Exemple #1
0
        #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]]