def loop_over_analyzed_data_for_figs(N_CELLS=30): FIG_LIST = [] for i in range(1, N_CELLS + 1): ##### LOADING THE DATA ##### data = np.load('../data/cell' + str(i) + '.npz') ##### FITTING OF THE PHENOMENOLOGICAL THRESHOLD ##### # two-steps procedure, see template_and_fitting.py # need SI units !!! P = fitting_Vthre_then_Fout(data['Fout'], 1e-3*data['muV'],\ 1e-3*data['sV'], data['TvN'],\ data['muGn'], data['Gl'], data['Cm'], data['El'], print_things=False) ##### PLOTTING ##### # see plotting_tools.py # need non SI units (electrophy units) !!! FIG = make_3d_and_2d_figs(P,\ data['Fout'], data['s_Fout'], data['muV'],\ data['sV'], data['TvN'], data['muGn'],\ data['Gl'], data['Cm'], data['El'], 'cell'+str(i)) FIG.savefig('../figures/cell' + str(i) + '.png', format='png') FIG_LIST.append(FIG) return FIG_LIST
def loop_over_analyzed_data_for_figs(N_CELLS=30): FIG_LIST = [] for i in range(1, N_CELLS+1): ##### LOADING THE DATA ##### data = np.load('../data/cell'+str(i)+'.npz') ##### FITTING OF THE PHENOMENOLOGICAL THRESHOLD ##### # two-steps procedure, see template_and_fitting.py # need SI units !!! P = fitting_Vthre_then_Fout(data['Fout'], 1e-3*data['muV'],\ 1e-3*data['sV'], data['TvN'],\ data['muGn'], data['Gl'], data['Cm'], data['El'], print_things=False) ##### PLOTTING ##### # see plotting_tools.py # need non SI units (electrophy units) !!! FIG = make_3d_and_2d_figs(P,\ data['Fout'], data['s_Fout'], data['muV'],\ data['sV'], data['TvN'], data['muGn'],\ data['Gl'], data['Cm'], data['El'], 'cell'+str(i)) FIG.savefig('../figures/cell'+str(i)+'.png', format='png') FIG_LIST.append(FIG) return FIG_LIST
def produce_reduced_data(): CELLS = [] OUTPUT = np.zeros((8, 30)) for i in range(1, 31): data = np.load('../data_firing_response/cell' + str(i) + '.npz') P = fitting_Vthre_then_Fout(data['Fout'], 1e-3*data['muV'],\ 1e-3*data['sV'], data['TvN'],\ data['muGn'], data['Gl'], data['Cm'], data['El'], print_things=True) E = get_mean_encoding_power(P, data['El'], data['Gl'], data['Cm']) CELLS.append({'Gl':data['Gl'], 'Cm':data['Cm'],\ 'Tm':data['Cm']/data['Gl'], 'P':P, 'E':E}) OUTPUT[:4, i - 1] = P OUTPUT[4:, i - 1] = E print(data['Gl'], data['Cm']) np.save('reduced_data.npy', CELLS) return OUTPUT
Stimulate a reconstructed cell with a shotnoise and study Vm dynamics """ ,formatter_class=argparse.RawTextHelpFormatter) parser.add_argument("NEURON",\ help="Choose a cell (e.g. 'cell1') or a model of neuron (e.g. 'LIF')", default='LIF') args = parser.parse_args() data = np.load('../data/'+args.NEURON+'.npz') ##### FITTING OF THE PHENOMENOLOGICAL THRESHOLD ##### # two-steps procedure, see template_and_fitting.py # need SI units !!! P = fitting_Vthre_then_Fout(data['Fout'], 1e-3*data['muV'],\ 1e-3*data['sV'], data['TvN'],\ data['muGn'], data['Gl'], data['Cm'], data['El'], print_things=True) print data['TvN_exp'] ##### PLOTTING ##### # see plotting_tools.py # need non SI units (electrophy units) !!! FIG = make_3d_and_2d_figs(P,\ data['Fout'], data['s_Fout'], data['muV'],\ data['sV'], data['TvN'], data['muGn'],\ data['Gl'], data['Cm'], data['El'], args.NEURON) plt.show()
parser = argparse.ArgumentParser( description=""" Stimulate a reconstructed cell with a shotnoise and study Vm dynamics """, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument("NEURON",\ help="Choose a cell (e.g. 'cell1') or a model of neuron (e.g. 'LIF')", default='LIF') args = parser.parse_args() data = np.load('data/' + args.NEURON + '.npz') ##### FITTING OF THE PHENOMENOLOGICAL THRESHOLD ##### # two-steps procedure, see template_and_fitting.py # need SI units !!! P = fitting_Vthre_then_Fout(data['Fout'], 1e-3*data['muV'],\ 1e-3*data['sV'], data['TvN'],\ data['muGn'], data['Gl'], data['Cm'], data['El'], print_things=True) ##### PLOTTING ##### # see plotting_tools.py # need non SI units (electrophy units) !!! FIG = make_3d_fig(P,\ data['Fout'], data['s_Fout'], data['muV'],\ data['sV'], data['TvN'], data['muGn'],\ data['Gl'], data['Cm'], data['El'], args.NEURON) plt.show()