xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

c445f7b208836c3fbce86b07e69b41707ccdba71

188

py

Python

codewars/7kyu/doha22/sort_numbers/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

null

codewars/7kyu/doha22/sort_numbers/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

2

2019-01-22T10:53:42.000Z

2019-01-31T08:02:48.000Z

codewars/7kyu/doha22/sort_numbers/test.py

doha22/Training_one

0cd7cf86c7da0f6175834146296b763d1841766b

[ "MIT" ]

13

2019-01-22T10:37:42.000Z

2019-01-25T13:30:43.000Z

import unittest from sort_numbers import solution def test_solution(benchmark): assert benchmark(solution,[1,2,10,5]) == [1,2,5,10] assert benchmark(solution,[3,1,4]) == [1,3,4]

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null

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0

5

c4622302bdc70f93ab1dc61522b6a6f34fd62ebf

3,395

py

Python

apps/worker/src/main/routes/groups/routes.py

aisportsbets/pygrid

eeee1e7ef6ba50a03f6f486992a45f7171976e5a

[ "Apache-2.0" ]

null

apps/worker/src/main/routes/groups/routes.py

aisportsbets/pygrid

eeee1e7ef6ba50a03f6f486992a45f7171976e5a

[ "Apache-2.0" ]

null

apps/worker/src/main/routes/groups/routes.py

aisportsbets/pygrid

eeee1e7ef6ba50a03f6f486992a45f7171976e5a

[ "Apache-2.0" ]

null

from .blueprint import groups_blueprint as group_route from flask import request, Response import json from syft.grid.messages.group_messages import ( CreateGroupMessage, DeleteGroupMessage, GetGroupMessage, GetGroupsMessage, UpdateGroupMessage, ) from ..auth import error_handler, token_required from ...core.task_handler import route_logic from ...core.node import node @group_route.route("", methods=["POST"]) @token_required def create_group_route(current_user): # Get request body content = request.get_json() if not content: content = {} content["current_user"] = current_user status_code, response_msg = error_handler( route_logic, CreateGroupMessage, current_user, content ) response = response_msg if isinstance(response_msg, dict) else response_msg.content return Response( json.dumps(response), status=status_code, mimetype="application/json", ) @group_route.route("", methods=["GET"]) @token_required def get_all_groups_routes(current_user): # Get request body content = request.get_json() if not content: content = {} content["current_user"] = current_user status_code, response_msg = error_handler( route_logic, GetGroupsMessage, current_user, content ) response = response_msg if isinstance(response_msg, dict) else response_msg.content return Response( json.dumps(response), status=status_code, mimetype="application/json", ) @group_route.route("/<group_id>", methods=["GET"]) @token_required def get_specific_group_route(current_user, group_id): # Get request body content = request.get_json() if not content: content = {} content["current_user"] = current_user content["group_id"] = group_id status_code, response_msg = error_handler( route_logic, GetGroupMessage, current_user, content ) response = response_msg if isinstance(response_msg, dict) else response_msg.content return Response( json.dumps(response), status=status_code, mimetype="application/json", ) @group_route.route("/<group_id>", methods=["PUT"]) @token_required def update_group_route(current_user, group_id): # Get request body content = request.get_json() if not content: content = {} content["current_user"] = current_user content["group_id"] = group_id status_code, response_msg = error_handler( route_logic, UpdateGroupMessage, current_user, content ) response = response_msg if isinstance(response_msg, dict) else response_msg.content return Response( json.dumps(response), status=status_code, mimetype="application/json", ) @group_route.route("/<group_id>", methods=["DELETE"]) @token_required def delete_group_route(current_user, group_id): # Get request body content = request.get_json() if not content: content = {} content["current_user"] = current_user content["group_id"] = group_id status_code, response_msg = error_handler( route_logic, DeleteGroupMessage, current_user, content ) response = response_msg if isinstance(response_msg, dict) else response_msg.content return Response( json.dumps(response), status=status_code, mimetype="application/json", )

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null

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5

675f9e34839bafe50b9710a36a0fac5e753048bc

1,472

py

Python

src/genie/libs/parser/iosxe/tests/ShowIpSlaSummary/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/iosxe/tests/ShowIpSlaSummary/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/iosxe/tests/ShowIpSlaSummary/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { 'ids': { '100': { 'state': 'active', 'type': 'icmp-jitter', 'destination': '192.0.2.2', 'rtt_stats': '100', 'rtt_stats_msecs': 100, 'return_code': 'OK', 'last_run': '22:49:53 PST Tue May 3 2011' }, '101': { 'state': 'active', 'type': 'udp-jitter', 'destination': '192.0.2.2', 'rtt_stats': '100', 'rtt_stats_msecs': 100, 'return_code': 'OK', 'last_run': '22:49:53 PST Tue May 3 2011' }, '102': { 'state': 'active', 'type': 'tcp-connect', 'destination': '192.0.2.2', 'rtt_stats': '-', 'return_code': 'NoConnection', 'last_run': '22:49:53 PST Tue May 3 2011' }, '103': { 'state': 'active', 'type': 'video', 'destination': '2001:db8:130f:d1c8::222', 'rtt_stats': '100', 'rtt_stats_msecs': 100, 'return_code': 'OK', 'last_run': '22:49:53 PST Tue May 3 2011' }, '104': { 'state': 'active', 'type': 'video', 'destination': '2001:db8:130f:d1c8::222', 'rtt_stats': '100', 'rtt_stats_msecs': 100, 'return_code': 'OK', 'last_run': '22:49:53 PST Tue May 3 2011' } } }

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0.160279

0.415082

1,472

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false

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null

0

1

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null

0

5

679541951dcef8c2672f484489eea87b2d68b4e2

659

py

Python

Mundo #02/ex68-a15-par-ou-impar-while.py

freitasSystemOutPrint/Python3

e5e88fbe8e7e0c5472573d2c901844270385194b

[ "MIT" ]

1

2020-06-02T07:31:16.000Z

Mundo #02/ex68-a15-par-ou-impar-while.py

freitasSystemOutPrint/Python3

e5e88fbe8e7e0c5472573d2c901844270385194b

[ "MIT" ]

null

Mundo #02/ex68-a15-par-ou-impar-while.py

freitasSystemOutPrint/Python3

e5e88fbe8e7e0c5472573d2c901844270385194b

[ "MIT" ]

null

rom random import randint jogador = contador = 0 while True: opcao = 'nula' computador = randint(1, 10) jogador = int(input('Escolha um número: ')) soma = computador + jogador while not opcao in 'PpIi': opcao = input('Par ou impar [p/i]: ') print(f'VOCÊ JOGOU {jogador} E O COMPUTADOR JOGOU {computador}! A soma deu {soma}.') if soma % 2 == 0 and opcao in 'pP': print('Deu PAR!\n') elif soma % 2 != 0 and opcao in 'Ii': print('Deu IMPAR!\n') else: break print('PARABÉNS, VOCÊ VENCEU. Vamos jogar novamente:') contador += 1 print(f'\nGAME OVER! VOCÊ PERDEU APÓS {contador} VITÓRIAS!'

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5

679ce48003106e7dee3fc070609e13a8520d5d39

238

py

Python

virtualis/methods/card_aware.py

valeriangalliat/virtualis-client

5972c3dd4abfe00f967a30bf84e5db47771e7034

[ "Unlicense" ]

null

virtualis/methods/card_aware.py

valeriangalliat/virtualis-client

5972c3dd4abfe00f967a30bf84e5db47771e7034

[ "Unlicense" ]

null

virtualis/methods/card_aware.py

valeriangalliat/virtualis-client

5972c3dd4abfe00f967a30bf84e5db47771e7034

[ "Unlicense" ]

null

from . import Request class CardAwareRequest(Request): def __init__(self, card): self.card = card def query(self): return { 'CardType': self.card.type, 'VCardId': self.card.id, }

18.307692

39

0.55042

25

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5.08

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0.336134

238

12

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0

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0.222222

false

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0.111111

0.555556

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null

1

0

null

0

1

0

1

0

5

e1dfa3b5ec1ef5b7a1c3dc7b8be7d9605ce94362

5,093

py

Python

test/cnnl/op_test/test_round.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

20

2022-03-01T11:40:51.000Z

2022-03-30T08:17:47.000Z

test/cnnl/op_test/test_round.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

null

test/cnnl/op_test/test_round.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

null

from __future__ import print_function import sys import os import unittest import logging import torch import torch_mlu.core.mlu_model as ct # pylint: disable=W0611 cur_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(cur_dir + "/../../") from common_utils import testinfo, TestCase # pylint: disable=C0413,C0411 logging.basicConfig(level=logging.DEBUG) class TestOps(TestCase): #@unittest.skip("not test") @testinfo() def test_round(self): shape_list = [(2,3,4,3,4,2,1),(2,3,4),(1,32,5,12,8), (2,128,10,6),(2,512,8),(1,100),(24,)] for i, _ in enumerate(shape_list): x = torch.randn(shape_list[i], dtype=torch.float) out_cpu = torch.round(x) out_mlu = torch.round(self.to_mlu(x)) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_channel_last(self): shape = (2,128,10,6) x = torch.randn(shape, dtype=torch.float).to(memory_format=torch.channels_last) out_cpu = torch.round(x) out_mlu = torch.round(self.to_mlu(x)) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_not_dense(self): shape_list = [(2,3,4),(1,32,5,12,8), (2,128,10,6)] for i, _ in enumerate(shape_list): x = torch.randn(shape_list[i], dtype=torch.float) out_cpu = torch.round(x[:, ... , :2]) out_mlu = torch.round(self.to_mlu(x)[:, ... , :2]) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_inplace(self): shape_list = [(2,3,4,3,4,2,1),(2,3,4),(1,32,5,12,8), (2,128,10,6),(2,512,8),(1,100),(24,)] for i, _ in enumerate(shape_list): x_cpu = torch.randn(shape_list[i], dtype=torch.float) x_mlu = x_cpu.to('mlu') out_cpu = torch.round_(x_cpu) out_mlu = torch.round_(x_mlu) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) x_cpu = torch.randn(shape_list[i], dtype=torch.float) x_mlu = x_cpu.to('mlu') x_cpu.round_() x_mlu.round_() self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_inplace_channel_last(self): shape_list = [(32,5,12,8), (2,128,10,6)] for i, _ in enumerate(shape_list): x_cpu = torch.randn(shape_list[i]).to(memory_format=torch.channels_last) x_mlu = x_cpu.to('mlu') out_cpu = torch.round_(x_cpu) out_mlu = torch.round_(x_mlu) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) x_cpu = torch.randn(shape_list[i]).to(memory_format=torch.channels_last) x_mlu = x_cpu.to('mlu') x_cpu.round_() x_mlu.round_() self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_inplace_not_dense(self): shape_list = [(2,3,4),(1,32,5,12,8), (2,128,10,6)] for i, _ in enumerate(shape_list): x_cpu = torch.randn(shape_list[i], dtype=torch.float) x_mlu = x_cpu.to('mlu') out_cpu = torch.round_(x_cpu[:, ... , :2]) out_mlu = torch.round_(x_mlu[:, ... , :2]) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) x_cpu = torch.randn(shape_list[i], dtype=torch.float) x_mlu = x_cpu.to('mlu') x_cpu[:, ... , :2].round_() x_mlu[:, ... , :2].round_() self.assertTensorsEqual(x_cpu, x_mlu.cpu(), 0) #@unittest.skip("not test") @testinfo() def test_round_out(self): shape_list = [(2,3,4,3,4,2,1),(2,3,4),(1,32,5,12,8), (2,128,10,6),(2,512,8),(1,100),(24,)] for i, _ in enumerate(shape_list): x_cpu = torch.randn(shape_list[i], dtype=torch.float) x_mlu = x_cpu.to('mlu') out_tmpcpu = torch.zeros(shape_list[i]) out_tmpmlu = torch.zeros(shape_list[i]).to('mlu') out_tmpcpu_2 = torch.zeros((1)) out_tmpmlu_2 = torch.zeros((1)).to('mlu') out_cpu = torch.round(x_cpu, out=out_tmpcpu) out_mlu = torch.round(x_mlu, out=out_tmpmlu) self.assertTensorsEqual(out_cpu, out_mlu.cpu(), 0) self.assertTensorsEqual(out_tmpcpu, out_tmpmlu.cpu(), 0) out_cpu_2 = torch.round(x_cpu, out=out_tmpcpu_2) out_mlu_2 = torch.round(x_mlu, out=out_tmpmlu_2) self.assertTensorsEqual(out_cpu_2, out_mlu_2.cpu(), 0) self.assertTensorsEqual(out_tmpcpu_2, out_tmpmlu_2.cpu(), 0) if __name__ == "__main__": unittest.main()

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null

0

5

223f7ea87976ca61e366ffb2400729d423e625bd

26

py

Python

base/test-show-scope/func-5.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

25

2015-04-16T04:31:49.000Z

2022-03-10T15:53:28.000Z

base/test-show-scope/func-5.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2018-11-21T22:40:02.000Z

2018-11-26T17:53:11.000Z

base/test-show-scope/func-5.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2021-03-26T03:36:19.000Z

def f(): x = 5 return x

6.5

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0.5

6

26

2.166667

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0

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0.346154

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3

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false

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1

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null

0

1

0

1

0

5

226d776dfbc372079f5b240419bd12599b1519c5

33

py

Python

Animation/__init__.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

Animation/__init__.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

Animation/__init__.py

olesmith/SmtC

dfae5097f02192b60aae05b9d02404fcfe893be3

[ "CC0-1.0" ]

null

from Main import Animation

11

31

0.69697

4

33

5.75

1

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0.30303

33

2

32

16.5

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0

1

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null

0

1

0

1

0

5

97fe769bbfc4a57abcb4e958b79cda544086d53e

89

py

Python

address/admin.py

Armestrong/Tourist_Attractions

8bcc60cd135cc92f32d3e82485cb45e1ecd4714a

[ "MIT" ]

null

address/admin.py

Armestrong/Tourist_Attractions

8bcc60cd135cc92f32d3e82485cb45e1ecd4714a

[ "MIT" ]

null

address/admin.py

Armestrong/Tourist_Attractions

8bcc60cd135cc92f32d3e82485cb45e1ecd4714a

[ "MIT" ]

null

from django.contrib import admin from .models import Addres admin.site.register(Addres)

17.8

32

0.820225

13

89

5.615385

0.692308

0

0.11236

89

4

33

22.25

0.924051

0

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true

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0.666667

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null

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1

0

null

0

1

0

1

0

1

0

5

3f0204cbedef38b7212139677031197af7161a4a

478

py

Python

spec/python/test_imports_circular_a.py

cherue/kaitai_struct_tests

86ff5169a24c8f5400453320721d86c675c53086

[ "MIT" ]

null

spec/python/test_imports_circular_a.py

cherue/kaitai_struct_tests

86ff5169a24c8f5400453320721d86c675c53086

[ "MIT" ]

null

spec/python/test_imports_circular_a.py

cherue/kaitai_struct_tests

86ff5169a24c8f5400453320721d86c675c53086

[ "MIT" ]

null

import unittest from imports_circular_a import ImportsCircularA class TestImportsCircularA(unittest.TestCase): def test_imports_circular_a(self): r = ImportsCircularA.from_file("src/fixed_struct.bin") self.assertEqual(r.code, 0x50) self.assertEqual(r.two.initial, 0x41) self.assertEqual(r.two.back_ref.code, 0x43) self.assertEqual(r.two.back_ref.two.initial, 0x4b) self.assertFalse(hasattr(r.two.back_ref.two, 'back_ref'))

34.142857

65

0.728033

64

478

5.265625

0.484375

0.178042

0.189911

0.169139

0.204748

0.154303

0

0.0275

0.16318

478

13

66

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0.815

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false

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0

1

0

1

0

1

0

5

3f0ebd6f9da90c31105befc8ab154a3d361d7fbb

18,456

py

Python

rh_DOGE_bot_2021_v1.py

0x00C0DE/robinhood_crypto_bot

831b1ae663a1819497fdf8e8034b87558fa97bb5

[ "MIT" ]

null

rh_DOGE_bot_2021_v1.py

0x00C0DE/robinhood_crypto_bot

831b1ae663a1819497fdf8e8034b87558fa97bb5

[ "MIT" ]

null

rh_DOGE_bot_2021_v1.py

0x00C0DE/robinhood_crypto_bot

831b1ae663a1819497fdf8e8034b87558fa97bb5

[ "MIT" ]

null

import robin_stocks import math import pyotp import sched import time import sys # Program description: # A Robinhood bot created to automatically monitor and trade crypto currency currently supported by Robinhood. # Works specifically for DOGE. # # This bot runs a scheduler every 5 minutes in order to update the prices on a 5 minute interval for a # list that will hold the previous prices for 30 minutes. # # This bot [REQUIRES] a individual to already have SET amount of shares of the current crypto they want to trade. # # Instructions after entering in login information (no particular order): # # 1. Fill in ticker (since this bot is specifically for DOGE, should be left alone) # 2. Fill in average_cost # 3. Fill in Shares2Buy amount (in dollars $) # 4. Fill in Shares2Sell amount (in dollars $) # 5. Fill in num_shares # # Some buying and selling errors will occur if a individual does not have enough shares to sell or enough money to buy. # If errors occur, simply re-update through redoing instructions above and restart the program. # Robinhood.login(username="example72", password="AnotherExample8") totp = pyotp.TOTP("Sauce").now() login = robin_stocks.login("", "") # Scheduler created to run every 5 mins s = sched.scheduler(time.time, time.sleep) # 5 min interval price history list, for every 30 minutes SE3P = [] Mazda = [] counter1 = 1.0 counter2 = 1.0 Shares2Sell = 0.00 Shares2Buy = 0.00 # step (5) # number of shares based on (total cost / Shares2Buy) # EX: ($2940 / 20) = 147 num_shares = 147 # step (2) # average cost average_cost = 0.363 def run(sc): # crypto currency ticker available on robinhood ticker = "DOGE" global SE3P global Mazda global Shares2Sell global Shares2Buy global counter1 global counter2 global average_cost global num_shares r = robin_stocks.crypto.get_crypto_quote(ticker, info="mark_price") #r = robin_stocks.robinhood.get_latest_price(ticker) print(ticker + ": $" + str(r)) SE3P.append(r) if len(SE3P) > 6: # if there are 5 or more elements in the list, rearrange positions Mazda = SE3P[1:6] SE3P = SE3P[-1:] SE3P = Mazda + SE3P print("Cleared and repositioned") if len(SE3P) == 1: #0min print("appended SE3P[0]") print(SE3P) elif len(SE3P) == 2: #SE3P.append(r) #5min print("appended SE3P[1]") print(SE3P) elif len(SE3P) == 3: #SE3P.append(r) #10min print("appended SE3P[2]") print(SE3P) elif len(SE3P) == 4: #SE3P.append(r) #15min print("appended SE3P[3]") print(SE3P) elif len(SE3P) == 5: #SE3P.append(r) #20min print("appended SE3P[4]") print(SE3P) elif len(SE3P) == 6: #SE3P.append(r) #25min print("appended SE3P[5]") print(SE3P) # BUY # if it's been less than 30 minutes since the start of the program if counter1 < 6: # For each 5 minutes, compare the inital price at the start of the program to the current price. # Each 5 minutes passed, checks if difference is larger than a manually set percentage, progressively increasing the difference boundary. # If the initial starting price when the program started multiplied by a set percentage, is greater than the current price AND # the current price is lower than the average cost minus a set percentage, then buy Shares2Buy amount in dollars. # If Bought, updates the average_cost and num_shares. if counter1 == 6: if float(SE3P[0])*1.005 > float(r) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif counter1 == 5: if float(SE3P[0])*1.0045 > float(r) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif counter1 == 4: if float(SE3P[0])*1.004 > float(r) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif counter1 == 3: if float(SE3P[0])*1.0035 > float(r) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif counter1 == 2: if float(SE3P[0])*1.003 > float(r) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) #BUY # if first price is < than 2nd # if it has been 30 minutes or more since the start of the program if counter1 >= 6: # For each 5 minutes, compare the current price to each index of the list in order starting with [1]-[5] since the current price would be [0]. # Each 5 minutes passed, checks if difference is larger than a manually set percentage, progressively increasing the difference boundary. # If the current price multiplied by a set percentage, is less than one of the indices AND # the current price is lower than the average cost minus a set percentage, then buy Shares2Buy amount in dollars. # If Bought, updates the average_cost and num_shares. if float(r)*1.004 < float(SE3P[0]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif float(r)*1.005 < float(SE3P[1]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif float(r)*1.006 < float(SE3P[2]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif float(r)*1.007 < float(SE3P[3]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif float(r)*1.008 < float(SE3P[4]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) elif float(r)*1.01 < float(SE3P[5]) and float(r) < float(average_cost-float(average_cost*float(0.015))): # instruction step (3) fill in amount in dollars in place of float(20) Shares2Buy = math.floor(float(20) / float(r)-1) crypto_BUY(ticker, Shares2Buy) print("bought:", r) tempval = average_cost*num_shares average_cost = tempval average_cost += float(r) num_shares += 1 average_cost /= float(num_shares) print("avg cost:" + str(average_cost)) #SELL # if it's been less than 30 minutes since the start of the program if counter2 < 6: # For each 5 minutes, compare the inital price at the start of the program to the current price. # Each 5 minutes passed, checks if difference is larger than a manually set percentage, progressively increasing the difference boundary. # If the initial starting price when the program started multiplied by a set percentage, is less than the current price AND # the current price is greater than the average cost plus a set percentage, then sell Shares2Sell amount in dollars. # If Sold, updates the average_cost and num_shares. if counter2 == 6: if float(SE3P[0])*1.0115 < float(r) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif counter2 == 5: if float(SE3P[0])*1.0105 < float(r) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif counter2 == 4: if float(SE3P[0])*1.0095 < float(r) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif counter2 == 3: if float(SE3P[0])*1.0085 < float(r) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif counter2 == 2: if float(SE3P[0])*1.0075 < float(r) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 #SELL # if it has been 30 minutes or more since the start of the program if counter2 >= 6: # For each 5 minutes, compare the current price to each index of the list in order starting with [1]-[5] since the current price would be [0]. # Each 5 minutes passed, checks if difference is larger than a manually set percentage, progressively increasing the difference boundary. # If the current price multiplied by a set percentage, is greater than one of the indices AND # the current price is greater than the average cost plus a set percentage, then sell Shares2Sell amount in dollars. # If Sold, updates the average_cost and num_shares. if float(r)*0.996 > float(SE3P[0]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif float(r)*0.995 > float(SE3P[1]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif float(r)*0.993 > float(SE3P[2]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif float(r)*0.991 > float(SE3P[3]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif float(r)*0.989 > float(SE3P[4]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 elif float(r)*0.987 > float(SE3P[5]) and float(r) > float(average_cost+float(average_cost*float(0.04))): # instruction step (4) fill in amount in dollars in place of float(100) Shares2Sell = math.floor(float(40) / float(r)+1) crypto_SELL(ticker, Shares2Sell) print("sold:", r) print("avg cost:", average_cost) num_shares -= 2.0 # Keeps track of counter print("c1:" + str(counter1)) print("c2:" + str(counter2)) counter1 += 1 counter2 += 1 if num_shares <= 0: sys.exit() # calls scheduler every 5 minutes s.enter(300, 1, run, (sc,)) # Functions to buy and sell crypto currency def crypto_BUY(ticker, amountD): r = robin_stocks.orders.order_buy_crypto_by_quantity(ticker, amountD) print(r) def crypto_SELL(ticker, amountD): r = robin_stocks.orders.order_sell_crypto_by_quantity(ticker, amountD) print(r) s.enter(1, 1, run, (s,)) s.run()

42.722222

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0.575856

2,400

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0.102083

0.12884

0.101382

0.08871

0.794547

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null

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1

0

null

0

5

3f1d0e1314da6292b201ab2f8278f0e1f894f6eb

109

py

Python

Programas do Curso/Desafio 5.py

carvalhopedro22/Programas-em-python-cursos-e-geral-

970e1ebe6cdd1e31f52dfd60328c2203d4de3ef1

[ "MIT" ]

null

Programas do Curso/Desafio 5.py

carvalhopedro22/Programas-em-python-cursos-e-geral-

970e1ebe6cdd1e31f52dfd60328c2203d4de3ef1

[ "MIT" ]

null

Programas do Curso/Desafio 5.py

carvalhopedro22/Programas-em-python-cursos-e-geral-

970e1ebe6cdd1e31f52dfd60328c2203d4de3ef1

[ "MIT" ]

null

n1 = int(input('Digite um numero: ')) print('Sucessor: {}'.format(n1+1)) print('Antecessor: {}'.format(n1-1))

36.333333

37

0.642202

16

109

4.375

0.6875

0.228571

0.257143

0

0.05

0.082569

109

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null

1

0

1

0

null

0

1

0

5

3f326a279f58306fa1c29be967df603190d5f9db

119

py

Python

cars/admin.py

Slawomir-Kwiatkowski/auto-maniac

0aad8bfbe0d4bc2f6a890dd5398cd8194248949d

[ "MIT" ]

3

2020-11-11T21:06:35.000Z

2022-03-02T16:11:29.000Z

cars/admin.py

Slawomir-Kwiatkowski/auto-maniac

0aad8bfbe0d4bc2f6a890dd5398cd8194248949d

[ "MIT" ]

1

2020-11-21T19:42:55.000Z

2020-12-01T18:50:49.000Z

cars/admin.py

Slawomir-Kwiatkowski/auto-maniac

0aad8bfbe0d4bc2f6a890dd5398cd8194248949d

[ "MIT" ]

2

2020-11-11T21:09:41.000Z

2020-12-13T16:29:30.000Z

from django.contrib import admin from .models import Car, Repair admin.site.register(Car) admin.site.register(Repair)

19.833333

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119

5.333333

0.555556

0.1875

0.354167

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0.10084

119

5

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null

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1

0

null

0

1

0

1

0

5

3f40396b0e504c043fd0c4aab4a71f0bba4a7892

90

py

Python

pub_data_visualization/load/tools/__init__.py

cre-os/pub-data-visualization

e5ec45e6397258646290836fc1a3b39ad69bf266

[ "MIT" ]

10

2020-10-08T11:35:49.000Z

2021-01-22T16:47:59.000Z

pub_data_visualization/load/tools/__init__.py

l-leo/pub-data-visualization

68eea00491424581b057495a7f0f69cf74e16e7d

[ "MIT" ]

3

2021-03-15T14:26:43.000Z

2021-12-02T15:27:49.000Z

pub_data_visualization/load/tools/__init__.py

cre-dev/pub-data-visualization

229bb7a543684be2cb06935299345ce3263da946

[ "MIT" ]

1

2021-01-22T16:47:10.000Z

""" Module to transform the load data. """ from .mean_load_delivery_period import *

12.857143

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12

90

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40

12.857143

0.833333

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true

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null

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1

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null

0

1

0

1

0

1

0

5

3f4603c4ecbbe2ef3bb1f2d8cde148f9daf7e067

76

py

Python

plugins/general/__init__.py

SumWum/HumanSona

37319bf7ba4f2b9a2d82038bbae3049c5137757a

[ "MIT" ]

null

plugins/general/__init__.py

SumWum/HumanSona

37319bf7ba4f2b9a2d82038bbae3049c5137757a

[ "MIT" ]

1

2020-06-07T14:43:49.000Z

plugins/general/__init__.py

SumWum/HumanSona

37319bf7ba4f2b9a2d82038bbae3049c5137757a

[ "MIT" ]

null

from .general import General def setup(bot): bot.add_cog(General(bot))

15.2

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76

4.5

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76

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1

0

1

0

1

0

5

58da351789bdee1cc05d36ed62b668c118e06a25

1,652

py

Python

examples_tests.py

bryanrios/Barcode_RFID_USB

4df6b7a17dfa1a16c35e37a8f03d093f71f19623

[ "MIT" ]

null

examples_tests.py

bryanrios/Barcode_RFID_USB

4df6b7a17dfa1a16c35e37a8f03d093f71f19623

[ "MIT" ]

null

examples_tests.py

bryanrios/Barcode_RFID_USB

4df6b7a17dfa1a16c35e37a8f03d093f71f19623

[ "MIT" ]

null

import unittest from black_rfid_reader import RFIDReader from lindy_bar_code_scanner import BarCodeReader class TestRFIDReader(unittest.TestCase): def test_if_raw_message_is_decoded(self): reader = RFIDReader(0x08ff, 0x0009, 84, 16, should_reset=False) raw_data = [0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] result = reader.decode_raw_data(raw_data) expected = '0027314141\n' self.assertEqual(expected, result) class TestBarCodeReader(unittest.TestCase): def test_if_raw_message_is_decoded(self): reader = BarCodeReader(0x03eb, 0x6201, 84, 6, should_reset=True) # Luke, Obi-Wan never told you what's in this raw_data... raw_data = [0, 0, 34, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0, 0, 36, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 31, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 40, 0, 0, 0] result = reader.decode_raw_data(raw_data) expected = '5903570132223\n' self.assertEqual(expected, result) if __name__ == '__main__': unittest.main()

55.066667

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0.639576

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0.541814

0.537102

0

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1,652

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0.015047

0

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0.105263

false

0

0.157895

0

0.368421

0

null

1

0

1

0

1

0

null

0

5

58f10a86e709248329bec71a72282b71f53526eb

28

py

Python

masterfile.py

ndc9jh/cs3240-labdemo

6ab150fa655327ae91d66881a31849e9a63fef22

[ "MIT" ]

null

masterfile.py

ndc9jh/cs3240-labdemo

6ab150fa655327ae91d66881a31849e9a63fef22

[ "MIT" ]

null

masterfile.py

ndc9jh/cs3240-labdemo

6ab150fa655327ae91d66881a31849e9a63fef22

[ "MIT" ]

null

#This file also does nothing

28

0.821429

5

28

4.6

1

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28

1

28

0.958333

0.964286

0

null

0

null

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5

451f1764c54080c9a50d3790e2d59afcede40e0b

194

py

Python

data/__init__.py

isLouisHsu/Pytorch_Retinaface

2db5e9aaec42d9605494032a5fa70fb7b82831de

[ "MIT" ]

null

data/__init__.py

isLouisHsu/Pytorch_Retinaface

2db5e9aaec42d9605494032a5fa70fb7b82831de

[ "MIT" ]

null

data/__init__.py

isLouisHsu/Pytorch_Retinaface

2db5e9aaec42d9605494032a5fa70fb7b82831de

[ "MIT" ]

null

from .wider_face import WiderFaceDetection, detection_collate from .ecust_hsfd import EcustHsfdDetection, detection_collate_hsfd, load_datacube from .data_augment import * from .config import *

38.8

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null

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1

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5

18c1a7deb28817e4e9f716090232c24f3c4a5b71

175

py

Python

bugtests/test310c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

bugtests/test310c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

bugtests/test310c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

""" [ #444292 ] local var binding overrides local import """ import support def foo(pickle): assert pickle == 1 import pickle assert pickle != 1 foo(1)

12.5

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18e744a2cfa978c6b7fe1cd5e8c2c8a4035d7311

164

py

Python

essentials/hackerank-1.py

priyeshkaratha/PythonProjects

b8f1aaa286eac4db8c8811c386e4bc120db1ad1a

[ "MIT" ]

null

essentials/hackerank-1.py

priyeshkaratha/PythonProjects

b8f1aaa286eac4db8c8811c386e4bc120db1ad1a

[ "MIT" ]

null

essentials/hackerank-1.py

priyeshkaratha/PythonProjects

b8f1aaa286eac4db8c8811c386e4bc120db1ad1a

[ "MIT" ]

null

def print_sequence(n): for i in range(1, n+1): print(i, end="") if __name__ == '__main__': n = int(input("Enter number :")) print_sequence(n)

18.222222

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7a0f5745022b9b4ae198282053af1c60380bb1d6

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py

Python

smart/forward_model/InterpolateModel.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

null

smart/forward_model/InterpolateModel.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

null

smart/forward_model/InterpolateModel.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

null

import smart import numpy as np import sys, os, os.path, time from astropy.table import Table from astropy.io import fits from numpy.linalg import inv, det from ..utils.interpolations import trilinear_interpolation ############################################################################################################## def InterpModel(teff, logg=4, metal=0, alpha=0, modelset='phoenix-aces-agss-cond-2011', instrument='nirspec', order='O33'): #print('Parameters', teff, logg, modelset, instrument, order) FULL_PATH = os.path.realpath(__file__) BASE, NAME = os.path.split(FULL_PATH) # Check the model set and instrument if instrument.lower() == 'nirspec': path = BASE + '/../libraries/%s/%s-O%s/'%(smart.ModelSets[modelset.lower()], instrument.upper(), order.upper()) else: path = BASE + '/../libraries/%s/%s-%s/'%(smart.ModelSets[modelset.lower()], instrument.upper(), order.upper()) Gridfile = BASE + '/../libraries/%s/%s_gridparams.csv'%(smart.ModelSets[modelset.lower()], smart.ModelSets[modelset.lower()]) if modelset.lower() == 'btsettl08': path = BASE + '/../libraries/btsettl08/NIRSPEC-O%s-RAW/'%order Gridfile = BASE + '/../libraries/btsettl08/btsettl08_gridparams.csv' # Read the grid file T1 = Table.read(Gridfile) ################################################################################### def GetModel(temp, wave=False, **kwargs): logg = kwargs.get('logg', 4.5) metal = kwargs.get('metal', 0) alpha = kwargs.get('alpha', 0) gridfile = kwargs.get('gridfile', None) instrument = kwargs.get('instrument', 'nirspec') order = kwargs.get('order', None) #print(temp, logg, metal, alpha) if gridfile is None: raise ValueError('Model gridfile must be provided.') if modelset.lower() == 'btsettl08': filename = 'btsettl08_t'+ str(int(temp.data[0])) + '_g' + '{0:.2f}'.format(float(logg)) + '_z-' + '{0:.2f}'.format(float(metal)) + '_en' + '{0:.2f}'.format(float(alpha)) + '_NIRSPEC-O' + str(order) + '-RAW.txt' elif modelset.lower() == 'sonora': if instrument.lower() == 'nirspec': filename = '%s'%smart.ModelSets[modelset.lower()] + '_t{0:03d}'.format(int(temp.data[0])) + '_g{0:.2f}'.format(float(logg)) + '_FeH0.00_Y0.28_CO1.00' + '_%s-O%s.fits'%(instrument.upper(), order.upper()) else: filename = '%s'%smart.ModelSets[modelset.lower()] + '_t{0:03d}'.format(int(temp.data[0])) + '_g{0:.2f}'.format(float(logg)) + '_FeH0.00_Y0.28_CO1.00' + '_%s-%s.fits'%(instrument.upper(), order.upper()) elif modelset.lower() == 'marcs-apogee-dr15': cm = kwargs.get('cm', 0) nm = kwargs.get('nm', 0) filename = '%s'%smart.ModelSets[modelset.lower()] + '_t{0:03d}'.format(int(temp.data[0])) + '_g{0:.2f}'.format(float(logg)) + '_z{0:.2f}'.format(float(metal)) + '_en{0:.2f}'.format(float(alpha)) + '_cm{0:.2f}'.format(float(cm)) + '_nm{0:.2f}'.format(float(nm)) + '_%s-%s.fits'%(instrument.upper(), order.upper()) else: filename = '%s'%smart.ModelSets[modelset.lower()] + '_t{0:03d}'.format(int(temp.data[0])) + '_g{0:.2f}'.format(float(logg)) + '_z{0:.2f}'.format(float(metal)) + '_en{0:.2f}'.format(float(alpha)) + '_%s-%s.fits'%(instrument.upper(), order.upper()) # Read in the model FITS file if modelset.lower() == 'btsettl08': Tab = Table.read(path+filename, format='ascii.tab', names=['wave', 'flux']) else: Tab = Table.read(path+filename) # Return the model (wave of flux) if wave: return Tab['wave'] else: return Tab['flux'] ################################################################################### # Check if the model already exists (grid point) if modelset.lower() == 'sonora': if (teff, logg) in zip(T1['teff'], T1['logg']): metal, ys = 0, 0.28 index0 = np.where( (T1['teff'] == teff) & (T1['logg'] == logg) & (T1['FeH'] == metal) & (T1['Y'] == ys) ) #flux2 = GetModel(T1['teff'][index0], T1['logg'][index0], T1['M_H'][index0], modelset=modelset ) #waves2 = GetModel(T1['teff'][index0], T1['logg'][index0], T1['M_H'][index0], modelset=modelset, wave=True) flux2 = GetModel(T1['teff'][index0], logg=T1['logg'][index0], metal=T1['FeH'][index0], alpha=T1['Y'][index0], instrument=instrument, order=order, gridfile=T1) waves2 = GetModel(T1['teff'][index0], logg=T1['logg'][index0], metal=T1['FeH'][index0], alpha=T1['Y'][index0], instrument=instrument, order=order, gridfile=T1, wave=True) return waves2, flux2 else: if (teff, logg, metal, alpha) in zip(T1['teff'], T1['logg'], T1['M_H'], T1['en']): index0 = np.where( (T1['teff'] == teff) & (T1['logg'] == logg) & (T1['M_H'] == metal) & (T1['en'] == alpha) ) #flux2 = GetModel(T1['teff'][index0], T1['logg'][index0], T1['M_H'][index0], modelset=modelset ) #waves2 = GetModel(T1['teff'][index0], T1['logg'][index0], T1['M_H'][index0], modelset=modelset, wave=True) flux2 = GetModel(T1['teff'][index0], logg=T1['logg'][index0], metal=T1['M_H'][index0], alpha=T1['en'][index0], instrument=instrument, order=order, gridfile=T1) waves2 = GetModel(T1['teff'][index0], logg=T1['logg'][index0], metal=T1['M_H'][index0], alpha=T1['en'][index0], instrument=instrument, order=order, gridfile=T1, wave=True) return waves2, flux2 try: if modelset.lower() == 'sonora': metal, alpha = 0, 0.28 # Get the nearest models to the gridpoint (teff) x0 = np.max(T1['teff'][np.where(T1['teff'] <= teff)]) x1 = np.min(T1['teff'][np.where(T1['teff'] >= teff)]) #print(x0, x1) # Get the nearest grid point to logg y0 = np.max(list(set(T1['logg'][np.where( (T1['teff'] == x0) & (T1['logg'] <= logg) )]) & set(T1['logg'][np.where( (T1['teff'] == x1) & (T1['logg'] <= logg) )]))) y1 = np.min(list(set(T1['logg'][np.where( (T1['teff'] == x0) & (T1['logg'] >= logg) )]) & set(T1['logg'][np.where( (T1['teff'] == x1) & (T1['logg'] >= logg) )]))) #print(y0, y1) # Get the nearest grid point to [M/H] z0 = np.max(list(set(T1['FeH'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] <= metal) )]) & set(T1['FeH'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] <= metal) )]))) z1 = np.min(list(set(T1['FeH'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] >= metal) )]) & set(T1['FeH'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] >= metal) )]))) #print(z0, z1) # Get the nearest grid point to Alpha t0 = np.max(list(set(T1['Y'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] <= alpha) )]) & set(T1['Y'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] <= alpha) )]))) t1 = np.min(list(set(T1['Y'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] >= alpha) )]) & set(T1['Y'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] >= alpha) )]))) #print(t0, t1) else: # Get the nearest models to the gridpoint (teff) x0 = np.max(T1['teff'][np.where(T1['teff'] <= teff)]) x1 = np.min(T1['teff'][np.where(T1['teff'] >= teff)]) #print('teff:', x0, teff, x1) # Get the nearest grid point to logg y0 = np.max(list(set(T1['logg'][np.where( (T1['teff'] == x0) & (T1['logg'] <= logg) )]) & set(T1['logg'][np.where( (T1['teff'] == x1) & (T1['logg'] <= logg) )]))) y1 = np.min(list(set(T1['logg'][np.where( (T1['teff'] == x0) & (T1['logg'] >= logg) )]) & set(T1['logg'][np.where( (T1['teff'] == x1) & (T1['logg'] >= logg) )]))) #print('logg:', y0, logg, y1) # Get the nearest grid point to [M/H] #print(metal) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) )]))) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) )]))) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] <= metal))]))) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] <= metal))]))) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] >= metal))]))) #print(list(set(T1['M_H'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] >= metal))]))) z0 = np.max(list(set(T1['M_H'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] <= metal) )]) & set(T1['M_H'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] <= metal) )]))) z1 = np.min(list(set(T1['M_H'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] >= metal) )]) & set(T1['M_H'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] >= metal) )]))) #print('metal:', z0, metal, z1) # Get the nearest grid point to Alpha #print(list(set(T1['en'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) )]))) #print(list(set(T1['en'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) )]))) #print(list(set(T1['en'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] <= alpha) )]))) #print(list(set(T1['en'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] <= alpha) )]))) #print(list(set(T1['en'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] >= alpha) )]))) #print(list(set(T1['en'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] >= alpha) )]))) t0 = np.max(list(set(T1['en'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] <= alpha) )]) & set(T1['en'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] <= alpha) )]))) t1 = np.min(list(set(T1['en'][np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] >= alpha) )]) & set(T1['en'][np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] >= alpha) )]))) #print('alpha:', z0, alpha, z1) except: raise ValueError('Model Parameters Teff: %0.3f, logg: %0.3f, [M/H]: %0.3f, Alpha: %0.3f are outside the model grid.'%(teff, logg, metal, alpha)) if modelset.lower() == 'sonora': # Get the 16 points ind0000 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] == t0) ) # 0000 ind1000 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] == t0) ) # 1000 ind0100 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['FeH'] == z0) & (T1['Y'] == t0) ) # 0100 ind0010 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z1) & (T1['Y'] == t0) ) # 0010 ind0001 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] == t1) ) # 0001 ind1001 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['FeH'] == z0) & (T1['Y'] == t1) ) # 1001 ind0101 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['FeH'] == z0) & (T1['Y'] == t1) ) # 0101 ind0011 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['FeH'] == z1) & (T1['Y'] == t1) ) # 0011 ind1011 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['FeH'] == z1) & (T1['Y'] == t1) ) # 1011 ind0111 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] == t1) ) # 0111 ind1111 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] == t1) ) # 1111 ind0110 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] == t0) ) # 0110 ind1010 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['FeH'] == z1) & (T1['Y'] == t0) ) # 1010 ind1100 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z0) & (T1['Y'] == t0) ) # 1100 ind1101 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z0) & (T1['Y'] == t1) ) # 1101 ind1110 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['FeH'] == z1) & (T1['Y'] == t0) ) # 1110 Points = [ [np.log10(T1['teff'][ind0000]), T1['logg'][ind0000], T1['FeH'][ind0000], T1['Y'][ind0000], np.log10(GetModel(T1['teff'][ind0000], logg=T1['logg'][ind0000], metal=T1['FeH'][ind0000], alpha=T1['Y'][ind0000], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1000]), T1['logg'][ind1000], T1['FeH'][ind1000], T1['Y'][ind1000], np.log10(GetModel(T1['teff'][ind1000], logg=T1['logg'][ind1000], metal=T1['FeH'][ind1000], alpha=T1['Y'][ind1000], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0100]), T1['logg'][ind0100], T1['FeH'][ind0100], T1['Y'][ind0100], np.log10(GetModel(T1['teff'][ind0100], logg=T1['logg'][ind0100], metal=T1['FeH'][ind0100], alpha=T1['Y'][ind0100], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0010]), T1['logg'][ind0010], T1['FeH'][ind0010], T1['Y'][ind0010], np.log10(GetModel(T1['teff'][ind0010], logg=T1['logg'][ind0010], metal=T1['FeH'][ind0010], alpha=T1['Y'][ind0010], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0001]), T1['logg'][ind0001], T1['FeH'][ind0001], T1['Y'][ind0001], np.log10(GetModel(T1['teff'][ind0001], logg=T1['logg'][ind0001], metal=T1['FeH'][ind0001], alpha=T1['Y'][ind0001], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1001]), T1['logg'][ind1001], T1['FeH'][ind1001], T1['Y'][ind1001], np.log10(GetModel(T1['teff'][ind1001], logg=T1['logg'][ind1001], metal=T1['FeH'][ind1001], alpha=T1['Y'][ind1001], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0101]), T1['logg'][ind0101], T1['FeH'][ind0101], T1['Y'][ind0101], np.log10(GetModel(T1['teff'][ind0101], logg=T1['logg'][ind0101], metal=T1['FeH'][ind0101], alpha=T1['Y'][ind0101], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0011]), T1['logg'][ind0011], T1['FeH'][ind0011], T1['Y'][ind0011], np.log10(GetModel(T1['teff'][ind0011], logg=T1['logg'][ind0011], metal=T1['FeH'][ind0011], alpha=T1['Y'][ind0011], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1011]), T1['logg'][ind1011], T1['FeH'][ind1011], T1['Y'][ind1011], np.log10(GetModel(T1['teff'][ind1011], logg=T1['logg'][ind1011], metal=T1['FeH'][ind1011], alpha=T1['Y'][ind1011], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0111]), T1['logg'][ind0111], T1['FeH'][ind0111], T1['Y'][ind0111], np.log10(GetModel(T1['teff'][ind0111], logg=T1['logg'][ind0111], metal=T1['FeH'][ind0111], alpha=T1['Y'][ind0111], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1111]), T1['logg'][ind1111], T1['FeH'][ind1111], T1['Y'][ind1111], np.log10(GetModel(T1['teff'][ind1111], logg=T1['logg'][ind1111], metal=T1['FeH'][ind1111], alpha=T1['Y'][ind1111], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0110]), T1['logg'][ind0110], T1['FeH'][ind0110], T1['Y'][ind0110], np.log10(GetModel(T1['teff'][ind0110], logg=T1['logg'][ind0110], metal=T1['FeH'][ind0110], alpha=T1['Y'][ind0110], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1010]), T1['logg'][ind1010], T1['FeH'][ind1010], T1['Y'][ind1010], np.log10(GetModel(T1['teff'][ind1010], logg=T1['logg'][ind1010], metal=T1['FeH'][ind1010], alpha=T1['Y'][ind1010], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1100]), T1['logg'][ind1100], T1['FeH'][ind1100], T1['Y'][ind1100], np.log10(GetModel(T1['teff'][ind1100], logg=T1['logg'][ind1100], metal=T1['FeH'][ind1100], alpha=T1['Y'][ind1100], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1101]), T1['logg'][ind1101], T1['FeH'][ind1101], T1['Y'][ind1101], np.log10(GetModel(T1['teff'][ind1101], logg=T1['logg'][ind1101], metal=T1['FeH'][ind1101], alpha=T1['Y'][ind1101], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1110]), T1['logg'][ind1110], T1['FeH'][ind1110], T1['Y'][ind1110], np.log10(GetModel(T1['teff'][ind1110], logg=T1['logg'][ind1110], metal=T1['FeH'][ind1110], alpha=T1['Y'][ind1110], instrument=instrument, order=order, gridfile=T1))], ] #print(Points) waves2 = GetModel(T1['teff'][ind1111], logg=T1['logg'][ind1111], metal=T1['FeH'][ind1111], alpha=T1['Y'][ind1111], instrument=instrument, order=order, gridfile=T1, wave=True) else: # Get the 16 points ind0000 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] == t0) ) # 0000 ind1000 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] == t0) ) # 1000 ind0100 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['M_H'] == z0) & (T1['en'] == t0) ) # 0100 ind0010 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z1) & (T1['en'] == t0) ) # 0010 ind0001 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] == t1) ) # 0001 ind1001 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['M_H'] == z0) & (T1['en'] == t1) ) # 1001 ind0101 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['M_H'] == z0) & (T1['en'] == t1) ) # 0101 ind0011 = np.where( (T1['teff'] == x0) & (T1['logg'] == y0) & (T1['M_H'] == z1) & (T1['en'] == t1) ) # 0011 ind1011 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['M_H'] == z1) & (T1['en'] == t1) ) # 1011 ind0111 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] == t1) ) # 0111 ind1111 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] == t1) ) # 1111 ind0110 = np.where( (T1['teff'] == x0) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] == t0) ) # 0110 ind1010 = np.where( (T1['teff'] == x1) & (T1['logg'] == y0) & (T1['M_H'] == z1) & (T1['en'] == t0) ) # 1010 ind1100 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z0) & (T1['en'] == t0) ) # 1100 ind1101 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z0) & (T1['en'] == t1) ) # 1101 ind1110 = np.where( (T1['teff'] == x1) & (T1['logg'] == y1) & (T1['M_H'] == z1) & (T1['en'] == t0) ) # 1110 Points = [ [np.log10(T1['teff'][ind0000]), T1['logg'][ind0000], T1['M_H'][ind0000], T1['en'][ind0000], np.log10(GetModel(T1['teff'][ind0000], logg=T1['logg'][ind0000], metal=T1['M_H'][ind0000], alpha=T1['en'][ind0000], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1000]), T1['logg'][ind1000], T1['M_H'][ind1000], T1['en'][ind1000], np.log10(GetModel(T1['teff'][ind1000], logg=T1['logg'][ind1000], metal=T1['M_H'][ind1000], alpha=T1['en'][ind1000], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0100]), T1['logg'][ind0100], T1['M_H'][ind0100], T1['en'][ind0100], np.log10(GetModel(T1['teff'][ind0100], logg=T1['logg'][ind0100], metal=T1['M_H'][ind0100], alpha=T1['en'][ind0100], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0010]), T1['logg'][ind0010], T1['M_H'][ind0010], T1['en'][ind0010], np.log10(GetModel(T1['teff'][ind0010], logg=T1['logg'][ind0010], metal=T1['M_H'][ind0010], alpha=T1['en'][ind0010], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0001]), T1['logg'][ind0001], T1['M_H'][ind0001], T1['en'][ind0001], np.log10(GetModel(T1['teff'][ind0001], logg=T1['logg'][ind0001], metal=T1['M_H'][ind0001], alpha=T1['en'][ind0001], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1001]), T1['logg'][ind1001], T1['M_H'][ind1001], T1['en'][ind1001], np.log10(GetModel(T1['teff'][ind1001], logg=T1['logg'][ind1001], metal=T1['M_H'][ind1001], alpha=T1['en'][ind1001], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0101]), T1['logg'][ind0101], T1['M_H'][ind0101], T1['en'][ind0101], np.log10(GetModel(T1['teff'][ind0101], logg=T1['logg'][ind0101], metal=T1['M_H'][ind0101], alpha=T1['en'][ind0101], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0011]), T1['logg'][ind0011], T1['M_H'][ind0011], T1['en'][ind0011], np.log10(GetModel(T1['teff'][ind0011], logg=T1['logg'][ind0011], metal=T1['M_H'][ind0011], alpha=T1['en'][ind0011], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1011]), T1['logg'][ind1011], T1['M_H'][ind1011], T1['en'][ind1011], np.log10(GetModel(T1['teff'][ind1011], logg=T1['logg'][ind1011], metal=T1['M_H'][ind1011], alpha=T1['en'][ind1011], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0111]), T1['logg'][ind0111], T1['M_H'][ind0111], T1['en'][ind0111], np.log10(GetModel(T1['teff'][ind0111], logg=T1['logg'][ind0111], metal=T1['M_H'][ind0111], alpha=T1['en'][ind0111], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1111]), T1['logg'][ind1111], T1['M_H'][ind1111], T1['en'][ind1111], np.log10(GetModel(T1['teff'][ind1111], logg=T1['logg'][ind1111], metal=T1['M_H'][ind1111], alpha=T1['en'][ind1111], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind0110]), T1['logg'][ind0110], T1['M_H'][ind0110], T1['en'][ind0110], np.log10(GetModel(T1['teff'][ind0110], logg=T1['logg'][ind0110], metal=T1['M_H'][ind0110], alpha=T1['en'][ind0110], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1010]), T1['logg'][ind1010], T1['M_H'][ind1010], T1['en'][ind1010], np.log10(GetModel(T1['teff'][ind1010], logg=T1['logg'][ind1010], metal=T1['M_H'][ind1010], alpha=T1['en'][ind1010], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1100]), T1['logg'][ind1100], T1['M_H'][ind1100], T1['en'][ind1100], np.log10(GetModel(T1['teff'][ind1100], logg=T1['logg'][ind1100], metal=T1['M_H'][ind1100], alpha=T1['en'][ind1100], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1101]), T1['logg'][ind1101], T1['M_H'][ind1101], T1['en'][ind1101], np.log10(GetModel(T1['teff'][ind1101], logg=T1['logg'][ind1101], metal=T1['M_H'][ind1101], alpha=T1['en'][ind1101], instrument=instrument, order=order, gridfile=T1))], [np.log10(T1['teff'][ind1110]), T1['logg'][ind1110], T1['M_H'][ind1110], T1['en'][ind1110], np.log10(GetModel(T1['teff'][ind1110], logg=T1['logg'][ind1110], metal=T1['M_H'][ind1110], alpha=T1['en'][ind1110], instrument=instrument, order=order, gridfile=T1))], ] #print(Points) waves2 = GetModel(T1['teff'][ind1111], logg=T1['logg'][ind1111], metal=T1['M_H'][ind1111], alpha=T1['en'][ind1111], instrument=instrument, order=order, gridfile=T1, wave=True) return waves2, smart.utils.interpolations.quadlinear_interpolation(np.log10(teff), logg, metal, alpha, Points) ################################################################################################################################################################################################

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null

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null

0

5

e149bcbe323162009740cdc117395402d5e41f29

6,428

py

Python

run_stepwise_experiment.py

enricomeloni/covid-tools

6920b8cfa0eb89bdb7e0ba96ecc74831185c44a7

[ "MIT" ]

1

2020-10-06T16:03:01.000Z

run_stepwise_experiment.py

enricomeloni/covid-tools

6920b8cfa0eb89bdb7e0ba96ecc74831185c44a7

[ "MIT" ]

3

2022-02-13T20:21:56.000Z

2022-02-27T10:19:23.000Z

run_stepwise_experiment.py

sailab-code/learning-sidarthe

6920b8cfa0eb89bdb7e0ba96ecc74831185c44a7

[ "MIT" ]

null

import os from collections import namedtuple from learning_models.stepwise_tied_sidarthe_extended import StepwiseTiedSidartheExtended, Param from experiments.sidarthe_extended_experiment import ExtendedSidartheExperiment if __name__ == "__main__": region = "Italy" n_epochs = 10000 t_step = 1.0 train_size = 190 # initial_params = { # "alpha": [Param(0.570, 4), Param(0.422, 18), Param(0.360, 6), Param(0.210, 73), Param(0.210, train_size-101)], # "beta": [Param(0.011, 4), Param(0.0057, 18), Param(0.005, train_size-38)], # "gamma": [Param(0.456, 4), Param(0.285, 18), Param(0.2, 6), Param(0.11, 10), Param(0.11, train_size-38)], # "delta": [Param(0.011, 4), Param(0.0057, 18), Param(0.005, train_size-38)], # "epsilon": [Param(0.171, 12), Param(0.143, 26), Param(0.2, train_size - 38)], # "theta": [Param(0.371, 38), Param(0.371, 63), Param(0.371, train_size - 101)], # "zeta": [Param(0.125, 22), Param(0.034, 16), Param(0.025, train_size - 38)], # "eta": [Param(0.125, 22), Param(0.034, 16), Param(0.025, train_size - 38)], # "mu": [Param(0.017, 22), Param(0.008, train_size - 22)], # "nu": [Param(0.027, 22), Param(0.015, train_size - 22)], # "tau": [Param(0.01, 4), Param(0.02, 18), Param(0.04, 6), Param(0.06, 10), Param(0.1, 30), Param(0.02, train_size - 68)], # "lambda": [Param(0.034, 22), Param(0.08, train_size - 22)], # # "lambda": [0.034], # "kappa": [Param(0.017, 22), Param(0.017, 16), Param(0.02, train_size - 38)], # # "kappa": [0.017], # "xi": [Param(0.017, 22), Param(0.017, 16), Param(0.02, train_size - 38)], # "rho": [Param(0.034, 22), Param(0.017, 16), Param(0.02, train_size - 38)], # # "rho": [0.034], # "sigma": [Param(0.017, 22), Param(0.017, 16), Param(0.01, train_size - 38)], # "phi": [Param(0.02, 4), Param(0.02, 18), Param(0.02, 16), Param(0.02, train_size - 38)], # "chi": [Param(0.02, 4), Param(0.02, 18), Param(0.02, 16), Param(0.02, train_size - 38)] # } # weekly wise_step = 7 initial_params = { "alpha": [Param(0.570, 4), Param(0.422, 18), Param(0.360, 6), Param(0.210, 73)] + [Param(0.21, wise_step) for i in range((train_size-101)//wise_step)], "beta": [Param(0.011, 4), Param(0.0057, 18), Param(0.005, 16)] + [Param(0.005, wise_step) for i in range((train_size-38)//wise_step)], "gamma": [Param(0.456, 4), Param(0.285, 18), Param(0.2, 6), Param(0.11, 10)] + [Param(0.11, wise_step) for i in range((train_size-38)//wise_step)], "delta": [Param(0.011, 4), Param(0.0057, 18), Param(0.005, 16)] + [Param(0.005, wise_step) for i in range((train_size-38)//wise_step)], "epsilon": [Param(0.171, 12), Param(0.143, 26)] + [Param(0.2, wise_step) for i in range((train_size-38)//wise_step)], "theta": [Param(0.371, wise_step) for i in range((train_size)//wise_step)], # "zeta": [Param(0.125, 22), Param(0.034, 16)] + [Param(0.025, wise_step) for i in range((train_size-38)//wise_step)], # "eta": [Param(0.125, 22), Param(0.034, 16)] + [Param(0.025, wise_step) for i in range((train_size-38)//wise_step)], # "mu": [Param(0.017, 22), Param(0.008, 16)] + [Param(0.008, wise_step) for i in range((train_size-38)//wise_step)], # "nu": [Param(0.027, 22), Param(0.015, 16)] + [Param(0.015, wise_step) for i in range((train_size-38)//wise_step)], # "tau": [Param(0.01, 4), Param(0.02, 18), Param(0.04, 6), Param(0.06, 10), Param(0.1, 30)] + [Param(0.025, wise_step) for i in range((train_size - 68)//wise_step)], # "lambda": [Param(0.034, 22), Param(0.08, 16)] + [Param(0.08, wise_step) for i in range((train_size-38)//wise_step)], # "zeta": [Param(0.125, wise_step) for _ in range(3)] + [Param(0.034, wise_step) for _ in range(3)] + [Param(0.025, wise_step) for i in range((train_size - 42) // wise_step)], "eta": [Param(0.125, wise_step) for _ in range(3)] + [Param(0.034, wise_step) for _ in range(3)] + [Param(0.025, wise_step) for i in range((train_size - 42) // wise_step)], "mu": [Param(0.017, wise_step) for _ in range(3)] + [Param(0.008, wise_step) for i in range((train_size - 21) // wise_step)], "nu": [Param(0.027, wise_step) for _ in range(3)] + [Param(0.015, wise_step) for i in range((train_size - 21) // wise_step)], "tau": [Param(0.01, 4), Param(0.02, 18), Param(0.04, 6), Param(0.06, 10), Param(0.1, 30)] + [Param(0.025, wise_step) for i in range((train_size - 68) // wise_step)], "lambda": [Param(0.034, wise_step) for _ in range(3)] + [Param(0.08, wise_step) for i in range((train_size - 21) // wise_step)], # "kappa": [Param(0.017, 22), Param(0.017, 16)] + [Param(0.02, wise_step) for i in range((train_size-38)//wise_step)], "kappa": [Param(0.02, wise_step) for i in range((train_size)//wise_step)], # "xi": [Param(0.017, 22), Param(0.017, 16)] + [Param(0.02, wise_step) for i in range((train_size-38)//wise_step)], "xi": [Param(0.02, wise_step) for i in range((train_size)//wise_step)], "rho": [Param(0.034, wise_step) for _ in range(3)] + [Param(0.08, wise_step) for i in range((train_size - 21) // wise_step)], "sigma": [Param(0.017, wise_step) for _ in range(6) for _ in range(3)] + [Param(0.01, wise_step) for i in range((train_size-42)//wise_step)], "phi": [Param(0.001, wise_step) for i in range((train_size)//wise_step)], "chi": [Param(0.001, wise_step) for i in range((train_size)//wise_step)] } loss_weights = { "d_weight": 3.5, "r_weight": 1., "t_weight": 1., "h_weight": 1., "e_weight": 1. } for k,v in loss_weights.items(): loss_weights[k] = 0.001 * v experiment_cls = ExtendedSidartheExperiment # switch class to change experiment: e.g. SidartheExperiment experiment = experiment_cls(region, n_epochs=n_epochs, time_step=t_step, runs_directory="stepwise_exps") experiment.run_exp( initial_params=initial_params, dataset_params={"train_size": train_size, "val_len": 22}, train_params={"momentum": True, "a": 0.0}, model_params={"model_cls": StepwiseTiedSidartheExtended, "der_1st_reg": 0, "bound_reg": 0, "bound_loss_type": "step"}, loss_weights=loss_weights ) # params can be set, no params => default configuration

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1,088

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0.104847

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6,428

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null

1

0

1

0

1

0

null

0

5

e16f91ae0147b0c77efb08e6b8b50a84759d74b1

491

py

Python

fishingapp_backend/api/serializers/__init__.py

Jimmyjtc001/fishingapp-backend

653ab1b1d61f43a84a67fbb5e581b8bd45f5b2b7

[ "BSD-3-Clause" ]

null

fishingapp_backend/api/serializers/__init__.py

Jimmyjtc001/fishingapp-backend

653ab1b1d61f43a84a67fbb5e581b8bd45f5b2b7

[ "BSD-3-Clause" ]

null

fishingapp_backend/api/serializers/__init__.py

Jimmyjtc001/fishingapp-backend

653ab1b1d61f43a84a67fbb5e581b8bd45f5b2b7

[ "BSD-3-Clause" ]

null

# from api.serializers.DashboardSerializer import from api.serializers.gateway.login_serializer import LoginSerializer from api.serializers.gateway.register_serializer import RegisterSerializer from api.serializers.pm.fishingspot_serializer import FishingSpotSerializer from api.serializers.pm.pm_serializer import SearchElementSerializer from api.serializers.homepage.homepage_serializer import UserLocationSerializer from api.serializers.pm.favorites_serializer import FavoritesSerializer

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5

e18378ec6772345b0c8fa4c3870b658c3cff39cc

392

py

Python

hooks/rthook-Crypto.py

bitProfessor/DEXBot

5d692fbf1acffeec46a82a12474b8a123e4c6370

[ "MIT" ]

1

2019-11-10T06:53:35.000Z

hooks/rthook-Crypto.py

g3d/DEXBot

a2b1462d78d7154cb10871a7cec9a44c8d6664de

[ "MIT" ]

null

hooks/rthook-Crypto.py

g3d/DEXBot

a2b1462d78d7154cb10871a7cec9a44c8d6664de

[ "MIT" ]

2

2021-02-13T10:58:33.000Z

2022-03-04T14:01:58.000Z

# Runtime hook for pycryptodome extensions import Crypto.Util._raw_api import importlib.machinery import os.path import sys def load_raw_lib(name, cdecl): for ext in importlib.machinery.EXTENSION_SUFFIXES: try: return Crypto.Util._raw_api.load_lib(os.path.join(sys._MEIPASS, name + ext), cdecl) except OSError: pass Crypto.Util._raw_api.load_pycryptodome_raw_lib = load_raw_lib

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5

e19e86dddb623ae8d49f96263e55228824dd9f3d

2,355

py

Python

caesar.py

SpicyMchaggis74/353

ed549674e15102c4b42ed3f11a9cee85704f21ee

[ "Unlicense" ]

null

caesar.py

SpicyMchaggis74/353

ed549674e15102c4b42ed3f11a9cee85704f21ee

[ "Unlicense" ]

null

caesar.py

SpicyMchaggis74/353

ed549674e15102c4b42ed3f11a9cee85704f21ee

[ "Unlicense" ]

null

# Sean Holmes import sys def encrypt(givenText, givenRotation): cipherText = "" # encrypted text //string givenText = givenText.lower() # get given text to lowercase # initialize loop index = 0 # index in given string while index < (len(givenText)): # begin encrypting value = (ord(givenText[index])) + givenRotation # use unicode value and rotate to given rotation if (value <= 122 and value >= 97): # if the value is in range a to z cipherText = cipherText + chr(value) # add char to cipher text elif (value >= 123): # if the value is above a-z cipherText = cipherText + chr(value - 26) # bring it back down to range and add to text else: # any other value not in a-z will be treated as ' ' cipherText = cipherText + " " index = index + 1 # increment to next char print(cipherText) # output encrypted text def decrypt(givenText, givenRotation): cipherText = "" # encrypted text //string givenText = givenText.lower() # get given text to lowercase # initialize loop index = 0 # index in given string while index < (len(givenText)): # begin encrypting value = (ord(givenText[index])) - givenRotation # rotate to given rotation if (value <= 122 and value >= 97): # if the value is in range a to z cipherText = cipherText + chr(value) # decrypt and add to text elif (value <= 97 and value >= 56): # if the value is below a-z cipherText = cipherText + chr(value + 26) # bring it back down to range and add to text else: # any other value not in a-z will be treated as ' ' cipherText = cipherText + " " index = index + 1 # increment to next char print(cipherText) # output decrypted text def decryptBrute(givenText): index = 25 # 26 total loops while (index != 0): decrypt(givenText, index) index = index - 1 argList = sys.argv # Mode: -e , -u , -d if (argList[1] == '-e'): # Encrypt with rotation encrypt(' '.join(argList[3:]), int(argList[2])) elif (argList[1] == '-u'): # Decrypt with rotation decrypt(' '.join(argList[3:]), int(argList[2])) elif (argList[1] == '-d'): # Brute force decrypt decryptBrute(' '.join(argList[2:])) else: print("Please enter -e -u or -d")

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bef9c7f74f7357d374613349eba84e3c2b338220

64

py

Python

transmogrify/tests/__init__.py

natgeosociety/Transmogrify

81d68c0c1693506ff9a2f59a8b610e0d00948636

[ "Apache-2.0" ]

null

transmogrify/tests/__init__.py

natgeosociety/Transmogrify

81d68c0c1693506ff9a2f59a8b610e0d00948636

[ "Apache-2.0" ]

null

transmogrify/tests/__init__.py

natgeosociety/Transmogrify

81d68c0c1693506ff9a2f59a8b610e0d00948636

[ "Apache-2.0" ]

null

# from base import * # NOQA # from autodetect import * # NOQA

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null

0

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5

55cd4f89c5a1ac8501ac5268f76c0c6439f505ee

8,886

py

Python

03_abstaining_classifier/plot.py

xiaozhanguva/Intrinsic_robustness_label_uncertainty

d9e32a3965ad6c696efd15b784876aed77a5e8a7

[ "MIT" ]

3

2021-09-11T03:21:07.000Z

2022-01-10T11:22:10.000Z

03_abstaining_classifier/plot.py

xiaozhanguva/Intrinsic_robustness_label_uncertainty

d9e32a3965ad6c696efd15b784876aed77a5e8a7

[ "MIT" ]

null

03_abstaining_classifier/plot.py

xiaozhanguva/Intrinsic_robustness_label_uncertainty

d9e32a3965ad6c696efd15b784876aed77a5e8a7

[ "MIT" ]

null

import torch import argparse import numpy as np import copy import os from robustbench.data import load_cifar10 import matplotlib.pyplot as plt def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--model_name', type=str, default='Carmon2019Unlabeled') parser.add_argument('--norm', type=str, default='Linf') parser.add_argument('--eps', type=float, default=8/255) parser.add_argument('--n_ex', type=int, default=1000, help='number of examples to evaluate on') parser.add_argument('--batch_size', type=int, default=500, help='batch size for evaluation') parser.add_argument('--data_dir', type=str, default='../00_data', help='where to store downloaded datasets') parser.add_argument('--model_dir', type=str, default='./models', help='where to store downloaded models') parser.add_argument('--device', type=str, default='cuda', help='device to use for computations') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() device = torch.device(args.device) if not os.path.exists('./figs'): os.makedirs('./figs') x_test, y_test = load_cifar10(args.n_ex, args.data_dir) x_test, y_test = x_test.to(device), y_test.to(device) y_soft_labels = np.load(args.data_dir+'/cifar10h-probs.npy')[:args.n_ex] probs_fstar = np.array([ y_soft_labels[i, y_test[i]] for i in range(len(y_test)) ]) y_soft_labels_copy = copy.deepcopy(y_soft_labels) for i in range(len(y_test)): y_soft_labels_copy[i, y_test[i]] = 0 probs_remain_top1 = np.max(y_soft_labels_copy, axis=1) lu_test = 1 - (probs_fstar - probs_remain_top1) normal_flags = np.load('./attack_log/std_'+args.norm+'_'+args.model_name+'.npy') robust_flags = np.load('./attack_log/rob_'+args.norm+'_'+args.model_name+'.npy') print(normal_flags) print(robust_flags) #### sort the examples by label uncertainty sorted_inds = np.argsort(lu_test) ## lu from smallest to largest (0->2) print(sorted_inds) ratio_arr = np.arange(0.01, 1.00, 0.01) acc_arr = [] rob_acc_arr = [] for ratio in ratio_arr: print('===== ratio: {:.2%}'.format(ratio)) n_selected = int(np.floor(ratio*len(lu_test))) inds_certain = sorted_inds[:n_selected] acc_certain = np.sum(normal_flags[inds_certain])/ len(inds_certain) rob_acc_certain = np.sum(robust_flags[inds_certain]) / len(inds_certain) print('Clean acc: {:.2%}, Robust acc: {:.2%}'.format(acc_certain, rob_acc_certain)) acc_arr.append(acc_certain) rob_acc_arr.append(rob_acc_certain) print('') plt.figure(figsize=(9, 6)) plt.plot(ratio_arr, acc_arr, linewidth=3.0, color='mediumblue') plt.plot(ratio_arr, rob_acc_arr, linewidth=3.0, color='orangered') plt.legend(['clean acc', 'robust acc'], fontsize=16, loc=3) plt.xticks(np.arange(0.00, 1.01, step=0.10), fontsize=14) plt.yticks(np.arange(0.55, 1.01, step=0.05), fontsize=14) # plt.xlim([0.0, 1.0]) plt.ylim([0.55, 1.0]) plt.xlabel("Percentage of Included Examples", fontsize=16) plt.ylabel("Accuracy", fontsize=16) plt.annotate('lu'+r'$\leq$'+'{:.2f}'.format(np.quantile(lu_test, 0.98)), (0.98, 0.55), color='black', fontsize=12, xycoords='data', xytext=(0.98, 0.59), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.annotate('lu'+r'$\leq$'+'{:.2f}'.format(np.quantile(lu_test, 0.90)), (0.90, 0.55), color='black', fontsize=12, xycoords='data', xytext=(0.84, 0.59), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.annotate('lu'+r'$\leq$'+'{:.2f}'.format(np.quantile(lu_test, 0.80)), (0.80, 0.55), color='black', fontsize=12, xycoords='data', xytext=(0.70, 0.59), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.savefig('./figs/certain_'+args.norm+'_'+args.model_name+'.png') #### plot the clean acc and robust acc w.r.t. label uncertain examples sorted_inds = np.argsort(-lu_test) ## lu from largest to smallest (0->2) print(sorted_inds) ratio_arr = np.arange(0.01, 1.00, 0.01) acc_arr = [] rob_acc_arr = [] for ratio in ratio_arr: print('===== ratio: {:.2%}'.format(ratio)) n_selected = int(np.floor(ratio*len(lu_test))) inds_uncertain = sorted_inds[:n_selected] acc_uncertain = np.sum(normal_flags[inds_uncertain])/ len(inds_uncertain) rob_acc_uncertain = np.sum(robust_flags[inds_uncertain]) / len(inds_uncertain) print('Clean acc: {:.2%}, Robust acc: {:.2%}'.format(acc_uncertain, rob_acc_uncertain)) acc_arr.append(acc_uncertain) rob_acc_arr.append(rob_acc_uncertain) plt.figure(figsize=(9, 6)) plt.plot(ratio_arr, acc_arr, linewidth=3.0, color='mediumblue') plt.plot(ratio_arr, rob_acc_arr, linewidth=3.0, color='orangered') plt.legend(['clean acc', 'robust acc'], fontsize=16, loc=4) plt.xticks(np.arange(0.00, 1.01, step=0.10), fontsize=14) plt.yticks(np.arange(0.0, 1.01, step=0.1), fontsize=14) # plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.0]) plt.xlabel("Percentage of Included Examples", fontsize=16) plt.ylabel("Accuracy", fontsize=16) plt.annotate('lu'+r'$\geq$'+'{:.2f}'.format(np.quantile(lu_test, 0.98)), (0.02, 0.0), color='black', fontsize=12, xycoords='data', xytext=(0.02, 0.1), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.annotate('lu'+r'$\geq$'+'{:.2f}'.format(np.quantile(lu_test, 0.90)), (0.1, 0.0), color='black', fontsize=12, xycoords='data', xytext=(0.16, 0.1), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.annotate('lu'+r'$\geq$'+'{:.2f}'.format(np.quantile(lu_test, 0.80)), (0.2, 0.0), color='black', fontsize=12, xycoords='data', xytext=(0.30, 0.1), arrowprops=dict(facecolor='black', arrowstyle='->'), horizontalalignment='center', verticalalignment='top',) plt.savefig('./figs/uncertain_'+args.norm+'_'+args.model_name+'.png') #### plot the clean acc and robust acc w.r.t. label certain examples q_lu_arr = np.arange(0.00, 1.00, 0.001) acc_arr = [] rob_acc_arr = [] for q_lu in q_lu_arr: lu_thres = np.quantile(lu_test, q_lu) print('===== q_lu: {:.2%}, lu threshold: {:.4f}'.format(q_lu, lu_thres)) inds_certain = np.argwhere(lu_test <= lu_thres).ravel() acc_certain = np.sum(normal_flags[inds_certain])/ len(inds_certain) rob_acc_certain = np.sum(robust_flags[inds_certain]) / len(inds_certain) print('Clean acc: {:.2%}, Robust acc: {:.2%}'.format(acc_certain, rob_acc_certain)) acc_arr.append(acc_certain) rob_acc_arr.append(rob_acc_certain) plt.figure(figsize=(9, 6)) plt.plot(q_lu_arr, acc_arr, linewidth=3.0) plt.plot(q_lu_arr, rob_acc_arr, linewidth=3.0) plt.legend(['clean acc', 'robust acc'], fontsize=16, loc=3) plt.xticks(np.arange(0.00, 1.01, step=0.10), fontsize=16) plt.yticks(np.arange(0.55, 1.01, step=0.05), fontsize=16) plt.xlabel("Ratio Threshold (certain)", fontsize=20) plt.ylabel("Accuracy", fontsize=20) plt.savefig('./figs/certain_'+args.norm+'_'+args.model_name+'.png') #### plot the clean acc and robust acc w.r.t. label uncertain examples q_lu_arr = np.arange(0.00, 1.00, 0.002) acc_arr = [] rob_acc_arr = [] for q_lu in q_lu_arr: lu_thres = np.quantile(lu_test, q_lu) print('===== q_lu: {:.2%}, lu threshold: {:.4f}'.format(q_lu, lu_thres)) inds_uncertain = np.argwhere(lu_test >= lu_thres).ravel() acc_uncertain = np.sum(normal_flags[inds_uncertain])/ len(inds_uncertain) rob_acc_uncertain = np.sum(robust_flags[inds_uncertain]) / len(inds_uncertain) print('Clean acc: {:.2%}, Robust acc: {:.2%}'.format(acc_uncertain, rob_acc_uncertain)) acc_arr.append(acc_uncertain) rob_acc_arr.append(rob_acc_uncertain) plt.figure(figsize=(9, 6)) plt.plot(1 - q_lu_arr, acc_arr, linewidth=3.0) plt.plot(1 - q_lu_arr, rob_acc_arr, linewidth=3.0) plt.legend(['clean acc', 'robust acc'], fontsize=16, loc=4) plt.xticks(np.arange(0.00, 1.01, step=0.10), fontsize=16) plt.yticks(np.arange(0.0, 1.01, step=0.1), fontsize=16) plt.xlabel("Ratio Threshold (uncertain)", fontsize=20) plt.ylabel("Accuracy", fontsize=20) plt.savefig('./figs/uncertain_'+args.norm+'_'+args.model_name+'.png')

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55fc205ca22d2339c5c28b90da7b59d0db871d34

89

py

Python

SolrClient/transport/__init__.py

cmilheim/SolrClient

32420fc2b8d17fd4457d957771b3ed05b6ac48cc

[ "Apache-2.0" ]

69

2015-10-16T20:49:53.000Z

2021-11-23T06:55:39.000Z

SolrClient/transport/__init__.py

cmilheim/SolrClient

32420fc2b8d17fd4457d957771b3ed05b6ac48cc

[ "Apache-2.0" ]

50

2015-11-21T16:20:15.000Z

2020-11-03T09:38:17.000Z

SolrClient/transport/__init__.py

cmilheim/SolrClient

32420fc2b8d17fd4457d957771b3ed05b6ac48cc

[ "Apache-2.0" ]

33

2015-11-23T01:37:44.000Z

2021-07-17T12:48:48.000Z

from .transportbase import TransportBase from .transportrequests import TransportRequests

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361ccb2491624d062cb73a1fbed74781df118442

103

py

Python

api/code/send_ping/lambda_function.py

mh35/tb08_chatapp

b148ea7e3db22471d4995c34a0378e1f7f8e0e8a

[ "MIT" ]

null

api/code/send_ping/lambda_function.py

mh35/tb08_chatapp

b148ea7e3db22471d4995c34a0378e1f7f8e0e8a

[ "MIT" ]

null

api/code/send_ping/lambda_function.py

mh35/tb08_chatapp

b148ea7e3db22471d4995c34a0378e1f7f8e0e8a

[ "MIT" ]

null

"""定期PING送信のLambdaプログラムです """ def lambda_handler(event, context): """Lambdaハンドラのメインプログラムです """

17.166667

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5

361fb689bbea5290334dc14f4244e4d9d3531327

57

py

Python

pybin/__main__.py

cefn/pybin

e8e221e321d2834d719e6f9867211313a53e4e37

[ "Apache-2.0", "MIT" ]

25

2017-09-01T07:37:05.000Z

2022-02-05T07:43:24.000Z

pybin/__main__.py

cefn/pybin

e8e221e321d2834d719e6f9867211313a53e4e37

[ "Apache-2.0", "MIT" ]

3

2018-05-21T13:17:08.000Z

2019-04-14T16:05:06.000Z

pybin/__main__.py

cefn/pybin

e8e221e321d2834d719e6f9867211313a53e4e37

[ "Apache-2.0", "MIT" ]

2

2017-12-10T05:37:56.000Z

2019-04-14T16:07:54.000Z

import sys from pybin.cli import pybin sys.exit(pybin())

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3677b5966781a78b2f7fc86451299574fe519a5f

4,806

py

Python

tests/unit/test_curriculum_learning.py

ganik/DeepSpeed

788e1c40e83beacfc4901e7daa1e097d2efb82bb

[ "MIT" ]

1

2022-02-12T06:27:26.000Z

tests/unit/test_curriculum_learning.py

ganik/DeepSpeed

788e1c40e83beacfc4901e7daa1e097d2efb82bb

[ "MIT" ]

null

tests/unit/test_curriculum_learning.py

ganik/DeepSpeed

788e1c40e83beacfc4901e7daa1e097d2efb82bb

[ "MIT" ]

null

import torch import torch.distributed as dist import deepspeed import argparse import pytest import json import os import numpy as np import time from .common import distributed_test from .simple_model import Curriculum_SimpleModel, random_dataloader, args_from_dict def test_curriculum_scheduler_fixed_discrete(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "Adam", "params": { "lr": 0.00015, "weight_decay": 0.01 } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, "curriculum_learning": { "enabled": True, "curriculum_type": "seqlen", "min_difficulty": 1, "max_difficulty": 5, "schedule_type": "fixed_discrete", "schedule_config": { "difficulty": [1, 2, 3, 4, 5], "max_step": [2, 4, 6, 8] } } } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 ground_truths = {1: 1, 2: 1, 3: 2, 4: 2, 5: 3, 6: 3, 7: 4, 8: 4} model = Curriculum_SimpleModel(hidden_dim) @distributed_test(world_size=[1, 2]) def _test_curriculum_scheduler_fixed_discrete(args, model, hidden_dim): model, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=20, hidden_dim=hidden_dim, device=model.device) for n, batch in enumerate(data_loader): loss, seqlen = model(batch[0], batch[1]) model.backward(loss) model.step() true_seqlen = 5 if n + 1 in ground_truths: true_seqlen = ground_truths[n + 1] print('at step {} the seqlen is {}'.format(n + 1, seqlen)) assert seqlen == true_seqlen, f"Incorrect curriculum schedule" _test_curriculum_scheduler_fixed_discrete(args=args, model=model, hidden_dim=hidden_dim) def test_curriculum_scheduler_fixed_linear(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "Adam", "params": { "lr": 0.00015, "weight_decay": 0.01 } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, "curriculum_learning": { "enabled": True, "curriculum_type": "seqlen", "min_difficulty": 2, "max_difficulty": 10, "schedule_type": "fixed_linear", "schedule_config": { "total_curriculum_step": 8, "difficulty_step": 2 } } } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 ground_truths = {1: 2, 2: 4, 3: 4, 4: 6, 5: 6, 6: 8, 7: 8, 8: 10, 9: 10, 10: 10} model = Curriculum_SimpleModel(hidden_dim) @distributed_test(world_size=[1, 2]) def _test_curriculum_scheduler_fixed_linear(args, model, hidden_dim): model, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=20, hidden_dim=hidden_dim, device=model.device) for n, batch in enumerate(data_loader): loss, seqlen = model(batch[0], batch[1]) model.backward(loss) model.step() if n + 1 in ground_truths: true_seqlen = ground_truths[n + 1] print('at step {} the seqlen is {}'.format(n + 1, seqlen)) assert seqlen == true_seqlen, f"Incorrect curriculum schedule" _test_curriculum_scheduler_fixed_linear(args=args, model=model, hidden_dim=hidden_dim)

35.865672

84

0.47732

463

4,806

4.684665

0.215983

0.058091

0.063624

0.077455

0.796681

0.748732

0.715537

0

0.041833

0.428007

4,806

133

85

36.135338

0.747181

0

0.624

0

0.134831

0.00437

0

0.016

1

0.032

false

0

0.088

0

0.12

0.032

0

null

0

1

0

null

0

5

7fc4a02fec6beef695b6b3021ba785d0fdd162fe

87

py

Python

blobcity/aicloud/__init__.py

lahdjirayhan/autoai

de4c0abfa07f5385ad566d6391fca38d17a22dc1

[ "Apache-2.0" ]

115

2021-09-01T03:55:40.000Z

2022-03-29T07:09:03.000Z

blobcity/aicloud/__init__.py

maxpark/autoai

c9f6d8d6cc2027b7918bae88862ea476cc49397f

[ "Apache-2.0" ]

87

2021-09-10T08:56:32.000Z

2022-02-01T07:18:05.000Z

blobcity/aicloud/__init__.py

maxpark/autoai

c9f6d8d6cc2027b7918bae88862ea476cc49397f

[ "Apache-2.0" ]

35

2021-09-01T04:16:18.000Z

2022-02-05T20:32:26.000Z

from .access_validation import set_token from .auto_save_yaml import send_yaml_to_cloud

43.5

46

0.896552

15

87

4.733333

0.8

0

0.08046

87

2

46

43.5

0.8875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7fec2354d9006d701a877403b234085d310dbe6d

117

py

Python

Spotify/config_censored.py

gur111/MtmPythonEnrichment

4919ec1a6a6ed975b9e522834aebbdb3510c1d52

[ "MIT" ]

null

Spotify/config_censored.py

gur111/MtmPythonEnrichment

4919ec1a6a6ed975b9e522834aebbdb3510c1d52

[ "MIT" ]

null

Spotify/config_censored.py

gur111/MtmPythonEnrichment

4919ec1a6a6ed975b9e522834aebbdb3510c1d52

[ "MIT" ]

null

SPOTIPY_CLIENT_ID = 'fa325***********************2365' SPOTIPY_CLIENT_SECRET = 'be4c0***********************8c4c'

58.5

58

0.478632

10

117

5.2

0.8

0.5

0

0.101852

0.076923

117

2

59

58.5

0.37963

0

0.542373

0

1

0

false

0

1

0

null

1

0

1

0

1

0

1

null

0

5

7ff3b56629d676b2640a4f07b308b8fbef4d060f

371

py

Python

src/ssm_document_generator/definition/parameters/assign_parameters_mixin.py

awslabs/aws-systems-manager-document-generator

2c041fd52342d95da4535fe3236e43933cc6e08d

[ "Apache-2.0" ]

54

2018-03-07T18:46:50.000Z

2022-01-26T04:35:56.000Z

src/ssm_document_generator/definition/parameters/assign_parameters_mixin.py

eshack94/aws-systems-manager-document-generator

2c041fd52342d95da4535fe3236e43933cc6e08d

[ "Apache-2.0" ]

2

2018-09-30T23:39:08.000Z

2020-04-03T17:15:21.000Z

src/ssm_document_generator/definition/parameters/assign_parameters_mixin.py

eshack94/aws-systems-manager-document-generator

2c041fd52342d95da4535fe3236e43933cc6e08d

[ "Apache-2.0" ]

12

2018-07-27T21:04:12.000Z

2021-10-20T18:02:57.000Z

class AssignParametersMixin: """ Adds the functionality to generate the code for passing the parameters in the following form: parameterName={{parameterName}} """ def generate_parameters_code(self): return super().generate_parameters_code() + \ [parameter.name + "={{" + parameter.name + "}}" for parameter in self.parameters]

37.1

97

0.673854

38

371

6.473684

0.552632

0.146341

0.178862

0

0.218329

371

9

98

41.222222

0.848276

0.336927

0

1

0

0.022124

0

1

0.25

false

0

0.25

0.75

0

null

0

1

0

1

0

null

0

1

0

1

0

5

3d216f2c2df923b6247b260be874ec1a3e4db684

221

py

Python

src/ufdl/annotations_plugin/common/component/util/__init__.py

waikato-ufdl/ufdl-annotations-plugin

9eb3d807e35215ad9cfbd4aa651d7f7142e83efe

[ "Apache-2.0" ]

null

src/ufdl/annotations_plugin/common/component/util/__init__.py

waikato-ufdl/ufdl-annotations-plugin

9eb3d807e35215ad9cfbd4aa651d7f7142e83efe

[ "Apache-2.0" ]

4

2020-07-29T04:09:13.000Z

2020-11-22T20:52:18.000Z

src/ufdl/annotations_plugin/common/component/util/__init__.py

waikato-ufdl/ufdl-annotations-plugin

9eb3d807e35215ad9cfbd4aa651d7f7142e83efe

[ "Apache-2.0" ]

null

""" Utilities for creating the common UFDL components. """ from ._typing import DatasetMethods from ._UFDLContextOptionsMixin import UFDLContextOptionsMixin from ._UFDLProjectSpecificMixin import UFDLProjectSpecificMixin

31.571429

63

0.859729

19

221

9.842105

0.684211

0

0.095023

221

6

64

36.833333

0.935

0.226244

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3d2c0774befc9bc891a7c0d982d986faa8fe20e9

107

py

Python

vnpy/chart/__init__.py

fakecoinbase/jojoquantslashjonpy

d92ba43b0de29eeef46c4eb0545a4eac465a280d

[ "MIT" ]

2

2020-08-17T18:56:07.000Z

2021-11-22T00:47:58.000Z

vnpy/chart/__init__.py

fakecoinbase/jojoquantslashjonpy

d92ba43b0de29eeef46c4eb0545a4eac465a280d

[ "MIT" ]

null

vnpy/chart/__init__.py

fakecoinbase/jojoquantslashjonpy

d92ba43b0de29eeef46c4eb0545a4eac465a280d

[ "MIT" ]

null

from .widget import ChartWidget from .item import CandleItem, VolumeItem # from .item import TechIndexItem

26.75

40

0.82243

13

107

6.769231

0.615385

0.181818

0.318182

0

0.130841

107

3

41

35.666667

0.946237

0.28972

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

3d5a4e0ae66bf8bcfe34873a6daa91e037800a37

108

py

Python

pylatex/command.py

sebastianhaas/PyLaTeX

e33ec1cd29a429dc5c984fb698af22ad3c153b61

[ "MIT" ]

null

pylatex/command.py

sebastianhaas/PyLaTeX

e33ec1cd29a429dc5c984fb698af22ad3c153b61

[ "MIT" ]

null

pylatex/command.py

sebastianhaas/PyLaTeX

e33ec1cd29a429dc5c984fb698af22ad3c153b61

[ "MIT" ]

null

# flake8: noqa """ Stub command module for backwards compatibility. """ from .base_classes import Command

13.5

48

0.75

13

108

6.153846

0.923077

0

0.010989

0.157407

108

7

49

15.428571

0.868132

0.574074

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e9ff3365d059f4aa1258c1ffc0716182f71bd512

54

py

Python

factor_vae/__init__.py

kiwi0fruit/jats-semi-supervised-pytorch

67e9bb85f09f8ef02e17e495784d1d9a71c3adec

[ "MIT" ]

null

factor_vae/__init__.py

kiwi0fruit/jats-semi-supervised-pytorch

67e9bb85f09f8ef02e17e495784d1d9a71c3adec

[ "MIT" ]

null

factor_vae/__init__.py

kiwi0fruit/jats-semi-supervised-pytorch

67e9bb85f09f8ef02e17e495784d1d9a71c3adec

[ "MIT" ]

null

from .module import Discriminator, FactorVAEContainer

27

53

0.87037

5

54

9.4

1

0

0.092593

54

1

54

0.959184

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1810f0f670445dfa7e529078f9272abb312fdd76

295

py

Python

run.py

oNguyenHaPhan/Flask_framgiatw_docker

e5ae30aea7e7ff9fff0ed27c1502b03786c640d1

[ "MIT" ]

null

run.py

oNguyenHaPhan/Flask_framgiatw_docker

e5ae30aea7e7ff9fff0ed27c1502b03786c640d1

[ "MIT" ]

null

run.py

oNguyenHaPhan/Flask_framgiatw_docker

e5ae30aea7e7ff9fff0ed27c1502b03786c640d1

[ "MIT" ]

null

#!venv/bin/python import sys import logging logging.basicConfig(stream=sys.stderr) from app import app from app import app as application if __name__ == '__main__': import logging logging.basicConfig(filename='error.log',level=logging.DEBUG) app.run(host='0.0.0.0', debug = True)

21.071429

65

0.738983

44

295

4.772727

0.568182

0.028571

0.190476

0.295238

0

0.01581

0.142373

295

13

66

22.692308

0.814229

0.054237

0

0.222222

0

0.086331

0

1

0

true

0

0.555556

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1810f55dec97a447cefa5f641661d31f8435450d

93

py

Python

sensibo_api/admin.py

eavior/django-server

80a702ba05ce73da3f4baff024635100cb1c1bed

[ "MIT" ]

null

sensibo_api/admin.py

eavior/django-server

80a702ba05ce73da3f4baff024635100cb1c1bed

[ "MIT" ]

null

sensibo_api/admin.py

eavior/django-server

80a702ba05ce73da3f4baff024635100cb1c1bed

[ "MIT" ]

null

from django.contrib import admin from sensibo_api import models # Register your models here.

23.25

32

0.827957

14

93

5.428571

0.785714

0

0.139785

93

3

33

31

0.95

0.27957

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

18188dbcfd6d9e7a27f2a1de418277314ed08fab

203

py

Python

3-1.py

DavidSenpai/aoc

479743a87f586e56f647299de78fb34c6260bb03

[ "MIT" ]

1

2022-02-17T07:45:50.000Z

3-1.py

DavidSenpai/aoc

479743a87f586e56f647299de78fb34c6260bb03

[ "MIT" ]

null

3-1.py

DavidSenpai/aoc

479743a87f586e56f647299de78fb34c6260bb03

[ "MIT" ]

2

2021-12-02T22:13:42.000Z

2021-12-13T08:14:31.000Z

print((lambda bits: int("".join(max(set(bit), key=bit.count) for bit in bits), base=2) * int("".join(min(set(bit), key=bit.count) for bit in bits), base=2))(tuple(zip(*open("input3.txt").readlines()))))

101.5

202

0.655172

37

203

3.594595

0.567568

0.105263

0.135338

0.180451

0.511278

0

0.016129

0.083744

203

1

203

0.698925

0

0.049261

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

1829bcd2579f93631f84eeca52f456ce775e55c2

84

py

Python

shopipy/exceptions.py

lowercase00/pyshopify

98fdd0ac30569725fc0ec32345a92cd84c510a6a

[ "MIT" ]

1

2021-10-17T02:09:22.000Z

shopipy/exceptions.py

lowercase00/shopipy

98fdd0ac30569725fc0ec32345a92cd84c510a6a

[ "MIT" ]

null

shopipy/exceptions.py

lowercase00/shopipy

98fdd0ac30569725fc0ec32345a92cd84c510a6a

[ "MIT" ]

null

class CredentialsError(Exception): pass class InvalidSetup(Exception): pass

16.8

34

0.761905

8

84

8

0.625

0.40625

0

0.166667

84

5

35

16.8

0.914286

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

1843fde2f9fc705427bbe7b70bc6914b1e8a8a4d

95

py

Python

passByReference.py

bourneagain/pythonBytes

be115162147e52718aacbfb9cd2763aa02754f28

[ "MIT" ]

1

2017-05-29T02:02:27.000Z

passByReference.py

bourneagain/pythonBytes

be115162147e52718aacbfb9cd2763aa02754f28

[ "MIT" ]

null

passByReference.py

bourneagain/pythonBytes

be115162147e52718aacbfb9cd2763aa02754f28

[ "MIT" ]

null

def passList(A): #A.append("this is end"); A=[123,3] A=[1,2,3] print A passList(A) print A

9.5

26

0.610526

21

95

2.761905

0.571429

0.310345

0

0.088608

0.168421

95

9

27

10.555556

0.64557

0.252632

0

0.333333

0

null

0.333333

0

null

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

5

185d14a17996730085443ef94d80b05de769561e

37

py

Python

Lesson9/random.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

2

2021-06-01T13:24:04.000Z

2021-06-01T13:27:47.000Z

Lesson9/random.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

Lesson9/random.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

#print(list(set(input().split()))[0])

37

0.621622

6

37

3.833333

1

0

0.027027

0

37

1

37

0.594595

0.972973

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

18821b0ca5a144b9f7a54abca0ce92da9d37b5f2

51

py

Python

spym/io/omicronscala/__init__.py

ns-rse/spym

5356d97d6baf774a3bdd8c03b436052b8d74dbd0

[ "MIT" ]

4

2021-02-08T08:47:52.000Z

2021-12-17T19:51:17.000Z

spym/io/omicronscala/__init__.py

ns-rse/spym

5356d97d6baf774a3bdd8c03b436052b8d74dbd0

[ "MIT" ]

4

2020-09-29T08:47:37.000Z

2021-07-15T13:56:43.000Z

spym/io/omicronscala/__init__.py

ns-rse/spym

5356d97d6baf774a3bdd8c03b436052b8d74dbd0

[ "MIT" ]

3

2021-07-10T18:42:06.000Z

2022-03-31T08:15:42.000Z

from ._methods import load, to_dataset, to_nexus

17

48

0.784314

8

51

4.625

0.875

0

0.156863

51

2

49

25.5

0.860465

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

43fec5706b6dfc0416030c763b8b11a232a99d5b

39

py

Python

PyPLS/__init__.py

showhei/show-time

5da4c4fd5270d7ba61425649129d2538dd50cb38

[ "MIT" ]

15

2019-05-01T03:20:50.000Z

2021-10-17T09:09:37.000Z

PyPLS/__init__.py

showhei/show-time

5da4c4fd5270d7ba61425649129d2538dd50cb38

[ "MIT" ]

1

2021-11-25T14:37:08.000Z

PyPLS/__init__.py

showhei/show-time

5da4c4fd5270d7ba61425649129d2538dd50cb38

[ "MIT" ]

5

2020-01-24T19:29:42.000Z

2021-04-27T08:58:22.000Z

## Written by xynx59 and Xinyue-Miranda

39

0.794872

6

39

5.166667

1

0

0.058824

0.128205

39

1

39

0.852941

0.923077

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

a10a5adbf154f5f2dccac9416f0907448fafd98c

82

py

Python

notebook_autorun/util.py

oscar6echo/notebook-autorun

e229bc99f21cecc26fceb9748cffc82ea2d0a7f9

[ "MIT" ]

null

notebook_autorun/util.py

oscar6echo/notebook-autorun

e229bc99f21cecc26fceb9748cffc82ea2d0a7f9

[ "MIT" ]

1

2017-12-12T16:59:17.000Z

2017-12-19T08:46:30.000Z

notebook_autorun/util.py

oscar6echo/notebook-autorun

e229bc99f21cecc26fceb9748cffc82ea2d0a7f9

[ "MIT" ]

null

import os def is_digit(s): """ """ return s.lstrip('+-').isdigit()

9.111111

35

0.487805

10

82

3.9

0.9

0

0.280488

82

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0

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5

a10dd1fc2ca4d01a124ab4ccb09d380ad9d39cfd

14,516

py

Python

model/VLAE.py

Jueast/VLAE_Pytorch

8373390008d611909997e4a3de8396f617d53a49

[ "MIT" ]

null

model/VLAE.py

Jueast/VLAE_Pytorch

8373390008d611909997e4a3de8396f617d53a49

[ "MIT" ]

null

model/VLAE.py

Jueast/VLAE_Pytorch

8373390008d611909997e4a3de8396f617d53a49

[ "MIT" ]

null

from model.abstract_VAE import VAE import numpy as np import torch import torch.nn as nn from torch.autograd import Variable from scipy.stats import norm class StableBCELoss(nn.modules.Module): def __init__(self): super(StableBCELoss, self).__init__() def forward(self, input, target): neg_abs = - input.abs() loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log() return loss.sum() class CNNEncodeLayer(nn.Module): def __init__(self, input, output, zdim, batchnorm, activacation, out_img_dims): super(CNNEncodeLayer, self).__init__() if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if batchnorm: main = nn.Sequential( nn.Conv2d(input, output, 4, stride=2, padding=1), nn.BatchNorm2d(output), self.act, ) main2 = nn.Sequential( nn.Conv2d(input, output, 4, stride=2, padding=1), nn.BatchNorm2d(output), self.act, ) else: main = nn.Sequential( nn.Conv2d(input, output, 4, stride=2, padding=1), self.act, ) main2 = nn.Sequential( nn.Conv2d(input, output, 4, stride=2, padding=1), self.act, ) self.main = main self.main2 = main2 self.fc1 = nn.Linear(output * out_img_dims[0] * out_img_dims[1], zdim) self.fc2 = nn.Linear(output * out_img_dims[0] * out_img_dims[1], zdim) self.out_img_dims = out_img_dims # print ("Not implemented now...") return def forward(self, x): h = self.main(x) h2 = self.main2(x) return h, self.fc1(h2.view(h2.size(0), -1)), self.fc2(h2.view(h2.size(0), -1)) class CNNDecodeLayer(nn.Module): def __init__(self, input, output, zdim, batchnorm, activacation, input_img_dims): super(CNNDecodeLayer, self).__init__() if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if input == 0: input = output self.fc = nn.Linear(zdim, input * input_img_dims[0] * input_img_dims[0]) else: self.fc = nn.Linear(zdim, input * input_img_dims[0] * input_img_dims[0]) input *= 2 if batchnorm: main = nn.Sequential( nn.ConvTranspose2d(input, output, 4, stride=2, padding=1), nn.BatchNorm2d(output), self.act, ) else: main = nn.Sequential( nn.ConvTranspose2d(input, output, 4, stride=2, padding=1), self.act, ) self.main = main self.input_img_dims = input_img_dims def forward(self, input, z): if input is None: input = self.act(self.fc(z).view(z.size(0), -1, self.input_img_dims[0], self.input_img_dims[1])) else: input = torch.cat([input, self.fc(z).view(z.size(0), -1, self.input_img_dims[0], self.input_img_dims[1])], 1) return self.main(input) class EncodeLayer(nn.Module): def __init__(self, input, output, zdim, batchnorm, activacation): super(EncodeLayer, self).__init__() if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if batchnorm: main = nn.Sequential( nn.Linear(input, output), nn.BatchNorm1d(output), self.act, ) else: main = nn.Sequential( nn.Linear(input, output), self.act, ) self.main = main self.fc1 = nn.Linear(output, zdim) self.fc2 = nn.Linear(output, zdim) def forward(self, x): h = self.main(x) return self.main(x),self.fc1(h), self.fc2(h) class DecodeLayer(nn.Module): def __init__(self, input, output, zdim, batchnorm, activacation): super(DecodeLayer, self).__init__() if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if input == 0: input = output self.fc = nn.Linear(zdim, input) else: self.fc = nn.Linear(zdim, input) input *= 2 if batchnorm: main = nn.Sequential( nn.Linear(input, output), nn.BatchNorm1d(output), self.act, ) else: main = nn.Sequential( nn.Linear(input, output), self.act, ) self.main = main def forward(self, input, z): if input is None: input = self.act(self.fc(z)) else: input = torch.cat([input, self.act(self.fc(z))], 1) return self.main(input) class VLAE(VAE): def __init__(self, input_dims, code_dims, beta=1.0, hidden=400, activacation="lrelu", decoder="Bernoulli", batchnorm=False): super(VLAE, self).__init__(input_dims, code_dims) self.name = "VLAE" self.nx = int(np.prod(input_dims)) self.nz = int(np.prod(code_dims)) self.beta = beta if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if decoder == "Bernoulli": self.reconstruct_loss = StableBCELoss() else: self.reconstruct_loss = nn.MSELoss() self.encode_layers = nn.ModuleList([EncodeLayer(self.nx, hidden, code_dims[1], batchnorm, activacation)]) self.decode_layers = nn.ModuleList([]) for i in range(code_dims[0]-1): el = EncodeLayer(hidden, hidden, code_dims[1], batchnorm, activacation) dl = DecodeLayer(hidden, hidden, code_dims[1], batchnorm, activacation) self.encode_layers.append(el) self.decode_layers.append(dl) self.fc1 = nn.Linear(code_dims[1], hidden) self.fc2 = nn.Linear(hidden, self.nx) def encode(self, x): h = x.view(x.size(0), -1) mu_list = [] logvar_list = [] for fc in self.encode_layers: h, mu, logvar = fc(h) mu_list.append(mu) logvar_list.append(logvar) return torch.cat(mu_list, dim=1), torch.cat(logvar_list, dim=1) def reparametrize(self, mu, logvar): std = logvar.mul(0.5).exp_() if isinstance(mu, torch.cuda.FloatTensor): eps = torch.cuda.FloatTensor(std.size()).normal_() else: eps = torch.FloatTensor(std.size()).normal_() # eps[:,-2:-1] = (eps[:,-2:-1] - mu.data[:,-2:-1]) / std.data[:,-2:-1] eps = Variable(eps) return eps.mul(std).add_(mu) def decode(self, z): zcode = list(torch.chunk(z, self.code_dims[0], dim=1))[::-1] h = self.act(self.fc1(zcode[0])) for z, fc in zip(zcode[1:], self.decode_layers): h = fc(h, z) return self.fc2(h) def forward(self, x): mu, logvar = self.encode(x.view(x.size(0), -1)) z = self.reparametrize(mu, logvar) return self.decode(z), mu, logvar, z def loss(self, recon_x, x, mu, logvar, z): x = x.view(x.size(0), -1) BCE = self.reconstruct_loss(recon_x, x) / x.size(0) # see Appendix B from VAE paper: # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014 # https://arxiv.org/abs/1312.6114 # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar) KLD = torch.sum(KLD_element).mul_(-0.5) / x.size(0) return BCE + self.beta * KLD, BCE, KLD def mutual_info_q(self, x): mu, logvar = self.encode(x.view(x.size(0), -1)) z = self.reparametrize(mu, logvar) l = z.size(0) z = z.repeat(l, 1, 1) mu = mu.unsqueeze(2).repeat(1,1,l).transpose(1,2) logvar = logvar.unsqueeze(2).repeat(1,1,l).transpose(1,2) p_matrix = ( - torch.sum((z - mu) ** 2 / logvar.exp(), dim=2) / 2.0 - 0.5 * torch.sum(logvar, dim=2)).exp_() p_split_matrix = (- (z - mu) ** 2 / logvar.exp() / 2.0 - 0.5 * logvar ).exp_() p_split_vector = torch.sum(p_split_matrix, dim=1) p_vector = torch.sum(p_matrix, dim=1) I = torch.FloatTensor([np.log(l)]) I_split = torch.FloatTensor([np.log(l)] * int(z.size(2))) for i in range(l): I += (p_matrix[i][i].log() - p_vector[i].log()).data / l I_split += (p_split_matrix[i][i].log() - p_split_vector[i].log()).data / l # q(z_i) is not independent.. # assert np.allclose(I.numpy(), np.sum(I_split.numpy())) return I, I_split class MMDVLAE(VLAE): def compute_kernel(self, x, y): x_size = x.size(0) y_size = y.size(0) dim = x.size(1) tiled_x = x.unsqueeze(1).repeat(1, y_size, 1) tiled_y = y.unsqueeze(0).repeat(x_size, 1, 1) return ((-(tiled_x - tiled_y) ** 2).mean(dim=2) / float(dim)).exp_() def compute_mmd(self, x, y, sigma_sqr=1.0): x_kernel = self.compute_kernel(x, x) y_kernel = self.compute_kernel(y, y) xy_kernel = self.compute_kernel(x, y) return torch.mean(x_kernel) + torch.mean(y_kernel) - 2 * torch.mean(xy_kernel) def loss(self, recon_x, x, mu, logvar, z): x = x.view(x.size(0), -1) BCE = self.reconstruct_loss(recon_x, x) / (x.size(0) * x.size(1)) true_samples = Variable(torch.FloatTensor(x.size(0), self.nz).normal_()) MMD = self.compute_mmd(true_samples, z) return BCE + self.beta * MMD , BCE, MMD class CNNVLAE(VAE): def __init__(self, input_dims, code_dims, beta=1.0, hidden=400, activacation="lrelu", decoder="Bernoulli", batchnorm=True): super(CNNVLAE, self).__init__(input_dims, code_dims) self.name = "CNNVLAE" self.nx = input_dims[0] self.nz = int(np.prod(code_dims)) self.beta = beta if activacation == "lrelu": self.act = nn.LeakyReLU() else: self.act = nn.ReLU() if decoder == "Bernoulli": self.reconstruct_loss = StableBCELoss() else: self.reconstruct_loss = nn.MSELoss() assert(input_dims[1] == input_dims[2]) l = input_dims[1] l = int(l/2) self.encode_layers = [CNNEncodeLayer(self.nx, hidden, code_dims[1], batchnorm, activacation, (l, l))] self.decode_layers = [CNNDecodeLayer(hidden*2, hidden, code_dims[1], batchnorm, activacation, (l, l))] self.conv2 = nn.ConvTranspose2d(hidden, 1, 1, 1) for i in range(code_dims[0]-2): l = int(l/2) el = CNNEncodeLayer(hidden, hidden * 2, code_dims[1], batchnorm, activacation, (l, l)) hidden *= 2 dl = CNNDecodeLayer(hidden * 2, hidden, code_dims[1], batchnorm, activacation, (l, l)) self.encode_layers.append(el) self.decode_layers.insert(0, dl) self.encode_layers = nn.ModuleList(self.encode_layers) self.decode_layers = nn.ModuleList(self.decode_layers) l = int(l/2) self.encode_layers.append(CNNEncodeLayer(hidden, hidden*2, code_dims[1], batchnorm, activacation, (l, l))) hidden *= 2 self.conv1 = CNNDecodeLayer(0, hidden, code_dims[1], batchnorm, activacation, (l, l)) def encode(self, x): h = x mu_list = [] logvar_list = [] for conv in self.encode_layers: h, mu, logvar = conv(h) mu_list.append(mu) logvar_list.append(logvar) return torch.cat(mu_list, dim=1), torch.cat(logvar_list, dim=1) def reparametrize(self, mu, logvar): std = logvar.mul(0.5).exp_() if isinstance(mu, torch.cuda.FloatTensor): eps = torch.cuda.FloatTensor(std.size()).normal_() else: eps = torch.FloatTensor(std.size()).normal_() # eps[:,-2:-1] = (eps[:,-2:-1] - mu.data[:,-2:-1]) / std.data[:,-2:-1] eps = Variable(eps) return eps.mul(std).add_(mu) def decode(self, z): zcode = list(torch.chunk(z, self.code_dims[0], dim=1))[::-1] h = self.act(self.conv1(None, zcode[0])) for z, conv in zip(zcode[1:], self.decode_layers): h = conv(h, z) return self.conv2(h) def forward(self, x): mu, logvar = self.encode(x) z = self.reparametrize(mu, logvar) return self.decode(z), mu, logvar, z def loss(self, recon_x, x, mu, logvar, z): x = x.view(x.size(0), -1) recon_x = recon_x.view(recon_x.size(0), -1) BCE = self.reconstruct_loss(recon_x, x) / x.size(0) # see Appendix B from VAE paper: # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014 # https://arxiv.org/abs/1312.6114 # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar) KLD = torch.sum(KLD_element).mul_(-0.5) / x.size(0) return BCE + self.beta * KLD, BCE, KLD def mutual_info_q(self, x): mu, logvar = self.encode(x) z = self.reparametrize(mu, logvar) z = z.view(z.size(0), -1) mu = mu.view(mu.size(0), -1) logvar = logvar.view(logvar.size(0), -1) l = z.size(0) z = z.repeat(l, 1, 1) mu = mu.unsqueeze(2).repeat(1,1,l).transpose(1,2) logvar = logvar.unsqueeze(2).repeat(1,1,l).transpose(1,2) p_matrix = ( - torch.sum((z - mu) ** 2 / logvar.exp(), dim=2) / 2.0 - 0.5 * torch.sum(logvar, dim=2)).exp_() p_split_matrix = (- (z - mu) ** 2 / logvar.exp() / 2.0 - 0.5 * logvar ).exp_() p_split_vector = torch.sum(p_split_matrix, dim=1) p_vector = torch.sum(p_matrix, dim=1) I = torch.FloatTensor([np.log(l)]) I_split = torch.FloatTensor([np.log(l)] * int(z.size(2))) for i in range(l): I += (p_matrix[i][i].log() - p_vector[i].log()).data / l I_split += (p_split_matrix[i][i].log() - p_split_vector[i].log()).data / l # q(z_i) is not independent.. # assert np.allclose(I.numpy(), np.sum(I_split.numpy())) return I, I_split

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5

a111b0e7cb321d1e71b0347115b8faab60668130

185

py

Python

bin/twigs/polytwigs-12345-butterfly-8x2-3.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/twigs/polytwigs-12345-butterfly-8x2-3.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/twigs/polytwigs-12345-butterfly-8x2-3.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

1

2022-01-02T16:54:14.000Z

#!/usr/bin/env python # $Id$ """ many solutions. """ import puzzler from puzzler.puzzles.polytwigs12345 import Polytwigs12345Butterfly8x2_3 puzzler.run(Polytwigs12345Butterfly8x2_3)

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5

a13061975cf47ec32848dfc9dc3b2ad41ad022d0

2,770

py

Python

example/compat.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

1

2020-10-27T21:30:31.000Z

example/compat.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

50

2021-07-26T08:20:07.000Z

2022-03-31T08:22:42.000Z

example/compat.py

sephiartlist/dynaconf

9c5f60b289c1f0fa3f899f1962a8fe5712c74eab

[ "MIT" ]

null

from dynaconf import LazySettings from dynaconf.utils import RENAMED_VARS # 0 given a bare settings settings = LazySettings(environments=True) # 1 Ensure all renamed vars exists in object for old, new in RENAMED_VARS.items(): assert old in settings assert new in settings # 2 Ensure pairs has the same value for old, new in RENAMED_VARS.items(): assert settings.get(old) == settings.get(new) assert getattr(settings, new) == getattr(settings, old) # 0 given a full old-style configured setting settings = LazySettings( environments=True, DYNACONF_NAMESPACE="FOO", DYNACONF_SETTINGS_MODULE="/tmp/foo.toml", PROJECT_ROOT="/tmp/", DYNACONF_SILENT_ERRORS=False, DYNACONF_ALWAYS_FRESH_VARS=["baz", "zaz", "caz"], BASE_NAMESPACE_FOR_DYNACONF="original", GLOBAL_ENV_FOR_DYNACONF="RAZAMANAZ", ) # 1 Ensure all renamed vars exists in object for old, new in RENAMED_VARS.items(): assert old in settings, old assert new in settings, new # 2 Ensure pairs has the same value for old, new in RENAMED_VARS.items(): assert settings.get(old) == settings.get(new), ( old, settings.get(old), new, settings.get(new), ) assert getattr(settings, new) == getattr(settings, old), ( new, getattr(settings, new), old, getattr(settings, old), ) settings = LazySettings( environments=True, DYNACONF_NAMESPACE="FOO", DYNACONF_SETTINGS_MODULE="foo.py", PROJECT_ROOT="/tmp", DYNACONF_SILENT_ERRORS=True, DYNACONF_ALWAYS_FRESH_VARS=["BAR"], GLOBAL_ENV_FOR_DYNACONF="BLARG", ) assert settings.ENV_FOR_DYNACONF == "FOO" assert settings.SETTINGS_FILE_FOR_DYNACONF == "foo.py" assert settings.ROOT_PATH_FOR_DYNACONF == "/tmp" assert settings.SILENT_ERRORS_FOR_DYNACONF is True assert settings.FRESH_VARS_FOR_DYNACONF == ["BAR"] assert settings.ENVVAR_PREFIX_FOR_DYNACONF == "BLARG" print(settings.ENV_FOR_DYNACONF) print(settings.SETTINGS_FILE_FOR_DYNACONF) print(settings.ROOT_PATH_FOR_DYNACONF) print(settings.SILENT_ERRORS_FOR_DYNACONF) print(settings.FRESH_VARS_FOR_DYNACONF) settings = LazySettings( environments=True, NAMESPACE="FOO", SETTINGS_MODULE="foo.py", PROJECT_ROOT="/tmp", DYNACONF_SILENT_ERRORS=True, DYNACONF_ALWAYS_FRESH_VARS=["BAR"], ) assert settings.ENV_FOR_DYNACONF == "FOO" assert settings.SETTINGS_FILE_FOR_DYNACONF == "foo.py" assert settings.ROOT_PATH_FOR_DYNACONF == "/tmp" assert settings.SILENT_ERRORS_FOR_DYNACONF is True assert settings.FRESH_VARS_FOR_DYNACONF == ["BAR"] print(settings.ENV_FOR_DYNACONF) print(settings.SETTINGS_FILE_FOR_DYNACONF) print(settings.ROOT_PATH_FOR_DYNACONF) print(settings.SILENT_ERRORS_FOR_DYNACONF) print(settings.FRESH_VARS_FOR_DYNACONF)

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a16b78d7e0085a0c47da2df931c404c602e12e4d

181

py

Python

securityheaders/checkers/cors/maxage/__init__.py

th3cyb3rc0p/securityheaders

941264be581dc01afe28f6416f2d7bed79aecfb3

[ "Apache-2.0" ]

151

2018-07-29T22:34:43.000Z

2022-03-22T05:08:27.000Z

securityheaders/checkers/cors/maxage/__init__.py

th3cyb3rc0p/securityheaders

941264be581dc01afe28f6416f2d7bed79aecfb3

[ "Apache-2.0" ]

5

2019-04-24T07:31:36.000Z

2021-04-15T14:31:23.000Z

securityheaders/checkers/cors/maxage/__init__.py

th3cyb3rc0p/securityheaders

941264be581dc01afe28f6416f2d7bed79aecfb3

[ "Apache-2.0" ]

42

2018-07-31T08:18:59.000Z

2022-03-28T08:18:32.000Z

from .checker import AccessControlMaxAgeChecker from .toolong import AccessControlMaxAgeTooLongChecker __all__ = ['AccessControlMaxAgeChecker','AccessControlMaxAgeTooLongChecker']

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5

a1789edd8716845b448e4257f042808ca4432f4b

55

py

Python

py2app_tests/app_with_scripts/helper1.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

193

2020-01-15T09:34:20.000Z

2022-03-18T19:14:16.000Z

py2app_tests/app_with_scripts/helper1.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

185

2020-01-15T08:38:27.000Z

2022-03-27T17:29:29.000Z

py2app_tests/app_with_scripts/helper1.py

flupke/py2app

8eb6c618f9c63d6ac970fb145a7f7782b71bcb4d

[ "MIT" ]

23

2020-01-24T14:47:18.000Z

2022-02-22T17:19:47.000Z

import curses print("Helper 1: %s"%(curses.__name__,))

18.333333

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1

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1

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5

a181f2c79e0680a8daab8d31b2083f94152674a4

106

py

Python

build/lib/Factor_analysis_pkg/__init__.py

kdk411/Factor_analysis

09f85bb77c83bced0dd344c990d85135c9780e8a

[ "MIT" ]

null

build/lib/Factor_analysis_pkg/__init__.py

kdk411/Factor_analysis

09f85bb77c83bced0dd344c990d85135c9780e8a

[ "MIT" ]

null

build/lib/Factor_analysis_pkg/__init__.py

kdk411/Factor_analysis

09f85bb77c83bced0dd344c990d85135c9780e8a

[ "MIT" ]

null

name = "Factor_analysis_pkg" from .EM_Factor_analysis import factor_analysis from .example import run_demo

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a18a224069313bf27582f6ad30c1d63f8ad1d5b9

187

py

Python

dynamically_import.py

iandmyhand/python-tester

b4fa2bd2706f3fb516388c9866e86fb7a1a9dfa4

[ "MIT" ]

1

2015-03-30T08:35:57.000Z

dynamically_import.py

iandmyhand/python-tester

b4fa2bd2706f3fb516388c9866e86fb7a1a9dfa4

[ "MIT" ]

null

dynamically_import.py

iandmyhand/python-tester

b4fa2bd2706f3fb516388c9866e86fb7a1a9dfa4

[ "MIT" ]

null

def dynamically_import(): mod = __import__('my_package.my_module', fromlist=['my_class']) klass = getattr(mod, 'my_class') if '__main__' == __name__: dynamically_import()

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a1b2ca8b0ee8536443694373b5e597f201db7c93

184

py

Python

py3plex/algorithms/hedwig/stats/__init__.py

SkBlaz/supertest

5d99034af820cc10c8f70271b55cc90c42328709

[ "BSD-3-Clause" ]

79

2018-10-22T14:54:04.000Z

2020-03-05T05:34:35.000Z

py3plex/algorithms/hedwig/stats/__init__.py

SkBlaz/supertest

5d99034af820cc10c8f70271b55cc90c42328709

[ "BSD-3-Clause" ]

6

2019-02-19T16:33:14.000Z

2019-12-16T10:23:25.000Z

py3plex/algorithms/hedwig/stats/__init__.py

SkBlaz/Py3Plex

5d99034af820cc10c8f70271b55cc90c42328709

[ "BSD-3-Clause" ]

16

2019-02-19T16:30:29.000Z

2020-02-13T05:57:16.000Z

from . import scorefunctions from . import adjustment from . import significance from .validate import Validate __all__ = ["scorefunctions", "adjustment", "significance", "Validate"]

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a1b371cbc484fc1b84f45a9bb95f96a177047d7c

190

py

Python

transcriptions/admin.py

productivity-in-tech/pit_transcriptor_django

8e6962c557a258d9e54e21569d7dfe9f79e6bfd6

[ "MIT" ]

3

2019-12-28T15:17:31.000Z

2020-03-27T01:30:32.000Z

transcriptions/admin.py

productivity-in-tech/pit_transcriptor_django

8e6962c557a258d9e54e21569d7dfe9f79e6bfd6

[ "MIT" ]

11

2020-03-24T18:11:10.000Z

2021-09-22T18:21:07.000Z

transcriptions/admin.py

productivity-in-tech/pit_transcriptor_django

8e6962c557a258d9e54e21569d7dfe9f79e6bfd6

[ "MIT" ]

1

2019-12-28T15:20:23.000Z

from django.contrib import admin from .models import Transcription, TranscriptionEdit # Register your models here. admin.site.register(Transcription) admin.site.register(TranscriptionEdit)

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0

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a1febb45905db7ff659d35b9f24078a4b3f87def

17

py

Python

pygcam/version.py

JGCRI/pygcam

e33042e7c9f33dfe471dc48e02965ecfb523bd83

[ "MIT" ]

18

2017-06-15T01:28:46.000Z

2022-03-30T03:13:35.000Z

pygcam/version.py

JGCRI/pygcam

e33042e7c9f33dfe471dc48e02965ecfb523bd83

[ "MIT" ]

8

2020-04-03T19:38:14.000Z

2022-03-03T17:12:17.000Z

pygcam/version.py

JGCRI/pygcam

e33042e7c9f33dfe471dc48e02965ecfb523bd83

[ "MIT" ]

10

2017-08-13T17:24:03.000Z

2022-03-06T13:39:25.000Z

VERSION="1.10.2"

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null

0

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b81a856329f5cc33ef14fc6b75260c4e1da95973

183

py

Python

nagl/utilities/__init__.py

SimonBoothroyd/nagl

3aacb69547baae822538a1728e1102513c79561f

[ "MIT" ]

4

2020-12-03T13:27:31.000Z

2022-03-07T14:17:06.000Z

nagl/utilities/__init__.py

SimonBoothroyd/nagl

3aacb69547baae822538a1728e1102513c79561f

[ "MIT" ]

37

2020-12-29T15:27:47.000Z

2022-03-30T13:52:09.000Z

nagl/utilities/__init__.py

SimonBoothroyd/nagl

3aacb69547baae822538a1728e1102513c79561f

[ "MIT" ]

1

2020-12-05T11:35:48.000Z

from nagl.utilities.utilities import ( MissingOptionalDependency, requires_package, temporary_cd, ) __all__ = [MissingOptionalDependency, requires_package, temporary_cd]

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62bf2d3e9e17825177b21ee63f06094a36439f67

119

py

Python

moleculex.py

sunhwan/MoleculeX

d718870a6f64fd7d6b110e3d6792e9d92c224da8

[ "MIT" ]

null

moleculex.py

sunhwan/MoleculeX

d718870a6f64fd7d6b110e3d6792e9d92c224da8

[ "MIT" ]

null

moleculex.py

sunhwan/MoleculeX

d718870a6f64fd7d6b110e3d6792e9d92c224da8

[ "MIT" ]

null

#!/usr/bin/env python import sys import moleculex.cli if __name__ == '__main__': moleculex.cli.run(sys.argv[1:])

14.875

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1

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1

0

5

62c1cde6a1154bcc0908e21442b7ee6735123736

115

py

Python

html/canvas/tools/gentest_union.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

14,668

2015-01-01T01:57:10.000Z

2022-03-31T23:33:32.000Z

html/canvas/tools/gentest_union.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

7,642

2018-05-28T09:38:03.000Z

2022-03-31T20:55:48.000Z

html/canvas/tools/gentest_union.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

5,941

2015-01-02T11:32:21.000Z

2022-03-31T16:35:46.000Z

from gentestutilsunion import genTestUtils_union genTestUtils_union('templates-new.yaml', 'name2dir-canvas.yaml')

28.75

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0

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115

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38.333333

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null

0

1

0

1

0

5

62c42ceb04c703f2ba1db0dd078669c603e171bd

10,872

py

Python

codebase/controller/ofa.py

qdmy/Adelaidet-Quantization

e88cf41c62dc3944d2bd57ffc1d365535b0a1c4b

[ "Apache-2.0" ]

null

codebase/controller/ofa.py

qdmy/Adelaidet-Quantization

e88cf41c62dc3944d2bd57ffc1d365535b0a1c4b

[ "Apache-2.0" ]

null

codebase/controller/ofa.py

qdmy/Adelaidet-Quantization

e88cf41c62dc3944d2bd57ffc1d365535b0a1c4b

[ "Apache-2.0" ]

null

import math import functools import torch import torch.nn as nn import torch.nn.functional as F from .base import BaseController from .gnn import EdgeGraphSAGE, NoEdgeGraph from codebase.core.arch_representation.ofa import N_LAYERS_PER_UNIT, N_AVAILABLE_RESOLUTIONS from codebase.core.dynamic_graph import DynamicGraph from codebase.torchutils.common import auto_device from codebase.torchutils import logger class OFAModificationController(BaseController): def __init__(self, K=10, n_steps=2, gnn_layers=2, in_features=201, has_resolution=False, n_layers=20, n_operations=10, hidden_size=64, temperature=None, tanh_constant=1.5, op_tanh_reduce=2.5, batch_size=1, device=auto_device, no_edge=False): super(OFAModificationController, self).__init__(hidden_size, device) self.K = K self.n_steps = n_steps self.has_resolution = has_resolution self.n_layers = n_layers self.n_operations = n_operations self.hidden_size = hidden_size self.temperature = temperature self.tanh_constant = tanh_constant self.op_tanh_reduce = op_tanh_reduce self.no_edge = no_edge self.batch_size = batch_size self.device = device self.gnn = EdgeGraphSAGE(gnn_layers, in_features, hidden_size, hidden_size) self.node_decision = nn.Linear(self.hidden_size, K) self.layer_decision = nn.Linear(self.hidden_size, n_layers) self.op_decision = nn.Linear(self.hidden_size, n_operations) self.emb_attn = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.hid_attn = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.v_attn = nn.Linear(self.hidden_size, 1, bias=False) self.node_embedding = nn.Embedding(K, self.hidden_size) self.layer_embedding = nn.Embedding(n_layers, self.hidden_size) self.op_embedding = nn.Embedding(n_operations, self.hidden_size) if self.has_resolution: self.resolution_decision = nn.Linear(self.hidden_size, N_AVAILABLE_RESOLUTIONS) self.resolution_embedding = nn.Embedding(n_operations, self.hidden_size) self.lstm = nn.LSTMCell(self.hidden_size, self.hidden_size) self.reset_parameters() def forward(self, graph: DynamicGraph, force_uniform=False): node_features = graph.node_features.to(device=self.device) edge_features = graph.edge_features.to(device=self.device) if self.no_edge: # print("no_edge") with torch.no_grad(): edge_features=edge_features.detach() edge_features.requires_grad=False edge_features.zero_() graph_features, _ = self.gnn(node_features, edge_features, graph.edge_index) hidden = self._zeros(self.batch_size), self._zeros(self.batch_size) edit_seq = [] logp_buf = [] entropy_buf = [] max_entropy = 0 # decide which node is most potential embed = graph_features.mean(dim=0, keepdim=True) if force_uniform: logits = torch.zeros(self.K, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) query = torch.tanh(self.emb_attn(graph_features) + self.hid_attn(hx)) logits = self.v_attn(query).view(-1) # (n_nodes,) logits = self._scale_attention(logits, self.temperature, self.tanh_constant) probs = F.softmax(logits, dim=-1) # logger.debug(f"Most Potential Node: {probs.tolist()}") action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) edit_seq.append(action.item()) logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.node_embedding(action) for step in range(self.n_steps): # which layer if force_uniform: logits = torch.zeros(self.n_layers, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) logits = self.layer_decision(hx).view(-1) logits = self._scale_attention(logits, self.temperature, self.tanh_constant, self.op_tanh_reduce) probs = F.softmax(logits, dim=-1) # logger.debug(f"Wchih Layer: {probs.tolist()}") action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) layer_index = action.item() logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.layer_embedding(action) if self.has_resolution and layer_index == 0: if force_uniform: logits = torch.zeros(N_AVAILABLE_RESOLUTIONS, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) logits = self.resolution_decision(hx).view(-1) logits = self._scale_attention(logits, self.temperature, self.tanh_constant, self.op_tanh_reduce) probs = F.softmax(logits, dim=-1) action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) op_index = action.item() logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.resolution_embedding(action) else: if force_uniform: logits = torch.zeros(self.n_operations, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) logits = self.op_decision(hx).view(-1) logits = self._scale_attention(logits, self.temperature, self.tanh_constant, self.op_tanh_reduce) is_front_split_line = 3 if self.has_resolution else 2 if layer_index % N_LAYERS_PER_UNIT < is_front_split_line: # here, the layer cannot be deleted, so we ignore the first decision (0,0) logits = logits[1:] probs = F.softmax(logits, dim=-1) # logger.debug(f"Wchih Operation: {probs.tolist()}") action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) if layer_index % N_LAYERS_PER_UNIT < is_front_split_line: op_index = action.item() + 1 else: op_index = action.item() logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.op_embedding(action) edit_seq.append((layer_index, op_index)) return edit_seq, sum(logp_buf), sum(entropy_buf), max_entropy class OFAController(BaseController): def __init__(self, n_layers=20, n_operations=10, has_resolution=False, hidden_size=64, temperature=None, tanh_constant=1.5, op_tanh_reduce=2.5, batch_size=1, device=auto_device): super(OFAController, self).__init__(hidden_size, device) self.n_layers = n_layers self.n_operations = n_operations self.has_resolution = has_resolution self.hidden_size = hidden_size self.temperature = temperature self.tanh_constant = tanh_constant self.op_tanh_reduce = op_tanh_reduce self.batch_size = batch_size self.device = device # self.gnn = EdgeGraphSAGE(gnn_layers, in_features, hidden_size, hidden_size) # self.node_decision = nn.Linear(self.hidden_size, K) # self.layer_decision = nn.Linear(self.hidden_size, n_layers) if self.has_resolution: self.resolution_decision = nn.Linear(self.hidden_size, N_AVAILABLE_RESOLUTIONS) self.op_decision = nn.Linear(self.hidden_size, n_operations) # self.emb_attn = nn.Linear(self.hidden_size, self.hidden_size, bias=False) # self.hid_attn = nn.Linear(self.hidden_size, self.hidden_size, bias=False) # self.v_attn = nn.Linear(self.hidden_size, 1, bias=False) # self.node_embedding = nn.Embedding(K, self.hidden_size) # self.layer_embedding = nn.Embedding(n_layers, self.hidden_size) if self.has_resolution: self.resolution_embedding = nn.Embedding(N_AVAILABLE_RESOLUTIONS, self.hidden_size) self.op_embedding = nn.Embedding(n_operations, self.hidden_size) self.lstm = nn.LSTMCell(self.hidden_size, self.hidden_size) self.reset_parameters() def forward(self, force_uniform=False): embed = self._zeros(self.batch_size) hidden = self._zeros(self.batch_size), self._zeros(self.batch_size) arch_seq = [] logp_buf = [] entropy_buf = [] max_entropy = 0 if self.has_resolution: if force_uniform: logits = torch.zeros(N_AVAILABLE_RESOLUTIONS, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) logits = self.resolution_decision(hx).view(-1) logits = self._scale_attention(logits, self.temperature, self.tanh_constant, self.op_tanh_reduce) probs = F.softmax(logits, dim=-1) action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) r = action.item() logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.resolution_embedding(action) arch_seq.append(r) for layer_index in range(self.n_layers): if force_uniform: logits = torch.zeros(self.n_operations, device=self.device) else: hx, cx = self.lstm(embed, hidden) hidden = (hx, cx) logits = self.op_decision(hx).view(-1) logits = self._scale_attention(logits, self.temperature, self.tanh_constant, self.op_tanh_reduce) if layer_index % N_LAYERS_PER_UNIT < 2: # here, the layer cannot be deleted, so we ignore the first decision (0,0) logits = logits[1:] probs = F.softmax(logits, dim=-1) action, select_log_p, entropy = self._impl(probs) max_entropy += math.log(probs.shape[0]) op = action.item() if layer_index % N_LAYERS_PER_UNIT < 2: op += 1 logp_buf.append(select_log_p) entropy_buf.append(entropy) embed = self.op_embedding(action) arch_seq.append(op) return arch_seq, sum(logp_buf), sum(entropy_buf), max_entropy

42.635294

117

0.621045

1,362

10,872

4.694567

0.112335

0.065687

0.070066

0.039412

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0.008352

0.284124

10,872

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null

0

1

0

null

0

5

1a194808796fa76974ff6fcc3460321ed1f98646

51

py

Python

mabel/adapters/mqtt/__init__.py

mabel-dev/mabel

ee1fdfcfe5fb87d2c5ce4f24b4b7113478ba1b8a

[ "Apache-2.0" ]

null

mabel/adapters/mqtt/__init__.py

mabel-dev/mabel

ee1fdfcfe5fb87d2c5ce4f24b4b7113478ba1b8a

[ "Apache-2.0" ]

287

2021-05-14T21:25:26.000Z

2022-03-30T12:02:51.000Z

mabel/adapters/mqtt/__init__.py

gva-jjoyce/mabel

eb99e02d0287b851e65ad9a75b5f4188805d4ec9

[ "Apache-2.0" ]

1

2021-04-29T18:18:20.000Z

from .mqtt_reader import MqttReader # type:ignore

25.5

50

0.803922

7

51

5.714286

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51

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0.909091

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true

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null

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1

0

1

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1

0

5

c51f39019ceb0ed730cbf59495c5e633ccb0420f

54,402

py

Python

openmoc/bgq/single/openmoc_bgq_single.py

samuelshaner/OpenMOC-shaner

52a9003eef0de0629aae4aa4030b5e8d2c3f9988

[ "MIT" ]

null

openmoc/bgq/single/openmoc_bgq_single.py

samuelshaner/OpenMOC-shaner

52a9003eef0de0629aae4aa4030b5e8d2c3f9988

[ "MIT" ]

null

openmoc/bgq/single/openmoc_bgq_single.py

samuelshaner/OpenMOC-shaner

52a9003eef0de0629aae4aa4030b5e8d2c3f9988

[ "MIT" ]

null

# This file was automatically generated by SWIG (http://www.swig.org). # Version 2.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_openmoc_bgq_single', [dirname(__file__)]) except ImportError: import _openmoc_bgq_single return _openmoc_bgq_single if fp is not None: try: _mod = imp.load_module('_openmoc_bgq_single', fp, pathname, description) finally: fp.close() return _mod _openmoc_bgq_single = swig_import_helper() del swig_import_helper else: import _openmoc_bgq_single del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 def castCellToCellFill(*args, **kwargs): return _openmoc_bgq_single.castCellToCellFill(*args, **kwargs) castCellToCellFill = _openmoc_bgq_single.castCellToCellFill def castCellToCellBasic(*args, **kwargs): return _openmoc_bgq_single.castCellToCellBasic(*args, **kwargs) castCellToCellBasic = _openmoc_bgq_single.castCellToCellBasic def castUniverseToLattice(*args, **kwargs): return _openmoc_bgq_single.castUniverseToLattice(*args, **kwargs) castUniverseToLattice = _openmoc_bgq_single.castUniverseToLattice def castLatticeToUniverse(*args, **kwargs): return _openmoc_bgq_single.castLatticeToUniverse(*args, **kwargs) castLatticeToUniverse = _openmoc_bgq_single.castLatticeToUniverse def cell_id(): return _openmoc_bgq_single.cell_id() cell_id = _openmoc_bgq_single.cell_id MATERIAL = _openmoc_bgq_single.MATERIAL FILL = _openmoc_bgq_single.FILL class Cell(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Cell, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Cell, name) def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _openmoc_bgq_single.delete_Cell __del__ = lambda self : None; def getUid(self): return _openmoc_bgq_single.Cell_getUid(self) def getId(self): return _openmoc_bgq_single.Cell_getId(self) def getType(self): return _openmoc_bgq_single.Cell_getType(self) def getUniverse(self): return _openmoc_bgq_single.Cell_getUniverse(self) def getNumSurfaces(self): return _openmoc_bgq_single.Cell_getNumSurfaces(self) def getSurfaces(self): return _openmoc_bgq_single.Cell_getSurfaces(self) def getNumFSRs(self): return _openmoc_bgq_single.Cell_getNumFSRs(self) def setUniverse(self, *args, **kwargs): return _openmoc_bgq_single.Cell_setUniverse(self, *args, **kwargs) def addSurface(self, *args, **kwargs): return _openmoc_bgq_single.Cell_addSurface(self, *args, **kwargs) def setSurfacePointer(self, *args, **kwargs): return _openmoc_bgq_single.Cell_setSurfacePointer(self, *args, **kwargs) def cellContainsPoint(self, *args, **kwargs): return _openmoc_bgq_single.Cell_cellContainsPoint(self, *args, **kwargs) def cellContainsCoords(self, *args, **kwargs): return _openmoc_bgq_single.Cell_cellContainsCoords(self, *args, **kwargs) def minSurfaceDist(self, *args, **kwargs): return _openmoc_bgq_single.Cell_minSurfaceDist(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Cell_toString(self) def printString(self): return _openmoc_bgq_single.Cell_printString(self) Cell_swigregister = _openmoc_bgq_single.Cell_swigregister Cell_swigregister(Cell) class CellBasic(Cell): __swig_setmethods__ = {} for _s in [Cell]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, CellBasic, name, value) __swig_getmethods__ = {} for _s in [Cell]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, CellBasic, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_CellBasic(*args, **kwargs) try: self.this.append(this) except: self.this = this def getMaterial(self): return _openmoc_bgq_single.CellBasic_getMaterial(self) def getNumRings(self): return _openmoc_bgq_single.CellBasic_getNumRings(self) def getNumSectors(self): return _openmoc_bgq_single.CellBasic_getNumSectors(self) def getNumFSRs(self): return _openmoc_bgq_single.CellBasic_getNumFSRs(self) def setNumRings(self, *args, **kwargs): return _openmoc_bgq_single.CellBasic_setNumRings(self, *args, **kwargs) def setNumSectors(self, *args, **kwargs): return _openmoc_bgq_single.CellBasic_setNumSectors(self, *args, **kwargs) def clone(self): return _openmoc_bgq_single.CellBasic_clone(self) def subdivideCell(self): return _openmoc_bgq_single.CellBasic_subdivideCell(self) def toString(self): return _openmoc_bgq_single.CellBasic_toString(self) def printString(self): return _openmoc_bgq_single.CellBasic_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_CellBasic __del__ = lambda self : None; CellBasic_swigregister = _openmoc_bgq_single.CellBasic_swigregister CellBasic_swigregister(CellBasic) class CellFill(Cell): __swig_setmethods__ = {} for _s in [Cell]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, CellFill, name, value) __swig_getmethods__ = {} for _s in [Cell]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, CellFill, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_CellFill(*args, **kwargs) try: self.this.append(this) except: self.this = this def getUniverseFillId(self): return _openmoc_bgq_single.CellFill_getUniverseFillId(self) def getUniverseFill(self): return _openmoc_bgq_single.CellFill_getUniverseFill(self) def getNumFSRs(self): return _openmoc_bgq_single.CellFill_getNumFSRs(self) def setUniverseFill(self, *args, **kwargs): return _openmoc_bgq_single.CellFill_setUniverseFill(self, *args, **kwargs) def setUniverseFillPointer(self, *args, **kwargs): return _openmoc_bgq_single.CellFill_setUniverseFillPointer(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.CellFill_toString(self) def printString(self): return _openmoc_bgq_single.CellFill_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_CellFill __del__ = lambda self : None; CellFill_swigregister = _openmoc_bgq_single.CellFill_swigregister CellFill_swigregister(CellFill) class Geometry(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Geometry, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Geometry, name) __repr__ = _swig_repr def __init__(self): this = _openmoc_bgq_single.new_Geometry() try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Geometry __del__ = lambda self : None; def getWidth(self): return _openmoc_bgq_single.Geometry_getWidth(self) def getHeight(self): return _openmoc_bgq_single.Geometry_getHeight(self) def getXMin(self): return _openmoc_bgq_single.Geometry_getXMin(self) def getXMax(self): return _openmoc_bgq_single.Geometry_getXMax(self) def getYMin(self): return _openmoc_bgq_single.Geometry_getYMin(self) def getYMax(self): return _openmoc_bgq_single.Geometry_getYMax(self) def getBCTop(self): return _openmoc_bgq_single.Geometry_getBCTop(self) def getBCBottom(self): return _openmoc_bgq_single.Geometry_getBCBottom(self) def getBCLeft(self): return _openmoc_bgq_single.Geometry_getBCLeft(self) def getBCRight(self): return _openmoc_bgq_single.Geometry_getBCRight(self) def getNumFSRs(self): return _openmoc_bgq_single.Geometry_getNumFSRs(self) def getNumEnergyGroups(self): return _openmoc_bgq_single.Geometry_getNumEnergyGroups(self) def getNumMaterials(self): return _openmoc_bgq_single.Geometry_getNumMaterials(self) def getFSRtoCellMap(self): return _openmoc_bgq_single.Geometry_getFSRtoCellMap(self) def getFSRtoMaterialMap(self): return _openmoc_bgq_single.Geometry_getFSRtoMaterialMap(self) def getMaxSegmentLength(self): return _openmoc_bgq_single.Geometry_getMaxSegmentLength(self) def getMinSegmentLength(self): return _openmoc_bgq_single.Geometry_getMinSegmentLength(self) def getMaterials(self): return _openmoc_bgq_single.Geometry_getMaterials(self) def getMaterial(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_getMaterial(self, *args, **kwargs) def getSurface(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_getSurface(self, *args, **kwargs) def getCell(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_getCell(self, *args, **kwargs) def getUniverse(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_getUniverse(self, *args, **kwargs) def getLattice(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_getLattice(self, *args, **kwargs) def addMaterial(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_addMaterial(self, *args, **kwargs) def addSurface(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_addSurface(self, *args, **kwargs) def addCell(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_addCell(self, *args, **kwargs) def addUniverse(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_addUniverse(self, *args, **kwargs) def addLattice(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_addLattice(self, *args, **kwargs) def findCellContainingCoords(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_findCellContainingCoords(self, *args, **kwargs) def findCellContainingFSR(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_findCellContainingFSR(self, *args, **kwargs) def findCell(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_findCell(self, *args, **kwargs) def findFSRId(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_findFSRId(self, *args, **kwargs) def subdivideCells(self): return _openmoc_bgq_single.Geometry_subdivideCells(self) def initializeFlatSourceRegions(self): return _openmoc_bgq_single.Geometry_initializeFlatSourceRegions(self) def segmentize(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_segmentize(self, *args, **kwargs) def computeFissionability(self, univ=None): return _openmoc_bgq_single.Geometry_computeFissionability(self, univ) def computePinPowers(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_computePinPowers(self, *args, **kwargs) def computePinPowersInUniverse(self, *args, **kwargs): return _openmoc_bgq_single.Geometry_computePinPowersInUniverse(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Geometry_toString(self) def printString(self): return _openmoc_bgq_single.Geometry_printString(self) Geometry_swigregister = _openmoc_bgq_single.Geometry_swigregister Geometry_swigregister(Geometry) UNIV = _openmoc_bgq_single.UNIV LAT = _openmoc_bgq_single.LAT class LocalCoords(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, LocalCoords, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, LocalCoords, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_LocalCoords(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_LocalCoords __del__ = lambda self : None; def getType(self): return _openmoc_bgq_single.LocalCoords_getType(self) def getUniverse(self): return _openmoc_bgq_single.LocalCoords_getUniverse(self) def getCell(self): return _openmoc_bgq_single.LocalCoords_getCell(self) def getLattice(self): return _openmoc_bgq_single.LocalCoords_getLattice(self) def getLatticeX(self): return _openmoc_bgq_single.LocalCoords_getLatticeX(self) def getLatticeY(self): return _openmoc_bgq_single.LocalCoords_getLatticeY(self) def getX(self): return _openmoc_bgq_single.LocalCoords_getX(self) def getY(self): return _openmoc_bgq_single.LocalCoords_getY(self) def getPoint(self): return _openmoc_bgq_single.LocalCoords_getPoint(self) def getNext(self): return _openmoc_bgq_single.LocalCoords_getNext(self) def getPrev(self): return _openmoc_bgq_single.LocalCoords_getPrev(self) def setType(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setType(self, *args, **kwargs) def setUniverse(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setUniverse(self, *args, **kwargs) def setCell(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setCell(self, *args, **kwargs) def setLattice(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setLattice(self, *args, **kwargs) def setLatticeX(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setLatticeX(self, *args, **kwargs) def setLatticeY(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setLatticeY(self, *args, **kwargs) def setX(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setX(self, *args, **kwargs) def setY(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setY(self, *args, **kwargs) def setNext(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setNext(self, *args, **kwargs) def setPrev(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_setPrev(self, *args, **kwargs) def getLowestLevel(self): return _openmoc_bgq_single.LocalCoords_getLowestLevel(self) def adjustCoords(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_adjustCoords(self, *args, **kwargs) def updateMostLocal(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_updateMostLocal(self, *args, **kwargs) def prune(self): return _openmoc_bgq_single.LocalCoords_prune(self) def copyCoords(self, *args, **kwargs): return _openmoc_bgq_single.LocalCoords_copyCoords(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.LocalCoords_toString(self) LocalCoords_swigregister = _openmoc_bgq_single.LocalCoords_swigregister LocalCoords_swigregister(LocalCoords) DEBUG = _openmoc_bgq_single.DEBUG INFO = _openmoc_bgq_single.INFO NORMAL = _openmoc_bgq_single.NORMAL SEPARATOR = _openmoc_bgq_single.SEPARATOR HEADER = _openmoc_bgq_single.HEADER TITLE = _openmoc_bgq_single.TITLE WARNING = _openmoc_bgq_single.WARNING CRITICAL = _openmoc_bgq_single.CRITICAL RESULT = _openmoc_bgq_single.RESULT UNITTEST = _openmoc_bgq_single.UNITTEST ERROR = _openmoc_bgq_single.ERROR def set_err(*args, **kwargs): return _openmoc_bgq_single.set_err(*args, **kwargs) set_err = _openmoc_bgq_single.set_err def setOutputDirectory(*args, **kwargs): return _openmoc_bgq_single.setOutputDirectory(*args, **kwargs) setOutputDirectory = _openmoc_bgq_single.setOutputDirectory def getOutputDirectory(): return _openmoc_bgq_single.getOutputDirectory() getOutputDirectory = _openmoc_bgq_single.getOutputDirectory def setLogfileName(*args, **kwargs): return _openmoc_bgq_single.setLogfileName(*args, **kwargs) setLogfileName = _openmoc_bgq_single.setLogfileName def getLogfileName(): return _openmoc_bgq_single.getLogfileName() getLogfileName = _openmoc_bgq_single.getLogfileName def setSeparatorCharacter(*args, **kwargs): return _openmoc_bgq_single.setSeparatorCharacter(*args, **kwargs) setSeparatorCharacter = _openmoc_bgq_single.setSeparatorCharacter def getSeparatorCharacter(): return _openmoc_bgq_single.getSeparatorCharacter() getSeparatorCharacter = _openmoc_bgq_single.getSeparatorCharacter def setHeaderCharacter(*args, **kwargs): return _openmoc_bgq_single.setHeaderCharacter(*args, **kwargs) setHeaderCharacter = _openmoc_bgq_single.setHeaderCharacter def getHeaderCharacter(): return _openmoc_bgq_single.getHeaderCharacter() getHeaderCharacter = _openmoc_bgq_single.getHeaderCharacter def setTitleCharacter(*args, **kwargs): return _openmoc_bgq_single.setTitleCharacter(*args, **kwargs) setTitleCharacter = _openmoc_bgq_single.setTitleCharacter def getTitleCharacter(): return _openmoc_bgq_single.getTitleCharacter() getTitleCharacter = _openmoc_bgq_single.getTitleCharacter def setLineLength(*args, **kwargs): return _openmoc_bgq_single.setLineLength(*args, **kwargs) setLineLength = _openmoc_bgq_single.setLineLength def setLogLevel(*args, **kwargs): return _openmoc_bgq_single.setLogLevel(*args, **kwargs) setLogLevel = _openmoc_bgq_single.setLogLevel def getLogLevel(): return _openmoc_bgq_single.getLogLevel() getLogLevel = _openmoc_bgq_single.getLogLevel def log_printf(*args, **kwargs): return _openmoc_bgq_single.log_printf(*args, **kwargs) log_printf = _openmoc_bgq_single.log_printf def createMultilineMsg(*args, **kwargs): return _openmoc_bgq_single.createMultilineMsg(*args, **kwargs) createMultilineMsg = _openmoc_bgq_single.createMultilineMsg SIGMA_T_THRESH = _openmoc_bgq_single.SIGMA_T_THRESH def material_id(): return _openmoc_bgq_single.material_id() material_id = _openmoc_bgq_single.material_id class Material(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Material, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Material, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Material(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Material __del__ = lambda self : None; def getUid(self): return _openmoc_bgq_single.Material_getUid(self) def getId(self): return _openmoc_bgq_single.Material_getId(self) def getNumEnergyGroups(self): return _openmoc_bgq_single.Material_getNumEnergyGroups(self) def getSigmaT(self): return _openmoc_bgq_single.Material_getSigmaT(self) def getSigmaA(self): return _openmoc_bgq_single.Material_getSigmaA(self) def getSigmaS(self): return _openmoc_bgq_single.Material_getSigmaS(self) def getSigmaF(self): return _openmoc_bgq_single.Material_getSigmaF(self) def getNuSigmaF(self): return _openmoc_bgq_single.Material_getNuSigmaF(self) def getChi(self): return _openmoc_bgq_single.Material_getChi(self) def isFissionable(self): return _openmoc_bgq_single.Material_isFissionable(self) def isDataAligned(self): return _openmoc_bgq_single.Material_isDataAligned(self) def getNumVectorGroups(self): return _openmoc_bgq_single.Material_getNumVectorGroups(self) def setNumEnergyGroups(self, *args, **kwargs): return _openmoc_bgq_single.Material_setNumEnergyGroups(self, *args, **kwargs) def setSigmaT(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaT(self, *args, **kwargs) def setSigmaA(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaA(self, *args, **kwargs) def setSigmaS(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaS(self, *args, **kwargs) def setSigmaF(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaF(self, *args, **kwargs) def setNuSigmaF(self, *args, **kwargs): return _openmoc_bgq_single.Material_setNuSigmaF(self, *args, **kwargs) def setChi(self, *args, **kwargs): return _openmoc_bgq_single.Material_setChi(self, *args, **kwargs) def setSigmaTByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaTByGroup(self, *args, **kwargs) def setSigmaAByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaAByGroup(self, *args, **kwargs) def setSigmaFByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaFByGroup(self, *args, **kwargs) def setNuSigmaFByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setNuSigmaFByGroup(self, *args, **kwargs) def setSigmaSByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setSigmaSByGroup(self, *args, **kwargs) def setChiByGroup(self, *args, **kwargs): return _openmoc_bgq_single.Material_setChiByGroup(self, *args, **kwargs) def checkSigmaT(self): return _openmoc_bgq_single.Material_checkSigmaT(self) def toString(self): return _openmoc_bgq_single.Material_toString(self) def printString(self): return _openmoc_bgq_single.Material_printString(self) def alignData(self): return _openmoc_bgq_single.Material_alignData(self) Material_swigregister = _openmoc_bgq_single.Material_swigregister Material_swigregister(Material) class Point(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Point, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Point, name) __repr__ = _swig_repr def __init__(self): this = _openmoc_bgq_single.new_Point() try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Point __del__ = lambda self : None; def setCoords(self, *args, **kwargs): return _openmoc_bgq_single.Point_setCoords(self, *args, **kwargs) def getX(self): return _openmoc_bgq_single.Point_getX(self) def getY(self): return _openmoc_bgq_single.Point_getY(self) def setX(self, *args, **kwargs): return _openmoc_bgq_single.Point_setX(self, *args, **kwargs) def setY(self, *args, **kwargs): return _openmoc_bgq_single.Point_setY(self, *args, **kwargs) def distance(self, *args, **kwargs): return _openmoc_bgq_single.Point_distance(self, *args, **kwargs) def distanceToPoint(self, *args, **kwargs): return _openmoc_bgq_single.Point_distanceToPoint(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Point_toString(self) Point_swigregister = _openmoc_bgq_single.Point_swigregister Point_swigregister(Point) LEONARD = _openmoc_bgq_single.LEONARD TABUCHI = _openmoc_bgq_single.TABUCHI class Quadrature(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Quadrature, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Quadrature, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Quadrature(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Quadrature __del__ = lambda self : None; def getNumPolarAngles(self): return _openmoc_bgq_single.Quadrature_getNumPolarAngles(self) def getType(self): return _openmoc_bgq_single.Quadrature_getType(self) def getSinTheta(self, *args, **kwargs): return _openmoc_bgq_single.Quadrature_getSinTheta(self, *args, **kwargs) def getWeight(self, *args, **kwargs): return _openmoc_bgq_single.Quadrature_getWeight(self, *args, **kwargs) def getMultiple(self, *args, **kwargs): return _openmoc_bgq_single.Quadrature_getMultiple(self, *args, **kwargs) def getSinThetas(self): return _openmoc_bgq_single.Quadrature_getSinThetas(self) def getWeights(self): return _openmoc_bgq_single.Quadrature_getWeights(self) def getMultiples(self): return _openmoc_bgq_single.Quadrature_getMultiples(self) def toString(self): return _openmoc_bgq_single.Quadrature_toString(self) Quadrature_swigregister = _openmoc_bgq_single.Quadrature_swigregister Quadrature_swigregister(Quadrature) FOUR_PI = _openmoc_bgq_single.FOUR_PI ONE_OVER_FOUR_PI = _openmoc_bgq_single.ONE_OVER_FOUR_PI class Solver(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Solver, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Solver, name) def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _openmoc_bgq_single.delete_Solver __del__ = lambda self : None; def getGeometry(self): return _openmoc_bgq_single.Solver_getGeometry(self) def getTrackGenerator(self): return _openmoc_bgq_single.Solver_getTrackGenerator(self) def getNumPolarAngles(self): return _openmoc_bgq_single.Solver_getNumPolarAngles(self) def getPolarQuadratureType(self): return _openmoc_bgq_single.Solver_getPolarQuadratureType(self) def getNumIterations(self): return _openmoc_bgq_single.Solver_getNumIterations(self) def getSourceConvergenceThreshold(self): return _openmoc_bgq_single.Solver_getSourceConvergenceThreshold(self) def getFSRScalarFlux(self, *args, **kwargs): return _openmoc_bgq_single.Solver_getFSRScalarFlux(self, *args, **kwargs) def getFSRScalarFluxes(self): return _openmoc_bgq_single.Solver_getFSRScalarFluxes(self) def setGeometry(self, *args, **kwargs): return _openmoc_bgq_single.Solver_setGeometry(self, *args, **kwargs) def setTrackGenerator(self, *args, **kwargs): return _openmoc_bgq_single.Solver_setTrackGenerator(self, *args, **kwargs) def setPolarQuadratureType(self, *args, **kwargs): return _openmoc_bgq_single.Solver_setPolarQuadratureType(self, *args, **kwargs) def setNumPolarAngles(self, *args, **kwargs): return _openmoc_bgq_single.Solver_setNumPolarAngles(self, *args, **kwargs) def setSourceConvergenceThreshold(self, *args, **kwargs): return _openmoc_bgq_single.Solver_setSourceConvergenceThreshold(self, *args, **kwargs) def useExponentialInterpolation(self): return _openmoc_bgq_single.Solver_useExponentialInterpolation(self) def useExponentialIntrinsic(self): return _openmoc_bgq_single.Solver_useExponentialIntrinsic(self) def convergeSource(self, *args, **kwargs): return _openmoc_bgq_single.Solver_convergeSource(self, *args, **kwargs) def computeFSRFissionRates(self, *args, **kwargs): return _openmoc_bgq_single.Solver_computeFSRFissionRates(self, *args, **kwargs) def printTimerReport(self): return _openmoc_bgq_single.Solver_printTimerReport(self) Solver_swigregister = _openmoc_bgq_single.Solver_swigregister Solver_swigregister(Solver) class CPUSolver(Solver): __swig_setmethods__ = {} for _s in [Solver]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, CPUSolver, name, value) __swig_getmethods__ = {} for _s in [Solver]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, CPUSolver, name) __repr__ = _swig_repr def __init__(self, geometry=None, track_generator=None): this = _openmoc_bgq_single.new_CPUSolver(geometry, track_generator) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_CPUSolver __del__ = lambda self : None; def getNumThreads(self): return _openmoc_bgq_single.CPUSolver_getNumThreads(self) def getFSRScalarFlux(self, *args, **kwargs): return _openmoc_bgq_single.CPUSolver_getFSRScalarFlux(self, *args, **kwargs) def getFSRScalarFluxes(self): return _openmoc_bgq_single.CPUSolver_getFSRScalarFluxes(self) def setNumThreads(self, *args, **kwargs): return _openmoc_bgq_single.CPUSolver_setNumThreads(self, *args, **kwargs) def computeFSRFissionRates(self, *args, **kwargs): return _openmoc_bgq_single.CPUSolver_computeFSRFissionRates(self, *args, **kwargs) CPUSolver_swigregister = _openmoc_bgq_single.CPUSolver_swigregister CPUSolver_swigregister(CPUSolver) class ThreadPrivateSolver(CPUSolver): __swig_setmethods__ = {} for _s in [CPUSolver]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, ThreadPrivateSolver, name, value) __swig_getmethods__ = {} for _s in [CPUSolver]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, ThreadPrivateSolver, name) __repr__ = _swig_repr def __init__(self, geometry=None, track_generator=None): this = _openmoc_bgq_single.new_ThreadPrivateSolver(geometry, track_generator) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_ThreadPrivateSolver __del__ = lambda self : None; ThreadPrivateSolver_swigregister = _openmoc_bgq_single.ThreadPrivateSolver_swigregister ThreadPrivateSolver_swigregister(ThreadPrivateSolver) ON_SURFACE_THRESH = _openmoc_bgq_single.ON_SURFACE_THRESH def surf_id(): return _openmoc_bgq_single.surf_id() surf_id = _openmoc_bgq_single.surf_id PLANE = _openmoc_bgq_single.PLANE CIRCLE = _openmoc_bgq_single.CIRCLE XPLANE = _openmoc_bgq_single.XPLANE YPLANE = _openmoc_bgq_single.YPLANE ZPLANE = _openmoc_bgq_single.ZPLANE QUADRATIC = _openmoc_bgq_single.QUADRATIC VACUUM = _openmoc_bgq_single.VACUUM REFLECTIVE = _openmoc_bgq_single.REFLECTIVE BOUNDARY_NONE = _openmoc_bgq_single.BOUNDARY_NONE class Surface(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Surface, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Surface, name) def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _openmoc_bgq_single.delete_Surface __del__ = lambda self : None; def getUid(self): return _openmoc_bgq_single.Surface_getUid(self) def getId(self): return _openmoc_bgq_single.Surface_getId(self) def getSurfaceType(self): return _openmoc_bgq_single.Surface_getSurfaceType(self) def getBoundaryType(self): return _openmoc_bgq_single.Surface_getBoundaryType(self) def getXMin(self): return _openmoc_bgq_single.Surface_getXMin(self) def getXMax(self): return _openmoc_bgq_single.Surface_getXMax(self) def getYMin(self): return _openmoc_bgq_single.Surface_getYMin(self) def getYMax(self): return _openmoc_bgq_single.Surface_getYMax(self) def setBoundaryType(self, *args, **kwargs): return _openmoc_bgq_single.Surface_setBoundaryType(self, *args, **kwargs) def evaluate(self, *args, **kwargs): return _openmoc_bgq_single.Surface_evaluate(self, *args, **kwargs) def intersection(self, *args, **kwargs): return _openmoc_bgq_single.Surface_intersection(self, *args, **kwargs) def isPointOnSurface(self, *args, **kwargs): return _openmoc_bgq_single.Surface_isPointOnSurface(self, *args, **kwargs) def isCoordOnSurface(self, *args, **kwargs): return _openmoc_bgq_single.Surface_isCoordOnSurface(self, *args, **kwargs) def getMinDistance(self, *args, **kwargs): return _openmoc_bgq_single.Surface_getMinDistance(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Surface_toString(self) def printString(self): return _openmoc_bgq_single.Surface_printString(self) Surface_swigregister = _openmoc_bgq_single.Surface_swigregister Surface_swigregister(Surface) class Plane(Surface): __swig_setmethods__ = {} for _s in [Surface]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, Plane, name, value) __swig_getmethods__ = {} for _s in [Surface]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, Plane, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Plane(*args, **kwargs) try: self.this.append(this) except: self.this = this def getXMin(self): return _openmoc_bgq_single.Plane_getXMin(self) def getXMax(self): return _openmoc_bgq_single.Plane_getXMax(self) def getYMin(self): return _openmoc_bgq_single.Plane_getYMin(self) def getYMax(self): return _openmoc_bgq_single.Plane_getYMax(self) def evaluate(self, *args, **kwargs): return _openmoc_bgq_single.Plane_evaluate(self, *args, **kwargs) def intersection(self, *args, **kwargs): return _openmoc_bgq_single.Plane_intersection(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Plane_toString(self) def printString(self): return _openmoc_bgq_single.Plane_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_Plane __del__ = lambda self : None; Plane_swigregister = _openmoc_bgq_single.Plane_swigregister Plane_swigregister(Plane) class XPlane(Plane): __swig_setmethods__ = {} for _s in [Plane]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, XPlane, name, value) __swig_getmethods__ = {} for _s in [Plane]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, XPlane, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_XPlane(*args, **kwargs) try: self.this.append(this) except: self.this = this def setX(self, *args, **kwargs): return _openmoc_bgq_single.XPlane_setX(self, *args, **kwargs) def getX(self): return _openmoc_bgq_single.XPlane_getX(self) def getXMin(self): return _openmoc_bgq_single.XPlane_getXMin(self) def getXMax(self): return _openmoc_bgq_single.XPlane_getXMax(self) def getYMin(self): return _openmoc_bgq_single.XPlane_getYMin(self) def getYMax(self): return _openmoc_bgq_single.XPlane_getYMax(self) def toString(self): return _openmoc_bgq_single.XPlane_toString(self) __swig_destroy__ = _openmoc_bgq_single.delete_XPlane __del__ = lambda self : None; XPlane_swigregister = _openmoc_bgq_single.XPlane_swigregister XPlane_swigregister(XPlane) class YPlane(Plane): __swig_setmethods__ = {} for _s in [Plane]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, YPlane, name, value) __swig_getmethods__ = {} for _s in [Plane]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, YPlane, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_YPlane(*args, **kwargs) try: self.this.append(this) except: self.this = this def setY(self, *args, **kwargs): return _openmoc_bgq_single.YPlane_setY(self, *args, **kwargs) def getY(self): return _openmoc_bgq_single.YPlane_getY(self) def getXMin(self): return _openmoc_bgq_single.YPlane_getXMin(self) def getXMax(self): return _openmoc_bgq_single.YPlane_getXMax(self) def getYMin(self): return _openmoc_bgq_single.YPlane_getYMin(self) def getYMax(self): return _openmoc_bgq_single.YPlane_getYMax(self) def toString(self): return _openmoc_bgq_single.YPlane_toString(self) def printString(self): return _openmoc_bgq_single.YPlane_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_YPlane __del__ = lambda self : None; YPlane_swigregister = _openmoc_bgq_single.YPlane_swigregister YPlane_swigregister(YPlane) class ZPlane(Plane): __swig_setmethods__ = {} for _s in [Plane]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, ZPlane, name, value) __swig_getmethods__ = {} for _s in [Plane]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, ZPlane, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_ZPlane(*args, **kwargs) try: self.this.append(this) except: self.this = this def setZ(self, *args, **kwargs): return _openmoc_bgq_single.ZPlane_setZ(self, *args, **kwargs) def getZ(self): return _openmoc_bgq_single.ZPlane_getZ(self) def getXMin(self): return _openmoc_bgq_single.ZPlane_getXMin(self) def getXMax(self): return _openmoc_bgq_single.ZPlane_getXMax(self) def getYMin(self): return _openmoc_bgq_single.ZPlane_getYMin(self) def getYMax(self): return _openmoc_bgq_single.ZPlane_getYMax(self) def toString(self): return _openmoc_bgq_single.ZPlane_toString(self) def printString(self): return _openmoc_bgq_single.ZPlane_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_ZPlane __del__ = lambda self : None; ZPlane_swigregister = _openmoc_bgq_single.ZPlane_swigregister ZPlane_swigregister(ZPlane) class Circle(Surface): __swig_setmethods__ = {} for _s in [Surface]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, Circle, name, value) __swig_getmethods__ = {} for _s in [Surface]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, Circle, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Circle(*args, **kwargs) try: self.this.append(this) except: self.this = this def getX0(self): return _openmoc_bgq_single.Circle_getX0(self) def getY0(self): return _openmoc_bgq_single.Circle_getY0(self) def getRadius(self): return _openmoc_bgq_single.Circle_getRadius(self) def getXMin(self): return _openmoc_bgq_single.Circle_getXMin(self) def getXMax(self): return _openmoc_bgq_single.Circle_getXMax(self) def getYMin(self): return _openmoc_bgq_single.Circle_getYMin(self) def getYMax(self): return _openmoc_bgq_single.Circle_getYMax(self) def evaluate(self, *args, **kwargs): return _openmoc_bgq_single.Circle_evaluate(self, *args, **kwargs) def intersection(self, *args, **kwargs): return _openmoc_bgq_single.Circle_intersection(self, *args, **kwargs) def toString(self): return _openmoc_bgq_single.Circle_toString(self) def printString(self): return _openmoc_bgq_single.Circle_printString(self) __swig_destroy__ = _openmoc_bgq_single.delete_Circle __del__ = lambda self : None; Circle_swigregister = _openmoc_bgq_single.Circle_swigregister Circle_swigregister(Circle) class Timer(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Timer, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Timer, name) __repr__ = _swig_repr def __init__(self): this = _openmoc_bgq_single.new_Timer() try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Timer __del__ = lambda self : None; __swig_getmethods__["Get"] = lambda x: _openmoc_bgq_single.Timer_Get if _newclass:Get = staticmethod(_openmoc_bgq_single.Timer_Get) def startTimer(self): return _openmoc_bgq_single.Timer_startTimer(self) def stopTimer(self): return _openmoc_bgq_single.Timer_stopTimer(self) def recordSplit(self, *args, **kwargs): return _openmoc_bgq_single.Timer_recordSplit(self, *args, **kwargs) def getTime(self): return _openmoc_bgq_single.Timer_getTime(self) def getSplit(self, *args, **kwargs): return _openmoc_bgq_single.Timer_getSplit(self, *args, **kwargs) def printSplits(self): return _openmoc_bgq_single.Timer_printSplits(self) def clearSplit(self, *args, **kwargs): return _openmoc_bgq_single.Timer_clearSplit(self, *args, **kwargs) def clearSplits(self): return _openmoc_bgq_single.Timer_clearSplits(self) Timer_swigregister = _openmoc_bgq_single.Timer_swigregister Timer_swigregister(Timer) def Timer_Get(): return _openmoc_bgq_single.Timer_Get() Timer_Get = _openmoc_bgq_single.Timer_Get class segment(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, segment, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, segment, name) __repr__ = _swig_repr __swig_setmethods__["_length"] = _openmoc_bgq_single.segment__length_set __swig_getmethods__["_length"] = _openmoc_bgq_single.segment__length_get if _newclass:_length = _swig_property(_openmoc_bgq_single.segment__length_get, _openmoc_bgq_single.segment__length_set) __swig_setmethods__["_material"] = _openmoc_bgq_single.segment__material_set __swig_getmethods__["_material"] = _openmoc_bgq_single.segment__material_get if _newclass:_material = _swig_property(_openmoc_bgq_single.segment__material_get, _openmoc_bgq_single.segment__material_set) __swig_setmethods__["_region_id"] = _openmoc_bgq_single.segment__region_id_set __swig_getmethods__["_region_id"] = _openmoc_bgq_single.segment__region_id_get if _newclass:_region_id = _swig_property(_openmoc_bgq_single.segment__region_id_get, _openmoc_bgq_single.segment__region_id_set) def __init__(self): this = _openmoc_bgq_single.new_segment() try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_segment __del__ = lambda self : None; segment_swigregister = _openmoc_bgq_single.segment_swigregister segment_swigregister(segment) class Track(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Track, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Track, name) __repr__ = _swig_repr def __init__(self): this = _openmoc_bgq_single.new_Track() try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Track __del__ = lambda self : None; def setValues(self, *args, **kwargs): return _openmoc_bgq_single.Track_setValues(self, *args, **kwargs) def setUid(self, *args, **kwargs): return _openmoc_bgq_single.Track_setUid(self, *args, **kwargs) def setPhi(self, *args, **kwargs): return _openmoc_bgq_single.Track_setPhi(self, *args, **kwargs) def setAzimAngleIndex(self, *args, **kwargs): return _openmoc_bgq_single.Track_setAzimAngleIndex(self, *args, **kwargs) def setReflIn(self, *args, **kwargs): return _openmoc_bgq_single.Track_setReflIn(self, *args, **kwargs) def setReflOut(self, *args, **kwargs): return _openmoc_bgq_single.Track_setReflOut(self, *args, **kwargs) def setBCIn(self, *args, **kwargs): return _openmoc_bgq_single.Track_setBCIn(self, *args, **kwargs) def setBCOut(self, *args, **kwargs): return _openmoc_bgq_single.Track_setBCOut(self, *args, **kwargs) def setTrackIn(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackIn(self, *args, **kwargs) def setTrackOut(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackOut(self, *args, **kwargs) def setTrackInI(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackInI(self, *args, **kwargs) def setTrackInJ(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackInJ(self, *args, **kwargs) def setTrackOutI(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackOutI(self, *args, **kwargs) def setTrackOutJ(self, *args, **kwargs): return _openmoc_bgq_single.Track_setTrackOutJ(self, *args, **kwargs) def getUid(self): return _openmoc_bgq_single.Track_getUid(self) def getEnd(self): return _openmoc_bgq_single.Track_getEnd(self) def getStart(self): return _openmoc_bgq_single.Track_getStart(self) def getPhi(self): return _openmoc_bgq_single.Track_getPhi(self) def getAzimAngleIndex(self): return _openmoc_bgq_single.Track_getAzimAngleIndex(self) def getSegment(self, *args, **kwargs): return _openmoc_bgq_single.Track_getSegment(self, *args, **kwargs) def getSegments(self): return _openmoc_bgq_single.Track_getSegments(self) def getNumSegments(self): return _openmoc_bgq_single.Track_getNumSegments(self) def getTrackIn(self): return _openmoc_bgq_single.Track_getTrackIn(self) def getTrackOut(self): return _openmoc_bgq_single.Track_getTrackOut(self) def getTrackInI(self): return _openmoc_bgq_single.Track_getTrackInI(self) def getTrackInJ(self): return _openmoc_bgq_single.Track_getTrackInJ(self) def getTrackOutI(self): return _openmoc_bgq_single.Track_getTrackOutI(self) def getTrackOutJ(self): return _openmoc_bgq_single.Track_getTrackOutJ(self) def isReflIn(self): return _openmoc_bgq_single.Track_isReflIn(self) def isReflOut(self): return _openmoc_bgq_single.Track_isReflOut(self) def getBCIn(self): return _openmoc_bgq_single.Track_getBCIn(self) def getBCOut(self): return _openmoc_bgq_single.Track_getBCOut(self) def contains(self, *args, **kwargs): return _openmoc_bgq_single.Track_contains(self, *args, **kwargs) def addSegment(self, *args, **kwargs): return _openmoc_bgq_single.Track_addSegment(self, *args, **kwargs) def clearSegments(self): return _openmoc_bgq_single.Track_clearSegments(self) def toString(self): return _openmoc_bgq_single.Track_toString(self) Track_swigregister = _openmoc_bgq_single.Track_swigregister Track_swigregister(Track) class TrackGenerator(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, TrackGenerator, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, TrackGenerator, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_TrackGenerator(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_TrackGenerator __del__ = lambda self : None; def getNumAzim(self): return _openmoc_bgq_single.TrackGenerator_getNumAzim(self) def getTrackSpacing(self): return _openmoc_bgq_single.TrackGenerator_getTrackSpacing(self) def getGeometry(self): return _openmoc_bgq_single.TrackGenerator_getGeometry(self) def getNumTracks(self): return _openmoc_bgq_single.TrackGenerator_getNumTracks(self) def getNumTracksArray(self): return _openmoc_bgq_single.TrackGenerator_getNumTracksArray(self) def getNumSegments(self): return _openmoc_bgq_single.TrackGenerator_getNumSegments(self) def getNumSegmentsArray(self): return _openmoc_bgq_single.TrackGenerator_getNumSegmentsArray(self) def getTracks(self): return _openmoc_bgq_single.TrackGenerator_getTracks(self) def getAzimWeights(self): return _openmoc_bgq_single.TrackGenerator_getAzimWeights(self) def containsTracks(self): return _openmoc_bgq_single.TrackGenerator_containsTracks(self) def retrieveTrackCoords(self, *args, **kwargs): return _openmoc_bgq_single.TrackGenerator_retrieveTrackCoords(self, *args, **kwargs) def retrieveSegmentCoords(self, *args, **kwargs): return _openmoc_bgq_single.TrackGenerator_retrieveSegmentCoords(self, *args, **kwargs) def setNumAzim(self, *args, **kwargs): return _openmoc_bgq_single.TrackGenerator_setNumAzim(self, *args, **kwargs) def setTrackSpacing(self, *args, **kwargs): return _openmoc_bgq_single.TrackGenerator_setTrackSpacing(self, *args, **kwargs) def setGeometry(self, *args, **kwargs): return _openmoc_bgq_single.TrackGenerator_setGeometry(self, *args, **kwargs) def generateTracks(self): return _openmoc_bgq_single.TrackGenerator_generateTracks(self) TrackGenerator_swigregister = _openmoc_bgq_single.TrackGenerator_swigregister TrackGenerator_swigregister(TrackGenerator) ON_LATTICE_CELL_THRESH = _openmoc_bgq_single.ON_LATTICE_CELL_THRESH TINY_MOVE = _openmoc_bgq_single.TINY_MOVE SIMPLE = _openmoc_bgq_single.SIMPLE LATTICE = _openmoc_bgq_single.LATTICE class Universe(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, Universe, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, Universe, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Universe(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Universe __del__ = lambda self : None; def addCell(self, *args, **kwargs): return _openmoc_bgq_single.Universe_addCell(self, *args, **kwargs) def getCell(self, *args, **kwargs): return _openmoc_bgq_single.Universe_getCell(self, *args, **kwargs) def getCells(self): return _openmoc_bgq_single.Universe_getCells(self) def getUid(self): return _openmoc_bgq_single.Universe_getUid(self) def getId(self): return _openmoc_bgq_single.Universe_getId(self) def getType(self): return _openmoc_bgq_single.Universe_getType(self) def getNumCells(self): return _openmoc_bgq_single.Universe_getNumCells(self) def getFSR(self, *args, **kwargs): return _openmoc_bgq_single.Universe_getFSR(self, *args, **kwargs) def getOrigin(self): return _openmoc_bgq_single.Universe_getOrigin(self) def getMaterialIds(self): return _openmoc_bgq_single.Universe_getMaterialIds(self) def getNestedUniverseIds(self): return _openmoc_bgq_single.Universe_getNestedUniverseIds(self) def getCellIds(self, *args, **kwargs): return _openmoc_bgq_single.Universe_getCellIds(self, *args, **kwargs) def isFissionable(self): return _openmoc_bgq_single.Universe_isFissionable(self) def setType(self, *args, **kwargs): return _openmoc_bgq_single.Universe_setType(self, *args, **kwargs) def setOrigin(self, *args, **kwargs): return _openmoc_bgq_single.Universe_setOrigin(self, *args, **kwargs) def setFissionability(self, *args, **kwargs): return _openmoc_bgq_single.Universe_setFissionability(self, *args, **kwargs) def findCell(self, *args, **kwargs): return _openmoc_bgq_single.Universe_findCell(self, *args, **kwargs) def computeFSRMaps(self): return _openmoc_bgq_single.Universe_computeFSRMaps(self) def subdivideCells(self): return _openmoc_bgq_single.Universe_subdivideCells(self) def toString(self): return _openmoc_bgq_single.Universe_toString(self) def printString(self): return _openmoc_bgq_single.Universe_printString(self) Universe_swigregister = _openmoc_bgq_single.Universe_swigregister Universe_swigregister(Universe) class Lattice(Universe): __swig_setmethods__ = {} for _s in [Universe]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, Lattice, name, value) __swig_getmethods__ = {} for _s in [Universe]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, Lattice, name) __repr__ = _swig_repr def __init__(self, *args, **kwargs): this = _openmoc_bgq_single.new_Lattice(*args, **kwargs) try: self.this.append(this) except: self.this = this __swig_destroy__ = _openmoc_bgq_single.delete_Lattice __del__ = lambda self : None; def getNumX(self): return _openmoc_bgq_single.Lattice_getNumX(self) def getNumY(self): return _openmoc_bgq_single.Lattice_getNumY(self) def getOrigin(self): return _openmoc_bgq_single.Lattice_getOrigin(self) def getUniverses(self): return _openmoc_bgq_single.Lattice_getUniverses(self) def getUniverse(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_getUniverse(self, *args, **kwargs) def getWidthX(self): return _openmoc_bgq_single.Lattice_getWidthX(self) def getWidthY(self): return _openmoc_bgq_single.Lattice_getWidthY(self) def getFSR(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_getFSR(self, *args, **kwargs) def getNestedUniverseIds(self): return _openmoc_bgq_single.Lattice_getNestedUniverseIds(self) def setLatticeCells(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_setLatticeCells(self, *args, **kwargs) def setUniversePointer(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_setUniversePointer(self, *args, **kwargs) def withinBounds(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_withinBounds(self, *args, **kwargs) def findCell(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_findCell(self, *args, **kwargs) def findNextLatticeCell(self, *args, **kwargs): return _openmoc_bgq_single.Lattice_findNextLatticeCell(self, *args, **kwargs) def computeFSRMaps(self): return _openmoc_bgq_single.Lattice_computeFSRMaps(self) def toString(self): return _openmoc_bgq_single.Lattice_toString(self) def printString(self): return _openmoc_bgq_single.Lattice_printString(self) Lattice_swigregister = _openmoc_bgq_single.Lattice_swigregister Lattice_swigregister(Lattice) # This file is compatible with both classic and new-style classes.

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null

0

1

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1

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5

c536b14f1525244574fca106ad6ff3a05154a32c

129

py

Python

authx/cache/__init__.py

theoohoho/authx

5c79983cedd4f33fe4c6c3d744c6857f830653fc

[ "MIT" ]

141

2021-11-08T18:59:57.000Z

2022-03-31T18:49:23.000Z

authx/cache/__init__.py

theoohoho/authx

5c79983cedd4f33fe4c6c3d744c6857f830653fc

[ "MIT" ]

85

2021-11-08T17:30:08.000Z

2022-03-24T18:23:51.000Z

authx/cache/__init__.py

theoohoho/authx

5c79983cedd4f33fe4c6c3d744c6857f830653fc

[ "MIT" ]

15

2021-11-09T00:01:13.000Z

2022-03-26T20:25:18.000Z

from authx.cache.config import basicConfig from authx.cache.redis import RedisBackend __all__ = ["RedisBackend", "basicConfig"]

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null

0

1

0

1

0

5

c54eba3f959dbfeab8babc52b8f50257695c23ae

86

py

Python

BluenetTestSuite/runtests.py

crownstone/bluenet-test-suite

2e1edcee18c0ee33231ea7a497c3165d69f016c8

[ "MIT" ]

null

BluenetTestSuite/runtests.py

crownstone/bluenet-test-suite

2e1edcee18c0ee33231ea7a497c3165d69f016c8

[ "MIT" ]

1

2021-02-15T10:55:27.000Z

BluenetTestSuite/runtests.py

crownstone/bluenet-test-suite

2e1edcee18c0ee33231ea7a497c3165d69f016c8

[ "MIT" ]

null

""" Runs all files in ./tests/ and collects stats on their successes. """ import sys

14.333333

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66

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1

0

true

0

1

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1

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0

null

0

1

0

1

0

1

0

5

c572b830a3f8b199f26eec2204c31528ba93dade

174

py

Python

tests/web_platform/CSS2/normal_flow/test_block_in_inline_insert_002_ref.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

71

2015-04-13T09:44:14.000Z

2019-03-24T01:03:02.000Z

tests/web_platform/CSS2/normal_flow/test_block_in_inline_insert_002_ref.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

35

2019-05-06T15:26:09.000Z

2022-03-28T06:30:33.000Z

tests/web_platform/CSS2/normal_flow/test_block_in_inline_insert_002_ref.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

139

2015-05-30T18:37:43.000Z

2019-03-27T17:14:05.000Z

from tests.utils import W3CTestCase class TestBlockInInlineInsert002Ref(W3CTestCase): vars().update(W3CTestCase.find_tests(__file__, 'block-in-inline-insert-002-ref'))

29

85

0.804598

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174

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0.85

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0.05625

0.08046

174

5

86

34.8

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0

1

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true

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0.333333

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0

null

0

1

0

1

0

1

0

5

c575eaa9155f837e1da6ef1b6a87650e317cc6c7

43

py

Python

central_server/api/__init__.py

BUPT-MAGA/central_server

1ecf0f2fe40651107fa010aac0e629b5b39b9fd0

[ "Apache-2.0" ]

null

central_server/api/__init__.py

BUPT-MAGA/central_server

1ecf0f2fe40651107fa010aac0e629b5b39b9fd0

[ "Apache-2.0" ]

null

central_server/api/__init__.py

BUPT-MAGA/central_server

1ecf0f2fe40651107fa010aac0e629b5b39b9fd0

[ "Apache-2.0" ]

null

from .slave import * from .center import *

14.333333

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6

43

5.166667

0.666667

0

0.186047

43

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22

21.5

0.885714

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3d6635c1b708d3ff65f86496d48f99a81be56387

26

py

Python

pgdiff/__init__.py

Onapsis/pgdiff

ee9f618bc339cbfaf7967103e95f9650273550f8

[ "MIT" ]

2

2020-05-11T16:42:48.000Z

2020-08-27T04:11:49.000Z

pgdiff/__init__.py

Onapsis/pgdiff

ee9f618bc339cbfaf7967103e95f9650273550f8

[ "MIT" ]

null

pgdiff/__init__.py

Onapsis/pgdiff

ee9f618bc339cbfaf7967103e95f9650273550f8

[ "MIT" ]

null

from .PgDiff import PgDiff

26

0.846154

4

26

5.5

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0

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26

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1

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null

0

1

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null

0

1

0

1

0

5

3d79c8931a6751d9c17bc142d541b260dfec66ab

36

py

Python

lib/Importer.py

assemblical/qtcreator_settings_sync

db2232a771fcfa597da6304566b500a203cd03c7

[ "MIT" ]

1

2016-06-15T14:43:35.000Z

lib/Importer.py

assemblical/qtcreator_settings_sync

db2232a771fcfa597da6304566b500a203cd03c7

[ "MIT" ]

null

lib/Importer.py

assemblical/qtcreator_settings_sync

db2232a771fcfa597da6304566b500a203cd03c7

[ "MIT" ]

null

# imports settings from exported dir

36

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0

null

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1

0

null

0

1

0

5

3d8cad379a2adca59dc59c3998c836db427c501d

3,356

py

Python

XYZHubConnector/modules/loader/space_loader.py

hererucis/xyz-qgis-plugin

5e1bd3a7a64b0bec8e36264b109155f6d90e87ea

[ "MIT" ]

1

2019-09-16T08:01:10.000Z

XYZHubConnector/xyz_qgis/loader/space_loader.py

deeplook/xyz-qgis-plugin

37b7d84992155fe35d9578b58c9d74a198eccb40

[ "MIT" ]

null

XYZHubConnector/xyz_qgis/loader/space_loader.py

deeplook/xyz-qgis-plugin

37b7d84992155fe35d9578b58c9d74a198eccb40

[ "MIT" ]

1

2019-09-13T15:05:17.000Z

# -*- coding: utf-8 -*- ############################################################################### # # Copyright (c) 2019 HERE Europe B.V. # # SPDX-License-Identifier: MIT # License-Filename: LICENSE # ############################################################################### from qgis.PyQt.QtCore import QThreadPool from ..controller import ChainController, NetworkFun, WorkerFun from ..network import net_handler class LoadSpaceController(ChainController): """ load space metadata (list space) Args: conn_info: token """ # Feature def __init__(self, network): super().__init__() self.pool = QThreadPool() # .globalInstance() will crash afterward self._config(network) def start(self, conn_info): super().start(conn_info) def _config(self, network): self.config_fun([ NetworkFun( network.list_spaces), WorkerFun( net_handler.on_received, self.pool), ]) class StatSpaceController(ChainController): """ get statistics of given space (count, byteSize, bbox) Args: conn_info: token """ # Feature def __init__(self, network): super().__init__() self.pool = QThreadPool() # .globalInstance() will crash afterward self._config(network) def start(self, conn_info): super().start(conn_info) def _config(self, network): self.config_fun([ NetworkFun( network.get_statistics), # NetworkFun( network.get_count), WorkerFun( net_handler.on_received, self.pool), ]) class DeleteSpaceController(ChainController): """ Delete space Args: conn_info: token + space_id """ # Feature def __init__(self, network): super().__init__() self.pool = QThreadPool() # .globalInstance() will crash afterward self._config(network) def start(self, conn_info): super().start(conn_info) def _config(self, network): self.config_fun([ NetworkFun( network.del_space), WorkerFun( net_handler.on_received, self.pool), ]) class EditSpaceController(ChainController): """ Edit space metadata Args: conn_info: token + space_id meta: new metadata/space_info (title, description) """ # Feature def __init__(self, network): super().__init__() self.pool = QThreadPool() # .globalInstance() will crash afterward self._config(network) def start(self, conn_info, meta): super().start(conn_info, meta) def _config(self, network): self.config_fun([ NetworkFun( network.edit_space), WorkerFun( net_handler.on_received, self.pool), ]) class CreateSpaceController(ChainController): """ Create new space Args: conn_info: token + space_id meta: new metadata/space_info (title, description) """ def __init__(self, network): super().__init__() self.pool = QThreadPool() # .globalInstance() will crash afterward self._config(network) def start(self, conn_info, meta): super().start(conn_info, meta) def _config(self, network): self.config_fun([ NetworkFun( network.add_space), WorkerFun( net_handler.on_received, self.pool), ])

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null

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1

0

5

3db92b912a585cb9f17302bc6c10d4b58c5ce247

189

py

Python

src/__init__.py

vaklyuenkov/classification

e2c008d47b608aaea428f4944a130161bcde0d1b

[ "Apache-2.0" ]

null

src/__init__.py

vaklyuenkov/classification

e2c008d47b608aaea428f4944a130161bcde0d1b

[ "Apache-2.0" ]

null

src/__init__.py

vaklyuenkov/classification

e2c008d47b608aaea428f4944a130161bcde0d1b

[ "Apache-2.0" ]

null

# flake8: noqa from catalyst.dl import registry from .experiment import Experiment from .runner import ModelRunner as Runner from .model import MultiHeadNet registry.Model(MultiHeadNet)

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0

null

0

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0

1

0

1

0

5

3dc12b281ce62b5f5649bf51e2c4c3d461be6879

104

py

Python

python/basic/importer.py

swenker/studio

298336a6327dbd3d496de6a9290d29ac8fe00514

[ "Apache-2.0" ]

1

2015-02-05T09:05:01.000Z

python/basic/importer.py

swenker/studio

298336a6327dbd3d496de6a9290d29ac8fe00514

[ "Apache-2.0" ]

null

python/basic/importer.py

swenker/studio

298336a6327dbd3d496de6a9290d29ac8fe00514

[ "Apache-2.0" ]

null

__author__ = 'samsung' import importee importee.init() print importee.a print importee._hide

11.555556

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0.721154

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104

5.833333

0.666667

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0

0.201923

104

9

24

11.555556

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0.072165

0

null

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0

1

0

null

0

1

0

1

0

5

3de8c2d588150168ee192459aac90ac03ecee1ef

160

py

Python

source/device_manager/tests/discovery_test.py

ElsevierSoftwareX/SOFTX-D-21-00051

7ae5ad60971129286e2ee452a797c26810a9cb0f

[ "MIT" ]

null

source/device_manager/tests/discovery_test.py

ElsevierSoftwareX/SOFTX-D-21-00051

7ae5ad60971129286e2ee452a797c26810a9cb0f

[ "MIT" ]

null

source/device_manager/tests/discovery_test.py

ElsevierSoftwareX/SOFTX-D-21-00051

7ae5ad60971129286e2ee452a797c26810a9cb0f

[ "MIT" ]

null

import os import sys sys.path.insert(0, os.path.abspath('.')) from source.device_manager.sila_auto_discovery.sila_auto_discovery import find servers = find()

20

78

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7

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0.00625

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0

1

0

1

0

5

9a8a1cd8b1db5a8cced377c0b4181ddbba1d6f56

80

py

Python

main.py

Halebane/Test_project

59d1c2a024cf9aae2b8e7d7517bc95ea19c952a1

[ "MIT" ]

null

main.py

Halebane/Test_project

59d1c2a024cf9aae2b8e7d7517bc95ea19c952a1

[ "MIT" ]

3

2022-02-21T11:15:15.000Z

2022-02-21T11:58:21.000Z

main.py

Halebane/Test_project

59d1c2a024cf9aae2b8e7d7517bc95ea19c952a1

[ "MIT" ]

null

print('hi all') print ('hello world') print('hi') print('hii') print('hello2')

11.428571

21

0.6375

12

80

4.25

0.583333

0.27451

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0.014085

0.1125

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7

22

11.428571

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0

0.345679

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9a8e897e6d7b3113e34dcc478e5b70fc503a51f0

178

py

Python

tardis/rest/hash_credentials/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

4

2018-05-22T13:22:06.000Z

2019-03-26T15:32:57.000Z

tardis/rest/hash_credentials/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

50

2018-05-18T11:46:39.000Z

2019-04-26T07:29:45.000Z

tardis/rest/hash_credentials/__main__.py

maxfischer2781/tardis

a83ba0a02d2f153a8ab95b84ec78bc6ababa57a5

[ "MIT" ]

2

2018-12-12T13:15:59.000Z

2018-12-17T08:18:15.000Z

from .hash_credentials import hash_credentials import typer def hash_credentials_cli(): typer.run(hash_credentials) if __name__ == "__main__": hash_credentials_cli()

16.181818

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0.775281

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178

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0.341463

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1

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null

0

1

0

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0

5

9a95386feeb6320db7799b5536c7b621fb56e7bf

8,271

py

Python

database/migrations/0003_auto_20180320_2058.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

1

2019-09-26T04:00:55.000Z

database/migrations/0003_auto_20180320_2058.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

2

2020-06-05T21:58:55.000Z

2021-06-10T21:45:08.000Z

database/migrations/0003_auto_20180320_2058.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

1

2019-09-26T03:55:06.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.5 on 2018-03-20 20:58 from __future__ import unicode_literals import database.validators from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('database', '0002_auto_20170923_1949'), ] operations = [ migrations.CreateModel( name='PipelineUpload', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('deleted', models.BooleanField(default=False)), ('created_on', models.DateTimeField(auto_now_add=True)), ('last_updated', models.DateTimeField(auto_now=True)), ('object_id', models.CharField(max_length=24, unique=True, validators=[database.validators.LengthValidator(24)])), ('file_name', models.TextField()), ('s3_path', models.TextField()), ('file_hash', models.CharField(max_length=128)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='PipelineUploadTags', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('deleted', models.BooleanField(default=False)), ('created_on', models.DateTimeField(auto_now_add=True)), ('last_updated', models.DateTimeField(auto_now=True)), ('tag', models.CharField(max_length=1024, validators=[database.validators.LengthValidator(1024)])), ('pipeline_upload', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='tags', to='database.PipelineUpload')), ], options={ 'abstract': False, }, ), migrations.AddField( model_name='chunkregistry', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='chunkregistry', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='decryptionkeyerror', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='decryptionkeyerror', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='devicesettings', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='devicesettings', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='encryptionerrormetadata', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='encryptionerrormetadata', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='fileprocesslock', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='fileprocesslock', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='filetoprocess', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='filetoprocess', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='lineencryptionerror', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='lineencryptionerror', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='participant', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='participant', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='researcher', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='researcher', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='study', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='study', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='survey', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='survey', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='surveyarchive', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='surveyarchive', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AddField( model_name='uploadtracking', name='created_on', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='uploadtracking', name='last_updated', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='chunkregistry', name='data_type', field=models.CharField(choices=[(b'accelerometer', b'accelerometer'), (b'bluetooth', b'bluetooth'), (b'calls', b'calls'), (b'gps', b'gps'), (b'identifiers', b'identifiers'), (b'app_log', b'app_log'), (b'power_state', b'power_state'), (b'survey_answers', b'survey_answers'), (b'survey_timings', b'survey_timings'), (b'texts', b'texts'), (b'audio_recordings', b'audio_recordings'), (b'wifi', b'wifi'), (b'proximity', b'proximity'), (b'gyro', b'gyro'), (b'magnetometer', b'magnetometer'), (b'devicemotion', b'devicemotion'), (b'reachability', b'reachability'), (b'ios_log', b'ios_log')], max_length=32), ), migrations.AddField( model_name='pipelineupload', name='study', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pipeline_uploads', to='database.Study'), ), ]

40.945545

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794

8,271

5.973552

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0.678684

0.666456

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0.008613

0.284125

8,271

201

613

41.149254

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null

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1

0

1

0

1

0

1

0

null

0

5

9ab669bff52da3b542216fac3731e365198d8ddc

51

py

Python

gym_cityflow/envs/__init__.py

MaxVanDijck/gym-cityflow

628ea37e48a3f639970801d22fe1d844a21ebc99

[ "MIT" ]

1

2021-08-04T21:40:53.000Z

gym_cityflow/envs/__init__.py

MaxVanDijck/gym-cityflow

628ea37e48a3f639970801d22fe1d844a21ebc99

[ "MIT" ]

null

gym_cityflow/envs/__init__.py

MaxVanDijck/gym-cityflow

628ea37e48a3f639970801d22fe1d844a21ebc99

[ "MIT" ]

null

from gym_cityflow.envs.cityflow_env import Cityflow

51

0.901961

8

51

5.5

0.75

0

0.058824

51

1

51

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9af2b0d5427f96e4f65518472ab8bed7816bc20b

76

py

Python

app/handlers/__init__.py

akrisanov/dataset-catalog

fa55ae2097dfd10f89021971ae9b14c830990c47

[ "MIT" ]

null

app/handlers/__init__.py

akrisanov/dataset-catalog

fa55ae2097dfd10f89021971ae9b14c830990c47

[ "MIT" ]

null

app/handlers/__init__.py

akrisanov/dataset-catalog

fa55ae2097dfd10f89021971ae9b14c830990c47

[ "MIT" ]

null

from .datasets import datasets_router from .services import services_router

25.333333

37

0.868421

10

76

6.4

0.5

0

0.105263

76

2

38

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b106993c0fc1404973a1c88f9f6016da9a468c68

404

py

Python

src/affe/utils.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

1

2020-12-02T06:16:00.000Z

src/affe/utils.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

null

src/affe/utils.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

null

import pandas as pd from .io.CTE import KEYCHAIN_SEPARATOR VERBOSITY = 1 def flatten_dict(d, separator=KEYCHAIN_SEPARATOR): return pd.json_normalize(d, sep=separator).to_dict(orient="records").pop() def keychain(*args, separator=KEYCHAIN_SEPARATOR): return separator.join(args) def debug_print(msg, level=1, V=VERBOSITY, **kwargs): if V >= level: print(msg + "\n") return

20.2

78

0.710396

57

404

4.912281

0.578947

0.182143

0.185714

0.228571

0

0.005935

0.165842

404

19

79

21.263158

0.824926

0

0.022277

0

1

0.272727

false

0

0.181818

0.727273

0.181818

0

null

0

1

0

null

0

1

0

1

0

5

b1240c319b7a8d48c0e54b56aff10d9f2a38bb86

2,489

py

Python

pyosim/static_optimization.py

sbourdon/pyosim

ce941432d47352c59eddf13f4ee949cca5cec60f

[ "Apache-2.0" ]

15

2018-05-26T00:07:57.000Z

2021-12-16T01:06:29.000Z

pyosim/static_optimization.py

sbourdon/pyosim

ce941432d47352c59eddf13f4ee949cca5cec60f

[ "Apache-2.0" ]

18

2018-04-05T13:59:55.000Z

2020-12-24T16:42:38.000Z

pyosim/static_optimization.py

pyomeca/pyosim

84b418ba276a9e5c499c44acd3fc4531103ad6c0

[ "Apache-2.0" ]

9

2018-10-10T11:30:49.000Z

2021-08-17T20:03:40.000Z

""" Static optimization class in pyosim """ from pyosim import AnalyzeTool class StaticOptimization(AnalyzeTool): """ Static Optimization in pyosim Parameters ---------- model_input : str Path to the osim model xml_input : str Path to the generic so xml xml_output : str Output path of the so xml xml_forces : str, optional Path to the generic forces sensor xml (Optional) ext_forces_dir : str, optional Path of the directory containing the external forces files (`.sto`) (Optional) muscle_forces_dir : str, optional Path of the directory containing the muscle forces files (`.sto`) (Optional) mot_files : str, Path, list Path or list of path to the directory containing the motion files (`.mot`) sto_output : Path, str Output directory xml_actuators: Path, str Actuators (Optional) prefix : str, optional Optional prefix to put in front of the output filename (typically model name) (Optional) low_pass : int, optional Cutoff frequency for an optional low pass filter on coordinates (Optional) remove_empty_files : bool, optional remove empty files i in `sto_output` if True (Optional) Examples -------- >>> from pathlib import Path >>> >>> from pyosim import StaticOptimization >>> >>> PROJECT_PATH = Path('../Misc/project_sample') >>> TEMPLATES_PATH = PROJECT_PATH / '_templates' >>> >>> participant = 'dapo' >>> model = 'wu' >>> trials = [ifile for ifile in (PROJECT_PATH / participant / '1_inverse_kinematic').glob('*.mot')] >>> >>> path_kwargs = { >>> 'model_input': f"{(PROJECT_PATH / participant / '_models' / model).resolve()}_scaled_markers.osim", >>> 'xml_input': f"{(TEMPLATES_PATH / model).resolve()}_so.xml", >>> 'xml_output': f"{(PROJECT_PATH / participant / '_xml' / model).resolve()}_so.xml", >>> 'xml_forces': f"{(TEMPLATES_PATH / 'forces_sensor.xml').resolve()}", >>> 'xml_actuators': f"{(TEMPLATES_PATH / f'{model}_actuators.xml').resolve()}", >>> 'ext_forces_dir': f"{(PROJECT_PATH / participant / '0_forces').resolve()}", >>> 'sto_output': f"{(PROJECT_PATH / participant / '3_static_optimization').resolve()}", >>> } >>> >>> StaticOptimization( >>> **path_kwargs, >>> mot_files=trials, >>> prefix=model, >>> low_pass=5 >>> ) """ pass

35.557143

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0.245078

2,489

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true

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null

0

1

0

null

0

1

0

1

0

5

b132a76689c95943a16253a38adba50636d09361

40

py

Python

test.py

ekutlu/raspberry-test

fc7a1f74764dc420f263de83b2eeb83110aff230

[ "MIT" ]

null

test.py

ekutlu/raspberry-test

fc7a1f74764dc420f263de83b2eeb83110aff230

[ "MIT" ]

null

test.py

ekutlu/raspberry-test

fc7a1f74764dc420f263de83b2eeb83110aff230

[ "MIT" ]

null

__author__ = 'emre' print "hello world"

13.333333

19

0.725

5

40

5

1

0

0.15

40

3

20

13.333333

0.735294

0

0.365854

0

null

0

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

b15686a64ddc5595d5b6bb95bf98a952650e456b

156

py

Python

cogs/utils/apis.py

bsquidwrd/Live-Bot

f28f028ddc371b86e19df6f603aa3b14bab93533

[ "MIT" ]

1

2019-02-27T10:38:46.000Z

cogs/utils/apis.py

bsquidwrd/Live-Bot

f28f028ddc371b86e19df6f603aa3b14bab93533

[ "MIT" ]

21

2017-08-03T01:01:31.000Z

2020-06-05T18:02:20.000Z

cogs/utils/apis.py

bsquidwrd/Live-Bot

f28f028ddc371b86e19df6f603aa3b14bab93533

[ "MIT" ]

null

class StreamAPI(object): 'https://www.googleapis.com/youtube/v3/search?part=snippet&channelId=[ChannelID]&type=video&eventType=live&key=[API_KEY]'

39

125

0.75

21

156

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0.076923

156

3

126

52

0.798611

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0

0.5

0.762821

0

1

0

true

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

b167579d44e76ef6cc1304432155211f1599a334

30

py

Python

build/v0.2.1/index.py

ryantwt07/Python-Math-Library

8320a73baf139e7ac0ba87ddcfa410af0dac8631

[ "MIT" ]

null

build/v0.2.1/index.py

ryantwt07/Python-Math-Library

8320a73baf139e7ac0ba87ddcfa410af0dac8631

[ "MIT" ]

null

build/v0.2.1/index.py

ryantwt07/Python-Math-Library

8320a73baf139e7ac0ba87ddcfa410af0dac8631

[ "MIT" ]

null

from lib import user user()

6

20

0.7

5

30

4.2

0.8

0

0.233333

30

4

21

7.5

0.913043

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b16da500a0f59e2f32cf245b40e907dc9eed5fd3

8,980

py

Python

tests/functional/test_queryset.py

timgates42/pyeqs

2e385c0a5d113af0e20be4d9393add2aabdd9565

[ "MIT" ]

null

tests/functional/test_queryset.py

timgates42/pyeqs

2e385c0a5d113af0e20be4d9393add2aabdd9565

[ "MIT" ]

null

tests/functional/test_queryset.py

timgates42/pyeqs

2e385c0a5d113af0e20be4d9393add2aabdd9565

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from sure import scenario from pyeqs import QuerySet from pyeqs.dsl import Term, ScriptScore, Sort from tests.helpers import prepare_data, cleanup_data, add_document @scenario(prepare_data, cleanup_data) def test_simple_search(context): """ Search with match_all query """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz"}) # And I do a search results = t[0:1] # Then I get a the expected results len(results).should.equal(1) results[0]['_source'].should.equal({"bar": "baz"}) @scenario(prepare_data, cleanup_data) def test_string_search(context): """ Search with string query """ # When create a queryset t = QuerySet("localhost", query="cheese", index="foo") # And there are records add_document("foo", {"bar": "banana"}) add_document("foo", {"bar": "cheese"}) # And I do a search results = t[0:10] # Then I get a the expected results len(results).should.equal(1) results[0]['_source'].should.equal({"bar": "cheese"}) @scenario(prepare_data, cleanup_data) def test_search_with_filter(context): """ Search with match_all query and filter """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz"}) add_document("foo", {"bar": "bazbaz"}) # And I do a search t.filter(Term("bar", "baz")) results = t[0:10] # Then I get a the expected results len(results).should.equal(1) results[0]['_source'].should.equal({"bar": "baz"}) @scenario(prepare_data, cleanup_data) def test_search_with_filter_and_scoring(context): """ Search with match_all query, filter and scoring """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz", "scoring_field": 1}) add_document("foo", {"bar": "baz", "scoring_field": 2}) add_document("foo", {"bar": "bazbaz", "scoring_field": 3}) # And I do a search t.filter(Term("bar", "baz")) score = ScriptScore("final_score = 0 + doc['scoring_field'].value;") t.score(score) results = t[0:10] # Then I get a the expected results len(results).should.equal(2) results[0]['_source'].should.equal({"bar": "baz", "scoring_field": 2}) results[1]['_source'].should.equal({"bar": "baz", "scoring_field": 1}) @scenario(prepare_data, cleanup_data) def test_search_with_scoring_min_score_and_track_scores(context): """ Search with match_all query and scoring with min_score and track_scores """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz", "scoring_field": 1}) add_document("foo", {"bar": "baz", "scoring_field": 2}) add_document("foo", {"bar": "baz", "scoring_field": 3}) # And I do a search score = ScriptScore("final_score = 0 + doc['scoring_field'].value;") t.score(score, min_score=2, track_scores=True) results = t[0:10] # Then I get a the expected results len(results).should.equal(2) results[0]['_source'].should.equal({"bar": "baz", "scoring_field": 3}) results[1]['_source'].should.equal({"bar": "baz", "scoring_field": 2}) @scenario(prepare_data, cleanup_data) def test_search_with_filter_and_scoring_and_sorting(context): """ Search with match_all query, filter, scoring, and sorting """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz", "scoring_field": 0, "sorting_field": 30}) add_document("foo", {"bar": "baz", "scoring_field": 1, "sorting_field": 20}) add_document("foo", {"bar": "baz", "scoring_field": 2, "sorting_field": 10}) add_document("foo", {"bar": "bazbaz", "scoring_field": 3, "sorting_field": 0}) # And I do a search t.filter(Term("bar", "baz")) score = ScriptScore("final_score = 0 + doc['scoring_field'].value;") t.score(score) sorting = Sort("sorting_field", order="desc") t.order_by(sorting) results = t[0:10] # Then I get a the expected results len(results).should.equal(3) results[0]['_source'].should.equal({"bar": "baz", "scoring_field": 0, "sorting_field": 30}) results[1]['_source'].should.equal({"bar": "baz", "scoring_field": 1, "sorting_field": 20}) results[2]['_source'].should.equal({"bar": "baz", "scoring_field": 2, "sorting_field": 10}) @scenario(prepare_data, cleanup_data) def test_search_with_filter_and_scoring_and_sorting_and_fields(context): """ Search with match_all query, filter, scoring, sorting, and fields """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz", "scoring_field": 0, "sorting_field": 30}) add_document("foo", {"bar": "baz", "scoring_field": 1, "sorting_field": 20}) add_document("foo", {"bar": "baz", "scoring_field": 2, "sorting_field": 10}) add_document("foo", {"bar": "bazbaz", "scoring_field": 3, "sorting_field": 0}) # And I do a search t.filter(Term("bar", "baz")) score = ScriptScore("final_score = 0 + doc['scoring_field'].value;") t.score(score) sorting = Sort("sorting_field", order="desc") t.order_by(sorting) t.only(["bar"]) results = t[0:10] # Then I get a the expected results len(results).should.equal(3) results[0]['fields'].should.equal({"bar": ["baz"]}) results[1]['fields'].should.equal({"bar": ["baz"]}) results[2]['fields'].should.equal({"bar": ["baz"]}) @scenario(prepare_data, cleanup_data) def test_wrappers(context): """ Search with wrapped match_all query """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": 1}) add_document("foo", {"bar": 2}) add_document("foo", {"bar": 3}) # And I do a search def wrapper_function(search_results): return list(map(lambda x: x['_source']['bar'] + 1, search_results)) t.wrappers(wrapper_function) t.order_by(Sort("bar", order="asc")) results = t[0:10] # Then I get a the expected results len(results).should.equal(3) results[0].should.equal(2) results[1].should.equal(3) results[2].should.equal(4) @scenario(prepare_data, cleanup_data) def test_search_as_queryset_with_filter(context): """ Search with match_all query and filter on a cloned queryset """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": "baz"}) add_document("foo", {"bar": "bazbaz"}) # And I do a filter on my new object my_search = t.objects.filter(Term("bar", "baz")) # And a different filter on my old object t.filter(Term("bar", "bazbaz")) # And I do a search results = my_search[0:10] # Then I get a the expected results len(results).should.equal(1) results[0]['_source'].should.equal({"bar": "baz"}) @scenario(prepare_data, cleanup_data) def test_search_with_iterator(context): """ Search using an iterator """ # When create a queryset t = QuerySet("localhost", index="foo") # And set iterator fetching to a small size t._per_request = 2 # And there are records add_document("foo", {"bar": 0}) add_document("foo", {"bar": 1}) add_document("foo", {"bar": 2}) add_document("foo", {"bar": 3}) add_document("foo", {"bar": 4}) # And I do a filter on my new object # And a different filter on my old object t.order_by(Sort("bar", order="asc")) # Then I get the expected results for (counter, result) in enumerate(t): result['_source'].should.equal({"bar": counter}) len(t).should.equal(5) t.count().should.equal(5) t.max_score().should_not.be(None) @scenario(prepare_data, cleanup_data) def test_post_query_actions(context): """ Search with match_all query with post query actions """ # When create a queryset t = QuerySet("localhost", index="foo") # And there are records add_document("foo", {"bar": 1}) add_document("foo", {"bar": 2}) add_document("foo", {"bar": 3}) # And I have a post query action global my_global_var my_global_var = 1 def action(self, results, start, stop): global my_global_var my_global_var += 1 t.post_query_actions(action) # And I do a search t.order_by(Sort("bar", order="asc")) results = t[0:10] # Then I get a the expected results len(results).should.equal(3) results[0]["_source"]["bar"].should.equal(1) results[1]["_source"]["bar"].should.equal(2) results[2]["_source"]["bar"].should.equal(3) my_global_var.should.equal(2)

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null

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5

b176a4acfa779ba34ebaa05209449e9361e85220

68

py

Python

src/vocles/__init__.py

NickleDave/vocles

897cff032af845b0a49af4a0bfdd1a3195b677a9

[ "BSD-3-Clause" ]

2

2022-01-17T14:43:38.000Z

2022-01-17T14:52:14.000Z

src/vocles/__init__.py

NickleDave/vocles

897cff032af845b0a49af4a0bfdd1a3195b677a9

[ "BSD-3-Clause" ]

2

2022-01-18T13:54:17.000Z

2022-01-21T13:41:02.000Z

src/vocles/__init__.py

NickleDave/vocles

897cff032af845b0a49af4a0bfdd1a3195b677a9

[ "BSD-3-Clause" ]

2

2022-02-12T13:48:51.000Z

2022-03-27T20:55:01.000Z

from .dataset import Dataset from .vocalization import Vocalization

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0

5

b1ab9fa9f5853d9e340df459d69cc78822aec02b

156

py

Python

boa3/model/builtin/interop/nativecontract/__init__.py

DanPopa46/neo3-boa

e4ef340744b5bd25ade26f847eac50789b97f3e9

[ "Apache-2.0" ]

null

boa3/model/builtin/interop/nativecontract/__init__.py

DanPopa46/neo3-boa

e4ef340744b5bd25ade26f847eac50789b97f3e9

[ "Apache-2.0" ]

null

boa3/model/builtin/interop/nativecontract/__init__.py

DanPopa46/neo3-boa

e4ef340744b5bd25ade26f847eac50789b97f3e9

[ "Apache-2.0" ]

null

from .CryptoLib import CryptoLibContract, CryptoLibMethod from .Oracle import OracleContract, OracleMethod from .StdLib import StdLibContract, StdLibMethod

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491dbf0d7ef595d9a7c58d13db06954d39c0efcd

352

py

Python

tests/entities/test_utils.py

Ahmed-Khaled-dev/modern-meerkats

e54304303c77928a2566f4e4c3eefb98135acfc7

[ "MIT" ]

1

2022-01-07T02:38:08.000Z

tests/entities/test_utils.py

Ahmed-Khaled-dev/modern-meerkats

e54304303c77928a2566f4e4c3eefb98135acfc7

[ "MIT" ]

null

tests/entities/test_utils.py

Ahmed-Khaled-dev/modern-meerkats

e54304303c77928a2566f4e4c3eefb98135acfc7

[ "MIT" ]

null

from app.entities.utils import get_loop_time def test_get_loop_time_returns_correct_time_value(): assert get_loop_time(2, 0) == 0 assert get_loop_time(2, 1) == 1 assert get_loop_time(2, 2) == 2 assert get_loop_time(2, 3) == 1 assert get_loop_time(2, 4) == 0 assert get_loop_time(2, 5) == 1 assert get_loop_time(2, 6) == 2

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95

py

Python

django_cradmin/apps/django_cradmin_js/__init__.py

appressoas/django_cradmin

0f8715afdfe1ad32e46033f442e622aecf6a4dec

[ "BSD-3-Clause" ]

11

2015-07-05T16:57:58.000Z

2020-11-24T16:58:19.000Z

django_cradmin/apps/django_cradmin_js/__init__.py

appressoas/django_cradmin

0f8715afdfe1ad32e46033f442e622aecf6a4dec

[ "BSD-3-Clause" ]

91

2015-01-08T22:38:13.000Z

2022-02-10T10:25:27.000Z

django_cradmin/apps/django_cradmin_js/__init__.py

appressoas/django_cradmin

0f8715afdfe1ad32e46033f442e622aecf6a4dec

[ "BSD-3-Clause" ]

3

2016-12-07T12:19:24.000Z

2018-10-03T14:04:18.000Z

# default_app_config = 'django_cradmin.apps.django_cradmin_js.apps.CradminJavascriptAppConfig'

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498b4db11b552f569d933b36e07f6aa906a8d8e7

101

py

Python

bots/__init__.py

mscroggs/MENACEpy

62adeb369b849bf0a067f7a0754995737b5cea86

[ "MIT" ]

2

2018-03-28T16:22:59.000Z

2020-01-08T16:02:29.000Z

bots/__init__.py

mscroggs/MENACEpy

62adeb369b849bf0a067f7a0754995737b5cea86

[ "MIT" ]

null

bots/__init__.py

mscroggs/MENACEpy

62adeb369b849bf0a067f7a0754995737b5cea86

[ "MIT" ]

1

2020-07-17T02:43:54.000Z

from .rando import Rando from .humans import QuiteGood, Good from .menace_killer import MenaceKiller

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null

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5

499bd2618b50afffd169f9549fcdd2132d8e2372

363

py

Python

examples/pipelayer_microservice/src/service/config/app_settings_factory.py

greater-than/PipeLayer

569f43b65992f8a32079835585b864d5fe0bb251

[ "BSD-2-Clause" ]

61

2021-02-03T02:54:18.000Z

2021-12-26T11:38:51.000Z

examples/pipelayer_microservice/src/service/config/app_settings_factory.py

greater-than/PipeLayer

569f43b65992f8a32079835585b864d5fe0bb251

[ "BSD-2-Clause" ]

1

2021-02-16T13:58:33.000Z

2021-02-18T12:56:32.000Z

examples/pipelayer_microservice/src/service/config/app_settings_factory.py

greater-than/PipeLayer

569f43b65992f8a32079835585b864d5fe0bb251

[ "BSD-2-Clause" ]

null

from typing import Any, Optional from pipelayer._patch.typing import Protocol from service.config.app_settings import AppSettings class ISettingsProvider(Protocol): def get(*args) -> Any: pass def create(provider: Optional[ISettingsProvider] = None) -> AppSettings: return AppSettings.parse_obj(provider.get()) if provider else AppSettings()

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0

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0

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0

null

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null

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0

1

0

5

b8c6e276c0543cc99e6dbaa57daeca5daee94dd3

394

py

Python

scrapyproject/models/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

2

2018-06-07T13:28:03.000Z

2018-12-10T14:04:53.000Z

scrapyproject/models/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

null

scrapyproject/models/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

null

""" Persist level for JapanCinemaStatusSpider """ from scrapyproject.models.models import (create_table, drop_table_if_exist, db_connect, Session) from scrapyproject.models.cinema import Cinema from scrapyproject.models.showing import Showing from scrapyproject.models.showing_booking import ShowingBooking from scrapyproject.models.movie import Movie

35.818182

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0

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1

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null

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1

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1

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5

b8f75b0025952cb1437d9c6ff962b821d5cc0ac8

51

py

Python

motionsaver/__init__.py

takeontom/motion-saver

743186dfb5c766a064d7892b54e82b08bfbbc657

[ "MIT" ]

null

motionsaver/__init__.py

takeontom/motion-saver

743186dfb5c766a064d7892b54e82b08bfbbc657

[ "MIT" ]

5

2021-03-18T20:44:06.000Z

2022-03-11T23:26:32.000Z

motionsaver/__init__.py

takeontom/motion-saver

743186dfb5c766a064d7892b54e82b08bfbbc657

[ "MIT" ]

null

from .motionsaver import MotionSaver # noqa: F401

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null

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0

1

0

1

0

5

7726f0ebd39f8eb5ff833acab30ad9291922372b

227

py

Python

syft/federated/__init__.py

abdulrahmanmazhar/PySyft

dc0ba29df83de3bd0cf59956b433b418a9731baa

[ "Apache-2.0" ]

null

syft/federated/__init__.py

abdulrahmanmazhar/PySyft

dc0ba29df83de3bd0cf59956b433b418a9731baa

[ "Apache-2.0" ]

1

2019-06-05T14:19:07.000Z

syft/federated/__init__.py

abmazhr/PySyft

dc0ba29df83de3bd0cf59956b433b418a9731baa

[ "Apache-2.0" ]

null

from syft.federated.plan import Plan from syft.federated.plan import func2plan from syft.federated.plan import method2plan from syft.federated.plan import make_plan __all__ = ["Plan", "func2plan", "method2plan", "make_plan"]

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null

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1

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5

772a75ab91757daebdd45b62ba757b5e7b491643

96

py

Python

venv/lib/python3.8/site-packages/setuptools/_deprecation_warning.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/setuptools/_deprecation_warning.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/setuptools/_deprecation_warning.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/8d/4f/7e/76d7efe9c2a6b5024e5cdf273f59a6ee038dc3990a12d88fb5bc276722

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0

1

0

null

1

0

1

0

5

77555cff7b1671ec1f5d37ac5180262645dc8ba3

1,880

py

Python

test-http/src/test/cve_tests/cve_as_org_admin.py

cristina479/cve-services

16794c7e04d7385ab0493bc9f838526c1401424d

[ "CC0-1.0" ]

null

test-http/src/test/cve_tests/cve_as_org_admin.py

cristina479/cve-services

16794c7e04d7385ab0493bc9f838526c1401424d

[ "CC0-1.0" ]

16

2021-05-14T23:52:16.000Z

2022-03-29T22:14:29.000Z

test-http/src/test/cve_tests/cve_as_org_admin.py

cristina479/cve-services

16794c7e04d7385ab0493bc9f838526c1401424d

[ "CC0-1.0" ]

null

import json import requests from src import env from src.utils import response_contains_json CVE_URL = '/api/cve' cve_id = 'CVE-1999-0001' update_cve_id = create_cve_id = 'CVE-2000-0008' #### GET /cve #### def test_get_all_cves(org_admin_headers): """ services api rejects requests for admin orgs """ res = requests.get( f'{env.AWG_BASE_URL}{CVE_URL}/', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') #### GET /cve/:id #### def test_get_cve(org_admin_headers): """ services api rejects requests for admin orgs """ res = requests.get( f'{env.AWG_BASE_URL}{CVE_URL}/{cve_id}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') #### POST /cve/:id #### def test_create_cve(org_admin_headers): """ services api rejects requests for admin orgs """ with open('./src/test/cve_tests/cve_record_fixtures/CVE-2000-0008_public.json') as json_file: data = json.load(json_file) res = requests.post( f'{env.AWG_BASE_URL}{CVE_URL}/{create_cve_id}', headers=org_admin_headers, json=data ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') #### PUT /cve/:id #### def test_update_cve_record(org_admin_headers): """ services api rejects requests for admin orgs """ with open('./src/test/cve_tests/cve_record_fixtures/CVE-2000-0008_public.json') as json_file: data = json.load(json_file) res = requests.put( f'{env.AWG_BASE_URL}{CVE_URL}/{update_cve_id}', headers=org_admin_headers, json=data ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY')

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null

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1

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null

0

5

620b3979b51a5f0c6be27adac0783dcff77e73dc

715

py

Python

COMP0016_2020_21_Team12-datasetsExperimentsAna/pwa/FADapp/pythonScripts/venv/Lib/site-packages/numpy/typing/tests/data/reveal/modules.py

anzhao920/MicrosoftProject15_Invictus

15f44eebb09561acbbe7b6730dfadf141e4c166d

[ "MIT" ]

null

COMP0016_2020_21_Team12-datasetsExperimentsAna/pwa/FADapp/pythonScripts/venv/Lib/site-packages/numpy/typing/tests/data/reveal/modules.py

anzhao920/MicrosoftProject15_Invictus

15f44eebb09561acbbe7b6730dfadf141e4c166d

[ "MIT" ]

null

COMP0016_2020_21_Team12-datasetsExperimentsAna/pwa/FADapp/pythonScripts/venv/Lib/site-packages/numpy/typing/tests/data/reveal/modules.py

anzhao920/MicrosoftProject15_Invictus

15f44eebb09561acbbe7b6730dfadf141e4c166d

[ "MIT" ]

1

2021-04-26T22:41:56.000Z

import numpy as np reveal_type(np) # E: ModuleType reveal_type(np.char) # E: ModuleType reveal_type(np.ctypeslib) # E: ModuleType reveal_type(np.emath) # E: ModuleType reveal_type(np.fft) # E: ModuleType reveal_type(np.lib) # E: ModuleType reveal_type(np.linalg) # E: ModuleType reveal_type(np.ma) # E: ModuleType reveal_type(np.matrixlib) # E: ModuleType reveal_type(np.polynomial) # E: ModuleType reveal_type(np.random) # E: ModuleType reveal_type(np.rec) # E: ModuleType reveal_type(np.testing) # E: ModuleType reveal_type(np.version) # E: ModuleType # TODO: Remove when annotations have been added to `np.testing.assert_equal` reveal_type(np.testing.assert_equal) # E: Any

34.047619

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0.73007

107

715

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0.592079

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0.156643

715

20

78

35.75

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true

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null

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null

0

1

0

1

0

5

622f836838574698c936717caf8d43fcf788b010

103

py

Python

edmondsonline/covid/__init__.py

khammrich/Prod-EdmondsOnline

5bc2771fee4086cae72fb250d658c4d622455428

[ "MIT" ]

null

edmondsonline/covid/__init__.py

khammrich/Prod-EdmondsOnline

5bc2771fee4086cae72fb250d658c4d622455428

[ "MIT" ]

1

2021-06-02T01:22:10.000Z

edmondsonline/covid/__init__.py

khammrich/Prod-EdmondsOnline

5bc2771fee4086cae72fb250d658c4d622455428

[ "MIT" ]

null

from flask import Blueprint bp = Blueprint('covid', __name__) from edmondsonline.covid import routes

17.166667

38

0.796117

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103

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0.135922

103

5

39

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null

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5

625380fda66bfd773001b17c8ee52fc48c200bec

209

py

Python

terzani/utils/types.py

JanMaxime/terzani_colorization

6538e0053c9119b1bf67da930f309b22cbdece30

[ "MIT" ]

null

terzani/utils/types.py

JanMaxime/terzani_colorization

6538e0053c9119b1bf67da930f309b22cbdece30

[ "MIT" ]

1

2020-12-16T14:16:16.000Z

2020-12-24T10:35:27.000Z

terzani/utils/types.py

JanMaxime/terzani_colorization

6538e0053c9119b1bf67da930f309b22cbdece30

[ "MIT" ]

null

class IIIF_Photo(object): def __init__(self, iiif, country): self.iiif = iiif self.country = country def get_photo_link(self): return self.iiif["images"][0]["resource"]["@id"]

26.125

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209

4.518519

0.555556

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209

7

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0.333333

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null

0

1

0

null

0

1

0

1

0

5

626458315526f01ec2c87c0b7d076c3552b41667

36

py

Python

cheope/dace/__init__.py

tiziano1590/cheops_analysis-package

d9e84ad43bfafd1f12a340dcaed89d4dad6b876b

[ "BSD-3-Clause" ]

1

2021-12-20T15:14:06.000Z

cheope/dace/__init__.py

tiziano1590/cheops_analysis-package

d9e84ad43bfafd1f12a340dcaed89d4dad6b876b

[ "BSD-3-Clause" ]

null

cheope/dace/__init__.py

tiziano1590/cheops_analysis-package

d9e84ad43bfafd1f12a340dcaed89d4dad6b876b

[ "BSD-3-Clause" ]

null

from .dace_search import DACESearch

18

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36

6

1

0

0.111111

36

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36

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1

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1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

62831dd496dad011ad0638fed7bad87dd5431cc0

181

py

Python

Training Modules/01 Introduction/1.09 Logical Operators/solution.py

cocobaco/Python3-by-practice

9255573ee7e68d20cde7a75e36b6bf5a8eb5b1f2

[ "Apache-2.0" ]

54

2020-07-20T13:52:50.000Z

2021-11-07T20:38:47.000Z

Training Modules/01 Introduction/1.09 Logical Operators/solution.py

cocobaco/Python3-by-practice

9255573ee7e68d20cde7a75e36b6bf5a8eb5b1f2

[ "Apache-2.0" ]

83

2020-09-25T15:21:55.000Z

2021-10-02T04:58:54.000Z

Training Modules/01 Introduction/1.09 Logical Operators/solution.py

cocobaco/Python3-by-practice

9255573ee7e68d20cde7a75e36b6bf5a8eb5b1f2

[ "Apache-2.0" ]

74

2020-09-25T19:16:29.000Z

2021-10-02T04:58:19.000Z

print (True and True) print (True and False) print (False and True) print (False and False) print (True or True) print (True or False) print (False or True) print (False or False)

18.1

23

0.729282

32

181

4.125

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0.272727

0.181818

0

0.18232

181

9

24

20.111111

0.891892

0

1

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true

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