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: ' \
+ str(np.mean(error_weights[:, j]))
print
for j, dt in enumerate(dts):
print 'Mean error in the fit with dt = '+str(dt)+' ms: ' \
+ str(np.mean(error_traces[:, j]))
def get_mean_rms_candidate_variables(self, candidate, optimal_candidate):
return np.mean(np.array([rms(candidate[k], optimal_candidate[k])
for k in range(len(optimal_candidate))]))