def append_to_file(self, h5file,currentTrial):
'''Returns True if successful '''
if currentTrial<1:
raise UserWarning('WARNING: No trials have been completed or currentTrial not updated.')
resultsDataGroup = h5file.require_group('resultsData')
resultsLabelsGroup = h5file.require_group('resultsLabels')
for key,item in self.iteritems():
dset = resultsDataGroup.create_dataset(key, data=item[:currentTrial])
for key,item in self.labels.iteritems():
# XXFIXME: Make sure items of self.labels are dictionaries
utils.append_dict_to_HDF5(resultsLabelsGroup,key,item)
def append_to_file(self, h5file, currentTrial):
'''Returns True if successful '''
if currentTrial < 1:
raise UserWarning(
'WARNING: No trials have been completed or currentTrial not updated.'
)
resultsDataGroup = h5file.require_group('resultsData')
resultsLabelsGroup = h5file.require_group('resultsLabels')
for key, item in self.iteritems():
dset = resultsDataGroup.create_dataset(key,
data=item[:currentTrial])
for key, item in self.labels.iteritems():
# XXFIXME: Make sure items of self.labels are dictionaries
utils.append_dict_to_HDF5(resultsLabelsGroup, key, item)
def append_to_file(self,h5file,currentTrial):
'''Append states definitions to open HDF5 file
It ignores currentTrial'''
statematGroup = h5file.create_group('/stateMatrix')
utils.append_dict_to_HDF5(statematGroup,'eventsNames',self.eventsDict)
utils.append_dict_to_HDF5(statematGroup,'outputsNames',self.outputsDict)
utils.append_dict_to_HDF5(statematGroup,'statesNames',self.statesNameToIndex)
utils.append_dict_to_HDF5(statematGroup,'extraTimersNames',self.extraTimersNameToIndex)
def append_to_file(self,h5file,currentTrial):
'''Append states definitions to open HDF5 file
It ignores currentTrial'''
statematGroup = h5file.create_group('/stateMatrix')
utils.append_dict_to_HDF5(statematGroup,'eventsNames',self.eventsDict)
utils.append_dict_to_HDF5(statematGroup,'outputsNames',self.outputsDict)
utils.append_dict_to_HDF5(statematGroup,'statesNames',self.statesNameToIndex)
utils.append_dict_to_HDF5(statematGroup,'extraTimersNames',self.extraTimersNameToIndex)
def append_to_file(self, h5file,currentTrial):
'''Append parameters' history to an HDF5 file.
It truncates data to the trial before currentTrial '''
dataParent = 'resultsData' # Parameters from each trial
itemsParent = 'resultsLabels' # Items in menu parameters
sessionParent = 'sessionData' # Parameters for the whole session
descriptionAttr = 'Description'
# FIXME: the contents of description should not be the label, but the
# description of the parameter (including its units)
trialDataGroup = h5file.require_group(dataParent)
menuItemsGroup = h5file.require_group(itemsParent)
sessionDataGroup = h5file.require_group(sessionParent)
# -- Append date/time and hostname --
dset = sessionDataGroup.create_dataset('hostname', data=socket.gethostname())
dateAndTime = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime())
dset = sessionDataGroup.create_dataset('date', data=dateAndTime)
# -- Append all other parameters --
for key,item in self.iteritems():
# -- Store parameters with history --
if item.history_enabled():
#h5file.createDataset(trialDataGroup, key, self.history[key], paramLabel)
if key not in self.history:
raise ValueError('No history was recorded for "{0}". '.format(key) +\
'Did you use paramgui.Container.update_history() correctly?')
dset = trialDataGroup.create_dataset(key, data=self.history[key][:currentTrial])
dset.attrs['Description'] = item.get_label()
if item.get_type()=='numeric':
dset.attrs['Units'] = item.get_units()
# FIXME: not very ObjectOriented to use getType
# the object should be able to save itself
if item.get_type()=='menu':
#h5file.createArray(menuItemsGroup, key, item.get_items(),
# '%s menu items'%paramLabel)
#menuItemsGroup.create_dataset(key, data=item.get_items())
menuList = item.get_items()
menuDict = dict(zip(menuList,range(len(menuList))))
utils.append_dict_to_HDF5(menuItemsGroup,key,menuDict)
dset.attrs['Description'] = '%s menu items'%item.get_label()
else: # -- Store parameters without history (Session parameters) --
if item.get_type()=='string':
dset = sessionDataGroup.create_dataset(key, data=np.string_(item.get_value()))
else:
dset = trialDataGroup.create_dataset(key, data=item.get_value())
dset.attrs['Description'] = item.get_label()