In [1]:

    

import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
import os
import scipy.io as sio
import sys
import time
import inspect
import pandas as pd
from tqdm import *


    

In [2]:

    

%load_ext autoreload
%autoreload 2


    

In [3]:

    

sys.path.append('../scripts/')
import bicorr as bicorr
import bicorr_plot as bicorr_plot
import bicorr_e as bicorr_e
import bicorr_math as bicorr_math


    

In [4]:

    

bhm_e, e_bin_edges, note = bicorr_e.load_bhm_e('../analysis/Cf072115_to_Cf072215b/datap')


    

In [5]:

    

det_df = bicorr.load_det_df()


    

In [6]:

    

dict_pair_to_index, dict_index_to_pair, dict_pair_to_angle = bicorr.build_dict_det_pair(det_df)


    

In [7]:

    

num_fissions = int(sio.loadmat(r'../analysis/Cf072115_to_Cf072215b/datap/num_fissions.mat')['num_fissions'])
num_fissions


    

    
Out[7]:





2194651200

In [8]:

    

bhp_e, norm_factor = bicorr_e.build_bhp_e(bhm_e,e_bin_edges,num_fissions=num_fissions,print_flag=True)


    

    




Creating bhp_e for...
pair_is =  all
energy bin width (MeV) =  0.025
length of pair_is =  990
norm_factor =  1357940430.0000002

In [9]:

    

bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, zoom_range=[0,6], title = "normalized bhp_e", show_flag = True)


    

    













    






<Figure size 576x396 with 0 Axes>

In [19]:

    

E_min = 0.75
E_max = 4


    

In [22]:

    

i_E_min = np.digitize(E_min,e_bin_edges)-1
i_E_max = np.digitize(E_max,e_bin_edges)-1
print(i_E_min,i_E_max)


    

    




30 160

In [27]:

    

centers = bicorr_math.calc_centers(e_bin_edges)[i_E_min:i_E_max]
centers.shape


    

    
Out[27]:





(130,)

In [31]:

    

X, Y = np.meshgrid(centers, centers)


    

In [32]:

    

H = bhp_e[i_E_min:i_E_max,i_E_min:i_E_max]


    

In [33]:

    

print(X.shape)
print(Y.shape)
print(H.shape)


    

    




(130, 130)
(130, 130)
(130, 130)

In [37]:

    

bicorr_math.calc_histogram_mean((X+Y)/2,H,True,True)


    

    




num:  0.01278272989117792
num_err:  0.16867930341498646
denom:  0.006244935943176828
denom_err:  0.07902490710641062
x_mean:  2.0468952776279856
x_mean_err: 37.422981528753866






    
Out[37]:





(2.0468952776279856, 37.422981528753866)

In [ ]:

    




    

In [55]:

    

th_bin_edges = np.arange(8,190,10)
th_bin_centers = bicorr_math.calc_centers(th_bin_edges)
E_min = 0.75
E_max = 4


    

In [42]:

    

i_E_min = np.digitize(E_min,e_bin_edges)-1
i_E_max = np.digitize(E_max,e_bin_edges)-1
print(i_E_min,i_E_max)


    

    




30 160

In [56]:

    

Esum_df = pd.DataFrame({'th_bin_center':th_bin_centers})
Esum_df['Eave'] = np.nan
Esum_df['Eave_err'] = np.nan


    

In [58]:

    

Esum_df.head()


    

    
Out[58]:







  

    

      

      
th_bin_center
      
Eave
      
Eave_err
    
  
  

    

      
0
      
13.0
      
NaN
      
NaN
    
    

      
1
      
23.0
      
NaN
      
NaN
    
    

      
2
      
33.0
      
NaN
      
NaN
    
    

      
3
      
43.0
      
NaN
      
NaN
    
    

      
4
      
53.0
      
NaN
      
NaN
    
  

In [59]:

    

for th_i in range(len(th_bin_edges)-1):
    th_min = th_bin_edges[th_i]
    th_max = th_bin_edges[th_i+1]
    
    pair_is = bicorr.generate_pair_is(det_df,th_min=th_min,th_max=th_max)
    if len(pair_is) > 5: 
        bhp_e = bicorr_e.build_bhp_e(bhm_e,e_bin_edges,pair_is=pair_is)[0]
        H = bhp_e[i_E_min:i_E_max,i_E_min:i_E_max]
        Esum_df.loc[th_i,'Eave'], Esum_df.loc[th_i,'Eave_err'] = bicorr_math.calc_histogram_mean((X+Y)/2,H,False,True)


    

In [62]:

    

plt.figure(figsize=(4,4))
ax = plt.gca()
ax.errorbar(Esum_df['th_bin_center'],Esum_df['Eave'],yerr=Esum_df['Eave_err'],fmt='.')
plt.xlabel('Angle (degrees)')
plt.ylabel('Average neutron energy (MeV)')
plt.show()


    

In [ ]:

    

Esum_df.to_csv()