def __init__(self, sedml_experiment):
"""
:rtype: None
:raises: RuntimeError
"""
self.name = ''
self.network = None
self.reportingInterval = 0.0
self.timeHorizon = 0.0
self.log = daeLogs.daePythonStdOutLog()
self.datareporter = pyDataReporting.daeNoOpDataReporter() #pyDataReporting.daeTCPIPDataReporter()
self.simulations = []
self.events_heap = []
self.average_firing_rate = {}
self.raster_plot_data = []
self.pathParser = CanonicalNameParser()
self.report_variables = {}
self.output_plots = []
self.number_of_equations = 0
self.number_of_neurones = 0
self.number_of_synapses = 0
self.spike_count = 0
self.minimal_delay = 1.0E10
heapify(self.events_heap)
# Create daetoolsPointNeuroneNetwork object and the simulation runtime information
self.processSEDMLExperiment(sedml_experiment)
# Connect the DataReporter
simName = self.name + strftime(" [%d.%m.%Y %H.%M.%S]", localtime())
if not self.datareporter.Connect("", simName):
raise RuntimeError('Cannot connect the data reporter')
# Set the random number generators of the daetoolsComponentSetup
daetoolsComponentSetup._random_number_generators = self.network.randomNumberGenerators
# Setup neurones
try:
self.log.Enabled = False
for group_name, group in self.network._groups.iteritems():
for projection_name, projection in group._projections.iteritems():
if projection.minimal_delay < self.minimal_delay:
self.minimal_delay = projection.minimal_delay
for population_name, population in group._populations.iteritems():
print("Creating simulations for: {0}...".format(population_name))
for neurone in population.neurones:
simulation = daetoolsPointNeuroneSimulation(neurone, population._parameters, {})
neurone.events_heap = self.events_heap
self.simulations.append(simulation)
self.number_of_neurones += 1
for (synapse, params) in neurone.incoming_synapses:
self.number_of_synapses += synapse.Nitems
self.simulations.sort()
if self.minimal_delay < self.reportingInterval:
raise RuntimeError('The minimal delay ({0}s) is greater than the reporting interval ({1}s)'.format(self.minimal_delay, self.reportingInterval))
except:
raise
finally:
self.log.Enabled = True
def __init__(self, sedml_experiment):
"""
:rtype: None
:raises: RuntimeError
"""
self.name = ''
self.network = None
self.reportingInterval = 0.0
self.timeHorizon = 0.0
self.log = daeLogs.daePythonStdOutLog()
self.datareporter = pyDataReporting.daeNoOpDataReporter(
) #pyDataReporting.daeTCPIPDataReporter()
self.simulations = []
self.events_heap = []
self.average_firing_rate = {}
self.raster_plot_data = []
self.pathParser = CanonicalNameParser()
self.report_variables = {}
self.output_plots = []
self.number_of_equations = 0
self.number_of_neurones = 0
self.number_of_synapses = 0
self.spike_count = 0
self.minimal_delay = 1.0E10
heapify(self.events_heap)
# Create daetoolsPointNeuroneNetwork object and the simulation runtime information
self.processSEDMLExperiment(sedml_experiment)
# Connect the DataReporter
simName = self.name + strftime(" [%d.%m.%Y %H.%M.%S]", localtime())
if not self.datareporter.Connect("", simName):
raise RuntimeError('Cannot connect the data reporter')
# Set the random number generators of the daetoolsComponentSetup
daetoolsComponentSetup._random_number_generators = self.network.randomNumberGenerators
# Setup neurones
try:
self.log.Enabled = False
for group_name, group in self.network._groups.iteritems():
for projection_name, projection in group._projections.iteritems(
):
if projection.minimal_delay < self.minimal_delay:
self.minimal_delay = projection.minimal_delay
for population_name, population in group._populations.iteritems(
):
print("Creating simulations for: {0}...".format(
population_name))
for neurone in population.neurones:
simulation = daetoolsPointNeuroneSimulation(
neurone, population._parameters, {})
neurone.events_heap = self.events_heap
self.simulations.append(simulation)
self.number_of_neurones += 1
for (synapse, params) in neurone.incoming_synapses:
self.number_of_synapses += synapse.Nitems
self.simulations.sort()
if self.minimal_delay < self.reportingInterval:
raise RuntimeError(
'The minimal delay ({0}s) is greater than the reporting interval ({1}s)'
.format(self.minimal_delay, self.reportingInterval))
except:
raise
finally:
self.log.Enabled = True
class daetoolsPointNeuroneNetworkSimulation(object):
"""
Simulates a network of neurones (specified by a daetoolsPointNeuroneNetworkSimulation object)
and produces outputs according to information in a SED-ML experiment object.
The daetoolsPointNeuroneNetworkSimulation object can be obtained from the SED-ML experiment object.
"""
def __init__(self, sedml_experiment):
"""
:rtype: None
:raises: RuntimeError
"""
self.name = ''
self.network = None
self.reportingInterval = 0.0
self.timeHorizon = 0.0
self.log = daeLogs.daePythonStdOutLog()
self.datareporter = pyDataReporting.daeNoOpDataReporter() #pyDataReporting.daeTCPIPDataReporter()
self.simulations = []
self.events_heap = []
self.average_firing_rate = {}
self.raster_plot_data = []
self.pathParser = CanonicalNameParser()
self.report_variables = {}
self.output_plots = []
self.number_of_equations = 0
self.number_of_neurones = 0
self.number_of_synapses = 0
self.spike_count = 0
self.minimal_delay = 1.0E10
heapify(self.events_heap)
# Create daetoolsPointNeuroneNetwork object and the simulation runtime information
self.processSEDMLExperiment(sedml_experiment)
# Connect the DataReporter
simName = self.name + strftime(" [%d.%m.%Y %H.%M.%S]", localtime())
if not self.datareporter.Connect("", simName):
raise RuntimeError('Cannot connect the data reporter')
# Set the random number generators of the daetoolsComponentSetup
daetoolsComponentSetup._random_number_generators = self.network.randomNumberGenerators
# Setup neurones
try:
self.log.Enabled = False
for group_name, group in self.network._groups.iteritems():
for projection_name, projection in group._projections.iteritems():
if projection.minimal_delay < self.minimal_delay:
self.minimal_delay = projection.minimal_delay
for population_name, population in group._populations.iteritems():
print("Creating simulations for: {0}...".format(population_name))
for neurone in population.neurones:
simulation = daetoolsPointNeuroneSimulation(neurone, population._parameters, {})
neurone.events_heap = self.events_heap
self.simulations.append(simulation)
self.number_of_neurones += 1
for (synapse, params) in neurone.incoming_synapses:
self.number_of_synapses += synapse.Nitems
self.simulations.sort()
if self.minimal_delay < self.reportingInterval:
raise RuntimeError('The minimal delay ({0}s) is greater than the reporting interval ({1}s)'.format(self.minimal_delay, self.reportingInterval))
except:
raise
finally:
self.log.Enabled = True
def initializeAndSolveInitial(self):
try:
self.log.Enabled = False
self.number_of_equations = 0
for i, simulation in enumerate(self.simulations):
print 'Setting up the neurone {0} out of {1} (total number of equations: {2})...'.format(i+1, self.number_of_neurones, self.number_of_equations), "\r",
sys.stdout.flush()
simulation.init(self.log, self.datareporter, self.reportingInterval, self.timeHorizon)
self.number_of_equations += simulation.daesolver.NumberOfVariables
simulation.SolveInitial()
simulation.CleanUpSetupData()
except:
raise
finally:
print '\n'
self.log.Enabled = True
# Run the garbage collector to free some memory
#print('garbage before collect:\n'.format(gc.garbage))
collected = gc.collect()
#print "Garbage collector: collected %d objects." % (collected)
#print('garbage after collect:\n'.format(gc.garbage))
def run(self):
# First create the reporting times list
reporting_times = numpy.arange(self.reportingInterval, self.timeHorizon, self.reportingInterval)
prev_time = 0.0
# Iterate over the queue. The (delayed) events will be added to the queue as they are trigerred.
for next_time in reporting_times:
self.log.Message("Simulating from {0:.5f}s to {1:.5f}s...".format(prev_time, next_time), 0)
try:
while True:
# Get the first item from the heap
(t_event, inlet_event_port, target_neurone) = heappop(self.events_heap)
# If out of the interval put it back
if t_event > next_time:
heappush(self.events_heap, (t_event, inlet_event_port, target_neurone))
break
#print('{0} --> {1}s (trigger event on {2})'.format(target_neurone.Name, t_event, inlet_event_port.CanonicalName))
# Integrate until next event time and report the data if there is a discontinuity
target_neurone.simulation.IntegrateUntilTime(t_event, pyActivity.eDoNotStopAtDiscontinuity, True)
# Trigger the event and reinitialize the system
inlet_event_port.ReceiveEvent(t_event)
target_neurone.simulation.Reinitialize()
self.spike_count += 1
except IndexError:
pass
# Integrate each neurone until the *next_time* is reached and report the data
for simulation in self.simulations:
#print('{0}........ {1} {2} {3}'.format(simulation.m.Name,
# simulation.CurrentTime,
# '<' if simulation.CurrentTime < next_time else '=' ,
# next_time))
if simulation.CurrentTime < next_time:
simulation.IntegrateUntilTime(next_time, pyActivity.eDoNotStopAtDiscontinuity, True)
simulation.ReportData(next_time)
# Set the progress
self.log.SetProgress(100.0 * next_time / self.timeHorizon)
prev_time = next_time
print('Simulation has ended successfuly.')
print('Processing the results...')
# Finally, process the results (generate 2D plots, raster plots, and other voodoo-mojo stuff)
self.processResults()
def processSEDMLExperiment(self, sedml_experiment):
if len(sedml_experiment.tasks) != 1:
raise RuntimeError('The number of SED-ML tasks must be one')
sedml_task = sedml_experiment.tasks[0]
self.name = sedml_task.simulation.name
ul_model = sedml_task.model.getUserLayerModel()
self.network = daetoolsPointNeuroneNetwork(ul_model)
self.reportingInterval = float(sedml_task.simulation.outputEndTime - sedml_task.simulation.outputStartTime) / (sedml_task.simulation.numberOfPoints - 1)
self.timeHorizon = float(sedml_task.simulation.outputEndTime)
for data_generator in sedml_experiment.data_generators:
for variable in data_generator.variables:
if variable.target:
try:
items = self.pathParser.parse(str(variable.target))
except Exception as e:
RuntimeError('Invalid SED-ML variable name: {0}'.format(variable.target))
if len(items) != 3:
raise RuntimeError('Invalid SED-ML variable name: {0}'.format(variable.target))
if items[0].Type != pathItem.typeID:
raise RuntimeError('Invalid SED-ML variable name: {0}'.format(variable.target))
group = self.network.getGroup(items[0].Name)
if items[1].Type != pathItem.typeIndexedID:
raise RuntimeError('Invalid SED-ML variable name: {0}'.format(variable.target))
population = group.getPopulation(items[1].Name)
neurone = population.getNeurone(items[1].Index)
if items[2].Type != pathItem.typeID:
raise RuntimeError('Invalid SED-ML variable name: {0}'.format(variable.target))
variable_name = items[2].Name
if variable_name in neurone.nineml_aliases:
dae_variable = neurone.nineml_aliases[variable_name]
elif variable_name in neurone.nineml_variables:
dae_variable = neurone.nineml_variables[variable_name]
elif variable_name in neurone.nineml_inlet_ports:
dae_variable = neurone.nineml_inlet_ports[variable_name]
elif variable_name in neurone.nineml_reduce_ports:
dae_variable = neurone.nineml_reduce_ports[variable_name]
else:
raise RuntimeError('Cannot find SED-ML variable: {0}'.format(variable.target))
self.report_variables[variable.target] = dae_variable
dae_variable.ReportingOn = True
elif variable.symbol:
if variable.symbol == 'urn:sedml:symbol:time':
self.report_variables['time'] = None
else:
raise RuntimeError('Unsupported SED-ML symbol: {0}'.format(variable.symbol))
else:
raise RuntimeError('Both SED-ML variable symbol and target are None')
for output in sedml_experiment.outputs:
variable_names = []
x_label = 'Time, s'
y_labels = []
x_log = False
y_log = False
for curve in output.curves:
if curve.logX:
x_log = True
if curve.logY:
y_log = True
x_dg = curve.xDataReference
y_dg = curve.yDataReference
if (len(x_dg.variables) != 1) or (not x_dg.variables[0].symbol) or (x_dg.variables[0].symbol != 'urn:sedml:symbol:time'):
raise RuntimeError('The number of variables in data referrence: {0} must be one with the symbol = urn:sedml:symbol:time'.format(x_dg.id))
for variable in y_dg.variables:
if not variable.target:
raise RuntimeError('SED-ML variable: {0} target is invalid'.format(variable.name))
if not variable.target in self.report_variables:
raise RuntimeError('SED-ML variable {0} does not exist in the network'.format(variable.name))
variable_names.append(self.report_variables[variable.target].CanonicalName)
y_labels.append(variable.name)
self.output_plots.append( (output.name, variable_names, x_label, y_labels, x_log, y_log) )
def processResults(self):
results_dir = '{0} {1}'.format(self.name, strftime("[%d.%m.%Y %H.%M.%S]", localtime()))
os.mkdir(results_dir)
print(' Total number of equations: {0:>10d}'.format(self.number_of_equations))
print(' Total number of neurones: {0:>10d}'.format(self.number_of_neurones))
print(' Total number of synapses: {0:>10d}'.format(self.number_of_synapses))
print(' Total number of spikes: {0:>10d}'.format(self.spike_count))
print(' Minimal network delay: {0:>10.6f}s'.format(self.minimal_delay))
# 1. Create a raster plot file (.ras)
neurone_index = 0
population_events = {}
for group_name, group in self.network._groups.iteritems():
for population_name, population in group._populations.iteritems():
count = 0
events = []
for neurone in population.neurones:
event_port = neurone.getOutletEventPort()
count += len(event_port.Events)
for (t, data) in event_port.Events:
self.raster_plot_data.append((t, neurone_index))
events.append((t, neurone_index))
neurone_index += 1
population_events[population_name] = sorted(events)
self.average_firing_rate[population_name] = count / (self.timeHorizon * len(population.neurones))
print(' [{0}] average firing rate: {1:.3f} Hz'.format(population_name, self.average_firing_rate[population_name]))
self.raster_plot_data.sort()
self.createRasterFile(os.path.join(results_dir, 'raster-plot.ras'), self.raster_plot_data, self.number_of_neurones)
# 2. Create a raster plot image (.png)
pop_times = []
pop_indexes = []
pop_names = []
for i, (population_name, events) in enumerate(population_events.iteritems()):
if len(events) > 0:
pop_times.append( [item[0] for item in events] )
pop_indexes.append( [item[1] for item in events] )
pop_names.append( population_name )
self.createRasterPlot(os.path.join(results_dir, 'raster-plot.png'), pop_times, pop_indexes, pop_names)
# 3. Create 2D plots (.png)
for (name, variable_names, x_label, y_labels, x_log, y_log) in self.output_plots:
x_values = []
y_values = []
for var_canonical_name in variable_names:
for variable in self.datareporter.Process.Variables:
if var_canonical_name == variable.Name:
x_values.append(variable.TimeValues)
y_values.append(variable.Values.reshape(len(variable.Values)))
self.create2DPlot(os.path.join(results_dir, '{0}.png'.format(name)), x_values, y_values, x_label, y_labels, x_log, y_log)
@staticmethod
def createRasterFile(filename, data, n):
"""
:param filename: string
:param data: list of floats
:param n: integer
:rtype: None
:raises: IOError
"""
f = open(filename, "w")
f.write('# size = {0}\n'.format(n))
f.write('# first_index = {0}\n'.format(0))
f.write('# first_id = {0}\n'.format(0))
f.write('# n = {0}\n'.format(len(data)))
f.write('# variable = spikes\n')
f.write('# last_id = {0}\n'.format(n - 1))
f.write('# dt = {0}\n'.format(0.0))
f.write('# label = {0}\n'.format('spikes'))
for t, index in data:
f.write('%.14e\t%d\n' % (t, index))
f.close()
@staticmethod
def createRasterPlot(filename, pop_times, pop_indexes, pop_names):
"""
:param filename: string
:param pop_times: 2D list (list of float lists)
:param pop_indexes: 2D list (list of integer lists)
:param pop_names: string list
:rtype: None
:raises: ValueError, IOError
"""
font = {'family' : 'serif',
'weight' : 'normal',
'size' : 8}
matplotlib.rc('font', **font)
params = {'axes.labelsize' : 10,
'legend.fontsize': 5,
'text.fontsize' : 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False}
matplotlib.rcParams.update(params)
colors = ['red', 'blue', 'green', 'black', 'c', 'm', 'k', 'y']
figure = Figure(figsize=(8, 6))
canvas = FigureCanvas(figure)
axes = figure.add_subplot(111)
axes.set_xlabel('Time, s')
axes.set_ylabel('Neurones')
axes.grid(True, linestyle = '-', color = '0.75')
i = 0
min_times = 1.0E10
max_times = 0.0
min_indexes = 1E10
max_indexes = 0
for (times, indexes, name) in zip(pop_times, pop_indexes, pop_names):
color = colors[i % len(colors)]
if len(times) > 0:
axes.scatter(times, indexes, label = name, marker = 's', s = 1, color = color)
min_times = min(min_times, min(times))
max_times = max(max_times, max(times))
min_indexes = min(min_indexes, min(indexes))
max_indexes = max(max_indexes, max(indexes)) + 1
i += 1
box = axes.get_position()
axes.set_position([box.x0, box.y0, box.width * 0.9, box.height])
axes.legend(loc = 'center left', bbox_to_anchor = (1, 0.5), ncol = 1, scatterpoints = 1, fancybox = True)
axes.set_xbound(lower = min_times, upper = max_times)
axes.set_ybound(lower = min_indexes, upper = max_indexes)
canvas.print_figure(filename, dpi = 300)
@staticmethod
def create2DPlot(filename, x_values, y_values, x_label, y_labels, x_log, y_log):
"""
:param filename: string
:param x_values: 2D list (list of float lists)
:param y_values: 2D list (list of float lists)
:param x_label: string
:param y_labels: string list
:param x_log: bool
:param y_log: bool
:rtype: None
:raises: ValueError, IOError
"""
font = {'family' : 'serif',
'weight' : 'normal',
'size' : 8}
matplotlib.rc('font', **font)
params = {'axes.labelsize': 9,
'legend.fontsize': 6,
'text.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False}
matplotlib.rcParams.update(params)
colors = ['blue', 'red', 'green', 'black', 'c', 'm', 'k', 'y']
figure = Figure(figsize=(8, 6))
canvas = FigureCanvas(figure)
axes = figure.add_subplot(111)
axes.set_xlabel(x_label)
axes.grid(True, linestyle = '-', color = '0.75')
i = 0
for (x, y, label) in zip(x_values, y_values, y_labels):
color = colors[i % len(colors)]
axes.plot(x, y, label = label,
color = color,
linewidth = 0.5,
linestyle = 'solid',
marker ='',
markersize = 0,
markerfacecolor = color,
markeredgecolor = color)
i += 1
axes.legend(loc = 0, ncol = 1, fancybox = True)
if x_log:
axes.set_xscale('log')
else:
axes.set_xscale('linear')
if y_log:
axes.set_yscale('log')
else:
axes.set_yscale('linear')
canvas.print_figure(filename, dpi = 300)
def finalize(self):
for simulation in self.simulations:
simulation.Finalize()
class daetoolsPointNeuroneNetworkSimulation(object):
"""
Simulates a network of neurones (specified by a daetoolsPointNeuroneNetworkSimulation object)
and produces outputs according to information in a SED-ML experiment object.
The daetoolsPointNeuroneNetworkSimulation object can be obtained from the SED-ML experiment object.
"""
def __init__(self, sedml_experiment):
"""
:rtype: None
:raises: RuntimeError
"""
self.name = ''
self.network = None
self.reportingInterval = 0.0
self.timeHorizon = 0.0
self.log = daeLogs.daePythonStdOutLog()
self.datareporter = pyDataReporting.daeNoOpDataReporter(
) #pyDataReporting.daeTCPIPDataReporter()
self.simulations = []
self.events_heap = []
self.average_firing_rate = {}
self.raster_plot_data = []
self.pathParser = CanonicalNameParser()
self.report_variables = {}
self.output_plots = []
self.number_of_equations = 0
self.number_of_neurones = 0
self.number_of_synapses = 0
self.spike_count = 0
self.minimal_delay = 1.0E10
heapify(self.events_heap)
# Create daetoolsPointNeuroneNetwork object and the simulation runtime information
self.processSEDMLExperiment(sedml_experiment)
# Connect the DataReporter
simName = self.name + strftime(" [%d.%m.%Y %H.%M.%S]", localtime())
if not self.datareporter.Connect("", simName):
raise RuntimeError('Cannot connect the data reporter')
# Set the random number generators of the daetoolsComponentSetup
daetoolsComponentSetup._random_number_generators = self.network.randomNumberGenerators
# Setup neurones
try:
self.log.Enabled = False
for group_name, group in self.network._groups.iteritems():
for projection_name, projection in group._projections.iteritems(
):
if projection.minimal_delay < self.minimal_delay:
self.minimal_delay = projection.minimal_delay
for population_name, population in group._populations.iteritems(
):
print("Creating simulations for: {0}...".format(
population_name))
for neurone in population.neurones:
simulation = daetoolsPointNeuroneSimulation(
neurone, population._parameters, {})
neurone.events_heap = self.events_heap
self.simulations.append(simulation)
self.number_of_neurones += 1
for (synapse, params) in neurone.incoming_synapses:
self.number_of_synapses += synapse.Nitems
self.simulations.sort()
if self.minimal_delay < self.reportingInterval:
raise RuntimeError(
'The minimal delay ({0}s) is greater than the reporting interval ({1}s)'
.format(self.minimal_delay, self.reportingInterval))
except:
raise
finally:
self.log.Enabled = True
def initializeAndSolveInitial(self):
try:
self.log.Enabled = False
self.number_of_equations = 0
for i, simulation in enumerate(self.simulations):
print 'Setting up the neurone {0} out of {1} (total number of equations: {2})...'.format(
i + 1, self.number_of_neurones,
self.number_of_equations), "\r",
sys.stdout.flush()
simulation.init(self.log, self.datareporter,
self.reportingInterval, self.timeHorizon)
self.number_of_equations += simulation.daesolver.NumberOfVariables
simulation.SolveInitial()
simulation.CleanUpSetupData()
except:
raise
finally:
print '\n'
self.log.Enabled = True
# Run the garbage collector to free some memory
#print('garbage before collect:\n'.format(gc.garbage))
collected = gc.collect()
#print "Garbage collector: collected %d objects." % (collected)
#print('garbage after collect:\n'.format(gc.garbage))
def run(self):
# First create the reporting times list
reporting_times = numpy.arange(self.reportingInterval,
self.timeHorizon,
self.reportingInterval)
prev_time = 0.0
# Iterate over the queue. The (delayed) events will be added to the queue as they are trigerred.
for next_time in reporting_times:
self.log.Message(
"Simulating from {0:.5f}s to {1:.5f}s...".format(
prev_time, next_time), 0)
try:
while True:
# Get the first item from the heap
(t_event, inlet_event_port,
target_neurone) = heappop(self.events_heap)
# If out of the interval put it back
if t_event > next_time:
heappush(self.events_heap,
(t_event, inlet_event_port, target_neurone))
break
#print('{0} --> {1}s (trigger event on {2})'.format(target_neurone.Name, t_event, inlet_event_port.CanonicalName))
# Integrate until next event time and report the data if there is a discontinuity
target_neurone.simulation.IntegrateUntilTime(
t_event, pyActivity.eDoNotStopAtDiscontinuity, True)
# Trigger the event and reinitialize the system
inlet_event_port.ReceiveEvent(t_event)
target_neurone.simulation.Reinitialize()
self.spike_count += 1
except IndexError:
pass
# Integrate each neurone until the *next_time* is reached and report the data
for simulation in self.simulations:
#print('{0}........ {1} {2} {3}'.format(simulation.m.Name,
# simulation.CurrentTime,
# '<' if simulation.CurrentTime < next_time else '=' ,
# next_time))
if simulation.CurrentTime < next_time:
simulation.IntegrateUntilTime(
next_time, pyActivity.eDoNotStopAtDiscontinuity, True)
simulation.ReportData(next_time)
# Set the progress
self.log.SetProgress(100.0 * next_time / self.timeHorizon)
prev_time = next_time
print('Simulation has ended successfuly.')
print('Processing the results...')
# Finally, process the results (generate 2D plots, raster plots, and other voodoo-mojo stuff)
self.processResults()
def processSEDMLExperiment(self, sedml_experiment):
if len(sedml_experiment.tasks) != 1:
raise RuntimeError('The number of SED-ML tasks must be one')
sedml_task = sedml_experiment.tasks[0]
self.name = sedml_task.simulation.name
ul_model = sedml_task.model.getUserLayerModel()
self.network = daetoolsPointNeuroneNetwork(ul_model)
self.reportingInterval = float(
sedml_task.simulation.outputEndTime -
sedml_task.simulation.outputStartTime) / (
sedml_task.simulation.numberOfPoints - 1)
self.timeHorizon = float(sedml_task.simulation.outputEndTime)
for data_generator in sedml_experiment.data_generators:
for variable in data_generator.variables:
if variable.target:
try:
items = self.pathParser.parse(str(variable.target))
except Exception as e:
RuntimeError(
'Invalid SED-ML variable name: {0}'.format(
variable.target))
if len(items) != 3:
raise RuntimeError(
'Invalid SED-ML variable name: {0}'.format(
variable.target))
if items[0].Type != pathItem.typeID:
raise RuntimeError(
'Invalid SED-ML variable name: {0}'.format(
variable.target))
group = self.network.getGroup(items[0].Name)
if items[1].Type != pathItem.typeIndexedID:
raise RuntimeError(
'Invalid SED-ML variable name: {0}'.format(
variable.target))
population = group.getPopulation(items[1].Name)
neurone = population.getNeurone(items[1].Index)
if items[2].Type != pathItem.typeID:
raise RuntimeError(
'Invalid SED-ML variable name: {0}'.format(
variable.target))
variable_name = items[2].Name
if variable_name in neurone.nineml_aliases:
dae_variable = neurone.nineml_aliases[variable_name]
elif variable_name in neurone.nineml_variables:
dae_variable = neurone.nineml_variables[variable_name]
elif variable_name in neurone.nineml_inlet_ports:
dae_variable = neurone.nineml_inlet_ports[
variable_name]
elif variable_name in neurone.nineml_reduce_ports:
dae_variable = neurone.nineml_reduce_ports[
variable_name]
else:
raise RuntimeError(
'Cannot find SED-ML variable: {0}'.format(
variable.target))
self.report_variables[variable.target] = dae_variable
dae_variable.ReportingOn = True
elif variable.symbol:
if variable.symbol == 'urn:sedml:symbol:time':
self.report_variables['time'] = None
else:
raise RuntimeError(
'Unsupported SED-ML symbol: {0}'.format(
variable.symbol))
else:
raise RuntimeError(
'Both SED-ML variable symbol and target are None')
for output in sedml_experiment.outputs:
variable_names = []
x_label = 'Time, s'
y_labels = []
x_log = False
y_log = False
for curve in output.curves:
if curve.logX:
x_log = True
if curve.logY:
y_log = True
x_dg = curve.xDataReference
y_dg = curve.yDataReference
if (len(x_dg.variables) != 1
) or (not x_dg.variables[0].symbol) or (
x_dg.variables[0].symbol != 'urn:sedml:symbol:time'):
raise RuntimeError(
'The number of variables in data referrence: {0} must be one with the symbol = urn:sedml:symbol:time'
.format(x_dg.id))
for variable in y_dg.variables:
if not variable.target:
raise RuntimeError(
'SED-ML variable: {0} target is invalid'.format(
variable.name))
if not variable.target in self.report_variables:
raise RuntimeError(
'SED-ML variable {0} does not exist in the network'
.format(variable.name))
variable_names.append(
self.report_variables[variable.target].CanonicalName)
y_labels.append(variable.name)
self.output_plots.append(
(output.name, variable_names, x_label, y_labels, x_log, y_log))
def processResults(self):
results_dir = '{0} {1}'.format(
self.name, strftime("[%d.%m.%Y %H.%M.%S]", localtime()))
os.mkdir(results_dir)
print(' Total number of equations: {0:>10d}'.format(
self.number_of_equations))
print(' Total number of neurones: {0:>10d}'.format(
self.number_of_neurones))
print(' Total number of synapses: {0:>10d}'.format(
self.number_of_synapses))
print(' Total number of spikes: {0:>10d}'.format(self.spike_count))
print(' Minimal network delay: {0:>10.6f}s'.format(
self.minimal_delay))
# 1. Create a raster plot file (.ras)
neurone_index = 0
population_events = {}
for group_name, group in self.network._groups.iteritems():
for population_name, population in group._populations.iteritems():
count = 0
events = []
for neurone in population.neurones:
event_port = neurone.getOutletEventPort()
count += len(event_port.Events)
for (t, data) in event_port.Events:
self.raster_plot_data.append((t, neurone_index))
events.append((t, neurone_index))
neurone_index += 1
population_events[population_name] = sorted(events)
self.average_firing_rate[population_name] = count / (
self.timeHorizon * len(population.neurones))
print(' [{0}] average firing rate: {1:.3f} Hz'.format(
population_name,
self.average_firing_rate[population_name]))
self.raster_plot_data.sort()
self.createRasterFile(os.path.join(results_dir, 'raster-plot.ras'),
self.raster_plot_data, self.number_of_neurones)
# 2. Create a raster plot image (.png)
pop_times = []
pop_indexes = []
pop_names = []
for i, (population_name,
events) in enumerate(population_events.iteritems()):
if len(events) > 0:
pop_times.append([item[0] for item in events])
pop_indexes.append([item[1] for item in events])
pop_names.append(population_name)
self.createRasterPlot(os.path.join(results_dir, 'raster-plot.png'),
pop_times, pop_indexes, pop_names)
# 3. Create 2D plots (.png)
for (name, variable_names, x_label, y_labels, x_log,
y_log) in self.output_plots:
x_values = []
y_values = []
for var_canonical_name in variable_names:
for variable in self.datareporter.Process.Variables:
if var_canonical_name == variable.Name:
x_values.append(variable.TimeValues)
y_values.append(
variable.Values.reshape(len(variable.Values)))
self.create2DPlot(
os.path.join(results_dir, '{0}.png'.format(name)), x_values,
y_values, x_label, y_labels, x_log, y_log)
@staticmethod
def createRasterFile(filename, data, n):
"""
:param filename: string
:param data: list of floats
:param n: integer
:rtype: None
:raises: IOError
"""
f = open(filename, "w")
f.write('# size = {0}\n'.format(n))
f.write('# first_index = {0}\n'.format(0))
f.write('# first_id = {0}\n'.format(0))
f.write('# n = {0}\n'.format(len(data)))
f.write('# variable = spikes\n')
f.write('# last_id = {0}\n'.format(n - 1))
f.write('# dt = {0}\n'.format(0.0))
f.write('# label = {0}\n'.format('spikes'))
for t, index in data:
f.write('%.14e\t%d\n' % (t, index))
f.close()
@staticmethod
def createRasterPlot(filename, pop_times, pop_indexes, pop_names):
"""
:param filename: string
:param pop_times: 2D list (list of float lists)
:param pop_indexes: 2D list (list of integer lists)
:param pop_names: string list
:rtype: None
:raises: ValueError, IOError
"""
font = {'family': 'serif', 'weight': 'normal', 'size': 8}
matplotlib.rc('font', **font)
params = {
'axes.labelsize': 10,
'legend.fontsize': 5,
'text.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False
}
matplotlib.rcParams.update(params)
colors = ['red', 'blue', 'green', 'black', 'c', 'm', 'k', 'y']
figure = Figure(figsize=(8, 6))
canvas = FigureCanvas(figure)
axes = figure.add_subplot(111)
axes.set_xlabel('Time, s')
axes.set_ylabel('Neurones')
axes.grid(True, linestyle='-', color='0.75')
i = 0
min_times = 1.0E10
max_times = 0.0
min_indexes = 1E10
max_indexes = 0
for (times, indexes, name) in zip(pop_times, pop_indexes, pop_names):
color = colors[i % len(colors)]
if len(times) > 0:
axes.scatter(times,
indexes,
label=name,
marker='s',
s=1,
color=color)
min_times = min(min_times, min(times))
max_times = max(max_times, max(times))
min_indexes = min(min_indexes, min(indexes))
max_indexes = max(max_indexes, max(indexes)) + 1
i += 1
box = axes.get_position()
axes.set_position([box.x0, box.y0, box.width * 0.9, box.height])
axes.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
ncol=1,
scatterpoints=1,
fancybox=True)
axes.set_xbound(lower=min_times, upper=max_times)
axes.set_ybound(lower=min_indexes, upper=max_indexes)
canvas.print_figure(filename, dpi=300)
@staticmethod
def create2DPlot(filename, x_values, y_values, x_label, y_labels, x_log,
y_log):
"""
:param filename: string
:param x_values: 2D list (list of float lists)
:param y_values: 2D list (list of float lists)
:param x_label: string
:param y_labels: string list
:param x_log: bool
:param y_log: bool
:rtype: None
:raises: ValueError, IOError
"""
font = {'family': 'serif', 'weight': 'normal', 'size': 8}
matplotlib.rc('font', **font)
params = {
'axes.labelsize': 9,
'legend.fontsize': 6,
'text.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': False
}
matplotlib.rcParams.update(params)
colors = ['blue', 'red', 'green', 'black', 'c', 'm', 'k', 'y']
figure = Figure(figsize=(8, 6))
canvas = FigureCanvas(figure)
axes = figure.add_subplot(111)
axes.set_xlabel(x_label)
axes.grid(True, linestyle='-', color='0.75')
i = 0
for (x, y, label) in zip(x_values, y_values, y_labels):
color = colors[i % len(colors)]
axes.plot(x,
y,
label=label,
color=color,
linewidth=0.5,
linestyle='solid',
marker='',
markersize=0,
markerfacecolor=color,
markeredgecolor=color)
i += 1
axes.legend(loc=0, ncol=1, fancybox=True)
if x_log:
axes.set_xscale('log')
else:
axes.set_xscale('linear')
if y_log:
axes.set_yscale('log')
else:
axes.set_yscale('linear')
canvas.print_figure(filename, dpi=300)
def finalize(self):
for simulation in self.simulations:
simulation.Finalize()
