def _getAggregationDS(self):
nm = '{0}/{1}'.format(self._rootDir, self.saveDataFileName)
try:
self._aggregationDS = DataStorage.open(nm, 'a')
return self._aggregationDS
except IOError as e:
return None
def _saveAllOptions(self):
'''Save the iterated options.'''
iterFileName = self.outputDir() + '/iterparams.h5'
log_info('root.submitters', 'Saving parameter iteration data: {0}'.format(iterFileName))
iterOut = DataStorage.open(iterFileName, 'w')
iterOut['items'] = self._ac.optionList()
iterOut.close()
def _getAggregationDS(self):
nm = '{0}/{1}'.format(self._rootDir, self.saveDataFileName)
try:
self._aggregationDS = DataStorage.open(nm, 'a')
return self._aggregationDS
except IOError as e:
return None
def _saveAllOptions(self):
'''Save the iterated options.'''
iterFileName = self.outputDir() + '/iterparams.h5'
log_info('root.submitters',
'Saving parameter iteration data: {0}'.format(iterFileName))
iterOut = DataStorage.open(iterFileName, 'w')
iterOut['items'] = self._ac.optionList()
iterOut.close()
def getBumpCurrentSlope(simLabel, threshold=0):
fileName = 'bump_slope_data/bump_slope_detailed_noise_{0}.h5'.format(simLabel)
msg = 'Loading bump vs. current slope from \'{0}\''.format(fileName)
log_info('getBumpCurrentlope (detailed)', msg)
ds = DataStorage.open(fileName, 'r')
slopes = ds['lineFitSlope'].flatten()
ds.close()
slopes[slopes < threshold] = np.nan
return slopes
def _retrieve_slopes(self, file_name, flatten):
'''Retrieve the slopes from a file ``file_name``.'''
ds = DataStorage.open(file_name, 'r')
slopes = ds['lineFitSlope']
if flatten:
slopes = slopes.flatten()
ds.close()
slopes[slopes < self.threshold] = np.nan
return slopes
def _retrieve_slopes(self, file_name, flatten):
'''Retrieve the slopes from a file ``file_name``.'''
ds = DataStorage.open(file_name, 'r')
slopes = ds['lineFitSlope']
if flatten:
slopes = slopes.flatten()
ds.close()
slopes[slopes < self.threshold] = np.nan
return slopes
def _open_iter_file(self):
'''Open the iteration metadata file for this parameter space.'''
fname = "{0}/iterparams.h5".format(self._rootDir)
try:
ds = DataStorage.open(fname, 'r')
except IOError as e:
job2DLogger.error("Could not open the metadata file. Check that "
"the file exists:\n\t%s", fname)
raise e
return ds
def getBumpCurrentSlope(simLabel, threshold=0):
fileName = 'bump_slope_data/bump_slope_detailed_noise_{0}.h5'.format(
simLabel)
msg = 'Loading bump vs. current slope from \'{0}\''.format(fileName)
log_info('getBumpCurrentlope (detailed)', msg)
ds = DataStorage.open(fileName, 'r')
slopes = ds['lineFitSlope'].flatten()
ds.close()
slopes[slopes < threshold] = np.nan
return slopes
def _open_iter_file(self):
'''Open the iteration metadata file for this parameter space.'''
fname = "{0}/iterparams.h5".format(self._rootDir)
try:
ds = DataStorage.open(fname, 'r')
except IOError as e:
job2DLogger.error(
"Could not open the metadata file. Check that "
"the file exists:\n\t%s", fname)
raise e
return ds
def saveSpikes(self, fileName):
'''
Save all the simulated spikes that have been recorded into a file.
Parameters
----------
fileName : string
Path and name of the file
'''
out = DataStorage.open(fileName, 'w')
d = self.getSpikes() # FIXME: what is d?
out.close()
def saveData(self):
output_fname = "{0}/{1}noise_sigma{2}_output.h5".format(
self.no.output_dir, self.no.fileNamePrefix,
int(self.no.noise_sigma))
out = self.getNetParams()
out['stateMon_e'] = nest.GetStatus(self.stateMon_e)
out['stateMon_i'] = nest.GetStatus(self.stateMon_i)
self.fixStateMon(out['stateMon_e'])
self.fixStateMon(out['stateMon_i'])
d = DataStorage.open(output_fname, 'w')
d.update(out)
d.close()
def _loadData(self):
if (self._dataLoaded):
return
try:
trialSetLogger.debug("Opening " + self._fileName)
self._ds = DataStorage.open(self._fileName, self._fileMode)
DataSpace.__init__(self, self._ds['trials'], key='trials')
except (IOError, KeyError) as e:
self._ds = None
msg = "Could not open file {0}. Creating an empty DataSet instead."
trialSetLogger.warn(msg.format(self._fileName))
trialSetLogger.warn("Error message: {0}".format(str(e)))
DataSpace.__init__(self, [], key='trials')
self._dataLoaded = True
def _loadData(self):
if (self._dataLoaded):
return
try:
trialSetLogger.debug("Opening " + self._fileName)
self._ds = DataStorage.open(self._fileName, self._fileMode)
DataSpace.__init__(self, self._ds['trials'], key='trials')
except (IOError, KeyError) as e:
self._ds = None
msg = "Could not open file {0}. Creating an empty DataSet instead."
trialSetLogger.warn(msg.format(self._fileName))
trialSetLogger.warn("Error message: {0}".format(str(e)))
DataSpace.__init__(self, [], key='trials')
self._dataLoaded = True
def saveAll(self, fileName):
'''
Save all the simulated data that has been recorded into a file.
Parameters
----------
fileName : string
Path and name of the file
'''
out = DataStorage.open(fileName, 'w')
d = self.getAllData()
for key, val in d.iteritems():
out[key] = val
out.close()
def saveData(self):
output_fname = "{0}/{1}noise_sigma{2}_output.h5".format(
self.no.output_dir, self.no.fileNamePrefix,
int(self.no.noise_sigma))
out = self.getNetParams()
out['stateMon_e'] = nest.GetStatus(self.stateMon_e)
out['stateMon_i'] = nest.GetStatus(self.stateMon_i)
self.fixStateMon(out['stateMon_e'])
self.fixStateMon(out['stateMon_i'])
d = DataStorage.open(output_fname, 'w')
d.update(out)
d.close()
def saveIterParams(self,
iterParams,
dimension_labels,
dimensions,
fileName='iterparams.h5',
dry_run=False):
'''
Save iterated parameters.
.. todo::
Check the consistency of iterParams, dimension_labels and
dimensions.
Parameters
----------
iterParams : dict
A dictionary of iterated parameters. {<name> : np.ndarray}
dimension_labels : list or tuple of strings
Labels of the dimensions. These have to reflect ``iterParams``.
dimensions : list of ints
Dimension for each label, must be the same length as
``dimension_labels``.
fileName : str, optional
An output file name, with an extension supported by the data_storage
package.
dry_run : bool
If ``True`` perform only the dry run.
'''
if ((dimension_labels is not None and dimensions is not None)
and len(dimension_labels) != len(dimensions)):
raise ValueError("len(dimension_labels) != len(dimensions)")
filePath = os.path.join(self.outputDir(), fileName)
log_info('root.submitters',
'Saving parameter iteration data to: {0}'.format(filePath))
if dry_run:
log_info('root.submitters', 'Dry run: not performing the save '
'actually.')
else:
o = DataStorage.open(filePath, 'w')
o['iterParams'] = iterParams
if dimension_labels is not None:
o['dimension_labels'] = list(dimension_labels)
if dimensions is not None:
o['dimensions'] = list(dimensions)
o.close()
def saveIterParams(self, iterParams, dimension_labels, dimensions,
fileName='iterparams.h5', dry_run=False):
'''
Save iterated parameters.
.. todo::
Check the consistency of iterParams, dimension_labels and
dimensions.
Parameters
----------
iterParams : dict
A dictionary of iterated parameters. {<name> : np.ndarray}
dimension_labels : list or tuple of strings
Labels of the dimensions. These have to reflect ``iterParams``.
dimensions : list of ints
Dimension for each label, must be the same length as
``dimension_labels``.
fileName : str, optional
An output file name, with an extension supported by the data_storage
package.
dry_run : bool
If ``True`` perform only the dry run.
'''
if ((dimension_labels is not None and dimensions is not None) and
len(dimension_labels) != len(dimensions)):
raise ValueError("len(dimension_labels) != len(dimensions)")
filePath = os.path.join(self.outputDir(), fileName)
log_info('root.submitters',
'Saving parameter iteration data to: {0}'.format(filePath))
if dry_run:
log_info('root.submitters', 'Dry run: not performing the save '
'actually.')
else:
o = DataStorage.open(filePath, 'w')
o['iterParams'] = iterParams
if dimension_labels is not None:
o['dimension_labels'] = list(dimension_labels)
if dimensions is not None:
o['dimensions'] = list(dimensions)
o.close()
def print_data(args):
'''Print data from a file or directory containing the parameter sweep.
Parameters
----------
args : argparse.Namespace
Arguments obtained from ``argparse.Argumentparser``.
Returns
-------
errno : int
Error number.
'''
data = DataStorage.open(args.path, 'r')
dataset_path = split_data_path(args.data)
if len(dataset_path) == 0:
print_data_type(data)
else:
print_data_type(data.get_item_chained(dataset_path))
return Errnum.success
def plot(self, *args, **kwargs):
myc= self._get_class_config()
tLimits = [5.e3, 6e3]
fig = self._get_final_fig(self.myc['fig_size'])
gs = gridspec.GridSpec(3, 1, height_ratios=(2.5, 1, 1))
data = DataStorage.open(self.myc['data_file'], 'r')
trial_data = data['trials'][0]
events_e = trial_data['spikeMon_e']['events']
ESpikes = PopulationSpikes(trial_data['net_attr']['net_Ne'],
events_e['senders'],
events_e['times'])
events_i = trial_data['spikeMon_i']['events']
ISpikes = PopulationSpikes(trial_data['net_attr']['net_Ni'],
events_i['senders'],
events_i['times'])
# EI Raster
ax_raster = fig.add_subplot(gs[0, 0])
rasters.plotEIRaster(
ESpikes, ISpikes,
ylabelPos=self.myc['ylabelPos'],
tLimits=tLimits,
markersize=self.config['scale_factor']*self.myc['markersize'],
yticks=True,
sigmaTitle=False,
ann_EI=False,
scaleBar=125, scaleX=.8, scaleY=-.05, scaleText='ms',
scaleTextYOffset=.03, scaleHeight=.01,
rasterized=False,
reshape_senders=False)
# EI rates
ax_erates = fig.add_subplot(gs[1, 0])
ax_irates = fig.add_subplot(gs[2, 0])
rasters.plot_avg_firing_rate_spikes(ESpikes,
ylabelPos=self.myc['ylabelPos'],
color='red',
tLimits=tLimits,
ax=ax_erates,
dt=.5,
winLen=2.)
rasters.plot_avg_firing_rate_spikes(ISpikes,
ylabelPos=self.myc['ylabelPos'],
color='blue',
tLimits=tLimits,
ax=ax_irates,
dt=.5,
winLen=2.)
gsl = .12
gsb = .02
gsr = .95
gst = .95
#fig.text(0.01, gst, 'B', size=16, weight='bold',
# va='bottom', ha='left')
gs.update(left=gsl, bottom=gsb, right=gsr, top=gst, hspace=.2)
ax_theta = fig.add_axes(Bbox.from_extents(gsl, gst - .015,
gsr, gst + .01))
t = np.arange(tLimits[0], tLimits[1]+self.dt, self.dt)
theta = self.const + .5 * (1. +
np.cos(2*np.pi*self.freq*1e-3*t - np.pi)) * (1 - self.const)
ax_theta.fill_between(t, theta, edgecolor='None',
color=self.myc['theta_color'])
ax_theta.set_xlim([tLimits[0], tLimits[1]])
ax_theta.set_ylim(-.02, 1.02)
ax_theta.axis('off')
plt.savefig(self.get_fname('pastoll_et_al_rasters.pdf'), dpi=300,
transparent=True)
plt.close(fig)
'''Main simulation run: Only export E and I connections.'''
from __future__ import absolute_import, print_function, division
from grid_cell_model.models.parameters import getOptParser
from grid_cell_model.models.gc_net_nest import BasicGridCellNetwork
from grid_cell_model.models.seeds import TrialSeedGenerator
from simtools.storage import DataStorage
parser = getOptParser()
(o, args) = parser.parse_args()
output_fname = "{0}/{1}job{2:05}_output.h5".format(o.output_dir,
o.fileNamePrefix, o.job_num)
d = DataStorage.open(output_fname, 'w')
seed_gen = TrialSeedGenerator(int(o.master_seed))
out = []
overalT = 0.
################################################################################
for trial_idx in range(o.ntrials):
print("\n\t\tStarting trial no. {0}\n".format(trial_idx))
seed_gen.set_generators(trial_idx)
d['master_seed'] = int(o.master_seed)
d['invalidated'] = 1
ei_net = BasicGridCellNetwork(o, simulationOpts=None)
ei_net.endConstruction()
ei_net.beginSimulation()
def plot(self, *args, **kwargs):
myc = self._get_class_config()
tLimits = [5.e3, 6e3]
fig = self._get_final_fig(self.myc['fig_size'])
gs = gridspec.GridSpec(3, 1, height_ratios=(2.5, 1, 1))
data = DataStorage.open(self.myc['data_file'], 'r')
trial_data = data['trials'][0]
events_e = trial_data['spikeMon_e']['events']
ESpikes = PopulationSpikes(trial_data['net_attr']['net_Ne'],
events_e['senders'], events_e['times'])
events_i = trial_data['spikeMon_i']['events']
ISpikes = PopulationSpikes(trial_data['net_attr']['net_Ni'],
events_i['senders'], events_i['times'])
# EI Raster
ax_raster = fig.add_subplot(gs[0, 0])
rasters.plotEIRaster(ESpikes,
ISpikes,
ylabelPos=self.myc['ylabelPos'],
tLimits=tLimits,
markersize=self.config['scale_factor'] *
self.myc['markersize'],
yticks=True,
sigmaTitle=False,
ann_EI=False,
scaleBar=125,
scaleX=.8,
scaleY=-.05,
scaleText='ms',
scaleTextYOffset=.03,
scaleHeight=.01,
rasterized=False,
reshape_senders=False)
# EI rates
ax_erates = fig.add_subplot(gs[1, 0])
ax_irates = fig.add_subplot(gs[2, 0])
rasters.plot_avg_firing_rate_spikes(ESpikes,
ylabelPos=self.myc['ylabelPos'],
color='red',
tLimits=tLimits,
ax=ax_erates,
dt=.5,
winLen=2.)
rasters.plot_avg_firing_rate_spikes(ISpikes,
ylabelPos=self.myc['ylabelPos'],
color='blue',
tLimits=tLimits,
ax=ax_irates,
dt=.5,
winLen=2.)
gsl = .12
gsb = .02
gsr = .95
gst = .95
#fig.text(0.01, gst, 'B', size=16, weight='bold',
# va='bottom', ha='left')
gs.update(left=gsl, bottom=gsb, right=gsr, top=gst, hspace=.2)
ax_theta = fig.add_axes(
Bbox.from_extents(gsl, gst - .015, gsr, gst + .01))
t = np.arange(tLimits[0], tLimits[1] + self.dt, self.dt)
theta = self.const + .5 * (1. + np.cos(2 * np.pi * self.freq * 1e-3 *
t - np.pi)) * (1 - self.const)
ax_theta.fill_between(t,
theta,
edgecolor='None',
color=self.myc['theta_color'])
ax_theta.set_xlim([tLimits[0], tLimits[1]])
ax_theta.set_ylim(-.02, 1.02)
ax_theta.axis('off')
plt.savefig(self.get_fname('pastoll_et_al_rasters.pdf'),
dpi=300,
transparent=True)
plt.close(fig)
def open_storage(tmpdir, file_name, mode):
'''Open the data storage in a temporary directory.'''
return DataStorage.open(str(tmpdir.join('test_basic_types.h5')), mode)
stats_visitor_e.visitDictDataSet(dummy_data_set)
ac_visitor.visitDictDataSet(dummy_data_set)
# Clean the state monitor
data['stateMonF_e'] = [data['stateMonF_e'][0]]
return data
parser = getOptParser()
(options, args) = parser.parse_args()
output_fname = "{0}/{1}job{2:05}_output.h5".format(options.output_dir,
options.fileNamePrefix,
options.job_num)
d = DataStorage.open(output_fname, 'a')
if "trials" not in d.keys():
d['trials'] = []
seed_gen = TrialSeedGenerator(int(options.master_seed))
overalT = 0.
###############################################################################
for trial_idx in range(len(d['trials']), options.ntrials):
print("\n\t\tStarting trial no. {0}\n".format(trial_idx))
seed_gen.set_generators(trial_idx)
d['master_seed'] = int(options.master_seed)
d['invalidated'] = 1
try:
ei_net = BasicGridCellNetwork(options, simulationOpts=None)
