-
Notifications
You must be signed in to change notification settings - Fork 46
/
ODYM_Functions.html
1746 lines (1572 loc) · 87.5 KB
/
ODYM_Functions.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.6.3" />
<title>modules.ODYM_Functions API documentation</title>
<meta name="description" content="Created on Thu Mar
2 17:33:00 2017 …" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>modules.ODYM_Functions</code></h1>
</header>
<section id="section-intro">
<p>Created on Thu Mar
2 17:33:00 2017</p>
<p>@author: spauliuk</p>
<details class="source">
<summary>Source code</summary>
<pre><code class="python"># -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 17:33:00 2017
@author: spauliuk
"""
"""
File ODYM_Functions
Check https://github.com/IndEcol/ODYM for latest version.
Contains class definitions for ODYM
standard abbreviation: msf (material-system-functions)
dependencies:
numpy >= 1.9
scipy >= 0.14
Repository for this class, documentation, and tutorials: https://github.com/IndEcol/ODYM
"""
import os
import logging
import numpy as np
import pandas as pd
import xlrd
import pypandoc
####################################
# Define functions #
####################################
def __version__(): # return version of this file
return str('1.0')
def function_logger(log_filename, log_pathname, file_level=logging.DEBUG, console_level=logging.WARNING):
"""
This is the logging routine of the model. It returns alogger that can be used by other functions to write to the
log(file).
:param file_level: Verbosity level for the logger's output file. This can be log.WARNING (default),
log.INFO, log.DEBUG
:param log_filename: The filename for the logfile.
:param log_pathname: The pathname for the logfile.
:param console_level: Verbosity level for the logger's output file.
out
:param logfile_type: Type of file to write. Markdown syntax is the default.
TODO: If other outputs types are desired, they can be converted via pandoc.
:return: A logger that can be used by other files to write to the log(file)
"""
log_file = os.path.join(log_pathname, log_filename)
# logging.basicConfig(format='%(levelname)s (%(filename)s <%(funcName)s>): %(message)s',
# filename=log_file,
# level=logging.INFO)
logger = logging.getLogger()
logger.handlers = [] # required if you don't want to exit the shell
logger.setLevel(file_level)
# The logger for console output
console_log = logging.StreamHandler() #StreamHandler logs to console
console_log.setLevel(console_level)
# console_log_format = logging.Formatter('%(message)s')
console_log_format = logging.Formatter('%(levelname)s (%(filename)s <%(funcName)s>): %(message)s')
console_log.setFormatter(console_log_format)
logger.addHandler(console_log)
# The logger for log file output
file_log = logging.FileHandler(log_file, mode='w', encoding=None, delay=False)
file_log.setLevel(file_level)
file_log_format = logging.Formatter('%(message)s\n')
file_log.setFormatter(file_log_format)
logger.addHandler(file_log)
return logger, console_log, file_log
def ensure_dir(f): # Checks whether a given directory f exists, and creates it if not
d = os.path.dirname(f)
if not os.path.exists(d):
os.makedirs(d)
def sort_index(mylist,direction): # returns index that sorts a list, either ascending or descending
if direction == 'ascending':
return sorted(range(len(mylist)), key=lambda k: mylist[k])
elif direction == 'descending':
return sorted(range(len(mylist)), key=lambda k: mylist[k], reverse=True)
else:
return None
def GroupingDict2Array(GroupingDict, ElementList):
'''
Tbd.
'''
NoOfItems = len(GroupingDict.keys())
GroupingList = []
for m in GroupingDict.keys():
GroupingList.append(m)
ElementContentArray = np.zeros((100,NoOfItems))
PosCount = 0
for m in GroupingList:
for n in GroupingDict[m].keys():
ElInd = ElementList.index(n)
ElementContentArray[ElInd,PosCount] = GroupingDict[m][n]
PosCount += 1
return GroupingList, ElementContentArray
def ListStringToListNumbers(ListStr):
"""
Extracts numbers from a string that looks like a list commant in python, and returns them as proper list
Examples: ListStringToListNumbers('[1,2,3]') yields [1,2,3]
"""
return [int(s) for s in ListStr[ListStr.find('['):ListStr.find(']')+1].replace('[',',').replace(']',',').split(',') if s.isdigit()]
def EvalItemSelectString(ItemSelectStr,IndexLength):
'''
Extract index item selection lists from ODYM datafile information
'''
if ItemSelectStr == 'All' or ItemSelectStr == 'ALL' or ItemSelectStr == 'all':
Res = 'all' # Selects all from list
elif ItemSelectStr.find('except') > -1: # type 'All except', return full list [0,1,2,5,6,7]
Res = np.arange(0,IndexLength)
b = ItemSelectStr[ItemSelectStr.find('['):ItemSelectStr.find(']')+1].replace('[',',').replace(']',',')
RemoveList = [int(s) for s in b.split(',') if s.isdigit()]
Res = np.delete(Res,RemoveList)
Res = Res.tolist()
elif ItemSelectStr.find(']') > -1: # type '[...]', return full list
Res = ItemSelectStr[ItemSelectStr.find('[')::]
elif ItemSelectStr.find(')') > -1: # type '[..:..)', return range a:b
Res = ItemSelectStr[ItemSelectStr.find('[')+1:-1]
else:
Res = 'ItemSelectString could not be detected.'
return Res
def MI_Tuple(value, Is):
"""
Define function for obtaining multiindex tuple from index value
value: flattened index position, Is: Number of values for each index dimension
Example: MI_Tuple(10, [3,4,2,6]) returns [0,0,1,4]
MI_Tuple is the inverse of Tuple_MI.
"""
IsValuesRev = []
CurrentValue = value
for m in range(0,len(Is)):
IsValuesRev.append(CurrentValue % Is[len(Is)-m-1])
CurrentValue = CurrentValue // Is[len(Is)-m-1]
return IsValuesRev[::-1]
def Tuple_MI(Tuple, IdxLength):
"""
Function to return the absolution position of a multiindex when the index tuple
and the index hierarchy and size are given.
Example: Tuple_MI([2,7,3],[100,10,5]) = 138
Tuple_MI is the inverse of MI_Tuple.
"""
# First, generate the index position offset values
A = IdxLength[1:] + IdxLength[:1] # Shift 1 to left
A[-1] = 1 # Replace lowest index by 1
A.reverse()
IdxPosOffset = np.cumproduct(A).tolist()
IdxPosOffset.reverse()
Position = np.sum([a*b for a,b in zip(Tuple,IdxPosOffset)])
return Position
def ModelIndexPositions_FromData(Positions,RowPos,ColPos):
"""
This function is needed to read data files into ODYM. It takes the positions of a given data point
in the parameter file and checks where in the model index structure this data points belongs,
if it is needed at all.
"""
TargetPosition = []
for m in range(0,len(Positions)):
if m < len(RowPos):
try:
TargetPosition.append(Positions[m].index(RowPos[m]))
except:
break
else:
try:
TargetPosition.append(Positions[m].index(ColPos[m-len(RowPos)]))
except:
break
return TargetPosition
def ReadParameter(ParPath, ThisPar, ThisParIx, IndexMatch, ThisParLayerSel, MasterClassification,
IndexTable, IndexTable_ClassificationNames, ScriptConfig, Mylog):
"""
This function reads a model parameter from the corresponding parameter file
"""
Parfile = xlrd.open_workbook(ParPath + '.xlsx')
ParHeader = Parfile.sheet_by_name('Cover')
IM = eval(IndexMatch) # List that matches model aspects to parameter indices
ri = 1 # row index
MetaData = {}
while True: # read cover sheet info
ThisItem = ParHeader.cell_value(ri,0)
if ThisItem != 'Dataset_RecordType':
MetaData[ThisItem] = ParHeader.cell_value(ri,1)
ri += 1
else:
break # terminate while loop when all meta information is read.
# Now we are in the row of Dataset_RecordType
# Check whether parameter file uses same classification:
if 'ODYM_Classifications_Master_' + \
ScriptConfig['Version of master classification'] != MetaData['Dataset_Classification_version_number']:
Mylog.critical('CLASSIFICATION FILE FATAL ERROR: Classification file of parameter ' + ThisPar +
' is not identical to the classification master file used for the current model run.')
if ParHeader.cell_value(ri,1) == 'List':
IList = []
IListMeaning = []
ci = 1 # column index
while True:
if ParHeader.cell_value(ri +1,ci) != '':
IList.append(ParHeader.cell_value(ri +1,ci))
IListMeaning.append(ParHeader.cell_value(ri +2,ci))
ci += 1
else:
break
# Re-Order indices to fit model aspect order:
IList = [IList[i] for i in IM]
IListMeaning = [IListMeaning[i] for i in IM]
ValueList = []
VIComment = []
ci = 1 # column index
while True:
if ParHeader.cell_value(ri +4,ci) != '':
ValueList.append(ParHeader.cell_value(ri +3,ci))
VIComment.append(ParHeader.cell_value(ri +4,ci))
ci += 1
else:
break
# Check whether all indices are present in the index table of the model
if set(IList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Index list of data file for parameter ' + ThisPar +
' contains indices that are not part of the current model run.')
# Check how well items match between model and data, select items to import
IndexSizesM = [] # List of dimension size for model
for m in range(0,len(ThisParIx)):
ThisDim = ThisParIx[m]
# Check whether index is present in parameter file:
ThisDimClassificationName = IndexTable.set_index('IndexLetter').ix[ThisDim].Classification.Name
if ThisDimClassificationName != IList[m]:
Mylog.error('CLASSIFICATION ERROR: Classification ' + ThisDimClassificationName + ' for aspect ' +
ThisDim + ' of parameter ' + ThisPar +
' must be identical to the specified classification of the corresponding parameter dimension, which is ' + IList[m])
break # Stop parsing parameter, will cause model to halt
IndexSizesM.append(IndexTable.set_index('IndexLetter').ix[ThisDim]['IndexSize'])
# Read parameter values into array:
Values = np.zeros((IndexSizesM))
ValIns = np.zeros((IndexSizesM)) # Array to check how many values are actually loaded
ValuesSheet = Parfile.sheet_by_name('Values_Master')
ColOffset = len(IList)
RowOffset = 1 # fixed for this format, different quantification layers (value, error, etc.) will be read later
cx = 0
while True:
try:
CV = ValuesSheet.cell_value(cx + RowOffset, ColOffset)
except:
break
TargetPosition = []
for mx in range(0,len(IList)): # mx iterates over the aspects of the parameter
CurrentItem = ValuesSheet.cell_value(cx + RowOffset, IM[mx])
try:
TargetPosition.append(IndexTable.set_index('IndexLetter').ix[ThisParIx[mx]].Classification.Items.index(CurrentItem))
except:
break # Current parameter value is not needed for model, outside scope for a certain aspect.
if len(TargetPosition) == len(ThisParIx):
Values[tuple(TargetPosition)] = CV
ValIns[tuple(TargetPosition)] = 1
cx += 1
Mylog.info('A total of ' + str(cx+1) + ' values was read from file for parameter ' + ThisPar + '.')
Mylog.info(str(ValIns.sum()) + ' of ' + str(np.prod(IndexSizesM)) + ' values for parameter ' + ThisPar + ' were assigned.')
### Table version ###
if ParHeader.cell_value(ri,1) == 'Table': # have 3 while loops, one for row indices, one for column indices, one for value layers
RIList = []
RISize = []
RIListMeaning = []
ci = 1 # column index
while True:
if ParHeader.cell_value(ri +1,ci) != '':
RIList.append(ParHeader.cell_value(ri +1,ci))
RISize.append(int(ParHeader.cell_value(ri +2,1)))
RIListMeaning.append(ParHeader.cell_value(ri +3,ci))
ci += 1
else:
break
RISize = RISize[0]
CIList = []
CISize = []
CIListMeaning = []
ci = 1 # column index
while True:
if ParHeader.cell_value(ri +4,ci) != '':
CIList.append(ParHeader.cell_value(ri +4,ci))
CISize.append(int(ParHeader.cell_value(ri +5,1)))
CIListMeaning.append(ParHeader.cell_value(ri +6,ci))
ci += 1
else:
break
CISize = CISize[0]
# Re-Order indices to fit model aspect order:
ComIList = RIList + CIList
ComIList = [ComIList[i] for i in IM]
ValueList = []
VIComment = []
ci = 1 # column index
while True:
if ParHeader.cell_value(ri +7,ci) != '':
ValueList.append(ParHeader.cell_value(ri +7,ci))
VIComment.append(ParHeader.cell_value(ri +8,ci))
ci += 1
else:
break
# Check whether all indices are present in the index table of the model
if set(RIList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Row index list of data file for parameter ' + ThisPar + ' contains indices that are not part of the current model run.')
if set(CIList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Column index list of data file for parameter ' + ThisPar + ' contains indices that are not part of the current model run.')
# Determine index letters for RIList and CIList
RIIndexLetter = []
for m in range(0,len(RIList)):
RIIndexLetter.append(ThisParIx[IM.index(m)])
CIIndexLetter = []
for m in range(0,len(CIList)):
CIIndexLetter.append(ThisParIx[IM.index(m+len(RIList))])
# Check how well items match between model and data, select items to import
IndexSizesM = [] # List of dimension size for model
for m in range(0,len(ThisParIx)):
ThisDim = ThisParIx[m]
ThisDimClassificationName = IndexTable.set_index('IndexLetter').ix[ThisDim].Classification.Name
if ThisDimClassificationName != ComIList[m]:
Mylog.error('CLASSIFICATION ERROR: Classification ' + ThisDimClassificationName + ' for aspect ' +
ThisDim + ' of parameter ' + ThisPar +
' must be identical to the specified classification of the corresponding parameter dimension, which is ' +
ComIList[m])
break # Stop parsing parameter, will cause model to halt
IndexSizesM.append(IndexTable.set_index('IndexLetter').ix[ThisDim]['IndexSize'])
# Read parameter values into array:
Values = np.zeros((IndexSizesM))
ValIns = np.zeros((IndexSizesM)) # Array to check how many values are actually loaded
ValuesSheet = Parfile.sheet_by_name(ValueList[ThisParLayerSel[0]])
ColOffset = len(RIList)
RowOffset = len(CIList)
RowNos = RISize
ColNos = CISize
TargetPos_R = []
for m in range(0,RowNos):
TP_RD = []
for mc in range(0,len(RIList)):
try:
CurrentItem = int(ValuesSheet.cell_value(m + RowOffset, mc))
except:
CurrentItem = ValuesSheet.cell_value(m + RowOffset, mc)
try:
IX = ThisParIx.find(RIIndexLetter[mc])
TPIX = IndexTable.set_index('IndexLetter').ix[RIIndexLetter[mc]].Classification.Items.index(CurrentItem)
TP_RD.append((IX,TPIX))
except:
TP_RD.append(None)
break
TargetPos_R.append(TP_RD)
TargetPos_C = []
for n in range(0,ColNos):
TP_CD = []
for mc in range(0,len(CIList)):
try:
CurrentItem = int(ValuesSheet.cell_value(mc, n + ColOffset))
except:
CurrentItem = ValuesSheet.cell_value(mc, n + ColOffset)
try:
IX = ThisParIx.find(CIIndexLetter[mc])
TPIX = IndexTable.set_index('IndexLetter').ix[CIIndexLetter[mc]].Classification.Items.index(CurrentItem)
TP_CD.append((IX,TPIX))
except:
TP_CD.append(None)
break
TargetPos_C.append(TP_CD)
for m in range(0,RowNos):
for n in range(0,ColNos):
TargetPosition = [0 for i in range(0,len(ComIList))]
try:
for i in range(0,len(RIList)):
TargetPosition[TargetPos_R[m][i][0]] = TargetPos_R[m][i][1]
for i in range(0,len(CIList)):
TargetPosition[TargetPos_C[n][i][0]] = TargetPos_C[n][i][1]
except:
TargetPosition = [0]
if len(TargetPosition) == len(ComIList):
Values[tuple(TargetPosition)] = ValuesSheet.cell_value(m + RowOffset, n + ColOffset)
ValIns[tuple(TargetPosition)] = 1
Mylog.info(str(ValIns.sum()) + ' of ' + str(np.prod(IndexSizesM)) + ' values for parameter ' + ThisPar +
' were assigned.')
return MetaData, Values
def ReadParameterV2(ParPath, ThisPar, ThisParIx, IndexMatch, ThisParLayerSel, MasterClassification,
IndexTable, IndexTable_ClassificationNames, ScriptConfig, Mylog, ParseUncertainty):
"""
This function reads a model parameter from the corresponding parameter file
"""
Parfile = xlrd.open_workbook(ParPath + '.xlsx')
ParHeader = Parfile.sheet_by_name('Cover')
IM = eval(IndexMatch) # List that matches model aspects to parameter indices
ri = 1 # row index
MetaData = {}
while True: # read cover sheet info
ThisItem = ParHeader.cell_value(ri,0)
if (ThisItem != '[Empty on purpose]' and ThisItem != 'Dataset_RecordType'):
MetaData[ThisItem] = ParHeader.cell_value(ri,1)
if ThisItem == 'Dataset_Unit':
if ParHeader.cell_value(ri,1) == 'GLOBAL':
MetaData['Unit_Global'] = ParHeader.cell_value(ri,2)
MetaData['Unit_Global_Comment'] = ParHeader.cell_value(ri,3)
if ThisItem == 'Dataset_Uncertainty':
# if LIST is specified, nothing happens here.
if ParHeader.cell_value(ri,1) == 'GLOBAL':
MetaData['Dataset_Uncertainty_Global'] = ParHeader.cell_value(ri,2)
if ParHeader.cell_value(ri,1) == 'TABLE':
MetaData['Dataset_Uncertainty_Sheet'] = ParHeader.cell_value(ri,2)
if ThisItem == 'Dataset_Comment':
if ParHeader.cell_value(ri,1) == 'GLOBAL':
MetaData['Dataset_Comment_Global'] = ParHeader.cell_value(ri,2)
ri += 1
else:
break # terminate while loop when all meta information is read.
# Now we are in the row of Dataset_RecordType
# Check whether parameter file uses same classification:
if ScriptConfig['Version of master classification'] != MetaData['Dataset_Classification_version_number']:
Mylog.critical('CLASSIFICATION FILE FATAL ERROR: Classification file of parameter ' + ThisPar +
' is not identical to the classification master file used for the current model run.')
# Continue parsing until line 'Dataset_RecordType' is found:
while True:
ThisItem = ParHeader.cell_value(ri,0)
if ThisItem == 'Dataset_RecordType':
break
else:
ri += 1
### List version ###
if ParHeader.cell_value(ri,1) == 'LIST':
IList = []
IListMeaning = []
RI_Start = ri + 2
while True:
if ParHeader.cell_value(RI_Start,0) != '':
IList.append(ParHeader.cell_value(RI_Start,0))
IListMeaning.append(ParHeader.cell_value(RI_Start,1))
RI_Start += 1
else:
break
# Re-Order indices to fit model aspect order:
IList = [IList[i] for i in IM]
IListMeaning = [IListMeaning[i] for i in IM]
ValueList = []
VIComment = []
RI_Start = ri + 2
while True:
if ParHeader.cell_value(RI_Start,2) != '':
ValueList.append(ParHeader.cell_value(RI_Start,2))
VIComment.append(ParHeader.cell_value(RI_Start,3))
RI_Start += 1
else:
break
# Check whether all indices are present in the index table of the model
if set(IList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Index list of data file for parameter ' + ThisPar +
' contains indices that are not part of the current model run.')
# Check how well items match between model and data, select items to import
IndexSizesM = [] # List of dimension size for model
for m in range(0,len(ThisParIx)):
ThisDim = ThisParIx[m]
# Check whether index is present in parameter file:
ThisDimClassificationName = IndexTable.set_index('IndexLetter').ix[ThisDim].Classification.Name
if ThisDimClassificationName != IList[m]:
Mylog.error('CLASSIFICATION ERROR: Classification ' + ThisDimClassificationName + ' for aspect ' +
ThisDim + ' of parameter ' + ThisPar +
' must be identical to the specified classification of the corresponding parameter dimension, which is ' + IList[m])
break # Stop parsing parameter, will cause model to halt
IndexSizesM.append(IndexTable.set_index('IndexLetter').ix[ThisDim]['IndexSize'])
# Read parameter values into array, uncertainty into list:
Values = np.zeros((IndexSizesM)) # Array for parameter values
Uncertainty = [None] * np.product(IndexSizesM) # parameter value uncertainties
ValIns = np.zeros((IndexSizesM)) # Array to check how many values are actually loaded
ValuesSheet = Parfile.sheet_by_name('Values_Master')
ColOffset = len(IList)
RowOffset = 1 # fixed for this format, different quantification layers (value, error, etc.) will be read later
cx = 0
while True:
try:
CV = ValuesSheet.cell_value(cx + RowOffset, ColOffset)
except:
break
TargetPosition = []
for mx in range(0,len(IList)): # mx iterates over the aspects of the parameter
CurrentItem = ValuesSheet.cell_value(cx + RowOffset, IM[mx])
try:
TargetPosition.append(IndexTable.set_index('IndexLetter').ix[ThisParIx[mx]].Classification.Items.index(CurrentItem))
except:
break # Current parameter value is not needed for model, outside scope for a certain aspect.
if len(TargetPosition) == len(ThisParIx):
Values[tuple(TargetPosition)] = CV
ValIns[tuple(TargetPosition)] = 1
Uncertainty[Tuple_MI(TargetPosition, IndexSizesM)] = ValuesSheet.cell_value(cx + RowOffset, ColOffset + 3)
cx += 1
Mylog.info('A total of ' + str(cx) + ' values was read from file for parameter ' + ThisPar + '.')
Mylog.info(str(ValIns.sum()) + ' of ' + str(np.prod(IndexSizesM)) + ' values for parameter ' + ThisPar + ' were assigned.')
### Table version ###
if ParHeader.cell_value(ri,1) == 'TABLE': # have 3 while loops, one for row indices, one for column indices, one for value layers
ColNos = int(ParHeader.cell_value(ri,5)) # Number of columns in dataset
RowNos = int(ParHeader.cell_value(ri,3)) # Number of rows in dataset
RI = ri + 2 # row where indices start
RIList = []
RIListMeaning = []
while True:
if ParHeader.cell_value(RI,0) != '':
RIList.append(ParHeader.cell_value(RI,0))
RIListMeaning.append(ParHeader.cell_value(RI,1))
RI += 1
else:
break
RI = ri + 2 # row where indices start
CIList = []
CIListMeaning = []
while True:
if ParHeader.cell_value(RI,2) != '':
CIList.append(ParHeader.cell_value(RI,2))
CIListMeaning.append(ParHeader.cell_value(RI,3))
RI += 1
else:
break
# Re-Order indices to fit model aspect order:
ComIList = RIList + CIList # List of all indices, both rows and columns
ComIList = [ComIList[i] for i in IM]
RI = ri + 2 # row where indices start
ValueList = []
VIComment = []
while True:
if ParHeader.cell_value(RI,4) != '':
ValueList.append(ParHeader.cell_value(RI,4))
VIComment.append(ParHeader.cell_value(RI,5))
RI += 1
else:
break
# Check whether all indices are present in the index table of the model
if set(RIList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Row index list of data file for parameter ' + ThisPar + ' contains indices that are not part of the current model run.')
if set(CIList).issubset(set(IndexTable_ClassificationNames)) is False:
Mylog.error('CLASSIFICATION ERROR: Column index list of data file for parameter ' + ThisPar + ' contains indices that are not part of the current model run.')
# Determine index letters for RIList and CIList
RIIndexLetter = []
for m in range(0,len(RIList)):
RIIndexLetter.append(ThisParIx[IM.index(m)])
CIIndexLetter = []
for m in range(0,len(CIList)):
CIIndexLetter.append(ThisParIx[IM.index(m+len(RIList))])
# Check how well items match between model and data, select items to import
IndexSizesM = [] # List of dimension size for model
for m in range(0,len(ThisParIx)):
ThisDim = ThisParIx[m]
ThisDimClassificationName = IndexTable.set_index('IndexLetter').ix[ThisDim].Classification.Name
if ThisDimClassificationName != ComIList[m]:
Mylog.error('CLASSIFICATION ERROR: Classification ' + ThisDimClassificationName + ' for aspect ' +
ThisDim + ' of parameter ' + ThisPar +
' must be identical to the specified classification of the corresponding parameter dimension, which is ' +
ComIList[m])
break # Stop parsing parameter, will cause model to halt
IndexSizesM.append(IndexTable.set_index('IndexLetter').ix[ThisDim]['IndexSize'])
# Read parameter values into array:
Values = np.zeros((IndexSizesM)) # Array for parameter values
Uncertainty = [None] * np.product(IndexSizesM) # parameter value uncertainties
ValIns = np.zeros((IndexSizesM)) # Array to check how many values are actually loaded, contains 0 or 1.
ValuesSheet = Parfile.sheet_by_name(ValueList[ThisParLayerSel[0]])
if ParseUncertainty == True:
if 'Dataset_Uncertainty_Sheet' in MetaData:
UncertSheet = Parfile.sheet_by_name(MetaData['Dataset_Uncertainty_Sheet'])
ColOffset = len(RIList)
RowOffset = len(CIList)
cx = 0
TargetPos_R = [] # Determine all row target positions in data array
for m in range(0,RowNos):
TP_RD = []
for mc in range(0,len(RIList)):
try:
CurrentItem = int(ValuesSheet.cell_value(m + RowOffset, mc)) # in case items come as int, e.g., years
except:
CurrentItem = ValuesSheet.cell_value(m + RowOffset, mc)
try:
IX = ThisParIx.find(RIIndexLetter[mc])
TPIX = IndexTable.set_index('IndexLetter').ix[RIIndexLetter[mc]].Classification.Items.index(CurrentItem)
TP_RD.append((IX,TPIX))
except:
TP_RD.append(None)
break
TargetPos_R.append(TP_RD)
TargetPos_C = [] # Determine all col target positions in data array
for n in range(0,ColNos):
TP_CD = []
for mc in range(0,len(CIList)):
try:
CurrentItem = int(ValuesSheet.cell_value(mc, n + ColOffset))
except:
CurrentItem = ValuesSheet.cell_value(mc, n + ColOffset)
try:
IX = ThisParIx.find(CIIndexLetter[mc])
TPIX = IndexTable.set_index('IndexLetter').ix[CIIndexLetter[mc]].Classification.Items.index(CurrentItem)
TP_CD.append((IX,TPIX))
except:
TP_CD.append(None)
break
TargetPos_C.append(TP_CD)
for m in range(0,RowNos): # Read values from excel template
for n in range(0,ColNos):
TargetPosition = [0 for i in range(0,len(ComIList))]
try:
for i in range(0,len(RIList)):
TargetPosition[TargetPos_R[m][i][0]] = TargetPos_R[m][i][1]
for i in range(0,len(CIList)):
TargetPosition[TargetPos_C[n][i][0]] = TargetPos_C[n][i][1]
except:
TargetPosition = [0]
if len(TargetPosition) == len(ComIList): # Read value if TargetPosition Tuple has same length as indexList
Values[tuple(TargetPosition)] = ValuesSheet.cell_value(m + RowOffset, n + ColOffset)
ValIns[tuple(TargetPosition)] = 1
# Add uncertainty
if ParseUncertainty == True:
if 'Dataset_Uncertainty_Global' in MetaData:
Uncertainty[Tuple_MI(TargetPosition, IndexSizesM)] = MetaData['Dataset_Uncertainty_Global']
if 'Dataset_Uncertainty_Sheet' in MetaData:
Uncertainty[Tuple_MI(TargetPosition, IndexSizesM)] = UncertSheet.cell_value(m + RowOffset, n + ColOffset)
cx += 1
Mylog.info('A total of ' + str(cx) + ' values was read from file for parameter ' + ThisPar + '.')
Mylog.info(str(ValIns.sum()) + ' of ' + str(np.prod(IndexSizesM)) + ' values for parameter ' + ThisPar +
' were assigned.')
if ParseUncertainty == True:
return MetaData, Values, Uncertainty
else:
return MetaData, Values
def ExcelSheetFill(Workbook, Sheetname, values, topcornerlabel=None,
rowlabels=None, collabels=None, Style=None,
rowselect=None, colselect=None):
Sheet = Workbook.add_sheet(Sheetname)
if topcornerlabel is not None:
if Style is not None:
Sheet.write(0,0,label = topcornerlabel, style = Style) # write top corner label
else:
Sheet.write(0,0,label = topcornerlabel) # write top corner label
if rowselect is None: # assign row select if not present (includes all rows in that case)
rowselect = np.ones((values.shape[0]))
if colselect is None: # assign col select if not present (includes all columns in that case)
colselect = np.ones((values.shape[1]))
if rowlabels is not None: # write row labels
rowindexcount = 0
for m in range(0,len(rowlabels)):
if rowselect[m] == 1: # True if True or 1
if Style is None:
Sheet.write(rowindexcount +1, 0, label = rowlabels[m])
else:
Sheet.write(rowindexcount +1, 0, label = rowlabels[m], style = Style)
rowindexcount += 1
if collabels is not None: # write column labels
colindexcount = 0
for m in range(0,len(collabels)):
if colselect[m] == 1: # True if True or 1
if Style is None:
Sheet.write(0, colindexcount +1, label = collabels[m])
else:
Sheet.write(0, colindexcount +1, label = collabels[m], style = Style)
colindexcount += 1
# write values:
rowindexcount = 0
for m in range(0,values.shape[0]): # for all rows
if rowselect[m] == 1:
colindexcount = 0
for n in range(0,values.shape[1]): # for all columns
if colselect[n] == 1:
Sheet.write(rowindexcount +1, colindexcount + 1, label=values[m, n])
colindexcount += 1
rowindexcount += 1
def ExcelExportAdd_tAB(Sheet,Data,rowoffset,coloffset,IName,UName,RName,FName,REName,ALabels,BLabels):
"""
This function exports a 3D array with aspects time, A, and B to a given excel sheet.
The t dimension is exported in one row, the A and B dimensions as several rows.
Each row starts with IName (indicator), UName (unit), RName (region),
FName (figure where data are used), REName (Resource efficiency scenario),
and then come the values for the dimensions A and B and from coloffset onwards, the time dimension.
Function is meant to be used multiple times, so a rowoffset is given, incremented, and returned for the next run.
"""
for m in range(0,len(ALabels)):
for n in range(0,len(BLabels)):
Sheet.write(rowoffset, 0, label = IName)
Sheet.write(rowoffset, 1, label = UName)
Sheet.write(rowoffset, 2, label = RName)
Sheet.write(rowoffset, 3, label = FName)
Sheet.write(rowoffset, 4, label = REName)
Sheet.write(rowoffset, 5, label = ALabels[m])
Sheet.write(rowoffset, 6, label = BLabels[n])
for t in range(0,Data.shape[0]):
Sheet.write(rowoffset, coloffset + t, label = Data[t,m,n])
rowoffset += 1
return rowoffset
def convert_log(file, file_format='html'):
"""
Converts the log file to a given file format
:param file: The filename and path
:param file_format: The desired format
"""
output_filename = os.path.splitext(file)[0] + '.' + file_format
output = pypandoc.convert_file(file, file_format, outputfile=output_filename)
assert output == ""
# The End</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="modules.ODYM_Functions.EvalItemSelectString"><code class="name flex">
<span>def <span class="ident">EvalItemSelectString</span></span>(<span>ItemSelectStr, IndexLength)</span>
</code></dt>
<dd>
<section class="desc"><p>Extract index item selection lists from ODYM datafile information</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def EvalItemSelectString(ItemSelectStr,IndexLength):
'''
Extract index item selection lists from ODYM datafile information
'''
if ItemSelectStr == 'All' or ItemSelectStr == 'ALL' or ItemSelectStr == 'all':
Res = 'all' # Selects all from list
elif ItemSelectStr.find('except') > -1: # type 'All except', return full list [0,1,2,5,6,7]
Res = np.arange(0,IndexLength)
b = ItemSelectStr[ItemSelectStr.find('['):ItemSelectStr.find(']')+1].replace('[',',').replace(']',',')
RemoveList = [int(s) for s in b.split(',') if s.isdigit()]
Res = np.delete(Res,RemoveList)
Res = Res.tolist()
elif ItemSelectStr.find(']') > -1: # type '[...]', return full list
Res = ItemSelectStr[ItemSelectStr.find('[')::]
elif ItemSelectStr.find(')') > -1: # type '[..:..)', return range a:b
Res = ItemSelectStr[ItemSelectStr.find('[')+1:-1]
else:
Res = 'ItemSelectString could not be detected.'
return Res</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Functions.ExcelExportAdd_tAB"><code class="name flex">
<span>def <span class="ident">ExcelExportAdd_tAB</span></span>(<span>Sheet, Data, rowoffset, coloffset, IName, UName, RName, FName, REName, ALabels, BLabels)</span>
</code></dt>
<dd>
<section class="desc"><p>This function exports a 3D array with aspects time, A, and B to a given excel sheet.
The t dimension is exported in one row, the A and B dimensions as several rows.
Each row starts with IName (indicator), UName (unit), RName (region),
FName (figure where data are used), REName (Resource efficiency scenario),
and then come the values for the dimensions A and B and from coloffset onwards, the time dimension.
Function is meant to be used multiple times, so a rowoffset is given, incremented, and returned for the next run.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def ExcelExportAdd_tAB(Sheet,Data,rowoffset,coloffset,IName,UName,RName,FName,REName,ALabels,BLabels):
"""
This function exports a 3D array with aspects time, A, and B to a given excel sheet.
The t dimension is exported in one row, the A and B dimensions as several rows.
Each row starts with IName (indicator), UName (unit), RName (region),
FName (figure where data are used), REName (Resource efficiency scenario),
and then come the values for the dimensions A and B and from coloffset onwards, the time dimension.
Function is meant to be used multiple times, so a rowoffset is given, incremented, and returned for the next run.
"""
for m in range(0,len(ALabels)):
for n in range(0,len(BLabels)):
Sheet.write(rowoffset, 0, label = IName)
Sheet.write(rowoffset, 1, label = UName)
Sheet.write(rowoffset, 2, label = RName)
Sheet.write(rowoffset, 3, label = FName)
Sheet.write(rowoffset, 4, label = REName)
Sheet.write(rowoffset, 5, label = ALabels[m])
Sheet.write(rowoffset, 6, label = BLabels[n])
for t in range(0,Data.shape[0]):
Sheet.write(rowoffset, coloffset + t, label = Data[t,m,n])
rowoffset += 1
return rowoffset</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Functions.ExcelSheetFill"><code class="name flex">
<span>def <span class="ident">ExcelSheetFill</span></span>(<span>Workbook, Sheetname, values, topcornerlabel=None, rowlabels=None, collabels=None, Style=None, rowselect=None, colselect=None)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def ExcelSheetFill(Workbook, Sheetname, values, topcornerlabel=None,
rowlabels=None, collabels=None, Style=None,
rowselect=None, colselect=None):
Sheet = Workbook.add_sheet(Sheetname)
if topcornerlabel is not None:
if Style is not None:
Sheet.write(0,0,label = topcornerlabel, style = Style) # write top corner label
else:
Sheet.write(0,0,label = topcornerlabel) # write top corner label
if rowselect is None: # assign row select if not present (includes all rows in that case)
rowselect = np.ones((values.shape[0]))
if colselect is None: # assign col select if not present (includes all columns in that case)
colselect = np.ones((values.shape[1]))
if rowlabels is not None: # write row labels
rowindexcount = 0
for m in range(0,len(rowlabels)):
if rowselect[m] == 1: # True if True or 1
if Style is None:
Sheet.write(rowindexcount +1, 0, label = rowlabels[m])
else:
Sheet.write(rowindexcount +1, 0, label = rowlabels[m], style = Style)
rowindexcount += 1
if collabels is not None: # write column labels
colindexcount = 0
for m in range(0,len(collabels)):
if colselect[m] == 1: # True if True or 1
if Style is None:
Sheet.write(0, colindexcount +1, label = collabels[m])
else:
Sheet.write(0, colindexcount +1, label = collabels[m], style = Style)
colindexcount += 1
# write values:
rowindexcount = 0
for m in range(0,values.shape[0]): # for all rows
if rowselect[m] == 1:
colindexcount = 0
for n in range(0,values.shape[1]): # for all columns
if colselect[n] == 1:
Sheet.write(rowindexcount +1, colindexcount + 1, label=values[m, n])
colindexcount += 1
rowindexcount += 1</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Functions.GroupingDict2Array"><code class="name flex">
<span>def <span class="ident">GroupingDict2Array</span></span>(<span>GroupingDict, ElementList)</span>
</code></dt>
<dd>
<section class="desc"><p>Tbd.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def GroupingDict2Array(GroupingDict, ElementList):
'''
Tbd.
'''
NoOfItems = len(GroupingDict.keys())
GroupingList = []
for m in GroupingDict.keys():
GroupingList.append(m)
ElementContentArray = np.zeros((100,NoOfItems))
PosCount = 0
for m in GroupingList:
for n in GroupingDict[m].keys():
ElInd = ElementList.index(n)
ElementContentArray[ElInd,PosCount] = GroupingDict[m][n]
PosCount += 1
return GroupingList, ElementContentArray</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Functions.ListStringToListNumbers"><code class="name flex">
<span>def <span class="ident">ListStringToListNumbers</span></span>(<span>ListStr)</span>
</code></dt>
<dd>
<section class="desc"><p>Extracts numbers from a string that looks like a list commant in python, and returns them as proper list
Examples: ListStringToListNumbers('[1,2,3]') yields [1,2,3]</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def ListStringToListNumbers(ListStr):
"""
Extracts numbers from a string that looks like a list commant in python, and returns them as proper list
Examples: ListStringToListNumbers('[1,2,3]') yields [1,2,3]
"""
return [int(s) for s in ListStr[ListStr.find('['):ListStr.find(']')+1].replace('[',',').replace(']',',').split(',') if s.isdigit()]</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Functions.MI_Tuple"><code class="name flex">
<span>def <span class="ident">MI_Tuple</span></span>(<span>value, Is)</span>
</code></dt>
<dd>
<section class="desc"><p>Define function for obtaining multiindex tuple from index value
value: flattened index position, Is: Number of values for each index dimension
Example: MI_Tuple(10, [3,4,2,6]) returns [0,0,1,4]
MI_Tuple is the inverse of Tuple_MI.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def MI_Tuple(value, Is):
"""