xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

1931d4d528117ea44e1d3581ceb8531be2ee01bc

44

py

Python

loady/__init__.py

rec/gitty

9d51a40c485fe7938342636705f3ab0595fc9e8c

[ "MIT" ]

2

2017-10-14T14:37:40.000Z

2018-02-24T14:06:25.000Z

loady/__init__.py

rec/gitty

9d51a40c485fe7938342636705f3ab0595fc9e8c

[ "MIT" ]

2

2017-08-13T13:38:21.000Z

2017-08-22T16:32:18.000Z

loady/__init__.py

rec/gitty

9d51a40c485fe7938342636705f3ab0595fc9e8c

[ "MIT" ]

null

from . import code, data, library, sys_path

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195a26905984bac75afed7a36fb6f25160a0b132

78

py

Python

forum/tests/unit_tests/test_forms.py

SH-anonta/Discussion-Forum

03c92916d4dd708ad76e0aa945aaecacb1eac30e

[ "MIT" ]

null

forum/tests/unit_tests/test_forms.py

SH-anonta/Discussion-Forum

03c92916d4dd708ad76e0aa945aaecacb1eac30e

[ "MIT" ]

null

forum/tests/unit_tests/test_forms.py

SH-anonta/Discussion-Forum

03c92916d4dd708ad76e0aa945aaecacb1eac30e

[ "MIT" ]

null

from django.contrib.auth.models import User from django.test import TestCase

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py

Python

tests/njoy_test.py

khurrumsaleem/sandy

74d4d62d808fd3637f0129a3f25c63db322d724e

[ "MIT" ]

30

2018-08-18T08:04:30.000Z

2022-03-23T12:48:46.000Z

tests/njoy_test.py

khurrumsaleem/sandy

74d4d62d808fd3637f0129a3f25c63db322d724e

[ "MIT" ]

59

2018-08-24T13:26:39.000Z

2022-03-29T13:12:05.000Z

tests/njoy_test.py

khurrumsaleem/sandy

74d4d62d808fd3637f0129a3f25c63db322d724e

[ "MIT" ]

9

2019-04-26T07:44:28.000Z

2021-12-08T08:32:11.000Z

# -*- coding: utf-8 -*- """ Created on Tue Mar 12 09:33:25 2019 @author: Luca Fiorito """ import pytest import os import sandy __author__ = "Luca Fiorito" @pytest.mark.njoy def test_get_njoy_from_environ(): exeold = None if "NJOY" in os.environ: exeold = os.environ["NJOY"] del os.environ["NJOY"] os.environ["NJOY"] = "/path/to/my_njoy.exe" exe = sandy.get_njoy() assert exe == "/path/to/my_njoy.exe" del os.environ["NJOY"] if exeold: os.environ["NJOY"] = exeold @pytest.mark.njoy def test_get_njoy_from_environ_error(): exe = None if "NJOY" in os.environ: exe = os.environ["NJOY"] del os.environ["NJOY"] with pytest.raises(Exception): sandy.get_njoy() if exe: os.environ["NJOY"] = exe @pytest.mark.njoy def test_njoy_process_dryrun(): """Test default options for njoy.process""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ broadr -21 -22 -23 / 225 1 0 0 0. / 0.001 / 293.6 / 0 / thermr 0 -23 -24 / 0 225 20 1 1 0 0 1 221 0 / 293.6 / 0.001 10 / heatr -21 -24 -25 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -25 -26 0 / 225 4 0 0 0 0 / 444 445 446 447 / gaspr -21 -26 -27 / purr -21 -27 -28 / 225 1 1 20 32 0 / 293.6 / 1.00E+10 / 0 / moder -28 30 / acer -21 -28 0 50 70 / 1 0 1 .02 0 / 'sandy runs acer'/ 225 293.6 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' assert outputs['tape50'] == '2003.02c' assert outputs['tape70'] == '2003.02c.xsd' @pytest.mark.njoy def test_njoy_process_no_broadr(): """Test njoy.process without broadr""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ thermr 0 -22 -23 / 0 225 20 1 1 0 0 1 221 0 / 293.6 / 0.001 10 / heatr -21 -23 -24 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -24 -25 0 / 225 4 0 0 0 0 / 444 445 446 447 / gaspr -21 -25 -26 / purr -21 -26 -27 / 225 1 1 20 32 0 / 293.6 / 1.00E+10 / 0 / moder -27 30 / acer -21 -27 0 50 70 / 1 0 1 .02 0 / 'sandy runs acer'/ 225 293.6 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' assert outputs['tape50'] == '2003.02c' assert outputs['tape70'] == '2003.02c.xsd' @pytest.mark.njoy def test_njoy_process_no_gaspr(): """Test njoy.process without gaspr""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ thermr 0 -22 -23 / 0 225 20 1 1 0 0 1 221 0 / 293.6 / 0.001 10 / heatr -21 -23 -24 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -24 -25 0 / 225 4 0 0 0 0 / 444 445 446 447 / purr -21 -25 -26 / 225 1 1 20 32 0 / 293.6 / 1.00E+10 / 0 / moder -26 30 / acer -21 -26 0 50 70 / 1 0 1 .02 0 / 'sandy runs acer'/ 225 293.6 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' assert outputs['tape50'] == '2003.02c' assert outputs['tape70'] == '2003.02c.xsd' @pytest.mark.njoy def test_njoy_process_no_thermr(): """Test njoy.process without thermr""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False, thermr=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ heatr -21 -22 -23 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -23 -24 0 / 225 4 0 0 0 0 / 444 445 446 447 / purr -21 -24 -25 / 225 1 1 20 32 0 / 293.6 / 1.00E+10 / 0 / moder -25 30 / acer -21 -25 0 50 70 / 1 0 1 .02 0 / 'sandy runs acer'/ 225 293.6 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' assert outputs['tape50'] == '2003.02c' assert outputs['tape70'] == '2003.02c.xsd' @pytest.mark.njoy def test_njoy_process_no_acer(): """Test njoy.process without acer""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ heatr -21 -22 -23 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -23 -24 0 / 225 4 0 0 0 0 / 444 445 446 447 / purr -21 -24 -25 / 225 1 1 20 32 0 / 293.6 / 1.00E+10 / 0 / moder -25 30 / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' @pytest.mark.njoy def test_njoy_process_no_purr(): """Test njoy.process without acer""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False, purr=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ heatr -21 -22 -23 0 / 225 7 0 0 0 0 / 302 303 304 318 402 442 443 / heatr -21 -23 -24 0 / 225 4 0 0 0 0 / 444 445 446 447 / moder -24 30 / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' @pytest.mark.njoy def test_njoy_process_no_heatr(): """Test njoy.process without heatr""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False, purr=False, heatr=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ moder -22 30 / stop""" assert input == text assert inputs['tape20'] == endftape assert outputs['tape30'] == '2003.pendf' @pytest.mark.njoy def test_njoy_process_no_keep_pendf(): """Test njoy.process and do not keep pendf""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") input, inputs, outputs = sandy.njoy.process(endftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False, purr=False, heatr=False, keep_pendf=False) text = """moder 20 -21 / reconr -21 -22 / 'sandy runs njoy'/ 225 0 0 / 0.001 0. / 0/ stop""" assert input == text assert inputs['tape20'] == endftape assert not outputs @pytest.mark.njoy def test_njoy_process_pendftape(): """Test njoy.process using argument pendftape (skip reconr)""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") pendftape = "pendf" input, inputs, outputs = sandy.njoy.process(endftape, pendftape=pendftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False, purr=False, heatr=False, keep_pendf=False) text = """moder 20 -21 / moder 99 -22 / stop""" assert input == text assert inputs['tape20'] == endftape assert inputs['tape99'] == pendftape @pytest.mark.njoy def test_njoy_process_temperatures(): """Test njoy.process for different temperatures""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") pendftape = "pendf" input, inputs, outputs = sandy.njoy.process(endftape, pendftape=pendftape, dryrun=True, gaspr=False, thermr=False, acer=False, purr=False, heatr=False, keep_pendf=False, temperatures=[300, 600.0000, 900.001]) text = """moder 20 -21 / moder 99 -22 / broadr -21 -22 -23 / 225 3 0 0 0. / 0.001 / 300.0 600.0 900.0 / 0 / stop""" assert input == text assert inputs['tape20'] == endftape assert inputs['tape99'] == pendftape assert not outputs @pytest.mark.njoy def test_njoy_process_acer(): """Test njoy.process for acer at different temperatures""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") pendftape = "pendf" input, inputs, outputs = sandy.njoy.process(endftape, pendftape=pendftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=True, purr=False, heatr=False, keep_pendf=False, temperatures=[300, 600.0000, 900.001]) text = """moder 20 -21 / moder 99 -22 / acer -21 -22 0 50 70 / 1 0 1 .03 0 / 'sandy runs acer'/ 225 300.0 / 1 1 / / acer -21 -22 0 51 71 / 1 0 1 .06 0 / 'sandy runs acer'/ 225 600.0 / 1 1 / / acer -21 -22 0 52 72 / 1 0 1 .09 0 / 'sandy runs acer'/ 225 900.0 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert inputs['tape99'] == pendftape assert outputs['tape50'] == '2003.03c' assert outputs['tape70'] == '2003.03c.xsd' assert outputs['tape51'] == '2003.06c' assert outputs['tape71'] == '2003.06c.xsd' assert outputs['tape52'] == '2003.09c' assert outputs['tape72'] == '2003.09c.xsd' @pytest.mark.njoy def test_njoy_process_suffixes(): """Test njoy.process for acer at different temperatures""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") pendftape = "pendf" input, inputs, outputs = sandy.njoy.process(endftape, pendftape="pendf", dryrun=True, broadr=False, gaspr=False, thermr=False, acer=True, purr=False, heatr=False, keep_pendf=False, temperatures=[300, 600.0000, 900.001], suffixes=["01", "02", "06"]) text = """moder 20 -21 / moder 99 -22 / acer -21 -22 0 50 70 / 1 0 1 .01 0 / 'sandy runs acer'/ 225 300.0 / 1 1 / / acer -21 -22 0 51 71 / 1 0 1 .02 0 / 'sandy runs acer'/ 225 600.0 / 1 1 / / acer -21 -22 0 52 72 / 1 0 1 .06 0 / 'sandy runs acer'/ 225 900.0 / 1 1 / / stop""" assert input == text assert inputs['tape20'] == endftape assert inputs['tape99'] == pendftape assert outputs['tape50'] == '2003.01c' assert outputs['tape70'] == '2003.01c.xsd' assert outputs['tape51'] == '2003.02c' assert outputs['tape71'] == '2003.02c.xsd' assert outputs['tape52'] == '2003.06c' assert outputs['tape72'] == '2003.06c.xsd' @pytest.mark.njoy def test_njoy_process_sig0(): """Test njoy.process for different sig0""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") pendftape = "pendf" input, inputs, outputs = sandy.njoy.process(endftape, pendftape=pendftape, dryrun=True, broadr=False, gaspr=False, thermr=False, acer=False, purr=True, heatr=False, keep_pendf=False, sig0=[1e10, 1E9, 100000000]) text = """moder 20 -21 / moder 99 -22 / purr -21 -22 -23 / 225 1 3 20 32 0 / 293.6 / 1.00E+10 1.00E+09 1.00E+08 / 0 / stop""" assert input == text assert inputs['tape20'] == endftape assert inputs['tape99'] == pendftape assert not outputs @pytest.mark.njoy @pytest.mark.njoy_exe def test_njoy_process(tmpdir): """Test njoy.process for ENDF/B-VIII.0 He-3. Check that desired outputs are produced and that xsdir files are correctly updated. """ endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") wdir = str(tmpdir) input, inputs, outputs = sandy.njoy.process(endftape, temperatures=[300, 600, 900], suffixes=["03", "06", "15"], tag="_b71", wdir=wdir, thermr=False) assert inputs['tape20'] == endftape assert outputs['tape30'] == os.path.join(wdir, '2003_b71.pendf') assert os.path.isfile(outputs['tape30']) assert outputs['tape50'] == os.path.join(wdir, '2003_b71.03c') assert os.path.isfile(outputs['tape50']) assert outputs['tape70'] == os.path.join(wdir, '2003_b71.03c.xsd') assert os.path.isfile(outputs['tape70']) assert outputs['tape51'] == os.path.join(wdir, '2003_b71.06c') assert os.path.isfile(outputs['tape51']) assert outputs['tape71'] == os.path.join(wdir, '2003_b71.06c.xsd') assert os.path.isfile(outputs['tape71']) assert outputs['tape52'] == os.path.join(wdir, '2003_b71.15c') assert os.path.isfile(outputs['tape52']) assert outputs['tape72'] == os.path.join(wdir, '2003_b71.15c.xsd') assert os.path.isfile(outputs['tape72']) for ace in ['2003_b71.03c', '2003_b71.06c', '2003_b71.15c']: xsdargs = open(os.path.join(wdir, ace) + ".xsd").read().split() assert len(xsdargs) == 10 assert xsdargs[0] == "2003{}".format(os.path.splitext(ace)[1]) assert xsdargs[2] == os.path.join(wdir, ace) assert xsdargs[3] == "0" @pytest.mark.njoy @pytest.mark.njoy_exe def test_njoy_process_1(tmpdir): """Test njoy.process for ENDF/B-VIII.0 He-3. Check that no output is produced. """ endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") wdir = str(tmpdir) input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, acer=False, keep_pendf=False) assert not os.listdir(wdir) @pytest.mark.njoy @pytest.mark.njoy_exe def test_njoy_process_2(tmpdir): """Test njoy.process for ENDF/B-VIII.0 Co-58m. Check that ZA of desired outputs is changed because isotope is metastable. """ endftape = os.path.join(os.path.dirname(__file__), "data", "n-027_Co_058m1.endf") wdir = str(tmpdir) input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, keep_pendf=True, route="1") assert inputs['tape20'] == endftape assert outputs['tape30'] == os.path.join(wdir, '27458.pendf') assert os.path.isfile(outputs['tape30']) assert outputs['tape50'] == os.path.join(wdir, '27458.02c') assert os.path.isfile(outputs['tape50']) assert outputs['tape70'] == os.path.join(wdir, '27458.02c.xsd') assert os.path.isfile(outputs['tape70']) xsdargs = open(outputs['tape70']).read().split() assert len(xsdargs) == 10 assert xsdargs[0] == "27458.02c" assert xsdargs[2] == outputs['tape50'] assert xsdargs[3] == "1" @pytest.mark.njoy @pytest.mark.njoy_exe def test_njoy_process_addpath(tmpdir): """Test add_path keyword""" endftape = os.path.join(os.path.dirname(__file__), "data", "n-002_He_003.endf") wdir = str(tmpdir) input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, gaspr=False, heatr=False, purr=False, addpath="") text = open(outputs['tape70']).read() assert text == '2003.02c 2.989032 2003.02c 0 1 1 7108 0 0 2.530E-08' input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, gaspr=False, heatr=False, purr=False, addpath="aaa") text = open(outputs['tape70']).read() assert text == '2003.02c 2.989032 aaa/2003.02c 0 1 1 7108 0 0 2.530E-08' input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, gaspr=False, heatr=False, purr=False, addpath=None) text = open(outputs['tape70']).read() assert text == '2003.02c 2.989032 {} 0 1 1 7108 0 0 2.530E-08'.format(outputs['tape50']) input, inputs, outputs = sandy.njoy.process(endftape, wdir=wdir, thermr=False, gaspr=False, heatr=False, purr=False) text = open(outputs['tape70']).read() assert text == '2003.02c 2.989032 {} 0 1 1 7108 0 0 2.530E-08'.format(outputs['tape50']) @pytest.mark.njoy def test_moder_1(): """Test moder with default parameters""" text = sandy.njoy._moder_input(-20111111, 21) assert text == 'moder\n-20111111 21 /\n' with pytest.raises(Exception): sandy.njoy._moder_input("aaa", 21) with pytest.raises(Exception): sandy.njoy._moder_input(-80, 15.5) @pytest.mark.njoy def test_reconr_1(): """Test reconr with default parameters""" text = sandy.njoy._reconr_input(-20, -21, 200) assert text == "reconr\n-20 -21 /\n'sandy runs njoy'/\n200 0 0 /\n0.001 0. /\n0/\n" @pytest.mark.njoy def test_reconr_2(): """Test reconr parameter err""" text = sandy.njoy._reconr_input(-20, -21, 200, err=10.0) assert text == "reconr\n-20 -21 /\n'sandy runs njoy'/\n200 0 0 /\n10.0 0. /\n0/\n" @pytest.mark.njoy def test_reconr_3(): """TTest reconr parameter header""" text = sandy.njoy._reconr_input(-20, -21, 200, header="aaa") assert text == "reconr\n-20 -21 /\n'aaa'/\n200 0 0 /\n0.001 0. /\n0/\n" @pytest.mark.njoy def test_broadr_1(): """Test broadr with default parameters""" text = sandy.njoy._broadr_input(-20, -21, -22, 200) assert text == 'broadr\n-20 -21 -22 /\n200 1 0 0 0. /\n0.001 /\n293.6 /\n0 /\n' @pytest.mark.njoy def test_broadr_2(): """Test broadr parameter temperatures""" text = sandy.njoy._broadr_input(-20, -21, -22, 200, temperatures=[900.51, 1E3]) assert text == 'broadr\n-20 -21 -22 /\n200 2 0 0 0. /\n0.001 /\n900.5 1000.0 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._broadr_input(-20, -21, -22, 200, temperatures=["aaa"]) @pytest.mark.njoy def test_broadr_3(): """Test broadr parameter err""" text = sandy.njoy._broadr_input(-20, -21, -22, 200, err=0.1) assert text == 'broadr\n-20 -21 -22 /\n200 1 0 0 0. /\n0.1 /\n293.6 /\n0 /\n' @pytest.mark.njoy def test_thermr_1(): """Test thermr with default parameters""" text = sandy.njoy._thermr_input(-20, -21, -22, 200) assert text == 'thermr\n-20 -21 -22 /\n0 200 20 1 1 0 0 1 221 0 /\n293.6 /\n0.001 10 /\n' @pytest.mark.njoy def test_thermr_2(): """Test thermr parameter temperatures""" text = sandy.njoy._thermr_input(-20, -21, -22, 200, temperatures=[900.51, 1E3]) assert text == 'thermr\n-20 -21 -22 /\n0 200 20 2 1 0 0 1 221 0 /\n900.5 1000.0 /\n0.001 10 /\n' @pytest.mark.njoy def test_thermr_3(): """Test thermr parameter err""" text = sandy.njoy._thermr_input(-20, -21, -22, 200, err=100) assert text == 'thermr\n-20 -21 -22 /\n0 200 20 1 1 0 0 1 221 0 /\n293.6 /\n100 10 /\n' @pytest.mark.njoy def test_thermr_4(): """Test thermr parameter angles""" text = sandy.njoy._thermr_input(-20, -21, -22, 200, angles=31) assert text == 'thermr\n-20 -21 -22 /\n0 200 31 1 1 0 0 1 221 0 /\n293.6 /\n0.001 10 /\n' with pytest.raises(Exception): sandy.njoy._thermr_input(-20, -21, -22, 200, angles=31.2) with pytest.raises(Exception): sandy.njoy._thermr_input(-20, -21, -22, 200, angles="aaa") @pytest.mark.njoy def test_thermr_5(): """Test thermr parameter emax""" text = sandy.njoy._thermr_input(-20, -21, -22, 200, emax=4) assert text == 'thermr\n-20 -21 -22 /\n0 200 20 1 1 0 0 1 221 0 /\n293.6 /\n0.001 4 /\n' @pytest.mark.njoy def test_thermr_6(): """Test thermr parameter iprint""" text = sandy.njoy._thermr_input(-20, -21, -22, 200, iprint=True) assert text == 'thermr\n-20 -21 -22 /\n0 200 20 1 1 0 0 1 221 1 /\n293.6 /\n0.001 10 /\n' with pytest.raises(Exception): sandy.njoy._thermr_input(-20, -21, -22, 200, iprint="aa") @pytest.mark.njoy def test_purr_1(): """Test purr with default parameters""" text = sandy.njoy._purr_input(-20, -21, -22, 200) assert text == 'purr\n-20 -21 -22 /\n200 1 1 20 32 0 /\n293.6 /\n1.00E+10 /\n0 /\n' @pytest.mark.njoy def test_purr_2(): """Test purr parameter temperatures""" text = sandy.njoy._purr_input(-20, -21, -22, 200, temperatures=[5, 10]) assert text == 'purr\n-20 -21 -22 /\n200 2 1 20 32 0 /\n5.0 10.0 /\n1.00E+10 /\n0 /\n' @pytest.mark.njoy def test_purr_3(): """Test purr parameter sig0""" text = sandy.njoy._purr_input(-20, -21, -22, 200, sig0=[1e8, 10.123]) assert text == 'purr\n-20 -21 -22 /\n200 1 2 20 32 0 /\n293.6 /\n1.00E+08 1.01E+01 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, sig0=["aa"]) @pytest.mark.njoy def test_purr_4(): """Test purr parameter bins""" text = sandy.njoy._purr_input(-20, -21, -22, 200, bins=5) assert text == 'purr\n-20 -21 -22 /\n200 1 1 5 32 0 /\n293.6 /\n1.00E+10 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, bins="aaa") with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, bins=20.1) @pytest.mark.njoy def test_purr_5(): """Test purr parameter iprint""" text = sandy.njoy._purr_input(-20, -21, -22, 200, iprint=True) assert text == 'purr\n-20 -21 -22 /\n200 1 1 20 32 1 /\n293.6 /\n1.00E+10 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, iprint="aa") @pytest.mark.njoy def test_purr_6(): """Test purr parameter ladders""" text = sandy.njoy._purr_input(-20, -21, -22, 200, ladders=2) assert text == 'purr\n-20 -21 -22 /\n200 1 1 20 2 0 /\n293.6 /\n1.00E+10 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, ladders="aaa") with pytest.raises(Exception): sandy.njoy._purr_input(-20, -21, -22, 200, ladders=20.1) @pytest.mark.njoy def test_gaspr_1(): """Test gaspr with default parameters""" text = sandy.njoy._gaspr_input(-20111111, 21, 0) assert text == 'gaspr\n-20111111 21 0 /\n' @pytest.mark.njoy def test_unresr_1(): """Test unresr with default parameters""" text = sandy.njoy._unresr_input(-20, -21, -22, 200) assert text == 'unresr\n-20 -21 -22 /\n200 1 1 0 /\n293.6 /\n1.00E+10 /\n0 /\n' @pytest.mark.njoy def test_unresr_2(): """Test unresr parameter temperatures""" text = sandy.njoy._unresr_input(-20, -21, -22, 200, temperatures=[5, 10]) assert text == 'unresr\n-20 -21 -22 /\n200 2 1 0 /\n5.0 10.0 /\n1.00E+10 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._unresr_input(-20, -21, -22, 200, temperatures=["aa"]) @pytest.mark.njoy def test_unresr_3(): """Test unresr parameter sig0""" text = sandy.njoy._unresr_input(-20, -21, -22, 200, sig0=[5, 10]) assert text == 'unresr\n-20 -21 -22 /\n200 1 2 0 /\n293.6 /\n5.00E+00 1.00E+01 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._unresr_input(-20, -21, -22, 200, sig0=["aa"]) @pytest.mark.njoy def test_unresr_4(): """Test unresr parameter iprint""" text = sandy.njoy._unresr_input(-20, -21, -22, 200, iprint=True) assert text == 'unresr\n-20 -21 -22 /\n200 1 1 1 /\n293.6 /\n1.00E+10 /\n0 /\n' with pytest.raises(Exception): sandy.njoy._unresr_input(-20, -21, -22, 200, iprint="aa") @pytest.mark.njoy def test_heatr_1(): """Test heatr with default parameters""" text = sandy.njoy._heatr_input(-20, -21, -22, 200, [10, 11]) assert text == 'heatr\n-20 -21 -22 0 /\n200 2 0 0 0 0 /\n10 11 /\n' with pytest.raises(Exception): sandy.njoy._unresr_input(-20, -21, -22, 200, ["aa"]) @pytest.mark.njoy def test_heatr_2(): """Test heatr parameter local""" text = sandy.njoy._heatr_input(-20, -21, -22, 200, [10, 11], local=True) assert text == 'heatr\n-20 -21 -22 0 /\n200 2 0 0 1 0 /\n10 11 /\n' with pytest.raises(Exception): sandy.njoy._heatr_input(-20, -21, -22, 200, [10, 11], local="aa") @pytest.mark.njoy def test_heatr_3(): """Test heatr parameter iprint""" text = sandy.njoy._heatr_input(-20, -21, -22, 200, [10, 11], iprint=True) assert text == 'heatr\n-20 -21 -22 0 /\n200 2 0 0 0 1 /\n10 11 /\n' with pytest.raises(Exception): sandy.njoy._heatr_input(-20, -21, -22, 200, [10, 11], iprint="aa") @pytest.mark.njoy def test_acer_1(): """Test acer with default parameters""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200) assert text == "acer\n-20 -21 0 -60 80 /\n1 0 1 .00 0 /\n'sandy runs acer'/\n200 293.6 /\n1 1 /\n/\n" @pytest.mark.njoy def test_acer_2(): """Test acer parameter temp""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, temp=-500) assert text == "acer\n-20 -21 0 -60 80 /\n1 0 1 .00 0 /\n'sandy runs acer'/\n200 -500.0 /\n1 1 /\n/\n" with pytest.raises(Exception): sandy.njoy._acer_input(-20, -21, -60, 80, 200, temp="aa") @pytest.mark.njoy def test_acer_3(): """Test acer parameter iprint""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, iprint=True) assert text == "acer\n-20 -21 0 -60 80 /\n1 1 1 .00 0 /\n'sandy runs acer'/\n200 293.6 /\n1 1 /\n/\n" with pytest.raises(Exception): sandy.njoy._acer_input(-20, -21, -60, 80, 200, iprint="aa") @pytest.mark.njoy def test_acer_4(): """Test acer parameter itype""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, itype=2) assert text == "acer\n-20 -21 0 -60 80 /\n1 0 2 .00 0 /\n'sandy runs acer'/\n200 293.6 /\n1 1 /\n/\n" with pytest.raises(Exception): sandy.njoy._acer_input(-20, -21, -60, 80, 200, itype="aa") @pytest.mark.njoy def test_acer_5(): """Test acer parameter suff""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, suff=5) assert text == "acer\n-20 -21 0 -60 80 /\n1 0 1 5 0 /\n'sandy runs acer'/\n200 293.6 /\n1 1 /\n/\n" @pytest.mark.njoy def test_acer_6(): """Test acer parameter header""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, header="Hi!") assert text == "acer\n-20 -21 0 -60 80 /\n1 0 1 .00 0 /\n'Hi!'/\n200 293.6 /\n1 1 /\n/\n" @pytest.mark.njoy def test_acer_7(): """Test acer parameter iprint""" text = sandy.njoy._acer_input(-20, -21, -60, 80, 200, photons=False) assert text == "acer\n-20 -21 0 -60 80 /\n1 0 1 .00 0 /\n'sandy runs acer'/\n200 293.6 /\n1 0 /\n/\n" with pytest.raises(Exception): sandy.njoy._acer_input(-20, -21, -60, 80, 200, photons="aa") @pytest.mark.njoy def test_process_proton(): """Test default options for njoy.process_proton""" endftape = os.path.join(os.path.dirname(__file__), "data", "O016-p.tendl") input, inputs, outputs = sandy.njoy.process_proton(endftape, dryrun=True) assert input == "acer\n20 20 0 50 70 /\n1 0 1 .00 0 /\n'sandy runs acer'/\n825 0.0 /\n1 1 /\n/\nstop" assert outputs['tape50'] == '8016.00h' assert outputs['tape70'] == '8016.00h.xsd' assert inputs["tape20"] == endftape @pytest.mark.njoy @pytest.mark.njoy_exe def test_process_proton_2(tmpdir): """Test njoy.process for TENDL-2015 O-16. Check that desired outputs are produced and that xsdir files are correctly updated. """ endftape = os.path.join(os.path.dirname(__file__), "data", "O016-p.tendl") wdir = str(tmpdir) input, inputs, outputs = sandy.njoy.process_proton(endftape, wdir=wdir) assert input == "acer\n20 20 0 50 70 /\n1 0 1 .00 0 /\n'sandy runs acer'/\n825 0.0 /\n1 1 /\n/\nstop" assert outputs['tape50'] == os.path.join(wdir, '8016.00h') assert os.path.isfile(outputs['tape50']) assert outputs['tape70'] == os.path.join(wdir, '8016.00h.xsd') assert os.path.isfile(outputs['tape70']) assert inputs["tape20"] == endftape xsdargs = open(outputs['tape70']).read().split() assert len(xsdargs) == 10 assert xsdargs[0] == "8016.00h" assert xsdargs[2] == outputs['tape50'] assert xsdargs[3] == "0" @pytest.mark.njoy def test_get_suffix(): """Test function get_suffix""" for tmp, ext in sandy.njoy.tmp2ext.items(): assert sandy.njoy.get_suffix(tmp, 0) == ext for tmp, ext in sandy.njoy.tmp2ext.items(): assert sandy.njoy.get_suffix(tmp, 1) == ext for tmp, ext in sandy.njoy.tmp2ext_meta.items(): assert sandy.njoy.get_suffix(tmp, 1, "aleph") == ext for tmp, ext in sandy.njoy.tmp2ext_meta.items(): assert sandy.njoy.get_suffix(tmp, 0, "aleph") == sandy.njoy.tmp2ext[tmp] with pytest.raises(Exception): sandy.njoy.get_suffix(150, 0) assert sandy.njoy.get_suffix(324, 0) == "03" assert sandy.njoy.get_suffix(326, 0) == "35" assert sandy.njoy.get_suffix(324, 2, method="aleph") == "31" assert sandy.njoy.get_suffix(326, 2, method="aleph") == "32"

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19a2b6cc1dbf409f45ce8d3c2201dfe55617b800

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Python

hip.py

putupradnya/streamlit-app

4f16166060f6fb7cb82053cd231c757f7a9db637

[ "MIT" ]

null

hip.py

putupradnya/streamlit-app

4f16166060f6fb7cb82053cd231c757f7a9db637

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null

hip.py

putupradnya/streamlit-app

4f16166060f6fb7cb82053cd231c757f7a9db637

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import hiplot as hip import streamlit as st st.button('Hit Me') st.button('Hit them') st.button('catch Me')

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py

Python

discord_dictionary_bot/exceptions.py

TychoTheTaco/Discord-Dictionary-Bot

c13e8955a39ce6ef49aecd7071a88ce9866d3a03

[ "MIT" ]

4

2021-03-29T23:35:04.000Z

2021-12-12T20:35:49.000Z

discord_dictionary_bot/exceptions.py

TychoTheTaco/Discord-Dictionary-Bot

c13e8955a39ce6ef49aecd7071a88ce9866d3a03

[ "MIT" ]

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2020-12-08T23:56:00.000Z

2021-05-15T03:37:33.000Z

discord_dictionary_bot/exceptions.py

TychoTheTaco/Discord-Dictionary-Bot

c13e8955a39ce6ef49aecd7071a88ce9866d3a03

[ "MIT" ]

4

2021-03-29T04:29:13.000Z

2021-12-12T20:37:56.000Z

class InsufficientPermissionsException(BaseException): def __init__(self, permissions): self._permissions = permissions @property def permissions(self): return self._permissions

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py

Python

terrascript/matchbox/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

4

2022-02-07T21:08:14.000Z

2022-03-03T04:41:28.000Z

terrascript/matchbox/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

null

terrascript/matchbox/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

2

2022-02-06T01:49:42.000Z

2022-02-08T14:15:00.000Z

# terrascript/matchbox/r.py import terrascript class matchbox_profile(terrascript.Resource): pass class matchbox_group(terrascript.Resource): pass

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py

Python

albumentations/core/__init__.py

rameshveer/convlib

39ea50493513ec962c6e793430bd782da243d0d1

[ "MIT" ]

null

albumentations/core/__init__.py

rameshveer/convlib

39ea50493513ec962c6e793430bd782da243d0d1

[ "MIT" ]

null

albumentations/core/__init__.py

rameshveer/convlib

39ea50493513ec962c6e793430bd782da243d0d1

[ "MIT" ]

null

from .composition import * from .serialization import * from .transforms_interface import *

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py

Python

rascil/processing_components/simulation/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

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2019-12-14T13:42:33.000Z

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rascil/processing_components/simulation/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

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2020-01-08T09:40:08.000Z

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rascil/processing_components/simulation/__init__.py

SKA-ScienceDataProcessor/rascil

bd3b47f779e18e184781e2928ad1539d1fdc1c9b

[ "Apache-2.0" ]

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2020-01-14T11:14:16.000Z

2020-09-15T05:21:06.000Z

from .configurations import * from .atmospheric_screen import * from .noise import * from .pointing import * from .rfi import * from .simulation_helpers import * from .surface import * from .testing_support import *

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py

Python

python/bistring/__init__.py

tavianator/bistring

b79007e96b971b44ca0d618b576d479010d4be9a

[ "MIT" ]

359

2019-07-08T20:53:06.000Z

2022-03-29T16:36:19.000Z

python/bistring/__init__.py

tavianator/bistring

b79007e96b971b44ca0d618b576d479010d4be9a

[ "MIT" ]

18

2019-07-12T16:29:40.000Z

2022-03-29T16:09:07.000Z

python/bistring/__init__.py

tavianator/bistring

b79007e96b971b44ca0d618b576d479010d4be9a

[ "MIT" ]

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2019-07-15T00:31:07.000Z

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py

Python

dns_catalog/__init__.py

illallangi/DNSCatalogHash

356d0d05c7280c1ab60ee2c894c6ca4aae6051ba

[ "MIT" ]

null

dns_catalog/__init__.py

illallangi/DNSCatalogHash

356d0d05c7280c1ab60ee2c894c6ca4aae6051ba

[ "MIT" ]

1

2020-09-25T07:04:26.000Z

2020-09-28T06:58:46.000Z

dns_catalog/__init__.py

illallangi/DNSCatalogHash

356d0d05c7280c1ab60ee2c894c6ca4aae6051ba

[ "MIT" ]

null

from .hash import hash

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fdca70e2b5d90246687310491475184d719b0a1a

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py

Python

venv/lib/python3.4/site-packages/zeep/__init__.py

zackszhu/SE343_Architecture-of-Enterprise-Applications

eae49d0c20ae4fc345e4d2dae8c053e8410729ad

[ "MIT" ]

null

venv/lib/python3.4/site-packages/zeep/__init__.py

zackszhu/SE343_Architecture-of-Enterprise-Applications

eae49d0c20ae4fc345e4d2dae8c053e8410729ad

[ "MIT" ]

null

venv/lib/python3.4/site-packages/zeep/__init__.py

zackszhu/SE343_Architecture-of-Enterprise-Applications

eae49d0c20ae4fc345e4d2dae8c053e8410729ad

[ "MIT" ]

null

from zeep.client import Client # noqa

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py

Python

Python/prac.py

shujanpannag/Random_Programs

77b7a8197e154926411d9939ef1e4effbc6eabfe

[ "MIT" ]

null

Python/prac.py

shujanpannag/Random_Programs

77b7a8197e154926411d9939ef1e4effbc6eabfe

[ "MIT" ]

null

Python/prac.py

shujanpannag/Random_Programs

77b7a8197e154926411d9939ef1e4effbc6eabfe

[ "MIT" ]

null

a,b,c = map(int, input().split()) print(a,b,c)

15.666667

33

0.574468

11

47

2.454545

0.727273

0.148148

0.222222

0

0.106383

47

3

34

15.666667

0.642857

0

1

0

true

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e3240197c4a72a7a1492565a111a2ad120ae3e98

29,564

py

Python

biorxiv/corpora_comparison/05_figure_generator_reviewer_request.py

danich1/annorxiver

8fab17e1c3ebce7b9e3fc54ea64585b37d9b3825

[ "CC0-1.0", "BSD-3-Clause" ]

4

2020-05-13T23:44:57.000Z

2021-07-04T23:51:46.000Z

biorxiv/corpora_comparison/05_figure_generator_reviewer_request.py

danich1/annorxiver

8fab17e1c3ebce7b9e3fc54ea64585b37d9b3825

[ "CC0-1.0", "BSD-3-Clause" ]

23

2020-03-23T18:35:25.000Z

2021-09-21T21:14:20.000Z

biorxiv/corpora_comparison/05_figure_generator_reviewer_request.py

danich1/annorxiver

8fab17e1c3ebce7b9e3fc54ea64585b37d9b3825

[ "CC0-1.0", "BSD-3-Clause" ]

3

2020-01-31T18:27:55.000Z

2020-05-29T20:26:22.000Z

# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # formats: ipynb,py # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.9.1+dev # kernelspec: # display_name: Python [conda env:annorxiver] # language: python # name: conda-env-annorxiver-py # --- # # Figures for Corpora Comparison between bioRxiv, Pubmed Central, New York Times # + # %load_ext autoreload # %autoreload 2 import numpy as np import pandas as pd from cairosvg import svg2png from IPython.display import Image import plotnine as p9 from annorxiver_modules.corpora_comparison_helper import ( calculate_confidence_intervals, create_lemma_count_df, plot_bargraph, plot_point_bar_figure, ) # - subset = 20 # # KL Divergence Graph kl_divergence_df = pd.read_csv( "output/comparison_stats/corpora_kl_divergence.tsv", sep="\t" ) kl_divergence_df.head() g = ( p9.ggplot( kl_divergence_df.replace( { "biorxiv_vs_pmc": "bioRxiv-PMC", "biorxiv_vs_nytac": "bioRxiv-NYTAC", "pmc_vs_nytac": "PMC-NYTAC", } ).rename(index=str, columns={"comparison": "Comparison"}) ) + p9.aes( x="factor(num_terms)", y="KL_divergence", fill="Comparison", color="Comparison", group="Comparison", ) + p9.geom_point(size=2) + p9.geom_line(linetype="dashed") + p9.scale_fill_brewer(type="qual", palette="Paired", direction=-1) + p9.scale_color_brewer( type="qual", palette="Paired", direction=-1, ) + p9.labs( x="Number of terms evaluated", y="Kullback–Leibler Divergence", ) + p9.theme_seaborn( context="paper", style="ticks", font_scale=1.8, ) + p9.theme(figure_size=(11, 8.5), text=p9.element_text(family="Arial")) ) g.save("output/svg_files/corpora_kl_divergence.svg") g.save("output/figures/corpora_kl_divergence.png", dpi=500) print(g) kl_divergence_special_char_df = pd.read_csv( "output/comparison_stats/corpora_kl_divergence_special_chars_removed.tsv", sep="\t" ) kl_divergence_special_char_df.head() g = ( p9.ggplot( kl_divergence_special_char_df.replace( { "biorxiv_vs_pmc": "bioRxiv-PMC", "biorxiv_vs_nytac": "bioRxiv-NYTAC", "pmc_vs_nytac": "PMC-NYTAC", } ).rename(index=str, columns={"comparison": "Comparison"}) ) + p9.aes( x="factor(num_terms)", y="KL_divergence", fill="Comparison", color="Comparison", group="Comparison", ) + p9.geom_point(size=2) + p9.geom_line(linetype="dashed") + p9.scale_fill_brewer(type="qual", palette="Paired", direction=-1) + p9.scale_color_brewer( type="qual", palette="Paired", direction=-1, ) + p9.labs( x="Number of terms evaluated", y="Kullback–Leibler Divergence", ) + p9.theme_seaborn( context="paper", style="ticks", font_scale=1.8, ) + p9.theme(figure_size=(11, 8.5), text=p9.element_text(family="Arial")) ) # g.save("output/svg_files/corpora_kl_divergence.svg") # g.save("output/figures/corpora_kl_divergence.png", dpi=500) print(g) # # bioRxiv vs Pubmed Central full_text_comparison = pd.read_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison.tsv", sep="\t" ) full_text_comparison.head() full_text_comparison_special_char = pd.read_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison_special_chars_removed.tsv", sep="\t", ) full_text_comparison_special_char.head() # ## Line Plots # ### Original full_plot_df = calculate_confidence_intervals(full_text_comparison) full_plot_df.to_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison_error_bars.tsv", sep="\t", index=False, ) full_plot_df.head() plot_df = ( full_plot_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_df.sort_values("odds_ratio", ascending=False).iloc[:-2].tail(subset) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_df.head() g = ( p9.ggplot( plot_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=(plot_df.sort_values("odds_ratio", ascending=True).lemma.tolist()) ) + p9.scale_x_continuous(limits=(-3, 3)) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="bioRxiv Enriched", x=1.5, y=2.5, size=18, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="PMC Enriched", x=-1.5, y=38.5, size=18, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.4, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="bioRxiv vs PMC log2(Odds Ratio)") ) g.save("output/svg_files/biorxiv_pmc_frequency_odds.svg") g.save("output/svg_files/biorxiv_pmc_frequency_odds.png", dpi=75) print(g) count_plot_df = ( create_lemma_count_df(plot_df, "bioRxiv", "pmc") .replace({"pmc": "PMC"}) .assign( repository=lambda x: pd.Categorical( x.repository.tolist(), categories=["bioRxiv", "PMC"] ) ) ) count_plot_df.to_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison_raw_counts.tsv", sep="\t", index=False, ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_df) g.save("output/svg_files/biorxiv_pmc_frequency_bar.svg") print(g) # + fig_output_path = "output/figures/biorxiv_vs_pubmed_central.png" fig = plot_point_bar_figure( "output/svg_files/biorxiv_pmc_frequency_odds.svg", "output/svg_files/biorxiv_pmc_frequency_bar.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # ### Special Char Removed full_plot_special_char_df = calculate_confidence_intervals( # Hard coded fix to remove duplicates # Next time use Spacy and lemmatize each token full_text_comparison_special_char.query("lemma != 'patient'").query( "lemma != 'groups'" ) ) full_plot_df.to_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison_special_char_removed_error_bars.tsv", sep="\t", index=False, ) full_plot_special_char_df.head() plot_special_char_df = ( full_plot_special_char_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_special_char_df.sort_values("odds_ratio", ascending=False).tail( subset ) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_special_char_df.head() g = ( p9.ggplot( plot_special_char_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=( plot_special_char_df.sort_values( "odds_ratio", ascending=True ).lemma.tolist() ) ) + p9.scale_x_continuous(limits=(-3, 3)) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="bioRxiv Enriched", x=1.5, y=2.5, size=18, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="PMC Enriched", x=-1.5, y=38.5, size=18, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.2, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), axis_text_y=p9.element_text(size=12), ) + p9.labs(y=None, x="bioRxiv vs PMC log2(Odds Ratio)") ) g.save("output/svg_files/biorxiv_pmc_frequency_odds_special_char_removed.svg") g.save("output/svg_files/biorxiv_pmc_frequency_odds_special_char_removed.png", dpi=75) print(g) count_plot_df = ( create_lemma_count_df(plot_special_char_df, "bioRxiv", "pmc") .replace({"pmc": "PMC"}) .assign( repository=lambda x: pd.Categorical( x.repository.tolist(), categories=["bioRxiv", "PMC"] ) ) ) count_plot_df.to_csv( "output/comparison_stats/biorxiv_vs_pmc_comparison_special_char_removed_raw_counts.tsv", sep="\t", index=False, ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_special_char_df) g.save("output/svg_files/biorxiv_pmc_frequency_bar_special_char_removed.svg") print(g) # + fig_output_path = "output/figures/biorxiv_vs_pubmed_central_special_char_removed.png" fig = plot_point_bar_figure( "output/svg_files/biorxiv_pmc_frequency_odds_special_char_removed.svg", "output/svg_files/biorxiv_pmc_frequency_bar_special_char_removed.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # # bioRxiv vs Reference full_text_comparison = pd.read_csv( "output/comparison_stats/biorxiv_nytac_comparison.tsv", sep="\t" ) full_text_comparison.head() full_text_comparison_special_char = pd.read_csv( "output/comparison_stats/biorxiv_nytac_comparison_special_chars_removed.tsv", sep="\t", ) full_text_comparison_special_char.head() # ## Line Plots # ### Original full_plot_df = calculate_confidence_intervals(full_text_comparison) full_plot_df.head() plot_df = ( full_plot_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_df.sort_values("odds_ratio", ascending=False).iloc[:-2].tail(subset) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_df.head() # + g = ( p9.ggplot( plot_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=(plot_df.sort_values("odds_ratio", ascending=True).lemma.tolist()) ) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=5, xend=17, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="bioRxiv Enriched", x=9, y=2.5, size=12, alpha=0.7) + p9.annotate( "segment", x=-5, xend=-17, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="NYTAC Enriched", x=-9, y=38.5, size=12, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.8, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="bioRxiv vs NYTAC log2(Odds Ratio)") ) g.save("output/svg_files/biorxiv_nytac_frequency_odds.svg") g.save("output/svg_files/biorxiv_nytac_frequency_odds.png", dpi=250) print(g) # - count_plot_df = create_lemma_count_df(plot_df, "bioRxiv", "NYTAC").assign( repository=lambda x: pd.Categorical( x.repository.tolist(), categories=["bioRxiv", "NYTAC"] ) ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_df) g.save("output/svg_files/biorxiv_nytac_frequency_bar.svg") print(g) # + fig_output_path = "output/figures/biorxiv_vs_reference.png" fig = plot_point_bar_figure( "output/svg_files/biorxiv_nytac_frequency_odds.svg", "output/svg_files/biorxiv_nytac_frequency_bar.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # ### Special Char Removed full_plot_special_char_df = calculate_confidence_intervals( full_text_comparison_special_char ) full_plot_special_char_df.head() plot_special_char_df = ( full_plot_special_char_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_special_char_df.sort_values("odds_ratio", ascending=False) .iloc[:-2] .tail(subset) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_special_char_df.head() g = ( p9.ggplot( plot_special_char_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=( plot_special_char_df.sort_values( "odds_ratio", ascending=True ).lemma.tolist() ) ) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="bioRxiv Enriched", x=1.5, y=2.5, size=12, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="NYTAC Enriched", x=-1.5, y=38.5, size=12, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.8, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="bioRxiv vs NYTAC log2(Odds Ratio)") ) g.save("output/svg_files/biorxiv_nytac_frequency_odds_special_char_removed.svg") g.save( "output/svg_files/biorxiv_nytac_frequency_odds_special_char_removed.png", dpi=250 ) print(g) count_plot_df = create_lemma_count_df(plot_special_char_df, "bioRxiv", "NYTAC").assign( repository=lambda x: pd.Categorical( x.repository.tolist(), categories=["bioRxiv", "nytac"] ) ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_special_char_df) g.save("output/svg_files/biorxiv_nytac_frequency_bar_special_char_removed.svg") print(g) # + fig_output_path = "output/figures/biorxiv_vs_reference_special_char_removed.png" fig = plot_point_bar_figure( "output/svg_files/biorxiv_nytac_frequency_odds_special_char_removed.svg", "output/svg_files/biorxiv_nytac_frequency_bar_special_char_removed.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # # PMC vs Reference full_text_comparison = pd.read_csv( "output/comparison_stats/pmc_nytac_comparison.tsv", sep="\t" ) full_text_comparison.head() full_text_comparison_special_char = pd.read_csv( "output/comparison_stats/pmc_nytac_comparison_special_chars_removed.tsv", sep="\t" ) full_text_comparison_special_char.head() # ## Line Plots # ### Original full_plot_df = calculate_confidence_intervals(full_text_comparison) full_plot_df.head() plot_df = ( full_plot_df.sort_values("odds_ratio", ascending=False) .drop([17, 154]) .head(subset) .append( full_plot_df.sort_values("odds_ratio", ascending=False).iloc[:-2].tail(subset) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_df.head() g = ( p9.ggplot( plot_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=(plot_df.sort_values("odds_ratio", ascending=True).lemma.tolist()) ) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=5, xend=17, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="PMC Enriched", x=9, y=2.5, size=12, alpha=0.7) + p9.annotate( "segment", x=-5, xend=-17, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="NYTAC Enriched", x=-9, y=38.5, size=12, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.8, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="PMC vs NYTAC log2(Odds Ratio)") ) g.save("output/svg_files/pmc_nytac_frequency_odds.svg") g.save("output/svg_files/pmc_nytac_frequency_odds.png", dpi=250) print(g) count_plot_df = create_lemma_count_df(plot_df, "pmc", "reference").replace( {"pmc": "PMC", "reference": "NYTAC"} ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_df) g.save("output/svg_files/pmc_nytac_frequency_bar.svg", dpi=75) print(g) # + fig_output_path = "output/figures/pmc_vs_reference.png" fig = plot_point_bar_figure( "output/svg_files/pmc_nytac_frequency_odds.svg", "output/svg_files/pmc_nytac_frequency_bar.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # ### Special Char Removed full_plot_special_char_df = calculate_confidence_intervals( full_text_comparison_special_char ) full_plot_special_char_df.head() plot_special_char_df = ( full_plot_special_char_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_special_char_df.sort_values("odds_ratio", ascending=False).tail( subset ) ) .replace("rna", "RNA") .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_special_char_df.head() g = ( p9.ggplot( plot_special_char_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=( plot_special_char_df.sort_values( "odds_ratio", ascending=True ).lemma.tolist() ) ) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="PMC Enriched", x=1.5, y=2.5, size=12, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="NYTAC Enriched", x=-1.5, y=38.5, size=12, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.8, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="PMC vs NYTAC log2(Odds Ratio)") ) g.save("output/svg_files/pmc_nytac_frequency_odds_special_char_removed.svg") g.save("output/svg_files/pmc_nytac_frequency_odds_special_char_removed.png", dpi=250) print(g) count_plot_df = ( create_lemma_count_df(plot_special_char_df, "nytac", "pmc") .replace({"pmc": "PMC"}) .assign( repository=lambda x: pd.Categorical( x.repository.tolist(), categories=["nytac", "PMC"] ) ) ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_special_char_df) g.save("output/svg_files/pmc_nytac_frequency_bar_special_char_removed.svg") print(g) # + fig_output_path = "output/figures/pmc_vs_reference_special_char_removed.png" fig = plot_point_bar_figure( "output/svg_files/pmc_nytac_frequency_odds_special_char_removed.svg", "output/svg_files/pmc_nytac_frequency_bar_special_char_removed.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # # Preprint vs Published preprint_published_comparison = pd.read_csv( "output/comparison_stats/preprint_to_published_comparison.tsv", sep="\t" ).assign(odds_ratio=lambda x: 1 / x.odds_ratio.values) preprint_published_comparison.head() preprint_published_comparison_special_char = pd.read_csv( "output/comparison_stats/preprint_to_published_comparison_special_chars_removed.tsv", sep="\t", ).assign(odds_ratio=lambda x: 1 / x.odds_ratio.values) preprint_published_comparison_special_char.head() # ## Line Plot # ### Original full_plot_df = calculate_confidence_intervals(preprint_published_comparison) full_plot_df.to_csv( "output/comparison_stats/preprint_vs_published_comparison_error_bars.tsv", sep="\t", index=False, ) full_plot_df.head() plot_df = ( full_plot_df.sort_values("odds_ratio", ascending=False) .iloc[3:] .head(subset) .append(full_plot_df.sort_values("odds_ratio", ascending=False).tail(subset)) .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_df.head() g = ( p9.ggplot( plot_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=(plot_df.sort_values("odds_ratio", ascending=True).lemma.tolist()) ) + p9.scale_x_continuous(limits=(-3, 3)) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="Published Enriched", x=1.5, y=2.5, size=18, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=39.5, yend=39.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.1), ) + p9.annotate("text", label="Preprint Enriched", x=-1.5, y=38.5, size=18, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.4, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="Preprint vs Published log2(Odds Ratio)") ) g.save("output/svg_files/preprint_published_frequency_odds.svg") g.save("output/svg_files/preprint_published_frequency_odds.png", dpi=250) print(g) count_plot_df = create_lemma_count_df(plot_df, "preprint", "published").replace( {"preprint": "Preprint", "published": "Published"} ) count_plot_df.to_csv( "output/comparison_stats/preprint_vs_published_comparison_raw_counts.tsv", sep="\t", index=False, ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_df) g.save("output/svg_files/preprint_published_frequency_bar.svg", dpi=75) print(g) # + fig_output_path = "output/figures/preprint_published_comparison.png" fig = plot_point_bar_figure( "output/svg_files/preprint_published_frequency_odds.svg", "output/svg_files/preprint_published_frequency_bar.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=75) Image(fig_output_path) # - # ### Special Char Removed full_plot_special_char_df = calculate_confidence_intervals( preprint_published_comparison_special_char ) full_plot_df.to_csv( "output/comparison_stats/preprint_vs_published_comparison_special_char_removed_error_bars.tsv", sep="\t", index=False, ) full_plot_special_char_df.head() plot_special_char_df = ( full_plot_special_char_df.sort_values("odds_ratio", ascending=False) .head(subset) .append( full_plot_special_char_df.sort_values("odds_ratio", ascending=False).tail( subset ) ) .assign( odds_ratio=lambda x: x.odds_ratio.apply(lambda x: np.log2(x)), lower_odds=lambda x: x.lower_odds.apply(lambda x: np.log2(x)), upper_odds=lambda x: x.upper_odds.apply(lambda x: np.log2(x)), ) ) plot_special_char_df.head() g = ( p9.ggplot( plot_special_char_df.assign(lemma=lambda x: pd.Categorical(x.lemma.tolist())), p9.aes( y="lemma", xmin="lower_odds", x="odds_ratio", xmax="upper_odds", yend="lemma", ), ) + p9.geom_errorbarh(color="#253494") + p9.scale_y_discrete( limits=( plot_special_char_df.sort_values( "odds_ratio", ascending=True ).lemma.tolist() ) ) + p9.scale_x_continuous(limits=(-3, 3)) + p9.geom_vline(p9.aes(xintercept=0), linetype="--", color="grey") + p9.annotate( "segment", x=0.5, xend=2.5, y=1.5, yend=1.5, colour="black", size=0.5, alpha=1, arrow=p9.arrow(length=0.2, angle=30), ) + p9.annotate("text", label="Published Enriched", x=1.5, y=2.5, size=18, alpha=0.7) + p9.annotate( "segment", x=-0.5, xend=-2.5, y=40, yend=40, colour="black", size=0.5, alpha=1, lineend="projecting", position=p9.position_dodge(width=5), arrow=p9.arrow(length=0.2, angle=30), ) + p9.annotate("text", label="Preprint Enriched", x=-1.5, y=38.5, size=18, alpha=0.7) + p9.theme_seaborn(context="paper", style="ticks", font_scale=1.4, font="Arial") + p9.theme( figure_size=(11, 8.5), panel_grid_minor=p9.element_blank(), ) + p9.labs(y=None, x="Preprint vs Published log2(Odds Ratio)") ) g.save("output/svg_files/preprint_published_frequency_odds_special_char_removed.svg") g.save( "output/svg_files/preprint_published_frequency_odds_special_char_removed.png", dpi=250, ) print(g) count_plot_df = create_lemma_count_df( plot_special_char_df, "preprint", "published" ).replace({"preprint": "Preprint", "published": "Published"}) count_plot_df.to_csv( "output/comparison_stats/preprint_vs_published_comparison_special_char_removed_raw_counts.tsv", sep="\t", index=False, ) count_plot_df.head() g = plot_bargraph(count_plot_df, plot_special_char_df) g.save( "output/svg_files/preprint_published_frequency_bar_special_char_removed.svg", dpi=75 ) print(g) # + fig_output_path = ( "output/figures/preprint_published_comparison_special_char_removed.png" ) fig = plot_point_bar_figure( "output/svg_files/preprint_published_frequency_odds_special_char_removed.svg", "output/svg_files/preprint_published_frequency_bar_special_char_removed.svg", ) # save generated SVG files svg2png(bytestring=fig.to_str(), write_to=fig_output_path, dpi=96) Image(fig_output_path)

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e3800e21228a99ca9787351b43f64ca1d4723802

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py

Python

src/pipupgrade/model/__init__.py

max-nicholson/pipupgrade

669cd4ecd4d858e6fb996e75af81960d0b35ccfb

[ "MIT" ]

517

2018-08-29T23:16:07.000Z

2022-03-20T16:06:37.000Z

src/pipupgrade/model/__init__.py

max-nicholson/pipupgrade

669cd4ecd4d858e6fb996e75af81960d0b35ccfb

[ "MIT" ]

117

2018-08-30T02:13:45.000Z

2022-03-30T15:47:52.000Z

src/pipupgrade/model/__init__.py

max-nicholson/pipupgrade

669cd4ecd4d858e6fb996e75af81960d0b35ccfb

[ "MIT" ]

35

2018-08-31T11:11:00.000Z

2022-01-29T21:20:46.000Z

# imports - module imports from pipupgrade.model.project import Project from pipupgrade.model.package import Package from pipupgrade.model.registry import Registry

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8b6c5feff130f309acd58056f0d664b3794b80f1

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py

Python

gui/views/ClientsGUIViews/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

gui/views/ClientsGUIViews/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

gui/views/ClientsGUIViews/__init__.py

Saldenisov/pyconlyse

1de301b4a4c15ee0bd19034aa8d5da1beacfd124

[ "MIT" ]

null

from .StepMotors import * from .SuperUser import * from .VD2Treatment import * from .ProjectManagers import *

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8bc700837bd7d2e73119c620d8271faca316b702

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py

Python

sskmeans/__init__.py

Eliot-M/sskmeans

57581709c8827d41411d85c073e43b39b552ee2f

[ "MIT" ]

null

sskmeans/__init__.py

Eliot-M/sskmeans

57581709c8827d41411d85c073e43b39b552ee2f

[ "MIT" ]

null

sskmeans/__init__.py

Eliot-M/sskmeans

57581709c8827d41411d85c073e43b39b552ee2f

[ "MIT" ]

null

from .samesizekmeans import SameSizeKmeans

11.25

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6

8be090be14859017c3114da637838c616c17565e

35

py

Python

poloniex/__init__.py

doubleDragon/quant

e992d1e2dc544b3106a87b08f4bd81eb16f75f4d

[ "Apache-2.0" ]

null

poloniex/__init__.py

doubleDragon/quant

e992d1e2dc544b3106a87b08f4bd81eb16f75f4d

[ "Apache-2.0" ]

null

poloniex/__init__.py

doubleDragon/quant

e992d1e2dc544b3106a87b08f4bd81eb16f75f4d

[ "Apache-2.0" ]

null

from poloniex.client import Client

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0

6

47848c7f25b0fc38781d9a96278c8395dad6e03d

70

py

Python

cptools/__init__.py

GentleCP/cptools

6816314f7e16168d3ce43c73e335a3c08590acb5

[ "MIT" ]

2

2021-04-17T13:16:38.000Z

2021-06-16T02:26:09.000Z

cptools/__init__.py

GentleCP/cptools

6816314f7e16168d3ce43c73e335a3c08590acb5

[ "MIT" ]

null

cptools/__init__.py

GentleCP/cptools

6816314f7e16168d3ce43c73e335a3c08590acb5

[ "MIT" ]

null

from .progress import * from .hello import * from .logger import *

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6

479cdb81cff60f17f92bca0680076e2ceac50a45

2,648

py

Python

flaskApp/auth/forms/__init__.py

kc446/kc446_flaskapp

41747879d2d3523ee1684076f9835f4166fefed4

[ "BSD-3-Clause" ]

null

flaskApp/auth/forms/__init__.py

kc446/kc446_flaskapp

41747879d2d3523ee1684076f9835f4166fefed4

[ "BSD-3-Clause" ]

null

flaskApp/auth/forms/__init__.py

kc446/kc446_flaskapp

41747879d2d3523ee1684076f9835f4166fefed4

[ "BSD-3-Clause" ]

null

from flask_wtf import FlaskForm from wtforms import validators from wtforms.fields import * class login_form(FlaskForm): email = EmailField('Email Address', [ validators.DataRequired(), #validators.Email() ], description="You need an email address to sign in!") password = PasswordField('Password', [ validators.DataRequired(), validators.length(min=6, max=35) ],description="You need a password to sign in!") submit = SubmitField() class register_form(FlaskForm): email = EmailField('Email Address', [ validators.DataRequired(), #validators.Email() ], description="You need an email address to sign up!") password = PasswordField('Create Password', [ validators.DataRequired(), #validators.length(min=6, max=35), validators.EqualTo('confirm', message='Passwords must match') ], description="Create a password.") confirm = PasswordField('Repeat Password', description="Please confirm your password.") submit = SubmitField() class create_user_form(FlaskForm): email = EmailField('Email Address', [validators.DataRequired()], description="You need to signup with an email") password = PasswordField('Create Password', [ validators.DataRequired(), validators.EqualTo('confirm', message='Passwords must match'), ], description="Create a password.") confirm = PasswordField('Repeat Password', description="Please retype your password to confirm it is correct") is_admin = BooleanField('Admin', render_kw={'value':'1'}) submit = SubmitField() class profile_form(FlaskForm): about = TextAreaField('About', [validators.length(min=6, max=300)], description="Tell us about yourself") submit = SubmitField() class user_edit_form(FlaskForm): about = TextAreaField('About', [validators.length(min=6, max=300)], description="Tell us about yourself") is_admin = BooleanField('Admin', render_kw={'value':'1'}) submit = SubmitField() class security_form(FlaskForm): email = EmailField('Email Address', [ validators.DataRequired(), #validators.Email() ], description="Change your email address") password = PasswordField('Create Password', [ validators.DataRequired(), validators.EqualTo('confirm', message='Passwords must match'), #validators.length(min=6, max=35) ], description="Create a password") confirm = PasswordField('Repeat Password', description="Please retype your password to confirm it is correct") submit = SubmitField() class csv_upload(FlaskForm): file = FileField() submit = SubmitField()

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6

47c346c9e9d38538fdc38db2f7de0f3cab10a211

19,878

py

Python

CompileRunTest.py

fcooper8472/CppRandomNumbers

91ae5132d70982619bba4424833ab6fd622106e1

[ "MIT" ]

null

CompileRunTest.py

fcooper8472/CppRandomNumbers

91ae5132d70982619bba4424833ab6fd622106e1

[ "MIT" ]

null

CompileRunTest.py

fcooper8472/CppRandomNumbers

91ae5132d70982619bba4424833ab6fd622106e1

[ "MIT" ]

null

import os import subprocess import matplotlib.pyplot as plt import numpy as np import scipy.stats # Define directories output_sample_dir = os.path.join('/tmp', 'CppRandomNumbers', 'RandomSamples') output_pdf_dir = os.path.join('/tmp', 'CppRandomNumbers', 'Pdf') script_dir = os.path.realpath(os.path.dirname(__file__)) build_dir = os.path.join(script_dir, 'Debug') # Define the C++ targets and associated data files pdf_exes_and_outputs = { 'pdf_beta': "Beta_alpha=2.6_beta=4.9", 'pdf_cauchy': "Cauchy_mu=7.4_sig=3.5", 'pdf_exponential': "Exponential_rate=0.4", 'pdf_gamma': "Gamma_alpha=2.6_beta=0.8", 'pdf_half_cauchy': "HalfCauchy_mu=0.0_sig=3.5", 'pdf_normal': "Normal_mean=8.9_std=2.3", 'pdf_student_t': "StudentT_location=4.2_scale=6.4_df=3.5", 'pdf_uniform': "Uniform_a=1.2_b=2.8", } sample_exes_and_outputs = { 'rand_beta': "Beta_alpha=1.23_beta=2.34", 'rand_cauchy': "Cauchy_mu=8.9_sigma=2.3", 'rand_exponential': "Exponential_rate=2.3", 'rand_gamma': "Gamma_alpha=4_beta=0.5", 'rand_half_cauchy': "HalfCauchy_mu=1.2_sigma=2.3", 'rand_normal': "Normal_mean=1.23_std=2.34", 'rand_student_t': "StudentT_df=4_mu=9.7_sigma=3.3", 'rand_uniform': "Uniform_a=1.23_b=2.34", } def main(): print('\n### Cleaning output directories') if os.path.isdir(output_sample_dir): for file in os.listdir(output_sample_dir): subprocess.call(['rm', file], cwd=output_sample_dir) if os.path.isdir(output_pdf_dir): for file in os.listdir(output_pdf_dir): subprocess.call(['rm', file], cwd=output_pdf_dir) print('\n### Making build directory') subprocess.call(['mkdir', '-p', build_dir]) print('\n### Running CMake') subprocess.call(['cmake', '..'], cwd=build_dir) print('\n### Building all') subprocess.call(['cmake', '--build', '.'], cwd=build_dir) #################################################################################################################### # PDF executables #################################################################################################################### print('\n### Running pdf executables...') for executable in pdf_exes_and_outputs.keys(): print(' {}'.format(executable)) subprocess.call(['./{}'.format(executable)], cwd=build_dir) # Verify all outputs exist print('\n### Verifying all pdf outputs exist') for val in pdf_exes_and_outputs.values(): output_file = os.path.join(output_pdf_dir, val) assert(os.path.isfile(output_file)) print('\n### Creating pdf graphs for...') pdf_plot_beta() pdf_plot_cauchy() pdf_plot_exponential() pdf_plot_gamma() pdf_plot_half_cauchy() pdf_plot_normal() pdf_plot_student_t() pdf_plot_uniform() # Verify all sample outputs have a graph print('\n### Verifying all graphs exist') for val in pdf_exes_and_outputs.values(): output_file = os.path.join(output_pdf_dir, '{}.svg'.format(val)) assert(os.path.isfile(output_file)) #################################################################################################################### #################################################################################################################### # Sample executables #################################################################################################################### print('\n### Running sample executables...') for executable in sample_exes_and_outputs.keys(): print(' {}'.format(executable)) subprocess.call(['./{}'.format(executable)], cwd=build_dir) # Verify all outputs exist print('\n### Verifying all sample outputs exist') for val in sample_exes_and_outputs.values(): output_file = os.path.join(output_sample_dir, val) assert(os.path.isfile(output_file)) print('\n### Creating sample graphs for...') sample_plot_beta() sample_plot_cauchy() sample_plot_exponential() sample_plot_gamma() sample_plot_half_cauchy() sample_plot_normal() sample_plot_student_t() sample_plot_uniform() # Verify all sample outputs have a graph print('\n### Verifying all graphs exist') for val in sample_exes_and_outputs.values(): output_file = os.path.join(output_sample_dir, '{}.svg'.format(val)) assert(os.path.isfile(output_file)) #################################################################################################################### print('\n### Done.') def pdf_plot_beta(): """ Plot the data from the C++ script against the scipy pdf, for the beta distribution """ print(' beta') raw_output = pdf_exes_and_outputs['pdf_beta'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_alpha = 2.6 cpp_beta = 4.9 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.beta.pdf(x, a=cpp_alpha, b=cpp_beta) scipy_log = scipy.stats.beta.logpdf(x, a=cpp_alpha, b=cpp_beta) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_cauchy(): """ Plot the data from the C++ script against the scipy pdf, for the cauchy distribution """ print(' cauchy') raw_output = pdf_exes_and_outputs['pdf_cauchy'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_mu = 7.4 cpp_sig = 3.5 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.cauchy.pdf(x, loc=cpp_mu, scale=cpp_sig) scipy_log = scipy.stats.cauchy.logpdf(x, loc=cpp_mu, scale=cpp_sig) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_exponential(): """ Plot the data from the C++ script against the scipy pdf, for the exponential distribution """ print(' exponential') raw_output = pdf_exes_and_outputs['pdf_exponential'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_rate = 0.4 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.expon.pdf(x, scale=1 / cpp_rate) scipy_log = scipy.stats.expon.logpdf(x, scale=1 / cpp_rate) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_gamma(): """ Plot the data from the C++ script against the scipy pdf, for the gamma distribution """ print(' gamma') raw_output = pdf_exes_and_outputs['pdf_gamma'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_alpha = 2.6 cpp_beta = 0.8 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.gamma.pdf(x, a=cpp_alpha, scale=1 / cpp_beta) scipy_log = scipy.stats.gamma.logpdf(x, a=cpp_alpha, scale=1 / cpp_beta) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_half_cauchy(): """ Plot the data from the C++ script against the scipy pdf, for the half cauchy distribution """ print(' half cauchy') raw_output = pdf_exes_and_outputs['pdf_half_cauchy'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_location = 0.0 cpp_scale = 3.5 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.halfcauchy.pdf(x, loc=cpp_location, scale=cpp_scale) scipy_log = scipy.stats.halfcauchy.logpdf(x, loc=cpp_location, scale=cpp_scale) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_normal(): """ Plot the data from the C++ script against the scipy pdf, for the normal distribution """ print(' normal') raw_output = pdf_exes_and_outputs['pdf_normal'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_mean = 8.9 cpp_std = 2.3 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.norm.pdf(x, loc=cpp_mean, scale=cpp_std) scipy_log = scipy.stats.norm.logpdf(x, loc=cpp_mean, scale=cpp_std) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_student_t(): """ Plot the data from the C++ script against the scipy pdf, for the student-t distribution """ print(' student-t') raw_output = pdf_exes_and_outputs['pdf_student_t'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_location = 4.2 cpp_scale = 6.4 cpp_df = 3.5 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.t.pdf(x, df=cpp_df, loc=cpp_location, scale=cpp_scale) scipy_log = scipy.stats.t.logpdf(x, df=cpp_df, loc=cpp_location, scale=cpp_scale) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def pdf_plot_uniform(): """ Plot the data from the C++ script against the scipy pdf, for the uniform distribution """ print(' uniform') raw_output = pdf_exes_and_outputs['pdf_uniform'] output_file = os.path.join(output_pdf_dir, raw_output) graph_name = os.path.join(output_pdf_dir, '{}.svg'.format(raw_output)) cpp_a = 1.2 cpp_b = 2.8 data = np.loadtxt(output_file, delimiter=',') x = data[:, 0] pdf = data[:, 1] log = data[:, 2] scipy_pdf = scipy.stats.uniform.pdf(x, loc=cpp_a, scale=cpp_b - cpp_a) scipy_log = scipy.stats.uniform.logpdf(x, loc=cpp_a, scale=cpp_b - cpp_a) mean = np.mean(log) plt.figure(figsize=(14, 6)) plt.subplot(121) plt.plot(x, scipy_pdf, 'orange') plt.plot(x, pdf, 'g:', linewidth=5) plt.title('pdf') plt.gca().set_facecolor('0.85') plt.subplot(122) plt.plot(x, scipy_log, 'orange') plt.plot(x, log, 'g:', linewidth=5) plt.title('log pdf') plt.gca().set_facecolor('0.85') plt.gca().set_ylim(mean - 0.1 * mean, mean + 0.1 * mean) plt.gcf().suptitle(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_beta(): """ Plot the data from the C++ script against the scipy pdf, for the beta distribution """ print(' beta') raw_output = sample_exes_and_outputs['rand_beta'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_alpha = 1.23 cpp_beta = 2.34 data = np.loadtxt(output_file) lower = 0.0 upper = 1.0 x = np.linspace(lower, upper, num=100) y = scipy.stats.beta.pdf(x, a=cpp_alpha, b=cpp_beta) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_cauchy(): """ Plot the data from the C++ script against the scipy pdf, for the cauchy distribution """ print(' cauchy') raw_output = sample_exes_and_outputs['rand_cauchy'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_mu = 8.9 cpp_sigma = 2.3 data = np.loadtxt(output_file) lower = np.quantile(data, 0.05) upper = np.quantile(data, 0.95) data = np.clip(data, lower, upper) x = np.linspace(lower, upper, num=100) y = scipy.stats.cauchy.pdf(x, loc=cpp_mu, scale=cpp_sigma) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_exponential(): """ Plot the data from the C++ script against the scipy pdf, for the exponential distribution """ print(' exponential') raw_output = sample_exes_and_outputs['rand_exponential'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_rate = 2.34 data = np.loadtxt(output_file) lower = 0.0 upper = np.quantile(data, 0.99) data = np.clip(data, lower, upper) x = np.linspace(lower, upper, num=100) y = scipy.stats.expon.pdf(x, scale=1. / cpp_rate) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_gamma(): """ Plot the data from the C++ script against the scipy pdf, for the gamma distribution """ print(' gamma') raw_output = sample_exes_and_outputs['rand_gamma'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_alpha = 4.0 cpp_beta = 0.5 data = np.loadtxt(output_file) lower = 0.0 upper = np.quantile(data, 0.99) data = np.clip(data, lower, upper) x = np.linspace(lower, upper, num=100) y = scipy.stats.gamma.pdf(x, a=cpp_alpha, scale=1 / cpp_beta) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_half_cauchy(): """ Plot the data from the C++ script against the scipy pdf, for the half cauchy distribution """ print(' half cauchy') raw_output = sample_exes_and_outputs['rand_half_cauchy'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_mu = 1.2 cpp_sigma = 2.3 data = np.loadtxt(output_file) lower = 0.0 upper = np.quantile(data, 0.9) data = np.clip(data, lower, upper) import math scale_fac = 1.0 / (0.5 + 0.31830988618379067154 * math.atan(cpp_mu / cpp_sigma)) x = np.linspace(lower, upper, num=100) y = scipy.stats.halfcauchy.pdf(x, loc=cpp_mu, scale=cpp_sigma) z = scale_fac / (math.pi * cpp_sigma * (1. + ((x - cpp_mu)/cpp_sigma) ** 2)) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.plot(x, z, 'g') plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_normal(): """ Plot the data from the C++ script against the scipy pdf, for the normal distribution """ print(' normal') raw_output = sample_exes_and_outputs['rand_normal'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_mean = 1.23 cpp_std = 2.34 data = np.loadtxt(output_file) lower = np.quantile(data, 0.005) upper = np.quantile(data, 0.995) data = np.clip(data, lower, upper) x = np.linspace(lower, upper, num=100) y = scipy.stats.norm.pdf(x, cpp_mean, cpp_std) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_student_t(): """ Plot the data from the C++ script against the scipy pdf, for the student t distribution """ print(' student-t') raw_output = sample_exes_and_outputs['rand_student_t'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_df = 4 cpp_mu = 9.7 cpp_sigma = 3.3 data = np.loadtxt(output_file) lower = np.quantile(data, 0.005) upper = np.quantile(data, 0.995) data = np.clip(data, lower, upper) x = np.linspace(lower, upper, num=100) y = scipy.stats.t.pdf(x, df=cpp_df, loc=cpp_mu, scale=cpp_sigma) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() def sample_plot_uniform(): """ Plot the data from the C++ script against the scipy pdf, for the uniform distribution """ print(' uniform') raw_output = sample_exes_and_outputs['rand_uniform'] output_file = os.path.join(output_sample_dir, raw_output) graph_name = os.path.join(output_sample_dir, '{}.svg'.format(raw_output)) cpp_a = 1.23 cpp_b = 2.34 data = np.loadtxt(output_file) lower = 1.23 upper = 2.34 scipy_loc = cpp_a scipy_scale = cpp_b - cpp_a x = np.linspace(lower, upper, num=100) y = scipy.stats.uniform.pdf(x, loc=scipy_loc, scale=scipy_scale) plt.hist(data, bins=25, density=True) plt.plot(x, y) plt.title(raw_output.replace('_', ' ')) plt.savefig(graph_name) plt.close() if __name__ == '__main__': main()

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py

Python

Exercises3/R-3.21.py

opnsesame/Data-Structures-and-Algorithms-Exercises

62f4066c6370225a41295ecb08e05258b08f6d7e

[ "Apache-2.0" ]

null

Exercises3/R-3.21.py

opnsesame/Data-Structures-and-Algorithms-Exercises

62f4066c6370225a41295ecb08e05258b08f6d7e

[ "Apache-2.0" ]

null

Exercises3/R-3.21.py

opnsesame/Data-Structures-and-Algorithms-Exercises

62f4066c6370225a41295ecb08e05258b08f6d7e

[ "Apache-2.0" ]

null

''' Show that nlogn is Ω(n). ''' nlogn > = c*n so nlogn is Ω(n)

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py

Python

samples/hello_world.py

corradosantoro/hephaestus

c40a534e3979cef7e6eeda1206aa93e25fdd55a8

[ "MIT" ]

4

2019-06-07T08:57:15.000Z

2021-08-30T10:40:23.000Z

samples/hello_world.py

corradosantoro/hephaestus

c40a534e3979cef7e6eeda1206aa93e25fdd55a8

[ "MIT" ]

null

samples/hello_world.py

corradosantoro/hephaestus

c40a534e3979cef7e6eeda1206aa93e25fdd55a8

[ "MIT" ]

1

2020-07-24T14:16:34.000Z

# # # from phidias.Types import * from phidias.Main import * from phidias.Lib import * class say_hello(Procedure): pass say_hello() >> [ show_line("Hello world from Phidias") ] PHIDIAS.run() PHIDIAS.shell(globals())

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py

Python

core/admin.py

0pi3/dirtysanta

29345c611d70e518fffe750d2a1cf1309897a7f9

[ "MIT" ]

null

core/admin.py

0pi3/dirtysanta

29345c611d70e518fffe750d2a1cf1309897a7f9

[ "MIT" ]

null

core/admin.py

0pi3/dirtysanta

29345c611d70e518fffe750d2a1cf1309897a7f9

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(GameSession) admin.site.register(GamePlayer)

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py

Python

model/__init__.py

SleuthKid/tie2misp

e8721de64cad2ab8bb01bf0b3af178a07afa6354

[ "BSD-3-Clause" ]

null

model/__init__.py

SleuthKid/tie2misp

e8721de64cad2ab8bb01bf0b3af178a07afa6354

[ "BSD-3-Clause" ]

null

model/__init__.py

SleuthKid/tie2misp

e8721de64cad2ab8bb01bf0b3af178a07afa6354

[ "BSD-3-Clause" ]

null

from .misp_event import MISPEvent from .misp_attribute import MISPAttribute from .misp_tag import MISPTag from .config import Config

26.6

41

0.849624

19

133

5.789474

0.526316

0.218182

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4

42

33.25

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1

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null

0

1

0

1

0

1

0

6

efb724b688e3322f29e91581b29a48bb5aefe60c

19

py

Python

course-1:basic-building-blocks/subject-3:integers/lesson-5:Converting strings to integers.py

regnart-tech-club/python

069df070059de662d4104de8192e45407a7e94ce

[ "Apache-2.0" ]

null

course-1:basic-building-blocks/subject-3:integers/lesson-5:Converting strings to integers.py

regnart-tech-club/python

069df070059de662d4104de8192e45407a7e94ce

[ "Apache-2.0" ]

null

course-1:basic-building-blocks/subject-3:integers/lesson-5:Converting strings to integers.py

regnart-tech-club/python

069df070059de662d4104de8192e45407a7e94ce

[ "Apache-2.0" ]

1

2016-04-03T00:53:37.000Z

print(int('2') + 3)

19

0.526316

4

19

2.5

1

0

0.117647

0.105263

19

1

19

0.470588

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0.05

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true

0

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null

0

1

0

1

0

null

0

1

0

1

0

6

efeeed0d3bc237a7efa5306749d7e1adea7690ff

89

py

Python

faker/providers/internet/en_US/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

12,077

2015-01-01T18:30:07.000Z

2022-03-31T23:22:01.000Z

faker/providers/internet/en_US/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

1,306

2015-01-03T05:18:55.000Z

2022-03-31T02:43:04.000Z

faker/providers/internet/en_US/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

1,855

2015-01-08T14:20:10.000Z

2022-03-25T17:23:32.000Z

from .. import Provider as InternetProvider class Provider(InternetProvider): pass

14.833333

43

0.775281

9

89

7.666667

0.777778

0

0.168539

89

5

44

17.8

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0

true

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null

0

1

0

null

0

1

0

1

0

6

ef349aa77e219103ce1b753873d754488fdbca57

9,079

py

Python

tests/test_ircmessage.py

FujiMakoto/IRC-Message-Formatter

832b705d192ebe9091acb552076a776efa80877c

[ "Unlicense", "MIT-0", "MIT" ]

5

2015-11-18T13:11:56.000Z

2019-06-30T14:08:08.000Z

tests/test_ircmessage.py

FujiMakoto/IRC-Message-Formatter

832b705d192ebe9091acb552076a776efa80877c

[ "Unlicense", "MIT-0", "MIT" ]

2

2020-02-04T07:42:34.000Z

2020-06-07T01:45:51.000Z

tests/test_ircmessage.py

FujiMakoto/IRC-Message-Formatter

832b705d192ebe9091acb552076a776efa80877c

[ "Unlicense", "MIT-0", "MIT" ]

5

2016-10-23T06:58:29.000Z

2021-05-01T17:47:02.000Z

from .config import IrcMessageTestCase import ircmessage class AttributeTests(IrcMessageTestCase): """ Basic attribute code tests """ def test_bold_with_reset(self): message = ircmessage.style('Hello, world!', bold=True) self.assertEqual(message, '\x02Hello, world!\x0F') def test_bold_without_reset(self): message = ircmessage.style('Hello, world!', bold=True, reset=False) self.assertEqual(message, '\x02Hello, world!') def test_italics_with_reset(self): message = ircmessage.style('Hello, world!', italics=True) self.assertEqual(message, '\x1DHello, world!\x0F') def test_italics_without_reset(self): message = ircmessage.style('Hello, world!', italics=True, reset=False) self.assertEqual(message, '\x1DHello, world!') def test_underline_with_reset(self): message = ircmessage.style('Hello, world!', underline=True) self.assertEqual(message, '\x1FHello, world!\x0F') def test_underline_without_reset(self): message = ircmessage.style('Hello, world!', underline=True, reset=False) self.assertEqual(message, '\x1FHello, world!') def test_complex_attributes_with_reset(self): message = ircmessage.style('Hello, world!', bold=True, italics=True, underline=True) self.assertEqual(message, '\x02\x1d\x1fHello, world!\x0F') def test_complex_attributes_without_reset(self): message = ircmessage.style('Hello, world!', bold=True, italics=True, underline=True, reset=False) self.assertEqual(message, '\x02\x1d\x1fHello, world!') class ColorTests(IrcMessageTestCase): """ Color code tests """ def test_fg_white(self): message = ircmessage.style('Hello, world!', fg='white') self.assertEqual(message, '\x0300Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.white) self.assertEqual(message, '\x0300Hello, world!\x0F') def test_fg_black(self): message = ircmessage.style('Hello, world!', fg='black') self.assertEqual(message, '\x0301Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.black) self.assertEqual(message, '\x0301Hello, world!\x0F') def test_fg_blue(self): message = ircmessage.style('Hello, world!', fg='blue') self.assertEqual(message, '\x0302Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.blue) self.assertEqual(message, '\x0302Hello, world!\x0F') def test_fg_green(self): message = ircmessage.style('Hello, world!', fg='green') self.assertEqual(message, '\x0303Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.green) self.assertEqual(message, '\x0303Hello, world!\x0F') def test_fg_red(self): message = ircmessage.style('Hello, world!', fg='red') self.assertEqual(message, '\x0304Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.red) self.assertEqual(message, '\x0304Hello, world!\x0F') def test_fg_brown(self): message = ircmessage.style('Hello, world!', fg='brown') self.assertEqual(message, '\x0305Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.brown) self.assertEqual(message, '\x0305Hello, world!\x0F') def test_fg_purple(self): message = ircmessage.style('Hello, world!', fg='purple') self.assertEqual(message, '\x0306Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.purple) self.assertEqual(message, '\x0306Hello, world!\x0F') def test_fg_orange(self): message = ircmessage.style('Hello, world!', fg='orange') self.assertEqual(message, '\x0307Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.orange) self.assertEqual(message, '\x0307Hello, world!\x0F') def test_fg_yellow(self): message = ircmessage.style('Hello, world!', fg='yellow') self.assertEqual(message, '\x0308Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.yellow) self.assertEqual(message, '\x0308Hello, world!\x0F') def test_fg_lime(self): message = ircmessage.style('Hello, world!', fg='lime') self.assertEqual(message, '\x0309Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.lime) self.assertEqual(message, '\x0309Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.light_green) self.assertEqual(message, '\x0309Hello, world!\x0F') def test_fg_teal(self): message = ircmessage.style('Hello, world!', fg='teal') self.assertEqual(message, '\x0310Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.teal) self.assertEqual(message, '\x0310Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.cyan) self.assertEqual(message, '\x0310Hello, world!\x0F') def test_fg_aqua(self): message = ircmessage.style('Hello, world!', fg='aqua') self.assertEqual(message, '\x0311Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.aqua) self.assertEqual(message, '\x0311Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.light_cyan) self.assertEqual(message, '\x0311Hello, world!\x0F') def test_fg_royal(self): message = ircmessage.style('Hello, world!', fg='royal') self.assertEqual(message, '\x0312Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.royal) self.assertEqual(message, '\x0312Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.light_blue) self.assertEqual(message, '\x0312Hello, world!\x0F') def test_fg_pink(self): message = ircmessage.style('Hello, world!', fg='pink') self.assertEqual(message, '\x0313Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.pink) self.assertEqual(message, '\x0313Hello, world!\x0F') def test_fg_grey(self): message = ircmessage.style('Hello, world!', fg='grey') self.assertEqual(message, '\x0314Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.grey) self.assertEqual(message, '\x0314Hello, world!\x0F') def test_fg_silver(self): message = ircmessage.style('Hello, world!', fg='silver') self.assertEqual(message, '\x0315Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.silver) self.assertEqual(message, '\x0315Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.light_grey) self.assertEqual(message, '\x0315Hello, world!\x0F') def test_bg(self): message = ircmessage.style('Hello, world!', bg='blue') self.assertEqual(message, '\x0301,02Hello, world!\x0F') message = ircmessage.style('Hello, world!', bg=ircmessage.colors.blue) self.assertEqual(message, '\x0301,02Hello, world!\x0F') def test_fg_and_bg(self): message = ircmessage.style('Hello, world!', fg='yellow', bg='blue') self.assertEqual(message, '\x0308,02Hello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.yellow, bg=ircmessage.colors.blue) self.assertEqual(message, '\x0308,02Hello, world!\x0F') def test_fg_and_bg_no_reset(self): message = ircmessage.style('Hello, world!', fg='yellow', bg='blue', reset=False) self.assertEqual(message, '\x0308,02Hello, world!') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.yellow, bg=ircmessage.colors.blue, reset=False) self.assertEqual(message, '\x0308,02Hello, world!') def test_fg_and_bg_with_attributes(self): message = ircmessage.style('Hello, world!', fg='yellow', bg='blue', bold=True, underline=True) self.assertEqual(message, '\x0308,02\x02\x1fHello, world!\x0F') message = ircmessage.style('Hello, world!', fg=ircmessage.colors.yellow, bg=ircmessage.colors.blue, bold=True, underline=True) self.assertEqual(message, '\x0308,02\x02\x1fHello, world!\x0F') def test_bad_color(self): self.assertRaises(TypeError, ircmessage.style, 'Hello, world!', 'bad_color') class UnstyleTests(IrcMessageTestCase): def test_unstyle_complex(self): message = '\x0308,02\x02\x1fHello, world!\x0F' self.assertEqual(ircmessage.unstyle(message), 'Hello, world!') def test_unstyle_nothing(self): message = 'Hello, world!' self.assertEqual(ircmessage.unstyle(message), message)

42.227907

120

0.66395

1,024

9,079

5.805664

0.083984

0.094197

0.181665

0.227082

0.899579

0.832632

0.787889

0.506308

0.456855

0.40471

0

0.037157

0.184822

9,079

214

121

42.425234

0.766113

0.004736

0

0.304054

0

0.234981

0.007662

0

0.378378

1

0.209459

false

0

0.013514

0

0.243243

0

null

0

1

0

null

0

1

0

6

ef635ca4cc16b1f0355e9eb705ed395edd560cc2

24,352

py

Python

DeepSaki/layers/sub_model_composites.py

sascha-kirch/DeepSaki

cfe6bd6537a2b0793d4db4041f2efb37d480cb4c

[ "MIT" ]

3

2021-12-23T09:08:19.000Z

2022-01-31T20:27:27.000Z

DeepSaki/layers/sub_model_composites.py

sascha-kirch/DeepSaki

cfe6bd6537a2b0793d4db4041f2efb37d480cb4c

[ "MIT" ]

1

2022-01-16T21:44:15.000Z

DeepSaki/layers/sub_model_composites.py

SaKi1309/DeepSaki

31c9c7ec86b1797abc23207c2a66cc66272f81fd

[ "MIT" ]

null

import tensorflow as tf import DeepSaki.layers class Encoder(tf.keras.layers.Layer): ''' Encoder sub-model combines convolutional blocks with down sample blocks. The spatial width is halfed with every level while the channel depth is doubled. args: - number_of_levels (optional, default:3): number of conv2D -> Downsampling pairs - filters (optional, default:64): defines the number of filters to which the input is exposed. - kernels: size of the convolutions kernels - limit_filters (optional, default:1024): limits the number of filters, which is doubled with every downsampling block - useResidualConv2DBlock (optional, default: False): ads a residual connection in parallel to the Conv2DBlock - downsampling(optional, default: "conv_stride_2"): describes the downsampling method used - split_kernels (optional, default: False): to decrease the number of parameters, a convolution with the kernel_size (kernel,kernel) can be splitted into two consecutive convolutions with the kernel_size (kernel,1) and (1,kernel) respectivly - numberOfConvs (optional, default: 1): number of consecutive convolutional building blocks, i.e. Conv2DBlock. - activation (optional, default: "leaky_relu"): string literal or tensorflow activation function object to obtain activation function - first_kernel (optional, default: 5): The first convolution can have a different kernel size, to e.g. increase the perceptive field, while the channel depth is still low. - useResidualIdentityBlock (optional, default: False): Whether or not to use the ResidualIdentityBlock instead of the Conv2DBlock - residual_cardinality (optional, default: 1): cardinality for the ResidualIdentityBlock - channelList (optional, default:None): alternativly to number_of_layers and filters, a list with the disired filters for each level can be provided. e.g. channel_list = [64, 128, 256] results in a 3-level Encoder with 64, 128, 256 filters for level 1, 2 and 3 respectivly. - useSpecNorm (optional, default: False): applies spectral normalization to convolutional and dense layers - use_bias (optional, default: True): determines whether convolutions and dense layers include a bias or not - dropout_rate (optional, default: 0): probability of the dropout layer. If the preceeding layer has more than one channel, spatial dropout is applied, otherwise standard dropout - useSelfAttention (optional, default: False): Determines whether to apply self-attention after the encoder before branching. - omit_skips (optional, default: 0): defines how many layers should not output a skip connection output. Requires outputSkips to be True. E.g. if omit_skips = 2, the first two levels do not output a skip connection, it starts at level 3. - padding (optional, default: "none"): padding type. Options are "none", "zero" or "reflection" - outputSkips (optional, default: False): Whether or not to output skip connections at each level - kernel_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the convolutions kernels. - gamma_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the normalization layers. ''' def __init__(self, number_of_levels = 3, filters = 64, limit_filters = 1024, useResidualConv2DBlock = False, downsampling = "conv_stride_2", kernels = 3, split_kernels = False, numberOfConvs = 2, activation = "leaky_relu", first_kernel = None, useResidualIdentityBlock = False, residual_cardinality = 1, channelList = None, useSpecNorm=False, use_bias = True, dropout_rate=0, useSelfAttention=False, omit_skips = 0, padding = "zero", outputSkips = False, kernel_initializer = DeepSaki.initializer.HeAlphaUniform(), gamma_initializer = DeepSaki.initializer.HeAlphaUniform() ): super(Encoder, self).__init__() self.number_of_levels = number_of_levels self.filters = filters self.limit_filters = limit_filters self.useResidualConv2DBlock = useResidualConv2DBlock self.downsampling = downsampling self.kernels = kernels self.split_kernels = split_kernels self.numberOfConvs = numberOfConvs self.activation = activation self.first_kernel = first_kernel self.useResidualIdentityBlock = useResidualIdentityBlock self.residual_cardinality = residual_cardinality self.channelList = channelList self.useSpecNorm = useSpecNorm self.dropout_rate = dropout_rate self.useSelfAttention = useSelfAttention self.omit_skips = omit_skips self.padding = padding self.outputSkips = outputSkips self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.gamma_initializer = gamma_initializer def build(self, input_shape): super(Encoder, self).build(input_shape) if self.channelList == None: self.channelList = [min(self.filters * 2**i, self.limit_filters) for i in range(self.number_of_levels)] else: self.number_of_levels = len(self.channelList) self.encoderBlocks = [] self.downSampleBlocks = [] if self.useSelfAttention: self.SA = DeepSaki.layers.ScalarGatedSelfAttention(useSpecNorm=self.useSpecNorm, intermediateChannel=None, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer) else: self.SA = None for i, ch in enumerate(self.channelList): if i == 0 and self.first_kernel: encoder_kernels = self.first_kernel else: encoder_kernels = self.kernels if self.useResidualIdentityBlock: self.encoderBlocks.append(DeepSaki.layers.ResidualIdentityBlock(filters =ch, activation = self.activation, kernels = encoder_kernels,numberOfBlocks=self.numberOfConvs, useSpecNorm=self.useSpecNorm,dropout_rate=self.dropout_rate, use_bias = self.use_bias, residual_cardinality = self.residual_cardinality,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) self.downSampleBlocks.append(DeepSaki.layers.ResBlockDown(activation = self.activation, useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) else: self.encoderBlocks.append(DeepSaki.layers.Conv2DBlock(filters=ch, useResidualConv2DBlock = self.useResidualConv2DBlock,kernels = encoder_kernels,split_kernels = self.split_kernels, activation = self.activation, numberOfConvs=self.numberOfConvs,useSpecNorm=self.useSpecNorm,dropout_rate=self.dropout_rate,padding=self.padding,use_bias = self.use_bias, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) self.downSampleBlocks.append(DeepSaki.layers.DownSampleBlock( downsampling = self.downsampling, activation=self.activation,kernels = encoder_kernels,useSpecNorm=self.useSpecNorm,padding=self.padding, use_bias = self.use_bias, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) def call(self, inputs): if not self.built: raise ValueError('This model has not yet been built.') x = inputs skips = [] for level in range(self.number_of_levels): if level == 3 and self.SA is not None: x = self.SA(x) skip = self.encoderBlocks[level](x) x = self.downSampleBlocks[level](skip) if self.outputSkips: if level >= self.omit_skips: # omit the first skip connection skips.append(skip) else: skips.append(None) if self.outputSkips: return x, skips else: return x def get_config(self): config = super(Encoder, self).get_config() config.update({ "number_of_levels":self.number_of_levels, "filters":self.filters, "limit_filters":self.limit_filters, "useResidualConv2DBlock":self.useResidualConv2DBlock, "downsampling":self.downsampling, "kernels":self.kernels, "split_kernels":self.split_kernels, "numberOfConvs":self.numberOfConvs, "activation":self.activation, "first_kernel":self.first_kernel, "useResidualIdentityBlock":self.useResidualIdentityBlock, "residual_cardinality":self.residual_cardinality, "channelList":self.channelList, "useSpecNorm":self.useSpecNorm, "use_bias":self.use_bias, "dropout_rate":self.dropout_rate, "useSelfAttention":self.useSelfAttention, "omit_skips":self.omit_skips, "padding":self.padding, "outputSkips":self.outputSkips, "kernel_initializer":self.kernel_initializer, "gamma_initializer":self.gamma_initializer }) return config #Testcode #layer = Encoder( number_of_levels = 5, filters = 64, limit_filters = 512, useSelfAttention = True,useResidualConv2DBlock = True, downsampling="max_pooling", kernels=3, split_kernels = True, numberOfConvs = 2,activation = "leaky_relu", first_kernel=3,useResidualIdentityBlock = True,useSpecNorm=True, omit_skips=2) #print(layer.get_config()) #DeepSaki.layers.helper.PlotLayer(layer,inputShape=(256,256,4)) class Bottleneck(tf.keras.layers.Layer): ''' Bottlenecks are sub-model blocks in auto-encoder-like models such as UNet or ResNet. It is composed of multiple convolution blocks which might have residuals args: - n_bottleneck_blocks (optional, default: 3): Number of consecutive convolution blocks - kernels: size of the convolutions kernels - split_kernels (optional, default: False): to decrease the number of parameters, a convolution with the kernel_size (kernel,kernel) can be splitted into two consecutive convolutions with the kernel_size (kernel,1) and (1,kernel) respectivly - numberOfConvs (optional, default: 2): number of consecutive convolutional building blocks, i.e. Conv2DBlock. - useResidualConv2DBlock (optional, default: True): ads a residual connection in parallel to the Conv2DBlock - useResidualIdentityBlock (optional, default: False): Whether or not to use the ResidualIdentityBlock instead of the Conv2DBlock - activation (optional, default: "leaky_relu"): string literal or tensorflow activation function object to obtain activation function - dropout_rate (optional, default: 0): probability of the dropout layer. If the preceeding layer has more than one channel, spatial dropout is applied, otherwise standard dropout - channelList (optional, default:None): alternativly to number_of_layers and filters, a list with the disired filters for each block can be provided. e.g. channel_list = [64, 128, 256] results in a 3-staged Bottleneck with 64, 128, 256 filters for stage 1, 2 and 3 respectivly. - useSpecNorm (optional, default: False): applies spectral normalization to convolutional and dense layers - use_bias (optional, default: True): determines whether convolutions and dense layers include a bias or not - residual_cardinality (optional, default: 1): cardinality for the ResidualIdentityBlock - padding (optional, default: "none"): padding type. Options are "none", "zero" or "reflection" - kernel_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the convolutions kernels. - gamma_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the normalization layers. ''' def __init__(self, n_bottleneck_blocks = 3, kernels = 3, split_kernels = False, numberOfConvs = 2, useResidualConv2DBlock = True, useResidualIdentityBlock = False, activation = "leaky_relu", dropout_rate = 0.2, channelList = None, useSpecNorm = False, use_bias = True, residual_cardinality = 1, padding = "zero", kernel_initializer = DeepSaki.initializer.HeAlphaUniform(), gamma_initializer = DeepSaki.initializer.HeAlphaUniform() ): super(Bottleneck, self).__init__() self.useResidualIdentityBlock = useResidualIdentityBlock self.n_bottleneck_blocks = n_bottleneck_blocks self.useResidualConv2DBlock = useResidualConv2DBlock self.kernels = kernels self.split_kernels = split_kernels self.numberOfConvs = numberOfConvs self.activation = activation self.dropout_rate = dropout_rate self.channelList = channelList self.useSpecNorm = useSpecNorm self.use_bias = use_bias self.residual_cardinality = residual_cardinality self.padding = padding self.kernel_initializer = kernel_initializer self.gamma_initializer = gamma_initializer def build(self, input_shape): super(Bottleneck, self).build(input_shape) if self.channelList == None: ch = input_shape[-1] self.channelList = [ch for i in range(self.n_bottleneck_blocks)] self.layers = [] for ch in self.channelList: if self.useResidualIdentityBlock: self.layers.append(DeepSaki.layers.ResidualIdentityBlock(activation = self.activation,filters=ch, kernels = self.kernels,numberOfBlocks=self.numberOfConvs,useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,residual_cardinality = self.residual_cardinality,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) else: self.layers.append(DeepSaki.layers.Conv2DBlock(filters=ch, useResidualConv2DBlock = self.useResidualConv2DBlock, kernels = self.kernels, split_kernels = self.split_kernels,numberOfConvs=self.numberOfConvs,activation=self.activation,useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) self.dropout = DeepSaki.layers.helper.dropout_func(self.channelList[-1], self.dropout_rate) def call(self, inputs): if not self.built: raise ValueError('This model has not yet been built.') x = inputs for layer in self.layers: x = layer(x) if self.dropout_rate > 0: x = self.dropout(x) return x def get_config(self): config = super(Bottleneck, self).get_config() config.update({ "useResidualIdentityBlock":self.useResidualIdentityBlock, "n_bottleneck_blocks":self.n_bottleneck_blocks, "useResidualConv2DBlock":self.useResidualConv2DBlock, "kernels":self.kernels, "split_kernels":self.split_kernels, "numberOfConvs":self.numberOfConvs, "activation":self.activation, "dropout_rate":self.dropout_rate, "useSpecNorm":self.useSpecNorm, "use_bias":self.use_bias, "channelList":self.channelList, "residual_cardinality":self.residual_cardinality, "padding": self.padding, "kernel_initializer":self.kernel_initializer, "gamma_initializer":self.gamma_initializer }) return config #Testcode #layer = Bottleneck(True, 3, False, 3,False,1, "leaky_relu" , dropout_rate = 0.2, channelList = None) #print(layer.get_config()) #DeepSaki.layers.helper.PlotLayer(layer,inputShape=(256,256,64)) class Decoder(tf.keras.layers.Layer): ''' Decoder sub-model combines convolutional blocks with up sample blocks. The spatial width is double with every level while the channel depth is halfed. args: - number_of_levels (optional, default:3): number of conv2D -> Upsampling pairs - upsampling(optional, default: "2D_upsample_and_conv"): describes the upsampling method used - filters (optional, default:64): defines the number of filters to which the input is exposed. - limit_filters (optional, default:1024): limits the number of filters - useResidualConv2DBlock (optional, default: False): ads a residual connection in parallel to the Conv2DBlock - kernels: size of the convolutions kernels - split_kernels (optional, default: False): to decrease the number of parameters, a convolution with the kernel_size (kernel,kernel) can be splitted into two consecutive convolutions with the kernel_size (kernel,1) and (1,kernel) respectivly - numberOfConvs (optional, default: 1): number of consecutive convolutional building blocks, i.e. Conv2DBlock. - activation (optional, default: "leaky_relu"): string literal or tensorflow activation function object to obtain activation function - dropout_rate (optional, default: 0): probability of the dropout layer. If the preceeding layer has more than one channel, spatial dropout is applied, otherwise standard dropout. In the decoder only applied to the first half of levels. - useResidualIdentityBlock (optional, default: False): Whether or not to use the ResidualIdentityBlock instead of the Conv2DBlock - residual_cardinality (optional, default: 1): cardinality for the ResidualIdentityBlock - channelList (optional, default:None): alternativly to number_of_layers and filters, a list with the disired filters for each level can be provided. e.g. channel_list = [64, 128, 256] results in a 3-level Decoder with 64, 128, 256 filters for level 1, 2 and 3 respectivly. - useSpecNorm (optional, default: False): applies spectral normalization to convolutional and dense layers - use_bias (optional, default: True): determines whether convolutions and dense layers include a bias or not - useSelfAttention (optional, default: False): Determines whether to apply self-attention after the encoder before branching. - enableSkipConnectionsInput (optional, default: False): Whether or not to input skip connections at each level - padding (optional, default: "none"): padding type. Options are "none", "zero" or "reflection" - kernel_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the convolutions kernels. - gamma_initializer (optional, default: DeepSaki.initializer.HeAlphaUniform()): Initialization of the normalization layers. ''' def __init__(self, number_of_levels = 3, upsampling = "2D_upsample_and_conv", filters = 64, limit_filters = 1024, useResidualConv2DBlock = False, kernels = 3, split_kernels = False, numberOfConvs = 2, activation = "leaky_relu", dropout_rate = 0.2, useResidualIdentityBlock = False, residual_cardinality = 1, channelList = None, useSpecNorm=False, use_bias = True, useSelfAttention=False, enableSkipConnectionsInput = False, padding = "zero", kernel_initializer = DeepSaki.initializer.HeAlphaUniform(), gamma_initializer = DeepSaki.initializer.HeAlphaUniform() ): super(Decoder, self).__init__() self.number_of_levels = number_of_levels self.filters = filters self.upsampling = upsampling self.limit_filters = limit_filters self.useResidualConv2DBlock = useResidualConv2DBlock self.kernels = kernels self.split_kernels = split_kernels self.numberOfConvs = numberOfConvs self.activation = activation self.useResidualIdentityBlock = useResidualIdentityBlock self.channelList = channelList self.useSpecNorm = useSpecNorm self.use_bias = use_bias self.dropout_rate = dropout_rate self.useSelfAttention = useSelfAttention self.enableSkipConnectionsInput = enableSkipConnectionsInput self.residual_cardinality = residual_cardinality self.padding = padding self.kernel_initializer = kernel_initializer self.gamma_initializer = gamma_initializer def build(self, input_shape): super(Decoder, self).build(input_shape) if self.channelList == None: self.channelList = [min(self.filters * 2**i, self.limit_filters) for i in reversed(range(self.number_of_levels))] else: self.number_of_levels = len(self.channelList) self.decoderBlocks = [] self.upSampleBlocks = [] self.dropouts = [] if self.useSelfAttention: self.SA =DeepSaki.layers.ScalarGatedSelfAttention(useSpecNorm=self.useSpecNorm, intermediateChannel=None, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer) else: self.SA = None for i, ch in enumerate(self.channelList): if i < int(self.number_of_levels/2): # ">" since i is reversed dropout_rate = self.dropout_rate else: dropout_rate = 0 if self.useResidualIdentityBlock: self.decoderBlocks.append(DeepSaki.layers.ResidualIdentityBlock(filters =ch, activation = self.activation, kernels = self.kernels,numberOfBlocks=self.numberOfConvs,useSpecNorm=self.useSpecNorm,dropout_rate=dropout_rate, use_bias = self.use_bias, residual_cardinality = self.residual_cardinality,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) self.upSampleBlocks.append(DeepSaki.layers.ResBlockUp(activation=self.activation,useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) else: self.decoderBlocks.append(DeepSaki.layers.Conv2DBlock(filters = ch,useResidualConv2DBlock=self.useResidualConv2DBlock, kernels = self.kernels,split_kernels=self.split_kernels, activation = self.activation,numberOfConvs=self.numberOfConvs, dropout_rate=dropout_rate,useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) self.upSampleBlocks.append(DeepSaki.layers.UpSampleBlock(kernels = self.kernels, upsampling = self.upsampling, split_kernels = self.split_kernels,activation=self.activation,useSpecNorm=self.useSpecNorm, use_bias = self.use_bias,padding = self.padding, kernel_initializer = self.kernel_initializer, gamma_initializer = self.gamma_initializer)) def call(self, inputs): if not self.built: raise ValueError('This model has not yet been built.') skipConnections = None if self.enableSkipConnectionsInput: x, skipConnections = inputs else: x = inputs for level in range(self.number_of_levels): if level == 3 and self.SA is not None: x = self.SA(x) x = self.upSampleBlocks[level](x) if skipConnections is not None: x = tf.keras.layers.concatenate([x, skipConnections[self.number_of_levels - (level+1)]]) x = self.decoderBlocks[level](x) return x def get_config(self): config = super(Decoder, self).get_config() config.update({ "number_of_levels":self.number_of_levels, "filters":self.filters, "limit_filters":self.limit_filters, "useResidualConv2DBlock":self.useResidualConv2DBlock, "upsampling":self.upsampling, "kernels":self.kernels, "split_kernels":self.split_kernels, "numberOfConvs":self.numberOfConvs, "activation":self.activation, "useResidualIdentityBlock":self.useResidualIdentityBlock, "residual_cardinality": self.residual_cardinality, "channelList":self.channelList, "useSpecNorm":self.useSpecNorm, "dropout_rate":self.dropout_rate, "useSelfAttention":self.useSelfAttention, "enableSkipConnectionsInput":self.enableSkipConnectionsInput, "padding": self.padding, "kernel_initializer":self.kernel_initializer, "gamma_initializer":self.gamma_initializer }) return config #Testcode #layer = Decoder( number_of_levels = 5, filters = 64, limit_filters = 2048, useSelfAttention = True,useResidualConv2DBlock = False, upsampling="depth_to_space", kernels=3, split_kernels = False, numberOfConvs = 2,activation = "leaky_relu",useResidualIdentityBlock = True,useSpecNorm=False, dropout_rate = 0.2) #print(layer.get_config()) #DeepSaki.layers.helper.PlotLayer(layer,inputShape=(256,256,4))

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32576549095cfa6eb04a2b7b770738910b48e0be

28

py

Python

code/todoist/__init__.py

vijai747/ToDoistDashboard

793de52c5774b30ea64855a41d85098e98ebbc23

[ "MIT" ]

2

2016-07-25T22:59:00.000Z

2017-01-02T00:55:07.000Z

code/todoist/__init__.py

vijai747/ToDoistDashboard

793de52c5774b30ea64855a41d85098e98ebbc23

[ "MIT" ]

2

2017-09-30T22:33:16.000Z

2017-09-30T23:16:42.000Z

code/todoist/__init__.py

vijai747/ToDoistDashboard

793de52c5774b30ea64855a41d85098e98ebbc23

[ "MIT" ]

null

from .api import TodoistAPI

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32740e68a3702b1b673cb7cc0c7e82fd08e6b6df

47

py

Python

src/plotting/pages/upload/upload_data.py

elric97/CmyPlot

ce4490d3075b2c6cb47ad8eb5f35add2e2b66a3f

[ "MIT" ]

1

2021-11-06T18:30:48.000Z

src/plotting/pages/upload/upload_data.py

freakNewton/CmyPlot

bc940a219137e9252e37655afef7435d6f913178

[ "MIT" ]

30

2021-09-03T21:46:54.000Z

2021-09-22T18:36:10.000Z

src/plotting/pages/upload/upload_data.py

freakNewton/CmyPlot

bc940a219137e9252e37655afef7435d6f913178

[ "MIT" ]

11

2021-09-26T16:09:42.000Z

2021-11-03T03:25:26.000Z

# TODO eventually pull data from user database

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32798f47eaf81b64ac3fce59d665a7a7f103e36a

28

py

Python

model/__init__.py

JiwonCocoder/label_transformer

165ce614269b29902cb9689b73c02fe53b9ff1f5

[ "MIT" ]

41

2021-02-03T04:55:24.000Z

2022-02-03T12:14:48.000Z

model/__init__.py

yuanwei0908/FeatMatch

9e7a20a5e1b4b1602b0c846bc2d5460454fa5741

[ "MIT" ]

1

2021-09-10T16:45:23.000Z

2021-09-11T07:27:55.000Z

model/__init__.py

yuanwei0908/FeatMatch

9e7a20a5e1b4b1602b0c846bc2d5460454fa5741

[ "MIT" ]

5

2021-02-04T01:29:04.000Z

2021-07-05T03:46:01.000Z

from .model import FeatMatch

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6

327f5b396996b81bd7ffa0e86da42074a39933fd

198

py

Python

solutions_automation/vdc/dashboard/taiga.py

threefoldtech/js-sdk

811f783ac34a60225175bab2d806802a87b9d5c7

[ "Apache-2.0" ]

13

2020-09-02T09:05:08.000Z

2022-03-12T02:43:24.000Z

solutions_automation/vdc/dashboard/taiga.py

threefoldtech/js-sdk

811f783ac34a60225175bab2d806802a87b9d5c7

[ "Apache-2.0" ]

1,998

2020-06-15T11:46:10.000Z

2022-03-24T22:12:41.000Z

solutions_automation/vdc/dashboard/taiga.py

threefoldtech/js-sdk

811f783ac34a60225175bab2d806802a87b9d5c7

[ "Apache-2.0" ]

8

2020-09-29T06:50:35.000Z

2021-06-14T03:30:52.000Z

from solutions_automation.vdc.dashboard.common import CommonChatBot from jumpscale.packages.vdc_dashboard.chats.taiga import TaigaDeploy class TaigaAutomated(CommonChatBot, TaigaDeploy): pass

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6

32b8f8f2bcd4df08eb7798bea52da0553862af7e

225

py

Python

assignments/assignment4/jupyter_notebook_config.py

Seraphirn/dlcourse_ai

f352fab5fd2fe28a063753947130e4b8b8aea14b

[ "MIT" ]

null

assignments/assignment4/jupyter_notebook_config.py

Seraphirn/dlcourse_ai

f352fab5fd2fe28a063753947130e4b8b8aea14b

[ "MIT" ]

null

assignments/assignment4/jupyter_notebook_config.py

Seraphirn/dlcourse_ai

f352fab5fd2fe28a063753947130e4b8b8aea14b

[ "MIT" ]

null

## Reload the webapp when changes are made to any Python src files. # Default: False c.NotebookApp.autoreload = True # c.NotebookApp.browser = 'google-chrome' # c.NotebookApp.browser = 'google-chrome --user-data-dir=/tmp/'

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null

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1

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6

32c1be5fec19deb386b588745a7a265145f5e529

26

py

Python

guardianapi/__init__.py

ankur-chouragade/openplatform-python

18b5243c77ecf1060993d43688a156b44acf2880

[ "BSD-2-Clause" ]

1

2016-05-09T04:17:30.000Z

guardianapi/__init__.py

ankur-chouragade/openplatform-python

18b5243c77ecf1060993d43688a156b44acf2880

[ "BSD-2-Clause" ]

null

guardianapi/__init__.py

ankur-chouragade/openplatform-python

18b5243c77ecf1060993d43688a156b44acf2880

[ "BSD-2-Clause" ]

1

2020-01-09T02:52:18.000Z

from client import Client

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6

08be1edf359fb20ca8a1febbf45971391d2aeb59

186

py

Python

django_sourcebook/tests/test_foia_request.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

django_sourcebook/tests/test_foia_request.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

django_sourcebook/tests/test_foia_request.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

from sourcebook.foia_sender import FoiaHandler from sourcebook.models import FoiaRequestBase, FoiaRequestItem from django import test class FoiaTemplateTests(test.TestCase): pass

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1

0

6

08c0f98ce69b6d0268e92cb5a0db28db791946d7

178

py

Python

config/views/__init__.py

0x213F/tip-jar

b9b2dd2f62d1780ed2ef10bbac6bdd9f5f883a6e

[ "MIT" ]

null

config/views/__init__.py

0x213F/tip-jar

b9b2dd2f62d1780ed2ef10bbac6bdd9f5f883a6e

[ "MIT" ]

8

2020-11-20T05:57:10.000Z

2020-12-08T17:11:54.000Z

config/views/__init__.py

0x213F/musician-tips

b9b2dd2f62d1780ed2ef10bbac6bdd9f5f883a6e

[ "MIT" ]

null

from .cart_view import MusicianCartView from .checkout_view import MusicianCheckoutView from .choose_view import MusicianChooseView from .receipt_view import MusicianReceiptView

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true

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null

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1

0

1

0

1

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6

08d278b717b0ea3b860c9f3b2dbaf051b8665b05

148

py

Python

lang/py/cookbook/v2/source/cb2_9_7_exm_2.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_9_7_exm_2.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_9_7_exm_2.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

import sys if sys.version >= '2.4': ## insert 2.4 definition of get_local_storage here else: ## insert 2.3 definition of get_local_storage here

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148

5

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null

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null

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1

0

1

0

6

08d98493b79d5e9b9ab9af13a2ddff22d8baa76b

71

py

Python

kv_config_reader/__init__.py

liuwilliamBUPT/bupt-ncov-report

6b8f025676a4b39890f81171f457505bcfcf750b

[ "MIT" ]

8

2020-09-01T12:45:33.000Z

2020-11-02T01:37:01.000Z

kv_config_reader/__init__.py

liuwilliamBUPT/bupt-ncov-report

6b8f025676a4b39890f81171f457505bcfcf750b

[ "MIT" ]

null

kv_config_reader/__init__.py

liuwilliamBUPT/bupt-ncov-report

6b8f025676a4b39890f81171f457505bcfcf750b

[ "MIT" ]

2

2020-09-03T02:02:42.000Z

2021-12-11T09:11:21.000Z

from .filler import * from .predef import * from .public_util import *

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0.169014

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0

null

0

1

0

1

0

1

0

6

08f1596a6c334b8509ed86f3cadb43d0a56fdd36

25

py

Python

lf3py/data/config.py

rog-works/lambda-fw

715b36fc2d8d0ea0388aa4ac1336dc8cd5543778

[ "CNRI-Python" ]

null

lf3py/data/config.py

rog-works/lambda-fw

715b36fc2d8d0ea0388aa4ac1336dc8cd5543778

[ "CNRI-Python" ]

15

2020-12-05T13:52:13.000Z

2020-12-19T10:14:40.000Z

lf3py/data/config.py

rog-works/lambda-fw

715b36fc2d8d0ea0388aa4ac1336dc8cd5543778

[ "CNRI-Python" ]

null

class Config(dict): pass

12.5

24

0.76

4

25

4.75

1

0

0.12

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1

25

0.863636

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null

0

1

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null

0

1

0

1

0

6

3eb24e561c99ccd825b8b54d0c57dfb678a067a5

202

py

Python

Framework/ResourceManagement/ImageLoader.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

1

2020-11-17T04:32:55.000Z

Framework/ResourceManagement/ImageLoader.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

null

Framework/ResourceManagement/ImageLoader.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

null

import pygame # loads image formats and returns surfaces class ImageLoader: def __init__(self): pass def get_asset (self, path): return pygame.image.load (path).convert_alpha()

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0.693069

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55

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1

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null

0

1

0

1

0

1

0

6

f5d57001ac35c38745c4272f375adbe264fb8e20

119

py

Python

mlddec/__init__.py

hjuinj/mlddec

c85df0952bfef3f652c7714067ed38385e877cd1

[ "MIT" ]

1

2019-09-27T02:00:50.000Z

mlddec/__init__.py

CHEMPHY/mlddec

92679b3e7552013d8dec3d75fa70d05dbb9f4527

[ "MIT" ]

null

mlddec/__init__.py

CHEMPHY/mlddec

92679b3e7552013d8dec3d75fa70d05dbb9f4527

[ "MIT" ]

null

from .utils import load_models, get_charges, add_charges_to_mol, visualise_charges, visualize_charges, validate_models

59.5

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0.87395

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119

5.647059

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119

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null

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0

1

0

1

0

6

eb2071e6fce69a06c177007f0f10a3f447ff2d93

110

py

Python

requirements_manager/errors.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

3

2021-06-02T09:45:00.000Z

2022-02-01T14:30:01.000Z

requirements_manager/errors.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

3

2021-05-26T11:46:28.000Z

2021-11-04T10:14:42.000Z

requirements_manager/errors.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

2

2021-06-04T15:02:14.000Z

2021-09-03T09:26:45.000Z

class PipfilePathDoesNotExistError(Exception): pass class NoPackagesInPipfileError(Exception): pass

15.714286

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0.8

8

110

11

0.625

0.295455

0

0.145455

110

6

47

18.333333

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0

1

0

true

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1

null

1

0

1

0

null

0

1

0

6

de284e918fe459f0fa9eefa3d9fd26745955e636

210

py

Python

torchflare/batch_mixers/__init__.py

earlbabson/torchflare

15db06d313a53a3ec4640869335ba87730562b28

[ "Apache-2.0" ]

1

2021-04-28T19:57:57.000Z

torchflare/batch_mixers/__init__.py

earlbabson/torchflare

15db06d313a53a3ec4640869335ba87730562b28

[ "Apache-2.0" ]

null

torchflare/batch_mixers/__init__.py

earlbabson/torchflare

15db06d313a53a3ec4640869335ba87730562b28

[ "Apache-2.0" ]

null

"""Imports for mixers.""" from torchflare.batch_mixers.mixers import CustomCollate, MixCriterion, cutmix, get_collate_fn, mixup __all__ = ["CustomCollate", "MixCriterion", "cutmix", "get_collate_fn", "mixup"]

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210

6.333333

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102

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0.795812

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0.27027

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0

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1

0

null

0

1

0

6

de71bc03890c2672bebf9de8a7304b26b3513897

78

py

Python

backend/__init__.py

tienthegainz/Face_for_Rice

7e126189fa3513407c3d56c48320e8d52c61e38e

[ "Apache-2.0" ]

null

backend/__init__.py

tienthegainz/Face_for_Rice

7e126189fa3513407c3d56c48320e8d52c61e38e

[ "Apache-2.0" ]

5

2021-06-08T21:25:34.000Z

2022-02-19T01:29:20.000Z

backend/__init__.py

tienthegainz/Face_for_Rice

7e126189fa3513407c3d56c48320e8d52c61e38e

[ "Apache-2.0" ]

2

2020-06-08T14:43:55.000Z

2020-08-25T07:28:32.000Z

from face_detector import FaceDetector from face_searcher import FaceSearcher

26

38

0.897436

10

78

6.8

0.7

0.235294

0

0.102564

78

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null

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1

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1

0

1

0

6

de85bcb6272d49e87e7c6bcd0e446799dc377a58

369

py

Python

chainercb/policies/__init__.py

rjagerman/chainercb

72f18146828e7ec3dbaec210ab2f41fe25033e42

[ "MIT" ]

1

2019-02-13T21:14:33.000Z

chainercb/policies/__init__.py

rjagerman/chainercb

72f18146828e7ec3dbaec210ab2f41fe25033e42

[ "MIT" ]

null

chainercb/policies/__init__.py

rjagerman/chainercb

72f18146828e7ec3dbaec210ab2f41fe25033e42

[ "MIT" ]

null

from chainercb.policies.epsilon_greedy import EpsilonGreedy from chainercb.policies.exploit import Exploit from chainercb.policies.explore import Explore from chainercb.policies.softmax import Softmax from chainercb.policies.linear_ucb import LinUCBPolicy from chainercb.policies.linear_thompson import ThompsonPolicy from chainercb.policies.adf_ucb import ADFUCBPolicy

52.714286

61

0.888889

46

369

7.043478

0.369565

0.280864

0.453704

0.166667

0

0.073171

369

7

62

52.714286

0.947368

0

1

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true

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1

0

null

0

1

0

1

0

6

debf0d83732668c4a9fcf5b694b968a634e0fae4

47

py

Python

gym_ds3/envs/__init__.py

EpiSci/SoCRATES

901a896c5a765e3cb56f290188cde71c8707192d

[ "MIT" ]

6

2021-09-24T13:40:39.000Z

2022-02-14T02:59:52.000Z

gym_ds3/envs/__init__.py

anonymous1958342/DS3Gym

71fbff5ea92ae9349ad440e2c25497d1d363e97b

[ "MIT" ]

null

gym_ds3/envs/__init__.py

anonymous1958342/DS3Gym

71fbff5ea92ae9349ad440e2c25497d1d363e97b

[ "MIT" ]

null

from gym_ds3.envs.core.ds3_env import DS3GymEnv

47

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47

4.333333

0.888889

0

0.068182

0.06383

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1

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0.818182

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true

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1

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1

0

null

0

1

0

1

0

1

0

6

dee643dabb9bc40614d1669576053bdb0c520ed6

10,206

py

Python

tests/plugins/polling/generic/test_plugin_polling_ping.py

mdrbh/panoptes

63a27ab60ce4315ccd6ee2f6d3aed3cdb74d888c

[ "Apache-2.0" ]

86

2018-10-01T18:13:24.000Z

2021-07-29T00:04:56.000Z

tests/plugins/polling/generic/test_plugin_polling_ping.py

mdrbh/panoptes

63a27ab60ce4315ccd6ee2f6d3aed3cdb74d888c

[ "Apache-2.0" ]

164

2018-10-03T02:01:15.000Z

2021-04-26T16:07:14.000Z

tests/plugins/polling/generic/test_plugin_polling_ping.py

mdrbh/panoptes

63a27ab60ce4315ccd6ee2f6d3aed3cdb74d888c

[ "Apache-2.0" ]

27

2018-10-03T22:43:06.000Z

2021-06-17T23:41:51.000Z

import unittest import logging import json from mock import Mock, patch, create_autospec from yahoo_panoptes.framework.utilities.helpers import ordered from yahoo_panoptes.framework.context import PanoptesContext from yahoo_panoptes.framework.resources import PanoptesResource from yahoo_panoptes.framework.plugins.context import PanoptesPluginWithEnrichmentContext from yahoo_panoptes.polling.polling_plugin import PanoptesPollingPluginConfigurationError from yahoo_panoptes.plugins.polling.generic.plugin_polling_ping import PluginPollingPing mock_time = Mock() mock_time.return_value = 1512629517.03121 TEST_PING_RESPONSE_SUCCESS = u"ping statistics ---\n" \ u"10 packets transmitted, 10 received, 0% packet loss, time 1439ms\n" \ u"rtt min/avg/max/mdev = 0.040/0.120/0.162/0.057 ms" TEST_PING_RESPONSE_FAILURE = u"ping statistics ---\n" \ u"10 packets transmitted, 0 received, 100% packet loss, time 10000ms\n" \ u"rtt min/avg/max/mdev = 0.0/0.0/0.0/0.0 ms" TEST_PLUGIN_RESULT_EXCEPTION = { u"resource": { u"resource_site": u"test_site", u"resource_class": u"test_class", u"resource_subclass": u"test_subclass", u"resource_type": u"test_type", u"resource_id": u"test_id", u"resource_endpoint": u"test_endpoint", u"resource_metadata": { u"_resource_ttl": u"604800" }, u"resource_creation_timestamp": 1512629517.03121, u"resource_plugin": u"test_plugin"}, u"dimensions": [], u"metrics": [ { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"ping_status", u"metric_value": 7 } ], u"metrics_group_type": u"ping", u"metrics_group_interval": 60, u"metrics_group_creation_timestamp": 1512629517.031, u"metrics_group_schema_version": u"0.2" } TEST_PLUGIN_RESULT_FAILURE = { u"resource": { u"resource_site": u"test_site", u"resource_class": u"test_class", u"resource_subclass": u"test_subclass", u"resource_type": u"test_type", u"resource_id": u"test_id", u"resource_endpoint": u"test_endpoint", u"resource_metadata": { u"_resource_ttl": u"604800" }, u"resource_creation_timestamp": 1512629517.03121, u"resource_plugin": u"test_plugin"}, u"dimensions": [], u"metrics": [ { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"ping_status", u"metric_value": 7 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"packet_loss_percent", u"metric_value": 100 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_minimum", u"metric_value": 0 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_average", u"metric_value": 0 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_maximum", u"metric_value": 0 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_standard_deviation", u"metric_value": 0 } ], u"metrics_group_type": u"ping", u"metrics_group_interval": 60, u"metrics_group_creation_timestamp": 1512629517.031, u"metrics_group_schema_version": u"0.2" } TEST_PLUGIN_RESULT_SUCCESS = { u"resource": { u"resource_site": u"test_site", u"resource_class": u"test_class", u"resource_subclass": u"test_subclass", u"resource_type": u"test_type", u"resource_id": u"test_id", u"resource_endpoint": u"test_endpoint", u"resource_metadata": { u"_resource_ttl": u"604800" }, u"resource_creation_timestamp": 1512629517.03121, u"resource_plugin": u"test_plugin"}, u"dimensions": [], u"metrics": [ { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"ping_status", u"metric_value": 0 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"packet_loss_percent", u"metric_value": 0 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_minimum", u"metric_value": 0.040 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_average", u"metric_value": 0.120 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_maximum", u"metric_value": 0.162 }, { u"metric_creation_timestamp": 1512629517.031, u"metric_type": u"gauge", u"metric_name": u"round_trip_standard_deviation", u"metric_value": 0.057 } ], u"metrics_group_type": u"ping", u"metrics_group_interval": 60, u"metrics_group_creation_timestamp": 1512629517.031, u"metrics_group_schema_version": u"0.2" } class TestPluginPollingPing(unittest.TestCase): @patch(u'yahoo_panoptes.framework.resources.time', mock_time) def setUp(self): self._panoptes_resource = PanoptesResource( resource_site=u'test_site', resource_class=u'test_class', resource_subclass=u'test_subclass', resource_type=u'test_type', resource_id=u'test_id', resource_endpoint=u'test_endpoint', resource_plugin=u'test_plugin' ) self._plugin_config = { u'Core': { u'name': u'Test Plugin', u'module': u'plugin_polling_ping' }, u'main': { u'resource_filter': u'resource_class = u"network"', u'execute_frequency': 60, } } self._panoptes_context = create_autospec(PanoptesContext) self._panoptes_plugin_context = create_autospec( PanoptesPluginWithEnrichmentContext, instance=True, spec_set=True, data=self._panoptes_resource, config=self._plugin_config, logger=logging.getLogger(__name__) ) @patch(u'yahoo_panoptes.framework.metrics.time', mock_time) @patch(u'yahoo_panoptes.framework.utilities.ping.subprocess.check_output', Mock(return_value=TEST_PING_RESPONSE_SUCCESS)) def test_plugin_ping_success(self): results = PluginPollingPing().run(self._panoptes_plugin_context) self.assertEqual(ordered(json.loads(list(results)[0].json)), ordered(TEST_PLUGIN_RESULT_SUCCESS)) @patch(u'yahoo_panoptes.framework.metrics.time', mock_time) @patch(u'yahoo_panoptes.framework.utilities.ping.subprocess.check_output', Mock(return_value=TEST_PING_RESPONSE_FAILURE)) def test_plugin_ping_failure(self): results = PluginPollingPing().run(self._panoptes_plugin_context) self.assertEqual(ordered(json.loads(list(results)[0].json)), ordered(TEST_PLUGIN_RESULT_FAILURE)) @patch(u'yahoo_panoptes.framework.metrics.time', mock_time) @patch(u'yahoo_panoptes.framework.utilities.ping.subprocess.check_output', Mock(side_effect=Exception)) def test_plugin_ping_exception(self): results = PluginPollingPing().run(self._panoptes_plugin_context) self.assertEqual(ordered(json.loads(list(results)[0].json)), ordered(TEST_PLUGIN_RESULT_EXCEPTION)) @patch(u'yahoo_panoptes.framework.metrics.time', mock_time) def test_plugin_ping_count_error(self): self._plugin_config = { u'Core': { u'name': u'Test Plugin', u'module': u'plugin_polling_ping' }, u'main': { u'resource_filter': u'resource_class = u"network"', u'execute_frequency': 60, u'count': "string" } } self._panoptes_context = create_autospec(PanoptesContext) self._panoptes_plugin_context = create_autospec( PanoptesPluginWithEnrichmentContext, instance=True, spec_set=True, data=self._panoptes_resource, config=self._plugin_config, logger=logging.getLogger(__name__) ) with self.assertRaises(PanoptesPollingPluginConfigurationError): results = PluginPollingPing().run(self._panoptes_plugin_context) @patch(u'yahoo_panoptes.framework.metrics.time', mock_time) def test_plugin_ping_timeout_error(self): self._plugin_config = { u'Core': { u'name': u'Test Plugin', u'module': u'plugin_polling_ping' }, u'main': { u'resource_filter': u'resource_class = u"network"', u'execute_frequency': 60, u'timeout': "string" } } self._panoptes_context = create_autospec(PanoptesContext) self._panoptes_plugin_context = create_autospec( PanoptesPluginWithEnrichmentContext, instance=True, spec_set=True, data=self._panoptes_resource, config=self._plugin_config, logger=logging.getLogger(__name__) ) with self.assertRaises(PanoptesPollingPluginConfigurationError): results = PluginPollingPing().run(self._panoptes_plugin_context)

36.45

107

0.614345

1,157

10,206

5.109767

0.113224

0.06157

0.086773

0.081191

0.835081

0.802436

0.801252

0.782307

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0.276406

10,206

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36.580645

0.749357

0

0.624506

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0.320008

0.112287

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0.019763

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0

0.039526

0

0.067194

0

null

0

1

0

null

0

6

dee7d686b0d81e8fb4936bd6364f8c0a98daa179

74

py

Python

Statistics/Skewness.py

sng23/miniproject2

3784cc1f92f85211676de9bc2dd914313103747a

[ "MIT" ]

null

Statistics/Skewness.py

sng23/miniproject2

3784cc1f92f85211676de9bc2dd914313103747a

[ "MIT" ]

null

Statistics/Skewness.py

sng23/miniproject2

3784cc1f92f85211676de9bc2dd914313103747a

[ "MIT" ]

1

2020-03-05T00:26:14.000Z

import scipy.stats def skewness(data): return scipy.stats.skew(data)

14.8

33

0.743243

11

74

5

0.727273

0.363636

0

0.148649

74

5

33

14.8

0.873016

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a0e77f69de2370d7779bafb20db0e97e6a5083b4

46

py

Python

dbdev_testproject/dbdev_testproject/production/settings.py

tworide/django_dbdev

9313e3ce39c64634577f4a63f11028cd01bb7b10

[ "BSD-3-Clause" ]

null

dbdev_testproject/dbdev_testproject/production/settings.py

tworide/django_dbdev

9313e3ce39c64634577f4a63f11028cd01bb7b10

[ "BSD-3-Clause" ]

4

2015-04-28T07:14:18.000Z

2018-02-28T18:53:25.000Z

dbdev_testproject/dbdev_testproject/production/settings.py

tworide/django_dbdev

9313e3ce39c64634577f4a63f11028cd01bb7b10

[ "BSD-3-Clause" ]

4

2015-01-05T18:58:30.000Z

2019-04-08T11:06:44.000Z

from dbdev_testproject.settings_base import *

23

45

0.869565

6

46

6.333333

1

0

0.086957

46

1

46

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a0fb5a513c351194183fa6fa1f22ab7f17381276

207

py

Python

ohsome2label/__init__.py

iboates/ohsome2label

a98c8716c6e40d322a7fdb310d119989c1ce216d

[ "MIT" ]

38

2020-07-05T10:13:05.000Z

2022-03-29T02:42:58.000Z

ohsome2label/__init__.py

iboates/ohsome2label

a98c8716c6e40d322a7fdb310d119989c1ce216d

[ "MIT" ]

8

2020-07-23T13:03:26.000Z

2022-01-24T17:55:25.000Z

ohsome2label/__init__.py

iboates/ohsome2label

a98c8716c6e40d322a7fdb310d119989c1ce216d

[ "MIT" ]

3

2020-11-11T06:32:04.000Z

2021-06-13T17:28:37.000Z

__version__ = '1.1.2' from .config import * from .utils import * from .label import * from .tile import * from .visualize import * from .palette import palette from .overpass import * from .quality import *

20.7

28

0.7343

29

207

5.103448

0.448276

0.405405

0

0.017442

0.169082

207

9

29

23

0.843023

0

0.024155

0

1

0

false

0.111111

0.888889

0

0.888889

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

9d081a3c368504e6f2039f1cb5ce1483ca154fde

103

py

Python

__init__.py

cxd/wave_weather_data_python

3b50137f6f8b185dd5f5672feb9645e179bc1490

[ "MIT" ]

null

__init__.py

cxd/wave_weather_data_python

3b50137f6f8b185dd5f5672feb9645e179bc1490

[ "MIT" ]

null

__init__.py

cxd/wave_weather_data_python

3b50137f6f8b185dd5f5672feb9645e179bc1490

[ "MIT" ]

null

from lib.ReadCsv import ReadCsv from lib.ReadData import ReadData from lib.ReadConfig import ReadConfig

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9d270403673652afbf5279688e5bc0302b049b37

152

py

Python

aliyunlogcli/exceptions.py

lichengseu/aliyun-log-cli

3ada08feda5d07638a00030b2dd402f2f23a6bcd

[ "MIT" ]

53

2017-11-22T07:06:17.000Z

2022-02-22T02:07:32.000Z

aliyunlogcli/exceptions.py

lichengseu/aliyun-log-cli

3ada08feda5d07638a00030b2dd402f2f23a6bcd

[ "MIT" ]

66

2017-11-23T04:27:56.000Z

2022-01-10T07:17:06.000Z

aliyunlogcli/exceptions.py

lichengseu/aliyun-log-cli

3ada08feda5d07638a00030b2dd402f2f23a6bcd

[ "MIT" ]

15

2017-11-23T08:35:51.000Z

2022-02-15T02:44:07.000Z

class CLIExceptionBase(Exception): pass class ConfigurationError(CLIExceptionBase): pass class IncompleteAccountInfoError(ConfigurationError): pass

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9d2f013e2aa59699d4429f622ae7c6218cbd6bd8

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py

Python

snakes/python/summer-league-2022/src/tests.py

es-na-battlesnake/snakes

b18eedeb538cc4cbf1b1f0ed9e7334471e469c48

[ "MIT" ]

3

2022-01-31T19:11:35.000Z

2022-02-01T22:37:24.000Z

snakes/python/summer-league-2022/src/tests.py

es-na-battlesnake/snakes

b18eedeb538cc4cbf1b1f0ed9e7334471e469c48

[ "MIT" ]

60

2022-01-31T17:53:01.000Z

2022-03-30T22:35:50.000Z

snakes/python/summer-league-2022/src/tests.py

es-na-battlesnake/snakes

b18eedeb538cc4cbf1b1f0ed9e7334471e469c48

[ "MIT" ]

null

""" Starter Unit Tests using the built-in Python unittest library. See https://docs.python.org/3/library/unittest.html You can expand these to cover more cases! To run the unit tests, use the following command in your terminal, in the folder where this file exists: python src/tests.py -v """ import unittest import logic class AvoidNeckTest(unittest.TestCase): def test_avoid_neck_all(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 5}, {"x": 5, "y": 5}] possible_moves = ["up", "down", "left", "right"] # Act result_moves = logic._avoid_my_neck(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 4) self.assertEqual(possible_moves, result_moves) def test_avoid_neck_left(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 4, "y": 5}, {"x": 3, "y": 5}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "down", "right"] # Act result_moves = logic._avoid_my_neck(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 3) self.assertEqual(expected, result_moves) def test_avoid_neck_right(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 6, "y": 5}, {"x": 7, "y": 5}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "down", "left"] # Act result_moves = logic._avoid_my_neck(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 3) self.assertEqual(expected, result_moves) def test_avoid_neck_up(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 6}, {"x": 5, "y": 7}] possible_moves = ["up", "down", "left", "right"] expected = ["down", "left", "right"] # Act result_moves = logic._avoid_my_neck(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 3) self.assertEqual(expected, result_moves) def test_avoid_neck_down(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 4}, {"x": 5, "y": 3}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "left", "right"] # Act result_moves = logic._avoid_my_neck(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 3) self.assertEqual(expected, result_moves) class AvoidBodyTest(unittest.TestCase): def test_avoid_self_right(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 4}, {"x": 6, "y": 4}, {"x": 6, "y": 5}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "left"] # Act result_moves = logic._avoid_my_body(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_self_left(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 4}, {"x": 4, "y": 4}, {"x": 4, "y": 5}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "right"] # Act result_moves = logic._avoid_my_body(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_self_up(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 4, "y": 5}, {"x": 4, "y": 6}, {"x": 5, "y": 6}] possible_moves = ["up", "down", "left", "right"] expected = ["down", "right"] # Act result_moves = logic._avoid_my_body(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_self_down(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 4, "y": 5}, {"x": 4, "y": 4}, {"x": 5, "y": 4}] possible_moves = ["up", "down", "left", "right"] expected = ["up", "right"] # Act result_moves = logic._avoid_my_body(test_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) class AvoidSnakeTest(unittest.TestCase): def test_avoid_snake_right(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 4, "y": 5}, {"x": 3, "y": 5}, {"x": 2, "y": 5}] other_snakes_body = ([ {"x": 5, "y": 3}, {"x": 6, "y": 3}, {"x": 6, "y": 2} ], [ {"x": 0, "y": 0}, {"x": 1, "y": 0}, {"x": 2, "y": 0} ], [ {"x": 6, "y": 5}, {"x": 6, "y": 4}, {"x": 6, "y": 3} ]) possible_moves = ["up", "down", "right"] expected = ["up", "down"] # Act result_moves = logic._avoid_snake(test_body, other_snakes_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_snake_left(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 6, "y": 5}, {"x": 7, "y": 5}, {"x": 8, "y": 5}] other_snakes_body = ([ {"x": 5, "y": 3}, {"x": 6, "y": 3}, {"x": 6, "y": 2} ], [ {"x": 0, "y": 0}, {"x": 1, "y": 0}, {"x": 2, "y": 0} ], [ {"x": 4, "y": 5}, {"x": 4, "y": 4}, {"x": 4, "y": 3} ]) possible_moves = ["up", "down", "left"] expected = ["up", "down"] # Act result_moves = logic._avoid_snake(test_body, other_snakes_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_snake_down(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 6}, {"x": 5, "y": 7}, {"x": 5, "y": 8}] other_snakes_body = ([ {"x": 3, "y": 5}, {"x": 2, "y": 5} ], [ {"x": 0, "y": 0}, {"x": 1, "y": 0}, {"x": 2, "y": 0} ], [ {"x": 5, "y": 4}, {"x": 6, "y": 4}, {"x": 6, "y": 3} ]) possible_moves = ["right", "down", "left"] expected = ["right", "left"] # Act result_moves = logic._avoid_snake(test_body, other_snakes_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_snake_up(self): # Arrange test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 4}, {"x": 5, "y": 3}, {"x": 5, "y": 2}] other_snakes_body = ([ {"x": 3, "y": 5}, {"x": 2, "y": 5} ], [ {"x": 0, "y": 0}, {"x": 1, "y": 0}, {"x": 2, "y": 0} ], [ {"x": 5, "y": 6}, {"x": 6, "y": 6}, {"x": 6, "y": 7} ]) possible_moves = ["up", "left", "right"] expected = ["left", "right"] # Act result_moves = logic._avoid_snake(test_body, other_snakes_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) def test_avoid_single_snake(self): test_body = [{"x": 5, "y": 5}, {"x": 5, "y": 4}, {"x": 5, "y": 3}, {"x": 5, "y": 2}] other_snakes_body = [] other_snakes_body.append([{"x": 5, "y": 6}, {"x": 6, "y": 6}, {"x": 6, "y": 7}]) possible_moves = ["up", "left", "right"] expected = ["left", "right"] # Act result_moves = logic._avoid_snake(test_body, other_snakes_body, possible_moves) # Assert self.assertEqual(len(result_moves), 2) self.assertEqual(expected, result_moves) class AvoidWallTest(unittest.TestCase): def test_avoid_wall_top_right(self): # Arrange test_body = [{"x": 10, "y": 10}, {"x": 9, "y": 10}, {"x": 8, "y": 10}, {"x": 7, "y": 10}] # Setup board object with width and height nested objects board = { "width": 10, "height": 10 } possible_moves = ["up", "right", "down"] expected = ["down"] # Act result_moves = logic._avoid_walls(test_body, possible_moves, board) # Assert self.assertEqual(len(result_moves), 1) self.assertEqual(expected, result_moves) if __name__ == "__main__": unittest.main()

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19d0dadf1374efc4cd17f05d6419891d018a279e

181

py

Python

whitelist.py

jshwi/gitspy

0c3f29cc74a3d1a52c64679f54dd97a41514491b

[ "MIT" ]

null

whitelist.py

jshwi/gitspy

0c3f29cc74a3d1a52c64679f54dd97a41514491b

[ "MIT" ]

9

2022-01-19T19:28:17.000Z

2022-03-03T19:33:28.000Z

whitelist.py

jshwi/gitspy

0c3f29cc74a3d1a52c64679f54dd97a41514491b

[ "MIT" ]

null

fixture_git # unused function (tests/conftest.py:44) fixture_mock_environment # unused function (tests/conftest.py:15) fixture_setup_git # unused function (tests/conftest.py:29)

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c20c36a60bf0764ba4a68c60b87db0642f33f1ac

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py

Python

handlers/users/__init__.py

YoshlikMedia/quiz-bot

2f694091f032dd31011f03351fe3f57a5b15af09

[ "Apache-2.0" ]

null

handlers/users/__init__.py

YoshlikMedia/quiz-bot

2f694091f032dd31011f03351fe3f57a5b15af09

[ "Apache-2.0" ]

null

handlers/users/__init__.py

YoshlikMedia/quiz-bot

2f694091f032dd31011f03351fe3f57a5b15af09

[ "Apache-2.0" ]

null

from . import help from . import start from . import add_quiz from . import get_poll from . import chooser from . import remove_keyboard from . import send_quiz

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c23367d5c6adbc8f60708cb4fdc80e3a809cadd5

28

py

Python

spiketoolkit/curation/__init__.py

Shawn-Guo-CN/spiketoolkit

11e60f3cd80c135c62e27538a4e141115a7e27ad

[ "MIT" ]

null

spiketoolkit/curation/__init__.py

Shawn-Guo-CN/spiketoolkit

11e60f3cd80c135c62e27538a4e141115a7e27ad

[ "MIT" ]

5

2019-02-15T20:16:43.000Z

2019-02-27T14:54:08.000Z

spiketoolkit/curation/__init__.py

Shawn-Guo-CN/spiketoolkit

11e60f3cd80c135c62e27538a4e141115a7e27ad

[ "MIT" ]

1

2019-02-15T14:40:54.000Z

from .curationlist import *

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dfe8d789b3b98d46a58ea9b5b838812ec050271d

2,263

py

Python

adsmutils/tests/test_service.py

nemanjamart/ADSMicroserviceUtils

eb0898c04b3232d42728bc4760b773b3084708c0

[ "MIT" ]

null

adsmutils/tests/test_service.py

nemanjamart/ADSMicroserviceUtils

eb0898c04b3232d42728bc4760b773b3084708c0

[ "MIT" ]

34

2017-11-09T14:59:09.000Z

2021-02-03T19:40:07.000Z

adsmutils/tests/test_service.py

nemanjamart/ADSMicroserviceUtils

eb0898c04b3232d42728bc4760b773b3084708c0

[ "MIT" ]

7

2017-11-09T18:33:42.000Z

2021-09-13T20:43:10.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, unicode_literals, division, print_function import unittest import mock from .base import TestCase, TestCaseDatabase class TestServices(TestCase): def test_readiness_probe(self): '''Tests for the existence of a /ready route, and that it returns properly formatted JSON data''' r = self.client.get(u'/ready') self.assertEqual(r.status_code, 200) self.assertEqual(r.json[u'ready'], True) def test_liveliness_probe(self): '''Tests for the existence of a /alive route, and that it returns properly formatted JSON data''' r = self.client.get(u'/alive') self.assertEqual(r.status_code, 200) self.assertEqual(r.json[u'alive'], True) class TestServicesWithDatabase(TestCaseDatabase): def test_readiness_probe(self): '''Tests for the existence of a /ready route, and that it returns properly formatted JSON data''' r = self.client.get(u'/ready') self.assertEqual(r.status_code, 200) self.assertEqual(r.json[u'ready'], True) def test_liveliness_probe(self): '''Tests for the existence of a /alive route, and that it returns properly formatted JSON data''' r = self.client.get(u'/alive') self.assertEqual(r.status_code, 200) self.assertEqual(r.json[u'alive'], True) def test_readiness_probe_with_db_failure(self): '''Tests for the existence of a /ready route, and that it returns properly formatted JSON data when database connection has been lost''' self.app._db_failure = mock.MagicMock(return_value=True) r = self.client.get(u'/ready') self.assertEqual(r.status_code, 503) self.assertEqual(r.json[u'ready'], False) def test_liveliness_probe_with_db_failure(self): '''Tests for the existence of a /alive route, and that it returns properly formatted JSON data when database connection has been lost''' self.app._db_failure = mock.MagicMock(return_value=True) r = self.client.get(u'/alive') self.assertEqual(r.status_code, 503) self.assertEqual(r.json[u'alive'], False) if __name__ == '__main__': unittest.main()

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a06bfb5f6cc4088ca63dc1aa280860edf86d8609

316

py

Python

tests/lcmap_fakes.py

lcmap/client-py

fc356d9b2917f8e2d0e73048c9bf86982caa6676

[ "NASA-1.3" ]

null

tests/lcmap_fakes.py

lcmap/client-py

fc356d9b2917f8e2d0e73048c9bf86982caa6676

[ "NASA-1.3" ]

null

tests/lcmap_fakes.py

lcmap/client-py

fc356d9b2917f8e2d0e73048c9bf86982caa6676

[ "NASA-1.3" ]

null

class FakeLCMAPHTTP(object): def __init__(self, fake_response): self.fake_response = fake_response def get(self, *args, **kwargs): return self.fake_response class FakeLCMAPRESTResponse(object): def __init__(self, data): self.data = data self.result = data["result"]

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a09da0f19e6c16c9ba97ce1d3a439ecda364aad6

931

py

Python

src/croudtech_python_aws_app_config/tests/redis_config_test.py

CroudTech/croudtech-python-aws-app-config

5ffb6f6ee5e953f55f2dfc3dc5751514803a3373

[ "MIT" ]

null

src/croudtech_python_aws_app_config/tests/redis_config_test.py

CroudTech/croudtech-python-aws-app-config

5ffb6f6ee5e953f55f2dfc3dc5751514803a3373

[ "MIT" ]

null

src/croudtech_python_aws_app_config/tests/redis_config_test.py

CroudTech/croudtech-python-aws-app-config

5ffb6f6ee5e953f55f2dfc3dc5751514803a3373

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest import json from croudtech_python_aws_app_config.redis_config import RedisConfig __author__ = "Jim Robinson" __copyright__ = "Jim Robinson" def test_redis_config(): redis_config_instance = RedisConfig( redis_host="127.0.0.1", redis_port=6379, app_name="test_app", environment="test" ) redis_database = redis_config_instance.get_redis_database() assert redis_database == 0 redis_config_instance = RedisConfig( redis_host="127.0.0.1", redis_port=6379, app_name="test_app", environment="test" ) redis_database = redis_config_instance.get_redis_database() assert redis_database == 0 redis_config_instance = RedisConfig( redis_host="127.0.0.1", redis_port=6379, app_name="test_app2", environment="test", ) redis_database = redis_config_instance.get_redis_database() assert redis_database == 1

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null

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26074f3c319c2b96f740231dc054eebfa2823f9d

33

py

Python

glue/plugins/tools/pv_slicer/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-12-17T07:58:35.000Z

glue/plugins/tools/pv_slicer/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

glue/plugins/tools/pv_slicer/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-08-04T14:10:12.000Z

from .pv_slicer import * # noqa

16.5

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261d25351c60f9e0e700ac6b5996e4787f07b0b5

201

py

Python

PythonClient/reinforcement_learning/airgym/envs/__init__.py

frietz58/AirSim

b960ac10bf1d2b80bf4fc5e9b32c94d2a71e11d8

[ "MIT" ]

null

PythonClient/reinforcement_learning/airgym/envs/__init__.py

frietz58/AirSim

b960ac10bf1d2b80bf4fc5e9b32c94d2a71e11d8

[ "MIT" ]

null

PythonClient/reinforcement_learning/airgym/envs/__init__.py

frietz58/AirSim

b960ac10bf1d2b80bf4fc5e9b32c94d2a71e11d8

[ "MIT" ]

null

from airgym.envs.airsim_env import AirSimEnv from airgym.envs.car_env import AirSimCarEnv from airgym.envs.drone_env import AirSimDroneEnv from airgym.envs.my_simple_drone_env import MyAirSimDroneEnv

33.5

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1

0

1

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1

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6

2641ba04e6e82394e4473862302135d6525f461f

197

py

Python

test_load_model.py

jaekookang/koclip

5e52ee9c80fb2862bf24ad3cdd4fd1548d56fb13

[ "Apache-2.0" ]

null

test_load_model.py

jaekookang/koclip

5e52ee9c80fb2862bf24ad3cdd4fd1548d56fb13

[ "Apache-2.0" ]

null

test_load_model.py

jaekookang/koclip

5e52ee9c80fb2862bf24ad3cdd4fd1548d56fb13

[ "Apache-2.0" ]

null

import os import requests import jax from koclip import load_koclip_custom os.environ['CUDA_VISIBLE_DEVICES'] = '1' model, processor = load_koclip_custom("koclip-base", cache_dir='koclip_model')

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197

9

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21.888889

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1

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1

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6

cd8241432430515b167aca1410c40382fb65db4c

86

py

Python

tests/test_user/__init__.py

nknaian/album_recs

b96981befb355261b4c02eadc8690863a1e9b285

[ "MIT" ]

2

2021-02-11T02:44:42.000Z

2021-02-25T01:37:48.000Z

tests/test_user/__init__.py

nknaian/album_recs

b96981befb355261b4c02eadc8690863a1e9b285

[ "MIT" ]

79

2020-10-06T12:47:42.000Z

2022-03-03T17:56:03.000Z

tests/test_user/__init__.py

nknaian/albumrecs

b96981befb355261b4c02eadc8690863a1e9b285

[ "MIT" ]

null

from tests import MusicrecsTestCase class UserTestCase(MusicrecsTestCase): pass

14.333333

38

0.813953

8

86

8.75

0.875

0

0.151163

86

5

39

17.2

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true

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0.666667

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null

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null

0

1

0

1

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6

f81d1787fa6c16f66e3ca14ee98764003fcd3ac3

152

py

Python

Code/source/objectDetection/__init__.py

Colorado-School-of-Mines-Robotics-Club/SpaceGrantMachineVision

992a8fd30ac9829ea2c941d758ba63ecd931f0e1

[ "MIT" ]

null

Code/source/objectDetection/__init__.py

Colorado-School-of-Mines-Robotics-Club/SpaceGrantMachineVision

992a8fd30ac9829ea2c941d758ba63ecd931f0e1

[ "MIT" ]

null

Code/source/objectDetection/__init__.py

Colorado-School-of-Mines-Robotics-Club/SpaceGrantMachineVision

992a8fd30ac9829ea2c941d758ba63ecd931f0e1

[ "MIT" ]

1

2022-02-09T05:13:11.000Z

from . import experimental from .featureDensity import * from .objectDetection import * from .contourDetection import * from .horizonDetection import *

25.333333

31

0.809211

15

152

8.2

0.466667

0.243902

0

0.131579

152

5

32

30.4

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0

1

0

true

0

1

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1

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1

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null

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6

f825a0ba159462363ac906c56e9e75c923114453

67

py

Python

src/vw-serving/src/vw_serving/sagemaker/config/__init__.py

yunzhe-tao/sagemaker-rl-container

36fac941f64006b9356880318066e28e207f56a8

[ "Apache-2.0" ]

65

2018-12-01T18:04:04.000Z

2022-02-01T19:44:32.000Z

src/vw-serving/src/vw_serving/sagemaker/config/__init__.py

yunzhe-tao/sagemaker-rl-container

36fac941f64006b9356880318066e28e207f56a8

[ "Apache-2.0" ]

28

2019-04-19T20:35:46.000Z

2021-05-27T23:22:20.000Z

src/vw-serving/src/vw_serving/sagemaker/config/__init__.py

yunzhe-tao/sagemaker-rl-container

36fac941f64006b9356880318066e28e207f56a8

[ "Apache-2.0" ]

38

2019-02-09T14:45:15.000Z

2022-03-11T07:06:21.000Z

from .config_helper import * # noqa from .status import * # noqa

22.333333

36

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67

5.111111

0.666667

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0

0.208955

67

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33.5

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1

0

null

0

1

0

1

0

1

0

6

f82a0249ab1e8fcf7682ab44555ca790b3c12a25

218

py

Python

webscrapbook/lib/shim/time.py

maxnikulin/PyWebScrapBook

8bcad37ce1c10969f3980125bf2641e247807f44

[ "MIT" ]

null

webscrapbook/lib/shim/time.py

maxnikulin/PyWebScrapBook

8bcad37ce1c10969f3980125bf2641e247807f44

[ "MIT" ]

null

webscrapbook/lib/shim/time.py

maxnikulin/PyWebScrapBook

8bcad37ce1c10969f3980125bf2641e247807f44

[ "MIT" ]

null

#!/usr/bin/env python3 """shim for time """ import time def time_ns(): # time.time_ns is available since Python 3.7 return int(time.time() * 1e9) if not hasattr(time, 'time_ns'): time.time_ns = time_ns

15.571429

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0.655963

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218

3.72973

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0.217391

0.202899

0.231884

0

0.028736

0.201835

218

13

49

16.769231

0.764368

0.357798

0

0.05303

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1

0.2

true

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0.2

0.6

0

null

1

0

null

0

1

0

1

0

6

f87df8502e7aae6214302c2eddcc4316660b964f

24

py

Python

src/cython/cyanodbc/__init__.py

cyanodbc/cyanodbc

6ed49ded15a545edf4b78886868daebc8c5d4874

[ "MIT" ]

2

2020-07-10T17:36:00.000Z

2020-08-12T14:57:48.000Z

src/cython/cyanodbc/__init__.py

detule/cyanodbc

e7713c3cc3333a018409ec50ee1e5836a8d85f06

[ "MIT" ]

15

2018-09-09T12:05:15.000Z

2020-07-07T12:06:16.000Z

src/cython/cyanodbc/__init__.py

detule/cyanodbc

e7713c3cc3333a018409ec50ee1e5836a8d85f06

[ "MIT" ]

1

2020-07-02T10:58:07.000Z

from ._cyanodbc import *

24

0.791667

3

24

6

1

0

0.125

24

1

24

0.857143

0

1

0

true

0

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null

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null

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0

1

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1

0

6

f8c4a61caf1065372f72d9bf7d044a6f87cb535e

39

py

Python

srlearn/__init__.py

rystrauss/symbolic-regression

c0c7e0afbddca30ea77a1d0758962f6349ee222d

[ "MIT" ]

4

2019-12-09T13:35:36.000Z

2021-12-19T02:13:01.000Z

srlearn/__init__.py

rystrauss/symbolic-regression

c0c7e0afbddca30ea77a1d0758962f6349ee222d

[ "MIT" ]

1

2020-10-08T08:33:51.000Z

2020-10-09T11:43:22.000Z

srlearn/__init__.py

rystrauss/symbolic-regression

c0c7e0afbddca30ea77a1d0758962f6349ee222d

[ "MIT" ]

1

2019-04-13T20:20:04.000Z

from .genetic import SymbolicRegressor

19.5

38

0.871795

4

39

8.5

1

0

0.102564

39

1

39

0.971429

0

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0

1

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null

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null

0

1

0

1

0

1

0

6

3e8b6d663b51a4501a673361492ababcad686e55

111

py

Python

my_functions.py

ricardgo403/literate-barnacle

1c008f0119eb58f09ac80a3796d97328037fc769

[ "MIT" ]

null

my_functions.py

ricardgo403/literate-barnacle

1c008f0119eb58f09ac80a3796d97328037fc769

[ "MIT" ]

null

my_functions.py

ricardgo403/literate-barnacle

1c008f0119eb58f09ac80a3796d97328037fc769

[ "MIT" ]

null

def add(a, b): return a + b def sub(a, b): if a > 0: return a - b else: return 0

11.1

20

0.423423

20

111

2.35

0.45

0.170213

0.340426

0

0.033333

0.459459

111

9

21

12.333333

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0.285714

false

0

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0

1

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null

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1

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1

0

null

0

1

0

1

0

6

e43c3e481258adf226fcd1efb41ce831aabcc125

33

py

Python

bodybuilder/__init__.py

alexsanjoseph/bodybuilder

c3e395537d101b07e517ee493a261c2cd3280fb7

[ "MIT" ]

1

2021-12-16T18:08:22.000Z

bodybuilder/__init__.py

alexsanjoseph/bodybuilder

c3e395537d101b07e517ee493a261c2cd3280fb7

[ "MIT" ]

null

bodybuilder/__init__.py

alexsanjoseph/bodybuilder

c3e395537d101b07e517ee493a261c2cd3280fb7

[ "MIT" ]

null

from .builder import BodyBuilder

16.5

32

0.848485

4

33

7

1

0

0.121212

33

1

33

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e476f90b5a752bbc444bd71bdc717fa918763916

15,343

py

Python

jason/figure2/plot_figure2.py

ajclaros/rl_legged_walker

26d0e124ef38045943449c2772b966571117683b

[ "MIT" ]

null

jason/figure2/plot_figure2.py

ajclaros/rl_legged_walker

26d0e124ef38045943449c2772b966571117683b

[ "MIT" ]

null

jason/figure2/plot_figure2.py

ajclaros/rl_legged_walker

26d0e124ef38045943449c2772b966571117683b

[ "MIT" ]

null

import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np def alpha_adj(color, alpha=0.25): """ Adjust alpha of color. """ return [color[0], color[1], color[2], alpha] def improvement_adj(init_fit, final_fit): """ Calculate improvement of y over x. """ #return x # if init_fit > final_fit: # print("it got worse") maxxed= max(0, 0.5 + (final_fit - init_fit) ) # minned = min(1, max(0, 0.5 + (final_fit - init_fit) )) # if not maxxed == minned: # print(maxxed, minned) return maxxed #return min(1, max(0, 0.5 + (final_fit - init_fit) )) ##################################################### legend_array=['much worse', 'worse', 'no change', 'better', 'much better'] # 500, 2000 # learning_duration=5000 # initflux=2 init_flux=4 learning_duration=5000 prefix="v1_plusminus8_by2" #prefix="v1_12x12x6" arrow_alpha=0.25 filename_prefix="10sec_" save_dat_filenames=[\ "v1_12x12x4_fig2_1.0k_initflux-1.dat",\ "v1_12x12x6_fig2_5.0k_initflux-1.dat",\ "v1_12x12x6_fig2_5.0k_initflux-2.dat",\ "v1_12x12x6_fig2_5.0k_initflux-4.dat",\ "v1_12x12x6_trial2_fig2_5.0k_initflux-1.dat",\ "v1_plusminus2_fig2_1.0k_initflux-1.dat",\ "v1_plusminus2_fig2_1.0k_initflux-2.dat",\ "v1_plusminus2_fig2_1.0k_initflux-4.dat",\ "v1_plusminus3_fig2_1.0k_initflux-1.dat",\ "v1_plusminus8_by2_fig2_5.0k_initflux-3.dat",\ "v1_plusminus8_by2_fig2_5.0k_initflux-4.dat"] #save_dat_filenames.append( f"{prefix}_fig2_{learning_duration/1000}k_initflux-{init_flux}.dat" ) write_figures=[0,1,2,3,4,5,6,7,8] write_figures=[7,8] loctext="lower left" img_dim=(3.5,2) for save_dat_filename in save_dat_filenames: plot_save_filename=f"plots/{save_dat_filename}".replace(".dat", "") #plot_save_filename=f"plots/{prefix}_fig2_{learning_duration/1000}k_initflux-{init_flux}" show_fitness_background=True data = np.genfromtxt(save_dat_filename,delimiter=",", dtype=float) cmap = plt.cm.gnuplot cmap = plt.cm.PiYG cmap = plt.cm.Spectral #cmap = plt.cm.turbo custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4), Line2D([0], [0], color=cmap(.25), lw=4), Line2D([0], [0], color=cmap(.5), lw=4), Line2D([0], [0], color=cmap(.75), lw=4), Line2D([0], [0], color=cmap(1.), lw=4)] first_time_index=13 threshold=0.5 success=0 for index in range(len(data)): final_fit=data[index][1] if final_fit>threshold: alpha=1 success+=1 ################################### 1 if 1 in write_figures: for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_fit, final_fit, color=[improvement,0,0,alpha] ) plt.scatter( init_fit, final_fit, color=alpha_adj(cmap(improvement) ) ) plt.plot( [0,100], [0,100], color=[.5,.5,.5,0.25] ) plt.title(f"success: {100*success/len(data):0.3f}%") plt.xlabel("init_fit") plt.ylabel("final_fit") plt.legend(custom_lines, legend_array,loc="upper right") plt.xlim(0,.8) plt.ylim(0,.8) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_init_fit_X_final_fit.png", dpi=300, \ bbox_inches='tight' ) plt.clf() #plt.show() ################################### 2 if 2 in write_figures: for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_dist, color=[final_fit,0,0,0.25] ) plt.scatter( init_dist, final_dist, color=alpha_adj(cmap(improvement) ) ) x_min = plt.xlim() y_min = plt.ylim() plt.xlim(0,x_min[1]) plt.ylim(0,y_min[1]) plt.plot( [0,100], [0,100], color=[.5,.5,.5,0.25] ) plt.title(f"success: {100*success/len(data):0.3f}%") plt.xlabel("init_dist") plt.ylabel("final_dist") plt.legend(custom_lines, legend_array,loc="upper right") plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_init_dist_X_final_dist.png", dpi=300, \ bbox_inches='tight' ) plt.clf() ################################### 3 if 3 in write_figures: #plt.show() # for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) #plt.scatter( init_dist, final_fit, color=cmap(improvement) ) plt.arrow( init_dist, init_fit, 0,final_fit-init_fit, color=alpha_adj(cmap(improvement),alpha=arrow_alpha ), head_width=0.04, head_length=0.01 ) plt.xlabel("init_dist") plt.ylabel("fitness") plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_init_dist_X_fit_change.png", dpi=300, \ bbox_inches='tight' ) plt.clf() #plt.show() ################################### 4 if 4 in write_figures: #ADD PLOT START AND STOP ################################ if show_fitness_background: #draw fitness plot and overlay... load="fitness_0_0__1_1.csv" fit_data = np.genfromtxt(load,delimiter=",", dtype=float, names=True) w_a_label=fit_data.dtype.names[0] w_b_label=fit_data.dtype.names[1] alpha=1 length=len(fit_data) plot_fit_data=np.zeros( (2,length ) ) colors=[] for i in range(len(fit_data)): plot_fit_data[0][i] = w_a = fit_data[i][0] plot_fit_data[1][i] = w_a = fit_data[i][1] fit=fit_data[i][2] if fit > 1.0: fit = 1.0 colors.append( [fit, fit, fit, alpha] ) wAs=plot_fit_data[0] wBs=plot_fit_data[1] plt.scatter(wAs, wBs, c=colors ) ####################################### for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] init_w00=data[index][4] init_w01=data[index][5] init_w10=data[index][6] init_w11=data[index][7] final_w00=data[index][8] final_w01=data[index][9] final_w10=data[index][10] final_w11=data[index][11] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) #plt.scatter( init_dist, final_fit, color=cmap(improvement) ) head_width=0.4 head_length=0.1 plt.arrow( init_w00, init_w11, final_w00-init_w00, final_w11-init_w11, color=alpha_adj(cmap(improvement),alpha=arrow_alpha ),\ head_width=head_width, head_length=head_length ) plt.xlabel("w00") plt.ylabel("w11") plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_w00_X_w11.png", dpi=300, \ bbox_inches='tight' ) plt.clf() #plt.show() ################################### 5 if 5 in write_figures: for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) plt.scatter( init_dist, init_fit, color=alpha_adj(cmap(improvement) ) ) plt.xlabel("init_dist") plt.ylabel("init_fit") plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_init_dist_X_init_fit.png", dpi=300, \ bbox_inches='tight' ) #plt.show() plt.clf() ################################### 6 if 6 in write_figures: for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] improvement=improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) plt.scatter( init_dist, final_fit, color=alpha_adj(cmap(improvement) ) ) plt.xlabel("init_dist") plt.ylabel("final_fit") plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_init_dist_X_final_fit.png", dpi=300, \ bbox_inches='tight' ) #plt.show() plt.clf() ################################### 7 if 7 in write_figures: cmap=plt.get_cmap("autumn") #ADD PLOT START AND STOP ################################ if show_fitness_background: #draw fitness plot and overlay... load=f"{filename_prefix}fitness_0_1__1_0.csv" fit_data = np.genfromtxt(load,delimiter=",", dtype=float, names=True) w_a_label=fit_data.dtype.names[0] w_b_label=fit_data.dtype.names[1] alpha=1 length=len(fit_data) plot_fit_data=np.zeros( (2,length ) ) colors=[] for i in range(len(fit_data)): plot_fit_data[0][i] = w_a = fit_data[i][0] plot_fit_data[1][i] = w_a = fit_data[i][1] fit=fit_data[i][2] if fit > 1.0: fit = 1.0 colors.append( [fit, fit, fit, alpha] ) wAs=plot_fit_data[0] wBs=plot_fit_data[1] plt.scatter(wAs, wBs, c=colors ) ####################################### for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] init_w00=data[index][4] init_w01=data[index][5] init_w10=data[index][6] init_w11=data[index][7] final_w00=data[index][8] final_w01=data[index][9] final_w10=data[index][10] final_w11=data[index][11] fitness_adj= min(1.0, final_fit/.7 ) #improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) #plt.scatter( init_dist, final_fit, color=cmap(improvement) ) head_width=0.4 head_length=0.1 #if improvement> threshold: plt.arrow( init_w00, init_w11, final_w00-init_w00, final_w11-init_w11, color=alpha_adj(cmap(fitness_adj),alpha=arrow_alpha ),\ head_width=head_width, head_length=head_length ) plt.xlabel("w00") plt.ylabel("w11") legend_array = ['zero', 'poor', 'high'] custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4), Line2D([0], [0], color=cmap(.5), lw=4), Line2D([0], [0], color=cmap(1.), lw=4)] plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_w00_X_w11_color-finalfitness{filename_prefix}.png", dpi=300, \ bbox_inches='tight' ) plt.clf() if 8 in write_figures: #ADD PLOT START AND STOP ################################ if show_fitness_background: #draw fitness plot and overlay... load=f"{filename_prefix}fitness_0_1__1_0.csv" fit_data = np.genfromtxt(load,delimiter=",", dtype=float, names=True) w_a_label=fit_data.dtype.names[0] w_b_label=fit_data.dtype.names[1] alpha=1 length=len(fit_data) plot_fit_data=np.zeros( (2,length ) ) colors=[] for i in range(len(fit_data)): plot_fit_data[0][i] = w_a = fit_data[i][0] plot_fit_data[1][i] = w_a = fit_data[i][1] fit=fit_data[i][2] if fit > 1.0: fit = 1.0 colors.append( [fit, fit, fit, alpha] ) wAs=plot_fit_data[0] wBs=plot_fit_data[1] plt.scatter(wAs, wBs, c=colors ) ####################################### for index in range(len(data)): #init_fit,final_fit,init_est_dist,final_est_dist init_fit=data[index][0] final_fit=data[index][1] init_dist=data[index][2] final_dist=data[index][3] init_w00=data[index][4] init_w01=data[index][5] init_w10=data[index][6] init_w11=data[index][7] final_w00=data[index][8] final_w01=data[index][9] final_w10=data[index][10] final_w11=data[index][11] fitness_adj= min(1.0, final_fit/.7 ) #improvement_adj(init_fit, final_fit) #plt.scatter( init_dist, final_fit, color=[improvement,0,0,0.25] ) #plt.scatter( init_dist, final_fit, color=cmap(improvement) ) head_width=0.4 head_length=0.1 #if improvement> threshold: plt.arrow( init_w01, init_w10, final_w01-init_w01, final_w10-init_w10, color=alpha_adj(cmap(fitness_adj),alpha=arrow_alpha ),\ head_width=head_width, head_length=head_length ) plt.xlabel("w01") plt.ylabel("w10") plt.legend(custom_lines, legend_array,loc=loctext) plt.rcParams["figure.figsize"] = img_dim plt.savefig(f"{plot_save_filename}_w01_X_w10_color-finalfitness{filename_prefix}.png", dpi=300, \ bbox_inches='tight' ) plt.clf()

34.71267

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2,070

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3.851208

0.08599

0.06435

0.030105

0.037632

0.848595

0.836678

0.824385

0.803939

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0.769443

0

0.053604

0.287493

15,343

441

157

34.791383

0.675631

0.134002

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0.698182

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1

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false

0

0.010909

0

0.025455

0

null

0

1

0

null

0

6

e4a28324bddfdac4026349647e84d8c90b4de748

121

py

Python

caos/_cli_commands/version_command.py

caotic-co/caos

27bdb25486cb37d26a821b7ff21d56526df8d6d2

[ "Apache-2.0" ]

null

caos/_cli_commands/version_command.py

caotic-co/caos

27bdb25486cb37d26a821b7ff21d56526df8d6d2

[ "Apache-2.0" ]

null

caos/_cli_commands/version_command.py

caotic-co/caos

27bdb25486cb37d26a821b7ff21d56526df8d6d2

[ "Apache-2.0" ]

null

from caos import __VERSION__ def show_version() -> None: print("You are using caos version {}".format(__VERSION__))

24.2

62

0.727273

16

121

4.9375

0.75

0

0.157025

121

5

62

24.2

0.77451

0

0.237705

0

1

0.333333

true

0

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0

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0.333333

1

0

null

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null

0

1

0

1

0

1

0

6

e4ad5aa6db9340ae13c8c05985546270b262a00a

19,997

py

Python

corehq/apps/data_interfaces/tests/test_utils.py

omari-funzone/commcare-hq

5edb462c891fc08e51c4babd7acdf12c0006a602

[ "BSD-3-Clause" ]

null

corehq/apps/data_interfaces/tests/test_utils.py

omari-funzone/commcare-hq

5edb462c891fc08e51c4babd7acdf12c0006a602

[ "BSD-3-Clause" ]

34

2020-12-11T18:51:17.000Z

2022-02-21T10:13:26.000Z

corehq/apps/data_interfaces/tests/test_utils.py

omari-funzone/commcare-hq

5edb462c891fc08e51c4babd7acdf12c0006a602

[ "BSD-3-Clause" ]

null

from unittest.case import TestCase from unittest.mock import Mock, patch from couchdbkit import ResourceNotFound from corehq.apps.data_interfaces.tasks import ( _get_repeat_record_ids, task_generate_ids_and_operate_on_payloads, ) from corehq.apps.data_interfaces.utils import ( _validate_record, operate_on_payloads, ) class TestUtils(TestCase): def test__get_ids_no_data(self): response = _get_repeat_record_ids(None, None, 'test_domain') self.assertEqual(response, []) @patch('corehq.apps.data_interfaces.tasks.get_repeat_records_by_payload_id') @patch('corehq.apps.data_interfaces.tasks.iter_repeat_records_by_repeater') def test__get_ids_payload_id_in_data(self, mock_iter_repeat_records_by_repeater, mock_get_repeat_records_by_payload_id): payload_id = Mock() _get_repeat_record_ids(payload_id, None, 'test_domain') self.assertEqual(mock_get_repeat_records_by_payload_id.call_count, 1) mock_get_repeat_records_by_payload_id.assert_called_with('test_domain', payload_id) self.assertEqual(mock_iter_repeat_records_by_repeater.call_count, 0) @patch('corehq.apps.data_interfaces.tasks.get_repeat_records_by_payload_id') @patch('corehq.apps.data_interfaces.tasks.iter_repeat_records_by_repeater') def test__get_ids_payload_id_not_in_data( self, mock_iter_repeat_records_by_repeater, mock_get_repeat_records_by_payload_id, ): REPEATER_ID = 'c0ffee' _get_repeat_record_ids(None, REPEATER_ID, 'test_domain') mock_get_repeat_records_by_payload_id.assert_not_called() mock_iter_repeat_records_by_repeater.assert_called_with('test_domain', REPEATER_ID) self.assertEqual(mock_iter_repeat_records_by_repeater.call_count, 1) @patch('corehq.motech.repeaters.models.RepeatRecord') def test__validate_record_record_does_not_exist(self, mock_RepeatRecord): mock_RepeatRecord.get.side_effect = [ResourceNotFound] response = _validate_record('id_1', 'test_domain') mock_RepeatRecord.get.assert_called_once() self.assertIsNone(response) @patch('corehq.motech.repeaters.models.RepeatRecord') def test__validate_record_invalid_domain(self, mock_RepeatRecord): mock_payload = Mock() mock_payload.domain = 'domain' mock_RepeatRecord.get.return_value = mock_payload response = _validate_record('id_1', 'test_domain') mock_RepeatRecord.get.assert_called_once() self.assertIsNone(response) @patch('corehq.motech.repeaters.models.RepeatRecord') def test__validate_record_success(self, mock_RepeatRecord): mock_payload = Mock() mock_payload.domain = 'test_domain' mock_RepeatRecord.get.return_value = mock_payload response = _validate_record('id_1', 'test_domain') mock_RepeatRecord.get.assert_called_once() self.assertEqual(response, mock_payload) class TestTasks(TestCase): def setUp(self): self.mock_payload_one, self.mock_payload_two = Mock(id='id_1'), Mock(id='id_2') self.mock_payload_ids = [self.mock_payload_one.id, self.mock_payload_two.id] @patch('corehq.apps.data_interfaces.tasks._get_repeat_record_ids') @patch('corehq.apps.data_interfaces.tasks.operate_on_payloads') def test_generate_ids_and_operate_on_payloads_success(self, mock_operate_on_payloads, mock__get_ids): payload_id = 'c0ffee' repeater_id = 'deadbeef' task_generate_ids_and_operate_on_payloads( payload_id, repeater_id, 'test_domain', 'test_action') mock__get_ids.assert_called_once() mock__get_ids.assert_called_with('c0ffee', 'deadbeef', 'test_domain') mock_record_ids = mock__get_ids('c0ffee', 'deadbeef', 'test_domain') mock_operate_on_payloads.assert_called_once() mock_operate_on_payloads.assert_called_with(mock_record_ids, 'test_domain', 'test_action', task=task_generate_ids_and_operate_on_payloads) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_false_resend(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'resend') expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully resend payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully resend 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_resend(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_true_resend(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'resend', from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully resend payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_resend(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_false_resend(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'resend', task=Mock()) expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully resend payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully resend 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_resend(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_true_resend(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'resend', task=Mock(), from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully resend payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_resend(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_false_cancel(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'cancel') expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully cancelled payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully cancel 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_cancel(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_true_cancel(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'cancel', from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully cancelled payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_cancel(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_false_cancel(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'cancel', task=Mock()) expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully cancelled payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully cancel 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_cancel(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_true_cancel(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'cancel', task=Mock(), from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully cancelled payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_cancel(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_false_requeue(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'requeue') expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully requeue 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_requeue(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_no_task_from_excel_true_requeue(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'requeue', from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 0) self._check_requeue(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_false_requeue(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'requeue', task=Mock()) expected_response = { 'messages': { 'errors': [], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], 'success_count_msg': _("Successfully requeue 1 form(s)") } } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_requeue(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_with_task_from_excel_true_requeue(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, None] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'requeue', task=Mock(), from_excel=True) expected_response = { 'errors': [], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 2) self._check_requeue(self.mock_payload_one, self.mock_payload_two, response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_throws_exception_resend(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, self.mock_payload_two] self.mock_payload_two.fire.side_effect = [Exception] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'resend', task=Mock(), from_excel=True) expected_response = { 'errors': [_("Could not perform action for payload (id={}): {}").format(self.mock_payload_two.id, Exception)], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 3) self.assertEqual(self.mock_payload_one.fire.call_count, 1) self.assertEqual(self.mock_payload_two.fire.call_count, 1) self.assertEqual(response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_throws_exception_cancel(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, self.mock_payload_two] self.mock_payload_two.cancel.side_effect = [Exception] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'cancel', task=Mock(), from_excel=True) expected_response = { 'errors': [_("Could not perform action for payload (id={}): {}").format(self.mock_payload_two.id, Exception)], 'success': [_('Successfully cancelled payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 3) self.assertEqual(self.mock_payload_one.cancel.call_count, 1) self.assertEqual(self.mock_payload_one.save.call_count, 1) self.assertEqual(self.mock_payload_two.cancel.call_count, 1) self.assertEqual(self.mock_payload_two.save.call_count, 0) self.assertEqual(response, expected_response) @patch('corehq.apps.data_interfaces.utils.DownloadBase') @patch('corehq.apps.data_interfaces.utils._validate_record') def test_operate_on_payloads_throws_exception_requeue(self, mock__validate_record, mock_DownloadBase): mock__validate_record.side_effect = [self.mock_payload_one, self.mock_payload_two] self.mock_payload_two.requeue.side_effect = [Exception] with patch('corehq.apps.data_interfaces.utils._') as _: response = operate_on_payloads(self.mock_payload_ids, 'test_domain', 'requeue', task=Mock(), from_excel=True) expected_response = { 'errors': [_("Could not perform action for payload (id={}): {}").format(self.mock_payload_two.id, Exception)], 'success': [_('Successfully requeue payload (id={})').format(self.mock_payload_one.id)], } self.assertEqual(mock_DownloadBase.set_progress.call_count, 3) self.assertEqual(self.mock_payload_one.requeue.call_count, 1) self.assertEqual(self.mock_payload_one.save.call_count, 1) self.assertEqual(self.mock_payload_two.requeue.call_count, 1) self.assertEqual(self.mock_payload_two.save.call_count, 0) self.assertEqual(response, expected_response) def _check_resend(self, mock_payload_one, mock_payload_two, response, expected_response): self.assertEqual(mock_payload_one.fire.call_count, 1) self.assertEqual(mock_payload_two.fire.call_count, 0) self.assertEqual(response, expected_response) def _check_cancel(self, mock_payload_one, mock_payload_two, response, expected_response): self.assertEqual(mock_payload_one.cancel.call_count, 1) self.assertEqual(mock_payload_one.save.call_count, 1) self.assertEqual(mock_payload_two.cancel.call_count, 0) self.assertEqual(mock_payload_two.save.call_count, 0) self.assertEqual(response, expected_response) def _check_requeue(self, mock_payload_one, mock_payload_two, response, expected_response): self.assertEqual(mock_payload_one.requeue.call_count, 1) self.assertEqual(mock_payload_one.save.call_count, 1) self.assertEqual(mock_payload_two.requeue.call_count, 0) self.assertEqual(mock_payload_two.save.call_count, 0) self.assertEqual(response, expected_response)

53.467914

121

0.697055

2,384

19,997

5.400587

0.042366

0.072699

0.111845

0.098796

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0.885437

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0.855456

0

0.003309

0.19908

19,997

373

122

53.61126

0.800574

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0.208781

0.123268

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0.085246

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0.016393

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0.108197

0

null

0

1

0

null

0

6

e4ba90c933da98a012315209c2a6490d8057af7b

29

py

Python

ChordDetection/__init__.py

belovm96/chord-detection

c1cc240dde41cd03c4e00ecc384b1d2670663783

[ "MIT" ]

10

2021-10-31T14:48:48.000Z

2022-02-13T16:17:29.000Z

ChordDetection/__init__.py

belovm96/chord-detection

c1cc240dde41cd03c4e00ecc384b1d2670663783

[ "MIT" ]

null

ChordDetection/__init__.py

belovm96/chord-detection

c1cc240dde41cd03c4e00ecc384b1d2670663783

[ "MIT" ]

1

2022-01-04T10:00:20.000Z

from .ChordDetection import *

29

0.827586

3

29

8

1

0

0.103448

29

1

29

0.923077

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null

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1

0

1

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1

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6

e4cdfb315f6bc0589810caae11930d862bf592bf

8,098

py

Python

tests/sklearn/test_SVMConverters.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

1

2018-04-10T02:30:47.000Z

tests/sklearn/test_SVMConverters.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

null

tests/sklearn/test_SVMConverters.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

1

2018-06-27T18:16:20.000Z

""" Tests scikit-linear converter. """ import unittest import onnxmltools from sklearn.datasets import load_iris from sklearn.svm import SVC, SVR, NuSVC, NuSVR from onnxmltools import convert_sklearn from onnxmltools.convert.common.data_types import FloatTensorType class TestSklearnSVM(unittest.TestCase): def _fit_binary_classification(self, model): iris = load_iris() X = iris.data[:, :3] y = iris.target y[y == 2] = 1 model.fit(X, y) return model def _fit_multi_classification(self, model): iris = load_iris() X = iris.data[:, :3] y = iris.target model.fit(X, y) return model def _check_attributes(self, node, attribute_test): attributes = node.attribute attribute_map = {} for attribute in attributes: attribute_map[attribute.name] = attribute for k, v in attribute_test.items(): self.assertTrue(k in attribute_map) if v is not None: attrib = attribute_map[k] if isinstance(v, str): self.assertEqual(attrib.s, v.encode(encoding='UTF-8')) elif isinstance(v, int): self.assertEqual(attrib.i, v) elif isinstance(v, float): self.assertEqual(attrib.f, v) elif isinstance(v, list): self.assertEqual(attrib.ints, v) else: self.fail('Unknown type') def test_convert_svmc_linear_binary(self): model = self._fit_binary_classification(SVC(kernel='linear', probability=False)) nodes = convert_sklearn(model, 'SVC', [('input', FloatTensorType([1, 1]))]).graph.node self.assertIsNotNone(nodes) self.assertEqual(len(nodes), 2) svc_node = nodes[0] self._check_attributes(svc_node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'LINEAR', 'post_transform': None, 'rho': None, 'support_vectors': None, 'vectors_per_class': None}) def test_convert_svmc_linear_multi(self): model = self._fit_multi_classification(SVC(kernel='linear', probability=False)) nodes = convert_sklearn(model, 'SVC', [('input', FloatTensorType([1, 1]))]).graph.node self.assertIsNotNone(nodes) self.assertEqual(len(nodes), 2) svc_node = nodes[0] self._check_attributes(svc_node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'LINEAR', 'post_transform': None, 'rho': None, 'support_vectors': None, 'vectors_per_class': None}) def test_convert_svmr_linear_binary(self): model = self._fit_binary_classification(SVR(kernel='linear')) nodes = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node self.assertIsNotNone(nodes) self._check_attributes(nodes[0], {'coefficients': None, 'kernel_params': None, 'kernel_type': 'LINEAR', 'post_transform': None, 'rho': None, 'support_vectors': None}) def test_convert_svmr_linear_multi(self): model = self._fit_multi_classification(SVR(kernel='linear')) node = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node[0] self.assertIsNotNone(node) self._check_attributes(node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'LINEAR', 'post_transform': None, 'rho': None, 'support_vectors': None}) def test_convert_nusvmc_binary(self): model = self._fit_binary_classification(NuSVC(probability=False)) nodes = convert_sklearn(model, 'SVC', [('input', FloatTensorType([1, 1]))]).graph.node self.assertIsNotNone(nodes) self.assertEqual(len(nodes), 2) svc_node = nodes[0] self._check_attributes(svc_node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'RBF', 'post_transform': None, 'rho': None, 'support_vectors': None, 'vectors_per_class': None}) def test_convert_nusvmc_multi(self): model = self._fit_multi_classification(NuSVC(probability=False)) nodes = convert_sklearn(model, 'SVC', [('input', FloatTensorType([1, 1]))]).graph.node self.assertIsNotNone(nodes) self.assertEqual(len(nodes), 2) svc_node = nodes[0] self._check_attributes(svc_node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'RBF', 'post_transform': None, 'rho': None, 'support_vectors': None, 'vectors_per_class': None}) def test_convert_nusvmr_binary(self): model = self._fit_binary_classification(NuSVR()) node = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node[0] self.assertIsNotNone(node) self._check_attributes(node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'RBF', 'post_transform': None, 'rho': None, 'support_vectors': None}) def test_convert_nusvmr_multi(self): model = self._fit_multi_classification(NuSVR()) node = convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]).graph.node[0] self.assertIsNotNone(node) self._check_attributes(node, {'coefficients': None, 'kernel_params': None, 'kernel_type': 'RBF', 'post_transform': None, 'rho': None, 'support_vectors': None}) def test_registration_convert_nusvr_model(self): model = self._fit_binary_classification(NuSVR()) model_onnx = onnxmltools.convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]) self.assertIsNotNone(model_onnx) def test_registration_convert_nusvc_model(self): model = self._fit_multi_classification(NuSVC(probability=False)) model_onnx = onnxmltools.convert_sklearn(model, 'SVC', [('input', FloatTensorType([1, 1]))]) self.assertIsNotNone(model_onnx) def test_registration_convert_svr_model(self): model = self._fit_multi_classification(SVR(kernel='linear')) model_onnx = onnxmltools.convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]) self.assertIsNotNone(model_onnx) def test_registration_convert_svc_model(self): model = self._fit_binary_classification(SVC(kernel='linear', probability=False)) model_onnx = onnxmltools.convert_sklearn(model, 'SVR', [('input', FloatTensorType([1, 1]))]) self.assertIsNotNone(model_onnx)

47.081395

100

0.518276

743

8,098

5.402423

0.13459

0.035874

0.038864

0.047833

0.806428

0.794968

0.792975

0.744145

0.708022

0.663926

0

0.008095

0.374537

8,098

171

101

47.356725

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0.003705

0

0.673469

0

0.098883

0

0.142857

1

0.102041

false

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0.040816

0

0.163265

0

null

0

1

0

null

0

6

e4f1c5288cc0fff7143f13da7287e1f41fa66cc1

102

py

Python

models/__init__.py

TrentBrick/RewardConditionedUDRL

fdb2ebacb4c3a886b64eea4cc1dd528e05f84e11

[ "MIT" ]

10

2020-11-10T12:54:43.000Z

2021-11-12T09:48:43.000Z

models/__init__.py

TrentBrick/RewardConditionedUDRL

fdb2ebacb4c3a886b64eea4cc1dd528e05f84e11

[ "MIT" ]

2

2021-03-10T01:51:11.000Z

2022-03-22T02:36:30.000Z

models/__init__.py

TrentBrick/RewardConditionedUDRL

fdb2ebacb4c3a886b64eea4cc1dd528e05f84e11

[ "MIT" ]

1

2020-11-29T17:08:18.000Z

from .upsd_model import UpsdModel, UpsdBehavior, UpsdHyper from .advantage_model import AdvantageModel

51

58

0.872549

12

102

7.25

0.75

0.252874

0

0.088235

102

2

59

51

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true

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null

0

1

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1

0

1

0

6

900226f99f428532ad8785114d75714e12bead8b

34

py

Python

wander/__init__.py

dominictarro/wander

43f49a6e0d023414b5dd1f412963c2f875ee52f2

[ "MIT" ]

null

wander/__init__.py

dominictarro/wander

43f49a6e0d023414b5dd1f412963c2f875ee52f2

[ "MIT" ]

null

wander/__init__.py

dominictarro/wander

43f49a6e0d023414b5dd1f412963c2f875ee52f2

[ "MIT" ]

null

from wander.wander import Wandbox

17

33

0.852941

5

34

5.8

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34

1

34

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true

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

9017d21fd06f96099a4da740f25145cf34916c0f

3,572

py

Python

tests/test_maildir.py

baverman/norless

9b63e184bb529b2a26695a76aa50a9f3936de9ff

[ "MIT" ]

null

tests/test_maildir.py

baverman/norless

9b63e184bb529b2a26695a76aa50a9f3936de9ff

[ "MIT" ]

null

tests/test_maildir.py

baverman/norless

9b63e184bb529b2a26695a76aa50a9f3936de9ff

[ "MIT" ]

null

from email.mime.text import MIMEText from norless.maildir import Maildir def test_dir_create(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) assert path.check() assert path.stat().mode & 0777 == 0700 for p in ('new', 'cur', 'tmp'): pp = path.join(p) assert pp.check() assert pp.stat().mode & 0777 == 0700 def test_adding_unseen_message(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) msgkey = md.add('msg') msgpath = path.join('new').join(msgkey) assert msgpath.check() assert msgpath.read() == 'msg' assert msgpath.stat().mode & 0777 == 0600 assert not path.join('tmp').listdir() assert md.get_flags(msgkey) == '' assert msgkey in md md._invalidate() assert md.get_flags(msgkey) == '' def test_adding_seen_message(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) msgkey = md.add('msg', 'S') msgpath = path.join('cur').join(msgkey + ':2,S') assert msgpath.check() assert msgpath.read() == 'msg' assert msgpath.stat().mode & 0777 == 0600 assert not path.join('tmp').listdir() assert md.get_flags(msgkey) == 'S' md._invalidate() assert md.get_flags(msgkey) == 'S' def test_adding_message_object(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) msgkey = md.add(MIMEText('boo')) msgpath = path.join('new').join(msgkey) assert 'boo' in msgpath.read() def test_message_discard(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) md.discard('garbage') msgkey = md.add('boo') msgpath = path.join('new').join(msgkey) msgpath.remove() assert not msgpath.check() md.discard(msgkey) assert msgkey not in md._toc msgkey = md.add('boo') msgpath = path.join('new').join(msgkey) assert msgpath.check() md.discard(msgkey) assert not msgpath.check() assert msgkey not in md._toc msgkey = md.add('boo') msgpath = path.join('new').join(msgkey) assert msgpath.check() md._invalidate() md.discard(msgkey) assert msgkey not in md._toc assert not msgpath.check() msgkey = md.add('boo', 'S') msgpath = path.join('cur').join(msgkey + ':2,S') assert msgpath.check() md.discard(msgkey) assert msgkey not in md._toc assert not msgpath.check() msgkey = md.add('boo', 'S') msgpath = path.join('cur').join(msgkey + ':2,S') assert msgpath.check() md._invalidate() md.discard(msgkey) assert msgkey not in md._toc assert not msgpath.check() def test_iterflags(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) k1 = md.add('boo') k2 = md.add('boo', 'S') k3 = md.add('boo', 'SF') result = set(md.iterflags()) assert result == set([(k1, ''), (k2, 'S'), (k3, 'SF')]) def test_add_flags(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) key = md.add('boo') md.add_flags(key, 'S') assert not path.join('new').join(key).check() assert path.join('cur').join(key + ':2,S').check() assert md.get_flags(key) == 'S' md._invalidate() assert md.get_flags(key) == 'S' def test_set_flags(tmpdir): path = tmpdir.join('inbox') md = Maildir(path.strpath) key = md.add('boo', 'R') md.set_flags(key, 'S') assert not path.join('new').join(key + ':2,R').check() assert path.join('cur').join(key + ':2,S').check() assert md.get_flags(key) == 'S' md._invalidate() assert md.get_flags(key) == 'S'

27.060606

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503

3,572

4.280318

0.125249

0.055736

0.037157

0.074315

0.775197

0.774733

0.73386

0.716674

0.640966

0

0.015697

0.215286

3,572

131

60

27.267176

0.752408

0

0.679245

0

0.052632

0

0.386792

0

null

0

0.018868

null

0

null

0

1

0

null

0

1

0

6

9030725c0bf975b2fd83c963f6f8309fe409c286

136

py

Python

django-demo/demo/models/__init__.py

lukyth/django-test

7080878c1b8b6edd955f7a0216fc5274e7adaa0f

[ "BSD-3-Clause" ]

null

django-demo/demo/models/__init__.py

lukyth/django-test

7080878c1b8b6edd955f7a0216fc5274e7adaa0f

[ "BSD-3-Clause" ]

null

django-demo/demo/models/__init__.py

lukyth/django-test

7080878c1b8b6edd955f7a0216fc5274e7adaa0f

[ "BSD-3-Clause" ]

null

import os os.environ.setdefault("DJANGO_SETTINGS_MODULE", "django_demo.settings") from .Bank import Bank # todo : add unit module here

22.666667

71

0.786765

20

136

5.2

0.7

0

0.117647

136

5

72

27.2

0.866667

0.198529

0

0.392523

0.205607

0

0.2

0

1

0

true

0

0.666667

0

0.666667

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null

0

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0

1

0

1

0

1

0

1

0

6

9036afdbef7ddef79aef149fd07ceae0f0eabd5d

34,338

py

Python

tests/unit/gapic/documentai_v1beta2/test_document_understanding_service.py

oflaeschen/python-documentai

ea83083c315d4a97c29df35955f9547e2f869114

[ "Apache-2.0" ]

1

2020-06-24T19:28:16.000Z

tests/unit/gapic/documentai_v1beta2/test_document_understanding_service.py

oflaeschen/python-documentai

ea83083c315d4a97c29df35955f9547e2f869114

[ "Apache-2.0" ]

null

tests/unit/gapic/documentai_v1beta2/test_document_understanding_service.py

oflaeschen/python-documentai

ea83083c315d4a97c29df35955f9547e2f869114

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import mock import grpc from grpc.experimental import aio import math import pytest from google import auth from google.api_core import client_options from google.api_core import future from google.api_core import grpc_helpers from google.api_core import grpc_helpers_async from google.api_core import operation_async from google.api_core import operations_v1 from google.auth import credentials from google.auth.exceptions import MutualTLSChannelError from google.cloud.documentai_v1beta2.services.document_understanding_service import ( DocumentUnderstandingServiceAsyncClient, ) from google.cloud.documentai_v1beta2.services.document_understanding_service import ( DocumentUnderstandingServiceClient, ) from google.cloud.documentai_v1beta2.services.document_understanding_service import ( transports, ) from google.cloud.documentai_v1beta2.types import document from google.cloud.documentai_v1beta2.types import document_understanding from google.cloud.documentai_v1beta2.types import geometry from google.longrunning import operations_pb2 from google.oauth2 import service_account from google.rpc import status_pb2 as status # type: ignore def client_cert_source_callback(): return b"cert bytes", b"key bytes" def test__get_default_mtls_endpoint(): api_endpoint = "example.googleapis.com" api_mtls_endpoint = "example.mtls.googleapis.com" sandbox_endpoint = "example.sandbox.googleapis.com" sandbox_mtls_endpoint = "example.mtls.sandbox.googleapis.com" non_googleapi = "api.example.com" assert DocumentUnderstandingServiceClient._get_default_mtls_endpoint(None) is None assert ( DocumentUnderstandingServiceClient._get_default_mtls_endpoint(api_endpoint) == api_mtls_endpoint ) assert ( DocumentUnderstandingServiceClient._get_default_mtls_endpoint(api_mtls_endpoint) == api_mtls_endpoint ) assert ( DocumentUnderstandingServiceClient._get_default_mtls_endpoint(sandbox_endpoint) == sandbox_mtls_endpoint ) assert ( DocumentUnderstandingServiceClient._get_default_mtls_endpoint( sandbox_mtls_endpoint ) == sandbox_mtls_endpoint ) assert ( DocumentUnderstandingServiceClient._get_default_mtls_endpoint(non_googleapi) == non_googleapi ) @pytest.mark.parametrize( "client_class", [DocumentUnderstandingServiceClient, DocumentUnderstandingServiceAsyncClient], ) def test_document_understanding_service_client_from_service_account_file(client_class): creds = credentials.AnonymousCredentials() with mock.patch.object( service_account.Credentials, "from_service_account_file" ) as factory: factory.return_value = creds client = client_class.from_service_account_file("dummy/file/path.json") assert client._transport._credentials == creds client = client_class.from_service_account_json("dummy/file/path.json") assert client._transport._credentials == creds assert client._transport._host == "us-documentai.googleapis.com:443" def test_document_understanding_service_client_get_transport_class(): transport = DocumentUnderstandingServiceClient.get_transport_class() assert transport == transports.DocumentUnderstandingServiceGrpcTransport transport = DocumentUnderstandingServiceClient.get_transport_class("grpc") assert transport == transports.DocumentUnderstandingServiceGrpcTransport @pytest.mark.parametrize( "client_class,transport_class,transport_name", [ ( DocumentUnderstandingServiceClient, transports.DocumentUnderstandingServiceGrpcTransport, "grpc", ), ( DocumentUnderstandingServiceAsyncClient, transports.DocumentUnderstandingServiceGrpcAsyncIOTransport, "grpc_asyncio", ), ], ) def test_document_understanding_service_client_client_options( client_class, transport_class, transport_name ): # Check that if channel is provided we won't create a new one. with mock.patch.object( DocumentUnderstandingServiceClient, "get_transport_class" ) as gtc: transport = transport_class(credentials=credentials.AnonymousCredentials()) client = client_class(transport=transport) gtc.assert_not_called() # Check that if channel is provided via str we will create a new one. with mock.patch.object( DocumentUnderstandingServiceClient, "get_transport_class" ) as gtc: client = client_class(transport=transport_name) gtc.assert_called() # Check the case api_endpoint is provided. options = client_options.ClientOptions(api_endpoint="squid.clam.whelk") with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options) patched.assert_called_once_with( api_mtls_endpoint="squid.clam.whelk", client_cert_source=None, credentials=None, host="squid.clam.whelk", ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS is # "never". os.environ["GOOGLE_API_USE_MTLS"] = "never" with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class() patched.assert_called_once_with( api_mtls_endpoint=client.DEFAULT_ENDPOINT, client_cert_source=None, credentials=None, host=client.DEFAULT_ENDPOINT, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS is # "always". os.environ["GOOGLE_API_USE_MTLS"] = "always" with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class() patched.assert_called_once_with( api_mtls_endpoint=client.DEFAULT_MTLS_ENDPOINT, client_cert_source=None, credentials=None, host=client.DEFAULT_MTLS_ENDPOINT, ) # Check the case api_endpoint is not provided, GOOGLE_API_USE_MTLS is # "auto", and client_cert_source is provided. os.environ["GOOGLE_API_USE_MTLS"] = "auto" options = client_options.ClientOptions( client_cert_source=client_cert_source_callback ) with mock.patch.object(transport_class, "__init__") as patched: patched.return_value = None client = client_class(client_options=options) patched.assert_called_once_with( api_mtls_endpoint=client.DEFAULT_MTLS_ENDPOINT, client_cert_source=client_cert_source_callback, credentials=None, host=client.DEFAULT_MTLS_ENDPOINT, ) # Check the case api_endpoint is not provided, GOOGLE_API_USE_MTLS is # "auto", and default_client_cert_source is provided. os.environ["GOOGLE_API_USE_MTLS"] = "auto" with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=True, ): patched.return_value = None client = client_class() patched.assert_called_once_with( api_mtls_endpoint=client.DEFAULT_MTLS_ENDPOINT, client_cert_source=None, credentials=None, host=client.DEFAULT_MTLS_ENDPOINT, ) # Check the case api_endpoint is not provided, GOOGLE_API_USE_MTLS is # "auto", but client_cert_source and default_client_cert_source are None. os.environ["GOOGLE_API_USE_MTLS"] = "auto" with mock.patch.object(transport_class, "__init__") as patched: with mock.patch( "google.auth.transport.mtls.has_default_client_cert_source", return_value=False, ): patched.return_value = None client = client_class() patched.assert_called_once_with( api_mtls_endpoint=client.DEFAULT_ENDPOINT, client_cert_source=None, credentials=None, host=client.DEFAULT_ENDPOINT, ) # Check the case api_endpoint is not provided and GOOGLE_API_USE_MTLS has # unsupported value. os.environ["GOOGLE_API_USE_MTLS"] = "Unsupported" with pytest.raises(MutualTLSChannelError): client = client_class() del os.environ["GOOGLE_API_USE_MTLS"] def test_document_understanding_service_client_client_options_from_dict(): with mock.patch( "google.cloud.documentai_v1beta2.services.document_understanding_service.transports.DocumentUnderstandingServiceGrpcTransport.__init__" ) as grpc_transport: grpc_transport.return_value = None client = DocumentUnderstandingServiceClient( client_options={"api_endpoint": "squid.clam.whelk"} ) grpc_transport.assert_called_once_with( api_mtls_endpoint="squid.clam.whelk", client_cert_source=None, credentials=None, host="squid.clam.whelk", ) def test_batch_process_documents(transport: str = "grpc"): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), transport=transport ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = document_understanding.BatchProcessDocumentsRequest() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._transport.batch_process_documents), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = operations_pb2.Operation(name="operations/spam") response = client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the response is the type that we expect. assert isinstance(response, future.Future) @pytest.mark.asyncio async def test_batch_process_documents_async(transport: str = "grpc_asyncio"): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials(), transport=transport ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = document_understanding.BatchProcessDocumentsRequest() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.batch_process_documents), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( operations_pb2.Operation(name="operations/spam") ) response = await client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the response is the type that we expect. assert isinstance(response, future.Future) def test_batch_process_documents_field_headers(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials() ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = document_understanding.BatchProcessDocumentsRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._transport.batch_process_documents), "__call__" ) as call: call.return_value = operations_pb2.Operation(name="operations/op") client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value") in kw["metadata"] @pytest.mark.asyncio async def test_batch_process_documents_field_headers_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = document_understanding.BatchProcessDocumentsRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.batch_process_documents), "__call__" ) as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( operations_pb2.Operation(name="operations/op") ) await client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value") in kw["metadata"] def test_batch_process_documents_flattened(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials() ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._transport.batch_process_documents), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = operations_pb2.Operation(name="operations/op") # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. client.batch_process_documents( requests=[ document_understanding.ProcessDocumentRequest(parent="parent_value") ] ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0].requests == [ document_understanding.ProcessDocumentRequest(parent="parent_value") ] def test_batch_process_documents_flattened_error(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials() ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): client.batch_process_documents( document_understanding.BatchProcessDocumentsRequest(), requests=[ document_understanding.ProcessDocumentRequest(parent="parent_value") ], ) @pytest.mark.asyncio async def test_batch_process_documents_flattened_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.batch_process_documents), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = operations_pb2.Operation(name="operations/op") call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( operations_pb2.Operation(name="operations/spam") ) # Call the method with a truthy value for each flattened field, # using the keyword arguments to the method. response = await client.batch_process_documents( requests=[ document_understanding.ProcessDocumentRequest(parent="parent_value") ] ) # Establish that the underlying call was made with the expected # request object values. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0].requests == [ document_understanding.ProcessDocumentRequest(parent="parent_value") ] @pytest.mark.asyncio async def test_batch_process_documents_flattened_error_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.batch_process_documents( document_understanding.BatchProcessDocumentsRequest(), requests=[ document_understanding.ProcessDocumentRequest(parent="parent_value") ], ) def test_process_document(transport: str = "grpc"): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), transport=transport ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = document_understanding.ProcessDocumentRequest() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._transport.process_document), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = document.Document( uri="uri_value", content=b"content_blob", mime_type="mime_type_value", text="text_value", ) response = client.process_document(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the response is the type that we expect. assert isinstance(response, document.Document) assert response.uri == "uri_value" assert response.content == b"content_blob" assert response.mime_type == "mime_type_value" assert response.text == "text_value" @pytest.mark.asyncio async def test_process_document_async(transport: str = "grpc_asyncio"): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials(), transport=transport ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = document_understanding.ProcessDocumentRequest() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.process_document), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( document.Document( uri="uri_value", content=b"content_blob", mime_type="mime_type_value", text="text_value", ) ) response = await client.process_document(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the response is the type that we expect. assert isinstance(response, document.Document) assert response.uri == "uri_value" assert response.content == b"content_blob" assert response.mime_type == "mime_type_value" assert response.text == "text_value" def test_process_document_field_headers(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials() ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = document_understanding.ProcessDocumentRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._transport.process_document), "__call__" ) as call: call.return_value = document.Document() client.process_document(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) == 1 _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value") in kw["metadata"] @pytest.mark.asyncio async def test_process_document_field_headers_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = document_understanding.ProcessDocumentRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.process_document), "__call__" ) as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(document.Document()) await client.process_document(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value") in kw["metadata"] def test_credentials_transport_error(): # It is an error to provide credentials and a transport instance. transport = transports.DocumentUnderstandingServiceGrpcTransport( credentials=credentials.AnonymousCredentials() ) with pytest.raises(ValueError): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), transport=transport ) def test_transport_instance(): # A client may be instantiated with a custom transport instance. transport = transports.DocumentUnderstandingServiceGrpcTransport( credentials=credentials.AnonymousCredentials() ) client = DocumentUnderstandingServiceClient(transport=transport) assert client._transport is transport def test_transport_get_channel(): # A client may be instantiated with a custom transport instance. transport = transports.DocumentUnderstandingServiceGrpcTransport( credentials=credentials.AnonymousCredentials() ) channel = transport.grpc_channel assert channel transport = transports.DocumentUnderstandingServiceGrpcAsyncIOTransport( credentials=credentials.AnonymousCredentials() ) channel = transport.grpc_channel assert channel def test_transport_grpc_default(): # A client should use the gRPC transport by default. client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials() ) assert isinstance( client._transport, transports.DocumentUnderstandingServiceGrpcTransport ) def test_document_understanding_service_base_transport(): # Instantiate the base transport. transport = transports.DocumentUnderstandingServiceTransport( credentials=credentials.AnonymousCredentials() ) # Every method on the transport should just blindly # raise NotImplementedError. methods = ("batch_process_documents", "process_document") for method in methods: with pytest.raises(NotImplementedError): getattr(transport, method)(request=object()) # Additionally, the LRO client (a property) should # also raise NotImplementedError with pytest.raises(NotImplementedError): transport.operations_client def test_document_understanding_service_auth_adc(): # If no credentials are provided, we should use ADC credentials. with mock.patch.object(auth, "default") as adc: adc.return_value = (credentials.AnonymousCredentials(), None) DocumentUnderstandingServiceClient() adc.assert_called_once_with( scopes=("https://www.googleapis.com/auth/cloud-platform",) ) def test_document_understanding_service_transport_auth_adc(): # If credentials and host are not provided, the transport class should use # ADC credentials. with mock.patch.object(auth, "default") as adc: adc.return_value = (credentials.AnonymousCredentials(), None) transports.DocumentUnderstandingServiceGrpcTransport(host="squid.clam.whelk") adc.assert_called_once_with( scopes=("https://www.googleapis.com/auth/cloud-platform",) ) def test_document_understanding_service_host_no_port(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="us-documentai.googleapis.com" ), ) assert client._transport._host == "us-documentai.googleapis.com:443" def test_document_understanding_service_host_with_port(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), client_options=client_options.ClientOptions( api_endpoint="us-documentai.googleapis.com:8000" ), ) assert client._transport._host == "us-documentai.googleapis.com:8000" def test_document_understanding_service_grpc_transport_channel(): channel = grpc.insecure_channel("http://localhost/") # Check that if channel is provided, mtls endpoint and client_cert_source # won't be used. callback = mock.MagicMock() transport = transports.DocumentUnderstandingServiceGrpcTransport( host="squid.clam.whelk", channel=channel, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=callback, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert not callback.called def test_document_understanding_service_grpc_asyncio_transport_channel(): channel = aio.insecure_channel("http://localhost/") # Check that if channel is provided, mtls endpoint and client_cert_source # won't be used. callback = mock.MagicMock() transport = transports.DocumentUnderstandingServiceGrpcAsyncIOTransport( host="squid.clam.whelk", channel=channel, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=callback, ) assert transport.grpc_channel == channel assert transport._host == "squid.clam.whelk:443" assert not callback.called @mock.patch("grpc.ssl_channel_credentials", autospec=True) @mock.patch("google.api_core.grpc_helpers.create_channel", autospec=True) def test_document_understanding_service_grpc_transport_channel_mtls_with_client_cert_source( grpc_create_channel, grpc_ssl_channel_cred ): # Check that if channel is None, but api_mtls_endpoint and client_cert_source # are provided, then a mTLS channel will be created. mock_cred = mock.Mock() mock_ssl_cred = mock.Mock() grpc_ssl_channel_cred.return_value = mock_ssl_cred mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel transport = transports.DocumentUnderstandingServiceGrpcTransport( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=client_cert_source_callback, ) grpc_ssl_channel_cred.assert_called_once_with( certificate_chain=b"cert bytes", private_key=b"key bytes" ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, ssl_credentials=mock_ssl_cred, scopes=("https://www.googleapis.com/auth/cloud-platform",), ) assert transport.grpc_channel == mock_grpc_channel @mock.patch("grpc.ssl_channel_credentials", autospec=True) @mock.patch("google.api_core.grpc_helpers_async.create_channel", autospec=True) def test_document_understanding_service_grpc_asyncio_transport_channel_mtls_with_client_cert_source( grpc_create_channel, grpc_ssl_channel_cred ): # Check that if channel is None, but api_mtls_endpoint and client_cert_source # are provided, then a mTLS channel will be created. mock_cred = mock.Mock() mock_ssl_cred = mock.Mock() grpc_ssl_channel_cred.return_value = mock_ssl_cred mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel transport = transports.DocumentUnderstandingServiceGrpcAsyncIOTransport( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint="mtls.squid.clam.whelk", client_cert_source=client_cert_source_callback, ) grpc_ssl_channel_cred.assert_called_once_with( certificate_chain=b"cert bytes", private_key=b"key bytes" ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, ssl_credentials=mock_ssl_cred, scopes=("https://www.googleapis.com/auth/cloud-platform",), ) assert transport.grpc_channel == mock_grpc_channel @pytest.mark.parametrize( "api_mtls_endpoint", ["mtls.squid.clam.whelk", "mtls.squid.clam.whelk:443"] ) @mock.patch("google.api_core.grpc_helpers.create_channel", autospec=True) def test_document_understanding_service_grpc_transport_channel_mtls_with_adc( grpc_create_channel, api_mtls_endpoint ): # Check that if channel and client_cert_source are None, but api_mtls_endpoint # is provided, then a mTLS channel will be created with SSL ADC. mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel # Mock google.auth.transport.grpc.SslCredentials class. mock_ssl_cred = mock.Mock() with mock.patch.multiple( "google.auth.transport.grpc.SslCredentials", __init__=mock.Mock(return_value=None), ssl_credentials=mock.PropertyMock(return_value=mock_ssl_cred), ): mock_cred = mock.Mock() transport = transports.DocumentUnderstandingServiceGrpcTransport( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint=api_mtls_endpoint, client_cert_source=None, ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, ssl_credentials=mock_ssl_cred, scopes=("https://www.googleapis.com/auth/cloud-platform",), ) assert transport.grpc_channel == mock_grpc_channel @pytest.mark.parametrize( "api_mtls_endpoint", ["mtls.squid.clam.whelk", "mtls.squid.clam.whelk:443"] ) @mock.patch("google.api_core.grpc_helpers_async.create_channel", autospec=True) def test_document_understanding_service_grpc_asyncio_transport_channel_mtls_with_adc( grpc_create_channel, api_mtls_endpoint ): # Check that if channel and client_cert_source are None, but api_mtls_endpoint # is provided, then a mTLS channel will be created with SSL ADC. mock_grpc_channel = mock.Mock() grpc_create_channel.return_value = mock_grpc_channel # Mock google.auth.transport.grpc.SslCredentials class. mock_ssl_cred = mock.Mock() with mock.patch.multiple( "google.auth.transport.grpc.SslCredentials", __init__=mock.Mock(return_value=None), ssl_credentials=mock.PropertyMock(return_value=mock_ssl_cred), ): mock_cred = mock.Mock() transport = transports.DocumentUnderstandingServiceGrpcAsyncIOTransport( host="squid.clam.whelk", credentials=mock_cred, api_mtls_endpoint=api_mtls_endpoint, client_cert_source=None, ) grpc_create_channel.assert_called_once_with( "mtls.squid.clam.whelk:443", credentials=mock_cred, ssl_credentials=mock_ssl_cred, scopes=("https://www.googleapis.com/auth/cloud-platform",), ) assert transport.grpc_channel == mock_grpc_channel def test_document_understanding_service_grpc_lro_client(): client = DocumentUnderstandingServiceClient( credentials=credentials.AnonymousCredentials(), transport="grpc" ) transport = client._transport # Ensure that we have a api-core operations client. assert isinstance(transport.operations_client, operations_v1.OperationsClient) # Ensure that subsequent calls to the property send the exact same object. assert transport.operations_client is transport.operations_client def test_document_understanding_service_grpc_lro_async_client(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials(), transport="grpc_asyncio" ) transport = client._client._transport # Ensure that we have a api-core operations client. assert isinstance(transport.operations_client, operations_v1.OperationsAsyncClient) # Ensure that subsequent calls to the property send the exact same object. assert transport.operations_client is transport.operations_client

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9036d409d625b1cf82857704338a567f45207e91

77

py

Python

py_tdlib/constructors/search_messages_filter_document.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/search_messages_filter_document.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/search_messages_filter_document.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class searchMessagesFilterDocument(Type): pass

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6

5f5373e3b965bebe4e8c6fec606f482a717be825

15,502

py

Python

titan/routes/v3/workflow.py

KhaosResearch/TITAN-API

98a66b211792f4b42680828644938b062de42579

[ "MIT" ]

null

titan/routes/v3/workflow.py

KhaosResearch/TITAN-API

98a66b211792f4b42680828644938b062de42579

[ "MIT" ]

null

titan/routes/v3/workflow.py

KhaosResearch/TITAN-API

98a66b211792f4b42680828644938b062de42579

[ "MIT" ]

null

import math import traceback from typing import Callable, Optional from fastapi import APIRouter, Depends, HTTPException, Query from motor.motor_asyncio import AsyncIOMotorClient from starlette.requests import Request from starlette.status import HTTP_404_NOT_FOUND from titan.auth import get_user_by_username from titan.database import get_connection from titan.logger import get_logger from titan.manager import WorkflowManager from titan.models.workflow import ( State, Task, WorkflowInDB, WorkflowInDBWithStatus, WorkflowRequest, WorkflowSearchResult, WorkflowStatusSearchResult, ) logger = get_logger(__name__) router = APIRouter() @router.post( "/new", summary="Creates new workflow", tags=["workflow"], response_model=WorkflowInDB, response_description="Workflow from database with associated metadata", status_code=201, ) async def new( username: str, workflow: WorkflowRequest = WorkflowRequest(), db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowInDB: """ Creates new workflow in database. If workflow is specified, inserts workflow in database instead. """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") workflow = await WorkflowManager().insert(db, username=username, workflow=workflow) return workflow @router.post( "/update", summary="Updates workflow with new content", tags=["workflow"], responses={ 404: {"description": "Workflow not found"}, }, response_model=WorkflowInDB, response_description="Workflow from database with associated metadata", status_code=201, ) async def update( username: str, workflow_id: str, workflow: WorkflowRequest, db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowInDB: """ Updates existing workflow in database. """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") exists = await WorkflowManager().find_one(db, username=username, workflow_id=workflow_id) if not exists: raise HTTPException(status_code=404, detail=f"Workflow {workflow_id} not found") workflow = await WorkflowManager().upsert(db, username=username, workflow_id=workflow_id, workflow=workflow) return workflow def _exclude_keys(dictionary, keys: list): """Filters a dict by excluding certain keys.""" key_set = set(dictionary.keys()) - set(keys) return {key: dictionary[key] for key in key_set} @router.get( "/get", summary="Gets workflow(s)", tags=["workflow"], responses={404: {"description": "No results matching query were found"}}, response_model=WorkflowSearchResult, response_description="Search result", status_code=200, ) async def get( request: Request, username: str, workflow_id: Optional[str] = None, page_size: int = Query(default=1, ge=1), page_num: int = Query(default=1, ge=1), db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowSearchResult: """ Retrieves workflows from database. This endpoint allows an arbitrary number of optional query parameters for filtering purposes, e.g.: ```?username=test&page_size=1&page_num=1&metadata.key=value&metadata.key2=value2``` """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") if workflow_id: workflows, total_count = await WorkflowManager().find( db, username=username, id=workflow_id, page_size=page_size, page_num=page_num ) else: query_params = request.query_params filtering = _exclude_keys(query_params, ["username", "workflow_id", "page_size", "page_num"]) workflows, total_count = await WorkflowManager().find( db, username=username, page_size=page_size, page_num=page_num, **filtering ) if not workflows: raise HTTPException(status_code=404, detail="No results matching query were found") return WorkflowSearchResult( workflows=workflows, pagination={ "page_size": len(workflows), "page_num": page_num, "page_count": math.ceil(total_count / page_size), "total_count": total_count, }, ) @router.get( "/status", summary="Gets workflow(s) execution states", tags=["workflow"], responses={404: {"description": "No results matching query were found"}}, response_model=WorkflowStatusSearchResult, response_description="Search result", status_code=200, ) async def status( request: Request, username: str, workflow_id: Optional[str] = None, page_size: int = Query(default=1, ge=1), page_num: int = Query(default=1, ge=1), exclude_key: list = Query(default=["operators", "links"]), db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowStatusSearchResult: """ Retrieves workflows from database including its execution status. This endpoint allows an arbitrary number of optional query parameters for filtering purposes, e.g.: ```?username=test&page_size=1&page_num=1&metadata.key=value&metadata.key2=value2``` """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") if workflow_id: workflows, total_count = await WorkflowManager().find( db, username=username, id=workflow_id, page_size=page_size, page_num=page_num ) else: query_params = request.query_params filtering = _exclude_keys(query_params, ["username", "workflow_id", "page_size", "page_num", "exclude_key"]) workflows, total_count = await WorkflowManager().find( db, username=username, page_size=page_size, page_num=page_num, **filtering ) if not workflows: raise HTTPException(status_code=404, detail="No results matching query were found") workflows_with_status = [] for workflow in workflows: workflow_as_dict = workflow.dict(exclude=set(exclude_key)) workflow_with_status = WorkflowInDBWithStatus(**workflow_as_dict, tasks=None, status=State.STATUS_UNKNOWN) # get tasks statuses from workflow # and derive global status try: assert workflow.executed, "Workflow has not been executed yet" # fetch tasks' statuses response, status_code = await WorkflowManager().status(workflow) assert status_code, "Could not establish connection with database" assert status_code == 200, "Status request failed" # read tasks tasks_with_status = [] tasks_statuses_only = [] for task in response["tasks"]: tasks_with_status.append(Task(**task)) task_status = task.get("status").upper() # compatibility with older DRAMA versions tasks_statuses_only.append(task_status) # append global status based on task statuses def _check(comp: Callable, stats: list) -> bool: return comp([s in stats for s in tasks_statuses_only]) # check global status if response.get("is_revoked"): workflow_status = State.STATUS_REVOKED elif _check(all, [State.STATUS_DONE]): workflow_status = State.STATUS_DONE elif _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_FAILED elif _check(all, [State.STATUS_PENDING]): workflow_status = State.STATUS_PENDING elif _check(any, [State.STATUS_PENDING]) and not _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_PENDING elif _check(any, [State.STATUS_RUNNING]) and not _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_RUNNING else: workflow_status = State.STATUS_UNKNOWN workflow_with_status = WorkflowInDBWithStatus( **workflow_as_dict, tasks=tasks_with_status, status=workflow_status ) except Exception: logger.error(traceback.format_exc()) workflows_with_status.append(workflow_with_status) return WorkflowStatusSearchResult( workflows=workflows_with_status, pagination={ "page_size": len(workflows_with_status), "page_num": page_num, "page_count": math.ceil(total_count / page_size), "total_count": total_count, }, ) @router.get( "/fstatus", summary="Gets workflow(s) execution states", tags=["workflow", "dev"], responses={404: {"description": "No results matching query were found"}}, response_model=WorkflowStatusSearchResult, response_description="Search result", status_code=200, ) async def status( request: Request, username: str, page_size: int = Query(default=1, ge=1), page_num: int = Query(default=1, ge=1), exclude_key: list = Query(default=["operators", "links"]), with_status: State = State.STATUS_DONE, db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowStatusSearchResult: user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") query_params = request.query_params filtering = _exclude_keys(query_params, ["username", "page_size", "page_num", "exclude_key", "with_status"]) workflows = WorkflowManager().find_all(db, username=username, **filtering) current_count, total_count = 0, 0 skips = page_size * (page_num - 1) workflows_with_status = [] async for workflow in workflows: workflow_as_dict = workflow.dict(exclude=set(exclude_key)) workflow_with_status = WorkflowInDBWithStatus(**workflow_as_dict, tasks=None, status=State.STATUS_UNKNOWN) # get tasks statuses from workflow # and derive global status try: assert workflow.executed, "Workflow has not been executed yet" # fetch tasks' statuses response, status_code = await WorkflowManager().status(workflow) assert status_code, "Could not establish connection with database" assert status_code == 200, "Status request failed" # read tasks tasks_with_status = [] tasks_statuses_only = [] for task in response["tasks"]: tasks_with_status.append(Task(**task)) task_status = task.get("status").upper() # compatibility with older DRAMA versions tasks_statuses_only.append(task_status) # append global status based on task statuses def _check(comp: Callable, stats: list) -> bool: return comp([s in stats for s in tasks_statuses_only]) # check global status if response.get("is_revoked"): workflow_status = State.STATUS_REVOKED elif _check(all, [State.STATUS_DONE]): workflow_status = State.STATUS_DONE elif _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_FAILED elif _check(all, [State.STATUS_PENDING]): workflow_status = State.STATUS_PENDING elif _check(any, [State.STATUS_PENDING]) and not _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_PENDING elif _check(any, [State.STATUS_RUNNING]) and not _check(any, [State.STATUS_FAILED]): workflow_status = State.STATUS_RUNNING else: workflow_status = State.STATUS_UNKNOWN workflow_with_status = WorkflowInDBWithStatus( **workflow_as_dict, tasks=tasks_with_status, status=workflow_status ) except Exception: logger.error(traceback.format_exc()) if workflow_with_status.status == with_status: if total_count >= skips and len(workflows_with_status) < page_size: current_count += 1 workflows_with_status.append(workflow_with_status) total_count += 1 # if page_size <= len(workflows_with_status): # break if not workflows_with_status: raise HTTPException(status_code=404, detail="No results matching query were found") return WorkflowStatusSearchResult( workflows=workflows_with_status, pagination={ "page_size": len(workflows_with_status), "page_num": page_num, "page_count": math.ceil(total_count / page_size), "total_count": total_count, }, ) @router.post( "/run", summary="Executes workflow", tags=["workflow"], responses={ 404: {"description": "Workflow not found"}, 500: {"description": "Missing key"}, }, response_model=WorkflowInDB, response_description="Workflow from database with associated metadata", status_code=200, ) async def run( username: str, workflow_id: str, db: AsyncIOMotorClient = Depends(get_connection), ) -> WorkflowInDB: """ Executes workflow from database. """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") # get workflow from db workflow = await WorkflowManager().find_one(db, username=username, workflow_id=workflow_id) if not workflow: raise HTTPException(status_code=404, detail=f"Workflow '{workflow_id}' not found") # execute try: await WorkflowManager().execute(db, workflow=workflow) except KeyError as err: logger.debug(f"There was an error executing the workflow '{workflow_id}'") raise HTTPException(status_code=500, detail=f"Missing key '{err.args[0]}'") workflow = await WorkflowManager().find_one(db, username=username, workflow_id=workflow_id) return workflow @router.post( "/revoke", summary="Revokes workflow execution", tags=["workflow"], responses={ 404: {"description": "Workflow not found"}, 500: {"description": "Workflow has not been executed yet"}, }, status_code=200, ) async def revoke( username: str, workflow_id: str, db: AsyncIOMotorClient = Depends(get_connection), ) -> None: """ Revoke workflow execution. """ user_by_username = await get_user_by_username(db, username) if not user_by_username: raise HTTPException(status_code=HTTP_404_NOT_FOUND, detail="Username not found") # get workflow from db workflow = await WorkflowManager().find_one(db, username=username, workflow_id=workflow_id) if not workflow: raise HTTPException(status_code=404, detail=f"Workflow {workflow_id} not found") # check status if not workflow.executed: raise HTTPException(status_code=404, detail=f"Workflow has not been executed yet") await WorkflowManager().revoke(workflow)

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null

0

1

0

null

0

6

5fa3df3e011ddfd3cb2125e7628207a46318ae3c

5,607

py

Python

NewsExtractors/Jsonabstract/default.py

kingking888/CommNewsExtractor

ab03d1de3d69bde8c25873cfbbe32913ec721894

[ "MIT" ]

17

2019-12-07T14:43:14.000Z

2021-09-07T06:43:55.000Z

NewsExtractors/Jsonabstract/default.py

kingking888/CommNewsExtractor

ab03d1de3d69bde8c25873cfbbe32913ec721894

[ "MIT" ]

3

2020-11-19T11:27:13.000Z

2021-12-13T20:28:03.000Z

NewsExtractors/Jsonabstract/default.py

kingking888/CommNewsExtractor

ab03d1de3d69bde8c25873cfbbe32913ec721894

[ "MIT" ]

5

2019-12-10T09:12:41.000Z

2021-11-03T08:26:24.000Z

CONTENT_KEYS = ['content', 'Content', 'description', 'text', 'html'] TITLE_KEYS = ['title', 'Title', 'topic', 'Topic'] TIME_KEYS = ['createtime', 'createTime', 'Createtime', 'CreateTime', 'publishtime', 'PublishTime', 'publish_time', 'UpdateTime', 'updateTime', 'updatetime', 'startTime', 'StartTime', 'newsTime', 'ctime', 'time'] AUTHOR_KEYS = ['author', 'Author', ] FROM_KEYS = ['source', 'Source', 'From', 'from', 'value_name'] AUTHOR_RE_RULES = [ "责编[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "责任编辑[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "作者[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "编辑[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "文[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "原创[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "撰文[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：]", "来源[：|:| |丨|/]\s*([\u4E00-\u9FA5a-zA-Z]{2,20})[^\u4E00-\u9FA5|:|：|<]" ] USELESS_TAG = ['style', 'script'] CLEAN_TAG = [r'\n', r'\t', r'\xa0', '/u3000'] ALL_DATETIME_PATTERN_DICT = { "A%Y-%m-%dT%H:%M:%S": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}T[0-1]?[0-9][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "A%Y-%m-%d %H:%M:%S": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "A%Y-%m-%dT%H:%M": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}T[2][0-3][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "B%Y-%m-%d %H:%M:%S": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "C%Y-%m-%d %H:%M:%S": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d[:|分][0-60]\d)", "B%Y-%m-%d %H:%M": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d分)", "A%Y-%m-%d %H:%M": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9])", "C%Y-%m-%d %H:%M": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3][:|时][0-5]?[0-9])", "D%Y-%m-%d %H:%M:%S": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "E%Y-%m-%d %H:%M:%S": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3]:[0-5]?[0-9]:[0-5]?[0-9])", "F%Y-%m-%d %H:%M:%S": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d[:|分][0-60]\d)", "D%Y-%m-%d %H:%M": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d分)", "E%Y-%m-%d %H:%M": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9])", "F%Y-%m-%d %H:%M": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3][:|时][0-5]?[0-9])", "G%Y-%m-%d %H:%M": "(\d{2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-9][:|时][0-9])", "G%Y-%m-%d %H:%M:%S": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "H%Y-%m-%d %H:%M:%S": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3][:|时][0-5]?[0-9][:|分][0-5]?[0-9])", "I%Y-%m-%d %H:%M:%S": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d[:|分][0-60]\d)", "H%Y-%m-%d %H:%M": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[1-24]\d[:|时][0-60]\d分)", "I%Y-%m-%d %H:%M": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[0-1]?[0-9][:|时][0-5]?[0-9])", "J%Y-%m-%d %H:%M": "(\d{1,2}[-|/|.|月]\d{1,2}[日|日 ]\s*?[2][0-3][:|时][0-5]?[0-9])", "A%Y-%m-%d": "(\d{4}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2})", "A%d-%m-%Y": "(\d{1,2}[-|/|.|日]\d{1,2}[-|/|.|月]\d{4})", "B%Y-%m-%d": "(\d{1,2}[-|/|.|年]\d{1,2}[-|/|.|月]\d{1,2})", "C%Y-%m-%d": "(\d{1,2}[-|/|.|年]\d{1,2})", "D%Y-%m-%d": "(\d{1,2}月\d{1,2}日)", "A%m-%d,%Y": "(\d{1,2}[-|/|.|月]\d{1,2},\d{4})", "L%Y-%m-%d %H:%M:%S": "(\d{1,2}[:|时]\d{1,2}[:|分]\d{1,2})", "a%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?年前)", "b%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?个月前)", "c%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?月前)", "d%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?周前)", "e%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?天内)", "f%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?天前)", "g%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?小[时|時]前)", "h%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?分[钟|鐘]前)", "i%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?秒[钟|鐘]前)", "o%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?hour\s*?ago)", "p%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?minutes\s*?ago)", "j%Y-%m-%d %H:%M:%S": "(\d{1,2}\s*?秒前)", "a%Y-%m-%d": "([今|昨|前]天\s*?\d{1,2}[:|时]\d{1,2}[:|分]\d{1,2})", "b%Y-%m-%d": "([今|昨|前]天\s*?\d{1,2}[:|时]\d{1,2}分)", "c%Y-%m-%d": "([今|昨|前]天\s*?\d{1,2}[:|时]\d{1,2})", "k%Y-%m-%d %H:%M:%S": '(前天)', "l%Y-%m-%d %H:%M:%S": '(昨天)', "m%Y-%m-%d %H:%M:%S": '(今天)', "n%Y-%m-%d %H:%M:%S": "(刚刚)", "%S %b %d, %Y, %I:%M%p": '(\d+ \w+ \d+, \d+, \d{1,2}[:|时]\d{1,2}[A|P|a|p][m|M])', "%S %b %d, %Y, %I:%M %p": '(\d+ \w+ \d+, \d+, \d{1,2}[:|时]\d{1,2}\s*?[A|P|a|p][m|M])', "%b %d, %Y, %I:%M%p": '(\w+ \d+, \d+, \d{1,2}[:|时]\d{1,2}[A|P|a|p][m|M])', "%b %d, %Y, %I:%M %p": '(\w+ \d+, \d+, \d{1,2}[:|时]\d{1,2}\s*?[A|P|a|p][m|M])', "%b %d, %Y %I:%M%p": '(\w+ \d+, \d+ \d{1,2}[:|时]\d{1,2}[A|P|a|p][m|M])', "%b %d, %Y %I:%M %p": '(\w+ \d+, \d+ \d{1,2}[:|时]\d{1,2}\s*?[A|P|a|p][m|M])', '%b %d, %Y - %I:%M%p': '(\w+ \d+, \d+ - \d{1,2}[:|时]\d{1,2}[A|P|a|p][m|M])', '%b %d, %Y - %I:%M %p': '(\w+ \d+, \d+ - \d{1,2}[:|时]\d{1,2}\s*?[A|P|a|p][m|M])', "%b %d, %Y": '(\w+ \d+, \d+)', } Month_Less_To_Full = { "January": "Jan", "February": "Feb", "March": "Mar", "April": "Apr", "May": "May", "June": "Jun", "July": "Jul", "August": "Aug", "September": "Sep", "October": "Oct", "November": "Nov", "December": "Dec", }

56.07

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0.342964

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5,607

1.453852

0.102975

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0.645855

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1

0

1

null

0

6

3970c6e61e6ec26dacc4cf097e9af4c6ee9c8580

21

py

Python

zipf/cli/__init__.py

LucaCappelletti94/zipf

956c3a1d56958384a02d5bb4671c6883cd9a25e3

[ "MIT" ]

3

2018-11-07T01:56:09.000Z

2020-05-31T12:24:09.000Z

zipf/cli/__init__.py

LucaCappelletti94/zipf

956c3a1d56958384a02d5bb4671c6883cd9a25e3

[ "MIT" ]

1

2018-05-15T15:58:06.000Z

zipf/cli/__init__.py

LucaCappelletti94/zipf

956c3a1d56958384a02d5bb4671c6883cd9a25e3

[ "MIT" ]

null

from .cli import Cli

10.5

20

0.761905

4

21

4

0.75

0

0.190476

21

1

21

0.941176

0

1

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true

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0

null

0

1

0

1

0

1

0

6

39a91c655da70ed468e49ea26a2b2692d0343fec

28

py

Python

tests/test_course.py

bibz/rudaux

9db516811823490a49845235fe236d56638acd17

[ "MIT" ]

1

2020-09-10T20:36:56.000Z

tests/test_course.py

bibz/rudaux

9db516811823490a49845235fe236d56638acd17

[ "MIT" ]

null

tests/test_course.py

bibz/rudaux

9db516811823490a49845235fe236d56638acd17

[ "MIT" ]

null

import pytest import rudaux

9.333333

13

0.857143

4

28

6

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28

3

14

9.333333

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null

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1

0

1

0

1

0

6

84090062db687dbc4d6306b62ceb741d0dcf01e8

42,280

py

Python

tests/test_make_recipe.py

gogetdata/ggd-cli

717d37643f3e29813f47eda68b9745459d9ef430

[ "MIT" ]

29

2016-04-23T13:28:51.000Z

2021-10-03T15:49:29.000Z

tests/test_make_recipe.py

gogetdata/ggd-cli

717d37643f3e29813f47eda68b9745459d9ef430

[ "MIT" ]

17

2016-04-22T15:45:33.000Z

2020-11-20T16:47:24.000Z

tests/test_make_recipe.py

gogetdata/ggd-cli

717d37643f3e29813f47eda68b9745459d9ef430

[ "MIT" ]

2

2016-05-26T01:54:51.000Z

2020-04-30T19:17:18.000Z

from __future__ import print_function import os import sys import subprocess as sp import pytest import yaml import tempfile import requests import argparse import json import re from argparse import Namespace from argparse import ArgumentParser import glob import contextlib import tarfile from helpers import CreateRecipe from ggd import utils from ggd import make_bash import oyaml if sys.version_info[0] == 3: from io import StringIO elif sys.version_info[0] == 2: from StringIO import StringIO #--------------------------------------------------------------------------------------------------------- ## enable socket #--------------------------------------------------------------------------------------------------------- from pytest_socket import enable_socket def pytest_enable_socket(): enable_socket() #--------------------------------------------------------------------------------------------------------- ## Test Label #--------------------------------------------------------------------------------------------------------- TEST_LABEL = "ggd-make-recipe-test" #--------------------------------------------------------------------------------------------------------- ## IO redirection #--------------------------------------------------------------------------------------------------------- ## Create a redirect_stdout that works for python 2 and 3. (Similar to contextlib.redirect_stdout in python 3) @contextlib.contextmanager def redirect_stdout(target): original = sys.stdout sys.stdout = target yield sys.stdout = original ## Create a redirect_stderr that works for python 2 and 3. (Similar to contextlib.redirect_stderr in python 3) @contextlib.contextmanager def redirect_stderr(target): original = sys.stderr sys.stderr = target yield sys.stderr = original #----------------------------------------------------------------------------------------------------------------------- # Unit test for ggd make-recipe #----------------------------------------------------------------------------------------------------------------------- def test_make_bash_test_bad_summary(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() ## test that make_bash fails when a bad summary is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "Please provide a thorough summary of the data package" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a bad summary is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary=' ', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "Please provide a thorough summary of the data package" in str(e) pass except Exception as e: print(e) assert False def test_make_bash_test_bad_name(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() ## test that make_bash fails when a bad name is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing name assert "The recipe name is required" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a bad name is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name=' ', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing name assert "The recipe name is required" in str(e) pass except Exception as e: print(e) assert False def test_make_bash_test_wildcards(): """ Test the main method of ggd make-recipe, make sure that a name with a wildcard raises and assertion error """ pytest_enable_socket() ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test.gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\".\" wildcard is not allowed in the recipe name" in str(e) assert "hg19-test.gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test?gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"?\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test?gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test*gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"*\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test*gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test[gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"[\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test[gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test]gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"]\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test]gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test{gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"{\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test{gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test}gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"}\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test}gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test!gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"!\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test!gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test+gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"+\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test+gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test^gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"^\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test^gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test$gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"$\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test$gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test(gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\"(\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test(gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False ## test that make_bash fails when a wild card is added to the name args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test)gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: ## Correctly raises an assetion error due to the missing summary assert "\")\" wildcard is not allowed in the recipe name. Please rename the recipe." in str(e) assert "hg19-test)gaps-ucsc-v1" in str(e) pass except Exception as e: print(e) assert False def test_make_bash_test_bad_genome_build(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() ## test that make_bash fails when a bad genome build is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg09', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: temp_stderr = StringIO() with redirect_stderr(temp_stderr): make_bash.make_bash((),args) except Exception as e: os.rmdir("{}-{}-{}-v{}".format("hg09","test-gaps","ucsc","1")) output = str(temp_stderr.getvalue().strip()) assert "ERROR: genome-build: hg09 not found in github repo for the Homo_sapiens species" in output ## test that make_bash fails when a bad genome build is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hgmm10', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script='recipe.sh', species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: temp_stderr = StringIO() with redirect_stderr(temp_stderr): make_bash.make_bash((),args) except Exception as e: os.rmdir("{}-{}-{}-v{}".format("hgmm10","test-gaps","ucsc","1")) output = temp_stderr.getvalue().strip() assert "ERROR: genome-build: hgmm10 not found in github repo for the Homo_sapiens species" in output def test_make_bash_test_bad_recipe(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() ## test that make_bash fails when a bad recipe is provided args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script='bad-recipe.sh', species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) with pytest.raises(SystemExit) as pytest_wrapped_e: make_bash.make_bash((), args) os.rmdir("{}-{}-{}-v{}".format("hg19","test-gaps","ucsc","1")) assert "SystemExit" in str(pytest_wrapped_e.exconly()) ## test that SystemExit was raised by sys.exit() assert pytest_wrapped_e.match("1") ## Check that the exit code is 1 def test_make_bash_missing_tags(): """ Test that there is an error when missing tags """ pytest_enable_socket() recipe = CreateRecipe( """ hg19-test-gaps-ucsc-v1: recipe.sh: | genome=https://raw.githubusercontent.com/gogetdata/ggd-recipes/master/genomes/Homo_sapiens/hg19/hg19.genome wget --quiet -O - http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/gap.txt.gz \\ | gzip -dc \\ | awk -v OFS="\t" 'BEGIN {print "#chrom\tstart\tend\tsize\ttype\tstrand"} {print $2,$3,$4,$7,$8,"+"}' \\ | gsort /dev/stdin $genome \\ | bgzip -c > gaps.bed.gz tabix gaps.bed.gz """, from_string=True) recipe.write_recipes() ggd_package = "hg19-test-gaps-ucsc-v1" recipe_file = os.path.join(recipe.recipe_dirs["hg19-test-gaps-ucsc-v1"],"recipe.sh") ## Bad coordinate args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="2-based-exclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: assert "2-based-exclusive is not an acceptable genomic coordinate base" in str(e) pass except Exception as e: print(e) assert False ## Emtpy data version args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: assert "Please provide the version of the data this recipe curates" in str(e) pass except Exception as e: print(e) assert False ## Empty data provider args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from USCS', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) try: assert make_bash.make_bash((),args) assert False except AssertionError as e: assert "The data provider is required" in str(e) pass except Exception as e: print(e) assert False def test_make_bash(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() recipe = CreateRecipe( """ hg19-test-gaps-ucsc-v1: recipe.sh: | genome=https://raw.githubusercontent.com/gogetdata/ggd-recipes/master/genomes/Homo_sapiens/hg19/hg19.genome wget --quiet -O - http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/gap.txt.gz \\ | gzip -dc \\ | awk -v OFS="\t" 'BEGIN {print "#chrom\tstart\tend\tsize\ttype\tstrand"} {print $2,$3,$4,$7,$8,"+"}' \\ | gsort /dev/stdin $genome \\ | bgzip -c > gaps.bed.gz tabix gaps.bed.gz """, from_string=True) recipe.write_recipes() ggd_package = "hg19-test-gaps-ucsc-v1" recipe_file = os.path.join(recipe.recipe_dirs["hg19-test-gaps-ucsc-v1"],"recipe.sh") args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=[], extra_file=[], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps', platform='noarch', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= [],final_file=[]) assert make_bash.make_bash((),args) new_recipe_file = os.path.join("./", ggd_package, "recipe.sh") assert os.path.exists(new_recipe_file) assert os.path.isfile(new_recipe_file) new_metayaml_file = os.path.join("./", ggd_package, "meta.yaml") assert os.path.exists(new_metayaml_file) assert os.path.isfile(new_metayaml_file) new_postlink_file = os.path.join("./", ggd_package, "post-link.sh") assert os.path.exists(new_postlink_file) assert os.path.isfile(new_postlink_file) new_checksums_file = os.path.join("./", ggd_package, "checksums_file.txt") assert os.path.exists(new_checksums_file) assert os.path.isfile(new_checksums_file) ## Test meta.yaml try: with open(new_metayaml_file, "r") as mf: yamldict = yaml.safe_load(mf) assert yamldict["build"]["noarch"] == "generic" assert yamldict["build"]["number"] == 0 assert yamldict["extra"]["authors"] == "me" assert yamldict["extra"]["extra-files"] == [] assert yamldict["package"]["name"] == ggd_package assert yamldict["package"]["version"] == "1" assert yamldict["requirements"]["build"] == ['gsort', 'htslib', 'zlib'] assert yamldict["requirements"]["run"] == ['gsort', 'htslib', 'zlib'] assert yamldict["source"]["path"] == "." assert yamldict["about"]["identifiers"]["genome-build"] == "hg19" assert yamldict["about"]["identifiers"]["species"] == "Homo_sapiens" assert yamldict["about"]["keywords"] == ['gaps','region'] assert yamldict["about"]["summary"] == "Assembly gaps from UCSC" assert yamldict["about"]["tags"]["genomic-coordinate-base"] == "0-based-inclusive" assert yamldict["about"]["tags"]["data-version"] == "27-Apr-2009" assert yamldict["about"]["tags"]["data-provider"] == "UCSC" assert yamldict["about"]["tags"]["file-type"] == [] assert yamldict["about"]["tags"]["final-files"] == [] assert yamldict["about"]["tags"]["final-file-sizes"] == {} assert yamldict["about"]["tags"]["ggd-channel"] == "genomics" except IOError as e: print(e) assert False ## Test post-link.sh try: with open(new_postlink_file, "r") as pf: recipe_dir = False pkd_dir = False dir_env_var = False file_env_var = False run_recipe_script = False file_extention = False rename_data = False for line in pf: ### Check the assignment of RECIPE_DIR if "RECIPE_DIR=" in line: assert line.strip() == """export RECIPE_DIR=$CONDA_ROOT/share/ggd/Homo_sapiens/hg19/hg19-test-gaps-ucsc-v1/1""" or line.strip() == """export RECIPE_DIR=$env_dir/share/ggd/Homo_sapiens/hg19/hg19-test-gaps-ucsc-v1/1""" recipe_dir = True ### Check the assigning of PKG_DIR to conform with proper file filtering for Linus and macOSX if "PKG_DIR=" in line: assert line.strip() == """PKG_DIR=`find "$CONDA_SOURCE_PREFIX/pkgs/" -name "$PKG_NAME-$PKG_VERSION*" | grep -v ".tar.bz2" | grep "$PKG_VERSION.*$PKG_BUILDNUM$"`""" pkd_dir = True ### Check enivornment variable setting if "recipe_env_dir_name=" in line: assert line.strip() == """recipe_env_dir_name="ggd_hg19-test-gaps-ucsc-v1_dir" """.strip() \ or line.strip() == """recipe_env_dir_name="$(echo "$recipe_env_dir_name" | sed 's/-/_/g' | sed 's/\./_/g')" """.strip() \ or line.strip() == """echo "export $recipe_env_dir_name=$RECIPE_DIR" >> $activate_dir/env_vars.sh""" dir_env_var = True if "recipe_env_file_name=" in line: assert line.strip() == """recipe_env_file_name="ggd_hg19-test-gaps-ucsc-v1_file" """.strip() \ or line.strip() == """recipe_env_file_name="$(echo "$recipe_env_file_name" | sed 's/-/_/g' | sed 's/\./_/g')" """.strip() \ or line.strip() == """if [[ ! -z "${recipe_env_file_name:-}" ]] """.strip() \ or line.strip() == """echo "export $recipe_env_file_name=$file_path" >> $activate_dir/env_vars.sh""" file_env_var = True #### Check that the recipe is being run if "bash $PKG_DIR" in line: assert line.strip() == """(cd $RECIPE_DIR && bash $PKG_DIR/info/recipe/recipe.sh)""" run_recipe_script = True ### Check taht the extention for the data files is being extracted if "ext=" in line: assert line.strip() == """ext="${f#*.}" """.strip() file_extention = True ### Check that the data file names are replaced with the ggd package name, but the extentions are kept if "(mv $f" in line: assert line.strip() == """(mv $f "hg19-test-gaps-ucsc-v1.$ext")""" rename_data = True assert recipe_dir assert pkd_dir assert dir_env_var assert file_env_var assert run_recipe_script assert file_extention assert rename_data except IOError as e: print(e) assert False os.remove(new_recipe_file) os.remove(new_metayaml_file) os.remove(new_postlink_file) os.remove(new_checksums_file) os.rmdir(ggd_package) def test_make_bash_all_params(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() recipe = CreateRecipe( """ hg19-test-gaps2-ucsc-v1: recipe.sh: | genome=https://raw.githubusercontent.com/gogetdata/ggd-recipes/master/genomes/Homo_sapiens/hg19/hg19.genome wget --quiet -O - http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/gap.txt.gz \\ | gzip -dc \\ | awk -v OFS="\t" 'BEGIN {print "#chrom\tstart\tend\tsize\ttype\tstrand"} {print $2,$3,$4,$7,$8,"+"}' \\ | gsort /dev/stdin $genome \\ | bgzip -c > gaps.bed.gz tabix gaps.bed.gz """, from_string=True) recipe.write_recipes() ggd_package = "hg19-test-gaps2-ucsc-v1" recipe_file = os.path.join(recipe.recipe_dirs["hg19-test-gaps2-ucsc-v1"],"recipe.sh") args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=['vt','samtools','bedtools'], extra_file=['not.a.real.extra.file'], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps2', platform='none', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="1-based-inclusive", file_type= [],final_file=[]) assert make_bash.make_bash((),args) new_recipe_file = os.path.join("./", ggd_package, "recipe.sh") assert os.path.exists(new_recipe_file) assert os.path.isfile(new_recipe_file) new_metayaml_file = os.path.join("./", ggd_package, "meta.yaml") assert os.path.exists(new_metayaml_file) assert os.path.isfile(new_metayaml_file) new_postlink_file = os.path.join("./", ggd_package, "post-link.sh") assert os.path.exists(new_postlink_file) assert os.path.isfile(new_postlink_file) new_checksums_file = os.path.join("./", ggd_package, "checksums_file.txt") assert os.path.exists(new_checksums_file) assert os.path.isfile(new_checksums_file) ## Test meta.yaml try: with open(new_metayaml_file, "r") as mf: yamldict = yaml.safe_load(mf) assert yamldict["build"]["number"] == 0 assert "noarch" not in yamldict["build"].keys() assert yamldict["extra"]["authors"] == "me" assert yamldict["extra"]["extra-files"] == ['{}.a.real.extra.file'.format(ggd_package)] assert yamldict["package"]["name"] == ggd_package assert yamldict["package"]["version"] == "1" assert yamldict["requirements"]["build"] == ['bedtools', 'gsort', 'htslib', 'samtools', 'vt', 'zlib'] assert yamldict["requirements"]["run"] == ['bedtools', 'gsort', 'htslib', 'samtools', 'vt', 'zlib'] assert yamldict["source"]["path"] == "." assert yamldict["about"]["identifiers"]["genome-build"] == "hg19" assert yamldict["about"]["identifiers"]["species"] == "Homo_sapiens" assert yamldict["about"]["keywords"] == ['gaps','region'] assert yamldict["about"]["summary"] == "Assembly gaps from UCSC" assert yamldict["about"]["tags"]["genomic-coordinate-base"] == "1-based-inclusive" assert yamldict["about"]["tags"]["data-version"] == "27-Apr-2009" assert yamldict["about"]["tags"]["file-type"] == [] ## Should be converted to lower case assert yamldict["about"]["tags"]["final-files"] == [] assert yamldict["about"]["tags"]["final-file-sizes"] == {} assert yamldict["about"]["tags"]["ggd-channel"] == "genomics" except IOError as e: print(e) assert False os.remove(new_recipe_file) os.remove(new_metayaml_file) os.remove(new_postlink_file) os.remove(new_checksums_file) os.rmdir(ggd_package) def test_make_bash_meta_yaml_key_order(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() recipe = CreateRecipe( """ hg19-test-gaps3-ucsc-v1: recipe.sh: | genome=https://raw.githubusercontent.com/gogetdata/ggd-recipes/master/genomes/Homo_sapiens/hg19/hg19.genome wget --quiet -O - http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/gap.txt.gz \\ | gzip -dc \\ | awk -v OFS="\t" 'BEGIN {print "#chrom\tstart\tend\tsize\ttype\tstrand"} {print $2,$3,$4,$7,$8,"+"}' \\ | gsort /dev/stdin $genome \\ | bgzip -c > gaps.bed.gz tabix gaps.bed.gz """, from_string=True) recipe.write_recipes() ggd_package = "hg19-test-gaps3-ucsc-v1" recipe_file = os.path.join(recipe.recipe_dirs["hg19-test-gaps3-ucsc-v1"],"recipe.sh") args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=['vt','samtools','bedtools'], extra_file=['not.a.real.extra.file'], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps3', platform='none', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= ["Bed"], final_file=["hg19-test-gaps3-ucsc-v1.bed.gz", "hg19-test-gaps3-ucsc-v1.bed.gz.tbi"]) assert make_bash.make_bash((),args) new_recipe_file = os.path.join("./", ggd_package, "recipe.sh") assert os.path.exists(new_recipe_file) assert os.path.isfile(new_recipe_file) new_metayaml_file = os.path.join("./", ggd_package, "meta.yaml") assert os.path.exists(new_metayaml_file) assert os.path.isfile(new_metayaml_file) new_postlink_file = os.path.join("./", ggd_package, "post-link.sh") assert os.path.exists(new_postlink_file) assert os.path.isfile(new_postlink_file) new_checksums_file = os.path.join("./", ggd_package, "checksums_file.txt") assert os.path.exists(new_checksums_file) assert os.path.isfile(new_checksums_file) ## Test that the keys in the meta.yaml file are in the correct order. ## Conda-build requires a strict order: https://github.com/conda/conda-build/issues/3267 try: ref_keys = ["build","extra","package","requirements","source","about"] index = 0 with open(new_metayaml_file, "r") as mf: for item in mf: item = item.strip().replace(":","") if item in ref_keys: assert ref_keys[index] == item ref_keys[index] = "Done" index += 1 assert index-1 == 5 ## Index - 1 because an additional 1 was added at the end. (Only index 0-5 exists) except IOError as e: print(e) assert False os.remove(new_recipe_file) os.remove(new_metayaml_file) os.remove(new_postlink_file) os.remove(new_checksums_file) os.rmdir(ggd_package) def test_make_bash_meta_yaml_ggd_dependency(): """ Test the main method of ggd make-recipe """ pytest_enable_socket() recipe = CreateRecipe( """ hg19-test-gaps4-ucsc-v1: recipe.sh: | genome=https://raw.githubusercontent.com/gogetdata/ggd-recipes/master/genomes/Homo_sapiens/hg19/hg19.genome wget --quiet -O - http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/gap.txt.gz \\ | gzip -dc \\ | awk -v OFS="\t" 'BEGIN {print "#chrom\tstart\tend\tsize\ttype\tstrand"} {print $2,$3,$4,$7,$8,"+"}' \\ | gsort /dev/stdin $genome \\ | bgzip -c > gaps.bed.gz tabix gaps.bed.gz """, from_string=True) recipe.write_recipes() ggd_package = "hg19-test-gaps4-ucsc-v1" recipe_file = os.path.join(recipe.recipe_dirs["hg19-test-gaps4-ucsc-v1"],"recipe.sh") ## grch37-gene-features-ensembl-v1 as a dependency args = Namespace(authors='me', channel='genomics', command='make-recipe', data_version='27-Apr-2009', data_provider="UCSC", dependency=['grch37-gene-features-ensembl-v1','hg38-chrom-mapping-ensembl2ucsc-ncbi-v1','vt','samtools','bedtools'], extra_file=['not.a.real.extra.file'], genome_build='hg19', package_version='1', keyword=['gaps', 'region'], name='test-gaps4', platform='none', script=recipe_file, species='Homo_sapiens', summary='Assembly gaps from UCSC', coordinate_base="0-based-inclusive", file_type= ["Bed"], final_file=["hg19-test-gaps4-ucsc-v1.bed.gz", "hg19-test-gaps4-ucsc-v1.bed.gz.tbi"]) assert make_bash.make_bash((),args) new_recipe_file = os.path.join("./", ggd_package, "recipe.sh") assert os.path.exists(new_recipe_file) assert os.path.isfile(new_recipe_file) new_metayaml_file = os.path.join("./", ggd_package, "meta.yaml") assert os.path.exists(new_metayaml_file) assert os.path.isfile(new_metayaml_file) new_postlink_file = os.path.join("./", ggd_package, "post-link.sh") assert os.path.exists(new_postlink_file) assert os.path.isfile(new_postlink_file) new_checksums_file = os.path.join("./", ggd_package, "checksums_file.txt") assert os.path.exists(new_checksums_file) assert os.path.isfile(new_checksums_file) ## Test meta.yaml has an ggd dependency in the run requirements and not the build requirements try: with open(new_metayaml_file, "r") as mf: yamldict = yaml.safe_load(mf) assert yamldict["requirements"]["build"] == ['bedtools', 'gsort', 'htslib', 'samtools', 'vt', 'zlib'] assert "grch37-gene-features-ensembl-v1" not in yamldict["requirements"]["build"] assert "hg38-chrom-mapping-ensembl2ucsc-ncbi-v1" not in yamldict["requirements"]["build"] assert yamldict["requirements"]["run"] == ['bedtools', 'grch37-gene-features-ensembl-v1', 'gsort', 'hg38-chrom-mapping-ensembl2ucsc-ncbi-v1', 'htslib', 'samtools', 'vt', 'zlib'] assert "grch37-gene-features-ensembl-v1" in yamldict["requirements"]["run"] assert "hg38-chrom-mapping-ensembl2ucsc-ncbi-v1" in yamldict["requirements"]["run"] except IOError as e: print(e) assert False os.remove(new_recipe_file) os.remove(new_metayaml_file) os.remove(new_postlink_file) os.remove(new_checksums_file) os.rmdir(ggd_package)

47.082405

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0.605227

5,256

42,280

4.730023

0.06583

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0.007964

0.01802

0.885322

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0.043189

0

null

0

1

0

null

0

6

845dce3929d2ae13e98764552dda60fa63da20ff

6,036

py

Python

authors/apps/articles/tests/test_comments.py

SilasKenneth/ah-technocrats

c199e6dd432bdb4a5e1152f90cb1716b09af2c4e

[ "BSD-3-Clause" ]

1

2018-12-04T15:29:57.000Z

authors/apps/articles/tests/test_comments.py

SilasKenneth/ah-technocrats

c199e6dd432bdb4a5e1152f90cb1716b09af2c4e

[ "BSD-3-Clause" ]

52

2018-11-27T08:00:25.000Z

2021-06-10T20:58:16.000Z

authors/apps/articles/tests/test_comments.py

SilasKenneth/ah-technocrats

c199e6dd432bdb4a5e1152f90cb1716b09af2c4e

[ "BSD-3-Clause" ]

4

2019-07-15T10:24:22.000Z

2020-02-04T19:15:12.000Z

import unittest from rest_framework import status from .base_test import BaseTestCase import unittest @unittest.skip("Skip this class") @unittest.skip("Not implemented") class TestComments(BaseTestCase): """ Class for testing comments. """ # test post comment def test_comment_creation(self): """ Test comment posting. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() self.assertEqual(response.status_code, status.HTTP_201_CREATED) def test_comment_creation_with_invalid_data(self): """ Test creating a comment using invalid data. """ self.user_signup() self.user_login() self.post_article() response = self.test_client.post(self.comment_url, self.invalid_comment_data, format='json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_commenting_on_non_existing_article(self): """ Test commenting on a missing article. """ self.user_signup() self.user_login() response = self.test_client.post(self.comment_url, self.invalid_comment_data, format='json') self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_commenting_by_a_non_user(self): """ Test a non-user cannot comment. """ response = self.test_client.post(self.comment_url, self.invalid_comment_data, format='json') self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) # test getting comment def test_getting_a_comment(self): """ Test getting a single comment successfully. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() response2 = self.test_client.get(self.comment_url) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_getting_a_non_existing_comment(self): """ Test getting a missing comment. """ self.user_signup() self.user_login() self.post_article() response = self.test_client.get(self.comment_url) self.assertEqual(response.status_code, status.HTTP_400_NOT_FOUND) def test_getting_comment_from_a_missing_article(self): """ Test getting comment from a non-existent article. """ self.user_signup() self.user_login() response2 = self.test_client.get(self.comment_url) self.assertEqual(response2.status_code, status.HTTP_400_BAD_REQUEST) def test_getting_all_comments(self): """ Test getting all comments to an article. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() response2 = self.test_client.get(self.comments_url) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_getting_all_comments_from_a_missing_article(self): """ Test getting all comments from a non-existent article. """ self.user_signup() self.user_login() response2 = self.test_client.get(self.comments_url) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) # test updating comment def test_updating_a_comment(self): """ Test editing an existing comment. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() response2 = self.test_client.put(self.comment_url, self.new_comment_data, format='json') self.assertEqual(response2.status_code, status.HTTP_200_OK) def test_updating_with_invalid_data(self): """ Test updating comment using invalid data. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() response2 = self.test_client.put(self.comment_url, self.invalid_comment_data, format='json') self.assertEqual(response2.status_code, status.HTTP_400_BAD_REQUEST) def test_updating_missing_comment(self): """ Test updating a non-existent comment. """ self.user_signup() self.user_login() self.post_article() response = self.test_client.put(self.comment_url, self.new_comment_data, format='json') self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_non_logged_in_user_cannot_update(self): """ Test a user has to login before updating. """ self.user_signup() self.post_article() response = self.test_client.put(self.comment_url, self.new_comment_data, format='json') self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) # test deleting comment def test_deleting_an_existing_comment(self): """ Method for testing deleting an existing comment. """ self.user_signup() self.user_login() self.post_article() response = self.post_comment() response2 = self.test_client.delete(self.comment_url) self.assertEqual(response2.status_code, status.HTTP_200_OK) def test_deleting_a_non_existing_comment(self): """ Method for testing deleting an existing comment. """ self.user_signup() self.user_login() self.post_article() response = self.test_client.delete(self.comment_url) self.assertEqual(response.status_code, status.HTTP_404_OK) def test_non_logged_in_user_deletting_comment(self): """ Test a user has to login before deleting. """ self.user_signup() self.post_article() response = self.post_comment() response2 = self.test_client.delete(self.comment_url) self.assertEqual(response2.status_code, status.HTTP_403_FORBIDDEN)

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6,036

5.160935

0.112792

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0.085288

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0.248012

6,036

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null

0

1

0

1

0

1

0

null

0

6

ffea90bb80af7ba411664e6cdfc7f9a1143f08df

94

py

Python

program 3.py

K-SUMANTH/Assigment-09-sumanth

747f8e7bf6696c5afb51c8aead5c563f67ff83ae

[ "BSL-1.0" ]

null

program 3.py

K-SUMANTH/Assigment-09-sumanth

747f8e7bf6696c5afb51c8aead5c563f67ff83ae

[ "BSL-1.0" ]

null

program 3.py

K-SUMANTH/Assigment-09-sumanth

747f8e7bf6696c5afb51c8aead5c563f67ff83ae

[ "BSL-1.0" ]

null

s = open("line.txt","r") print(s.readline()) print(s.readline()) print(s.readline()) s.close()

18.8

24

0.648936

16

94

3.8125

0.5

0.295082

0.688525

0.622951

0.688525

0

0.06383

94

5

25

18.8

0.693182

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0.6

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1

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false

0

0.6

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null

1

0

1

0

null

0

1

0

6

080c858d00f86a39d52b803ce10b81f6ba6aedf7

70

py

Python

grobber/blueprints/__init__.py

fossabot/grobber

6279888d605af5962bc51995e979cea74134011f

[ "MIT" ]

1

2018-07-08T21:35:04.000Z

grobber/blueprints/__init__.py

fossabot/grobber

6279888d605af5962bc51995e979cea74134011f

[ "MIT" ]

9

2018-07-01T20:06:33.000Z

2018-10-05T18:29:00.000Z

grobber/blueprints/__init__.py

fossabot/grobber

6279888d605af5962bc51995e979cea74134011f

[ "MIT" ]

1

2018-06-27T21:02:21.000Z

from .anime import anime_blueprint from .debug import debug_blueprint

23.333333

34

0.857143

10

70

5.8

0.5

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70

2

35

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true

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1

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null

0

1

0

1

0

1

0

6

082073c197cfe930a8438506603659f171d426fd

344

py

Python

tests/test_miniml_utils.py

ppavlidis/rnaseq-pipeline

8de5506dd86091c7c35b99781cfb4b054325a22a

[ "Unlicense" ]

null

tests/test_miniml_utils.py

ppavlidis/rnaseq-pipeline

8de5506dd86091c7c35b99781cfb4b054325a22a

[ "Unlicense" ]

null

tests/test_miniml_utils.py

ppavlidis/rnaseq-pipeline

8de5506dd86091c7c35b99781cfb4b054325a22a

[ "Unlicense" ]

null

from rnaseq_pipeline.miniml_utils import * def test_collect_geo_samples(): collect_geo_samples('tests/data/GSE100007_family.xml') collect_geo_samples('tests/data/GSM69846.xml') def test_collect_geo_samples_info(): collect_geo_samples_info('tests/data/GSE100007_family.xml') collect_geo_samples_info('tests/data/GSM69846.xml')

34.4

63

0.80814

49

344

5.244898

0.367347

0.233463

0.396887

0.245136

0.762646

0.474708

0.342412

0

0.070064

0.087209

344

9

64

38.222222

0.748408

0

0.313953

0

1

0.285714

true

0

0.142857

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0.428571

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null

1

0

1

0

null

0

1

0

6

f2b09e865102f133e2d4ead5fef1a0318796a92c

4,212

py

Python

ideaman_mail/MailSender.py

LibRec-Practical/ideaman-offline

f8341fc9ca77adcc1191c01037dda18c02d77b29

[ "MIT" ]

1

2021-06-21T06:41:12.000Z

ideaman_mail/MailSender.py

LibRec-Practical/ideaman-offline

f8341fc9ca77adcc1191c01037dda18c02d77b29

[ "MIT" ]

null

ideaman_mail/MailSender.py

LibRec-Practical/ideaman-offline

f8341fc9ca77adcc1191c01037dda18c02d77b29

[ "MIT" ]

null

# coding: utf-8 import smtplib import time from datetime import datetime from email.header import Header from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from ideaman_mail.config import * def sendEmail(subject,text): """ @subject:邮件标题 @text：邮件文本 """ # 通过Header对象编码的文本，包含utf-8编码信息和Base64编码信息。以下中文名测试ok subject=Header(subject, 'utf-8').encode() # 构造邮件对象MIMEMultipart对象 # 下面的主题，发件人，收件人，日期是显示在邮件页面上的。 msg = MIMEMultipart('mixed') msg['Subject'] = subject msg['From'] = '{} <{}>'.format(username,username) # 收件人为多个收件人,通过join将列表转换为以;为间隔的字符串 msg['To'] = ";".join(receiver) msg['Date']= time.strftime("%Y-%m-%d", time.localtime()) # 构造文字内容 text_plain = MIMEText(text, 'plain', 'utf-8') msg.attach(text_plain) # 发送邮件 smtp = smtplib.SMTP() smtp.connect('smtp.163.com') # 我们用set_debuglevel(1)就可以打印出和SMTP服务器交互的所有信息。 smtp.set_debuglevel(1) smtp.login(username, password) smtp.sendmail(sender, receiver, msg.as_string()) smtp.quit() if __name__ == '__main__': start_prediction ,end_prediction= 1577894400000,1578412800000 subject = 'Arxiv 本周推荐论文5篇 : {start_date}-{end_date}'.format( start_date=datetime.fromtimestamp(start_prediction / 1000).strftime("%Y.%m.%d"), end_date=datetime.fromtimestamp(end_prediction / 1000).strftime("%Y.%m.%d") ) string = """ 1. Advanced Intelligent Systems for Surgical Robotics Mai Thanh Thai, Phuoc Thien Phan, Shing Wong, Nigel H. Lovell, Thanh Nho Do https://arxiv.org/abs/2001.00285v1 Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. This paper will overview a historical development of surgery from conventional open to robotic-assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems. It will also revisit available autonomous surgical platforms with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic-assisted surgical systems towards the achievement of fully autonomous operation. 2. Advanced Intelligent Systems for Surgical Robotics Mai Thanh Thai, Phuoc Thien Phan, Shing Wong, Nigel H. Lovell, Thanh Nho Do https://arxiv.org/abs/2001.00285v1 Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. This paper will overview a historical development of surgery from conventional open to robotic-assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems. It will also revisit available autonomous surgical platforms with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic-assisted surgical systems towards the achievement of fully autonomous operation. 3. Advanced Intelligent Systems for Surgical Robotics Mai Thanh Thai, Phuoc Thien Phan, Shing Wong, Nigel H. Lovell, Thanh Nho Do https://arxiv.org/abs/2001.00285v1 Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. This paper will overview a historical development of surgery from conventional open to robotic-assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems. It will also revisit available autonomous surgical platforms with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic-assisted surgical systems towards the achievement of fully autonomous operation. """ sendEmail(subject,string)

62.865672

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5.963168

0.305709

0.035207

0.048178

0.010191

0.7168

0.701359

0

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4,212

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737

62.865672

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0.053419

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0.26087

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0

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false

0.021739

0.152174

0

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0

null

0

1

0

1

0

null

0

6

4b5fc44a55884ffea8379d87a13fa23bca38f915

46

py

Python

multilingual_t5/bn_en/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/bn_en/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/bn_en/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

"""bn_en dataset.""" from .bn_en import BnEn

11.5

23

0.673913

8

46

3.625

0.75

0.275862

0

0.152174

46

3

24

15.333333

0.74359

0.304348

0

1

0

true

0

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0

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1

0

1

0

null

0

1

0

1

0

6

4b77c91a197482a4b94a317d4dd2a11924a649e0

166

py

Python

evaluation/__init__.py

aretius/control-over-copying

762fe3949d02ff9487bf11f861f7651ff641719f

[ "BSD-3-Clause" ]

39

2019-11-23T07:48:43.000Z

2021-11-06T16:17:58.000Z

evaluation/__init__.py

aretius/control-over-copying

762fe3949d02ff9487bf11f861f7651ff641719f

[ "BSD-3-Clause" ]

9

2019-12-11T10:23:39.000Z

2021-02-23T19:28:04.000Z

evaluation/__init__.py

aretius/control-over-copying

762fe3949d02ff9487bf11f861f7651ff641719f

[ "BSD-3-Clause" ]

9

2019-12-11T10:28:58.000Z

2020-12-31T16:38:03.000Z

from .Bleu import Bleu from .Rouge import Rouge from .evaluation import evaluate, evalFile, evalList __all__ = ["Rouge", "Bleu", "evaluate", "evalFile", "evalList"]

27.666667

63

0.73494

20

166

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0.45

0.271186

0.40678

0

0.13253

166

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33.2

0.819444

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0

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false

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1

0

null

0

1

0

1

0

6

4b9ac4a768f25c4fdd64fe4e9583c349f6df7336

30

py

Python

f5_lbaas_dashboard/api/__init__.py

F5Networks/f5-lbaas-dashboard

06d891260778a77ecf31b9f16d68fe7197162699

[ "Apache-2.0" ]

null

f5_lbaas_dashboard/api/__init__.py

F5Networks/f5-lbaas-dashboard

06d891260778a77ecf31b9f16d68fe7197162699

[ "Apache-2.0" ]

null

f5_lbaas_dashboard/api/__init__.py

F5Networks/f5-lbaas-dashboard

06d891260778a77ecf31b9f16d68fe7197162699

[ "Apache-2.0" ]

null

from . import lbaasv2 # noqa

15

29

0.7

4

30

5.25

1

0

0.043478

0.233333

30

1

30

0.869565

0.133333

0

1

0

true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

4baefdcf2c50887172bfbc47ef0dd9fa5f57ca5a

256

py

Python

playground/control/exceptions.py

phlax/playground

ca661f7adcc2c3502f63e630c96e87e31aa9309a

[ "Apache-2.0" ]

8

2020-11-23T21:08:32.000Z

2021-12-18T10:37:25.000Z

playground/control/exceptions.py

phlax/playground

ca661f7adcc2c3502f63e630c96e87e31aa9309a

[ "Apache-2.0" ]

273

2020-11-23T19:27:06.000Z

2020-12-21T17:34:49.000Z

playground/control/exceptions.py

phlax/playground

ca661f7adcc2c3502f63e630c96e87e31aa9309a

[ "Apache-2.0" ]

2

2020-11-24T09:49:29.000Z

2020-12-30T10:39:10.000Z

# -*- coding: utf-8 -*- # todo: improve validationerror interface class ValidationError(Exception): pass class PlaygroundError(Exception): pass class PlaygroundValidationError(Exception): pass class PlaytimeError(Exception): pass

12.190476

43

0.722656

23

256

8.043478

0.565217

0.281081

0.291892

0

0.004808

0.1875

256

20

44

12.8

0.884615

0.238281

0

0.5

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true

0.5

0

0.5

0

1

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null

1

0

null

0

1

0

1

0

6

29bbfa05d17c8fbc4bfcbe0182658e1a84867d60

126

py

Python

falcon_versioning/invalid_version_error.py

FreakinFacu/falcon_versioning

73b255352ec2f26ee7ffe79faa6db3737f5631a6

[ "MIT" ]

null

falcon_versioning/invalid_version_error.py

FreakinFacu/falcon_versioning

73b255352ec2f26ee7ffe79faa6db3737f5631a6

[ "MIT" ]

null

falcon_versioning/invalid_version_error.py

FreakinFacu/falcon_versioning

73b255352ec2f26ee7ffe79faa6db3737f5631a6

[ "MIT" ]

null

class InvalidVersionError(Exception): def __init__(self, invalid_version): self.invalid_version = invalid_version

31.5

46

0.769841

13

126

6.923077

0.615385

0.466667

0.4

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126

3

47

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0

1

0.333333

false

0

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0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

29fb2c917ac76218fb288098f1138bf94da6c5e4

36,954

py

Python

test/test_evaluation/test_train_evaluator.py

wsyjwps1983/autosklearn

2e29ebaca6bc26fa838f7c3b8b13960c600884e4

[ "BSD-3-Clause" ]

null

test/test_evaluation/test_train_evaluator.py

wsyjwps1983/autosklearn

2e29ebaca6bc26fa838f7c3b8b13960c600884e4

[ "BSD-3-Clause" ]

null

test/test_evaluation/test_train_evaluator.py

wsyjwps1983/autosklearn

2e29ebaca6bc26fa838f7c3b8b13960c600884e4

[ "BSD-3-Clause" ]

1

2019-04-01T11:53:20.000Z

import copy import queue import multiprocessing import os import sys import unittest import unittest.mock from ConfigSpace import Configuration import numpy as np from sklearn.cross_validation import StratifiedKFold, ShuffleSplit from smac.tae.execute_ta_run import StatusType from autosklearn.evaluation import get_last_result, TrainEvaluator, eval_holdout, \ eval_iterative_holdout, eval_cv, eval_partial_cv from autosklearn.util import backend from autosklearn.util.pipeline import get_configuration_space from autosklearn.constants import * this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import get_regression_datamanager, BaseEvaluatorTest, \ get_binary_classification_datamanager, get_dataset_getters, \ get_multiclass_classification_datamanager class Dummy(object): def __init__(self): self.name = 'dummy' class TestTrainEvaluator(BaseEvaluatorTest, unittest.TestCase): _multiprocess_can_split_ = True @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_holdout(self, pipeline_mock): D = get_binary_classification_datamanager() D.name = 'test' kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_holdout') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss() duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(result, 1.7142857142857144) self.assertEqual(pipeline_mock.fit.call_count, 1) # three calls because of the holdout, the validation and the test set self.assertEqual(pipeline_mock.predict_proba.call_count, 3) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.model.fit.call_count, 1) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_iterative_holdout(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) class SideEffect(object): def __init__(self): self.fully_fitted_call_count = 0 def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 return self.fully_fitted_call_count > 5 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted pipeline_mock.pre_transform.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) class LossSideEffect(object): def __init__(self): self.losses = [1.0, 0.8, 0.6, 0.4, 0.2, 0.0] self.iteration = 0 def side_effect(self, *args): self.iteration += 1 return self.losses[self.iteration] evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect evaluator.fit_predict_and_loss(iterative=True) self.assertEqual(evaluator.file_output.call_count, 5) for i in range(1, 6): duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertAlmostEqual(result, 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 5) self.assertEqual([cal[1]['n_iter'] for cal in pipeline_mock.iterative_fit.call_args_list], [2, 4, 8, 16, 32]) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 15) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 5) self.assertEqual(evaluator.model.fit.call_count, 0) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_iterative_holdout_interuption(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) class SideEffect(object): def __init__(self): self.fully_fitted_call_count = 0 def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 if self.fully_fitted_call_count == 3: raise ValueError() return self.fully_fitted_call_count > 5 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted pipeline_mock.pre_transform.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_interuption') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) class LossSideEffect(object): def __init__(self): self.losses = [1.0, 0.8, 0.6, 0.4, 0.2, 0.0] self.iteration = 0 def side_effect(self, *args): self.iteration += 1 return self.losses[self.iteration] evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect self.assertRaises(ValueError, evaluator.fit_predict_and_loss, iterative=True) self.assertEqual(evaluator.file_output.call_count, 2) for i in range(1, 3): duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertAlmostEqual(result, 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 2) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 6) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 2) self.assertEqual(evaluator.model.fit.call_count, 0) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_iterative_holdout_not_iterative(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = False pipeline_mock.pre_transform.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_not_iterative') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss(iterative=True) self.assertEqual(evaluator.file_output.call_count, 1) duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertAlmostEqual(result, 1.7142857142857144) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(evaluator.model.predict_proba.call_count, 3) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], 7) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(evaluator.model.fit.call_count, 1) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_cv(self, pipeline_mock): D = get_binary_classification_datamanager() kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, n_folds=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_cv') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss() duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) self.assertEqual(result, 0.92753623188405787) self.assertEqual(pipeline_mock.fit.call_count, 5) # Fifteen calls because of the holdout, the validation and the test set self.assertEqual(pipeline_mock.predict_proba.call_count, 15) self.assertEqual(evaluator.file_output.call_args[0][0].shape[0], D.data['Y_train'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][1].shape[0], D.data['Y_valid'].shape[0]) self.assertEqual(evaluator.file_output.call_args[0][2].shape[0], D.data['Y_test'].shape[0]) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether # the if block in which model assignment is done is accessed self.assertTrue(evaluator._added_empty_model) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_partial_cv(self, pipeline_mock): D = get_binary_classification_datamanager() kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, n_folds=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_partial_cv') D = get_binary_classification_datamanager() D.name = 'test' configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.partial_fit_predict_and_loss(1) duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 0) self.assertEqual(result, 0.93333333333333335) self.assertEqual(pipeline_mock.fit.call_count, 1) self.assertEqual(pipeline_mock.predict_proba.call_count, 3) # The model prior to fitting is saved, this cannot be directly tested # because of the way the mock module is used. Instead, we test whether # the if block in which model assignment is done is accessed self.assertTrue(evaluator._added_empty_model) @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_iterative_partial_cv(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, n_folds=3) class SideEffect(object): def __init__(self): self.fully_fitted_call_count = 0 def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 return self.fully_fitted_call_count > 5 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted pipeline_mock.pre_transform.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_partial_cv') configuration = unittest.mock.Mock(spec=Configuration) backend_api = backend.create(output_dir, output_dir) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=False, output_y_test=True) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) class LossSideEffect(object): def __init__(self): self.losses = [1.0, 0.8, 0.6, 0.4, 0.2, 0.0] self.iteration = 0 def side_effect(self, *args): self.iteration += 1 return self.losses[self.iteration] evaluator._loss = unittest.mock.Mock() evaluator._loss.side_effect = LossSideEffect().side_effect evaluator.partial_fit_predict_and_loss(fold=1, iterative=True) # No file output here! self.assertEqual(evaluator.file_output.call_count, 0) for i in range(1, 6): duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertAlmostEqual(result, 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 5) self.assertEqual([cal[1]['n_iter'] for cal in pipeline_mock.iterative_fit.call_args_list], [2, 4, 8, 16, 32]) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertFalse(hasattr(evaluator, 'model')) self.assertEqual(pipeline_mock.iterative_fit.call_count, 5) # fifteen calls because of the holdout, the validation and the test set # and a total of five calls because of five iterations of fitting self.assertEqual(pipeline_mock.predict_proba.call_count, 15) @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('os.makedirs') def test_file_output(self, makedirs_mock, backend_mock): D = get_regression_datamanager() D.name = 'test' configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), n_folds=5, shuffle=True, random_state=1) evaluator = TrainEvaluator(D, backend_mock, queue=queue_, configuration=configuration, cv=kfold, with_predictions=True, all_scoring_functions=True, output_y_test=True) backend_mock.get_model_dir.return_value = True evaluator.model = 'model' evaluator.Y_optimization = D.data['Y_train'] rval = evaluator.file_output(D.data['Y_train'], D.data['Y_valid'], D.data['Y_test']) self.assertEqual(rval, (None, None)) self.assertEqual(backend_mock.save_targets_ensemble.call_count, 1) self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 3) self.assertEqual(makedirs_mock.call_count, 1) self.assertEqual(backend_mock.save_model.call_count, 1) # Check for not containing NaNs - that the models don't predict nonsense # for unseen data D.data['Y_valid'][0] = np.NaN rval = evaluator.file_output(D.data['Y_train'], D.data['Y_valid'], D.data['Y_test']) self.assertEqual(rval, (1.0, 'Model predictions for validation set contains NaNs.')) D.data['Y_train'][0] = np.NaN rval = evaluator.file_output(D.data['Y_train'], D.data['Y_valid'], D.data['Y_test']) self.assertEqual(rval, (1.0, 'Model predictions for optimization set contains NaNs.')) @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_classification(self, mock, backend_mock): D = get_binary_classification_datamanager() configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, cv=kfold, subsample=10) train_indices = np.arange(69, dtype=int) train_indices1 = evaluator.subsample_indices(train_indices) evaluator.subsample = 20 train_indices2 = evaluator.subsample_indices(train_indices) evaluator.subsample = 30 train_indices3 = evaluator.subsample_indices(train_indices) evaluator.subsample = 67 train_indices4 = evaluator.subsample_indices(train_indices) # Common cases for ti in train_indices1: self.assertIn(ti, train_indices2) for ti in train_indices2: self.assertIn(ti, train_indices3) for ti in train_indices3: self.assertIn(ti, train_indices4) # Corner cases evaluator.subsample = 0 self.assertRaisesRegex(ValueError, 'The train_size = 0 should be ' 'greater or equal to the number ' 'of classes = 2', evaluator.subsample_indices, train_indices) # With equal or greater it should return a non-shuffled array of indices evaluator.subsample = 69 train_indices5 = evaluator.subsample_indices(train_indices) self.assertTrue(np.all(train_indices5 == train_indices)) evaluator.subsample = 68 self.assertRaisesRegex(ValueError, 'The test_size = 1 should be greater' ' or equal to the number of ' 'classes = 2', evaluator.subsample_indices, train_indices) @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_subsample_indices_regression(self, mock, backend_mock): D = get_regression_datamanager() configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, cv=kfold, subsample=30) train_indices = np.arange(69, dtype=int) train_indices3 = evaluator.subsample_indices(train_indices) evaluator.subsample = 67 train_indices4 = evaluator.subsample_indices(train_indices) # Common cases for ti in train_indices3: self.assertIn(ti, train_indices4) # Corner cases evaluator.subsample = 0 train_indices5 = evaluator.subsample_indices(train_indices) np.testing.assert_allclose(train_indices5, np.array([])) # With equal or greater it should return a non-shuffled array of indices evaluator.subsample = 69 train_indices6 = evaluator.subsample_indices(train_indices) np.testing.assert_allclose(train_indices6, train_indices) @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_predict_proba_binary_classification(self, mock, backend_mock): D = get_binary_classification_datamanager() mock.predict_proba.side_effect = lambda y, batch_size: np.array([[0.1, 0.9]] * 7) mock.side_effect = lambda **kwargs: mock configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, cv=kfold) evaluator.fit_predict_and_loss() Y_optimization_pred = backend_mock.save_predictions_as_npy.call_args_list[0][0][0] print(Y_optimization_pred) for i in range(7): self.assertEqual(0.9, Y_optimization_pred[i][1]) def test_get_results(self): backend_mock = unittest.mock.Mock(spec=backend.Backend) backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau' D = get_binary_classification_datamanager() kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) queue_ = multiprocessing.Queue() for i in range(5): queue_.put((i * 1, 1 - (i * 0.2), 0, "", StatusType.SUCCESS)) result = get_last_result(queue_) self.assertEqual(result[0], 4) self.assertAlmostEqual(result[1], 0.2) def test_datasets(self): for getter in get_dataset_getters(): testname = '%s_%s' % (os.path.basename(__file__). replace('.pyc', '').replace('.py', ''), getter.__name__) with self.subTest(testname): backend_mock = unittest.mock.Mock(spec=backend.Backend) backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau' D = getter() D_ = copy.deepcopy(D) y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() kfold = ShuffleSplit(n=len(y), n_iter=5, random_state=1) queue_ = multiprocessing.Queue() evaluator = TrainEvaluator(D_, backend_mock, queue_, cv=kfold) evaluator.fit_predict_and_loss() duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) self.assertTrue(np.isfinite(result)) class FunctionsTest(unittest.TestCase): def setUp(self): self.queue = multiprocessing.Queue() self.configuration = get_configuration_space( {'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False}).get_default_configuration() self.data = get_multiclass_classification_datamanager() self.tmp_dir = os.path.join(os.path.dirname(__file__), '.test_holdout_functions') self.n = len(self.data.data['Y_train']) self.y = self.data.data['Y_train'].flatten() self.backend = unittest.mock.Mock() self.backend.get_model_dir.return_value = 'udiaetzrpduaeirdaetr' self.backend.output_directory = 'duapdbaetpdbe' def test_eval_holdout(self): kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) eval_holdout(self.queue, self.configuration, self.data, self.backend, kfold, 1, 1, None, True, False, True, None, None, False) info = get_last_result(self.queue) self.assertAlmostEqual(info[1], 0.095, places=3) self.assertEqual(info[2], 1) self.assertNotIn('bac_metric', info[3]) def test_eval_holdout_all_loss_functions(self): kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) eval_holdout(self.queue, self.configuration, self.data, self.backend, kfold, 1, 1, None, True, True, True, None, None, False) info = get_last_result(self.queue) fixture = {'f1_metric': 0.0954545454545, 'pac_metric': 0.203125867166, 'acc_metric': 0.0909090909091, 'auc_metric': 0.0197868008145, 'bac_metric': 0.0954545454545, 'num_run': 1} rval = {i.split(':')[0]: float(i.split(':')[1]) for i in info[3].split(';')} for key, value in fixture.items(): self.assertAlmostEqual(rval[key], fixture[key]) self.assertIn('duration', rval) self.assertAlmostEqual(info[1], 0.095, places=3) self.assertEqual(info[2], 1) # def test_eval_holdout_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) # eval_holdout(self.queue, self.configuration, self.data, # backend_api, 1, 1, 43, True, False, True, None, None, # False) # info = self.queue.get() # self.assertAlmostEqual(info[1], 0.1) # self.assertEqual(info[2], 1) def test_eval_holdout_iterative_fit_no_timeout(self): kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) eval_iterative_holdout(self.queue, self.configuration, self.data, self.backend, kfold, 1, 1, None, True, False, True, None, None, False) info = get_last_result(self.queue) self.assertAlmostEqual(info[1], 0.09545454545454557) self.assertEqual(info[2], 1) # def test_eval_holdout_iterative_fit_on_subset_no_timeout(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) # eval_iterative_holdout(self.queue, self.configuration, # self.data, backend_api, 1, 1, 43, True, False, # True, None, None, False) # # info = self.queue.get() # self.assertAlmostEqual(info[1], 0.1) # self.assertEqual(info[2], 1) def test_eval_cv(self): cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1) eval_cv(queue=self.queue, config=self.configuration, data=self.data, backend=self.backend, seed=1, num_run=1, cv=cv, subsample=None, with_predictions=True, all_scoring_functions=False, output_y_test=True, include=None, exclude=None, disable_file_output=False) info = get_last_result(self.queue) self.assertAlmostEqual(info[1], 0.063004032258064502) self.assertEqual(info[2], 1) self.assertNotIn('bac_metric', info[3]) def test_eval_cv_all_loss_functions(self): cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1) eval_cv(queue=self.queue, config=self.configuration, data=self.data, backend=self.backend, seed=1, num_run=1, cv=cv, subsample=None, with_predictions=True, all_scoring_functions=True, output_y_test=True, include=None, exclude=None, disable_file_output=False) info = get_last_result(self.queue) fixture = {'f1_metric': 0.0635080645161, 'pac_metric': 0.165226664054, 'acc_metric': 0.06, 'auc_metric': 0.0154405176049, 'bac_metric': 0.0630040322581, 'num_run': 1} rval = {i.split(':')[0]: float(i.split(':')[1]) for i in info[3].split(';')} for key, value in fixture.items(): self.assertAlmostEqual(rval[key], fixture[key]) self.assertIn('duration', rval) self.assertAlmostEqual(info[1], 0.063004032258064502) self.assertEqual(info[2], 1) # def test_eval_cv_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) # eval_cv(queue=self.queue, config=self.configuration, data=self.data, # backend=backend_api, seed=1, num_run=1, folds=5, subsample=45, # with_predictions=True, all_scoring_functions=False, # output_y_test=True, include=None, exclude=None, # disable_file_output=False) # info = self.queue.get() # self.assertAlmostEqual(info[1], 0.063004032258064502) # self.assertEqual(info[2], 1) def test_eval_partial_cv(self): cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1, n_folds=5) results = [0.071428571428571508, 0.15476190476190488, 0.08333333333333337, 0.16666666666666674, 0.0] for fold in range(5): eval_partial_cv(queue=self.queue, config=self.configuration, data=self.data, backend=self.backend, seed=1, num_run=1, instance=fold, cv=cv, subsample=None, with_predictions=True, all_scoring_functions=False, output_y_test=True, include=None, exclude=None, disable_file_output=False) info = get_last_result(self.queue) results.append(info[1]) self.assertAlmostEqual(info[1], results[fold]) self.assertEqual(info[2], 1) # def test_eval_partial_cv_on_subset_no_timeout(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) # # results = [0.071428571428571508, # 0.15476190476190488, # 0.08333333333333337, # 0.16666666666666674, # 0.0] # for fold in range(5): # eval_partial_cv(queue=self.queue, config=self.configuration, # data=self.data, backend=backend_api, # seed=1, num_run=1, instance=fold, folds=5, # subsample=80, with_predictions=True, # all_scoring_functions=False, output_y_test=True, # include=None, exclude=None, # disable_file_output=False) # # info = self.queue.get() # self.assertAlmostEqual(info[1], results[fold]) # self.assertEqual(info[2], 1) # # results = [0.071428571428571508, # 0.15476190476190488, # 0.16666666666666674, # 0.0, # 0.0] # for fold in range(5): # eval_partial_cv(queue=self.queue, config=self.configuration, # data=self.data, backend=backend_api, # seed=1, num_run=1, instance=fold, folds=5, # subsample=43, with_predictions=True, # all_scoring_functions=False, output_y_test=True, # include=None, exclude=None, # disable_file_output=False) # # info = self.queue.get() # self.assertAlmostEqual(info[1], results[fold]) # self.assertEqual(info[2], 1)

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0.032775

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null

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null

0

6

4b151ef5e4a98f631d58547a11f09498073c36ac

9,434

py

Python

tests/contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/test_michelson_coding_KT1KVn.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2020-08-11T02:31:24.000Z

tests/contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/test_michelson_coding_KT1KVn.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2020-12-30T16:44:56.000Z

tests/contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/test_michelson_coding_KT1KVn.py

tqtezos/pytezos

a4ac0b022d35d4c9f3062609d8ce09d584b5faa8

[ "MIT" ]

1

2022-03-20T19:01:00.000Z

from unittest import TestCase from tests import get_data from pytezos.michelson.micheline import michelson_to_micheline from pytezos.michelson.formatter import micheline_to_michelson class MichelsonCodingTestKT1KVn(TestCase): def setUp(self): self.maxDiff = None def test_michelson_parse_code_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/code_KT1KVn.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/code_KT1KVn.tz')) self.assertEqual(expected, actual) def test_michelson_format_code_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/code_KT1KVn.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/code_KT1KVn.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_code_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/code_KT1KVn.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_storage_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/storage_KT1KVn.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/storage_KT1KVn.tz')) self.assertEqual(expected, actual) def test_michelson_format_storage_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/storage_KT1KVn.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/storage_KT1KVn.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_storage_KT1KVn(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/storage_KT1KVn.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_oodpad(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oodpad.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oodpad.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_oodpad(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oodpad.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oodpad.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_oodpad(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oodpad.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_op5JXz(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_op5JXz.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_op5JXz.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_op5JXz(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_op5JXz.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_op5JXz.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_op5JXz(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_op5JXz.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_opWTsh(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opWTsh.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opWTsh.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_opWTsh(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opWTsh.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opWTsh.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_opWTsh(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opWTsh.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_oovB4n(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oovB4n.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oovB4n.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_oovB4n(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oovB4n.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oovB4n.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_oovB4n(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_oovB4n.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_opVpjK(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opVpjK.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opVpjK.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_opVpjK(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opVpjK.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opVpjK.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_opVpjK(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opVpjK.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_ooSTG6(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_ooSTG6.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_ooSTG6.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_ooSTG6(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_ooSTG6.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_ooSTG6.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_ooSTG6(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_ooSTG6.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_parameter_opPcx1(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opPcx1.json') actual = michelson_to_micheline(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opPcx1.tz')) self.assertEqual(expected, actual) def test_michelson_format_parameter_opPcx1(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opPcx1.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opPcx1.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_parameter_opPcx1(self): expected = get_data( path='contracts/KT1KVn5cHLPuLoEDmiLEXGfMtNihLtcJtEpM/parameter_opPcx1.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual)

46.935323

89

0.734683

880

9,434

7.563636

0.05

0.048377

0.074369

0.135216

0.963341

0.947416

0

0.018203

0.190587

9,434

200

90

47.17

0.853457

0

0.639053

0

0.316833

0

0.159763

1

0.16568

false

0

0.023669

0

0.195266

0

null

0

1

0

null

0

6

d9f59edd851d7c015a147804b83deeb8cddc454a

188

py

Python

__init__.py

edbarnard/thorlabs_powermeter

869b41da8bf2b2ec36cfcfd3acfb3a6e2ade871b

[ "MIT" ]

null

__init__.py

edbarnard/thorlabs_powermeter

869b41da8bf2b2ec36cfcfd3acfb3a6e2ade871b

[ "MIT" ]

null

__init__.py

edbarnard/thorlabs_powermeter

869b41da8bf2b2ec36cfcfd3acfb3a6e2ade871b

[ "MIT" ]

null

from __future__ import absolute_import from .thorlabs_powermeter import ThorlabsPowerMeterHW from ScopeFoundryHW.thorlabs_powermeter.powermeter_optimizer import PowerMeterOptimizerMeasure

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py

Python

cmapPy/math/tests/test_fast_cov.py

dblyon/cmapPy

d310d092dbf0a0596448c9bd1f75ffff0bb92f09

[ "BSD-3-Clause" ]

1

2021-07-21T21:33:35.000Z

cmapPy/math/tests/test_fast_cov.py

dblyon/cmapPy

d310d092dbf0a0596448c9bd1f75ffff0bb92f09

[ "BSD-3-Clause" ]

10

2022-03-14T18:40:45.000Z

2022-03-22T12:45:02.000Z

cmapPy/math/tests/test_fast_cov.py

Cellular-Longevity/cmapPy

abd4349f28af6d035f69fe8c399fde7bef8dd635

[ "BSD-3-Clause" ]

null

import unittest import logging import cmapPy.pandasGEXpress.setup_GCToo_logger as setup_logger import cmapPy.math.fast_cov as fast_cov import numpy import tempfile import os logger = logging.getLogger(setup_logger.LOGGER_NAME) class TestFastCov(unittest.TestCase): @staticmethod def build_standard_x_y(): x = numpy.array([[1,2,3], [5,7,11]], dtype=float) logger.debug("x: {}".format(x)) logger.debug("x.shape: {}".format(x.shape)) y = numpy.array([[13, 17, 19], [23, 29, 31]], dtype=float) logger.debug("y: {}".format(y)) logger.debug("y.shape: {}".format(y.shape)) return x, y @staticmethod def build_nan_containing_x_y(): x = numpy.array([[1,numpy.nan,2], [3,5,7], [11,13,17]], dtype=float) logger.debug("x:\n{}".format(x)) logger.debug("x.shape: {}".format(x.shape)) y = numpy.array([[19, 23, 29], [31, 37, 41], [numpy.nan, 43, 47]], dtype=float) logger.debug("y:\n{}".format(y)) logger.debug("y.shape: {}".format(y.shape)) return x, y def test_validate_inputs(self): shape = (3,2) #happy path just x x = numpy.zeros(shape) fast_cov.validate_inputs(x, None, None) x = numpy.zeros(1) fast_cov.validate_inputs(x, None, None) #unhappy path just x, x does not have shape attribute with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(None, None, None) logger.debug("unhappy path just x, x does not have shape attribute - context.exception: {}".format(context.exception)) self.assertIn("x needs to be numpy array-like", str(context.exception)) #unhappy path x does not have shape attribute, y does not have shape attribute with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(None, 3, None) logger.debug("unhappy path x does not have shape attribute, y does not have shape attribute - context.exception: {}".format(context.exception)) self.assertIn("x needs to be numpy array-like", str(context.exception)) self.assertIn("y needs to be numpy array-like", str(context.exception)) #happy path x and y x = numpy.zeros(shape) y = numpy.zeros(shape) fast_cov.validate_inputs(x, y, None) #happy path y different shape from x y = numpy.zeros((3,1)) fast_cov.validate_inputs(x, y, None) #unhappy path y different shape from x, invalid axis with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(x, y.T, None) logger.debug("unhappy path y different shape from x, invalid axis - context.exception: {}".format(context.exception)) self.assertIn("the number of rows in the x and y matrices must be the same", str(context.exception)) with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(x.T, y, None) logger.debug("unhappy path y different shape from x, invalid axis - context.exception: {}".format(context.exception)) self.assertIn("the number of rows in the x and y matrices must be the same", str(context.exception)) #happy path with x, destination x = numpy.zeros(shape) dest = numpy.zeros((shape[1], shape[1])) fast_cov.validate_inputs(x, None, dest) #unhappy path with x, destination wrong size dest = numpy.zeros((shape[1]+1, shape[1])) with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(x, None, dest) logger.debug("unhappy path incorrrect shape of destination for provided x - context.exception: {}".format(context.exception)) self.assertIn("x and destination provided", str(context.exception)) self.assertIn("destination must have shape matching", str(context.exception)) #happy path with x, y, destination x = numpy.zeros(shape) y = numpy.zeros((shape[0], shape[1]+1)) dest = numpy.zeros((shape[1], shape[1]+1)) fast_cov.validate_inputs(x, y, dest) #unhappy path x, y, destination wrong size dest = numpy.zeros((shape[1], shape[1]+2)) with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.validate_inputs(x, y, dest) logger.debug("unhappy path incorrrect shape of destination for provided x, y - context.exception: {}".format(context.exception)) self.assertIn("x, y, and destination provided", str(context.exception)) self.assertIn("destination must have number of", str(context.exception)) def test_fast_cov_check_validations_run(self): #unhappy path check that input validation checks are run with self.assertRaises(fast_cov.CmapPyMathFastCovInvalidInputXY) as context: fast_cov.fast_cov(None, None) logger.debug("unhappy path check that input validation checks are run - context.exception: {}".format(context.exception)) def test_fast_cov_just_x(self): logger.debug("*************happy path just x") x, _ = TestFastCov.build_standard_x_y() ex = numpy.cov(x, rowvar=False) logger.debug("expected ex: {}".format(ex)) r = fast_cov.fast_cov(x) logger.debug("r: {}".format(r)) self.assertTrue(numpy.allclose(ex, r)) #happy path just x, uses destination dest = numpy.zeros((x.shape[1], x.shape[1])) r = fast_cov.fast_cov(x, destination=dest) logger.debug("happy path just x, uses destination - r: {}".format(r)) self.assertIs(dest, r) self.assertTrue(numpy.allclose(ex, dest)) #happy path just x, uses destination which is a different type dest = dest.astype(numpy.float16) r = fast_cov.fast_cov(x, destination=dest) logger.debug("happy path, just x, uses destination which is a different type - r: {}".format(r)) self.assertIs(dest, r) self.assertTrue(numpy.allclose(ex, dest)) #happy path just x, uses destination that is a numpy.memmap outfile = tempfile.mkstemp() logger.debug("happy path, just x, uses destination which is a numpy.memmap - outfile: {}".format(outfile)) dest = numpy.memmap(outfile[1], dtype="float16", mode="w+", shape=ex.shape) dest_array = numpy.asarray(dest) r = fast_cov.fast_cov(x, destination=dest_array) dest.flush() logger.debug(" - r: {}".format(r)) os.close(outfile[0]) os.remove(outfile[1]) #happy path just x, transposed ex = numpy.cov(x, rowvar=True) logger.debug("happy path just x, transposed, expected ex: {}".format(ex)) r = fast_cov.fast_cov(x.T) logger.debug("r: {}".format(r)) self.assertTrue(numpy.allclose(ex, r)) def test_fast_cov_x_and_y(self): logger.debug("*************happy path x and y") x, y = TestFastCov.build_standard_x_y() combined = numpy.hstack([x, y]) logger.debug("combined: {}".format(combined)) logger.debug("combined.shape: {}".format(combined.shape)) off_diag_ind = int(combined.shape[1] / 2) raw_ex = numpy.cov(combined, rowvar=False) logger.debug("raw expected produced from numpy.cov on full combined - raw_ex: {}".format(raw_ex)) ex = raw_ex[:off_diag_ind, off_diag_ind:] logger.debug("expected ex: {}".format(ex)) r = fast_cov.fast_cov(x, y) logger.debug("r: {}".format(r)) self.assertTrue(numpy.allclose(ex, r)) #happy path x, y, and destination dest = numpy.zeros((x.shape[1], y.shape[1])) r = fast_cov.fast_cov(x, y, dest) logger.debug("happy path x, y, and destination - r: {}".format(r)) self.assertIs(dest, r) self.assertTrue(numpy.allclose(ex, dest)) #happy path x and y, other direction combined = numpy.hstack([x.T, y.T]) off_diag_ind = int(combined.shape[1] / 2) raw_ex = numpy.cov(combined, rowvar=False) logger.debug("happy path x and y, other direction, raw expected produced from numpy.cov on full combined - raw_ex: {}".format(raw_ex)) ex = raw_ex[:off_diag_ind, off_diag_ind:] logger.debug("expected ex: {}".format(ex)) r = fast_cov.fast_cov(x.T, y.T) logger.debug("r: {}".format(r)) self.assertTrue(numpy.allclose(ex, r)) def test_fast_cov_x_and_y_different_shapes(self): logger.debug("*************happy path x and y different shapes") x, _ = TestFastCov.build_standard_x_y() y = numpy.array([[13, 17, 19, 23, 41], [23, 29, 31, 37, 43]]) logger.debug("y.shape: {}".format(y.shape)) logger.debug("y:\n{}".format(y)) combined = numpy.hstack([x, y]) logger.debug("combined: {}".format(combined)) logger.debug("combined.shape: {}".format(combined.shape)) raw_ex = numpy.cov(combined, rowvar=False) logger.debug("raw expected produced from numpy.cov on full combined - raw_ex: {}".format(raw_ex)) logger.debug("raw_ex.shape: {}".format(raw_ex.shape)) ex = raw_ex[:x.shape[1], -y.shape[1]:] logger.debug("expected ex: {}".format(ex)) logger.debug("ex.shape: {}".format(ex.shape)) r = fast_cov.fast_cov(x, y) logger.debug("r: {}".format(r)) self.assertTrue(numpy.allclose(ex, r)) #happy path x and y different shapes, using destination dest = numpy.zeros((x.shape[1], y.shape[1])) r = fast_cov.fast_cov(x, y, dest) logger.debug("happy path x and y different shapes, using destination - r: {}".format(r)) self.assertIs(dest, r) self.assertTrue(numpy.allclose(ex, dest)) def test_fast_cov_1D_arrays(self): logger.debug("*****************happy path test_fast_cov_1D_arrays") x = numpy.array(range(3)) logger.debug("x.shape: {}".format(x.shape)) r = fast_cov.fast_cov(x) logger.debug("r: {}".format(r)) self.assertEqual(1., r[0][0]) y = numpy.array(range(3,6)) logger.debug("y.shape: {}".format(y.shape)) r = fast_cov.fast_cov(x, y) logger.debug("r: {}".format(r)) self.assertEqual(1., r[0][0]) def test_calculate_non_mask_overlaps(self): x = numpy.zeros((3,3)) x[0,1] = numpy.nan x = numpy.ma.array(x, mask=numpy.isnan(x)) logger.debug("happy path x has 1 nan - x:\n{}".format(x)) r = fast_cov.calculate_non_mask_overlaps(x.mask, x.mask) logger.debug("r:\n{}".format(r)) expected = numpy.array([[3,2,3], [2,2,2], [3,2,3]], dtype=int) self.assertTrue(numpy.array_equal(expected, r)) def test_nan_fast_cov_just_x(self): logger.debug("*************happy path just x") x, _ = TestFastCov.build_nan_containing_x_y() ex_with_nan = numpy.cov(x, rowvar=False) logger.debug("expected with nan's - ex_with_nan:\n{}".format(ex_with_nan)) r = fast_cov.nan_fast_cov(x) logger.debug("r:\n{}".format(r)) non_nan_locs = ~numpy.isnan(ex_with_nan) self.assertTrue(numpy.allclose(ex_with_nan[non_nan_locs], r[non_nan_locs])) check_nominal_nans = [] u = x[1:, 1] for i in range(3): t = x[1:, i] c = numpy.cov(t, u, bias=False)[0,1] check_nominal_nans.append(c) logger.debug("calculate entries that would be nan - check_nominal_nans: {}".format(check_nominal_nans)) self.assertTrue(numpy.allclose(check_nominal_nans, r[:, 1])) self.assertTrue(numpy.allclose(check_nominal_nans, r[1, :])) def test_nan_fast_cov_x_and_y(self): logger.debug("*************happy path x and y") x, y = TestFastCov.build_nan_containing_x_y() combined = numpy.hstack([x, y]) logger.debug("combined:\n{}".format(combined)) logger.debug("combined.shape: {}".format(combined.shape)) off_diag_ind = int(combined.shape[1] / 2) raw_ex = numpy.cov(combined, rowvar=False) logger.debug("raw expected produced from numpy.cov on full combined - raw_ex:\n{}".format(raw_ex)) ex = raw_ex[:off_diag_ind, off_diag_ind:] logger.debug("expected ex:\n{}".format(ex)) r = fast_cov.nan_fast_cov(x, y) logger.debug("r:\n{}".format(r)) non_nan_locs = ~numpy.isnan(ex) logger.debug("ex[non_nan_locs]: {}".format(ex[non_nan_locs])) logger.debug("r[non_nan_locs]: {}".format(r[non_nan_locs])) self.assertTrue(numpy.allclose(ex[non_nan_locs], r[non_nan_locs])) check_nominal_nans = [] t = x[1:, 1] for i in [1,2]: u = y[1:, i] c = numpy.cov(t,u) check_nominal_nans.append(c[0,1]) logger.debug("calculate entries that would be nan - check_nominal_nans: {}".format(check_nominal_nans)) logger.debug("r values to compare to - r[1, 1:]: {}".format(r[1, 1:])) self.assertTrue(numpy.allclose(check_nominal_nans, r[1, 1:])) check_nominal_nans = [] u = y[:2, 0] for i in [0, 2]: t = x[:2, i] c = numpy.cov(t,u) check_nominal_nans.append(c[0,1]) logger.debug("calculate entries that would be nan - check_nominal_nans: {}".format(check_nominal_nans)) logger.debug("r values to compare to - r[[0,2], 0]: {}".format(r[[0,2], 0])) self.assertTrue(numpy.allclose(check_nominal_nans, r[[0,2], 0])) self.assertTrue(numpy.isnan(r[1,0]), """expect this entry to be nan b/c for the intersection of x[:,1] and y[:,0] there is only one entry in common, therefore covariance is undefined""") def test_nan_fast_cov_x_and_y_different_shapes(self): logger.debug("*************happy path x and y different shapes") x, t = TestFastCov.build_nan_containing_x_y() y = numpy.zeros((t.shape[0], t.shape[1]+1)) y[:, :t.shape[1]] = t y[:, t.shape[1]] = [53, 59, 61] logger.debug("y.shape: {}".format(y.shape)) logger.debug("y:\n{}".format(y)) combined = numpy.hstack([x, y]) logger.debug("combined:\n{}".format(combined)) logger.debug("combined.shape: {}".format(combined.shape)) raw_ex = numpy.cov(combined, rowvar=False) logger.debug("raw expected produced from numpy.cov on full combined - raw_ex:\n{}".format(raw_ex)) logger.debug("raw_ex.shape: {}".format(raw_ex.shape)) ex = raw_ex[:x.shape[1], -y.shape[1]:] logger.debug("expected ex:\n{}".format(ex)) logger.debug("ex.shape: {}".format(ex.shape)) r = fast_cov.nan_fast_cov(x, y) logger.debug("r:\n{}".format(r)) non_nan_locs = ~numpy.isnan(ex) logger.debug("ex[non_nan_locs]: {}".format(ex[non_nan_locs])) logger.debug("r[non_nan_locs]: {}".format(r[non_nan_locs])) self.assertTrue(numpy.allclose(ex[non_nan_locs], r[non_nan_locs])) check_nominal_nans = [] t = x[1:, 1] for i in [1,2,3]: u = y[1:, i] c = numpy.cov(t,u) check_nominal_nans.append(c[0,1]) logger.debug("calculate entries that would be nan - check_nominal_nans: {}".format(check_nominal_nans)) logger.debug("r values to compare to - r[1, 1:]: {}".format(r[1, 1:])) self.assertTrue(numpy.allclose(check_nominal_nans, r[1, 1:])) check_nominal_nans = [] u = y[:2, 0] for i in [0, 2]: t = x[:2, i] c = numpy.cov(t,u) check_nominal_nans.append(c[0,1]) logger.debug("calculate entries that would be nan - check_nominal_nans: {}".format(check_nominal_nans)) logger.debug("r values to compare to - r[[0,2], 0]: {}".format(r[[0,2], 0])) self.assertTrue(numpy.allclose(check_nominal_nans, r[[0,2], 0])) self.assertTrue(numpy.isnan(r[1,0]), """expect this entry to be nan b/c for the intersection of x[:,1] and y[:,0] there is only one entry in common, therefore covariance is undefined""") def test_nan_fast_cov_all_nan(self): x = numpy.zeros(3) x[:] = numpy.nan x = x[:, numpy.newaxis] logger.debug("x:\n{}".format(x)) r = fast_cov.nan_fast_cov(x) logger.debug("r:\n{}".format(r)) self.assertEqual(0, numpy.sum(numpy.isnan(r))) def test_nan_fast_cov_1D_arrays(self): logger.debug("*****************happy path test_nan_fast_cov_1D_arrays") x = numpy.array(range(3)) logger.debug("x.shape: {}".format(x.shape)) r = fast_cov.nan_fast_cov(x) logger.debug("r: {}".format(r)) self.assertEqual(1., r[0][0]) y = numpy.array(range(3,6)) logger.debug("y.shape: {}".format(y.shape)) r = fast_cov.fast_cov(x, y) logger.debug("r: {}".format(r)) self.assertEqual(1., r[0][0]) if __name__ == "__main__": setup_logger.setup(verbose=True) unittest.main()

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