In [1]:

    

import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import imageio
import pandas as pd
import seaborn as sns
sns.set(style='ticks')


    

    




C:\Users\pfsch\Anaconda3\lib\importlib\_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)

In [2]:

    

sys.path.append('../../scripts')


    

In [3]:

    

import bicorr as bicorr
import bicorr_e as bicorr_e
import bicorr_plot as bicorr_plot
import bicorr_sums as bicorr_sums
import bicorr_math as bicorr_math


    

In [4]:

    

%load_ext autoreload
%autoreload 2


    

In [5]:

    

os.getcwd()


    

    
Out[5]:





'C:\\Users\\pfsch\\Box Sync\\Projects\\fnpc\\analysis\\Cf072115_to_Cf072215b'

In [6]:

    

det_df = bicorr.load_det_df('../../meas_info/det_df_pairs_angles.csv')
det_df.head()


    

    
Out[6]:







  

    

      

      
d1
      
d2
      
d1d2
      
angle
    
  
  

    

      
0
      
1
      
2
      
102
      
15.0
    
    

      
1
      
1
      
3
      
103
      
30.0
    
    

      
2
      
1
      
4
      
104
      
45.0
    
    

      
3
      
1
      
5
      
105
      
60.0
    
    

      
4
      
1
      
6
      
106
      
75.0
    
  

In [7]:

    

num_fissions = int(int(sio.loadmat('datap/num_fissions.mat')['num_fissions'])*float(sio.loadmat('datap/fc_efficiency.mat')['fc_efficiency']))
print(num_fissions)


    

    




1422133977

In [36]:

    

npzfile = np.load('datap/bhp_nn_by_pair_1ns.npz')
pair_is = npzfile['pair_is']
bhp_nn_pos = npzfile['bhp_nn_pos']
bhp_nn_neg = npzfile['bhp_nn_neg']
dt_bin_edges = npzfile['dt_bin_edges']


    

In [37]:

    

bhp_nn_diff = np.subtract(bhp_nn_pos.astype(np.int32),bhp_nn_neg[:,::-1,::-1].astype(np.int32))


    

In [10]:

    

bhp_nn_diff.shape


    

    
Out[10]:





(861, 200, 200)

In [11]:

    

a = bhp_nn_neg
b = bhp_nn_pos
print(a.shape, b.shape)

fraction_negative = np.divide(a,b,out=np.zeros_like(a), where=b!=0)
fraction_negative.shape


    

    




(861, 200, 200) (861, 200, 200)






    
Out[11]:





(861, 200, 200)

In [12]:

    

a = np.sum(bhp_nn_neg)
b = np.sum(bhp_nn_pos)
print(a, b)

fraction_negative = np.divide(a,b,out=np.zeros_like(a), where=b!=0)
fraction_negative


    

    




209861.0 8300712.0






    
Out[12]:





array(0.02528229)

In [13]:

    

bicorr_math.convert_energy_to_time(0.62)


    

    
Out[13]:





97.14588258389688

In [14]:

    

bicorr_math.convert_energy_to_time(6)


    

    
Out[14]:





31.228032150250982

In [15]:

    

i_min = int(np.digitize(30,dt_bin_edges))
i_max = int(np.digitize(100,dt_bin_edges))
print(i_min,i_max)


    

    




31 101

In [16]:

    

a = np.sum(bhp_nn_neg[i_min:i_max,:,:])
b = np.sum(bhp_nn_pos[i_min:i_max,:,:])
print(a, b)

fraction_negative = np.divide(a,b,out=np.zeros_like(a), where=b!=0)
fraction_negative


    

    




16362.0 667054.0






    
Out[16]:





array(0.02452875)

In [17]:

    

norm_factor = num_fissions * len(pair_is) * np.power((dt_bin_edges[1]-dt_bin_edges[0]),2)
bhp = np.sum(bhp_nn_diff,axis=0)/norm_factor


    

In [18]:

    

vmin = np.min(bhp[np.nonzero(bhp)])
vmax = np.max(bhp)
print(vmin,vmax)


    

    




-1.551707777724951e-11 4.909930084043371e-09

In [20]:

    

ax = bicorr_plot.bhp_plot(bhp,dt_bin_edges,show_flag=False,
                     vmin = 1e-12, vmax=4e-9, clear=False)
ax.text(10,175,'EXPERIMENT', size=10, backgroundcolor='lightgray')
ax.text(165,175,'(a)',size=15,backgroundcolor='lightgray')
bicorr_plot.save_fig_to_folder('bhm_all_normed',dpi=150)
plt.show()


    

In [42]:

    

all_pairs = pair_is
all_pairs.shape


    

    
Out[42]:





(861,)

In [27]:

    

with_to_without = dict(zip(all_pairs,np.arange(861)))


    

In [28]:

    

pair_is = bicorr.generate_pair_is(det_df, th_min=10, th_max=20, ignore_fc_neighbors_flag=True)
pair_is_without = [with_to_without[pair_is[i]] for i in range(len(pair_is))]


    

In [29]:

    

norm_factor = num_fissions * len(pair_is) * np.power((dt_bin_edges[1]-dt_bin_edges[0]),2)
bhp = np.sum(bhp_nn_diff[pair_is_without,:,:],axis=0)/norm_factor


    

In [30]:

    

bicorr_plot.bhp_plot(bhp,dt_bin_edges,show_flag=True,
                     save_flag=True,
                     save_filename = 'bhm_15_normed')


    

    






<Figure size 432x288 with 0 Axes>






    













    
Out[30]:





<matplotlib.axes._subplots.AxesSubplot at 0x1d1d9822d30>

In [26]:

    

pair_is = bicorr.generate_pair_is(det_df, th_min=40, th_max=50, ignore_fc_neighbors_flag=True)
pair_is_without = [with_to_without[pair_is[i]] for i in range(len(pair_is))]


    

In [27]:

    

norm_factor = num_fissions * len(pair_is) * np.power((dt_bin_edges[1]-dt_bin_edges[0]),2)
bhp = np.sum(bhp_nn_diff[pair_is_without,:,:],axis=0)/norm_factor


    

In [28]:

    

bicorr_plot.bhp_plot(bhp,dt_bin_edges,show_flag=True,
                     save_flag=True,
                     save_filename = 'bhm_45_normed')


    

    













    
Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x1c64ada0>






    






<Figure size 576x396 with 0 Axes>

In [43]:

    

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


    

In [44]:

    

bhp_e, norm_factor = bicorr_e.build_bhp_e(bhm_e,e_bin_edges,pair_is=all_pairs,num_fissions=num_fissions)


    

In [45]:

    

vmin = np.min(bhp_e[np.nonzero(bhp_e)])
vmax = np.max(bhp_e)
print(vmin, vmax)


    

    




1.3067012865052215e-09 3.132947004524919e-05

In [46]:

    

ax = bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, show_flag = False, 
                       vmin = 1e-8, vmax=1e-6, zoom_range =[0,6],
                       clear_flag=False)
ax.text(0.25,5.25,'EXPERIMENT', size=10, backgroundcolor='lightgray') 
ax.text(4.85,5.25,'(c)',size=15,backgroundcolor='lightgray')
bicorr_plot.save_fig_to_folder(fig_filename='bhm_e_all')
plt.show()


    

In [33]:

    

bhm_e.shape


    

    
Out[33]:





(990, 1, 600, 600)

In [34]:

    

pair_is = bicorr.generate_pair_is(det_df, th_min=10, th_max=20, ignore_fc_neighbors_flag=True)


    

In [35]:

    

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


    

In [36]:

    

vmin = np.min(bhp_e[np.nonzero(bhp_e)])
vmax = np.max(bhp_e)
print(vmin, vmax)


    

    




2.8126745192024896e-08 8.519591118664342e-05

In [37]:

    

bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, show_flag = True, 
                       vmin = 1e-8, vmax=1e-5, zoom_range =[0,6],
                       save_flag = True, save_filename = 'bhm_e_15')


    

    













    
Out[37]:





<matplotlib.axes._subplots.AxesSubplot at 0x3136d9b0>






    






<Figure size 576x396 with 0 Axes>

In [32]:

    

pair_is = bicorr.generate_pair_is(det_df, th_min=40, th_max=50, ignore_fc_neighbors_flag=True)


    

In [33]:

    

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


    

In [34]:

    

vmin = np.min(bhp_e[np.nonzero(bhp_e)])
vmax = np.max(bhp_e)
print(vmin, vmax)


    

    




1.121608053953385e-08 4.023208089530792e-05

In [35]:

    

bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, show_flag = True, 
                       vmin = 1e-8, vmax=1e-5, zoom_range =[0,6],
                       save_flag = True, save_filename = 'bhm_e_45')


    

    













    
Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0x2fadce48>






    






<Figure size 576x396 with 0 Axes>

In [ ]: