hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2d4056a01e207872fd1a18d0e6fd911eebfd7a5a | 27 | py | Python | Taekwon/Python/baseGrammar/codeup017.py | sonnysorry/codingtest | 478e0168e3209eb97b6b16910027bf12ccc3ccd0 | [
"MIT"
] | 2 | 2021-09-27T19:10:36.000Z | 2021-11-09T05:40:39.000Z | Taekwon/Python/baseGrammar/codeup017.py | sonnysorry/codingtest | 478e0168e3209eb97b6b16910027bf12ccc3ccd0 | [
"MIT"
] | 1 | 2021-11-15T14:56:54.000Z | 2021-11-15T14:56:54.000Z | Taekwon/Python/baseGrammar/codeup017.py | sonnysorry/codingtest | 478e0168e3209eb97b6b16910027bf12ccc3ccd0 | [
"MIT"
] | null | null | null | s = input()
print(s, s, s)
| 9 | 14 | 0.518519 | 6 | 27 | 2.333333 | 0.5 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.222222 | 27 | 2 | 15 | 13.5 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
741a4acc7ed13a285570ae5847afbd1cf1f5ddd8 | 142 | py | Python | python-teste/uteis/numeros/__init__.py | zmixtv1/cev-Python | edce04f86d943d9af070bf3c5e89575ff796ec9e | [
"MIT"
] | null | null | null | python-teste/uteis/numeros/__init__.py | zmixtv1/cev-Python | edce04f86d943d9af070bf3c5e89575ff796ec9e | [
"MIT"
] | null | null | null | python-teste/uteis/numeros/__init__.py | zmixtv1/cev-Python | edce04f86d943d9af070bf3c5e89575ff796ec9e | [
"MIT"
] | null | null | null | def fatorial(n):
f = 1
for c in range(1,n+1):
f*=c
return f
def dobro(n):
return n*2
def triplo(n):
return n*3
| 10.923077 | 26 | 0.507042 | 28 | 142 | 2.571429 | 0.5 | 0.194444 | 0.222222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054945 | 0.359155 | 142 | 12 | 27 | 11.833333 | 0.736264 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.222222 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
742d82e0a4ddad0cb9de5355563f6c1abde0c796 | 71 | py | Python | run.py | colemickens/plex-mpv-shim | 53b0767af1f6a533730ba9f9c2ada97f76d6b905 | [
"MIT"
] | null | null | null | run.py | colemickens/plex-mpv-shim | 53b0767af1f6a533730ba9f9c2ada97f76d6b905 | [
"MIT"
] | null | null | null | run.py | colemickens/plex-mpv-shim | 53b0767af1f6a533730ba9f9c2ada97f76d6b905 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
from plex_mpv_shim.mpv_shim import main
main()
| 14.2 | 39 | 0.774648 | 13 | 71 | 4 | 0.769231 | 0.269231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015873 | 0.112676 | 71 | 4 | 40 | 17.75 | 0.809524 | 0.295775 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
74346c9e602328251041b223cf6371f54adab9ef | 217 | py | Python | apps/archives/management/commands/populate_from_metadata.py | dhmit/computation_hist | 73265df00d1ba7952942be16f7b84e2a6692b359 | [
"BSD-3-Clause"
] | 5 | 2018-11-16T20:23:19.000Z | 2020-10-02T21:54:03.000Z | apps/archives/management/commands/populate_from_metadata.py | dhmit/computation_hist | 73265df00d1ba7952942be16f7b84e2a6692b359 | [
"BSD-3-Clause"
] | 236 | 2018-11-17T01:56:47.000Z | 2019-12-05T01:57:03.000Z | apps/archives/management/commands/populate_from_metadata.py | dhmit/computation_hist | 73265df00d1ba7952942be16f7b84e2a6692b359 | [
"BSD-3-Clause"
] | 26 | 2018-11-09T21:16:25.000Z | 2019-06-11T04:38:12.000Z | from django.core.management.base import BaseCommand
from utilities.metadata_parser import populate_from_metadata
class Command(BaseCommand):
def handle(self, *args, **options):
populate_from_metadata()
| 24.111111 | 60 | 0.78341 | 26 | 217 | 6.346154 | 0.692308 | 0.145455 | 0.242424 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.138249 | 217 | 8 | 61 | 27.125 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
74366cf09efda2ca14eaf646fd072e717b22c75b | 62,898 | py | Python | experiments/experiments_toy/convergence/nmtf_vb.py | ThomasBrouwer/BNMTF | 34df0c3cebc5e67a5e39762b9305b75d73a2a0e0 | [
"Apache-2.0"
] | 16 | 2017-04-19T12:04:47.000Z | 2021-12-03T00:50:43.000Z | experiments/experiments_toy/convergence/nmtf_vb.py | ThomasBrouwer/BNMTF | 34df0c3cebc5e67a5e39762b9305b75d73a2a0e0 | [
"Apache-2.0"
] | 1 | 2017-04-20T11:26:16.000Z | 2017-04-20T11:26:16.000Z | experiments/experiments_toy/convergence/nmtf_vb.py | ThomasBrouwer/BNMTF | 34df0c3cebc5e67a5e39762b9305b75d73a2a0e0 | [
"Apache-2.0"
] | 8 | 2015-12-15T05:29:43.000Z | 2019-06-05T03:14:11.000Z | """
Recover the toy dataset using VB.
We can plot the MSE, R2 and Rp as it converges, on the entire dataset.
We have I=100, J=80, K=5, L=5, and no test data.
We give flatter priors (1/10) than what was used to generate the data (1).
"""
import sys, os
project_location = os.path.dirname(__file__)+"/../../../../"
sys.path.append(project_location)
from BNMTF.code.models.bnmtf_vb_optimised import bnmtf_vb_optimised
import numpy, matplotlib.pyplot as plt
##########
input_folder = project_location+"BNMTF/data_toy/bnmtf/"
iterations = 1000
init_FG = 'kmeans'
init_S = 'random'
I, J, K, L = 100, 80, 5, 5
alpha, beta = 1., 1.
lambdaF = numpy.ones((I,K))
lambdaS = numpy.ones((K,L))/100.
lambdaG = numpy.ones((J,L))
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
# Load in data
R = numpy.loadtxt(input_folder+"R.txt")
M = numpy.ones((I,J))
# Give the same random initialisation
numpy.random.seed(3)
# Run the Gibbs sampler
BNMTF = bnmtf_vb_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S=init_S,init_FG=init_FG)
expF = BNMTF.expF
expS = BNMTF.expS
expG = BNMTF.expG
BNMTF.run(iterations)
# Plot the tau expectation values to check convergence
plt.plot(BNMTF.all_exp_tau)
# Extract the performances across all iterations
print "vb_all_performances = %s" % BNMTF.all_performances
'''
vb_all_performances = {'R^2': [0.5640738157611842, 0.9377691999406352, 0.9408616526339433, 0.9430220793430073, 0.9470802255399074, 0.9571789776719599, 0.9699605812528003, 0.9760635099999311, 0.9780471604119425, 0.9787745785769697, 0.979117655140942, 0.9793122320248447, 0.979442553107223, 0.9795472233135014, 0.979648952673204, 0.9797655930929889, 0.9799155560333175, 0.9801217342260696, 0.9804162662940422, 0.9808526205819487, 0.9819319610355989, 0.9883851974229043, 0.9927280833303324, 0.9943854996577983, 0.9951025786041116, 0.9954912151488043, 0.9957322765339365, 0.9958948665516809, 0.9960117359091515, 0.9961000533536801, 0.9961690968954828, 0.9962241531745667, 0.99626859811864, 0.9963048133316338, 0.9963345643705713, 0.9963591903469013, 0.9963797151827377, 0.9963969268499755, 0.9964114391780778, 0.9964237375900546, 0.996434209643847, 0.9964431599981138, 0.9964508155559995, 0.996457354367668, 0.9964629624582434, 0.9964678286215591, 0.9964721063969654, 0.996475907295325, 0.99647931219445, 0.9964823824310624, 0.9964851667312676, 0.996487705117511, 0.9964900311672186, 0.9964921734346631, 0.9964941564358143, 0.9964960013849578, 0.9964977267761304, 0.9964993488594914, 0.9965008820420387, 0.9965023392310542, 0.9965037321325098, 0.9965050715132098, 0.9965063674335122, 0.9965076294563019, 0.9965088668371014, 0.9965100886995745, 0.9965113042001487, 0.9965125226850421, 0.9965137538426868, 0.9965150078544832, 0.9965162955471342, 0.996517628550607, 0.9965190194670926, 0.9965204820579312, 0.9965220314567188, 0.9965236844164703, 0.9965254595953849, 0.9965273778790019, 0.9965294627288181, 0.9965317405443034, 0.9965342410312057, 0.9965369975820632, 0.9965400476870969, 0.9965434333988121, 0.9965472018720952, 0.9965514059981773, 0.9965561051490333, 0.9965613660480214, 0.9965672637796902, 0.9965738829443865, 0.9965813189522302, 0.9965896794384514, 0.9965990857683592, 0.9966096745817756, 0.996621599300098, 0.9966350314922495, 0.996650162001835, 0.9966672018239541, 0.9966863828669749, 0.9967079587212216, 0.9967322049970244, 0.9967594157860322, 0.9967898486203957, 0.9968234582482813, 0.9968600394848183, 0.9968997925125103, 0.9969429129051841, 0.9969894633351029, 0.9970390496467505, 0.9970907715916443, 0.9971441390111052, 0.9971988275554147, 0.9972544815671623, 0.9973106652893031, 0.9973668994104018, 0.9974227548883532, 0.9974778074542953, 0.9975315122761891, 0.9975833407602679, 0.9976330186925664, 0.9976803836313127, 0.9977252643229142, 0.9977674551442283, 0.9978067220965289, 0.997842913594705, 0.9978760084783472, 0.9979062234875882, 0.9979338070813549, 0.9979589365280958, 0.9979817851513406, 0.9980025423994768, 0.9980213908840214, 0.9980385023704782, 0.9980540422060519, 0.998068170573196, 0.9980810404176625, 0.9980927947554629, 0.9981035644323842, 0.9981134643810351, 0.9981225912678804, 0.9981310315540833, 0.9981388683467812, 0.9981461777978892, 0.9981530250938878, 0.9981594645089605, 0.9981655413284088, 0.9981712938324677, 0.9981767548673257, 0.998181952970361, 0.9981869130724069, 0.9981916568965817, 0.9981962032452021, 0.9982005683087833, 0.9982047660234573, 0.998208808440723, 0.998212706066437, 0.9982164681440581, 0.9982201028769475, 0.9982236175973503, 0.998227018894956, 0.9982303127177583, 0.9982335044549455, 0.998236599007962, 0.9982396008529871, 0.9982425140962873, 0.9982453425230426, 0.9982480896399896, 0.9982507587122063, 0.9982533527944328, 0.9982558747573481, 0.9982583273092119, 0.9982607130132615, 0.9982630343012271, 0.9982652934833643, 0.9982674927554878, 0.9982696342036269, 0.9982717198070855, 0.9982737514407948, 0.9982757308778633, 0.9982776597930821, 0.9982795397678634, 0.9982813722967135, 0.9982831587949812, 0.9982849006073462, 0.998286599016399, 0.9982882552507024, 0.9982898704918727, 0.9982914458804258, 0.9982929825203287, 0.9982944814823463, 0.9982959438063724, 0.9982973705029683, 0.9982987625543319, 0.9983001209148931, 0.9983014465116915, 0.9983027402446546, 0.9983040029868545, 0.9983052355847971, 0.9983064388587723, 0.9983076136032801, 0.9983087605875367, 0.9983098805560608, 0.9983109742293322, 0.9983120423045171, 0.9983130854562497, 0.9983141043374615, 0.9983150995802418, 0.9983160717967194, 0.9983170215799447, 0.9983179495047556, 0.9983188561286076, 0.9983197419923511, 0.9983206076209412, 0.9983214535240735, 0.9983222801967421, 0.9983230881197275, 0.9983238777600265, 0.9983246495712377, 0.9983254039939213, 0.99832614145595, 0.9983268623728604, 0.9983275671482174, 0.9983282561739929, 0.9983289298309594, 0.9983295884890947, 0.9983302325079924, 0.9983308622372733, 0.9983314780169886, 0.998332080178013, 0.9983326690424192, 0.9983332449238355, 0.9983338081277798, 0.9983343589519735, 0.9983348976866314, 0.9983354246147319, 0.9983359400122664, 0.9983364441484703, 0.998336937286037, 0.9983374196813162, 0.9983378915844994, 0.9983383532397929, 0.9983388048855795, 0.9983392467545709, 0.9983396790739527, 0.9983401020655205, 0.9983405159458104, 0.9983409209262237, 0.9983413172131462, 0.9983417050080625, 0.998342084507667, 0.9983424559039703, 0.9983428193844011, 0.9983431751319055, 0.9983435233250402, 0.998343864138061, 0.9983441977410042, 0.9983445242997604, 0.998344843976135, 0.9983451569278963, 0.9983454633088038, 0.9983457632686134, 0.9983460569530538, 0.9983463445037694, 0.9983466260582231, 0.9983469017495589, 0.998347171706421, 0.9983474360527407, 0.9983476949075069, 0.9983479483845552, 0.9983481965924337, 0.9983484396344227, 0.9983486776088085, 0.9983489106095028, 0.9983491387270654, 0.9983493620501037, 0.9983495806669156, 0.9983497946671315, 0.9983500041430674, 0.9983502091905337, 0.9983504099089694, 0.9983506064009307, 0.998350798771102, 0.9983509871250609, 0.9983511715680246, 0.9983513522037443, 0.998351529133639, 0.9983517024561878, 0.9983518722665616, 0.9983520386564475, 0.998352201714017, 0.9983523615239931, 0.9983525181677794, 0.9983526717236249, 0.9983528222668043, 0.9983529698698028, 0.9983531146024947, 0.9983532565323145, 0.9983533957244161, 0.9983535322418174, 0.9983536661455348, 0.9983537974947034, 0.9983539263466867, 0.9983540527571751, 0.998354176780275, 0.9983542984685895, 0.998354417873291, 0.9983545350441878, 0.998354650029783, 0.9983547628773302, 0.9983548736328832, 0.9983549823413419, 0.9983550890464947, 0.9983551937910575, 0.9983552966167104, 0.9983553975641312, 0.9983554966730271, 0.9983555939821651, 0.9983556895293988, 0.998355783351696, 0.9983558754851626, 0.9983559659650671, 0.9983560548258632, 0.99835614210121, 0.9983562278239937, 0.9983563120263464, 0.9983563947396643, 0.9983564759946257, 0.9983565558212073, 0.9983566342486998, 0.9983567113057225, 0.9983567870202373, 0.9983568614195613, 0.9983569345303774, 0.9983570063787459, 0.9983570769901122, 0.9983571463893147, 0.9983572146005905, 0.9983572816475791, 0.998357347553325, 0.9983574123402773, 0.9983574760302877, 0.998357538644606, 0.9983576002038733, 0.9983576607281138, 0.9983577202367226, 0.9983577787484541, 0.9983578362814075, 0.9983578928530128, 0.9983579484800172, 0.9983580031784732, 0.9983580569637308, 0.9983581098504356, 0.9983581618525346, 0.9983582129832932, 0.9983582632553257, 0.9983583126806415, 0.9983583612707091, 0.9983584090365404, 0.9983584559887941, 0.9983585021378966, 0.998358547494179, 0.9983585920680239, 0.9983586358700156, 0.9983586789110849, 0.9983587212026415, 0.9983587627566831, 0.9983588035858756, 0.9983588437035973, 0.9983588831239448, 0.9983589218617016, 0.9983589599322703, 0.9983589973515764, 0.9983590341359494, 0.9983590703019898, 0.9983591058664314, 0.998359140846004, 0.9983591752573063, 0.9983592091166904, 0.9983592424401632, 0.9983592752433054, 0.9983593075412093, 0.9983593393484327, 0.9983593706789696, 0.9983594015462347, 0.9983594319630591, 0.9983594619416964, 0.9983594914938354, 0.9983595206306187, 0.9983595493626659, 0.9983595777000978, 0.9983596056525639, 0.9983596332292688, 0.9983596604389993, 0.9983596872901507, 0.9983597137907517, 0.9983597399484886, 0.9983597657707273, 0.9983597912645348, 0.9983598164366984, 0.9983598412937438, 0.998359865841952, 0.9983598900873744, 0.9983599140358468, 0.9983599376930029, 0.9983599610642856, 0.9983599841549585, 0.998360006970116, 0.9983600295146919, 0.9983600517934694, 0.998360073811088, 0.9983600955720513, 0.9983601170807339, 0.9983601383413884, 0.9983601593581507, 0.9983601801350462, 0.9983602006759954, 0.9983602209848185, 0.9983602410652409, 0.9983602609208975, 0.9983602805553369, 0.9983602999720262, 0.9983603191743551, 0.998360338165639, 0.9983603569491234, 0.9983603755279875, 0.998360393905347, 0.9983604120842581, 0.9983604300677198, 0.9983604478586774, 0.9983604654600247, 0.9983604828746068, 0.9983605001052225, 0.998360517154626, 0.9983605340255295, 0.9983605507206041, 0.9983605672424823, 0.9983605835937585, 0.9983605997769905, 0.998360615794701, 0.9983606316493775, 0.9983606473434739, 0.99836066287941, 0.9983606782595734, 0.9983606934863182, 0.9983607085619663, 0.9983607234888074, 0.9983607382690984, 0.9983607529050643, 0.9983607673988978, 0.998360781752759, 0.9983607959687759, 0.9983608100490441, 0.9983608239956265, 0.9983608378105541, 0.9983608514958249, 0.9983608650534047, 0.998360878485227, 0.9983608917931931, 0.998360904979172, 0.9983609180450007, 0.9983609309924847, 0.9983609438233977, 0.9983609565394822, 0.9983609691424497, 0.9983609816339808, 0.9983609940157261, 0.9983610062893058, 0.9983610184563109, 0.9983610305183027, 0.9983610424768143, 0.99836105433335, 0.9983610660893861, 0.998361077746372, 0.9983610893057294, 0.9983611007688542, 0.9983611121371155, 0.9983611234118576, 0.9983611345943989, 0.9983611456860337, 0.998361156688032, 0.9983611676016404, 0.9983611784280817, 0.9983611891685565, 0.998361199824243, 0.9983612103962973, 0.9983612208858547, 0.9983612312940289, 0.9983612416219138, 0.9983612518705827, 0.9983612620410895, 0.9983612721344692, 0.9983612821517374, 0.9983612920938918, 0.9983613019619116, 0.9983613117567591, 0.9983613214793787, 0.9983613311306981, 0.9983613407116287, 0.9983613502230656, 0.998361359665888, 0.9983613690409597, 0.9983613783491294, 0.9983613875912309, 0.9983613967680836, 0.9983614058804925, 0.9983614149292488, 0.9983614239151304, 0.9983614328389013, 0.998361441701313, 0.9983614505031039, 0.9983614592450001, 0.9983614679277153, 0.9983614765519515, 0.9983614851183987, 0.9983614936277356, 0.9983615020806297, 0.9983615104777374, 0.9983615188197041, 0.9983615271071652, 0.9983615353407453, 0.9983615435210593, 0.9983615516487119, 0.998361559724298, 0.9983615677484033, 0.9983615757216043, 0.9983615836444679, 0.9983615915175524, 0.9983615993414074, 0.9983616071165741, 0.9983616148435847, 0.9983616225229636, 0.9983616301552274, 0.9983616377408844, 0.9983616452804354, 0.9983616527743735, 0.9983616602231845, 0.998361667627347, 0.9983616749873323, 0.9983616823036049, 0.9983616895766223, 0.9983616968068355, 0.9983617039946889, 0.9983617111406203, 0.9983617182450615, 0.9983617253084377, 0.9983617323311685, 0.9983617393136673, 0.9983617462563418, 0.998361753159594, 0.99836176002382, 0.998361766849411, 0.9983617736367524, 0.9983617803862244, 0.9983617870982019, 0.9983617937730552, 0.9983618004111492, 0.998361807012844, 0.998361813578495, 0.9983618201084529, 0.9983618266030636, 0.9983618330626688, 0.9983618394876056, 0.9983618458782066, 0.9983618522348003, 0.9983618585577112, 0.9983618648472591, 0.9983618711037604, 0.998361877327527, 0.9983618835188673, 0.9983618896780856, 0.9983618958054826, 0.9983619019013553, 0.9983619079659969, 0.9983619139996972, 0.9983619200027425, 0.9983619259754154, 0.9983619319179956, 0.9983619378307589, 0.9983619437139782, 0.9983619495679231, 0.9983619553928602, 0.9983619611890525, 0.9983619669567604, 0.9983619726962412, 0.998361978407749, 0.9983619840915354, 0.9983619897478487, 0.9983619953769347, 0.9983620009790362, 0.9983620065543934, 0.9983620121032438, 0.9983620176258222, 0.9983620231223609, 0.9983620285930894, 0.998362034038235, 0.998362039458022, 0.998362044852673, 0.9983620502224073, 0.9983620555674426, 0.9983620608879937, 0.9983620661842735, 0.9983620714564923, 0.9983620767048581, 0.9983620819295771, 0.998362087130853, 0.9983620923088872, 0.9983620974638794, 0.9983621025960269, 0.9983621077055248, 0.9983621127925667, 0.9983621178573435, 0.9983621229000446, 0.9983621279208572, 0.9983621329199667, 0.9983621378975565, 0.9983621428538079, 0.9983621477889008, 0.9983621527030128, 0.9983621575963199, 0.9983621624689961, 0.9983621673212141, 0.998362172153144, 0.9983621769649549, 0.9983621817568138, 0.998362186528886, 0.998362191281335, 0.9983621960143229, 0.9983622007280097, 0.9983622054225543, 0.9983622100981132, 0.9983622147548417, 0.9983622193928934, 0.9983622240124201, 0.998362228613572, 0.9983622331964978, 0.998362237761344, 0.9983622423082561, 0.9983622468373775, 0.99836225134885, 0.9983622558428137, 0.9983622603194067, 0.9983622647787657, 0.9983622692210251, 0.998362273646318, 0.9983622780547752, 0.9983622824465256, 0.998362286821696, 0.9983622911804113, 0.9983622955227941, 0.9983622998489645, 0.9983623041590407, 0.9983623084531379, 0.9983623127313689, 0.9983623169938436, 0.9983623212406693, 0.9983623254719497, 0.9983623296877855, 0.9983623338882738, 0.9983623380735079, 0.9983623422435773, 0.998362346398567, 0.9983623505385576, 0.9983623546636247, 0.9983623587738387, 0.9983623628692645, 0.9983623669499609, 0.9983623710159805, 0.9983623750673688, 0.9983623791041641, 0.9983623831263974, 0.9983623871340909, 0.9983623911272582, 0.998362395105904, 0.9983623990700231, 0.9983624030196001, 0.9983624069546091, 0.9983624108750131, 0.9983624147807638, 0.9983624186718014, 0.9983624225480541, 0.9983624264094383, 0.9983624302558587, 0.9983624340872084, 0.9983624379033694, 0.9983624417042135, 0.9983624454896026, 0.9983624492593904, 0.9983624530134237, 0.9983624567515439, 0.9983624604735888, 0.9983624641793956, 0.9983624678688026, 0.9983624715416519, 0.998362475197793, 0.9983624788370848, 0.9983624824593991, 0.9983624860646233, 0.9983624896526635, 0.998362493223447, 0.9983624967769242, 0.9983625003130716, 0.9983625038318923, 0.9983625073334177, 0.9983625108177083, 0.9983625142848535, 0.9983625177349709, 0.9983625211682061, 0.9983625245847314, 0.9983625279847431, 0.9983625313684609, 0.9983625347361239, 0.9983625380879897, 0.9983625414243307, 0.9983625447454316, 0.998362548051587, 0.9983625513430984, 0.9983625546202718, 0.9983625578834152, 0.998362561132837, 0.9983625643688429, 0.9983625675917348, 0.9983625708018095, 0.9983625739993562, 0.9983625771846568, 0.9983625803579835, 0.998362583519599, 0.998362586669755, 0.9983625898086925, 0.9983625929366411, 0.9983625960538185, 0.9983625991604305, 0.9983626022566712, 0.9983626053427226, 0.998362608418755, 0.9983626114849271, 0.998362614541386, 0.9983626175882677, 0.9983626206256971, 0.998362623653789, 0.9983626266726473, 0.9983626296823669, 0.9983626326830328, 0.9983626356747217, 0.9983626386575015, 0.9983626416314328, 0.998362644596569, 0.9983626475529569, 0.9983626505006377, 0.9983626534396471, 0.998362656370017, 0.9983626592917749, 0.9983626622049463, 0.9983626651095538, 0.9983626680056193, 0.9983626708931638, 0.9983626737722087, 0.9983626766427766, 0.9983626795048918, 0.998362682358581, 0.9983626852038748, 0.9983626880408071, 0.9983626908694166, 0.9983626936897474, 0.998362696501849, 0.998362699305777, 0.9983627021015943, 0.9983627048893698, 0.9983627076691804, 0.9983627104411106, 0.9983627132052523, 0.9983627159617059, 0.99836271871058, 0.9983627214519921, 0.9983627241860681, 0.9983627269129435, 0.9983627296327628, 0.9983627323456811, 0.9983627350518635, 0.9983627377514864, 0.9983627404447383, 0.9983627431318202, 0.998362745812947, 0.9983627484883493, 0.9983627511582736, 0.9983627538229849, 0.9983627564827682, 0.9983627591379305, 0.9983627617888028, 0.9983627644357423, 0.9983627670791354, 0.9983627697193987, 0.9983627723569829, 0.998362774992374, 0.9983627776260956, 0.9983627802587106, 0.9983627828908227, 0.998362785523077, 0.9983627881561598, 0.9983627907907981, 0.9983627934277578, 0.9983627960678411, 0.9983627987118817, 0.9983628013607413, 0.9983628040153014, 0.9983628066764573, 0.9983628093451089, 0.9983628120221512, 0.998362814708464, 0.998362817404902, 0.9983628201122826, 0.9983628228313761, 0.9983628255628945, 0.9983628283074821, 0.9983628310657063, 0.99836283383805, 0.9983628366249052, 0.998362839426568, 0.9983628422432356, 0.9983628450750047, 0.9983628479218715, 0.9983628507837333, 0.9983628536603922, 0.998362856551559, 0.998362859456859, 0.9983628623758392, 0.998362865307975, 0.9983628682526788, 0.9983628712093084, 0.9983628741771747, 0.9983628771555516, 0.9983628801436829, 0.9983628831407917, 0.9983628861460869, 0.9983628891587715, 0.9983628921780477, 0.9983628952031244, 0.9983628982332212, 0.9983629012675739, 0.9983629043054381, 0.9983629073460926, 0.9983629103888422, 0.9983629134330203, 0.9983629164779899, 0.9983629195231455, 0.9983629225679134, 0.9983629256117525, 0.9983629286541545, 0.998362931694643, 0.9983629347327737, 0.9983629377681337, 0.9983629408003404, 0.9983629438290408, 0.9983629468539099, 0.9983629498746504, 0.9983629528909906, 0.9983629559026839, 0.9983629589095065, 0.9983629619112574, 0.9983629649077558, 0.9983629678988408, 0.9983629708843699, 0.9983629738642172, 0.9983629768382732, 0.9983629798064427, 0.9983629827686447, 0.9983629857248103, 0.9983629886748826, 0.9983629916188154, 0.9983629945565722, 0.9983629974881257, 0.9983630004134569, 0.9983630033325541, 0.9983630062454127, 0.9983630091520341, 0.9983630120524255, 0.9983630149465992, 0.9983630178345719, 0.998363020716365, 0.9983630235920028, 0.9983630264615135, 0.9983630293249282, 0.9983630321822804, 0.9983630350336061, 0.9983630378789435, 0.9983630407183323, 0.9983630435518138, 0.9983630463794309, 0.9983630492012275, 0.9983630520172487, 0.9983630548275403, 0.9983630576321486, 0.9983630604311211, 0.9983630632245052, 0.9983630660123488, 0.9983630687947004, 0.9983630715716083, 0.9983630743431212, 0.9983630771092878, 0.9983630798701568, 0.9983630826257769, 0.9983630853761966, 0.9983630881214642, 0.9983630908616283, 0.9983630935967369, 0.9983630963268378, 0.9983630990519788, 0.9983631017722072, 0.99836310448757, 0.9983631071981144, 0.9983631099038867, 0.9983631126049329, 0.9983631153012994, 0.9983631179930313, 0.9983631206801743, 0.9983631233627729, 0.9983631260408717, 0.9983631287145152, 0.998363131383747, 0.9983631340486107, 0.9983631367091493, 0.9983631393654058, 0.9983631420174226, 0.9983631446652418, 0.9983631473089051, 0.9983631499484539, 0.9983631525839293, 0.9983631552153719, 0.9983631578428221, 0.99836316046632, 0.9983631630859051, 0.9983631657016168, 0.9983631683134938, 0.9983631709215751, 0.9983631735258988, 0.9983631761265027, 0.9983631787234246, 0.9983631813167018, 0.9983631839063709, 0.9983631864924687, 0.9983631890750313, 0.9983631916540948, 0.9983631942296946, 0.998363196801866, 0.9983631993706438, 0.9983632019360626, 0.9983632044981567, 0.9983632070569598, 0.9983632096125056, 0.9983632121648273, 0.9983632147139576, 0.9983632172599292, 0.9983632198027741, 0.9983632223425242, 0.998363224879211, 0.9983632274128658, 0.9983632299435192, 0.9983632324712017, 0.9983632349959434, 0.998363237517774, 0.9983632400367228, 0.9983632425528189, 0.9983632450660911, 0.9983632475765672, 0.9983632500842755, 0.9983632525892433, 0.9983632550914977, 0.9983632575910654, 0.9983632600879727, 0.9983632625822454, 0.998363265073909, 0.9983632675629885, 0.9983632700495085, 0.9983632725334931, 0.9983632750149658, 0.9983632774939497, 0.9983632799704676, 0.9983632824445414, 0.9983632849161929, 0.9983632873854428, 0.9983632898523117, 0.9983632923168195, 0.9983632947789852, 0.9983632972388274, 0.998363299696364, 0.9983633021516118, 0.9983633046045876, 0.9983633070553066, 0.9983633095037837, 0.9983633119500328, 0.9983633143940667, 0.9983633168358974, 0.9983633192755359, 0.9983633217129919, 0.9983633241482743, 0.9983633265813906, 0.9983633290123469, 0.9983633314411482, 0.9983633338677979, 0.9983633362922979, 0.9983633387146487], 'MSE': [249.84196048717777, 35.666279410735257, 33.893904931879369, 32.655701621325903, 30.329860140737836, 24.541971910184063, 17.216463573530408, 13.718697809450013, 12.581810130350162, 12.164905647400296, 11.968278501706594, 11.856760836795848, 11.782070038464834, 11.722080502422484, 11.663776450988701, 11.59692643783332, 11.510978320085998, 11.392811608038564, 11.224006723291566, 10.973919404155478, 10.355318033733178, 6.6567807841140354, 4.1677467033130231, 3.217833255648443, 2.8068544793131771, 2.5841155850837199, 2.4459563020750212, 2.3527712394253673, 2.2857900154314406, 2.2351727222197457, 2.1956018625957303, 2.1640475095864891, 2.1385748209467779, 2.1178188302745737, 2.1007676455477831, 2.086653783092066, 2.0748904033974505, 2.0650258968790509, 2.0567084600390251, 2.0496598828481138, 2.0436580445910595, 2.0385283365397235, 2.0341407139101833, 2.0303931309807988, 2.0271789713487336, 2.0243900317832213, 2.0219383144222407, 2.0197599091505909, 2.0178084625447568, 2.0160488207472822, 2.0144530574115569, 2.012998234511691, 2.0116651078490739, 2.010437312140521, 2.0093007965716772, 2.0082434026190472, 2.0072545308129053, 2.0063248677147709, 2.0054461562556938, 2.0046109988973635, 2.0038126866073611, 2.0030450486171998, 2.0023023190435065, 2.001579017119099, 2.0008698382351708, 2.0001695533549078, 1.999472914663766, 1.9987745655725748, 1.9980689533594447, 1.997350242767989, 1.9966122287003967, 1.9958482456843534, 1.9950510710382892, 1.9942128177382694, 1.9933248122807357, 1.9923774530262617, 1.9913600464187677, 1.990260622348822, 1.9890657343511715, 1.9877602521227031, 1.9863271504361737, 1.9847472910578652, 1.9829991872529318, 1.9810587375090087, 1.9788989159908037, 1.976489409197081, 1.9737961893242131, 1.9707810152772263, 1.9674008539200487, 1.9636072183360478, 1.959345426213813, 1.9545537886759334, 1.9491627477380404, 1.943093991148388, 1.9362595886471627, 1.928561209100083, 1.9198894744929851, 1.9101234574044899, 1.8991302444116407, 1.8867644955323879, 1.8728682504796277, 1.8572729567856259, 1.8398310152931669, 1.8205683611425183, 1.7996025917442131, 1.7768189766897409, 1.7521054269372034, 1.7254260231919563, 1.6970066674310804, 1.6673633182135073, 1.6367768997180241, 1.6054333061912802, 1.5735363752273583, 1.5413358518483282, 1.5091064433623673, 1.4770940462642013, 1.4455418213643945, 1.4147620277326347, 1.3850576178129828, 1.3565857515953685, 1.3294395291783687, 1.3037171009711608, 1.2795363155703467, 1.2570312844438494, 1.2362888854226406, 1.2173212461520053, 1.2000041465653506, 1.1841951876334267, 1.169792771658714, 1.1566975618907678, 1.1448009799552097, 1.1339983658863593, 1.1241912758839945, 1.1152849446615267, 1.1071875670013429, 1.099811485249536, 1.0930747327752985, 1.0869023180565998, 1.0812283698766434, 1.0759974858760777, 1.0711601126503938, 1.0666686190848145, 1.0624793603044482, 1.0585549760435875, 1.054864360190904, 1.0513815584244632, 1.0480846312296042, 1.0449547535013282, 1.0419755696392328, 1.0391327910583994, 1.0364139676087529, 1.0338083231362571, 1.0313065783863029, 1.0289007461568562, 1.0265839193836424, 1.0243500768552316, 1.0221939208766708, 1.0201107498633146, 1.018096361492635, 1.0161469790221065, 1.0142591934836938, 1.0124299161770196, 1.0106563379419633, 1.0089358933499837, 1.0072662289806895, 1.0056451754371716, 1.0040707229070753, 1.0025410000792554, 1.0010542561932227, 0.9996088459797744, 0.99820321725637229, 0.99683590095731467, 0.99550550338740107, 0.9942107004719446, 0.99295023372592339, 0.99172290758513315, 0.99052758765316651, 0.98936319935239547, 0.98822872646287685, 0.98712320911308271, 0.98604574094992259, 0.984995465428254, 0.98397157137060232, 0.98297328810204054, 0.98199988053255649, 0.98105064453721258, 0.9801249028973964, 0.97922200195018738, 0.97834130898019389, 0.97748221030087623, 0.97664410991883521, 0.97582642865206348, 0.97502860357442378, 0.97425008767447263, 0.97349034963874947, 0.97274887369275687, 0.9720251594534689, 0.97131872176373901, 0.97062909049163182, 0.9699558102862097, 0.96929844028716028, 0.96865655378924131, 0.96802973786456892, 0.9674175929469887, 0.96681973238387531, 0.96623578196144333, 0.96566537941076269, 0.96510817390309556, 0.96456382554404818, 0.9640320048773392, 0.96351239240900344, 0.96300467816200175, 0.96250856126917395, 0.9620237496093097, 0.96154995948692812, 0.96108691535261559, 0.96063434955703042, 0.96019200212967049, 0.95975962057253561, 0.95933695965956067, 0.95892378123454902, 0.95851985400269868, 0.95812495331349401, 0.95773886093524285, 0.95736136482297163, 0.95699225888298223, 0.95663134273754846, 0.95627842149332154, 0.95593330551661615, 0.95559581021801454, 0.95526575584816142, 0.95494296730572703, 0.95462727395809788, 0.95431850947464336, 0.95401651167220125, 0.95372112237208495, 0.9534321872677034, 0.95314955580191318, 0.95287308105313184, 0.95260261962930104, 0.9523380315688138, 0.95207918024758886, 0.95182593229164691, 0.95157815749442609, 0.95133572873840733, 0.95109852192040933, 0.95086641588022791, 0.95063929233225453, 0.95041703579970194, 0.95019953355131004, 0.94998667554033811, 0.94977835434577296, 0.94957446511599908, 0.94937490551498238, 0.94917957567167344, 0.94898837813319903, 0.9488012178230476, 0.94861800200570245, 0.94843864025959634, 0.94826304446069309, 0.94809112877955382, 0.94792280969476084, 0.94775800602590154, 0.94759663898885771, 0.94743863227511493, 0.94728391215483432, 0.94713240759934714, 0.94698405041274625, 0.94683877535243799, 0.94669652020648298, 0.94655722578184054, 0.94642083574781888, 0.94628729628086405, 0.94615655547861699, 0.94602856255747103, 0.9459032669114813, 0.94578061717075745, 0.945660560426271, 0.94554304176635517, 0.9454280041997587, 0.94531538894837031, 0.9452051360145286, 0.94509718488870775, 0.94499147526792593, 0.94488794768919404, 0.94478654402647311, 0.94468720783847271, 0.9445898845799876, 0.94449452170251147, 0.94440106867237128, 0.94430947693237655, 0.94421969982770326, 0.94413169251162421, 0.94404541184194257, 0.94396081627547612, 0.94387786576506671, 0.94379652166198158, 0.94371674662486771, 0.94363850453614373, 0.94356176042560436, 0.94348648040116168, 0.9434126315861896, 0.94334018206290227, 0.94326910082130855, 0.9431993577130553, 0.94313092340968718, 0.94306376936481207, 0.94299786777972494, 0.94293319157202204, 0.94286971434696742, 0.9428074103711257, 0.94274625454811889, 0.94268622239616473, 0.9426272900272582, 0.94256943412775474, 0.94251263194019519, 0.94245686124627936, 0.94240210035084582, 0.94234832806670976, 0.94229552370037861, 0.94224366703844809, 0.94219273833473371, 0.94214271829798868, 0.94209358808025756, 0.94204532926573881, 0.94199792386028669, 0.94195135428130794, 0.94190560334833107, 0.94186065427398524, 0.94181649065564121, 0.94177309646755214, 0.94173045605362926, 0.94168855412088925, 0.94164737573360047, 0.9416069063082122, 0.94156713160911631, 0.94152803774536298, 0.94148961116836904, 0.9414518386707299, 0.94141470738620703, 0.94137820479097356, 0.94134231870613339, 0.94130703730157073, 0.94127234910105484, 0.94123824298853165, 0.94120470821537661, 0.94117173440836421, 0.94113931157785036, 0.94110743012558662, 0.94107608085140138, 0.94104525495772495, 0.94101494405079578, 0.94098514013714207, 0.94095583561392293, 0.9409270232515935, 0.94089869616727873, 0.94087084778793195, 0.94084347180228933, 0.94081656210169096, 0.94079011271038282, 0.9407641177072612, 0.94073857114175896, 0.94071346694781999, 0.94068879886059154, 0.9406645603408923, 0.94064074451242818, 0.94061734411604347, 0.94059435148427784, 0.94057175853787245, 0.94054955680408237, 0.94052773745515983, 0.94050629136374697, 0.94048520917097322, 0.94046448136250638, 0.94044409834786713, 0.94042405053857303, 0.94040432842151511, 0.94038492262483753, 0.94036582397452362, 0.94034702354070632, 0.94032851267368212, 0.94031028302991937, 0.94029232658900697, 0.94027463566264702, 0.94025720289689141, 0.94024002126892103, 0.94022308407955468, 0.94020638494255604, 0.94018991777165051, 0.94017367676617292, 0.94015765639579796, 0.94014185138507456, 0.94012625669801819, 0.94011086752307904, 0.94009567925874449, 0.94008068749985008, 0.9400658880246876, 0.94005127678299572, 0.94003684988476321, 0.94002260358987588, 0.94000853429857523, 0.93999463854265486, 0.93998091297735131, 0.93996735437393619, 0.93995395961283534, 0.93994072567735476, 0.93992764964784803, 0.93991472869637605, 0.93990196008174431, 0.93988934114491118, 0.93987686930468872, 0.93986454205377967, 0.93985235695505887, 0.9398403116380375, 0.93982840379560539, 0.93981663118089476, 0.93980499160434428, 0.9397934829308936, 0.93978210307732535, 0.93977085000968374, 0.93975972174089373, 0.93974871632837786, 0.93973783187186444, 0.93972706651124904, 0.93971641842451692, 0.93970588582587355, 0.93969546696381778, 0.93968516011945347, 0.93967496360481906, 0.93966487576134372, 0.93965489495839316, 0.93964501959198143, 0.93963524808347965, 0.93962557887858567, 0.93961601044627474, 0.93960654127793264, 0.93959716988655162, 0.93958789480609217, 0.93957871459089148, 0.93956962781516706, 0.93956063307264592, 0.93955172897624306, 0.93954291415782309, 0.93953418726800153, 0.93952554697606405, 0.93951699196983252, 0.93950852095566106, 0.93950013265839405, 0.93949182582138735, 0.93948359920653468, 0.93947545159424328, 0.93946738178349798, 0.93945938859188893, 0.93945147085557934, 0.93944362742932908, 0.93943585718646971, 0.93942815901887777, 0.93942053183691876, 0.93941297456936435, 0.93940548616331154, 0.93939806558408678, 0.93939071181507894, 0.93938342385762985, 0.93937620073084926, 0.93936904147145583, 0.93936194513354676, 0.93935491078842348, 0.93934793752436929, 0.93934102444638923, 0.93933417067600644, 0.93932737535098854, 0.93932063762511087, 0.93931395666787898, 0.93930733166427383, 0.93930076181447297, 0.93929424633360759, 0.93928778445144001, 0.93928137541213697, 0.93927501847398331, 0.93926871290908109, 0.93926245800310915, 0.93925625305505112, 0.9392500973769069, 0.93924399029345917, 0.93923793114196197, 0.9392319192719385, 0.9392259540448904, 0.93922003483405436, 0.93921416102415567, 0.93920833201117748, 0.93920254720209029, 0.93919680601466304, 0.93919110787718152, 0.93918545222827499, 0.9391798385166571, 0.93917426620092381, 0.93916873474935481, 0.93916324363967851, 0.93915779235889041, 0.93915238040305704, 0.93914700727710276, 0.93914167249463754, 0.93913637557777496, 0.93913111605692312, 0.93912589347065223, 0.93912070736548137, 0.93911555729573493, 0.93911044282334943, 0.93910536351775675, 0.93910031895568002, 0.93909530872102065, 0.93909033240465589, 0.93908538960435028, 0.93908047992457533, 0.93907560297636761, 0.93907075837721865, 0.93906594575091196, 0.93906116472740309, 0.93905641494268532, 0.93905169603868177, 0.93904700766309823, 0.93904234946931908, 0.93903772111628581, 0.93903312226838098, 0.93902855259530715, 0.93902401177201056, 0.93901949947852115, 0.9390150153999044, 0.93901055922609022, 0.93900613065184768, 0.93900172937664284, 0.93899735510455007, 0.93899300754414405, 0.93898868640844979, 0.93898439141479784, 0.93898012228478989, 0.93897587874417643, 0.93897166052277259, 0.93896746735441516, 0.93896329897682707, 0.93895915513157546, 0.93895503556399773, 0.93895094002309887, 0.93894686826150153, 0.93894282003536123, 0.9389387951043201, 0.93893479323139273, 0.93893081418294833, 0.93892685772860529, 0.9389229236412161, 0.93891901169673464, 0.93891512167423075, 0.93891125335577297, 0.93890740652640803, 0.93890358097406912, 0.93889977648956524, 0.93889599286648096, 0.93889222990114729, 0.93888848739259245, 0.93888476514248176, 0.93888106295508167, 0.93887738063717774, 0.93887371799807318, 0.93887007484951368, 0.93886645100565269, 0.93886284628300132, 0.93885926050038249, 0.93885569347891284, 0.9388521450419226, 0.93884861501496775, 0.93884510322572112, 0.93884160950400264, 0.9388381336817061, 0.93883467559277733, 0.93883123507314914, 0.9388278119607536, 0.9388244060954466, 0.93882101731901468, 0.93881764547508961, 0.93881429040917574, 0.93881095196856557, 0.93880763000235623, 0.93880432436138828, 0.93880103489822353, 0.93879776146713745, 0.93879450392406139, 0.93879126212657582, 0.93878803593387394, 0.93878482520673889, 0.93878162980754065, 0.93877844960015711, 0.938775284450025, 0.93877213422403971, 0.93876899879059073, 0.93876587801950961, 0.93876277178205902, 0.93875967995092258, 0.93875660240013992, 0.93875353900514291, 0.93875048964270458, 0.93874745419092165, 0.93874443252920414, 0.93874142453823683, 0.93873843010000058, 0.93873544909772155, 0.9387324814158472, 0.9387295269400685, 0.93872658555727018, 0.93872365715552319, 0.93872074162407626, 0.93871783885333038, 0.93871494873483019, 0.938712071161249, 0.9387092060263762, 0.93870635322509643, 0.93870351265339225, 0.93870068420830588, 0.93869786778795494, 0.93869506329150809, 0.93869227061914828, 0.93868948967212384, 0.93868672035267731, 0.93868396256406317, 0.93868121621054035, 0.93867848119735087, 0.93867575743072662, 0.93867304481787661, 0.93867034326696763, 0.93866765268714492, 0.9386649729885036, 0.93866230408210005, 0.93865964587993789, 0.9386569982949764, 0.93865436124113732, 0.93865173463328377, 0.93864911838724507, 0.93864651241981178, 0.93864391664874958, 0.93864133099279901, 0.9386387553716955, 0.93863618970618223, 0.93863363391803467, 0.93863108793006111, 0.93862855166614256, 0.93862602505126835, 0.93862350801153993, 0.93862100047424968, 0.93861850236787869, 0.93861601362216451, 0.93861353416817506, 0.93861106393832361, 0.93860860286647729, 0.9386061508880108, 0.93860370793989201, 0.93860127396074777, 0.93859884889103296, 0.93859643267306525, 0.93859402525119517, 0.93859162657190831, 0.93858923658398552, 0.93858685523866503, 0.9385844824897871, 0.93858211829399218, 0.93857976261092702, 0.93857741540342698, 0.93857507663775919, 0.9385727462838398, 0.93857042431551685, 0.93856811071078605, 0.93856580545207502, 0.93856350852654069, 0.93856121992631913, 0.93855893964881543, 0.93855666769701274, 0.93855440407969681, 0.9385521488117351, 0.93854990191430332, 0.93854766341507923, 0.93854543334840934, 0.93854321175539002, 0.93854099868392082, 0.93853879418865527, 0.9385365983308257, 0.93853441117804715, 0.93853223280388853, 0.93853006328741495, 0.93852790271248587, 0.93852575116695558, 0.93852360874173568, 0.93852147552963971, 0.93851935162411204, 0.93851723711781954, 0.93851513210112014, 0.93851303666044272, 0.93851095087659064, 0.93850887482305301, 0.93850680856431257, 0.93850475215421725, 0.93850270563450555, 0.93850066903343832, 0.93849864236466629, 0.93849662562631708, 0.93849461880033458, 0.93849262185214755, 0.93849063473058303, 0.93848865736807219, 0.93848668968117044, 0.93848473157131129, 0.93848278292577092, 0.93848084361887829, 0.93847891351333979, 0.93847699246167293, 0.93847508030774063, 0.93847317688831078, 0.93847128203461272, 0.93846939557382592, 0.9384675173305479, 0.93846564712819347, 0.93846378479020531, 0.93846193014124568, 0.93846008300822092, 0.93845824322121152, 0.93845641061421992, 0.93845458502593204, 0.93845276630020003, 0.93845095428656888, 0.93844914884062658, 0.93844734982431055, 0.93844555710611099, 0.93844377056122552, 0.93844199007163298, 0.93844021552615442, 0.93843844682043254, 0.93843668385687007, 0.93843492654455629, 0.938433174799161, 0.93843142854275508, 0.93842968770367186, 0.93842795221629116, 0.93842622202085413, 0.93842449706314468, 0.93842277729430656, 0.9384210626705406, 0.93841935315276159, 0.93841764870631528, 0.93841594930063355, 0.93841425490883101, 0.93841256550737517, 0.93841088107566328, 0.93840920159560171, 0.93840752705118902, 0.93840585742806371, 0.93840419271308828, 0.93840253289384257, 0.93840087795821481, 0.93839922789393382, 0.93839758268808615, 0.9383959423267455, 0.93839430679448399, 0.93839267607399246, 0.93839105014568913, 0.9383894289873892, 0.93838781257390314, 0.93838620087680968, 0.93838459386412032, 0.93838299150007887, 0.93838139374491158, 0.93837980055466397, 0.93837821188100234, 0.9383766276710942, 0.93837504786744874, 0.93837347240778102, 0.938371901224892, 0.93837033424649252, 0.93836877139503849, 0.93836721258753508, 0.93836565773526781, 0.93836410674351489, 0.9383625595111631, 0.93836101593029109, 0.93835947588561064, 0.93835793925388034, 0.9383564059031525, 0.93835487569199116, 0.93835334846853413, 0.93835182406944539, 0.93835030231879801, 0.93834878302687941, 0.93834726598888307, 0.93834575098356765, 0.938344237771916, 0.93834272609577951, 0.93834121567660056, 0.93833970621426621, 0.93833819738611413, 0.93833668884617205, 0.93833518022478191, 0.93833367112854282, 0.93833216114078521, 0.93833064982254333, 0.9383291367141855, 0.93832762133762815, 0.93832610319929954, 0.93832458179376055, 0.93832305660798365, 0.93832152712623718, 0.93831999283556489, 0.93831845323159291, 0.93831690782466803, 0.93831535614617545, 0.9383137977547199, 0.9383122322421571, 0.93831065923925938, 0.93830907842082711, 0.93830748951009957, 0.93830589228245276, 0.9383042865681136, 0.93830267225408004, 0.93830104928495439, 0.93829941766290947, 0.93829777744671727, 0.93829612874993085, 0.93829447173828429, 0.93829280662642545, 0.93829113367410233, 0.93828945318187085, 0.9382877654865236, 0.93828607095625904, 0.93828436998581866, 0.93828266299155771, 0.93828095040666881, 0.93827923267657953, 0.9382775102545251, 0.93827578359752251, 0.93827405316257162, 0.93827231940325551, 0.93827058276673503, 0.9382688436910499, 0.93826710260284674, 0.93826535991544113, 0.93826361602720609, 0.93826187132032457, 0.93826012615977672, 0.93825838089265734, 0.93825663584770302, 0.93825489133502527, 0.93825314764609802, 0.93825140505383209, 0.93824966381284913, 0.9382479241598437, 0.93824618631408596, 0.93824445047794813, 0.93824271683755345, 0.93824098556341684, 0.93823925681117826, 0.93823753072231197, 0.93823580742488932, 0.93823408703428512, 0.93823236965398105, 0.93823065537622519, 0.93822894428280279, 0.93822723644571404, 0.93822553192785108, 0.93822383078364158, 0.9382221330596705, 0.93822043879528638, 0.93821874802313776, 0.93821706076974121, 0.93821537705594404, 0.93821369689743661, 0.93821202030515305, 0.9382103472857356, 0.93820867784188966, 0.93820701197272605, 0.93820534967416047, 0.93820369093915001, 0.93820203575802819, 0.93820038411876439, 0.93819873600718429, 0.93819709140721075, 0.93819545030107809, 0.93819381266949409, 0.93819217849186543, 0.938190547746377, 0.9381889204102084, 0.93818729645962506, 0.93818567587010204, 0.93818405861645437, 0.93818244467291045, 0.93818083401319541, 0.93817922661064368, 0.93817762243823011, 0.93817602146868351, 0.938174423674486, 0.93817282902797539, 0.93817123750135889, 0.93816964906677813, 0.93816806369632555, 0.93816648136210268, 0.9381649020361984, 0.93816332569079119, 0.93816175229810483, 0.93816018183045835, 0.93815861426028724, 0.93815704956013546, 0.93815548770268209, 0.93815392866077751, 0.93815237240740079, 0.93815081891571706, 0.93814926815905064, 0.93814772011091363, 0.93814617474499984, 0.93814463203518339, 0.93814309195554135, 0.93814155448034819, 0.9381400195840629, 0.93813848724134319, 0.9381369574270696, 0.93813543011628897, 0.93813390528427953, 0.93813238290650147, 0.93813086295863246, 0.93812934541653825, 0.93812783025629776, 0.93812631745418762, 0.93812480698667433, 0.93812329883045398, 0.93812179296238907, 0.93812028935955949, 0.93811878799925008, 0.93811728885892509, 0.9381157919162596, 0.93811429714911654, 0.93811280453556978, 0.93811131405387904, 0.93810982568249213, 0.93810833940006988, 0.93810685518544956, 0.93810537301767138, 0.93810389287597395, 0.93810241473978229, 0.93810093858870525, 0.93809946440256686, 0.93809799216136236, 0.93809652184529935, 0.93809505343476407, 0.93809358691034184, 0.93809212225281335, 0.93809065944313896, 0.93808919846249927, 0.93808773929224654, 0.93808628191394428, 0.93808482630934631, 0.9380833724604003, 0.93808192034926752, 0.93808046995828565, 0.93807902127001719, 0.93807757426721206, 0.93807612893283976, 0.93807468525006221, 0.93807324320226271, 0.93807180277302993, 0.93807036394618259, 0.93806892670572672, 0.93806749103591536, 0.93806605692123257, 0.93806462434636662, 0.93806319329626064, 0.93806176375608608, 0.93806033571126679, 0.93805890914747692, 0.93805748405063971, 0.938056060406953, 0.93805463820287638, 0.93805321742515468, 0.93805179806083383, 0.93805038009722896, 0.9380489635220004, 0.93804754832310178, 0.93804613448883956, 0.93804472200785205, 0.93804331086915627, 0.93804190106213259, 0.93804049257656275, 0.93803908540263903, 0.93803767953099682, 0.93803627495270991, 0.93803487165933641, 0.93803346964294043, 0.93803206889610702, 0.9380306694119861, 0.93802927118430801, 0.93802787420741862, 0.93802647847632425, 0.93802508398672091, 0.93802369073503822, 0.93802229871847465, 0.93802090793504989, 0.93801951838364162, 0.93801813006404955], 'Rp': [0.93577179442291958, 0.96909596999980718, 0.97006798079822532, 0.97112995718622852, 0.97319823201471622, 0.97836384132782406, 0.98487976866350735, 0.98796846437474206, 0.98896665631063407, 0.98933243464266796, 0.98950510364898114, 0.98960308966193278, 0.98966874750254086, 0.98972151171303613, 0.98977282787172272, 0.98983170179024127, 0.98990742739422233, 0.99001156349832087, 0.99016033691347516, 0.99038074107836538, 0.99092591904524174, 0.99418145283974302, 0.9963625213796018, 0.99719117894828624, 0.99754943088890069, 0.99774372734024619, 0.99786430907985757, 0.99794566513579275, 0.9980041562792018, 0.99804836361851423, 0.99808292567340662, 0.99811048659646073, 0.99813273569819261, 0.99815086493461114, 0.99816575792125406, 0.9981780849526819, 0.9981883587017486, 0.9981969737593962, 0.99820423749622955, 0.9982103929595062, 0.9982156342370474, 0.99822011411112399, 0.99822394644117163, 0.99822722006825537, 0.99823002761816804, 0.99823246344599692, 0.99823460448729218, 0.99823650667175434, 0.99823821056678963, 0.99823974692674944, 0.9982411401707274, 0.99824241034671923, 0.99824357426840515, 0.998244646230802, 0.99824563850549064, 0.99824656171054482, 0.99824742510204345, 0.99824823681268915, 0.99824900405238781, 0.9982497332801854, 0.99825043035365346, 0.99825110066016809, 0.99825174923357352, 0.99825238085885259, 0.99825300016744378, 0.99825361172516747, 0.99825422011459453, 0.99825483001359916, 0.99825544627143958, 0.99825607398390026, 0.9982567185692528, 0.99825738584697621, 0.99825808212237288, 0.99825881428066343, 0.99825958989488861, 0.99826041735165227, 0.99826130599633511, 0.9982622662960271, 0.99826331001424473, 0.99826445039081835, 0.99826570232379719, 0.998267082557309, 0.99826860988534682, 0.99827030538356898, 0.99827219267981604, 0.99827429827231251, 0.99827665190341974, 0.99827928699656499, 0.99828224116293995, 0.99828555678059527, 0.99828928164347475, 0.99829346967135901, 0.99829818166489159, 0.99830348608046882, 0.9983094597860741, 0.99831618874510664, 0.99832376857770722, 0.99833230499252334, 0.99834191415923168, 0.99835272308976619, 0.99836486981171957, 0.99837850158955566, 0.99839374717490847, 0.99841058288162288, 0.99842890578611343, 0.99844881765238158, 0.9984704168411801, 0.9984937381440524, 0.99851858550774208, 0.99854449776326559, 0.99857122927143638, 0.99859862014874823, 0.99862649345176635, 0.99865463159629353, 0.99868279392477233, 0.99871076454911445, 0.99873833075704932, 0.99876521996111889, 0.99879116824420033, 0.9988160383216047, 0.99883974855876145, 0.9988622134447851, 0.99888333097272564, 0.99890298466914695, 0.99892109823822872, 0.99893766024119146, 0.99895277967566265, 0.99896658186887777, 0.99897915557663652, 0.99899058735693036, 0.99900097214645089, 0.99901040149724429, 0.99901896146470825, 0.99902673485661531, 0.99903380189016511, 0.99904023916663087, 0.99904611833849433, 0.99905150499819562, 0.99905645671485166, 0.99906102174718159, 0.9990652432308148, 0.99906916277377678, 0.99907281857450669, 0.99907624331440459, 0.99907946416673954, 0.99908250377097174, 0.99908538123702884, 0.99908811294471511, 0.99909071312187825, 0.99909319420686249, 0.99909556705058133, 0.99909784105643096, 0.99910002432925715, 0.99910212384968455, 0.9991041456561921, 0.99910609501322689, 0.99910797655215156, 0.99910979438229097, 0.99911155217545433, 0.99911325323052813, 0.99911490052426155, 0.99911649675308489, 0.99911804436893936, 0.99911954561083649, 0.99912100253269553, 0.99912241702785298, 0.99912379085053171, 0.99912512563418376, 0.99912642290722298, 0.99912768410611175, 0.99912891058617481, 0.99913010363025612, 0.9991312644554079, 0.99913239421787714, 0.9991334940165536, 0.99913456489520092, 0.99913560784399291, 0.99913662380060431, 0.99913761365152587, 0.99913857823381758, 0.99913951833766179, 0.99914043470969371, 0.9991413280569138, 0.99914219905111112, 0.99914304833315892, 0.99914387651712189, 0.99914468419381575, 0.99914547193373904, 0.99914624028928389, 0.9991469897964097, 0.99914772097577242, 0.99914843433339473, 0.99914913036117681, 0.99914980953711496, 0.99915047232550269, 0.99915111917700017, 0.99915175052880878, 0.99915236680476771, 0.99915296841554291, 0.99915355575885645, 0.99915412921969793, 0.99915468917066552, 0.99915523597228051, 0.99915576997334954, 0.99915629151136354, 0.99915680091293613, 0.99915729849419044, 0.99915778456125626, 0.99915825941068181, 0.99915872332990452, 0.99915917659759246, 0.99915961948415088, 0.99916005225197924, 0.99916047515585837, 0.99916088844322115, 0.99916129235435691, 0.99916168712269471, 0.99916207297495907, 0.99916245013137828, 0.99916281880581848, 0.99916317920602138, 0.99916353153371784, 0.99916387598489831, 0.99916421274993139, 0.99916454201381133, 0.99916486395633375, 0.99916517875234023, 0.99916548657187032, 0.99916578758040475, 0.99916608193899881, 0.99916636980450768, 0.99916665132971638, 0.9991669266635389, 0.99916719595110759, 0.99916745933394313, 0.99916771695009587, 0.99916796893421056, 0.99916821541767376, 0.99916845652868991, 0.99916869239238881, 0.99916892313092531, 0.99916914886350594, 0.99916936970653059, 0.99916958577362425, 0.99916979717572207, 0.99917000402113054, 0.99917020641557408, 0.99917040446228755, 0.99917059826204735, 0.99917078791322755, 0.99917097351187212, 0.99917115515173394, 0.99917133292429838, 0.99917150691889101, 0.999171677222639, 0.99917184392059122, 0.99917200709569665, 0.99917216682881849, 0.99917232319882676, 0.99917247628251338, 0.9991726261546644, 0.99917277288797657, 0.99917291655306184, 0.99917305721837057, 0.99917319495011636, 0.99917332981220053, 0.99917346186611233, 0.99917359117079196, 0.9991737177826685, 0.9991738417555398, 0.99917396314072304, 0.99917408198722357, 0.99917419834217247, 0.99917431225132469, 0.99917442375980237, 0.99917453291285252, 0.99917463975657483, 0.99917474433850073, 0.99917484670781187, 0.99917494691534836, 0.99917504501323162, 0.99917514105435656, 0.99917523509179218, 0.99917532717818991, 0.99917541736528481, 0.99917550570352998, 0.99917559224183317, 0.99917567702740817, 0.99917576010575748, 0.99917584152060401, 0.99917592131400024, 0.99917599952638825, 0.99917607619661819, 0.99917615136213433, 0.99917622505898684, 0.99917629732195101, 0.99917636818461997, 0.99917643767945863, 0.99917650583786943, 0.99917657269030469, 0.99917663826624548, 0.99917670259434022, 0.99917676570235436, 0.99917682761733695, 0.99917688836553964, 0.9991769479725463, 0.99917700646321261, 0.99917706386178717, 0.99917712019188842, 0.9991771754765264, 0.99917722973814938, 0.99917728299864772, 0.99917733527936037, 0.99917738660115141, 0.99917743698432837, 0.99917748644876014, 0.99917753501383411, 0.99917758269846946, 0.99917762952115208, 0.99917767549994874, 0.99917772065248267, 0.99917776499599487, 0.99917780854730276, 0.99917785132286763, 0.99917789333874441, 0.99917793461064119, 0.99917797515392481, 0.99917801498355141, 0.99917805411418248, 0.99917809256012646, 0.99917813033539182, 0.99917816745361365, 0.99917820392814216, 0.99917823977200593, 0.99917827499795064, 0.99917830961838316, 0.99917834364541491, 0.9991783770908671, 0.99917840996627516, 0.99917844228285413, 0.99917847405152138, 0.99917850528290464, 0.99917853598730411, 0.99917856617474443, 0.99917859585489466, 0.99917862503712085, 0.99917865373048009, 0.9991786819436419, 0.99917870968498546, 0.99917873696250314, 0.99917876378385884, 0.99917879015635136, 0.99917881608693937, 0.99917884158224368, 0.99917886664856848, 0.99917889129187332, 0.99917891551793769, 0.99917893933224755, 0.99917896274015527, 0.99917898574689967, 0.99917900835768947, 0.99917903057777124, 0.99917905241247573, 0.9991790738673455, 0.99917909494815049, 0.99917911566096318, 0.99917913601219122, 0.99917915600858154, 0.99917917565723968, 0.99917919496559804, 0.99917921394138776, 0.99917923259259578, 0.99917925092737059, 0.99917926895402387, 0.99917928668086886, 0.99917930411625555, 0.99917932126841691, 0.99917933814549753, 0.99917935475543085, 0.99917937110594979, 0.99917938720455091, 0.99917940305845077, 0.99917941867459059, 0.99917943405963838, 0.99917944921995205, 0.99917946416163295, 0.99917947889046699, 0.99917949341201295, 0.99917950773153896, 0.99917952185408643, 0.99917953578446894, 0.99917954952724075, 0.99917956308679046, 0.99917957646731137, 0.99917958967279941, 0.99917960270708528, 0.99917961557384394, 0.99917962827659323, 0.99917964081872723, 0.99917965320349567, 0.99917966543403314, 0.99917967751336256, 0.99917968944438529, 0.99917970122992006, 0.99917971287268159, 0.99917972437529956, 0.99917973574030516, 0.99917974697017531, 0.99917975806728021, 0.99917976903395833, 0.99917977987243745, 0.99917979058490003, 0.99917980117347516, 0.99917981164024461, 0.99917982198719424, 0.99917983221629525, 0.99917984232945356, 0.9991798523285379, 0.99917986221535648, 0.99917987199168079, 0.99917988165926264, 0.99917989121978157, 0.99917990067490925, 0.99917991002626971, 0.9991799192754478, 0.9991799284240247, 0.99917993747351463, 0.99917994642543273, 0.99917995528125747, 0.99917996404244169, 0.99917997271040981, 0.99917998128657948, 0.99917998977233424, 0.9991799981690237, 0.99918000647799543, 0.99918001470058471, 0.99918002283808316, 0.99918003089179452, 0.99918003886296924, 0.99918004675287053, 0.99918005456272574, 0.99918006229375445, 0.99918006994714892, 0.99918007752409033, 0.99918008502576006, 0.99918009245330042, 0.9991800998078284, 0.99918010709048333, 0.99918011430233689, 0.99918012144450175, 0.99918012851802385, 0.99918013552395424, 0.99918014246332731, 0.99918014933715504, 0.99918015614644995, 0.99918016289217204, 0.99918016957530464, 0.99918017619679422, 0.99918018275756249, 0.99918018925852814, 0.99918019570060035, 0.99918020208465297, 0.99918020841156063, 0.99918021468217633, 0.99918022089733438, 0.99918022705785492, 0.99918023316455162, 0.99918023921820742, 0.99918024521960458, 0.99918025116950415, 0.99918025706865876, 0.99918026291778872, 0.99918026871762766, 0.99918027446888213, 0.99918028017224414, 0.99918028582840235, 0.99918029143801035, 0.99918029700173172, 0.99918030252020817, 0.99918030799407487, 0.99918031342395042, 0.99918031881044023, 0.99918032415414038, 0.99918032945563362, 0.99918033471551471, 0.99918033993432742, 0.99918034511263065, 0.99918035025098062, 0.9991803553498857, 0.999180360409894, 0.99918036543150279, 0.99918037041523622, 0.99918037536157944, 0.99918038027101341, 0.99918038514403862, 0.99918038998109182, 0.99918039478266651, 0.99918039954920079, 0.99918040428113253, 0.99918040897892069, 0.99918041364297505, 0.99918041827372861, 0.9991804228715977, 0.99918042743698199, 0.99918043197028561, 0.99918043647191024, 0.99918044094222924, 0.99918044538162221, 0.9991804497904887, 0.9991804541691659, 0.99918045851803472, 0.999180462837435, 0.99918046712773068, 0.99918047138924726, 0.99918047562234491, 0.99918047982734393, 0.9991804840045726, 0.99918048815434723, 0.99918049227699046, 0.99918049637280992, 0.99918050044211271, 0.99918050448520557, 0.99918050850237872, 0.99918051249391571, 0.99918051646011175, 0.99918052040125904, 0.99918052431761772, 0.99918052820947001, 0.99918053207709667, 0.99918053592073708, 0.99918053974066867, 0.99918054353714969, 0.99918054731042327, 0.99918055106074766, 0.99918055478836665, 0.9991805584935175, 0.99918056217643536, 0.99918056583737458, 0.99918056947654788, 0.99918057309417974, 0.99918057669050864, 0.9991805802657443, 0.99918058382011654, 0.9991805873538282, 0.99918059086710209, 0.99918059436013984, 0.99918059783314206, 0.99918060128632669, 0.99918060471987946, 0.99918060813401288, 0.99918061152890492, 0.99918061490475263, 0.99918061826175453, 0.99918062160008525, 0.99918062491993387, 0.99918062822147347, 0.99918063150489989, 0.99918063477038066, 0.99918063801808854, 0.99918064124820272, 0.99918064446088095, 0.99918064765631265, 0.99918065083463314, 0.99918065399603762, 0.99918065714066151, 0.99918066026867958, 0.99918066338024436, 0.99918066647550852, 0.99918066955462614, 0.99918067261775245, 0.99918067566503865, 0.99918067869662419, 0.99918068171266006, 0.99918068471329857, 0.99918068769866708, 0.99918069066890869, 0.99918069362417072, 0.99918069656459263, 0.99918069949029775, 0.99918070240142098, 0.99918070529810699, 0.99918070818047633, 0.99918071104866579, 0.9991807139028096, 0.99918071674301345, 0.9991807195694149, 0.99918072238213551, 0.9991807251813013, 0.99918072796702406, 0.99918073073943414, 0.99918073349864756, 0.99918073624477755, 0.99918073897793525, 0.99918074169824633, 0.99918074440581717, 0.99918074710076399, 0.99918074978318994, 0.9991807524532067, 0.99918075511093074, 0.99918075775645032, 0.99918076038989279, 0.9991807630113525, 0.9991807656209275, 0.99918076821873358, 0.99918077080486967, 0.99918077337942057, 0.99918077594249921, 0.99918077849420306, 0.99918078103463559, 0.99918078356387896, 0.99918078608202843, 0.99918078858918724, 0.99918079108545022, 0.99918079357089207, 0.99918079604563603, 0.99918079850973829, 0.99918080096330963, 0.99918080340643634, 0.99918080583919022, 0.99918080826167666, 0.99918081067397591, 0.99918081307616724, 0.99918081546834026, 0.99918081785057389, 0.99918082022295562, 0.99918082258556862, 0.99918082493848104, 0.99918082728177815, 0.99918082961554588, 0.99918083193985818, 0.99918083425478721, 0.99918083656041545, 0.99918083885681197, 0.99918084114406647, 0.99918084342222901, 0.99918084569138876, 0.99918084795161144, 0.9991808502029772, 0.99918085244553789, 0.99918085467938456, 0.99918085690457259, 0.99918085912116483, 0.99918086132924588, 0.99918086352886348, 0.99918086572008002, 0.99918086790297922, 0.99918087007759959, 0.99918087224401919, 0.99918087440228576, 0.99918087655246846, 0.99918087869461614, 0.99918088082878187, 0.99918088295502594, 0.99918088507340952, 0.99918088718395781, 0.99918088928673565, 0.99918089138179755, 0.99918089346917316, 0.99918089554891509, 0.99918089762105644, 0.99918089968563084, 0.9991809017426887, 0.99918090379225488, 0.99918090583435049, 0.99918090786901403, 0.99918090989625896, 0.99918091191610792, 0.99918091392857566, 0.99918091593367642, 0.9991809179314014, 0.99918091992177416, 0.99918092190478169, 0.99918092388041257, 0.99918092584866236, 0.99918092780950096, 0.99918092976292083, 0.99918093170888767, 0.99918093364734628, 0.9991809355782858, 0.99918093750164583, 0.99918093941737973, 0.999180941325432, 0.99918094322572337, 0.99918094511821698, 0.9991809470028179, 0.99918094887946318, 0.999180950748076, 0.99918095260857964, 0.99918095446089306, 0.9991809563049332, 0.99918095814063568, 0.99918095996792455, 0.99918096178671145, 0.99918096359695352, 0.99918096539858847, 0.99918096719156746, 0.99918096897585418, 0.99918097075142043, 0.999180972518244, 0.99918097427634001, 0.99918097602570244, 0.99918097776637149, 0.99918097949837525, 0.99918098122178578, 0.99918098293664781, 0.99918098464307337, 0.99918098634113783, 0.99918098803095756, 0.99918098971265246, 0.99918099138633987, 0.99918099305216246, 0.99918099471028687, 0.99918099636082491, 0.99918099800395732, 0.9991809996398191, 0.99918100126858622, 0.99918100289040135, 0.9991810045054319, 0.99918100611383387, 0.99918100771575313, 0.99918100931133047, 0.9991810109007262, 0.99918101248407165, 0.99918101406149795, 0.99918101563313821, 0.99918101719911379, 0.99918101875955356, 0.99918102031454936, 0.99918102186420843, 0.99918102340863302, 0.99918102494791938, 0.99918102648215468, 0.9991810280114054, 0.99918102953576537, 0.99918103105529321, 0.99918103257004209, 0.99918103408008863, 0.99918103558547222, 0.99918103708624773, 0.99918103858245289, 0.99918104007412778, 0.99918104156131549, 0.99918104304404076, 0.9991810445223176, 0.99918104599619173, 0.99918104746567737, 0.99918104893079418, 0.99918105039154703, 0.99918105184797057, 0.99918105330006524, 0.9991810547478458, 0.99918105619132369, 0.99918105763051035, 0.99918105906541599, 0.99918106049605737, 0.99918106192243938, 0.99918106334458634, 0.99918106476250745, 0.99918106617622049, 0.99918106758575287, 0.99918106899111392, 0.99918107039233617, 0.99918107178944948, 0.9991810731824905, 0.99918107457147731, 0.99918107595646066, 0.99918107733748129, 0.99918107871459483, 0.99918108008784412, 0.99918108145727969, 0.99918108282297213, 0.99918108418498164, 0.99918108554337626, 0.9991810868982407, 0.99918108824963969, 0.99918108959767604, 0.99918109094243024, 0.99918109228399854, 0.99918109362249607, 0.99918109495802954, 0.99918109629072005, 0.99918109762071183, 0.99918109894811802, 0.9991811002731058, 0.99918110159583862, 0.99918110291648099, 0.99918110423522899, 0.99918110555229189, 0.99918110686788131, 0.99918110818223793, 0.99918110949561378, 0.9991811108082943, 0.99918111212056349, 0.99918111343274441, 0.99918111474518345, 0.99918111605821569, 0.99918111737224491, 0.99918111868764847, 0.99918112000485493, 0.99918112132428505, 0.99918112264637826, 0.99918112397159486, 0.99918112530036984, 0.99918112663317438, 0.99918112797044412, 0.99918112931260483, 0.99918113066008563, 0.99918113201327585, 0.999181133372543, 0.99918113473822856, 0.9991811361106161, 0.99918113748996262, 0.99918113887649251, 0.99918114027034421, 0.99918114167165473, 0.99918114308048356, 0.99918114449685835, 0.99918114592073859, 0.99918114735205399, 0.99918114879069497, 0.99918115023650189, 0.99918115168927513, 0.99918115314878075, 0.99918115461477575, 0.99918115608695079, 0.99918115756500125, 0.99918115904859039, 0.9991811605373756, 0.99918116203099971, 0.99918116352907838, 0.99918116503123977, 0.99918116653711209, 0.99918116804629908, 0.99918116955842828, 0.99918117107312465, 0.99918117259002137, 0.99918117410876717, 0.99918117562898801, 0.99918117715036425, 0.99918117867256695, 0.99918118019529267, 0.99918118171822534, 0.99918118324109206, 0.99918118476361306, 0.99918118628554031, 0.99918118780664811, 0.99918118932669586, 0.99918119084547385, 0.99918119236278824, 0.99918119387846738, 0.99918119539233841, 0.99918119690424712, 0.99918119841405217, 0.99918119992161969, 0.99918120142684563, 0.99918120292960988, 0.99918120442981628, 0.99918120592738102, 0.99918120742224281, 0.99918120891431461, 0.99918121040353325, 0.99918121188985465, 0.99918121337323274, 0.99918121485362854, 0.99918121633100243, 0.99918121780533453, 0.99918121927659376, 0.99918122074478211, 0.99918122220985162, 0.99918122367182372, 0.99918122513067642, 0.99918122658641595, 0.99918122803903187, 0.99918122948853838, 0.9991812309349335, 0.99918123237823564, 0.99918123381843782, 0.99918123525557268, 0.99918123668963044, 0.99918123812064663, 0.99918123954862859, 0.99918124097358996, 0.99918124239555295, 0.99918124381454809, 0.99918124523056717, 0.99918124664365993, 0.99918124805383179, 0.99918124946111109, 0.99918125086551413, 0.99918125226706522, 0.99918125366579369, 0.99918125506172295, 0.99918125645486644, 0.99918125784526368, 0.99918125923291745, 0.99918126061786983, 0.99918126200013224, 0.99918126337974, 0.99918126475670177, 0.99918126613105906, 0.99918126750281877, 0.99918126887202818, 0.99918127023868131, 0.99918127160282899, 0.99918127296447623, 0.99918127432365822, 0.99918127568038229, 0.99918127703468484, 0.99918127838659376, 0.99918127973611659, 0.99918128108329352, 0.99918128242812809, 0.99918128377065785, 0.99918128511089432, 0.99918128644886672, 0.99918128778459758, 0.99918128911810344, 0.99918129044941284, 0.99918129177854276, 0.99918129310550541, 0.99918129443035231, 0.9991812957530618, 0.99918129707368109, 0.9991812983922409, 0.99918129970873337, 0.99918130102319525, 0.99918130233564906, 0.99918130364610325, 0.99918130495458934, 0.9991813062611149, 0.99918130756571621, 0.99918130886839385, 0.99918131016918654, 0.99918131146809208, 0.99918131276513944, 0.99918131406036281, 0.99918131535375054, 0.99918131664534227, 0.99918131793514608, 0.99918131922318953, 0.99918132050947883, 0.99918132179404362, 0.99918132307689023, 0.99918132435804141, 0.99918132563752005, 0.99918132691533457, 0.99918132819150118, 0.99918132946604499, 0.99918133073897641, 0.99918133201030312, 0.99918133328006309, 0.9991813345482633, 0.99918133581490698, 0.99918133708001677, 0.99918133834362455, 0.99918133960572242, 0.99918134086634203, 0.99918134212548759, 0.99918134338317977, 0.99918134463942332, 0.99918134589425001, 0.9991813471476565, 0.99918134839967354, 0.99918134965031247, 0.99918135089956206, 0.99918135214746495, 0.99918135339402137, 0.99918135463925495, 0.99918135588316936, 0.99918135712577194, 0.99918135836708788, 0.99918135960711951, 0.99918136084588627, 0.99918136208339015, 0.99918136331965279, 0.9991813645546751, 0.99918136578849004, 0.99918136702107929, 0.99918136825247228, 0.99918136948266745, 0.99918137071167679, 0.99918137193952206, 0.99918137316620448, 0.99918137439172316, 0.99918137561609865, 0.99918137683933894, 0.99918137806143481, 0.99918137928241468, 0.99918138050227667, 0.99918138172102455, 0.99918138293866421, 0.99918138415520208, 0.99918138537064149, 0.99918138658499323, 0.99918138779825039, 0.99918138901042275, 0.99918139022150609, 0.99918139143149554, 0.99918139264041106, 0.99918139384823434]}
''' | 1,084.448276 | 61,546 | 0.85044 | 6,230 | 62,898 | 8.58138 | 0.5 | 0.000673 | 0.000898 | 0.000823 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.879748 | 0.050717 | 62,898 | 58 | 61,547 | 1,084.448276 | 0.015643 | 0.002703 | 0 | 0 | 0 | 0 | 0.114007 | 0.022801 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.115385 | null | null | 0.038462 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
7461e2a02e2fcdbecac68110c623bae2655cafcb | 88 | py | Python | saver.py | e13/afilecreature | f51b4665acf73a82b9e18474bae8f56d491d1fcd | [
"MIT"
] | null | null | null | saver.py | e13/afilecreature | f51b4665acf73a82b9e18474bae8f56d491d1fcd | [
"MIT"
] | null | null | null | saver.py | e13/afilecreature | f51b4665acf73a82b9e18474bae8f56d491d1fcd | [
"MIT"
] | null | null | null | class Saver:
def __init__(self, config):
pass
def go(self):
pass | 17.6 | 31 | 0.545455 | 11 | 88 | 4 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.363636 | 88 | 5 | 32 | 17.6 | 0.785714 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0.4 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
74683d90edf387bf69da70b424cb38cb641abc4b | 54 | py | Python | wsu/__init__.py | wsucougpy/package-demo | 76987b2857d57095ab8a05adc801eff29330ae54 | [
"MIT"
] | null | null | null | wsu/__init__.py | wsucougpy/package-demo | 76987b2857d57095ab8a05adc801eff29330ae54 | [
"MIT"
] | null | null | null | wsu/__init__.py | wsucougpy/package-demo | 76987b2857d57095ab8a05adc801eff29330ae54 | [
"MIT"
] | null | null | null | from .scraper import scrape
from .visual import plot
| 13.5 | 27 | 0.796296 | 8 | 54 | 5.375 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 54 | 3 | 28 | 18 | 0.955556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
7495b57424a4e3f2c06c974d00077295d29eb7b9 | 38 | py | Python | src/sharpen/__main__.py | forgetfulyoshi/sharpener | 2f7294f9aa57b609594bbcfe32535f5e45665bb6 | [
"Unlicense"
] | null | null | null | src/sharpen/__main__.py | forgetfulyoshi/sharpener | 2f7294f9aa57b609594bbcfe32535f5e45665bb6 | [
"Unlicense"
] | null | null | null | src/sharpen/__main__.py | forgetfulyoshi/sharpener | 2f7294f9aa57b609594bbcfe32535f5e45665bb6 | [
"Unlicense"
] | null | null | null | from . import cli
cli.sharpen_image()
| 12.666667 | 19 | 0.763158 | 6 | 38 | 4.666667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.131579 | 38 | 2 | 20 | 19 | 0.848485 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7773656199c4462387d3fee4adb0e27dca90dc78 | 24 | py | Python | seonmul/__init__.py | AChusuei/seonmul | 9a0dee2b6c37ea766dcc9fe79b1ddab0e699c9ba | [
"MIT"
] | null | null | null | seonmul/__init__.py | AChusuei/seonmul | 9a0dee2b6c37ea766dcc9fe79b1ddab0e699c9ba | [
"MIT"
] | null | null | null | seonmul/__init__.py | AChusuei/seonmul | 9a0dee2b6c37ea766dcc9fe79b1ddab0e699c9ba | [
"MIT"
] | null | null | null | from .seonmul import app | 24 | 24 | 0.833333 | 4 | 24 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 24 | 1 | 24 | 24 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
77bff67d387eee63249f982304f80e2c9be2fde3 | 171 | py | Python | data_loaders/__init__.py | Hhhhhhhhhhao/image-cartoonization | 073b51656b96b069496917d212119caad7bf4728 | [
"MIT"
] | null | null | null | data_loaders/__init__.py | Hhhhhhhhhhao/image-cartoonization | 073b51656b96b069496917d212119caad7bf4728 | [
"MIT"
] | null | null | null | data_loaders/__init__.py | Hhhhhhhhhhao/image-cartoonization | 073b51656b96b069496917d212119caad7bf4728 | [
"MIT"
] | null | null | null | from .diff_aug import DiffAugment
from .data_loader import CartoonDataLoader, CartoonGANDataLoader, CartoonDefaultDataLoader, StarCartoonDataLoader, ClassifierDataLoader
| 42.75 | 135 | 0.888889 | 14 | 171 | 10.714286 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076023 | 171 | 3 | 136 | 57 | 0.949367 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
77c22082f3b5be1c31cca4c392210352bb9088b6 | 165 | py | Python | backend/app/admin.py | raauhl/simplifymarkets | 9b2a608606a29b8be431b2b6e6ddac1dca3bb180 | [
"Apache-2.0"
] | null | null | null | backend/app/admin.py | raauhl/simplifymarkets | 9b2a608606a29b8be431b2b6e6ddac1dca3bb180 | [
"Apache-2.0"
] | null | null | null | backend/app/admin.py | raauhl/simplifymarkets | 9b2a608606a29b8be431b2b6e6ddac1dca3bb180 | [
"Apache-2.0"
] | 1 | 2022-01-29T00:08:13.000Z | 2022-01-29T00:08:13.000Z | from django.contrib import admin
from . models import employee, knowledge
# Register your models here.
admin.site.register(employee)
admin.site.register(knowledge)
| 23.571429 | 40 | 0.812121 | 22 | 165 | 6.090909 | 0.545455 | 0.134328 | 0.253731 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.109091 | 165 | 6 | 41 | 27.5 | 0.911565 | 0.157576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
77c816f36935ff16e56e847598da864268243d70 | 1,962 | py | Python | models/__init__.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 5 | 2020-08-26T20:12:00.000Z | 2020-12-11T16:39:22.000Z | models/__init__.py | RaenonX/Jelly-Bot | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 234 | 2019-12-14T03:45:19.000Z | 2020-08-26T18:55:19.000Z | models/__init__.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 2 | 2019-10-23T15:21:15.000Z | 2020-05-22T09:35:55.000Z | """Implementations of various data models including the data to be stored into MongoDB or the result objects."""
# noinspection PyUnresolvedReferences
from .field import OID_KEY, ModelDefaultValueExt
# noinspection PyUnresolvedReferences
from ._base import Model
# noinspection PyUnresolvedReferences
from .ar import (
AutoReplyModuleModel, AutoReplyContentModel, AutoReplyModuleTagModel, AutoReplyModuleExecodeModel,
AutoReplyTagPopularityScore, UniqueKeywordCountResult
)
# noinspection PyUnresolvedReferences
from .channel import ChannelModel, ChannelConfigModel, ChannelCollectionModel
# noinspection PyUnresolvedReferences
from .exctnt import ExtraContentModel
# noinspection PyUnresolvedReferences
from .execode import ExecodeEntryModel
# noinspection PyUnresolvedReferences
from .prof import (
ChannelProfileModel, ChannelProfileConnectionModel,
PermissionPromotionRecordModel, ChannelProfileListEntry
)
# noinspection PyUnresolvedReferences
from .rpdata import PendingRepairDataModel
# noinspection PyUnresolvedReferences
from .shorturl import ShortUrlRecordModel
# noinspection PyUnresolvedReferences
from .stats import (
# result base
DailyResult, HourlyResult,
# bot feature usage
BotFeatureUsageResult, BotFeatureHourlyAvgResult, BotFeaturePerUserUsageResult,
# models
APIStatisticModel, MessageRecordModel, BotFeatureUsageModel,
# messages
MemberMessageCountEntry, MemberMessageCountResult, HourlyIntervalAverageMessageResult, DailyMessageResult,
MemberMessageByCategoryEntry, MemberMessageByCategoryResult, MemberDailyMessageResult, MeanMessageResultGenerator,
CountBeforeTimeResult
)
# noinspection PyUnresolvedReferences
from .timer import TimerModel, TimerListResult
# noinspection PyUnresolvedReferences
from .user import APIUserModel, OnPlatformUserModel, RootUserModel, RootUserConfigModel, set_uname_cache
# noinspection PyUnresolvedReferences
from .rmc import RemoteControlEntryModel
| 43.6 | 118 | 0.853211 | 137 | 1,962 | 12.189781 | 0.613139 | 0.264671 | 0.295808 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108563 | 1,962 | 44 | 119 | 44.590909 | 0.954831 | 0.316514 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 0 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
77fe25341594b92c99182e169fdece9925d23d08 | 201 | py | Python | Modulos.py | Mpase01/Python-Exercicios | b79564cfb697e7fa3827cd695cef443f3a09af11 | [
"MIT"
] | null | null | null | Modulos.py | Mpase01/Python-Exercicios | b79564cfb697e7fa3827cd695cef443f3a09af11 | [
"MIT"
] | null | null | null | Modulos.py | Mpase01/Python-Exercicios | b79564cfb697e7fa3827cd695cef443f3a09af11 | [
"MIT"
] | null | null | null | # Para chamar uma biblioteca usamos a tag -----> import, (nome da biblioteca).
# Para chamar uma função especifica da biblioteca usamos o ---> From, (nome biblioteca), import, (nome da função).
pip
| 33.5 | 114 | 0.706468 | 28 | 201 | 5.071429 | 0.535714 | 0.140845 | 0.183099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.174129 | 201 | 5 | 115 | 40.2 | 0.855422 | 0.940299 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
7ad19b33aa4f457659e6ab23b7cca8940953ed60 | 67 | py | Python | atcoder/abc/a090.py | tomato-300yen/coding | db6f440a96d8c83f486005c650461a69f27e3926 | [
"MIT"
] | null | null | null | atcoder/abc/a090.py | tomato-300yen/coding | db6f440a96d8c83f486005c650461a69f27e3926 | [
"MIT"
] | null | null | null | atcoder/abc/a090.py | tomato-300yen/coding | db6f440a96d8c83f486005c650461a69f27e3926 | [
"MIT"
] | null | null | null | S = [input() for _ in range(3)]
print(S[0][0] + S[1][1] + S[2][2])
| 22.333333 | 34 | 0.477612 | 16 | 67 | 1.9375 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.127273 | 0.179104 | 67 | 2 | 35 | 33.5 | 0.436364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
7ad589f4b38dbf8f83b6fa5e1b30d81253b0f9b7 | 318 | py | Python | ass3-airplane_det/mmdet/models/bbox_heads_rotated/__init__.py | Rooooyy/BUAA_PR | 5b4d12dc786c3fdc469ae59e0b099e8095aee550 | [
"BSD-2-Clause"
] | 2 | 2021-06-09T16:21:53.000Z | 2021-08-30T02:31:56.000Z | mmdet/models/bbox_heads_rotated/__init__.py | jedibobo/S2ANet-custom-dataset | 869b196d4c33713a5c61bd80064d10a453fb76ef | [
"Apache-2.0"
] | null | null | null | mmdet/models/bbox_heads_rotated/__init__.py | jedibobo/S2ANet-custom-dataset | 869b196d4c33713a5c61bd80064d10a453fb76ef | [
"Apache-2.0"
] | null | null | null | from .bbox_head_rotated import BBoxHeadRotated
from .convfc_bbox_head_rotated import ConvFCBBoxHeadRotated, SharedFCBBoxHeadRotated
from .double_bbox_head_rotated import DoubleConvFCBBoxHeadRotated
__all__ = [
'BBoxHeadRotated', 'ConvFCBBoxHeadRotated', 'SharedFCBBoxHeadRotated', 'DoubleConvFCBBoxHeadRotated'
]
| 39.75 | 104 | 0.861635 | 26 | 318 | 10.076923 | 0.461538 | 0.091603 | 0.171756 | 0.240458 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081761 | 318 | 7 | 105 | 45.428571 | 0.89726 | 0 | 0 | 0 | 0 | 0 | 0.27044 | 0.22327 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
bb21dca2970e1960a983b0565a1691b50d9c82e3 | 176 | wsgi | Python | docker/playbooks/roles/demo_app/files/app/demo.wsgi | AlanCLo/play_ansible | 5fc73e2c4a548739f4e1fab7a395d39d123fd8bb | [
"MIT"
] | null | null | null | docker/playbooks/roles/demo_app/files/app/demo.wsgi | AlanCLo/play_ansible | 5fc73e2c4a548739f4e1fab7a395d39d123fd8bb | [
"MIT"
] | null | null | null | docker/playbooks/roles/demo_app/files/app/demo.wsgi | AlanCLo/play_ansible | 5fc73e2c4a548739f4e1fab7a395d39d123fd8bb | [
"MIT"
] | null | null | null | import os
os.environ['DATABASE_URI'] = 'postgresql:///postgres:admin123@postgres:5432/demo'
import sys
sys.path.insert(0, '/var/www/demo')
from app import app as application
| 22 | 81 | 0.755682 | 27 | 176 | 4.888889 | 0.740741 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.050314 | 0.096591 | 176 | 7 | 82 | 25.142857 | 0.779874 | 0 | 0 | 0 | 0 | 0 | 0.426136 | 0.284091 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.6 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
bb42cec2e74e2a4b7c6f83963b8880049500278c | 369 | py | Python | structominer/__init__.py | aGHz/structominer | 74d576f2eac1a33341765868923f38b57883f5d2 | [
"MIT"
] | 9 | 2015-07-02T00:11:05.000Z | 2020-09-07T22:03:25.000Z | structominer/__init__.py | aGHz/structominer | 74d576f2eac1a33341765868923f38b57883f5d2 | [
"MIT"
] | null | null | null | structominer/__init__.py | aGHz/structominer | 74d576f2eac1a33341765868923f38b57883f5d2 | [
"MIT"
] | null | null | null | from .document import Document
from .exc import ParsingError, ErrorHandlingFailure
from .fields import (
Field,
ElementsField, ElementField,
StringsField, TextField, IntField, FloatField, DateField, DateTimeField, StructuredTextField,
URLField, StructuredField,
ListField, DictField, StructuredListField, StructuredDictField,
ElementsOperation)
| 36.9 | 97 | 0.794038 | 29 | 369 | 10.103448 | 0.827586 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146341 | 369 | 9 | 98 | 41 | 0.930159 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
24b2bb23a41d230f37d79be635011670c91925b0 | 202 | py | Python | similarity/data/loaders/__init__.py | asappresearch/rationale-alignment | 8d2bf06ba4c121863833094d5d4896bf34a9a73e | [
"MIT"
] | 38 | 2020-05-28T04:30:06.000Z | 2022-03-26T12:47:37.000Z | similarity/data/loaders/__init__.py | asappresearch/rationale-alignment | 8d2bf06ba4c121863833094d5d4896bf34a9a73e | [
"MIT"
] | 1 | 2021-04-26T12:46:47.000Z | 2021-11-11T08:28:51.000Z | similarity/data/loaders/__init__.py | asappresearch/rationale-alignment | 8d2bf06ba4c121863833094d5d4896bf34a9a73e | [
"MIT"
] | 5 | 2020-09-14T09:12:28.000Z | 2022-03-31T08:16:42.000Z | from similarity.data.loaders.askubuntu import AskUbuntuDataLoader
from similarity.data.loaders.multinews import MultiNewsDataLoader
__all__ = [
"AskUbuntuDataLoader",
"MultiNewsDataLoader",
]
| 22.444444 | 65 | 0.806931 | 17 | 202 | 9.352941 | 0.588235 | 0.176101 | 0.226415 | 0.314465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.118812 | 202 | 8 | 66 | 25.25 | 0.893258 | 0 | 0 | 0 | 0 | 0 | 0.188119 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
24cdc30eeffe2b1ce1ec0740e52ceaed33850406 | 146 | py | Python | ifthen/statements/thens/0004.py | tinyx/yitao.io | 8a3a75016e417b4c158bca0ceae98a589b2adff2 | [
"MIT"
] | null | null | null | ifthen/statements/thens/0004.py | tinyx/yitao.io | 8a3a75016e417b4c158bca0ceae98a589b2adff2 | [
"MIT"
] | 12 | 2020-06-05T19:26:11.000Z | 2022-03-11T23:33:24.000Z | ifthen/statements/thens/0004.py | tinyx/yitao.io | 8a3a75016e417b4c158bca0ceae98a589b2adff2 | [
"MIT"
] | null | null | null | def execute(operating_player, opponent_player):
operating_player.hp = operating_player.hp + 5
opponent_player.hp = opponent_player.hp - 5
| 36.5 | 49 | 0.773973 | 20 | 146 | 5.35 | 0.35 | 0.299065 | 0.317757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016 | 0.143836 | 146 | 3 | 50 | 48.666667 | 0.84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
24e5dc8f565704e39ae9ad1fdbcfff9513245326 | 3,293 | py | Python | misc/vanilla_cnn_sequential.py | platonic-realm/UM-Dissertation | 3f64f6990cc5465ec12bf49a8b34cfd46ac4b70f | [
"MIT"
] | null | null | null | misc/vanilla_cnn_sequential.py | platonic-realm/UM-Dissertation | 3f64f6990cc5465ec12bf49a8b34cfd46ac4b70f | [
"MIT"
] | null | null | null | misc/vanilla_cnn_sequential.py | platonic-realm/UM-Dissertation | 3f64f6990cc5465ec12bf49a8b34cfd46ac4b70f | [
"MIT"
] | null | null | null | from varname.helpers import Wrapper
from tensorflow.python.keras import Sequential, regularizers
from tensorflow.python.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Activation, BatchNormalization, Dropout
import datasets
from train import train_model
weight_decay = 1e-4
def create_vanilla_cnn(input_shape, no_of_classes):
model = Sequential()
model.add(Conv2D(16, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay),
input_shape=input_shape))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Conv2D(16, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Conv2D(32, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(256, activation='tanh'))
model.add(Dropout(0.25))
model.add(Dense(no_of_classes, activation='softmax'))
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
return model
def train_vanilla_seq_cnn_cifar10(epochs, iteration_id):
ds_train, ds_validation, ds_test, ds_class_no, ds_class_names = datasets.get_cifar_10()
input_shape = ds_train.element_spec[0].shape[1:]
vanilla_cnn_cifar10 = Wrapper(create_vanilla_cnn(input_shape, ds_class_no))
train_model(vanilla_cnn_cifar10.value, vanilla_cnn_cifar10.name,
ds_train, ds_validation, ds_test, ds_class_names, epochs, iteration_id)
def train_vanilla_seq_cnn_cifar100(epochs, iteration_id):
ds_train, ds_validation, ds_test, ds_class_no, ds_class_names = datasets.get_cifar_100()
input_shape = ds_train.element_spec[0].shape[1:]
vanilla_cnn_cifar100 = Wrapper(create_vanilla_cnn(input_shape, ds_class_no))
train_model(vanilla_cnn_cifar100.value, vanilla_cnn_cifar100.name,
ds_train, ds_validation, ds_test, ds_class_names, epochs, iteration_id)
def train_vanilla_seq_cnn_imagenet64(epochs, iteration_id):
ds_train, ds_validation, ds_test, ds_class_no, ds_class_names = datasets.get_imagenet_64()
input_shape = ds_train.element_spec[0].shape[1:]
vanilla_cnn_cifar100 = Wrapper(create_vanilla_cnn(input_shape, ds_class_no))
train_model(vanilla_cnn_cifar100.value, vanilla_cnn_cifar100.name,
ds_train, ds_validation, ds_test, ds_class_names, epochs, iteration_id)
| 41.683544 | 120 | 0.737322 | 451 | 3,293 | 5.088692 | 0.18847 | 0.097603 | 0.036601 | 0.033987 | 0.75817 | 0.737691 | 0.737691 | 0.737691 | 0.737691 | 0.737691 | 0 | 0.037063 | 0.13969 | 3,293 | 78 | 121 | 42.217949 | 0.773032 | 0 | 0 | 0.47541 | 0 | 0 | 0.029153 | 0.009414 | 0 | 0 | 0 | 0 | 0 | 1 | 0.065574 | false | 0 | 0.081967 | 0 | 0.163934 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
7019ffefad5480953a687f8e63fb0c326ab71b67 | 44 | py | Python | mdut/__init__.py | nkantar/mdut | aab41175378004915b71b689c6a48c86c2785124 | [
"MIT"
] | 3 | 2022-01-03T07:35:28.000Z | 2022-02-17T18:36:03.000Z | mdut/__init__.py | ssklyg36/mdut | 98874be1ea422e23fbb61e46c205718afd026cbf | [
"MIT"
] | 8 | 2022-01-03T21:32:21.000Z | 2022-01-09T17:59:42.000Z | mdut/__init__.py | ssklyg36/mdut | 98874be1ea422e23fbb61e46c205718afd026cbf | [
"MIT"
] | 1 | 2022-01-09T13:03:39.000Z | 2022-01-09T13:03:39.000Z | from .mdut import inline, reference # noqa
| 22 | 43 | 0.75 | 6 | 44 | 5.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 44 | 1 | 44 | 44 | 0.916667 | 0.090909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7040c634f443b7da01a42eff19d8766541c24279 | 24 | py | Python | torchez/model/__init__.py | kishalxd/torchez | a3e7e4e4659787132134dd9982aa1cdec14b2e88 | [
"MIT"
] | 57 | 2021-04-04T10:31:36.000Z | 2022-03-30T03:13:07.000Z | torchez/model/__init__.py | kishalxd/torchez | a3e7e4e4659787132134dd9982aa1cdec14b2e88 | [
"MIT"
] | 5 | 2021-04-16T13:31:20.000Z | 2022-02-28T01:26:25.000Z | torchez/model/__init__.py | kishalxd/torchez | a3e7e4e4659787132134dd9982aa1cdec14b2e88 | [
"MIT"
] | 6 | 2021-04-08T07:43:14.000Z | 2022-02-09T06:49:24.000Z | from .model import Model | 24 | 24 | 0.833333 | 4 | 24 | 5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 24 | 1 | 24 | 24 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
705388aaa4b757d1981b794b29f42dc31c697717 | 138 | py | Python | {{cookiecutter.project_slug}}/app_{{ cookiecutter.app_slug }}/apps.py | mk-dv/cookiecutter-django-2-simpliest | f586de3f857aa22614956bf3219dd5e094165034 | [
"BSD-3-Clause"
] | null | null | null | {{cookiecutter.project_slug}}/app_{{ cookiecutter.app_slug }}/apps.py | mk-dv/cookiecutter-django-2-simpliest | f586de3f857aa22614956bf3219dd5e094165034 | [
"BSD-3-Clause"
] | 2 | 2020-11-29T08:47:41.000Z | 2020-11-29T21:46:19.000Z | {{cookiecutter.project_slug}}/app_{{ cookiecutter.app_slug }}/apps.py | mk-dv/cookiecutter-django-2-simpliest | f586de3f857aa22614956bf3219dd5e094165034 | [
"BSD-3-Clause"
] | null | null | null | from django.apps import AppConfig
class App{{ cookiecutter.app_config_slug }}Config(AppConfig):
name = '{{ cookiecutter.app_slug}}'
| 23 | 61 | 0.746377 | 17 | 138 | 5.882353 | 0.647059 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 138 | 5 | 62 | 27.6 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0.188406 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.333333 | null | null | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7058f80965c3ca8cfcf4aa1bc57b484438b614f4 | 20 | py | Python | hello_world.py | Chapulcu/profiles-rest-api | 8fa7234cc5fe072d2a76a8f603d35b7f7c945001 | [
"MIT"
] | null | null | null | hello_world.py | Chapulcu/profiles-rest-api | 8fa7234cc5fe072d2a76a8f603d35b7f7c945001 | [
"MIT"
] | 4 | 2021-03-19T00:00:27.000Z | 2021-06-04T22:31:09.000Z | hello_world.py | Chapulcu/profiles-rest-api | 8fa7234cc5fe072d2a76a8f603d35b7f7c945001 | [
"MIT"
] | null | null | null | print('Merhaba Lo!') | 20 | 20 | 0.7 | 3 | 20 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 20 | 1 | 20 | 20 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0.52381 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
70791a9a8b1afbb15b011ea2a1d8345889772ee5 | 138 | py | Python | tests/test_openccbinary.py | starofrainnight/openccbinary | a07d3f335fdf52a3472402d19a6ed89e0491b476 | [
"Apache-2.0"
] | null | null | null | tests/test_openccbinary.py | starofrainnight/openccbinary | a07d3f335fdf52a3472402d19a6ed89e0491b476 | [
"Apache-2.0"
] | null | null | null | tests/test_openccbinary.py | starofrainnight/openccbinary | a07d3f335fdf52a3472402d19a6ed89e0491b476 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `openccbinary` package."""
import pytest
def test_empty():
assert True
| 12.545455 | 39 | 0.630435 | 18 | 138 | 4.777778 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00885 | 0.181159 | 138 | 10 | 40 | 13.8 | 0.752212 | 0.550725 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
5640e6ad9ca8ea0e28d49f592e07501192d1f3e9 | 12,740 | py | Python | kerasAC/interpret/profile_shap.py | kundajelab/kerasAC | 6aa6573f5f07659bfd68deca37de77e47612020e | [
"MIT"
] | 6 | 2018-10-30T20:33:14.000Z | 2020-10-07T05:28:47.000Z | kerasAC/interpret/profile_shap.py | kundajelab/kerasAC | 6aa6573f5f07659bfd68deca37de77e47612020e | [
"MIT"
] | 3 | 2019-07-01T19:23:30.000Z | 2021-04-19T00:55:54.000Z | kerasAC/interpret/profile_shap.py | kundajelab/kerasAC | 6aa6573f5f07659bfd68deca37de77e47612020e | [
"MIT"
] | 9 | 2018-09-24T16:17:42.000Z | 2022-02-25T20:04:35.000Z | #much of this code taken from Alex Tseng, all credit to Alex.
from .helpers import dinuc_shuffle
import shap
import tensorflow as tf
import numpy as np
def create_background_counts_chip(model_inputs,bg_size=10,seed=1234):
input_seq=model_inputs[0]
cont_counts = model_inputs[1]
rng = np.random.RandomState(seed)
input_seq_bg = dinuc_shuffle(input_seq, bg_size, rng=rng)
cont_counts_bg = np.tile(cont_counts, (bg_size, 1))
return [input_seq_bg, cont_counts_bg]
def create_background_chip(model_inputs, bg_size=10, seed=1234):
"""
From a pair of single inputs to the model, generates the set of background
inputs to perform interpretation against.
Arguments:
`model_inputs`: a pair of two entries; the first is a single one-hot
encoded input sequence of shape I x 4; the second is the set of
control profiles for the model, shaped T x O x 2
`bg_size`: the number of background examples to generate.
Returns a pair of arrays as a list, where the first array is G x I x 4, and
the second array is G x T x O x 2; these are the background inputs. The
background for the input sequences is randomly dinuceotide-shuffles of the
original sequence. The background for the control profiles is the same as
the originals.
"""
input_seq=model_inputs[0]
cont_profs = model_inputs[1]
rng = np.random.RandomState(seed)
input_seq_bg = dinuc_shuffle(input_seq, bg_size, rng=rng)
cont_prof_bg = np.tile(cont_profs, (bg_size, 1, 1))
return [input_seq_bg, cont_prof_bg]
def create_background_chip_1(model_inputs, bg_size=1, seed=1234):
input_seq=model_inputs[0]
cont_profs = model_inputs[1]
rng = np.random.RandomState(seed)
input_seq_bg = dinuc_shuffle(input_seq, bg_size, rng=rng)
cont_prof_bg = np.tile(cont_profs, (bg_size, 1, 1))
return [input_seq_bg, cont_prof_bg]
def create_background_atac(model_inputs, bg_size=10, seed=1234):
"""
From a pair of single inputs to the model, generates the set of background
inputs to perform interpretation against.
Arguments:
`model_inputs`: a pair of two entries; the first is a single one-hot
encoded input sequence of shape I x 4; the second is the set of
control profiles for the model, shaped T x O x 2
`bg_size`: the number of background examples to generate.
Returns a pair of arrays as a list, where the first array is G x I x 4, and
the second array is G x T x O x 2; these are the background inputs. The
background for the input sequences is randomly dinuceotide-shuffles of the
original sequence. The background for the control profiles is the same as
the originals.
"""
input_seq= model_inputs[0]
rng = np.random.RandomState(seed)
input_seq_bg = dinuc_shuffle(input_seq, bg_size, rng=rng)
return [input_seq_bg]
def create_background_atac_1(model_inputs, bg_size=1, seed=1234):
input_seq= model_inputs[0]
rng = np.random.RandomState(seed)
input_seq_bg = dinuc_shuffle(input_seq, bg_size, rng=rng)
return [input_seq_bg]
def combine_mult_and_diffref_chip(mult, orig_inp, bg_data):
"""
Computes the hypothetical contribution of any base along the input sequence
to the final output, given the multipliers for the input sequence
background. This will simulate all possible base identities as compute a
"difference-from-reference" for each possible base, averaging the product
of the multipliers with the differences, over the base identities. For the
control profiles, the returned contribution is 0.
Arguments:
`mult`: multipliers for the background data; a pair of a G x I x 4 array
and a G x T x O x 2 array
`orig_inp`: the target inputs to compute contributions for; a pair of an
I x 4 array and a T x O x 2 array
`bg_data`: the background data; a pair of a G x I x 4 array and a
G x T x O x 2 array
Returns a pair of importance scores as a list: an I x 4 array and a
T x O x 2 zero-array.
This function is necessary for this specific implementation of DeepSHAP. In
the original DeepSHAP, the final step is to take the difference of the input
sequence to each background sequence, and weight this difference by the
contribution multipliers for the background sequence. However, all
differences to the background would be only for the given input sequence
(i.e. the actual importance scores). To get the hypothetical importance
scores efficiently, we try every possible base for the input sequence, and
for each one, compute the difference-from-reference and weight by the
multipliers separately. This allows us to compute the hypothetical scores
in just one pass, instead of running DeepSHAP many times. To get the actual
scores for the original input, simply extract the entries for the bases in
the real input sequence.
"""
# Reassign arguments to better names; this specific implementation of
# DeepSHAP requires the arguments to have the above names
input_seq_bg_mults, cont_profs_bg_mults= mult
input_seq, cont_profs= orig_inp
input_seq_bg, cont_profs_bg = bg_data
# Allocate array to store hypothetical scores, one set for each background
# reference (i.e. each difference-from-reference)
input_seq_hyp_scores_eachdiff = np.empty_like(input_seq_bg,dtype='float64')
# Loop over the 4 input bases
for i in range(input_seq.shape[-1]):
# Create hypothetical input of all one type of base
hyp_input_seq = np.zeros_like(input_seq)
hyp_input_seq[:, i] = 1
# Compute difference from reference for each reference
diff_from_ref = np.expand_dims(hyp_input_seq, axis=0) - input_seq_bg
# Shape: G x I x 4
# Weight difference-from-reference by multipliers
contrib = diff_from_ref * input_seq_bg_mults
# Sum across bases axis; this computes the actual importance score AS IF
# the target sequence were all that base
input_seq_hyp_scores_eachdiff[:, :, i] = np.sum(contrib, axis=-1)
# Average hypothetical scores across background
# references/diff-from-references
input_seq_hyp_scores = np.mean(input_seq_hyp_scores_eachdiff, axis=0)
cont_profs_hyp_scores = np.zeros_like(cont_profs) # All 0s
return [input_seq_hyp_scores,cont_profs_hyp_scores]
def combine_mult_and_diffref_atac(mult, orig_inp, bg_data):
"""
Computes the hypothetical contribution of any base along the input sequence
to the final output, given the multipliers for the input sequence
background. This will simulate all possible base identities as compute a
"difference-from-reference" for each possible base, averaging the product
of the multipliers with the differences, over the base identities. For the
control profiles, the returned contribution is 0.
Arguments:
`mult`: multipliers for the background data; a pair of a G x I x 4 array
and a G x T x O x 2 array
`orig_inp`: the target inputs to compute contributions for; a pair of an
I x 4 array and a T x O x 2 array
`bg_data`: the background data; a pair of a G x I x 4 array and a
G x T x O x 2 array
Returns a pair of importance scores as a list: an I x 4 array and a
T x O x 2 zero-array.
This function is necessary for this specific implementation of DeepSHAP. In
the original DeepSHAP, the final step is to take the difference of the input
sequence to each background sequence, and weight this difference by the
contribution multipliers for the background sequence. However, all
differences to the background would be only for the given input sequence
(i.e. the actual importance scores). To get the hypothetical importance
scores efficiently, we try every possible base for the input sequence, and
for each one, compute the difference-from-reference and weight by the
multipliers separately. This allows us to compute the hypothetical scores
in just one pass, instead of running DeepSHAP many times. To get the actual
scores for the original input, simply extract the entries for the bases in
the real input sequence.
"""
# Reassign arguments to better names; this specific implementation of
# DeepSHAP requires the arguments to have the above names
input_seq_bg_mults = mult[0]
input_seq = orig_inp[0]
input_seq_bg = bg_data[0]
# Allocate array to store hypothetical scores, one set for each background
# reference (i.e. each difference-from-reference)
input_seq_hyp_scores_eachdiff = np.empty_like(input_seq_bg,dtype='float64')
# Loop over the 4 input bases
for i in range(input_seq.shape[-1]):
# Create hypothetical input of all one type of base
hyp_input_seq = np.zeros_like(input_seq)
hyp_input_seq[:, i] = 1
# Compute difference from reference for each reference
diff_from_ref = np.expand_dims(hyp_input_seq, axis=0) - input_seq_bg
# Shape: G x I x 4
# Weight difference-from-reference by multipliers
contrib = diff_from_ref * input_seq_bg_mults
# Sum across bases axis; this computes the actual importance score AS IF
# the target sequence were all that base
input_seq_hyp_scores_eachdiff[:, :, i] = np.sum(contrib, axis=-1)
# Average hypothetical scores across background
# references/diff-from-references
input_seq_hyp_scores = np.mean(input_seq_hyp_scores_eachdiff, axis=0)
return [input_seq_hyp_scores]
def create_explainer(model, ischip, task_index=None,bg_size=10,session=None):
"""
Given a trained Keras model, creates a Shap DeepExplainer that returns
hypothetical scores for the input sequence.
Arguments:
`model`: a model from `profile_model.profile_tf_binding_predictor`
`task_index`: a specific task index (0-indexed) to perform explanations
from (i.e. explanations will only be from the specified outputs); by
default explains all tasks
Returns a function that takes in input sequences and control profiles, and
outputs hypothetical scores for the input sequences.
"""
prof_output = model.output[0] # Shape: B x T x O x 2 (logits)
# As a slight optimization, instead of explaining the logits, explain
# the logits weighted by the probabilities after passing through the
# softmax; this exponentially increases the weight for high-probability
# positions, and exponentially reduces the weight for low-probability
# positions, resulting in a more cleaner signal
# First, center/mean-normalize the logits so the contributions are
# normalized, as a softmax would do
logits = prof_output - \
tf.reduce_mean(prof_output, axis=1, keepdims=True)
# Stop gradients flowing to softmax, to avoid explaining those
logits_stopgrad = tf.stop_gradient(logits)
probs = tf.nn.softmax(logits_stopgrad, axis=1)
logits_weighted = logits * probs # Shape: B x T x O x 2
if task_index is not None:
logits_weighted = logits_weighted[:,:, task_index : task_index + 1]
prof_sum = tf.reduce_sum(logits_weighted, axis=(1, 2))
if ischip==True:
if bg_size==10:
create_background=create_background_chip
elif bg_size==1:
create_background=create_background_chip_1
combine_mult_and_diffref=combine_mult_and_diffref_chip
model_input=[model.input[0],model.input[1]]
else:
if bg_size==10:
create_background=create_background_atac
elif bg_size==1:
create_background=create_background_atac_1
combine_mult_and_diffref=combine_mult_and_diffref_atac
model_input=model.input
explainer = shap.DeepExplainer(
model=(model_input, prof_sum),
data=create_background,
combine_mult_and_diffref=combine_mult_and_diffref,
session=session
)
def explain_fn(input_seqs,control_profile):
"""
Given input sequences and control profiles, returns hypothetical scores
for the input sequences.
Arguments:
`input_seqs`: a B x I x 4 array
`cont_profs`: a B x T x O x 4 array
Returns a B x I x 4 array containing hypothetical importance scores for
each of the B input sequences.
"""
if control_profile is not None:
return explainer.shap_values([input_seqs,control_profile], progress_message=None)
else:
return explainer.shap_values([input_seqs], progress_message=None)
return explain_fn
| 47.894737 | 93 | 0.715306 | 1,972 | 12,740 | 4.467039 | 0.136917 | 0.047224 | 0.02838 | 0.006811 | 0.79748 | 0.770689 | 0.746396 | 0.743898 | 0.71041 | 0.700647 | 0 | 0.011711 | 0.229199 | 12,740 | 265 | 94 | 48.075472 | 0.885336 | 0.581947 | 0 | 0.459184 | 0 | 0 | 0.002909 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.091837 | false | 0 | 0.040816 | 0 | 0.234694 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
5676591c73275fe4aca04a49c9362340b8dcce47 | 80 | py | Python | azbankgateways/models/__init__.py | lordmahyar/az-iranian-bank-gateways | e9eb7101f2b91318847d63d783c22c4a8d430ba3 | [
"MIT"
] | 196 | 2020-12-07T11:29:19.000Z | 2022-03-23T09:32:56.000Z | azbankgateways/models/__init__.py | lordmahyar/az-iranian-bank-gateways | e9eb7101f2b91318847d63d783c22c4a8d430ba3 | [
"MIT"
] | 25 | 2021-01-13T11:56:35.000Z | 2022-03-14T19:41:51.000Z | azbankgateways/models/__init__.py | lordmahyar/az-iranian-bank-gateways | e9eb7101f2b91318847d63d783c22c4a8d430ba3 | [
"MIT"
] | 44 | 2021-01-08T18:27:47.000Z | 2022-03-22T03:36:04.000Z | from .banks import Bank
from .enum import BankType, CurrencyEnum, PaymentStatus
| 26.666667 | 55 | 0.825 | 10 | 80 | 6.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 80 | 2 | 56 | 40 | 0.942857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3b0ca5db6f637ecf016ac40d3a9f5b28d8842de7 | 336 | py | Python | Mundo-1/ex018.py | Gabriel-Leao/Exercicios-de-python | 71933d24ab938d9cd2f4d64dc784b79cb8e756d2 | [
"MIT"
] | null | null | null | Mundo-1/ex018.py | Gabriel-Leao/Exercicios-de-python | 71933d24ab938d9cd2f4d64dc784b79cb8e756d2 | [
"MIT"
] | null | null | null | Mundo-1/ex018.py | Gabriel-Leao/Exercicios-de-python | 71933d24ab938d9cd2f4d64dc784b79cb8e756d2 | [
"MIT"
] | null | null | null | import math
angulo = float(input('Digite o ângulo que você deseja: '))
print(f'O ângulo de {angulo:.1f} tem o seno de {math.sin(math.radians(angulo)):.2f}')
print(f'O ângulo de {angulo:.1f} tem o cosseno de {math.cos(math.radians(angulo)):.2f}')
print(f'O ângulo de {angulo:.1f} tem a tangente de {math.tan(math.radians(angulo)):.2f}')
| 56 | 89 | 0.702381 | 62 | 336 | 3.806452 | 0.403226 | 0.118644 | 0.088983 | 0.165254 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.381356 | 0 | 0.020134 | 0.113095 | 336 | 5 | 90 | 67.2 | 0.771812 | 0 | 0 | 0 | 0 | 0.6 | 0.78869 | 0.321429 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 0.2 | 0.6 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
3b120b0438b3fa44c0371b6461c023da69d14bb6 | 5,317 | py | Python | setup.py | analogbit/urban-colonization | 7651522fb1bc4009b362147473903b46c9105679 | [
"MIT"
] | 2 | 2015-02-22T10:55:13.000Z | 2015-03-15T16:31:06.000Z | setup.py | analogbit/urban-colonization | 7651522fb1bc4009b362147473903b46c9105679 | [
"MIT"
] | null | null | null | setup.py | analogbit/urban-colonization | 7651522fb1bc4009b362147473903b46c9105679 | [
"MIT"
] | null | null | null | from server import db, create_user, model
db.create_all()
a = model.Lichen("wolfeyes", "Wolf Eyes", "/static/img/lichen_wolf_eyes.jpg")
a.description = "The 'brown eyes' are the fruiting bodies where spores are made for reproduction. The lichen is a combination of fungus and algae (or, sometimes, cyanobacteria), but only the fungal partner reproduces sexually and produces spores - then the new generation has to find its algal partner all over again. Wolf lichens are so named because of their common use as poisons for wolves and foxes in Europe centuries ago. The lichen, with its toxic vulpinic acid, was mixed with ground glass and meat, apparently a deadly combination."
a.passcode = "passcode"
a.hint = "This can be found hanging high above with a sunny disposition. They were named in Latin for their split-end appearance."
db.session.add(a)
a = model.Lichen("bryoria", "Bryoria", "/static/img/bryoria.jpg")
a.description = "Wila (Bryoria fremontii), like almost all of the 23 other species of Bryoria found in North America, is a dark brown hair lichen that grow on trees (mostly conifers). Differentiating the different species of Bryoria can be difficult. The simplest characteristic that distinguishes wila from the other species of Bryoria is that its main branches grow to be quite thick (greater than 0.4 mm wide), and usually become somewhat flattened, twisted, and wrinkled in older specimens. Other species of Bryoria usually have narrower main branches. Wila can also grow to be a lot longer than other species of Bryoria, and is the only species in this genus in North America that regularly grows longer than 20 cm (occasionally reaching 90 cm in length). Wila is often slightly darker in colour than most other species of Bryoria, although there is much variation in this characteristic. Soredia and apothecia are uncommon, but when they are present they are very distinctive, as they are both bright yellow."
a.passcode = "passcode"
a.hint = "Do finding these colonies have you stumped...literally?"
db.session.add(a)
a = model.Lichen("parientina", "Maritime Sunburst Lichen", "/static/img/parientina.jpg")
a.description = "The outer 'skin' of the lichen, the cortex, is composed of closely packed fungal hyphae and serves to protect the thallus from water loss due to evaporation as well as harmful effects of high levels of irradiation. In Xanthoria parietina, the thickness of the thalli is known to vary depending on the habitat is which it grows. Thalli are much thinner in shady locations than in those exposed to full sunshine; this has the effect of protecting the algae that cannot tolerate high light intensities. The lichen pigment parietin gives this species a deep yellow or orange-red color."
a.passcode = "passcode"
a.hint = "Adept in the art of camouflage, this lichen you 'wood' not see otherwise."
db.session.add(a)
a = model.Lichen("parmotrema", "Scatter-Rag Lichens", "/static/img/parmotrema.jpg")
a.description = "Ascospores are simple, hyaline, and often small. Conidia generally arise laterally from the joints of conidiogenous hyphae (Parmelia-type), but arise terminally from these joints in a small number of species (Psora-type). The conidia can have a broad range of shapes: cylindrical to bacilliform, bifusiform, fusiform, sublageniform, unciform, filiform, or curved. Pycnidia are immersed or rarely emergent from the upper cortex, are produced along the lamina or margins, pyriform in shape, and dark-brown to black in colour.[6]"
a.passcode = "passcode"
a.hint = "Long, silvery-green, tendril like ruffles welcome the public and 'shields' all those who enter."
db.session.add(a)
a = model.Lichen("unidentified", "Unidentified", "/static/img/unidentified.jpg")
a.description = "Is this a fake lichen?"
a.passcode = "passcode"
a.hint = "We found a hidden niche where soldiers, pixies and reindeer cohabitate beautifully."
db.session.add(a)
a = model.Lichen("cladonia", "Cup Lichen", "/static/img/cladonia.jpg")
a.description = "Cladonia (cup lichen) is a genus of moss-like lichens in the family Cladoniaceae. They are the primary food source for reindeer and caribou. Cladonia species are of economic importance to reindeer-herders, such as the Sami in Scandinavia or the Nenets in Russia. Antibiotic compounds are extracted from some species to create antibiotic cream. The light green species Cladonia stellaris is used in flower decorations."
a.passcode = "passcode"
a.hint = "Growing low and facing West, these golden gems glow. They can be found rockin' it--metal style."
db.session.add(a)
a = model.Lichen("farinaceae", "Cup Lichen", "/static/img/farinaceae.jpg")
a.description = "Cladonia (cup lichen) is a genus of moss-like lichens in the family Cladoniaceae. They are the primary food source for reindeer and caribou. Cladonia species are of economic importance to reindeer-herders, such as the Sami in Scandinavia or the Nenets in Russia. Antibiotic compounds are extracted from some species to create antibiotic cream. The light green species Cladonia stellaris is used in flower decorations."
a.passcode = "passcode"
a.hint = "Capable of thriving on inhospitable surfaces, this particular specimen has a hobby of hanging out near windows."
db.session.add(a)
db.session.commit()
create_user('admin', 'admin@example.com', 'password')
| 106.34 | 1,015 | 0.783525 | 839 | 5,317 | 4.959476 | 0.420739 | 0.017304 | 0.020187 | 0.030281 | 0.245614 | 0.219178 | 0.219178 | 0.183129 | 0.183129 | 0.183129 | 0 | 0.001984 | 0.146699 | 5,317 | 49 | 1,016 | 108.510204 | 0.915142 | 0 | 0 | 0.410256 | 0 | 0.230769 | 0.855181 | 0.034794 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.205128 | 0.076923 | 0 | 0.076923 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
3b216a133622106612940c6160ca90e68c8f610a | 177 | py | Python | cape/client/__init__.py | control-q/cape-client | dbc12d602d95b05be43875bae9b15967d407ec75 | [
"MIT"
] | 6 | 2018-01-09T13:30:14.000Z | 2019-05-02T02:18:12.000Z | cape/client/__init__.py | control-q/cape-client | dbc12d602d95b05be43875bae9b15967d407ec75 | [
"MIT"
] | 2 | 2018-10-24T10:18:00.000Z | 2020-06-22T08:16:35.000Z | cape/client/__init__.py | control-q/cape-client | dbc12d602d95b05be43875bae9b15967d407ec75 | [
"MIT"
] | 9 | 2018-09-27T14:03:36.000Z | 2020-06-29T03:45:05.000Z | """
Cape API client module.
This module provides a python interface to the Cape API: http://thecape.ai
"""
from .client import CapeClient
from .exceptions import CapeException
| 22.125 | 74 | 0.774011 | 25 | 177 | 5.48 | 0.76 | 0.10219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146893 | 177 | 7 | 75 | 25.285714 | 0.907285 | 0.559322 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3b275406bdf09ddd9a2e9a43f5d6cb647eba87dd | 176 | py | Python | megumin/utils/functions.py | davitudoplugins1234/WhiterKang | f4779d2c440849fa97e7014cd856f885b0abbc87 | [
"MIT"
] | 2 | 2022-02-01T17:55:44.000Z | 2022-03-27T17:21:55.000Z | megumin/utils/functions.py | davitudoplugins1234/WhiterKang | f4779d2c440849fa97e7014cd856f885b0abbc87 | [
"MIT"
] | null | null | null | megumin/utils/functions.py | davitudoplugins1234/WhiterKang | f4779d2c440849fa97e7014cd856f885b0abbc87 | [
"MIT"
] | 3 | 2022-01-29T20:04:03.000Z | 2022-02-01T18:17:40.000Z | import random
# funções futuras
def rand_array(array: list, string: bool = True):
random_num = random.choice(array)
return str(random_num) if string else random_num
| 19.555556 | 52 | 0.738636 | 26 | 176 | 4.846154 | 0.653846 | 0.214286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 176 | 8 | 53 | 22 | 0.875 | 0.085227 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
3b2e14526ce99b482b4b68f58fc70216a656adf0 | 165 | py | Python | glearn/viewers/__init__.py | glennpow/glearn | e50046cb76173668fec12c20b446be7457482528 | [
"MIT"
] | null | null | null | glearn/viewers/__init__.py | glennpow/glearn | e50046cb76173668fec12c20b446be7457482528 | [
"MIT"
] | null | null | null | glearn/viewers/__init__.py | glennpow/glearn | e50046cb76173668fec12c20b446be7457482528 | [
"MIT"
] | null | null | null | def load_view_controller(config, render=True):
from glearn.viewers.viewer_controller import ViewerController
return ViewerController(config, render=render)
| 33 | 65 | 0.818182 | 19 | 165 | 6.947368 | 0.736842 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115152 | 165 | 4 | 66 | 41.25 | 0.90411 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3b46df78d72efad7bd462c2b0c7dfcd4b04f394b | 141 | py | Python | symba/core/context.py | lycantropos/symba | 279bf86311d50fde55d17c843391f9f83ea31ddf | [
"MIT"
] | 2 | 2021-03-15T12:23:15.000Z | 2022-03-26T21:20:54.000Z | symba/core/context.py | lycantropos/symba | 279bf86311d50fde55d17c843391f9f83ea31ddf | [
"MIT"
] | null | null | null | symba/core/context.py | lycantropos/symba | 279bf86311d50fde55d17c843391f9f83ea31ddf | [
"MIT"
] | null | null | null | import math
from contextvars import ContextVar
sqrt_evaluator = ContextVar('sqrt_evaluator',
default=math.sqrt)
| 23.5 | 46 | 0.666667 | 14 | 141 | 6.571429 | 0.571429 | 0.304348 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.276596 | 141 | 5 | 47 | 28.2 | 0.901961 | 0 | 0 | 0 | 0 | 0 | 0.099291 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
3b4c450bc70298b68801ca5d7e09237265be29d4 | 81 | py | Python | config/production_setting.py | qiuchen100/moviecat | 926a965fe73b408c8d18ebc2070f201ae3958d7f | [
"Apache-2.0"
] | null | null | null | config/production_setting.py | qiuchen100/moviecat | 926a965fe73b408c8d18ebc2070f201ae3958d7f | [
"Apache-2.0"
] | null | null | null | config/production_setting.py | qiuchen100/moviecat | 926a965fe73b408c8d18ebc2070f201ae3958d7f | [
"Apache-2.0"
] | null | null | null | """
created by 邱晨 on 2020/5/3 11:00 上午.
"""
from config.base_setting import * | 20.25 | 39 | 0.654321 | 15 | 81 | 3.466667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 0.197531 | 81 | 4 | 40 | 20.25 | 0.646154 | 0.432099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
8e5cb101ccfb6179d71bafddee23ec2ff193d737 | 151 | py | Python | app/customer/__init__.py | codacy-badger/FASTFOODFAST-API | 3ddb2715dd2b19bf0eae823b5a17c3a01e963a53 | [
"MIT"
] | 1 | 2018-10-05T12:36:17.000Z | 2018-10-05T12:36:17.000Z | app/customer/__init__.py | codacy-badger/FASTFOODFAST-API | 3ddb2715dd2b19bf0eae823b5a17c3a01e963a53 | [
"MIT"
] | 1 | 2018-09-06T17:06:27.000Z | 2018-09-06T20:39:59.000Z | app/customer/__init__.py | codacy-badger/FASTFOODFAST-API | 3ddb2715dd2b19bf0eae823b5a17c3a01e963a53 | [
"MIT"
] | 8 | 2018-09-10T12:04:58.000Z | 2020-08-06T17:57:12.000Z | from flask import Blueprint
from .customer_views import PostOrders, Order, CustomersOrderHistory
customer_blueprint = Blueprint("customer", __name__)
| 30.2 | 68 | 0.84106 | 16 | 151 | 7.5625 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.099338 | 151 | 4 | 69 | 37.75 | 0.889706 | 0 | 0 | 0 | 0 | 0 | 0.05298 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 5 |
8eb2503f9506a38384b839bd601d5cd1b70f708b | 141 | py | Python | tests/unit_tests/test_obsoletesource.py | realead/obsoletesource | 59e28adc1477eea430b1cad2118446bb1748363a | [
"Unlicense"
] | null | null | null | tests/unit_tests/test_obsoletesource.py | realead/obsoletesource | 59e28adc1477eea430b1cad2118446bb1748363a | [
"Unlicense"
] | null | null | null | tests/unit_tests/test_obsoletesource.py | realead/obsoletesource | 59e28adc1477eea430b1cad2118446bb1748363a | [
"Unlicense"
] | null | null | null | import unittest
class obsoletesourceTester(unittest.TestCase):
def test_test_me(self):
import obsoletesource.obsoletesource as t
| 20.142857 | 47 | 0.787234 | 16 | 141 | 6.8125 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.156028 | 141 | 6 | 48 | 23.5 | 0.915966 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.5 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d902ac02573397dd9bda879efcea2b1626828f18 | 181 | py | Python | mainapp/admin.py | Alenjojo/Kitabe | edbb2c761b1592dd086e9761bf284d8565e6f7c4 | [
"MIT"
] | 119 | 2020-11-09T04:03:04.000Z | 2022-03-31T05:03:20.000Z | mainapp/admin.py | PranavChauhan22/Kitabe | 19fcd0ce0dbd2ee6e60b7d8f9d08ef3c97f83853 | [
"MIT"
] | 207 | 2020-11-17T09:37:37.000Z | 2022-01-16T13:17:16.000Z | mainapp/admin.py | PranavChauhan22/Kitabe | 19fcd0ce0dbd2ee6e60b7d8f9d08ef3c97f83853 | [
"MIT"
] | 186 | 2020-11-09T08:01:04.000Z | 2022-03-23T19:58:05.000Z | from django.contrib import admin
from mainapp.models import UserRating, SaveForLater
# Register your models here.
admin.site.register(UserRating)
admin.site.register(SaveForLater)
| 25.857143 | 51 | 0.834254 | 23 | 181 | 6.565217 | 0.565217 | 0.119205 | 0.225166 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093923 | 181 | 6 | 52 | 30.166667 | 0.920732 | 0.143646 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
d91a52d81879c03dfbea6045d8abf2dc1343f092 | 99 | py | Python | ast/testdata/assign_aug.py | MaxTurchin/pycopy-lib | d7a69fc2a28031e2ca475c29239f715c1809d8cc | [
"PSF-2.0"
] | 126 | 2019-07-19T14:42:41.000Z | 2022-03-21T22:22:19.000Z | ast/testdata/assign_aug.py | MaxTurchin/pycopy-lib | d7a69fc2a28031e2ca475c29239f715c1809d8cc | [
"PSF-2.0"
] | 38 | 2019-08-28T01:46:31.000Z | 2022-03-17T05:46:51.000Z | ast/testdata/assign_aug.py | MaxTurchin/pycopy-lib | d7a69fc2a28031e2ca475c29239f715c1809d8cc | [
"PSF-2.0"
] | 55 | 2019-08-02T09:32:33.000Z | 2021-12-22T11:25:51.000Z | a += 1
a -= 2
a *= 3
a /= 4
a //= 5
a %= 6
a **= 7
a |= 8
a ^= 9
a &= 10
a >>= 11
a <<= 12
a @= 13
| 7.071429 | 8 | 0.30303 | 26 | 99 | 1.153846 | 0.538462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.283333 | 0.393939 | 99 | 13 | 9 | 7.615385 | 0.216667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d927853d72df83af76c5c2c510b9ab4a85be4e97 | 100 | py | Python | snippets/python/suppress_subprocess_output.py | GhostLyrics/doc | 6f81ef7417ff34104de4e09adab20b669ba579d8 | [
"MIT"
] | 3 | 2020-12-01T13:43:03.000Z | 2021-05-05T11:45:19.000Z | snippets/python/suppress_subprocess_output.py | GhostLyrics/doc | 6f81ef7417ff34104de4e09adab20b669ba579d8 | [
"MIT"
] | null | null | null | snippets/python/suppress_subprocess_output.py | GhostLyrics/doc | 6f81ef7417ff34104de4e09adab20b669ba579d8 | [
"MIT"
] | null | null | null | import subprocess
subprocess.run(['example'], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
| 33.333333 | 81 | 0.82 | 11 | 100 | 7.454545 | 0.636364 | 0.414634 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 100 | 2 | 82 | 50 | 0.863158 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
d976d04441d7de90e0a8fed5c02c697737755d09 | 413 | py | Python | QUANTAXIS/QASchedule/schedulefunc.py | B34nK0/QUANTAXIS | 94162f0f863682e443ef8ae11f5b54da6f93421b | [
"MIT"
] | 6,322 | 2017-03-22T09:34:20.000Z | 2022-03-31T05:26:45.000Z | QUANTAXIS/QASchedule/schedulefunc.py | B34nK0/QUANTAXIS | 94162f0f863682e443ef8ae11f5b54da6f93421b | [
"MIT"
] | 690 | 2018-01-02T06:44:54.000Z | 2022-03-25T02:06:22.000Z | QUANTAXIS/QASchedule/schedulefunc.py | B34nK0/QUANTAXIS | 94162f0f863682e443ef8ae11f5b54da6f93421b | [
"MIT"
] | 2,183 | 2018-01-02T10:32:10.000Z | 2022-03-30T00:57:31.000Z | ##
import os
import toml
def read_config(file):
config = toml.loads(file)
@read_config
def before_trading():
pass
@read_config
def on_trading():
"""
trading_day
"""
pass
@read_config
def after_1530():
"""
start 15:31
"""
pass
@read_config
def before_nighttrading():
"""
start 8:30
"""
pass
@read_config
def before_nighttrading():
pass
| 9.833333 | 30 | 0.59322 | 50 | 413 | 4.66 | 0.42 | 0.257511 | 0.27897 | 0.291845 | 0.300429 | 0.300429 | 0 | 0 | 0 | 0 | 0 | 0.037415 | 0.288136 | 413 | 41 | 31 | 10.073171 | 0.755102 | 0.082324 | 0 | 0.631579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.315789 | false | 0.263158 | 0.105263 | 0 | 0.421053 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
d9823b6c1b8af387adb125655287d6001cd104b9 | 13,675 | py | Python | two_hidden_layers/snakegame.py | jamiechang917/SnakeAI | dae1b8f86bd529eafbaab8fa981a83e3a9f55bde | [
"MIT"
] | 1 | 2020-06-17T12:48:10.000Z | 2020-06-17T12:48:10.000Z | two_hidden_layers/snakegame.py | jamiechang917/SnakeAI | dae1b8f86bd529eafbaab8fa981a83e3a9f55bde | [
"MIT"
] | null | null | null | two_hidden_layers/snakegame.py | jamiechang917/SnakeAI | dae1b8f86bd529eafbaab8fa981a83e3a9f55bde | [
"MIT"
] | null | null | null | '''
SnakeGame
@Author: JamieChang
@Date: 2020/06/02
'''
import numpy as np
import random
class Snake():
def __init__(self,ID,length,mapsize,foods):
self.ID = ID
self.direction = "DIRECTION"
self.snake_position = [("y_postion","x_position")]
self.food_position = set() # "set" form, there could be multiple foods
self.HP = mapsize**2-length
self.score = 0
self.steps = 0
self.mapsize = mapsize
self.foods = foods
self.length = length
self.layers = {} #{"Input":,"Hidden1":,"Hidden2":,"Output":}
self.generate_snake_position()
for _ in range(foods):
self.generate_food_position()
def init_parameters(self): # initialize some parameters of snake
self.HP = self.mapsize**2-self.length
self.score = 0
self.steps = 0
self.direction = "DIRECTION"
self.snake_position = [("y_postion","x_position")]
self.food_position = set() # "set" form, there could be multiple foods
self.generate_snake_position()
for _ in range(self.foods):
self.generate_food_position()
def generate_snake_position(self):
head = [(random.randint(1,self.mapsize-2),random.randint(1,self.mapsize-2))] #(y_position, x_position)
if head[0][0] <= self.mapsize//2:
if head[0][1] <= self.mapsize//2:
if random.randint(0,1) == 0:
head.extend([(head[0][0],head[0][1]+i) for i in range(1,self.length)])
self.snake_position = head
self.direction = "L"
else:
head.extend([(head[0][0]+i,head[0][1]) for i in range(1,self.length)])
self.snake_position = head
self.direction = "U"
elif head[0][1] > self.mapsize//2:
if random.randint(0,1) == 0:
head.extend([(head[0][0],head[0][1]-i) for i in range(1,self.length)])
self.snake_position = head
self.direction = "R"
else:
head.extend([(head[0][0]+i,head[0][1]) for i in range(1,self.length)])
self.snake_position = head
self.direction = "U"
elif head[0][0] > self.mapsize//2:
if head[0][1] <= self.mapsize//2:
if random.randint(0,1) == 0:
head.extend([(head[0][0],head[0][1]+i) for i in range(1,self.length)])
self.snake_position = head
self.direction = "L"
else:
head.extend([(head[0][0]-i,head[0][1]) for i in range(1,self.length)])
self.snake_position = head
self.direction = "D"
elif head[0][1] > self.mapsize//2:
if random.randint(0,1) == 0:
head.extend([(head[0][0],head[0][1]-i) for i in range(1,self.length)])
self.snake_position = head
self.direction = "R"
else:
head.extend([(head[0][0]-i,head[0][1]) for i in range(1,self.length)])
self.snake_position = head
self.direction = "D"
def generate_food_position(self):
while True:
food = (random.randint(0,self.mapsize-1),random.randint(0,self.mapsize-1))
if food not in set(self.snake_position)|self.food_position:
self.food_position.add(food)
break
if set(self.snake_position)|self.food_position == set([(i,j) for i in range(self.mapsize) for j in range(self.mapsize)]):
break
def move(self,keep_length=True):
if self.direction == "U":
new_head = [(self.snake_position[0][0]-1,self.snake_position[0][1])]
elif self.direction == "D":
new_head = [(self.snake_position[0][0]+1,self.snake_position[0][1])]
elif self.direction == "L":
new_head = [(self.snake_position[0][0],self.snake_position[0][1]-1)]
elif self.direction == "R":
new_head = [(self.snake_position[0][0],self.snake_position[0][1]+1)]
if keep_length==True:
new_head.extend(self.snake_position[:-1]) # chop off the last part of snake
self.snake_position = new_head
self.HP -= 1
self.steps += 1
else:
new_head.extend(self.snake_position)
self.snake_position = new_head
self.HP -= 1
self.steps += 1
def check_food_collisions(self):
if self.snake_position[0] in self.food_position:
return True
return False
def check_collisions(self):
if self.snake_position[0][0] in {-1,self.mapsize} or self.snake_position[0][1] in {-1,self.mapsize}:
return "Border"
elif self.snake_position[0] in self.snake_position[1:]:
return "Body"
#==================GameLogic=================#
def perform_actions(self):
if bool(self.check_food_collisions()) == True:
self.food_position.remove(self.snake_position[0])
self.generate_food_position()
self.move(keep_length=False)
self.score += 1
self.HP += self.mapsize**2- (len(self.snake_position)+len(self.food_position))
else:
self.move()
collision = self.check_collisions()
if bool(collision) is True:
return collision
if self.HP == 0:
return "HP"
#==============================================#
def ML_output_4_directions(self): # binary output (0 or 1)
# initialize
head_location = self.snake_position[0]
food_direction = np.array([0,0,0,0]) #[Up,Down,Left,Right]
head_direction = np.array([0,0,0,0]) #[Up,Down,Left,Right]
border_detection = np.array([0,0,0,0]) #[Up,Down,Left,Right]
body_detection = np.array([0,0,0,0]) #[Up,Down,Left,Right]
# food direction
for food in self.food_position:
if head_location[0]-food[0] < 0:
if head_location[1] - food[1] < 0:
food_direction[1],food_direction[3] = 1,1
elif head_location[1] - food[1] > 0:
food_direction[1],food_direction[2] = 1,1
elif head_location[1] == food[1]:
food_direction[1] = 1
elif head_location[0]-food[0] > 0:
if head_location[1] - food[1] < 0:
food_direction[0],food_direction[3] = 1,1
elif head_location[1] - food[1] > 0:
food_direction[0],food_direction[2] = 1,1
elif head_location[1] == food[1]:
food_direction[0] = 1
elif head_location[0] == food[0]:
if head_location[1] - food[1] < 0:
food_direction[3] = 1
elif head_location[1] - food[1] > 0:
food_direction[2] = 1
elif head_location[1] == food[1]:
food_direction = np.zeros(shape=(4))
# distance to border of four directions
if head_location[0]+1 == self.mapsize : border_detection[1] =1
elif head_location[0]-1 == -1 : border_detection[0] =1
elif head_location[1]+1 == self.mapsize : border_detection[3] =1
elif head_location[1]-1 == -1 : border_detection[2] =1
# head direction
if self.direction == "U": head_direction[0] = 1
elif self.direction == "D": head_direction[1] = 1
elif self.direction == "L": head_direction[2] = 1
elif self.direction == "R": head_direction[3] = 1
# body detection
for body in self.snake_position[1:]:
if body[0] == head_location[0]:
if body[1] > head_location[1]: body_detection[3] = 1
elif body[1] < head_location[1]: body_detection[2] = 1
if body[1] == head_location[1]:
if body[0] > head_location[0]: body_detection[1] = 1
elif body[0] < head_location[0]: body_detection[0] = 1
return np.concatenate([head_direction,food_direction,body_detection,border_detection])
def ML_output_8_directions(self): # binary output (0 or 1), 28neurons
# initialize
head_location = self.snake_position[0]
food_direction = np.array([0,0,0,0,0,0,0,0]) #[Up,Down,Left,Right,LU,RU,LD,RD]
head_direction = np.array([0,0,0,0]) #[Up,Down,Left,Right]
border_detection = np.array([0,0,0,0,0,0,0,0]) #[Up,Down,Left,Right,LU,RU,LD,RD]
body_detection = np.array([0,0,0,0,0,0,0,0]) #[Up,Down,Left,Right,LU,RU,LD,RD]
# food direction
for food in self.food_position:
if head_location[0] < food[0]:
if head_location[1] < food[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
food_direction[4] = 1
elif head_location[1] > food[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
food_direction[5] = 1
elif head_location[1] == food[1]:
food_direction[1] = 1
elif head_location[0] > food[0]:
if head_location[1] < food[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
food_direction[6] = 1
elif head_location[1] > food[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
food_direction[7] = 1
elif head_location[1] == food[1]:
food_direction[0] = 1
elif head_location[0] == food[0]:
if head_location[1] < food[1]:
food_direction[3] = 1
elif head_location[1] > food[1]:
food_direction[2] = 1
# distance to border of four directions
if head_location[0]+1 == self.mapsize : border_detection[1] =1
elif head_location[0]-1 == -1 : border_detection[0] =1
elif head_location[1]+1 == self.mapsize : border_detection[3] =1
elif head_location[1]-1 == -1 : border_detection[2] =1
elif head_location == (0,0): border_detection[4] = 1
elif head_location == (self.mapsize-1,0): border_detection[6] = 1
elif head_location == (0,self.mapsize-1): border_detection[5] = 1
elif head_location == (self.mapsize-1,self.mapsize-1): border_detection[7] = 1
# head direction
if self.direction == "U": head_direction[0] = 1
elif self.direction == "D": head_direction[1] = 1
elif self.direction == "L": head_direction[2] = 1
elif self.direction == "R": head_direction[3] = 1
# body detection
for body in self.snake_position[1:]:
if body[0] == head_location[0]:
if body[1] > head_location[1]: body_detection[3] = 1
elif body[1] < head_location[1]: body_detection[2] = 1
elif body[1] == head_location[1]:
if body[0] > head_location[0]: body_detection[1] = 1
elif body[0] < head_location[0]: body_detection[0] = 1
elif body[0] < head_location[0]:
if body[1] < head_location[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
body_detection[4] = 1
elif body[1] > head_location[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
body_detection[5] = 1
elif body[0] > head_location[0]:
if body[1] < head_location[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
body_detection[6] = 1
elif body[1] > head_location[1]:
if np.abs(head_location[0]-food[0])==np.abs(head_location[1]-food[1]):
body_detection[7] = 1
return np.concatenate([head_direction,food_direction,body_detection,border_detection])
def ML_output_global(self):
import GUI
global_map = GUI.draw_map(snake=self,mapsize=self.mapsize)
return global_map.flatten()
def ML_output_simple(self):
head_location = self.snake_position[0]
collision_detection = np.array([0,0,0,0])
for body in self.snake_position[1:]:
if body[0] == head_location[0]:
if body[1] > head_location[1]: collision_detection[3] = 1
elif body[1] < head_location[1]: collision_detection[2] = 1
if body[1] == head_location[1]:
if body[0] > head_location[0]: collision_detection[1] = 1
elif body[0] < head_location[0]: collision_detection[0] = 1
if head_location[0]+1 == self.mapsize : collision_detection[1] =1
elif head_location[0]-1 == -1 : collision_detection[0] =1
elif head_location[1]+1 == self.mapsize : collision_detection[3] =1
elif head_location[1]-1 == -1 : collision_detection[2] =1
return collision_detection | 48.66548 | 134 | 0.52936 | 1,792 | 13,675 | 3.891183 | 0.070313 | 0.141116 | 0.082031 | 0.068263 | 0.834648 | 0.80195 | 0.760505 | 0.700846 | 0.691094 | 0.665997 | 0 | 0.053335 | 0.332285 | 13,675 | 281 | 135 | 48.66548 | 0.710327 | 0.058282 | 0 | 0.57551 | 0 | 0 | 0.007005 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.04898 | false | 0 | 0.012245 | 0 | 0.106122 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d9914ee9fad7d29e733755d11e245b98afbe4141 | 194 | py | Python | tackle/__init__.py | geometry-labs/tackle-box | 83424a10416955ba983f0c14ec89bd79673a4282 | [
"BSD-3-Clause"
] | 1 | 2021-04-13T23:10:11.000Z | 2021-04-13T23:10:11.000Z | tackle/__init__.py | geometry-labs/tackle-box | 83424a10416955ba983f0c14ec89bd79673a4282 | [
"BSD-3-Clause"
] | 4 | 2021-01-27T00:06:12.000Z | 2021-02-12T01:20:32.000Z | tackle/__init__.py | geometry-labs/tackle-box | 83424a10416955ba983f0c14ec89bd79673a4282 | [
"BSD-3-Clause"
] | 1 | 2021-05-07T05:07:29.000Z | 2021-05-07T05:07:29.000Z | """Main package for tackle box."""
__version__ = "0.2.0-alpha.1"
from tackle.models import BaseHook
from tackle.models import Field
from tackle.main import tackle
__all__ = [
'tackle',
]
| 16.166667 | 34 | 0.716495 | 28 | 194 | 4.678571 | 0.571429 | 0.229008 | 0.244275 | 0.335878 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.024691 | 0.164948 | 194 | 11 | 35 | 17.636364 | 0.783951 | 0.14433 | 0 | 0 | 0 | 0 | 0.11875 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.428571 | 0 | 0.428571 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
796093989629c4276d5a6b45ccc6fba6a9345bd0 | 886 | py | Python | aiotdlib/api/functions/request_password_recovery.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 37 | 2021-05-04T10:41:41.000Z | 2022-03-30T13:48:05.000Z | aiotdlib/api/functions/request_password_recovery.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 13 | 2021-07-17T19:54:51.000Z | 2022-02-26T06:50:00.000Z | aiotdlib/api/functions/request_password_recovery.py | jraylan/aiotdlib | 4528fcfca7c5c69b54a878ce6ce60e934a2dcc73 | [
"MIT"
] | 7 | 2021-09-22T21:27:11.000Z | 2022-02-20T02:33:19.000Z | # =============================================================================== #
# #
# This file has been generated automatically!! Do not change this manually! #
# #
# =============================================================================== #
from __future__ import annotations
from pydantic import Field
from ..base_object import BaseObject
class RequestPasswordRecovery(BaseObject):
"""
Requests to send a 2-step verification password recovery code to an email address that was previously set up
"""
ID: str = Field("requestPasswordRecovery", alias="@type")
@staticmethod
def read(q: dict) -> RequestPasswordRecovery:
return RequestPasswordRecovery.construct(**q)
| 36.916667 | 112 | 0.452596 | 62 | 886 | 6.387097 | 0.806452 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001647 | 0.314898 | 886 | 23 | 113 | 38.521739 | 0.650741 | 0.579007 | 0 | 0 | 1 | 0 | 0.081871 | 0.067251 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0.5 | 0.375 | 0.125 | 0.875 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 5 |
79664a7c209cbcef1bd1fb3d514b4f54e3152514 | 2,854 | py | Python | tests/lattice/test_graphene_lattice.py | PROMNY/pymc_pp | b5b5a611a0b66cf4ae2732c31b1531e8ae132a0e | [
"MIT"
] | 2 | 2019-03-25T07:31:55.000Z | 2020-08-29T16:49:15.000Z | tests/lattice/test_graphene_lattice.py | PROMNY/pymc_pp | b5b5a611a0b66cf4ae2732c31b1531e8ae132a0e | [
"MIT"
] | null | null | null | tests/lattice/test_graphene_lattice.py | PROMNY/pymc_pp | b5b5a611a0b66cf4ae2732c31b1531e8ae132a0e | [
"MIT"
] | 1 | 2020-08-29T17:07:07.000Z | 2020-08-29T17:07:07.000Z | import numpy as np
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(
os.path.dirname(__file__), '../../pymc')))
import lattice
class TestGrapheneLattice():
"""Basic test cases."""
def test_adj_matrix_graphene(self):
a = lattice.GrapheneLattice(4)
res = np.asarray([
[0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
[1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
[0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.],
[1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1.],
[1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],
[0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0.]])
np.testing.assert_array_equal(a.adj_matrix, res, verbose=False)
def test_pos_matrix_graphene(self):
a = lattice.GrapheneLattice(4)
res = np.asarray([[0.5, 2.59807621],
[1.5, 2.59807621],
[3.5, 2.59807621],
[4.5, 2.59807621],
[0., 1.73205081],
[2., 1.73205081],
[3., 1.73205081],
[5., 1.73205081],
[0.5, 0.8660254],
[1.5, 0.8660254],
[3.5, 0.8660254],
[4.5, 0.8660254],
[0., 0.],
[2., 0.],
[3., 0.],
[5., 0.]])
np.testing.assert_array_almost_equal(a.pos_matrix, res, verbose=False)
def test_sub_matrix_graphene(self):
a = lattice.GrapheneLattice(4)
res = np.asarray([[0, 2, 5, 7, 8, 10, 13, 15],
[1, 3, 4, 6, 9, 11, 12, 14]])
np.testing.assert_array_almost_equal(a.sub_matrix, res, verbose=False)
| 46.786885 | 78 | 0.325508 | 428 | 2,854 | 2.114486 | 0.130841 | 0.364641 | 0.40442 | 0.39337 | 0.61989 | 0.596685 | 0.534807 | 0.464088 | 0.464088 | 0.462983 | 0 | 0.245697 | 0.409601 | 2,854 | 60 | 79 | 47.566667 | 0.291395 | 0.005957 | 0 | 0.058824 | 0 | 0 | 0.003532 | 0 | 0 | 0 | 0 | 0 | 0.058824 | 1 | 0.058824 | false | 0 | 0.078431 | 0 | 0.156863 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
797d9714083a5c3aaaf9023afdea6a67a2025b78 | 136 | py | Python | Metrics/__init__.py | nimRobotics/EyeTrackingMetrics | 94717702b00250cc4a9e1df4eec950b4133d0c15 | [
"MIT"
] | 6 | 2019-12-16T17:13:06.000Z | 2021-09-14T04:44:54.000Z | Metrics/__init__.py | nimRobotics/EyeTrackingMetrics | 94717702b00250cc4a9e1df4eec950b4133d0c15 | [
"MIT"
] | null | null | null | Metrics/__init__.py | nimRobotics/EyeTrackingMetrics | 94717702b00250cc4a9e1df4eec950b4133d0c15 | [
"MIT"
] | 1 | 2019-12-16T17:13:08.000Z | 2019-12-16T17:13:08.000Z | from .convexhull import ConvexHull
from .nni import NNI
from .spatialdensity import SpatialDensity
from .entropy import GazeEntropy
| 27.2 | 43 | 0.823529 | 16 | 136 | 7 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.147059 | 136 | 4 | 44 | 34 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
798b53a926ac3a56699dc045c8070a01d3a5683c | 117 | py | Python | auctioning_platform/payments/payments/config.py | nhdinh/smp-modulith | 84c31b36e9b449fe3135f3802c1bbc362e7fb459 | [
"MIT"
] | 299 | 2019-09-13T23:03:29.000Z | 2022-03-24T09:20:43.000Z | auctioning_platform/payments/payments/config.py | winston-won/clean-architecture | 95546b29c9a5ff0e16c7f67b9ab736749e87f8ae | [
"MIT"
] | 109 | 2019-11-03T12:16:16.000Z | 2021-07-26T08:32:28.000Z | auctioning_platform/payments/payments/config.py | winston-won/clean-architecture | 95546b29c9a5ff0e16c7f67b9ab736749e87f8ae | [
"MIT"
] | 38 | 2019-09-13T23:03:34.000Z | 2022-03-24T09:21:10.000Z | from dataclasses import dataclass
@dataclass(repr=False)
class PaymentsConfig:
username: str
password: str
| 14.625 | 33 | 0.760684 | 13 | 117 | 6.846154 | 0.846154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.179487 | 117 | 7 | 34 | 16.714286 | 0.927083 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.2 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
79950060a312736b630bc8771d4ee6fe430bb88c | 157 | py | Python | auto_addons/__init__.py | jmartinezespza/odoo-docker | 288064e879c2a3910197c8b91473358e0bb25928 | [
"MIT"
] | null | null | null | auto_addons/__init__.py | jmartinezespza/odoo-docker | 288064e879c2a3910197c8b91473358e0bb25928 | [
"MIT"
] | null | null | null | auto_addons/__init__.py | jmartinezespza/odoo-docker | 288064e879c2a3910197c8b91473358e0bb25928 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright 2015 Elico Corp
# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).
from . import addons
from . import tests
| 26.166667 | 63 | 0.687898 | 25 | 157 | 4.32 | 0.88 | 0.185185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 0.152866 | 157 | 5 | 64 | 31.4 | 0.759399 | 0.694268 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
79c1ea2746b3756feab2b7f3eabb34edd8c02c74 | 163 | py | Python | src/wann_genetic/individual/__init__.py | plonerma/wann-genetic | c4a8a1db81665b2549994d615e1d347dbe00226a | [
"MIT"
] | null | null | null | src/wann_genetic/individual/__init__.py | plonerma/wann-genetic | c4a8a1db81665b2549994d615e1d347dbe00226a | [
"MIT"
] | null | null | null | src/wann_genetic/individual/__init__.py | plonerma/wann-genetic | c4a8a1db81665b2549994d615e1d347dbe00226a | [
"MIT"
] | null | null | null | from .genes import Genes, RecurrentGenes
from .individual_base import IndividualBase, RecurrentIndividualBase
from .numpy import Individual, RecurrentIndividual
| 27.166667 | 68 | 0.858896 | 16 | 163 | 8.6875 | 0.625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104294 | 163 | 5 | 69 | 32.6 | 0.952055 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
8de848a70c9811cb5d523046c28c5495e37ffd1a | 219 | py | Python | python/byteport/clients.py | gebart/byteport-api | 38504af42bd91ffafed4d813af14ccf88fdfe56d | [
"BSD-2-Clause"
] | null | null | null | python/byteport/clients.py | gebart/byteport-api | 38504af42bd91ffafed4d813af14ccf88fdfe56d | [
"BSD-2-Clause"
] | 2 | 2015-02-13T13:43:53.000Z | 2015-04-20T07:57:16.000Z | python/byteport/clients.py | gebart/byteport-api | 38504af42bd91ffafed4d813af14ccf88fdfe56d | [
"BSD-2-Clause"
] | 1 | 2017-12-18T01:38:46.000Z | 2017-12-18T01:38:46.000Z | from http_clients import *
# For backward compatibility only
# NOTE: The MQTT and STOMP client code has additional dependencies outside of standard Python
# hence no good idea to include them in the "clients.py" file
| 31.285714 | 93 | 0.789954 | 34 | 219 | 5.058824 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173516 | 219 | 6 | 94 | 36.5 | 0.950276 | 0.835616 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
5c1162c7e91f1b13f40f5bdc1a2db72ffdf04df7 | 290 | py | Python | page_fragments/abstract_drawable.py | abamaxa/docvision_generator | 8017f29c7d908cb80ddcd59e345a222271fa74de | [
"MIT"
] | 2 | 2020-02-06T17:30:41.000Z | 2020-08-04T10:35:46.000Z | page_fragments/abstract_drawable.py | abamaxa/docvision_generator | 8017f29c7d908cb80ddcd59e345a222271fa74de | [
"MIT"
] | null | null | null | page_fragments/abstract_drawable.py | abamaxa/docvision_generator | 8017f29c7d908cb80ddcd59e345a222271fa74de | [
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
class AbstractDrawable(ABC) :
@abstractmethod
def get_element_size(self) :
pass
@abstractmethod
def calculate_dimensions(self, draw, size) :
pass
@abstractmethod
def render(self, draw) :
pass | 20.714286 | 48 | 0.631034 | 29 | 290 | 6.206897 | 0.551724 | 0.283333 | 0.233333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.303448 | 290 | 14 | 49 | 20.714286 | 0.891089 | 0 | 0 | 0.545455 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.272727 | false | 0.272727 | 0.090909 | 0 | 0.454545 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
5c1842f7cf1c55b3d0b680d1b80005af29e7eb6f | 436 | py | Python | EllisApproach/paths.py | shayenne/VoiceDetection | 5b9ce0950da245fa9488301e3a024b06f363f4db | [
"MIT"
] | null | null | null | EllisApproach/paths.py | shayenne/VoiceDetection | 5b9ce0950da245fa9488301e3a024b06f363f4db | [
"MIT"
] | null | null | null | EllisApproach/paths.py | shayenne/VoiceDetection | 5b9ce0950da245fa9488301e3a024b06f363f4db | [
"MIT"
] | null | null | null | import os
os.environ["MUSIC_PATH"] = "/home/compmus/MIR-1K/Wavfile/abjones_1_01.wav"
os.environ["VOCAL_PATH"] = "/home/compmus/MIR-1K/vocal-nonvocalLabel/abjones_1_01.vocal"
os.environ["AUDIO_PATH"] = "/home/compmus/MIR-1K/Wavfile/"
os.environ["ANNOT_PATH"] = "/home/compmus/MIR-1K/vocal-nonvocalLabel/"
os.environ["FEATURE_PATH"] = "/home/compmus/MIR-1K/spectrogram/"
os.environ["LABEL_PATH"] = "/home/compmus/MIR-1K/PitchLabel/"
| 39.636364 | 88 | 0.740826 | 66 | 436 | 4.742424 | 0.333333 | 0.172524 | 0.28754 | 0.345048 | 0.543131 | 0.415335 | 0.242812 | 0 | 0 | 0 | 0 | 0.029197 | 0.057339 | 436 | 10 | 89 | 43.6 | 0.73236 | 0 | 0 | 0 | 0 | 0 | 0.690367 | 0.548165 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.142857 | 0 | 0.142857 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
5c374758ec77b023c9b7fde399134426f745edc4 | 394 | py | Python | bugtests/test096.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | bugtests/test096.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | bugtests/test096.py | doom38/jython_v2.2.1 | 0803a0c953c294e6d14f9fc7d08edf6a3e630a15 | [
"CNRI-Jython"
] | null | null | null | """
Test the imp module (unfinished)
"""
import support
import sys
""" Does not work, will never work.
import imp
i = imp.find_module("test096j")
r = imp.load_module("test096j", i[0], i[1], i[2])
print r
print dir(r)
print sys.modules['test096j']
i = imp.find_module("test096j")
r = imp.load_module("test096j", i[0], i[1], i[2])
print r
print dir(r)
print sys.modules['test096j']
""" | 13.586207 | 49 | 0.662437 | 67 | 394 | 3.835821 | 0.358209 | 0.217899 | 0.062257 | 0.108949 | 0.684825 | 0.684825 | 0.684825 | 0.684825 | 0.684825 | 0.684825 | 0 | 0.072072 | 0.154822 | 394 | 29 | 50 | 13.586207 | 0.6997 | 0.081218 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
5c4a24cc9915300104c50b6177357fd878d7abf3 | 150 | py | Python | analysis_engine/scripts/print_last_close_date.py | virdesai/stock-analysis-engine | 0ca501277c632150717ca499121a34f8f8c71ccb | [
"Apache-2.0"
] | 819 | 2018-09-16T20:33:11.000Z | 2022-03-30T21:18:23.000Z | analysis_engine/scripts/print_last_close_date.py | gvpathi/stock-analysis-engine | 0ca501277c632150717ca499121a34f8f8c71ccb | [
"Apache-2.0"
] | 14 | 2018-09-16T20:52:25.000Z | 2020-09-06T12:36:36.000Z | analysis_engine/scripts/print_last_close_date.py | gvpathi/stock-analysis-engine | 0ca501277c632150717ca499121a34f8f8c71ccb | [
"Apache-2.0"
] | 226 | 2018-09-16T20:04:32.000Z | 2022-03-31T01:41:14.000Z | #!/usr/bin/env python
from analysis_engine.utils import last_close
last_close_str = last_close().strftime('%Y-%m-%d %H:%M:%S')
print(last_close_str)
| 25 | 59 | 0.746667 | 27 | 150 | 3.888889 | 0.703704 | 0.342857 | 0.228571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08 | 150 | 5 | 60 | 30 | 0.76087 | 0.133333 | 0 | 0 | 0 | 0 | 0.131783 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
30c1a5558564d3348d5ee5eb1fd296f3f29fb622 | 19,581 | py | Python | tests/assets/test_account.py | zsluedem/MonkTrader | 760942a59919b34c876467bc0eb4afb30689cbc1 | [
"MIT"
] | 2 | 2018-11-17T06:39:36.000Z | 2019-01-18T13:14:15.000Z | tests/assets/test_account.py | zsluedem/MonkTrader | 760942a59919b34c876467bc0eb4afb30689cbc1 | [
"MIT"
] | 37 | 2018-11-04T15:05:04.000Z | 2019-03-09T09:26:30.000Z | tests/assets/test_account.py | zsluedem/MonkTrader | 760942a59919b34c876467bc0eb4afb30689cbc1 | [
"MIT"
] | null | null | null | #
# MIT License
#
# Copyright (c) 2018 WillQ
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
from typing import List, TypeVar
from unittest.mock import MagicMock
import pytest
from monkq.assets.account import FutureAccount
from monkq.assets.instrument import FutureInstrument, Instrument # noqa
from monkq.assets.order import FutureLimitOrder
from monkq.assets.positions import FuturePosition
from monkq.assets.trade import Trade
from monkq.exchange.base import BaseExchange # noqa: F401
from ..utils import random_string
T_INSTRUMENT = TypeVar('T_INSTRUMENT', bound="Instrument")
T_EXCHANGE = TypeVar('T_EXCHANGE', bound="BaseExchange")
def test_future_account_deal(exchange: MagicMock, future_instrument: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
assert account.position_margin == 0
assert account.order_margin == 0
assert account.unrealised_pnl == 0
assert account.wallet_balance == 10000
assert account.margin_balance == 10000
assert account.available_balance == 10000
assert account.total_capital == 10000
# open a position
order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=11)
open_orders.append(order1)
assert account.order_margin == 60.5
trade1 = Trade(order=order1, exec_price=11, exec_quantity=100, trade_id=random_string(6))
account.deal(trade1)
open_orders.remove(order1)
assert account.wallet_balance == 9997.25
assert account.position_margin == pytest.approx(52.50, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == -102.5
assert account.margin_balance == 9894.75
assert account.available_balance == 9842.25
assert account.total_capital == 9894.75
# more on a position
order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=200, price=10.5)
open_orders.append(order2)
assert account.order_margin == 115.5
trade2 = Trade(order=order2, exec_price=10.5, exec_quantity=200, trade_id=random_string(6))
account.deal(trade2)
open_orders.remove(order2)
assert account.wallet_balance == 9992.0
assert account.position_margin == pytest.approx(157.50, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == -207.5
assert account.margin_balance == 9784.5
assert account.available_balance == 9627.0
assert account.total_capital == 9784.5
# close part
order3 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=10)
open_orders.append(order3)
assert account.order_margin == 0
trade3 = Trade(order=order3, exec_price=10, exec_quantity=-100, trade_id=random_string(6))
account.deal(trade3)
open_orders.remove(order3)
assert account.wallet_balance == pytest.approx(9922.8333, 0.0001)
assert account.position_margin == pytest.approx(105, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == pytest.approx(-138.3333, 0.0001)
assert account.margin_balance == pytest.approx(9784.5000, 0.0001)
assert account.available_balance == pytest.approx(9679.5000, 0.0001)
assert account.total_capital == pytest.approx(9784.5000, 0.0001)
# close and open
order4 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-300, price=11)
open_orders.append(order4)
assert account.order_margin == 60.5
trade4 = Trade(order=order4, exec_price=11, exec_quantity=-300, trade_id=random_string(6))
account.deal(trade4)
open_orders.remove(order4)
assert account.wallet_balance == pytest.approx(9981.2513, 0.0001)
assert account.position_margin == pytest.approx(52.5, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == pytest.approx(97.5, 0.0001)
assert account.margin_balance == pytest.approx(10078.7513, 0.0001)
assert account.available_balance == pytest.approx(10026.2513, 0.0001)
assert account.total_capital == pytest.approx(10078.7513, 0.0001)
# get more
order5 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=10.5)
open_orders.append(order5)
assert account.order_margin == 57.75
trade5 = Trade(order=order5, exec_price=10.5, exec_quantity=-100, trade_id=random_string(6))
account.deal(trade5)
open_orders.remove(order5)
assert account.wallet_balance == pytest.approx(9978.6263, 0.0001)
assert account.position_margin == pytest.approx(105, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == pytest.approx(145.0, 0.0001)
assert account.margin_balance == pytest.approx(10123.6263, 0.0001)
assert account.available_balance == pytest.approx(10018.6263, 0.0001)
assert account.total_capital == pytest.approx(10123.6263, 0.0001)
# close and open
order6 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=300, price=10.5)
open_orders.append(order6)
assert account.order_margin == 57.75
trade6 = Trade(order=order6, exec_price=10.5, exec_quantity=300, trade_id=random_string(6))
account.deal(trade6)
open_orders.remove(order6)
assert account.wallet_balance == pytest.approx(10020.7513, 0.0001)
assert account.position_margin == pytest.approx(52.5, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == pytest.approx(-52.5, 0.0001)
assert account.margin_balance == pytest.approx(9968.2513, 0.0001)
assert account.available_balance == pytest.approx(9915.7513, 0.0001)
assert account.total_capital == pytest.approx(9968.2513, 0.0001)
# close
order7 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=9)
open_orders.append(order7)
assert account.order_margin == 0
trade7 = Trade(order=order7, exec_price=9, exec_quantity=-100, trade_id=random_string(6))
account.deal(trade7)
open_orders.remove(order7)
assert account.wallet_balance == pytest.approx(9868.5013, 0.0001)
assert account.position_margin == 0
assert account.order_margin == 0
assert account.unrealised_pnl == 0
assert account.margin_balance == pytest.approx(9868.5013, 0.0001)
assert account.available_balance == pytest.approx(9868.5013, 0.0001)
assert account.total_capital == pytest.approx(9868.5013, 0.0001)
# close
order8 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-150, price=11)
open_orders.append(order8)
assert account.order_margin == 90.75
trade8 = Trade(order=order8, exec_price=11, exec_quantity=-150, trade_id=random_string(6))
account.deal(trade8)
open_orders.remove(order8)
assert account.wallet_balance == pytest.approx(9864.3763, 0.0001)
assert account.position_margin == pytest.approx(78.7500, 0.0001)
assert account.order_margin == 0
assert account.unrealised_pnl == pytest.approx(146.25, 0.0001)
assert account.margin_balance == pytest.approx(10010.6263, 0.0001)
assert account.available_balance == pytest.approx(9931.8763, 0.0001)
assert account.total_capital == pytest.approx(10010.6263, 0.0001)
order9 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=150, price=11)
open_orders.append(order9)
assert account.order_margin == 0
trade9 = Trade(order=order9, exec_price=11, exec_quantity=150, trade_id=random_string(6))
account.deal(trade9)
open_orders.remove(order9)
assert account.wallet_balance == pytest.approx(9860.2513, 0.0001)
assert account.position_margin == 0
assert account.order_margin == 0
assert account.unrealised_pnl == 0
assert account.margin_balance == pytest.approx(9860.2513, 0.0001)
assert account.available_balance == pytest.approx(9860.2513, 0.0001)
assert account.total_capital == pytest.approx(9860.2513, 0.0001)
def test_future_account_order_margin_two_direction(exchange: MagicMock, future_instrument: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
untraded_order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=5)
untraded_order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=20)
open_orders.extend([untraded_order1, untraded_order2])
assert account.order_margin == 110.0
untraded_order3 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=5)
open_orders.append(untraded_order3)
assert account.order_margin == 110.0
untraded_order4 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=16)
open_orders.append(untraded_order4)
assert account.order_margin == 143
open_orders.clear()
def test_future_account_order_margin_long_position(exchange: MagicMock, future_instrument: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=11)
open_orders.append(order1)
assert account.order_margin == 60.5
trade1 = Trade(order=order1, exec_price=11, exec_quantity=100, trade_id=random_string(6))
account.deal(trade1)
open_orders.remove(order1)
untraded_order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=9)
untraded_order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-90, price=11)
open_orders.extend([untraded_order1, untraded_order2])
assert account.order_margin == 49.5
untraded_order3 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=12)
open_orders.append(untraded_order3)
assert account.order_margin == 59.4
untraded_order4 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=10)
open_orders.append(untraded_order4)
assert account.order_margin == 104.5
untraded_order5 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-50, price=19)
open_orders.append(untraded_order5)
assert account.order_margin == 111.65
trade2 = Trade(untraded_order2, exec_price=11, exec_quantity=-30, trade_id=random_string(6))
untraded_order2.deal(trade2)
assert account.order_margin == 111.65
untraded_order6 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=20)
open_orders.append(untraded_order6)
assert account.order_margin == 214.5
def test_future_account_order_margin_short_position(exchange: MagicMock, future_instrument: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=11)
open_orders.append(order1)
assert account.order_margin == 60.5
trade1 = Trade(order=order1, exec_price=11, exec_quantity=-100, trade_id=random_string(6))
account.deal(trade1)
open_orders.remove(order1)
untraded_order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=9)
untraded_order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-90, price=11)
open_orders.extend([untraded_order1, untraded_order2])
assert account.order_margin == 54.45
untraded_order3 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=12)
open_orders.append(untraded_order3)
assert account.order_margin == 66.0
untraded_order4 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=10)
open_orders.append(untraded_order4)
assert account.order_margin == 109.45
untraded_order5 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=50, price=19)
open_orders.append(untraded_order5)
assert account.order_margin == 118.25
trade2 = Trade(untraded_order2, exec_price=11, exec_quantity=-30, trade_id=random_string(6))
untraded_order2.deal(trade2)
assert account.order_margin == 98.45
untraded_order6 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=20)
open_orders.append(untraded_order6)
assert account.order_margin == 208.45
def test_future_account_order_margin_multiple_instruments(exchange: MagicMock, future_instrument: FutureInstrument,
future_instrument2: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
untraded_order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=10)
untraded_order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument2,
quantity=-90, price=11)
untraded_order3 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-100, price=9)
open_orders.extend([untraded_order3, untraded_order1, untraded_order2])
assert account.order_margin == 66.385
def test_future_accoutn_order_margin_leverage(exchange: MagicMock, future_instrument: FutureInstrument) -> None:
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=11)
open_orders.append(order1)
assert account.order_margin == 60.5
trade1 = Trade(order=order1, exec_price=11, exec_quantity=100, trade_id=random_string(6))
account.deal(trade1)
open_orders.remove(order1)
position = account.positions[future_instrument]
position.set_leverage(5)
untraded_order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=5)
untraded_order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=-200, price=20)
open_orders.extend([untraded_order1, untraded_order2])
assert account.order_margin == pytest.approx(410)
def test_future_account_position_margin(exchange: MagicMock, future_instrument: FutureInstrument,
future_instrument2: FutureInstrument) -> None:
# test the position margin of the account when the account have two different positions
open_orders: List[FutureLimitOrder] = []
exchange.get_open_orders = MagicMock(return_value=open_orders)
exchange.last_price = MagicMock(return_value=10)
account = FutureAccount(exchange=exchange, position_cls=FuturePosition, wallet_balance=10000)
order1 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument,
quantity=100, price=11)
order2 = FutureLimitOrder(order_id=random_string(6), account=account, instrument=future_instrument2,
quantity=-200, price=18)
trade1 = Trade(order=order1, exec_price=11, exec_quantity=100, trade_id=random_string(6))
trade2 = Trade(order=order2, exec_price=18, exec_quantity=-200, trade_id=random_string(6))
account.deal(trade1)
account.deal(trade2)
assert account.position_margin == pytest.approx(74.0)
position1 = account.positions[future_instrument]
position1.set_leverage(4)
assert account.position_margin == pytest.approx(271.5)
| 49.69797 | 118 | 0.717532 | 2,407 | 19,581 | 5.640631 | 0.115912 | 0.096708 | 0.052589 | 0.056345 | 0.802902 | 0.781469 | 0.720262 | 0.716432 | 0.67769 | 0.620756 | 0 | 0.073731 | 0.188908 | 19,581 | 393 | 119 | 49.824427 | 0.781136 | 0.064144 | 0 | 0.493243 | 0 | 0 | 0.002406 | 0 | 0 | 0 | 0 | 0 | 0.341216 | 1 | 0.023649 | false | 0 | 0.033784 | 0 | 0.057432 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
30f9fa7a6050d03395602281f06a3da07c71dded | 35 | py | Python | python/testData/surround/SurroundNewline.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 2 | 2018-12-29T09:53:39.000Z | 2018-12-29T09:53:42.000Z | python/testData/surround/SurroundNewline.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/surround/SurroundNewline.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | <selection>a = 1
</selection>
a = 2 | 11.666667 | 16 | 0.628571 | 6 | 35 | 3.666667 | 0.666667 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068966 | 0.171429 | 35 | 3 | 17 | 11.666667 | 0.689655 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
eb4b3866972f26fd369ccf15f596563e21ffbdb2 | 49 | py | Python | src/python/src/rmq/exceptions/exceptions.py | halimov-oa/scrapy-boilerplate | fe3c552fed26bedb0618c245ab923aa34a89ac9d | [
"MIT"
] | 34 | 2019-12-13T10:31:39.000Z | 2022-03-09T15:59:07.000Z | src/python/src/rmq/exceptions/exceptions.py | halimov-oa/scrapy-boilerplate | fe3c552fed26bedb0618c245ab923aa34a89ac9d | [
"MIT"
] | 49 | 2020-02-25T19:41:09.000Z | 2022-02-27T12:05:25.000Z | src/python/src/rmq/exceptions/exceptions.py | halimov-oa/scrapy-boilerplate | fe3c552fed26bedb0618c245ab923aa34a89ac9d | [
"MIT"
] | 23 | 2019-12-23T15:19:42.000Z | 2022-03-09T16:00:15.000Z | class ConsumedDataCorrupted(Exception):
pass
| 16.333333 | 39 | 0.795918 | 4 | 49 | 9.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 49 | 2 | 40 | 24.5 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
eb8f922449f43ad69a0dd20717312c745e897aea | 46 | py | Python | wellknown/appInfo.py | sdyiheng/SimplePythonWebApp | fc3188796a1e48a42a0c22b5f3b430c0de1be87a | [
"MIT"
] | null | null | null | wellknown/appInfo.py | sdyiheng/SimplePythonWebApp | fc3188796a1e48a42a0c22b5f3b430c0de1be87a | [
"MIT"
] | null | null | null | wellknown/appInfo.py | sdyiheng/SimplePythonWebApp | fc3188796a1e48a42a0c22b5f3b430c0de1be87a | [
"MIT"
] | null | null | null | '''应用程序信息'''
class AppInfo(object):
pass
| 9.2 | 22 | 0.608696 | 5 | 46 | 5.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195652 | 46 | 4 | 23 | 11.5 | 0.756757 | 0.130435 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
ebbda6fa511c1d98b2f279d0c40456401783a1b7 | 635 | py | Python | src/tests/metrics/test_precision.py | lab-a1/pyai | 0d05324fdf0ac07117eb5f4fde6b90d6cec10479 | [
"WTFPL"
] | null | null | null | src/tests/metrics/test_precision.py | lab-a1/pyai | 0d05324fdf0ac07117eb5f4fde6b90d6cec10479 | [
"WTFPL"
] | null | null | null | src/tests/metrics/test_precision.py | lab-a1/pyai | 0d05324fdf0ac07117eb5f4fde6b90d6cec10479 | [
"WTFPL"
] | null | null | null | from pyai import metrics
import numpy as np
def test_precision_1():
y_true = np.array([1, 1, 0, 1, 0, 0])
y_hat = np.array([1, 1, 1, 1, 1, 1])
precision = metrics.precision(y_true, y_hat)
assert round(precision, 3) == 0.5
def test_precision_2():
y_true = np.array([1, 1, 0, 1, 0, 0])
y_hat = np.array([1, 1, 1, 0, 0, 0])
precision = metrics.precision(y_true, y_hat)
assert round(precision, 3) == 0.667
def test_precision_3():
y_true = np.array([1, 1, 0, 1, 0, 0])
y_hat = np.array([0, 0, 0, 0, 0, 0])
precision = metrics.precision(y_true, y_hat)
assert round(precision, 3) == 0
| 23.518519 | 48 | 0.601575 | 118 | 635 | 3.084746 | 0.186441 | 0.054945 | 0.10989 | 0.123626 | 0.75 | 0.733516 | 0.733516 | 0.733516 | 0.733516 | 0.733516 | 0 | 0.100205 | 0.229921 | 635 | 26 | 49 | 24.423077 | 0.644172 | 0 | 0 | 0.352941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.176471 | 1 | 0.176471 | false | 0 | 0.117647 | 0 | 0.294118 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
ccf26601b7c977269ed371f7369b2c29c9a8b7b6 | 278 | py | Python | ControlPanel/Apps/Main/views.py | PatyLuPrz/unicollagen | 92206648f691581efee4f0ea82817729670bcd0f | [
"MIT"
] | null | null | null | ControlPanel/Apps/Main/views.py | PatyLuPrz/unicollagen | 92206648f691581efee4f0ea82817729670bcd0f | [
"MIT"
] | 10 | 2019-12-04T23:46:06.000Z | 2022-02-10T10:00:30.000Z | ControlPanel/Apps/Main/views.py | PatyLuPrz/unicollagen | 92206648f691581efee4f0ea82817729670bcd0f | [
"MIT"
] | null | null | null | from django.shortcuts import render
from django.http import HttpResponse
from django.contrib.auth.decorators import login_required
def login(request):
return render(request, 'Main/login.html')
@login_required
def menu(request):
return render(request, 'Main/menu.html') | 27.8 | 57 | 0.791367 | 38 | 278 | 5.736842 | 0.473684 | 0.137615 | 0.146789 | 0.238532 | 0.275229 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115108 | 278 | 10 | 58 | 27.8 | 0.886179 | 0 | 0 | 0 | 0 | 0 | 0.103943 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.375 | 0.25 | 0.875 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 5 |
696043a4e6bd5d6c706c4c6b8e060723d1dedd01 | 137 | py | Python | setup.py | PhilipChicco/wsshisto | 26957499d23d81d18853d6608dfa8db217435672 | [
"MIT"
] | null | null | null | setup.py | PhilipChicco/wsshisto | 26957499d23d81d18853d6608dfa8db217435672 | [
"MIT"
] | null | null | null | setup.py | PhilipChicco/wsshisto | 26957499d23d81d18853d6608dfa8db217435672 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
from setuptools import setup, find_packages
setup(name='graph_research', version='1.0', packages=find_packages()) | 27.4 | 69 | 0.773723 | 20 | 137 | 5.15 | 0.8 | 0.23301 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015873 | 0.080292 | 137 | 5 | 69 | 27.4 | 0.801587 | 0.145985 | 0 | 0 | 0 | 0 | 0.145299 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
15eec3d16253dfb895c02fad3fc276281f7e3243 | 271 | py | Python | nn_partition/nn_partition/propagators/Propagator.py | StanfordASL/nn_robustness_analysis | 2e03d98efb3ee848b4d8b277968e162513abbd0f | [
"MIT"
] | 36 | 2021-02-17T22:46:14.000Z | 2022-03-28T08:36:27.000Z | nn_partition/nn_partition/propagators/Propagator.py | zhouzhiqian/nn_robustness_analysis | cff947c1b6c6b586a004d13387bb2fe31131dcab | [
"MIT"
] | null | null | null | nn_partition/nn_partition/propagators/Propagator.py | zhouzhiqian/nn_robustness_analysis | cff947c1b6c6b586a004d13387bb2fe31131dcab | [
"MIT"
] | 9 | 2021-06-03T09:03:54.000Z | 2022-03-07T15:12:03.000Z | class Propagator:
def __init__(self, input_shape=None):
self.input_shape = input_shape
@property
def network(self):
return self._network
@network.setter
def network(self, network):
self._network = self.torch2network(network)
| 22.583333 | 51 | 0.667897 | 31 | 271 | 5.548387 | 0.419355 | 0.255814 | 0.162791 | 0.255814 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004878 | 0.243542 | 271 | 11 | 52 | 24.636364 | 0.834146 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.111111 | 0.555556 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
c611df3db5cbc94f2bd9a5d94fb60d3d3ef4b034 | 132 | py | Python | interpret/attr/__init__.py | ttumiel/interpret | aeecb00bf65376668a48895cb707beb6dd8fb7ab | [
"MIT"
] | 14 | 2019-10-28T18:49:31.000Z | 2021-03-25T12:13:35.000Z | interpret/attr/__init__.py | ttumiel/interpret | aeecb00bf65376668a48895cb707beb6dd8fb7ab | [
"MIT"
] | null | null | null | interpret/attr/__init__.py | ttumiel/interpret | aeecb00bf65376668a48895cb707beb6dd8fb7ab | [
"MIT"
] | null | null | null | from .attribute import Attribute
from .gradcam import Gradcam
from .guidedback import GuidedBackProp
from .gradient import Gradient
| 26.4 | 38 | 0.848485 | 16 | 132 | 7 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121212 | 132 | 4 | 39 | 33 | 0.965517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c62a3e8403ec1bed8cca7debced4a7c3af82c7cb | 142 | py | Python | xnbread/__init__.py | dolkow/xnbread | 4dbd88727dfdbdeab8a4a754647e48299be309a7 | [
"MIT"
] | null | null | null | xnbread/__init__.py | dolkow/xnbread | 4dbd88727dfdbdeab8a4a754647e48299be309a7 | [
"MIT"
] | null | null | null | xnbread/__init__.py | dolkow/xnbread | 4dbd88727dfdbdeab8a4a754647e48299be309a7 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
#coding=utf8
from .container import read_payload, decode_payload, dump
from . import readers
from . import exceptions
| 20.285714 | 57 | 0.788732 | 20 | 142 | 5.5 | 0.75 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016129 | 0.126761 | 142 | 6 | 58 | 23.666667 | 0.870968 | 0.225352 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d6acd25a249bd4c398f0e40c543fb5f54f608976 | 119 | py | Python | test/integration/expected_out_single_line/indexed_percent.py | Inveracity/flynt | b975b6f61893d5db1114d68fbb5d212c4e11aeb8 | [
"MIT"
] | 487 | 2019-06-10T17:44:56.000Z | 2022-03-26T01:28:19.000Z | test/integration/expected_out_single_line/indexed_percent.py | Inveracity/flynt | b975b6f61893d5db1114d68fbb5d212c4e11aeb8 | [
"MIT"
] | 118 | 2019-07-03T12:26:39.000Z | 2022-03-06T22:40:17.000Z | test/integration/expected_out_single_line/indexed_percent.py | Inveracity/flynt | b975b6f61893d5db1114d68fbb5d212c4e11aeb8 | [
"MIT"
] | 25 | 2019-07-10T08:39:58.000Z | 2022-03-03T14:44:15.000Z | def test_context_binding(app):
@app.route("/")
def index():
return f"Hello {flask.request.args[name]}!" | 29.75 | 51 | 0.621849 | 16 | 119 | 4.5 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.193277 | 119 | 4 | 51 | 29.75 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0.283333 | 0.225 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.25 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
d6ba1db789c0bfc9b327be04f9f15dc3264af42a | 43 | py | Python | src/strigiform/app/__init__.py | X-McKay/strigiform | 5db74c99c6778303ec98f30f77097b9cb0cd7a36 | [
"MIT"
] | null | null | null | src/strigiform/app/__init__.py | X-McKay/strigiform | 5db74c99c6778303ec98f30f77097b9cb0cd7a36 | [
"MIT"
] | 76 | 2021-10-31T21:14:46.000Z | 2022-03-30T18:32:49.000Z | src/strigiform/app/__init__.py | X-McKay/kingfisher | 5db74c99c6778303ec98f30f77097b9cb0cd7a36 | [
"MIT"
] | null | null | null | """Provider for webapps and dashboards."""
| 21.5 | 42 | 0.72093 | 5 | 43 | 6.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116279 | 43 | 1 | 43 | 43 | 0.815789 | 0.837209 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d6dd2bf75e61593205d981c83d98bd69eb94e67c | 126 | py | Python | corrector/utils/cut_word/load_dict.py | mamachengcheng/corrector | e87c49f7dd7d9f236084e963906f414f72a884c9 | [
"MIT"
] | 4 | 2020-11-11T14:08:56.000Z | 2022-02-15T01:31:27.000Z | corrector/utils/cut_word/load_dict.py | mamachengcheng/corrector | e87c49f7dd7d9f236084e963906f414f72a884c9 | [
"MIT"
] | null | null | null | corrector/utils/cut_word/load_dict.py | mamachengcheng/corrector | e87c49f7dd7d9f236084e963906f414f72a884c9 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
corrector.utils.cut_word.load_dict
~~~~~~~~~~~~~~~
该模块用于加载分词词典
"""
def load_dict():
return
| 10.5 | 34 | 0.555556 | 14 | 126 | 4.785714 | 0.857143 | 0.238806 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009434 | 0.15873 | 126 | 11 | 35 | 11.454545 | 0.622642 | 0.674603 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
ba7e4def11076284320d27af0b77672d58fe61d2 | 59 | py | Python | TUI/TCC/Catalog/__init__.py | ApachePointObservatory/stui | cfaaa9bcec9da9ac21bad1b9a2c7db2a739ffc97 | [
"BSD-3-Clause"
] | 2 | 2019-05-07T04:33:57.000Z | 2021-12-16T19:54:02.000Z | TUI/TCC/Catalog/__init__.py | sdss/snafui | 0793b036122755396f06f449080d9cdad7d508ec | [
"BSD-3-Clause"
] | 5 | 2018-05-29T20:14:50.000Z | 2020-02-17T21:58:30.000Z | TUI/TCC/Catalog/__init__.py | r-owen/TUI | 8f130368254161a2748167b7c8260cc24170c28c | [
"BSD-3-Clause"
] | 2 | 2019-10-18T22:02:54.000Z | 2020-09-26T04:20:26.000Z | """Catalog of user objects"""
from CatalogMenuWdg import *
| 19.666667 | 29 | 0.745763 | 7 | 59 | 6.285714 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135593 | 59 | 2 | 30 | 29.5 | 0.862745 | 0.389831 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
baa075160705190addf164577b14eb6984609200 | 299 | py | Python | web_app/serializers.py | babakatea/ACME_backend | 5a7eb3ffdbdd1b63b03705c21087a920a1aa94e2 | [
"BSD-3-Clause"
] | null | null | null | web_app/serializers.py | babakatea/ACME_backend | 5a7eb3ffdbdd1b63b03705c21087a920a1aa94e2 | [
"BSD-3-Clause"
] | null | null | null | web_app/serializers.py | babakatea/ACME_backend | 5a7eb3ffdbdd1b63b03705c21087a920a1aa94e2 | [
"BSD-3-Clause"
] | null | null | null | from rest_framework import serializers
from .models import *
class parcelSerializer(serializers.ModelSerializer):
class Meta:
model = Parcel
fields = '__all__'
class userSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = '__all__'
| 19.933333 | 52 | 0.692308 | 28 | 299 | 7.071429 | 0.571429 | 0.262626 | 0.313131 | 0.353535 | 0.40404 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.240803 | 299 | 14 | 53 | 21.357143 | 0.872247 | 0 | 0 | 0.4 | 0 | 0 | 0.046823 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
bac0cd92a35a05c646b1c0dd994ed8e564636e08 | 1,836 | py | Python | dense/densenet/standard_densenets.py | yamrzou/pytorch-densenet-tiramisu | 1ebe7c64a603e594888922b90020b85404738bed | [
"MIT"
] | 50 | 2018-03-11T15:32:00.000Z | 2022-03-29T08:48:40.000Z | dense/densenet/standard_densenets.py | ibrahimgh25/EL-GAN-Implementation | bff0766e682a6441bb27b3a3aa5cf136202564b5 | [
"MIT"
] | 2 | 2018-12-17T17:04:04.000Z | 2021-04-21T13:36:40.000Z | dense/densenet/standard_densenets.py | ibrahimgh25/EL-GAN-Implementation | bff0766e682a6441bb27b3a3aa5cf136202564b5 | [
"MIT"
] | 16 | 2018-03-12T17:56:44.000Z | 2022-01-06T07:51:20.000Z | from .densenet import DenseNet
class DenseNet121(DenseNet):
def __init__(self, dropout: float = 0.0):
super(DenseNet121, self).__init__(
in_channels=3,
output_classes=1000,
initial_num_features=64,
dropout=dropout,
dense_blocks_growth_rates=32,
dense_blocks_bottleneck_ratios=4,
dense_blocks_num_layers=(6, 12, 24, 16),
transition_blocks_compression_factors=0.5
)
class DenseNet169(DenseNet):
def __init__(self, dropout: float = 0.0):
super(DenseNet169, self).__init__(
in_channels=3,
output_classes=1000,
initial_num_features=64,
dropout=dropout,
dense_blocks_growth_rates=32,
dense_blocks_bottleneck_ratios=4,
dense_blocks_num_layers=(6, 12, 32, 32),
transition_blocks_compression_factors=0.5
)
class DenseNet201(DenseNet):
def __init__(self, dropout: float = 0.0):
super(DenseNet201, self).__init__(
in_channels=3,
output_classes=1000,
initial_num_features=64,
dropout=dropout,
dense_blocks_growth_rates=32,
dense_blocks_bottleneck_ratios=4,
dense_blocks_num_layers=(6, 12, 48, 32),
transition_blocks_compression_factors=0.5
)
class DenseNet161(DenseNet):
def __init__(self, dropout: float = 0.0):
super(DenseNet161, self).__init__(
in_channels=3,
output_classes=1000,
initial_num_features=64,
dropout=dropout,
dense_blocks_growth_rates=48,
dense_blocks_bottleneck_ratios=4,
dense_blocks_num_layers=(6, 12, 36, 24),
transition_blocks_compression_factors=0.5
)
| 31.118644 | 53 | 0.611111 | 204 | 1,836 | 5.029412 | 0.215686 | 0.128655 | 0.05848 | 0.074074 | 0.87037 | 0.87037 | 0.835283 | 0.795322 | 0.711501 | 0.563353 | 0 | 0.084972 | 0.307734 | 1,836 | 58 | 54 | 31.655172 | 0.722266 | 0 | 0 | 0.632653 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.081633 | false | 0 | 0.020408 | 0 | 0.183673 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
2445692d7f1749e1aeb86c27a09884616105e446 | 200 | py | Python | bci_framework/extensions/data_analysis/__init__.py | UN-GCPDS/bci-framework- | b51f530967561738dc34752acf6add20cbb02283 | [
"BSD-2-Clause"
] | null | null | null | bci_framework/extensions/data_analysis/__init__.py | UN-GCPDS/bci-framework- | b51f530967561738dc34752acf6add20cbb02283 | [
"BSD-2-Clause"
] | null | null | null | bci_framework/extensions/data_analysis/__init__.py | UN-GCPDS/bci-framework- | b51f530967561738dc34752acf6add20cbb02283 | [
"BSD-2-Clause"
] | null | null | null | """
=============
Data Analysis
=============
"""
from .data_analysis import DataAnalysis, Feedback
from .utils import loop_consumer, fake_loop_consumer, thread_this, subprocess_this, marker_slicing
| 22.222222 | 98 | 0.705 | 22 | 200 | 6.090909 | 0.681818 | 0.179104 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 200 | 8 | 99 | 25 | 0.744444 | 0.205 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
246c51db4e45223f271046b09b3ca1abca4ad051 | 270 | py | Python | src/baboon_tracking/mixins/preprocessed_frame_mixin.py | radioactivebean0/baboon-tracking | 062351c514073aac8e1207b8b46ca89ece987928 | [
"MIT"
] | 6 | 2019-07-15T19:10:59.000Z | 2022-02-01T04:25:26.000Z | src/baboon_tracking/mixins/preprocessed_frame_mixin.py | radioactivebean0/baboon-tracking | 062351c514073aac8e1207b8b46ca89ece987928 | [
"MIT"
] | 86 | 2019-07-02T17:59:46.000Z | 2022-02-01T23:23:08.000Z | src/baboon_tracking/mixins/preprocessed_frame_mixin.py | radioactivebean0/baboon-tracking | 062351c514073aac8e1207b8b46ca89ece987928 | [
"MIT"
] | 7 | 2019-10-16T12:58:21.000Z | 2022-03-08T00:31:32.000Z | """
Mixin for returning preprocessed frames.
"""
from baboon_tracking.models.frame import Frame
class PreprocessedFrameMixin:
"""
Mixin for returning preprocessed frames.
"""
def __init__(self):
self.processed_frame: Frame = None
| 19.285714 | 47 | 0.67037 | 27 | 270 | 6.481481 | 0.666667 | 0.091429 | 0.194286 | 0.331429 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.244444 | 270 | 13 | 48 | 20.769231 | 0.857843 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
79f1de6bf0b0773b9fea60775ef13bf752323c9e | 23 | py | Python | PDaS/__init__.py | Seenivasanseeni/PyDas | cdae21c6b542254a921a692902d7c3920bd87b6a | [
"MIT"
] | null | null | null | PDaS/__init__.py | Seenivasanseeni/PyDas | cdae21c6b542254a921a692902d7c3920bd87b6a | [
"MIT"
] | 1 | 2017-08-02T15:24:51.000Z | 2017-08-02T15:41:47.000Z | PDaS/__init__.py | Seenivasanseeni/PyDas | cdae21c6b542254a921a692902d7c3920bd87b6a | [
"MIT"
] | 1 | 2017-08-07T13:01:59.000Z | 2017-08-07T13:01:59.000Z | print("Impoting PyDas") | 23 | 23 | 0.782609 | 3 | 23 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043478 | 23 | 1 | 23 | 23 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0.583333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
0315a5bc145f09f35caca8aa1e0e15234c9a4683 | 7,798 | py | Python | src/cova/api/sagemaker.py | danirivas/cova-tuner | e7eaf7e75f0c15ce35c449fb67529c9c73386817 | [
"Apache-2.0"
] | 1 | 2021-08-28T14:21:20.000Z | 2021-08-28T14:21:20.000Z | src/cova/api/sagemaker.py | danirivas/cova-tuner | e7eaf7e75f0c15ce35c449fb67529c9c73386817 | [
"Apache-2.0"
] | 1 | 2021-11-03T15:44:44.000Z | 2021-11-03T15:44:44.000Z | src/cova/api/sagemaker.py | danirivas/cova-tuner | e7eaf7e75f0c15ce35c449fb67529c9c73386817 | [
"Apache-2.0"
] | 2 | 2021-04-16T06:09:26.000Z | 2021-11-09T09:13:16.000Z | """This module implements functions related to the usage of AWS Sagemaker"""
import json
import logging
import time
import sagemaker
from sagemaker import ModelPackage
logger = logging.getLogger(__name__)
class ModelPackageArnProvider:
"""This class provides ARNs to SSD and YOLOv3 models for different regions of AWS Sagemaker."""
@staticmethod
def get_yolov3_model_package_arn(current_region: str) -> str:
"""Returns ARN for YOLOv3 model in the specified region.
Args:
current_region (str): AWS region
Returns:
str: ARN for YOLOv3 in the specified region.
"""
mapping = {
"sa-east-1": "arn:aws:sagemaker:sa-east-1:270155090741:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ap-south-1": "arn:aws:sagemaker:ap-south-1:077584701553:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ap-northeast-2": "arn:aws:sagemaker:ap-northeast-2:745090734665:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ap-southeast-1": "arn:aws:sagemaker:ap-southeast-1:192199979996:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ap-southeast-2": "arn:aws:sagemaker:ap-southeast-2:666831318237:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ap-northeast-1": "arn:aws:sagemaker:ap-northeast-1:977537786026:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"ca-central-1": "arn:aws:sagemaker:ca-central-1:470592106596:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"eu-central-1": "arn:aws:sagemaker:eu-central-1:446921602837:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"eu-west-1": "arn:aws:sagemaker:eu-west-1:985815980388:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"eu-west-2": "arn:aws:sagemaker:eu-west-2:856760150666:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"eu-west-3": "arn:aws:sagemaker:eu-west-3:843114510376:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"eu-north-1": "arn:aws:sagemaker:eu-north-1:136758871317:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"us-east-1": "arn:aws:sagemaker:us-east-1:865070037744:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"us-east-2": "arn:aws:sagemaker:us-east-2:057799348421:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"us-west-1": "arn:aws:sagemaker:us-west-1:382657785993:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
"us-west-2": "arn:aws:sagemaker:us-west-2:594846645681:model-package/gluoncv-yolo3-darknet531547760-bdf604d6d9c12bf6194b6ae534a638b2",
}
return mapping[current_region]
@staticmethod
def get_ssd_model_package_arn(current_region: str) -> str:
"""Returns ARN for SSD-Resnet50 model in the specified region.
Args:
current_region (str): AWS region
Returns:
str: ARN for SSD-Resnet50 in the specified region.
"""
mapping = {
"sa-east-1": "arn:aws:sagemaker:sa-east-1:270155090741:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ap-south-1": "arn:aws:sagemaker:ap-south-1:077584701553:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ap-northeast-2": "arn:aws:sagemaker:ap-northeast-2:745090734665:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ap-southeast-1": "arn:aws:sagemaker:ap-southeast-1:192199979996:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ap-southeast-2": "arn:aws:sagemaker:ap-southeast-2:666831318237:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ap-northeast-1": "arn:aws:sagemaker:ap-northeast-1:977537786026:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"ca-central-1": "arn:aws:sagemaker:ca-central-1:470592106596:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"eu-central-1": "arn:aws:sagemaker:eu-central-1:446921602837:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"eu-west-1": "arn:aws:sagemaker:eu-west-1:985815980388:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"eu-west-2": "arn:aws:sagemaker:eu-west-2:856760150666:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"eu-west-3": "arn:aws:sagemaker:eu-west-3:843114510376:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"eu-north-1": "arn:aws:sagemaker:eu-north-1:136758871317:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"us-east-1": "arn:aws:sagemaker:us-east-1:865070037744:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"us-east-2": "arn:aws:sagemaker:us-east-2:057799348421:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"us-west-1": "arn:aws:sagemaker:us-west-1:382657785993:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
"us-west-2": "arn:aws:sagemaker:us-west-2:594846645681:model-package/gluoncv-ssd-resnet501547760463-0f9e6796d2438a1d64bb9b15aac57bc0",
}
return mapping[current_region]
def deploy_model(
role,
num_instances,
model_arn,
instance_type,
model_name,
output_path,
max_concurrent_transforms=2,
):
model = ModelPackage(
role=role, model_package_arn=model_arn, sagemaker_session=sagemaker.Session()
)
# model.deploy(num_instances, instance_type, endpoint_name=model_name)
transformer = model.transformer(
instance_count=num_instances,
instance_type=instance_type,
output_path=output_path,
max_concurrent_transforms=max_concurrent_transforms,
)
return model, transformer
def batch_transform(data, transformer, batch_output, content_type):
ts0 = time.time()
transformer.transform(
data=data,
data_type="S3Prefix",
content_type=content_type,
input_filter="$",
join_source="None",
output_filter="$",
)
ts_create = time.time() - ts0
ts0 = time.time()
transformer.wait()
ts_exec = time.time() - ts0
logger.info(
f"Batch Transform job created in {ts_create:.2f} seconds and executed in {ts_exec:.2f} seconds."
)
assert batch_output == transformer.output_path
output = transformer.output_path
return output
def invoke_DL_endpoint(
image_path, runtime, endpoint_name, content_type="image/png", bounding_box="no"
):
img = open(image_path, "rb").read()
response = runtime.invoke_endpoint(
EndpointName=endpoint_name,
Body=bytearray(img),
ContentType=content_type,
CustomAttributes='{"threshold": 0.2}',
Accept="json",
)
result = json.loads(response["Body"].read().decode("utf-8"))
return result
def get_default_bucket() -> str:
"""Returns default bucket of the Sagemaker session.
Returns:
str: default bucket in s3 of the Sagemaker session.
"""
return sagemaker.Session().default_bucket()
| 52.33557 | 156 | 0.730572 | 858 | 7,798 | 6.554779 | 0.175991 | 0.07468 | 0.085349 | 0.056899 | 0.725818 | 0.714083 | 0.676387 | 0.619844 | 0.412518 | 0.412518 | 0 | 0.216069 | 0.152475 | 7,798 | 148 | 157 | 52.689189 | 0.634892 | 0.086048 | 0 | 0.097087 | 0 | 0.320388 | 0.617613 | 0.54696 | 0 | 0 | 0 | 0 | 0.009709 | 1 | 0.058252 | false | 0 | 0.048544 | 0 | 0.174757 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
0316a31cc00c3ba6e4f05bebfc215ba42fe83658 | 43 | py | Python | tests/__init__.py | datagazing/disambigufile | 7ada4fc15be64d986ec3b8ca912911521461b0f6 | [
"MIT"
] | null | null | null | tests/__init__.py | datagazing/disambigufile | 7ada4fc15be64d986ec3b8ca912911521461b0f6 | [
"MIT"
] | null | null | null | tests/__init__.py | datagazing/disambigufile | 7ada4fc15be64d986ec3b8ca912911521461b0f6 | [
"MIT"
] | null | null | null | """Unit test package for disambigufile."""
| 21.5 | 42 | 0.72093 | 5 | 43 | 6.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116279 | 43 | 1 | 43 | 43 | 0.815789 | 0.837209 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
0345c149d726c294feae19bca46a80b8f09946bd | 62 | py | Python | ABC/171/a.py | fumiyanll23/AtCoder | 362ca9fcacb5415c1458bc8dee5326ba2cc70b65 | [
"MIT"
] | null | null | null | ABC/171/a.py | fumiyanll23/AtCoder | 362ca9fcacb5415c1458bc8dee5326ba2cc70b65 | [
"MIT"
] | null | null | null | ABC/171/a.py | fumiyanll23/AtCoder | 362ca9fcacb5415c1458bc8dee5326ba2cc70b65 | [
"MIT"
] | null | null | null | if(str(input()).islower()):
print("a")
else:
print("A") | 15.5 | 28 | 0.532258 | 9 | 62 | 3.666667 | 0.777778 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 62 | 4 | 29 | 15.5 | 0.634615 | 0 | 0 | 0 | 0 | 0 | 0.033333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
0356b4f01e63670fd3f8b65de93cc32274a33683 | 36 | py | Python | zfs_test/replicate_test/snapshot_test/__init__.py | tuffnatty/zfs-replicate | 4b618e5a2babb141c7da7be0e9b72511b5dd2190 | [
"BSD-2-Clause"
] | 11 | 2018-09-07T03:40:47.000Z | 2021-07-03T08:10:36.000Z | zfs_test/replicate_test/snapshot_test/__init__.py | tuffnatty/zfs-replicate | 4b618e5a2babb141c7da7be0e9b72511b5dd2190 | [
"BSD-2-Clause"
] | 68 | 2018-09-07T02:28:54.000Z | 2021-03-19T20:01:13.000Z | zfs_test/replicate_test/snapshot_test/__init__.py | tuffnatty/zfs-replicate | 4b618e5a2babb141c7da7be0e9b72511b5dd2190 | [
"BSD-2-Clause"
] | 7 | 2020-05-02T13:24:34.000Z | 2022-02-07T02:29:17.000Z | """zfs.replicate.snapshot tests."""
| 18 | 35 | 0.694444 | 4 | 36 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.055556 | 36 | 1 | 36 | 36 | 0.735294 | 0.805556 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
06243c525fbd7a9f0b8f324f546561eeeb479ef0 | 383 | py | Python | pybitcointools/__init__.py | liyicong763/Ferproof_for_Python | 38747fd5bc0f50553e90594936d6044bb4d10fea | [
"MIT"
] | null | null | null | pybitcointools/__init__.py | liyicong763/Ferproof_for_Python | 38747fd5bc0f50553e90594936d6044bb4d10fea | [
"MIT"
] | null | null | null | pybitcointools/__init__.py | liyicong763/Ferproof_for_Python | 38747fd5bc0f50553e90594936d6044bb4d10fea | [
"MIT"
] | 2 | 2018-02-22T19:05:27.000Z | 2018-11-18T17:54:38.000Z | from pybitcointools.py2specials import *
from pybitcointools.py3specials import *
from pybitcointools.main import *
from pybitcointools.transaction import *
from pybitcointools.deterministic import *
from pybitcointools.bci import *
from pybitcointools.composite import *
from pybitcointools.stealth import *
from pybitcointools.blocks import *
from pybitcointools.mnemonic import *
| 34.818182 | 42 | 0.843342 | 40 | 383 | 8.075 | 0.325 | 0.557276 | 0.668731 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005831 | 0.104439 | 383 | 10 | 43 | 38.3 | 0.93586 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
067f1c438634613dbd48da44ae998cbd4949acd3 | 13 | py | Python | apps/csvimport/models.py | jfterpstra/onepercentclub-site | 43e8e01ac4d3d1ffdd5959ebd048ce95bb2dba0e | [
"BSD-3-Clause"
] | 7 | 2015-01-02T19:31:14.000Z | 2021-03-22T17:30:23.000Z | apps/csvimport/models.py | jfterpstra/onepercentclub-site | 43e8e01ac4d3d1ffdd5959ebd048ce95bb2dba0e | [
"BSD-3-Clause"
] | 1 | 2015-03-06T08:34:59.000Z | 2015-03-06T08:34:59.000Z | apps/csvimport/models.py | jfterpstra/onepercentclub-site | 43e8e01ac4d3d1ffdd5959ebd048ce95bb2dba0e | [
"BSD-3-Clause"
] | null | null | null | """ Stub """
| 6.5 | 12 | 0.307692 | 1 | 13 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.230769 | 13 | 1 | 13 | 13 | 0.4 | 0.307692 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
ebeaa871e5b28f5415fd239df08c87bfd8820919 | 134 | py | Python | numbers/random number.py | paniseven/py-playground | e42ffaaaeadbc0ec014709bbf76faf9d7b28c20b | [
"Unlicense"
] | null | null | null | numbers/random number.py | paniseven/py-playground | e42ffaaaeadbc0ec014709bbf76faf9d7b28c20b | [
"Unlicense"
] | null | null | null | numbers/random number.py | paniseven/py-playground | e42ffaaaeadbc0ec014709bbf76faf9d7b28c20b | [
"Unlicense"
] | null | null | null | import random
print(random.randrange(1, 10))
# there is no actual random function but you can import a module that can be used
| 22.333333 | 82 | 0.738806 | 23 | 134 | 4.304348 | 0.826087 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.028302 | 0.208955 | 134 | 5 | 83 | 26.8 | 0.90566 | 0.589552 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 5 |
ebf48e5301645715563f944e42df3d46188c302a | 233 | py | Python | dartcms/apps/siteusers/views.py | astrikov-d/dartcms | 41af3ecfcff73d3fb6a483e3a6ca1c4acb6278fa | [
"MIT"
] | 26 | 2015-01-12T09:47:32.000Z | 2021-04-15T14:09:49.000Z | dartcms/apps/siteusers/views.py | astrikov-d/dartcms | 41af3ecfcff73d3fb6a483e3a6ca1c4acb6278fa | [
"MIT"
] | 41 | 2016-07-04T06:55:31.000Z | 2019-07-31T14:11:53.000Z | dartcms/apps/siteusers/views.py | astrikov-d/dartcms | 41af3ecfcff73d3fb6a483e3a6ca1c4acb6278fa | [
"MIT"
] | 12 | 2015-01-20T09:51:53.000Z | 2021-01-26T16:51:47.000Z | from dartcms.apps.users.views import \
ChangePasswordView as CMSChangePasswordView
from django.urls import reverse_lazy
class ChangePasswordView(CMSChangePasswordView):
success_url = reverse_lazy('dartcms:siteusers:index')
| 29.125 | 57 | 0.824034 | 25 | 233 | 7.56 | 0.72 | 0.116402 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111588 | 233 | 7 | 58 | 33.285714 | 0.913043 | 0 | 0 | 0 | 0 | 0 | 0.098712 | 0.098712 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.4 | 0.4 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 5 |
88d4f375a390020e95d8c5f31584e39c1986a788 | 22 | py | Python | modules/lm/__init__.py | tom-pelsmaeker/vae-lagging-encoder | b190239019a94c85858d188a0853886eb48ce4be | [
"MIT"
] | 173 | 2018-12-21T16:34:04.000Z | 2022-02-22T08:47:28.000Z | modules/lm/__init__.py | tom-pelsmaeker/vae-lagging-encoder | b190239019a94c85858d188a0853886eb48ce4be | [
"MIT"
] | 11 | 2019-01-12T22:15:20.000Z | 2020-09-21T03:34:42.000Z | modules/lm/__init__.py | tom-pelsmaeker/vae-lagging-encoder | b190239019a94c85858d188a0853886eb48ce4be | [
"MIT"
] | 28 | 2019-01-03T16:11:41.000Z | 2021-02-17T20:28:04.000Z | from .lm_lstm import * | 22 | 22 | 0.772727 | 4 | 22 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 22 | 1 | 22 | 22 | 0.842105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
002786200e61d5578ecb57e8c4ea8e4b4f125921 | 2,281 | py | Python | tests/system/action/topic/test_create.py | reiterl/openslides-backend | d36667f00087ae8baf25853d4cef18a5e6dc7b3b | [
"MIT"
] | null | null | null | tests/system/action/topic/test_create.py | reiterl/openslides-backend | d36667f00087ae8baf25853d4cef18a5e6dc7b3b | [
"MIT"
] | null | null | null | tests/system/action/topic/test_create.py | reiterl/openslides-backend | d36667f00087ae8baf25853d4cef18a5e6dc7b3b | [
"MIT"
] | null | null | null | from tests.system.action.base import BaseActionTestCase
class TopicSystemTest(BaseActionTestCase):
def test_create(self) -> None:
self.create_model("meeting/1", {"name": "test"})
response = self.client.post(
"/",
json=[
{
"action": "topic.create",
"data": [{"meeting_id": 1, "title": "test"}],
}
],
)
self.assert_status_code(response, 200)
self.assert_model_exists("topic/1")
topic = self.get_model("topic/1")
self.assertEqual(topic.get("meeting_id"), 1)
self.assertEqual(topic.get("agenda_item_id"), 1)
self.assert_model_exists("agenda_item/1")
agenda_item = self.get_model("agenda_item/1")
self.assertEqual(agenda_item.get("meeting_id"), 1)
self.assertEqual(agenda_item.get("content_object_id"), "topic/1")
self.assert_model_exists("list_of_speakers/1", {"content_object_id": "topic/1"})
def test_create_more_fields(self) -> None:
self.create_model("meeting/1", {"name": "test"})
response = self.client.post(
"/",
json=[
{
"action": "topic.create",
"data": [
{
"meeting_id": 1,
"title": "test",
"agenda_type": 2,
"agenda_duration": 60,
}
],
}
],
)
self.assert_status_code(response, 200)
self.assert_model_exists("topic/1")
topic = self.get_model("topic/1")
self.assertEqual(topic.get("meeting_id"), 1)
self.assertEqual(topic.get("agenda_item_id"), 1)
self.assertTrue(topic.get("agenda_type") is None)
self.assert_model_exists("agenda_item/1")
agenda_item = self.get_model("agenda_item/1")
self.assertEqual(agenda_item.get("meeting_id"), 1)
self.assertEqual(agenda_item.get("content_object_id"), "topic/1")
self.assertEqual(agenda_item["type"], 2)
self.assertEqual(agenda_item["duration"], 60)
self.assertEqual(agenda_item["weight"], 10000)
| 39.327586 | 88 | 0.533976 | 241 | 2,281 | 4.813278 | 0.207469 | 0.12931 | 0.124138 | 0.150862 | 0.762931 | 0.712069 | 0.712069 | 0.712069 | 0.712069 | 0.712069 | 0 | 0.025424 | 0.327488 | 2,281 | 57 | 89 | 40.017544 | 0.730769 | 0 | 0 | 0.574074 | 0 | 0 | 0.180184 | 0 | 0 | 0 | 0 | 0 | 0.351852 | 1 | 0.037037 | false | 0 | 0.018519 | 0 | 0.074074 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
ccb1a9b7a348210876f155500c2c55a03db07689 | 374 | py | Python | static/brythonlib/cs1graphics/event_trigger.py | pythonpad/vue-pythonpad-runner | 52decba9607b3b7b050ee0bf6dd4ef07ae644587 | [
"MIT"
] | 3 | 2021-01-26T16:18:45.000Z | 2021-09-15T00:57:12.000Z | static/brythonlib/cs1graphics/event_trigger.py | pythonpad/vue-pythonpad-runner | 52decba9607b3b7b050ee0bf6dd4ef07ae644587 | [
"MIT"
] | null | null | null | static/brythonlib/cs1graphics/event_trigger.py | pythonpad/vue-pythonpad-runner | 52decba9607b3b7b050ee0bf6dd4ef07ae644587 | [
"MIT"
] | 2 | 2021-01-26T16:18:47.000Z | 2021-10-21T20:45:20.000Z | class _EventTrigger(object):
def __init__(self):
pass
def raiseEventError(self):
raise NotImplementedError('cs1graphics in Pythonpad does not support events.')
def addHandler(self, handler):
self.raiseEventError()
def removeHandler(self, handler):
self.raiseEventError()
def wait(self):
self.raiseEventError()
| 23.375 | 86 | 0.668449 | 36 | 374 | 6.805556 | 0.583333 | 0.232653 | 0.122449 | 0.244898 | 0.269388 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003521 | 0.240642 | 374 | 15 | 87 | 24.933333 | 0.859155 | 0 | 0 | 0.272727 | 0 | 0 | 0.131016 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.454545 | false | 0.090909 | 0 | 0 | 0.545455 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
aed225707fc89c64ed2c28eb2111a0302fbd25b1 | 171 | py | Python | commons/apps.py | bhagirath1312/ich_bau | d37fe7aa3379f312a4d8b5f3d4715dd334b9adb0 | [
"Apache-2.0"
] | 1 | 2021-11-25T19:37:01.000Z | 2021-11-25T19:37:01.000Z | commons/apps.py | bhagirath1312/ich_bau | d37fe7aa3379f312a4d8b5f3d4715dd334b9adb0 | [
"Apache-2.0"
] | 197 | 2017-09-06T22:54:20.000Z | 2022-02-05T00:04:13.000Z | commons/apps.py | bhagirath1312/ich_bau | d37fe7aa3379f312a4d8b5f3d4715dd334b9adb0 | [
"Apache-2.0"
] | 2 | 2017-11-08T02:13:03.000Z | 2020-09-30T19:48:12.000Z | # base app for pluggable aaplications
from django.apps import AppConfig
class BaseAppConfig(AppConfig):
def get_site_index_html_block( self, request ):
pass
| 21.375 | 51 | 0.760234 | 22 | 171 | 5.727273 | 0.954545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.187135 | 171 | 7 | 52 | 24.428571 | 0.906475 | 0.204678 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0.25 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
4e0803d7d304c7933079cc2ae577ba0ff2cff8fb | 149 | py | Python | examples/apk_download.py | riquedev/WhatsAppManifest | bcbbd48f6f9152024a54172886876d3a725a3a62 | [
"MIT"
] | 15 | 2020-03-11T17:31:12.000Z | 2021-11-19T03:26:09.000Z | examples/apk_download.py | riquedev/WhatsAppManifest | bcbbd48f6f9152024a54172886876d3a725a3a62 | [
"MIT"
] | 5 | 2021-03-31T19:43:15.000Z | 2022-03-12T00:18:38.000Z | examples/apk_download.py | riquedev/WhatsAppManifest | bcbbd48f6f9152024a54172886876d3a725a3a62 | [
"MIT"
] | 4 | 2020-03-11T01:52:57.000Z | 2021-03-16T04:14:33.000Z | from WhatsAppManifest.tools import APKPureDownload
apk_pure = APKPureDownload()
apk_pure.download_apk("com.whatsapp.w4b", path=".", file_name=None)
| 29.8 | 67 | 0.805369 | 19 | 149 | 6.105263 | 0.789474 | 0.310345 | 0.37931 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007246 | 0.073826 | 149 | 4 | 68 | 37.25 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0.114094 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
9d6f73153d0ef72cc28de77ccc20d6ccc45145ac | 8,356 | py | Python | sql_to_ibis/tests/expression_generation/test_conditionals.py | zbrookle/sql_to_ibis | 5d29ff903fd61f7c652f7763f5cd58b76f9a083f | [
"BSD-3-Clause"
] | 25 | 2020-06-11T22:44:06.000Z | 2021-11-23T13:02:16.000Z | sql_to_ibis/tests/expression_generation/test_conditionals.py | zbrookle/sql_to_ibis | 5d29ff903fd61f7c652f7763f5cd58b76f9a083f | [
"BSD-3-Clause"
] | 40 | 2020-06-12T16:35:47.000Z | 2022-02-23T20:48:19.000Z | sql_to_ibis/tests/expression_generation/test_conditionals.py | zbrookle/sql_to_ibis | 5d29ff903fd61f7c652f7763f5cd58b76f9a083f | [
"BSD-3-Clause"
] | 3 | 2020-08-30T13:43:06.000Z | 2020-10-03T11:38:47.000Z | from typing import List
import ibis
from ibis.expr.operations import Literal
import pytest
from sql_to_ibis import query
from sql_to_ibis.tests.utils import assert_ibis_equal_show_diff, assert_state_not_change
@assert_state_not_change
def test_where_clause(forest_fires):
"""
Test where clause
:return:
"""
my_table = query("""select * from forest_fires where month = 'mar'""")
ibis_table = forest_fires[forest_fires.month == "mar"]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_all_boolean_ops_clause(forest_fires):
"""
Test where clause
:return:
"""
my_table = query(
"""select * from forest_fires where month = 'mar' and temp > 8.0 and rain >= 0
and area != 0 and dc < 100 and ffmc <= 90.1
"""
)
ibis_table = forest_fires[
(forest_fires.month == "mar")
& (forest_fires.temp > 8.0)
& (forest_fires.rain >= 0)
& (forest_fires.area != ibis.literal(0))
& (forest_fires.DC < 100)
& (forest_fires.FFMC <= 90.1)
]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_having_multiple_conditions(forest_fires):
"""
Test having clause
:return:
"""
my_table = query(
"select min(temp) from forest_fires having min(temp) > 2 and max(dc) < 200"
)
having_condition = (forest_fires.temp.min() > 2) & (forest_fires.DC.max() < 200)
ibis_table = forest_fires.aggregate(
metrics=forest_fires.temp.min().name("_col0"),
having=having_condition,
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_having_multiple_conditions_with_or(forest_fires):
"""
Test having clause
:return:
"""
my_table = query(
"select min(temp) from forest_fires having min(temp) > 2 and "
"max(dc) < 200 or max(dc) > 1000"
)
having_condition = (forest_fires.temp.min() > 2) & (forest_fires.DC.max() < 200) | (
(forest_fires.DC.max() > 1000)
)
ibis_table = forest_fires.aggregate(
metrics=forest_fires.temp.min().name("_col0"),
having=having_condition,
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_having_one_condition(forest_fires):
"""
Test having clause
:return:
"""
my_table = query("select min(temp) from forest_fires having min(temp) > 2")
min_aggregate = forest_fires.temp.min()
ibis_table = forest_fires.aggregate(
min_aggregate.name("_col0"), having=(min_aggregate > 2)
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_having_with_group_by(forest_fires):
"""
Test having clause
:return:
"""
my_table = query(
"select min(temp) from forest_fires group by day having min(temp) > 5"
)
ibis_table = (
forest_fires.groupby("day")
.having(forest_fires.temp.min() > 5)
.aggregate(forest_fires.temp.min().name("_col0"))
.drop(["day"])
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_between_operator(forest_fires):
"""
Test using between operator
:return:
"""
my_table = query(
"""
select * from forest_fires
where wind between 5 and 6
"""
)
ibis_table = forest_fires.filter(forest_fires.wind.between(5, 6))
assert_ibis_equal_show_diff(ibis_table, my_table)
in_list_params = pytest.mark.parametrize(
"sql,ibis_expr_list",
[
(
"('fri', 'sun')",
[ibis.literal("fri"), ibis.literal("sun")],
),
(
"('fri', 'sun', 'sat')",
[ibis.literal("fri"), ibis.literal("sun"), ibis.literal("sat")],
),
],
)
@assert_state_not_change
@in_list_params
def test_in_operator(forest_fires, sql: str, ibis_expr_list: List[Literal]):
"""
Test using in operator in a sql query
:return:
"""
my_table = query(
f"""
select * from forest_fires where day in {sql}
"""
)
ibis_table = forest_fires[forest_fires.day.isin(ibis_expr_list)]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_in_operator_expression_numerical(forest_fires):
"""
Test using in operator in a sql query
:return:
"""
my_table = query(
"""
select * from forest_fires where X in (5, 9)
"""
)
ibis_table = forest_fires[forest_fires.X.isin((ibis.literal(5), ibis.literal(9)))]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
@in_list_params
def test_not_in_operator(forest_fires, sql: str, ibis_expr_list: List[Literal]):
"""
Test using in operator in a sql query
:return:
"""
my_table = query(
f"""
select * from forest_fires where day not in {sql}
"""
)
ibis_table = forest_fires[forest_fires.day.notin(ibis_expr_list)]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_case_statement_w_name(forest_fires):
"""
Test using case statements
:return:
"""
my_table = query(
"""
select case when wind > 5 then 'strong'
when wind = 5 then 'mid'
else 'weak' end as wind_strength
from
forest_fires
"""
)
ibis_table = forest_fires.projection(
ibis.case()
.when(forest_fires.wind > 5, "strong")
.when(forest_fires.wind == 5, "mid")
.else_("weak")
.end()
.name("wind_strength")
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_case_statement_w_no_name(forest_fires):
"""
Test using case statements
:return:
"""
my_table = query(
"""
select case when wind > 5 then 'strong' when wind = 5 then 'mid' else 'weak' end
from forest_fires
"""
)
ibis_table = forest_fires.projection(
ibis.case()
.when(forest_fires.wind > 5, "strong")
.when(forest_fires.wind == 5, "mid")
.else_("weak")
.end()
.name("_col0")
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_case_statement_w_other_columns_as_result(forest_fires):
"""
Test using case statements
:return:
"""
my_table = query(
"""
select case when wind > 5 then month when wind = 5 then 'mid' else day end
from forest_fires
"""
)
ibis_table = forest_fires.projection(
ibis.case()
.when(forest_fires.wind > 5, forest_fires.month)
.when(forest_fires.wind == 5, "mid")
.else_(forest_fires.day)
.end()
.name("_col0")
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_filter_on_non_selected_column(forest_fires):
my_table = query("select temp from forest_fires where month = 'mar'")
ibis_table = forest_fires[forest_fires.month == "mar"].projection(
[forest_fires.temp]
)
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_boolean_order_of_operations_with_parens(forest_fires):
"""
Test boolean order of operations with parentheses
:return:
"""
my_table = query(
"select * from forest_fires "
"where (month = 'oct' and day = 'fri') or "
"(month = 'nov' and day = 'tue')"
)
ibis_table = forest_fires[
((forest_fires.month == "oct") & (forest_fires.day == "fri"))
| ((forest_fires.month == "nov") & (forest_fires.day == "tue"))
]
assert_ibis_equal_show_diff(ibis_table, my_table)
@assert_state_not_change
def test_case_statement_with_same_conditions(forest_fires):
"""
Test using case statements
:return:
"""
my_table = query(
"""
select case when wind > 5 then month when wind > 5 then 'mid' else day end
from forest_fires
"""
)
ibis_table = forest_fires.projection(
ibis.case()
.when(forest_fires.wind > 5, forest_fires.month)
.when(forest_fires.wind > 5, "mid")
.else_(forest_fires.day)
.end()
.name("_col0")
)
assert_ibis_equal_show_diff(ibis_table, my_table)
| 26.696486 | 88 | 0.637506 | 1,100 | 8,356 | 4.502727 | 0.11 | 0.193216 | 0.051484 | 0.065213 | 0.790228 | 0.772461 | 0.732082 | 0.724813 | 0.716939 | 0.667474 | 0 | 0.012548 | 0.246529 | 8,356 | 312 | 89 | 26.782051 | 0.774142 | 0.065342 | 0 | 0.475676 | 0 | 0.005405 | 0.114925 | 0 | 0 | 0 | 0 | 0 | 0.178378 | 1 | 0.086486 | false | 0 | 0.032432 | 0 | 0.118919 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
9db00e355f66473f4830492655bc0b07d7eb2c17 | 30 | py | Python | gnomad/resources/__init__.py | tpoterba/gnomad_methods | 95dbb4844bd625619492026713a474882d87fcb7 | [
"MIT"
] | null | null | null | gnomad/resources/__init__.py | tpoterba/gnomad_methods | 95dbb4844bd625619492026713a474882d87fcb7 | [
"MIT"
] | null | null | null | gnomad/resources/__init__.py | tpoterba/gnomad_methods | 95dbb4844bd625619492026713a474882d87fcb7 | [
"MIT"
] | null | null | null | from .resource_utils import *
| 15 | 29 | 0.8 | 4 | 30 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 30 | 1 | 30 | 30 | 0.884615 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
9dbdfbfada01ad548eeb5c62042f3022aa1c8b7f | 149 | py | Python | pysagereader/__init__.py | LandonRieger/pySAGE | 1752c3bef53ab854846d4d8d2ac1dcf9a8e8bcb1 | [
"MIT"
] | 1 | 2018-06-15T22:44:40.000Z | 2018-06-15T22:44:40.000Z | pysagereader/__init__.py | LandonRieger/pySAGE | 1752c3bef53ab854846d4d8d2ac1dcf9a8e8bcb1 | [
"MIT"
] | 4 | 2016-06-09T12:30:32.000Z | 2018-09-06T04:15:41.000Z | pysagereader/__init__.py | LandonRieger/pySAGE | 1752c3bef53ab854846d4d8d2ac1dcf9a8e8bcb1 | [
"MIT"
] | null | null | null | from pysagereader.sage_ii_reader import SAGEIILoaderV700
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
| 29.8 | 56 | 0.852349 | 19 | 149 | 6.157895 | 0.578947 | 0.282051 | 0.307692 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.022059 | 0.087248 | 149 | 4 | 57 | 37.25 | 0.838235 | 0 | 0 | 0 | 0 | 0 | 0.04698 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
9dea478ec5858d6bb7732599cf093d108f48afaa | 182 | py | Python | core/models/dataparallel.py | tridivb/attention_based_tbn | 8fc32216664833c48579c9bd8b63fdf5aa5a7711 | [
"MIT"
] | 7 | 2020-07-20T08:29:45.000Z | 2020-08-04T14:00:15.000Z | core/models/dataparallel.py | tridivb/attention_based_tbn | 8fc32216664833c48579c9bd8b63fdf5aa5a7711 | [
"MIT"
] | null | null | null | core/models/dataparallel.py | tridivb/attention_based_tbn | 8fc32216664833c48579c9bd8b63fdf5aa5a7711 | [
"MIT"
] | null | null | null | from torch.nn import DataParallel
class DataParallel(DataParallel):
def get_loss(self, criterion, target, preds):
return self.module.get_loss(criterion, target, preds)
| 26 | 61 | 0.752747 | 23 | 182 | 5.869565 | 0.652174 | 0.103704 | 0.296296 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159341 | 182 | 6 | 62 | 30.333333 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.25 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
9dec8953e224fa17f0a133aa23f732b8dba1f193 | 423 | py | Python | hrwros_ws/devel/.private/hrwros_msgs/lib/python2.7/dist-packages/hrwros_msgs/msg/__init__.py | AshfakYeafi/ros | 7895302251088b7945e359f60a9c617e5170a72e | [
"MIT"
] | null | null | null | hrwros_ws/devel/.private/hrwros_msgs/lib/python2.7/dist-packages/hrwros_msgs/msg/__init__.py | AshfakYeafi/ros | 7895302251088b7945e359f60a9c617e5170a72e | [
"MIT"
] | null | null | null | hrwros_ws/devel/.private/hrwros_msgs/lib/python2.7/dist-packages/hrwros_msgs/msg/__init__.py | AshfakYeafi/ros | 7895302251088b7945e359f60a9c617e5170a72e | [
"MIT"
] | null | null | null | from ._CounterWithDelayAction import *
from ._CounterWithDelayActionFeedback import *
from ._CounterWithDelayActionGoal import *
from ._CounterWithDelayActionResult import *
from ._CounterWithDelayFeedback import *
from ._CounterWithDelayGoal import *
from ._CounterWithDelayResult import *
from ._ObjectDetection import *
from ._RobotTrajectories import *
from ._SensorInformation import *
from ._TargetToolPoses import *
| 35.25 | 46 | 0.843972 | 33 | 423 | 10.484848 | 0.393939 | 0.289017 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104019 | 423 | 11 | 47 | 38.454545 | 0.912929 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3b049fb6049d01ec463e1f9c497b8695ef1f647b | 31 | py | Python | mousedb/groups/views.py | BridgesLab/mousedb | 7e423991f72c89468010c99865e3c70c22044df3 | [
"BSD-3-Clause"
] | 2 | 2016-01-18T06:49:35.000Z | 2016-12-16T17:00:27.000Z | mousedb/groups/views.py | davebridges/mousedb | 2a33f6d15d88b1540b05f7232b154fdbf8568580 | [
"BSD-3-Clause"
] | 12 | 2016-03-07T14:47:09.000Z | 2019-06-07T17:11:33.000Z | mousedb/groups/views.py | BridgesLab/mousedb | 7e423991f72c89468010c99865e3c70c22044df3 | [
"BSD-3-Clause"
] | 1 | 2019-08-19T14:53:28.000Z | 2019-08-19T14:53:28.000Z | # The groups app has no views.
| 15.5 | 30 | 0.709677 | 6 | 31 | 3.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225806 | 31 | 1 | 31 | 31 | 0.916667 | 0.903226 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d17788f763bc6bca9431e7f3c0ec19c67df7f1a3 | 191 | py | Python | gilderoy/__init__.py | bash/gilderoy | 3d9b192838a407371400c7b6dd5e35db34209abc | [
"MIT"
] | 2 | 2018-04-03T19:40:30.000Z | 2018-11-24T14:07:13.000Z | gilderoy/__init__.py | bash/gilderoy | 3d9b192838a407371400c7b6dd5e35db34209abc | [
"MIT"
] | 5 | 2018-11-22T10:58:50.000Z | 2018-12-04T15:17:12.000Z | gilderoy/__init__.py | bash/gilderoy | 3d9b192838a407371400c7b6dd5e35db34209abc | [
"MIT"
] | 1 | 2018-11-22T10:02:39.000Z | 2018-11-22T10:02:39.000Z | from .render import render
from .assets import build_assets
from .constants import *
from .sitemap import render_sitemap
from .config import get_config, process_config
from .main import main
| 27.285714 | 46 | 0.827225 | 28 | 191 | 5.5 | 0.392857 | 0.155844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.13089 | 191 | 6 | 47 | 31.833333 | 0.927711 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d17e6a24354c1d83db299fcaf0e9d73a42951574 | 638 | py | Python | app/addrbookapp/tests.py | kumarisneha/django_on_docker | 192ed48e1c1dd3b2dbc2bd80763466c937473af6 | [
"MIT"
] | null | null | null | app/addrbookapp/tests.py | kumarisneha/django_on_docker | 192ed48e1c1dd3b2dbc2bd80763466c937473af6 | [
"MIT"
] | 5 | 2021-03-30T14:08:36.000Z | 2021-09-22T19:29:37.000Z | app/addrbookapp/tests.py | kumarisneha/django_on_docker | 192ed48e1c1dd3b2dbc2bd80763466c937473af6 | [
"MIT"
] | null | null | null | from django.test import TestCase
# from addrbookapp.models import Address
# class AddressTestCase(TestCase):
# def setUp(self):
# Address.objects.create(user="sneha", address="patna",
# email_id="xyz@gmail.com", phone_number="2345678901")
# def test_user_add_email_phone_no(self):
# '''Get an Address object to test'''
# addr = Address.objects.get(id=1)
# print(addr)
# self.assertEquals(addr.user, 'sneha')
# self.assertEquals(addr.email_id, 'xyz@gmail.com')
# self.assertEquals(addr.address, 'patna')
# self.assertEquals(addr.phone_number, '2345678901') | 39.875 | 63 | 0.655172 | 77 | 638 | 5.311688 | 0.467532 | 0.156479 | 0.195599 | 0.07335 | 0.08802 | 0 | 0 | 0 | 0 | 0 | 0 | 0.04142 | 0.205329 | 638 | 16 | 64 | 39.875 | 0.765286 | 0.904389 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d1872638ab833269f505aa26ce0451476476fa90 | 139 | py | Python | .history/functions/online_ops_20211218104952.py | mihailgaberov/michelangelo | 1a1dc945d2e1dc15397b8b7296768980e05b5acd | [
"MIT"
] | null | null | null | .history/functions/online_ops_20211218104952.py | mihailgaberov/michelangelo | 1a1dc945d2e1dc15397b8b7296768980e05b5acd | [
"MIT"
] | null | null | null | .history/functions/online_ops_20211218104952.py | mihailgaberov/michelangelo | 1a1dc945d2e1dc15397b8b7296768980e05b5acd | [
"MIT"
] | null | null | null | import requests
import wikipedia
import pywhatkit as kit
from email.message import EmailMessage
import smtplib
from decouple import config
| 19.857143 | 38 | 0.863309 | 19 | 139 | 6.315789 | 0.684211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129496 | 139 | 6 | 39 | 23.166667 | 0.991736 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d19f130b574025671db5e9eee652ffe75187eb74 | 902 | py | Python | apps/web/github/github_pages.py | timo95/knausj_talon | 735b6fda064b12c832fce2ba4ca8e0186e7db48a | [
"MIT"
] | 1 | 2021-09-08T05:45:03.000Z | 2021-09-08T05:45:03.000Z | apps/web/github/github_pages.py | timo95/knausj_talon | 735b6fda064b12c832fce2ba4ca8e0186e7db48a | [
"MIT"
] | null | null | null | apps/web/github/github_pages.py | timo95/knausj_talon | 735b6fda064b12c832fce2ba4ca8e0186e7db48a | [
"MIT"
] | null | null | null | from talon import Context, actions
# Issues, pull requests (query "page")
# /<user>/<repository>/issues
# /<user>/<repository>/pulls
ctx = Context()
ctx.matches = r"""
app: github
browser.path: /^\/[-\w]+\/[-\w]+\/(issues|pulls)\/?/
"""
ctx.tags = ["user.pages"]
@ctx.action_class("user")
class UserActions:
# user.pages
def page_current(): return int(actions.user.browser_url_query().get("page", "1"))
def page_jump(number: int): actions.user.browser_set_url_query("page", number)
# Search (query "p")
# [/<user>/<repository>]/search
ctx = Context()
ctx.matches = r"""
app: github
browser.path: /^(\/[-\w]+\/[-\w]+)?\/search\/?/
"""
ctx.tags = ["user.pages"]
@ctx.action_class("user")
class UserActions:
# user.pages
def page_current(): return int(actions.user.browser_url_query().get("p", "1"))
def page_jump(number: int): actions.user.browser_set_url_query("p", number)
| 26.529412 | 85 | 0.654102 | 123 | 902 | 4.666667 | 0.308943 | 0.062718 | 0.097561 | 0.146341 | 0.710801 | 0.710801 | 0.710801 | 0.710801 | 0.710801 | 0.710801 | 0 | 0.002519 | 0.119734 | 902 | 33 | 86 | 27.333333 | 0.720403 | 0.179601 | 0 | 0.666667 | 0 | 0 | 0.228142 | 0.096995 | 0 | 0 | 0 | 0 | 0 | 1 | 0.190476 | false | 0 | 0.047619 | 0.095238 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d1bd64ed909d615d43420d79476b3b66d0c79bdf | 89 | py | Python | mtools/test/test_util_presplit.py | akung0324/mtools | f72f904dc942cdbe19748f0d9de50dfdcf3d0889 | [
"Apache-2.0"
] | 1,522 | 2015-01-04T01:00:47.000Z | 2022-03-31T14:12:40.000Z | mtools/test/test_util_presplit.py | akung0324/mtools | f72f904dc942cdbe19748f0d9de50dfdcf3d0889 | [
"Apache-2.0"
] | 444 | 2015-01-07T02:06:27.000Z | 2022-03-31T09:03:13.000Z | mtools/test/test_util_presplit.py | akung0324/mtools | f72f904dc942cdbe19748f0d9de50dfdcf3d0889 | [
"Apache-2.0"
] | 361 | 2015-01-04T01:00:51.000Z | 2022-03-17T13:50:59.000Z | #import mtools.util.presplit
def test_presplit():
"""Test stub."""
assert True
| 12.714286 | 28 | 0.651685 | 11 | 89 | 5.181818 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.202247 | 89 | 6 | 29 | 14.833333 | 0.802817 | 0.426966 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d1fe4420d8f35cfafcd02ed78d91b79853ccf6ce | 147 | py | Python | src/onegov/core/orm/func.py | politbuero-kampagnen/onegov-cloud | 20148bf321b71f617b64376fe7249b2b9b9c4aa9 | [
"MIT"
] | null | null | null | src/onegov/core/orm/func.py | politbuero-kampagnen/onegov-cloud | 20148bf321b71f617b64376fe7249b2b9b9c4aa9 | [
"MIT"
] | null | null | null | src/onegov/core/orm/func.py | politbuero-kampagnen/onegov-cloud | 20148bf321b71f617b64376fe7249b2b9b9c4aa9 | [
"MIT"
] | null | null | null | from sqlalchemy.sql.functions import ReturnTypeFromArgs
class unaccent(ReturnTypeFromArgs):
""" Produce a UNACCENT expression. """
pass
| 18.375 | 55 | 0.755102 | 14 | 147 | 7.928571 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.163265 | 147 | 7 | 56 | 21 | 0.902439 | 0.204082 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 5 |
06164c89971bda33d348f3dab0f581d35715b701 | 27 | py | Python | paddlepalm/downloader.py | baajur/PALM | 2555c0e2a5fab1b702ae8d1c7612bef48c65af38 | [
"Apache-2.0"
] | 136 | 2019-09-24T05:38:55.000Z | 2022-02-14T01:38:51.000Z | paddlepalm/downloader.py | baajur/PALM | 2555c0e2a5fab1b702ae8d1c7612bef48c65af38 | [
"Apache-2.0"
] | 21 | 2019-11-21T12:24:03.000Z | 2021-03-23T09:34:15.000Z | paddlepalm/downloader.py | baajur/PALM | 2555c0e2a5fab1b702ae8d1c7612bef48c65af38 | [
"Apache-2.0"
] | 28 | 2019-09-24T05:39:36.000Z | 2022-02-14T01:42:58.000Z | from ._downloader import *
| 13.5 | 26 | 0.777778 | 3 | 27 | 6.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 27 | 1 | 27 | 27 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
ae11abfc0ce3cd3218b1b542fc2155354934e86d | 102 | py | Python | djblog_wordpress_importer/__init__.py | ninjaotoko/djblog_wordpress_importer | 2c59310d5b69e5cad4063656137d330918898bd8 | [
"BSD-3-Clause"
] | null | null | null | djblog_wordpress_importer/__init__.py | ninjaotoko/djblog_wordpress_importer | 2c59310d5b69e5cad4063656137d330918898bd8 | [
"BSD-3-Clause"
] | null | null | null | djblog_wordpress_importer/__init__.py | ninjaotoko/djblog_wordpress_importer | 2c59310d5b69e5cad4063656137d330918898bd8 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding:utf-8 -*-
from djblog_wordpress_importer import DjblogImporter, DjblogPost, DjblogAuthor
| 34 | 78 | 0.794118 | 11 | 102 | 7.181818 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.01087 | 0.098039 | 102 | 2 | 79 | 51 | 0.847826 | 0.196078 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
ae1a931bbb0b027375025423926e6a04731303dd | 12,631 | py | Python | pybind/nos/v7_1_0/vlan/classifier/rule/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/nos/v7_1_0/vlan/classifier/rule/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/nos/v7_1_0/vlan/classifier/rule/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | 1 | 2021-11-05T22:15:42.000Z | 2021-11-05T22:15:42.000Z |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
import mac
import proto
class rule(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-vlan - based on the path /vlan/classifier/rule. Each member element of
the container is represented as a class variable - with a specific
YANG type.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__ruleid','__mac','__proto',)
_yang_name = 'rule'
_rest_name = 'rule'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__mac = YANGDynClass(base=mac.mac, is_container='container', presence=False, yang_name="mac", rest_name="mac", parent=self, choice=(u'class-type', u'mac'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'MAC address classification by source MAC address', u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
self.__proto = YANGDynClass(base=proto.proto, is_container='container', presence=False, yang_name="proto", rest_name="proto", parent=self, choice=(u'class-type', u'proto'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-compact-syntax': None, u'info': u'Proto - specify an ethernet protocol\n classification', u'cli-sequence-commands': None, u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
self.__ruleid = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..256']}), is_leaf=True, yang_name="ruleid", rest_name="ruleid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='uint32', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'vlan', u'classifier', u'rule']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'vlan', u'classifier', u'rule']
def _get_ruleid(self):
"""
Getter method for ruleid, mapped from YANG variable /vlan/classifier/rule/ruleid (uint32)
"""
return self.__ruleid
def _set_ruleid(self, v, load=False):
"""
Setter method for ruleid, mapped from YANG variable /vlan/classifier/rule/ruleid (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_ruleid is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_ruleid() directly.
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..256']}), is_leaf=True, yang_name="ruleid", rest_name="ruleid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """ruleid must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..256']}), is_leaf=True, yang_name="ruleid", rest_name="ruleid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='uint32', is_config=True)""",
})
self.__ruleid = t
if hasattr(self, '_set'):
self._set()
def _unset_ruleid(self):
self.__ruleid = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..256']}), is_leaf=True, yang_name="ruleid", rest_name="ruleid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='uint32', is_config=True)
def _get_mac(self):
"""
Getter method for mac, mapped from YANG variable /vlan/classifier/rule/mac (container)
"""
return self.__mac
def _set_mac(self, v, load=False):
"""
Setter method for mac, mapped from YANG variable /vlan/classifier/rule/mac (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_mac is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_mac() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=mac.mac, is_container='container', presence=False, yang_name="mac", rest_name="mac", parent=self, choice=(u'class-type', u'mac'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'MAC address classification by source MAC address', u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """mac must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=mac.mac, is_container='container', presence=False, yang_name="mac", rest_name="mac", parent=self, choice=(u'class-type', u'mac'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'MAC address classification by source MAC address', u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)""",
})
self.__mac = t
if hasattr(self, '_set'):
self._set()
def _unset_mac(self):
self.__mac = YANGDynClass(base=mac.mac, is_container='container', presence=False, yang_name="mac", rest_name="mac", parent=self, choice=(u'class-type', u'mac'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'MAC address classification by source MAC address', u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
def _get_proto(self):
"""
Getter method for proto, mapped from YANG variable /vlan/classifier/rule/proto (container)
"""
return self.__proto
def _set_proto(self, v, load=False):
"""
Setter method for proto, mapped from YANG variable /vlan/classifier/rule/proto (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_proto is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_proto() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=proto.proto, is_container='container', presence=False, yang_name="proto", rest_name="proto", parent=self, choice=(u'class-type', u'proto'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-compact-syntax': None, u'info': u'Proto - specify an ethernet protocol\n classification', u'cli-sequence-commands': None, u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """proto must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=proto.proto, is_container='container', presence=False, yang_name="proto", rest_name="proto", parent=self, choice=(u'class-type', u'proto'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-compact-syntax': None, u'info': u'Proto - specify an ethernet protocol\n classification', u'cli-sequence-commands': None, u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)""",
})
self.__proto = t
if hasattr(self, '_set'):
self._set()
def _unset_proto(self):
self.__proto = YANGDynClass(base=proto.proto, is_container='container', presence=False, yang_name="proto", rest_name="proto", parent=self, choice=(u'class-type', u'proto'), path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-compact-syntax': None, u'info': u'Proto - specify an ethernet protocol\n classification', u'cli-sequence-commands': None, u'cli-suppress-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-vlan', defining_module='brocade-vlan', yang_type='container', is_config=True)
ruleid = __builtin__.property(_get_ruleid, _set_ruleid)
mac = __builtin__.property(_get_mac, _set_mac)
proto = __builtin__.property(_get_proto, _set_proto)
__choices__ = {u'class-type': {u'mac': [u'mac'], u'proto': [u'proto']}}
_pyangbind_elements = {'ruleid': ruleid, 'mac': mac, 'proto': proto, }
| 63.155 | 637 | 0.709208 | 1,709 | 12,631 | 5.036279 | 0.114687 | 0.04415 | 0.032532 | 0.025096 | 0.768096 | 0.735332 | 0.722784 | 0.712676 | 0.712676 | 0.693621 | 0 | 0.008411 | 0.152878 | 12,631 | 199 | 638 | 63.472362 | 0.795981 | 0.113689 | 0 | 0.4 | 0 | 0.022222 | 0.353661 | 0.125227 | 0 | 0 | 0 | 0 | 0 | 1 | 0.088889 | false | 0 | 0.074074 | 0 | 0.296296 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.