# convert units
dvdt_sc, i_inj_sc, _, _, cell_area = convert_units(fitter.cell.soma.L, fitter.cell.soma.diam,
fitter.cell.soma.cm, dvdt_exp,
i_exp, np.zeros(len(t_exp)))
# extract part of current injection
current_inj = np.nonzero(i_exp)[0]
idx_start = current_inj[0]
idx_end = current_inj[-1] / dt_exp
dvdt_cut = dvdt_sc[idx_start:idx_end]
t_cut = t_exp[idx_start:idx_end]
i_exp_cut = i_inj_sc[idx_start:idx_end]
currents_cut = np.zeros(len(t_cut))
# linear regression
weights, residual, y, X = linear_regression(dvdt_cut, i_exp_cut, currents_cut, i_pas=0, cell_area=cell_area)
# plots
pl.figure()
pl.plot(t_exp, dvdt_exp, 'k')
pl.plot(t_cut, dvdt_cut, 'r')
pl.title('Visualize cut of dV/dt')
pl.show()
plot_fit(y, X, weights, t_cut, [])
# transform into change in cell area
# 1) area_new = area_old * cm_fit
area_new = cell_area * weights[-1]
# 2) r=L = np.sqrt(area_new / (2*np.pi*1e-8))
r = np.sqrt(area_new / (2*np.pi*1e-8))
dvdt_newdt = np.concatenate((np.array([(v_newdt[1]-v_newdt[0])/dt]), np.diff(v_newdt)/dt))
i_newdt = data_newdt.i.values
celsius = problem.simulation_params['celsius']
# get currents
candidate = np.ones(len(problem.path_variables)) # gbars should be 1
problem.update_cell(candidate)
currents_newdt = currents_given_v(v_newdt, t_newdt, problem.cell.soma, channel_list, ion_list, celsius)
# convert units
dvdt_sc, i_inj_sc, currents_sc, Cm, _ = convert_units(problem.cell.soma.L, problem.cell.soma.diam,
problem.cell.soma.cm, dvdt_newdt,
i_newdt, currents_newdt)
# linear regression
weights, residual, y, X = linear_regression(dvdt_sc, i_inj_sc, currents_sc, i_pas=0, Cm=Cm)
# plots
#plot_fit(y, X, weights, t_newdt, channel_list)
# compute error
error_traces[i, j] = rms(y, np.sum(np.array(currents), 0))
error_tmp = [rms(weights_model[k], weights[k]) for k in range(len(weights_model))]
error_weights[i, j] = np.mean(error_tmp)
# compute mean error of all models
print
for j, dt in enumerate(dts):
print 'Mean error in the weights with dt = '+str(dt)+' ms: ' \
