def saveECD(LoggerList, FullPN):
Data = LoggerList[0].getData()
Data = np.matrix(Data)
Data = Data[:, 0]
Data = Data.tolist()
rabelrows = ["time"]
for i in range(len(LoggerList)):
Next_Data = LoggerList[i].getData()
rabel = LoggerList[i].getName()
if len(Next_Data) > 1:
rabelrows.append(rabel)
else:
pass
for j in range(len(Data)):
if len(Next_Data) > RECORDING_TIME * 200:
Data[j].append(Next_Data[2 * j][1])
elif len(Next_Data) > RECORDING_TIME * 100:
Data[j].append(Next_Data[j][1])
else:
pass
Data.insert(0, rabelrows)
Data = np.array(Data)
ECDFile = ECDDataFile(Data)
ECDFile.setDataName(VmLogger.getName())
ECDFile.setNote(FullPN + "is" + str(VALUE_OF_GX))
FullPN = FullPN.replace(":", "_")
FullPN = FullPN.replace("/", "_")
filename = FullPN + str(VALUE_OF_GX) + ".ecd"
ECDFile.save(filename)
def saveECD(LoggerList, FullPN):
Data = LoggerList[0].getData()
Data = np.matrix(Data)
Data = Data[:, 0]
Data = Data.tolist()
rabelrows = ['time']
for i in range(len(LoggerList)):
Next_Data = LoggerList[i].getData()
rabel = LoggerList[i].getName()
if len(Next_Data) > 1:
rabelrows.append(rabel)
else:
pass
for j in range(len(Data)):
if len(Next_Data) > RECORDING_TIME * 200:
Data[j].append(Next_Data[2 * j][1])
elif len(Next_Data) > RECORDING_TIME * 100:
Data[j].append(Next_Data[j][1])
else:
pass
Data.insert(0, rabelrows)
Data = np.array(Data)
ECDFile = ECDDataFile(Data)
ECDFile.setDataName(VmLogger.getName())
ECDFile.setNote(FullPN + 'is' + str(VALUE_OF_GX))
FullPN = FullPN.replace(':', '_')
FullPN = FullPN.replace('/', '_')
filename = FullPN + str(VALUE_OF_GX) + '.ecd'
ECDFile.save(filename)
VmLogger.setLoggerPolicy([1, 0.1, 1, 1000000])
run(RECORDING_TIME)
#save simulation log of membrane potential in files as csv and ecd.
pretimecourse = VmLogger.getData()
precsvfile = open('Vm' + '_' + FullPN_GX + '_' + str(VALUE_OF_GX) + '.csv',
'w')
writer = csv.writer(precsvfile)
pretimecourse_csv = np.matrix(pretimecourse)
pretimecourse_csv = pretimecourse_csv.tolist()
name = ['time', 'Vm']
pretimecourse_csv.insert(0, ['time', 'Vm'])
writer.writerows(pretimecourse_csv)
pretimecourse = ECDDataFile(pretimecourse)
pretimecourse.setDataName(VmLogger.getName())
pretimecourse.setNote(VmLogger.getName())
pretimecourse.save('Vm' + '_' + FullPN_GX + '_' + str(VALUE_OF_GX) + '.ecd')
#save simulation log of all variable except for membrane potential in a file as csv.
saveCSV(allLoggerList, FullPN_GX)
#
print currentnumber
if currentnumber == endnumber - 1:
print "end"
else:
print "continue"
print FullPN_GX + '_' + str(VALUE_OF_GX)
print endnumber
aTimeCouse = ECDDataFile() aTimeCouse.load( _DATA_ + os.sep + TRAINING_DATA_FILE_LIST[i] ) aTrainingTimeCourseList.append( aTimeCouse ) # -------------------------------------------------------- # (6) save predicted time-course # -------------------------------------------------------- aPredictedTimeCouseList = [] for i in range( len(aLoggerList) ): aTimeCouse = ECDDataFile( aLoggerList[i].getData(START_TIME, \ DULATION + INTERVAL, \ INTERVAL) ) aTimeCouse.setDataName( aLoggerList[i].getName() ) aTimeCouse.setNote( 'Predicted %s' %VARIABLE_LIST_FOR_LOGGER[i] ) aTimeCouse.save( PREFIX_OF_PREDICTED_TIMECOURSE + \ TRAINING_DATA_FILE_LIST[i] ) aPredictedTimeCouseList.append( aTimeCouse ) # -------------------------------------------------------- # (7) calculate the difference between the training and # the prediction simulated time-course. # -------------------------------------------------------- aDifference = 0.0 for i in range(aTrainingTimeCourseList[0].getSize()[1]):
# run for recording simulation log of all variable in this model.
VmLogger.setLoggerPolicy([1, 0.1, 1, 1000000])
run(RECORDING_TIME)
# save simulation log of membrane potential in files as csv and ecd.
pretimecourse = VmLogger.getData()
precsvfile = open("Vm" + "_" + FullPN_GX + "_" + str(VALUE_OF_GX) + ".csv", "w")
writer = csv.writer(precsvfile)
pretimecourse_csv = np.matrix(pretimecourse)
pretimecourse_csv = pretimecourse_csv.tolist()
name = ["time", "Vm"]
pretimecourse_csv.insert(0, ["time", "Vm"])
writer.writerows(pretimecourse_csv)
pretimecourse = ECDDataFile(pretimecourse)
pretimecourse.setDataName(VmLogger.getName())
pretimecourse.setNote(VmLogger.getName())
pretimecourse.save("Vm" + "_" + FullPN_GX + "_" + str(VALUE_OF_GX) + ".ecd")
# save simulation log of all variable except for membrane potential in a file as csv.
saveCSV(allLoggerList, FullPN_GX)
#
print currentnumber
if currentnumber == endnumber - 1:
print "end"
else:
print "continue"
print FullPN_GX + "_" + str(VALUE_OF_GX)
print endnumber
# print some values
message( 't= \t%s' % getCurrentTime() )
message( 'S:Value= \t%s' % S.getProperty( 'Value' ) )
message( 'S:MolarConc= \t%s' % S.getProperty( 'MolarConc' ) )
# run
duration = 1000
message( '\n' )
message( 'run %s sec.\n' % duration )
run( duration )
# print results
message( 't= \t%s' % getCurrentTime() )
message( 'S:Value= \t%s' % S.getProperty( 'Value' ) )
message( 'S:MolarConc= \t%s' % S.getProperty( 'MolarConc' ) )
message( '\n' )
from ecell.ECDDataFile import *
message('saving S.ecd..')
aDataFile = ECDDataFile( S_Logger.getData(0,2000,.5) )
aDataFile.setDataName( S_Logger.getName() )
aDataFile.setNote( '' )
aDataFile.save( 'S.ecd' )
#message('loading')
#aNewFile = ECDDataFile()
#aNewFile.load( 'S.ecd' )
#print aNewFile.getData()[:10]
# --------------------------------------------------------
run( DULATION + INTERVAL )
step( 1 )
# --------------------------------------------------------
# (6) save predicted time-course
# --------------------------------------------------------
predictedTimeCouseDict = {}
for FullPN in TRAINING_TIME_COURSE_DATA_DICT.keys():
aTimeCouse = ECDDataFile( aLoggerDict[ FullPN ].getData(
START_TIME, DULATION + INTERVAL, INTERVAL) )
aTimeCouse.setDataName( aLoggerDict[ FullPN ].getName() )
aTimeCouse.setNote( 'Predicted {}'.format( FullPN ) )
aTimeCouse.save( PREFIX_OF_PREDICTED_TIMECOURSE + \
TRAINING_TIME_COURSE_DATA_DICT[ FullPN ]['ECDFileName'] )
predictedTimeCouseDict[ FullPN ] = aTimeCouse
# --------------------------------------------------------
# (7) calculate the difference between the training and
# the prediction simulated time-course.
# --------------------------------------------------------
aDifference = 0.0
data_size = None
for FullPN in TRAINING_TIME_COURSE_DATA_DICT.keys():
# print some values
message( 't= \t%s' % getCurrentTime() )
message( 'S:Value= \t%s' % S.getProperty( 'Value' ) )
message( 'S:MolarConc= \t%s' % S.getProperty( 'MolarConc' ) )
# run
duration = 1000
message( '\n' )
message( 'run %s sec.\n' % duration )
run( duration )
# print results
message( 't= \t%s' % getCurrentTime() )
message( 'S:Value= \t%s' % S.getProperty( 'Value' ) )
message( 'S:MolarConc= \t%s' % S.getProperty( 'MolarConc' ) )
message( '\n' )
from ecell.ECDDataFile import *
message('saving S.ecd..')
aDataFile = ECDDataFile( S_Logger.getData(0,2000,.5) )
aDataFile.setDataName( S_Logger.getName() )
aDataFile.setNote( '' )
aDataFile.save( 'S.ecd' )
#message('loading')
#aNewFile = ECDDataFile()
#aNewFile.load( 'S.ecd' )
#print aNewFile.getData()[:10]
# --------------------------------------------------------
# (3) create logger stubs
# --------------------------------------------------------
logger_dict = {}
for a_target_FullPN in TARGET:
logger_dict[a_target_FullPN] = createLoggerStub(a_target_FullPN)
logger_dict[a_target_FullPN].create()
# --------------------------------------------------------
# (4) run
# --------------------------------------------------------
run(11.0)
step(1)
# --------------------------------------------------------
# (6) save predicted time-course
# --------------------------------------------------------
aPredictedTimeCouseList = []
for a_FullPN, a_Logger in logger_dict.items():
a_time_course = ECDDataFile(a_Logger.getData(0, 11, 0.01))
a_time_course.setDataName(a_Logger.getName())
a_time_course.setNote('Predicted {}'.format(a_FullPN))
a_time_course.save('{}.ecd'.format(a_FullPN.split(':')[2]))
# end of this file
