#pl.plot(t_newdt, v_newdt)
#pl.show()
# compute parameter
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)
v_exp = problem.data.v.values
t_exp = problem.data.t.values
i_exp = problem.data.i.values
dt = t_exp[1] - t_exp[0]
dvdt = np.concatenate((np.array([(v_exp[1]-v_exp[0])/dt]), np.diff(v_exp) / dt))
candidate = np.ones(len(problem.path_variables))
problem.update_cell(candidate)
channel_list = get_channel_list(problem.cell, 'soma')
ion_list = get_ionlist(channel_list)
celsius = problem.simulation_params['celsius']
currents = currents_given_v(v_exp, t_exp, problem.cell.soma, channel_list, ion_list, celsius)
# convert units
dvdt_sc, i_inj_sc, currents_sc, Cm, cell_area = convert_units(problem.cell.soma.L, problem.cell.soma.diam,
problem.cell.soma.cm, dvdt, i_exp,
currents)
# linear regression
weights, residual, y, X = linear_regression(dvdt_sc, i_inj_sc, currents_sc, i_pas=0, Cm=Cm)
#weights, residual, y, X = linear_regression(dvdt_sc, i_inj_sc, currents_sc, i_pas=0, Cm=None, cell_area=cell_area)
# output
print 'channels: ' + str(channel_list)
print 'weights: ' + str(weights)
# plot fit
# plot in three parts
merge_points = [0, 22999, 26240, 550528]
for i in range(len(merge_points)-1):
y_plot = y[merge_points[i]:merge_points[i+1]]
