xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

d4e88474ab017434713d6764dcf24407a16b343a

89

py

Python

tests/__init__.py

clu-ling/clu-phontools

304510150c6f9a4b0e1372bc9275630b7f976aeb

[ "Apache-2.0" ]

null

tests/__init__.py

clu-ling/clu-phontools

304510150c6f9a4b0e1372bc9275630b7f976aeb

[ "Apache-2.0" ]

3

2021-06-15T23:32:30.000Z

2021-09-01T18:49:20.000Z

tests/__init__.py

clu-ling/clu-phontools

304510150c6f9a4b0e1372bc9275630b7f976aeb

[ "Apache-2.0" ]

1

2021-06-18T05:48:29.000Z

# -*- coding: utf-8 -*- # TODO: add phrases here for eas of use in multiple test suites

22.25

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5

be197aedf2a5e4757e87902f3ea6ae392963f8ff

196

py

Python

dotloop/detail.py

spentaur/dotloop-python

5374ab5f5e16f9b826438a9c4f051a4be53d433b

[ "MIT" ]

null

dotloop/detail.py

spentaur/dotloop-python

5374ab5f5e16f9b826438a9c4f051a4be53d433b

[ "MIT" ]

null

dotloop/detail.py

spentaur/dotloop-python

5374ab5f5e16f9b826438a9c4f051a4be53d433b

[ "MIT" ]

1

2021-07-28T14:28:17.000Z

from .bases import DotloopObject class Detail(DotloopObject): def get(self): return self.fetch('get') def patch(self, **kwargs): return self.fetch('patch', json=kwargs)

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5

076df1c420f981336caaeec495ef473bdcab621d

13,968

py

Python

parsetab.py

MrSuicideParrot/pyGoCompiler

5133a2ff5221f3b79f6a82439b1f06be30c89b52

[ "MIT" ]

null

parsetab.py

MrSuicideParrot/pyGoCompiler

5133a2ff5221f3b79f6a82439b1f06be30c89b52

[ "MIT" ]

null

parsetab.py

MrSuicideParrot/pyGoCompiler

5133a2ff5221f3b79f6a82439b1f06be30c89b52

[ "MIT" ]

null

# parsetab.py # This file is automatically generated. Do not edit. _tabversion = '3.10' _lr_method = 'LALR' _lr_signature = 'nonassocLESSMOREEQUALSTOMOREEQUALLESSEQUALNOTEQUALleftPLUSMINUSleftTIMESDIVIDEleftANDORrightUMINUSINT FLOAT PLUS MINUS TIMES DIVIDE EQUALS LPAREN RPAREN LCURLBRACKET RCURLBRACKET ID COMMENT STRING ASSIGN SEMICOLON COMMA POINT NOT EQUALSTO MORE LESS MOREEQUAL LESSEQUAL NOTEQUAL AND OR INCREMENT DECREMENT BREAK CASE CHAN CONST CONTINUE DEFAULT DEFER ELSE FALLTHROUGH FOR FUNC GO GOTO IF IMPORT INTERFACE MAP PACKAGE RANGE RETURN SELECT STRUCT SWITCH TYPE VAR MAIN FMT PRINT SCAN TRUE FALSEstatement : PACKAGE MAIN IMPORT STRING FUNC MAIN LPAREN RPAREN LCURLBRACKET list RCURLBRACKET\n | PACKAGE MAIN IMPORT STRING FUNC MAIN LPAREN RPAREN LCURLBRACKET RCURLBRACKETlist : inst\n | inst listassignment : ID ASSIGN expressionAR\n | ID ASSIGN expressionBo\n | ID EQUALS expressionAR\n | ID EQUALS expressionBo\n | ID INCREMENT\n | ID DECREMENTinst : FOR expressionBo LCURLBRACKET list RCURLBRACKET\n | FOR assignment SEMICOLON expressionBo SEMICOLON assignment LCURLBRACKET list RCURLBRACKETinst : assignment SEMICOLONinst : IF expressionBo LCURLBRACKET list RCURLBRACKET ELSE LCURLBRACKET list RCURLBRACKET\n | IF expressionBo LCURLBRACKET list RCURLBRACKETlistID : expressionAR\n | expressionBo\n | expressionBo COMMA listID\n | expressionAR COMMA listIDIDlist : ID\n | ID COMMA IDlistinst : FMT POINT PRINT LPAREN listID RPAREN SEMICOLON\n | FMT POINT SCAN LPAREN IDlist RPAREN SEMICOLON\n | FMT POINT PRINT LPAREN RPAREN SEMICOLON\n | FMT POINT SCAN LPAREN RPAREN SEMICOLONexpressionAR : expressionAR PLUS expressionAR\n | expressionAR MINUS expressionAR\n | expressionAR TIMES expressionAR\n | expressionAR DIVIDE expressionAR\n | IDexpressionAR : INTexpressionAR : MINUS expressionAR %prec UMINUSexpressionAR : FLOATexpressionAR : LPAREN expressionAR RPARENexpressionBo : expressionAR MORE expressionAR\n | expressionAR LESS expressionAR\n | expressionAR MOREEQUAL expressionAR\n | expressionAR LESSEQUAL expressionAR\n | expressionBo NOTEQUAL expressionBo\n | expressionAR NOTEQUAL expressionAR\n | expressionBo EQUALSTO expressionBo\n | expressionAR EQUALSTO expressionAR\n | expressionBo AND expressionBo\n | expressionBo OR expressionBoexpressionBo : NOT expressionBo %prec UMINUSexpressionBo : TRUE\n | FALSEexpressionBo : LPAREN expressionBo RPAREN' _lr_action_items = {'PACKAGE':([0,],[2,]),'$end':([1,12,19,],[0,-2,-1,]),'MAIN':([2,6,],[3,7,]),'IMPORT':([3,],[4,]),'STRING':([4,],[5,]),'FUNC':([5,],[6,]),'LPAREN':([7,14,16,24,27,29,36,37,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,60,62,63,66,89,106,107,],[8,27,27,27,27,60,66,66,27,27,27,27,27,60,60,60,60,60,60,60,60,60,60,60,89,90,66,66,66,66,]),'RPAREN':([8,25,26,30,31,34,56,57,58,59,70,71,72,73,75,76,77,78,79,80,81,82,83,84,85,86,87,89,90,91,95,97,98,99,101,114,115,117,],[9,-46,-47,-31,-33,-30,-45,85,86,-32,-39,-41,-43,-44,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,86,96,100,86,104,-16,-17,108,-20,-19,-18,-21,]),'LCURLBRACKET':([9,21,25,26,30,31,33,34,38,39,56,59,64,65,67,68,70,71,72,73,75,76,77,78,79,80,81,82,83,84,85,86,102,103,],[10,40,-46,-47,-31,-33,61,-30,-9,-10,-45,-32,-5,-6,-7,-8,-39,-41,-43,-44,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,111,112,]),'RCURLBRACKET':([10,11,13,20,32,69,88,92,94,105,109,113,116,118,119,120,121,],[12,19,-3,-4,-13,92,94,-11,-15,-24,-25,-22,-23,120,121,-12,-14,]),'FOR':([10,13,32,40,61,92,94,105,109,111,112,113,116,120,121,],[14,14,-13,14,14,-11,-15,-24,-25,14,14,-22,-23,-12,-14,]),'IF':([10,13,32,40,61,92,94,105,109,111,112,113,116,120,121,],[16,16,-13,16,16,-11,-15,-24,-25,16,16,-22,-23,-12,-14,]),'FMT':([10,13,32,40,61,92,94,105,109,111,112,113,116,120,121,],[17,17,-13,17,17,-11,-15,-24,-25,17,17,-22,-23,-12,-14,]),'ID':([10,13,14,16,24,27,29,32,36,37,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,60,61,66,89,90,92,93,94,105,106,107,109,110,111,112,113,116,120,121,],[18,18,28,34,34,34,34,-13,34,34,18,34,34,34,34,34,34,34,34,34,34,34,34,34,34,34,34,18,34,34,101,-11,18,-15,-24,34,34,-25,101,18,18,-22,-23,-12,-14,]),'NOT':([14,16,24,27,36,37,41,42,43,44,45,66,89,106,107,],[24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,]),'TRUE':([14,16,24,27,36,37,41,42,43,44,45,66,89,106,107,],[25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,]),'FALSE':([14,16,24,27,36,37,41,42,43,44,45,66,89,106,107,],[26,26,26,26,26,26,26,26,26,26,26,26,26,26,26,]),'INT':([14,16,24,27,29,36,37,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,60,66,89,106,107,],[30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,]),'MINUS':([14,16,23,24,27,28,29,30,31,34,36,37,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,58,59,60,64,66,67,75,76,77,78,79,80,81,82,83,84,86,87,89,91,97,106,107,],[29,29,53,29,29,-30,29,-31,-33,-30,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,53,-32,29,53,29,53,53,53,53,53,53,53,-26,-27,-28,-29,-34,53,29,53,53,29,29,]),'FLOAT':([14,16,24,27,29,36,37,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,60,66,89,106,107,],[31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,]),'SEMICOLON':([15,22,25,26,30,31,34,38,39,56,59,64,65,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,96,100,104,108,],[32,45,-46,-47,-31,-33,-30,-9,-10,-45,-32,-5,-6,-7,-8,-39,-41,-43,-44,93,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,105,109,113,116,]),'POINT':([17,],[35,]),'ASSIGN':([18,28,],[36,36,]),'EQUALS':([18,28,],[37,37,]),'INCREMENT':([18,28,],[38,38,]),'DECREMENT':([18,28,],[39,39,]),'NOTEQUAL':([21,23,25,26,28,30,31,33,34,56,57,58,59,64,65,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,91,97,98,],[41,50,-46,-47,-30,-31,-33,41,-30,-45,41,50,-32,50,41,50,41,None,None,-43,-44,41,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,50,50,41,]),'EQUALSTO':([21,23,25,26,28,30,31,33,34,56,57,58,59,64,65,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,91,97,98,],[42,51,-46,-47,-30,-31,-33,42,-30,-45,42,51,-32,51,42,51,42,None,None,-43,-44,42,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,51,51,42,]),'AND':([21,25,26,30,31,33,34,56,57,59,65,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,98,],[43,-46,-47,-31,-33,43,-30,-45,43,-32,43,43,43,43,-43,-44,43,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,43,]),'OR':([21,25,26,30,31,33,34,56,57,59,65,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,98,],[44,-46,-47,-31,-33,44,-30,-45,44,-32,44,44,44,44,-43,-44,44,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,44,]),'MORE':([23,28,30,31,34,58,59,64,67,81,82,83,84,86,91,97,],[46,-30,-31,-33,-30,46,-32,46,46,-26,-27,-28,-29,-34,46,46,]),'LESS':([23,28,30,31,34,58,59,64,67,81,82,83,84,86,91,97,],[47,-30,-31,-33,-30,47,-32,47,47,-26,-27,-28,-29,-34,47,47,]),'MOREEQUAL':([23,28,30,31,34,58,59,64,67,81,82,83,84,86,91,97,],[48,-30,-31,-33,-30,48,-32,48,48,-26,-27,-28,-29,-34,48,48,]),'LESSEQUAL':([23,28,30,31,34,58,59,64,67,81,82,83,84,86,91,97,],[49,-30,-31,-33,-30,49,-32,49,49,-26,-27,-28,-29,-34,49,49,]),'PLUS':([23,28,30,31,34,58,59,64,67,75,76,77,78,79,80,81,82,83,84,86,87,91,97,],[52,-30,-31,-33,-30,52,-32,52,52,52,52,52,52,52,52,-26,-27,-28,-29,-34,52,52,52,]),'TIMES':([23,28,30,31,34,58,59,64,67,75,76,77,78,79,80,81,82,83,84,86,87,91,97,],[54,-30,-31,-33,-30,54,-32,54,54,54,54,54,54,54,54,54,54,-28,-29,-34,54,54,54,]),'DIVIDE':([23,28,30,31,34,58,59,64,67,75,76,77,78,79,80,81,82,83,84,86,87,91,97,],[55,-30,-31,-33,-30,55,-32,55,55,55,55,55,55,55,55,55,55,-28,-29,-34,55,55,55,]),'COMMA':([25,26,30,31,34,56,59,70,71,72,73,75,76,77,78,79,80,81,82,83,84,85,86,97,98,101,],[-46,-47,-31,-33,-30,-45,-32,-39,-41,-43,-44,-35,-36,-37,-38,-40,-42,-26,-27,-28,-29,-48,-34,106,107,110,]),'PRINT':([35,],[62,]),'SCAN':([35,],[63,]),'ELSE':([94,],[103,]),} _lr_action = {} for _k, _v in _lr_action_items.items(): for _x,_y in zip(_v[0],_v[1]): if not _x in _lr_action: _lr_action[_x] = {} _lr_action[_x][_k] = _y del _lr_action_items _lr_goto_items = {'statement':([0,],[1,]),'list':([10,13,40,61,111,112,],[11,20,69,88,118,119,]),'inst':([10,13,40,61,111,112,],[13,13,13,13,13,13,]),'assignment':([10,13,14,40,61,93,111,112,],[15,15,22,15,15,102,15,15,]),'expressionBo':([14,16,24,27,36,37,41,42,43,44,45,66,89,106,107,],[21,33,56,57,65,68,70,71,72,73,74,57,98,98,98,]),'expressionAR':([14,16,24,27,29,36,37,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,60,66,89,106,107,],[23,23,23,58,59,64,67,23,23,23,23,23,75,76,77,78,79,80,81,82,83,84,87,91,97,97,97,]),'listID':([89,106,107,],[95,114,115,]),'IDlist':([90,110,],[99,117,]),} _lr_goto = {} for _k, _v in _lr_goto_items.items(): for _x, _y in zip(_v[0], _v[1]): if not _x in _lr_goto: _lr_goto[_x] = {} _lr_goto[_x][_k] = _y del _lr_goto_items _lr_productions = [ ("S' -> statement","S'",1,None,None,None), ('statement -> PACKAGE MAIN IMPORT STRING FUNC MAIN LPAREN RPAREN LCURLBRACKET list RCURLBRACKET','statement',11,'p_statement_expr','plintax.py',17), ('statement -> PACKAGE MAIN IMPORT STRING FUNC MAIN LPAREN RPAREN LCURLBRACKET RCURLBRACKET','statement',10,'p_statement_expr','plintax.py',18), ('list -> inst','list',1,'p_list','plintax.py',26), ('list -> inst list','list',2,'p_list','plintax.py',27), ('assignment -> ID ASSIGN expressionAR','assignment',3,'p_assignment','plintax.py',34), ('assignment -> ID ASSIGN expressionBo','assignment',3,'p_assignment','plintax.py',35), ('assignment -> ID EQUALS expressionAR','assignment',3,'p_assignment','plintax.py',36), ('assignment -> ID EQUALS expressionBo','assignment',3,'p_assignment','plintax.py',37), ('assignment -> ID INCREMENT','assignment',2,'p_assignment','plintax.py',38), ('assignment -> ID DECREMENT','assignment',2,'p_assignment','plintax.py',39), ('inst -> FOR expressionBo LCURLBRACKET list RCURLBRACKET','inst',5,'p_inst_For','plintax.py',53), ('inst -> FOR assignment SEMICOLON expressionBo SEMICOLON assignment LCURLBRACKET list RCURLBRACKET','inst',9,'p_inst_For','plintax.py',54), ('inst -> assignment SEMICOLON','inst',2,'p_inst_assignment','plintax.py',62), ('inst -> IF expressionBo LCURLBRACKET list RCURLBRACKET ELSE LCURLBRACKET list RCURLBRACKET','inst',9,'p_inst_If','plintax.py',67), ('inst -> IF expressionBo LCURLBRACKET list RCURLBRACKET','inst',5,'p_inst_If','plintax.py',68), ('listID -> expressionAR','listID',1,'p_listID','plintax.py',75), ('listID -> expressionBo','listID',1,'p_listID','plintax.py',76), ('listID -> expressionBo COMMA listID','listID',3,'p_listID','plintax.py',77), ('listID -> expressionAR COMMA listID','listID',3,'p_listID','plintax.py',78), ('IDlist -> ID','IDlist',1,'p_IDlist','plintax.py',85), ('IDlist -> ID COMMA IDlist','IDlist',3,'p_IDlist','plintax.py',86), ('inst -> FMT POINT PRINT LPAREN listID RPAREN SEMICOLON','inst',7,'p_inst_func','plintax.py',102), ('inst -> FMT POINT SCAN LPAREN IDlist RPAREN SEMICOLON','inst',7,'p_inst_func','plintax.py',103), ('inst -> FMT POINT PRINT LPAREN RPAREN SEMICOLON','inst',6,'p_inst_func','plintax.py',104), ('inst -> FMT POINT SCAN LPAREN RPAREN SEMICOLON','inst',6,'p_inst_func','plintax.py',105), ('expressionAR -> expressionAR PLUS expressionAR','expressionAR',3,'p_expressionAR_binop','plintax.py',115), ('expressionAR -> expressionAR MINUS expressionAR','expressionAR',3,'p_expressionAR_binop','plintax.py',116), ('expressionAR -> expressionAR TIMES expressionAR','expressionAR',3,'p_expressionAR_binop','plintax.py',117), ('expressionAR -> expressionAR DIVIDE expressionAR','expressionAR',3,'p_expressionAR_binop','plintax.py',118), ('expressionAR -> ID','expressionAR',1,'p_expressionAR_binop','plintax.py',119), ('expressionAR -> INT','expressionAR',1,'p_expressionAR_int','plintax.py',134), ('expressionAR -> MINUS expressionAR','expressionAR',2,'p_expressionAR_inverse','plintax.py',139), ('expressionAR -> FLOAT','expressionAR',1,'p_expressionAR_float','plintax.py',144), ('expressionAR -> LPAREN expressionAR RPAREN','expressionAR',3,'p_expressionAR_group','plintax.py',149), ('expressionBo -> expressionAR MORE expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',156), ('expressionBo -> expressionAR LESS expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',157), ('expressionBo -> expressionAR MOREEQUAL expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',158), ('expressionBo -> expressionAR LESSEQUAL expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',159), ('expressionBo -> expressionBo NOTEQUAL expressionBo','expressionBo',3,'p_expressionBo_binop','plintax.py',160), ('expressionBo -> expressionAR NOTEQUAL expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',161), ('expressionBo -> expressionBo EQUALSTO expressionBo','expressionBo',3,'p_expressionBo_binop','plintax.py',162), ('expressionBo -> expressionAR EQUALSTO expressionAR','expressionBo',3,'p_expressionBo_binop','plintax.py',163), ('expressionBo -> expressionBo AND expressionBo','expressionBo',3,'p_expressionBo_binop','plintax.py',164), ('expressionBo -> expressionBo OR expressionBo','expressionBo',3,'p_expressionBo_binop','plintax.py',165), ('expressionBo -> NOT expressionBo','expressionBo',2,'p_expressionBo_inverse','plintax.py',186), ('expressionBo -> TRUE','expressionBo',1,'p_expressionBo_int','plintax.py',192), ('expressionBo -> FALSE','expressionBo',1,'p_expressionBo_int','plintax.py',193), ('expressionBo -> LPAREN expressionBo RPAREN','expressionBo',3,'p_expressionBo_group','plintax.py',201), ]

179.076923

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077a4f9fec09dfc396b0a5f4c0029a8eb995111d

20

py

Python

checkov/version.py

smrojas/checkov

d83f5024bf771fa8d795f9a303e603ed107895e1

[ "Apache-2.0" ]

null

checkov/version.py

smrojas/checkov

d83f5024bf771fa8d795f9a303e603ed107895e1

[ "Apache-2.0" ]

null

checkov/version.py

smrojas/checkov

d83f5024bf771fa8d795f9a303e603ed107895e1

[ "Apache-2.0" ]

null

version = '2.0.962'

10

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4

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null

0

5

07b1e5140f06db85db1ad17da65d7d9126e6b75c

164

py

Python

tests/web_platform/CSS2/positioning/test_absolute_replaced_width.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

null

tests/web_platform/CSS2/positioning/test_absolute_replaced_width.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

null

tests/web_platform/CSS2/positioning/test_absolute_replaced_width.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

1

2020-01-16T01:56:41.000Z

from tests.utils import W3CTestCase class TestAbsoluteReplacedWidth(W3CTestCase): vars().update(W3CTestCase.find_tests(__file__, 'absolute-replaced-width-'))

27.333333

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0

5

07b617bf989ca1e103a481abb7a84443c8a9d841

9,839

py

Python

horovod/mxnet/mpi_ops.py

gate2k1/horovod

38e91bee84efbb5b563a4928027a75dc3974633b

[ "Apache-2.0" ]

12

2020-06-04T20:23:49.000Z

2022-03-18T18:22:59.000Z

horovod/mxnet/mpi_ops.py

gate2k1/horovod

38e91bee84efbb5b563a4928027a75dc3974633b

[ "Apache-2.0" ]

4

2020-12-04T21:00:38.000Z

2022-01-22T12:49:30.000Z

horovod/mxnet/mpi_ops.py

gate2k1/horovod

38e91bee84efbb5b563a4928027a75dc3974633b

[ "Apache-2.0" ]

8

2020-07-25T15:25:47.000Z

2022-03-17T02:27:15.000Z

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function # Load all the necessary MXNet C types. import ctypes import os import mxnet as mx from mxnet.base import c_str, check_call, string_types from horovod.common.util import get_ext_suffix from horovod.common.basics import HorovodBasics as _HorovodBasics _basics = _HorovodBasics(__file__, 'mpi_lib') # import basic methods init = _basics.init shutdown = _basics.shutdown size = _basics.size local_size = _basics.local_size rank = _basics.rank local_rank = _basics.local_rank mpi_threads_supported = _basics.mpi_threads_supported mpi_enabled = _basics.mpi_enabled mpi_built = _basics.mpi_built gloo_enabled = _basics.gloo_enabled gloo_built = _basics.gloo_built nccl_built = _basics.nccl_built ddl_built = _basics.ddl_built mlsl_built = _basics.mlsl_built dll_path = os.path.join(os.path.dirname(__file__), 'mpi_lib' + get_ext_suffix()) MPI_MXNET_LIB_CTYPES = ctypes.CDLL(dll_path, ctypes.RTLD_GLOBAL) def allreduce(tensor, average=True, name=None, priority=0): """ A function that performs averaging or summation of the input tensor over all the Horovod processes. The input tensor is not modified. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. name: A name of the reduction operation. priority: The priority of this operation. Higher priority operations are likely to be executed before other operations. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ output = mx.nd.zeros(shape=tensor.shape, ctx=tensor.context, dtype=tensor.dtype) c_in = tensor.handle c_out = output.handle if isinstance(name, string_types): check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allreduce_async( c_in, c_out, c_str(name), ctypes.c_bool(average), ctypes.c_int(priority))) else: check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allreduce_async( c_in, c_out, name, ctypes.c_bool(average), ctypes.c_int(priority))) return output def allreduce_(tensor, average=True, name=None, priority=0): """ A function that performs in-place averaging or summation of the input tensor over all the Horovod processes. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to average and sum. average: A flag indicating whether to compute average or summation, defaults to average. name: A name of the reduction operation. priority: The priority of this operation. Higher priority operations are likely to be executed before other operations. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ c_in = tensor.handle c_out = tensor.handle if isinstance(name, string_types): check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allreduce_async( c_in, c_out, c_str(name), ctypes.c_bool(average), ctypes.c_int(priority))) else: check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allreduce_async( c_in, c_out, name, ctypes.c_bool(average), ctypes.c_int(priority))) return tensor def allgather(tensor, name=None, priority=0): """ A function that concatenates the input tensor with the same input tensor on all other Horovod processes. The input tensor is not modified. The concatenation is done on the first dimension, so the input tensors on the different processes must have the same rank and shape, except for the first dimension, which is allowed to be different. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to allgather. name: A name of the allgather operation. priority: The priority of this operation. Higher priority operations are likely to be executed before other operations. Returns: A tensor of the same type as `tensor`, concatenated on dimension zero across all processes. The shape is identical to the input shape, except for the first dimension, which may be greater and is the sum of all first dimensions of the tensors in different Horovod processes. """ assert(isinstance(tensor, mx.nd.NDArray)) output = mx.nd.zeros(shape=tensor.shape, ctx=tensor.context, dtype=tensor.dtype) c_in = tensor.handle c_out = output.handle if isinstance(name, string_types): check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allgather_async( c_in, c_out, c_str(name), ctypes.c_int(priority))) else: check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_allgather_async( c_in, c_out, name, ctypes.c_int(priority))) return output def broadcast(tensor, root_rank, name=None, priority=0): """ A function that broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The input tensor is not modified. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. priority: The priority of this operation. Higher priority operations are likely to be executed before other operations. Returns: A tensor of the same shape and type as `tensor`, with the value broadcasted from root rank. """ output = mx.nd.zeros(shape=tensor.shape, ctx=tensor.context, dtype=tensor.dtype) c_in = tensor.handle c_out = output.handle if isinstance(name, string_types): check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_broadcast_async( c_in, c_out, c_str(name), ctypes.c_int(root_rank), ctypes.c_int(priority))) else: check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_broadcast_async( c_in, c_out, name, ctypes.c_int(root_rank), ctypes.c_int(priority))) return output def broadcast_(tensor, root_rank, name=None, priority=0): """ A function that broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The operation is performed in-place. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. priority: The priority of this operation. Higher priority operations are likely to be executed before other operations. Returns: A tensor of the same shape and type as `tensor`, with the value broadcasted from root rank. """ c_in = tensor.handle c_out = tensor.handle if isinstance(name, string_types): check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_broadcast_async( c_in, c_out, c_str(name), ctypes.c_int(root_rank), ctypes.c_int(priority))) else: check_call(MPI_MXNET_LIB_CTYPES.horovod_mxnet_broadcast_async( c_in, c_out, name, ctypes.c_int(root_rank), ctypes.c_int(priority))) return tensor

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0.01087

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null

0

1

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null

0

5

07bbe2b1aa9fb904fa03aafaef516ff019879db1

2,111

py

Python

TUNIBrain/University_API_requests.py

woroko/TUNIBot

e53dd66b11c732e2524a5022026156990fc9702a

[ "MIT" ]

null

TUNIBrain/University_API_requests.py

woroko/TUNIBot

e53dd66b11c732e2524a5022026156990fc9702a

[ "MIT" ]

null

TUNIBrain/University_API_requests.py

woroko/TUNIBot

e53dd66b11c732e2524a5022026156990fc9702a

[ "MIT" ]

null

import requests, time #Loads University API-data and saves it into json and xml files. def save_API_data(): #Is there a delay between API calls or not delay = False #Implementation of study modules #URL and API-key for the request url = 'https://opendata.uta.fi:8443/apiman-gateway/UTA/opintojaksot/1.0' headers = {'X-API-Key':'***REMOVED***', 'content-type': 'application/json'} #Makes API-request and saves data into json-file. r = requests.post(url, data="{}", headers=headers) file = open("jsons/uta_course_implementations.json","w", encoding="utf-8") file.write(r.content.decode('utf-8')) file.close() if(delay): time.sleep(30) #Open university studies #URL and API-key for the request url = 'https://opendata.uta.fi:8443/apiman-gateway/UTA/tarjontaavoin/1.0' headers = {'X-API-Key':'***REMOVED***', 'content-type': 'application/json'} #Makes API-request and saves data into xml-file. r = requests.post(url, data="{}", headers=headers) file = open("xmls/available_studies_at_open_university.xml","w") file.write(r.content.decode('utf-8')) file.close() if(delay): time.sleep(30) #Exchange information #URL and API-key for the request url = 'https://opendata.uta.fi:8443/apiman-gateway/UTA/kvkohteet/1.0' headers = {'X-API-Key':'***REMOVED***', 'content-type': 'application/json'} #Makes API-request and saves data into json-file. r = requests.post(url, data="{}", headers=headers) file = open("jsons/exchange_destinations_and_programs.json","w") file.write(r.content.decode('utf-8')) file.close() if(delay): time.sleep(30) #Cross-institutional studies #URL and API-key for the request url = 'https://opendata.uta.fi:8443/apiman-gateway/UTA/tarjontat3/1.0' headers = {'X-API-Key':'***REMOVED***', 'content-type': 'application/json'} #Makes API-request and saves data into xml-file. r = requests.post(url, data="{}", headers=headers) file = open("xmls/cross-institutional_studies.xml","w") file.write(r.content.decode('utf-8')) file.close() save_API_data()

35.183333

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0.028289

0.033946

0.722065

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2,111

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0

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0

0.058824

0

null

0

1

0

null

0

5

07d8501b967166ba513130165b51a34fc3df5995

167

py

Python

skhep/utils/__init__.py

scikit-hep/scikit-hep

b22d876b276ff88730b359309b64f123549c1f07

[ "BSD-3-Clause" ]

150

2016-11-14T14:09:29.000Z

2022-03-18T16:37:03.000Z

skhep/utils/__init__.py

scikit-hep/scikit-hep

b22d876b276ff88730b359309b64f123549c1f07

[ "BSD-3-Clause" ]

123

2017-01-30T10:03:04.000Z

2022-03-31T06:26:09.000Z

skhep/utils/__init__.py

scikit-hep/scikit-hep

b22d876b276ff88730b359309b64f123549c1f07

[ "BSD-3-Clause" ]

41

2017-01-11T11:42:56.000Z

2021-12-06T22:38:32.000Z

# -*- coding: utf-8 -*- # Licensed under a 3-clause BSD style license, see LICENSE. """ Module for miscellaneous and general utilities. """ from .exceptions import *

20.875

59

0.700599

22

167

5.318182

0.954545

0

0.014388

0.167665

167

7

60

23.857143

0.827338

0.766467

0

1

0

true

0

1

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1

0

1

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null

0

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0

null

0

1

0

1

0

1

0

5

07e0219f4b208c5a23a96b4f1b282e05a91e80a0

655

py

Python

src/prefect/environments/execution/__init__.py

tedmiston/prefect

a2cb40c28c942b1d170db42a55bab99598a4dcd6

[ "ECL-2.0", "Apache-2.0" ]

1

2020-05-10T14:32:32.000Z

src/prefect/environments/execution/__init__.py

tedmiston/prefect

a2cb40c28c942b1d170db42a55bab99598a4dcd6

[ "ECL-2.0", "Apache-2.0" ]

null

src/prefect/environments/execution/__init__.py

tedmiston/prefect

a2cb40c28c942b1d170db42a55bab99598a4dcd6

[ "ECL-2.0", "Apache-2.0" ]

null

""" Execution environments encapsulate the logic for where your Flow should execute in Prefect Cloud. Currently, we recommend all users deploy their Flow using the `RemoteEnvironment` configured with the appropriate choice of executor. """ from prefect.environments.execution.base import Environment from prefect.environments.execution.dask import DaskKubernetesEnvironment from prefect.environments.execution.fargate import FargateTaskEnvironment from prefect.environments.execution.k8s import KubernetesJobEnvironment from prefect.environments.execution.local import LocalEnvironment from prefect.environments.execution.remote import RemoteEnvironment

50.384615

101

0.865649

74

655

7.662162

0.567568

0.116402

0.243386

0.338624

0

0.001678

0.090076

655

12

102

54.583333

0.949664

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0

1

0

true

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

07f4c5293621d596f516737c6f52b2659e861b18

90

py

Python

app/reports/__init__.py

pmcheng/reportdiff_flask

5091cdf4d7c1a627ff262578440d67dcd1e47965

[ "Apache-2.0" ]

null

app/reports/__init__.py

pmcheng/reportdiff_flask

5091cdf4d7c1a627ff262578440d67dcd1e47965

[ "Apache-2.0" ]

null

app/reports/__init__.py

pmcheng/reportdiff_flask

5091cdf4d7c1a627ff262578440d67dcd1e47965

[ "Apache-2.0" ]

null

from flask import Blueprint reports=Blueprint('reports',__name__) from . import routes

22.5

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0.788889

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null

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0

1

0

null

0

1

0

1

0

5

ed0aaf2fdce40b2a4f476ea2a2aaabe6a10843af

295

py

Python

jwql/website/apps/jwql/monitor_pages/__init__.py

cracraft/jwql

030c1663bc433465e01ad803e1578a2bc53035f4

[ "BSD-3-Clause" ]

42

2018-10-03T13:38:18.000Z

2022-03-11T12:19:32.000Z

jwql/website/apps/jwql/monitor_pages/__init__.py

cracraft/jwql

030c1663bc433465e01ad803e1578a2bc53035f4

[ "BSD-3-Clause" ]

723

2018-08-29T18:29:49.000Z

2022-03-31T21:09:20.000Z

jwql/website/apps/jwql/monitor_pages/__init__.py

cracraft/jwql

030c1663bc433465e01ad803e1578a2bc53035f4

[ "BSD-3-Clause" ]

30

2018-08-29T18:17:32.000Z

2022-03-10T19:43:39.000Z

from .monitor_bad_pixel_bokeh import BadPixelMonitor from .monitor_bias_bokeh import BiasMonitor from .monitor_dark_bokeh import DarkMonitor from .monitor_filesystem_bokeh import MonitorFilesystem from .monitor_mast_bokeh import MastMonitor from .monitor_readnoise_bokeh import ReadnoiseMonitor

42.142857

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0.898305

37

295

6.810811

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0.261905

0

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295

6

56

49.166667

0.929889

0

1

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true

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1

0

null

0

1

0

1

0

1

0

5

ed37e3d75025f33febb04ea7541c8c8fb224cf35

11,400

py

Python

tests/integration/test_materialize_mysql_database/test.py

edani/ClickHouse

17a8a4e9664fabed5b370b37e148139ba698acf5

[ "Apache-2.0" ]

2

2019-09-05T17:17:27.000Z

2020-09-06T20:27:32.000Z

tests/integration/test_materialize_mysql_database/test.py

edani/ClickHouse

17a8a4e9664fabed5b370b37e148139ba698acf5

[ "Apache-2.0" ]

null

tests/integration/test_materialize_mysql_database/test.py

edani/ClickHouse

17a8a4e9664fabed5b370b37e148139ba698acf5

[ "Apache-2.0" ]

1

2022-03-29T06:54:31.000Z

import os import os.path as p import subprocess import time import pwd import re import pymysql.cursors import pytest from helpers.cluster import ClickHouseCluster, get_docker_compose_path import docker from . import materialize_with_ddl DOCKER_COMPOSE_PATH = get_docker_compose_path() cluster = ClickHouseCluster(__file__) clickhouse_node = cluster.add_instance('node1', user_configs=["configs/users.xml"], with_mysql=False, stay_alive=True) @pytest.fixture(scope="module") def started_cluster(): try: cluster.start() yield cluster finally: cluster.shutdown() class MySQLNodeInstance: def __init__(self, user='root', password='clickhouse', ip_address='127.0.0.1', port=3308, docker_compose=None, project_name=cluster.project_name): self.user = user self.port = port self.ip_address = ip_address self.password = password self.mysql_connection = None # lazy init self.docker_compose = docker_compose self.project_name = project_name def alloc_connection(self): if self.mysql_connection is None: self.mysql_connection = pymysql.connect(user=self.user, password=self.password, host=self.ip_address, port=self.port, autocommit=True) else: if self.mysql_connection.ping(): self.mysql_connection = pymysql.connect(user=self.user, password=self.password, host=self.ip_address, port=self.port, autocommit=True) return self.mysql_connection def query(self, execution_query): with self.alloc_connection().cursor() as cursor: cursor.execute(execution_query) def create_min_priv_user(self, user, password): self.query("CREATE USER '" + user + "'@'%' IDENTIFIED BY '" + password + "'") self.grant_min_priv_for_user(user) def grant_min_priv_for_user(self, user, db='priv_err_db'): self.query("GRANT REPLICATION SLAVE, REPLICATION CLIENT, RELOAD ON *.* TO '" + user + "'@'%'") self.query("GRANT SELECT ON " + db + ".* TO '" + user + "'@'%'") def result(self, execution_query): with self.alloc_connection().cursor() as cursor: result = cursor.execute(execution_query) if result is not None: print(cursor.fetchall()) def query_and_get_data(self, executio_query): with self.alloc_connection().cursor() as cursor: cursor.execute(executio_query) return cursor.fetchall() def close(self): if self.mysql_connection is not None: self.mysql_connection.close() def wait_mysql_to_start(self, timeout=60): start = time.time() while time.time() - start < timeout: try: self.alloc_connection() print("Mysql Started") return except Exception as ex: print("Can't connect to MySQL " + str(ex)) time.sleep(0.5) subprocess.check_call(['docker-compose', 'ps', '--services', 'all']) raise Exception("Cannot wait MySQL container") @pytest.fixture(scope="module") def started_mysql_5_7(): docker_compose = os.path.join(DOCKER_COMPOSE_PATH, 'docker_compose_mysql_5_7_for_materialize_mysql.yml') mysql_node = MySQLNodeInstance('root', 'clickhouse', '127.0.0.1', 3308, docker_compose) try: subprocess.check_call( ['docker-compose', '-p', cluster.project_name, '-f', docker_compose, 'up', '--no-recreate', '-d']) mysql_node.wait_mysql_to_start(120) yield mysql_node finally: mysql_node.close() subprocess.check_call(['docker-compose', '-p', cluster.project_name, '-f', docker_compose, 'down', '--volumes', '--remove-orphans']) @pytest.fixture(scope="module") def started_mysql_8_0(): docker_compose = os.path.join(DOCKER_COMPOSE_PATH, 'docker_compose_mysql_8_0_for_materialize_mysql.yml') mysql_node = MySQLNodeInstance('root', 'clickhouse', '127.0.0.1', 33308, docker_compose) try: subprocess.check_call( ['docker-compose', '-p', cluster.project_name, '-f', docker_compose, 'up', '--no-recreate', '-d']) mysql_node.wait_mysql_to_start(120) yield mysql_node finally: mysql_node.close() subprocess.check_call(['docker-compose', '-p', cluster.project_name, '-f', docker_compose, 'down', '--volumes', '--remove-orphans']) def test_materialize_database_dml_with_mysql_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.dml_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.materialize_mysql_database_with_datetime_and_decimal(clickhouse_node, started_mysql_5_7, "mysql1") def test_materialize_database_dml_with_mysql_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.dml_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.materialize_mysql_database_with_datetime_and_decimal(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_materialize_database_ddl_with_mysql_5_7(started_cluster, started_mysql_5_7): try: materialize_with_ddl.drop_table_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.create_table_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.rename_table_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.alter_add_column_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.alter_drop_column_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") # mysql 5.7 cannot support alter rename column # materialize_with_ddl.alter_rename_column_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.alter_rename_table_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") materialize_with_ddl.alter_modify_column_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") except: print((clickhouse_node.query( "select '\n', thread_id, query_id, arrayStringConcat(arrayMap(x -> concat(demangle(addressToSymbol(x)), '\n ', addressToLine(x)), trace), '\n') AS sym from system.stack_trace format TSVRaw"))) raise def test_materialize_database_ddl_with_mysql_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.drop_table_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.create_table_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.rename_table_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.alter_add_column_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.alter_drop_column_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.alter_rename_table_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.alter_rename_column_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") materialize_with_ddl.alter_modify_column_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_materialize_database_ddl_with_empty_transaction_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.query_event_with_empty_transaction(clickhouse_node, started_mysql_5_7, "mysql1") def test_materialize_database_ddl_with_empty_transaction_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.query_event_with_empty_transaction(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_select_without_columns_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.select_without_columns(clickhouse_node, started_mysql_5_7, "mysql1") def test_select_without_columns_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.select_without_columns(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_insert_with_modify_binlog_checksum_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.insert_with_modify_binlog_checksum(clickhouse_node, started_mysql_5_7, "mysql1") def test_insert_with_modify_binlog_checksum_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.insert_with_modify_binlog_checksum(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_materialize_database_err_sync_user_privs_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.err_sync_user_privs_with_materialize_mysql_database(clickhouse_node, started_mysql_5_7, "mysql1") def test_materialize_database_err_sync_user_privs_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.err_sync_user_privs_with_materialize_mysql_database(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_network_partition_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.network_partition_test(clickhouse_node, started_mysql_5_7, "mysql1") def test_network_partition_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.network_partition_test(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_mysql_kill_sync_thread_restore_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.mysql_kill_sync_thread_restore_test(clickhouse_node, started_mysql_5_7, "mysql1") def test_mysql_kill_sync_thread_restore_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.mysql_kill_sync_thread_restore_test(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_mysql_killed_while_insert_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.mysql_killed_while_insert(clickhouse_node, started_mysql_5_7, "mysql1") def test_mysql_killed_while_insert_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.mysql_killed_while_insert(clickhouse_node, started_mysql_8_0, "mysql8_0") def test_clickhouse_killed_while_insert_5_7(started_cluster, started_mysql_5_7): materialize_with_ddl.clickhouse_killed_while_insert(clickhouse_node, started_mysql_5_7, "mysql1") def test_clickhouse_killed_while_insert_8_0(started_cluster, started_mysql_8_0): materialize_with_ddl.clickhouse_killed_while_insert(clickhouse_node, started_mysql_8_0, "mysql8_0")

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ed50d70748e98ace5f21f18ef65fcce0b964334f

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py

Python

2. Existing Conversion Efficiency (EC), Appendix A/DL_FP_EC.py

rioarya/Land-Conversion_Woody-Biomass-Utilization-Scenarios

0042fd4333212e65735f3643ecb59971d1bd9466

[ "MIT" ]

null

2. Existing Conversion Efficiency (EC), Appendix A/DL_FP_EC.py

rioarya/Land-Conversion_Woody-Biomass-Utilization-Scenarios

0042fd4333212e65735f3643ecb59971d1bd9466

[ "MIT" ]

null

2. Existing Conversion Efficiency (EC), Appendix A/DL_FP_EC.py

rioarya/Land-Conversion_Woody-Biomass-Utilization-Scenarios

0042fd4333212e65735f3643ecb59971d1bd9466

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Dec 13 15:21:55 2019 @author: raryapratama """ #%% #Step (1): Import Python libraries, set land conversion scenarios general parameters import numpy as np import matplotlib.pyplot as plt from scipy.integrate import quad import seaborn as sns import pandas as pd #DL_FP_S1 Scenario ##Set parameters #Parameters for primary forest initAGB = 233 #source: van Beijma et al. (2018) initAGB_min = 233-72 initAGB_max = 233 + 72 #parameters for timber plantation. Source: Khasanah et al. (2015) tf = 201 a = 0.082 b = 2.53 #%% #Step (2_1): C loss from the harvesting/clear cut df1_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') df1_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') df1_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') df1_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') dfE_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') t = range(0,tf,1) c_firewood_energy_S1_Ac7 = df1_Ac7['Firewood_other_energy_use'].values c_firewood_energy_S1_Ac18 = df1_Ac18['Firewood_other_energy_use'].values c_firewood_energy_S1_Tgr40 = df1_Tgr40['Firewood_other_energy_use'].values c_firewood_energy_S1_Tgr60 = df1_Tgr60['Firewood_other_energy_use'].values c_firewood_energy_E_Hbr40 = dfE_Hbr40['Firewood_other_energy_use'].values #%% #Step (2_2): C loss from the harvesting/clear cut as wood pellets dfE = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') c_pellets_Hbr_40y = dfE['Wood_pellets'].values #%% #Step (3): Aboveground biomass (AGB) decomposition #Ac_7y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') tf = 201 t = np.arange(tf) def decomp_S1_Ac_7y(t,remainAGB_S1_Ac_7y): return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Ac_7y #set zero matrix output_decomp_S1_Ac_7y = np.zeros((len(t),len(df['C_remainAGB'].values))) for i,remain_part_S1_Ac_7y in enumerate(df['C_remainAGB'].values): #print(i,remain_part) output_decomp_S1_Ac_7y[i:,i] = decomp_S1_Ac_7y(t[:len(t)-i],remain_part_S1_Ac_7y) print(output_decomp_S1_Ac_7y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Ac_7y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1))) i = 0 while i < tf: subs_matrix_S1_Ac_7y[:,i] = np.diff(output_decomp_S1_Ac_7y[:,i]) i = i + 1 print(subs_matrix_S1_Ac_7y[:,:4]) print(len(subs_matrix_S1_Ac_7y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Ac_7y = subs_matrix_S1_Ac_7y.clip(max=0) print(subs_matrix_S1_Ac_7y[:,:4]) #make the results as absolute values subs_matrix_S1_Ac_7y = abs(subs_matrix_S1_Ac_7y) print(subs_matrix_S1_Ac_7y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Ac_7y = np.zeros((len(t)-200,len(df['C_remainAGB'].values))) print(zero_matrix_S1_Ac_7y) subs_matrix_S1_Ac_7y = np.vstack((zero_matrix_S1_Ac_7y, subs_matrix_S1_Ac_7y)) print(subs_matrix_S1_Ac_7y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Ac_7y = (tf,1) decomp_tot_S1_Ac_7y = np.zeros(matrix_tot_S1_Ac_7y) i = 0 while i < tf: decomp_tot_S1_Ac_7y[:,0] = decomp_tot_S1_Ac_7y[:,0] + subs_matrix_S1_Ac_7y[:,i] i = i + 1 print(decomp_tot_S1_Ac_7y[:,0]) #S1_Ac_18y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') tf = 201 t = np.arange(tf) def decomp_S1_Ac_18y(t,remainAGB_S1_Ac_18y): return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Ac_18y #set zero matrix output_decomp_S1_Ac_18y = np.zeros((len(t),len(df['C_remainAGB'].values))) for i,remain_part_S1_Ac_18y in enumerate(df['C_remainAGB'].values): #print(i,remain_part) output_decomp_S1_Ac_18y[i:,i] = decomp_S1_Ac_18y(t[:len(t)-i],remain_part_S1_Ac_18y) print(output_decomp_S1_Ac_18y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Ac_18y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1))) i = 0 while i < tf: subs_matrix_S1_Ac_18y[:,i] = np.diff(output_decomp_S1_Ac_18y[:,i]) i = i + 1 print(subs_matrix_S1_Ac_18y[:,:4]) print(len(subs_matrix_S1_Ac_18y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Ac_18y = subs_matrix_S1_Ac_18y.clip(max=0) print(subs_matrix_S1_Ac_18y[:,:4]) #make the results as absolute values subs_matrix_S1_Ac_18y = abs(subs_matrix_S1_Ac_18y) print(subs_matrix_S1_Ac_18y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Ac_18y = np.zeros((len(t)-200,len(df['C_remainAGB'].values))) print(zero_matrix_S1_Ac_18y) subs_matrix_S1_Ac_18y = np.vstack((zero_matrix_S1_Ac_18y, subs_matrix_S1_Ac_18y)) print(subs_matrix_S1_Ac_18y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Ac_18y = (tf,1) decomp_tot_S1_Ac_18y = np.zeros(matrix_tot_S1_Ac_18y) i = 0 while i < tf: decomp_tot_S1_Ac_18y[:,0] = decomp_tot_S1_Ac_18y[:,0] + subs_matrix_S1_Ac_18y[:,i] i = i + 1 print(decomp_tot_S1_Ac_18y[:,0]) #S1_Tgr_40y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') tf = 201 t = np.arange(tf) def decomp_S1_Tgr_40y(t,remainAGB_S1_Tgr_40y): return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Tgr_40y #set zero matrix output_decomp_S1_Tgr_40y = np.zeros((len(t),len(df['C_remainAGB'].values))) for i,remain_part_S1_Tgr_40y in enumerate(df['C_remainAGB'].values): #print(i,remain_part) output_decomp_S1_Tgr_40y[i:,i] = decomp_S1_Tgr_40y(t[:len(t)-i],remain_part_S1_Tgr_40y) print(output_decomp_S1_Tgr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Tgr_40y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1))) i = 0 while i < tf: subs_matrix_S1_Tgr_40y[:,i] = np.diff(output_decomp_S1_Tgr_40y[:,i]) i = i + 1 print(subs_matrix_S1_Tgr_40y[:,:4]) print(len(subs_matrix_S1_Tgr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Tgr_40y = subs_matrix_S1_Tgr_40y.clip(max=0) print(subs_matrix_S1_Tgr_40y[:,:4]) #make the results as absolute values subs_matrix_S1_Tgr_40y = abs(subs_matrix_S1_Tgr_40y) print(subs_matrix_S1_Tgr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Tgr_40y = np.zeros((len(t)-200,len(df['C_remainAGB'].values))) print(zero_matrix_S1_Tgr_40y) subs_matrix_S1_Tgr_40y = np.vstack((zero_matrix_S1_Tgr_40y, subs_matrix_S1_Tgr_40y)) print(subs_matrix_S1_Tgr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Tgr_40y = (tf,1) decomp_tot_S1_Tgr_40y = np.zeros(matrix_tot_S1_Tgr_40y) i = 0 while i < tf: decomp_tot_S1_Tgr_40y[:,0] = decomp_tot_S1_Tgr_40y[:,0] + subs_matrix_S1_Tgr_40y[:,i] i = i + 1 print(decomp_tot_S1_Tgr_40y[:,0]) #S1_Tgr_60y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') tf = 201 t = np.arange(tf) def decomp_S1_Tgr_60y(t,remainAGB_S1_Tgr_60y): return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Tgr_60y #set zero matrix output_decomp_S1_Tgr_60y = np.zeros((len(t),len(df['C_remainAGB'].values))) for i,remain_part_S1_Tgr_60y in enumerate(df['C_remainAGB'].values): #print(i,remain_part) output_decomp_S1_Tgr_60y[i:,i] = decomp_S1_Tgr_60y(t[:len(t)-i],remain_part_S1_Tgr_60y) print(output_decomp_S1_Tgr_60y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Tgr_60y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1))) i = 0 while i < tf: subs_matrix_S1_Tgr_60y[:,i] = np.diff(output_decomp_S1_Tgr_60y[:,i]) i = i + 1 print(subs_matrix_S1_Tgr_60y[:,:4]) print(len(subs_matrix_S1_Tgr_60y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Tgr_60y = subs_matrix_S1_Tgr_60y.clip(max=0) print(subs_matrix_S1_Tgr_60y[:,:4]) #make the results as absolute values subs_matrix_S1_Tgr_60y = abs(subs_matrix_S1_Tgr_60y) print(subs_matrix_S1_Tgr_60y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Tgr_60y = np.zeros((len(t)-200,len(df['C_remainAGB'].values))) print(zero_matrix_S1_Tgr_60y) subs_matrix_S1_Tgr_60y = np.vstack((zero_matrix_S1_Tgr_60y, subs_matrix_S1_Tgr_60y)) print(subs_matrix_S1_Tgr_60y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Tgr_60y = (tf,1) decomp_tot_S1_Tgr_60y = np.zeros(matrix_tot_S1_Tgr_60y) i = 0 while i < tf: decomp_tot_S1_Tgr_60y[:,0] = decomp_tot_S1_Tgr_60y[:,0] + subs_matrix_S1_Tgr_60y[:,i] i = i + 1 print(decomp_tot_S1_Tgr_60y[:,0]) #E df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') tf = 201 t = np.arange(tf) def decomp_E_Hbr_40y(t,remainAGB_E_Hbr_40y): return (1-(1-np.exp(-a*t))**b)*remainAGB_E_Hbr_40y #set zero matrix output_decomp_E_Hbr_40y = np.zeros((len(t),len(df['C_remainAGB'].values))) for i,remain_part_E_Hbr_40y in enumerate(df['C_remainAGB'].values): #print(i,remain_part) output_decomp_E_Hbr_40y[i:,i] = decomp_E_Hbr_40y(t[:len(t)-i],remain_part_E_Hbr_40y) print(output_decomp_E_Hbr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_E_Hbr_40y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1))) i = 0 while i < tf: subs_matrix_E_Hbr_40y[:,i] = np.diff(output_decomp_E_Hbr_40y[:,i]) i = i + 1 print(subs_matrix_E_Hbr_40y[:,:4]) print(len(subs_matrix_E_Hbr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_E_Hbr_40y = subs_matrix_E_Hbr_40y.clip(max=0) print(subs_matrix_E_Hbr_40y[:,:4]) #make the results as absolute values subs_matrix_E_Hbr_40y = abs(subs_matrix_E_Hbr_40y) print(subs_matrix_E_Hbr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_E_Hbr_40y = np.zeros((len(t)-200,len(df['C_remainAGB'].values))) print(zero_matrix_E_Hbr_40y) subs_matrix_E_Hbr_40y = np.vstack((zero_matrix_E_Hbr_40y, subs_matrix_E_Hbr_40y)) print(subs_matrix_E_Hbr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_E_Hbr_40y = (tf,1) decomp_tot_E_Hbr_40y = np.zeros(matrix_tot_E_Hbr_40y) i = 0 while i < tf: decomp_tot_E_Hbr_40y[:,0] = decomp_tot_E_Hbr_40y[:,0] + subs_matrix_E_Hbr_40y[:,i] i = i + 1 print(decomp_tot_E_Hbr_40y[:,0]) #plotting t = np.arange(0,tf) plt.plot(t,decomp_tot_S1_Ac_7y,label='Ac_7y') plt.plot(t,decomp_tot_S1_Ac_18y,label='Ac_18y') plt.plot(t,decomp_tot_S1_Tgr_40y,label='Tgr_40y') plt.plot(t,decomp_tot_S1_Tgr_60y,label='Tgr_60y') plt.plot(t,decomp_tot_E_Hbr_40y,label='E_Hbr_40y') plt.xlim(0,200) plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) plt.show() #%% #Step (4): Dynamic stock model of in-use wood materials from dynamic_stock_model import DynamicStockModel df1_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') df1_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') df1_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') df1_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') dfE_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') #product lifetime #paper P = 4 #furniture F = 20 #building materials B = 35 TestDSM1_Ac7 = DynamicStockModel(t = df1_Ac7['Year'].values, i = df1_Ac7['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([P]), 'StdDev': np.array([0.3*P])}) TestDSM1_Ac18 = DynamicStockModel(t = df1_Ac18['Year'].values, i = df1_Ac18['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([F]), 'StdDev': np.array([0.3*F])}) TestDSM1_Tgr40 = DynamicStockModel(t = df1_Tgr40['Year'].values, i = df1_Tgr40['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])}) TestDSM1_Tgr60 = DynamicStockModel(t = df1_Tgr60['Year'].values, i = df1_Tgr60['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])}) TestDSME_Hbr40 = DynamicStockModel(t = dfE_Hbr40['Year'].values, i = dfE_Hbr40['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])}) CheckStr1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.dimension_check() CheckStr1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.dimension_check() CheckStr1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.dimension_check() CheckStr1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.dimension_check() CheckStrE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.dimension_check() Stock_by_cohort1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_s_c_inflow_driven() Stock_by_cohort1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_s_c_inflow_driven() Stock_by_cohort1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_s_c_inflow_driven() Stock_by_cohort1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_s_c_inflow_driven() Stock_by_cohortE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_s_c_inflow_driven() S1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_stock_total() S1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_stock_total() S1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_stock_total() S1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_stock_total() SE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_stock_total() O_C1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_o_c_from_s_c() O_C1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_o_c_from_s_c() O_C1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_o_c_from_s_c() O_C1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_o_c_from_s_c() O_CE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_o_c_from_s_c() O1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_outflow_total() O1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_outflow_total() O1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_outflow_total() O1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_outflow_total() OE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_outflow_total() DS1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_stock_change() DS1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_stock_change() DS1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_stock_change() DS1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_stock_change() DSE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_stock_change() Bal1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.check_stock_balance() Bal1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.check_stock_balance() Bal1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.check_stock_balance() Bal1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.check_stock_balance() BalE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.check_stock_balance() #print output flow print(TestDSM1_Ac7.o) print(TestDSM1_Ac18.o) print(TestDSM1_Tgr40.o) print(TestDSM1_Tgr60.o) print(TestDSME_Hbr40.o) #%% #Step (5): Biomass growth ## one-year gap between rotation cycle # A. crassicarpa (Source: Anitha et al., 2015; Adiriono, 2009). Code: Ac tf_Ac_7y = 8 tf_Ac_18y = 19 A1 = range(1,tf_Ac_7y,1) A2 = range(1,tf_Ac_18y,1) #calculate the biomass and carbon content of A. crassicarpa over time (7y) def Y_Ac_7y(A1): return 44/12*1000*np.exp(4.503-(2.559/A1)) output_Y_Ac_7y = np.array([Y_Ac_7y(A1i) for A1i in A1]) print(output_Y_Ac_7y) #insert 0 value to the first element of the output result output_Y_Ac_7y = np.insert(output_Y_Ac_7y,0,0) print(output_Y_Ac_7y) #calculate the biomass and carbon content of A. crassicarpa over time (18y) def Y_Ac_18y(A2): return 44/12*1000*np.exp(4.503-(2.559/A2)) output_Y_Ac_18y = np.array([Y_Ac_18y(A2i) for A2i in A2]) print(output_Y_Ac_18y) #insert 0 value to the first element of the output result output_Y_Ac_18y = np.insert(output_Y_Ac_18y,0,0) print(output_Y_Ac_18y) ##26 times 8-year cycle (+1 year gap after the FP harvest)of new AGB of A. crassicarpa (7y), zero year gap between the cycle counter_7y = range(0,26,1) y_Ac_7y = [] for i in counter_7y: y_Ac_7y.append(output_Y_Ac_7y) flat_list_Ac_7y = [] for sublist in y_Ac_7y: for item in sublist: flat_list_Ac_7y.append(item) #the length of the list is now 208, so we remove the last 7 elements of the list to make the len=tf flat_list_Ac_7y = flat_list_Ac_7y[:len(flat_list_Ac_7y)-7] print(len(flat_list_Ac_7y)) ##11 times 19-year cycle (+1 year gap after the FP harvest) of new AGB of A. crassicarpa (18y), zero year gap between the cycle counter_18y = range(0,11,1) y_Ac_18y = [] for i in counter_18y: y_Ac_18y.append(output_Y_Ac_18y) flat_list_Ac_18y = [] for sublist in y_Ac_18y: for item in sublist: flat_list_Ac_18y.append(item) #the length of the list is now 209, so we remove the last 8 elements of the list to make the len=tf flat_list_Ac_18y = flat_list_Ac_18y[:len(flat_list_Ac_18y)-8] #####Check the flat list length for Hbr ## T. grandis (Source: Anitha et al., 2015; Adiriono, 2009). Code: Tgr tf_Tgr_40y = 41 tf_Tgr_60y = 61 T1 = range(0,tf_Tgr_40y,1) T2 = range(0,tf_Tgr_60y,1) #calculate the biomass and carbon content of T. grandis over time (40y) def Y_Tgr_40y(T1): return 44/12*1000*2.114*(T1**0.941) output_Y_Tgr_40y = np.array([Y_Tgr_40y(T1i) for T1i in T1]) print(output_Y_Tgr_40y) #calculate the biomass and carbon content of T. grandis over time (60y) def Y_Tgr_60y(T2): return 44/12*1000*2.114*(T2**0.941) output_Y_Tgr_60y = np.array([Y_Tgr_60y(T2i) for T2i in T2]) print(output_Y_Tgr_60y) ##5 times 41-year cycle of new AGB of T. grandis (40y), zero year gap between the cycle counter_40y = range(0,5,1) y_Tgr_40y = [] for i in counter_40y: y_Tgr_40y.append(output_Y_Tgr_40y) flat_list_Tgr_40y = [] for sublist in y_Tgr_40y: for item in sublist: flat_list_Tgr_40y.append(item) #the length of the list is now 205, so we remove the last 4 elements of the list to make the len=tf flat_list_Tgr_40y = flat_list_Tgr_40y[:len(flat_list_Tgr_40y)-4] ##4 times 60-year cycle of new AGB of T. grandis (60y), zero year gap between the cycle counter_60y = range(0,4,1) y_Tgr_60y = [] for i in counter_60y: y_Tgr_60y.append(output_Y_Tgr_60y) flat_list_Tgr_60y = [] for sublist in y_Tgr_60y: for item in sublist: flat_list_Tgr_60y.append(item) #the length of the list is now 244, so we remove the last 43 elements of the list to make the len=tf flat_list_Tgr_60y = flat_list_Tgr_60y[:len(flat_list_Tgr_60y)-43] ## H. brasiliensis (Source: Guillaume et al., 2018). Code: Hbr tf_Hbr_40y = 41 H1 = range(0,tf_Hbr_40y,1) #calculate the biomass and carbon content of H. brasiliensis over time (40y) def Y_Hbr_40y(H1): return 44/12*1000*1.55*H1 output_Y_Hbr_40y = np.array([Y_Hbr_40y(H1i) for H1i in H1]) print(output_Y_Hbr_40y) ##5 times 40-year cycle of new AGB of H. brasiliensis (40y), zero year gap between the cycle counter_40y = range(0,5,1) y_Hbr_40y = [] for i in counter_40y: y_Hbr_40y.append(output_Y_Hbr_40y) flat_list_Hbr_40y = [] for sublist in y_Hbr_40y: for item in sublist: flat_list_Hbr_40y.append(item) #the length of the list is now 205, so we remove the last 4 elements of the list to make the len=tf flat_list_Hbr_40y = flat_list_Hbr_40y[:len(flat_list_Hbr_40y)-4] #plotting t = range (0,tf,1) plt.xlim([0, 200]) plt.plot(t, flat_list_Ac_7y, color='lightcoral') plt.plot(t, flat_list_Ac_18y, color='deeppink') plt.plot(t, flat_list_Hbr_40y, color='darkviolet') plt.plot(t, flat_list_Tgr_40y) plt.plot(t, flat_list_Tgr_60y, color='seagreen') #plt.fill_between(t, flat_list_nucleus, flat_list_plasma, color='darkseagreen', alpha='0.4') plt.xlabel('Time (year)') plt.ylabel('AGB (tC/ha)') plt.show() ##Yearly sequestration ## A. crassicarpa (7y) #find the yearly sequestration by calculating the differences between elements in list 'flat_list_Ac_7y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) flat_list_Ac_7y = [p - q for q, p in zip(flat_list_Ac_7y, flat_list_Ac_7y[1:])] #since there is no sequestration between the replanting year (e.g., year 7 to 8), we have to replace negative numbers in 'flat_list_Ac_7y' with 0 values flat_list_Ac_7y = [0 if i < 0 else i for i in flat_list_Ac_7y] #insert 0 value to the list as the first element, because there is no sequestration in year 0 var = 0 flat_list_Ac_7y.insert(0,var) #make 'flat_list_Ac_7y' elements negative numbers to denote sequestration flat_list_Ac_7y = [ -x for x in flat_list_Ac_7y] print(flat_list_Ac_7y) ##A. crassicarpa (18y) #find the yearly sequestration by calculating the differences between elements in list 'flat_list_Ac_18y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) flat_list_Ac_18y = [t - u for u, t in zip(flat_list_Ac_18y, flat_list_Ac_18y[1:])] #since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Ac_18y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) flat_list_Ac_18y = [0 if i < 0 else i for i in flat_list_Ac_18y] #insert 0 value to the list as the first element, because there is no sequestration in year 0 var = 0 flat_list_Ac_18y.insert(0,var) #make 'flat_list_plasma' elements negative numbers to denote sequestration flat_list_Ac_18y = [ -x for x in flat_list_Ac_18y] print(flat_list_Ac_18y) ##T. grandis (40y) #find the yearly sequestration by calculating the differences between elements in list 'flat_list_Tgr_40y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) flat_list_Tgr_40y = [b - c for c, b in zip(flat_list_Tgr_40y, flat_list_Tgr_40y[1:])] #since there is no sequestration between the replanting year (e.g., year 40 to 41), we have to replace negative numbers in 'flat_list_Tgr_40y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) flat_list_Tgr_40y = [0 if i < 0 else i for i in flat_list_Tgr_40y] #insert 0 value to the list as the first element, because there is no sequestration in year 0 var = 0 flat_list_Tgr_40y.insert(0,var) #make 'flat_list_plasma' elements negative numbers to denote sequestration flat_list_Tgr_40y = [-x for x in flat_list_Tgr_40y] print(flat_list_Tgr_40y) ##T. grandis (60y) #find the yearly sequestration by calculating the differences between elements in list 'flat_list_Tgr_60y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) flat_list_Tgr_60y = [k - l for l, k in zip(flat_list_Tgr_60y, flat_list_Tgr_60y[1:])] #since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Tgr_60y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) flat_list_Tgr_60y = [0 if i < 0 else i for i in flat_list_Tgr_60y] #insert 0 value to the list as the first element, because there is no sequestration in year 0 var = 0 flat_list_Tgr_60y.insert(0,var) #make 'flat_list_plasma' elements negative numbers to denote sequestration flat_list_Tgr_60y = [ -x for x in flat_list_Tgr_60y] print(flat_list_Tgr_60y) ##H. brasiliensis (40y) #find the yearly sequestration by calculating the differences between elements in list 'flat_list_Hbr_40y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) flat_list_Hbr_40y = [c - d for d, c in zip(flat_list_Hbr_40y, flat_list_Hbr_40y[1:])] #since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Hbr_40y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) flat_list_Hbr_40y = [0 if i < 0 else i for i in flat_list_Hbr_40y] #insert 0 value to the list as the first element, because there is no sequestration in year 0 var = 0 flat_list_Hbr_40y.insert(0,var) #make 'flat_list_plasma' elements negative numbers to denote sequestration flat_list_Hbr_40y = [ -x for x in flat_list_Hbr_40y] print(flat_list_Hbr_40y) #%% #Step (6): post-harvest processing of wood #post-harvest wood processing df1_Ac_7y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') df1_Ac_18y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') df1_Tgr_40y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') dfl_Tgr_60y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') dfE_Hbr_40y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') t = range(0,tf,1) PH_Emissions_HWP1_Ac_7y = df1_Ac_7y['PH_Emissions_HWP'].values PH_Emissions_HWP1_Ac_18y = df1_Ac_18y['PH_Emissions_HWP'].values PH_Emissions_HWP1_Tgr_40y = df1_Tgr_40y['PH_Emissions_HWP'].values PH_Emissions_HWP1_Tgr_60y = dfl_Tgr_60y['PH_Emissions_HWP'].values PH_Emissions_HWPE_Hbr_40y = dfE_Hbr_40y ['PH_Emissions_HWP'].values #%% #Step (7_1): landfill gas decomposition (CH4) #CH4 decomposition hl = 20 #half-live k = (np.log(2))/hl #S1_Ac_7y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') tf = 201 t = np.arange(tf) def decomp_CH4_S1_Ac_7y(t,remainAGB_CH4_S1_Ac_7y): return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Ac_7y #set zero matrix output_decomp_CH4_S1_Ac_7y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values))) for i,remain_part_CH4_S1_Ac_7y in enumerate(df['Landfill_decomp_CH4'].values): #print(i,remain_part) output_decomp_CH4_S1_Ac_7y[i:,i] = decomp_CH4_S1_Ac_7y(t[:len(t)-i],remain_part_CH4_S1_Ac_7y) print(output_decomp_CH4_S1_Ac_7y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_CH4_S1_Ac_7y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1))) i = 0 while i < tf: subs_matrix_CH4_S1_Ac_7y[:,i] = np.diff(output_decomp_CH4_S1_Ac_7y[:,i]) i = i + 1 print(subs_matrix_CH4_S1_Ac_7y[:,:4]) print(len(subs_matrix_CH4_S1_Ac_7y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_CH4_S1_Ac_7y = subs_matrix_CH4_S1_Ac_7y.clip(max=0) print(subs_matrix_CH4_S1_Ac_7y[:,:4]) #make the results as absolute values subs_matrix_CH4_S1_Ac_7y = abs(subs_matrix_CH4_S1_Ac_7y) print(subs_matrix_CH4_S1_Ac_7y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_CH4_S1_Ac_7y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values))) print(zero_matrix_CH4_S1_Ac_7y) subs_matrix_CH4_S1_Ac_7y = np.vstack((zero_matrix_CH4_S1_Ac_7y, subs_matrix_CH4_S1_Ac_7y)) print(subs_matrix_CH4_S1_Ac_7y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_CH4_S1_Ac_7y = (tf,1) decomp_tot_CH4_S1_Ac_7y = np.zeros(matrix_tot_CH4_S1_Ac_7y) i = 0 while i < tf: decomp_tot_CH4_S1_Ac_7y[:,0] = decomp_tot_CH4_S1_Ac_7y[:,0] + subs_matrix_CH4_S1_Ac_7y[:,i] i = i + 1 print(decomp_tot_CH4_S1_Ac_7y[:,0]) #S1_Ac_18y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') tf = 201 t = np.arange(tf) def decomp_CH4_S1_Ac_18y(t,remainAGB_CH4_S1_Ac_18y): return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Ac_18y #set zero matrix output_decomp_CH4_S1_Ac_18y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values))) for i,remain_part_CH4_S1_Ac_18y in enumerate(df['Landfill_decomp_CH4'].values): #print(i,remain_part) output_decomp_CH4_S1_Ac_18y[i:,i] = decomp_CH4_S1_Ac_18y(t[:len(t)-i],remain_part_CH4_S1_Ac_18y) print(output_decomp_CH4_S1_Ac_18y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_CH4_S1_Ac_18y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1))) i = 0 while i < tf: subs_matrix_CH4_S1_Ac_18y[:,i] = np.diff(output_decomp_CH4_S1_Ac_18y[:,i]) i = i + 1 print(subs_matrix_CH4_S1_Ac_18y[:,:4]) print(len(subs_matrix_CH4_S1_Ac_18y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_CH4_S1_Ac_18y = subs_matrix_CH4_S1_Ac_18y.clip(max=0) print(subs_matrix_CH4_S1_Ac_18y[:,:4]) #make the results as absolute values subs_matrix_CH4_S1_Ac_18y = abs(subs_matrix_CH4_S1_Ac_18y) print(subs_matrix_CH4_S1_Ac_18y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_CH4_S1_Ac_18y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values))) print(zero_matrix_CH4_S1_Ac_18y) subs_matrix_CH4_S1_Ac_18y = np.vstack((zero_matrix_CH4_S1_Ac_18y, subs_matrix_CH4_S1_Ac_18y)) print(subs_matrix_CH4_S1_Ac_18y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_CH4_S1_Ac_18y = (tf,1) decomp_tot_CH4_S1_Ac_18y = np.zeros(matrix_tot_CH4_S1_Ac_18y) i = 0 while i < tf: decomp_tot_CH4_S1_Ac_18y[:,0] = decomp_tot_CH4_S1_Ac_18y[:,0] + subs_matrix_CH4_S1_Ac_18y[:,i] i = i + 1 print(decomp_tot_CH4_S1_Ac_18y[:,0]) #S1_Tgr_40y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') tf = 201 t = np.arange(tf) def decomp_CH4_S1_Tgr_40y(t,remainAGB_CH4_S1_Tgr_40y): return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Tgr_40y #set zero matrix output_decomp_CH4_S1_Tgr_40y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values))) for i,remain_part_CH4_S1_Tgr_40y in enumerate(df['Landfill_decomp_CH4'].values): #print(i,remain_part) output_decomp_CH4_S1_Tgr_40y[i:,i] = decomp_CH4_S1_Tgr_40y(t[:len(t)-i],remain_part_CH4_S1_Tgr_40y) print(output_decomp_CH4_S1_Tgr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_CH4_S1_Tgr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1))) i = 0 while i < tf: subs_matrix_CH4_S1_Tgr_40y[:,i] = np.diff(output_decomp_CH4_S1_Tgr_40y[:,i]) i = i + 1 print(subs_matrix_CH4_S1_Tgr_40y[:,:4]) print(len(subs_matrix_CH4_S1_Tgr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_CH4_S1_Tgr_40y = subs_matrix_CH4_S1_Tgr_40y.clip(max=0) print(subs_matrix_CH4_S1_Tgr_40y[:,:4]) #make the results as absolute values subs_matrix_CH4_S1_Tgr_40y = abs(subs_matrix_CH4_S1_Tgr_40y) print(subs_matrix_CH4_S1_Tgr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_CH4_S1_Tgr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values))) print(zero_matrix_CH4_S1_Tgr_40y) subs_matrix_CH4_S1_Tgr_40y = np.vstack((zero_matrix_CH4_S1_Tgr_40y, subs_matrix_CH4_S1_Tgr_40y)) print(subs_matrix_CH4_S1_Tgr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_CH4_S1_Tgr_40y = (tf,1) decomp_tot_CH4_S1_Tgr_40y = np.zeros(matrix_tot_CH4_S1_Tgr_40y) i = 0 while i < tf: decomp_tot_CH4_S1_Tgr_40y[:,0] = decomp_tot_CH4_S1_Tgr_40y[:,0] + subs_matrix_CH4_S1_Tgr_40y[:,i] i = i + 1 print(decomp_tot_CH4_S1_Tgr_40y[:,0]) #S1_Tgr_60y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') tf = 201 t = np.arange(tf) def decomp_CH4_S1_Tgr_60y(t,remainAGB_CH4_S1_Tgr_60y): return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Tgr_60y #set zero matrix output_decomp_CH4_S1_Tgr_60y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values))) for i,remain_part_CH4_S1_Tgr_60y in enumerate(df['Landfill_decomp_CH4'].values): #print(i,remain_part) output_decomp_CH4_S1_Tgr_60y[i:,i] = decomp_CH4_S1_Tgr_60y(t[:len(t)-i],remain_part_CH4_S1_Tgr_60y) print(output_decomp_CH4_S1_Tgr_60y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_CH4_S1_Tgr_60y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1))) i = 0 while i < tf: subs_matrix_CH4_S1_Tgr_60y[:,i] = np.diff(output_decomp_CH4_S1_Tgr_60y[:,i]) i = i + 1 print(subs_matrix_CH4_S1_Tgr_60y[:,:4]) print(len(subs_matrix_CH4_S1_Tgr_60y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_CH4_S1_Tgr_60y = subs_matrix_CH4_S1_Tgr_60y.clip(max=0) print(subs_matrix_CH4_S1_Tgr_60y[:,:4]) #make the results as absolute values subs_matrix_CH4_S1_Tgr_60y = abs(subs_matrix_CH4_S1_Tgr_60y) print(subs_matrix_CH4_S1_Tgr_60y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_CH4_S1_Tgr_60y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values))) print(zero_matrix_CH4_S1_Tgr_60y) subs_matrix_CH4_S1_Tgr_60y = np.vstack((zero_matrix_CH4_S1_Tgr_60y, subs_matrix_CH4_S1_Tgr_60y)) print(subs_matrix_CH4_S1_Tgr_60y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_CH4_S1_Tgr_60y = (tf,1) decomp_tot_CH4_S1_Tgr_60y = np.zeros(matrix_tot_CH4_S1_Tgr_60y) i = 0 while i < tf: decomp_tot_CH4_S1_Tgr_60y[:,0] = decomp_tot_CH4_S1_Tgr_60y[:,0] + subs_matrix_CH4_S1_Tgr_60y[:,i] i = i + 1 print(decomp_tot_CH4_S1_Tgr_60y[:,0]) #E df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') tf = 201 t = np.arange(tf) def decomp_CH4_E_Hbr_40y(t,remainAGB_CH4_E_Hbr_40y): return (1-(1-np.exp(-k*t)))*remainAGB_CH4_E_Hbr_40y #set zero matrix output_decomp_CH4_E_Hbr_40y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values))) for i,remain_part_CH4_E_Hbr_40y in enumerate(df['Landfill_decomp_CH4'].values): #print(i,remain_part) output_decomp_CH4_E_Hbr_40y[i:,i] = decomp_CH4_E_Hbr_40y(t[:len(t)-i],remain_part_CH4_E_Hbr_40y) print(output_decomp_CH4_E_Hbr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_CH4_E_Hbr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1))) i = 0 while i < tf: subs_matrix_CH4_E_Hbr_40y[:,i] = np.diff(output_decomp_CH4_E_Hbr_40y[:,i]) i = i + 1 print(subs_matrix_CH4_E_Hbr_40y[:,:4]) print(len(subs_matrix_CH4_E_Hbr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_CH4_E_Hbr_40y = subs_matrix_CH4_E_Hbr_40y.clip(max=0) print(subs_matrix_CH4_E_Hbr_40y[:,:4]) #make the results as absolute values subs_matrix_CH4_E_Hbr_40y = abs(subs_matrix_CH4_E_Hbr_40y) print(subs_matrix_CH4_E_Hbr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_CH4_E_Hbr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values))) print(zero_matrix_CH4_E_Hbr_40y) subs_matrix_CH4_E_Hbr_40y = np.vstack((zero_matrix_CH4_E_Hbr_40y, subs_matrix_CH4_E_Hbr_40y)) print(subs_matrix_CH4_E_Hbr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_CH4_E_Hbr_40y = (tf,1) decomp_tot_CH4_E_Hbr_40y = np.zeros(matrix_tot_CH4_E_Hbr_40y) i = 0 while i < tf: decomp_tot_CH4_E_Hbr_40y[:,0] = decomp_tot_CH4_E_Hbr_40y[:,0] + subs_matrix_CH4_E_Hbr_40y[:,i] i = i + 1 print(decomp_tot_CH4_E_Hbr_40y[:,0]) #plotting t = np.arange(0,tf) plt.plot(t,decomp_tot_CH4_S1_Ac_7y,label='Ac_7y') plt.plot(t,decomp_tot_CH4_S1_Ac_18y,label='Ac_18y') plt.plot(t,decomp_tot_CH4_S1_Tgr_40y,label='Tgr_40y') plt.plot(t,decomp_tot_CH4_S1_Tgr_60y,label='Tgr_60y') plt.plot(t,decomp_tot_CH4_E_Hbr_40y,label='E_Hbr_40y') plt.xlim(0,200) plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) plt.show() #%% #Step (7_2): landfill gas decomposition (CO2) #CO2 decomposition hl = 20 #half-live k = (np.log(2))/hl #S1_Ac_7y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') tf = 201 t = np.arange(tf) def decomp_S1_Ac_7y(t,remainAGB_S1_Ac_7y): return (1-(1-np.exp(-k*t)))*remainAGB_S1_Ac_7y #set zero matrix output_decomp_S1_Ac_7y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values))) for i,remain_part_S1_Ac_7y in enumerate(df['Landfill_decomp_CO2'].values): #print(i,remain_part) output_decomp_S1_Ac_7y[i:,i] = decomp_S1_Ac_7y(t[:len(t)-i],remain_part_S1_Ac_7y) print(output_decomp_S1_Ac_7y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Ac_7y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1))) i = 0 while i < tf: subs_matrix_S1_Ac_7y[:,i] = np.diff(output_decomp_S1_Ac_7y[:,i]) i = i + 1 print(subs_matrix_S1_Ac_7y[:,:4]) print(len(subs_matrix_S1_Ac_7y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Ac_7y = subs_matrix_S1_Ac_7y.clip(max=0) print(subs_matrix_S1_Ac_7y[:,:4]) #make the results as absolute values subs_matrix_S1_Ac_7y = abs(subs_matrix_S1_Ac_7y) print(subs_matrix_S1_Ac_7y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Ac_7y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values))) print(zero_matrix_S1_Ac_7y) subs_matrix_S1_Ac_7y = np.vstack((zero_matrix_S1_Ac_7y, subs_matrix_S1_Ac_7y)) print(subs_matrix_S1_Ac_7y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Ac_7y = (tf,1) decomp_tot_CO2_S1_Ac_7y = np.zeros(matrix_tot_S1_Ac_7y) i = 0 while i < tf: decomp_tot_CO2_S1_Ac_7y[:,0] = decomp_tot_CO2_S1_Ac_7y[:,0] + subs_matrix_S1_Ac_7y[:,i] i = i + 1 print(decomp_tot_CO2_S1_Ac_7y[:,0]) #S1_Ac_18y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') tf = 201 t = np.arange(tf) def decomp_S1_Ac_18y(t,remainAGB_S1_Ac_18y): return (1-(1-np.exp(-k*t)))*remainAGB_S1_Ac_18y #set zero matrix output_decomp_S1_Ac_18y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values))) for i,remain_part_S1_Ac_18y in enumerate(df['Landfill_decomp_CO2'].values): #print(i,remain_part) output_decomp_S1_Ac_18y[i:,i] = decomp_S1_Ac_18y(t[:len(t)-i],remain_part_S1_Ac_18y) print(output_decomp_S1_Ac_18y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Ac_18y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1))) i = 0 while i < tf: subs_matrix_S1_Ac_18y[:,i] = np.diff(output_decomp_S1_Ac_18y[:,i]) i = i + 1 print(subs_matrix_S1_Ac_18y[:,:4]) print(len(subs_matrix_S1_Ac_18y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Ac_18y = subs_matrix_S1_Ac_18y.clip(max=0) print(subs_matrix_S1_Ac_18y[:,:4]) #make the results as absolute values subs_matrix_S1_Ac_18y = abs(subs_matrix_S1_Ac_18y) print(subs_matrix_S1_Ac_18y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Ac_18y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values))) print(zero_matrix_S1_Ac_18y) subs_matrix_S1_Ac_18y = np.vstack((zero_matrix_S1_Ac_18y, subs_matrix_S1_Ac_18y)) print(subs_matrix_S1_Ac_18y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Ac_18y = (tf,1) decomp_tot_CO2_S1_Ac_18y = np.zeros(matrix_tot_S1_Ac_18y) i = 0 while i < tf: decomp_tot_CO2_S1_Ac_18y[:,0] = decomp_tot_CO2_S1_Ac_18y[:,0] + subs_matrix_S1_Ac_18y[:,i] i = i + 1 print(decomp_tot_CO2_S1_Ac_18y[:,0]) #S1_Tgr_40y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') tf = 201 t = np.arange(tf) def decomp_S1_Tgr_40y(t,remainAGB_S1_Tgr_40y): return (1-(1-np.exp(-k*t)))*remainAGB_S1_Tgr_40y #set zero matrix output_decomp_S1_Tgr_40y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values))) for i,remain_part_S1_Tgr_40y in enumerate(df['Landfill_decomp_CO2'].values): #print(i,remain_part) output_decomp_S1_Tgr_40y[i:,i] = decomp_S1_Tgr_40y(t[:len(t)-i],remain_part_S1_Tgr_40y) print(output_decomp_S1_Tgr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Tgr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1))) i = 0 while i < tf: subs_matrix_S1_Tgr_40y[:,i] = np.diff(output_decomp_S1_Tgr_40y[:,i]) i = i + 1 print(subs_matrix_S1_Tgr_40y[:,:4]) print(len(subs_matrix_S1_Tgr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Tgr_40y = subs_matrix_S1_Tgr_40y.clip(max=0) print(subs_matrix_S1_Tgr_40y[:,:4]) #make the results as absolute values subs_matrix_S1_Tgr_40y = abs(subs_matrix_S1_Tgr_40y) print(subs_matrix_S1_Tgr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Tgr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values))) print(zero_matrix_S1_Tgr_40y) subs_matrix_S1_Tgr_40y = np.vstack((zero_matrix_S1_Tgr_40y, subs_matrix_S1_Tgr_40y)) print(subs_matrix_S1_Tgr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Tgr_40y = (tf,1) decomp_tot_CO2_S1_Tgr_40y = np.zeros(matrix_tot_S1_Tgr_40y) i = 0 while i < tf: decomp_tot_CO2_S1_Tgr_40y[:,0] = decomp_tot_CO2_S1_Tgr_40y[:,0] + subs_matrix_S1_Tgr_40y[:,i] i = i + 1 print(decomp_tot_CO2_S1_Tgr_40y[:,0]) #S2_Tgr_60y df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') tf = 201 t = np.arange(tf) def decomp_S1_Tgr_60y(t,remainAGB_S1_Tgr_60y): return (1-(1-np.exp(-k*t)))*remainAGB_S1_Tgr_60y #set zero matrix output_decomp_S1_Tgr_60y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values))) for i,remain_part_S1_Tgr_60y in enumerate(df['Landfill_decomp_CO2'].values): #print(i,remain_part) output_decomp_S1_Tgr_60y[i:,i] = decomp_S1_Tgr_60y(t[:len(t)-i],remain_part_S1_Tgr_60y) print(output_decomp_S1_Tgr_60y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_S1_Tgr_60y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1))) i = 0 while i < tf: subs_matrix_S1_Tgr_60y[:,i] = np.diff(output_decomp_S1_Tgr_60y[:,i]) i = i + 1 print(subs_matrix_S1_Tgr_60y[:,:4]) print(len(subs_matrix_S1_Tgr_60y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_S1_Tgr_60y = subs_matrix_S1_Tgr_60y.clip(max=0) print(subs_matrix_S1_Tgr_60y[:,:4]) #make the results as absolute values subs_matrix_S1_Tgr_60y = abs(subs_matrix_S1_Tgr_60y) print(subs_matrix_S1_Tgr_60y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_S1_Tgr_60y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values))) print(zero_matrix_S1_Tgr_60y) subs_matrix_S1_Tgr_60y = np.vstack((zero_matrix_S1_Tgr_60y, subs_matrix_S1_Tgr_60y)) print(subs_matrix_S1_Tgr_60y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_S1_Tgr_60y = (tf,1) decomp_tot_CO2_S1_Tgr_60y = np.zeros(matrix_tot_S1_Tgr_60y) i = 0 while i < tf: decomp_tot_CO2_S1_Tgr_60y[:,0] = decomp_tot_CO2_S1_Tgr_60y[:,0] + subs_matrix_S1_Tgr_60y[:,i] i = i + 1 print(decomp_tot_CO2_S1_Tgr_60y[:,0]) #E df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') tf = 201 t = np.arange(tf) def decomp_E_Hbr_40y(t,remainAGB_E_Hbr_40y): return (1-(1-np.exp(-k*t)))*remainAGB_E_Hbr_40y #set zero matrix output_decomp_E_Hbr_40y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values))) for i,remain_part_E_Hbr_40y in enumerate(df['Landfill_decomp_CO2'].values): #print(i,remain_part) output_decomp_E_Hbr_40y[i:,i] = decomp_E_Hbr_40y(t[:len(t)-i],remain_part_E_Hbr_40y) print(output_decomp_E_Hbr_40y[:,:4]) #find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1' #(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list) # https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements #difference between element, subs_matrix_E_Hbr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1))) i = 0 while i < tf: subs_matrix_E_Hbr_40y[:,i] = np.diff(output_decomp_E_Hbr_40y[:,i]) i = i + 1 print(subs_matrix_E_Hbr_40y[:,:4]) print(len(subs_matrix_E_Hbr_40y)) #since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward), #we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913) subs_matrix_E_Hbr_40y = subs_matrix_E_Hbr_40y.clip(max=0) print(subs_matrix_E_Hbr_40y[:,:4]) #make the results as absolute values subs_matrix_E_Hbr_40y = abs(subs_matrix_E_Hbr_40y) print(subs_matrix_E_Hbr_40y[:,:4]) #insert row of zeros into the first row of the subs_matrix zero_matrix_E_Hbr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values))) print(zero_matrix_E_Hbr_40y) subs_matrix_E_Hbr_40y = np.vstack((zero_matrix_E_Hbr_40y, subs_matrix_E_Hbr_40y)) print(subs_matrix_E_Hbr_40y[:,:4]) #sum every column of the subs_matrix into one vector matrix matrix_tot_E_Hbr_40y = (tf,1) decomp_tot_CO2_E_Hbr_40y = np.zeros(matrix_tot_E_Hbr_40y) i = 0 while i < tf: decomp_tot_CO2_E_Hbr_40y[:,0] = decomp_tot_CO2_E_Hbr_40y[:,0] + subs_matrix_E_Hbr_40y[:,i] i = i + 1 print(decomp_tot_CO2_E_Hbr_40y[:,0]) #plotting t = np.arange(0,tf) plt.plot(t,decomp_tot_CO2_S1_Ac_7y,label='Ac_7y') plt.plot(t,decomp_tot_CO2_S1_Ac_18y,label='Ac_18y') plt.plot(t,decomp_tot_CO2_S1_Tgr_40y,label='Tgr_40y') plt.plot(t,decomp_tot_CO2_S1_Tgr_60y,label='Tgr_60y') plt.plot(t,decomp_tot_CO2_E_Hbr_40y,label='E_Hbr_40y') plt.xlim(0,200) plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) plt.show() #%% #Step (8): Sum the emissions and sequestration (net carbon balance), CO2 and CH4 are separated #https://stackoverflow.com/questions/52703442/python-sum-values-from-multiple-lists-more-than-two #C_loss + C_remainAGB + C_remainHWP + PH_Emissions_PO Emissions_S1_Ac_7y = [c_firewood_energy_S1_Ac7, decomp_tot_S1_Ac_7y[:,0], TestDSM1_Ac7.o, PH_Emissions_HWP1_Ac_7y, decomp_tot_CO2_S1_Ac_7y[:,0]] Emissions_S1_Ac_18y = [c_firewood_energy_S1_Ac18, decomp_tot_S1_Ac_18y[:,0], TestDSM1_Ac18.o, PH_Emissions_HWP1_Ac_18y, decomp_tot_CO2_S1_Ac_18y[:,0]] Emissions_S1_Tgr_40y = [c_firewood_energy_S1_Tgr40, decomp_tot_S1_Tgr_40y[:,0], TestDSM1_Tgr40.o, PH_Emissions_HWP1_Tgr_40y, decomp_tot_CO2_S1_Tgr_40y[:,0]] Emissions_S1_Tgr_60y = [c_firewood_energy_S1_Tgr60, decomp_tot_S1_Tgr_60y[:,0], TestDSM1_Tgr60.o, PH_Emissions_HWP1_Tgr_60y, decomp_tot_CO2_S1_Tgr_60y[:,0]] Emissions_E_Hbr_40y = [c_firewood_energy_E_Hbr40, c_pellets_Hbr_40y, decomp_tot_E_Hbr_40y[:,0], TestDSME_Hbr40.o, PH_Emissions_HWPE_Hbr_40y, decomp_tot_CO2_E_Hbr_40y[:,0]] Emissions_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Emissions_S1_Ac_7y)] Emissions_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Emissions_S1_Ac_18y)] Emissions_DL_FP_S1_Tgr_40y = [sum(x) for x in zip(*Emissions_S1_Tgr_40y)] Emissions_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Emissions_S1_Tgr_60y)] Emissions_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Emissions_E_Hbr_40y)] #CH4_S1_Ac_7y Emissions_CH4_DL_FP_S1_Ac_7y = decomp_tot_CH4_S1_Ac_7y[:,0] #CH4_S1_Ac_18y Emissions_CH4_DL_FP_S1_Ac_18y = decomp_tot_CH4_S1_Ac_18y[:,0] #CH4_S1_Tgr_40y Emissions_CH4_DL_FP_S1_Tgr_40y = decomp_tot_CH4_S1_Tgr_40y[:,0] #CH4_S1_Tgr_60y Emissions_CH4_DL_FP_S1_Tgr_60y = decomp_tot_CH4_S1_Tgr_60y[:,0] #CH4_E_Hbr_40y Emissions_CH4_DL_FP_E_Hbr_40y = decomp_tot_CH4_E_Hbr_40y[:,0] #%% #Step (9): Generate the excel file (emissions_seq_scenarios.xlsx) from Step (8) calculation #print year column year = [] for x in range (0, tf): year.append(x) print (year) #print CH4 emission column import itertools lst = [0] Emissions_CH4 = list(itertools.chain.from_iterable(itertools.repeat(x, tf) for x in lst)) print(Emissions_CH4) #print emission ref lst1 = [0] Emission_ref = list(itertools.chain.from_iterable(itertools.repeat(x, tf) for x in lst1)) print(Emission_ref) #replace the first element with 1 to denote the emission reference as year 0 (for dynGWP calculation) Emission_ref[0] = 1 print(Emission_ref) Col1 = year Col2_S1_Ac_7y = Emissions_DL_FP_S1_Ac_7y Col2_S1_Ac_18y = Emissions_DL_FP_S1_Ac_18y Col2_S1_Tgr_40y = Emissions_DL_FP_S1_Tgr_40y Col2_S1_Tgr_60y = Emissions_DL_FP_S1_Tgr_60y Col2_E_Hbr_40y = Emissions_DL_FP_E_Hbr_40y Col3_S1_Ac_7y = Emissions_CH4_DL_FP_S1_Ac_7y Col3_S1_Ac_18y = Emissions_CH4_DL_FP_S1_Ac_18y Col3_S1_Tgr_40y = Emissions_CH4_DL_FP_S1_Tgr_40y Col3_S1_Tgr_60y = Emissions_CH4_DL_FP_S1_Tgr_60y Col3_E_Hbr_40y = Emissions_CH4_DL_FP_E_Hbr_40y Col4 = Emission_ref Col5 = flat_list_Ac_7y Col6 = flat_list_Ac_18y Col7 = flat_list_Tgr_40y Col8 = flat_list_Tgr_60y Col9 = flat_list_Hbr_40y #A. crassicarpa df1_Ac_7y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Ac_7y,'kg_CH4':Col3_S1_Ac_7y,'kg_CO2_seq':Col5,'emission_ref':Col4}) df1_Ac_18y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Ac_18y,'kg_CH4':Col3_S1_Ac_18y,'kg_CO2_seq':Col6,'emission_ref':Col4}) #T. grandis df1_Tgr_40y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Tgr_40y,'kg_CH4':Col3_S1_Tgr_40y,'kg_CO2_seq':Col7,'emission_ref':Col4}) df1_Tgr_60y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Tgr_60y,'kg_CH4':Col3_S1_Tgr_60y,'kg_CO2_seq':Col8,'emission_ref':Col4}) #H. brasiliensis dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_E_Hbr_40y,'kg_CH4':Col3_E_Hbr_40y,'kg_CO2_seq':Col9,'emission_ref':Col4}) writer = pd.ExcelWriter('emissions_seq_DL_FP_S1.xlsx', engine = 'xlsxwriter') df1_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_S1_Ac_7y', header=True, index=False ) df1_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_S1_Ac_18y', header=True, index=False) df1_Tgr_40y.to_excel(writer, sheet_name = 'DL_FP_S1_Tgr_40y', header=True, index=False) df1_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_S1_Tgr_60y', header=True, index=False) dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_Hbr_40y', header=True, index=False) writer.save() writer.close() #df1.to_excel('test.xlsx', 'nuclues', header=True, index=False) #df2.to_excel('test.xlsx', 'plasma', header=True, index=False) #%% ## DYNAMIC LCA # Step (10): Set General Parameters for Dynamic LCA calculation aCH4 = 0.129957e-12; # methane - instantaneous radiative forcing per unit mass [W/m2 /kgCH4] TauCH4 = 12; # methane - lifetime (years) aCO2 = 0.0018088e-12; # CO2 - instantaneous radiative forcing per unit mass [W/m2 /kgCO2] TauCO2 = [172.9, 18.51, 1.186]; # CO2 parameters according to Bern carbon cycle-climate model aBern = [0.259, 0.338, 0.186]; # CO2 parameters according to Bern carbon cycle-climate model a0Bern = 0.217; # CO2 parameters according to Bern carbon cycle-climate model tf = 202 #until 202 because we want to get the DCF(t-i) until DCF(201) to determine the impact from the emission from the year 200 (There is no DCF(0)) #%% #Step (11): Bern 2.5 CC Model, determine atmospheric load (C(t)) for GHG (CO2 and CH4) t = range(0,tf,1) ## CO2 calculation formula # time dependant atmospheric load for CO2, Bern model def C_CO2(t): return a0Bern + aBern[0]*np.exp(-t/TauCO2[0]) + aBern[1]*np.exp(-t/TauCO2[1]) + aBern[2]*np.exp(-t/TauCO2[2]) output_CO2 = np.array([C_CO2(ti) for ti in t]) print(output_CO2) ## CH4 calculation formula # time dependant atmospheric load for non-CO2 GHGs (Methane) def C_CH4(t): return np.exp(-t/TauCH4) output_CH4 = np.array([C_CH4(ti) for ti in t]) plt.xlim([0, 200]) plt.ylim([0,1.1]) plt.plot(t, output_CO2, output_CH4) plt.xlabel('Time (year)') plt.ylabel('Fraction of CO$_2$') plt.show() output_CH4.size #%% #determine the C(t) for CO2 s = [] t = np.arange(0,tf,1) for i in t: s.append(quad(C_CO2,i-1,i)) res_list_CO2 = [x[0] for x in s] len(res_list_CO2) #%% #determine the C(t) for CH4 s = [] for i in t: s.append(quad(C_CH4,i-1,i)) res_list_CH4 = [p[0] for p in s] #plot plt.xlim([0, 200]) plt.ylim([0,1.5]) plt.plot(t, res_list_CO2, res_list_CH4) plt.show() #%% #Step (12): Determine dynamic characterization factors (DCF) for CO2 and CH4 DCF_inst_CO2 = aCO2 * np.array(res_list_CO2) print(DCF_inst_CO2) DCF_inst_CH4 = aCH4 * np.array(res_list_CH4) plt.xlim([0, 200]) plt.ylim([0,4e-15]) plt.plot(t, DCF_inst_CO2, DCF_inst_CH4) plt.xlabel('Time (year)') plt.ylabel('DCF_inst (10$^{-15}$ W/m$^2$.kg CO$_2$)') plt.show() len(DCF_inst_CO2) #%% #Step (13): import emission data from emissions_seq_scenarios.xlsx (Step (9)) ##wood-based #read S1_Ac_7y df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Ac_7y') # can also index sheet by name or fetch all sheets emission_CO2_S1_Ac_7y = df['kg_CO2'].tolist() emission_CH4_S1_Ac_7y = df['kg_CH4'].tolist() emission_CO2_seq_S1_Ac_7y = df['kg_CO2_seq'].tolist() emission_CO2_ref = df['emission_ref'].tolist() #read S1_Ac_18y df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Ac_18y') emission_CO2_S1_Ac_18y = df['kg_CO2'].tolist() emission_CH4_S1_Ac_18y = df['kg_CH4'].tolist() emission_CO2_seq_S1_Ac_18y = df['kg_CO2_seq'].tolist() #read S1_Tgr_40y df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Tgr_40y') # can also index sheet by name or fetch all sheets emission_CO2_S1_Tgr_40y = df['kg_CO2'].tolist() emission_CH4_S1_Tgr_40y = df['kg_CH4'].tolist() emission_CO2_seq_S1_Tgr_40y = df['kg_CO2_seq'].tolist() #read S1_Tgr_60y df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Tgr_60y') emission_CO2_S1_Tgr_60y = df['kg_CO2'].tolist() emission_CH4_S1_Tgr_60y = df['kg_CH4'].tolist() emission_CO2_seq_S1_Tgr_60y = df['kg_CO2_seq'].tolist() #read E_Hbr_40y df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_E_Hbr_40y') # can also index sheet by name or fetch all sheets emission_CO2_E_Hbr_40y = df['kg_CO2'].tolist() emission_CH4_E_Hbr_40y = df['kg_CH4'].tolist() emission_CO2_seq_E_Hbr_40y = df['kg_CO2_seq'].tolist() #%% #Step (14): import emission data from the counter-use of non-renewable materials/energy scenarios (NR) #read S1_Ac_7y df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_7y') emissions_NonRW_S1_Ac_7y = df['NonRW_emissions'].tolist() emissions_NonRW_S1_Ac_7y_seq = df['kg_CO2_seq'].tolist() emission_CO2_ref = df['emission_ref'].tolist() #read S1_Ac_18y df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_18y') emissions_NonRW_S1_Ac_18y = df['NonRW_emissions'].tolist() emissions_NonRW_S1_Ac_18y_seq = df['kg_CO2_seq'].tolist() #read S1_Tgr_40y df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') # can also index sheet by name or fetch all sheets emissions_NonRW_S1_Tgr_40y = df['NonRW_emissions'].tolist() emissions_NonRW_S1_Tgr_40y_seq = df['kg_CO2_seq'].tolist() #read S1_Tgr_60y df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') emissions_NonRW_S1_Tgr_60y = df['NonRW_emissions'].tolist() emissions_NonRW_S1_Tgr_60y_seq = df['kg_CO2_seq'].tolist() #read E_Hbr_40y df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_E_Hbr_40y') # can also index sheet by name or fetch all sheets emissions_NonRW_E_Hbr_40y = df['NonRW_emissions'].tolist() emissions_NonRW_E_Hbr_40y_seq = df['kg_CO2_seq'].tolist() #%% #Step (15): Determine the time elapsed dynamic characterization factors, DCF(t-ti), for CO2 and CH4 #DCF(t-i) CO2 matrix = (tf-1,tf-1) DCF_CO2_ti = np.zeros(matrix) for t in range(0,tf-1): i = -1 while i < t: DCF_CO2_ti[i+1,t] = DCF_inst_CO2[t-i] i = i + 1 print(DCF_CO2_ti) #sns.heatmap(DCF_CO2_ti) DCF_CO2_ti.shape #DCF(t-i) CH4 matrix = (tf-1,tf-1) DCF_CH4_ti = np.zeros(matrix) for t in range(0,tf-1): i = -1 while i < t: DCF_CH4_ti[i+1,t] = DCF_inst_CH4[t-i] i = i + 1 print(DCF_CH4_ti) #sns.heatmap(DCF_CH4_ti) DCF_CH4_ti.shape #%% # Step (16): Calculate instantaneous global warming impact (GWI) ##Wood-based #S1_Ac_7y t = np.arange(0,tf-1,1) matrix_GWI_S1_Ac_7y = (tf-1,3) GWI_inst_S1_Ac_7y = np.zeros(matrix_GWI_S1_Ac_7y) for t in range(0,tf-1): GWI_inst_S1_Ac_7y[t,0] = np.sum(np.multiply(emission_CO2_S1_Ac_7y,DCF_CO2_ti[:,t])) GWI_inst_S1_Ac_7y[t,1] = np.sum(np.multiply(emission_CH4_S1_Ac_7y,DCF_CH4_ti[:,t])) GWI_inst_S1_Ac_7y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Ac_7y,DCF_CO2_ti[:,t])) matrix_GWI_tot_S1_Ac_7y = (tf-1,1) GWI_inst_tot_S1_Ac_7y = np.zeros(matrix_GWI_tot_S1_Ac_7y) GWI_inst_tot_S1_Ac_7y[:,0] = np.array(GWI_inst_S1_Ac_7y[:,0] + GWI_inst_S1_Ac_7y[:,1] + GWI_inst_S1_Ac_7y[:,2]) print(GWI_inst_tot_S1_Ac_7y[:,0]) t = np.arange(0,tf-1,1) #S1_Ac_18y t = np.arange(0,tf-1,1) matrix_GWI_S1_Ac_18y = (tf-1,3) GWI_inst_S1_Ac_18y = np.zeros(matrix_GWI_S1_Ac_18y) for t in range(0,tf-1): GWI_inst_S1_Ac_18y[t,0] = np.sum(np.multiply(emission_CO2_S1_Ac_18y,DCF_CO2_ti[:,t])) GWI_inst_S1_Ac_18y[t,1] = np.sum(np.multiply(emission_CH4_S1_Ac_18y,DCF_CH4_ti[:,t])) GWI_inst_S1_Ac_18y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Ac_18y,DCF_CO2_ti[:,t])) matrix_GWI_tot_S1_Ac_18y = (tf-1,1) GWI_inst_tot_S1_Ac_18y = np.zeros(matrix_GWI_tot_S1_Ac_18y) GWI_inst_tot_S1_Ac_18y[:,0] = np.array(GWI_inst_S1_Ac_18y[:,0] + GWI_inst_S1_Ac_18y[:,1] + GWI_inst_S1_Ac_18y[:,2]) print(GWI_inst_tot_S1_Ac_18y[:,0]) #S1_Tgr_40y t = np.arange(0,tf-1,1) matrix_GWI_S1_Tgr_40y = (tf-1,3) GWI_inst_S1_Tgr_40y = np.zeros(matrix_GWI_S1_Tgr_40y) for t in range(0,tf-1): GWI_inst_S1_Tgr_40y[t,0] = np.sum(np.multiply(emission_CO2_S1_Tgr_40y,DCF_CO2_ti[:,t])) GWI_inst_S1_Tgr_40y[t,1] = np.sum(np.multiply(emission_CH4_S1_Tgr_40y,DCF_CH4_ti[:,t])) GWI_inst_S1_Tgr_40y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Tgr_40y,DCF_CO2_ti[:,t])) matrix_GWI_tot_S1_Tgr_40y = (tf-1,1) GWI_inst_tot_S1_Tgr_40y = np.zeros(matrix_GWI_tot_S1_Tgr_40y) GWI_inst_tot_S1_Tgr_40y[:,0] = np.array(GWI_inst_S1_Tgr_40y[:,0] + GWI_inst_S1_Tgr_40y[:,1] + GWI_inst_S1_Tgr_40y[:,2]) print(GWI_inst_tot_S1_Tgr_40y[:,0]) #S1_Tgr_60y t = np.arange(0,tf-1,1) matrix_GWI_S1_Tgr_60y = (tf-1,3) GWI_inst_S1_Tgr_60y = np.zeros(matrix_GWI_S1_Tgr_60y) for t in range(0,tf-1): GWI_inst_S1_Tgr_60y[t,0] = np.sum(np.multiply(emission_CO2_S1_Tgr_60y,DCF_CO2_ti[:,t])) GWI_inst_S1_Tgr_60y[t,1] = np.sum(np.multiply(emission_CH4_S1_Tgr_60y,DCF_CH4_ti[:,t])) GWI_inst_S1_Tgr_60y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Tgr_60y,DCF_CO2_ti[:,t])) matrix_GWI_tot_S1_Tgr_60y = (tf-1,1) GWI_inst_tot_S1_Tgr_60y = np.zeros(matrix_GWI_tot_S1_Tgr_60y) GWI_inst_tot_S1_Tgr_60y[:,0] = np.array(GWI_inst_S1_Tgr_60y[:,0] + GWI_inst_S1_Tgr_60y[:,1] + GWI_inst_S1_Tgr_60y[:,2]) print(GWI_inst_tot_S1_Tgr_60y[:,0]) #E_Hbr_40y t = np.arange(0,tf-1,1) matrix_GWI_E_Hbr_40y = (tf-1,3) GWI_inst_E_Hbr_40y = np.zeros(matrix_GWI_E_Hbr_40y) for t in range(0,tf-1): GWI_inst_E_Hbr_40y[t,0] = np.sum(np.multiply(emission_CO2_E_Hbr_40y,DCF_CO2_ti[:,t])) GWI_inst_E_Hbr_40y[t,1] = np.sum(np.multiply(emission_CH4_E_Hbr_40y,DCF_CH4_ti[:,t])) GWI_inst_E_Hbr_40y[t,2] = np.sum(np.multiply(emission_CO2_seq_E_Hbr_40y,DCF_CO2_ti[:,t])) matrix_GWI_tot_E_Hbr_40y = (tf-1,1) GWI_inst_tot_E_Hbr_40y = np.zeros(matrix_GWI_tot_E_Hbr_40y) GWI_inst_tot_E_Hbr_40y[:,0] = np.array(GWI_inst_E_Hbr_40y[:,0] + GWI_inst_E_Hbr_40y[:,1] + GWI_inst_E_Hbr_40y[:,2]) print(GWI_inst_tot_E_Hbr_40y[:,0]) ##NonRW #S1_Ac_7y t = np.arange(0,tf-1,1) matrix_GWI_NonRW_S1_Ac_7y = (tf-1,2) GWI_inst_NonRW_S1_Ac_7y = np.zeros(matrix_GWI_NonRW_S1_Ac_7y) for t in range(0,tf-1): GWI_inst_NonRW_S1_Ac_7y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Ac_7y,DCF_CO2_ti[:,t])) GWI_inst_NonRW_S1_Ac_7y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Ac_7y_seq,DCF_CO2_ti[:,t])) matrix_GWI_tot_NonRW_S1_Ac_7y = (tf-1,1) GWI_inst_tot_NonRW_S1_Ac_7y = np.zeros(matrix_GWI_tot_NonRW_S1_Ac_7y) GWI_inst_tot_NonRW_S1_Ac_7y[:,0] = np.array(GWI_inst_NonRW_S1_Ac_7y[:,0] + GWI_inst_NonRW_S1_Ac_7y[:,1]) print(GWI_inst_tot_NonRW_S1_Ac_7y[:,0]) #S1_Ac_18y t = np.arange(0,tf-1,1) matrix_GWI_NonRW_S1_Ac_18y = (tf-1,2) GWI_inst_NonRW_S1_Ac_18y = np.zeros(matrix_GWI_NonRW_S1_Ac_18y) for t in range(0,tf-1): GWI_inst_NonRW_S1_Ac_18y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Ac_18y,DCF_CO2_ti[:,t])) GWI_inst_NonRW_S1_Ac_18y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Ac_18y_seq,DCF_CO2_ti[:,t])) matrix_GWI_tot_NonRW_S1_Ac_18y = (tf-1,1) GWI_inst_tot_NonRW_S1_Ac_18y = np.zeros(matrix_GWI_tot_NonRW_S1_Ac_18y) GWI_inst_tot_NonRW_S1_Ac_18y[:,0] = np.array(GWI_inst_NonRW_S1_Ac_18y[:,0] + GWI_inst_NonRW_S1_Ac_18y[:,1]) print(GWI_inst_tot_NonRW_S1_Ac_18y[:,0]) #S1_Tgr_40y t = np.arange(0,tf-1,1) matrix_GWI_NonRW_S1_Tgr_40y = (tf-1,2) GWI_inst_NonRW_S1_Tgr_40y = np.zeros(matrix_GWI_NonRW_S1_Tgr_40y) for t in range(0,tf-1): GWI_inst_NonRW_S1_Tgr_40y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_40y,DCF_CO2_ti[:,t])) GWI_inst_NonRW_S1_Tgr_40y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_40y_seq,DCF_CO2_ti[:,t])) matrix_GWI_tot_NonRW_S1_Tgr_40y = (tf-1,1) GWI_inst_tot_NonRW_S1_Tgr_40y = np.zeros(matrix_GWI_tot_NonRW_S1_Tgr_40y) GWI_inst_tot_NonRW_S1_Tgr_40y[:,0] = np.array(GWI_inst_NonRW_S1_Tgr_40y[:,0] + GWI_inst_NonRW_S1_Tgr_40y[:,1]) print(GWI_inst_tot_NonRW_S1_Tgr_40y[:,0]) #S1_Tgr_60y t = np.arange(0,tf-1,1) matrix_GWI_NonRW_S1_Tgr_60y = (tf-1,2) GWI_inst_NonRW_S1_Tgr_60y = np.zeros(matrix_GWI_NonRW_S1_Tgr_60y) for t in range(0,tf-1): GWI_inst_NonRW_S1_Tgr_60y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_60y,DCF_CO2_ti[:,t])) GWI_inst_NonRW_S1_Tgr_60y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_60y_seq,DCF_CO2_ti[:,t])) matrix_GWI_tot_NonRW_S1_Tgr_60y = (tf-1,1) GWI_inst_tot_NonRW_S1_Tgr_60y = np.zeros(matrix_GWI_tot_NonRW_S1_Tgr_60y) GWI_inst_tot_NonRW_S1_Tgr_60y[:,0] = np.array(GWI_inst_NonRW_S1_Tgr_60y[:,0] + GWI_inst_NonRW_S1_Tgr_60y[:,1]) print(GWI_inst_tot_NonRW_S1_Tgr_60y[:,0]) #E_Hbr_40y t = np.arange(0,tf-1,1) matrix_GWI_NonRW_E_Hbr_40y = (tf-1,2) GWI_inst_NonRW_E_Hbr_40y = np.zeros(matrix_GWI_NonRW_E_Hbr_40y) for t in range(0,tf-1): GWI_inst_NonRW_E_Hbr_40y[t,0] = np.sum(np.multiply(emissions_NonRW_E_Hbr_40y,DCF_CO2_ti[:,t])) GWI_inst_NonRW_E_Hbr_40y[t,1] = np.sum(np.multiply(emissions_NonRW_E_Hbr_40y_seq,DCF_CO2_ti[:,t])) matrix_GWI_tot_NonRW_E_Hbr_40y = (tf-1,1) GWI_inst_tot_NonRW_E_Hbr_40y = np.zeros(matrix_GWI_tot_NonRW_E_Hbr_40y) GWI_inst_tot_NonRW_E_Hbr_40y[:,0] = np.array(GWI_inst_NonRW_E_Hbr_40y[:,0] + GWI_inst_NonRW_E_Hbr_40y[:,1]) print(GWI_inst_tot_NonRW_E_Hbr_40y[:,0]) t = np.arange(0,tf-1,1) #create zero list to highlight the horizontal line for 0 def zerolistmaker(n): listofzeros = [0] * (n) return listofzeros #convert to flat list GWI_inst_tot_NonRW_S1_Ac_7y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Ac_7y for item in sublist]) GWI_inst_tot_NonRW_S1_Ac_18y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Ac_18y for item in sublist]) GWI_inst_tot_NonRW_S1_Tgr_60y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Tgr_60y for item in sublist]) GWI_inst_tot_NonRW_E_Hbr_40y = np.array([item for sublist in GWI_inst_tot_NonRW_E_Hbr_40y for item in sublist]) GWI_inst_tot_S1_Ac_7y = np.array([item for sublist in GWI_inst_tot_S1_Ac_7y for item in sublist]) GWI_inst_tot_S1_Ac_18y = np.array([item for sublist in GWI_inst_tot_S1_Ac_18y for item in sublist]) GWI_inst_tot_S1_Tgr_60y = np.array([item for sublist in GWI_inst_tot_S1_Tgr_60y for item in sublist]) GWI_inst_tot_E_Hbr_40y = np.array([item for sublist in GWI_inst_tot_E_Hbr_40y for item in sublist]) plt.plot(t, GWI_inst_tot_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55) plt.plot(t, GWI_inst_tot_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55) #plt.plot(t, GWI_inst_tot_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55) plt.plot(t, GWI_inst_tot_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55) plt.plot(t, GWI_inst_tot_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55) plt.plot(t, GWI_inst_tot_S1_Ac_7y, color='olive', label='M_EC_Ac_7y') plt.plot(t, GWI_inst_tot_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y') #plt.plot(t, GWI_inst_tot_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y') plt.plot(t, GWI_inst_tot_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y') plt.plot(t, GWI_inst_tot_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y') plt.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75) #plt.fill_between(t, GWI_inst_tot_NonRW_E_Hbr_40y, GWI_inst_tot_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3) #plt.fill_between(t, GWI_inst_tot_NonRW_S1_Ac_7y, GWI_inst_tot_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3) plt.grid(True) plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) plt.xlim(0,200) plt.ylim(-1e-9,1.4e-9) plt.title('Instantaneous GWI, DL_FP_EC') plt.xlabel('Time (year)') #plt.ylabel('GWI_inst (10$^{-12}$ W/m$^2$)') plt.ylabel('GWI_inst (W/m$^2$)')# plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWI_inst_NonRW_DL_FP_S1', dpi=300) plt.show() #%% #Step (17): Calculate cumulative global warming impact (GWI) ##Wood-based GWI_cum_S1_Ac_7y = np.cumsum(GWI_inst_tot_S1_Ac_7y) GWI_cum_S1_Ac_18y = np.cumsum(GWI_inst_tot_S1_Ac_18y) GWI_cum_S1_Tgr_40y = np.cumsum(GWI_inst_tot_S1_Tgr_40y) GWI_cum_S1_Tgr_60y = np.cumsum(GWI_inst_tot_S1_Tgr_60y) GWI_cum_E_Hbr_40y = np.cumsum(GWI_inst_tot_E_Hbr_40y) ##NonRW GWI_cum_NonRW_S1_Ac_7y = np.cumsum(GWI_inst_tot_NonRW_S1_Ac_7y) GWI_cum_NonRW_S1_Ac_18y = np.cumsum(GWI_inst_tot_NonRW_S1_Ac_18y) GWI_cum_NonRW_S1_Tgr_40y = np.cumsum(GWI_inst_tot_NonRW_S1_Tgr_40y) GWI_cum_NonRW_S1_Tgr_60y = np.cumsum(GWI_inst_tot_NonRW_S1_Tgr_60y) GWI_cum_NonRW_E_Hbr_40y = np.cumsum(GWI_inst_tot_NonRW_E_Hbr_40y) #print(GWI_cum_NonRW_S1_Ac_18y) plt.xlabel('Time (year)') #plt.ylabel('GWI_cum (10$^{-10}$ W/m$^2$)') plt.ylabel('GWI_cum (W/m$^2$)') plt.xlim(0,200) plt.ylim(-1e-7,1.5e-7) plt.title('Cumulative GWI, DL_FP_EC') plt.plot(t, GWI_cum_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55) plt.plot(t, GWI_cum_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55) #plt.plot(t, GWI_cum_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55) plt.plot(t, GWI_cum_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55) plt.plot(t, GWI_cum_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55) plt.plot(t, GWI_cum_S1_Ac_7y, color='olive', label='M_EC_Ac_7y') plt.plot(t, GWI_cum_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y') #plt.plot(t, GWI_cum_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y') plt.plot(t, GWI_cum_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y') plt.plot(t, GWI_cum_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y') plt.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75) plt.grid(True) #plt.fill_between(t, GWI_cum_NonRW_S1_Tgr_60y, GWI_cum_NonRW_S1_Ac_7y, color='lightcoral', alpha=0.3) plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWI_cum_NonRW_DL_FP_EC', dpi=300) plt.show() #%% #Step (18): Determine the Instantenous and Cumulative GWI for the emission reference (1 kg CO2 emission at time zero) before performing dynamic GWP calculation t = np.arange(0,tf-1,1) matrix_GWI_ref = (tf-1,1) GWI_inst_ref = np.zeros(matrix_GWI_ref) for t in range(0,tf-1): GWI_inst_ref[t,0] = np.sum(np.multiply(emission_CO2_ref,DCF_CO2_ti[:,t])) #print(GWI_inst_ref[:,0]) len(GWI_inst_ref) #determine the GWI cumulative for the emission reference t = np.arange(0,tf-1,1) GWI_cum_ref = np.cumsum(GWI_inst_ref[:,0]) #print(GWI_cum_ref) plt.xlabel('Time (year)') plt.ylabel('GWI_cum_ref (10$^{-13}$ W/m$^2$.kgCO$_2$)') plt.plot(t, GWI_cum_ref) len(GWI_cum_ref) #%% #Step (19): Calculate dynamic global warming potential (GWPdyn) ##Wood-based GWP_dyn_cum_S1_Ac_7y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Ac_7y, GWI_cum_ref)] GWP_dyn_cum_S1_Ac_18y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Ac_18y, GWI_cum_ref)] GWP_dyn_cum_S1_Tgr_40y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Tgr_40y, GWI_cum_ref)] GWP_dyn_cum_S1_Tgr_60y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Tgr_60y, GWI_cum_ref)] GWP_dyn_cum_E_Hbr_40y = [x/(y*1000) for x,y in zip(GWI_cum_E_Hbr_40y, GWI_cum_ref)] ##NonRW GWP_dyn_cum_NonRW_S1_Ac_7y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Ac_7y, GWI_cum_ref)] GWP_dyn_cum_NonRW_S1_Ac_18y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Ac_18y, GWI_cum_ref)] GWP_dyn_cum_NonRW_S1_Tgr_40y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Tgr_40y, GWI_cum_ref)] GWP_dyn_cum_NonRW_S1_Tgr_60y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Tgr_60y, GWI_cum_ref)] GWP_dyn_cum_NonRW_E_Hbr_40y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_E_Hbr_40y, GWI_cum_ref)] #print(GWP_dyn_cum_NonRW_S1_Ac_18y) fig=plt.figure() fig.show() ax=fig.add_subplot(111) ax.plot(t, GWP_dyn_cum_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55) ax.plot(t, GWP_dyn_cum_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55) #ax.plot(t, GWP_dyn_cum_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55) ax.plot(t, GWP_dyn_cum_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55) ax.plot(t, GWP_dyn_cum_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55) ax.plot(t, GWP_dyn_cum_S1_Ac_7y, color='olive', label='M_EC_Ac_7y') ax.plot(t, GWP_dyn_cum_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y') #ax.plot(t, GWP_dyn_cum_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y') ax.plot(t, GWP_dyn_cum_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y') ax.plot(t, GWP_dyn_cum_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y') ax.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75) #plt.fill_between(t, GWP_dyn_cum_NonRW_S1_Ac_7y, GWP_dyn_cum_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3) plt.grid(True) ax.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax.set_xlim(0,200) ax.set_ylim(-750,1000) #ax.set_ylim(-600,1500) ax.set_xlabel('Time (year)') ax.set_ylabel('GWP$_{dyn}$ (t-CO$_2$-eq)') ax.set_title('Dynamic GWP, DL_FP_EC') plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_cum_NonRW_DL_FP_S1', dpi=300) plt.draw() #%% #Step (20): Exporting the data behind result graphs to Excel year = [] for x in range (0, 201): year.append(x) ### Create Column Col1 = year ##GWI_Inst #GWI_inst from wood-based scenarios Col_GI_1 = GWI_inst_tot_S1_Ac_7y Col_GI_2 = GWI_inst_tot_S1_Ac_18y Col_GI_3 = GWI_inst_tot_S1_Tgr_60y Col_GI_4 = GWI_inst_tot_E_Hbr_40y #print(Col_GI_1) #print(np.shape(Col_GI_1)) #GWI_inst from counter use scenarios Col_GI_5 = GWI_inst_tot_NonRW_S1_Ac_7y Col_GI_6 = GWI_inst_tot_NonRW_S1_Ac_18y Col_GI_7 = GWI_inst_tot_NonRW_S1_Tgr_60y Col_GI_8 = GWI_inst_tot_NonRW_E_Hbr_40y #print(Col_GI_7) #print(np.shape(Col_GI_7)) #create column results ##GWI_cumulative #GWI_cumulative from wood-based scenarios Col_GC_1 = GWI_cum_S1_Ac_7y Col_GC_2 = GWI_cum_S1_Ac_18y Col_GC_3 = GWI_cum_S1_Tgr_60y Col_GC_4 = GWI_cum_E_Hbr_40y #GWI_cumulative from counter use scenarios Col_GC_5 = GWI_cum_NonRW_S1_Ac_7y Col_GC_6 = GWI_cum_NonRW_S1_Ac_18y Col_GC_7 = GWI_cum_NonRW_S1_Tgr_60y Col_GC_8 = GWI_cum_NonRW_E_Hbr_40y #create column results ##GWPdyn #GWPdyn from wood-based scenarios Col_GWP_1 = GWP_dyn_cum_S1_Ac_7y Col_GWP_2 = GWP_dyn_cum_S1_Ac_18y Col_GWP_3 = GWP_dyn_cum_S1_Tgr_60y Col_GWP_4 = GWP_dyn_cum_E_Hbr_40y #GWPdyn from counter use scenarios Col_GWP_5 = GWP_dyn_cum_NonRW_S1_Ac_7y Col_GWP_6 = GWP_dyn_cum_NonRW_S1_Ac_18y Col_GWP_7 = GWP_dyn_cum_NonRW_S1_Tgr_60y Col_GWP_8 = GWP_dyn_cum_NonRW_E_Hbr_40y #Create colum results dfM_EC_GI = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (W/m2)':Col_GI_1, 'M_EC_Ac_18y (W/m2)':Col_GI_2, 'M_EC_Tgr_60y (W/m2)':Col_GI_3, 'E_EC_Hbr_40y (W/m2)':Col_GI_4, 'NR_M_EC_Ac_7y (W/m2)':Col_GI_5, 'NR_M_EC_Ac_18y (W/m2)':Col_GI_6, 'NR_M_EC_Tgr_60y (W/m2)':Col_GI_7, 'NR_E_EC_Hbr_40y (W/m2)':Col_GI_8}) dfM_EC_GC = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (W/m2)':Col_GC_1, 'M_EC_Ac_18y (W/m2)':Col_GC_2, 'M_EC_Tgr_60y (W/m2)':Col_GC_3, 'E_EC_Hbr_40y (W/m2)':Col_GC_4, 'NR_M_EC_Ac_7y (W/m2)':Col_GC_5, 'NR_M_EC_Ac_18y (W/m2)':Col_GC_6, 'NR_M_EC_Tgr_60y (W/m2)':Col_GC_7, 'NR_E_EC_Hbr_40y (W/m2)':Col_GC_8}) dfM_EC_GWPdyn = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (t-CO2-eq)':Col_GWP_1, 'M_EC_Ac_18y (t-CO2-eq)':Col_GWP_2, 'M_EC_Tgr_60y (t-CO2-eq)':Col_GWP_3, 'E_EC_Hbr_40y (t-CO2-eq)':Col_GWP_4, 'NR_M_EC_Ac_7y (t-CO2-eq)':Col_GWP_5, 'NR_M_EC_Ac_18y (t-CO2-eq)':Col_GWP_6, 'NR_M_EC_Tgr_60y (t-CO2-eq)':Col_GWP_7, 'NR_E_EC_Hbr_40y (t-CO2-eq)':Col_GWP_8}) #Export to excel writer = pd.ExcelWriter('GraphResults_DL_FP_EC.xlsx', engine = 'xlsxwriter') dfM_EC_GI.to_excel(writer, sheet_name = 'GWI_Inst_DL_FP_EC', header=True, index=False ) dfM_EC_GC.to_excel(writer, sheet_name = 'Cumulative GWI_DL_FP_EC', header=True, index=False ) dfM_EC_GWPdyn.to_excel(writer, sheet_name = 'GWPdyn_DL_FP_EC', header=True, index=False ) writer.save() writer.close() #%% #Step (21): Generate the excel file for the individual carbon emission and sequestration flows #print year column year = [] for x in range (0, 201): year.append(x) print (year) division = 1000*44/12 division_CH4 = 1000*16/12 #M_Ac_7y c_firewood_energy_S1_Ac7 = [x/division for x in c_firewood_energy_S1_Ac7] decomp_tot_S1_Ac_7y[:,0] = [x/division for x in decomp_tot_S1_Ac_7y[:,0]] TestDSM1_Ac7.o = [x/division for x in TestDSM1_Ac7.o] PH_Emissions_HWP1_Ac_7y = [x/division for x in PH_Emissions_HWP1_Ac_7y] #OC_storage_S1_Ac7 = [x/division for x in OC_storage_S1_Ac7] flat_list_Ac_7y = [x/division for x in flat_list_Ac_7y] decomp_tot_CO2_S1_Ac_7y[:,0] = [x/division for x in decomp_tot_CO2_S1_Ac_7y[:,0]] decomp_tot_CH4_S1_Ac_7y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Ac_7y[:,0]] #M_Ac_18y c_firewood_energy_S1_Ac18 = [x/division for x in c_firewood_energy_S1_Ac18] decomp_tot_S1_Ac_18y[:,0] = [x/division for x in decomp_tot_S1_Ac_18y[:,0]] TestDSM1_Ac18.o = [x/division for x in TestDSM1_Ac18.o] PH_Emissions_HWP1_Ac_18y = [x/division for x in PH_Emissions_HWP1_Ac_18y] #OC_storage_S1_Ac18 = [x/division for x in OC_storage_S1_Ac18] flat_list_Ac_18y = [x/division for x in flat_list_Ac_18y] decomp_tot_CO2_S1_Ac_18y[:,0] = [x/division for x in decomp_tot_CO2_S1_Ac_18y[:,0]] decomp_tot_CH4_S1_Ac_18y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Ac_18y[:,0]] #M_Tgr_60y c_firewood_energy_S1_Tgr60 = [x/division for x in c_firewood_energy_S1_Tgr60] decomp_tot_S1_Tgr_60y[:,0] = [x/division for x in decomp_tot_S1_Tgr_60y[:,0]] TestDSM1_Tgr60.o = [x/division for x in TestDSM1_Tgr60.o] PH_Emissions_HWP1_Tgr_60y = [x/division for x in PH_Emissions_HWP1_Tgr_60y] #OC_storage_S1_Tgr60 = [x/division for x in OC_storage_S1_Tgr60] flat_list_Tgr_60y = [x/division for x in flat_list_Tgr_60y] decomp_tot_CO2_S1_Tgr_60y[:,0] = [x/division for x in decomp_tot_CO2_S1_Tgr_60y[:,0]] decomp_tot_CH4_S1_Tgr_60y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Tgr_60y[:,0]] #E_Hbr_40y c_firewood_energy_E_Hbr40 = [x/division for x in c_firewood_energy_E_Hbr40] c_pellets_Hbr_40y = [x/division for x in c_pellets_Hbr_40y] decomp_tot_E_Hbr_40y[:,0] = [x/division for x in decomp_tot_E_Hbr_40y[:,0]] TestDSME_Hbr40.o = [x/division for x in TestDSME_Hbr40.o] PH_Emissions_HWPE_Hbr_40y = [x/division for x in PH_Emissions_HWPE_Hbr_40y] ##ColumnOC_storage_E_Hbr40 = [x/division for x in OC_storage_E_Hbr40] flat_list_Hbr_40y = [x/division for x in flat_list_Hbr_40y] decomp_tot_CO2_E_Hbr_40y[:,0] = [x/division for x in decomp_tot_CO2_E_Hbr_40y[:,0]] decomp_tot_CH4_E_Hbr_40y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_E_Hbr_40y[:,0]] #landfill aggregate flows Landfill_decomp_DL_FP_S1_Ac_7y = decomp_tot_CH4_S1_Ac_7y, decomp_tot_CO2_S1_Ac_7y Landfill_decomp_DL_FP_S1_Ac_18y = decomp_tot_CH4_S1_Ac_18y, decomp_tot_CO2_S1_Ac_18y Landfill_decomp_DL_FP_S1_Tgr_60y = decomp_tot_CH4_S1_Tgr_60y, decomp_tot_CO2_S1_Tgr_60y Landfill_decomp_DL_FP_E_Hbr_40y = decomp_tot_CH4_E_Hbr_40y, decomp_tot_CO2_E_Hbr_40y Landfill_decomp_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Ac_7y)] Landfill_decomp_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Ac_18y)] Landfill_decomp_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Tgr_60y)] Landfill_decomp_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_E_Hbr_40y)] Landfill_decomp_DL_FP_S1_Ac_7y = [item for sublist in Landfill_decomp_DL_FP_S1_Ac_7y for item in sublist] Landfill_decomp_DL_FP_S1_Ac_18y = [item for sublist in Landfill_decomp_DL_FP_S1_Ac_18y for item in sublist] Landfill_decomp_DL_FP_S1_Tgr_60y = [item for sublist in Landfill_decomp_DL_FP_S1_Tgr_60y for item in sublist] Landfill_decomp_DL_FP_E_Hbr_40y = [item for sublist in Landfill_decomp_DL_FP_E_Hbr_40y for item in sublist] #M_Ac_7y Column1 = year Column2 = c_firewood_energy_S1_Ac7 Column3 = decomp_tot_S1_Ac_7y[:,0] Column4 = TestDSM1_Ac7.o Column5 = PH_Emissions_HWP1_Ac_7y #Column6_1 = OC_storage_S1_Ac7 Column6 = Landfill_decomp_DL_FP_S1_Ac_7y Column7 = flat_list_Ac_7y #M_Ac_18y Column8 = c_firewood_energy_S1_Ac18 Column9 = decomp_tot_S1_Ac_18y[:,0] Column10 = TestDSM1_Ac18.o Column11 = PH_Emissions_HWP1_Ac_18y #Column12_1 = OC_storage_S1_Ac18 Column12 = Landfill_decomp_DL_FP_S1_Ac_18y Column13 = flat_list_Ac_18y #M_Tgr_60y Column14 = c_firewood_energy_S1_Tgr60 Column15 = decomp_tot_S1_Tgr_60y[:,0] Column16 = TestDSM1_Tgr60.o Column17 = PH_Emissions_HWP1_Tgr_60y #Column18_1 = OC_storage_S1_Tgr60 Column18 = Landfill_decomp_DL_FP_S1_Tgr_60y Column19 = flat_list_Tgr_60y #E_Hbr_40y Column20 = c_firewood_energy_E_Hbr40 Column20_1 = c_pellets_Hbr_40y Column21 = decomp_tot_E_Hbr_40y[:,0] Column22 = TestDSME_Hbr40.o Column23 = PH_Emissions_HWPE_Hbr_40y #Column24_1 = OC_storage_E_Hbr40 Column24 = Landfill_decomp_DL_FP_E_Hbr_40y Column25 = flat_list_Hbr_40y #create columns dfM_Ac_7y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column7, # '9: Landfill storage (t-C)':Column6_1, 'F1-0: Residue decomposition (t-C)':Column3, 'F6-0-1: Emissions from firewood/other energy use (t-C)':Column2, 'F8-0: Operational stage/processing emissions (t-C)':Column5, 'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column4, 'F7-0: Landfill gas decomposition (t-C)':Column6}) dfM_Ac_18y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column13, # '9: Landfill storage (t-C)':Column12_1, 'F1-0: Residue decomposition (t-C)':Column9, 'F6-0-1: Emissions from firewood/other energy use (t-C)':Column8, 'F8-0: Operational stage/processing emissions (t-C)':Column11, 'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column10, 'F7-0: Landfill gas decomposition (t-C)':Column12}) dfE_Tgr_60y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column19, # '9: Landfill storage (t-C)':Column18_1, 'F1-0: Residue decomposition (t-C)':Column15, 'F6-0-1: Emissions from firewood/other energy use (t-C)':Column14, 'F8-0: Operational stage/processing emissions (t-C)':Column17, 'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column16, 'F7-0: Landfill gas decomposition (t-C)':Column18}) dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column25, # '9: Landfill storage (t-C)':Column24_1, 'F1-0: Residue decomposition (t-C)':Column21, 'F6-0-1: Emissions from firewood/other energy use (t-C)':Column20, 'F8-0: Operational stage/processing emissions (t-C)':Column23, 'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column22, 'F7-0: Landfill gas decomposition (t-C)':Column24, 'F4-0: Emissions from wood pellets use (t-C)':Column20_1}) writer = pd.ExcelWriter('C_flows_DL_FP_EC.xlsx', engine = 'xlsxwriter') dfM_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_M_Ac_7y (EC)', header=True, index=False) dfM_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_M_Ac_18y (EC)', header=True, index=False) dfE_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_M_Tgr_60y (EC)', header=True, index=False) dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_Hbr_40y (EC)', header=True, index=False) writer.save() writer.close() #%% #Step (22): Plot of the individual carbon emission and sequestration flows for normal and symlog-scale graphs #DL_FP_M_EC_Ac_7y (Existing conversion efficiency) fig=plt.figure() fig.show() ax1_s=fig.add_subplot(111) #plot ax1_s.plot(t, flat_list_Ac_7y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax1_s.plot(t, OC_storage_S1_Ac7, color='darkturquoise', label='9: Landfill storage') ax1_s.plot(t, decomp_tot_S1_Ac_7y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax1_s.plot(t, c_firewood_energy_S1_Ac7, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax1_s.plot(t, PH_Emissions_HWP1_Ac_7y, color='orange', label='F8-0: Operational stage/processing emissions') ax1_s.plot(t, TestDSM1_Ac7.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax1_s.plot(t, Landfill_decomp_DL_FP_S1_Ac_7y, color='yellow', label='F7-0: Landfill gas decomposition') ax1_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax1_s.set_xlim(-1,200) ax1_s.set_yscale('symlog') ax1_s.set_xlabel('Time (year)') ax1_s.set_ylabel('C flows (t-C) (symlog)') ax1_s.set_title('Carbon flow, DL_FP_M_EC_Ac_7y (EC) (symlog-scale)') plt.show() #%% #plot for the individual carbon flows #DL_FP_M_EC_Ac_7y (Existing conversion efficiency) fig=plt.figure() fig.show() ax1=fig.add_subplot(111) ax1.plot(t, flat_list_Ac_7y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax1.plot(t, OC_storage_S1_Ac7, color='darkturquoise', label='9: Landfill storage') ax1.plot(t, decomp_tot_S1_Ac_7y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax1.plot(t, c_firewood_energy_S1_Ac7, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax1.plot(t, PH_Emissions_HWP1_Ac_7y, color='orange', label='F8-0: Operational stage/processing emissions') ax1.plot(t, TestDSM1_Ac7.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax1.plot(t, Landfill_decomp_DL_FP_S1_Ac_7y, color='yellow', label='F7-0: Landfill gas decomposition') ax1.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax1.set_xlim(0,200) ax1.set_xlabel('Time (year)') ax1.set_ylabel('C flows(t-C)') ax1.set_title('Carbon flow, DL_FP_M_Ac_7y (EC)') #plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M') plt.draw() #%% #plot for the individual carbon flows - test for symlog-scale graphs #DL_FP_M_EC_Ac_18y (Existing conversion efficiency) fig=plt.figure() fig.show() ax2_s=fig.add_subplot(111) #plot ax2_s.plot(t, flat_list_Ac_18y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax2_s.plot(t, OC_storage_S1_Ac18, color='darkturquoise', label='9: Landfill storage') ax2_s.plot(t, decomp_tot_S1_Ac_18y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax2_s.plot(t, c_firewood_energy_S1_Ac18, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax2_s.plot(t, PH_Emissions_HWP1_Ac_18y, color='orange', label='F8-0: Operational stage/processing emissions') ax2_s.plot(t, TestDSM1_Ac18.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax2_s.plot(t, Landfill_decomp_DL_FP_S1_Ac_18y, color='yellow', label='F7-0: Landfill gas decomposition') ax2_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax2_s.set_xlim(-1,200) ax2_s.set_yscale('symlog') ax2_s.set_xlabel('Time (year)') ax2_s.set_ylabel('C flows (t-C) (symlog)') ax2_s.set_title('Carbon flow, DL_FP_M_EC_Ac_18y (EC) (symlog-scale)') plt.show() #%% #plot for the individual carbon flows #DL_FP_M_EC_Ac_18y (Existing conversion efficiency) fig=plt.figure() fig.show() ax2=fig.add_subplot(111) #plot ax2.plot(t, flat_list_Ac_18y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax2.plot(t, OC_storage_S1_Ac18, color='darkturquoise', label='9: Landfill storage') ax2.plot(t, decomp_tot_S1_Ac_18y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax2.plot(t, c_firewood_energy_S1_Ac18, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax2.plot(t, PH_Emissions_HWP1_Ac_18y, color='orange', label='F8-0: Operational stage/processing emissions') ax2.plot(t, TestDSM1_Ac18.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax2.plot(t, Landfill_decomp_DL_FP_S1_Ac_18y, color='yellow', label='F7-0: Landfill gas decomposition') ax2.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax2.set_xlim(0,200) ax2.set_xlabel('Time (year)') ax2.set_ylabel('C flows(t-C)') ax2.set_title('Carbon flow, DL_FP_M_Ac_18y (EC)') #plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M') plt.draw() #%% #plot for the individual carbon flows - test for symlog-scale graphs #DL_FP_M_EC_Tgr_60y (Existing conversion efficiency) fig=plt.figure() fig.show() ax3_s=fig.add_subplot(111) #plot ax3_s.plot(t, flat_list_Tgr_60y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax3_s.plot(t, OC_storage_S1_Tgr60, color='darkturquoise', label='9: Landfill storage') ax3_s.plot(t, decomp_tot_S1_Tgr_60y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax3_s.plot(t, c_firewood_energy_S1_Tgr60, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax3_s.plot(t, PH_Emissions_HWP1_Tgr_60y, color='orange', label='F8-0: Operational stage/processing emissions') ax3_s.plot(t, TestDSM1_Tgr60.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax3_s.plot(t, Landfill_decomp_DL_FP_S1_Tgr_60y, color='yellow', label='F7-0: Landfill gas decomposition') ax3_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax3_s.set_xlim(-1,200) ax3_s.set_yscale('symlog') ax3_s.set_xlabel('Time (year)') ax3_s.set_ylabel('C flows (t-C) (symlog)') ax3_s.set_title('Carbon flow, DL_FP_M_EC_Tgr_60y (EC) (symlog-scale)') plt.show() #%% #plot for the individual carbon flows #DL_FP_M_EC_Tgr_60y (Existing conversion efficiency) fig=plt.figure() fig.show() ax3=fig.add_subplot(111) #plot ax3.plot(t, flat_list_Tgr_60y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax3.plot(t, OC_storage_S1_Tgr60, color='darkturquoise', label='9: Landfill storage') ax3.plot(t, decomp_tot_S1_Tgr_60y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax3.plot(t, c_firewood_energy_S1_Tgr60, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax3.plot(t, PH_Emissions_HWP1_Tgr_60y, color='orange', label='F8-0: Operational stage/processing emissions') ax3.plot(t, TestDSM1_Tgr60.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow') ax3.plot(t, Landfill_decomp_DL_FP_S1_Tgr_60y, color='yellow', label='F7-0: Landfill gas decomposition') ax3.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax3.set_xlim(0,200) ax3.set_xlabel('Time (year)') ax3.set_ylabel('C flows(t-C)') ax3.set_title('Carbon flow, DL_FP_M_Tgr_60y (EC)') #plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M') plt.draw() #%% #plot for the individual carbon flows - test for symlog-scale graphs #DL_FP_E_EC_Hbr_40y (Existing conversion efficiency) fig=plt.figure() fig.show() ax4_s=fig.add_subplot(111) #plot ax4_s.plot(t, flat_list_Hbr_40y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax4_s.plot(t, OC_storage_E_Hbr40, color='darkturquoise', label='9: Landfill storage') ax4_s.plot(t, decomp_tot_E_Hbr_40y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax4_s.plot(t, c_firewood_energy_E_Hbr40, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax4_s.plot(t, PH_Emissions_HWPE_Hbr_40y, color='orange', label='F8-0: Operational stage/processing emissions') ax4_s.plot(t, Landfill_decomp_DL_FP_E_Hbr_40y, color='yellow', label='F7-0: Landfill gas decomposition') ax4_s.plot(t, c_pellets_Hbr_40y, color='slategrey', label='F4-0: Emissions from wood pellets use') #ax4_s.plot(t, TestDSME_Hbr40.o, label='in-use stock output') ax4_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax4_s.set_xlim(-1,200) ax4_s.set_yscale('symlog') ax4_s.set_xlabel('Time (year)') ax4_s.set_ylabel('C flows (t-C) (symlog)') ax4_s.set_title('Carbon flow, DL_FP_E_EC_Hbr_40y (EC) (symlog-scale)') plt.show() #%% #plot for the individual carbon flows #DL_FP_E_Hbr_40y (Existing conversion efficiency) fig=plt.figure() fig.show() ax4=fig.add_subplot(111) #plot ax4.plot(t, flat_list_Hbr_40y, color='darkkhaki', label='F0-1: Biomass C sequestration') #ax4.plot(t, OC_storage_E_Hbr40, color='darkturquoise', label='9: Landfill storage') ax4.plot(t, decomp_tot_E_Hbr_40y[:,0], color='lightcoral', label='F1-0: Residue decomposition') ax4.plot(t, c_firewood_energy_E_Hbr40, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use') ax4.plot(t, PH_Emissions_HWPE_Hbr_40y, color='orange', label='F8-0: Operational stage/processing emissions') ax4.plot(t, Landfill_decomp_DL_FP_E_Hbr_40y, color='yellow', label='F7-0: Landfill gas decomposition') ax4.plot(t, c_pellets_Hbr_40y, color='slategrey', label='F4-0: Emissions from wood pellets use') #ax_g.plot(t, TestDSME_Hbr40.o, label='in-use stock output') ax4.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax4.set_xlim(0,200) ax4.set_xlabel('Time (year)') ax4.set_ylabel('C flows(t-C)') ax4.set_title('Carbon flow, DL_FP_E_Hbr_40y (EC)') #plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M') #%% #Step (23): Generate the excel file for the net carbon balance Agg_Cflow_S1_Ac_7y = [c_firewood_energy_S1_Ac7, decomp_tot_S1_Ac_7y[:,0], TestDSM1_Ac7.o, PH_Emissions_HWP1_Ac_7y, Landfill_decomp_DL_FP_S1_Ac_7y, flat_list_Ac_7y] Agg_Cflow_S1_Ac_18y = [c_firewood_energy_S1_Ac18, decomp_tot_S1_Ac_18y[:,0], TestDSM1_Ac18.o, PH_Emissions_HWP1_Ac_18y, Landfill_decomp_DL_FP_S1_Ac_18y, flat_list_Ac_18y] Agg_Cflow_S1_Tgr_60y = [c_firewood_energy_S1_Tgr60, decomp_tot_S1_Tgr_60y[:,0], TestDSM1_Tgr60.o, PH_Emissions_HWP1_Tgr_60y, Landfill_decomp_DL_FP_S1_Tgr_60y, flat_list_Tgr_60y] Agg_Cflow_E_Hbr_40y = [c_firewood_energy_E_Hbr40, c_pellets_Hbr_40y, decomp_tot_E_Hbr_40y[:,0], TestDSME_Hbr40.o, PH_Emissions_HWPE_Hbr_40y, Landfill_decomp_DL_FP_E_Hbr_40y, flat_list_Hbr_40y] Agg_Cflow_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Agg_Cflow_S1_Ac_7y)] Agg_Cflow_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Agg_Cflow_S1_Ac_18y)] Agg_Cflow_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Agg_Cflow_S1_Tgr_60y)] Agg_Cflow_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Agg_Cflow_E_Hbr_40y)] #create column year year = [] for x in range (0, 201): year.append(x) print (year) #Create colum results dfM_DL_FP_EC = pd.DataFrame.from_dict({'Year':year,'M_EC_Ac_7y (t-C)':Agg_Cflow_DL_FP_S1_Ac_7y, 'M_EC_Ac_18y (t-C)':Agg_Cflow_DL_FP_S1_Ac_18y, 'M_EC_Tgr_60y (t-C)':Agg_Cflow_DL_FP_S1_Tgr_60y, 'E_EC_Hbr_40y (t-C)':Agg_Cflow_DL_FP_E_Hbr_40y}) #Export to excel writer = pd.ExcelWriter('AggCFlow_DL_FP_EC.xlsx', engine = 'xlsxwriter') dfM_DL_FP_EC.to_excel(writer, sheet_name = 'DL_FP_EC', header=True, index=False) writer.save() writer.close() #%% #Step (24): Plot the net carbon balance fig=plt.figure() fig.show() ax5=fig.add_subplot(111) # plot ax5.plot(t, Agg_Cflow_DL_FP_S1_Ac_7y, color='orange', label='M_EC_Ac_7y') ax5.plot(t, Agg_Cflow_DL_FP_S1_Ac_18y, color='darkturquoise', label='M_EC_Ac_18y') ax5.plot(t, Agg_Cflow_DL_FP_S1_Tgr_60y, color='lightcoral', label='M_EC_Tgr_60y') ax5.plot(t, Agg_Cflow_DL_FP_E_Hbr_40y, color='mediumseagreen', label='E_EC_Hbr_40y') ax5.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75) ax5.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False) ax5.set_xlim(-1,200) ax5.set_ylim(-25,220) #ax5.set_yscale('symlog') ax5.set_xlabel('Time (year)') ax5.set_ylabel('C flows (t-C)') ax5.set_title('Net carbon balance, DL_FP_EC') plt.show() #%% #Step (25): Generate the excel file for documentation of individual carbon flows in the system definition (Fig. 1) #print year column year = [] for x in range (0, 201): year.append(x) print (year) df2_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y') df2_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y') df2_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') df2_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') dfE2_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y') Column1 = year division = 1000*44/12 division_CH4 = 1000*16/12 ## S1_Ac_7y ## define the input flow for the landfill (F5-7) OC_storage_S1_Ac7 = df1_Ac7['Other_C_storage'].values OC_storage_S1_Ac7 = [x/division for x in OC_storage_S1_Ac7] OC_storage_S1_Ac7 = [abs(number) for number in OC_storage_S1_Ac7] C_LF_S1_Ac7 = [x*1/0.82 for x in OC_storage_S1_Ac7] ## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3) HWP_S1_Ac7 = [x/division for x in df1_Ac7['Input_PF'].values] HWP_S1_Ac7_energy = [x*1/3 for x in c_firewood_energy_S1_Ac7] HWP_S1_Ac7_landfill = [x*1/0.82 for x in OC_storage_S1_Ac7] HWP_S1_Ac7_sum = [HWP_S1_Ac7, HWP_S1_Ac7_energy, HWP_S1_Ac7_landfill] HWP_S1_Ac7_sum = [sum(x) for x in zip(*HWP_S1_Ac7_sum )] #in-use stocks (S-4) TestDSM1_Ac7.s = [x/division for x in TestDSM1_Ac7.s] #TestDSM1_Ac7.i = [x/division for x in TestDSM1_Ac7.i] #calculate the F1-2 #In general, F1-2 = F2-3 + F2-6, #To split the F1-2 to F1a-2 and F1c-2, we need to differentiate the flow for the initial land conversion (PF) and the subsequent land type (FP) #create F1a-2 #tf = 201 #zero_PF_S2_Ac_7y = (tf,1) #PF_S2_Ac_7y = np.zeros(zero_PF_S2_Ac_7y) #PF_S2_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S2_Ac7_sum, [x*2/3 for x in c_firewood_energy_S2_Ac7])][0:8] #create F1c-2 #zero_FP_S2_Ac_7y = (tf,1) #FP_S2_Ac_7y = np.zeros(zero_FP_S2_Ac_7y) #FP_S2_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S2_Ac7_sum, [x*2/3 for x in c_firewood_energy_S2_Ac7])][8:tf] # calculate C stocks in landfill (S-7) tf = 201 zero_matrix_stocks_S1_Ac_7y = (tf,1) stocks_S1_Ac_7y = np.zeros(zero_matrix_stocks_S1_Ac_7y) i = 0 stocks_S1_Ac_7y[0] = C_LF_S1_Ac7[0] - Landfill_decomp_DL_FP_S1_Ac_7y[0] while i < tf-1: stocks_S1_Ac_7y[i+1] = np.array(C_LF_S1_Ac7[i+1] - Landfill_decomp_DL_FP_S1_Ac_7y[i+1] + stocks_S1_Ac_7y[i]) i = i + 1 ## calculate aggregate flow of logged wood (F1-2) HWP_logged_S1_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S1_Ac7_sum, [x*2/3 for x in c_firewood_energy_S1_Ac7])] ## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c) tf = 201 zero_matrix_ForCstocks_S1_Ac_7y = (tf,1) ForCstocks_S1_Ac_7y = np.zeros(zero_matrix_ForCstocks_S1_Ac_7y) i = 0 ForCstocks_S1_Ac_7y[0] = initAGB - flat_list_Ac_7y[0] - decomp_tot_S1_Ac_7y[0] - HWP_logged_S1_Ac_7y[0] while i < tf-1: ForCstocks_S1_Ac_7y[i+1] = np.array(ForCstocks_S1_Ac_7y[i] - flat_list_Ac_7y[i+1] - decomp_tot_S1_Ac_7y[i+1] - HWP_logged_S1_Ac_7y[i+1]) i = i + 1 ##NonRW materials/energy amount (F9-0-1) df1_amount_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_7y') NonRW_amount_S1_Ac_7y = df1_amount_Ac7['NonRW_amount'].values NonRW_amount_S1_Ac_7y = [x/1000 for x in NonRW_amount_S1_Ac_7y] ##NonRW emissions (F9-0-2) emissions_NonRW_S1_Ac_7y = [x/division for x in emissions_NonRW_S1_Ac_7y] #create columns dfM_Ac_7y = pd.DataFrame.from_dict({'Year':Column1, 'F0-1 (t-C)': flat_list_Ac_7y, 'F1-0 (t-C)': decomp_tot_S1_Ac_7y[:,0], #'F1a-2 (t-C)': PF_S1_Ac_7y, #'F1c-2 (t-C)': FP_S1_Ac_7y, 'F1-2 (t-C)': HWP_logged_S1_Ac_7y, 'St-1 (t-C)':ForCstocks_S1_Ac_7y[:,0], 'F2-3 (t-C)': HWP_S1_Ac7_sum, 'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Ac7], 'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Ac7_sum, [x*1/0.82 for x in OC_storage_S1_Ac7], [x*1/3 for x in c_firewood_energy_S1_Ac7])], 'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Ac7], 'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Ac7], # 'F4-0 (t-C)':, 'St-4 (t-C)': TestDSM1_Ac7.s, #'S-4-i (t-C)': TestDSM1_Ac7.i, 'F4-5 (t-C)': TestDSM1_Ac7.o, 'F5-6 (t-C)': TestDSM1_Ac7.o, 'F5-7 (t-C)': C_LF_S1_Ac7, 'F6-0-1 (t-C)': c_firewood_energy_S1_Ac7, 'F6-0-2 (t-C)': TestDSM1_Ac7.o, 'St-7 (t-C)': stocks_S1_Ac_7y[:,0], 'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Ac_7y, 'F8-0 (t-C)': PH_Emissions_HWP1_Ac_7y, 'S9-0 (t)': NonRW_amount_S1_Ac_7y, 'F9-0 (t-C)': emissions_NonRW_S1_Ac_7y, }) ##S1_Ac_18y ## define the input flow for the landfill (F5-7) OC_storage_S1_Ac18 = df1_Ac18['Other_C_storage'].values OC_storage_S1_Ac18 = [x/division for x in OC_storage_S1_Ac18] OC_storage_S1_Ac18 = [abs(number) for number in OC_storage_S1_Ac18] C_LF_S1_Ac18 = [x*1/0.82 for x in OC_storage_S1_Ac18] ## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3) HWP_S1_Ac18 = [x/division for x in df1_Ac18['Input_PF'].values] HWP_S1_Ac18_energy = [x*1/3 for x in c_firewood_energy_S1_Ac18] HWP_S1_Ac18_landfill = [x*1/0.82 for x in OC_storage_S1_Ac18] HWP_S1_Ac18_sum = [HWP_S1_Ac18, HWP_S1_Ac18_energy, HWP_S1_Ac18_landfill] HWP_S1_Ac18_sum = [sum(x) for x in zip(*HWP_S1_Ac18_sum )] ## in-use stocks (S-4) TestDSM1_Ac18.s = [x/division for x in TestDSM1_Ac18.s] #TestDSM1_Ac18.i = [x/division for x in TestDSM1_Ac18.i] #calculate C stocks in landfill (S-7) tf = 201 zero_matrix_stocks_S1_Ac_18y = (tf,1) stocks_S1_Ac_18y = np.zeros(zero_matrix_stocks_S1_Ac_18y) i = 0 stocks_S1_Ac_18y[0] = C_LF_S1_Ac18[0] - Landfill_decomp_DL_FP_S1_Ac_18y[0] while i < tf-1: stocks_S1_Ac_18y[i+1] = np.array(C_LF_S1_Ac18[i+1] - Landfill_decomp_DL_FP_S1_Ac_18y[i+1] + stocks_S1_Ac_18y[i]) i = i + 1 ## calculate aggregate flow of logged wood (F1-2) HWP_logged_S1_Ac_18y = [x1+x2 for (x1,x2) in zip(HWP_S1_Ac18_sum, [x*2/3 for x in c_firewood_energy_S1_Ac18])] ## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c) tf = 201 zero_matrix_ForCstocks_S1_Ac_18y = (tf,1) ForCstocks_S1_Ac_18y = np.zeros(zero_matrix_ForCstocks_S1_Ac_18y) i = 0 ForCstocks_S1_Ac_18y[0] = initAGB - flat_list_Ac_18y[0] - decomp_tot_S1_Ac_18y[0] - HWP_logged_S1_Ac_18y[0] while i < tf-1: ForCstocks_S1_Ac_18y[i+1] = np.array(ForCstocks_S1_Ac_18y[i] - flat_list_Ac_18y[i+1] - decomp_tot_S1_Ac_18y[i+1] - HWP_logged_S1_Ac_18y[i+1]) i = i + 1 ##NonRW materials/energy amount (F9-0-1) df1_amount_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_18y') NonRW_amount_S1_Ac_18y = df1_amount_Ac18['NonRW_amount'].values NonRW_amount_S1_Ac_18y = [x/1000 for x in NonRW_amount_S1_Ac_18y] ##NonRW emissions (F9-0-2) emissions_NonRW_S1_Ac_18y = [x/division for x in emissions_NonRW_S1_Ac_18y] #create columns dfM_Ac_18y = pd.DataFrame.from_dict({'Year':Column1, 'F0-1 (t-C)': flat_list_Ac_18y, 'F1-0 (t-C)': decomp_tot_S1_Ac_18y[:,0], #'F1a-2 (t-C)': PF_S1_Ac_18y, #'F1c-2 (t-C)': FP_S1_Ac_18y, 'F1-2 (t-C)': HWP_logged_S1_Ac_18y, 'St-1 (t-C)':ForCstocks_S1_Ac_18y[:,0], 'F2-3 (t-C)': HWP_S1_Ac18_sum, 'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Ac18], 'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Ac18_sum, [x*1/0.82 for x in OC_storage_S1_Ac18], [x*1/3 for x in c_firewood_energy_S1_Ac18])], 'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Ac18], 'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Ac18], # 'F4-0 (t-C)':, 'St-4 (t-C)': TestDSM1_Ac18.s, #'S-4-i (t-C)': TestDSM1_Ac7.i, 'F4-5 (t-C)': TestDSM1_Ac18.o, 'F5-6 (t-C)': TestDSM1_Ac18.o, 'F5-7 (t-C)': C_LF_S1_Ac18, 'F6-0-1 (t-C)': c_firewood_energy_S1_Ac18, 'F6-0-2 (t-C)': TestDSM1_Ac18.o, 'St-7 (t-C)': stocks_S1_Ac_18y[:,0], 'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Ac_18y, 'F8-0 (t-C)': PH_Emissions_HWP1_Ac_18y, 'S9-0 (t)': NonRW_amount_S1_Ac_18y, 'F9-0 (t-C)': emissions_NonRW_S1_Ac_18y, }) ##S1_Tgr_60y ## define the input flow for the landfill (F5-7) OC_storage_S1_Tgr60 = df1_Tgr60['Other_C_storage'].values OC_storage_S1_Tgr60 = [x/division for x in OC_storage_S1_Tgr60] OC_storage_S1_Tgr60 = [abs(number) for number in OC_storage_S1_Tgr60] C_LF_S1_Tgr60 = [x*1/0.82 for x in OC_storage_S1_Tgr60] ## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3) HWP_S1_Tgr60 = [x/division for x in df1_Tgr60['Input_PF'].values] HWP_S1_Tgr60_energy = [x*1/3 for x in c_firewood_energy_S1_Tgr60] HWP_S1_Tgr60_landfill = [x*1/0.82 for x in OC_storage_S1_Tgr60] HWP_S1_Tgr60_sum = [HWP_S1_Tgr60, HWP_S1_Tgr60_energy, HWP_S1_Tgr60_landfill] HWP_S1_Tgr60_sum = [sum(x) for x in zip(*HWP_S1_Tgr60_sum )] ## in-use stocks (S-4) TestDSM1_Tgr60.s = [x/division for x in TestDSM1_Tgr60.s] #TestDSM1_Tgr60.i = [x/division for x in TestDSM1_Tgr60.i] ## calculate C stocks in landfill (S-7) tf = 201 zero_matrix_stocks_S1_Tgr_60y = (tf,1) stocks_S1_Tgr_60y = np.zeros(zero_matrix_stocks_S1_Tgr_60y) i = 0 stocks_S1_Tgr_60y[0] = C_LF_S1_Tgr60[0] - Landfill_decomp_DL_FP_S1_Tgr_60y[0] while i < tf-1: stocks_S1_Tgr_60y[i+1] = np.array(C_LF_S1_Tgr60[i+1] - Landfill_decomp_DL_FP_S1_Tgr_60y[i+1] + stocks_S1_Tgr_60y[i]) i = i + 1 #print(stocks_S2_Ac_7y[:]) #print(type(stocks_S2_Ac_7y)) #print(type(C_LF_S2_Ac7)) #print(type(Landfill_decomp_PF_FP_S2_Ac_7y)) ## calculate aggregate flow of logged wood (F1-2) HWP_logged_S1_Tgr_60y = [x1+x2 for (x1,x2) in zip(HWP_S1_Tgr60_sum, [x*2/3 for x in c_firewood_energy_S1_Tgr60])] ## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c) tf = 201 zero_matrix_ForCstocks_S1_Tgr_60y = (tf,1) ForCstocks_S1_Tgr_60y = np.zeros(zero_matrix_ForCstocks_S1_Tgr_60y) i = 0 ForCstocks_S1_Tgr_60y[0] = initAGB - flat_list_Tgr_60y[0] - decomp_tot_S1_Tgr_60y[0] - HWP_logged_S1_Tgr_60y[0] while i < tf-1: ForCstocks_S1_Tgr_60y[i+1] = np.array(ForCstocks_S1_Tgr_60y[i] - flat_list_Tgr_60y[i+1] - decomp_tot_S1_Tgr_60y[i+1] - HWP_logged_S1_Tgr_60y[i+1]) i = i + 1 ##NonRW materials/energy amount (F9-0-1) df1_amount_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_60y') NonRW_amount_S1_Tgr_60y = df1_amount_Tgr60['NonRW_amount'].values NonRW_amount_S1_Tgr_60y = [x/1000 for x in NonRW_amount_S1_Tgr_60y] ##NonRW emissions (F9-0-2) emissions_NonRW_S1_Tgr_60y = [x/division for x in emissions_NonRW_S1_Tgr_60y] #create columns dfM_Tgr_60y = pd.DataFrame.from_dict({'Year':Column1, 'F0-1 (t-C)': flat_list_Tgr_60y, 'F1-0 (t-C)': decomp_tot_S1_Tgr_60y[:,0], #'F1a-2 (t-C)': PF_S1_Tgr_60y, #'F1c-2 (t-C)': FP_S1_Tgr_60y, 'F1-2 (t-C)': HWP_logged_S1_Tgr_60y, 'St-1 (t-C)':ForCstocks_S1_Tgr_60y[:,0], 'F2-3 (t-C)': HWP_S1_Tgr60_sum, 'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Tgr60], 'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Tgr60_sum, [x*1/0.82 for x in OC_storage_S1_Tgr60], [x*1/3 for x in c_firewood_energy_S1_Tgr60])], 'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Tgr60], 'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Tgr60], # 'F4-0 (t-C)':, 'St-4 (t-C)': TestDSM1_Tgr60.s, #'S-4-i (t-C)': TestDSM1_Tgr60.i, 'F4-5 (t-C)': TestDSM1_Tgr60.o, 'F5-6 (t-C)': TestDSM1_Tgr60.o, 'F5-7 (t-C)': C_LF_S1_Tgr60, 'F6-0-1 (t-C)': c_firewood_energy_S1_Tgr60, 'F6-0-2 (t-C)': TestDSM1_Tgr60.o, 'St-7 (t-C)': stocks_S1_Tgr_60y[:,0], 'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Tgr_60y, 'F8-0 (t-C)': PH_Emissions_HWP1_Tgr_60y, 'S9-0 (t)': NonRW_amount_S1_Tgr_60y, 'F9-0 (t-C)': emissions_NonRW_S1_Tgr_60y, }) ##S1_E_Hbr_40y ## define the input flow for the landfill (F5-7) OC_storage_E_Hbr40 = dfE_Hbr40['Other_C_storage'].values OC_storage_E_Hbr40 = [x/division for x in OC_storage_E_Hbr40] OC_storage_E_Hbr40 = [abs(number) for number in OC_storage_E_Hbr40] C_LF_E_Hbr40 = [x*1/0.82 for x in OC_storage_E_Hbr40] ## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3) HWP_E_Hbr40 = [x/division for x in dfE_Hbr40['Wood_pellets'].values] HWP_E_Hbr40_energy = [x*1/3 for x in c_firewood_energy_E_Hbr40] HWP_E_Hbr40_landfill = [x*1/0.82 for x in OC_storage_E_Hbr40] HWP_E_Hbr40_sum = [HWP_E_Hbr40, HWP_E_Hbr40_energy, HWP_E_Hbr40_landfill] HWP_E_Hbr40_sum = [sum(x) for x in zip(*HWP_E_Hbr40_sum )] ## in-use stocks (S-4) TestDSME_Hbr40.s = [x/division for x in TestDSME_Hbr40.s] ## calculate C stocks in landfill (S-7) tf = 201 zero_matrix_stocks_E_Hbr_40y = (tf,1) stocks_E_Hbr_40y = np.zeros(zero_matrix_stocks_E_Hbr_40y) i = 0 stocks_E_Hbr_40y[0] = C_LF_E_Hbr40[0] - Landfill_decomp_DL_FP_E_Hbr_40y[0] while i < tf-1: stocks_E_Hbr_40y[i+1] = np.array(C_LF_E_Hbr40[i+1] - Landfill_decomp_DL_FP_E_Hbr_40y[i+1] + stocks_E_Hbr_40y[i]) i = i + 1 ## calculate aggregate flow of logged wood (F1-2) HWP_logged_E_Hbr_40y = [x1+x2 for (x1,x2) in zip(HWP_E_Hbr40_sum, [x*2/3 for x in c_firewood_energy_E_Hbr40])] #calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c) tf = 201 zero_matrix_ForCstocks_E_Hbr_40y = (tf,1) ForCstocks_E_Hbr_40y = np.zeros(zero_matrix_ForCstocks_E_Hbr_40y) i = 0 ForCstocks_E_Hbr_40y[0] = initAGB - flat_list_Hbr_40y[0] - decomp_tot_E_Hbr_40y[0] - HWP_logged_E_Hbr_40y[0] while i < tf-1: ForCstocks_E_Hbr_40y[i+1] = np.array(ForCstocks_E_Hbr_40y[i] - flat_list_Hbr_40y[i+1] - decomp_tot_E_Hbr_40y[i+1] - HWP_logged_E_Hbr_40y[i+1]) i = i + 1 ##NonRW materials/energy amount (F9-0-1) dfE_amount_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_E_Hbr_40y') NonRW_amount_E_Hbr_40y = dfE_amount_Hbr40['NonRW_amount'].values NonRW_amount_E_Hbr_40y = [x/1000 for x in NonRW_amount_E_Hbr_40y] ##NonRW emissions (F9-0-2) emissions_NonRW_E_Hbr_40y = [x/division for x in emissions_NonRW_E_Hbr_40y] #create columns dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Column1, 'F0-1 (t-C)': flat_list_Hbr_40y, 'F1-0 (t-C)': decomp_tot_E_Hbr_40y[:,0], #'F1a-2 (t-C)': PF_S2_Tgr_60y, #'F1c-2 (t-C)': FP_S2_Tgr_60y, 'F1-2 (t-C)': HWP_logged_E_Hbr_40y, 'St-1 (t-C)':ForCstocks_E_Hbr_40y[:,0], 'F2-3 (t-C)': HWP_E_Hbr40_sum, 'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_E_Hbr40], 'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_E_Hbr40_sum, [x*1/0.82 for x in OC_storage_E_Hbr40], [x*1/3 for x in c_firewood_energy_E_Hbr40])], 'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_E_Hbr40], 'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_E_Hbr40], 'F4-0 (t-C)': c_pellets_Hbr_40y, 'St-4 (t-C)': TestDSME_Hbr40.s, #'S-4-i (t-C)': TestDSME_Hbr40.i, 'F4-5 (t-C)': TestDSME_Hbr40.o, 'F5-6 (t-C)': TestDSME_Hbr40.o, 'F5-7 (t-C)': C_LF_E_Hbr40, 'F6-0-1 (t-C)': c_firewood_energy_E_Hbr40, 'F6-0-2 (t-C)': TestDSME_Hbr40.o, 'St-7 (t-C)': stocks_E_Hbr_40y[:,0], 'F7-0 (t-C)': Landfill_decomp_DL_FP_E_Hbr_40y, 'F8-0 (t-C)': PH_Emissions_HWPE_Hbr_40y, 'S9-0 (t)': NonRW_amount_E_Hbr_40y, 'F9-0 (t-C)': emissions_NonRW_E_Hbr_40y, }) writer = pd.ExcelWriter('C_flows_SysDef_DL_FP_EC.xlsx', engine = 'xlsxwriter') dfM_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Ac_7y', header=True, index=False) dfM_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Ac_18y', header=True, index=False) dfM_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Tgr_60y', header=True, index=False) dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_EC_Hbr_40y', header=True, index=False) writer.save() writer.close() #%%

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ed5dc2a53343033ed8ace3353be469af11dc06bd

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Python

projects/vdk-plugins/vdk-trino/tests/test_vdk_trino_lineage_utils.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

100

2021-10-04T09:32:04.000Z

2022-03-30T11:23:53.000Z

projects/vdk-plugins/vdk-trino/tests/test_vdk_trino_lineage_utils.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

208

2021-10-04T16:56:40.000Z

2022-03-31T10:41:44.000Z

projects/vdk-plugins/vdk-trino/tests/test_vdk_trino_lineage_utils.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

14

2021-10-11T14:15:13.000Z

2022-03-11T13:39:17.000Z

# Copyright 2021 VMware, Inc. # SPDX-License-Identifier: Apache-2.0 import json from vdk.plugin.trino.lineage_utils import _get_input_tables_from_explain from vdk.plugin.trino.lineage_utils import _get_lineage_table_from_plan from vdk.plugin.trino.lineage_utils import _lineage_table_from_name from vdk.plugin.trino.lineage_utils import get_rename_table_lineage_from_query from vdk.plugin.trino.lineage_utils import is_heartbeat_query def test_is_heartbeat_query(): assert is_heartbeat_query("select 1") assert is_heartbeat_query("select 'aaa'") assert not is_heartbeat_query("select * from a_table") def test_lineage_table_from_name(): lineage_table = _lineage_table_from_name( table_name="test_table", schema="default_schema", catalog="default_catalog" ) assert lineage_table.catalog == "default_catalog" assert lineage_table.schema == "default_schema" assert lineage_table.table == "test_table" def test_lineage_table_from_name_and_schema(): lineage_table = _lineage_table_from_name( table_name="test_schema.test_table", schema="default_schema", catalog="default_catalog", ) assert lineage_table.catalog == "default_catalog" assert lineage_table.schema == "test_schema" assert lineage_table.table == "test_table" def test_lineage_table_from_name_and_schema_and_catalog(): lineage_table = _lineage_table_from_name( table_name="test_catalog.test_schema.test_table", schema="default_schema", catalog="default_catalog", ) assert lineage_table.catalog == "test_catalog" assert lineage_table.schema == "test_schema" assert lineage_table.table == "test_table" def test_get_lineage_table_from_plan(): table_dict = json.loads( """ { "catalog": "test_catalog", "schemaTable": { "schema": "test_schema", "table": "test_table" } } """ ) lineage_table = _get_lineage_table_from_plan(table_dict) assert lineage_table.catalog == "test_catalog" assert lineage_table.schema == "test_schema" assert lineage_table.table == "test_table" def test_get_input_tables_from_explain(): explain_io_json = """ { "inputTableColumnInfos" : [ { "table" : { "catalog" : "hive", "schemaTable" : { "schema" : "history", "table" : "palexiev2" } }, "columnConstraints" : [ ], "estimate" : { "outputRowCount" : 0.0, "outputSizeInBytes" : 0.0, "cpuCost" : 0.0, "maxMemory" : 0.0, "networkCost" : 0.0 } }, { "table" : { "catalog" : "hive", "schemaTable" : { "schema" : "history", "table" : "palexiev" } }, "columnConstraints" : [ ], "estimate" : { "outputRowCount" : 0.0, "outputSizeInBytes" : 0.0, "cpuCost" : 0.0, "maxMemory" : 0.0, "networkCost" : 0.0 } } ], "estimate" : { "outputRowCount" : 0.0, "outputSizeInBytes" : 0.0, "cpuCost" : 0.0, "maxMemory" : 0.0, "networkCost" : 0.0 } } """ explain_dict = json.loads(explain_io_json) lineage_tables = _get_input_tables_from_explain( explain_dict["inputTableColumnInfos"] ) table1 = lineage_tables[0] assert table1.catalog == "hive" assert table1.schema == "history" assert table1.table == "palexiev2" table2 = lineage_tables[1] assert table2.catalog == "hive" assert table2.schema == "history" assert table2.table == "palexiev" def test_get_rename_table_lineage_from_query(): query = "alter table tbl_from rename to tbl_to" lineage_data = get_rename_table_lineage_from_query( query, "test_schema", "test_catalog" ) assert lineage_data is not None assert lineage_data.query == query assert lineage_data.query_type == "rename_table" assert lineage_data.query_status == "OK" assert lineage_data.input_tables is not None assert len(lineage_data.input_tables) == 1 assert lineage_data.input_tables[0].table == "tbl_from" assert lineage_data.input_tables[0].schema == "test_schema" assert lineage_data.input_tables[0].catalog == "test_catalog" assert lineage_data.output_table is not None assert lineage_data.output_table.table == "tbl_to" assert lineage_data.output_table.schema == "test_schema" assert lineage_data.output_table.catalog == "test_catalog" def test_get_rename_table_lineage_from_query_with_schema(): query = "alter table test_schema.tbl_from rename to test_schema.tbl_to" lineage_data = get_rename_table_lineage_from_query( query, "wrong_schema", "test_catalog" ) assert lineage_data is not None assert lineage_data.query == query assert lineage_data.query_type == "rename_table" assert lineage_data.query_status == "OK" assert lineage_data.input_tables is not None assert len(lineage_data.input_tables) == 1 assert lineage_data.input_tables[0].table == "tbl_from" assert lineage_data.input_tables[0].schema == "test_schema" assert lineage_data.input_tables[0].catalog == "test_catalog" assert lineage_data.output_table is not None assert lineage_data.output_table.table == "tbl_to" assert lineage_data.output_table.schema == "test_schema" assert lineage_data.output_table.catalog == "test_catalog" def test_get_rename_table_lineage_from_query_full_names(): query = "alter table test_catalog.test_schema.tbl_from rename to test_catalog.test_schema.tbl_to" lineage_data = get_rename_table_lineage_from_query( query, "wrong_schema", "wrong_catalog" ) assert lineage_data is not None assert lineage_data.query == query assert lineage_data.query_type == "rename_table" assert lineage_data.query_status == "OK" assert lineage_data.input_tables is not None assert len(lineage_data.input_tables) == 1 assert lineage_data.input_tables[0].table == "tbl_from" assert lineage_data.input_tables[0].schema == "test_schema" assert lineage_data.input_tables[0].catalog == "test_catalog" assert lineage_data.output_table is not None assert lineage_data.output_table.table == "tbl_to" assert lineage_data.output_table.schema == "test_schema" assert lineage_data.output_table.catalog == "test_catalog" def test_get_rename_table_lineage_from_query_false_cases(): assert ( get_rename_table_lineage_from_query( "alter table tbl1 add column col1 int", "test_schema", "test_catalog" ) is None ) assert ( get_rename_table_lineage_from_query( "alter table tbl1 rename column col1 to col2", "test_schema", "test_catalog", ) is None ) assert ( get_rename_table_lineage_from_query( "alter view view1 rename to view2", "test_schema", "test_catalog" ) is None )

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ed7434d2a3bb706cb2fbdda02b7f5c9aa27cb512

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py

Python

G4 Localizer/a2_Camera/cameraModel.py

cbrahana/FRC-Localizer-Systems

740c88ec6e0af490e703e8a5c544434c0f33ee0b

[ "MIT" ]

null

G4 Localizer/a2_Camera/cameraModel.py

cbrahana/FRC-Localizer-Systems

740c88ec6e0af490e703e8a5c544434c0f33ee0b

[ "MIT" ]

null

G4 Localizer/a2_Camera/cameraModel.py

cbrahana/FRC-Localizer-Systems

740c88ec6e0af490e703e8a5c544434c0f33ee0b

[ "MIT" ]

null

class Camera: def __init__(): #Need to have all information necessary to calibrate camera input into here as arguements #Calibrate Camera - constant #Locate Camera Relative to Centerpoint - constant #Define GPIO pins for synchronization - constant #Change camera settings - tunable return None

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ed7765e2ab92212613889bc15172427bc5f6b297

56

py

Python

utime/hypnogram/__init__.py

aluquecerp/U-Time

c792259825b57e49544684ce2997f3ac8db84c6e

[ "MIT" ]

138

2019-11-20T02:31:17.000Z

2022-03-23T04:31:51.000Z

utime/hypnogram/__init__.py

amiyapatanaik/U-Time

a9ed4892da77d165a71dbfef1d069d782c909757

[ "MIT" ]

46

2019-12-04T03:13:28.000Z

2022-03-31T13:10:48.000Z

utime/hypnogram/__init__.py

amiyapatanaik/U-Time

a9ed4892da77d165a71dbfef1d069d782c909757

[ "MIT" ]

42

2019-11-26T16:02:26.000Z

2022-01-06T11:01:32.000Z

from .hypnograms import SparseHypnogram, DenseHypnogram

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212

py

Python

emos/__init__.py

CubeFlix/emos

7f84100908e78384c82777ec3bee0cc1b130cefb

[ "MIT" ]

1

2021-05-26T17:41:07.000Z

emos/__init__.py

CubeFlix/emos

7f84100908e78384c82777ec3bee0cc1b130cefb

[ "MIT" ]

null

emos/__init__.py

CubeFlix/emos

7f84100908e78384c82777ec3bee0cc1b130cefb

[ "MIT" ]

null

""" - EMOS Main Source Code - (C) Cubeflix 2021 (EMOS) """ # Imports from .misc import * from .memory import * from .cpu import * from .operatingsystem import * from .computer import * from .screen import *

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9c3aab888d3f65753a4185dde758c2dad29802a9

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py

Python

terrascript/spotinst/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/spotinst/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/spotinst/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/spotinst/__init__.py import terrascript class spotinst(terrascript.Provider): pass

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9c4506a37458e4187e29a1ff1afd9c962565ea1d

122

py

Python

pafd/admin.py

kavinzhao/fduhole

508922cfa0558c58b95206dd8fbf51d10525fa1e

[ "Apache-2.0" ]

9

2021-04-14T12:08:38.000Z

2021-12-16T08:14:40.000Z

pafd/admin.py

kavinzhao/fduhole

508922cfa0558c58b95206dd8fbf51d10525fa1e

[ "Apache-2.0" ]

9

2021-04-18T09:48:25.000Z

2021-11-26T07:43:22.000Z

pafd/admin.py

kavinzhao/fduhole

508922cfa0558c58b95206dd8fbf51d10525fa1e

[ "Apache-2.0" ]

4

2021-07-15T02:10:42.000Z

2022-01-22T02:12:11.000Z

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

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9c7d79b032a1a7393980f36ae8b429df16d52012

2,376

py

Python

migrations/versions/0d14cc3a1cf1_add_private_key_and_csr_to_certificate_.py

cloud-gov/legacy-domain-certificate-renewer

6b008fdc8e1277cfe4449626e6c488d11fc4857c

[ "CC0-1.0" ]

1

2021-11-16T17:25:21.000Z

migrations/versions/0d14cc3a1cf1_add_private_key_and_csr_to_certificate_.py

cloud-gov/legacy-domain-certificate-renewer

6b008fdc8e1277cfe4449626e6c488d11fc4857c

[ "CC0-1.0" ]

1

2021-12-22T19:04:34.000Z

migrations/versions/0d14cc3a1cf1_add_private_key_and_csr_to_certificate_.py

cloud-gov/legacy-domain-certificate-renewer

6b008fdc8e1277cfe4449626e6c488d11fc4857c

[ "CC0-1.0" ]

null

"""add private key and csr to certificate, track certificate in operation Revision ID: 0d14cc3a1cf1 Revises: 531893054cdf Create Date: 2021-09-10 23:03:20.631787 """ from alembic import op import sqlalchemy as sa import sqlalchemy_utils # revision identifiers, used by Alembic. revision = "0d14cc3a1cf1" down_revision = "531893054cdf" branch_labels = None depends_on = None def upgrade(engine_name): globals()["upgrade_%s" % engine_name]() def downgrade(engine_name): globals()["downgrade_%s" % engine_name]() def upgrade_cdn(): op.add_column( "certificates", sa.Column( "private_key_pem", sqlalchemy_utils.types.encrypted.encrypted_type.StringEncryptedType(), nullable=True, ), ) op.add_column("certificates", sa.Column("csr_pem", sa.Text(), nullable=True)) op.add_column( "operations", sa.Column("certificate_id", sa.Integer(), nullable=True) ) op.create_foreign_key( op.f("fk_operations_certificate_id_certificates"), "operations", "certificates", ["certificate_id"], ["id"], ) def downgrade_cdn(): op.drop_constraint( op.f("fk_operations_certificate_id_certificates"), "operations", type_="foreignkey", ) op.drop_column("operations", "certificate_id") op.drop_column("certificates", "csr_pem") op.drop_column("certificates", "private_key_pem") def upgrade_domain(): op.add_column( "certificates", sa.Column( "private_key_pem", sqlalchemy_utils.types.encrypted.encrypted_type.StringEncryptedType(), nullable=True, ), ) op.add_column("certificates", sa.Column("csr_pem", sa.Text(), nullable=True)) op.add_column( "operations", sa.Column("certificate_id", sa.Integer(), nullable=True) ) op.create_foreign_key( op.f("fk_operations_certificate_id_certificates"), "operations", "certificates", ["certificate_id"], ["id"], ) def downgrade_domain(): op.drop_constraint( op.f("fk_operations_certificate_id_certificates"), "operations", type_="foreignkey", ) op.drop_column("operations", "certificate_id") op.drop_column("certificates", "csr_pem") op.drop_column("certificates", "private_key_pem")

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92bcda30a2c5b17469f5f875c18b381cd8272fad

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py

Python

courses/backend/django-for-everybody/Web Application Technologies and Django/resources/dj4e-samples/tagme/admin.py

Nahid-Hassan/fullstack-software-development

892ffb33e46795061ea63378279a6469de317b1a

[ "CC0-1.0" ]

297

2019-01-25T08:44:08.000Z

2022-03-29T18:46:08.000Z

courses/backend/django-for-everybody/Web Application Technologies and Django/resources/dj4e-samples/tagme/admin.py

Nahid-Hassan/fullstack-software-development

892ffb33e46795061ea63378279a6469de317b1a

[ "CC0-1.0" ]

22

2019-05-06T14:21:04.000Z

2022-02-21T10:05:25.000Z

courses/backend/django-for-everybody/Web Application Technologies and Django/resources/dj4e-samples/tagme/admin.py

Nahid-Hassan/fullstack-software-development

892ffb33e46795061ea63378279a6469de317b1a

[ "CC0-1.0" ]

412

2019-02-12T20:44:43.000Z

2022-03-30T04:23:25.000Z

from django.contrib import admin # Register your models here. from tagme.models import Forum, Comment admin.site.register(Forum) admin.site.register(Comment)

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92c04fd9a550435a0d5f9a86e5aa73256572f8b4

207

py

Python

decomp/semantics/__init__.py

esteng/decomp

a6996b379e4a5e1a70a28b2b6f86bf39160ee10b

[ "MIT" ]

48

2019-10-01T13:33:24.000Z

2022-02-14T13:58:57.000Z

decomp/semantics/__init__.py

esteng/decomp

a6996b379e4a5e1a70a28b2b6f86bf39160ee10b

[ "MIT" ]

15

2019-10-01T15:01:36.000Z

2021-05-25T17:23:22.000Z

decomp/semantics/__init__.py

esteng/decomp

a6996b379e4a5e1a70a28b2b6f86bf39160ee10b

[ "MIT" ]

9

2020-03-02T17:54:17.000Z

2021-06-17T19:53:53.000Z

""" Module for representing PredPatt and UDS graphs This module represents PredPatt and UDS graphs using networkx. It incorporates the dependency parse-based graphs from the syntax module as subgraphs. """

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92c161d079c65843de6ca5fac529e3cb181171a2

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py

Python

{{cookiecutter.package_name}}/{{cookiecutter.package_name}}/__init__.py

kevinbache/python-data-project

867cb9650d86c4049f382a54d5c02210c6901e59

[ "MIT" ]

null

{{cookiecutter.package_name}}/{{cookiecutter.package_name}}/__init__.py

kevinbache/python-data-project

867cb9650d86c4049f382a54d5c02210c6901e59

[ "MIT" ]

null

{{cookiecutter.package_name}}/{{cookiecutter.package_name}}/__init__.py

kevinbache/python-data-project

867cb9650d86c4049f382a54d5c02210c6901e59

[ "MIT" ]

null

"""{{ cookiecutter.package_name }} - {{ cookiecutter.package_description }}""" __version__ = '{{ cookiecutter.package_version }}' __author__ = '{{ cookiecutter.author_name }} <{{ cookiecutter.author_email }}>' __all__ = []

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5

92cbe41d5ffac041aa53916a162299bb2ec2306e

1,196

py

Python

examples/demo_gen_annular_FPM.py

kian1377/falco-python

a9666629845fc72957cd89339f924b9cfb7ce6f5

[ "Apache-2.0" ]

4

2019-05-22T22:24:01.000Z

2021-07-21T13:32:36.000Z

examples/demo_gen_annular_FPM.py

kian1377/falco-python

a9666629845fc72957cd89339f924b9cfb7ce6f5

[ "Apache-2.0" ]

11

2018-06-22T01:05:07.000Z

2021-11-03T13:46:25.000Z

examples/demo_gen_annular_FPM.py

kian1377/falco-python

a9666629845fc72957cd89339f924b9cfb7ce6f5

[ "Apache-2.0" ]

2

2018-06-21T23:58:06.000Z

2021-07-13T21:25:23.000Z

import sys sys.path.insert(0,"../") import falco import numpy as np import matplotlib.pyplot as plt inputs = {} inputs["FPMampFac"] = 0. inputs["pixresFPM"] = 3 inputs["rhoInner"] = 6.5 inputs["centering"] = 'pixel' # %% With Outer Ring inputs["rhoOuter"] = 20.0 fpm = falco.mask.falco_gen_annular_FPM(inputs) plt.imshow(fpm); plt.colorbar(); plt.pause(0.1) if("centering" in inputs.keys()): # Check symmetry if inputs["centering"]=='pixel': plt.imshow(fpm[1::,1::]-np.fliplr(fpm[1::,1::])); plt.colorbar(); plt.pause(0.1) #--Check centering elif inputs["centering"]=='interpixel': plt.imshow(fpm-np.fliplr(fpm)); plt.colorbar(); plt.pause(0.1) #--Check centering # %% Without Outer Ring inputs["rhoOuter"] = np.Infinity fpm = falco.mask.falco_gen_annular_FPM(inputs) plt.imshow(fpm); plt.colorbar(); plt.pause(0.1) if("centering" in inputs.keys()): # Check symmetry if inputs["centering"]=='pixel': plt.imshow(fpm[1::,1::]-np.fliplr(fpm[1::,1::])); plt.colorbar(); plt.pause(0.1) #--Check centering elif inputs["centering"]=='interpixel': plt.imshow(fpm-np.fliplr(fpm)); plt.colorbar(); plt.pause(0.1) #--Check centering

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92cfb8ab0843c53700a6fac8d59707f34be2d7ed

45

py

Python

reqsit/__init__.py

mattyWuh/reqsit

c4b6075c0f2e0e47263c5f9d5966c3b382148de1

[ "MIT" ]

1

2021-11-15T18:52:56.000Z

tests/__init__.py

mattyWuh/reqsit

c4b6075c0f2e0e47263c5f9d5966c3b382148de1

[ "MIT" ]

null

tests/__init__.py

mattyWuh/reqsit

c4b6075c0f2e0e47263c5f9d5966c3b382148de1

[ "MIT" ]

null

"""Copyright matt witt 2021, MIT License."""

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92fa8ba51f239bdaf98242c556145a65055f137c

324

py

Python

tests/linter.py

Conor-Behard333/covid_alarm_clock

a53c58164da2bc8f0fd257e4aa5a6662abab44fa

[ "MIT" ]

null

tests/linter.py

Conor-Behard333/covid_alarm_clock

a53c58164da2bc8f0fd257e4aa5a6662abab44fa

[ "MIT" ]

null

tests/linter.py

Conor-Behard333/covid_alarm_clock

a53c58164da2bc8f0fd257e4aa5a6662abab44fa

[ "MIT" ]

null

"""Used to test the formatting of the code using pylint""" import pylint.lint pylint_opts = ['../logger_setup.py', '../main.py', '../api_handling/get_config_info.py', '../api_handling/get_covid_info.py', '../api_handling/get_news_info.py', '../api_handling/get_weather_info.py'] pylint.lint.Run(pylint_opts)

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133393bc0891217bd2d5ce2124d5af09e5ccdcbc

212

py

Python

first_app/admin.py

Vedant1202/Django-Demo

6c7e21d210c9b32bcb5ecb8e7cce2a33ac5c21cc

[ "MIT" ]

null

first_app/admin.py

Vedant1202/Django-Demo

6c7e21d210c9b32bcb5ecb8e7cce2a33ac5c21cc

[ "MIT" ]

null

first_app/admin.py

Vedant1202/Django-Demo

6c7e21d210c9b32bcb5ecb8e7cce2a33ac5c21cc

[ "MIT" ]

null

from django.contrib import admin from first_app.models import Topic, Webpage, AccessRecord # Register your models here. admin.site.register(Topic) admin.site.register(Webpage) admin.site.register(AccessRecord)

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133807713aa0a037db3203058038d938705abaad

6,644

py

Python

unit_test/test_snfolds.py

melloddy/MELLODDY-TUNERv1

37a70402bee53fc4aa221257213e87ac4d6d750a

[ "MIT" ]

24

2020-08-28T14:58:15.000Z

2021-12-30T14:40:16.000Z

unit_test/test_snfolds.py

melloddy/MELLODDY-TUNERv1

37a70402bee53fc4aa221257213e87ac4d6d750a

[ "MIT" ]

null

unit_test/test_snfolds.py

melloddy/MELLODDY-TUNERv1

37a70402bee53fc4aa221257213e87ac4d6d750a

[ "MIT" ]

6

2021-04-02T12:51:15.000Z

2022-03-15T21:44:57.000Z

import os import unittest import sys from melloddy_tuner.utils.config import ConfigDict, SecretDict from melloddy_tuner.utils.scaffold_folding import ScaffoldFoldAssign from melloddy_tuner.utils.df_transformer import DfTransformer import filecmp from pathlib import Path curDir = os.path.dirname(os.path.abspath(__file__)) print(curDir) class SNFoldCalculationTests(unittest.TestCase): ############################ #### setup and teardown #### ############################ # executed after each test def tearDown(self): pass def setUp(self): self.config = ConfigDict( config_path=Path( os.path.join(curDir, "reference_files", "example_parameters.json") ) ).get_parameters() self.keys = SecretDict( key_path=Path(os.path.join(curDir, "reference_files", "example_key.json")) ).get_secrets() # def defineConfig(self,fp=3): # if(fp==3): # tuner.config.parameters.get_parameters(path=curDir+"/../tests/structure_preparation_test/example_parameters.json") # else: # tuner.config.parameters.get_parameters(path=curDir+"/input/ecfp2_param.json") # # def defineConfigNewSecret(self): # tuner.config.parameters.get_parameters(path=curDir+"/input/new_secret_param.json") ############### #### tests #### ############### def test_calculate_snfold_single_hard(self): """test the single claculation based on hard coded parameters""" input_smiles = ( "Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C" # imatinib ) sa = ScaffoldFoldAssign(nfolds=5, secret="melloddy") result_actual = sa.calculate_single(input_smiles) result_expected = ( "O=C(Nc1cccc(Nc2nccc(-c3cccnc3)n2)c1)c1ccc(CN2CCNCC2)cc1", "c1ccc(Nc2nccc(-c3cccnc3)n2)cc1", 2, True, None, ) self.assertEqual(result_actual, result_expected) def test_calculate_snfold_single_config(self): """test the single claculation based on config file conent""" input_smiles = ( "Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C" # imatinib ) sa = ScaffoldFoldAssign( nfolds=self.config["scaffold_folding"]["nfolds"], secret=self.keys["key"] ) result_actual = sa.calculate_single(input_smiles) result_expected = ( "O=C(Nc1cccc(Nc2nccc(-c3cccnc3)n2)c1)c1ccc(CN2CCNCC2)cc1", "c1ccc(Nc2nccc(-c3cccnc3)n2)cc1", 2, True, None, ) self.assertEqual(result_actual, result_expected) def test_calculate_sn_fold_multiple(self): infile = os.path.join(curDir, "input", "test_sn_fold_input.csv") outfile = os.path.join(curDir, "output", "tmp", "sn_fold_output.csv") output_columns = [ "murcko_smiles", "sn_smiles", "fold_id", "success", "error_message", ] output_types = ["object", "object", "int", "bool", "object"] sa = ScaffoldFoldAssign( nfolds=self.config["scaffold_folding"]["nfolds"], secret=self.keys["key"] ) dt = DfTransformer( sa, input_columns={"canonical_smiles": "smiles"}, output_columns=output_columns, output_types=output_types, ) dt.process_file(infile, outfile) result = filecmp.cmp( os.path.join(curDir, "output", "test_sn_fold_output.csv"), os.path.join(outfile), shallow=False, ) self.assertEqual(result, True) def test_calculate_sn_fold_multiple_split(self): infile = os.path.join(curDir, "input", "test_sn_fold_input.csv") outfile = os.path.join(curDir, "output", "tmp", "sn_fold_output.OK.csv") errfile = os.path.join(curDir, "output", "tmp", "sn_fold_output.failed.csv") output_columns = [ "murcko_smiles", "sn_smiles", "fold_id", "success", "error_message", ] output_types = ["object", "object", "int", "bool", "object"] sa = ScaffoldFoldAssign( nfolds=self.config["scaffold_folding"]["nfolds"], secret=self.keys["key"] ) dt = DfTransformer( sa, input_columns={"canonical_smiles": "smiles"}, output_columns=output_columns, output_types=output_types, success_column="success", ) dt.process_file(infile, outfile, error_file=errfile) result_OK = filecmp.cmp( os.path.join(curDir, "output", "test_sn_fold_output.OK.csv"), os.path.join(outfile), shallow=False, ) result_failed = filecmp.cmp( os.path.join(curDir, "output", "test_sn_fold_output.failed.csv"), os.path.join(errfile), shallow=False, ) self.assertEqual(result_OK & result_failed, True) def test_calculate_sn_fold_multiple_split_par(self): infile = os.path.join(curDir, "input", "test_sn_fold_input.csv") outfile = os.path.join( curDir, "output", "tmp", "sn_fold_output_parallel.OK.csv" ) errfile = os.path.join( curDir, "output", "tmp", "sn_fold_output.parallel.failed.csv" ) output_columns = [ "murcko_smiles", "sn_smiles", "fold_id", "success", "error_message", ] output_types = ["object", "object", "int", "bool", "object"] sa = ScaffoldFoldAssign( nfolds=self.config["scaffold_folding"]["nfolds"], secret=self.keys["key"] ) dt = DfTransformer( sa, input_columns={"canonical_smiles": "smiles"}, output_columns=output_columns, output_types=output_types, success_column="success", nproc=2, ) dt.process_file(infile, outfile, error_file=errfile) result_OK = filecmp.cmp( os.path.join(curDir, "output", "test_sn_fold_output.OK.csv"), os.path.join(outfile), shallow=False, ) result_failed = filecmp.cmp( os.path.join(curDir, "output", "test_sn_fold_output.failed.csv"), os.path.join(errfile), shallow=False, ) self.assertEqual(result_OK & result_failed, True) # if __name__ == "__main__": # unittest.main()

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state_management/__init__.py

johnurbanik/prospector

a014a5b8767320e2a3937a79db2d364f8e6da1c8

[ "MIT" ]

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state_management/__init__.py

johnurbanik/prospector

a014a5b8767320e2a3937a79db2d364f8e6da1c8

[ "MIT" ]

null

state_management/__init__.py

johnurbanik/prospector

a014a5b8767320e2a3937a79db2d364f8e6da1c8

[ "MIT" ]

null

from state_management.question_state import QuestionManager

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py

Python

tuto/00_hello.py

gb6612/py_tuto

37f171287c025903cc9003c266618aadcb3f0052

[ "MIT" ]

null

tuto/00_hello.py

gb6612/py_tuto

37f171287c025903cc9003c266618aadcb3f0052

[ "MIT" ]

null

tuto/00_hello.py

gb6612/py_tuto

37f171287c025903cc9003c266618aadcb3f0052

[ "MIT" ]

null

# Print something to the terminal print("Hey there! I'm your very first python script") # you can comment this way... input("Press Enter to continue...")

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py

Python

src/anu/data/pipelines/__init__.py

ankitskvmdam/anu

699598fb60dcc23f6cccd5abb30a03b294d21598

[ "MIT" ]

null

src/anu/data/pipelines/__init__.py

ankitskvmdam/anu

699598fb60dcc23f6cccd5abb30a03b294d21598

[ "MIT" ]

null

src/anu/data/pipelines/__init__.py

ankitskvmdam/anu

699598fb60dcc23f6cccd5abb30a03b294d21598

[ "MIT" ]

null

"""Pipeline related modules stay here."""

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13855a0431b7e519d6002edd45076f4b8a4e3e82

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py

Python

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/core/serializers.py

e-dang/cookiecutter-django

2ba986296de1d5a086e73cde746d6fc7366f149c

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/core/serializers.py

e-dang/cookiecutter-django

2ba986296de1d5a086e73cde746d6fc7366f149c

[ "BSD-3-Clause" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/core/serializers.py

e-dang/cookiecutter-django

2ba986296de1d5a086e73cde746d6fc7366f149c

[ "BSD-3-Clause" ]

null

from rest_framework import serializers class DetailResponseSerializer(serializers.Serializer): detail = serializers.CharField(read_only=True) class NonFieldErrorResponseSerializer(serializers.Serializer): non_field_errors = serializers.CharField(read_only=True)

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py

Python

ci/infra/testrunner/kubectl/__init__.py

manuelbuil/skuba

71770c969f59275d6f7fb7a788635fcce6900bee

[ "Apache-2.0" ]

72

2019-07-18T13:01:36.000Z

2022-03-05T04:14:06.000Z

ci/infra/testrunner/kubectl/__init__.py

manuelbuil/skuba

71770c969f59275d6f7fb7a788635fcce6900bee

[ "Apache-2.0" ]

602

2019-07-18T13:48:04.000Z

2021-09-27T14:10:30.000Z

ci/infra/testrunner/kubectl/__init__.py

manuelbuil/skuba

71770c969f59275d6f7fb7a788635fcce6900bee

[ "Apache-2.0" ]

90

2019-07-18T09:27:52.000Z

2020-12-08T15:57:27.000Z

from kubectl.kubectl import Kubectl

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py

Python

experiments/performance_eval/DeepDTA/data_utils/__init_.py

giosumarin/compare_dnn_compression

7de9760af6b2031c588dc6c09bbfc3ca33fd14da

[ "Apache-2.0" ]

1

2022-03-18T11:05:15.000Z

experiments/performance_eval/DeepDTA/data_utils/__init_.py

giosumarin/compare_dnn_compression

7de9760af6b2031c588dc6c09bbfc3ca33fd14da

[ "Apache-2.0" ]

null

experiments/performance_eval/DeepDTA/data_utils/__init_.py

giosumarin/compare_dnn_compression

7de9760af6b2031c588dc6c09bbfc3ca33fd14da

[ "Apache-2.0" ]

null

from data_utils import datahelper_noflag, davis_dataset

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py

Python

venv/lib/python3.8/site-packages/pip/_vendor/chardet/euctwprober.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/pip/_vendor/chardet/euctwprober.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/pip/_vendor/chardet/euctwprober.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/d7/7a/7a/10fe3245ac6a9cfe221edc47389e91db3c47ab5fe6f214d18f3559f797

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5

b92fe2d7eaa82c839735f3a432ffead8cb48f5c4

435

py

Python

gpseqc/__init__.py

ggirelli/gpseq-centrality-estimate

9e911c360e2abddc688ea3fb4390bc0f8e2bfed3

[ "MIT" ]

1

2020-08-21T07:19:49.000Z

gpseqc/__init__.py

ggirelli/gpseq-centrality-estimate

9e911c360e2abddc688ea3fb4390bc0f8e2bfed3

[ "MIT" ]

1

2019-01-29T08:21:21.000Z

gpseqc/__init__.py

ggirelli/gpseq-centrality-estimate

9e911c360e2abddc688ea3fb4390bc0f8e2bfed3

[ "MIT" ]

2

2020-07-16T11:09:22.000Z

2020-08-21T07:19:54.000Z

# -*- coding: utf-8 -*- ''' @author: Gabriele Girelli @email: gigi.ga90@gmail.com @description: GPSeq Centrality Estimation package. ''' # DEPENDENCIES ================================================================= from gpseqc import bed, centrality, cutsite_domain # END ========================================================================== ################################################################################

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py

Python

netbox_secretstore/utils/tables.py

Onemind-Services-LLC/netbox-secretstore

cfe8b813ed5997b0b8566d00cca90991fc87b55b

[ "Apache-2.0" ]

null

netbox_secretstore/utils/tables.py

Onemind-Services-LLC/netbox-secretstore

cfe8b813ed5997b0b8566d00cca90991fc87b55b

[ "Apache-2.0" ]

null

netbox_secretstore/utils/tables.py

Onemind-Services-LLC/netbox-secretstore

cfe8b813ed5997b0b8566d00cca90991fc87b55b

[ "Apache-2.0" ]

null

from netbox.tables import columns class PluginButtonsColumn(columns.ActionsColumn): pass

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py

Python

EJERCICIO2/animal.py

Germiprogramer/PRIMERPARCIAL

01a2c95a4b259829ad04e5439beae53e6734b034

[ "Apache-2.0" ]

null

EJERCICIO2/animal.py

Germiprogramer/PRIMERPARCIAL

01a2c95a4b259829ad04e5439beae53e6734b034

[ "Apache-2.0" ]

null

EJERCICIO2/animal.py

Germiprogramer/PRIMERPARCIAL

01a2c95a4b259829ad04e5439beae53e6734b034

[ "Apache-2.0" ]

null

class Animal: def __init__(self, nombre, tamaño): self.nombre = nombre self.tamaño = tamaño def get_nombre(self): return self.nombre def set_nombre(self, a): self.nombre = a

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null

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b95aac929561cfbf233253bbd8e7225b97cea630

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py

Python

deploy/resources/deployrpm.py

zhxiaohe/starwars_api

f1b729e819eb19e5eb59630bed56b13127eb1ef2

[ "MIT" ]

null

deploy/resources/deployrpm.py

zhxiaohe/starwars_api

f1b729e819eb19e5eb59630bed56b13127eb1ef2

[ "MIT" ]

null

deploy/resources/deployrpm.py

zhxiaohe/starwars_api

f1b729e819eb19e5eb59630bed56b13127eb1ef2

[ "MIT" ]

null

from flask.ext.restful import Resource from common.util import login_required class DeployManager(Resource): method_decorators = [login_required] def get(self): return 'Auth Token success'

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5

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566

py

Python

tasks/dirs.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

2

2020-08-27T08:40:22.000Z

2021-04-14T15:42:09.000Z

tasks/dirs.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

tasks/dirs.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

import os import seaice.nasateam as nt def this_dir(): return os.path.dirname(os.path.abspath(__file__)) def parent_dir(): return os.path.dirname(this_dir()) def default_config_dir(): return os.path.join(parent_dir(), 'seaice.tools', 'configs') def output_image_dir(): return os.path.join(parent_dir(), 'test_output_images') def output_dir(): return os.path.join(parent_dir(), 'test_output') def data_dir(): return os.path.join(parent_dir(), 'data', 'xlsify') def datastore_directory(): return nt.DATA_STORE_BASE_DIRECTORY

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5

b98c77ac54f0888487feee1d4446a0c0fcee1a1e

81

py

Python

segmentfault/apps/circle/tasks.py

Yookyiss/segmentfault

8fb7890c8b650ac34541a8fb14c3cd9bef98d120

[ "MIT" ]

null

segmentfault/apps/circle/tasks.py

Yookyiss/segmentfault

8fb7890c8b650ac34541a8fb14c3cd9bef98d120

[ "MIT" ]

12

2020-02-12T01:14:42.000Z

2022-03-11T23:54:43.000Z

segmentfault/apps/circle/tasks.py

Yookyiss/segmentfault

8fb7890c8b650ac34541a8fb14c3cd9bef98d120

[ "MIT" ]

null

# -*- coding:utf-8 -*- # @Time : 2019/7/29 11:39 PM # @Author : __wutonghe__

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null

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5

b99ab66bd66c9cd85ce315540478197f26f753a7

248

py

Python

backend/main/views.py

adityamittl/ThankHut

caf76c12f90b08c1b7d449930bf51a975cd8959f

[ "MIT" ]

null

backend/main/views.py

adityamittl/ThankHut

caf76c12f90b08c1b7d449930bf51a975cd8959f

[ "MIT" ]

null

backend/main/views.py

adityamittl/ThankHut

caf76c12f90b08c1b7d449930bf51a975cd8959f

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def index(request): return render(request, 'index.html') def note(request): return render(request, 'note.html') def send(request): return render(request, 'send.html')

15.5

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b9cde4d25a4ccb5a12098cf998eae765df2e7aed

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Python

server/camphoric/migrations/0015_auto_20201128_0134.py

willfulbard/camphoric

32e30e88b97a905dbde00229a34f01cb05316e08

[ "MIT" ]

2

2020-09-25T01:20:14.000Z

2021-08-18T18:49:47.000Z

server/camphoric/migrations/0015_auto_20201128_0134.py

camphoric/camphoric

31ad94d0da61cd649fe55b74adfb83196eef0011

[ "MIT" ]

57

2020-05-30T03:22:56.000Z

2022-03-07T01:52:11.000Z

server/camphoric/migrations/0015_auto_20201128_0134.py

camphoric/camphoric

31ad94d0da61cd649fe55b74adfb83196eef0011

[ "MIT" ]

1

2020-01-24T04:30:07.000Z

# Generated by Django 3.1.2 on 2020-11-28 01:34 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('camphoric', '0014_auto_20201025_0058'), ] operations = [ migrations.AlterField( model_name='camper', name='attributes', field=models.JSONField(null=True), ), migrations.AlterField( model_name='deposit', name='attributes', field=models.JSONField(null=True), ), migrations.AlterField( model_name='event', name='camper_pricing_logic', field=models.JSONField(help_text='JsonLogic Camper-level pricing components', null=True), ), migrations.AlterField( model_name='event', name='camper_schema', field=models.JSONField(help_text='JSON schema for Camper.attributes', null=True), ), migrations.AlterField( model_name='event', name='confirmation_email_template', field=models.JSONField(default=list, help_text='JsonLogic template'), ), migrations.AlterField( model_name='event', name='confirmation_page_template', field=models.JSONField(default=list, help_text='JsonLogic template'), ), migrations.AlterField( model_name='event', name='deposit_schema', field=models.JSONField(help_text='JSON schema for Deposit.attributes', null=True), ), migrations.AlterField( model_name='event', name='payment_schema', field=models.JSONField(help_text='JSON schema for Payment.attributes', null=True), ), migrations.AlterField( model_name='event', name='pricing', field=models.JSONField(help_text='key-value object with pricing variables', null=True), ), migrations.AlterField( model_name='event', name='registration_pricing_logic', field=models.JSONField(help_text='JsonLogic Registration-level pricing components', null=True), ), migrations.AlterField( model_name='event', name='registration_schema', field=models.JSONField(help_text='JSON schema for Registration.attributes', null=True), ), migrations.AlterField( model_name='event', name='registration_ui_schema', field=models.JSONField(help_text='react-jsonschema-form uiSchema for registration form', null=True), ), migrations.AlterField( model_name='payment', name='attributes', field=models.JSONField(null=True), ), migrations.AlterField( model_name='registration', name='attributes', field=models.JSONField(null=True), ), migrations.AlterField( model_name='registration', name='client_reported_pricing', field=models.JSONField(null=True), ), migrations.AlterField( model_name='registration', name='server_pricing_results', field=models.JSONField(null=True), ), migrations.AlterField( model_name='registrationtype', name='invitation_email_template', field=models.JSONField(null=True), ), ]

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b9e19abcd38a18b4cb4b386c5ea53016f616a174

480

py

Python

src/python/pants/backend/experimental/python/lint/autoflake/register.py

bastianwegge/pants

43f0b90d41622bee0ed22249dbaffb3ff4ad2eb2

[ "Apache-2.0" ]

null

src/python/pants/backend/experimental/python/lint/autoflake/register.py

bastianwegge/pants

43f0b90d41622bee0ed22249dbaffb3ff4ad2eb2

[ "Apache-2.0" ]

22

2022-01-27T09:59:50.000Z

2022-03-30T07:06:49.000Z

src/python/pants/backend/experimental/python/lint/autoflake/register.py

ryanking/pants

e45b00d2eb467b599966bca262405a5d74d27bdd

[ "Apache-2.0" ]

null

# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). """Autoformatter for removing unused Python imports. See https://github.com/myint/autoflake for details. """ from pants.backend.python.lint.autoflake import rules as autoflake_rules from pants.backend.python.lint.autoflake import skip_field, subsystem def rules(): return (*autoflake_rules.rules(), *skip_field.rules(), *subsystem.rules())

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6a130d9c4062cb3d5da7db7c6acb1a9244178580

140

py

Python

while_loop/lab/sum_numbers.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

while_loop/lab/sum_numbers.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

while_loop/lab/sum_numbers.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

target_num = int(input()) sum_nums = 0 while sum_nums < target_num: input_num = int(input()) sum_nums += input_num print(sum_nums)

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null

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5

6a29d67e627d3f690a26eb927960da3aa98f0cd7

8,083

py

Python

tests/test_message_handler/test_strategies/test_utils.py

Zapix/mtpylon

b268a4e2d1bc641cace1962ea68de73c1156e44c

[ "MIT" ]

9

2021-11-10T08:53:51.000Z

2021-12-15T12:03:44.000Z

tests/test_message_handler/test_strategies/test_utils.py

Zapix/mtpylon

b268a4e2d1bc641cace1962ea68de73c1156e44c

[ "MIT" ]

123

2020-10-22T07:08:20.000Z

2021-09-29T15:26:22.000Z

tests/test_message_handler/test_strategies/test_utils.py

Zapix/mtpylon

b268a4e2d1bc641cace1962ea68de73c1156e44c

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest from mtpylon import long, int128 from mtpylon.messages import UnencryptedMessage, EncryptedMessage from mtpylon.serialization import CallableFunc from mtpylon.message_handler.strategies.utils import ( is_unencrypted_message, is_rpc_call_message, is_container_message, is_msgs_ack, ) from mtpylon.service_schema.functions import req_pq, ping from mtpylon.service_schema.constructors import ( MsgsAck, MessageContainer, Message ) from tests.simpleschema import set_task @pytest.mark.parametrize( 'message', [ pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data=CallableFunc( func=req_pq, params={'nonce': int128(234234)} ), ), id='unencrypted message' ), ] ) def test_is_unencrypted_message_true(message): assert is_unencrypted_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data='wrong data', ), id='unencrypted message wrong rpc call' ), pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data=CallableFunc( func=set_task, params={'content': 'hello world!'} ), ), id='unencrypted message wrong rpc call' ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data='Wrong message data' ), id='encrypted message' ) ] ) def test_is_unencrypted_message_false(message): assert not is_unencrypted_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=CallableFunc( func=set_task, params={'content': 'hello world!'} ) ), id='encrypted message' ), pytest.param( Message( msg_id=long(0x60a4d9830000001c), seqno=9, bytes=16, body=CallableFunc( func=set_task, params={'content': 'hello world'} ) ), id='message constructor' ), ] ) def test_is_rpc_call_true(message): assert is_rpc_call_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data=CallableFunc( func=req_pq, params={'nonce': int128(234234)} ), ), id='unencrypted message' ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data='Wrong message data' ), id='encrypted message wrong data' ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data='some un expected data' ), id='encrypted message ping call' ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=CallableFunc( func=ping, params={'ping_id': long(111)}, ) ), id='encrypted message ping call' ) ] ) def test_is_rpc_call_message_false(message): assert not is_rpc_call_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data=CallableFunc( func=req_pq, params={'nonce': int128(234234)} ), ), id='unencrypted message' ), ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=CallableFunc( func=ping, params={'ping_id': long(111)}, ) ), id='encrypted message ping call' ) ] ) def test_is_not_container_message(message): assert not is_container_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=MessageContainer( messages=[ Message( msg_id=long(0x5e0b700a00000000), seqno=7, bytes=20, body=MsgsAck( msg_ids=[ long(1621416313) ] ), ), Message( msg_id=long(0x60a4d9830000001c), seqno=9, bytes=16, body=CallableFunc( func=set_task, params={'content': 'hello world'} ) ), ] ) ), ), ] ) def test_is_container_message(message): assert is_container_message(message) @pytest.mark.parametrize( 'message', [ pytest.param( UnencryptedMessage( message_id=long(0x51e57ac42770964a), message_data=CallableFunc( func=req_pq, params={'nonce': int128(234234)} ), ), id='unencrypted message' ), pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=CallableFunc( func=ping, params={'ping_id': long(111)}, ) ), id='encrypted message ping call' ) ] ) def test_is_not_msgs_ack(message): assert not is_msgs_ack(message) @pytest.mark.parametrize( 'message', [ pytest.param( EncryptedMessage( message_id=long(0x51e57ac42770964a), session_id=long(1), salt=long(2), seq_no=0, message_data=MsgsAck( msg_ids=[ long(0x51e57ac42770964a), long(0x60a4d9830000001c), ] ) ), id='encrypted msgs ack' ) ] ) def test_is_msgs_ack(message): assert is_msgs_ack(message)

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6a2b01f2dd148676aeb7010da99d2ca3c7268928

95

py

Python

ip_rep/database_connector/views.py

logicbomb-1/ARTIF

8f5cc38ab2e986ee39cbc0328aac0d825c1915b4

[ "MIT" ]

205

2021-06-21T13:49:14.000Z

2022-02-17T05:10:50.000Z

ip_rep/database_connector/views.py

0xDivyanshu/ARTIF

946ea8ea7b60e2d81b35ae820a07241ecf25e9b0

[ "MIT" ]

3

2021-07-11T08:08:01.000Z

2021-09-22T20:01:03.000Z

ip_rep/database_connector/views.py

0xDivyanshu/ARTIF

946ea8ea7b60e2d81b35ae820a07241ecf25e9b0

[ "MIT" ]

35

2021-06-21T10:35:07.000Z

2021-10-11T07:39:12.000Z

from django.shortcuts import render from pymongo import MongoClient # Create your views here.

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1

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5

6a37d0bd32839f17d66049b5827401a0cbb335da

31

py

Python

py/desispec/_version.py

echaussidon/desispec

8a8bd59653861509dd630ffc8e1cd6c67f6cdd51

[ "BSD-3-Clause" ]

null

py/desispec/_version.py

echaussidon/desispec

8a8bd59653861509dd630ffc8e1cd6c67f6cdd51

[ "BSD-3-Clause" ]

null

py/desispec/_version.py

echaussidon/desispec

8a8bd59653861509dd630ffc8e1cd6c67f6cdd51

[ "BSD-3-Clause" ]

null

__version__ = '0.49.1.dev6472'

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1

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null

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5

6a3d9d092ffdf84d2327df789bc2cdc2c5ecf639

439

py

Python

tests/test_mocks_generator.py

yairhoresh/bias-detector

0bc28f646c98aee33c93a6c0999a0b05c4882a61

[ "MIT" ]

50

2021-02-02T19:27:01.000Z

2021-12-22T22:10:02.000Z

tests/test_mocks_generator.py

yairhoresh/bias-detector

0bc28f646c98aee33c93a6c0999a0b05c4882a61

[ "MIT" ]

1

2022-02-24T08:03:39.000Z

tests/test_mocks_generator.py

LaudateCorpus1/bias-detector

c9229b8928f3bdee582039622dc142b623b37467

[ "MIT" ]

10

2021-02-04T23:33:26.000Z

2022-02-28T17:13:30.000Z

from .mocks_generator import * def test_generate_mocks(): first_names_mock, last_names_mock, zip_codes_mock, emails_mock, y_scores_mock, y_pred_mock, y_true_mock = generate_mocks(10) assert len(first_names_mock) == 10 assert len(last_names_mock) == 10 assert len(zip_codes_mock) == 10 assert len(emails_mock) == 10 assert len(y_scores_mock) == 10 assert len(y_pred_mock) == 10 assert len(y_true_mock) == 10

39.909091

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1

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5

e003c3686dbddde727f2a55cd6d3e1b10d25e58e

201

py

Python

src/dfd/datasets/modifications/__init__.py

cicheck/dfd

b02752f958cfea2f85222e2b4b3ba7e265a6152d

[ "MIT" ]

null

src/dfd/datasets/modifications/__init__.py

cicheck/dfd

b02752f958cfea2f85222e2b4b3ba7e265a6152d

[ "MIT" ]

2

2021-12-31T17:44:20.000Z

2021-12-31T19:51:11.000Z

src/dfd/datasets/modifications/__init__.py

cicheck/dfd

b02752f958cfea2f85222e2b4b3ba7e265a6152d

[ "MIT" ]

null

"""Definitions of frame level modifications. Each modification takes as input single frame and outputs modified frame. """ from dfd.datasets.modifications.definitions.clahe import CLAHEModification

25.125

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1

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1

0

5

e008ef0dfb77ddde56a895453636f91569106be5

170

py

Python

comtypes/test/setup.py

phuslu/pyMSAA

611bc4c31e0d6ba36f0f0bebdc6e6be14b994eb0

[ "MIT" ]

23

2015-05-28T15:31:35.000Z

2022-02-16T07:51:34.000Z

comtypes/test/setup.py

kar98kar/pyMSAA

611bc4c31e0d6ba36f0f0bebdc6e6be14b994eb0

[ "MIT" ]

3

2020-05-19T03:00:52.000Z

2020-11-03T09:22:51.000Z

comtypes/test/setup.py

kar98kar/pyMSAA

611bc4c31e0d6ba36f0f0bebdc6e6be14b994eb0

[ "MIT" ]

13

2016-08-26T23:00:40.000Z

2022-03-03T09:58:36.000Z

# all the unittests can be converted to exe-files. from distutils.core import setup import glob import py2exe setup(name='test_*', console=glob.glob("test_*.py"))

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5

e03bf7f0b753932fd48e0d252639cad3d22fea6d

86

py

Python

pyFiDEL/__init__.py

sungcheolkim78/pyFiDEL

670067b12a2efd276e23382251ec612af678731f

[ "Apache-2.0" ]

null

pyFiDEL/__init__.py

sungcheolkim78/pyFiDEL

670067b12a2efd276e23382251ec612af678731f

[ "Apache-2.0" ]

null

pyFiDEL/__init__.py

sungcheolkim78/pyFiDEL

670067b12a2efd276e23382251ec612af678731f

[ "Apache-2.0" ]

null

from .simulator import SimClassifier from .pcr import PCR from .ensemble import FiDEL

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5

e073e43fb191ceedd3b610406c7ce78e3814aeae

27

py

Python

msgflow/__init__.py

noriyukipy/smilechat

a9c0ef93c35b2a1f3e9d1700391ae865544adfbc

[ "MIT" ]

2

2020-09-19T07:57:28.000Z

2020-09-20T10:41:42.000Z

msgflow/__init__.py

noriyukipy/smilechat

a9c0ef93c35b2a1f3e9d1700391ae865544adfbc

[ "MIT" ]

null

msgflow/__init__.py

noriyukipy/smilechat

a9c0ef93c35b2a1f3e9d1700391ae865544adfbc

[ "MIT" ]

2

2020-09-20T10:41:51.000Z

2020-11-09T06:15:32.000Z

from .bot import Messenger

13.5

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null

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0

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5

0ec07f317ec396e2b695fa4589614010547b3cb8

151

py

Python

pdfplay/views.py

dbads/pdf_processing

5ab60cf0ad8e93c8619ed3cc77ddd420f0a3abf5

[ "MIT" ]

null

pdfplay/views.py

dbads/pdf_processing

5ab60cf0ad8e93c8619ed3cc77ddd420f0a3abf5

[ "MIT" ]

9

2020-02-12T03:23:57.000Z

2022-01-13T01:58:36.000Z

pdfplay/views.py

dbads/pdf_processing

5ab60cf0ad8e93c8619ed3cc77ddd420f0a3abf5

[ "MIT" ]

null

from django.shortcuts import render, get_object_or_404, redirect def pdf_play(request): return render(request, 'pdf_play.html', { })

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0

5

0ef313666d9a8df945780e013fe54f9e8669cfeb

133

py

Python

attic/gui/accounting/__init__.py

awacha/cct

be1adbed2533df15c778051f3f4f9da0749c873a

[ "BSD-3-Clause" ]

1

2015-11-04T16:37:39.000Z

attic/gui/accounting/__init__.py

awacha/cct

be1adbed2533df15c778051f3f4f9da0749c873a

[ "BSD-3-Clause" ]

null

attic/gui/accounting/__init__.py

awacha/cct

be1adbed2533df15c778051f3f4f9da0749c873a

[ "BSD-3-Clause" ]

1

2020-03-05T02:50:43.000Z

from .authenticatordialog import AuthenticatorDialog from .projectmanager import ProjectManager from .usermanager import UserManager

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1

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5

1609886486077d333ed1462e0148def80f39d1ed

207

py

Python

read.py

pedrocaseiro/twitch-bot

12661901a0bea463508334250c9886abc3ca776b

[ "MIT" ]

1

2017-01-08T17:43:18.000Z

read.py

pedrocaseiro/twitch-bot

12661901a0bea463508334250c9886abc3ca776b

[ "MIT" ]

null

read.py

pedrocaseiro/twitch-bot

12661901a0bea463508334250c9886abc3ca776b

[ "MIT" ]

null

import string def getUser(line): separate = line.split(":", 2) user = separate[1].split("!", 1)[0] return user def getMessage(line): separate = line.split(":", 2) message = separate[2] return message

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null

0

1

0

null

0

1

0

1

0

5

161cfa9f4872e789ebc8897cfa9cfb2e456af9ed

67

py

Python

stubs/asn1crypto/algos.py

joernheissler/chipcard

1ec1da0a1575f8d9735ae14fb54df6a20e654fbb

[ "MIT" ]

1

2020-04-23T09:13:33.000Z

stubs/asn1crypto/algos.py

joernheissler/chipcard

1ec1da0a1575f8d9735ae14fb54df6a20e654fbb

[ "MIT" ]

null

stubs/asn1crypto/algos.py

joernheissler/chipcard

1ec1da0a1575f8d9735ae14fb54df6a20e654fbb

[ "MIT" ]

null

from .core import Sequence class DHParameters(Sequence): ...

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1

0

null

0

1

0

1

0

1

0

5

161de2dc5dd7767243b2e68991638c51774852bf

57

py

Python

rio_viz/templates/__init__.py

dzanaga/rio-viz

88ec2dec0d69c9d13e5485af7315cc7b56a25963

[ "MIT" ]

null

rio_viz/templates/__init__.py

dzanaga/rio-viz

88ec2dec0d69c9d13e5485af7315cc7b56a25963

[ "MIT" ]

null

rio_viz/templates/__init__.py

dzanaga/rio-viz

88ec2dec0d69c9d13e5485af7315cc7b56a25963

[ "MIT" ]

null

"""rio_viz.templates""" from .template import * # noqa

14.25

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0

null

0

1

0

1

0

1

0

5

16574e03be20d970ce586fdad8a048a93410bda9

120

py

Python

amd64-linux/lib/python/mod_pmppc_components_gcommands.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

1

2020-06-15T10:41:18.000Z

amd64-linux/lib/python/mod_pmppc_components_gcommands.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

null

amd64-linux/lib/python/mod_pmppc_components_gcommands.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

3

2020-08-10T10:25:02.000Z

2021-09-12T01:12:09.000Z

30

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120

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0.091667

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1

0

1

0

null

0

1

0

1

0

5

1659437885a7b7d145d957a21b895af0ef1a74c1

140

py

Python

src/tutorial/todo/admin.py

okwrtdsh/django_tutorial

3125b9a9b1d606626507d01142c4437d03909e0a

[ "MIT" ]

null

src/tutorial/todo/admin.py

okwrtdsh/django_tutorial

3125b9a9b1d606626507d01142c4437d03909e0a

[ "MIT" ]

null

src/tutorial/todo/admin.py

okwrtdsh/django_tutorial

3125b9a9b1d606626507d01142c4437d03909e0a

[ "MIT" ]

null

from django.contrib import admin from tutorial.todo.models import ToDoUser, ToDo admin.site.register(ToDoUser) admin.site.register(ToDo)

17.5

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1

0

1

0

null

0

1

0

1

0

5

1661fb8cb169b10d9c0631cbd2b573355ff40f8d

47

py

Python

cli_toolkit/tests/__init__.py

hile/cli-toolkit

3dbd6b97e69cc0b9a3b9facb33e0a4e6b7d1bc33

[ "PSF-2.0" ]

null

cli_toolkit/tests/__init__.py

hile/cli-toolkit

3dbd6b97e69cc0b9a3b9facb33e0a4e6b7d1bc33

[ "PSF-2.0" ]

null

cli_toolkit/tests/__init__.py

hile/cli-toolkit

3dbd6b97e69cc0b9a3b9facb33e0a4e6b7d1bc33

[ "PSF-2.0" ]

null

""" Unit testing utilities for cli-toolkit """

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null

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1

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1

0

null

0

1

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5

1675ea3cb14efbd54dd264b56885fd47e00fd832

106

py

Python

pystac/models/__init__.py

geobeyond/py-stac

f15f3511737c35fca27da21b38a98960e21f4293

[ "Apache-2.0" ]

null

pystac/models/__init__.py

geobeyond/py-stac

f15f3511737c35fca27da21b38a98960e21f4293

[ "Apache-2.0" ]

null

pystac/models/__init__.py

geobeyond/py-stac

f15f3511737c35fca27da21b38a98960e21f4293

[ "Apache-2.0" ]

null

from .asset import Asset from .item import Item from .link import Link from .properties import Properties

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null

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1

0

1

0

1

0

5

16a98307cd93bbd4adf4ccec212b1f7a2f14ae74

40

py

Python

flagger/exception.py

bbenabbes/flagger

d2ff30821b4fdef008f6e6ae6b4fb752e40c2d7e

[ "MIT" ]

1

2019-05-15T11:07:27.000Z

flagger/exception.py

bbenabbes/flagger

d2ff30821b4fdef008f6e6ae6b4fb752e40c2d7e

[ "MIT" ]

null

flagger/exception.py

bbenabbes/flagger

d2ff30821b4fdef008f6e6ae6b4fb752e40c2d7e

[ "MIT" ]

1

2020-01-03T10:14:56.000Z

class FlaggerError(Exception): pass

13.333333

30

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0

null

0

1

0

null

0

1

0

5

bc4cbbda9a587e154bdcd6b9d5f851ad3d51d31d

841

py

Python

dplex.py

Dream7-Kim/graduation_code

be1808f90589c08d7283a8e12f52e22a5749c27d

[ "MIT" ]

null

dplex.py

Dream7-Kim/graduation_code

be1808f90589c08d7283a8e12f52e22a5749c27d

[ "MIT" ]

null

dplex.py

Dream7-Kim/graduation_code

be1808f90589c08d7283a8e12f52e22a5749c27d

[ "MIT" ]

null

import jax.numpy as np import logging def deinsum(subscript, aa, bb): real = np.einsum(subscript, aa[0], bb[0]) - np.einsum(subscript, aa[1], bb[1]) imag = np.einsum(subscript, aa[0], bb[1]) + np.einsum(subscript, aa[1], bb[0]) return np.stack([real, imag], axis=0) def deinsum_ord(subscript, aa, bb): real = np.einsum(subscript, aa, bb[0]) imag = np.einsum(subscript, aa, bb[1]) return np.stack([real, imag], axis=0) def dabs(aa): return aa[0]**2 + aa[1]**2 # 因为是纵向叠加所以aa[0]是第一行 def dconj(aa): return dplex(aa.val[0], -aa.val[1]) def dtomine(aa): return np.stack([np.real(aa), np.imag(aa)], axis=0) def dconstruct(aa, bb): return np.stack([aa, bb], axis=0) # 纵向叠加数组 def ddivide(a, bb): real = a * bb[0] / dabs(bb) imag = -a * bb[1] / dabs(bb) return np.stack([real, imag], axis=0)

29

82

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149

841

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0.171206

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0.22179

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0

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841

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0.717423

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null

0

1

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null

0

1

0

1

0

5

bc68ca7afbfdf0fb3c5a9889f8e42dcfb454d170

182

py

Python

pyleus/__init__.py

dapuck/pyleus

f5c4a06cf8351d0c6bc28b07edbe99025455409c

[ "Apache-2.0" ]

166

2015-01-14T16:06:37.000Z

2021-11-15T12:17:11.000Z

pyleus/__init__.py

WenbinTan/pyleus

8ab87e2d18b8b6a7e0471ceefdbb3ff23a576cce

[ "Apache-2.0" ]

105

2015-01-16T19:59:06.000Z

2016-05-13T19:40:45.000Z

pyleus/__init__.py

WenbinTan/pyleus

8ab87e2d18b8b6a7e0471ceefdbb3ff23a576cce

[ "Apache-2.0" ]

62

2015-01-19T07:42:24.000Z

2021-06-05T21:02:09.000Z

from __future__ import absolute_import import pkg_resources __version__ = '0.3.0' BASE_JAR = "pyleus-base.jar" BASE_JAR_PATH = pkg_resources.resource_filename('pyleus', BASE_JAR)

20.222222

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1

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0

1

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5

bcb715d3c6b6fa6d597e8d6b17001f53b6a1ae57

11,165

py

Python

sdk/lusid/models/__init__.py

mneedham/lusid-sdk-python

edabec16b357ba3fc48a53f3faacb4f94b18843e

[ "MIT" ]

null

sdk/lusid/models/__init__.py

mneedham/lusid-sdk-python

edabec16b357ba3fc48a53f3faacb4f94b18843e

[ "MIT" ]

null

sdk/lusid/models/__init__.py

mneedham/lusid-sdk-python

edabec16b357ba3fc48a53f3faacb4f94b18843e

[ "MIT" ]

null

# coding: utf-8 # flake8: noqa """ LUSID API FINBOURNE Technology # noqa: E501 The version of the OpenAPI document: 0.11.2808 Contact: info@finbourne.com Generated by: https://openapi-generator.tech """ from __future__ import absolute_import # import models into model package from lusid.models.access_controlled_action import AccessControlledAction from lusid.models.access_controlled_resource import AccessControlledResource from lusid.models.action_id import ActionId from lusid.models.adjust_holding import AdjustHolding from lusid.models.adjust_holding_request import AdjustHoldingRequest from lusid.models.annul_quotes_response import AnnulQuotesResponse from lusid.models.change import Change from lusid.models.complete_portfolio import CompletePortfolio from lusid.models.complete_relation import CompleteRelation from lusid.models.constituents_adjustment_header import ConstituentsAdjustmentHeader from lusid.models.create_cut_label_definition_request import CreateCutLabelDefinitionRequest from lusid.models.create_derived_transaction_portfolio_request import CreateDerivedTransactionPortfolioRequest from lusid.models.create_portfolio_details import CreatePortfolioDetails from lusid.models.create_portfolio_group_request import CreatePortfolioGroupRequest from lusid.models.create_property_definition_request import CreatePropertyDefinitionRequest from lusid.models.create_reference_portfolio_request import CreateReferencePortfolioRequest from lusid.models.create_relation_definition_request import CreateRelationDefinitionRequest from lusid.models.create_relation_request import CreateRelationRequest from lusid.models.create_transaction_portfolio_request import CreateTransactionPortfolioRequest from lusid.models.currency_and_amount import CurrencyAndAmount from lusid.models.cut_label_definition import CutLabelDefinition from lusid.models.cut_local_time import CutLocalTime from lusid.models.data_type import DataType from lusid.models.date_range import DateRange from lusid.models.delete_instrument_response import DeleteInstrumentResponse from lusid.models.delete_relation_request import DeleteRelationRequest from lusid.models.deleted_entity_response import DeletedEntityResponse from lusid.models.error_detail import ErrorDetail from lusid.models.execution_request import ExecutionRequest from lusid.models.expanded_group import ExpandedGroup from lusid.models.file_response import FileResponse from lusid.models.get_instruments_response import GetInstrumentsResponse from lusid.models.get_quotes_response import GetQuotesResponse from lusid.models.get_reference_portfolio_constituents_response import GetReferencePortfolioConstituentsResponse from lusid.models.holding_adjustment import HoldingAdjustment from lusid.models.holdings_adjustment import HoldingsAdjustment from lusid.models.holdings_adjustment_header import HoldingsAdjustmentHeader from lusid.models.i_unit_definition_dto import IUnitDefinitionDto from lusid.models.id_selector_definition import IdSelectorDefinition from lusid.models.identifier_part_schema import IdentifierPartSchema from lusid.models.instrument import Instrument from lusid.models.instrument_definition import InstrumentDefinition from lusid.models.instrument_id_type_descriptor import InstrumentIdTypeDescriptor from lusid.models.instrument_id_value import InstrumentIdValue from lusid.models.label_value_set import LabelValueSet from lusid.models.legal_entity import LegalEntity from lusid.models.link import Link from lusid.models.lusid_instrument import LusidInstrument from lusid.models.lusid_problem_details import LusidProblemDetails from lusid.models.lusid_validation_problem_details import LusidValidationProblemDetails from lusid.models.metric_value import MetricValue from lusid.models.model_property import ModelProperty from lusid.models.output_transaction import OutputTransaction from lusid.models.paged_resource_list_of_cut_label_definition import PagedResourceListOfCutLabelDefinition from lusid.models.paged_resource_list_of_instrument import PagedResourceListOfInstrument from lusid.models.paged_resource_list_of_legal_entity import PagedResourceListOfLegalEntity from lusid.models.paged_resource_list_of_portfolio_group_search_result import PagedResourceListOfPortfolioGroupSearchResult from lusid.models.paged_resource_list_of_portfolio_search_result import PagedResourceListOfPortfolioSearchResult from lusid.models.paged_resource_list_of_property_definition_search_result import PagedResourceListOfPropertyDefinitionSearchResult from lusid.models.perpetual_property import PerpetualProperty from lusid.models.portfolio import Portfolio from lusid.models.portfolio_cash_flow import PortfolioCashFlow from lusid.models.portfolio_details import PortfolioDetails from lusid.models.portfolio_group import PortfolioGroup from lusid.models.portfolio_group_properties import PortfolioGroupProperties from lusid.models.portfolio_group_search_result import PortfolioGroupSearchResult from lusid.models.portfolio_holding import PortfolioHolding from lusid.models.portfolio_properties import PortfolioProperties from lusid.models.portfolio_reconciliation_request import PortfolioReconciliationRequest from lusid.models.portfolio_search_result import PortfolioSearchResult from lusid.models.portfolios_reconciliation_request import PortfoliosReconciliationRequest from lusid.models.processed_command import ProcessedCommand from lusid.models.property_definition import PropertyDefinition from lusid.models.property_definition_search_result import PropertyDefinitionSearchResult from lusid.models.property_interval import PropertyInterval from lusid.models.property_value import PropertyValue from lusid.models.quote import Quote from lusid.models.quote_id import QuoteId from lusid.models.quote_series_id import QuoteSeriesId from lusid.models.realised_gain_loss import RealisedGainLoss from lusid.models.reconciliation_break import ReconciliationBreak from lusid.models.reference_portfolio_constituent import ReferencePortfolioConstituent from lusid.models.reference_portfolio_constituent_request import ReferencePortfolioConstituentRequest from lusid.models.relation import Relation from lusid.models.relation_definition import RelationDefinition from lusid.models.resource_id import ResourceId from lusid.models.resource_list_of_access_controlled_resource import ResourceListOfAccessControlledResource from lusid.models.resource_list_of_change import ResourceListOfChange from lusid.models.resource_list_of_constituents_adjustment_header import ResourceListOfConstituentsAdjustmentHeader from lusid.models.resource_list_of_data_type import ResourceListOfDataType from lusid.models.resource_list_of_holdings_adjustment_header import ResourceListOfHoldingsAdjustmentHeader from lusid.models.resource_list_of_i_unit_definition_dto import ResourceListOfIUnitDefinitionDto from lusid.models.resource_list_of_instrument_id_type_descriptor import ResourceListOfInstrumentIdTypeDescriptor from lusid.models.resource_list_of_portfolio import ResourceListOfPortfolio from lusid.models.resource_list_of_portfolio_cash_flow import ResourceListOfPortfolioCashFlow from lusid.models.resource_list_of_portfolio_group import ResourceListOfPortfolioGroup from lusid.models.resource_list_of_portfolio_search_result import ResourceListOfPortfolioSearchResult from lusid.models.resource_list_of_processed_command import ResourceListOfProcessedCommand from lusid.models.resource_list_of_property_definition import ResourceListOfPropertyDefinition from lusid.models.resource_list_of_property_interval import ResourceListOfPropertyInterval from lusid.models.resource_list_of_quote import ResourceListOfQuote from lusid.models.resource_list_of_reconciliation_break import ResourceListOfReconciliationBreak from lusid.models.resource_list_of_relation import ResourceListOfRelation from lusid.models.resource_list_of_scope_definition import ResourceListOfScopeDefinition from lusid.models.scope_definition import ScopeDefinition from lusid.models.side_configuration_data import SideConfigurationData from lusid.models.stream import Stream from lusid.models.target_tax_lot import TargetTaxLot from lusid.models.target_tax_lot_request import TargetTaxLotRequest from lusid.models.transaction import Transaction from lusid.models.transaction_configuration_data import TransactionConfigurationData from lusid.models.transaction_configuration_data_request import TransactionConfigurationDataRequest from lusid.models.transaction_configuration_movement_data import TransactionConfigurationMovementData from lusid.models.transaction_configuration_movement_data_request import TransactionConfigurationMovementDataRequest from lusid.models.transaction_configuration_type_alias import TransactionConfigurationTypeAlias from lusid.models.transaction_price import TransactionPrice from lusid.models.transaction_property_mapping import TransactionPropertyMapping from lusid.models.transaction_property_mapping_request import TransactionPropertyMappingRequest from lusid.models.transaction_query_parameters import TransactionQueryParameters from lusid.models.transaction_request import TransactionRequest from lusid.models.transaction_set_configuration_data import TransactionSetConfigurationData from lusid.models.update_cut_label_definition_request import UpdateCutLabelDefinitionRequest from lusid.models.update_instrument_identifier_request import UpdateInstrumentIdentifierRequest from lusid.models.update_portfolio_group_request import UpdatePortfolioGroupRequest from lusid.models.update_portfolio_request import UpdatePortfolioRequest from lusid.models.update_property_definition_request import UpdatePropertyDefinitionRequest from lusid.models.upsert_instrument_properties_response import UpsertInstrumentPropertiesResponse from lusid.models.upsert_instrument_property_request import UpsertInstrumentPropertyRequest from lusid.models.upsert_instruments_response import UpsertInstrumentsResponse from lusid.models.upsert_legal_entity_request import UpsertLegalEntityRequest from lusid.models.upsert_portfolio_executions_response import UpsertPortfolioExecutionsResponse from lusid.models.upsert_portfolio_transactions_response import UpsertPortfolioTransactionsResponse from lusid.models.upsert_quote_request import UpsertQuoteRequest from lusid.models.upsert_quotes_response import UpsertQuotesResponse from lusid.models.upsert_reference_portfolio_constituents_request import UpsertReferencePortfolioConstituentsRequest from lusid.models.upsert_reference_portfolio_constituents_response import UpsertReferencePortfolioConstituentsResponse from lusid.models.upsert_transaction_properties_response import UpsertTransactionPropertiesResponse from lusid.models.user import User from lusid.models.version import Version from lusid.models.version_summary_dto import VersionSummaryDto from lusid.models.versioned_resource_list_of_output_transaction import VersionedResourceListOfOutputTransaction from lusid.models.versioned_resource_list_of_portfolio_holding import VersionedResourceListOfPortfolioHolding from lusid.models.versioned_resource_list_of_transaction import VersionedResourceListOfTransaction

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11,165

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bcb7571f4904175d49f4329483b1baabf1cc4fce

214

py

Python

timeline/dbindexes.py

asrashley/ieee-802-11-timeline

b4375dbde023dee214642e18c09318e9383a2bcf

[ "Apache-2.0" ]

null

timeline/dbindexes.py

asrashley/ieee-802-11-timeline

b4375dbde023dee214642e18c09318e9383a2bcf

[ "Apache-2.0" ]

null

timeline/dbindexes.py

asrashley/ieee-802-11-timeline

b4375dbde023dee214642e18c09318e9383a2bcf

[ "Apache-2.0" ]

1

2020-06-01T07:46:12.000Z

from models import DenormalizedProjectBallots from dbindexer.lookups import StandardLookup from dbindexer.api import register_index #register_index(DenormalizedProjectBallots, {'project__pk': StandardLookup(), #})

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5

bcbb11d9e39ab6341c66fb1dc0f07d1556cf7992

8,570

py

Python

raiden_contracts/tests/test_channel_close.py

anmolshl/raiden-contracts

fd752a1e7ef2a77ce90a1a6cf87cebcca66d5038

[ "MIT" ]

null

raiden_contracts/tests/test_channel_close.py

anmolshl/raiden-contracts

fd752a1e7ef2a77ce90a1a6cf87cebcca66d5038

[ "MIT" ]

null

raiden_contracts/tests/test_channel_close.py

anmolshl/raiden-contracts

fd752a1e7ef2a77ce90a1a6cf87cebcca66d5038

[ "MIT" ]

null

import pytest from eth_tester.exceptions import TransactionFailed from raiden_contracts.constants import ( TEST_SETTLE_TIMEOUT_MIN, EVENT_CHANNEL_CLOSED, CHANNEL_STATE_NONEXISTENT, CHANNEL_STATE_SETTLED, CHANNEL_STATE_OPENED, CHANNEL_STATE_CLOSED, ) from raiden_contracts.utils.events import check_channel_closed from .fixtures.config import fake_bytes, fake_hex def test_close_nonexistent_channel( token_network, get_accounts, ): (A, B) = get_accounts(2) (_, settle_block_number, state) = token_network.functions.getChannelInfo(A, B).call() assert state == CHANNEL_STATE_NONEXISTENT assert settle_block_number == 0 with pytest.raises(TransactionFailed): token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) def test_close_settled_channel( web3, token_network, create_channel, channel_deposit, get_accounts, ): (A, B) = get_accounts(2) create_channel(A, B, TEST_SETTLE_TIMEOUT_MIN) channel_deposit(A, 5, B) (_, _, state) = token_network.functions.getChannelInfo(A, B).call() assert state == CHANNEL_STATE_OPENED token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) web3.testing.mine(TEST_SETTLE_TIMEOUT_MIN) token_network.functions.settleChannel( A, 0, 0, fake_bytes(32), B, 0, 0, fake_bytes(32), ).transact({'from': A}) (_, settle_block_number, state) = token_network.functions.getChannelInfo(A, B).call() assert state == CHANNEL_STATE_SETTLED assert settle_block_number == 0 with pytest.raises(TransactionFailed): token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) def test_close_wrong_signature( token_network, create_channel, channel_deposit, get_accounts, create_balance_proof, ): (A, B, C) = get_accounts(3) deposit_A = 6 transferred_amount = 5 nonce = 3 locksroot = fake_hex(32, '03') channel_identifier = create_channel(A, B)[0] channel_deposit(A, deposit_A, B) # Create balance proofs balance_proof = create_balance_proof( channel_identifier, C, transferred_amount, 0, nonce, locksroot, ) with pytest.raises(TransactionFailed): token_network.functions.closeChannel(B, *balance_proof).transact({'from': A}) def test_close_call_twice_fail( token_network, create_channel, channel_deposit, get_accounts, ): (A, B) = get_accounts(2) create_channel(A, B) channel_deposit(A, 5, B) token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) with pytest.raises(TransactionFailed): token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) def test_close_wrong_sender( token_network, create_channel, channel_deposit, get_accounts, ): (A, B, C) = get_accounts(3) create_channel(A, B) channel_deposit(A, 5, B) with pytest.raises(TransactionFailed): token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': C}) def test_close_first_argument_is_for_partner_transfer( token_network, create_channel, get_accounts, create_balance_proof, ): (A, B) = get_accounts(2) # Create channel channel_identifier = create_channel(A, B, settle_timeout=TEST_SETTLE_TIMEOUT_MIN)[0] # Create balance proofs balance_proof = create_balance_proof( channel_identifier, B, ) # closeChannel fails, if the provided balance proof is from the same participant who closes with pytest.raises(TransactionFailed): token_network.functions.closeChannel(B, *balance_proof).transact({'from': B}) # Else, closeChannel works with this balance proof token_network.functions.closeChannel(B, *balance_proof).transact({'from': A}) def test_close_first_participant_can_close( token_network, create_channel, get_accounts, ): (A, B) = get_accounts(2) create_channel(A, B) token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) def test_close_second_participant_can_close( token_network, create_channel, get_accounts, ): (A, B) = get_accounts(2) create_channel(A, B) token_network.functions.closeChannel( A, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': B}) def test_close_channel_state( token_network, create_channel, channel_deposit, get_accounts, get_block, create_balance_proof, ): (A, B) = get_accounts(2) settle_timeout = TEST_SETTLE_TIMEOUT_MIN deposit_A = 20 transferred_amount = 5 nonce = 3 locksroot = fake_hex(32, '03') # Create channel and deposit channel_identifier = create_channel(A, B, settle_timeout)[0] channel_deposit(A, deposit_A, B) # Create balance proofs balance_proof = create_balance_proof( channel_identifier, B, transferred_amount, 0, nonce, locksroot, ) (_, settle_block_number, state) = token_network.functions.getChannelInfo(A, B).call() assert settle_block_number == settle_timeout assert state == CHANNEL_STATE_OPENED ( _, _, A_is_the_closer, A_balance_hash, A_nonce, ) = token_network.functions.getChannelParticipantInfo(A, B).call() assert A_is_the_closer is False assert A_balance_hash == fake_bytes(32) assert A_nonce == 0 txn_hash = token_network.functions.closeChannel(B, *balance_proof).transact({'from': A}) (_, settle_block_number, state) = token_network.functions.getChannelInfo(A, B).call() assert settle_block_number == settle_timeout + get_block(txn_hash) assert state == CHANNEL_STATE_CLOSED ( _, _, A_is_the_closer, A_balance_hash, A_nonce, ) = token_network.functions.getChannelParticipantInfo(A, B).call() assert A_is_the_closer is True assert A_balance_hash == fake_bytes(32) assert A_nonce == 0 ( _, _, B_is_the_closer, B_balance_hash, B_nonce, ) = token_network.functions.getChannelParticipantInfo(B, A).call() assert B_is_the_closer is False assert B_balance_hash == balance_proof[0] assert B_nonce == nonce def test_close_channel_event_no_offchain_transfers( get_accounts, token_network, create_channel, event_handler, ): ev_handler = event_handler(token_network) (A, B) = get_accounts(2) channel_identifier = create_channel(A, B)[0] # No off-chain transfers have occured # There is no signature data here, because it was never provided to A txn_hash = token_network.functions.closeChannel( B, fake_bytes(32), 0, fake_bytes(32), fake_bytes(64), ).transact({'from': A}) ev_handler.add(txn_hash, EVENT_CHANNEL_CLOSED, check_channel_closed(channel_identifier, A)) ev_handler.check() def test_close_channel_event( get_accounts, token_network, create_channel, channel_deposit, create_balance_proof, event_handler, ): ev_handler = event_handler(token_network) (A, B) = get_accounts(2) deposit_A = 10 channel_identifier = create_channel(A, B)[0] channel_deposit(A, deposit_A, B) balance_proof = create_balance_proof(channel_identifier, B, 5, 0, 3) txn_hash = token_network.functions.closeChannel(B, *balance_proof).transact({'from': A}) ev_handler.add(txn_hash, EVENT_CHANNEL_CLOSED, check_channel_closed(channel_identifier, A)) ev_handler.check()

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null

0

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4c7190701e0b1bec12530d62480fcec0b3bbdc47

32

py

Python

brotab/tests/mocks.py

craigevil/brotab

9d21332af716b73a8bc5dee90b7ea26baeb99a7c

[ "MIT" ]

3

2022-03-30T01:29:07.000Z

2022-03-30T10:41:36.000Z

brotab/tests/mocks.py

craigevil/brotab

9d21332af716b73a8bc5dee90b7ea26baeb99a7c

[ "MIT" ]

null

brotab/tests/mocks.py

craigevil/brotab

9d21332af716b73a8bc5dee90b7ea26baeb99a7c

[ "MIT" ]

null

class BrowserPortMock: pass

10.666667

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0

1

0

null

0

1

0

5

4c7801deb524a1c11b2e33ff460041710f97750c

199

py

Python

tests/__init__.py

ekhtiar/airflow

9410715c81a2a16dcd04e7cce56d75747bb19ff6

[ "Apache-2.0" ]

null

tests/__init__.py

ekhtiar/airflow

9410715c81a2a16dcd04e7cce56d75747bb19ff6

[ "Apache-2.0" ]

null

tests/__init__.py

ekhtiar/airflow

9410715c81a2a16dcd04e7cce56d75747bb19ff6

[ "Apache-2.0" ]

1

2019-12-12T06:44:14.000Z

from __future__ import absolute_import from .configuration import * from .core import * from .jobs import * from .models import * from .operators import * from .contrib import * from .utils import *

22.111111

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null

0

1

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1

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5

d5e4ed175f3d8cf00270349bde7092b3e63d44da

43

py

Python

models/cells/PC2001Miyasho/__init__.py

HarshKhilawala/cerebmodels

d2a2f2ef947ef9dc23ddce6e55159240cd3233cb

[ "BSD-3-Clause" ]

null

models/cells/PC2001Miyasho/__init__.py

HarshKhilawala/cerebmodels

d2a2f2ef947ef9dc23ddce6e55159240cd3233cb

[ "BSD-3-Clause" ]

9

2020-03-24T17:09:03.000Z

2021-05-17T16:11:17.000Z

models/cells/PC2001Miyasho/__init__.py

myHBPwork/cerebmodels

371ea7f1bbe388f1acade17c7128b8ca6ab8fb7a

[ "BSD-3-Clause" ]

1

2021-05-21T03:08:41.000Z

# ~/models/cells/PC2001Miyasho/__init__.py

21.5

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0

1

0

null

0

1

0

5

910f33eefb3f8b53f4490be32eb382248cee0c0c

3,913

py

Python

ai/causalcell/models/translation_models.py

Bertinus/causal_cell_embedding

417b55749130fc7b7832fd3ee4c49feff4a04593

[ "MIT" ]

null

ai/causalcell/models/translation_models.py

Bertinus/causal_cell_embedding

417b55749130fc7b7832fd3ee4c49feff4a04593

[ "MIT" ]

null

ai/causalcell/models/translation_models.py

Bertinus/causal_cell_embedding

417b55749130fc7b7832fd3ee4c49feff4a04593

[ "MIT" ]

null

from ai.causalcell.models.utils import * import ai.causalcell.utils.register as register import torch from ai.causalcell.models.autoencoder import VariationalAutoEncoder import torch.nn as nn import copy @register.setmodelname('env_trans_VAE') class TranslationVariationalAutoEncoder(VariationalAutoEncoder): """ VAE that adapts to each environment by translating one of the latent variables. The prior in latent space depends on the translation (after the translation is applied) """ def __init__(self, enc_layers, dec_layers, aux_layers, optimizer_params, beta=1, dropout=0, norm='none', softmax=True, temperature=1): """ :param softmax: if True, a softmax is used to normalize env_mu so that the absolute values of env_mu sum to 1 :param temperature: temperature of the softmax """ self.enc_layers = copy.deepcopy(enc_layers) self.dec_layers = copy.deepcopy(dec_layers) self.enc_layers[0] += aux_layers[0] # The encoder has to take the fingerprint as input super(TranslationVariationalAutoEncoder, self).__init__(self.enc_layers, self.dec_layers, optimizer_params, beta=beta, dropout=dropout, norm=norm) if softmax: self.softmax = nn.Softmax(dim=1) self.temperature = temperature else: if temperature != 1: print("If Softmax is False, Temperature is set to 1") self.temperature = 1 self.softmax = Dummy() self.env_prior_mu = LinearLayers(layers=aux_layers, dropout=dropout, norm=norm, activate_final=False) def forward(self, x, fingerprint, compound=0, line=0): mu, logvar = self.embed(torch.cat((x, fingerprint), dim=1)) env_mu = self.env_prior_mu(fingerprint) alpha = self.softmax(1 / self.temperature * torch.abs(env_mu)) env_mu = alpha * env_mu # Make mu sparse z = self.reparameterize(mu, logvar) x_prime = self.decoder(z + env_mu) return {'z': z, 'x_prime': x_prime, 'x': x, 'mu': mu, 'logvar': logvar, 'env_mu': env_mu} @register.setmodelname('no_env_input_trans_VAE') class NoEnvInputTranslationVariationalAutoEncoder(VariationalAutoEncoder): """ VAE that adapts to each environment by translating one of the latent variables. The prior in latent space depends on the translation (after the translation is applied) """ def __init__(self, enc_layers, dec_layers, aux_layers, beta=1, dropout=0, norm='none', softmax=True, temperature=1): """ :param softmax: if True, a softmax is used to normalize env_mu so that the absolute values of env_mu sum to 1 :param temperature: temperature of the softmax """ super(NoEnvInputTranslationVariationalAutoEncoder, self).__init__(enc_layers, dec_layers, beta=beta, dropout=dropout, norm=norm) if softmax: self.softmax = nn.Softmax(dim=1) self.temperature = temperature else: if temperature != 1: print("If Softmax is False, Temperature is set to 1") self.temperature = 1 self.softmax = Dummy() self.env_prior_mu = LinearLayers(layers=aux_layers, dropout=dropout, norm=norm, activate_final=False) def forward(self, x, fingerprint, compound=0, line=0): mu, logvar = self.embed(x) env_mu = self.env_prior_mu(fingerprint) alpha = self.softmax(1 / self.temperature * torch.abs(env_mu)) env_mu = alpha * env_mu # Make mu sparse z = self.reparameterize(mu, logvar) x_prime = self.decoder(z + env_mu) return {'z': z, 'x_prime': x_prime, 'x': x, 'mu': mu, 'logvar': logvar, 'env_mu': env_mu}

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0

null

0

1

0

null

0

5

911cb0825001751b3234282d0109171076d7c383

2,574

py

Python

pytils/test/test_utils.py

yamnikov-oleg/pytils

d09ecdfca203aa67814487fb07792a000995b072

[ "MIT" ]

null

pytils/test/test_utils.py

yamnikov-oleg/pytils

d09ecdfca203aa67814487fb07792a000995b072

[ "MIT" ]

null

pytils/test/test_utils.py

yamnikov-oleg/pytils

d09ecdfca203aa67814487fb07792a000995b072

[ "MIT" ]

1

2022-02-19T08:36:07.000Z

# -*- coding: utf-8 -*- """ Unit-tests for pytils.utils """ import decimal import unittest import pytils class ChecksTestCase(unittest.TestCase): """ Test case for check_* utils """ def testCheckLength(self): """ Unit-test for pytils.utils.check_length """ self.assertEqual(pytils.utils.check_length("var", 3), None) self.assertRaises(ValueError, pytils.utils.check_length, "var", 4) self.assertRaises(ValueError, pytils.utils.check_length, "var", 2) self.assertRaises(ValueError, pytils.utils.check_length, (1,2), 3) def testCheckPositive(self): """ Unit-test for pytils.utils.check_positive """ self.assertEqual(pytils.utils.check_positive(0), None) self.assertEqual(pytils.utils.check_positive(1), None) self.assertEqual(pytils.utils.check_positive(1, False), None) self.assertEqual(pytils.utils.check_positive(1, strict=False), None) self.assertEqual(pytils.utils.check_positive(1, True), None) self.assertEqual(pytils.utils.check_positive(1, strict=True), None) self.assertEqual(pytils.utils.check_positive(decimal.Decimal("2.0")), None) self.assertEqual(pytils.utils.check_positive(2.0), None) self.assertRaises(ValueError, pytils.utils.check_positive, -2) self.assertRaises(ValueError, pytils.utils.check_positive, -2.0) self.assertRaises(ValueError, pytils.utils.check_positive, decimal.Decimal("-2.0")) self.assertRaises(ValueError, pytils.utils.check_positive, 0, True) class SplitValuesTestCase(unittest.TestCase): def testClassicSplit(self): """ Unit-test for pytils.utils.split_values, classic split """ self.assertEqual(("Раз", "Два", "Три"), pytils.utils.split_values("Раз,Два,Три")) self.assertEqual(("Раз", "Два", "Три"), pytils.utils.split_values("Раз, Два,Три")) self.assertEqual(("Раз", "Два", "Три"), pytils.utils.split_values(" Раз, Два, Три ")) self.assertEqual(("Раз", "Два", "Три"), pytils.utils.split_values(" Раз, \nДва,\n Три ")) def testEscapedSplit(self): """ Unit-test for pytils.utils.split_values, split with escaping """ self.assertEqual(("Раз,Два", "Три,Четыре", "Пять,Шесть"), pytils.utils.split_values("Раз\,Два,Три\,Четыре,Пять\,Шесть")) self.assertEqual(("Раз, Два", "Три", "Четыре"), pytils.utils.split_values("Раз\, Два, Три, Четыре")) if __name__ == '__main__': unittest.main()

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null

0

1

0

null

0

1

0

5

912e06e33a6a27013d2009e2907af76625e51bb0

28

py

Python

src/LuauAST/types/nodes.py

Roblox-py/roblox-py

64d6d129c5d6a8edb43410242333fe539d34a1d9

[ "MIT" ]

2

2021-01-08T20:43:36.000Z

2021-05-24T14:31:20.000Z

src/LuauAST/types/nodes.py

Roblox-py/roblox-py

64d6d129c5d6a8edb43410242333fe539d34a1d9

[ "MIT" ]

null

src/LuauAST/types/nodes.py

Roblox-py/roblox-py

64d6d129c5d6a8edb43410242333fe539d34a1d9

[ "MIT" ]

null

import luau from 'LuauAST'

9.333333

26

0.75

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1

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2

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null

0

1

0

1

0

5

e66b274323ce013e38bd7639b7d4c83f71b1e91f

120

py

Python

bin/CoverView.py

RahmanTeamDevelopment/CoverView

ece798725ac9ff7dfd74fcf1daee62fb6aca0f69

[ "MIT" ]

2

2018-04-10T07:57:09.000Z

2018-04-23T09:11:39.000Z

bin/CoverView.py

RahmanTeamDevelopment/CoverView

ece798725ac9ff7dfd74fcf1daee62fb6aca0f69

[ "MIT" ]

22

2017-05-12T15:02:04.000Z

2020-06-17T09:14:28.000Z

bin/CoverView.py

RahmanTeamDevelopment/CoverView

ece798725ac9ff7dfd74fcf1daee62fb6aca0f69

[ "MIT" ]

null

#!env/bin/python import sys import coverview_.main if __name__ == "__main__": coverview_.main.main(sys.argv[1:])

13.333333

38

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17

120

4.411765

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true

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1

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null

0

1

0

1

0

5

e67bf8add2ad41c33a8611539dc4145ca9b9a12c

286

py

Python

pyvi/tests/utils.py

Julian/PyVi

5e9e087146e0baffbe791bacccbfd4b840cdeb5f

[ "MIT" ]

3

2018-07-26T09:52:31.000Z

2019-07-02T14:29:31.000Z

pyvi/tests/utils.py

Julian/PyVi

5e9e087146e0baffbe791bacccbfd4b840cdeb5f

[ "MIT" ]

null

pyvi/tests/utils.py

Julian/PyVi

5e9e087146e0baffbe791bacccbfd4b840cdeb5f

[ "MIT" ]

null

class StubCursor(object): column = 0 row = 0 def __iter__(self): return iter([self.row, self.column]) @property def coords(self): return self.row, self.column @coords.setter def coords(self, coords): self.row, self.column = coords

19.066667

44

0.601399

36

286

4.666667

0.361111

0.125

0.196429

0.303571

0.27381

0

0.009804

0.286713

286

14

45

20.428571

0.813725

0

1

0.272727

false

0

0.181818

0.727273

0

null

0

1

0

null

0

1

0

1

0

5

e699117a41937afdb0b4f4577b801c50bd7a19ec

11,349

py

Python

scripts/heatmap_fig/pathdip.py

hui2000ji/scETM

0a34c345d70b262ebc38e033bae683fa4929ed3e

[ "BSD-3-Clause" ]

24

2021-07-09T12:59:31.000Z

2022-03-04T22:31:41.000Z

scripts/heatmap_fig/pathdip.py

hui2000ji/scETM

0a34c345d70b262ebc38e033bae683fa4929ed3e

[ "BSD-3-Clause" ]

3

2021-09-07T11:14:19.000Z

2022-02-15T01:38:09.000Z

scripts/heatmap_fig/pathdip.py

hui2000ji/scETM

0a34c345d70b262ebc38e033bae683fa4929ed3e

[ "BSD-3-Clause" ]

3

2021-12-02T23:44:37.000Z

2022-02-11T16:46:45.000Z

# Note: Works with Python 3 and up import urllib.request, urllib.parse # ######################################################################################################## # class library mirDIP_Http # # ######################################################################################################## class pathDIP_Http: url = "http://ophid.utoronto.ca/pathDIP/Http_API" map = {} # results will be here def __init__(self): return def searchOnUniprot_IDs(self, IDs, component, sources): self.sendPost('Uniprot ID', IDs, component, sources) return def searchOnGenesymbols(self, IDs, component, sources): self.sendPost('Gene Symbol', IDs, component, sources) return def searchOnEntrez_IDs(self, IDs, component, sources): self.sendPost('Egid', IDs, component, sources) return # .. serve POST request def sendPost(self, typeChoice, IDs, component, sources): params = { 'typeChoice' : typeChoice, 'IDs' : IDs, 'TableName' : component, 'DataSet' : sources} params = bytes( urllib.parse.urlencode( params ).encode() ) response = '' try: handler = urllib.request.urlopen(self.url, params) except Exception: traceback.print_exc() else: self.response = handler.read().decode('utf-8') ## print(self.response) self.makeMap() return def makeMap(self): ENTRY_DEL = 0x01 KEY_DEL = 0x02 arr = self.response.split(chr(ENTRY_DEL)) for str in arr: arrKeyValue = str.split(chr(KEY_DEL)); if len(arrKeyValue) > 1: self.map[arrKeyValue[0]] = arrKeyValue[1] return def getGeneratedAt(self): if "GeneratedAt" in self.map: return self.map["GeneratedAt"] else: return '' def getIDs(self): if "IDs" in self.map: return self.map["IDs"] else: return '' def getDataComponent(self): if "TableName" in self.map: return self.map["TableName"] else: return '' def getSources(self): if "DataSet" in self.map: return self.map["DataSet"] else: return '' def getPathwayAnalysisSize(self): if "SummarySize" in self.map: return self.map["SummarySize"] else: return '' def getPathwayAnalysis(self): if "Summary" in self.map: return self.map["Summary"] else: return '' def getDetailsSize(self): if "DetailsSize" in self.map: return self.map["DetailsSize"] else: return '' def getDetails(self): if "Details" in self.map: return self.map["Details"] else: return '' ######################################################################################################### # Pick-up right sample for your search # ######################################################################################################### # Note: Adjust 'ophid.utoronto.ca' if you are using our development server. # ########################################### # Example of search on Uniprot IDs # # ########################################### # Uniprot IDs # - Comma delimited. # - Mind case. IDs = "O15379,P15692,P13236,P13236,P13236,Q96P48,O00468" # Data component # - Use the only one of those five: # Literature curated (core) pathway memberships # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.95 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.95 # - Mind exact spelling and spaces. component = "Literature curated (core) pathway memberships" # Data sources (Note: Data source list was updated for full list of 22 available sources) # - Use some or all of those: # ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways # - Comma delimited. # - Mind exact spelling. sources = "ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways" o = pathDIP_Http() o.searchOnUniprot_IDs(IDs, component, sources) # print results print("\r\n Search on Uniprot IDs: \r\n") print("Generated at: " + o.getGeneratedAt()) print("IDs: " + o.getIDs()) print("DataComponent: " + o.getDataComponent()) print("Sources: " + o.getSources()) print(); print("Pathway enrichment analysis results size: " + o.getPathwayAnalysisSize()); print("Pathway enrichment analysis results ('q-value (FDR: BH-method) less than 0.05'): \r\n" + o.getPathwayAnalysis()); # formatted as tab-delimited spreadsheet print("Detailed table size: " + o.getDetailsSize()); print("Pathway annotations for the full input list: \r\n" + o.getDetails()); # formatted as tab-delimited spreadsheet # ########################################### # Example of search on Gene Symbols # # ########################################### # Gene Symbols # - Use HUGO Gene nomenclature # - Comma delimited. # - Mind case. IDs = "HDAC3, VEGFA, CCL4, DP141L, ARAP1, MARK4, ZZZ3, AGRN, MAPRKE1, CRABP1, HIST1H1C, RACGAPI" # Data component # - Use the only one of those five: # Literature curated (core) pathway memberships # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.95 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.95 # - Mind exact spelling and spaces. component = "Literature curated (core) pathway memberships" # Data sources (Note: Data source list was updated for full list of 22 available sources) # - Use some or all of those: # ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways # - Comma delimited. # - Mind exact spelling. sources = "ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways" o = pathDIP_Http() o.searchOnGenesymbols(IDs, component, sources); # print results print("\r\n Search on Gene Symbols: \r\n") print("Generated at: " + o.getGeneratedAt()) print("IDs: " + o.getIDs()) print("DataComponent: " + o.getDataComponent()) print("Sources: " + o.getSources()) print(); print("Pathway enrichment analysis results size: " + o.getPathwayAnalysisSize()) print("Pathway enrichment analysis results ('q-value (FDR: BH-method) less than 0.05'): \r\n" + o.getPathwayAnalysis()) # formatted as tab-delimited spreadsheet print("Detailed table size: " + o.getDetailsSize()) print("Pathway annotations for the full input list: \r\n" + o.getDetails()) # formatted as tab-delimited spreadsheet # ########################################### # Example of search on Entrez Gene IDs # # ########################################### # Entrez Gene ID # - Comma delimited. # - Mind case.) IDs = "375790,8841,6351,7422" # Data component # - Use the only one of those five: # Literature curated (core) pathway memberships # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.95 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.99 # Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.95 # - Mind exact spelling and spaces. component = "Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.99" # Data sources # Data sources (Note: Data source list was updated for full list of 22 available sources) # - Use some or all of those: # ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways # - Comma delimited. # - Mind exact spelling. sources = "ACSN2,BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,Panther_Pathway,PharmGKB,PID,RB-Pathways,REACTOME,stke,systems-biology.org,SignaLink2.0,SIGNOR2.0,SMPDB,Spike,UniProt_Pathways,WikiPathways" o = pathDIP_Http() o.searchOnEntrez_IDs(IDs, component, sources) # print results print("\r\n Search on Entrez Gene ID: \r\n") print("Generated at: " + o.getGeneratedAt()) print("IDs: " + o.getIDs()) print("DataComponent: " + o.getDataComponent()) print("Sources: " + o.getSources()) print(); print("Pathway enrichment analysis results size: " + o.getPathwayAnalysisSize()) print("Pathway enrichment analysis results ('q-value (FDR: BH-method) less than 0.05'): \r\n" + o.getPathwayAnalysis()) # formatted as tab-delimited spreadsheet print("Details Size: " + o.getDetailsSize()) print("Pathway annotations for the full input list: \r\n" + o.getDetails()) # formatted as tab-delimited spreadsheet

40.532143

215

0.627456

1,215

11,349

5.835391

0.1893

0.016784

0.049506

0.062341

0.779972

0.761495

0.731735

0.721016

0

0.018005

0.22187

11,349

280

216

40.532143

0.784849

0.413164

0

0.416

0

0.056

0.34706

0.114896

0

0.001356

0

1

0.112

false

0

0.008

0.32

0.248

0

null

0

1

0

null

0

5

fc150a4185036a1af01114a91bef1afb12d1ddb4

96

py

Python

Str_Repr/test2.py

DSNR/snippets

12006dd083be60c6444d8b5ca48fd917005e081b

[ "MIT" ]

null

Str_Repr/test2.py

DSNR/snippets

12006dd083be60c6444d8b5ca48fd917005e081b

[ "MIT" ]

null

Str_Repr/test2.py

DSNR/snippets

12006dd083be60c6444d8b5ca48fd917005e081b

[ "MIT" ]

null

a = [1,2,3,4] b = 'sample string' print str(a) print repr(a) print str(b) print repr(b)

12

20

0.583333

20

96

2.8

0.55

0.285714

0

0.054795

0.239583

96

8

21

12

0.712329

0

0.144444

0

null

0

null

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

5

fc9957732fd3e2ee4f929fdf84f63035260fd64c

5,341

py

Python

code/G2/cfg.py

HJHjiahao/FCGP

fcb511157e9014d1ead778fc402900bd101c6425

[ "MIT" ]

null

code/G2/cfg.py

HJHjiahao/FCGP

fcb511157e9014d1ead778fc402900bd101c6425

[ "MIT" ]

null

code/G2/cfg.py

HJHjiahao/FCGP

fcb511157e9014d1ead778fc402900bd101c6425

[ "MIT" ]

null

''' Function: 配置文件 ''' import os '''字体''' FONTPATH = os.path.join(os.getcwd(), 'G2/resources/font/font.ttf') '''图片''' BULLET_IMAGE_PATHS = { 'up': os.path.join(os.getcwd(), 'G2/resources/images/bullet/bullet_up.png'), 'down': os.path.join(os.getcwd(), 'G2/resources/images/bullet/bullet_down.png'), 'left': os.path.join(os.getcwd(), 'G2/resources/images/bullet/bullet_left.png'), 'right': os.path.join(os.getcwd(), 'G2/resources/images/bullet/bullet_right.png') } ENEMY_TANK_IMAGE_PATHS = { '1': [os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_1_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_1_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_1_2.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_1_3.png')], '2': [os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_2_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_2_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_2_2.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_2_3.png')], '3': [os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_3_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_3_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_3_2.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_3_3.png')], '4': [os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_4_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_4_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_4_2.png'), os.path.join(os.getcwd(), 'G2/resources/images/enemyTank/enemy_4_3.png')] } PLAYER_TANK_IMAGE_PATHS = { 'player1': [os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T1_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T1_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T1_2.png')], 'player2': [os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T2_0.png'), os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T2_1.png'), os.path.join(os.getcwd(), 'G2/resources/images/playerTank/tank_T2_2.png')] } FOOD_IMAGE_PATHS = { 'boom': os.path.join(os.getcwd(), 'G2/resources/images/food/food_boom.png'), 'clock': os.path.join(os.getcwd(), 'G2/resources/images/food/food_clock.png'), 'gun': os.path.join(os.getcwd(), 'G2/resources/images/food/food_gun.png'), 'iron': os.path.join(os.getcwd(), 'G2/resources/images/food/food_iron.png'), 'protect': os.path.join(os.getcwd(), 'G2/resources/images/food/food_protect.png'), 'star': os.path.join(os.getcwd(), 'G2/resources/images/food/food_star.png'), 'tank': os.path.join(os.getcwd(), 'G2/resources/images/food/food_tank.png') } HOME_IMAGE_PATHS = [os.path.join(os.getcwd(), 'G2/resources/images/home/home1.png'), os.path.join(os.getcwd(), 'G2/resources/images/home/home_destroyed.png')] SCENE_IMAGE_PATHS = { 'brick': os.path.join(os.getcwd(), 'G2/Gresources/images/scene/brick.png'), 'ice': os.path.join(os.getcwd(), 'G2/resources/images/scene/ice.png'), 'iron': os.path.join(os.getcwd(), 'G2/resources/images/scene/iron.png'), 'river1': os.path.join(os.getcwd(), 'G2/resources/images/scene/river1.png'), 'river2': os.path.join(os.getcwd(), 'G2/resources/images/scene/river2.png'), 'tree': os.path.join(os.getcwd(), 'G2/resources/images/scene/tree.png') } OTHER_IMAGE_PATHS = { 'appear': os.path.join(os.getcwd(), 'G2/resources/images/others/appear.png'), 'background': os.path.join(os.getcwd(), 'G2/resources/images/others/background.png'), 'boom_dynamic': os.path.join(os.getcwd(), 'G2/resources/images/others/boom_dynamic.png'), 'boom_static': os.path.join(os.getcwd(), 'G2/resources/images/others/boom_static.png'), 'gameover': os.path.join(os.getcwd(), 'G2/resources/images/others/gameover.png'), 'logo': os.path.join(os.getcwd(), 'G2/resources/images/others/logo.png'), 'mask': os.path.join(os.getcwd(), 'G2/resources/images/others/mask.png'), 'protect': os.path.join(os.getcwd(), 'G2/resources/images/others/protect.png'), 'tip': os.path.join(os.getcwd(), 'G2/resources/images/others/tip.png'), 'gamebar': os.path.join(os.getcwd(), 'G2/resources/images/others/gamebar.png') } '''声音''' AUDIO_PATHS = { 'add': os.path.join(os.getcwd(), 'G2/resources/audios/add.wav'), 'bang': os.path.join(os.getcwd(), 'G2/resources/audios/bang.wav'), 'blast': os.path.join(os.getcwd(), 'G2/resources/audios/blast.wav'), 'fire': os.path.join(os.getcwd(), 'G2/resources/audios/fire.wav'), 'Gunfire': os.path.join(os.getcwd(), 'G2/resources/audios/Gunfire.wav'), 'hit': os.path.join(os.getcwd(), 'G2/resources/audios/hit.wav'), 'start': os.path.join(os.getcwd(), 'G2/resources/audios/start.wav') } '''屏幕''' WIDTH = 630 HEIGHT = 630 BORDER_LEN = 3 GRID_SIZE = 24 PANEL_WIDTH = 150 TITLE = '坦克大战' '''关卡''' LEVELFILEDIR = os.path.join(os.getcwd(), 'G2/modules/levels')

58.054348

95

0.662048

800

5,341

4.32

0.11625

0.104167

0.173611

0.208333

0.782986

0.771412

0.763021

0.69213

0.536169

0

0.027148

0.124134

5,341

92

96

58.054348

0.711629

0.004119

0

0.473006

0.432089

0

1

0

false

0

0.012346

0

0.012346

0

null

0

1

0

1

0

1

null

0

5

5d9d2fb6d86b823a1620b3c1acd96472abd70e42

4,094

py

Python

tapiriik/testing/stddevice.py

Decathlon/exercisync

e9df9d4f2210fff8cfc8b34e2e5f9d09d84bddef

[ "Apache-2.0" ]

null

tapiriik/testing/stddevice.py

Decathlon/exercisync

e9df9d4f2210fff8cfc8b34e2e5f9d09d84bddef

[ "Apache-2.0" ]

null

tapiriik/testing/stddevice.py

Decathlon/exercisync

e9df9d4f2210fff8cfc8b34e2e5f9d09d84bddef

[ "Apache-2.0" ]

null

from unittest import TestCase from tapiriik.services.devices import Device from tapiriik.services.Decathlon.decathlon import DecathlonService class StdDeviceTest(TestCase): def test_fit_manufacturer_with_no_fit_device_return_provided_manufacturer_none_product(self): undefined_fit_device_and_decathlon_fit_manufacturer_std_device = { "@context": "/v2/contexts/UserDevice", "@id": "/v2/user_devices/eu23218ff9b8010d294e", "@type": "UserDevice", "id": "eu23218ff9b8010d294e", "serial": "30:67:71:B8:DB:02", "fitManufacturer": 310, "fitDevice": None, "model": "/v2/device_models/99", "firmware": "/v2/firmware/9928", "user": "/v2/users/eu200a4d76c4eab29015", } # When hubDevice = DecathlonService.convertStdDeviceToHubDevice(undefined_fit_device_and_decathlon_fit_manufacturer_std_device) # Then self.assertIsNotNone(hubDevice) self.assertIsInstance(hubDevice, Device) self.assertEqual(hubDevice.Manufacturer, "decathlon") self.assertIsNone(hubDevice.Product) def test_fit_manufacturer_with_fit_device_return_provided_manufacturer_and_provided_product(self): undefined_fit_device_and_decathlon_fit_manufacturer_std_device = { "@context": "/v2/contexts/UserDevice", "@id": "/v2/user_devices/eu23218ff9b8010d294e", "@type": "UserDevice", "id": "eu23218ff9b8010d294e", "serial": "30:67:71:B8:DB:02", "fitManufacturer": 23, "fitDevice": 15, "model": "/v2/device_models/18", "firmware": "/v2/firmware/9928", "user": "/v2/users/eu200a4d76c4eab29015", } # When hubDevice = DecathlonService.convertStdDeviceToHubDevice(undefined_fit_device_and_decathlon_fit_manufacturer_std_device) # Then self.assertIsNotNone(hubDevice) self.assertIsInstance(hubDevice, Device) self.assertEqual(hubDevice.Manufacturer, "suunto") self.assertEqual(hubDevice.Product, 15) def test_undefined_fit_manufacturer_and_fit_device_with_model_return_default_manufacturer_and_model_id_as_product(self): undefined_fit_device_and_fit_manufacturer_but_model_std_device = { "@context": "/v2/contexts/UserDevice", "@id": "/v2/user_devices/eu23218ff9b8010d294e", "@type": "UserDevice", "id": "eu23218ff9b8010d294e", "serial": "30:67:71:B8:DB:02", "fitManufacturer": None, "fitDevice": None, "model": "/v2/device_models/99", "firmware": "/v2/firmware/9928", "user": "/v2/users/eu200a4d76c4eab29015" } # When hubDevice = DecathlonService.convertStdDeviceToHubDevice(undefined_fit_device_and_fit_manufacturer_but_model_std_device) # Then self.assertIsNotNone(hubDevice) self.assertIsInstance(hubDevice, Device) self.assertEqual(hubDevice.Manufacturer, "decathlon") self.assertEqual(hubDevice.Product, 99) def test_unknown_manufacturer_with_fut_device_return_developper_manufacturer_and_fit_product(self): unknown_manufacturer_std_device = { "@context": "/v2/contexts/UserDevice", "@id": "/v2/user_devices/eu23218ff9b8010d294e", "@type": "UserDevice", "id": "eu23218ff9b8010d294e", "serial": "30:67:71:B8:DB:02", "fitManufacturer": 294, "fitDevice": 5, "model": "/v2/device_models/99", "firmware": "/v2/firmware/9928", "user": "/v2/users/eu200a4d76c4eab29015" } # When hubDevice = DecathlonService.convertStdDeviceToHubDevice(unknown_manufacturer_std_device) # Then self.assertIsNotNone(hubDevice) self.assertIsInstance(hubDevice, Device) self.assertIsNone(hubDevice.Manufacturer) self.assertIsNone(hubDevice.Product)

38.622642

128

0.648999

378

4,094

6.730159

0.187831

0.053066

0.042453

0.049528

0.775943

0.727987

0.723664

0

0.079729

0.243283

4,094

106

129

38.622642

0.741446

0.010503

0

0.644737

0

0.242394

0.089043

0

0.210526

1

0.052632

false

0

0.039474

0

0.105263

0

null

0

1

0

null

0

5

5dc888ea421123a6cedbb0e30ce1181f1fb47307

146

py

Python

encode/tests/__init__.py

Ircam-Web/django-encode

2c1c9d843865ec99fb5b45631d6f08a9c7cb86ce

[ "MIT" ]

11

2015-03-11T20:48:13.000Z

2021-12-14T14:17:39.000Z

encode/tests/__init__.py

Ircam-Web/django-encode

2c1c9d843865ec99fb5b45631d6f08a9c7cb86ce

[ "MIT" ]

2

2015-11-24T22:10:06.000Z

2017-05-26T09:27:02.000Z

encode/tests/__init__.py

Ircam-Web/django-encode

2c1c9d843865ec99fb5b45631d6f08a9c7cb86ce

[ "MIT" ]

2

2019-08-09T17:29:41.000Z

2020-08-31T16:47:27.000Z

# Copyright Collab 2012-2016 # See LICENSE for details. """ Tests for the :py:mod:`encode` project. """ from .celery import app # flake8: noqa

16.222222

39

0.691781

21

146

4.809524

0.952381

0

0.075

0.178082

146

8

40

18.25

0.766667

0.719178

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5deda6cdf80bdf5091832ea3ef23ddc56fbe2e17

361

py

Python

dev_tools/test_manager.py

SocialSisterYi/Alconna

3e1d986ca5486dfd3c7bd80118a75364ab6831b8

[ "MIT" ]

null

dev_tools/test_manager.py

SocialSisterYi/Alconna

3e1d986ca5486dfd3c7bd80118a75364ab6831b8

[ "MIT" ]

null

dev_tools/test_manager.py

SocialSisterYi/Alconna

3e1d986ca5486dfd3c7bd80118a75364ab6831b8

[ "MIT" ]

null

from dev_tools.test_alconna_1 import * from dev_tools.test_alconna_2 import * print("\n\n## ------------- Test Manager -------------## \n\n") print(command_manager.all_command_help(max_length=6, page=3, pages="[%d/%d]")) print("\n") print(command_manager.broadcast("cmd.北京天气")) print(command_manager.require("/pip")) print(command_manager.command_help("/pip"))

40.111111

78

0.698061

54

361

4.407407

0.481481

0.201681

0.319328

0.134454

0.193277

0

0.011765

0.058172

361

9

79

40.111111

0.688235

0

0.218232

0

1

0

true

0

0.25

0

0.25

0.75

0

null

1

0

1

0

null

0

1

0

1

0

5

b90048453095acbe52948585025b7df36f6ee00b

75

py

Python

7_kyu/sum_of_two_lowest_positive_integers.py

nik4nd/codewars

efae95f1f9fbd5f31fc62b1b4f5a7d1ee511ced0

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

7_kyu/sum_of_two_lowest_positive_integers.py

nik4nd/codewars

efae95f1f9fbd5f31fc62b1b4f5a7d1ee511ced0

[ "MIT" ]

null

7_kyu/sum_of_two_lowest_positive_integers.py

nik4nd/codewars

efae95f1f9fbd5f31fc62b1b4f5a7d1ee511ced0

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

def sum_two_smallest_numbers(numbers): return sum(sorted(numbers)[:2])

25

38

0.76

11

75

4.909091

0.727273

0

0.014925

0.106667

75

2

39

37.5

0.791045

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f8db7637e59854334ce81efa34272b135f36f33f

49,872

py

Python

octavia/tests/unit/network/drivers/neutron/test_allowed_address_pairs.py

zjchao/octavia

e07031fa78604568c6e2112cb4cb147661bc57d7

[ "Apache-2.0" ]

null

octavia/tests/unit/network/drivers/neutron/test_allowed_address_pairs.py

zjchao/octavia

e07031fa78604568c6e2112cb4cb147661bc57d7

[ "Apache-2.0" ]

null

octavia/tests/unit/network/drivers/neutron/test_allowed_address_pairs.py

zjchao/octavia

e07031fa78604568c6e2112cb4cb147661bc57d7

[ "Apache-2.0" ]

1

2021-12-27T13:18:38.000Z

# Copyright 2015 Rackspace # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import copy import mock from neutronclient.common import exceptions as neutron_exceptions from novaclient.client import exceptions as nova_exceptions from oslo_config import cfg from oslo_config import fixture as oslo_fixture from oslo_utils import uuidutils from octavia.common import clients from octavia.common import constants from octavia.common import data_models from octavia.common import exceptions from octavia.network import base as network_base from octavia.network import data_models as network_models from octavia.network.drivers.neutron import allowed_address_pairs from octavia.network.drivers.neutron import base as neutron_base from octavia.tests.common import constants as t_constants from octavia.tests.common import data_model_helpers as dmh from octavia.tests.unit import base class TestAllowedAddressPairsDriver(base.TestCase): k_session = None driver = None SUBNET_ID_1 = "5" SUBNET_ID_2 = "8" FIXED_IP_ID_1 = "6" FIXED_IP_ID_2 = "8" NETWORK_ID_1 = "7" NETWORK_ID_2 = "10" IP_ADDRESS_1 = "10.0.0.2" IP_ADDRESS_2 = "12.0.0.2" AMPHORA_ID = "1" LB_ID = "2" COMPUTE_ID = "3" ACTIVE = "ACTIVE" LB_NET_IP = "10.0.0.2" LB_NET_PORT_ID = "6" HA_PORT_ID = "8" HA_IP = "12.0.0.2" PORT_ID = uuidutils.generate_uuid() DEVICE_ID = uuidutils.generate_uuid() def setUp(self): super(TestAllowedAddressPairsDriver, self).setUp() with mock.patch('octavia.common.clients.neutron_client.Client', autospec=True) as neutron_client: with mock.patch('stevedore.driver.DriverManager.driver', autospec=True): client = neutron_client(clients.NEUTRON_VERSION) client.list_extensions.return_value = { 'extensions': [ {'alias': allowed_address_pairs.AAP_EXT_ALIAS}, {'alias': neutron_base.SEC_GRP_EXT_ALIAS} ] } self.k_session = mock.patch( 'keystoneauth1.session.Session').start() self.driver = allowed_address_pairs.AllowedAddressPairsDriver() @mock.patch('octavia.network.drivers.neutron.base.BaseNeutronDriver.' '_check_extension_enabled', return_value=False) def test_check_aap_loaded(self, mock_check_ext): self.assertRaises(network_base.NetworkException, self.driver._check_aap_loaded) def test_get_interfaces_to_unplug(self): if1 = network_models.Interface() if1.network_id = 'if1-net' if1.port_id = 'if1-port' if1.fixed_ips = [network_models.FixedIP(ip_address='10.0.0.1')] if2 = network_models.Interface() if2.network_id = 'if2-net' if2.port_id = 'if2-port' if2.fixed_ips = [network_models.FixedIP(ip_address='11.0.0.1')] interfaces = [if1, if2] unpluggers = self.driver._get_interfaces_to_unplug( interfaces, 'if1-net') self.assertEqual([if1], unpluggers) unpluggers = self.driver._get_interfaces_to_unplug( interfaces, 'if1-net', ip_address='10.0.0.1') self.assertEqual([if1], unpluggers) unpluggers = self.driver._get_interfaces_to_unplug( interfaces, 'if1-net', ip_address='11.0.0.1') self.assertEqual([], unpluggers) unpluggers = self.driver._get_interfaces_to_unplug( interfaces, 'if3-net') self.assertEqual([], unpluggers) def test_deallocate_vip(self): lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip sec_grp_id = 'lb-sec-grp1' show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} delete_port = self.driver.neutron_client.delete_port delete_sec_grp = self.driver.neutron_client.delete_security_group list_security_groups = self.driver.neutron_client.list_security_groups security_groups = { 'security_groups': [ {'id': sec_grp_id} ] } list_security_groups.return_value = security_groups self.driver.deallocate_vip(vip) calls = [mock.call(vip.port_id)] for amp in lb.amphorae: calls.append(mock.call(amp.vrrp_port_id)) delete_port.assert_has_calls(calls, any_order=True) delete_sec_grp.assert_called_once_with(sec_grp_id) def test_deallocate_vip_no_port(self): lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip sec_grp_id = 'lb-sec-grp1' show_port = self.driver.neutron_client.show_port port = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} show_port.side_effect = [port, Exception] list_security_groups = self.driver.neutron_client.list_security_groups security_groups = { 'security_groups': [ {'id': sec_grp_id} ] } list_security_groups.return_value = security_groups self.driver.deallocate_vip(vip) self.driver.neutron_client.update_port.assert_not_called() def test_deallocate_vip_port_deleted(self): lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip sec_grp_id = 'lb-sec-grp1' show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} delete_port = self.driver.neutron_client.delete_port delete_port.side_effect = neutron_exceptions.NotFound delete_sec_grp = self.driver.neutron_client.delete_security_group list_security_groups = self.driver.neutron_client.list_security_groups security_groups = { 'security_groups': [ {'id': sec_grp_id} ] } list_security_groups.return_value = security_groups self.driver.deallocate_vip(vip) calls = [mock.call(vip.port_id)] for amp in lb.amphorae: calls.append(mock.call(amp.vrrp_port_id)) delete_port.assert_has_calls(calls, any_order=True) delete_sec_grp.assert_called_once_with(sec_grp_id) def test_deallocate_vip_no_sec_group(self): lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} delete_port = self.driver.neutron_client.delete_port delete_sec_grp = self.driver.neutron_client.delete_security_group list_security_groups = self.driver.neutron_client.list_security_groups security_groups = { 'security_groups': [] } list_security_groups.return_value = security_groups self.driver.deallocate_vip(vip) delete_port.assert_called_with(vip.port_id) delete_sec_grp.assert_not_called() def test_deallocate_vip_when_delete_port_fails(self): lb = dmh.generate_load_balancer_tree() vip = data_models.Vip(port_id='1') vip.load_balancer = lb show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} delete_port = self.driver.neutron_client.delete_port delete_port.side_effect = [None, None, TypeError] self.assertRaises(network_base.DeallocateVIPException, self.driver.deallocate_vip, vip) def test_deallocate_vip_when_secgrp_has_allocated_ports(self): max_retries = 1 conf = oslo_fixture.Config(cfg.CONF) conf.config(group="networking", max_retries=max_retries) lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} delete_port = self.driver.neutron_client.delete_port list_ports = self.driver.neutron_client.list_ports list_security_groups = self.driver.neutron_client.list_security_groups delete_sec_grp = self.driver.neutron_client.delete_security_group security_groups = { 'security_groups': [ {'id': t_constants.MOCK_SECURITY_GROUP_ID} ] } list_security_groups.return_value = security_groups delete_grp_results = [ network_base.DeallocateVIPException for _ in range(max_retries + 1)] # Total tries = max_retries + 1 delete_grp_results.append(None) delete_sec_grp.side_effect = delete_grp_results list_ports.side_effect = [{ "ports": [t_constants.MOCK_NEUTRON_PORT['port'], t_constants.MOCK_NEUTRON_PORT2['port']]}] self.driver.deallocate_vip(vip) # First we expect the amp's ports to be deleted dp_calls = [mock.call(amp.vrrp_port_id) for amp in lb.amphorae] # Then after the SG delete fails, extra hanging-on ports are removed dp_calls.append(mock.call(t_constants.MOCK_PORT_ID)) # Lastly we remove the vip port dp_calls.append(mock.call(vip.port_id)) self.assertEqual(len(dp_calls), delete_port.call_count) delete_port.assert_has_calls(dp_calls) dsg_calls = [mock.call(t_constants.MOCK_SECURITY_GROUP_ID) for _ in range(max_retries + 2)] # Max fail + one success self.assertEqual(len(dsg_calls), delete_sec_grp.call_count) delete_sec_grp.assert_has_calls(dsg_calls) def test_deallocate_vip_when_port_not_found(self): lb = dmh.generate_load_balancer_tree() vip = data_models.Vip(port_id='1') vip.load_balancer = lb show_port = self.driver.neutron_client.show_port show_port.side_effect = neutron_exceptions.PortNotFoundClient self.driver.deallocate_vip(vip) def test_deallocate_vip_when_port_not_found_for_update(self): lb = dmh.generate_load_balancer_tree() vip = data_models.Vip(port_id='1') vip.load_balancer = lb show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'device_owner': allowed_address_pairs.OCTAVIA_OWNER}} update_port = self.driver.neutron_client.update_port update_port.side_effect = neutron_exceptions.PortNotFoundClient self.driver.deallocate_vip(vip) def test_deallocate_vip_when_port_not_owned_by_octavia(self): lb = dmh.generate_load_balancer_tree() lb.vip.load_balancer = lb vip = lb.vip sec_grp_id = 'lb-sec-grp1' show_port = self.driver.neutron_client.show_port show_port.return_value = {'port': { 'id': vip.port_id, 'device_owner': 'neutron:LOADBALANCERV2', 'security_groups': [sec_grp_id]}} update_port = self.driver.neutron_client.update_port delete_sec_grp = self.driver.neutron_client.delete_security_group list_security_groups = self.driver.neutron_client.list_security_groups security_groups = { 'security_groups': [ {'id': sec_grp_id} ] } list_security_groups.return_value = security_groups self.driver.deallocate_vip(vip) expected_port_update = {'port': {'security_groups': []}} update_port.assert_called_once_with(vip.port_id, expected_port_update) delete_sec_grp.assert_called_once_with(sec_grp_id) def test_deallocate_vip_when_vip_port_not_found(self): lb = dmh.generate_load_balancer_tree() vip = data_models.Vip(port_id='1') vip.load_balancer = lb admin_project_id = 'octavia' session_mock = mock.MagicMock() session_mock.get_project_id.return_value = admin_project_id self.k_session.return_value = session_mock show_port = self.driver.neutron_client.show_port show_port.side_effect = neutron_exceptions.PortNotFoundClient self.driver.deallocate_vip(vip) def test_plug_vip_errors_when_nova_cant_find_network_to_attach(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = { 'subnet': { 'id': lb.vip.subnet_id } } list_security_groups = self.driver.neutron_client.list_security_groups lsc_side_effect = [ None, { 'security_groups': [ {'id': 'lb-sec-grp1'} ] } ] list_security_groups.side_effect = lsc_side_effect network_attach = self.driver.compute.attach_network_or_port network_attach.side_effect = nova_exceptions.NotFound(404, "Network") self.assertRaises(network_base.PlugVIPException, self.driver.plug_vip, lb, lb.vip) def test_plug_vip_errors_when_neutron_cant_find_port_to_update(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = { 'subnet': { 'id': lb.vip.subnet_id } } list_security_groups = self.driver.neutron_client.list_security_groups lsc_side_effect = [ None, { 'security_groups': [ {'id': 'lb-sec-grp1'} ] } ] list_security_groups.side_effect = lsc_side_effect network_attach = self.driver.compute.attach_network_or_port network_attach.return_value = t_constants.MOCK_NOVA_INTERFACE update_port = self.driver.neutron_client.update_port update_port.side_effect = neutron_exceptions.PortNotFoundClient self.assertRaises(network_base.PortNotFound, self.driver.plug_vip, lb, lb.vip) def test_plug_vip(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = { 'subnet': { 'id': t_constants.MOCK_VIP_SUBNET_ID, 'network_id': t_constants.MOCK_VIP_NET_ID } } list_ports = self.driver.neutron_client.list_ports port1 = t_constants.MOCK_MANAGEMENT_PORT1['port'] port2 = t_constants.MOCK_MANAGEMENT_PORT2['port'] list_ports.side_effect = [{'ports': [port1]}, {'ports': [port2]}] network_attach = self.driver.compute.attach_network_or_port network_attach.side_effect = [t_constants.MOCK_VRRP_INTERFACE1, t_constants.MOCK_VRRP_INTERFACE2] list_security_groups = self.driver.neutron_client.list_security_groups list_security_groups.return_value = { 'security_groups': [ {'id': 'lb-sec-grp1'} ] } update_port = self.driver.neutron_client.update_port expected_aap = {'port': {'allowed_address_pairs': [{'ip_address': lb.vip.ip_address}]}} amps = self.driver.plug_vip(lb, lb.vip) self.assertEqual(5, update_port.call_count) for amp in amps: update_port.assert_any_call(amp.vrrp_port_id, expected_aap) self.assertIn(amp.vrrp_ip, [t_constants.MOCK_VRRP_IP1, t_constants.MOCK_VRRP_IP2]) self.assertEqual(lb.vip.ip_address, amp.ha_ip) def _set_safely(self, obj, name, value): if isinstance(obj, dict): current = obj.get(name) self.addCleanup(obj.update, {name: current}) obj.update({name: value}) else: current = getattr(obj, name) self.addCleanup(setattr, obj, name, current) setattr(obj, name, value) def test_plug_vip_on_mgmt_net(self): lb = dmh.generate_load_balancer_tree() lb.vip.subnet_id = t_constants.MOCK_MANAGEMENT_SUBNET_ID show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = { 'subnet': { 'id': t_constants.MOCK_MANAGEMENT_SUBNET_ID, 'network_id': t_constants.MOCK_MANAGEMENT_NET_ID } } list_ports = self.driver.neutron_client.list_ports port1 = t_constants.MOCK_MANAGEMENT_PORT1['port'] port2 = t_constants.MOCK_MANAGEMENT_PORT2['port'] self._set_safely(t_constants.MOCK_MANAGEMENT_FIXED_IPS1[0], 'ip_address', lb.amphorae[0].lb_network_ip) self._set_safely(t_constants.MOCK_MANAGEMENT_FIXED_IPS2[0], 'ip_address', lb.amphorae[1].lb_network_ip) list_ports.side_effect = [{'ports': [port1]}, {'ports': [port2]}] network_attach = self.driver.compute.attach_network_or_port self._set_safely(t_constants.MOCK_VRRP_INTERFACE1, 'net_id', t_constants.MOCK_MANAGEMENT_NET_ID) self._set_safely(t_constants.MOCK_VRRP_FIXED_IPS1[0], 'subnet_id', t_constants.MOCK_MANAGEMENT_SUBNET_ID) self._set_safely(t_constants.MOCK_VRRP_INTERFACE2, 'net_id', t_constants.MOCK_MANAGEMENT_NET_ID) self._set_safely(t_constants.MOCK_VRRP_FIXED_IPS2[0], 'subnet_id', t_constants.MOCK_MANAGEMENT_SUBNET_ID) network_attach.side_effect = [t_constants.MOCK_VRRP_INTERFACE1, t_constants.MOCK_VRRP_INTERFACE2] list_security_groups = self.driver.neutron_client.list_security_groups list_security_groups.return_value = { 'security_groups': [ {'id': 'lb-sec-grp1'} ] } update_port = self.driver.neutron_client.update_port expected_aap = {'port': {'allowed_address_pairs': [{'ip_address': lb.vip.ip_address}]}} amps = self.driver.plug_vip(lb, lb.vip) self.assertEqual(5, update_port.call_count) for amp in amps: update_port.assert_any_call(amp.vrrp_port_id, expected_aap) self.assertIn(amp.vrrp_ip, [t_constants.MOCK_VRRP_IP1, t_constants.MOCK_VRRP_IP2]) self.assertEqual(lb.vip.ip_address, amp.ha_ip) def test_allocate_vip_when_port_already_provided(self): show_port = self.driver.neutron_client.show_port show_port.return_value = t_constants.MOCK_NEUTRON_PORT fake_lb_vip = data_models.Vip( port_id=t_constants.MOCK_PORT_ID, subnet_id=t_constants.MOCK_SUBNET_ID) fake_lb = data_models.LoadBalancer(id='1', vip=fake_lb_vip) vip = self.driver.allocate_vip(fake_lb) self.assertIsInstance(vip, data_models.Vip) self.assertEqual(t_constants.MOCK_IP_ADDRESS, vip.ip_address) self.assertEqual(t_constants.MOCK_SUBNET_ID, vip.subnet_id) self.assertEqual(t_constants.MOCK_PORT_ID, vip.port_id) self.assertEqual(fake_lb.id, vip.load_balancer_id) def test_allocate_vip_when_port_creation_fails(self): fake_lb_vip = data_models.Vip( subnet_id=t_constants.MOCK_SUBNET_ID) fake_lb = data_models.LoadBalancer(id='1', vip=fake_lb_vip) create_port = self.driver.neutron_client.create_port create_port.side_effect = Exception self.assertRaises(network_base.AllocateVIPException, self.driver.allocate_vip, fake_lb) @mock.patch('octavia.network.drivers.neutron.base.BaseNeutronDriver.' '_check_extension_enabled', return_value=True) def test_allocate_vip_when_no_port_provided(self, mock_check_ext): port_create_dict = copy.deepcopy(t_constants.MOCK_NEUTRON_PORT) port_create_dict['port']['device_owner'] = ( allowed_address_pairs.OCTAVIA_OWNER) port_create_dict['port']['device_id'] = 'lb-1' create_port = self.driver.neutron_client.create_port create_port.return_value = port_create_dict show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = {'subnet': { 'id': t_constants.MOCK_SUBNET_ID, 'network_id': t_constants.MOCK_NETWORK_ID }} fake_lb_vip = data_models.Vip(subnet_id=t_constants.MOCK_SUBNET_ID, network_id=t_constants.MOCK_NETWORK_ID) fake_lb = data_models.LoadBalancer(id='1', vip=fake_lb_vip, project_id='test-project') vip = self.driver.allocate_vip(fake_lb) exp_create_port_call = { 'port': { 'name': 'octavia-lb-1', 'network_id': t_constants.MOCK_NETWORK_ID, 'device_id': 'lb-1', 'device_owner': allowed_address_pairs.OCTAVIA_OWNER, 'admin_state_up': False, 'project_id': 'test-project', 'fixed_ips': [{'subnet_id': t_constants.MOCK_SUBNET_ID}] } } create_port.assert_called_once_with(exp_create_port_call) self.assertIsInstance(vip, data_models.Vip) self.assertEqual(t_constants.MOCK_IP_ADDRESS, vip.ip_address) self.assertEqual(t_constants.MOCK_SUBNET_ID, vip.subnet_id) self.assertEqual(t_constants.MOCK_PORT_ID, vip.port_id) self.assertEqual(fake_lb.id, vip.load_balancer_id) @mock.patch('octavia.network.drivers.neutron.base.BaseNeutronDriver.' '_check_extension_enabled', return_value=False) def test_allocate_vip_when_no_port_provided_tenant(self, mock_check_ext): port_create_dict = copy.deepcopy(t_constants.MOCK_NEUTRON_PORT) port_create_dict['port']['device_owner'] = ( allowed_address_pairs.OCTAVIA_OWNER) port_create_dict['port']['device_id'] = 'lb-1' create_port = self.driver.neutron_client.create_port create_port.return_value = port_create_dict show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = {'subnet': { 'id': t_constants.MOCK_SUBNET_ID, 'network_id': t_constants.MOCK_NETWORK_ID }} fake_lb_vip = data_models.Vip(subnet_id=t_constants.MOCK_SUBNET_ID, network_id=t_constants.MOCK_NETWORK_ID) fake_lb = data_models.LoadBalancer(id='1', vip=fake_lb_vip, project_id='test-project') vip = self.driver.allocate_vip(fake_lb) exp_create_port_call = { 'port': { 'name': 'octavia-lb-1', 'network_id': t_constants.MOCK_NETWORK_ID, 'device_id': 'lb-1', 'device_owner': allowed_address_pairs.OCTAVIA_OWNER, 'admin_state_up': False, 'tenant_id': 'test-project', 'fixed_ips': [{'subnet_id': t_constants.MOCK_SUBNET_ID}] } } create_port.assert_called_once_with(exp_create_port_call) self.assertIsInstance(vip, data_models.Vip) self.assertEqual(t_constants.MOCK_IP_ADDRESS, vip.ip_address) self.assertEqual(t_constants.MOCK_SUBNET_ID, vip.subnet_id) self.assertEqual(t_constants.MOCK_PORT_ID, vip.port_id) self.assertEqual(fake_lb.id, vip.load_balancer_id) def test_unplug_vip_errors_when_update_port_cant_find_port(self): lb = dmh.generate_load_balancer_tree() list_ports = self.driver.neutron_client.list_ports show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = t_constants.MOCK_SUBNET port1 = t_constants.MOCK_NEUTRON_PORT['port'] port2 = { 'id': '4', 'network_id': '3', 'fixed_ips': [{'ip_address': '10.0.0.2'}] } list_ports.return_value = {'ports': [port1, port2]} update_port = self.driver.neutron_client.update_port update_port.side_effect = neutron_exceptions.PortNotFoundClient self.assertRaises(network_base.UnplugVIPException, self.driver.unplug_vip, lb, lb.vip) def test_unplug_vip_errors_when_update_port_fails(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = t_constants.MOCK_SUBNET port1 = t_constants.MOCK_NEUTRON_PORT['port'] port2 = { 'id': '4', 'network_id': '3', 'fixed_ips': [{'ip_address': '10.0.0.2'}] } list_ports = self.driver.neutron_client.list_ports list_ports.return_value = {'ports': [port1, port2]} update_port = self.driver.neutron_client.update_port update_port.side_effect = TypeError self.assertRaises(network_base.UnplugVIPException, self.driver.unplug_vip, lb, lb.vip) def test_unplug_vip_errors_when_vip_subnet_not_found(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.side_effect = neutron_exceptions.NotFound self.assertRaises(network_base.PluggedVIPNotFound, self.driver.unplug_vip, lb, lb.vip) def test_unplug_vip(self): lb = dmh.generate_load_balancer_tree() show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = t_constants.MOCK_SUBNET update_port = self.driver.neutron_client.update_port port1 = t_constants.MOCK_NEUTRON_PORT['port'] port2 = { 'id': '4', 'network_id': '3', 'fixed_ips': [{'ip_address': '10.0.0.2'}] } list_ports = self.driver.neutron_client.list_ports list_ports.return_value = {'ports': [port1, port2]} get_port = self.driver.neutron_client.get_port get_port.side_effect = neutron_exceptions.NotFound self.driver.unplug_vip(lb, lb.vip) self.assertEqual(len(lb.amphorae), update_port.call_count) clear_aap = {'port': {'allowed_address_pairs': []}} update_port.assert_has_calls([mock.call(port1.get('id'), clear_aap), mock.call(port1.get('id'), clear_aap)]) def test_plug_network_when_compute_instance_cant_be_found(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id network_attach = self.driver.compute.attach_network_or_port network_attach.side_effect = nova_exceptions.NotFound( 404, message='Instance not found') self.assertRaises(network_base.AmphoraNotFound, self.driver.plug_network, t_constants.MOCK_COMPUTE_ID, net_id) def test_plug_network_when_network_cant_be_found(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id network_attach = self.driver.compute.attach_network_or_port network_attach.side_effect = nova_exceptions.NotFound( 404, message='Network not found') self.assertRaises(network_base.NetworkException, self.driver.plug_network, t_constants.MOCK_COMPUTE_ID, net_id) def test_plug_network_when_interface_attach_fails(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id network_attach = self.driver.compute.attach_network_or_port network_attach.side_effect = TypeError self.assertRaises(network_base.PlugNetworkException, self.driver.plug_network, t_constants.MOCK_COMPUTE_ID, net_id) def test_plug_network(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id network_attach = self.driver.compute.attach_network_or_port network_attach.return_value = t_constants.MOCK_NOVA_INTERFACE oct_interface = self.driver.plug_network( t_constants.MOCK_COMPUTE_ID, net_id) exp_ips = [fixed_ip.get('ip_address') for fixed_ip in t_constants.MOCK_NOVA_INTERFACE.fixed_ips] actual_ips = [fixed_ip.ip_address for fixed_ip in oct_interface.fixed_ips] self.assertEqual(exp_ips, actual_ips) self.assertEqual(t_constants.MOCK_COMPUTE_ID, oct_interface.compute_id) self.assertEqual(net_id, oct_interface.network_id) def test_unplug_network_when_compute_port_cant_be_found(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id list_ports = self.driver.neutron_client.list_ports list_ports.return_value = {'ports': []} self.assertRaises(network_base.NetworkNotFound, self.driver.unplug_network, t_constants.MOCK_COMPUTE_ID, net_id) def test_unplug_network_when_list_ports_fails(self): net_id = t_constants.MOCK_NOVA_INTERFACE.net_id list_ports = self.driver.neutron_client.list_ports list_ports.side_effect = Exception self.assertRaises(network_base.NetworkException, self.driver.unplug_network, t_constants.MOCK_COMPUTE_ID, net_id) def test_unplug_network(self): list_ports = self.driver.neutron_client.list_ports port1 = t_constants.MOCK_NEUTRON_PORT['port'] port2 = { 'id': '4', 'network_id': '3', 'fixed_ips': [{'ip_address': '10.0.0.2'}] } list_ports.return_value = {'ports': [port1, port2]} port_detach = self.driver.compute.detach_port self.driver.unplug_network(t_constants.MOCK_COMPUTE_ID, port2.get('network_id')) port_detach.assert_called_once_with( compute_id=t_constants.MOCK_COMPUTE_ID, port_id=port2.get('id')) def test_update_vip(self): listeners = [data_models.Listener(protocol_port=80, peer_port=1024, protocol=constants.PROTOCOL_TCP), data_models.Listener(protocol_port=443, peer_port=1025, protocol=constants.PROTOCOL_TCP), data_models.Listener(protocol_port=50, peer_port=1026, protocol=constants.PROTOCOL_UDP)] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': [{'id': 'secgrp-1'}]} fake_rules = { 'security_group_rules': [ {'id': 'rule-80', 'port_range_max': 80, 'protocol': 'tcp'}, {'id': 'rule-22', 'port_range_max': 22, 'protocol': 'tcp'} ] } list_rules = self.driver.neutron_client.list_security_group_rules list_rules.return_value = fake_rules delete_rule = self.driver.neutron_client.delete_security_group_rule create_rule = self.driver.neutron_client.create_security_group_rule self.driver.update_vip(lb) delete_rule.assert_called_once_with('rule-22') expected_create_rule_1 = { 'security_group_rule': { 'security_group_id': 'secgrp-1', 'direction': 'ingress', 'protocol': 'tcp', 'port_range_min': 1024, 'port_range_max': 1024, 'ethertype': 'IPv4' } } expected_create_rule_udp_peer = { 'security_group_rule': { 'security_group_id': 'secgrp-1', 'direction': 'ingress', 'protocol': 'tcp', 'port_range_min': 1026, 'port_range_max': 1026, 'ethertype': 'IPv4' } } expected_create_rule_2 = { 'security_group_rule': { 'security_group_id': 'secgrp-1', 'direction': 'ingress', 'protocol': 'tcp', 'port_range_min': 1025, 'port_range_max': 1025, 'ethertype': 'IPv4' } } expected_create_rule_3 = { 'security_group_rule': { 'security_group_id': 'secgrp-1', 'direction': 'ingress', 'protocol': 'tcp', 'port_range_min': 443, 'port_range_max': 443, 'ethertype': 'IPv4' } } expected_create_rule_udp = { 'security_group_rule': { 'security_group_id': 'secgrp-1', 'direction': 'ingress', 'protocol': 'udp', 'port_range_min': 50, 'port_range_max': 50, 'ethertype': 'IPv4' } } create_rule.assert_has_calls([mock.call(expected_create_rule_1), mock.call(expected_create_rule_udp_peer), mock.call(expected_create_rule_2), mock.call(expected_create_rule_3), mock.call(expected_create_rule_udp)], any_order=True) def test_update_vip_when_listener_deleted(self): listeners = [data_models.Listener(protocol_port=80, protocol=constants.PROTOCOL_TCP), data_models.Listener( protocol_port=443, protocol=constants.PROTOCOL_TCP, provisioning_status=constants.PENDING_DELETE), data_models.Listener( protocol_port=50, protocol=constants.PROTOCOL_UDP, provisioning_status=constants.PENDING_DELETE)] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': [{'id': 'secgrp-1'}]} fake_rules = { 'security_group_rules': [ {'id': 'rule-80', 'port_range_max': 80, 'protocol': 'tcp'}, {'id': 'rule-22', 'port_range_max': 443, 'protocol': 'tcp'}, {'id': 'rule-udp-50', 'port_range_max': 50, 'protocol': 'tcp'} ] } list_rules = self.driver.neutron_client.list_security_group_rules list_rules.return_value = fake_rules delete_rule = self.driver.neutron_client.delete_security_group_rule create_rule = self.driver.neutron_client.create_security_group_rule self.driver.update_vip(lb) delete_rule.assert_has_calls( [mock.call('rule-22'), mock.call('rule-udp-50')]) self.assertTrue(create_rule.called) def test_update_vip_when_no_listeners(self): listeners = [] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': [{'id': 'secgrp-1'}]} fake_rules = { 'security_group_rules': [ {'id': 'all-egress', 'protocol': None, 'direction': 'egress'}, {'id': 'ssh-rule', 'protocol': 'tcp', 'port_range_max': 22} ] } list_rules = self.driver.neutron_client.list_security_group_rules list_rules.return_value = fake_rules delete_rule = self.driver.neutron_client.delete_security_group_rule self.driver.update_vip(lb) delete_rule.assert_called_once_with('ssh-rule') def test_update_vip_when_security_group_rule_deleted(self): listeners = [] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': [{'id': 'secgrp-1'}]} fake_rules = { 'security_group_rules': [ {'id': 'all-egress', 'protocol': None, 'direction': 'egress'}, {'id': 'ssh-rule', 'protocol': 'tcp', 'port_range_max': 22} ] } list_rules = self.driver.neutron_client.list_security_group_rules list_rules.return_value = fake_rules delete_rule = self.driver.neutron_client.delete_security_group_rule delete_rule.side_effect = neutron_exceptions.NotFound self.driver.update_vip(lb) delete_rule.assert_called_once_with('ssh-rule') def test_update_vip_when_security_group_missing(self): listeners = [] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': []} self.assertRaises(exceptions.MissingVIPSecurityGroup, self.driver.update_vip, lb) @mock.patch('octavia.network.drivers.neutron.allowed_address_pairs.' 'AllowedAddressPairsDriver._update_security_group_rules') def test_update_vip_for_delete_when_security_group_missing(self, update_rules): listeners = [] vip = data_models.Vip(ip_address='10.0.0.2') lb = data_models.LoadBalancer(id='1', listeners=listeners, vip=vip) list_sec_grps = self.driver.neutron_client.list_security_groups list_sec_grps.return_value = {'security_groups': []} self.driver.update_vip(lb, for_delete=True) update_rules.assert_not_called() def test_failover_preparation(self): original_dns_integration_state = self.driver.dns_integration_enabled self.driver.dns_integration_enabled = False ports = {"ports": [ {"fixed_ips": [{"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1}, {"fixed_ips": [{"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2}]} self.driver.neutron_client.list_ports.return_value = ports self.driver.neutron_client.show_port = mock.Mock( side_effect=self._failover_show_port_side_effect) port_update = self.driver.neutron_client.update_port amphora = data_models.Amphora( id=self.AMPHORA_ID, load_balancer_id=self.LB_ID, compute_id=self.COMPUTE_ID, status=self.ACTIVE, lb_network_ip=self.LB_NET_IP, ha_port_id=self.HA_PORT_ID, ha_ip=self.HA_IP) self.driver.failover_preparation(amphora) self.assertFalse(port_update.called) self.driver.dns_integration_enabled = original_dns_integration_state def test_failover_preparation_dns_integration(self): ports = {"ports": [ {"fixed_ips": [{"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1}, {"fixed_ips": [{"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2}]} original_dns_integration_state = self.driver.dns_integration_enabled self.driver.dns_integration_enabled = True self.driver.neutron_client.list_ports.return_value = ports self.driver.neutron_client.show_port = mock.Mock( side_effect=self._failover_show_port_side_effect) port_update = self.driver.neutron_client.update_port amphora = data_models.Amphora( id=self.AMPHORA_ID, load_balancer_id=self.LB_ID, compute_id=self.COMPUTE_ID, status=self.ACTIVE, lb_network_ip=self.LB_NET_IP, ha_port_id=self.HA_PORT_ID, ha_ip=self.HA_IP) self.driver.failover_preparation(amphora) port_update.assert_called_once_with(ports['ports'][1].get('id'), {'port': {'dns_name': ''}}) self.driver.dns_integration_enabled = original_dns_integration_state def _failover_show_port_side_effect(self, port_id): if port_id == self.LB_NET_PORT_ID: return {"fixed_ips": [{"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1} if port_id == self.HA_PORT_ID: return {"fixed_ips": [{"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2} def test_plug_port(self): port = mock.MagicMock() port.id = self.PORT_ID network_attach = self.driver.compute.attach_network_or_port network_attach.return_value = t_constants.MOCK_NOVA_INTERFACE amphora = data_models.Amphora( id=self.AMPHORA_ID, load_balancer_id=self.LB_ID, compute_id=self.COMPUTE_ID, status=self.ACTIVE, lb_network_ip=self.LB_NET_IP, ha_port_id=self.HA_PORT_ID, ha_ip=self.HA_IP) self.driver.plug_port(amphora, port) network_attach.assert_called_once_with(compute_id=amphora.compute_id, network_id=None, ip_address=None, port_id=self.PORT_ID) # NotFound cases network_attach.side_effect = nova_exceptions.NotFound( 1, message='Instance') self.assertRaises(network_base.AmphoraNotFound, self.driver.plug_port, amphora, port) network_attach.side_effect = nova_exceptions.NotFound( 1, message='Network') self.assertRaises(network_base.NetworkNotFound, self.driver.plug_port, amphora, port) network_attach.side_effect = nova_exceptions.NotFound( 1, message='bogus') self.assertRaises(network_base.PlugNetworkException, self.driver.plug_port, amphora, port) # Already plugged case should not raise an exception network_attach.side_effect = nova_exceptions.Conflict(1) self.driver.plug_port(amphora, port) # Unknown error case network_attach.side_effect = TypeError self.assertRaises(network_base.PlugNetworkException, self.driver.plug_port, amphora, port) def test_get_network_configs(self): amphora_mock = mock.MagicMock() load_balancer_mock = mock.MagicMock() vip_mock = mock.MagicMock() amphora_mock.status = constants.DELETED load_balancer_mock.amphorae = [amphora_mock] show_port = self.driver.neutron_client.show_port show_port.return_value = t_constants.MOCK_NEUTRON_PORT fake_subnet = {'subnet': { 'id': t_constants.MOCK_SUBNET_ID, 'gateway_ip': t_constants.MOCK_IP_ADDRESS, 'cidr': t_constants.MOCK_CIDR}} show_subnet = self.driver.neutron_client.show_subnet show_subnet.return_value = fake_subnet configs = self.driver.get_network_configs(load_balancer_mock) self.assertEqual({}, configs) vip_mock.port_id = 1 amphora_mock.id = 222 amphora_mock.status = constants.ACTIVE amphora_mock.vrrp_port_id = 2 amphora_mock.vrrp_ip = "10.0.0.1" amphora_mock.ha_port_id = 3 amphora_mock.ha_ip = "10.0.0.2" load_balancer_mock.amphorae = [amphora_mock] configs = self.driver.get_network_configs(load_balancer_mock) self.assertEqual(1, len(configs)) config = configs[222] # TODO(ptoohill): find a way to return different items for multiple # calls to the same method, right now each call to show subnet # will return the same values if a method happens to call it # multiple times for different subnets. We should be able to verify # different requests get different expected data. expected_port_id = t_constants.MOCK_NEUTRON_PORT['port']['id'] self.assertEqual(expected_port_id, config.ha_port.id) self.assertEqual(expected_port_id, config.vrrp_port.id) expected_subnet_id = fake_subnet['subnet']['id'] self.assertEqual(expected_subnet_id, config.ha_subnet.id) self.assertEqual(expected_subnet_id, config.vrrp_subnet.id) @mock.patch('time.sleep') def test_wait_for_port_detach(self, mock_sleep): amphora = data_models.Amphora( id=self.AMPHORA_ID, load_balancer_id=self.LB_ID, compute_id=self.COMPUTE_ID, status=self.ACTIVE, lb_network_ip=self.LB_NET_IP, ha_port_id=self.HA_PORT_ID, ha_ip=self.HA_IP) ports = {"ports": [ {"fixed_ips": [{"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1}, {"fixed_ips": [{"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2}]} show_port_1_without_device_id = {"fixed_ips": [ {"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1, "device_id": ''} show_port_2_with_device_id = {"fixed_ips": [ {"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2, "device_id": self.DEVICE_ID} show_port_2_without_device_id = {"fixed_ips": [ {"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2, "device_id": None} self.driver.neutron_client.list_ports.return_value = ports port_mock = mock.MagicMock() port_mock.get = mock.Mock( side_effect=[show_port_1_without_device_id, show_port_2_with_device_id, show_port_2_with_device_id, show_port_2_without_device_id]) self.driver.neutron_client.show_port.return_value = port_mock self.driver.wait_for_port_detach(amphora) self.assertEqual(1, mock_sleep.call_count) @mock.patch('time.time') @mock.patch('time.sleep') def test_wait_for_port_detach_timeout(self, mock_sleep, mock_time): mock_time.side_effect = [1, 2, 6] conf = oslo_fixture.Config(cfg.CONF) conf.config(group="networking", port_detach_timeout=5) amphora = data_models.Amphora( id=self.AMPHORA_ID, load_balancer_id=self.LB_ID, compute_id=self.COMPUTE_ID, status=self.ACTIVE, lb_network_ip=self.LB_NET_IP, ha_port_id=self.HA_PORT_ID, ha_ip=self.HA_IP) ports = {"ports": [ {"fixed_ips": [{"subnet_id": self.SUBNET_ID_1, "ip_address": self.IP_ADDRESS_1}], "id": self.FIXED_IP_ID_1, "network_id": self.NETWORK_ID_1}, {"fixed_ips": [{"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2}]} show_port_1_with_device_id = {"fixed_ips": [ {"subnet_id": self.SUBNET_ID_2, "ip_address": self.IP_ADDRESS_2}], "id": self.FIXED_IP_ID_2, "network_id": self.NETWORK_ID_2, "device_id": self.DEVICE_ID} self.driver.neutron_client.list_ports.return_value = ports port_mock = mock.MagicMock() port_mock.get = mock.Mock( return_value=show_port_1_with_device_id) self.driver.neutron_client.show_port.return_value = port_mock self.assertRaises(network_base.TimeoutException, self.driver.wait_for_port_detach, amphora)

47.497143

79

0.634424

6,105

49,872

4.787551

0.061097

0.054742

0.052929

0.071609

0.811824

0.770973

0.738299

0.707746

0.67473

0.660018

0

0.01238

0.271174

49,872

1,049

80

47.542421

0.791735

0.023159

0

0.606029

0

0.079467

0.011091

0

0.000953

0.087318

1

0.047817

false

0

0.018711

0

0.090437

0

null

0

1

0

1

0

null

0

5

5d0548637a72123ee6c5ee2b4ec9a20c5d0fd264

63

py

Python

wpiformat/wpiformat/test/__init__.py

prateekma/styleguide

962c6cd6e316b71156d80e5751e8a76a01b60668

[ "BSD-3-Clause" ]

19

2018-08-23T06:42:33.000Z

2022-01-31T05:09:19.000Z

wpiformat/wpiformat/test/__init__.py

prateekma/styleguide

962c6cd6e316b71156d80e5751e8a76a01b60668

[ "BSD-3-Clause" ]

34

2016-08-22T20:38:35.000Z

2021-12-20T20:15:39.000Z

wpiformat/wpiformat/test/__init__.py

prateekma/styleguide

962c6cd6e316b71156d80e5751e8a76a01b60668

[ "BSD-3-Clause" ]

12

2016-08-19T07:07:58.000Z

2021-12-08T06:21:30.000Z

import pytest pytest.register_assert_rewrite("test.tasktest")

15.75

47

0.84127

8

63

6.375

0.875

0

0.063492

63

3

48

21

0.864407

0

0.206349

0

0.5

1

0

true

0

0.5

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0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5d34639467c1896ea95c4a819c169800445ee5c3

108

py

Python

test/test_cover.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

test/test_cover.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

test/test_cover.py

NicolasAbroad/wnscraper

87d5aa8e3a26aa0846a289d378848e1eb1d13304

[ "Apache-2.0" ]

null

from unittest import TestCase class TestCoverGeneration(TestCase): def test_cover(self): pass

15.428571

36

0.731481

12

108

6.5

0.916667

0

0.212963

108

6

37

18

0.917647

0

1

0.25

false

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5d5f5dded1ca55810114fc5874e044aa5a04d3f1

52

py

Python

kivymd/uix/pickers/colorpicker/__init__.py

marvelous-benji/KivyMD

4ab8dd339902597eaa9f8a4f9a80d8a6eb7d6053

[ "MIT" ]

1,111

2015-07-15T02:31:09.000Z

2022-03-29T17:22:02.000Z

kivymd/uix/pickers/colorpicker/__init__.py

marvelous-benji/KivyMD

4ab8dd339902597eaa9f8a4f9a80d8a6eb7d6053

[ "MIT" ]

706

2015-06-10T22:24:13.000Z

2022-03-31T16:22:39.000Z

kivymd/uix/pickers/colorpicker/__init__.py

marvelous-benji/KivyMD

4ab8dd339902597eaa9f8a4f9a80d8a6eb7d6053

[ "MIT" ]

561

2015-07-15T04:57:23.000Z

2022-03-31T17:14:31.000Z

from .colorpicker import MDColorPicker # NOQA F401

26

51

0.807692

6

52

7

1

0

0.068182

0.153846

52

1

52

0.886364

0.173077

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true

0

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1

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0

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null

0

1

0

1

0

1

0

5

5d70d76958a440aaf73c049d1471abebb73bf4f7

9,242

py

Python

backends/ubpf/tests/ptf/ipv4_actions_test.py

anasyrmia/p4c-1

2bf2f615fdaaf4efed1f2f8ab0b3f3261cface60

[ "Apache-2.0" ]

487

2016-12-22T03:33:27.000Z

2022-03-29T06:36:45.000Z

backends/ubpf/tests/ptf/ipv4_actions_test.py

anasyrmia/p4c-1

2bf2f615fdaaf4efed1f2f8ab0b3f3261cface60

[ "Apache-2.0" ]

2,114

2016-12-18T11:36:27.000Z

2022-03-31T22:33:23.000Z

backends/ubpf/tests/ptf/ipv4_actions_test.py

anasyrmia/p4c-1

2bf2f615fdaaf4efed1f2f8ab0b3f3261cface60

[ "Apache-2.0" ]

456

2016-12-20T14:01:11.000Z

2022-03-30T19:26:05.000Z

#!/usr/bin/env python # Copyright 2019 Orange # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ptf.mask import Mask from ptf.packet import TCP, IP, Ether from base_test import P4rtOVSBaseTest from ptf.testutils import send_packet, verify_packets, simple_ip_packet class Ipv4Test(P4rtOVSBaseTest): def setUp(self): P4rtOVSBaseTest.setUp(self) self.del_flows() self.unload_bpf_program() self.load_bpf_program(path_to_program="build/test-ipv4-actions.o") self.add_bpf_prog_flow(1,2) self.add_bpf_prog_flow(2,1) class Ipv4SetVersionTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="0 0 0 0 5 0 0 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", version=4) / TCP() / "Ala a un chat" exp_pkt = Ether() / IP(src="192.168.1.1", version=5) / TCP() / "Ala a un chat" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetIhlTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="1 0 0 0 15 0 0 0 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ihl=10) exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ihl=15) mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetDiffservTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="2 0 0 0 255 0 0 0 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_tos=10) exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_tos=255) mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetIdentificationTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="3 0 0 0 211 0 0 0 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_id=10) exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_id=211) mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetFlagsTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="4 0 0 0 7 0 0 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", flags=0) / TCP() / "Ala a un chat" exp_pkt = Ether() / IP(src="192.168.1.1", flags=7) / TCP() / "Ala a un chat" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetFragOffsetTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="5 0 0 0 13 0 0 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", frag=0) / TCP() / "Ala ma kota" exp_pkt = Ether() / IP(src="192.168.1.1", frag=13) / TCP() / "Ala ma kota" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetTtlTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="6 0 0 0 60 0 0 0 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ttl=64) exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ttl=60) mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetProtocolTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="7 0 0 0 55 0 0 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1") / TCP() / "Ala ma kota" exp_pkt = Ether() / IP(src="192.168.1.1", proto=55) / TCP() / "Ala ma kota" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetSrcTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="8 0 0 0 2 2 168 192 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1") exp_pkt = simple_ip_packet(ip_src="192.168.2.2") mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetDstTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="9 0 0 0 2 2 168 192 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_dst="192.168.1.2") exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_dst="192.168.2.2") mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetSrcDstTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="10 0 0 0 10 10 10 10 10 10 10 10") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_dst="192.168.1.2") exp_pkt = simple_ip_packet(ip_src="10.10.10.10", ip_dst="10.10.10.10") mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetIhlDiffservTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="11 0 0 0 15 26 0 0 0 0 0 0") def runTest(self): pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ihl=10, ip_tos=0) exp_pkt = simple_ip_packet(ip_src="192.168.1.1", ip_ihl=15, ip_tos=26) mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetFragmentOffsetFlagTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="12 0 0 0 13 0 7 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", frag=0, flags=0) / TCP() / "Ala ma kota" exp_pkt = Ether() / IP(src="192.168.1.1", frag=13, flags=7) / TCP() / "Ala ma kota" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetFlagsTtlTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="13 0 0 0 7 50 0 0 0 0 0 0") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", flags=0, ttl=64) / TCP() / "Ala ma kota" exp_pkt = Ether() / IP(src="192.168.1.1", flags=7, ttl=50) / TCP() / "Ala ma kota" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2]) class Ipv4SetFragOffsetSrcTest(Ipv4Test): def setUp(self): Ipv4Test.setUp(self) self.update_bpf_map(map_id=0, key="1 1 168 192", value="14 0 0 0 255 31 0 0 255 255 255 255") def runTest(self): pkt = Ether() / IP(src="192.168.1.1", frag=0) / TCP() / "Ala ma kota" exp_pkt = Ether() / IP(src="255.255.255.255", frag=8191) / TCP() / "Ala ma kota" mask = Mask(exp_pkt) mask.set_do_not_care_scapy(IP, 'chksum') mask.set_do_not_care_scapy(TCP, 'chksum') send_packet(self, (0, 1), pkt) verify_packets(self, mask, device_number=0, ports=[2])

30.301639

101

0.623134

1,572

9,242

3.494275

0.108142

0.03641

0.038777

0.031313

0.777535

0.769161

0.765338

0.761697

0.754597

0

0.114241

0.235663

9,242

304

102

30.401316

0.663364

0.061026

0

0.617978

0

0.140235

0.002886

0

1

0.174157

false

0

0.022472

0

0.286517

0

null

0

1

0

null

0

5

538411c050ddde830cdd9c0c658ed57c0d8116ee

106

py

Python

route/file.py

dubovinszky/route-calculator

e2da6e351a25fcf4ebf98dc05b1d651ed291b7e8

[ "MIT" ]

null

route/file.py

dubovinszky/route-calculator

e2da6e351a25fcf4ebf98dc05b1d651ed291b7e8

[ "MIT" ]

null

route/file.py

dubovinszky/route-calculator

e2da6e351a25fcf4ebf98dc05b1d651ed291b7e8

[ "MIT" ]

null

def get_file_contents(file_path): with open(file_path, 'r') as content: return content.read()

26.5

41

0.688679

16

106

4.3125

0.75

0.231884

0

0.198113

106

3

42

35.333333

0.811765

0

0.009434

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

539ece508c5d9bf18f86fbbe46df243f8857393d

67

py

Python

code/__init__.py

nju-websoft/DRESSED

52d22630213ea1389c3f3663812afde1b833c328

[ "Apache-2.0" ]

9

2019-12-06T15:05:54.000Z

2022-03-11T06:25:54.000Z

code/__init__.py

nju-websoft/DRESSED

52d22630213ea1389c3f3663812afde1b833c328

[ "Apache-2.0" ]

null

code/__init__.py

nju-websoft/DRESSED

52d22630213ea1389c3f3663812afde1b833c328

[ "Apache-2.0" ]

5

2020-03-18T15:11:09.000Z

2022-03-11T06:25:57.000Z

''' @file: __init__.py.py @author: qxLiu @time: 2020/3/14 9:37 '''

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53b7abacea5bf83e18f9b16489c5fd5f74b2de38

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py

Python

project/cloudmesh/cluster/providers/kubernetes.py

cybertraining-dsc/fa19-516-153

e6e3952a74f8a711221ea8e1c461567526267d51

[ "Apache-2.0" ]

null

project/cloudmesh/cluster/providers/kubernetes.py

cybertraining-dsc/fa19-516-153

e6e3952a74f8a711221ea8e1c461567526267d51

[ "Apache-2.0" ]

null

project/cloudmesh/cluster/providers/kubernetes.py

cybertraining-dsc/fa19-516-153

e6e3952a74f8a711221ea8e1c461567526267d51

[ "Apache-2.0" ]

1

2019-09-20T02:13:45.000Z

from .meta_cluster import MetaCluster class KubernetesCluster(metaclass=MetaCluster): def add(self): pass

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53edc9b4ed1c50ded5cb107d5d5785efa2cd75be

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py

Python

imagepy/core/roi/__init__.py

BioinfoTongLI/imagepy

b86f33f20e872ee8b86471a9ddfbd5ad064fd64d

[ "BSD-4-Clause" ]

2

2020-04-17T12:57:55.000Z

2020-04-17T12:57:57.000Z

imagepy/core/roi/__init__.py

BioinfoTongLI/imagepy

b86f33f20e872ee8b86471a9ddfbd5ad064fd64d

[ "BSD-4-Clause" ]

null

imagepy/core/roi/__init__.py

BioinfoTongLI/imagepy

b86f33f20e872ee8b86471a9ddfbd5ad064fd64d

[ "BSD-4-Clause" ]

null

from .lineroi import LineRoi from .ovalroi import OvalRoi from .pointroi import PointRoi from .polygonroi import PolygonRoi from .rectangleroi import RectangleRoi

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53f861359ac0ab1c41de871cc265263a28577a95

46

py

Python

ML.1/src/lab3.py

jfnavarro/old_python_courses

fb500e8eeae6c5d10bf77e1ff52725627527222a

[ "MIT" ]

1

2018-02-20T03:26:35.000Z

ML.1/src/lab3.py

jfnavarro/BioInfo_ML_courses

fb500e8eeae6c5d10bf77e1ff52725627527222a

[ "MIT" ]

null

ML.1/src/lab3.py

jfnavarro/BioInfo_ML_courses

fb500e8eeae6c5d10bf77e1ff52725627527222a

[ "MIT" ]

null

''' Created on Oct 1, 2011 @author: jose '''

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1

0

null

0

1

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5

54de6899bf45aff4c7554c74bbd7016c3880c7c0

75

py

Python

init_db.py

Armalon/BaseFlask

53679da1099cc2bfc310905651fa7fa9fa77ff35

[ "MIT" ]

null

init_db.py

Armalon/BaseFlask

53679da1099cc2bfc310905651fa7fa9fa77ff35

[ "MIT" ]

2

2021-10-06T19:53:48.000Z

2022-02-13T17:56:22.000Z

init_db.py

Armalon/BaseFlask

53679da1099cc2bfc310905651fa7fa9fa77ff35

[ "MIT" ]

null

# Init Database from chat_schema.sql from server import init_db init_db()

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0.146667

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1

0

null

0

1

0

1

0

5

070d76276f82b26642f6f27d090a142d69125400

1,065

py

Python

ucb_cs61A/lab/lab12/tests/smallest-int.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

null

ucb_cs61A/lab/lab12/tests/smallest-int.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

1

2017-07-31T08:15:26.000Z

ucb_cs61A/lab/lab12/tests/smallest-int.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

1

2019-10-06T16:52:31.000Z

test = { 'name': 'smallest-int', 'points': 0, 'suites': [ { 'cases': [ { 'code': r""" sqlite> SELECT * FROM smallest_int; 11/11/2015 10:01:03|7 11/11/2015 13:53:36|7 11/11/2015 14:52:07|7 11/11/2015 15:36:00|7 11/11/2015 15:46:03|7 11/11/2015 16:11:56|7 11/11/2015 17:42:09|7 11/11/2015 11:49:59|8 11/12/2015 14:30:09|8 11/11/2015 9:57:49|9 11/11/2015 10:29:15|10 11/11/2015 11:18:22|10 11/11/2015 16:56:15|10 11/11/2015 10:04:51|11 11/11/2015 10:27:47|11 11/11/2015 11:04:43|11 11/11/2015 12:27:14|11 11/11/2015 12:52:33|11 11/11/2015 13:05:03|11 11/11/2015 13:48:29|11 """, 'hidden': False, 'locked': False } ], 'ordered': False, 'scored': True, 'setup': r""" sqlite> .read lab12.sql """, 'teardown': '', 'type': 'sqlite' } ] }

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1,065

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071b1c765565478df1dd406b50fb44be86335821

31

py

Python

socfaker/__init__.py

priamai/soc-faker

51b587f0cec52212136905280406e915006d2afc

[ "MIT" ]

122

2020-02-21T16:06:54.000Z

2022-03-21T13:53:03.000Z

socfaker/__init__.py

priamai/soc-faker

51b587f0cec52212136905280406e915006d2afc

[ "MIT" ]

13

2020-01-29T16:37:05.000Z

2022-01-27T21:30:10.000Z

socfaker/__init__.py

priamai/soc-faker

51b587f0cec52212136905280406e915006d2afc

[ "MIT" ]

20

2020-04-10T11:59:29.000Z

2022-02-10T09:20:26.000Z

from .socfaker import SocFaker

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1

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0.962963

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null

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1

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1

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5

07487c7a0365aa5c7fa0fd09d78c5b10eed24166

134

py

Python

robosuite/devices/__init__.py

clj003/mod_surreal2

1c91ed1d85acdb8b82dae46c49153d547301d2d9

[ "MIT" ]

null

robosuite/devices/__init__.py

clj003/mod_surreal2

1c91ed1d85acdb8b82dae46c49153d547301d2d9

[ "MIT" ]

null

robosuite/devices/__init__.py

clj003/mod_surreal2

1c91ed1d85acdb8b82dae46c49153d547301d2d9

[ "MIT" ]

null

# Takes away spacemouse since not a mac from .device import Device from .keyboard import Keyboard #from .spacemouse import SpaceMouse

26.8

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134

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4

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true

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null

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null

0

1

0

1

0

1

0

5

ab2236b24a74fa00d1828dadec89603cf1ec0fc7

145

py

Python

Feature/Feature.py

NunoXu/UnbabelChallenge2016

680131bb80e1cb0a8d52033b3e75d0ed0e0eb70a

[ "MIT" ]

null

Feature/Feature.py

NunoXu/UnbabelChallenge2016

680131bb80e1cb0a8d52033b3e75d0ed0e0eb70a

[ "MIT" ]

5

2021-03-31T18:23:08.000Z

2022-01-13T00:38:59.000Z

Feature/Feature.py

NunoXu/UnbabelChallenge2016

680131bb80e1cb0a8d52033b3e75d0ed0e0eb70a

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod class Feature(metaclass=ABCMeta): @abstractmethod def evaluate(self, sentence): pass

14.5

39

0.710345

15

145

6.866667

0.866667

0.407767

0

0.22069

145

9

40

16.111111

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0

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1

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null

0

1

0

1

0

5

ab2fb75563fa16e8f35357f6b2a5ff4c87cfa2a9

83

py

Python

src/kernel-graphql/riotapi/views.py

pseudonym117/kernel-graphql

0c0fc05ca84c525f1515953d77d853455db14fb1

[ "MIT" ]

1

2021-03-17T16:35:09.000Z

src/kernel-graphql/riotapi/views.py

pseudonym117/kernel-graphql

0c0fc05ca84c525f1515953d77d853455db14fb1

[ "MIT" ]

1

2021-06-02T00:14:18.000Z

src/kernel-graphql/riotapi/views.py

pseudonym117/kernel-graphql

0c0fc05ca84c525f1515953d77d853455db14fb1

[ "MIT" ]

null

from . import riotapi @riotapi.route('/') def index(): return 'hello world!'

11.857143

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5.3

0.9

0

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6

26

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0

1

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true

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0.25

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0

1

0

null

0

1

0

null

0

1

0

1

0

5

ab33ac6effdd8027526afc44891f3d0dd3ae6976

713

py

Python

test/test_model_statistics.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

17

2020-02-04T16:42:40.000Z

2021-11-11T14:37:32.000Z

test/test_model_statistics.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

null

test/test_model_statistics.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

4

2020-02-04T12:59:04.000Z

2021-05-30T14:10:54.000Z

from unittest import TestCase from brnolm.runtime.model_statistics import scaled_int_str class ScaledIntRepreTests(TestCase): def test_order_0(self): self.assertEqual(scaled_int_str(0), '0') def test_order_1(self): self.assertEqual(scaled_int_str(10), '10') def test_order_2(self): self.assertEqual(scaled_int_str(210), '210') def test_order_3(self): self.assertEqual(scaled_int_str(3210), '3.2k') def test_order_4(self): self.assertEqual(scaled_int_str(43210), '43.2k') def test_order_5(self): self.assertEqual(scaled_int_str(543210), '543.2k') def test_order_6(self): self.assertEqual(scaled_int_str(6543210), '6.5M')

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