fit_logic_standalone.py 文件源码

def linear_testing():
    x_axis = np.linspace(1, 51, 100)
    x_nice = np.linspace(x_axis[0], x_axis[-1], 100)
    mod, params = qudi_fitting.make_linear_model()
    print('Parameters of the model', mod.param_names, ' with the independet variable', mod.independent_vars)

    params['slope'].value = 2  # + abs(np.random.normal(0,1))
    params['offset'].value = 50 #+ abs(np.random.normal(0, 200))
    #print('\n', 'beta', params['beta'].value, '\n', 'lifetime',
          #params['lifetime'].value)
    data_noisy = (mod.eval(x=x_axis, params=params)
                  + 10 * np.random.normal(size=x_axis.shape))

    result = qudi_fitting.make_linear_fit(axis=x_axis, data=data_noisy, add_parameters=None)
    plt.plot(x_axis, data_noisy, 'ob')
    plt.plot(x_nice, mod.eval(x=x_nice, params=params), '-g')
    print(result.fit_report())
    plt.plot(x_axis, result.best_fit, '-r', linewidth=2.0)
    plt.plot(x_axis, result.init_fit, '-y', linewidth=2.0)

    plt.show()