xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

2a96b605630a5e4d56fbbd6a6840963b201cc254

4,145

py

Python

mit-ml/netflix/main.py

stepinski/machinelearning

1f84883a25616da4cd76bb4655267efd3421e561

[ "MIT" ]

null

mit-ml/netflix/main.py

stepinski/machinelearning

1f84883a25616da4cd76bb4655267efd3421e561

[ "MIT" ]

null

mit-ml/netflix/main.py

stepinski/machinelearning

1f84883a25616da4cd76bb4655267efd3421e561

[ "MIT" ]

null

import numpy as np import kmeans import common import naive_em import em X = np.loadtxt("toy_data.txt") # TODO: Your code here # for K in [1,2,3,4]: # for seed in [0,1,2,3,4]: # mixture,post=common.init(X, K, seed) # mixture, post, cost=kmeans.run(X,mixture,post) # common.plot(X,mixture,post,title='K=%s seed=%s cost=%s'%(K,seed,cost)) # print('K=%s seed=%s cost=%s'%(K,seed,cost)) for K in [1,2,3,4]: maxcost=-100000 for seed in [0]: mixture,post=common.init(X, K, seed) mixture, post, cost=naive_em.run(X,mixture,post) print(common.bic(X,mixture,cost)) # common.plot(X,mixture,post,title='EM K=%s seed=%s cost=%s'%(K,seed,cost)) # mixture,post=common.init(X, K, seed) # mixture, post, cost=kmeans.run(X,mixture,post) # common.plot(X,mixture,post,title='kmeansK=%s seed=%s cost=%s'%(K,seed,cost)) # print('K=%s seed=%s cost=%s'%(K,seed,cost)) # if cost>maxcost: maxcost=cost # print("maxll %s"%maxcost) # K=3 # seed=0 # mixture,post=common.init(X, K, seed) # # print(mixture) # # posts,ll = naive_em.estep(X,mixture) # # print('ll = %s'%ll) # # print(posts) # # for K in [1,2,3,4]: # # for seed in [0,1,2,3,4]: # # mixture,post=common.init(X, K, seed) # # mixture, post, cost=kmeans.run(X,mixture,post) # # #common.plot(X,mixture,post,title='K=%s seed=%s cost=%s'%(K,seed,cost)) # # print('K=%s seed=%s cost=%s'%(K,seed,cost)) # K=5 # X = np.loadtxt("lasttestx.txt") # mu=np.array( [[-0.60787456, 0.09534884], # [ 0.53830805, -0.24498689], # [ 0.4983494, -0.94992061], # [-0.66868763, -0.9861811 ], # [-0.15367443, -0.44492439]]) # var=np.array([0.66695384, 0.30533997, 1.00062913, 1.639639,0.61075705]) # p=np.array([0.12075413,0.26092829, 0.19481629, 0.23742157, 0.18607972]) # mixture = common.GaussianMixture(mu, var, p) # posts,ll = naive_em.estep(X,mixture) # print('ll = %s'%ll) # print(posts) # newmixt=naive_em.mstep(X,posts) # print(newmixt) # n=X.shape[0] # llt=0.0 # # print("startar") # # for i in range(n): # # for j in range(K): # # llt+=np.log(mixture.p[j]*Gaussian(mixture[i,j], var[j],X[i]) # # # print(pn) # # # print(pn.sum()) # # llt+=np.log(pn) # # print("test %.20f"%llt) # # print(llt) # # print('fin') # # # Output: # # # post:[[0.03939317 0.66938479 0.1207385 0.05606209 0.11442145] # # # [0.1284887 0.46274858 0.09891089 0.08438805 0.22546379] # # # [0.12250705 0.49162696 0.09739513 0.0799134 0.20855745] # # # [0.0496701 0.65425613 0.1051122 0.05541291 0.13554867] # # # [0.09493723 0.56463229 0.10373629 0.07240648 0.16428772] # # # [0.17238229 0.41053463 0.10757978 0.10210253 0.20740077] # # # [0.20502453 0.35053858 0.10083158 0.10866167 0.23494364] # # # [0.04599863 0.66358567 0.10870553 0.05489602 0.12681415] # # # [0.11717788 0.40929401 0.18426962 0.13553269 0.15372579] # # # [0.19227515 0.37045863 0.11410566 0.11445474 0.20870582] # # # [0.13920751 0.47399095 0.10541633 0.08908133 0.19230388]] # # # LL:-25.086211 # # tst=np.array([[0.03939317, 0.66938479, 0.1207385, 0.05606209, 0.11442145], # # [0.1284887, 0.46274858, 0.09891089, 0.08438805, 0.22546379], # # [0.12250705, 0.49162696, 0.09739513, 0.0799134 , 0.20855745], # # [0.0496701, 0.65425613, 0.1051122 , 0.05541291, 0.13554867], # # [0.09493723, 0.56463229, 0.10373629, 0.07240648, 0.16428772], # # [0.17238229, 0.41053463, 0.10757978, 0.10210253, 0.20740077], # # [0.20502453, 0.35053858, 0.10083158, 0.10866167, 0.23494364], # # [0.04599863, 0.66358567, 0.10870553, 0.05489602, 0.12681415], # # [0.11717788, 0.40929401, 0.18426962, 0.13553269, 0.15372579], # # [0.19227515, 0.37045863, 0.11410566, 0.11445474, 0.20870582], # # [0.13920751, 0.47399095, 0.10541633, 0.08908133, 0.19230388]]) # # n=tst.shape[0] # # print(n) # # llt=0.0 # # print("bla") # # for i in range(n): # # print(tst[i,:]) # # print(np.log(tst[i,:]).sum()) # # print("end") # # pn=np.log(tst[i,:]).sum() # # # print(pn) # # # print(pn.sum()) # # llt+=pn # # print("test %.20f"%llt) # # print(llt)

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py

Python

watson/__main__.py

hiiwave/Watson

297fd7f2561180ebb20e615be87c89e7d3b597e7

[ "MIT" ]

1

2020-12-30T00:17:51.000Z

watson/__main__.py

hiiwave/Watson

297fd7f2561180ebb20e615be87c89e7d3b597e7

[ "MIT" ]

1

2018-06-15T11:01:24.000Z

watson/__main__.py

hiiwave/Watson

297fd7f2561180ebb20e615be87c89e7d3b597e7

[ "MIT" ]

2

2018-06-13T13:46:51.000Z

2019-04-19T16:38:04.000Z

try: from watson import cli except ImportError: from . import cli cli.cli()

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2ada7143d3c49139b2e6552f30902b63f53c5839

280

py

Python

atnlp/model/__init__.py

wedavey/atnlp

002497f27abfcdac9701aa324301d482dbf4df0e

[ "MIT" ]

null

atnlp/model/__init__.py

wedavey/atnlp

002497f27abfcdac9701aa324301d482dbf4df0e

[ "MIT" ]

null

atnlp/model/__init__.py

wedavey/atnlp

002497f27abfcdac9701aa324301d482dbf4df0e

[ "MIT" ]

null

"""Model building, training, tuning .. automodule:: atnlp.model.embed :members: .. automodule:: atnlp.model.grid :members: .. automodule:: atnlp.model.io :members: .. automodule:: atnlp.model.tune :members: .. automodule:: atnlp.model.wordmatch :members: """

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6

2aee13416e3fc2b8d2940df3050d66ec6ef36a60

480

py

Python

apps/Aeon_Account/forms.py

kprsajuuk/Aeon-Citadel

c6ceae23bc3902de19aa8b7689e88bae81082e88

[ "MIT" ]

null

apps/Aeon_Account/forms.py

kprsajuuk/Aeon-Citadel

c6ceae23bc3902de19aa8b7689e88bae81082e88

[ "MIT" ]

4

2020-02-11T22:27:00.000Z

2021-04-08T19:04:22.000Z

apps/Aeon_Account/forms.py

kprsajuuk/Aeon-Citadel

c6ceae23bc3902de19aa8b7689e88bae81082e88

[ "MIT" ]

null

from django import forms class UserForm(forms.Form): username = forms.CharField(label="用户名", max_length=128) password = forms.CharField(label="密码", max_length=256, widget=forms.PasswordInput) class RegisterForm(forms.Form): username = forms.CharField(label="用户名", max_length=128) password = forms.CharField(label="密码", max_length=256, widget=forms.PasswordInput) confirm_password = forms.CharField(label="确认密码", max_length=256, widget=forms.PasswordInput)

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6

2aff40569c9a18c4091d9968921a41d23c43bbae

7,338

py

Python

OmniDB/OmniDB_app/tests.old/bak_test_views.py

lejmr/OmniDB

52c1c5a726a322f537a8e65f71d77ce322344d35

[ "MIT" ]

2,982

2016-04-12T13:33:50.000Z

2022-03-31T14:16:43.000Z

OmniDB/OmniDB_app/tests.old/bak_test_views.py

lejmr/OmniDB

52c1c5a726a322f537a8e65f71d77ce322344d35

[ "MIT" ]

704

2016-04-30T14:44:11.000Z

2022-03-18T09:39:41.000Z

OmniDB/OmniDB_app/tests.old/bak_test_views.py

lejmr/OmniDB

52c1c5a726a322f537a8e65f71d77ce322344d35

[ "MIT" ]

452

2016-04-25T23:50:25.000Z

2022-03-28T15:03:52.000Z

from django.test import TestCase, Client from django.http import JsonResponse import json class Login(TestCase): def test_sign_in_ok(self): c = Client() response = c.post('/sign_in/', {'data': '{"p_username": "admin", "p_pwd": "admin"}'}) assert 200 == response.status_code data = json.loads(response.content.decode()) assert 0 <= data['v_data'] session = c.session assert 'admin' == session['omnidb_session'].v_user_name def test_sign_in_nok(self): c = Client() response = c.post('/sign_in/', {'data': '{"p_username": "admin", "p_pwd": "ad"}'}) assert 200 == response.status_code data = json.loads(response.content.decode()) assert -1 == data['v_data'] class Connections(TestCase): def test_connections_nosession(self): c = Client() response = c.post('/connections/', follow=True) assert '/login/' == response.redirect_chain[0][0] assert 302 == response.redirect_chain[0][1] def test_get_connections_nosession(self): c = Client() response = c.post('/get_connections/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_save_connections_nosession(self): c = Client() response = c.post('/save_connections/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_test_connection_nosession(self): c = Client() response = c.post('/test_connection/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] class Users(TestCase): def test_get_users_nosession(self): c = Client() response = c.post('/get_users/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_new_user_nosession(self): c = Client() response = c.post('/new_user/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_remove_user_nosession(self): c = Client() response = c.post('/remove_user/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_save_users_nosession(self): c = Client() response = c.post('/save_users/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] class Workspace(TestCase): def test_workspace_nosession(self): c = Client() response = c.post('/workspace/', follow=True) assert '/login/' == response.redirect_chain[0][0] assert 302 == response.redirect_chain[0][1] def test_save_config_user_nosession(self): c = Client() response = c.post('/save_config_user/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_get_database_list_nosession(self): c = Client() response = c.post('/get_database_list/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_renew_password_nosession(self): c = Client() response = c.post('/renew_password/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_draw_graph_nosession(self): c = Client() response = c.post('/draw_graph/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_alter_table_data_nosession(self): c = Client() response = c.post('/alter_table_data/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_save_alter_table_nosession(self): c = Client() response = c.post('/save_alter_table/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_start_edit_data_nosession(self): c = Client() response = c.post('/start_edit_data/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_get_completions_nosession(self): c = Client() response = c.post('/get_completions/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_get_completions_table_nosession(self): c = Client() response = c.post('/get_completions_table/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_get_command_list_nosession(self): c = Client() response = c.post('/get_command_list/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_clear_command_list_nosession(self): c = Client() response = c.post('/clear_command_list/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] class TreeSnippets(TestCase): def test_get_node_children_nosession(self): c = Client() response = c.post('/get_node_children/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_get_snippet_text_nosession(self): c = Client() response = c.post('/get_snippet_text/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_new_node_snippet_nosession(self): c = Client() response = c.post('/new_node_snippet/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_delete_node_snippet_nosession(self): c = Client() response = c.post('/delete_node_snippet/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_save_snippet_text_nosession(self): c = Client() response = c.post('/save_snippet_text/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id'] def test_rename_node_snippet_nosession(self): c = Client() response = c.post('/rename_node_snippet/') assert 200 == response.status_code data = json.loads(response.content.decode()) assert 1 == data['v_error_id']

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6

6310e5307cf1d3aae3108be158062e7e1d98446c

215

py

Python

run_SEDML.py

vporubsky/COMBINE_2020_reproducibility

43ae5cda128845fbb1d63bc22dc97c0afff04c0c

[ "Apache-2.0" ]

null

run_SEDML.py

vporubsky/COMBINE_2020_reproducibility

43ae5cda128845fbb1d63bc22dc97c0afff04c0c

[ "Apache-2.0" ]

null

run_SEDML.py

vporubsky/COMBINE_2020_reproducibility

43ae5cda128845fbb1d63bc22dc97c0afff04c0c

[ "Apache-2.0" ]

null

'''run_SEDML.py This script executes the simulation experiment specified with SED-ML in the file sars_cov2_infection_simulation.xml.''' import tellurium as te te.executeSEDML('sars_cov2_infection_simulation.xml')

26.875

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6312eafb4e53d5c869f173248582d7675fc23fa3

117

py

Python

tests/test_huya.py

hldh214/stream-recorder

f4a9f22418e62cdc3f3ef050004f925dc3b2902c

[ "MIT" ]

5

2020-06-15T15:42:17.000Z

2021-04-29T08:24:56.000Z

tests/test_huya.py

hldh214/stream-recorder

f4a9f22418e62cdc3f3ef050004f925dc3b2902c

[ "MIT" ]

5

2019-10-29T08:46:50.000Z

2022-03-06T13:46:40.000Z

tests/test_huya.py

hldh214/stream-recorder

f4a9f22418e62cdc3f3ef050004f925dc3b2902c

[ "MIT" ]

1

2020-03-12T07:54:14.000Z

import recorder.source.huya as huya def test_get_stream(): assert huya.get_stream('a_non_exist_room') is False

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2d86988f73c2d711eb3b53c84fba0447f0c62c56

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py

Python

hypixelio/models/recent_games/__init__.py

FoxNerdSaysMoo/HypixelIO

aca8fd6535c0afb2bb733172db2dcbd68590118d

[ "MIT" ]

16

2020-10-28T01:49:31.000Z

2022-03-13T23:19:31.000Z

hypixelio/models/recent_games/__init__.py

FoxNerdSaysMoo/HypixelIO

aca8fd6535c0afb2bb733172db2dcbd68590118d

[ "MIT" ]

20

2021-03-17T07:32:14.000Z

2022-03-07T02:48:00.000Z

hypixelio/models/recent_games/__init__.py

FoxNerdSaysMoo/HypixelIO

aca8fd6535c0afb2bb733172db2dcbd68590118d

[ "MIT" ]

5

2020-10-21T13:53:27.000Z

2021-09-02T15:47:45.000Z

from .recent_games import RecentGames

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py

Python

hellopypa/__init__.py

mittelholcz/pypaexperiment

5d70bbf02a7176f7be7fb9e37611f2bbddcb533d

[ "MIT" ]

3

2020-02-24T23:13:17.000Z

2021-05-17T09:27:58.000Z

hellopypa/__init__.py

mittelholcz/pypaexperiment

5d70bbf02a7176f7be7fb9e37611f2bbddcb533d

[ "MIT" ]

null

hellopypa/__init__.py

mittelholcz/pypaexperiment

5d70bbf02a7176f7be7fb9e37611f2bbddcb533d

[ "MIT" ]

null

from hellopypa.hellopypa import hello from hellopypa.version import __version__

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932eb9d1f88db99e30f358d1bdca823d952b11ef

21

py

Python

wacom/__init__.py

AgenttiX/linux-scripts

a3703ea78a2cb1a5689a6efbaa0519602900d568

[ "MIT" ]

3

2021-05-01T16:39:02.000Z

2021-11-04T12:28:18.000Z

wacom/__init__.py

AgenttiX/linux-scripts

a3703ea78a2cb1a5689a6efbaa0519602900d568

[ "MIT" ]

null

wacom/__init__.py

AgenttiX/linux-scripts

a3703ea78a2cb1a5689a6efbaa0519602900d568

[ "MIT" ]

null

from .wacom import *

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py

Python

webBlog/admin.py

JordanBRoberts/python-theBand

1e475a45a42b210c722ab43c0b966d7b58d97a9d

[ "MIT" ]

null

webBlog/admin.py

JordanBRoberts/python-theBand

1e475a45a42b210c722ab43c0b966d7b58d97a9d

[ "MIT" ]

null

webBlog/admin.py

JordanBRoberts/python-theBand

1e475a45a42b210c722ab43c0b966d7b58d97a9d

[ "MIT" ]

null

from django.contrib import admin from .models import Post from markdownx.admin import MarkdownxModelAdmin from .models import Question, Choice admin.site.register(Question) admin.site.register(Choice) admin.site.register(Post,MarkdownxModelAdmin )

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py

Python

tests/drive_decoder.py

lzamparo/SeqDemote

3eaf18e88c9dc6a3d1a69444ecdba9f9b5d9682a

[ "MIT" ]

1

2019-04-16T12:25:09.000Z

tests/drive_decoder.py

lzamparo/SeqDemote

3eaf18e88c9dc6a3d1a69444ecdba9f9b5d9682a

[ "MIT" ]

null

tests/drive_decoder.py

lzamparo/SeqDemote

3eaf18e88c9dc6a3d1a69444ecdba9f9b5d9682a

[ "MIT" ]

null

from __future__ import print_function import data_load_utils as utils from nose.tools import eq_ import dna_io import numpy as np import khmer rando_3mer_string = 'ATGGGGTAGAGAATGGGGTAGAGA' AAA_vs_lc_file = './test_data/AAA_vs_lc.fa' lc_vs_UC_file = './test_data/lc_vs_UC.fa' def test_rando_decoding(): ktable = khmer.new_ktable(3) rando_vec = dna_io.dna_one_hot_kmer(rando_3mer_string, 3, ktable) kmer_decoder = {i: ktable.reverse_hash(i) for i in range(0, ktable.n_entries())} result = dna_io.kmer_vecs_to_dna(rando_vec,3, kmer_decoder) print("expected: ", rando_3mer_string, " and got: ", result[0]) def test_end_to_end_3mer(): # choose a random test file test_files = utils.get_test_data_files() arr = np.arange(len(test_files)) np.random.shuffle(arr) test_file = test_files[arr[0]] test_seqs = utils.load_data_from_file(test_file,trunc=9) # encode encoded_test_seqs = [] for seq in test_seqs: encoded_test_seqs.append(dna_io.dna_one_hot_kmer(seq,3)) # stack into matrices train_seqs = np.vstack(encoded_test_seqs) # decode decoded_test_seqs = dna_io.kmer_vecs_to_dna(train_seqs, 3) print("length of decoded test seqs is ", len(decoded_test_seqs)) print("length of first element is ", len(decoded_test_seqs[0])) print("first decoded seq: ", decoded_test_seqs[0]) print("first test seq equals first decoded seq?: ", str(test_seqs[0] == decoded_test_seqs[0])) # compare elementwise agreement = True for enc, dec in zip(test_seqs,decoded_test_seqs): agreement = agreement and (enc == dec) if not agreement: print("Expected ", enc, " ||||||||| but got ", dec) def test_kmer_end_to_end_AAA_vs_lc(): test_seqs = utils.load_data_from_file(AAA_vs_lc_file) # encode encoded_test_seqs = [] for seq in test_seqs: encoded_test_seqs.append(dna_io.dna_one_hot_kmer(seq,3)) # stack into matrices, check the shapes match train_seqs = np.vstack(encoded_test_seqs) # decode decoded_test_seqs = dna_io.kmer_vecs_to_dna(train_seqs, 3) # compare elementwise agreement = True for enc, dec in zip(test_seqs,decoded_test_seqs): agreement = agreement and (enc == dec) if not agreement: print("Expected ", enc, " ||||||||| but got ", dec) def test_base_end_to_end_AAA_vs_lc(): test_seqs = utils.load_data_from_file(AAA_vs_lc_file) # encode encoded_test_seqs = [] for seq in test_seqs: encoded_test_seqs.append(dna_io.dna_one_hot(seq)) # stack into matrices, check the shapes match train_seqs = np.vstack(encoded_test_seqs) # decode decoded_test_seqs = dna_io.vecs2dna(train_seqs) # compare elementwise agreement = True for enc, dec in zip(test_seqs,decoded_test_seqs): agreement = agreement and (enc == dec) if not agreement: print("Expected ", enc, " ||||||||| but got ", dec) def test_kmer_end_to_end_caps_vs_lc(): test_seqs = utils.load_data_from_file(lc_vs_UC_file) # encode encoded_test_seqs = [] for seq in test_seqs: encoded_test_seqs.append(dna_io.dna_one_hot_kmer(seq,3)) # stack into matrices, check the shapes match try: train_seqs = np.vstack(encoded_test_seqs) except ValueError: print("Caught a value error in vstack! Dumping shape info: ") for i,elem in enumerate(encoded_test_seqs): print("element ", i, " has shape ", str(elem.shape)) assert False # decode try: decoded_test_seqs = dna_io.kmer_vecs_to_dna(train_seqs, 3) except ValueError: print("Caught a value error in decoding, dunno why") assert False # compare elementwise agreement = True for enc, dec in zip(test_seqs,decoded_test_seqs): agreement = agreement and (enc == dec) if not agreement: print("Expected ", enc, " ||||||||| but got ", dec) assert agreement def test_base_end_to_end_caps_vs_lc(): test_seqs = utils.load_data_from_file(lc_vs_UC_file) # encode encoded_test_seqs = [] for seq in test_seqs: encoded_test_seqs.append(dna_io.dna_one_hot(seq)) # stack into matrices, check the shapes match try: train_seqs = np.vstack(encoded_test_seqs) except ValueError: print("Caught a value error in vstack! Dumping shape info: ") for i,elem in enumerate(encoded_test_seqs): print("element ", i, " has shape ", str(elem.shape)) assert False # decode try: decoded_test_seqs = dna_io.vecs2dna(train_seqs) except ValueError: print("Caught a value error in decoding, dunno why") assert False # compare elementwise agreement = True for enc, dec in zip(test_seqs,decoded_test_seqs): agreement = agreement and (enc == dec) if not agreement: print("Expected ", enc, " ||||||||| but got ", dec) assert agreement if __name__ == "__main__": #test_base_end_to_end_AAA_vs_lc() test_kmer_end_to_end_AAA_vs_lc() #test_base_end_to_end_caps_vs_lc() #test_kmer_end_to_end_caps_vs_lc()

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fafdaf6eacbc23fc3781fe86046ab4c2680dcec6

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py

Python

changes/db/funcs/__init__.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

443

2015-01-03T16:28:39.000Z

2021-04-26T16:39:46.000Z

changes/db/funcs/__init__.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

12

2015-07-30T19:07:16.000Z

2016-11-07T23:11:21.000Z

changes/db/funcs/__init__.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

47

2015-01-09T10:04:00.000Z

2020-11-18T17:58:19.000Z

from .coalesce import * # NOQA

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py

Python

test/python/hybridsimulator/test_hybrid_standalone_simulator.py

cda-tum/ddsim

79743fb581bd9d2b3cd9d0a460914257c8a8e584

[ "MIT" ]

5

2022-03-03T05:18:03.000Z

2022-03-30T00:36:06.000Z

test/python/hybridsimulator/test_hybrid_standalone_simulator.py

cda-tum/ddsim

79743fb581bd9d2b3cd9d0a460914257c8a8e584

[ "MIT" ]

9

2022-02-28T17:01:45.000Z

2022-03-25T16:07:58.000Z

test/python/hybridsimulator/test_hybrid_standalone_simulator.py

cda-tum/ddsim

79743fb581bd9d2b3cd9d0a460914257c8a8e584

[ "MIT" ]

2

2022-03-03T07:30:19.000Z

2022-03-07T09:46:53.000Z

import unittest from qiskit import * from mqt import ddsim class MQTStandaloneHybridSimulatorTest(unittest.TestCase): def setUp(self) -> None: q = QuantumRegister(4) circ = QuantumCircuit(q) circ.h(q) circ.cz(3, 1) circ.cz(2, 0) circ.measure_all(inplace=True) self.circuit = circ def test_standalone_amplitude_mode(self): sim = ddsim.HybridCircuitSimulator(self.circuit, mode=ddsim.HybridMode.amplitude) result = sim.simulate(2048) self.assertEqual(len(result.keys()), 16) def test_standalone_amplitude_mode_with_seed(self): sim = ddsim.HybridCircuitSimulator(self.circuit, seed=1337, mode=ddsim.HybridMode.amplitude) result = sim.simulate(2048) self.assertEqual(len(result.keys()), 16) def test_standalone_dd_mode(self): sim = ddsim.HybridCircuitSimulator(self.circuit, mode=ddsim.HybridMode.DD) result = sim.simulate(2048) self.assertEqual(len(result.keys()), 16) def test_standalone_dd_mode_with_seed(self): sim = ddsim.HybridCircuitSimulator(self.circuit, seed=1337, mode=ddsim.HybridMode.DD) result = sim.simulate(2048) self.assertEqual(len(result.keys()), 16)

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87a29acef03f2c2152e5ce3fedbcccbb63cc7989

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py

Python

once-for-all-GM/data/dataset/__init__.py

skhu101/GM-NAS

cf2c8f8201690d929ec1d286c7f7cbc53e76012b

[ "MIT" ]

4

2022-03-17T09:06:42.000Z

2022-03-24T02:38:01.000Z

once-for-all-GM/data/dataset/__init__.py

skhu101/GM-NAS

cf2c8f8201690d929ec1d286c7f7cbc53e76012b

[ "MIT" ]

null

once-for-all-GM/data/dataset/__init__.py

skhu101/GM-NAS

cf2c8f8201690d929ec1d286c7f7cbc53e76012b

[ "MIT" ]

null

import time from .single_label_dataset import *

12.25

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6

87ae822a90616ff3b47d26046e397dd2646928ac

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py

Python

pyqtgraph/console/__init__.py

hishizuka/pyqtgraph

4820625d93ffb41f324431d0d29b395cf91f339e

[ "MIT" ]

2,762

2015-01-02T14:34:10.000Z

2022-03-30T14:06:07.000Z

pyqtgraph/console/__init__.py

hishizuka/pyqtgraph

4820625d93ffb41f324431d0d29b395cf91f339e

[ "MIT" ]

1,901

2015-01-12T03:20:30.000Z

2022-03-31T16:33:36.000Z

pyqtgraph/console/__init__.py

hishizuka/pyqtgraph

4820625d93ffb41f324431d0d29b395cf91f339e

[ "MIT" ]

1,038

2015-01-01T04:05:49.000Z

2022-03-31T11:57:51.000Z

from .Console import ConsoleWidget

17.5

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87c637b0b3966192259acc511da4033b40ab83af

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py

Python

discord_bot.py

longqua69/python-bots

55151a0da037192e800db231808d6ec6702a3185

[ "MIT" ]

null

discord_bot.py

longqua69/python-bots

55151a0da037192e800db231808d6ec6702a3185

[ "MIT" ]

3

2021-08-10T02:21:24.000Z

2021-08-21T13:08:27.000Z

discord_bot.py

longqua69/python-bots

55151a0da037192e800db231808d6ec6702a3185

[ "MIT" ]

null

def main(): """Main program for Discord bot""" pass if __name__ == '__main__': main()

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87f597d8f843d48654509c426025236074efa13f

96

py

Python

venv/lib/python3.8/site-packages/requests/_internal_utils.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/requests/_internal_utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/requests/_internal_utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/67/1d/cf/9c451c7327ec07e89ed759d95405bca82949cb4831d6a34c13bae04f5f

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35619f1b9276b76d0d0ffd60fe8a91c98eb98181

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py

Python

Log/__init__.py

10000ms/aiohttp_mongodb_unit

5163b3e34b1648ea3a2d6135fb367debd6ed87a7

[ "MIT" ]

null

Log/__init__.py

10000ms/aiohttp_mongodb_unit

5163b3e34b1648ea3a2d6135fb367debd6ed87a7

[ "MIT" ]

null

Log/__init__.py

10000ms/aiohttp_mongodb_unit

5163b3e34b1648ea3a2d6135fb367debd6ed87a7

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os def get_logfile(name): return os.path.join(os.path.dirname(__file__), name)

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py

Python

src/text_normalizer/config/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/config/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/config/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

from ._load import * from ._types import * from .config import *

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py

Python

models/discriminators.py

Nikronic/Deep-Halftoning

9564c592abf139ccab2791c1dbb354505edab5f9

[ "MIT" ]

null

models/discriminators.py

Nikronic/Deep-Halftoning

9564c592abf139ccab2791c1dbb354505edab5f9

[ "MIT" ]

1

2021-11-07T12:13:38.000Z

models/discriminators.py

Nikronic/Deep-Halftoning

9564c592abf139ccab2791c1dbb354505edab5f9

[ "MIT" ]

null

# %% import library import torch import torch.nn as nn from models.layers import CL, CBL, C # %% discriminator one class DiscriminatorOne(nn.Module): def __init__(self, input_channel=3, output_channel=1): """ Consists of a CL module followed by repetitive CBL modules and finally a C class to match the final needed classes. :param input_channel: number of input channels of input images to network. :param output_channel: number of output channels of input images to network. """ super(DiscriminatorOne, self).__init__() self.cl = CL(input_channel=input_channel, output_channel=128, kernel_size=4, stride=2, padding=1) self.cbl0 = CBL(input_channel=128, output_channel=256, kernel_size=4, stride=2, padding=1) self.cbl1 = CBL(input_channel=256, output_channel=512, kernel_size=4, stride=2, padding=1) self.cbl2 = CBL(input_channel=512, output_channel=1024, kernel_size=4, stride=2, padding=1) self.cbl3 = CBL(input_channel=1024, output_channel=2048, kernel_size=4, stride=2, padding=1) self.final = C(input_channel=2048, output_channel=output_channel, kernel_size=1, stride=1, padding=0, activation=None) def forward(self, x): x = self.cl(x) x = self.cbl0(x) x = self.cbl1(x) x = self.cbl2(x) x = self.cbl3(x) x = self.final(x) return x # %% discriminator two class DiscriminatorTwo(nn.Module): def __init__(self, input_channel=9, output_channel=1): """ Consists of a CL module followed by repetitive CBL modules and finally a C class to match the final needed classes. :param input_channel: number of input channels of input images to network which is concatenation of Ih, Id, and Io RGB vectors. :param output_channel: number of output channels of input images to network. """ super(DiscriminatorTwo, self).__init__() self.cl = CL(input_channel=input_channel, output_channel=128, kernel_size=5, stride=2, padding=0) self.cbl0 = CBL(input_channel=128, output_channel=256, kernel_size=5, stride=2, padding=0) self.cbl1 = CBL(input_channel=256, output_channel=512, kernel_size=5, stride=2, padding=0) self.cbl2 = CBL(input_channel=512, output_channel=1024, kernel_size=5, stride=2, padding=0) self.cbl3 = CBL(input_channel=1024, output_channel=2048, kernel_size=5, stride=2, padding=0) self.final = C(input_channel=2048, output_channel=output_channel, kernel_size=4, stride=1, padding=0, activation='None') def forward(self, x): x = self.cl(x) x = self.cbl0(x) x = self.cbl1(x) x = self.cbl2(x) x = self.cbl3(x) x = self.final(x) return x # %% tests # z = torch.randn(size=(1, 3, 256, 256)) # d1 = DiscriminatorOne() # z = d1(z) # z.size()

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35c78c6ac1dc3452b23f6936d26948ab29e1dc57

244

py

Python

src/game/scripts/ui/__init__.py

ShoaibSyed1/project-pokemon

6916962cf0be478c2a229b6620e9425d707c2b29

[ "MIT" ]

null

src/game/scripts/ui/__init__.py

ShoaibSyed1/project-pokemon

6916962cf0be478c2a229b6620e9425d707c2b29

[ "MIT" ]

null

src/game/scripts/ui/__init__.py

ShoaibSyed1/project-pokemon

6916962cf0be478c2a229b6620e9425d707c2b29

[ "MIT" ]

null

from game.scripts.ui.button import Button from game.scripts.ui.controller import UiController from game.scripts.ui.label import Label from game.scripts.ui.status_panel import StatusPanel from game.scripts.ui.textbox import Textbox, TextboxState

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0.193237

0.362319

0.410628

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0.081967

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0

null

0

1

0

1

0

1

0

6

35e1cd0318462ad29dc13fd397785f299ec922bf

48

py

Python

pynexus/segments/__init__.py

ve-interactive/Pynexus

553ffe8c7c4e6b7094bf67672be5d6613c4524e7

[ "Apache-2.0" ]

1

2016-10-19T15:04:17.000Z

pynexus/segments/__init__.py

ve-interactive/Pynexus

553ffe8c7c4e6b7094bf67672be5d6613c4524e7

[ "Apache-2.0" ]

null

pynexus/segments/__init__.py

ve-interactive/Pynexus

553ffe8c7c4e6b7094bf67672be5d6613c4524e7

[ "Apache-2.0" ]

null

from .api import SegmentAPI, SegmentUploadError

24

47

0.854167

5

48

8.2

1

0

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48

1

48

0.953488

0

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0

true

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1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

6

ea18cbc8b782dfce269da5bd6a10c97abf3dc581

93

py

Python

rlearn/reporting/_search/__init__.py

AlgoWit/research-learn

81ff420accf3929d0d9ae400a6b69eb88e8ae592

[ "MIT" ]

1

2019-11-15T16:02:35.000Z

rlearn/reporting/_search/__init__.py

AlgoWit/research-learn

81ff420accf3929d0d9ae400a6b69eb88e8ae592

[ "MIT" ]

6

2019-11-29T12:09:39.000Z

2020-08-13T17:14:09.000Z

rlearn/reporting/_search/__init__.py

AlgoWit/research-learn

81ff420accf3929d0d9ae400a6b69eb88e8ae592

[ "MIT" ]

2

2019-11-29T11:50:38.000Z

2020-03-16T12:15:23.000Z

from ._results import report_model_search_results __all__ = ['report_model_search_results']

23.25

49

0.849462

12

93

5.666667

0.583333

0.323529

0.5

0.705882

0

0.086022

93

3

50

31

0.8

0

0.290323

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1

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false

0

0.5

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null

1

0

1

0

null

0

1

0

6

ea52cc9b9a69c84a14f717ee2a0267de05f32d98

86

py

Python

katas/kyu_6/digital_root.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_6/digital_root.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_6/digital_root.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

def digital_root(n): return n if n <= 10 else digital_root(sum(map(int, str(n))))

28.666667

64

0.662791

17

86

3.235294

0.705882

0.4

0

0.028169

0.174419

86

2

65

43

0.746479

0

1

0.5

false

0

0.5

1

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null

1

0

1

0

null

0

1

0

1

0

6

ea6966d2533ff6afa4f859f5986a4c5b5c210518

43

py

Python

variational_lstm/VariationalRecurrentNeuralNetwork/dataset/__init__.py

noahlozevski/variational-RNN

629c36869e7f81b4c40451edd9521a797d8b4db6

[ "MIT" ]

null

variational_lstm/VariationalRecurrentNeuralNetwork/dataset/__init__.py

noahlozevski/variational-RNN

629c36869e7f81b4c40451edd9521a797d8b4db6

[ "MIT" ]

null

variational_lstm/VariationalRecurrentNeuralNetwork/dataset/__init__.py

noahlozevski/variational-RNN

629c36869e7f81b4c40451edd9521a797d8b4db6

[ "MIT" ]

null

from .dataset import get_dataset, processed

43

0.860465

6

43

6

0.833333

0

0.093023

43

1

43

0.923077

0

1

0

true

0

1

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

5760c322d8eacb72591ec114fcfa5cd3bf61acd9

250

py

Python

summarize/nn/beam_search/length_penalizers/__init__.py

danieldeutsch/summarize

f36a86d58f381ff1f607f356dad3d6ef7b0e0224

[ "Apache-2.0" ]

15

2019-11-01T11:49:44.000Z

2021-01-19T06:59:32.000Z

summarize/nn/beam_search/length_penalizers/__init__.py

CogComp/summary-cloze

b38e3e8c7755903477fd92a4cff27125cbf5553d

[ "Apache-2.0" ]

2

2020-03-30T07:54:01.000Z

2021-11-15T16:27:42.000Z

summarize/nn/beam_search/length_penalizers/__init__.py

CogComp/summary-cloze

b38e3e8c7755903477fd92a4cff27125cbf5553d

[ "Apache-2.0" ]

3

2019-12-06T05:57:51.000Z

2019-12-11T11:34:21.000Z

from summarize.nn.beam_search.length_penalizers.length_penalizer import LengthPenalizer from summarize.nn.beam_search.length_penalizers.average import AverageLengthPenalizer from summarize.nn.beam_search.length_penalizers.wu import WuLengthPenalizer

62.5

87

0.904

31

250

7.064516

0.451613

0.178082

0.205479

0.260274

0.561644

0

0.048

250

3

88

83.333333

0.920168

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

579bd88e341215fd3d0161fbdb3f71ac2c48b382

36

py

Python

asgi_cors_middleware/__init__.py

mrkiura/asgi-cors-middleware

3d70de6e3c0ffc7701757d3229e5e8a2f10cdaa5

[ "MIT" ]

1

2021-08-01T21:17:35.000Z

asgi_cors_middleware/__init__.py

mrkiura/asgi-cors-middleware

3d70de6e3c0ffc7701757d3229e5e8a2f10cdaa5

[ "MIT" ]

null

asgi_cors_middleware/__init__.py

mrkiura/asgi-cors-middleware

3d70de6e3c0ffc7701757d3229e5e8a2f10cdaa5

[ "MIT" ]

null

from .middleware import CorsASGIApp

18

35

0.861111

4

36

7.75

1

0

0.111111

36

1

36

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

57c4ca956a5c9ed087233b0c44e8a59c68bb4932

148

py

Python

HackerRank Solutions/Regex/Introduction/Matching Word & Non-Word Character.py

DevashishPathrabe/Competetive-Coding

91049459359854b7834cbfb31415682600dc9c57

[ "MIT" ]

null

HackerRank Solutions/Regex/Introduction/Matching Word & Non-Word Character.py

DevashishPathrabe/Competetive-Coding

91049459359854b7834cbfb31415682600dc9c57

[ "MIT" ]

null

HackerRank Solutions/Regex/Introduction/Matching Word & Non-Word Character.py

DevashishPathrabe/Competetive-Coding

91049459359854b7834cbfb31415682600dc9c57

[ "MIT" ]

null

Regex_Pattern = r"\w\w\w\W\w\w\w\w\w\w\w\w\w\w\W\w\w\w" # Do not delete 'r'. import re print(str(bool(re.search(Regex_Pattern, input()))).lower())

29.6

76

0.641892

36

148

2.583333

0.416667

0.365591

0.516129

0.645161

0.193548

0

0.087838

148

5

77

29.6

0.688889

0.121622

0

0.333333

0.27907

0

1

0

false

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0.333333

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1

0

1

0

null

0

1

0

6

57e7cc547c17451d7ce4e4b862fad2e29ca3adc1

20,419

py

Python

models/ssadgm.py

kuleshov/deep-learning-models

fa755f54f5692739f71c969b1ae489b17de84ae3

[ "MIT" ]

28

2017-03-01T12:35:33.000Z

2021-03-23T23:53:07.000Z

models/ssadgm.py

kuleshov/deep-learning-model-zoo

fa755f54f5692739f71c969b1ae489b17de84ae3

[ "MIT" ]

null

models/ssadgm.py

kuleshov/deep-learning-model-zoo

fa755f54f5692739f71c969b1ae489b17de84ae3

[ "MIT" ]

22

2017-03-01T13:41:55.000Z

2021-03-19T05:46:09.000Z

import time import pickle import numpy as np import theano import theano.tensor as T import lasagne from semisup_model import SemiSupModel from lasagne.layers import batch_norm from layers.sampling import GaussianSampleLayer from layers.shape import RepeatLayer from distributions import log_bernoulli, log_normal, log_normal2 # ---------------------------------------------------------------------------- class SSADGM(SemiSupModel): """Auxiliary Deep Generative Model (semi-supervised version)""" def __init__(self, X_labeled, y_labeled, n_out, n_superbatch=12800, model='bernoulli', opt_alg='adam', opt_params={'lr' : 1e-3, 'b1': 0.9, 'b2': 0.99}): # save model that wil be created self.model = model self.n_out = n_out self.n_sample = 3 # monte-carlo samples; need to make this a command-line param SemiSupModel.__init__(self, X_labeled, y_labeled, n_out, n_superbatch, opt_alg, opt_params) def create_model(self, L, yl, n_dim, n_out, n_chan=1): # params n_lat = 100 # latent stochastic variables n_aux = 10 # auxiliary variables n_hid = 100 # size of hidden layer in encoder/decoder n_sam = self.n_sample # number of monte-carlo samples n_vis = n_dim * n_dim * n_chan # total dimensionality of ouput hid_nl = lasagne.nonlinearities.rectify relu_shift = lambda av: T.nnet.relu(av+10)-10 # for numerical stability # save this for later (hack; should be saved elsewhere) self.n_out = n_out self.n_aux = n_aux # create input layers l_qx_in = lasagne.layers.InputLayer(shape=(None, n_chan, n_dim, n_dim)) l_qy_in = lasagne.layers.InputLayer(shape=(None, n_out)) # create q(a|x) l_qa_hid1 = (lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl)) l_qa_hid2 = (lasagne.layers.DenseLayer( l_qa_hid1, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl)) l_qa_mu = lasagne.layers.DenseLayer( l_qa_hid2, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_qa_logsigma = lasagne.layers.DenseLayer( l_qa_hid2, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) l_qa_mu = lasagne.layers.ReshapeLayer( RepeatLayer(l_qa_mu, n_ax=1, n_rep=n_sam), shape=(-1, n_aux)) l_qa_logsigma = lasagne.layers.ReshapeLayer( RepeatLayer(l_qa_logsigma, n_ax=1, n_rep=n_sam), shape=(-1, n_aux)) l_qa = GaussianSampleLayer(l_qa_mu, l_qa_logsigma) # create q(y|a,x) l_qy_hid1a = lasagne.layers.DenseLayer( l_qa, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qy_hid1b = lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qy_hid1b = lasagne.layers.ReshapeLayer( RepeatLayer(l_qy_hid1b, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_qy_hid2 = (lasagne.layers.ElemwiseSumLayer( [l_qy_hid1a, l_qy_hid1b])) l_qy_hid2 = lasagne.layers.NonlinearityLayer(l_qy_hid2, hid_nl) l_qy_hid3 = (lasagne.layers.DenseLayer( l_qy_hid2, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl)) l_qy_mu = lasagne.layers.DenseLayer( l_qy_hid3, num_units=n_out, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=lasagne.nonlinearities.softmax) # create q(z|a,x,y) l_qz_hid1a = lasagne.layers.DenseLayer( l_qa, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1b = lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1b = lasagne.layers.ReshapeLayer( RepeatLayer(l_qz_hid1b, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_qz_hid1c = lasagne.layers.DenseLayer( l_qy_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1c = lasagne.layers.ReshapeLayer( RepeatLayer(l_qz_hid1c, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_qz_hid2 = (lasagne.layers.ElemwiseSumLayer( # [l_qz_hid1a, l_qz_hid1b])) [l_qz_hid1a, l_qz_hid1b, l_qz_hid1c])) l_qz_hid2 = lasagne.layers.NonlinearityLayer(l_qz_hid2, hid_nl) # l_qz_hid3 = (lasagne.layers.DenseLayer( # l_qz_hid2, num_units=n_hid, # W=lasagne.init.GlorotNormal('relu'), # b=lasagne.init.Normal(1e-3), # nonlinearity=hid_nl)) l_qz_mu = lasagne.layers.DenseLayer( l_qz_hid2, num_units=n_lat, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_qz_logsigma = lasagne.layers.DenseLayer( l_qz_hid2, num_units=n_lat, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) l_qz = GaussianSampleLayer(l_qz_mu, l_qz_logsigma) # create the decoder network # create p(x|z,y) l_px_hid1a = lasagne.layers.DenseLayer( l_qz, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_px_hid1b = lasagne.layers.DenseLayer( l_qy_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_px_hid1b = lasagne.layers.ReshapeLayer( RepeatLayer(l_px_hid1b, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_px_hid2 = (lasagne.layers.ElemwiseSumLayer( # [l_px_hid1a])) [l_px_hid1a, l_px_hid1b])) l_px_hid2 = lasagne.layers.NonlinearityLayer(l_px_hid2, hid_nl) # l_px_hid3 = (lasagne.layers.DenseLayer( # l_px_hid2, num_units=n_hid, # W=lasagne.init.GlorotNormal('relu'), # b=lasagne.init.Normal(1e-3), # nonlinearity=hid_nl)) l_px_mu, l_px_logsigma = None, None if self.model == 'bernoulli': l_px_mu = lasagne.layers.DenseLayer(l_px_hid2, num_units=n_vis, nonlinearity = lasagne.nonlinearities.sigmoid, W=lasagne.init.GlorotUniform(), b=lasagne.init.Normal(1e-3)) elif self.model == 'gaussian': l_px_mu = lasagne.layers.DenseLayer( l_px_hid2, num_units=n_vis, nonlinearity=None) l_px_logsigma = lasagne.layers.DenseLayer( l_px_hid2, num_units=n_vis, nonlinearity=relu_shift) # create p(a|z,x,y) l_pa_hid1a = lasagne.layers.DenseLayer( l_qz, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_pa_hid1b = lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_pa_hid1b = lasagne.layers.ReshapeLayer( RepeatLayer(l_pa_hid1b, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_pa_hid1c = lasagne.layers.DenseLayer( l_qy_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_pa_hid1c = lasagne.layers.ReshapeLayer( RepeatLayer(l_pa_hid1c, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_pa_hid2 = (lasagne.layers.ElemwiseSumLayer( # [l_pa_hid1a, l_pa_hid1b])) [l_pa_hid1a, l_pa_hid1b, l_pa_hid1c])) l_pa_hid2 = lasagne.layers.NonlinearityLayer(l_pa_hid2, hid_nl) # l_pa_hid3 = (lasagne.layers.DenseLayer( # l_pa_hid2, num_units=n_hid, # nonlinearity=hid_nl, # W=lasagne.init.GlorotNormal('relu'), # b=lasagne.init.Normal(1e-3))) l_pa_mu = lasagne.layers.DenseLayer( l_pa_hid2, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_pa_logsigma = lasagne.layers.DenseLayer( l_pa_hid2, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) return l_px_mu, l_px_logsigma, l_pa_mu, l_pa_logsigma, \ l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, \ l_qy_mu, l_qa, l_qz, l_qx_in, l_qy_in def create_model2(self, X, Y, n_dim, n_out, n_chan=1): # params n_lat = 200 # latent stochastic variables n_aux = 10 # auxiliary variables n_hid = 100 # size of hidden layer in encoder/decoder n_sam = self.n_sample # number of monte-carlo samples n_out = n_dim * n_dim * n_chan # total dimensionality of ouput hid_nl = lasagne.nonlinearities.rectify relu_shift = lambda av: T.nnet.relu(av+10)-10 # for numerical stability # create the encoder network self.n_aux = n_aux self.n_out = n_out # create input layers l_qx_in = lasagne.layers.InputLayer(shape=(None, n_chan, n_dim, n_dim)) l_qy_in = lasagne.layers.InputLayer(shape=(None, n_out)) # create q(a|x) l_qa_hid1 = (lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl)) # l_qa_hid2 = (lasagne.layers.DenseLayer( # l_qa_hid1, num_units=n_hid, # W=lasagne.init.GlorotNormal('relu'), # b=lasagne.init.Normal(1e-3), # nonlinearity=hid_nl)) l_qa_mu = lasagne.layers.DenseLayer( l_qa_hid1, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_qa_logsigma = lasagne.layers.DenseLayer( l_qa_hid1, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) l_qa_mu = lasagne.layers.ReshapeLayer( RepeatLayer(l_qa_mu, n_ax=1, n_rep=n_sam), shape=(-1, n_aux)) l_qa_logsigma = lasagne.layers.ReshapeLayer( RepeatLayer(l_qa_logsigma, n_ax=1, n_rep=n_sam), shape=(-1, n_aux)) l_qa = GaussianSampleLayer(l_qa_mu, l_qa_logsigma) # create q(z|a,x,y) l_qz_hid1a = lasagne.layers.DenseLayer( l_qa, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1b = lasagne.layers.DenseLayer( l_qx_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1b = lasagne.layers.ReshapeLayer( RepeatLayer(l_qz_hid1b, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_qz_hid1c = lasagne.layers.DenseLayer( l_qy_in, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_qz_hid1c = lasagne.layers.ReshapeLayer( RepeatLayer(l_qz_hid1c, n_ax=1, n_rep=n_sam), shape=(-1, n_hid)) l_qz_hid2 = (lasagne.layers.ElemwiseSumLayer( [l_qz_hid1a, l_qz_hid1b])) # [l_qz_hid1a, l_qz_hid1b, l_qz_hid1c])) l_qz_hid2 = lasagne.layers.NonlinearityLayer(l_qz_hid2, hid_nl) # l_qz_hid3 = (lasagne.layers.DenseLayer( # l_qz_hid2, num_units=n_hid, # W=lasagne.init.GlorotNormal('relu'), # b=lasagne.init.Normal(1e-3), # nonlinearity=hid_nl)) l_qz_mu = lasagne.layers.DenseLayer( l_qz_hid2, num_units=n_lat, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_qz_logsigma = lasagne.layers.DenseLayer( l_qz_hid2, num_units=n_lat, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) l_qz = GaussianSampleLayer(l_qz_mu, l_qz_logsigma) # create the decoder network # create p(x|z) l_px_hid = lasagne.layers.DenseLayer( l_qz, num_units=n_hid, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3), nonlinearity=hid_nl) l_px_mu, l_px_logsigma = None, None if self.model == 'bernoulli': l_px_mu = lasagne.layers.DenseLayer(l_px_hid, num_units=n_out, nonlinearity = lasagne.nonlinearities.sigmoid, W=lasagne.init.GlorotUniform(), b=lasagne.init.Normal(1e-3)) elif self.model == 'gaussian': l_px_mu = lasagne.layers.DenseLayer( l_px_hid, num_units=n_out, nonlinearity=None) l_px_logsigma = lasagne.layers.DenseLayer( l_px_hid, num_units=n_out, nonlinearity=relu_shift) # create p(a|z) l_pa_hid = lasagne.layers.DenseLayer( l_qz, num_units=n_hid, nonlinearity=hid_nl, W=lasagne.init.GlorotNormal('relu'), b=lasagne.init.Normal(1e-3)) l_pa_mu = lasagne.layers.DenseLayer( l_pa_hid, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=None) l_pa_logsigma = lasagne.layers.DenseLayer( l_pa_hid, num_units=n_aux, W=lasagne.init.GlorotNormal(), b=lasagne.init.Normal(1e-3), nonlinearity=relu_shift) return l_px_mu, l_px_logsigma, l_pa_mu, l_pa_logsigma, \ l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, \ l_qa, l_qa, l_qz, l_qx_in, None def create_objectives(self, X, L, yu, yl, deterministic=False): # load data dimensions n_lbl, n_chan, n_dim, _ = L.shape n_vis = n_dim * n_dim * n_chan n_unl = X.shape[0] n_sam = self.n_sample n_out = self.n_out n_aux = self.n_aux # duplicate entries to take into account multiple mc samples x = L.flatten(2) x = x.dimshuffle(0,'x',1).repeat(n_sam, axis=1).reshape((-1, n_vis)) yl = lasagne.utils.one_hot(yl, m=n_out) yl_rep = yl.dimshuffle(0,'x',1).repeat(n_sam, axis=1).reshape((-1, n_out)) # load network l_px_mu, l_px_logsigma, l_pa_mu, l_pa_logsigma, \ l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, \ l_qy_mu, l_qa, l_qz, l_qx_in, l_qy_in = self.network # first, we construct the ELBO on the labeled examples # load network output input_asgn = { l_qx_in : L, l_qy_in : yl } # input_asgn = { l_qx_in : L } pa_mu, pa_logsigma, qz_mu, qz_logsigma, qa_mu, qa_logsigma, qy_mu, a, z \ = lasagne.layers.get_output( [ l_pa_mu, l_pa_logsigma, l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, l_qy_mu, l_qa, l_qz ], input_asgn, deterministic=deterministic) if self.model == 'bernoulli': px_mu = lasagne.layers.get_output(l_px_mu, input_asgn, deterministic=deterministic) elif self.model == 'gaussian': px_mu, px_logsigma = lasagne.layers.get_output([l_px_mu, l_px_logsigma], input_asgn, deterministic=deterministic) # entropy term log_qa_given_x = log_normal2(a, qa_mu, qa_logsigma).sum(axis=1) log_qz_given_ayx = log_normal2(z, qz_mu, qz_logsigma).sum(axis=1) log_qza_given_xy = log_qz_given_ayx + log_qa_given_x # log-probability term z_prior_sigma = T.cast(T.ones_like(qz_logsigma), dtype=theano.config.floatX) z_prior_mu = T.cast(T.zeros_like(qz_mu), dtype=theano.config.floatX) y_prior = T.cast(T.ones((n_lbl*n_sam, n_out)) / n_out, dtype=theano.config.floatX) log_pz = log_normal(z, z_prior_mu, z_prior_sigma).sum(axis=1) log_pa_given_z = log_normal2(a, pa_mu, pa_logsigma).sum(axis=1) log_py = -lasagne.objectives.categorical_crossentropy(y_prior, yl_rep) if self.model == 'bernoulli': log_px_given_z = log_bernoulli(x, px_mu).sum(axis=1) elif self.model == 'gaussian': log_px_given_z = log_normal2(x, px_mu, px_logsigma).sum(axis=1) log_paxzy = log_pa_given_z + log_px_given_z + log_pz + log_py # compute the evidence lower bound elbo_lbl = T.mean(log_paxzy - log_qza_given_xy, axis=0) # next, we build the elbo on the unlabeled examples # n_rep # we are going to replicate the batch n_out times, once for each label I = T.eye(n_out) t = I.reshape((n_out, 1, n_out)).repeat(n_unl, axis=1).reshape((-1, n_out)) U = X.dimshuffle(('x', 0, 1, 2, 3)).repeat(10, axis=0) \ .reshape((-1, n_chan, n_dim, n_dim)) # duplicate entries to take into account multiple mc samples u = U.flatten(2) u = u.dimshuffle(0,'x',1).repeat(n_sam, axis=1).reshape((-1, n_vis)) t_rep = t.dimshuffle(0,'x',1).repeat(n_sam, axis=1).reshape((-1, n_out)) yu = lasagne.utils.one_hot(yu, m=n_out) yu_rep = yu.dimshuffle(0,'x',1).repeat(n_sam, axis=1).reshape((-1, n_out)) # load network output # not going to try to be fancy right now (commenting this out): # a_unl = get_output(l_qa, X) # a_unl_rep = a_unl.reshape((1, n_unl*n_sam, n_aux)) \ # .repeat(n_out, axis=0).reshape((-1, n_aux)) input_asgn = { l_qx_in : U, l_qy_in : t } pa_mu, pa_logsigma, qz_mu, qz_logsigma, qa_mu, qa_logsigma, qy_mu, a, z \ = lasagne.layers.get_output( [ l_pa_mu, l_pa_logsigma, l_qz_mu, l_qz_logsigma, l_qa_mu, l_qa_logsigma, l_qy_mu, l_qa, l_qz ], input_asgn, deterministic=deterministic) if self.model == 'bernoulli': px_mu = lasagne.layers.get_output(l_px_mu, input_asgn, deterministic=deterministic) elif self.model == 'gaussian': px_mu, px_logsigma = lasagne.layers.get_output([l_px_mu, l_px_logsigma], input_asgn, deterministic=deterministic) # entropy term log_qa_given_x = log_normal2(a, qa_mu, qa_logsigma).sum(axis=1) log_qz_given_ayx = log_normal2(z, qz_mu, qz_logsigma).sum(axis=1) log_qy_given_ax = log_bernoulli(t_rep, qy_mu).sum(axis=1) log_qza_given_xy = log_qz_given_ayx + log_qa_given_x + log_qy_given_ax # log-probability term z_prior_sigma = T.cast(T.ones_like(qz_logsigma), dtype=theano.config.floatX) z_prior_mu = T.cast(T.zeros_like(qz_mu), dtype=theano.config.floatX) y_prior = T.cast(T.ones((n_out*n_unl*n_sam, n_out)) / n_out, dtype=theano.config.floatX) log_pz = log_normal(z, z_prior_mu, z_prior_sigma).sum(axis=1) log_pa_given_z = log_normal2(a, pa_mu, pa_logsigma).sum(axis=1) log_py = -lasagne.objectives.categorical_crossentropy(y_prior, t_rep) if self.model == 'bernoulli': log_px_given_z = log_bernoulli(u, px_mu).sum(axis=1) elif self.model == 'gaussian': log_px_given_z = log_normal2(u, px_mu, px_logsigma).sum(axis=1) log_paxzy = log_pa_given_z + log_px_given_z + log_pz + log_py # compute the evidence lower bound elbo_unl = T.mean(log_paxzy - log_qza_given_xy, axis=0) # compute the total lower bound elbo = elbo_lbl + elbo_unl # in case we want regularization: # l2_reg = 0.0 # for p in self.get_params(): # if 'W' not in str(p): continue # l2_reg += log_normal(p, 0, 1).sum() # elbo_lbl += l2_reg # finally, compute the accuracy y_pred = lasagne.layers.get_output(l_qy_mu, X, deterministic=True) \ .reshape((n_unl, n_sam, n_out)) \ .mean(axis=1) acc = lasagne.objectives.categorical_accuracy(y_pred, yu).mean() return -elbo, acc def create_gradients(self, loss, deterministic=False): grads = SemiSupModel.create_gradients(self, loss, deterministic) # combine and clip gradients clip_grad = 1 max_norm = 5 mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm) cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads] return cgrads def get_params(self): l_px_mu = self.network[0] l_pa_mu = self.network[2] params = lasagne.layers.get_all_params(l_px_mu, trainable=True) params0 = lasagne.layers.get_all_params(l_pa_mu, trainable=True) for param in params0: if param not in params: params.append(param) return params

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py

Python

epytope/Data/pssms/smmpmbec/mat/A_11_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

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2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

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2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

A_11_01_8 = {0: {'A': -0.218, 'C': 0.09, 'E': 0.12, 'D': 0.063, 'G': -0.112, 'F': 0.179, 'I': -0.081, 'H': 0.135, 'K': 0.1, 'M': -0.027, 'L': 0.049, 'N': -0.003, 'Q': -0.069, 'P': 0.092, 'S': -0.444, 'R': 0.123, 'T': -0.336, 'W': 0.168, 'V': -0.104, 'Y': 0.277}, 1: {'A': 0.305, 'C': 0.105, 'E': 0.131, 'D': 0.089, 'G': 0.163, 'F': 0.056, 'I': -0.273, 'H': 0.084, 'K': 0.149, 'M': -0.151, 'L': -0.213, 'N': -0.051, 'Q': -0.139, 'P': -0.048, 'S': 0.072, 'R': 0.312, 'T': -0.015, 'W': -0.174, 'V': -0.106, 'Y': -0.295}, 2: {'A': 0.121, 'C': -0.053, 'E': 0.002, 'D': -0.044, 'G': -0.255, 'F': -0.099, 'I': 0.176, 'H': -0.049, 'K': -0.051, 'M': 0.041, 'L': 0.052, 'N': -0.302, 'Q': 0.108, 'P': 0.124, 'S': 0.06, 'R': 0.4, 'T': -0.003, 'W': -0.129, 'V': 0.07, 'Y': -0.172}, 3: {'A': -0.023, 'C': -0.376, 'E': -0.081, 'D': -0.13, 'G': -0.205, 'F': -0.608, 'I': 0.251, 'H': -0.079, 'K': 0.31, 'M': 0.17, 'L': 0.162, 'N': -0.003, 'Q': 0.437, 'P': 0.166, 'S': 0.158, 'R': 0.147, 'T': 0.256, 'W': -0.265, 'V': 0.283, 'Y': -0.569}, 4: {'A': 0.1, 'C': 0.169, 'E': -0.094, 'D': -0.097, 'G': 0.101, 'F': -0.043, 'I': -0.191, 'H': 0.061, 'K': -0.242, 'M': 0.071, 'L': -0.13, 'N': 0.3, 'Q': 0.33, 'P': -0.165, 'S': 0.069, 'R': 0.225, 'T': -0.235, 'W': 0.037, 'V': -0.303, 'Y': 0.038}, 5: {'A': 0.188, 'C': -0.028, 'E': -0.124, 'D': -0.04, 'G': 0.008, 'F': -0.386, 'I': -0.284, 'H': -0.154, 'K': 0.282, 'M': -0.005, 'L': 0.044, 'N': 0.127, 'Q': -0.033, 'P': 0.068, 'S': 0.235, 'R': 0.226, 'T': 0.054, 'W': -0.095, 'V': 0.126, 'Y': -0.207}, 6: {'A': 0.299, 'C': -0.029, 'E': 0.065, 'D': 0.233, 'G': 0.086, 'F': -0.651, 'I': -0.013, 'H': -0.011, 'K': -0.036, 'M': -0.11, 'L': -0.108, 'N': 0.011, 'Q': -0.014, 'P': 0.203, 'S': 0.255, 'R': -0.045, 'T': 0.242, 'W': -0.216, 'V': 0.106, 'Y': -0.266}, 7: {'A': 0.091, 'C': 0.141, 'E': 0.146, 'D': 0.096, 'G': 0.012, 'F': 0.268, 'I': 0.24, 'H': -0.239, 'K': -0.969, 'M': 0.063, 'L': -0.013, 'N': -0.004, 'Q': -0.005, 'P': 0.015, 'S': -0.041, 'R': -0.52, 'T': 0.105, 'W': 0.159, 'V': 0.166, 'Y': 0.29}, -1: {'con': 4.47965}}

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Python

torchbench/semantic_segmentation/__init__.py

xperience-ai/torchbench

c2cff1ede9a746680ac583b0aa190b748cd15a52

[ "Apache-2.0" ]

143

2019-08-12T17:24:02.000Z

2022-03-31T09:39:34.000Z

torchbench/semantic_segmentation/__init__.py

xperience-ai/torchbench

c2cff1ede9a746680ac583b0aa190b748cd15a52

[ "Apache-2.0" ]

6

2019-08-12T18:03:56.000Z

2021-05-05T10:53:40.000Z

torchbench/semantic_segmentation/__init__.py

xperience-ai/torchbench

c2cff1ede9a746680ac583b0aa190b748cd15a52

[ "Apache-2.0" ]

18

2019-08-24T19:33:09.000Z

2022-01-20T02:15:00.000Z

__all__ = ["ADE20K", "CamVid", "Cityscapes", "PASCALContext", "PASCALVOC"] from torchbench.semantic_segmentation.ade20k import ADE20K from torchbench.semantic_segmentation.camvid import CamVid from torchbench.semantic_segmentation.cityscapes import Cityscapes from torchbench.semantic_segmentation.pascalcontext import PASCALContext from torchbench.semantic_segmentation.pascalvoc import PASCALVOC

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Python

resources/dot_PyCharm/system/python_stubs/-762174762/PySide/QtGui/QPen.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

1

2020-04-20T02:27:20.000Z

resources/dot_PyCharm/system/python_stubs/cache/8cdc475d469a13122bc4bc6c3ac1c215d93d5f120f5cc1ef33a8f3088ee54d8e/PySide/QtGui/QPen.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

resources/dot_PyCharm/system/python_stubs/cache/8cdc475d469a13122bc4bc6c3ac1c215d93d5f120f5cc1ef33a8f3088ee54d8e/PySide/QtGui/QPen.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

# encoding: utf-8 # module PySide.QtGui # from C:\Python27\lib\site-packages\PySide\QtGui.pyd # by generator 1.147 # no doc # imports import PySide.QtCore as __PySide_QtCore import Shiboken as __Shiboken class QPen(__Shiboken.Object): # no doc def brush(self, *args, **kwargs): # real signature unknown pass def capStyle(self, *args, **kwargs): # real signature unknown pass def color(self, *args, **kwargs): # real signature unknown pass def dashOffset(self, *args, **kwargs): # real signature unknown pass def dashPattern(self, *args, **kwargs): # real signature unknown pass def isCosmetic(self, *args, **kwargs): # real signature unknown pass def isSolid(self, *args, **kwargs): # real signature unknown pass def joinStyle(self, *args, **kwargs): # real signature unknown pass def miterLimit(self, *args, **kwargs): # real signature unknown pass def setBrush(self, *args, **kwargs): # real signature unknown pass def setCapStyle(self, *args, **kwargs): # real signature unknown pass def setColor(self, *args, **kwargs): # real signature unknown pass def setCosmetic(self, *args, **kwargs): # real signature unknown pass def setDashOffset(self, *args, **kwargs): # real signature unknown pass def setDashPattern(self, *args, **kwargs): # real signature unknown pass def setJoinStyle(self, *args, **kwargs): # real signature unknown pass def setMiterLimit(self, *args, **kwargs): # real signature unknown pass def setStyle(self, *args, **kwargs): # real signature unknown pass def setWidth(self, *args, **kwargs): # real signature unknown pass def setWidthF(self, *args, **kwargs): # real signature unknown pass def style(self, *args, **kwargs): # real signature unknown pass def swap(self, *args, **kwargs): # real signature unknown pass def width(self, *args, **kwargs): # real signature unknown pass def widthF(self, *args, **kwargs): # real signature unknown pass def __copy__(self, *args, **kwargs): # real signature unknown pass def __eq__(self, y): # real signature unknown; restored from __doc__ """ x.__eq__(y) <==> x==y """ pass def __ge__(self, y): # real signature unknown; restored from __doc__ """ x.__ge__(y) <==> x>=y """ pass def __gt__(self, y): # real signature unknown; restored from __doc__ """ x.__gt__(y) <==> x>y """ pass def __init__(self, *args, **kwargs): # real signature unknown pass def __le__(self, y): # real signature unknown; restored from __doc__ """ x.__le__(y) <==> x<=y """ pass def __lshift__(self, y): # real signature unknown; restored from __doc__ """ x.__lshift__(y) <==> x<<y """ pass def __lt__(self, y): # real signature unknown; restored from __doc__ """ x.__lt__(y) <==> x<y """ pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __ne__(self, y): # real signature unknown; restored from __doc__ """ x.__ne__(y) <==> x!=y """ pass def __repr__(self): # real signature unknown; restored from __doc__ """ x.__repr__() <==> repr(x) """ pass def __rlshift__(self, y): # real signature unknown; restored from __doc__ """ x.__rlshift__(y) <==> y<<x """ pass def __rrshift__(self, y): # real signature unknown; restored from __doc__ """ x.__rrshift__(y) <==> y>>x """ pass def __rshift__(self, y): # real signature unknown; restored from __doc__ """ x.__rshift__(y) <==> x>>y """ pass

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Python

usaspending_api/disaster/tests/integration/test_spending_by_geography.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

1

2020-08-14T04:14:32.000Z

usaspending_api/disaster/tests/integration/test_spending_by_geography.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

null

usaspending_api/disaster/tests/integration/test_spending_by_geography.py

jbuendiallc/usaspending-api

f827870cbca4b6a6e16f1c5272bb2ff73a113d76

[ "CC0-1.0" ]

null

import json import pytest from rest_framework import status from usaspending_api.awards.v2.lookups.lookups import grant_type_mapping, contract_type_mapping, loan_type_mapping from usaspending_api.common.helpers.generic_helper import get_time_period_message from usaspending_api.search.tests.data.utilities import setup_elasticsearch_test def _get_shape_code_for_sort(result_dict): return result_dict["shape_code"] def post(client, **kwargs): url = "/api/v2/disaster/spending_by_geography/" request_body = {} filters = {} if kwargs.get("def_codes"): filters["def_codes"] = kwargs["def_codes"] if kwargs.get("award_type_codes"): filters["award_type_codes"] = kwargs["award_type_codes"] request_body["filter"] = filters if kwargs.get("geo_layer"): request_body["geo_layer"] = kwargs["geo_layer"] if kwargs.get("geo_layer_filters"): request_body["geo_layer_filters"] = kwargs["geo_layer_filters"] if kwargs.get("spending_type"): request_body["spending_type"] = kwargs["spending_type"] resp = client.post(url, content_type="application/json", data=json.dumps(request_body)) return resp @pytest.mark.django_db def test_spending_by_geography_failure_with_missing_fields( client, monkeypatch, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) # Test required "def_codes" in filter object resp = post(client, geo_layer="state", geo_layer_filters=["SC-01"], spending_type="obligation") assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY assert resp.data["detail"] == "Missing value: 'filter|def_codes' is a required field" # Test required "geo_layer" string resp = post(client, def_codes=["L"], geo_layer_filters=["SC-01"], spending_type="obligation") assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY assert resp.data["detail"] == "Missing value: 'geo_layer' is a required field" # Test required "spending_type" string resp = post(client, def_codes=["L"], geo_layer="state", geo_layer_filters=["SC-01"]) assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY assert resp.data["detail"] == "Missing value: 'spending_type' is a required field" @pytest.mark.django_db def test_spending_by_geography_failure_with_invalid_fields( client, monkeypatch, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) # Test invalid "geo_layer" string resp = post(client, def_codes=["L"], geo_layer="NOT VALID", geo_layer_filters=["SC-01"], spending_type="obligation") assert resp.status_code == status.HTTP_400_BAD_REQUEST assert resp.data["detail"] == "Field 'geo_layer' is outside valid values ['state', 'county', 'district']" # Test invalid "spending_type" string resp = post(client, def_codes=["L"], geo_layer="state", geo_layer_filters=["SC-01"], spending_type="NOT VALID") assert resp.status_code == status.HTTP_400_BAD_REQUEST assert ( resp.data["detail"] == "Field 'spending_type' is outside valid values ['obligation', 'outlay', 'face_value_of_loan']" ) # Test invalid "award_type_codes" string resp = post( client, award_type_codes=["NOT VALID"], def_codes=["L"], geo_layer="state", geo_layer_filters=["SC-01"], spending_type="obligation", ) assert resp.status_code == status.HTTP_400_BAD_REQUEST assert "Field 'filter|award_type_codes' is outside valid values " in resp.data["detail"] @pytest.mark.django_db def test_correct_response_with_different_geo_filters( client, monkeypatch, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) test_cases = [ _test_correct_response_for_county, _test_correct_response_for_district, _test_correct_response_for_state, ] for test in test_cases: test(client) def _test_correct_response_for_county(client): resp = post( client, def_codes=["L", "M"], geo_layer="county", geo_layer_filters=["45001", "45005"], spending_type="obligation", ) expected_response = { "geo_layer": "county", "spending_type": "obligation", "results": [ { "amount": 2000220.0, "display_name": "Charleston", "per_capita": 2000220.0, "population": 1, "shape_code": "45001", "award_count": 3, }, { "amount": 200000.0, "display_name": "Test Name", "per_capita": 20000.0, "population": 10, "shape_code": "45005", "award_count": 1, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_district(client): resp = post( client, def_codes=["L", "M"], geo_layer="district", geo_layer_filters=["4510", "4550", "5350"], spending_type="obligation", ) expected_response = { "geo_layer": "district", "spending_type": "obligation", "results": [ { "amount": 2000000.0, "display_name": "SC-10", "per_capita": None, "population": None, "shape_code": "4510", "award_count": 1, }, { "amount": 200200.0, "display_name": "SC-50", "per_capita": 2002.0, "population": 100, "shape_code": "4550", "award_count": 2, }, { "amount": 22000.0, "display_name": "WA-50", "per_capita": 22.0, "population": 1000, "shape_code": "5350", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_state(client): resp = post(client, def_codes=["L", "M"], geo_layer="state", geo_layer_filters=["WA"], spending_type="obligation",) expected_response = { "geo_layer": "state", "spending_type": "obligation", "results": [ { "amount": 22000.0, "display_name": "Washington", "per_capita": 2.2, "population": 10000, "shape_code": "WA", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response @pytest.mark.django_db def test_correct_response_with_different_spending_types( client, monkeypatch, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) test_cases = [ _test_correct_response_for_obligation, _test_correct_response_for_outlay, _test_correct_response_for_face_value_of_loan, ] for test in test_cases: test(client) def _test_correct_response_for_obligation(client): resp = post( client, def_codes=["L", "M"], geo_layer="state", geo_layer_filters=["SC", "WA"], spending_type="obligation", ) expected_response = { "geo_layer": "state", "spending_type": "obligation", "results": [ { "amount": 2200220.0, "display_name": "South Carolina", "per_capita": 2200.22, "population": 1000, "shape_code": "SC", "award_count": 4, }, { "amount": 22000.0, "display_name": "Washington", "per_capita": 2.2, "population": 10000, "shape_code": "WA", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_outlay(client): resp = post( client, def_codes=["L", "M"], geo_layer="state", geo_layer_filters=["SC", "WA"], spending_type="outlay", ) expected_response = { "geo_layer": "state", "spending_type": "outlay", "results": [ { "amount": 1100110.0, "display_name": "South Carolina", "per_capita": 1100.11, "population": 1000, "shape_code": "SC", "award_count": 4, }, { "amount": 11000.0, "display_name": "Washington", "per_capita": 1.1, "population": 10000, "shape_code": "WA", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_face_value_of_loan(client): resp = post( client, def_codes=["L", "M"], geo_layer="state", geo_layer_filters=["SC", "WA"], spending_type="face_value_of_loan", ) expected_response = { "geo_layer": "state", "spending_type": "face_value_of_loan", "results": [ { "amount": 330.0, "display_name": "South Carolina", "per_capita": 0.33, "population": 1000, "shape_code": "SC", "award_count": 4, }, { "amount": 0.0, "display_name": "Washington", "per_capita": 0.0, "population": 10000, "shape_code": "WA", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def test_correct_response_for_award_type_codes(client, monkeypatch, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) test_cases = [ _test_correct_response_of_loans, _test_correct_response_of_contracts, _test_correct_response_of_grants, ] for test in test_cases: test(client) def _test_correct_response_of_loans(client): resp = post( client, award_type_codes=list(loan_type_mapping.keys()), def_codes=["L", "M"], geo_layer="county", geo_layer_filters=["45001", "45005"], spending_type="obligation", ) expected_response = { "geo_layer": "county", "spending_type": "obligation", "results": [ { "amount": 220.0, "display_name": "Charleston", "per_capita": 220.0, "population": 1, "shape_code": "45001", "award_count": 2, } ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_of_contracts(client): resp = post( client, award_type_codes=list(contract_type_mapping.keys()), def_codes=["L", "M"], geo_layer="district", geo_layer_filters=["4510", "4550", "5350"], spending_type="obligation", ) expected_response = { "geo_layer": "district", "spending_type": "obligation", "results": [ { "amount": 2000000.0, "display_name": "SC-10", "per_capita": None, "population": None, "shape_code": "4510", "award_count": 1, }, { "amount": 200000.0, "display_name": "SC-50", "per_capita": 2000.0, "population": 100, "shape_code": "4550", "award_count": 1, }, { "amount": 22000.0, "display_name": "WA-50", "per_capita": 22.0, "population": 1000, "shape_code": "5350", "award_count": 2, }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_of_grants(client): resp = post( client, award_type_codes=list(grant_type_mapping.keys()), def_codes=["L", "M"], geo_layer="state", geo_layer_filters=["SC", "WA"], spending_type="obligation", ) expected_response = { "geo_layer": "state", "spending_type": "obligation", "results": [], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def test_correct_response_of_empty_list(client, monkeypatch, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) test_cases = [ _test_correct_response_of_empty_list_for_county, _test_correct_response_of_empty_list_for_district, _test_correct_response_of_empty_list_for_state, ] for test in test_cases: test(client) def _test_correct_response_of_empty_list_for_county(client): resp = post( client, def_codes=["N"], geo_layer="county", geo_layer_filters=["45001", "45005"], spending_type="obligation" ) expected_response = { "geo_layer": "county", "spending_type": "obligation", "results": [], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" assert resp.json() == expected_response def _test_correct_response_of_empty_list_for_district(client): resp = post( client, def_codes=["N"], geo_layer="district", geo_layer_filters=["4510", "4550", "5350"], spending_type="obligation", ) expected_response = { "geo_layer": "district", "spending_type": "obligation", "results": [], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" assert resp.json() == expected_response def _test_correct_response_of_empty_list_for_state(client): resp = post(client, def_codes=["N"], geo_layer="state", geo_layer_filters=["WA"], spending_type="obligation") expected_response = { "geo_layer": "state", "spending_type": "obligation", "results": [], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" assert resp.json() == expected_response def test_correct_response_without_geo_filters(client, monkeypatch, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) test_cases = [ _test_correct_response_for_county_without_geo_filters, _test_correct_response_for_district_without_geo_filters, _test_correct_response_for_state_without_geo_filters, ] for test in test_cases: test(client) def _test_correct_response_for_county_without_geo_filters(client): resp = post(client, def_codes=["L", "M"], geo_layer="county", spending_type="obligation",) expected_response = { "spending_type": "obligation", "geo_layer": "county", "results": [ { "amount": 2000220.0, "award_count": 3, "display_name": "Charleston", "per_capita": 2000220.0, "population": 1, "shape_code": "45001", }, { "amount": 200000.0, "award_count": 1, "display_name": "Test Name", "per_capita": 20000.0, "population": 10, "shape_code": "45005", }, { "amount": 22000.0, "award_count": 2, "display_name": "Test Name", "per_capita": 220.0, "population": 100, "shape_code": "53005", }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_district_without_geo_filters(client): resp = post(client, def_codes=["L", "M"], geo_layer="district", spending_type="obligation",) expected_response = { "spending_type": "obligation", "geo_layer": "district", "results": [ { "amount": 2000000.0, "award_count": 1, "display_name": "SC-10", "per_capita": None, "population": None, "shape_code": "4510", }, { "amount": 200200.0, "award_count": 2, "display_name": "SC-50", "per_capita": 2002.0, "population": 100, "shape_code": "4550", }, { "amount": 20.0, "award_count": 1, "display_name": "SC-90", "per_capita": 20.0, "population": 1, "shape_code": "4590", }, { "amount": 22000.0, "award_count": 2, "display_name": "WA-50", "per_capita": 22.0, "population": 1000, "shape_code": "5350", }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response def _test_correct_response_for_state_without_geo_filters(client): resp = post(client, def_codes=["L", "M"], geo_layer="state", spending_type="obligation",) expected_response = { "spending_type": "obligation", "geo_layer": "state", "results": [ { "amount": 2200220.0, "award_count": 4, "display_name": "South Carolina", "per_capita": 2200.22, "population": 1000, "shape_code": "SC", }, { "amount": 22000.0, "award_count": 2, "display_name": "Washington", "per_capita": 2.2, "population": 10000, "shape_code": "WA", }, ], "messages": [get_time_period_message()], } assert resp.status_code == status.HTTP_200_OK, "Failed to return 200 Response" resp_json = resp.json() resp_json["results"].sort(key=_get_shape_code_for_sort) assert resp_json == expected_response

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py

Python

indra/preassembler/grounding_mapper/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

136

2016-02-11T22:06:37.000Z

2022-03-31T17:26:20.000Z

indra/preassembler/grounding_mapper/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

748

2016-02-03T16:27:56.000Z

2022-03-09T14:27:54.000Z

indra/preassembler/grounding_mapper/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

56

2015-08-28T14:03:44.000Z

2022-02-04T06:15:55.000Z

from .mapper import *

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py

Python

gobayes/parsers/__init__.py

jni/gobayes

35b938c6ade3b2c2cfb7e4e675c6966cc902620f

[ "MIT" ]

null

gobayes/parsers/__init__.py

jni/gobayes

35b938c6ade3b2c2cfb7e4e675c6966cc902620f

[ "MIT" ]

null

gobayes/parsers/__init__.py

jni/gobayes

35b938c6ade3b2c2cfb7e4e675c6966cc902620f

[ "MIT" ]

null

import annotation import obo

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py

Python

train.py

DetectionBLWX/YOLOv3

0a9787670d109ef7d7fb668550e5715749f1ef5a

[ "MIT" ]

3

2020-03-19T04:13:28.000Z

2020-10-11T16:55:33.000Z

train.py

DetectionBLWX/YOLOv3

0a9787670d109ef7d7fb668550e5715749f1ef5a

[ "MIT" ]

null

train.py

DetectionBLWX/YOLOv3

0a9787670d109ef7d7fb668550e5715749f1ef5a

[ "MIT" ]

null

''' Function: train the model Author: Charles ''' import torch

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1

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null

0

1

0

1

0

1

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6

10bd9cac6bbebf36e90df32f21f17582ab199711

47

py

Python

src/meltano/core/tracking/__init__.py

siilats/meltano

404605c83f441c3fc2b729e26416c6caa8b0ed0b

[ "MIT" ]

122

2021-06-21T17:30:29.000Z

2022-03-25T06:21:38.000Z

src/meltano/core/tracking/__init__.py

siilats/meltano

404605c83f441c3fc2b729e26416c6caa8b0ed0b

[ "MIT" ]

38

2019-12-09T06:53:33.000Z

2022-03-29T22:29:19.000Z

src/meltano/core/tracking/__init__.py

siilats/meltano

404605c83f441c3fc2b729e26416c6caa8b0ed0b

[ "MIT" ]

21

2021-06-22T10:08:15.000Z

2022-03-18T08:57:02.000Z

from .ga_tracker import GoogleAnalyticsTracker

23.5

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1

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6

529c8a7387e501f4965aa613fd7cd5e9545f8694

8,056

py

Python

models/users.py

sandygoreraza/FlaskTaskManagementSampleApp

22e82a5013b0b9178e2a3acfe6d51367d9804bfe

[ "MIT" ]

null

models/users.py

sandygoreraza/FlaskTaskManagementSampleApp

22e82a5013b0b9178e2a3acfe6d51367d9804bfe

[ "MIT" ]

null

models/users.py

sandygoreraza/FlaskTaskManagementSampleApp

22e82a5013b0b9178e2a3acfe6d51367d9804bfe

[ "MIT" ]

null

import sqlite3 def check_user(email,password): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users WHERE email = ? and password = ?;",(email,password)) ##print("Fetching single row") record = cursor.fetchone() if record: ##print(record[1]) print fullname message=True; else: message = False; return message; def user_details_diplayID(id): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ## defining the Query query = "SELECT * FROM users WHERE user_id=?" ## getting records from the table cursor.execute(query, [id]) ## fetching all records from the 'cursor' object records = cursor.fetchone() if records == "None": tasks = "" else: tasks = records return tasks def reassignTask(user_id,task_id): """ Update reassign task table :param conn: Connection to the SQLite database :return: """ connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ###delete user first try: cursor.execute("update tasks set user_id=? where task_id=?", [user_id,task_id]) connection.commit() print("Task successlly reassigned ") cursor.close() except sqlite3.Error as error: print("Failed to reassign task, error: ", error) finally: if connection: connection.close() return True; def get_userid(email): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users WHERE email = ?;",[email]) ##print("Fetching single row") record = cursor.fetchone() return record[0]; def TotalSignUps(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users;") ##print("Fetching all row") record = len(cursor.fetchall()) return record; def SignUpsDisplayLimit5(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users ORDER BY user_id DESC LIMIT 5;") ##print("Fetching all row") record = cursor.fetchall() return record; def SignUpsDisplay(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users;") ##print("Fetching all row") record = cursor.fetchall() return record; def UserReassignList(exclude_id): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users WHERE user_id != ?;",[exclude_id]) ##print("Fetching all row") record = cursor.fetchall() return record; def TotalSignUps_last_24hr(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ## defining the Query query = 'SELECT * FROM users WHERE timestamp > datetime(\'now\', \'-24 hour\') ORDER BY user_id DESC LIMIT 5;' ##query = "SELECT * FROM tasks" ## getting records from the table cursor.execute(query) ## fetching all records from the 'cursor' object records = len(cursor.fetchall()) if records == "None": tasks = "" else: tasks = records return tasks def TotalSignUps_last_24hr_display(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ## defining the Query query = 'SELECT * FROM users WHERE timestamp > datetime(\'now\', \'-24 hour\') ORDER BY user_id DESC LIMIT 5;' ##query = "SELECT * FROM tasks" ## getting records from the table cursor.execute(query) ## fetching all records from the 'cursor' object records = cursor.fetchall() if records == "None": tasks = "" else: tasks = records return tasks def TotalSignUps_last_Week(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ## defining the Query query = 'SELECT * FROM users WHERE timestamp BETWEEN datetime(\'now\', \'-6 days\') AND datetime(\'now\', \'localtime\') ORDER BY user_id DESC LIMIT 5;' ##query = "SELECT * FROM tasks" ## getting records from the table cursor.execute(query) ## fetching all records from the 'cursor' object records = cursor.fetchall() if records == "None": tasks = "" else: tasks = records return tasks def TodaySignUps(): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ## defining the Query query = 'SELECT * FROM users WHERE timestamp >= datetime(\'now\', \'start of day\') ORDER BY user_id DESC LIMIT 5;' ##query = "SELECT * FROM tasks" ## getting records from the table cursor.execute(query) ## fetching all records from the 'cursor' object records = cursor.fetchall() if records == "None": tasks = "" else: tasks = records return tasks def check_email_exist(email): connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() cursor.execute("SELECT * FROM users WHERE email = ?;",[email]) ##print("Fetching single row") record = cursor.fetchone() if record: ##print(record[1]) print fullname message=True; else: message = False; return message; def delete_user_with_tasks(user_id): """ Delete all rows in the tasks table :param conn: Connection to the SQLite database :return: """ connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() ###delete user first try: cursor.execute("DELETE FROM users WHERE user_id = ?;",[user_id]) cursor.execute("DELETE FROM tasks WHERE user_id = ?;",[user_id]) connection.commit() print("Successfully Deleted user with associated tasks : ", cursor.rowcount) cursor.close() except sqlite3.Error as error: print("deletion of user with associated tasks error : ", error) finally: if connection: connection.close() return True; def new_user(fname,email,password,ctime): try: connection = sqlite3.connect('flask_sandy.db' , check_same_thread = False) cursor = connection.cursor() sqlite_insert_query = """ INSERT INTO users( fullname, email, password, timestamp ) VALUES(?,?,?,?);""" if check_email_exist(email): print("Email already taken. Please use a different email address.") else: # This is the qmark style: new_user= cursor.execute(sqlite_insert_query,[fname,email,password,ctime]) connection.commit() print("New user successfully added : ", cursor.rowcount) cursor.close() except sqlite3.Error as error: print("Failed to add new user : ", error) finally: if connection: connection.close()

24.192192

160

0.589623

860

8,056

5.419767

0.151163

0.044626

0.077237

0.093328

0.795752

0.780305

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0.754988

0.744261

0

0.006606

0.304742

8,056

332

161

24.26506

0.825567

0.133441

0

0.708861

0

0.210092

0

1

0.094937

false

0.031646

0.006329

0

0.189873

0.044304

0

null

0

1

0

1

0

null

0

6

52bdb8ac23c9ca40f1c17c887b23c7a5532e183e

4,221

py

Python

tests/test_rest_term_create.py

oarepo/flask-taxonomies

50d704adfc2d5c80d90b040e147abeb00fff3766

[ "MIT" ]

null

tests/test_rest_term_create.py

oarepo/flask-taxonomies

50d704adfc2d5c80d90b040e147abeb00fff3766

[ "MIT" ]

39

2019-06-17T08:01:29.000Z

2021-06-25T15:21:59.000Z

tests/test_rest_term_create.py

oarepo/flask-taxonomies

50d704adfc2d5c80d90b040e147abeb00fff3766

[ "MIT" ]

4

2019-08-16T09:55:28.000Z

2020-07-07T06:18:54.000Z

from sqlalchemy_utils.types.json import json def term_create_test(api, client, sample_taxonomy): term = client.put('/api/2.0/taxonomies/test/aaa', data=json.dumps({ 'title': 'test aaa title' }), content_type='application/json') exp = { 'links': { 'self': 'http://localhost/api/2.0/taxonomies/test/aaa', }, 'title': 'test aaa title' } assert json.loads(term.data) == exp taxonomies = client.get('/api/2.0/taxonomies/test/aaa') assert json.loads(taxonomies.data) == exp term = client.put('/api/2.0/taxonomies/test/aaa/bbb', data=json.dumps({ 'title': 'test bbb title' }), content_type='application/json') exp = { 'ancestors': [ { 'links': { 'self': 'http://localhost/api/2.0/taxonomies/test/aaa' }, 'title': 'test aaa title' } ], 'links': { 'self': 'http://localhost/api/2.0/taxonomies/test/aaa/bbb' }, 'title': 'test bbb title' } assert json.loads(term.data) == exp taxonomies = client.get('/api/2.0/taxonomies/test/aaa/bbb') assert json.loads(taxonomies.data) == exp def term_create_post_test(api, client, sample_taxonomy): term = client.post('/api/2.0/taxonomies/test', data=json.dumps({ 'title': 'test aaa title', 'slug': 'aaa' }), content_type='application/json') exp = { 'links': { 'self': 'http://localhost/api/2.0/taxonomies/test/aaa', }, 'title': 'test aaa title' } assert json.loads(term.data) == exp taxonomies = client.get('/api/2.0/taxonomies/test/aaa') assert json.loads(taxonomies.data) == exp term = client.post('/api/2.0/taxonomies/test/aaa', data=json.dumps({ 'title': 'test bbb title', 'slug': 'bbb' }), content_type='application/json') exp = { 'ancestors': [ { 'links': {'self': 'http://localhost/api/2.0/taxonomies/test/aaa'}, 'title': 'test aaa title' } ], 'links': {'self': 'http://localhost/api/2.0/taxonomies/test/aaa/bbb'}, 'title': 'test bbb title' } assert json.loads(term.data) == exp taxonomies = client.get('/api/2.0/taxonomies/test/aaa/bbb') assert json.loads(taxonomies.data) == exp def term_create_on_deleted_test(api, client, sample_taxonomy): client.delete('/api/2.0/taxonomies/test/b') resp = client.put('/api/2.0/taxonomies/test/b', data='{}', content_type='application/json') assert resp.status_code == 409 assert resp.json['reason'] == 'deleted-term-exists' resp = client.put('/api/2.0/taxonomies/test/b?representation:include=del', data='{}', content_type='application/json') assert resp.status_code == 200 def term_create_term_only_test(api, client, sample_taxonomy): resp = client.put('/api/2.0/taxonomies/test/b', data='{}', content_type='application/json', headers={'If-None-Match': '*'}) assert resp.status_code == 412 assert resp.json['reason'] == 'term-exists' resp = client.put('/api/2.0/taxonomies/test/c', data='{}', content_type='application/json', headers={'If-None-Match': '*'}) assert resp.status_code == 201 def term_update_term_only_test(api, client, sample_taxonomy): resp = client.put('/api/2.0/taxonomies/test/c', data='{}', content_type='application/json', headers={'If-Match': '*'}) assert resp.status_code == 412 assert resp.json['reason'] == 'term-does-not-exist' resp = client.put('/api/2.0/taxonomies/test/b', data='{}', content_type='application/json', headers={'If-Match': '*'}) assert resp.status_code == 200 # updated existing term

37.026316

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4,221

4.611691

0.135699

0.038026

0.047533

0.142598

0.907651

0.892712

0.883658

0.82345

0.8067

0.757356

0

0.020457

0.305141

4,221

113

96

37.353982

0.732697

0.004975

0

0.608247

0

0.303478

0.10505

0

0.175258

1

0.051546

false

0

0.010309

0

0.061856

0

null

0

1

0

null

0

6

52d0c63b5966cf6157264fead2da18c09d1bbf5b

87

py

Python

source/tests/metacall_configuration_exec_path_test/data/scripts/metacall_configuration_exec_path_test.py

Zedonboy/core

79a4d959659a0f96b940b28d44476943de120d95

[ "Apache-2.0" ]

null

source/tests/metacall_configuration_exec_path_test/data/scripts/metacall_configuration_exec_path_test.py

Zedonboy/core

79a4d959659a0f96b940b28d44476943de120d95

[ "Apache-2.0" ]

null

source/tests/metacall_configuration_exec_path_test/data/scripts/metacall_configuration_exec_path_test.py

Zedonboy/core

79a4d959659a0f96b940b28d44476943de120d95

[ "Apache-2.0" ]

null

#!/usr/bin/python3.5 def hello_world(text): return 'Python hello_world: ' + text

17.4

38

0.678161

13

87

4.384615

0.769231

0.350877

0.491228

0

0.027778

0.172414

87

4

39

21.75

0.763889

0.218391

0

0.31746

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

eabf9cd1ca91475b4b7e22ca2d686c0cf1db7a23

97

py

Python

stimuli/Python/one_file_per_item/jap/42_# math_if 6.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

6

2020-04-24T08:16:51.000Z

2021-11-01T09:50:46.000Z

stimuli/Python/one_file_per_item/jap/42_# math_if 6.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

null

stimuli/Python/one_file_per_item/jap/42_# math_if 6.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

4

2021-02-17T20:21:31.000Z

2022-02-14T12:43:23.000Z

ooki_kazu, chisa_kazu = 64, 16 if ooki_kazu % chisa_kazu == 0: print(1) else: print(0)

12.125

31

0.628866

17

97

3.352941

0.588235

0.280702

0.45614

0.596491

0

0.09589

0.247423

97

7

32

13.857143

0.684932

0

1

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true

0

0.4

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0

null

1

0

1

0

null

0

1

0

6

eae86aacea5179af47108390f7af6d7507fb5714

12,537

py

Python

src/jk_simpleexec/simpleexec.py

jkpubsrc/python-module-jk-simpleexec

ec5d4f643ad0c8c97679c482aaf2f17d475d9321

[ "Apache-1.1" ]

null

src/jk_simpleexec/simpleexec.py

jkpubsrc/python-module-jk-simpleexec

ec5d4f643ad0c8c97679c482aaf2f17d475d9321

[ "Apache-1.1" ]

null

src/jk_simpleexec/simpleexec.py

jkpubsrc/python-module-jk-simpleexec

ec5d4f643ad0c8c97679c482aaf2f17d475d9321

[ "Apache-1.1" ]

null

import os import sys import subprocess import typing from .CommandResult import CommandResult from .TextDataProcessingPolicy import TextDataProcessingPolicy from ._DebugValveToFile import _DebugValveToFile from . import _common as _common # # Synchroneously invokes the specified command on the local machine. Output of STDOUT and STDERR is collected and returned by the <c>CommandResult</c> return object. # # NOTE: Despite for <c>cmdPath</c> and <c>cmdArgs</c> do not rely on the order of the arguments. If you need to specify them invoke them by name as they are # intended to be invoked that way! Their order might be changed unnoticed in other versions of this API. # # NOTE: This method is deprecated and should no longer be called. Please use the new <c>invokeCmd2()</c> instead. # # @param string cmdPath (required) The (absolute) path to the program to invoke. # @param string[] cmdArgs (required) A list of arguments. Specify <c>None</c> if you do not want to have any arguments. # Please note that there is no shell to interprete these commands. # @param string onErrorExceptionMsg If you specify an error message here an exception is thrown. If <c>None</c> is specified # <c>None</c> will be returned and no exception will be thrown. # @param str|bytes[] dataToPipeAsStdIn (optional) Either a string or binary data (or None) that should be passed on to the application invoked usint STDIN. # If string data is presented it is automatically encoded using UTF-8 # @param str workingDirectory (optional) If you specify a working directory here this function will change to this working directory # specified in <c>workingDirector</c> and return to the previous one after the command has been completed. # @return CommandOutput Returns an object that contains the exit status, STDOUT and STDERR data. # def invokeCmd( cmdPath:str, cmdArgs:list, bRemoveTrailingNewLinesFromStdOut:bool = True, bRemoveTrailingNewLinesFromStdErr:bool = True, dataToPipeAsStdIn:typing.Union[str,bytes,bytearray] = None, workingDirectory:str = None, ) -> CommandResult: assert isinstance(cmdPath, str) if cmdArgs is not None: assert isinstance(cmdArgs, (list, tuple)) if workingDirectory: assert isinstance(workingDirectory, str) returnToDirectory = os.getcwd() else: returnToDirectory = None try: if workingDirectory: os.chdir(workingDirectory) if dataToPipeAsStdIn: if isinstance(dataToPipeAsStdIn, str): dataToPipeAsStdIn = dataToPipeAsStdIn.encode("utf-8") elif isinstance(dataToPipeAsStdIn, (bytes, bytearray)): pass else: raise Exception("Can only pipe string data and byte arrays!") cmd = [] cmd.append(cmdPath) if cmdArgs is not None: cmd.extend(cmdArgs) if _common.debugValve: _common.debugValve("================================================================================================================================") _common.debugValve("EXECUTING:", cmd) if dataToPipeAsStdIn: p = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) p.stdin.write(dataToPipeAsStdIn) else: p = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=None) (stdout, stderr) = p.communicate() output = [] stdOutData = stdout.decode("utf-8") if _common.debugValve: _common.debugValve("STDOUT:") _common.debugValve(stdOutData) for line in stdOutData.split("\n"): output.append(line.rstrip()) if bRemoveTrailingNewLinesFromStdOut: while (len(output) > 0) and (len(output[len(output) - 1]) == 0): del output[len(output) - 1] outputErr = [] stdErrData = stderr.decode("utf-8") if _common.debugValve != None: _common.debugValve("STDERR:") _common.debugValve(stdErrData) for line in stdErrData.split("\n"): outputErr.append(line.rstrip()) if bRemoveTrailingNewLinesFromStdErr: while (len(outputErr) > 0) and (len(outputErr[len(outputErr) - 1]) == 0): del outputErr[len(outputErr) - 1] if _common.debugValve != None: _common.debugValve("RETURN CODE:", p.returncode) return CommandResult(cmdPath, cmdArgs, output, outputErr, p.returncode) finally: if returnToDirectory: os.chdir(returnToDirectory) # # # Synchroneously invokes the specified command on the local machine. Output of STDOUT and STDERR is collected and returned by the <c>CommandResult</c> return object. # # NOTE: This method is deprecated and should no longer be called. Please use the new <c>invokeCmd2()</c> instead. # # @param string cmdPath (required) The (absolute) path to the program to invoke. # @param string[] cmdArgs (required) A list of arguments. Specify <c>None</c> if you do not want to have any arguments. # Please note that there is no shell to interprete these commands. # @param str|bytes[] dataToPipeAsStdIn (optional) Either a string or binary data (or None) that should be passed on to the application invoked usint STDIN. # If string data is presented it is automatically encoded using UTF-8 # @param str workingDirectory (optional) If you specify a working directory here this function will change to this working directory # specified in <c>workingDirector</c> and return to the previous one after the command has been completed. # @param TextDataProcessingPolicy stdOutProcessing (optional) If specified you can override defaults of the STDOUT preprocessing that can already be done by this function. # @param TextDataProcessingPolicy stdErrProcessing (optional) If specified you can override defaults of the STDERR preprocessing that can already be done by this function. # @param * log (optional) You can specify a logger here. This logger will receive a notice about what command is going to be executed. # For this to work the logger object specified here is checked against the following criterias (in the order listed): # * have a method named `notice(..)` expecting a single string argument - the log message # * have a method named `info(..)` expecting a single string argument - the log message # * is callable (= is a method itself) expecting a single string argument - the log message # # @return CommandOutput Returns an object that contains the exit status, (preprocessed) STDOUT and (preprocessed) STDERR data. # def invokeCmd1( cmdPath:str, cmdArgs:list, dataToPipeAsStdIn:typing.Union[str,bytes,bytearray] = None, workingDirectory:str = None, stdOutProcessing:TextDataProcessingPolicy = None, stdErrProcessing:TextDataProcessingPolicy = None, log = None, ) -> CommandResult: return invokeCmd2( cmdPath=cmdPath, cmdArgs=cmdArgs, dataToPipeAsStdIn=dataToPipeAsStdIn, workingDirectory=workingDirectory, stdOutProcessing=stdOutProcessing, stdErrProcessing=stdErrProcessing, shell=False, log=log, ) # # # Synchroneously invokes the specified command on the local machine. Output of STDOUT and STDERR is collected and returned by the <c>CommandResult</c> return object. # # @param string cmdPath (required) The (absolute) path to the program to invoke. # @param string[] cmdArgs (required) A list of arguments. Specify <c>None</c> if you do not want to have any arguments. # Please note that there is no shell to interprete these commands. # @param str|bytes[] dataToPipeAsStdIn (optional) Either a string or binary data (or None) that should be passed on to the application invoked usint STDIN. # If string data is presented it is automatically encoded using UTF-8 # @param str workingDirectory (optional) If you specify a working directory here this function will change to this working directory # specified in <c>workingDirector</c> and return to the previous one after the command has been completed. # @param TextDataProcessingPolicy stdOutProcessing (optional) If specified you can override defaults of the STDOUT preprocessing that can already be done by this function. # @param TextDataProcessingPolicy stdErrProcessing (optional) If specified you can override defaults of the STDERR preprocessing that can already be done by this function. # @param * log (optional) You can specify a logger here. This logger will receive a notice about what command is going to be executed. # For this to work the logger object specified here is checked against the following criterias (in the order listed): # * have a method named `notice(..)` expecting a single string argument - the log message # * have a method named `info(..)` expecting a single string argument - the log message # * is callable (= is a method itself) expecting a single string argument - the log message # @param bool shell If set to `True` interpret the specified command by a shell. (This is then equivalent to `subprocess.Popen(..)` # with `shell = True`.) # # @return CommandOutput Returns an object that contains the exit status, (preprocessed) STDOUT and (preprocessed) STDERR data. # def invokeCmd2( *argv, cmdPath:str, cmdArgs:list, dataToPipeAsStdIn:typing.Union[str,bytes,bytearray] = None, workingDirectory:str = None, stdOutProcessing:TextDataProcessingPolicy = None, stdErrProcessing:TextDataProcessingPolicy = None, shell:bool = False, log = None, ) -> CommandResult: if len(argv) > 0: raise Exception("For compatibility with future changes please invoke this method with named arguments only!") stdOutProcessing = _common.DEFAULT_STDOUT_PROCESSING.override(stdOutProcessing) stdErrProcessing = _common.DEFAULT_STDERR_PROCESSING.override(stdErrProcessing) if stdErrProcessing is not None: assert isinstance(stdErrProcessing, TextDataProcessingPolicy) assert isinstance(cmdPath, str) if cmdArgs is not None: assert isinstance(cmdArgs, (list, tuple)) if workingDirectory is not None: assert isinstance(workingDirectory, str) returnToDirectory = os.getcwd() else: returnToDirectory = None try: if workingDirectory: os.chdir(workingDirectory) if dataToPipeAsStdIn: if isinstance(dataToPipeAsStdIn, str): dataToPipeAsStdIn = dataToPipeAsStdIn.encode("utf-8") elif isinstance(dataToPipeAsStdIn, (bytes, bytearray)): pass else: raise Exception("Can only pipe string data and byte arrays!") # build list of arguments cmd = [] cmd.append(cmdPath) if cmdArgs is not None: cmd.extend(cmdArgs) # write log message if logger is specified if log: printFunc = getattr(log, "notice", None) if printFunc is None: printFunc = getattr(log, "info", None) if printFunc is None: assert callable(log) printFunc = log printFunc("run: " + cmdPath + " " + str(cmdArgs)) # write data to debug valve if _common.debugValve: _common.debugValve("================================================================================================================================") _common.debugValve("EXECUTING:", cmd + " " + str(cmdArgs)) # run the processes if dataToPipeAsStdIn: p = subprocess.Popen(cmd, shell=shell, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) p.stdin.write(dataToPipeAsStdIn) else: p = subprocess.Popen(cmd, shell=shell, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=None) (stdout, stderr) = p.communicate() # process stdout stdOutData = stdout.decode("utf-8") if _common.debugValve: _common.debugValve("STDOUT:") _common.debugValve(stdOutData) stdOutData = _common.processCmdOutput(stdOutData, stdOutProcessing) # process stderr stdErrData = stderr.decode("utf-8") if _common.debugValve: _common.debugValve("STDERR:") _common.debugValve(stdErrData) stdErrData = _common.processCmdOutput(stdErrData, stdErrProcessing) # ---- if _common.debugValve != None: _common.debugValve("RETURN CODE:", p.returncode) return CommandResult(cmdPath, cmdArgs, stdOutData, stdErrData, p.returncode) finally: if returnToDirectory: os.chdir(returnToDirectory) #

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Python

src/py4geo/client/controllers/__init__.py

manuelep/py4geo

ad1b25f89b2f254d7270d05123fb3e6cb91186a9

[ "Apache-2.0" ]

null

src/py4geo/client/controllers/__init__.py

manuelep/py4geo

ad1b25f89b2f254d7270d05123fb3e6cb91186a9

[ "Apache-2.0" ]

null

src/py4geo/client/controllers/__init__.py

manuelep/py4geo

ad1b25f89b2f254d7270d05123fb3e6cb91186a9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from ... import settings if settings.IS_H3_INSTALLED: from . import advanced from . import base

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py

Python

simclr/__init__.py

martinmamql/SimCLR-2

60fb488a1914ec97af2bd01c85a9ec64e804db1e

[ "MIT" ]

496

2020-03-10T11:29:19.000Z

2022-03-30T04:52:08.000Z

simclr/__init__.py

martinmamql/SimCLR-2

60fb488a1914ec97af2bd01c85a9ec64e804db1e

[ "MIT" ]

34

2020-03-12T15:03:02.000Z

2022-01-10T18:46:05.000Z

simclr/__init__.py

martinmamql/SimCLR-2

60fb488a1914ec97af2bd01c85a9ec64e804db1e

[ "MIT" ]

125

2020-03-11T21:50:37.000Z

2022-03-16T08:24:58.000Z

from .simclr import SimCLR

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py

Python

plant/models/__init__.py

Kradukman/beesUlb

1234658af3aff7d2f580212c01d8acec96167078

[ "MIT" ]

null

plant/models/__init__.py

Kradukman/beesUlb

1234658af3aff7d2f580212c01d8acec96167078

[ "MIT" ]

null

plant/models/__init__.py

Kradukman/beesUlb

1234658af3aff7d2f580212c01d8acec96167078

[ "MIT" ]

null

from . import family from . import genus from . import specie from . import wizard

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py

Python

ZiggeoAnalytics.py

Ziggeo/ZiggeoPythonSdk

7c1e46bdd0649bdd58707747279da40783f14f8b

[ "Apache-2.0" ]

3

2018-07-17T16:38:17.000Z

2020-10-31T19:56:47.000Z

ZiggeoAnalytics.py

Ziggeo/ZiggeoPythonSdk

7c1e46bdd0649bdd58707747279da40783f14f8b

[ "Apache-2.0" ]

8

2015-08-20T15:59:13.000Z

2022-01-17T13:08:45.000Z

ZiggeoAnalytics.py

Ziggeo/ZiggeoPythonSdk

7c1e46bdd0649bdd58707747279da40783f14f8b

[ "Apache-2.0" ]

7

2015-08-12T14:32:12.000Z

2019-10-30T05:26:51.000Z

class ZiggeoAnalytics: def __init__(self, application): self.__application = application def get(self, data = None): return self.__application.connect.postJSON('/v1/analytics/get', data)

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dcbaa9235e7d2b3b3d4fd4af496531aab3394aa5

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py

Python

src/multi_task_retrieval/open_qa_p_level.py

ethanjperez/semanticRetrievalMRS

765e00d6e7693e0eaba20ef1407fad0be4a7a92b

[ "MIT" ]

61

2019-09-19T03:04:32.000Z

2022-03-08T03:59:28.000Z

src/multi_task_retrieval/open_qa_p_level.py

ethanjperez/semanticRetrievalMRS

765e00d6e7693e0eaba20ef1407fad0be4a7a92b

[ "MIT" ]

13

2019-09-19T12:11:01.000Z

2020-12-28T17:51:43.000Z

src/multi_task_retrieval/open_qa_p_level.py

ethanjperez/semanticRetrievalMRS

765e00d6e7693e0eaba20ef1407fad0be4a7a92b

[ "MIT" ]

10

2019-09-20T05:07:28.000Z

2022-01-12T08:12:08.000Z

import copy import os import random from pathlib import Path import torch from allennlp.data.iterators import BasicIterator from allennlp.nn.util import move_to_device from pytorch_pretrained_bert import BertTokenizer, BertModel, BertAdam from tqdm import tqdm from data_utils.readers.bert_reader_content_selection import BertContentSelectionReader import datetime import config from bert_model_variances.bert_multilayer_output import BertMultiLayerSeqClassification from evaluation import ext_hotpot_eval, fever_scorer from fever_sampler import fever_p_level_sampler from fever_sampler import fever_sampler_utils from flint import torch_util from hotpot_data_analysis.fullwiki_provided_upperbound import append_gt_downstream_to_get_upperbound_from_doc_retri from hotpot_fact_selection_sampler import sampler_utils as hotpot_sampler_utils from hotpot_fact_selection_sampler.sampler_full_wiki import down_sample_neg from neural_modules.model_EMA import get_ema_gpu_id_list, EMA from open_domain_sampler import p_sampler as open_domain_p_sampler from open_domain_sampler import od_sample_utils from evaluation import open_domain_qa_eval from span_prediction_task_utils.squad_utils import get_squad_question_selection_forward_list from utils import common, list_dict_data_tool, save_tool from data_utils.exvocab import ExVocabulary def select_top_k_and_to_results_dict(scored_dict, merged_field_name='merged_field', score_field_name='score', item_field_name='element', top_k=5): results_dict = {'sp_doc': dict(), 'scored_results': dict()} for key, value in scored_dict.items(): fitems_dict = value[merged_field_name] scored_element_list = [] for item in fitems_dict.values(): score = item[score_field_name] element = item[item_field_name] scored_element_list.append((score, element)) # score is index 0. results_dict['scored_results'][key] = scored_element_list sorted_e_list = sorted(scored_element_list, key=lambda x: x[0], reverse=True) results_dict['sp_doc'][key] = [e for s, e in sorted_e_list[:top_k]] return results_dict def eval_model(model, data_iter, device_num, with_probs=False, make_int=False, show_progress=False): print("Evaluating ...") with torch.no_grad(): model.eval() totoal_size = 0 y_pred_list = [] y_fid_list = [] y_pid_list = [] y_element_list = [] y_logits_list = [] y_probs_list = [] for batch_idx, batch in tqdm(enumerate(data_iter), disable=(not show_progress)): batch = move_to_device(batch, device_num) eval_paired_sequence = batch['paired_sequence'] eval_paired_segments_ids = batch['paired_segments_ids'] eval_labels_ids = batch['label'] eval_att_mask, _ = torch_util.get_length_and_mask(eval_paired_sequence) s1_span = batch['bert_s1_span'] s2_span = batch['bert_s2_span'] out = model(eval_paired_sequence, token_type_ids=eval_paired_segments_ids, attention_mask=eval_att_mask, mode=BertMultiLayerSeqClassification.ForwardMode.EVAL, labels=eval_labels_ids) y_pid_list.extend(list(batch['qid'])) y_fid_list.extend(list(batch['fid'])) y_element_list.extend(list(batch['item'])) y_pred_list.extend(torch.max(out, 1)[1].view(out.size(0)).tolist()) y_logits_list.extend(out.view(out.size(0)).tolist()) if with_probs: y_probs_list.extend(torch.sigmoid(out).view(out.size(0)).tolist()) totoal_size += out.size(0) result_items_list = [] assert len(y_pred_list) == len(y_fid_list) assert len(y_pred_list) == len(y_pid_list) assert len(y_pred_list) == len(y_element_list) assert len(y_pred_list) == len(y_logits_list) if with_probs: assert len(y_pred_list) == len(y_probs_list) for i in range(len(y_pred_list)): r_item = dict() r_item['fid'] = y_fid_list[i] r_item['qid'] = y_pid_list[i] if not make_int else int(y_pid_list[i]) r_item['score'] = y_logits_list[i] r_item['element'] = y_element_list[i] if with_probs: r_item['prob'] = y_probs_list[i] result_items_list.append(r_item) return result_items_list def eval_open_qa_procedure(biterator, dev_instances, model, device_num, ema_device_num, dev_list, dev_o_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, dataset_name): print(f"Eval {dataset_name}!") # dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) # # cur_eval_results_list = eval_model(model, dev_iter, device_num, make_int=True, with_probs=True) # copied_dev_o_dict = copy.deepcopy(dev_o_dict) # copied_dev_d_list = copy.deepcopy(dev_list) # list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, # 'qid', 'fid', check=True) # # cur_results_dict_th0_5 = od_sample_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, # score_field_name='prob', # top_k=5, filter_value=0.5) # # list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, # cur_results_dict_th0_5, # 'id', 'predicted_docids') # # mode = {'standard': False, 'check_doc_id_correct': True} # strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, # max_evidence=5) # score_05 = { # 'ss': strict_score, # 'pr': pr, 'rec': rec, 'f1': f1, # } # # list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, # 'qid', 'fid', check=True) # # cur_results_dict_th0_2 = fever_sampler_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, # score_field_name='prob', # top_k=5, filter_value=0.2) # # list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, # cur_results_dict_th0_2, # 'id', 'predicted_docids') # # mode = {'standard': False, 'check_doc_id_correct': True} # strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, # max_evidence=5) # score_02 = { # 'ss': strict_score, # 'pr': pr, 'rec': rec, 'f1': f1, # } # # logging_item = { # 'step:': update_step, # 'epoch': epoch_i, # 'score_02': score_02, # 'score_05': score_05, # 'time': str(datetime.datetime.now()) # } # # print(logging_item) # # s02_ss_score = score_02['ss'] # s05_ss_score = score_05['ss'] # # if not debug_mode: # save_file_name = f'i({update_step})|e({epoch_i})' \ # f'|v02_ofever({s02_ss_score})' \ # f'|v05_ofever({s05_ss_score})|seed({seed})' # # # print(save_file_name) # logging_agent.incorporate_results({}, save_file_name, logging_item) # logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") # # model_to_save = model.module if hasattr(model, 'module') else model # output_model_file = Path(file_path_prefix) / save_file_name # torch.save(model_to_save.state_dict(), str(output_model_file)) if do_ema and ema is not None: ema_model = ema.get_inference_model() master_device_num = ema_device_num ema_inference_device_ids = get_ema_gpu_id_list(master_device_num=master_device_num) ema_model = ema_model.to(master_device_num) ema_model = torch.nn.DataParallel(ema_model, device_ids=ema_inference_device_ids) dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(ema_model, dev_iter, master_device_num, make_int=False, with_probs=True) copied_dev_o_dict = copy.deepcopy(dev_o_dict) copied_dev_d_list = copy.deepcopy(dev_list) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_top10 = od_sample_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=10, filter_value=0.01) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_top10, 'qid', 'pred_p_list') t10_recall = open_domain_qa_eval.qa_paragraph_eval_v1(copied_dev_d_list, dev_list) top_10_recall = { 'recall': t10_recall, } list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_top20 = od_sample_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=20, filter_value=0.01) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_top20, 'qid', 'pred_p_list') t20_recall = open_domain_qa_eval.qa_paragraph_eval_v1(copied_dev_d_list, dev_list) top_20_recall = { 'top_20_recall': t20_recall, } logging_item = { 'label': 'ema', 'step:': update_step, 'epoch': epoch_i, 'dataset_name': dataset_name, 'top10': top_10_recall, 'top20': top_20_recall, 'time': str(datetime.datetime.now()) } print(logging_item) common.save_jsonl(cur_eval_results_list, Path(file_path_prefix) / Path( f"i({update_step})|e({epoch_i})|{dataset_name}|top10({t10_recall})|top20({t20_recall})|seed({seed})_eval_results.jsonl")) if not debug_mode: save_file_name = f'i({update_step})|e({epoch_i})|{dataset_name}' \ f'|top10({t10_recall})' \ f'|top20({t20_recall})|seed({seed})' # print(save_file_name) logging_agent.incorporate_results({}, save_file_name, logging_item) logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") model_to_save = ema_model.module if hasattr(ema_model, 'module') else ema_model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) def separate_eval_open_qa_procedure(biterator, dev_instances, model, device_num, dev_list, dev_o_dict, dataset_name, save_path=None, tag=""): print(f"Eval {dataset_name}!") dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(model, dev_iter, device_num, make_int=False, with_probs=True, show_progress=True) if save_path is not None: model_file = str(Path(model_path).stem) save_filename = Path(save_path) / f"{model_file}_{dataset_name}_{tag}_p_level_eval.jsonl" common.save_jsonl(cur_eval_results_list, Path(save_filename)) else: pass copied_dev_o_dict = copy.deepcopy(dev_o_dict) copied_dev_d_list = copy.deepcopy(dev_list) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_top10 = od_sample_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=10, filter_value=0.01) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_top10, 'qid', 'pred_p_list') t10_recall = open_domain_qa_eval.qa_paragraph_eval_v1(copied_dev_d_list, dev_list) top_10_recall = { 'recall': t10_recall, } list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_top20 = od_sample_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=20, filter_value=0.01) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_top20, 'qid', 'pred_p_list') t20_recall = open_domain_qa_eval.qa_paragraph_eval_v1(copied_dev_d_list, dev_list) top_20_recall = { 'top_20_recall': t20_recall, } logging_item = { 'label': 'ema', 'dataset_name': dataset_name, 'top10': top_10_recall, 'top20': top_20_recall, 'time': str(datetime.datetime.now()) } print(logging_item) def eval_fever_procedure(biterator, dev_instances, model, device_num, ema_device_num, dev_list, dev_o_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed): print("Eval FEVER!") dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(model, dev_iter, device_num, make_int=True, with_probs=True) copied_dev_o_dict = copy.deepcopy(dev_o_dict) copied_dev_d_list = copy.deepcopy(dev_list) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_th0_5 = fever_sampler_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.5) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_th0_5, 'id', 'predicted_docids') # mode = {'standard': False, 'check_doc_id_correct': True} strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, max_evidence=5) score_05 = { 'ss': strict_score, 'pr': pr, 'rec': rec, 'f1': f1, } list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_th0_2 = fever_sampler_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.2) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_th0_2, 'id', 'predicted_docids') # mode = {'standard': False, 'check_doc_id_correct': True} strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, max_evidence=5) score_02 = { 'ss': strict_score, 'pr': pr, 'rec': rec, 'f1': f1, } logging_item = { 'step:': update_step, 'epoch': epoch_i, 'score_02': score_02, 'score_05': score_05, 'time': str(datetime.datetime.now()) } print(logging_item) s02_ss_score = score_02['ss'] s05_ss_score = score_05['ss'] if not debug_mode: save_file_name = f'i({update_step})|e({epoch_i})' \ f'|v02_ofever({s02_ss_score})' \ f'|v05_ofever({s05_ss_score})|seed({seed})' # print(save_file_name) logging_agent.incorporate_results({}, save_file_name, logging_item) logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") model_to_save = model.module if hasattr(model, 'module') else model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) if do_ema and ema is not None: ema_model = ema.get_inference_model() master_device_num = ema_device_num ema_inference_device_ids = get_ema_gpu_id_list(master_device_num=master_device_num) ema_model = ema_model.to(master_device_num) ema_model = torch.nn.DataParallel(ema_model, device_ids=ema_inference_device_ids) dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(ema_model, dev_iter, master_device_num, make_int=True, with_probs=True) copied_dev_o_dict = copy.deepcopy(dev_o_dict) copied_dev_d_list = copy.deepcopy(dev_list) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_th0_5 = fever_sampler_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.5) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_th0_5, 'id', 'predicted_docids') # mode = {'standard': False, 'check_doc_id_correct': True} strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, max_evidence=5) score_05 = { 'ss': strict_score, 'pr': pr, 'rec': rec, 'f1': f1, } list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) cur_results_dict_th0_2 = fever_sampler_utils.select_top_k_and_to_results_dict(copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.2) list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_dev_d_list, cur_results_dict_th0_2, 'id', 'predicted_docids') strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list, dev_list, max_evidence=5) score_02 = { 'ss': strict_score, 'pr': pr, 'rec': rec, 'f1': f1, } logging_item = { 'label': 'ema', 'step:': update_step, 'epoch': epoch_i, 'score_02': score_02, 'score_05': score_05, 'time': str(datetime.datetime.now()) } print(logging_item) s02_ss_score = score_02['ss'] s05_ss_score = score_05['ss'] if not debug_mode: save_file_name = f'i({update_step})|e({epoch_i})' \ f'|v02_ofever({s02_ss_score})' \ f'|v05_ofever({s05_ss_score})|seed({seed})' # print(save_file_name) logging_agent.incorporate_results({}, save_file_name, logging_item) logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") model_to_save = ema_model.module if hasattr(ema_model, 'module') else ema_model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) def eval_hotpot_procedure(biterator, dev_instances, model, device_num, ema_device_num, dev_list, dev_o_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed): print("Eval HOTPOT!") dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(model, dev_iter, device_num, with_probs=True) copied_dev_o_dict = copy.deepcopy(dev_o_dict) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) # Top_5 cur_results_dict_top5 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5) upperbound_results_dict_top5 = append_gt_downstream_to_get_upperbound_from_doc_retri( cur_results_dict_top5, dev_list) cur_results_dict_top10 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=10) upperbound_results_dict_top10 = append_gt_downstream_to_get_upperbound_from_doc_retri( cur_results_dict_top10, dev_list) _, metrics_top5 = ext_hotpot_eval.eval(cur_results_dict_top5, dev_list, verbose=False) _, metrics_top5_UB = ext_hotpot_eval.eval(upperbound_results_dict_top5, dev_list, verbose=False) _, metrics_top10 = ext_hotpot_eval.eval(cur_results_dict_top10, dev_list, verbose=False) _, metrics_top10_UB = ext_hotpot_eval.eval(upperbound_results_dict_top10, dev_list, verbose=False) top5_doc_recall = metrics_top5['doc_recall'] top5_UB_sp_recall = metrics_top5_UB['sp_recall'] top10_doc_recall = metrics_top10['doc_recall'] top10_Ub_sp_recall = metrics_top10_UB['sp_recall'] logging_item = { 'step:': update_step, 'epoch': epoch_i, 'top5': metrics_top5, 'top5_UB': metrics_top5_UB, 'top10': metrics_top10, 'top10_UB': metrics_top10_UB, 'time': str(datetime.datetime.now()) } print(logging_item) if not debug_mode: save_file_name = f'i({update_step})|e({epoch_i})' \ f'|t5_doc_recall({top5_doc_recall})|t5_sp_recall({top5_UB_sp_recall})' \ f'|t10_doc_recall({top10_doc_recall})|t5_sp_recall({top10_Ub_sp_recall})|seed({seed})' # print(save_file_name) logging_agent.incorporate_results({}, save_file_name, logging_item) logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") model_to_save = model.module if hasattr(model, 'module') else model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) if do_ema and ema is not None: ema_model = ema.get_inference_model() master_device_num = ema_device_num ema_inference_device_ids = get_ema_gpu_id_list(master_device_num=master_device_num) ema_model = ema_model.to(master_device_num) ema_model = torch.nn.DataParallel(ema_model, device_ids=ema_inference_device_ids) dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False) cur_eval_results_list = eval_model(ema_model, dev_iter, master_device_num, with_probs=True) copied_dev_o_dict = copy.deepcopy(dev_o_dict) list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True) # Top_5 cur_results_dict_top5 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5) upperbound_results_dict_top5 = append_gt_downstream_to_get_upperbound_from_doc_retri( cur_results_dict_top5, dev_list) cur_results_dict_top10 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=10) upperbound_results_dict_top10 = append_gt_downstream_to_get_upperbound_from_doc_retri( cur_results_dict_top10, dev_list) _, metrics_top5 = ext_hotpot_eval.eval(cur_results_dict_top5, dev_list, verbose=False) _, metrics_top5_UB = ext_hotpot_eval.eval(upperbound_results_dict_top5, dev_list, verbose=False) _, metrics_top10 = ext_hotpot_eval.eval(cur_results_dict_top10, dev_list, verbose=False) _, metrics_top10_UB = ext_hotpot_eval.eval(upperbound_results_dict_top10, dev_list, verbose=False) top5_doc_recall = metrics_top5['doc_recall'] top5_UB_sp_recall = metrics_top5_UB['sp_recall'] top10_doc_recall = metrics_top10['doc_recall'] top10_Ub_sp_recall = metrics_top10_UB['sp_recall'] logging_item = { 'label': 'ema', 'step:': update_step, 'epoch': epoch_i, 'top5': metrics_top5, 'top5_UB': metrics_top5_UB, 'top10': metrics_top10, 'top10_UB': metrics_top10_UB, 'time': str(datetime.datetime.now()) } print(logging_item) if not debug_mode: save_file_name = f'ema_i({update_step})|e({epoch_i})' \ f'|t5_doc_recall({top5_doc_recall})|t5_sp_recall({top5_UB_sp_recall})' \ f'|t10_doc_recall({top10_doc_recall})|t5_sp_recall({top10_Ub_sp_recall})|seed({seed})' # print(save_file_name) logging_agent.incorporate_results({}, save_file_name, logging_item) logging_agent.logging_to_file(Path(file_path_prefix) / "log.json") model_to_save = ema_model.module if hasattr(ema_model, 'module') else ema_model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) def multitask_open_qa_model_go(): seed = 12 torch.manual_seed(seed) # bert_model_name = 'bert-large-uncased' bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert' bert_model_name = 'bert-base-uncased' lazy = True # lazy = True forward_size = 64 # batch_size = 64 batch_size = 128 gradient_accumulate_step = int(batch_size / forward_size) warmup_proportion = 0.1 learning_rate = 3e-5 num_train_epochs = 3 eval_frequency = 10000 hotpot_pos_ratio = 0.25 do_lower_case = True max_l = 254 hotpot_train_size = None fever_train_size = None curatedtrec_train_size = 0 webq_train_size = 0 squad_train_size = 0 wikimovie_train_size = 0 squad_v11_pos_size = None # hotpot_train_size = 0 # fever_train_size = 0 # squad_train_size = 80_000 # squad_v11_pos_size = 0 experiment_name = f'mtr_open_qa_p_level_(num_train_epochs:{num_train_epochs})' debug_mode = False do_ema = True open_qa_paras = { 'webq': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': 0.25}, 'curatedtrec': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': 0.25}, 'squad': {'upstream_top_k': 40, 'distant_gt_top_k': 1, 'down_sample_ratio': 0.25}, 'wikimovie': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': 0.25}, } # est_datasize = 900_000 num_class = 1 # num_train_optimization_steps device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device_num = 0 if torch.cuda.is_available() else -1 n_gpu = torch.cuda.device_count() unk_token_num = {'tokens': 1} # work around for initiating vocabulary. vocab = ExVocabulary(unk_token_num=unk_token_num) vocab.add_token_to_namespace("false", namespace="labels") # 0 vocab.add_token_to_namespace("true", namespace="labels") # 1 vocab.add_token_to_namespace("hidden", namespace="labels") vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels') # Load Hotpot Dataset # hotpot_train_list = common.load_json(config.TRAIN_FILE) # hotpot_dev_list = common.load_json(config.DEV_FULLWIKI_FILE) # hotpot_dev_o_dict = list_dict_data_tool.list_to_dict(hotpot_dev_list, '_id') # Load Hotpot upstream paragraph forward item hotpot_dev_fitems_list = common.load_jsonl( config.PDATA_ROOT / "content_selection_forward" / "hotpot_dev_p_level_unlabeled.jsonl") hotpot_train_fitems_list = common.load_jsonl( config.PDATA_ROOT / "content_selection_forward" / "hotpot_train_p_level_labeled.jsonl") hotpot_train_fitems_list = hotpot_sampler_utils.field_name_convert(hotpot_train_fitems_list, 'doc_t', 'element') hotpot_dev_fitems_list = hotpot_sampler_utils.field_name_convert(hotpot_dev_fitems_list, 'doc_t', 'element') # Load FEVER Dataset # fever_train_list = common.load_json(config.FEVER_TRAIN) # fever_dev_list = common.load_jsonl(config.FEVER_DEV) # fever_dev_o_dict = list_dict_data_tool.list_to_dict(fever_dev_list, 'id') train_ruleterm_doc_results = common.load_jsonl( config.PRO_ROOT / "results/doc_retri_results/fever_results/merged_doc_results/m_doc_train.jsonl") dev_ruleterm_doc_results = common.load_jsonl( config.PRO_ROOT / "results/doc_retri_results/fever_results/merged_doc_results/m_doc_dev.jsonl") fever_train_fitems_list = fever_p_level_sampler.get_paragraph_forward_pair('train', train_ruleterm_doc_results, is_training=True, debug=debug_mode, ignore_non_verifiable=True) fever_dev_fitems_list = fever_p_level_sampler.get_paragraph_forward_pair('dev', dev_ruleterm_doc_results, is_training=False, debug=debug_mode, ignore_non_verifiable=False) # Load Open QA Dataset. webq_test_fitem_list = open_domain_p_sampler.prepare_forward_data('webq', 'test', False, debug=debug_mode, upstream_top_k=40) webq_train_fitem_list = open_domain_p_sampler.prepare_forward_data('webq', 'train', True, open_qa_paras['webq']['upstream_top_k'], open_qa_paras['webq']['distant_gt_top_k'], open_qa_paras['webq']['down_sample_ratio'], debug=debug_mode) webq_test_gt_list = common.load_jsonl(config.OPEN_WEBQ_TEST_GT) curatedtrec_test_fitem_list = open_domain_p_sampler.prepare_forward_data('curatedtrec', 'test', False, upstream_top_k=40, debug=debug_mode) curatedtrec_train_fitem_list = open_domain_p_sampler.prepare_forward_data('curatedtrec', 'train', True, open_qa_paras['curatedtrec'][ 'upstream_top_k'], open_qa_paras['curatedtrec'][ 'distant_gt_top_k'], open_qa_paras['curatedtrec'][ 'down_sample_ratio'], debug=debug_mode) curatedtrec_test_gt_list = common.load_jsonl(config.OPEN_CURATEDTERC_TEST_GT) squad_dev_fitem_list = open_domain_p_sampler.prepare_forward_data('squad', 'dev', False, upstream_top_k=40, debug=debug_mode) squad_train_fitem_list = open_domain_p_sampler.prepare_forward_data('squad', 'train', True, open_qa_paras['squad'][ 'upstream_top_k'], open_qa_paras['squad'][ 'distant_gt_top_k'], open_qa_paras['squad'][ 'down_sample_ratio'], debug=debug_mode) squad_dev_gt_list = common.load_jsonl(config.OPEN_SQUAD_DEV_GT) wikimovie_test_fitem_list = open_domain_p_sampler.prepare_forward_data('wikimovie', 'test', False, upstream_top_k=40, debug=debug_mode) wikimovie_train_fitem_list = open_domain_p_sampler.prepare_forward_data('wikimovie', 'train', True, open_qa_paras['wikimovie'][ 'upstream_top_k'], open_qa_paras['wikimovie'][ 'distant_gt_top_k'], open_qa_paras['wikimovie'][ 'down_sample_ratio'], debug=debug_mode) wikimovie_test_gt_list = common.load_jsonl(config.OPEN_WIKIM_TEST_GT) # Load squadv11 forward: squad_v11_pos_fitems = get_squad_question_selection_forward_list(common.load_json(config.SQUAD_TRAIN_1_1)) if debug_mode: webq_test_gt_list = webq_test_gt_list[:50] curatedtrec_test_gt_list = curatedtrec_test_gt_list[:50] squad_dev_gt_list = squad_dev_gt_list[:50] wikimovie_test_gt_list = wikimovie_test_gt_list[:50] # hotpot_dev_list = hotpot_dev_list[:10] hotpot_dev_fitems_list = hotpot_dev_fitems_list[:296] hotpot_train_fitems_list = hotpot_train_fitems_list[:300] # fever_dev_list = fever_dev_list[:100] eval_frequency = 2 webq_test_gt_dict = list_dict_data_tool.list_to_dict(webq_test_gt_list, 'question') curatedtrec_test_gt_dict = list_dict_data_tool.list_to_dict(curatedtrec_test_gt_list, 'question') squad_dev_gt_dict = list_dict_data_tool.list_to_dict(squad_dev_gt_list, 'question') wikimovie_test_gt_dict = list_dict_data_tool.list_to_dict(wikimovie_test_gt_list, 'question') # Down_sample for hotpot. hotpot_sampled_train_list = down_sample_neg(hotpot_train_fitems_list, ratio=hotpot_pos_ratio) if hotpot_train_size is None: hotpot_est_datasize = len(hotpot_sampled_train_list) else: hotpot_est_datasize = hotpot_train_size if fever_train_size is None: fever_est_datasize = len(fever_train_fitems_list) else: fever_est_datasize = fever_train_size sampled_squad_v11_pos_fitems = squad_v11_pos_fitems[:squad_v11_pos_size] webq_est_datasize = len(webq_train_fitem_list[:webq_train_size]) curatedtrec_est_datasize = len(curatedtrec_train_fitem_list[:curatedtrec_train_size]) squad_est_datasize = len(squad_train_fitem_list[:squad_train_size]) wikimovie_est_datasize = len(wikimovie_train_fitem_list[:wikimovie_train_size]) print("Hotpot Train Size:", hotpot_est_datasize) print("Fever Train Size:", fever_est_datasize) print("WebQ Train Size:", webq_est_datasize) print("TREC Train Size:", curatedtrec_est_datasize) print("SQuAD Train Size:", squad_est_datasize) print("WikiMovie Train Size:", wikimovie_est_datasize) print("SQuADv11 pos size:", len(sampled_squad_v11_pos_fitems)) est_datasize = hotpot_est_datasize + fever_est_datasize + webq_est_datasize + curatedtrec_est_datasize + \ len(sampled_squad_v11_pos_fitems) + squad_est_datasize + wikimovie_est_datasize bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=do_lower_case, cache_dir=bert_pretrain_path) bert_cs_reader = BertContentSelectionReader(bert_tokenizer, lazy, is_paired=True, example_filter=lambda x: len(x['context']) == 0, max_l=max_l, element_fieldname='element') bert_encoder = BertModel.from_pretrained(bert_model_name, cache_dir=bert_pretrain_path) model = BertMultiLayerSeqClassification(bert_encoder, num_labels=num_class, num_of_pooling_layer=1, act_type='tanh', use_pretrained_pooler=True, use_sigmoid=True) ema = None if do_ema: ema = EMA(model, model.named_parameters(), device_num=1) model.to(device) if n_gpu > 1: model = torch.nn.DataParallel(model) # param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \ num_train_epochs if debug_mode: num_train_optimization_steps = 100 print("Estimated training size", est_datasize) print("Number of optimization steps:", num_train_optimization_steps) optimizer = BertAdam(optimizer_grouped_parameters, lr=learning_rate, warmup=warmup_proportion, t_total=num_train_optimization_steps) # hotpot_dev_instances = bert_cs_reader.read(hotpot_dev_fitems_list) # fever_dev_instances = bert_cs_reader.read(fever_dev_fitems_list) webq_test_instance = bert_cs_reader.read(webq_test_fitem_list) curatedtrec_test_instance = bert_cs_reader.read(curatedtrec_test_fitem_list) squad_dev_instance = bert_cs_reader.read(squad_dev_fitem_list) wikimovie_test_instance = bert_cs_reader.read(wikimovie_test_fitem_list) biterator = BasicIterator(batch_size=forward_size) biterator.index_with(vocab) forbackward_step = 0 update_step = 0 logging_agent = save_tool.ScoreLogger({}) file_path_prefix = '.' if not debug_mode: # # # Create Log File file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}") # Save the source code. script_name = os.path.basename(__file__) with open(os.path.join(file_path_prefix, script_name), 'w') as out_f, open(__file__, 'r') as it: out_f.write(it.read()) out_f.flush() # # # Log File end for epoch_i in range(num_train_epochs): print("Epoch:", epoch_i) # sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio) hotpot_sampled_train_list = down_sample_neg(hotpot_train_fitems_list, ratio=hotpot_pos_ratio) random.shuffle(hotpot_sampled_train_list) hotpot_sampled_train_list = hotpot_sampled_train_list[:hotpot_train_size] random.shuffle(fever_train_fitems_list) fever_train_fitems_list = fever_train_fitems_list[:fever_train_size] random.shuffle(squad_v11_pos_fitems) sampled_squad_v11_pos_fitems = squad_v11_pos_fitems[:squad_v11_pos_size] all_train_data = hotpot_sampled_train_list + fever_train_fitems_list + sampled_squad_v11_pos_fitems # all_train_data = [] webq_train_fitem_list = open_domain_p_sampler.prepare_forward_data('webq', 'train', True, open_qa_paras['webq']['upstream_top_k'], open_qa_paras['webq']['distant_gt_top_k'], open_qa_paras['webq']['down_sample_ratio'], debug=debug_mode) curatedtrec_train_fitem_list = open_domain_p_sampler.prepare_forward_data('curatedtrec', 'train', True, open_qa_paras['curatedtrec'][ 'upstream_top_k'], open_qa_paras['curatedtrec'][ 'distant_gt_top_k'], open_qa_paras['curatedtrec'][ 'down_sample_ratio'], debug=debug_mode) squad_train_fitem_list = open_domain_p_sampler.prepare_forward_data('squad', 'train', True, open_qa_paras['squad'][ 'upstream_top_k'], open_qa_paras['squad'][ 'distant_gt_top_k'], open_qa_paras['squad'][ 'down_sample_ratio'], debug=debug_mode) wikimovie_train_fitem_list = open_domain_p_sampler.prepare_forward_data('wikimovie', 'train', True, open_qa_paras['wikimovie'][ 'upstream_top_k'], open_qa_paras['wikimovie'][ 'distant_gt_top_k'], open_qa_paras['wikimovie'][ 'down_sample_ratio'], debug=debug_mode) random.shuffle(squad_train_fitem_list) squad_train_fitem_list = squad_train_fitem_list[:squad_train_size] random.shuffle(wikimovie_train_fitem_list) wikimovie_train_fitem_list = wikimovie_train_fitem_list[:wikimovie_train_size] random.shuffle(curatedtrec_train_fitem_list) curatedtrec_train_fitem_list = curatedtrec_train_fitem_list[:curatedtrec_train_size] random.shuffle(webq_train_fitem_list) webq_train_fitem_list = webq_train_fitem_list[:webq_train_size] all_train_data = all_train_data + webq_train_fitem_list + curatedtrec_train_fitem_list + \ squad_train_fitem_list + wikimovie_train_fitem_list print("Current all train size:", len(all_train_data)) random.shuffle(all_train_data) train_instance = bert_cs_reader.read(all_train_data) train_iter = biterator(train_instance, num_epochs=1, shuffle=True) for batch in tqdm(train_iter): model.train() batch = move_to_device(batch, device_num) paired_sequence = batch['paired_sequence'] paired_segments_ids = batch['paired_segments_ids'] labels_ids = batch['label'] att_mask, _ = torch_util.get_length_and_mask(paired_sequence) s1_span = batch['bert_s1_span'] s2_span = batch['bert_s2_span'] loss = model(paired_sequence, token_type_ids=paired_segments_ids, attention_mask=att_mask, mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN, labels=labels_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if gradient_accumulate_step > 1: loss = loss / gradient_accumulate_step loss.backward() forbackward_step += 1 if forbackward_step % gradient_accumulate_step == 0: optimizer.step() if ema is not None and do_ema: updated_model = model.module if hasattr(model, 'module') else model ema(updated_model.named_parameters()) optimizer.zero_grad() update_step += 1 if update_step % eval_frequency == 0: print("Update steps:", update_step) eval_open_qa_procedure(biterator, webq_test_instance, model, device_num, 1, webq_test_gt_list, webq_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'webq') eval_open_qa_procedure(biterator, curatedtrec_test_instance, model, device_num, 1, curatedtrec_test_gt_list, curatedtrec_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'curatedtrec') eval_open_qa_procedure(biterator, squad_dev_instance, model, device_num, 1, squad_dev_gt_list, squad_dev_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'squad') eval_open_qa_procedure(biterator, wikimovie_test_instance, model, device_num, 1, wikimovie_test_gt_list, wikimovie_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'wikimovie') # Eval FEVER # eval_fever_procedure(biterator, fever_dev_instances, model, device_num, 1, fever_dev_list, # fever_dev_o_dict, debug_mode, logging_agent, update_step, epoch_i, # file_path_prefix, # do_ema, ema, seed) # eval_hotpot_procedure(biterator, hotpot_dev_instances, model, device_num, 1, hotpot_dev_list, # hotpot_dev_o_dict, debug_mode, logging_agent, update_step, epoch_i, # file_path_prefix, do_ema, ema, seed) epoch_i = num_train_epochs - 1 eval_open_qa_procedure(biterator, webq_test_instance, model, device_num, 1, webq_test_gt_list, webq_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'webq') eval_open_qa_procedure(biterator, curatedtrec_test_instance, model, device_num, 1, curatedtrec_test_gt_list, curatedtrec_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'curatedtrec') eval_open_qa_procedure(biterator, squad_dev_instance, model, device_num, 1, squad_dev_gt_list, squad_dev_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'squad') eval_open_qa_procedure(biterator, wikimovie_test_instance, model, device_num, 1, wikimovie_test_gt_list, wikimovie_test_gt_dict, debug_mode, logging_agent, update_step, epoch_i, file_path_prefix, do_ema, ema, seed, 'wikimovie') if not debug_mode: print("Final Saving.") save_file_name = f'i({update_step})|e({num_train_epochs})_final_model' model_to_save = model.module if hasattr(model, 'module') else model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) if do_ema and ema is not None: print("Final EMA Saving") ema_model = ema.get_inference_model() save_file_name = f'i({update_step})|e({num_train_epochs})_final_ema_model' model_to_save = ema_model.module if hasattr(ema_model, 'module') else ema_model output_model_file = Path(file_path_prefix) / save_file_name torch.save(model_to_save.state_dict(), str(output_model_file)) def selective_eval(model_path): seed = 12 torch.manual_seed(seed) # bert_model_name = 'bert-large-uncased' bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert' bert_model_name = 'bert-base-uncased' lazy = True # lazy = True forward_size = 128 do_lower_case = True max_l = 264 debug_mode = False open_qa_paras = { 'webq': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': None}, 'curatedtrec': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': None}, 'squad': {'upstream_top_k': 30, 'distant_gt_top_k': 1, 'down_sample_ratio': None}, 'wikimovie': {'upstream_top_k': 40, 'distant_gt_top_k': 2, 'down_sample_ratio': None}, } num_class = 1 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device_num = 0 if torch.cuda.is_available() else -1 n_gpu = torch.cuda.device_count() unk_token_num = {'tokens': 1} # work around for initiating vocabulary. vocab = ExVocabulary(unk_token_num=unk_token_num) vocab.add_token_to_namespace("false", namespace="labels") # 0 vocab.add_token_to_namespace("true", namespace="labels") # 1 vocab.add_token_to_namespace("hidden", namespace="labels") vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels') # Load Open QA Dataset. webq_test_fitem_list = open_domain_p_sampler.prepare_forward_data('webq', 'test', False, debug=debug_mode, upstream_top_k=40) webq_train_fitem_list = open_domain_p_sampler.prepare_forward_data('webq', 'train', True, open_qa_paras['webq']['upstream_top_k'], open_qa_paras['webq']['distant_gt_top_k'], open_qa_paras['webq']['down_sample_ratio'], debug=debug_mode) webq_test_gt_list = common.load_jsonl(config.OPEN_WEBQ_TEST_GT) webq_train_gt_list = common.load_jsonl(config.OPEN_WEBQ_TRAIN_GT) curatedtrec_test_fitem_list = open_domain_p_sampler.prepare_forward_data('curatedtrec', 'test', False, upstream_top_k=40, debug=debug_mode) curatedtrec_train_fitem_list = open_domain_p_sampler.prepare_forward_data('curatedtrec', 'train', True, open_qa_paras['curatedtrec'][ 'upstream_top_k'], open_qa_paras['curatedtrec'][ 'distant_gt_top_k'], open_qa_paras['curatedtrec'][ 'down_sample_ratio'], debug=debug_mode) curatedtrec_test_gt_list = common.load_jsonl(config.OPEN_CURATEDTERC_TEST_GT) curatedtrec_train_gt_list = common.load_jsonl(config.OPEN_CURATEDTERC_TRAIN_GT) squad_dev_fitem_list = open_domain_p_sampler.prepare_forward_data('squad', 'dev', False, upstream_top_k=40, debug=debug_mode) squad_train_fitem_list = open_domain_p_sampler.prepare_forward_data('squad', 'train', True, open_qa_paras['squad'][ 'upstream_top_k'], open_qa_paras['squad'][ 'distant_gt_top_k'], open_qa_paras['squad'][ 'down_sample_ratio'], debug=debug_mode) squad_dev_gt_list = common.load_jsonl(config.OPEN_SQUAD_DEV_GT) squad_train_gt_list = common.load_jsonl(config.OPEN_SQUAD_TRAIN_GT) wikimovie_test_fitem_list = open_domain_p_sampler.prepare_forward_data('wikimovie', 'test', False, upstream_top_k=40, debug=debug_mode) wikimovie_train_fitem_list = open_domain_p_sampler.prepare_forward_data('wikimovie', 'train', True, open_qa_paras['wikimovie'][ 'upstream_top_k'], open_qa_paras['wikimovie'][ 'distant_gt_top_k'], open_qa_paras['wikimovie'][ 'down_sample_ratio'], debug=debug_mode) wikimovie_test_gt_list = common.load_jsonl(config.OPEN_WIKIM_TEST_GT) wikimovie_train_gt_list = common.load_jsonl(config.OPEN_WIKIM_TRAIN_GT) # Load squadv11 forward: webq_test_gt_dict = list_dict_data_tool.list_to_dict(webq_test_gt_list, 'question') curatedtrec_test_gt_dict = list_dict_data_tool.list_to_dict(curatedtrec_test_gt_list, 'question') squad_dev_gt_dict = list_dict_data_tool.list_to_dict(squad_dev_gt_list, 'question') wikimovie_test_gt_dict = list_dict_data_tool.list_to_dict(wikimovie_test_gt_list, 'question') webq_train_gt_dict = list_dict_data_tool.list_to_dict(webq_train_gt_list, 'question') curatedtrec_train_gt_dict = list_dict_data_tool.list_to_dict(curatedtrec_train_gt_list, 'question') squad_train_gt_dict = list_dict_data_tool.list_to_dict(squad_train_gt_list, 'question') wikimovie_train_gt_dict = list_dict_data_tool.list_to_dict(wikimovie_train_gt_list, 'question') webq_est_datasize = len(webq_train_fitem_list) curatedtrec_est_datasize = len(curatedtrec_train_fitem_list) print("WebQ Train Size:", webq_est_datasize) print("TREC Train Size:", curatedtrec_est_datasize) bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=do_lower_case, cache_dir=bert_pretrain_path) bert_cs_reader = BertContentSelectionReader(bert_tokenizer, lazy, is_paired=True, example_filter=lambda x: len(x['context']) == 0, max_l=max_l, element_fieldname='element') bert_encoder = BertModel.from_pretrained(bert_model_name, cache_dir=bert_pretrain_path) model = BertMultiLayerSeqClassification(bert_encoder, num_labels=num_class, num_of_pooling_layer=1, act_type='tanh', use_pretrained_pooler=True, use_sigmoid=True) model.load_state_dict(torch.load(model_path)) model.to(device) if n_gpu > 1: model = torch.nn.DataParallel(model) # webq_test_instance = bert_cs_reader.read(webq_test_fitem_list) curatedtrec_test_instance = bert_cs_reader.read(curatedtrec_test_fitem_list) squad_dev_instance = bert_cs_reader.read(squad_dev_fitem_list) wikimovie_test_instance = bert_cs_reader.read(wikimovie_test_fitem_list) webq_train_instance = bert_cs_reader.read(webq_train_fitem_list) curatedtrec_train_instance = bert_cs_reader.read(curatedtrec_train_fitem_list) squad_train_instance = bert_cs_reader.read(squad_train_fitem_list) wikimovie_train_instance = bert_cs_reader.read(wikimovie_train_fitem_list) print('webq:', len(webq_train_fitem_list)) print('curatedtrec:', len(curatedtrec_train_fitem_list)) print('squad:', len(squad_train_fitem_list)) print('wikimovie:', len(wikimovie_train_fitem_list)) biterator = BasicIterator(batch_size=forward_size) biterator.index_with(vocab) # separate_eval_open_qa_procedure(biterator, curatedtrec_test_instance, model, 0, # curatedtrec_test_gt_list, curatedtrec_test_gt_dict, 'curatedtrec', # save_path=".", tag='test') # # separate_eval_open_qa_procedure(biterator, curatedtrec_train_instance, model, 0, # curatedtrec_train_gt_list, curatedtrec_train_gt_dict, 'curatedtrec', # save_path=".", tag='train') # separate_eval_open_qa_procedure(biterator, squad_train_instance, model, 0, # squad_train_gt_list, squad_train_gt_dict, 'squad', # save_path=".", tag='train') # separate_eval_open_qa_procedure(biterator, wikimovie_train_instance, model, 0, # wikimovie_train_gt_list, wikimovie_train_gt_dict, 'wikimovie', # save_path=".", tag='train') separate_eval_open_qa_procedure(biterator, webq_train_instance, model, 0, webq_train_gt_list, webq_train_gt_dict, 'webq', save_path=".", tag='train') if __name__ == '__main__': multitask_open_qa_model_go() # model_path = config.PRO_ROOT / "saved_models/05-12-20:32:15_mtr_open_qa_p_level_(num_train_epochs:3)/i(3837)|e(3)_final_ema_model" # selective_eval(model_path)

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false

0.001205

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0.044578

0.037349

0

null

0

1

0

null

0

6

f4baca57e8362b9cd8b6c4bd08adcb042b71eede

8,760

py

Python

rasotools/plot/profile.py

MBlaschek/rasotools

a8b954518a1e39b554f850aac0f5bd8fa1f23dc6

[ "MIT" ]

1

2019-10-06T22:26:43.000Z

rasotools/plot/profile.py

MBlaschek/rasotools

a8b954518a1e39b554f850aac0f5bd8fa1f23dc6

[ "MIT" ]

null

rasotools/plot/profile.py

MBlaschek/rasotools

a8b954518a1e39b554f850aac0f5bd8fa1f23dc6

[ "MIT" ]

1

2020-04-19T13:47:52.000Z

# -*- coding: utf-8 -*- __all__ = ['var', 'winds', 'boxplot', 'bars'] def var(data, dim='plev', ax=None, logy=False, yticklabels=None, showna=False, **kwargs): import numpy as np from xarray import DataArray from ._helpers import line, set_labels, get_info from ..fun import message if not isinstance(data, DataArray): raise ValueError('Requires a DataArray', type(data)) if dim not in data.dims: raise ValueError('Requires a datetime dimension', dim) if data.ndim > 1: raise ValueError('Too many dimensions', data.dims, data.shape) values = data.values levels = data[dim].values.copy() lev_units = data[dim].attrs.get('units', 'Pa') if lev_units == 'Pa': levels = levels.astype(float) / 100. message('Converting', lev_units, 'to', 'hPa', levels, **kwargs) lev_units = 'hPa' if yticklabels is not None: yticklabels = np.int_(np.asarray(yticklabels) / 100) itx = np.isfinite(values) kwargs.update({'marker': kwargs.get('marker', 'o')}) set_labels(kwargs, xlabel=get_info(data), title=get_info(data), ylabel=dim + ' [%s]' %lev_units) ax = line(values[itx], levels[itx], ax=ax, **kwargs) if np.sum(itx) != np.size(levels) and showna: itmp = ax.get_xlim() ax.plot([itmp[1]]*np.sum(~itx), levels[~itx], marker=kwargs.get('marker','x'), c='red') if logy: ax.set_yscale('log') if np.diff(ax.get_ylim())[0] > 0: ax.invert_yaxis() ax.set_yticks(levels) if yticklabels is not None: yticklabels = np.asarray(yticklabels) # can not calc on list ax.set_yticks(yticklabels) ax.set_yticklabels(np.int_(yticklabels)) else: ax.set_yticks(levels[::2]) ax.set_yticklabels(np.int_(levels[::2])) ax.set_ylim(*kwargs.get('ylim', (None, None))) # fixed ax.set_xlim(*kwargs.get('xlim', (None, None))) return ax def winds(data, u='u', v='v', dim='plev', barbs=True, ax=None, logy=False, yticklabels=None, showna=True, **kwargs): import numpy as np from xarray import Dataset import matplotlib.pyplot as plt from ._helpers import set_labels, line from ..fun import message if not isinstance(data, Dataset): raise ValueError('Requires a Dataset', type(data)) if dim not in data.dims: raise ValueError('Requires a datetime dimension', dim) if u not in data.data_vars: raise ValueError('Requires a u-wind component', u) if v not in data.data_vars: raise ValueError('Requires a v-wind component', v) if data[u].ndim > 1 or data[v].ndim > 1: raise ValueError('Too many dimensions', data.dims, data.shape) uvalues = data[u].values vvalues = data[v].values levels = data[dim].values.copy() lev_units = data[dim].attrs.get('units', 'Pa') if lev_units == 'Pa': levels = levels.astype(float) / 100. message('Converting', lev_units, 'to', 'hPa', levels, **kwargs) lev_units = 'hPa' if yticklabels is not None: yticklabels = np.int_(np.asarray(yticklabels) / 100) itx = np.isfinite(uvalues) & np.isfinite(vvalues) set_labels(kwargs, xlabel='Winds ['+data[u].attrs.get('units','m/s')+']', title='Winds', ylabel=dim + ' [%s]' %lev_units) if barbs: if ax is None: f, ax = plt.subplots() # 1D SNHT PLOT speed = np.sqrt(uvalues * uvalues + vvalues * vvalues) ax.barbs(np.zeros_like(levels[itx]), levels[itx], uvalues[itx], vvalues[itx], speed[itx], alpha=kwargs.get('alpha', 1)) else: ax = line(uvalues[itx], levels[itx], label='u-wind', ax=ax, **kwargs) ax = line(vvalues[itx], levels[itx], label='v-wind', ax=ax, **kwargs) ax.legend() ax.grid('gray', ls='--') ax.set_title(kwargs.get('title')) ax.set_ylabel(kwargs.get('ylabel')) ax.set_xlabel(kwargs.get('xlabel')) if logy: ax.set_yscale('log') if np.diff(ax.get_ylim())[0] > 0: ax.invert_yaxis() ax.set_yticks(levels, minor=True) if yticklabels is not None: yticklabels = np.asarray(yticklabels) # can not calc on list ax.set_yticks(yticklabels) ax.set_yticklabels(np.int_(yticklabels)) else: ax.set_yticks(levels[::2]) ax.set_yticklabels(np.int_(levels[::2])) ax.set_ylim(*kwargs.get('ylim', (None, None))) # fixed ax.set_xlim(*kwargs.get('xlim', (None, None))) return ax def boxplot(data, dim='plev', ax=None, vline=None, yticklabels=None, logy=False, **kwargs): import numpy as np import pandas as pd from xarray import DataArray import matplotlib.pyplot as plt from ._helpers import set_labels, get_info if not isinstance(data, DataArray): raise ValueError('Requires a DataArray', type(data)) if dim not in data.dims: raise ValueError('Requires a level dimension', dim) if data.ndim != 2: raise ValueError('Too many/few dimensions', data.dims, data.shape) if ax is None: fig, ax = plt.subplots() axis = data.dims.index(dim) dims = list(data.dims) dims.remove(dim) odim = dims[0] levels = data[dim].values.copy() lev_units = data[dim].attrs.get('units', 'Pa') if lev_units == 'Pa': levels = levels.astype(float) / 100. lev_units = 'hPa' if yticklabels is not None: yticklabels = np.int_(np.asarray(yticklabels) / 100) levels = levels.astype(int) if axis == 0: idata = pd.DataFrame(data.values.T, index=data[odim].values, columns=levels) else: idata = pd.DataFrame(data.values, index=data[odim].values, columns=levels) set_labels(kwargs, xlabel=get_info(data), title=get_info(data), ylabel=dim + ' [%s]' % lev_units) idata = idata.sort_index(axis=1, ascending=False) idata.boxplot(ax=ax, vert=False, return_type='axes', sym='+') if vline is not None: ax.axvline(x=vline, color='k', lw=1) ax.grid(ls='--') ax.set_title(kwargs.get('title')) ax.set_ylabel(kwargs.get('ylabel')) ax.set_xlabel(kwargs.get('xlabel')) if logy: ax.set_yscale('log') if yticklabels is not None: yticklabels = np.asarray(yticklabels) # can not calc on list ax.set_yticks(yticklabels) ax.set_yticklabels(np.int_(yticklabels)) # for label in ax.yaxis.get_ticklabels(): # if int(label.get_text()) not in yticklabels: # label.set_visible(False) # else: # for label in ax.yaxis.get_ticklabels()[::2]: # label.set_visible(False) ax.set_ylim(*kwargs.get('ylim', (None, None))) # fixed ax.set_xlim(*kwargs.get('xlim', (None, None))) return ax def bars(data, dim='plev', ax=None, vline=None, yticklabels=None, logy=False, use_levels=False, bar_kwargs={}, **kwargs): import numpy as np from xarray import DataArray import matplotlib.pyplot as plt from ._helpers import set_labels, get_info if not isinstance(data, DataArray): raise ValueError('Requires a DataArray', type(data)) if dim not in data.dims: raise ValueError('Requires a level dimension', dim) if data.ndim != 1: raise ValueError('Too many/few dimensions', data.dims, data.shape) if ax is None: fig, ax = plt.subplots() levels = data[dim].values.copy() lev_units = data[dim].attrs.get('units', 'Pa') if lev_units == 'Pa': levels = levels.astype(float) / 100. lev_units = 'hPa' levels = levels.astype(int) set_labels(kwargs, xlabel=get_info(data), title=get_info(data), ylabel=dim + ' [%s]' % lev_units) if use_levels: ax.barh(levels, data.values, align='center', **bar_kwargs) # ax.set_yticklabels([str(i) for i in levels]) else: ax.barh(np.arange(1, levels.size+1), data.values, align='center', **bar_kwargs) ax.set_yticklabels([str(i) for i in levels]) if logy: ax.set_yscale('log') if np.diff(levels)[0] > 0: ax.invert_yaxis() if vline is not None: ax.axvline(x=vline, color='k', lw=1) ax.grid(ls='--') ax.set_title(kwargs.get('title')) ax.set_ylabel(kwargs.get('ylabel')) ax.set_xlabel(kwargs.get('xlabel')) if yticklabels is not None: for label in ax.yaxis.get_ticklabels(): if int(label.get_text()) not in yticklabels: label.set_visible(False) # else: # for label in ax.yaxis.get_ticklabels()[::2]: # label.set_visible(False) ax.set_ylim(*kwargs.get('ylim', (None, None))) # fixed ax.set_xlim(*kwargs.get('xlim', (None, None))) return ax

33.822394

121

0.615525

1,232

8,760

4.285714

0.135552

0.033144

0.043561

0.045455

0.800568

0.775947

0.754735

0.741098

0.722917

0.689394

0

0.007318

0.235616

8,760

258

122

33.953488

0.781213

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false

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0.092784

0

0.134021

0

null

0

1

0

null

0

6

f4bb302bb6eb9ac0da14024bae916a128fa80280

159

py

Python

branch/branch/doctype/company_branch/test_company_branch.py

Solufy-ERPNext-Apps/branch

6f565b4669acb96c211ae40024db15e198c50be9

[ "MIT" ]

null

branch/branch/doctype/company_branch/test_company_branch.py

Solufy-ERPNext-Apps/branch

6f565b4669acb96c211ae40024db15e198c50be9

[ "MIT" ]

null

branch/branch/doctype/company_branch/test_company_branch.py

Solufy-ERPNext-Apps/branch

6f565b4669acb96c211ae40024db15e198c50be9

[ "MIT" ]

null

17.666667

56

0.779874

20

159

6.2

0.85

0

0.029412

0.144654

159

8

57

19.875

0.882353

0.528302

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

f4f9dbd265712cf93b378f2ef68c10a94ecec0fb

5,845

py

Python

indy_common/test/auth/metadata/test_error_messages.py

jandayanan/indy-node

98d0c8165d45b2e38b95c82a134d835085e56a9f

[ "Apache-2.0" ]

null

indy_common/test/auth/metadata/test_error_messages.py

jandayanan/indy-node

98d0c8165d45b2e38b95c82a134d835085e56a9f

[ "Apache-2.0" ]

null

indy_common/test/auth/metadata/test_error_messages.py

jandayanan/indy-node

98d0c8165d45b2e38b95c82a134d835085e56a9f

[ "Apache-2.0" ]

null

import os from collections import OrderedDict import pytest from indy_common.authorize.auth_constraints import AuthConstraint, IDENTITY_OWNER, AuthConstraintOr from indy_common.test.auth.metadata.helper import set_auth_constraint, PLUGIN_FIELD, build_req_and_action, Action from plenum.common.constants import TRUSTEE, STEWARD from plenum.common.exceptions import UnauthorizedClientRequest MAX_SIG_COUNT = 3 def test_plugin_simple_error_msg_no_plugin_field(write_auth_req_validator): set_auth_constraint(write_auth_req_validator, AuthConstraint(role=IDENTITY_OWNER, sig_count=1, need_to_be_owner=True, metadata={PLUGIN_FIELD: 2})) req, actions = build_req_and_action(Action(author=IDENTITY_OWNER, endorser=None, sigs={IDENTITY_OWNER: 1}, is_owner=True, amount=None, extra_sigs=False)) with pytest.raises(UnauthorizedClientRequest) as excinfo: write_auth_req_validator.validate(req, actions) assert ("missing required plugin field") in str(excinfo.value) def test_plugin_simple_error_msg_extra_plugin_field(write_auth_req_validator): set_auth_constraint(write_auth_req_validator, AuthConstraint(role=IDENTITY_OWNER, sig_count=1, need_to_be_owner=True)) req, actions = build_req_and_action(Action(author=IDENTITY_OWNER, endorser=None, sigs={IDENTITY_OWNER: 1}, is_owner=True, amount=5, extra_sigs=False)) with pytest.raises(UnauthorizedClientRequest) as excinfo: write_auth_req_validator.validate(req, actions) assert ("plugin field must be absent") in str(excinfo.value) def test_plugin_simple_error_msg_not_enough_amount(write_auth_req_validator): set_auth_constraint(write_auth_req_validator, AuthConstraint(role=IDENTITY_OWNER, sig_count=1, need_to_be_owner=True, metadata={PLUGIN_FIELD: 10})) req, actions = build_req_and_action(Action(author=IDENTITY_OWNER, endorser=None, sigs={IDENTITY_OWNER: 1}, is_owner=True, amount=5, extra_sigs=False)) with pytest.raises(UnauthorizedClientRequest) as excinfo: write_auth_req_validator.validate(req, actions) assert ("not enough amount in plugin field") in str(excinfo.value) def test_plugin_or_error_msg_not_enough_amount(write_auth_req_validator): set_auth_constraint(write_auth_req_validator, AuthConstraintOr(auth_constraints=[ AuthConstraint(role=TRUSTEE, sig_count=1, need_to_be_owner=False), AuthConstraint(role=STEWARD, sig_count=1, need_to_be_owner=False, metadata={PLUGIN_FIELD: 10}), ])) req, actions = build_req_and_action(Action(author=STEWARD, endorser=None, sigs={STEWARD: 1}, is_owner=True, amount=5, extra_sigs=False)) with pytest.raises(UnauthorizedClientRequest) as excinfo: write_auth_req_validator.validate(req, actions) expected = os.linesep.join([ "Rule for this action is: 1 TRUSTEE signature is required OR 1 STEWARD signature is required with additional metadata new_field 10", "Failed checks:", "Constraint: 1 TRUSTEE signature is required, Error: Not enough TRUSTEE signatures", "Constraint: 1 STEWARD signature is required with additional metadata new_field 10, Error: not enough amount in plugin field" ]) assert expected in str(excinfo.value.args[0]) def test_plugin_or_error_msg_not_enough_amount_multiple_metadata_fields(write_auth_req_validator): set_auth_constraint(write_auth_req_validator, AuthConstraintOr(auth_constraints=[ AuthConstraint(role=TRUSTEE, sig_count=1, need_to_be_owner=False), AuthConstraint(role=STEWARD, sig_count=1, need_to_be_owner=False, metadata=OrderedDict([ (PLUGIN_FIELD, 10), ("aaa", "bbb") ])) ])) req, actions = build_req_and_action(Action(author=STEWARD, endorser=None, sigs={STEWARD: 1}, is_owner=True, amount=5, extra_sigs=False)) with pytest.raises(UnauthorizedClientRequest) as excinfo: write_auth_req_validator.validate(req, actions) expected = os.linesep.join([ "Rule for this action is: 1 TRUSTEE signature is required OR 1 STEWARD signature is required with additional metadata new_field 10 aaa bbb", "Failed checks:", "Constraint: 1 TRUSTEE signature is required, Error: Not enough TRUSTEE signatures", "Constraint: 1 STEWARD signature is required with additional metadata new_field 10 aaa bbb, Error: not enough amount in plugin field" ]) assert expected in str(excinfo.value.args[0])

54.12037

148

0.586142

609

5,845

5.334975

0.155993

0.041551

0.055402

0.096953

0.869191

0.862111

0.848569

0.820252

0

0.011076

0.35124

5,845

107

149

54.626168

0.845728

0

0.681818

0

0.045455

0.137725

0

0.056818

1

0.056818

false

0

0.079545

0

0.136364

0

null

0

1

0

null

0

6

7629d48fd0957bc5bc602b45e10f06cb745fcb5a

174

py

Python

0x04-python-more_data_structures/7-update_dictionary.py

coding-max/holbertonschool-higher_level_programming

392fed1ae686642b6cca6bb6752050882bbf79fc

[ "MIT" ]

1

2021-04-26T03:45:12.000Z

0x04-python-more_data_structures/7-update_dictionary.py

coding-max/holbertonschool-higher_level_programming

392fed1ae686642b6cca6bb6752050882bbf79fc

[ "MIT" ]

null

0x04-python-more_data_structures/7-update_dictionary.py

coding-max/holbertonschool-higher_level_programming

392fed1ae686642b6cca6bb6752050882bbf79fc

[ "MIT" ]

1

2022-02-02T02:44:35.000Z

#!/usr/bin/python3 def update_dictionary(a_dictionary, key, value): "replaces or adds key/value in a dictionary" a_dictionary[key] = value return (a_dictionary)

24.857143

48

0.724138

25

174

4.88

0.56

0.360656

0.344262

0.393443

0.47541

0

0.006944

0.172414

174

6

49

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0.840278

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0

1

0.25

false

0

0.5

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null

1

0

1

0

null

0

1

0

6

5207acee4876fca9a5de45333f4d2751e4051962

112,581

py

Python

app/views.py

joelsegoviacrespo/control_aforo_migrado

be90d1d45a20f735e7ef20449c4ab91ca05b5d85

[ "MIT" ]

null

app/views.py

joelsegoviacrespo/control_aforo_migrado

be90d1d45a20f735e7ef20449c4ab91ca05b5d85

[ "MIT" ]

null

app/views.py

joelsegoviacrespo/control_aforo_migrado

be90d1d45a20f735e7ef20449c4ab91ca05b5d85

[ "MIT" ]

null

from django.contrib.auth.decorators import login_required from django.shortcuts import render, get_object_or_404, redirect from django.template import loader from django.http import HttpResponse, JsonResponse from django import template from camaras.models import Camaras from camara_zona.models import CamaraZona from django.forms.models import model_to_dict from instalacion.models import Instalacion from cliente.models import Cliente from display.models import Display from monitor.models import Monitor import sys import http.client import mimetypes from pip._vendor import requests import logging import base64 from django.conf import settings import json import urllib.request import calendar import datetime import pytz from datetime import date from aforoInfo.models import AforoInfo from django.core import serializers from rest_framework.renderers import JSONRenderer from camaras_historico.models import CamarasHistorico from datetime import date from datetime import datetime, timedelta from json import dumps import re from operator import add import Constantes from calendar import monthrange from Fecha import Fecha import simplejson as simplejson from usuarios_red.models import UsuariosRed from jornada_laboral.models import JornadaLaboral serial_camara = "Q2GV-4YBM-YWWJ" def TimeConverter(millis): #si ven este codigo, si se que se puede hacer mucho mas corto pero por razones que desconozco por los momentos no quiere funcionar de esa manera tsToString =str(millis) x=re.split('Timestamp|,',tsToString) y= x[1] z=y.split('(') final =z[1] lasMillis = int(final) #print(lasMillis) seconds=(lasMillis/1000)%60 seconds = int(seconds) minutes=(lasMillis/(1000*60))%60 minutes = int(minutes) hours=(lasMillis/(1000*60*60))%24 result =("%d:%d:%d" % (hours, minutes, seconds)) newResult = datetime.strptime(result, '%H:%M:%S') return newResult def grafica_semana(mydate, mydate1 ,mydate2,fecha_limite,fecha_limite_minima,seriales): return 0 def grafica_semana2(mydate, mydate1 ,mydate2,fecha_limite,fecha_limite_minima,seriales): #print('la fecha actual es',mydate2) mySerials=[] e= 0 if (type(seriales )is list): for i in seriales: aux = seriales[e] mySerials.append(aux) #print(mySerials) e=e +1 else: mySerials.append(seriales) #print(seriales) #print('indiceeeeeeeeeeeeeeeeeee') #print(seriales) #print('estoy recibiendo de argumento:',mydate, mydate1 ,mydate2,) #mydate = str(today.strftime("%Y-%m-%d")) #mydate1 = datetime.today() #mydate2 = datetime.today().strftime('%A') # print('estoy recibiendo la siguente informacion: mi fecha dereferencia es:',mydate, 'mydate1:' ,mydate1, 'mydate2:', mydate2,'fecha limite:',fecha_limite,'fecha limite minima:',fecha_limite_minima) #no me queria funcionar el indice i contador= 0 int(contador) #filtro del limite de la semana if fecha_limite_minima.strftime('%A') == 'Saturday' and datetime.strptime(mydate, '%Y-%m-%d').strftime('%A') == 'Saturday': while fecha_limite_minima <= datetime.strptime(mydate, '%Y-%m-%d'): fecha_limite_minima =fecha_limite_minima+timedelta(days=1) #print(fecha_limite_minima) else: while fecha_limite_minima.strftime('%A') != 'Sunday': fecha_limite_minima =fecha_limite_minima+timedelta(days=1) #print('fecha_limite_minima') #print(fecha_limite_minima) dias_semana_total=[0,0,0,0,0,0,0,0,0,0] for i in mySerials: #print("se está ejecutando este cilco for el primero") dias_semana=[0,0,0,0,0,0,0] varTemporal0= 0000 varTemporal1= 0000 varTemporal2= 0000 varTemporal3= 0000 varTemporal4= 0000 varTemporal5= 0000 varTemporal6= 0000 myLocalSunday=datetime.strptime(str(mydate), '%Y-%m-%d') #print('mylocalsunday') #print(myLocalSunday) while myLocalSunday.strftime('%A') != 'Sunday': myLocalSunday =(myLocalSunday-timedelta(days=1)) #print(myLocalSunday) #print(myLocalSunday) for e in myCamaras.objects.all(): #print("se está ejecutando este cilco for el SEGUNDO") comparativeDate = 0000 if (e.serial_camara == mySerials[contador]): if(mydate2== 'Sunday'): #print('la fecha del documento es:',e.fecha , 'y la de referencia es:',mydate) if(str(e.fecha)==str(mydate) ): if datetime.strptime(e.fecha, '%Y-%m-%d') <= fecha_limite and datetime.strptime(e.fecha, '%Y-%m-%d') >= fecha_limite_minima: if datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Sunday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal1 : dias_semana.append(e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print('esto viene de ese documento',e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas, 'de la fecha',e.fecha, 'con el id', e._id) varTemporal0 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) dias_semana.append(0) #print('------------------ esto es la lista en total---', dias_semana, 'de este dia', e.fecha,'este valor',e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) return dias_semana else: pass else: #print("primera condicional") if datetime.strptime(e.fecha, '%Y-%m-%d') <= fecha_limite and datetime.strptime(e.fecha, '%Y-%m-%d') >= myLocalSunday: # print('fechas segun el rango') # print(e.fecha) #print("segunda condicional") if datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Monday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal1 : #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en lunes") # print('se agrego al dia lunes el valor',e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) dias_semana.pop(1) dias_semana.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Tuesday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal2) : dias_semana.pop(2) #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en martes") dias_semana.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Thursday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal3) : dias_semana.pop(3) #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en miercoles") dias_semana.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Wednesday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal4) : dias_semana.pop(4) #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en jueves") dias_semana.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Friday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal5): dias_semana.pop(5) #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en viernes") dias_semana.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Saturday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal6) : dias_semana.pop(6) #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en sabado") dias_semana.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas if (datetime.strptime(e.fecha, '%Y-%m-%d').strftime('%A') == 'Sunday' and e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas != varTemporal0): #print("esta guardando datos"+ str(e.zonas_camara[0].nro_personas) + "en domingo") dias_semana.pop(0) #print('se agrego al dia domingo el valor',e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) dias_semana.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass #print(dias_semana) dias_semana_total= list( map(add, dias_semana, dias_semana_total) ) #print("dias_semana_total000000000000000000000000000000000000") #print(dias_semana_total) return dias_semana_total def grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite,fecha_limite_minima,seriales): return [] def grafica_semana_actual_acumulada2(mydate, mydate1 ,mydate2,fecha_limite,fecha_limite_minima,seriales): datos_semana_acumuladas= grafica_semana(mydate, mydate1 ,mydate2,fecha_limite,fecha_limite_minima,seriales) datosSemana=[] a=0 today = date.today() myLocalDate = str(today.strftime("%Y-%m-%d")) nw=today.weekday() if (mydate != myLocalDate): #print('no es el dia de hoy') nw= 5 else: if nw == 6: nw= -1 #print(nw) diaDeHoy = (int(today.strftime("%d"))) for i in datos_semana_acumuladas: if(a == 0): #print('primera condicional') datosSemana.append(datos_semana_acumuladas[a]) a=a+1 elif (a>nw+1): pass else: datosSemana.append(datos_semana_acumuladas[a]+datosSemana[a-1]) a=a+1 #print(datosSemana) return datosSemana def esteMesActual(seriales, mydate, boolean): return [] def esteMesActual2(seriales, mydate, boolean): #inicio el array today = date.today() i = 0 aux = 0 myLocalDate = str(today.strftime("%m")) fechaAComparar = datetime.strptime(mydate, '%Y-%m-%d') ready = str(fechaAComparar.strftime("%m")) if (myLocalDate != ready and boolean == True ): if(i == aux): i=i+1 date_time_obj = datetime.strptime(mydate, '%Y-%m-%d') #obtengo la cantidad de dias por mes weekDay,myMonthrange=monthrange(int(date_time_obj.strftime("%Y")),int(date_time_obj.strftime("%m"))) dateToFunction(today-timedelta(month=i)) else: myLocalDate = ready aux = i elif(myLocalDate != ready and boolean == False ): if(i == aux): i=i+1 date_time_obj = datetime.strptime(mydate, '%Y-%m-%d') #obtengo la cantidad de dias por mes weekDay,myMonthrange=monthrange(int(date_time_obj.strftime("%Y")),int(date_time_obj.strftime("%m"))) dateToFunction(today+timedelta(month=i)) else: myLocalDate = ready aux = i else: today = date.today() weekDay,myMonthrange=monthrange(int(today.strftime("%Y")), int(today.strftime("%m"))) #obtengo e l dia de hoy diaDeHoy = (int(today.strftime("%d"))) #print(int(today.strftime("%Y"))) #print(int(today.strftime("%m"))) #print(myMonthrange) #print("el dia de hoy tiene esta cantidad de dias en su mes", myMonthrange) #una vez obtenida la cantidad de dias de este me se hacen las condicionales respectivas switch_casos = { 31: dias31(today,seriales), 30: dias30(today,seriales), 29: dias29(today,seriales), 28: dias28(today,seriales), } esteMes = switch_casos.get(myMonthrange,default()) #print ('total') #print (esteMes) #alamcenar lo que retorne en una variable y retornarla datosMes=[] a=0 for i in esteMes: if(a == 0): #print('primera condicional') datosMes.append(esteMes[a]) a=a+1 elif (a>diaDeHoy-1): pass else: datosMes.append(esteMes[a]) a=a+1 #print(datosMes) return datosMes def esteMesAcumulado(seriales,mydate,state): return [] def esteMesAcumulado2(seriales,mydate,state): #inicio el array date_time_obj = datetime.strptime(mydate, '%Y-%m-%d') #obtengo la cantidad de dias por mes NWeekDay,NMonthRange=monthrange(int(date_time_obj.strftime("%Y")),int(date_time_obj.strftime("%m"))) #obtengo la cantidad de dias por mes today = date.today() weekDay,myMonthrange=monthrange(int(today.strftime("%Y")), int(today.strftime("%m"))) #obtengo el dia de hoy diaDeHoy = (int(today.strftime("%d"))) #print(int(today.strftime("%Y"))) #print(int(today.strftime("%m"))) #print(myMonthrange) #print("el dia de hoy tiene esta cantidad de dias en su mes", myMonthrange) #una vez obtenida la cantidad de dias de este me se hacen las condicionales respectivas switch_casos = { 31: dias31(today,seriales), 30: dias30(today,seriales), 29: dias29(today,seriales), 28: dias28(today,seriales), } esteMes = switch_casos.get(myMonthrange,default()) #print ('total') #print (esteMes) #alamcenar lo que retorne en una variable y retornarla datosSemanaAcum=[] a=0 for i in esteMes: if(a == 0): # print('primera condicional') datosSemanaAcum.append(esteMes[a]) a=a+1 elif (a>diaDeHoy-1): pass else: datosSemanaAcum.append(esteMes[a]+datosSemanaAcum[a-1]) a=a+1 #print(datosSemanaAcum) return datosSemanaAcum def dias31(today,seriales): contador= 0 int(contador) datos_semana_total=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for i in seriales: datos_semana=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for e in myCamaras.objects.all(): #print('fecha e') #separar la fecha para usar el mes if e.fecha is not None : fechaASeparar = str(e.fecha) separado= fechaASeparar.split('-') #print('fecha a separar') #print(separado) date = separado[1] else: pass comparative = str(today.strftime("%m")) # print('fecha',date, 'a comparar', comparative) if (e.serial_camara == seriales[contador] and date !=0): #print(seriales[contador]) if date == comparative: #obtener el dia de esa fecha diaASeparar =str(e.fecha) diaSeparado = diaASeparar.split('-') dia = diaSeparado[2] #aqui se filtra por dias if(int(dia)==1): datos_semana.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) elif(int(dia)==2): # print('esto pasa') datos_semana.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==3): datos_semana.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==4): datos_semana.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==5): datos_semana.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==6): datos_semana.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==7): datos_semana.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==8): datos_semana.insert(7,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==9): datos_semana.insert(8,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) # print(datos_semana) elif(int(dia)==10): datos_semana.insert(9,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==11): datos_semana.insert(10,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==12): datos_semana.insert(11,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==13): datos_semana.insert(12,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==14): datos_semana.insert(13,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==15): datos_semana.insert(14,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==16): datos_semana.insert(15,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==17): datos_semana.insert(16,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==18): datos_semana.insert(17,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==19): datos_semana.insert(18,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==20): datos_semana.insert(19,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==21): datos_semana.insert(20,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==22): datos_semana.insert(21,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==23): datos_semana.insert(22,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==24): datos_semana.insert(23,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==25): datos_semana.insert(24,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==26): datos_semana.insert(25,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==27): datos_semana.insert(26,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==28): datos_semana.insert(27,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==29): datos_semana.insert(28,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==30): datos_semana.insert(29,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==31): datos_semana.insert(30,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) else: #print(datos_semana) pass total= list( map(add, datos_semana, datos_semana_total) ) contador=contador+1 #print('total') #print(total) return total def dias30(today,seriales): contador= 0 int(contador) datos_semana_total=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for i in seriales: datos_semana=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for e in myCamaras.objects.all(): #print('fecha e') #separar la fecha para usar el mes if e.fecha is not None : fechaASeparar = str(e.fecha) separado= fechaASeparar.split('-') #print('fecha a separar') #print(separado) date = separado[1] else: pass comparative = str(today.strftime("%m")) #print('fecha',date, 'a comparar', comparative) if (e.serial_camara == seriales[contador] and date !=0): #print(seriales[contador]) if date == comparative: #obtener el dia de esa fecha diaASeparar =str(e.fecha) diaSeparado = diaASeparar.split('-') dia = diaSeparado[2] #aqui se filtra por dias if(int(dia)==1): datos_semana.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) elif(int(dia)==2): #print('esto pasa') datos_semana.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==3): datos_semana.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==4): datos_semana.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==5): datos_semana.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==6): datos_semana.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==7): datos_semana.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==8): datos_semana.insert(7,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==9): datos_semana.insert(8,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==10): datos_semana.insert(9,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==11): datos_semana.insert(10,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==12): datos_semana.insert(11,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==13): datos_semana.insert(12,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==14): datos_semana.insert(13,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==15): datos_semana.insert(14,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==16): datos_semana.insert(15,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==17): datos_semana.insert(16,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==18): datos_semana.insert(17,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==19): datos_semana.insert(18,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) # print(datos_semana) elif(int(dia)==20): datos_semana.insert(19,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==21): datos_semana.insert(20,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==22): datos_semana.insert(21,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==23): datos_semana.insert(22,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==24): datos_semana.insert(23,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==25): datos_semana.insert(24,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==26): datos_semana.insert(25,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==27): datos_semana.insert(26,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==28): datos_semana.insert(27,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==29): datos_semana.insert(28,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==30): datos_semana.insert(29,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) else: # print(datos_semana) pass total= list( map(add, datos_semana, datos_semana_total) ) contador=contador+1 #print('total') #print(total) return total def dias29(today,seriales): contador= 0 int(contador) datos_semana_total=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for i in seriales: datos_semana=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for e in myCamaras.objects.all(): #print('fecha e') #separar la fecha para usar el mes if e.fecha is not None : fechaASeparar = str(e.fecha) separado= fechaASeparar.split('-') #print('fecha a separar') #print(separado) date = separado[1] else: pass comparative = str(today.strftime("%m")) #print('fecha',date, 'a comparar', comparative) if (e.serial_camara == seriales[contador] and date !=0): #print(seriales[contador]) if date == comparative: #obtener el dia de esa fecha diaASeparar =str(e.fecha) diaSeparado = diaASeparar.split('-') dia = diaSeparado[2] #aqui se filtra por dias if(int(dia)==1): datos_semana.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) elif(int(dia)==2): #print('esto pasa') datos_semana.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==3): datos_semana.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==4): datos_semana.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==5): datos_semana.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==6): datos_semana.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==7): datos_semana.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==8): datos_semana.insert(7,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==9): datos_semana.insert(8,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==10): datos_semana.insert(9,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==11): datos_semana.insert(10,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==12): datos_semana.insert(11,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==13): datos_semana.insert(12,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==14): datos_semana.insert(13,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==15): datos_semana.insert(14,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==16): datos_semana.insert(15,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==17): datos_semana.insert(16,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==18): datos_semana.insert(17,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==19): datos_semana.insert(18,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==20): datos_semana.insert(19,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==21): datos_semana.insert(20,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==22): datos_semana.insert(21,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==23): datos_semana.insert(22,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==24): datos_semana.insert(23,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==25): datos_semana.insert(24,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==26): datos_semana.insert(25,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==27): datos_semana.insert(26,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==28): datos_semana.insert(27,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==29): datos_semana.insert(28,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) else: #print(datos_semana) pass total= list( map(add, datos_semana, datos_semana_total) ) contador=contador+1 #print('total') #print(total) return total def dias28(today,seriales): contador= 0 int(contador) datos_semana_total=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for i in seriales: datos_semana=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for e in myCamaras.objects.all(): #print('fecha e') #separar la fecha para usar el mes if e.fecha is not None : fechaASeparar = str(e.fecha) separado= fechaASeparar.split('-') #print('fecha a separar') #print(separado) date = separado[1] else: pass comparative = str(today.strftime("%m")) #print('fecha',date, 'a comparar', comparative) if (e.serial_camara == seriales[contador] and date !=0): #print(seriales[contador]) if date == comparative: #obtener el dia de esa fecha diaASeparar =str(e.fecha) diaSeparado = diaASeparar.split('-') dia = diaSeparado[2] #aqui se filtra por dias if(int(dia)==1): datos_semana.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) elif(int(dia)==2): #print('esto pasa') datos_semana.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==3): datos_semana.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==4): datos_semana.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==5): datos_semana.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==6): datos_semana.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==7): datos_semana.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==8): datos_semana.insert(7,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) # print(datos_semana) elif(int(dia)==9): datos_semana.insert(8,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==10): datos_semana.insert(9,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==11): datos_semana.insert(10,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==12): datos_semana.insert(11,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==13): datos_semana.insert(12,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==14): datos_semana.insert(13,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==15): datos_semana.insert(14,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==16): datos_semana.insert(15,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==17): datos_semana.insert(16,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==18): datos_semana.insert(17,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==19): datos_semana.insert(18,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==20): datos_semana.insert(19,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==21): datos_semana.insert(20,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==22): datos_semana.insert(21,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==23): datos_semana.insert(22,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==24): datos_semana.insert(23,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==25): datos_semana.insert(24,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==26): datos_semana.insert(25,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==27): datos_semana.insert(26,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) elif(int(dia)==28): datos_semana.insert(27,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) #print(datos_semana) else: #print(datos_semana) pass total= list( map(add, datos_semana, datos_semana_total) ) contador=contador+1 #print('total') #print(total) return total def default(): defecto = "" myHoraDeecremental = datetime.strptime('00:00:00',"%H:%M:%S") #print(myHoraDeecremental) myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) #print('Hora decremental de prueba') #print(myHoraDeecremental) if myHoraDeecremental == datetime.strptime('23:50:00',"%H:%M:%S"): pass #print('se cumple la condicional') else: pass #print('no se cumplio') # HORAS--------------------------------------------------------------------------------------------------------------------- def grafica_horas(mydate): return [0,0,0,0,0,0,0,0,0,0] def grafica_horas2(mydate): varTemporal1 = 0 varTemporal2 = 0 varTemporal3 = 0 varTemporal4 = 0 varTemporal5 = 0 varTemporal6 = 0 varTemporal7 = 0 varTemporal8 = 0 #print('se esta ejecutandoooooooooooooooooooooo') now = datetime.now() #hora de referencia myhora = now.strftime("%H:%M:%S") #hora que va a decrementar myHoraDeecremental = datetime.strptime('23:00:00',"%H:%M:%S") #hora indice que aunmenta el decremento horaIncremental ='00:00:00' #hora de referencia #myrefHour=datetime.strptime('23:59:59',"%H:%M:%S") myrefHour=datetime.strptime('00:00:00',"%H:%M:%S") myrefHour1=datetime.strptime('03:00:00',"%H:%M:%S") myrefHour2=datetime.strptime('06:00:00',"%H:%M:%S") myrefHour3=datetime.strptime('09:00:00',"%H:%M:%S") myrefHour4=datetime.strptime('12:00:00',"%H:%M:%S") myrefHour5=datetime.strptime('15:00:00',"%H:%M:%S") myrefHour6=datetime.strptime('18:00:00',"%H:%M:%S") myrefHour7=datetime.strptime('21:00:00',"%H:%M:%S") myrefHour8=datetime.strptime('21:59:59',"%H:%M:%S") #print('si ves esto sobre una fecha...') #print(myrefHour) datos_horas=[0,0,0,0,0,0,0,0,0,0] datos_horas_acumuladas=[1,2,3,4,5,6,7] #print(myhora) for e in myCamaras.objects.all(): if (e.serial_camara == serial_camara): if(e.fecha==mydate ): #print('se esta ejecutandoooooooooooooooooooooo') #print(e.zonas_camara[0].nro_personas) tiempo =(TimeConverter(e.ts)) if TimeConverter(e.ts) <=myrefHour1 and TimeConverter(e.ts)> myrefHour8: #print('se esta ejecutando la primera condicional') #varConverter = TimeConverter(var) #TimeConverter(str(e.ts)) datos_horas.insert(0,e.zonas_camara[0].nro_personas) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) datos_horas.append(0) #print('hey----------------se cumplio la primera condicion, esta temprano') #print(varConverter) else: #while myHoraDeecremental >= myrefHour: #for i in datos_horas_acumuladas: #print(e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) if TimeConverter(e.ts) >= myrefHour and TimeConverter(e.ts) <= myrefHour1: #print('hey----------------se cumplio la segunda y la hora ronda la 1 y las 3') #print(Time) if varTemporal1 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): datos_horas.pop(0) myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.insert(0,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal1 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass #horaIncremental+timedelta('00:10:00') #print(horaIncremental) if TimeConverter(e.ts) > myrefHour1 and TimeConverter(e.ts) <= myrefHour2: #print('hey----------------se cumplio la segunda y la hora ronda la 3 y las 6') if varTemporal2 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): datos_horas.pop(1) datos_horas.insert(1,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) varTemporal2 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour2 and TimeConverter(e.ts) <= myrefHour3: #print('hey----------------se cumplio la segunda y la hora ronda la 6 y las 9') if varTemporal3 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): datos_horas.pop(2) datos_horas.insert(2,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) varTemporal3 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour3 and TimeConverter(e.ts) <= myrefHour4: #print('hey----------------se cumplio la segunda y la hora ronda la 9 y las 12') if varTemporal4 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.pop(3) datos_horas.insert(3,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal4 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour4 and TimeConverter(e.ts) <= myrefHour5: #print('hey----------------se cumplio la segunda y la hora ronda la 12 y las 15') if varTemporal5 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.pop(4) datos_horas.insert(4,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal5 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour5 and TimeConverter(e.ts) <= myrefHour6: #print('hey----------------se cumplio la segunda y la hora ronda la 15 y las 18') if varTemporal6 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.pop(5) datos_horas.insert(5,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal6 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour6 and TimeConverter(e.ts) <= myrefHour7: #print('hey----------------se cumplio la segunda y la hora ronda la 18 y las 21') if varTemporal7 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.pop(6) datos_horas.insert(6,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal7 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass if TimeConverter(e.ts) > myrefHour7 and TimeConverter(e.ts) <= myrefHour8: #print('hey----------------se cumplio la segunda y la hora ronda la 21 y las 11 y 59') if varTemporal8 != (e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas): myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) datos_horas.pop(7) datos_horas.insert(7,e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas) varTemporal8 = e.zonas_camara[0].nro_personas + e.zonas_camara[1].nro_personas else: pass else: #print('no se cumplieron ningunas de las anteriores') myHoraDeecremental = myHoraDeecremental-timedelta(minutes=10) return datos_horas #HORAS ACUMULADAS-------------------------------------------------------------------------------------------------------------------------------- def grafica_horas_acumuladas(mydate): return [] def grafica_horas_acumuladas2(mydate): today = date.today() myLocalDate = str(today.strftime("%Y-%m-%d")) datosHoras=[] datos_horas_acumuladas = grafica_horas(mydate) now = datetime.now() #hora de referencia myhora = now.strftime("%H:%M:%S") myHoraDeecremental = datetime.strptime(myhora,"%H:%M:%S").time() #print(myHoraDeecremental) #horas de referencia para comparar myrefHour=datetime.strptime('00:00:00',"%H:%M:%S").time() myrefHour1=datetime.strptime('03:00:00',"%H:%M:%S").time() myrefHour2=datetime.strptime('06:00:00',"%H:%M:%S").time() myrefHour3=datetime.strptime('09:00:00',"%H:%M:%S").time() myrefHour4=datetime.strptime('12:00:00',"%H:%M:%S").time() myrefHour5=datetime.strptime('15:00:00',"%H:%M:%S").time() myrefHour6=datetime.strptime('18:00:00',"%H:%M:%S").time() myrefHour7=datetime.strptime('21:00:00',"%H:%M:%S").time() myrefHour8=datetime.strptime('21:59:59',"%H:%M:%S").time() nw=3 #print(myHoraDeecremental, ' ',myrefHour) if(mydate == myLocalDate ): if (myHoraDeecremental >= myrefHour and myHoraDeecremental < myrefHour1): nw = 1 elif (myHoraDeecremental >= myrefHour1 and myHoraDeecremental < myrefHour2): nw =2 elif (myHoraDeecremental >= myrefHour2 and myHoraDeecremental < myrefHour3): nw = 3 #print('se estan comparando') elif (myHoraDeecremental >= myrefHour3 and myHoraDeecremental < myrefHour4): nw = 4 elif (myHoraDeecremental >= myrefHour4 and myHoraDeecremental < myrefHour5): nw = 5 #print('se estan comparando') elif (myHoraDeecremental >= myrefHour5 and myHoraDeecremental < myrefHour6): nw = 6 #print('se estan comparando') elif (myHoraDeecremental >= myrefHour6 and myHoraDeecremental < myrefHour7): nw = 7 #print('se estan comparando') elif (myHoraDeecremental >= myrefHour7 and myHoraDeecremental < myrefHour8): nw = 8 else: nw=8 a=0 for i in datos_horas_acumuladas: if(a == 0): #print('primera condicional') datosHoras.append(datos_horas_acumuladas[a]) a=a+1 elif (a>nw+1): pass else: datosHoras.append(datos_horas_acumuladas[a]+datosHoras[a-1]) a=a+1 #print('datosHoras') # print(datosHoras) #print(datosHoras) return datosHoras #print(grafica_horas_acumuladas(mydate)) @login_required(login_url="/login/") def index(request): #TODO: Orientarlo a un cliente en particular camarasAll = Camaras.objects.all() fecha_actual = Fecha.getFechaActual().strftime("%d-%m-%Y") info_grafica_semana = grafica_semana("", "" ,"","","","") info_grafica_semana_acumulada = grafica_semana_actual_acumulada("", "" ,"","","","") esteMes = esteMesActual("","","") MyesteMesAcumulado = esteMesAcumulado("","","") info_grafica_horas = grafica_horas("") info_grafica_horas_acumulado= grafica_horas_acumuladas("") datosDevolver= {'camaras':camarasAll,"fecha_actual":fecha_actual,'info_grafica_semana': info_grafica_semana,'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,'info_grafica_semana_acumulada':info_grafica_semana_acumulada,'estemes':esteMes,'estemesacumulado':MyesteMesAcumulado} return render(request, "index.html",datosDevolver ) @login_required(login_url="/login/") def index2(request): #argumentos ordenados #para el primer parametro hoy = date.today() today = date.today() mydate = str(today.strftime("%Y-%m-%d")) semana_a_restar = (hoy-timedelta(weeks=1)) #para el segundo parametro mydate1 = datetime.today() semana_a_restar_str = datetime.strptime(str(hoy-timedelta(weeks=1)),"%Y-%m-%d") #para el tercer parametro mydate2 = datetime.today().strftime('%A') mydate3 = (datetime.today()-timedelta(weeks=1)).strftime('%A') #para el cuarto parametro hoy0= datetime.today() hoy1= datetime.today() #para el quinto parametro fecha_limite_minima0 =(hoy0-timedelta(weeks=1)) fecha_limite_minima1 =(hoy1-timedelta(weeks=1)) '''for e in Cliente.objects.all(): if(Cliente.objects.filter(nif=request.user.profile.cliente.nif).first() is not None ): display = Cliente.objects.filter(nif=request.user.profile.cliente.nif).first() id_display = e.nif print('que imprime esto') print(request.user.profile.cliente.nif)''' if (request.user.profile.rol== Constantes.SUPERUSUARIO): mySerial=['Q2GV-4YBM-YWWJ','Q2HV-B24V-ZKN5'] mylist= mySerial else: mySerial=[] for e in Cliente.objects.all(): if(Cliente.objects.filter(nif=request.user.profile.cliente.nif).first() is not None ): id_display = e.nif for i in Instalacion.objects.all(): for o in Camaras. objects.all(): #print('pasa algo') if (Instalacion.objects.filter(cliente__startswith={'nif': request.user.profile.cliente.nif}) is not None): if(i.cliente.nif ==request.user.profile.cliente.nif): #display = Instalacion.objects.filter(nif=id_display).first() #print('----------que imprime esto--------------------') #print(request.user.profile.cliente.nif) MyInstalacion= i.nombre_comercial #print(MyInstalacion) else: print('') pass if (Camaras.objects.filter(instalacion__startswith={'nombre': MyInstalacion}) is not None): #print('intalacion.nombre',o.instalacion.nombre,'mi instalacion var',MyInstalacion ) if str(o.instalacion.nombre) == MyInstalacion: #print('------tenemos una camara con esas caracteristicas') mySerial.append(o.serial_camara) #else: #print('no encontro nada--------------------') #print('el serial es:') mylist=list(dict.fromkeys(mySerial)) #print(mylist) fecha_limite0 = hoy0 fecha_limite =(hoy1-timedelta(weeks=1)) camarasAll = Camaras.objects.all() #info_grafica_semana = grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) #info_grafica_semana_pasada = grafica_semana_pasada(semana_a_restar, semana_a_restar_str,mydate3,fecha_limite,fecha_limite_minima1,mylist) #----------------Para las graficas de la tarjeta superior derecha (HOY)------------------ info_grafica_horas = grafica_horas(mydate) info_grafica_horas_acumulado= grafica_horas_acumuladas(mydate) #----------------Para las graficas de la tarjeta superior derecha (ESTA SEMANA)------------------ info_grafica_semana = grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) info_grafica_semana_acumulada = grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) #-------------Para las graficas de la tarjeta superior derecha (ESTE MES)------------------------ esteMes = esteMesActual(mylist,mydate,1) MyesteMesAcumulado = esteMesAcumulado(mylist,mydate,1) indice = 0 datosDevolver= {'camaras':camarasAll,'info_grafica_semana': info_grafica_semana,'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,'info_grafica_semana_acumulada':info_grafica_semana_acumulada,'estemes':esteMes,'estemesacumulado':MyesteMesAcumulado} #de ejemplo mylist=['Q2GV-4YBM-YWWJ','Q2HV-B24V-ZKN5'] if len(mylist) > 1: for i in mylist: info_camara1=grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) info_grafica_semana_acumulada1 = grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print('info camara a agragar al diccionario') #print(info_camara1) #nombre = "infocamara" + str(indice) #nombre1 ="infocamaraAcumulada"+ str(indice) #globals() [nombre] = info_camara1 #globals() [nombre1]= info_grafica_semana_acumulada1 #print("infocamara" + str(indice)) datosDevolver["infocamara" + str(indice)] = grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print( grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice])) #print(grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice])) datosDevolver["infocamaraAcumulada"+str(indice)]=grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) indice = indice+1 else: for i in mylist: info_camara1=grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) info_grafica_semana_acumulada1 = grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print('info camara a agragar al diccionario') #print(info_camara1) #nombre = "infocamara" + str(indice) #nombre1 ="infocamaraAcumulada"+ str(indice) #globals() [nombre] = info_camara1 #globals() [nombre1]= info_grafica_semana_acumulada1 #print("infocamara" + str(indice)) datosDevolver["infocamara" + str(indice)] = grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print( grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice])) #print(grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice])) datosDevolver["infocamaraAcumulada"+str(indice)]=grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) indice = indice+1 datosDevolver["infocamara1"]=[0,0,0,0,0,0,0,0,0,0] datosDevolver["infocamaraAcumulada1"]=[0,0,0,0,0,0,0,0,0,0] #grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print (datosDevolver) return render(request, "index.html",datosDevolver ) @login_required(login_url="/login/") def pages(request): context = {} # All resource paths end in .html. # Pick out the html file name from the url. And load that template. try: load_template = request.path.split('/')[-1] html_template = loader.get_template(load_template) return HttpResponse(html_template.render(context, request)) except template.TemplateDoesNotExist: html_template = loader.get_template('error-404.html') return HttpResponse(html_template.render(context, request)) except: html_template = loader.get_template( 'error-500.html' ) return HttpResponse(html_template.render(context, request)) indiceTiempo=0 @login_required(login_url="/login/") def back(request): print("back@@@@@@@@@@@@@@@@@") global indiceTiempo print("indiceTiempo INICIAL: ",indiceTiempo) indiceTiempo = indiceTiempo -1 indiceTiempoNP= abs(indiceTiempo) print("indiceTiempoNP: ",indiceTiempoNP) mySerial=['Q2GV-4YBM-YWWJ'] mylist= mySerial hoy = date.today() hoy0= datetime.today() hoy1= datetime.today() today = date.today() mydate = str((today-timedelta(days=indiceTiempoNP)).strftime("%Y-%m-%d")) mydateToShow = str((today-timedelta(days=indiceTiempoNP)).strftime("%d-%m-%Y")) mydate1 = (datetime.today()-timedelta(days=indiceTiempoNP)) mydate2 = (datetime.today()-timedelta(days=indiceTiempoNP)).strftime('%A') fecha_limite0 = hoy0-timedelta(days=indiceTiempoNP) fecha_limite_minima0 =(hoy0-timedelta(days=indiceTiempo)) fecha_limite_minima1 =(hoy1-timedelta(days=indiceTiempo)) fecha_limite =(hoy1-timedelta(days=indiceTiempo)) info_grafica_horas = grafica_horas(mydate) info_grafica_horas_acumulado= grafica_horas_acumuladas(mydate) info_grafica_semana= grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) info_grafica_semana_acumulada= grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) esteMes= esteMesActual(mylist,mydate,False) MyesteMesAcumulado= esteMesAcumulado(mylist,mydate,False) #print(info_grafica_horas) #print(info_grafica_horas_acumulado) #print('HOLAAAAAAAAAAAAAAAAAAA') #print( info_grafica_horas) #print(info_grafica_horas_acumulado) fecha = mydateToShow return_sub_array = {'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,' info_grafica_semana': info_grafica_semana,'info_grafica_semana_acumulada':info_grafica_semana_acumulada,'estemes':esteMes,'estemesacumulado':MyesteMesAcumulado,'fecha':fecha} #print('return_sub_array') #print(return_sub_array) return HttpResponse( json.dumps(return_sub_array)) @login_required(login_url="/login/") def ahead(request): global indiceTiempo print("indiceTiempo: ",indiceTiempo) indiceTiempo = indiceTiempo +1 print("indiceTiempo2: ",indiceTiempo) indiceTiempoNP= abs(indiceTiempo) mySerial=['Q2GV-4YBM-YWWJ'] mylist= mySerial hoy = date.today() hoy0= datetime.today() hoy1= datetime.today() today = date.today() mydate = str((today+timedelta(days=indiceTiempoNP)).strftime("%Y-%m-%d")) mydateToShow = str((today+timedelta(days=indiceTiempoNP)).strftime("%d-%m-%Y")) mydate1 = (datetime.today()+timedelta(days=indiceTiempoNP)) mydate2 = (datetime.today()+timedelta(days=indiceTiempoNP)).strftime('%A') fecha_limite0 = hoy0-timedelta(days=indiceTiempoNP) fecha_limite_minima0 =(hoy0+timedelta(days=indiceTiempo)) fecha_limite_minima1 =(hoy1+timedelta(days=indiceTiempo)) fecha_limite =(hoy1-timedelta(days=indiceTiempo)) info_grafica_horas = grafica_horas(mydate) info_grafica_horas_acumulado= grafica_horas_acumuladas(mydate) info_grafica_semana= grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) info_grafica_semana_acumulada= grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) esteMes= esteMesActual(mylist,mydate,True) MyesteMesAcumulado= esteMesAcumulado(mylist,mydate,True) fecha = mydateToShow #print(info_grafica_horas) #print(info_grafica_horas_acumulado) #print('HOLAAAAAAAAAAAAAAAAAAA') #print( info_grafica_horas) #print(info_grafica_horas_acumulado) return_sub_array = {'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,' info_grafica_semana': info_grafica_semana,'info_grafica_semana_acumulada':info_grafica_semana_acumulada,'estemes':esteMes,'estemesacumulado':MyesteMesAcumulado,'fecha':fecha} #print('return_sub_array') #print(return_sub_array) return HttpResponse( json.dumps(return_sub_array)) @login_required(login_url="/login/") def hello(request): return HttpResponse('Hello World!') #--------------------------------Informacion Para index_base.html @login_required(login_url="/login/") def pitarnfo(request): #print("AHHHHHHHHHHHHHHHHHHHHHHHHHHHH") if (request.user.profile.rol== Constantes.SUPERUSUARIO): mySerial=['Q2GV-4YBM-YWWJ'] mylist= mySerial else: mySerial=[] for e in Cliente.objects.all(): if(Cliente.objects.filter(nif=request.user.profile.cliente.nif).first() is not None ): id_display = e.nif for i in Instalacion.objects.all(): for o in Camaras. objects.all(): #print('pasa algo') if (Instalacion.objects.filter(cliente__startswith={'nif': request.user.profile.cliente.nif}) is not None): if(i.cliente.nif ==request.user.profile.cliente.nif): #display = Instalacion.objects.filter(nif=id_display).first() #print('----------que imprime esto--------------------') #print(request.user.profile.cliente.nif) MyInstalacion= i.nombre_comercial #print(MyInstalacion) else: #print('***************************no se parece') pass if (Camaras.objects.filter(instalacion__startswith={'nombre': MyInstalacion}) is not None): #print('intalacion.nombre',o.instalacion.nombre,'mi instalacion var',MyInstalacion ) if str(o.instalacion.nombre) == MyInstalacion: # print('------tenemos una camara con esas caracteristicas') mySerial.append(o.serial_camara) else: #print('no encontro nada--------------------') pass #print('el serial es:') mylist=list(dict.fromkeys(mySerial)) #print(mylist) #argumentos ordenados #para el primer parametro hoy = date.today() today = date.today() mydate = str(today.strftime("%Y-%m-%d")) semana_a_restar = (hoy-timedelta(weeks=1)) #para el segundo parametro mydate1 = datetime.today() semana_a_restar_str = datetime.strptime(str(hoy-timedelta(weeks=1)),"%Y-%m-%d") #para el tercer parametro mydate2 = datetime.today().strftime('%A') mydate3 = (datetime.today()-timedelta(weeks=1)).strftime('%A') #para el cuarto parametro hoy0= datetime.today() hoy1= datetime.today() #para el quinto parametro fecha_limite_minima0 =(hoy0-timedelta(weeks=1)) fecha_limite_minima1 =(hoy1-timedelta(weeks=1)) fecha_limite0 =(hoy0) today = date.today() mydate = str(today.strftime("%Y-%m-%d")) mydate1 = datetime.today() mydate2 = datetime.today().strftime('%A') #graficas horas y horas acumuladas para las tarjeta Inferiores----------------------------------------------------------------- info_grafica_horas = grafica_horas(mydate) info_grafica_horas_acumulado= grafica_horas_acumuladas(mydate) #------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------ info_grafica_semana_acumulada = grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist) #print('info grafica de la semana acumulada-----------------------------------------------------------------------------------------') #print(info_grafica_semana_acumulada) #graficas inferiores indice= 0 datosDevolver={'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,'info_grafica_semana_acumulada':info_grafica_semana_acumulada} #datosDevolver= {'camaras':camarasAll,'info_grafica_semana': info_grafica_semana,'info_grafica_horas':info_grafica_horas,'info_grafica_horas_acumulado':info_grafica_horas_acumulado,'info_grafica_semana_acumulada':info_grafica_semana_acumulada,'estemes':esteMes,'estemesacumulado':MyesteMesAcumulado} #de ejemplo mylist=['Q2GV-4YBM-YWWJ','Q2HV-B24V-ZKN5'] for i in mylist: info_camara1=grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) info_grafica_semana_acumulada1 = grafica_semana_actual_acumulada(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print('info camara a agragar al diccionario') #print(info_camara1) nombre = "infocamara" + str(indice) globals() [nombre] = info_camara1 #print("infocamara" + str(indice)) datosDevolver["infocamara" + str(indice)] =[1,2,3,4,5,6,7] indice = indice+1 #grafica_semana(mydate, mydate1 ,mydate2,fecha_limite0,fecha_limite_minima0,mylist[indice]) #print("AHHHHHHHHHHHHHHHHHHHHHHHHHHHH") #print(datosDevolver) return render(request, "includes\index_base.html", datosDevolver ) def hfs(request): #print("hfs") if hasattr(request.user.profile, 'cliente') and hasattr(request.user.profile.cliente, 'get_id') and (request.user.profile.cliente.get_id() is not None): if hasattr(request.user.profile, 'instalacion') and hasattr(request.user.profile.instalacion, 'get_id') and (request.user.profile.instalacion.get_id() is not None): id_instalacion = request.user.profile.instalacion.get_id() display = Display.objects.filter(instalacion={'nif_cliente': request.user.profile.cliente.nif} and {'nombre': request.user.profile.instalacion.nombre_comercial}).first() id_display = display.get_id() # id_display = "5f67fbcd37cb6511302af8ee" else: #print("else") display = Display.objects.filter(instalacion={'nif_cliente': request.user.profile.cliente.nif}).first() id_display = display.get_id() # id_display = "5f67fbcd37cb6511302af8ee" else: #print("estoy en el else") cliente = Cliente.objects.first() instalacion = Instalacion.objects.filter(cliente={'nif': cliente.nif}).first() display = Display.objects.filter(instalacion={'nif_cliente': cliente.nif} and {'nombre': instalacion.nombre_comercial}).first() id_display = display.get_id() # id_display = "5f67fbcd37cb6511302af8ee" #print("id_display: ",id_display) data = { 'embebido':False, 'id_display': id_display } dataJSON = dumps(data) return render(request, 'hzfullscreen_bu.html', {'data': dataJSON}) def hfsEmbebido(request): #print("hfsEmbebido") if hasattr(request.user.profile, 'cliente') and hasattr(request.user.profile.cliente, 'get_id') and (request.user.profile.cliente.get_id() is not None): if hasattr(request.user.profile, 'instalacion') and hasattr(request.user.profile.instalacion, 'get_id') and (request.user.profile.instalacion.get_id() is not None): id_instalacion = request.user.profile.instalacion.get_id() display = Display.objects.filter(instalacion={'nif_cliente': request.user.profile.cliente.nif} and {'nombre': request.user.profile.instalacion.nombre_comercial}).first() id_display = display.get_id() #id_display = "5f67fbcd37cb6511302af8ee" else: #print("estoy en el else1") display = Display.objects.filter(instalacion={'nif_cliente': request.user.profile.cliente.nif}).first() #id_display = "5f67fbcd37cb6511302af8ee" id_display = display.get_id() else: #print("estoy en el else2") cliente = Cliente.objects.first() #print("cliente",cliente.nif) instalacion = Instalacion.objects.filter(cliente={'nif': cliente.nif}).first() #print("instalacion",instalacion.nombre_comercial) display = Display.objects.filter(instalacion={'nif_cliente': cliente.nif} and {'nombre': instalacion.nombre_comercial}).first() #print("display",display) #id_display = "5f67fbcd37cb6511302af8ee" id_display = display.get_id() #print("id_display: ",id_display) data = { 'embebido':True, 'id_display': id_display } dataJSON = dumps(data) return render(request, 'hzfullscreen_bu.html', {'data': dataJSON}) def generar_estadistica_generales(request,fecha_str,operacion): fecha = "" derecha_disabled = False array_red_ethernet = [] array_red_wifi = [] result = [] if request.method == 'GET': try: try: if request.method == 'GET': #print("GET") #print("fecha_str: ",fecha_str) fecha_convert = datetime.strptime(fecha_str, "%d-%m-%Y").date() #print("fecha_convert: ",fecha_convert) if (operacion == 'atras'): fecha = (fecha_convert - timedelta(days=1)).strftime("%d-%m-%Y") #print("fecha: ",fecha) elif (operacion == 'delante'): fecha = (fecha_convert + timedelta(days=1)).strftime("%d-%m-%Y") #print("fecha: ",fecha) derecha_disabled = (fecha == Fecha.getFechaActual().strftime("%d-%m-%Y")) fecha_consultar = datetime.strptime(fecha, "%d-%m-%Y").date() result = generar_estadistica_conteo_red(array_red_ethernet,array_red_wifi,fecha_consultar) array_red_ethernet = result[0] array_red_wifi = result[1] hora_apertura = result[2] hora_cierre = result[3] except Exception as e: print('%s (%s)' % (e, type(e))) pass estadistica_js = { "fecha" : str(fecha), "derecha_disabled": derecha_disabled, "array_red_ethernet" : array_red_ethernet, "array_red_wifi" : array_red_wifi, "hora_apertura" : hora_apertura, "hora_cierre" : hora_cierre, } return HttpResponse(simplejson.dumps(estadistica_js), content_type='application/json') except Exception as e: print('%s (%s)' % (e, type(e))) return JsonResponse({'m': fecha_str, 'error:': 'parametros erroneos'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) pass return JsonResponse({'m': fecha_str, 'error:': 'parametros erroneos 2'}, status=status.HTTP_400_BAD_REQUEST) def generar_estadistica_conteo_red(array_red_ethernet,array_red_wifi,fecha): #print("generar_estadistica_conteo_red") intervalo = get_intervaloPeriodo(fecha) start_date = intervalo[0] end_date = intervalo[1] #print("start_date",start_date) #print("end_date",end_date) parametros = setParametros() tiempo_medicion = parametros[0] #print("tiempo_medicion",tiempo_medicion) tiempo_medicion_parametro = parametros[1] #print("tiempo_medicion_parametro",tiempo_medicion_parametro) array_tiempo = parametros[2] #print("array_tiempo",array_tiempo) query = getQuery(start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo) #print("QUERY") #print(query) usuariosRed = UsuariosRed.objects.mongo_aggregate(query) lista = list(usuariosRed) #print(list) jornada = getHorarioLaboral() hora_apertura = jornada[0] hora_cierre = jornada[1] #print("hora_apertura: ",hora_apertura) #print("hora_cierre: ",hora_cierre) array_red_ethernet = [0] * len(array_tiempo) array_red_wifi = [0] * len(array_tiempo) """ for i in range(len(array_red_ethernet)): print("i: ",i) print("array_red_ethernet[i]: ",array_red_ethernet[i]) """ for dispositivoConectados in lista: #print("dispositivoConectados") #print(dispositivoConectados) result: OrderedDict[str, int] = dispositivoConectados #print("RESULTADOS!!!!!!!!!!!!!!!") #print(result['_id']) #result_tiempo: OrderedDict[str, str] = result['_id'] result_tiempo: OrderedDict[str, int] = dispositivoConectados nro_usuarios_ethernet = result['nro_usuarios_ethernet'] nro_usuarios_wifi = result['nro_usuarios_wifi'] result_hora: OrderedDict[str, str] = result['_id'] #print("result_hora: ",result_tiempo) #print("tiempo_medicion: ",tiempo_medicion) hora = result_hora[tiempo_medicion] #print("hora: ",hora) #if hora in nro_usuarios_ethernet = int(result_tiempo['nro_usuarios_ethernet']) nro_usuarios_wifi = int(result_tiempo['nro_usuarios_wifi']) #print("nro_usuarios_ethernet: ",nro_usuarios_ethernet) array_red_ethernet[hora] = nro_usuarios_ethernet array_red_wifi[hora] = nro_usuarios_wifi #print("ARREGLO DE PRESION ARTERIAL QUE VA PARA ESTADISTICA") #for i in range(len(array_presion_arterial_sys_hora)): #print("i: ",i) #print("array_presion_arterial_sys_hora[i]: ",array_presion_arterial_sys_hora[i]) #print("array_presion_arterial_dia_hora[i]: ",array_presion_arterial_dia_hora[i]) return array_red_ethernet,array_red_wifi,hora_apertura,hora_cierre def get_intervaloPeriodo(fecha_consulta): #print("fecha_consulta:",fecha_consulta) start_date = Fecha.get_start_day(fecha_consulta) end_date = Fecha.get_end_day(fecha_consulta) #print("start_date: ",start_date) #print("end_date: ",end_date) return start_date,end_date def getQuery(start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo): query = [ { "$match": { "fecha": { "$gte": start_date, "$lte": end_date }, } }, { "$project": { "date": { "$dateToString": { "format": "%Y-%m-%d", "date": "$fecha" } }, tiempo_medicion: { tiempo_medicion_parametro: "$fecha" }, "nro_usuarios_ethernet": "$nro_usuarios_ethernet", "nro_usuarios_wifi": "$nro_usuarios_wifi" } }, { "$match":{ tiempo_medicion:{"$in":array_tiempo} } }, { "$group": { "_id": { tiempo_medicion: tiempo_medicion_parametro, "date": "$date" }, "nro_usuarios_ethernet": { "$max": "$nro_usuarios_ethernet" }, "nro_usuarios_wifi": { "$max": "$nro_usuarios_wifi" }, } } ] return query def get_array_tiempo(ultimo_dia): array_tiempo = [] rango = ultimo_dia + 1 for i in range(0,rango): array_tiempo.append(i) return array_tiempo def setParametrosAF(periodo_estadistica): if (periodo_estadistica == 1): tiempo_medicion = "hour" tiempo_medicion_parametro = "$hour" array_tiempo = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] #Por semana if (periodo_estadistica == 2): tiempo_medicion = "dayOfWeek" tiempo_medicion_parametro = "$dayOfWeek" array_tiempo = [1,2,3,4,5,6,7] if (periodo_estadistica == 3): tiempo_medicion = "dayOfMonth" tiempo_medicion_parametro = "$dayOfMonth" ultimo_dia = last_day_of_month(datetime.today()) array_tiempo = get_array_tiempo(ultimo_dia) return tiempo_medicion,tiempo_medicion_parametro,array_tiempo def setParametros(): tiempo_medicion = "hour" tiempo_medicion_parametro = "$hour" array_tiempo = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] return tiempo_medicion,tiempo_medicion_parametro,array_tiempo def getHorarioLaboral(): #print("getHorarioLaboral") jornada = JornadaLaboral.objects.all()[0] #print("jornada") #print(jornada) hora_apertura = int(str(jornada.hora_apertura)[:2]) hora_cierre = int(str(jornada.hora_cierre)[:2]) #print("hora_apertura HL: ",hora_apertura) #print("hora_cierre: HL",hora_cierre) return hora_apertura,hora_cierre """ if (request.user.profile.rol == Constantes.ADMINISTRADOR) and hasattr(request.user.profile, 'cliente') and (request.user.profile.cliente.get_id() is not None): jornadaTodos = JornadaLaboral.objects.all().filter(instalacion={'nif_cliente': request.user.profile.cliente.nif}) elif (request.user.profile.rol > Constantes.ADMINISTRADOR) and hasattr(request.user.profile, 'cliente') and (request.user.profile.cliente.get_id() is not None): if hasattr(request.user.profile, 'instalacion') and hasattr(request.user.profile.instalacion, 'get_id') and (request.user.profile.instalacion.get_id() is not None): jornadaTodos = JornadaLaboral.objects.all().filter(instalacion={'nif_cliente': request.user.profile.cliente.nif} and {'nombre': request.user.profile.instalacion.nombre_comercial}) """ def getQueryTotalAforo(verdadero,start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo): query =[ { "$match": { "suma_total_aforo": { "$eq": verdadero }, "fecha": { "$gte": start_date, "$lte": end_date } } }, { "$project": { "date": { "$dateToString": { "format": "%Y-%m-%d", "date": "$fecha" } }, tiempo_medicion: { tiempo_medicion_parametro: "$fecha" }, "nro_personas": "$nro_personas" } }, { "$match":{ tiempo_medicion:{"$in":array_tiempo} } }, { "$group": { "_id": { tiempo_medicion: tiempo_medicion_parametro, "date": "$date", }, "total_nro_personas": { "$sum": "$nro_personas" } } } ] return query def generar_estadistica_total_aforo(periodo_estadistica): #print("generar_estadistica_total_aforo") intervalo = get_intervaloPeriodo_AF(periodo_estadistica) start_date = intervalo[0] end_date = intervalo[1] #print("start_date",start_date) #print("end_date",end_date) parametros = setParametrosAF(periodo_estadistica) tiempo_medicion = parametros[0] #print("tiempo_medicion",tiempo_medicion) tiempo_medicion_parametro = parametros[1] #print("tiempo_medicion_parametro",tiempo_medicion_parametro) array_tiempo = parametros[2] #print("array_tiempo",array_tiempo) query = getQueryTotalAforo(True,start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo) #print("QUERY") #print(query) camarasHistorico = CamarasHistorico.objects.mongo_aggregate(query) lista = list(camarasHistorico) #print(list) jornada = getHorarioLaboral() hora_apertura = jornada[0] hora_cierre = jornada[1] #print("hora_apertura: ",hora_apertura) #print("hora_cierre: ",hora_cierre) array_total_aforo = [0] * len(array_tiempo) array_total_persona = [0] * len(array_tiempo) """ for i in range(len(array_red_ethernet)): print("i: ",i) print("array_red_ethernet[i]: ",array_red_ethernet[i]) """ for nro_personas in lista: #print("dispositivoConectados") #print(dispositivoConectados) #result: OrderedDict[str, int] = nro_personas #print("RESULTADOS!!!!!!!!!!!!!!!") #print(result['_id']) #result_tiempo: OrderedDict[str, str] = result['_id'] result_tiempo: OrderedDict[str, int] = nro_personas #nro_personas = result['total_nro_personas'] result_hora: OrderedDict[str, str] = result_tiempo['_id'] #print("result_hora: ",result_tiempo) #print("tiempo_medicion: ",tiempo_medicion) hora = result_hora[tiempo_medicion] #print("hora: ",hora) #if hora in total_nro_personas = int(result_tiempo['total_nro_personas']) #nro_usuarios_wifi = int(result_tiempo['nro_usuarios_wifi']) #print("nro_usuarios_ethernet: ",nro_usuarios_ethernet) array_total_aforo[hora] = total_nro_personas #print("ARREGLO DE TOTAL NÚMEROS DE PERSONAS QUE VA PARA ESTADISTICA") #for i in range(len(array_total_aforo)): #print("i: ",i) #print("array_total_aforo[i]: ",array_total_aforo[i]) return array_total_aforo,array_total_persona,hora_apertura,hora_cierre def totalAforo(request,periodo_estadistica): result = [] if request.method == 'GET': try: #print("periodo_estadistica: ",periodo_estadistica) try: nro_aforo = 0 result = generar_estadistica_total_aforo(periodo_estadistica) array_total_aforo = result[0] array_total_persona = result[1] hora_apertura = result[2] hora_cierre = result[3] except Exception as e: print('%s (%s)' % (e, type(e))) pass red_js = { "array_total_aforo" : array_total_aforo, "array_total_persona" : array_total_persona, "hora_apertura" : hora_apertura, "hora_cierre" : hora_cierre, } return HttpResponse(simplejson.dumps(red_js), content_type='application/json') except Exception as e: print('%s (%s)' % (e, type(e))) return JsonResponse({'m': periodo_estadistica, 'error:': 'parametros erroneos'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) pass return JsonResponse({'m': periodo_estadistica, 'error:': 'parametros erroneos 2'}, status=status.HTTP_400_BAD_REQUEST) def get_intervaloPeriodo_AF(periodo_estadistica): if (periodo_estadistica == 1): start_date = Fecha.get_start_day(Fecha.getFechaActual()) end_date = Fecha.get_end_day(Fecha.getFechaActual()) elif (periodo_estadistica == 2): dia_semana = datetime.today().weekday() inicio_semana = datetime.today() - timedelta(days=dia_semana) fin_semana =inicio_semana + timedelta(days=6) start_date = Fecha.get_start_day(inicio_semana) end_date = Fecha.get_end_day(fin_semana) elif (periodo_estadistica == 3): inicio_mes = datetime.today() - timedelta(days=(datetime.today().day-1)) fin_mes = last_date_of_month(datetime.today()) start_date = Fecha.get_start_day(inicio_mes) end_date = Fecha.get_end_day(fin_mes) return start_date,end_date def totalAforoDia(request,fecha_str,operacion): fecha = "" derecha_disabled = False array_total_aforo = [] result = [] if request.method == 'GET': try: try: if request.method == 'GET': #print("GET") #print("fecha_str: ",fecha_str) fecha_convert = datetime.strptime(fecha_str, "%d-%m-%Y").date() #print("fecha_convert: ",fecha_convert) if (operacion == 'atras'): fecha = (fecha_convert - timedelta(days=1)).strftime("%d-%m-%Y") #print("fecha: ",fecha) elif (operacion == 'delante'): fecha = (fecha_convert + timedelta(days=1)).strftime("%d-%m-%Y") #print("fecha: ",fecha) derecha_disabled = (fecha == Fecha.getFechaActual().strftime("%d-%m-%Y")) fecha_consultar = datetime.strptime(fecha, "%d-%m-%Y").date() result = generar_estadistica_aforo_dia(array_total_aforo,fecha_consultar) array_total_aforo = result[0] array_total_persona = result[1] hora_apertura = result[2] hora_cierre = result[3] except Exception as e: print('%s (%s)' % (e, type(e))) pass estadistica_js = { "fecha" : str(fecha), "derecha_disabled": derecha_disabled, "array_total_aforo" : array_total_aforo, "array_total_persona" : array_total_persona, "hora_apertura" : hora_apertura, "hora_cierre" : hora_cierre, } return HttpResponse(simplejson.dumps(estadistica_js), content_type='application/json') except Exception as e: print('%s (%s)' % (e, type(e))) return JsonResponse({'m': fecha_str, 'error:': 'parametros erroneos'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) pass return JsonResponse({'m': fecha_str, 'error:': 'parametros erroneos 2'}, status=status.HTTP_400_BAD_REQUEST) def generar_estadistica_aforo_dia(array_total_aforo,fecha): #print("generar_estadistica_aforo_dia") intervalo = get_intervaloPeriodo(fecha) start_date = intervalo[0] end_date = intervalo[1] #print("start_date",start_date) #print("end_date",end_date) parametros = setParametros() tiempo_medicion = parametros[0] #print("tiempo_medicion",tiempo_medicion) tiempo_medicion_parametro = parametros[1] #print("tiempo_medicion_parametro",tiempo_medicion_parametro) array_tiempo = parametros[2] #print("array_tiempo",array_tiempo) query = getQueryTotalAforo(True,start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo) #print("QUERY") #print(query) camarasHistorico = CamarasHistorico.objects.mongo_aggregate(query) lista = list(camarasHistorico) #print(lista) jornada = getHorarioLaboral() hora_apertura = jornada[0] hora_cierre = jornada[1] #print("hora_apertura dia: ",hora_apertura) #print("hora_cierre dia: ",hora_cierre) array_total_aforo = [0] * len(array_tiempo) array_total_persona = [0] * len(array_tiempo) """ for i in range(len(array_red_ethernet)): print("i: ",i) print("array_red_ethernet[i]: ",array_red_ethernet[i]) """ for nro_personas in lista: #print("dispositivoConectados") #print(dispositivoConectados) #result: OrderedDict[str, int] = nro_personas #print("RESULTADOS!!!!!!!!!!!!!!!") #print(result['_id']) #result_tiempo: OrderedDict[str, str] = result['_id'] result_tiempo: OrderedDict[str, int] = nro_personas #nro_personas = result['total_nro_personas'] result_hora: OrderedDict[str, str] = result_tiempo['_id'] #print("result_hora: ",result_tiempo) #print("tiempo_medicion: ",tiempo_medicion) hora = result_hora[tiempo_medicion] #print("hora: ",hora) #if hora in total_nro_personas = int(result_tiempo['total_nro_personas']) #nro_usuarios_wifi = int(result_tiempo['nro_usuarios_wifi']) #print("nro_usuarios_ethernet: ",nro_usuarios_ethernet) array_total_aforo[hora] = total_nro_personas #print("ARREGLO DE PRESION ARTERIAL QUE VA PARA ESTADISTICA") #for i in range(len(array_presion_arterial_sys_hora)): #print("i: ",i) #print("array_presion_arterial_sys_hora[i]: ",array_presion_arterial_sys_hora[i]) #print("array_presion_arterial_dia_hora[i]: ",array_presion_arterial_dia_hora[i]) return array_total_aforo,array_total_persona,hora_apertura,hora_cierre def aforoZona(request,periodo_estadistica): result = [] dict_zonas_camara = {} hora_apertura = 0 hora_cierre= 0 if request.method == 'GET': try: #print("periodo_estadistica: ",periodo_estadistica) try: nro_aforo = 0 result = generar_estadistica_aforo_zona(periodo_estadistica) dict_zonas_camara = result[0] hora_apertura = result[1] hora_cierre = result[2] except Exception as e: print('%s (%s)' % (e, type(e))) pass red_js = { "dict_zonas_camara" : dict_zonas_camara, "hora_apertura" : hora_apertura, "hora_cierre" : hora_cierre, } return HttpResponse(simplejson.dumps(red_js), content_type='application/json') except Exception as e: print('%s (%s)' % (e, type(e))) return JsonResponse({'m': periodo_estadistica, 'error:': 'parametros erroneos'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) pass return JsonResponse({'m': periodo_estadistica, 'error:': 'parametros erroneos 2'}, status=status.HTTP_400_BAD_REQUEST) def generar_estadistica_total_aforo(periodo_estadistica): #print("generar_estadistica_total_aforo") intervalo = get_intervaloPeriodo_AF(periodo_estadistica) start_date = intervalo[0] end_date = intervalo[1] #print("start_date",start_date) #print("end_date",end_date) parametros = setParametrosAF(periodo_estadistica) tiempo_medicion = parametros[0] #print("tiempo_medicion",tiempo_medicion) tiempo_medicion_parametro = parametros[1] #print("tiempo_medicion_parametro",tiempo_medicion_parametro) array_tiempo = parametros[2] #print("array_tiempo",array_tiempo) query = getQueryTotalAforo(True,start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo) #print("QUERY") #print(query) camarasHistorico = CamarasHistorico.objects.mongo_aggregate(query) lista = list(camarasHistorico) #print("####lista###") #print(lista) jornada = getHorarioLaboral() hora_apertura = jornada[0] hora_cierre = jornada[1] #print("hora_apertura: ",hora_apertura) #print("hora_cierre: ",hora_cierre) array_total_aforo = [0] * len(array_tiempo) array_total_persona = [0] * len(array_tiempo) """ for i in range(len(array_red_ethernet)): print("i: ",i) print("array_red_ethernet[i]: ",array_red_ethernet[i]) """ for nro_personas in lista: #print("dispositivoConectados") #print(dispositivoConectados) #result: OrderedDict[str, int] = nro_personas #print("RESULTADOS!!!!!!!!!!!!!!!") #print(result['_id']) #result_tiempo: OrderedDict[str, str] = result['_id'] result_tiempo: OrderedDict[str, int] = nro_personas #nro_personas = result['total_nro_personas'] result_hora: OrderedDict[str, str] = result_tiempo['_id'] #print("result_hora: ",result_tiempo) #print("tiempo_medicion: ",tiempo_medicion) hora = result_hora[tiempo_medicion] total_nro_personas = int(result_tiempo['total_nro_personas']) #print("hora: ",hora) #if hora in if (periodo_estadistica ==2): if (hora-2 == -1): hora = len(array_tiempo)-1 else: hora = hora-2 if (periodo_estadistica ==3): if (hora-1 >= 0): hora = hora-1 if (periodo_estadistica !=1 and hora-1 >= -1): array_total_aforo[hora] = total_nro_personas elif (periodo_estadistica ==1): array_total_aforo[hora] = total_nro_personas #nro_usuarios_wifi = int(result_tiempo['nro_usuarios_wifi']) #print("nro_usuarios_ethernet: ",nro_usuarios_ethernet) #print("ARREGLO DE TOTAL NÚMEROS DE PERSONAS QUE VA PARA ESTADISTICA") #for i in range(len(array_total_aforo)): #print("i: ",i) #print("array_total_aforo[i]: ",array_total_aforo[i]) return array_total_aforo,array_total_persona,hora_apertura,hora_cierre def generar_estadistica_aforo_zona(periodo_estadistica): #print("generar_estadistica_aforo_zona") intervalo = get_intervaloPeriodo_AF(periodo_estadistica) start_date = intervalo[0] end_date = intervalo[1] #print("start_date",start_date) #print("end_date",end_date) parametros = setParametrosAF(periodo_estadistica) tiempo_medicion = parametros[0] #print("tiempo_medicion",tiempo_medicion) tiempo_medicion_parametro = parametros[1] #print("tiempo_medicion_parametro",tiempo_medicion_parametro) array_tiempo = parametros[2] #print("array_tiempo",array_tiempo) #zonas_camara = ["Interior Planta 3","Office Planta 3","Despacho Planta 3","Aforo de Planta 3","Aforo Planta 7","Ocupación Mesa"] result_zona = getZonasXCamaras() zonas_camara = result_zona[0] #print("zonas_camara: ",zonas_camara) query = getQueryAforoZona(start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo,zonas_camara) #print("QUERY") #print(query) camarasHistorico = CamarasHistorico.objects.mongo_aggregate(query) lista = list(camarasHistorico) #print("#############LISTA###############") #print(lista) jornada = getHorarioLaboral() hora_apertura = jornada[0] hora_cierre = jornada[1] #print("hora_apertura: ",hora_apertura) #print("hora_cierre: ",hora_cierre) #array_zonas_camara = ["Planta 3-Aforo Interior planta 3","Planta 3-Aforo Office planta 3","Planta 3-Aforo Despacho planta 3","Planta 3-Aforo de Planta","Planta 7-Aforo Planta 7","Planta 7-Ocupación Mesa"] camaras_zonas_camaras = result_zona[1] #print("!!!!camaras_zonas_camaras!!!!!: ",camaras_zonas_camaras) dict_zonas_camara = {} for zona_camara in camaras_zonas_camaras: #print("zona_camara: ",zona_camara) dict_zonas_camara[zona_camara] = [0] * len(array_tiempo) #print("!!!!!!!!!!dict_zonas_camara!!!!!!!!!!!!") #print(dict_zonas_camara) """ for i in range(len(array_red_ethernet)): print("i: ",i) print("array_red_ethernet[i]: ",array_red_ethernet[i]) """ try: for nro_personas in lista: #print("nro_personas") #print(nro_personas) result: OrderedDict[str, int] = nro_personas #print("RESULTADOS!!!!!!!!!!!!!!!") #print(result['_id']) result_tiempo: OrderedDict[str, str] = result['_id'] result_tiempo: OrderedDict[str, int] = nro_personas #print("result_tiempo!!!!") #print(result_tiempo) result: OrderedDict[str, str] = result_tiempo['_id'] #print("result_hora: ",result_tiempo) #print("tiempo_medicion: ",tiempo_medicion) hora = result[tiempo_medicion] #print("hora: ",hora) #if hora in total_nro_personas = int(result_tiempo['nro_personas']) #print("total_nro_personas: ",total_nro_personas) nombre_zona_camara = result['nombre_zona_camara'] #print('nombre_zona_camara: ',nombre_zona_camara) nombre_camara = result['nombre_camara'] #print('nombre_camara: ',nombre_camara) zonaxcamara = str(nombre_camara)+"-"+str(nombre_zona_camara) #print("zonaxcamara: ",zonaxcamara) #array_total_aforo[hora] = total_nro_personas #dict_zonas_camara[nombre_zona_camara] = nombre_camara dict_zonas_camara[zonaxcamara][hora] = total_nro_personas #nro_usuarios_wifi = int(result_tiempo['nro_usuarios_wifi']) #print("nro_usuarios_ethernet: ",nro_usuarios_ethernet) except KeyError: pass #print("dict_zonas_camara no encuentra el indice") #print("ARREGLO DE TOTAL NÚMEROS DE PERSONAS QUE VA PARA ESTADISTICA") #print("??????? dict_zonas_camara ?????????") #print(dict_zonas_camara) return dict_zonas_camara,hora_apertura,hora_cierre def getQueryAforoZona(start_date,end_date,tiempo_medicion,tiempo_medicion_parametro,array_tiempo,array_zona_camara): query =[ { "$match": { "nombre_zona_camara": { "$in": array_zona_camara }, "fecha": { "$gte": start_date, "$lte": end_date } } }, { "$project": { "date": { "$dateToString": { "format": "%Y-%m-%d", "date": "$fecha" } }, tiempo_medicion: { tiempo_medicion_parametro: "$fecha" }, "nro_personas": "$nro_personas", "nombre_zona_camara": "$nombre_zona_camara", "nombre_camara": "$nombre_camara", } }, { "$match":{ tiempo_medicion:{"$in":array_tiempo} } }, { "$group": { "_id": { tiempo_medicion: tiempo_medicion_parametro, "date": "$date", "nombre_zona_camara" :"$nombre_zona_camara", "nombre_camara": "$nombre_camara", }, "nro_personas": { "$max": "$nro_personas" } } } ] return query def getZonasXCamaras(): zonas_camaras = [] camaras_zonas_camaras = [] camarasAll = Camaras.objects.all() for camaras in camarasAll: for zonas_camara in camaras.zonas_camara: if (zonas_camara.zona_fisica == 'True'): zonas_camaras.append(zonas_camara.nombre_zona_camara) camaras_zonas_camaras.append(camaras.nombre_camara+"-"+zonas_camara.nombre_zona_camara) return zonas_camaras,camaras_zonas_camaras def last_day_of_month(date_value): return monthrange(date_value.year, date_value.month)[1] def last_date_of_month(date_value): return date_value.replace(day = last_day_of_month(date_value))

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py

Python

python-web/ModelFoms/books_app/books_app/book/admin.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

python-web/ModelFoms/books_app/books_app/book/admin.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

python-web/ModelFoms/books_app/books_app/book/admin.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

from django.contrib import admin from books_app.book.models import Book @admin.register(Book) class BookAdmin(admin.ModelAdmin): pass

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26

py

Python

vnpy/api/xtp/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

19,529

2015-03-02T12:17:35.000Z

2022-03-31T17:18:27.000Z

vnpy/api/xtp/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

2,186

2015-03-04T23:16:33.000Z

2022-03-31T03:44:01.000Z

vnpy/api/xtp/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

8,276

2015-03-02T05:21:04.000Z

2022-03-31T13:13:13.000Z

from vnpy_xtp.api import *

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525c970f6acae3bee957b43cb56fa17e7ecf2df3

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py

Python

hume/storage/local/__init__.py

open-home-iot/hume

03755d86363dfce4e7521223795b90754f21a6d7

[ "MIT" ]

2

2021-12-27T15:14:42.000Z

2021-12-27T15:14:48.000Z

hume/storage/local/__init__.py

open-home-iot/hume

03755d86363dfce4e7521223795b90754f21a6d7

[ "MIT" ]

4

2019-08-03T08:58:15.000Z

2021-06-09T15:49:49.000Z

hume/storage/local/__init__.py

megacorpincorporated/hume

40093cc7e5e79dbac8386e2e5f7f7a41c7e516e8

[ "MIT" ]

null

from .local_storage import LocalStorage # noqa

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bfe2b8cddb4896e8b25c7144daf7480541e64013

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py

Python

apps/products/permissions/__init__.py

Haizza1/RandomCameras-Backend

b679e2c685a9f9582f5f1a6b4c79c91c2328e326

[ "MIT" ]

1

2021-06-09T01:35:59.000Z

apps/products/permissions/__init__.py

Haizza1/RandomCameras-Backend

b679e2c685a9f9582f5f1a6b4c79c91c2328e326

[ "MIT" ]

null

apps/products/permissions/__init__.py

Haizza1/RandomCameras-Backend

b679e2c685a9f9582f5f1a6b4c79c91c2328e326

[ "MIT" ]

null

from .products import IsStaff

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bfeaa0ad516ab21223ee4b4f12cff883f33a4049

686

py

Python

api/permissions/roleplay.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

8

2019-08-27T20:08:22.000Z

2021-07-23T22:49:47.000Z

api/permissions/roleplay.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

73

2020-03-11T18:07:29.000Z

2022-03-28T18:07:47.000Z

api/permissions/roleplay.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

4

2020-02-22T19:44:17.000Z

2022-03-08T09:42:45.000Z

from rest_framework import permissions class IsInPlayersOrStaff(permissions.BasePermission): def has_object_permission(self, request, view, obj) -> bool: """ Checks if user is staff or user in players. """ user = request.user if user.is_staff: return True return user in obj.players.all() class IsInGameMastersOrStaff(permissions.BasePermission): def has_object_permission(self, request, view, obj) -> bool: """ Checks if user is staff or user in game masters. """ user = request.user if user.is_staff: return True return user in obj.game_masters

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bff6edba013e415e8309849b12390227ff7b72da

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py

Python

biosimulations_dispatch/sbatch/__init__.py

gmarupilla/Biosimulations_Dispatch

a6ac4061725214a9a0ab5ad7c033d00de13dcb5d

[ "MIT" ]

null

biosimulations_dispatch/sbatch/__init__.py

gmarupilla/Biosimulations_Dispatch

a6ac4061725214a9a0ab5ad7c033d00de13dcb5d

[ "MIT" ]

null

biosimulations_dispatch/sbatch/__init__.py

gmarupilla/Biosimulations_Dispatch

a6ac4061725214a9a0ab5ad7c033d00de13dcb5d

[ "MIT" ]

null

# TODO: Add Sbatch utils in this package

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872414a91700f084a49927d4f22922593fe8a639

49

py

Python

python/extend_to_c/fastprime_test.py

zeroam/TIL

43e3573be44c7f7aa4600ff8a34e99a65cbdc5d1

[ "MIT" ]

null

python/extend_to_c/fastprime_test.py

zeroam/TIL

43e3573be44c7f7aa4600ff8a34e99a65cbdc5d1

[ "MIT" ]

null

python/extend_to_c/fastprime_test.py

zeroam/TIL

43e3573be44c7f7aa4600ff8a34e99a65cbdc5d1

[ "MIT" ]

null

import fastprime print(fastprime.kthPrime(10000))

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8739983584fb70543c979540490e065ea951e157

38

py

Python

easy_spotify/__init__.py

OktarianTB/easy_spotify

76fbc3cf668c264354d732b8773291eef2bb0f45

[ "MIT" ]

null

easy_spotify/__init__.py

OktarianTB/easy_spotify

76fbc3cf668c264354d732b8773291eef2bb0f45

[ "MIT" ]

null

easy_spotify/__init__.py

OktarianTB/easy_spotify

76fbc3cf668c264354d732b8773291eef2bb0f45

[ "MIT" ]

null

from easy_spotify.main import Spotify

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6

5e8148677dde2553034fc1694b9c1b6cad5e2b0a

125

py

Python

vkbottle/framework/framework/handler/__init__.py

LouisPython217/vkbottle

3541bbdb66f32c2d3567b0047c36b706ac72bb3b

[ "MIT" ]

null

vkbottle/framework/framework/handler/__init__.py

LouisPython217/vkbottle

3541bbdb66f32c2d3567b0047c36b706ac72bb3b

[ "MIT" ]

null

vkbottle/framework/framework/handler/__init__.py

LouisPython217/vkbottle

3541bbdb66f32c2d3567b0047c36b706ac72bb3b

[ "MIT" ]

null

from . import handler from . import user from .handler import Handler from .middleware import Middleware, MiddlewareExecutor

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1

0

1

0

6

5e99b53c0d452a585225cbcea988bf7143dcd4e1

47

py

Python

prototypes/tiles.py

ericl16384/botsBuildBots

8aead6131273488a0b6e8096e931b5a645411e97

[ "MIT" ]

null

prototypes/tiles.py

ericl16384/botsBuildBots

8aead6131273488a0b6e8096e931b5a645411e97

[ "MIT" ]

null

prototypes/tiles.py

ericl16384/botsBuildBots

8aead6131273488a0b6e8096e931b5a645411e97

[ "MIT" ]

1

2021-02-07T03:01:13.000Z

# Imports import classes # Prototypes pass

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py

Python

CangJie/SE/__init__.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

2

2020-03-04T02:27:29.000Z

2020-05-22T04:07:24.000Z

CangJie/SE/__init__.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

null

CangJie/SE/__init__.py

bigdata-ustc/CangJie

a3264082fa0432d257b5c4722b14c55f9092a411

[ "MIT" ]

1

2022-03-12T00:31:59.000Z

# coding: utf-8 # 2020/1/8 @ tongshiwei

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py

Python

tests/test_config_attributes.py

gaby/wireguard

7270aa50f990b3c03a453622cbf05e39e4e08e60

[ "MIT" ]

11

2021-06-29T00:18:59.000Z

2022-03-24T22:19:51.000Z

tests/test_config_attributes.py

gaby/wireguard

7270aa50f990b3c03a453622cbf05e39e4e08e60

[ "MIT" ]

2

2021-11-28T18:17:30.000Z

2022-01-03T01:44:47.000Z

tests/test_config_attributes.py

gaby/wireguard

7270aa50f990b3c03a453622cbf05e39e4e08e60

[ "MIT" ]

7

2021-09-13T01:54:19.000Z

2022-02-14T12:48:55.000Z

import pytest from unittest.mock import ( call, mock_open, patch, ) from subnet import ip_network, IPv4Network, IPv4Address from wireguard import ( Config, ServerConfig, Peer, Server, ) from wireguard.utils import IPAddressSet def test_description(): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, ) config = Config(peer) wg_config = config.local_config assert config.description == '# test-peer' # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.description = None assert config.description is None # Don't like the `dummy` value here, but pytest gets confused about the test arguments otherwise @pytest.mark.parametrize( ('dns', 'dummy',), [ (['2.2.2.2', '3.3.3.3', '1.1.1.1',], None,), (('3.3.3.3', '1.1.1.1', '2.2.2.2',), None,), ({'1.1.1.1', '2.2.2.2', '3.3.3.3',}, None,), ]) def test_multiple_dns(dns, dummy): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, dns=dns, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') # Because the set of DNS entries could return in any order, check that at least one is present assert ( 'DNS = 1.1.1.1,2.2.2.2,3.3.3.3' in config_lines or 'DNS = 1.1.1.1,3.3.3.3,2.2.2.2' in config_lines or 'DNS = 2.2.2.2,1.1.1.1,3.3.3.3' in config_lines or 'DNS = 2.2.2.2,3.3.3.3,1.1.1.1' in config_lines or 'DNS = 3.3.3.3,1.1.1.1,2.2.2.2' in config_lines or 'DNS = 3.3.3.3,2.2.2.2,1.1.1.1' in config_lines ) # Check that these don't appear anywhere at all because of how basic this config is for option in ['PreUp', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.dns = None assert config.dns is None def test_dns(): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, dns='8.8.8.8', ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert 'DNS = 8.8.8.8' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['PreUp', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.dns = None assert config.dns is None def test_pre_up(): address = '192.168.0.2' command = 'some-iptables-command' peer = Peer( 'test-peer', address=address, pre_up=command, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert f'PreUp = {command}' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.pre_up = None assert config.pre_up is None def test_pre_down(): address = '192.168.0.2' command = 'some-iptables-command' peer = Peer( 'test-peer', address=address, pre_down=command, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert f'PreDown = {command}' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.pre_down = None assert config.pre_down is None def test_post_up(): address = '192.168.0.2' command = 'some-iptables-command' peer = Peer( 'test-peer', address=address, post_up=command, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert f'PostUp = {command}' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.post_up = None assert config.post_up is None def test_post_down(): address = '192.168.0.2' command = 'some-iptables-command' peer = Peer( 'test-peer', address=address, post_down=command, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert f'PostDown = {command}' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PreDown', 'SaveConfig', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.post_down = None assert config.post_down is None @pytest.mark.parametrize( ('save_config', 'expected_output',), [ (True, 'SaveConfig = true',), (False, 'SaveConfig = false',), ]) def test_save_config(save_config, expected_output): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, save_config=save_config, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert expected_output in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PreDown', 'PostDown', 'MTU', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.save_config = None assert config.save_config is None def test_mtu(): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, mtu=1280, ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert 'MTU = 1280' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'Table', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.mtu = None assert config.mtu is None def test_table(): address = '192.168.0.2' peer = Peer( 'test-peer', address=address, table='off', ) config = Config(peer) wg_config = config.local_config config_lines = wg_config.split('\n') assert 'Table = off' in config_lines # Check that these don't appear anywhere at all because of how basic this config is for option in ['DNS', 'PreUp', 'PostUp', 'PreDown', 'PostDown', 'SaveConfig', 'MTU', 'AllowedIPs', 'Endpoint', 'PersistentKeepalive', 'PresharedKey', 'PublicKey']: assert f'{option} =' not in wg_config peer.table = None assert config.table is None

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0da8aad757e74d70971c56ba67a461ba8af30bb1

99

py

Python

nesmdb/__init__.py

NotOkay3272/nesmdb3

42445848be59ee4501b69800fb6803271e4bb224

[ "MIT" ]

null

nesmdb/__init__.py

NotOkay3272/nesmdb3

42445848be59ee4501b69800fb6803271e4bb224

[ "MIT" ]

null

nesmdb/__init__.py

NotOkay3272/nesmdb3

42445848be59ee4501b69800fb6803271e4bb224

[ "MIT" ]

null

from __future__ import absolute_import from . import apu from . import convert from . import cycle

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1

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1

0

6

0db0fbd84bad90ce99da18d32337d4c28d599c85

12,792

py

Python

supriya/ugens/delay.py

butayama/supriya

0c197324ecee4232381221880d1f40e109bb756c

[ "MIT" ]

191

2015-11-13T02:28:42.000Z

2022-03-29T10:26:44.000Z

supriya/ugens/delay.py

butayama/supriya

0c197324ecee4232381221880d1f40e109bb756c

[ "MIT" ]

130

2016-01-04T16:59:02.000Z

2022-02-26T15:37:20.000Z

supriya/ugens/delay.py

butayama/supriya

0c197324ecee4232381221880d1f40e109bb756c

[ "MIT" ]

22

2016-05-04T10:32:16.000Z

2022-02-26T19:22:45.000Z

import collections from supriya import CalculationRate from supriya.synthdefs import PureUGen, UGen class AllpassC(PureUGen): """ A cubic-interpolating allpass delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> allpass_c = supriya.ugens.AllpassC.ar(source=source) >>> allpass_c AllpassC.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class AllpassL(PureUGen): """ A linear interpolating allpass delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> allpass_l = supriya.ugens.AllpassL.ar(source=source) >>> allpass_l AllpassL.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class AllpassN(PureUGen): """ A non-interpolating allpass delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> allpass_n = supriya.ugens.AllpassN.ar(source=source) >>> allpass_n AllpassN.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufAllpassC(PureUGen): """ A buffer-based cubic-interpolating allpass delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufAllpassC.ar( ... buffer_id=buffer_id, source=source, ... ) BufAllpassC.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufAllpassL(PureUGen): """ A buffer-based linear-interpolating allpass delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufAllpassL.ar( ... buffer_id=buffer_id, source=source, ... ) BufAllpassL.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufAllpassN(PureUGen): """ A buffer-based non-interpolating allpass delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufAllpassN.ar( ... buffer_id=buffer_id, source=source, ... ) BufAllpassN.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufCombC(PureUGen): """ A buffer-based cubic-interpolating comb delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufCombC.ar( ... buffer_id=buffer_id, source=source, ... ) BufCombC.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.CONTROL, CalculationRate.AUDIO) class BufCombL(PureUGen): """ A buffer-based linear-interpolating comb delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufCombL.ar( ... buffer_id=buffer_id, source=source, ... ) BufCombL.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.CONTROL, CalculationRate.AUDIO) class BufCombN(PureUGen): """ A buffer-based non-interpolating comb delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufCombN.ar( ... buffer_id=buffer_id, source=source, ... ) BufCombN.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.CONTROL, CalculationRate.AUDIO) class BufDelayC(PureUGen): """ A buffer-based cubic-interpolating delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufDelayC.ar( ... buffer_id=buffer_id, source=source, ... ) BufDelayC.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufDelayL(PureUGen): """ A buffer-based linear-interpolating delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufDelayL.ar( ... buffer_id=buffer_id, source=source, ... ) BufDelayL.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class BufDelayN(PureUGen): """ A buffer-based non-interpolating delay line unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.BufDelayN.ar( ... buffer_id=buffer_id, source=source, ... ) BufDelayN.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class CombC(PureUGen): """ A cubic-interpolating comb delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.CombC.ar(source=source) CombC.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class CombL(PureUGen): """ A linear interpolating comb delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.CombL.ar(source=source) CombL.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class CombN(PureUGen): """ A non-interpolating comb delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.CombN.ar(source=source) CombN.ar() """ _ordered_input_names = collections.OrderedDict( [ ("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2), ("decay_time", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class DelTapRd(UGen): """ A delay tap reader unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.SoundIn.ar(0) >>> tapin = supriya.ugens.DelTapWr.ar(buffer_id=buffer_id, source=source,) :: >>> tapin DelTapWr.ar() :: >>> tapout = supriya.ugens.DelTapRd.ar( ... buffer_id=buffer_id, phase=tapin, delay_time=0.1, interpolation=True, ... ) :: >>> tapout DelTapRd.ar() """ _ordered_input_names = collections.OrderedDict( [ ("buffer_id", None), ("phase", None), ("delay_time", 0.0), ("interpolation", 1.0), ] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class DelTapWr(UGen): """ A delay tap writer unit generator. :: >>> buffer_id = 0 >>> source = supriya.ugens.SoundIn.ar(0) >>> tapin = supriya.ugens.DelTapWr.ar(buffer_id=buffer_id, source=source,) :: >>> tapin DelTapWr.ar() :: >>> tapout = supriya.ugens.DelTapRd.ar( ... buffer_id=buffer_id, phase=tapin, delay_time=0.1, interpolation=True, ... ) :: >>> tapout DelTapRd.ar() """ _ordered_input_names = collections.OrderedDict( [("buffer_id", None), ("source", None)] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class DelayC(PureUGen): """ A cubic-interpolating delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.DelayC.ar(source=source) DelayC.ar() """ _ordered_input_names = collections.OrderedDict( [("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2)] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class DelayL(PureUGen): """ A linear-interpolating delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.DelayL.ar(source=source) DelayL.ar() """ _ordered_input_names = collections.OrderedDict( [("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2)] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class DelayN(PureUGen): """ A non-interpolating delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.DelayN.ar(source=source) DelayN.ar() """ _ordered_input_names = collections.OrderedDict( [("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2)] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class Delay1(PureUGen): """ A one-sample delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.Delay1.ar(source=source) Delay1.ar() """ _ordered_input_names = collections.OrderedDict([("source", None)]) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL) class Delay2(PureUGen): """ A two-sample delay line unit generator. :: >>> source = supriya.ugens.In.ar(bus=0) >>> supriya.ugens.Delay2.ar(source=source) Delay2.ar() """ _ordered_input_names = collections.OrderedDict( [("source", None), ("maximum_delay_time", 0.2), ("delay_time", 0.2)] ) _valid_calculation_rates = (CalculationRate.AUDIO, CalculationRate.CONTROL)

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0.778307

0

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false

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0

null

0

1

0

null

0

6

0dd543a187e78033060f7117d4c8653863cf15f7

18,971

py

Python

tronx/modules/admin.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

4

2022-03-07T07:27:04.000Z

2022-03-29T05:59:57.000Z

tronx/modules/admin.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

null

tronx/modules/admin.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

3

2022-03-05T15:24:51.000Z

2022-03-14T08:48:05.000Z

import time import asyncio import html from datetime import datetime, timedelta from pyrogram.types import Message, ChatPermissions from pyrogram.errors import ( UserAdminInvalid, UsernameInvalid, UserNotParticipant, UsernameNotOccupied, ) from tronx import app, gen app.CMD_HELP.update( {"admin" : ( "admin", { "ban [username | id | reply] [time]" : "bans a user, use it as timeban too", "banall [confirm]" : "Ban all members in by one command", "unban" : "unbans a user", "mute [username | id | reply] [time]" : "restricts a user from talking in groups", "unmute" : "unrestricts a user from talking in groups", "promote" : "promote a member to admin", "demote" : "demote a admin to a member", "pin" : "pin a message in group", "kick" : "kick a user out of your groups.", "unpin" : "unpin a pinned message.", "unpin all" : "unpin all pinned messages in one command" } ) } ) private = ("private", "bot") def to_seconds(format, number): # number: int, format: s, m, h, d format_set = {"s": number, "m": number*60, "h": number*60*60, "d": number*60*60*24} return int(format_set[format]) @app.on_message(gen("ban", allow = ["sudo", "channel"])) async def ban_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False cmd = m.command ban_time = False if app.long() == 1 and not reply: return await app.send_edit("Reply or give some id | username after command.", text_type=["mono"], delme=4) if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not an admin here or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) if app.long() > 1: arg = cmd[1] ban_time = to_seconds(arg[-1], int(arg.replace(arg[-1], ""))) elif not reply: if app.long() > 1: user = await app.get_chat_member(m.chat.id, cmd[1]) if app.long() > 2: arg = cmd[2] ban_time = to_seconds(arg[-1], int(arg.replace(arg[-1], ""))) if user: if user.user.is_self: return await app.send_edit("You can't ban yourself !", text_type=["mono"], delme=4) elif user.status == "administrator": return await app.send_edit("How am i supposed to ban an admin ?", text_type=["mono"], delme=4) elif user.status == "creator": return await app.send_edit("How am i supposed to ban a creator of a group ?", text_type=["mono"], delme=4) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) await app.send_edit("⏳ • Hold on . . .", text_type=["mono"]) if ban_time: await app.ban_chat_member(m.chat.id, user.user.id, datetime.now() + timedelta(ban_time)) await app.send_edit(f"Banned {user.user.mention} for {arg}", delme=4) else: await app.ban_chat_member(m.chat.id, user.user.id) await app.send_edit(f"Banned {user.user.mention} in this chat.", delme=4) except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("banall", allow = ["channel"])) async def banall_handler(_, m: Message): try: if await app.check_private(): return if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not an admin or you don't have enough admin rights.", text_type=["mono"], delme=4) count = 0 data = [] data.clear() if app.long() == 1: return await app.send_edit("Use '`confirm`' text after command to ban all members.", text_type=["mono"], delme=4) elif app.long() > 1 and m.command[1] == "confirm": async for x in app.iter_chat_members(m.chat.id): if x.status == "member": await app.ban_chat_member(m.chat.id, x.user.id) count += 1 await app.send_edit(f"Banned {x.user.mention} . . .") await app.send_edit(f"Banned {count} members !") elif app.long() > 1 and m.command[1] != "confirm": await app.send_edit("Use '`confirm`' text after command to ban all members.", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("unban", allow = ["sudo", "channel"])) async def unban_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False if not reply and app.long() == 1: return await app.send_edit("Reply to a user or give me the username | id of that user.", text_type=["mono"], delme=4) if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not an admin or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) elif not reply: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) if user: if user.user.is_self: return await app.send_edit("You can't unban yourself !", text_type=["mono"], delme=4) elif user.status == "administrator": return await app.send_edit("How am i supposed to unban an admin ?", text_type=["mono"], delme=4) elif user.status == "creator": return await app.send_edit("How am i supposed to unban a creator of a group ?", text_type=["mono"], delme=4) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) await app.send_edit("Unbanning . . .", text_type=["mono"]) done = await app.unban_chat_member(m.chat.id, user.user.id) if done: await app.send_edit(f"Unbanned {user.user.mention} in this chat.", delme=4) else: await app.send_edit("Failed to unabn this user.", text_type=["mono"], delme=4) except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) async def mute_user(chat_id, user_id, duration=datetime.now()): return await app.restrict_chat_member( chat_id=chat_id, user_id=user_id, permissions=ChatPermissions( can_send_messages=False, can_send_media_messages=False, can_send_other_messages=False, can_add_web_page_previews=False, can_send_polls=False, can_change_info=False, can_invite_users=True, can_pin_messages=False ), until_date=duration ) @app.on_message(gen("mute", allow = ["sudo"])) async def mute_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False mute_time = False cmd = m.command if not reply and app.long() == 1: return await app.send_edit("Reply to a user or give me username | id of that user.", text_type=["mono"], delme=4) if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not an admin or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) if app.long() > 1: arg = cmd[1] mute_time = to_seconds(arg[-1], int(arg.replace(arg[-1], ""))) elif not reply: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) if app.long() > 2: arg = cmd[2] mute_time = to_seconds(arg[-1], int(arg.replace(arg[-1], ""))) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) if user: if user.user.is_self: return await app.send_edit("You can't mute yourself !", text_type=["mono"], delme=4) elif user.status == "administrator": return await app.send_edit("How am i supposed to mute an admin ?", text_type=["mono"], delme=4) elif user.status == "creator": return await app.send_edit("How am i supposed to mute a creator of a group ?", text_type=["mono"], delme=4) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) if mute_time: await mute_user(m.chat.id, user.user.id, datetime.now() + timedelta(mute_time)) await app.send_edit(f"Muted {user.user.mention} for {arg}") else: await mute_user(m.chat.id, user.user.id) await app.send_edit(f"Muted {user.user.mention} in this chat for forever.", delme=4) except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("unmute", allow = ["sudo"])) async def unmute_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False if not reply and app.long() == 1: return await app.send_edit("Reply to a user or give me the username | id of that user.", text_type=["mono"], delme=4) if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not an admin or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) elif not reply: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) if user: if user.user.is_self: return await app.send_edit("You can't unmute yourself !", text_type=["mono"], delme=4) elif user.status == "administrator": return await app.send_edit("How do i unmute an admin ?", text_type=["mono"], delme=4) elif user.status == "creator": return await app.send_edit("How do i unmute a creator ?", text_type=["mono"], delme=4) else: return await app.send_edit("Something went wrong !", text_type=["mono"], delme=4) await app.restrict_chat_member( m.chat.id, user.user.id, permissions=ChatPermissions( can_send_messages=True, can_send_media_messages=True, can_send_other_messages=True, can_add_web_page_previews=True, can_send_polls=True, can_change_info=False, can_invite_users=True, can_pin_messages=False ) ) await app.send_edit(f"Unmuted {user.user.mention} in this chat.", delme=4) except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("kick", allow = ["sudo", "channel"])) async def kick_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False if not reply and app.long() == 1: return await app.send_edit("Reply to a user or give me username | id of that user.", text_type=["mono"], delme=4) if await app.IsAdmin("can_restrict_members") is False: return await app.send_edit("You're not admin or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) else: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) if user: if user.user.is_self: return await app.send_edit("You can't kick yourself !", text_type=["mono"]) elif user.status == "administrator": return await app.send_edit("How am i supposed to kick an admin ?", text_type=["mono"]) elif user.status == "creator": return await app.send_edit("How am i supposed to kick a creator of a group ?", text_type=["mono"]) else: return await app.send_edit("Something went wrong.", text_type=["mono"], delme=4) await app.send_edit("Kicking . . .", text_type=["mono"]) done = await app.kick_user(m.chat.id, user.user.id) if done: await app.send_edit(f"Kicked {user.user.mention} in this chat.", delme=4) else: await app.send_edit("Failed to kick to user.", text_type=["mono"], delme=4) except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("pin", allow = ["sudo", "channel"])) async def pin_handler(_, m: Message): try: arg = True cmd = m.command reply = m.reply_to_message if app.long() > 1: arg = False if cmd[1] == "loud" else True if m.chat.type in private: if not reply: return await app.send_edit("Reply to some message, so that i can pin that message.", text_type=["mono"], delme=4) done = await reply.pin(disable_notification=arg) if done: return await app.send_edit("Pinned message !", text_type=["mono"], delme=4) else: return await app.send_edit("Failed to pin message.", text_type=["mono"], delme=4) if await app.IsAdmin("can_pin_messages") is False: return await app.send_edit("You're not an admin here or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: await app.send_edit("⏳ • Hold on . . .", text_type=["mono"]) done = await reply.pin(disable_notification=arg) if done: await app.send_edit("Pinned message.", text_type=["mono"], delme=4) else: await app.send_edit("Failed to pin message.", text_type=["mono"], delme=4) else: await app.send_edit("Reply to a message so that I can pin that message.", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("unpin", allow = ["sudo", "channel"])) async def unpin_handler(_, m: Message): try: cmd = m.command reply = m.reply_to_message if not reply and app.long() == 1: return await app.send_edit("Reply to a message or use `all` as a prefix to unpin all pinned message.", text_type=["mono"], delme=4) if reply: m = await app.send_edit("⏳ • Hold on . . .", text_type=["mono"]) done = await reply.unpin() if done: await app.send_edit("Unpinned message.", text_type=["mono"]) else: await app.send_edit("Failed to unpin message.", text_type=["mono"], delme=4) elif not reply and app.long() > 1: if cmd[1] == "all": done = await app.unpin_all_chat_messages(m.chat.id) if done: await app.send_edit("Unpinned all pinned messages . . .", text_type=["mono"]) else: await app.send_edit("Failed to unpin all messages.", text_type=["mono"], delme=4) elif cmd[1] != "all": await app.send_edit("Reply to a pinned message to unpin or use `all` as a suffix to unpin all pinned messages.", text_type=["mono"], delme=4) else: await app.send_edit("Failed to unpin all messages.", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("promote", allow = ["sudo", "channel"])) async def promote_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False if app.long() == 1 and not reply: return await app.send_edit("Reply to user or give me username | id of that user.", text_type=["mono"], delme=4) if await app.IsAdmin("can_promote_members") is False: return await app.send_edit("You're not admin or you don't have enough admin rights.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) else: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) if user: if user.user.is_self: return await app.send_edit("You can't promote yourself !", text_type=["mono"]) elif user.status == "administrator": return await app.send_edit("How am i supposed to promote already promoted user ?", text_type=["mono"]) elif user.status == "creator": return await app.send_edit("How am i supposed to promote a creator of a group ? wth ?", text_type=["mono"]) else: return await app.send_edit("Something went wrong !", text_type=["mono"]) await app.promote_chat_member( m.chat.id, user.user.id, can_change_info=True, can_manage_voice_chats=True, can_manage_chat=True, can_delete_messages=True, can_edit_messages=True, can_invite_users=True, can_promote_members=False, can_restrict_members=True, can_pin_messages=True, can_post_messages=True, ) app.send_edit("Promoting . . .", text_type=["mono"]) await app.send_edit(f"Promoted {user.user.mention} in this chat !") except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen("demote", allow = ["sudo", "channel"])) async def demote_handler(_, m: Message): try: if await app.check_private(): return reply = m.reply_to_message user = False if await app.IsAdmin("can_promote_members") is False: return await app.send_edit("You're not an admin here or you don't have enough rights.", text_type=["mono"], delme=4) if app.long() == 1 and not reply: return await app.send_edit("Reply to user or give me username | id of that user.", text_type=["mono"], delme=4) if reply: user = await app.get_chat_member(m.chat.id, reply.from_user.id) else: if app.long() > 1: user = await app.get_chat_member(m.chat.id, m.command[1]) if user: if user.user.is_self: return await app.send_edit("You can't demote yourself !", text_type=["mono"]) elif user.status == "creator": return await app.send_edit("How am i supposed to demote a creator of a group ?", text_type=["mono"]) else: return await app.send_edit("Something went wrong !", text_type=["mono"]) await app.promote_chat_member( m.chat.id, user.user.id, can_change_info=False, can_manage_voice_chats=False, can_manage_chat=False, can_delete_messages=False, can_edit_messages=False, can_invite_users=False, can_promote_members=False, can_restrict_members=False, can_pin_messages=False, can_post_messages=False, ) await app.send_edit("Demoting . . .", text_type=["mono"]) await app.send_edit(f"Demoted {user.user.mention} in this chat !") except (UsernameInvalid, UsernameNotOccupied): await app.send_edit("The provided username | id is invalid !", text_type=["mono"], delme=4) except UserNotParticipant: await app.send_edit("This user doesn't exist in this group !", text_type=["mono"], delme=4) except Exception as e: await app.error(e)

33.876786

145

0.685994

3,059

18,971

4.116051

0.067996

0.092129

0.082996

0.11945

0.841156

0.812962

0.78985

0.7638

0.749107

0.714081

0

0.008472

0.172474

18,971

559

146

33.937388

0.793172

0.001634

0

0.594533

0

0.002278

0.255848

0

1

0.002278

false

0

0.015945

0

0.157175

0

null

0

1

0

null

0

6

2176ce637b2bd17a5c8839cc010a502783fd8075

55

py

Python

Waveforms/results/hSymmetric_2_0.py

keefemitman/PostNewtonian

853d6577cb0002da5eebe1cb55f0c28fbc114324

[ "MIT" ]

18

2015-03-26T01:04:36.000Z

2022-02-01T19:26:21.000Z

Waveforms/results/hSymmetric_2_0.py

keefemitman/PostNewtonian

853d6577cb0002da5eebe1cb55f0c28fbc114324

[ "MIT" ]

4

2015-01-08T23:46:29.000Z

2017-09-20T19:13:51.000Z

Waveforms/results/hSymmetric_2_0.py

keefemitman/PostNewtonian

853d6577cb0002da5eebe1cb55f0c28fbc114324

[ "MIT" ]

3

2016-05-13T02:36:14.000Z

2021-11-23T21:36:32.000Z

sqrt(30)*sqrt(pi)*nu*(-3*r(0)*v(0)**2 + 1)*v(0)**12/375

55

0.509091

16

55

1.75

0.75

0.142857

0

0.245283

0.036364

55

1

55

0.283019

0

1

0

true

0

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

6

21eabc188d5d5da2a67651d5a8c2d252f25fd32f

8,953

py

Python

tests/library/mpi/redistribute_test.py

meshtag/dace

e6751ee6a4f6356b47b93065d43cefb3fd54ebaa

[ "BSD-3-Clause" ]

1

2022-03-11T13:36:34.000Z

tests/library/mpi/redistribute_test.py

meshtag/dace

e6751ee6a4f6356b47b93065d43cefb3fd54ebaa

[ "BSD-3-Clause" ]

null

tests/library/mpi/redistribute_test.py

meshtag/dace

e6751ee6a4f6356b47b93065d43cefb3fd54ebaa

[ "BSD-3-Clause" ]

null

# Copyright 2019-2022 ETH Zurich and the DaCe authors. All rights reserved. import dace from dace.sdfg import utils import numpy as np import pytest @pytest.mark.mpi def test_redistribute_matrix_2d_2d(): """ _______________________ _______________________ | | | | | | | | | | | | | |___________|___________| | | | | | | | | |_____|_____|_____|_____| -> |___________|___________| | | | | | | | | | | | | | |___________|___________| | | | | | | | | |_____|_____|_____|_____| |___________|___________| """ P = dace.symbol('P', dace.int32) @dace.program def matrix_2d_2d(A: dace.int32[4 * P, 16]): a_grid = dace.comm.Cart_create([2, P // 2]) b_grid = dace.comm.Cart_create([P // 2, 2]) B = np.empty_like(A, shape=(16, 4 * P)) a_arr = dace.comm.Subarray((8 * P, 8 * P), A, process_grid=a_grid) b_arr = dace.comm.Subarray((8 * P, 8 * P), B, process_grid=b_grid) rdistr = dace.comm.Redistribute(A, a_arr, B, b_arr) return B from mpi4py import MPI commworld = MPI.COMM_WORLD rank = commworld.Get_rank() size = commworld.Get_size() even_size = (size // 2) * 2 if size < 2: raise ValueError("Please run this test with at least two processes.") sdfg = None if rank == 0: sdfg = matrix_2d_2d.to_sdfg() func = utils.distributed_compile(sdfg, commworld) A = np.arange(64 * even_size * even_size, dtype=np.int32).reshape(8 * even_size, 8 * even_size) lA = A.reshape(2, 4 * even_size, even_size // 2, 16).transpose(0, 2, 1, 3) lB = A.reshape(even_size // 2, 16, 2, 4 * even_size).transpose(0, 2, 1, 3) if rank < even_size: B = func(A=lA[rank // (even_size // 2), rank % (even_size // 2)].copy(), P=even_size) else: B = func(A=np.zeros((1, ), dtype=np.int32), P=even_size) if rank < even_size: assert (np.array_equal(B, lB[rank // 2, rank % 2])) @pytest.mark.mpi def test_redistribute_matrix_2d_2d_2(): """ _______________________ _______________________ | | | | | |_______________________| | | | | | |_______________________| | | | | | |_______________________| |_____|_____|_____|_____| -> |_______________________| | | | | | |_______________________| | | | | | |_______________________| | | | | | |_______________________| |_____|_____|_____|_____| |_______________________| """ P = dace.symbol('P', dace.int32) @dace.program def matrix_2d_2d_2(A: dace.int32[4 * P, 16]): a_grid = dace.comm.Cart_create([2, P // 2]) b_grid = dace.comm.Cart_create([P, 1]) B = np.empty_like(A, shape=(8, 8 * P)) a_arr = dace.comm.Subarray((8 * P, 8 * P), A, process_grid=a_grid) b_arr = dace.comm.Subarray((8 * P, 8 * P), B, process_grid=b_grid) rdistr = dace.comm.Redistribute(A, a_arr, B, b_arr) return B from mpi4py import MPI commworld = MPI.COMM_WORLD rank = commworld.Get_rank() size = commworld.Get_size() even_size = (size // 2) * 2 if size < 2: raise ValueError("Please run this test with at least two processes.") sdfg = None if rank == 0: sdfg = matrix_2d_2d_2.to_sdfg() func = utils.distributed_compile(sdfg, commworld) A = np.arange(64 * even_size * even_size, dtype=np.int32).reshape(8 * even_size, 8 * even_size) lA = A.reshape(2, 4 * even_size, even_size // 2, 16).transpose(0, 2, 1, 3) lB = A.reshape(even_size, 8, 1, 8 * even_size).transpose(0, 2, 1, 3) if rank < even_size: B = func(A=lA[rank // (even_size // 2), rank % (even_size // 2)].copy(), P=even_size) else: B = func(A=np.zeros((1, ), dtype=np.int32), P=even_size) if rank < even_size: assert (np.array_equal(B, lB[rank, 0])) @pytest.mark.mpi def test_redistribute_matrix_2d_2d_3(): """ The numbers are example tile IDs, NOT MPI ranks. _______________________ ___________ |0 |1 |2 |3 | |0 |4 | | | | | | | | | | | | | | | | | |_____|_____|_____|_____| -> |_____|_____| |4 |5 |6 |7 | |1 |5 | | | | | | | | | | | | | | | | | |_____|_____|_____|_____| |_____|_____| |2 |6 | | | | | | | |_____|_____| |3 |7 | | | | | | | |_____|_____| """ P = dace.symbol('P', dace.int32) @dace.program def matrix_2d_2d_3(A: dace.int32[4 * P, 16]): a_grid = dace.comm.Cart_create([2, P // 2]) b_grid = dace.comm.Cart_create([P // 2, 2]) B = np.empty_like(A) a_arr = dace.comm.Subarray((8 * P, 8 * P), A, process_grid=a_grid) b_arr = dace.comm.Subarray((8 * P, 8 * P), B, process_grid=b_grid, correspondence=(1, 0)) rdistr = dace.comm.Redistribute(A, a_arr, B, b_arr) return B from mpi4py import MPI commworld = MPI.COMM_WORLD rank = commworld.Get_rank() size = commworld.Get_size() even_size = (size // 2) * 2 if size < 2: raise ValueError("Please run this test with at least two processes.") sdfg = None if rank == 0: sdfg = matrix_2d_2d_3.to_sdfg() func = utils.distributed_compile(sdfg, commworld) A = np.arange(64 * even_size * even_size, dtype=np.int32).reshape(8 * even_size, 8 * even_size) lA = A.reshape(2, 4 * even_size, even_size // 2, 16).transpose(0, 2, 1, 3) lB = A.reshape(2, 4 * even_size, even_size // 2, 16).transpose(2, 0, 1, 3) if rank < even_size: B = func(A=lA[rank // (even_size // 2), rank % (even_size // 2)].copy(), P=even_size) else: B = func(A=np.zeros((1, ), dtype=np.int32), P=even_size) if rank < even_size: assert (np.array_equal(B, lB[rank // 2, rank % 2])) @pytest.mark.mpi def test_redistribute_vector_2d_2d(): """ The numbers are example tile IDs, NOT MPI ranks. "(r)" means that the tile is a replica. ____________________ _______________________ ___________ |____________________| -> |0____|1____|2____|3____| -> |0 __|zero_| |0(r)_|1(r)_|2(r)_|3(r)_| |1____|zero_| |2____|zero_| |3____|zero_| """ P = dace.symbol('P', dace.int32) @dace.program def vector_2d_2d(A: dace.int32[8 * P]): a_grid = dace.comm.Cart_create([2, P // 2]) a_scatter_grid = dace.comm.Cart_sub(a_grid, [False, True], exact_grid=0) a_bcast_grid = dace.comm.Cart_sub(a_grid, [True, False]) b_grid = dace.comm.Cart_create([P // 2, 2]) b_scatter_grid = dace.comm.Cart_sub(b_grid, [True, False], exact_grid=0) b_bcast_grid = dace.comm.Cart_sub(b_grid, [False, True]) lA = np.empty_like(A, shape=(16, )) a_subarr = dace.comm.BlockScatter(A, lA, a_scatter_grid, a_bcast_grid) lB = np.zeros_like(A, shape=(16, )) b_subarr = dace.comm.Subarray((8 * P, ), lB, process_grid=b_scatter_grid) redistr = dace.comm.Redistribute(lA, a_subarr, lB, b_subarr) return lB from mpi4py import MPI commworld = MPI.COMM_WORLD rank = commworld.Get_rank() size = commworld.Get_size() even_size = (size // 2) * 2 if size < 2: raise ValueError("Please run this test with at least two processes.") sdfg = None if rank == 0: sdfg = vector_2d_2d.to_sdfg() func = utils.distributed_compile(sdfg, commworld) A = np.arange(8 * even_size, dtype=np.int32) lB_ref = A.reshape(even_size // 2, 16) if rank < even_size: lB = func(A=A, P=even_size) else: lB = func(A=np.zeros((1, ), dtype=np.int32), P=even_size) if rank < even_size: if rank % 2 == 0: assert (np.array_equal(lB, lB_ref[rank // 2])) else: assert (np.array_equal(lB, np.zeros_like(lB))) if __name__ == "__main__": test_redistribute_matrix_2d_2d() test_redistribute_matrix_2d_2d_2() test_redistribute_matrix_2d_2d_3() test_redistribute_vector_2d_2d()

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null

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21fe45d5efa70f7699459dc77c19fdfe068e9cd1

88

py

Python

posts/utils.py

patreeceeo/showposter

8fbbd038c9819a4b523fb1c9e7b646bfcb19bb44

[ "MIT" ]

null

posts/utils.py

patreeceeo/showposter

8fbbd038c9819a4b523fb1c9e7b646bfcb19bb44

[ "MIT" ]

7

2021-03-18T22:36:20.000Z

2022-03-12T00:25:46.000Z

posts/utils.py

patreeceeo/showposter

8fbbd038c9819a4b523fb1c9e7b646bfcb19bb44

[ "MIT" ]

null

import secrets def get_slug(length=7): return secrets.token_urlsafe()[:length]

9.777778

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30,199

py

Python

spark_fhir_schemas/r4/resources/compartmentdefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/resources/compartmentdefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/resources/compartmentdefinition.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import ( StructType, StructField, StringType, ArrayType, BooleanType, DataType, ) # This file is auto-generated by generate_schema so do not edit it manually # noinspection PyPep8Naming class CompartmentDefinitionSchema: """ A compartment definition that defines how resources are accessed on a server. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = None, extension_depth: int = 0, max_extension_depth: Optional[int] = 2, include_modifierExtension: Optional[bool] = False, use_date_for: Optional[List[str]] = None, parent_path: Optional[str] = "", ) -> Union[StructType, DataType]: """ A compartment definition that defines how resources are accessed on a server. resourceType: This is a CompartmentDefinition resource id: The logical id of the resource, as used in the URL for the resource. Once assigned, this value never changes. meta: The metadata about the resource. This is content that is maintained by the infrastructure. Changes to the content might not always be associated with version changes to the resource. implicitRules: A reference to a set of rules that were followed when the resource was constructed, and which must be understood when processing the content. Often, this is a reference to an implementation guide that defines the special rules along with other profiles etc. language: The base language in which the resource is written. text: A human-readable narrative that contains a summary of the resource and can be used to represent the content of the resource to a human. The narrative need not encode all the structured data, but is required to contain sufficient detail to make it "clinically safe" for a human to just read the narrative. Resource definitions may define what content should be represented in the narrative to ensure clinical safety. contained: These resources do not have an independent existence apart from the resource that contains them - they cannot be identified independently, and nor can they have their own independent transaction scope. extension: May be used to represent additional information that is not part of the basic definition of the resource. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. modifierExtension: May be used to represent additional information that is not part of the basic definition of the resource and that modifies the understanding of the element that contains it and/or the understanding of the containing element's descendants. Usually modifier elements provide negation or qualification. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer is allowed to define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. Applications processing a resource are required to check for modifier extensions. Modifier extensions SHALL NOT change the meaning of any elements on Resource or DomainResource (including cannot change the meaning of modifierExtension itself). url: An absolute URI that is used to identify this compartment definition when it is referenced in a specification, model, design or an instance; also called its canonical identifier. This SHOULD be globally unique and SHOULD be a literal address at which at which an authoritative instance of this compartment definition is (or will be) published. This URL can be the target of a canonical reference. It SHALL remain the same when the compartment definition is stored on different servers. version: The identifier that is used to identify this version of the compartment definition when it is referenced in a specification, model, design or instance. This is an arbitrary value managed by the compartment definition author and is not expected to be globally unique. For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is not available. There is also no expectation that versions can be placed in a lexicographical sequence. name: A natural language name identifying the compartment definition. This name should be usable as an identifier for the module by machine processing applications such as code generation. status: The status of this compartment definition. Enables tracking the life-cycle of the content. experimental: A Boolean value to indicate that this compartment definition is authored for testing purposes (or education/evaluation/marketing) and is not intended to be used for genuine usage. date: The date (and optionally time) when the compartment definition was published. The date must change when the business version changes and it must change if the status code changes. In addition, it should change when the substantive content of the compartment definition changes. publisher: The name of the organization or individual that published the compartment definition. contact: Contact details to assist a user in finding and communicating with the publisher. description: A free text natural language description of the compartment definition from a consumer's perspective. useContext: The content was developed with a focus and intent of supporting the contexts that are listed. These contexts may be general categories (gender, age, ...) or may be references to specific programs (insurance plans, studies, ...) and may be used to assist with indexing and searching for appropriate compartment definition instances. purpose: Explanation of why this compartment definition is needed and why it has been designed as it has. code: Which compartment this definition describes. search: Whether the search syntax is supported,. resource: Information about how a resource is related to the compartment. """ if extension_fields is None: extension_fields = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueUrl", "valueReference", "valueCodeableConcept", "valueAddress", ] from spark_fhir_schemas.r4.simple_types.id import idSchema from spark_fhir_schemas.r4.complex_types.meta import MetaSchema from spark_fhir_schemas.r4.simple_types.uri import uriSchema from spark_fhir_schemas.r4.simple_types.code import codeSchema from spark_fhir_schemas.r4.complex_types.narrative import NarrativeSchema from spark_fhir_schemas.r4.complex_types.resourcelist import ResourceListSchema from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema from spark_fhir_schemas.r4.simple_types.datetime import dateTimeSchema from spark_fhir_schemas.r4.complex_types.contactdetail import ( ContactDetailSchema, ) from spark_fhir_schemas.r4.simple_types.markdown import markdownSchema from spark_fhir_schemas.r4.complex_types.usagecontext import UsageContextSchema from spark_fhir_schemas.r4.complex_types.compartmentdefinition_resource import ( CompartmentDefinition_ResourceSchema, ) if ( max_recursion_limit and nesting_list.count("CompartmentDefinition") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["CompartmentDefinition"] my_parent_path = ( parent_path + ".compartmentdefinition" if parent_path else "compartmentdefinition" ) schema = StructType( [ # This is a CompartmentDefinition resource StructField("resourceType", StringType(), True), # The logical id of the resource, as used in the URL for the resource. Once # assigned, this value never changes. StructField( "id", idSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".id", ), True, ), # The metadata about the resource. This is content that is maintained by the # infrastructure. Changes to the content might not always be associated with # version changes to the resource. StructField( "meta", MetaSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ), True, ), # A reference to a set of rules that were followed when the resource was # constructed, and which must be understood when processing the content. Often, # this is a reference to an implementation guide that defines the special rules # along with other profiles etc. StructField( "implicitRules", uriSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".implicitrules", ), True, ), # The base language in which the resource is written. StructField( "language", codeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".language", ), True, ), # A human-readable narrative that contains a summary of the resource and can be # used to represent the content of the resource to a human. The narrative need # not encode all the structured data, but is required to contain sufficient # detail to make it "clinically safe" for a human to just read the narrative. # Resource definitions may define what content should be represented in the # narrative to ensure clinical safety. StructField( "text", NarrativeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ), True, ), # These resources do not have an independent existence apart from the resource # that contains them - they cannot be identified independently, and nor can they # have their own independent transaction scope. StructField( "contained", ArrayType( ResourceListSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # May be used to represent additional information that is not part of the basic # definition of the resource. To make the use of extensions safe and manageable, # there is a strict set of governance applied to the definition and use of # extensions. Though any implementer can define an extension, there is a set of # requirements that SHALL be met as part of the definition of the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # May be used to represent additional information that is not part of the basic # definition of the resource and that modifies the understanding of the element # that contains it and/or the understanding of the containing element's # descendants. Usually modifier elements provide negation or qualification. To # make the use of extensions safe and manageable, there is a strict set of # governance applied to the definition and use of extensions. Though any # implementer is allowed to define an extension, there is a set of requirements # that SHALL be met as part of the definition of the extension. Applications # processing a resource are required to check for modifier extensions. # # Modifier extensions SHALL NOT change the meaning of any elements on Resource # or DomainResource (including cannot change the meaning of modifierExtension # itself). StructField( "modifierExtension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # An absolute URI that is used to identify this compartment definition when it # is referenced in a specification, model, design or an instance; also called # its canonical identifier. This SHOULD be globally unique and SHOULD be a # literal address at which at which an authoritative instance of this # compartment definition is (or will be) published. This URL can be the target # of a canonical reference. It SHALL remain the same when the compartment # definition is stored on different servers. StructField( "url", uriSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".url", ), True, ), # The identifier that is used to identify this version of the compartment # definition when it is referenced in a specification, model, design or # instance. This is an arbitrary value managed by the compartment definition # author and is not expected to be globally unique. For example, it might be a # timestamp (e.g. yyyymmdd) if a managed version is not available. There is also # no expectation that versions can be placed in a lexicographical sequence. StructField("version", StringType(), True), # A natural language name identifying the compartment definition. This name # should be usable as an identifier for the module by machine processing # applications such as code generation. StructField("name", StringType(), True), # The status of this compartment definition. Enables tracking the life-cycle of # the content. StructField("status", StringType(), True), # A Boolean value to indicate that this compartment definition is authored for # testing purposes (or education/evaluation/marketing) and is not intended to be # used for genuine usage. StructField("experimental", BooleanType(), True), # The date (and optionally time) when the compartment definition was published. # The date must change when the business version changes and it must change if # the status code changes. In addition, it should change when the substantive # content of the compartment definition changes. StructField( "date", dateTimeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".date", ), True, ), # The name of the organization or individual that published the compartment # definition. StructField("publisher", StringType(), True), # Contact details to assist a user in finding and communicating with the # publisher. StructField( "contact", ArrayType( ContactDetailSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # A free text natural language description of the compartment definition from a # consumer's perspective. StructField( "description", markdownSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".description", ), True, ), # The content was developed with a focus and intent of supporting the contexts # that are listed. These contexts may be general categories (gender, age, ...) # or may be references to specific programs (insurance plans, studies, ...) and # may be used to assist with indexing and searching for appropriate compartment # definition instances. StructField( "useContext", ArrayType( UsageContextSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # Explanation of why this compartment definition is needed and why it has been # designed as it has. StructField( "purpose", markdownSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".purpose", ), True, ), # Which compartment this definition describes. StructField("code", StringType(), True), # Whether the search syntax is supported,. StructField("search", BooleanType(), True), # Information about how a resource is related to the compartment. StructField( "resource", ArrayType( CompartmentDefinition_ResourceSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] if not include_modifierExtension: schema.fields = [ c if c.name != "modifierExtension" else StructField("modifierExtension", StringType(), True) for c in schema.fields ] return schema

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1

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null

0

6

1d09fea47ba605fe28c960c26a5fe85a628caf58

158

py

Python

aiohttp_charts/utils/path.py

nanvel/aiohttp-charts

1d9e1bccaf886fdd9641e41cd13fb0f1586865b5

[ "MIT" ]

null

aiohttp_charts/utils/path.py

nanvel/aiohttp-charts

1d9e1bccaf886fdd9641e41cd13fb0f1586865b5

[ "MIT" ]

null

aiohttp_charts/utils/path.py

nanvel/aiohttp-charts

1d9e1bccaf886fdd9641e41cd13fb0f1586865b5

[ "MIT" ]

null

import os.path PROJECT_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..') def rel(*path): return os.path.join(PROJECT_ROOT, *path)

17.555556

77

0.696203

25

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4.16

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6

df3bdec335bfc5860301a9b66320993d79e85ba5

47

py

Python

resources/scripts/import_mpl.py

rkrishnasanka/cello

27f6354f41cd2997610e79e2a41ded61f2c3fa91

[ "BSD-2-Clause" ]

786

2016-03-31T20:08:42.000Z

2022-03-26T21:50:11.000Z

resources/scripts/import_mpl.py

rkrishnasanka/cello

27f6354f41cd2997610e79e2a41ded61f2c3fa91

[ "BSD-2-Clause" ]

42

2016-04-03T19:15:10.000Z

2021-02-03T19:27:07.000Z

resources/scripts/import_mpl.py

rkrishnasanka/cello

27f6354f41cd2997610e79e2a41ded61f2c3fa91

[ "BSD-2-Clause" ]

164

2016-04-01T12:00:09.000Z

2022-01-17T19:02:38.000Z

import matplotlib as mpl print mpl.__version__

15.666667

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5.142857

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2

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null

0

1

0

1

0

1

0

6

df62962080da80cb87c43300da26281ad7d21daa

38

py

Python

app/template_db/template_engine/connectors/pdf_publiposting/__init__.py

Plawn/petit_publipost_gateway

e0a09207ae5bcad1623f8e7662e004ad9b59ffbe

[ "Apache-2.0" ]

null

app/template_db/template_engine/connectors/pdf_publiposting/__init__.py

Plawn/petit_publipost_gateway

e0a09207ae5bcad1623f8e7662e004ad9b59ffbe

[ "Apache-2.0" ]

7

2021-06-22T09:48:59.000Z

2022-01-10T16:08:00.000Z

app/template_db/template_engine/connectors/pdf_publiposting/__init__.py

Plawn/petit_publiposter

e0a09207ae5bcad1623f8e7662e004ad9b59ffbe

[ "Apache-2.0" ]

null

from .pdf_template import PDFTemplator

38

0.894737

5

38

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1

0

0.078947

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1

38

0.942857

0

1

0

true

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

df8deb0a9683910b45d253ef5837a30bba3b6b13

133

py

Python

implicit/__init__.py

EthanRosenthal/implicit

ad2be694a9da6411732a939f5a959c9856050ae7

[ "MIT" ]

16

2016-10-29T13:19:08.000Z

2022-03-16T14:13:58.000Z

implicit/__init__.py

BHamoudeh/implicit

ad2be694a9da6411732a939f5a959c9856050ae7

[ "MIT" ]

null

implicit/__init__.py

BHamoudeh/implicit

ad2be694a9da6411732a939f5a959c9856050ae7

[ "MIT" ]

12

2016-10-25T14:33:26.000Z

2022-03-21T06:47:14.000Z

from .implicit import alternating_least_squares, ALS __version__ = '0.1.4' __all__ = [alternating_least_squares, ALS, __version__]

22.166667

55

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133

5.294118

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0.511111

0.577778

0.733333

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133

5

56

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null

1

0

1

0

null

0

1

0

6

10d945c3a912107ec945d407524c2bd518e2bed3

97

py

Python

p2p/__init__.py

zixuanzh/py-evm

de05e73036c663e85083316bc503549044792892

[ "MIT" ]

null

p2p/__init__.py

zixuanzh/py-evm

de05e73036c663e85083316bc503549044792892

[ "MIT" ]

null

p2p/__init__.py

zixuanzh/py-evm

de05e73036c663e85083316bc503549044792892

[ "MIT" ]

null

# This is to ensure we call setup_trace_logging() before anything else. import evm # noqa: F401

32.333333

71

0.762887

16

97

4.5

1

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0.175258

97

2

72

48.5

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0

1

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true

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

d5072fcc6cb2c5fd881535e2dbdfbfdc3fe84184

22,650

py

Python

tests/test_alara.py

makoziol0/pyne

660b1bdd608d9b227d6a432737303f7e82af4a25

[ "MIT" ]

null

tests/test_alara.py

makoziol0/pyne

660b1bdd608d9b227d6a432737303f7e82af4a25

[ "MIT" ]

58

2019-01-07T16:13:26.000Z

2019-05-09T15:56:26.000Z

tests/test_alara.py

makoziol0/pyne

660b1bdd608d9b227d6a432737303f7e82af4a25

[ "MIT" ]

null

"""alara module tests""" import os import nose import subprocess from nose.tools import assert_almost_equal from nose.tools import assert_equal, assert_true, with_setup from nose.plugins.skip import SkipTest import numpy as np from numpy.testing import assert_array_equal import tables as tb import warnings import filecmp # mesh specific imports from pyne.mesh import HAVE_PYMOAB from pyne.mesh import Mesh, StatMesh, MeshError from pyne.alara import mesh_to_fluxin, photon_source_to_hdf5, \ photon_source_hdf5_to_mesh, mesh_to_geom, num_density_to_mesh, \ irradiation_blocks, record_to_geom, phtn_src_energy_bounds from pyne.material import Material from pyne.utils import QAWarning warnings.simplefilter("ignore", QAWarning) thisdir = os.path.dirname(__file__) def test_write_fluxin_single(): """This function tests the flux_mesh_to_fluxin function for a single energy group case. """ if not HAVE_PYMOAB: raise SkipTest output_name = "fluxin.out" forward_fluxin = os.path.join(thisdir, "files_test_alara", "fluxin_single_forward.txt") output = os.path.join(os.getcwd(), output_name) flux_mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1, 2], [0, 1]]) tag_flux = flux_mesh.tag(name="flux", size=1, dtype=float) flux_data = [1, 2, 3, 4] ves = flux_mesh.structured_iterate_hex("xyz") for i, ve in enumerate(ves): flux_mesh.flux[i] = flux_data[i] # test forward writting mesh_to_fluxin(flux_mesh, "flux", output_name, False) with open(output) as f: written = f.readlines() with open(forward_fluxin) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(output): os.remove(output) def test_write_fluxin_multiple(): """This function tests the flux_mesh_to_fluxin function for a multiple energy group case. """ if not HAVE_PYMOAB: raise SkipTest output_name = "fluxin.out" forward_fluxin = os.path.join(thisdir, "files_test_alara", "fluxin_multiple_forward.txt") reverse_fluxin = os.path.join(thisdir, "files_test_alara", "fluxin_multiple_reverse.txt") output = os.path.join(os.getcwd(), output_name) flux_mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1], [0, 1]]) flux_data = [[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14]] flux_mesh.tag("flux", flux_data, 'nat_mesh', size=7, dtype=float) # test forward writting mesh_to_fluxin(flux_mesh, "flux", output_name, False) with open(output) as f: written = f.readlines() with open(forward_fluxin) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(output): os.remove(output) # test reverse writting mesh_to_fluxin(flux_mesh, "flux", output_name, True) with open(output) as f: written = f.readlines() with open(reverse_fluxin) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(output): os.remove(output) def test_write_fluxin_multiple_subvoxel(): """This function tests the flux_mesh_to_fluxin function for a multiple energy group case under sub-voxel r2s. """ if not HAVE_PYMOAB: raise SkipTest output_name = "fluxin_subvoxel.out" forward_fluxin = os.path.join(thisdir, "files_test_alara", "fluxin_multiple_forward_subvoxel.txt") reverse_fluxin = os.path.join(thisdir, "files_test_alara", "fluxin_multiple_reverse_subvoxel.txt") output = os.path.join(os.getcwd(), output_name) flux_mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1, 2], [0, 1]]) flux_data = [[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14], [15, 16, 17, 18, 19, 20, 21], [22, 23, 24, 25, 26, 27, 28]] flux_mesh.tag("flux", flux_data, 'nat_mesh', size=7, dtype=float) cell_fracs = np.zeros(6, dtype=[('idx', np.int64), ('cell', np.int64), ('vol_frac', np.float64), ('rel_error', np.float64)]) cell_fracs[:] = [(0, 11, 1, 0.0), (1, 11, 0.5, 0.0), (1, 12, 0.5, 0.0), (2, 11, 0.5, 0.0), (2, 13, 0.5, 0.0), (3, 13, 1, 0.0)] cell_mats = {11: Material({'H': 1.0}, density=1.0), 12: Material({'He': 1.0}, density=1.0), 13: Material({}, density=0.0, metadata={'name': 'void'})} # test forward writting mesh_to_fluxin(flux_mesh, "flux", output_name, False, True, cell_fracs, cell_mats) with open(output) as f: written = f.readlines() with open(forward_fluxin) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(output): os.remove(output) # test reverse writting mesh_to_fluxin(flux_mesh, "flux", output_name, True, True, cell_fracs, cell_mats) with open(output) as f: written = f.readlines() with open(reverse_fluxin) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(output): os.remove(output) def test_photon_source_to_hdf5(): """Tests the function photon_source_to_hdf5. """ filename = os.path.join(thisdir, "files_test_alara", "phtn_src") photon_source_to_hdf5(filename, chunkshape=(10,)) assert_true(os.path.exists(filename + '.h5')) with tb.open_file(filename + '.h5') as h5f: obs = h5f.root.data[:] with open(filename, 'r') as f: lines = f.readlines() count = 0 old = "" for i, row in enumerate(obs): ls = lines[i].strip().split('\t') if ls[0] != 'TOTAL' and old == 'TOTAL': count += 1 assert_equal(count, row['idx']) assert_equal(ls[0].strip(), row['nuc'].decode()) assert_equal(ls[1].strip(), row['time'].decode()) assert_array_equal(np.array(ls[2:], dtype=np.float64), row['phtn_src']) old = ls[0] if os.path.isfile(filename + '.h5'): os.remove(filename + '.h5') def test_photon_source_hdf5_to_mesh(): """Tests the function photon source_h5_to_mesh.""" if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "phtn_src") photon_source_to_hdf5(filename, chunkshape=(10,)) assert_true(os.path.exists(filename + '.h5')) mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1, 2], [0, 1]]) tags = {('1001', 'shutdown'): 'tag1', ('TOTAL', '1.0 h'): 'tag2'} photon_source_hdf5_to_mesh(mesh, filename + '.h5', tags) # create lists of lists of expected results tag1_answers = [[1] + [0] * 41, [2] + [0] * 41, [3] + [0] * 41, [4] + [0] * 41] tag2_answers = [[5] + [0] * 41, [6] + [0] * 41, [7] + [0] * 41, [8] + [0] * 41] ves = list(mesh.structured_iterate_hex("xyz")) for i, ve in enumerate(ves): assert_array_equal(mesh.tag1[ve], tag1_answers[i]) assert_array_equal(mesh.tag2[ve], tag2_answers[i]) if os.path.isfile(filename + '.h5'): os.remove(filename + '.h5') def test_photon_source_hdf5_to_mesh_subvoxel(): """Tests the function photon source_h5_to_mesh under sub-voxel r2s condition.""" if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "phtn_src") photon_source_to_hdf5(filename, chunkshape=(10,)) assert_true(os.path.exists(filename + '.h5')) sub_voxel = True mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1, 2], [0, 1]]) cell_fracs = np.zeros(6, dtype=[('idx', np.int64), ('cell', np.int64), ('vol_frac', np.float64), ('rel_error', np.float64)]) cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 11, 0.5, 0.0), (1, 12, 0.5, 0.0), (2, 11, 0.5, 0.0), (2, 13, 0.5, 0.0), (3, 13, 1.0, 0.0)] cell_mats = {11: Material({'H': 1.0}, density=1.0), 12: Material({'He': 1.0}, density=1.0), 13: Material({}, density=0.0, metadata={'name': 'void'})} mesh.tag_cell_fracs(cell_fracs) tags = {('1001', 'shutdown'): 'tag1', ('TOTAL', '1 h'): 'tag2'} photon_source_hdf5_to_mesh(mesh, filename + '.h5', tags, sub_voxel=sub_voxel, cell_mats=cell_mats) # create lists of lists of expected results tag1_answers = [[1.0] + [0.0] * 41 + [0.0] * 42, [2.0] + [0.0] * 41 + [3.0] + [0.0] * 41, [4.0] + [0.0] * 41 + [0.0] * 42, [0.0] * 42 * 2] tag2_answers = [[5.0] + [0.0] * 41 + [0.0] * 42, [6.0] + [0.0] * 41 + [7.0] + [0.0] * 41, [8.0] + [0.0] * 41 + [0.0] * 42, [0.0] * 42 * 2] for i, _, ve in mesh: assert_array_equal(mesh.tag1[ve], tag1_answers[i]) assert_array_equal(mesh.tag2[ve], tag2_answers[i]) if os.path.isfile(filename + '.h5'): os.remove(filename + '.h5') def test_photon_source_hdf5_to_mesh_subvoxel_size1(): """Tests the function photon source_h5_to_mesh under sub-voxel r2s condition.""" if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "phtn_src") photon_source_to_hdf5(filename, chunkshape=(10,)) assert_true(os.path.exists(filename + '.h5')) sub_voxel = True mesh = Mesh(structured=True, structured_coords=[[0, 1, 2], [0, 1, 2], [0, 1]]) cell_fracs = np.zeros(4, dtype=[('idx', np.int64), ('cell', np.int64), ('vol_frac', np.float64), ('rel_error', np.float64)]) cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 12, 1.0, 0.0), (2, 13, 1.0, 0.0), (3, 14, 1.0, 0.0)] cell_mats = {11: Material({'H': 1.0}, density=1.0), 12: Material({'He': 1.0}, density=1.0), 13: Material({'He': 1.0}, density=1.0), 14: Material({}, density=0.0, metadata={'name': 'void'})} mesh.tag_cell_fracs(cell_fracs) tags = {('1001', 'shutdown'): 'tag1', ('TOTAL', '1 h'): 'tag2'} photon_source_hdf5_to_mesh(mesh, filename + '.h5', tags, sub_voxel=sub_voxel, cell_mats=cell_mats) # create lists of lists of expected results tag1_answers = [[1.0] + [0.0] * 41, [2.0] + [0.0] * 41, [3.0] + [0.0] * 41, [0.0] * 42] tag2_answers = [[5.0] + [0.0] * 41, [6.0] + [0.0] * 41, [7.0] + [0.0] * 41, [0.0] * 42] for i, _, ve in mesh: assert_array_equal(mesh.tag1[ve], tag1_answers[i]) assert_array_equal(mesh.tag2[ve], tag2_answers[i]) if os.path.isfile(filename + '.h5'): os.remove(filename + '.h5') def test_record_to_geom(): if not HAVE_PYMOAB: raise SkipTest expected_geom = os.path.join(thisdir, "files_test_alara", "alara_record_geom.txt") expected_matlib = os.path.join(thisdir, "files_test_alara", "alara_record_matlib.txt") geom = os.path.join(os.getcwd(), "alara_record_geom") matlib = os.path.join(os.getcwd(), "alara_record_matlib") cell_fracs = np.zeros(11, dtype=[('idx', np.int64), ('cell', np.int64), ('vol_frac', np.float64), ('rel_error', np.float64)]) cell_mats = {11: Material({'H1': 1.0, 'K39': 1.0}, density=1.1, metadata={'name': 'fake_mat'}), 12: Material({'H1': 0.1, 'O16': 1.0}, density=1.2, metadata={'name': 'water'}), 13: Material({'He4': 42.0}, density=1.3, metadata={'name': 'helium'}), 14: Material({}, density=0.0, metadata={'name': 'void'}), 15: Material({}, density=0.0, metadata={'name': 'void'}), 16: Material({'H1': 1.0, 'K39': 1.0}, density=1.1, metadata={'name': 'fake_mat'})} cell_fracs[:] = [(0, 11, 0.55, 0.0), (0, 12, 0.45, 0.0), (1, 11, 0.2, 0.0), (1, 12, 0.3, 0.0), (1, 13, 0.5, 0.0), (2, 11, 0.15, 0.0), (2, 14, 0.01, 0.0), (2, 15, 0.04, 0.0), (2, 16, 0.8, 0.0), (3, 11, 0.55, 0.0), (3, 12, 0.45, 0.0)] m = Mesh(structured_coords=[[-1, 0, 1], [-1, 0, 1], [0, 1]], structured=True, mats=None) record_to_geom(m, cell_fracs, cell_mats, geom, matlib) assert(filecmp.cmp(geom, expected_geom)) if os.path.isfile(geom): os.remove(geom) assert(filecmp.cmp(matlib, expected_matlib)) if os.path.isfile(matlib): os.remove(matlib) def test_record_to_geom_subvoxel(): if not HAVE_PYMOAB: raise SkipTest expected_geom = os.path.join(thisdir, "files_test_alara", "alara_record_geom_subvoxel.txt") expected_matlib = os.path.join(thisdir, "files_test_alara", "alara_record_matlib_subvoxel.txt") geom = os.path.join(os.getcwd(), "alara_record_geom") matlib = os.path.join(os.getcwd(), "alara_record_matlib") cell_fracs = np.zeros(11, dtype=[('idx', np.int64), ('cell', np.int64), ('vol_frac', np.float64), ('rel_error', np.float64)]) cell_mats = {11: Material({'H1': 1.0, 'K39': 1.0}, density=1.1, metadata={'name': 'fake_mat'}), 12: Material({'H1': 0.1, 'O16': 1.0}, density=1.2, metadata={'name': 'water'}), 13: Material({'He4': 42.0}, density=1.3, metadata={'name': 'helium'}), 14: Material({}, density=0.0, metadata={'name': 'void'}), 15: Material({}, density=0.0, metadata={'name': 'void'}), 16: Material({'H1': 1.0, 'K39': 1.0}, density=1.1, metadata={'name': 'fake_mat'})} cell_fracs[:] = [(0, 11, 0.55, 0.0), (0, 12, 0.45, 0.0), (1, 11, 0.2, 0.0), (1, 12, 0.3, 0.0), (1, 13, 0.5, 0.0), (2, 11, 0.15, 0.0), (2, 14, 0.01, 0.0), (2, 15, 0.04, 0.0), (2, 16, 0.8, 0.0), (3, 11, 0.55, 0.0), (3, 12, 0.45, 0.0)] m = Mesh(structured_coords=[[-1, 0, 1], [-1, 0, 1], [0, 1]], structured=True, mats=None) record_to_geom(m, cell_fracs, cell_mats, geom, matlib, sub_voxel=True) assert(filecmp.cmp(geom, expected_geom)) if os.path.isfile(geom): os.remove(geom) assert(filecmp.cmp(matlib, expected_matlib)) if os.path.isfile(matlib): os.remove(matlib) def test_mesh_to_geom(): if not HAVE_PYMOAB: raise SkipTest expected_geom = os.path.join(thisdir, "files_test_alara", "alara_geom.txt") expected_matlib = os.path.join(thisdir, "files_test_alara", "alara_matlib.txt") geom = os.path.join(os.getcwd(), "alara_geom") matlib = os.path.join(os.getcwd(), "alara_matlib") mats = { 0: Material({'H1': 1.0, 'K39': 1.0}, density=1.1), 1: Material({'H1': 0.1, 'O16': 1.0}, density=1.2), 2: Material({'He4': 42.0}, density=1.3), 3: Material({'Tm171': 171.0}, density=1.4), } m = Mesh(structured_coords=[[-1, 0, 1], [-1, 0, 1], [0, 1]], structured=True, mats=mats) mesh_to_geom(m, geom, matlib) with open(expected_geom) as f: written = f.readlines() with open(geom) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(geom): os.remove(geom) with open(expected_matlib) as f: written = f.readlines() with open(matlib) as f: expected = f.readlines() assert_equal(written, expected) if os.path.isfile(matlib): os.remove(matlib) def test_num_den_to_mesh_shutdown(): if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "num_density_output.txt") m = Mesh(structured=True, structured_coords=[[0, 1], [0, 1], [0, 1, 2]]) with open(filename) as f: lines = f.readlines() num_density_to_mesh(lines, 'shutdown', m) # expected composition results: exp_comp_0 = {10010000: 5.3390e+19, 10020000: 3.0571e+17, 10030000: 1.2082e+12, 20030000: 7.4323e+09, 20040000: 7.1632e+02} exp_comp_1 = {10010000: 4.1240e+13, 10020000: 4.7443e+11, 10030000: 2.6627e+13, 20030000: 8.3547e+10, 20040000: 2.6877e+19} # actual composition results act_comp_0 = m.mats[0].to_atom_frac() act_comp_1 = m.mats[1].to_atom_frac() assert_equal(len(exp_comp_0), len(act_comp_0)) for key, value in exp_comp_0.iteritems(): assert_almost_equal(value/act_comp_0[key], 1.0, 15) assert_equal(len(exp_comp_1), len(act_comp_1)) for key, value in exp_comp_1.iteritems(): assert_almost_equal(value/act_comp_1[key], 1.0, 15) # compare densities exp_density_0 = 8.96715E-05 exp_density_1 = 1.785214E-04 assert_almost_equal(exp_density_0, m.mats[0].density) assert_almost_equal(exp_density_1, m.mats[1].density) def test_num_den_to_mesh_stdout(): if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "num_density_output.txt") m = Mesh(structured=True, structured_coords=[[0, 1], [0, 1], [0, 1, 2]]) p = subprocess.Popen(["cat", filename], stdout=subprocess.PIPE) lines, err = p.communicate() num_density_to_mesh(lines.split('\n'), 'shutdown', m) # expected composition results: exp_comp_0 = {10010000: 5.3390e+19, 10020000: 3.0571e+17, 10030000: 1.2082e+12, 20030000: 7.4323e+09, 20040000: 7.1632e+02} exp_comp_1 = {10010000: 4.1240e+13, 10020000: 4.7443e+11, 10030000: 2.6627e+13, 20030000: 8.3547e+10, 20040000: 2.6877e+19} # actual composition results act_comp_0 = m.mats[0].to_atom_frac() act_comp_1 = m.mats[1].to_atom_frac() assert_equal(len(exp_comp_0), len(act_comp_0)) for key, value in exp_comp_0.iteritems(): assert_almost_equal(value/act_comp_0[key], 1.0, 15) assert_equal(len(exp_comp_1), len(act_comp_1)) for key, value in exp_comp_1.iteritems(): assert_almost_equal(value/act_comp_1[key], 1.0, 15) # compare densities exp_density_0 = 8.96715E-05 exp_density_1 = 1.785214E-04 assert_almost_equal(exp_density_0, m.mats[0].density) assert_almost_equal(exp_density_1, m.mats[1].density) def test_num_den_to_mesh_1_y(): if not HAVE_PYMOAB: raise SkipTest filename = os.path.join(thisdir, "files_test_alara", "num_density_output.txt") m = Mesh(structured=True, structured_coords=[[0, 1], [0, 1], [0, 1, 2]]) num_density_to_mesh(filename, '1 y', m) # expected results: exp_comp_0 = {10010000: 5.3390e+19, 10020000: 3.0571e+17, 10030000: 1.1424e+12, 20030000: 7.3260e+10, 20040000: 7.1632e+02} exp_comp_1 = {10010000: 4.1240e+13, 10020000: 4.7443e+11, 10030000: 2.5176e+13, 20030000: 1.5343e+12, 20040000: 2.6877e+19} # actual results act_comp_0 = m.mats[0].to_atom_frac() act_comp_1 = m.mats[1].to_atom_frac() assert_equal(len(exp_comp_0), len(act_comp_0)) for key, value in exp_comp_0.iteritems(): assert_almost_equal(value/act_comp_0[key], 1.0, 15) assert_equal(len(exp_comp_1), len(act_comp_1)) for key, value in exp_comp_1.iteritems(): assert_almost_equal(value/act_comp_1[key], 1.0, 15) # compare densities exp_density_0 = 8.96715E-05 exp_density_1 = 1.78521E-04 assert_almost_equal(exp_density_0, m.mats[0].density) assert_almost_equal(exp_density_1, m.mats[1].density) def test_irradiation_blocks(): # actual results act = irradiation_blocks("matlib", "isolib", "FEINDlib CINDER CINDER90 THERMAL", ["1 h", "0.5 y"], "fluxin.out", "1 y", output="number_density") exp = ("material_lib matlib\n" "element_lib isolib\n" "data_library FEINDlib CINDER CINDER90 THERMAL\n" "\n" "cooling\n" " 1 h\n" " 0.5 y\n" "end\n" "\n" "flux flux_1 fluxin.out 1.0 0 default\n" "schedule simple_schedule\n" " 1 y flux_1 pulse_once 0 s\n" "end\n" "\n" "pulsehistory pulse_once\n" " 1 0.0 s\n" "end\n" "\n" "output zone\n" " units Ci cm3\n" " number_density\n" "end\n" "\n" "truncation 1e-12\n" "impurity 5e-06 0.001\n" "dump_file dump_file\n") assert_equal(act, exp) def test_phtn_src_energy_bounds(): input_file = os.path.join(thisdir, "files_test_alara", "alara_other_blocks.txt") e_bounds = phtn_src_energy_bounds(input_file) expected_e_bounds = [0, 1.00E4, 2.00E4, 5.00E4, 1.00E5, 2.00E5, 3.00E5, 4.00E5, 6.00E5, 8.00E5, 1.00E6, 1.22E6, 1.44E6, 1.66E6, 2.00E6, 2.50E6, 3.00E6, 4.00E6, 5.00E6, 6.50E6, 8.00E6, 1.00E7, 1.20E7, 1.40E7, 2.00E7] assert_array_equal(e_bounds, expected_e_bounds)

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d50ed916ac257773d6677b1889312e1fd159226a

36

py

Python

core/tests/utils/callers/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

core/tests/utils/callers/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

core/tests/utils/callers/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

from .base_caller import BaseCaller

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d513ad0fa63e416ea4756d93260cdbdf42a7ce43

137

py

Python

tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.missing_data.test_missing_data_air_passengers_generic as gen gen.test_air_passengers_missing_data('Interpolate', 'Median')

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py

Python

compare_stats.py

tkent198/modRSW_EnKF

fc9f0bcc6f753a05fed245d4d2987cd3a34078ad

[ "MIT" ]

3

2019-10-11T09:30:59.000Z

2021-11-05T08:39:19.000Z

compare_stats.py

tkent198/modRSW_EnKF

fc9f0bcc6f753a05fed245d4d2987cd3a34078ad

[ "MIT" ]

null

compare_stats.py

tkent198/modRSW_EnKF

fc9f0bcc6f753a05fed245d4d2987cd3a34078ad

[ "MIT" ]

5

2017-09-03T18:16:26.000Z

2021-11-05T08:39:24.000Z

################################################################## # Summary diagnostics of idealised enkf experiments # inc. summary plots a la Poterjoy and Zhang ################################################################## ''' Each directory has i*j*k experiments with different parameter combinations. This script produces summary plots for comparison. Specify: dirname If DIAGS.npy exists, straight to plotting. If not, calculate statistics and save before plotting. ''' ## generic modules import os import errno import numpy as np import matplotlib.pyplot as plt ## custom modules from parameters import * from analysis_diag_stats import ave_stats ################################################################## dirname = '/test_enkf' # LOAD DATA FROM GIVEN DIRECTORY cwd = os.getcwd() dirn = str(cwd+dirname) figsdir = str(dirn+'/figs') #check if dir exixts, if not make it try: os.makedirs(figsdir) except OSError as exception: if exception.errno != errno.EEXIST: raise #TEST CASE: parameters for outer loop loc = [1e-10] add_inf = [0.2] inf = [1.01, 1.05, 1.1] loc = ['inf'] ii=0 ################################################################## # if LOAD: try: DIAGS = np.load(str(dirn+'/DIAGS.npy')) DIAGS = np.roll(DIAGS,1,2) spr_fc = DIAGS[0,:,:,:] spr_an = DIAGS[1,:,:,:] err_fc = DIAGS[2,:,:,:] err_an = DIAGS[3,:,:,:] rmse_fc = DIAGS[4,:,:,:] rmse_an = DIAGS[5,:,:,:] crps_fc = DIAGS[6,:,:,:] crps_an = DIAGS[7,:,:,:] OI = DIAGS[8,:,:,:] ################################################################## # if NOT: except: spr_fc = np.empty([len(loc),len(add_inf),len(inf)]) spr_an = np.empty([len(loc),len(add_inf),len(inf)]) err_fc = np.empty([len(loc),len(add_inf),len(inf)]) err_an = np.empty([len(loc),len(add_inf),len(inf)]) rmse_fc = np.empty([len(loc),len(add_inf),len(inf)]) rmse_an = np.empty([len(loc),len(add_inf),len(inf)]) crps_fc = np.empty([len(loc),len(add_inf),len(inf)]) crps_an = np.empty([len(loc),len(add_inf),len(inf)]) OI = np.empty([len(loc),len(add_inf),len(inf)]) for i in range(0,len(loc)): for j in range(0,len(add_inf)): for k in range(0,len(inf)): spr_fc[i,j,k], err_fc[i,j,k], rmse_fc[i,j,k], crps_fc[i,j,k], spr_an[i,j,k], err_an[i,j,k], rmse_an[i,j,k], crps_an[i,j,k], OI[i,j,k] = ave_stats(i, j, k, dirname) DIAGS = np.empty([9,len(loc),len(add_inf),len(inf)]) DIAGS[0,:,:,:] = spr_fc DIAGS[1,:,:,:] = spr_an DIAGS[2,:,:,:] = err_fc DIAGS[3,:,:,:] = err_an DIAGS[4,:,:,:] = rmse_fc DIAGS[5,:,:,:] = rmse_an DIAGS[6,:,:,:] = crps_fc DIAGS[7,:,:,:] = crps_an DIAGS[8,:,:,:] = OI np.save(str(dirn+'/DIAGS'),DIAGS) print ' ' print ' *** Summary diagnostics saved in :', dirn print ' ' DIAGS = np.roll(DIAGS,1,2) spr_fc = DIAGS[0,:,:,:] spr_an = DIAGS[1,:,:,:] err_fc = DIAGS[2,:,:,:] err_an = DIAGS[3,:,:,:] rmse_fc = DIAGS[4,:,:,:] rmse_an = DIAGS[5,:,:,:] crps_fc = DIAGS[6,:,:,:] crps_an = DIAGS[7,:,:,:] OI = DIAGS[8,:,:,:] ################################################################## fs = 14 cpar = 0.06 tick_loc_y = [0] tick_loc_x = [0,1,2] tick_lab_y = np.roll(add_inf,1) tick_lab_x = inf ################################################################## print ' *** PLOT: STATS matrix with AME ***' ################################################################## fig, axes = plt.subplots(2, 2, figsize=(10,10)) im=axes[0,0].matshow(err_fc[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = err_fc[ii,y_val,x_val] if c == np.nanmin(err_an[ii,:,:]): axes[0,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[0,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[0,0].set_xticks(tick_loc_x) axes[0,0].set_yticks(tick_loc_y) axes[0,0].set_xticklabels(tick_lab_x,fontsize=14) axes[0,0].set_yticklabels(tick_lab_y,fontsize=14) axes[0,0].set_title('err_fc') #axes[0,0].grid() im=axes[1,0].matshow(spr_fc[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = spr_fc[ii,y_val,x_val] if c == np.nanmin(spr_fc[ii,:,:]): axes[1,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[1,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[1,0].set_xticks(tick_loc_x) axes[1,0].set_yticks(tick_loc_y) axes[1,0].set_xticklabels(tick_lab_x,fontsize=14) axes[1,0].set_yticklabels(tick_lab_y,fontsize=14) axes[1,0].set_title('spr_fc') #axes[1,0].grid() im=axes[0,1].matshow(err_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = err_an[ii,y_val,x_val] if c == np.nanmin(err_an[ii,:,:]): axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[0,1].set_xticks(tick_loc_x) axes[0,1].set_yticks(tick_loc_y) axes[0,1].set_xticklabels(tick_lab_x,fontsize=14) axes[0,1].set_yticklabels(tick_lab_y,fontsize=14) axes[0,1].set_title('err_an') #axes[0,1].grid() im=axes[1,1].matshow(spr_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = spr_an[ii,y_val,x_val] if c == np.nanmin(spr_an[ii,:,:]): axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[1,1].set_xticks(tick_loc_x) axes[1,1].set_yticks(tick_loc_y) axes[1,1].set_xticklabels(tick_lab_x,fontsize=14) axes[1,1].set_yticklabels(tick_lab_y,fontsize=14) axes[1,1].set_title('spr_an') #axes[1,1].grid() im.set_clim(0,cpar) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) ################################################################## name = "/spr_mae_summary%d.pdf" %(ii+1) f_name = str(figsdir+name) plt.savefig(f_name) print ' ' print ' *** %s saved to %s' %(name,figsdir) print ' ' ################################################################## print ' *** PLOT: STATS matrix with RMSE ***' ################################################################## cpar = np.nanmax(np.maximum(rmse_fc[ii,:,:],spr_fc[ii,:,:])) cpar = np.round(cpar+0.025,2) print cpar fig, axes = plt.subplots(2, 2, figsize=(12,10)) im=axes[0,0].matshow(rmse_fc[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c =rmse_fc[ii,y_val,x_val] if c == np.nanmin(rmse_fc[ii,:,:]): axes[0,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[0,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[0,0].set_xticks(tick_loc_x) axes[0,0].set_yticks(tick_loc_y) axes[0,0].set_xticklabels(tick_lab_x,fontsize=14) axes[0,0].set_yticklabels(tick_lab_y,fontsize=14) axes[0,0].set_title('rmse_fc') #axes[0,0].grid() im=axes[1,0].matshow(spr_fc[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = spr_fc[ii,y_val,x_val] if c == np.nanmin(spr_fc[ii,:,:]): axes[1,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[1,0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[1,0].set_xticks(tick_loc_x) axes[1,0].set_yticks(tick_loc_y) axes[1,0].set_xticklabels(tick_lab_x,fontsize=14) axes[1,0].set_yticklabels(tick_lab_y,fontsize=14) axes[1,0].set_title('spr_fc') #axes[1,0].grid() im=axes[0,1].matshow(rmse_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = rmse_an[ii,y_val,x_val] if c == np.nanmin(rmse_an[ii,:,:]): axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[0,1].set_xticks(tick_loc_x) axes[0,1].set_yticks(tick_loc_y) axes[0,1].set_xticklabels(tick_lab_x,fontsize=14) axes[0,1].set_yticklabels(tick_lab_y,fontsize=14) axes[0,1].set_title('rmse_an') #axes[0,1].grid() im=axes[1,1].matshow(spr_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = spr_an[ii,y_val,x_val] if c == np.nanmin(spr_an[ii,:,:]): axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[1,1].set_xticks(tick_loc_x) axes[1,1].set_yticks(tick_loc_y) axes[1,1].set_xticklabels(tick_lab_x,fontsize=14) axes[1,1].set_yticklabels(tick_lab_y,fontsize=14) axes[1,1].set_title('spr_an') #axes[1,1].grid() im.set_clim(0,cpar) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) ################################################################## name = "/spr_rmse_summary%d.pdf" %(ii+1) f_name = str(figsdir+name) plt.savefig(f_name) print ' ' print ' *** %s saved to %s' %(name,figsdir) print ' ' ################################################################## print ' *** PLOT: STATS matrix with CRPS ***' ################################################################## cpar = np.nanmax(np.maximum(crps_fc[ii,:,:],crps_fc[ii,:,:])) cpar = np.round(cpar+0.005,2) print cpar fig, axes = plt.subplots(1, 2, figsize=(12,7)) im=axes[0].matshow(crps_fc[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c =crps_fc[ii,y_val,x_val] if c == np.nanmin(crps_fc[ii,:,:]): axes[0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[0].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[0].set_xticks(tick_loc_x) axes[0].set_yticks(tick_loc_y) axes[0].set_xticklabels(tick_lab_x,fontsize=14) axes[0].set_yticklabels(tick_lab_y,fontsize=14) axes[0].set_title('crps_fc') #axes[0,0].grid() im=axes[1].matshow(crps_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = crps_an[ii,y_val,x_val] if c == np.nanmin(crps_an[ii,:,:]): axes[1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes[1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) axes[1].set_xticks(tick_loc_x) axes[1].set_yticks(tick_loc_y) axes[1].set_xticklabels(tick_lab_x,fontsize=14) axes[1].set_yticklabels(tick_lab_y,fontsize=14) axes[1].set_title('crps_an') #axes[1,0].grid() #im=axes[0,1].matshow(rmse_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) #y, x = np.meshgrid(tick_loc_y,tick_loc_x) #for x_val, y_val in zip(x.flatten(), y.flatten()): # c = rmse_an[ii,y_val,x_val] # if c == np.min(rmse_an[ii,:,:]): # axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') # else: # axes[0,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) #axes[0,1].set_xticks(tick_loc_x) #axes[0,1].set_yticks(tick_loc_y) #axes[0,1].set_xticklabels(tick_lab_x,fontsize=14) #axes[0,1].set_yticklabels(tick_lab_y,fontsize=14) #axes[0,1].set_title('rmse_an') ##axes[0,1].grid() # #im=axes[1,1].matshow(spr_an[ii,:,:],cmap='hot_r',vmin=0,vmax=cpar) #y, x = np.meshgrid(tick_loc_y,tick_loc_x) #for x_val, y_val in zip(x.flatten(), y.flatten()): # c = spr_an[ii,y_val,x_val] # if c == np.min(spr_an[ii,:,:]): # axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs,weight='bold') # else: # axes[1,1].text(x_val, y_val, '%.3g'%c, va='center', ha='center',fontsize=fs) #axes[1,1].set_xticks(tick_loc_x) #axes[1,1].set_yticks(tick_loc_y) #axes[1,1].set_xticklabels(tick_lab_x,fontsize=14) #axes[1,1].set_yticklabels(tick_lab_y,fontsize=14) #axes[1,1].set_title('spr_an') ##axes[1,1].grid() im.set_clim(0,cpar) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) ################################################################## name = "/crps_summary%d.pdf" %(ii+1) f_name = str(figsdir+name) plt.savefig(f_name) print ' ' print ' *** %s saved to %s' %(name,figsdir) print ' ' ################################################################## print ' *** PLOT: OI matrix ***' ################################################################## cpar = np.nanmax(OI[ii,:,:]) cpar = np.round(cpar+5,-1) print cpar fig = plt.figure(figsize=(7,7)) axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1) im = axes.matshow(OI[ii,:,:],cmap='hot_r',vmin=0, vmax=cpar) y, x = np.meshgrid(tick_loc_y,tick_loc_x) for x_val, y_val in zip(x.flatten(), y.flatten()): c = OI[ii,y_val,x_val] if c == np.nanmax(OI[ii,:,:]): axes.text(x_val, y_val, '%.1f'%c, va='center', ha='center',fontsize=fs,weight='bold') else: axes.text(x_val, y_val, '%.1f'%c, va='center', ha='center',fontsize=fs) axes.set_xticks(tick_loc_x) axes.set_yticks(tick_loc_y) axes.set_xticklabels(tick_lab_x,fontsize=14) axes.set_yticklabels(tick_lab_y,fontsize=14) fig.colorbar(im) ################################################################## name = "/OI_summary%d.pdf" %(ii+1) f_name = str(figsdir+name) plt.savefig(f_name) print ' ' print ' *** %s saved to %s' %(name,figsdir) print ' ' ################################################################## #plt.show() ''' fig, ax = plt.subplots() min_val, max_val, diff = 0., 5., 1. #imshow portion N_points = (max_val - min_val) / diff print 'N_points =', N_points imshow_data = np.random.rand(N_points, N_points) ax.imshow(imshow_data, interpolation='nearest') #text portion ind_array = np.arange(min_val, max_val, diff) x, y = np.meshgrid(ind_array, ind_array) for x_val, y_val in zip(x.flatten(), y.flatten()): c = imshow_data[x_val,y_val] ax.text(x_val, y_val, c, va='center', ha='center') #set tick marks for grid ax.set_xticks(np.arange(min_val-diff/2, max_val-diff/2)) ax.set_yticks(np.arange(min_val-diff/2, max_val-diff/2)) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_xlim(min_val-diff/2, max_val-diff/2) ax.set_ylim(min_val-diff/2, max_val-diff/2) ax.grid() plt.show() '''

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6

1da76cde90485a852c5d0ab3877006e9c49ec009

105

py

Python

obsidion/cogs/info/__init__.py

Darkflame72/Minecraft-Discord

4e701df9820d18c9f2b8a4863145e2af36729505

[ "MIT" ]

1

2020-02-29T22:37:01.000Z

obsidion/cogs/info/__init__.py

Darkflame72/Minecraft-Discord

4e701df9820d18c9f2b8a4863145e2af36729505

[ "MIT" ]

1

2020-03-27T05:49:37.000Z

2020-03-27T05:51:25.000Z

obsidion/cogs/info/__init__.py

Darkflame72/Minecraft-Discord

4e701df9820d18c9f2b8a4863145e2af36729505

[ "MIT" ]

1

2020-03-27T05:53:17.000Z

"""Info.""" from .info import Info def setup(bot) -> None: """Setup.""" bot.add_cog(Info(bot))

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0

null

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0

1

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1

0

6

d561f4f916cb858a59cdf7cac23c702050823c1f

155

py

Python

discum/gateway/__init__.py

firewood-b/Discord-S.C.U.M

1beb8c25ab245a1389431a5206eafb9b4a95df0f

[ "MIT" ]

null

discum/gateway/__init__.py

firewood-b/Discord-S.C.U.M

1beb8c25ab245a1389431a5206eafb9b4a95df0f

[ "MIT" ]

null

discum/gateway/__init__.py

firewood-b/Discord-S.C.U.M

1beb8c25ab245a1389431a5206eafb9b4a95df0f

[ "MIT" ]

null

from .event import * from .gateway import * from .parse import * from .request import * from .response import * from .session import * from .types import *

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1

0

6

d56556f3aaeff55e98df45ccc55ec1ec0bb111ce

49,412

py

Python

Code_HPC/Sparcle.py

sandhya212/Sparcle_for_spot_reassignments

6de771bab2dab0c40aa14b8cb7f4e1774394d9bc

[ "MIT" ]

1

2021-02-16T16:22:03.000Z

Code_HPC/Sparcle.py

sandhya212/Sparcle_for_spot_reassignments

6de771bab2dab0c40aa14b8cb7f4e1774394d9bc

[ "MIT" ]

1

2021-04-12T10:15:49.000Z

2021-04-13T15:00:37.000Z

Code_HPC/Sparcle.py

sandhya212/Sparcle_for_spot_reassignments

6de771bab2dab0c40aa14b8cb7f4e1774394d9bc

[ "MIT" ]

null

## Merfish assignment code ## Code author SP ## ## ## 5th Feb 2019 - clustering methods ## 6th Feb 2019 - cluster moments extraction ## 9th Feb 2019 - normality tests check, find nearest cells to an mRNA ## 10th Feb 2019 - adding Gaussian copula to work with marginals ## 10th Mar 2019 - loop added to take in multiple FOVs ## 20th Mar 2019 - tidying up plots ## 11th July 2019 - parallelising the loops ## does not use the scRNA seq cluster moments ## ## ##June - July 2020 - redoing the code with edits, adding Merfish start, distance ub, ## ## July 31st 2020 - only printing the assignments in FOVs in loop Overall ############ #### Main code ############ t1 = time.time() path_temp = os.path.join(path_data + '/barcode_metadata.csv') mRNA_metadata_t = np.array(pd.read_csv(path_temp).as_matrix()) Merfish_CM_list = [] mRNA_assign_list = [] closest_cells_list = [] mRNA_coords_list = [] CoM_image_coords_list =[] cell_metadata_t_list = [] mRNA_all_list = [] num_cell_fov = np.zeros(num_fov, dtype=int) ########################################### ######## Build the mRNA stats per FOV ##### ########################################### inputs = range(num_fov) def processFOV(f): print('Fov is: ', str(f)) if (f < 10): f_name = str("/cell_metadata/cell_metadata_fov_00"+str(f)+".csv") elif (f <= 99): f_name = str("/cell_metadata/cell_metadata_fov_0"+str(f)+".csv") else: f_name = str("/cell_metadata/cell_metadata_fov_"+str(f)+".csv") mRNA_all = np.where(mRNA_metadata_t[:,1] == f) mRNA_all = np.array(mRNA_all).flatten() print("number of mRNA rows in Fov ",str(f),"= ", mRNA_all.shape) #return [mRNA_all_list.append(mRNA_all)] #read in mRNA needed for clustering #mRNA_certain = np.where((mRNA_metadata_t[:,1] == f) & (mRNA_metadata_t[:,10] ==1))# & (mRNA_metadata_t[:,5] ==1.5)) #fov_id = 0 and in_feature = 1 and z-plane = 2 #mRNA_certain = np.array(mRNA_certain).flatten() #print("number of mRNA rows in Fov ",str(f)," for clustering = ", mRNA_certain.shape) #print("number of unique genes or barcodes in Fov ",str(f),"(for clustering) = ", np.unique(mRNA_metadata_t[mRNA_certain,0]).shape) #read in mRNA needed for reassignment mRNA_assign = np.where((mRNA_metadata_t[:,1] == f) & (mRNA_metadata_t[:,10] ==0))# & (mRNA_metadata_t[:,5] ==1.5)) #fov_id = 0 and in_feature = 0 and z-plane = 2 mRNA_assign = np.array(mRNA_assign).flatten() print("number of mRNA rows in Fov ",str(f)," for reassignment = ", mRNA_assign.shape) print("number of unique genes or barcodes in Fov ",str(f)," (for reassignment) = ", np.unique(mRNA_metadata_t[mRNA_assign,0]).shape) #mRNA_assign_list.append(mRNA_assign) # Build the count matrix of cells x genes per FOV path_temp = os.path.join(path_data + f_name) cell_metadata_t = np.array(pd.read_csv(path_temp).as_matrix()) #.astype("float") #cell_metadata_t_list.append(cell_metadata_t) num_cells = cell_metadata_t.shape[0] num_genes = np.unique(mRNA_metadata_t[mRNA_all,0]).shape[0] Merfish_CM = np.zeros([num_cells, num_genes], dtype=float) #this has to be a matrix with ALL the genes for i in range(num_cells): feat_id = cell_metadata_t[i,1] #print("cell is", feat_id) CM_row = np.where(cell_metadata_t[:,1]==feat_id) #print("cell row is", CM_row) #for 'f' fov, all the spots that are to be considered for clustering and for a given cell (feat_id) feat_rows = np.where((mRNA_metadata_t[:,1]==f) & (mRNA_metadata_t[:,9]==feat_id) & (mRNA_metadata_t[:,10]==1))# & (mRNA_metadata_t[:,5] == 1.5)) #fov id, which cell and in/out of cell #feat_rows = np.where((mRNA_metadata_t[:, 1] == 0) & (mRNA_metadata_t[:, 9] == feat_id)) feat_rows = np.array(feat_rows).flatten() num_rows = feat_rows.shape[0] for j in range(num_rows): bc_id = mRNA_metadata_t[feat_rows[j],0]-1 #telling which gene it is and where the entry should go to in the count matrix #print("gene is", bc_id) #Merfish_CM[CM_row,int(bc_id)] = Merfish_CM[CM_row,int(bc_id)] + mRNA_metadata_t[feat_rows[j],2]/mRNA_metadata_t[feat_rows[j],6] #area normalised values #intensity-based count matrix #Merfish_CM[CM_row,int(bc_id)] = Merfish_CM[CM_row,int(bc_id)] + mRNA_metadata_t[feat_rows[j],2] #count matrix Merfish_CM[CM_row,int(bc_id)] = Merfish_CM[CM_row,int(bc_id)] + 1 #Merfish_CM_list.append(Merfish_CM) return f_name, mRNA_all, mRNA_assign, cell_metadata_t, num_cells, num_genes, Merfish_CM #num_cores = multiprocessing.cpu_count() t1 = time.time() results = Parallel(n_jobs=num_cores)(delayed(processFOV)(f) for f in inputs) t2 = time.time() print('Time taken to execute FOV consolidation:', str(t2-t1),'secs') print('Time taken to execute FOV consolidation:', str((t2-t1)/60),'mins') #collect the results ff_name=[item[0] for item in results] mRNA_all_list=[item[1] for item in results] mRNA_assign_list=[item[2] for item in results] cell_metadata_t_list=[item[3] for item in results] num_cells_list=[item[4] for item in results] num_genes_list=[item[5] for item in results] Merfish_CM_list=[item[6] for item in results] ## added 20th June 2020 total_mRNA = 0 total_dang_mRNA = 0 for kl in range(num_fov): total_mRNA = total_mRNA + mRNA_all_list[kl].shape[0] total_dang_mRNA = total_dang_mRNA + mRNA_assign_list[kl].shape[0] print('total mRNA across all FOVs:', str(total_mRNA)) print('total dangling mRNA across all FOVs:',str(total_dang_mRNA)) ### cell_cumsum = np.cumsum(num_cells_list) cell_cumsum = np.hstack((0,cell_cumsum)) #1st July 2020 Merfish_CM = np.zeros((1,Merfish_genes)) #will have all the Count matrices consolidated #Merfish_CM = np.zeros((1,num_genes_list[0])) #will have all the Count matrices consolidated for f in range(num_fov): print("Number of cells in Fov "+str(f)+" is:"+str(Merfish_CM_list[f].shape[0])) Merfish_CM = np.concatenate((Merfish_CM,Merfish_CM_list[f]),axis=0) Merfish_CM = np.delete(Merfish_CM, (0), axis=0) #remove rows that have 0 rowsum row_sums_CM = Merfish_CM.sum(axis=1,keepdims=True) rows_remove = np.where(row_sums_CM==0) Merfish_CM = np.delete(Merfish_CM,(rows_remove),axis=0) #### added 20th June 2020 print("rows_remove") print(rows_remove) print('cell_cumsum b4') print(cell_cumsum) #adjust for cells removed per bin so that cumsum reflects the correct number of cells per FOV for i in range(len(rows_remove[0])): print('i is', str(i)) for j in range(i+1): print('j is', str(j)) if ((j+1) < (len(cell_cumsum))): if (rows_remove[0][i] < cell_cumsum[j+1]): cell_cumsum[j+1] = cell_cumsum[j+1] - 1 print(cell_cumsum) continue continue print('cell_cumsum after') print(cell_cumsum) ################## M = zscore(Merfish_CM,axis=1) M_upd = M num_cells = M.shape[0] num_genes = Merfish_genes #M.shape[1] print(M.shape) # added 20th June 2020 df_data = pd.DataFrame(Merfish_CM) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_JeffMerfish_fovall.csv'), header=None, index=None) df_data = pd.DataFrame(M) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_M_fovall.csv'), header=None, index=None) tsne = TSNE(n_components=2, verbose=1)#, perplexity=30, n_iter=900) X_2d = tsne.fit_transform(M) if(bayes_MM): Mod_dpgmm=BayesianGaussianMixture(n_components=num_comp, covariance_type='full',weight_concentration_prior_type='dirichlet_process').fit(M) cluster_asgn = Mod_dpgmm.predict(M) else: communities, graph, Q = pg.cluster(M)#, k=num_comp, min_cluster_size=1) cluster_asgn = communities df_clusterlabel = pd.DataFrame(cluster_asgn) df_clusterlabel.to_csv(os.path.join(path_python + '/count_data/cluster_labels_b4.csv'), header=None, index=None) unique, counts = np.unique(cluster_asgn, return_counts=True) #t-SNE plots target_ids = range(len(unique)) plt.figure(figsize=(8,8),frameon=False,dpi=dpi_set) plt.axis('off') #colors = 'r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple' for i, label in zip(target_ids, unique): plt.scatter(X_2d[ cluster_asgn==(i), 0], X_2d[ cluster_asgn==(i), 1], color=cm[i], label=label,s=s_size) plt.legend() plt.title('t-SNE') plt.show() file_name = os.path.join(path_python+'/figures_python/tSNE_labels.png') plt.savefig(file_name,dpi=dpi_set) df_data = pd.DataFrame(X_2d) df_data.to_csv(os.path.join(path_python + '/count_data/tSNE_embedding_pre.csv'), header=None, index=None) ############################### ## construct the cluster moments ############################### mean_mat = [] #1 x genes per entry. K x genes overall cov_list = [] # genes x genes per entry. K genes x genes overall joint_mat = [] # samples X genes per entry. K samples x genes overall U_mat = [] # samples X genes per entry. K samples x genes overall num_genes = Merfish_genes ## for i in range(len(unique)): #target_ids: print(i) rows = np.where(cluster_asgn==unique[i]) rows = np.array(rows).flatten() if (rows.shape[0] > 1): mean_mat.append(np.mean(M[rows, :], axis=0)) # added 3rd july #cov_list.append(np.cov(M[rows, 0:num_genes].T)) #joint_mat.append(np.random.multivariate_normal(mean_mat[i][0:num_genes], cov_list[i], num_unif_samples)) #####joint_mat.append(multivariate_normal.logpdf(mean_mat[i][0:10], cov_list[i], num_unif_samples)) cov_mat = np.cov(M[rows, 0:num_genes].T) cov_mat = np.dot(cov_mat,cov_mat.T) np.fill_diagonal(cov_mat, np.diag(cov_mat)+0.01) if (givens): (Q, R) = givens_rotation(cov_mat) cov_list.append(R) else: cov_list.append(cov_mat) len(mRNA_assign_list[0]) ################################ ## Iteration 0 ################# ################################ #inputs = range(num_fov) def processIter0(f): #mRNA_asgn_counter = np.zeros([num_fov,1]) #added 1st July 2020 asgn_stats = np.array([0,0,0,0]) #added , iter, fov, p, cellitwentto print('Fov is: ', str(f)) num_cells = cell_metadata_t_list[f].shape[0] #perspective projection if z==1: z_x = 6 z_y = 7 else: z_x = 6 + 2**(z-1) z_y = 7 + 2**(z-1) rw_x_boundary = cell_metadata_t_list[f][:,z_x] rw_y_boundary = cell_metadata_t_list[f][:,z_y] CoM_stage_coords = np.zeros([num_cells, 2], dtype=np.float) CoM_image_coords = np.zeros([num_cells, 2], dtype=np.float) #fig1 = (f+1)*100+(f+1) '''nx31 if(fig2p==1): fig2 = (f+1)*200+(f+1) fig4 = (f+1)*400+(f+1) plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) plt.title(str(f)) plt.axis('off') ''' print('plotting cells') for i in range(num_cells): #i will be i+2th row in the xls #print("Cell= " + str(i)) if(is_nan(rw_x_boundary[i]) | is_nan(rw_y_boundary[i])): #entries were empty: no segmentation found print("skip cell ", str(i)) else: m = rw_x_boundary[i].split(';') n = rw_y_boundary[i].split(';') num_coord = m.__len__() # this can be done smarter. for now I cast every string float to float and place into a list m_list = [] n_list = [] for j in range(num_coord-1): if( (float(m[j])!=float(m[j])) or (float(n[j])!=float(n[j]))): print("Coord NaN is at = " + str(j)) #continue else: m_list.append(float(m[j])) n_list.append(float(n[j])) #to plot the stage coords #for k in np.arange(0, m_list.__len__()-1, 2): #print(k) #plt.figure(fig1,figsize=(10,10),dpi=dpi_set) # connectpoints(m_list, n_list, k, k + 1) x_coord = np.array(m_list) # (256,) y_coord = np.array(n_list) # (256,) f1 = np.vstack((x_coord, y_coord)) # (2, 256) cX = sum(f1[0, :]) / f1.shape[1] cY = sum(f1[1, :]) / f1.shape[1] '''nx31 CoM_stage_coords[i, 0] = cX CoM_stage_coords[i, 1] = cY ''' #plt.figure(fig1,dpi=dpi_set,figsize=(10,10)) #axes = plt.gca() #axes.add_patch(Polygon(np.transpose(f1),closed=True, facecolor=colours[cluster_asgn[i]])) #plt.axis('off') #to project to the image coords im_list = [] in_list = [] for s in range(m_list.__len__()): a = np.array([[f1[0, s], f1[1, s]]], dtype='float32') a = np.array([a]) pointsOut = cv2.perspectiveTransform(a, h) im_list.append(pointsOut[0,0,0]) in_list.append(pointsOut[0,0,1]) ''' for k in np.arange(0, im_list.__len__()-1, 2): plt.figure(fig2,figsize=(10,10),dpi=dpi_set) connectpoints(im_list, in_list, k, k + 1) ''' ix_coord = np.array(im_list) # (256,) iy_coord = np.array(in_list) # (256,) f_image = np.vstack((ix_coord, iy_coord)) # (2, 256) cX_image = sum(f_image[0, :]) / f_image.shape[1] cY_image = sum(f_image[1, :]) / f_image.shape[1] CoM_image_coords[i,0] = cX_image CoM_image_coords[i,1] = cY_image if(fig2p==1): plt.figure(fig2) #,dpi=dpi_set,figsize=(10,10)) axes = plt.gca() #axes.add_patch(Polygon(np.transpose(f_image),closed=True, facecolor=colours[cluster_asgn[i]])) axes.add_patch(Polygon(np.transpose(f_image),closed=True, facecolor=cm[cluster_asgn[i]])) #plt.title('fig2') #plt.axis('off') '''nx31 ## plot mRNA inside the cell ## feat_id = cell_metadata_t_list[f][i,1] feat_rows = np.where((mRNA_metadata_t[:,1]==f) & (mRNA_metadata_t[:,9]==feat_id) & (mRNA_metadata_t[:,10]==1)) #fov id, which cell and in/out of cell #feat_rows = np.where((mRNA_metadata_t[:, 1] == 0) & (mRNA_metadata_t[:, 9] == feat_id)) feat_rows = np.array(feat_rows).flatten() num_rows = feat_rows.shape[0] for j in range(num_rows): x_stage_coord = mRNA_metadata_t[feat_rows[j],3] y_stage_coord = mRNA_metadata_t[feat_rows[j],4] a = np.array([[x_stage_coord, y_stage_coord]], dtype='float32') a = np.array([a]) #perspective proj pointsOut = cv2.perspectiveTransform(a, h) x_image_coord = pointsOut[0,0,0] y_image_coord = pointsOut[0,0,1] #plt.plot(x_image_coord, y_image_coord,'o',markersize=2, c=colours[cluster_asgn[i]],alpha=0.5) if ((i+cell_cumsum[f]) < M.shape[0]):#added 20th June 2020 plt.plot(x_image_coord, y_image_coord,'.',markersize=4, color=cm[cluster_asgn[i+cell_cumsum[f]]]) ''' #CoM_image_coords_list.append(CoM_image_coords) # find all mRNA coords after projection print("find all mRNA coords after projection") l2_norm_mat = np.zeros([num_cells, len(mRNA_assign_list[f])],dtype=float) closest_cells = [] #getting all the mRNA coords in that specific fov mRNA_coords = np.zeros([len(mRNA_all_list[f]), 2], dtype=np.float) for j in range(len(mRNA_all_list[f])): #get mRNA CoM m_x = mRNA_metadata_t[mRNA_all_list[f][j],3] m_y = mRNA_metadata_t[mRNA_all_list[f][j],4] a = np.array([[m_x, m_y]], dtype='float32') a = np.array([a]) #perspective proj pointsOut = cv2.perspectiveTransform(a, h) mRNA_coords[j, 0] = pointsOut[0,0,0] mRNA_coords[j, 1] = pointsOut[0,0,1] #mRNA_coords_list.append(mRNA_coords) #added # all mRNA to be assigned asgn_mRNA = np.intersect1d(mRNA_assign_list[f],mRNA_all_list[f]) # per mRNA to be assigned, do: print("per mRNA to be assigned do: loop") for l in range(0,len(asgn_mRNA)): # mock cell creation # print("create mock cell") #print(l) p = np.array(np.where(asgn_mRNA[l]==mRNA_all_list[f])).flatten() xc = mRNA_coords[p, 0] yc = mRNA_coords[p, 1] l_xc = xc - win_size r_xc = xc + win_size l_yc = yc - win_size u_yc = yc + win_size #ys = mRNA_coords[:,1][(mRNA_coords[:,1] >= l_yc) & (mRNA_coords[:,1] <= u_yc)] #xs = [np.where((mRNA_coords[:,0] >= l_xc) & (mRNA_coords[:,0] <= r_xc))] #ys = [np.where((mRNA_coords[:,1] >= l_yc) & (mRNA_coords[:,1] <= u_yc))] # finding its nearest cells #print("find its nearest cells") for i in range(num_cells): l2_norm_mat[i,l] = distance.euclidean([xc,yc], [CoM_image_coords[i,0],CoM_image_coords[i,1]]) ##21st June 2020 check #print('distances 1') #print(l2_norm_mat[0:10,0:10]) #23rd June 2020 #closest_cells.append(np.argsort(l2_norm_mat[:,l])[0:neigh_size]) #this is the order of cells (ie cell id) but in Python. For real world, +1 closest_cells.append(np.argsort(l2_norm_mat[:,l])[np.where((np.sort(l2_norm_mat[:,l])[0:neigh_size]<dist_ub)==True)]) #1st quadrant ##print("build the mock cell") #for the circle ind = np.where(pow(mRNA_coords[:,0]-xc,2) + pow(mRNA_coords[:,1]-yc,2) <= pow(win_size,2)) #ind = [np.where((mRNA_coords[:,1] >= l_yc) & (mRNA_coords[:,1] <= u_yc) & (mRNA_coords[:,0] >= l_xc) & (mRNA_coords[:,0] <= r_xc))] ind = np.array(ind).flatten() num_mRNA_neighs = ind.shape[0] ##########mockcell weighted l2_norm_mat_1 =np.zeros([1, num_mRNA_neighs],dtype=float) for i in range(num_mRNA_neighs): l2_norm_mat_1[0,i] = distance.euclidean([xc,yc], [mRNA_coords[ind[i],0],mRNA_coords[ind[i],1]]) mock_cell = np.zeros((1,num_genes)) #note change #####weighted_mc - 9th sep 2020 if (wgt_mock_cell): for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1/l2_norm_mat_1[0,k] # 9th sep 2020 else: #unweighted mock cell for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1 # 9th sep 2020 ''' #mock_cell = np.zeros((1,num_genes)) #num_genes = 10 mock_cell = np.zeros((1,num_genes)) #note change for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1 #mock_cell = mock_cell[:,0:10] #note change // ''' ######## # MLE with all K clusters #print("computing ML") mle_mockcell = np.zeros((1,len(unique))) for u in range(len(unique)): #mle_mockcell[0,u] = multivariate_normal.logpdf(zscore(mock_cell[0,0:10],axis=0), mean_mat[u], cov_list[u]) mle_mockcell[0,u] = multivariate_normal.logpdf(zscore(mock_cell,axis=1), mean_mat[u][0:num_genes], cov_list[u]) #print(mle_mockcell) mle_k = np.argmin(mle_mockcell) #assign to closest cell of that ML cluster cc = np.array(closest_cells[l]).flatten() mle_cc = np.array(np.where(cluster_asgn[cc]==mle_k)).flatten() #plot this line from mRNA to assign to cell if (len(mle_cc)!=0): #means there are cells in the neighbourhood of mRNA that are of the same class cell_to_map = cc[mle_cc[0]] #np.where(cell_metadata_t_list[1][cell_to_map,1] ==cell_metadata_t_list[1][:,1]) = cell_to_map # meaning cell_to_map indexes cell_metadata_t[f] #plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) if ((cell_to_map+cell_cumsum[f]) < M.shape[0]): #added 20th June 2020 '''nx31 plt.plot(xc, yc,'x',markersize=4, c=cm[cluster_asgn[cell_to_map+cell_cumsum[f]]],alpha=0.5) x_temp = [CoM_image_coords[cell_to_map,0],xc] y_temp = [CoM_image_coords[cell_to_map,1],yc] ''' mRNA_asgn_counter[f] = mRNA_asgn_counter[f] + 1 #nx31 plt.plot(x_temp, y_temp, linewidth=1, c=cm[cluster_asgn[cell_to_map+cell_cumsum[f]]]) #if (f==0): # M_upd[cell_to_map, int(mRNA_metadata_t[p, 0])-1] = M_upd[cell_to_map, int(mRNA_metadata_t[p, 0])-1] + 1 #else: # M_upd[cell_to_map+int(Merfish_CM_list[f-1].shape[0]), int(mRNA_metadata_t[mRNA_all_list[f-1].shape+p, 0])-1] = M_upd[cell_to_map+int(Merfish_CM_list[f-1].shape[0]), int(mRNA_metadata_t[mRNA_all_list[f-1].shape+p, 0])-1] + 1 #M_upd[cell_metadata_t_list[f][cell_to_map,1]-1, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] = M_upd[cell_metadata_t_list[f][cell_to_map,1]-1, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] + 1 # -ve 1 only because I need to update a Python structure! Merfish_CM_list[f][cell_to_map, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] = Merfish_CM_list[f][cell_to_map, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] + 1 a = np.array([0,f,p,cell_to_map]) #added asgn_stats = np.vstack((asgn_stats,a)) #added '''nx31 else: #print(l) #print('no match with any cluster') #plt.figure(fig4)#,dpi=dpi_set,figsize=(12,12)) plt.plot(xc,yc, '.', markersize=4, c='black')#, alpha=0.5) ''' '''nx31 plt.figure(fig4) plt.title("Iter_"+str(0)+"_"+"Fov_"+str(f)) plt.axis('off') file_name = os.path.join(path_python+'/figures_python/mRNA_assigned_seg_all_Iter=0_Fov='+str(f)+'.png') plt.savefig(file_name,dpi=dpi_set) ''' asgn_stats = np.delete(asgn_stats, (0), axis=0) #added #asgn_stats_list.append(asgn_stats) # added return CoM_image_coords, mRNA_coords, asgn_stats,mRNA_asgn_counter #20th June 2020 t3 = time.time() results1 = Parallel(n_jobs=num_cores)(delayed(processIter0)(f) for f in inputs) t4 = time.time() print('Time taken to perform Iter 0:', str(t4-t3),'secs') print('Time taken to perform Iter 0:', str((t4-t3)/60),'mins') CoM_image_coords_list=[item[0] for item in results1] mRNA_coords_list=[item[1] for item in results1] asgn_stats_list=[item[2] for item in results1] mRNA_asgn_counter_list=[item[3] for item in results1] #20th June 2020 asgn_stats_listoflists.append(asgn_stats_list) #### added below from scrna-seq code on 20th June 2020 # added # 17th, 18th Nov 2019 sd = [] for si in range(num_fov): print(mRNA_asgn_counter_list[si]) if (np.all(mRNA_asgn_counter_list[si]==0)): print(si) sd.append(si) ss = np.delete(range(num_fov),sd) #s=np.array(mRNA_asgn_counter_list).flatten() #print('s is ', s) #ss = np.array(np.where(s!=0)).flatten() #s = s[ss] #mRNA_asgn_counter_list = s print('ss is',ss) print('len of ss ', len(ss)) num_f = len(ss) print('num_f ', num_f) print('Total time to execute code across', str(iterations),'Iterations and',str(num_fov), 'FOVs is:', str((t4-t1)/60),'minutes') print('assigned stats in Iter 0 across FOVs are:', str(mRNA_asgn_counter_list)) #print('Total time to execute code across', str(iterations),'Iterations and',str(num_fov), 'FOVs is:', str((t4-t1)/60),'minutes') #print('assigned stats in Iter 0 across FOVs are:', str(mRNA_asgn_counter_list)) #print('number of mRNA to be assigned per FOV:', str(mRNA_assign_list)) #num_f = num_fov - 1 num_fov = len(ss) print('num_fov ', num_fov) for kk in range(0,num_f): print('% of mRNA assigned over total dangling mRNAs in FOV ', str(ss[kk]),'is ', str(int(len(mRNA_asgn_counter_list[ss[kk]]))/len(mRNA_assign_list[ss[kk]]))) print('% of mRNA assigned over total mRNAs in FOV ', str(ss[kk]),'is ', str(int(len(mRNA_asgn_counter_list[ss[kk]]))/len(mRNA_all_list[ss[kk]]))) #### added end ########################################## ######### Iterations ##################### ########################################## def processItern(f): print('Fov is: ', str(f)) asgn_stats = np.array([iter1,f,0,0]) # iter, fov, p, cellitwentto mRNA_asgn_counter = 0 num_cells = cell_metadata_t_list[f].shape[0] '''nx31 if(fig2p==1): fig2 = (f+1)*200+(f+1) #fig4 = (iter1+f+1)*4+(iter1+f+1) fig4 = (iter1+f+1)*4+f # cells are always the same per fov. so this is ok, no need to keep track of the iteration plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) plt.title(str(f)) plt.axis('off') ''' CoM_im_coords = CoM_image_coords_list[f] ''' for i in range(num_cells): feat_id = cell_metadata_t_list[f][i,1] feat_rows = np.where((mRNA_metadata_t[:,1]==f) & (mRNA_metadata_t[:,9]==feat_id) & (mRNA_metadata_t[:,10]==1)) #fov id, which cell and in/out of cell#feat_rows = np.where((mRNA_metadata_t[:, 1] == 0) & (mRNA_metadata_t[:, 9] == feat_id)) feat_rows = np.array(feat_rows).flatten() num_rows = feat_rows.shape[0] for j in range(num_rows): x_stage_coord = mRNA_metadata_t[feat_rows[j],3] y_stage_coord = mRNA_metadata_t[feat_rows[j],4] a = np.array([[x_stage_coord, y_stage_coord]], dtype='float32') a = np.array([a]) pointsOut = cv2.perspectiveTransform(a, h) x_image_coord = pointsOut[0,0,0] y_image_coord = pointsOut[0,0,1] plt.plot(x_image_coord, y_image_coord,'o',markersize=2, color=cm.colors[cluster_asgn[i]]) ''' #find l2-norm of each gene candidate to every cell #do just for image coordinates, it should be the same cells in the stage coords as wel l2_norm_mat =np.zeros([num_cells, len(mRNA_assign_list[f])],dtype=float) closest_cells = [] mRNA_im_coords = mRNA_coords_list[f] #added # all mRNA are always the same per fov. so this is ok, no need to keep track of the iteration #new mRNA assign list per fov #added to_rem_mRNA = asgn_stats_listoflists[iter1-1][f] len_rem_mRNA = len(to_rem_mRNA) print('len_rem_mRNA ',str(len_rem_mRNA)) to_rem_mRNA_1=np.array([0]) for l in range(len_rem_mRNA): pp = np.array(np.where(mRNA_all_list[f][to_rem_mRNA[l,2]]==mRNA_assign_list[f])).flatten() to_rem_mRNA_1 = np.concatenate((to_rem_mRNA_1,pp)) to_rem_mRNA_1=np.delete(to_rem_mRNA_1,(0),axis=0) mRNA_assign_list[f]=np.delete(mRNA_assign_list[f],to_rem_mRNA_1,axis=0).copy() #added ,copy() 1st July 2020 print('mRNA_assign_list[f] ', str(len(mRNA_assign_list[f]))) # all mRNA to be assigned asgn_mRNA = np.intersect1d(mRNA_assign_list[f],mRNA_all_list[f]) print('asgn_mRNA ',str(len(asgn_mRNA))) # per mRNA to be assigned, do: print("per mRNA to be assigned do: loop") for l in range(0,len(asgn_mRNA)): #//added 1st July 2020 p = np.array(np.where(asgn_mRNA[l]==mRNA_all_list[f])).flatten() xc = mRNA_im_coords[p, 0] yc = mRNA_im_coords[p, 1] l_xc = xc - win_size r_xc = xc + win_size l_yc = yc - win_size u_yc = yc + win_size for i in range(num_cells): l2_norm_mat[i,l] = distance.euclidean([xc,yc], [CoM_im_coords[i,0],CoM_im_coords[i,1]]) ##21st June 2020 check #print('distances 1') #print(l2_norm_mat[0:10,0:10]) #23rd June 2020 #closest_cells.append(np.argsort(l2_norm_mat[:,l])[0:neigh_size]) #this is the order of cells (ie cell id) but in Python. For real world, +1 closest_cells.append(np.argsort(l2_norm_mat[:,l])[np.where((np.sort(l2_norm_mat[:,l])[0:neigh_size]<dist_ub)==True)]) #1st quadrant #print("build the mock cell") # for a square # ind = [np.where((mRNA_im_coords[:,1] >= l_yc) & (mRNA_im_coords[:,1] <= u_yc) & (mRNA_im_coords[:,0] >= l_xc) & (mRNA_im_coords[:,0] <= r_xc))] # for a circle ind = np.where(pow(mRNA_im_coords[:,0]-xc,2) + pow(mRNA_im_coords[:,1]-yc,2) <= pow(win_size,2)) ind = np.array(ind).flatten() num_mRNA_neighs = ind.shape[0] ##########mockcell weighted l2_norm_mat_1 =np.zeros([1, num_mRNA_neighs],dtype=float) for i in range(num_mRNA_neighs): l2_norm_mat_1[0,i] = distance.euclidean([xc,yc], [mRNA_im_coords[ind[i],0],mRNA_im_coords[ind[i],1]]) mock_cell = np.zeros((1,num_genes)) #note change #####weighted_mc - 9th sep 2020 if (wgt_mock_cell): for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1/l2_norm_mat_1[0,k] # 9th sep 2020 else: #unweighted mock cell for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1 # 9th sep 2020 ''' #mock_cell = np.zeros((1,num_genes)) #num_genes = 10 mock_cell = np.zeros((1,num_genes)) for k in range(num_mRNA_neighs): bc_id = mRNA_metadata_t[mRNA_all_list[f][ind[k]],0]-1 #telling which gene it is and where the entry should go to in the count matrix if(bc_id < num_genes): mock_cell[0,int(bc_id)] = mock_cell[0,int(bc_id)] + 1 ''' ######## # MLE with all K clusters #print("computing ML") mle_mockcell = np.zeros((1,len(unique))) #for u in range(len(unique)): for u in range(len(mean_mat)): #mle_mockcell[0,u] = multivariate_normal.logpdf(zscore(mock_cell[0,0:10],axis=0), mean_mat[u], cov_list[u]) mle_mockcell[0,u] = multivariate_normal.logpdf(zscore(mock_cell,axis=1), mean_mat[u][0:num_genes], cov_list[u])#print(mle_mockcell) mle_k = np.argmin(mle_mockcell) cc = np.array(closest_cells[l]).flatten() mle_cc = np.array(np.where(cluster_asgn[cc]==mle_k)).flatten() #plot this line from mRNA to assign to cell if (len(mle_cc)!=0): #means there are cells in the neighbourhood of mRNA that are of the same class cell_to_map = cc[mle_cc[0]] #plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) if ((cell_to_map+cell_cumsum[f]) < M_upd.shape[0]): #added 20th June 2020 '''nx31 plt.plot(xc, yc,'x',markersize=4, c=cm[cluster_asgn[cell_to_map+cell_cumsum[f]]],alpha=0.5) x_temp = [CoM_im_coords[cell_to_map,0],xc] y_temp = [CoM_im_coords[cell_to_map,1],yc] ''' mRNA_asgn_counter = mRNA_asgn_counter + 1 #nx31 plt.plot(x_temp, y_temp, linewidth=1, c=cm[cluster_asgn[cell_to_map+cell_cumsum[f]]]) #Merfish_CM[cell_to_map+cell_cumsum[f], int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] = Merfish_CM[cell_to_map+cell_cumsum[f], int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] + 1 Merfish_CM_list[f][cell_to_map, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] = Merfish_CM_list[f][cell_to_map, int(mRNA_metadata_t[mRNA_all_list[f][p],0]-1)] + 1 a = np.array([iter1,f,p,cell_to_map]) #added asgn_stats = np.vstack((asgn_stats,a)) #added '''nx31 else: plt.figure(fig4)#,dpi=dpi_set,figsize=(12,12)) plt.plot(xc,yc, '.', markersize=4, c='black')#, alpha=0.5) ''' '''nx31 plt.figure(fig4)#,dpi=dpi_set,figsize=(12,12)) plt.title("Iter_"+str(iter1)+"_"+"Fov_"+str(f)) plt.axis('off') file_name = os.path.join(path_python+'/figures_python/mRNA_assigned_seg_all_Iter='+str(iter1)+'_Fov='+str(f)+'.png') plt.savefig(file_name) ''' asgn_stats = np.delete(asgn_stats, (0), axis=0) #asgn_stats_list.append(asgn_stats) #end of all fov return asgn_stats, mRNA_asgn_counter#, mRNA_assign_list # added mRNA_assign_list 1st July 2020 for iter1 in range(1,iterations): #mRNA_assign_listoflists = []#added on 2nd June 2020 ##commneted the above on 4th July 2020 print('Iteration is: ', str(iter1)) #asgn_stats_list = [] Merfish_CM = np.zeros((1,Merfish_genes)) #will have all the Count matrices consolidated for f in range(num_fov): Merfish_CM = np.concatenate((Merfish_CM,Merfish_CM_list[f]),axis=0) #20th june 2020 Merfish_CM = np.delete(Merfish_CM, (0), axis=0) #remove rows that have 0 rowsum row_sums_CM = Merfish_CM.sum(axis=1,keepdims=True) rows_remove = np.where(row_sums_CM==0) Merfish_CM = np.delete(Merfish_CM,(rows_remove),axis=0) #added 20th June 2020 cell_cumsum = np.cumsum(num_cells_list) cell_cumsum = np.hstack((0,cell_cumsum)) for i in range(len(rows_remove[0])): print('i is', str(i)) for j in range(i+1): print('j is', str(j)) if ((j+1) < (len(cell_cumsum))): if (rows_remove[0][i] < cell_cumsum[j+1]): cell_cumsum[j+1] = cell_cumsum[j+1] - 1 print(cell_cumsum) continue continue #end add M_upd = zscore(Merfish_CM,axis=1) num_cells = M_upd.shape[0] num_genes = M_upd.shape[1] #save M_upd df_data = pd.DataFrame(M_upd) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_Mupd_fovall_Iter_'+str(iter1)+'.csv'), header=None, index=None) #save count matrix df_data = pd.DataFrame(Merfish_CM) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_Merfish_fovall_Iter_'+str(iter1)+'.csv'), header=None, index=None) #perform clustering for the 'sure' pixels if(bayes_MM): Mod_dpgmm=BayesianGaussianMixture(n_components=num_comp, covariance_type='full',weight_concentration_prior_type='dirichlet_process').fit(M_upd) cluster_asgn = Mod_dpgmm.predict(M_upd) else: communities, graph, Q = pg.cluster(M_upd)#, k=num_comp, min_cluster_size=1) cluster_asgn = communities df_clusterlabel = pd.DataFrame(cluster_asgn) df_clusterlabel.to_csv(os.path.join(path_python + '/count_data/cluster_labels_postreasgn_Iter_'+str(iter1)+'.csv'), header=None, index=None) X_2d = tsne.fit_transform(M_upd) df_data = pd.DataFrame(X_2d) df_data.to_csv(os.path.join(path_python + '/count_data/tSNE_embedding_post.csv'), header=None, index=None) unique, counts = np.unique(cluster_asgn, return_counts=True) #t-SNE plots target_ids = range(len(unique)) plt.figure(figsize=(8,8),frameon=False,dpi=dpi_set) plt.axis('off') #colors = 'r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple' for i, label in zip(target_ids, unique): plt.scatter(X_2d[ cluster_asgn==(i), 0], X_2d[ cluster_asgn==(i), 1], color=cm[i], label=label,s=s_size) plt.legend() plt.title('t-SNE') plt.show() file_name = os.path.join(path_python+'/figures_python/tSNE_labels_postreasgn_newcoord_Iter_'+str(iter1)+'.png') plt.savefig(file_name,dpi=dpi_set) #mRNA_asgn_counter = np.zeros([num_fov,1]) #####added 20th June 2020 ##### recomputing the cluster moments based on the revised Merfish CMq ##### mean_mat = [] #1 x genes per entry. K x genes overall cov_list = [] # genes x genes per entry. K genes x genes overall for i in range(len(unique)): #target_ids: print(i) #rows = np.where(cluster_asgn==unique[i]) rows = np.where(cluster_asgn==unique[i]) rows = np.array(rows).flatten() if (rows.shape[0] > 1): mean_mat.append(np.mean(M_upd[rows, :], axis=0)) cov_mat = np.cov(M_upd[rows, 0:num_genes].T) cov_mat = np.dot(cov_mat,cov_mat.T) np.fill_diagonal(cov_mat, np.diag(cov_mat)+0.01) if (givens): (Q, R) = givens_rotation(cov_mat) cov_list.append(R) else: cov_list.append(cov_mat) ##add end t3 = time.time() ########## 21st Feb 2021 inputs = ss ########## results = Parallel(n_jobs=num_cores)(delayed(processItern)(f) for f in inputs) t4 = time.time() print('Time taken to perform Iteration',str(iter1),'is:', str(t4-t3),'secs') print('Time taken to perform all Iteration:',str(iter1),'is:', str((t4-t3)/60),'mins') asgn_stats_list = [item[0] for item in results] #results#[item[0] for item in results] assigned_percent = [item[1] for item in results] ## added 2nd July 2020 #mRNA_assign_listoflists = [item[2] for item in results] #mRNA_assign_listoflists.append(item[2] for item in results) ## added 4th July 2020 #mRNA_assign_list = [] ## added end asgn_stats_listoflists.append(asgn_stats_list) ## added 20th June 2020 ### added ### 17th Nov 2019 #s=np.array(mRNA_asgn_counter_list).flatten() #11th Dec 2019 - added to remove ss just accessing FOV 0 ss= np.array(np.where(np.array(assigned_percent).flatten()!=0)).flatten() #ss = np.array(np.where(assigned_percent!=0)).flatten() #new set of FOVs #s = s[ss] #mRNA_asgn_counter_list = s print('within processItern loop') print('ss is ',str(ss)) num_f = len(ss) print('num_f is ',str(num_f)) print(ss) #num_f = num_fov - 1 #num_fov = len(ss) ##end add print("assigned_percent ",str(assigned_percent)) ''' if (not np.any(assigned_percent)): inputs=ss break if len(ss)!=0: for kk in range(0,num_fov): print('% of mRNA assigned over total dangling mRNAs in FOV ', str(ss[kk]),'is ', str(int(assigned_percent[ss[kk]])/len(mRNA_assign_list[ss[kk]]))) print('% of mRNA assigned over total mRNAs in FOV ', str(ss[kk]),'is ', str(int(assigned_percent[ss[kk]])/len(mRNA_all_list[ss[kk]]))) ''' inputs = ss print('Overall runtime is:', str((t4-t1)/60),'mins') #final Merfish_CM updated Merfish_CM = np.zeros((1,Merfish_genes)) #will have all the Count matrices consolidated for f in range(num_fov): Merfish_CM = np.concatenate((Merfish_CM,Merfish_CM_list[f]),axis=0) #20th june 2020 Merfish_CM = np.delete(Merfish_CM, (0), axis=0) #remove rows that have 0 rowsum row_sums_CM = Merfish_CM.sum(axis=1,keepdims=True) rows_remove = np.where(row_sums_CM==0) Merfish_CM = np.delete(Merfish_CM,(rows_remove),axis=0) #added 20th June 2020 '''cell_cumsum = np.cumsum(num_cells_list) cell_cumsum = np.hstack((0,cell_cumsum)) for i in range(len(rows_remove[0])): print('i is', str(i)) for j in range(i+1): print('j is', str(j)) if ((j+1) < (len(cell_cumsum))): if (rows_remove[0][i] < cell_cumsum[j+1]): cell_cumsum[j+1] = cell_cumsum[j+1] - 1 print(cell_cumsum) continue continue #end add ''' M_upd = zscore(Merfish_CM,axis=1) num_cells = M_upd.shape[0] #num_genes = M_upd.shape[1] num_genes = Merfish_genes #nx31 #save M_upd df_data = pd.DataFrame(M_upd) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_Mupd_fovall_Iter_'+str(iterations)+'.csv'), header=None, index=None) #save count matrix df_data = pd.DataFrame(Merfish_CM) df_data.to_csv(os.path.join(path_python + '/count_data/count_matrix_Merfish_fovall_Iter_'+str(iterations)+'.csv'), header=None, index=None) #perform clustering for the 'sure' pixels if(bayes_MM): Mod_dpgmm=BayesianGaussianMixture(n_components=num_comp, covariance_type='full',weight_concentration_prior_type='dirichlet_process').fit(M_upd) cluster_asgn = Mod_dpgmm.predict(M_upd) else: communities, graph, Q = pg.cluster(M_upd)#, k=num_comp, min_cluster_size=1) cluster_asgn = communities df_clusterlabel = pd.DataFrame(cluster_asgn) df_clusterlabel.to_csv(os.path.join(path_python + '/count_data/cluster_labels_postreasgn_Iter_'+str(iterations)+'.csv'), header=None, index=None) X_2d = tsne.fit_transform(M_upd) df_data = pd.DataFrame(X_2d) df_data.to_csv(os.path.join(path_python + '/count_data/tSNE_embedding_post_'+str(iterations)+'.csv'), header=None, index=None) unique, counts = np.unique(cluster_asgn, return_counts=True) #t-SNE plots target_ids = range(len(unique)) plt.figure(figsize=(10,10),frameon=False,dpi=dpi_set) plt.axis('off') #colors = 'r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple' for i, label in zip(target_ids, unique): plt.scatter(X_2d[ cluster_asgn==(i), 0], X_2d[ cluster_asgn==(i), 1], color=cm[i], label=label,s=s_size) plt.legend() plt.title('t-SNE') plt.show() file_name = os.path.join(path_python+'/figures_python/tSNE_labels_postreasgn_newcoord_Iter_'+str(iterations)+'.png') plt.savefig(file_name,dpi=dpi_set) ################################################################### ## parallelise the overall merging of mRNA assignments per FOV #### ################################################################### def processOverall(f): #asgn_stats = np.array([iter1,0,0,0]) #added if(fig2p==1): fig2 = (f+1)*200+(f+1) #fig4 = (iter1+f+1)*4+(iter1+f+1) fig4 = (iter1+f+1)*4+f #fig2 = (f+1)*200+(f+1) #fig4 = (f+1)*400+(f+1) plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) plt.title(str(f)) plt.axis('off') print('Fov is: ', str(f)) num_cells = cell_metadata_t_list[f].shape[0] #perspective projection if z==1: z_x = 6 z_y = 7 else: z_x = 6 + 2**(z-1) z_y = 7 + 2**(z-1) rw_x_boundary = cell_metadata_t_list[f][:,z_x] rw_y_boundary = cell_metadata_t_list[f][:,z_y] CoM_stage_coords = np.zeros([num_cells, 2], dtype=np.float) CoM_image_coords = np.zeros([num_cells, 2], dtype=np.float) #fig1 = (f+1)*100+(f+1) print('plotting cells') for i in range(num_cells): #i will be i+2th row in the xls #print("Cell= " + str(i)) if(is_nan(rw_x_boundary[i]) | is_nan(rw_y_boundary[i])): #entries were empty: no segmentation found print("skip cell ", str(i)) else: m = rw_x_boundary[i].split(';') n = rw_y_boundary[i].split(';') num_coord = m.__len__() # this can be done smarter. for now I cast every string float to float and place into a list m_list = [] n_list = [] for j in range(num_coord-1): if( (float(m[j])!=float(m[j])) or (float(n[j])!=float(n[j]))): print("Coord NaN is at = " + str(j)) #continue else: m_list.append(float(m[j])) n_list.append(float(n[j])) #to plot the stage coords #for k in np.arange(0, m_list.__len__()-1, 2): #print(k) #plt.figure(fig1,figsize=(10,10),dpi=dpi_set) # connectpoints(m_list, n_list, k, k + 1) x_coord = np.array(m_list) # (256,) y_coord = np.array(n_list) # (256,) f1 = np.vstack((x_coord, y_coord)) # (2, 256) cX = sum(f1[0, :]) / f1.shape[1] cY = sum(f1[1, :]) / f1.shape[1] CoM_stage_coords[i, 0] = cX CoM_stage_coords[i, 1] = cY im_list = [] in_list = [] for s in range(m_list.__len__()): a = np.array([[f1[0, s], f1[1, s]]], dtype='float32') a = np.array([a]) pointsOut = cv2.perspectiveTransform(a, h) im_list.append(pointsOut[0,0,0]) in_list.append(pointsOut[0,0,1]) for k in np.arange(0, im_list.__len__()-1, 2): plt.figure(fig4,figsize=(10,10),dpi=dpi_set) connectpoints(im_list, in_list, k, k + 1) ix_coord = np.array(im_list) # (256,) iy_coord = np.array(in_list) # (256,) f_image = np.vstack((ix_coord, iy_coord)) # (2, 256) cX_image = sum(f_image[0, :]) / f_image.shape[1] cY_image = sum(f_image[1, :]) / f_image.shape[1] CoM_image_coords[i,0] = cX_image CoM_image_coords[i,1] = cY_image if(fig2p==1): plt.figure(fig2) # ,dpi=dpi_set,figsize=(10,10)) axes = plt.gca() #axes.add_patch(Polygon(np.transpose(f_image),closed=True, facecolor=colours[cluster_asgn[i]])) axes.add_patch(Polygon(np.transpose(f_image),closed=True, facecolor=cm.colors[cluster_asgn[i]])) ## plot mRNA inside the cell ## feat_id = cell_metadata_t_list[f][i,1] feat_rows = np.where((mRNA_metadata_t[:,1]==f) & (mRNA_metadata_t[:,9]==feat_id) & (mRNA_metadata_t[:,10]==1)) #fov id, which cell and in/out of cell #feat_rows = np.where((mRNA_metadata_t[:, 1] == 0) & (mRNA_metadata_t[:, 9] == feat_id)) feat_rows = np.array(feat_rows).flatten() num_rows = feat_rows.shape[0] for j in range(num_rows): x_stage_coord = mRNA_metadata_t[feat_rows[j],3] y_stage_coord = mRNA_metadata_t[feat_rows[j],4] a = np.array([[x_stage_coord, y_stage_coord]], dtype='float32') a = np.array([a]) #perspective proj pointsOut = cv2.perspectiveTransform(a, h) x_image_coord = pointsOut[0,0,0] y_image_coord = pointsOut[0,0,1] #plt.plot(x_image_coord, y_image_coord,'o',markersize=2, c=colours[cluster_asgn[i]],alpha=0.5) if ((i+cell_cumsum[f]) < M_upd.shape[0]): plt.plot(x_image_coord, y_image_coord,'.',markersize=4, color=cm[cluster_asgn[i+cell_cumsum[f]]]) mRNA_im_coords = mRNA_coords_list[f] #added # all mRNA are always the same per fov. so this is ok, no need to keep track of the iteration plt.figure(fig4,dpi=dpi_set,figsize=(12,12)) for itert in range(iterations): print("within final mergen iteration is:", str(itert)) if (f < len(asgn_stats_listoflists[itert])): to_plot_mRNA = asgn_stats_listoflists[itert][f] len_tp = len(to_plot_mRNA) #if (len_tp > 0): if (isinstance(to_plot_mRNA[0], np.ndarray)): print('len_tp', len_tp) #nx31 print('to_plot_mRNA', to_plot_mRNA) for l in range(len_tp): pp = to_plot_mRNA[l,2] cell_pp = to_plot_mRNA[l,3] if(pp < mRNA_im_coords.shape[0]): xc = mRNA_im_coords[pp, 0] yc = mRNA_im_coords[pp, 1] if(cell_pp < CoM_image_coords.shape[0]): if ((cell_pp+cell_cumsum[f]) < M_upd.shape[0]): plt.plot(xc, yc,'x',markersize=4, c=cm[cluster_asgn[cell_pp+cell_cumsum[f]]],alpha=0.5) x_temp = [CoM_image_coords[cell_pp,0],xc] y_temp = [CoM_image_coords[cell_pp,1],yc] plt.plot(x_temp, y_temp, linewidth=1, c=cm[cluster_asgn[cell_pp+cell_cumsum[f]]]) plt.figure(fig4) plt.title("Final_plot_Fov_"+str(f)) plt.axis('off') file_name = os.path.join(path_python+'/figures_python/ ='+str(f)+'.png') plt.savefig(file_name,dpi=dpi_set) t5 = time.time() inputs = range(num_fov) results = Parallel(n_jobs=num_cores)(delayed(processOverall)(f) for f in inputs) t6 = time.time() print('Time taken to perform overall merge is',str(t6-t5),'secs') print('Time taken to perform overall merge is',str((t6-t5)/60),'mins') print('Overall runtime is:', str((t6-t1)/60),'mins')

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49,412

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null

0

1

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null

0

6

d57da9de211b8a215dc7a2219e0142665bdbed84

31

py

Python

djtest/djtest/settings.py

petroleyum/dj2example

459fe77d72bd2c0a4e0cb583927839344e39e0de

[ "Apache-2.0" ]

null

djtest/djtest/settings.py

petroleyum/dj2example

459fe77d72bd2c0a4e0cb583927839344e39e0de

[ "Apache-2.0" ]

null

djtest/djtest/settings.py

petroleyum/dj2example

459fe77d72bd2c0a4e0cb583927839344e39e0de

[ "Apache-2.0" ]

null

from .default_settings import *

31

0.83871

4

31

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0.096774

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31

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null

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null

0

1

0

1

0

1

0

6

63452017db8d3639d13554b995b2b32b9b15941c

5,945

py

Python

skcv.py

jjepsuomi/LPO-SCV

168be0f8d284f1716be5861e5699b14e6b924732

[ "MIT" ]

null

skcv.py

jjepsuomi/LPO-SCV

168be0f8d284f1716be5861e5699b14e6b924732

[ "MIT" ]

null

skcv.py

jjepsuomi/LPO-SCV

168be0f8d284f1716be5861e5699b14e6b924732

[ "MIT" ]

null

def warn(*args, **kwargs): pass import warnings warnings.warn = warn import numpy as np from rlscore.learner import RLS from sklearn.neighbors.regression import KNeighborsRegressor from rlscore.measure import cindex from rlscore.utilities.cross_validation import random_folds from utils import distanceMatrix, dzfolds, visualize_skcv, plotRes_skcv """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" --- SPATIAL K-FOLD CROSS VALIDATION FOR RIDGE REGRESSION --- DESCRIPTION: - This function will implement spatial k-fold cross validation for ridge regression model and produces a performance table with two columns: prediction range, concordance index. INPUT: 'coordinates': a n-by-2 array consisting from data point coordinates 'Xdata': a n-by-m matrix containing predictor data (columns as features) 'Ydata': a n-by-1 matrix of output values 'number_of_folds': integer number of cross validation folds 'dzradii': list of dead zone radiuses to be used 'regparams': list of regularization parameters to be tried 'visualization': boolean on whether visualization wanted or not. If True, results in longer calculation OUTPUT: 'performanceTable': a r-by-2 matrix containing prediction results. The number of rows (r) is determined by the number of dead zone radiuses in 'dzradii'. First corresponds to prediction range, second to concordance index """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" def skcv_rls(coordinates, Xdata, Ydata, number_of_folds, dzradii, regparam, visualization): print("Starting skcv-rls analysis...") # Calculate sorted pairwise distance matrix and indexes performanceTable = np.zeros([len(dzradii),2]) data_distances, data_distance_indexes = distanceMatrix(coordinates) folds = random_folds(len(Ydata), number_of_folds) for rind, dzradius in enumerate(dzradii): print("Analysis ongoing, dead zone radius: " + str(dzradius) + "m / " + str(dzradii[len(dzradii)-1]) + "m") # Calculate dead zone folds dz_folds = dzfolds(dzradius, folds, data_distances, data_distance_indexes) learner = RLS(Xdata, Ydata, regparam=regparam) P = np.zeros(Ydata.shape) for fold_id, dz_fold in enumerate(dz_folds): preds = learner.holdout(dz_fold) if preds.ndim == 0: P[folds[fold_id]] = preds else: P[folds[fold_id]] = preds[0:len(folds[fold_id])] if visualization: # Check for visualization testcoords = coordinates[folds[fold_id],:] dzcoords = coordinates[dz_fold, :] visualize_skcv(coordinates, testcoords, dzcoords, dzradius) perf = cindex(Ydata, P) performanceTable[rind,0] = dzradius performanceTable[rind, 1] = perf plotRes_skcv(performanceTable, rind, number_of_folds, "rls") print("Analysis done.") return performanceTable """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" --- SPATIAL K-FOLD CROSS VALIDATION FOR K-NEAREST NEIGHBOR --- DESCRIPTION: - This function will implement spatial k-fold cross validation for k-nearest neighbor model and produces a performance table with three columns: prediction range, concordance index. INPUT: 'coordinates': a n-by-2 array consisting from data point coordinates 'Xdata': a n-by-m matrix containing predictor data (columns as features) 'Ydata': a n-by-1 matrix of output values 'number_of_folds': integer number of cross validation folds 'dzradii': list of dead zone radiuses to be used 'klist': list of k values to be tried 'visualization': boolean on whether visualization wanted or not. If True, result in longer calculation OUTPUT: 'performanceTable': a r-by-2 matrix containing prediction results. The number of rows (r) is determined by the number of dead zone radiuses in 'dzradii'. First corresponds to prediction range, second to concordance index """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" def skcv_knn(coordinates, Xdata, Ydata, number_of_folds, dzradii, k_neighbors, visualization): print("Starting skcv-knn analysis...") # Calculate sorted pairwise distance matrix and indexes performanceTable = np.zeros([len(dzradii),2]) data_distances, data_distance_indexes = distanceMatrix(coordinates) folds = random_folds(len(Ydata), number_of_folds) for rind, dzradius in enumerate(dzradii): print("Analysis ongoing, dead zone radius: " + str(dzradius) + "m / " + str(dzradii[len(dzradii)-1]) + "m") # Calculate dead zone folds dz_folds = dzfolds(dzradius, folds, data_distances, data_distance_indexes) # Initialize performance variables P = np.zeros(Ydata.shape) for fold_id, dz_fold in enumerate(dz_folds): X_tr = np.delete(Xdata, dz_fold, axis=0) Y_tr = np.delete(Ydata, dz_fold, axis=0) learner = KNeighborsRegressor(n_neighbors=k_neighbors) learner.fit(X_tr, Y_tr) preds = learner.predict(Xdata[dz_fold]) if preds.ndim == 0: P[folds[fold_id]] = preds else: P[folds[fold_id]] = preds[0:len(folds[fold_id])] if visualization: # Check for visualization testcoords = coordinates[folds[fold_id],:] dzcoords = coordinates[dz_fold, :] visualize_skcv(coordinates, testcoords, dzcoords, dzradius) perf = cindex(Ydata, P) performanceTable[rind,0] = dzradius performanceTable[rind, 1] = perf plotRes_skcv(performanceTable, rind, number_of_folds, "K-nn") print("Analysis done.") return performanceTable

49.132231

116

0.643566

703

5,945

5.344239

0.226174

0.029811

0.027682

0.0181

0.816609

0.795848

0.754325

0.738355

0.721853

0

0.004331

0.223213

5,945

120

117

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0.009524

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0.114286

0.057143

0

null

0

1

0

null

0

6

635739029debab13876c10161662d411d7e6178e

9,392

py

Python

tests/data_generator/tests_varr_sorting.py

usert5432/vlne

e3cafd30ecce3a2dbc4a37cc4257d07fb1a1785d

[ "MIT" ]

null

tests/data_generator/tests_varr_sorting.py

usert5432/vlne

e3cafd30ecce3a2dbc4a37cc4257d07fb1a1785d

[ "MIT" ]

null

tests/data_generator/tests_varr_sorting.py

usert5432/vlne

e3cafd30ecce3a2dbc4a37cc4257d07fb1a1785d

[ "MIT" ]

null

"""Test correctness of the prong sorting by the `DataProngSorter` decorator""" import unittest import numpy as np from vlne.data.data_generator import DataProngSorter from .tests_data_generator_base import ( DictLoader, DataGenerator, TestsDataGeneratorBase ) class TestsVarrSorting(TestsDataGeneratorBase, unittest.TestCase): """Test correctness of the `DataProngSorter` decorator""" @staticmethod def _make_sorted_dgen( data, batch_size, prong_sorter, input_vars, is3d = True ): kwargs = {} if is3d: kwargs = { 'vars_input_png3d' : input_vars } input_name = 'input_png3d' else: kwargs = { 'vars_input_png2d' : input_vars } input_name = 'input_png2d' dgen = DataGenerator( DictLoader(data), batch_size = batch_size, **kwargs ) return DataProngSorter(dgen, prong_sorter, input_name, input_vars) def test_single_var_sorter_batch_size_1_ascending(self): """ Test single var prong sorting in ascending order when batch_size=1 """ data = { 'var' : [[4,1], [1,2,3,4], [4,3,2,1], [2,1], []] } batch_size = 1 batch_data = [ { 'input_png3d' : [[[1],[4]]] }, { 'input_png3d' : [[[1],[2],[3],[4]]] }, { 'input_png3d' : [[[1],[2],[3],[4]]] }, { 'input_png3d' : [[[1],[2]]] }, { 'input_png3d' : np.empty((1, 0, 1)) }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '+var', [ 'var' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_single_var_sorter_batch_size_1_descending(self): """ Test single var prong sorting in descending order when batch_size=1 """ data = { 'var' : [[4,1], [1,2,3,4], [4,3,2,1], [2,1], []] } batch_size = 1 batch_data = [ { 'input_png3d' : [[[4],[1]]] }, { 'input_png3d' : [[[4],[3],[2],[1]]] }, { 'input_png3d' : [[[4],[3],[2],[1]]] }, { 'input_png3d' : [[[2],[1]]] }, { 'input_png3d' : np.empty((1, 0, 1)) }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '-var', [ 'var' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_single_var_sorter_batch_size_3_ascending(self): """ Test single var prong sorting in ascending order when batch_size=3 """ data = { 'var' : [[4,1], [1,2,3,4], [4,3,2,1], [2,1], []] } batch_size = 3 batch_data = [ { 'input_png3d' : [ [ [1], [4], [np.nan], [np.nan]], [ [1], [2], [3], [4]], [ [1], [2], [3], [4]], ] }, { 'input_png3d' : [ [ [1], [2] ], [ [np.nan], [np.nan] ], ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '+var', [ 'var' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_single_var_sorter_batch_size_3_descending(self): """ Test single var prong sorting in descending order when batch_size=3 """ data = { 'var' : [[4,1], [1,2,3,4], [4,3,2,1], [2,1], []] } batch_size = 3 batch_data = [ { 'input_png3d' : [ [ [4], [1], [np.nan], [np.nan]], [ [4], [3], [2], [1]], [ [4], [3], [2], [1]], ] }, { 'input_png3d' : [ [ [2], [1] ], [ [np.nan], [np.nan] ], ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '-var', [ 'var' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_multi_var_sorter_batch_size_1_ascending(self): """ Test multi var prong sorting in ascending order when batch_size=1 """ data = { 'data1' : [[6,3,6,2],[2,5],[1],[4,2],[3,8,4]], 'sortby' : [[8,9,3,7],[5,1],[3],[2,7],[7,1,3]], 'data2' : [[1,6,2,1],[4,2],[2],[8,4],[1,9,8]], } batch_size = 1 batch_data = [ { 'input_png3d' : [ [ [6,3,2], [2,7,1], [6,8,1], [3,9,6] ] ] }, { 'input_png3d' : [ [ [5,1,2], [2,5,4] ] ] }, { 'input_png3d' : [ [ [1,3,2] ] ] }, { 'input_png3d' : [ [ [4,2,8], [2,7,4] ] ] }, { 'input_png3d' : [ [ [8,1,9], [4,3,8], [3,7,1] ] ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '+sortby', [ 'data1', 'sortby', 'data2' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_multi_var_sorter_batch_size_1_descending(self): """ Test multi var prong sorting in descending order when batch_size=1 """ data = { 'data1' : [[6,3,6,2],[2,5],[1],[4,2],[3,8,4]], 'sortby' : [[8,9,3,7],[5,1],[3],[2,7],[7,1,3]], 'data2' : [[1,6,2,1],[4,2],[2],[8,4],[1,9,8]], } batch_size = 1 batch_data = [ { 'input_png3d' : [ [ [3,9,6], [6,8,1], [2,7,1], [6,3,2] ] ] }, { 'input_png3d' : [ [ [2,5,4], [5,1,2] ] ] }, { 'input_png3d' : [ [ [1,3,2] ] ] }, { 'input_png3d' : [ [ [2,7,4], [4,2,8] ] ] }, { 'input_png3d' : [ [ [3,7,1], [4,3,8], [8,1,9] ] ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '-sortby', [ 'data1', 'sortby', 'data2' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_multi_var_sorter_batch_size_3_ascending(self): """ Test multi var prong sorting in ascending order when batch_size=3 """ data = { 'data1' : [[6,3,6,2],[2,5],[1],[4,2],[3,8,4]], 'sortby' : [[8,9,3,7],[5,1],[3],[2,7],[7,1,3]], 'data2' : [[1,6,2,1],[4,2],[2],[8,4],[1,9,8]], } missing = [ np.nan, np.nan, np.nan ] batch_size = 3 batch_data = [ { 'input_png3d' : [ [ [6,3,2], [2,7,1], [6,8,1], [3,9,6] ], [ [5,1,2], [2,5,4], missing, missing ], [ [1,3,2], missing, missing, missing ], ] }, { 'input_png3d' : [ [ [4,2,8], [2,7,4], missing ], [ [8,1,9], [4,3,8], [3,7,1] ], ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '+sortby', [ 'data1', 'sortby', 'data2' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_multi_var_sorter_batch_size_3_descending(self): """ Test multi var prong sorting in descending order when batch_size=3 """ data = { 'data1' : [[6,3,6,2],[2,5],[1],[4,2],[3,8,4]], 'sortby' : [[8,9,3,7],[5,1],[3],[2,7],[7,1,3]], 'data2' : [[1,6,2,1],[4,2],[2],[8,4],[1,9,8]], } missing = [ np.nan, np.nan, np.nan ] batch_size = 3 batch_data = [ { 'input_png3d' : [ [ [3,9,6], [6,8,1], [2,7,1], [6,3,2] ], [ [2,5,4], [5,1,2], missing, missing ], [ [1,3,2], missing, missing, missing ], ] }, { 'input_png3d' : [ [ [2,7,4], [4,2,8], missing ], [ [3,7,1], [4,3,8], [8,1,9] ], ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '-sortby', [ 'data1', 'sortby', 'data2' ] ) self._compare_dgen_to_batch_data(dgen, batch_data) def test_multi_var_sorter_batch_size_3_descending_png2d(self): """ Test multi var 2D prong sorting in descending order when batch_size=3 """ data = { 'data1' : [[6,3,6,2],[2,5],[1],[4,2],[3,8,4]], 'sortby' : [[8,9,3,7],[5,1],[3],[2,7],[7,1,3]], 'data2' : [[1,6,2,1],[4,2],[2],[8,4],[1,9,8]], } missing = [ np.nan, np.nan, np.nan ] batch_size = 3 batch_data = [ { 'input_png2d' : [ [ [3,9,6], [6,8,1], [2,7,1], [6,3,2] ], [ [2,5,4], [5,1,2], missing, missing ], [ [1,3,2], missing, missing, missing ], ] }, { 'input_png2d' : [ [ [2,7,4], [4,2,8], missing ], [ [3,7,1], [4,3,8], [8,1,9] ], ] }, ] dgen = TestsVarrSorting._make_sorted_dgen( data, batch_size, '-sortby', [ 'data1', 'sortby', 'data2' ], False ) self._compare_dgen_to_batch_data(dgen, batch_data) if __name__ == '__main__': unittest.main()

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6364e1e96fc00634802574bde6e4578f8d0baa02

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Python

AltZSC/__init__.py

PrithivirajDamodaran/Alt-ZSC

3e487742a0b4d742e25959871e0b96b54ffc4777

[ "MIT" ]

33

2022-03-05T10:17:46.000Z

2022-03-17T11:32:22.000Z

AltZSC/__init__.py

techthiyanes/Alt-ZSC

f3338d8380055608bf12090224794fc70df289ea

[ "MIT" ]

1

2022-03-14T14:36:33.000Z

2022-03-20T12:12:05.000Z

AltZSC/__init__.py

techthiyanes/Alt-ZSC

f3338d8380055608bf12090224794fc70df289ea

[ "MIT" ]

3

2022-03-06T03:36:11.000Z

2022-03-12T16:38:39.000Z

from AltZSC.ZeroShotTextClassification import ZeroShotTextClassification

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63bb88378cc43a8b0e73ecf4b840e4064dbdd70d

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py

Python

rwmutex/__init__.py

BenJetson/py-rwmutex

5890b1089336569ca7f25dbb0a21a9e299010954

[ "MIT" ]

null

rwmutex/__init__.py

BenJetson/py-rwmutex

5890b1089336569ca7f25dbb0a21a9e299010954

[ "MIT" ]

null

rwmutex/__init__.py

BenJetson/py-rwmutex

5890b1089336569ca7f25dbb0a21a9e299010954

[ "MIT" ]

null

from .mux import RWLock

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898b5a8b195bba5ed1cb3161e334bbfe9902c98d

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Python

monitoring/prober/rid/v2/test_subscription_validation.py

interuss/InterUSS-Platform

099abaa1159c4c143f8f1fde6b88956c86608281

[ "Apache-2.0" ]

null

monitoring/prober/rid/v2/test_subscription_validation.py

interuss/InterUSS-Platform

099abaa1159c4c143f8f1fde6b88956c86608281

[ "Apache-2.0" ]

null

monitoring/prober/rid/v2/test_subscription_validation.py

interuss/InterUSS-Platform

099abaa1159c4c143f8f1fde6b88956c86608281

[ "Apache-2.0" ]

null

"""Subscription input validation tests: - check we can't create too many SUBS (common.MAX_SUBS_PER_AREA) - check we can't create the SUB with a huge area - check we can't create the SUB with missing fields - check we can't create the SUB with a time_start in the past - check we can't create the SUB with a time_start after time_end """ import datetime from monitoring.monitorlib.infrastructure import default_scope from monitoring.monitorlib import rid_v2 from monitoring.monitorlib.rid_v2 import SCOPE_DP, SUBSCRIPTION_PATH from monitoring.prober.infrastructure import register_resource_type from . import common SUB_TYPE = register_resource_type(350, 'Subscription') MULTI_SUB_TYPES = [register_resource_type(351 + i, 'Subscription limit Subscription {}'.format(i)) for i in range(11)] BASE_URL = 'http://example.com/rid/v2' def test_ensure_clean_workspace(ids, session_ridv2): resp = session_ridv2.get('{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), scope=SCOPE_DP) if resp.status_code == 200: version = resp.json()['subscription']['version'] resp = session_ridv2.delete('{}/{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE), version), scope=SCOPE_DP) assert resp.status_code == 200, resp.content elif resp.status_code == 404: # As expected. pass else: assert False, resp.content @default_scope(SCOPE_DP) def test_create_sub_empty_vertices(ids, session_ridv2): time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(seconds=10) resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), json={ 'extents': { 'volume': { 'outline_polygon': { 'vertices': [], }, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }) assert resp.status_code == 400, resp.content @default_scope(SCOPE_DP) def test_create_sub_missing_outline_polygon(ids, session_ridv2): time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(seconds=10) resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), json={ 'extents': { 'volume': { 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }) assert resp.status_code == 400, resp.content @default_scope(SCOPE_DP) def test_create_sub_with_huge_area(ids, session_ridv2): time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(seconds=10) resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), json={ 'extents': { 'volume': { 'outline_polygon': { 'vertices': common.HUGE_VERTICES, }, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }) assert resp.status_code == 400, resp.content @default_scope(SCOPE_DP) def test_create_too_many_subs(ids, session_ridv2): """ASTM Compliance Test: DSS0050_MAX_SUBS_PER_AREA.""" time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(seconds=30) # create 1 more than the max allowed Subscriptions per area versions = [] for index in range(rid_v2.MAX_SUB_PER_AREA + 1): resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(MULTI_SUB_TYPES[index])), json={ 'extents': { 'volume': { 'outline_polygon': { 'vertices': [ { "lat": 37.440, "lng": -131.745, }, { "lat": 37.459, "lng": -131.745, }, { "lat": 37.459, "lng": -131.706, }, { "lat": 37.440, "lng": -131.706, }, ], }, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }) if index < rid_v2.MAX_SUB_PER_AREA: assert resp.status_code == 200, resp.content resp_json = resp.json() assert 'subscription' in resp_json assert 'version' in resp_json['subscription'] versions.append(resp_json['subscription']['version']) else: assert resp.status_code == 429, resp.content # clean up Subscription-limit Subscriptions for index in range(rid_v2.MAX_SUB_PER_AREA): resp = session_ridv2.delete('{}/{}/{}'.format(SUBSCRIPTION_PATH, ids(MULTI_SUB_TYPES[index]), versions[index])) assert resp.status_code == 200 @default_scope(SCOPE_DP) def test_create_sub_with_too_long_end_time(ids, session_ridv2): """ASTM Compliance Test: DSS0060_MAX_SUBS_DURATION.""" time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(hours=(rid_v2.MAX_SUB_TIME_HRS + 1)) resp = session_ridv2.put( "{}/{}".format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), json={ "extents": { "volume": { "outline_polygon": {"vertices": common.VERTICES}, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }, ) assert resp.status_code == 400, resp.content @default_scope(SCOPE_DP) def test_update_sub_with_too_long_end_time(ids, session_ridv2): """ASTM Compliance Test: DSS0060_MAX_SUBS_DURATION.""" time_start = datetime.datetime.utcnow() time_end = time_start + datetime.timedelta(seconds=10) resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), json={ "extents": { "volume": { "outline_polygon": {"vertices": common.VERTICES}, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }, ) assert resp.status_code == 200, resp.content time_end = time_start + datetime.timedelta(hours=(rid_v2.MAX_SUB_TIME_HRS + 1)) resp = session_ridv2.put( '{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)) + '/' + resp.json()["subscription"]["version"], json={ "extents": { "volume": { "outline_polygon": {"vertices": common.VERTICES}, 'altitude_lower': rid_v2.Altitude.make(20), 'altitude_upper': rid_v2.Altitude.make(400), }, 'time_start': rid_v2.Time.make(time_start), 'time_end': rid_v2.Time.make(time_end), }, 'uss_base_url': BASE_URL }, ) assert resp.status_code == 400, resp.content @default_scope(SCOPE_DP) def test_delete(ids, session_ridv2): resp = session_ridv2.get('{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE)), scope=SCOPE_DP) if resp.status_code == 200: version = resp.json()['subscription']['version'] resp = session_ridv2.delete('{}/{}/{}'.format(SUBSCRIPTION_PATH, ids(SUB_TYPE), version), scope=SCOPE_DP) assert resp.status_code == 200, resp.content elif resp.status_code == 404: # As expected. pass

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py

Python

lagom/experiment/__init__.py

lkylych/lagom

64777be7f09136072a671c444b5b3fbbcb1b2f18

[ "MIT" ]

null

lagom/experiment/__init__.py

lkylych/lagom

64777be7f09136072a671c444b5b3fbbcb1b2f18

[ "MIT" ]

null

lagom/experiment/__init__.py

lkylych/lagom

64777be7f09136072a671c444b5b3fbbcb1b2f18

[ "MIT" ]

null

from .config import Config from .base_experiment_worker import BaseExperimentWorker from .base_experiment_master import BaseExperimentMaster from .run_experiment import run_experiment

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989900402cc02df789827561dda1476d0da8d910

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py

Python

mitreattack/collections/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

137

2021-04-06T17:40:20.000Z

2022-03-30T18:27:44.000Z

mitreattack/collections/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

33

2021-04-07T13:41:39.000Z

2022-03-25T14:37:40.000Z

mitreattack/collections/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

29

2021-04-06T21:14:40.000Z

2022-03-31T15:26:27.000Z

from .index_to_markdown import * from .collection_to_index import * from .stix_to_collection import *

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py

Python

tests/test_wps_generic_raster_subset.py

fossabot/raven

b5ed6258a4c09ac4d132873d6b8b4a1d82d2131b

[ "MIT" ]

29

2018-08-13T20:16:41.000Z

2022-03-17T02:31:38.000Z

tests/test_wps_generic_raster_subset.py

fossabot/raven

b5ed6258a4c09ac4d132873d6b8b4a1d82d2131b

[ "MIT" ]

359

2018-05-31T00:37:53.000Z

2022-03-26T04:35:43.000Z

tests/test_wps_generic_raster_subset.py

fossabot/raven

b5ed6258a4c09ac4d132873d6b8b4a1d82d2131b

[ "MIT" ]

10

2019-06-17T18:07:46.000Z

2022-02-15T02:01:32.000Z

import tempfile import rasterio as rio from metalink import download as md from pywps import Service from pywps.tests import assert_response_success from ravenpy.utilities.testdata import get_local_testdata from raven.processes import RasterSubsetProcess from .common import CFG_FILE, client_for, get_output class TestGenericRasterSubsetProcess: def test_simple(self): client = client_for( Service(processes=[RasterSubsetProcess()], cfgfiles=CFG_FILE) ) fields = [ "shape=file@xlink:href=file://{shape}", "raster=file@xlink:href=file://{raster}", "band={band}", "select_all_touching={touches}", ] datainputs = ";".join(fields).format( shape=get_local_testdata("watershed_vector/Basin_test.zip"), raster=get_local_testdata( "earthenv_dem_90m/earthenv_dem90_southernQuebec.tiff" ), band=1, touches=True, ) resp = client.get( service="WPS", request="Execute", version="1.0.0", identifier="raster-subset", datainputs=datainputs, ) assert_response_success(resp) out = get_output(resp.xml) assert {"raster"}.issubset([*out]) raster_dir = md.get(out["raster"], path=tempfile.mkdtemp()) assert len(raster_dir) == 1 bounds = list() for f in raster_dir: raster = rio.open(f) assert raster.bounds bounds.append(raster.bounds) assert len(set(bounds)) == len(raster_dir) def test_multiple_features_metalink(self): client = client_for( Service(processes=[RasterSubsetProcess()], cfgfiles=CFG_FILE) ) fields = [ "shape=file@xlink:href=file://{shape}", "raster=file@xlink:href=file://{raster}", "band={band}", "select_all_touching={touches}", ] datainputs = ";".join(fields).format( shape=get_local_testdata("donneesqc_mrc_poly/mrc_subset.gml"), raster=get_local_testdata( "earthenv_dem_90m/earthenv_dem90_southernQuebec.tiff" ), band=1, touches=True, ) resp = client.get( service="WPS", request="Execute", version="1.0.0", identifier="raster-subset", datainputs=datainputs, ) assert_response_success(resp) out = get_output(resp.xml) assert {"raster"}.issubset([*out]) raster_dir = md.get(out["raster"], path=tempfile.mkdtemp()) assert len(raster_dir) == 6 bounds = list() for f in raster_dir: raster = rio.open(f) assert raster.bounds bounds.append(raster.bounds) assert len(set(bounds)) == len(raster_dir)

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null

0

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0

1

0

null

0

6

7f2f51ab1949018c4edefec9f5e3a4af7b77cb25

61

py

Python

config/__init__.py

yonjansanjay8888/Sanjay-Lama

5243a492a2db4c71634b6ddc51a34b90ab51ef44

[ "MIT" ]

1

2018-02-18T04:22:40.000Z

config/__init__.py

yonjansanjay8888/Sanjay-Lama

5243a492a2db4c71634b6ddc51a34b90ab51ef44

[ "MIT" ]

null

config/__init__.py

yonjansanjay8888/Sanjay-Lama

5243a492a2db4c71634b6ddc51a34b90ab51ef44

[ "MIT" ]

null

from .config import Config from .dev_config import DevConfig

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33

0.836066

9

61

5.555556

0.555556

0.48

0

0.131148

61

2

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1

0

true

0

1

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0

null

0

1

0

1

0

1

0

6

7f70b4af63c7a2291b3084071c121aa054ab10d3

6,633

py

Python

resources.py

elitonfilho/deleteFeatures

55860c91b83f77d6975688fd7edd6e133bba369b

[ "MIT" ]

null

resources.py

elitonfilho/deleteFeatures

55860c91b83f77d6975688fd7edd6e133bba369b

[ "MIT" ]

4

2020-02-18T14:42:24.000Z

2020-02-19T13:05:42.000Z

resources.py

elitonfilho/deleteFeatures

55860c91b83f77d6975688fd7edd6e133bba369b

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Resource object code # # Created by: The Resource Compiler for PyQt5 (Qt v5.14.1) # # WARNING! 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\x83\xc9\xe4\x96\x32\x51\x14\x29\x16\x19\x0c\x1e\x4a\x64\xf0\x14\ \x59\x3c\x1c\x0b\xe2\xe2\xfe\x7e\x26\xe7\x3b\x9d\x73\x3e\xef\x73\ \x3a\x9f\x2f\x4b\xe2\x0f\x6f\x27\xe3\x54\x8b\x43\xe9\xa2\xc3\x0b\ \x5b\x42\x47\x2a\x79\x95\x47\xc7\x59\x6b\x18\x4e\x05\x0c\x2a\xb5\ \x4a\x9f\x70\xa0\x3c\x51\x9e\xb5\x29\x0c\xc1\xa2\x30\x93\x08\x8c\ \x0b\x6b\x32\xd0\xee\x5c\x98\x2c\x2a\x1f\xf5\xe2\x46\xd7\x7b\xda\ \xeb\x5a\x98\x53\xfa\xa3\x38\x63\x4a\x78\x90\xfb\x5c\xec\x76\x22\ \xac\xa8\xfe\x26\x2f\x33\x2f\x5c\x19\x28\x6c\x34\xdb\x15\xb6\x35\ \x16\x58\xb9\x2c\x1c\xe9\xfc\x69\x75\xad\x55\x61\x4f\xdd\x47\xa5\ \xc2\x86\xb0\xab\xe9\xb7\xd3\xca\xad\x08\xb3\x1f\xf9\x84\xb0\xae\ \xa6\x98\x1b\x0d\x6e\xdd\xab\x78\x3b\xf6\xcc\xb3\xb6\x24\xbf\xf3\ \x42\x0f\xa8\x17\xf6\x0b\xdb\xd9\x6f\xc0\x1d\xaa\x40\x25\x2e\x92\ \x81\x84\xf8\x5f\xa0\xe4\x6f\x40\xbf\x3c\x6e\x93\x77\x2e\x08\x39\ \xd3\x9e\x84\xfc\xef\x7f\xfc\x15\xb8\x14\xae\x8d\xa4\xbb\x6a\x41\ \x08\xcb\x5a\xd3\xda\x30\xe6\xac\xd8\xec\x57\x98\xe8\x63\xb0\x7c\ \xd1\x97\xb0\x00\x00\x00\x25\x74\x45\x58\x74\x64\x61\x74\x65\x3a\ \x63\x72\x65\x61\x74\x65\x00\x32\x30\x32\x30\x2d\x30\x32\x2d\x31\ \x37\x54\x31\x34\x3a\x31\x37\x3a\x31\x37\x2b\x30\x30\x3a\x30\x30\ \x75\x9e\x22\x7c\x00\x00\x00\x25\x74\x45\x58\x74\x64\x61\x74\x65\ \x3a\x6d\x6f\x64\x69\x66\x79\x00\x32\x30\x32\x30\x2d\x30\x32\x2d\ \x31\x37\x54\x31\x34\x3a\x31\x37\x3a\x31\x37\x2b\x30\x30\x3a\x30\ \x30\x04\xc3\x9a\xc0\x00\x00\x00\x19\x74\x45\x58\x74\x53\x6f\x66\ \x74\x77\x61\x72\x65\x00\x77\x77\x77\x2e\x69\x6e\x6b\x73\x63\x61\ \x70\x65\x2e\x6f\x72\x67\x9b\xee\x3c\x1a\x00\x00\x00\x00\x49\x45\ \x4e\x44\xae\x42\x60\x82\ " qt_resource_name = b"\ \x00\x07\ \x07\x3b\xe0\xb3\ \x00\x70\ \x00\x6c\x00\x75\x00\x67\x00\x69\x00\x6e\x00\x73\ \x00\x0e\ \x03\xd2\x81\xe3\ \x00\x64\ \x00\x65\x00\x6c\x00\x65\x00\x74\x00\x65\x00\x46\x00\x65\x00\x61\x00\x74\x00\x75\x00\x72\x00\x65\x00\x73\ \x00\x17\ \x07\x12\x29\xc7\ \x00\x66\ \x00\x69\x00\x6c\x00\x74\x00\x65\x00\x72\x00\x5f\x00\x69\x00\x6e\x00\x74\x00\x65\x00\x72\x00\x73\x00\x65\x00\x63\x00\x74\x00\x69\ \x00\x6f\x00\x6e\x00\x2e\x00\x70\x00\x6e\x00\x67\ \x00\x12\ \x0d\x31\xdc\x87\ \x00\x66\ \x00\x69\x00\x6c\x00\x74\x00\x65\x00\x72\x00\x5f\x00\x63\x00\x6c\x00\x69\x00\x70\x00\x70\x00\x65\x00\x72\x00\x2e\x00\x70\x00\x6e\ \x00\x67\ " qt_resource_struct_v1 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ \x00\x00\x00\x14\x00\x02\x00\x00\x00\x02\x00\x00\x00\x03\ \x00\x00\x00\x36\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ \x00\x00\x00\x6a\x00\x00\x00\x00\x00\x01\x00\x00\x01\xe9\ " qt_resource_struct_v2 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x14\x00\x02\x00\x00\x00\x02\x00\x00\x00\x03\ \x00\x00\x00\x00\x00\x00\x00\x00\ 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1

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1

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null

1

0

6

f6827bc18d5b55605c2e335e75a5503e7adac16c

20,156

py

Python

tests/graph/terraform/graph_builder/graph_components/test_blocks.py

ismailyenigul/checkov

b65daa796e166568fdd02591ab5232e567f4cd36

[ "Apache-2.0" ]

5

2021-07-29T18:08:40.000Z

2022-03-21T04:39:32.000Z

tests/graph/terraform/graph_builder/graph_components/test_blocks.py

ismailyenigul/checkov

b65daa796e166568fdd02591ab5232e567f4cd36

[ "Apache-2.0" ]

null

tests/graph/terraform/graph_builder/graph_components/test_blocks.py

ismailyenigul/checkov

b65daa796e166568fdd02591ab5232e567f4cd36

[ "Apache-2.0" ]

2

2021-08-23T13:25:36.000Z

2021-11-05T21:44:52.000Z

from unittest import TestCase from checkov.terraform.graph_builder.graph_components.block_types import BlockType from checkov.terraform.graph_builder.graph_components.blocks import Block class TestBlocks(TestCase): def test_update_inner_attribute_1(self): config = {'aws_security_group': { 'test': {'name': ['test'], 'vpc_id': ['${aws_vpc.vpc_main.id}'], 'tags': [{'Name': 'test'}], 'description': ['test - Elasticsearch Cluster'], 'ingress': [ {'from_port': [443], 'to_port': [443], 'protocol': ['tcp'], 'security_groups': [['${aws_security_group.test.id}', '${data.aws_security_group.test.id}']]}]}}} block = Block(name='aws_security_group.test', config=config, path='test_path', block_type=BlockType.RESOURCE, attributes=config['aws_security_group']['test']) block.update_inner_attribute(attribute_key='ingress.security_groups.0', nested_attributes=block.attributes, value_to_update='sg-0') block.update_inner_attribute(attribute_key='ingress.security_groups.1', nested_attributes=block.attributes, value_to_update='sg-1') self.assertEqual('sg-0', block.attributes['ingress.security_groups.0'], f"failed to update ingress.security_groups.0, got {block.attributes['ingress.security_groups.0']}") self.assertEqual('sg-1', block.attributes['ingress.security_groups.1'], f"failed to update ingress.security_groups.1, got {block.attributes['ingress.security_groups.1']}") self.assertEqual('sg-0', block.attributes['ingress']['security_groups'][0], f"failed to update block.attributes['ingress']['security_groups'][0], got {block.attributes['ingress']['security_groups'][0]}") self.assertEqual('sg-1', block.attributes['ingress']['security_groups'][1], f"failed to update block.attributes['ingress']['security_groups'][1], got {block.attributes['ingress']['security_groups'][1]}") def test_update_inner_attribute_2(self): config = {'aws_security_group': {'test': {'name': ['test'], 'vpc_id': ['${aws_vpc.vpc_main.id}'], 'ingress': [ {'from_port': [53], 'to_port': [53], 'protocol': ['udp'], 'security_groups': [ ['${data.test1.id}', '${data.test2.id}', '${data.test3.id}', '${data.test4.id}', '${data.test5.id}', '${data.test6.id}']], 'cidr_blocks': [['test1', '${var.test2}', '${var.test4}']]}, {'from_port': [53], 'to_port': [53], 'protocol': ['tcp'], 'security_groups': [ ['${data.test1.id}', '${data.test2.id}', '${data.test3.id}', '${data.test4.id}', '${data.test5.id}', '${data.test6.id}']], 'cidr_blocks': [['test', '${var.test}', '${var.v3}']]}]}}} block = Block(name='aws_security_group.test', config=config, path='test_path', block_type=BlockType.RESOURCE, attributes=config['aws_security_group']['test']) block.update_inner_attribute(attribute_key='ingress.0.cidr_blocks.1', nested_attributes=block.attributes, value_to_update='sg-1') self.assertEqual('sg-1', block.attributes['ingress.0.cidr_blocks.1'], f"failed to update ingress.0.cidr_blocks.1, got {block.attributes['ingress.0.cidr_blocks.1']}") self.assertEqual('sg-1', block.attributes['ingress'][0]['cidr_blocks'][1], f"failed to update block.attributes['ingress'][0]['cidr_blocks'][1], got {block.attributes['ingress'][0]['cidr_blocks'][1]}") def test_update_inner_attribute_3(self): config = {'aws_iam_policy_document': {'vcs_webhook_step_function_execution_policy': {'statement': [ {'actions': [['events:DescribeRule', 'events:PutRule', 'events:PutTargets']], 'effect': ['Allow'], 'resources': [[ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule']]}, {'actions': [['states:StartExecution']], 'effect': ['Allow'], 'resources': [[ 'arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:${module.consts.bc_checkov_scanner_step_function_name}*']]}, {'actions': [['lambda:InvokeFunction']], 'effect': ['Allow'], 'resources': [ '${formatlist("%s%s","arn:aws:lambda:${var.region}:${data.aws_caller_identity.current.account_id}:function:",concat([\'${local.vcs_webhook_lambda_name}\', \'${local.customer_api_lambda}\']))}']}]}}} block = Block(name='aws_iam_policy_document.vcs_webhook_step_function_execution_policy', config=config, path='test_path', block_type=BlockType.DATA, attributes=config['aws_iam_policy_document']['vcs_webhook_step_function_execution_policy']) err = block.update_inner_attribute(attribute_key='statement.1.resources.0', nested_attributes={'statement': [{'actions': ['events:DescribeRule', 'events:PutRule', 'events:PutTargets'], 'effect': 'Allow', 'resources': [ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule']}, {'actions': 'states:StartExecution', 'effect': 'Allow', 'resources': 'arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:bc-vcs-scanner-sfn*'}, {'actions': 'lambda:InvokeFunction', 'effect': 'Allow', 'resources': '${formatlist("%s%s","arn:aws:lambda:${var.region}:${data.aws_caller_identity.current.account_id}:function:",concat([\'${local.vcs_webhook_lambda_name}\', \'${local.customer_api_lambda}\']))}'}], 'statement.0': { 'actions': ['events:DescribeRule', 'events:PutRule', 'events:PutTargets'], 'effect': 'Allow', 'resources': [ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule']}, 'statement.0.actions': ['events:DescribeRule', 'events:PutRule', 'events:PutTargets'], 'statement.0.actions.0': 'events:DescribeRule', 'statement.0.actions.1': 'events:PutRule', 'statement.0.actions.2': 'events:PutTargets', 'statement.0.effect': 'Allow', 'statement.0.resources': [ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule'], 'statement.0.resources.0': 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'statement.0.resources.1': 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule', 'statement.1': { 'resources': 'arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:bc-vcs-scanner-sfn*'}, 'statement.1.actions': 'states:StartExecution', 'statement.1.actions.0': 'states:StartExecution', 'statement.1.effect': 'Allow', 'statement.1.resources': 'arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:bc-vcs-scanner-sfn*', 'statement.1.resources.0': 'arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:bc-vcs-scanner-sfn*', 'statement.2': {'actions': 'lambda:InvokeFunction', 'effect': 'Allow', 'resources': '${formatlist("%s%s","arn:aws:lambda:${var.region}:${data.aws_caller_identity.current.account_id}:function:",concat([\'${local.vcs_webhook_lambda_name}\', \'${local.customer_api_lambda}\']))}'}, 'statement.2.actions': 'lambda:InvokeFunction', 'statement.2.actions.0': 'lambda:InvokeFunction', 'statement.2.effect': 'Allow', 'statement.2.resources': '${formatlist("%s%s","arn:aws:lambda:${var.region}:${data.aws_caller_identity.current.account_id}:function:",concat([\'${local.vcs_webhook_lambda_name}\', \'${local.customer_api_lambda}\']))}'}, value_to_update='arn:aws:states:${var.region}:${data.aws_caller_identity.current.account_id}:stateMachine:bc-vcs-scanner-sfn*') self.assertIsNone(err) self.assertIn(block.attributes['statement.0.resources.1'], [ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule'] ) self.assertIn(block.attributes['statement.0.resources.0'], [ 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForECSTaskRule', 'arn:aws:events:${var.region}:${data.aws_caller_identity.current.account_id}:rule/StepFunctionsGetEventsForStepFunctionsExecutionRule'] ) def test_update_complex_key(self): config = {'labels': [{'app.kubernetes.io/name': '${local.name}', 'app.kubernetes.io/instance': 'hpa', 'app.kubernetes.io/version': '1.0.0', 'app.kubernetes.io/managed-by': 'terraform'}]} attributes = {'labels': {'app.kubernetes.io/name': '${local.name}', 'app.kubernetes.io/instance': 'hpa', 'app.kubernetes.io/version': '1.0.0', 'app.kubernetes.io/managed-by': 'terraform'}, 'labels.app.kubernetes.io/name': '${local.name}', 'labels.app.kubernetes.io/instance': 'hpa', 'labels.app.kubernetes.io/version': '1.0.0', 'labels.app.kubernetes.io/managed-by': 'terraform'} block = Block(name='test_local_name', config=config, path='', block_type=BlockType.LOCALS, attributes=attributes) err = block.update_inner_attribute(attribute_key="labels.app.kubernetes.io/name", nested_attributes=attributes, value_to_update="dummy value") self.assertEqual(None, err) def test_update_complex_key2(self): config = {} attributes = {'var.owning_account': {'route_to': None, 'route_to_cidr_blocks': '${local.allowed_cidrs}', 'static_routes': None, 'subnet_ids': '${local.own_vpc.private_subnet_ids}', 'subnet_route_table_ids': '${local.own_vpc.private_route_table_ids}', 'transit_gateway_vpc_attachment_id': None, 'vpc_cidr': '${local.own_vpc.vpc_cidr}', 'vpc_id': '${local.own_vpc.vpc_id}'}} block = Block(name='test_local_name', config=config, path='', block_type=BlockType.LOCALS, attributes=attributes) value_to_update = "test" err = block.update_inner_attribute(attribute_key="var.owning_account.vpc_cidr", nested_attributes=attributes, value_to_update=value_to_update) self.assertEqual(None, err) self.assertDictEqual(block.attributes, {'var.owning_account': {'route_to': None, 'route_to_cidr_blocks': '${local.allowed_cidrs}', 'static_routes': None, 'subnet_ids': '${local.own_vpc.private_subnet_ids}', 'subnet_route_table_ids': '${local.own_vpc.private_route_table_ids}', 'transit_gateway_vpc_attachment_id': None, 'vpc_cidr': 'test', 'vpc_id': '${local.own_vpc.vpc_id}'}}) def test_update_inner_attribute_bad_index(self): config = {'aws_security_group': { 'test': {}}} nested_attributes = {'provisioner/remote-exec.connection': {'private_key': '${file(var.ssh_key_path)}', 'user': 'ec2-user'}, 'provisioner/remote-exec.connection.private_key': '${file(var.ssh_key_path)}', 'provisioner/remote-exec.connection.user': 'ec2-user', 'provisioner/remote-exec.inline': ['command'], 'provisioner/remote-exec.inline.0': 'command0', 'provisioner/remote-exec.inline.1': 'command1', 'provisioner/remote-exec.inline.2': 'command2', 'provisioner/remote-exec.inline.3': 'command3', 'provisioner/remote-exec.inline.4': 'command4'} block = Block(name='aws_security_group.test', config=config, path='test_path', block_type=BlockType.RESOURCE, attributes=nested_attributes) block.update_inner_attribute(attribute_key='provisioner/remote-exec.inline.3', nested_attributes=nested_attributes, value_to_update='new_command_3') self.assertEqual('new_command_3', block.attributes['provisioner/remote-exec.inline.3'], f"failed to update provisioner/remote-exec.inline.3, got {block.attributes['provisioner/remote-exec.inline.3']}") def test_update_inner_attribute_bad_map_entry(self): config = {'aws_security_group': { 'test': {}}} nested_attributes = {'triggers': {'change_endpoint_name': '${md5("my_dev_endpoint")}', 'change_extra_jars_s3_path': '${md5()}', 'change_extra_python_libs_s3_path': '${md5()}', 'change_number_of_nodes': '${md5("2")}', 'change_public_keys': '${md5("${var.glue_endpoint_public_keys}")}', 'change_region': '${md5("us-east-1")}', 'change_role': '${md5("arn:aws:iam::111111111111:role/my_role")}', 'change_security_configuration': '${md5()}', 'change_security_group_ids': '${md5("${var.glue_endpoint_security_group_ids}")}', 'change_subnet_id': '${md5()}'}, 'provisioner/local-exec': {'command': "echo 'info: destroy ignored because part of apply'", 'when': 'destroy'}, 'provisioner/local-exec.command': "echo 'info: destroy ignored because part of apply'", 'provisioner/local-exec.environment': {'endpoint_name': '${var.glue_endpoint_name}', 'extra_jars_s3_path': '${var.glue_endpoint_extra_jars_libraries}', 'extra_python_libs_s3_path': '${var.glue_endpoint_extra_python_libraries}', 'number_of_nodes': '${var.glue_endpoint_number_of_dpus}', 'public_keys': '${join(",",var.glue_endpoint_public_keys)}', 'region': '${var.aws_region}', 'role_arn': '${var.glue_endpoint_role}', 'security_configuration': '${var.glue_endpoint_security_configuration}', 'security_group_ids': '${join(",",var.glue_endpoint_security_group_ids)}', 'subnet_id': '${var.glue_endpoint_subnet_id}'}, 'provisioner/local-exec.environment.endpoint_name': 'my_dev_endpoint', 'provisioner/local-exec.environment.extra_jars_s3_path': '', 'provisioner/local-exec.environment.extra_python_libs_s3_path': '', 'provisioner/local-exec.environment.number_of_nodes': 2, 'provisioner/local-exec.environment.public_keys': '${join(",",var.glue_endpoint_public_keys)}', 'provisioner/local-exec.environment.region': 'us-east-1', 'provisioner/local-exec.environment.role_arn': 'arn:aws:iam::111111111111:role/my_role', 'provisioner/local-exec.environment.security_configuration': '', 'provisioner/local-exec.environment.security_group_ids': '${join(",",var.glue_endpoint_security_group_ids)}', 'provisioner/local-exec.environment.subnet_id': '', 'provisioner/local-exec.when': 'destroy', 'resource_type': ['null_resource'], 'triggers.change_endpoint_name': '${md5("my_dev_endpoint")}', 'triggers.change_extra_jars_s3_path': '${md5()}', 'triggers.change_extra_python_libs_s3_path': '${md5()}', 'triggers.change_number_of_nodes': '${md5("2")}', 'triggers.change_public_keys': '${md5("${var.glue_endpoint_public_keys}")}', 'triggers.change_region': '${md5("us-east-1")}', 'triggers.change_role': '${md5("arn:aws:iam::111111111111:role/my_role")}', 'triggers.change_security_configuration': '${md5()}', 'triggers.change_security_group_ids': '${md5("${var.glue_endpoint_security_group_ids}")}', 'triggers.change_subnet_id': '${md5()}'} block = Block(name='null_resource.glue_endpoint_apply', config=config, path='test_path', block_type=BlockType.RESOURCE, attributes=nested_attributes) attribute_key = 'provisioner/local-exec.environment.security_configuration' block.update_inner_attribute(attribute_key=attribute_key, nested_attributes=nested_attributes, value_to_update='') self.assertEqual('', block.attributes[attribute_key], f"failed to update provisioner/remote-exec.inline.3, got {block.attributes[attribute_key]}")

107.212766

2,786

0.564497

1,956

20,156

5.557771

0.101227

0.014902

0.0287

0.035323

0.830742

0.791831

0.718609

0.656701

0.623862

0.601325

0

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0.285622

20,156

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0

1

0

1

0

1

0

null

0

6

f6945e590bd971b136eb7c0e2841942f95cf8730

5,945

py

Python

test_autolens/unit/pipeline/phase/dataset/test_result_dataset.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

test_autolens/unit/pipeline/phase/dataset/test_result_dataset.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

test_autolens/unit/pipeline/phase/dataset/test_result_dataset.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

from os import path import autolens as al import numpy as np import pytest from autolens.mock import mock pytestmark = pytest.mark.filterwarnings( "ignore:Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of " "`arr[seq]`. In the future this will be interpreted as an arrays index, `arr[np.arrays(seq)]`, which will result " "either in an error or a different result." ) directory = path.dirname(path.realpath(__file__)) class TestResult: def test__results_of_phase_include_mask__available_as_property( self, imaging_7x7, mask_7x7, samples_with_result ): phase_imaging_7x7 = al.PhaseImaging( galaxies=dict(lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)), search=mock.MockSearch("test_phase", samples=samples_with_result), settings=al.SettingsPhaseImaging( settings_masked_imaging=al.SettingsMaskedImaging(sub_size=2) ), ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert (result.mask == mask_7x7).all() def test__results_of_phase_include_positions__available_as_property( self, imaging_7x7, mask_7x7, samples_with_result ): phase_imaging_7x7 = al.PhaseImaging( search=mock.MockSearch("test_phase", samples=samples_with_result) ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert result.positions == None phase_imaging_7x7 = al.PhaseImaging( galaxies=dict(lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)), search=mock.MockSearch("test_phase", samples=samples_with_result), settings=al.SettingsPhaseImaging( settings_lens=al.SettingsLens(positions_threshold=1.0) ), ) imaging_7x7.positions = al.GridIrregularGrouped([[(1.0, 1.0)]]) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert (result.positions[0] == np.array([1.0, 1.0])).all() def test__results_of_phase_include_pixelization__available_as_property( self, imaging_7x7, mask_7x7 ): lens = al.Galaxy(redshift=0.5, light=al.lp.EllipticalSersic(intensity=1.0)) source = al.Galaxy( redshift=1.0, pixelization=al.pix.VoronoiMagnification(shape=(2, 3)), regularization=al.reg.Constant(), ) tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) samples = mock.MockSamples(max_log_likelihood_instance=tracer) phase_imaging_7x7 = al.PhaseImaging( settings=al.SettingsPhaseImaging(), search=mock.MockSearch("test_phase", samples=samples), ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert isinstance(result.pixelization, al.pix.VoronoiMagnification) assert result.pixelization.shape == (2, 3) lens = al.Galaxy(redshift=0.5, light=al.lp.EllipticalSersic(intensity=1.0)) source = al.Galaxy( redshift=1.0, pixelization=al.pix.VoronoiBrightnessImage(pixels=6), regularization=al.reg.Constant(), ) source.hyper_galaxy_image = np.ones(9) tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) samples = mock.MockSamples(max_log_likelihood_instance=tracer) phase_imaging_7x7 = al.PhaseImaging( settings=al.SettingsPhaseImaging(), search=mock.MockSearch("test_phase", samples=samples), ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert isinstance(result.pixelization, al.pix.VoronoiBrightnessImage) assert result.pixelization.pixels == 6 def test__results_of_phase_include_pixelization_grid__available_as_property( self, imaging_7x7, mask_7x7 ): galaxy = al.Galaxy(redshift=0.5, light=al.lp.EllipticalSersic(intensity=1.0)) tracer = al.Tracer.from_galaxies(galaxies=[galaxy]) samples = mock.MockSamples(max_log_likelihood_instance=tracer) phase_imaging_7x7 = al.PhaseImaging( galaxies=dict(lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)), search=mock.MockSearch("test_phase_2", samples=samples), ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert result.max_log_likelihood_pixelization_grids_of_planes == [None] lens = al.Galaxy(redshift=0.5, light=al.lp.EllipticalSersic(intensity=1.0)) source = al.Galaxy( redshift=1.0, pixelization=al.pix.VoronoiBrightnessImage(pixels=6), regularization=al.reg.Constant(), ) source.hyper_galaxy_image = np.ones(9) tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) samples = mock.MockSamples(max_log_likelihood_instance=tracer) phase_imaging_7x7 = al.PhaseImaging( galaxies=dict(lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)), settings=al.SettingsPhaseImaging(), search=mock.MockSearch("test_phase_2", samples=samples), ) result = phase_imaging_7x7.run( dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults() ) assert result.max_log_likelihood_pixelization_grids_of_planes[-1].shape == ( 6, 2, )

36.25

119

0.639865

676

5,945

5.400888

0.183432

0.071213

0.065735

0.051767

0.800055

0.783073

0.751849

0.72172

0.707204

0

0.031717

0.257527

5,945

163

120

36.472393

0.795424

0

0.570248

0

0.016529

0.05863

0.003805

0

0.07438

1

0.033058

false

0

0.041322

0

0.082645

0

null

0

1

0

null

0

6

f6b02273806f5ec0ed4568a44dee2253fcfc939a

45

py

Python

nxtools/caspar/__init__.py

immstudios/nxtools

3a9e911fe141b989d8163cf50327a2c190a248bd

[ "MIT" ]

2

2020-02-24T18:43:17.000Z

2022-02-15T12:32:50.000Z

nxtools/caspar/__init__.py

immstudios/nxtools

3a9e911fe141b989d8163cf50327a2c190a248bd

[ "MIT" ]

null

nxtools/caspar/__init__.py

immstudios/nxtools

3a9e911fe141b989d8163cf50327a2c190a248bd

[ "MIT" ]

2

2020-04-27T22:12:33.000Z

2020-08-04T03:53:38.000Z

from .caspar import CasparCG, CasparResponse

22.5

44

0.844444

5

45

7.6

1

0

0.111111

45

1

45

0.95

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f6fd6818e17f233d898e521697b1f47c2c4caa0b

260

py

Python

clients/oathkeeper/python/ory_oathkeeper_client/api/__init__.py

mojotalantikite/sdk

00fc86e98570e88911cfc66ce76637f8f1ac9dbe

[ "Apache-2.0" ]

null

clients/oathkeeper/python/ory_oathkeeper_client/api/__init__.py

mojotalantikite/sdk

00fc86e98570e88911cfc66ce76637f8f1ac9dbe

[ "Apache-2.0" ]

null

clients/oathkeeper/python/ory_oathkeeper_client/api/__init__.py

mojotalantikite/sdk

00fc86e98570e88911cfc66ce76637f8f1ac9dbe

[ "Apache-2.0" ]

null

from __future__ import absolute_import # flake8: noqa # import apis into api package from ory_oathkeeper_client.api.api_api import ApiApi from ory_oathkeeper_client.api.health_api import HealthApi from ory_oathkeeper_client.api.version_api import VersionApi

28.888889

60

0.861538

39

260

5.384615

0.461538

0.1

0.242857

0.328571

0.371429

0

0.004292

0.103846

260

8

61

32.5

0.896996

0.157692

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

101895c2d847ae020ebdb7fe1a75081bb4e8fa88

366

py

Python

ggpy/cruft/autocode/GdlLiteral.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

1

2015-01-26T19:07:45.000Z

ggpy/cruft/autocode/GdlLiteral.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

null

ggpy/cruft/autocode/GdlLiteral.py

hobson/ggpy

4e6e6e876c3a4294cd711647051da2d9c1836b60

[ "MIT" ]

null

#!/usr/bin/env python """ generated source for module GdlLiteral """ # package: org.ggp.base.util.gdl.grammar @SuppressWarnings("serial") class GdlLiteral(Gdl): """ generated source for class GdlLiteral """ def isGround(self): """ generated source for method isGround """ def __str__(self): """ generated source for method toString """

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