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()
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