xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

e6b492a8569382b674ad38edea3efaacc9af93df

412

py

Python

tests/lightgbm/model-server/serve.py

basisai/bedrock-express

273b6377f080e1f6125dfd8ec465a8aaf3dee468

[ "Apache-2.0" ]

9

2020-10-22T06:42:38.000Z

2020-10-22T08:38:17.000Z

tests/lightgbm/model-server/serve.py

basisai/bedrock-express

273b6377f080e1f6125dfd8ec465a8aaf3dee468

[ "Apache-2.0" ]

69

2020-10-23T02:15:36.000Z

2022-03-31T00:03:18.000Z

tests/lightgbm/model-server/serve.py

basisai/bedrock-express

273b6377f080e1f6125dfd8ec465a8aaf3dee468

[ "Apache-2.0" ]

1

2021-09-28T01:36:41.000Z

import pickle from typing import List, Optional from bedrock_client.bedrock.model import BaseModel class Model(BaseModel): def __init__(self, path: Optional[str] = None): with open(path or "/artefact/model.pkl", "rb") as f: self.model = pickle.load(f) def predict(self, features: List[List[float]]) -> List[float]: return self.model.predict_proba(features)[:, 0].tolist()

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4

e6fd74084df825010079f5238c86ccfc9f4993cb

420

py

Python

venv/lib/python3.8/site-packages/mathpy/numerical/__init__.py

sonakshibhalla/sonakshicode

5242d1b128a6be3d184b5c64cf5f9448ccdc49be

[ "MIT" ]

null

venv/lib/python3.8/site-packages/mathpy/numerical/__init__.py

sonakshibhalla/sonakshicode

5242d1b128a6be3d184b5c64cf5f9448ccdc49be

[ "MIT" ]

null

venv/lib/python3.8/site-packages/mathpy/numerical/__init__.py

sonakshibhalla/sonakshicode

5242d1b128a6be3d184b5c64cf5f9448ccdc49be

[ "MIT" ]

null

from __future__ import absolute_import from .differentiation import forward_difference, backward_difference, central_difference, \ approximate_derivative_finite from .integration import trapezoidal_rule, simpsons_rule, composite_simpsons_rule, \ composite_trapezoidal from .polynomial import horner_eval, lagrange_interpolate, neville, divided_differences from .roots import newtonraph, bisection, secant

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155

py

Python

objects/__init__.py

ionicsolutions/pyfecs

38bf28decc5caf6c1f94263c9788880dd9c17707

[ "Apache-2.0" ]

1

2021-07-31T04:27:09.000Z

objects/__init__.py

ionicsolutions/pyfecs

38bf28decc5caf6c1f94263c9788880dd9c17707

[ "Apache-2.0" ]

null

objects/__init__.py

ionicsolutions/pyfecs

38bf28decc5caf6c1f94263c9788880dd9c17707

[ "Apache-2.0" ]

1

2018-10-30T01:09:08.000Z

"""PyFECS models a FECS sequence as a structure of instances of Python classes. This structure can then be modified and compiled to FPGA instructions. """

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4

fc1314cdf21eebc306d3ca68c6a19aea68021097

5,750

py

Python

python/find_vsan_storage_ctrl_queue_depth.py

droorda/vmware-scripts

1bdd04ca419b874d6ebd90ba11bf44de13e886d4

[ "BSD-2-Clause" ]

null

python/find_vsan_storage_ctrl_queue_depth.py

droorda/vmware-scripts

1bdd04ca419b874d6ebd90ba11bf44de13e886d4

[ "BSD-2-Clause" ]

null

python/find_vsan_storage_ctrl_queue_depth.py

droorda/vmware-scripts

1bdd04ca419b874d6ebd90ba11bf44de13e886d4

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # Author: William Lam # Website: www.williamlam.com # Product: VMware vSphere + VSAN # Description: This script extracts the queue depth of a VSAN Storage Controller if found in the VSAN HCL (offline list) # Reference: http://www.williamlam.com/2014/06/community-vsan-storage-controller-queue-depth-list.html import json import os from xml.etree import ElementTree as ET show_non_vsan_hcl_ctr = False #VSAN HCL Supported Controllers vsan_controllers = '{"103C:323B:103c:3355": "Smart Array P220i", "1000:005B:1014:041D": "ServeRAID M5115 SAS/SATA Controller for IBM Flex System (90Y4390)", "1000:0065:1000:30C0": "SAS9201-16i", "1000:0079:8086:9261": "Intel RAID Controller RS2BL080", "1000:005B:8086:351D": "Intel RAID Controller RMS25CB080N", "1000:005B:1000:1F31": "PERC H710P Adapter", "1000:005B:1734:11E5": "SAS RAID HDD Module (D2816C )", "1000:005B:1734:11E4": "RAID Ctrl SAS 6G 1GB (D3116C)", "1000:005B:1000:9270": "MegaRAID SAS 9270-8i", "1000:005B:1000:9271": "UCS-RAID9271CV-8I", "1000:005B:1000:9272": "MegaRAID SAS 9272-8i", "1000:0072:1000:3040": "SAS9210-8i", "1000:005B:1000:9275": "MegaRAID SAS 9271-8iCC", "1000:005B:1000:9276": "MegaRAID SAS 9271-4i", "1000:0087:1000:3040": "SAS9207-4i4e", "1000:0079:8086:9276": "Intel RAID Controller RS2WG160", "103C:323B:103c:3351": "Smart Array P420", "1000:0079:1000:9268": "LSI MegaRAID SAS 9260CV-8i", "103c:323b:103c:3353": "Smart Array P822", "103C:323B:103c:3354": "Smart Array P420i", "1000:0087:8086:3060": "Intel RAID Controller RS25FB044", "1000:005B:1000:9268": "MegaRAID SAS 9265CV-8i", "1000:0087:1000:3050": "SAS9217-8i", "1000:0079:1000:9267": "LSI MegaRAID SAS 9260CV-4i", "1000:0079:1000:9264": "LSI MegaRAID SAS 9264-8i", "1000:0087:1000:3020": "SAS9207-8i", "1000:0079:1000:9263": "LSI MegaRAID SAS 9261-8i", "1000:0079:15d9:0070": "SMC2108", "1000:005B:1028:1F35": "PERC H710 Adapter", "1000:005B:15d9:0690": "SMC2208", "1000:0079:1000:9282": "LSI MegaRAID SAS 9280-4i4e", "1000:005B:1014:040B": "ServeRAID M5110 SAS/SATA Controller for IBM System x (81Y4481)", "1000:005b:1137:008d": "LSI 2208R", "1000:0070:1000:3010": "SAS9211-4i", "1000:0087:1000:3060": "SAS9217-4i4e", "1000:0079:1000:9277": "LSI MegaRAID SAS 9280-16i4e", "1000:005B:1734:11D3": "RAID Ctrl SAS 6G 1GB (D3116)", "1000:005B:1734:11D4": "SAS RAID HDD Module (D2816)", "1000:0072:1000:3050": "SAS9211-8i", "1000:005B:8086:9265": "Intel RAID Controller RS25DB080", "1000:005B:1000:9267": "MegaRAID SAS 9267-8i", "1000:005B:1000:9266": "MegaRAID SAS 9266-8i", "1000:005B:1000:9265": "MegaRAID SAS 9265-8i", "1000:0086:15d9:0691": "SMC2308", "1000:0079:8086:9290": "Intel RAID Controller RS2SG244", "1000:005B:1000:9269": "MegaRAID SAS 9266-4i", "1000:005B:8086:351C": "Intel RAID Controller RMS25PB080N", "1000:0087:8086:3518": "Intel RAID Controller RMS25KB080", "1000:0087:8086:3519": "Intel RAID Controller RMS25KB040", "1000:0072:1000:3020": "SAS9211-8i", "1000:0079:1734:1176": "RAID Crtl SAS 6G 5/6 512MB", "1000:0079:1014:03c7": "IBM ServeRAID-M5014 SAS/SATA Controller", "1000:0079:8086:350B": "Intel RAID Controller RMS2MH080", "1000:0079:1734:11B3": "PY SAS RAID Mezz Card 6Gb", "1000:0079:1000:9276": "LSI MegaRAID SAS 9260-16i", "1000:0087:8087:3516": "Intel RAID Controller RMS25JB080", "1000:0079:1000:9261": "LSI MegaRAID SAS 9260-8i", "1000:0079:1014:03b3": "ServeRAID M5025 SAS/SATA Controller (46M0830)", "1000:0079:1014:0411": "ServeRAID M5016 SAS/SATA Controller for IBM System x (90Y4304)", "1000:005b:1000:9273": "MegaRAID SAS 9270CV-8i", "1000:0087:8086:3517": "Intel RAID Controller RMS25JB040", "1000:0079:1000:9262": "LSI MegaRAID SAS 9262-8i", "1000:005B:8086:3514": "Intel RAID Controller RMS25CB040", "1000:005B:8086:3515": "Intel RAID Controller RMS25CB080", "1000:0072:1028:1F1D": "Dell PERC H200 Adapter", "1000:0079:1000:9290": "LSI MegaRAID SAS 9280-24i4e", "1000:005B:8086:3510": "Intel RAID Controller RMS25PB080", "1000:0072:1000:3060": "SAS9212-4i4e"}' json_data = json.loads(vsan_controllers) #run esxcfg-info -s -F xml and store output to /tmp/esxcfginfo.xml os.system("esxcfg-info -s -F xml > /tmp/esxcfginfo.xml") # Load up XML output root = ET.parse("/tmp/esxcfginfo.xml").getroot() # SCSI Adatpers root starts at 'all-scsi-iface' for allscsiadapters in root.findall('all-scsi-iface'): for allscsiadapter in allscsiadapters: scsiinterfaces = allscsiadapter.find('scsi-interface') for scsiinterface in scsiinterfaces: if scsiinterface.get('name') == 'queue-depth': queue_depth = scsiinterface.text pcidevices = scsiinterfaces.find('pci-device') if pcidevices != None: for pcidevice in pcidevices: if pcidevice.get('name') == 'vendor-id': vendor_id = pcidevice.text if pcidevice.get('name') == 'device-id': device_id = pcidevice.text if pcidevice.get('name') == 'sub-vendor-id': sub_vendor_id = pcidevice.text if pcidevice.get('name') == 'sub-device-id': sub_device_id = pcidevice.text if pcidevice.get('name') == 'vendor-name': vendor_name = pcidevice.text if pcidevice.get('name') == 'device-name': device_name = pcidevice.text # used for non-VSAN HCL storage controllers adapter = vendor_name + " " + device_name custom_pci_id = (vendor_id + ":" + device_id + ":" + sub_vendor_id + ":" + sub_device_id).replace('0x','') if custom_pci_id in json_data: print "VSAN HCL: Yes" print "Adapter: " + json_data[custom_pci_id] print "Identifier: " + custom_pci_id print "QueueDepth: " + queue_depth + "\n" if show_non_vsan_hcl_ctr: print "VSAN HCL: No" print "Adapter: " + adapter print "Identifier: " + custom_pci_id print "QueueDepth: " + queue_depth + "\n"

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null

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4

fc38b2952fbf119278b23ba53a38c4e21b95fc1b

1,263

py

Python

Tests/test_statistics.py

jimishapatel/statistic

73430aeb5c11ae2683047bab16dc976cc7b3e403

[ "MIT" ]

null

Tests/test_statistics.py

jimishapatel/statistic

73430aeb5c11ae2683047bab16dc976cc7b3e403

[ "MIT" ]

null

Tests/test_statistics.py

jimishapatel/statistic

73430aeb5c11ae2683047bab16dc976cc7b3e403

[ "MIT" ]

1

2019-12-22T07:27:47.000Z

import unittest from Statistics.statistics import Statistics class MyTestCase(unittest.TestCase): def setUp(self) -> None: self.statistics = Statistics('Tests/Data/statistics.csv') def test_instantiate_calculator(self): self.assertIsInstance(self.statistics, Statistics) def test_decorator_calculator(self): self.assertIsInstance(self.statistics, Statistics) def test_mean(self): self.assertEqual(self.statistics.mean(), 0.25) def test_median(self): self.assertEqual(self.statistics.median(), 4) def test_mode(self): self.assertEqual(self.statistics.mod(), 1) def test_popstand(self): self.assertEqual(round(self.statistics.popstand(), 4), 0.2233) def test_vpop(self): self.assertEqual(self.statistics.vpop(), 0.0498442) def test_confidence(self): self.assertEqual(self.statistics.confidence(), 0.2232581) def test_popuvar(self): self.assertEqual(self.statistics.confidence(), 0.2232581) def test_samplestand(self): self.assertEqual(self.statistics.confidence(), 0.2232581) def test_zscore(self): self.assertEqual(self.statistics.confidence(), 0.2232581) if __name__ == '__main__': unittest.main()

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fc4ef406430e8f41589fc87b90a4fe7776f6b861

28

py

Python

venv-lib/lib/python3.7/enum.py

migmaciasdiaz/venvs

bcdbb75931cb27fc4b5b30f12fc44be85952157e

[ "MIT" ]

2

2020-03-30T14:17:10.000Z

2020-10-04T12:33:00.000Z

venv-lib/lib/python3.7/enum.py

migmaciasdiaz/venvs

bcdbb75931cb27fc4b5b30f12fc44be85952157e

[ "MIT" ]

1

2020-11-24T03:31:13.000Z

venv/lib/python3.7/enum.py

wensu425/aws-eb-webapp

4b149c75c11fe5b33c9a080313ec336fabb45824

[ "MIT" ]

1

2021-05-04T09:18:22.000Z

/usr/lib64/python3.7/enum.py

28

0.785714

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28

3.666667

1

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null

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1

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4

fc59ea037d6526648755b25f4fb9aba7764b26c7

344

py

Python

src/network/bo/messages/connection_accepted.py

TimmMoetz/blockchain-lab

02bb55cc201586dbdc8fdc252a32381f525e83ff

[ "RSA-MD" ]

2

2021-11-08T12:00:02.000Z

2021-11-12T18:37:52.000Z

src/network/bo/messages/connection_accepted.py

TimmMoetz/blockchain-lab

02bb55cc201586dbdc8fdc252a32381f525e83ff

[ "RSA-MD" ]

null

src/network/bo/messages/connection_accepted.py

TimmMoetz/blockchain-lab

02bb55cc201586dbdc8fdc252a32381f525e83ff

[ "RSA-MD" ]

1

2022-03-28T13:49:37.000Z

from .message import Message class Connection_accepted(Message): def __init__(self) -> None: super().__init__() self._name = "connection-accepted" def to_dict(self): return { "name": self.get_name() } @staticmethod def from_dict(dict): return Connection_accepted()

20.235294

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4

5d826d4c386cd459637ce9e23eb52cc44c3880f1

121

py

Python

tests/cases/test_indexing.py

MiguelMarcelino/py2many

9b040b2a157e265df9c053eaf3e5cd644d3e30d0

[ "MIT" ]

2

2022-02-02T11:37:53.000Z

2022-03-30T18:19:06.000Z

tests/cases/test_indexing.py

MiguelMarcelino/py2many

9b040b2a157e265df9c053eaf3e5cd644d3e30d0

[ "MIT" ]

25

2022-02-28T21:19:11.000Z

2022-03-23T21:26:20.000Z

tests/cases/test_indexing.py

MiguelMarcelino/py2many

9b040b2a157e265df9c053eaf3e5cd644d3e30d0

[ "MIT" ]

null

if __name__ == "__main__": a = [1,2,3] i = -1 print(a[-1]) for i in range(-1,-4,-1): print(a[i])

17.285714

29

0.429752

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0

1

0

null

0

4

5d852685fb3ae99a9448f0ad9bc94151a4a37757

264

py

Python

abc032_d.py

Lockdef/kyopro-code

2d943a87987af05122c556e173e5108a0c1c77c8

[ "MIT" ]

null

abc032_d.py

Lockdef/kyopro-code

2d943a87987af05122c556e173e5108a0c1c77c8

[ "MIT" ]

null

abc032_d.py

Lockdef/kyopro-code

2d943a87987af05122c556e173e5108a0c1c77c8

[ "MIT" ]

null

N, W = map(int, input().split()) vw = [list(map(int, input().split())) for _ in range(N)] dp = [[0] * W for _ in range(N)] for i in range(N): for w in range(W): if w >= vw[i][1]: dp[i + 1][w] = max(dp[i][w - vw[i][1]] + vw[i][0], dp[i][w])

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null

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5d8c423482f860eb783401948ef4ef79bec03dc3

224

py

Python

djangosaml2/apps.py

mz-techops/banhammer

02476db3d2bb617dbe50827687065fbea7553caf

[ "BSD-3-Clause" ]

3

2018-03-09T23:29:25.000Z

2020-11-25T15:34:13.000Z

djangosaml2/apps.py

whyallyn/banhammer

59fc81b15d9950a7a40279a9d1df8101c58df569

[ "BSD-3-Clause" ]

3

2018-05-08T01:10:43.000Z

2021-03-19T21:56:36.000Z

djangosaml2/apps.py

whyallyn/banhammer

59fc81b15d9950a7a40279a9d1df8101c58df569

[ "BSD-3-Clause" ]

2

2018-05-10T15:07:24.000Z

2018-06-20T16:24:00.000Z

"""Register Djangosaml2 as Django app.""" from __future__ import unicode_literals from django.apps import AppConfig class Djangosaml2Config(AppConfig): """BanHammer Djangosaml2 Django app.""" name = 'djangosaml2'

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null

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1

0

4

5da9aedf0ea72f033604e2d2cea3c170efe10297

124

py

Python

alignments/forms.py

LDWLab/DESIRE

fda3e10c6fdfe5ce47c151d506239dbc9f65f2f0

[ "MIT" ]

null

alignments/forms.py

LDWLab/DESIRE

fda3e10c6fdfe5ce47c151d506239dbc9f65f2f0

[ "MIT" ]

null

alignments/forms.py

LDWLab/DESIRE

fda3e10c6fdfe5ce47c151d506239dbc9f65f2f0

[ "MIT" ]

null

from django import forms class ParalogForm(forms.Form): your_name = forms.CharField(label='Your name', max_length=100)

24.8

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null

0

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1

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4

5dad52037c697b1dec7a92cbb77cab50e0aa001c

177

py

Python

setup.py

thomascbrs/mpc-tsid

a20da07fd285a628c6dd32afd76075e3963bf005

[ "BSD-2-Clause" ]

null

setup.py

thomascbrs/mpc-tsid

a20da07fd285a628c6dd32afd76075e3963bf005

[ "BSD-2-Clause" ]

null

setup.py

thomascbrs/mpc-tsid

a20da07fd285a628c6dd32afd76075e3963bf005

[ "BSD-2-Clause" ]

1

2021-06-30T06:31:26.000Z

from distutils.core import setup from Cython.Build import cythonize modules = ["FootTrajectoryGenerator.py"] setup(name='MPC TSID app', ext_modules=cythonize(modules))

19.666667

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8

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null

0

1

0

4

5db24472973d7bd82732f63f526e4a3fbf0abe5c

96

py

Python

code/arc020_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/arc020_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/arc020_1_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

A,B=map(int,input().split()) A,B=abs(A),abs(B) print("Ant" if A<B else "Bug" if A>B else "Draw")

32

49

0.614583

24

96

2.458333

0.541667

0.135593

0.271186

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0.104167

96

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0.103093

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null

0

1

0

4

5dde527c31104a93d7ee67e853c6a768a778e966

291

py

Python

jupyter/jupyter_config.py

eleflow/estudo_cartola

ce1d3d7cac4be4cdda9afcd1b826d3cb506051c2

[ "Apache-2.0" ]

1

2022-01-25T21:40:00.000Z

jupyter/jupyter_config.py

eleflow/estudo_cartola

ce1d3d7cac4be4cdda9afcd1b826d3cb506051c2

[ "Apache-2.0" ]

null

jupyter/jupyter_config.py

eleflow/estudo_cartola

ce1d3d7cac4be4cdda9afcd1b826d3cb506051c2

[ "Apache-2.0" ]

null

# Kernel config c.IPKernelApp.pylab = 'inline' # if you want plotting support always in your notebook c = get_config() c.NotebookApp.ip = '*' c.NotebookApp.port = 8888 c.NotebookApp.open_browser = False c.NotebookApp.token = '' c.NotebookApp.password = u'' c.notebookApp.open_browser = True

32.333333

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291

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291

9

87

32.333333

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1

0

1

0

1

0

null

0

1

0

4

5de372e7bf5ec6726b3342d073bea54dd533dd1b

353

py

Python

slixmpp/plugins/xep_0422/__init__.py

anirudhrata/slixmpp

1fcee0e80a212eeb274d2f560e69099d8a61bf7f

[ "BSD-3-Clause" ]

86

2016-07-04T13:26:02.000Z

2022-02-19T10:26:21.000Z

slixmpp/plugins/xep_0422/__init__.py

anirudhrata/slixmpp

1fcee0e80a212eeb274d2f560e69099d8a61bf7f

[ "BSD-3-Clause" ]

10

2016-09-30T18:55:41.000Z

2020-05-01T14:22:47.000Z

slixmpp/plugins/xep_0422/__init__.py

anirudhrata/slixmpp

1fcee0e80a212eeb274d2f560e69099d8a61bf7f

[ "BSD-3-Clause" ]

45

2016-09-30T18:48:41.000Z

2022-03-18T21:39:33.000Z

# Slixmpp: The Slick XMPP Library # Copyright (C) 2020 Mathieu Pasquet <mathieui@mathieui.net> # This file is part of Slixmpp. # See the file LICENSE for copying permission. from slixmpp.plugins.base import register_plugin from slixmpp.plugins.xep_0422.stanza import * from slixmpp.plugins.xep_0422.fastening import XEP_0422 register_plugin(XEP_0422)

32.090909

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353

5.283019

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353

10

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null

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1

0

1

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0

4

5defa2a16accaa03aefed3c4e8c56728f8986a20

616

py

Python

tests/fixtures/python3_marshmallow/string_property_default.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

21

2018-06-15T16:08:57.000Z

2022-02-11T16:16:11.000Z

tests/fixtures/python3_marshmallow/string_property_default.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

14

2018-08-09T18:02:19.000Z

2022-01-24T18:04:17.000Z

tests/fixtures/python3_marshmallow/string_property_default.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

4

2018-11-30T18:19:10.000Z

2021-11-18T04:04:36.000Z

from marshmallow import Schema, fields as fields_, post_load from typing import Optional, List, Any class TestSchema(Schema): x = fields_.String(default="42") @post_load def make_test(self, test): return Test(test) class Test: def __init__(self, test: dict): self.x: str = test.get("x", "42") def to_json(self): return TestSchema(strict=True).dumps(self).data def to_dict(self): return TestSchema(strict=True).dump(self).data @staticmethod def from_json(json: str, only=None): return TestSchema(strict=True, only=only).loads(json).data

23.692308

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616

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616

25

67

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null

0

1

0

1

0

4

5df61c4fd0bc46260132ad4d0503d0c1e21aabe5

146

py

Python

demo.py

Sjord/whey

f06477f32820679bc9e860d77a0f1d46c7adade4

[ "MIT" ]

16

2021-03-11T06:32:46.000Z

2022-02-04T22:31:17.000Z

demo.py

Sjord/whey

f06477f32820679bc9e860d77a0f1d46c7adade4

[ "MIT" ]

24

2021-02-22T21:07:28.000Z

2022-03-19T03:11:48.000Z

demo.py

Sjord/whey

f06477f32820679bc9e860d77a0f1d46c7adade4

[ "MIT" ]

2

2021-08-08T17:34:39.000Z

2021-09-21T08:53:23.000Z

# stdlib from pprint import pprint # this package from whey.config import load_toml config = load_toml("example_pyproject.toml") pprint(config)

16.222222

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0.794521

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146

5.380952

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146

8

45

18.25

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1

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4

5d1432ad4d14446fda8f8193c6c0c3dc34fb1ae3

103

py

Python

src/jazzit/settings.py

gtaylor/jazzit

52256d5d9a477dc95da5c41daaf31020a662cb90

[ "Apache-2.0" ]

null

src/jazzit/settings.py

gtaylor/jazzit

52256d5d9a477dc95da5c41daaf31020a662cb90

[ "Apache-2.0" ]

null

src/jazzit/settings.py

gtaylor/jazzit

52256d5d9a477dc95da5c41daaf31020a662cb90

[ "Apache-2.0" ]

null

import os os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "hide" current_dir, _ = os.path.split(__file__)

17.166667

49

0.757282

15

103

4.6

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0.106796

103

5

50

20.6

0.75

0

0.291262

0.252427

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

5d1785f4be7d5bb3de775b36a61eb4856ff1615c

34

py

Python

data/studio21_generated/introductory/4083/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/4083/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/4083/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

def performant_smallest(arr, n):

17

32

0.764706

5

34

5

1

0

0.117647

34

2

33

17

0.833333

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

4

5d1ad2ffc5e8705faf35b91223fca80efa0cf3ef

450

py

Python

pettingzoo/utils/__init__.py

mlanas/PettingZoo

58d47c68057bdf37720f961c1a372b4671b8b777

[ "Apache-2.0" ]

1

2021-09-13T17:47:48.000Z

pettingzoo/utils/__init__.py

mlanas/PettingZoo

58d47c68057bdf37720f961c1a372b4671b8b777

[ "Apache-2.0" ]

null

pettingzoo/utils/__init__.py

mlanas/PettingZoo

58d47c68057bdf37720f961c1a372b4671b8b777

[ "Apache-2.0" ]

null

from .agent_selector import agent_selector from .average_total_reward import average_total_reward from .conversions import from_parallel, to_parallel from .env import AECEnv, ParallelEnv from .random_demo import random_demo from .save_observation import save_observation from .wrappers import (AssertOutOfBoundsWrapper, BaseWrapper, CaptureStdoutWrapper, ClipOutOfBoundsWrapper, OrderEnforcingWrapper, TerminateIllegalWrapper)

50

94

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47

450

7.744681

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0.071429

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450

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true

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4

5d1bd4e074bd20cc1ae5c7735f06f86bc04863ec

447

py

Python

perilib/hal/__init__.py

perilib/perilib-python-core

a67de15ab715186f8072782f09ac0acff76a2955

[ "MIT" ]

2

2020-06-11T16:30:35.000Z

2021-12-03T04:23:43.000Z

perilib/hal/__init__.py

perilib/perilib-python-hal

a67de15ab715186f8072782f09ac0acff76a2955

[ "MIT" ]

null

perilib/hal/__init__.py

perilib/perilib-python-hal

a67de15ab715186f8072782f09ac0acff76a2955

[ "MIT" ]

null

""" This module provides the lowest-level framework for defining protocols and packets, including data type definitions that all protocols inherit. Submodules include extended classes for streaming protocols such as what you typically need for devices that communicate over UART or USB CDC (virtual serial). TODO: Support for register-based protocols used by many I2C slaves """ # .py files from .UartManager import * from .UartStream import *

29.8

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5.68254

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0

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447

14

77

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0.944444

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null

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1

0

1

0

1

0

4

5d44a1ff6af7b25ca22ad63b4c87af03a52bdfdb

250

py

Python

tests/test_byUsername.py

crazygmr101/TikTok-Api

32734422fecd9c3fb4eadad451d050fe1ab22e2a

[ "MIT" ]

9

2020-08-27T18:35:22.000Z

2022-01-18T20:19:22.000Z

tests/test_byUsername.py

crazygmr101/TikTok-Api

32734422fecd9c3fb4eadad451d050fe1ab22e2a

[ "MIT" ]

null

tests/test_byUsername.py

crazygmr101/TikTok-Api

32734422fecd9c3fb4eadad451d050fe1ab22e2a

[ "MIT" ]

1

2021-06-11T06:48:33.000Z

from TikTokApi import TikTokApi def test_trending(): api = TikTokApi() assert abs(len(api.byUsername('therock', 5))-5) <= 2 assert abs(len(api.byUsername('therock', 10))-10) <= 2 assert abs(len(api.byUsername('therock', 20))-20) <= 2

35.714286

58

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36

250

4.555556

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0.219512

0.27439

0.597561

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0.16

250

7

59

35.714286

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0

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null

0

1

0

1

0

null

0

1

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4

536f7aa75d0bc8fe5b540ba6765621f8996993dc

1,008

py

Python

website/website.py

alomk/FSDD

071b6654298699aa340575cdd63ed24514ecdb31

[ "MIT" ]

null

website/website.py

alomk/FSDD

071b6654298699aa340575cdd63ed24514ecdb31

[ "MIT" ]

null

website/website.py

alomk/FSDD

071b6654298699aa340575cdd63ed24514ecdb31

[ "MIT" ]

null

from flask import Flask, render_template from flask_bootstrap import Bootstrap from flask_login import LoginManager app = Flask(__name__) login_manager = LoginManager() login_manager.init_app(app) login_manager.login_view = "users.login" login_manager.login_message = u"hello" @login_manager.user_loader def load_user(user_id): return User.get(user_id) @app.route("/") @app.route("/index") @app.route("/logout") def index(): return render_template('index.html') @app.route("/search", methods=['GET', 'POST']) def search(): return render_template('search.html') @app.route("/fresh") def fresh(): return render_template('fresh.html') @app.route("/expire") def deliver(): return render_template('expire.html') @app.route("/location") def stock(): return render_template('location.html') @app.route('/login', methods=['GET', 'POST']) def login(): return render_template('login.html') @app.errorhandler(404) def page_not_found(e): return render_template('404.html'), 404

22.4

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null

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1

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1

0

4

537c0c673c75c20dace9d4cbc7c043487b8f44de

284

py

Python

django_cockroachdb_gis/introspection.py

mlazowik/django-cockroachdb

e6a01c093f6f22f3764f845f04504434cb6aaa8c

[ "Apache-2.0" ]

94

2019-11-21T06:52:08.000Z

2022-01-03T14:14:40.000Z

django_cockroachdb_gis/introspection.py

mlazowik/django-cockroachdb

e6a01c093f6f22f3764f845f04504434cb6aaa8c

[ "Apache-2.0" ]

83

2019-11-14T19:25:28.000Z

2022-03-12T17:41:57.000Z

django_cockroachdb_gis/introspection.py

mlazowik/django-cockroachdb

e6a01c093f6f22f3764f845f04504434cb6aaa8c

[ "Apache-2.0" ]

15

2019-11-21T06:52:48.000Z

2022-02-06T02:22:05.000Z

from django.contrib.gis.db.backends.postgis.introspection import ( PostGISIntrospection, ) from django_cockroachdb.introspection import ( DatabaseIntrospection as CockroachIntrospection, ) class DatabaseIntrospection(CockroachIntrospection, PostGISIntrospection): pass

23.666667

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284

10.130435

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true

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0

1

null

0

null

0

1

0

4

5385cc621aaafc0c7547b40aa9945c1d37f2c4a2

25,600

py

Python

egret/model_library/transmission/tx_calc.py

kdheepak/Egret

f982247637137e098191453e682376adc1b4c2ac

[ "BSD-3-Clause" ]

null

egret/model_library/transmission/tx_calc.py

kdheepak/Egret

f982247637137e098191453e682376adc1b4c2ac

[ "BSD-3-Clause" ]

1

2019-05-23T02:56:29.000Z

egret/model_library/transmission/tx_calc.py

kdheepak/Egret

f982247637137e098191453e682376adc1b4c2ac

[ "BSD-3-Clause" ]

2

2019-11-18T20:18:51.000Z

2020-05-08T15:56:17.000Z

# ___________________________________________________________________________ # # EGRET: Electrical Grid Research and Engineering Tools # Copyright 2019 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # This software is distributed under the Revised BSD License. # ___________________________________________________________________________ """ This module collects some helper functions useful for performing different computations for transmission models """ import math import numpy as np import scipy as sp from math import cos, sin from egret.model_library.defn import BasePointType, ApproximationType def calculate_conductance(branch): rs = branch['resistance'] xs = branch['reactance'] return rs / (rs**2 + xs**2) def calculate_susceptance(branch): rs = branch['resistance'] xs = branch['reactance'] return -xs / (rs**2 + xs**2) def calculate_y_matrix_from_branch(branch): rs = branch['resistance'] xs = branch['reactance'] bs = branch['charging_susceptance'] tau = 1.0 shift = 0.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] shift = branch['transformer_phase_shift'] return calculate_y_matrix(rs, xs, bs, tau, shift) def calculate_y_matrix(rs, xs, bc, tau, shift): """ Compute the y matrix from various branch properties Parameters ---------- rs : float Branch resistance xs : float Branch reactance bc : float Branch charging susceptance tau : float Branch transformer tap ratio shift : float Branch transformer phase shift Returns ------- list : list of floats representing the y matrix [Y(ifr,vfr), Y(ifr,vfj), Y(ifr,vtr), Y(ifr,vtj), Y(ifj,vfr), Y(ifj,vfj), Y(ifj,vtr), Y(ifj,vtj), Y(itr,vfr), Y(itr,vfj), Y(itr,vtr), Y(itr,vtj), Y(itj,vfr), Y(itj,vfj), Y(itj,vtr), Y(itj,vtj)] """ bc = bc/2 tr = tau * math.cos(math.radians(shift)) tj = tau * math.sin(math.radians(shift)) mag = rs**2 + xs**2 a = rs/(tau**2*mag) # c1 b = (1/tau**2) * (xs/mag - bc) # c2 c = (-rs*tr - xs*tj)/(tau**2 * mag) # c3 d = (rs*tj - xs*tr)/(tau**2 * mag) # c4 e = -b # -c2 f = a # c1 g = -d # -c4 h = c # c3 i = (xs*tj - rs*tr)/(tau**2 * mag) # c7 j = (-rs*tj - xs*tr)/(tau**2 * mag) # c8 k = rs/mag # c5 l = xs/mag - bc # c6 m = -j # -c8 n = i # c7 o = -l # -c6 p = k # c5 # y = [a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p] y_dict = {} y_dict[('ifr', 'vfr')] = a y_dict[('ifr', 'vfj')] = b y_dict[('ifr', 'vtr')] = c y_dict[('ifr', 'vtj')] = d y_dict[('ifj', 'vfr')] = e y_dict[('ifj', 'vfj')] = f y_dict[('ifj', 'vtr')] = g y_dict[('ifj', 'vtj')] = h y_dict[('itr', 'vfr')] = i y_dict[('itr', 'vfj')] = j y_dict[('itr', 'vtr')] = k y_dict[('itr', 'vtj')] = l y_dict[('itj', 'vfr')] = m y_dict[('itj', 'vfj')] = n y_dict[('itj', 'vtr')] = o y_dict[('itj', 'vtj')] = p return y_dict def calculate_ifr(vfr, vfj, vtr, vtj, y_matrix): """ Compute ifr from voltages and the y_matrix (computed from the branch properties using :py:meth:`calculate_branch_y_matrix`) """ ifr = y_matrix['ifr', 'vfr'] * vfr + y_matrix['ifr', 'vfj'] * vfj + \ y_matrix['ifr', 'vtr'] * vtr + y_matrix['ifr', 'vtj'] * vtj return ifr def calculate_ifj(vfr, vfj, vtr, vtj, y_matrix): """ Compute ify from voltages and the y_matrix (computed from the branch properties using :py:meth:`calculate_branch_y_matrix`) """ ifj = y_matrix['ifj', 'vfr'] * vfr + y_matrix['ifj', 'vfj'] * vfj + \ y_matrix['ifj', 'vtr'] * vtr + y_matrix['ifj', 'vtj'] * vtj return ifj def calculate_itr(vfr, vfj, vtr, vtj, y_matrix): """ Compute itr from voltages and the y_matrix (computed from the branch properties using :py:meth:`calculate_branch_y_matrix`) """ itr = y_matrix['itr', 'vfr'] * vfr + y_matrix['itr', 'vfj'] * vfj + \ y_matrix['itr', 'vtr'] * vtr + y_matrix['itr', 'vtj'] * vtj return itr def calculate_itj(vfr, vfj, vtr, vtj, y_matrix): """ Compute itj from voltages and the y_matrix (computed from the branch properties using :py:meth:`calculate_branch_y_matrix`) """ itj = y_matrix['itj', 'vfr'] * vfr + y_matrix['itj', 'vfj'] * vfj + \ y_matrix['itj', 'vtr'] * vtr + y_matrix['itj', 'vtj'] * vtj return itj def calculate_ir(p, q, vr, vj): """ Compute ir from power flows and voltages """ ir = (q*vj+p*vr)/(vj**2 + vr**2) return ir def calculate_ij(p, q, vr, vj): """ Compute ij from power flows and voltages """ ij = (p*vj-q*vr)/(vj**2 + vr**2) return ij def calculate_p(ir, ij, vr, vj): """ Compute real power flow from currents and voltages """ p = vr * ir + vj * ij return p def calculate_q(ir, ij, vr, vj): """ Compute reactive power flow from currents and voltages """ q = vj * ir - vr * ij return q def calculate_vr_from_vm_va(vm, va): """ Compute the value of vr from vm and va Parameters ---------- vm : float The value of voltage magnitude (per) va : float The value of voltage angle (degrees) Returns ------- float : the value of vr or None if either vm or va (or both) is None """ if vm is not None and va is not None: vr = vm * math.cos(va*math.pi/180) return vr return None def calculate_vj_from_vm_va(vm, va): """ Compute the value of vj from vm and va Parameters ---------- vm : float The value of voltage magnitude (per) va : float The value of voltage angle (degrees) Returns ------- float : the value of vj or None if either vm or va (or both) is None """ if vm is not None and va is not None: vj = vm * math.sin(va*math.pi/180) return vj return None def calculate_vm_from_vj_vr(vj,vr): """ Compute the value of vm from vj and vr Parameters ---------- vj : float The value of the imaginary part of the voltage phasor (per) vr : float The value of the real part of the voltage phasor (per) Returns ------- float : the value of the voltage magnitude vm or None if either vj or vr (or both) is None """ if vj is not None and vr is not None: vm = math.sqrt(vj**2 + vr**2) return vm return None def calculate_va_from_vj_vr(vj, vr): """ Compute the value of va from vj and vr Parameters ---------- vj : float The value of the imaginary part of the voltage phasor (per) vr : float The value of the real part of the voltage phasor (per) Returns ------- float : the value of the voltage angle va in degrees or None if either vj or vr (or both) is None """ if vj is not None and vr is not None: va = math.degrees(math.atan(vj/vr)) return va return None def _calculate_J11(branches,buses,index_set_branch,index_set_bus,mapping_bus_to_idx,base_point=BasePointType.FLATSTART,approximation_type=ApproximationType.PTDF): """ Compute the power flow Jacobian for partial derivative of real power flow to voltage angle """ _len_bus = len(index_set_bus) _len_branch = len(index_set_branch) data = [] row = [] col = [] for idx_row, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] if approximation_type == ApproximationType.PTDF: x = branch['reactance'] b = -1/(tau*x) elif approximation_type == ApproximationType.PTDF_LOSSES: b = calculate_susceptance(branch)/tau if base_point == BasePointType.FLATSTART: vn = 1. vm = 1. tn = 0. tm = 0. elif base_point == BasePointType.SOLUTION: # TODO: check that we are loading the correct values (or results) vn = buses[from_bus]['vm'] vm = buses[to_bus]['vm'] tn = buses[from_bus]['va'] tm = buses[to_bus]['va'] val = -b * vn * vm * cos(tn - tm) idx_col = mapping_bus_to_idx[from_bus] row.append(idx_row) col.append(idx_col) data.append(val) idx_col = mapping_bus_to_idx[to_bus] row.append(idx_row) col.append(idx_col) data.append(-val) J11 = sp.sparse.coo_matrix( (data, (row,col)), shape=(_len_branch, _len_bus)) return J11.tocsr() def _calculate_L11(branches,buses,index_set_branch,index_set_bus,mapping_bus_to_idx,base_point=BasePointType.FLATSTART): """ Compute the power flow Jacobian for partial derivative of real power losses to voltage angle """ _len_bus = len(index_set_bus) _len_branch = len(index_set_branch) row = [] col = [] data = [] for idx_row, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] g = calculate_conductance(branch)/tau if base_point == BasePointType.FLATSTART: vn = 1. vm = 1. tn = 0. tm = 0. elif base_point == BasePointType.SOLUTION: # TODO: check that we are loading the correct values (or results) vn = buses[from_bus]['vm'] vm = buses[to_bus]['vm'] tn = buses[from_bus]['va'] tm = buses[to_bus]['va'] val = 2 * g * vn * vm * sin(tn - tm) idx_col = mapping_bus_to_idx[from_bus] row.append(idx_row) col.append(idx_col) data.append(val) idx_col = mapping_bus_to_idx[to_bus] row.append(idx_row) col.append(idx_col) data.append(-val) L11 = sp.sparse.coo_matrix((data,(row,col)),shape=(_len_branch,_len_bus)) return L11.tocsr() def calculate_phi_constant(branches,index_set_branch,index_set_bus,approximation_type=ApproximationType.PTDF, mapping_bus_to_idx=None): """ Compute the phase shifter constant for fixed phase shift transformers """ _len_bus = len(index_set_bus) if mapping_bus_to_idx is None: mapping_bus_to_idx = {bus_n: i for i, bus_n in enumerate(index_set_bus)} _len_branch = len(index_set_branch) row_from = [] row_to = [] col = [] data = [] for idx_col, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 shift = 0.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] shift = math.radians(branch['transformer_phase_shift']) b = 0. if approximation_type == ApproximationType.PTDF: x = branch['reactance'] b = -(1/x)*(shift/tau) elif approximation_type == ApproximationType.PTDF_LOSSES: b = calculate_susceptance(branch)*(shift/tau) row_from.append(mapping_bus_to_idx[from_bus]) row_to.append(mapping_bus_to_idx[to_bus]) col.append(idx_col) data.append(b) phi_from = sp.sparse.coo_matrix((data,(row_from,col)), shape=(_len_bus,_len_branch)) phi_to = sp.sparse.coo_matrix((data,(row_to,col)), shape=(_len_bus,_len_branch)) return phi_from.tocsr(), phi_to.tocsr() def calculate_phi_loss_constant(branches,index_set_branch,index_set_bus,approximation_type=ApproximationType.PTDF_LOSSES, mapping_bus_to_idx=None): """ Compute the phase shifter constant for fixed phase shift transformers """ _len_bus = len(index_set_bus) if mapping_bus_to_idx is None: mapping_bus_to_idx = {bus_n: i for i, bus_n in enumerate(index_set_bus)} _len_branch = len(index_set_branch) row_from = [] row_to = [] col = [] data = [] for idx_col, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 shift = 0.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] shift = math.radians(branch['transformer_phase_shift']) g = 0. if approximation_type == ApproximationType.PTDF: r = branch['resistance'] g = (1/r)*(1/tau)*shift**2 elif approximation_type == ApproximationType.PTDF_LOSSES: g = calculate_conductance(branch)*(1/tau)*shift**2 row_from.append(mapping_bus_to_idx[from_bus]) row_to.append(mapping_bus_to_idx[to_bus]) col.append(idx_col) data.append(g) phi_loss_from = sp.sparse.coo_matrix((data,(row_from,col)),shape=(_len_bus,_len_branch)) phi_loss_to = sp.sparse.coo_matrix((data,(row_to,col)),shape=(_len_bus,_len_branch)) return phi_loss_from.tocsr(), phi_loss_to.tocsr() def _calculate_pf_constant(branches,buses,index_set_branch,base_point=BasePointType.FLATSTART): """ Compute the power flow constant for the taylor series expansion of real power flow as a convex combination of the from/to directions, i.e., pf = 0.5*g*((tau*vn)^2 - vm^2) - tau*vn*vm*b*sin(tn-tm-shift) """ _len_branch = len(index_set_branch) ## this will be fully dense pf_constant = np.zeros(_len_branch) for idx_row, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 shift = 0.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] shift = math.radians(branch['transformer_phase_shift']) g = calculate_conductance(branch) b = calculate_susceptance(branch)/tau if base_point == BasePointType.FLATSTART: vn = 1. vm = 1. tn = 0. tm = 0. elif base_point == BasePointType.SOLUTION: # TODO: check that we are loading the correct values (or results) vn = buses[from_bus]['vm'] vm = buses[to_bus]['vm'] tn = buses[from_bus]['va'] tm = buses[to_bus]['va'] pf_constant[idx_row] = 0.5 * g * ((vn/tau) ** 2 - vm ** 2) \ - b * vn * vm * (sin(tn - tm + shift) - cos(tn - tm + shift)*(tn - tm)) return pf_constant def _calculate_pfl_constant(branches,buses,index_set_branch,base_point=BasePointType.FLATSTART): """ Compute the power losses constant for the taylor series expansion of real power losses as a convex combination of the from/to directions, i.e., pfl = g*((tau*vn)^2 + vm^2) - 2*tau*vn*vm*g*cos(tn-tm-shift) """ _len_branch = len(index_set_branch) ## this will be fully dense pfl_constant = np.zeros(_len_branch) for idx_row, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] to_bus = branch['to_bus'] tau = 1.0 shift = 0.0 if branch['branch_type'] == 'transformer': tau = branch['transformer_tap_ratio'] shift = math.radians(branch['transformer_phase_shift']) _g = calculate_conductance(branch) g = _g/tau g2 = _g/tau**2 if base_point == BasePointType.FLATSTART: vn = 1. vm = 1. tn = 0. tm = 0. elif base_point == BasePointType.SOLUTION: # TODO: check that we are loading the correct values (or results) vn = buses[from_bus]['vm'] vm = buses[to_bus]['vm'] tn = buses[from_bus]['va'] tm = buses[to_bus]['va'] pfl_constant[idx_row] = g2 * (vn ** 2) + _g * (vm ** 2) \ - 2 * g * vn * vm * (sin(tn - tm + shift) * (tn - tm) + cos(tn - tm + shift)) return pfl_constant def calculate_ptdf(branches,buses,index_set_branch,index_set_bus,reference_bus,base_point=BasePointType.FLATSTART,sparse_index_set_branch=None,mapping_bus_to_idx=None): """ Calculates the sensitivity of the voltage angle to real power injections Parameters ---------- branches: dict{} The dictionary of branches for the test case buses: dict{} The dictionary of buses for the test case index_set_branch: list The list of keys for branches for the test case index_set_bus: list The list of keys for buses for the test case reference_bus: key value The reference bus key value base_point: egret.model_library_defn.BasePointType The base-point type for calculating the PTDF matrix sparse_index_set_branch: list The list of keys for branches needed to compute a sparse PTDF matrix If this is None, a dense PTDF matrix is returned mapping_bus_to_idx: dict A map from bus names to indices for matrix construction. If None, will be inferred from index_set_bus. """ _len_bus = len(index_set_bus) if mapping_bus_to_idx is None: mapping_bus_to_idx = {bus_n: i for i, bus_n in enumerate(index_set_bus)} _len_branch = len(index_set_branch) _ref_bus_idx = mapping_bus_to_idx[reference_bus] J = _calculate_J11(branches,buses,index_set_branch,index_set_bus,mapping_bus_to_idx,base_point,approximation_type=ApproximationType.PTDF) A = calculate_adjacency_matrix_transpose(branches,index_set_branch,index_set_bus,mapping_bus_to_idx) M = A@J ref_bus_row = sp.sparse.coo_matrix(([1],([0],[_ref_bus_idx])), shape=(1,_len_bus)) ref_bus_col = sp.sparse.coo_matrix(([1],([_ref_bus_idx],[0])), shape=(_len_bus,1)) J0 = sp.sparse.bmat([[M,ref_bus_col],[ref_bus_row,0]], format='coo') if sparse_index_set_branch is None or len(sparse_index_set_branch) == _len_branch: ## the resulting matrix after inversion will be fairly dense, ## the scipy documenation recommends using dense for the inversion ## as well try: SENSI = np.linalg.inv(J0.A) except np.linalg.LinAlgError: print("Matrix not invertible. Calculating pseudo-inverse instead.") SENSI = np.linalg.pinv(J0.A,rcond=1e-7) SENSI = SENSI[:-1,:-1] PTDF = np.matmul(J.A,SENSI) elif len(sparse_index_set_branch) < _len_branch: B = np.array([], dtype=np.int64).reshape(_len_bus + 1,0) _sparse_mapping_branch = {i: branch_n for i, branch_n in enumerate(index_set_branch) if branch_n in sparse_index_set_branch} ## TODO: Maybe just keep the sparse PTDFs as a dict of ndarrays? ## Right now the return type depends on the options ## passed in for idx, branch_name in _sparse_mapping_branch.items(): b = np.zeros((_len_branch,1)) b[idx] = 1 _tmp = np.matmul(J.transpose(),b) _tmp = np.vstack([_tmp,0]) B = np.concatenate((B,_tmp), axis=1) row_idx = list(_sparse_mapping_branch.keys()) PTDF = sp.sparse.lil_matrix((_len_branch,_len_bus)) _ptdf = sp.sparse.linalg.spsolve(J0.transpose().tocsr(), B).T PTDF[row_idx] = _ptdf[:,:-1] return PTDF def calculate_ptdf_ldf(branches,buses,index_set_branch,index_set_bus,reference_bus,base_point=BasePointType.SOLUTION,sparse_index_set_branch=None,mapping_bus_to_idx=None): """ Calculates the sensitivity of the voltage angle to real power injections and losses on the lines. Includes the calculation of the constant term for the quadratic losses on the lines. Parameters ---------- branches: dict{} The dictionary of branches for the test case buses: dict{} The dictionary of buses for the test case index_set_branch: list The list of keys for branches for the test case index_set_bus: list The list of keys for buses for the test case reference_bus: key value The reference bus key value base_point: egret.model_library_defn.BasePointType The base-point type for calculating the PTDF and LDF matrix sparse_index_set_branch: list The list of keys for branches needed to compute a sparse PTDF matrix mapping_bus_to_idx: dict A map from bus names to indices for matrix construction. If None, will be inferred from index_set_bus. """ _len_bus = len(index_set_bus) if mapping_bus_to_idx is None: mapping_bus_to_idx = {bus_n: i for i, bus_n in enumerate(index_set_bus)} _len_branch = len(index_set_branch) _ref_bus_idx = mapping_bus_to_idx[reference_bus] J = _calculate_J11(branches,buses,index_set_branch,index_set_bus,mapping_bus_to_idx,base_point,approximation_type=ApproximationType.PTDF_LOSSES) L = _calculate_L11(branches,buses,index_set_branch,index_set_bus,mapping_bus_to_idx,base_point) Jc = _calculate_pf_constant(branches,buses,index_set_branch,base_point) Lc = _calculate_pfl_constant(branches,buses,index_set_branch,base_point) if np.all(Jc == 0) and np.all(Lc == 0): return np.zeros((_len_branch, _len_bus)), np.zeros((_len_branch, _len_bus)), np.zeros((1,_len_branch)) A = calculate_adjacency_matrix_transpose(branches,index_set_branch,index_set_bus, mapping_bus_to_idx) AA = calculate_absolute_adjacency_matrix(A) M1 = A@J M2 = AA@L M = M1 + 0.5 * M2 ref_bus_row = sp.sparse.coo_matrix(([1],([0],[_ref_bus_idx])), shape=(1,_len_bus)) ref_bus_col = sp.sparse.coo_matrix(([1],([_ref_bus_idx],[0])), shape=(_len_bus,1)) J0 = sp.sparse.bmat([[M,ref_bus_col],[ref_bus_row,0]], format='coo') if sparse_index_set_branch is None or len(sparse_index_set_branch) == _len_branch: ## the resulting matrix after inversion will be fairly dense, ## the scipy documenation recommends using dense for the inversion ## as well try: SENSI = np.linalg.inv(J0.A) except np.linalg.LinAlgError: print("Matrix not invertible. Calculating pseudo-inverse instead.") SENSI = np.linalg.pinv(J0.A,rcond=1e-7) pass SENSI = SENSI[:-1,:-1] PTDF = np.matmul(J.A, SENSI) LDF = np.matmul(L.A, SENSI) elif len(sparse_index_set_branch) < _len_branch: B_J = np.array([], dtype=np.int64).reshape(_len_bus + 1, 0) B_L = np.array([], dtype=np.int64).reshape(_len_bus + 1, 0) _sparse_mapping_branch = {i: branch_n for i, branch_n in enumerate(index_set_branch) if branch_n in sparse_index_set_branch} for idx, branch_name in _sparse_mapping_branch.items(): b = np.zeros((_len_branch, 1)) b[idx] = 1 _tmp_J = np.matmul(J.transpose(), b) _tmp_J = np.vstack([_tmp_J, 0]) B_J = np.concatenate((B_J, _tmp_J), axis=1) _tmp_L = np.matmul(L.transpose(), b) _tmp_L = np.vstack([_tmp_L, 0]) B_L = np.concatenate((B_L, _tmp_L), axis=1) row_idx = list(_sparse_mapping_branch.keys()) PTDF = sp.sparse.lil_matrix((_len_branch, _len_bus)) _ptdf = sp.sparse.linalg.spsolve(J0.transpose().tocsr(), B_J).T PTDF[row_idx] = _ptdf[:, :-1] LDF = sp.sparse.lil_matrix((_len_branch, _len_bus)) _ldf = sp.sparselinalg.spsolve(J0.transpose().tocsr(), B_L).T LDF[row_idx] = _ldf[:, :-1] M1 = A@Jc M2 = AA@Lc M = M1 + 0.5 * M2 LDF_constant = -LDF@M + Lc return PTDF, LDF, LDF_constant def calculate_adjacency_matrix_transpose(branches,index_set_branch,index_set_bus, mapping_bus_to_idx): """ Calculates the adjacency matrix where (-1) represents flow from the bus and (1) represents flow to the bus for a given branch """ _len_bus = len(index_set_bus) _len_branch = len(index_set_branch) row = [] col = [] data = [] for idx_col, branch_name in enumerate(index_set_branch): branch = branches[branch_name] from_bus = branch['from_bus'] row.append(mapping_bus_to_idx[from_bus]) col.append(idx_col) data.append(-1) to_bus = branch['to_bus'] row.append(mapping_bus_to_idx[to_bus]) col.append(idx_col) data.append(1) adjacency_matrix = sp.sparse.coo_matrix((data,(row,col)), shape=(_len_bus, _len_branch)) return adjacency_matrix.tocsr() def calculate_absolute_adjacency_matrix(adjacency_matrix): """ Calculates the absolute value of the adjacency matrix """ return sp.absolute(adjacency_matrix)

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Section07_Bridge/Practice/VectorRenderer.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

1

2020-10-20T07:41:51.000Z

Section07_Bridge/Practice/VectorRenderer.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

Section07_Bridge/Practice/VectorRenderer.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

from Section07_Bridge.Practice.Renderer import Renderer class VectorRenderer(Renderer): @property def what_to_render_as(self): return 'lines'

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py

Python

netforce_general/netforce_general/models/__init__.py

nfco/netforce

35252eecd0a6633ab9d82162e9e3ff57d4da029a

[ "MIT" ]

27

2015-09-30T23:53:30.000Z

2021-06-07T04:56:25.000Z

netforce_general/netforce_general/models/__init__.py

nfco/netforce

35252eecd0a6633ab9d82162e9e3ff57d4da029a

[ "MIT" ]

191

2015-10-08T11:46:30.000Z

2019-11-14T02:24:36.000Z

netforce_general/netforce_general/models/__init__.py

nfco/netforce

35252eecd0a6633ab9d82162e9e3ff57d4da029a

[ "MIT" ]

32

2015-10-01T03:59:43.000Z

2022-01-13T07:31:05.000Z

# Copyright (c) 2012-2015 Netforce Co. Ltd. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE # OR OTHER DEALINGS IN THE SOFTWARE. from . import tag from . import tag_type from . import base_user from . import permission from . import profile from . import model from . import field from . import profile_access from . import field_access from . import share_access from . import role from . import login from . import product_categ from . import product from . import settings from . import uom from . import cron_job from . import activity from . import translation from . import translation_field from . import import_data from . import sequence from . import sequence_running from . import country from . import province from . import district from . import subdistrict from . import postal_code from . import language from . import feedback from . import attach from . import log from . import view from . import change_passwd from . import forgot_passwd from . import user_group from . import report_template from . import workflow_rule from . import address from . import bank_account from . import company_type from . import bank from . import share_record from . import ws_listener from . import ws_event from . import inline_help from . import wkf_rule from . import create_db from . import copy_db from . import upgrade_db from . import delete_db from . import field_cache from . import user_pref from . import company from . import select_company from . import field_value from . import field_default from . import view_layout from . import action from . import update_ui from . import update_db from . import print_wizard from . import send_wizard from . import approve_wizard from . import template from . import theme from . import module from . import import_module from . import script from . import import_inline_help from . import reason_code from . import menu_access from . import ui_params from . import report_custom

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53999565d746fdb9f01307d8f9db882556e4efc8

182

py

Python

books/python-3-oop-packt/Chapter9/9_23_generate_colors.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

73

2016-09-15T23:07:04.000Z

2022-03-05T15:09:48.000Z

books/python-3-oop-packt/Chapter9/9_23_generate_colors.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

34

2019-12-16T16:53:24.000Z

2022-01-13T02:29:30.000Z

books/python-3-oop-packt/Chapter9/9_23_generate_colors.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

51

2016-10-07T20:47:51.000Z

2021-12-22T21:00:24.000Z

from random import random def generate_colors(count=100): for i in range(count): yield (random(), random(), random()) for color in generate_colors(): print(color)

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0.6

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null

0

4

53b2d5e83eb0462eaa3f62c2a5d55353f00658a5

837

py

Python

collectors/NSXTMgmtServiceAlertCollector.py

sapcc/vrops-exporter

c342f319e1f69590fa514ba07c783e586aeb223a

[ "Apache-2.0" ]

18

2019-10-24T03:36:50.000Z

2022-01-22T20:39:42.000Z

collectors/NSXTMgmtServiceAlertCollector.py

richardtief/vrops-exporter

7e7543f5561f85aab1828e4c9d6fada4b687639f

[ "Apache-2.0" ]

55

2019-10-16T09:51:36.000Z

2022-03-28T11:46:08.000Z

collectors/NSXTMgmtServiceAlertCollector.py

richardtief/vrops-exporter

7e7543f5561f85aab1828e4c9d6fada4b687639f

[ "Apache-2.0" ]

19

2019-10-15T14:07:27.000Z

2022-02-17T21:41:14.000Z

from collectors.AlertCollector import AlertCollector class NSXTMgmtServiceAlertCollector(AlertCollector): def __init__(self): super().__init__() self.vrops_entity_name = 'nsxt_mgmt_service' self.label_names = ['nsxt_mgmt_cluster', 'nsxt_adapter', 'nsxt_mgmt_node', 'nsxt_mgmt_service'] self.resourcekind = ["ManagementService"] def get_resource_uuids(self): return self.get_nsxt_mgmt_service_by_target() def get_labels(self, resource_id, project_ids): return [self.nsxt_mgmt_service[resource_id]['mgmt_cluster_name'], self.nsxt_mgmt_service[resource_id]['nsxt_adapter_name'], self.nsxt_mgmt_service[resource_id]['mgmt_node_name'], self.nsxt_mgmt_service[resource_id]['name']] if resource_id in self.nsxt_mgmt_service else []

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1

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4

53cda44ed70383d7e26e0707e6a0013540c209e7

336

py

Python

acloud-guru/func.py

wiltonpaulo/python-fullcourse

5befe60221a2e6f8a567a11e2f449245c11b3447

[ "MIT" ]

null

acloud-guru/func.py

wiltonpaulo/python-fullcourse

5befe60221a2e6f8a567a11e2f449245c11b3447

[ "MIT" ]

null

acloud-guru/func.py

wiltonpaulo/python-fullcourse

5befe60221a2e6f8a567a11e2f449245c11b3447

[ "MIT" ]

null

class Car: """ Docstring describing the class """ def __init__(self, car_name) -> None: self.car_name = car_name def __str__(self) -> str: return f"Soh uma string mano {self.car_name}" def anda(self): return f"Anda {self.car_name}" car = Car("ecosport") print(car.anda()) print(car)

17.684211

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0.185185

0.232804

0.148148

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1

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4

53d3eb49383b275cbf7c7041d9e96584de27c7cf

99

py

Python

matplotsoccer/__init__.py

TomDecroos/matplotsoccer

fac4c5554d873f57a8997d735220da653bb3593b

[ "MIT" ]

78

2019-08-24T12:47:44.000Z

2022-02-10T06:26:47.000Z

matplotsoccer/__init__.py

eddwebster/matplotsoccer

fac4c5554d873f57a8997d735220da653bb3593b

[ "MIT" ]

3

2019-08-23T10:24:16.000Z

2021-04-18T07:50:46.000Z

matplotsoccer/__init__.py

eddwebster/matplotsoccer

fac4c5554d873f57a8997d735220da653bb3593b

[ "MIT" ]

11

2019-08-29T10:39:15.000Z

2021-09-30T17:28:01.000Z

from matplotsoccer.fns import ( spadl_config, field, heatmap, heatmap_green, actions, count, )

33

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99

6.083333

0.916667

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true

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0

4

54ccab2d303d6e6c5cdb84bf878f4d7012f41a43

704

py

Python

tha2/nn/batch_module/batch_input_model_factory.py

jyoonab/EasyVtuber

6a1341b632f1cf56a570bbf2689cbd568360a46c

[ "MIT" ]

151

2021-12-15T15:24:01.000Z

2022-03-28T07:40:57.000Z

tha2/nn/batch_module/batch_input_model_factory.py

jyoonab/EasyVtuber

6a1341b632f1cf56a570bbf2689cbd568360a46c

[ "MIT" ]

2

2021-12-19T04:04:20.000Z

2021-12-25T06:33:02.000Z

tha2/nn/batch_module/batch_input_model_factory.py

jyoonab/EasyVtuber

6a1341b632f1cf56a570bbf2689cbd568360a46c

[ "MIT" ]

14

2021-12-16T20:17:27.000Z

2022-01-04T14:16:10.000Z

from typing import Dict, Set from tha2.nn.batch_module.batch_input_module import BatchInputModule, BatchInputModuleFactory class BatchInputModelFactory: def __init__(self, module_factories: Dict[str, BatchInputModuleFactory]): self.module_factories = module_factories def get_module_names(self) -> Set[str]: return set(self.module_factories.keys()) def create(self) -> Dict[str, BatchInputModule]: output = {} for name in self.module_factories: output[name] = self.module_factories[name].create() return output def get_module_factory(self, module_name) -> BatchInputModuleFactory: return self.module_factories[module_name]

35.2

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null

0

1

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1

0

4

54fbf6107ef062b972ad67d8d4db3e9f59e246c9

1,011

py

Python

module_04_datetime/psl_04.01_dt_03_formatting_date.py

CodingGearsCourses/Python-3-Standard-Library-Essentials

8b80bc8b77fa477b6ccbe2886ed9239c2defdfda

[ "Apache-2.0" ]

null

module_04_datetime/psl_04.01_dt_03_formatting_date.py

CodingGearsCourses/Python-3-Standard-Library-Essentials

8b80bc8b77fa477b6ccbe2886ed9239c2defdfda

[ "Apache-2.0" ]

null

module_04_datetime/psl_04.01_dt_03_formatting_date.py

CodingGearsCourses/Python-3-Standard-Library-Essentials

8b80bc8b77fa477b6ccbe2886ed9239c2defdfda

[ "Apache-2.0" ]

null

# -------------------------------- # CodingGears.io # -------------------------------- # Datetime Module from datetime import datetime # TODO: datetime.now now = datetime.now() print("Now : {}".format(now)) # TODO: Formatting - Day of the week print("Day of the week : " + now.strftime("%a")) print("Day of the week : " + now.strftime("%A")) # TODO: Formatting - Month print("Month : " + now.strftime("%m")) print("Month : " + now.strftime("%b")) print("Month : " + now.strftime("%B")) # TODO: Formatting - Day print("Day : " + now.strftime("%d")) print("Month : " + now.strftime("%B")) print("Day : " + now.strftime("%A, %B %d")) # TODO: Formatting - Year print("Year YY : " + now.strftime("%y")) print("Year YYYY : " + now.strftime("%Y")) # TODO: Formatting - Date print("Date : " + now.strftime("%A, %B %d, %y")) print("Date : " + now.strftime("%A, %B %d, %Y")) print("Date : " + now.strftime("%m-%d-%Y")) print("Date : " + now.strftime("%m/%d/%Y")) print("Date : " + now.strftime("%b/%d/%Y"))

26.605263

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null

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0

1

0

4

07012362c3c536035406bfb2731ddb1b140e054c

217

py

Python

apps/node/src/app/main/routes/general.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

7

2020-04-20T22:22:08.000Z

2020-07-25T17:32:08.000Z

apps/node/src/app/main/routes/general.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

3

2020-04-24T21:20:57.000Z

2020-05-28T09:17:02.000Z

apps/node/src/app/main/routes/general.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

4

2020-04-24T22:32:37.000Z

2020-05-25T19:29:20.000Z

"""All Network routes (REST API).""" from flask import render_template from .. import main_routes @main_routes.route("/", methods=["GET"]) def index(): """Main Page.""" return render_template("index.html")

19.727273

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null

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null

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0

1

0

1

0

4

072858bf1bc0e8144d9e75151c8c5c8aa93df558

20

py

Python

processlst/__init__.py

bucricket/projectMASlst2

9976ce0958e9dff7d5d8475a9242f743edc8a6b3

[ "BSD-3-Clause" ]

null

processlst/__init__.py

bucricket/projectMASlst2

9976ce0958e9dff7d5d8475a9242f743edc8a6b3

[ "BSD-3-Clause" ]

null

processlst/__init__.py

bucricket/projectMASlst2

9976ce0958e9dff7d5d8475a9242f743edc8a6b3

[ "BSD-3-Clause" ]

2

2017-05-25T09:06:02.000Z

2018-12-03T11:33:30.000Z

__version__='0.4.0'

10

19

0.7

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20

2.5

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0

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0.05

20

1

20

0.368421

0

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false

0

1

0

null

0

1

0

1

0

null

0

4

4adffb15a6230bdf98eea40adca1afdf593262df

38

py

Python

DjangoStrannikVk/DjangoStrannikVk/__init__.py

StrannikVK/strannikvk.github.io

8c76c54b36da292b4341e2bc3ab5602d098a53e6

[ "MIT" ]

null

DjangoStrannikVk/DjangoStrannikVk/__init__.py

StrannikVK/strannikvk.github.io

8c76c54b36da292b4341e2bc3ab5602d098a53e6

[ "MIT" ]

null

DjangoStrannikVk/DjangoStrannikVk/__init__.py

StrannikVK/strannikvk.github.io

8c76c54b36da292b4341e2bc3ab5602d098a53e6

[ "MIT" ]

null

""" Package for DjangoStrannikVk. """

9.5

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12.666667

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true

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null

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1

0

null

0

1

0

4

4af1a1aa84b2fe0447c5ec459f04ea22b47e22e6

668

py

Python

tests/test_unittests.py

retxxxirt/django-fixtures

8a8d3c1ac49291716c02efe56ed0b9697b93370c

[ "MIT" ]

null

tests/test_unittests.py

retxxxirt/django-fixtures

8a8d3c1ac49291716c02efe56ed0b9697b93370c

[ "MIT" ]

null

tests/test_unittests.py

retxxxirt/django-fixtures

8a8d3c1ac49291716c02efe56ed0b9697b93370c

[ "MIT" ]

null

from django_fixtures.decorators import exclude_fixtures from django_fixtures.unittests import FixturesTestCase class FixturesUnittestsTestCase(FixturesTestCase): fixtures = ('app_a.GeneratedNumbers', 'app_a.OSMFullData') def test_count_of_numbers(self): self.assertEqual(len(self.fixtures.generated_numbers), 100) def test_count_of_objects(self): from tests.project.app_a.models import OSMData self.assertEqual(OSMData.objects.count(), 8) @exclude_fixtures('app_a.OSMFullData') def test_exclude_fixtures(self): from tests.project.app_a.models import OSMData self.assertEqual(OSMData.objects.count(), 0)

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4afbb18e83fe583c88cd10e2330649c5bd3947c6

118

py

Python

readable/settings/development.py

amalchuk/readable

ce5397039516299a105ed975d79d9e62d0fe747f

[ "MIT" ]

null

readable/settings/development.py

amalchuk/readable

ce5397039516299a105ed975d79d9e62d0fe747f

[ "MIT" ]

null

readable/settings/development.py

amalchuk/readable

ce5397039516299a105ed975d79d9e62d0fe747f

[ "MIT" ]

null

from readable.settings.common import * # Core Settings: DEBUG: bool = True DEBUG_PROPAGATE_EXCEPTIONS: bool = True

14.75

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null

0

1

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4

ab0527ac26c6a03751f5e5d8b55a155d59e8af59

5,789

py

Python

python3_sample_project/app/migrations/0001_initial.py

laborautonomo/django-admin-sortable

ba552549c26461bcbd0e624e4b45e078e2c51d6c

[ "MS-PL", "Naumen", "Condor-1.1", "Apache-1.1" ]

1

2015-11-05T17:33:04.000Z

python3_sample_project/app/migrations/0001_initial.py

laborautonomo/django-admin-sortable

ba552549c26461bcbd0e624e4b45e078e2c51d6c

[ "MS-PL", "Naumen", "Condor-1.1", "Apache-1.1" ]

null

python3_sample_project/app/migrations/0001_initial.py

laborautonomo/django-admin-sortable

ba552549c26461bcbd0e624e4b45e078e2c51d6c

[ "MS-PL", "Naumen", "Condor-1.1", "Apache-1.1" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import adminsortable.fields class Migration(migrations.Migration): dependencies = [ ('contenttypes', '0001_initial'), ] operations = [ migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('title', models.CharField(max_length=50)), ], options={ 'ordering': ['order'], 'verbose_name_plural': 'Categories', 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Component', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('title', models.CharField(max_length=50)), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Credit', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('first_name', models.CharField(max_length=30)), ('last_name', models.CharField(max_length=30)), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='GenericNote', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('title', models.CharField(max_length=50)), ('object_id', models.PositiveIntegerField(verbose_name='Content id')), ('content_type', models.ForeignKey(related_name='generic_notes', verbose_name='Content type', to='contenttypes.ContentType')), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Note', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('text', models.CharField(max_length=100)), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Person', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('first_name', models.CharField(max_length=50)), ('last_name', models.CharField(max_length=50)), ('is_board_member', models.BooleanField(verbose_name='Board Member', default=False)), ], options={ 'ordering': ['order'], 'verbose_name_plural': 'People', 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Project', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('title', models.CharField(max_length=50)), ('description', models.TextField()), ('category', adminsortable.fields.SortableForeignKey(to='app.Category')), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.CreateModel( name='Widget', fields=[ ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')), ('order', models.PositiveIntegerField(editable=False, default=1, db_index=True)), ('title', models.CharField(max_length=50)), ], options={ 'ordering': ['order'], 'abstract': False, }, bases=(models.Model,), ), migrations.AddField( model_name='note', name='project', field=models.ForeignKey(to='app.Project'), preserve_default=True, ), migrations.AddField( model_name='credit', name='project', field=models.ForeignKey(to='app.Project'), preserve_default=True, ), migrations.AddField( model_name='component', name='widget', field=adminsortable.fields.SortableForeignKey(to='app.Widget'), preserve_default=True, ), ]

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0

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0

null

0

1

0

1

0

null

0

4

ab3e5ec01fafc1a1d808ddfa7236ee80623e262d

3,265

py

Python

generated/machine.py

cpwood/Pico-Stub

176af2962b4701805c81afed2e540d39e1adad82

[ "Apache-2.0" ]

19

2021-01-25T23:56:09.000Z

2022-02-21T13:55:16.000Z

generated/machine.py

cpwood/Pico-Stub

176af2962b4701805c81afed2e540d39e1adad82

[ "Apache-2.0" ]

18

2021-02-06T09:03:09.000Z

2021-10-04T16:36:35.000Z

generated/machine.py

cpwood/Pico-Stub

176af2962b4701805c81afed2e540d39e1adad82

[ "Apache-2.0" ]

6

2021-01-26T08:41:47.000Z

2021-04-27T11:33:33.000Z

""" Module: 'machine' on micropython-rp2-1.15 """ # MCU: {'family': 'micropython', 'sysname': 'rp2', 'version': '1.15.0', 'build': '', 'mpy': 5637, 'port': 'rp2', 'platform': 'rp2', 'name': 'micropython', 'arch': 'armv7m', 'machine': 'Raspberry Pi Pico with RP2040', 'nodename': 'rp2', 'ver': '1.15', 'release': '1.15.0'} # Stubber: 1.3.9 class ADC: '' CORE_TEMP = 4 def read_u16(): pass class I2C: '' def init(): pass def readfrom(): pass def readfrom_into(): pass def readfrom_mem(): pass def readfrom_mem_into(): pass def readinto(): pass def scan(): pass def start(): pass def stop(): pass def write(): pass def writeto(): pass def writeto_mem(): pass def writevto(): pass class PWM: '' def deinit(): pass def duty_ns(): pass def duty_u16(): pass def freq(): pass PWRON_RESET = 1 class Pin: '' ALT = 3 IN = 0 IRQ_FALLING = 4 IRQ_RISING = 8 OPEN_DRAIN = 2 OUT = 1 PULL_DOWN = 2 PULL_UP = 1 def high(): pass def init(): pass def irq(): pass def low(): pass def off(): pass def on(): pass def toggle(): pass def value(): pass class SPI: '' LSB = 0 MSB = 1 def deinit(): pass def init(): pass def read(): pass def readinto(): pass def write(): pass def write_readinto(): pass class Signal: '' def off(): pass def on(): pass def value(): pass class SoftI2C: '' def init(): pass def readfrom(): pass def readfrom_into(): pass def readfrom_mem(): pass def readfrom_mem_into(): pass def readinto(): pass def scan(): pass def start(): pass def stop(): pass def write(): pass def writeto(): pass def writeto_mem(): pass def writevto(): pass class SoftSPI: '' LSB = 0 MSB = 1 def deinit(): pass def init(): pass def read(): pass def readinto(): pass def write(): pass def write_readinto(): pass class Timer: '' ONE_SHOT = 0 PERIODIC = 1 def deinit(): pass def init(): pass class UART: '' INV_RX = 2 INV_TX = 1 def any(): pass def read(): pass def readinto(): pass def readline(): pass def sendbreak(): pass def write(): pass class WDT: '' def feed(): pass WDT_RESET = 3 def bootloader(): pass def deepsleep(): pass def disable_irq(): pass def enable_irq(): pass def freq(): pass def idle(): pass def lightsleep(): pass mem16 = None mem32 = None mem8 = None def reset(): pass def reset_cause(): pass def soft_reset(): pass def time_pulse_us(): pass def unique_id(): pass

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0.464931

370

3,265

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0.047138

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0.418683

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285

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null

1

0

1

0

null

0

1

0

1

0

4

ab4036034b5cd8ee94bb3335e14992b7b52653ec

71

py

Python

Contest/ABC138/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC138/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC138/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 print("red" if int(input()) < 3200 else input())

35.5

48

0.661972

12

71

3.916667

0.916667

0

0.079365

0.112676

71

2

48

35.5

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0

0.06

0

1

0

true

0

1

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null

0

1

0

null

0

1

0

1

0

4

ab5e0f4adf474568f3f9176f20ed81b0da1f7063

174

py

Python

Event/EventLogger.py

dimiroul/BacktestFramePublic

15c48c98e67cb32550d4bfca3ce40e2e0c84c67d

[ "MIT" ]

null

Event/EventLogger.py

dimiroul/BacktestFramePublic

15c48c98e67cb32550d4bfca3ce40e2e0c84c67d

[ "MIT" ]

null

Event/EventLogger.py

dimiroul/BacktestFramePublic

15c48c98e67cb32550d4bfca3ce40e2e0c84c67d

[ "MIT" ]

null

from Logger.Logger import (LoggerStringUnit) # 定义EVENT_LOGGER为回测框架使用的事件记录模块，作为全局变量 EVENT_LOGGER: LoggerStringUnit = LoggerStringUnit(head_="event_datetime,event_type,info")

34.8

89

0.856322

18

174

8

0.666667

0

0.063218

174

4

90

43.5

0.883436

0.201149

0

0.218978

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

db65924b53241dee41fead4ede71f27d988d5915

1,960

py

Python

mlist/test/test_binary_search.py

fwu03/Software_Testing_Python

51d559c9649481fdff94f35e027c3424e9fd600f

[ "MIT" ]

null

mlist/test/test_binary_search.py

fwu03/Software_Testing_Python

51d559c9649481fdff94f35e027c3424e9fd600f

[ "MIT" ]

5

2019-02-15T23:31:10.000Z

2019-03-05T20:17:03.000Z

mlist/test/test_binary_search.py

fwu03/Software_Testing_Python

51d559c9649481fdff94f35e027c3424e9fd600f

[ "MIT" ]

null

# test_binary_search.py import pytest from mlist import binary_search def test_format(): with pytest.raises(TypeError): binary_search.binary_search("hello", [1,2,3,4,6,8]) # return ERROR if input x is a string with pytest.raises(TypeError): binary_search.binary_search(3.0, [1,3,8,9,15]) # return ERROR if input x is a float with pytest.raises(TypeError): binary_search.binary_search(4, [[1,2], [3, 5]]) # return ERROR if input lst is a nested list with pytest.raises(TypeError): binary_search.binary_search(4, [1,2, "hello",6,8]) # return ERROR if input lst contains strings with pytest.raises(TypeError): binary_search.binary_search(3, [1,3.0,8,9,15]) # return ERROR if input lst contains float with pytest.raises(ValueError): binary_search.binary_search(3, [3,1,7,2,8]) # return ERROR if input lst is not sorted with pytest.raises(TypeError): binary_search.binary_search(3, 'a') # return ERROR if input lst is not a list def test_values(): with pytest.raises(ValueError): binary_search.binary_search(3000, [1,3,25,36,800,900]) # return ERROR if input x is over 1000 with pytest.raises(ValueError): binary_search.binary_search(3, [1,3,25,36,800,5550]) # return ERROR if input lst contains values over 1000 def test_output(): # assert return contain correct values assert binary_search.binary_search(3, [1,2,3,4]) == [True,3,2], "Assertion Failed, the output is wrong" # assert return contain correct values assert binary_search.binary_search(3, [1,2,4]) == [False,3,None], "Assertion Failed, the output is wrong" # assert return is a list assert isinstance(binary_search.binary_search(3, [1,2,3,4]), list), "Assertion Failed, the output should be a list" # assert return length is 3 assert len(binary_search.binary_search(2, [2,3])) == 3, "Assertion Failed, the output has incorrect length"

49

119

0.694898

313

1,960

4.249201

0.220447

0.252632

0.17594

0.234586

0.714286

0.702256

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0.388722

0.17594

0

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0.189796

1,960

39

120

50.25641

0.775189

0.263265

0

0.333333

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0.148148

1

0.111111

true

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0.074074

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0.185185

0

null

1

0

1

0

1

0

null

0

1

0

4

db6b94d3861119df93a29a4660786f1d427f50b8

156

py

Python

Krakatau-master/Krakatau/Krakatau/ssa/ssa_jumps/placeholder.py

orneryhippo/saturdays

525ce086452e96a01d1762418c79d4c84fd605b5

[ "Apache-2.0" ]

null

Krakatau-master/Krakatau/Krakatau/ssa/ssa_jumps/placeholder.py

orneryhippo/saturdays

525ce086452e96a01d1762418c79d4c84fd605b5

[ "Apache-2.0" ]

null

Krakatau-master/Krakatau/Krakatau/ssa/ssa_jumps/placeholder.py

orneryhippo/saturdays

525ce086452e96a01d1762418c79d4c84fd605b5

[ "Apache-2.0" ]

null

from .base import BaseJump class Placeholder(BaseJump): def __init__(self, parent, *args, **kwargs): super(Placeholder, self).__init__(parent)

26

49

0.711538

18

156

5.722222

0.722222

0

0.166667

156

5

50

31.2

0.792308

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

db8b9b6974df6efe7b7e6e5f091bc2811d20b17f

101

py

Python

mzcli/__main__.py

quodlibetor/mzcli

8c69419a36d73ab990f8bba4fb26a11b549699cc

[ "BSD-3-Clause" ]

8

2020-05-04T22:23:52.000Z

2022-03-21T04:02:35.000Z

mzcli/__main__.py

quodlibetor/mzcli

8c69419a36d73ab990f8bba4fb26a11b549699cc

[ "BSD-3-Clause" ]

7

2020-02-18T18:21:42.000Z

2021-12-10T14:00:38.000Z

mzcli/__main__.py

quodlibetor/mzcli

8c69419a36d73ab990f8bba4fb26a11b549699cc

[ "BSD-3-Clause" ]

null

""" mzcli package main entry point """ from .main import cli if __name__ == "__main__": cli()

10.1

30

0.633663

13

101

4.307692

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0

0.227723

101

9

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11.222222

0.717949

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true

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0.333333

0

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0

1

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null

0

1

0

null

0

1

0

1

0

4

db91d8112dba93ea9883b738997a69fb0c8e77d2

45,565

py

Python

PyLib/GameUnitTests.py

Lyapunov/Adventure-engine

f8786e6fc2bbb8b363178520759ef42a46b02615

[ "MIT" ]

null

PyLib/GameUnitTests.py

Lyapunov/Adventure-engine

f8786e6fc2bbb8b363178520759ef42a46b02615

[ "MIT" ]

null

PyLib/GameUnitTests.py

Lyapunov/Adventure-engine

f8786e6fc2bbb8b363178520759ef42a46b02615

[ "MIT" ]

null

import unittest import json from GameObject import Game from GameObject import GameObject from GameObject import GameObjectAttribute from GameObject import GameObjectUseAction from GameObject import GameObjectRevealAction from GameObject import GamePassageRevealAction from GameObject import GamePassage from GameObject import GameSyntaxChecker from GameObject import GameSolver from GameObject import GameEncoder from GameObject import GameDecoder class GameUnitTests(unittest.TestCase): # TODO: (IDEA) descriptions and images, the entire view should be a completely separated layer, # which just portrays the game objects according to their attributes # TODO: Write serializer # TODO: Write view layer - first step is an object that returns an empty hash of texts indexed by game names def setUp( self ): # Test game1, just to start with something self.game1_text_blueprints = """ [ [{"obj_content": {"attributes": [], "childObjects": [{"obj_content": {"attributes": ["immobile"], "childObjects": [], "name": "table"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "candle"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "match"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "bird"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "stone"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": ["immobile", "invisible"], "childObjects": [], "name": "picture"}, "obj_name": "GameObject"}], "name": "dark_room"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [{"obj_content": {"attributes": ["immobile"], "childObjects": [{"obj_content": {"attributes": [], "childObjects": [], "name": "knife"}, "obj_name": "GameObject"}], "name": "cabinet"}, "obj_name": "GameObject"}], "name": "bathroom"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "secret_room"}, "obj_name": "GameObject"}], [{"obj_content": {"attributes": [], "childObjects": [], "name": "burning_candle"}, "obj_name": "GameObject"}, {"obj_content": {"attributes": [], "childObjects": [], "name": "injured_bird"}, "obj_name": "GameObject"}], [{"obj_content": {"room_name2": "bathroom", "room_name1": "dark_room", "direction2": "S", "attributes": [], "direction1": "N", "identifier": 11}, "obj_name": "GamePassage"}, {"obj_content": {"room_name2": "secret_room", "room_name1": "dark_room", "direction2": "E", "attributes": ["invisible"], "direction1": "W", "identifier": 12}, "obj_name": "GamePassage"}], [{"obj_content": {"subjectname": "candle", "toolname": "match", "resultname": "burning_candle"}, "obj_name": "GameObjectUseAction"}, {"obj_content": {"subjectname": "bird", "toolname": "stone", "resultname": "injured_bird"}, "obj_name": "GameObjectUseAction"}, {"obj_content": {"subjectname": "picture", "toolname": "", "identifier": 12}, "obj_name": "GamePassageRevealAction"}], [{"obj_content": {"subjectname": "picture", "toolname": "burning_candle"}, "obj_name": "GameObjectRevealAction"}], "secret_room", {"go#dark_room": "dark_room", "go#bathroom": "bathroom"} ] """ self.game1 = Game( [ [ GameObject( 'dark_room', [], [ GameObject( 'table', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'candle' ), GameObject( 'match' ), GameObject( 'bird' ), GameObject( 'stone' ), GameObject( 'picture', [GameObjectAttribute.IMMOBILE, GameObjectAttribute.INVISIBLE] ) ] ), GameObject( 'bathroom', [], [ GameObject( 'cabinet', [GameObjectAttribute.IMMOBILE], [ GameObject( 'knife' ) ] ) ] ), GameObject( 'secret_room' ) ], [ GameObject( 'burning_candle' ), GameObject( 'injured_bird' ) ], [ GamePassage( 11, 'dark_room', 'bathroom' , 'N', 'S' ), GamePassage( 12, 'dark_room', 'secret_room', 'W', 'E', [GameObjectAttribute.INVISIBLE] ) ], [ GameObjectUseAction( 'candle', 'match', 'burning_candle' ), GameObjectUseAction( 'bird', 'stone', 'injured_bird' ), GamePassageRevealAction( 'picture', '', 12 ) ], [ GameObjectRevealAction( 'picture', 'burning_candle' ) ], 'secret_room', { 'go#dark_room' : 'dark_room', 'go#bathroom' : 'bathroom' } ] ); assert ( self.game1.look() == 'dark_room' ) assert ( self.game1.has( 'burning_candle' ) is None ) assert ( self.game1.has( 'candle' ) is None ) assert ( self.game1.has( 'match' ) is None ) assert ( 'candle' in self.game1.stuffs() ) assert ( 'match' in self.game1.stuffs() ) assert ( 'table' in self.game1.stuffs() ) assert ( not 'picture' in self.game1.stuffs() ) assert ( self.game1.directions() == [['N', 'bathroom']] ) assert ( self.game1.won() == 0 ) def test_syntax_checker_wrong_game_1(self): # there is no room game_internal = Game( [ [], [], [], [], [], '', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'must have at least one room' ) def test_syntax_checker_wrong_game_2(self): # starting in the ending room game_internal = Game( [ [ GameObject( 'room1', [], []) ], [], [], [], [], 'room1', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'cannot start in the ending room' ) def test_syntax_checker_wrong_game_3(self): # starting in the ending room game_internal = Game( [ [ GameObject( 'room1', [], []) ], [], [], [], [], 'final_room', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'final room does not exist' ) def test_syntax_checker_wrong_game_4(self): game_internal = Game( [ [ GameObject( 'starting_room' ), GameObject( 'final_room' ) ], [], [], [], [], 'final_room', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'final room is not reachable' ) def test_syntax_checker_wrong_game_5(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomC', 'roomD', 'N', 'S' ) ], [], [], 'roomD', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'final room is not reachable' ) def test_syntax_checker_wrong_game_6(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomB', 'roomC', 'N', 'S' ) ], [], [], 'roomD', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'final room is not reachable' ) def test_syntax_checker_wrong_game_7(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ), GameObject( 'roomE' ), GameObject( 'roomF' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomA', 'roomE', 'E', 'W' ), GamePassage(13, 'roomE', 'roomB', 'N', 'E' ), GamePassage(14, 'roomD', 'roomE', 'N', 'S' ), GamePassage(15, 'roomC', 'roomF', 'E', 'W' ) ], [], [], 'roomF', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'final room is not reachable' ) def test_syntax_checker_wrong_game_8(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomA', 'roomB', 'W', 'S' ) ], [], [], 'roomB', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'multiple passages between the rooms roomA, roomB' ) def test_syntax_checker_wrong_game_9(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomB', 'roomA', 'W', 'S' ) ], [], [], 'roomB', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'multiple passages between the rooms roomA, roomB' ) def test_syntax_checker_wrong_game_10(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(11, 'roomB', 'roomC', 'W', 'S' ) ], [], [], 'roomC', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'passage identifiers are not unique, 11' ) def test_syntax_checker_wrong_game_11(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomC', 'roomD', 'N', 'S' ) ], [], [], 'roomB', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'not all rooms are accessible, roomC' ) def test_syntax_checker_wrong_game_12(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'button', [GameObjectAttribute.IMMOBILE], [] ) ] ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'button', '', 13 ) ], [], 'roomB', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == 'invalid passage identifiers in an action, 13' ) def test_syntax_checker_wrong_game_13(self): game_internal = Game( [ [ GameObject( 'roomA',[], [ GameObject( 'button1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'button2', [GameObjectAttribute.IMMOBILE], [] ) ] ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'button', '', 11 ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found invalid object in an action, button' ) def test_syntax_checker_wrong_game_14(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'button1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'button1', [GameObjectAttribute.IMMOBILE], [] ) ] ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'button1', '', 11 ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found two objects with the same name, button1' ) def test_syntax_checker_wrong_game_15(self): game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomC' ), GameObject( 'roomB' ), GameObject( 'roomC' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomB', 'roomC', 'N', 'S' ) ], [], [], 'roomC', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found two objects with the same name, roomC' ) def test_syntax_checker_wrong_game16(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( '', '', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found an action without actors' ) def test_syntax_checker_wrong_game17(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'door', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found invalid action with the same actor twice, door' ) def test_syntax_checker_wrong_game_18(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'button1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'button2', [GameObjectAttribute.IMMOBILE], [] ) ] ), GameObject( 'roomB' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'button1', '', 11 ), GamePassageRevealAction( 'button1', '', 11 ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found multiple actions for the same actor, button1' ) def test_syntax_checker_wrong_game_19(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'button1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'button2', [GameObjectAttribute.IMMOBILE], [] ) ] ), GameObject( 'roomB' ) ], [ GameObject( 'broken button' ) ], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'button1', '', 11 ), GameObjectUseAction( '', 'button1', 'broken button' ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found multiple actions for the same actor, button1' ) def test_syntax_checker_wrong_game_20(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'handle1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'handle2', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'crowbar' ) ] ), GameObject( 'roomB' ) ], [ GameObject( 'broken handle' ) ], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'handle1', 'crowbar', 11 ), GameObjectUseAction( 'handle2', 'crowbar', 'broken handle' ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found multiple actions for the same actor, crowbar' ) def test_syntax_checker_wrong_game_21(self): game_internal = Game( [ [ GameObject( 'roomA', [], [ GameObject( 'handle1', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'handle2', [GameObjectAttribute.IMMOBILE], [] ), GameObject( 'crowbar' ) ] ), GameObject( 'roomB' ) ], [ GameObject( 'handle1' ) ], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'handle1', 'crowbar', 11 ), GameObjectUseAction( 'handle2', 'crowbar', 'handle1' ) ], [], 'roomB', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'found two objects with the same name, handle1' ) def test_syntax_checker_wrong_game22(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'key', [GameObjectAttribute.IMMOBILE] ) ] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'not top level stuffs cannot have attributes, key' ) def test_syntax_checker_wrong_game23(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'keypart1' ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'keypart2' ) ] ) ] ), GameObject( 'ending_room' ) ], [ GameObject( 'key', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ), GameObjectUseAction( 'keypart1', 'keypart2', 'key' ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'not top level stuffs cannot have attributes, key' ) def test_syntax_checker_wrong_game24(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'key', [GameObjectAttribute.INVISIBLE] ) ] ), GameObject( 'middle_room' , [], [ GameObject( 'burning_candle' ), GameObject( 'door', [GameObjectAttribute.IMMOBILE] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'middle_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ), GamePassage( 12, 'starting_room', 'middle_room' , 'N', 'S' ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [ GameObjectRevealAction( 'burning_candle', 'key') ], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'subjects of revealing actions must be invisible initially, burning_candle' ) def test_syntax_checker_wrong_game25(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' , [GameObjectAttribute.IMMOBILE] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == "action actor key must be mobile" ) def test_syntax_checker_wrong_game26(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE, GameObjectAttribute.INVISIBLE] ), GameObject( 'key' ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'there must be exactly one action for each invisible object which reveals it, door' ) def test_syntax_checker_wrong_game27(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' , [] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'key', 'door', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == "action actor door must be mobile" ) def test_syntax_checker_wrong_game28(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' , [] ) ] ), GameObject( 'ending_room' ) ], [ GameObject( 'broken_key' ) ], [ GamePassage( 11, 'starting_room', 'strange_room' , 'W', 'E', [] ), GamePassage( 12, 'strange_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GameObjectUseAction( 'door', 'key', 'broken_key' ) ], [ GamePassageRevealAction( 'broken_key', '', 12 )], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == "found not existing room in a passage: strange_room" ) def test_syntax_checker_wrong_game29(self): game_internal = Game( [ [ GameObject( 'starting_room' ), GameObject( 'final/room' ) ], [], [ GamePassage( 11, 'starting_room', 'final/room', 'N', 'S' ) ], [], [], 'final/room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == 'game object names can contain only lower case alphabets and _, final/room' ) def test_syntax_checker_good_game1(self): # minimal valid game game_internal = Game( [ [ GameObject( 'starting_room' ), GameObject( 'final_room' ) ], [], [ GamePassage( 11, 'starting_room', 'final_room', 'N', 'S' ) ], [], [], 'final_room', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == '' ) assert ( GameSolver().solve( game_internal ) == [ [ 'go', 'N' ] ] ) def test_syntax_checker_good_game_2(self): # testing whether final room is accessible game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ), GameObject( 'roomE' ), GameObject( 'roomF' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomA', 'roomE', 'E', 'W' ), GamePassage(13, 'roomD', 'roomC', 'E', 'W' ), GamePassage(14, 'roomE', 'roomB', 'N', 'E' ), GamePassage(15, 'roomD', 'roomE', 'N', 'S' ), GamePassage(16, 'roomC', 'roomF', 'E', 'W' ) ], [], [], 'roomF', {} ] ) assert ( GameSyntaxChecker().check( game_internal ) == '' ) assert ( GameSolver().solve( game_internal ) == [ [ 'go', 'N' ], [ 'go', 'E' ], [ 'go', 'S' ], [ 'go', 'E' ], [ 'go', 'E' ] ] ) def test_syntax_checker_good_game3(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', '', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) assert ( GameSolver().solve( game_internal ) == [['use', '', 'door'], ['go', 'N']] ) def test_syntax_checker_good_game4(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) assert ( GameSolver().solve( game_internal ) == [['take', 'key'], ['use', 'door', 'key'], ['go', 'N']] ) def test_syntax_checker_good_game5(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'key' ) ] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) solution = GameSolver().solve( game_internal ) assert ( solution == [['open', 'box'], ['take', 'key'], ['use', 'door', 'key'], ['go', 'N']] ) def test_syntax_checker_good_game6(self): game_internal = Game( [ [ GameObject( 'starting_room' ), GameObject( 'middle_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'key' ) ] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'middle_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ), GamePassage( 12, 'starting_room', 'middle_room' , 'N', 'S' ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) solution = GameSolver().solve( game_internal ) assert ( solution == [['go', 'N'],['open', 'box'], ['take', 'key'], ['use', 'door', 'key'], ['go', 'N']] ) def test_syntax_checker_good_game7(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'key', [GameObjectAttribute.INVISIBLE] ) ] ), GameObject( 'middle_room' , [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'burning_candle' ) ] ) ] ), GameObject( 'ending_room' ) ], [], [ GamePassage( 11, 'middle_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ), GamePassage( 12, 'starting_room', 'middle_room' , 'N', 'S' ) ], [ GamePassageRevealAction( 'door', 'key', 11 ) ], [ GameObjectRevealAction( 'key', 'burning_candle') ], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) solution = GameSolver().solve( game_internal ) assert ( solution == [['go', 'N'], ['open', 'box'], ['take', 'burning_candle'], ['go', 'S'], ['take', 'key'], ['go', 'N'], ['use', 'door', 'key'], ['go', 'N']] ) def test_syntax_checker_good_game8(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'keypart1' ), GameObject( 'box', [GameObjectAttribute.IMMOBILE], [GameObject( 'keypart2' ) ] ) ] ), GameObject( 'ending_room' ) ], [ GameObject( 'key' ) ], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GamePassageRevealAction( 'door', 'key', 11 ), GameObjectUseAction( 'keypart1', 'keypart2', 'key' ) ], [], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == '' ) solution = GameSolver().solve( game_internal ) assert ( solution == [['take', 'keypart1'], ['open', 'box'], ['take', 'keypart2'], ['use', 'keypart1', 'keypart2'], ['use', 'door', 'key'], ['go', 'N']] ) # Here use action + passage reval view = use passage reveal, so it is just # pure complication. game10 may make the possibility of the separaton more meaningful. def test_syntax_checker_good_game9(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' , [] ) ] ), GameObject( 'ending_room' ) ], [ GameObject( 'broken_key' ) ], [ GamePassage( 11, 'starting_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GameObjectUseAction( 'door', 'key', 'broken_key' ) ], [ GamePassageRevealAction( 'broken_key', '', 11 )], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == "" ) assert ( GameSolver().solve( game_internal ) == [['take', 'key'], ['use', 'door', 'key'], ['go', 'N']] ) def test_syntax_checker_good_game10(self): game_internal = Game( [ [ GameObject( 'starting_room', [], [ GameObject( 'door', [GameObjectAttribute.IMMOBILE] ), GameObject( 'key' , [] ) ] ), GameObject( 'strange_room' ), GameObject( 'ending_room' ) ], [ GameObject( 'broken_key' ) ], [ GamePassage( 11, 'starting_room', 'strange_room' , 'W', 'E', [] ), GamePassage( 12, 'strange_room', 'ending_room' , 'N', 'S', [GameObjectAttribute.INVISIBLE] ) ], [ GameObjectUseAction( 'door', 'key', 'broken_key' ) ], [ GamePassageRevealAction( 'broken_key', '', 12 )], 'ending_room', {} ] ) verdict = GameSyntaxChecker().check( game_internal ) assert ( verdict == "" ) assert ( GameSolver().solve( game_internal ) == [['take', 'key'], ['use', 'door', 'key'], ['go', 'W'], ['go', 'N']] ) def test_take_and_drop_existing_object(self): subject = self.game1.do_it( 'take', 'candle' ) assert ( not subject is None ) assert ( not self.game1.has( 'candle' ) is None ) assert ( not 'candle' in self.game1.stuffs() ) subject = self.game1.do_it( 'drop', 'candle' ) assert ( not subject is None ) assert ( self.game1.has( 'candle' ) is None ) def test_trying_take_not_existing_object(self): subject = self.game1.do_it( 'take', 'banana' ) assert ( subject is None ) assert ( self.game1.has( 'banana' ) is None ) def test_trying_take_immobile_object(self): subject = self.game1.do_it( 'take', 'table' ) assert ( subject is None ) assert ( self.game1.has( 'table' ) is None ) def test_action_hit_the_bird_with_the_stone(self): self.game1.do_it( 'take', 'stone' ) object1 = self.game1.do_it( 'use', 'stone', 'bird' ) assert ( not object1 is None ) assert ( not 'bird' in self.game1.stuffs() ) assert ( self.game1.has( 'stone' ) is None ) assert ( 'injured_bird' in self.game1.stuffs() ) object2 = self.game1.do_it( 'use', 'stone', 'bird' ) assert ( object2 is None ) def test_action_hit_the_bird_with_the_stone_but_both_are_in_inventory(self): self.game1.do_it( 'take', 'stone' ) self.game1.do_it( 'take', 'bird' ) object1 = self.game1.do_it( 'use', 'stone', 'bird' ) assert ( not self.game1.has( 'injured_bird' ) is None ) def test_action_hit_the_bird_with_the_stone_but_use_params_are_reversed(self): self.game1.do_it( 'take', 'stone' ) self.game1.do_it( 'use', 'bird', 'stone' ) assert ( 'injured_bird' in self.game1.stuffs() ) def test_room_goes_light_from_dark_if_we_burn_the_candle_without_taking_it_first(self): self.game1.do_it( 'take', 'match' ) self.game1.do_it( 'use', 'candle', 'match' ) assert( not 'candle' in self.game1.stuffs() ) assert( 'burning_candle' in self.game1.stuffs() ) assert( 'picture' in self.game1.stuffs() ) def test_room_goes_light_from_dark_if_we_burn_the_candle_with_taking_it_first(self): self.game1.do_it( 'take', 'candle' ) self.game1.do_it( 'take', 'match' ) self.game1.do_it( 'use', 'candle', 'match' ) assert ( not self.game1.has( 'burning_candle' ) is None ) assert( 'picture' in self.game1.stuffs() ) def test_moving_between_rooms(self): self.game1.do_it( 'go', 'N') assert( self.game1.look() == 'bathroom' ) assert ( self.game1.directions() == [['S', 'dark_room']] ) self.game1.do_it( 'go', 'S') assert( self.game1.look() == 'dark_room' ) def test_opening_objects(self): self.game1.do_it( 'go', 'N') assert( not 'knife' in self.game1.stuffs() ) assert ( self.game1.do_it( 'open', 'cabinet' ) ) assert( 'knife' in self.game1.stuffs() ) def test_moving_between_rooms_and_carrying_object(self): subject = self.game1.do_it( 'take', 'candle') self.game1.do_it( 'go', 'N') self.game1.do_it( 'drop', 'candle') self.game1.do_it( 'go', 'S') assert( self.game1.look() == 'dark_room' ) assert( not 'candle' in self.game1.stuffs() ) def test_recognizing_a_new_object_through_a_view_and_it_becomes_permanent(self): self.game1.do_it( 'take', 'match' ) object1 = self.game1.do_it( 'use', 'candle', 'match' ) self.game1.do_it( 'take', 'burning_candle') self.game1.do_it( 'go', 'N') self.game1.do_it( 'drop', 'burning_candle') self.game1.do_it( 'go', 'S') assert( self.game1.look() == 'dark_room' ) assert( 'picture' in self.game1.stuffs() ) def test_finding_a_new_passage(self): self.test_recognizing_a_new_object_through_a_view_and_it_becomes_permanent() assert( 'picture' in self.game1.stuffs() ) self.game1.do_it( 'use','picture') assert ( self.game1.directions() == [['N', 'bathroom'], ['W', 'secret_room']] ) def test_winning_the_game(self): self.test_finding_a_new_passage() self.game1.do_it( 'go', 'W') assert ( self.game1.won() == 1 ) def test_solver_on_full_game(self): verdict = GameSyntaxChecker().check( self.game1 ) assert ( verdict == '' ) solution = GameSolver().solve( self.game1 ) assert ( solution == [ ['take', 'candle'], ['take', 'match'], ['take', 'bird'], ['take', 'stone'], ['use', 'candle', 'match'], ['use', 'bird', 'stone'], ['use', '', 'picture'], ['go', 'W']] ) def test_json_serializer_deserializer(self): game1_text_blueprints_reconstructed = json.dumps( self.game1.get_blueprints(), cls=GameEncoder ); array_game_description_reconstructed = GameDecoder().decode( game1_text_blueprints_reconstructed ); assert( self.game1.get_blueprints() == array_game_description_reconstructed ) def test_json_deserializer_serializer(self): array_game_description = GameDecoder().decode( self.game1_text_blueprints ); text_game_description2 = json.dumps( array_game_description, cls=GameEncoder ); array_game_description2 = GameDecoder().decode( text_game_description2 ); assert( array_game_description == array_game_description2 ) def test_json_game_deserializer_serializer_1(self): game_internal_text = '[[{"obj_content": {"attributes": [], "childObjects": [], "name": "roomA"}, "obj_name": "GameObject"},\ {"obj_content": {"attributes": [], "childObjects": [], "name": "roomB"}, "obj_name": "GameObject"},\ {"obj_content": {"attributes": [], "childObjects": [], "name": "roomC"}, "obj_name": "GameObject"},\ {"obj_content": {"attributes": [], "childObjects": [], "name": "roomD"}, "obj_name": "GameObject"},\ {"obj_content": {"attributes": [], "childObjects": [], "name": "roomE"}, "obj_name": "GameObject"},\ {"obj_content": {"attributes": [], "childObjects": [], "name": "roomF"}, "obj_name": "GameObject"}],\ [],\ [{"obj_content": {"room_name2": "roomB", "room_name1": "roomA", "direction2": "S", "attributes": [], "direction1": "N", "identifier": 11},\ "obj_name": "GamePassage"},\ {"obj_content": {"room_name2": "roomE", "room_name1": "roomA", "direction2": "W", "attributes": [], "direction1": "E", "identifier": 12},\ "obj_name": "GamePassage"},\ {"obj_content": {"room_name2": "roomB", "room_name1": "roomE", "direction2": "E", "attributes": [], "direction1": "N", "identifier": 13},\ "obj_name": "GamePassage"},\ {"obj_content": {"room_name2": "roomE", "room_name1": "roomD", "direction2": "S", "attributes": [], "direction1": "N", "identifier": 14},\ "obj_name": "GamePassage"},\ {"obj_content": {"room_name2": "roomF", "room_name1": "roomC", "direction2": "W", "attributes": [], "direction1": "E", "identifier": 15},\ "obj_name": "GamePassage"}], [], [], "roomF", {}]' game_from_text = Game( GameDecoder().decode( game_internal_text ) ) game_internal = Game( [ [ GameObject( 'roomA' ), GameObject( 'roomB' ), GameObject( 'roomC' ), GameObject( 'roomD' ), GameObject( 'roomE' ), GameObject( 'roomF' ) ], [], [ GamePassage(11, 'roomA', 'roomB', 'N', 'S' ), GamePassage(12, 'roomA', 'roomE', 'E', 'W' ), GamePassage(13, 'roomE', 'roomB', 'N', 'E' ), GamePassage(14, 'roomD', 'roomE', 'N', 'S' ), GamePassage(15, 'roomC', 'roomF', 'E', 'W' ) ], [], [], 'roomF', {} ] ) assert( game_internal == game_from_text ) def test_json_game_deserializer_serializer_2(self): game_from_text = Game( GameDecoder().decode( self.game1_text_blueprints ) ) assert( self.game1 == game_from_text ) if __name__ == '__main__' : unittest.main()

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dba687d4fe645d6ead2435d80e320a12d165b7d2

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py

Python

temp/mapjdeo.py

doublejtoh/mission_ide

dc5ee5ee26dfad428fcf9744195d1ed910b50341

[ "MIT" ]

null

temp/mapjdeo.py

doublejtoh/mission_ide

dc5ee5ee26dfad428fcf9744195d1ed910b50341

[ "MIT" ]

null

temp/mapjdeo.py

doublejtoh/mission_ide

dc5ee5ee26dfad428fcf9744195d1ed910b50341

[ "MIT" ]

null

s = input('as: ') print(s)

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dba88a0578acd65c4de18a45c2a669259e8852e4

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py

Python

tests/integration/test_async_http_client.py

TimonPeng/solana-py

e2d53a61fd4bfd30a8d3726aa87398c04b248b38

[ "MIT" ]

null

tests/integration/test_async_http_client.py

TimonPeng/solana-py

e2d53a61fd4bfd30a8d3726aa87398c04b248b38

[ "MIT" ]

null

tests/integration/test_async_http_client.py

TimonPeng/solana-py

e2d53a61fd4bfd30a8d3726aa87398c04b248b38

[ "MIT" ]

null

"""Tests for the HTTP API Client.""" import pytest import solana.system_program as sp from solana.rpc.api import DataSliceOpt from solana.transaction import Transaction from .utils import assert_valid_response, aconfirm_transaction, AIRDROP_AMOUNT, generate_expected_meta_after_airdrop @pytest.mark.integration @pytest.mark.asyncio async def test_request_air_drop(alt_stubbed_sender, test_http_client_async): """Test air drop to alt_stubbed_sender.""" resp = await test_http_client_async.request_airdrop(alt_stubbed_sender.public_key(), AIRDROP_AMOUNT) assert_valid_response(resp) resp = await aconfirm_transaction(test_http_client_async, resp["result"]) assert_valid_response(resp) expected_meta = generate_expected_meta_after_airdrop(resp) assert resp["result"]["meta"] == expected_meta @pytest.mark.integration @pytest.mark.asyncio async def test_send_transaction_and_get_balance(alt_stubbed_sender, alt_stubbed_receiver, test_http_client_async): """Test sending a transaction to localnet.""" # Create transfer tx to transfer lamports from stubbed sender to alt_stubbed_receiver transfer_tx = Transaction().add( sp.transfer( sp.TransferParams( from_pubkey=alt_stubbed_sender.public_key(), to_pubkey=alt_stubbed_receiver, lamports=1000 ) ) ) resp = await test_http_client_async.send_transaction(transfer_tx, alt_stubbed_sender) assert_valid_response(resp) # Confirm transaction resp = await aconfirm_transaction(test_http_client_async, resp["result"]) assert_valid_response(resp) expected_meta = { "err": None, "fee": 5000, "innerInstructions": [], "logMessages": [ "Program 11111111111111111111111111111111 invoke [1]", "Program 11111111111111111111111111111111 success", ], "postBalances": [9999994000, 954, 1], "postTokenBalances": [], "preBalances": [10000000000, 0, 1], "preTokenBalances": [], "rewards": [ { "commission": None, "lamports": -46, "postBalance": 954, "pubkey": "J3dxNj7nDRRqRRXuEMynDG57DkZK4jYRuv3Garmb1i98", "rewardType": "Rent", } ], "status": {"Ok": None}, } assert resp["result"]["meta"] == expected_meta # Check balances resp = await test_http_client_async.get_balance(alt_stubbed_sender.public_key()) assert_valid_response(resp) assert resp["result"]["value"] == 9999994000 resp = await test_http_client_async.get_balance(alt_stubbed_receiver) assert_valid_response(resp) assert resp["result"]["value"] == 954 @pytest.mark.integration @pytest.mark.asyncio async def test_send_raw_transaction_and_get_balance(alt_stubbed_sender, alt_stubbed_receiver, test_http_client_async): """Test sending a raw transaction to localnet.""" # Get a recent blockhash resp = await test_http_client_async.get_recent_blockhash() assert_valid_response(resp) recent_blockhash = resp["result"]["value"]["blockhash"] # Create transfer tx transfer lamports from stubbed sender to alt_stubbed_receiver transfer_tx = Transaction(recent_blockhash=recent_blockhash).add( sp.transfer( sp.TransferParams( from_pubkey=alt_stubbed_sender.public_key(), to_pubkey=alt_stubbed_receiver, lamports=1000 ) ) ) # Sign transaction transfer_tx.sign(alt_stubbed_sender) # Send raw transaction resp = await test_http_client_async.send_raw_transaction(transfer_tx.serialize()) assert_valid_response(resp) # Confirm transaction resp = await aconfirm_transaction(test_http_client_async, resp["result"]) assert_valid_response(resp) expected_meta = { "err": None, "fee": 5000, "innerInstructions": [], "logMessages": [ "Program 11111111111111111111111111111111 invoke [1]", "Program 11111111111111111111111111111111 success", ], "postBalances": [9999988000, 1954, 1], "postTokenBalances": [], "preBalances": [9999994000, 954, 1], "preTokenBalances": [], "rewards": [], "status": {"Ok": None}, } assert resp["result"]["meta"] == expected_meta # Check balances resp = await test_http_client_async.get_balance(alt_stubbed_sender.public_key()) assert_valid_response(resp) assert resp["result"]["value"] == 9999988000 resp = await test_http_client_async.get_balance(alt_stubbed_receiver) assert_valid_response(resp) assert resp["result"]["value"] == 1954 @pytest.mark.integration @pytest.mark.asyncio async def test_get_block_commitment(test_http_client_async): """Test get block commitment.""" resp = await test_http_client_async.get_block_commitment(5) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_block_time(test_http_client_async): """Test get block time.""" resp = await test_http_client_async.get_block_time(5) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_cluster_nodes(test_http_client_async): """Test get cluster nodes.""" resp = await test_http_client_async.get_cluster_nodes() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_confirmed_block(test_http_client_async): """Test get confirmed block.""" resp = await test_http_client_async.get_confirmed_block(1) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_confirmed_block_with_encoding(test_http_client_async): """Test get confrimed block with encoding.""" resp = await test_http_client_async.get_confirmed_block(1, encoding="base64") assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_confirmed_blocks(test_http_client_async): """Test get confirmed blocks.""" resp = await test_http_client_async.get_confirmed_blocks(5, 10) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_confirmed_signature_for_address2(test_http_client_async): """Test get confirmed signature for address2.""" resp = await test_http_client_async.get_confirmed_signature_for_address2( "Vote111111111111111111111111111111111111111", limit=1 ) assert_valid_response(resp) # TODO(michael): This RPC call is only available in solana-core v1.7 or newer. # @pytest.mark.integration # @pytest.mark.asyncio # async def test_get_signatures_for_address(test_http_client_async_async): # """Test get signatures for addresses.""" # resp = await test_http_client_async_async.get_signatures_for_address( # "Vote111111111111111111111111111111111111111", limit=1 # ) # assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_epoch_info(test_http_client_async): """Test get epoch info.""" resp = await test_http_client_async.get_epoch_info() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_epoch_schedule(test_http_client_async): """Test get epoch schedule.""" resp = await test_http_client_async.get_epoch_schedule() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_fee_calculator_for_blockhash(test_http_client_async): """Test get fee calculator for blockhash.""" resp = await test_http_client_async.get_recent_blockhash() assert_valid_response(resp) resp = await test_http_client_async.get_fee_calculator_for_blockhash(resp["result"]["value"]["blockhash"]) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_slot(test_http_client_async): """Test get slot.""" resp = await test_http_client_async.get_slot() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_fees(test_http_client_async): """Test get fees.""" resp = await test_http_client_async.get_fees() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_first_available_block(test_http_client_async): """Test get first available block.""" resp = await test_http_client_async.get_first_available_block() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_genesis_hash(test_http_client_async): """Test get genesis hash.""" resp = await test_http_client_async.get_genesis_hash() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_identity(test_http_client_async): """Test get identity.""" resp = await test_http_client_async.get_genesis_hash() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_inflation_governor(test_http_client_async): """Test get inflation governor.""" resp = await test_http_client_async.get_inflation_governor() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_inflation_rate(test_http_client_async): """Test get inflation rate.""" resp = await test_http_client_async.get_inflation_rate() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_largest_accounts(test_http_client_async): """Test get largest accounts.""" resp = await test_http_client_async.get_largest_accounts() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_leader_schedule(test_http_client_async): """Test get leader schedule.""" resp = await test_http_client_async.get_leader_schedule() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_minimum_balance_for_rent_exemption(test_http_client_async): """Test get minimum balance for rent exemption.""" resp = await test_http_client_async.get_minimum_balance_for_rent_exemption(50) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_slot_leader(test_http_client_async): """Test get slot leader.""" resp = await test_http_client_async.get_slot_leader() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_supply(test_http_client_async): """Test get slot leader.""" resp = await test_http_client_async.get_supply() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_transaction_count(test_http_client_async): """Test get transactinon count.""" resp = await test_http_client_async.get_transaction_count() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_version(test_http_client_async): """Test get version.""" resp = await test_http_client_async.get_version() assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_account_info(alt_stubbed_sender, test_http_client_async): """Test get_account_info.""" resp = await test_http_client_async.get_account_info(alt_stubbed_sender.public_key()) assert_valid_response(resp) resp = await test_http_client_async.get_account_info(alt_stubbed_sender.public_key(), encoding="jsonParsed") assert_valid_response(resp) resp = await test_http_client_async.get_account_info(alt_stubbed_sender.public_key(), data_slice=DataSliceOpt(1, 1)) assert_valid_response(resp) @pytest.mark.integration @pytest.mark.asyncio async def test_get_vote_accounts(test_http_client_async): """Test get vote accounts.""" resp = await test_http_client_async.get_vote_accounts() assert_valid_response(resp)

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916cabad219d7b77533fff8043a3a972535766c3

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py

Python

pyretri/extract/splitter/__init__.py

dongan-beta/PyRetri

8756d5d5813a5211b58855373b6c6cd33d7a11f6

[ "Apache-2.0" ]

1,063

2020-04-21T12:42:05.000Z

2022-03-31T06:32:50.000Z

pyretri/extract/splitter/__init__.py

dongan-beta/PyRetri

8756d5d5813a5211b58855373b6c6cd33d7a11f6

[ "Apache-2.0" ]

39

2020-05-07T07:24:19.000Z

2022-02-02T23:49:23.000Z

pyretri/extract/splitter/__init__.py

dongan-beta/PyRetri

8756d5d5813a5211b58855373b6c6cd33d7a11f6

[ "Apache-2.0" ]

174

2020-04-26T04:33:11.000Z

2022-03-17T02:58:45.000Z

# -*- coding: utf-8 -*- from yacs.config import CfgNode from .splitter_impl.identity import Identity from .splitter_impl.pcb import PCB from .splitter_base import SplitterBase __all__ = [ 'SplitterBase', 'Identity', 'PCB', ]

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9183d01977f3b641e68197d0966e83729339627d

72

py

Python

makenew_serverless_python/__init__.py

makenew/serverless-python

cbfc860ab82de76c8a0a95167e5eea2fec665816

[ "MIT" ]

6

2019-04-30T16:44:20.000Z

2021-12-25T23:23:41.000Z

makenew_serverless_python/__init__.py

makenew/serverless-python

cbfc860ab82de76c8a0a95167e5eea2fec665816

[ "MIT" ]

15

2020-03-28T20:29:38.000Z

2021-09-11T07:19:46.000Z

makenew_serverless_python/__init__.py

makenew/serverless-python

cbfc860ab82de76c8a0a95167e5eea2fec665816

[ "MIT" ]

3

2019-11-21T08:29:14.000Z

2021-01-10T17:22:41.000Z

""" Package skeleton for a Python Serverless project on AWS Lambda. """

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9189832ab2f5c095f75e48d8971bf6b836144730

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py

Python

dns_messages/dns_objects/__init__.py

wahlflo/dns-messages

f42a1c1d0c933f44ca819b9a7f6e54daf48a7140

[ "MIT" ]

null

dns_messages/dns_objects/__init__.py

wahlflo/dns-messages

f42a1c1d0c933f44ca819b9a7f6e54daf48a7140

[ "MIT" ]

null

dns_messages/dns_objects/__init__.py

wahlflo/dns-messages

f42a1c1d0c933f44ca819b9a7f6e54daf48a7140

[ "MIT" ]

null

from .rr_data import * from .question import Question from .dns_message import DnsMessage from .opcode import OPCODE from .rocde import RCODE

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py

Python

overview_table/models.py

danmcelroy/VoSeq

e22bd5d971154170bf3f4f24b684b95a12418637

[ "BSD-3-Clause" ]

2

2019-08-20T04:16:12.000Z

2020-08-25T02:05:12.000Z

overview_table/models.py

danmcelroy/VoSeq

e22bd5d971154170bf3f4f24b684b95a12418637

[ "BSD-3-Clause" ]

65

2016-09-27T23:14:51.000Z

2022-03-19T14:17:58.000Z

overview_table/models.py

danmcelroy/VoSeq

e22bd5d971154170bf3f4f24b684b95a12418637

[ "BSD-3-Clause" ]

4

2018-07-02T16:57:44.000Z

2021-03-23T02:12:15.000Z

from django.db import models class OverviewTable(models.Model): """Bulk create does not work on inherited models so we create a new one.""" sequence_string = models.TextField(help_text="HTML string of cells with " "length of sequences for each " "gene.") o_code = models.CharField(max_length=300) orden = models.TextField(blank=True) superfamily = models.TextField(blank=True) family = models.TextField(blank=True) subfamily = models.TextField(blank=True) genus = models.TextField(blank=True) species = models.TextField(blank=True) def __str__(self): return "OverviewTable: {0}".format(self.o_code)

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null

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null

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918e75300fe04c031168c214fb97059362ff96a6

92

py

Python

2014/12/social-security-benefits-age/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

14

2015-05-08T13:41:51.000Z

2021-02-24T12:34:55.000Z

2014/12/social-security-benefits-age/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

null

2014/12/social-security-benefits-age/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

7

2015-04-04T04:45:54.000Z

2021-02-18T11:12:48.000Z

#!/usr/bin/env python COPY_GOOGLE_DOC_KEY = '1rn4wrIqnhLzikYukOvdxlbcPZL0h6GCLluwEZ57sKPM'

23

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1

null

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37cfe0f36dfa4aeba7a668c5f8cc7b32ad2c5a2c

23

py

Python

mathbox/calculus/__init__.py

freedeaths/mathbox-py

e294dc1b916bb634807378883b1ba941a924bec5

[ "MIT" ]

7

2021-12-23T07:03:12.000Z

2021-12-31T06:35:34.000Z

mathbox/calculus/__init__.py

freedeaths/mathbox-py

e294dc1b916bb634807378883b1ba941a924bec5

[ "MIT" ]

8

2021-12-23T06:12:19.000Z

2022-01-07T15:01:47.000Z

mathbox/calculus/__init__.py

freedeaths/mathbox-py

e294dc1b916bb634807378883b1ba941a924bec5

[ "MIT" ]

null

""" Calculus module """

7.666667

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23

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7.666667

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null

0

1

0

null

0

1

0

4

53093447ee14888e947d2e785d197e90e5ea4039

142

py

Python

dropcopy-run.py

juniorbl/dropcopy

5ea76a8e2322bd86d5abed0adb50ac43ced8520a

[ "MIT" ]

1

2015-10-25T19:27:26.000Z

dropcopy-run.py

juniorbl/dropcopy

5ea76a8e2322bd86d5abed0adb50ac43ced8520a

[ "MIT" ]

null

dropcopy-run.py

juniorbl/dropcopy

5ea76a8e2322bd86d5abed0adb50ac43ced8520a

[ "MIT" ]

null

#!/usr/bin/env python import gobject from dropcopy.dropcopy import Dropcopy if __name__ == "__main__": gobject.threads_init() Dropcopy()

14.2

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4

5316135edababd5a0e6953a5060fe8f54f7e214d

1,174

py

Python

check-engine-lib/checkengine/_constraints/_Constraint.py

mikulskibartosz/correct-horse

383cf4106605cc6f94e800bdc707789c0cedbe95

[ "MIT" ]

14

2020-07-06T05:37:02.000Z

2021-06-30T16:59:31.000Z

check-engine-lib/checkengine/_constraints/_Constraint.py

mikulskibartosz/correct-horse

383cf4106605cc6f94e800bdc707789c0cedbe95

[ "MIT" ]

1

2021-10-15T23:33:34.000Z

2021-10-16T10:15:06.000Z

check-engine-lib/checkengine/_constraints/_Constraint.py

mikulskibartosz/correct-horse

383cf4106605cc6f94e800bdc707789c0cedbe95

[ "MIT" ]

4

2020-10-08T05:14:32.000Z

2021-07-02T14:07:46.000Z

from typing import List, Tuple from abc import ABC, abstractmethod import random import string from pyspark.sql import DataFrame def _generate_constraint_column_name(constraint_type, column_name): random_suffix = ''.join(random.choice(string.ascii_lowercase) for i in range(12)) return f"__checkengine__{column_name}_{constraint_type}_{random_suffix}" class _Constraint(ABC): def __init__(self, column_name: str): self.column_name = column_name self.constraint_column_name = _generate_constraint_column_name(self.constraint_name(), column_name) @abstractmethod def constraint_name(self): pass @abstractmethod def prepare_df_for_check(self, data_frame: DataFrame) -> DataFrame: return data_frame @abstractmethod def filter_success(self, data_frame: DataFrame) -> DataFrame: return data_frame @abstractmethod def filter_failure(self, data_frame: DataFrame) -> DataFrame: return data_frame def validate_self(self, data_frame: DataFrame, df_columns: List[str]) -> Tuple[bool, str]: return self.column_name in df_columns, f"There is no '{self.column_name}' column"

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0

null

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1

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null

0

1

0

1

0

4

532600249eae56c852a821cfdfb9697d65d358b1

157

py

Python

src/try_django/bin/django-admin.py

aiegoo/django2-udemy

7bf383dee57b15859193c3d0e1d9e7987eef58b6

[ "MIT" ]

null

src/try_django/bin/django-admin.py

aiegoo/django2-udemy

7bf383dee57b15859193c3d0e1d9e7987eef58b6

[ "MIT" ]

8

2020-02-12T03:26:47.000Z

2021-09-08T01:40:27.000Z

src/try_django/bin/django-admin.py

aiegoo/django2-udemy

7bf383dee57b15859193c3d0e1d9e7987eef58b6

[ "MIT" ]

null

#!/root/repos/udemy/django2/try_django/bin/python3 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

26.166667

50

0.789809

21

157

5.333333

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0

0.014085

0.095541

157

5

51

31.4

0.774648

0.312102

0

0.074766

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true

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null

0

1

0

null

0

1

0

1

0

4

5333748b2cd7514623c36006a3669b18f1d773f5

154

py

Python

progressivis/core/column_update.py

jdfekete/progressivis

3bc79ce229cd628ef0aa4663136a674743697b47

[ "BSD-2-Clause" ]

51

2015-09-14T16:31:02.000Z

2022-01-12T17:56:53.000Z

progressivis/core/column_update.py

jdfekete/progressivis

3bc79ce229cd628ef0aa4663136a674743697b47

[ "BSD-2-Clause" ]

10

2017-11-15T15:10:05.000Z

2022-01-19T07:36:43.000Z

progressivis/core/column_update.py

jdfekete/progressivis

3bc79ce229cd628ef0aa4663136a674743697b47

[ "BSD-2-Clause" ]

5

2017-11-14T20:20:56.000Z

2020-01-22T06:26:51.000Z

""" Manage changes in table columns """ from collections import namedtuple ColumnUpdate = namedtuple('ColumnUpdate', ['created', 'updated', 'deleted'])

19.25

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154

7

77

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1

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null

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1

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4

534e972c0d201ac2543e11e122e220987d2b9b24

221

py

Python

muteria/drivers/criteria/tools_by_languages/c/gcov/__init__.py

muteria/muteria

2cb72ff04548b011bce9296833bceb295199ae8e

[ "MIT" ]

5

2020-05-06T03:13:01.000Z

2021-12-09T22:39:26.000Z

muteria/drivers/criteria/tools_by_languages/c/gcov/__init__.py

muteria/muteria

2cb72ff04548b011bce9296833bceb295199ae8e

[ "MIT" ]

6

2019-11-27T18:38:09.000Z

2021-12-16T20:40:50.000Z

muteria/drivers/criteria/tools_by_languages/c/gcov/__init__.py

muteria/muteria

2cb72ff04548b011bce9296833bceb295199ae8e

[ "MIT" ]

4

2019-06-24T08:54:36.000Z

2022-03-31T15:38:35.000Z

from muteria.drivers.criteria.tools_by_languages.c.gcov.gcov \ import CriteriaToolGCov #from .gcov import CriteriaToolGCov StaticCriteriaTool = CriteriaToolGCov

27.625

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4

53537535ec28ff1ec23375c42a5518508554689f

151

py

Python

picturic/schemas.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

4

2021-11-24T21:48:29.000Z

2021-12-07T00:44:44.000Z

picturic/schemas.py

ThePokerFaCcCe/myblog

9b24f381148b7f3262dd59e320f5e1600d1af68f

[ "MIT" ]

null

picturic/schemas.py

ThePokerFaCcCe/myblog

9b24f381148b7f3262dd59e320f5e1600d1af68f

[ "MIT" ]

null

PICTURE_DEFAULT = { "image": { "url": str, "name": str }, "thumbnail": { "url": str, "name": str } }

11.615385

19

0.364238

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4.5

0.583333

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151

12

20

12.583333

0.650602

0

0.4

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false

0

1

0

null

1

0

null

0

4

5357be7e4858fa080a891cb56fb6d833d39d5199

222

py

Python

about/views.py

Gao-Chuan/blog

4dcbf0720e50be7f11841778889301b7dcb14e2d

[ "Apache-2.0" ]

null

about/views.py

Gao-Chuan/blog

4dcbf0720e50be7f11841778889301b7dcb14e2d

[ "Apache-2.0" ]

null

about/views.py

Gao-Chuan/blog

4dcbf0720e50be7f11841778889301b7dcb14e2d

[ "Apache-2.0" ]

null

import datetime from django.shortcuts import render # Create your views here. def about(request): data = {} data['date'] = str(datetime.datetime.now()).split('.')[0] return render(request, 'about.html', data)

24.666667

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9

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null

0

1

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null

0

1

0

1

0

4

5367e683e65a9d614614547f9df3d58193bf63db

323

py

Python

src/model/mutual_net.py

TencentYoutuResearch/SelfSupervisedLearning-DSM

655a0a23a47bf2559f3d435384ae59a8871a5ff5

[ "Apache-2.0" ]

27

2021-01-07T11:09:33.000Z

2021-08-31T02:46:23.000Z

src/model/mutual_net.py

TencentYoutuResearch/SelfSupervisedLearning-DSM

655a0a23a47bf2559f3d435384ae59a8871a5ff5

[ "Apache-2.0" ]

null

src/model/mutual_net.py

TencentYoutuResearch/SelfSupervisedLearning-DSM

655a0a23a47bf2559f3d435384ae59a8871a5ff5

[ "Apache-2.0" ]

3

2021-01-08T08:31:06.000Z

2021-11-26T04:10:23.000Z

import torch.nn as nn class MutualNet(nn.Module): def __init__(self, embeddingnet): super(MutualNet, self).__init__() self.embeddingnet = embeddingnet def forward(self, x, y, z): feature_x = self.embeddingnet(x) feature_y = self.embeddingnet(y) return feature_x, feature_y

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0.820408

0

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0

null

1

0

null

0

1

0

1

0

4

536cb6f7a3412ce3428828ed95647de166c57fbb

1,176

py

Python

grr/core/grr_response_core/config/__init__.py

tsehori/grr

048506f22f74642bfe61749069a45ddf496fdab3

[ "Apache-2.0" ]

1

2021-07-01T01:43:06.000Z

grr/core/grr_response_core/config/__init__.py

tsehori/grr

048506f22f74642bfe61749069a45ddf496fdab3

[ "Apache-2.0" ]

44

2021-05-14T22:49:24.000Z

2022-03-13T21:54:02.000Z

grr/core/grr_response_core/config/__init__.py

tsehori/grr

048506f22f74642bfe61749069a45ddf496fdab3

[ "Apache-2.0" ]

1

2020-06-25T14:25:54.000Z

#!/usr/bin/env python # Lint as: python3 """This module will load all the configuration parameters.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals # pylint: disable=unused-import from grr_response_core.config import acls from grr_response_core.config import api from grr_response_core.config import artifacts from grr_response_core.config import build from grr_response_core.config import checks from grr_response_core.config import client from grr_response_core.config import config from grr_response_core.config import contexts from grr_response_core.config import data_store from grr_response_core.config import gui from grr_response_core.config import local from grr_response_core.config import logging from grr_response_core.config import output_plugins from grr_response_core.config import server from grr_response_core.config import test # pylint: enable=unused-import from grr_response_core.lib import config_lib # By this time it's guaranteed that all configuration options # and filters are imported and known to the config system. CONFIG = config_lib._CONFIG # pylint: disable=protected-access

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null

0

1

0

1

0

4

727dd4d203f65f5147f46d42f8735192c28f5ebd

16,149

py

Python

prepare_data/gen_hard_bbox_rnet_onet.py

thaiph99/MTCNN

d6acfcdba972beb47751d63a34f3cf168d0488d2

[ "MIT" ]

null

prepare_data/gen_hard_bbox_rnet_onet.py

thaiph99/MTCNN

d6acfcdba972beb47751d63a34f3cf168d0488d2

[ "MIT" ]

null

prepare_data/gen_hard_bbox_rnet_onet.py

thaiph99/MTCNN

d6acfcdba972beb47751d63a34f3cf168d0488d2

[ "MIT" ]

null

# coding:utf-8 import sys import numpy as np import cv2 import os import argparse import pickle rootPath = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../")) sys.path.insert(0, rootPath) from detection.MtcnnDetector_plate import MtcnnDetector_plate from detection.MtcnnDetector import MtcnnDetector from detection.fcn_detector import FcnDetector from detection.detector_plate import Detector from tools.loader import TestLoader from training.mtcnn_config import config from training.mtcnn_plate_model import P_Net, R_Net from tools.common_utils import IoU, convert_to_square, enlarge_det def read_wider_annotation(widerImagesPath, annoTxtPath): data = dict() images = [] bboxes = [] labelfile = open(annoTxtPath, 'r') while True: # image path imagepath = labelfile.readline().strip('\n') if not imagepath: break imagepath = os.path.join(widerImagesPath, imagepath) images.append(imagepath) # face numbers nums = labelfile.readline().strip('\n') one_image_bboxes = [] for i in range(int(nums)): bb_info = labelfile.readline().strip('\n').split(' ') # only need x, y, w, h face_box = [float(bb_info[i]) for i in range(4)] xmin = face_box[0] ymin = face_box[1] xmax = xmin + face_box[2] ymax = ymin + face_box[3] one_image_bboxes.append([xmin, ymin, xmax, ymax]) bboxes.append(one_image_bboxes) data['images'] = images # all image pathes data['bboxes'] = bboxes # all image bboxes return data def read_plate_annotation(plateImagesPath, annoTxtPath): data = dict() images = [] bboxes = [] labelfile = open(annoTxtPath, 'r') lines = labelfile.readlines() for line in lines: line = line.strip('\n') components = line.split(' ') # image path imagepath = os.path.join(plateImagesPath, components[0]) images.append(imagepath) # plate numbers bboxes.append([components[1], components[2], components[3], components[4]]) data['images'] = images data['bboxes'] = bboxes return data def __save_data(stage, data, save_path): im_idx_list = data['images'] gt_boxes_list = data['bboxes'] num_of_images = len(im_idx_list) # save files saveFolder = os.path.join(rootPath, "tmp/data/%s/" % (stage)) print(">>>>>> Gen hard samples for %s..." % (stage)) typeName = ["pos", "neg", "part"] saveFiles = {} for tp in typeName: _saveFolder = os.path.join(saveFolder, tp) if not os.path.isdir(_saveFolder): os.makedirs(_saveFolder) saveFiles[tp] = open(os.path.join(saveFolder, "{}.txt".format(tp)), 'w') # read detect result det_boxes = pickle.load(open(os.path.join(save_path, 'detections.pkl'), 'rb')) assert len(det_boxes) == num_of_images, "incorrect detections or ground truths" # index of neg, pos and part face, used as their image names n_idx, p_idx, d_idx = 0, 0, 0 total_idx = 0 for im_idx, dets, gts in zip(im_idx_list, det_boxes, gt_boxes_list): gts = np.array(gts, dtype=np.float32).reshape(-1, 4) if dets.shape[0] == 0: continue img = cv2.imread(im_idx) total_idx += 1 # change to square dets = convert_to_square(dets) dets[:, 0:4] = np.round(dets[:, 0:4]) neg_num = 0 for box in dets: x_left, y_top, x_right, y_bottom, _ = box.astype(int) width = x_right - x_left + 1 height = y_bottom - y_top + 1 # ignore box that is too small or beyond image border if width < 20 or x_left < 0 or y_top < 0 or x_right > img.shape[1] - 1 or y_bottom > img.shape[0] - 1: continue # compute intersection over union(IoU) between current box and all gt boxes Iou = IoU(box, gts) cropped_im = img[y_top:y_bottom + 1, x_left:x_right + 1, :] image_size = 24 if stage == "rnet" else 48 resized_im = cv2.resize(cropped_im, (image_size, image_size), interpolation=cv2.INTER_LINEAR) # save negative images and write label # Iou with all gts must below 0.3 if np.max(Iou) < 0.3 and neg_num < 60: # now to save it save_file = os.path.join(saveFolder, "neg", "%s.jpg" % n_idx) saveFiles['neg'].write(save_file + ' 0\n') cv2.imwrite(save_file, resized_im) n_idx += 1 neg_num += 1 else: # find gt_box with the highest iou idx = np.argmax(Iou) assigned_gt = gts[idx] x1, y1, x2, y2 = assigned_gt # compute bbox reg label offset_x1 = (x1 - x_left) / float(width) offset_y1 = (y1 - y_top) / float(height) offset_x2 = (x2 - x_right) / float(width) offset_y2 = (y2 - y_bottom) / float(height) # save positive and part-face images and write labels if np.max(Iou) >= 0.65: save_file = os.path.join(saveFolder, "pos", "%s.jpg" % p_idx) saveFiles['pos'].write(save_file + ' 1 %.2f %.2f %.2f %.2f\n' % (offset_x1, offset_y1, offset_x2, offset_y2)) cv2.imwrite(save_file, resized_im) p_idx += 1 elif np.max(Iou) >= 0.4: save_file = os.path.join(saveFolder, "part", "%s.jpg" % d_idx) saveFiles['part'].write(save_file + ' -1 %.2f %.2f %.2f %.2f\n' % (offset_x1, offset_y1, offset_x2, offset_y2)) cv2.imwrite(save_file, resized_im) d_idx += 1 printStr = "\r[{}] pos: {} neg: {} part:{}".format(total_idx, p_idx, n_idx, d_idx) sys.stdout.write(printStr) sys.stdout.flush() for f in saveFiles.values(): f.close() print('\n') def __save_plate_data(stage, data, save_path): im_idx_list = data['images'] gt_boxes_list = data['bboxes'] num_of_images = len(im_idx_list) # save files saveFolder = os.path.join(rootPath, "tmp/data/%s/" % (stage)) print(">>>>>> Gen hard samples for %s..." % (stage)) typeName = ["pos", "neg", "part"] saveFiles = {} for tp in typeName: _saveFolder = os.path.join(saveFolder, tp) if not os.path.isdir(_saveFolder): os.makedirs(_saveFolder) saveFiles[tp] = open(os.path.join(saveFolder, "{}.txt".format(tp)), 'w') # read detect result det_boxes = pickle.load(open(os.path.join(save_path, 'detections.pkl'), 'rb')) assert len(det_boxes) == num_of_images, "incorrect detections or ground truths" # index of neg, pos and part face, used as their image names n_idx, p_idx, d_idx = 0, 0, 0 total_idx = 0 for im_idx, dets, gts in zip(im_idx_list, det_boxes, gt_boxes_list): gts = np.array(gts, dtype=np.float32).reshape(-1, 4) if dets.shape[0] == 0: continue img = cv2.imread(im_idx) total_idx += 1 # enlarge the det box to w:h==3:1 size dets = enlarge_det(dets) dets[:, 0:4] = np.round(dets[:, 0:4]) neg_num = 0 for box in dets: x_left, y_top, x_right, y_bottom, _ = box.astype(int) width = x_right - x_left + 1 height = y_bottom - y_top + 1 # ignore box that is too small or beyond image border if width < 20 or x_left < 0 or y_top < 0 or x_right > img.shape[1] - 1 or y_bottom > img.shape[0] - 1: continue # compute intersection over union(IoU) between current box and all gt boxes Iou = IoU(box, gts) cropped_im = img[y_top:y_bottom + 1, x_left:x_right + 1, :] image_size = 24 if stage == "rnet" else 48 resized_im = cv2.resize(cropped_im, (image_size * 3, image_size), interpolation=cv2.INTER_LINEAR) # save negative images and write label # Iou with all gts must below 0.3 if np.max(Iou) < 0.3 and neg_num < 60: # now to save it save_file = os.path.join(saveFolder, "neg", "%s.jpg" % n_idx) saveFiles['neg'].write(save_file + ' 0\n') cv2.imwrite(save_file, resized_im) n_idx += 1 neg_num += 1 else: # find gt_box with the highest iou idx = np.argmax(Iou) assigned_gt = gts[idx] x1, y1, x2, y2 = assigned_gt # compute bbox reg label offset_x1 = (x1 - x_left) / float(width) offset_y1 = (y1 - y_top) / float(height) offset_x2 = (x2 - x_right) / float(width) offset_y2 = (y2 - y_bottom) / float(height) # save positive and part-face images and write labels if np.max(Iou) >= 0.65: save_file = os.path.join(saveFolder, "pos", "%s.jpg" % p_idx) saveFiles['pos'].write(save_file + ' 1 %.2f %.2f %.2f %.2f\n' % (offset_x1, offset_y1, offset_x2, offset_y2)) cv2.imwrite(save_file, resized_im) p_idx += 1 elif np.max(Iou) >= 0.4: save_file = os.path.join(saveFolder, "part", "%s.jpg" % d_idx) saveFiles['part'].write(save_file + ' -1 %.2f %.2f %.2f %.2f\n' % (offset_x1, offset_y1, offset_x2, offset_y2)) cv2.imwrite(save_file, resized_im) d_idx += 1 printStr = "\r[{}] pos: {} neg: {} part:{}".format(total_idx, p_idx, n_idx, d_idx) sys.stdout.write(printStr) sys.stdout.flush() for f in saveFiles.values(): f.close() print('\n') def test_net(batch_size, stage, thresh, min_size, stride): print(">>>>>> Detect bbox for %s..." % (stage)) detectors = [None, None, None] if stage in ["rnet", "onet"]: modelPath = os.path.join(rootPath, 'tmp/model/pnet/') a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('pnet-') and b.endswith('.index')] maxEpoch = max(map(int, a)) modelPath = os.path.join(modelPath, "pnet-%d" % (maxEpoch)) print("Use PNet model: %s" % (modelPath)) PNet = FcnDetector(P_Net, modelPath) detectors[0] = PNet if stage in ["onet"]: modelPath = os.path.join(rootPath, 'tmp/model/rnet/') a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('rnet-') and b.endswith('.index')] maxEpoch = max(map(int, a)) modelPath = os.path.join(modelPath, "rnet-%d" % (maxEpoch)) print("Use RNet model: %s" % (modelPath)) RNet = Detector(R_Net, 24, batch_size, modelPath) detectors[1] = RNet # read annatation(type:dict) widerImagesPath = os.path.join(rootPath, "dataset", "WIDER_train", "images") annoTxtPath = os.path.join(rootPath, "dataset", "wider_face_train_bbx_gt.txt") data = read_wider_annotation(widerImagesPath, annoTxtPath) mtcnn_detector = MtcnnDetector(detectors=detectors, min_size=min_size, stride=stride, threshold=thresh) test_data = TestLoader(data['images']) # do detect detections, _ = mtcnn_detector.detect_plate(test_data) # save detect result save_path = os.path.join(rootPath, "tmp/data", stage) if not os.path.exists(save_path): os.makedirs(save_path) save_file = os.path.join(save_path, "detections.pkl") with open(save_file, 'wb') as f: pickle.dump(detections, f, 1) print("\nDone! Start to do OHEM...") __save_data(stage, data, save_path) def test_net_plate(batch_size, stage, thresh, min_size, stride): print(">>>>>> Detect bbox for %s..." % (stage)) detectors = [None, None, None] if stage in ["rnet", "onet"]: modelPath = os.path.join(rootPath, 'tmp/model/pnet/') a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('pnet-') and b.endswith('.index')] maxEpoch = max(map(int, a)) modelPath = os.path.join(modelPath, "pnet-%d" % (maxEpoch)) print("Use PNet model: %s" % (modelPath)) PNet = FcnDetector(P_Net, modelPath) detectors[0] = PNet if stage in ["onet"]: modelPath = os.path.join(rootPath, 'tmp/model/rnet/') a = [b[5:-6] for b in os.listdir(modelPath) if b.startswith('rnet-') and b.endswith('.index')] maxEpoch = max(map(int, a)) modelPath = os.path.join(modelPath, "rnet-%d" % (maxEpoch)) print("Use RNet model: %s" % (modelPath)) RNet = Detector(R_Net, 24, batch_size, modelPath) detectors[1] = RNet # read annotation(type:dict) plateImagesPath = os.path.join(rootPath, "dataset") annoTxtPath = os.path.join(rootPath, "dataset", "anno_file.txt") data = read_plate_annotation(plateImagesPath, annoTxtPath) mtcnn_detector = MtcnnDetector_plate(detectors=detectors, min_size=min_size, stride=stride, threshold=thresh) test_data = TestLoader(data['images']) print("shape of test data:", np.shape(test_data)) # do detect detections, _ = mtcnn_detector.detect_plate(test_data) # save detect result save_path = os.path.join(rootPath, "tmp/data", stage) if not os.path.exists(save_path): os.makedirs(save_path) save_file = os.path.join(save_path, "detections.pkl") with open(save_file, 'wb') as f: pickle.dump(detections, f, 1) print("\nDone! Start to do OHEM...") __save_plate_data(stage, data, save_path) def parse_args(): parser = argparse.ArgumentParser(description='Create hard bbox sample...', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--stage', dest='stage', help='working stage, can be rnet, onet', default='unknow', type=str) parser.add_argument('--gpus', dest='gpus', help='specify gpu to run. eg: --gpus=0,1', default='0 ', type=str) parser.add_argument('--mydata', dest='mydata', help='data type(default training data or my data)', default=False, type=bool) parser.add_argument('--lmnum', dest='lmnum', help='number of landmarks in one bounding box', default=5, type=int) args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() stage = args.stage gpus = args.gpus # set GPU if gpus: os.environ["CUDA_VISIBLE_DEVICES"] = gpus if stage == "rnet": batchSize = 1 threshold = [0.4, 0.05] minSize = 24 stride = 2 elif stage == "onet": batchSize = 1 threshold = [0.4, 0.05] minSize = 48 stride = 2 else: raise Exception("Invaild stage...Please use --stage") if args.mydata == False: test_net( batchSize, # test batch_size stage, # can be 'rnet' or 'onet' threshold, # cls threshold minSize, # min_face stride) elif args.lmnum == 4: test_net_plate( batchSize, # test batch_size stage, # can be 'rnet' or 'onet' threshold, # cls threshold minSize, # min_plate stride)

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Python

phasing/io/VariantPhaser.py

Zuhayr-PacBio/cDNA_Cupcake

2cbbcf91f363e8a385a485b2c832b466cce2a323

[ "BSD-3-Clause-Clear" ]

null

phasing/io/VariantPhaser.py

Zuhayr-PacBio/cDNA_Cupcake

2cbbcf91f363e8a385a485b2c832b466cce2a323

[ "BSD-3-Clause-Clear" ]

null

phasing/io/VariantPhaser.py

Zuhayr-PacBio/cDNA_Cupcake

2cbbcf91f363e8a385a485b2c832b466cce2a323

[ "BSD-3-Clause-Clear" ]

null

__author__ = 'etseng@pacb.com' import pdb from collections import defaultdict, namedtuple, Counter from csv import DictReader import vcf import pysam from Bio.Seq import Seq from Bio import SeqIO from cupcake.io.BioReaders import GMAPSAMReader from .coordinate_mapper import get_base_to_base_mapping_from_sam __VCF_EXAMPLE__ = \ """ ##fileformat=VCFv4.2 ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 20 1 . G A,T . PASS AF=0.5;DB GT """ def type_fa_or_fq(file): file = file.upper() if file.endswith('.FA') or file.endswith('.FASTA'): return 'fasta' else: return 'fastq' class VariantPhaser(object): def __init__(self, vc): """ :param vc: MPileUPVariant instance. """ self.vc = vc self.min_var_pos = min(vc.variant) # mininum 0-based position of a called variant self.max_var_pos = max(vc.variant) # maximum 0-based position of a called variant self.accepted_vars_by_pos = {} # 0-based pos --> list of accepted, (NOT strand sense) base self.count_of_vars_by_pos = {} # 0-based pos --> (NOT strand sense, but ref-based) base --> count self.accepted_pos = [] # sorted list of variant positions (0-based, ref) # process vc.variant which is # dict of 0-based pos --> desc list of (base, count) # ex: {1565: [('a', 49), ('g', 36)]} # lower case means at pos 1565, we expect - strand mapping and # seq base is 'T' on the sense strand # this converts to self.accepted_vars_by_pos[1565] = ['A', 'G'] # later, when we are matchin back to transcript seq, need to watch for strand! for pos, vars in vc.variant.items(): self.accepted_vars_by_pos[pos] = [_base.upper() for _base,_count in vars] self.count_of_vars_by_pos[pos] = dict((_base.upper(), _count) for _base,_count in vars) self.accepted_pos = list(self.accepted_vars_by_pos.keys()) self.accepted_pos.sort() self.haplotypes = Haplotypes(self.accepted_pos, self.vc.ref_base, self.count_of_vars_by_pos) self.seq_hap_info = {} # haplotype assignment, key: (CCS) seqid, value: haplotype index def phase_variant(self, sam_filename, input_fa_or_fq, output_prefix, partial_ok=False): """ :param sam_filename: CCS SAM filename. Can be unsorted. :param input_fa_or_fq: Input CCS fasta/fastq filename. :param output_prefix: Output prefix. Writes to xxx.log. :param partial_ok: default False. if True, (CCS) reads don't need to cover all SNP positions. For each alignment: 1. discard if did not map to the strand expected 2. discard if did not map to the full range of variants (unless <partial_ok> is True) 3. discard if at var positions have non-called bases (outliers) """ f_log = open(output_prefix+'.log', 'w') seq_dict = SeqIO.to_dict(SeqIO.parse(open(input_fa_or_fq), type_fa_or_fq(input_fa_or_fq))) for r in GMAPSAMReader(sam_filename, True, query_len_dict=dict((k, len(seq_dict[k].seq)) for k in seq_dict)): if r.sID == '*': f_log.write("Ignore {0} because: unmapped.\n".format(r.qID)) continue if r.flag.strand != self.vc.expected_strand: f_log.write("Ignore {0} because: strand is {1}.\n".format(r.qID, r.flag.strand)) continue # ignore if not partial_ok and (r.sStart > self.min_var_pos or r.sEnd < self.max_var_pos): f_log.write("Ignore {0} because: aln too short, from {1}-{2}.\n".format(r.qID, r.sStart+1, r.sEnd)) continue i, msg = self.match_haplotype(r, str(seq_dict[r.qID].seq).upper(), partial_ok) if i is None: # read is rejected for reason listed in <msg> f_log.write("Ignore {0} because: {1}.\n".format(r.qID, msg)) continue else: f_log.write("{0} phased: haplotype {1}={2}\n".format(r.qID, i, self.haplotypes[i])) print("{0} has haplotype {1}:{2}".format(r.qID, i, self.haplotypes[i])) self.seq_hap_info[r.qID] = i def match_haplotype(self, r, s, partial_ok=False): """ Match an alignment record to existing haplotypes or create a new one. Helper function for self.phase_variant() :param r: CCS alignment (SAM record) :param s: CCS sequence (in strand), must be plain str and every base is upper case :param partial_ok: default False. if True, (CCS) reads don't need to cover all SNP positions. :return: (haplotype_index, msg) or (None, msg) if variants don't match w/ called SNPs """ assert type(s) is str and str.isupper(s) assert r.flag.strand == self.vc.expected_strand # m: mapping of 0-based seq --> 0-based ref position # rev_map: mapping of 0-based ref position --> 0-based seq m = get_base_to_base_mapping_from_sam(r.segments, r.cigar, r.qStart, r.qEnd, r.flag.strand) ref_m = dict((v,k) for k,v in m.items()) # go through each variant # <hap> to represent the concatenated string of all variant positions for this seq # ex: if there are three var positions, a hap would be "ATG" or "A?G" (if partial_ok is True), etc. hap = '' impute_later = False for ref_pos in self.accepted_pos: if ref_pos not in ref_m: if partial_ok: # read does not cover one of the SNP positions, so use "?" hap += "?" else: return None, "Does not have base at ref_pos {0}.\n".format(ref_pos) else: base = s[ref_m[ref_pos]] if self.vc.expected_strand == '-': # must convert the base to the rev comp base = str(Seq(base).reverse_complement()).upper() if base in self.accepted_vars_by_pos[ref_pos]: hap += base else: # contains a base at a variant position that is not called. Try to impute. hap += base impute_later = True if all(b=='?' for b in hap): return None, "Does not cover any variant base." if impute_later: impute_i = self.haplotypes.impute_haplotype(hap, min_score=3) if impute_i is None: return None, "Seq {0} contained non-called variant. Impute failed.\n".format(hap) else: return impute_i, "IMPUTED" return self.haplotypes.match_or_add_haplotype(hap_string=hap) def phase_isoforms(read_stat_filename, seqids, phaser): """ :param read_stat_filename: the .read_stat file that has columns <id> and <pbid>, where <id> is CCS id and <pbid> is PB.X.Y :param seqids: CCS IDs that were used to create the haplotypes. :param phaser: VariantPhaser object that contains the haplotype and seqid->haplotype information. :return: list of (isoform, dict of haplotype count), ex: {'PB.45.1': {0:10, 1:20}} which means PB.45.1 has haplotype 0 supported by 10 CCS reads and hap 1 supported by 20 CCS reads. *NOTE* currently uses FL CCS reads only (even if the SNPs may have been called by FL+nFL CCS SAM) """ result = {} # dict of (isoform, dict of haplotype_index --> CCS count supporting it # from read stat, gather which isoforms have which (CCS) seq members. isoforms = defaultdict(lambda: []) # key: PB.X.Y, value: list of seqid members for r in DictReader(open(read_stat_filename), delimiter='\t'): if r['id'] in seqids and r['is_fl']=='Y': isoforms[r['pbid']].append(r['id']) # for each isoform, look at the CCS membership to know which haplotypes are expressed for _iso, _seqids in isoforms.items(): tally = defaultdict(lambda: 0) # haplotype index --> count (of CCS) for seqid in _seqids: if seqid in phaser.seq_hap_info: # some CCS (seqids) may not have been used by the phaser, so account for that tally[phaser.seq_hap_info[seqid]] += 1 if len(tally) > 0: result[_iso] = dict(tally) return result class Haplotypes(object): """ Storing haplotypes for a loci. self.haplotype[i] is the i-th haplotype. if N = len(self.haplotype[i]), then there are N variants along the loci. self.hap_var_positions[j] means that the j-th variant corressponds to (0-based) position on the ref genome. """ def __init__(self, var_positions, ref_at_pos, count_of_vars_by_pos): """ :param var_positions: sorted list of (0-based) variant positions :param ref_at_pos: dict of (0-based) variant position --> ref base at this position :param count_of_vars_by_pos: 0-based pos --> (NOT strand sense, but ref-based) base --> count """ self.haplotypes = [] # haplotypes, where haplotypes[i] is the i-th distinct haplotype of all var concat self.hap_var_positions = var_positions self.ref_at_pos = ref_at_pos # dict of (0-based) pos --> ref base self.alt_at_pos = None # init: None, later: dict of (0-based) pos --> unique list of alt bases self.count_of_vars_by_pos = count_of_vars_by_pos self.haplotype_vcf_index = None # init: None, later: dict of (hap index) --> (0-based) var pos --> phase (0 for ref, 1+ for alt) # sanity check: all variant positions must be present self.sanity_check() def __getitem__(self, ith): """ Returns the <i>-th haplotype """ return self.haplotypes[ith] def __str__(self): return """ var positions: {pp} haplotypes: \n{h} """.format(pp=",".join(map(str,self.hap_var_positions)), h="\n".join(self.haplotypes)) def sanity_check(self): """ Sanity check the following: -- variant positions are properly recorded and concordant -- alt bases are truly alt and unique -- all haplotypes are the same length """ for pos in self.hap_var_positions: assert pos in self.ref_at_pos if self.alt_at_pos is not None: for pos in self.alt_at_pos: # ref base must not be in alt assert self.ref_at_pos[pos] not in self.alt_at_pos[pos] # alt bases must be unique assert len(self.alt_at_pos[pos]) == len(set(self.alt_at_pos[pos])) if len(self.haplotypes) >= 1: n = len(self.haplotypes[0]) assert n == len(self.hap_var_positions) for hap_str in self.haplotypes[1:]: assert len(hap_str) == n def match_or_add_haplotype(self, hap_string): """ If <hap_string> is an existing haplotype, return the index. Otherwise, add to known haplotypes and return the new index. :return: <index>, "FOUND" or "NEW" """ if hap_string in self.haplotypes: i = self.haplotypes.index(hap_string) return i, "FOUND" else: i = len(self.haplotypes) self.haplotypes.append(hap_string) return i, "NEW" def impute_haplotype(self, hap_string, min_score): """ :param hap_string: a hap string with '?'s. :param min_sim: minimum similarity with existing haplotype to accept assignment :return: <index> of an existing haplotype, or None if not sufficiently matched Impute haplotype and only return a match if: (a) score (similarity) is >= min_score (b) the matching score for the best one is higher than the second best match """ sim_tuple = namedtuple('sim_tuple', 'index score') sims = [] # list of sim_tuple hap_str_len = len(hap_string) for i in range(len(self.haplotypes)): # Liz note: currently NOT checking whether existing haplotypes have '?'. I'm assuming no '?'. score = sum((hap_string[k]==self.haplotypes[i][k]) for k in range(hap_str_len)) if score > 0: sims.append(sim_tuple(index=i, score=score)) if len(sims) == 0: return None sims.sort(key=lambda x: x.score, reverse=True) if sims[0].score >= min_score and (len(sims)==1 or sims[0].score > sims[1].score): return sims[0].index else: return None def get_haplotype_vcf_assignment(self): """ Must be called before self.write_haplotype_to_vcf() This is preparing for writing out VCF. We need to know, for each variant position, the ref base (already filled in self.ref_at_pos) and the alt bases (self.alt_at_pos). For each haplotype in (self.haplotype), we need to know the whether the i-th variant is the ref (index 0), or some alt base (index 1 and onwards). Propagates two variables: self.haplotype_vcf_index: hap index --> pos --> phase index (0 for ref, 1+ for alt) self.alt_at_pos: dict of <0-based pos> --> alt bases (not is not ref) at this position """ self.haplotype_vcf_index = [{} for i in range(len(self.haplotypes))] self.alt_at_pos = {} # what happens in the case of partial phasing # ex: self.haplotypes[0] = "A?G", this means when it comes to the second pos, pos2, # in the VCF we would want to write out .|. for diploid, . for haploid, etc # so let's set self.haplotype_vcf_index[0][pos2] = '.' to indicate that for i,pos in enumerate(self.hap_var_positions): ref = self.ref_at_pos[pos] # need to go through the haplotype bases, if ref is already represented, then don't put it in alt self.alt_at_pos[pos] = [] for hap_i, hap_str in enumerate(self.haplotypes): base = hap_str[i] if base=='?': # means this haplotype does not cover this position! self.haplotype_vcf_index[hap_i][pos] = '.' elif base==ref: # is the ref base self.haplotype_vcf_index[hap_i][pos] = 0 else: # is an alt base, see if it's already there if base in self.alt_at_pos[pos]: j = self.alt_at_pos[pos].index(base) self.haplotype_vcf_index[hap_i][pos] = j + 1 # always +1, buz alt starts at 1 (0 is ref) else: j = len(self.alt_at_pos[pos]) self.alt_at_pos[pos].append(base) self.haplotype_vcf_index[hap_i][pos] = j + 1 # always +1, buz alt starts at 1 (0 is ref) # in the case where partial_ok=False, it's possible some alt are never presented by a haplotype # we must check that all variants are presented here for _base in self.count_of_vars_by_pos[pos]: if (_base not in self.ref_at_pos[pos]) and (_base not in self.alt_at_pos[pos]): self.alt_at_pos[pos].append(_base) def write_haplotype_to_vcf(self, fake_genome_mapping_filename, isoform_tally, output_prefix): """ The following functions must first be called first: -- self.get_haplotype_vcf_assignment """ if self.haplotype_vcf_index is None or self.alt_at_pos is None: raise Exception("Must call self.get_haplotype_vcf_assignment() first!") self.sanity_check() name_isoforms = list(isoform_tally.keys()) name_isoforms.sort() # write a fake VCF example so we can read the headers in with open('template.vcf', 'w') as f: f.write(__VCF_EXAMPLE__) reader = vcf.VCFReader(open('template.vcf')) reader.samples = name_isoforms f_vcf = vcf.Writer(open(output_prefix+'.vcf', 'w'), reader) # human readable text: # first line: assoc VCF filename # second line: haplotype, list of sorted isoforms # third line onwards: haplotype and assoc count f_human = open(output_prefix+'.human_readable.txt', 'w') f_human.write("Associated VCF file: {0}.vcf\n".format(output_prefix)) f_human.write("haplotype\t{samples}\n".format(samples="\t".join(name_isoforms))) for hap_index,hap_str in enumerate(self.haplotypes): f_human.write(hap_str) for _iso in name_isoforms: if hap_index in isoform_tally[_iso]: f_human.write("\t{0}".format(isoform_tally[_iso][hap_index])) else: f_human.write("\t0") f_human.write('\n') f_human.close() # read fake genome mapping file fake_map = {} # 0-based position on fake --> (chr, 0-based ref position) with open(fake_genome_mapping_filename) as f: for line in f: fake_pos, ref_chr, ref_pos = line.strip().split(',') fake_map[int(fake_pos)] = (ref_chr, int(ref_pos)) # for each position, write out the ref and alt bases # then fill in for each isoform (aka "sample"): # if this isoform only shows one allele, then it's just that allele (0 for ref, 1+ otherwise) # if this isoform shows 2+ allele, then the first allele is indicated by self.haplotypes[0] for i,pos in enumerate(self.hap_var_positions): ref_chr, ref_pos = fake_map[pos] total_count = sum(self.count_of_vars_by_pos[pos].values()) alt_freq = ["{0:.2f}".format(self.count_of_vars_by_pos[pos][b]*1./total_count) for b in self.alt_at_pos[pos]] rec = vcf.model._Record(CHROM=ref_chr, POS=ref_pos+1, ID='.', REF=self.ref_at_pos[pos], ALT=[vcf.model._Substitution(b) for b in self.alt_at_pos[pos]], QUAL='.', FILTER='PASS', INFO={'AF':alt_freq, 'DP':total_count}, FORMAT="GT:HQ", sample_indexes=None) samp_ft = vcf.model.make_calldata_tuple(['GT', 'HQ']) rec.samples = [] for _iso in name_isoforms: # isoform_tally[_iso] is a dict of haplotype index --> count # the index for thos base at this pos would thus be haplotype_vcf_index[hap_index][i] # we always need to show the phases in haplotype index order sorted hap_indices = list(isoform_tally[_iso].keys()) hap_indices.sort() genotype = "|".join(str(self.haplotype_vcf_index[hap_index][pos]) for hap_index in hap_indices) counts = ",".join(str(isoform_tally[_iso][hap_index]) for hap_index in hap_indices) rec.samples.append(vcf.model._Call(rec, _iso, samp_ft(*[genotype, counts]))) f_vcf.write_record(rec) f_vcf.close() def get_base_to_base_mapping_from_aligned_pairs(reftuple, qLen, strand): """ Returns: dict of 0-based position --> 0-based ref position """ cur_genome_loc = reftuple[0][1] mapping = {} for qpos, rpos in reftuple: if qpos is not None and rpos is not None: mapping[qpos] = (rpos, True) elif qpos is not None: mapping[qpos] = (cur_genome_loc, None) if rpos is not None: cur_genome_loc = rpos if strand == '-': mapping = dict((qLen-1-k, v) for k,v in mapping.items()) for k in mapping: mapping[k] = mapping[k][0] return mapping class MagVariantPhaser(object): def __init__(self, vc): """ :param vc: MPileUPVariant instance. """ self.vc = vc self.min_var_pos = min(vc.variant) # mininum 0-based position of a called variant self.max_var_pos = max(vc.variant) # maximum 0-based position of a called variant self.accepted_vars_by_pos = {} # 0-based pos --> list of accepted, (NOT strand sense) base self.count_of_vars_by_pos = {} # 0-based pos --> (NOT strand sense, but ref-based) base --> count self.accepted_pos = [] # sorted list of variant positions (0-based, ref) # process vc.variant which is # dict of 0-based pos --> desc list of (base, count) # ex: {1565: [('a', 49), ('g', 36)]} # lower case means at pos 1565, we expect - strand mapping and # seq base is 'T' on the sense strand # this converts to self.accepted_vars_by_pos[1565] = ['A', 'G'] # later, when we are matchin back to transcript seq, need to watch for strand! for pos, vars in vc.variant.items(): self.accepted_vars_by_pos[pos] = [_base.upper() for _base,_count in vars] self.count_of_vars_by_pos[pos] = dict((_base.upper(), _count) for _base,_count in vars) self.accepted_pos = list(self.accepted_vars_by_pos.keys()) self.accepted_pos.sort() self.haplotypes = MagHaplotypes(self.accepted_pos, [self.vc.ref_name[p] for p in self.accepted_pos], self.vc.ref_base, self.count_of_vars_by_pos) self.seq_hap_info = {} # haplotype assignment, key: (CCS) seqid, value: haplotype index def phase_variant(self, sam_filename, coordstr, output_prefix, partial_ok=False): """ :param sam_filename: CCS SAM filename. Can be unsorted. :param coordstr: list of [contig, start, end] :param output_prefix: Output prefix. Writes to xxx.log. :param partial_ok: default False. if True, (CCS) reads don't need to cover all SNP positions. For each alignment: 1. discard if did not map to the strand expected 2. discard if did not map to the full range of variants (unless <partial_ok> is True) 3. discard if at var positions have non-called bases (outliers) """ f_log = open(output_prefix+'.log', 'a+') contig, start, end = coordstr secondary_align_counts = 0 tot_align_counts = 0 with pysam.AlignmentFile(sam_filename, 'rb') as samfile: for s in samfile.fetch(contig, start, end): tot_align_counts += 1 if s.reference_name == '*': f_log.write("Ignore {0} because: unmapped.\n".format(s.query_name)) continue if not partial_ok and (s.reference_start > self.min_var_pos or s.reference_end < self.max_var_pos): f_log.write("Ignore {0} because: aln too short, from {1}-{2}.\n".format(s.query_name, s.referenc_start+1, s.reference_end)) continue if s.is_secondary: secondary_align_counts += 1 continue seqstr = s.query_sequence.upper() i, msg = self.match_haplotype(s, seqstr, partial_ok) if i is None: # read is rejected for reason listed in <msg> f_log.write("Ignore {0} because: {1}.\n".format(s.query_name, msg)) continue else: f_log.write("{0} phased: haplotype {1}={2}\n".format(s.query_name, i, self.haplotypes[i])) print("{0} has haplotype {1}:{2}".format(s.query_name, i, self.haplotypes[i])) self.seq_hap_info[s.query_name] = i f_log.write(f'Encountered {secondary_align_counts} out of {tot_align_counts} read alignments') def match_haplotype(self, r, s, partial_ok=False): """ Match an alignment record to existing haplotypes or create a new one. Helper function for self.phase_variant() :param r: CCS alignment (pysam record) :param s: CCS sequence (in strand), must be plain str and every base is upper case :param partial_ok: default False. if True, (CCS) reads don't need to cover all SNP positions. :return: (haplotype_index, msg) or (None, msg) if variants don't match w/ called SNPs """ try: assert type(s) is str and str.isupper(s) except Exception as e: print(f'exception: {s}') # m: mapping of 0-based seq --> 0-based ref position # rev_map: mapping of 0-based ref position --> 0-based seq strand = '-' if r.is_reverse else '+' m = get_base_to_base_mapping_from_aligned_pairs(r.get_aligned_pairs(), len(r.query_sequence), strand) ref_m = dict((v,k) for k,v in m.items()) # go through each variant # <hap> to represent the concatenated string of all variant positions for this seq # ex: if there are three var positions, a hap would be "ATG" or "A?G" (if partial_ok is True), etc. hap = '' impute_later = False for ref_pos in self.accepted_pos: if ref_pos not in ref_m: if partial_ok: # read does not cover one of the SNP positions, so use "?" hap += "?" else: return None, "Does not have base at ref_pos {0}.\n".format(ref_pos) else: base = s[ref_m[ref_pos]] if base in self.accepted_vars_by_pos[ref_pos]: hap += base else: # contains a base at a variant position that is not called. Try to impute. hap += base impute_later = True if all(b=='?' for b in hap): return None, "Does not cover any variant base." if impute_later: impute_i = self.haplotypes.impute_haplotype(hap, min_score=3) if impute_i is None: return None, "Seq {0} contained non-called variant. Impute failed.\n".format(hap) else: return impute_i, "IMPUTED" return self.haplotypes.match_or_add_haplotype(hap_string=hap) class MagHaplotypes(object): """ Storing haplotypes for a loci. self.haplotype[i] is the i-th haplotype. if N = len(self.haplotype[i]), then there are N variants along the loci. self.hap_var_positions[j] means that the j-th variant corressponds to (0-based) position on the ref genome. """ def __init__(self, var_positions, chrs, ref_at_pos, count_of_vars_by_pos): """ :param var_positions: sorted list of (0-based) variant positions :param ref_at_pos: dict of (0-based) variant position --> ref base at this position :param count_of_vars_by_pos: 0-based pos --> (NOT strand sense, but ref-based) base --> count """ self.haplotypes = [] # haplotypes, where haplotypes[i] is the i-th distinct haplotype of all var concat self.hap_var_positions = var_positions self.ref_at_pos = ref_at_pos # dict of (0-based) pos --> ref base self.alt_at_pos = None # init: None, later: dict of (0-based) pos --> unique list of alt bases self.count_of_vars_by_pos = count_of_vars_by_pos self.haplotype_vcf_index = None # init: None, later: dict of (hap index) --> (0-based) var pos --> phase (0 for ref, 1+ for alt) self.chrs = chrs # contig names where chrs[i] is the i-th contig name # sanity check: all variant positions must be present self.sanity_check() def __getitem__(self, ith): """ Returns the <i>-th haplotype """ return self.haplotypes[ith] def __str__(self): return """ var positions: {pp} haplotypes: \n{h} """.format(pp=",".join(map(str,self.hap_var_positions)), h="\n".join(self.haplotypes)) def sanity_check(self): """ Sanity check the following: -- variant positions are properly recorded and concordant -- alt bases are truly alt and unique -- all haplotypes are the same length """ for pos in self.hap_var_positions: assert pos in self.ref_at_pos if self.alt_at_pos is not None: for pos in self.alt_at_pos: # ref base must not be in alt assert self.ref_at_pos[pos] not in self.alt_at_pos[pos] # alt bases must be unique assert len(self.alt_at_pos[pos]) == len(set(self.alt_at_pos[pos])) if len(self.haplotypes) >= 1: n = len(self.haplotypes[0]) assert n == len(self.hap_var_positions) for hap_str in self.haplotypes[1:]: assert len(hap_str) == n def match_or_add_haplotype(self, hap_string): """ If <hap_string> is an existing haplotype, return the index. Otherwise, add to known haplotypes and return the new index. :return: <index>, "FOUND" or "NEW" """ if hap_string in self.haplotypes: i = self.haplotypes.index(hap_string) return i, "FOUND" else: i = len(self.haplotypes) self.haplotypes.append(hap_string) return i, "NEW" def impute_haplotype(self, hap_string, min_score): """ :param hap_string: a hap string with '?'s. :param min_sim: minimum similarity with existing haplotype to accept assignment :return: <index> of an existing haplotype, or None if not sufficiently matched Impute haplotype and only return a match if: (a) score (similarity) is >= min_score (b) the matching score for the best one is higher than the second best match """ sim_tuple = namedtuple('sim_tuple', 'index score') sims = [] # list of sim_tuple hap_str_len = len(hap_string) for i in range(len(self.haplotypes)): # Liz note: currently NOT checking whether existing haplotypes have '?'. I'm assuming no '?'. score = sum((hap_string[k]==self.haplotypes[i][k]) for k in range(hap_str_len)) if score > 0: sims.append(sim_tuple(index=i, score=score)) if len(sims) == 0: return None sims.sort(key=lambda x: x.score, reverse=True) if sims[0].score >= min_score and (len(sims)==1 or sims[0].score > sims[1].score): return sims[0].index else: return None def get_haplotype_vcf_assignment(self): """ Must be called before self.write_haplotype_to_vcf() This is preparing for writing out VCF. We need to know, for each variant position, the ref base (already filled in self.ref_at_pos) and the alt bases (self.alt_at_pos). For each haplotype in (self.haplotype), we need to know the whether the i-th variant is the ref (index 0), or some alt base (index 1 and onwards). Propagates two variables: self.haplotype_vcf_index: hap index --> pos --> phase index (0 for ref, 1+ for alt) self.alt_at_pos: dict of <0-based pos> --> alt bases (not is not ref) at this position """ self.haplotype_vcf_index = [{} for i in range(len(self.haplotypes))] self.alt_at_pos = {} # what happens in the case of partial phasing # ex: self.haplotypes[0] = "A?G", this means when it comes to the second pos, pos2, # in the VCF we would want to write out .|. for diploid, . for haploid, etc # so let's set self.haplotype_vcf_index[0][pos2] = '.' to indicate that for i,pos in enumerate(self.hap_var_positions): ref = self.ref_at_pos[pos] # need to go through the haplotype bases, if ref is already represented, then don't put it in alt self.alt_at_pos[pos] = [] for hap_i, hap_str in enumerate(self.haplotypes): base = hap_str[i] if base=='?': # means this haplotype does not cover this position! self.haplotype_vcf_index[hap_i][pos] = '.' elif base==ref: # is the ref base self.haplotype_vcf_index[hap_i][pos] = 0 else: # is an alt base, see if it's already there if base in self.alt_at_pos[pos]: j = self.alt_at_pos[pos].index(base) self.haplotype_vcf_index[hap_i][pos] = j + 1 # always +1, buz alt starts at 1 (0 is ref) else: j = len(self.alt_at_pos[pos]) self.alt_at_pos[pos].append(base) self.haplotype_vcf_index[hap_i][pos] = j + 1 # always +1, buz alt starts at 1 (0 is ref) # in the case where partial_ok=False, it's possible some alt are never presented by a haplotype # we must check that all variants are presented here for _base in self.count_of_vars_by_pos[pos]: if (_base not in self.ref_at_pos[pos]) and (_base not in self.alt_at_pos[pos]): self.alt_at_pos[pos].append(_base) def write_haplotype_to_humanreadable(self, contig, f_human1, f_human2, seq_hap_info): """ The following functions must first be called first: -- self.get_haplotype_vcf_assignment f_human1 : human readable tab file handle, one SNP per line f_human2: human readable tab file handle, one allele per line """ if self.haplotype_vcf_index is None or self.alt_at_pos is None: raise Exception("Must call self.get_haplotype_vcf_assignment() first!") self.sanity_check() hap_count = Counter() for ccs_id, hap_index in seq_hap_info.items(): hap_count[hap_index] += 1 # f_human1.write("haplotype\thapIdx\tcontig\tpos\tvarIdx\tbase\tcount\n") # f_human2.write("haplotype\thapIdx\tcontig\tcount\n") for hap_index,hap_str in enumerate(self.haplotypes): f_human2.write(f'{hap_str}\t{hap_index}\t{contig}\t') f_human2.write(str(hap_count[hap_index]) + '\n') for pos_index,pos in enumerate(self.hap_var_positions): i = self.haplotype_vcf_index[hap_index][pos] if i == '.': # means this haplotype does not include this position, skip! continue assert type(i) is int f_human1.write(f'{hap_str}\t{hap_index}\t{contig}\t') f_human1.write(str(pos+1)+'\t') f_human1.write(str(pos_index+1)+'\t') if i == 0: base = self.ref_at_pos[pos] f_human1.write("REF\t") else: base = self.alt_at_pos[pos][i-1] f_human1.write("ALT" + str(i-1) + '\t') #if i>0: pdb.set_trace() f_human1.write(str(self.count_of_vars_by_pos[pos][base]) + '\n')

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729240850edac5cd8418657f0002acc9a4f8d5d5

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py

Python

fedot/core/operations/evaluation/operation_implementations/models/ts_implementations.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

fedot/core/operations/evaluation/operation_implementations/models/ts_implementations.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

fedot/core/operations/evaluation/operation_implementations/models/ts_implementations.py

bahia14/Fedot_Times_Series_Forecast

995751068733541ba2f546065082709ce0fb63ae

[ "BSD-3-Clause" ]

null

from typing import Optional import numpy as np from scipy import stats from statsmodels.tsa.api import STLForecast from statsmodels.tsa.ar_model import AutoReg from statsmodels.tsa.arima.model import ARIMA from fedot.core.log import Log from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import _ts_to_table from fedot.core.operations.evaluation. \ operation_implementations.implementation_interfaces import ModelImplementation from fedot.core.repository.dataset_types import DataTypesEnum class ARIMAImplementation(ModelImplementation): def __init__(self, log: Log = None, **params: Optional[dict]): super().__init__(log) self.params = params self.arima = None self.lmbda = None self.scope = None self.actual_ts_len = None self.sts = None # TODO for some configuration of p,d,q got ValueError def fit(self, input_data): """ Class fit arima model on data :param input_data: data with features, target and ids to process """ source_ts = np.array(input_data.features) # Save actual time series length self.actual_ts_len = len(source_ts) self.sts = source_ts # Apply box-cox transformation for positive values min_value = np.min(source_ts) if min_value > 0: pass else: # Making a shift to positive values self.scope = abs(min_value) + 1 source_ts = source_ts + self.scope transformed_ts, self.lmbda = stats.boxcox(source_ts) if not self.params: # Default data self.params = {'p': 2, 'd': 0, 'q': 2} p = int(self.params.get('p')) d = int(self.params.get('d')) q = int(self.params.get('q')) params = {'order': (p, d, q)} self.arima = ARIMA(transformed_ts, **params).fit() return self.arima def predict(self, input_data, is_fit_pipeline_stage: bool): """ Method for time series prediction on forecast length :param input_data: data with features, target and ids to process :param is_fit_pipeline_stage: is this fit or predict stage for pipeline :return output_data: output data with smoothed time series """ parameters = input_data.task.task_params forecast_length = parameters.forecast_length old_idx = input_data.idx target = input_data.target # For training pipeline get fitted data if is_fit_pipeline_stage: fitted_values = self.arima.fittedvalues fitted_values = self._inverse_boxcox(predicted=fitted_values, lmbda=self.lmbda) # Undo shift operation fitted_values = self._inverse_shift(fitted_values) diff = int(self.actual_ts_len - len(fitted_values)) # If first elements skipped if diff != 0: # Fill nans with first values first_element = fitted_values[0] first_elements = [first_element] * diff first_elements.extend(list(fitted_values)) fitted_values = np.array(first_elements) _, predict = _ts_to_table(idx=old_idx, time_series=fitted_values, window_size=forecast_length) new_idx, target_columns = _ts_to_table(idx=old_idx, time_series=target, window_size=forecast_length) # Update idx and target input_data.idx = new_idx input_data.target = target_columns # For predict stage we can make prediction else: start_id = old_idx[-1] - forecast_length + 1 end_id = old_idx[-1] predicted = self.arima.predict(start=start_id, end=end_id) predicted = self._inverse_boxcox(predicted=predicted, lmbda=self.lmbda) # Undo shift operation predict = self._inverse_shift(predicted) # Convert one-dim array as column predict = np.array(predict).reshape(1, -1) new_idx = np.arange(start_id, end_id + 1) # Update idx input_data.idx = new_idx # Update idx and features output_data = self._convert_to_output(input_data, predict=predict, data_type=DataTypesEnum.table) return output_data def get_params(self): return self.params @staticmethod def _inverse_boxcox(predicted, lmbda): """ Method apply inverse Box-Cox transformation """ if lmbda == 0: return np.exp(predicted) else: return np.exp(np.log(lmbda * predicted + 1) / lmbda) def _inverse_shift(self, values): """ Method apply inverse shift operation """ if self.scope is None: pass else: values = values - self.scope return values class AutoRegImplementation(ModelImplementation): def __init__(self, log: Log = None, **params: Optional[dict]): super().__init__(log) self.params = params self.actual_ts_len = None self.autoreg = None def fit(self, input_data): """ Class fit arima model on data :param input_data: data with features, target and ids to process """ source_ts = np.array(input_data.features) self.actual_ts_len = len(source_ts) if not self.params: # Default data self.params = {'lag_1': 12, 'lag_2': 60} lag_1 = int(self.params.get('lag_1')) lag_2 = int(self.params.get('lag_2')) params = {'lags': [lag_1, lag_2]} self.autoreg = AutoReg(source_ts, **params).fit() return self.autoreg def predict(self, input_data, is_fit_pipeline_stage: bool): """ Method for time series prediction on forecast length :param input_data: data with features, target and ids to process :param is_fit_pipeline_stage: is this fit or predict stage for pipeline :return output_data: output data with smoothed time series """ parameters = input_data.task.task_params forecast_length = parameters.forecast_length old_idx = input_data.idx target = input_data.target if is_fit_pipeline_stage: fitted = self.autoreg.predict(start=old_idx[0], end=old_idx[-1]) # First n elements in time series are skipped diff = self.actual_ts_len - len(fitted) # Fill nans with first values first_element = fitted[0] first_elements = [first_element] * diff first_elements.extend(list(fitted)) fitted = np.array(first_elements) _, predict = _ts_to_table(idx=old_idx, time_series=fitted, window_size=forecast_length) new_idx, target_columns = _ts_to_table(idx=old_idx, time_series=target, window_size=forecast_length) # Update idx and target input_data.idx = new_idx input_data.target = target_columns # For predict stage we can make prediction else: start_id = old_idx[-1] - forecast_length + 1 end_id = old_idx[-1] predicted = self.autoreg.predict(start=start_id, end=end_id) # Convert one-dim array as column predict = np.array(predicted).reshape(1, -1) new_idx = np.arange(start_id, end_id + 1) # Update idx input_data.idx = new_idx # Update idx and features output_data = self._convert_to_output(input_data, predict=predict, data_type=DataTypesEnum.table) return output_data def get_params(self): return self.params class STLForecastARIMAImplementation(ModelImplementation): def __init__(self, log: Log = None, **params: Optional[dict]): super().__init__(log) self.params = params self.model = None self.lmbda = None self.scope = None self.actual_ts_len = None self.sts = None def fit(self, input_data): """ Class fit STLForecast arima model on data :param input_data: data with features, target and ids to process """ source_ts = np.array(input_data.features) # Save actual time series length self.actual_ts_len = len(source_ts) self.sts = source_ts if not self.params: # Default data self.params = {'p': 2, 'd': 0, 'q': 2, 'period': 365} p = int(self.params.get('p')) d = int(self.params.get('d')) q = int(self.params.get('q')) period = int(self.params.get('period')) params = {'period': period, 'model_kwargs': {'order': (p, d, q)}} self.model = STLForecast(source_ts, ARIMA, **params).fit() return self.model def predict(self, input_data, is_fit_pipeline_stage: bool): """ Method for time series prediction on forecast length :param input_data: data with features, target and ids to process :param is_fit_pipeline_stage: is this fit or predict stage for pipeline :return output_data: output data with smoothed time series """ parameters = input_data.task.task_params forecast_length = parameters.forecast_length old_idx = input_data.idx target = input_data.target # For training pipeline get fitted data if is_fit_pipeline_stage: fitted_values = self.model.get_prediction(start=old_idx[0], end=old_idx[-1]).predicted_mean diff = int(self.actual_ts_len) - len(fitted_values) # If first elements skipped if diff != 0: # Fill nans with first values first_element = fitted_values[0] first_elements = [first_element] * diff first_elements.extend(list(fitted_values)) fitted_values = np.array(first_elements) _, predict = _ts_to_table(idx=old_idx, time_series=fitted_values, window_size=forecast_length) new_idx, target_columns = _ts_to_table(idx=old_idx, time_series=target, window_size=forecast_length) # Update idx and target input_data.idx = new_idx input_data.target = target_columns # For predict stage we can make prediction else: start_id = old_idx[-1] - forecast_length + 1 end_id = old_idx[-1] predicted = self.model.get_prediction(start=start_id, end=end_id).predicted_mean # Convert one-dim array as column predict = np.array(predicted).reshape(1, -1) new_idx = np.arange(start_id, end_id + 1) # Update idx input_data.idx = new_idx # Update idx and features output_data = self._convert_to_output(input_data, predict=predict, data_type=DataTypesEnum.table) return output_data def get_params(self): return self.params

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null

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72e834e553061b8aebab53754e351698630f6498

609

py

Python

scripts/tests/setup.py

NDevTK/cel

e97226416b6e12245564bfc1c3631d610d62f052

[ "BSD-3-Clause" ]

null

scripts/tests/setup.py

NDevTK/cel

e97226416b6e12245564bfc1c3631d610d62f052

[ "BSD-3-Clause" ]

null

scripts/tests/setup.py

NDevTK/cel

e97226416b6e12245564bfc1c3631d610d62f052

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # Copyright 2018 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. ''' Installs all dependencies required to run test.py. ''' import subprocess # TODO: Add Windows support subprocess.check_call(['apt-get', 'install', 'python-pip']) subprocess.check_call(['pip', 'install', 'absl-py']) subprocess.check_call(['pip', 'install', 'google-api-python-client']) subprocess.check_call(['pip', 'install', 'grpc-google-iam-admin-v1']) subprocess.check_call(['pip', 'install', 'grpc-google-iam-v1'])

33.833333

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609

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null

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1

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1

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4

f402cceedb38ed018e632aec19ad1a850b040623

1,124

py

Python

tests/test_events.py

jvanbrug/cs143sim

4e510a7669a534b55b606cbe175142104ae4a92c

[ "MIT" ]

null

tests/test_events.py

jvanbrug/cs143sim

4e510a7669a534b55b606cbe175142104ae4a92c

[ "MIT" ]

null

tests/test_events.py

jvanbrug/cs143sim

4e510a7669a534b55b606cbe175142104ae4a92c

[ "MIT" ]

null

from simpy.core import Environment from cs143sim.events import FlowStart from cs143sim.events import LinkAvailable from cs143sim.events import PacketReceipt from cs143sim.events import RoutingTableOutdated from test_actors import basic_flow from test_actors import basic_link from test_actors import basic_packet from test_actors import basic_router def basic_environment(): return Environment() def basic_flow_start(): FlowStart(env=basic_environment(), delay=1.0, flow=basic_flow()) def basic_link_available(): LinkAvailable(env=basic_environment(), delay=1.0, link=basic_link()) def basic_packet_receipt(): PacketReceipt(env=basic_environment(), delay=1.0, receiver=basic_router(), packet=basic_packet()) def basic_update_routing_table(): RoutingTableOutdated(env=basic_environment(), delay=1.0, router=basic_router()) def test_flow_start(): basic_flow_start() def test_link_available(): basic_link_available() def test_packet_receipt(): basic_packet_receipt() def test_update_routing_table(): basic_update_routing_table()

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null

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1

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4

f41e1355b328be6cadaf3784932fcdefc9064e64

303

py

Python

WEEKS/CD_Sata-Structures/_MISC/misc-examples/03_csBinarySearchTreeInOrderSuccessor.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/03_csBinarySearchTreeInOrderSuccessor.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/03_csBinarySearchTreeInOrderSuccessor.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

""" What does the phrase "in-order successor" mean when we are talking about a node in a binary search tree? A - the node that has the next lowest value B - the node that has the maximum value C - the node that has the minimuin value D - the node that has the next highest value answer is : """

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null

0

1

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4

f424c41dabf2730fc770b299307eb3c76316defe

135

py

Python

send_email/apps.py

AthmanZiri/django-site

04c6e0967b628b8ebef1ca1caae8cee83c1a2f07

[ "MIT" ]

null

send_email/apps.py

AthmanZiri/django-site

04c6e0967b628b8ebef1ca1caae8cee83c1a2f07

[ "MIT" ]

null

send_email/apps.py

AthmanZiri/django-site

04c6e0967b628b8ebef1ca1caae8cee83c1a2f07

[ "MIT" ]

null

from __future__ import unicode_literals from django.apps import AppConfig class SendEmailConfig(AppConfig): name = 'send_email'

16.875

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null

0

1

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1

0

4

f47f90ec352390a446cb134ed76b2f6da04ebc27

209

py

Python

nngen/onnx/shape.py

m1kit/nngen

c96457aa0f1681a8ba7c12881ea3d455eccffde0

[ "Apache-2.0" ]

7

2020-06-08T13:36:13.000Z

2021-12-24T06:55:30.000Z

nngen/onnx/shape.py

m1kit/nngen

c96457aa0f1681a8ba7c12881ea3d455eccffde0

[ "Apache-2.0" ]

null

nngen/onnx/shape.py

m1kit/nngen

c96457aa0f1681a8ba7c12881ea3d455eccffde0

[ "Apache-2.0" ]

1

2021-03-12T03:51:56.000Z

from __future__ import absolute_import from __future__ import print_function from __future__ import division def Shape(visitor, node): input = visitor.visit(node.input[0]) return tuple(input.shape)

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null

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1

0

4

be6ed34db6e4f27b954b3ede51144930fffd1e0e

11,518

py

Python

echopype/tests/visualize/test_plot.py

mbdunn/echopype

a53290801d1ca062d45c00ca2c541d54682dd40a

[ "Apache-2.0" ]

null

echopype/tests/visualize/test_plot.py

mbdunn/echopype

a53290801d1ca062d45c00ca2c541d54682dd40a

[ "Apache-2.0" ]

null

echopype/tests/visualize/test_plot.py

mbdunn/echopype

a53290801d1ca062d45c00ca2c541d54682dd40a

[ "Apache-2.0" ]

null

import echopype import echopype.visualize from echopype.testing import TEST_DATA_FOLDER import pytest from xarray.plot.facetgrid import FacetGrid from matplotlib.collections import QuadMesh import xarray as xr import numpy as np ek60_path = TEST_DATA_FOLDER / "ek60" ek80_path = TEST_DATA_FOLDER / "ek80_new" azfp_path = TEST_DATA_FOLDER / "azfp" ad2cp_path = TEST_DATA_FOLDER / "ad2cp" param_args = ("filepath", "sonar_model", "azfp_xml_path", "range_kwargs") param_testdata = [ ( ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw", "EK60", None, {}, ), ( ek60_path / "DY1002_EK60-D20100318-T023008_rep_freq.raw", "EK60", None, {}, ), ( ek80_path / "echopype-test-D20211004-T235930.raw", "EK80", None, {'waveform_mode': 'BB', 'encode_mode': 'complex'}, ), ( ek80_path / "D20211004-T233354.raw", "EK80", None, {'waveform_mode': 'CW', 'encode_mode': 'power'}, ), ( ek80_path / "D20211004-T233115.raw", "EK80", None, {'waveform_mode': 'CW', 'encode_mode': 'complex'}, ), ( azfp_path / "17082117.01A", "AZFP", azfp_path / "17041823.XML", {}, ), # Will always need env variables pytest.param( ad2cp_path / "raw" / "090" / "rawtest.090.00001.ad2cp", "AD2CP", None, {}, marks=pytest.mark.xfail( run=False, reason="Not supported at the moment", ), ), ] @pytest.mark.parametrize(param_args, param_testdata) def test_plot_multi( filepath, sonar_model, azfp_xml_path, range_kwargs, ): # TODO: Need to figure out how to compare the actual rendered plots ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path) plots = echopype.visualize.create_echogram(ed) assert isinstance(plots, list) is True assert all(isinstance(plot, FacetGrid) for plot in plots) is True @pytest.mark.parametrize(param_args, param_testdata) def test_plot_single( filepath, sonar_model, azfp_xml_path, range_kwargs, ): # TODO: Need to figure out how to compare the actual rendered plots ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path) plots = echopype.visualize.create_echogram( ed, channel=ed.beam.channel[0].values ) assert isinstance(plots, list) is True if ( sonar_model.lower() == 'ek80' and range_kwargs['encode_mode'] == 'complex' ): assert all(isinstance(plot, FacetGrid) for plot in plots) is True else: assert all(isinstance(plot, QuadMesh) for plot in plots) is True @pytest.mark.parametrize(param_args, param_testdata) def test_plot_multi_get_range( filepath, sonar_model, azfp_xml_path, range_kwargs, ): # TODO: Need to figure out how to compare the actual rendered plots ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path) if ed.sonar_model.lower() == 'azfp': avg_temperature = ( ed.environment['temperature'].mean('time1').values ) env_params = { 'temperature': avg_temperature, 'salinity': 27.9, 'pressure': 59, } range_kwargs['env_params'] = env_params plots = echopype.visualize.create_echogram( ed, get_range=True, range_kwargs=range_kwargs ) assert isinstance(plots, list) is True assert all(isinstance(plot, FacetGrid) for plot in plots) is True # Beam shape check if ( sonar_model.lower() == 'ek80' and range_kwargs['encode_mode'] == 'complex' ): assert plots[0].axes.shape[-1] > 1 else: assert plots[0].axes.shape[-1] == 1 # Channel shape check assert ed.beam.channel.shape[0] == len(plots) @pytest.mark.parametrize(param_args, param_testdata) def test_plot_Sv( filepath, sonar_model, azfp_xml_path, range_kwargs, ): # TODO: Need to figure out how to compare the actual rendered plots ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path) if ed.sonar_model.lower() == 'azfp': avg_temperature = ( ed.environment['temperature'].mean('time1').values ) env_params = { 'temperature': avg_temperature, 'salinity': 27.9, 'pressure': 59, } range_kwargs['env_params'] = env_params if 'azfp_cal_type' in range_kwargs: range_kwargs.pop('azfp_cal_type') Sv = echopype.calibrate.compute_Sv(ed, **range_kwargs) plots = echopype.visualize.create_echogram(Sv) assert isinstance(plots, list) is True assert all(isinstance(plot, FacetGrid) for plot in plots) is True @pytest.mark.parametrize(param_args, param_testdata) def test_plot_mvbs( filepath, sonar_model, azfp_xml_path, range_kwargs, ): # TODO: Need to figure out how to compare the actual rendered plots ed = echopype.open_raw(filepath, sonar_model, azfp_xml_path) if ed.sonar_model.lower() == 'azfp': avg_temperature = ( ed.environment['temperature'].mean('time1').values ) env_params = { 'temperature': avg_temperature, 'salinity': 27.9, 'pressure': 59, } range_kwargs['env_params'] = env_params if 'azfp_cal_type' in range_kwargs: range_kwargs.pop('azfp_cal_type') Sv = echopype.calibrate.compute_Sv(ed, **range_kwargs) mvbs = echopype.preprocess.compute_MVBS(Sv, ping_time_bin='10S') plots = [] try: plots = echopype.visualize.create_echogram(mvbs) except Exception as e: assert isinstance(e, ValueError) assert str(e) == "Ping time must have a length that is greater or equal to 2" # noqa if len(plots) > 0: assert all(isinstance(plot, FacetGrid) for plot in plots) is True @pytest.mark.parametrize( ("water_level", "expect_warning"), [ (True, False), ([True], True), (False, True), (xr.DataArray(np.array(50.0)).expand_dims({'channel': 3}), False), (xr.DataArray(np.array(50.0)), False), (10, False), (30.5, False), ], ) def test_water_level_echodata(water_level, expect_warning): from echopype.echodata import EchoData from echopype.visualize.api import _add_water_level filepath = ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw" sonar_model = "EK60" range_kwargs = {} echodata = echopype.open_raw( sonar_model=sonar_model, raw_file=filepath, xml_path=None ) range_in_meter = echodata.compute_range( env_params=range_kwargs.get('env_params', {}), azfp_cal_type=range_kwargs.get('azfp_cal_type', None), ek_waveform_mode=range_kwargs.get('waveform_mode', 'CW'), ek_encode_mode=range_kwargs.get('encode_mode', 'power'), ) single_array = range_in_meter.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11', ping_time='2017-07-19T21:13:47.984999936').values no_input_water_level = False if isinstance(water_level, list): water_level = water_level[0] echodata.platform = echodata.platform.drop_vars('water_level') no_input_water_level = True if isinstance(water_level, xr.DataArray): if 'channel' in water_level.dims: original_array = single_array + water_level.isel(channel=0).values elif isinstance(water_level, bool) and water_level is True: if no_input_water_level is False: original_array = ( single_array + echodata.platform.water_level.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11', time3='2017-07-19T21:13:47.984999936').values ) else: original_array = single_array elif water_level is not False and isinstance(water_level, (int, float)): original_array = single_array + water_level else: original_array = single_array results = None try: if expect_warning: with pytest.warns(UserWarning): results = _add_water_level( range_in_meter=range_in_meter, water_level=water_level, data_type=EchoData, platform_data=echodata.platform, ) else: results = _add_water_level( range_in_meter=range_in_meter, water_level=water_level, data_type=EchoData, platform_data=echodata.platform, ) except Exception as e: assert isinstance(e, ValueError) assert str(e) == 'Water level must have any of these dimensions: channel, ping_time, range_sample' # noqa if isinstance(results, xr.DataArray): final_array = results.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11', ping_time='2017-07-19T21:13:47.984999936').values print(f"original_array = {original_array}") print(f"results = {results}") assert np.array_equal(original_array, final_array) @pytest.mark.parametrize( ("water_level", "expect_warning"), [ (True, True), (False, True), (xr.DataArray(np.array(50.0)).expand_dims({'channel': 3}), False), (xr.DataArray(np.array(50.0)), False), (10, False), (30.5, False), ], ) def test_water_level_Sv_dataset(water_level, expect_warning): from echopype.visualize.api import _add_water_level filepath = ek60_path / "ncei-wcsd" / "Summer2017-D20170719-T211347.raw" sonar_model = "EK60" range_kwargs = {} echodata = echopype.open_raw( sonar_model=sonar_model, raw_file=filepath, xml_path=None ) Sv = echopype.calibrate.compute_Sv(echodata, **range_kwargs) ds = Sv.set_coords('echo_range') range_in_meter = ds.echo_range single_array = range_in_meter.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11', ping_time='2017-07-19T21:13:47.984999936').values if isinstance(water_level, xr.DataArray): if 'channel' in water_level.dims: original_array = single_array + water_level.isel(channel=0).values elif not isinstance(water_level, bool) and isinstance(water_level, (int, float)): original_array = single_array + water_level else: original_array = single_array results = None try: if expect_warning: with pytest.warns(UserWarning): results = _add_water_level( range_in_meter=range_in_meter, water_level=water_level, data_type=xr.Dataset, ) else: results = _add_water_level( range_in_meter=range_in_meter, water_level=water_level, data_type=xr.Dataset, ) except Exception as e: assert isinstance(e, ValueError) assert str(e) == 'Water level must have any of these dimensions: channel, ping_time, range_sample' # noqa if isinstance(results, xr.DataArray): final_array = results.sel(channel='GPT 18 kHz 009072058c8d 1-1 ES18-11', ping_time='2017-07-19T21:13:47.984999936').values assert np.array_equal(original_array, final_array)

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11,518

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0.021059

0.03539

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0.707517

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0

0.054044

0.272269

11,518

348

115

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null

0

1

0

1

0

null

0

4

be909d3398cbd0bc40faa3b82a76c4c1dbe5bc23

132

py

Python

bracketology/__init__.py

stahl085/bracketology

3311241fa82eb41a5529ac5e126171850f715032

[ "MIT" ]

2

2020-02-26T07:02:04.000Z

2020-03-09T19:21:37.000Z

Bracketology/bracketology/__init__.py

andrewargeros/minnemudac-2021

2e96b598d3b231937f1fda871fecaeb44d236a7e

[ "MIT" ]

1

2020-03-09T02:39:25.000Z

2021-03-16T02:56:58.000Z

Bracketology/bracketology/__init__.py

andrewargeros/minnemudac-2021

2e96b598d3b231937f1fda871fecaeb44d236a7e

[ "MIT" ]

4

2020-02-26T03:35:39.000Z

2021-04-09T00:46:33.000Z

__version__ = '0.0.8' from bracketology.brackets import Team, Game, SubBracket16, FinalFour, Bracket import bracketology.simulators

33

78

0.818182

16

132

6.5

0.8125

0

0.042017

0.098485

132

3

79

44

0.831933

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0.037879

0

1

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false

0

0.666667

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0.666667

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null

0

1

0

null

0

1

0

1

0

4

fe3c1f9a570d1238ee435cb1cfdbb65470ae8432

565

py

Python

facebook_lite_app/views.py

B339r1p/fb-lite

2930ef3ef487250e74664df6642b2c398f256de9

[ "MIT" ]

null

facebook_lite_app/views.py

B339r1p/fb-lite

2930ef3ef487250e74664df6642b2c398f256de9

[ "MIT" ]

null

facebook_lite_app/views.py

B339r1p/fb-lite

2930ef3ef487250e74664df6642b2c398f256de9

[ "MIT" ]

null

from django.shortcuts import render from rest_framework import viewsets from .serializers import PostSerializer,CommentSerializer,LikeSerializer from .models import Post,Comment,Like # Create your views here. class PostViewSet(viewsets.ModelViewSet): queryset = Post.objects.all() serializer_class = PostSerializer class CommentViewSet(viewsets.ModelViewSet): queryset = Comment.objects.all() serializer_class = CommentSerializer class LikeViewSet(viewsets.ModelViewSet): queryset = Like.objects.all() serializer_class = LikeSerializer

28.25

72

0.8

59

565

7.59322

0.491525

0.133929

0.1875

0.167411

0

0.130973

565

20

73

28.25

0.912424

0.040708

0

1

0

false

0

0.307692

0

1

0

null

0

1

0

null

0

1

0

1

0

4

fe41b910ef8a962de3f0c491a4edbf423b0dc5cd

172

py

Python

Python/242valid_anagram.py

Apocrypse/LeetCode

3ada2605ce8c8f6dadebf37a30c9c00a0d1ede39

[ "MIT" ]

4

2020-03-17T03:08:51.000Z

2022-03-14T17:33:28.000Z

Python/242valid_anagram.py

Apocrypse/LeetCode

3ada2605ce8c8f6dadebf37a30c9c00a0d1ede39

[ "MIT" ]

null

Python/242valid_anagram.py

Apocrypse/LeetCode

3ada2605ce8c8f6dadebf37a30c9c00a0d1ede39

[ "MIT" ]

3

2021-04-29T16:51:02.000Z

2022-03-19T17:37:56.000Z

class Solution: def isAnagram(self, s, t): """ :type s: str :type t: str :rtype: bool """ return sorted(s) == sorted(t)

19.111111

37

0.44186

20

172

3.8

0.65

0

0.418605

172

8

38

21.5

0.76

0.22093

0

1

0.333333

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

fe4b9404ba9f3a23c8d01ee95ddce3f6a7b0ffc9

105

py

Python

IotApp/test.py

greenSyntax/AWS-RaspberryPi

ac09fa90eb61c525be94e44f0580a669782c221e

[ "MIT" ]

null

IotApp/test.py

greenSyntax/AWS-RaspberryPi

ac09fa90eb61c525be94e44f0580a669782c221e

[ "MIT" ]

null

IotApp/test.py

greenSyntax/AWS-RaspberryPi

ac09fa90eb61c525be94e44f0580a669782c221e

[ "MIT" ]

null

import threading def printit(): threading.Timer(2.0, printit).start() print "Hello World" printit()

11.666667

38

0.714286

14

105

5.357143

0.785714

0

0.022222

0.142857

105

8

39

13.125

0.811111

0

0.105769

0

null

0

0.2

null

0.8

1

0

null

0

1

0

null

0

1

0

1

0

4

fe6bfa3bd83df9e7ddc136154e1615f0e3edc7dd

94

py

Python

tests/test_ansible_task_worker.py

benthomasson/ansible-task-worker

33189b503e010df93adf486fde8c0eec9c436e18

[ "Apache-2.0" ]

null

tests/test_ansible_task_worker.py

benthomasson/ansible-task-worker

33189b503e010df93adf486fde8c0eec9c436e18

[ "Apache-2.0" ]

10

2020-01-05T19:08:49.000Z

2021-11-15T17:47:59.000Z

tests/test_ansible_task_worker.py

benthomasson/ansible-task-worker

33189b503e010df93adf486fde8c0eec9c436e18

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `ansible_task_worker` package."""

18.8

46

0.62766

13

94

4.384615

1

0

0.012195

0.12766

94

4

47

23.5

0.682927

0.882979

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

fe6dff8becd7dd38f4f1f68d27c8e924eb891859

9,016

py

Python

tests/test_run_mypy.py

bsamseth/jsonschema-typed

54d2f8c250fdba14422e44545c7cd1254218045e

[ "MIT" ]

14

2020-02-26T17:48:33.000Z

2022-02-04T13:25:24.000Z

tests/test_run_mypy.py

bsamseth/jsonschema-typed

54d2f8c250fdba14422e44545c7cd1254218045e

[ "MIT" ]

5

2020-08-10T20:37:59.000Z

2021-07-08T08:55:15.000Z

tests/test_run_mypy.py

bsamseth/jsonschema-typed

54d2f8c250fdba14422e44545c7cd1254218045e

[ "MIT" ]

3

2020-07-26T18:57:58.000Z

2021-09-30T18:41:04.000Z

""" This type of test attempts to run mypy on selected sources and asserts that the output is consistent. This does not (yet) test sufficient edge cases, and more testing should be done. # TODO: Add more test cases. """ import os import pytest from mypy import api from typing import List, Tuple, TypedDict class Expect(TypedDict): normal: str error: str exit_status: int case_directory = os.path.join(os.path.dirname(__file__), "cases") cases: List[Tuple[str, Expect]] = [ ( "from_readme.py", Expect( normal=""" note: Revealed type is 'TypedDict('FooSchema', {'title'?: builtins.str, 'awesome'?: builtins.int})' error: TypedDict "FooSchema" has no key 'description' error: Argument 2 has incompatible type "None"; expected "int" """, error="", exit_status=1, ), ), ( "check_required.py", Expect( normal=""" error: Key 'awesome' missing for TypedDict "FooSchema" note: Revealed type is 'TypedDict('FooSchema', {'title'?: builtins.str, 'awesome': builtins.int})' error: TypedDict "FooSchema" has no key 'description' error: Argument 2 has incompatible type "None"; expected "int" """, error="", exit_status=1, ), ), ( "alias.py", Expect( normal=""" note: Revealed type is 'TypedDict('FooSchema', {'title'?: builtins.str, 'awesome'?: builtins.int})' error: TypedDict "FooSchema" has no key 'description' error: Argument 2 has incompatible type "None"; expected "int" """, error="", exit_status=1, ), ), ( "nonetype.py", Expect( normal=""" note: Revealed type is 'TypedDict('NoneSchema', {'title'?: builtins.str, 'awesome'?: Union[builtins.list[Any], None]})' error: Argument 2 has incompatible type "int"; expected "Optional[List[Any]]" """, error="", exit_status=1, ), ), ( "nested.py", Expect( normal=""" note: Revealed type is 'TypedDict('NestedFooSchema', {'title': builtins.str, 'awesome'?: TypedDict({'nested'?: TypedDict({'thing': builtins.str}), 'thing': builtins.int})})' note: Revealed type is 'TypedDict('NestedFooSchemaAwesome', {'nested'?: TypedDict({'thing': builtins.str}), 'thing': builtins.int})' note: Revealed type is 'TypedDict('NestedFooSchemaAwesomeNested', {'thing': builtins.str})' """, error="", exit_status=1, ), ), ( "hard.py", Expect( normal=""" note: Revealed type is 'TypedDict('ComplicatedJson', {'num': builtins.int, 'status': builtins.list[TypedDict({'code'?: Union[Literal['success'], Literal['failure']], 'diagnostics'?: builtins.list[TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})], 'message'?: builtins.str, 'module': Union[Literal['m1'], Literal['m2']]})]})' note: Revealed type is 'builtins.int' note: Revealed type is 'builtins.list[TypedDict({'code'?: Union[Literal['success'], Literal['failure']], 'diagnostics'?: builtins.list[TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})], 'message'?: builtins.str, 'module': Union[Literal['m1'], Literal['m2']]})]' note: Revealed type is 'TypedDict({'code'?: Union[Literal['success'], Literal['failure']], 'diagnostics'?: builtins.list[TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})], 'message'?: builtins.str, 'module': Union[Literal['m1'], Literal['m2']]})' note: Revealed type is 'builtins.list[TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})]' note: Revealed type is 'TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})' note: Revealed type is 'TypedDict('ComplicatedJsonStatusDiagnostics', {'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})' """, error="", exit_status=1, ), ), ( "optional_typed_dict.py", Expect( normal=""" note: Revealed type is 'TypedDict('FooSchema', {'title'?: builtins.str, 'awesome'?: builtins.int})' error: TypedDict "FooSchema" has no key 'description' error: Argument 2 has incompatible type "None"; expected "int" """, error="", exit_status=1, ), ), ( "optional_typed_dict_hard_mode.py", Expect( normal=""" note: Revealed type is 'TypedDict('ComplicatedJson', {'num'?: builtins.int, 'status'?: builtins.list[TypedDict({'code'?: Union[Literal['success'], Literal['failure']], 'diagnostics'?: builtins.list[TypedDict({'field'?: builtins.str, 'illegal_value'?: builtins.str, 'level': Union[Literal['info'], Literal['warn'], Literal['error']], 'mismatch_fields'?: builtins.list[builtins.str], 'ids'?: builtins.list[TypedDict({'id': builtins.int, 'thing_type'?: Union[Literal['A'], Literal['B']]})]})], 'message'?: builtins.str, 'module': Union[Literal['m1'], Literal['m2']]})]})' error: TypedDict "ComplicatedJson" has no key 'description' error: Argument 2 has incompatible type "None"; expected "int" """, error="", exit_status=1, ), ), ( "outer_array.py", Expect( normal=""" note: Revealed type is 'builtins.list[TypedDict({'a_number'?: builtins.int, 'a_string': builtins.str, 'nested_array_of_numbers'?: builtins.list[builtins.list[builtins.float]]})]' note: Revealed type is 'TypedDict('ArrayOfObjects', {'a_number'?: builtins.int, 'a_string': builtins.str, 'nested_array_of_numbers'?: builtins.list[builtins.list[Union[builtins.int, builtins.float]]]})' """, error="", exit_status=1, ), ), ( "tuple.py", Expect( normal=""" tests/cases/tuple.py:9: error: List item 0 has incompatible type "List[str]"; expected "Optional[Tuple[Optional[str], Optional[str]]]" tests/cases/tuple.py:10: error: List item 0 has incompatible type "Tuple[str, str, str]"; expected "Optional[Tuple[Optional[str], Optional[str]]]" tests/cases/tuple.py:11: error: List item 0 has incompatible type "int"; expected "Optional[Tuple[Optional[str], Optional[str]]]" tests/cases/tuple.py:12: error: List item 0 has incompatible type "Tuple[int, int]"; expected "Optional[Tuple[Optional[str], Optional[str]]]" tests/cases/tuple.py:15: note: Revealed type is 'builtins.list[Union[Tuple[Union[builtins.str, None], Union[builtins.str, None]], None]]' Found 4 errors in 1 file (checked 1 source file) """, error="", exit_status=1, ), ), ] @pytest.mark.parametrize("case_file, expected", cases) def test_cases(case_file: str, expected: Expect): normal_report, error_report, exit_status = api.run( ["--show-traceback", os.path.join(case_directory, case_file)] ) for line in expected["normal"].strip().splitlines(): assert line.strip() in normal_report for line in expected["error"].strip().splitlines(): assert line.strip() in error_report assert exit_status == expected["exit_status"]

54.313253

584

0.594388

985

9,016

5.371574

0.148223

0.079002

0.057456

0.064638

0.764317

0.759214

0.731053

0.69571

0.665848

0

0.005755

0.229148

9,016

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585

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0.023514

0

0.557823

0

0.142857

0.774102

0.216007

0

0.006061

0.020408

1

0.006803

false

0

0.027211

0

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null

0

1

0

1

0

1

0

null

0

4

fe6f7534b411ea5987584e0cec59e6f82308cf33

1,898

py

Python

test/pytest/test_bech32.py

kcorlidy/importaddress

e63aeb2e288df29435d9321a0a90e4c905b851fa

[ "Apache-2.0" ]

null

test/pytest/test_bech32.py

kcorlidy/importaddress

e63aeb2e288df29435d9321a0a90e4c905b851fa

[ "Apache-2.0" ]

null

test/pytest/test_bech32.py

kcorlidy/importaddress

e63aeb2e288df29435d9321a0a90e4c905b851fa

[ "Apache-2.0" ]

1

2021-05-03T23:42:18.000Z

from importaddress.segwit_addr import encode, decode import pytest valid = [ ["BC1QW508D6QEJXTDG4Y5R3ZARVARY0C5XW7KV8F3T4", "0014751e76e8199196d454941c45d1b3a323f1433bd6"], ["tb1qrp33g0q5c5txsp9arysrx4k6zdkfs4nce4xj0gdcccefvpysxf3q0sl5k7", "00201863143c14c5166804bd19203356da136c985678cd4d27a1b8c6329604903262"], ["bc1pw508d6qejxtdg4y5r3zarvary0c5xw7kw508d6qejxtdg4y5r3zarvary0c5xw7k7grplx", "5128751e76e8199196d454941c45d1b3a323f1433bd6751e76e8199196d454941c45d1b3a323f1433bd6"], ["BC1SW50QA3JX3S", "6002751e"], ["bc1zw508d6qejxtdg4y5r3zarvaryvg6kdaj", "5210751e76e8199196d454941c45d1b3a323"], ["tb1qqqqqp399et2xygdj5xreqhjjvcmzhxw4aywxecjdzew6hylgvsesrxh6hy", "0020000000c4a5cad46221b2a187905e5266362b99d5e91c6ce24d165dab93e86433"] ] invalid = [ "tc1qw508d6qejxtdg4y5r3zarvary0c5xw7kg3g4ty" # Invalid human-readable part "bc1qw508d6qejxtdg4y5r3zarvary0c5xw7kv8f3t5" # Invalid checksum "BC13W508D6QEJXTDG4Y5R3ZARVARY0C5XW7KN40WF2" # Invalid witness version "bc1rw5uspcuh" # Invalid program length "bc10w508d6qejxtdg4y5r3zarvary0c5xw7kw508d6qejxtdg4y5r3zarvary0c5xw7kw5rljs90" # Invalid program length "BC1QR508D6QEJXTDG4Y5R3ZARVARYV98GJ9P" # Invalid program length for witness version 0 (per BIP141) "tb1qrp33g0q5c5txsp9arysrx4k6zdkfs4nce4xj0gdcccefvpysxf3q0sL5k7" # Mixed case "bc1zw508d6qejxtdg4y5r3zarvaryvqyzf3du" # zero padding of more than 4 bits "tb1qrp33g0q5c5txsp9arysrx4k6zdkfs4nce4xj0gdcccefvpysxf3pjxtptv" # Non-zero padding in 8-to-5 conversion "bc1gmk9yu" # Empty data section ] def test_encode(): for c,p in valid: p = bytes.fromhex(p) l0 = p[0] - 0x50 if p[0] else 0 assert c.lower() == encode(c[:2].lower(), l0, p[2:]) def test_decode(): for c,p in valid: c = c.lower() p = bytes.fromhex(p)[2:] assert bytes(decode(c[:2], c)[1]) == p def test_decode_invalid(): for c in invalid: c = c.lower() with pytest.raises(Exception): bytes(decode(c[:2], c)[1])

42.177778

167

0.818757

153

1,898

10.124183

0.509804

0.027114

0.038735

0.009038

0.034861

0.019367

0

0.26331

0.089568

1,898

44

168

43.136364

0.633102

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0

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0.054054

1

0.081081

false

0

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0.135135

0

1

null

0

1

0

1

null

0

4

fe70feb29bf8d55c18b23df33560a15791015eca

161

py

Python

pywind/lib/filter.py

augustand/fdslight

f3d82465aaa27160438b22f9b474be8c5dc100cc

[ "BSD-2-Clause" ]

null

pywind/lib/filter.py

augustand/fdslight

f3d82465aaa27160438b22f9b474be8c5dc100cc

[ "BSD-2-Clause" ]

null

pywind/lib/filter.py

augustand/fdslight

f3d82465aaa27160438b22f9b474be8c5dc100cc

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 class _drop_html_event(object): pass def drop_html_event(sts): """过滤到HTML的事件属性 :param sts: :return: """ pass

12.384615

31

0.608696

20

161

4.65

0.75

0.172043

0.27957

0

0.008403

0.26087

161

12

32

13.416667

0.773109

0.354037

0

0.5

0

1

0.25

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

fe7e2b32b5faa9daaf437a5ad761058903432372

646

py

Python

secateur/forms.py

funkybob/secateur

9bda9ab7b9ddd8c1c43e5d2081342222f28eaaaf

[ "MIT" ]

null

secateur/forms.py

funkybob/secateur

9bda9ab7b9ddd8c1c43e5d2081342222f28eaaaf

[ "MIT" ]

null

secateur/forms.py

funkybob/secateur

9bda9ab7b9ddd8c1c43e5d2081342222f28eaaaf

[ "MIT" ]

null

from django import forms class Disconnect(forms.Form): pass class BlockAccountsForm(forms.Form): screen_name = forms.CharField(help_text="The Twitter username.") duration = forms.IntegerField( min_value=1, max_value=52, initial=6, help_text="How long to block the accounts (in weeks)", ) block_account = forms.BooleanField(required=False) mute_account = forms.BooleanField(required=False) block_followers = forms.BooleanField(required=False) mute_followers = forms.BooleanField(required=False) class Search(forms.Form): screen_name = forms.CharField(help_text="Username")

26.916667

68

0.716718

78

646

5.794872

0.512821

0.150442

0.221239

0.265487

0.535398

0.181416

0

0.007605

0.185759

646

23

69

28.086957

0.851711

0

0.108359

0

1

0

false

0.058824

0

0.647059

0

null

0

1

0

null

0

1

0

1

0

4

feb3d82764064b9c5ad416fba4e417a1542bf938

150

py

Python

old/application/__init__.py

UNSW-CEEM/market-control-room

a64228084b50e1eb662a20f939c02bc01a9c195a

[ "MIT" ]

1

2021-07-28T03:07:55.000Z

old/application/__init__.py

luke-marshall/market-control-room

a64228084b50e1eb662a20f939c02bc01a9c195a

[ "MIT" ]

6

2021-03-09T01:07:34.000Z

2022-02-26T09:59:28.000Z

old/application/__init__.py

UNSW-CEEM/market-control-room

a64228084b50e1eb662a20f939c02bc01a9c195a

[ "MIT" ]

null

from flask import Flask, request app = Flask(__name__, static_url_path='') @app.route('/') def root(): return app.send_static_file('monitor.html')

18.75

44

0.726667

22

150

4.590909

0.772727

0

0.113333

150

7

45

21.428571

0.759399

0

0.087248

0

1

0.2

false

0

0.2

0.6

0

1

0

null

0

1

0

null

0

1

0

4

feb5b11dc902ec538bd96c1ad4a739b143dbc66c

26,199

py

Python

cvat/apps/engine/migrations/0001_initial.py

irisradgroup/cvat

cc427b122a0382e501154f4caf4afe813ee7e3b1

[ "Intel", "MIT" ]

null

cvat/apps/engine/migrations/0001_initial.py

irisradgroup/cvat

cc427b122a0382e501154f4caf4afe813ee7e3b1

[ "Intel", "MIT" ]

null

cvat/apps/engine/migrations/0001_initial.py

irisradgroup/cvat

cc427b122a0382e501154f4caf4afe813ee7e3b1

[ "Intel", "MIT" ]

null

# Generated by Django 3.1.13 on 2021-09-01 22:47 import cvat.apps.engine.models from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='AttributeSpec', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=64)), ('mutable', models.BooleanField()), ('input_type', models.CharField(choices=[('checkbox', 'CHECKBOX'), ('radio', 'RADIO'), ('number', 'NUMBER'), ('text', 'TEXT'), ('select', 'SELECT')], max_length=16)), ('default_value', models.CharField(max_length=128)), ('values', models.CharField(max_length=4096)), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='CloudStorage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('provider_type', models.CharField(choices=[('AWS_S3_BUCKET', 'AWS_S3'), ('AZURE_CONTAINER', 'AZURE_CONTAINER'), ('GOOGLE_DRIVE', 'GOOGLE_DRIVE')], max_length=20)), ('resource', models.CharField(max_length=63)), ('display_name', models.CharField(max_length=63)), ('created_date', models.DateTimeField(auto_now_add=True)), ('updated_date', models.DateTimeField(auto_now=True)), ('credentials', models.CharField(max_length=500)), ('credentials_type', models.CharField(choices=[('TEMP_KEY_SECRET_KEY_TOKEN_SET', 'TEMP_KEY_SECRET_KEY_TOKEN_SET'), ('ACCOUNT_NAME_TOKEN_PAIR', 'ACCOUNT_NAME_TOKEN_PAIR'), ('ANONYMOUS_ACCESS', 'ANONYMOUS_ACCESS')], max_length=29)), ('specific_attributes', models.CharField(blank=True, max_length=50)), ('description', models.TextField(blank=True)), ('owner', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='cloud_storages', to=settings.AUTH_USER_MODEL)), ], options={ 'default_permissions': (), 'unique_together': {('provider_type', 'resource', 'credentials')}, }, ), migrations.CreateModel( name='Data', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('chunk_size', models.PositiveIntegerField(null=True)), ('size', models.PositiveIntegerField(default=0)), ('image_quality', models.PositiveSmallIntegerField(default=50)), ('start_frame', models.PositiveIntegerField(default=0)), ('stop_frame', models.PositiveIntegerField(default=0)), ('frame_filter', models.CharField(blank=True, default='', max_length=256)), ('compressed_chunk_type', models.CharField(choices=[('video', 'VIDEO'), ('imageset', 'IMAGESET'), ('list', 'LIST')], default=cvat.apps.engine.models.DataChoice['IMAGESET'], max_length=32)), ('original_chunk_type', models.CharField(choices=[('video', 'VIDEO'), ('imageset', 'IMAGESET'), ('list', 'LIST')], default=cvat.apps.engine.models.DataChoice['IMAGESET'], max_length=32)), ('storage_method', models.CharField(choices=[('cache', 'CACHE'), ('file_system', 'FILE_SYSTEM')], default=cvat.apps.engine.models.StorageMethodChoice['FILE_SYSTEM'], max_length=15)), ('storage', models.CharField(choices=[('cloud_storage', 'CLOUD_STORAGE'), ('local', 'LOCAL'), ('share', 'SHARE')], default=cvat.apps.engine.models.StorageChoice['LOCAL'], max_length=15)), ('cloud_storage', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='data', to='engine.cloudstorage')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Image', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('path', models.CharField(default='', max_length=1024)), ('frame', models.PositiveIntegerField()), ('width', models.PositiveIntegerField()), ('height', models.PositiveIntegerField()), ('data', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='images', to='engine.data')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Job', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('status', models.CharField(choices=[('annotation', 'ANNOTATION'), ('validation', 'VALIDATION'), ('completed', 'COMPLETED')], default=cvat.apps.engine.models.StatusChoice['ANNOTATION'], max_length=32)), ('assignee', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ('reviewer', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='review_job_set', to=settings.AUTH_USER_MODEL)), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Label', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', cvat.apps.engine.models.SafeCharField(max_length=64)), ('color', models.CharField(default='', max_length=8)), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='LabeledImage', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('frame', models.PositiveIntegerField()), ('group', models.PositiveIntegerField(null=True)), ('source', models.CharField(choices=[('auto', 'AUTO'), ('manual', 'MANUAL')], default='manual', max_length=16, null=True)), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.job')), ('label', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.label')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='LabeledShape', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('frame', models.PositiveIntegerField()), ('group', models.PositiveIntegerField(null=True)), ('source', models.CharField(choices=[('auto', 'AUTO'), ('manual', 'MANUAL')], default='manual', max_length=16, null=True)), ('type', models.CharField(choices=[('rectangle', 'RECTANGLE'), ('polygon', 'POLYGON'), ('polyline', 'POLYLINE'), ('points', 'POINTS'), ('cuboid', 'CUBOID')], max_length=16)), ('occluded', models.BooleanField(default=False)), ('z_order', models.IntegerField(default=0)), ('points', cvat.apps.engine.models.FloatArrayField()), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.job')), ('label', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.label')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='LabeledTrack', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('frame', models.PositiveIntegerField()), ('group', models.PositiveIntegerField(null=True)), ('source', models.CharField(choices=[('auto', 'AUTO'), ('manual', 'MANUAL')], default='manual', max_length=16, null=True)), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.job')), ('label', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.label')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='Project', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', cvat.apps.engine.models.SafeCharField(max_length=256)), ('bug_tracker', models.CharField(blank=True, default='', max_length=2000)), ('created_date', models.DateTimeField(auto_now_add=True)), ('updated_date', models.DateTimeField(auto_now_add=True)), ('status', models.CharField(choices=[('annotation', 'ANNOTATION'), ('validation', 'VALIDATION'), ('completed', 'COMPLETED')], default=cvat.apps.engine.models.StatusChoice['ANNOTATION'], max_length=32)), ('assignee', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ('owner', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Task', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', cvat.apps.engine.models.SafeCharField(max_length=256)), ('mode', models.CharField(max_length=32)), ('bug_tracker', models.CharField(blank=True, default='', max_length=2000)), ('created_date', models.DateTimeField(auto_now_add=True)), ('updated_date', models.DateTimeField(auto_now=True)), ('overlap', models.PositiveIntegerField(null=True)), ('segment_size', models.PositiveIntegerField(default=0)), ('status', models.CharField(choices=[('annotation', 'ANNOTATION'), ('validation', 'VALIDATION'), ('completed', 'COMPLETED')], default=cvat.apps.engine.models.StatusChoice['ANNOTATION'], max_length=32)), ('dimension', models.CharField(choices=[('3d', 'DIM_3D'), ('2d', 'DIM_2D')], default=cvat.apps.engine.models.DimensionType['DIM_2D'], max_length=2)), ('subset', models.CharField(blank=True, default='', max_length=64)), ('assignee', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='assignees', to=settings.AUTH_USER_MODEL)), ('data', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='tasks', to='engine.data')), ('owner', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='owners', to=settings.AUTH_USER_MODEL)), ('project', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='tasks', related_query_name='task', to='engine.project')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='TrackedShape', fields=[ ('type', models.CharField(choices=[('rectangle', 'RECTANGLE'), ('polygon', 'POLYGON'), ('polyline', 'POLYLINE'), ('points', 'POINTS'), ('cuboid', 'CUBOID')], max_length=16)), ('occluded', models.BooleanField(default=False)), ('z_order', models.IntegerField(default=0)), ('points', cvat.apps.engine.models.FloatArrayField()), ('id', models.BigAutoField(primary_key=True, serialize=False)), ('frame', models.PositiveIntegerField()), ('outside', models.BooleanField(default=False)), ('track', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.labeledtrack')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='TrainingProject', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('host', models.CharField(max_length=256)), ('username', models.CharField(max_length=256)), ('password', models.CharField(max_length=256)), ('training_id', models.CharField(max_length=64)), ('enabled', models.BooleanField(null=True)), ('project_class', models.CharField(blank=True, choices=[('OD', 'Object Detection')], max_length=2, null=True)), ], ), migrations.CreateModel( name='Video', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('path', models.CharField(default='', max_length=1024)), ('width', models.PositiveIntegerField()), ('height', models.PositiveIntegerField()), ('data', models.OneToOneField(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='video', to='engine.data')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='TrainingProjectLabel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('training_label_id', models.CharField(max_length=64)), ('cvat_label', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='training_project_label', to='engine.label')), ], ), migrations.CreateModel( name='TrainingProjectImage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idx', models.PositiveIntegerField()), ('training_image_id', models.CharField(max_length=64)), ('task', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.task')), ], ), migrations.CreateModel( name='TrackedShapeAttributeVal', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('value', cvat.apps.engine.models.SafeCharField(max_length=4096)), ('shape', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.trackedshape')), ('spec', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.attributespec')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='ServerFile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('file', models.CharField(max_length=1024)), ('data', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='server_files', to='engine.data')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Segment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('start_frame', models.IntegerField()), ('stop_frame', models.IntegerField()), ('task', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.task')), ], options={ 'default_permissions': (), }, ), migrations.CreateModel( name='Review', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('estimated_quality', models.FloatField()), ('status', models.CharField(choices=[('accepted', 'ACCEPTED'), ('rejected', 'REJECTED'), ('review_further', 'REVIEW_FURTHER')], max_length=16)), ('assignee', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='reviewed', to=settings.AUTH_USER_MODEL)), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.job')), ('reviewer', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='reviews', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='RemoteFile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('file', models.CharField(max_length=1024)), ('data', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='remote_files', to='engine.data')), ], options={ 'default_permissions': (), }, ), migrations.AddField( model_name='project', name='training_project', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='engine.trainingproject'), ), migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rating', models.FloatField(default=0.0)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='LabeledTrackAttributeVal', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('value', cvat.apps.engine.models.SafeCharField(max_length=4096)), ('spec', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.attributespec')), ('track', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.labeledtrack')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='LabeledShapeAttributeVal', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('value', cvat.apps.engine.models.SafeCharField(max_length=4096)), ('shape', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.labeledshape')), ('spec', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.attributespec')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.CreateModel( name='LabeledImageAttributeVal', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('value', cvat.apps.engine.models.SafeCharField(max_length=4096)), ('image', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.labeledimage')), ('spec', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.attributespec')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.AddField( model_name='label', name='project', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='engine.project'), ), migrations.AddField( model_name='label', name='task', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='engine.task'), ), migrations.CreateModel( name='JobCommit', fields=[ ('id', models.BigAutoField(primary_key=True, serialize=False)), ('version', models.PositiveIntegerField(default=0)), ('timestamp', models.DateTimeField(auto_now=True)), ('message', models.CharField(default='', max_length=4096)), ('author', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='commits', to='engine.job')), ], options={ 'abstract': False, 'default_permissions': (), }, ), migrations.AddField( model_name='job', name='segment', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.segment'), ), migrations.CreateModel( name='Issue', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('frame', models.PositiveIntegerField()), ('position', cvat.apps.engine.models.FloatArrayField()), ('created_date', models.DateTimeField(auto_now_add=True)), ('resolved_date', models.DateTimeField(blank=True, null=True)), ('job', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.job')), ('owner', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='issues', to=settings.AUTH_USER_MODEL)), ('resolver', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='resolved_issues', to=settings.AUTH_USER_MODEL)), ('review', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='engine.review')), ], ), migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('message', models.TextField(default='')), ('created_date', models.DateTimeField(auto_now_add=True)), ('updated_date', models.DateTimeField(auto_now=True)), ('author', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ('issue', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.issue')), ], ), migrations.AddField( model_name='attributespec', name='label', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='engine.label'), ), migrations.CreateModel( name='RelatedFile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('path', models.FileField(max_length=1024, storage=cvat.apps.engine.models.MyFileSystemStorage(), upload_to=cvat.apps.engine.models.upload_path_handler)), ('data', models.ForeignKey(default=1, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_files', to='engine.data')), ('primary_image', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_files', to='engine.image')), ], options={ 'default_permissions': (), 'unique_together': {('data', 'path')}, }, ), migrations.AlterUniqueTogether( name='label', unique_together={('task', 'name')}, ), migrations.CreateModel( name='ClientFile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('file', models.FileField(max_length=1024, storage=cvat.apps.engine.models.MyFileSystemStorage(), upload_to=cvat.apps.engine.models.upload_path_handler)), ('data', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='client_files', to='engine.data')), ], options={ 'default_permissions': (), 'unique_together': {('data', 'file')}, }, ), migrations.AlterUniqueTogether( name='attributespec', unique_together={('label', 'name')}, ), ]

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feb5dc7aa6e32742e66af2f8c2cc1c87bac34007

25,331

py

Python

google/cloud/aiplatform/matching_engine/matching_engine_index.py

nayaknishant/python-aiplatform

309b3b9d1688a62b0c60aada1e7de1d131fb163e

[ "Apache-2.0" ]

1

2022-03-30T05:23:29.000Z

google/cloud/aiplatform/matching_engine/matching_engine_index.py

xxxtrillionarie/GCP_MLOps_VertexAI_Workshop

d0d719c0bf557b908eb63f3a245db2f47b136eb3

[ "Apache-2.0" ]

null

google/cloud/aiplatform/matching_engine/matching_engine_index.py

xxxtrillionarie/GCP_MLOps_VertexAI_Workshop

d0d719c0bf557b908eb63f3a245db2f47b136eb3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2022 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. # from typing import Dict, List, Optional, Sequence, Tuple from google.auth import credentials as auth_credentials from google.protobuf import field_mask_pb2 from google.cloud.aiplatform import base from google.cloud.aiplatform.compat.types import ( matching_engine_deployed_index_ref as gca_matching_engine_deployed_index_ref, matching_engine_index as gca_matching_engine_index, ) from google.cloud.aiplatform import initializer from google.cloud.aiplatform.matching_engine import matching_engine_index_config from google.cloud.aiplatform import utils _LOGGER = base.Logger(__name__) class MatchingEngineIndex(base.VertexAiResourceNounWithFutureManager): """Matching Engine index resource for Vertex AI.""" client_class = utils.IndexClientWithOverride _resource_noun = "indexes" _getter_method = "get_index" _list_method = "list_indexes" _delete_method = "delete_index" _parse_resource_name_method = "parse_index_path" _format_resource_name_method = "index_path" def __init__( self, index_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ): """Retrieves an existing index given an index name or ID. Example Usage: my_index = aiplatform.MatchingEngineIndex( index_name='projects/123/locations/us-central1/indexes/my_index_id' ) or my_index = aiplatform.MatchingEngineIndex( index_name='my_index_id' ) Args: index_name (str): Required. A fully-qualified index resource name or a index ID. Example: "projects/123/locations/us-central1/indexes/my_index_id" or "my_index_id" when project and location are initialized or passed. project (str): Optional. Project to retrieve index from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve index from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to retrieve this Index. Overrides credentials set in aiplatform.init. """ super().__init__( project=project, location=location, credentials=credentials, resource_name=index_name, ) self._gca_resource = self._get_gca_resource(resource_name=index_name) @property def description(self) -> str: """Description of the index.""" self._assert_gca_resource_is_available() return self._gca_resource.description @classmethod @base.optional_sync() def _create( cls, display_name: str, contents_delta_uri: str, config: matching_engine_index_config.MatchingEngineIndexConfig, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), sync: bool = True, ) -> "MatchingEngineIndex": """Creates a MatchingEngineIndex resource. Args: display_name (str): Required. The display name of the Index. The name can be up to 128 characters long and can be consist of any UTF-8 characters. contents_delta_uri (str): Required. Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Google Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://docs.google.com/document/d/12DLVB6Nq6rdv8grxfBsPhUA283KWrQ9ZenPBp0zUC30 config (matching_engine_index_config.MatchingEngineIndexConfig): Required. The configuration with regard to the algorithms used for efficient search. description (str): Optional. The description of the Index. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Index. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Index(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. project (str): Optional. Project to create EntityType in. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to create EntityType in. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to create EntityTypes. Overrides credentials set in aiplatform.init. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. encryption_spec (str): Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key. sync (bool): Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Returns: MatchingEngineIndex - Index resource object """ gapic_index = gca_matching_engine_index.Index( display_name=display_name, description=description, metadata={ "config": config.as_dict(), "contentsDeltaUri": contents_delta_uri, }, ) if labels: utils.validate_labels(labels) gapic_index.labels = labels api_client = cls._instantiate_client(location=location, credentials=credentials) create_lro = api_client.create_index( parent=initializer.global_config.common_location_path( project=project, location=location ), index=gapic_index, metadata=request_metadata, ) _LOGGER.log_create_with_lro(cls, create_lro) created_index = create_lro.result() _LOGGER.log_create_complete(cls, created_index, "index") index_obj = cls( index_name=created_index.name, project=project, location=location, credentials=credentials, ) return index_obj def update_metadata( self, display_name: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), ) -> "MatchingEngineIndex": """Updates the metadata for this index. Args: display_name (str): Optional. The display name of the Index. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): Optional. The description of the Index. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Indexs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Index (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. Returns: MatchingEngineIndex - The updated index resource object. """ self.wait() update_mask = list() if labels: utils.validate_labels(labels) update_mask.append("labels") if display_name is not None: update_mask.append("display_name") if description is not None: update_mask.append("description") update_mask = field_mask_pb2.FieldMask(paths=update_mask) gapic_index = gca_matching_engine_index.Index( name=self.resource_name, display_name=display_name, description=description, labels=labels, ) _LOGGER.log_action_start_against_resource( "Updating", "index", self, ) update_lro = self.api_client.update_index( index=gapic_index, update_mask=update_mask, metadata=request_metadata, ) _LOGGER.log_action_started_against_resource_with_lro( "Update", "index", self.__class__, update_lro ) self._gca_resource = update_lro.result() _LOGGER.log_action_completed_against_resource("index", "Updated", self) return self def update_embeddings( self, contents_delta_uri: str, is_complete_overwrite: Optional[bool] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), ) -> "MatchingEngineIndex": """Updates the embeddings for this index. Args: contents_delta_uri (str): Required. Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Google Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://docs.google.com/document/d/12DLVB6Nq6rdv8grxfBsPhUA283KWrQ9ZenPBp0zUC30 is_complete_overwrite (str): Optional. If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. Returns: MatchingEngineIndex - The updated index resource object. """ self.wait() update_mask = list() if contents_delta_uri or is_complete_overwrite: update_mask.append("metadata") update_mask = field_mask_pb2.FieldMask(paths=update_mask) gapic_index = gca_matching_engine_index.Index( name=self.resource_name, metadata={ "contentsDeltaUri": contents_delta_uri, "isCompleteOverwrite": is_complete_overwrite, }, ) _LOGGER.log_action_start_against_resource( "Updating", "index", self, ) update_lro = self.api_client.update_index( index=gapic_index, update_mask=update_mask, metadata=request_metadata, ) _LOGGER.log_action_started_against_resource_with_lro( "Update", "index", self.__class__, update_lro ) self._gca_resource = update_lro.result() _LOGGER.log_action_completed_against_resource("index", "Updated", self) return self @property def deployed_indexes( self, ) -> List[gca_matching_engine_deployed_index_ref.DeployedIndexRef]: """Returns a list of deployed index references that originate from this index. Returns: List[gca_matching_engine_deployed_index_ref.DeployedIndexRef] - Deployed index references """ self.wait() return self._gca_resource.deployed_indexes @classmethod def create_tree_ah_index( cls, display_name: str, contents_delta_uri: str, dimensions: int, approximate_neighbors_count: int, leaf_node_embedding_count: Optional[int] = None, leaf_nodes_to_search_percent: Optional[float] = None, distance_measure_type: Optional[ matching_engine_index_config.DistanceMeasureType ] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), sync: bool = True, ) -> "MatchingEngineIndex": """Creates a MatchingEngineIndex resource that uses the tree-AH algorithm. Example Usage: my_index = aiplatform.Index.create_tree_ah_index( display_name="my_display_name", contents_delta_uri="gs://my_bucket/embeddings", dimensions=1, approximate_neighbors_count=150, distance_measure_type="SQUARED_L2_DISTANCE", leaf_node_embedding_count=100, leaf_nodes_to_search_percent=50, description="my description", labels={ "label_name": "label_value" }, ) Args: display_name (str): Required. The display name of the Index. The name can be up to 128 characters long and can be consist of any UTF-8 characters. contents_delta_uri (str): Required. Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Google Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://docs.google.com/document/d/12DLVB6Nq6rdv8grxfBsPhUA283KWrQ9ZenPBp0zUC30 dimensions (int): Required. The number of dimensions of the input vectors. approximate_neighbors_count (int): Required. The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. leaf_node_embedding_count (int): Optional. Number of embeddings on each leaf node. The default value is 1000 if not set. leaf_nodes_to_search_percent (float): Optional. The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set. distance_measure_type (matching_engine_index_config.DistanceMeasureType): Optional. The distance measure used in nearest neighbor search. description (str): Optional. The description of the Index. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Index. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Index(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. project (str): Optional. Project to create EntityType in. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to create EntityType in. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to create EntityTypes. Overrides credentials set in aiplatform.init. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. encryption_spec (str): Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key. sync (bool): Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Returns: MatchingEngineIndex - Index resource object """ algorithm_config = matching_engine_index_config.TreeAhConfig( leaf_node_embedding_count=leaf_node_embedding_count, leaf_nodes_to_search_percent=leaf_nodes_to_search_percent, ) config = matching_engine_index_config.MatchingEngineIndexConfig( dimensions=dimensions, algorithm_config=algorithm_config, approximate_neighbors_count=approximate_neighbors_count, distance_measure_type=distance_measure_type, ) return cls._create( display_name=display_name, contents_delta_uri=contents_delta_uri, config=config, description=description, labels=labels, project=project, location=location, credentials=credentials, request_metadata=request_metadata, sync=sync, ) @classmethod def create_brute_force_index( cls, display_name: str, contents_delta_uri: str, dimensions: int, distance_measure_type: Optional[ matching_engine_index_config.DistanceMeasureType ] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), sync: bool = True, ) -> "MatchingEngineIndex": """Creates a MatchingEngineIndex resource that uses the brute force algorithm. Example Usage: my_index = aiplatform.Index.create_brute_force_index( display_name="my_display_name", contents_delta_uri="gs://my_bucket/embeddings", dimensions=1, approximate_neighbors_count=150, distance_measure_type="SQUARED_L2_DISTANCE", description="my description", labels={ "label_name": "label_value" }, ) Args: display_name (str): Required. The display name of the Index. The name can be up to 128 characters long and can be consist of any UTF-8 characters. contents_delta_uri (str): Required. Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Google Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://docs.google.com/document/d/12DLVB6Nq6rdv8grxfBsPhUA283KWrQ9ZenPBp0zUC30 dimensions (int): Required. The number of dimensions of the input vectors. distance_measure_type (matching_engine_index_config.DistanceMeasureType): Optional. The distance measure used in nearest neighbor search. description (str): Optional. The description of the Index. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Index. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Index(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. project (str): Optional. Project to create EntityType in. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to create EntityType in. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to create EntityTypes. Overrides credentials set in aiplatform.init. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. encryption_spec (str): Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key. sync (bool): Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Returns: MatchingEngineIndex - Index resource object """ algorithm_config = matching_engine_index_config.BruteForceConfig() config = matching_engine_index_config.MatchingEngineIndexConfig( dimensions=dimensions, algorithm_config=algorithm_config, distance_measure_type=distance_measure_type, ) return cls._create( display_name=display_name, contents_delta_uri=contents_delta_uri, config=config, description=description, labels=labels, project=project, location=location, credentials=credentials, request_metadata=request_metadata, sync=sync, )

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227da6004fc76cd422c9164eb2afc40bcf2e2618

63

py

Python

panopticon/django/__init__.py

mobify/python-panopticon

081f341c5fcb6d7d1c438d66cd1dd8a8083ea8a1

[ "MIT" ]

3

2016-04-16T05:08:13.000Z

2017-06-20T19:10:06.000Z

panopticon/django/__init__.py

elbaschid/python-panopticon

081f341c5fcb6d7d1c438d66cd1dd8a8083ea8a1

[ "MIT" ]

4

2016-05-26T22:57:45.000Z

2016-06-23T21:40:57.000Z

panopticon/django/__init__.py

elbaschid/python-panopticon

081f341c5fcb6d7d1c438d66cd1dd8a8083ea8a1

[ "MIT" ]

2

2016-03-25T12:44:31.000Z

2018-02-14T22:16:26.000Z

default_app_config = "panopticon.django.apps.PanopticonConfig"

31.5

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null

0

1

0

1

null

0

4

228051f708c9ce73e4ea5fed3e1eb55ca03f21e0

91

py

Python

dropdown/apps.py

earthpyy/django-rest-dropdown

5222753f0e5b9ec5b589d6576cc9860f23a54e6d

[ "MIT" ]

2

2022-01-11T03:23:22.000Z

2022-01-21T08:18:27.000Z

dropdown/apps.py

earthpyy/django-rest-dropdown

5222753f0e5b9ec5b589d6576cc9860f23a54e6d

[ "MIT" ]

null

dropdown/apps.py

earthpyy/django-rest-dropdown

5222753f0e5b9ec5b589d6576cc9860f23a54e6d

[ "MIT" ]

null

from django.apps import AppConfig class DropdownConfig(AppConfig): name = 'dropdown'

15.166667

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6.9

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0.164835

91

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null

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0

1

0

4

2292624cde27e72457034e44e1127862ef316d4f

462

py

Python

src/kemokrw/client.py

Kemok-Repos/kemokrw

bfe2a82e2ef5d3580ed5dfe65129b30bd3fc4971

[ "MIT" ]

null

src/kemokrw/client.py

Kemok-Repos/kemokrw

bfe2a82e2ef5d3580ed5dfe65129b30bd3fc4971

[ "MIT" ]

null

src/kemokrw/client.py

Kemok-Repos/kemokrw

bfe2a82e2ef5d3580ed5dfe65129b30bd3fc4971

[ "MIT" ]

null

from abc import ABC, abstractmethod class ApiClient(ABC): """Clase Extract Encapsula el manejo de una API para simplificar los procesos de autenticación y de llamadas a los endpoints. Métodos ------- get(): Realiza una solicitud a un endpoint utilizando el verbo GET. """ @abstractmethod def get(self): pass def post(self): pass def put(self): pass def delete(self): pass

18.48

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null

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1

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1

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1

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4

229297867251239fe769ebd85fa258512c95d035

30,702

py

Python

bayes_opt/functions.py

ntienvu/KnowingOptimumValue_BO

42225cb9d61c1225bd757fe9dd02834a0bc7a3e6

[ "MIT" ]

14

2020-06-30T00:36:14.000Z

2022-01-11T13:15:53.000Z

bayes_opt/functions.py

ntienvu/KnowingOptimumValue_BO

42225cb9d61c1225bd757fe9dd02834a0bc7a3e6

[ "MIT" ]

null

bayes_opt/functions.py

ntienvu/KnowingOptimumValue_BO

42225cb9d61c1225bd757fe9dd02834a0bc7a3e6

[ "MIT" ]

2

2020-10-17T15:27:06.000Z

2021-02-27T10:34:04.000Z

# -*- coding: utf-8 -*- import numpy as np from collections import OrderedDict from scipy.stats import multivariate_normal from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D def reshape(x,input_dim): ''' Reshapes x into a matrix with input_dim columns ''' x = np.array(x) if x.size ==input_dim: x = x.reshape((1,input_dim)) return x class functions: def plot(self): bounds=self.bounds if isinstance(bounds,dict): # Get the name of the parameters keys = bounds.keys() arr_bounds = [] for key in keys: arr_bounds.append(bounds[key]) arr_bounds = np.asarray(arr_bounds) else: arr_bounds=np.asarray(bounds) X=np.array([np.arange(x[0], x[1], 0.01) for x in arr_bounds]) X=X.reshape(-1,2) X1=np.array([X[:,0]]) X2=np.array([X[:,1]]) X1, X2 = np.meshgrid(X1, X2) y=np.zeros([X1.shape[1],X2.shape[1]]) #print(y.shape) #print(X1.shape) #print(X2.shape) for ii in range(0,X1.shape[1]): for jj in range(0,X2.shape[1]): Xij=np.array([X1[ii,ii],X2[jj,jj]]) #print(Xij) y[ii,jj]=self.func(Xij) # f1=plt.figure(1) # ax=plt.axes(projection='3d') # ax.plot_surface(X1,X2,y) plt.contourf(X1,X2,y,levels=np.arange(0,35,1)) plt.colorbar() def findSdev(self): num_points_per_dim=100 bounds=self.bounds if isinstance(bounds,dict): # Get the name of the parameters keys = bounds.keys() arr_bounds = [] for key in keys: arr_bounds.append(bounds[key]) arr_bounds = np.asarray(arr_bounds) else: arr_bounds=np.asarray(bounds) X=np.array([np.random.uniform(x[0], x[1], size=num_points_per_dim) for x in arr_bounds]) X=X.reshape(num_points_per_dim,-1) y=self.func(X) sdv=np.std(y) #maxima=np.max(y) #minima=np.min(y) return sdv class saddlepoint(functions): def __init__(self): self.input_dim=2 self.bounds=OrderedDict({'x1':(-1,1),'x2':(-1,1)}) self.fstar=0 self.min=0 self.ismax=1 self.name='saddlepoint' def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] fval=X[:,0]*X[:,0]-X[:,1]*X[:,1] return fval*self.ismax class sin(functions): def __init__(self,sd=None): self.input_dim=1 self.bounds={'x':(-1,15)} #self.bounds={'x':(0,1)} self.fstar=11 self.min=0 self.ismax=1 self.name='sin' if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd def func(self,x): x=np.asarray(x) fval=np.sin(x) return fval*self.ismax class sincos(functions): def __init__(self,sd=None): self.input_dim=1 self.bounds={'x':(-1,2)} #self.bounds={'x':(0,1)} self.fstar=11 self.min=0 self.ismax=1 self.name='sincos' if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd def func(self,x): x=np.asarray(x) fval=x*np.sin(x)+x*np.cos(2*x) return fval*self.ismax class fourier(functions): ''' Forrester function. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,sd=None): self.bounds = {'x':(0,10)} self.sd=0 self.input_dim = 1 self.ismax=-1 self.min = 4.795 ## approx self.fstar = -9.5083483926941064*self.ismax ## approx self.name='fourier' if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd def func(self,X): X=np.asarray(X) X = X.reshape((len(X),1)) n = X.shape[0] fval = X*np.sin(X)+X*np.cos(2*X) if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return self.ismax*fval.reshape(n,1) + noise class branin(functions): def __init__(self,sd=None): if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd self.input_dim=2 #if sd==None: self.sd = 0 #else: self.sd=sd self.bounds=OrderedDict([('x1',(-5,10)),('x2',(0,15))]) #self.bounds=OrderedDict([('x1',(-20,70)),('x2',(-50,50))]) self.ismax=-1 self.fstar=0.397887*self.ismax self.min=[9.424,2.475] self.name='branin' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) n=X.shape[0] if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] a=1 b=5.1/(4*np.pi**2) c=5/np.pi r=6 s=10 t=1/(8*np.pi) fx=a*(x2-b*x1*x1+c*x1-r)**2+s*(1-t)*np.cos(x1)+s if self.sd==0: return fx*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fx*self.ismax+np.ravel(noise) class forrester(functions): ''' Forrester function. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self, sd=None): if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd self.ismax=-1 self.input_dim = 1 self.min = 0.78 ## approx self.fstar = -6.03*self.ismax ## approx self.bounds = {'x':(0,1)} self.name='forrester' #self.sd=0 def func(self,X): X=np.asarray(X) X = X.reshape((len(X),1)) n = X.shape[0] fval = ((6*X -2)**2)*np.sin(12*X-4) if self.sd!=0: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) else: return fval*self.ismax class rosenbrock(functions): ''' rosenbrock function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=0): if sd==0: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = OrderedDict([('x1',(-2.048,2.048)),('x2',(-2.048,2.048))]) else: self.bounds = bounds self.min = [(0, 0)] self.ismax=-1 self.fstar = 0 self.name = 'Rosenbrock' #self.sd=self.findSdev() def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) n=X.shape[0] n=1 if len(X.shape)==1:# one observation x1=X[0] x2=X[1] else:# multiple observations x1=X[:,0] x2=X[:,1] n=X.shape[0] fx = 100*(x2-x1**2)**2 + (x1-1)**2 if self.sd==0: return fx*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fx*self.ismax+np.ravel(noise) class beale(functions): ''' beale function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=None): if sd==None: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = OrderedDict({'x1':(-1,1),'x2':(-1,1)}) else: self.bounds = bounds self.min = [(3, 0.5)] self.ismax=-1 self.fstar = 0 self.name = 'Beale' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] fval = (1.5-x1+x1*x2)**2+(2.25-x1+x1*x2**2)**2+(2.625-x1+x1*x2**3)**2 n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class dropwave(functions): ''' dropwave function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=None): if sd==None: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = OrderedDict([('x1',(-5.12,5.12)),('x2',(-5.12,5.12))]) else: self.bounds = bounds self.min = [(0, 0)] self.ismax=-1 self.fstar = -1*self.ismax self.name = 'dropwave' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) n=1 if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] fval = - (1+np.cos(12*np.sqrt(x1**2+x2**2))) / (0.5*(x1**2+x2**2)+2) n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class cosines(functions): ''' Cosines function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=None): if sd==None or sd==0: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = OrderedDict([('x1',(0,1)),('x2',(0,1))]) else: self.bounds = bounds self.min = [(0.31426205, 0.30249864)] self.ismax=1 self.fstar = -1.59622468*self.ismax self.name = 'Cosines' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] #X = reshape(X,self.input_dim) #n = X.shape[0] u = 1.6*x1-0.5 v = 1.6*x2-0.5 fval = 1-(u**2 + v**2 - 0.3*np.cos(3*np.pi*u) - 0.3*np.cos(3*np.pi*v) ) return self.ismax*fval class goldstein(functions): ''' Goldstein function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=None): if sd==None or sd==0: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = {'x1':(-2,2),'x2':(-2,2)} else: self.bounds = bounds self.ismax=-1 self.min = [(0,-1)] self.fstar = 3*self.ismax self.name = 'Goldstein' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] fact1a = (x1 + x2 + 1)**2 fact1b = 19 - 14*x1 + 3*x1**2 - 14*x2 + 6*x1*x2 + 3*x2**2 fact1 = 1 + fact1a*fact1b fact2a = (2*x1 - 3*x2)**2 fact2b = 18 - 32*x1 + 12*x1**2 + 48*x2 - 36*x1*x2 + 27*x2**2 fact2 = 30 + fact2a*fact2b fval = fact1*fact2 n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class sixhumpcamel(functions): ''' Six hump camel function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd self.input_dim = 2 if bounds == None: self.bounds = OrderedDict([('x1',(-3,3)),('x2',(-2,2))]) else: self.bounds = bounds self.min = [(0.0898,-0.7126),(-0.0898,0.7126)] self.ismax=-1 self.fstar = -1.0316*self.ismax self.name = 'Six-hump camel' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) n=1 if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] term1 = (4-2.1*x1**2+(x1**4)/3) * x1**2 term2 = x1*x2 term3 = (-4+4*x2**2) * x2**2 fval = term1 + term2 + term3 n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class mccormick(functions): ''' Mccormick function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd self.input_dim = 2 if bounds == None: self.bounds = [(-1.5,4),(-3,4)] else: self.bounds = bounds self.min = [(-0.54719,-1.54719)] self.ismax=-1 self.fstar = -1.9133*self.ismax self.name = 'Mccormick' def func(self,X): X = reshape(X,self.input_dim) x1=X[:,0] x2=X[:,1] term1 = np.sin(x1 + x2) term2 = (x1 - x2)**2 term3 = -1.5*x1 term4 = 2.5*x2 fval = term1 + term2 + term3 + term4 + 1 n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class powers(functions): ''' Powers function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: self.sd=self.findSdev() self.input_dim = 2 if bounds == None: self.bounds = [(-1,1),(-1,1)] else: self.bounds = bounds self.min = [(0,0)] self.fstar = 0 #if sd==None: self.sd = 0 #else: self.sd=sd self.name = 'Sum of Powers' def func(self,x): x = reshape(x,self.input_dim) n = x.shape[0] if x.shape[1] != self.input_dim: return 'wrong input dimension' else: x1 = x[:,0] x2 = x[:,1] fval = abs(x1)**2 + abs(x2)**3 if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,self.sd,n).reshape(n,1) return fval.reshape(n,1) + noise class eggholder(functions): def __init__(self,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: #self.sd=self.findSdev() self.sd=sd self.input_dim = 2 #self.bounds = {'x1':(-512,512),'x2':(-512,512)} self.bounds = [(-512,512),(-512,512)] self.min = [(512,404.2319)] self.ismax=-1 self.fstar = -959.6407*self.ismax self.name = 'Egg-holder' def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) if len(X.shape)==1: x1=X[0] x2=X[1] else: x1=X[:,0] x2=X[:,1] fval = -(x2+47) * np.sin(np.sqrt(abs(x2+x1/2+47))) + -x1 * np.sin(np.sqrt(abs(x1-(x2+47)))) n=X.shape[0] if self.sd==0: return fval*self.ismax else: noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) return fval*self.ismax+np.ravel(noise) class alpine1(functions): ''' Alpine1 function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,input_dim,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: self.sd=self.findSdev() if bounds == None: self.bounds = bounds =[(-10,10)]*input_dim else: self.bounds = bounds self.min = [(0)]*input_dim self.input_dim = input_dim self.ismax=-1 self.fstar = -46*self.ismax self.name='alpine1' def func(self,X): X = reshape(X,self.input_dim) #n = X.shape[0] temp=(X*np.sin(X) + 0.1*X) if len(temp.shape)<=1: fval=np.sum(temp) else: fval = np.sum(temp,axis=1) n=X.shape[0] if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,self.sd,n).reshape(n,1) return self.ismax*fval.reshape(n,1) + noise class alpine2(functions): ''' Alpine2 function :param bounds: the box constraints to define the domain in which the function is optimized. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,input_dim,bounds=None,sd=0): if sd==None or sd==0: self.sd=0 else: self.sd=self.findSdev() if bounds == None: self.bounds = bounds =[(1,10)]*input_dim else: self.bounds = bounds self.min = [(7.917)]*input_dim self.ismax=-1 self.fstar = self.ismax*(-2.808**input_dim) self.input_dim = input_dim self.name='Alpine2' def internal_func(self,X): fval = np.cumprod(np.sqrt(X))[self.input_dim-1]*np.cumprod(np.sin(X))[self.input_dim-1] #fval = np.cumprod(np.sqrt(X)*np.sin(X)) return fval def func(self,X): X=np.asarray(X) X = reshape(X,self.input_dim) #n = X.shape[0] #fval = np.cumprod(np.sqrt(X),axis=1)[:,self.input_dim-1]*np.cumprod(np.sin(X),axis=1)[:,self.input_dim-1] #fval = np.cumprod(np.sqrt(X))[:,self.input_dim-1]*np.cumprod(np.sin(X))[:,self.input_dim-1] fval=[self.ismax*self.internal_func(val) for idx, val in enumerate(X)] fval=np.asarray(fval) #noise = np.random.normal(0,0.1*self.sd,n).reshape(n,1) n=X.shape[0] if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,self.sd,n).reshape(n,1) return self.ismax*fval.reshape(n,1) + noise class gSobol(functions): ''' gSolbol function :param a: one-dimensional array containing the coefficients of the function. :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self,a,bounds=None,sd=None): self.a = a self.input_dim = len(self.a) if bounds == None: self.bounds =[(-4,6)]*self.input_dim else: self.bounds = bounds if not (self.a>0).all(): return 'Wrong vector of coefficients, they all should be positive' self.S_coef = (1/(3*((1+self.a)**2))) / (np.prod(1+1/(3*((1+self.a)**2)))-1) if sd==None: self.sd = 0 else: self.sd=sd self.ismax=-1 self.fstar=0# in correct #self.fstar=20*self.ismax# in correct self.name='gSobol' def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] aux = (abs(4*X-2)+np.ones(n).reshape(n,1)*self.a)/(1+np.ones(n).reshape(n,1)*self.a) fval = np.cumprod(aux,axis=1)[:,self.input_dim-1] n=X.shape[0] if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,self.sd,n).reshape(n,1) return self.ismax*fval.reshape(n,1) + noise ##### class ackley(functions): ''' Ackley function :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self, input_dim, bounds=None,sd=None): self.input_dim = input_dim if sd==None or sd==0: self.sd=0 else: self.sd=sd #self.sd=self.findSdev() if bounds == None: self.bounds =[(-32.768,32.768)]*self.input_dim else: self.bounds = bounds self.min = [(0.)*self.input_dim] self.fstar = 0 self.ismax=-1 self.name='ackley' def func(self,X): X = reshape(X,self.input_dim) #print X #n = X.shape[0] fval = (20+np.exp(1)-20*np.exp(-0.2*np.sqrt((X**2).sum(1)/self.input_dim))-np.exp(np.cos(2*np.pi*X).sum(1)/self.input_dim)) n=X.shape[0] if self.sd ==0: noise = np.zeros(n).reshape(n,1) else: noise = np.random.normal(0,self.sd,n).reshape(n,1) return self.ismax*fval.reshape(n,1) + noise ##### class hartman_6d: ''' Ackley function :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self, bounds=None,sd=None): if sd==None or sd==0: self.sd=0 else: self.sd=sd #self.sd=self.findSdev() self.input_dim = 6 if bounds == None: self.bounds =[(0,1)]*self.input_dim else: self.bounds = bounds self.min = [(0.)*self.input_dim] self.ismax=-1 #self.fstar = -3.32237*self.ismax self.fstar = -3.05*self.ismax self.name='hartman_6d' def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] alpha = [1.0, 1.2, 3.0, 3.2]; A = [[10, 3, 17, 3.5, 1.7, 8], [0.05, 10, 17, 0.1, 8, 14], [3, 3.5, 1.7, 10, 17, 8], [17, 8, 0.05, 10, 0.1, 14]] A=np.asarray(A) P = [[1312, 1696, 5569, 124, 8283, 5886], [2329, 4135, 8307, 3736, 1004, 9991], [2348, 1451, 3522, 2883, 3047, 6650], [4047, 8828, 8732, 5743, 1091, 381]] P=np.asarray(P) c=10**(-4) P=np.multiply(P,c) outer = 0; fval =np.zeros((n,1)) for idx in range(n): outer = 0; for ii in range(4): inner = 0; for jj in range(6): xj = X[idx,jj] Aij = A[ii, jj] Pij = P[ii, jj] inner = inner + Aij*(xj-Pij)**2 new = alpha[ii] * np.exp(-inner) outer = outer + new fval[idx] = -(2.58 + outer) / 1.94; noise = np.random.normal(0,self.sd,n).reshape(n,1) if n==1: return self.ismax*(fval[0][0])+noise else: return self.ismax*(fval)+noise """ class hartman_4d: ''' Ackley function :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self, bounds=None,sd=None): self.input_dim = 4 if bounds == None: self.bounds =[(0,1)]*self.input_dim else: self.bounds = bounds self.min = [(0.)*self.input_dim] self.fstar = -3.32237 self.ismax=-1 self.name='hartman_4d' def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] alpha = [1.0, 1.2, 3.0, 3.2]; A = [[10, 3, 17, 3.5, 1.7, 8], [0.05, 10, 17, 0.1, 8, 14], [3, 3.5, 1.7, 10, 17, 8], [17, 8, 0.05, 10, 0.1, 14]] A=np.asarray(A) P = [[1312, 1696, 5569, 124, 8283, 5886], [2329, 4135, 8307, 3736, 1004, 9991], [2348, 1451, 3522, 2883, 3047, 6650], [4047, 8828, 8732, 5743, 1091, 381]] P=np.asarray(P) c=10**(-4) P=np.multiply(P,c) outer = 0; fval =np.zeros((n,1)) for idx in range(n): X_idx=X[idx,:] outer = 0; for ii in range(4): inner = 0; for jj in range(4): xj = X_idx[jj] Aij = A[ii, jj] Pij = P[ii, jj] inner = inner + Aij*(xj-Pij)**2 new = alpha[ii] * np.exp(-inner) outer = outer + new fval[idx] = (1.1 - outer) / 0.839; if n==1: return self.ismax*(fval[0][0]) else: return self.ismax*(fval) """ class hartman_3d(functions): ''' hartman_3d: function :param sd: standard deviation, to generate noisy evaluations of the function. ''' def __init__(self, bounds=None,sd=None): if sd==None or sd==0: self.sd=0 else: self.sd=sd #self.sd=self.findSdev() self.input_dim = 3 if bounds == None: self.bounds =[(0,1)]*self.input_dim else: self.bounds = bounds self.min = [0.114614,0.555649,0.852547] self.ismax=-1 self.fstar = -3.86278*self.ismax #self.fstar = -3.7078*self.ismax self.name='hartman_3d' def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] alpha = [1.0, 1.2, 3.0, 3.2]; A = [[3.0, 10, 30], [0.1, 10, 35], [3.0, 10, 30], [0.1, 10, 35]] A=np.asarray(A) P = [[3689, 1170, 2673], [4699, 4387, 7470], [1091, 8732, 5547], [381, 5743, 8828]] P=np.asarray(P) c=10**(-4) P=np.multiply(P,c) outer = 0; fval =np.zeros((n,1)) for idx in range(n): outer = 0; for ii in range(4): inner = 0; for jj in range(3): xj = X[idx,jj] Aij = A[ii, jj] Pij = P[ii, jj] inner = inner + Aij*(xj-Pij)**2 new = alpha[ii] * np.exp(-inner) outer = outer + new fval[idx] = -outer; noise = np.random.normal(0,self.sd,n).reshape(n,1) #noise=0 if n==1: return self.ismax*(fval[0][0])+noise else: return self.ismax*(fval)+noise class mixture(functions): ''' a scalable gaussian mixture function :param sd: standard deviation to generate noisy exaluations of the functions :param peaks: number of gaussian peaks used ''' def __init__(self,bounds=None, peaks=3): self.input_dim=2 self.peaks=peaks self.sd=0 if bounds == None: self.bounds =[(0,1)]*self.input_dim else: self.bounds = bounds self.min = [(0.)*self.input_dim] self.fstar=-1 self.ismax=-1 self.name="mixture" self.sd=self.findSdev() def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] y=2*multivariate_normal.pdf(X,mean=[0.5,0.5],cov=0.07*np.eye(2)) if self.peaks>=2: y+=1.8*multivariate_normal.pdf(X,mean=[0.2,0.2],cov=0.03*np.eye(2)) if self.peaks>=3: y+=1.7*multivariate_normal.pdf(X,mean=[0.7,0.7],cov=0.07*np.eye(2)) if self.peaks>=4: y+=1*multivariate_normal.pdf(X,mean=[0.8,0.5],cov=0.02*np.eye(2)) if self.peaks>=5: y+=1.7*multivariate_normal.pdf(X,mean=[0.4,0.6],cov=0.005*np.eye(2)) if self.peaks>=6: y+=1.75*multivariate_normal.pdf(X,mean=[0.3,0.4],cov=0.0012*np.eye(2)) if self.peaks>=7: y+=1.75*multivariate_normal.pdf(X,mean=[0.9,0.8],cov=0.01*np.eye(2)) if self.peaks>=8: y+=1.75*multivariate_normal.pdf(X,mean=[0.2,0.6],cov=0.01*np.eye(2)) if self.peaks>=9: y+=1.75*multivariate_normal.pdf(X,mean=[0.9,0.3],cov=0.01*np.eye(2)) return y class gaussian(functions): ''' a scalable gaussian mixture function :param sd: standard deviation to generate noisy exaluations of the functions :param peaks: number of gaussian peaks used ''' def __init__(self,bounds=None, dim=3): self.input_dim=dim self.sd=0 if bounds == None: self.bounds =[(0,1)]*self.input_dim else: self.bounds = bounds self.min = [(0.)*self.input_dim] self.fstar=-1 self.ismax=-1 self.name="gaussian" self.sd=self.findSdev() def func(self,X): X = reshape(X,self.input_dim) n = X.shape[0] noise = np.random.normal(0,self.sd,n).reshape(n,1) y=multivariate_normal.pdf(X,mean=0.5*np.ones(self.input_dim),cov=np.eye(self.input_dim)) return y

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null

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22aaa82a2864215312f103824e22eea0738b7ea9

208

py

Python

args_kwargs.py

jjberg83/python_eksperimenter

ea26a6bd4a0cf71e69cbf5015a06db30de811b45

[ "MIT" ]

null

args_kwargs.py

jjberg83/python_eksperimenter

ea26a6bd4a0cf71e69cbf5015a06db30de811b45

[ "MIT" ]

null

args_kwargs.py

jjberg83/python_eksperimenter

ea26a6bd4a0cf71e69cbf5015a06db30de811b45

[ "MIT" ]

null

""" https://edabit.com/challenge/ogjDWJAT2kTXEzkD5 https://www.programiz.com/python-programming/args-and-kwargs#:~:text=Python%20has%20*args%20which%20allow,to%20pass%20variable%20length%20arguments. """

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22c01daa2fa85b8fe837a2ec1d3ac93b1b19ddaf

143

py

Python

src/pcc_data_exchange/plugins/sinopia/__init__.py

LD4P/pcc-data-exchange

bb0de1c85928e582ab51f469740b06cc0870d413

[ "Apache-2.0" ]

null

src/pcc_data_exchange/plugins/sinopia/__init__.py

LD4P/pcc-data-exchange

bb0de1c85928e582ab51f469740b06cc0870d413

[ "Apache-2.0" ]

null

src/pcc_data_exchange/plugins/sinopia/__init__.py

LD4P/pcc-data-exchange

bb0de1c85928e582ab51f469740b06cc0870d413

[ "Apache-2.0" ]

null

from airflow.plugins_manager import AirflowPlugin from flask import Blueprint class SinopiaPlugin(AirflowPlugin): name = "sinopia_plugin"

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22c72b9f198ab9f1bc3185a4d6e873e252d1aef5

195

py

Python

cegs_portal/search/views/index.py

ReddyLab/cegs-portal

a83703a3557167be328c24bfb866b6aa019ba059

[ "MIT" ]

null

cegs_portal/search/views/index.py

ReddyLab/cegs-portal

a83703a3557167be328c24bfb866b6aa019ba059

[ "MIT" ]

null

cegs_portal/search/views/index.py

ReddyLab/cegs-portal

a83703a3557167be328c24bfb866b6aa019ba059

[ "MIT" ]

null

from django.shortcuts import render from cegs_portal.search.forms import SearchForm def index(request): form = SearchForm() return render(request, "search/index.html", {"form": form})

21.666667

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a3a6a16c4407acc39270d40d3c50a5347af46f25

7,323

py

Python

sdl2/test/sdlgfx_test.py

papagiannakis/py-sdl2

c8ff267761ce19d7714e72a4a3eb97a375c06fc6

[ "CC0-1.0" ]

222

2017-08-19T00:51:59.000Z

2022-02-05T19:39:33.000Z

sdl2/test/sdlgfx_test.py

Sahil-pixel/py-sdl2

e5c8cbaccfda4f20f35f58bc8d00e0f533b30c3b

[ "CC0-1.0" ]

103

2017-08-20T17:13:05.000Z

2022-02-05T20:20:01.000Z

sdl2/test/sdlgfx_test.py

Sahil-pixel/py-sdl2

e5c8cbaccfda4f20f35f58bc8d00e0f533b30c3b

[ "CC0-1.0" ]

54

2017-08-20T17:13:00.000Z

2022-01-14T23:51:13.000Z

import os import sys import pytest from sdl2 import SDL_Init, SDL_Quit, SDL_INIT_VIDEO from sdl2 import surface sdlgfx = pytest.importorskip("sdl2.sdlgfx") class TestSDLGFX(object): __tags__ = ["sdl", "sdlgfx"] @classmethod def setup_class(cls): if SDL_Init(SDL_INIT_VIDEO) != 0: raise pytest.skip('Video subsystem not supported') @classmethod def teardown_class(cls): SDL_Quit() @pytest.mark.skip("not implemented") def test_FPSManager(self): pass @pytest.mark.skip("not implemented") def test_SDL_initFramerate(self): pass @pytest.mark.skip("not implemented") def test_SDL_getFramerate(self): pass @pytest.mark.skip("not implemented") def test_SDL_setFramerate(self): pass @pytest.mark.skip("not implemented") def test_SDL_getFramecount(self): pass @pytest.mark.skip("not implemented") def test_SDL_framerateDelay(self): pass @pytest.mark.skip("not implemented") def test_pixelColor(self): pass @pytest.mark.skip("not implemented") def test_pixelRGBA(self): pass @pytest.mark.skip("not implemented") def test_hlineColor(self): pass @pytest.mark.skip("not implemented") def test_hlineRGBA(self): pass @pytest.mark.skip("not implemented") def test_vlineColor(self): pass @pytest.mark.skip("not implemented") def test_vlineRGBA(self): pass @pytest.mark.skip("not implemented") def test_rectangleColor(self): pass @pytest.mark.skip("not implemented") def test_rectangleRGBA(self): pass @pytest.mark.skip("not implemented") def test_roundedRectangleColor(self): pass @pytest.mark.skip("not implemented") def test_roundedRectangleRGBA(self): pass @pytest.mark.skip("not implemented") def test_boxColor(self): pass @pytest.mark.skip("not implemented") def test_boxRGBA(self): pass @pytest.mark.skip("not implemented") def test_roundedBoxColor(self): pass @pytest.mark.skip("not implemented") def test_roundedBoxRGBA(self): pass @pytest.mark.skip("not implemented") def test_lineColor(self): pass @pytest.mark.skip("not implemented") def test_lineRGBA(self): pass @pytest.mark.skip("not implemented") def test_aalineColor(self): pass @pytest.mark.skip("not implemented") def test_aalineRGBA(self): pass @pytest.mark.skip("not implemented") def test_thickLineColor(self): pass @pytest.mark.skip("not implemented") def test_thickLineRGBA(self): pass @pytest.mark.skip("not implemented") def test_circleColor(self): pass @pytest.mark.skip("not implemented") def test_circleRGBA(self): pass @pytest.mark.skip("not implemented") def test_arcColor(self): pass @pytest.mark.skip("not implemented") def test_arcRGBA(self): pass @pytest.mark.skip("not implemented") def test_aacircleColor(self): pass @pytest.mark.skip("not implemented") def test_aacircleRGBA(self): pass @pytest.mark.skip("not implemented") def test_filledCircleColor(self): pass @pytest.mark.skip("not implemented") def test_filledCircleRGBA(self): pass @pytest.mark.skip("not implemented") def test_ellipseColor(self): pass @pytest.mark.skip("not implemented") def test_ellipseRGBA(self): pass @pytest.mark.skip("not implemented") def test_aaellipseColor(self): pass @pytest.mark.skip("not implemented") def test_aaellipseRGBA(self): pass @pytest.mark.skip("not implemented") def test_filledEllipseColor(self): pass @pytest.mark.skip("not implemented") def test_filledEllipseRGBA(self): pass @pytest.mark.skip("not implemented") def test_pieColor(self): pass @pytest.mark.skip("not implemented") def test_pieRGBA(self): pass @pytest.mark.skip("not implemented") def test_filledPieColor(self): pass @pytest.mark.skip("not implemented") def test_filledPieRGBA(self): pass @pytest.mark.skip("not implemented") def test_trigonColor(self): pass @pytest.mark.skip("not implemented") def test_trigonRGBA(self): pass @pytest.mark.skip("not implemented") def test_aatrigonColor(self): pass @pytest.mark.skip("not implemented") def test_aatrigonRGBA(self): pass @pytest.mark.skip("not implemented") def test_filledTrigonColor(self): pass @pytest.mark.skip("not implemented") def test_filledTrigonRGBA(self): pass @pytest.mark.skip("not implemented") def test_polygonColor(self): pass @pytest.mark.skip("not implemented") def test_polygonRGBA(self): pass @pytest.mark.skip("not implemented") def test_aapolygonColor(self): pass @pytest.mark.skip("not implemented") def test_aapolygonRGBA(self): pass @pytest.mark.skip("not implemented") def test_filledPolygonColor(self): pass @pytest.mark.skip("not implemented") def test_filledPolygonRGBA(self): pass @pytest.mark.skip("not implemented") def test_texturedPolygon(self): pass @pytest.mark.skip("not implemented") def test_bezierColor(self): pass @pytest.mark.skip("not implemented") def test_bezierRGBA(self): pass @pytest.mark.skip("not implemented") def test_gfxPrimitivesSetFont(self): pass @pytest.mark.skip("not implemented") def test_gfxPrimitivesSetFontRotation(self): pass @pytest.mark.skip("not implemented") def test_characterColor(self): pass @pytest.mark.skip("not implemented") def test_characterRGBA(self): pass @pytest.mark.skip("not implemented") def test_stringColor(self): pass @pytest.mark.skip("not implemented") def test_stringRGBA(self): pass @pytest.mark.skip("not implemented") def test_rotozoomSurface(self): pass @pytest.mark.skip("not implemented") def test_rotozoomSurfaceXY(self): pass @pytest.mark.skip("not implemented") def test_rotozoomSurfaceSize(self): pass @pytest.mark.skip("not implemented") def test_rotozoomSurfaceSizeXY(self): pass @pytest.mark.skip("not implemented") def test_zoomSurface(self): pass @pytest.mark.skip("not implemented") def test_zoomSurfaceSize(self): pass @pytest.mark.skip("not implemented") def test_shrinkSurface(self): pass def test_rotateSurface90Degrees(self): w, h = 470, 530 sf = surface.SDL_CreateRGBSurface(0, w, h, 32, 0, 0, 0, 0) assert isinstance(sf.contents, surface.SDL_Surface) rotsf = sdlgfx.rotateSurface90Degrees(sf, 1) assert isinstance(rotsf.contents, surface.SDL_Surface) assert rotsf.contents.w == h assert rotsf.contents.h == w surface.SDL_FreeSurface(rotsf) surface.SDL_FreeSurface(sf)

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4

a3c60fff8cfc39e43dade01a5dc17b43609ec2d6

326

py

Python

leaderboard_entries/pytorch_likelihood.py

philomenec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

413

2018-09-18T17:49:44.000Z

2022-03-23T12:25:41.000Z

leaderboard_entries/pytorch_likelihood.py

aliang-rec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

15

2018-11-08T17:04:21.000Z

2021-11-30T19:20:27.000Z

leaderboard_entries/pytorch_likelihood.py

aliang-rec/reco-gym

f8553d197f42ec2f415aefce48525d0e9b10ddaa

[ "Apache-2.0" ]

81

2018-09-22T02:28:55.000Z

2022-03-30T14:03:01.000Z

from recogym import build_agent_init from recogym.agents import PyTorchMLRAgent, pytorch_mlr_args pytorch_mlr_args['n_epochs'] = 30 pytorch_mlr_args['learning_rate'] = 0.01, pytorch_mlr_args['ll_IPS'] = False, pytorch_mlr_args['alpha'] = 1.0 agent = build_agent_init('PyTorchMLRAgent', PyTorchMLRAgent, {**pytorch_mlr_args})

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null

1

0

1

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null

0

4

a3c68b80a3ed2cfc8cf74f7679a0fb5d0fcf63c3

284

py

Python

movielist_app/modelsv1.py

rhedwan/BuildingDjangoAPI

09c7513ef43390435c7de78e8812083796b9a0fe

[ "MIT" ]

null

movielist_app/modelsv1.py

rhedwan/BuildingDjangoAPI

09c7513ef43390435c7de78e8812083796b9a0fe

[ "MIT" ]

null

movielist_app/modelsv1.py

rhedwan/BuildingDjangoAPI

09c7513ef43390435c7de78e8812083796b9a0fe

[ "MIT" ]

null

from django.db import models # Create your models here. class Movie(models.Model): name = models.CharField(max_length=50) description = models.CharField(max_length=200) active= models.BooleanField(default = True) def __str__(self): return self.name

23.666667

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a3cc0b790418490fcaefb984a7158ac9ce6c761f

74

py

Python

websauna/depot/models.py

ooduor/websauna.depot

4992c28a8e35d4b22f7ff3a8b042fa74fca4ede4

[ "MIT" ]

null

websauna/depot/models.py

ooduor/websauna.depot

4992c28a8e35d4b22f7ff3a8b042fa74fca4ede4

[ "MIT" ]

null

websauna/depot/models.py

ooduor/websauna.depot

4992c28a8e35d4b22f7ff3a8b042fa74fca4ede4

[ "MIT" ]

null

"""Websauna Depot models. Place your SQLAlchemy models in this file. """

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43

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1

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null

0

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4

a3eec4439daefdc979cf5f3a43952191078a58e0

73

py

Python

test.py

188806879/TBD44

f30fdf95d09b788383fe41218b1ebf9237e6ed0f

[ "MIT" ]

null

test.py

188806879/TBD44

f30fdf95d09b788383fe41218b1ebf9237e6ed0f

[ "MIT" ]

null

test.py

188806879/TBD44

f30fdf95d09b788383fe41218b1ebf9237e6ed0f

[ "MIT" ]

null

num = 10 print(num) print("手下修") print("我是经理谁谁敢动我的东西？") ni shi sha bi

9.125

22

0.657534

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73

4

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73

7

23

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0

null

0

null

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0

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0

null

0

1

0

1

0

4

a3ef74c5d4110df90cf0f4fc463e4fbe85c5f274

154

py

Python

myvenv/bin/django-admin.py

qq565999484/django_learn

520bc9ddfb6a4d78e85e0a29871838bb7fb66c80

[ "Apache-2.0" ]

null

myvenv/bin/django-admin.py

qq565999484/django_learn

520bc9ddfb6a4d78e85e0a29871838bb7fb66c80

[ "Apache-2.0" ]

null

myvenv/bin/django-admin.py

qq565999484/django_learn

520bc9ddfb6a4d78e85e0a29871838bb7fb66c80

[ "Apache-2.0" ]

null

#!/Users/ios2/django_learn/myvenv/bin/python3.6 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

25.666667

47

0.785714

21

154

5.190476

0.857143

0

0.021583

0.097403

154

5

48

30.8

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0

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1

0

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null

0

1

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null

0

1

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1

0

4

43366a63035d965e61ae290aa23425ac6be0c20d

162

py

Python

vizdoomaze/envs/vizdoomazethree7.py

fanyuzeng/Vizdoomaze

5b444f2d861c908c4d96ae374bcce660d364f22e

[ "MIT" ]

3

2020-09-25T16:00:49.000Z

2020-10-29T10:32:30.000Z

vizdoomaze/envs/vizdoomazethree7.py

fanyuzeng/Vizdoomaze

5b444f2d861c908c4d96ae374bcce660d364f22e

[ "MIT" ]

null

vizdoomaze/envs/vizdoomazethree7.py

fanyuzeng/Vizdoomaze

5b444f2d861c908c4d96ae374bcce660d364f22e

[ "MIT" ]

1

2021-12-17T07:50:47.000Z

from vizdoomaze.envs.vizdoomenv import VizdoomEnv class vizdoomazeThree7(VizdoomEnv): def __init__(self): super(vizdoomazeThree7, self).__init__(60)

27

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162

6.882353

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162

6

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null

0

1

0

null

0

1

0

1

0

4

433d637616dae001912b5dba289fae6e35c542e6

191

py

Python

_service/utils/isValidUrl.py

Psyconical/msdocs-python-flask-webapp-quickstart

70d31722b9b3ccafc19d2e2089fa05d99fa2514b

[ "MIT" ]

null

_service/utils/isValidUrl.py

Psyconical/msdocs-python-flask-webapp-quickstart

70d31722b9b3ccafc19d2e2089fa05d99fa2514b

[ "MIT" ]

null

_service/utils/isValidUrl.py

Psyconical/msdocs-python-flask-webapp-quickstart

70d31722b9b3ccafc19d2e2089fa05d99fa2514b

[ "MIT" ]

null

from urllib.parse import urlparse def is_valid(url): # Function to check the provided link whether it works parsed = urlparse(url) return bool(parsed.netloc) and bool(parsed.scheme)

38.2

74

0.759162

29

191

4.965517

0.827586

0.138889

0

0.167539

191

5

75

38.2

0.90566

0.272251

0

1

0.25

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null

0

1

0

null

0

1

0

1

0

4

433e24cde0a8783bbd3ad046738396dd5049fe1d

243

py

Python

foreignform/models.py

uditagarwal/django-foreignform

45916b7e2413b38e2d33ce263913a4d01e932ddf

[ "MIT" ]

14

2018-06-27T04:44:14.000Z

2021-10-05T17:55:01.000Z

foreignform/models.py

uditagarwal/django-foreignform

45916b7e2413b38e2d33ce263913a4d01e932ddf

[ "MIT" ]

15

2018-04-16T13:50:04.000Z

2021-10-05T13:37:07.000Z

foreignform/models.py

uditagarwal/django-foreignform

45916b7e2413b38e2d33ce263913a4d01e932ddf

[ "MIT" ]

9

2018-07-02T09:40:49.000Z

2021-10-05T11:35:39.000Z

from django.db import models from .fields import JSONField class ForeignFormBaseModel(models.Model): jsonSchema = JSONField(blank=True, null=True) uiSchema = JSONField(blank=True, null=True) class Meta: abstract = True

20.25

49

0.72428

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243

6.068966

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0.159091

0.204545

0.25

0.295455

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0.193416

243

11

50

22.090909

0.897959

0

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false

0

0.285714

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null

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null

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4