xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

b530bd6817587ae7bf40c4c98c7e591a1c852149

22

py

Python

hqc/__init__.py

leockl/helstrom-quantum-centroid-classifier

9f5d056e98c255aa0bbc9e22ffea6c66627c3189

[ "BSD-3-Clause" ]

7

2020-01-25T02:43:41.000Z

2021-10-21T21:17:03.000Z

hqc/__init__.py

leockl/helstrom-quantum-centroid-classifier

9f5d056e98c255aa0bbc9e22ffea6c66627c3189

[ "BSD-3-Clause" ]

1

2020-01-27T07:15:44.000Z

2020-01-27T07:16:03.000Z

hqc/__init__.py

leockl/helstrom-quantum-centroid-classifier

9f5d056e98c255aa0bbc9e22ffea6c66627c3189

[ "BSD-3-Clause" ]

2

2020-01-27T07:14:17.000Z

2020-12-17T08:04:22.000Z

from .hqc import HQC

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1

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6

b54a19fb77035d3d0a72edba4b80d0425d565c25

39

py

Python

amocrm_asterisk_ng/telephony/impl/instances/asterisk_16/ami_handlers/ami_store/impl/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/telephony/impl/instances/asterisk_16/ami_handlers/ami_store/impl/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/telephony/impl/instances/asterisk_16/ami_handlers/ami_store/impl/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

from .AmiStoreImpl import AmiStoreImpl

19.5

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6

b54c79250fa758411c671299b102752ca7202fea

79

py

Python

foundry/__init__.py

MLMI2-CSSI/foundry

d72f3af2591f678149e303ab217d6badcda92f09

[ "MIT" ]

10

2020-09-11T01:40:46.000Z

2022-02-24T05:02:35.000Z

foundry/__init__.py

MLMI2-CSSI/foundry

d72f3af2591f678149e303ab217d6badcda92f09

[ "MIT" ]

73

2020-02-14T20:11:56.000Z

2022-03-31T17:16:18.000Z

foundry/__init__.py

MLMI2-CSSI/foundry

d72f3af2591f678149e303ab217d6badcda92f09

[ "MIT" ]

4

2020-06-24T20:11:27.000Z

2022-01-29T02:08:07.000Z

from .foundry import Foundry from . import models from . import xtract_method

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6

b56593bd8246cc536ddb96a956890292cedf9933

45

py

Python

FlyBIDS/__init__.py

PennLINC/FlyBIDS

0b44d624c75f537c668d75664c239c51100bdf8d

[ "MIT" ]

null

FlyBIDS/__init__.py

PennLINC/FlyBIDS

0b44d624c75f537c668d75664c239c51100bdf8d

[ "MIT" ]

null

FlyBIDS/__init__.py

PennLINC/FlyBIDS

0b44d624c75f537c668d75664c239c51100bdf8d

[ "MIT" ]

1

2021-11-25T21:33:13.000Z

from FlyBIDS.BIDSLayout import FlyBIDSLayout

22.5

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6

b56b8373a52a1ad06a645595f23ac13235e600ec

204

py

Python

SimPEG/PF/__init__.py

kimjaed/simpeg

b8d716f86a4ea07ba3085fabb24c2bc974788040

[ "MIT" ]

3

2020-11-27T03:18:28.000Z

2022-03-18T01:29:58.000Z

SimPEG/PF/__init__.py

kimjaed/simpeg

b8d716f86a4ea07ba3085fabb24c2bc974788040

[ "MIT" ]

null

SimPEG/PF/__init__.py

kimjaed/simpeg

b8d716f86a4ea07ba3085fabb24c2bc974788040

[ "MIT" ]

1

2020-05-26T17:00:53.000Z

from . import MagAnalytics from . import GravAnalytics from . import BaseMag from . import Magnetics from . import BaseGrav from . import Gravity from . import MagneticsDriver from . import GravityDriver

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1

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6

b59393335f0033da58f47547bc08f9edcb4ee947

1,086

py

Python

100 Days of Code/Day 007/hangman_art.py

jburke234/learning

f192e1ab5d6ec4a7d7dfc66d9e8d1b170e6685ea

[ "MIT" ]

null

100 Days of Code/Day 007/hangman_art.py

jburke234/learning

f192e1ab5d6ec4a7d7dfc66d9e8d1b170e6685ea

[ "MIT" ]

null

100 Days of Code/Day 007/hangman_art.py

jburke234/learning

f192e1ab5d6ec4a7d7dfc66d9e8d1b170e6685ea

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Mar 30 23:08:01 2021 @author: James """ stages = [''' +---+ | | O | /|\ | / \ | | ========= ''', ''' +---+ | | O | /|\ | / | | ========= ''', ''' +---+ | | O | /|\ | | | ========= ''', ''' +---+ | | O | /| | | | =========''', ''' +---+ | | O | | | | | ========= ''', ''' +---+ | | O | | | | ========= ''', ''' +---+ | | | | | | ========= '''] logo = ''' _ | | | |__ __ _ _ __ __ _ _ __ ___ __ _ _ __ | '_ \ / _` | '_ \ / _` | '_ ` _ \ / _` | '_ \ | | | | (_| | | | | (_| | | | | | | (_| | | | | |_| |_|\__,_|_| |_|\__, |_| |_| |_|\__,_|_| |_| __/ | |___/ '''

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6

a91d6f20ef10bea5a5871eb7093356312447b1f0

92

py

Python

install/super_prove/lib/pyzz/__init__.py

ljbrooks/superkb_release

cd8c476ba687dea3cdd979eb4b1a7bd9471ece66

[ "MIT" ]

null

install/super_prove/lib/pyzz/__init__.py

ljbrooks/superkb_release

cd8c476ba687dea3cdd979eb4b1a7bd9471ece66

[ "MIT" ]

null

install/super_prove/lib/pyzz/__init__.py

ljbrooks/superkb_release

cd8c476ba687dea3cdd979eb4b1a7bd9471ece66

[ "MIT" ]

null

from _pyzz import * from pyzz import * import utils import bmc import primitives import tt

11.5

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null

0

1

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6

a9617ae27481bbd8ad002b5466041f24da135099

4,506

py

Python

src/PDBParser.py

yingyulou/PDBTools

7dadc1916d9a5c71b6e05a6e52c08820344640ec

[ "MIT" ]

8

2018-10-22T05:20:06.000Z

2021-08-17T11:01:29.000Z

src/PDBParser.py

yingyulou/PDBTools

7dadc1916d9a5c71b6e05a6e52c08820344640ec

[ "MIT" ]

null

src/PDBParser.py

yingyulou/PDBTools

7dadc1916d9a5c71b6e05a6e52c08820344640ec

[ "MIT" ]

3

2018-12-15T04:41:41.000Z

2020-08-09T06:30:01.000Z

#!/usr/bin/env python3 # coding=UTF-8 ''' PDBParser ========= PDB parser function define. ''' # Import Python Lib from os.path import splitext, basename from numpy import array # Import PDBTools from .Protein import Protein from .Chain import Chain from .Residue import Residue from .Atom import Atom from .Util import IsH ################################################################################ # Parse PDB File ################################################################################ def Load(pdbFilePath, parseHBool = False): proObj = Protein(splitext(basename(pdbFilePath))[0]) lastChainName = None lastResName = None lastResNum = None lastResIns = None with open(pdbFilePath) as f: for line in f: if line[:4] != 'ATOM': continue atomName = line[12:16].strip() if IsH(atomName) and not parseHBool: continue atomNum = int(line[6:11]) atomAltLoc = line[16].strip() resName = line[17:20].strip() chainName = line[21].strip() resNum = int(line[22:26]) resIns = line[26].strip() atomCoord = array((float(line[30:38]), float(line[38:46]), float(line[46:54]))) atomOccupancy = line[54:60].strip() atomTempFactor = line[60:66].strip() atomElement = line[76:78].strip() atomCharge = line[78:80].strip() if chainName != lastChainName: lastChainName, lastResNum, lastResName, lastResIns = chainName, resNum, resName, resIns chainObj = Chain(chainName, proObj) resObj = Residue(resName, resNum, resIns, chainObj) elif lastResNum != resNum or lastResName != resName or lastResIns != resIns: lastResNum, lastResName, lastResIns = resNum, resName, resIns resObj = Residue(resName, resNum, resIns, chainObj) Atom(atomName, atomNum, atomCoord, atomAltLoc, atomOccupancy, atomTempFactor, atomElement, atomCharge, resObj) return proObj ################################################################################ # Parse PDB File With Model ################################################################################ def LoadModel(pdbFilePath, parseHBool = False): pdbIdStr = splitext(basename(pdbFilePath))[0] proObj = Protein(pdbIdStr) proObjList = [proObj] lastChainName = None lastResName = None lastResNum = None lastResIns = None with open(pdbFilePath) as f: for line in f: if line[:5] == 'MODEL': proObj = Protein(pdbIdStr, int(line[10:14])) proObjList.append(proObj) lastChainName = None lastResName = None lastResNum = None lastResIns = None continue elif line[:4] != 'ATOM': continue atomName = line[12:16].strip() if IsH(atomName) and not parseHBool: continue atomNum = int(line[6:11]) atomAltLoc = line[16].strip() resName = line[17:20].strip() chainName = line[21].strip() resNum = int(line[22:26]) resIns = line[26].strip() atomCoord = array((float(line[30:38]), float(line[38:46]), float(line[46:54]))) atomOccupancy = line[54:60].strip() atomTempFactor = line[60:66].strip() atomElement = line[76:78].strip() atomCharge = line[78:80].strip() if chainName != lastChainName: lastChainName, lastResNum, lastResName, lastResIns = chainName, resNum, resName, resIns chainObj = Chain(chainName, proObj) resObj = Residue(resName, resNum, resIns, chainObj) elif lastResNum != resNum or lastResName != resName or lastResIns != resIns: lastResNum, lastResName, lastResIns = resNum, resName, resIns resObj = Residue(resName, resNum, resIns, chainObj) Atom(atomName, atomNum, atomCoord, atomAltLoc, atomOccupancy, atomTempFactor, atomElement, atomCharge, resObj) if not proObjList[0]: proObjList.pop(0) return proObjList

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null

0

6

8d225e482f7c312fd2cf296e450e24a5936008f1

41

py

Python

tests/test_bot.py

dead-beef/telegram-bot

3abe33e179ddc65093ec55a4fb53d64d948d4e86

[ "MIT" ]

null

tests/test_bot.py

dead-beef/telegram-bot

3abe33e179ddc65093ec55a4fb53d64d948d4e86

[ "MIT" ]

null

tests/test_bot.py

dead-beef/telegram-bot

3abe33e179ddc65093ec55a4fb53d64d948d4e86

[ "MIT" ]

null

import pytest from bot.bot import Bot

6.833333

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true

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1

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6

8d511c517334c2f9153ee7a1500daa5144ecd02a

2,516

py

Python

python/ql/test/library-tests/frameworks/stdlib/xml_dom.py

adityasharad/ql

439dcc0731ae665402466a13daf12737ea3a2a44

[ "MIT" ]

643

2018-08-03T11:16:54.000Z

2020-04-27T23:10:55.000Z

python/ql/test/library-tests/frameworks/stdlib/xml_dom.py

DirtyApexAlpha/codeql

4c59b0d2992ee0d90cc2f46d6a85ac79e1d57f21

[ "MIT" ]

1,880

2018-08-03T11:28:32.000Z

2020-04-28T13:18:51.000Z

python/ql/test/library-tests/frameworks/stdlib/xml_dom.py

DirtyApexAlpha/codeql

4c59b0d2992ee0d90cc2f46d6a85ac79e1d57f21

[ "MIT" ]

218

2018-08-03T11:16:58.000Z

2020-04-24T02:24:00.000Z

from io import StringIO import xml.dom.minidom import xml.dom.pulldom import xml.sax x = "some xml" # minidom xml.dom.minidom.parse(StringIO(x)) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' decodeOutput=xml.dom.minidom.parse(..) getAPathArgument=StringIO(..) xml.dom.minidom.parse(file=StringIO(x)) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' decodeOutput=xml.dom.minidom.parse(..) getAPathArgument=StringIO(..) xml.dom.minidom.parseString(x) # $ decodeFormat=XML decodeInput=x xmlVuln='XML bomb' decodeOutput=xml.dom.minidom.parseString(..) xml.dom.minidom.parseString(string=x) # $ decodeFormat=XML decodeInput=x xmlVuln='XML bomb' decodeOutput=xml.dom.minidom.parseString(..) # pulldom xml.dom.pulldom.parse(StringIO(x))['START_DOCUMENT'][1] # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' decodeOutput=xml.dom.pulldom.parse(..) getAPathArgument=StringIO(..) xml.dom.pulldom.parse(stream_or_string=StringIO(x))['START_DOCUMENT'][1] # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' decodeOutput=xml.dom.pulldom.parse(..) getAPathArgument=StringIO(..) xml.dom.pulldom.parseString(x)['START_DOCUMENT'][1] # $ decodeFormat=XML decodeInput=x xmlVuln='XML bomb' decodeOutput=xml.dom.pulldom.parseString(..) xml.dom.pulldom.parseString(string=x)['START_DOCUMENT'][1] # $ decodeFormat=XML decodeInput=x xmlVuln='XML bomb' decodeOutput=xml.dom.pulldom.parseString(..) # These are based on SAX parses, and you can specify your own, so you can expose yourself to XXE (yay/) parser = xml.sax.make_parser() parser.setFeature(xml.sax.handler.feature_external_ges, True) xml.dom.minidom.parse(StringIO(x), parser) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' xmlVuln='DTD retrieval' xmlVuln='XXE' decodeOutput=xml.dom.minidom.parse(..) getAPathArgument=StringIO(..) xml.dom.minidom.parse(StringIO(x), parser=parser) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' xmlVuln='DTD retrieval' xmlVuln='XXE' decodeOutput=xml.dom.minidom.parse(..) getAPathArgument=StringIO(..) xml.dom.pulldom.parse(StringIO(x), parser) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' xmlVuln='DTD retrieval' xmlVuln='XXE' decodeOutput=xml.dom.pulldom.parse(..) getAPathArgument=StringIO(..) xml.dom.pulldom.parse(StringIO(x), parser=parser) # $ decodeFormat=XML decodeInput=StringIO(..) xmlVuln='XML bomb' xmlVuln='DTD retrieval' xmlVuln='XXE' decodeOutput=xml.dom.pulldom.parse(..) getAPathArgument=StringIO(..)

78.625

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null

0

1

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null

0

6

a5c338ae59d3c029cd2f1e9bc28acc6734a93b08

79

py

Python

gblog/common/exceptions.py

nanvel/gblog

c0eb2f597645dda6c1d8631c0e31921ed37e38f4

[ "MIT" ]

1

2017-01-11T11:02:03.000Z

gblog/common/exceptions.py

nanvel/gblog

c0eb2f597645dda6c1d8631c0e31921ed37e38f4

[ "MIT" ]

3

2015-04-13T07:06:47.000Z

2015-04-14T02:13:50.000Z

gblog/common/exceptions.py

nanvel/gblog

c0eb2f597645dda6c1d8631c0e31921ed37e38f4

[ "MIT" ]

null

from tornado.web import HTTPError class GBlogException(HTTPError): pass

11.285714

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79

6.777778

0.888889

0

0.177215

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true

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null

0

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1

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6

571e14b4cf52410b3562b5a1e223074fb632d203

25,943

py

Python

pybind/slxos/v17r_2_00/cpu_state/summary/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/cpu_state/summary/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/cpu_state/summary/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class summary(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-RAS-operational - based on the path /cpu-state/summary. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: Overall CPU utilization summary """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__summary_cpu_load_average_one_min','__summary_cpu_load_average_five_min','__summary_cpu_load_average_fifteen_min','__summary_cpu_util_current','__summary_cpu_util_current_user','__summary_cpu_util_current_kernel','__summary_cpu_util_current_iowait',) _yang_name = 'summary' _rest_name = 'summary' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__summary_cpu_util_current_user = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-user", rest_name="summary-cpu-util-current-user", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_load_average_fifteen_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-fifteen-min", rest_name="summary-cpu-load-average-fifteen-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_load_average_one_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-one-min", rest_name="summary-cpu-load-average-one-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_util_current_kernel = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-kernel", rest_name="summary-cpu-util-current-kernel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_util_current = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current", rest_name="summary-cpu-util-current", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_load_average_five_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-five-min", rest_name="summary-cpu-load-average-five-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) self.__summary_cpu_util_current_iowait = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-iowait", rest_name="summary-cpu-util-current-iowait", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'cpu-state', u'summary'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'cpu-state', u'summary'] def _get_summary_cpu_load_average_one_min(self): """ Getter method for summary_cpu_load_average_one_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_one_min (decimal64) YANG Description: CPU load average in the last one minute """ return self.__summary_cpu_load_average_one_min def _set_summary_cpu_load_average_one_min(self, v, load=False): """ Setter method for summary_cpu_load_average_one_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_one_min (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_load_average_one_min is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_load_average_one_min() directly. YANG Description: CPU load average in the last one minute """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-one-min", rest_name="summary-cpu-load-average-one-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_load_average_one_min must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-one-min", rest_name="summary-cpu-load-average-one-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_load_average_one_min = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_load_average_one_min(self): self.__summary_cpu_load_average_one_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-one-min", rest_name="summary-cpu-load-average-one-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_load_average_five_min(self): """ Getter method for summary_cpu_load_average_five_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_five_min (decimal64) YANG Description: CPU load average in the last five minute """ return self.__summary_cpu_load_average_five_min def _set_summary_cpu_load_average_five_min(self, v, load=False): """ Setter method for summary_cpu_load_average_five_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_five_min (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_load_average_five_min is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_load_average_five_min() directly. YANG Description: CPU load average in the last five minute """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-five-min", rest_name="summary-cpu-load-average-five-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_load_average_five_min must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-five-min", rest_name="summary-cpu-load-average-five-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_load_average_five_min = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_load_average_five_min(self): self.__summary_cpu_load_average_five_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-five-min", rest_name="summary-cpu-load-average-five-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_load_average_fifteen_min(self): """ Getter method for summary_cpu_load_average_fifteen_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_fifteen_min (decimal64) YANG Description: CPU load average in the last fifteen minute """ return self.__summary_cpu_load_average_fifteen_min def _set_summary_cpu_load_average_fifteen_min(self, v, load=False): """ Setter method for summary_cpu_load_average_fifteen_min, mapped from YANG variable /cpu_state/summary/summary_cpu_load_average_fifteen_min (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_load_average_fifteen_min is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_load_average_fifteen_min() directly. YANG Description: CPU load average in the last fifteen minute """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-fifteen-min", rest_name="summary-cpu-load-average-fifteen-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_load_average_fifteen_min must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-fifteen-min", rest_name="summary-cpu-load-average-fifteen-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_load_average_fifteen_min = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_load_average_fifteen_min(self): self.__summary_cpu_load_average_fifteen_min = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-load-average-fifteen-min", rest_name="summary-cpu-load-average-fifteen-min", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_util_current(self): """ Getter method for summary_cpu_util_current, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current (decimal64) YANG Description: Current total CPU utilization percentage """ return self.__summary_cpu_util_current def _set_summary_cpu_util_current(self, v, load=False): """ Setter method for summary_cpu_util_current, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_util_current is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_util_current() directly. YANG Description: Current total CPU utilization percentage """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current", rest_name="summary-cpu-util-current", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_util_current must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current", rest_name="summary-cpu-util-current", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_util_current = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_util_current(self): self.__summary_cpu_util_current = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current", rest_name="summary-cpu-util-current", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_util_current_user(self): """ Getter method for summary_cpu_util_current_user, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_user (decimal64) YANG Description: Current CPU utilization percentage of user processes """ return self.__summary_cpu_util_current_user def _set_summary_cpu_util_current_user(self, v, load=False): """ Setter method for summary_cpu_util_current_user, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_user (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_util_current_user is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_util_current_user() directly. YANG Description: Current CPU utilization percentage of user processes """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-user", rest_name="summary-cpu-util-current-user", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_util_current_user must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-user", rest_name="summary-cpu-util-current-user", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_util_current_user = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_util_current_user(self): self.__summary_cpu_util_current_user = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-user", rest_name="summary-cpu-util-current-user", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_util_current_kernel(self): """ Getter method for summary_cpu_util_current_kernel, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_kernel (decimal64) YANG Description: Current CPU utilization percentage of kernel processes """ return self.__summary_cpu_util_current_kernel def _set_summary_cpu_util_current_kernel(self, v, load=False): """ Setter method for summary_cpu_util_current_kernel, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_kernel (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_util_current_kernel is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_util_current_kernel() directly. YANG Description: Current CPU utilization percentage of kernel processes """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-kernel", rest_name="summary-cpu-util-current-kernel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_util_current_kernel must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-kernel", rest_name="summary-cpu-util-current-kernel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_util_current_kernel = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_util_current_kernel(self): self.__summary_cpu_util_current_kernel = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-kernel", rest_name="summary-cpu-util-current-kernel", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) def _get_summary_cpu_util_current_iowait(self): """ Getter method for summary_cpu_util_current_iowait, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_iowait (decimal64) YANG Description: Current CPU utilization percentage of iowait """ return self.__summary_cpu_util_current_iowait def _set_summary_cpu_util_current_iowait(self, v, load=False): """ Setter method for summary_cpu_util_current_iowait, mapped from YANG variable /cpu_state/summary/summary_cpu_util_current_iowait (decimal64) If this variable is read-only (config: false) in the source YANG file, then _set_summary_cpu_util_current_iowait is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_summary_cpu_util_current_iowait() directly. YANG Description: Current CPU utilization percentage of iowait """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-iowait", rest_name="summary-cpu-util-current-iowait", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """summary_cpu_util_current_iowait must be of a type compatible with decimal64""", 'defined-type': "decimal64", 'generated-type': """YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-iowait", rest_name="summary-cpu-util-current-iowait", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False)""", }) self.__summary_cpu_util_current_iowait = t if hasattr(self, '_set'): self._set() def _unset_summary_cpu_util_current_iowait(self): self.__summary_cpu_util_current_iowait = YANGDynClass(base=RestrictedPrecisionDecimalType(precision=2), is_leaf=True, yang_name="summary-cpu-util-current-iowait", rest_name="summary-cpu-util-current-iowait", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-RAS-operational', defining_module='brocade-RAS-operational', yang_type='decimal64', is_config=False) summary_cpu_load_average_one_min = __builtin__.property(_get_summary_cpu_load_average_one_min) summary_cpu_load_average_five_min = __builtin__.property(_get_summary_cpu_load_average_five_min) summary_cpu_load_average_fifteen_min = __builtin__.property(_get_summary_cpu_load_average_fifteen_min) summary_cpu_util_current = __builtin__.property(_get_summary_cpu_util_current) summary_cpu_util_current_user = __builtin__.property(_get_summary_cpu_util_current_user) summary_cpu_util_current_kernel = __builtin__.property(_get_summary_cpu_util_current_kernel) summary_cpu_util_current_iowait = __builtin__.property(_get_summary_cpu_util_current_iowait) _pyangbind_elements = {'summary_cpu_load_average_one_min': summary_cpu_load_average_one_min, 'summary_cpu_load_average_five_min': summary_cpu_load_average_five_min, 'summary_cpu_load_average_fifteen_min': summary_cpu_load_average_fifteen_min, 'summary_cpu_util_current': summary_cpu_util_current, 'summary_cpu_util_current_user': summary_cpu_util_current_user, 'summary_cpu_util_current_kernel': summary_cpu_util_current_kernel, 'summary_cpu_util_current_iowait': summary_cpu_util_current_iowait, }

71.46832

500

0.778322

3,527

25,943

5.38588

0.050184

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null

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1

0

null

0

6

573dcc511aa24922ff01ccebb31593dc8a2b77d3

49

py

Python

handlers/api/__init__.py

kocsob/tornado-template

ef71439b0526fd532684743ef6365dc16d90a26a

[ "Apache-2.0" ]

null

handlers/api/__init__.py

kocsob/tornado-template

ef71439b0526fd532684743ef6365dc16d90a26a

[ "Apache-2.0" ]

null

handlers/api/__init__.py

kocsob/tornado-template

ef71439b0526fd532684743ef6365dc16d90a26a

[ "Apache-2.0" ]

null

from example_apihandler import ExampleApiHandler

24.5

48

0.918367

5

49

8.8

1

0

0.081633

49

1

49

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true

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null

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1

0

1

0

1

0

6

27949839a80a8e23ab08afb28d8946ec11809ce3

15,126

py

Python

lammps_interface/BTW.py

zmzeng/lammps_interface

07da45c444cadcab06683f3fea0fe4a781377365

[ "MIT" ]

74

2016-10-19T23:42:21.000Z

2022-03-31T08:05:54.000Z

lammps_interface/BTW.py

zmzeng/lammps_interface

07da45c444cadcab06683f3fea0fe4a781377365

[ "MIT" ]

44

2017-01-22T02:25:12.000Z

2021-12-08T03:25:51.000Z

lammps_interface/BTW.py

mwitman1/lammps_interface

4ebea5493df9e7f2381b7cad3cd5b6b2ae698a27

[ "MIT" ]

46

2016-08-10T09:22:41.000Z

2022-03-01T03:33:14.000Z

""" Parameters for BTW-FF. """ #### BTW-FF atom types and properties ##### BTW_atoms = { #FF_num at_num at_mass valance vdW_rad[A] epsilo[kcal/mol] H-bond charge atom_type description "21" :( 1 , 1.008 , 1.0 , 1.62 , 0.02 , 0.923 , 0.622 ), # H H-Oi "75" :( 8 , 15.9994 , 4.0 , 1.82 , 0.059 , 0 , -1.242 ), # O O-H "170":( 8 , 15.9994 , 2.0 , 1.82 , 0.059 , 0 , -1.0908 ), # O O-Carboxylate "171":( 8 , 15.9994 , 4.0 , 1.82 , 0.059 , 0 , -1.1145 ), # O O-inorganic "172":( 30 , 65.38 , 4.0 , 2.29 , 0.276 , 0 , 1.281 ), # Zn Zn "192":( 40 , 91.224 , 8.0 , 3.52 , 0.367 , 0 , 2.601 ), # Zr Zr "185":( 29 , 63.546 , 5.0 , 2.29 , 0.276 , 0 , 1.0358 ), # Cu Cu "902":( 6 , 12.0 , 3.0 , 1.96 , 0.056 , 0 , -0.0114 ), # C Calpha "903":( 6 , 12 , 3.0 , 1.96 , 0.056 , 0 , -0.0124 ), # C C-doublephenolligand "913":( 6 , 12.0 , 3.0 , 1.94 , 0.056 , 0 , 1.5398 ), # C Cacid "912":( 6 , 12.0 , 3.0 , 1.96 , 0.056 , 0 , -0.0228 ), # C Cbenzene "915":( 1 , 1.008 , 1.0 , 1.62 , 0.02 , 0.923 , 0.1582 ) # H Hbenzene } #### BONDs in BTW-FF #### BTW_bonds = { #FF_type k[mdyne] r[A] "21_75" :( 3.630 , 0.989), "75_192" :( 5.500 , 2.276), "170_172":( 3.665 , 2.009), "170_192":( 5.821 , 2.338), "170_185":( 5.091 , 1.969), "170_913":( 5.999 , 1.299), "171_172":( 4.329 , 2.039), "171_192":( 5.809 , 2.192), "185_185":( 4.349 , 2.422), "902_912":( 4.500 , 1.389), "902_913":( 5.299 , 1.485), "903_903":( 5.899 , 1.465), "903_912":( 5.999 , 1.389), "912_912":( 4.500 , 1.389), "912_915":( 5.150 , 1.101) } #### ANGLES in BTW-FF #### BTW_angles = { # at1_atcen_at2 k[mdyne/rad^2], Theta1[degree] , Theta2[degree], Theta3[degree], Ksb1 , Ksb2 , Kss "21_75_192" :( 2.099, 116.848 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "75_192_75" :( 2.099, 123.230 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "75_192_170" :( 2.099, 89.658 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "75_192_171" :( 2.099, 71.110 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_172_170":( 1.000, 110.103 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_172_171":( 3.000, 113.584 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_192_170":( 2.099, 73.103 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_192_171":( 2.099, 84.318 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_185_185":( 4.299, 87.822 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), #"170_185_170":( 0.05, 175.945 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ),# Fourier equation used instead "170_913_170":( 2.867, 126.299 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "170_913_902":( 1.867, 117.082 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "171_192_171":( 2.099, 91.479 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "172_170_913":( 3.022, 130.606 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "172_171_172":( 1.198, 110.992 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "185_170_913":( 3.322, 120.962 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "192_75_192" :( 2.099, 103.406 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "192_170_913":( 2.099, 139.820 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "192_171_192":( 2.099, 118.408 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "902_912_902":( 0.06, 121.797 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), # Added from MM3 "902_912_912":( 0.060, 121.582 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "902_912_915":( 0.090, 119.859 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "902_913_170":( 1.867, 117.082 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "903_903_912":( 5.00 , 122.690 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "903_912_912":( 5.00 , 122.904 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "903_912_915":( 5.00 , 120.00 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "912_902_912":( 0.000, 119.406 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "912_902_913":( 0.360, 121.797 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "912_903_912":( 5.00 , 117.621 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ), "912_912_915":( 0.49 , 120.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ) # Added from MM3 } #### DIHEDRALs in BTW-FF BTW_dihedrals = { #at1_at2_at3_at4 k1 , t1 , n1 , k2 , t2 , n2 , k3 , t3 , n3 , k4 , t4 , n4 "170_913_902_912":( 0.0 , 0.0 , 1 , 2.5 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "915_912_902_913":( 0.0 , 0.0 , 1 , 1.999 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "913_902_912_912":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "912_902_912_912":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "912_902_912_915":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "902_912_912_915":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "902_912_912_902":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "903_903_912_915":( 0.0 , 0.0 , 1 , 6.999 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "903_903_912_912":( 0.0 , 0.0 , 1 , 6.9 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "912_903_903_912":( 0.0 , 0.0 , 1 , 6.9 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "912_903_912_912":( 0.0 , 0.0 , 1 , 4.930 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "912_903_912_915":( 0.0 , 0.0 , 1 , 4.930 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "915_912_912_903":( 0.0 , 0.0 , 1 , 4.930 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "902_912_912_903":( 0.0 , 0.0 , 1 , 5.930 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "902_912_912_912":( 0.0 , 0.0 , 1 , 8.030 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "171_172_170_913":( 0.0 , 0.0 , 1 , 4.690 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "172_170_913_170":( 0.0 , 0.0 , 1 , 2.176 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_172_170_913":( 0.0 , 0.0 , 1 , 0.860 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "172_170_913_902":( 0.0 , 0.0 , 1 , 0.072 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_172_171_172":( 0.0 , 0.0 , 1 , 1.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_185_170_913":( 0.0 , 0.0 , 1 , 0.860 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "185_170_913_902":( 0.0 , 0.0 , 1 , 0.072 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_913_170_185":( 0.0 , 0.0 , 1 , 5.805 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "913_170_185_185":( 0.0 , 0.0 , 1 , 0.850 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_185_185_170":( 0.0 , 0.0 , 1 , 2.071 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_185_185_170":( 0.0 , 0.0 , 1 , 2.071 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "171_192_170_913":( 0.0 , 0.0 , 1 , 2.064 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_170_913_170":( 0.0 , 0.0 , 1 , 2.017 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "915_912_902_913":( 0.0 , 0.0 , 1 , 1.999 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_192_170_913":( 0.0 , 0.0 , 1 , 0.860 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_170_913_902":( 0.0 , 0.0 , 1 , 0.072 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "170_192_171_192":( 0.0 , 0.0 , 1 , 1.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "913_170_192_75" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "21_75_192_170" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "21_75_191_170" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_75_192_170" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_75_192_75" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_75_192_171" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "21_75_192_75" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_75_192_171" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_171_192_75" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_171_192_171":( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "21_75_192_171" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_75_192_171" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "192_171_192_75" :( 0.0 , 0.0 , 1 , 5.000 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), "915_912_912_915":( 0.0 , 0.0 , 1 , 11.5 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), # Added from MM3 "902_912_902_913":( 0.0 , 0.0 , 1 , 8.03 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), # Added from MM3 "902_912_902_912":( 0.0 , 0.0 , 1 , 8.03 , 180.0 , 2 , 0.0 , 0.0 , 3 , 0.0 , 0.0 , 4 ), # Added from MM3 } #### OUT-OF-PLANE bending in BTW-FF BTW_opbends = { #at1_at2_at3_at4 K_opb, phi , Ka1 , ka2 , ka3 ### """ ### H ### / ### C = C ### \ ### C ### """ "902_912_912_915":( 0.0 , 0.0 , 0.24 , 0.300 , 0.0 ),# BTW-FF coefficient is 0.0 while in MM3 is 0.2 "902_912_915_912":( 0.0 , 0.0 , 0.24 , 0.300 , 0.0 ),# BTW-FF coefficient is 0.0 while in MM3 is 0.2 "902_912_902_915":( 0.0 , 0.0 , 0.24 , 0.300 , 0.0 ),# BTW-FF coefficient is 0.0 while in MM3 is 0.2 "902_912_915_902":( 0.0 , 0.0 , 0.24 , 0.300 , 0.0 ),# BTW-FF coefficient is 0.0 while in MM3 is 0.2 "903_912_915_912":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "903_912_912_915":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_912_915_903":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_912_903_915":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_912_902_915":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_912_915_902":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "915_912_912_902":( 0.11, 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "915_912_902_912":( 0.11, 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "915_912_902_902":( 0.11, 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "915_912_912_903":( 0.11, 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "915_912_903_912":( 0.11, 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 ### """ ### O ### / ### C = C ### \ ### O ### """ "170_913_170_902":( 1.5 , 0.0 , 0.00 , 0.0 , 0.0 ),# "170_913_902_170":( 1.5 , 0.0 , 0.00 , 0.0 , 0.0 ),# "902_913_170_170":( 0.0 , 0.0 , 0.00 , 0.0 , 0.0 ),# BTW-FF coefficient is 0.0 while in MM3 is 0.2 ### ----------- ######## ----------- ### O | C | ######## C | C | ### \| / | ## ## \| / | ### |C = C | ## Or ## |C = C | ### /| \ | ## ## /| \ | ### O | C | ######## C | C | ### ----------- ######## ----------- "912_902_912_913":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_902_913_912":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "913_902_912_912":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "903_903_912_912":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_903_912_903":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 "912_903_903_912":( 0.2 , 0.0 , 0.24 , 0.300 , 0.0 ),# Added from MM3 # # UIO special opbend # "192_171_192_192":( 2.0 , 0.0 , 0.00 , 0.00 , 0.0 ) } BTW_charges = { "Cu Paddlewheel_185" :( 1.0358 ), # Cu Cu "Cu Paddlewheel_170" :( -1.0908 ), # O O-Carboxylate "Cu Paddlewheel_902" :( -0.0114 ), # C Calpha "Cu Paddlewheel_913" :( 1.5398 ), # C Cacid "Cu Paddlewheel_912" :( -0.0228 ), # C Cbenzene "Cu Paddlewheel_915" :( 0.1582 ), # H Hbenzene "Zn4O_170" :( -1.1513 ), # O O-Carboxylate "Zn4O_171" :( -1.1145 ), # O O-inorganic "Zn4O_902" :( -0.0081 ), # C Calpha "Zn4O_913" :( 1.4972 ), # C Cacid "Zn4O_912" :( -0.0536 ), # C Cbenzene "Zn4O_915" :( 0.1259 ), # H Hbenzene "Zn4O_172" :( 1.281 ), # Zn Zn "IRMOF10_170" :( -1.1630 ), # O O-Carboxylate "IRMOF10_902" :( -0.0279 ), # C Calpha "IRMOF10_913" :( 1.5377 ), # C Cacid "IRMOF10_912" :( -0.0460 ), # C Cbenzene "IRMOF10_915" :( 0.1047 ), # H Hbenzene "IRMOF10_172" :( 1.2954 ), # Zn Zn "IRMOF10_171" :( -1.2144 ), # O O-inorganic "IRMOF10_903" :( -0.0124 ), # C C-doublephenolligand "Zr_UiO_170" :( -1.181 ), # O O-Carboxylate "Zr_UiO_902" :( -0.056 ), # C Calpha "Zr_UiO_913" :( 1.576 ), # C Cacid "Zr_UiO_912" :( -0.058 ), # C Cbenzene "Zr_UiO_915" :( 0.129 ), # H Hbenzene "Zr_UiO_75" :( -1.242 ), # O O-H "Zr_UiO_192" :( 2.601 ), # Zr Zr "Zr_UiO_21" :( 0.622 ), # H H-Oi "Zr_UiO_171" :( -1.189 ), # O O-inorganic "Zr_UiO_903" :( -0.035 ), # C C-doublephenolligand "TFF_171" :( -1.186 ), # O O-inorganic "TFF_172" :( 1.291 ), # Zn Zn "TFF_170" :( -1.154 ), # O O-Carboxylate "TFF_913" :( 1.539 ), # C Cacid "TFF_912" :( -0.050 ), # C Cbenzene "TFF_902" :( -0.008 ), # C Calpha "TFF_915" :( 0.118 ), # H Hbenzene "TFF_192" :( 2.605 ), # Zr Zr "TFF_75" :( -1.243 ), # O O-H "TFF_21" :( 0.622 ), # H O-H "TFF_903" :( -0.0124 ), # C C-doublephenolligand !!!!!! temporary! }

67.526786

133

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27a39de9fbea8f36a4994c7d2701499b02d10873

307

py

Python

netutils_linux_hardware/__init__.py

AlexeyAB/netutils-linux

f97a919ecd765c50c364415ba43eeb09e8e829ed

[ "MIT" ]

1

2019-02-09T23:37:41.000Z

netutils_linux_hardware/__init__.py

shildenbrand/PyNetUtils

feafd5cf11ae9402bcdd1e38db38478a3ed0dee1

[ "MIT" ]

null

netutils_linux_hardware/__init__.py

shildenbrand/PyNetUtils

feafd5cf11ae9402bcdd1e38db38478a3ed0dee1

[ "MIT" ]

1

2020-05-28T07:47:20.000Z

from netutils_linux_hardware import netdev from netutils_linux_hardware import parsers from netutils_linux_hardware import interrupts from netutils_linux_hardware.reader import Reader from netutils_linux_hardware.assessor import Assessor __all__ = ['parsers', 'netdev', 'interrupts', 'Reader', 'Assessor']

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1

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27b0f7108012cf23bcdcf9f11468342c9e1cb9b0

275

py

Python

src/waldur_auth_social/extension.py

ahti87/waldur-mastermind

772268e62dfd8eadb387b2ec3789785817a6e621

[ "MIT" ]

null

src/waldur_auth_social/extension.py

ahti87/waldur-mastermind

772268e62dfd8eadb387b2ec3789785817a6e621

[ "MIT" ]

null

src/waldur_auth_social/extension.py

ahti87/waldur-mastermind

772268e62dfd8eadb387b2ec3789785817a6e621

[ "MIT" ]

null

from waldur_core.core import WaldurExtension class AuthSocialExtension(WaldurExtension): @staticmethod def django_app(): return 'waldur_auth_social' @staticmethod def django_urls(): from .urls import urlpatterns return urlpatterns

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6

27d3fc4bd038d42e3e283e83f2dd7d3819a32962

113

py

Python

socialnews/loadtest.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

30

2015-01-18T16:34:03.000Z

2021-05-23T20:05:54.000Z

socialnews/loadtest.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

null

socialnews/loadtest.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

11

2015-02-21T10:45:41.000Z

2021-01-24T21:08:20.000Z

from django.core.management import setup_environ import settings setup_environ(settings) import models

14.125

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0.93617

0

1

0

true

0

0.75

0

0.75

0

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

fd8bd7cc5f572899ab277c4ecf147622ffd96904

213

py

Python

dewaveADCP/__init__.py

apaloczy/dewaveADCP

f37702905ccaeb5a4ecc738cba9ee46cd76cd03f

[ "MIT" ]

4

2019-09-25T21:39:50.000Z

2022-02-16T19:11:21.000Z

dewaveADCP/__init__.py

apaloczy/dewaveADCP

f37702905ccaeb5a4ecc738cba9ee46cd76cd03f

[ "MIT" ]

null

dewaveADCP/__init__.py

apaloczy/dewaveADCP

f37702905ccaeb5a4ecc738cba9ee46cd76cd03f

[ "MIT" ]

1

2021-12-10T12:32:26.000Z

from . import VerticalDetrend from . import VarianceFit from . import StructureFunction from . import CospectraFit from . import AdaptiveFiltering from . import beam2earth from . import stress from . import utils

23.666667

31

0.812207

24

213

7.208333

0.416667

0.462428

0

0.005525

0.150235

213

8

32

26.625

0.950276

0

1

0

true

0

1

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null

1

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1

0

null

0

1

0

1

0

1

0

6

fdb254829993dd33e8b019985ab64d68f1148c5f

119

py

Python

tictactoe/adapters/match_channel.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

1

2020-09-30T21:03:37.000Z

tictactoe/adapters/match_channel.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

null

tictactoe/adapters/match_channel.py

pitzer42/nano_tcg

c984b253b8a53a707460aac21c10f140d16d902e

[ "MIT" ]

null

from gloop.adapters.match_channel import MatchClientChannel class TicTacToeMatchClient(MatchClientChannel): pass

19.833333

59

0.848739

11

119

9.090909

0.909091

0

0.109244

119

5

60

23.8

0.943396

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true

0.333333

0

0.666667

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null

0

1

0

null

0

1

0

1

0

6

e30f5285c593fa7b807efa56263d5f7715d02464

5,674

py

Python

tests/search_api/test_stats.py

EnriqueSoria/pydoof

e5a2b7129e6c18e92b69501946be35cd386fcb47

[ "MIT" ]

null

tests/search_api/test_stats.py

EnriqueSoria/pydoof

e5a2b7129e6c18e92b69501946be35cd386fcb47

[ "MIT" ]

null

tests/search_api/test_stats.py

EnriqueSoria/pydoof

e5a2b7129e6c18e92b69501946be35cd386fcb47

[ "MIT" ]

null

from unittest import mock import unittest from pydoof.search_api import stats class TestStats(unittest.TestCase): @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_init_session(self, APIClientMock): hashid = 'aab32d8' session_id = 'SESSION_ID' stats.init_session(hashid, session_id) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/init', query_params={'session_id': session_id} ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_log_checkout(self, APIClientMock): hashid = 'aab32d8' session_id = 'SESSION_ID' stats.log_checkout(hashid, session_id) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/checkout', query_params={'session_id': session_id} ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_log_redirect_minimum_requirements(self, APIClientMock): hashid = 'aab32d8' redirection_id = 'ID' session_id = 'SESSION_ID' stats.log_redirect(hashid, redirection_id, session_id) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/redirect', query_params={ 'id': redirection_id, 'session_id': session_id } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_log_redirect(self, APIClientMock): hashid = 'aab32d8' redirection_id = 'ID' session_id = 'SESSION_ID' query = 'QUERY' stats.log_redirect(hashid, redirection_id, session_id, query) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/redirect', query_params={ 'id': redirection_id, 'session_id': session_id, 'query': query } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_click_stats_minimum_requirements(self, APIClientMock): hashid = 'aab32d8' dfid = 'ID' session_id = 'SESSION_ID' stats.click_stats(hashid, dfid, session_id) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/click', query_params={ 'dfid': dfid, 'session_id': session_id } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_click_stats(self, APIClientMock): hashid = 'aab32d8' dfid = 'ID' session_id = 'SESSION_ID' query = 'QUERY' stats.click_stats(hashid, dfid, session_id, query) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/click', query_params={ 'dfid': dfid, 'session_id': session_id, 'query': query } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_log_banner_image_click_minimum_requirements(self, APIClientMock): hashid = 'aab32d8' redirection_id = 'ID' session_id = 'SESSION_ID' stats.log_banner_image_click(hashid, redirection_id, session_id) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/image', query_params={ 'id': redirection_id, 'session_id': session_id } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_log_banner_image_click(self, APIClientMock): hashid = 'aab32d8' redirection_id = 'ID' session_id = 'SESSION_ID' query = 'QUERY' stats.log_banner_image_click(hashid, redirection_id, session_id, query) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/image', query_params={ 'id': redirection_id, 'session_id': session_id, 'query': query } ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_add_to_cart(self, APIClientMock): hashid = 'aab32d8' index_name = 'product' session_id = '4affa6' amount = 2 item_id = 1235 title = 'Product' price = 12.99 stats.add_to_cart( hashid, index_name, session_id, item_id, amount, title, price ) APIClientMock.return_value.put.assert_called_once_with( f'/6/{hashid}/stats/cart/{session_id}', query_params={ 'index': index_name, 'id': item_id, 'amount': amount, 'title': title, 'price': price} ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_remove_from_cart(self, APIClientMock): hashid = 'aab32d8' index_name = 'product' session_id = '4affa6' amount = 2 item_id = 1235 stats.remove_from_cart( hashid, index_name, session_id, item_id, amount ) APIClientMock.return_value.patch.assert_called_once_with( f'/6/{hashid}/stats/cart/{session_id}', query_params={'index': index_name, 'id': item_id, 'amount': amount} ) @mock.patch('pydoof.search_api.stats.SearchAPIClient') def test_clear_cart(self, APIClientMock): hashid = 'aab32d8' session_id = '4affa6' stats.clear_cart(hashid, session_id) APIClientMock.return_value.delete.assert_called_once_with( f'/6/{hashid}/stats/cart/{session_id}' )

31.005464

79

0.603983

617

5,674

5.246353

0.098865

0.136237

0.101946

0.088971

0.908248

0.907013

0.87612

0.862218

0.852641

0.747915

0

0.015897

0.290448

5,674

182

80

31.175824

0.788127

0

0.609589

0

0.193162

0.127952

0

0.075342

1

0.075342

false

0

0.020548

0

0.10274

0

null

0

1

0

null

0

6

e3276ff249e741a061ca24242060f8f4ae6a4c80

764

py

Python

xmlns/rev/status.py

danja/danja.github.io

26662fdf910b8121e14b8470fc4abb94707c574a

[ "Apache-2.0" ]

null

xmlns/rev/status.py

danja/danja.github.io

26662fdf910b8121e14b8470fc4abb94707c574a

[ "Apache-2.0" ]

null

xmlns/rev/status.py

danja/danja.github.io

26662fdf910b8121e14b8470fc4abb94707c574a

[ "Apache-2.0" ]

null

import urllib u="""http://xmlarmyknife.org/api/rdf/sparql/query?default-graph-uri=http%3A%2F%2Fxmlns.com%2Ffoaf%2F0.1%2Findex.rdf&query=PREFIX+rdf%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0D%0APREFIX+rdfs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0D%0APREFIX+vs%3A+%3Chttp%3A%2F%2Fwww.w3.org%2F2003%2F06%2Fsw-vocab-status%2Fns%23%3E%0D%0ASELECT+DISTINCT+%3Fstatus+%3Fx+%3Flabel%0D%0AWHERE+%7B%0D%0A%7B%0D%0A++%3Fx+a+rdf%3AProperty+.%0D%0A++%3Fx+rdfs%3Alabel+%3Flabel+.%0D%0A++%3Fx+vs%3Aterm_status+%3Fstatus+.%0D%0A%7D+UNION+%7B%0D%0A++%3Fx+a+rdfs%3AClass+.%0D%0A++%3Fx+rdfs%3Alabel+%3Flabel+.%0D%0A++%3Fx+vs%3Aterm_status+%3Fstatus+.%0D%0A%7D%0D%0A%7D%0D%0AORDER+BY+%3Fstatus&format=html""" print urllib.unquote(u)

191

723

0.747382

146

764

3.89726

0.458904

0.070299

0.073814

0.063269

0.362039

0.326889

0.210896

0

0.160053

0.010471

764

3

724

254.666667

0.592593

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null

0

0.333333

null

0.333333

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null

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0

1

0

null

0

1

0

1

0

6

e32921374d9783826f5e1170d9bfc9c62fb0dbee

64

py

Python

py_tdlib/constructors/user_type_regular.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/user_type_regular.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/user_type_regular.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

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

10.666667

28

0.765625

8

64

6.125

0.875

0

0.15625

64

5

29

12.8

0.907407

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

e358234aec1716efc24fdaf2b86796526ef63b2e

5,547

py

Python

blmath/geometry/transform/test_coordinate_manager.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

[ "BSD-2-Clause" ]

6

2019-09-28T16:48:34.000Z

2022-03-25T17:05:46.000Z

blmath/geometry/transform/test_coordinate_manager.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

[ "BSD-2-Clause" ]

6

2019-09-09T16:42:02.000Z

2021-06-25T15:25:50.000Z

blmath/geometry/transform/test_coordinate_manager.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

[ "BSD-2-Clause" ]

4

2017-05-09T16:15:07.000Z

2019-02-15T14:15:30.000Z

# pylint: disable=invalid-unary-operand-type import unittest import numpy as np from blmath.geometry.transform.test_composite import create_cube_verts class TestCoordinateManager(unittest.TestCase): def test_coordinate_manager_forward(self): from blmath.geometry.transform.coordinate_manager import CoordinateManager cube_v = create_cube_verts([1., 0., 0.], 4.) cube_floor_point = np.array([3., 0., 2.]) # as lace.mesh.floor_point coordinate_manager = CoordinateManager() coordinate_manager.tag_as('source') coordinate_manager.translate(-cube_floor_point) coordinate_manager.scale(2) coordinate_manager.tag_as('floored_and_scaled') coordinate_manager.translate(np.array([0., -4., 0.])) coordinate_manager.tag_as('centered_at_origin') coordinate_manager.source = cube_v floored_and_scaled_v = coordinate_manager.do_transform( cube_v, from_tag='source', to_tag='floored_and_scaled' ) # Sanity check np.testing.assert_array_almost_equal(cube_v[0], [1., 0., 0.]) np.testing.assert_array_almost_equal(cube_v[6], [5., 4., 4.]) np.testing.assert_array_almost_equal(floored_and_scaled_v[0], [-4., 0., -4.]) np.testing.assert_array_almost_equal(floored_and_scaled_v[6], [4., 8., 4.]) centered_at_origin_v_1 = coordinate_manager.do_transform( cube_v, from_tag='source', to_tag='centered_at_origin' ) centered_at_origin_v_2 = coordinate_manager.do_transform( floored_and_scaled_v, from_tag='floored_and_scaled', to_tag='centered_at_origin' ) np.testing.assert_array_almost_equal(centered_at_origin_v_1[0], [-4., -4., -4.]) np.testing.assert_array_almost_equal(centered_at_origin_v_1[6], [4., 4., 4.]) np.testing.assert_array_almost_equal(centered_at_origin_v_2[0], [-4., -4., -4.]) np.testing.assert_array_almost_equal(centered_at_origin_v_2[6], [4., 4., 4.]) source_v_1 = coordinate_manager.do_transform( floored_and_scaled_v, from_tag='floored_and_scaled', to_tag='source' ) source_v_2 = coordinate_manager.do_transform( centered_at_origin_v_1, from_tag='centered_at_origin', to_tag='source' ) np.testing.assert_array_almost_equal(source_v_1, cube_v) np.testing.assert_array_almost_equal(source_v_2, cube_v) def test_coordinate_manager_forward_with_attrs(self): from blmath.geometry.transform.coordinate_manager import CoordinateManager cube_v = create_cube_verts([1., 0., 0.], 4.) cube_floor_point = np.array([3., 0., 2.]) # as lace.mesh.floor_point coordinate_manager = CoordinateManager() coordinate_manager.tag_as('source') coordinate_manager.translate(-cube_floor_point) coordinate_manager.scale(2) coordinate_manager.tag_as('floored_and_scaled') coordinate_manager.translate(np.array([0., -4., 0.])) coordinate_manager.tag_as('centered_at_origin') coordinate_manager.source = cube_v # Sanity check np.testing.assert_array_almost_equal(cube_v[0], [1., 0., 0.]) np.testing.assert_array_almost_equal(cube_v[6], [5., 4., 4.]) floored_and_scaled_v = coordinate_manager.floored_and_scaled np.testing.assert_array_almost_equal(floored_and_scaled_v[0], [-4., 0., -4.]) np.testing.assert_array_almost_equal(floored_and_scaled_v[6], [4., 8., 4.]) centered_at_origin_v = coordinate_manager.centered_at_origin np.testing.assert_array_almost_equal(centered_at_origin_v[0], [-4., -4., -4.]) np.testing.assert_array_almost_equal(centered_at_origin_v[6], [4., 4., 4.]) source_v = coordinate_manager.source np.testing.assert_array_almost_equal(source_v, cube_v) def test_coordinate_manager_forward_on_mesh(self): from mock import MagicMock from blmath.geometry.transform.coordinate_manager import CoordinateManager cube_v = create_cube_verts([1., 0., 0.], 4.) cube_floor_point = np.array([3., 0., 2.]) # as lace.mesh.floor_point # By default a magic mock will always have any attribute it's asked for; # here we set the spec property so that it will not respond to having a copy method # when the CoodinateManager looks for it. cube = MagicMock(spec=['v', 'other_thing'], v=cube_v, other_thing=np.array([-9.])) coordinate_manager = CoordinateManager() coordinate_manager.tag_as('source') coordinate_manager.translate(-cube_floor_point) coordinate_manager.scale(2) coordinate_manager.tag_as('floored_and_scaled') coordinate_manager.translate(np.array([0., -4., 0.])) coordinate_manager.tag_as('centered_at_origin') coordinate_manager.source = cube # Sanity check np.testing.assert_array_almost_equal(cube.v[0], [1., 0., 0.]) np.testing.assert_array_almost_equal(cube.v[6], [5., 4., 4.]) np.testing.assert_array_equal(cube.other_thing, [-9.]) floored_and_scaled = coordinate_manager.floored_and_scaled np.testing.assert_array_almost_equal(floored_and_scaled.v[0], [-4., 0., -4.]) np.testing.assert_array_almost_equal(floored_and_scaled.v[6], [4., 8., 4.]) np.testing.assert_array_equal(floored_and_scaled.other_thing, [-9.])

42.669231

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4.571805

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0.099424

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0.756484

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0.206959

5,547

129

92

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0.761764

0.062917

0

0.553191

0

0.052053

0

0.244681

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0.031915

false

0

0.074468

0

0.117021

0

null

0

1

0

null

0

6

e380a94587fb8a7cbcbda297628c0dc5c28d53b0

77

py

Python

templates/models_header.py

fecitpotentiam/djmodels_creator

dd46f00b054eabc9dc4a65c4e0b32a0c174bcf5a

[ "MIT" ]

1

2020-04-12T14:17:35.000Z

templates/models_header.py

fecitpotentiam/djmodels_creator

dd46f00b054eabc9dc4a65c4e0b32a0c174bcf5a

[ "MIT" ]

null

templates/models_header.py

fecitpotentiam/djmodels_creator

dd46f00b054eabc9dc4a65c4e0b32a0c174bcf5a

[ "MIT" ]

null

header = """from django.db import models from django.db.models import * """

15.4

40

0.701299

11

77

4.909091

0.545455

0.37037

0.444444

0

0.155844

77

5

41

15.4

0.830769

0

0.794872

0

1

0

false

0

0.666667

0

0.666667

0

1

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null

1

0

1

0

1

0

null

0

1

0

6

8bc321bd3ec6dc7e868a4ec6d304ac8febd2aa9b

343

py

Python

gym_pybullet_drones/envs/single_agent_rl/__init__.py

Yashupadhyay603/gym-pybullet-drones

30317a176672f0a462cc249b4e5d17a7078ea3d2

[ "MIT" ]

1

2021-06-10T07:43:55.000Z

gym_pybullet_drones/envs/single_agent_rl/__init__.py

Yashupadhyay603/gym-pybullet-drones

30317a176672f0a462cc249b4e5d17a7078ea3d2

[ "MIT" ]

null

gym_pybullet_drones/envs/single_agent_rl/__init__.py

Yashupadhyay603/gym-pybullet-drones

30317a176672f0a462cc249b4e5d17a7078ea3d2

[ "MIT" ]

1

2021-04-01T01:56:45.000Z

from gym_pybullet_drones.envs.single_agent_rl.BaseSingleAgentAviary import BaseSingleAgentAviary from gym_pybullet_drones.envs.single_agent_rl.TakeoffAviary import TakeoffAviary from gym_pybullet_drones.envs.single_agent_rl.HoverAviary import HoverAviary from gym_pybullet_drones.envs.single_agent_rl.FlyThruGateAviary import FlyThruGateAviary

85.75

96

0.921283

44

343

6.818182

0.295455

0.093333

0.2

0.28

0.506667

0

0.043732

343

4

97

85.75

0.914634

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

4756cb1fdc7950deeba4df6617bbbf5e41336350

111

py

Python

translatable/exceptions.py

artscoop/django-translatable

5b5c818120bb7afe1f7639fa3181991307fbb3e8

[ "BSD-3-Clause" ]

null

translatable/exceptions.py

artscoop/django-translatable

5b5c818120bb7afe1f7639fa3181991307fbb3e8

[ "BSD-3-Clause" ]

null

translatable/exceptions.py

artscoop/django-translatable

5b5c818120bb7afe1f7639fa3181991307fbb3e8

[ "BSD-3-Clause" ]

1

2021-01-05T15:16:46.000Z

from django.core.exceptions import ObjectDoesNotExist class MissingTranslation(ObjectDoesNotExist): pass

18.5

53

0.837838

10

111

9.3

0.9

0

0.117117

111

5

54

22.2

0.94898

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

4777476a90e0453eb0ade1e3682444b53e53b386

489

py

Python

fault/logging.py

makaimann/fault

8c805415f398e64971d18fbd3014bc0b59fb38b8

[ "BSD-3-Clause" ]

31

2018-07-16T15:03:14.000Z

2022-03-10T08:36:09.000Z

fault/logging.py

makaimann/fault

8c805415f398e64971d18fbd3014bc0b59fb38b8

[ "BSD-3-Clause" ]

216

2018-07-18T20:00:34.000Z

2021-10-05T17:40:47.000Z

fault/logging.py

makaimann/fault

8c805415f398e64971d18fbd3014bc0b59fb38b8

[ "BSD-3-Clause" ]

10

2019-02-17T00:56:58.000Z

2021-11-05T13:31:37.000Z

from __future__ import absolute_import from __future__ import print_function import logging import traceback import inspect import sys log = logging.getLogger("fault") def info(message, *args, **kwargs): log.info(message, *args, **kwargs) def debug(message, *args, **kwargs): log.debug(message, *args, **kwargs) def warning(message, *args, **kwargs): log.warning(message, *args, **kwargs) def error(message, *args, **kwargs): log.error(message, *args, **kwargs)

18.111111

41

0.705521

62

489

5.403226

0.33871

0.262687

0.40597

0.238806

0

0.151329

489

26

42

18.807692

0.807229

0

0.010225

0

1

0.266667

false

0

0.4

0

0.666667

0.066667

0

null

1

0

null

0

1

0

1

0

1

0

6

477eec003a7a24a49da2ed718fed04a481eb8b2f

46

py

Python

collagen/data/utils/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

4

2019-05-14T14:44:51.000Z

2020-03-13T08:37:48.000Z

collagen/data/utils/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

26

2019-04-21T20:35:22.000Z

2022-03-12T00:32:57.000Z

collagen/data/utils/__init__.py

MIPT-Oulu/Collagen

0cbc4285d60e5c9fcc89f629fcf4321e80b7452c

[ "MIT" ]

1

2019-05-14T14:53:28.000Z

from ._utils import * from .datasets import *

15.333333

23

0.73913

6

46

5.5

0.666667

0

0.173913

46

2

24

23

0.868421

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

478e0739c7312ba55495eedcbda7e015ef9d2b3b

127

py

Python

week1/exe1.py

mikealford/ktbyers

05f11dd0aa7f3b1a75013d923fadeac2bba6e083

[ "Apache-2.0" ]

null

week1/exe1.py

mikealford/ktbyers

05f11dd0aa7f3b1a75013d923fadeac2bba6e083

[ "Apache-2.0" ]

null

week1/exe1.py

mikealford/ktbyers

05f11dd0aa7f3b1a75013d923fadeac2bba6e083

[ "Apache-2.0" ]

null

ip_addr1 = '192.168.5.1' ip_addr2 = '192.168.5.2' ip_addr3 = '192.168.5.3' print(ip_addr1 + ' ' + ip_addr2 + ' ' + ip_addr3)

18.142857

49

0.606299

25

127

2.84

0.44

0.253521

0.295775

0

0.285714

0.173228

127

6

50

21.166667

0.390476

0

0.277778

0

1

0

false

0

0.25

1

0

null

1

0

1

0

1

0

1

0

null

0

6

47c51f9940cc37c23ef4e9e8c6195a90db47282e

11,908

py

Python

tests/test_figure_area.py

rbardaji/graffiti

e10490a58b7eff041ff8212784f05daa076e3f53

[ "MIT" ]

null

tests/test_figure_area.py

rbardaji/graffiti

e10490a58b7eff041ff8212784f05daa076e3f53

[ "MIT" ]

null

tests/test_figure_area.py

rbardaji/graffiti

e10490a58b7eff041ff8212784f05daa076e3f53

[ "MIT" ]

null

import unittest from run import app from config import test_token class ResourceTest(unittest.TestCase): def setUp(self): self.app = app.test_client() # Add some data to the DB query = '/admin_data/R' for i in range(3): payload = { 'platform_code': 'test_platform', 'parameter': 'test_parameter', 'depth': 10, 'depth_qc': 1, 'time': f'3000-03-09T21:4{i}:00Z', "time_qc": 1, "lat": 20, "lat_qc": 1, "lon": 20, "lon_qc": 1, "value": i, "qc": 1 } self.app.post(query, json=payload, headers={'Authorization': test_token}) for i in range(3): payload = { 'platform_code': 'test_platform2', 'parameter': 'test_parameter', 'depth': 10, 'depth_qc': 1, 'time': f'3000-03-09T21:4{i}:00Z', "time_qc": 1, "lat": 20, "lat_qc": 1, "lon": 20, "lon_qc": 1, "value": i, "qc": 1 } self.app.post(query, json=payload, headers={'Authorization': test_token}) for i in range(3): payload = { 'platform_code': 'test_platform2', 'parameter': 'test_parameter2', 'depth': 10, 'depth_qc': 1, 'time': f'3000-03-09T21:4{i}:00Z', "time_qc": 1, "lat": 20, "lat_qc": 1, "lon": 20, "lon_qc": 1, "value": i, "qc": 1 } self.app.post(query, json=payload, headers={'Authorization': test_token}) def test_get_area_201(self): """ GET figure/area/test_platform/test_parameter should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_multiplatform(self): """ GET figure/area/test_platform,test_platform2/test_parameter should return a status_code = 201 """ query = 'figure/area/test_platform,test_platform2/test_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_multiplatform_with_parameter_for_one(self): """ GET figure/area/test_platform,test_platform2/test_parameter2 should return a status_code = 201 """ query = 'figure/area/test_platform,test_platform2/test_parameter2' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_404_multiplatform_bad_parameter(self): """ GET figure/area/test_platform,test_platform2/bad_parameter should return a status_code = 404 """ query = 'figure/area/test_platform,test_platform2/bad_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(404, response.status_code) def test_get_area_404_bad_parameter(self): """ GET figure/area/test_platform/bad_parameter should return a status_code = 404 """ query = 'figure/area/test_platform/bad_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(404, response.status_code) def test_get_area_404_bad_platform(self): """ GET figure/area/bad_platform/test_parameter should return a status_code = 404 """ query = 'figure/area/test_platform/bad_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(404, response.status_code) def test_get_area_404_bad_platform_and_parameter(self): """ GET figure/area/bad_platform/test_parameter should return a status_code = 404 """ query = 'figure/area/bad_platform/bad_parameter' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(404, response.status_code) def test_get_area_201_depth_min(self): """ GET figure/area/test_platform/test_parameter?depth_min=0 should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?depth_min=0' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_depth_max(self): """ GET figure/area/test_platform/test_parameter?depth_max=20 should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?depth_max=20' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_time_min(self): """ GET figure/area/test_platform/test_parameter?time_min=2000-01-01T00:00:00Z should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?time_min=2000-01-01T00:00:00Z' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_time_max(self): """ GET figure/area/test_platform/test_parameter?time_max=4000-01-01T00:00:00Z should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?time_max=4000-01-01T00:00:00Z' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_qc(self): """ GET figure/area/test_platform/test_parameter?qc=1 should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?qc=1' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_all(self): """ GET figure/area/test_platform/test_parameter?... should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?qc=1&' + \ 'time_max=4000-01-01T00:00:00Z&time_min=2000-01-01T00:00:00Z&' + \ 'depth_max=20&depth_min=0&template=plotly_white' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_ggplot2(self): """ GET figure/area/test_platform/test_parameter? template=ggplot2 should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=ggplot2' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_seaborn(self): """ GET figure/area/test_platform/test_parameter? template=seaborn should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=seaborn' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_simple_white(self): """ GET figure/area/test_platform/test_parameter? template=simple_white should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=simple_white' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_plotly(self): """ GET figure/area/test_platform/test_parameter? template=plotly should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=plotly' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_plotly_white(self): """ GET figure/area/test_platform/test_parameter? template=plotly_white should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=plotly_white' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_plotly_dark(self): """ GET figure/area/test_platform/test_parameter? template=plotly_dark should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=plotly_dark' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_presentation(self): """ GET figure/area/test_platform/test_parameter? template=presentation should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=presentation' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_xgridoff(self): """ GET figure/area/test_platform/test_parameter? template=xgridoff should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=xgridoff' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_ygridoff(self): """ GET figure/area/test_platform/test_parameter? template=ygridoff should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=ygridoff' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def test_get_area_201_template_gridon(self): """ GET figure/area/test_platform/test_parameter? template=gridon should return a status_code = 201 """ query = 'figure/area/test_platform/test_parameter?template=gridon' response = self.app.get(query, headers={'Authorization': test_token}) self.assertEqual(201, response.status_code) def tearDown(self): """ Delete all generated data """ for i in range(3): query = '/admin_data/R/test_platform_test_parameter_10_' + \ f'3000-03-09T21:4{i}:00Z' self.app.delete(query, headers={'Authorization': test_token}) for i in range(3): query = '/admin_data/R/test_platform2_test_parameter_10_' + \ f'3000-03-09T21:4{i}:00Z' self.app.delete(query, headers={'Authorization': test_token}) for i in range(3): query = '/admin_data/R/test_platform2_test_parameter2_10_' + \ f'3000-03-09T21:4{i}:00Z' self.app.delete(query, headers={'Authorization': test_token})

38.289389

88

0.615553

1,406

11,908

4.962304

0.066145

0.065931

0.086284

0.135588

0.950838

0.947112

0.940519

0.901964

0.816827

0

0.051374

0.275865

11,908

310

89

38.412903

0.757741

0.184918

0

0.624242

0

0.006061

0.262459

0.18447

0

0.139394

1

0.151515

false

0

0.018182

0

0.175758

0

null

0

1

0

null

0

6

9a6c12d4caf225bc212d0b255bd69c14714335c4

62

py

Python

park/envs/load_balance/__init__.py

utkarsh5k/park

e7eba74f532204564df42a8e82a65ed025ce3b30

[ "MIT" ]

180

2019-04-30T05:50:32.000Z

2022-03-28T01:32:07.000Z

park/envs/load_balance/__init__.py

utkarsh5k/park

e7eba74f532204564df42a8e82a65ed025ce3b30

[ "MIT" ]

21

2019-05-03T17:42:54.000Z

2022-01-25T19:31:42.000Z

park/envs/load_balance/__init__.py

utkarsh5k/park

e7eba74f532204564df42a8e82a65ed025ce3b30

[ "MIT" ]

42

2019-05-01T15:15:19.000Z

2021-11-19T05:27:09.000Z

from park.envs.load_balance.load_balance import LoadBalanceEnv

62

0.903226

9

62

6

0.777778

0.407407

0

0.048387

62

1

62

0.915254

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

9a6c98160d9006d50265a5d1795be152d3926ccc

92

py

Python

lib/model/__init__.py

n2westman/CS410_Project

f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83

[ "MIT" ]

null

lib/model/__init__.py

n2westman/CS410_Project

f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83

[ "MIT" ]

null

lib/model/__init__.py

n2westman/CS410_Project

f8cfd5ab4d07354f3bb5f712e848853fbc9d7f83

[ "MIT" ]

null

from .wordrepr import * from .model import * from .VAT import * from .meanteachers import *

18.4

27

0.73913

12

92

5.666667

0.5

0.441176

0

0.173913

92

4

28

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d025ae46a357921dda5cb2e082f425fb68138faf

452

py

Python

AJAX/apidemo/ui/views.py

shivampip/FrontEnd

b43e5088baaa3accb9210a3093e982035c58cff1

[ "MIT" ]

null

AJAX/apidemo/ui/views.py

shivampip/FrontEnd

b43e5088baaa3accb9210a3093e982035c58cff1

[ "MIT" ]

null

AJAX/apidemo/ui/views.py

shivampip/FrontEnd

b43e5088baaa3accb9210a3093e982035c58cff1

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def index(request): return render(request, "index.html") def text(request): return render(request, "text.html") def rating(request): return render(request, "rating.html") def slider(request): return render(request, "slider.html") def dialog(request): return render(request, "dialog.html") def progress(request): return render(request, "progress.html")

18.08

43

0.70354

57

452

5.578947

0.333333

0.245283

0.358491

0.490566

0

0.170354

452

25

43

18.08

0.848

0.050885

0

0.151869

0

1

0.461538

false

0

0.076923

0.461538

1

0

null

1

0

null

0

1

0

1

0

6

d051d79133dfe795c74d49312c0c994054af6515

3,480

py

Python

build_dataset/build-stackoverflow/statistics.py

Faffola/emotions-online-qa

46935a8e0489b677769def629294d00dd860f5a1

[ "MIT" ]

null

build_dataset/build-stackoverflow/statistics.py

Faffola/emotions-online-qa

46935a8e0489b677769def629294d00dd860f5a1

[ "MIT" ]

null

build_dataset/build-stackoverflow/statistics.py

Faffola/emotions-online-qa

46935a8e0489b677769def629294d00dd860f5a1

[ "MIT" ]

null

import csv # Calcola la media del sentiment score (positivo e negativo) per ogni topic # SPECIFICA PER IL RAGGRUPPAMENTO PER TOPIC def mean_sentiscore_per_topic(file_name, output_file): dict_reader = csv.DictReader(open(file_name, 'r'), delimiter=';') topic_posscore_mean = {} # Media positive sentiment score per ogni topic topic_negscore_mean = {} # Media negative sentiment score per ogni topic topic_numb = {} # Conta il numero di post per ogni topic (utilizzato per calcolare la media) for row in dict_reader: topic = row['Topic'] if topic_numb.has_key(topic): topic_numb[topic] += 1 topic_posscore_mean[topic] += int(row['SentimentPositiveScore']) topic_negscore_mean[topic] += int(row['SentimentNegativeScore']) else: topic_numb[topic] = 1 topic_posscore_mean[topic] = int(row['SentimentPositiveScore']) topic_negscore_mean[topic] = int(row['SentimentNegativeScore']) out = open(output_file, 'w') for topic in sorted(topic_numb, key=int): topic_posscore_mean[topic] = float(topic_posscore_mean[topic]) / float(topic_numb[topic]) topic_negscore_mean[topic] = float(topic_negscore_mean[topic]) / float(topic_numb[topic]) out.write("Topic -> " + topic) out.write("\nNumber of posts -> " + str(topic_numb[topic])) out.write("\n\tMean sentiment positive score -> " + str(topic_posscore_mean[topic])) out.write("\n\tMean sentiment negative score -> " + str(topic_negscore_mean[topic])) out.write("\n\n") print "Topic -> " + topic print "\n\tMean sentiment positive score -> ", "|" * int(topic_posscore_mean[topic] / 0.1) print "\n\tMean sentiment negative score -> ", "|" * int((topic_negscore_mean[topic] / 0.1) * -1) print "\n" # Calcola la media del sentiment score (positivo e negativo) per ogni valore del campo group_by # es. group_by = "Topic" def mean_sentiscore(file_name, output_file, group_by): dict_reader = csv.DictReader(open(file_name, 'r'), delimiter=';') topic_posscore_mean = {} # Media positive sentiment score per ogni topic topic_negscore_mean = {} # Media negative sentiment score per ogni topic topic_numb = {} # Conta il numero di post per ogni topic (utilizzato per calcolare la media) for row in dict_reader: group_val = row[group_by] if topic_numb.has_key(group_val): topic_numb[group_val] += 1 topic_posscore_mean[group_val] += int(row['SentimentPositiveScore']) topic_negscore_mean[group_val] += int(row['SentimentNegativeScore']) else: topic_numb[group_val] = 1 topic_posscore_mean[group_val] = int(row['SentimentPositiveScore']) topic_negscore_mean[group_val] = int(row['SentimentNegativeScore']) out = open(output_file, 'w') for group_val in sorted(topic_numb): topic_posscore_mean[group_val] = float(topic_posscore_mean[group_val]) / float(topic_numb[group_val]) topic_negscore_mean[group_val] = float(topic_negscore_mean[group_val]) / float(topic_numb[group_val]) out.write(group_by + " -> " + group_val) out.write("\nNumber of posts -> " + str(topic_numb[group_val])) out.write("\n\tMean sentiment positive score -> " + str(topic_posscore_mean[group_val])) out.write("\n\tMean sentiment negative score -> " + str(topic_negscore_mean[group_val])) out.write("\n\n") print group_by, " -> " + group_val print "\n\tMean sentiment positive score -> ", "|" * int(topic_posscore_mean[group_val] / 0.1) print "\n\tMean sentiment negative score -> ", "|" * int((topic_negscore_mean[group_val] / 0.1) * -1) print "\n"

45.789474

103

0.724713

495

3,480

4.856566

0.141414

0.073211

0.099002

0.054908

0.867304

0.821547

0.747088

0.704659

0.648087

0.630616

0

0.004693

0.142816

3,480

75

104

46.4

0.801207

0.162931

0

0.315789

0

0.195382

0.060648

0

null

0

0.017544

null

0.140351

0

null

0

1

0

1

0

null

0

1

0

6

d06b7d082cbdf1b9163e2dc07fc03a82988d5394

224

py

Python

models/networks/__init__.py

kevinchoy/oct-schlemm-seg

e8b78695521dc65a7bbd1bcdb65b0a6200af25b3

[ "BSD-4-Clause-UC" ]

1

2021-11-17T01:54:53.000Z

models/networks/__init__.py

kevinchoy/oct-schlemm-seg

e8b78695521dc65a7bbd1bcdb65b0a6200af25b3

[ "BSD-4-Clause-UC" ]

1

2022-01-24T18:20:04.000Z

models/networks/__init__.py

kevinchoy/oct-schlemm-seg

e8b78695521dc65a7bbd1bcdb65b0a6200af25b3

[ "BSD-4-Clause-UC" ]

null

from .UNet2dFusedAttentionDsvMultiscale import * from .UNet2dAttentionDsvMultiscale import * from .UNet2d import * from .ResUNet2d import * from .DRAGUNet import * from .LFDRAGUNet import * from .UNet2dAttentionDsv import *

28

48

0.8125

21

224

8.666667

0.428571

0.32967

0

0.02551

0.125

224

7

49

32

0.903061

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d077c9af42896d230847e4a68f9e4f1074582769

131

py

Python

mvatv/exception/exceptions.py

Ilcyb/MvATv

ac358a736dd9f571aa04c73687e674d1cf8663e9

[ "0BSD" ]

null

mvatv/exception/exceptions.py

Ilcyb/MvATv

ac358a736dd9f571aa04c73687e674d1cf8663e9

[ "0BSD" ]

1

2021-06-01T21:42:13.000Z

mvatv/exception/exceptions.py

Ilcyb/MvATv

ac358a736dd9f571aa04c73687e674d1cf8663e9

[ "0BSD" ]

null

class CantPlugingError(Exception): pass class NoResourceError(Exception): pass class SearchInfoError(Exception): pass

16.375

34

0.763359

12

131

8.333333

0.5

0.39

0.36

0

0.167939

131

8

35

16.375

0.917431

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

6

d08100ce567c8dc777a98c80c17f35416bce02c4

3,939

py

Python

server/tests/test_project.py

OpenChemistry/experimentaldataplatform

f45a7ee4f9087a3e8fa61374ade4bd7b04584f61

[ "BSD-3-Clause" ]

2

2018-10-10T20:38:14.000Z

2020-07-01T13:14:59.000Z

server/tests/test_project.py

OpenChemistry/experimentaldataplatform

f45a7ee4f9087a3e8fa61374ade4bd7b04584f61

[ "BSD-3-Clause" ]

23

2018-09-06T22:31:53.000Z

2021-05-24T13:22:04.000Z

server/tests/test_project.py

OpenChemistry/edp

f45a7ee4f9087a3e8fa61374ade4bd7b04584f61

[ "BSD-3-Clause" ]

null

import pytest import datetime import json from pytest_girder.assertions import assertStatus, assertStatusOk @pytest.mark.plugin('edp') def test_create_public(server, user, project_request): from girder.plugins.edp.models.project import Project r = server.request('/edp/projects', method='POST', body=json.dumps(project_request), type='application/json', user=user) assertStatus(r, 201) assert '_id' in r.json project = Project().load(r.json['_id'], force=True) assert project['owner'] == user['_id'] assert project_request.items() <= project.items() @pytest.mark.plugin('edp') def test_create_private(server, user, project_request): from girder.plugins.edp.models.project import Project project_request['public'] = False r = server.request('/edp/projects', method='POST', body=json.dumps(project_request), type='application/json', user=user) assertStatus(r, 201) assert '_id' in r.json project = Project().load(r.json['_id'], force=True) assert project_request.items() <= project.items() @pytest.mark.plugin('edp') def test_update(server, user, project): from girder.plugins.edp.models.project import Project updates = { 'title': 'Nothing to see here.' } r = server.request('/edp/projects/%s' % project['_id'], method='PATCH', body=json.dumps(updates), type='application/json', user=user) assertStatusOk(r) project = Project().load(r.json['_id'], force=True) assert updates.items() <= project.items() @pytest.mark.plugin('edp') def test_update_non_existent(server, user, project): from girder.plugins.edp.models.project import Project updates = { 'title': 'Nothing to see here.' } non_existent = '5ae71e1ff657102b11ce2233' r = server.request('/edp/projects/%s' % non_existent, method='PATCH', body=json.dumps(updates), type='application/json', user=user) assertStatus(r, 400) @pytest.mark.plugin('edp') def test_delete(server, user, project): from girder.plugins.edp.models.project import Project r = server.request('/edp/projects/%s' % project['_id'], method='DELETE', user=user) assertStatusOk(r) project = Project().load(project['_id'], force=True) assert project is None @pytest.mark.plugin('edp') def test_delete_with_cycle(server, user, project, cycle): from girder.plugins.edp.models.project import Project from girder.plugins.edp.models.cycle import Cycle r = server.request('/edp/projects/%s' % project['_id'], method='DELETE', user=user) assertStatusOk(r) project = Project().load(project['_id'], force=True) assert project is None cycle = Cycle().load(cycle['_id'], force=True) assert cycle is None @pytest.mark.plugin('edp') def test_find(server, user, project): from girder.plugins.edp.models.project import Project r = server.request('/edp/projects', method='GET', user=user) assertStatusOk(r) assert len(r.json) == 1 @pytest.mark.plugin('edp') def test_find_owner(server, user, admin, project): from girder.plugins.edp.models.project import Project params = { 'owner': admin['_id'] } r = server.request('/edp/projects', params=params, method='GET', user=user) assertStatusOk(r) assert len(r.json) == 0 params['owner'] = user['_id'] r = server.request('/edp/projects', params=params, method='GET', user=user) assertStatusOk(r) assert len(r.json) == 1 @pytest.mark.plugin('edp') def test_get(server, user, admin, project): r = server.request('/edp/projects/%s' % project['_id'], method='GET', user=user) assertStatusOk(r) assert project.items() <= r.json.items()

30.3

88

0.639249

486

3,939

5.096708

0.135802

0.02826

0.05652

0.068631

0.842551

0.821558

0.763424

0.721437

0.669762

0

0.009064

0.215791

3,939

129

89

30.534884

0.792813

0

0.670213

0

0.106145

0.006094

0

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1

0.095745

false

0

0.138298

0

0.234043

0

null

0

1

0

null

0

6

d097c0cab34056141857fa9380638f16b78d47a5

218

py

Python

Doc/PREPRODUCTION/django-simple-email-confirmation-develop/simple_email_confirmation/__init__.py

zerxen/django_boilerplate

c29214ba02497b559a1f5c57b880e71b0f9041ea

[ "Unlicense" ]

null

Doc/PREPRODUCTION/django-simple-email-confirmation-develop/simple_email_confirmation/__init__.py

zerxen/django_boilerplate

c29214ba02497b559a1f5c57b880e71b0f9041ea

[ "Unlicense" ]

3

2020-02-12T01:06:54.000Z

2021-06-10T20:32:53.000Z

venv/lib/python3.6/site-packages/simple_email_confirmation/__init__.py

zerxen/django_boilerplate

c29214ba02497b559a1f5c57b880e71b0f9041ea

[ "Unlicense" ]

null

__version__ = '0.23' __all__ = [ 'email_confirmed', 'unconfirmed_email_created', 'primary_email_changed', ] from .signals import ( email_confirmed, unconfirmed_email_created, primary_email_changed, )

18.166667

70

0.733945

23

218

6.173913

0.565217

0.197183

0.352113

0.422535

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0

0.016484

0.165138

218

11

71

19.818182

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0

0.298165

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0

1

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false

0

0.111111

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0.111111

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1

0

null

0

1

0

1

0

null

0

6

d0a976b98b93980f7287fab38b593f0caa250dc4

213

py

Python

tests/detector/config_module_missing/__init__.py

dadaloop82/viseron

1c6c446a4856e16c0e2ed6b9323d169fbdcae20f

[ "MIT" ]

399

2020-08-31T21:13:07.000Z

2022-03-31T18:54:26.000Z

tests/detector/config_module_missing/__init__.py

dadaloop82/viseron

1c6c446a4856e16c0e2ed6b9323d169fbdcae20f

[ "MIT" ]

157

2020-09-01T18:59:56.000Z

2022-03-25T07:14:19.000Z

tests/detector/config_module_missing/__init__.py

dadaloop82/viseron

1c6c446a4856e16c0e2ed6b9323d169fbdcae20f

[ "MIT" ]

53

2020-09-01T07:35:59.000Z

2022-03-28T23:21:16.000Z

"""Dummy module that is missing the Config class.""" from viseron.detector import AbstractObjectDetection class ObjectDetection(AbstractObjectDetection): """Dummy module that is missing the Config class."""

30.428571

56

0.784038

24

213

6.958333

0.583333

0.131737

0.179641

0.203593

0.45509

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0.131455

213

6

57

35.5

0.902703

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1

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0.5

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1

0

null

0

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null

0

1

0

1

0

1

0

6

ef2f59346362d55b3180a7f4dcc1d4e9f3b4df6e

1,660

py

Python

tests/test_complex_example.py

valerybriz/vanguard-kit

3d3516537215e7195fc4df3acc9b9c9209d01781

[ "MIT" ]

null

tests/test_complex_example.py

valerybriz/vanguard-kit

3d3516537215e7195fc4df3acc9b9c9209d01781

[ "MIT" ]

null

tests/test_complex_example.py

valerybriz/vanguard-kit

3d3516537215e7195fc4df3acc9b9c9209d01781

[ "MIT" ]

null

from vanguardkit import create_html_tree, calcuate_html_tree_distance def test_calculate_impact_when_a_branch_changes(): with open("tests/html_examples/complex_example_a.html") as example_a: with open("tests/html_examples/complex_example_b.html") as example_b: a_tree = create_html_tree(example_a) b_tree = create_html_tree(example_b) assert calcuate_html_tree_distance(a_tree, b_tree) > 1 def test_calculate_difference_between_div_class_branch_a(): with open("tests/html_examples/complex_example_a.html") as example_a: with open("tests/html_examples/complex_example_b.html") as example_b: a_tree = create_html_tree(example_a, specific_tag="div", class_="branch-a") b_tree = create_html_tree(example_b, specific_tag="div", class_="branch-a") assert calcuate_html_tree_distance(a_tree, b_tree) == 4 def test_calculate_difference_between_div_class_branch_b(): with open("tests/html_examples/complex_example_a.html") as example_a: with open("tests/html_examples/complex_example_b.html") as example_b: a_tree = create_html_tree(example_a, specific_tag="div", class_="branch-b") b_tree = create_html_tree(example_b, specific_tag="div", class_="branch-b") assert calcuate_html_tree_distance(a_tree, b_tree) == 0

48.823529

77

0.604819

204

1,660

4.45098

0.166667

0.096916

0.10793

0.112335

0.886564

0.884361

0.75

0.705947

0

0.002655

0.319277

1,660

33

78

50.30303

0.800885

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0.666667

0

0.178313

0.151807

0

0.111111

1

0.111111

false

0

0.037037

0

0.148148

0

null

0

1

0

null

0

6

327340e2bbb58c862390c309a564713307935f41

44

py

Python

src/pygmy/validator/__init__.py

TinLe/pygmy

54ff3ed4ce0dbbe2868556e7fbf8bde97baa8b07

[ "MIT" ]

null

src/pygmy/validator/__init__.py

TinLe/pygmy

54ff3ed4ce0dbbe2868556e7fbf8bde97baa8b07

[ "MIT" ]

null

src/pygmy/validator/__init__.py

TinLe/pygmy

54ff3ed4ce0dbbe2868556e7fbf8bde97baa8b07

[ "MIT" ]

null

from pygmy.validator.link import LinkSchema

22

43

0.863636

6

44

6.333333

1

0

0.090909

44

1

44

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3274a5cb2371e1e1ab85b901cac29cb999cf3510

41

py

Python

src/dirtyfields/__init__.py

idonethis/django-dirtyfields

e2ac2976fda6914ce18f4d3e8423ce0aa8395c8f

[ "BSD-3-Clause" ]

null

src/dirtyfields/__init__.py

idonethis/django-dirtyfields

e2ac2976fda6914ce18f4d3e8423ce0aa8395c8f

[ "BSD-3-Clause" ]

null

src/dirtyfields/__init__.py

idonethis/django-dirtyfields

e2ac2976fda6914ce18f4d3e8423ce0aa8395c8f

[ "BSD-3-Clause" ]

1

2019-01-25T09:32:36.000Z

from dirtyfields import DirtyFieldsMixin

20.5

40

0.902439

4

41

9.25

1

0

0.097561

41

1

41

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3296391d0a170976a567e976dcee5c6f913da986

130

py

Python

insurancecompany/insurancecompany/views.py

karthikpalavalli/csci5448

4d2c84f5ee9080e032e7d73c33c7378f8a813938

[ "MIT" ]

null

insurancecompany/insurancecompany/views.py

karthikpalavalli/csci5448

4d2c84f5ee9080e032e7d73c33c7378f8a813938

[ "MIT" ]

null

insurancecompany/insurancecompany/views.py

karthikpalavalli/csci5448

4d2c84f5ee9080e032e7d73c33c7378f8a813938

[ "MIT" ]

null

from django.http import HttpResponse def insurance_home(request): return HttpResponse('Welcome to the Insurance Company!')

18.571429

60

0.784615

16

130

6.3125

0.875

0

0.146154

130

6

61

21.666667

0.90991

0

0.255814

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

32b452acbbfe615e9a5381fb36b72bdd26aaca7c

44

py

Python

bede/tagging/__init__.py

SeanMatthewNolan/Bede

5fa0396be35007ecea14acdaecaca8e1810cd8f8

[ "MIT" ]

null

bede/tagging/__init__.py

SeanMatthewNolan/Bede

5fa0396be35007ecea14acdaecaca8e1810cd8f8

[ "MIT" ]

null

bede/tagging/__init__.py

SeanMatthewNolan/Bede

5fa0396be35007ecea14acdaecaca8e1810cd8f8

[ "MIT" ]

null

from .classes import Library, Document, Tag

22

43

0.795455

6

44

5.833333

1

0

0.136364

44

1

44

0.921053

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3ee28d430d403c4e3d588ec75331f15931a46fa5

43

py

Python

server/train.py

bfortuner/label-ai

f05896c2b2c2d282763ee7db54b5f66066073961

[ "MIT" ]

1

2017-08-26T20:08:12.000Z

server/train.py

bfortuner/label-ai

f05896c2b2c2d282763ee7db54b5f66066073961

[ "MIT" ]

null

server/train.py

bfortuner/label-ai

f05896c2b2c2d282763ee7db54b5f66066073961

[ "MIT" ]

1

2018-04-11T16:42:53.000Z

import os import pandas as pd import utils

10.75

19

0.813953

8

43

4.375

0.75

0

0.186047

43

3

20

14.333333

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

411419506936de1073e70bbf89af8ec45e9823fe

52

py

Python

examples/pytest/directory/test_func.py

Gnonpi/balto

18d51f0a6ba90bc2083b34518d1ced5c2e86b7a0

[ "MIT" ]

16

2018-10-07T11:45:05.000Z

2021-11-03T05:22:47.000Z

examples/pytest/directory/test_func.py

Lothiraldan/litr

6a4b57ebd95d5bc968f9d4057de81138d59dcae2

[ "MIT" ]

15

2018-10-08T13:29:24.000Z

2021-09-11T10:01:52.000Z

examples/pytest/directory/test_func.py

Lothiraldan/litr

6a4b57ebd95d5bc968f9d4057de81138d59dcae2

[ "MIT" ]

5

2018-10-08T09:11:17.000Z

2019-11-28T14:04:14.000Z

import pytest def test_success(): assert True

8.666667

19

0.711538

7

52

5.142857

1

0

0.230769

52

5

20

10.4

0.9

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

eb1426e1b13bc25c028345d203f9f70614a058d2

23

py

Python

copypastor/config/__init__.py

Venomen/copypastor

add35e6b4ea17632c2b678a629d541b74b55ff0e

[ "MIT" ]

null

copypastor/config/__init__.py

Venomen/copypastor

add35e6b4ea17632c2b678a629d541b74b55ff0e

[ "MIT" ]

null

copypastor/config/__init__.py

Venomen/copypastor

add35e6b4ea17632c2b678a629d541b74b55ff0e

[ "MIT" ]

null

from ..config import *

11.5

22

0.695652

3

23

5.333333

1

0

0.173913

23

1

23

0.842105

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

eb20888f33e247664aa8e2e3ea3b6e7699167653

102

py

Python

Codewars/8kyu/find-the-integral/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/find-the-integral/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/find-the-integral/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 integrate = lambda c, e: f'{c / (e + 1) if c % (e + 1) else c // (e + 1)}x^{e + 1}'

25.5

83

0.441176

23

102

1.956522

0.565217

0.177778

0.2

0

0.09589

0.284314

102

3

84

34

0.520548

0.137255

0

1

0.639535

0

1

0

false

0

1

0

1

null

0

1

0

1

0

1

0

null

0

6

de16af77c5421f63d2c8fb579146a25773d7f440

69

py

Python

numertweak/__init__.py

kmedian/numertweak

ecbe6a6d02e0f921ec9a22ec9b6563a72e5628e0

[ "MIT" ]

null

numertweak/__init__.py

kmedian/numertweak

ecbe6a6d02e0f921ec9a22ec9b6563a72e5628e0

[ "MIT" ]

1

2019-03-23T21:49:57.000Z

2019-08-15T10:05:10.000Z

numertweak/__init__.py

kmedian/numertweak

ecbe6a6d02e0f921ec9a22ec9b6563a72e5628e0

[ "MIT" ]

null

from .get_cols import get_cols from .load_pandas import load_dataset

23

37

0.855072

12

69

4.583333

0.583333

0.254545

0

0.115942

69

2

38

34.5

0.901639

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

de3ea6a2d8b27eaae34ba46cbf89151a21bb824e

128

py

Python

qlist/qlist.py

QsonLabs/open-qlist-py

f6c9ae430b1b36846a7765b920edc0fe2fbe1260

[ "Apache-2.0" ]

null

qlist/qlist.py

QsonLabs/open-qlist-py

f6c9ae430b1b36846a7765b920edc0fe2fbe1260

[ "Apache-2.0" ]

null

qlist/qlist.py

QsonLabs/open-qlist-py

f6c9ae430b1b36846a7765b920edc0fe2fbe1260

[ "Apache-2.0" ]

null

import os def main(): """Prints info about qlist""" print("qlist enterprise available at - https://www.qsonlabs.com")

18.285714

69

0.664063

17

128

5

0.941176

0

0.1875

128

6

70

21.333333

0.817308

0.179688

0

0.565657

0

1

0.333333

true

0

0.333333

0

0.666667

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

de552914dfa4dda55c16c915d4c8109959b5b29c

2,479

py

Python

d06.py

f-koehler/adventofcode

b1f5f36b64e1e0e9decc3a3941cf207096d0102e

[ "MIT" ]

1

2020-07-01T16:10:06.000Z

d06.py

f-koehler/adventofcode

b1f5f36b64e1e0e9decc3a3941cf207096d0102e

[ "MIT" ]

null

d06.py

f-koehler/adventofcode

b1f5f36b64e1e0e9decc3a3941cf207096d0102e

[ "MIT" ]

null

#!/bin/env python3 import re def part1(): lights = [] for x in range(0, 1000): lights.append([False for y in range(0, 1000)]) regex = re.compile(r"^(?P<command>turn\son|turn\soff|toggle)\s(?P<x1>\d+),(?P<y1>\d+)\s+through\s+(?P<x2>\d+),(?P<y2>\d+)$") with open("d06.txt") as f: commands = f.read().splitlines() for cmd in commands: m = regex.match(cmd) grpdict = m.groupdict() c = grpdict["command"] x1 = int(grpdict["x1"]) y1 = int(grpdict["y1"]) x2 = int(grpdict["x2"]) y2 = int(grpdict["y2"]) if c == "turn on": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] = True elif c == "turn off": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] = False elif c == "toggle": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] = not lights[x][y] turned_on = 0 for x in range(0, 1000): for y in range(0, 1000): if lights[x][y]: turned_on += 1 print(turned_on) def part2(): lights = [] for x in range(0, 1000): lights.append([0 for y in range(0, 1000)]) regex = re.compile(r"^(?P<command>turn\son|turn\soff|toggle)\s(?P<x1>\d+),(?P<y1>\d+)\s+through\s+(?P<x2>\d+),(?P<y2>\d+)$") with open("d06.txt") as f: commands = f.read().splitlines() for cmd in commands: m = regex.match(cmd) grpdict = m.groupdict() c = grpdict["command"] x1 = int(grpdict["x1"]) y1 = int(grpdict["y1"]) x2 = int(grpdict["x2"]) y2 = int(grpdict["y2"]) if c == "turn on": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] += 1 elif c == "turn off": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] -= 1 if lights[x][y] < 0: lights[x][y] = 0 elif c == "toggle": for x in range(x1, x2+1): for y in range(y1, y2+1): lights[x][y] += 2 brightness = 0 for x in range(0, 1000): for y in range(0, 1000): brightness += lights[x][y] print(brightness) if __name__ == "__main__": part1() part2()

32.618421

128

0.454215

367

2,479

3.038147

0.171662

0.125561

0.078924

0.098655

0.860987

0.832287

0.7713

0

0.075544

0.369907

2,479

75

129

33.053333

0.638284

0.006858

0

0.676056

0

0.028169

0.120276

0.08208

0

1

0.028169

false

0

0.014085

0

0.042254

0.028169

0

null

0

1

0

null

0

6

deb11fddab76b8741ef4b0d45c26405f53f00b5b

6,975

py

Python

test_preprocessor.py

xingdi-eric-yuan/gata

059cd2e486adfdb5edc3e2df628d573ee9a3796b

[ "MIT" ]

1

2021-04-28T03:31:07.000Z

test_preprocessor.py

xingdi-eric-yuan/gata

059cd2e486adfdb5edc3e2df628d573ee9a3796b

[ "MIT" ]

null

test_preprocessor.py

xingdi-eric-yuan/gata

059cd2e486adfdb5edc3e2df628d573ee9a3796b

[ "MIT" ]

1

2021-04-28T03:32:57.000Z

import pytest import torch from preprocessor import SpacyPreprocessor @pytest.mark.parametrize( "batch,expected_preprocessed,expected_mask", [ ( ["My name is Peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["my name is peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["My name is Peter", "Is my name David?"], torch.tensor([[2, 3, 4, 5, 0], [4, 2, 3, 1, 1]]), torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]).float(), ), ], ) def test_spacy_preprocessor_preprocess(batch, expected_preprocessed, expected_mask): sp = SpacyPreprocessor(["<pad>", "<unk>", "my", "name", "is", "peter"]) preprocessed, mask = sp.preprocess(batch) assert preprocessed.equal(expected_preprocessed) assert mask.equal(expected_mask) @pytest.mark.parametrize( "batch,expected_preprocessed,expected_mask", [ ( ["My name is Peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["my name is peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["My name is Peter", "Is my name David?"], torch.tensor([[2, 3, 4, 5, 0], [4, 2, 3, 1, 1]]), torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]).float(), ), ], ) def test_spacy_preprocessor_decode(batch, expected_preprocessed, expected_mask): sp = SpacyPreprocessor( ["<pad>", "<unk>", "my", "name", "is", "peter", "david", "?"] ) preprocessed, _ = sp.preprocess(batch) assert sp.decode(preprocessed.tolist()) == [" ".join(sp.tokenize(s)) for s in batch] def test_spacy_preprocessor_load_from_file(): sp = SpacyPreprocessor.load_from_file("vocabs/word_vocab.txt") assert len(sp.word_to_id_dict) == 772 @pytest.mark.parametrize("batch_size", list(range(3))) @pytest.mark.parametrize( "raw_str,cleaned", [ (None, "nothing"), ("double spaces!", "double spaces!"), ("many spaces!", "many spaces!"), (" ", "nothing"), ( "\n\n\n" " ________ ________ __ __ ________ \n" " | \\| \\| \\ | \\| \\ \n" " \\$$$$$$$$| $$$$$$$$| $$ | $$ \\$$$$$$$$ \n" " | $$ | $$__ \\$$\\/ $$ | $$ \n" " | $$ | $$ \\ >$$ $$ | $$ \n" " | $$ | $$$$$ / $$$$\\ | $$ \n" " | $$ | $$_____ | $$ \\$$\\ | $$ \n" " | $$ | $$ \\| $$ | $$ | $$ \n" " \\$$ \\$$$$$$$$ \\$$ \\$$ \\$$ \n" " __ __ ______ _______ __ _______ \n" " | \\ _ | \\ / \\ | \\ | \\ | \\ \n" " | $$ / \\ | $$| $$$$$$\\| $$$$$$$\\| $$ | $$$$$$$\\\n" " | $$/ $\\| $$| $$ | $$| $$__| $$| $$ | $$ | $$\n" " | $$ $$$\\ $$| $$ | $$| $$ $$| $$ | $$ | $$\n" " | $$ $$\\$$\\$$| $$ | $$| $$$$$$$\\| $$ | $$ | $$\n" " | $$$$ \\$$$$| $$__/ $$| $$ | $$| $$_____ | $$__/ $$\n" " | $$$ \\$$$ \\$$ $$| $$ | $$| $$ \\| $$ $$\n" " \\$$ \\$$ \\$$$$$$ \\$$ \\$$ \\$$$$$$$$ \\$$$$$$$" " \n\n" "You are hungry! Let's cook a delicious meal. " "Check the cookbook in the kitchen for the recipe. " "Once done, enjoy your meal!\n\n" "-= Kitchen =-\n" "If you're wondering why everything seems so normal all of a sudden, " "it's because you've just shown up in the kitchen.\n\n" "You can see a closed fridge, which looks conventional, " "right there by you. " "You see a closed oven right there by you. Oh, great. Here's a table. " "Unfortunately, there isn't a thing on it. Hm. " "Oh well You scan the room, seeing a counter. The counter is vast. " "On the counter you can make out a cookbook and a knife. " "You make out a stove. Looks like someone's already been here and " "taken everything off it, though. Sometimes, just sometimes, " "TextWorld can just be the worst.\n\n\n", "You are hungry! Let's cook a delicious meal. " "Check the cookbook in the kitchen for the recipe. " "Once done, enjoy your meal! -= Kitchen =- " "If you're wondering why everything seems so normal all of a sudden, " "it's because you've just shown up in the kitchen. " "You can see a closed fridge, which looks conventional, " "right there by you. You see a closed oven right there by you. " "Oh, great. Here's a table. Unfortunately, there isn't a thing on it. " "Hm. Oh well You scan the room, seeing a counter. The counter is vast. " "On the counter you can make out a cookbook and a knife. " "You make out a stove. " "Looks like someone's already been here and taken everything off it, " "though. Sometimes, just sometimes, TextWorld can just be the worst.", ), ], ) def test_spacy_preprocessor_clean(raw_str, cleaned, batch_size): sp = SpacyPreprocessor.load_from_file("vocabs/word_vocab.txt") assert sp.clean(raw_str) == cleaned assert sp.batch_clean([raw_str] * batch_size) == [cleaned] * batch_size @pytest.mark.parametrize( "batch,expected_preprocessed,expected_mask", [ ( ["$$$$$$$ My name is Peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["my name is peter"], torch.tensor([[2, 3, 4, 5]]), torch.tensor([[1, 1, 1, 1]]).float(), ), ( ["My name\n is Peter", "$$$$$$$Is my name \n\nDavid?"], torch.tensor([[2, 3, 4, 5, 0], [4, 2, 3, 1, 1]]), torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]).float(), ), ], ) def test_spacy_preprocessor_clean_preprocess( batch, expected_preprocessed, expected_mask ): sp = SpacyPreprocessor(["<pad>", "<unk>", "my", "name", "is", "peter"]) preprocessed, mask = sp.clean_and_preprocess(batch) assert preprocessed.equal(expected_preprocessed) assert mask.equal(expected_mask)

43.59375

88

0.440143

729

6,975

4.035665

0.196159

0.028552

0.027532

0.025833

0.837525

0.82087

0

0.025653

0.37405

6,975

159

89

43.867925

0.648191

0

0.432432

0

0.006757

0.474122

0.023656

0

0.054054

1

0.033784

false

0

0.02027

0

0.054054

0

null

0

1

0

null

0

6

9d264587324672c1423c1263e03d06f48f9fce7b

352

py

Python

pedal/__init__.py

acbart/python-analysis

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

14

2019-08-22T03:40:23.000Z

2022-03-13T00:30:53.000Z

pedal/__init__.py

pedal-edu/pedal

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

74

2019-09-12T04:35:56.000Z

2022-01-26T19:21:32.000Z

pedal/__init__.py

acbart/python-analysis

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

2

2018-09-16T22:39:15.000Z

2018-09-17T12:53:28.000Z

""" A package for analyzing student code. """ import sys import os # Core Commands from pedal.core.report import MAIN_REPORT from pedal.core.submission import Submission from pedal.core.commands import * from pedal.source import * from pedal.sandbox.commands import * from pedal.cait import * from pedal.assertions.commands import * student: Sandbox

20.705882

44

0.792614

50

352

5.56

0.4

0.226619

0.215827

0.165468

0

0.133523

352

16

45

22

0.911475

0.147727

0

0.1

1

0

true

0

0.9

0

0.9

0

null

1

0

null

0

1

0

1

0

1

0

6

c24714b9098420ab2a075741c1a1ef5a78b697e0

17,620

py

Python

tests/test_vault_core.py

speraxdev/USDs

8ff2dfaf2173fadacf49619473d681707fc8507c

[ "MIT" ]

null

tests/test_vault_core.py

speraxdev/USDs

8ff2dfaf2173fadacf49619473d681707fc8507c

[ "MIT" ]

null

tests/test_vault_core.py

speraxdev/USDs

8ff2dfaf2173fadacf49619473d681707fc8507c

[ "MIT" ]

null

#!/usr/bin/python3 import pytest import brownie from brownie import Wei, Contract, reverts, SperaxTokenL2 def test_chi_redeem(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax core_proxy.chiRedeem(vault_proxy, {'from': owner_l2}) def test_mint_usds(sperax, owner_l2, accounts, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax ( usdt_strategy, wbtc_strategy, weth_strategy, ) = strategy_proxies ( two_hops_buyback, three_hops_buyback ) = buybacks invalid_coll = brownie.convert.to_address('0x0000000000000000000000000000000000000000') deadline = 1637632800 + brownie.chain.time() amount = 100000 slippage_collateral = 1000000000000000000000000000000 slippage_spa = 1000000000000000000000000000000 spa.approve(vault_proxy.address, slippage_spa, {'from': owner_l2 }) weth_erc20 = brownie.interface.IERC20(weth.address) weth_erc20.approve(vault_proxy.address, slippage_spa, {'from': owner_l2}) #collateral not addedd with reverts(): vault_proxy.mintBySpecifyingUSDsAmt( invalid_coll, int(amount), slippage_collateral, slippage_spa, deadline, {'from': owner_l2} ) #zero amount with reverts("Amount needs to be greater than 0"): vault_proxy.mintBySpecifyingUSDsAmt( weth.address, 0, slippage_collateral, slippage_spa, deadline, {'from': owner_l2} ) with reverts('Deadline expired'): vault_proxy.mintBySpecifyingUSDsAmt( weth.address, int(amount), slippage_collateral, slippage_spa, 0, {'from': owner_l2} ) txn = vault_proxy.mintBySpecifyingUSDsAmt( weth.address, int(amount), slippage_collateral, slippage_spa, deadline, {'from': owner_l2} ) txn = vault_proxy.updateAllocationPermission(True, {'from': owner_l2}) txn = vault_proxy.allocate({'from': owner_l2}) assert txn.events["CollateralAllocated"]["allocateAmount"] > 0 vault_proxy.updateCollateralInfo( weth, weth_strategy, True, 80, two_hops_buyback, True, {'from': owner_l2} ) txn = vault_proxy.allocate({'from': owner_l2}) vault_proxy.updateCollateralInfo( weth, weth_strategy, False, 80, two_hops_buyback, True, {'from': owner_l2} ) with reverts('Rebase paused'): txn = vault_proxy.rebase({'from': owner_l2}) vault_proxy.updateRebasePermission(True, {'from': owner_l2}) with reverts('Caller is not a rebaser'): vault_proxy.rebase({'from': owner_l2}) vault_proxy.grantRole(vault_proxy.REBASER_ROLE(), owner_l2, {'from': owner_l2}) txn = vault_proxy.rebase({'from': owner_l2}) vault_proxy.revokeRole(vault_proxy.REBASER_ROLE(), owner_l2, {'from': owner_l2}) with reverts('Caller is not a rebaser'): vault_proxy.rebase({'from': owner_l2}) vault_proxy.renounceRole(vault_proxy.REBASER_ROLE(), owner_l2, {'from': owner_l2}) core_proxy.chiRedeem(vault_proxy, {'from': owner_l2}) def test_mint_spa(sperax, weth, owner_l2, accounts): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax invalid_coll = brownie.convert.to_address('0x0000000000000000000000000000000000000000') deadline = 1637632800 + brownie.chain.time() amount = 1000 slippage_collateral = 1000000000000000000000000000000 slippage_usds = 10 spa.approve(vault_proxy.address, slippage_collateral, {'from': owner_l2 }) weth_erc20 = brownie.interface.IERC20(weth.address) weth_erc20.approve(vault_proxy.address, slippage_collateral, {'from': owner_l2}) with reverts(): vault_proxy.mintBySpecifyingSPAamt( invalid_coll, int(amount), slippage_usds, slippage_collateral, deadline, {'from': owner_l2} ) with reverts("Amount needs to be greater than 0"): vault_proxy.mintBySpecifyingSPAamt( weth.address, 0, slippage_usds, slippage_collateral, deadline, {'from': owner_l2} ) vault_proxy.mintBySpecifyingSPAamt( weth.address, int(amount), slippage_usds, slippage_collateral, deadline, {'from': owner_l2} ) def test_mint_collateral(sperax, weth, owner_l2, accounts): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax invalid_coll = brownie.convert.to_address('0x0000000000000000000000000000000000000000') deadline = 1637632800 + brownie.chain.time() amount = 10000 slippage_collateral = 10 slippage_coll = 1000000000000000000000000000000 spa.approve(vault_proxy.address, slippage_coll, {'from': owner_l2}) weth_erc20 = brownie.interface.IERC20(weth.address) weth_erc20.approve(vault_proxy.address, slippage_coll, {'from': owner_l2}) with reverts(): vault_proxy.mintBySpecifyingCollateralAmt( invalid_coll, int(amount), slippage_collateral, slippage_coll, deadline, {'from': owner_l2} ) with reverts("Amount needs to be greater than 0"): vault_proxy.mintBySpecifyingCollateralAmt( weth.address, 0, slippage_collateral, slippage_coll, deadline, {'from': owner_l2} ) vault_proxy.mintBySpecifyingCollateralAmt( weth.address, int(amount), slippage_collateral, slippage_coll, deadline, {'from': owner_l2} ) def test_allow_allocate(sperax, accounts, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax txn = vault_proxy.updateAllocationPermission(True, {'from': owner_l2}) assert txn.events["AllocationPermssionChanged"]["permission"] == True def test_vault_core_fail_allocate(sperax, accounts, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax with reverts('Allocate paused'): txn = vault_proxy.allocate({'from': owner_l2}) with reverts('Ownable: caller is not the owner'): txn = vault_proxy.updateAllocationPermission(True, {'from': owner_l2}) txn = vault_proxy.allocate({'from': accounts[5]}) def test_upgrage_collateral(sperax, accounts, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax (two_hops_buyback, three_hops_buyback) = buybacks collateralAddr = weth.address defaultStrategyAddr = brownie.convert.to_address('0x0000000000000000000000000000000000000000') invalid_coll = brownie.convert.to_address('0x0000000000000000000000000000000000000000') allocationAllowed = True allocatePercentage = 0 buyBackAddr = two_hops_buyback.address rebaseAllowed = True with reverts('Ownable: caller is not the owner'): vault_proxy.updateCollateralInfo( collateralAddr, defaultStrategyAddr, allocationAllowed, allocatePercentage, buyBackAddr, rebaseAllowed, {'from': accounts[5]}) with reverts('Collateral not added'): vault_proxy.updateCollateralInfo( invalid_coll, defaultStrategyAddr, allocationAllowed, allocatePercentage, buyBackAddr, rebaseAllowed, {'from': owner_l2}) txn = vault_proxy.updateCollateralInfo( collateralAddr, defaultStrategyAddr, allocationAllowed, allocatePercentage, buyBackAddr, rebaseAllowed, {'from': owner_l2}) assert txn.events["CollateralChanged"]["collateralAddr"] == collateralAddr assert txn.events["CollateralChanged"]["addded"] == True def test_vault_core_add_collatral(sperax, accounts, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax collateralAddr = weth.address defaultStrategyAddr = brownie.convert.to_address('0x0000000000000000000000000000000000000000') allocationAllowed = True allocatePercentage = 0 buyBackAddr = (two_hops_buyback, three_hops_buyback) = buybacks rebaseAllowed = True with reverts('Collateral added'): vault_proxy.addCollateral( collateralAddr, defaultStrategyAddr, allocationAllowed, allocatePercentage, two_hops_buyback, rebaseAllowed, {'from': owner_l2}) def test_add_strategy(sperax, accounts, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax ( usdt_strategy, wbtc_strategy, weth_strategy, ) = strategy_proxies defaultStrategyAddr = brownie.convert.to_address('0x0000000000000000000000000000000000000000') txn = vault_proxy.addStrategy(defaultStrategyAddr, {'from': owner_l2}) assert txn.events["StrategyAdded"]["added"] == True with reverts('Strategy added'): vault_proxy.addStrategy(defaultStrategyAddr, {'from': owner_l2}) def test_update_strategy_rwd_buyback_addr(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax ( usdt_strategy, wbtc_strategy, weth_strategy, ) = strategy_proxies ( two_hops_buyback, three_hops_buyback ) = buybacks defaultStrategyAddr = brownie.convert.to_address('0x0000000000000000000000000000000000000000') with reverts('Strategy not added'): txn = vault_proxy.updateStrategyRwdBuybackAddr( defaultStrategyAddr, two_hops_buyback, {'from': owner_l2}) vault_proxy.addStrategy(defaultStrategyAddr, {'from': owner_l2}) txn = vault_proxy.updateStrategyRwdBuybackAddr( defaultStrategyAddr, two_hops_buyback, {'from': owner_l2}) assert txn.events["StrategyRwdBuyBackUpdateded"]["strategyAddr"] == defaultStrategyAddr assert txn.events["StrategyRwdBuyBackUpdateded"]["buybackAddr"] == two_hops_buyback.address def test_reedem(sperax, accounts, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax deadline = brownie.chain.time() + 2000 amount = 1000000 invalid_coll = brownie.convert.to_address('0x0000000000000000000000000000000000000000') ( usdt_strategy, wbtc_strategy, weth_strategy, ) = strategy_proxies ( two_hops_buyback, three_hops_buyback ) = buybacks vault_proxy.updateCollateralInfo( weth, weth_strategy, True, 80, two_hops_buyback, True, {'from': owner_l2} ) amount = 100000 slippage_collateral = 10 slippage_spa = 10 with reverts('Amount needs to be greater than 0'): vault_proxy.redeem(weth.address, 0, slippage_collateral, slippage_spa, deadline, {'from': owner_l2}) with reverts(): vault_proxy.redeem(invalid_coll, amount, slippage_collateral, slippage_spa, deadline, {'from': owner_l2}) txn = spa.setMintable( vault_proxy, True, {'from': owner_l2} ) expired_deadline = brownie.chain.time() - 200 with reverts('Deadline expired'): vault_proxy.redeem(weth.address, amount, slippage_collateral, slippage_spa, expired_deadline, {'from': owner_l2}) txn = vault_proxy.redeem(weth.address, amount, slippage_collateral, slippage_spa, deadline, {'from': owner_l2}) def test_reedem_collateral_from_strategy(sperax, accounts, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax deadline = brownie.chain.time() + 2000 amount = 100000 slippage_collateral = 10 slippage_spa = 10 txn = spa.setMintable( vault_proxy, True, {'from': owner_l2} ) ( usdt_strategy, wbtc_strategy, weth_strategy, ) = strategy_proxies ( two_hops_buyback, three_hops_buyback ) = buybacks vault_proxy.updateCollateralInfo( weth, weth_strategy, True, 80, two_hops_buyback, True, {'from': owner_l2} ) txn = vault_proxy.redeem(weth.address, amount, slippage_collateral, slippage_spa, deadline, {'from': owner_l2}) def test_vault_core_allocate(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax txn = vault_proxy.updateAllocationPermission(True, {'from': owner_l2}) txn = vault_proxy.allocate({'from': owner_l2}) def test_vault_core_tools_spa_amount_calculator(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax with reverts('invalid valueType'): txn = core_proxy.SPAAmountCalculator(1, 10000, vault_proxy, 3000,{'from': owner_l2}) amount = core_proxy.SPAAmountCalculator.call(0, 10000, vault_proxy, 3000,{'from': owner_l2}) assert amount > 0 amount = core_proxy.SPAAmountCalculator.call(0, 10000, vault_proxy, 0,{'from': owner_l2}) assert amount > 0 def test_usds_amount_calculator(sperax, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax amt = core_proxy.USDsAmountCalculator.call(2, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) assert amt > 0 txn = core_proxy.USDsAmountCalculator.call(2, 10000, vault_proxy, weth, 0,{'from': owner_l2}) assert amt > 0 with reverts('invalid valueType'): amt = core_proxy.USDsAmountCalculator.call(0, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) amt = core_proxy.USDsAmountCalculator.call(1, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) assert amt > 0 def test_colla_dept_amount_calculator(sperax, owner_l2, weth): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax amt = core_proxy.collaDeptAmountCalculator.call(1, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) assert amt > 0 amt = core_proxy.collaDeptAmountCalculator.call(1, 10000, vault_proxy, weth, 0,{'from': owner_l2}) assert amt > 0 amt = core_proxy.collaDeptAmountCalculator.call(0, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) assert amt > 0 amt = core_proxy.collaDeptAmountCalculator.call(0, 10000, vault_proxy, weth, 0,{'from': owner_l2}) assert amt > 0 amt = core_proxy.collaDeptAmountCalculator.call(1, 10000, vault_proxy, weth, 3000,{'from': owner_l2}) assert amt > 0 def test_calculate_swapfeein(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax txn = vault_proxy.updateSwapInOutFeePermission(True, False, {'from': owner_l2}) fee = core_proxy.calculateSwapFeeIn.call(vault_proxy, {'from': owner_l2}) assert fee > 0 def test_calculate_swapfeeout(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax txn = vault_proxy.updateSwapInOutFeePermission(False, True, {'from': owner_l2}) fee = core_proxy.calculateSwapFeeOut.call(vault_proxy, {'from': owner_l2}) fee > 0 def test_chi_target(sperax, owner_l2): ( spa, usds_proxy, core_proxy, vault_proxy, oracle_proxy, strategy_proxies, buybacks, bancor ) = sperax core_proxy.chiTarget(10, 1000, 1000000, vault_proxy, {'from':owner_l2 }) core_proxy.chiTarget(10, 100000, 10000, vault_proxy, {'from':owner_l2 })

25.760234

120

0.627128

1,743

17,620

6.065404

0.086632

0.08986

0.073874

0.028755

0.850927

0.813091

0.761256

0.707813

0.671585

0.600643

0

0.068085

0.282293

17,620

683

121

25.79795

0.767911

0.002781

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null

0

1

0

1

0

1

0

null

0

6

dfcf2eebc224f9a4902fb9f63d6a34e7318eeb31

86

py

Python

autopandas_v2/generators/ml/traindata/dsl/values.py

chyanju/autopandas

16080ad12f0e8e7b0a614671aea1ed57b3fed7fe

[ "BSD-3-Clause" ]

16

2019-08-13T02:49:44.000Z

2022-02-08T03:14:34.000Z

autopandas_v2/generators/ml/traindata/dsl/values.py

chyanju/autopandas

16080ad12f0e8e7b0a614671aea1ed57b3fed7fe

[ "BSD-3-Clause" ]

2

2020-09-25T22:40:40.000Z

2022-02-09T23:42:53.000Z

autopandas_v2/generators/ml/traindata/dsl/values.py

chyanju/autopandas

16080ad12f0e8e7b0a614671aea1ed57b3fed7fe

[ "BSD-3-Clause" ]

3

2021-07-06T10:30:36.000Z

2022-01-11T23:21:31.000Z

from autopandas_v2.generators.dsl.values import Value class NewInp(Value): pass

14.333333

53

0.77907

12

86

5.5

0.916667

0

0.013699

0.151163

86

5

54

17.2

0.890411

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

dfdc265cece3387c5def226e2381c70c1faca9cc

655

py

Python

dltk/core/modules/__init__.py

mseitzer/DLTK

3237aa6c7ed63aa177ca90eafcc076d144155a34

[ "Apache-2.0" ]

17

2019-03-24T08:36:56.000Z

2021-12-28T11:42:56.000Z

dltk/core/modules/__init__.py

mseitzer/DLTK

3237aa6c7ed63aa177ca90eafcc076d144155a34

[ "Apache-2.0" ]

null

dltk/core/modules/__init__.py

mseitzer/DLTK

3237aa6c7ed63aa177ca90eafcc076d144155a34

[ "Apache-2.0" ]

6

2019-05-19T10:37:18.000Z

2021-12-04T05:13:01.000Z

from __future__ import division from __future__ import absolute_import from __future__ import print_function from dltk.core.modules.activations import * from dltk.core.modules.base import * from dltk.core.modules.batch_normalization import * from dltk.core.modules.bilinear_upsample import * from dltk.core.modules.convolution import * from dltk.core.modules.graph_convolution import * from dltk.core.modules.linear import * from dltk.core.modules.residual_units import * from dltk.core.modules.tranposed_convolution import * from dltk.core.modules.summaries import * from dltk.core.modules.losses import * from dltk.core.modules.regularization import *

38.529412

53

0.833588

91

655

5.791209

0.274725

0.227704

0.273245

0.432638

0.58444

0.204934

0

0.09313

655

16

54

40.9375

0.887205

0

1

0

true

0

1

0

1

0.066667

0

null

1

0

null

0

1

0

1

0

1

0

6

dff1b65592bababd6411646b44e68712bcac45fb

26

py

Python

pyglmnet/__init__.py

jasmainak/pyglmnet

95733c8d661632542a294722048209c85121c0ab

[ "MIT" ]

1

2018-04-10T19:42:51.000Z

pyglmnet/__init__.py

jasmainak/pyglmnet

95733c8d661632542a294722048209c85121c0ab

[ "MIT" ]

3

2019-11-04T15:36:40.000Z

2019-11-07T19:05:35.000Z

pyglmnet/__init__.py

jasmainak/pyglmnet

95733c8d661632542a294722048209c85121c0ab

[ "MIT" ]

1

2017-06-01T15:40:56.000Z

from .pyglmnet import GLM

13

25

0.807692

4

26

5.25

1

0

0.153846

26

1

26

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5f03ba44a58ac81bd5cb22140fb908fcc7bc7e86

32

py

Python

common/scheduler/celery.py

universalengineer/blockchain

65274c0a7b26bb30ce38109e3ecc566a5e72a0ac

[ "MIT" ]

null

common/scheduler/celery.py

universalengineer/blockchain

65274c0a7b26bb30ce38109e3ecc566a5e72a0ac

[ "MIT" ]

null

common/scheduler/celery.py

universalengineer/blockchain

65274c0a7b26bb30ce38109e3ecc566a5e72a0ac

[ "MIT" ]

1

2020-03-29T02:42:19.000Z

from datetime import timedelta

10.666667

30

0.84375

4

32

6.75

1

0

0.15625

32

2

31

16

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5f0f0e5d282cc7951a002f9f322b9a30c8bbb27c

108

py

Python

tseries_patterns/ml/rf/__init__.py

tr8dr/patterns

757a0b9d4936a0c6af633af6f16c0ca8ee676bb0

[ "MIT" ]

127

2020-07-12T21:48:20.000Z

2022-03-27T21:12:26.000Z

tseries_patterns/ml/rf/__init__.py

kumprj/tseries-patterns

99c5279d1a06e4ab0fe92f2a04102d09ae6300c7

[ "MIT" ]

11

2020-08-08T05:17:16.000Z

2022-02-23T13:29:23.000Z

tseries_patterns/ml/rf/__init__.py

kumprj/tseries-patterns

99c5279d1a06e4ab0fe92f2a04102d09ae6300c7

[ "MIT" ]

46

2020-07-22T20:50:55.000Z

2021-12-16T00:57:50.000Z

#from .DeepRandomForest import DeepRandomForest from .RelabeledRandomForest import RelabeledRandomForest

18

56

0.87037

8

108

11.75

0.5

0

0.101852

108

5

57

21.6

0.969072

0.425926

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

a04800324bdf2a4bf36134f5d2f8d64b3e0a27e6

16

py

Python

config/version.py

veltzer/python-sigfd

250cefa5bfb21bdb05e4cdc266f872f68a46be29

[ "MIT" ]

null

config/version.py

veltzer/python-sigfd

250cefa5bfb21bdb05e4cdc266f872f68a46be29

[ "MIT" ]

null

config/version.py

veltzer/python-sigfd

250cefa5bfb21bdb05e4cdc266f872f68a46be29

[ "MIT" ]

null

tup = (1, 3, 5)

8

15

0.375

4

16

1.5

1

0

0.272727

0.3125

16

1

16

0.272727

0

1

0

false

0

1

null

0

1

0

1

0

1

0

null

0

6

a053ca5b1be6511e086f224b55f60408a50b1bc4

1,588

py

Python

plot.py

piotr147/SnakeGameAI_RL

7180a7e874fdd1125262ad541986960bdfb6a038

[ "MIT" ]

null

plot.py

piotr147/SnakeGameAI_RL

7180a7e874fdd1125262ad541986960bdfb6a038

[ "MIT" ]

null

plot.py

piotr147/SnakeGameAI_RL

7180a7e874fdd1125262ad541986960bdfb6a038

[ "MIT" ]

null

import pandas as pd import matplotlib.pyplot as plt def plot1(): df = pd.read_csv('waz1_rozrz_stan.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 1, rozrzeszony stan") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz1_rozrz_stan") df = pd.read_csv('waz2_rozrz_stan.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 2, rozrzeszony stan") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz2_rozrz_stan") df = pd.read_csv('waz3_rozrz_stan.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 3, rozrzeszony stan") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz3_rozrz_stan") df = pd.read_csv('waz1_walls.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 1, ściany") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz1_walls") df = pd.read_csv('waz2_walls.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 2, ściany") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz2_walls") df = pd.read_csv('waz3_walls.csv') plt.figure(figsize=(14, 8)) plt.plot(df.iloc[1:, 1:]) plt.title("Wąż numer 3, ściany") plt.xlabel('Numer gry') plt.ylabel('Wynik') #plt.show() plt.savefig("waz3_walls") if(__name__=="__main__"): plot1()

25.206349

46

0.611461

246

1,588

3.817073

0.166667

0.025559

0.051118

0.070288

0.924388

0.853035

0.789137

0

0.039308

0.198992

1,588

62

47

25.612903

0.698899

0.037783

0

0.510638

0

0.26956

0

1

0.021277

false

0

0.042553

0

0.06383

0

null

0

1

0

null

0

6

a095f1e7d371dcd8a66a00eb3526f7705b19e0fe

64

py

Python

multilingual_t5/baseline_pa/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_pa/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_pa/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

"""baseline_pa dataset.""" from .baseline_pa import BaselinePa

16

35

0.765625

8

64

5.875

0.75

0.425532

0

0.109375

64

3

36

21.333333

0.824561

0.3125

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

a09e4a5f049ca43f26f3bf91d9c1114e07b1013b

968

py

Python

python/auto it/wrapper.py

lucaszdevzn/learning3

a1382bce05e0f4420b56a9cb06b712b90dc70390

[ "MIT" ]

1

2020-07-20T04:01:46.000Z

python/auto it/wrapper.py

lucaszdevzn/learning3

a1382bce05e0f4420b56a9cb06b712b90dc70390

[ "MIT" ]

null

python/auto it/wrapper.py

lucaszdevzn/learning3

a1382bce05e0f4420b56a9cb06b712b90dc70390

[ "MIT" ]

null

#coding=utf-8 import logging from functools import wraps def log_call(f): @wraps(f) def call(*args): (ret_code, ret_value, error_msg, log_path) = f(*args) param_desc = ', '.join(list(map(lambda x:str(x), args))) if ret_code == 0: logging.info('(%i) %s.%s(%s)' % (ret_code, f.__module__, f.__name__, param_desc), log_path) else: logging.error('(%i) %s.%s(%s) : %s' % (ret_code, f.__module__, f.__name__, param_desc, error_msg), log_path) return (ret_code, ret_value) return call def log_call_error(f): @wraps(f) def call(*args): (ret_code, ret_value, error_msg, log_path) = f(*args) param_desc = ', '.join(list(map(lambda x:str(x), args))) if ret_code != 0: logging.error('(%i) %s.%s(%s) : %s' % (ret_code, f.__module__, f.__name__, param_desc, error_msg), log_path) return (ret_code, ret_value) return call

35.851852

121

0.576446

145

968

3.475862

0.248276

0.125

0.029762

0.119048

0.825397

0

0.004184

0.259298

968

27

122

35.851852

0.698745

0.012397

0

0.636364

0

0.060215

0

1

0.181818

false

0

0.090909

0

0.454545

0

null

0

1

0

null

0

6

a0b6df1625b4ac59e5176aadb43bd2a6017c66b6

122

py

Python

astwro/starlist/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

[ "MIT" ]

6

2017-06-15T20:34:51.000Z

2020-04-15T14:21:43.000Z

astwro/starlist/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

[ "MIT" ]

18

2017-08-15T20:53:55.000Z

2020-10-05T23:40:34.000Z

astwro/starlist/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

[ "MIT" ]

2

2017-11-06T15:33:53.000Z

2020-10-02T21:06:05.000Z

from .daofiles import * from .fileformats import * from .ds9 import * from ._version import __version__, __version_info__

24.4

51

0.795082

15

122

5.8

0.466667

0.344828

0

0.009524

0.139344

122

4

52

30.5

0.819048

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

261c000447f6e13de4b57749f6a40e377eebd986

211

py

Python

djangosite/home/admin.py

awcrosby/jobpost_data

465a5f1e44febb01477dab4d0935c6c43a70fe97

[ "MIT" ]

null

djangosite/home/admin.py

awcrosby/jobpost_data

465a5f1e44febb01477dab4d0935c6c43a70fe97

[ "MIT" ]

null

djangosite/home/admin.py

awcrosby/jobpost_data

465a5f1e44febb01477dab4d0935c6c43a70fe97

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import JobSite, QueryLoc, ScraperParams admin.site.register(ScraperParams) admin.site.register(JobSite) admin.site.register(QueryLoc)

23.444444

52

0.819905

27

211

6.407407

0.481481

0.156069

0.294798

0.346821

0

0.094787

211

8

53

26.375

0.905759

0.123223

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

26301f016317936457218b36d65d4ff1c6571819

97

py

Python

src/operations/sub.py

macielti/tests-python

23378f147258c612227b3c9f1017e3d7bd33674e

[ "MIT" ]

1

2021-03-13T23:41:34.000Z

src/operations/sub.py

macielti/unittests-python

23378f147258c612227b3c9f1017e3d7bd33674e

[ "MIT" ]

null

src/operations/sub.py

macielti/unittests-python

23378f147258c612227b3c9f1017e3d7bd33674e

[ "MIT" ]

null

class SubOperation: def difference(self, number1, number2): return number1 - number2

24.25

43

0.701031

10

97

6.8

0.8

0.411765

0

0.053333

0.226804

97

4

44

24.25

0.853333

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

266f558453e18d54a17797c67db4f203e8de1bc3

355

py

Python

chainchomp_service_layer/__init__.py

trashtatur/chainchomp_service_layer

45a981c4eb1f851eb8ab84b7b40e890940f7331d

[ "MIT" ]

null

chainchomp_service_layer/__init__.py

trashtatur/chainchomp_service_layer

45a981c4eb1f851eb8ab84b7b40e890940f7331d

[ "MIT" ]

null

chainchomp_service_layer/__init__.py

trashtatur/chainchomp_service_layer

45a981c4eb1f851eb8ab84b7b40e890940f7331d

[ "MIT" ]

null

from chainchomp_service_layer.service_layer.ConnectionThread import ConnectionThread from chainchomp_service_layer.service_layer.ServiceLayerInterface import ServiceLayerInterface from chainchomp_service_layer.resolver.ChainfileNameResolver import ChainfileNameResolver interface = ServiceLayerInterface(ConnectionThread(), ChainfileNameResolver())

59.166667

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0.898592

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0.192926

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null

0

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0

1

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6

cd33ea67b836867711f067d4429419f4ea80034d

19

py

Python

zhusuan/variational/__init__.py

McGrady00H/Zhusuan-Jittor

53c3d2b09c9a575806da966e557e99d533b7d35f

[ "MIT" ]

12

2021-07-02T15:27:04.000Z

2021-12-28T05:59:04.000Z

zhusuan/variational/__init__.py

thu-ml/Zhusuan-Jittor

e73c6e3081afde305b9caba80858543abf168466

[ "MIT" ]

1

2021-07-29T08:50:00.000Z

zhusuan/variational/__init__.py

thu-ml/Zhusuan-Jittor

e73c6e3081afde305b9caba80858543abf168466

[ "MIT" ]

2

2021-08-17T12:05:15.000Z

2022-01-12T09:47:49.000Z

from .elbo import *

19

0.736842

3

19

4.666667

1

0

0.157895

19

1

19

0.875

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1

0

true

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null

0

1

0

1

0

1

0

6

cd5d45ffa7d1d9568aeff23bf5a30ff14169ce02

3,255

py

Python

06_Banner/python/test_banner.py

roygilliam/basic-computer-games

4b3f00ce738c612d702eaf12683a0d9cd76563a5

[ "Unlicense" ]

null

06_Banner/python/test_banner.py

roygilliam/basic-computer-games

4b3f00ce738c612d702eaf12683a0d9cd76563a5

[ "Unlicense" ]

1

2022-03-24T20:16:26.000Z

06_Banner/python/test_banner.py

roygilliam/basic-computer-games

4b3f00ce738c612d702eaf12683a0d9cd76563a5

[ "Unlicense" ]

1

2022-03-11T14:14:06.000Z

import io from _pytest.monkeypatch import MonkeyPatch from _pytest.capture import CaptureFixture from banner import print_banner def test_print_banner(monkeypatch: MonkeyPatch) -> None: horizontal = "1" vertical = "1" centered = "1" char = "*" statement = "O" # only capital letters set_page = "2" monkeypatch.setattr( "sys.stdin", io.StringIO( f"{horizontal}\n{vertical}\n{centered}\n{char}\n{statement}\n{set_page}" ), ) print_banner() def test_print_banner_horizontal_0( monkeypatch: MonkeyPatch, capsys: CaptureFixture ) -> None: horizontal = "1" vertical = "1" centered = "1" char = "*" statement = "O" # only capital letters set_page = "2" monkeypatch.setattr( "sys.stdin", io.StringIO( f"0\n{horizontal}\n{vertical}\n{centered}\n{char}\n{statement}\n{set_page}" ), ) print_banner() captured = capsys.readouterr() assert "Please enter a number greater than zero" in captured.out def test_print_banner_vertical_0( monkeypatch: MonkeyPatch, capsys: CaptureFixture ) -> None: horizontal = "1" vertical = "1" centered = "1" char = "*" statement = "O" # only capital letters set_page = "2" monkeypatch.setattr( "sys.stdin", io.StringIO( f"{horizontal}\n0\n{vertical}\n{centered}\n{char}\n{statement}\n{set_page}" ), ) print_banner() captured = capsys.readouterr() assert "Please enter a number greater than zero" in captured.out def test_print_banner_centered( monkeypatch: MonkeyPatch, capsys: CaptureFixture ) -> None: horizontal = "1" vertical = "1" centered = "Y" char = "*" statement = "O" # only capital letters set_page = "2" monkeypatch.setattr( "sys.stdin", io.StringIO( f"{horizontal}\n{vertical}\n{centered}\n{char}\n{statement}\n{set_page}" ), ) print_banner() captured = capsys.readouterr() expected = ( "Horizontal Vertical Centered Character " "(type 'ALL' if you want character being printed) Statement Set page " " *****\n" " * *\n" " * *\n" " * *\n" " * *\n" " * *\n" " *****\n\n\n" ) assert captured.out.split("\n") == expected.split("\n") def test_print_banner_all_statement( monkeypatch: MonkeyPatch, capsys: CaptureFixture ) -> None: horizontal = "1" vertical = "1" centered = "1" char = "UNIT TESTING" statement = "ALL" # only capital letters set_page = "2" monkeypatch.setattr( "sys.stdin", io.StringIO( f"{horizontal}\n{vertical}\n{centered}\n{char}\n{statement}\n{set_page}" ), ) print_banner()

29.590909

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3,255

5.142405

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0.074462

0.012923

0.014769

0.792

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3,255

109

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null

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0

1

0

null

0

6

cd8a8ba6a1479b3e8b8dcc536407743cf0f6f114

45

py

Python

numpy_version.py

Effie375/NumPy

14e24ff34caa1753cc2b0f87e2586b0541361731

[ "MIT" ]

null

numpy_version.py

Effie375/NumPy

14e24ff34caa1753cc2b0f87e2586b0541361731

[ "MIT" ]

null

numpy_version.py

Effie375/NumPy

14e24ff34caa1753cc2b0f87e2586b0541361731

[ "MIT" ]

1

2022-03-12T09:28:24.000Z

import numpy as np print(np.__version__)

11.25

22

0.733333

7

45

4.142857

0.857143

0

0.2

45

3

23

15

0.805556

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true

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0.5

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null

0

1

0

1

0

1

0

6

cd93b32dcffaa6f3f80a687c0108edea08176c28

39

py

Python

__init__.py

gregnero/color

fe86eed3bd222ec91772364f27820a7300c4732c

[ "CC0-1.0" ]

null

__init__.py

gregnero/color

fe86eed3bd222ec91772364f27820a7300c4732c

[ "CC0-1.0" ]

null

__init__.py

gregnero/color

fe86eed3bd222ec91772364f27820a7300c4732c

[ "CC0-1.0" ]

null

from .colorPalette import colorPalette

19.5

38

0.871795

4

39

8.5

0.75

0

0.102564

39

1

39

0.971429

0

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true

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null

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1

0

1

0

6

cd94781edde6fc9d6823dc1c9f5ad61afe069d58

109

py

Python

melodiam/auth/__init__.py

HarrySky/melodiam

53a5ce6a5472e88939402023d907aa120ae02bf9

[ "Unlicense" ]

null

melodiam/auth/__init__.py

HarrySky/melodiam

53a5ce6a5472e88939402023d907aa120ae02bf9

[ "Unlicense" ]

6

2020-10-05T15:27:18.000Z

2020-10-06T15:47:59.000Z

melodiam/auth/__init__.py

HarrySky/melodiam

53a5ce6a5472e88939402023d907aa120ae02bf9

[ "Unlicense" ]

null

from melodiam.auth.application import api # noqa: F401 from melodiam.auth.models import Token # noqa: F401

36.333333

55

0.779817

16

109

5.3125

0.625

0.282353

0.376471

0

0.064516

0.146789

109

2

56

54.5

0.849462

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null

0

1

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1

0

6

26959314dae862e55260a15812438d1f8b7d1158

97

py

Python

accounts/models.py

AhmedElmougy/socialwebsite

4d53e8d17b78958d52f078156ebc9c2019f00fd3

[ "BSD-3-Clause" ]

1

2021-01-24T18:40:19.000Z

accounts/models.py

AhmedElmougy/socialwebsite

4d53e8d17b78958d52f078156ebc9c2019f00fd3

[ "BSD-3-Clause" ]

null

accounts/models.py

AhmedElmougy/socialwebsite

4d53e8d17b78958d52f078156ebc9c2019f00fd3

[ "BSD-3-Clause" ]

null

from django.db import models from django.contrib import auth # Create your models here.

9.7

31

0.731959

14

97

5.071429

0.714286

0.28169

0

0.226804

97

9

32

10.777778

0.946667

0.247423

0

1

0

true

0

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1

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null

1

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null

0

1

0

1

0

1

0

6

f84901fb5e1a70caa87bc7a730a920a8e6d3c5d8

112

py

Python

xbee/thread/ieee.py

PowerFlex/python-xbee-intercept

0c07f3a5f16f479ad7c925cd31638598030cf5a7

[ "MIT" ]

65

2015-12-06T02:38:28.000Z

2017-09-05T16:46:07.000Z

xbee/thread/ieee.py

PowerFlex/python-xbee-intercept

0c07f3a5f16f479ad7c925cd31638598030cf5a7

[ "MIT" ]

44

2015-10-23T15:33:54.000Z

2017-09-01T06:39:50.000Z

xbee/thread/ieee.py

PowerFlex/python-xbee-intercept

0c07f3a5f16f479ad7c925cd31638598030cf5a7

[ "MIT" ]

43

2015-12-15T02:52:21.000Z

2017-06-24T17:14:53.000Z

from xbee.thread.base import XBeeBase import xbee.backend as _xbee class XBee(_xbee.XBee, XBeeBase): pass

16

37

0.767857

17

112

4.941176

0.588235

0.190476

0

0.160714

112

6

38

18.666667

0.893617

0

1

0

true

0.25

0.5

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0.75

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null

0

1

0

null

0

1

0

1

0

6

6ef4c228b8c9b9a4377f745d33c41704c7950df3

179

py

Python

src/reminder/blueprint.py

arnulfojr/sanic-persistance-patterns

c3c433014401725ab60f1dde3c35848f9ce3ef88

[ "MIT" ]

null

src/reminder/blueprint.py

arnulfojr/sanic-persistance-patterns

c3c433014401725ab60f1dde3c35848f9ce3ef88

[ "MIT" ]

null

src/reminder/blueprint.py

arnulfojr/sanic-persistance-patterns

c3c433014401725ab60f1dde3c35848f9ce3ef88

[ "MIT" ]

null

from sanic.blueprints import Blueprint blueprint = Blueprint('reminders', url_prefix='/reminders', strict_slashes=False) # register controllers from reminder import controller

22.375

81

0.815642

20

179

7.2

0.75

0.25

0

0.106145

179

7

82

25.571429

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0

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0

1

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false

0

0.666667

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null

1

0

1

0

null

0

1

0

1

0

6

3e18728a33ed66f97c8fb02061926c3855cd018d

11,426

py

Python

utils.py

lehommee/DPDNet

d12a61cab4204010e8dd446a507819172e15a5cf

[ "Apache-2.0" ]

15

2020-02-18T14:11:24.000Z

2021-12-06T13:36:55.000Z

utils.py

lehommee/DPDNet

d12a61cab4204010e8dd446a507819172e15a5cf

[ "Apache-2.0" ]

6

2020-02-18T19:08:38.000Z

2021-06-24T01:16:33.000Z

utils.py

lehommee/DPDNet

d12a61cab4204010e8dd446a507819172e15a5cf

[ "Apache-2.0" ]

5

2020-02-27T08:43:12.000Z

2021-03-08T15:31:28.000Z

from tensorflow.keras.preprocessing import image from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, Reshape from tensorflow.keras.models import Model import numpy as np from scipy.ndimage import rotate import tensorflow.keras import tensorflow.keras.layers as layers import tensorflow.keras.backend as K import scipy.io import math from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.layers import Conv2D,Add,SeparableConv2D, MaxPooling2D,concatenate,ZeroPadding2D,Cropping2D,Dropout,Lambda,Reshape,Input,Concatenate, concatenate,Conv3D,BatchNormalization,Activation,UpSampling2D,Conv2DTranspose from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing import image from tensorflow.keras.utils import plot_model from tensorflow.keras.models import Sequential, load_model,Model from skimage import data, img_as_float from skimage import exposure from tensorflow.keras.preprocessing.image import ImageDataGenerator import tensorflow as tf from tensorflow.keras.applications.resnet50 import ResNet50 import matplotlib.pylab as plt import numpy as np import random import scipy import cv2 as cv from skimage.transform import rescale, resize, downscale_local_mean from scipy import ndimage from tensorflow.keras.models import Sequential, load_model,Model from scipy import * import imageio import os def to_rgb3(im): # we can use dstack and an array copy # this has to be slow, we create an array with # 3x the data we need and truncate afterwards im=im*(255/np.max(im)) return np.asarray(np.dstack((im, im, im)), dtype=np.uint8) def encoding_identity_block(input_tensor, kernel_size, filters, stage, block): filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = SeparableConv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = SeparableConv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = SeparableConv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = layers.add([x, input_tensor]) x = Activation('relu')(x) return x def decoding_identity_block(input_tensor, kernel_size, filters, stage, block): filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2DTranspose(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2DTranspose(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv2DTranspose(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = layers.add([x, input_tensor]) x = Activation('relu')(x) return x def encoding_conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2,2)): filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = SeparableConv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = SeparableConv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = SeparableConv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = SeparableConv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = Activation('relu')(x) return x def decoding_conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2,2)): filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x=UpSampling2D(size=strides, data_format=None)(input_tensor) x = SeparableConv2D(filters1, (1, 1))(x) x = BatchNormalization(axis=bn_axis)(x) x = Activation('relu')(x) x = SeparableConv2D(filters2, kernel_size,padding='same')(x) x = BatchNormalization(axis=bn_axis)(x) x = Activation('relu')(x) x = SeparableConv2D(filters3, (1,1))(x) x = BatchNormalization(axis=bn_axis)(x) shortcut = UpSampling2D(size=strides, data_format=None)(input_tensor) shortcut = SeparableConv2D(filters3, (1, 1) )(shortcut) shortcut = BatchNormalization(axis=bn_axis)(shortcut) x = layers.add([x, shortcut]) x = Activation('relu')(x) return x def refunit(divider,ch,img_y,img_x): image_input = Input(shape=(int(img_y/divider), int(img_x/divider), ch)) x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')(image_input) x = BatchNormalization(axis=3, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3))(x) x = encoding_conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = encoding_conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = decoding_conv_block(x, 3, [512, 512, 128], stage=6, block='a') x = decoding_conv_block(x, 3, [256, 256, 64], stage=7, block='a') x=ZeroPadding2D(padding=(0,1),data_format=None)(x) x = UpSampling2D(size=(3, 3))(x) x = Cropping2D(cropping=((2, 2), (1, 1)), data_format=None)(x) x = Conv2DTranspose(1, (3, 3), padding='same', name='c8o')(x) x = Activation('sigmoid')(x) modelo = Model(inputs=image_input, outputs=x) modelo.summary() return modelo def fastrefunit(divider,ch,img_y,img_x): image_input = Input(shape=(int(img_y/divider), int(img_x/divider), ch)) x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')(image_input) x = BatchNormalization(axis=3, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3))(x) x = encoding_conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = encoding_conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = decoding_conv_block(x, 3, [512, 512, 128], stage=6, block='a') x = decoding_conv_block(x, 3, [256, 256, 64], stage=7, block='a') x = UpSampling2D(size=(3, 3))(x) x = Cropping2D(cropping=((1, 1), (2, 2)), data_format=None)(x) x = Conv2DTranspose(1, (3, 3), padding='same', name='c8o')(x) x = Activation('sigmoid')(x) modelo = Model(inputs=image_input, outputs=x) modelo.summary() return modelo def load_valdata(divider,canales,batch_size,lengthdataset,path,img_y,img_x): valinput=[] valoutput=[] multiplier=6 counter=0 l1=["validation/imagenes/seq-P01-M04-A0002-G00-C00-S0101/image%04d.png","validation/imagenes/seq-P05-M04-A0001-G03-C00-S0030/image%04d.png","validation/imagenes/seq-P00-M02-A0032-G00-C00-S0037/image%04d.png","validation/imagenes/seq-P00-M02-A0032-G00-C00-S0036/image%04d.png"] l2=["validation/gaussianas/seq-P01-M04-A0002-G00-C00-S0101/image%04d.png","validation/gaussianas/seq-P05-M04-A0001-G03-C00-S0030/image%04d.png","validation/gaussianas/seq-P00-M02-A0032-G00-C00-S0037/image%04d.png","validation/gaussianas/seq-P00-M02-A0032-G00-C00-S0036/image%04d.png"] l3=[741,509,920,868] while 1: valinput = [] valoutput= [] for j in range(batch_size*counter+1, batch_size*(counter+1)+1): ind=np.uint16(rand()*4) j=np.uint16(rand()*(l3[ind]-5))+1 img_path = path+l1[ind] % (j) imgc = imageio.imread(img_path) imgc = cv.resize(imgc, (int(img_x/divider), int(img_y/divider))) xc = image.img_to_array(imgc) xc = xc / 65536 if(canales is 3): xc=np.asarray(np.dstack((xc, xc, xc)), dtype=np.float64) valinput.append(xc) img_path = path+l2[ind] % (j) imgc = image.load_img(img_path, grayscale=True, target_size=(int(img_y/divider), int(img_x/divider), 1)) xc = image.img_to_array(imgc) xc = cv.blur(xc, (3, 3)) xc = np.expand_dims(xc, axis=2) xc = xc / 255 valoutput.append(xc) valinput=np.array(valinput) valoutput=np.array(valoutput) yield valinput,[valoutput,valoutput] def TrainGen(divider,canales,batch_size,lengthdataset,path,img_y,img_x): counter=0 while 1: X = [] Y= [] for j in range(batch_size*counter+1, batch_size*(counter+1)+1): j=math.floor(rand()*(lengthdataset-5))+1 img_path = path+"train/imagenes/image%05d.png" % (j) imgc = imageio.imread(img_path) imgc = cv.resize(imgc, (int(img_x / divider), int(img_y / divider))) xc = image.img_to_array(imgc) xc = xc / 65536 if (canales is 3): xc = np.asarray(np.dstack((xc, xc, xc)), dtype=np.float64) X.append(xc) img_path = path+"train/gaussianas/image%05d.png" % (j) imgc = image.load_img(img_path, grayscale=True, target_size=(int(img_y/divider), int(img_x/divider), 1)) xc = image.img_to_array(imgc) xc = cv.blur(xc, (3, 3)) xc = np.expand_dims(xc, axis=2) xcaux = np.copy(xc) xcaux = abs(xcaux - 255) xc = xc / 255 Y.append(xc) X = np.array(X) Y= np.array(Y) counter = counter + 1 yield X,[Y,Y] def to_rgb3(im): # we can use dstack and an array copy # this has to be slow, we create an array with # 3x the data we need and truncate afterwards im=im*(255/np.max(im)) return np.asarray(np.dstack((im, im, im)), dtype=np.uint8) def test(divider,canales,path,img_x,img_y): valinput=[] valoutput=[] multiplier=6 counter=0 valinput = [] valoutput= [] for j in range(1,741,1): img_path = path+"validation/imagenes/seq-P01-M04-A0002-G00-C00-S0101/image%04d.png" % (j) imgc = imageio.imread(img_path) imgc = cv.resize(imgc, (int(img_x / divider), int(img_y / divider))) xc = image.img_to_array(imgc) xc = xc / 65536 valinput.append(xc) img_path = path+"validation/gaussianas/seq-P01-M04-A0002-G00-C00-S0101/image%04d.png" % (j) imgc = image.load_img(img_path, grayscale=True, target_size=(int(img_y/divider), int(img_x/divider), 1)) xc = image.img_to_array(imgc) xc = cv.blur(xc, (3, 3)) xc = np.expand_dims(xc, axis=2) xc = xc / 255 valoutput.append(xc) valinput=np.array(valinput) valoutput=np.array(valoutput) return valinput,valoutput

38.732203

288

0.665412

1,706

11,426

4.325322

0.130129

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0.043637

0.053124

0.833582

0.793197

0.759724

0.755658

0.728554

0.696842

0

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0.185017

11,426

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38.863946

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1

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0.214876

0

null

0

1

0

null

0

6

3e1f239d34ee1a3335ed744a17231773fa3bc34a

8,589

py

Python

test/functional/whc_tokenrevokenegtive.py

quangdo3112/wormhole

c911826b43b5de666b9ae0e69b9f9deb95039a9a

[ "MIT" ]

78

2018-07-16T14:55:15.000Z

2022-02-26T10:43:23.000Z

test/functional/whc_tokenrevokenegtive.py

quangdo3112/wormhole

c911826b43b5de666b9ae0e69b9f9deb95039a9a

[ "MIT" ]

14

2018-07-20T02:17:45.000Z

2019-05-13T09:50:13.000Z

test/functional/whc_tokenrevokenegtive.py

quangdo3112/wormhole

c911826b43b5de666b9ae0e69b9f9deb95039a9a

[ "MIT" ]

28

2018-07-17T01:50:37.000Z

2021-11-04T08:04:56.000Z

#!/usr/bin/env python3 # Copyleft (c) 2017 eric sun from test_framework.test_framework import BitcoinTestFramework from test_framework.util import (assert_equal, assert_raises_rpc_error) from test_framework.authproxy import JSONRPCException import time class WHC_TOKEN_MANAGE(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 1 self.tip = None self.setup_clean_chain = True def token_manage_test(self): # generate 200whc for node[0] address = self.nodes[0].getnewaddress("") address_dst = self.nodes[0].getnewaddress("") self.nodes[0].generatetoaddress(110, address) self.nodes[0].whc_burnbchgetwhc(4) self.nodes[0].sendtoaddress(address_dst, 10) self.nodes[0].generatetoaddress(1, address) self.nodes[0].whc_sendissuancefixed(address, 1, 1, 0, "", "", "whctoken", "", "", "500") managed_trans_id = self.nodes[0].whc_sendissuancemanaged(address, 1, 1, 0, "", "", "managede token", "", "") self.nodes[0].generatetoaddress(1, address) managed_trans = self.nodes[0].whc_gettransaction(managed_trans_id) managed_property_id = managed_trans["propertyid"] # exp3: token value exceed max item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_grant(managed_property_id, "50", "") p = payload[:16] + "ffffffffffffffff" + payload[32:] ret = self.nodes[0].whc_createrawtx_opreturn(ret, p) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is False assert trans["invalidreason"] == "Value out of range or zero" else: assert False # exp3: token value exceed max item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_grant(managed_property_id, "50", "") ret = self.nodes[0].whc_createrawtx_opreturn(ret, payload) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is True else: assert False # not managed token item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_revoke(managed_property_id, "20", "") p = payload[:15] + '6' + payload[16:] ret = self.nodes[0].whc_createrawtx_opreturn(ret, p) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is False else: assert False # not managed token issuer item = self.getSpent(address_dst) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_revoke(managed_property_id, "20", "") ret = self.nodes[0].whc_createrawtx_opreturn(ret, payload) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id2 = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id2) assert trans["valid"] is False assert trans["invalidreason"] == "Sender is not the issuer of the property" else: assert False # no issuer raise a change issuer action item = self.getSpent(address_dst) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_changeissuer(managed_property_id) ret = self.nodes[0].whc_createrawtx_opreturn(ret, payload) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) ret = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(ret) assert trans["valid"] is False assert trans["invalidreason"] == "Sender is not the issuer of the property" else: assert False # issuer is himself item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_changeissuer(managed_property_id) ret = self.nodes[0].whc_createrawtx_opreturn(ret, payload) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].whc_createrawtx_reference(ret, address) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is True else: assert False # property id not exit item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_changeissuer(managed_property_id) p = payload[:15] + 'f' ret = self.nodes[0].whc_createrawtx_opreturn(ret, p) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].whc_createrawtx_reference(ret, address_dst) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is False assert trans["invalidreason"] == "Property does not exist" else: assert False # token revoke > token left item = self.getSpent(address) if item: ret = self.nodes[0].whc_createrawtx_input("", item["txid"], item["vout"]) payload = self.nodes[0].whc_createpayload_revoke(managed_property_id, "60", "") ret = self.nodes[0].whc_createrawtx_opreturn(ret, payload) ret = self.nodes[0].whc_createrawtx_reference(ret, item["address"], round(float(item["amount"]) - 0.01, 8)) ret = self.nodes[0].signrawtransactionwithwallet(ret) trans_id = self.nodes[0].sendrawtransaction(ret["hex"]) self.nodes[0].generatetoaddress(1, address) trans = self.nodes[0].whc_gettransaction(trans_id) assert trans["valid"] is False assert trans["invalidreason"] == "Sender has insufficient balance" else: assert False def getSpent(self, addr): item = None ret = self.nodes[0].listunspent() for it in ret: if it["address"] == addr and it["amount"] > 1: item = it break return item def run_test(self): self.token_manage_test() if __name__ == '__main__': WHC_TOKEN_MANAGE().main()

48.801136

119

0.61765

1,025

8,589

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0.134511

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0

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8,589

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null

0

1

0

1

0

null

0

6

3e579ffbb8e4d476ba95a772cec7ac3d69c3a5d6

2,193

py

Python

elasticsearch/es.py

dumingcode/my-fintech-idc

5546b2b2ab8b66224941ae1600e4ffd3ce571ec6

[ "MIT" ]

null

elasticsearch/es.py

dumingcode/my-fintech-idc

5546b2b2ab8b66224941ae1600e4ffd3ce571ec6

[ "MIT" ]

7

2019-07-10T10:49:48.000Z

2021-12-13T20:02:04.000Z

elasticsearch/es.py

dumingcode/my-fintech-idc

5546b2b2ab8b66224941ae1600e4ffd3ce571ec6

[ "MIT" ]

null

import requests import json from loguru import logger from config import cons as ct def create_index(index_name: str): ret_jsons = None try: html = requests.put( ct.conf('ES')['url'] + index_name) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons def create_index_setting(index_name: str, settings: str): ret_jsons = None try: headers = {"Content-Type": "application/json"} html = requests.put( ct.conf('ES')['url'] + index_name, data=settings, headers=headers) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons def delete_index(index_name: str): ret_jsons = None try: html = requests.delete(ct.conf('ES')['url'] + index_name) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons def create_mapping(index_name: str, mapping_obj: str): ret_jsons = None headers = {"Content-Type": "application/json"} try: html = requests.post( ct.conf('ES')['url'] + index_name + '/_doc/_mapping', data=mapping_obj, headers=headers) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons def insert_document(index_name: str, document: str, key: str): ret_jsons = None headers = {"Content-Type": "application/json"} try: html = requests.put( ct.conf('ES')['url'] + index_name + '/_doc/' + key, data=document, headers=headers) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons def delete_document(index_name: str, key: str): ret_jsons = None try: html = requests.delete( ct.conf('ES')['url'] + index_name + '/_doc/' + key) ret_jsons = json.loads(html.text) except Exception as err: logger.error(err) return None return ret_jsons

26.743902

65

0.609667

282

2,193

4.588652

0.163121

0.111283

0.055641

0.069552

0.814529

0.775116

0.765842

0.748068

0.738794

0.711747

0

0.279526

2,193

81

66

27.074074

0.818987

0

0.695652

0

0.063839

0

1

0.086957

false

0

0.057971

0

0.318841

0

null

0

1

0

null

0

6

e40fc710fd4003d8817c42800be2ac5d6e249eda

145

py

Python

olfactory/detection/__init__.py

OctaveLauby/olfactory

679b67459c12002041a8f77e1bdffe33d776500b

[ "Apache-2.0" ]

null

olfactory/detection/__init__.py

OctaveLauby/olfactory

679b67459c12002041a8f77e1bdffe33d776500b

[ "Apache-2.0" ]

null

olfactory/detection/__init__.py

OctaveLauby/olfactory

679b67459c12002041a8f77e1bdffe33d776500b

[ "Apache-2.0" ]

null

from .drop import detect_drop from .xregularity import reg_bounds, stepreg_bounds from .yregularity import detect_elbow, detect_iso, detect_leap

36.25

62

0.855172

21

145

5.619048

0.571429

0.20339

0

0.103448

145

3

63

48.333333

0.907692

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e412ebdb8c6aac3f1006ccdb7daa0f01cc4ae7d5

129

py

Python

tests/dao/test_base.py

kmjbyrne/flask-kbpc

859ea26146ea69cfff7699c75a0612388b84c756

[ "MIT" ]

null

tests/dao/test_base.py

kmjbyrne/flask-kbpc

859ea26146ea69cfff7699c75a0612388b84c756

[ "MIT" ]

3

2020-05-29T01:28:25.000Z

2021-04-30T21:05:42.000Z

tests/dao/test_base.py

kmjbyrne/flask-kbpc

859ea26146ea69cfff7699c75a0612388b84c756

[ "MIT" ]

null

import unittest class TestDAOBase(unittest.TestCase): def setUp(self) -> None: pass def tearDown(self) -> None: pass

14.333333

37

0.689922

16

129

5.5625

0.6875

0.179775

0.269663

0

0.20155

129

8

38

16.125

0.864078

0

1

0.5

false

0.5

0.25

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

e45d3a8ccb81b704f80870f0d94418c49fb243be

4,812

py

Python

test/hyperLSTM_unittest.py

Ar-Kareem/Sketch-RNN

350824040715ea281182de01bca467130f326566

[ "MIT" ]

1

2020-07-05T17:18:36.000Z

test/hyperLSTM_unittest.py

Ar-Kareem/Sketch-RNN

350824040715ea281182de01bca467130f326566

[ "MIT" ]

null

test/hyperLSTM_unittest.py

Ar-Kareem/Sketch-RNN

350824040715ea281182de01bca467130f326566

[ "MIT" ]

null

import unittest import torch import torch.nn as nn import hyperLSTM class MyTestCase(unittest.TestCase): def test_lstm_single(self): torch.manual_seed(42) pytorch_lstm = nn.LSTM(5, 2) lstm = hyperLSTM.LSTM(5, 2, forget_bias=0) lstm.wx.weight.data = dict(pytorch_lstm.named_parameters())['weight_ih_l0'] lstm.wh.weight.data = dict(pytorch_lstm.named_parameters())['weight_hh_l0'] lstm.wh.bias.data = dict(pytorch_lstm.named_parameters())['bias_ih_l0'] + dict(pytorch_lstm.named_parameters())['bias_hh_l0'] input_ = torch.normal(torch.ones(1, 1, 5)) state = (torch.ones(2) * 2, torch.ones(2) * 3) state_torch = (torch.ones(1, 1, 2) * 2, torch.ones(1, 1, 2) * 3) lstm_out = lstm(input_, state) pytorch_lstm = pytorch_lstm(input_, state_torch) self.assertTrue(torch.allclose(lstm_out[1][0].data, pytorch_lstm[1][0].data), "Short term memory differs") self.assertTrue(torch.allclose(lstm_out[1][1].data, pytorch_lstm[1][1].data), "Long term memory differs") def test_lstm_long(self): torch.manual_seed(42) pytorch_lstm = nn.LSTM(5, 2) lstm = hyperLSTM.LSTM(5, 2, forget_bias=0) lstm.wx.weight.data = dict(pytorch_lstm.named_parameters())['weight_ih_l0'] lstm.wh.weight.data = dict(pytorch_lstm.named_parameters())['weight_hh_l0'] lstm.wh.bias.data = dict(pytorch_lstm.named_parameters())['bias_ih_l0'] + dict(pytorch_lstm.named_parameters())['bias_hh_l0'] input_ = torch.normal(torch.ones(10, 1, 5)) state = (torch.ones(2) * 2, torch.ones(2) * 3) state_torch = (torch.ones(1, 1, 2) * 2, torch.ones(1, 1, 2) * 3) lstm_out = lstm(input_, state) pytorch_lstm = pytorch_lstm(input_, state_torch) self.assertTrue(torch.allclose(lstm_out[1][0].data, pytorch_lstm[1][0].data), "Short term memory differs") self.assertTrue(torch.allclose(lstm_out[1][1].data, pytorch_lstm[1][1].data), "Long term memory differs") def test_lstm_multple_states(self): torch.manual_seed(42) pytorch_lstm = nn.LSTM(5, 2) lstm = hyperLSTM.LSTM(5, 2, forget_bias=0) lstm.wx.weight.data = dict(pytorch_lstm.named_parameters())['weight_ih_l0'] lstm.wh.weight.data = dict(pytorch_lstm.named_parameters())['weight_hh_l0'] lstm.wh.bias.data = dict(pytorch_lstm.named_parameters())['bias_ih_l0'] + dict(pytorch_lstm.named_parameters())['bias_hh_l0'] input_ = torch.normal(torch.ones(3, 10, 5)) state = (torch.ones(10, 2) * 2, torch.ones(10, 2) * 3) state[0][0,1] = 2 state[0][1,1] = 3 state[1][0,0] = 4 state[1][1,0] = 5 state_torch = (state[0].clone().unsqueeze(0), state[1].clone().unsqueeze(0)) lstm_out = lstm(input_, state) pytorch_lstm_out = pytorch_lstm(input_, state_torch) self.assertTrue(torch.allclose(lstm_out[1][0].data, pytorch_lstm_out[1][0].data), "Short term memory differs") self.assertTrue(torch.allclose(lstm_out[1][1].data, pytorch_lstm_out[1][1].data), "Long term memory differs") state[1][1,0] = 5.1 lstm_out = lstm(input_, state) pytorch_lstm_out = pytorch_lstm(input_, state_torch) self.assertFalse(torch.allclose(lstm_out[1][0].data, pytorch_lstm_out[1][0].data), "Short term memory should be different") self.assertFalse(torch.allclose(lstm_out[1][1].data, pytorch_lstm_out[1][1].data), "Long term memory should be different") def test_lstm_empty_state(self): torch.manual_seed(42) pytorch_lstm = nn.LSTM(5, 2) lstm = hyperLSTM.LSTM(5, 2, forget_bias=0) lstm.wx.weight.data = dict(pytorch_lstm.named_parameters())['weight_ih_l0'] lstm.wh.weight.data = dict(pytorch_lstm.named_parameters())['weight_hh_l0'] lstm.wh.bias.data = dict(pytorch_lstm.named_parameters())['bias_ih_l0'] + dict(pytorch_lstm.named_parameters())['bias_hh_l0'] input_ = torch.normal(torch.ones(3, 10, 5)) state_torch = (torch.zeros(1, 10, 2), torch.zeros(1, 10, 2)) lstm_out = lstm(input_) pytorch_lstm_out = pytorch_lstm(input_, state_torch) self.assertTrue(torch.allclose(lstm_out[1][0].data, pytorch_lstm_out[1][0].data), "Short term memory differs") self.assertTrue(torch.allclose(lstm_out[1][1].data, pytorch_lstm_out[1][1].data), "Long term memory differs") def test_hyper_lstm_not_crashing(self): torch.manual_seed(42) lstm = hyperLSTM.HyperLSTM(5, 2, layer_norm=True, dropout=0.1) input_ = torch.normal(torch.ones(3, 10, 5)) lstm_out = lstm(input_) if __name__ == '__main__': unittest.main()

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Python

python/testData/refactoring/rename/renameLocalWithComprehension.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/refactoring/rename/renameLocalWithComprehension.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/rename/renameLocalWithComprehension.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

parameter_lists_copy = [m for m in parameter_lists] for <caret>m in parameter_lists_copy: if param_index >= len(m.GetParameters()): parameter_lists.remove(m)

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py

Python

pyKinectTools/configs/__init__.py

colincsl/pyKinectTools

a84bb5b7ff9dd613576415932865c2ad435520b3

[ "BSD-2-Clause-FreeBSD" ]

33

2015-04-07T16:28:04.000Z

2021-11-22T00:28:43.000Z

pyKinectTools/dataset_readers/__init__.py

colincsl/pyKinectTools

a84bb5b7ff9dd613576415932865c2ad435520b3

[ "BSD-2-Clause-FreeBSD" ]

null

pyKinectTools/dataset_readers/__init__.py

colincsl/pyKinectTools

a84bb5b7ff9dd613576415932865c2ad435520b3

[ "BSD-2-Clause-FreeBSD" ]

13

2015-04-07T16:28:34.000Z

2021-04-26T08:04:36.000Z

# __all__ = ["algs", "utils"]

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e4d67884a93ae28baf78b86f1ae83c7ab6dbcd76

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py

Python

fancyfuncs.py

Nicksname/fancyfuncs

1fbedec9ae6c2be9e580e13c1c872d9378b9ba8f

[ "MIT" ]

null

fancyfuncs.py

Nicksname/fancyfuncs

1fbedec9ae6c2be9e580e13c1c872d9378b9ba8f

[ "MIT" ]

null

fancyfuncs.py

Nicksname/fancyfuncs

1fbedec9ae6c2be9e580e13c1c872d9378b9ba8f

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function from timefuncs import * # noqa

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py

Python

BluePlug/__init__.py

liufeng3486/BluePlug

c7c5c769ed35c71ebc542d34848d6bf309abd051

[ "MIT" ]

1

2019-01-27T04:08:05.000Z

BluePlug/__init__.py

liufeng3486/BluePlug

c7c5c769ed35c71ebc542d34848d6bf309abd051

[ "MIT" ]

5

2021-03-18T21:35:20.000Z

2022-01-13T00:58:18.000Z

BluePlug/__init__.py

liufeng3486/BluePlug

c7c5c769ed35c71ebc542d34848d6bf309abd051

[ "MIT" ]

null

from .demo_test import * from .QtWork import * from .SubMom import * from .BaseClass import * from .img import test_iamge

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Python

3.py

hydrapheetz/adventofcode-soutions

02bcf5411da3944e3cbfd7db6fec5333aed46650

[ "Unlicense" ]

null

3.py

hydrapheetz/adventofcode-soutions

02bcf5411da3944e3cbfd7db6fec5333aed46650

[ "Unlicense" ]

null

3.py

hydrapheetz/adventofcode-soutions

02bcf5411da3944e3cbfd7db6fec5333aed46650

[ "Unlicense" ]

null

house_map = {} problem_input = open("input_3.txt", "r").read() #problem_input = "^v^v^v^v^v" def process_map(): global house_map coords = (0,0) for house in problem_input: presents = house_map.setdefault(coords, 0) house_map[coords] = presents+1 if (house == "v"): coords = (coords[0], coords[1]+1) house_map.setdefault(coords, 0) house_map[coords] = presents+1 elif (house == ">"): coords = (coords[0]+1, coords[1]) house_map.setdefault(coords, 0) house_map[coords] = presents+1 elif (house == "<"): coords = (coords[0]-1, coords[1]) house_map.setdefault(coords, 0) house_map[coords] = presents+1 elif (house == "^"): coords = (coords[0], coords[1]-1) house_map.setdefault(coords, 0) house_map[coords] = presents+1 def process_robo(): global house_map coords = (0,0) robo_coords = (0,0) robo_move = False presents = house_map.setdefault(coords, 0) robo_presents = house_map.setdefault(robo_coords,0) house_map[coords] = presents+1 house_map[robo_coords] = presents+1 for house in problem_input: if (house == "v"): if (not robo_move): coords = (coords[0], coords[1]+1) house_map.setdefault(coords, 0) house_map[coords] = presents+1 else: robo_coords = (robo_coords[0], robo_coords[1]+1) house_map.setdefault(robo_coords,0) house_map[robo_coords] = presents+1 elif (house == ">"): if (not robo_move): coords = (coords[0]+1, coords[1]) house_map.setdefault(coords, 0) house_map[coords] = presents+1 else: robo_coords = (robo_coords[0]+1, robo_coords[1]) house_map.setdefault(robo_coords, 0) house_map[robo_coords] = presents+1 elif (house == "<"): if (not robo_move): coords = (coords[0]-1, coords[1]) house_map.setdefault(coords, 0) house_map[coords] = presents+1 else: robo_coords = (robo_coords[0]-1, robo_coords[1]) house_map.setdefault(robo_coords, 0) house_map[robo_coords] = presents+1 elif (house == "^"): if (not robo_move): coords = (coords[0], coords[1]-1) house_map.setdefault(coords, 0) house_map[coords] = presents+1 else: robo_coords = (robo_coords[0], robo_coords[1]-1) house_map.setdefault(robo_coords, 0) house_map[robo_coords] = presents+1 robo_move = (not robo_move) process_map() print(len(house_map.values())) house_map = {} # problem_input = "^v^v^v^v^v" process_robo() print(house_map.keys()) print(len(house_map.values()))

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py

Python

testapi/admin.py

matheusmatos/django-rest-models

18da71bd921064279b03129aac38d3fbb9e29ae2

[ "BSD-2-Clause" ]

61

2016-12-05T09:09:49.000Z

2022-03-09T13:23:06.000Z

testapi/admin.py

matheusmatos/django-rest-models

18da71bd921064279b03129aac38d3fbb9e29ae2

[ "BSD-2-Clause" ]

51

2016-12-07T10:19:52.000Z

2022-03-11T23:35:23.000Z

testapi/admin.py

matheusmatos/django-rest-models

18da71bd921064279b03129aac38d3fbb9e29ae2

[ "BSD-2-Clause" ]

18

2017-03-11T18:07:17.000Z

2022-03-09T13:14:40.000Z

# -*- coding: utf-8 -*- from django.contrib import admin from testapi.models import Menu, Pizza, Topping admin.site.register([Pizza, Topping, Menu])

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py

Python

contractor/lib/ip_test.py

T3kton/contractor

dd78f5b770ee7b5c41cddfc0a61869908b96e385

[ "Apache-2.0" ]

5

2019-02-15T15:55:56.000Z

2020-08-02T03:36:44.000Z

contractor/lib/ip_test.py

T3kton/contractor

dd78f5b770ee7b5c41cddfc0a61869908b96e385

[ "Apache-2.0" ]

4

2017-05-17T22:18:41.000Z

2020-05-10T03:46:33.000Z

contractor/lib/ip_test.py

T3kton/contractor

dd78f5b770ee7b5c41cddfc0a61869908b96e385

[ "Apache-2.0" ]

4

2017-05-09T21:05:51.000Z

2020-09-25T16:37:20.000Z

import pytest from contractor.lib.ip import IpIsV4, StrToIp, IpToStr, CIDRNetwork, CIDRNetmask, CIDRNetmaskToPrefix, CIDRNetworkSize, CIDRNetworkBounds, CIDRNetworkRange def test_isv4(): assert IpIsV4( 281470681743360 ) assert IpIsV4( 281470681743361 ) assert not IpIsV4( 1 ) assert not IpIsV4( 0 ) assert not IpIsV4( 281470681743359 ) def test_strtoip(): with pytest.raises( ValueError ): StrToIp( '127' ) with pytest.raises( ValueError ): StrToIp( '127.00.1' ) with pytest.raises( ValueError ): StrToIp( '127.0.1' ) with pytest.raises( ValueError ): StrToIp( 'a.0.0.0' ) with pytest.raises( ValueError ): StrToIp( '0.a.0.0' ) with pytest.raises( ValueError ): StrToIp( '0.0.a.0' ) with pytest.raises( ValueError ): StrToIp( '0.0.0.a' ) with pytest.raises( ValueError ): StrToIp( '256.0.0.0' ) with pytest.raises( ValueError ): StrToIp( '0.256.0.0' ) with pytest.raises( ValueError ): StrToIp( '0.0.256.0' ) with pytest.raises( ValueError ): StrToIp( '0.0.0.256' ) with pytest.raises( ValueError ): StrToIp( '0.0.0.-1' ) with pytest.raises( ValueError ): StrToIp( '0.0.-1.0' ) with pytest.raises( ValueError ): StrToIp( '0.-1.0.0' ) with pytest.raises( ValueError ): StrToIp( '-1.0.0.0' ) assert StrToIp( '0.0.0.0' ) == 281470681743360 assert StrToIp( '127.0.0.1' ) == 281472812449793 assert StrToIp( '1.2.3.4' ) == 281470698652420 assert StrToIp( ':ffff:0.0.0.0' ) == 281470681743360 assert StrToIp( ':ffff:127.0.0.1' ) == 281472812449793 assert StrToIp( ':ffff:1.2.3.4' ) == 281470698652420 with pytest.raises( ValueError ): StrToIp( ':fff:0.0.0.0' ) with pytest.raises( ValueError ): StrToIp( ':' ) with pytest.raises( ValueError ): StrToIp( ':::' ) with pytest.raises( ValueError ): StrToIp( '::x' ) assert StrToIp( '::' ) == 0 assert StrToIp( '::1' ) == 1 assert StrToIp( '::a' ) == 10 assert StrToIp( '::ffff' ) == 65535 assert StrToIp( '2001:db8:0:0:1:0:0:1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:0db8:0:0:1:0:0:1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:db8::1:0:0:1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:db8::0:1:0:0:1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:0db8::1:0:0:1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:db8:0:0:1::1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:db8:0000:0:1::1' ) == 42540766411282592856904266426630537217 assert StrToIp( '2001:DB8:0:0:1::1' ) == 42540766411282592856904266426630537217 with pytest.raises( ValueError ): StrToIp( '2001:db8::1::1' ) assert StrToIp( '2001:db8:0:0:0:0:0:1' ) == 42540766411282592856903984951653826561 assert StrToIp( '2001:DB8:0:0:0:0:0:1' ) == 42540766411282592856903984951653826561 assert StrToIp( '2001:db8:0:0:0::1' ) == 42540766411282592856903984951653826561 assert StrToIp( '2001:db8:0:0::1' ) == 42540766411282592856903984951653826561 assert StrToIp( '2001:db8:0::1' ) == 42540766411282592856903984951653826561 assert StrToIp( '2001:db8::1' ) == 42540766411282592856903984951653826561 with pytest.raises( ValueError ): StrToIp( '::db8::1' ) with pytest.raises( ValueError ): StrToIp( '2001:db8::0::1' ) assert StrToIp( '2001:0:0:0:1:0:0:1' ) == 42540488161975842760550637900276957185 assert StrToIp( '2001::1:0:0:1' ) == 42540488161975842760550637900276957185 assert StrToIp( '2001:0:0:0:1::1' ) == 42540488161975842760550637900276957185 assert StrToIp( '2001:0:1:0:1:0:1:0' ) == 42540488161977051686370252529451728896 assert StrToIp( '2001::' ) == 42540488161975842760550356425300246528 assert StrToIp( None ) is None with pytest.raises( ValueError ): StrToIp( 1 ) with pytest.raises( ValueError ): StrToIp( 42540766411282592856904266426630537217 ) with pytest.raises( ValueError ): StrToIp( 281472812449793 ) def test_iptostr(): assert IpToStr( 0 ) == '::' assert IpToStr( 1 ) == '::1' assert IpToStr( 10 ) == '::a' assert IpToStr( 65535 ) == '::ffff' assert IpToStr( 0, False ) == '::' assert IpToStr( 1, False ) == '::1' assert IpToStr( 10, False ) == '::a' assert IpToStr( 65535, False ) == '::ffff' assert IpToStr( 0, True ) == '::' assert IpToStr( 1, True ) == '::1' assert IpToStr( 10, True ) == '::a' assert IpToStr( 65535, True ) == '::ffff' assert IpToStr( 42540766411282592856903984951653826561 ) == '2001:db8::1' assert IpToStr( 42540766411282592856904266426630537217 ) == '2001:db8::1:0:0:1' assert IpToStr( 42540488161975842760550637900276957185 ) == '2001::1:0:0:1' assert IpToStr( 42540488161977051686370252529451728896 ) == '2001:0:1:0:1:0:1:0' assert IpToStr( 42540488161975842760550356425300246528 ) == '2001::' assert IpToStr( 42540766411282592856903984951653826561, False ) == '2001:db8::1' assert IpToStr( 42540766411282592856904266426630537217, False ) == '2001:db8::1:0:0:1' assert IpToStr( 42540488161975842760550637900276957185, False ) == '2001::1:0:0:1' assert IpToStr( 42540488161977051686370252529451728896, False ) == '2001:0:1:0:1:0:1:0' assert IpToStr( 42540488161975842760550356425300246528, False ) == '2001::' assert IpToStr( 42540766411282592856903984951653826561, True ) == '2001:db8::1' assert IpToStr( 42540766411282592856904266426630537217, True ) == '2001:db8::1:0:0:1' assert IpToStr( 42540488161975842760550637900276957185, True ) == '2001::1:0:0:1' assert IpToStr( 42540488161977051686370252529451728896, True ) == '2001:0:1:0:1:0:1:0' assert IpToStr( 42540488161975842760550356425300246528, True ) == '2001::' assert IpToStr( 281470681743360 ) == '0.0.0.0' assert IpToStr( 281470681743361 ) == '0.0.0.1' assert IpToStr( 281472812449793 ) == '127.0.0.1' assert IpToStr( 281470698652420 ) == '1.2.3.4' assert IpToStr( 281470681743360, False ) == '0.0.0.0' assert IpToStr( 281470681743361, False ) == '0.0.0.1' assert IpToStr( 281472812449793, False ) == '127.0.0.1' assert IpToStr( 281470698652420, False ) == '1.2.3.4' assert IpToStr( 281470681743360, True ) == ':ffff:0.0.0.0' assert IpToStr( 281470681743361, True ) == ':ffff:0.0.0.1' assert IpToStr( 281472812449793, True ) == ':ffff:127.0.0.1' assert IpToStr( 281470698652420, True ) == ':ffff:1.2.3.4' assert IpToStr( StrToIp( '1:1:1:1:2:3:4:5' ) ) == '1:1:1:1:2:3:4:5' assert IpToStr( StrToIp( '2:2:2:2:2:3:4:5' ) ) == '2:2:2:2:2:3:4:5' assert IpToStr( StrToIp( '0:0:0:0:2:3:4:5' ) ) == '::2:3:4:5' assert IpToStr( StrToIp( '1:2:2:2:2:3:4:5' ) ) == '1:2:2:2:2:3:4:5' assert IpToStr( StrToIp( '1:0:0:0:0:3:4:5' ) ) == '1::3:4:5' assert IpToStr( StrToIp( '1:1:1:1:0:0:4:5' ) ) == '1:1:1:1::4:5' assert IpToStr( StrToIp( '1:1:1:1:0:0:0:0' ) ) == '1:1:1:1::' with pytest.raises( ValueError ): IpToStr( -1 ) with pytest.raises( ValueError ): IpToStr( 0x100000000000000000000000000000000 ) with pytest.raises( ValueError ): IpToStr( '0' ) assert IpToStr( None ) is None def test_cidrnetwork(): assert CIDRNetwork( 24, False ) == StrToIp( '0.0.0.255' ) assert CIDRNetwork( 25, False ) == StrToIp( '0.0.0.127' ) assert CIDRNetwork( 26, False ) == StrToIp( '0.0.0.63' ) assert CIDRNetwork( 27, False ) == StrToIp( '0.0.0.31' ) assert CIDRNetwork( 23, False ) == StrToIp( '0.0.1.255' ) assert CIDRNetwork( 32, False ) == StrToIp( '0.0.0.0' ) assert CIDRNetwork( 31, False ) == StrToIp( '0.0.0.1' ) assert CIDRNetwork( 8, False ) == StrToIp( '0.255.255.255' ) assert CIDRNetwork( 8, True ) == StrToIp( '00ff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ) assert CIDRNetwork( 16, True ) == StrToIp( '0:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ) assert CIDRNetwork( 128, True ) == StrToIp( '::' ) assert CIDRNetwork( 127, True ) == StrToIp( '::1' ) assert CIDRNetwork( 120, True ) == StrToIp( '::ff' ) assert CIDRNetwork( 32, True ) == StrToIp( '::ffff:ffff:ffff:ffff:ffff:ffff' ) assert CIDRNetwork( 64, True ) == StrToIp( '::ffff:ffff:ffff:ffff' ) assert CIDRNetwork( 96, True ) == StrToIp( '::ffff:ffff' ) assert CIDRNetwork( 80, True ) == StrToIp( '::ffff:ffff:ffff' ) with pytest.raises( ValueError ): CIDRNetwork( -1, True ) with pytest.raises( ValueError ): CIDRNetwork( -1, False ) with pytest.raises( ValueError ): CIDRNetwork( 33, False ) with pytest.raises( ValueError ): CIDRNetwork( 129, True ) with pytest.raises( ValueError ): CIDRNetwork( 'a', False ) with pytest.raises( ValueError ): CIDRNetwork( 'a', True ) def test_cidrnetmask(): assert CIDRNetmask( 24, False ) == StrToIp( '255.255.255.0' ) assert CIDRNetmask( 25, False ) == StrToIp( '255.255.255.128' ) assert CIDRNetmask( 26, False ) == StrToIp( '255.255.255.192' ) assert CIDRNetmask( 27, False ) == StrToIp( '255.255.255.224' ) assert CIDRNetmask( 23, False ) == StrToIp( '255.255.254.0' ) assert CIDRNetmask( 32, False ) == StrToIp( '255.255.255.255' ) assert CIDRNetmask( 31, False ) == StrToIp( '255.255.255.254' ) assert CIDRNetmask( 8, False ) == StrToIp( '255.0.0.0' ) assert CIDRNetmask( 8, True ) == StrToIp( 'ff00::' ) assert CIDRNetmask( 16, True ) == StrToIp( 'ffff::' ) assert CIDRNetmask( 128, True ) == StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ) assert CIDRNetmask( 127, True ) == StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:fffe' ) assert CIDRNetmask( 120, True ) == StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ff00' ) assert CIDRNetmask( 32, True ) == StrToIp( 'ffff:ffff::' ) assert CIDRNetmask( 64, True ) == StrToIp( 'ffff:ffff:ffff:ffff::' ) assert CIDRNetmask( 96, True ) == StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff::' ) assert CIDRNetmask( 80, True ) == StrToIp( 'ffff:ffff:ffff:ffff:ffff::' ) with pytest.raises( ValueError ): CIDRNetmask( -1, True ) with pytest.raises( ValueError ): CIDRNetmask( -1, False ) with pytest.raises( ValueError ): CIDRNetmask( 33, False ) with pytest.raises( ValueError ): CIDRNetmask( 129, True ) with pytest.raises( ValueError ): CIDRNetmask( 'a', False ) with pytest.raises( ValueError ): CIDRNetmask( 'a', True ) def test_cidrnetmasktoprefix(): assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.0' ) ) == 24 assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.128' ) ) == 25 assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.192' ) ) == 26 assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.224' ) ) == 27 assert CIDRNetmaskToPrefix( StrToIp( '255.255.254.0' ) ) == 23 assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.255' ) ) == 32 assert CIDRNetmaskToPrefix( StrToIp( '255.255.255.254' ) ) == 31 assert CIDRNetmaskToPrefix( StrToIp( '255.0.0.0' ) ) == 8 assert CIDRNetmaskToPrefix( StrToIp( 'ff00::' ) ) == 8 assert CIDRNetmaskToPrefix( StrToIp( 'ffff::' ) ) == 16 assert CIDRNetmaskToPrefix( StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ) ) == 128 assert CIDRNetmaskToPrefix( StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:fffe' ) ) == 127 assert CIDRNetmaskToPrefix( StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ff00' ) ) == 120 assert CIDRNetmaskToPrefix( StrToIp( '10.0.0.3' ) ) == 1 # yea, with CIDR are relying on leading bits being set with pytest.raises( ValueError ): CIDRNetmaskToPrefix( '127.0.0.1' ) def test_cidrnetworksize(): assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 32 ) == 1 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 32, True ) == 1 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 32, False ) == 1 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 31 ) == 2 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 31, True ) == 2 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 31, False ) == 2 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 30 ) == 2 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 30, True ) == 4 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 30, False ) == 2 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 29 ) == 6 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 29, True ) == 8 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 29, False ) == 6 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 8 ) == 16777214 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 8, True ) == 16777216 assert CIDRNetworkSize( StrToIp( '3.2.5.3' ), 8, False ) == 16777214 assert CIDRNetworkSize( StrToIp( '::3' ), 128 ) == 1 assert CIDRNetworkSize( StrToIp( '::3' ), 128, True ) == 1 assert CIDRNetworkSize( StrToIp( '::3' ), 128, False ) == 1 assert CIDRNetworkSize( StrToIp( '::3' ), 127 ) == 2 assert CIDRNetworkSize( StrToIp( '::3' ), 127, True ) == 2 assert CIDRNetworkSize( StrToIp( '::3' ), 127, False ) == 2 assert CIDRNetworkSize( StrToIp( '::3' ), 126 ) == 2 assert CIDRNetworkSize( StrToIp( '::3' ), 126, True ) == 4 assert CIDRNetworkSize( StrToIp( '::3' ), 126, False ) == 2 assert CIDRNetworkSize( StrToIp( '::3' ), 125 ) == 6 assert CIDRNetworkSize( StrToIp( '::3' ), 125, True ) == 8 assert CIDRNetworkSize( StrToIp( '::3' ), 125, False ) == 6 assert CIDRNetworkSize( StrToIp( '::3' ), 8 ) == 1329227995784915872903807060280344574 assert CIDRNetworkSize( StrToIp( '::3' ), 8, True ) == 1329227995784915872903807060280344576 assert CIDRNetworkSize( StrToIp( '::3' ), 8, False ) == 1329227995784915872903807060280344574 with pytest.raises( ValueError ): CIDRNetworkSize( -1, 0 ) with pytest.raises( ValueError ): CIDRNetworkSize( 0x100000000000000000000000000000000, 0 ) with pytest.raises( ValueError ): CIDRNetworkSize( StrToIp( '255.255.255.255' ), 33 ) with pytest.raises( ValueError ): CIDRNetworkSize( StrToIp( '0.0.0.0' ), 33 ) with pytest.raises( ValueError ): CIDRNetworkSize( StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ), 129 ) with pytest.raises( ValueError ): CIDRNetworkSize( StrToIp( '::' ), 129 ) with pytest.raises( ValueError ): CIDRNetworkSize( '::', 129 ) def test_cidrnetworkbounds(): assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 8 ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 8, False ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 8, True ) == ( StrToIp( '10.0.0.0' ), StrToIp( '10.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 8 ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 8, False ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 8, True ) == ( StrToIp( '10.0.0.0' ), StrToIp( '10.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 8 ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 8, False ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.255.255.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 8, True ) == ( StrToIp( '10.0.0.0' ), StrToIp( '10.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 24 ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.0.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 24, False ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.0.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 24, True ) == ( StrToIp( '10.0.0.0' ), StrToIp( '10.0.0.255' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 24 ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.0.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 24, False ) == ( StrToIp( '10.0.0.1' ), StrToIp( '10.0.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 24, True ) == ( StrToIp( '10.0.0.0' ), StrToIp( '10.0.0.255' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 24 ) == ( StrToIp( '10.3.0.1' ), StrToIp( '10.3.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 24, False ) == ( StrToIp( '10.3.0.1' ), StrToIp( '10.3.0.254' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 24, True ) == ( StrToIp( '10.3.0.0' ), StrToIp( '10.3.0.255' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 112 ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 112, False ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 112, True ) == ( StrToIp( '2001::' ), StrToIp( '2001::ffff' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 112 ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 112, False ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 112, True ) == ( StrToIp( '2001::' ), StrToIp( '2001::ffff' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 112 ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 112, False ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fffe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 112, True ) == ( StrToIp( '2001::' ), StrToIp( '2001::ffff' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 120 ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 120, False ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 120, True ) == ( StrToIp( '2001::' ), StrToIp( '2001::ff' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 120 ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 120, False ) == ( StrToIp( '2001::1' ), StrToIp( '2001::fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 120, True ) == ( StrToIp( '2001::' ), StrToIp( '2001::ff' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 120 ) == ( StrToIp( '2001::f001' ), StrToIp( '2001::f0fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 120, False ) == ( StrToIp( '2001::f001' ), StrToIp( '2001::f0fe' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 120, True ) == ( StrToIp( '2001::f000' ), StrToIp( '2001::f0ff' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 32 ) == ( StrToIp( '10.3.2.5' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 32, False ) == ( StrToIp( '10.3.2.5' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 32, True ) == ( StrToIp( '10.3.2.5' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 31 ) == ( StrToIp( '10.3.2.4' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 31, False ) == ( StrToIp( '10.3.2.4' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 31, True ) == ( StrToIp( '10.3.2.4' ), StrToIp( '10.3.2.5' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 128 ) == ( StrToIp( '2001::f009' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 128, False ) == ( StrToIp( '2001::f009' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 128, True ) == ( StrToIp( '2001::f009' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 127 ) == ( StrToIp( '2001::f008' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 127, False ) == ( StrToIp( '2001::f008' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 127, True ) == ( StrToIp( '2001::f008' ), StrToIp( '2001::f009' ) ) assert CIDRNetworkBounds( StrToIp( '254.0.0.0' ), 8, True ) == ( StrToIp( '254.0.0.0' ), StrToIp( '254.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '255.0.0.0' ), 8, True ) == ( StrToIp( '255.0.0.0' ), StrToIp( '255.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '1.2.3.4' ), 0, True ) == ( StrToIp( '0.0.0.0' ), StrToIp( '255.255.255.255' ) ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 0, True ) == ( StrToIp( '::' ), StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ) ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 8, False, True ) == ( 1, 16777214 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 8, True, True ) == ( 0, 16777215 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 8, False, True ) == ( 1, 16777214 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 8, True, True ) == ( 0, 16777215 ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 8, False, True ) == ( 1, 16777214 ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 8, True, True ) == ( 0, 16777215 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 24, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.0' ), 24, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 24, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '10.0.0.1' ), 24, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 24, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '10.3.0.0' ), 24, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 112, False, True ) == ( 1, 65534 ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 112, True, True ) == ( 0, 65535 ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 112, False, True ) == ( 1, 65534 ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 112, True, True ) == ( 0, 65535 ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 112, False, True ) == ( 1, 65534 ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 112, True, True ) == ( 0, 65535 ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 120, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 120, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 120, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '2001::1' ), 120, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 120, False, True ) == ( 1, 254 ) assert CIDRNetworkBounds( StrToIp( '2001::f001' ), 120, True, True ) == ( 0, 255 ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 32, False, True ) == ( 0, 0 ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 32, True, True ) == ( 0, 0 ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 31, False, True ) == ( 0, 1 ) assert CIDRNetworkBounds( StrToIp( '10.3.2.5' ), 31, True, True ) == ( 0, 1 ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 128, False, True ) == ( 0, 0 ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 128, True, True ) == ( 0, 0 ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 127, False, True ) == ( 0, 1 ) assert CIDRNetworkBounds( StrToIp( '2001::f009' ), 127, True, True ) == ( 0, 1 ) assert CIDRNetworkBounds( StrToIp( '254.0.0.0' ), 8, True, True ) == ( 0, 16777215 ) assert CIDRNetworkBounds( StrToIp( '255.0.0.0' ), 8, True, True ) == ( 0, 16777215 ) assert CIDRNetworkBounds( StrToIp( '1.2.3.4' ), 0, True, True ) == ( 0, 4294967295 ) assert CIDRNetworkBounds( StrToIp( '2001::' ), 0, True, True ) == ( 0, 340282366920938463463374607431768211455 ) with pytest.raises( ValueError ): CIDRNetworkBounds( -1, 0 ) with pytest.raises( ValueError ): CIDRNetworkBounds( 0x100000000000000000000000000000000, 0 ) with pytest.raises( ValueError ): CIDRNetworkBounds( StrToIp( '255.255.255.255' ), 33 ) with pytest.raises( ValueError ): CIDRNetworkBounds( StrToIp( '0.0.0.0' ), 33 ) with pytest.raises( ValueError ): CIDRNetworkBounds( StrToIp( 'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff' ), 129 ) with pytest.raises( ValueError ): CIDRNetworkBounds( StrToIp( '::' ), 129 ) with pytest.raises( ValueError ): CIDRNetworkBounds( '::', 129 ) def test_cidrnetworkrange(): assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32 ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32, True ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32, False ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31 ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31, True ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31, False ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30 ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30, True ) ) == [ StrToIp( '169.254.1.0' ), StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30, False ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29 ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29, True ) ) == [ StrToIp( '169.254.1.0' ), StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ), StrToIp( '169.254.1.7' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29, False ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128 ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128, True ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128, False ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127 ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127, True ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127, False ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126 ) ) == [ StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126, True ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ), StrToIp( '2::7' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126, False ) ) == [ StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125 ) ) == [ StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125, True ) ) == [ StrToIp( '2::0' ), StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ), StrToIp( '2::7' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125, False ) ) == [ StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ) ] def test_cidrnetworksizerange(): # NOTE: becarefull with the prefixes here, this can generate some pretty large networks even a /8 in ipv4 can make this test take a while assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 32 ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 32 ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 32, True ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 32, True ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 32, False ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 32, False ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 31 ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 31 ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 31, True ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 31, True ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 31, False ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 31, False ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 30 ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 30 ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 30, True ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 30, True ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 30, False ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 30, False ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 16 ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 16 ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 16, True ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 16, True ) ) ) assert CIDRNetworkSize( StrToIp( '34.54.23.12' ), 16, False ) == len( list( CIDRNetworkRange( StrToIp( '34.54.23.12' ), 16, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 128 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 128 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 128, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 128, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 128, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 128, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 127 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 127 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 127, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 127, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 127, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 127, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 126 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 126 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 126, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 126, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 126, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 126, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 125 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 125 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 125, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 125, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 125, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 125, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 124 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 124 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 124, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 124, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 124, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 124, False ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 120 ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 120 ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 120, True ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 120, True ) ) ) assert CIDRNetworkSize( StrToIp( '1:2:3::' ), 120, False ) == len( list( CIDRNetworkRange( StrToIp( '1:2:3::' ), 120, False ) ) ) assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32 ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32, True ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 32, False ) ) == [ StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31 ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31, True ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 31, False ) ) == [ StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30 ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30, True ) ) == [ StrToIp( '169.254.1.0' ), StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 30, False ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29 ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29, True ) ) == [ StrToIp( '169.254.1.0' ), StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ), StrToIp( '169.254.1.7' ) ] assert list( CIDRNetworkRange( StrToIp( '169.254.1.3' ), 29, False ) ) == [ StrToIp( '169.254.1.1' ), StrToIp( '169.254.1.2' ), StrToIp( '169.254.1.3' ), StrToIp( '169.254.1.4' ), StrToIp( '169.254.1.5' ), StrToIp( '169.254.1.6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128 ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128, True ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 128, False ) ) == [ StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127 ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127, True ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 127, False ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126 ) ) == [ StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126, True ) ) == [ StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ), StrToIp( '2::7' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 126, False ) ) == [ StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125 ) ) == [ StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125, True ) ) == [ StrToIp( '2::0' ), StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ), StrToIp( '2::7' ) ] assert list( CIDRNetworkRange( StrToIp( '2::5' ), 125, False ) ) == [ StrToIp( '2::1' ), StrToIp( '2::2' ), StrToIp( '2::3' ), StrToIp( '2::4' ), StrToIp( '2::5' ), StrToIp( '2::6' ) ]

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39a46b87fdecb6fa9bd1031229883f4e29c4876b

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py

Python

fs/expose/wsgi/__init__.py

rimrim/pyfs

ce9f3c76468a0779a0517ea7d7c191caf1bffd25

[ "BSD-3-Clause" ]

1

2021-07-15T22:45:17.000Z

fs/expose/wsgi/__init__.py

rimrim/pyfs

ce9f3c76468a0779a0517ea7d7c191caf1bffd25

[ "BSD-3-Clause" ]

null

fs/expose/wsgi/__init__.py

rimrim/pyfs

ce9f3c76468a0779a0517ea7d7c191caf1bffd25

[ "BSD-3-Clause" ]

null

from .wsgi import serve_fs

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py

Python

src/heuristics.py

sampreets3/pyjulia-pddl

8508690312535d90b59297279268d8a754e4a212

[ "Apache-2.0" ]

null

src/heuristics.py

sampreets3/pyjulia-pddl

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[ "Apache-2.0" ]

null

src/heuristics.py

sampreets3/pyjulia-pddl

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[ "Apache-2.0" ]

null

def zero_heuristic(state, pddl): return 0

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py

Python

train/dewarp.py

hhu-machine-learning/hdc2021-psfnn

275eec198e57c49dcd3eee3e7b09ee36d1655ede

[ "MIT" ]

2

2021-11-04T15:45:41.000Z

2021-11-04T15:47:22.000Z

train/dewarp.py

hhu-machine-learning/hdc2021-psfnn

275eec198e57c49dcd3eee3e7b09ee36d1655ede

[ "MIT" ]

1

2021-11-04T15:40:40.000Z

2021-11-16T07:44:57.000Z

train/dewarp.py

hhu-machine-learning/hdc2021-psfnn

275eec198e57c49dcd3eee3e7b09ee36d1655ede

[ "MIT" ]

null

import torch import torch.nn.functional as F def get_dewarping_matrix(step): if step == 0: return [1.0074838399887085, 0.0007350334199145436, 0.0018522378522902727, 0.0011821923544630408, -9.216999023919925e-05, -2.1575890059466474e-05, -0.0008361316868104041, -2.0978655811632052e-05, 2.024814057222102e-05, -9.610102279111743e-05, 0.005001508630812168, 1.0126982927322388, -0.002687784843146801, 0.0004823343479074538, -0.0003023565514013171, -0.00017967041640076786, 5.8112331316806376e-05, 0.0004127228748984635, -0.00010364824265707284, -3.341829142300412e-05, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step == 1: return [1.0001074075698853, 0.00026503356639295816, 0.0014267113292589784, -0.000140930904308334, -0.00024006569583434612, -0.002316687721759081, -8.164918835973367e-05, 0.00024537910940125585, 0.0002223470073658973, 0.0020410853903740644, 0.005251534283161163, 1.0058963298797607, -0.004370789974927902, -0.000786478107329458, 0.00014022525283508003, -0.0018259487114846706, -0.0007027303799986839, 0.002358881291002035, -0.00045202276669442654, -0.004688096232712269, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step == 2: return [0.9938850998878479, 0.0011783745139837265, 0.0, 0.001726538990624249, 0.00071810552617535, -0.0006797484820708632, -0.0037932987324893475, -0.00037118676118552685, -0.0031556240282952785, -0.003694966435432434, 0.005031114909797907, 1.0014299154281616, -0.00625627301633358, -0.0025116801261901855, 0.00016182185208890587, -0.007379685062915087, -0.0018687976989895105, 0.002322555286809802, 0.005523629952222109, -0.029866278171539307, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step == 3: return [0.9869324564933777, 0.00224688439629972, -0.0008935428340919316, 0.002959209494292736, -0.0022612223401665688, 0.0018939843866974115, -0.004060809034854174, 0.0017625142354518175, -0.006656560115516186, -0.009651963599026203, 0.00016188605513889343, 1.0035479068756104, -0.010218928568065166, 0.0005651656538248062, -0.0009788924362510443, 0.0014329419936984777, 0.008163115940988064, 0.005938321352005005, 0.008032983168959618, -0.08853603154420853, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step == 4: return [0.983738362789154, 0.0023218116257339716, -0.0017126877792179585, 0.001369951176457107, -0.004269269295036793, 0.004380248486995697, -0.016033707186579704, 0.0067543284967541695, -0.016424868255853653, -0.01624421216547489, 7.708399789407849e-05, 0.994385302066803, -0.012866039760410786, -0.001022055745124817, 0.0037307552993297577, 0.0027339875232428312, 0.009606639854609966, -0.008584169670939445, 0.013230630196630955, -0.09363924711942673, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step == 5: return [0.9786288738250732, 0.003588682971894741, -0.0023918221704661846, 0.004777341615408659, -0.0037737672682851553, 0.002030150732025504, -0.013176627457141876, -0.010321627371013165, -0.026121007278561592, -0.015811236575245857, 0.001201795064844191, 1.0035192966461182, -0.01841144822537899, 0.008479919284582138, 0.003908892627805471, 0.0044402433559298515, 0.015674248337745667, 0.005413076840341091, 0.008270949125289917, -0.18248037993907928, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step <= 10: return [0.9777207374572754, 0.003674966050311923, -0.000865395471919328, 0.00839876476675272, -0.00921174418181181, -0.00444203382357955, -0.024727338925004005, -0.007308421190828085, -0.05595914274454117, -0.009856735356152058, -0.0010057302424684167, 1.0023410320281982, -0.01852775737643242, 0.0016161234816536307, -0.0016956499312072992, 0.002951698610559106, 0.026358529925346375, -0.017851702868938446, -0.004329687915742397, -0.18836215138435364, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] if step >= 11: return [0.9716978073120117, 0.056364890187978745, -0.029213212430477142, 0.03584786504507065, 0.0005257084267213941, -0.0900191217660904, -0.09005090594291687, -0.07786328345537186, -0.13387863337993622, -0.0701950192451477, 0.001431028125807643, 0.9523154497146606, -0.04134024307131767, 0.004861794412136078, 0.013068363070487976, 0.018636401742696762, 0.00844097975641489, -0.008905373513698578, -0.0029179020784795284, -0.10307711362838745, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] def warp(blurry, params): M = params.reshape(10, 10) h, w = blurry.shape x = torch.linspace(-1, 1, w, device=blurry.device) y = torch.linspace(-1, 1, h, device=blurry.device) y, x = torch.meshgrid(y, x, indexing="ij") features = [x, y, 1, x*x, x*y, y*y, x*x*x, x*x*y, x*y*y, y*y*y] x_warped = sum(weight * feature for weight, feature in zip(M[0], features)) y_warped = sum(weight * feature for weight, feature in zip(M[1], features)) grid = torch.stack([x_warped, y_warped], dim=2) blurry_warp = F.grid_sample( blurry[None, None, :, :], grid[None, :, :, :], mode='bicubic', align_corners=True, padding_mode='border')[0, 0, :, :] return blurry_warp def loss(sharp, blurry, M): blurry_warped = warp(blurry, M) blurry_warped_centered = blurry_warped - blurry_warped.mean() sharp_centered = sharp - sharp.mean() return torch.mean(torch.square(blurry_warped_centered - sharp_centered)) def main(): from load_hdc import load_hdc device = torch.device("cuda") sample = 2 for step in range(1, 10): sharp = load_hdc(step=step, cam=1, sample=sample, font="Times") blurry = load_hdc(step=step, cam=2, sample=sample, font="Times") sharp = torch.tensor(sharp, device=device) blurry = torch.tensor(blurry, device=device) M_eye = torch.eye(10, device=device).ravel() M_dewarp = torch.tensor(get_dewarping_matrix(step), device=device) loss_baseline = loss(sharp, blurry, M_eye).item() loss_warped = loss(sharp, blurry, M_dewarp).item() print("step", step) print("baseline:", loss_baseline) print("dewarped:", loss_warped) print() if __name__ == "__main__": main()

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8,865

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6

f23d333bf368163c918b3042745f184a91f16d6f

33

py

Python

concurrent_execution/_subprocess/subprocess_pyfile_only/02-worin-pipe-capture-err-otpt.py

codingEzio/code_python_standard_library

90ea086fa13ccde4f69bb5abb87450f07c2c5bbf

[ "MIT" ]

null

concurrent_execution/_subprocess/subprocess_pyfile_only/02-worin-pipe-capture-err-otpt.py

codingEzio/code_python_standard_library

90ea086fa13ccde4f69bb5abb87450f07c2c5bbf

[ "MIT" ]

null

concurrent_execution/_subprocess/subprocess_pyfile_only/02-worin-pipe-capture-err-otpt.py

codingEzio/code_python_standard_library

90ea086fa13ccde4f69bb5abb87450f07c2c5bbf

[ "MIT" ]

null

import subprocess # placeholder

8.25

17

0.818182

3

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null

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1

0

1

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1

0

6

f2522f4dae02a99bf22bdace69bb17bbbf762443

5,000

py

Python

battlesmithbackend/base/models.py

craigrmills/Capstone_Backend

b40d4c869a7f2e2ec2e746c159b269fcfd1d8db8

[ "MIT" ]

null

battlesmithbackend/base/models.py

craigrmills/Capstone_Backend

b40d4c869a7f2e2ec2e746c159b269fcfd1d8db8

[ "MIT" ]

null

battlesmithbackend/base/models.py

craigrmills/Capstone_Backend

b40d4c869a7f2e2ec2e746c159b269fcfd1d8db8

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User from django.db.models.fields import related # Create your models here. class Product(models.Model): user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True) name = models.CharField(max_length=200, null=True, blank=True) image = models.ImageField(null=True, blank=True, default='/placeholder.png') category = models.CharField(max_length=200, null=True, blank=True) description = models.TextField(null=True, blank=True) rating = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) numReviews = models.IntegerField(null=True, blank=True, default=0) price = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) countInStock = models.IntegerField(null=True, blank=True, default=0) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return self.name class Review(models.Model): product = models.ForeignKey(Product, on_delete=models.SET_NULL, null=True) user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True) name = models.CharField(max_length=200, null=True, blank=True) rating = models.IntegerField(null=True, blank=True, default=0) comment = models.TextField(null=True, blank=True) createdAt = models.DateTimeField(auto_now_add=True) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self.rating) class Order(models.Model): user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True) paymentMethod = models.CharField(max_length=200, null=True, blank=True) taxPrice = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) shippingPrice = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) totalPrice = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) isPaid = models.BooleanField(default=False) paidAt = models.DateTimeField(auto_now_add=False, null=True, blank=True) isDelivered = models.BooleanField(default=False) deliveredAt = models.DateTimeField( auto_now_add=False, null=True, blank=True) createdAt = models.DateTimeField(auto_now_add=True) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self.createdAt) class OrderItem(models.Model): product = models.ForeignKey(Product, on_delete=models.SET_NULL, null=True) order = models.ForeignKey(Order, on_delete=models.SET_NULL, null=True) name = models.CharField(max_length=200, null=True, blank=True) qty = models.IntegerField(null=True, blank=True, default=0) price = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) image = models.CharField(max_length=200, null=True, blank=True) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self.name) class ShippingAddress(models.Model): order = models.OneToOneField( Order, on_delete=models.CASCADE, null=True, blank=True) address = models.CharField(max_length=200, null=True, blank=True) city = models.CharField(max_length=200, null=True, blank=True) postalCode = models.CharField(max_length=200, null=True, blank=True) country = models.CharField(max_length=200, null=True, blank=True) shippingPrice = models.DecimalField( max_digits=7, decimal_places=2, null=True, blank=True) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self.address) class Faction(models.Model): name = models.CharField(max_length=200, null=True, blank=True) numPlayed = models.IntegerField(null=True, blank=True, default=0) winRate = models.IntegerField(null=True, blank=True, default=0) _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self.name) class Game(models.Model): player1 = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="player1") player2 = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="player2") p1Faction = models.ForeignKey( Faction, on_delete=models.SET_NULL, null=True, related_name="p1Faction") p2Faction = models.ForeignKey( Faction, on_delete=models.SET_NULL, null=True, related_name="p2Faction") p1Score = models.IntegerField(null=True, blank=True, default=0) p2Score = models.IntegerField(null=True, blank=True, default=0) loser = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="winner") winner = models.ForeignKey( User, on_delete=models.SET_NULL, null=True, related_name="loser") _id = models.AutoField(primary_key=True, editable=False) def __str__(self): return str(self._id)

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0

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null

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1

0

null

0

1

0

6

f284b0c336bfbe5b609e8c33f52f7147355d99fe

4,518

py

Python

JsonFormatting.py

shaoshixin/Tool-Set

44a920b4351c4add1cdf247effad7cdcc22573f1

[ "Apache-2.0" ]

null

JsonFormatting.py

shaoshixin/Tool-Set

44a920b4351c4add1cdf247effad7cdcc22573f1

[ "Apache-2.0" ]

null

JsonFormatting.py

shaoshixin/Tool-Set

44a920b4351c4add1cdf247effad7cdcc22573f1

[ "Apache-2.0" ]

null

import json class JsonFormatting: def __init__(self): self.json = {"tools":[{"id":"toc","text":"图层","tooltip":"","enable":1},{"id":"view","text":"地图操作","tooltip":"","enable":1},{"id":"zoomIn","text":"放大","tooltip":"","enable":1},{"id":"zoomOut","text":"缩小","tooltip":"","enable":1},{"id":"clickZoomIn","text":"点击放大","tooltip":"","enable":1},{"id":"clickZoomOut","text":"点击缩小","tooltip":"","enable":1},{"id":"clickCenterAt","text":"点击居中","tooltip":"","enable":1},{"id":"customLocate","text":"自定义范围","tooltip":"","enable":1},{"id":"pan","text":"漫游","tooltip":"","enable":1},{"id":"zoomToFullExtent","text":"全图","tooltip":"","enable":1},{"id":"zoomToPrevExtent","text":"前图","tooltip":"","enable":1},{"id":"zoomToNextExtent","text":"后图","tooltip":"","enable":1},{"id":"tools","text":"工具","tooltip":"","enable":1},{"id":"distanceMeasure","text":"测距","tooltip":"","enable":1},{"id":"areaMeasure","text":"测面","tooltip":"","enable":1},{"id":"print","text":"打印","tooltip":"","enable":1},{"id":"mapSaveAs","text":"地图另存为","tooltip":"","enable":1},{"id":"pipeLineQuery","text":"管线查询","tooltip":"","enable":1},{"id":"identify","text":"快速查询","tooltip":"","enable":1},{"id":"identify","text":"快速查询","tooltip":"","enable":1},{"id":"spatialQuery","text":"空间查询","tooltip":"","enable":2},{"id":"query","text":"管线明细","tooltip":"","enable":2},{"id":"conditionQuery","text":"条件查询","tooltip":"","enable":1},{"id":"pipeStatistic","text":"管线统计","tooltip":"","enable":1},{"id":"pipePointStatistic","text":"管点统计","tooltip":"","enable":1},{"id":"pipeLineStatistic","text":"管线统计","tooltip":"","enable":1},{"id":"spatialStatistic","text":"空间统计","tooltip":"","enable":1},{"id":"fractureAnalysis","text":"断面分析","tooltip":"","enable":1},{"id":"tranSectionAnalysis","text":"横断面分析","tooltip":"","enable":1},{"id":"verticalsectionAnalysis","text":"纵断面分析","tooltip":"","enable":1},{"id":"spatialAnalysis","text":"空间分析","tooltip":"","enable":1},{"id":"pipeBurstAnalysis","text":"爆管分析","tooltip":"","enable":2},{"id":"connectivityAnalysis","text":"连通性分析","tooltip":"","enable":1},{"id":"bufferParamAnalysis","text":"缓冲区分析","tooltip":"","enable":1},{"id":"sectionAnalysis","text":"剖面分析","tooltip":"","enable":1},{"id":"pipeLineCollision","text":"管线碰撞","tooltip":"","enable":1},{"id":"pipeDisTance","text":"间距分析","tooltip":"","enable":1},{"id":"overburdenDepthAnalysis","text":"覆土深度分析","tooltip":"","enable":1},{"id":"crossingAnalysis","text":"交叉口分析","tooltip":"","enable":1},{"id":"parallelDistanceAnalysis","text":"水平净距分析","tooltip":"","enable":2},{"id":"dataUpload","text":"数据上报","tooltip":"","enable":1},{"id":"uploadFile","text":"数据上报","tooltip":"","enable":1},{"id":"uploadFileManage","text":"上报数据处理","tooltip":"","enable":1},{"id":"statUploadData","text":"上报统计","tooltip":"","enable":1},{"id":"uploadDataResult","text":"上报结果","tooltip":"","enable":1},{"id":"polling","text":"管线巡检","tooltip":"","enable":1},{"id":"pollingInfo","text":"巡检信息查看","tooltip":"","enable":1},{"id":"pollingThematic","text":"巡检专题图","tooltip":"","enable":1},{"id":"warning","text":"报废预警","tooltip":"","enable":1},{"id":"emergencyProcessing","text":"应急处理","tooltip":"","enable":1},{"id":"emergencyProcessingInfo","text":"应急信息查看","tooltip":"","enable":1},{"id":"emergencyProcessingThematic","text":"应急专题图","tooltip":"","enable":1},{"id":"accidentStatistic","text":"事故统计分析","tooltip":"","enable":1},{"id":"refreash","text":"刷新","tooltip":"","enable":1}],"groupTools":[["toc",{"id":"view","text":"地图操作","items":["zoomIn","zoomOut","clickZoomIn","clickZoomOut","clickCenterAt","customLocate","pan","zoomToFullExtent","zoomToPrevExtent","zoomToNextExtent"]},{"id":"tools","text":"工具","items":["distanceMeasure","areaMeasure","print","mapSaveAs"]}],[{"id":"pipeLineQuery","text":"管线查询","items":["identify","spatialQuery","query","conditionQuery"]},{"id":"pipeStatistic","text":"管线统计","items":["pipePointStatistic","pipeLineStatistic","spatialStatistic"]}],[{"id":"fractureAnalysis","text":"断面分析","items":["tranSectionAnalysis","verticalsectionAnalysis"]},{"id":"spatialAnalysis","text":"空间分析","items":["pipeBurstAnalysis","valveClosedAnalysis","connectivityAnalysis","bufferParamAnalysis","sectionAnalysis"]},{"id":"pipeLineCollision","text":"管线碰撞","items":["pipeDisTance","overburdenDepthAnalysis","crossingAnalysis","parallelDistanceAnalysis"]}],[{"id":"dataUpload","text":"数据上报","items":["uploadFile","uploadFileManage","statUploadData","uploadDataResult"]}],["refreash"]]} self.go() def go(self): js = json.dumps(self.json) print(js)

347.538462

4,362

0.631253

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4,518

5.94572

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0.125

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null

1

0

1

0

null

0

1

0

6

f2b5e92799c7c339c27055f252e1f991b123a63c

164

py

Python

conlo/__init__.py

kira607/config_loader

024f33d48fee1635dfa9ed286f84bb96f22c134a

[ "MIT" ]

null

conlo/__init__.py

kira607/config_loader

024f33d48fee1635dfa9ed286f84bb96f22c134a

[ "MIT" ]

null

conlo/__init__.py

kira607/config_loader

024f33d48fee1635dfa9ed286f84bb96f22c134a

[ "MIT" ]

null

from .config_file import ConfigFile from .config_format import ConfigFormat, ConfigFormatType from .data_dir import DataDir from .config_loader import ConfigLoader

32.8

57

0.865854

21

164

6.571429

0.619048

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164

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0

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1

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null

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0

null

0

1

0

1

0

1

0

6

4b436be6efe22133f999bbf691697cb3d912dfb0

523

py

Python

carpedm/data/__init__.py

SimulatedANeal/carpedm

22bd5d28cfff50d7462e2a8e1b8dc1675e2a4c89

[ "MIT" ]

2

2020-09-30T04:59:06.000Z

2021-03-30T20:42:44.000Z

carpedm/data/__init__.py

SimulatedANeal/carpedm

22bd5d28cfff50d7462e2a8e1b8dc1675e2a4c89

[ "MIT" ]

null

carpedm/data/__init__.py

SimulatedANeal/carpedm

22bd5d28cfff50d7462e2a8e1b8dc1675e2a4c89

[ "MIT" ]

1

2018-05-25T07:15:16.000Z

# # Copyright (C) 2018 Neal Digre. # # This software may be modified and distributed under the terms # of the MIT license. See the LICENSE file for details. # Bring in subpackages. from carpedm.data.download import * from carpedm.data.io import * from carpedm.data.lang import * from carpedm.data.meta import * from carpedm.data.ops import * from carpedm.data.preproc import * from carpedm.data.providers import * from carpedm.data.util import * # Easy access to sample data from carpedm.data.small import path as sample

27.526316

63

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81

523

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0.24505

0.334158

0.363861

0

0.009029

0.152964

523

18

64

29.055556

0.902935

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0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

4b678c6f375a767f4053c3c745bcd381e59ab591

43

py

Python

odoo-13.0/odoo/tests/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/odoo/tests/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/odoo/tests/__init__.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

from . import common from .common import *

14.333333

21

0.744186

6

43

5.333333

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0

0.186047

43

2

22

21.5

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true

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null

0

1

0

1

0

1

0

6

4b689b4dcbbca47ba738d490ccf9d38f46e63ce4

45,754

py

Python

tmu/tsetlin_machine.py

cair/tmu

f662aea05726f8fb99182496220c75e75e5d0e2d

[ "MIT" ]

14

2021-12-07T13:44:18.000Z

2021-12-30T23:23:26.000Z

tmu/tsetlin_machine.py

cair/tmu

f662aea05726f8fb99182496220c75e75e5d0e2d

[ "MIT" ]

null

tmu/tsetlin_machine.py

cair/tmu

f662aea05726f8fb99182496220c75e75e5d0e2d

[ "MIT" ]

2

2021-12-11T11:28:50.000Z

2022-02-21T22:20:21.000Z

# Copyright (c) 2022 Ole-Christoffer Granmo # 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. # This code implements the Convolutional Tsetlin Machine from paper arXiv:1905.09688 # https://arxiv.org/abs/1905.09688 import sys import numpy as np from tmu.clause_bank import ClauseBank from tmu.weight_bank import WeightBank from scipy.sparse import csr_matrix from sys import maxsize from time import time class TMBasis(): def __init__(self, number_of_clauses, T, s, type_iii_feedback=False, focused_negative_sampling=False, output_balancing=False, d=200.0, platform='CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, number_of_state_bits_ind=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): self.number_of_clauses = number_of_clauses self.number_of_state_bits_ta = number_of_state_bits_ta self.number_of_state_bits_ind = number_of_state_bits_ind self.T = int(T) self.s = s self.type_iii_feedback = type_iii_feedback self.focused_negative_sampling= focused_negative_sampling self.output_balancing = output_balancing self.d = d self.platform = platform self.patch_dim = patch_dim self.feature_negation = feature_negation self.boost_true_positive_feedback = boost_true_positive_feedback self.weighted_clauses = weighted_clauses self.clause_drop_p = clause_drop_p self.literal_drop_p = literal_drop_p self.X_train = np.zeros(0, dtype=np.uint32) self.X_test = np.zeros(0, dtype=np.uint32) self.initialized = False def clause_co_occurrence(self, X, percentage=False): clause_outputs = csr_matrix(self.transform(X)) if percentage: return clause_outputs.transpose().dot(clause_outputs).multiply(1.0/clause_outputs.sum(axis=0)) else: return clause_outputs.transpose().dot(clause_outputs) def transform(self, X): encoded_X = self.clause_bank.prepare_X(X) transformed_X = np.empty((X.shape[0], self.number_of_clauses), dtype=np.uint32) for e in range(X.shape[0]): transformed_X[e,:] = self.clause_bank.calculate_clause_outputs_predict(encoded_X, e) return transformed_X def transform_patchwise(self, X): encoded_X = tmu.tools.encode(X, X.shape[0], self.number_of_patches, self.clause_bank.number_of_ta_chunks, self.dim, self.patch_dim, 0) transformed_X = np.empty((X.shape[0], self.number_of_clauses*self.number_of_patches), dtype=np.uint32) for e in range(X.shape[0]): transformed_X[e,:] = self.clause_bank.calculate_clause_outputs_patchwise(encoded_X, e) return transformed_X.reshape((X.shape[0], self.number_of_clauses, self.number_of_patches)) def literal_clause_frequency(self): clause_active = np.ones(self.number_of_clauses, dtype=np.uint32) return self.clause_bank.calculate_literal_clause_frequency(clause_active) def get_ta_action(self, clause, ta): return self.clause_bank.get_ta_action(clause, ta) def get_ta_state(self, clause, ta): return self.clause_bank.get_ta_state(clause, ta) def set_ta_state(self, clause, ta, state): return self.clause_bank.set_ta_state(clause, ta, state) class TMClassifier(TMBasis): def __init__(self, number_of_clauses, T, s, type_iii_feedback=False, d=200.0, platform='CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, number_of_state_bits_ind=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): super().__init__(number_of_clauses, T, s, type_iii_feedback=type_iii_feedback, d=d, platform=platform, patch_dim=patch_dim, feature_negation=feature_negation, boost_true_positive_feedback=boost_true_positive_feedback, number_of_state_bits_ta=number_of_state_bits_ta, number_of_state_bits_ind=number_of_state_bits_ind, weighted_clauses=weighted_clauses, clause_drop_p = clause_drop_p, literal_drop_p = literal_drop_p) def initialize(self, X, Y): self.number_of_classes = int(np.max(Y) + 1) self.weight_banks = [] for i in range(self.number_of_classes): self.weight_banks.append(WeightBank(np.concatenate((np.ones(self.number_of_clauses//2, dtype=np.int32), -1*np.ones(self.number_of_clauses//2, dtype=np.int32))))) self.clause_banks = [] if self.platform == 'CPU': for i in range(self.number_of_classes): self.clause_banks.append(ClauseBank(X, self.number_of_clauses, self.number_of_state_bits_ta, self.number_of_state_bits_ind, self.patch_dim)) elif self.platform == 'CUDA': from tmu.clause_bank_cuda import ClauseBankCUDA for i in range(self.number_of_classes): self.clause_banks.append(ClauseBankCUDA(X, self.number_of_clauses, self.number_of_state_bits_ta, self.patch_dim)) else: print("Unknown Platform") sys.exit(-1) self.positive_clauses = np.concatenate((np.ones(self.number_of_clauses//2, dtype=np.int32), np.zeros(self.number_of_clauses//2, dtype=np.int32))) self.negative_clauses = np.concatenate((np.zeros(self.number_of_clauses//2, dtype=np.int32), np.ones(self.number_of_clauses//2, dtype=np.int32))) def fit(self, X, Y, shuffle=True): if self.initialized == False: self.initialize(X, Y) self.initialized = True if not np.array_equal(self.X_train, X): self.encoded_X_train = self.clause_banks[0].prepare_X(X) self.X_train = X.copy() Ym = np.ascontiguousarray(Y).astype(np.uint32) clause_active = [] for i in range(self.number_of_classes): # Clauses are dropped based on their weights class_clause_active = np.ascontiguousarray(np.ones(self.number_of_clauses, dtype=np.int32)) clause_score = np.abs(self.weight_banks[i].get_weights()) deactivate = np.random.choice(np.arange(self.number_of_clauses), size=int(self.number_of_clauses*self.clause_drop_p), p = clause_score / clause_score.sum()) for d in range(deactivate.shape[0]): class_clause_active[deactivate[d]] = 0 clause_active.append(class_clause_active) # Literals are dropped based on their frequency literal_active = (np.zeros(self.clause_banks[0].number_of_ta_chunks, dtype=np.uint32) | ~0).astype(np.uint32) literal_clause_frequency = self.literal_clause_frequency() if literal_clause_frequency.sum() > 0: deactivate = np.random.choice(np.arange(self.clause_banks[0].number_of_literals), size=int(self.clause_banks[0].number_of_literals*self.literal_drop_p), p = literal_clause_frequency / literal_clause_frequency.sum()) else: deactivate = np.random.choice(np.arange(self.clause_banks[0].number_of_literals), size=int(self.clause_banks[0].number_of_literals*self.literal_drop_p)) for d in range(deactivate.shape[0]): ta_chunk = deactivate[d] // 32 chunk_pos = deactivate[d] % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) if not self.feature_negation: for k in range(self.clause_banks[0].number_of_literals//2, self.clause_banks[0].number_of_literals): ta_chunk = k // 32 chunk_pos = k % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) literal_active = literal_active.astype(np.uint32) shuffled_index = np.arange(X.shape[0]) if shuffle: np.random.shuffle(shuffled_index) for e in shuffled_index: target = Ym[e] clause_outputs = self.clause_banks[target].calculate_clause_outputs_update(literal_active, self.encoded_X_train, e) class_sum = np.dot(clause_active[target] * self.weight_banks[target].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) update_p = (self.T - class_sum)/(2*self.T) if self.weighted_clauses: self.weight_banks[target].increment(clause_outputs, update_p, clause_active[target], False) self.clause_banks[target].type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, clause_active[target]*self.positive_clauses, literal_active, self.encoded_X_train, e) self.clause_banks[target].type_ii_feedback(update_p, clause_active[target]*self.negative_clauses, literal_active, self.encoded_X_train, e) if self.type_iii_feedback: self.clause_banks[target].type_iii_feedback(update_p, self.d, clause_active[target]*self.positive_clauses, literal_active, self.encoded_X_train, e, 1) self.clause_banks[target].type_iii_feedback(update_p, self.d, clause_active[target]*self.negative_clauses, literal_active, self.encoded_X_train, e, 0) not_target = np.random.randint(self.number_of_classes) while not_target == target: not_target = np.random.randint(self.number_of_classes) clause_outputs = self.clause_banks[not_target].calculate_clause_outputs_update(literal_active, self.encoded_X_train, e) class_sum = np.dot(clause_active[not_target] * self.weight_banks[not_target].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) update_p = (self.T + class_sum)/(2*self.T) if self.weighted_clauses: self.weight_banks[not_target].decrement(clause_outputs, update_p, clause_active[not_target], False) self.clause_banks[not_target].type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, clause_active[not_target]*self.negative_clauses, literal_active, self.encoded_X_train, e) self.clause_banks[not_target].type_ii_feedback(update_p, clause_active[not_target]*self.positive_clauses, literal_active, self.encoded_X_train, e) if self.type_iii_feedback: self.clause_banks[not_target].type_iii_feedback(update_p, self.d, clause_active[not_target]*self.negative_clauses, literal_active, self.encoded_X_train, e, 1) self.clause_banks[not_target].type_iii_feedback(update_p, self.d, clause_active[not_target]*self.positive_clauses, literal_active, self.encoded_X_train, e, 0) return def predict(self, X): if not np.array_equal(self.X_test, X): self.encoded_X_test = self.clause_banks[0].prepare_X(X) self.X_test = X.copy() Y = np.ascontiguousarray(np.zeros(X.shape[0], dtype=np.uint32)) for e in range(X.shape[0]): max_class_sum = -self.T max_class = 0 for i in range(self.number_of_classes): class_sum = np.dot(self.weight_banks[i].get_weights(), self.clause_banks[i].calculate_clause_outputs_predict(self.encoded_X_test, e)).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) if class_sum > max_class_sum: max_class_sum = class_sum max_class = i Y[e] = max_class return Y def transform(self, X): encoded_X = self.clause_banks[0].prepare_X(X) transformed_X = np.empty((X.shape[0], self.number_of_classes, self.number_of_clauses), dtype=np.uint32) for e in range(X.shape[0]): for i in range(self.number_of_classes): transformed_X[e,i,:] = self.clause_banks[i].calculate_clause_outputs_predict(encoded_X, e) return transformed_X.reshape((X.shape[0], self.number_of_classes*self.number_of_clauses)) def transform_patchwise(self, X): encoded_X = tmu.tools.encode(X, X.shape[0], self.number_of_patches, self.number_of_ta_chunks, self.dim, self.patch_dim, 0) transformed_X = np.empty((X.shape[0], self.number_of_classes, self.number_of_clauses//2*self.number_of_patches), dtype=np.uint32) for e in range(X.shape[0]): for i in range(self.number_of_classes): transformed_X[e,i,:] = self.clause_bank[i].calculate_clause_outputs_patchwise(encoded_X, e) return transformed_X.reshape((X.shape[0], self.number_of_classes*self.number_of_clauses, self.number_of_patches)) def literal_clause_frequency(self): clause_active = np.ones(self.number_of_clauses, dtype=np.uint32) literal_frequency = np.zeros(self.clause_banks[0].number_of_literals, dtype=np.uint32) for i in range(self.number_of_classes): literal_frequency += self.clause_banks[i].calculate_literal_clause_frequency(clause_active) return literal_frequency def literal_importance(self, the_class, negated_features=False, negative_polarity=False): literal_frequency = np.zeros(self.clause_banks[0].number_of_literals, dtype=np.uint32) if negated_features: if negative_polarity: literal_frequency[self.clause_banks[the_class].number_of_literals//2:] += self.clause_banks[the_class].calculate_literal_clause_frequency(self.negative_clauses)[self.clause_banks[the_class].number_of_literals//2:] else: literal_frequency[self.clause_banks[the_class].number_of_literals//2:] += self.clause_banks[the_class].calculate_literal_clause_frequency(self.positive_clauses)[self.clause_banks[the_class].number_of_literals//2:] else: if negative_polarity: literal_frequency[:self.clause_banks[the_class].number_of_literals//2] += self.clause_banks[the_class].calculate_literal_clause_frequency(self.negative_clauses)[:self.clause_banks[the_class].number_of_literals//2] else: literal_frequency[:self.clause_banks[the_class].number_of_literals//2] += self.clause_banks[the_class].calculate_literal_clause_frequency(self.positive_clauses)[:self.clause_banks[the_class].number_of_literals//2] return literal_frequency def clause_precision(self, the_class, polarity, X, Y): clause_outputs = self.transform(X).reshape(X.shape[0], self.number_of_classes, 2, self.number_of_clauses//2)[:,the_class, polarity,:] if polarity == 0: true_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) else: true_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) return np.where(true_positive_clause_outputs + false_positive_clause_outputs == 0, 0, true_positive_clause_outputs/(true_positive_clause_outputs + false_positive_clause_outputs)) def clause_recall(self, the_class, polarity, X, Y): clause_outputs = self.transform(X).reshape(X.shape[0], self.number_of_classes, 2, self.number_of_clauses//2)[:,the_class, polarity,:] if polarity == 0: true_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) else: true_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) return true_positive_clause_outputs / Y[Y==the_class].shape[0] def get_weight(self, the_class, polarity, clause): if polarity == 0: return self.weight_banks[the_class].get_weights()[clause] else: return self.weight_banks[the_class].get_weights()[self.number_of_clauses//2 + clause] def set_weight(self, the_class, polarity, clause, weight): if polarity == 0: self.weight_banks[the_class].get_weights()[clause] = weight else: self.weight_banks[the_class].get_weights()[self.number_of_clauses//2 + clause] = weight def get_ta_action(self, the_class, polarity, clause, ta): if polarity == 0: return self.clause_banks[the_class].get_ta_action(clause, ta) else: return self.clause_banks[the_class].get_ta_action(self.number_of_clauses//2 + clause, ta) def get_ta_state(self, the_class, polarity, clause, ta): if polarity == 0: return self.clause_banks[the_class].get_ta_state(clause, ta) else: return self.clause_banks[the_class].get_ta_state(self.number_of_clauses//2 + clause, ta) def set_ta_state(self, the_class, polarity, clause, ta, state): if polarity == 0: return self.clause_banks[the_class].set_ta_state(clause, ta, state) else: return self.clause_banks[the_class].set_ta_state(self.number_of_clauses//2 + clause, ta, state) class TMCoalescedClassifier(TMBasis): def __init__(self, number_of_clauses, T, s, type_iii_feedback=False, focused_negative_sampling=False, output_balancing=False, d=200.0, platform = 'CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, number_of_state_bits_ind=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): super().__init__(number_of_clauses, T, s, type_iii_feedback=type_iii_feedback, focused_negative_sampling=focused_negative_sampling, output_balancing=output_balancing, d=d, platform = platform, patch_dim=patch_dim, feature_negation=feature_negation, boost_true_positive_feedback=boost_true_positive_feedback, number_of_state_bits_ta=number_of_state_bits_ta, number_of_state_bits_ind=number_of_state_bits_ind, weighted_clauses=weighted_clauses, clause_drop_p = clause_drop_p, literal_drop_p = literal_drop_p) def initialize(self, X, Y): self.number_of_classes = int(np.max(Y) + 1) if self.platform == 'CPU': self.clause_bank = ClauseBank(X, self.number_of_clauses, self.number_of_state_bits_ta, self.number_of_state_bits_ind, self.patch_dim) elif self.platform == 'CUDA': from tmu.clause_bank_cuda import ClauseBankCUDA self.clause_bank = ClauseBankCUDA(X, self.number_of_clauses, self.number_of_state_bits_ta, self.patch_dim) else: print("Unknown Platform") sys.exit(-1) self.weight_banks = [] for i in range(self.number_of_classes): self.weight_banks.append(WeightBank(np.random.choice([-1,1], size=self.number_of_clauses).astype(np.int32))) def update(self, target, e): clause_outputs = self.clause_bank.calculate_clause_outputs_update(self.literal_active, self.encoded_X_train, e) class_sum = np.dot(self.clause_active * self.weight_banks[target].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) update_p = (self.T - class_sum)/(2*self.T) type_iii_feedback_selection = np.random.choice(2) self.clause_bank.type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, self.clause_active*(self.weight_banks[target].get_weights() >= 0), self.literal_active, self.encoded_X_train, e) self.clause_bank.type_ii_feedback(update_p, self.clause_active*(self.weight_banks[target].get_weights() < 0), self.literal_active, self.encoded_X_train, e) self.weight_banks[target].increment(clause_outputs, update_p, self.clause_active, True) if self.type_iii_feedback and type_iii_feedback_selection == 0: self.clause_bank.type_iii_feedback(update_p, self.d, self.clause_active*(self.weight_banks[target].get_weights() >= 0), self.literal_active, self.encoded_X_train, e, 1) self.clause_bank.type_iii_feedback(update_p, self.d, self.clause_active*(self.weight_banks[target].get_weights() < 0), self.literal_active, self.encoded_X_train, e, 0) for i in range(self.number_of_classes): if i == target: self.update_ps[i] = 0.0 else: self.update_ps[i] = np.dot(self.clause_active * self.weight_banks[i].get_weights(), clause_outputs).astype(np.int32) self.update_ps[i] = np.clip(self.update_ps[i], -self.T, self.T) self.update_ps[i] = 1.0*(self.T + self.update_ps[i])/(2*self.T) if self.update_ps.sum() == 0: return if self.focused_negative_sampling: not_target = np.random.choice(self.number_of_classes, p=self.update_ps/self.update_ps.sum()) update_p = self.update_ps[not_target] else: not_target = np.random.randint(self.number_of_classes) while not_target == target: not_target = np.random.randint(self.number_of_classes) update_p = self.update_ps[not_target] self.clause_bank.type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, self.clause_active * (self.weight_banks[not_target].get_weights() < 0), self.literal_active, self.encoded_X_train, e) self.clause_bank.type_ii_feedback(update_p, self.clause_active*(self.weight_banks[not_target].get_weights() >= 0), self.literal_active, self.encoded_X_train, e) if self.type_iii_feedback and type_iii_feedback_selection == 1: self.clause_bank.type_iii_feedback(update_p, self.d, self.clause_active*(self.weight_banks[not_target].get_weights() < 0), self.literal_active, self.encoded_X_train, e, 1) self.clause_bank.type_iii_feedback(update_p, self.d, self.clause_active*(self.weight_banks[not_target].get_weights() >= 0), self.literal_active, self.encoded_X_train, e, 0) self.weight_banks[not_target].decrement(clause_outputs, update_p, self.clause_active, True) def fit(self, X, Y, shuffle=True): if self.initialized == False: self.initialize(X, Y) self.initialized = True if not np.array_equal(self.X_train, X): self.encoded_X_train = self.clause_bank.prepare_X(X) self.X_train = X.copy() Ym = np.ascontiguousarray(Y).astype(np.uint32) # Clauses are dropped based on their weights self.clause_active = np.ones(self.number_of_clauses, dtype=np.uint32) clause_score = np.zeros(self.number_of_clauses, dtype=np.int32) for i in range(self.number_of_classes): clause_score += np.abs(self.weight_banks[i].get_weights()) deactivate = np.random.choice(np.arange(self.number_of_clauses), size=int(self.number_of_clauses*self.clause_drop_p), p = clause_score / clause_score.sum()) for d in range(deactivate.shape[0]): self.clause_active[deactivate[d]] = 0 # Literals are dropped based on their frequency self.literal_active = (np.zeros(self.clause_bank.number_of_ta_chunks, dtype=np.uint32) | ~0).astype(np.uint32) literal_clause_frequency = self.literal_clause_frequency() if literal_clause_frequency.sum() > 0: deactivate = np.random.choice(np.arange(self.clause_bank.number_of_literals), size=int(self.clause_bank.number_of_literals*self.literal_drop_p), p = literal_clause_frequency / literal_clause_frequency.sum()) else: deactivate = np.random.choice(np.arange(self.clause_bank.number_of_literals), size=int(self.clause_bank.number_of_literals*self.literal_drop_p)) for d in range(deactivate.shape[0]): ta_chunk = deactivate[d] // 32 chunk_pos = deactivate[d] % 32 self.literal_active[ta_chunk] &= (~(1 << chunk_pos)) if not self.feature_negation: for k in range(self.clause_bank.number_of_literals//2, self.clause_bank.number_of_literals): ta_chunk = k // 32 chunk_pos = k % 32 self.literal_active[ta_chunk] &= (~(1 << chunk_pos)) self.literal_active = self.literal_active.astype(np.uint32) self.update_ps = np.empty(self.number_of_classes) shuffled_index = np.arange(X.shape[0]) if shuffle: np.random.shuffle(shuffled_index) class_observed = np.zeros(self.number_of_classes, dtype=np.uint32) example_indexes = np.zeros(self.number_of_classes, dtype=np.uint32) example_counter = 0 for e in shuffled_index: if self.output_balancing: if class_observed[Ym[e]] == 0: example_indexes[Ym[e]] = e class_observed[Ym[e]] = 1 example_counter += 1 else: example_indexes[example_counter] = e example_counter += 1 if example_counter == self.number_of_classes: example_counter = 0 for i in range(self.number_of_classes): class_observed[i] = 0 batch_example = example_indexes[i] self.update(Ym[batch_example], batch_example) return def predict(self, X): if not np.array_equal(self.X_test, X): self.encoded_X_test = self.clause_bank.prepare_X(X) self.X_test = X.copy() Y = np.ascontiguousarray(np.zeros(X.shape[0], dtype=np.uint32)) for e in range(X.shape[0]): max_class_sum = -self.T max_class = 0 clause_outputs = self.clause_bank.calculate_clause_outputs_predict(self.encoded_X_test, e) for i in range(self.number_of_classes): class_sum = np.dot(self.weight_banks[i].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) if class_sum > max_class_sum: max_class_sum = class_sum max_class = i Y[e] = max_class return Y def clause_precision(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) weights = self.weight_banks[the_class].get_weights() if positive_polarity == 0: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) return np.where(true_positive_clause_outputs + false_positive_clause_outputs == 0, 0, 1.0*true_positive_clause_outputs/(true_positive_clause_outputs + false_positive_clause_outputs)) def clause_recall(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) weights = self.weight_banks[the_class].get_weights() if positive_polarity == 0: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==the_class].sum(axis=0) else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y!=the_class].sum(axis=0) return true_positive_clause_outputs / Y[Y==the_class].shape[0] def get_weight(self, the_class, clause): return self.weight_banks[the_class].get_weights()[clause] def set_weight(self, the_class, clause, weight): self.weight_banks[the_class].get_weights()[clause] = weight class TMMultiChannelClassifier(TMBasis): def __init__(self, number_of_clauses, global_T, T, s, platform = 'CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): super().__init__(number_of_clauses, T, s, platform = platform, patch_dim=patch_dim, feature_negation=feature_negation, boost_true_positive_feedback=boost_true_positive_feedback, number_of_state_bits_ta=number_of_state_bits_ta, weighted_clauses=weighted_clauses, clause_drop_p = clause_drop_p, literal_drop_p = literal_drop_p) self.global_T = global_T def initialize(self, X, Y): self.number_of_classes = int(np.max(Y) + 1) if self.platform == 'CPU': self.clause_bank = ClauseBank(X[0], self.number_of_clauses, self.number_of_state_bits_ta, self.number_of_state_bits_ind, self.patch_dim) elif self.platform == 'CUDA': from tmu.clause_bank_cuda import ClauseBankCUDA self.clause_bank = ClauseBankCUDA(X[0], self.number_of_clauses, self.number_of_state_bits_ta, self.patch_dim) else: print("Unknown Platform") sys.exit(-1) self.weight_banks = [] for i in range(self.number_of_classes): self.weight_banks.append(WeightBank(np.random.choice([-1,1], size=self.number_of_clauses).astype(np.int32))) self.X_train = {} self.X_test = {} for c in range(X.shape[0]): self.X_train[c] = np.zeros(0, dtype=np.uint32) self.X_test[c] = np.zeros(0, dtype=np.uint32) self.encoded_X_train = {} self.encoded_X_test = {} def fit(self, X, Y, shuffle=True): if self.initialized == False: self.initialize(X, Y) self.initialized = True for c in range(X.shape[0]): if not np.array_equal(self.X_train[c], X[c]): self.encoded_X_train[c] = self.clause_bank.prepare_X(X[c]) self.X_train[c] = X[c].copy() Ym = np.ascontiguousarray(Y).astype(np.uint32) # Clauses are dropped based on their weights clause_active = np.ones(self.number_of_clauses, dtype=np.uint32) clause_score = np.zeros(self.number_of_clauses, dtype=np.int32) for i in range(self.number_of_classes): clause_score += np.abs(self.weight_banks[i].get_weights()) deactivate = np.random.choice(np.arange(self.number_of_clauses), size=int(self.number_of_clauses*self.clause_drop_p), p = clause_score / clause_score.sum()) for d in range(deactivate.shape[0]): clause_active[deactivate[d]] = 0 # Literals are dropped based on their frequency literal_active = (np.zeros(self.clause_bank.number_of_ta_chunks, dtype=np.uint32) | ~0).astype(np.uint32) literal_clause_frequency = self.literal_clause_frequency() if literal_clause_frequency.sum() > 0: deactivate = np.random.choice(np.arange(self.clause_bank.number_of_literals), size=int(self.clause_bank.number_of_literals*self.literal_drop_p), p = literal_clause_frequency / literal_clause_frequency.sum()) else: deactivate = np.random.choice(np.arange(self.clause_bank.number_of_literals), size=int(self.clause_bank.number_of_literals*self.literal_drop_p)) for d in range(deactivate.shape[0]): ta_chunk = deactivate[d] // 32 chunk_pos = deactivate[d] % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) if not self.feature_negation: for k in range(self.clause_bank.number_of_literals//2, self.clause_bank.number_of_literals): ta_chunk = k // 32 chunk_pos = k % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) literal_active = literal_active.astype(np.uint32) local_class_sum = np.empty(X.shape[0], dtype=np.int32) shuffled_index = np.arange(X.shape[1]) if shuffle: np.random.shuffle(shuffled_index) for e in shuffled_index: target = Ym[e] clause_outputs = [] for c in range(X.shape[0]): clause_outputs.append(self.clause_bank.calculate_clause_outputs_update(literal_active, self.encoded_X_train[c], e).copy()) global_class_sum = 0 for c in range(X.shape[0]): local_class_sum[c] = np.dot(clause_active * self.weight_banks[target].get_weights(), clause_outputs[c]).astype(np.int32) local_class_sum[c] = np.clip(local_class_sum[c], -self.T, self.T) global_class_sum += local_class_sum[c] global_class_sum = np.clip(global_class_sum, -self.global_T[target][0], self.global_T[target][1]) global_update_p = 1.0*(self.global_T[target][1] - global_class_sum)/(self.global_T[target][0]+self.global_T[target][1]) for c in range(X.shape[0]): local_update_p = 1.0*(self.T - local_class_sum[c])/(2*self.T) update_p = np.minimum(local_update_p, global_update_p) self.clause_bank.type_i_feedback(update_p, self.s[target], self.boost_true_positive_feedback, clause_active*(self.weight_banks[target].get_weights() >= 0), literal_active, self.encoded_X_train[c], e) self.clause_bank.type_ii_feedback(update_p, clause_active*(self.weight_banks[target].get_weights() < 0), literal_active, self.encoded_X_train[c], e) self.weight_banks[target].increment(clause_outputs[c], update_p, clause_active, True) not_target = np.random.randint(self.number_of_classes) while not_target == target: not_target = np.random.randint(self.number_of_classes) global_class_sum = 0.0 for c in range(X.shape[0]): local_class_sum[c] = np.dot(clause_active * self.weight_banks[not_target].get_weights(), clause_outputs[c]).astype(np.int32) local_class_sum[c] = np.clip(local_class_sum[c], -self.T, self.T) global_class_sum += local_class_sum[c] global_class_sum = np.clip(global_class_sum, -self.global_T[not_target][0], self.global_T[not_target][1]) global_update_p = 1.0*(self.global_T[not_target][0] + global_class_sum)/(self.global_T[not_target][0]+self.global_T[not_target][1]) for c in range(X.shape[0]): local_update_p = 1.0*(self.T + local_class_sum[c])/(2*self.T) update_p = np.minimum(local_update_p, global_update_p) self.clause_bank.type_i_feedback(update_p, self.s[not_target], self.boost_true_positive_feedback, clause_active * (self.weight_banks[not_target].get_weights() < 0), literal_active, self.encoded_X_train[c], e) self.clause_bank.type_ii_feedback(update_p, clause_active*(self.weight_banks[not_target].get_weights() >= 0), literal_active, self.encoded_X_train[c], e) self.weight_banks[not_target].decrement(clause_outputs[c], update_p, clause_active, True) return def predict(self, X): for c in range(X.shape[0]): if not np.array_equal(self.X_test[c], X[c]): self.encoded_X_test[c] = self.clause_bank.prepare_X(X[c]) self.X_test[c] = X[c].copy() Y = np.ascontiguousarray(np.zeros(X.shape[1], dtype=np.uint32)) for e in range(X.shape[1]): max_class_sum = -maxsize max_class = 0 clause_outputs = [] for c in range(X.shape[0]): clause_outputs.append(self.clause_bank.calculate_clause_outputs_predict(self.encoded_X_test[c], e).copy()) for i in range(self.number_of_classes): global_class_sum = 1 for c in range(X.shape[0]): local_class_sum = np.dot(self.weight_banks[i].get_weights(), clause_outputs[c]).astype(np.int32) local_class_sum = np.clip(local_class_sum, -self.T, self.T) global_class_sum *= local_class_sum >= 0 global_class_sum = np.clip(global_class_sum, -self.global_T[i][0], self.global_T[i][1]) if global_class_sum > max_class_sum: max_class_sum = global_class_sum max_class = i Y[e] = max_class return Y def clause_precision(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) weights = self.weight_banks[the_class].get_weights() if positive_polarity == 0: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = clause_outputs[Y!=the_class].sum(axis=0) false_positive_clause_outputs = clause_outputs[Y==the_class].sum(axis=0) return np.where(true_positive_clause_outputs + false_positive_clause_outputs == 0, 0, 1.0*true_positive_clause_outputs/(true_positive_clause_outputs + false_positive_clause_outputs)) def clause_recall(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) weights = self.weight_banks[the_class].get_weights() if positive_polarity == 0: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==the_class].sum(axis=0) else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y!=the_class].sum(axis=0) return true_positive_clause_outputs / Y[Y==the_class].shape[0] def get_weight(self, the_class, clause): return self.weight_banks[the_class].get_weights()[clause] def set_weight(self, the_class, clause, weight): self.weight_banks[the_class].get_weights()[clause] = weight class TMOneVsOneClassifier(TMBasis): def __init__(self, number_of_clauses, T, s, platform = 'CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): super().__init__(number_of_clauses, T, s, platform = platform, patch_dim=patch_dim, feature_negation=feature_negation, boost_true_positive_feedback=boost_true_positive_feedback, number_of_state_bits_ta=number_of_state_bits_ta, weighted_clauses=weighted_clauses, clause_drop_p = clause_drop_p, literal_drop_p = literal_drop_p) def initialize(self, X, Y): self.number_of_classes = int(np.max(Y) + 1) self.number_of_outputs = self.number_of_classes * (self.number_of_classes-1) if self.platform == 'CPU': self.clause_bank = ClauseBank(X, self.number_of_clauses, self.number_of_state_bits_ta, self.number_of_state_bits_ind, self.patch_dim) elif self.platform == 'CUDA': from tmu.clause_bank_cuda import ClauseBankCUDA self.clause_bank = ClauseBankCUDA(X, self.number_of_clauses, self.number_of_state_bits_ta, self.patch_dim) else: print("Unknown Platform") sys.exit(-1) self.weight_banks = [] for i in range(self.number_of_outputs): self.weight_banks.append(WeightBank(np.ones(self.number_of_clauses).astype(np.int32))) def fit(self, X, Y, shuffle=True): if self.initialized == False: self.initialize(X, Y) self.initialized = True if not np.array_equal(self.X_train, X): self.encoded_X_train = self.clause_bank.prepare_X(X) self.X_train = X.copy() Ym = np.ascontiguousarray(Y).astype(np.uint32) clause_active = np.ascontiguousarray(np.random.choice(2, self.number_of_clauses, p=[self.clause_drop_p, 1.0 - self.clause_drop_p]).astype(np.int32)) literal_active = (np.zeros(self.clause_bank.number_of_ta_chunks, dtype=np.uint32) | ~0).astype(np.uint32) if not self.feature_negation: for k in range(self.clause_bank.number_of_literals//2, self.clause_bank.number_of_literals): ta_chunk = k // 32 chunk_pos = k % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) literal_active = literal_active.astype(np.uint32) shuffled_index = np.arange(X.shape[0]) if shuffle: np.random.shuffle(shuffled_index) for e in shuffled_index: clause_outputs = self.clause_bank.calculate_clause_outputs_update(literal_active, self.encoded_X_train, e) target = Ym[e] not_target = np.random.randint(self.number_of_classes) while not_target == target: not_target = np.random.randint(self.number_of_classes) output = target * (self.number_of_classes-1) + not_target - (not_target > target) class_sum = np.dot(clause_active * self.weight_banks[output].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) update_p = (self.T - class_sum)/(2*self.T) self.clause_bank.type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, clause_active*(self.weight_banks[output].get_weights() >= 0), literal_active, self.encoded_X_train, e) self.clause_bank.type_ii_feedback(update_p, clause_active*(self.weight_banks[output].get_weights() < 0), literal_active, self.encoded_X_train, e) self.weight_banks[output].increment(clause_outputs, update_p, clause_active, True) output = not_target * (self.number_of_classes-1) + target - (target > not_target) class_sum = np.dot(clause_active * self.weight_banks[output].get_weights(), clause_outputs).astype(np.int32) class_sum = np.clip(class_sum, -self.T, self.T) update_p = (self.T + class_sum)/(2*self.T) self.clause_bank.type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, clause_active * (self.weight_banks[output].get_weights() < 0), literal_active, self.encoded_X_train, e) self.clause_bank.type_ii_feedback(update_p, clause_active*(self.weight_banks[output].get_weights() >= 0), literal_active, self.encoded_X_train, e) self.weight_banks[output].decrement(clause_outputs, update_p, clause_active, True) return def predict(self, X): if not np.array_equal(self.X_test, X): self.encoded_X_test = self.clause_bank.prepare_X(X) self.X_test = X.copy() Y = np.ascontiguousarray(np.zeros(X.shape[0], dtype=np.uint32)) for e in range(X.shape[0]): clause_outputs = self.clause_bank.calculate_clause_outputs_predict(self.encoded_X_test, e) max_class_sum = -self.T*self.number_of_classes max_class = 0 for i in range(self.number_of_classes): class_sum = 0 for output in range(i * (self.number_of_classes - 1), (i+1) * (self.number_of_classes-1)): output_sum = np.dot(self.weight_banks[output].get_weights(), clause_outputs).astype(np.int32) output_sum = np.clip(output_sum, -self.T, self.T) class_sum += output_sum if class_sum > max_class_sum: max_class_sum = class_sum max_class = i Y[e] = max_class return Y def clause_precision(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) precision = np.zeros((self.number_of_classes - 1, self.number_of_clauses)) for i in range(self.number_of_classes - 1): other_class = i + (i >= the_class) output = the_class * (self.number_of_classes - 1) + i weights = self.weight_banks[output].get_weights() if positive_polarity: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==the_class].sum(axis=0) false_positive_clause_outputs = positive_clause_outputs[Y==other_class].sum(axis=0) precision[i] = np.where(true_positive_clause_outputs + false_positive_clause_outputs == 0, 0, true_positive_clause_outputs/(true_positive_clause_outputs + false_positive_clause_outputs)) else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==other_class].sum(axis=0) false_positive_clause_outputs = positive_clause_outputs[Y==the_class].sum(axis=0) precision[i] = np.where(true_positive_clause_outputs + false_positive_clause_outputs == 0, 0, true_positive_clause_outputs/(true_positive_clause_outputs + false_positive_clause_outputs)) return precision def clause_recall(self, the_class, positive_polarity, X, Y): clause_outputs = self.transform(X) recall = np.zeros((self.number_of_classes - 1, self.number_of_clauses)) for i in range(self.number_of_classes - 1): other_class = i + (i >= the_class) output = the_class * (self.number_of_classes - 1) + i weights = self.weight_banks[output].get_weights() if positive_polarity: positive_clause_outputs = (weights >= 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==the_class].sum(axis=0) recall[i] = true_positive_clause_outputs / Y[Y==the_class].shape[0] else: positive_clause_outputs = (weights < 0)[:,np.newaxis].transpose() * clause_outputs true_positive_clause_outputs = positive_clause_outputs[Y==other_class].sum(axis=0) recall[i] = true_positive_clause_outputs / Y[Y==other_class].shape[0] return recall def get_weight(self, output, clause): return self.weight_banks[output].get_weights()[clause] def set_weight(self, output, weight): self.weight_banks[output].get_weights()[output] = weight class TMRegressor(TMBasis): def __init__(self, number_of_clauses, T, s, platform='CPU', patch_dim=None, feature_negation=True, boost_true_positive_feedback=1, number_of_state_bits_ta=8, weighted_clauses=False, clause_drop_p = 0.0, literal_drop_p = 0.0): super().__init__(number_of_clauses, T, s, platform=platform, patch_dim=patch_dim, feature_negation=feature_negation, boost_true_positive_feedback=boost_true_positive_feedback, number_of_state_bits_ta=number_of_state_bits_ta, weighted_clauses=weighted_clauses, clause_drop_p = clause_drop_p, literal_drop_p = literal_drop_p) def initialize(self, X, Y): self.max_y = np.max(Y) self.min_y = np.min(Y) if self.platform == 'CPU': self.clause_bank = ClauseBank(X, self.number_of_clauses, self.number_of_state_bits_ta, self.number_of_state_bits_ind, self.patch_dim) elif self.platform == 'CUDA': from tmu.clause_bank_cuda import ClauseBankCUDA self.clause_bank = ClauseBankCUDA(X, self.number_of_clauses, self.number_of_state_bits_ta, self.patch_dim) else: print("Unknown Platform") sys.exit(-1) self.weight_bank = WeightBank(np.ones(self.number_of_clauses).astype(np.int32)) def fit(self, X, Y, shuffle=True): if self.initialized == False: self.initialize(X, Y) self.initialized = True if not np.array_equal(self.X_train, X): self.encoded_X_train = self.clause_bank.prepare_X(X) self.X_train = X.copy() encoded_Y = np.ascontiguousarray(((Y - self.min_y)/(self.max_y - self.min_y)*self.T).astype(np.int32)) clause_active = np.ascontiguousarray(np.random.choice(2, self.number_of_clauses, p=[self.clause_drop_p, 1.0 - self.clause_drop_p]).astype(np.int32)) literal_active = (np.zeros(self.clause_bank.number_of_ta_chunks, dtype=np.uint32) | ~0).astype(np.uint32) if not self.feature_negation: for k in range(self.clause_bank.number_of_literals//2, self.clause_bank.number_of_literals): ta_chunk = k // 32 chunk_pos = k % 32 literal_active[ta_chunk] &= (~(1 << chunk_pos)) literal_active = literal_active.astype(np.uint32) shuffled_index = np.arange(X.shape[0]) if shuffle: np.random.shuffle(shuffled_index) for e in shuffled_index: clause_outputs = self.clause_bank.calculate_clause_outputs_update(literal_active, self.encoded_X_train, e) pred_y = np.dot(clause_active * self.weight_bank.get_weights(), clause_outputs).astype(np.int32) pred_y = np.clip(pred_y, 0, self.T) prediction_error = pred_y - encoded_Y[e]; update_p = (1.0*prediction_error/self.T)**2 if pred_y < encoded_Y[e]: self.clause_bank.type_i_feedback(update_p, self.s, self.boost_true_positive_feedback, clause_active, literal_active, self.encoded_X_train, e) if self.weighted_clauses: self.weight_bank.increment(clause_outputs, update_p, clause_active, False) elif pred_y > encoded_Y[e]: self.clause_bank.type_ii_feedback(update_p, clause_active, literal_active, self.encoded_X_train, e) if self.weighted_clauses: self.weight_bank.decrement(clause_outputs, update_p, clause_active, False) return def predict(self, X): if not np.array_equal(self.X_test, X): self.encoded_X_test = self.clause_bank.prepare_X(X) self.X_test = X.copy() Y = np.ascontiguousarray(np.zeros(X.shape[0])) for e in range(X.shape[0]): clause_outputs = self.clause_bank.calculate_clause_outputs_predict(self.encoded_X_test, e) pred_y = np.dot(self.weight_bank.get_weights(), clause_outputs).astype(np.int32) Y[e] = 1.0*pred_y * (self.max_y - self.min_y)/(self.T) + self.min_y return Y def get_weight(self, clause): return self.weight_bank.get_weights()[clause] def set_weight(self, clause, weight): self.weight_banks.get_weights()[clause] = weight

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4b71b02c448bd0e0d0e5e3f089e91681339dbe6b

132

py

Python

src/10000/10992.py3.py

upple/BOJ

e6dbf9fd17fa2b458c6a781d803123b14c18e6f1

[ "MIT" ]

8

2018-04-12T15:54:09.000Z

2020-06-05T07:41:15.000Z

src/10000/10992.py3.py

upple/BOJ

e6dbf9fd17fa2b458c6a781d803123b14c18e6f1

[ "MIT" ]

null

src/10000/10992.py3.py

upple/BOJ

e6dbf9fd17fa2b458c6a781d803123b14c18e6f1

[ "MIT" ]

null

n = int(input()) print(' '*(n-1)+'*') for i in range(1, n-1): print(' '*(n-i-1)+'*'+' '*(2*i-1)+'*') if n>1: print('*'*(2*n-1))

22

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29a2e1942a02a2f73d5f928c43adbe8eb185c867

34

py

Python

__init__.py

joegagliardo/sqldf

2d90496a0fc041ac989b3ee538f4a43a283dceb2

[ "Apache-2.0" ]

2

2022-03-23T14:05:43.000Z

2022-03-31T05:09:24.000Z

__init__.py

joegagliardo/sqldf

2d90496a0fc041ac989b3ee538f4a43a283dceb2

[ "Apache-2.0" ]

2

2022-03-23T14:34:13.000Z

2022-03-31T06:37:40.000Z

__init__.py

joegagliardo/sqldf

2d90496a0fc041ac989b3ee538f4a43a283dceb2

[ "Apache-2.0" ]

null

from bettersql.sqldf import sqldf

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