def read_integrated_data(filename, listOfEntities, dataSet=None):
"""
DEPRECTATED! Just for reminiscing in the past... :)
Reads the results of a Fortran-BioParkin run.
The list of SBML entities is used to associate columns in the entityData file
with their corresponding entityData entities.
format:
C{[time [WHITESPACE] par 1 [WHITESPACE] par n}
e.g.:
C{0.0000000000000000E+00 1.4999999999999999E-02 0.0000000000000000E+00}
"""
try:
csv.register_dialect("SimulationData", delimiter=' ', quotechar='"', skipinitialspace=True)
reader = csv.reader(open(filename), dialect="SimulationData")
time = []
values = {}
for (i, row) in enumerate(reader): # we don't use the i... yet
for (j, value) in enumerate(row):
if j == 0: # jump over the 0th row
time.append(value) # time value is always in the 0th row
continue
columnID = j - 1 # possibility to compensate for column offset
entity = listOfEntities[columnID]
if values.has_key(entity):
values[entity].append(value) # append to existing list
else:
values[entity] = [value] # create list for the first time
# create final data structure
dataMap = {}
if len(values) > 0: # if we have dataMap
for entity in values.keys():
entityData = EntityData()
if dataSet:
entityData.setAssociatedDataSet(dataSet)
entityData.dataDescriptors = time
entityData.datapoints = values[entity]
entityData.type = entitydata.TYPE_SIMULATED
entityData.sbmlEntity = entity.wrappedEntity
entityData.originalFilename = filename
dataMap[entity] = entityData
return dataMap
except Exception, e:
logging.error("Error while trying to read simulation dataMap file: %s" % e)
def read_integrated_data(filename, listOfEntities, dataSet=None):
'''
Reads the results of a Fortran-BioParkin run.
The list of SBML entities is used to associate columns in the entityData file
with their corresponding entityData entities.
format:
C{[time [WHITESPACE] par 1 [WHITESPACE] par n}
e.g.:
C{0.0000000000000000E+00 1.4999999999999999E-02 0.0000000000000000E+00}
'''
try:
csv.register_dialect("SimulationData",
delimiter=' ',
quotechar='"',
skipinitialspace=True)
reader = csv.reader(open(filename), dialect="SimulationData")
time = []
values = {}
for (i, row) in enumerate(reader): # we don't use the i... yet
for (j, value) in enumerate(row):
if j == 0: # jump over the 0th row
time.append(value) # time value is always in the 0th row
continue
columnID = j - 1 # possibility to compensate for column offset
entity = listOfEntities[columnID]
if values.has_key(entity):
values[entity].append(value) # append to existing list
else:
values[entity] = [value] # create list for the first time
# create final data structure
dataMap = {}
if len(values) > 0: # if we have dataMap
for entity in values.keys():
entityData = EntityData()
if dataSet:
entityData.setAssociatedDataSet(dataSet)
entityData.dataDescriptors = time
entityData.datapoints = values[entity]
entityData.type = entitydata.TYPE_SIMULATED
entityData.sbmlEntity = entity.wrappedEntity
entityData.originalFilename = filename
dataMap[entity] = entityData
return dataMap
except Exception, e:
logging.error(
"Error while trying to read simulation dataMap file: %s" % e)
#def read_integrated_data_old(filename):
# '''
# OLD VERSION, DON'T USE!
# Only for looking up old code!
#
# Reads the results of a Fortran-BioParkin run.
#
# input format, e.g. see:
# /home/bzfwadem/workspace/BioParkin/compute/temp/P_gnrh_rec4_Solution.dat
#
# format:
# time \t par 1 \t par n
#
# '''
# data = {}
#
# #with csv.reader(open(filename), delimiter=' ', quotechar='"') as reader:
# try:
# csv.register_dialect("SimulationData", delimiter=' ', quotechar='"', skipinitialspace=True)
# reader = csv.reader(open(filename), dialect="SimulationData")
# time = []
# values = {}
# for (i, row) in enumerate(reader): # we don't use the i... yet
#
# for (j, value) in enumerate(row):
# if j == 0: # jump over the 0th row
# time.append(value) # time value is always in the 0th row
# continue
#
# columnID = j # possibility to compensate for column offset
# # we will use the column index as the key
# # TODO: We need something better than the column index. Column indices should be matched to previously loaded experimental data (or parts of the SBML model?)
# if values.has_key(columnID):
# values[columnID].append(value) # append to existing list
# else:
# values[columnID] = [value] # create list for the first time
#
# # create final data structure
# if len(values) > 0: # if we have data
# for dataID in values.keys():
# #if not data.has_key(dataID):
# entry = {services.dataservice.TIME: time,
# services.dataservice.VALUE: values[dataID]}
# data[str(dataID)] = entry #str: because the experimental data's keys are strings
# #else:
# # entry = data[dataID]
# # entry[Services.DataService.TIME] = time
# # entry[Services.DataService.VALUE].append(value)
#
# except Exception, e:
# logging.error("Error while trying to read simulation data file: %s" % e)
# # finally:
# # reader.close() # does not exist and does not seem to be necessary
#
#
# #
# # dictReader = csv.DictReader(open(filename), ["time", range(nrCols)-1])
# # unformattedData = dictReader.
#
# # TODO
# # for row in reader:
# # #time = float(row[0])
# # for (i, value) in row.enumerate_stripped():
# # if i == 0:
# # time = float(row[0])
# # else:
#
# return data
def read_integrated_data(filename, listOfEntities, dataSet=None):
'''
Reads the results of a Fortran-BioParkin run.
The list of SBML entities is used to associate columns in the entityData file
with their corresponding entityData entities.
format:
C{[time [WHITESPACE] par 1 [WHITESPACE] par n}
e.g.:
C{0.0000000000000000E+00 1.4999999999999999E-02 0.0000000000000000E+00}
'''
try:
csv.register_dialect("SimulationData", delimiter=' ', quotechar='"', skipinitialspace=True)
reader = csv.reader(open(filename), dialect="SimulationData")
time = []
values = {}
for (i, row) in enumerate(reader): # we don't use the i... yet
for (j, value) in enumerate(row):
if j == 0: # jump over the 0th row
time.append(value) # time value is always in the 0th row
continue
columnID = j - 1 # possibility to compensate for column offset
entity = listOfEntities[columnID]
if values.has_key(entity):
values[entity].append(value) # append to existing list
else:
values[entity] = [value] # create list for the first time
# create final data structure
dataMap = {}
if len(values) > 0: # if we have dataMap
for entity in values.keys():
entityData = EntityData()
if dataSet:
entityData.setAssociatedDataSet(dataSet)
entityData.dataDescriptors = time
entityData.datapoints = values[entity]
entityData.type = entitydata.TYPE_SIMULATED
entityData.sbmlEntity = entity.wrappedEntity
entityData.originalFilename = filename
dataMap[entity] = entityData
return dataMap
except Exception, e:
logging.error("Error while trying to read simulation dataMap file: %s" % e)
#def read_integrated_data_old(filename):
# '''
# OLD VERSION, DON'T USE!
# Only for looking up old code!
#
# Reads the results of a Fortran-BioParkin run.
#
# input format, e.g. see:
# /home/bzfwadem/workspace/BioParkin/compute/temp/P_gnrh_rec4_Solution.dat
#
# format:
# time \t par 1 \t par n
#
# '''
# data = {}
#
# #with csv.reader(open(filename), delimiter=' ', quotechar='"') as reader:
# try:
# csv.register_dialect("SimulationData", delimiter=' ', quotechar='"', skipinitialspace=True)
# reader = csv.reader(open(filename), dialect="SimulationData")
# time = []
# values = {}
# for (i, row) in enumerate(reader): # we don't use the i... yet
#
# for (j, value) in enumerate(row):
# if j == 0: # jump over the 0th row
# time.append(value) # time value is always in the 0th row
# continue
#
# columnID = j # possibility to compensate for column offset
# # we will use the column index as the key
# # TODO: We need something better than the column index. Column indices should be matched to previously loaded experimental data (or parts of the SBML model?)
# if values.has_key(columnID):
# values[columnID].append(value) # append to existing list
# else:
# values[columnID] = [value] # create list for the first time
#
# # create final data structure
# if len(values) > 0: # if we have data
# for dataID in values.keys():
# #if not data.has_key(dataID):
# entry = {services.dataservice.TIME: time,
# services.dataservice.VALUE: values[dataID]}
# data[str(dataID)] = entry #str: because the experimental data's keys are strings
# #else:
# # entry = data[dataID]
# # entry[Services.DataService.TIME] = time
# # entry[Services.DataService.VALUE].append(value)
#
# except Exception, e:
# logging.error("Error while trying to read simulation data file: %s" % e)
# # finally:
# # reader.close() # does not exist and does not seem to be necessary
#
#
# #
# # dictReader = csv.DictReader(open(filename), ["time", range(nrCols)-1])
# # unformattedData = dictReader.
#
# # TODO
# # for row in reader:
# # #time = float(row[0])
# # for (i, value) in row.enumerate_stripped():
# # if i == 0:
# # time = float(row[0])
# # else:
#
# return data