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()
