fit_logic_standalone.py 文件源码

def double_poissonian_testing():
    """ Testing of double poissonian with self created data.
    First version of double poissonian fit."""

    start=100
    stop=300
    num_points=int((stop-start)+1)*100
    x = np.linspace(start, stop, num_points)

    # double poissonian
    mod,params = qudi_fitting.make_multiplepoissonian_model(no_of_functions=2)
    print('Parameters of the model',mod.param_names)
    parameter=Parameters()
    parameter.add('p0_mu',value=200)
    parameter.add('p1_mu',value=240)
    parameter.add('p0_amplitude',value=1)
    parameter.add('p1_amplitude',value=1)
    data_noisy = ( np.array(mod.eval(x=x,params=parameter)) *
                   np.array((1+0.2*np.random.normal(size=x.shape) )*
                   parameter['p1_amplitude'].value) )


    #make the filter an extra function shared and usable for other functions
    gaus=gaussian(10,10)
    data_smooth = filters.convolve1d(data_noisy, gaus/gaus.sum(),mode='mirror')

    result = qudi_fitting.make_doublepoissonian_fit(x, data_noisy)
    print(result.fit_report())

    plt.figure()
    plt.plot(x, data_noisy, '-b', label='noisy data')
    plt.plot(x, data_smooth, '-g', label='smoothed data')
    plt.plot(x,result.init_fit,'-y', label='initial values')
    plt.plot(x,result.best_fit,'-r',linewidth=2.0, label='fit')
    plt.xlabel('counts')
    plt.ylabel('occurences')
    plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
               ncol=2, mode="expand", borderaxespad=0.)
    plt.show()