I am trying to generate 1000 sets of data points using fake_exp_y_values
generator. All I am trying to do is to calculate the chi2
values for all 1000 iterations with different fitting models. But the way I code it right now only gives me back one chi2
value. I don’t understand what is wrong.
Here is my code:
true_mean = 5 #Assume that we know the perfect fitting model is gaussian #with mean value 5. And use try_mean with different mean values to see how #does the chi2 behave true_amp = 100 true_wid = 2 true_offset =10 x_values = np.array([i for i in np.arange(0,10,0.4)]) exact_y_values = np.array([true_offset + true_amp* np.exp(-((i-true_mean)/true_wid)**2) for i in x_values]) def func (x_values,offset,amp,mean,wid): return (offset + amp*np.exp(-((i-mean)/wid)**2)) try_mean=np.linspace(2,8,25) y_values=func(x_values,true_offset,true_amp,try_mean,true_wid) for i in range (1000): chi2=np.zeros(1000) fake_exp_y_values = np.array([np.random.poisson(y) for y in exact_y_values]) residuals=fake_exp_y_values-y_values y_err=np.clip(np.sqrt(fake_exp_y_values),1,9999) pulls=residuals/y_err chi2[i]=np.sum(pulls**2)
Yet the returned chi2
list is:
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Answer
This code 1000 times creates an array with 1000 zeros and inserts one value in each of them, but only keeps the last one:
for i in range (1000): chi2=np.zeros(1000) # [...] chi2[i]=np.sum(pulls**2)
You only want to create one array and use it in every one of the 1000 iterations:
chi2 = np.zeros(1000) for i in range(1000): # [...] chi2[i] = np.sum(pulls**2)