def convertFromFmi(fmuFilename, fmi=None):
''' Returns data to initialize an MTSF result file from an FMU.
The call to initialize an MTSF result file is
pyMtsf.MTSF(resultFileName, modelDescription, modelVariables, experimentSetup, simpleTypes, units, enumerationsMatrix)
The missing data is resultFileName and experimentSetup to be specified before initializing the MTSF object.
Inputs Type:
fmuFilename String
fmi FMIDescription [optional]
if fmi is given, then fmuFilename is ignored. Otherwise the FMI description is loaded from the given file.
Outputs
modelDescription pyMtsf.ModelDescription
modelVariables pyMtsf.ModelVariables
simpleTypes list of pyMtsf.SimpleType
units list of pyMtsf.Unit
enumerationsMatrix list of pyMtsf.Enumeration
'''
def _None2Str(x):
if x is None:
return ''
else:
return x
# Load FMIDescription if necessary
if fmi is None:
fmuFile = zipfile.ZipFile(os.getcwd() + u'\\' + fmuFilename + u'.fmu', 'r')
fmi = FMIDescription(fmuFile.open('modelDescription.xml'))
# Prepare some variables
allSeriesNames = [x.name for x in StandardSeriesForFmi]
variable = collections.OrderedDict()
simpleTypes = []
units = []
enumerationsMatrix = []
variable['Time'] = pyMtsf.ScalarModelVariable('Continuous Time', 'input', 0, 'continuous', allSeriesNames.index('Continuous'), pyMtsf.StandardCategoryNames.index(pyMtsf.CategoryMapping['Real']), None, 0)
variable['TimeDiscrete'] = pyMtsf.ScalarModelVariable('Discrete Time at events', 'input', 0, 'discrete', allSeriesNames.index('Discrete'), pyMtsf.StandardCategoryNames.index(pyMtsf.CategoryMapping['Real']), None, 0)
# Alias
for var in fmi.scalarVariables.values():
if var.alias is None or var.alias.lower() == "noalias":
var.alias = 'NOAlias' # To guarantee that this variable is the first
# one in sorted order
referenceList = [(x, y.valueReference, y.alias) for x, y in fmi.scalarVariables.iteritems()]
referenceList.sort(key=itemgetter(2))
referenceList.sort(key=itemgetter(1))
for index in xrange(len(referenceList)):
variableName = referenceList[index][0]
if referenceList[index][2] in ['alias', 'negatedAlias']:
valueReference = referenceList[index][1]
prevValueReference = referenceList[index - 1][1]
if prevValueReference != valueReference:
raise ValueError("No original variable found for alias " + variableName)
if referenceList[index - 1][2] == "NOAlias":
originName = referenceList[index - 1][0]
else:
originName = fmi.scalarVariables[referenceList[index - 1][0]].aliasName
fmi.scalarVariables[variableName].aliasName = originName
else:
fmi.scalarVariables[variableName].aliasName = None
# Types and display units
uniqueSimpleType = []
for fmiVariableName, fmiVariable in fmi.scalarVariables.iteritems():
type = fmiVariable.type
unitList = [_None2Str(type.unit) + _None2Str(type.displayUnit)]
if fmi.units.has_key(type.unit):
for displayUnitName, displayUnit in fmi.units[type.unit].iteritems():
if displayUnitName != type.unit:
unitList.append(displayUnitName + '{:.16e}'.format(displayUnit.gain) + '{:.16e}'.format(displayUnit.offset))
# unitList.sort()
dataType = type.type
enumerations = ''
if dataType == 'Enumeration':
enumerations = ''.join([_None2Str(x[0]) + _None2Str(x[1]) for x in type.item])
uniqueSimpleType.append((fmiVariableName, type, _None2Str(type.name) + str(pyMtsf.DataType[dataType]) + _None2Str(type.quantity) + str(type.relativeQuantity), ''.join(unitList), enumerations))
# Simple Types
uniqueSimpleType.sort(key=itemgetter(3))
uniqueSimpleType.sort(key=itemgetter(2))
lastUniqueStr = ''
rowIndex = dict()
lastIndex = -1
uniqueDisplayUnit = []
uniqueEnumerations = []
for s in uniqueSimpleType:
fmiVariableName = s[0]
type = s[1]
uniqueStr = s[2] + s[3] + s[4]
if uniqueStr == lastUniqueStr:
rowIndex[fmiVariableName] = lastIndex
else:
lastUniqueStr = uniqueStr
lastIndex += 1
rowIndex[fmiVariableName] = lastIndex
uniqueDisplayUnit.append((type, lastIndex, s[3]))
uniqueEnumerations.append((type, lastIndex, s[4]))
dataType = type.type
simpleTypes.append(pyMtsf.SimpleType(type.name, pyMtsf.DataType[dataType], type.quantity, type.relativeQuantity, -1, type.description))
# Units
uniqueDisplayUnit.sort(key=itemgetter(2))
lastUniqueStr = ''
startRow = -1
for s in uniqueDisplayUnit:
type = s[0]
k = s[1]
uniqueStr = s[2]
if uniqueStr == lastUniqueStr:
simpleTypes[k].unitOrEnumerationRow = startRow
else:
lastUniqueStr = uniqueStr
if uniqueStr != '': # There is a unit definition
startRow = len(units)
units.append(pyMtsf.Unit(type.unit, 1.0, 0.0, 0))
if fmi.units.has_key(type.unit):
for displayUnitName, displayUnit in fmi.units[type.unit].iteritems():
if displayUnitName != type.unit:
if type.displayUnit is not None and type.displayUnit != '' and type.displayUnit == displayUnitName:
mode = 2 # DefaultDisplayUnit
else:
mode = 1 # DisplayUnit
units.append(pyMtsf.Unit(displayUnitName, displayUnit.gain, displayUnit.offset, mode))
simpleTypes[k].unitOrEnumerationRow = startRow
else:
startRow = -1
# Enumerations
uniqueEnumerations.sort(key=itemgetter(2))
lastUniqueStr = ''
startRow = -1
for s in uniqueEnumerations:
type = s[0]
k = s[1]
uniqueStr = s[2]
if uniqueStr != '':
if uniqueStr == lastUniqueStr:
simpleTypes[k].unitOrEnumerationRow = startRow
else:
lastUniqueStr = uniqueStr
startRow = len(enumerationsMatrix)
j = 0
for enum in type.item:
j += 1
if j == 1:
firstEntry = 1
else:
firstEntry = 0
enumerationsMatrix.append(pyMtsf.Enumeration(enum[0], j, enum[1], firstEntry))
simpleTypes[k].unitOrEnumerationRow = startRow
# Iterate over all fmi-variables
for fmiVariableName, fmiVariable in fmi.scalarVariables.iteritems():
variableType = fmiVariable.type.type
if variableType != "String": # Do not support strings
variability = fmiVariable.variability
aliasNegated = 0
aliasName = fmiVariable.aliasName
if aliasName is not None:
if fmiVariable.alias == 'negatedAlias':
aliasNegated = 1
# Due to possibly insufficient information in xml-file
variability = fmi.scalarVariables[aliasName].variability
categoryIndex = pyMtsf.StandardCategoryNames.index(pyMtsf.CategoryMapping[variableType])
if variability in ['constant', 'parameter']:
seriesIndex = allSeriesNames.index('Fixed')
elif variability == 'discrete':
seriesIndex = allSeriesNames.index('Discrete')
else:
seriesIndex = allSeriesNames.index('Continuous')
causality = fmiVariable.causality
# Due to FMI 1.0; in vers. 2.0 this should not be necessary
if causality is None:
causality = 'local'
if variability == 'parameter':
causality = 'parameter'
variability = 'fixed'
if causality in ['internal', 'none']:
causality = 'local'
simpleTypeRow = rowIndex[fmiVariableName]
variable[fmiVariableName] = pyMtsf.ScalarModelVariable(fmiVariable.description,
causality,
simpleTypeRow,
variability,
seriesIndex, categoryIndex,
aliasName, aliasNegated)
# Some basics for independent time variables
startRow = len(units)
units.append(pyMtsf.Unit('s', 1.0, 0.0, 0))
units.append(pyMtsf.Unit('ms', 0.001, 0.0, 1))
units.append(pyMtsf.Unit('min', 60.0, 0.0, 1))
units.append(pyMtsf.Unit('h', 3600.0, 0.0, 1))
units.append(pyMtsf.Unit('d', 86400.0, 0.0, 1))
simpleTypes.append(pyMtsf.SimpleType('Time', pyMtsf.DataType["Real"], 'Time', False, startRow, ''))
variable['Time'].simpleTypeRow = len(simpleTypes) - 1
variable['TimeDiscrete'].simpleTypeRow = len(simpleTypes) - 1
modelDescription = pyMtsf.ModelDescription(fmi.modelName, fmi.description, fmi.author, fmi.version, fmi.generationTool, fmi.generationDateAndTime, fmi.variableNamingConvention)
modelVariables = pyMtsf.ModelVariables(variable, StandardSeriesForFmi, pyMtsf.StandardCategoryNames)
return modelDescription, modelVariables, simpleTypes, units, enumerationsMatrix
def convertFromDymolaMatFile(matFilename, mtsfFilename=None):
''' Converts a Dymola result file (in mat-format) into the MTSF format.
Returns the filename of the new result file
'''
# Define file name of result file
if mtsfFilename is None:
if len(matFilename) >= 4:
if matFilename[-4:] == '.mat':
resultFileName = matFilename[:-4] + '.mtsf'
else:
resultFileName = matFilename + '.mtsf'
else:
resultFileName = matFilename + '.mtsf'
else:
if '.mtsf' in mtsfFilename:
if mtsfFilename[-5:] == '.mtsf':
resultFileName = mtsfFilename
else:
resultFileName = mtsfFilename + '.mtsf'
else:
resultFileName = mtsfFilename + '.mtsf'
from ..DymolaMat import DymolaMat
# Load mat-file
res = DymolaMat.Results(matFilename)
# Define basic structure of result file
variable = collections.OrderedDict()
# Search for aliases
sortedVariables = [(i, res._dataInfo[i, 0], abs(res._dataInfo[i, 1]))
for i in xrange(len(res._name))]
sortedVariables.sort(key=itemgetter(2))
sortedVariables.sort(key=itemgetter(1))
aliasName = [None for i in xrange(len(res._name))]
for i, var in enumerate(sortedVariables):
index = var[0]
alias = None
j = i
while j > 0:
if sortedVariables[j - 1][1] != var[1] or sortedVariables[
j - 1][2] != var[2]:
break
else:
j -= 1
if j < i:
alias = res._name[sortedVariables[j][0]]
aliasName[index] = alias
dataIndexFixed = []
dataIndexContinuous = []
categoryIndex = pyMtsf.StandardCategoryNames.index(
pyMtsf.CategoryMapping['Real'])
for index, variableName in enumerate(res._name):
aliasNegated = False
if res._dataInfo[index, 0] == 1:
variability = 'fixed'
seriesIndex = 0 # Fixed
else:
variability = 'continuous'
seriesIndex = 1 # Continuous
if res._dataInfo[index, 1] < 0:
aliasNegated = True
if aliasName[index] is None:
if variability == 'fixed':
dataIndexFixed.append(abs(res._dataInfo[index, 1]) - 1)
else:
dataIndexContinuous.append(abs(res._dataInfo[index, 1]) - 1)
variable[variableName] = pyMtsf.ScalarModelVariable(
res._description[index],
'option',
0, # may be set later
variability,
seriesIndex,
categoryIndex,
aliasName[index],
aliasNegated)
modelVariables = pyMtsf.ModelVariables(variable,
MtsfFmi.StandardSeriesForFmi,
pyMtsf.StandardCategoryNames)
timeData = res.data("Time")
modelVariables.allSeries[1].initialRows = len(timeData) # Continuous
simpleTypes = []
units = []
enumerations = []
simpleTypes.append(
pyMtsf.SimpleType('Real without unit', pyMtsf.DataType["Real"], '',
False, -1, '')) # No unit
unitList = [(index, unit) for index, unit in enumerate(res._unit)]
unitList.sort(key=itemgetter(1))
preUnit = ''
for x in unitList:
index = x[0]
unit = x[1]
if unit != '':
if preUnit != unit:
units.append(pyMtsf.Unit(unit, 1.0, 0.0, 0))
simpleTypes.append(
pyMtsf.SimpleType('Real, Unit = ' + unit,
pyMtsf.DataType["Real"], '', False,
len(units) - 1, ''))
preUnit = unit
modelVariables.variable[
res._name[index]].simpleTypeRow = len(simpleTypes) - 1
else:
modelVariables.variable[
res._name[index]].simpleTypeRow = len(simpleTypes) - 1
experimentSetup = pyMtsf.ExperimentSetup(
startTime=timeData[0],
stopTime=timeData[-1],
algorithm="",
relativeTolerance='',
author="",
description="",
generationDateAndTime=time.strftime("%a, %d %b %Y %H:%M:%S",
time.gmtime()),
generationTool="Python",
machine=os.getenv('COMPUTERNAME'),
cpuTime="")
modelDescription = pyMtsf.ModelDescription(resultFileName[:-5], '', '', '',
'', '', 'structured')
# Create result object
mtsf = pyMtsf.MTSF(resultFileName, modelDescription, modelVariables,
experimentSetup, simpleTypes, units, enumerations)
# Write numeric data
fixedValues = numpy.ndarray((len(dataIndexFixed, )))
for i, index in enumerate(dataIndexFixed):
fixedValues[i] = res._data[0][0, index]
continuousValues = numpy.ndarray((len(timeData), len(dataIndexContinuous)))
for i, index in enumerate(dataIndexContinuous):
continuousValues[:, i] = res._data[1][:, index]
mtsf.results.series['Fixed'].category[
pyMtsf.CategoryMapping['Real']].writeData(fixedValues)
mtsf.results.series['Continuous'].category[
pyMtsf.CategoryMapping['Real']].writeData(continuousValues)
# Close file
mtsf.close()
return resultFileName
def convertFromFmi(fmuFilename, fmi=None):
''' Returns data to initialize an MTSF result file from an FMU.
The call to initialize an MTSF result file is
pyMtsf.MTSF(resultFileName, modelDescription, modelVariables, experimentSetup, simpleTypes, units, enumerationsMatrix)
The missing data is resultFileName and experimentSetup to be specified before initializing the MTSF object.
Inputs Type:
fmuFilename String
fmi FMIDescription [optional]
if fmi is given, then fmuFilename is ignored. Otherwise the FMI description is loaded from the given file.
Outputs
modelDescription pyMtsf.ModelDescription
modelVariables pyMtsf.ModelVariables
simpleTypes list of pyMtsf.SimpleType
units list of pyMtsf.Unit
enumerationsMatrix list of pyMtsf.Enumeration
'''
def _None2Str(x):
if x is None:
return ''
else:
return x
# Load FMIDescription if necessary
if fmi is None:
fmuFile = zipfile.ZipFile(
os.path.join(os.getcwd(), fmuFilename + u'.fmu'), 'r')
fmi = FMIDescription(fmuFile.open('modelDescription.xml'))
# Prepare some variables
allSeriesNames = [x.name for x in StandardSeriesForFmi]
variable = collections.OrderedDict()
simpleTypes = []
units = []
enumerationsMatrix = []
variable['Time'] = pyMtsf.ScalarModelVariable(
'Continuous Time', 'input', 0, 'continuous',
allSeriesNames.index('Continuous'),
pyMtsf.StandardCategoryNames.index(pyMtsf.CategoryMapping['Real']),
None, 0)
variable['TimeDiscrete'] = pyMtsf.ScalarModelVariable(
'Discrete Time at events', 'input', 0, 'discrete',
allSeriesNames.index('Discrete'),
pyMtsf.StandardCategoryNames.index(pyMtsf.CategoryMapping['Real']),
None, 0)
# Searching aliases
referenceList = [(x, fmi.scalarVariables[x].valueReference)
for x in fmi.scalarVariables.keys()]
referenceList.sort(key=itemgetter(1))
alias = dict()
if len(referenceList) > 1:
origin = None
alias[referenceList[0][0]] = None
for index in xrange(len(referenceList) - 1):
variableName = referenceList[index][0]
valueReference = referenceList[index][1]
nextVariableName = referenceList[index + 1][0]
nextValueReference = referenceList[index + 1][1]
if valueReference == nextValueReference:
if origin is None:
origin = variableName
alias[nextVariableName] = origin
else:
origin = None
alias[nextVariableName] = None
'''
# Types and display units
uniqueSimpleType = []
for fmiVariableName, fmiVariable in fmi.scalarVariables.iteritems():
type = fmiVariable.type
unitList = [_None2Str(type.unit) + _None2Str(type.displayUnit)]
if fmi.units.has_key(type.unit):
for displayUnitName, displayUnit in fmi.units[type.unit].iteritems():
if displayUnitName != type.unit:
unitList.append(displayUnitName + '{:.16e}'.format(displayUnit.factor) + '{:.16e}'.format(displayUnit.offset))
# unitList.sort()
dataType = type.basicType
enumerations = ''
if dataType == 'Enumeration':
enumerations = ''.join([_None2Str(x[0]) + _None2Str(x[1]) for x in type.item])
uniqueSimpleType.append((fmiVariableName, type, _None2Str(type.name) + str(pyMtsf.DataType[dataType]) + _None2Str(type.quantity) + str(type.relativeQuantity), ''.join(unitList), enumerations))
# Simple Types
uniqueSimpleType.sort(key=itemgetter(3))
uniqueSimpleType.sort(key=itemgetter(2))
lastUniqueStr = ''
rowIndex = dict()
lastIndex = -1
uniqueDisplayUnit = []
uniqueEnumerations = []
for s in uniqueSimpleType:
fmiVariableName = s[0]
type = s[1]
uniqueStr = s[2] + s[3] + s[4]
if uniqueStr == lastUniqueStr:
rowIndex[fmiVariableName] = lastIndex
else:
lastUniqueStr = uniqueStr
lastIndex += 1
rowIndex[fmiVariableName] = lastIndex
uniqueDisplayUnit.append((type, lastIndex, s[3]))
uniqueEnumerations.append((type, lastIndex, s[4]))
dataType = type.type
simpleTypes.append(pyMtsf.SimpleType(type.name, pyMtsf.DataType[dataType], type.quantity, type.relativeQuantity, -1, type.description))
# Units
uniqueDisplayUnit.sort(key=itemgetter(2))
lastUniqueStr = ''
startRow = -1
for s in uniqueDisplayUnit:
type = s[0]
k = s[1]
uniqueStr = s[2]
if uniqueStr == lastUniqueStr:
simpleTypes[k].unitOrEnumerationRow = startRow
else:
lastUniqueStr = uniqueStr
if uniqueStr != '': # There is a unit definition
startRow = len(units)
units.append(pyMtsf.Unit(type.unit, 1.0, 0.0, 0))
if fmi.units.has_key(type.unit):
for displayUnitName, displayUnit in fmi.units[type.unit].iteritems():
if displayUnitName != type.unit:
if type.displayUnit is not None and type.displayUnit != '' and type.displayUnit == displayUnitName:
mode = 2 # DefaultDisplayUnit
else:
mode = 1 # DisplayUnit
units.append(pyMtsf.Unit(displayUnitName, displayUnit.gain, displayUnit.offset, mode))
simpleTypes[k].unitOrEnumerationRow = startRow
else:
startRow = -1
# Enumerations
uniqueEnumerations.sort(key=itemgetter(2))
lastUniqueStr = ''
startRow = -1
for s in uniqueEnumerations:
type = s[0]
k = s[1]
uniqueStr = s[2]
if uniqueStr != '':
if uniqueStr == lastUniqueStr:
simpleTypes[k].unitOrEnumerationRow = startRow
else:
lastUniqueStr = uniqueStr
startRow = len(enumerationsMatrix)
j = 0
for enum in type.item:
j += 1
if j == 1:
firstEntry = 1
else:
firstEntry = 0
enumerationsMatrix.append(pyMtsf.Enumeration(enum[0], j, enum[1], firstEntry))
simpleTypes[k].unitOrEnumerationRow = startRow
'''
simpleTypes.append(
pyMtsf.SimpleType('Real', pyMtsf.DataType['Real'], 'Real', False, -1,
''))
simpleTypes.append(
pyMtsf.SimpleType('Integer', pyMtsf.DataType['Integer'], 'Integer',
False, -1, ''))
simpleTypes.append(
pyMtsf.SimpleType('Boolean', pyMtsf.DataType['Boolean'], 'Boolean',
False, -1, ''))
# Iterate over all fmi-variables
for fmiVariableName, fmiVariable in fmi.scalarVariables.iteritems():
variableType = fmiVariable.type.basicType
if variableType != "String": # Do not support strings
aliasNegated = 0 # Not supported in FMI 2.0
aliasName = alias[fmiVariableName]
categoryIndex = pyMtsf.StandardCategoryNames.index(
pyMtsf.CategoryMapping[variableType])
variability = fmiVariable.variability
if variability == 'tunable':
variability = 'fixed'
if variability in ['constant', 'fixed']:
seriesIndex = allSeriesNames.index('Fixed')
elif variability in ['discrete']:
seriesIndex = allSeriesNames.index('Discrete')
else:
seriesIndex = allSeriesNames.index('Continuous')
causality = fmiVariable.causality
if causality == 'calculatedParameter':
causality = 'parameter'
if causality == 'independent':
causality = 'option'
if variableType == 'Real':
simpleTypeRow = 0
elif variableType == 'Integer':
simpleTypeRow = 1
elif variableType == 'Boolean':
simpleTypeRow = 2
elif variableType == 'Enumeration':
simpleTypeRow = 1 # Integer
variable[fmiVariableName] = pyMtsf.ScalarModelVariable(
fmiVariable.description, causality, simpleTypeRow, variability,
seriesIndex, categoryIndex, aliasName, aliasNegated)
# Some basics for independent time variables
startRow = len(units)
units.append(pyMtsf.Unit('s', 1.0, 0.0, 0))
units.append(pyMtsf.Unit('ms', 0.001, 0.0, 1))
units.append(pyMtsf.Unit('min', 60.0, 0.0, 1))
units.append(pyMtsf.Unit('h', 3600.0, 0.0, 1))
units.append(pyMtsf.Unit('d', 86400.0, 0.0, 1))
simpleTypes.append(
pyMtsf.SimpleType('Time', pyMtsf.DataType["Real"], 'Time', False,
startRow, ''))
variable['Time'].simpleTypeRow = len(simpleTypes) - 1
variable['TimeDiscrete'].simpleTypeRow = len(simpleTypes) - 1
modelDescription = pyMtsf.ModelDescription(
_None2Str(fmi.modelName), _None2Str(fmi.description),
_None2Str(fmi.author), _None2Str(fmi.version),
_None2Str(fmi.generationTool), _None2Str(fmi.generationDateAndTime),
fmi.variableNamingConvention)
modelVariables = pyMtsf.ModelVariables(variable, StandardSeriesForFmi,
pyMtsf.StandardCategoryNames)
return modelDescription, modelVariables, simpleTypes, units, enumerationsMatrix