Python utils

class python_utils.SpikeSim(path, sim_fname, neglect_t, neglect_t_end=-1, config_fname='')

Class loading and parsing files given by a simulation. The main attributes are the simulation parameters and results:

• end_t: end time of simulation

• dt: time resolution of the simulation

• input_mode: external input mode:

  • 0 (base mode): each neuron receives an indipendent poisson signal with mean frequency = SubNetwork::ext_in_rate

  • 1 (not implemented)

• data: dictionary with spike times corresponding to each population; data[‘pop’] is a list of np.arrays each containing the activity of a neuron

• subnets: a list of the SubNetworks in the simulation

MeanActivity()

Method computing the mean spiking activity of the subnets

getParameterValues()

Method initializing the simulation parameters

histogram(pop='', res=1.0, save_img='')

Method showing or saving the spiking activity of a given subnet

Parameters:

• pop (float) – desidered population; if ‘all’ is passed all population are showed.

• res – time width of each bin in the histogram

• save_img (string) – path and name of the file to be saved

info()

Method printing the simulation parameters

loadData()

Method loading spike times for each SubNetwork

necglectTime()

Method removing the spikes occurring before t_start and after t_end (if > 0)

python_utils.load_pkl(path)

Function loading an object from a pickle file.

Parameters:

path (string) – path to the object to be loaded

python_utils.newReadSpikes(file, n)

Function reading spike times in the format produced by the simulation

Parameters:

file (string) – path to the file with spike times

python_utils.readSpikes(file)

Function reading spike times in the format produced by the simulation

Parameters:

file (string) – path to the file with spike times

python_utils.save_pkl(obj, path)

Function saving an object as a pickle file.

Parameters:

• obj (generic) – python object (list, dictionary…) to be saved

• path (string) – path to the object to be saved