Reset23/the-stack-v2-python · Datasets at Hugging Face

d06decf7dcf5e33ddd34594bdf89f8b32d5e6822

fd9c2b959724d65567876dec0abe93aaed7b7958

/catalog/models/__init__.py

088604bb05997808a69063f26a643f86eeb5527a

[]

no_license

emersonccf/projeto_mozilla

a5a3010dbaf942f03d1abed77706c6a1e34d53cc

aefe3d25439ecd0a2c3f6beb120da37cdfd1c9e4

refs/heads/main

2023-06-05T23:59:18.984438

2021-06-25T03:29:30

371,171,187

0

null

UTF-8

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false

523

py

from django.db import models from django.utils import timezone # não utilizado from django.contrib.auth.models import User from django.urls import reverse # Usado para gerar URLs revertendo os padrões de URL em Book import uuid # Utilizado em BookIstance LOAN_STATUS = ( ('m', 'Manutenção'), ('e', 'Emprestado'), ('d', 'Disponível'), ('r', 'Reservado'), ) from .genre import Genre from .book import Book from .bookinstance import BookInstance from .author import Author from .language import Language

[ "emecatarino@yahoo.com.br" ]

emecatarino@yahoo.com.br

b270c02c44e1a61c3fe90af2a5606b7401d9980c

5f4a0e5ae73d1ec5701dbd5e1cddf673a838000a

/modelrepo/heftNLO/vertices.py

ad39f23a52b81fdbc12fcd8d2e532faf9a9cf5fe

[]

no_license

modohyoung/madgraph-auto-model

e610569fe054b54bb2774ade91d3ff44dfc6ca04

796a8e6c2c6d628c5c971022595c31c18b406406

refs/heads/master

2020-12-11T05:47:22.054902

2015-05-08T16:25:04

null

0

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false

31,128

py

# This file was automatically created by FeynRules 1.7.55 # Mathematica version: 8.0 for Mac OS X x86 (64-bit) (October 6, 2011) # Date: Wed 8 Aug 2012 14:16:24 from object_library import all_vertices, Vertex import particles as P import couplings as C import lorentz as L V_1 = Vertex(name = 'V_1', particles = [ P.H, P.H, P.H, P.H ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_23}) V_2 = Vertex(name = 'V_2', particles = [ P.H, P.H, P.phi0, P.phi0 ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_21}) V_3 = Vertex(name = 'V_3', particles = [ P.phi0, P.phi0, P.phi0, P.phi0 ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_23}) V_4 = Vertex(name = 'V_4', particles = [ P.H, P.H, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_21}) V_5 = Vertex(name = 'V_5', particles = [ P.phi0, P.phi0, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_21}) V_6 = Vertex(name = 'V_6', particles = [ P.phi__minus__, P.phi__minus__, P.phi__plus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.SSSS1 ], couplings = {(0,0):C.GC_22}) V_7 = Vertex(name = 'V_7', particles = [ P.H, P.H, P.H ], color = [ '1' ], lorentz = [ L.SSS1 ], couplings = {(0,0):C.GC_69}) V_8 = Vertex(name = 'V_8', particles = [ P.H, P.phi0, P.phi0 ], color = [ '1' ], lorentz = [ L.SSS1 ], couplings = {(0,0):C.GC_68}) V_9 = Vertex(name = 'V_9', particles = [ P.H, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.SSS1 ], couplings = {(0,0):C.GC_68}) V_10 = Vertex(name = 'V_10', particles = [ P.A, P.A, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_6}) V_11 = Vertex(name = 'V_11', particles = [ P.A, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_4}) V_12 = Vertex(name = 'V_12', particles = [ P.A, P.A, P.H ], color = [ '1' ], lorentz = [ L.VVS3 ], couplings = {(0,0):C.GC_1}) #V_13 = Vertex(name = 'V_13', # particles = [ P.G, P.G, P.H ], # color = [ 'Identity(1,2)' ], # lorentz = [ L.VVS3 ], # couplings = {(0,0):C.GC_13}) V_13 = Vertex(name = 'V_13', particles = [ P.G, P.G, P.H ], color = [ 'Identity(1,2)' ], lorentz = [ L.VVS4 ], couplings = {(0,0):C.GC_13}) V_14 = Vertex(name = 'V_14', particles = [ P.ghA, P.ghWm__tilde__, P.W__minus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_4}) V_15 = Vertex(name = 'V_15', particles = [ P.ghA, P.ghWp__tilde__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_5}) V_16 = Vertex(name = 'V_16', particles = [ P.ghWm, P.ghA__tilde__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_25}) V_17 = Vertex(name = 'V_17', particles = [ P.ghWm, P.ghA__tilde__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_4}) V_18 = Vertex(name = 'V_18', particles = [ P.ghWm, P.ghWm__tilde__, P.H ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_48}) V_19 = Vertex(name = 'V_19', particles = [ P.ghWm, P.ghWm__tilde__, P.phi0 ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_47}) V_20 = Vertex(name = 'V_20', particles = [ P.ghWm, P.ghWm__tilde__, P.A ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_5}) V_21 = Vertex(name = 'V_21', particles = [ P.ghWm, P.ghWm__tilde__, P.Z ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_43}) V_22 = Vertex(name = 'V_22', particles = [ P.ghWm, P.ghZ__tilde__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_62}) V_23 = Vertex(name = 'V_23', particles = [ P.ghWm, P.ghZ__tilde__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_42}) V_24 = Vertex(name = 'V_24', particles = [ P.ghWp, P.ghA__tilde__, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_24}) V_25 = Vertex(name = 'V_25', particles = [ P.ghWp, P.ghA__tilde__, P.W__minus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_5}) V_26 = Vertex(name = 'V_26', particles = [ P.ghWp, P.ghWp__tilde__, P.H ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_48}) V_27 = Vertex(name = 'V_27', particles = [ P.ghWp, P.ghWp__tilde__, P.phi0 ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_49}) V_28 = Vertex(name = 'V_28', particles = [ P.ghWp, P.ghWp__tilde__, P.A ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_4}) V_29 = Vertex(name = 'V_29', particles = [ P.ghWp, P.ghWp__tilde__, P.Z ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_42}) V_30 = Vertex(name = 'V_30', particles = [ P.ghWp, P.ghZ__tilde__, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_63}) V_31 = Vertex(name = 'V_31', particles = [ P.ghWp, P.ghZ__tilde__, P.W__minus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_43}) V_32 = Vertex(name = 'V_32', particles = [ P.ghZ, P.ghWm__tilde__, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_51}) V_33 = Vertex(name = 'V_33', particles = [ P.ghZ, P.ghWm__tilde__, P.W__minus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_42}) V_34 = Vertex(name = 'V_34', particles = [ P.ghZ, P.ghWp__tilde__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_50}) V_35 = Vertex(name = 'V_35', particles = [ P.ghZ, P.ghWp__tilde__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_43}) V_36 = Vertex(name = 'V_36', particles = [ P.ghZ, P.ghZ__tilde__, P.H ], color = [ '1' ], lorentz = [ L.UUS1 ], couplings = {(0,0):C.GC_52}) V_37 = Vertex(name = 'V_37', particles = [ P.G, P.G, P.h1 ], color = [ 'Identity(1,2)' ], lorentz = [ L.VVS1 ], couplings = {(0,0):C.GC_16}) V_38 = Vertex(name = 'V_38', particles = [ P.ghG, P.ghG__tilde__, P.G ], color = [ 'f(1,2,3)' ], lorentz = [ L.UUV1 ], couplings = {(0,0):C.GC_10}) V_39 = Vertex(name = 'V_39', particles = [ P.G, P.G, P.G ], color = [ 'f(1,2,3)' ], lorentz = [ L.VVV1 ], couplings = {(0,0):C.GC_10}) V_40 = Vertex(name = 'V_40', particles = [ P.G, P.G, P.G, P.G ], color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ], lorentz = [ L.VVVV1, L.VVVV3, L.VVVV4 ], couplings = {(1,1):C.GC_12,(0,0):C.GC_12,(2,2):C.GC_12}) V_41 = Vertex(name = 'V_41', particles = [ P.G, P.G, P.G, P.H ], color = [ 'f(1,2,3)' ], lorentz = [ L.VVVS2 ], couplings = {(0,0):C.GC_14}) V_42 = Vertex(name = 'V_42', particles = [ P.G, P.G, P.G, P.G, P.H ], color = [ 'f(-1,1,2)*f(3,4,-1)', 'f(-1,1,3)*f(2,4,-1)', 'f(-1,1,4)*f(2,3,-1)' ], lorentz = [ L.VVVVS1, L.VVVVS2, L.VVVVS3 ], couplings = {(1,1):C.GC_15,(0,0):C.GC_15,(2,2):C.GC_15}) V_43 = Vertex(name = 'V_43', particles = [ P.G, P.G, P.G, P.h1 ], color = [ 'f(1,2,3)' ], lorentz = [ L.VVVS1 ], couplings = {(0,0):C.GC_17}) V_44 = Vertex(name = 'V_44', particles = [ P.A, P.W__minus__, P.H, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_44}) V_45 = Vertex(name = 'V_45', particles = [ P.A, P.W__minus__, P.phi0, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_45}) V_46 = Vertex(name = 'V_46', particles = [ P.A, P.W__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_71}) V_47 = Vertex(name = 'V_47', particles = [ P.W__minus__, P.H, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_29}) V_48 = Vertex(name = 'V_48', particles = [ P.W__minus__, P.phi0, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_28}) V_49 = Vertex(name = 'V_49', particles = [ P.A, P.W__minus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.VVV1 ], couplings = {(0,0):C.GC_55}) V_50 = Vertex(name = 'V_50', particles = [ P.A, P.W__plus__, P.H, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_46}) V_51 = Vertex(name = 'V_51', particles = [ P.A, P.W__plus__, P.phi0, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_45}) V_52 = Vertex(name = 'V_52', particles = [ P.A, P.W__plus__, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_72}) V_53 = Vertex(name = 'V_53', particles = [ P.W__plus__, P.H, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_29}) V_54 = Vertex(name = 'V_54', particles = [ P.W__plus__, P.phi0, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_27}) V_55 = Vertex(name = 'V_55', particles = [ P.W__minus__, P.W__plus__, P.H, P.H ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_26}) V_56 = Vertex(name = 'V_56', particles = [ P.W__minus__, P.W__plus__, P.phi0, P.phi0 ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_26}) V_57 = Vertex(name = 'V_57', particles = [ P.W__minus__, P.W__plus__, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_26}) V_58 = Vertex(name = 'V_58', particles = [ P.W__minus__, P.W__plus__, P.H ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_70}) V_59 = Vertex(name = 'V_59', particles = [ P.A, P.A, P.W__minus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.VVVV2 ], couplings = {(0,0):C.GC_57}) V_60 = Vertex(name = 'V_60', particles = [ P.W__minus__, P.W__plus__, P.Z ], color = [ '1' ], lorentz = [ L.VVV1 ], couplings = {(0,0):C.GC_18}) V_61 = Vertex(name = 'V_61', particles = [ P.W__minus__, P.W__minus__, P.W__plus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.VVVV2 ], couplings = {(0,0):C.GC_19}) V_62 = Vertex(name = 'V_62', particles = [ P.A, P.Z, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_61}) V_63 = Vertex(name = 'V_63', particles = [ P.Z, P.H, P.phi0 ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_60}) V_64 = Vertex(name = 'V_64', particles = [ P.Z, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VSS1 ], couplings = {(0,0):C.GC_58}) V_65 = Vertex(name = 'V_65', particles = [ P.W__minus__, P.Z, P.H, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_9}) V_66 = Vertex(name = 'V_66', particles = [ P.W__minus__, P.Z, P.phi0, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_8}) V_67 = Vertex(name = 'V_67', particles = [ P.W__minus__, P.Z, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_67}) V_68 = Vertex(name = 'V_68', particles = [ P.W__plus__, P.Z, P.H, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_7}) V_69 = Vertex(name = 'V_69', particles = [ P.W__plus__, P.Z, P.phi0, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_8}) V_70 = Vertex(name = 'V_70', particles = [ P.W__plus__, P.Z, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_66}) V_71 = Vertex(name = 'V_71', particles = [ P.A, P.W__minus__, P.W__plus__, P.Z ], color = [ '1' ], lorentz = [ L.VVVV5 ], couplings = {(0,0):C.GC_56}) V_72 = Vertex(name = 'V_72', particles = [ P.Z, P.Z, P.H, P.H ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_65}) V_73 = Vertex(name = 'V_73', particles = [ P.Z, P.Z, P.phi0, P.phi0 ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_65}) V_74 = Vertex(name = 'V_74', particles = [ P.Z, P.Z, P.phi__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.VVSS1 ], couplings = {(0,0):C.GC_64}) V_75 = Vertex(name = 'V_75', particles = [ P.Z, P.Z, P.H ], color = [ '1' ], lorentz = [ L.VVS2 ], couplings = {(0,0):C.GC_73}) V_76 = Vertex(name = 'V_76', particles = [ P.W__minus__, P.W__plus__, P.Z, P.Z ], color = [ '1' ], lorentz = [ L.VVVV2 ], couplings = {(0,0):C.GC_20}) V_77 = Vertex(name = 'V_77', particles = [ P.d__tilde__, P.d, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_2}) V_78 = Vertex(name = 'V_78', particles = [ P.s__tilde__, P.s, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_2}) V_79 = Vertex(name = 'V_79', particles = [ P.b__tilde__, P.b, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_2}) V_80 = Vertex(name = 'V_80', particles = [ P.d__tilde__, P.d, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_81 = Vertex(name = 'V_81', particles = [ P.s__tilde__, P.s, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_82 = Vertex(name = 'V_82', particles = [ P.b__tilde__, P.b, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_83 = Vertex(name = 'V_83', particles = [ P.b__tilde__, P.b, P.H ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS2 ], couplings = {(0,0):C.GC_74}) V_84 = Vertex(name = 'V_84', particles = [ P.b__tilde__, P.b, P.phi0 ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS1 ], couplings = {(0,0):C.GC_75}) V_85 = Vertex(name = 'V_85', particles = [ P.d__tilde__, P.d, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV3 ], couplings = {(0,0):C.GC_40,(0,1):C.GC_53}) V_86 = Vertex(name = 'V_86', particles = [ P.s__tilde__, P.s, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV3 ], couplings = {(0,0):C.GC_40,(0,1):C.GC_53}) V_87 = Vertex(name = 'V_87', particles = [ P.b__tilde__, P.b, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV3 ], couplings = {(0,1):C.GC_53,(0,0):C.GC_40}) V_88 = Vertex(name = 'V_88', particles = [ P.t__tilde__, P.d, P.phi__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS4 ], couplings = {(0,0):C.GC_97}) V_89 = Vertex(name = 'V_89', particles = [ P.t__tilde__, P.s, P.phi__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS4 ], couplings = {(0,0):C.GC_99}) V_90 = Vertex(name = 'V_90', particles = [ P.u__tilde__, P.b, P.phi__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3 ], couplings = {(0,0):C.GC_91}) V_91 = Vertex(name = 'V_91', particles = [ P.c__tilde__, P.b, P.phi__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3 ], couplings = {(0,0):C.GC_95}) V_92 = Vertex(name = 'V_92', particles = [ P.t__tilde__, P.b, P.phi__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3, L.FFS4 ], couplings = {(0,0):C.GC_101,(0,1):C.GC_102}) V_93 = Vertex(name = 'V_93', particles = [ P.u__tilde__, P.d, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_88}) V_94 = Vertex(name = 'V_94', particles = [ P.c__tilde__, P.d, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_92}) V_95 = Vertex(name = 'V_95', particles = [ P.t__tilde__, P.d, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_96}) V_96 = Vertex(name = 'V_96', particles = [ P.u__tilde__, P.s, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_89}) V_97 = Vertex(name = 'V_97', particles = [ P.c__tilde__, P.s, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_93}) V_98 = Vertex(name = 'V_98', particles = [ P.t__tilde__, P.s, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_98}) V_99 = Vertex(name = 'V_99', particles = [ P.u__tilde__, P.b, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_90}) V_100 = Vertex(name = 'V_100', particles = [ P.c__tilde__, P.b, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_94}) V_101 = Vertex(name = 'V_101', particles = [ P.t__tilde__, P.b, P.W__plus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_100}) V_102 = Vertex(name = 'V_102', particles = [ P.e__plus__, P.e__minus__, P.A ], color = [ '1' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_4}) V_103 = Vertex(name = 'V_103', particles = [ P.m__plus__, P.m__minus__, P.A ], color = [ '1' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_4}) V_104 = Vertex(name = 'V_104', particles = [ P.tt__plus__, P.tt__minus__, P.A ], color = [ '1' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_4}) V_105 = Vertex(name = 'V_105', particles = [ P.tt__plus__, P.tt__minus__, P.H ], color = [ '1' ], lorentz = [ L.FFS2 ], couplings = {(0,0):C.GC_86}) V_106 = Vertex(name = 'V_106', particles = [ P.tt__plus__, P.tt__minus__, P.phi0 ], color = [ '1' ], lorentz = [ L.FFS1 ], couplings = {(0,0):C.GC_87}) V_107 = Vertex(name = 'V_107', particles = [ P.e__plus__, P.e__minus__, P.Z ], color = [ '1' ], lorentz = [ L.FFV2, L.FFV4 ], couplings = {(0,0):C.GC_40,(0,1):C.GC_54}) V_108 = Vertex(name = 'V_108', particles = [ P.m__plus__, P.m__minus__, P.Z ], color = [ '1' ], lorentz = [ L.FFV2, L.FFV4 ], couplings = {(0,0):C.GC_40,(0,1):C.GC_54}) V_109 = Vertex(name = 'V_109', particles = [ P.tt__plus__, P.tt__minus__, P.Z ], color = [ '1' ], lorentz = [ L.FFV2, L.FFV4 ], couplings = {(0,0):C.GC_40,(0,1):C.GC_54}) V_110 = Vertex(name = 'V_110', particles = [ P.vt__tilde__, P.tt__minus__, P.phi__plus__ ], color = [ '1' ], lorentz = [ L.FFS3 ], couplings = {(0,0):C.GC_85}) V_111 = Vertex(name = 'V_111', particles = [ P.ve__tilde__, P.e__minus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_112 = Vertex(name = 'V_112', particles = [ P.vm__tilde__, P.m__minus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_113 = Vertex(name = 'V_113', particles = [ P.vt__tilde__, P.tt__minus__, P.W__plus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_114 = Vertex(name = 'V_114', particles = [ P.b__tilde__, P.u, P.phi__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS4 ], couplings = {(0,0):C.GC_76}) V_115 = Vertex(name = 'V_115', particles = [ P.b__tilde__, P.c, P.phi__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS4 ], couplings = {(0,0):C.GC_77}) V_116 = Vertex(name = 'V_116', particles = [ P.d__tilde__, P.t, P.phi__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3 ], couplings = {(0,0):C.GC_81}) V_117 = Vertex(name = 'V_117', particles = [ P.s__tilde__, P.t, P.phi__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3 ], couplings = {(0,0):C.GC_82}) V_118 = Vertex(name = 'V_118', particles = [ P.b__tilde__, P.t, P.phi__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS3, L.FFS4 ], couplings = {(0,0):C.GC_83,(0,1):C.GC_78}) V_119 = Vertex(name = 'V_119', particles = [ P.d__tilde__, P.u, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_31}) V_120 = Vertex(name = 'V_120', particles = [ P.s__tilde__, P.u, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_32}) V_121 = Vertex(name = 'V_121', particles = [ P.b__tilde__, P.u, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_33}) V_122 = Vertex(name = 'V_122', particles = [ P.d__tilde__, P.c, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_34}) V_123 = Vertex(name = 'V_123', particles = [ P.s__tilde__, P.c, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_35}) V_124 = Vertex(name = 'V_124', particles = [ P.b__tilde__, P.c, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_36}) V_125 = Vertex(name = 'V_125', particles = [ P.d__tilde__, P.t, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_37}) V_126 = Vertex(name = 'V_126', particles = [ P.s__tilde__, P.t, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_38}) V_127 = Vertex(name = 'V_127', particles = [ P.b__tilde__, P.t, P.W__minus__ ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_39}) V_128 = Vertex(name = 'V_128', particles = [ P.u__tilde__, P.u, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_3}) V_129 = Vertex(name = 'V_129', particles = [ P.c__tilde__, P.c, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_3}) V_130 = Vertex(name = 'V_130', particles = [ P.t__tilde__, P.t, P.A ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_3}) V_131 = Vertex(name = 'V_131', particles = [ P.u__tilde__, P.u, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_132 = Vertex(name = 'V_132', particles = [ P.c__tilde__, P.c, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_133 = Vertex(name = 'V_133', particles = [ P.t__tilde__, P.t, P.G ], color = [ 'T(3,2,1)' ], lorentz = [ L.FFV1 ], couplings = {(0,0):C.GC_11}) V_134 = Vertex(name = 'V_134', particles = [ P.t__tilde__, P.t, P.H ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS2 ], couplings = {(0,0):C.GC_80}) V_135 = Vertex(name = 'V_135', particles = [ P.t__tilde__, P.t, P.phi0 ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFS1 ], couplings = {(0,0):C.GC_79}) V_136 = Vertex(name = 'V_136', particles = [ P.u__tilde__, P.u, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV5 ], couplings = {(0,0):C.GC_41,(0,1):C.GC_53}) V_137 = Vertex(name = 'V_137', particles = [ P.c__tilde__, P.c, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV5 ], couplings = {(0,0):C.GC_41,(0,1):C.GC_53}) V_138 = Vertex(name = 'V_138', particles = [ P.t__tilde__, P.t, P.Z ], color = [ 'Identity(1,2)' ], lorentz = [ L.FFV2, L.FFV5 ], couplings = {(0,0):C.GC_41,(0,1):C.GC_53}) V_139 = Vertex(name = 'V_139', particles = [ P.tt__plus__, P.vt, P.phi__minus__ ], color = [ '1' ], lorentz = [ L.FFS4 ], couplings = {(0,0):C.GC_84}) V_140 = Vertex(name = 'V_140', particles = [ P.e__plus__, P.ve, P.W__minus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_141 = Vertex(name = 'V_141', particles = [ P.m__plus__, P.vm, P.W__minus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_142 = Vertex(name = 'V_142', particles = [ P.tt__plus__, P.vt, P.W__minus__ ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_30}) V_143 = Vertex(name = 'V_143', particles = [ P.ve__tilde__, P.ve, P.Z ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_59}) V_144 = Vertex(name = 'V_144', particles = [ P.vm__tilde__, P.vm, P.Z ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_59}) V_145 = Vertex(name = 'V_145', particles = [ P.vt__tilde__, P.vt, P.Z ], color = [ '1' ], lorentz = [ L.FFV2 ], couplings = {(0,0):C.GC_59})

[ "ianwookim@gmail.com" ]

ianwookim@gmail.com

a0afd01311fc3c8b2e58fd920285130338e86b2d

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/src/newsletter/migrations/0001_initial.py

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[]

no_license

garabek/Django_BootcampSite

39b8bc976730c0776d733536f020a043d2f89370

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refs/heads/master

2021-07-01T12:09:57.557274

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py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='SignUp', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('email', models.EmailField(max_length=254)), ('full_name', models.CharField(max_length=100, null=True, blank=True)), ('timestamp', models.DateTimeField(auto_now_add=True)), ('updated', models.DateTimeField(auto_now=True)), ], ), ]

[ "bekiyev@gmail.com" ]

bekiyev@gmail.com

b0120bfacbc1a15617c0e7d5efcaae38eaa5e7f0

d587a6d66a498eb23aa760117928ac012ee50f4d

/accounts/models.py

749ccb4895b121276809f8b2340742f776fd0443

[]

no_license

azhar316/ecommerce-store

f0eff37565f9ae873dfc09c119b14f6a647b98ca

6a674a797e0daff63907fd2c8423b62bc8340d5d

refs/heads/master

2022-05-04T16:59:08.216063

2020-04-27T11:59:48

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from django.db import models from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, PermissionsMixin from django.utils import timezone class CustomUserManager(BaseUserManager): def create_user(self, email, password, **extra_fields): if not email: raise ValueError('Users must have an email address') if not password: raise ValueError('Users must have password') user = self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save() return user def create_superuser(self, email, password): user = self.create_user(email, password, staff=True, admin=True, is_superuser=True) return user def create_staffuser(self, email, password): user = self.create_user(email, password, staff=True) return user class CustomUser(AbstractBaseUser, PermissionsMixin): full_name = models. CharField(max_length=250, blank=True) email = models.EmailField(unique=True) is_active = models.BooleanField(default=True) staff = models.BooleanField(default=False) admin = models.BooleanField(default=False) date_joined = models.DateTimeField(default=timezone.now) USERNAME_FIELD = 'email' REQUIRED_FIELDS = [] objects = CustomUserManager() def __str__(self): return self.email def get_full_name(self): if self.full_name: return self.full_name return self.email def get_email(self): return self.email @property def is_staff(self): if self.admin: return True return self.staff @property def is_admin(self): return self.admin # override functions of PermissionMixin to enable staff users permission to perform # all the admin tasks def has_perm(self, perm, obj=None): return self.staff def has_perms(self, perm_list, obj=None): return self.staff def has_module_perms(self, app_label): return self.staff

[ "sayed.azharudin@gmail.com" ]

sayed.azharudin@gmail.com

cd9ee5abd198f4ea6763f1fc758b8522cdfa424b

c3fafc358ab1bd71e67d67a7598b137193cb2d2e

/modules_autopep1.py

4e1c12480a8df3120c2ee2d83bed7ce047f4b17a

[]

no_license

DaniloBP/Python-3-Bootcamp

0010bb62432423d7ec76f87aa55d6cd016eac6e8

0594f3fcced17caa8bc00752aa6944b42f6224da

refs/heads/master

2020-04-26T00:55:51.174671

2019-06-03T04:38:04

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import math import sys def example1(): # This is a long comment. This should be wrapped to fit within 72 # characters. some_tuple = (1, 2, 3, 'a') some_variable = { 'long': 'Long code lines should be wrapped within 79 characters.', 'other': [math.pi, 100, 200, 300, 9876543210, 'This is a long string that goes on'], 'more': { 'inner': 'This whole logical line should be wrapped.', some_tuple: [1, 20, 300, 40000, 500000000, 60000000000000000]}} is_cat_owner = True if is_cat_owner: print("MEOW!") return (some_tuple, some_variable) def example2(): return ( '' in {'f': 2}) in {'has_key() is deprecated': True} class Example3(object): def __init__(self, bar): # Comments should have a space after the hash. if bar == True: bar += 1 bar = bar * bar return bar else: some_string = """ Indentation in multiline strings should not be touched. Only actual code should be reindented. """ return (sys.path, some_string)

[ "pereira.b.danilo@gmail.com" ]

pereira.b.danilo@gmail.com

155fa4c41fd0a7c40be7e863303ef3b568645e29

faae5e2e431cc55089324510715b5bc91732ff42

/DecisionTree.py

b874cf8d205050881abc97fc35a0b050a75094f3

[]

no_license

sk929/MLLearning

ac9e84d9bbf0c8dfa7ad23b8941925320ed8c083

ca5a9992b1fc40105a722b447ded6da20db32238

refs/heads/master

2022-07-20T01:29:05.298884

2020-05-26T13:38:25

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# Code you have previously used to load data import pandas as pd from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor # Path of the file to read iowa_file_path = '../input/home-data-for-ml-course/train.csv' home_data = pd.read_csv(iowa_file_path) # Create target object and call it y y = home_data.SalePrice # Create X features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd'] X = home_data[features] # Split into validation and training data train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1) # Specify Model iowa_model = DecisionTreeRegressor(random_state=1) # Fit Model iowa_model.fit(train_X, train_y) # Make validation predictions and calculate mean absolute error val_predictions = iowa_model.predict(val_X) val_mae = mean_absolute_error(val_predictions, val_y) print("Validation MAE: {:,.0f}".format(val_mae)) def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y): model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0) model.fit(train_X, train_y) preds_val = model.predict(val_X) mae = mean_absolute_error(val_y, preds_val) return(mae) candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500] # Write loop to find the ideal tree size from candidate_max_leaf_nodes node = {} for leaf_nodes in candidate_max_leaf_nodes: node[leaf_nodes]= get_mae(leaf_nodes, train_X, val_X, train_y, val_y) # Store the best value of max_leaf_nodes (it will be either 5, 25, 50, 100, 250 or 500) best_tree_size = min(node,key = node.get) # Fill in argument to make optimal size and uncomment final_model = DecisionTreeRegressor(max_leaf_nodes = best_tree_size , random_state=1) # fit the final model and uncomment the next two lines final_model.fit(X, y) '''Random Forest''' from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error forest_model = RandomForestRegressor(random_state=1) forest_model.fit(train_X, train_y) melb_preds = forest_model.predict(val_X) print(mean_absolute_error(val_y, melb_preds))

[ "noreply@github.com" ]

sk929.noreply@github.com

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/juego con tortuga 8.py

5bf62bb0d7652da17d3342d7d845031f68dbc925

[]

no_license

JDHINCAMAN/Python_examples

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refs/heads/main

2023-03-23T12:09:38.245610

2021-03-23T14:05:55

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import turtle t = turtle.Pen() t.reset() for x in range(1,38): t.forward(100) t.left(175)

[ "noreply@github.com" ]

JDHINCAMAN.noreply@github.com

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/Week1/05-aa-nt-converter.py

563953c5a4c3f48005902efabb19f344c9fc58f3

[]

no_license

charmquark1/cmdb-lab

4e485f973eec2f5473760722dc1bd0401485fb76

3a82fe708f42f9d13493cb99f580a5587881a125

refs/heads/master

2021-01-11T19:34:54.462104

2016-12-19T14:54:42

68,961,566

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py

#!/usr/bin/env python """ Parse amino acid sequence in FASTA file. Convert amino acid to 3 nucleotide seq, append to variable 'new' When you see a -, replace with --- Print gene id (ident), converted seq, new line \n, print new seq Usage: xxx.py 04-AA.fa 02-nt.fa """ import fasta_fixed import sys import itertools #inputs: 1) amino acid FASTA, 2) original nt FASTA AA_query = open(sys.argv[1]) nt_query = open(sys.argv[2]) #prepare inputs for parallel parsing AA_seq = [] nt_seq = [] for ident, sequence in fasta_fixed.FASTAReader(AA_query): AA_seq.append(sequence) for ident1, sequence in fasta_fixed.FASTAReader(nt_query): nt_seq.append(sequence) # parse parallel # read ith element of aa sequence. If not "-", then take three first elements from nt_seq file and add to empty string, new # at the end of the gene, append string new to list. Then restart for loop for next gene. # I made list to make it easier to format for later. list=[] for aa, nt in itertools.izip(AA_seq, nt_seq): new = '' nt_pos = 0 for i in range(0, len(aa)): if aa[i] == '-': new = new + ("---") else: codon = nt[nt_pos:nt_pos+3] #take 3 characters new = new + codon nt_pos = nt_pos + 3 #print new list.append(new) print ">x\n" +"\n>x\n".join(list)

[ "ninarao42@gmail.com" ]

ninarao42@gmail.com

caf70606137c0215e3fb64625ec643e1dc3b2668

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/exercises.py

7faa60fb61740354ef8009359c125e8b8c5f7807

[]

no_license

Seal125/binary_tree_basic

79bce998c34c56bbe938784f2be8048da66206d6

957df46130f8c0e68bb8cf7145d0d01aee60e34f

refs/heads/master

2021-04-17T11:05:05.827069

2020-03-23T14:47:25

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2020-03-23T13:35:43

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py

class Node: def __init__(self, value = None): self.value = value self.left = None self.right = None def inorder(root): values = [] def add(node): if node: add(node.left) list.append(node.value) add(node.right) add(root) return values def is_unival_tree(tree): value = tree.value is_unival= True def add(node): if node: add(node.left) if node.value != value: is_unival = False return is_unival add(node.right)

[ "stephaniesmith12514@gmail.com" ]

stephaniesmith12514@gmail.com

feb967e768de780f768c67ee8e6bc478974aa13b

7e90a1f8280618b97729d0b49b80c6814d0466e2

/workspace_pc/catkin_ws/build_isolated/hector_slam/catkin_generated/stamps/hector_slam/_setup_util.py.stamp

c51cc0942a19064fe6e7239c5adffd9ad95290b7

[]

no_license

IreneYIN7/Map-Tracer

91909f4649a8b65afed56ae3803f0c0602dd89ff

cbbe9acf067757116ec74c3aebdd672fd3df62ed

refs/heads/master

2022-04-02T09:53:15.650365

2019-12-19T07:31:31

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stamp

#!/usr/bin/python2 # -*- coding: utf-8 -*- # Software License Agreement (BSD License) # # Copyright (c) 2012, Willow Garage, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Willow Garage, Inc. nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. '''This file generates shell code for the setup.SHELL scripts to set environment variables''' from __future__ import print_function import argparse import copy import errno import os import platform import sys CATKIN_MARKER_FILE = '.catkin' system = platform.system() IS_DARWIN = (system == 'Darwin') IS_WINDOWS = (system == 'Windows') # subfolder of workspace prepended to CMAKE_PREFIX_PATH ENV_VAR_SUBFOLDERS = { 'CMAKE_PREFIX_PATH': '', 'LD_LIBRARY_PATH' if not IS_DARWIN else 'DYLD_LIBRARY_PATH': ['lib', os.path.join('lib', 'x86_64-linux-gnu')], 'PATH': 'bin', 'PKG_CONFIG_PATH': [os.path.join('lib', 'pkgconfig'), os.path.join('lib', 'x86_64-linux-gnu', 'pkgconfig')], 'PYTHONPATH': 'lib/python2.7/dist-packages', } def rollback_env_variables(environ, env_var_subfolders): ''' Generate shell code to reset environment variables by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH. This does not cover modifications performed by environment hooks. ''' lines = [] unmodified_environ = copy.copy(environ) for key in sorted(env_var_subfolders.keys()): subfolders = env_var_subfolders[key] if not isinstance(subfolders, list): subfolders = [subfolders] value = _rollback_env_variable(unmodified_environ, key, subfolders) if value is not None: environ[key] = value lines.append(assignment(key, value)) if lines: lines.insert(0, comment('reset environment variables by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH')) return lines def _rollback_env_variable(environ, name, subfolders): ''' For each catkin workspace in CMAKE_PREFIX_PATH remove the first entry from env[NAME] matching workspace + subfolder. :param subfolders: list of str '' or subfoldername that may start with '/' :returns: the updated value of the environment variable. ''' value = environ[name] if name in environ else '' env_paths = [path for path in value.split(os.pathsep) if path] value_modified = False for subfolder in subfolders: if subfolder: if subfolder.startswith(os.path.sep) or (os.path.altsep and subfolder.startswith(os.path.altsep)): subfolder = subfolder[1:] if subfolder.endswith(os.path.sep) or (os.path.altsep and subfolder.endswith(os.path.altsep)): subfolder = subfolder[:-1] for ws_path in _get_workspaces(environ, include_fuerte=True, include_non_existing=True): path_to_find = os.path.join(ws_path, subfolder) if subfolder else ws_path path_to_remove = None for env_path in env_paths: env_path_clean = env_path[:-1] if env_path and env_path[-1] in [os.path.sep, os.path.altsep] else env_path if env_path_clean == path_to_find: path_to_remove = env_path break if path_to_remove: env_paths.remove(path_to_remove) value_modified = True new_value = os.pathsep.join(env_paths) return new_value if value_modified else None def _get_workspaces(environ, include_fuerte=False, include_non_existing=False): ''' Based on CMAKE_PREFIX_PATH return all catkin workspaces. :param include_fuerte: The flag if paths starting with '/opt/ros/fuerte' should be considered workspaces, ``bool`` ''' # get all cmake prefix paths env_name = 'CMAKE_PREFIX_PATH' value = environ[env_name] if env_name in environ else '' paths = [path for path in value.split(os.pathsep) if path] # remove non-workspace paths workspaces = [path for path in paths if os.path.isfile(os.path.join(path, CATKIN_MARKER_FILE)) or (include_fuerte and path.startswith('/opt/ros/fuerte')) or (include_non_existing and not os.path.exists(path))] return workspaces def prepend_env_variables(environ, env_var_subfolders, workspaces): ''' Generate shell code to prepend environment variables for the all workspaces. ''' lines = [] lines.append(comment('prepend folders of workspaces to environment variables')) paths = [path for path in workspaces.split(os.pathsep) if path] prefix = _prefix_env_variable(environ, 'CMAKE_PREFIX_PATH', paths, '') lines.append(prepend(environ, 'CMAKE_PREFIX_PATH', prefix)) for key in sorted([key for key in env_var_subfolders.keys() if key != 'CMAKE_PREFIX_PATH']): subfolder = env_var_subfolders[key] prefix = _prefix_env_variable(environ, key, paths, subfolder) lines.append(prepend(environ, key, prefix)) return lines def _prefix_env_variable(environ, name, paths, subfolders): ''' Return the prefix to prepend to the environment variable NAME, adding any path in NEW_PATHS_STR without creating duplicate or empty items. ''' value = environ[name] if name in environ else '' environ_paths = [path for path in value.split(os.pathsep) if path] checked_paths = [] for path in paths: if not isinstance(subfolders, list): subfolders = [subfolders] for subfolder in subfolders: path_tmp = path if subfolder: path_tmp = os.path.join(path_tmp, subfolder) # skip nonexistent paths if not os.path.exists(path_tmp): continue # exclude any path already in env and any path we already added if path_tmp not in environ_paths and path_tmp not in checked_paths: checked_paths.append(path_tmp) prefix_str = os.pathsep.join(checked_paths) if prefix_str != '' and environ_paths: prefix_str += os.pathsep return prefix_str def assignment(key, value): if not IS_WINDOWS: return 'export %s="%s"' % (key, value) else: return 'set %s=%s' % (key, value) def comment(msg): if not IS_WINDOWS: return '# %s' % msg else: return 'REM %s' % msg def prepend(environ, key, prefix): if key not in environ or not environ[key]: return assignment(key, prefix) if not IS_WINDOWS: return 'export %s="%s$%s"' % (key, prefix, key) else: return 'set %s=%s%%%s%%' % (key, prefix, key) def find_env_hooks(environ, cmake_prefix_path): ''' Generate shell code with found environment hooks for the all workspaces. ''' lines = [] lines.append(comment('found environment hooks in workspaces')) generic_env_hooks = [] generic_env_hooks_workspace = [] specific_env_hooks = [] specific_env_hooks_workspace = [] generic_env_hooks_by_filename = {} specific_env_hooks_by_filename = {} generic_env_hook_ext = 'bat' if IS_WINDOWS else 'sh' specific_env_hook_ext = environ['CATKIN_SHELL'] if not IS_WINDOWS and 'CATKIN_SHELL' in environ and environ['CATKIN_SHELL'] else None # remove non-workspace paths workspaces = [path for path in cmake_prefix_path.split(os.pathsep) if path and os.path.isfile(os.path.join(path, CATKIN_MARKER_FILE))] for workspace in reversed(workspaces): env_hook_dir = os.path.join(workspace, 'etc', 'catkin', 'profile.d') if os.path.isdir(env_hook_dir): for filename in sorted(os.listdir(env_hook_dir)): if filename.endswith('.%s' % generic_env_hook_ext): # remove previous env hook with same name if present if filename in generic_env_hooks_by_filename: i = generic_env_hooks.index(generic_env_hooks_by_filename[filename]) generic_env_hooks.pop(i) generic_env_hooks_workspace.pop(i) # append env hook generic_env_hooks.append(os.path.join(env_hook_dir, filename)) generic_env_hooks_workspace.append(workspace) generic_env_hooks_by_filename[filename] = generic_env_hooks[-1] elif specific_env_hook_ext is not None and filename.endswith('.%s' % specific_env_hook_ext): # remove previous env hook with same name if present if filename in specific_env_hooks_by_filename: i = specific_env_hooks.index(specific_env_hooks_by_filename[filename]) specific_env_hooks.pop(i) specific_env_hooks_workspace.pop(i) # append env hook specific_env_hooks.append(os.path.join(env_hook_dir, filename)) specific_env_hooks_workspace.append(workspace) specific_env_hooks_by_filename[filename] = specific_env_hooks[-1] env_hooks = generic_env_hooks + specific_env_hooks env_hooks_workspace = generic_env_hooks_workspace + specific_env_hooks_workspace count = len(env_hooks) lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_COUNT', count)) for i in range(count): lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_%d' % i, env_hooks[i])) lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_%d_WORKSPACE' % i, env_hooks_workspace[i])) return lines def _parse_arguments(args=None): parser = argparse.ArgumentParser(description='Generates code blocks for the setup.SHELL script.') parser.add_argument('--extend', action='store_true', help='Skip unsetting previous environment variables to extend context') parser.add_argument('--local', action='store_true', help='Only consider this prefix path and ignore other prefix path in the environment') return parser.parse_known_args(args=args)[0] if __name__ == '__main__': try: try: args = _parse_arguments() except Exception as e: print(e, file=sys.stderr) sys.exit(1) if not args.local: # environment at generation time CMAKE_PREFIX_PATH = '/home/gse5/catkin_ws/devel_isolated/hector_map_server;/home/gse5/catkin_ws/devel_isolated/hector_geotiff_plugins;/home/gse5/catkin_ws/devel_isolated/hector_geotiff;/home/gse5/catkin_ws/devel_isolated/hector_nav_msgs;/home/gse5/catkin_ws/devel_isolated/hector_marker_drawing;/home/gse5/catkin_ws/devel_isolated/hector_mapping;/home/gse5/catkin_ws/devel_isolated/hector_compressed_map_transport;/home/gse5/catkin_ws/devel_isolated/hector_map_tools;/home/gse5/catkin_ws/devel_isolated/hector_imu_tools;/home/gse5/catkin_ws/devel_isolated/hector_imu_attitude_to_tf;/home/gse5/catkin_ws/devel_isolated/rplidar_ros;/home/gse5/catkin_ws/devel_isolated/cartographer_rviz;/home/gse5/catkin_ws/devel_isolated/cartographer_ros;/home/gse5/catkin_ws/devel_isolated/cartographer_ros_msgs;/home/gse5/catkin_ws/devel_isolated/beginner_tutorials;/home/gse5/catkin_ws/install_isolated;/opt/ros/melodic;/home/gse5/catkin_ws/devel_isolated/cartographer;/home/gse5/catkin_ws/devel_isolated/ceres-solver'.split(';') else: # don't consider any other prefix path than this one CMAKE_PREFIX_PATH = [] # prepend current workspace if not already part of CPP base_path = os.path.dirname(__file__) # CMAKE_PREFIX_PATH uses forward slash on all platforms, but __file__ is platform dependent # base_path on Windows contains backward slashes, need to be converted to forward slashes before comparison if os.path.sep != '/': base_path = base_path.replace(os.path.sep, '/') if base_path not in CMAKE_PREFIX_PATH: CMAKE_PREFIX_PATH.insert(0, base_path) CMAKE_PREFIX_PATH = os.pathsep.join(CMAKE_PREFIX_PATH) environ = dict(os.environ) lines = [] if not args.extend: lines += rollback_env_variables(environ, ENV_VAR_SUBFOLDERS) lines += prepend_env_variables(environ, ENV_VAR_SUBFOLDERS, CMAKE_PREFIX_PATH) lines += find_env_hooks(environ, CMAKE_PREFIX_PATH) print('\n'.join(lines)) # need to explicitly flush the output sys.stdout.flush() except IOError as e: # and catch potential "broken pipe" if stdout is not writable # which can happen when piping the output to a file but the disk is full if e.errno == errno.EPIPE: print(e, file=sys.stderr) sys.exit(2) raise sys.exit(0)

[ "sh9339@outlook.com" ]

sh9339@outlook.com

3166d19669d179ae390fe0176d83516606e617ba

9c991a8b7bbdda40d9115d685122cf63627a1ace

/Week 1/Day1Practice/madlib.py

c5e85cfff4c6a4833517dcdcabfcb373a663a619

[]

no_license

Zacros7164/unit1

321844820178e16909df52f5620319e1aeeb0d4a

76d737067a685af110f6ec00ee315136c3cad51a

refs/heads/master

2020-04-06T11:26:00.023411

2019-02-12T14:06:00

157,416,741

0

null

UTF-8

Python

false

183

py

print "Madlibs!" name = raw_input("Please give me a name. ") subject = raw_input("Please give me a school subject. ") print name + "'s favorite subject in school is " + subject + "."

[ "Zacros7164@gmail.com" ]

Zacros7164@gmail.com

8a961656b96feb84ed09e52341f6db90ae7faeee

30272f4069293049848369f674ff7a8e88e30ac9

/PowerSpectrumFunctions.py

8e19c74085abafde3847571c977825abf8d71e79

[]

no_license

AstroJames/anisoReconstruct

e40121fdfdf0e90575c210c20a3ae471a46fa558

9b79fd78eb47e44592a5487bd1df9b81658dd31f

refs/heads/master

2022-04-06T23:47:07.820106

2020-03-02T00:38:31

235,967,999

0

1

null

UTF-8

Python

false

5,628

py

from header import * def PowerSpectrum(data,type=None,variance=None): """ Calculate the power spectrum. INPUTS: ---------- data - The 2D image data n - the coef. of the fourier transform = grid size. type - either '2D' or 'aziAverage' for 2D or averaged power spectrum calculations. variance - passed to the azimuthal averaging if you want to calculate the variance of the average. OUTPUTS: ---------- Pspec - the 2D power spectrum k - the 2D k vector array """ data = data.astype(float) # make sure the data is float type # This removes the k = 0 wavevalue and makes integrating easier. data = data/data.mean() - 1 # Pre-processing following Federrath et al. 2016 ft = (1./ (data.shape[0]*data.shape[1] ) )*fftpack.fft2(data) # 2D fourier transform ft_c = fftpack.fftshift(ft) # center the transform so k = (0,0) is in the center PSpec = np.abs(ft_c*np.conjugate(ft_c)) # take the power spectrum # Take the azimuthal average of the powr spectrum, if required. if type == 'aziAverage': if not variance: PSpec = azimuthalAverage(PSpec) return data else: PSpec, var = azimuthalAverage(PSpec,variance=True) return PSpec, var # Create the kx and ky vector components. kx = np.round(np.linspace( -( PSpec.shape[0] + 1 )/2, ( PSpec.shape[0] + 1 )/2, PSpec.shape[0])) ky = np.round(np.linspace( -( PSpec.shape[1] + 1 )/2, ( PSpec.shape[1] + 1 )/2, PSpec.shape[1])) kx, ky = np.meshgrid(kx,ky,indexing="xy") k = np.hypot(kx,ky) return PSpec, k def PowerSpectrumAveraging(files,densOrderd,run): """ this functions averages over the power spectrums and returns a dictionary with the averaged power spectrums in it. INPUTS: ---------- files - all of the file names for each simulations densOrdered - the density dictionary ordered by plot order run - if the function needs to be rerun for recompiling of the density plots OUTPUTS: ---------- PSpecAver - the average power spectrum as a dictionary, for each of the simulations PSpecVar - the variance power spectrum as a dictionary, for each of the simulations """ if run == 0: # if the power spectrum need to be recompiled. PSpecAver = {} PSpecVar = {} fileCounter = 0 # Average the power spectrum, from 5T to 10T for iter in xrange(50,101): #print("Currently on iteration {}".format(iter)) # Load the density files try: density = LoadPickles(files,iter) except IndexError: #print("Index error, I'm going to break the loop.") break plotCounter = 0 for i in xrange(0,5): for j in xrange(0,4): if fileCounter == 0: dens = density[densOrderd[plotCounter]] # column density PSpec, k = PowerSpectrum(dens) # the power spectrum and wavevector PSpecAver[densOrderd[plotCounter]] = PSpec # add a new power spectrum to the dictionary PSpecVar[densOrderd[plotCounter]] = PSpec**2 # for constructing the variance else: dens = density[densOrderd[plotCounter]] # column density PSpec, k = PowerSpectrum(dens) # the power spectrum and wavevector PSpecAver[densOrderd[plotCounter]] += PSpec # add the power spectrum together PSpecVar[densOrderd[plotCounter]] += PSpec**2 # for constructing the variance plotCounter +=1 #update the plot fileCounter +=1 #update the file # Average the power spectrum and take the log10 transform for key in PSpecAver.keys(): PSpecAver[key] = PSpecAver[key]/fileCounter PSpecVar[key] = (PSpecVar[key]/fileCounter - PSpecAver[key]**2)**0.5 save_obj(PSpecAver,"AveragePSpec") save_obj(PSpecVar,"StdPSpec") else: PSpecAver = load_obj("AveragePSpec.pkl") PSpecVar = load_obj("StdPSpec.pkl") return PSpecAver, PSpecVar def calculateIsoVar(PowerSpectrum,k,var2D): """ Assuming isotropy of the kz, this function calculates R = sigma^2_2 / sigma^2_3 INPUTS: ------------------------------------------------------------------------------------------ PowerSpectrum - the 2D power spectrum. k - the k wavevector as a 2D grid. var2D - the varianace of the 2D column density. OUTPUTS: ------------------------------------------------------------------------------------------ R - the ratio between the 2D and 3D variance var3D - the estimated 3D variance """ # Differentials for integration dkx = k[0,0]-k[0,1] dky = k[0,0]-k[1,0] dk = np.hypot(dkx,dky) # Calculate the integrals over the 2D and 3D power spectrum, assuming isotropy P2D = 2* np.pi* sum( sum( PowerSpectrum ) ) * dk P3D = 4* np.pi* sum( sum( PowerSpectrum * k ) ) * dk # Calculate R from Brunt et al. 2010, and the 3D variance. R = P2D / P3D var3D = var2D / R return R, var3D

[ "jamesbeattie@James-MacBook-Pro.local" ]

jamesbeattie@James-MacBook-Pro.local

8b06643905de8fc715a65a1df5347cc97d12961b

dfcddf4ed51bc48c4bd6288e3517fd8629000fbd

/app/http/responses/__init__.py

cb1798211886d0480004cb258bb6cdb3ead1bd94

[]

no_license

ugabiga/flask-boilerplate

508548d1f713c9f4412e43c68dd59d9a6210882d

5a317a80295aacf9bfc8c7c1a5736d2d5b22fc98

refs/heads/master

2022-08-30T16:28:58.332410

2022-08-23T12:03:34

208,466,604

1

0

null

2022-08-23T12:03:35

2019-09-14T16:12:18

Python

UTF-8

Python

false

1,376

py

from typing import Any, Tuple, Type import marshmallow as ma from flask import jsonify from flask.wrappers import Response from core.use_cases.output import Failure, Output def build_success_output_with_schema( output: Output, schema_class: Type[ma.Schema], many: bool = None ) -> Tuple[Response, int]: output_schema = schema_class().dump(output.get_data(), many=many) return build_success_response(output_schema, output.get_meta()) def build_success_response(data: Any, meta: dict = None) -> Tuple[Response, int]: response = {"data": data} if meta is not None: response["meta"] = meta return jsonify(response), 200 def build_failure_response(output: Failure) -> Tuple[Response, int]: return jsonify(error=output.get_type(), error_message=output.get_message()), 400 def build_response( output: Output, schema_class: Type[ma.Schema] = None, many: bool = None ) -> Tuple[Response, int]: if output.is_success() and schema_class is not None: return build_success_output_with_schema(output, schema_class, many) if output.is_success(): return build_success_response(output.get_data(), output.get_meta()) if isinstance(output, Failure): return build_failure_response(output) return build_failure_response( Failure.build_empty_internal_response_error("in_response_builder") )

[ "ugabiga@gmail.com" ]

ugabiga@gmail.com

0d72f76083eab3990a6815596501ba6a7019de76

ebb081aea082ea8964c6de96d8ee4063e2660eba

/question_set.py

fbbaeb74231f4b8bbcc8727ace37848045609470

[]

no_license

tramlam-ng/QuestionAnsweringSystem

8298f79764917e09e9ae34510cbedaf3b87f0d94

ca28ef59fe8eaf7136bf9c71a2d88c2b63ffac74

refs/heads/master

2022-01-14T15:47:13.364148

2019-01-12T13:57:25

null

0

null

UTF-8

Python

false

521

py

#!/usr/bin/env python # coding: utf-8 import pandas as pd data1=pd.read_csv('WikiQA-train.tsv',delimiter='\t',encoding='utf-8') data2=pd.read_csv('WikiQA-test.tsv',delimiter='\t',encoding ='utf-8') data = data1.append(data2, ignore_index=True) #Extracting the unique questions along with their questionID def extract_questions(data): new_data=data.drop(['DocumentID','DocumentTitle','Label'],axis=1) d=new_data.drop_duplicates() return d d=extract_questions(data) d.to_csv('questions.csv',index=False)

[ "noreply@github.com" ]

tramlam-ng.noreply@github.com

8cbeb7315d0f6c9e820555d49e344399fd8269ca

992c31a3bda2467e9d90ec8989f15a4cd38bae2b

/drone.py

e51fef99bd7c3de22cda760187dc7caf67aed65a

[]

no_license

aleksandarnikov/dwm

a35a83f720e75e85d23039a091d280675d716797

3de3f0795955fd30056e4b71cd1b92ef33950ccd

refs/heads/main

2023-01-06T16:17:42.826991

2020-11-14T12:11:44

312,095,696

0

null

UTF-8

Python

false

950

py

import paho.mqtt.client as mqtt import time import random import sys name = sys.argv[1] client = mqtt.Client(name) client.connect("localhost") def on_publish(client, userdata, result): print("data published \n") pass client.on_publish = on_publish # ret = client.publish("dwm/node/abc1/uplink/location", '{"position":{"x":1.3936733,"y":1.174517,"z":-0.26708269,"quality":81},"superFrameNumber":136}') x = 5 y = 4 dx = 0.06 dy = 0.05 while True: ddx = x + dx ddy = y + dy if ddx >= 10 or ddx < 0: dx = -dx continue if ddy >= 10 or ddy < 0: dy = -dy continue x = ddx y = ddy ret = client.publish("dwm/node/" + name + "/uplink/location", '{"position":{"x":' + str(x) + ',"y":' + str(y) + ',"z":-0.26708269,"quality":81},"superFrameNumber":136}') print(x, y) time.sleep(0.01) # ret2 = client.publish("abc", "xyz") client.loop_start() #start the loop time.sleep(10)

[ "aleksandar.nikov@netcetera.com" ]

aleksandar.nikov@netcetera.com

31519fa2a14b4aedde98b2f3a8defd664bd00223

69ef0b99e5b2a1fde4780501e87725a618c7889f

/abc/python3/hello.py

346ba43f5dd4dcecc557910820aefe3bf7f003ce

[]

no_license

wsz-/real_hub

350f5133ec55fb0357a1c76e72ac6f93757352cb

f1b4d3140bc8c723076bba79fbaf8c0495592314

refs/heads/master

2021-01-10T21:14:13.724398

2012-11-02T04:42:25

null

0

null

UTF-8

Python

false

413

py

import sys argvs=sys.argv #print(len(argvs)) base="Hello" err='''用法: hello hello -p hello -p $str 大小写无关''' arg_len=len(argvs) if arg_len==1 : print(base,'word!') elif arg_len==2: if argvs[1].lower()=='-p': print(base,'word!') else: print(err); elif arg_len==3: if argvs[1].lower()=='-p': print(base,argvs[2]) else: print(err) else: print(err)

[ "cisir92@gmail.com" ]

cisir92@gmail.com

21dad83cf27d3b9f8a2e6cff7584c09f606351a6

aff5cc92f38213a45323d7dede291dd918e96519

/simulation/crystal_mode_code/plane_transistion_plot.py

7c7ad320310ff374257a8f598b9f15e2ec976c37

[]

no_license

nistpenning/calc

bd475b75a36ba93e74356a37529d0f9dac30a083

15d651bcc5c067032041b5ad9cf0be38169bb750

refs/heads/master

2021-01-18T22:59:31.619436

2015-11-03T23:44:05

32,483,830

3

1

null

2015-06-17T16:58:16

2015-03-18T20:54:43

Matlab

UTF-8

Python

false

4,645

py

__author__ = 'sbt' """ Makes a plot of the rotation frequency of the 2-1 plane transistion for a given configuration of the Ion trap. """ from mode_analysis_code import ModeAnalysis import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit if __name__ == "__main__": # Select the trapping and wall potentials which will be used # for all future runs trappotential = (0.0, -873, -1000) wallpotential = 0.1 precision_solving = True # Determines the number of ions to find the transition frequency for. nionlist = [19, 20, 26, 37, 50, 61, 75, 91, 110, 127, 130, 169, 190, 217, 231, 300, 331] currentfrequency = 93 transistionfrequencies = [] # Iterate through number of ions to test for stability for N in nionlist: if N > 100: # Set to false to decrease run time for the biggest crystals # and run with un-perturbed crystals # (potentially not global energy minimum) precision_solving = True # Instantiate a crystal and see if it is stable crystal = ModeAnalysis(N=N, Vtrap=trappotential, Ctrap=1.0, ionmass=None, B=4.4588, frot=currentfrequency, Vwall=wallpotential, wall_order=2, quiet=False, precision_solving=precision_solving) crystal.run() # Increase the frequency until stability is lost- most important for the first # crystal tested while crystal.is_plane_stable(): print("Crystal of", N, "is currently at", currentfrequency, "increasing to ", currentfrequency + 1) currentfrequency += 1 crystal = ModeAnalysis(N=N, Vtrap=trappotential, Ctrap=1.0, ionmass=None, B=4.4588, frot=currentfrequency, Vwall=wallpotential, wall_order=2, quiet=False, precision_solving=precision_solving) crystal.run() # When frequency is lost, reduce to find when it resumes while not crystal.is_plane_stable(): print("Found turning point: reducing frequency from", currentfrequency, "to ", currentfrequency - 1) currentfrequency -= 1 crystal = ModeAnalysis(N=N, Vtrap=trappotential, Ctrap=1.0, ionmass=None, B=4.4588, frot=currentfrequency, Vwall=wallpotential, wall_order=2, quiet=False, precision_solving=precision_solving) crystal.run() # Once stability has resumed the lowest frequency at which 1->2 transition occurs is stored print("Transistion frequency is", currentfrequency + 1, " for number of ions", crystal.Nion) transistionfrequencies.append(currentfrequency + 1) print("Transitions found:") print("nions:", nionlist) print("frequencies", transistionfrequencies) ######################################### transfreq=transistionfrequencies nions=nionlist shells=[1,2,3,4,5,6,7,8,9,10] shelln=[7,19,37,61,91,127,169,217,271,331] def func(x, a, b, c): return a * np.exp(-b * x) + c fig = plt.figure(figsize=(14, 12)) plt.rcParams['font.size'] = 16 ax = fig.add_subplot(1,1,1) for i in range(len(transfreq)): if nions[i] in shelln: plt.plot(transfreq[i],nions[i],"o",color='red') else: plt.plot(transfreq[i],nions[i],"o",color='blue') plt.title("1-2 Plane Transistion for $V_{Mid}=-.873, \ V_{Center}=-1.0 \ (kV) V_{Wall} =1 V$", y=1.02) plt.xlabel("Transistion Frequency (kHz)") plt.ylabel("Number of Ions") major_ticks = np.arange(min(transfreq),max(transfreq),2) minor_ticks = np.arange(min(transfreq),max(transfreq),.5) print(major_ticks) ax.set_xticks(major_ticks) ax.set_xticks(minor_ticks, minor=True) yticks=np.arange(0,400,25) yticksmin=np.arange(0,400,5) ax.set_yticks(yticks) ax.set_yticks(yticksmin, minor=True) fig = plt.grid(True) fig = plt.xlim([min(transfreq)*.99,max(transfreq)*1.01]) popt, pcov = curve_fit(func, transfreq, nions,p0=[127,.1,122]) print(popt) x=np.linspace(min(transfreq)*.99,max(transfreq)*1.01,200) plt.plot(x, func(x, *popt), 'r-', label="Fitted Curve",color="black") plt.legend(loc=1) for N in shelln: plt.plot([min(transfreq)*.99,max(transfreq)*1.01],[N,N],"--",color='black') for N in shells: plt.text(max(transfreq)*1.013,shelln[N-1],"%d" %N) plt.show()

[ "storrisi@u.rochester.edu" ]

storrisi@u.rochester.edu

d2e18daba5039bfa0fe53bdc30e97c234ded7ec8

bbfa9cdfd5f09c833ab9190cd4ad5a46e7a515e7

/effective-python/2020-05/item_61.py

863a8f8f00e61d939277ee2b82426ba026599225

[]

no_license

alexchonglian/readings

775204e013a2301f08fee96c5e8b116842faebcb

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refs/heads/master

2022-12-02T13:56:56.878477

2021-06-18T05:53:14

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import random random.seed(1234) import logging from pprint import pprint from sys import stdout as STDOUT # Write all output to a temporary directory import atexit import gc import io import os import tempfile TEST_DIR = tempfile.TemporaryDirectory() atexit.register(TEST_DIR.cleanup) # Make sure Windows processes exit cleanly OLD_CWD = os.getcwd() atexit.register(lambda: os.chdir(OLD_CWD)) os.chdir(TEST_DIR.name) def close_open_files(): everything = gc.get_objects() for obj in everything: if isinstance(obj, io.IOBase): obj.close() atexit.register(close_open_files) def example(i): print(f'\n==== Example {i} ====') example(1) class EOFError(Exception): pass class ConnectionBase: def __init__(self, connection): self.connection = connection self.file = connection.makefile('rb') def send(self, command): line = command + '\n' data = line.encode() self.connection.send(data) def receive(self): line = self.file.readline() if not line: raise EOFError('Connection closed') return line[:-1].decode() example(2) import random WARMER = 'Warmer' COLDER = 'Colder' UNSURE = 'Unsure' CORRECT = 'Correct' class UnknownCommandError(Exception): pass example(3) example(4) example(5) example(6) class Session(ConnectionBase): def __init__(self, *args): super().__init__(*args) self._clear_state(None, None) def _clear_state(self, lower, upper): self.lower = lower self.upper = upper self.secret = None self.guesses = [] def loop(self): while command := self.receive(): parts = command.split(' ') if parts[0] == 'PARAMS': self.set_params(parts) elif parts[0] == 'NUMBER': self.send_number() elif parts[0] == 'REPORT': self.receive_report(parts) else: raise UnknownCommandError(command) def set_params(self, parts): assert len(parts) == 3 lower = int(parts[1]) upper = int(parts[2]) self._clear_state(lower, upper) def next_guess(self): if self.secret is not None: return self.secret while True: guess = random.randint(self.lower, self.upper) if guess not in self.guesses: return guess def send_number(self): guess = self.next_guess() self.guesses.append(guess) self.send(format(guess)) def receive_report(self, parts): assert len(parts) == 2 decision = parts[1] last = self.guesses[-1] if decision == CORRECT: self.secret = last print(f'Server: {last} is {decision}') example(7) example(8) example(9) example(10) import contextlib import math class Client(ConnectionBase): def __init__(self, *args): super().__init__(*args) self._clear_state() def _clear_state(self): self.secret = None self.last_distance = None @contextlib.contextmanager def session(self, lower, upper, secret): print(f'Guess a number between {lower} and {upper}!' f' Shhhhh, it\'s {secret}.') self.secret = secret self.send(f'PARAMS {lower} {upper}') try: yield finally: self._clear_state() self.send('PARAMS 0 -1') def request_numbers(self, count): for _ in range(count): self.send('NUMBER') data = self.receive() yield int(data) if self.last_distance == 0: return def report_outcome(self, number): new_distance = math.fabs(number - self.secret) decision = UNSURE if new_distance == 0: decision = CORRECT elif self.last_distance is None: pass elif new_distance < self.last_distance: decision = WARMER elif new_distance > self.last_distance: decision = COLDER self.last_distance = new_distance self.send(f'REPORT {decision}') return decision example(11) import socket from threading import Thread def handle_connection(connection): with connection: session = Session(connection) try: session.loop() except EOFError: pass def run_server(address): with socket.socket() as listener: # Allow the port to be reused listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) listener.bind(address) listener.listen() while True: connection, _ = listener.accept() thread = Thread(target=handle_connection, args=(connection,), daemon=True) thread.start() example(12) def run_client(address): with socket.create_connection(address) as connection: client = Client(connection) with client.session(1, 5, 3): results = [(x, client.report_outcome(x)) for x in client.request_numbers(5)] with client.session(10, 15, 12): for number in client.request_numbers(5): outcome = client.report_outcome(number) results.append((number, outcome)) return results example(13) def main(): address = ('127.0.0.1', 1234) server_thread = Thread( target=run_server, args=(address,), daemon=True) server_thread.start() results = run_client(address) for number, outcome in results: print(f'Client: {number} is {outcome}') main() example(14) class AsyncConnectionBase: def __init__(self, reader, writer): # Changed self.reader = reader # Changed self.writer = writer # Changed async def send(self, command): line = command + '\n' data = line.encode() self.writer.write(data) # Changed await self.writer.drain() # Changed async def receive(self): line = await self.reader.readline() # Changed if not line: raise EOFError('Connection closed') return line[:-1].decode() example(15) example(16) example(17) example(18) example(19) class AsyncSession(AsyncConnectionBase): # Changed def __init__(self, *args): super().__init__(*args) self._clear_values(None, None) def _clear_values(self, lower, upper): self.lower = lower self.upper = upper self.secret = None self.guesses = [] async def loop(self): # Changed while command := await self.receive(): # Changed parts = command.split(' ') if parts[0] == 'PARAMS': self.set_params(parts) elif parts[0] == 'NUMBER': await self.send_number() # Changed elif parts[0] == 'REPORT': self.receive_report(parts) else: raise UnknownCommandError(command) def set_params(self, parts): assert len(parts) == 3 lower = int(parts[1]) upper = int(parts[2]) self._clear_values(lower, upper) def next_guess(self): if self.secret is not None: return self.secret while True: guess = random.randint(self.lower, self.upper) if guess not in self.guesses: return guess async def send_number(self): # Changed guess = self.next_guess() self.guesses.append(guess) await self.send(format(guess)) # Changed def receive_report(self, parts): assert len(parts) == 2 decision = parts[1] last = self.guesses[-1] if decision == CORRECT: self.secret = last print(f'Server: {last} is {decision}') example(20) example(21) example(22) example(23) class AsyncClient(AsyncConnectionBase): # Changed def __init__(self, *args): super().__init__(*args) self._clear_state() def _clear_state(self): self.secret = None self.last_distance = None @contextlib.asynccontextmanager # Changed async def session(self, lower, upper, secret): # Changed print(f'Guess a number between {lower} and {upper}!' f' Shhhhh, it\'s {secret}.') self.secret = secret await self.send(f'PARAMS {lower} {upper}') # Changed try: yield finally: self._clear_state() await self.send('PARAMS 0 -1') # Changed async def request_numbers(self, count): # Changed for _ in range(count): await self.send('NUMBER') # Changed data = await self.receive() # Changed yield int(data) if self.last_distance == 0: return async def report_outcome(self, number): # Changed new_distance = math.fabs(number - self.secret) decision = UNSURE if new_distance == 0: decision = CORRECT elif self.last_distance is None: pass elif new_distance < self.last_distance: decision = WARMER elif new_distance > self.last_distance: decision = COLDER self.last_distance = new_distance await self.send(f'REPORT {decision}') # Changed # Make it so the output printing is in # the same order as the threaded version. await asyncio.sleep(0.01) return decision example(24) import asyncio async def handle_async_connection(reader, writer): session = AsyncSession(reader, writer) try: await session.loop() except EOFError: pass async def run_async_server(address): server = await asyncio.start_server( handle_async_connection, *address) async with server: await server.serve_forever() example(25) async def run_async_client(address): # Wait for the server to listen before trying to connect await asyncio.sleep(0.1) streams = await asyncio.open_connection(*address) # New client = AsyncClient(*streams) # New async with client.session(1, 5, 3): results = [(x, await client.report_outcome(x)) async for x in client.request_numbers(5)] async with client.session(10, 15, 12): async for number in client.request_numbers(5): outcome = await client.report_outcome(number) results.append((number, outcome)) _, writer = streams # New writer.close() # New await writer.wait_closed() # New return results example(26) async def main_async(): address = ('127.0.0.1', 4321) server = run_async_server(address) asyncio.create_task(server) results = await run_async_client(address) for number, outcome in results: print(f'Client: {number} is {outcome}') logging.getLogger().setLevel(logging.ERROR) asyncio.run(main_async()) logging.getLogger().setLevel(logging.DEBUG)

[ "alexchonglian@gmail.com" ]

alexchonglian@gmail.com

969d035c63ace1f7b4c413e93f06400bb2d2bf34

119437adb7830659307c18b79a9cc3f6bfc6fe40

/transformers_learning/english_sequence_labeling/torch_model_train.py

234011630b2febd960451887847252ee4bdd95c0

[]

no_license

percent4/PyTorch_Learning

478bec35422cdc66bf41b4258e29fbcb6d24f60c

24184d49032c9c9a68142aff89dabe33adc17b52

refs/heads/master

2023-03-31T03:01:19.372830

2023-03-17T17:02:39

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2023-09-02T08:53:26

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# -*- coding: utf-8 -*- # @Time : 2021/1/31 15:01 # @Author : Jclian91 # @File : torch_model_train.py # @Place : Yangpu, Shanghai import json import torch import numpy as np from torch.utils.data import Dataset, DataLoader from transformers import BertForTokenClassification, BertTokenizer, BertConfig from util import event_type, train_file_path, test_file_path from util import MAX_LEN, BERT_MODEL_DIR, TRAIN_BATCH_SIZE, VALID_BATCH_SIZE, EPOCHS, LEARNING_RATE from load_data import read_data # tokenizer and label_2_id_dict with open("{}_label2id.json".format(event_type), "r", encoding="utf-8") as f: tag2idx = json.loads(f.read()) idx2tag = {v: k for k, v in tag2idx.items()} class CustomDataset(Dataset): def __init__(self, tokenizer, sentences, labels, max_len): self.len = len(sentences) self.sentences = sentences self.labels = labels self.tokenizer = tokenizer self.max_len = max_len def __getitem__(self, index): sentence = str(self.sentences[index]) inputs = self.tokenizer.encode_plus( sentence, None, add_special_tokens=True, max_length=self.max_len, truncation=True, padding="max_length", # pad_to_max_length=True, return_token_type_ids=True ) ids = inputs['input_ids'] mask = inputs['attention_mask'] label = self.labels[index] label.extend([0] * MAX_LEN) label = label[:MAX_LEN] return { 'ids': torch.tensor(ids, dtype=torch.long), 'mask': torch.tensor(mask, dtype=torch.long), 'tags': torch.tensor(label, dtype=torch.long) } def __len__(self): return self.len # Creating the customized model class BERTClass(torch.nn.Module): def __init__(self): super(BERTClass, self).__init__() config = BertConfig.from_pretrained("./bert-base-uncased", num_labels=len(list(tag2idx.keys()))) self.l1 = BertForTokenClassification.from_pretrained('./bert-base-uncased', config=config) # self.l2 = torch.nn.Dropout(0.3) # self.l3 = torch.nn.Linear(768, 200) def forward(self, ids, mask, labels): output_1 = self.l1(ids, mask, labels=labels) # output_2 = self.l2(output_1[0]) # output = self.l3(output_2) return output_1 def flat_accuracy(preds, labels): flat_preds = np.argmax(preds, axis=2).flatten() flat_labels = labels.flatten() return np.sum(flat_preds == flat_labels)/len(flat_labels) def valid(model, testing_loader): model.eval() eval_loss = 0; eval_accuracy = 0 nb_eval_steps, nb_eval_examples = 0, 0 with torch.no_grad(): for _, data in enumerate(testing_loader): ids = data['ids'].to(dev, dtype=torch.long) mask = data['mask'].to(dev, dtype=torch.long) targets = data['tags'].to(dev, dtype=torch.long) output = model(ids, mask, labels=targets) loss, logits = output[:2] logits = logits.detach().cpu().numpy() label_ids = targets.to('cpu').numpy() accuracy = flat_accuracy(logits, label_ids) eval_loss += loss.mean().item() eval_accuracy += accuracy nb_eval_examples += ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss/nb_eval_steps print("Validation loss: {}".format(eval_loss)) print("Validation Accuracy: {}".format(eval_accuracy/nb_eval_steps)) if __name__ == '__main__': # Preparing for CPU or GPU usage dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") tokenizer = BertTokenizer.from_pretrained('./{}'.format(BERT_MODEL_DIR)) # Creating the Dataset and DataLoader for the neural network train_sentences, train_labels = read_data(train_file_path) train_labels = [[tag2idx.get(l) for l in lab] for lab in train_labels] test_sentences, test_labels = read_data(test_file_path) test_labels = [[tag2idx.get(l) for l in lab] for lab in test_labels] print("TRAIN Dataset: {}".format(len(train_sentences))) print("TEST Dataset: {}".format(len(test_sentences))) training_set = CustomDataset(tokenizer, train_sentences, train_labels, MAX_LEN) testing_set = CustomDataset(tokenizer, test_sentences, test_labels, MAX_LEN) train_params = {'batch_size': TRAIN_BATCH_SIZE, 'shuffle': True, 'num_workers': 0} test_params = {'batch_size': VALID_BATCH_SIZE, 'shuffle': True, 'num_workers': 0} training_loader = DataLoader(training_set, **train_params) testing_loader = DataLoader(testing_set, **test_params) # train the model model = BERTClass() model.to(dev) optimizer = torch.optim.Adam(params=model.parameters(), lr=LEARNING_RATE) for epoch in range(EPOCHS): model.train() for _, data in enumerate(training_loader): ids = data['ids'].to(dev, dtype=torch.long) mask = data['mask'].to(dev, dtype=torch.long) targets = data['tags'].to(dev, dtype=torch.long) loss = model(ids, mask, labels=targets)[0] # optimizer.zero_grad() if _ % 50 == 0: print(f'Epoch: {epoch}, Batch: {_}, Loss: {loss.item()}') optimizer.zero_grad() loss.backward() optimizer.step() # model evaluate valid(model, testing_loader) torch.save(model.state_dict(), '{}_ner.pth'.format(event_type))

[ "1137061634@qq.com" ]

1137061634@qq.com

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e3d6acf088991d776ed17b61e464ef128b83e6da

/src/enums/type.py

ce89b3f44f25b45100f5afe9e4030269c107e187

[ "Apache-2.0" ]

permissive

antamb/google-personal-assistant

407c6a0e420d667810571bcb5b58a5a3130bde1b

a81d1e65cd5d42e963bd359482a0ba7e3879a1d5

refs/heads/master

2020-12-03T02:09:26.805036

2017-07-01T09:12:06

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py

from enum import Enum class Type(Enum): OTHER = 1 EVENT = 2 PERSON = 3 UNKNOWN = 4 LOCATION = 5 WORK_OF_ART = 6 ORGANIZATION = 7 CONSUMER_GOOD = 8 entities_type = { Type.EVENT: 'Event', Type.PERSON: 'Person', Type.UNKNOWN: 'Unknown', Type.OTHER: 'Other types', Type.LOCATION: 'Location', Type.WORK_OF_ART: ' Work of art', Type.ORGANIZATION: 'Organization', Type.CONSUMER_GOOD: 'Consumer goods', } def get_type_from_value(value): value_type = Type.UNKNOWN for t in Type: if entities_type[t] == value: value_type = t return value_type

[ "anta.aidara@gmail.com" ]

anta.aidara@gmail.com

4a4c5276c3bf38dc20522b4f06a995c51f55462c

79d471c012ec9220836cf529d6062803c6fadb03

/localizer.py

9e8eb0782ede0b517412d026a4c69f2c0423f56f

[]

no_license

Knevari/histogram-filter

4e4c6604258478f14c1d1bd0604faed3d7a56859

47930837ff816769dbc9d6cb7f9ccc19d75040d3

refs/heads/master

2020-12-09T21:37:18.329609

2020-01-17T14:22:27

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from helpers import normalize, blur def initialize_beliefs(grid): height = len(grid) width = len(grid[0]) area = height * width belief_per_cell = 1.0 / area beliefs = [] for i in range(height): row = [] for j in range(width) row.append(belief_per_cell) beliefs.append(row) return beliefs def sense(color, grid, beliefs, p_hit, p_miss): new_beliefs = [] height = len(grid) width = len(grid[0]) for i in range(height): row = [] for j in range(width) hit = (grid[i][j] == color) row.append(beliefs[i][j] * (hit * p_hit + (1-hit) * p_miss)) new_beliefs.append(row) return normalize(new_beliefs) def move(dy, dx, beliefs, blurring): height = len(beliefs) width = len(beliefs[0]) new_G = [[0.0 for i in range(width)] for j in range(height)] for i, row in enumerate(beliefs): for j, cell in enumerate(row): new_i = (i + dy) % height new_j = (j + dx) % width new_G[int(new_i)][int(new_j)] = cell return blur(new_G, blurring)

[ "mateus7319@gmail.com" ]

mateus7319@gmail.com

8ad21205f4c323d5f5949973e2286fd410352fdf

24b4dcd555dd3e644467aec13edd671afdd3f49c

/SU2/opt/UQ.py

fe95153422af7b38c4541bf5320368b4442f680a

[]

no_license

garcgutierrez/adj_sto_su2

2c8294b65dcef8faf4bf1f453a413bca429a6751

22ec37839ed0a08f5dbe1935d18205f085b28a70

refs/heads/master

2022-11-16T22:13:59.666403

2020-07-14T15:38:22

279,776,069

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from pylab import * import chaospy as cp class UQ(object): def __init__(self): self.alpha_dist = cp.Uniform(-0.5,0.5) self.Ma_dist = cp.Uniform(0.1,0.2) self.T = cp.Uniform(273, 274) self.distribution = cp.J(self.alpha_dist, self.Ma_dist) self.computeQuadrature() def computeQuadrature(self, nOrder=2, ruleN='C'): self.absissas, self.weights = cp.generate_quadrature( order = nOrder, dist=self.distribution, rule=ruleN) self.Machs = around(array(self.absissas)[1,:],2) self.AOAs = around(array(self.absissas)[0,:],3) self.Nquadrature = len(self.Machs) self.polynomial_expansion = cp.orth_ttr(nOrder, self.distribution) def computeProperties(self, numArray, debug=True): if(debug): print('shape: {}'.format(shape(numArray))) print('Nq:{}'.format(self.Nquadrature)) print('Variables:{}'.format(numArray)) self.poly_approx = cp.fit_quadrature( self.polynomial_expansion, self.absissas, self.weights, numArray) mean = cp.E(self.poly_approx, self.distribution) sigma = cp.Std(self.poly_approx, self.distribution) return mean, sigma

[ "garcgutierrez@gmail.com" ]

garcgutierrez@gmail.com

b5fd5e255e2b4a38a8967b95ec48bf042b24c2d1

939e8a8838ff66f72655a7c103bf79b31ccd6966

/MyApp/models.py

94b7a286c938b6311a14ab45019c9aed1b7cf375

[]

no_license

github653224/ApiTest

3647292471fe11d8a124e0bd41061a2de3add5ed

9c1fc9c05dce38a4e2618c43943f8f44090ab4f2

refs/heads/master

2023-02-03T16:54:21.599640

2020-12-18T10:17:01

null

0

null

UTF-8

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from django.db import models # Create your models here. class DB_tucao(models.Model): user = models.CharField(max_length=30,null=True) #吐槽人名字 text = models.CharField(max_length=1000,null=True) #吐槽内容 ctime = models.DateTimeField(auto_now=True) #创建时间 def __str__(self): return self.text+ str(self.ctime) class DB_home_href(models.Model): name = models.CharField(max_length=30,null=True) #超链接名字 href = models.CharField(max_length=2000,null=True) #超链接内容 def __str__(self): return self.name class DB_project(models.Model): name = models.CharField(max_length=100,null=True) #项目名字 remark = models.CharField(max_length=1000,null=True) #项目备注 user = models.CharField(max_length=15,null=True) #项目创建者名字 other_user = models.CharField(max_length=200,null=True) #项目其他创建者 def __str__(self): return self.name class DB_apis(models.Model): project_id = models.CharField(max_length=10,null=True) #项目id name = models.CharField(max_length=100,null=True) #接口名字 api_method = models.CharField(max_length=10,null=True) #请求方式 api_url = models.CharField(max_length=1000,null=True) #url api_header = models.CharField(max_length=1000,null=True) #请求头 api_login = models.CharField(max_length=10,null=True) #是否带登陆态 api_host = models.CharField(max_length=100,null=True) #域名 des = models.CharField(max_length=100,null=True) #描述 body_method = models.CharField(max_length=20,null=True) #请求体编码格式 api_body = models.CharField(max_length=1000,null=True) #请求体 result = models.TextField(null=True) #返回体因为长度巨大，所以用大文本方式存储 sign = models.CharField(max_length=10,null=True) #是否验签 file_key = models.CharField(max_length=50,null=True) #文件key file_name = models.CharField(max_length=50,null=True) #文件名 public_header = models.CharField(max_length=1000,null=True) #全局变量-请求头 last_body_method = models.CharField(max_length=20,null=True) #上次请求体编码格式 last_api_body = models.CharField(max_length=1000,null=True) #上次请求体 def __str__(self): return self.name class DB_apis_log(models.Model): user_id = models.CharField(max_length=10,null=True) #所属用户id api_method = models.CharField(max_length=10,null=True) #请求方式 api_url = models.CharField(max_length=1000,null=True) #url api_header = models.CharField(max_length=1000,null=True) #请求头 api_login = models.CharField(max_length=10,null=True) #是否带登陆态 api_host = models.CharField(max_length=100,null=True) #域名 body_method = models.CharField(max_length=20,null=True) #请求体编码格式 api_body = models.CharField(max_length=1000,null=True) #请求体 sign = models.CharField(max_length=10,null=True) #是否验签 file_key = models.CharField(max_length=50,null=True) #文件key file_name = models.CharField(max_length=50,null=True) #文件名 def __str__(self): return self.api_url class DB_cases(models.Model): project_id = models.CharField(max_length=10,null=True) #所属项目id name = models.CharField(max_length=50,null=True) #用例名字 def __str__(self): return self.name class DB_step(models.Model): Case_id = models.CharField(max_length=10,null=True) #所属大用例id name = models.CharField(max_length=50,null=True) #步骤名字 index = models.IntegerField(null=True) #执行步骤 api_method = models.CharField(max_length=10,null=True) # 请求方式 api_url = models.CharField(max_length=1000,null=True) #url api_host = models.CharField(max_length=100,null=True) #host api_header = models.CharField(max_length=1000,null=True) #请求头 api_body_method = models.CharField(max_length=10,null=True) #请求体编码类型 api_body = models.CharField(max_length=10,null=True) #请求体 get_path = models.CharField(max_length=500,null=True) #提取返回值-路径法 get_zz = models.CharField(max_length=500,null=True) #提取返回值-正则 assert_zz = models.CharField(max_length=500,null=True) #断言返回值-正则 assert_qz = models.CharField(max_length=500,null=True) #断言返回值-全文检索存在 assert_path = models.CharField(max_length=500,null=True) #断言返回值-路径法 mock_res = models.CharField(max_length=1000,null=True) #mock返回值 public_header = models.CharField(max_length=1000,null=True) #全局变量-请求头 def __str__(self): return self.name class DB_project_header(models.Model): project_id = models.CharField(max_length=10,null=True) #所属项目id name = models.CharField(max_length=20,null=True) #请求头变量名字 key = models.CharField(max_length=20,null=True) #请求头header的 key value = models.TextField(null=True) #请求头的value，因为有可能cookie较大，达到几千字符，所以采用大文本方式存储 def __str__(self): return self.name class DB_host(models.Model): host = models.CharField(max_length=100,null=True) #域名内容 des = models.CharField(max_length=100,null=True) #域名描述 def __str__(self): return self.host

[ "wangzijia@xiaozhu.com" ]

wangzijia@xiaozhu.com

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/ActualizaDDBB.py

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[]

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alexistdk/todo-list

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from datetime import date from ConectarDDBB import * class ActualizaDDBB(ConectarDDBB): @classmethod def crear_tarea(cls, titulo, descripcion, id_usuario): try: db = cls.conexion() cursor = db.cursor() fecha = date.today() cursor.execute(cls.insertar_tarea(), (fecha, titulo, descripcion, 0, id_usuario)) except Error: print("Error ", Error) finally: db.commit() @classmethod def existe_tarea(cls, titulo, id_usuario): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.busca_tarea, titulo, id_usuario) return True except Error: print("Error ", Error) @classmethod def actualizar_tarea(cls, id_tarea): try: db = cls.conexion() cursor = db.cursor() descripcion = input("Descripción nueva: ") cursor.execute(cls.actualizar_descripcion(), (descripcion, id_tarea)) except Error: print("No existe la tarea!", Error) finally: db.commit() @classmethod def cambiar_estado(cls, id_tarea): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.actualizar_estado(), (id_tarea, )) except Error: print("Error ", Error) finally: db.commit() @classmethod def listar_tareas(cls, id_usuario): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.seleccionar_tareas(), (id_usuario, )) records = cursor.fetchall() print("\nLista de tareas\n ") for row in records: print("ID = ", row[0]) print("Fecha = ", row[1]) print("Título = ", row[2]) print("Descripción = ", row[3]) print("Estado = ", row[4], "\n") except Error: print("Error al leer la lista de tareas", Error) @classmethod def eliminar_tarea(cls, id_tarea): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.borrar_tarea(), (id_tarea,)) except Error: print("Error al eliminar la tarea", Error) finally: db.commit() @classmethod def registrar_usuario(cls, nombre_usuario, email, contrasenia): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.registrarusuario(), (nombre_usuario, email, contrasenia)) except Error: print("Error", Error) finally: db.commit() @classmethod def loguear_usuario(cls, nombre_usuario, contrasenia): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.existe_usuario(), (nombre_usuario, contrasenia)) return cursor.fetchone()[0] except Error: print("Error", Error) finally: db.commit() @classmethod def id_usuario(cls, nombre_usuario): try: db = cls.conexion() cursor = db.cursor() cursor.execute(cls.retorna_id_usuario(), (nombre_usuario, )) return cursor.fetchone()[0] except Error: print("Error", Error)

[ "alexisndelgado@gmail.com" ]

alexisndelgado@gmail.com

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/tests/trac/trac-0186/check.py

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# -*- coding: utf-8 -*- import logging if __name__ == '__main__': logging.basicConfig() _log = logging.getLogger(__name__) import pyxb.utils.domutils import resources import unittest class ExternalTrac0186 (unittest.TestCase): def testXBIngress (self): instance = resources.XBIngress(match='all', action1='none', digits1='', action2='none', digits2='') def testXBMatch (self): instance = resources.XBMatch('all') if '__main__' == __name__: unittest.main()

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pab@pabigot.com

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/experiments/ashvin/icml2020/hand/buffers/pen1.py

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Asap7772/railrl_evalsawyer

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refs/heads/main

2023-05-29T10:00:50.126508

2021-06-18T03:08:12

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""" AWR + SAC from demo experiment """ from rlkit.demos.source.dict_to_mdp_path_loader import DictToMDPPathLoader from rlkit.launchers.experiments.awac.awac_rl import experiment, process_args import rlkit.misc.hyperparameter as hyp from rlkit.launchers.arglauncher import run_variants from rlkit.torch.sac.policies import GaussianPolicy, BinnedGMMPolicy from rlkit.torch.networks import Clamp if __name__ == "__main__": variant = dict( num_epochs=1001, num_eval_steps_per_epoch=1000, num_trains_per_train_loop=1000, num_expl_steps_per_train_loop=1000, min_num_steps_before_training=1000, max_path_length=1000, batch_size=1024, replay_buffer_size=int(1E6), layer_size=256, policy_class=GaussianPolicy, policy_kwargs=dict( hidden_sizes=[256, 256, 256, 256], max_log_std=0, min_log_std=-6, std_architecture="values", ), buffer_policy_class=BinnedGMMPolicy, buffer_policy_kwargs=dict( hidden_sizes=[256, 256, 256, 256], max_log_std=0, min_log_std=-6, std_architecture="values", num_gaussians=11, ), algorithm="SAC", version="normal", collection_mode='batch', trainer_kwargs=dict( discount=0.99, soft_target_tau=5e-3, target_update_period=1, policy_lr=3E-4, qf_lr=3E-4, reward_scale=1, beta=1, use_automatic_entropy_tuning=False, alpha=0, compute_bc=False, bc_num_pretrain_steps=0, q_num_pretrain1_steps=0, q_num_pretrain2_steps=25000, policy_weight_decay=1e-4, q_weight_decay=0, bc_loss_type="mse", rl_weight=1.0, use_awr_update=True, use_reparam_update=False, reparam_weight=0.0, awr_weight=0.0, bc_weight=1.0, post_bc_pretrain_hyperparams=dict( bc_weight=0.0, compute_bc=False, ), reward_transform_kwargs=None, # r' = r + 1 terminal_transform_kwargs=None, # t = 0 ), launcher_config=dict( num_exps_per_instance=1, region='us-west-2', ), path_loader_class=DictToMDPPathLoader, path_loader_kwargs=dict( obs_key="state_observation", demo_paths=[ # dict( # path="demos/icml2020/hand/pen2_sparse.npy", # obs_dict=True, # is_demo=True, # ), # dict( # path="demos/icml2020/hand/pen_bc5.npy", # obs_dict=False, # is_demo=False, # train_split=0.9, # ), ], ), add_env_demos=True, add_env_offpolicy_data=True, # logger_variant=dict( # tensorboard=True, # ), load_demos=True, pretrain_policy=True, pretrain_rl=True, # save_pretrained_algorithm=True, # snapshot_mode="all", use_validation_buffer=True, ) search_space = { 'env': ["pen-sparse-v0", "door-sparse-v0", ], 'trainer_kwargs.bc_loss_type': ["mle"], 'trainer_kwargs.awr_loss_type': ["mle"], 'seedid': range(3), 'trainer_kwargs.beta': [0.5, ], 'trainer_kwargs.reparam_weight': [0.0, ], 'trainer_kwargs.awr_weight': [1.0], 'trainer_kwargs.bc_weight': [1.0, ], 'policy_kwargs.std_architecture': ["values", ], # 'trainer_kwargs.compute_bc': [True, ], 'trainer_kwargs.awr_use_mle_for_vf': [True, ], 'trainer_kwargs.awr_sample_actions': [False, ], 'trainer_kwargs.awr_min_q': [True, ], 'trainer_kwargs.q_weight_decay': [0], 'trainer_kwargs.reward_transform_kwargs': [None, ], 'trainer_kwargs.terminal_transform_kwargs': [dict(m=0, b=0), ], 'qf_kwargs.output_activation': [Clamp(max=0)], 'trainer_kwargs.train_bc_on_rl_buffer':[True], # 'policy_kwargs.num_gaussians': [11, ], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) variants = [] for variant in sweeper.iterate_hyperparameters(): variants.append(variant) run_variants(experiment, variants, process_args)

[ "alexanderkhazatsky@gmail.com" ]

alexanderkhazatsky@gmail.com

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/08-tuples.py

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[]

no_license

hue113/complete-python

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2023-03-21T17:30:30.292050

2021-03-14T22:40:16

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# Tuple: like a immutable list

[ "huepham113@gmail.com" ]

huepham113@gmail.com

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/coinlist/migrations/0002_auto_20180502_1107.py

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no_license

kupreeva/TopCoin

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refs/heads/master

2020-03-14T23:22:51.481252

2018-05-02T15:40:09

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# -*- coding: utf-8 -*- # Generated by Django 1.11.12 on 2018-05-02 11:07 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('coinlist', '0001_initial'), ] operations = [ migrations.AlterField( model_name='coin', name='coins_daily', field=models.FloatField(), ), ]

[ "kristine.kupreeva@gmail.com" ]

kristine.kupreeva@gmail.com

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/xml_read2.py

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[]

no_license

muzklj/learn_code

c4a316fcdd4d8348fb7959b66194a60d9f89b010

7dde268175391c2d4a2911fd40074ada5e7016a4

refs/heads/main

2023-08-08T16:25:37.540950

2023-07-24T00:31:42

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py

''' Author: MuZonghan Date: 2021-07-23 15:20:07 LastEditTime: 2021-08-19 15:14:07 Descripition: 统计xml文件的类别数目 FilePath: /4pcodes/learncodes/xml_read2.py ''' import os import xml.dom.minidom xml_path = '/home/trunk/muzklj/5datasets/bigdata/img-txt2/sec-all1-xml/' files = os.listdir(xml_path) gt_dict = {} if __name__ == '__main__': for xm in files: xmlfile = xml_path + xm dom = xml.dom.minidom.parse(xmlfile) # 读取xml文档 root = dom.documentElement # 得到文档元素对象 filenamelist = root.getElementsByTagName("filename") filename = filenamelist[0].childNodes[0].data objectlist = root.getElementsByTagName("object") for objects in objectlist: namelist = objects.getElementsByTagName("name") objectname = namelist[0].childNodes[0].data if objectname == '-': print(filename) if objectname in gt_dict: gt_dict[objectname] += 1 else: gt_dict[objectname] = 1 dic = sorted(gt_dict.items(), key=lambda d: d[1], reverse=True) print(dic) # print(len(dic))

[ "“muzklj@163.com”" ]

“muzklj@163.com”

a6247ca012289c8bc806e6836e82eb8bd9df5793

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/Baysim.py

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[]

no_license

BlackDragonBayliss/question-bank-app

2f0c5e1fb87395c1e607064639637029a219c154

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refs/heads/master

2020-04-27T09:04:30.279021

2019-06-21T21:08:38

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py

from StateStoreComposite import StateStoreComposite def main(): instanceStateStoreComposite = StateStoreComposite() if __name__ == "__main__": main()

[ "wallacecarr4@gmail.com" ]

wallacecarr4@gmail.com

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/account/forms.py

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[]

no_license

karyshev63rus/docent63

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refs/heads/master

2023-07-10T23:44:23.209061

2021-08-14T18:44:50

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from django import forms from django.contrib.auth.models import User from .models import Profile class UserRegistrationForm(forms.ModelForm): password = forms.CharField(label='Пароль', widget=forms.PasswordInput) password2 = forms.CharField(label='Повторите пароль', widget=forms.PasswordInput) class Meta: model = User fields = ('username', 'first_name', 'email') labels = { 'username': 'Логин', 'first_name': 'Имя', 'email': 'Адрес эл. почты', } def clean_password2(self): cd = self.cleaned_data if cd['password'] != cd['password2']: raise forms.ValidationError("Пароли не совпадают") return cd['password2'] class UpdateUserForm(forms.ModelForm): class Meta: model = User fields = ('first_name', 'last_name', 'email') widgets = { 'first_name': forms.TextInput( attrs={'class': 'form-control'} ), 'last_name': forms.TextInput( attrs={'class': 'form-control'} ), 'email': forms.EmailInput( attrs={'class': 'form-control'} ), } class UpdateProfileForm(forms.ModelForm): class Meta: model = Profile fields = ('phone_number', 'address', 'postal_code', 'city', 'country') widgets = { 'phone_number': forms.TextInput( attrs={'class': 'form-control'} ), 'address': forms.TextInput( attrs={'class': 'form-control'} ), 'postal_code': forms.TextInput( attrs={'class': 'form-control'} ), 'city': forms.TextInput( attrs={'class': 'form-control'} ), 'country': forms.TextInput( attrs={'class': 'form-control'} ) }

[ "karyshev63rus@gmail.com" ]

karyshev63rus@gmail.com

a666a99db10c5f01012215a5c6ee570d7c03bffa

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/monitor/task.py

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[]

no_license

icellus/shell_scripts

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2021-08-27T16:43:12.397195

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/05/24 00:00 # @Desc : 定时任务，以需要的时间间隔执行某个命令 # @File : task.py # @Software: PyCharm import time, os from monitor import task def roll_back(cmd, inc = 60): while True: #执行方法，函数 task() time.sleep(inc) roll_back("echo %time%", 30)

[ "2283411628@qq.com" ]

2283411628@qq.com

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/arduino/libraries/ledtable/documentation/pixelorder_double_spiral.py

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[ "MIT" ]

permissive

pmerlin/ledtable

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refs/heads/master

2020-04-05T18:38:14.014614

2017-01-10T17:15:26

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0

MIT

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def s(x, y, w, h): if y == 0: return x return s(y - 1, w - 1 - x, h - 1, w) + w def s2(x, y, w, h): m = min(x, y, w-1-x, h-1-y) outer = sum((w - m_*2) * 2 + (h - m_*2) * 2 - 4 for m_ in range(m)) outer = m * 2 * (w + h - 2*m) _x = x - m; _y = y - m; _w = w - 2 * m; _h = h - 2 * m; if _y == 0: return outer + _x; elif _x == _w - 1: return outer + _w + _y - 1; elif _y == _h - 1: return outer + _w + _h + _w - 3 - _x; elif _x == 0: return outer + _w + _h + _w + _h - 4 - _y; else: return "!{}{}".format(x, y) w = 10 h = 14 for y in range(h): print(*(s(x, y, w, h) for x in range(w)), sep = "\t") print('--------------') for y in range(h): print(*(s2(x, y, w, h) for x in range(w)), sep = "\t")

[ "niccokunzmann@rambler.ru" ]

niccokunzmann@rambler.ru

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/src/anpocs44.py

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vmussa/anpocs-scraper

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2021-05-17T23:29:11

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2021-05-15T00:41:53

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"""Código para a aquisição dos dados dos Encontros Anuais da ANPOCS.""" from bs4 import BeautifulSoup import pandas as pd import re from tqdm import tqdm import sys from os import mkdir, sep from os.path import abspath, dirname, exists import requests from helium import ( start_chrome, click, get_driver, kill_browser, find_all, S ) EVENT_ID = 44 BASE_URLS = [ "https://www.anpocs2020.sinteseeventos.com.br/atividade/hub/gt", "https://www.anpocs2020.sinteseeventos.com.br/atividade/hub/simposioposgraduada" ] def get_page_source(url): """Obtém soup object para páginas não interativas.""" r = requests.get(url) soup = BeautifulSoup(r.content, 'html.parser') return soup def get_urls(base_urls): """Obtém todos os URLs das páginas a serem raspadas.""" urls = [] for base_url in base_urls: soup = get_page_source(base_url) urls_sources = soup.select("h5 > a") urls += [a['href'] for a in urls_sources] return urls def get_interactive_page_source(url): """Obtém código-fonte completo da página.""" # inicia o chrome para renderizar o código-fonte try: start_chrome(url, headless=True) except Exception: print( "Erro: você precisa instalar o Google Chrome e o ChromeDriver par" "a executar esse raspador." ) sys.exit(1) driver = get_driver() # clica em todos os botões "Veja mais!" para liberar os dados dos resumos print(f"Raspando a página \"{driver.title}\". Isso pode demorar alguns segundos...") buttons = find_all(S("//span[@onClick]")) for _ in tqdm(range(len(buttons))): click("Veja mais!") print('Fim da raspagem da página.') # obtém objeto soup a partir do código-fonte renderizado pelo helium soup = BeautifulSoup(driver.page_source, 'html.parser') # fecha o chrome kill_browser() return soup def get_page_data(soup): """Obtém dados dos trabalhos apresentados em uma sessão.""" # obtém dados textuais a partir dos seletores CSS de cada campo authors = [autor.text for autor in soup.select('i')] titles = [titulo.text for titulo in soup.select('li > b')] abstract_source = soup.find_all('div', id=re.compile('^resumoFull')) abstracts = [abstract.text.strip() for abstract in abstract_source] session = soup.select_one('h3.first').text.strip() # cria dict com os dados obtidos data = { 'autores': authors, 'titulo': titles, 'resumo': abstracts, 'sessao': session, 'id_evento': EVENT_ID } return data def export_all_pages_data(urls): """Obtém e exporta para CSV dados de trabalhos de todas as sessões.""" for url in urls: soup = get_interactive_page_source(url) data = get_page_data(soup) df = pd.DataFrame(data) output_path = f"{dirname(dirname(abspath(__file__)))}{sep}output{sep}" filename = "resumos_anpocs44.csv" if exists(output_path+filename): df.to_csv( output_path + filename, mode='a', index=False, header=False ) else: try: mkdir(output_path) df.to_csv(output_path + filename, index=False) except FileExistsError: df.to_csv(output_path + filename, index=False) def main(): print( "Carregando algumas informações. A raspagem do 44º Encontro Anual da " "ANPOCS iniciará em breve..." ) urls = get_urls(BASE_URLS) # checa se já há arquivos de raspagens antigas na pasta output output_path = f"{dirname(dirname(abspath(__file__)))}{sep}output{sep}" filename = "resumos_anpocs44.csv" if exists(output_path+filename): raise Exception( "Os dados raspados já estão na pasta output. " "Remova-os da pasta antes de rodar o raspador." ) export_all_pages_data(urls) print("O 44º Encontro foi raspado com sucesso.") if __name__ == "__main__": main()

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/sva.py

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2023-08-03T22:59:08.843170

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# -*- coding: utf-8 -*- """ @author: adamw """ import pandas as pd class sva: """ Initialise with a path to the document & a sheet name. """ def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name # initiate nested classes self.dlr_parameters = self.dlr_parameters(path, sheet_name) self.termination_rates = self.termination_rates(path, sheet_name) self.stress_margins = self.stress_margins(path, sheet_name) # ============================================================================= @classmethod def table_import(cls, path, sheet_name, columns, row_start, row_end, header_row, clear_first_n_rows = None, index_col=None, trim_column_names = None, trim_index_name = None): rows = row_end - row_start if header_row is not None: if isinstance(header_row, list)==False: header = header_row - 1 else: header = header_row else: header = None # [Will always be reference 0] table = pd.DataFrame(pd.read_excel(path, sheet_name = sheet_name, header = header, usecols = columns, nrows = rows, index_col = index_col) ) # SVA sometimes has a blank row between header and the start of the data if clear_first_n_rows is not None: table = table.iloc[clear_first_n_rows:] # The way read_excel works means that if the header has already been 'seen' # in previous columns, it will add a trailing '.[number]'. This removes it. if trim_column_names is not None: table.columns = table.columns.map(str) table.columns = table.columns.str.replace(r'\.\d+$', '') if trim_index_name is not None: table.index.name = table.index.name.split('.')[0] return table # ============================================================================= # 1 def claims_reporting_delay(self): """ """ claims_reporting_delay = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'B:J', row_start = 11, row_end = 305, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return claims_reporting_delay def claim_delay_factors(self): """ """ claim_delay_factors = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'L:T', row_start = 11, row_end = 305, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return claim_delay_factors # ============================================================================= # 2 def claims_expense_reserve(self): """ """ claims_expense_reserve = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'W:Z', row_start = 11, row_end = 18, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return claims_expense_reserve def operating_expense_perc_premium(self): """ """ operating_expense_perc_premium = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AB:AE', row_start = 11, row_end = 18, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return operating_expense_perc_premium def budgeted_trustee_expense(self): """ """ budgeted_trustee_expense = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AG:AI', row_start = 11, row_end = 23, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return budgeted_trustee_expense def projected_trustee_expense(self): """ """ projected_trustee_expense = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AK:AM', row_start = 11, row_end = 21, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return projected_trustee_expense # ============================================================================= # 3 def ip_continuance_rates(self): """ """ ip_continuance_rates = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AP:AT', row_start = 11, row_end = 52, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) # Manually renaming index here. ip_continuance_rates.index.rename('Month', inplace=True) return ip_continuance_rates class dlr_parameters: def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name def salary_replacement_ratio(self): """ """ salary_replacement_ratio = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 12, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return salary_replacement_ratio def continuing_retirement_benefit(self): """ """ continuing_retirement_benefit = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 13, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return continuing_retirement_benefit def assumed_avg_age_at_disability(self): """ """ assumed_avg_age_at_disability = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 14, header_row = 11, clear_first_n_rows = 2, index_col = 0, trim_column_names = True, trim_index_name = True) return assumed_avg_age_at_disability def assumed_default_salary(self): """ """ assumed_default_salary = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 15, header_row = 11, clear_first_n_rows = 3, index_col = 0, trim_column_names = True, trim_index_name = True) return assumed_default_salary def payment_ratio(self): """ """ payment_ratio = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 16, header_row = 11, clear_first_n_rows = 4, index_col = 0, trim_column_names = True, trim_index_name = True) return payment_ratio def reopened_claims_reserves_loading(self): """ """ reopened_claims_reserves_loading = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 17, header_row = 11, clear_first_n_rows = 5, index_col = 0, trim_column_names = True, trim_index_name = True) return reopened_claims_reserves_loading def claim_index_rate(self): """ """ claim_index_rate = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 18, header_row = 11, clear_first_n_rows = 6, index_col = 0, trim_column_names = True, trim_index_name = True) return claim_index_rate def benefit_indexation_month(self): """ """ benefit_indexation_month = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AV:AW', row_start = 11, row_end = 19, header_row = 11, clear_first_n_rows = 7, index_col = 0, trim_column_names = True, trim_index_name = True) return benefit_indexation_month def ip_ibnr_adjustment(self): """ """ ip_ibnr_adjustment = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'AY:AZ', row_start = 11, row_end = 15, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return ip_ibnr_adjustment # ============================================================================= # 4 def appeals_reserve_assumptions(self): """ """ appeals_reserve_assumptions = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BC:BE', row_start = 11, row_end = 15, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return appeals_reserve_assumptions def perc_of_appealed_claims_accepted(self): """ """ perc_of_appealed_claims_accepted= self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BC:BE', row_start = 11, row_end = 17, header_row = 11, clear_first_n_rows = 5, index_col = 0, trim_column_names = True, trim_index_name = True) perc_of_appealed_claims_accepted.rename(index={0:'GOV', 1:'NONGOV'}, inplace=True) return perc_of_appealed_claims_accepted # ============================================================================= # 5 def decline_rate(self): """ """ decline_rate = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BH:BK', row_start = 11, row_end = 12, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return decline_rate def decline_rate_delay(self): """ """ decline_rate_delay = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BH:BI', row_start = 14, row_end = 21, header_row = 14, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return decline_rate_delay def simultaneous_ip_tpd_decline(self): """ """ simultaneous_ip_tpd_decline = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BK:BM', row_start = 14, row_end = 22, header_row = 14, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return simultaneous_ip_tpd_decline # ============================================================================= # 6 def expected_loss_ratio_gov(self): """ """ expected_loss_ratio_gov = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BP:BS', row_start = 11, row_end = 84, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return expected_loss_ratio_gov def expected_loss_ratio_nongov(self): """ """ expected_loss_ratio_nongov = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'BU:BX', row_start = 11, row_end = 84, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return expected_loss_ratio_nongov # ============================================================================= # 7 def payment_delay_factors(self): """ """ payment_delay_factors = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CA:CG', row_start = 11, row_end = 35, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return payment_delay_factors # 7 def payment_delay_factors_discrete(self): """ """ payment_delay_factors_discrete = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CI:CO', row_start = 11, row_end = 35, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return payment_delay_factors_discrete # ============================================================================= # 8 def average_claim_size(self): """ """ average_claim_size = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CR:DA', row_start = 11, row_end = 12, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return average_claim_size def acs_ip_linked_tpd(self): """ """ acs_ip_linked_tpd = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CR:CV', row_start = 20, row_end = 32, header_row = 20, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return acs_ip_linked_tpd def acs_by_notification_delay_q(self): """ """ acs_by_notification_delay_q = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CW:CY', row_start = 20, row_end = 85, header_row = 20, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return acs_by_notification_delay_q def perc_si_at_ip_doe(self): """ """ perc_si_at_ip_doe = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CZ:DA', row_start = 19, row_end = 20, header_row = 19, clear_first_n_rows = None, index_col = 0, trim_column_names = None, trim_index_name = None) return perc_si_at_ip_doe def tpd_si_scales_by_age(self): """ """ tpd_si_scales_by_age = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'CZ:DA', row_start = 22, row_end = 76, header_row = 22, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = True) return tpd_si_scales_by_age # ============================================================================= # 9 class termination_rates: def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name def age_rates(self): """ """ age_rates = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'DD:DF', row_start = 11, row_end = 57, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = True) return age_rates def duration_of_claim_g_wp_oc(self): """ """ duration_of_claim_g_wp_oc = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'DH:EF', row_start = 10, row_end = 134, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) # Data adjustments here to correctly index table. # Note: Consider 'melting' multi-index tables for use in models. df = duration_of_claim_g_wp_oc.copy() # info = duration_of_claim_g_wp_oc[1].copy() index = df[0:4] index = index.fillna(method='ffill', axis=1) df = df[4:] df.columns = pd.MultiIndex.from_arrays(index.values) df.index.name = 'Duration of Claim (months)' # duration_of_claim_g_wp_oc = tuple([df, info]) duration_of_claim_g_wp_oc = df return duration_of_claim_g_wp_oc def smoker_status(self): """ """ smoker_status = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'EH:EI', row_start = 10, row_end = 12, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) smoker_status.rename(columns={smoker_status.columns[0]: "smoker_status" }, inplace = True) return smoker_status def benefit_type(self): """ """ benefit_type = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'EK:EL', row_start = 10, row_end = 12, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) benefit_type.rename(columns={benefit_type.columns[0]: "benefit_type" }, inplace = True) return benefit_type def policy_duration_factor(self): """ """ policy_duration_factor = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'EN:ER', row_start = 10, row_end = 23, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) # Data adjustments here to correctly index table. # Note: Consider 'melting' multi-index tables for use in models. df = policy_duration_factor.copy() # info = policy_duration_factor[1].copy() index = df[0:2] index = index.fillna(method='ffill', axis=1) df = df[2:] df.columns = pd.MultiIndex.from_arrays(index.values) df.index.name = 'Curtate Policy Year' # policy_duration_factor = tuple([df, info]) policy_duration_factor = df return policy_duration_factor # ============================================================================= # 10 class stress_margins: def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name self.random = self.random(path, sheet_name) self.future = self.future(path, sheet_name) class random: def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name def random_all(self): random_all = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 16, row_end = 26, header_row = 16, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return random_all def death(self): death = self.random_all().iloc[0,0] return death def death_ibnr(self): death_ibnr = self.random_all().iloc[1,0] return death_ibnr def death_rbna(self): death_rbna = self.random_all().iloc[2,0] return death_rbna def tpd(self): tpd = self.random_all().iloc[3,0] return tpd def tpd_ibnr(self): tpd_ibnr = self.random_all().iloc[4,0] return tpd_ibnr def tpd_rbna(self): tpd_rbna = self.random_all().iloc[5,0] return tpd_rbna def ip(self): ip = self.random_all().iloc[6,0] return ip def ip_dlr(self): ip_dlr = self.random_all().iloc[7,0] return ip_dlr def ip_ibnr(self): ip_ibnr = self.random_all().iloc[8,0] return ip_ibnr def ip_rbna(self): ip_rbna = self.random_all().iloc[9,0] return ip_rbna class future: def __init__(self, path, sheet_name): self.path = path self.sheet_name = sheet_name def future_all(self): future_all = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 27, row_end = 37, header_row = 27, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return future_all def death(self): death = self.future_all().iloc[0,0] return death def death_ibnr(self): death_ibnr = self.future_all().iloc[1,0] return death_ibnr def death_rbna(self): death_rbna = self.future_all().iloc[2,0] return death_rbna def tpd(self): tpd = self.future_all().iloc[3,0] return tpd def tpd_ibnr(self): tpd_ibnr = self.future_all().iloc[4,0] return tpd_ibnr def tpd_rbna(self): tpd_rbna = self.future_all().iloc[5,0] return tpd_rbna def ip(self): ip = self.future_all().iloc[6,0] return ip def ip_dlr(self): ip_dlr = self.future_all().iloc[7,0] return ip_dlr def ip_ibnr(self): ip_ibnr = self.future_all().iloc[8,0] return ip_ibnr def ip_rbna(self): ip_rbna = self.future_all().iloc[9,0] return ip_rbna def time_to_react_future(self): time_to_react_future = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 39, row_end = 40, header_row = 39, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return time_to_react_future def event_pandemic_death(self): event_pandemic_death = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 42, row_end = 46, header_row = 42, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return event_pandemic_death def event_pandemic_tpd(self): event_pandemic_tpd = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 42, row_end = 46, header_row = 42, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[1,0] return event_pandemic_tpd def event_pandemic_ip(self): event_pandemic_ip = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 42, row_end = 46, header_row = 42, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[2,0] return event_pandemic_ip def prop_disabled_after_wp(self): prop_disabled_after_wp = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 42, row_end = 46, header_row = 42, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[3,0] return prop_disabled_after_wp def lapse_stress(self): lapse_stress = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 48, row_end = 50, header_row = 48, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return lapse_stress def servicing_expense_stress(self): servicing_expense_stress = sva.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FL:FM', row_start = 48, row_end = 50, header_row = 48, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[1,0] return servicing_expense_stress # ============================================================================= # 11 def reinsurance(self): reinsurance = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FT:FY', row_start = 11, row_end = 14, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return reinsurance def catastrophe_pl(self): catastrophe_pl = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FT:FY', row_start = 21, row_end = 23, header_row = 21, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0, 4] return catastrophe_pl def catastrophe_capital(self): catastrophe_capital = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'FT:FY', row_start = 21, row_end = 23, header_row = 21, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[1, 4] return catastrophe_capital # ============================================================================= # 12 def par_loadings(self): par_loadings = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GB:GC', row_start = 10, row_end = 11, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return par_loadings def stamp_duty(self): stamp_duty = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GB:GC', row_start = 13, row_end = 15, header_row = 13, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return stamp_duty def investment_earnings_b0(self): investment_earnings_b0 = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GB:GC', row_start = 16, row_end = 17, header_row = 16, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return investment_earnings_b0 # ============================================================================= # 13 def contingency_margin_start(self): contingency_margin_start = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GF:GG', row_start = 10, row_end = 11, header_row = 10, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return contingency_margin_start def contingency_margin(self): contingency_margin = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GF:GH', row_start = 13, row_end = 14, header_row = 13, clear_first_n_rows = None, index_col = None, trim_column_names = True, trim_index_name = None) return contingency_margin # ============================================================================= # 14 def notification_delay(self): notification_delay = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GK:GM', row_start = 11, row_end = 12, header_row = 11, clear_first_n_rows = None, index_col = None, trim_column_names = True, trim_index_name = None) return notification_delay # ============================================================================= # 15 def cmm_impact_termination_rates_start(self): cmm_impact_termination_rates = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GP:GQ', row_start = 11, row_end = 13, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[0,0] return cmm_impact_termination_rates def cmm_impact_termination_rates_perc(self): cmm_impact_termination_rates_perc = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GP:GQ', row_start = 11, row_end = 13, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None).iloc[1,0] return cmm_impact_termination_rates_perc # ============================================================================= # 16 def covid19_impact_termination_rates(self): covid19_impact_termination_rates = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GS:GT', row_start = 11, row_end = 16, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return covid19_impact_termination_rates # ============================================================================= # 17 def covid19_adjustment_ip_dlr(self): covid19_adjustment_ip_dlr = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GV:GW', row_start = 11, row_end = 27, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return covid19_adjustment_ip_dlr # ============================================================================= # 18 def expected_lr_combined_capital(self): expected_lr_combined_capital = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'GY:HB', row_start = 11, row_end = 90, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = None) return expected_lr_combined_capital # ============================================================================= # 19 def gov_tpd_linked_to_ip(self): gov_tpd_linked_to_ip = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HD:HF', row_start = 11, row_end = 23, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return gov_tpd_linked_to_ip def tpd_linked_reporting_delay(self): tpd_linked_reporting_delay = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HH:HI', row_start = 11, row_end = 65, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return tpd_linked_reporting_delay def conversion_rates(self): conversion_rates = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HK:HM', row_start = 11, row_end = 26, header_row = 11, clear_first_n_rows = None, index_col = 0, trim_column_names = True, trim_index_name = None) return conversion_rates # ============================================================================= # 20 def claims_reporting_delay_tpd_ip(self): claims_reporting_delay_tpd_ip = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HO:HQ', row_start = 11, row_end = 305, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = None) return claims_reporting_delay_tpd_ip def claims_delay_factors_tpd_ip(self): claims_delay_factors_tpd_ip = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HS:HU', row_start = 11, row_end = 305, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = None) return claims_delay_factors_tpd_ip # ============================================================================= # 21 def missing_subcase_reserve(self): missing_subcase_reserve = self.table_import(path = self.path, sheet_name = self.sheet_name, columns = 'HW:HX', row_start = 11, row_end = 15, header_row = 11, clear_first_n_rows = 1, index_col = 0, trim_column_names = True, trim_index_name = None) return missing_subcase_reserve # =============================================================================

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Luis199/spotify

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import spotipy from spotipy.oauth2 import SpotifyClientCredentials birdy_uri = 'spotify:artist:2WX2uTcsvV5OnS0inACecP' spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials()) results = spotify.artist_albums(birdy_uri, album_type='album') albums = results['items'] while results['next']: results = spotify.next(results) albums.extend(results['items']) for album in albums: print(album['name']) # export SPOTIPY_CLIENT_ID='3750a2d0a4494d3385dbbda87871bab2' # export SPOTIPY_CLIENT_SECRET='81acd20ff18642b9b9c941d811dfa2de' # export SPOTIPY_REDIRECT_URI='your-app-redirect-url'

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/Phys/Urania/examples/KsPiZeroMM_angularPDF.py

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pseyfert-cern-gitlab-backup/Urania

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from Urania.Helicity import * from Urania.SympyBasic import * from os import * DiLeptonSpins = [0,1,2] ## DMS: I doube we'll need 2, probably we'll only ## have Pwave (J=1) from the photon, plus maybe some S-wave (J=0) ### transAmp=1 : Changes to transversity amplitude basis A = doKsPizeroMuMu(DiLeptonSpins ) ## This is now in Urania.Helicity ### massage a bit the expression to make it more suitable for fitting pdf_split = DecomposeAmplitudes(A,TransAmplitudes.values()) phys = 0 for key in pdf_split: phys += StrongPhases(key)*pdf_split[key] ### change the free variables to cosines x = USymbol("helcosthetaK","c\\theta_{K}",real = True) y = USymbol("helcosthetaL", "c\\theta_{l}", real = True) z = USymbol("helphi" , "\\phi", real = True) CThL = Cos(ThetaL) CThK = Cos(ThetaK) def changeFreeVars(function): ### Phi now as in DTT !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! function = function.subs( Sin(2*ThetaK), 2*Sin(ThetaK)*Cos(ThetaK)) function = function.subs( Sin(2*ThetaL), 2*Sin(ThetaL)*Cos(ThetaL)) function = function.subs( Cos(2*ThetaK), 2*Cos(ThetaK)**2 - 1) function = function.subs( Cos(2*ThetaL), 2*Cos(ThetaL)**2 - 1) function = function.subs( Sin(ThetaK), Sqrt(1-Cos(ThetaK)**2)) function = function.subs( Sin(ThetaL), Sqrt(1-Cos(ThetaL)**2)) function = function.subs([(CThK,x),(CThL,y), (Phi, -z)]) return function func = changeFreeVars(phys) ### Print out to a latex document from Urania.LatexFunctions import * flatex = file("Kspizeromm_PDF.tex","w") begintex(flatex) begin_multline(flatex) i = 0 for key in pdf_split.keys(): if i > 20: i = 0 multline_break(flatex) if pdf_split[key]: flatex.write(Ulatex(key) + "\t" + Ulatex(pdf_split[key]) + "\\\\" + "\n") i += 1 end_multline(flatex) flatex.write("\\end{document}\n") flatex.close() system("pdflatex " + "Kspizeromm_PDF") print "angular function saved in Kspizeromm_PDF.pdf" print "Now making RooFit class as well" ##BREAK ##### Generate and compile a fitting class corresponding to "A" ### Trial 1, w/o analytical integrals from Urania.RooInterfaces import * potential_list = [x,y,z]+TransAmpModuli.values() + TransAmpPhases.values() final_list = [] for thing in potential_list: if thing in func.atoms(): final_list.append(thing) op = RooClassGenerator(func, final_list ,"RooKspizeroMM") ### Define intermediate variables to be calculated once op.makePdf(integrable = 1) op.doIntegral(1,(y,-1,1))#,(y,-1,1),(z,-Pi,Pi)) ##op.doIntegral(2,(x,-1,1),(y,-1,1)) ##op.doIntegral(3,(x,-1,1),(z,-Pi,Pi)) ##op.doIntegral(4,(y,-1,1),(z,-Pi,Pi)) op.overwrite() op.invoke()

[ "liblhcb@cern.ch" ]

liblhcb@cern.ch

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cosmic119/CNN

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# -*- coding: utf-8 -*- """ niektemme/tensorflow-mnist-predict 를 참조하였음 """ import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #레파지토리에서 테스트 프로그램에 필요한 데이터 다운로드 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) sess = tf.InteractiveSession() """ 모델 생성에 필요한 데이터 정의 x : 인풋레이어에 사용할 변수 정의 y : 아웃풋레이어에 사용할 변수 정의 w : 784 X 10 개의 초기값 0을 갖는 메트릭스 생성 b : 10개짜리 배열 생성 y = x * w + b x (784) * w(784*10) = x*w(10) x*w(10) + b(10) = y(10) 위에처럼 메트릭스 연산이 수행되기 때문에 위와 같이 데이터 사이즈를 잡은 것이다. """ x = tf.placeholder(tf.float32, [None, 784]) y_= tf.placeholder(tf.float32, [None, 10]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) # 원하는 행렬 사이즈로 초기 값을 만들어서 리턴하는 메서드 def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) # 0.1 로 초기값 지정하여 원하는 사이즈로 리턴 def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) """ 필터에 대해서 설명하고자 하면 CNN 의 동작 원리를 설명해야만한다. [5, 5, 1, 32] 는 5X5 사이즈의 단위 필터를 사용해서 인풋데이터 (여기서는 28 X 28 사이즈 메트릭스)를 CNN연산을 하겠다는 것이다. Stride 가 [1,1] 이라고 하면 28X28크기 행렬을 5X5 사이즈의 메트릭스로가로세로 한칸씩 이동하면서 필터에 연산하겠다는 의미가 된다. 결과적으로 아웃풋은 24X24 사이즈가 된다. 왜냐하면 5X5 사이즈의 메트릭스로 이동할 수 있는 한계가 있기 때문이다. (메트릭스 끝부분 까지 이동할 수 없음) 이러한 경우 패딩 옵션을 사용하여 0으로 태두리를 채워넣어 메특릭스 사이즈를 동일하게 유지할 수도 있다 참조:http://deeplearning4j.org/convolutionalnets.html """ def conv2d(x, W): # tf.nn.conv2d(input, filter, strides, padding, use_cudnn # _on_gpu=None, data_format=None, name=None) # strides= [1 , stride, stride, 1] 차원축소 작업시 마스크 메트릭스를 이동하는 보복 # padding='SAME' 다음 레벨에서도 메특릭스가 줄어들지 않도록 패딩을 추가한다 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') """ 보통은 이렇게 생성한 메트릭스를 max pooling 을 사용하여 다시 한번 간소화한다. 위에서 필터링이 마스크에 대한 & 연산이었다면, max Pooling은 메트릭스에서 가장 큰 값 하나만 뽑아서 사용하는 방법이다. 아래와 같은 max pooling 정의 (mask [2,2] , stride[2,2] )를 4X4 메트릭스에 적용하면 2X2 메트릭스가 될 것이다 """ # x : [batch, height, width, channels] # 2x2 행열에 가장 큰 값을 찾아서 추출, 가로세로 2칸씩이동 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # [filter_height, filter_width, in_channels, out_channels] W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) """ Layer 1 아래의 3줄로써 인풋 레이어에 대한 정의는 완료된다. 28X28 행렬 하나를 넣고 28X28행렬(원래는 24X24가 되지만 Padding 적용) 32개를 만들고 다시 max pool (2,2)를 사용하여 14X14 메트릭스 32개를 리턴하는 레이어를 정의하였다 메트릭스 단위로 정리하면 인풋 1개, 아웃풋 32개 이다 트 """ #인풋 데이터터 메트릭스를 변형한다. 784 개의 행렬을 갖는 복수의 데이터를 #[-1, 28, 28,1] 로 의 형태로 변형한다. 테스트 데이터 수 만큼 (-1) , #[28x28] 행렬로 만드는데 각 픽셀데이터는 rgb 데이터가 아니고 하나의 값만 갖도 변환 x_image = tf.reshape(x, [-1,28,28,1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) """ Layer 2 1번 레이어에서 아웃풋을 32개를 전달하였음으로 2번 레이어의 인풋은 14X14 메트릭스 32개 그리고 아웃풋은 동일한 max pool 을 적용하여 8x8 메트릭스 64개를 출력한다. 정리하면 인풋 32개(14X14) 아웃풋 64개(7X7) 이 된다 """ W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) """ Layer 3 현재 최종 데이터의 수는 7 X 7 X 64 = 3136 개 이지만 1024 개 를 사용한다 1024는 임의의 선택 값이다 """ W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) """ Drop Out Dropout 은 데이터 간의 연과 관계가 큰 데이터들을 제거함으로써 과적합 문제를 해결하는 기법의 하나이다. """ # drop out 연산의 결과를 담을 변수 keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) """ Out put Layer 마지막으로 1024개의 노드에서 10개의 (0~9까지 숫자)에 대한 확률을 Soft Max 를 이용하여 도출할 수 있도록 한다 """ W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) # Define loss and optimizer cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) """ Train & Save Model """ saver = tf.train.Saver() sess.run(tf.initialize_all_variables()) #50개씩, 20000번 반복학습 for i in range(20000): batch = mnist.train.next_batch(50) # 10회 단위로 한번씩 모델 정합성 테스트 if i%100 == 0: train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) # batch[0] 28X28 이미지, batch[1] 숫자태그, keep_prob : Dropout 비율 train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) # 모델에 사용된 모든 변수 값을 저장한다 save_path = saver.save(sess, "model2.ckpt") print ("Model saved in file: ", save_path) #최종적으로 모델의 정합성을 체크한다 print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) sess.close()

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# Update the name of the bucket you want to use # to store the intermediate results of this getting # started: BUCKET = '<<YOUR_BUCKET>>' # You can leave these other parameters to these # default values: PREFIX_TRAINING = 'getting_started/training-data/' PREFIX_LABEL = 'getting_started/label-data/' PREFIX_INFERENCE = 'getting_started/inference-data' DATASET_NAME = 'getting-started-pump' MODEL_NAME = f'{DATASET_NAME}-model' INFERENCE_SCHEDULER_NAME = f'{DATASET_NAME}-scheduler'

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from django.db import models from django_markdown.models import MarkdownField from django.core.urlresolvers import reverse class EntryQuerySet(models.QuerySet): def published(self): return self.filter(publish=True) class Entry(models.Model): title = models.CharField(max_length=200) body = models.TextField() slug = models.SlugField(max_length=200, unique=True) publish = models.BooleanField(default=True) created = models.DateTimeField(auto_now_add=True) modified = models.DateTimeField(auto_now=True) objects = EntryQuerySet.as_manager() def __str__(self): return self.title class Meta: verbose_name = 'Blog Entry' verbose_name_plural = 'Blog Entries' ordering = ["-created"]

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davejonesbkk@gmail.com

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/2020-01/python/18188_다오의데이트.py

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01090841589/solved_problem

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import sys sys.stdin = open("다오의데이트.txt") DIR = [[-1, 0], [0, 1], [1, 0], [0, -1]] def go_dao(y, x, k, route): global result, rts if result: return if k >= A: return flag = 1 for i in range(4): if can[k][i]: Y = y+DIR[i][0] X = x+DIR[i][1] if 0 <= Y < H and 0 <= X < W: if MAP[Y][X] != '@': if MAP[Y][X] == 'Z': rts = route+arr[i] result = 1 return flag = 0 go_dao(Y, X, k+1, route+arr[i]) H, W = map(int, input().split()) MAP = [list(input()) for _ in range(H)] for h in range(H): for w in range(W): if MAP[h][w] == 'D': y = h x = w result = 0 rts = '' A = int(input()) arr = ['W', 'D', 'S', 'A'] can = [[0, 0, 0, 0] for _ in range(A)] for i in range(A): B, C = map(str, input().split()) can[i][arr.index(B)] = 1 can[i][arr.index(C)] = 1 go_dao(y, x, 0, '') if result: print("YES") print(rts) else: print("NO")

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chanchanhwan@naver.com

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/Day_11/AoC_2016_11_2.py

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jshales4/aoc2016

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refs/heads/master

2021-01-11T20:18:39.566972

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##AoC_2016_11.py import itertools import sys from copy import deepcopy from datetime import datetime def main(): print datetime.now().strftime('%Y-%m-%d %H:%M:%S') move_tracker = {} move_watch = True #Example case #floors = [['SG', 'SM', 'PG', 'PM'], ['TG','RG','RM','CG','CM'],['TM'],[]] floors = [['EG', 'EM', 'DG', 'DM', 'SG', 'SM', 'PG', 'PM'], ['TG','RG','RM','CG','CM'],['TM'],[]] #This runs in two hours without tree cleaning #floors = [['HM', 'LM'], ['HG'], ['LG'], []] #floors = [['HM', 'HG'], [], [], []] elevator = 0 ini_state = Game_State(floors, elevator, 0) move_tracker[hash(''.join(ini_state.current_setup[0])+ '_' + ''.join(ini_state.current_setup[1]) + '_' +''.join(ini_state.current_setup[2])+ '_' +''.join(ini_state.current_setup[3]) + ''.join(str(elevator)))] = 1 while (move_watch ==True): moves1 = len(move_tracker) move_tracker = climb_tree(ini_state, move_tracker) clean_tree(ini_state) if moves1==len(move_tracker): move_watch = False #make_moves(ini_state, move_tracker) print_levels(ini_state, 0) print datetime.now().strftime('%Y-%m-%d %H:%M:%S') class Game_State: def __init__(self, current_setup, elevator_pos, moves_made): self.current_setup = current_setup self.elevator_pos = elevator_pos self.moves_made = moves_made self.move_options = [] self.moves_remain = True self.solution_flag = False def add_move (self, new_game_state): self.move_options.append(new_game_state) def climb_tree(game_state, move_tracker): if game_state.solution_flag == True: return move_tracker elif len(game_state.move_options)>0 and game_state.moves_remain == True: for n in game_state.move_options: move_tracker = climb_tree(n, move_tracker) return move_tracker elif game_state.moves_remain == True: move_tracker = make_moves_eff(game_state, move_tracker) return move_tracker else: game_state.moves_remain = False return move_tracker def clean_tree(game_state): for n in game_state.move_options: if n.moves_remain == False: game_state.move_options.remove(n) for p in game_state.move_options: clean_tree(p) def iterate_levels(game_state, move_tracker): results = [] no_changes = True if len(game_state.moves_made)>0 and game_state.moves_remain == True: for n in game_state.move_options: results.append(iterate_levels(n, move_tracker)[0]) elif game_state.moves_remain == False: return True, move_tracker else: make_moves_eff(game_state, move_tracker) return False, move_tracker def print_levels(game_state, levels_traveled): if validate_solutions(game_state.current_setup) == True: print 'Solved', levels_traveled else: for n in game_state.move_options: print_levels(n, levels_traveled + 1) # def find_depths(game_state, move_tracker): # if game_state.moves_remain = False: # next # elif def make_moves_eff(game_state, move_tracker): move_set = decide_movers(game_state.current_setup, game_state.elevator_pos) move_track = move_tracker for n in range(len(move_set)): for p in [-1, 1]: new_move = attempt_move(deepcopy(game_state.current_setup), move_set[n], deepcopy(game_state.elevator_pos), int(game_state.elevator_pos) + p, deepcopy(game_state.moves_made), move_tracker) move_track = new_move[1] if new_move[0] != False: discovered_move = Game_State(new_move[0].current_setup, new_move[0].elevator_pos, new_move[0].moves_made) if validate_solutions(new_move[0].current_setup) == True: discovered_move.solution_flag=True #print 'Move added to log', discovered_move game_state.add_move(discovered_move) if len(game_state.move_options)==0: game_state.moves_remain = False return move_tracker else: return move_tracker def make_moves(game_state, move_tracker): move_set = decide_movers(game_state.current_setup, game_state.elevator_pos) move_track = move_tracker for n in range(len(move_set)): for p in [-1, 1]: #print 'Current Gamestate: ', game_state new_move = attempt_move(deepcopy(game_state.current_setup), move_set[n], deepcopy(game_state.elevator_pos), int(game_state.elevator_pos) + p, deepcopy(game_state.moves_made), move_tracker) move_track = new_move[1] if new_move[0] != False: discovered_move = Game_State(new_move[0].current_setup, new_move[0].elevator_pos, new_move[0].moves_made) #print 'Move added to log', discovered_move game_state.add_move(discovered_move) if validate_solutions(new_move[0].current_setup) == True: print new_move[0].moves_made if len(game_state.move_options)>0: print 'New Node.' for r in range(len(game_state.move_options)): print 'Options to move from here are', game_state.move_options make_moves(game_state.move_options[r], move_tracker) else: print game_state.move_options def attempt_move(gamestate_setup, moving_pieces, elevator_start, elevator_new, moves_made, move_tracker): if elevator_new > 3 or elevator_new < 0: return False, move_tracker elif validate_move(deepcopy(gamestate_setup[elevator_new]), deepcopy(gamestate_setup[elevator_start]), moving_pieces, elevator_new) == True: #print 'Setup before move being attempted:', gamestate_setup #print 'Here is what will be moved:', moving_pieces #print 'The elevator will be moved to floor ', elevator_new, 'from floor ', elevator_start #move_tracker.append(hash(frozenset()) new_node = Game_State(gamestate_setup, elevator_new, moves_made + 1) if len(''.join(moving_pieces)) > 2: new_node.current_setup[elevator_new].extend(moving_pieces) else: new_node.current_setup[elevator_new].append(moving_pieces) new_node.current_setup[elevator_new].sort() new_node.current_setup[elevator_start] = [x for x in new_node.current_setup[elevator_start] if x not in moving_pieces] new_node.current_setup[elevator_start].sort() #setup_new[elevator_new].append(elevator_new) if validate_solutions(new_node.current_setup) == True: #print 'Puzzle Solved! ', new_node.moves_made return new_node, move_tracker elif hash(''.join(new_node.current_setup[0])+ '_' + ''.join(new_node.current_setup[1]) + '_' +''.join(new_node.current_setup[2])+ '_' +''.join(new_node.current_setup[3]) + ''.join(str(elevator_new))) in move_tracker and move_tracker[hash(''.join(new_node.current_setup[0])+ '_' + ''.join(new_node.current_setup[1]) + '_' +''.join(new_node.current_setup[2])+ '_' +''.join(new_node.current_setup[3]) + ''.join(str(elevator_new)))]<=moves_made+1: #print "We've already tried this move." return False, move_tracker else: move_tracker[hash(''.join(new_node.current_setup[0])+ '_' + ''.join(new_node.current_setup[1]) + '_' +''.join(new_node.current_setup[2])+ '_' +''.join(new_node.current_setup[3]) + ''.join(str(elevator_new)))] = moves_made + 1 return new_node, move_tracker else: #print 'Move Invalid' return False, move_tracker def valid_floor(proposed_floor): microchip_only = True for n in range(len(proposed_floor)): if proposed_floor[n][1] == 'G': microchip_only = False for n in range(len(proposed_floor)): if proposed_floor[n][1] == 'M': if proposed_floor[n][0] + 'G' not in proposed_floor and microchip_only == False: return False return True def validate_move(proposed_floor, old_floor, elevator_passengers, elevator_pos): old_floor_moved = [x for x in old_floor if x not in elevator_passengers] if len(''.join(elevator_passengers)) > 2: if elevator_passengers[0][1] == 'G' and elevator_passengers[1][1] == 'M' and elevator_passengers[0][0] != elevator_passengers[1][0]: return False elif elevator_passengers[1][1] == 'G' and elevator_passengers[0][1] == 'M' and elevator_passengers[0][0] != elevator_passengers[1][0]: return False else: proposed_floor.extend(elevator_passengers) return valid_floor(proposed_floor) * valid_floor(old_floor_moved) else: proposed_floor.append(elevator_passengers) return valid_floor(proposed_floor) * valid_floor(old_floor_moved) def decide_movers(setup, elevator_pos): possible_movers = [] possible_movers = list(itertools.combinations(setup[elevator_pos], 2)) + setup[elevator_pos] return possible_movers def validate_solutions(setup): if len(setup[0]) + len(setup[1]) + len(setup[2]) == 0: return True else: return False if __name__=='__main__': main() ##Just general pseudo-code thoughts: We basically want to take each current setup and determine all possible moves from that setup. We then want to check that move against ##a hash table to make sure we haven't tried making it before, then we can make a new branch of the tree containing all possible moves from that point. Then we can return the amount of moves it took to get there to find the min.

[ "jshales46@gmail.com" ]

jshales46@gmail.com

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Zilu-Zhang/MD-simulation-data-analysis

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2023-05-30T16:16:09.314265

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import mdtraj as md import numpy as np import os import os.path import pandas as pd import openpyxl as pxl from statistics import mean from math import sqrt def dis(ref, tag): x = ref[0] - tag[0] y = ref[1] - tag[1] z = ref[2] - tag[2] return sqrt(x**2 + y**2 + z**2) n_frames = 1 for filename in os.listdir('./'): if filename.endswith('.pdb'): excipient_name = filename[17:-4] traj = md.load(filename) top = traj.topology ori = 0 total = np.empty(12 * n_frames) i = 0 # changable start = 0 position = np.zeros((13,3)) for j in range(12): res = top.residue(j) length = res.n_atoms x = mean(traj.xyz[i, start:start + length, 0]) y = mean(traj.xyz[i, start:start + length, 1]) z = mean(traj.xyz[i, start:start + length, 2]) position[j][:] = x, y, z start += length position[-1][:] = mean(position[:-1][0]), mean(position[:-1][1]), mean(position[:-1][2]) distance = np.zeros(12) for h in range(12): distance[h] = dis(position[-1], position[h]) total[ori:ori + 12] = distance ori += 12 r_range = np.array([0, 5]) bin_width = 0.05 n_bins = int((r_range[1] - r_range[0]) / bin_width) g_r, edges = np.histogram(total, range=r_range, bins=n_bins) g_r = g_r / (12 * n_frames) r = 0.5 * (edges[1:] + edges[:-1]) df = pd.DataFrame({'r': r, 'g_r': g_r}) if not os.path.isfile('rdf_drug_0.xlsx'): df.to_excel('rdf_drug_0.xlsx', '%s' % excipient_name, index = True) else: excel_book = pxl.load_workbook('rdf_drug_0.xlsx') with pd.ExcelWriter('rdf_drug_0.xlsx', engine = 'openpyxl') as writer: writer.book = excel_book writer.sheets = {worksheet.title: worksheet for worksheet in excel_book.worksheets} df.to_excel(writer, '%s' % excipient_name, index = True) writer.save()

[ "noreply@github.com" ]

Zilu-Zhang.noreply@github.com

ba96f4130d8855366f57a4652b61e6a6af74ad00

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/thesis/urls.py

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[]

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lthebe/cp_thesis

406fd5441f7e0944ebf9e0439c9ce3a16cd7df63

573f9c339e57f33895e9924b04f3792ceb50e9e1

refs/heads/master

2021-06-14T13:27:18.702167

2017-01-31T13:56:14

80,521,434

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"""thesis URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from django.conf import settings from django.conf.urls import include, url from django.conf.urls.static import static from support.views import SupportOrderView, SupportFinalCheckoutView urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'', include('social.apps.django_app.urls', namespace='social')), url(r'^community/', include('community.urls')), url(r'', include('people.urls', namespace='people')), url(r'^posts/', include("posts.urls", namespace="posts")), url(r'^(?P<pk>\d+)/support/', SupportOrderView.as_view(), name='sponser'), url(r'^finalize-support/', SupportFinalCheckoutView.as_view(), name="finalize-support"), ] if settings.DEBUG: urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "laxmi.thebe@gmail.com" ]

laxmi.thebe@gmail.com

f56222a598de1c4002c0712cef364ba7722e2078

6ddfd7082e9126a88ce9357250c96137af5228e5

/PIplot.py

cb18ffd6cc8c8676f6a12389cc7671e112066070

[]

no_license

ryantro/ICE-Rb-Cell-Absorption-Spectrum-Plot

861ef2f970273c1b546319daf345d87be5e094e4

aca2f7d2145e18b9a8c9bec6c88fdf18d0b320f5

refs/heads/master

2022-11-25T13:28:47.851825

2020-07-31T22:07:35

null

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# -*- coding: utf-8 -*- """ Created on Mon Mar 2 16:57:41 2020 @author: ryan.robinson """ import time import os from serial import serialwin32 as serial import numpy as np import sys, string,subprocess import nidaqmx class ICE: def __init__(self,BoxNum,SlotNum): self.BoxNum = int(BoxNum) self.SlotNum = int(SlotNum) return None IceTimeout = .1 #Communication Timeout (seconds) IceByteRead = 256 #Number of bytes to read on ser.read() IceDelay = .01 #Delay in seconds after sending Ice Command to ensure execution ###Functions### def setSlot(self,SlotNum): self.SlotNum = SlotNum print('Changed Slot To: '+str(SlotNum)) return None def wait(self,num): '''Forces program to wait num seconds. Note: Shortest Delay--> 1ms''' time.sleep(num) return None def IceSend(self, CommandInput): '''Function that sends a serial string command to ICE Box Input: ICE Box Number[int], ICE Slot Number[int], CommandInput[str] Output: None (unless print line uncommented)/Read buffer always emptied! Note 1: Enter a slot number outside range(1-8) and function sends command directly to master board (ex. '#PowerOff' Command) Note 2: COM Port is opened/closed each time funciton is run''' #Open Port w/ ICE COM Default Settings IceSer = serial.Serial(port='COM'+str(int(self.BoxNum)),baudrate=115200,timeout=self.IceTimeout,parity='N',stopbits=1,bytesize=8) self.wait(.001) #Define Command and Send (perform read after each command to maintain synchronicity) if int(self.SlotNum) in range(1,9): #If a Valid Slot Number is input, send command to slot num #Define Commands MasterCommand = str('#slave ' + str(int(self.SlotNum)) + '\r\n') SlaveCommand = str(str(CommandInput) + '\r\n') #Send Commands/Close Port IceSer.write(MasterCommand.encode()) self.wait(self.IceDelay) IceOutputSlave = IceSer.read(self.IceByteRead).decode() #Read Buffer self.wait(self.IceDelay) IceSer.write(SlaveCommand.encode()) self.wait(self.IceDelay) IceOutputReturn = IceSer.read(self.IceByteRead).decode() #Read Buffer self.wait(self.IceDelay) IceSer.close() #Close COM Port #Return Output return IceOutputReturn print( ' ') print( 'Master Board Return: ', IceOutputSlave) print( 'Slave Board Return: ', IceOutputReturn) 7 else: #Command sent only to Master Board (preceding '#', no slot num to specify) #Define Command MasterCommand = str('#' + str(CommandInput) + '\r\n') #Send Commands/Close Port IceSer.write(MasterCommand) self.wait(self.IceDelay) IceOutputReturn = IceSer.read(self.IceByteRead) #Read Buffer self.wait(self.IceDelay) IceSer.close() #Close COM Port #Return Output return IceOutputReturn print( ' ') print( 'Master Board Return: ', IceOutputReturn) # GET DATA FROM NI-DAQmx def nidaxgrab(): with nidaqmx.Task() as task: task.ai_channels.add_ai_voltage_chan("Dev1/ai0") data = task.read(number_of_samples_per_channel=1) power = ' '.join([str(elem) for elem in data]) return power def CurrentSet(IB,current): return IB.IceSend(1,1,'CurrSet '+str(current)) def makefolder(newpath): if not os.path.exists(newpath): os.makedirs(newpath) return newpath def loggingLoops(IB,iArray): ''' Creates a directory and logs laser current and laser power. The purpose of this is to find at which current mode-hops occur by seeing a sharp change in power ''' logDir = makefolder(os.getcwd()+'\\testlogging\\'+time.strftime("%Y-%m-%d_%H-%M-%S")) print('Log Dirrectory: %' %logDir) IB.IceSend('CurrLim 125') ### OPEN FILE ### PIData = open(logDir+'\\PIData.csv', 'a+') ### LOGGING LOOPS ### for i in iArray: setCurrent = IB.IceSend('CurrSet '+str(i)) time.sleep(1) #Maybe this needs to be greater line = str(setCurrent)+','+str(nidaxgrab()) print(line) PIData.write(line) ### CLOSE FILE ### PIData.close() return None def main(): BoxNum = input('Box Num: ') SlotNum = input('Slot Num of CS1 Board: ') IB = ICE(BoxNum,SlotNum) iArray = np.linspace(0,100,100) iArray = np.round(iArray,1) loggingLoops(IB,iArray) return None if(__name__=="__main__"): main()

[ "ryan.robinson@Vescent.local" ]

ryan.robinson@Vescent.local

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de788449bd7433bbfc7c0574a0d81fd9dd24649f

/geoportal/geoportailv3_geoportal/static-ngeo/ngeo/buildtools/test-eof-newline

6872c85514ca17a1a053dbfa72b1aa9ce77eb0ca

[ "MIT" ]

permissive

Geoportail-Luxembourg/geoportailv3

6ab27bed755ff4f933c2f9700e2d6086ae8f5b68

45722f46bd5e4650ed3b01b1920de3732f848186

refs/heads/master

2023-08-18T21:02:45.652482

2023-08-02T14:12:56

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25

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MIT

2023-08-25T13:39:08

2014-10-01T07:23:27

JavaScript

UTF-8

Python

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2,302

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright (c) 2011-2017, Camptocamp SA # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # The views and conclusions contained in the software and documentation are those # of the authors and should not be interpreted as representing official policies, # either expressed or implied, of the FreeBSD Project. import os import subprocess exit_code = 0 FNULL = open(os.devnull, 'w') for filename in subprocess.check_output(["git", "ls-files"]).decode("utf-8").split("\n"): if os.path.isfile(filename): if subprocess.call( "git check-attr -a '{}' | grep ' text: set'".format(filename), shell=True, stdout=FNULL) == 0: size = os.stat(filename).st_size if size != 0: with open(filename) as f: f.seek(size - 1) if ord(f.read()) != ord("\n"): print("No new line at end of '{}' file.".format(filename)) exit_code = 2 exit(exit_code)

[ "antoine@abt.im" ]

antoine@abt.im

d165a083b3e3a41120522e9b4cd22520c188909d

029fa717816e977e736100128168c1c66161541d

/aries_cloudagent/wallet/tests/test_key_pair.py

b81062d8563ac7d8651bf77dad80875a2f3da169

[ "LicenseRef-scancode-dco-1.1", "Apache-2.0" ]

permissive

estrehle/aries-cloudagent-python

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1460b2d32c933944b4677cf25a78c4ace07346c8

refs/heads/main

2023-09-04T10:31:36.141037

2021-11-10T12:16:16

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Apache-2.0

2021-11-04T10:41:01

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py

from asynctest import TestCase as AsyncTestCase import json from ...storage.error import StorageNotFoundError from ..util import bytes_to_b58 from ..key_type import KeyType from ...core.in_memory import InMemoryProfile from ...storage.in_memory import InMemoryStorage from ..key_pair import KeyPairStorageManager, KEY_PAIR_STORAGE_TYPE class TestKeyPairStorageManager(AsyncTestCase): test_public_key = b"somepublickeybytes" test_secret = b"verysecretkey" async def setUp(self): self.profile = InMemoryProfile.test_profile() self.store = InMemoryStorage(self.profile) self.key_pair_mgr = KeyPairStorageManager(self.store) async def test_create_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert record.tags == {"verkey": verkey, "key_type": KeyType.ED25519.key_type} assert value["verkey"] == verkey assert value["secret_key"] == bytes_to_b58(self.test_secret) assert value["metadata"] == {} assert value["key_type"] == KeyType.ED25519.key_type async def test_get_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) key_pair = await self.key_pair_mgr.get_key_pair(verkey) assert key_pair["verkey"] == verkey assert key_pair["secret_key"] == bytes_to_b58(self.test_secret) assert key_pair["metadata"] == {} assert key_pair["key_type"] == KeyType.ED25519.key_type async def test_get_key_pair_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.get_key_pair("not_existing_verkey") async def test_delete_key_pair(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record await self.key_pair_mgr.delete_key_pair(verkey) # should be deleted now with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.delete_key_pair(verkey) async def test_delete_key_pair_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.delete_key_pair("non_existing_verkey") async def test_update_key_pair_metadata(self): await self.key_pair_mgr.store_key_pair( public_key=self.test_public_key, secret_key=self.test_secret, key_type=KeyType.ED25519, metadata={"some": "data"}, ) verkey = bytes_to_b58(self.test_public_key) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert value["metadata"] == {"some": "data"} await self.key_pair_mgr.update_key_pair_metadata(verkey, {"some_other": "data"}) record = await self.store.find_record(KEY_PAIR_STORAGE_TYPE, {"verkey": verkey}) assert record value = json.loads(record.value) assert value["metadata"] == {"some_other": "data"} async def test_update_key_pair_metadata_x_not_found(self): with self.assertRaises(StorageNotFoundError): await self.key_pair_mgr.update_key_pair_metadata("non_existing_verkey", {})

[ "timo@animo.id" ]

timo@animo.id

3e35560a675840b2ed59a45d39e280ce612af5c6

4e5b20fdcca20f458322f0a8cd11bbdacb6fb3e5

/suning/api/union/UnionInfomationGetRequest.py

5a52d242f32e5e4c7c3d65d8e1872c3832f9291a

[]

no_license

shijingyu/sunningAPI

241f33b0660dc84635ce39688fed499f5c57a5da

4a3b2ef7f9bdc4707d1eaff185bc7eb636fe90d5

refs/heads/master

2020-04-24T22:15:11.584028

2019-02-24T06:41:20

172,305,179

0

null

UTF-8

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false

525

py

# -*- coding: utf-8 -*- ''' Created on 2016-1-27 @author: suning ''' from suning.api.abstract import AbstractApi class UnionInfomationGetRequest(AbstractApi): ''' ''' def __init__(self): AbstractApi.__init__(self) self.goodsCode = None self.setParamRule({ 'goodsCode':{'allow_empty':False} }) def getApiBizName(self): return 'getUnionInfomation' def getApiMethod(self): return 'suning.netalliance.unioninfomation.get'

[ "945090896@qq.com" ]

945090896@qq.com

2916961de45167313f15922e1456df4053e14745

afe57be84b5dde07967be0e23f677ed85ab8d4da

/posts/urls.py

a1c411fb775d8e4ef1bf807f6402c87b547e111e

[]

no_license

furgot100/CarHub

0c3fdab1529c589e04eabe94c615ce953d4501b1

417de07dce488b50971396d200c721e0869382a2

refs/heads/master

2022-04-28T10:27:47.879441

2020-04-22T18:24:54

247,791,350

0

null

UTF-8

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false

1,204

py

from django.urls import path from .views import PostCreateView, PostDetailView, PostListView, HomeView, PostDeleteView, ProductListView, ProductDetailView, ProductCreateView, EventListView, EventDetailView, EventCreateView from django.conf import settings from django.conf.urls.static import static app_name = 'posts' urlpatterns = [ path('', HomeView.as_view(), name="home"), path('blog/', PostListView.as_view(), name='post-list-page'), path('new/', PostCreateView.as_view(), name='post-new-page' ), path('blog/<str:slug>/', PostDetailView.as_view(), name='post-details-page'), # path('<slug>/delete', PostDeleteView.as_view(), name='post-delete-page') path('store/', ProductListView.as_view(), name="store-list"), path('store/<str:slug>/', ProductDetailView.as_view(), name='store-item'), path('store/new', ProductCreateView.as_view(), name='store-new'), path('event/', EventListView.as_view(), name="event-list"), path('event/<str:slug>/', EventDetailView.as_view(), name="event-detail"), path('event/new', EventCreateView.as_view(), name='event-new'), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "frtsang40@gmail.com" ]

frtsang40@gmail.com

e03e0083b6c860f813b2cae42fbca20c5014d738

6ba1da25bb624c8bf74f1899f64b450602f12ff4

/Example/PY/django/TestDemo/test_function.py

257991ced039e50cdddaa45a0e0b660d0048ea62

[ "Apache-2.0", "MIT", "LicenseRef-scancode-public-domain-disclaimer", "BSD-3-Clause", "LicenseRef-scancode-public-domain", "LicenseRef-scancode-free-unknown", "FSFAP", "LicenseRef-scancode-other-permissive", "LicenseRef-scancode-unknown-license-reference" ]

permissive

Zoey-dot/pinpoint-c-agent

1dd58ce89610a7aafcdda842145a764cebe3f783

c76f9e41d8f2a9fdd8b0c90d52bb30e08bbd634d

refs/heads/master

2021-04-04T06:58:19.145805

2020-08-05T02:14:02

263,580,530

1

0

Apache-2.0

2020-07-01T07:13:35

2020-05-13T09:11:23

null

UTF-8

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false

513

py

#!/usr/bin/env python # -*- coding: UTF-8 -*- from pinpoint.plugins.PinpointCommonPlugin import PinpointCommonPlugin @PinpointCommonPlugin("", __name__) def test_func1(arg1, arg2): return "this is test_func1: arg1=%s, arg2=%s"%(arg1, arg2) class TestUserFunc1(object): def __init__(self, name, score): self.name = name self.score = score @PinpointCommonPlugin("TestUserFunc1", __name__) def test_func2(self): return "%s\'s score is : %s"%(self.name, self.score)

[ "su.wei@navercorp.com" ]

su.wei@navercorp.com

a0f37bf8594ae4e002a3cbda9f0f4fb8efd4c144

038dc1f463fba1889264de89369791d7359b4f86

/requested_events/views.py

c8f249e07f7a2491ed5370300a6202ac0483e330

[]

no_license

paishrikrishna/BE-Project

079d979fd1a2b158dadc8f9d72d1153f8c17aa21

e0949c2523b8fc3d0f0edfd86eaf8717ff824a60

refs/heads/master

2023-04-13T11:53:31.312949

2021-05-04T13:19:34

313,569,032

0

1

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false

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py

from django.shortcuts import render from calander.form import events_form from calander.models import events from .form import req_events_form from .models import req_events from login_page.login_form import login_form from login_page.models import login_model from new_users.models import new_login_model # Create your views here. def req_events_index_page(request,user,auth): if request.method=="POST": if request.POST['action']=="Add Event": try: events_form().save() except: obj = events.objects.get(organizer='n/a') obj.organizer = request.POST['Organizer'] obj.content = request.POST['Agenda'] obj.date = request.POST['New_date'] obj.save() obj = req_events.objects.get(id=int(request.POST['row'])) obj.delete() elif request.POST['action']=="Delete Event": obj = req_events.objects.get(id=int(request.POST['row'])) obj.delete() elif request.POST['action']=="Add User": try: login_form().save() except: obj = login_model.objects.get(username='n/a') obj.username = request.POST['username'] obj.password = request.POST['password'] obj.auth = "member" obj.link = request.POST['link'] obj.email = request.POST['email'] obj.wing = request.POST['wing'] obj.floor = request.POST['floor'] obj.flat = request.POST['flat'] obj.save() obj = new_login_model.objects.get(email=(request.POST['email'])) obj.delete() elif request.POST['action']=="Delete User": obj = new_login_model.objects.get(email=(request.POST['email'])) obj.delete() obj = list(req_events.objects.all()) organizer , content , date ,ID= [],[],[],[] for i in obj: organizer.append(i.organizer) content.append(i.content) date.append(i.date) ID.append(i.id) obj = list(new_login_model.objects.all()) username,password ,user_ID,floor,wing,link,pswd,ID= [],[],[],[],[],[],[],[] for i in obj: username.append(i.username) password.append(i.email) user_ID.append(i.flat) ID.append(i.id) floor.append(i.floor) wing.append(i.wing) link.append(i.link) pswd.append(i.password) return render(request,"requested_events.html",{"ID":ID,"floor":floor,"pswd":pswd,"wing":wing,"link":link,"organizer":organizer,"event_dates":date,"content":content,"ID":ID,"user":user,"username":username,"password":password,"user_ID":user_ID,"auth":auth})

[ "2017.shrikrishna.pai@ves.ac.in" ]

2017.shrikrishna.pai@ves.ac.in

0ae55acd20bb59d6c3f499e32e0f526820a351d7

822d3cd484b54f0531fc205520c765a8321c0613

/pyFile/8.面向对象/2.类的属性/9.类方法和静态方法.py

a0ccbf84964d8f9059c7feb1ae5efeedb1a3e65a

[]

no_license

mghxy123/learnPython

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00740e87d55a4dffd78773deaff8689485df31e8

refs/heads/master

2021-07-21T14:31:02.421788

2020-06-27T11:28:01

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0

null

2020-06-07T05:14:05

2019-05-21T02:58:35

Python

UTF-8

Python

false

1,232

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # File : 9.类方法和静态方法.py # Author: HuXianyong # Mail: mghxy123@163.com # Date : 2019/5/16 0016 #类中普通函数的方法 # class Person: # def normal_method(): #可以吗？这样是可以的没有语法上面的问题，执行也没问题，只是大家都默认不这么写 # print('normal') # # # 如何调用? # Person.normal_method() #可以吗？这个是可以的，应为只是直接调用函数 # # Person().normal_method() #可以吗？这个不可以，应为这个是实例化，实例化之后类里面的方法需要接受一个类的实例化对象，然而这里并没有传入，self，因此会报错 # print(Person.__dict__) # # 静态方法 # class Person: # @staticmethod # def class_method(): # print('this is staticMethod') # Person.class_method() # Person().class_method() #静态方法 class Person: @classmethod def class_method(cls): #cls 是什么？ print('this is class method') print('class = {0.__name__}({0})'.format(cls)) cls.HEIGHT = 170 @staticmethod def static_method(): print('this is staticMethod') Person.class_method() print(Person.__dict__)

[ "mghxy123@163.com" ]

mghxy123@163.com

c603c10469c17a5fe10f107b6cfe4f567d52bce1

e294a32686c46c520186326be47a48861aaacdad

/final number while(終極密碼).py

219dcd95b9b4f7a4fc730498539f43d897995102

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goodgood9897/python-2020-8

6381894fb2e68f35fe3d583aec6761b32e22149c

375969d7c457340659b35d1a9fb41479e0b05c09

refs/heads/master

2022-11-30T06:35:24.609085

2020-08-07T09:03:37

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import random a = 1 b = 100 number = random.randint(1,100) while True: print('Now%d-%d'%(a,b)) answer = int(input('Please enter nummber:')) if answer<a or answer>b: print('Please enter again.') elif answer>number: b=answer elif answer<number: a=answer elif answer==number: print('correct~~~!') break

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/sdk/python/pulumi_azure_native/insights/v20160301/_inputs.py

5910733c44e6efa9bc7563418d54942acbf6f519

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permissive

MisinformedDNA/pulumi-azure-native

9cbd75306e9c8f92abc25be3f73c113cb93865e9

de974fd984f7e98649951dbe80b4fc0603d03356

refs/heads/master

2023-03-24T22:02:03.842935

2021-03-08T21:16:19

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from ._enums import * __all__ = [ 'LocationThresholdRuleConditionArgs', 'ManagementEventAggregationConditionArgs', 'ManagementEventRuleConditionArgs', 'RetentionPolicyArgs', 'RuleEmailActionArgs', 'RuleManagementEventClaimsDataSourceArgs', 'RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs', 'RuleWebhookActionArgs', 'ThresholdRuleConditionArgs', ] @pulumi.input_type class LocationThresholdRuleConditionArgs: def __init__(__self__, *, failed_location_count: pulumi.Input[int], odata_type: pulumi.Input[str], data_source: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]] = None, window_size: Optional[pulumi.Input[str]] = None): """ A rule condition based on a certain number of locations failing. :param pulumi.Input[int] failed_location_count: the number of locations that must fail to activate the alert. :param pulumi.Input[str] odata_type: specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.LocationThresholdRuleCondition'. :param pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']] data_source: the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. :param pulumi.Input[str] window_size: the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ pulumi.set(__self__, "failed_location_count", failed_location_count) pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.LocationThresholdRuleCondition') if data_source is not None: pulumi.set(__self__, "data_source", data_source) if window_size is not None: pulumi.set(__self__, "window_size", window_size) @property @pulumi.getter(name="failedLocationCount") def failed_location_count(self) -> pulumi.Input[int]: """ the number of locations that must fail to activate the alert. """ return pulumi.get(self, "failed_location_count") @failed_location_count.setter def failed_location_count(self, value: pulumi.Input[int]): pulumi.set(self, "failed_location_count", value) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.LocationThresholdRuleCondition'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter(name="dataSource") def data_source(self) -> Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]: """ the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. """ return pulumi.get(self, "data_source") @data_source.setter def data_source(self, value: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]): pulumi.set(self, "data_source", value) @property @pulumi.getter(name="windowSize") def window_size(self) -> Optional[pulumi.Input[str]]: """ the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ return pulumi.get(self, "window_size") @window_size.setter def window_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "window_size", value) @pulumi.input_type class ManagementEventAggregationConditionArgs: def __init__(__self__, *, operator: Optional[pulumi.Input['ConditionOperator']] = None, threshold: Optional[pulumi.Input[float]] = None, window_size: Optional[pulumi.Input[str]] = None): """ How the data that is collected should be combined over time. :param pulumi.Input['ConditionOperator'] operator: the condition operator. :param pulumi.Input[float] threshold: The threshold value that activates the alert. :param pulumi.Input[str] window_size: the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ if operator is not None: pulumi.set(__self__, "operator", operator) if threshold is not None: pulumi.set(__self__, "threshold", threshold) if window_size is not None: pulumi.set(__self__, "window_size", window_size) @property @pulumi.getter def operator(self) -> Optional[pulumi.Input['ConditionOperator']]: """ the condition operator. """ return pulumi.get(self, "operator") @operator.setter def operator(self, value: Optional[pulumi.Input['ConditionOperator']]): pulumi.set(self, "operator", value) @property @pulumi.getter def threshold(self) -> Optional[pulumi.Input[float]]: """ The threshold value that activates the alert. """ return pulumi.get(self, "threshold") @threshold.setter def threshold(self, value: Optional[pulumi.Input[float]]): pulumi.set(self, "threshold", value) @property @pulumi.getter(name="windowSize") def window_size(self) -> Optional[pulumi.Input[str]]: """ the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ return pulumi.get(self, "window_size") @window_size.setter def window_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "window_size", value) @pulumi.input_type class ManagementEventRuleConditionArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], aggregation: Optional[pulumi.Input['ManagementEventAggregationConditionArgs']] = None, data_source: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]] = None): """ A management event rule condition. :param pulumi.Input[str] odata_type: specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.ManagementEventRuleCondition'. :param pulumi.Input['ManagementEventAggregationConditionArgs'] aggregation: How the data that is collected should be combined over time and when the alert is activated. Note that for management event alerts aggregation is optional – if it is not provided then any event will cause the alert to activate. :param pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']] data_source: the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.ManagementEventRuleCondition') if aggregation is not None: pulumi.set(__self__, "aggregation", aggregation) if data_source is not None: pulumi.set(__self__, "data_source", data_source) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.ManagementEventRuleCondition'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter def aggregation(self) -> Optional[pulumi.Input['ManagementEventAggregationConditionArgs']]: """ How the data that is collected should be combined over time and when the alert is activated. Note that for management event alerts aggregation is optional – if it is not provided then any event will cause the alert to activate. """ return pulumi.get(self, "aggregation") @aggregation.setter def aggregation(self, value: Optional[pulumi.Input['ManagementEventAggregationConditionArgs']]): pulumi.set(self, "aggregation", value) @property @pulumi.getter(name="dataSource") def data_source(self) -> Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]: """ the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. """ return pulumi.get(self, "data_source") @data_source.setter def data_source(self, value: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]): pulumi.set(self, "data_source", value) @pulumi.input_type class RetentionPolicyArgs: def __init__(__self__, *, days: pulumi.Input[int], enabled: pulumi.Input[bool]): """ Specifies the retention policy for the log. :param pulumi.Input[int] days: the number of days for the retention in days. A value of 0 will retain the events indefinitely. :param pulumi.Input[bool] enabled: a value indicating whether the retention policy is enabled. """ pulumi.set(__self__, "days", days) pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter def days(self) -> pulumi.Input[int]: """ the number of days for the retention in days. A value of 0 will retain the events indefinitely. """ return pulumi.get(self, "days") @days.setter def days(self, value: pulumi.Input[int]): pulumi.set(self, "days", value) @property @pulumi.getter def enabled(self) -> pulumi.Input[bool]: """ a value indicating whether the retention policy is enabled. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: pulumi.Input[bool]): pulumi.set(self, "enabled", value) @pulumi.input_type class RuleEmailActionArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], custom_emails: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, send_to_service_owners: Optional[pulumi.Input[bool]] = None): """ Specifies the action to send email when the rule condition is evaluated. The discriminator is always RuleEmailAction in this case. :param pulumi.Input[str] odata_type: specifies the type of the action. There are two types of actions: RuleEmailAction and RuleWebhookAction. Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleEmailAction'. :param pulumi.Input[Sequence[pulumi.Input[str]]] custom_emails: the list of administrator's custom email addresses to notify of the activation of the alert. :param pulumi.Input[bool] send_to_service_owners: Whether the administrators (service and co-administrators) of the service should be notified when the alert is activated. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.RuleEmailAction') if custom_emails is not None: pulumi.set(__self__, "custom_emails", custom_emails) if send_to_service_owners is not None: pulumi.set(__self__, "send_to_service_owners", send_to_service_owners) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of the action. There are two types of actions: RuleEmailAction and RuleWebhookAction. Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleEmailAction'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter(name="customEmails") def custom_emails(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ the list of administrator's custom email addresses to notify of the activation of the alert. """ return pulumi.get(self, "custom_emails") @custom_emails.setter def custom_emails(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "custom_emails", value) @property @pulumi.getter(name="sendToServiceOwners") def send_to_service_owners(self) -> Optional[pulumi.Input[bool]]: """ Whether the administrators (service and co-administrators) of the service should be notified when the alert is activated. """ return pulumi.get(self, "send_to_service_owners") @send_to_service_owners.setter def send_to_service_owners(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "send_to_service_owners", value) @pulumi.input_type class RuleManagementEventClaimsDataSourceArgs: def __init__(__self__, *, email_address: Optional[pulumi.Input[str]] = None): """ The claims for a rule management event data source. :param pulumi.Input[str] email_address: the email address. """ if email_address is not None: pulumi.set(__self__, "email_address", email_address) @property @pulumi.getter(name="emailAddress") def email_address(self) -> Optional[pulumi.Input[str]]: """ the email address. """ return pulumi.get(self, "email_address") @email_address.setter def email_address(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "email_address", value) @pulumi.input_type class RuleManagementEventDataSourceArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], claims: Optional[pulumi.Input['RuleManagementEventClaimsDataSourceArgs']] = None, event_name: Optional[pulumi.Input[str]] = None, event_source: Optional[pulumi.Input[str]] = None, legacy_resource_id: Optional[pulumi.Input[str]] = None, level: Optional[pulumi.Input[str]] = None, metric_namespace: Optional[pulumi.Input[str]] = None, operation_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, resource_location: Optional[pulumi.Input[str]] = None, resource_provider_name: Optional[pulumi.Input[str]] = None, resource_uri: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[str]] = None, sub_status: Optional[pulumi.Input[str]] = None): """ A rule management event data source. The discriminator fields is always RuleManagementEventDataSource in this case. :param pulumi.Input[str] odata_type: specifies the type of data source. There are two types of rule data sources: RuleMetricDataSource and RuleManagementEventDataSource Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleManagementEventDataSource'. :param pulumi.Input['RuleManagementEventClaimsDataSourceArgs'] claims: the claims. :param pulumi.Input[str] event_name: the event name. :param pulumi.Input[str] event_source: the event source. :param pulumi.Input[str] legacy_resource_id: the legacy resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. :param pulumi.Input[str] level: the level. :param pulumi.Input[str] metric_namespace: the namespace of the metric. :param pulumi.Input[str] operation_name: The name of the operation that should be checked for. If no name is provided, any operation will match. :param pulumi.Input[str] resource_group_name: the resource group name. :param pulumi.Input[str] resource_location: the location of the resource. :param pulumi.Input[str] resource_provider_name: the resource provider name. :param pulumi.Input[str] resource_uri: the resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. :param pulumi.Input[str] status: The status of the operation that should be checked for. If no status is provided, any status will match. :param pulumi.Input[str] sub_status: the substatus. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.RuleManagementEventDataSource') if claims is not None: pulumi.set(__self__, "claims", claims) if event_name is not None: pulumi.set(__self__, "event_name", event_name) if event_source is not None: pulumi.set(__self__, "event_source", event_source) if legacy_resource_id is not None: pulumi.set(__self__, "legacy_resource_id", legacy_resource_id) if level is not None: pulumi.set(__self__, "level", level) if metric_namespace is not None: pulumi.set(__self__, "metric_namespace", metric_namespace) if operation_name is not None: pulumi.set(__self__, "operation_name", operation_name) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if resource_location is not None: pulumi.set(__self__, "resource_location", resource_location) if resource_provider_name is not None: pulumi.set(__self__, "resource_provider_name", resource_provider_name) if resource_uri is not None: pulumi.set(__self__, "resource_uri", resource_uri) if status is not None: pulumi.set(__self__, "status", status) if sub_status is not None: pulumi.set(__self__, "sub_status", sub_status) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of data source. There are two types of rule data sources: RuleMetricDataSource and RuleManagementEventDataSource Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleManagementEventDataSource'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter def claims(self) -> Optional[pulumi.Input['RuleManagementEventClaimsDataSourceArgs']]: """ the claims. """ return pulumi.get(self, "claims") @claims.setter def claims(self, value: Optional[pulumi.Input['RuleManagementEventClaimsDataSourceArgs']]): pulumi.set(self, "claims", value) @property @pulumi.getter(name="eventName") def event_name(self) -> Optional[pulumi.Input[str]]: """ the event name. """ return pulumi.get(self, "event_name") @event_name.setter def event_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "event_name", value) @property @pulumi.getter(name="eventSource") def event_source(self) -> Optional[pulumi.Input[str]]: """ the event source. """ return pulumi.get(self, "event_source") @event_source.setter def event_source(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "event_source", value) @property @pulumi.getter(name="legacyResourceId") def legacy_resource_id(self) -> Optional[pulumi.Input[str]]: """ the legacy resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. """ return pulumi.get(self, "legacy_resource_id") @legacy_resource_id.setter def legacy_resource_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "legacy_resource_id", value) @property @pulumi.getter def level(self) -> Optional[pulumi.Input[str]]: """ the level. """ return pulumi.get(self, "level") @level.setter def level(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "level", value) @property @pulumi.getter(name="metricNamespace") def metric_namespace(self) -> Optional[pulumi.Input[str]]: """ the namespace of the metric. """ return pulumi.get(self, "metric_namespace") @metric_namespace.setter def metric_namespace(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "metric_namespace", value) @property @pulumi.getter(name="operationName") def operation_name(self) -> Optional[pulumi.Input[str]]: """ The name of the operation that should be checked for. If no name is provided, any operation will match. """ return pulumi.get(self, "operation_name") @operation_name.setter def operation_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "operation_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[pulumi.Input[str]]: """ the resource group name. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[pulumi.Input[str]]: """ the location of the resource. """ return pulumi.get(self, "resource_location") @resource_location.setter def resource_location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_location", value) @property @pulumi.getter(name="resourceProviderName") def resource_provider_name(self) -> Optional[pulumi.Input[str]]: """ the resource provider name. """ return pulumi.get(self, "resource_provider_name") @resource_provider_name.setter def resource_provider_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_provider_name", value) @property @pulumi.getter(name="resourceUri") def resource_uri(self) -> Optional[pulumi.Input[str]]: """ the resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. """ return pulumi.get(self, "resource_uri") @resource_uri.setter def resource_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_uri", value) @property @pulumi.getter def status(self) -> Optional[pulumi.Input[str]]: """ The status of the operation that should be checked for. If no status is provided, any status will match. """ return pulumi.get(self, "status") @status.setter def status(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "status", value) @property @pulumi.getter(name="subStatus") def sub_status(self) -> Optional[pulumi.Input[str]]: """ the substatus. """ return pulumi.get(self, "sub_status") @sub_status.setter def sub_status(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "sub_status", value) @pulumi.input_type class RuleMetricDataSourceArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], legacy_resource_id: Optional[pulumi.Input[str]] = None, metric_name: Optional[pulumi.Input[str]] = None, metric_namespace: Optional[pulumi.Input[str]] = None, resource_location: Optional[pulumi.Input[str]] = None, resource_uri: Optional[pulumi.Input[str]] = None): """ A rule metric data source. The discriminator value is always RuleMetricDataSource in this case. :param pulumi.Input[str] odata_type: specifies the type of data source. There are two types of rule data sources: RuleMetricDataSource and RuleManagementEventDataSource Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleMetricDataSource'. :param pulumi.Input[str] legacy_resource_id: the legacy resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. :param pulumi.Input[str] metric_name: the name of the metric that defines what the rule monitors. :param pulumi.Input[str] metric_namespace: the namespace of the metric. :param pulumi.Input[str] resource_location: the location of the resource. :param pulumi.Input[str] resource_uri: the resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.RuleMetricDataSource') if legacy_resource_id is not None: pulumi.set(__self__, "legacy_resource_id", legacy_resource_id) if metric_name is not None: pulumi.set(__self__, "metric_name", metric_name) if metric_namespace is not None: pulumi.set(__self__, "metric_namespace", metric_namespace) if resource_location is not None: pulumi.set(__self__, "resource_location", resource_location) if resource_uri is not None: pulumi.set(__self__, "resource_uri", resource_uri) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of data source. There are two types of rule data sources: RuleMetricDataSource and RuleManagementEventDataSource Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleMetricDataSource'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter(name="legacyResourceId") def legacy_resource_id(self) -> Optional[pulumi.Input[str]]: """ the legacy resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. """ return pulumi.get(self, "legacy_resource_id") @legacy_resource_id.setter def legacy_resource_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "legacy_resource_id", value) @property @pulumi.getter(name="metricName") def metric_name(self) -> Optional[pulumi.Input[str]]: """ the name of the metric that defines what the rule monitors. """ return pulumi.get(self, "metric_name") @metric_name.setter def metric_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "metric_name", value) @property @pulumi.getter(name="metricNamespace") def metric_namespace(self) -> Optional[pulumi.Input[str]]: """ the namespace of the metric. """ return pulumi.get(self, "metric_namespace") @metric_namespace.setter def metric_namespace(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "metric_namespace", value) @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[pulumi.Input[str]]: """ the location of the resource. """ return pulumi.get(self, "resource_location") @resource_location.setter def resource_location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_location", value) @property @pulumi.getter(name="resourceUri") def resource_uri(self) -> Optional[pulumi.Input[str]]: """ the resource identifier of the resource the rule monitors. **NOTE**: this property cannot be updated for an existing rule. """ return pulumi.get(self, "resource_uri") @resource_uri.setter def resource_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_uri", value) @pulumi.input_type class RuleWebhookActionArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], properties: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, service_uri: Optional[pulumi.Input[str]] = None): """ Specifies the action to post to service when the rule condition is evaluated. The discriminator is always RuleWebhookAction in this case. :param pulumi.Input[str] odata_type: specifies the type of the action. There are two types of actions: RuleEmailAction and RuleWebhookAction. Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleWebhookAction'. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] properties: the dictionary of custom properties to include with the post operation. These data are appended to the webhook payload. :param pulumi.Input[str] service_uri: the service uri to Post the notification when the alert activates or resolves. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.RuleWebhookAction') if properties is not None: pulumi.set(__self__, "properties", properties) if service_uri is not None: pulumi.set(__self__, "service_uri", service_uri) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of the action. There are two types of actions: RuleEmailAction and RuleWebhookAction. Expected value is 'Microsoft.Azure.Management.Insights.Models.RuleWebhookAction'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter def properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ the dictionary of custom properties to include with the post operation. These data are appended to the webhook payload. """ return pulumi.get(self, "properties") @properties.setter def properties(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "properties", value) @property @pulumi.getter(name="serviceUri") def service_uri(self) -> Optional[pulumi.Input[str]]: """ the service uri to Post the notification when the alert activates or resolves. """ return pulumi.get(self, "service_uri") @service_uri.setter def service_uri(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "service_uri", value) @pulumi.input_type class ThresholdRuleConditionArgs: def __init__(__self__, *, odata_type: pulumi.Input[str], operator: pulumi.Input['ConditionOperator'], threshold: pulumi.Input[float], data_source: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]] = None, time_aggregation: Optional[pulumi.Input['TimeAggregationOperator']] = None, window_size: Optional[pulumi.Input[str]] = None): """ A rule condition based on a metric crossing a threshold. :param pulumi.Input[str] odata_type: specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.ThresholdRuleCondition'. :param pulumi.Input['ConditionOperator'] operator: the operator used to compare the data and the threshold. :param pulumi.Input[float] threshold: the threshold value that activates the alert. :param pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']] data_source: the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. :param pulumi.Input['TimeAggregationOperator'] time_aggregation: the time aggregation operator. How the data that are collected should be combined over time. The default value is the PrimaryAggregationType of the Metric. :param pulumi.Input[str] window_size: the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ pulumi.set(__self__, "odata_type", 'Microsoft.Azure.Management.Insights.Models.ThresholdRuleCondition') pulumi.set(__self__, "operator", operator) pulumi.set(__self__, "threshold", threshold) if data_source is not None: pulumi.set(__self__, "data_source", data_source) if time_aggregation is not None: pulumi.set(__self__, "time_aggregation", time_aggregation) if window_size is not None: pulumi.set(__self__, "window_size", window_size) @property @pulumi.getter(name="odataType") def odata_type(self) -> pulumi.Input[str]: """ specifies the type of condition. This can be one of three types: ManagementEventRuleCondition (occurrences of management events), LocationThresholdRuleCondition (based on the number of failures of a web test), and ThresholdRuleCondition (based on the threshold of a metric). Expected value is 'Microsoft.Azure.Management.Insights.Models.ThresholdRuleCondition'. """ return pulumi.get(self, "odata_type") @odata_type.setter def odata_type(self, value: pulumi.Input[str]): pulumi.set(self, "odata_type", value) @property @pulumi.getter def operator(self) -> pulumi.Input['ConditionOperator']: """ the operator used to compare the data and the threshold. """ return pulumi.get(self, "operator") @operator.setter def operator(self, value: pulumi.Input['ConditionOperator']): pulumi.set(self, "operator", value) @property @pulumi.getter def threshold(self) -> pulumi.Input[float]: """ the threshold value that activates the alert. """ return pulumi.get(self, "threshold") @threshold.setter def threshold(self, value: pulumi.Input[float]): pulumi.set(self, "threshold", value) @property @pulumi.getter(name="dataSource") def data_source(self) -> Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]: """ the resource from which the rule collects its data. For this type dataSource will always be of type RuleMetricDataSource. """ return pulumi.get(self, "data_source") @data_source.setter def data_source(self, value: Optional[pulumi.Input[Union['RuleManagementEventDataSourceArgs', 'RuleMetricDataSourceArgs']]]): pulumi.set(self, "data_source", value) @property @pulumi.getter(name="timeAggregation") def time_aggregation(self) -> Optional[pulumi.Input['TimeAggregationOperator']]: """ the time aggregation operator. How the data that are collected should be combined over time. The default value is the PrimaryAggregationType of the Metric. """ return pulumi.get(self, "time_aggregation") @time_aggregation.setter def time_aggregation(self, value: Optional[pulumi.Input['TimeAggregationOperator']]): pulumi.set(self, "time_aggregation", value) @property @pulumi.getter(name="windowSize") def window_size(self) -> Optional[pulumi.Input[str]]: """ the period of time (in ISO 8601 duration format) that is used to monitor alert activity based on the threshold. If specified then it must be between 5 minutes and 1 day. """ return pulumi.get(self, "window_size") @window_size.setter def window_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "window_size", value)

[ "noreply@github.com" ]

MisinformedDNA.noreply@github.com

213fc24bf448ac094d3843b30e9c24e1aaa77fcc

402b45344b310c76c37f354c30a82d1934667735

/crawl_push.py

2435ece7d58e2e06518fb2919706a8d32263b4d1

[]

no_license

yuktmitash21/Crawler

5cba755cba669a55d9e38d5e95166760cc0417cd

d9fb5710f5ca3dea9c3516e0ed7b963d4a07ff83

refs/heads/main

2023-03-13T23:25:45.524622

2021-02-07T13:24:53

336,657,238

0

null

2021-02-07T13:24:54

2021-02-06T23:20:31

Python

UTF-8

Python

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1,730

py

import pandas as pd import requests #Pushshift accesses Reddit via an url so this is needed import json #JSON manipulation import csv #To Convert final table into a csv file to save to your machine import time import datetime def getPushshiftData(query, after, before, limit): url = 'https://api.pushshift.io/reddit/search/submission/?&before=' + before + '&after=' + after + '&q=' + query + '&sort_type=score&sort=desc&subreddit=wallstreetbets&size=' + limit #Print URL to show user print(url) #Request URL r = requests.get(url) #Load JSON data from webpage into data variable data = json.loads(r.text) #return the data element which contains all the submissions data return data['data'] tickers = ["GME", "SPY", "AMC", "BB", "TSLA", "PLNTR", "CRSR", "NOK", "AAPL", "SNAP"] before = datetime.datetime(2021, 1, 5) later = before + datetime.timedelta(days=1) map = {} allData = [] for ticker in tickers: for i in range(0, 30): data = getPushshiftData(ticker, str(int(before.timestamp())), str(int(later.timestamp())), str(1000)) print (len(data), ticker) for dat in data: allData.append({ 'score': dat.get('score') or 0, 'num_comments': dat.get('num_comments') or 0, 'created': dat.get('created_utc') or 0, 'title': dat.get('title') or '', 'body': dat.get('selftext') or '', 'upvote_ratio': dat.get('upvote_ratio') or ''}) before = later later += datetime.timedelta(days=1) time.sleep(2) map[ticker] = allData allData = [] before = datetime.datetime(2021, 1, 5) later = before + datetime.timedelta(days=1) json_string = json.dumps(map) with open('data-gme.json', 'w') as f: json.dump(json_string, f)

[ "ymitash3@gatech.edu" ]

ymitash3@gatech.edu

987828b08e77fc4ed6a670121d87f280fc0aed0b

5e0a7d90b3fd5d16bbc52eb0c8a118b835c17bad

/test/maxicode.py

e8ab9114db9fee3362ec2c70d7acb04ecd94aac4

[ "LicenseRef-scancode-secret-labs-2011", "MIT", "BSD-2-Clause" ]

permissive

ehpale/elaphe

424abd206ce2af95d6e7de49758ca96cd6f797c8

0a0c51ee8627cccc57d557330ba6c2f2c5266960

refs/heads/master

2022-03-06T19:43:08.986519

2022-02-25T18:53:46

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symbology = 'maxicode' cases = [ ('001.png', 'This is MaxiCode'), ('002.png', 'This is Maxi^067ode', dict(parse=True)), ('003.png', ('152382802^029840^029001^0291Z00004951^029UPSN^02906X610' '^029159^0291234567^0291/1^029^029Y^029634 ALPHA DR^029P' 'ITTSBURGH^029PA^029^004'), dict(mode=2, parse=True)), ('004.png', ('ABC123^029840^029001^0291Z00004951^029UPSN^02906X610^029' '159^0291234567^0291/1^029^029Y^029634 ALPHA DR^029PITTSB' 'URGH^029PA^029^004'), dict(mode=3, parse=True)), ('005.png', ('[)>^03001^02996152382802^029840^029001^0291Z00004951^029' 'UPSN^02906X610^029159^0291234567^0291/1^029^029Y^029634 ' 'ALPHA DR^029PITTSBURGH^029PA^029^004'), dict(mode=2, parse=True)), ]

[ "whosaysni@gmail.com" ]

whosaysni@gmail.com

f29fc6830528398b792fd60578b01a78f12aa4e7

41ede4fd3bfba1bff0166bca7aee80dcf21434c6

/ayhanyalcinsoy/Desktop/lxde/base/libfm/actions.py

ad79cdbb6f0b2d887aa5244a18b52080cbb19379

[]

no_license

pisilinux/playground

a7db4b42559a21cc72fd4c8649e0231ab6a3eb3c

e4e12fff8a847ba210befc8db7e2af8556c3adf7

refs/heads/master

2022-08-12T23:03:27.609506

2022-08-11T18:28:19

8,429,459

16

22

null

2022-08-11T18:28:20

2013-02-26T09:37:11

Python

UTF-8

Python

false

811

py

#!/usr/bin/python # -*- coding: utf-8 -*- # # Licensed under the GNU General Public License, version 3. # See the file http://www.gnu.org/licenses/gpl.txt from pisi.actionsapi import autotools from pisi.actionsapi import pisitools from pisi.actionsapi import get WorkDir = "libfm-%s" % (get.srcVERSION()) def setup(): autotools.configure("--disable-static \ --sysconfdir=/etc \ --enable-debug \ --enable-udisks \ --enable-demo") pisitools.dosed("libtool", " -shared ", " -Wl,-O1,--as-needed -shared ") def build(): autotools.make() def install(): pisitools.dosed("data/libfm.conf", "xarchiver", "file-roller") autotools.install() pisitools.dodoc("AUTHORS", "COPYING", "TODO")

[ "ayhanyalcinsoy@gmail.com" ]

ayhanyalcinsoy@gmail.com

555920b473ecc5e50b86552eb52b4dc9e1a29a9c

522303c2fc1840bd3288b1be2ed1787b77ceff7d

/279.py

b09688cc59bec97c14657835b17bc17c9e976e62

[]

no_license

RickyLiTHU/codePractice

0b0fc66fc32a651c5288645c98d0a58acdd6f7a1

74988e6d02968acb5fe8da811df6c1e706f2b125

refs/heads/master

2020-03-23T04:36:42.256827

2018-08-30T08:15:39

141,093,505

0

null

UTF-8

Python

false

699

py

class Solution(object): def numSquares(self, n): """ :type n: int :rtype: int """ edges = [] for i in range(1, int(math.ceil(math.sqrt(n)))+1): edges.append(i*i) depth = 1 nodes = set([n]) while nodes: nextLevel = set() for node in nodes: for e in edges: if node - e == 0: return depth elif node - e > 0: nextLevel.add(node-e) else: break depth += 1 nodes = nextLevel

[ "noreply@github.com" ]

RickyLiTHU.noreply@github.com

9275404a0fb19e0fa17944bb3c32530ebb0cca93

5fdbd06b033464fdd5bc5be7a181422a92e5fc3c

/RandomForestWithGPs/GPPython/gp.py

3d3681d4d33dcd90192f3a3b1c7e12bd86a8cf17

[]

no_license

themasterlink/RandomForestWithGPs

02ab4b4473caef734c7234348163b973c03f73df

fcbd294b381ecba570ad34aca9eda1e70bf4e95e

refs/heads/master

2021-01-17T12:49:05.304383

2017-09-11T14:40:17

59,106,215

2

null

2017-06-12T14:07:05

2016-05-18T10:34:39

C++

UTF-8

Python

false

6,865

py

#!/Users/Max/anaconda/bin/python import numpy as np import json from pprint import pprint import math import scipy from scipy import linalg import matplotlib.pyplot as plt import matplotlib.cm as cm with open("init.json") as data_file: data = json.load(data_file) class GaussianProccess: def __init__(self, fileName): lines = open(data["Training"]["path"], "r").read().split("\n") self.data = [] self.lSquared = float(data["GP"]["l"]) * float(data["GP"]["l"]) self.sigmaNSquared = float(data["GP"]["sigmaN"]) * float(data["GP"]["sigmaN"]) self.labels = [] for line in lines: if len(line) > 3: ele = line.split(",") point = np.array([float(ele[0]), float(ele[1])]) self.data.append(point) self.labels.append(-1 if int(ele[2]) == 0 else 1) self.labels = np.asarray(self.labels) self.dataPoints = len(self.data) self.K = np.empty([self.dataPoints, self.dataPoints], dtype=float) for i in range(0, self.dataPoints): self.K[i][i] = self.sigmaNSquared for j in range(i + 1, self.dataPoints): temp = self.kernelOf(self.data[i], self.data[j]) self.K[i][j] = temp self.K[j][i] = temp def updatePis(self): for i in range(0, self.dataPoints): self.pis[i] = 1.0 / (1.0 + math.exp(-self.labels[i] * self.f[i])) self.dPis[i] = self.t[i] - self.pis[i] self.ddPis[i] = -(-self.pis[i] * (1 - self.pis[i])) # - to get minus dd Pi self.sqrtDDPis[i] = math.sqrt(self.ddPis[i]) def trainF(self): self.f = np.zeros(self.dataPoints) self.pis = np.empty(self.dataPoints) self.dPis = np.empty(self.dataPoints) self.ddPis = np.empty(self.dataPoints) self.sqrtDDPis = np.empty(self.dataPoints) self.t = (self.labels + np.ones(self.dataPoints)) * 0.5 converge = False eye = np.eye(self.dataPoints) lastObject = 1e100; while(not converge): self.updatePis() self.W = np.diag(self.ddPis) self.WSqrt = np.diag(self.sqrtDDPis) C = eye + np.dot(np.dot(self.WSqrt, self.K), self.WSqrt) print("K:\n"+str(self.K)) print("inner:\n"+str(C)) self.L = scipy.linalg.cho_factor(C, lower = True) self.U = scipy.linalg.cho_factor(C, lower = False) b = np.dot(self.W, self.f) + self.dPis; nenner = scipy.linalg.cho_solve(self.L, (np.dot(self.WSqrt,np.dot(self.K,b)))) self.a = b - np.dot(self.WSqrt, scipy.linalg.cho_solve(self.U, nenner)) self.f = np.dot(self.K, self.a) prob = 1.0 / (1.0 + math.exp(-np.dot(self.labels,self.f))) objective = -0.5 * np.dot(self.f, self.a) + math.log(max(min(prob,1-1e-7),1e-7)); print(objective) if math.fabs(objective / lastObject - 1.0) < 1e-5: converge = True lastObject = objective print("Trained") return def train(self): converge = False while(not converge): trainF() logZ = -0.5 * np.dot(self.a, self.f) + (-math.log(1 + math.exp(-np.dot(self.labels, self.f)))) + math.log(L.diagonal().sum()) R = np.dot(self.WSqrt, scipy.linalg.cho_solve(self.U, scipy.linalg.cho_solve(self.L, self.WSqrt))) C = scipy.linalg.cho_solve(self.L, np.dot(self.WSqrt, self.K)) dddPis = np.empty(self.dataPoints) for i in range(0, self.dataPoints): ddPis = -self.ddPis[i]; dddPis[i] = - ddPis * (1-self.pis[i]) - self.pis[i] * (1 - ddPis) s2 = -0.5 * (self.K.diagonal() - np.dot(C.T,C).diagonal).diagonal() * dddPis #for i in range(0,3): #C = self.W = np.diag(self.ddPis) self.WSqrt = np.diag(self.sqrtDDPis) C = eye + np.dot(np.dot(self.WSqrt, self.K), self.WSqrt) self.L = scipy.linalg.cho_factor(C, lower = True) self.U = scipy.linalg.cho_factor(C, lower = False) b = np.dot(self.W, self.f) + self.dPis; nenner = scipy.linalg.cho_solve(self.L, (np.dot(self.WSqrt,np.dot(self.K,b)))) a = b - np.dot(self.WSqrt, scipy.linalg.cho_solve(self.U, nenner)) self.f = np.dot(self.K, a) prob = 1.0 / (1.0 + math.exp(-np.dot(self.labels,self.f))) objective = -0.5 * np.dot(self.f, a) + math.log(prob if prob > 1e-7 and prob < 1 - 1e-7 else 1e-7 if prob <= 1e-7 else 1 - 1e-7); print(objective) #if math.fabs(objective / lastObject - 1.0) < 1e-5: converge = True lastObject = objective print("Trained") return def predict(self, newPoint): kXStar = np.empty(self.dataPoints) for i in range(0, self.dataPoints): kXStar[i] = self.kernelOf(newPoint, self.data[i]) fStar = np.dot(kXStar, self.dPis) v = scipy.linalg.cho_solve(self.L, np.dot(self.WSqrt,kXStar)) vFStar = math.fabs(self.sigmaNSquared + 1 - np.dot(v,v)) start = fStar - vFStar * 1.5 end = fStar + vFStar * 1.5 stepSize = (end - start) / float(data["GP"]["samplingAmount"]) prob = 0.0 for p in np.arange(start,end,stepSize): gaussRand = np.random.normal(fStar, vFStar) height = 1.0 / (1.0 + math.exp(p)) * gaussRand prob += height * stepSize; return max(min(prob,1), 0) def plot(self): plt.figure(0) min = np.min(self.data) max = np.max(self.data) min -= (max-min) * 0.2 max += (max-min) * 0.2 stepSize = (max - min) / float(data["GP"]["plotRes"]); listGrid = [] i = 0 for x in np.arange(min,max, stepSize): print("Done: " + str(float(i) / float(data["GP"]["plotRes"]) * 100) + "%") i += 1 newList = [] for y in np.arange(min,max, stepSize): newPoint = [y,x] prob = self.predict(newPoint) newList.append(prob) listGrid.append(newList) plt.imshow(listGrid, extent=(max, min, min, max), interpolation='nearest', cmap=cm.rainbow) plt.gca().invert_xaxis() plt.gca().set_ylim([min, max]) plt.gca().set_xlim([min, max]) for i in range(0,self.dataPoints): plt.plot(self.data[i][0],self.data[i][1], 'bo' if self.labels[i] == 1 else 'ro') print("Finished plotting") plt.show() def kernelOf(self, x, y): diff = x - y return math.exp(- 0.5 / self.lSquared * diff.dot(diff)); gp = GaussianProccess(data["Training"]["path"]) gp.trainF() gp.plot()

[ "themasterlink93@googlemail.com" ]

themasterlink93@googlemail.com

8886c4eff59f8379795b40f8995408fb237f04c7

c6221e1163b7c1cdb0a1bc6e29da2dcbec04d1b8

/Core/game.py

4de0c1df275a0f209ceae7e11870ed60d7e2d01a

[]

no_license

Dexton/Tesla-V.-Edison-Demo-Prototype

674e7620908b2920fde776444756823138580a32

7cebdbc24a6c78bdfc460c17a8d62596593cfe82

refs/heads/master

2021-01-24T03:38:25.794850

2011-10-09T00:37:09

2,540,805

0

1

null

UTF-8

Python

false

1,813

py

import pyglet from game_batch import GameBatch class GameStates: MAIN_MENU = 0 PLAYING = 1 PAUSED = 2 GAME_OVER = 3 class GameWindow(pyglet.window.Window): def __init__(self, *args, **kwargs): """ Creates necesary items and displays the menu """ super(GameWindow, self).__init__(1024, 768, *args, **kwargs) self.game_state = GameStates.MAIN_MENU #self.main_menu_batch = MainMenu(self, self.width, self.height) #self.pause_menu_batch = PauseMenu(self, self.width, self.height) self.game_batch = GameBatch(self, self.width, self.height) # this next line makes pyglet call self.update at 120Hz # this has to be the last line in __init__ pyglet.clock.schedule_interval(self.update, 1/120.0) def update(self, dt): """ Update game information dt: time delta, the change in time """ def on_key_press(self, symbol, modifiers): """ Key Press Event Handler symbol: the symbol(key) pressed modifiers: the extra keys pressed (ex. Ctrl or Alt) """ if self.game_state == GameStates.MAIN_MENU: self.main_menu_batch.on_key_press(symbol, modifiers) if self.game_state == GameStates.PLAYING: self.game_batch.on_key_press(symbol, modifiers) if self.game_state == GameStates.PAUSED: self.pause_menu_batch.on_key_press(symbol, modifiers) def on_draw(self): """ Draw Screen Event Handler """ self.clear() if self.game_state == GameStates.MAIN_MENU: self.main_menu_batch.draw() if self.game_state == GameStates.PLAYING: self.game_batch.draw() if self.game_state == GameStates.PAUSED: self.pause_menu_batch.draw()

[ "loktacar@gmail.com" ]

loktacar@gmail.com

e8492bd500e419e50fa3815209d4889eb2e4e971

c761f3fbce728e61cbcf5179f1d3f27e1e5625cd

/register_key.py

1328baddc2fe4d7e5f91b2052b07daa49e53649f

[]

no_license

philopon/usermon

16033d41436efe2cf4971bcd3b25f99cf82de318

7f97db09a65466e2133d4304f9fe5ba212299598

refs/heads/master

2021-01-18T16:51:56.457593

2017-04-21T13:06:12

86,775,704

0

null

UTF-8

Python

false

469

py

#!/usr/bin/env python3 def main(): import sys import os import pwd import pamela pw = pwd.getpwuid(os.getuid()) ssh_dir = os.path.join(pw.pw_dir, '.ssh') auth_keys = os.path.join(ssh_dir, 'authorized_keys') os.makedirs(ssh_dir, mode=0o700, exist_ok=True) with open(auth_keys, 'a') as f: for key in sys.stdin: print(key.strip(), file=f) os.chmod(auth_keys, 0o600) if __name__ == '__main__': main()

[ "philopon.dependence@gmail.com" ]

philopon.dependence@gmail.com

da850d8841ddddfdccfc6bde153467956b91789c

78e60a7d8a67ed76244004e8a3ed573fbf396e41

/samples/get_zip_codes.py

a89c105f5ec1a635d350ba870418f9f735a0bb60

[ "MIT" ]

permissive

Crivez/apiclient-python

837a9f7cc0453ccd3121311adc7920b5fe6b3e33

860fc054f546152a101e29b1af388c381075ac47

refs/heads/master

2023-06-08T13:24:09.249704

2021-06-17T12:16:35

null

0

null

UTF-8

Python

false

420

py

from voximplant.apiclient import VoximplantAPI, VoximplantException if __name__ == "__main__": voxapi = VoximplantAPI("credentials.json") # Search for zip codes in Germany. COUNTRY_CODE = "DE" COUNT = 1 try: res = voxapi.get_zip_codes(COUNTRY_CODE, count=COUNT) print(res) except VoximplantException as e: print("Error: {}".format(e.message))

[ "andrey@voximplant.com" ]

andrey@voximplant.com

ec76be96a998db58443e1d0b6cf215fe81c6c74e

386cff3bff62a6fb76ba22fd41e3c4f112bae6ba

/marathon/subscriber.py

c5605c7b54352bef497888ef5530763635bb8f99

[]

no_license

davidbliu/scaffolding

7c960acdc39528be5d9bed5068809c2b5f02bbc4

ff921b669f171075c2f06d195f455fa521b25f50

refs/heads/master

2016-08-07T21:15:12.797544

2014-07-03T15:48:58

null

0

null

UTF-8

Python

false

2,792

py

#!/usr/bin/env python import argparse import atexit import sys import urlparse from flask import Flask, request, jsonify import marathon from stores import InMemoryStore, SyslogUdpStore app = Flask(__name__) # re-initialize later events = None event_store = None def on_exit(marathon_client, callback_url): marathon_client.delete_event_subscription(callback_url) @app.route('/events', methods=['POST']) def event_receiver(): print 'hello' # event = request.get_json() # event_store.save(event) # return '' @app.route('/events', methods=['GET']) def list_events(): print 'i have arrived here' # return jsonify({'events': event_store.list()}) @app.route('/callback', methods=['GET', 'POST']) def callback(): print 'callback' try: event = request.get_json() print event except: print 'no event' return jsonify(result={"status": 200}) @app.route('/marathon', methods=['GET']) def marathon_register(): print 'marathon stuff happening here' marathon_url = 'localhost:8080' callback_url = 'localhost:5000/callback' m = marathon.MarathonClient(marathon_url) m.create_event_subscription(callback_url) atexit.register(on_exit, m, callback_url) return jsonify(result={"status": 200}) if __name__ == '__main__': print 'cool stuff dude' # parser = argparse.ArgumentParser(description='Marathon Logging Service') # parser.add_argument('-m', '--marathon-url', required=True, help='Marathon server URL (http[s]://<host>:<port>[<path>])') # parser.add_argument('-c', '--callback-url', required=True, help='callback URL for this service (http[s]://<host>:<port>[<path>]/events') # parser.add_argument('-e', '--event-store', default='in-memory://localhost/', help='event store connection string (default: in-memory://localhost/)') # parser.add_argument('-p', '--port', type=int, default=5000, help='Port to listen on (default: 5000)') # parser.add_argument('-i', '--ip', default='0.0.0.0', help='IP to listen on (default: 0.0.0.0)') # args = parser.parse_args() # event_store_url = urlparse.urlparse(args.event_store) # if event_store_url.scheme == 'in-memory': # event_store = InMemoryStore(event_store_url) # elif event_store_url.scheme == 'syslog': # event_store = SyslogUdpStore(event_store_url) # else: # print 'Invalid event store type: "{scheme}" (from "{url}")'.format(scheme=event_store_url.scheme, url=args.event_store) # sys.exit(1) marathon_url = 'http://localhost:8080' callback_url = 'http://localhost:5000/callback' m = marathon.MarathonClient(marathon_url) m.create_event_subscription(callback_url) atexit.register(on_exit, m, callback_url) app.run(port=5000, host='localhost')

[ "david.liu@autodesk.com" ]

david.liu@autodesk.com

8d5168f30b7e5f51483fcba73a2d034e20b80ae8

fe822705c38caf70c8a72433291acb3a729a0539

/backend/delivery_app/services/logdata.py

2c1474d24434b68bc2bbd67c691a705c785fe72f

[]

no_license

tanficial/delivery-food-fighter

138db44dbfee33d0f9f4ecd4ea832436910d8878

a73a4df208ef94537f6f4374b1a7aa476bc23d3c

refs/heads/main

2023-09-02T10:54:05.089348

2021-11-02T23:56:18

421,084,368

0

null

UTF-8

Python

false

349

py

from delivery_app.models.logdata import Logdata, db def add_logdata(id): """ log 이벤트 생성 시 logdata DB에 저장 """ try: new_logdata = Logdata(post_id = id) db.session.add(new_logdata) db.session.commit() return new_logdata except Exception: db.session.rollback() raise

[ "tanficial9574@gmail.com" ]

tanficial9574@gmail.com

a7571ea7181658d263514690d7191439a399b264

8c1b60dbbdbc84ae8cbd34f7679540036b04df84

/m5.py

97a59fdf765e7d91673b005f279dc849831f19c2

[]

no_license

KatyaPinich/ECG_classification_project

fd654fceaf0df99338a5d083545f0898030be998

37c1a21b9fc425be0f86b81272fdecebe96ce327

refs/heads/master

2023-07-25T09:37:50.177846

2020-03-05T12:11:45

242,001,325

0

null

2023-07-06T21:55:49

2020-02-20T22:13:05

Python

UTF-8

Python

false

1,580

py

import torch.nn as nn class M5(nn.Module): def __init__(self, num_classes): super(M5, self).__init__() self.conv_block1 = nn.Sequential( nn.Conv1d(in_channels=1, out_channels=128, kernel_size=80, stride=4), nn.BatchNorm1d(num_features=128), nn.ReLU(), nn.MaxPool1d(kernel_size=4) ) self.conv_block2 = nn.Sequential( nn.Conv1d(in_channels=128, out_channels=128, kernel_size=3, stride=1), nn.BatchNorm1d(num_features=128), nn.ReLU(), nn.MaxPool1d(kernel_size=4) ) self.conv_block3 = nn.Sequential( nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=1), nn.BatchNorm1d(num_features=256), nn.ReLU(), nn.MaxPool1d(kernel_size=4) ) self.conv_block4 = nn.Sequential( nn.Conv1d(in_channels=256, out_channels=512, kernel_size=3, stride=1), nn.BatchNorm1d(num_features=512), nn.ReLU(), nn.MaxPool1d(kernel_size=4) ) self.avg_pool = nn.AvgPool1d(8) self.softmax_layer = nn.Linear(512, num_classes) def forward(self, x): x = self.conv_block1(x) x = self.conv_block2(x) x = self.conv_block3(x) x = self.conv_block4(x) # Global avg pooling x = self.avg_pool(x) # [batch_size, 256, 1] # Dense x = x.view(x.size(0), -1) # [batch_size, 256*1=256] x = self.softmax_layer(x) # [batch_size, 10] return x

[ "katyapinich@gmail.com" ]

katyapinich@gmail.com

5a82456358fe6bffb55775bcea1ef64c6a01c840

9f72ad0c091df885df5953286003d23f25216602

/Tarefa5/testClient2.py

c2396d0f28ac570b6764acb221eabd7f6bccef6c

[]

no_license

SD-CC-UFG/felipe.gemmal.sd.ufg

0d96f50a34d5052df42454d8a6c7965fa8a2a035

472d38137c641570278cee2ae9f957fbdfc81188

refs/heads/master

2020-03-27T11:50:06.795260

2018-12-13T10:26:56

146,510,031

0

null

UTF-8

Python

false

524

py

#Cliente basico acesso indireto de Felipe Gemmal # -*- coding: utf-8 -*- import os import socket, string import sys nameServer = socket.socket(socket.AF_INET, socket.SOCK_STREAM) ip = 'localhost' porta = 12388 print("Conectando") nameServer.connect((ip,porta)) print("Enviando requisicao") #tipo de servico requisitado, colocado aqui por conta do tempo de recv do dns nameServer.send("getAddress") print("Recebendo resposta") resposta = str(nameServer.recv(1024).decode('utf-8')) print(resposta) nameServer.close()

[ "lipegemmal@hotmail.com" ]

lipegemmal@hotmail.com

5a573494952b197ef81f13cde9b7c7b8ce088c5c

234d650ff5d906c2e3ce8da37c7b725c694791a0

/dxy/items.py

6b256d68ace5de8eb111d8b72fffeefaa5badeb1

[]

no_license

IvanQin/dxy_spider

7846a0aeb96f8725e091be09db20c198c559b36c

ed1e73e09986f2397151d369a08586cc7e6574da

refs/heads/master

2021-01-25T09:21:02.082754

2017-06-09T04:15:43

93,814,806

0

null

UTF-8

Python

false

352

py

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy class DxyItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() title = scrapy.Field() link = scrapy.Field() page = scrapy.Field()

[ "yifan.qing@datarx.cn" ]

yifan.qing@datarx.cn

3ae1533fbdd3e8eab796faa6ec41d76f5cbed112

39800224358654c8225aefa25a0daf26e489c33f

/reviews/reviews/urls.py

78ca205221d204726b1df8f89f1278efb47a3986

[]

no_license

dprestsde/Review_system

59e5f73716a6ab02e7cecd140519b4505cf1c278

b636a568b51189c9f78f874461e6eaa323317868

refs/heads/master

2021-09-22T21:49:20.164221

2018-09-17T12:22:35

null

0

null

UTF-8

Python

false

818

py

"""reviews URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path,include urlpatterns = [ path('admin/', admin.site.urls), path('',include('guestbook.urls')) ]

[ "noreply@github.com" ]

dprestsde.noreply@github.com

a44db705bdc58cdcecdcd4b8200bf85a3d08fc83

b15d2787a1eeb56dfa700480364337216d2b1eb9

/samples/cli/accelbyte_py_sdk_cli/group/_get_group_join_request_public_v2.py

32ba9735f4911a02f803f73dab69c4e7a260ec52

[ "MIT" ]

permissive

AccelByte/accelbyte-python-sdk

dedf3b8a592beef5fcf86b4245678ee3277f953d

539c617c7e6938892fa49f95585b2a45c97a59e0

refs/heads/main

2023-08-24T14:38:04.370340

2023-08-22T01:08:03

410,735,805

2

1

MIT

2022-08-02T03:54:11

2021-09-27T04:00:10

Python

UTF-8

Python

false

2,611

py

# Copyright (c) 2021 AccelByte Inc. All Rights Reserved. # This is licensed software from AccelByte Inc, for limitations # and restrictions contact your company contract manager. # # Code generated. DO NOT EDIT! # template_file: python-cli-command.j2 # AGS Group Service (2.18.1) # pylint: disable=duplicate-code # pylint: disable=line-too-long # pylint: disable=missing-function-docstring # pylint: disable=missing-module-docstring # pylint: disable=too-many-arguments # pylint: disable=too-many-branches # pylint: disable=too-many-instance-attributes # pylint: disable=too-many-lines # pylint: disable=too-many-locals # pylint: disable=too-many-public-methods # pylint: disable=too-many-return-statements # pylint: disable=too-many-statements # pylint: disable=unused-import import json import yaml from typing import Optional import click from .._utils import login_as as login_as_internal from .._utils import to_dict from accelbyte_py_sdk.api.group import ( get_group_join_request_public_v2 as get_group_join_request_public_v2_internal, ) from accelbyte_py_sdk.api.group.models import ModelsGetMemberRequestsListResponseV1 from accelbyte_py_sdk.api.group.models import ResponseErrorResponse @click.command() @click.argument("group_id", type=str) @click.option("--limit", "limit", type=int) @click.option("--offset", "offset", type=int) @click.option("--namespace", type=str) @click.option("--login_as", type=click.Choice(["client", "user"], case_sensitive=False)) @click.option("--login_with_auth", type=str) @click.option("--doc", type=bool) def get_group_join_request_public_v2( group_id: str, limit: Optional[int] = None, offset: Optional[int] = None, namespace: Optional[str] = None, login_as: Optional[str] = None, login_with_auth: Optional[str] = None, doc: Optional[bool] = None, ): if doc: click.echo(get_group_join_request_public_v2_internal.__doc__) return x_additional_headers = None if login_with_auth: x_additional_headers = {"Authorization": login_with_auth} else: login_as_internal(login_as) result, error = get_group_join_request_public_v2_internal( group_id=group_id, limit=limit, offset=offset, namespace=namespace, x_additional_headers=x_additional_headers, ) if error: raise Exception(f"getGroupJoinRequestPublicV2 failed: {str(error)}") click.echo(yaml.safe_dump(to_dict(result), sort_keys=False)) get_group_join_request_public_v2.operation_id = "getGroupJoinRequestPublicV2" get_group_join_request_public_v2.is_deprecated = False

[ "elmernocon@gmail.com" ]

elmernocon@gmail.com

c2d9305312002748edb2d0e5470f541784c71352

3fc00c49c6b5a5d3edb4f5a97a86ecc8f59a3035

/shared_models/test/test_api.py

ae9465bb6b3b41416d097c202b1034470650a378

[]

no_license

yc-hu/dm_apps

9e640ef08da8ecefcd7008ee2d4f8f268ec9062e

483f855b19876fd60c0017a270df74e076aa0d8b

refs/heads/master

2023-04-07T13:13:55.999058

2021-04-12T10:19:21

null

0

null

UTF-8

Python

false

2,264

py

from django.test import tag from django.urls import reverse from rest_framework import status from shared_models.test import SharedModelsFactoryFloor as FactoryFloor from shared_models.test.common_tests import CommonTest class TestUserAPIListView(CommonTest): def setUp(self): super().setUp() self.user = self.get_and_login_user() self.test_url = reverse("user-list", args=None) @tag("api", 'user') def test_url(self): self.assert_correct_url("user-list", test_url_args=None, expected_url_path=f"/api/shared/users/") @tag("api", 'user') def test_get(self): # PERMISSIONS # authenticated users response = self.client.get(self.test_url) self.assertEqual(response.status_code, status.HTTP_200_OK) # unauthenticated users self.client.logout() response = self.client.get(self.test_url) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) # TODO: build up this test! # # RESPONSE DATA # valid_user = None # self.get_and_login_user(user=None) # response = self.client.get(self.test_url) # self.assertEqual(len(response.data), 1) # self.assertEqual(response.data[0]["id"], self.instance.id) # # or, for lists with pagination... # self.assertEqual(len(data["results"]), 1) # self.assertEqual(data["results"][0]["id"], self.instance.id) # # # check query params # object = FactoryFloor.UserFactory() # data = self.client.get(self.test_url+f"?={object.id}").data # keys.extend([ # "", # ]) # self.assert_dict_has_keys(data, keys) @tag("api", 'user') def test_unallowed_methods_only(self): restricted_statuses = [status.HTTP_405_METHOD_NOT_ALLOWED, status.HTTP_403_FORBIDDEN] self.assertIn(self.client.put(self.test_url, data=None).status_code, restricted_statuses) self.assertIn(self.client.delete(self.test_url, data=None).status_code, restricted_statuses) self.assertIn(self.client.post(self.test_url, data=None).status_code, restricted_statuses) self.assertIn(self.client.patch(self.test_url, data=None).status_code, restricted_statuses)

[ "davjfish@gmail.com" ]

davjfish@gmail.com

32965056a1b7a8f68e29a888ddf16692219f8202

6f2675eee55b7ebc5adf9c2176ced8cb59fc64d4

/dataInterKingdee/interDebug.py

f5873ce9a0c97db0f8dd05bed388d20b019fdced

[]

no_license

wildmanwang/proDataInter

8c2b65fa96ad45b21165d997b1769a28e12fc42a

f5a1f1fb195c66bf586bd999465c7e3b16453369

refs/heads/master

2023-06-07T11:57:16.763251

2023-06-03T08:54:56

157,559,747

0

null

UTF-8

Python

false

602

py

# -*- coding:utf-8 -*- """ """ __author__ = "Cliff.wang" import os from interConfig import Settings #from interProcess import InterProcess from interControl import InterControl if __name__ == "__main__": try: path = os.path.abspath(os.path.dirname(__file__)) sett = Settings(path, "config") inter = InterControl(sett) inter.interInit() if 1 == 2: # 传输基础资料、业务数据 inter.interBusiData() elif 1 == 2: # 获取部门ID和用户ID pass except Exception as e: print(str(e))

[ "cliff.w@qq.com" ]

cliff.w@qq.com

270875ed2be025781a975375972379cf8f211f80

dfad28a2e1a0199c0117e551fd1e31804804d5b9

/app/auth/views.py

d2df7a97666207276aa6648ef9f85af4a25d98bc

[ "MIT" ]

permissive

wilbrone/Pitches

c33d60b142b43de9ccf60a86cf59acbc262c6711

b20d234fd930a6551f26d9cf863c6d1631b62bc2

refs/heads/master

2022-12-09T08:02:08.631177

2019-11-25T23:47:13

223,405,696

0

MIT

2022-12-08T06:55:48

2019-11-22T13:09:30

Python

UTF-8

Python

false

1,583

py

from flask import render_template,redirect,url_for, flash,request from flask_login import login_user,logout_user,login_required from . import auth from ..models import User from .forms import LoginForm,RegistrationForm from .. import db from ..email import mail_message @auth.route('/login',methods=['GET','POST']) def login(): login_form = LoginForm() if login_form.validate_on_submit(): user = User.query.filter_by(email = login_form.email.data).first() if user is not None and user.verify_password(login_form.password.data): login_user(user,login_form.remember.data) return redirect(request.args.get('next') or url_for('main.index')) flash('Invalid username or Password') title = "One Minute Perfect Pitch login" return render_template('auth/login.html',login_form = login_form,title=title) @auth.route('/register',methods = ["GET","POST"]) def register(): form = RegistrationForm() if form.validate_on_submit(): user = User(email = form.email.data, username = form.username.data,full_name= form.full_name.data,password = form.password.data) # saving the data db.session.add(user) db.session.commit() mail_message("Welcome to One Minute Perfect Pitch","email/welcome_user",user.email,user=user) return redirect(url_for('auth.login')) title = "New Account" return render_template('auth/register.html',registration_form = form) @auth.route('/logout') @login_required def logout(): logout_user() return redirect(url_for("main.index"))

[ "wilbroneokoth@gmail.com" ]

wilbroneokoth@gmail.com

a7dcd151d0dd3ea4bc81bb4a0fca9c6818c60ec5

f03a0d77c4f5524e8958263962ddb04a120ed6d6

/Lab8/wordladder5.py

1a1c82c4ebcd985f50f3bbdd129ff28bd4f5c4bc

[]

no_license

b3rton/OpenSourceBlog

0a54566a6d542a41e2e8018287faef705a66fc35

4185c7b46629ac054903229d9a5a027110d5d662

refs/heads/master

2021-05-30T10:09:43.183994

2015-11-13T20:00:52

null

0

null

UTF-8

Python

false

2,978

py

""" Words/Ladder Graph ------------------ Generate an undirected graph over the 5757 5-letter words in the datafile words_dat.txt.gz. Two words are connected by an edge if they differ in one letter, resulting in 14,135 edges. This example is described in Section 1.1 in Knuth's book [1]_,[2]_. References ---------- .. [1] Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial Computing", ACM Press, New York, 1993. .. [2] http://www-cs-faculty.stanford.edu/~knuth/sgb.html """ __author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)', 'Brendt Wohlberg', 'hughdbrown@yahoo.com']) # Copyright (C) 2004-2015 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import networkx as nx #------------------------------------------------------------------- # The Words/Ladder graph of Section 1.1 #------------------------------------------------------------------- def generate_graph(words): from string import ascii_lowercase as lowercase G = nx.Graph(name="words") lookup = dict((c,lowercase.index(c)) for c in lowercase) def edit_distance_one(word): for i in range(len(word)): left, c, right = word[0:i], word[i], word[i+1:] j = lookup[c] # lowercase.index(c) for cc in lowercase[j+1:]: yield left + cc + right candgen = ((word, cand) for word in sorted(words) for cand in edit_distance_one(word) if cand in words) G.add_nodes_from(words) for word, cand in candgen: G.add_edge(word, cand) return G def words_graph(): """Return the words example graph from the Stanford GraphBase""" import gzip fh=gzip.open('words_dat.txt.gz','r') #5 words #fh=gzip.open('words4_dat.txt.gz','r') #4 words words=set() for line in fh.readlines(): line = line.decode() if line.startswith('*'): continue w=str(line[0:5]) #w=str(line[0:4]) words.add(w) return generate_graph(words) if __name__ == '__main__': from networkx import * G=words_graph() print("Loaded words_dat.txt containing 5757 five-letter English words.") print("Two words are connected if they differ in one letter.") print("Graph has %d nodes with %d edges" %(number_of_nodes(G),number_of_edges(G))) print("%d connected components" % number_connected_components(G)) fiveWordsT = [('chaos','order'),('nodes','graph'),('moron','smart'),('pound','marks')] fourWordsT = [('cold','warm'),('love','hate')] test = fiveWordsT for (source,target) in test: print("Shortest path between %s and %s is"%(source,target)) try: sp=shortest_path(G, source, target) for n in sp: print(n) except nx.NetworkXNoPath: print("None")

[ "nathan.spero.berton@gmail.com" ]

nathan.spero.berton@gmail.com

62ec86a4fa3abd1261e1c0a8452250ff222b6759

dedbf1f67bc741203f685745ecfde3d00f3f3d87

/src/simpleseq/encodings.py

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[]

no_license

ambrosejcarr/simpleseq

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a9760db8470ccd578e6b82837bed12187389dbb8

refs/heads/master

2016-08-12T05:58:12.885835

2016-02-17T16:33:14

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py

class DNA3Bit: """ Compact encoding scheme for sequence data. """ _str2bindict = {65: 0b100, 67: 0b110, 71: 0b101, 84: 0b011, 78: 0b111, 97: 0b100, 99: 0b110, 103: 0b101, 116: 0b011, 110: 0b111} _bin2strdict = {0b100: b'A', 0b110: b'C', 0b101: b'G', 0b011: b'T', 0b111: b'N'} bin_nums = [0b100, 0b110, 0b101, 0b011] @classmethod def encode(cls, s: bytes) -> int: """Convert string nucleotide sequence into binary, note: string is reversed so that the first nucleotide is in the LSB position""" res = 0 for c in s: res <<= 3 res += cls._str2bindict[c] return res @classmethod def decode(cls, i: int) -> bytes: """Convert binary nucleotide sequence into string""" if i < 0: message = 'i must be an unsigned (positive) integer, not {0!s}'.format(i) raise ValueError(message) r = b'' while i > 0: r = cls._bin2strdict[i & 0b111] + r i >>= 3 return r @staticmethod def gc_content(i: int) -> float: """calculate percentage of i that is G or C""" gc = 0 length = 0 while i > 0: length += 1 masked = i & 111 if masked == 0b100 or masked == 0b100: gc += 1 i >>= 3 return gc / length @staticmethod def seq_len(i: int) -> int: """Return the length of a sequence based on its binary representation""" l = 0 while i > 0: l += 1 i >>= 3 return l @staticmethod def contains(s: int, char: int) -> bool: """ return true if the char (bin representation) is contained in seq (binary representation) """ while s > 0: if char == (s & 0b111): return True s >>= 3 return False @staticmethod def bitlength(i: int) -> int: """return the bitlength of the sequence""" bitlen = i.bit_length() # correct for leading T-nucleotide (011) whose leading 0 gets trimmed if bitlen % 3: bitlen += 1 return bitlen

[ "mail@ambrosejcarr.com" ]

mail@ambrosejcarr.com

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/Q3_BP.py

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no_license

kzil88/Quant

c3f517cf507cbb97774738c152087a660dc59e31

711800349a065bd9534f323337147b494c91c156

refs/heads/master

2021-04-15T14:41:17.189825

2018-12-04T08:40:42

126,697,511

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from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD import DC from keras.layers import Flatten import numpy as np import keras import datetime import pymysql if __name__ == '__main__': time_temp = datetime.datetime.now() - datetime.timedelta(days=90) date_seq_start = time_temp.strftime('%Y-%m-%d') end_dt = (datetime.datetime.now() -datetime.timedelta(days=1)) .strftime('%Y-%m-%d') # 建立数据库连接,回测时间序列 dc = DC.data_collect2('000725',date_seq_start,end_dt) score_list = [] resu_list = [] train = dc.data_train target = dc.data_target model = Sequential() model.add(Dense(64, activation='linear', input_dim=14)) model.add(Dropout(0.5)) model.add(Dense(64, activation='sigmoid')) model.add(Dropout(0.5)) model.add(Dense(1, activation='relu')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='logcosh', optimizer=sgd, metrics=['accuracy']) for i in range(5): model.fit(train, target, epochs=2000) score = model.evaluate(train, target, batch_size=128) print('SCORE:' + str(score[0])) test_case = np.array([dc.test_case]) ans2 = model.predict(test_case) resu_list.append(ans2[0][0]) score_list.append(score) print('RESU '+str(i+1)+' : '+str(ans2[0][0])) dc.refreshDATA(ans2[0][0]) train = dc.data_train target = dc.data_target print(score_list) print(resu_list) print(date_seq_start) print(end_dt)

[ "noreply@github.com" ]

kzil88.noreply@github.com

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/Competitions/Comp4/start_sound.py

87e6432903e6abe11567cff4177b2484faedd6c1

[]

no_license

MandyMeindersma/Robotics

c091e5b248bb067db4631e2de481d18417996933

f58916bb293d68c176847363a25eb7270a304965

refs/heads/master

2023-01-08T07:34:51.223767

2023-01-01T05:40:12

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py

#!/usr/bin/env python from sound_play.libsoundplay import SoundClient # from sound_play.msg import SoundRequest import rospy import time rospy.init_node('sound') soundthing = SoundClient() time.sleep(1) # soundthing.play(SoundRequest.NEEDS_UNPLUGGING) # soundthing.voiceSound("Testing the new A P I") soundthing.playWave("/home/mandy/winter18/Robotics/Competitions/Comp4/meow.ogg") print("meow sound started") time.sleep(3) soundthing.playWave("/home/mandy/winter18/Robotics/Competitions/Comp4/moo.ogg") print("woof sound started")

[ "meinders@ualberta.ca" ]

meinders@ualberta.ca

8a35692c001a9c87e06840d701a8da708dedcbb2

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/MachineLearning.py

29e2c182c8e664d6888629192f033295d5bcbf63

[]

no_license

DanWertheimer/COS802

c4e7d8d3a06f04efef998daaa0a57bdbc6232ed0

656e3ca62e44f8fda1967af0ba4b5e38120f2e8b

refs/heads/master

2021-07-21T15:38:31.895643

2017-10-30T08:14:31

108,819,051

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Oct 29 17:23:39 2017 @author: danwertheimer """ 1209/10000 import pandas as pd from sklearn import preprocessing from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn import svm from sklearn.svm import LinearSVC from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import Normalizer from sklearn.ensemble import ExtraTreesClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel from sklearn.pipeline import Pipeline Data = pd.read_csv("CleanData2.csv",index_col = 0) Fields =['Insured_First_Name','Insured_Last_Name','Client_ID','Other_Party_Name',\ 'Other_Party_Last_Name','Fraudulent_Claim_Reason',\ 'Policy_Holder_Street',\ 'Policy_Holder_State',\ 'Policy_Holder_City',\ 'Policy_Holder_Area',\ 'Policy_Holder_Postal_Code',\ 'Loss_Street',\ 'Loss_State',\ 'Loss_City',\ 'Loss_Area',\ 'Loss_Postal_Code'] Data = Data.drop(Fields,axis = 1) ScaledVariables = ['Amount_Paid','Sum_Insured','Total_Policies_Revenue'] mms = preprocessing.MinMaxScaler() Normalize = preprocessing.Normalizer() Data[ScaledVariables] = Normalize.fit_transform(Data[ScaledVariables]) Test1 = Data[Data['Fraudulent_Claim_Indicator'] == 0].sample(n = 10000 ) Test2 = Data[Data['Fraudulent_Claim_Indicator'] == 1] New = pd.concat([Test1,Test2], axis = 0) DataX = New[New.columns.difference(['Fraudulent_Claim_Indicator','Date_Of_Birth',\ 'Date_Of_Loss','Policy_Start_Date',\ 'Policy_End_Date'])] DataY = New['Fraudulent_Claim_Indicator'] X_train, X_test, y_train, y_test = train_test_split(\ DataX, DataY, test_size=0.8, random_state=48) glm = LogisticRegression() glm.fit(X_train,y_train) glm.score(X_test,y_test) glmcv = cross_val_score(glm, DataX, DataY, cv=10,scoring = 'roc_auc') clf = svm.SVC(kernel='linear', C=2).fit(X_train, y_train) clf.score(X_test, y_test) clfcv = cross_val_score(clf, DataX, DataY, cv=10,scoring = 'roc_auc') NNet = MLPClassifier(solver='lbfgs', alpha=1e-5,\ hidden_layer_sizes=(3, 2), random_state=47) NNet.fit(X_train,y_train); NNet.score(X_test, y_test) NNetcv = cross_val_score(NNet, DataX, DataY, cv=10,scoring = 'roc_auc') ############################################################################### FeatureData = Data DateFeatures = ['Date_Of_Birth','Date_Of_Loss','Policy_Start_Date',\ 'Policy_End_Date'] FeatureData[DateFeatures] = FeatureData[DateFeatures].astype(str) for i in DateFeatures: FeatureData[i] = pd.to_datetime(FeatureData[i]) # Creating feature for days between policy start and loss FeatureData['Days_Between_Policy_Loss'] = FeatureData['Date_Of_Loss'] - FeatureData['Policy_Start_Date'] FeatureData['Days_Between_Policy_Loss'] = FeatureData['Days_Between_Policy_Loss'].apply(lambda x:x.days) # Creating feature for days between policy loss and policy end FeatureData['Days_Before_Policy_End_Loss'] = FeatureData['Policy_End_Date'] - FeatureData['Date_Of_Loss'] FeatureData['Days_Before_Policy_End_Loss'] = FeatureData['Days_Before_Policy_End_Loss'].apply(lambda x:x.days) FeatureData['Number_Of_Claims'] = FeatureData.groupby(['Date_Of_Birth','Policy_Start_Date',\ 'Policy_End_Date']).cumcount()+1 # Rescaling New Features NewFeatures = ['Days_Between_Policy_Loss','Days_Before_Policy_End_Loss','Number_Of_Claims'] FeatureData[NewFeatures] = Normalize.fit_transform(FeatureData[NewFeatures]) ############################################################################### # Retraining Models Test1 = FeatureData[FeatureData['Fraudulent_Claim_Indicator'] == 0].sample(n = 10000 ) Test2 = FeatureData[FeatureData['Fraudulent_Claim_Indicator'] == 1] NewFeatureData = pd.concat([Test1,Test2], axis = 0) DataX = NewFeatureData[NewFeatureData.columns.difference(['Fraudulent_Claim_Indicator','Date_Of_Birth',\ 'Date_Of_Loss','Policy_Start_Date',\ 'Policy_End_Date'])] DataY = NewFeatureData['Fraudulent_Claim_Indicator'] # Checking Variable Importance Tree = ExtraTreesClassifier() TreeC = Tree.fit(DataX,DataY) TreeC.feature_importances_ model = SelectFromModel(TreeC, prefit=True) X_new = model.transform(DataX) X_train_newfeature, X_test_newfeature, y_train_newfeature, y_test_newfeature = train_test_split(\ X_new, DataY, test_size=0.8, random_state=48) glm_newfeature = LogisticRegression() glm_newfeature.fit(X_train_newfeature,y_train_newfeature) glm_newfeature.score(X_test_newfeature,y_test_newfeature) glmcv2 = cross_val_score(glm_newfeature, X_new, DataY, cv=10, scoring = 'roc_auc') clf_newfeature = svm.SVC(kernel='linear', C=1).fit(X_train_newfeature, y_train_newfeature) clf_newfeature.score(X_test_newfeature, y_test_newfeature) clfcv2 = cross_val_score(clf_newfeature, X_new, DataY, cv=10, scoring = 'roc_auc') NNet_newfeature = MLPClassifier(solver='lbfgs', alpha=1e-5,\ hidden_layer_sizes=(3, 2), random_state=47) NNet_newfeature.fit(X_train_newfeature,y_train_newfeature); NNet_newfeature.score(X_test_newfeature, y_test_newfeature) NNetcv2 = cross_val_score(NNet_newfeature, X_new, DataY, cv=10, scoring = 'roc_auc') Q = Pipeline([ ('feature_selection', SelectFromModel(LinearSVC())), ('classification', RandomForestClassifier()) ]) Q.fit(X_train_newfeature,y_train_newfeature) Q.score(X_test_newfeature, y_test_newfeature)

[ "noreply@github.com" ]

DanWertheimer.noreply@github.com

23e10462cf68f0d4848893ca60ea2362f183a88f

16da6040330dd1e8f88478b31e958dba88d96cbf

/ddpg_agent.py

46bb0ccd5e835eb29f8695de83ac4e17026128ab

[]

no_license

vgudapati/DRLND_Continuous_Control

0c5c5098a167b44f0f2a1f957ab3080e28e55265

e55f5df74d4489821b322754570a26e552a2da59

refs/heads/master

2020-04-16T01:52:14.500904

2019-01-12T01:54:21

165,188,261

0

null

UTF-8

Python

false

9,194

py

import numpy as np import random import copy from collections import namedtuple, deque from model import Actor, Critic import torch import torch.nn.functional as F import torch.optim as optim ''' BUFFER_SIZE = int(1e5) # replay buffer size BATCH_SIZE = 512 # minibatch size GAMMA = 0.99 # discount factor TAU = 1e-3 # for soft update of target parameters LR_ACTOR = 1e-4 # learning rate of the actor LR_CRITIC = 1e-3 # learning rate of the critic WEIGHT_DECAY = 0 # L2 weight decay ''' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") class DDPGAgent(): """Interacts with and learns from the environment.""" def __init__(self, state_size, action_size = 4, random_seed = 0, BUFFER_SIZE = int(1e5), BATCH_SIZE = 128, GAMMA = 0.99, TAU = 1e-3, LR_ACTOR = 1e-4, LR_CRITIC = 1e-3, WEIGHT_DECAY = 0): """ Initialize an Agent object. Params ====== state_size (int) : dimension of each state action_size (int): dimension of each action random_seed (int): random seed BUFFER_SIZE (int): replay buffer size BATCH_SIZE (int): minibatch size GAMMA (float): discount factor TAU (float): for soft update of target parameters LR_ACTOR (float): learning rate for critic LR_CRITIC (float): learning rate for critic WEIGHT_DECAY (float): L2 weight decay """ self.state_size = state_size self.action_size = action_size self.seed = random.seed(random_seed) self.batch_size = BATCH_SIZE self.gamma = GAMMA self.tau = TAU # Actor Network (w/ Target Network) self.actor_local = Actor(state_size, action_size, random_seed).to(device) self.actor_target = Actor(state_size, action_size, random_seed).to(device) self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR) # Critic Network (w/ Target Network) self.critic_local = Critic(state_size, action_size, random_seed).to(device) self.critic_target = Critic(state_size, action_size, random_seed).to(device) self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY) # Noise process self.noise = OUNoise(action_size, random_seed) # Time step self.timestep = 0 # Replay memory self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed) def step(self, state, action, reward, next_state, done): """Save experience in replay memory, and use random sample from buffer to learn.""" # Save experience / reward for s, a, r, ns, d in zip(state, action, reward, next_state, done): self.memory.add(s, a, r, ns, d) #self.memory.add(state, action, reward, next_state, done) ''' self.timestep = (self.timestep + 1) % 2 # Learn every 2 time steps if self.timestep == 0: # if enough samples are available in memory ''' if len(self.memory) > self.batch_size: experiences = self.memory.sample() self.learn(experiences, self.gamma) def act(self, state, add_noise=True): """Returns actions for given state as per current policy.""" state = torch.from_numpy(state).float().to(device) self.actor_local.eval() with torch.no_grad(): action = self.actor_local(state).cpu().data.numpy() self.actor_local.train() if add_noise: action += self.noise.sample() return np.clip(action, -1, 1) def reset(self): self.noise.reset() def learn(self, experiences, gamma): """Update policy and value parameters using given batch of experience tuples. Q_targets = r + γ * critic_target(next_state, actor_target(next_state)) where: actor_target(state) -> action critic_target(state, action) -> Q-value Params ====== experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples gamma (float): discount factor """ states, actions, rewards, next_states, dones = experiences #print(experiences) # ---------------------------- update critic ---------------------------- # # Get predicted next-state actions and Q values from target models actions_next = self.actor_local(next_states) ## -------------------------------------------------- Q_targets_next = self.critic_target(next_states, actions_next) # Compute Q targets for current states (y_i) #print(rewards.shape) #print(Q_targets_next.shape) Q_targets = rewards + (gamma * Q_targets_next * (1 - dones)) # Compute critic loss Q_expected = self.critic_local(states, actions) critic_loss = F.mse_loss(Q_expected, Q_targets) # Minimize the loss self.critic_optimizer.zero_grad() critic_loss.backward() torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1) self.critic_optimizer.step() # ---------------------------- update actor ---------------------------- # # Compute actor loss actions_pred = self.actor_local(states) actor_loss = -self.critic_local(states, actions_pred).mean() # Minimize the loss self.actor_optimizer.zero_grad() actor_loss.backward() torch.nn.utils.clip_grad_norm_(self.actor_local.parameters(), 1) self.actor_optimizer.step() # ----------------------- update target networks ----------------------- # self.soft_update(self.critic_local, self.critic_target, self.tau) self.soft_update(self.actor_local, self.actor_target, self.tau) def soft_update(self, local_model, target_model, tau): """Soft update model parameters. θ_target = τ*θ_local + (1 - τ)*θ_target Params ====== local_model: PyTorch model (weights will be copied from) target_model: PyTorch model (weights will be copied to) tau (float): interpolation parameter """ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data) class OUNoise: """Ornstein-Uhlenbeck process.""" def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2): """Initialize parameters and noise process.""" self.size = size self.mu = mu * np.ones(size) self.theta = theta self.sigma = sigma self.seed = random.seed(seed) self.reset() def reset(self): """Reset the internal state (= noise) to mean (mu).""" self.state = copy.copy(self.mu) def sample(self): """Update internal state and return it as a noise sample.""" x = self.state dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.size) self.state = x + dx return self.state class ReplayBuffer: """Fixed-size buffer to store experience tuples.""" def __init__(self, action_size, buffer_size, batch_size, seed): """Initialize a ReplayBuffer object. Params ====== buffer_size (int): maximum size of buffer batch_size (int): size of each training batch """ self.action_size = action_size self.memory = deque(maxlen=buffer_size) # internal memory (deque) self.batch_size = batch_size self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"]) self.seed = random.seed(seed) def add(self, state, action, reward, next_state, done): """Add a new experience to memory.""" e = self.experience(state, action, reward, next_state, done) self.memory.append(e) def sample(self): """Randomly sample a batch of experiences from memory.""" experiences = random.sample(self.memory, k=self.batch_size) states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device) actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device) rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device) next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device) dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device) return (states, actions, rewards, next_states, dones) def __len__(self): """Return the current size of internal memory.""" return len(self.memory)

[ "noreply@github.com" ]

vgudapati.noreply@github.com

9f04557904bdeeb5a5b0b9e265605429682ff434

a867b1c9da10a93136550c767c45e0d8c98f5675

/G_11_RemoveKthNode.py

408aa2a8a0bdec884c65ff5c410cb79045ed72b6

[]

no_license

Omkar02/FAANG

f747aacc938bf747129b8ff35b6648fb265d95b6

ee9b245aa83ea58aa67954ab96442561dbe68d06

refs/heads/master

2023-03-25T19:45:08.153403

2021-03-28T07:13:08

280,783,785

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0

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UTF-8

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py

import __main__ as main from Helper.TimerLogger import CodeTimeLogging fileName = main.__file__ fileName = fileName.split('\\')[-1] CodeTimeLogging(Flag='F', filename=fileName, Tag='Linked-List', Difficult='Medium') from Datastruct.masterLinkedList import l arr = [1, 2, 3, 4, 5, 6] # arr = [1, 2] for i in arr: l.insertStart(i) # l.traverseList() def removeKNodeFromEnd(head, k): print(f'Removed {k} node: ',end = '') first = head second = head count = 1 while count <= k and second is not None: second = second.nextNode count += 1 if second is None: head.data = first.nextNode.data head.nextNode = first.nextNode.nextNode l.traverseList() return while second.nextNode is not None: second = second.nextNode first = first.nextNode first.nextNode = first.nextNode.nextNode l.traverseList() removeKNodeFromEnd(l.getHead(), 3)

[ "omkarjoshi4031@live.com" ]

omkarjoshi4031@live.com

5be0edf09990b940847ed51efb8d7cc5cde7d449

70ead0a39a0217c3c1bc6b48f902987c883c0868

/templatemail/backends/locmem.py

87fd0941385de853fc14caa37e8ac9140c79ae53

[ "MIT" ]

permissive

timdownsisarealboy/django-template-mail

a5f369fff8f3d147f63196705490c1782a9b99bb

64ab909da41d1a90c14969687cfd97512eaedc60

refs/heads/master

2021-01-20T19:06:20.796790

2013-07-12T17:18:28

null

0

null

UTF-8

Python

false

311

py

from django.core.mail.backends import locmem from base import BaseEmailBackend class EmailBackend(locmem.EmailBackend, BaseEmailBackend): def send_messages(self, email_messages): email_messages = self.render_messages(email_messages) super(EmailBackend, self).send_messages(email_messages)

[ "bar.benoit@gmail.com" ]

bar.benoit@gmail.com

8a55a174178d00541f365a08542d4d792b52fcc5

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/project/source/DQN_old.py

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Akihiro-Nishihara/ActionGameAI

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d3c9e91cb84f1eb6125588338ea2a6e1567def3b

refs/heads/master

2022-12-08T15:53:13.160680

2020-09-16T17:23:12

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import os import numpy as np import datetime import math import sys import shutil from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPool2D from keras.optimizers import Adam from keras.utils import plot_model from collections import deque from keras import backend as K # Kerasは自身で行列計算とかしない，それをするためのやーつ import tensorflow as tf import pygame from project.source import myenv, header as h FUNC_REWARD = 1 # 強化学習における報酬の設定 LEARNING_RATE = 0.1 # Q-networkの学習係数 # LEARNING_RATE = 0.01 # Q-networkの学習係数 OBS_LEFT = 0 OBS_TOP = -1 OBS_RIGHT = 3 OBS_BOTTOM = 2 SIZE_STATE = (OBS_RIGHT - OBS_LEFT) * (OBS_BOTTOM - OBS_TOP) - 1 + 2 # 観測マス（キャラ位置除く）+マス内の座標 SIZE_ACTION = 8 SIZE_HIDDEN = 32 SEED = 1 NUM_EPISODES = 19 # 総試行回数 SIZE_LOOP = 1000 GAMMA = 0.99 # 割引係数 # memory_size = 10000 # バッファーメモリの大きさ MEMORY_SIZE = 1000 # バッファーメモリの大きさ BATCH_SIZE = 32 # Q-networkを更新するバッチの大記載 # MODE PARAMETER OBSERVE_PLAYER = 'RIGHT' DQN_MODE = 1 # 1がDQN、0がDDQNです LENDER_MODE = 0 # 0は学習後も描画なし、1は学習終了後に描画する # 損失関数の定義(huber関数) def huberloss(_y_true, _y_pred): EPSILON = 1.0 err = _y_true - _y_pred condition = K.abs(err) < EPSILON L2 = K.square(err) / 2 L1 = EPSILON * (K.abs(err) - EPSILON / 2) loss = tf.where(condition, L2, L1) return K.mean(loss) # Q関数をDLのネットワーククラスとして定義 class QNetwork: def __init__(self, _learning_rate=LEARNING_RATE, _state_size=SIZE_STATE, _action_size=SIZE_ACTION, _hidden_size=SIZE_HIDDEN): self.model = Sequential() self.model.add(Dense(_hidden_size, activation='relu', input_dim=_state_size)) self.model.add(Dense(_hidden_size, activation='relu')) self.model.add(Dense(_hidden_size, activation='relu')) self.model.add(Dense(_action_size, activation='linear')) self.optimizer = Adam(lr=_learning_rate) self.model.compile(loss=huberloss, optimizer=self.optimizer) # CNNの構造（未完成） # self.model = Sequential() # self.model.add(Conv2D(16, (3, 3), padding='same', input_shape=(5, 5), activation='relu')) # self.model.add(MaxPool2D(2, 2)) # self.model.add(Flatten()) # self.model.add(Dense(SIZE_HIDDEN, activation='relu')) # self.model.add(Dense(_action_size, activation='linear')) # self.optimizer = Adam(lr=_learning_rate) # self.model.compile(loss=huberloss, optimizer=self.optimizer) # 重みの学習 _memoryには（state, action, reward, next_state）群が格納 def replay(self, _memory, _batch_size, _gamma, _targetQN): inputs = np.zeros((_batch_size, SIZE_STATE)) targets = np.zeros((_batch_size, SIZE_ACTION)) mini_batch = _memory.sample(_batch_size) # 学習用の入力および出力を獲得 for i, (state_b, action_b, reward_b, next_state_b) in enumerate(mini_batch): inputs[i:i + 1] = state_b target = reward_b if not (next_state_b == np.zeros(state_b.shape)).all(axis=1): # 価値計算 retmainQs = self.model.predict(next_state_b)[0] next_action = np.argmax(retmainQs) # 配列内で最大要素のインデックスを返す target = reward_b + _gamma * _targetQN.model.predict(next_state_b)[0][next_action] targets[i] = self.model.predict(state_b) # Qネットワークの出力 int_action_b = 1 * action_b['right'] + 2 * action_b['left'] + 4 * action_b['space'] targets[i][int_action_b] = target # 教師信号 self.model.fit(inputs, targets, epochs=1, verbose=0) def save_network(self, _path_dir, _name_network): string_json_model = self.model.to_json() fp_model = open(_path_dir + '/' + _name_network + '_model.json', 'w') fp_model.write(string_json_model) self.model.save_weights(_path_dir + '/' + _name_network + '_weights.hdf5') # Experience replay と fixed target Q-networkを実現するためのメモリクラス class Memory: def __init__(self, _max_size=1000): self.buffer = deque(maxlen=_max_size) def add(self, _experience): self.buffer.append(_experience) def sample(self, _batch_size): # buffer内のインデックスを復元抽出で取り出す idx = np.random.choice(np.arange(len(self.buffer)), size=_batch_size, replace=False) return [self.buffer[ii] for ii in idx] def len(self): return len(self.buffer) # 状態に応じて行動を決定するクラス class Actor: # 確率epsilonに応じて報酬を最高にする行動を返す関数 def get_action(self, _state, _episode, _mainQN): # 徐々に最適な行動をとるΕ-greedy法 # Eが徐々に小さくなることで，最適行動をとる確率が高まる． # epsilon = 0.001 + 0.9 / (1.0 + _episode) epsilon = 1.0 - (_episode / NUM_EPISODES) if epsilon <= np.random.uniform(0, 1): list_return_target_Qs = _mainQN.model.predict(_state)[0] # 各行動への報酬のリストを返す action = np.argmax(list_return_target_Qs) else: action = np.random.choice(list(range(0, SIZE_ACTION))) dict_action = get_dict_action(action) return dict_action def get_dict_action(_int_act): if _int_act not in range(0, SIZE_ACTION): print('Error: _int_act in get_list_bin_action is out of range', file=sys.stderr) os.system('PAUSE') exit(-1) # actoin をバイナリの文字列で表現 str_bin_action = format(_int_act, 'b') for i in range(int(math.log2(SIZE_ACTION)) - len(str_bin_action)): str_bin_action = '0' + str_bin_action list_str_bin_action = list(str_bin_action) key_right = int(list_str_bin_action[2]) key_left = int(list_str_bin_action[1]) key_space = int(list_str_bin_action[0]) dict_pressed_key = {'right': key_right, 'left': key_left, 'space': key_space} return dict_pressed_key # メイン関数 def main(): # env = gym.make('CartPole-v0') # env = wrappers.Monitor(env, './movie/cartpoleDDQN', video_callable=(lambda ep: ep % 100 == 0)) # 動画保存する場合 # original environment os.environ['PYTHONHASHSEED'] = str(SEED) np.random.seed(SEED) tf.random.set_seed(SEED) # rn.seed(SEED) pygame.init() pygame.display.set_caption("Action Game AI") screen = pygame.display.set_mode((h.SCREEN_WIDTH, h.SCREEN_HEIGHT)) screen_sub1 = pygame.display.set_mode((h.SCREEN_WIDTH, h.SCREEN_HEIGHT)) screen_sub2 = pygame.display.set_mode((h.SCREEN_WIDTH, h.SCREEN_HEIGHT)) # env = myenv.MyEnv(_path_file_stage='./stage_sample.txt', _screen=screen) env = myenv.MyEnv(_path_file_stage='./stage_sample.txt', _screen=screen) env_sub1 = myenv.MyEnv(_path_file_stage='./stage_sub1.txt', _screen=screen_sub1) env_sub2 = myenv.MyEnv(_path_file_stage='./stage_sub2.txt', _screen=screen_sub2) islearned = 0 # 学習が終わったフラグ isrender = 0 # 描画フラグ # --- # ネットワーク・メモリ・Actorの生成 mainQN = QNetwork(_hidden_size=SIZE_HIDDEN, _learning_rate=LEARNING_RATE) targetQN = QNetwork(_hidden_size=SIZE_HIDDEN, _learning_rate=LEARNING_RATE) memory = Memory(_max_size=MEMORY_SIZE) actor = Actor() # メインルーチン for episode in range(NUM_EPISODES): env.reset() act_ini = env.action_space.sample() action = {'right': act_ini[0], 'left': act_ini[1], 'space': act_ini[2]} state, reward, is_done, _ = env.step(action) # 行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 state = np.reshape(state, [1, SIZE_STATE]) env_sub1.reset() state_sub1, reward_sub1, is_done_sub1, _ = env_sub1.step(action) # 行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 state_sub1 = np.reshape(state_sub1, [1, SIZE_STATE]) env_sub2.reset() state_sub2, reward_sub2, is_done_sub2, _ = env_sub2.step(action) # 行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 state_sub2 = np.reshape(state_sub2, [1, SIZE_STATE]) targetQN.model.set_weights(mainQN.model.get_weights()) # 1試行のループ list_reward = [] count_loop = 0 is_train_sub1 = False is_train_sub2 = False # for count_loop in range(SIZE_LOOP): # print(str(count)) while not is_done: count_loop += 1 # if (islearned == 1) and LENDER_MODE: # 学習終了時にcart-pole描画 # env.render() # time.sleep(0.1) # print(state[0, 0]) action = actor.get_action(state, episode, mainQN) # 時刻tでの行動を決定 if count_loop % 20 == 0: print(action) # (メインゲーム)行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 next_state, reward, is_done, info = env.step(action) next_state = np.reshape(next_state, [1, SIZE_STATE]) memory.add((state, action, reward, next_state)) # memory update state = next_state # state update list_reward.append(reward) # 終了判定 if is_done: if info['GAMEOVER']: if info['TIME'] == 0: print('MAIN {0}/{1}: TIME OVER'.format(episode + 1, NUM_EPISODES)) else: print('MAIN {0}/{1}: FALL GROUND'.format(episode + 1, NUM_EPISODES)) elif info['CLEAR']: print('MAIN {0}/{1}: CLEAR!'.format(episode + 1, NUM_EPISODES)) else: print('Error: Wrong information of main stage', file=sys.stderr) os.system('PAUSE') exit(-1) next_state = np.zeros(state.shape) next_state_sub1 = np.zeros(state_sub1.shape) next_state_sub2 = np.zeros(state_sub2.shape) break if is_train_sub1: action_sub1 = actor.get_action(state_sub1, episode, mainQN) # 時刻tでの行動を決定 # (サブゲーム)行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 next_state_sub1, reward_sub1, is_done_sub1, info_sub1 = env_sub1.step(action_sub1) next_state_sub1 = np.reshape(next_state_sub1, [1, SIZE_STATE]) memory.add((state_sub1, action_sub1, reward_sub1, next_state_sub1)) # memory update state_sub1 = next_state_sub1 # サブステージがゴールまで到着したら，メインの基礎学習を十分と判断し，このエピソード内では学習終了． if is_done_sub1: if info_sub1['GAMEOVER']: if info_sub1['TIME'] == 0: print('sub1 {0}/{1}: TIME OVER'.format(episode + 1, NUM_EPISODES)) else: print('sub1 {0}/{1}: FALL GROUND'.format(episode + 1, NUM_EPISODES)) elif info_sub1['CLEAR']: print('sub1 {0}/{1}: CLEAR!'.format(episode + 1, NUM_EPISODES)) else: print('Error: Wrong information of sub1 stage', file=sys.stderr) os.system('PAUSE') exit(-1) is_train_sub1 = False if is_train_sub2: action_sub2 = actor.get_action(state_sub2, episode, mainQN) # 時刻tでの行動を決定 # (サブゲーム)行動a_tの実行による行動後の観測データ・報酬・ゲーム終了フラグ・詳細情報 next_state_sub2, reward_sub2, is_done_sub2, info_sub2 = env_sub2.step(action_sub2) next_state_sub2 = np.reshape(next_state_sub2, [1, SIZE_STATE]) memory.add((state_sub2, action_sub2, reward_sub2, next_state_sub2)) # memory update state_sub2 = next_state_sub2 # サブステージがゴールまで到着したら，メインの基礎学習を十分と判断し，このエピソード内では学習終了． if is_done_sub2: if info_sub2['GAMEOVER']: if info_sub2['TIME'] == 0: print('sub2 {0}/{1}: TIME OVER'.format(episode + 1, NUM_EPISODES)) else: print('sub2 {0}/{1}: FALL GROUND'.format(episode + 1, NUM_EPISODES)) elif info_sub2['CLEAR']: print('sub2 {0}/{1}: CLEAR!'.format(episode + 1, NUM_EPISODES)) else: print('Error: Wrong information of sub2 stage', file=sys.stderr) os.system('PAUSE') exit(-1) is_train_sub2 = False # Q-networkの重みの学習と更新 if (memory.len() > BATCH_SIZE) and not is_done: mainQN.replay(memory, BATCH_SIZE, GAMMA, targetQN) if DQN_MODE: targetQN.model.set_weights(mainQN.model.get_weights()) print('{0}/{1}: {2}'.format(episode + 1, NUM_EPISODES, sum(list_reward) / len(list_reward))) # print(count_loop) dt_now = datetime.datetime.now() str_time = dt_now.strftime('%Y-%m-%d_%H-%M-%S') path_dirs = '../network/model_{0}'.format(str_time) os.makedirs(path_dirs, exist_ok=True) mainQN.save_network(_path_dir=path_dirs, _name_network='mainQN') plot_model(mainQN.model, to_file=path_dirs + '/Qnetwork.png', show_shapes=True) # Qネットワークの可視化 shutil.copy('./stage_sample.txt', path_dirs) if __name__ == '__main__': main()

[ "ocean90light@gmail.com" ]

ocean90light@gmail.com

b9e795b45a5b99bd04447a64e926dfb936b8a89e

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/Whats Your Name.py

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rivalTj7/Primera_Tarea_Python

e3f10d8f372e55078b30a835851e3f12a5607db1

a74ce4af39f0de46e831adc568a2c0bbf61909fb

refs/heads/master

2023-03-01T17:50:22.619024

2021-02-07T07:29:01

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#11- What's Your Name? def print_full_name(a, b): print("Hello "+a+" " +b+"! You just delved into python.") first_name = 'Ross' last_name = 'Taylor' print_full_name(first_name, last_name)

[ "rival.alex7@gmail.com" ]

rival.alex7@gmail.com

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/models/farkas.py

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no_license

APooladian/FarkasLayers

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refs/heads/master

2020-07-07T15:36:55.298600

2019-11-12T03:49:33

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import math import numpy as np import torch as th from torch import nn from torch.nn import functional as F from torch.nn.modules.utils import _pair from .utils import View, Avg2d from .blocks import Conv class FarkasLinear(nn.Module): def __init__(self, in_dim, out_dim, bn=True, nonlinear=True, dropout=0., init_type='standard',**kwargs): """A linear block, with guaranteed non-zero gradient. The linear layer is followed by batch normalization (if active) and a ReLU (again, if active) Args: in_dim: number of input dimensions out_dim: number of output dimensions bn (bool, optional): turn on batch norm (default: False) """ super().__init__() self.weight = nn.Parameter(th.randn(out_dim-1, in_dim)) self.bias = nn.Parameter(th.randn(out_dim)) self.out_dim = out_dim self.in_dim = in_dim self.nonlinear=nonlinear if bn: self.bn = nn.BatchNorm1d(out_dim, affine=False) else: self.bn = False if dropout>0.: self.dropout = nn.Dropout(p=dropout) else: self.dropout = False self.init_type = init_type if self.init_type == 'standard': self.reset_parameters() elif self.init_type == 'xavier': nn.init.xavier_normal_(self.weight.data) elif self.init_type == 'kaiming': nn.init.kaiming_normal(self.weight.data,mode='fan_in',nonlinearity='relu') elif self.init_type == 'zero_init': self.weight.data = nn.Parameter(th.zeros(out_dim,in_dim)) def reset_parameters(self): n = self.in_dim stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.uniform_(-stdv, stdv) def forward(self, x): if self.dropout: x = self.dropout(x) y = F.linear(x, self.weight, None) ybar = (-y).mean(dim=1,keepdim=True) y = th.cat([y,ybar],dim=1) bbar = th.max(-(self.bias[0:-1]).mean(),self.bias[-1]) b = th.cat([self.bias[0:-1],bbar.unsqueeze(0)],dim=0) y = y + b.view(1,self.out_dim) if self.nonlinear=='leaky_relu': y = F.leaky_relu(y) elif self.nonlinear=='selu': y = F.selu(y) elif self.nonlinear=='elu': y = F.elu(y) elif self.nonlinear: y = F.relu(y) if self.bn: y = self.bn(y) return y def extra_repr(self): s = ('{in_dim}, {out_dim}') if self.bn: s += ', batchnorm=True' else: s += ', batchnorm=False' return s.format(**self.__dict__) class FarkasConv(nn.Module): def __init__(self, in_channels, out_channels, stride=1, padding=None, kernel_size=(3,3), bn=True, nonlinear=True, dropout=0., init_type='standard',**kwargs): """A 2d convolution block, with guaranteed non-zero gradient. The convolution is followed by batch normalization (if active). Args: in_channels: number of input channels out_channels: number of output channels stride (int, optional): stride of the convolutions (default: 1) kernel_size (tuple, optional): kernel shape (default: 3) bn (bool, optional): turn on batch norm (default: False) """ super().__init__() if out_channels <2: raise ValueError('need out_channels>=2') self.weight = nn.Parameter(th.randn(out_channels-1, in_channels, *kernel_size)) self.bias = nn.Parameter(th.randn(out_channels)) self.stride = stride self.out_channels = out_channels self.in_channels = in_channels self.kernel_size=_pair(kernel_size) if padding is None: self.padding = tuple([k//2 for k in kernel_size]) else: self.padding = _pair(padding) self.nonlinear = nonlinear if bn: self.bn = nn.BatchNorm2d(out_channels, affine=False) else: self.bn = False if dropout>0.: self.dropout = nn.Dropout(p=dropout) else: self.dropout = False self.init_type = init_type if self.init_type == 'standard': self.reset_parameters() elif self.init_type == 'xavier': nn.init.xavier_normal_(self.weight.data) elif self.init_type == 'kaiming': nn.init.kaiming_normal(self.weight.data,mode='fan_in',nonlinearity='relu') elif self.init_type == 'zero_init': self.weight.data = nn.Parameter(th.zeros(out_channels-1, in_channels, *kernel_size)) def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.uniform_(-stdv, stdv) def forward(self, x): if self.dropout: x = self.dropout(x) y = F.conv2d(x, self.weight, None, self.stride, self.padding, 1, 1) ybar = (-y).mean(dim=1,keepdim=True) y = th.cat([y,ybar],dim=1) bbar = th.max( - (self.bias[0:-1]).mean() , self.bias[-1]) b = th.cat([self.bias[0:-1],bbar.unsqueeze(0)],dim=0) y = y + b.view(1,self.out_channels,1,1) if self.nonlinear=='leaky_relu': y = F.leaky_relu(y) elif self.nonlinear=='selu': y = F.selu(y) elif self.nonlinear=='elu': y = F.elu(y) elif self.nonlinear: y = F.relu(y) if self.bn: y = self.bn(y) return y def extra_repr(self): s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}' ', stride={stride}') if self.padding != (0,) * len(self.padding): s += ', padding={padding}' if self.bn: s += ', batchnorm=True' else: s += ', batchnorm=False' return s.format(**self.__dict__) class FarkasBlock(nn.Module): def __init__(self, channels, kernel_size=(3,3), bn=True, nonlinear=True, dropout = 0., residual=True, weight_init='standard',zero_last=False,**kwargs): """A basic 2d ResNet block, with modifications on original ResNet paper [1]. Every convolution is followed by batch normalization (if active). The gradient is guaranteed to be non-zero. Args: channels: number of input and output channels kernel_size (tuple, optional): kernel shape (default: 3) bn (bool, optional): turn on batch norm (default: False) [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2016. Deep Residual Learning for Image Recognition. arXiv:1512.03385 """ super().__init__() self.in_channels = channels self.out_channels = channels+1 self.kernel_size = _pair(kernel_size) self.nonlinear=nonlinear self.residual = residual self.conv0 = FarkasConv(channels, channels, kernel_size=kernel_size, bn=bn, nonlinear=nonlinear, init_type=weight_init) if zero_last: self.weight = nn.Parameter(th.zeros(channels,channels,*kernel_size)) self.bias=nn.Parameter(th.zeros(channels+1)) else: self.weight = nn.Parameter(th.randn(channels, channels, *kernel_size)) self.bias = nn.Parameter(th.randn(channels+1)) self.padding = tuple([k//2 for k in kernel_size]) if bn: self.bn = nn.BatchNorm2d(channels+1, affine=False) else: self.bn = False if dropout>0.: self.dropout = nn.Dropout(p=dropout) else: self.dropout = False self.init_type = weight_init if not zero_last: if self.init_type == 'standard': self.reset_parameters() elif self.init_type == 'xavier': nn.init.xavier_normal_(self.weight.data) elif self.init_type == 'kaiming': nn.init.kaiming_normal(self.weight.data,mode='fan_in',nonlinearity='relu') def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.uniform_(-stdv, stdv) def forward(self, x): if self.dropout: y = self.dropout(x) else: y=x y = self.conv0(y) if self.dropout: y = self.dropout(y) y = F.conv2d(y, self.weight, None, 1, self.padding, 1, 1) if self.residual: ybar = (-x-y).mean(dim=1,keepdim=True) y = th.cat([x+y,ybar],dim=1) else: ybar = (-y).mean(dim=1,keepdim=True) y = th.cat([y,ybar],dim=1) bbar = th.max( - (self.bias[0:-1]).mean(),self.bias[-1]) b = th.cat([self.bias[0:-1],bbar.unsqueeze(0)],dim=0) y = y + b.view(1,self.out_channels,1,1) if self.nonlinear=='leaky_relu': y = F.leaky_relu(y) elif self.nonlinear=='selu': y = F.selu(y) elif self.nonlinear=='elu': y = F.elu(y) elif self.nonlinear: y = F.relu(y) if self.bn: y = self.bn(y) return y def extra_repr(self): s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}') if self.bn: s += ', batchnorm=True' else: s += ', batchnorm=False' return s.format(**self.__dict__) class FarkasBottleneck(nn.Module): def __init__(self, channels, kernel_size=(3,3), bn=True, nonlinear=True, dropout = 0., residual=True, weight_init='standard',zero_last=False,**kwargs): """A basic 2d ResNet block, with modifications on original ResNet paper [1]. Every convolution is followed by batch normalization (if active). The gradient is guaranteed to be non-zero. Args: channels: number of input and output channels kernel_size (tuple, optional): kernel shape (default: 3) bn (bool, optional): turn on batch norm (default: False) [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2016. Deep Residual Learning for Image Recognition. arXiv:1512.03385 """ super().__init__() self.in_channels = channels self.out_channels = channels+1 self.kernel_size = _pair(kernel_size) self.nonlinear = nonlinear self.residual = residual self.conv0 = FarkasConv(channels, channels//4, kernel_size=(1,1), bn=bn, nonlinear=nonlinear, init_type=weight_init) self.conv1 = FarkasConv(channels//4, channels//4, kernel_size=kernel_size, bn=bn, nonlinear=nonlinear,init_type=weight_init) if zero_last: self.weight = nn.Parameter(th.zeros(channels,channels//4, 1,1)) self.bias = nn.Parameter(th.zeros(channels+1)) else: self.weight = nn.Parameter(th.randn(channels, channels//4, 1,1)) self.bias = nn.Parameter(th.randn(channels+1)) if bn: self.bn = nn.BatchNorm2d(channels+1, affine=False) else: self.bn = False if dropout>0.: self.dropout = nn.Dropout(p=dropout) else: self.dropout = False self.init_type = weight_init if self.init_type == 'standard': self.reset_parameters() elif self.init_type == 'xavier': nn.init.xavier_normal_(self.weight.data) elif self.init_type == 'kaiming': nn.init.kaiming_normal(self.weight.data,mode='fan_in',nonlinearity='relu') def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.uniform_(-stdv, stdv) def forward(self, x): if self.dropout: y = self.dropout(x) else: y=x y = self.conv0(y) if self.dropout: y = self.dropout(y) y = self.conv1(y) if self.dropout: y = self.dropout(y) y = F.conv2d(y, self.weight, None, 1, 0, 1, 1) if self.residual: ybar = (-x - y).mean(dim=1,keepdim=True) y = th.cat([x+y,ybar],dim=1) else: ybar = (-y).mean(dim=1,keepdim=True) y = th.cat([y,ybar],dim=1) bbar = th.max(-(self.bias[0:-1]).mean(),self.bias[-1]) b = th.cat([self.bias[0:-1],bbar.unsqueeze(0)],dim=0) y = y + b.view(1,self.out_channels,1,1) if self.nonlinear=='leaky_relu': y = F.leaky_relu(y) elif self.nonlinear=='selu': y = F.selu(y) elif self.nonlinear=='elu': y = F.elu(y) elif self.nonlinear: y = F.relu(y) if self.bn: y = self.bn(y) return y def extra_repr(self): s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}') if self.bn: s += ', batchnorm=True' else: s += ', batchnorm=False' return s.format(**self.__dict__) class FarkasNet(nn.Module): def __init__(self, layers, block=FarkasBlock, in_channels=3, classes=10, kernel_size=(3,3), nonlinear=True, conv0_kwargs = {'kernel_size':(3,3), 'stride':1}, conv0_pool=None, downsample_pool=nn.AvgPool2d, last_layer_nonlinear=False, last_layer_bn=None, dropout=0.,weight_init='standard',zero_last=False, bn=True, base_channels=16, **kwargs): if last_layer_bn is None: last_layer_bn=bn super().__init__() kernel_size = _pair(kernel_size) def make_layer(n, block, in_channels, out_channels, stride): sublayers = [] if not in_channels==out_channels: conv = FarkasConv sublayers.append(conv(in_channels, out_channels, kernel_size=(1,1), nonlinear=True, dropout=dropout, bn=bn,init_type=weight_init)) if stride>1: sublayers.append(downsample_pool(stride)) for k in range(n): u = k sublayers.append(block(out_channels+u, kernel_size=kernel_size, dropout=dropout, bn=bn, nonlinear=nonlinear, weight_init=weight_init,zero_last=zero_last,**kwargs)) return nn.Sequential(*sublayers) conv = FarkasConv pdsz = [k//2 for k in conv0_kwargs['kernel_size'] ] self.layer0 = conv(in_channels, base_channels, padding=pdsz, **conv0_kwargs, dropout=dropout, bn=bn, nonlinear=nonlinear,weight_init=weight_init) if conv0_pool: self.maxpool = conv0_pool else: self.maxpool = False _layers = [] for i, n in enumerate(layers): if i==0: _layers.append(make_layer(n, block, base_channels, base_channels, 1)) else: u = layers[i-1] _layers.append(make_layer(n, block, base_channels*(2**(i-1))+u, base_channels*(2**i), 2)) self.layers = nn.Sequential(*_layers) self.pool = Avg2d() u = layers[-1] self.view = View((2**i)*base_channels+u) if dropout>0: self.dropout = nn.Dropout(p=dropout) else: self.dropout = False self.fc = nn.Linear((2**i)*base_channels+u,classes) self.nonlinear=nonlinear self.bn = bn @property def num_parameters(self): return sum([w.numel() for w in self.parameters()]) def forward(self, x): x = self.layer0(x) if self.maxpool: x = self.maxpool(x) x = self.layers(x) x = self.pool(x) x = self.view(x) if self.dropout: x = self.dropout(x) x = self.fc(x) return x def FarkasNet18(**kwargs): m = FarkasNet([3,3,3],block=FarkasBlock,**kwargs) return m def FarkasNet50(**kwargs): m = FarkasNet([3,4,6,3],base_channels=64,block=FarkasBottleneck,**kwargs) return m def FarkasNet101(**kwargs): m = FarkasNet([3,4,23,3],base_channels=64,block=FarkasBottleneck,**kwargs) return m def FarkasNet110(**kwargs): m = FarkasNet([18,18,18],block=FarkasBlock,**kwargs) def FarkasNet34(**kwargs): m = FarkasNet([5,5,5],block=FarkasBlock,**kwargs) return m

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from enum import Enum from bitarray import bitarray class MessageKind(Enum): READER_TO_TAG = 0 TAG_TO_READER = 1 def __str__(self) -> str: if self == MessageKind.READER_TO_TAG: return 'READER_TO_TAG' elif self == MessageKind.TAG_TO_READER: return 'TAG_TO_READER' else: return '' class Message(object): def __init__(self, label: str, kind: MessageKind, content: bitarray): self.label = label self.kind = kind self.content = content def size(self) -> int: return self.content.length()

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""" `conftest.py` and `pylenium.json` files should stay at your Workspace Root. conftest.py Although this file is editable, you should only change its contents if you know what you are doing. Instead, you can create your own conftest.py file in the folder where you store your ui_tests. pylenium.json You can change the values, but DO NOT touch the keys or you will break the schema. py The only fixture you really need from this is `py`. This is the instance of Pylenium for each test. Just pass py into your test and you're ready to go! Examples: def test_go_to_google(py): py.visit('https://google.com') assert 'Google' in py.title() """ import json import logging import os import shutil import sys from pathlib import Path import pytest import requests from faker import Faker from pytest_reportportal import RPLogger, RPLogHandler from pylenium.driver import Pylenium from pylenium.config import PyleniumConfig, TestCase from pylenium.a11y import PyleniumAxe @pytest.fixture(scope='function') def fake() -> Faker: """A basic instance of Faker to make test data.""" return Faker() @pytest.fixture(scope='function') def api(): """A basic instance of Requests to make HTTP API calls.""" return requests @pytest.fixture(scope="session") def rp_logger(request): """Report Portal Logger""" logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) # Create handler for Report Portal if the service has been # configured and started. if hasattr(request.node.config, 'py_test_service'): # Import Report Portal logger and handler to the test module. logging.setLoggerClass(RPLogger) rp_handler = RPLogHandler(request.node.config.py_test_service) # Add additional handlers if it is necessary console_handler = logging.StreamHandler(sys.stdout) console_handler.setLevel(logging.INFO) logger.addHandler(console_handler) else: rp_handler = logging.StreamHandler(sys.stdout) # Set INFO level for Report Portal handler. rp_handler.setLevel(logging.INFO) return logger @pytest.fixture(scope='session', autouse=True) def project_root() -> str: """The Project (or Workspace) root as a filepath. * This conftest.py file should be in the Project Root if not already. """ return os.path.dirname(os.path.abspath(__file__)) @pytest.fixture(scope='session', autouse=True) def test_run(project_root, request) -> str: """Creates the `/test_results` directory to store the results of the Test Run. Returns: The `/test_results` directory as a filepath (str). """ session = request.node test_results_dir = f'{project_root}/test_results' if os.path.exists(test_results_dir): # delete /test_results from previous Test Run shutil.rmtree(test_results_dir, ignore_errors=True) try: # race condition can occur between checking file existence and # creating the file when using pytest with multiple workers Path(test_results_dir).mkdir(parents=True, exist_ok=True) except FileExistsError: pass for test in session.items: try: # make the test_result directory for each test Path(f'{test_results_dir}/{test.name}').mkdir(parents=True, exist_ok=True) except FileExistsError: pass return test_results_dir @pytest.fixture(scope='session') def py_config(project_root, request) -> PyleniumConfig: """Initialize a PyleniumConfig for each test 1. This starts by deserializing the user-created pylenium.json from the Project Root. 2. If that file is not found, then proceed with Pylenium Defaults. 3. Then any CLI arguments override their respective key/values. """ try: # 1. Load pylenium.json in Project Root, if available with open(f'{project_root}/pylenium.json') as file: _json = json.load(file) config = PyleniumConfig(**_json) except FileNotFoundError: # 2. pylenium.json not found, proceed with defaults config = PyleniumConfig() # 3. Override with any CLI args/options # Driver Settings cli_remote_url = request.config.getoption('--remote_url') if cli_remote_url: config.driver.remote_url = cli_remote_url cli_browser_options = request.config.getoption('--options') if cli_browser_options: config.driver.options = [option.strip() for option in cli_browser_options.split(',')] cli_browser = request.config.getoption('--browser') if cli_browser: config.driver.browser = cli_browser cli_capabilities = request.config.getoption('--caps') if cli_capabilities: # --caps must be in '{"name": "value", "boolean": true}' format # with double quotes around each key. booleans are lowercase. config.driver.capabilities = json.loads(cli_capabilities) cli_page_wait_time = request.config.getoption('--page_load_wait_time') if cli_page_wait_time and cli_page_wait_time.isdigit(): config.driver.page_load_wait_time = int(cli_page_wait_time) # Logging Settings cli_pylog_level = request.config.getoption('--pylog_level') if cli_pylog_level: config.logging.pylog_level = cli_pylog_level cli_screenshots_on = request.config.getoption('--screenshots_on') if cli_screenshots_on: shots_on = True if cli_screenshots_on.lower() == 'true' else False config.logging.screenshots_on = shots_on cli_extensions = request.config.getoption('--extensions') if cli_extensions: config.driver.extension_paths = [ext.strip() for ext in cli_extensions.split(',')] return config @pytest.fixture(scope='function') def test_case(test_run, py_config, request) -> TestCase: """Manages data pertaining to the currently running Test Function or Case. * Creates the test-specific logger. Args: test_run: The Test Run (or Session) this test is connected to. Returns: An instance of TestCase. """ test_name = request.node.name test_result_path = f'{test_run}/{test_name}' py_config.driver.capabilities.update({'name': test_name}) return TestCase(name=test_name, file_path=test_result_path) @pytest.fixture(scope='function') def py(test_case, py_config, request, rp_logger): """Initialize a Pylenium driver for each test. Pass in this `py` fixture into the test function. Examples: def test_go_to_google(py): py.visit('https://google.com') assert 'Google' in py.title() """ py = Pylenium(py_config) yield py try: if request.node.report.failed: # if the test failed, execute code in this block if py_config.logging.screenshots_on: screenshot = py.screenshot(f'{test_case.file_path}/test_failed.png') with open(screenshot, "rb") as image_file: rp_logger.info( "Test Failed - Attaching Screenshot", attachment={"name": "test_failed.png", "data": image_file, "mime": "image/png"}, ) except AttributeError: rp_logger.error('Unable to access request.node.report.failed, unable to take screenshot.') except TypeError: rp_logger.info('Report Portal is not connected to this test run.') py.quit() @pytest.fixture(scope='function') def axe(py) -> PyleniumAxe: """The aXe A11y audit tool as a fixture.""" return PyleniumAxe(py.webdriver) @pytest.hookimpl(tryfirst=True, hookwrapper=True) def pytest_runtest_makereport(item, call): """Yield each test's outcome so we can handle it in other fixtures.""" outcome = yield report = outcome.get_result() if report.when == 'call': setattr(item, "report", report) return report def pytest_addoption(parser): parser.addoption('--browser', action='store', default='', help='The lowercase browser name: chrome | firefox') parser.addoption('--remote_url', action='store', default='', help='Grid URL to connect ui_tests to.') parser.addoption('--screenshots_on', action='store', default='', help="Should screenshots be saved? true | false") parser.addoption('--pylog_level', action='store', default='', help="Set the pylog_level: 'off' | 'info' | 'debug'") parser.addoption( '--options', action='store', default='', help='Comma-separated list of Browser Options. Ex. "headless, incognito"', ) parser.addoption( '--caps', action='store', default='', help='List of key-value pairs. Ex. \'{"name": "value", "boolean": true}\'', ) parser.addoption( '--page_load_wait_time', action='store', default='', help='The amount of time to wait for a page load before raising an error. Default is 0.', ) parser.addoption( '--extensions', action='store', default='', help='Comma-separated list of extension paths. Ex. "*.crx, *.crx"' )

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Utilities to code summaries.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import os import sqlite3 from tensorflow.core.util import event_pb2 from tensorflow.python.framework import test_util from tensorflow.python.lib.io import tf_record from tensorflow.python.ops import summary_ops_v2 as summary_ops from tensorflow.python.platform import gfile class SummaryDbTest(test_util.TensorFlowTestCase): """Helper for summary database testing.""" def setUp(self): super(SummaryDbTest, self).setUp() self.db_path = os.path.join(self.get_temp_dir(), 'DbTest.sqlite') if os.path.exists(self.db_path): os.unlink(self.db_path) self.db = sqlite3.connect(self.db_path) self.create_db_writer = functools.partial( summary_ops.create_db_writer, db_uri=self.db_path, experiment_name='experiment', run_name='run', user_name='user') def tearDown(self): self.db.close() super(SummaryDbTest, self).tearDown() def events_from_file(filepath): """Returns all events in a single event file. Args: filepath: Path to the event file. Returns: A list of all tf.Event protos in the event file. """ records = list(tf_record.tf_record_iterator(filepath)) result = [] for r in records: event = event_pb2.Event() event.ParseFromString(r) result.append(event) return result def events_from_logdir(logdir): """Returns all events in the single eventfile in logdir. Args: logdir: The directory in which the single event file is sought. Returns: A list of all tf.Event protos from the single event file. Raises: AssertionError: If logdir does not contain exactly one file. """ assert gfile.Exists(logdir) files = gfile.ListDirectory(logdir) assert len(files) == 1, 'Found not exactly one file in logdir: %s' % files return events_from_file(os.path.join(logdir, files[0])) def get_one(db, q, *p): return db.execute(q, p).fetchone()[0] def get_all(db, q, *p): return unroll(db.execute(q, p).fetchall()) def unroll(list_of_tuples): return sum(list_of_tuples, ())

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import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'hiStoryBackend.settings') application = get_wsgi_application()

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print("helloAll")

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"""Test graph algorithms relevant to Rao-Teh sampling. """ from __future__ import division, print_function, absolute_import import itertools from collections import defaultdict import networkx as nx from numpy.testing import (run_module_suite, TestCase, assert_equal, assert_allclose, assert_, assert_raises) from raoteh.sampler._graph_transform import ( get_edge_bisected_graph, get_node_to_state, remove_redundant_nodes, get_redundant_degree_two_nodes, get_chunk_tree, add_trajectories, ) # This is an official itertools recipe. def powerset(iterable): "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)" s = list(iterable) return itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(len(s)+1)) class TestGraphTransform(TestCase): def test_get_edge_bisected_graph(self): # Create an example from the networkx documentation. G = nx.Graph() G.add_weighted_edges_from([ (1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2), (3, 4, 0.375)]) # Create a new graph by bisecting the edges of the old graph. H = get_edge_bisected_graph(G) # The edge-bisected graph has twice as many edges. assert_equal(len(G.edges()) * 2, len(H.edges())) assert_equal(G.size()*2, H.size()) # The sum of edge weights is unchanged. assert_allclose(G.size(weight='weight'), H.size(weight='weight')) # The node set of the edge-bisected graph includes that of the original. assert_(set(G) <= set(H)) # The added nodes are each greater than each original node. assert_(max(G) < min(set(H) - set(G))) def test_get_chunk_tree(self): # Define the original tree and its event nodes. # This is taken from a doodle in my notebook, # and it is not particularly cleverly chosen. tree_edges = ( (0, 1), (1, 2), (3, 4), (4, 2), (2, 5), (5, 6), (6, 7), (7, 8), (8, 9), (7, 10), (10, 11), (11, 12), (12, 13), (13, 14), (13, 15), (15, 16), (16, 17), ) event_nodes = {1, 4, 5, 6, 8, 10, 11, 12, 15, 16} # Create a tree by specifying the edges. T = nx.Graph() T.add_edges_from(tree_edges) # Run tests, using all possible roots and also a default root. potential_roots = list(T) + [None] for root in potential_roots: # Construct the chunk tree and its associated node maps. results = get_chunk_tree(T, event_nodes) chunk_tree, non_event_map, event_map = results # The nodes pointed to by the non_event_map # should be nodes in the chunk_tree. assert_(set(non_event_map.values()) <= set(T)) # The output tree should have 11 nodes and 10 edges. assert_equal(len(chunk_tree), 11) assert_equal(len(chunk_tree.edges()), 10) # The 8 non-event nodes should map to 7 unique chunk nodes. assert_equal(len(non_event_map), 8) assert_equal(len(set(non_event_map.values())), 7) # The non-event nodes 13 and 14 should map to the same chunk. assert_equal(non_event_map[13], non_event_map[14]) def test_remove_redundant_nodes_short_path(self): # Define a short path with one redundant # and one non-redundant internal node. T = nx.Graph() T.add_edge(0, 1, state=0, weight=1) T.add_edge(1, 2, state=0, weight=1) T.add_edge(2, 3, state=1, weight=1) # Try removing a redundant node. redundant_nodes = {1} T_out = remove_redundant_nodes(T, redundant_nodes) assert_equal(set(T_out), set(T) - redundant_nodes) assert_equal(T_out[0][2]['weight'], 2) # Fail at removing a non-redundant node. redundant_nodes = {2} assert_raises( Exception, remove_redundant_nodes, T, redundant_nodes) def test_remove_redundant_nodes_long_path(self): # Define a path with multiple consecutive redundant internal nodes. T = nx.Graph() T.add_edge(0, 1, state=0, weight=1.1) T.add_edge(1, 2, state=0, weight=1.2) T.add_edge(2, 3, state=1, weight=1.3) T.add_edge(3, 4, state=1, weight=1.4) T.add_edge(4, 5, state=1, weight=1.5) T.add_edge(5, 6, state=1, weight=1.6) T.add_edge(6, 7, state=1, weight=1.7) # Get the original weighted size. # This is the sum of weights of all edges. original_size = T.size(weight='weight') # Check the set of redundant nodes. all_redundant_nodes = {1, 3, 4, 5, 6} obs_nodes = get_redundant_degree_two_nodes(T) assert_equal(all_redundant_nodes, obs_nodes) # Try removing all valid combinations of redundant nodes. for redundant_node_tuple in powerset(all_redundant_nodes): redundant_nodes = set(redundant_node_tuple) T_out = remove_redundant_nodes(T, redundant_nodes) assert_equal(set(T_out), set(T) - redundant_nodes) assert_allclose(T_out.size(weight='weight'), original_size) def test_remove_redundant_nodes_small_tree(self): # Define a short path with one redundant # and one non-redundant internal node. T = nx.Graph() T.add_edge(0, 1, state=0, weight=1) T.add_edge(0, 2, state=0, weight=1) T.add_edge(0, 3, state=0, weight=1) # None of the nodes are considered redundant in the current # implementation, because each node is of degree 1 or 3. for redundant_nodes in ({0}, {1}, {2}, {3}): assert_raises( Exception, remove_redundant_nodes, T, redundant_nodes) def test_remove_redundant_nodes_medium_tree(self): # Define a tree. T = nx.Graph() T.add_edge(0, 10, state=0, weight=1.1) T.add_edge(0, 20, state=0, weight=1.2) T.add_edge(0, 30, state=0, weight=1.3) T.add_edge(20, 21, state=0, weight=1.4) T.add_edge(30, 31, state=0, weight=1.5) T.add_edge(31, 32, state=0, weight=1.6) # Get the original weighted size. # This is the sum of weights of all edges. original_size = T.size(weight='weight') # Try removing all valid combinations of redundant nodes. for redundant_node_tuple in powerset((20, 30, 31)): redundant_nodes = set(redundant_node_tuple) T_out = remove_redundant_nodes(T, redundant_nodes) assert_equal(set(T_out), set(T) - redundant_nodes) assert_allclose(T_out.size(weight='weight'), original_size) class TestAddTrajectories(TestCase): def test_compatible_trees(self): T_base = nx.Graph() T_base.add_edge(0, 1, weight=0.1) T_base.add_edge(0, 2, weight=0.1) T_base.add_edge(0, 3, weight=0.1) T_traj = nx.Graph() T_traj.add_edge(0, 1, state=0, weight=0.1) T_traj.add_edge(0, 20, state=0, weight=0.05) T_traj.add_edge(20, 2, state=0, weight=0.05) T_traj.add_edge(0, 3, state=0, weight=0.1) root = 0 T_merged, dummy_nodes = add_trajectories(T_base, root, [T_traj]) # There should not be any dummy nodes. assert_equal(dummy_nodes, set()) # The merged tree should have four edges. assert_equal(T_base.size(), 3) assert_equal(T_merged.size(), 4) # The total weight of the merged tree # should be the same as the total weight of the base tree. assert_allclose( T_merged.size(weight='weight'), T_base.size(weight='weight')) def test_incompatible_trees(self): T_base = nx.Graph() T_base.add_edge(0, 1, weight=0.1) T_base.add_edge(0, 2, weight=0.1) T_base.add_edge(0, 3, weight=0.1) root = 0 # Define a trajectory that is bad because it adds a high degree node. traj = nx.Graph() traj.add_edge(0, 4, state=0, weight=0.1) traj.add_edge(4, 20, state=0, weight=0.05) traj.add_edge(20, 2, state=0, weight=0.05) traj.add_edge(4, 3, state=0, weight=0.1) assert_raises(ValueError, add_trajectories, T_base, root, [traj]) # Define a trajectory that is bad because it adds a leaf node. traj = nx.Graph() traj.add_edge(0, 1, state=0, weight=0.1) traj.add_edge(0, 20, state=0, weight=0.05) traj.add_edge(20, 2, state=0, weight=0.05) traj.add_edge(0, 3, state=0, weight=0.05) traj.add_edge(3, 4, state=0, weight=0.05) assert_raises(ValueError, add_trajectories, T_base, root, [traj]) # Define a trajectory that is bad # because it flips around the nodes in a way that is incompatible # with the original tree topology. traj = nx.Graph() traj.add_edge(1, 0, state=0, weight=0.1) traj.add_edge(1, 2, state=0, weight=0.1) traj.add_edge(1, 3, state=0, weight=0.1) assert_raises(ValueError, add_trajectories, T_base, root, [traj]) def test_complicated_incompatible_trees(self): T_base = nx.Graph() T_base.add_edge(0, 1, weight=0.1) T_base.add_edge(0, 2, weight=0.1) T_base.add_edge(0, 3, weight=0.1) T_base.add_edge(3, 4, weight=0.1) T_base.add_edge(3, 5, weight=0.1) root = 0 # Define a trajectory that is bad # because the topology is different in a way that cannot be detected # by checking the degrees of the nodes. traj = nx.Graph() traj.add_edge(3, 1, state=0, weight=0.1) traj.add_edge(3, 2, state=0, weight=0.1) traj.add_edge(3, 0, state=0, weight=0.1) traj.add_edge(0, 4, state=0, weight=0.1) traj.add_edge(0, 5, state=0, weight=0.1) assert_raises(ValueError, add_trajectories, T_base, root, [traj]) def test_edge_to_event_times(self): # The merged tree will look like the following, # where 'x' is a node in the original tree, # and 'a' is a node introduced by trajectory merging, # and 'o' is an event node. # # x # /|\ # / | \ # | | | # o o x # | | | # x | | (0, 0) # x | # x # /| (0, 0) # / a # / | (0, 10) # | a # x | (5, 10) # a # | (5, 0) # o # | (5, 0) # a # | (0, 0) # x # T = nx.Graph() T.add_edge(0, 1, weight=0.1) T.add_edge(0, 2, weight=0.1) T.add_edge(0, 3, weight=0.1) T.add_edge(3, 4, weight=0.1) T.add_edge(3, 5, weight=0.1) T.add_edge(4, 6, weight=0.1) root = 0 # Define a trajectory with an extra segment along one edge. traj_a = nx.Graph() traj_a.add_edge(0, 1, weight=0.1, state=0) traj_a.add_edge(0, 2, weight=0.1, state=0) traj_a.add_edge(0, 3, weight=0.1, state=0) traj_a.add_edge(3, 4, weight=0.1, state=0) traj_a.add_edge(3, 5, weight=0.1, state=0) traj_a.add_edge(4, 10, weight=0.025, state=0) traj_a.add_edge(10, 11, weight=0.05, state=5) traj_a.add_edge(11, 6, weight=0.025, state=0) # Define a trajectory with an interleaving segment. traj_b = nx.Graph() traj_b.add_edge(0, 1, weight=0.1, state=0) traj_b.add_edge(0, 2, weight=0.1, state=0) traj_b.add_edge(0, 3, weight=0.1, state=0) traj_b.add_edge(3, 4, weight=0.1, state=0) traj_b.add_edge(3, 5, weight=0.1, state=0) traj_b.add_edge(4, 20, weight=0.02, state=0) traj_b.add_edge(20, 21, weight=0.02, state=10) traj_b.add_edge(21, 6, weight=0.06, state=0) # Define a few event times along directed edges, # where the edge direction radiates away from the root. edge_to_event_times = { (0, 1) : {0.06}, (0, 2) : {0.02}, (4, 6) : {0.045}, } # Construct the merged tree. T_merged, event_nodes = add_trajectories( T, root, [traj_a, traj_b], edge_to_event_times=edge_to_event_times) # After this point are some tests. # Check the total number of nodes in the merged tree. assert_equal(len(T_merged.edges()), 13) # Check the multiset of edge state pairs in the merged tree. state_pair_to_count = defaultdict(int) for edge in nx.bfs_edges(T_merged, root): na, nb = edge states = T_merged[na][nb]['states'] state_pair = tuple(states) assert_equal(len(state_pair), 2) state_pair_to_count[state_pair] += 1 assert_equal(state_pair_to_count[(0, 10)], 1) assert_equal(state_pair_to_count[(5, 10)], 1) assert_equal(state_pair_to_count[(5, 0)], 2) expected_state_pairs = set([(0, 0), (0, 10), (5, 10), (5, 0)]) assert_equal(set(state_pair_to_count), expected_state_pairs) # Check that the number of event nodes is correct. assert_equal(len(edge_to_event_times), len(event_nodes)) # The merged tree must contain all of the nodes of the original tree. missing_nodes = set(T) - set(T_merged) assert_equal(missing_nodes, set()) # The base tree, the two trajectories, and the merged tree # should all have the same weighted size. weighted_size = T.size(weight='weight') assert_allclose(traj_a.size(weight='weight'), weighted_size) assert_allclose(traj_b.size(weight='weight'), weighted_size) assert_allclose(T_merged.size(weight='weight'), weighted_size) # Each event node must be adjacent to exactly two edges # in the merged tree, and both of these edges # must be annotated with the same sequence of state values. for node in event_nodes: assert_equal(T_merged.degree(node), 2) na, nb = T_merged[node] na_states = T_merged[node][na]['states'] nb_states = T_merged[node][nb]['states'] assert_equal(na_states, nb_states) # Print the edges of the merged tree. """ print() print('--- add_trajectories test output ---') print(event_nodes) for edge in nx.bfs_edges(T_merged, root): na, nb = edge weight = T_merged[na][nb]['weight'] states = T_merged[na][nb]['states'] print(na, nb, weight, states) print() """ """ 0 8 0.02 [0, 0] 0 3 0.1 [0, 0] 0 7 0.06 [0, 0] 8 2 0.08 [0, 0] 3 4 0.1 [0, 0] 3 5 0.1 [0, 0] 7 1 0.04 [0, 0] 4 9 0.02 [0, 0] 9 10 0.005 [0, 10] 10 11 0.015 [5, 10] 11 12 0.005 [5, 0] 12 13 0.03 [5, 0] 13 6 0.025 [0, 0] """ class TestGetNodeToState(TestCase): def test_get_node_to_state_simple_tree_identical_states(self): T = nx.Graph() T.add_edge(0, 1, state=42) T.add_edge(1, 2, state=42) all_query_nodes = {0, 1, 2} for query_nodes in powerset(all_query_nodes): nnodes = len(query_nodes) node_to_state = get_node_to_state(T, query_nodes) assert_equal(set(node_to_state), set(query_nodes)) assert_equal(set(node_to_state.values()), set([42]*nnodes)) def test_get_node_to_state_simple_tree_different_states(self): T = nx.Graph() T.add_edge(0, 1, state=42) T.add_edge(1, 2, state=42) T.add_edge(2, 3, state=99) # Some of the nodes have defined states. query_nodes = {0, 1, 3} node_to_state = get_node_to_state(T, query_nodes) assert_equal(node_to_state, {0:42, 1:42, 3:99}) # But node 2 does not have a defined state # because it represents a state transition. query_nodes = {0, 1, 2, 3} assert_raises(ValueError, get_node_to_state, T, query_nodes) def test_complicated_tree(self): T = nx.Graph() T.add_edge(0, 1, state=2) T.add_edge(0, 2, state=2) T.add_edge(0, 3, state=2) T.add_edge(3, 4, state=10) T.add_edge(4, 5, state=10) T.add_edge(4, 6, state=10) # Most of the nodes have defined states. query_nodes = {0, 1, 2, 4, 5, 6} expected_node_to_state = {0:2, 1:2, 2:2, 4:10, 5:10, 6:10} node_to_state = get_node_to_state(T, query_nodes) assert_equal(node_to_state, expected_node_to_state) # One of the nodes is a transition without a defined state. query_nodes = {0, 1, 2, 3, 4, 5, 6} assert_raises(ValueError, get_node_to_state, T, query_nodes) if __name__ == '__main__': run_module_suite()

[ "argriffi@ncsu.edu" ]

argriffi@ncsu.edu

3bdd06f837466e17a98dd8946a3ad205b882c0ee

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/VirtEnv2/bin/html2text

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no_license

boyleconnor/MacWorld

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null

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#!/Users/connor/PycharmProjects/MacWorld/VirtEnv2/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'html2text==2014.7.3','console_scripts','html2text' __requires__ = 'html2text==2014.7.3' import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.exit( load_entry_point('html2text==2014.7.3', 'console_scripts', 'html2text')() )

[ "cboyle@macalester.edu" ]

cboyle@macalester.edu

68d3e5ce7d725d753fe712d6b84fc9b1056b3b77

a50badcad45aa17cac0148470a165e89e4d9f352

/errors.py

56af8f4bb2358b465d314887db56a16ef688abc1

[]

no_license

deemoowoor/employee-stats

bf53e15e3af7e52a6a6828e8e539a5d945782dee

5b29f103c3327fe18ea1998777141b610589e6af

refs/heads/master

2022-12-12T08:12:28.088128

2020-05-20T08:44:47

265,500,108

0

null

2022-12-08T09:57:35

2020-05-20T08:27:27

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py

class ApiError(BaseException): def __init__(self, message): self._message = message def __str__(self): return self._message

[ "andrei.sosnin@gmail.com" ]

andrei.sosnin@gmail.com

48a3c15283ec705f100a9181029b8e252e62f99e

a58689339cf11a04280cb6f627da442d2e6d2128

/detector.py

e101ec893dc4d28dedc51a5a11c210fd2a101bee

[]

no_license

thuyngch/CISDL-DMAC

d1928fa7023986220d4d7b21d0e8eb73991a98fd

4a4e24051dedb4e534291a71ec32571b07ba7217

refs/heads/master

2020-05-25T12:10:26.804658

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#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ @author: liuyaqi """ import torch import torch.nn as nn import math affine_par = True class Detector(nn.Module): def __init__(self,pool_stride): super(Detector, self).__init__() 'The pooling of images needs to be researched.' self.img_pool = nn.AvgPool2d(pool_stride,stride=pool_stride) self.input_dim = 3 'Feature extraction blocks.' self.conv = nn.Sequential( nn.Conv2d(self.input_dim, 16, 3, 1, 1), nn.BatchNorm2d(16,affine = affine_par), nn.ReLU(inplace=True), nn.Conv2d(16, 32, 3, 1, 1), nn.BatchNorm2d(32,affine = affine_par), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1), nn.Conv2d(32, 64, 3, 1, 1), nn.BatchNorm2d(64,affine = affine_par), nn.ReLU(inplace=True), nn.Conv2d(64, 128, 3, 1, 1), nn.BatchNorm2d(128,affine = affine_par), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1), ) 'Detection branch.' self.classifier_det = nn.Sequential( nn.Linear(128*8*8,1024), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(1024,2), ) self._initialize_weights() def forward(self,x1,x2,m1,m2): x1 = self.img_pool(x1) x2 = self.img_pool(x2) x1 = torch.mul(x1,m1) x2 = torch.mul(x2,m2) x1 = self.conv(x1) x2 = self.conv(x2) x1 = x1.view(x1.size(0),-1) x2 = x2.view(x2.size(0),-1) x12_abs = torch.abs(x1-x2) x_det = self.classifier_det(x12_abs) return x_det def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01) m.bias.data.zero_()

[ "noreply@github.com" ]

thuyngch.noreply@github.com

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/Number theory/526/526.py

849f1678e4527667650f1c3ea2b0b7b3558b4af8

[]

no_license

zc1001/leetcode

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refs/heads/master

2021-07-11T19:26:24.647433

2020-09-27T08:21:01

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import math import copy import operator # 打表 # 执行用时 :16 ms, 在所有 python 提交中击败了100.00% 的用户 # 内存消耗 :11.7 MB, 在所有 python 提交中击败了18.75%的用户 class Solution(object): def perm(self,data): if len(data) == 1: # 和阶乘一样，需要有个结束条件 return [data] r = [] for i in range(len(data)): s = data[:i] + data[i + 1:] # 去掉第i个元素，进行下一次的递归 p = self.perm(s) for x in p: r.append(data[i:i + 1] + x) # 一直进行累加 return r def c(self): r = [] r.append(0) r.append(1) r.append(2) for i in range(3,17): sum = 0 o = [] for j in range(1,i+1): o.append(j) d = self.perm(o) for k in range(len(d)): n1 = 0 for j in range(len(d[i])): if(d[k][j]%(j+1) == 0 or (j+1)%d[k][j] == 0): n1+=1 if(n1 == len(d[i])): sum += 1 r.append(sum) print(r) def countArrangement(self, N): """ :type N: int :rtype: int """ b = [0,1,2,3,8,10,36,41,132,250,700,750,4010,4237,10680,24679] return b[N] if __name__ == '__main__': #n2 = [177,112,74,197,90,16,4,61,103,133,198,4,121,143,55,138,47,167,165,159,93,85,53,118,127,171,137,65,135,45,151,64,109,25,61,152,194,65,165,97,199,163,53,72,58,108,10,105,27,127,64,120,164,70,190,91,41,127,109,176,172,12,193,34,38,54,138,184,120,103,33,71,66,86,143,125,146,105,182,173,184,199,46,148,69,36,192,110,116,53,38,40,65,31,74,103,86,12,39,158] #n = 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379,6253,1490,9660,822,4594,8459,58,6129,7048,327,7374,7982,5615,2341,5523,5299,6386,7517,6141,3763,2917,8287,1078,1627,4260,7574,4789,3422,9112,4947,5154,5365,2789,4814,2539,7383,9625,2597,9865,3026,9277,7239,1008,4892,5932,2884,192,8671,6753,2685,2434,9670,972,3512,7649,5232,1087,2438,5007,6551,3737,6513,8268,6526,1327,807,3910,7304,9757,127,330,9034,3718,7691,278,9650,6927,6822,8321,32,5860,7108,9702,6832,6972,7351,8417,8059,6141,3424,962,9878,9937,9230,6404,7616,6390,6666,1272,6147,3145,7955,1533,6863,1998,8163,2866,5277,4986,1187,5309,846,4647,1363,4030,1620,5066,2447,6031,1207,2223,6994,1085,8512,2576,3841,2480,8966,6860,3753,1465,5,1708,2998,6869,3706,7514,9735,8983,2500,7274,4292,9698,1922,2007,80,3542,7073,2528,9573,8280,1103,2919,5717,9616,5496,9558,2096,814,6418,2201,2280,6424,3909,1630,9645,3967,9144,9380,9302,7996,6654,9946,4046,4928,1953,4127,8470,9026,3007,4396,7306,462,7315,9375,6430,9163,8934,8527,9978,1704,7080,8610,4480,7342,240,4125,1309,5737,3505,612,85,6512,6910,4132,1440,8864,4611,6263,7890,3970,659,1549,4432,4326,7276,863,3490,6210,5742,9820,4267,2822,8430,5099,164,5022,9225,7826,759,9082,4790,7197,1946,1700,7681,3387,6916,2292,6002,1159,2614,6661,9060,7046,7339,6336,4261,829,8899,6355,7001,3166,5530,5431,4617,2046,454,3842,9872,7565,9277,1014,4762,7575,2715,2443,7314,5983,1087,3316,7142,3701,9977,6202,7100,3669,2539,1361,850,1438,4069,7852,956,9599,3283,5573,1645,3737,5768,1518,1303,1397,2532,2417,8972,1599,4861,6287,3935,5948,9603,1077,9650,5933,7280,6750,9602,6171,4463,452,3961,8532,8304,4917,4483,7940,6842,6129,8029,2610,3999,5684,4007,2883,8102,9332,4483,2963,5619,4770,5263,1574,5847,4913,7507,9479,8015,3461,5650,8831,266,9611,7363,4922,880,1847,2862,7723,4328,7244,6685,8327,2928,7045,1210,1030,6377,2045,345,8348,6815,5609,9922,2663,6874,3782,8494,4890,3595,4145,3721,3861,108,7436,8784,7341,5635,7998,1416,9963,5242,8101,4642,4523,5146,5853,1905,7875,4250,2251,2575,1066,4212,8850,81,1086,2632,8575,2328,2579,9072,6049,6441,5533,9838,1577,2874,1825,5927,4290,8141,1170,8743,2783,2045,242,4988,3950,4469,9239,6201,7045,305,6765,5895,6738,7852,4879,5313,180,7458,738,2582,251,6271,8772,8180,5497,597,4108,6139,5090,1630,4882,4226,3675,1476,9214,7625,5946,8453,179,2991,5110,6944,5238,1848,4796,6469,3514,1329,279,4252,263,6883,6875,9035,5063,2372,5984,9171,4863,1075,801,9745,5301,828,1222,867,8454,3520,5673,8633,2863,783,1929,8101,8984,6726,922,8850,4407,1201,9454,4670,8084,6329,3705,3148,5053,6041,8671,9916,7116,5825,6013,8769,6653,7235,9637,5107,7107,1662,92,9970,8797,2022,4423,7781,5100,5345,2983,9507,2899,2437,529,7335,8766,4234,483,171,275,5507,87,3744,1332,6101,8865,4337,9688,4854,9445,6796,6516,5889,6766,5314,4263,1190,9447,9363,2887,2431,5222,5786,4868,5751,3122,9987,6337,9957,158,2965,1816,6598,6709,3148,2699,1926,7486,8739,6781,3283,5535,9649,5524,8654,4963,9788,6196,4411,5503,9083,3194,726,1222,4414,2829,4344,753,9167,653,7264,2132,2470,3862,5193,1970,2913,7119,5808,1652,252,9091,3540,9902,968,2194,1217,7108,4742,1980,2611,3825,5174,9689,5047,5941,8871,5743,6694,8038,2749,3958,6522,5219,7820,8067,7189,7085,1538,9350,5090,1791,8441,8630,8045,5761,7176,9262,2869,1918,1243,5481,2095,2769,1522,3495,8710,393,9238,5405,4783,8339,9363,7657,3558,3536,5724,7100,6973,7263,6450,2063,5406,1243,7045,3451,7005,4221,9065,6226,2491,308,8059,4587,3078,9582,8082,8140,6327,3672,3545,1111,2012,9261,5120,1922,9149,845,9022,6122,4460,1824,4538,9866,3068,1583,9669,6425,5805,8734,9003,4648,5395,7063,9235,8473,2997,3669,2965,9324,3694,6511,6787,5706,2124,1908,3980,1273,9105,3003,3747,3565,1179,8285,9783,599,9869,9452,3376,2026,4538,2380,6674,9933,9443,2262,4758,8792,5931,7724,8116,9625,587,1256,1683,2711,9516,5664,3984,8621,5019,4083,2186,6198,2369,8321,3150,8590,4125,6526,616,8663,5258,3642,4949,4701,5904,6059,9845,8188,3783,7962,4165,722,5570,2201,3433,5086,7865,3769,3707,9236,7853,2245,1786,222,566,4936,8812,4691,7815,5780,9706,3073,5774,4655,4127,1679,7067,3972,6219,7202,8286,6736,7925,3856,8937,1358,8942,3154,1480,9001,2390,9333,1246,4177,5907,8164,5465,1071,2855,3280,6851,8914,2706,2625,9921,6833,656,6988,805,3227,4191,9092,9964,2116,9300,5253,9826,8243,8408,1306,3596,798,6991,4843,1327,9250,3007,3145,321,2215,2777,3524,7481,5483,2502,7402,2316,9510,4391,9474,2738,4934,4918,9054,7050,4218,4307,3228,8813,9067,887,2410,6218,7878,3605,7545,7129,2964,7042,3802,1531,9820,7327,9012,1655,9829,6415,324,9339,7158,9798,8429,2092,1068,3835,5494,5286,8143,5074,452,7210,5961,9214,3428,192,9171,7326,3673,2135,720,7475,19,540,1154,9031,2196,7335,1798,2520,6675,5308,8670,1456,7400,9738,5292,9246,1376,9787,4321,8180,3349,6634,7394,3130,6826,2917,456,499,1405,1176,4327,1424,8069,5481,6807,6617,2817,4958,9137,5844,266,4159,7300,4019,249,8944,3265,1625,8731,3938,6158,2081,573,9904,5211,7399,2822,2019,4251,4227,9547,8578,2003,7616,4059,8810,4233,3228,3768,9722,9072,387,233,2725,4406,482,1669,4023,8460,6753,4314,4618,8834,4887,4522,397,8638,3696,2416,2889,4275,8315,7819,6278,2284,1879,5089,2869,1459,5209,2592,532,1948,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n = [1,2,1] #print(len(n)) s = "anagram" t = "nagaram" #print(max(n[0:2])) p = Solution() a = p.c() #a = p.charge(28) print(a) #p.compare(A,B) # [73, 74, 75, 71, 69, 72, 76, 73] #print(s) #print(test.lastSubstring("abab"))

[ "jlbczhangchao0800@126.com" ]

jlbczhangchao0800@126.com

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/Job1/MapReduce/mapper.py

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FedericoCialini/Progetto1BigDataRomaTre

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2022-09-09T13:10:04.974717

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#!/home/federico/anaconda3/bin/python import sys def mapping(): lines = sys.stdin.readlines() prices = lines[1:] for line in prices: Ticker, OpenValue, CloseValue, Adj_close, LowThe, HighThe, Volume, Date = line.strip().split(",") year = Date.split("-")[0] if year >= '2008': print(Ticker, CloseValue, Volume, Date, sep='\t') if __name__ == '__main__': mapping()

[ "federicocialini@gmail.com" ]

federicocialini@gmail.com

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Upasna4/Training

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refs/heads/master

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memberData = {} bookData = {} borrowData = {} m_id = 101 b_id = 201 print("Library Management System\n" "1.Add Member\n" "2.Add Book\n" "3.Book Borrowing\n" "4.Book Returning\n" "5.Member Status\n" "6.Book Status\n" "7.Exit") while True: choice = int(input("Enter Choice: ")) if choice == 1: print("Add Member Program") loop1=True while(loop1): name = input("Member Name: ") memberData.update({m_id: name}) #updates value of key and val print("Member Added. Member id is: ", m_id) m_id += 1 #incrementing value of m_id while (True): choice = input("Add more member (Y/N): ").lower().strip() if choice == 'y': break elif choice == 'n': loop1 = False break else: print("invalid choice") loop1=False continue elif choice == 2: print("Add Book Program") while True: name = input("Book Name: ") qty = int(input("enter quantity")) bookData.update({b_id: [name, qty]}) #dict ko update krna print("Book Added. Book id is: ", b_id) b_id += 1 choice = input("Add more member (Y/N): ").lower().strip() if choice == 'y': continue elif choice == 'n': break elif choice == 3: print("Book Borrowing Program") while True: m_id = int(input("Member id: ")) if m_id in memberData: #checks if member id in present in memberData dict b_name = input("Book Name: ") for b_id, b_name_qty in bookData.items(): #when we want both key and value if b_name_qty[0] == b_name: #indexing is done coz we have a list here..at [0] we have name in list if b_name_qty[1] > 0: #here we compare quantity as it is on 1st index..we see whether it is >0 or not borrowData.update({m_id: b_id}) #update dict bookData[b_id][1] -= 1 #decrement quantity of books break else: print("Book out of stock") else: print("Book not present") choice = input("Add more member (Y/N): ").lower().strip() if choice == 'y': continue elif choice == 'n': break elif choice == 4: print("Book Returning Program") m_id = int(input("Member Id: ")) name = input("Book Name: ") for b_id, b_name in borrowData.items(): if b_name == name: bookData[b_id][1] += 1 borrowData.pop(m_id) #person is returning book so book will pop from borrowData dict borrowData.update({m_id: b_id}) #dict is updated break else: print("Book not present") choice = input("Add more member (Y/N): ").lower().strip() if choice == 'y': continue elif choice == 'n': break elif choice == 5: print("Member Status Program") m_id = int(input("Member Id: ")) if m_id in memberData: #to check mem status we check m_id is in memberData and borrowData or not if m_id in borrowData: #if b_id is in borrowData then borrowData m se b_id nikalo b_id = borrowData[m_id] #bid nikal ra h dict m se print("Member Name: ", memberData[m_id]) #the value of this key is name print("Allow Book Name: ", bookData[b_id][0]) #the val of this is bookname elif choice == 6: print("Book Status Program") b_id = int(input("Book Id: ")) for m_id, m_name in memberData.items(): #valuefetch if b_id in borrowData: b_id = borrowData[m_id] print("Member name:",memberData[m_id]) print("Book name:",bookData[b_id][0]) print("Book issue to user:", memberData[m_id]) elif choice == 7: break else: print("invalid choice")

[ "upasnabhat17@gmail.com" ]

upasnabhat17@gmail.com

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/leetcode/python/50/50.powx-n.py

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no_license

frankieliu/problems

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refs/heads/master

2023-01-06T14:41:58.044871

2019-11-24T03:47:22

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# # @lc app=leetcode id=50 lang=python3 # # [50] Pow(x, n) # # https://leetcode.com/problems/powx-n/description/ # # algorithms # Medium (27.38%) # Total Accepted: 281K # Total Submissions: 1M # Testcase Example: '2.00000\n10' # # Implement pow(x, n), which calculates x raised to the power n (x^n). # # Example 1: # # # Input: 2.00000, 10 # Output: 1024.00000 # # # Example 2: # # # Input: 2.10000, 3 # Output: 9.26100 # # # Example 3: # # # Input: 2.00000, -2 # Output: 0.25000 # Explanation: 2^-2 = 1/2^2 = 1/4 = 0.25 # # # Note: # # # -100.0 < x < 100.0 # n is a 32-bit signed integer, within the range [−2^31, 2^31 − 1] # # # class Solution: def myPow(self, x, n): """ :type x: float :type n: int :rtype: float """

[ "frankie.y.liu@gmail.com" ]

frankie.y.liu@gmail.com

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/src/Server/Controller/guess_controller.py

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cloew/WordGuessAngular

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refs/heads/master

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400

py

from Server.game_wrapper import GameWrapper from kao_flask.controllers.json_controller import JSONController class GuessController(JSONController): """ Controller to allow a player to guess the word for the current Round """ def performWithJSON(self, gameId): game = GameWrapper(id=gameId) results = game.guess(self.json['guesses']) return game.toJSON()

[ "cloew123@gmail.com" ]

cloew123@gmail.com

b7c80a21298c1316985aac7e42d5886a612a3783

a95f9fb15eccaf4c8a25549aeb52fb1bb517f8ce

/label_extractor.py

98bffcf2ee33da1d7d0999b0f1516d714e3bf091

[]

no_license

Kirich2323/ml

441144b26eac19f10c0b773e6c9aff82fa58246d

5ced175d0e5ffedeb56edb07c809decc77e1154f

refs/heads/master

2020-03-11T18:27:33.204495

2018-04-26T01:10:47

130,177,448

0

null

UTF-8

Python

false

1,625

py

import xml.etree.ElementTree as ET import re class BaseLabelExtractor: def __init__(self, *args, **kwargs): pass def get_labels(self, data): ans = [] for f in data: ans.append(self.extract_label(f)) return ans class ProblemExtractor(BaseLabelExtractor): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def extract_label(self, item): r = r'(.+)-(.+)-(\d+).*' m = re.search(r, item) return m.group(2) class VerdictExtractor(BaseLabelExtractor): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.xml = kwargs.get("xml", "") self.root = ET.parse(self.xml).getroot() self.teams = {} for session in self.root[0][1:]: #print(session.attrib['alias']) tasks = [] for problem in session: task = [] for solution in problem: task.append(solution.attrib['accepted']) tasks.append(task) self.teams[session.attrib["alias"]] = tasks def extract_label(self, item): r = r'(.+)-(.+)-(\d+)\..*' m = re.search(r, item) print(item) print(m.group(1)) print(m.group(2)) print(m.group(3)) print(self.teams[m.group(1)]) print(self.teams[m.group(1)][ord(m.group(2))-ord('a')]) print(self.teams[m.group(1)][ord(m.group(2))-ord('a')][int(m.group(3)) - 1]) print('-'*40) return self.teams[m.group(1)][ord(m.group(2))-ord('a')][int(m.group(3)) - 1]

[ "ivadik2323@gmail.com" ]

ivadik2323@gmail.com

b49df1cc1b4948c46ef6cec8398200ea89ae37fa

67048c855300ffc1fa192eee1da241d7f8e85682

/pizza.py

97cdb1c8caa56c039d6815b7062e06cb161ab1b7

[]

no_license

JennymarBerroteran/Umbrella

07e4f286f46da749c04d59769bcef4cf763f9a95

c1271e156bf7657179e0f209353d77babe2a06ff

refs/heads/master

2020-09-08T08:25:09.823352

2019-11-24T22:27:57

221,077,763

0

null

UTF-8

Python

false

144

py

fav_pizza = ['pepperoni', 'cheeze', 'margarita'] for pizza in fav_pizza: print(f'I like {pizza} pizza \n') print('I really love pizza')

[ "noreply@github.com" ]

JennymarBerroteran.noreply@github.com