fit_logic_standalone.py 文件源码

def poissonian_testing():
    start=0
    stop=30
    mu=8
    num_points=1000
    x = np.array(np.linspace(start, stop, num_points))
#            x = np.array(x,dtype=np.int64)
    mod,params = qudi_fitting.make_poissonian_model()
    print('Parameters of the model',mod.param_names)

    p=Parameters()
    p.add('mu',value=mu)
    p.add('amplitude',value=200.)

    data_noisy=(mod.eval(x=x,params=p) *
                np.array((1+0.001*np.random.normal(size=x.shape) *
                p['amplitude'].value ) ) )

    print('all int',all(isinstance(item, (np.int32,int, np.int64)) for item in x))
    print('int',isinstance(x[1], int),float(x[1]).is_integer())
    print(type(x[1]))
    #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_poissonian_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()