def print_results(self, print_v=True):
"""
# # # # # # # # # # # # # # # # #
# P R I N T R E S U L T S #
# # # # # # # # # # # # # # # # #
"""
if print_v:
if self.pc_id == 0:
print "print_v to file: %s.v" % (self.params["exc_volt_fn_base"])
self.exc_pop_view.print_v("%s.v" % (self.params["exc_volt_fn_base"]), compatible_output=False)
if self.pc_id == 0:
print "Printing inhibitory membrane potentials"
self.inh_pop_view.print_v("%s.v" % (self.params["inh_volt_fn_base"]), compatible_output=False)
if self.pc_id == 0:
print "Printing excitatory spikes"
self.exc_pop.printSpikes(self.params["exc_spiketimes_fn_merged"] + ".ras")
if self.pc_id == 0:
print "Printing inhibitory spikes"
self.inh_pop.printSpikes(self.params["inh_spiketimes_fn_merged"] + ".ras")
self.times["t_print"] = self.timer.diff()
if self.pc_id == 0:
print "calling pyNN.end() ...."
end()
self.times["t_end"] = self.timer.diff()
if self.pc_id == 0:
self.times["t_all"] = 0.0
for k in self.times.keys():
self.times["t_all"] += self.times[k]
self.n_cells = {}
self.n_cells["n_exc"] = self.params["n_exc"]
self.n_cells["n_inh"] = self.params["n_inh"]
self.n_cells["n_cells"] = self.params["n_cells"]
self.n_cells["n_proc"] = self.n_proc
output = {"times": self.times, "n_cells_proc": self.n_cells}
print "Proc %d Simulation time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (
self.pc_id,
self.times["t_sim"],
(self.times["t_sim"]) / 60.0,
self.params["n_cells"],
self.params["n_exc"],
self.params["n_inh"],
)
print "Proc %d Full pyNN run time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (
self.pc_id,
self.times["t_all"],
(self.times["t_all"]) / 60.0,
self.params["n_cells"],
self.params["n_exc"],
self.params["n_inh"],
)
fn = utils.convert_to_url(params["folder_name"] + "times_dict_np%d.py" % self.n_proc)
output = ntp.ParameterSet(output)
output.save(fn)
def get_parameter(self, param_name):
"""
For all simulations (in self.dirs_to_process) get the according parameter value
"""
for i_, folder in enumerate(self.dirs_to_process):
param_fn = folder + '/Parameters/simulation_parameters.info'
param_fn = utils.convert_to_url(param_fn)
param_dict = NTP.ParameterSet(param_fn)
value = param_dict[param_name]
self.param_space[i_][param_name] = value
def print_results(self, print_v=True):
"""
# # # # # # # # # # # # # # # # #
# P R I N T R E S U L T S #
# # # # # # # # # # # # # # # # #
"""
if print_v:
if self.pc_id == 0:
print 'print_v to file: %s.v' % (self.params['exc_volt_fn_base'])
self.exc_pop_view.print_v("%s.v" % (self.params['exc_volt_fn_base']), compatible_output=False)
if self.pc_id == 0:
print "Printing inhibitory membrane potentials"
self.inh_pop_view.print_v("%s.v" % (self.params['inh_volt_fn_base']), compatible_output=False)
print 'DEBUG printing anticipatory cells', self.anticipatory_record
if self.anticipatory_record == True:
print 'print_v to file: %s' % (self.params['exc_volt_anticipation'])
self.exc_pop_view_anticipation.print_v("%s" % (self.params['exc_volt_anticipation']), compatible_output=False)
print 'print_gsyn to file: %s' % (self.params['exc_gsyn_anticipation'])
self.exc_pop_view_anticipation.print_gsyn("%s" % (self.params['exc_gsyn_anticipation']), compatible_output=False)
if self.pc_id == 0:
print "Printing excitatory spikes"
self.exc_pop.printSpikes(self.params['exc_spiketimes_fn_merged'] + '.ras')
if self.pc_id == 0:
print "Printing inhibitory spikes"
self.inh_pop.printSpikes(self.params['inh_spiketimes_fn_merged'] + '.ras')
self.times['t_print'] = self.timer.diff()
if self.pc_id == 0:
print "calling pyNN.end() ...."
end()
self.times['t_end'] = self.timer.diff()
if self.pc_id == 0:
self.times['t_all'] = 0.
for k in self.times.keys():
self.times['t_all'] += self.times[k]
self.n_cells = {}
self.n_cells['n_exc'] = self.params['n_exc']
self.n_cells['n_inh'] = self.params['n_inh']
self.n_cells['n_cells'] = self.params['n_cells']
self.n_cells['n_proc'] = self.n_proc
output = {'times' : self.times, 'n_cells_proc' : self.n_cells}
print "Proc %d Simulation time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (self.pc_id, self.times['t_sim'], (self.times['t_sim'])/60., self.params['n_cells'], self.params['n_exc'], self.params['n_inh'])
print "Proc %d Full pyNN run time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (self.pc_id, self.times['t_all'], (self.times['t_all'])/60., self.params['n_cells'], self.params['n_exc'], self.params['n_inh'])
fn = utils.convert_to_url(params['folder_name'] + 'times_dict_np%d.py' % self.n_proc)
def write_parameters_to_file(self, fn=None):
if not (os.path.isdir(self.params['folder_name'])):
print 'Creating folder:\n\t%s' % self.params['folder_name']
self.create_folders()
if fn == None:
fn = self.params['params_fn']
print 'Writing parameters to: %s' % (fn)
self.ParamSet = ntp.ParameterSet(self.params)
fn = utils.convert_to_url(fn)
self.ParamSet.save(fn)
output_file = file(self.params['params_fn_json'], 'w')
d = json.dump(self.params, output_file)
def print_results(self, print_v=True):
"""
# # # # # # # # # # # # # # # # #
# P R I N T R E S U L T S
# # # # # # # # # # # # # # # # #
"""
if print_v:
if self.pc_id == 0:
print 'print_v to file: %s.v' % (self.params['exc_volt_fn_base'])
self.exc_pop_view.print_v("%s.v" % (self.params['exc_volt_fn_base']), compatible_output=False)
if self.pc_id == 0:
print "Printing excitatory spikes"
self.exc_pop.printSpikes(self.params['exc_spiketimes_fn_merged'] + '.ras')
# print a dummy file for inhibitory
np.savetxt(self.params['inh_spiketimes_fn_merged'] + '.ras', np.array([]))
self.times['t_print'] = self.timer.diff()
if self.pc_id == 0:
print "calling pyNN.end() ...."
end()
self.times['t_end'] = self.timer.diff()
if self.pc_id == 0:
self.times['t_all'] = 0.
for k in self.times.keys():
self.times['t_all'] += self.times[k]
self.n_cells = {}
self.n_cells['n_exc'] = self.params['n_exc']
self.n_cells['n_inh'] = self.params['n_inh']
self.n_cells['n_cells'] = self.params['n_cells']
self.n_cells['n_proc'] = self.n_proc
output = {'times' : self.times, 'n_cells_proc' : self.n_cells}
print "Proc %d Simulation time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (self.pc_id, self.times['t_sim'], (self.times['t_sim'])/60., self.params['n_cells'], self.params['n_exc'], self.params['n_inh'])
print "Proc %d Full pyNN run time: %d sec or %.1f min for %d cells (%d exc %d inh)" % (self.pc_id, self.times['t_all'], (self.times['t_all'])/60., self.params['n_cells'], self.params['n_exc'], self.params['n_inh'])
fn = utils.convert_to_url(params['folder_name'] + 'times_dict_np%d.py' % self.n_proc)
output = ntp.ParameterSet(output)
output.save(fn)
import utils
import os
rcP= { 'axes.labelsize' : 24,
'label.fontsize': 24,
'xtick.labelsize' : 24,
'ytick.labelsize' : 24,
'axes.titlesize' : 32,
'legend.fontsize': 9}
if len(sys.argv) > 1:
param_fn = sys.argv[1]
if os.path.isdir(param_fn):
param_fn += '/Parameters/simulation_parameters.info'
import NeuroTools.parameters as NTP
fn_as_url = utils.convert_to_url(param_fn)
print 'Loading parameters from', param_fn
params = NTP.ParameterSet(fn_as_url)
else:
print '\nPlotting the default parameters given in simulation_parameters.py\n'
import simulation_parameters
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
def plot_input_spikes(ax, shift=0, m='o', c='k', ms=2):
"""
Shift could be used when plotting in the same axis as the output spikes
"""
n_cells = params['n_exc']
if len(sys.argv) > 1:
if len(sys.argv[1]) == 2:
conn_type = sys.argv[1]
assert (conn_type in conn_types), 'Non-existant conn_type %s' % conn_type
try:
param_fn = sys.argv[2]
except:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
else:
param_fn = sys.argv[1]
if os.path.isdir(param_fn):
param_fn += '/Parameters/simulation_parameters.info'
print 'Trying to load parameters from', param_fn
import NeuroTools.parameters as NTP
params = NTP.ParameterSet(utils.convert_to_url(param_fn))
else:
print '\nLoading the parameters currently in simulation_parameters.py\n'
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
# get the connection type either from sys.argv[1] or [2]
if conn_type == None:
conn_types = ['ee', 'ei', 'ie', 'ii']
else:
conn_types = [conn_type]
print 'Processing conn_types', conn_types
CA = ConnectivityAnalyser(params, comm)
def plot_outgoing_connections(conn_type):
