# -*- 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
import ODYM_Classes as msc
####################################
# 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 TableWithFlowsToShares(Table,axis):
"""
Given a 2D-table with flow values that sum up to a total,
either along the columns (= across rows, axis =0) or along the rows (=across the columns, axis =1).
The function then converts the flows into shares (between 0 and 1), that each element has in the column sum (axis =0)
or the row sum (axis =1).
Only makes sense if all table entries have the same sign, that is not checked by the function.
"""
Shares = np.zeros(Table.shape)
if axis == 0: # shares along columns
colsum = Table.sum(axis=0)
Divisor = np.einsum('b,a->ab',colsum,np.ones(Table.shape[0]))
if axis == 1: # shares along rows
rowsum = Table.sum(axis=1)
Divisor = np.einsum('a,b->ab',rowsum,np.ones(Table.shape[1]))
Divided = np.divide(1, Divisor, out=np.zeros_like(Divisor), where=Divisor!=0)
Shares = Table * Divided
return Shares
def DetermineElementComposition_All_Oth(me):
"""
Given an array of flows of materials (rows) broken down into chem. elements (columns),
where the first element is "all" and the last element is "other",
the function determines the share of each element in the material, and fills nonexistent rows with a 1 for all and other, resp.
"""
result = np.zeros(me.shape)
Shares = TableWithFlowsToShares(me[:,1::],1)
SharesSum = Shares.sum(axis=1)
result[:,0] = 1
result[:,1::] = Shares.copy()
for m in range(0,me.shape[0]):
if SharesSum[m] == 0:
result[m,-1] = 1
return result
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 ParseModelControl(Model_Configsheet,ScriptConfig):
""" Parse the RECC and ODYM model control parameters from the ODYM config sheet. """
SCix = 0
# search for script config list entry
while Model_Configsheet.cell_value(SCix, 1) != 'General Info':
SCix += 1
SCix += 2 # start on first data row
while len(Model_Configsheet.cell_value(SCix, 3)) > 0:
ScriptConfig[Model_Configsheet.cell_value(SCix, 2)] = Model_Configsheet.cell_value(SCix,3)
SCix += 1
SCix = 0
# search for script config list entry
while Model_Configsheet.cell_value(SCix, 1) != 'Software version selection':
SCix += 1
SCix += 2 # start on first data row
while len(Model_Configsheet.cell_value(SCix, 3)) > 0:
ScriptConfig[Model_Configsheet.cell_value(SCix, 2)] = Model_Configsheet.cell_value(SCix,3)
SCix += 1
return ScriptConfig
def ParseClassificationFile_Main(Classsheet,Mylog):
""" Parse the ODYM classification file, format version
"""
ci = 1 # column index to start with
MasterClassification = {} # Dict of master classifications
while True:
TheseItems = []
ri = 10 # row index to start with
try:
ThisName = Classsheet.cell_value(0,ci)
ThisDim = Classsheet.cell_value(1,ci)
ThisID = Classsheet.cell_value(3,ci)
ThisUUID = Classsheet.cell_value(4,ci)
TheseItems.append(Classsheet.cell_value(ri,ci)) # read the first classification item
except:
Mylog.info('End of file or formatting error while reading the classification file in column ' + str(ci) + '. Check if all classifications are present. If yes, you are good to go!')
break
while True:
ri += 1
try:
ThisItem = Classsheet.cell_value(ri, ci)
except:
break
if ThisItem != '':
TheseItems.append(ThisItem)
MasterClassification[ThisName] = msc.Classification(Name = ThisName, Dimension = ThisDim, ID = ThisID, UUID = ThisUUID, Items = TheseItems)
ci += 1
return MasterClassification
def ParseConfigFile(Model_Configsheet,ScriptConfig,Mylog):
"""
Standard routine to parse the ODYM model config file.
"""
ITix = 0
# search for index table entry
while True:
if Model_Configsheet.cell_value(ITix, 1) == 'Index Table':
break
else:
ITix += 1
IT_Aspects = []
IT_Description = []
IT_Dimension = []
IT_Classification = []
IT_Selector = []
IT_IndexLetter = []
ITix += 2 # start on first data row
while True:
if len(Model_Configsheet.cell_value(ITix,2)) > 0:
IT_Aspects.append(Model_Configsheet.cell_value(ITix,2))
IT_Description.append(Model_Configsheet.cell_value(ITix,3))
IT_Dimension.append(Model_Configsheet.cell_value(ITix,4))
IT_Classification.append(Model_Configsheet.cell_value(ITix,5))
IT_Selector.append(Model_Configsheet.cell_value(ITix,6))
IT_IndexLetter.append(Model_Configsheet.cell_value(ITix,7))
ITix += 1
else:
break
Mylog.info('Read parameter list from model config sheet.')
PLix = 0
while True: # search for parameter list entry
if Model_Configsheet.cell_value(PLix, 1) == 'Model Parameters':
break
else:
PLix += 1
PL_Names = []
PL_Description = []
PL_Version = []
PL_IndexStructure = []
PL_IndexMatch = []
PL_IndexLayer = []
PLix += 2 # start on first data row
while True:
if len(Model_Configsheet.cell_value(PLix,2)) > 0:
PL_Names.append(Model_Configsheet.cell_value(PLix,2))
PL_Description.append(Model_Configsheet.cell_value(PLix,3))
PL_Version.append(Model_Configsheet.cell_value(PLix,4))
PL_IndexStructure.append(Model_Configsheet.cell_value(PLix,5))
PL_IndexMatch.append(Model_Configsheet.cell_value(PLix,6))
PL_IndexLayer.append(ListStringToListNumbers(Model_Configsheet.cell_value(PLix,7))) # strip numbers out of list string
PLix += 1
else:
break
Mylog.info('Read process list from model config sheet.')
PrLix = 0
# search for process list entry
while True:
if Model_Configsheet.cell_value(PrLix, 1) == 'Process Group List':
break
else:
PrLix += 1
PrL_Number = []
PrL_Name = []
PrL_Comment = []
PrL_Type = []
PrLix += 2 # start on first data row
while True:
if Model_Configsheet.cell_value(PrLix,2) != '':
try:
PrL_Number.append(int(Model_Configsheet.cell_value(PrLix,2)))
except:
PrL_Number.append(Model_Configsheet.cell_value(PrLix,2))
PrL_Name.append(Model_Configsheet.cell_value(PrLix,3))
PrL_Type.append(Model_Configsheet.cell_value(PrLix,4))
PrL_Comment.append(Model_Configsheet.cell_value(PrLix,5))
PrLix += 1
else:
break
Mylog.info('Read model run control from model config sheet.')
PrLix = 0
# search for model flow control entry
while True:
if Model_Configsheet.cell_value(PrLix, 1) == 'Model flow control':
break
else:
PrLix += 1
# start on first data row
PrLix += 2
while True:
if Model_Configsheet.cell_value(PrLix, 2) != '':
try:
ScriptConfig[Model_Configsheet.cell_value(PrLix, 2)] = Model_Configsheet.cell_value(PrLix,3)
except:
None
PrLix += 1
else:
break
Mylog.info('Read model output control from model config sheet.')
PrLix = 0
# search for model flow control entry
while True:
if Model_Configsheet.cell_value(PrLix, 1) == 'Model output control':
break
else:
PrLix += 1
# start on first data row
PrLix += 2
while True:
if Model_Configsheet.cell_value(PrLix, 2) != '':
try:
ScriptConfig[Model_Configsheet.cell_value(PrLix, 2)] = Model_Configsheet.cell_value(PrLix,3)
except:
None
PrLix += 1
else:
break
return IT_Aspects,IT_Description,IT_Dimension,IT_Classification,IT_Selector,IT_IndexLetter,PL_Names,PL_Description,PL_Version,PL_IndexStructure,PL_IndexMatch,PL_IndexLayer,PrL_Number,PrL_Name,PrL_Comment,PrL_Type,ScriptConfig
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
