xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

15ccf0ad411c06a2a10f53568a644a0ac7444a57

175

py

Python

lickport_array_interface/__init__.py

peterpolidoro/lickport_array_python

380eafdbe010f5f94230cac5332a3094d833f94f

[ "BSD-3-Clause" ]

null

lickport_array_interface/__init__.py

peterpolidoro/lickport_array_python

380eafdbe010f5f94230cac5332a3094d833f94f

[ "BSD-3-Clause" ]

null

lickport_array_interface/__init__.py

peterpolidoro/lickport_array_python

380eafdbe010f5f94230cac5332a3094d833f94f

[ "BSD-3-Clause" ]

1

2021-10-01T18:51:17.000Z

''' This Python package (lickport_array_interface) creates a class named LickportArrayInterface. ''' from .lickport_array_interface import LickportArrayInterface, __version__

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py

Python

tests/interpreter/test_gradcam.py

Christophe-Jia/InterpretDL

5736cb880d3c9bd79241d2ea6cb0490d9e8b089d

[ "Apache-2.0" ]

107

2020-07-02T14:25:01.000Z

2022-03-31T18:49:01.000Z

tests/interpreter/test_gradcam.py

Christophe-Jia/InterpretDL

5736cb880d3c9bd79241d2ea6cb0490d9e8b089d

[ "Apache-2.0" ]

22

2020-07-28T01:57:21.000Z

2022-03-31T07:51:36.000Z

tests/interpreter/test_gradcam.py

Christophe-Jia/InterpretDL

5736cb880d3c9bd79241d2ea6cb0490d9e8b089d

[ "Apache-2.0" ]

23

2020-07-10T05:08:39.000Z

2022-03-31T10:00:04.000Z

import unittest from paddle.vision.models import mobilenet_v2 import numpy as np from paddle.vision.models.resnet import resnet50 import interpretdl as it from tests.utils import assert_arrays_almost_equal class TestGradCAM(unittest.TestCase): def test_cv(self): paddle_model = mobilenet_v2(pretrained=True) img_path = 'imgs/catdog.jpg' algo = it.GradCAMInterpreter(paddle_model, use_cuda=False) exp = algo.interpret(img_path, 'features.18.2', visual=False) result = np.array([exp.mean(), exp.std(), exp.min(), exp.max(), *exp.shape]) desired = np.array([7.08578909e-06, 9.28105146e-06, 0.00000000e+00, 3.74892770e-05, 1.00000000e+00, 7.00000000e+00, 7.00000000e+00]) assert_arrays_almost_equal(self, result, desired) def test_cv_class(self): paddle_model = mobilenet_v2(pretrained=True) img_path = 'imgs/catdog.jpg' algo = it.GradCAMInterpreter(paddle_model, use_cuda=False) exp = algo.interpret(img_path, 'features.18.2', label=282, visual=False) result = np.array([exp.mean(), exp.std(), exp.min(), exp.max(), *exp.shape]) desired = np.array([5.12873930e-06, 7.74075761e-06, 0.00000000e+00, 2.88265182e-05, 1.00000000e+00, 7.00000000e+00, 7.00000000e+00]) assert_arrays_almost_equal(self, result, desired) def test_cv_layer(self): paddle_model = mobilenet_v2(pretrained=True) img_path = 'imgs/catdog.jpg' algo = it.GradCAMInterpreter(paddle_model, use_cuda=False) exp = algo.interpret(img_path, 'features.16.conv.3', visual=False) result = np.array([exp.mean(), exp.std(), exp.min(), exp.max(), *exp.shape]) desired = np.array([2.97199367e-05, 3.79896701e-05, 0.00000000e+00, 1.25247447e-04, 1.00000000e+00, 7.00000000e+00, 7.00000000e+00]) assert_arrays_almost_equal(self, result, desired) def test_cv_layer_2(self): paddle_model = mobilenet_v2(pretrained=True) img_path = 'imgs/catdog.jpg' algo = it.GradCAMInterpreter(paddle_model, use_cuda=False) exp = algo.interpret(img_path, 'features.8.conv.3', visual=False) result = np.array([exp.mean(), exp.std(), exp.min(), exp.max(), *exp.shape]) desired = np.array([1.13254619e-05, 1.62324668e-05, 0.00000000e+00, 6.76311683e-05, 1.00000000e+00, 1.40000000e+01, 1.40000000e+01]) assert_arrays_almost_equal(self, result, desired, 2e-3) def test_cv_multiple_inputs(self): paddle_model = mobilenet_v2(pretrained=True) img_path = ['imgs/catdog.jpg', 'imgs/catdog.jpg'] algo = it.GradCAMInterpreter(paddle_model, use_cuda=False) exp = algo.interpret(img_path, 'features.18.2', visual=False) result = np.array([exp.mean(), exp.std(), exp.min(), exp.max(), *exp.shape]) desired = np.array([7.08578864e-06, 9.28105146e-06, 0.00000000e+00, 3.74892770e-05, 2.00000000e+00, 7.00000000e+00, 7.00000000e+00]) assert_arrays_almost_equal(self, result, desired) if __name__ == '__main__': unittest.main()

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Python

bindings/pydairlib/multibody/__init__.py

DavidDePauw1/dairlib

3c75c8f587927b12a58f2e88dda61cc0e7dc82a3

[ "BSD-3-Clause" ]

32

2019-04-15T03:10:26.000Z

2022-03-28T17:27:03.000Z

bindings/pydairlib/multibody/__init__.py

DavidDePauw1/dairlib

3c75c8f587927b12a58f2e88dda61cc0e7dc82a3

[ "BSD-3-Clause" ]

157

2019-02-21T03:13:57.000Z

2022-03-09T19:13:59.000Z

bindings/pydairlib/multibody/__init__.py

DavidDePauw1/dairlib

3c75c8f587927b12a58f2e88dda61cc0e7dc82a3

[ "BSD-3-Clause" ]

22

2019-03-02T22:31:42.000Z

2022-03-10T21:28:50.000Z

# Importing everything in this directory to this package from .multibody import *

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d6cf665c210e285ad471f8209e2220b1dc0d3b18

231

py

Python

8kyu/Sum of positive.py

walkgo/codewars_tasks

4c0ab6f0e1d2181318fc15b12dd55ef565ecd223

[ "MIT" ]

null

8kyu/Sum of positive.py

walkgo/codewars_tasks

4c0ab6f0e1d2181318fc15b12dd55ef565ecd223

[ "MIT" ]

null

8kyu/Sum of positive.py

walkgo/codewars_tasks

4c0ab6f0e1d2181318fc15b12dd55ef565ecd223

[ "MIT" ]

null

def positive_sum(arr): positive_list = [] for i in arr: if i > 0: positive_list.append(i) return(sum(positive_list)) # Best Practices def positive_sum(arr): return sum(x for x in arr if x > 0)

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d6eba3ce3823719c04ea0546f80127e90d56a123

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py

Python

doudian/__init__.py

minibear2021/doudian

c770299dd2bd92814851de6e0f73b2c18c71d130

[ "MIT" ]

5

2021-12-01T16:05:16.000Z

2022-03-11T10:19:10.000Z

doudian/__init__.py

minibear2021/doudian

c770299dd2bd92814851de6e0f73b2c18c71d130

[ "MIT" ]

null

doudian/__init__.py

minibear2021/doudian

c770299dd2bd92814851de6e0f73b2c18c71d130

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .core import DouDian from .exception import CodeError, ShopIdError, TokenError from .type import AppType

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py

Python

lambdata/__init__.py

PatrickRaborn/Lambdata

4799075fda0db5e7c25e9ed6d92a353063ea14d8

[ "MIT" ]

null

lambdata/__init__.py

PatrickRaborn/Lambdata

4799075fda0db5e7c25e9ed6d92a353063ea14d8

[ "MIT" ]

null

lambdata/__init__.py

PatrickRaborn/Lambdata

4799075fda0db5e7c25e9ed6d92a353063ea14d8

[ "MIT" ]

null

"""lambdata - A collections of DS helper functions""" import pandas as pd import numpy as np def increment(x): return x + 1 COLORS =['Blue', 'Mauve', 'Cyan', 'Teal'] def df_cleaner(df): '''Cleans pd.DataFrame''' # TODO - Implement pass

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ba3d99d3abec6f230fa43db72127273ec053d09d

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py

Python

step 319.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

null

step 319.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

null

step 319.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

1

2020-09-11T16:05:46.000Z

import math print (math.sqrt(64)) import random print (random.randint(0,100))

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py

Python

notes/components/__init__.py

diefenbach/cba-notes

48adfa6fa98246212fcfe350f3b9392ec44ad3ef

[ "BSD-3-Clause" ]

null

notes/components/__init__.py

diefenbach/cba-notes

48adfa6fa98246212fcfe350f3b9392ec44ad3ef

[ "BSD-3-Clause" ]

null

notes/components/__init__.py

diefenbach/cba-notes

48adfa6fa98246212fcfe350f3b9392ec44ad3ef

[ "BSD-3-Clause" ]

null

from . login import Login from . main_menu import MainMenu from . note_edit import NoteEdit from . note_display import NoteDisplay from . tag_explorer import TagExplorer

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py

Python

AntPool/__init__.py

cclauss/AntPool

a28e8f5e93801166ab7402793d49292effdb0dc3

[ "MIT" ]

3

2022-03-20T02:15:50.000Z

2022-03-22T22:39:17.000Z

AntPool/__init__.py

cclauss/AntPool

a28e8f5e93801166ab7402793d49292effdb0dc3

[ "MIT" ]

1

2022-03-23T09:43:31.000Z

AntPool/__init__.py

cclauss/AntPool

a28e8f5e93801166ab7402793d49292effdb0dc3

[ "MIT" ]

1

2022-03-20T06:31:40.000Z

from AntPool.AntPool import AntPoolExecutor, __version__, __author__

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ba6ed4d11cec8302e387de32994ab02c2e93cf08

5,935

py

Python

huya.py

Ltre/SomeUseful

a03009ebd8c8080abdab5d639dede4c638e32f62

[ "MIT" ]

1

2021-01-03T07:45:25.000Z

huya.py

Ltre/SomeUseful

a03009ebd8c8080abdab5d639dede4c638e32f62

[ "MIT" ]

null

huya.py

Ltre/SomeUseful

a03009ebd8c8080abdab5d639dede4c638e32f62

[ "MIT" ]

null

import requests,os,time import toml def get_headers(header_raw): return dict(line.split(": ", 1) for line in header_raw.split("\n") if line != '') def get_cookies(cookie_raw): return dict(line.split("=", 1) for line in cookie_raw.split("; ")) '''hcookies = {"Cookie": "SoundValue=0.50; alphaValue=0.80; __yamid_tt1=0.5630173980060627; __yamid_new=C8736F6698800001A3314BF01CD08350; udb_guiddata=4d0af64ce63b43f29a7a5975d914b205; first_username_flag=35184377273454hy_first_1; udb_accdata=undefined; Hm_lvt_51700b6c722f5bb4cf39906a596ea41f=1576679026,1576732023,1577338814,1577958774; guid=0ad6867c39df195e6201245925596308; udb_passdata=3; __yasmid=0.5630173980060627; isInLiveRoom=true; web_qrlogin_confirm_id=7c2b76b3-3478-4027-8623-8a781b8bdb42; udb_other=%7B%22lt%22%3A%221583155678597%22%2C%22isRem%22%3A%221%22%7D; udb_uid=1199513272235; yyuid=1199513272235; udb_passport=35184377273454hy; username=35184377273454hy; udb_version=1.0; udb_origin=0; udb_status=1; rep_cnt=17; h_unt=1583155740; __yaoldyyuid=1199513272235; _yasids=__rootsid%3DC8D04226B2600001E867E8201711C0A0; huya_flash_rep_cnt=16; udb_biztoken=AQCPgSb_RAp2Lkq_LRPzj-_3SooD7ucFltQSNoc09Z6JU3sujkVXK9djBMBhMuMScB6e27Y9xm4GX-U-j5aUATaeg26L4-ghXpi0qjWVMLkx0oC7WNpy2LIrs6RC9e6Z4UM3b0EkQEooqZDHMPRs6eiVfRvCtOuYqQjLoCUCxLtrSKPwC2Fsso4qAZFonDKQijGLUuD8WsAmj8kYe4T3XQ77DF15J0UEJPTi8iLWHmqCjLq3Sn4ewLBV8rInE6gyW7KVR398oLwQHTJJIqaPNQnG3eBTdFxqe3Pk2hcWfrjeBtlek34BpsyOap59iH6fn6rFgQTTW0ZnTt4-_FohOZAb; PHPSESSID=8q4kdj213rm41gjatak7lhg383; undefined=undefined; huya_web_rep_cnt=16" }''' hcookies_raw = toml.load("/root/u/huya.conf")['hcookies_raw'] hcookies=get_cookies(hcookies_raw) headers={"Accept":"application/json, text/javascript, */*; q=0.01","User-Agent":"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/534.20 (KHTML, like Gecko) Chrome/11.0.672.0 Safari/534.20 QBWebViewUA/2 QBWebViewType/1 WKType/1","Referer":"http//i.huya.com/","Accept-Language":"zh-cn"} url = 'https://fw.huya.com/dispatch?do=subscribeList&uid=1199513272235&page=1&pageSize=1000' if not os.path.exists('huser.txt'): os.makedirs('huser.txt') namelist = open('huser.txt').read().splitlines() if namelist: print(len(namelist)) upurl='https://udblgn.huya.com/web/cookieExchange' upheaders_raw='''user-agent: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36 content-type: application/json accept: */* origin: https://udblgn.huya.com sec-fetch-site: same-origin sec-fetch-mode: cors referer: https://udblgn.huya.com/proxy.html''' upheaders=get_headers(upheaders_raw) d={"uri":20009,"version":"1.0","context":"1","appId":"5002","lcid":"2052","data":{"info":"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"}} s=requests.session() s.cookies.update(hcookies) while 1: try: r = s.get(url,timeout=10) data = r.json()['result'] dlist = data['list'] liveCount = data['liveCount'] with open('huser.txt','a') as f: for i in dlist: name = str(i['profileRoom']) if name not in namelist: print(time.strftime('%Y_%m_%d-%H:%M:%S'),name) f.write(name) f.write('\n') if not name in namelist: namelist.append(name) except Exception as e: if not 'time' in str(e): print(e,r.json()) while 1: try: r = s.post(upurl,headers=upheaders,data=d,allow_redirects=False,timeout=10) print(r.status_code,r.headers) break except: print('登录失败') finally: r.close() time.sleep(5)

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ba7926b5ec5d11b5c5fe529b4b531ee228243847

39

py

Python

custom_components/classificationbox/__init__.py

LucaKaufmann/HomeAssistant-Config

3be0ab0a91a2ff188abf1e0a9d0dd4dea7d30d45

[ "MIT" ]

19

2018-05-30T08:07:26.000Z

2020-11-29T13:31:20.000Z

custom_components/classificationbox/__init__.py

LucaKaufmann/Home-AssistantConfig

3be0ab0a91a2ff188abf1e0a9d0dd4dea7d30d45

[ "MIT" ]

6

2018-05-30T17:56:20.000Z

2022-03-14T12:07:42.000Z

custom_components/classificationbox/__init__.py

LucaKaufmann/Home-AssistantConfig

3be0ab0a91a2ff188abf1e0a9d0dd4dea7d30d45

[ "MIT" ]

7

2018-07-25T09:56:54.000Z

2022-03-14T11:59:37.000Z

"""The classificationbox component."""

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ba7a028a544be2bb9a1f6b38fd1f8856465ebe85

430

py

Python

repositorybots/events/IssueEvent.py

conda/conda-bots

a68cff7b0318093328e355e18871518c050f5493

[ "BSD-3-Clause" ]

2

2021-09-27T02:29:26.000Z

2021-10-20T19:10:39.000Z

repositorybots/events/IssueEvent.py

conda/conda-bots

a68cff7b0318093328e355e18871518c050f5493

[ "BSD-3-Clause" ]

14

2021-09-09T21:16:05.000Z

2022-03-28T09:31:09.000Z

repositorybots/events/IssueEvent.py

conda/conda-bots

a68cff7b0318093328e355e18871518c050f5493

[ "BSD-3-Clause" ]

2

2021-09-09T12:11:48.000Z

2022-01-28T20:25:26.000Z

from abc import ABC, abstractmethod class IssueEvent(ABC): @property @abstractmethod def github_conn(self): pass @property @abstractmethod def event_body(self): pass @abstractmethod def get_pull_request_author(self): pass @abstractmethod def add_comment(self, comment_body): pass @abstractmethod def add_label(self, label_name): pass

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5

ba85cc834390d71bcb89c34cf27ebb83c9e68da1

152

py

Python

sampleapi/books/admin.py

zachtib/SampleApi

becdae90501af62d655ffb6fe66719d519f37ccb

[ "Apache-2.0" ]

1

2016-10-05T19:13:05.000Z

sampleapi/books/admin.py

zachtib/SampleApi

becdae90501af62d655ffb6fe66719d519f37ccb

[ "Apache-2.0" ]

null

sampleapi/books/admin.py

zachtib/SampleApi

becdae90501af62d655ffb6fe66719d519f37ccb

[ "Apache-2.0" ]

null

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

16.888889

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5.454545

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152

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1

0

null

0

1

0

1

0

5

bae5caf131b8f7e02ea76b01a1df77510179fefc

69

py

Python

UVRatio/ui/__init__.py

chrisdevito/UVRatio

10411e07d2de47ee760996db484a8185323b63cc

[ "MIT" ]

null

UVRatio/ui/__init__.py

chrisdevito/UVRatio

10411e07d2de47ee760996db484a8185323b63cc

[ "MIT" ]

null

UVRatio/ui/__init__.py

chrisdevito/UVRatio

10411e07d2de47ee760996db484a8185323b63cc

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from UVRatio.ui import ui

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1

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5

baf4e55bff83bb830427d583fc4923d31c074397

135

py

Python

tests/inner_tests/fixture_hook/test_fixture_hook_examples.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

tests/inner_tests/fixture_hook/test_fixture_hook_examples.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

tests/inner_tests/fixture_hook/test_fixture_hook_examples.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

import pytest @pytest.fixture def fixture_to_filter_out(): pass def test_using_fixture(fixture_to_filter_out): assert True

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135

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1

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null

0

1

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1

0

5

24078583aeebc925b2bf44e90ccd4c75785ac7ca

203

py

Python

fastapi_depends/__init__.py

troyan-dy/fastapi-depends

aa42aec82e36cc7be0ddc5a51a331563ac412708

[ "MIT" ]

null

fastapi_depends/__init__.py

troyan-dy/fastapi-depends

aa42aec82e36cc7be0ddc5a51a331563ac412708

[ "MIT" ]

null

fastapi_depends/__init__.py

troyan-dy/fastapi-depends

aa42aec82e36cc7be0ddc5a51a331563ac412708

[ "MIT" ]

1

2022-03-02T19:38:55.000Z

from fastapi_depends.dep_container import DepContainer from fastapi_depends.fake_request import FakeRequest from fastapi_depends.simple import inject __all__ = ("FakeRequest", "inject", "DepContainer")

33.833333

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null

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5

24253d68faf65d827fff17d1bf9982957787807a

1,338

py

Python

scripts/field/angelic_tutoA.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/field/angelic_tutoA.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/field/angelic_tutoA.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# Created by MechAviv # Map ID :: 940012010 # Hidden Street : Decades Later sm.curNodeEventEnd(True) sm.setTemporarySkillSet(0) sm.setInGameDirectionMode(True, True, False, False) sm.removeSkill(60011219) if not "1" in sm.getQRValue(25807): sm.levelUntil(10) sm.setJob(6500) sm.createQuestWithQRValue(25807, "1") sm.resetStats() # Unhandled Stat Changed [HP] Packet: 00 00 00 04 00 00 00 00 00 00 C2 00 00 00 FF 00 00 00 00 # Unhandled Stat Changed [MHP] Packet: 00 00 00 08 00 00 00 00 00 00 C2 00 00 00 FF 00 00 00 00 # Unhandled Stat Changed [MMP] Packet: 00 00 00 20 00 00 00 00 00 00 71 00 00 00 FF 00 00 00 00 # Unhandled Stat Changed [MHP] Packet: 00 00 00 08 00 00 00 00 00 00 58 01 00 00 FF 00 00 00 00 # Unhandled Stat Changed [HP] Packet: 00 00 00 04 00 00 00 00 00 00 58 01 00 00 FF 00 00 00 00 sm.addSP(5, True) # [INVENTORY_GROW] [01 1C ] # [INVENTORY_GROW] [02 1C ] # [INVENTORY_GROW] [03 1C ] # [INVENTORY_GROW] [04 1C ] sm.giveSkill(60011216, 1, 1) sm.giveSkill(60011218, 1, 1) sm.giveSkill(60011220, 1, 1) sm.giveSkill(60011222, 1, 1) sm.sendDelay(300) sm.showFieldEffect("kaiser/text0", 0) sm.sendDelay(4200) sm.setTemporarySkillSet(0) sm.setInGameDirectionMode(False, True, False, False) # [FORCED_STAT_RESET] [] sm.warp(940011020, 0)

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100

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true

0

null

1

0

1

0

null

0

1

0

5

032326801fa121b2d59e5299e76d4c135cb76a9e

233

py

Python

zentral/contrib/mdm/views/__init__.py

janheise/zentral

cd809483573301e7d1aa5d3fc2da2c74a62405ab

[ "Apache-2.0" ]

634

2015-10-30T00:55:40.000Z

2022-03-31T02:59:00.000Z

zentral/contrib/mdm/views/__init__.py

janheise/zentral

cd809483573301e7d1aa5d3fc2da2c74a62405ab

[ "Apache-2.0" ]

145

2015-11-06T00:17:33.000Z

2022-03-16T13:30:31.000Z

zentral/contrib/mdm/views/__init__.py

janheise/zentral

cd809483573301e7d1aa5d3fc2da2c74a62405ab

[ "Apache-2.0" ]

103

2015-11-07T07:08:49.000Z

2022-03-18T17:34:36.000Z

from .dep import * # NOQA from .inventory import * # NOQA from .management import * # NOQA from .mdm import * # NOQA from .ota import * # NOQA from .scep import * # NOQA from .setup import * # NOQA from .user import * # NOQA

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8

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null

0

1

0

1

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5

032416e827d0402ce0a5416a4c729d44d0b88d64

165

py

Python

autofront/tests/import_script.py

JimmyLamothe/autofront

d179e54411f5d53046a5fa52b4430e09b01ebaca

[ "BSD-3-Clause" ]

1

2020-11-16T22:18:03.000Z

autofront/tests/import_script.py

JimmyLamothe/autofront

d179e54411f5d53046a5fa52b4430e09b01ebaca

[ "BSD-3-Clause" ]

null

autofront/tests/import_script.py

JimmyLamothe/autofront

d179e54411f5d53046a5fa52b4430e09b01ebaca

[ "BSD-3-Clause" ]

null

import sys import os from simple_functions import foo, return_value print('sys.path: ' + str(sys.path)) print('cwd: ' + os.getcwd()) print('__file__: ' + __file__)

20.625

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165

7

47

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null

0

1

0

1

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1

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5

032b1c40e52bd66083e531b48405bb610b740f02

125

py

Python

awspice/modules/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

1

2020-08-04T18:22:41.000Z

awspice/modules/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

null

awspice/modules/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

2

2019-04-03T16:56:19.000Z

2019-05-06T19:41:26.000Z

# -*- coding: utf-8 -*- from .finder import FinderModule from .security import SecurityModule from .stats import StatsModule

25

36

0.768

15

125

6.4

0.733333

0

0.009259

0.136

125

4

37

31.25

0.87963

0.168

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0349d8001089d30a6847530414266cfab287457c

39

py

Python

livereload/management/__init__.py

Fantomas42/django-livereload

1170b6729667a6164e5e47776781b2a7f6b2c0d3

[ "BSD-3-Clause" ]

63

2015-01-02T03:07:50.000Z

2022-01-06T13:53:07.000Z

livereload/management/__init__.py

Fantomas42/django-livereload

1170b6729667a6164e5e47776781b2a7f6b2c0d3

[ "BSD-3-Clause" ]

12

2015-02-26T20:04:17.000Z

2021-08-25T05:24:04.000Z

livereload/management/__init__.py

Fantomas42/django-livereload

1170b6729667a6164e5e47776781b2a7f6b2c0d3

[ "BSD-3-Clause" ]

18

2015-02-24T22:23:51.000Z

2017-01-22T16:00:25.000Z

"""Management for django-livereload"""

19.5

38

0.74359

4

39

7.25

1

0

0.076923

39

1

39

0.805556

0.820513

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

037bfd92ee3af53e6f7f943cc52fccf9bc98b1aa

209

py

Python

limis/core/environment.py

pstreck/limis

2316bacc10f0cc7fb17774511ca6f695d2e6c195

[ "MIT" ]

null

limis/core/environment.py

pstreck/limis

2316bacc10f0cc7fb17774511ca6f695d2e6c195

[ "MIT" ]

null

limis/core/environment.py

pstreck/limis

2316bacc10f0cc7fb17774511ca6f695d2e6c195

[ "MIT" ]

null

""" limis core - environment Environment variables set for a project. """ LIMIS_PROJECT_NAME_ENVIRONMENT_VARIABLE = 'LIMIS_PROJECT_NAME' LIMIS_PROJECT_SETTINGS_ENVIRONMENT_VARIABLE = 'LIMIS_PROJECT_SETTINGS'

26.125

70

0.842105

25

209

6.56

0.44

0.292683

0.195122

0.378049

0

0.090909

209

7

71

29.857143

0.863158

0.315789

0

0.296296

0.162963

0

1

0

false

0

1

0

null

1

0

1

0

null

0

5

3012262a4b028060d7bdd2e986f7c5314aa0cee6

216

py

Python

seventh/flask/formatters.py

iamjillsanluis/flasklab

d6f71e3e42ab72462b04df62b4f67474c4ee5b6f

[ "MIT" ]

null

seventh/flask/formatters.py

iamjillsanluis/flasklab

d6f71e3e42ab72462b04df62b4f67474c4ee5b6f

[ "MIT" ]

6

2020-05-03T00:16:26.000Z

2020-07-30T01:51:38.000Z

seventh/flask/formatters.py

iamjillsanluis/flasklab

d6f71e3e42ab72462b04df62b4f67474c4ee5b6f

[ "MIT" ]

null

def response_json(target): def decorator(*args, **kwargs): response = target(*args, **kwargs) # TODO: you can add your error handling in here return response.json() return decorator

24

55

0.634259

26

216

5.230769

0.653846

0.176471

0

0.263889

216

8

56

27

0.855346

0.208333

0

0.125

0

1

0.4

false

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

30204f58f0fa4bbaaf76a5ce66241e7607f1212e

79

py

Python

sfybook/mbasic_home_header/__init__.py

Scr44gr/sfybook

8662aee8b324a9074fdd3313c00c90e189a7c544

[ "Apache-2.0" ]

1

2020-09-06T14:58:00.000Z

sfybook/mbasic_home_header/__init__.py

Scr44gr/sfybook

8662aee8b324a9074fdd3313c00c90e189a7c544

[ "Apache-2.0" ]

null

sfybook/mbasic_home_header/__init__.py

Scr44gr/sfybook

8662aee8b324a9074fdd3313c00c90e189a7c544

[ "Apache-2.0" ]

null

""" Author: Scr44gr """ from sfybook.mbasic_home_header.pages import Pages

15.8

51

0.734177

10

79

5.6

0.9

0

0.029851

0.151899

79

4

52

19.75

0.80597

0.189873

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

302f2a140ca2f8982db3d2ff2b0371362eecaee9

169

py

Python

nlp_project/src/crf.py

Lord-Cthulhu/NLP_Tokenizer

7d766e5bd5a88d1f49636fd19bfb3b6bcbeb6342

[ "RSA-MD" ]

null

nlp_project/src/crf.py

Lord-Cthulhu/NLP_Tokenizer

7d766e5bd5a88d1f49636fd19bfb3b6bcbeb6342

[ "RSA-MD" ]

null

nlp_project/src/crf.py

Lord-Cthulhu/NLP_Tokenizer

7d766e5bd5a88d1f49636fd19bfb3b6bcbeb6342

[ "RSA-MD" ]

null

import tensorflow as tf from keras_crf import CRFModel from keras.layers import LSTM, Embedding, Dense, Dropout, Bidirectional from keras_contrib.layers import CRF

18.777778

71

0.816568

24

169

5.666667

0.625

0.198529

0

0.147929

169

8

72

21.125

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

305d9c2046a0862470e8df3755376ec710c07dec

16

py

Python

Serial.py

rohanbaba/RMCS-220X-RPi

885fc2b1186682cbe39f02f452eea11ae24ffb0b

[ "MIT" ]

null

Serial.py

rohanbaba/RMCS-220X-RPi

885fc2b1186682cbe39f02f452eea11ae24ffb0b

[ "MIT" ]

null

Serial.py

rohanbaba/RMCS-220X-RPi

885fc2b1186682cbe39f02f452eea11ae24ffb0b

[ "MIT" ]

null

print("Serial")

8

15

0.6875

2

16

5.5

1

0

0.0625

16

1

16

0.733333

0

0.375

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

306ac51f7b4b05c17e7481f1a1c1252ac2e373da

56

py

Python

Ene-Jun-2021/pena-balderas-bryan/prueba.py

bryanbalderas/DAS_Sistemas

1e31f088c0de7134471025a5730b0abfc19d936e

[ "MIT" ]

41

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ene-Jun-2021/pena-balderas-bryan/prueba.py

bryanbalderas/DAS_Sistemas

1e31f088c0de7134471025a5730b0abfc19d936e

[ "MIT" ]

67

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ene-Jun-2021/pena-balderas-bryan/prueba.py

bryanbalderas/DAS_Sistemas

1e31f088c0de7134471025a5730b0abfc19d936e

[ "MIT" ]

210

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

print('prueba para clase de DAS,cambio para nuevo pull')

56

0.785714

10

56

4.4

0.9

0

0.125

56

1

56

0.897959

0

0.824561

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

306d21d685f6390715be2f03976b50fd3da5049d

105

py

Python

core/backend/user/admin.py

Djacket/djacket

8f5258ae34ab2fb2849324145681e6d4932a22ba

[ "MIT" ]

85

2016-02-19T06:46:29.000Z

2022-03-25T20:20:47.000Z

core/backend/user/admin.py

Djacket/djacket

8f5258ae34ab2fb2849324145681e6d4932a22ba

[ "MIT" ]

15

2016-04-08T02:46:11.000Z

2022-01-29T08:20:45.000Z

core/backend/user/admin.py

Djacket/djacket

8f5258ae34ab2fb2849324145681e6d4932a22ba

[ "MIT" ]

20

2016-04-08T02:39:08.000Z

2021-12-16T14:05:28.000Z

from django.contrib import admin from user.models import UserProfile admin.site.register(UserProfile)

15

35

0.828571

14

105

6.214286

0.714286

0

0.114286

105

6

36

17.5

0.935484

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3080b8a3c10dc880fcbc82fe92a34df9b8dd2b6c

21

py

Python

python/testData/copyPaste/Indent7709.dst.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/copyPaste/Indent7709.dst.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/copyPaste/Indent7709.dst.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

a = 1 <caret> b = 2

4.2

7

0.428571

5

21

1.8

1

0

0.153846

0.380952

21

4

8

5.25

0.538462

0

null

0

null

0

1

null

0

1

0

null

0

1

0

5

061e768ebbd1f3a14a7b9d3348eeff85f5bb0a7c

8,176

py

Python

tests/unit/test_download_hashes.py

jamezpolley/pip

0b9beab59c1bd5b634e198e919b9173690fe1d65

[ "MIT" ]

1

2019-06-27T11:57:35.000Z

tests/unit/test_download_hashes.py

jamezpolley/pip

0b9beab59c1bd5b634e198e919b9173690fe1d65

[ "MIT" ]

1

2021-08-07T12:15:25.000Z

tests/unit/test_download_hashes.py

jamezpolley/pip

0b9beab59c1bd5b634e198e919b9173690fe1d65

[ "MIT" ]

1

2020-01-06T15:39:00.000Z

import pytest from pip.download import _get_hash_from_file, _check_hash from pip.exceptions import InstallationError from pip.index import Link def test_get_hash_from_file_md5(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#md5=d41d8cd98f00b204e9800998ecf8427e" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 16 assert download_hash.hexdigest() == "d41d8cd98f00b204e9800998ecf8427e" def test_get_hash_from_file_sha1(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha1=da39a3ee5e6b4b0d3255bfef95601890afd80709" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 20 assert download_hash.hexdigest() == ( "da39a3ee5e6b4b0d3255bfef95601890afd80709" ) def test_get_hash_from_file_sha224(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha224=d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 28 assert download_hash.hexdigest() == ( "d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f" ) def test_get_hash_from_file_sha384(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha384=38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e" "1da274edebfe76f65fbd51ad2f14898b95b" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 48 assert download_hash.hexdigest() == ( "38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e1da274e" "debfe76f65fbd51ad2f14898b95b" ) def test_get_hash_from_file_sha256(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852" "b855" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 32 assert download_hash.hexdigest() == ( "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" ) def test_get_hash_from_file_sha512(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha512=cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36" "ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 64 assert download_hash.hexdigest() == ( "cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0" "d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e" ) def test_get_hash_from_file_unknown(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#unknown_hash=d41d8cd98f00b204e9800998ecf8427e" ) download_hash = _get_hash_from_file(file_path, file_link) assert download_hash is None def test_check_hash_md5_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#md5=d41d8cd98f00b204e9800998ecf8427e" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_md5_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#md5=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hash_sha1_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha1=da39a3ee5e6b4b0d3255bfef95601890afd80709" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha1_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha1=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hash_sha224_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha224=d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f'" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha224_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha224=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hash_sha384_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha384=38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6" "e1da274edebfe76f65fbd51ad2f14898b95b" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha384_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha384=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hash_sha256_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b785" "2b855" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha256_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha256=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hash_sha512_valid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha512=cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36c" "e9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e" ) download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha512_invalid(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha512=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, file_link) def test_check_hasher_mismsatch(data): file_path = data.packages.join("gmpy-1.15.tar.gz") file_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#md5=d41d8cd98f00b204e9800998ecf8427e" ) other_link = Link( "http://testserver/gmpy-1.15.tar.gz" "#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b785" "2b855" ) download_hash = _get_hash_from_file(file_path, file_link) with pytest.raises(InstallationError): _check_hash(download_hash, other_link)

30.969697

79

0.731409

1,007

8,176

5.595829

0.072493

0.073824

0.050932

0.07276

0.768412

0.764508

0.737178

0

0.159386

0.162794

8,176

263

80

31.087452

0.663842

0

0.56044

0

0.319839

0.180528

0

0.071429

1

0.10989

false

0

0.021978

0

0.131868

0

null

0

1

0

null

0

5

063364c6842c15d8b14becc36b89cf99f7946a78

61

py

Python

dynd/benchmarks/__init__.py

mwiebe/dynd-python

45ffecaf7887761a5634140f0ed120b33ace58a3

[ "BSD-2-Clause" ]

93

2015-01-29T14:00:57.000Z

2021-11-23T14:37:27.000Z

dynd/benchmarks/__init__.py

ContinuumIO/dynd-python

bae7afb8eb604b0bce09befc9e896c8ec8357aaa

[ "BSD-2-Clause" ]

143

2015-01-04T12:30:24.000Z

2016-09-29T18:36:22.000Z

dynd/benchmarks/__init__.py

ContinuumIO/dynd-python

bae7afb8eb604b0bce09befc9e896c8ec8357aaa

[ "BSD-2-Clause" ]

20

2015-06-08T11:54:46.000Z

2021-03-09T07:57:25.000Z

try: from pycuda import autoinit except ImportError: pass

15.25

29

0.786885

8

61

6

1

0

0.180328

61

4

30

15.25

0.96

0

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true

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0

null

0

1

0

null

0

1

0

5

063b61ac2b90fe757f49547346fd44173e48ddd3

37

py

Python

tests/__init__.py

smitchandarana/FredGdp

fe836d7949e265666d4acc2dbb712864d0cfd083

[ "MIT" ]

null

tests/__init__.py

smitchandarana/FredGdp

fe836d7949e265666d4acc2dbb712864d0cfd083

[ "MIT" ]

null

tests/__init__.py

smitchandarana/FredGdp

fe836d7949e265666d4acc2dbb712864d0cfd083

[ "MIT" ]

null

"""Unit test package for fredgdp."""

18.5

36

0.675676

5

37

5

1

0

0.135135

37

1

37

0.78125

0.810811

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

06ad6373313bb361891ab628ca6299807dd5fef0

63

py

Python

src/hub/dataload/sources/umls/__init__.py

mlebeur/mygene.info

e71ca89c2b1c546c260101286ad5419503fd6653

[ "Apache-2.0" ]

78

2017-05-26T08:38:25.000Z

2022-02-25T08:55:31.000Z

src/hub/dataload/sources/umls/__init__.py

mlebeur/mygene.info

e71ca89c2b1c546c260101286ad5419503fd6653

[ "Apache-2.0" ]

105

2017-05-18T21:57:13.000Z

2022-03-18T21:41:47.000Z

src/hub/dataload/sources/umls/__init__.py

mlebeur/mygene.info

e71ca89c2b1c546c260101286ad5419503fd6653

[ "Apache-2.0" ]

19

2017-06-12T18:31:54.000Z

2021-11-10T00:04:43.000Z

from .upload import UMLSUploader from .dump import UMLSDumper

15.75

32

0.825397

8

63

6.5

0.75

0

0.142857

63

3

33

21

0.962963

0

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0

1

0

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null

0

1

0

1

0

1

0

5

06b201d6220d6dc039a1006bf5ad0cd54def299c

24

py

Python

lib/model/siamese_net/proposal_target_layer.py

YeLyuUT/FastVOD

707dcf0d88a901d2db0b7cf24096801fbdd8735c

[ "MIT" ]

1

2020-05-12T14:07:00.000Z

lib/model/siamese_net/proposal_target_layer.py

YeLyuUT/FastVOD

707dcf0d88a901d2db0b7cf24096801fbdd8735c

[ "MIT" ]

null

lib/model/siamese_net/proposal_target_layer.py

YeLyuUT/FastVOD

707dcf0d88a901d2db0b7cf24096801fbdd8735c

[ "MIT" ]

1

2019-12-18T09:43:48.000Z

# sample training pairs.

24

0.791667

3

24

6.333333

1

0

0.125

24

1

24

0.904762

0.916667

0

null

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1

0

1

0

null

0

1

0

5

ebf7acb38f0bc056d0475f87693461b7eb5ed277

5,319

py

Python

pendulopodes/dynamics.py

nwunderly/pendulopodes

e9daa8c76625b806fe81d2c623a34493390a62d0

[ "MIT" ]

1

2021-05-06T00:19:52.000Z

pendulopodes/dynamics.py

nwunderly/pendulopodes

e9daa8c76625b806fe81d2c623a34493390a62d0

[ "MIT" ]

null

pendulopodes/dynamics.py

nwunderly/pendulopodes

e9daa8c76625b806fe81d2c623a34493390a62d0

[ "MIT" ]

null

import numpy as np from .constants import g """ Assumptions: (constant) unit length unit mass rotate about origin 2-dimensional (for now) theta = 0 at (x, y) = (1, 0) Coordinate system: | | ------------|------------> Y | | | | | V X Angular kinematics: (RADIANS) theta: angle omega: angular velocity alpha: angular acceleration """ def inertial_to_polar(x, y): r = np.sqrt(x**2 + y**2) theta = np.arctan(y/x) return r, theta def polar_to_inertial(r, theta): x = r * np.cos(theta) y = r * np.sin(theta) return x, y # def eqm_single_simple_pendulum(theta, omega): # """Single-element simple pendulum equation of motion. # (Shoutout to Derek Paley) # # tension = m*g*cos(theta)+m*l*theta # alpha = -g/l*sin(theta # """ # alpha = -g*np.sin(theta) # # return omega, alpha # # # def system_single_simple_pendulum(t, y): # """System of differential equations for a single-element simple pendulum. # # y = [theta, omega] # y_dot = [omega, alpha] # """ # theta, omega = y # # omega, alpha = eqm_single_simple_pendulum(theta, omega) # y_dot = [omega, alpha] # # return y_dot # # # def eqm_double_simple_pendulum(theta1, omega1, theta2, omega2): # """Two-element simple pendulum equation of motion. # # Equations found at https://www.myphysicslab.com/pendulum/double-pendulum-en.html # """ # alpha1_n = -g*(2*m1 + m2)*np.sin(theta1) - m2*g*np.sin(theta1 - 2*theta2) - 2*np.sin(theta1 - theta2)*m2*(omega2**2 * l2 + omega1**2 * l1 * np.cos(theta1 - theta2)) # alpha1_d = l1 * (2*m1 + m2 - m2*np.cos(2*theta1 - 2*theta2)) # alpha1 = alpha1_n / alpha1_d # # alpha2_n = 2*np.sin(theta1 - theta2) * (omega1**2 * l1 * (m1 + m2) + g*(m1 + m2) * np.cos(theta1) + omega2**2 * l2 * m2 * np.cos(theta1 - theta2)) # alpha2_d = l2 * (2*m1 + m2 - m2*np.cos(2*theta1 - 2*theta2)) # alpha2 = alpha2_n / alpha2_d # # return omega1, alpha1, omega2, alpha2 # # # def system_double_simple_pendlum(t, y): # """System of differential equations for a two-element simple pendulum. # # y = [theta1, omega1, theta2, omega2] # y_dot = [omega1, alpha1, omega2, alpha2] # """ # theta1, omega1, theta2, omega2 = y # # omega1, alpha1, omega2, alpha2 = eqm_double_simple_pendulum(theta1, omega1, theta2, omega2) # y_dot = [omega1, alpha1, omega2, alpha2] # # return y_dot class NElementPendulum: def __init__(self, element_count, *, length=(1,), mass=(1,), theta0=(np.pi/2,), omega0=(0,)): if element_count > 1: assert element_count % len(length) == 0 assert element_count % len(mass) == 0 assert element_count % len(theta0) == 0 assert element_count % len(omega0) == 0 self.element_count = element_count self.length = length*(element_count//len(length)) self.mass = mass*(element_count//len(mass)) self.theta0 = theta0*(element_count//len(theta0)) self.omega0 = omega0*(element_count//len(omega0)) def eqm_single_simple_pendulum(self, theta, omega): """Single-element simple pendulum equation of motion. (Shoutout to Derek Paley) tension = m*g*cos(theta)+m*l*theta alpha = -g/l*sin(theta """ alpha = -g / self.length[0] * np.sin(theta) return omega, alpha def system_single_simple_pendulum(self, t, y): """System of differential equations for a single-element simple pendulum. y = [theta, omega] y_dot = [omega, alpha] """ theta, omega = y omega, alpha = self.eqm_single_simple_pendulum(theta, omega) y_dot = [omega, alpha] return y_dot def eqm_double_simple_pendulum(self, theta1, omega1, theta2, omega2): """Two-element simple pendulum equation of motion. Equations found at https://www.myphysicslab.com/pendulum/double-pendulum-en.html """ m1 = self.mass[0] m2 = self.mass[1] l1 = self.length[0] l2 = self.length[1] alpha1_n = -g * (2 * m1 + m2) * np.sin(theta1) - m2 * g * np.sin(theta1 - 2 * theta2) - 2 * np.sin(theta1 - theta2) * m2 * ( omega2 ** 2 * l2 + omega1 ** 2 * l1 * np.cos(theta1 - theta2)) alpha1_d = l1 * (2 * m1 + m2 - m2 * np.cos(2 * theta1 - 2 * theta2)) alpha1 = alpha1_n / alpha1_d alpha2_n = 2 * np.sin(theta1 - theta2) * ( omega1 ** 2 * l1 * (m1 + m2) + g * (m1 + m2) * np.cos(theta1) + omega2 ** 2 * l2 * m2 * np.cos(theta1 - theta2)) alpha2_d = l2 * (2 * m1 + m2 - m2 * np.cos(2 * theta1 - 2 * theta2)) alpha2 = alpha2_n / alpha2_d return omega1, alpha1, omega2, alpha2 def system_double_simple_pendlum(self, t, y): """System of differential equations for a two-element simple pendulum. y = [theta1, omega1, theta2, omega2] y_dot = [omega1, alpha1, omega2, alpha2] """ theta1, omega1, theta2, omega2 = y omega1, alpha1, omega2, alpha2 = self.eqm_double_simple_pendulum(theta1, omega1, theta2, omega2) y_dot = [omega1, alpha1, omega2, alpha2] return y_dot

30.568966

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0.582816

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5,319

4.241913

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0.055703

0.06366

0.765252

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0.726127

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0.059221

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5,319

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171

30.745665

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0

null

0

1

0

null

0

5

2371bd1c2612bfb16e91537d3cbfc4b5744328a2

203

py

Python

utils/lock.py

DrugowitschLab/motion-structure-identification

908f084b36c7387daf0cbfe75f16bab70cf96db9

[ "MIT" ]

null

utils/lock.py

DrugowitschLab/motion-structure-identification

908f084b36c7387daf0cbfe75f16bab70cf96db9

[ "MIT" ]

null

utils/lock.py

DrugowitschLab/motion-structure-identification

908f084b36c7387daf0cbfe75f16bab70cf96db9

[ "MIT" ]

null

class Lock: def __init__(self): self.locked = False def lock(self, msg): assert not self.locked, msg self.locked = True def unlock(self): self.locked = False

20.3

35

0.576355

26

203

4.346154

0.461538

0.353982

0.247788

0.336283

0

0.330049

203

10

36

20.3

0.830882

0

0.25

0

0.125

1

0.375

false

0

0.5

0

1

0

null

1

0

null

0

1

0

5

23764f172090631ac31ec7f17f09516d1e0246f6

124

py

Python

tests/test_scripts/__init__.py

morganwl/turnovertools

ea911853033ed5087b40852b5adc3b8f5d0a903d

[ "MIT" ]

null

tests/test_scripts/__init__.py

morganwl/turnovertools

ea911853033ed5087b40852b5adc3b8f5d0a903d

[ "MIT" ]

3

2021-03-22T00:44:24.000Z

2021-06-26T19:32:31.000Z

tests/test_scripts/__init__.py

morganwl/turnovertools

ea911853033ed5087b40852b5adc3b8f5d0a903d

[ "MIT" ]

null

"""Test suites for the various scripts that interface with the turnovertools libraries.""" from .test_insert_umid import *

24.8

62

0.790323

17

124

5.647059

0.882353

0

0.137097

124

4

63

31

0.897196

0.677419

0

1

0

true

0

1

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

88dfd5ee854bcd48d4fa1dc40aaa126ba1e06da6

193

py

Python

matchzoo/models/__init__.py

JacobPolloreno/MatchZoo

e49d619a52b2e96b6f0e8e76164d76f623210198

[ "Apache-2.0" ]

null

matchzoo/models/__init__.py

JacobPolloreno/MatchZoo

e49d619a52b2e96b6f0e8e76164d76f623210198

[ "Apache-2.0" ]

null

matchzoo/models/__init__.py

JacobPolloreno/MatchZoo

e49d619a52b2e96b6f0e8e76164d76f623210198

[ "Apache-2.0" ]

null

from .naive_model import NaiveModel from .dssm_model import DSSMModel from .cdssm_model import CDSSMModel from .dense_baseline_model import DenseBaselineModel from .arci_model import ArcIModel

32.166667

52

0.870466

26

193

6.230769

0.538462

0.339506

0

0.103627

193

5

53

38.6

0.936416

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

88fd9e5a7fa3b960439e0b6e392fa703ec132322

1,111

py

Python

aitoolbox/experiment/core_metrics/regression.py

mv1388/AIToolbox

c64ac4810a02d230ce471d86b758e82ea232a7e7

[ "MIT" ]

3

2019-10-12T12:24:09.000Z

2020-08-02T02:42:43.000Z

aitoolbox/experiment/core_metrics/regression.py

mv1388/aitoolbox

1060435e6cbdfd19abcb726c4080b663536b7467

[ "MIT" ]

3

2020-04-10T14:07:07.000Z

2020-04-22T19:04:38.000Z

aitoolbox/experiment/core_metrics/regression.py

mv1388/aitoolbox

1060435e6cbdfd19abcb726c4080b663536b7467

[ "MIT" ]

null

from aitoolbox.experiment.core_metrics.abstract_metric import AbstractBaseMetric from sklearn.metrics import mean_squared_error, mean_absolute_error class MeanSquaredErrorMetric(AbstractBaseMetric): def __init__(self, y_true, y_predicted): """Model prediction MSE Args: y_true (numpy.array or list): ground truth targets y_predicted (numpy.array or list): predicted targets """ AbstractBaseMetric.__init__(self, y_true, y_predicted, metric_name='Mean_squared_error') def calculate_metric(self): return mean_squared_error(self.y_true, self.y_predicted) class MeanAbsoluteErrorMetric(AbstractBaseMetric): def __init__(self, y_true, y_predicted): """Model prediction MAE Args: y_true (numpy.array or list): ground truth targets y_predicted (numpy.array or list): predicted targets """ AbstractBaseMetric.__init__(self, y_true, y_predicted, metric_name='Mean_absolute_error') def calculate_metric(self): return mean_absolute_error(self.y_true, self.y_predicted)

34.71875

97

0.718272

133

1,111

5.62406

0.285714

0.053476

0.072193

0.069519

0.705882

0.532086

0

0.205221

1,111

31

98

35.83871

0.847112

0.251125

0

0.333333

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0.333333

false

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0.166667

0.833333

0

null

0

1

0

null

0

1

0

1

0

5

0004a1432c50a486fee18e368a1eb6ed79f33d00

39

py

Python

modules/2.79/bpy/types/TextureNodeTexBlend.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/TextureNodeTexBlend.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/TextureNodeTexBlend.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class TextureNodeTexBlend: pass

6.5

26

0.717949

3

39

9.333333

1

0

0.25641

39

5

27

7.8

0.965517

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

000a6432772d70f9ddc37eab2166d959e80a9b9e

32

py

Python

test/login.py

jinhongyi/test007

b48750ed30a690ceb1ec739b9d181b6ecb82b0a0

[ "MIT" ]

null

test/login.py

jinhongyi/test007

b48750ed30a690ceb1ec739b9d181b6ecb82b0a0

[ "MIT" ]

null

test/login.py

jinhongyi/test007

b48750ed30a690ceb1ec739b9d181b6ecb82b0a0

[ "MIT" ]

null

num1=10 num2=20 num3=30 num4=40

6.4

7

0.75

8

32

3

1

0

0.428571

0.125

32

4

8

0.428571

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

cc52c5c7709da52f4cca0eba70ca5b79485d6260

87

py

Python

grove/helper/__init__.py

chousemath/grove.py

ebab518ace0c8efe34a56078c9a876368d80781f

[ "MIT" ]

null

grove/helper/__init__.py

chousemath/grove.py

ebab518ace0c8efe34a56078c9a876368d80781f

[ "MIT" ]

null

grove/helper/__init__.py

chousemath/grove.py

ebab518ace0c8efe34a56078c9a876368d80781f

[ "MIT" ]

null

from .helper import SlotHelper from .os_sched import * # __all__ = [ 'SlotHelper' ]

12.428571

30

0.701149

10

87

5.6

0.7

0

0.195402

87

6

31

14.5

0.8

0.298851

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

cc52f4467a8fefa5e93405ceaba9750cb564e846

693

py

Python

db/queries.py

hamedsh/healthCheck

8f6b8ffffc1f1d8849a58b4966e54d30ead9556b

[ "Apache-2.0" ]

null

db/queries.py

hamedsh/healthCheck

8f6b8ffffc1f1d8849a58b4966e54d30ead9556b

[ "Apache-2.0" ]

null

db/queries.py

hamedsh/healthCheck

8f6b8ffffc1f1d8849a58b4966e54d30ead9556b

[ "Apache-2.0" ]

null

QUERIES = { 'add_service': 'insert into services(name, type, repeat_period, metadata, status) values("{name}", {type}, {repeat_period}, "{metadata}", 1)', 'services_last_row_id': 'select max(id) from services', 'get_active_services': 'select services.id, services.name, services.type, service_types.Type, services.repeat_period, services.metadata from services INNER join service_types on services.type = service_types.id', 'get_active_services_type': 'select services.id, services.name, services.type, service_types.Type, services.repeat_period, services.metadata from services INNER join service_types on services.type = service_types.id where services.type = {}' }

77

245

0.756133

92

693

5.48913

0.293478

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0.150495

0.190099

0.716832

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0

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0.122655

693

8

246

86.625

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0

1

0

1

0

1

0

1

0

null

0

5

cc81aa64d2274c4ce850357fafef27da6f801644

127

py

Python

SampleInvoiceCRUDUsingDict/apps.py

juned8236/quickbook

32757c911d176131d71ccd532c07378950962053

[ "Apache-2.0" ]

null

SampleInvoiceCRUDUsingDict/apps.py

juned8236/quickbook

32757c911d176131d71ccd532c07378950962053

[ "Apache-2.0" ]

2

2020-06-06T00:52:36.000Z

2021-06-10T22:40:04.000Z

SampleInvoiceCRUDUsingDict/apps.py

juned8236/quickbook

32757c911d176131d71ccd532c07378950962053

[ "Apache-2.0" ]

null

from django.apps import AppConfig class SampleinvoicecrudusingdictConfig(AppConfig): name = 'SampleInvoiceCRUDUsingDict'

21.166667

50

0.826772

10

127

10.5

0.9

0

0.11811

127

5

51

25.4

0.9375

0

0.204724

0

1

0

false

0

0.333333

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

cca1569749bc6de7062709323854b9efde4deb62

1,032

py

Python

tests/test_face_recognition.py

bagashvilit/facial_recognition_bias

abe87302b1d452cbd9100773a16c127f4d2ab546

[ "MIT" ]

null

tests/test_face_recognition.py

bagashvilit/facial_recognition_bias

abe87302b1d452cbd9100773a16c127f4d2ab546

[ "MIT" ]

1

2021-11-15T04:09:22.000Z

tests/test_face_recognition.py

bagashvilit/facial_recognition_bias

abe87302b1d452cbd9100773a16c127f4d2ab546

[ "MIT" ]

1

2021-11-17T05:10:08.000Z

import pickle import cv2 import joblib import pytest import sklearn from pyimagesearch.rgbhistogram import RGBHistogram @pytest.mark.parametrize( "input_image,expected_gender", [("tests/images/17_1_0.jpg", 1), ("tests/images/23_1_2.jpg", 1)], ) def test_SVM(input_image, expected_gender): desc = RGBHistogram([8, 8, 8]) model = pickle.load(open("src/SVM/SVM_model.pkl", "rb")) image = cv2.imread(input_image) features = desc.describe(image) gender = (model.predict([features.flatten()]))[0] assert gender == expected_gender @pytest.mark.parametrize( "input_image,expected_gender", [("tests/images/17_1_0.jpg", 1), ("tests/images/23_1_2.jpg", 1)], ) def test_Random_Forest(input_image, expected_gender): desc = RGBHistogram([8, 8, 8]) model = pickle.load(open("src/RandomForest/RandomForest_model.pkl", "rb")) image = cv2.imread(input_image) features = desc.describe(image) gender = (model.predict([features.flatten()]))[0] assert gender == expected_gender

25.170732

78

0.700581

140

1,032

4.985714

0.307143

0.08596

0.103152

0.137536

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0

0.035308

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1,032

40

79

25.8

0.759681

0

0.571429

0

0.203488

0.199612

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0

0.285714

0

null

0

1

0

null

0

5

ccbf427b7c08bffd5472d1329684dbdf347f1cf4

128

py

Python

test/smoke_test_server.py

carbonblack/cb-lastline-connector

0129c4c8737248b83bdae817eafd9873fb8cae65

[ "MIT" ]

13

2016-04-01T02:00:29.000Z

2021-06-10T07:12:12.000Z

test/smoke_test_server.py

carbonblack/cb-lastline-connector

0129c4c8737248b83bdae817eafd9873fb8cae65

[ "MIT" ]

5

2015-12-14T19:24:23.000Z

2021-07-29T14:15:28.000Z

test/smoke_test_server.py

carbonblack/cb-lastline-connector

0129c4c8737248b83bdae817eafd9873fb8cae65

[ "MIT" ]

12

2016-02-02T06:25:12.000Z

2021-06-10T07:12:26.000Z

from flask import Flask from utils.mock_server import get_mocked_server app = Flask(__name__) server = get_mocked_server(app)

18.285714

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0.820313

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128

4.8

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0.125

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null

0

1

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1

0

null

0

1

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5

aeed49e8fabd2ce9076d8c02009a3bcf54efd7ed

173

py

Python

control_citas/apps/doctor/admin.py

mariomtzjr/agenda-medica

a36eaf79507d63e35f8f14796c916f0f5aaa36d4

[ "MIT" ]

null

control_citas/apps/doctor/admin.py

mariomtzjr/agenda-medica

a36eaf79507d63e35f8f14796c916f0f5aaa36d4

[ "MIT" ]

null

control_citas/apps/doctor/admin.py

mariomtzjr/agenda-medica

a36eaf79507d63e35f8f14796c916f0f5aaa36d4

[ "MIT" ]

null

from django.contrib import admin from apps.doctor.models import Doctor # Register your models here. @admin.register(Doctor) class DoctorAdmin(admin.ModelAdmin): pass

17.3

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173

5.913043

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173

9

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19.222222

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1

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null

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null

0

1

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5

4e3d90d85e0e65b12dcc087a2dc8241b6026b8de

38

py

Python

python/testData/psi/ResetAfterSemicolon.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/psi/ResetAfterSemicolon.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/psi/ResetAfterSemicolon.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

if True: import tmp2; import tmp1

12.666667

28

0.684211

6

38

4.333333

0.833333

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0.071429

0.263158

38

2

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0

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1

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null

0

1

0

1

0

5

9d99b6e17bd9c32f9f0117b93b7211d398781a03

119

py

Python

d6/d6.py

mwm021/Advent-of-Code-2021

9908b95ea6503c1b31fa26845e8ee5d0ad474718

[ "MIT" ]

null

d6/d6.py

mwm021/Advent-of-Code-2021

9908b95ea6503c1b31fa26845e8ee5d0ad474718

[ "MIT" ]

null

d6/d6.py

mwm021/Advent-of-Code-2021

9908b95ea6503c1b31fa26845e8ee5d0ad474718

[ "MIT" ]

null

import pandas as pd import os import csv import numpy as np def d6_1(): pass def d6_2(): pass d6_1() d6_2()

8.5

19

0.655462

24

119

3.083333

0.541667

0.135135

0

0.090909

0.260504

119

13

20

9.153846

0.75

0

0.2

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1

0.2

true

0.2

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0.6

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1

0

null

0

null

0

1

0

1

0

5

9dd169aac1b5bf47d2d437164010983d95f3fb44

103

py

Python

artbot/proxy/__init__.py

skielred/ArtCompanion

6b3d41dcdbd1151778324cf8068e0ce74cfab09c

[ "MIT" ]

1

2021-05-04T09:18:17.000Z

artbot/proxy/__init__.py

skielred/ArtCompanion

6b3d41dcdbd1151778324cf8068e0ce74cfab09c

[ "MIT" ]

6

2021-04-18T01:03:50.000Z

2021-08-30T14:18:30.000Z

artbot/proxy/__init__.py

skielred/ArtCompanion

6b3d41dcdbd1151778324cf8068e0ce74cfab09c

[ "MIT" ]

null

from .cog import ProxyCog def init(bot): bot.add_cog(ProxyCog(bot)) from . import pixiv, twitter

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16

103

4.5625

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6

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null

0

1

0

1

0

1

0

5

9dd9b88bd13b0fb7392b2a258acf22d9a4ae3cf5

174

py

Python

ticketer/processor.py

gavinB-orange/ticketer

4e49d928dd6a4d22134dcbf989e84fd335f45307

[ "Apache-2.0" ]

null

ticketer/processor.py

gavinB-orange/ticketer

4e49d928dd6a4d22134dcbf989e84fd335f45307

[ "Apache-2.0" ]

null

ticketer/processor.py

gavinB-orange/ticketer

4e49d928dd6a4d22134dcbf989e84fd335f45307

[ "Apache-2.0" ]

null

# contains the processing code for the ticketer. class Processor(object): def __init__(self): pass def say(self, message): return message + "\n"

14.5

48

0.626437

21

174

5

0.809524

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174

11

49

15.818182

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0.264368

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1

0.4

false

0.2

0

0.2

0.8

0

1

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null

0

null

0

1

0

1

0

1

0

5

9ddda570c754203d55e826630562afac6c1ee49d

251

py

Python

muddery/events/base_event_action.py

noahzaozao/muddery

294da6fb73cb04c62e5ba6eefe49b595ca76832a

[ "BSD-3-Clause" ]

null

muddery/events/base_event_action.py

noahzaozao/muddery

294da6fb73cb04c62e5ba6eefe49b595ca76832a

[ "BSD-3-Clause" ]

null

muddery/events/base_event_action.py

noahzaozao/muddery

294da6fb73cb04c62e5ba6eefe49b595ca76832a

[ "BSD-3-Clause" ]

null

""" Event action's base class. """ class BaseEventAction(object): """ Event action's base class. """ key = "" name = "" def func(self, event, character): """ Event action's function. """ pass

13.944444

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0.494024

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251

4.96

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0.290323

0.258065

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0

0.354582

251

17

38

14.764706

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0.310757

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1

0.2

false

0.2

0

0.8

0

1

0

null

1

0

null

0

1

0

5

9de4a6fd7fb969be970653874bef2adabed0bf35

1,639

py

Python

fastconv/conv2d/conv2d.py

wueric/fastconv

7b34f09eb83439241737e764b93e584d582ca917

[ "MIT" ]

null

fastconv/conv2d/conv2d.py

wueric/fastconv

7b34f09eb83439241737e764b93e584d582ca917

[ "MIT" ]

null

fastconv/conv2d/conv2d.py

wueric/fastconv

7b34f09eb83439241737e764b93e584d582ca917

[ "MIT" ]

null

import numpy as np from . import imageconv_cpp def batch_parallel_2Dconv_same(batched_images: np.ndarray, filter_coeffs: np.ndarray, pad_values: float) -> np.ndarray: ''' Performs batched 2D "same" convolution of images in parallel :param batched_images: np.ndarray shape (batch, height, width), dtype either np.float32 or np.float64 :param filter_coeffs: np.ndarray of shape (kern_height, kern_width), dtype either np.float32 or np.float64 :param pad_values: float, value to pad the border by to produce a "same" convolution :return: ''' if batched_images.ndim != 3: raise ValueError("batched_images must have ndim 3") if filter_coeffs.ndim != 2: raise ValueError("filter_coeffs must have ndim 2") return imageconv_cpp.batch_smallfilter_2dconv_same(batched_images, filter_coeffs, pad_values) def batch_parallel_2Dconv_valid(batched_images : np.ndarray, filter_coeffs: np.ndarray) -> np.ndarray: ''' Performs batched 2D "same" convolution of images in parallel :param batched_images: np.ndarray shape (batch, height, width), dtype either np.float32 or np.float64 :param filter_coeffs: np.ndarray of shape (kern_height, kern_width), dtype either np.float32 or np.float64 :return: ''' if batched_images.ndim != 3: raise ValueError("batched_images must have ndim 3") if filter_coeffs.ndim != 2: raise ValueError("filter_coeffs must have ndim 2") return imageconv_cpp.batch_smallfilter_2dconv_shrink(batched_images, filter_coeffs)

39.97561

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0

null

0

1

0

null

0

5

d194f64a2b9a375402e064dbd7dcb5d7db4e0e27

99

py

Python

VacationPy/api_keys.py

scarlett014j/python-api-challenge

be3fbe4dbc274caccbafd548d222526609e66420

[ "ADSL" ]

null

VacationPy/api_keys.py

scarlett014j/python-api-challenge

be3fbe4dbc274caccbafd548d222526609e66420

[ "ADSL" ]

null

VacationPy/api_keys.py

scarlett014j/python-api-challenge

be3fbe4dbc274caccbafd548d222526609e66420

[ "ADSL" ]

null

# OpenWeatherMap API Key weather_api_key = "Put Key Here" # Google API Key g_key = "Put key here"

16.5

32

0.727273

17

99

4.058824

0.470588

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0.376812

0

0.191919

99

5

33

19.8

0.8625

0.373737

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0.40678

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false

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1

0

null

1

0

1

0

null

0

5

d1d1daaef4159ea769be23a4d98c1743c882c3a1

31

py

Python

Warmup/Q08 - Mini-Max Sum/python.py

utkarshg6/SMVDU-HackerRank

1f8764be28cd8170841b134bcf9c68e349ba79bc

[ "MIT" ]

2

2018-07-01T21:12:59.000Z

2018-09-05T16:05:24.000Z

Warmup/Q08 - Mini-Max Sum/python.py

utkarshg6/SMVDU-HackerRank

1f8764be28cd8170841b134bcf9c68e349ba79bc

[ "MIT" ]

4

2018-02-20T06:45:49.000Z

2018-03-29T20:55:53.000Z

Warmup/Q08 - Mini-Max Sum/python.py

utkarshg6/SMVDU-HackerRank

1f8764be28cd8170841b134bcf9c68e349ba79bc

[ "MIT" ]

3

2018-02-19T11:35:30.000Z

2018-03-27T15:23:18.000Z

#Q08 - Mini-Max Sum || Warmup

31

0.612903

5

31

3.8

1

0

0.083333

0.225806

31

1

31

0.708333

0.967742

0

null

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null

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null

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null

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null

true

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null

0

1

0

1

0

null

0

1

0

5

060b0413bfc93fa8d01d87750fba3b06589bbcac

422

py

Python

General/Modules/Macros/FZJveinThickness/least_sqr/LA.py

IBG-2/phenoVein

534330747c54a35966b68951526fa2e381fb924d

[ "BSD-3-Clause" ]

1

2020-08-18T02:18:19.000Z

General/Modules/Macros/FZJveinThickness/least_sqr/LA.py

IBG-2/phenoVein

534330747c54a35966b68951526fa2e381fb924d

[ "BSD-3-Clause" ]

null

General/Modules/Macros/FZJveinThickness/least_sqr/LA.py

IBG-2/phenoVein

534330747c54a35966b68951526fa2e381fb924d

[ "BSD-3-Clause" ]

null

#import Scientific_numerics_package_id #package = Scientific_numerics_package_id.getNumericsPackageName() #del Scientific_numerics_package_id #if package == "Numeric": # from LinearAlgebra import * #elif package == "NumPy": from numpy.oldnumeric.linear_algebra import * #elif package == "Numarray": # from numarray.linear_algebra import * #else: # raise ImportError("Unknown numerics package " + package)

21.1

66

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422

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0.241158

0.26045

0

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true

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null

0

1

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1

0

5

ae13549bd1d53ac93ac7e0223628341a17825e06

120

py

Python

dynamo_pandas/serde/__init__.py

divideby0/dynamo-pandas

3a28921bb2ecaecab20ee8cd919f4c686a81e2b5

[ "MIT" ]

10

2021-04-25T17:13:36.000Z

2022-03-28T21:03:10.000Z

dynamo_pandas/serde/__init__.py

divideby0/dynamo-pandas

3a28921bb2ecaecab20ee8cd919f4c686a81e2b5

[ "MIT" ]

30

2021-03-07T23:03:41.000Z

2021-12-23T14:41:49.000Z

dynamo_pandas/serde/__init__.py

divideby0/dynamo-pandas

3a28921bb2ecaecab20ee8cd919f4c686a81e2b5

[ "MIT" ]

3

2021-04-15T21:21:22.000Z

2022-03-04T23:32:13.000Z

from .serde import TypeDeserializer from .serde import TypeSerializer __all__ = ["TypeDeserializer", "TypeSerializer"]

24

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8.454545

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null

0

1

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1

0

5

ae4cce99bc5d43d017c1635ff12184238f8ce688

40,873

py

Python

test/TestHyGraph.py

shoaibkamil/OLD-kdt-specializer

85074ec1990df980d25096ea8c55dd81350e531e

[ "BSD-3-Clause" ]

1

2021-11-15T02:11:33.000Z

test/TestHyGraph.py

shoaibkamil/OLD-kdt-specializer

85074ec1990df980d25096ea8c55dd81350e531e

[ "BSD-3-Clause" ]

null

test/TestHyGraph.py

shoaibkamil/OLD-kdt-specializer

85074ec1990df980d25096ea8c55dd81350e531e

[ "BSD-3-Clause" ]

null

import unittest from kdt import * from kdt import pyCombBLAS as pcb class HyGraphTests(unittest.TestCase): def initializeGraph(self, nvert, nedge, i, j, v=1): """ Initialize a graph with edge weights equal to one or the input value. """ iInd = ParVec(nedge) jInd = ParVec(nedge) if type(v) == int or type(v) == float: vInd = ParVec(nedge, v) else: vInd = ParVec(nedge) for ind in range(nedge): iInd[ind] = i[ind] jInd[ind] = j[ind] if type(v) != int and type(v) != float: vInd[ind] = v[ind] return HyGraph(iInd, jInd, vInd, nvert) class ConstructorTests(HyGraphTests): def test_toDiGraph(self): nvert = 7 nSEdge = 9 # #SimpleEdge origI = [0, 0, 1, 1, 2, 1, 2, 2, 3] origJ = [1, 2, 2, 3, 3, 4, 4, 5, 6] origV = 1 G = self.initializeGraph(nvert, nSEdge, origI, origJ, origV) diG = G.toDiGraph() [di, dj, dv] = diG.toParVec() diExpected = [1, 2, 1, 2, 3, 4, 2, 3, 4, 5, 2, 3, 4, 5, 3, 4, 5, 6] djExpected = [1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6] dvExpected = [1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1] self.assertEqual(7, diG.nvert()) self.assertEqual(18, diG.nedge()) self.assertEqual(18, len(diExpected)) for ind in range(nvert): self.assertEqual(diExpected[ind], di[ind]) self.assertEqual(djExpected[ind], dj[ind]) self.assertEqual(dvExpected[ind], dv[ind]) class PageRankTests(HyGraphTests): def test_connected(self): G = DiGraph.fullyConnected(10) pr = G.pageRank() for prv in pr: self.assertAlmostEqual(0.1, prv, 7) def test_simple(self): # This test is drawn from the PageRank example at # http://en.wikipedia.org/wiki/File:PageRanks-Example.svg. nvert = 11 nedge = 17 i = [1, 2, 3, 3, 4, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 10] j = [2, 1, 0, 1, 1, 3, 5, 1, 4, 1, 4, 1, 4, 1, 4, 4, 4] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) pr = G.pageRank(0.0001) expected = [0.032814, 0.38440, 0.34291, 0.03909, 0.08089, 0.03909, \ 0.01617, 0.01617, 0.01617, 0.01617, 0.01617] for ind in range(nvert): self.assertAlmostEqual(pr[ind], expected[ind], 4) def test_simple_loops(self): # This test is drawn from the PageRank example at # http://en.wikipedia.org/wiki/File:PageRanks-Example.svg. # # The difference between this and the previous test is that # this test includes several self loops to verify they have no # effect on the outcome. nvert = 11 nedge = 21 i = [1, 1, 2, 3, 3, 4, 4, 4, 4, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 10, 10] j = [1, 2, 1, 0, 1, 1, 3, 4, 5, 1, 4, 1, 4, 1, 4, 7, 1, 4, 4, 4, 10] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) pr = G.pageRank(0.0001) expected = [0.032814, 0.38440, 0.34291, 0.03909, 0.08089, 0.03909, \ 0.01617, 0.01617, 0.01617, 0.01617, 0.01617] for ind in range(nvert): self.assertAlmostEqual(pr[ind], expected[ind], 4) class NormalizeEdgeWeightsTests(HyGraphTests): def no_edge_graph(self): nvert = 4 nedge = 0 i = [] j = [] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) return self.initializeGraph(nvert, nedge, i, j) def test_no_edges_default(self): G = self.no_edge_graph() G.normalizeEdgeWeights() self.assertEqual(G.nedge(), 0) def test_no_edges_out(self): G = self.no_edge_graph() G.normalizeEdgeWeights(DiGraph.Out) self.assertEqual(G.nedge(), 0) def test_no_edges_in(self): G = self.no_edge_graph() G.normalizeEdgeWeights(DiGraph.In) self.assertEqual(G.nedge(), 0) def small_test_graph(self): # 1 0 1 0 # 0 0 0 1 # 0 1 0 1 # 1 0 0 0 nvert = 4 nedge = 6 i = [0, 3, 2, 0, 1, 2] j = [0, 0, 1, 2, 3, 3] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) return [nvert, nedge, i, j, self.initializeGraph(nvert, nedge, i, j)] def test_small_default(self): [nvert, nedge, i, j, G] = self.small_test_graph() G.normalizeEdgeWeights() [iInd, jInd, eW] = G.toParVec() w = [0.5, 1., 0.5, 0.5, 1., 0.5] for ind in range(nedge): self.assertEqual(i[ind], iInd[ind]) self.assertEqual(j[ind], jInd[ind]) self.assertEqual(eW[ind], w[ind]) def test_small_out(self): [nvert, nedge, i, j, G] = self.small_test_graph() G.normalizeEdgeWeights(DiGraph.Out) [iInd, jInd, eW] = G.toParVec() w = [0.5, 1., 0.5, 0.5, 1., 0.5] for ind in range(nedge): self.assertEqual(i[ind], iInd[ind]) self.assertEqual(j[ind], jInd[ind]) self.assertEqual(eW[ind], w[ind]) def test_small_in(self): [nvert, nedge, i, j, G] = self.small_test_graph() G.normalizeEdgeWeights(DiGraph.In) [iInd, jInd, eW] = G.toParVec() w = [0.5, 0.5, 1., 1., 0.5, 0.5] for ind in range(nedge): self.assertEqual(i[ind], iInd[ind]) self.assertEqual(j[ind], jInd[ind]) self.assertEqual(eW[ind], w[ind]) class DegreeTests(HyGraphTests): def test_degree_no_edges(self): nvert = 4 nedge = 0 i = [] j = [] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) deg = G.degree() for ind in range(nvert): self.assertEqual(deg[ind], 0) def test_degree_simple(self): nvert = 11 nedge = 17 i = [0, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 4, 4, 4, 4, 4, 5] j = [3, 2, 3, 4, 5, 6, 7, 8, 1, 4, 5, 6, 7, 8, 9, 10, 4] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) deg = G.degree() degExpected = [0, 1, 1, 2, 3, 2, 2, 2, 2, 1, 1] for ind in range(nvert): self.assertEqual(deg[ind], degExpected[ind]) def test_npin_no_edges(self): nvert = 4 nedge = 0 i = [] j = [] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) npin = G.npin() for ind in range(nedge): self.assertEqual(npin[ind], 0) def test_npin_simple(self): nSvert = 11 nSedge = 17 i = [0, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 4, 4, 4, 4, 4, 5] j = [3, 2, 3, 4, 5, 6, 7, 8, 1, 4, 5, 6, 7, 8, 9, 10, 4] self.assertEqual(len(i), nSedge) self.assertEqual(len(j), nSedge) G = self.initializeGraph(nSvert, nSedge, i, j) nvert = G.nvert() self.assertEqual(11, nvert) nedge = G.nedge() self.assertEqual(6, nedge) npin = G.npin() npinExpected = [1, 7, 1, 1, 6, 1] for ind in range(nedge): self.assertEqual(npin[ind], npinExpected[ind]) def test_rank_no_edges(self): nvert = 4 nedge = 0 i = [] j = [] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) rank = G.rank() self.assertEqual(rank, 0) def test_rank_simple(self): nSvert = 11 nSedge = 17 i = [0, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 4, 4, 4, 4, 4, 5] j = [3, 2, 3, 4, 5, 6, 7, 8, 1, 4, 5, 6, 7, 8, 9, 10, 4] self.assertEqual(len(i), nSedge) self.assertEqual(len(j), nSedge) G = self.initializeGraph(nSvert, nSedge, i, j) nvert = G.nvert() self.assertEqual(11, nvert) nedge = G.nedge() self.assertEqual(6, nedge) rank = G.rank() self.assertEqual(rank, 7) def test_antirank_no_edges(self): nvert = 4 nedge = 0 i = [] j = [] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) G = self.initializeGraph(nvert, nedge, i, j) antirank = G.antirank() self.assertEqual(antirank, 0) def test_antirank_simple(self): nSvert = 11 nSedge = 17 i = [0, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 4, 4, 4, 4, 4, 5] j = [3, 2, 3, 4, 5, 6, 7, 8, 1, 4, 5, 6, 7, 8, 9, 10, 4] self.assertEqual(len(i), nSedge) self.assertEqual(len(j), nSedge) G = self.initializeGraph(nSvert, nSedge, i, j) nvert = G.nvert() self.assertEqual(11, nvert) nedge = G.nedge() self.assertEqual(6, nedge) antirank = G.antirank() self.assertEqual(antirank, 1) class CentralityTests(HyGraphTests): def test_exactBC_twoDTorus(self): n = 16 G = DiGraph.twoDTorus(n) nv = G.nvert() bc = G.centrality('exactBC',normalize=True) bcExpected = 0.0276826 for ind in range(nv): self.assertAlmostEqual(bc[ind],bcExpected, 6) def test_approxBC_twoDTorus(self): n = 16 G = DiGraph.twoDTorus(n) nv = G.nvert() bc = G.centrality('approxBC',sample=1.0, normalize=True) bcExpected = 0.0276826 for ind in range(nv): self.assertAlmostEqual(bc[ind],bcExpected, 6) def test_approxBC_twoDTorus_sample(self): n = 16 G = DiGraph.twoDTorus(n) nv = G.nvert() bc = G.centrality('approxBC',sample=0.05, normalize=True) bcExpected = 0.0276 for ind in range(nv): self.assertAlmostEqual(bc[ind],bcExpected,2) class BFSTreeTests(HyGraphTests): def test_bfsTree(self): nvert = 7 nSEdge = 9 # #SimpleEdge origI = [0, 0, 1, 1, 2, 1, 2, 2, 3] origJ = [1, 2, 2, 3, 3, 4, 4, 5, 6] origV = 1 G = self.initializeGraph(nvert, nSEdge, origI, origJ, origV) root = 1 parentsExpected = [-1, 1, 1, 2, 2, 4, -1] parents = G.bfsTree(root) self.assertEqual(len(parentsExpected), len(parents)) for ind in range(len(parents)): self.assertEqual(parentsExpected[ind], parents[ind]) class IsBFSTreeTests(HyGraphTests): def test_isBfsTree(self): nvert = 7 nSEdge = 9 # #SimpleEdge origI = [0, 0, 1, 1, 2, 1, 2, 2, 3] origJ = [1, 2, 2, 3, 3, 4, 4, 5, 6] origV = 1 G = self.initializeGraph(nvert, nSEdge, origI, origJ, origV) root = 1 parents = G.bfsTree(root) ret = G.isBfsTree(root, parents) self.assertTrue(type(ret)==tuple) [ret2, levels] = ret self.assertTrue(ret2) levelsExpected = [-1, 0, 1, 2, 2, 3, -1] self.assertEqual(len(levelsExpected),len(levels)) for i in range(len(levels)): self.assertEqual(levelsExpected[i],levels[i]) class NeighborsTests(HyGraphTests): def test_neighbors(self): nvert = 8 nedge = 13 i = [1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 7, 7] j = [2, 4, 5, 7, 6, 1, 3, 7, 6, 3, 3, 4, 5] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) neighborsExpected = [0, 1, 0, 1, 0, 0, 0, 1] G = self.initializeGraph(nvert, nedge, i, j) neighbors = G.neighbors(4) for ind in range(nvert): self.assertEqual(neighbors[ind], neighborsExpected[ind]) def test_neighbors_2hop(self): nvert = 8 nedge = 12 i = [1, 1, 2, 2, 4, 4, 4, 5, 6, 7, 7, 7] j = [2, 4, 5, 7, 1, 3, 7, 6, 3, 3, 4, 5] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) neighborsExpected = [0, 1, 1, 1, 1, 1, 0, 1] G = self.initializeGraph(nvert, nedge, i, j) neighbors = G.neighbors(4, nhop=2) for ind in range(nvert): self.assertEqual(neighbors[ind], neighborsExpected[ind]) class PathsHopTests(HyGraphTests): def test_pathsHop(self): nvert = 8 nedge = 13 i = [1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 7, 7] j = [2, 4, 5, 7, 6, 1, 3, 7, 6, 3, 3, 4, 5] self.assertEqual(len(i), nedge) self.assertEqual(len(j), nedge) neighborsExpected = [-1, 4, -1, 4, -1, 2, -1, 4] G = self.initializeGraph(nvert, nedge, i, j) tmp = ParVec.range(8) starts = (tmp == 2) | (tmp == 4) neighbors = G.pathsHop(starts) for ind in range(nvert): self.assertEqual(neighbors[ind], neighborsExpected[ind]) class LoadTests(HyGraphTests): def test_load(self): G = HyGraph.load('testfiles/UFlorida_Pajek_Sandi_sandi.mtx') G._T(); # swap so edges are papers, not authors self.assertEqual(G.nvert(),314) self.assertEqual(G.nedge(),360) [i, j, v] = G.toParVec() self.assertEqual(len(i),613) self.assertEqual(len(j),613) self.assertEqual(len(v),613) expectedNdx = [100, 200, 300, 400, 500, 600] expectedI = [ 23, 13, 222, 244, 310, 352] expectedJ = [ 63, 121, 155, 204, 252, 309] expectedV = 1 for ind in range(len(expectedNdx)): self.assertEqual(i[expectedNdx[ind]], expectedI[ind]) self.assertEqual(j[expectedNdx[ind]], expectedJ[ind]) self.assertEqual(v[expectedNdx[ind]], expectedV) def test_load_bad_file(self): self.assertRaises(IOError, DiGraph.load, 'not_a_real_file.mtx') # def test_UFget_simple_unsym(self): # G = UFget('Pajek/CSphd') # self.assertEqual(G.nvert(), 1882) # self.assertEqual(G.nedge(), 1740) # # def test_UFget_simple_sym(self): # G = UFget('Pajek/dictionary28') # self.assertEqual(G.nvert(), 52652) # self.assertEqual(G.nedge(), 178076) class MaxTests(HyGraphTests): def test_max_out(self): nvert = 9 nedge = 19 i = [0, 1, 1, 2, 1, 3, 2, 3, 3, 4, 6, 8, 7, 8, 1, 1, 1, 1, 1] j = [1, 0, 2, 1, 3, 1, 3, 2, 4, 3, 8, 6, 8, 7, 4, 5, 6, 7, 8] v = [01, 10, 12, 21, 13, 31, 23, 32, 34, 43, 68, 1.6e10, 78, 87, 14, 15, 16, 17, 18] G = self.initializeGraph(nvert, nedge, i, j, v) self.assertEqual(G.nvert(), nvert) self.assertEqual(G.nedge(), nedge) outmax = G.max(dir=DiGraph.Out) inmax = G.max(dir=DiGraph.In) outmaxExpected = [1, 18, 23, 34, 43, 0, 68, 78, 1.6e10] inmaxExpected = [10, 31, 32, 43, 34, 15, 1.6e+10, 87, 78] self.assertEqual(len(outmax), len(outmaxExpected)) self.assertEqual(len(inmax), len(inmaxExpected)) for ind in range(len(outmax)): self.assertEqual(outmax[ind], outmaxExpected[ind]) self.assertEqual(inmax[ind], inmaxExpected[ind]) class MinTests(HyGraphTests): def test_min_out(self): nvert = 9 nedge = 19 i = [0, 1, 1, 2, 1, 3, 2, 3, 3, 4, 6, 8, 7, 8, 1, 1, 1, 1, 1] j = [1, 0, 2, 1, 3, 1, 3, 2, 4, 3, 8, 6, 8, 7, 4, 5, 6, 7, 8] v = [-01, -10, -12, -21, -13, -31, -23, -32, -34, -43, -68, -1.6e10, -78, -87, -14, -15, -16, -17, -18] G = self.initializeGraph(nvert, nedge, i, j, v) self.assertEqual(G.nvert(), nvert) self.assertEqual(G.nedge(), nedge) outmin = G.min(dir=DiGraph.Out) inmin = G.min(dir=DiGraph.In) outminExpected = [-1, -18, -23, -34, -43, 0, -68, -78, -1.6e10] inminExpected = [-10, -31, -32, -43, -34, -15, -1.6e+10, -87, -78] self.assertEqual(len(outmin), len(outminExpected)) self.assertEqual(len(inmin), len(inminExpected)) for ind in range(len(outmin)): self.assertEqual(outmin[ind], outminExpected[ind]) self.assertEqual(inmin[ind], inminExpected[ind]) class BuiltInMethodTests(HyGraphTests): def test_HyGraph_simple(self): # ensure that a simple HyGraph constructor creates the number, source/ # destination, and value pairs expected. nvert = 7 nSEdge = 9 # #SimpleEdge origI = [0, 0, 1, 1, 2, 1, 2, 2, 3] origJ = [1, 2, 2, 3, 3, 4, 4, 5, 6] origV = [1, 2, 12, 13, 23, 14, 24, 25, 46] G = self.initializeGraph(nvert, nSEdge, origI, origJ, origV) self.assertEqual(7, G.nvert()) self.assertEqual(4, G.nedge()) [actualI, actualJ, actualV] = G.toParVec() self.assertEqual(len(origI), len(actualI)) self.assertEqual(len(origJ), len(actualJ)) self.assertEqual(len(origV), len(actualV)) for ind in range(len(origI)): self.assertEqual(origI[ind], actualI[ind]) self.assertEqual(origJ[ind], actualJ[ind]) self.assertEqual(origV[ind], actualV[ind]) def test_HyGraph_no_verts(self): nvert = 0 nedge = 6 i = [0, 3, 2, 2, 1, 3] j = [0, 0, 1, 2, 3, 3] self.assertRaises(KeyError, self.initializeGraph, nvert, nedge, i, j) # def test_indexing_simple_scalar_scalar(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 2, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = 2 # G2 = G[ndx,ndx] # [actualI, actualJ, actualV] = G2.toParVec() # expI = [0] # expJ = [0] # expV = [21] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_indexing_simple_scalar_null(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = 2 # G2 = G[ndx,ndx] # [actualI, actualJ, actualV] = G2.toParVec() # expI = [] # expJ = [] # expV = [] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_indexing_simple_ParVeclen1_scalar(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 2, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(1) # ndx[0] = 2 # G2 = G[ndx,ndx] # [actualI, actualJ, actualV] = G2.toParVec() # expI = [0] # expJ = [0] # expV = [21] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_indexing_simple_ParVeclen1_null(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(1) # ndx[0] = 2 # G2 = G[ndx,ndx] # [actualI, actualJ, actualV] = G2.toParVec() # expI = [] # expJ = [] # expV = [] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_indexing_simple_ParVeclenk(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(3) # ndx[0] = 2 # ndx[1] = 3 # ndx[2] = 4 # G2 = G[ndx,ndx] # [actualI, actualJ, actualV] = G2.toParVec() # expI = [1, 0, 2, 1] # expJ = [0, 1, 1, 2] # expV = [32, 23, 43, 34] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_subgraph_simple_scalar_scalar(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 2, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = 2 # G2 = G.subgraph(ndx,ndx) # [actualI, actualJ, actualV] = G2.toParVec() # expI = [0] # expJ = [0] # expV = [21] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_subgraph_simple_scalar_null(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = 2 # G2 = G.subgraph(ndx,ndx) # [actualI, actualJ, actualV] = G2.toParVec() # expI = [] # expJ = [] # expV = [] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_subgraph_simple_ParVeclen1_scalar(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 2, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(1) # ndx[0] = 2 # G2 = G.subgraph(ndx,ndx) # [actualI, actualJ, actualV] = G2.toParVec() # expI = [0] # expJ = [0] # expV = [21] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_subgraph_simple_ParVeclen1_null(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(1) # ndx[0] = 2 # G2 = G.subgraph(ndx,ndx) # [actualI, actualJ, actualV] = G2.toParVec() # expI = [] # expJ = [] # expV = [] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_subgraph_simple_ParVeclenk(self): # # ensure that a simple DiGraph constructor creates the number, source/ # # destination, and value pairs expected. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # ndx = ParVec(3) # ndx[0] = 2 # ndx[1] = 3 # ndx[2] = 4 # G2 = G.subgraph(ndx,ndx) # [actualI, actualJ, actualV] = G2.toParVec() # expI = [1, 0, 2, 1] # expJ = [0, 1, 1, 2] # expV = [32, 23, 43, 34] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_DiGraph_duplicates(self): # # ensure that a DiGraph constructor creates the number, source/ # # destination, and value pairs expected when 3 input edges have # # the same source and destination. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 3, 3, 3, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # expI = [1, 0, 2, 3, 1, 3, 3, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # expJ = [0, 1, 1, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # expV = [10, 1, 21, 31, 12, 32, 79, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G = self.initializeGraph(nvert, nedge, origI, origJ, origV) # [actualI, actualJ, actualV] = G.toParVec() # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(origI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_add_simple(self): # # ensure that DiGraph addition creates the number, source/ # # destination, and value pairs expected when all edges are # # in both DiGraphs. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV1 = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # origV2 = [11, 2, 22, 32, 13, 33, 14, 24, 44, 15, 35, 16, 17, (1.6e+10)+1, # 18, 88, 19, 69, 79] # expV = [21, 3, 43, 63, 25, 65, 27, 47, 87, 29, 69, 31, 33, (3.2e+10)+1, # 35, 175, 37, 137, 157] # G1 = self.initializeGraph(nvert, nedge, origI, origJ, origV1) # G2 = self.initializeGraph(nvert, nedge, origI, origJ, origV2) # G3 = G1+G2 # [actualI, actualJ, actualV] = G3.toParVec() # self.assertEqual(len(origI), len(actualI)) # self.assertEqual(len(origJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(origI)): # self.assertEqual(origI[ind], actualI[ind]) # self.assertEqual(origJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_add_union(self): # # ensure that DiGraph addition creates the number, source/ # # destination, and value pairs expected when some edges are not # # in both DiGraphs. # nvert1 = 9 # nedge1 = 19 # origI1 = [1, 0, 2, 4, 1, 3, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ1 = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV1 = [10, 1, 21, 41, 12, 32, 13, 23, 33, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) # nvert2 = 9 # nedge2 = 19 # origI2 = [7, 3, 6, 8, 5, 7, 4, 5, 6, 5, 7, 7, 2, 7, 2, 7, 0, 2, 5] # origJ2 = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV2 = [70, 31, 61, 81, 52, 72, 43, 53, 63, 54, 74, 75, 26, 1.6e+10, # 27, 77, 8, 28, 58] # G2 = self.initializeGraph(nvert2, nedge2, origI2, origJ2, origV2) # G3 = G1 + G2 # [actualI, actualJ, actualV] = G3.toParVec() # expNvert = 9 # expNedge = 38 # expI = [1, 7, 0, 2, 3, 4, 6, 8, 1, 3, 5, 7, 1, 2, 3, 4, 5, 6, 1, 3, 5, # 7, 1, 7, 1, 2, 7, 8, 1, 2, 7, 8, 0, 1, 2, 5, 6, 7] # expJ = [0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, # 4, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8] # expV = [10,70, 1,21,31,41,61,81,12,32,52,72,13,23,33,43,53,63,14,34,54, # 74,15,75,16,26,1.6e+10,1.6e+10,17,27,77,87,8,18,28,58,68,78] # [actualI, actualJ, actualV] = G3.toParVec() # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertEqual(expV[ind], actualV[ind]) # # def test_multiply_simple(self): # # ensure that DiGraph addition creates the number, source/ # # destination, and value pairs expected when all edges are # # in both DiGraphs. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV1 = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 7.3, # 17, 87, 18, 68, 78] # origV2 = [11, 2, 22, 32, 13, 33, 14, 24, 44, 15, 35, 16, 17, 8.3, # 18, 88, 19, 69, 79] # expV = [110, 2, 462, 992, 156, 1056, 182, 552, 1892, 210, 1190, 240, # 272, 60.59, 306, 7656, 342, 4692, 6162] # G1 = self.initializeGraph(nvert, nedge, origI, origJ, origV1) # G2 = self.initializeGraph(nvert, nedge, origI, origJ, origV2) # G3 = G1*G2 # [actualI, actualJ, actualV] = G3.toParVec() # self.assertEqual(len(origI), len(actualI)) # self.assertEqual(len(origJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(origI)): # self.assertEqual(origI[ind], actualI[ind]) # self.assertEqual(origJ[ind], actualJ[ind]) # self.assertAlmostEqual(expV[ind], actualV[ind]) # # def test_multiply_intersection(self): # # ensure that DiGraph addition creates the number, source/ # # destination, and value pairs expected when some edges are not # # in both DiGraphs. # nvert1 = 9 # nedge1 = 19 # origI1 = [1, 0, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] # origJ1 = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV1 = [10, 1, 41, 61, 12, 52, 13, 23, 33, 14, 34, 15, 16, 7.7, # 17, 87, 8, 68, 78] # G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) # nvert2 = 9 # nedge2 = 19 # origI2 = [7, 3, 4, 8, 5, 7, 3, 5, 6, 3, 7, 7, 2, 8, 2, 7, 0, 2, 5] # origJ2 = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV2 = [70, 31, 41, 81, 52, 72, 33, 53, 63, 34, 74, 75, 26, 7.7, # 27, 77, 8, 28, 58] # G2 = self.initializeGraph(nvert2, nedge2, origI2, origJ2, origV2) # G3 = G1*G2 # [actualI, actualJ, actualV] = G3.toParVec() # expNvert = 9 # expNedge = 6 # expI = [4, 5, 3, 3, 8, 0] # expJ = [1, 2, 3, 4, 6, 8] # expV = [1681, 2704, 1089, 1156, 59.29, 64] # self.assertEqual(len(expI), len(actualI)) # self.assertEqual(len(expJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(expI)): # self.assertEqual(expI[ind], actualI[ind]) # self.assertEqual(expJ[ind], actualJ[ind]) # self.assertAlmostEqual(expV[ind], actualV[ind]) # # def test_div_simple(self): # # ensure that DiGraph addition creates the number, source/ # # destination, and value pairs expected when all edges are # # in both DiGraphs. # nvert = 9 # nedge = 19 # origI = [1, 0, 2, 3, 1, 3, 1, 2, 4, 1, 3, 1, 1, 8, 1, 8, 1, 6, 7] # origJ = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] # origV1 = [10, 1, 21, 31, 12, 32, 13, 23, 43, 14, 34, 15, 16, 1.6e+10, # 17, 87, 18, 68, 78] # origV2 = [11, 2, 22, 32, 13, 33, 14, 24, 44, 15, 35, 16, 17, (1.6e+10)+1, # 18, 88, 19, 69, 79] # expV = [0.9090909091, 0.5, 0.9545454545, 0.96875, 0.92307692, 0.96969696, # 0.92857142, 0.95833333, 0.97727272, 0.93333333, 0.97142857, 0.93750000, # 0.94117647, 1, 0.94444444, 0.98863636, 0.94736842, 0.98550724, 0.98734177] # G1 = self.initializeGraph(nvert, nedge, origI, origJ, origV1) # G2 = self.initializeGraph(nvert, nedge, origI, origJ, origV2) # G3 = G1/G2 # [actualI, actualJ, actualV] = G3.toParVec() # self.assertEqual(len(origI), len(actualI)) # self.assertEqual(len(origJ), len(actualJ)) # self.assertEqual(len(expV), len(actualV)) # for ind in range(len(origI)): # self.assertEqual(origI[ind], actualI[ind]) # self.assertEqual(origJ[ind], actualJ[ind]) # self.assertAlmostEqual(expV[ind], actualV[ind]) class GeneralPurposeTests(HyGraphTests): def test_multNot(self): nvert1 = 9 nedge1 = 19 origI1 = [1, 0, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] origJ1 = [0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] origV1 = [10, 1, 41, 61, 12, 52, 13, 23, 33, 14, 34, 15, 1.6, 8.6, 17, 87, 8, 68, 78] G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) nvert2 = 9 nedge2 = 10 origI2 = [7, 0, 4, 8, 5, 2, 7, 8, 1, 7] origJ2 = [0, 1, 1, 1, 2, 3, 5, 6, 7, 8] origV2 = [70, 1, 41, 81, 52, 23, 75, 8.6, 17, 78] G2 = self.initializeGraph(nvert2, nedge2, origI2, origJ2, origV2) G3 = G1.mulNot(G2) [actualI, actualJ, actualV] = G3.toParVec() expNvert = 9 expNedge = 13 expI = [1, 6, 1, 1, 3, 1, 3, 1, 1, 8, 0, 6] expJ = [0, 1, 2, 3, 3, 4, 4, 5, 6, 7, 8, 8] expV = [10, 61, 12, 13, 33, 14, 34, 15, 1.6, 87, 8, 68] self.assertEqual(len(expI), len(actualI)) self.assertEqual(len(expJ), len(actualJ)) self.assertEqual(len(expV), len(actualV)) for ind in range(len(expI)): self.assertEqual(expI[ind], actualI[ind]) self.assertEqual(expJ[ind], actualJ[ind]) self.assertAlmostEqual(expV[ind], actualV[ind]) def test_scale_out(self): nvert1 = 9 nedge1 = 19 origI1 = [0, 1, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] origJ1 = [1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] origV1 = [10, 1, 41, 61, 12, 52, 13, 23, 33, 14, 34, 15, 1.6, 8.6, 17, 87, 8, 68, 78] G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) vec1 = SpParVec(nvert1) # vec[0] null, scaling a null column in G1 vec1[1] = 1 vec1[2] = 2 vec1[3] = 3 vec1[4] = 4 vec1[5] = 5 # vec[6] null, scaling a non-null column in G1 vec1[7] = 7 vec1[8] = 8 G1.scale(vec1, dir=DiGraph.Out) [actualI, actualJ, actualV] = G1.toParVec() expI = [0, 1, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] expJ = [1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] expV = [10, 1, 164, 61, 12, 260, 13, 46, 99, 14, 102, 15, 1.6, 68.8, 17, 696, 8, 68, 546] self.assertEqual(len(expI), len(actualI)) self.assertEqual(len(expJ), len(actualJ)) self.assertEqual(len(expV), len(actualV)) for ind in range(len(expI)): self.assertEqual(expI[ind], actualI[ind]) self.assertEqual(expJ[ind], actualJ[ind]) self.assertAlmostEqual(expV[ind], actualV[ind]) def test_scale_in(self): nvert1 = 9 nedge1 = 19 origI1 = [0, 1, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] origJ1 = [1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] origV1 = [10, 1, 41, 61, 12, 52, 13, 23, 33, 14, 34, 15, 1.6, 8.6, 17, 87, 8, 68, 78] G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) vec1 = SpParVec(nvert1) # vec[0] null, scaling a null column in G1 vec1[1] = 1 vec1[2] = 2 vec1[3] = 3 vec1[4] = 4 vec1[5] = 5 # vec[6] null, scaling a non-null column in G1 vec1[7] = 7 vec1[8] = 8 G1.scale(vec1, dir=DiGraph.In) [actualI, actualJ, actualV] = G1.toParVec() expI = [0, 1, 4, 6, 1, 5, 1, 2, 3, 1, 3, 1, 1, 8, 1, 8, 0, 6, 7] expJ = [1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8, 8, 8] expV = [10, 1, 41, 61, 24, 104, 39, 69, 99, 56, 136, 75, 1.6, 8.6, 119, 609, 64, 544, 624] self.assertEqual(len(expI), len(actualI)) self.assertEqual(len(expJ), len(actualJ)) self.assertEqual(len(expV), len(actualV)) for ind in range(len(expI)): self.assertEqual(expI[ind], actualI[ind]) self.assertEqual(expJ[ind], actualJ[ind]) self.assertAlmostEqual(expV[ind], actualV[ind]) class LinearAlgebraTests(HyGraphTests): def test_matMul_1row1col(self): nvert1 = 16 nedge1 = 4 origI1 = [0, 0, 0, 0] origJ1 = [1, 3, 4, 12] origV1 = [1, 1, 1, 1] G1 = self.initializeGraph(nvert1, nedge1, origI1, origJ1, origV1) nvert2 = 16 nedge2 = 4 origI2 = [1, 3, 4, 12] origJ2 = [0, 0, 0, 0] origV2 = [1, 1, 1, 1] G2 = self.initializeGraph(nvert2, nedge2, origI2, origJ2, origV2) G3 = G1._SpMM(G2) self.assertEqual(G1.nvert(), G3.nvert()) [i3, j3, v3] = G3.toParVec() expLen = 1 self.assertEqual(len(i3),expLen) self.assertEqual(len(j3),expLen) self.assertEqual(len(v3),expLen) expectedI = [0] expectedJ = [0] expectedV = [4] for ind in range(len(expectedI)): self.assertEqual(i3[ind], expectedI[ind]) self.assertEqual(j3[ind], expectedJ[ind]) self.assertEqual(v3[ind], expectedV[ind]) def test_matMul_simple(self): G = DiGraph.load('testfiles/small_nonsym_fp.mtx') GT = G.copy() GT._T() G2 = G._SpMM(GT) self.assertEqual(G.nvert(),9) [i2, j2, v2] = G2.toParVec() self.assertEqual(len(i2),30) self.assertEqual(len(j2),30) self.assertEqual(len(v2),30) expectedI = [0, 2, 3, 1, 2, 3, 4, 6, 7, 8, 0, 1, 2, 3, 4, 0, 1, 2, 3, 1, 2, 4, 1, 6, 7, 1, 6, 7, 1, 8] expectedJ = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 6, 6, 6, 7, 7, 7, 8, 8] expectedV = [0.0001, 0.0001, 0.0001, 0.001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 1.6e+8, 0.0001, 0.0001, 0.0002, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0003, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 1.6e+8, 2.56e+20] for ind in range(len(expectedI)): self.assertEqual(i2[ind], expectedI[ind]) self.assertEqual(j2[ind], expectedJ[ind]) self.assertAlmostEqual(v2[ind], expectedV[ind], places=3) def runTests(verbosity = 1): testSuite = suite() unittest.TextTestRunner(verbosity=verbosity).run(testSuite) def suite(): suite = unittest.TestSuite() suite.addTests(unittest.TestLoader().loadTestsFromTestCase(BuiltInMethodTests)) suite.addTests(unittest.TestLoader().loadTestsFromTestCase(ConstructorTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(PageRankTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(NormalizeEdgeWeightsTests)) suite.addTests(unittest.TestLoader().loadTestsFromTestCase(DegreeTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(CentralityTests)) suite.addTests(unittest.TestLoader().loadTestsFromTestCase(BFSTreeTests)) suite.addTests(unittest.TestLoader().loadTestsFromTestCase(IsBFSTreeTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(NeighborsTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(PathsHopTests)) suite.addTests(unittest.TestLoader().loadTestsFromTestCase(LoadTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(MaxTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(MinTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(GeneralPurposeTests)) #suite.addTests(unittest.TestLoader().loadTestsFromTestCase(LinearAlgebraTests)) return suite if __name__ == '__main__': runTests()

37.224954

91

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ae5383134b36479c90b4a4739b3cd0e4533ed365

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py

Python

yxf_yixue/bazi/_fenxi.py

lamzuzuzu/yxf_yixue_py

90eb077f195b543f93a507f28b0a4c016cb0c92f

[ "MIT" ]

20

2019-01-08T08:13:39.000Z

2021-12-23T09:04:14.000Z

yxf_yixue/bazi/_fenxi.py

lamzuzuzu/yxf_yixue_py

90eb077f195b543f93a507f28b0a4c016cb0c92f

[ "MIT" ]

null

yxf_yixue/bazi/_fenxi.py

lamzuzuzu/yxf_yixue_py

90eb077f195b543f93a507f28b0a4c016cb0c92f

[ "MIT" ]

13

2019-04-22T03:25:13.000Z

2022-01-04T05:43:48.000Z

#!/usr/bin/python3 # -*- coding: utf-8 -*- from ..utils import Db, Db2Cdata class Chuantongfenxi: def __init__(self): self.pan = None self.db = Db() self.db2cdata = Db2Cdata() def fenxi(self, pan): self.pan = pan self.pan['标签'] = '传统分析' self._wangshuai() self._geju() self._yongshen() self._qushu() return self.pan def _wangshuai(self): pass def _geju(self): pass def _yongshen(self): pass def _qushu(self): pass def output_addition(self): map_str = '' return map_str class Lianghuafenxi(Chuantongfenxi): def __init__(self): super(Lianghuafenxi, self).__init__() self.pan = None self.db = Db() self.db2cdata = Db2Cdata() def fenxi(self, pan): self.pan = pan self.pan['标签'] = '量化分析' self.pan['量化分析'] = {} self.pan['量化分析']['八字权重表'] = self.db.get_tabledict_dict("[八字-八字权重]") self.pan['量化分析']['旺衰权重表'] = self.db.get_tabledict_dict("[八字-旺衰权重]") self.pan['量化分析']['八字传统定格表'] = self.db.get_tabledict_dict("[八字-八字传统定格表]") # self.pan['量化分析']['八字量化取用表'] = self.db.get_tabledict_dict("[八字-八字量化取用表]") self.pan['量化分析']['五行'] = self.pan['五行'] # 存储五行（六亲）量化值 self.pan['量化分析']['天干'] = self.pan['天干'] # 后面会把所有地支转化为天干，存储十神量化值 self.pan['量化分析']['六亲'] = {} self.pan['量化分析']['十神'] = {} for wuxing in self.pan['量化分析']['五行']: self.pan['量化分析']['六亲'][self.pan['量化分析']['五行'][wuxing]['六亲']] = self.pan['量化分析']['五行'][wuxing] for tiangan in self.pan['量化分析']['天干']: self.pan['量化分析']['十神'][self.pan['量化分析']['天干'][tiangan]['十神']] = self.pan['量化分析']['天干'][tiangan] self.pan['量化分析']['旺衰'] = {} self.pan['量化分析']['八字格局'] = '' self.pan['量化分析']['格局序号'] = '' self.pan['量化分析']['取用格局'] = '' self.pan['量化分析']['八字喜忌'] = {} self.pan['量化分析']['建议取用'] = {} self.pan['量化分析']['建议取数'] = '' self._wangshuai() self._geju() self._yongshen() self._qushu() return self.pan def _wangshuai(self): # 此处采用新浪博客“留指爪”的方法，原文没有提及五行自身旺衰的变化，我认为需要添加此逻辑 # 1.八字旺衰：初始化八字权重系数之天干 for name in ['年干', '月干', '日干', '时干']: # 配置四柱的天干权值 for item in self.pan['量化分析']['八字权重表']: if name == self.pan['量化分析']['八字权重表'][item]['宫位']: self.pan['八字单字'][name]['系数'] = 1.0 * float(self.pan['量化分析']['八字权重表'][item]['权重']) # 2.八字旺衰：初始化八字权重系数之地支（藏干） for name in ['年支', '月支', '日支', '时支']: # 配置四柱的地支藏干权值 for item in self.pan['量化分析']['八字权重表']: if name == self.pan['量化分析']['八字权重表'][item]['宫位']: self.pan['八字单字'][name]['藏干']['藏干1系数'] = float(self.pan['八字单字'][name]['藏干']['藏干1系数']) * float( self.pan['量化分析']['八字权重表'][item]['权重']) self.pan['八字单字'][name]['藏干']['藏干2系数'] = float(self.pan['八字单字'][name]['藏干']['藏干2系数']) * float( self.pan['量化分析']['八字权重表'][item]['权重']) self.pan['八字单字'][name]['藏干']['藏干3系数'] = float(self.pan['八字单字'][name]['藏干']['藏干3系数']) * float( self.pan['量化分析']['八字权重表'][item]['权重']) # 3.八字旺衰：初始化天干本来系数（初始化八字权重系数之后） for tiangan in self.pan['量化分析']['天干']: self.pan['量化分析']['天干'][tiangan]['权重'] = 0 for name in ['年干', '月干', '日干', '时干']: # 把四柱天干的系数更新到十神量化值 if tiangan == self.pan['八字单字'][name]['宫主']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['八字单字'][name]['系数']) # 4.八字旺衰：地支藏干系数转化到天干系数 for name in ['年支','月支','日支','时支']: # 把四柱地支藏干的系数更新到十神量化值 if self.pan['八字单字'][name]['藏干']['藏干1'] != '无': # 地支藏干1 for tiangan in self.pan['量化分析']['天干']: if tiangan == self.pan['八字单字'][name]['藏干']['藏干1']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['八字单字'][name]['藏干']['藏干1系数']) if self.pan['八字单字'][name]['藏干']['藏干2'] != '无': # 地支藏干2 for tiangan in self.pan['量化分析']['天干']: if tiangan == self.pan['八字单字'][name]['藏干']['藏干2']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['八字单字'][name]['藏干']['藏干2系数']) if self.pan['八字单字'][name]['藏干']['藏干3'] != '无': # 地支藏干3 for tiangan in self.pan['量化分析']['天干']: if tiangan == self.pan['八字单字'][name]['藏干']['藏干3']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['八字单字'][name]['藏干']['藏干3系数']) # 5.八字旺衰：干支关系转化到天干系数 for guanxi in self.pan['干支关系']: # 根据干支关系化生的天干及其系数，更新到十神量化值 for zuhe in self.pan['干支关系'][guanxi]: for tiangan in self.pan['量化分析']['天干']: if self.pan['干支关系'][guanxi][zuhe].get('化', None): if tiangan == self.pan['干支关系'][guanxi][zuhe]['化']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['干支关系'][guanxi][zuhe]['化系数']) if self.pan['干支关系'][guanxi][zuhe].get('化1', None): if tiangan == self.pan['干支关系'][guanxi][zuhe]['化1']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['干支关系'][guanxi][zuhe]['化1系数']) if self.pan['干支关系'][guanxi][zuhe].get('化2', None): if tiangan == self.pan['干支关系'][guanxi][zuhe]['化2']: self.pan['量化分析']['天干'][tiangan]['权重'] += float(self.pan['干支关系'][guanxi][zuhe]['化2系数']) # 6.八字旺衰：旺衰权重系数转化到天干系数（依月支） for tiangan in self.pan['量化分析']['天干']: for item in self.pan['量化分析']['旺衰权重表']: if self.pan['量化分析']['天干'][tiangan]['旺衰'] == item: self.pan['量化分析']['天干'][tiangan]['权重'] *= float(self.pan['量化分析']['旺衰权重表'][item]['权重']) # 7.八字旺衰：天干系数（十神权值）归一化 sum = 0 for tiangan in self.pan['量化分析']['天干']: sum += self.pan['量化分析']['天干'][tiangan]['权重'] for tiangan in self.pan['量化分析']['天干']: self.pan['量化分析']['天干'][tiangan]['归一'] = self.pan['量化分析']['天干'][tiangan]['权重']/sum*100 # 8.八字旺衰：六亲权重（天干十神的简单归并） for wuxing in self.pan['量化分析']['五行']: self.pan['量化分析']['五行'][wuxing]['权重'] = 0 for tiangan in self.pan['量化分析']['天干']: if self.pan['量化分析']['天干'][tiangan]['五行'] == wuxing: self.pan['量化分析']['五行'][wuxing]['权重'] += self.pan['量化分析']['天干'][tiangan]['权重'] self.pan['量化分析']['五行'][wuxing]['归一'] = self.pan['量化分析']['五行'][wuxing]['权重']/sum*100 # 1.日主旺衰：己生助 self.pan['量化分析']['旺衰']['己生助'] = 0 for wuxing in self.pan['量化分析']['五行']: if self.pan['量化分析']['五行'][wuxing]['六亲'] in ['比劫', '印枭']: self.pan['量化分析']['旺衰']['己生助'] += self.pan['量化分析']['五行'][wuxing]['归一'] # 2.日主旺衰：克泄耗 self.pan['量化分析']['旺衰']['克泄耗'] = 0 for wuxing in self.pan['量化分析']['五行']: if self.pan['量化分析']['五行'][wuxing]['六亲'] in ['官杀', '财星', '食伤']: self.pan['量化分析']['旺衰']['克泄耗'] += self.pan['量化分析']['五行'][wuxing]['归一'] # 3.日主旺衰：阴气 self.pan['量化分析']['旺衰']['阴气'] = 0 for tiangan in self.pan['量化分析']['天干']: if tiangan in ['乙', '丁', '己', '辛', '癸']: self.pan['量化分析']['旺衰']['阴气'] += self.pan['量化分析']['天干'][tiangan]['归一'] # 4.日主旺衰：阳气 self.pan['量化分析']['旺衰']['阳气'] = 0 for tiangan in self.pan['量化分析']['天干']: if tiangan in ['甲', '丙', '戊', '庚', '壬']: self.pan['量化分析']['旺衰']['阳气'] += self.pan['量化分析']['天干'][tiangan]['归一'] # 5.日主旺衰：分段判定。这里的百分比是很重要的参数 self.pan['量化分析']['旺衰']['日干'] = '无' if self.pan['量化分析']['旺衰']['己生助'] < 17: self.pan['量化分析']['旺衰']['日干'] = '极弱' elif 17 < self.pan['量化分析']['旺衰']['己生助'] <= 37: self.pan['量化分析']['旺衰']['日干'] = '弱' elif 37 < self.pan['量化分析']['旺衰']['己生助'] <= 47: self.pan['量化分析']['旺衰']['日干'] = '偏弱' elif 47 < self.pan['量化分析']['旺衰']['己生助'] <= 53: self.pan['量化分析']['旺衰']['日干'] = '中' elif 53 < self.pan['量化分析']['旺衰']['己生助'] <= 63: self.pan['量化分析']['旺衰']['日干'] = '偏强' elif 63 < self.pan['量化分析']['旺衰']['己生助'] <= 83: self.pan['量化分析']['旺衰']['日干'] = '强' elif 83 < self.pan['量化分析']['旺衰']['己生助']: self.pan['量化分析']['旺衰']['日干'] = '极强' def _geju(self): # 八字格局 for tiangan in self.pan['量化分析']['八字传统定格表']: if self.pan['八字单字']['日干']['宫主'] == tiangan: geju_str = self.pan['量化分析']['八字传统定格表'][tiangan][self.pan['八字单字']['月支']['宫主']] self.pan['量化分析']['八字格局'] = geju_str.split(' ')[0] self.pan['量化分析']['格局序号'] = geju_str.split(' ')[1] if len(geju_str.split(' ')) >= 3: self.pan['量化分析']['八字格局'] += geju_str.split(' ')[2] # 取用格局 if self.pan['量化分析']['旺衰']['日干'] == '极弱': self.pan['量化分析']['取用格局'] = '从弱' elif self.pan['量化分析']['旺衰']['日干'] == '极强': self.pan['量化分析']['取用格局'] = '从强' elif self.pan['量化分析']['旺衰']['日干'] == '中': self.pan['量化分析']['取用格局'] = '通关' else: self.pan['量化分析']['取用格局'] = '扶抑' def _yongshen(self): # 取用不能仅靠量化，需要分类： # 扶抑： # 日干弱多官杀，不能克制官杀，而应当泄掉官杀，所以取印枭 # 日干弱多财星，需要比劫来帮身 # 日干弱多食伤，需要印枭克制 # 日干强多印枭，需要财星泄日干以及制印枭 # 日干强多比劫，需要官杀 # 从格： # 日干极强从强，极弱从弱 # 通关： # 日干中和，多财印需要官杀通关，多印食需要比劫通关，多官比需要印枭通关 # 调候： # 日干中和且不需要通关，木火性燥取金水，金水性寒取木火 # 1.喜忌 # 1.1.扶抑喜忌 if self.pan['量化分析']['取用格局'] == '扶抑': # 1.1.1.日干弱（财官食必大于53） if self.pan['量化分析']['旺衰']['日干'] in ['弱', '偏弱']: if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['财星']['归一']\ or self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: # 取印枭 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['官杀'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['财星'] else: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['食伤'] elif self.pan['量化分析']['六亲']['财星']['归一'] > self.pan['量化分析']['六亲']['官杀']['归一'] \ or self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: # 取比劫 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['财星'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['食伤'] else: # 取印枭 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['食伤'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['财星']['归一']: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['财星'] # 1.1.2.日干强（印比必大于53） elif self.pan['量化分析']['旺衰']['日干'] in ['强', '偏强']: if self.pan['量化分析']['六亲']['印枭']['归一'] >= self.pan['量化分析']['六亲']['比劫']['归一']: # 取财星 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['财星'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['比劫'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['食伤'] else: # 取官杀 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['官杀'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['比劫'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['财星'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['食伤'] # 1.2.从格喜忌（与扶抑喜忌对应相反） elif self.pan['量化分析']['取用格局'] in ['从弱', '从强']: # 1.2.1.从弱喜克泄耗 if self.pan['量化分析']['取用格局'] == '从弱': if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['财星']['归一'] \ or self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['官杀'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['财星'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['食伤'] elif self.pan['量化分析']['六亲']['财星']['归一'] > self.pan['量化分析']['六亲']['官杀']['归一'] \ or self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['财星'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['食伤'] else: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['食伤'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['财星']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['财星'] # 1.2.2.从强喜生助 elif self.pan['量化分析']['取用格局'] == '从强': if self.pan['量化分析']['六亲']['印枭']['归一'] >= self.pan['量化分析']['六亲']['比劫']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['财星'] self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['比劫'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['官杀'] else: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['食伤'] else: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['官杀'] self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['比劫'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['财星'] else: self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['食伤'] # 1.3.通关喜忌 elif self.pan['量化分析']['旺衰']['日干'] == '中': if self.pan['量化分析']['六亲']['财星']['归一'] + self.pan['量化分析']['六亲']['印枭']['归一'] >= 53: # 取官杀 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['官杀'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['比劫'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['印枭']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['财星'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['印枭'] else: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['财星'] elif self.pan['量化分析']['六亲']['印枭']['归一'] + self.pan['量化分析']['六亲']['食伤']['归一'] >= 53: # 取比劫 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['官杀'] if self.pan['量化分析']['六亲']['印枭']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['印枭'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['食伤'] else: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['食伤'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['印枭'] elif self.pan['量化分析']['六亲']['官杀']['归一'] + self.pan['量化分析']['六亲']['比劫']['归一'] >= 53: # 取印枭 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['六亲']['印枭'] if self.pan['量化分析']['六亲']['财星']['归一'] >= self.pan['量化分析']['六亲']['食伤']['归一']: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['财星'] else: self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['六亲']['食伤'] if self.pan['量化分析']['六亲']['官杀']['归一'] >= self.pan['量化分析']['六亲']['比劫']['归一']: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['官杀'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['比劫'] else: self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['六亲']['比劫'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['六亲']['官杀'] # 1.4.调候喜忌 else: if self.pan['量化分析']['五行']['木']['归一'] + self.pan['量化分析']['五行']['火']['归一'] >= self.pan['量化分析']['五行']['金']['归一'] + self.pan['量化分析']['五行']['水']['归一']: # 取水金 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['五行']['水'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['五行']['金'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['五行']['火'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['五行']['木'] else: # 取火木 self.pan['量化分析']['八字喜忌']['喜1'] = self.pan['量化分析']['五行']['火'] self.pan['量化分析']['八字喜忌']['喜2'] = self.pan['量化分析']['五行']['木'] self.pan['量化分析']['八字喜忌']['忌1'] = self.pan['量化分析']['五行']['水'] self.pan['量化分析']['八字喜忌']['忌2'] = self.pan['量化分析']['五行']['金'] # 2.通过喜忌取用 self.pan['量化分析']['建议取用'] = {'五行': self.pan['量化分析']['八字喜忌']['喜1']['五行'], '六亲': self.pan['量化分析']['八字喜忌']['喜1']['六亲']} tmp_list = [] for tiangan in self.pan['量化分析']['天干']: if self.pan['量化分析']['建议取用']['五行'] == self.pan['量化分析']['天干'][tiangan]['五行']: tmp_list.append(self.pan['量化分析']['天干'][tiangan]) if tmp_list[0]['归一'] >= tmp_list[1]['归一']: self.pan['量化分析']['建议取用']['天干'] = tmp_list[0]['天干'] self.pan['量化分析']['建议取用']['十神'] = tmp_list[0]['十神'] else: self.pan['量化分析']['建议取用']['天干'] = tmp_list[1]['天干'] self.pan['量化分析']['建议取用']['十神'] = tmp_list[1]['十神'] def _qushu(self): self.pan['量化分析']['建议取数'] = self.pan['量化分析']['五行'][self.pan['量化分析']['八字喜忌']['喜1']['五行']]['五行数']+self.pan['量化分析']['五行'][self.pan['量化分析']['八字喜忌']['喜2']['五行']]['五行数'] def output_addition(self): map_str = '' map_str += '\n\n【量化分析】\n' map_str += '六亲力量：' for i in self.pan['量化分析']['五行']: map_str += str(self.pan['量化分析']['五行'][i]['六亲']) map_str += str(i) map_str += str(round(self.pan['量化分析']['五行'][i]['归一'],2))+'%' map_str += ';' map_str += '\n' map_str += '十神力量：' for i in self.pan['量化分析']['天干']: map_str += str(self.pan['量化分析']['天干'][i]['十神']) map_str += str(i) map_str += str(round(self.pan['量化分析']['天干'][i]['归一'],2))+'%' map_str += ';' map_str += '\n' map_str += '命主强弱：' map_str += '己生助'+str(round(self.pan['量化分析']['旺衰']['己生助'],2))+'%;' map_str += '克泄耗'+str(round(self.pan['量化分析']['旺衰']['克泄耗'],2))+'%;' map_str += '阴气'+str(round(self.pan['量化分析']['旺衰']['阴气'],2))+'%;' map_str += '阳气'+str(round(self.pan['量化分析']['旺衰']['阳气'],2))+'%;' map_str += '命主'+str(self.pan['量化分析']['旺衰']['日干'])+';' map_str += '\n' map_str += '八字格局：' map_str += str(self.pan['量化分析']['八字格局'])+'格;' map_str += '\n' map_str += '取用格局：' map_str += str(self.pan['量化分析']['取用格局']) + '格;' map_str += '\n' map_str += '八字喜忌：喜'+self.pan['量化分析']['八字喜忌']['喜1']['五行']+self.pan['量化分析']['八字喜忌']['喜2']['五行']+';忌'+self.pan['量化分析']['八字喜忌']['忌1']['五行']+self.pan['量化分析']['八字喜忌']['忌2']['五行']+';' map_str += '\n' map_str += '建议取用：'+self.pan['量化分析']['建议取用']['六亲']+self.pan['量化分析']['建议取用']['五行']+';'+self.pan['量化分析']['建议取用']['十神']+self.pan['量化分析']['建议取用']['天干']+';' map_str += '\n' map_str += '建议取数：'+self.pan['量化分析']['建议取数']+';' map_str += '\n' # # 测试 # for k in self.pan['量化分析']: # map_str += str(k)+': ' # map_str += str(self.pan['量化分析'][k]) # map_str += '\n\n' return map_str

55.411192

184

0.421533

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22,774

3.328666

0.086804

0.301788

0.424501

0.170873

0.822292

0.745741

0.635016

0.589274

0.551104

0.509989

0

0.013319

0.287872

22,774

410

185

55.546341

0.573067

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0.171957

0

1

0.040346

false

0.011527

0.002882

0

0.060519

0

null

1

0

null

0

5

ae68c3ea3965e3f719035242f837e69f2231c6ed

52

py

Python

backend/types/files_convertor.py

Exanis/cannelloni

43adcaf468d95ca774a82e1d2fea3877f0b648a4

[ "MIT" ]

1

2017-03-16T16:10:37.000Z

backend/types/files_convertor.py

Exanis/cannelloni

43adcaf468d95ca774a82e1d2fea3877f0b648a4

[ "MIT" ]

null

backend/types/files_convertor.py

Exanis/cannelloni

43adcaf468d95ca774a82e1d2fea3877f0b648a4

[ "MIT" ]

null

# -*- coding: utf8 -*- "Files-related convertors"

10.4

26

0.615385

5

52

6.4

1

0

0.023256

0.173077

52

4

27

13

0.72093

0.884615

0

0.827586

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

882c1c50ab8e9b8d6006732ce22c17b592419b38

19,098

py

Python

tests/unit/modules/test_yumpkg.py

xiaowei582648206/saltx

1d17b030b973ce5422e0fbe7e17c98c7ca91c49b

[ "Apache-2.0" ]

1

2022-02-09T06:40:14.000Z

tests/unit/modules/test_yumpkg.py

xiaowei582648206/saltx

1d17b030b973ce5422e0fbe7e17c98c7ca91c49b

[ "Apache-2.0" ]

null

tests/unit/modules/test_yumpkg.py

xiaowei582648206/saltx

1d17b030b973ce5422e0fbe7e17c98c7ca91c49b

[ "Apache-2.0" ]

4

2020-11-04T06:28:05.000Z

2022-02-09T10:54:49.000Z

# -*- coding: utf-8 -*- # Import Python Libs from __future__ import absolute_import # Import Salt Testing Libs from tests.support.mixins import LoaderModuleMockMixin from tests.support.unit import TestCase, skipIf from tests.support.mock import ( Mock, MagicMock, patch, NO_MOCK, NO_MOCK_REASON ) # Import Salt libs import salt.modules.yumpkg as yumpkg LIST_REPOS = { 'base': { 'file': '/etc/yum.repos.d/CentOS-Base.repo', 'gpgcheck': '1', 'gpgkey': 'file:///etc/pki/rpm-gpg/RPM-GPG-KEY-CentOS-7', 'mirrorlist': 'http://mirrorlist.centos.org/?release=$releasever&arch=$basearch&repo=os&infra=$infra', 'name': 'CentOS-$releasever - Base' }, 'base-source': { 'baseurl': 'http://vault.centos.org/centos/$releasever/os/Source/', 'enabled': '0', 'file': '/etc/yum.repos.d/CentOS-Sources.repo', 'gpgcheck': '1', 'gpgkey': 'file:///etc/pki/rpm-gpg/RPM-GPG-KEY-CentOS-7', 'name': 'CentOS-$releasever - Base Sources' }, 'updates': { 'file': '/etc/yum.repos.d/CentOS-Base.repo', 'gpgcheck': '1', 'gpgkey': 'file:///etc/pki/rpm-gpg/RPM-GPG-KEY-CentOS-7', 'mirrorlist': 'http://mirrorlist.centos.org/?release=$releasever&arch=$basearch&repo=updates&infra=$infra', 'name': 'CentOS-$releasever - Updates' }, 'updates-source': { 'baseurl': 'http://vault.centos.org/centos/$releasever/updates/Source/', 'enabled': '0', 'file': '/etc/yum.repos.d/CentOS-Sources.repo', 'gpgcheck': '1', 'gpgkey': 'file:///etc/pki/rpm-gpg/RPM-GPG-KEY-CentOS-7', 'name': 'CentOS-$releasever - Updates Sources' } } @skipIf(NO_MOCK, NO_MOCK_REASON) class YumTestCase(TestCase, LoaderModuleMockMixin): ''' Test cases for salt.modules.yumpkg ''' def setup_loader_modules(self): return { yumpkg: { '__context__': { 'yum_bin': 'yum', }, '__grains__': { 'osarch': 'x86_64', 'os_family': 'RedHat', 'osmajorrelease': 7, }, } } def test_latest_version_with_options(self): with patch.object(yumpkg, 'list_pkgs', MagicMock(return_value={})): # with fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.latest_version( 'foo', refresh=False, fromrepo='good', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '--disablerepo=*', '--enablerepo=good', '--branch=foo', 'list', 'available', 'foo'], ignore_retcode=True, output_loglevel='trace', python_shell=False) # without fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.latest_version( 'foo', refresh=False, enablerepo='good', disablerepo='bad', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '--disablerepo=bad', '--enablerepo=good', '--branch=foo', 'list', 'available', 'foo'], ignore_retcode=True, output_loglevel='trace', python_shell=False) def test_list_repo_pkgs_with_options(self): ''' Test list_repo_pkgs with and without fromrepo NOTE: mock_calls is a stack. The most recent call is indexed with 0, while the first call would have the highest index. ''' really_old_yum = MagicMock(return_value='3.2.0') older_yum = MagicMock(return_value='3.4.0') newer_yum = MagicMock(return_value='3.4.5') list_repos_mock = MagicMock(return_value=LIST_REPOS) kwargs = {'output_loglevel': 'trace', 'ignore_retcode': True, 'python_shell': False} with patch.object(yumpkg, 'list_repos', list_repos_mock): # Test with really old yum. The fromrepo argument has no effect on # the yum commands we'd run. with patch.dict(yumpkg.__salt__, {'cmd.run': really_old_yum}): cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_repo_pkgs('foo') # We should have called cmd.run_all twice self.assertEqual(len(cmd.mock_calls), 2) # Check args from first call self.assertEqual( cmd.mock_calls[1][1], (['yum', '--quiet', 'list', 'available'],) ) # Check kwargs from first call self.assertEqual(cmd.mock_calls[1][2], kwargs) # Check args from second call self.assertEqual( cmd.mock_calls[0][1], (['yum', '--quiet', 'list', 'installed'],) ) # Check kwargs from second call self.assertEqual(cmd.mock_calls[0][2], kwargs) # Test with really old yum. The fromrepo argument has no effect on # the yum commands we'd run. with patch.dict(yumpkg.__salt__, {'cmd.run': older_yum}): cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_repo_pkgs('foo') # We should have called cmd.run_all twice self.assertEqual(len(cmd.mock_calls), 2) # Check args from first call self.assertEqual( cmd.mock_calls[1][1], (['yum', '--quiet', '--showduplicates', 'list', 'available'],) ) # Check kwargs from first call self.assertEqual(cmd.mock_calls[1][2], kwargs) # Check args from second call self.assertEqual( cmd.mock_calls[0][1], (['yum', '--quiet', '--showduplicates', 'list', 'installed'],) ) # Check kwargs from second call self.assertEqual(cmd.mock_calls[0][2], kwargs) # Test with newer yum. We should run one yum command per repo, so # fromrepo would limit how many calls we make. with patch.dict(yumpkg.__salt__, {'cmd.run': newer_yum}): # When fromrepo is used, we would only run one yum command, for # that specific repo. cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_repo_pkgs('foo', fromrepo='base') # We should have called cmd.run_all once self.assertEqual(len(cmd.mock_calls), 1) # Check args self.assertEqual( cmd.mock_calls[0][1], (['yum', '--quiet', '--showduplicates', 'repository-packages', 'base', 'list', 'foo'],) ) # Check kwargs self.assertEqual(cmd.mock_calls[0][2], kwargs) # Test enabling base-source and disabling updates. We should # get two calls, one for each enabled repo. Because dict # iteration order will vary, different Python versions will be # do them in different orders, which is OK, but it will just # mean that we will have to check both the first and second # mock call both times. cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_repo_pkgs( 'foo', enablerepo='base-source', disablerepo='updates') # We should have called cmd.run_all twice self.assertEqual(len(cmd.mock_calls), 2) for repo in ('base', 'base-source'): for index in (0, 1): try: # Check args self.assertEqual( cmd.mock_calls[index][1], (['yum', '--quiet', '--showduplicates', 'repository-packages', repo, 'list', 'foo'],) ) # Check kwargs self.assertEqual(cmd.mock_calls[index][2], kwargs) break except AssertionError: continue else: self.fail("repo '{0}' not checked".format(repo)) def test_list_upgrades_dnf(self): ''' The subcommand should be "upgrades" with dnf ''' with patch.dict(yumpkg.__context__, {'yum_bin': 'dnf'}): # with fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_upgrades( refresh=False, fromrepo='good', branch='foo') cmd.assert_called_once_with( ['dnf', '--quiet', '--disablerepo=*', '--enablerepo=good', '--branch=foo', 'list', 'upgrades'], output_loglevel='trace', ignore_retcode=True, python_shell=False) # without fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_upgrades( refresh=False, enablerepo='good', disablerepo='bad', branch='foo') cmd.assert_called_once_with( ['dnf', '--quiet', '--disablerepo=bad', '--enablerepo=good', '--branch=foo', 'list', 'upgrades'], output_loglevel='trace', ignore_retcode=True, python_shell=False) def test_list_upgrades_yum(self): ''' The subcommand should be "updates" with yum ''' # with fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_upgrades( refresh=False, fromrepo='good', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '--disablerepo=*', '--enablerepo=good', '--branch=foo', 'list', 'updates'], output_loglevel='trace', ignore_retcode=True, python_shell=False) # without fromrepo cmd = MagicMock(return_value={'retcode': 0, 'stdout': ''}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.list_upgrades( refresh=False, enablerepo='good', disablerepo='bad', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '--disablerepo=bad', '--enablerepo=good', '--branch=foo', 'list', 'updates'], output_loglevel='trace', ignore_retcode=True, python_shell=False) def test_refresh_db_with_options(self): with patch('salt.utils.pkg.clear_rtag', Mock()): # With check_update=True we will do a cmd.run to run the clean_cmd, and # then a separate cmd.retcode to check for updates. # with fromrepo clean_cmd = Mock() update_cmd = MagicMock(return_value=0) with patch.dict(yumpkg.__salt__, {'cmd.run': clean_cmd, 'cmd.retcode': update_cmd}): yumpkg.refresh_db( check_update=True, fromrepo='good', branch='foo') clean_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'clean', 'expire-cache', '--disablerepo=*', '--enablerepo=good', '--branch=foo'], python_shell=False) update_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'check-update', '--setopt=autocheck_running_kernel=false', '--disablerepo=*', '--enablerepo=good', '--branch=foo'], output_loglevel='trace', ignore_retcode=True, python_shell=False) # without fromrepo clean_cmd = Mock() update_cmd = MagicMock(return_value=0) with patch.dict(yumpkg.__salt__, {'cmd.run': clean_cmd, 'cmd.retcode': update_cmd}): yumpkg.refresh_db( check_update=True, enablerepo='good', disablerepo='bad', branch='foo') clean_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'clean', 'expire-cache', '--disablerepo=bad', '--enablerepo=good', '--branch=foo'], python_shell=False) update_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'check-update', '--setopt=autocheck_running_kernel=false', '--disablerepo=bad', '--enablerepo=good', '--branch=foo'], output_loglevel='trace', ignore_retcode=True, python_shell=False) # With check_update=False we will just do a cmd.run for the clean_cmd # with fromrepo clean_cmd = Mock() with patch.dict(yumpkg.__salt__, {'cmd.run': clean_cmd}): yumpkg.refresh_db( check_update=False, fromrepo='good', branch='foo') clean_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'clean', 'expire-cache', '--disablerepo=*', '--enablerepo=good', '--branch=foo'], python_shell=False) # without fromrepo clean_cmd = Mock() with patch.dict(yumpkg.__salt__, {'cmd.run': clean_cmd}): yumpkg.refresh_db( check_update=False, enablerepo='good', disablerepo='bad', branch='foo') clean_cmd.assert_called_once_with( ['yum', '--quiet', '--assumeyes', 'clean', 'expire-cache', '--disablerepo=bad', '--enablerepo=good', '--branch=foo'], python_shell=False) def test_install_with_options(self): parse_targets = MagicMock(return_value=({'foo': None}, 'repository')) with patch.object(yumpkg, 'list_pkgs', MagicMock(return_value={})), \ patch.object(yumpkg, 'list_holds', MagicMock(return_value=[])), \ patch.dict(yumpkg.__salt__, {'pkg_resource.parse_targets': parse_targets}), \ patch('salt.utils.systemd.has_scope', MagicMock(return_value=False)): # with fromrepo cmd = MagicMock(return_value={'retcode': 0}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.install( refresh=False, fromrepo='good', branch='foo') cmd.assert_called_once_with( ['yum', '-y', '--disablerepo=*', '--enablerepo=good', '--branch=foo', 'install', 'foo'], output_loglevel='trace', python_shell=False, redirect_stderr=True) # without fromrepo cmd = MagicMock(return_value={'retcode': 0}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.install( refresh=False, enablerepo='good', disablerepo='bad', branch='foo') cmd.assert_called_once_with( ['yum', '-y', '--disablerepo=bad', '--enablerepo=good', '--branch=foo', 'install', 'foo'], output_loglevel='trace', python_shell=False, redirect_stderr=True) def test_upgrade_with_options(self): with patch.object(yumpkg, 'list_pkgs', MagicMock(return_value={})), \ patch('salt.utils.systemd.has_scope', MagicMock(return_value=False)): # with fromrepo cmd = MagicMock(return_value={'retcode': 0}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.upgrade( refresh=False, fromrepo='good', exclude='kernel*', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '-y', '--disablerepo=*', '--enablerepo=good', '--branch=foo', '--exclude=kernel*', 'upgrade'], output_loglevel='trace', python_shell=False) # without fromrepo cmd = MagicMock(return_value={'retcode': 0}) with patch.dict(yumpkg.__salt__, {'cmd.run_all': cmd}): yumpkg.upgrade( refresh=False, enablerepo='good', disablerepo='bad', exclude='kernel*', branch='foo') cmd.assert_called_once_with( ['yum', '--quiet', '-y', '--disablerepo=bad', '--enablerepo=good', '--branch=foo', '--exclude=kernel*', 'upgrade'], output_loglevel='trace', python_shell=False)

43.208145

115

0.484239

1,796

19,098

4.943764

0.13196

0.030409

0.060818

0.047077

0.796937

0.765852

0.74389

0.731839

0.720126

0.708638

0

0.006066

0.387109

19,098

441

116

43.306122

0.752499

0.099644

0

0.687688

0

0.006006

0.196609

0.029277

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false

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0.045045

0

null

0

1

0

null

0

5

88430f5062b8c05301fc75c14108244b19a5a701

91

py

Python

keras_losses/__init__.py

CyberZHG/keras-losses

20f6adaf65770c031f63e69570ec96814c4591e8

[ "MIT" ]

9

2018-10-11T03:02:18.000Z

2021-02-23T03:22:06.000Z

keras_losses/__init__.py

CyberZHG/keras-losses

20f6adaf65770c031f63e69570ec96814c4591e8

[ "MIT" ]

null

keras_losses/__init__.py

CyberZHG/keras-losses

20f6adaf65770c031f63e69570ec96814c4591e8

[ "MIT" ]

2

2019-01-03T08:49:17.000Z

2021-08-12T10:27:12.000Z

from .ranking import * from .weighted import * from .coral import * __version__ = '0.5.0'

15.166667

23

0.703297

13

91

4.615385

0.615385

0.333333

0

0.04

0.175824

91

5

24

18.2

0.76

0

0.054945

0

1

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false

0

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0

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1

0

1

0

null

0

1

0

1

0

5

8848ce1af35d57bbbee16305fa556dad09e4aa2c

1,278

py

Python

platform/core/polyaxon/tracker/events/operation_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/tracker/events/operation_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/tracker/events/operation_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

import tracker from events.registry import operation_run tracker.subscribe(operation_run.OperationRunCreatedEvent) tracker.subscribe(operation_run.OperationRunUpdatedEvent) tracker.subscribe(operation_run.OperationRunCleanedTriggeredEvent) tracker.subscribe(operation_run.OperationRunViewedEvent) tracker.subscribe(operation_run.OperationRunArchivedEvent) tracker.subscribe(operation_run.OperationRunRestoredEvent) tracker.subscribe(operation_run.OperationRunSkippedEvent) tracker.subscribe(operation_run.OperationRunDeletedEvent) tracker.subscribe(operation_run.OperationRunDeletedTriggeredEvent) tracker.subscribe(operation_run.OperationRunStoppedEvent) tracker.subscribe(operation_run.OperationRunStoppedTriggeredEvent) tracker.subscribe(operation_run.OperationRunResumedEvent) tracker.subscribe(operation_run.OperationRunResumedTriggeredEvent) tracker.subscribe(operation_run.OperationRunRestartedEvent) tracker.subscribe(operation_run.OperationRunRestartedTriggeredEvent) tracker.subscribe(operation_run.OperationRunSkippedTriggeredEvent) tracker.subscribe(operation_run.OperationRunNewStatusEvent) tracker.subscribe(operation_run.OperationRunSucceededEvent) tracker.subscribe(operation_run.OperationRunFailedEvent) tracker.subscribe(operation_run.OperationRunDoneEvent)

51.12

68

0.91471

108

1,278

10.62963

0.259259

0.219512

0.43554

0.487805

0

0.021909

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true

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null

1

0

null

0

1

0

5

ee2c74f607e6bb8601355e5b5bbfee305252b336

99

py

Python

emiz/weather/avwx/__init__.py

theendsofinvention/emiz

98b210dd36053ce8062d54e8c501ca4715cd78b5

[ "MIT" ]

null

emiz/weather/avwx/__init__.py

theendsofinvention/emiz

98b210dd36053ce8062d54e8c501ca4715cd78b5

[ "MIT" ]

5

2020-03-24T16:34:15.000Z

2020-06-26T08:31:46.000Z

emiz/weather/avwx/__init__.py

theendsofinvention/emiz

98b210dd36053ce8062d54e8c501ca4715cd78b5

[ "MIT" ]

1

2018-04-01T16:02:13.000Z

# coding=utf-8 """ Access to AVWX API https://avwx.rest/documentation """ from .avwx import AVWX

11

31

0.69697

15

99

4.6

0.8

0

0.011905

0.151515

99

8

32

12.375

0.809524

0.656566

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ee3f26d10e4fd3ce4f4b971ca0eac1d96f87faf4

123

py

Python

{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}/public/handlers.py

drgarcia1986/cookiecutter-muffin

7aa861787b4280477a726da99cf9de4047b01d91

[ "MIT" ]

3

2016-06-24T21:14:37.000Z

2017-03-07T05:36:33.000Z

{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}/public/handlers.py

drgarcia1986/cookiecutter-muffin

7aa861787b4280477a726da99cf9de4047b01d91

[ "MIT" ]

null

{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}/public/handlers.py

drgarcia1986/cookiecutter-muffin

7aa861787b4280477a726da99cf9de4047b01d91

[ "MIT" ]

null

from .. import app @app.register('/', methods=['GET']) def index(request): return app.ps.jinja2.render('index.html')

17.571429

45

0.658537

17

123

4.764706

0.823529

0

0.009346

0.130081

123

6

46

20.5

0.747664

0

0.113821

0

1

0.25

false

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ee55530ac7ae7f4f055a981126d2a2981b0523f1

7,890

py

Python

aoc2021/inputs/DATA_4.py

catalystcf/freezing-archer

9d87ced30d04436d2a05ed8ff29ced2c4a438f03

[ "MIT" ]

37

2016-12-14T19:01:47.000Z

2021-12-06T15:26:54.000Z

aoc2021/inputs/DATA_4.py

catalystcf/freezing-archer

9d87ced30d04436d2a05ed8ff29ced2c4a438f03

[ "MIT" ]

4

2016-08-01T05:19:52.000Z

2017-01-07T07:47:43.000Z

aoc2021/inputs/DATA_4.py

catalystcf/freezing-archer

9d87ced30d04436d2a05ed8ff29ced2c4a438f03

[ "MIT" ]

3

2016-12-02T09:20:42.000Z

2021-12-01T13:31:07.000Z

27,14,70,7,85,66,65,57,68,23,33,78,4,84,25,18,43,71,76,61,34,82,93,74,26,15,83,64,2,35,19,97,32,47,6,51,99,20,77,75,56,73,80,86,55,36,13,95,52,63,79,72,9,10,16,8,69,11,50,54,81,22,45,1,12,88,44,17,62,0,96,94,31,90,39,92,37,40,5,98,24,38,46,21,30,49,41,87,91,60,48,29,59,89,3,42,58,53,67,28 31 23 52 26 8 27 89 37 80 46 97 19 63 34 79 13 59 45 12 73 42 25 22 6 39 27 71 24 3 0 79 42 32 72 62 99 52 11 92 33 38 22 16 44 39 35 26 76 49 58 39 19 82 53 57 52 98 69 77 23 1 40 18 66 83 34 85 28 48 16 15 93 38 96 27 74 50 88 84 99 34 2 11 25 17 57 4 19 83 1 59 77 42 36 33 73 22 23 37 55 98 91 56 84 78 45 21 24 83 40 46 58 8 67 4 33 97 55 7 86 2 68 64 27 69 68 29 14 49 26 4 21 87 71 32 58 5 17 46 93 45 96 8 83 2 78 91 9 20 42 49 81 19 48 37 38 23 45 82 92 93 99 67 66 42 40 74 25 56 16 21 47 26 75 61 53 66 72 30 34 55 82 77 6 92 60 56 8 22 88 5 71 49 29 74 28 2 32 84 73 52 31 24 68 41 48 82 19 29 65 51 91 97 39 80 3 55 43 40 38 20 89 53 45 75 29 74 19 89 18 32 88 93 46 63 91 4 94 64 5 57 54 49 36 40 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35 59 39 19 22 27 21 58 57 41 54 75 13 82 50 32 23 5 99 66 10 7 19 45 66 78 38 57 40 73 87 58 30 99 53 83 64 1 8 56 95 70 77 16 18 82 72 83 95 37 35 54 59 92 21 79 7 81 86 29 41 52 99 42 57 71 3 15 75 34 77 7 70 5 69 4 34 60 40 73 6 74 54 67 32 38 93 62 17 51 86 57 88 99 3 16 42 74 11 34 7 82 47 71 31 58 69 23 43 4 64 32 19 98 93 41 63 97 8 85 48 63 54 34 38 86 4 27 15 49 0 61 77 53 98 74 62 23 88 97 37 93 28 25 50 13 56 82 41 27 79 23 31 64 7 65 52 98 93 16 57 88 49 10 11 62 43 95 53 51 83 41 10 87 54 86 19 22 13 40 17 37 27 45 29 63 83 85 81 90 7 57 88 47 66 56 67 44 54 88 89 20 46 61 28 92 86 49 60 83 95 42 78 97 51 96 11 62 4 26 31 18 68 87 26 70 62 84 11 33 90 0 45 66 83 6 20 19 27 44 55 52 8 5 7 3 54 94 88 76 92 13 98 22 33 26 95 62 53 81 24 29 69 15 87 25 61 40 84 90 93 7 31 3 28 46 20 51 21 18 38 30 92 39 70 61 27 88 35 96 74 23 5 66 11 42 40 61 90 57 54 41 14 99 62 59 92 10 48 81 52 22 29 77 18 87 31 79 25 94 13 17 26 44 98 57 74 83 51 14 11 76 91 96 64 33 43 45 92 72 27 66 3 28 20 40 88 82 44 71 55 83 47 51 76 24 86 19 42 34 99 30 31 87 48 62 98 53 68 9 21 3 31 6 41 61 24 77 81 96 44 78 73 1 98 11 40 80 27 65 92 62 67 2 30 10 78 46 50 65 56 84 16 32 58 86 22 12 54 99 35 9 43 55 10 94 66 81 59 92 76 78 3 55 23 83 13 42 94 91 22 14 37 31 67 71 8 61 57 34 43 74 50 0 39 65 78 16 13 91 34 14 74 86 3 97 12 89 58 65 51 29 57 48 44 93 95 1 42 39 92 93 96 16 85 25 59 3 70 19 17 21 84 58 38 86 57 10 35 95 79 81 44 73 63 9 22 1 96 7 93 40 49 2 4 66 87 21 17 32 48 44 28 42 99 26 69 8 85 86 75 21 31 37 87 28 89 43 74 83 57 95 29 92 88 35 94 25 97 81 50 15 19 73 45 63 92 62 67 95 57 30 8 4 39 64 99 31 70 63 96 25 53 24 93 35 34 51 82 91 28 41 30 20 56 46 16 32 98 60 35 67 9 43 42 88 78 90 71 5 29 49 31 37 63 18 80 40 88 5 62 3 6 74 71 97 19 58 63 59 38 50 64 34 68 45 25 30 21 33 83 10 65 67 17 50 21 51 18 68 59 29 78 77 99 76 62 35 96 7 95 82 53 42 49 69 74 65 89 6 1 18 30 72 75 24 60 50 52 55 82 68 99 4 61 22 9 37 84 57 87 96 85 56 72 2 9 38 98 12 4 34 45 74 97 86 18 94 64 70 68 91 41 58 39 66 34 13 26 80 29 0 4 21 60 90 39 73 12 2 19 64 44 61 88 45 59 50 8 91 49 34 85 55 2 75 10 15 89 12 63 90 29 87 73 71 38 17 84 45 9 97 98 77 23 61 47 43 22 58 1 63 44 2 94 99 33 81 51 49 13 38 86 42 91 23 7 67 68 39 84 4 26 12 38 41 43 16 88 71 99 50 24 19 77 98 23 73 44 10 51 56 42 30 52 59 57 16 9 62 27 26 65 56 10 82 0 74 78 12 99 77 18 38 5 37 7 60 40 90 23 14 69 18 51 8 21 79 60 36 12 68 44 59 45 16 90 50 85 25 70 91 31 30 54 26 24 40 51 72 63 31 60 62 25 96 9 44 35 28 91 97 4 34 81 2 61 68 94 52 86 0 57 95 88 94 36 38 25 35 19 13 6 8 61 98 45 85 86 69 97 41 32 7 15 59 41 82 19 29 34 44 96 6 91 76 69 21 32 94 98 4 10 88 30 2 8 74 56 65 99 36 91 73 15 54 62 55 40 27 44 11 60 95 61 46 31 32 21 41 35 74 86 83 89 79 2 96 94 82 68 39 83 49 30 15 62 11 86 99 59 51 80 12 72 58 87 66 98 53 29 44 71 18 63 85 11 75 60 66 13 36 9 94 57 8 10 12 32 3 86 4 29 54 70 21 27 33 76 83 67 77 29 65 39 44 52 34 25 93 64 35 4 57 92 84 41 51 88 96 0 21 91 82 3 26 23 8 36 20 73 54 39 60 34 57 49 99 97 69 43 41 93 95 80 63 73 77 4 9 22 17 33 15 86 79 38 16 99 98 30 64 92 76 50 68 83 85 52 87 88 57 53 13 36 76 7 10 91 3 22 8 84 56 73 59 62 80 85 38 33 68 97 47 14 96 36 8 98 43 70 85 95 31 1 51 33 41 78 89 56 76 16 15 34 82 12 18 39 4 3 98 49 41 30 95 68 89 81 48 84 15 19 90 66 14 32 1 88 34 64 73 65 6 20 86 22 18 13 74 34 75 4 60 88 46 25 97 54 94 91 42 67 40 11 81 5 12 49 48 15 82 91 18 73 57 97 50 34 16 66 29 43 81 20 15 19 44 85 4 32 90 58 39 53 42 50 53 83 10 0 93 16 84 23 13 89 63 75 69 51 65 35 67 56 70 4 37 29 47 38

13.106312

289

0.62725

2,600

7,890

1.903462

0.038462

0.003233

0.002425

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0.980388

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7,890

601

290

13.12812

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null

0

1

0

1

0

null

1

0

1

0

5

ee613069e72fc22445ed875ffca215565dc7a91f

175

py

Python

script/resumeQuartHeure.py

nicolasleger/velibstats

850028cd6aa8ba86f7fe597433f8fc6aca211aa0

[ "MIT" ]

null

script/resumeQuartHeure.py

nicolasleger/velibstats

850028cd6aa8ba86f7fe597433f8fc6aca211aa0

[ "MIT" ]

null

script/resumeQuartHeure.py

nicolasleger/velibstats

850028cd6aa8ba86f7fe597433f8fc6aca211aa0

[ "MIT" ]

null

from resumeLib import debuterCalculResumeOfResume, debuterCalculResumeOfResumeConso import datetime debuterCalculResumeOfResume(5, 15) debuterCalculResumeOfResumeConso(5, 15)

35

83

0.897143

13

175

12.076923

0.615385

0.038217

0

0.036585

0.062857

175

5

84

35

0.920732

0

1

0

true

0

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0

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null

0

1

0

null

0

1

0

1

0

5

ee80953493527f46a32b0f63bf2a5af30c960fb3

206

py

Python

payrolls/admin.py

aaronmatei/Payroll-System-Django

5605e6a152c56cd171c43dfd07ff0a99eea65b4d

[ "bzip2-1.0.6" ]

null

payrolls/admin.py

aaronmatei/Payroll-System-Django

5605e6a152c56cd171c43dfd07ff0a99eea65b4d

[ "bzip2-1.0.6" ]

null

payrolls/admin.py

aaronmatei/Payroll-System-Django

5605e6a152c56cd171c43dfd07ff0a99eea65b4d

[ "bzip2-1.0.6" ]

2

2020-09-08T07:12:34.000Z

2021-11-19T08:25:22.000Z

from django.contrib import admin from .models import Department,Employee,Payrolls # Register your models here. admin.site.register(Department) admin.site.register(Employee) admin.site.register(Payrolls)

20.6

48

0.820388

27

206

6.259259

0.481481

0.159763

0.301775

0

0.092233

206

9

49

22.888889

0.903743

0.126214

0

1

0

true

0

0.4

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0

1

0

1

0

null

0

1

0

1

0

5

c98d2659ca34adf8804849290193ff37893a2510

166

py

Python

lib/aws_sso_lib/__init__.py

OlafConijn/aws-sso-util

2df0fff8d4a8a43d76fa31d429bd8d6c9657f144

[ "Apache-2.0" ]

null

lib/aws_sso_lib/__init__.py

OlafConijn/aws-sso-util

2df0fff8d4a8a43d76fa31d429bd8d6c9657f144

[ "Apache-2.0" ]

null

lib/aws_sso_lib/__init__.py

OlafConijn/aws-sso-util

2df0fff8d4a8a43d76fa31d429bd8d6c9657f144

[ "Apache-2.0" ]

null

__version__ = '1.0.0' from .sso import get_boto3_session, login, list_available_accounts, list_available_roles from .assignments import Assignment, list_assignments

33.2

88

0.837349

23

166

5.565217

0.695652

0.203125

0

0.026667

0.096386

166

4

89

41.5

0.826667

0

0.03012

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c9a0d41b5bbddafbc70c7d347c3aa3f83f90bd72

197

py

Python

P1/admin.py

rmohsen/webb

cc84260b9c7a5e3bb3a58f75d8c9c606288ef99a

[ "MIT" ]

null

P1/admin.py

rmohsen/webb

cc84260b9c7a5e3bb3a58f75d8c9c606288ef99a

[ "MIT" ]

null

P1/admin.py

rmohsen/webb

cc84260b9c7a5e3bb3a58f75d8c9c606288ef99a

[ "MIT" ]

null

from django.contrib import admin from P1.models import Post, Post_Word, Comment admin.site.register(Post) admin.site.register(Post_Word) admin.site.register(Comment) # Register your models here.

21.888889

46

0.807107

30

197

5.233333

0.466667

0.171975

0.324841

0.267516

0

0.00565

0.101523

197

8

47

24.625

0.881356

0.13198

0

1

0

true

0

0.4

0

0.4

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c9ac24322c18e222d1f8d5518921b3f10721bd4c

53

py

Python

code/sir/__init__.py

FrederikWR/course-02443-stochastic-virus-outbreak

4f1d7f1fa4aa197b31ed86c4daf420d5a637974e

[ "MIT" ]

null

code/sir/__init__.py

FrederikWR/course-02443-stochastic-virus-outbreak

4f1d7f1fa4aa197b31ed86c4daf420d5a637974e

[ "MIT" ]

null

code/sir/__init__.py

FrederikWR/course-02443-stochastic-virus-outbreak

4f1d7f1fa4aa197b31ed86c4daf420d5a637974e

[ "MIT" ]

null

from .parameter_estimator import ParameterEstimator

17.666667

51

0.886792

5

53

9.2

1

0

0.09434

53

2

52

26.5

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c9d55bc304681f3f1c2bd6c64d5c641d9fc76f45

114

py

Python

controlpanel/controlpanel/ros_check/admin.py

filesmuggler/acc_web_server

2497cbfdb08db30d4eb1ca842cee7c2f65ff7470

[ "MIT" ]

null

controlpanel/controlpanel/ros_check/admin.py

filesmuggler/acc_web_server

2497cbfdb08db30d4eb1ca842cee7c2f65ff7470

[ "MIT" ]

null

controlpanel/controlpanel/ros_check/admin.py

filesmuggler/acc_web_server

2497cbfdb08db30d4eb1ca842cee7c2f65ff7470

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import Car admin.site.register(Car)

14.25

32

0.780702

17

114

5.235294

0.647059

0

0.149123

114

7

33

16.285714

0.917526

0.22807

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a004c1872468383f76e4f9663e23c1a8f8bd4a36

223

py

Python

oauth2_provider/contrib/rest_framework/__init__.py

grnspace/django-oauth-toolkit

3d876563a2528eadac0f832f360a0b269b99b94e

[ "BSD-2-Clause-FreeBSD" ]

4

2017-01-09T17:01:28.000Z

2021-06-29T21:26:15.000Z

oauth2_provider/contrib/rest_framework/__init__.py

grnspace/django-oauth-toolkit

3d876563a2528eadac0f832f360a0b269b99b94e

[ "BSD-2-Clause-FreeBSD" ]

7

2018-03-14T19:40:42.000Z

2020-09-08T16:36:45.000Z

oauth2_provider/contrib/rest_framework/__init__.py

grnspace/django-oauth-toolkit

3d876563a2528eadac0f832f360a0b269b99b94e

[ "BSD-2-Clause-FreeBSD" ]

7

2018-03-07T14:02:15.000Z

2020-08-13T10:15:37.000Z

# flake8: noqa from .authentication import OAuth2Authentication from .permissions import ( TokenHasScope, TokenHasReadWriteScope, TokenMatchesOASRequirements, TokenHasResourceScope, IsAuthenticatedOrTokenHasScope )

31.857143

71

0.843049

14

223

13.428571

0.857143

0

0.010101

0.112108

223

6

72

37.166667

0.939394

0.053812

0

1

0

true

0

0.4

0

0.4

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

4e57f5bac31573d74b2919bfee097904f4d03779

253

py

Python

clinicadl/clinicadl/tools/inputs/filename_types.py

yogeshmj/AD-DL

76b9b564061581effe8f3698992bfea3ffb055fa

[ "MIT" ]

112

2019-10-21T14:50:35.000Z

2022-03-29T03:15:47.000Z

clinicadl/clinicadl/tools/inputs/filename_types.py

yogeshmj/AD-DL

76b9b564061581effe8f3698992bfea3ffb055fa

[ "MIT" ]

136

2019-10-17T17:40:55.000Z

2021-06-30T14:53:29.000Z

clinicadl/clinicadl/tools/inputs/filename_types.py

yogeshmj/AD-DL

76b9b564061581effe8f3698992bfea3ffb055fa

[ "MIT" ]

49

2019-11-26T13:57:52.000Z

2022-03-20T13:17:42.000Z

# coding: utf8 FILENAME_TYPE = {'full': '_T1w_space-MNI152NLin2009cSym_res-1x1x1_T1w', 'cropped': '_T1w_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w', 'skull_stripped': '_space-Ixi549Space_desc-skullstripped_T1w'}

42.166667

84

0.703557

27

253

6.074074

0.62963

0.097561

0.317073

0

0.140097

0.181818

253

5

85

50.6

0.652174

0.047431

0

0.677824

0.573222

0

1

0

false

0

1

0

null

0

1

0

1

0

1

null

0

5

4ebe0768d71d86c4de32f67a0ba3b59392f95b9a

78

py

Python

schema_matcher_api/featurizer.py

columbustech/schema_matcher_api

bba5d87e924c41a17fbb4ccf0319628d00d047e2

[ "BSD-3-Clause" ]

null

schema_matcher_api/featurizer.py

columbustech/schema_matcher_api

bba5d87e924c41a17fbb4ccf0319628d00d047e2

[ "BSD-3-Clause" ]

null

schema_matcher_api/featurizer.py

columbustech/schema_matcher_api

bba5d87e924c41a17fbb4ccf0319628d00d047e2

[ "BSD-3-Clause" ]

null

from .application import Application class Featurizer(Application): pass

15.6

36

0.794872

8

78

7.75

0.75

0

0.153846

78

4

37

19.5

0.939394

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

14fd74098ee45fc94bc1faf505ed7c4749b2a43b

73

py

Python

src/cortex_skill_utils/api_spec/__init__.py

wmcabee-cs/cortex-skill-utils

e97eb6decb3dae6647154ee459e6cf7987cbc93f

[ "MIT" ]

null

src/cortex_skill_utils/api_spec/__init__.py

wmcabee-cs/cortex-skill-utils

e97eb6decb3dae6647154ee459e6cf7987cbc93f

[ "MIT" ]

2

2021-03-25T22:40:33.000Z

2021-06-01T23:50:42.000Z

src/cortex_skill_utils/api_spec/__init__.py

wmcabee-cs/cortex-skill-utils

e97eb6decb3dae6647154ee459e6cf7987cbc93f

[ "MIT" ]

null

from .factory import define_api_spec from .decorate import cortex_action

24.333333

36

0.863014

11

73

5.454545

0.818182

0

0.109589

73

2

37

36.5

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0903fe4b6d2e5793af697ebd7ba1c3b954fe634f

315

py

Python

bin/cheers/controllers/forms.py

shutingrz/cheers

e8885e1dc2e7e717bc0ac13b4678a9cfe4d99a88

[ "MIT" ]

null

bin/cheers/controllers/forms.py

shutingrz/cheers

e8885e1dc2e7e717bc0ac13b4678a9cfe4d99a88

[ "MIT" ]

null

bin/cheers/controllers/forms.py

shutingrz/cheers

e8885e1dc2e7e717bc0ac13b4678a9cfe4d99a88

[ "MIT" ]

null

from cheers.util import Util from flask_wtf import FlaskForm from wtforms import StringField, validators class LoginForm(FlaskForm): user_id = StringField('user_id', [validators.length(min=1, max=Util.MaxUserIdLength)]) password = StringField('password', [validators.length(min=1, max=Util.MaxUserPassLength)])

35

91

0.796825

40

315

6.2

0.525

0.048387

0.153226

0.16129

0.217742

0

0.006993

0.092063

315

8

92

39.375

0.86014

0

0.047771

0

1

0

false

0.166667

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

091af48f28617e60426403ff086000b9cb130ff5

121

py

Python

NoticeBoard/admin.py

MdJunaidMahmood/IITR-Campus-guide

feee7217e2170b23da88c80d6e452d0d897be56e

[ "MIT" ]

1

2021-08-19T10:04:06.000Z

NoticeBoard/admin.py

MdJunaidMahmood/IITR-Campus-guide

feee7217e2170b23da88c80d6e452d0d897be56e

[ "MIT" ]

null

NoticeBoard/admin.py

MdJunaidMahmood/IITR-Campus-guide

feee7217e2170b23da88c80d6e452d0d897be56e

[ "MIT" ]

2

2021-07-10T04:41:50.000Z

2021-08-19T10:22:08.000Z

from django.contrib import admin from .models import message admin.site.register(message) # Register your models here.

17.285714

32

0.801653

17

121

5.705882

0.647059

0

0.132231

121

6

33

20.166667

0.92381

0.214876

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

091c645374e305a19472f540f545194e398b8a50

456

py

Python

sdk/python/pulumi_azure_nextgen/azurestack/v20170601/__init__.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/azurestack/v20170601/__init__.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/azurestack/v20170601/__init__.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** # Export this package's modules as members: from .customer_subscription import * from .get_customer_subscription import * from .get_registration import * from .get_registration_activation_key import * from .list_product_details import * from .registration import * from . import outputs

35.076923

80

0.769737

65

456

5.261538

0.661538

0.175439

0.114035

0.175439

0.192982

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0.002577

0.149123

456

12

81

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0.445175

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1

0

null

0

1

0

1

0

1

0

5

092ca9729723cd440bef589f6a111ec05d74dc14

1,926

py

Python

Python/POO/ex009.py

henrique-tavares/Coisas

f740518b1bedec5b0ea8c12ae07a2cac21eb51ae

[ "MIT" ]

1

2020-02-07T20:39:26.000Z

Python/POO/ex009.py

neptune076/Coisas

85c064cc0e134465aaf6ef41acf747d47f108fc9

[ "MIT" ]

null

Python/POO/ex009.py

neptune076/Coisas

85c064cc0e134465aaf6ef41acf747d47f108fc9

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod class Animal(metaclass=ABCMeta): __slots__ = () @property @abstractmethod def nome(self): pass @property @abstractmethod def peso(self): pass @property @abstractmethod def idade(self): pass @abstractmethod def comer(self): pass @abstractmethod def locomover(self): pass class Pessoa(Animal): __slots__ = ("_nome", "_peso", "_idade") def __init__(self, nome, peso, idade): self.nome = nome self.peso = peso self.idade = idade @property def nome(self): return self._nome @nome.setter def nome(self, nome): self._nome = nome @property def peso(self): return self._peso @peso.setter def peso(self, peso): self._peso = peso @property def idade(self): return self._idade @idade.setter def idade(self, idade): self._idade = idade def comer(self, comida): print(f"{self.nome} está comendo {comida}") def locomover(self): print(f"{self.nome} está andando sobre duas pernas") class Gato(Animal): __slots__ = ("_nome", "_peso", "_idade") def __init__(self, nome, peso, idade): self.nome = nome self.peso = peso self.idade = idade @property def nome(self): return self._nome @nome.setter def nome(self, nome): self._nome = nome @property def peso(self): return self._peso @peso.setter def peso(self, peso): self._peso = peso @property def idade(self): return self._idade @idade.setter def idade(self, idade): self._idade = idade def comer(self): print(f"{self.nome} está comendo sua ração") def locomover(self): print(f"{self.nome} está andando sobre quatro patas")

17.509091

61

0.577882

224

1,926

4.799107

0.160714

0.104186

0.066977

0.052093

0.779535

0.71814

0.671628

0

0.316199

1,926

109

62

17.669725

0.816249

0

0.87013

0

0.095535

0

1

0.298701

false

0.064935

0.012987

0.077922

0.467532

0.051948

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

11995caf6d09b033583358c91e92ff3c5b50573f

127

py

Python

dyalog_kernel/__main__.py

RojerGS/dyalog-jupyter-kernel

068bd0761001f7516144f44982a66ca5f18c1634

[ "MIT" ]

49

2018-03-29T15:55:54.000Z

2022-03-25T01:25:00.000Z

dyalog_kernel/__main__.py

RojerGS/dyalog-jupyter-kernel

068bd0761001f7516144f44982a66ca5f18c1634

[ "MIT" ]

52

2018-02-12T14:36:55.000Z

2022-03-10T09:45:33.000Z

dyalog_kernel/__main__.py

RojerGS/dyalog-jupyter-kernel

068bd0761001f7516144f44982a66ca5f18c1634

[ "MIT" ]

20

2018-06-26T16:06:21.000Z

2022-03-06T00:17:18.000Z

from ipykernel.kernelapp import IPKernelApp from . import DyalogKernel IPKernelApp.launch_instance(kernel_class=DyalogKernel)

25.4

54

0.874016

14

127

7.785714

0.714286

0

0.07874

127

4

55

31.75

0.931624

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

11ad3422973fecf8220b7572910b4ca937ad24c2

170

py

Python

week04/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week04/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week04/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

1

2019-11-27T20:28:19.000Z

score = float(input("백분위(0~100)점수를 입력해 주세요 >>")) if score <= 30: print("당신의 학점은 A입니다.") elif score <= 70: print("당신의 학점은 B입니다.") else: print("당신의 학점은 C입니다.")

21.25

48

0.588235

28

170

3.571429

0.714286

0.24

0.33

0

0.059701

0.211765

170

8

49

21.25

0.686567

0

0.368421

0

1

0

false

0

0.428571

1

0

null

1

0

1

0

null

0

1

0

5

ee9262265da91013d798ff0f2f0b3e2cdb2c6ce2

36

py

Python

octopuslab_installer/__init__.py

octopusengine/octopus-init-lite

025f7be16dad6a055501e889b5f1c280363e2aa0

[ "MIT" ]

2

2020-09-14T08:19:02.000Z

2020-09-15T16:40:27.000Z

octopuslab_installer/__init__.py

octopusengine/octopus-init-lite

025f7be16dad6a055501e889b5f1c280363e2aa0

[ "MIT" ]

null

octopuslab_installer/__init__.py

octopusengine/octopus-init-lite

025f7be16dad6a055501e889b5f1c280363e2aa0

[ "MIT" ]

null

from .octopuslab_installer import *

18

35

0.833333

4

36

7.25

1

0

0.111111

36

1

36

0.90625

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ee965940ded3f5ed146c1d130dde816cc6a561c5

638

py

Python

allencv/modules/image_encoders/image_encoder.py

sethah/allencv

1bdc27359f81290e96b290ccda11f7a9905ebf14

[ "Apache-2.0" ]

8

2019-05-09T02:48:54.000Z

2022-02-14T03:58:54.000Z

allencv/modules/image_encoders/image_encoder.py

sethah/allencv

1bdc27359f81290e96b290ccda11f7a9905ebf14

[ "Apache-2.0" ]

null

allencv/modules/image_encoders/image_encoder.py

sethah/allencv

1bdc27359f81290e96b290ccda11f7a9905ebf14

[ "Apache-2.0" ]

null

from overrides import overrides from typing import Sequence import torch from allennlp.common import Registrable from allencv.modules.encoder_base import _EncoderBase class ImageEncoder(_EncoderBase, Registrable): @overrides def forward(self, # pylint: disable=arguments-differ image: torch.Tensor) -> Sequence[torch.Tensor]: raise NotImplementedError def get_input_channels(self) -> int: raise NotImplementedError def get_output_channels(self) -> Sequence[int]: raise NotImplementedError def get_output_scales(self) -> Sequence[int]: raise NotImplementedError

25.52

63

0.733542

68

638

6.75

0.485294

0.20915

0.176471

0.196078

0.281046

0.169935

0

0.200627

638

25

64

25.52

0.9

0.050157

0

0.25

0

1

0.25

false

0

0.3125

0

0.625

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

ee9e7ca829ddf175218577e5532856083ce64e24

215

py

Python

plugins/Foosun_cms.py

cflq3/getcms

6cf07da0ea3ec644866df715cff1f311a46ee378

[ "MIT" ]

22

2016-09-01T08:27:07.000Z

2021-01-11T13:32:59.000Z

plugins/Foosun_cms.py

cflq3/getcms

6cf07da0ea3ec644866df715cff1f311a46ee378

[ "MIT" ]

null

plugins/Foosun_cms.py

cflq3/getcms

6cf07da0ea3ec644866df715cff1f311a46ee378

[ "MIT" ]

20

2015-11-07T19:09:48.000Z

2018-05-02T03:10:41.000Z

#!/usr/bin/env python # encoding: utf-8 def run(whatweb, pluginname): whatweb.recog_from_file(pluginname, "sysImages/css/PagesCSS.css", "foosun") whatweb.recog_from_file(pluginname, "Tags.html", "Foosun")

26.875

79

0.730233

29

215

5.275862

0.689655

0.156863

0.20915

0.261438

0.392157

0

0.005236

0.111628

215

7

80

30.714286

0.795812

0.167442

0

0.267045

0.147727

0

1

0.333333

false

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

eeeaaef7c1da337a27d82420844848f279263bb2

168

py

Python

4_Backwoods_Forest/174-Forest_Cannon_Dancing/cannon_dance.py

katitek/Code-Combat

fbda1ac0ae4a2e2cbfce21492a2caec8098f1bef

[ "MIT" ]

null

4_Backwoods_Forest/174-Forest_Cannon_Dancing/cannon_dance.py

katitek/Code-Combat

fbda1ac0ae4a2e2cbfce21492a2caec8098f1bef

[ "MIT" ]

null

4_Backwoods_Forest/174-Forest_Cannon_Dancing/cannon_dance.py

katitek/Code-Combat

fbda1ac0ae4a2e2cbfce21492a2caec8098f1bef

[ "MIT" ]

null

def onSpawn(): while True: pet.moveXY(48, 8) pet.moveXY(12, 8) pet.on("spawn", onSpawn) while True: hero.say("Run!!!") hero.say("Faster!")

16.8

25

0.547619

24

168

3.833333

0.625

0.26087

0.347826

0

0.048

0.255952

168

9

26

18.666667

0.688

0

0.25

0

0.107143

0

1

0.125

true

0

0.125

0

1

0

null

1

0

1

0

null

0

1

0

5

e10ae62a8a83b2696d543a218c2d553876a0cad2

14,221

py

Python

tests/subscription_manager/endpoints/test_users.py

eurocontrol-swim/subscription-manager

95700334cb5d58957043c6c487b56b1dd6641ec0

[ "BSD-3-Clause" ]

null

tests/subscription_manager/endpoints/test_users.py

eurocontrol-swim/subscription-manager

95700334cb5d58957043c6c487b56b1dd6641ec0

[ "BSD-3-Clause" ]

null

tests/subscription_manager/endpoints/test_users.py

eurocontrol-swim/subscription-manager

95700334cb5d58957043c6c487b56b1dd6641ec0

[ "BSD-3-Clause" ]

null

""" Copyright 2019 EUROCONTROL ========================================== Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ========================================== Editorial note: this license is an instance of the BSD license template as provided by the Open Source Initiative: http://opensource.org/licenses/BSD-3-Clause Details on EUROCONTROL: http://www.eurocontrol.int """ import json from unittest import mock import pytest from sqlalchemy.exc import IntegrityError from werkzeug.security import check_password_hash from subscription_manager import BASE_PATH from swim_backend.auth.auth import HASH_METHOD from swim_backend.db import db_save from subscription_manager.db.users import get_user_by_id from tests.subscription_manager.utils import make_user, make_basic_auth_header from tests.conftest import DEFAULT_LOGIN_PASS __author__ = "EUROCONTROL (SWIM)" @pytest.fixture def generate_user(session): def _generate_user(is_admin=False): user = make_user(is_admin=is_admin) return db_save(session, user) return _generate_user def basic_auth_header(user): return make_basic_auth_header(user.username, DEFAULT_LOGIN_PASS) def test_get_user__user_does_not_exist__returns_404(test_client, test_admin_user): url = f'{BASE_PATH}/users/123456' response = test_client.get(url, headers=basic_auth_header(test_admin_user)) assert 404 == response.status_code def test_get_user__unauthorized_user__returns_401(test_client, generate_user): user = generate_user() user.password = 'password' url = f'{BASE_PATH}/users/{user.id}' response = test_client.get(url, headers=make_basic_auth_header('fake_username', 'fake_password')) assert 401 == response.status_code response_data = json.loads(response.data) assert 'Invalid credentials' == response_data['detail'] def test_get_user__non_admin_user__returns_403(test_client, generate_user): user = generate_user(is_admin=False) url = f'{BASE_PATH}/users/{user.id}' response = test_client.get(url, headers=make_basic_auth_header(user.username, 'password')) assert 403 == response.status_code response_data = json.loads(response.data) assert 'Admin rights required' == response_data['detail'] def test_get_user__user_exists_and_is_returned(test_client, generate_user, test_admin_user): user = generate_user() url = f'{BASE_PATH}/users/{user.id}' response = test_client.get(url, headers=basic_auth_header(test_admin_user)) assert 200 == response.status_code response_data = json.loads(response.data) assert user.username == response_data['username'] assert user.active == response_data['active'] assert user.is_admin == response_data['is_admin'] def test_get_users__unauthorized_user__returns_401(test_client, generate_user): users = [generate_user(), generate_user()] url = f'{BASE_PATH}/users/' response = test_client.get(url, headers=make_basic_auth_header('fake_username', 'fake_password')) assert 401 == response.status_code response_data = json.loads(response.data) assert 'Invalid credentials' == response_data['detail'] def test_get_users__non_admin_user__returns_403(test_client, generate_user, test_user): users = [generate_user(), generate_user()] url = f'{BASE_PATH}/users/' response = test_client.get(url, headers=basic_auth_header(test_user)) assert 403 == response.status_code response_data = json.loads(response.data) assert 'Admin rights required' == response_data['detail'] def test_get_users__users_exist_and_are_returned_as_list(test_client, generate_user, test_admin_user): users = [generate_user(), generate_user()] url = f'{BASE_PATH}/users/' response = test_client.get(url, headers=basic_auth_header(test_admin_user)) assert 200 == response.status_code response_data = json.loads(response.data) assert isinstance(response_data, list) assert 3 == len(response_data) # plus the test_user @pytest.mark.parametrize('missing_property', ['username', 'password']) def test_post_user__missing_required_property__returns_400(test_client, missing_property, test_admin_user): user_data = { 'username': 'username', 'password': 'password' } del user_data[missing_property] url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 400 == response.status_code response_data = json.loads(response.data) assert f"'{missing_property}' is a required property" == response_data['detail'] @mock.patch('swim_backend.auth.passwords.is_strong', return_value=False) def test_post_user__password_is_not_strong_enough__returns_400(mock_password_is_strong, test_client, test_admin_user): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 400 == response.status_code response_data = json.loads(response.data) assert f"password is not strong enough" == response_data['detail'] @mock.patch('subscription_manager.db.users.save_user', side_effect=IntegrityError(None, None, None)) @mock.patch('swim_backend.auth.passwords.is_strong', return_value=True) def test_post_user__db_error__returns_409(mock_password_is_strong, mock_create_user, test_client, generate_user, test_admin_user): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 409 == response.status_code response_data = json.loads(response.data) assert "Error while saving user in DB" == response_data['detail'] def test_post_user__unauthorized_user__returns_401(test_client): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=make_basic_auth_header('fake_username', 'fake_password')) assert 401 == response.status_code response_data = json.loads(response.data) assert 'Invalid credentials' == response_data['detail'] def test_post_user__non_admin_user__returns_403(test_client, test_user): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_user)) assert 403 == response.status_code response_data = json.loads(response.data) assert 'Admin rights required' == response_data['detail'] @mock.patch('swim_backend.auth.passwords.is_strong', return_value=True) def test_post_user__user_is_saved_in_db(mock_password_is_strong, test_client, test_admin_user): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/' response = test_client.post(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 201 == response.status_code response_data = json.loads(response.data) assert 'id' in response_data assert isinstance(response_data['id'], int) assert user_data['username'] == response_data['username'] db_user = get_user_by_id(response_data['id']) assert db_user is not None assert user_data['username'] == db_user.username assert db_user.password.startswith(HASH_METHOD) assert check_password_hash(db_user.password, 'password') is True @mock.patch('subscription_manager.db.users.save_user', side_effect=IntegrityError(None, None, None)) @mock.patch('swim_backend.auth.passwords.is_strong', return_value=True) def test_put_user__db_error__returns_409(mock_password_is_strong, mock_update_user, test_client, generate_user, test_admin_user): user = generate_user() user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 409 == response.status_code response_data = json.loads(response.data) assert "Error while saving user in DB" == response_data['detail'] def test_put_user__user_does_not_exist__returns_404(test_client, test_admin_user): user_data = { 'username': 'username', 'password': 'password' } url = f'{BASE_PATH}/users/1234' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 404 == response.status_code response_data = json.loads(response.data) assert "User with id 1234 does not exist" == response_data['detail'] def test_put_user__unauthorized_user__returns_401(test_client, generate_user): user = generate_user() user_data = { 'username': 'new username', } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=make_basic_auth_header('fake_username', 'fake_password')) assert 401 == response.status_code response_data = json.loads(response.data) assert 'Invalid credentials' == response_data['detail'] @mock.patch('swim_backend.auth.passwords.is_strong', return_value=False) def test_put_user__update_password__password_not_strong_enough__returns_400(mock_password_is_strong, test_client, generate_user, test_admin_user): user = generate_user() user_data = { 'password': 'new password', } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 400 == response.status_code response_data = json.loads(response.data) assert 'password is not strong enough' == response_data['detail'] def test_put_user__non_admin_user__returns_403(test_client, generate_user, test_user): user = generate_user() user_data = { 'username': 'new username', } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_user)) assert 403 == response.status_code response_data = json.loads(response.data) assert 'Admin rights required' == response_data['detail'] def test_put_user__user_is_updated(test_client, generate_user, test_admin_user): user = generate_user() user_data = { 'username': 'new username', } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 200 == response.status_code response_data = json.loads(response.data) assert user_data['username'] == response_data['username'] db_user = get_user_by_id(response_data['id']) assert user_data['username'] == db_user.username @mock.patch('swim_backend.auth.passwords.is_strong', return_value=True) def test_put_user__new_password_is_updated_and_hashed_correctly(mock_password_is_strong, test_client, generate_user, test_admin_user): user = generate_user() user_data = { 'password': 'new password', } url = f'{BASE_PATH}/users/{user.id}' response = test_client.put(url, data=json.dumps(user_data), content_type='application/json', headers=basic_auth_header(test_admin_user)) assert 200 == response.status_code response_data = json.loads(response.data) db_user = get_user_by_id(response_data['id']) assert db_user.password.startswith(HASH_METHOD) assert check_password_hash(db_user.password, 'new password') is True

35.200495

121

0.711553

1,869

14,221

5.092028

0.133226

0.080698

0.032783

0.025218

0.754545

0.743196

0.730377

0.707996

0.689083

0.680361

0

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null

0

1

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5

e1102ba3880791221f38dda8e9386b654dfa95fe

451

py

Python

EgammaAnalysis/CSA07Skims/python/EgammaZPlusEMOrJetPath_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-08-09T08:42:11.000Z

EgammaAnalysis/CSA07Skims/python/EgammaZPlusEMOrJetPath_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

null

EgammaAnalysis/CSA07Skims/python/EgammaZPlusEMOrJetPath_cff.py

nistefan/cmssw

ea13af97f7f2117a4f590a5e654e06ecd9825a5b

[ "Apache-2.0" ]

1

2019-04-03T19:23:27.000Z

import FWCore.ParameterSet.Config as cms from EgammaAnalysis.CSA07Skims.EgammaZJetToEleHLT_cfi import * from EgammaAnalysis.CSA07Skims.EgammaZJetToMuHLT_cfi import * from EgammaAnalysis.CSA07Skims.EgammaZJetToElePlusProbe_cfi import * from EgammaAnalysis.CSA07Skims.EgammaZJetToMuPlusProbe_cfi import * electronFilterZPath = cms.Path(EgammaZJetToEleHLT+EgammaZJetToElePlusProbe) muonFilterZPath = cms.Path(EgammaZJetToMuHLT+EgammaZJetToMuPlusProbe)

45.1

75

0.884701

40

451

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0.182278

0.283544

0.205063

0.281013

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null

0

1

0

1

0

5

012119d9c53adbee1d9e47a9f364a943e81b1e93

31

py

Python

Sim_ATAV/simulation_configurator/__init__.py

SahilDhull/autonomous

378fc7d6c5a9c34c4e915f080fb78ed5c11195d6

[ "MIT" ]

3

2020-02-28T12:04:26.000Z

2022-02-27T00:42:56.000Z

Sim_ATAV/vehicle_control/trial_controller/__init__.py

SahilDhull/autonomous

378fc7d6c5a9c34c4e915f080fb78ed5c11195d6

[ "MIT" ]

null

Sim_ATAV/vehicle_control/trial_controller/__init__.py

SahilDhull/autonomous

378fc7d6c5a9c34c4e915f080fb78ed5c11195d6

[ "MIT" ]

null

# Author: Cumhur Erkan Tuncali

15.5

30

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31

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31

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null

0

1

0

1

0

null

0

1

0

5

012bac8ea112937cc08973a4ca408097202d790f

2,943

py

Python

test/test_queue.py

shenxiangzhuang/ToyData

66489267cc7a438215e0d30d751ae2a54301b513

[ "MIT" ]

4

2020-02-02T08:11:14.000Z

2020-04-07T15:40:45.000Z

test/test_queue.py

shenxiangzhuang/ToyData

66489267cc7a438215e0d30d751ae2a54301b513

[ "MIT" ]

null

test/test_queue.py

shenxiangzhuang/ToyData

66489267cc7a438215e0d30d751ae2a54301b513

[ "MIT" ]

null

import unittest from toydata.Queue import ArrayQueue, LinkedQueue, ArrayDeque, LinkedDeque class testArrayQueue(unittest.TestCase): def test_init(self): q = ArrayQueue() self.assertTrue(q.is_empty()) def test_is_empty(self): q = ArrayQueue() self.assertTrue(q.is_empty()) q.enqueue(1) self.assertFalse(q.is_empty()) def test_first(self): q = ArrayQueue() q.enqueue(1) self.assertEqual(q.first(), 1) q.enqueue(2) self.assertEqual(q.first(), 1) def test_dequeue(self): q = ArrayQueue() q.enqueue(1) q.dequeue() self.assertTrue(q.is_empty) def test_enqueue(self): q = ArrayQueue() q.enqueue(1) self.assertEqual(q.first(), 1) class testLinkedQueue(unittest.TestCase): def test_init(self): q = LinkedQueue() self.assertTrue(q.is_empty()) def test_is_empty(self): q = LinkedQueue() self.assertTrue(q.is_empty()) q.enqueue(1) self.assertFalse(q.is_empty()) def test_first(self): q = LinkedQueue() q.enqueue(1) self.assertEqual(q.first(), 1) q.enqueue(2) self.assertEqual(q.first(), 1) def test_dequeue(self): q = LinkedQueue() q.enqueue(1) q.dequeue() self.assertTrue(q.is_empty) def test_enqueue(self): q = LinkedQueue() q.enqueue(1) self.assertEqual(q.first(), 1) class testArrayDeque(unittest.TestCase): def test_add_last(self): q = ArrayDeque() q.add_last(1) self.assertEqual(q.last(), 1) def test_last(self): q = ArrayDeque() q.add_last(1) self.assertEqual(q.last(), 1) q.add_last(2) self.assertEqual(q.last(), 2) def test_delete_last(self): q = ArrayDeque() q.add_last(1) q.add_last(2) q.delete_last() self.assertEqual(q.last(), 1) def test_add_first(self): q = ArrayDeque() q.add_first(1) q.add_first(0) self.assertEqual(q.last(), 1) def test_first(self): q = ArrayDeque() q.add_first(1) self.assertEqual(q.first(), 1) def test_delete_first(self): q = ArrayDeque() q.add_last(1) q.add_first(0) q.delete_first() self.assertEqual(q.first(), 1) class testLinkedDeque(unittest.TestCase): def test_first(self): q = LinkedDeque() q.insert_first(1) self.assertEqual(q.first(), 1) q.insert_first(2) self.assertEqual(q.first(), 2) q.delete_first() self.assertEqual(q.first(), 1) def test_last(self): q = LinkedDeque() q.insert_last(1) self.assertEqual(q.last(), 1) q.insert_last(2) self.assertEqual(q.last(), 2) q.delete_last() self.assertEqual(q.last(), 1)

24.122951

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0.092838

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0.187539

0.142505

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0.297995

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1

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1

0

1

0

null

0

5

017cbe791572c6ae9f94b822d09e39c61e77c88b

30

py

Python

Sort/14_10_Counting_Sort.py

misscindy/Interview

eab43da97e61fcc3d0278408f8f4ea709eed14e6

[ "CC0-1.0" ]

null

Sort/14_10_Counting_Sort.py

misscindy/Interview

eab43da97e61fcc3d0278408f8f4ea709eed14e6

[ "CC0-1.0" ]

1

2015-04-23T20:05:24.000Z

2015-04-23T20:07:45.000Z

Sort/14_10_Counting_Sort.py

misscindy/Interview

eab43da97e61fcc3d0278408f8f4ea709eed14e6

[ "CC0-1.0" ]

null

# Given a list of person class

30

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1

30

0.958333

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1

0

1

0

null

0

1

0

5

09b165468ddbf64822e467edf519bdacea35f62a

261

py

Python

core/admin.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

null

core/admin.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

2

2022-03-31T14:34:44.000Z

2022-03-31T14:35:17.000Z

core/admin.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

null

from django.contrib import admin import core.models admin.site.register(core.models.Category) admin.site.register(core.models.Product) admin.site.register(core.models.Pharmacy) admin.site.register(core.models.Available) admin.site.register(core.models.Review)

29

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261

5.710526

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0.391705

0.483871

0.62212

0

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261

8

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32.625

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true

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0.285714

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0.285714

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null

1

0

1

0

null

0

1

0

5

09b89554b7ff4b7ec3fea7631109b0962ddc10d3

26

py

Python

python/testData/stubs/ParameterAnnotation.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/stubs/ParameterAnnotation.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/stubs/ParameterAnnotation.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def func(x: int): pass

13

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5

26

3

1

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26

2

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13

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0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

09b9b2544018554e9275dd22d34fd0a62bc0fe5e

53,806

py

Python

StackEPI/ML/EPIconst.py

20032303092/StackEPI

106c0f3142f55aead0259a5f7f4d21a14fb8dcef

[ "CECILL-B" ]

null

StackEPI/ML/EPIconst.py

20032303092/StackEPI

106c0f3142f55aead0259a5f7f4d21a14fb8dcef

[ "CECILL-B" ]

null

StackEPI/ML/EPIconst.py

20032303092/StackEPI

106c0f3142f55aead0259a5f7f4d21a14fb8dcef

[ "CECILL-B" ]

null

class EPIconst: class FeatureName: pseknc = "pseknc" cksnap = "cksnap" dpcp = "dpcp" eiip = "eiip" kmer = "kmer" tpcp = "tpcp" all = sorted([pseknc, cksnap, dpcp, eiip, kmer, tpcp]) class CellName: K562 = "K562" NHEK = "NHEK" IMR90 = "IMR90" HeLa_S3 = "HeLa-S3" HUVEC = "HUVEC" GM12878 = "GM12878" all = sorted([GM12878, HeLa_S3, HUVEC, IMR90, K562, NHEK]) class MethodName: ensemble = "meta" xgboost = "xgboost" svm = "svm" deepforest = "deepforest" lightgbm = "lightgbm" rf = "rf" all = sorted([lightgbm, rf, xgboost, svm, deepforest]) class ModelInitParams: logistic = {"n_jobs": 13, } mlp = {} deepforest = {"n_jobs": 13, "use_predictor": False, "random_state": 1, "predictor": 'forest', "verbose": 0} lightgbm = {"n_jobs": 13, 'max_depth': -1, 'num_leaves': 31, 'min_child_samples': 20, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'objective': None, 'n_estimators': 100, 'learning_rate': 0.1, 'device': 'gpu', 'boosting_type': 'gbdt', 'class_weight': None, 'importance_type': 'split', 'min_child_weight': 0.001, 'random_state': None, 'subsample_for_bin': 200000, 'silent': True} rf = {"n_jobs": 13, 'n_estimators': 100, "max_depth": None, 'min_samples_split': 2, "min_samples_leaf": 1, 'max_features': 'auto'} svm = {"probability": True} xgboost = {'learning_rate': 0.1, 'n_estimators': 500, 'max_depth': 5, 'min_child_weight': 1, 'seed': 0, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1, 'use_label_encoder': False, 'eval_metric': 'logloss', 'tree_method': 'gpu_hist'} class BaseModelParams: GM12878_cksnap_deepforest = {"max_layers": 20, "n_estimators": 5, "n_trees": 250} GM12878_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 125, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} GM12878_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'} GM12878_cksnap_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1} GM12878_cksnap_rf = {'n_estimators': 340, 'max_depth': 114, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'sqrt'} "----------------------------------------------" GM12878_dpcp_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 300} GM12878_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 331, 'max_bin': 135, 'min_child_samples': 190, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.9, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} GM12878_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'} GM12878_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3, 'learning_rate': 0.1} GM12878_dpcp_rf = {'n_estimators': 150, 'max_depth': 88, 'min_samples_leaf': 1, 'min_samples_split': 3, 'max_features': "sqrt"} "----------------------------------------------" GM12878_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300} GM12878_eiip_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} GM12878_eiip_rf = {'n_estimators': 280, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 7, 'max_features': "sqrt"} GM12878_eiip_svm = {'C': 1.0, 'gamma': 2048.0, 'kernel': 'rbf'} GM12878_eiip_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" GM12878_kmer_deepforest = {'max_layers': 25, 'n_estimators': 5, 'n_trees': 400} GM12878_kmer_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40, 'colsample_bytree': 1.0, 'subsample': 0.8, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} GM12878_kmer_rf = {'n_estimators': 170, 'max_depth': 41, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'sqrt'} GM12878_kmer_svm = {'C': 2.0, 'gamma': 128.0, 'kernel': 'rbf'} GM12878_kmer_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" GM12878_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400} GM12878_pseknc_lightgbm = {'max_depth': 11, 'num_leaves': 291, 'max_bin': 185, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150} GM12878_pseknc_rf = {'n_estimators': 250, 'max_depth': 41, 'min_samples_leaf': 2, 'min_samples_split': 6, 'max_features': 'log2'} GM12878_pseknc_svm = {'C': 0.5, 'gamma': 1024.0, 'kernel': 'rbf'} GM12878_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.1, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.1} "----------------------------------------------" GM12878_tpcp_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 100} GM12878_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 175, 'min_child_samples': 80, 'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} GM12878_tpcp_rf = {'n_estimators': 250, 'max_depth': 89, 'min_samples_leaf': 2, 'min_samples_split': 9, 'max_features': "log2"} GM12878_tpcp_svm = {'C': 16.0, 'gamma': 64.0, 'kernel': 'rbf'} GM12878_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "==============================================" HeLa_S3_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 300} HeLa_S3_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 105, 'min_child_samples': 80, 'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.1, 'reg_lambda': 0.1, 'min_split_gain': 0.4, 'learning_rate': 0.1, 'n_estimators': 150} HeLa_S3_cksnap_svm = {'C': 128.0, 'gamma': 128.0, 'kernel': 'rbf'} HeLa_S3_cksnap_rf = {'n_estimators': 340, 'max_depth': 44, 'min_samples_leaf': 1, 'min_samples_split': 5, 'max_features': 'sqrt'} HeLa_S3_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 3, 'reg_lambda': 0.5, 'learning_rate': 0.1} "----------------------------------------------" HeLa_S3_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 400} HeLa_S3_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 221, 'max_bin': 155, 'min_child_samples': 180, 'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 200} HeLa_S3_dpcp_rf = {'n_estimators': 70, 'max_depth': 32, 'min_samples_leaf': 1, 'min_samples_split': 8, 'max_features': 'sqrt'} HeLa_S3_dpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'} HeLa_S3_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" HeLa_S3_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200} HeLa_S3_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 5, 'min_child_samples': 110, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100} HeLa_S3_eiip_rf = {'n_estimators': 180, 'max_depth': 138, 'min_samples_leaf': 6, 'min_samples_split': 10, 'max_features': 'sqrt'} HeLa_S3_eiip_svm = {'C': 2.0, 'gamma': 1024.0, 'kernel': 'rbf'} HeLa_S3_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" HeLa_S3_kmer_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200} HeLa_S3_kmer_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 165, 'min_child_samples': 90, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 0.001, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125} HeLa_S3_kmer_rf = {'n_estimators': 240, 'max_depth': 77, 'min_samples_leaf': 2, 'min_samples_split': 2, 'max_features': 'sqrt'} HeLa_S3_kmer_svm = {'C': 8.0, 'gamma': 128.0, 'kernel': 'rbf'} HeLa_S3_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" HeLa_S3_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200} HeLa_S3_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 261, 'max_bin': 25, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} HeLa_S3_pseknc_rf = {'n_estimators': 330, 'max_depth': 118, 'min_samples_leaf': 1, 'min_samples_split': 8, 'max_features': 'log2'} HeLa_S3_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'} HeLa_S3_pseknc_xgboost = {'n_estimators': 750, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.1, 'reg_lambda': 2, 'learning_rate': 0.1} "----------------------------------------------" HeLa_S3_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 250} HeLa_S3_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 341, 'max_bin': 45, 'min_child_samples': 10, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 250} HeLa_S3_tpcp_rf = {'n_estimators': 320, 'max_depth': 99, 'min_samples_leaf': 1, 'min_samples_split': 10, 'max_features': 'sqrt'} HeLa_S3_tpcp_svm = {'C': 4.0, 'gamma': 32.0, 'kernel': 'rbf'} HeLa_S3_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "==============================================" HUVEC_cksnap_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 200} HUVEC_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 271, 'max_bin': 45, 'min_child_samples': 10, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.5, 'reg_lambda': 0.5, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175} HUVEC_cksnap_rf = {'n_estimators': 270, 'max_depth': 38, 'min_samples_leaf': 2, 'min_samples_split': 2, 'max_features': "auto"} HUVEC_cksnap_svm = {'C': 8.0, 'gamma': 64.0, 'kernel': 'rbf'} HUVEC_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" HUVEC_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 400} HUVEC_dpcp_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 245, 'min_child_samples': 30, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.5, 'reg_lambda': 0.3, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200} HUVEC_dpcp_rf = {'n_estimators': 300, 'max_depth': 61, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'log2'} HUVEC_dpcp_svm = {'C': 4.0, 'gamma': 16.0, 'kernel': 'rbf'} HUVEC_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3, 'learning_rate': 0.1} "----------------------------------------------" HUVEC_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 300} HUVEC_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} HUVEC_eiip_rf = {'n_estimators': 310, 'max_depth': 28, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'sqrt'} HUVEC_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'} HUVEC_eiip_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1} "----------------------------------------------" HUVEC_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300} HUVEC_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 5, 'min_child_samples': 170, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.5, 'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125} HUVEC_kmer_rf = {'n_estimators': 230, 'max_depth': 59, 'min_samples_leaf': 1, 'min_samples_split': 4, 'max_features': 'auto'} HUVEC_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'} HUVEC_kmer_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1} "----------------------------------------------" HUVEC_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400} HUVEC_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 115, 'min_child_samples': 190, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175} HUVEC_pseknc_rf = {'n_estimators': 310, 'max_depth': 42, 'min_samples_leaf': 2, 'min_samples_split': 7, 'max_features': 'sqrt'} HUVEC_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'} HUVEC_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" HUVEC_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150} HUVEC_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 35, 'min_child_samples': 190, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150} HUVEC_tpcp_rf = {'n_estimators': 330, 'max_depth': 121, 'min_samples_leaf': 2, 'min_samples_split': 5, 'max_features': "sqrt"} HUVEC_tpcp_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'} HUVEC_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "==============================================" IMR90_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 250} IMR90_cksnap_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 95, 'min_child_samples': 60, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.3, 'learning_rate': 0.1, 'n_estimators': 225} IMR90_cksnap_rf = {'n_estimators': 280, 'max_depth': 124, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'auto'} IMR90_cksnap_svm = {'C': 16.0, 'gamma': 16.0, 'kernel': 'rbf'} IMR90_cksnap_xgboost = {'n_estimators': 900, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.4, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0.1, 'learning_rate': 0.1} "----------------------------------------------" IMR90_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200} IMR90_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 281, 'max_bin': 115, 'min_child_samples': 20, 'colsample_bytree': 0.7, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.5, 'learning_rate': 0.1, 'n_estimators': 125} IMR90_dpcp_rf = {'n_estimators': 70, 'max_depth': 116, 'min_samples_leaf': 1, 'min_samples_split': 9, 'max_features': 'log2'} IMR90_dpcp_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'} IMR90_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.05, 'reg_lambda': 0.1, 'learning_rate': 0.1} "----------------------------------------------" IMR90_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 350} IMR90_eiip_lightgbm = {'max_depth': 13, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 50, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 80, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.4, 'learning_rate': 0.2, 'n_estimators': 200} IMR90_eiip_rf = {'n_estimators': 240, 'max_depth': 78, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'auto'} IMR90_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'} IMR90_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" IMR90_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 250} IMR90_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 175, 'min_child_samples': 120, 'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 0.7, 'reg_lambda': 0.9, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200} IMR90_kmer_rf = {'n_estimators': 280, 'max_depth': 79, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'auto'} IMR90_kmer_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'} IMR90_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.2, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" IMR90_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300} IMR90_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 15, 'min_child_samples': 50, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} IMR90_pseknc_rf = {'n_estimators': 240, 'max_depth': 96, 'min_samples_leaf': 3, 'min_samples_split': 4, 'max_features': 'auto'} IMR90_pseknc_svm = {'C': 4.0, 'gamma': 1024.0, 'kernel': 'rbf'} IMR90_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.2, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" IMR90_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300} IMR90_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 35, 'min_child_samples': 60, 'colsample_bytree': 0.6, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.5, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 100} IMR90_tpcp_rf = {'n_estimators': 290, 'max_depth': 71, 'min_samples_leaf': 5, 'min_samples_split': 4, 'max_features': 'auto'} IMR90_tpcp_svm = {'C': 1.0, 'gamma': 512.0, 'kernel': 'rbf'} IMR90_tpcp_xgboost = {'n_estimators': 950, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.5, 'learning_rate': 0.1} "==============================================" K562_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 400} K562_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 225, 'min_child_samples': 60, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 250} K562_cksnap_rf = {'n_estimators': 330, 'max_depth': 109, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'sqrt'} K562_cksnap_svm = {'C': 16.0, 'gamma': 32.0, 'kernel': 'rbf'} K562_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 0.05, 'learning_rate': 0.1} "----------------------------------------------" K562_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 150} K562_dpcp_lightgbm = {'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 1e-05, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 225} K562_dpcp_rf = {'n_estimators': 240, 'max_depth': 127, 'min_samples_leaf': 1, 'min_samples_split': 6, 'max_features': 'sqrt'} K562_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'} K562_dpcp_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.05, 'learning_rate': 0.1} "----------------------------------------------" K562_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 150} K562_eiip_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 225, 'min_child_samples': 110, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 150} K562_eiip_rf = {'n_estimators': 120, 'max_depth': 93, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': 'auto'} K562_eiip_svm = {'C': 2.0, 'gamma': 1024.0, 'kernel': 'rbf'} K562_eiip_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0, 'learning_rate': 0.1} "----------------------------------------------" K562_kmer_deepforest = {'max_layers': 15, 'n_estimators': 5, 'n_trees': 150} K562_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 5, 'min_child_samples': 70, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} K562_kmer_rf = {'n_estimators': 290, 'max_depth': 137, 'min_samples_leaf': 10, 'min_samples_split': 7, 'max_features': "auto"} K562_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'} K562_kmer_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0, 'learning_rate': 0.1} "----------------------------------------------" K562_pseknc_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 300} K562_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 65, 'min_child_samples': 200, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150} K562_pseknc_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 1, 'min_samples_split': 6, 'max_features': 'log2'} K562_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'} K562_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1} "----------------------------------------------" K562_tpcp_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 300} K562_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 105, 'min_child_samples': 130, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200} K562_tpcp_rf = {'n_estimators': 280, 'max_depth': 143, 'min_samples_leaf': 5, 'min_samples_split': 2, 'max_features': 'sqrt'} K562_tpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'} K562_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 1, 'learning_rate': 0.1} "==============================================" NHEK_cksnap_deepforest = {"max_layers": 20, "n_estimators": 5, "n_trees": 400} NHEK_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 205, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 75} NHEK_cksnap_rf = {'n_estimators': 300, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': 'auto'} NHEK_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'} NHEK_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" NHEK_dpcp_deepforest = {"max_layers": 10, "n_estimators": 8, "n_trees": 200} NHEK_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 301, 'max_bin': 145, 'min_child_samples': 70, 'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.9, 'reg_lambda': 1.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150} NHEK_dpcp_rf = {'n_estimators': 300, 'max_depth': 138, 'min_samples_leaf': 1, 'min_samples_split': 5, 'max_features': 'auto'} NHEK_dpcp_svm = {'C': 8.0, 'gamma': 16.0, 'kernel': 'rbf'} NHEK_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.5, 'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1} "----------------------------------------------" NHEK_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 100} NHEK_eiip_lightgbm = {'max_depth': 11, 'num_leaves': 231, 'max_bin': 255, 'min_child_samples': 70, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} NHEK_eiip_rf = {'n_estimators': 230, 'max_depth': 56, 'min_samples_leaf': 2, 'min_samples_split': 6, 'max_features': 'log2'} NHEK_eiip_svm = {'C': 8.0, 'gamma': 512.0, 'kernel': 'rbf'} NHEK_eiip_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1} "----------------------------------------------" NHEK_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200} NHEK_kmer_lightgbm = {'max_depth': 13, 'num_leaves': 261, 'max_bin': 115, 'min_child_samples': 60, 'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.001, 'min_split_gain': 1.0, 'learning_rate': 0.1, 'n_estimators': 150} NHEK_kmer_rf = {'n_estimators': 60, 'max_depth': 117, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': "auto"} NHEK_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'} NHEK_kmer_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1} "----------------------------------------------" NHEK_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150} NHEK_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 271, 'max_bin': 155, 'min_child_samples': 20, 'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 60, 'reg_alpha': 0.1, 'reg_lambda': 1e-05, 'min_split_gain': 0.7, 'learning_rate': 0.1, 'n_estimators': 75} NHEK_pseknc_rf = {'n_estimators': 190, 'max_depth': 85, 'min_samples_leaf': 1, 'min_samples_split': 10, 'max_features': 'auto'} NHEK_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'} NHEK_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.1, 'reg_lambda': 3, 'learning_rate': 0.1} "----------------------------------------------" NHEK_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200} NHEK_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 241, 'max_bin': 15, 'min_child_samples': 90, 'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 40, 'reg_alpha': 0.001, 'reg_lambda': 0.001, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100} NHEK_tpcp_rf = {'n_estimators': 120, 'max_depth': 115, 'min_samples_leaf': 1, 'min_samples_split': 4, 'max_features': 'auto'} NHEK_tpcp_svm = {'C': 1.0, 'gamma': 128.0, 'kernel': 'rbf'} NHEK_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.01, 'learning_rate': 0.1} class MetaModelParams: ################# GM12878 ###################### GM12878_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 32} GM12878_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 8} GM12878_6f5m_prob_logistic = {'C': 2.900000000000001} GM12878_4f2m_prob_logistic = {'C': 0.9000000000000001} GM12878_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400} GM12878_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 200} GM12878_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 200, 'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 30, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 50} GM12878_4f2m_prob_lightgbm = {'max_depth': 11, 'num_leaves': 311, 'max_bin': 85, 'min_child_samples': 150, 'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 75} GM12878_6f5m_prob_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 9, 'min_samples_split': 5, 'max_features': 'auto'} GM12878_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 53, 'min_samples_leaf': 6, 'min_samples_split': 7, 'max_features': 'log2'} GM12878_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'} GM12878_4f2m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'} GM12878_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1} GM12878_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.05} ################# HeLa_S3 ###################### HeLa_S3_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'relu', 'hidden_layer_sizes': 32} HeLa_S3_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': (16, 32)} HeLa_S3_6f5m_prob_logistic = {'C': 1.9000000000000004} HeLa_S3_4f2m_prob_logistic = {'C': 0.5000000000000001} HeLa_S3_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 10, 'n_trees': 400} HeLa_S3_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400} HeLa_S3_6f5m_prob_lightgbm = {'max_depth': 5, 'num_leaves': 281, 'max_bin': 175, 'min_child_samples': 180, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 150} HeLa_S3_4f2m_prob_lightgbm = {'max_depth': 3, 'num_leaves': 311, 'max_bin': 35, 'min_child_samples': 20, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 1.0, 'n_estimators': 125} HeLa_S3_6f5m_prob_rf = {'n_estimators': 130, 'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'sqrt'} HeLa_S3_4f2m_prob_rf = {'n_estimators': 210, 'max_depth': 117, 'min_samples_leaf': 2, 'min_samples_split': 5, 'max_features': 'auto'} HeLa_S3_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'} HeLa_S3_4f2m_prob_svm = {'C': 0.25, 'gamma': 0.0625, 'kernel': 'rbf'} HeLa_S3_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'learning_rate': 0.1} HeLa_S3_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'learning_rate': 0.1} ################# HUVEC ######################## HUVEC_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': 8} HUVEC_4f2m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': (8, 16)} HUVEC_6f5m_prob_logistic = {'C': 2.900000000000001} HUVEC_4f2m_prob_logistic = {'C': 0.9000000000000001} HUVEC_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 250} HUVEC_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400} HUVEC_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 311, 'max_bin': 45, 'min_child_samples': 170, 'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 10, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100} HUVEC_4f2m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 261, 'max_bin': 45, 'min_child_samples': 180, 'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 10, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 200} HUVEC_6f5m_prob_rf = {'n_estimators': 290, 'max_depth': 105, 'min_samples_leaf': 5, 'min_samples_split': 2, 'max_features': 'log2'} HUVEC_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'log2'} HUVEC_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'} HUVEC_4f2m_prob_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'} HUVEC_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.02, 'learning_rate': 0.05} HUVEC_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.02, 'learning_rate': 0.01} ################# IMR90 ######################## IMR90_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'identity', 'hidden_layer_sizes': (16, 32)} IMR90_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': (8, 16)} IMR90_6f5m_prob_logistic = {'C': 2.5000000000000004} IMR90_4f2m_prob_logistic = {'C': 2.5000000000000004} IMR90_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 8, 'n_trees': 300} IMR90_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 200} IMR90_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 85, 'min_child_samples': 70, 'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250} IMR90_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 55, 'min_child_samples': 60, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 30, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 175} IMR90_6f5m_prob_rf = {'n_estimators': 340, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 3, 'max_features': 'log2'} IMR90_4f2m_prob_rf = {'n_estimators': 270, 'max_depth': 120, 'min_samples_leaf': 10, 'min_samples_split': 7, 'max_features': 'log2'} IMR90_6f5m_prob_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'} IMR90_4f2m_prob_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'} IMR90_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.9, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.05} IMR90_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.07} ################# K562 ######################### K562_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'logistic', 'hidden_layer_sizes': (8, 16)} K562_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': 8} K562_6f5m_prob_logistic = {'C': 2.900000000000001} K562_4f2m_prob_logistic = {'C': 0.1} K562_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400} K562_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 300} K562_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 65, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.07, 'n_estimators': 75} K562_4f2m_prob_lightgbm = {'max_depth': 13, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.75, 'n_estimators': 175} K562_6f5m_prob_rf = {'n_estimators': 180, 'max_depth': 35, 'min_samples_leaf': 7, 'min_samples_split': 5, 'max_features': 'log2'} K562_4f2m_prob_rf = {'n_estimators': 80, 'max_depth': 130, 'min_samples_leaf': 6, 'min_samples_split': 5, 'max_features': 'log2'} K562_6f5m_prob_svm = {'C': 0.5, 'gamma': 0.0625, 'kernel': 'rbf'} K562_4f2m_prob_svm = {'C': 1.0, 'gamma': 0.0625, 'kernel': 'rbf'} K562_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.1} K562_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.01} ################# NHEK ######################### NHEK_6f5m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 32} NHEK_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': (16, 32)} NHEK_6f5m_prob_logistic = {'C': 0.9000000000000001} NHEK_4f2m_prob_logistic = {'C': 0.1} NHEK_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 50} NHEK_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 50} NHEK_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 291, 'max_bin': 45, 'min_child_samples': 140, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 1.0, 'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200} NHEK_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 35, 'min_child_samples': 100, 'colsample_bytree': 0.8, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.07, 'n_estimators': 100} NHEK_6f5m_prob_rf = {'n_estimators': 70, 'max_depth': 106, 'min_samples_leaf': 10, 'min_samples_split': 9, 'max_features': 'log2'} NHEK_4f2m_prob_rf = {'n_estimators': 130, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 4, 'max_features': 'sqrt'} NHEK_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'} NHEK_4f2m_prob_svm = {'C': 2.0, 'gamma': 16.0, 'kernel': 'rbf'} NHEK_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.07} NHEK_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0.4, 'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0.05, 'reg_lambda': 1, 'learning_rate': 1.0} if __name__ == '_main_': print(getattr(EPIconst.BaseModelParams, "NHEK_tpcp_deepforest"))

74.626907

120

0.511653

6,553

53,806

3.855028

0.039829

0.08491

0.049402

0.04489

0.902621

0.873565

0.793841

0.716966

0.688504

0.60589

0

0.107977

0.30032

53,806

720

121

74.730556

0.563046

0.000781

0

0.339089

0

0.376714

0.030083

0

1

0

false

0

0.00157

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0.00157

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null

0

1

0

1

0

null

0

5

09f85089f4c897040aa05567bd2bf236e62b47f9

46

py

Python

nes/bus/devices/joy/__init__.py

Hexadorsimal/pynes

dbb3d40c1240fa27f70fa798bcec09188755eec2

[ "MIT" ]

1

2017-05-13T18:57:09.000Z

nes/bus/devices/joy/__init__.py

Hexadorsimal/py6502

dbb3d40c1240fa27f70fa798bcec09188755eec2

[ "MIT" ]

7

2020-10-24T17:16:56.000Z

2020-11-01T14:10:23.000Z

nes/bus/devices/joy/__init__.py

Hexadorsimal/pynes

dbb3d40c1240fa27f70fa798bcec09188755eec2

[ "MIT" ]

null

from .joy1 import Joy1 from .joy2 import Joy2

15.333333

22

0.782609

8

46

4.5

0.5

0

0.105263

0.173913

46

2

23

0.842105

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

09ffb6ae881a61b59ef28ab49ae63c006b7f6b86

308

py

Python

testproject/testproject/views.py

vicalloy/django-lb-adminlte

ba6ae6fd83e0882937c70326975783c46a73a812

[ "MIT" ]

3

2017-04-25T10:15:16.000Z

2021-02-12T20:06:29.000Z

testproject/testproject/views.py

vicalloy/django-lb-adminlte

ba6ae6fd83e0882937c70326975783c46a73a812

[ "MIT" ]

null

testproject/testproject/views.py

vicalloy/django-lb-adminlte

ba6ae6fd83e0882937c70326975783c46a73a812

[ "MIT" ]

null

from django.shortcuts import render from .forms import LeaveForm def base(request): return render(request, 'base.html') def base_ext(request): return render(request, 'base_ext.html') def form(request): ctx = { 'form': LeaveForm() } return render(request, 'form.html', ctx)

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61cd983afeb411788d362af451169a18e6532a23

215

py

Python

landlab/components/stream_power/__init__.py

SiccarPoint/landlab

4150db083a0426b3647e31ffa80dfefb5faa5a60

[ "MIT" ]

1

2015-08-17T19:29:50.000Z

landlab/components/stream_power/__init__.py

laijingtao/landlab

871151bff814e672b4f09f091b6347367758c764

[ "MIT" ]

1

2016-03-02T01:24:41.000Z

landlab/components/stream_power/__init__.py

SiccarPoint/landlab

4150db083a0426b3647e31ffa80dfefb5faa5a60

[ "MIT" ]

2

2017-07-03T20:21:13.000Z

2018-09-06T23:58:19.000Z

from .stream_power import StreamPowerEroder from .fastscape_stream_power import FastscapeEroder from .sed_flux_dep_incision import SedDepEroder __all__ = ['StreamPowerEroder', 'FastscapeEroder', 'SedDepEroder', ]

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61d3fe4569768281efbb3893be53a50d23435123

274

py

Python

ifs/source/confd.py

cbednarski/ifs-python

9629ba857b1c397fc1a1f13eeee46e5427fb2744

[ "0BSD" ]

6

2016-03-29T21:12:43.000Z

2021-05-01T18:34:10.000Z

ifs/source/confd.py

cbednarski/ifs-python

9629ba857b1c397fc1a1f13eeee46e5427fb2744

[ "0BSD" ]

2

2015-08-12T01:34:51.000Z

2015-08-25T19:23:17.000Z

ifs/source/confd.py

cbednarski/ifs-python

9629ba857b1c397fc1a1f13eeee46e5427fb2744

[ "0BSD" ]

null

version = '0.11.0' version_cmd = 'confd -version' download_url = 'https://github.com/kelseyhightower/confd/releases/download/vVERSION/confd-VERSION-linux-amd64' install_script = """ chmod +x confd-VERSION-linux-amd64 mv -f confd-VERSION-linux-amd64 /usr/local/bin/confd """

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61d4d009fe33d72d82bc543c24b4dfe79633eceb

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py

Python

double3/double3sdk/documentation/documentation.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/documentation/documentation.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/documentation/documentation.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

from double3sdk.double_api import _DoubleAPI class _Documentation: pass

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1133682c338aa6e81e5d76b2b8d1c64cc8853828

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py

Python

tests/run_tests/driver_mag_test.py

MiraGeoscience/mirageoscience-apps

8c445ec8f2391349aa4cac6c705426301b3c31ca

[ "MIT" ]

null

tests/run_tests/driver_mag_test.py

MiraGeoscience/mirageoscience-apps

8c445ec8f2391349aa4cac6c705426301b3c31ca

[ "MIT" ]

null

tests/run_tests/driver_mag_test.py

MiraGeoscience/mirageoscience-apps

8c445ec8f2391349aa4cac6c705426301b3c31ca

[ "MIT" ]

null

# Copyright (c) 2022 Mira Geoscience Ltd. # # This file is part of geoapps. # # geoapps is distributed under the terms and conditions of the MIT License # (see LICENSE file at the root of this source code package). import numpy as np from geoh5py.workspace import Workspace from SimPEG import utils from geoapps.utils import get_inversion_output from geoapps.utils.testing import check_target, setup_inversion_workspace # import pytest # pytest.skip("eliminating conflicting test.", allow_module_level=True) # To test the full run and validate the inversion. # Move this file out of the test directory and run. target_run = { "data_norm": 11.707134, "phi_d": 1.598, "phi_m": 8.824e-6, } def test_susceptibility_run( tmp_path, n_grid_points=2, max_iterations=1, pytest=True, refinement=(2,), ): from geoapps.inversion.driver import InversionDriver from geoapps.inversion.potential_fields import MagneticScalarParams np.random.seed(0) inducing_field = (50000.0, 90.0, 0.0) # Run the forward geoh5, mesh, model, survey, topography = setup_inversion_workspace( tmp_path, background=0.0, anomaly=0.05, refinement=refinement, n_electrodes=n_grid_points, n_lines=n_grid_points, flatten=False, ) params = MagneticScalarParams( forward_only=True, geoh5=geoh5, mesh=model.parent.uid, topography_object=topography.uid, inducing_field_strength=inducing_field[0], inducing_field_inclination=inducing_field[1], inducing_field_declination=inducing_field[2], resolution=0.0, z_from_topo=False, data_object=survey.uid, starting_model_object=model.parent.uid, starting_model=model.uid, ) params.workpath = tmp_path fwr_driver = InversionDriver(params) fwr_driver.run() geoh5 = Workspace(geoh5.h5file) tmi = geoh5.get_entity("Iteration_0_tmi")[0] # Run the inverse np.random.seed(0) params = MagneticScalarParams( geoh5=geoh5, mesh=mesh.uid, topography_object=topography.uid, inducing_field_strength=inducing_field[0], inducing_field_inclination=inducing_field[1], inducing_field_declination=inducing_field[2], resolution=0.0, data_object=tmi.parent.uid, starting_model=1e-4, s_norm=0.0, x_norm=0.0, y_norm=0.0, z_norm=0.0, gradient_type="components", lower_bound=0.0, tmi_channel_bool=True, z_from_topo=False, tmi_channel=tmi.uid, tmi_uncertainty=4.0, max_iterations=max_iterations, initial_beta_ratio=1e0, ) params.workpath = tmp_path driver = InversionDriver(params) driver.run() run_ws = Workspace(driver.params.geoh5.h5file) output = get_inversion_output( driver.params.geoh5.h5file, driver.params.ga_group.uid ) output["data"] = tmi.values if pytest: check_target(output, target_run) nan_ind = np.isnan(run_ws.get_entity("Iteration_0_model")[0].values) inactive_ind = run_ws.get_entity("active_cells")[0].values == 0 assert np.all(nan_ind == inactive_ind) else: return fwr_driver.starting_model, driver.inverse_problem.model target_mvi_run = { "data_norm": 8.943476, "phi_d": 0.00776, "phi_m": 4.674e-6, } def test_magnetic_vector_run( tmp_path, n_grid_points=2, max_iterations=1, pytest=True, refinement=(2,), ): from geoapps.inversion.driver import InversionDriver from geoapps.inversion.potential_fields import MagneticVectorParams np.random.seed(0) inducing_field = (50000.0, 90.0, 0.0) # Run the forward geoh5, mesh, model, survey, topography = setup_inversion_workspace( tmp_path, background=0.0, anomaly=0.05, refinement=refinement, n_electrodes=n_grid_points, n_lines=n_grid_points, ) params = MagneticVectorParams( forward_only=True, geoh5=geoh5, mesh=model.parent.uid, topography_object=topography.uid, inducing_field_strength=inducing_field[0], inducing_field_inclination=inducing_field[1], inducing_field_declination=inducing_field[2], resolution=0.0, z_from_topo=False, data_object=survey.uid, starting_model_object=model.parent.uid, starting_model=model.uid, starting_inclination=45, starting_declination=270, ) fwr_driver = InversionDriver(params) fwr_driver.run() geoh5 = Workspace(geoh5.h5file) tmi = geoh5.get_entity("Iteration_0_tmi")[0] # Run the inverse params = MagneticVectorParams( geoh5=geoh5, mesh=mesh.uid, topography_object=topography.uid, inducing_field_strength=inducing_field[0], inducing_field_inclination=inducing_field[1], inducing_field_declination=inducing_field[2], resolution=0.0, data_object=tmi.parent.uid, starting_model=1e-4, s_norm=0.0, x_norm=1.0, y_norm=1.0, z_norm=1.0, gradient_type="components", tmi_channel_bool=True, z_from_topo=False, tmi_channel=tmi.uid, tmi_uncertainty=4.0, max_iterations=max_iterations, initial_beta_ratio=1e1, prctile=100, ) driver = InversionDriver(params) driver.run() run_ws = Workspace(driver.params.geoh5.h5file) # Re-open the workspace and get iterations output = get_inversion_output( driver.params.geoh5.h5file, driver.params.ga_group.uid ) output["data"] = tmi.values if pytest: check_target(output, target_mvi_run) nan_ind = np.isnan(run_ws.get_entity("Iteration_0_amplitude_model")[0].values) inactive_ind = run_ws.get_entity("active_cells")[0].values == 0 assert np.all(nan_ind == inactive_ind) else: return fwr_driver.starting_model, utils.spherical2cartesian( driver.inverse_problem.model ) if __name__ == "__main__": # Full run m_start, m_rec = test_susceptibility_run( "./", n_grid_points=20, max_iterations=30, pytest=False, refinement=(4, 8) ) residual = np.linalg.norm(m_rec - m_start) / np.linalg.norm(m_start) * 100.0 assert ( residual < 15.0 ), f"Deviation from the true solution is {residual:.2f}%. Validate the solution!" print("Susceptibility model is within 15% of the answer. Well done you!") m_start, m_rec = test_magnetic_vector_run( "./", n_grid_points=20, max_iterations=30, pytest=False, refinement=(4, 8) ) residual = np.linalg.norm(m_rec - m_start) / np.linalg.norm(m_start) * 100.0 assert ( residual < 50.0 ), f"Deviation from the true solution is {residual:.2f}%. Validate the solution!" print("MVI model is within 50% of the answer. Done!")

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