In [1]:

    

import os
import sys
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np


    

In [2]:

    

os.getcwd()


    

    
Out[2]:





'C:\\Users\\pfschus\\Box Sync\\Projects\\fnpc\\analysis\\ipol'

In [4]:

    

os.listdir('../../scripts')


    

    
Out[4]:





['bicorr.py',
 'bicorr_e.py',
 'bicorr_main.py',
 'bicorr_math.py',
 'bicorr_plot.py',
 'bicorr_sim.py',
 'bicorr_sums.py',
 'combine_bhm_e.py',
 'combine_singles_hist.py',
 'combine_sparse_bhm.py',
 'fastpsd.i',
 'general.py',
 'plot_Asym_energy_space.py',
 'plot_counts_vs_angle_E.py',
 'plot_E_slices.py',
 'plot_slices_bhp_e.py',
 'process_all.sh',
 'psdcoeff.i',
 'readme.md',
 'scripts_from_flux.sh',
 'scripts_to_flux.sh',
 'sphinx',
 'tqdm',
 'wattE.m',
 '__pycache__']

In [5]:

    

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


    

In [6]:

    

import bicorr as bicorr
import bicorr_plot as bicorr_plot


    

In [7]:

    

%load_ext autoreload
%autoreload 2


    

In [8]:

    

import seaborn as sns
sns.set(style='ticks')


    

In [9]:

    

det_df = bicorr.load_det_df('../../meas_info/det_df_pairs_angles.csv',plot_flag=True)


    

    













    






<Figure size 576x396 with 0 Axes>






    













    






<Figure size 576x396 with 0 Axes>

In [10]:

    

chList, fcList, detList, num_dets, num_det_pairs = bicorr.build_ch_lists(print_flag=True)


    

    




Fission chamber channels: [ 0 16 32]
Detector channels: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25
 26 27 28 29 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]
Number of detectors: 45
Number of detector pairs: 990

In [11]:

    

os.listdir()


    

    
Out[11]:





['.ipynb_checkpoints',
 'check_sums.ipynb',
 'create_bhp_nn_1ns.ipynb',
 'datap',
 'fig',
 'plot_bhm_and_bhm_e.ipynb']

In [12]:

    

sparse_bhm, dt_bin_edges, note = bicorr.load_sparse_bhm(filepath='datap')


    

In [13]:

    

sparse_bhm.nbytes


    

    
Out[13]:





175199673

In [14]:

    

bhm = bicorr.revive_sparse_bhm(sparse_bhm, det_df, dt_bin_edges)


    

In [15]:

    

bhm.shape


    

    
Out[15]:





(990, 4, 800, 800)

In [16]:

    

print(bhm.shape)
print(dt_bin_edges[1]-dt_bin_edges[0])


    

    




(990, 4, 800, 800)
0.25

In [17]:

    

bhm, dt_bin_edges = bicorr.coarsen_bhm(bhm,dt_bin_edges,4,True)


    

    




Dimensions of bicorr_hist_master:  (990, 4, 800, 800)
Width of time bin in (ns):  0.25
Width of coarse time bin in (ns):  1.0
Condensing bhm from shape  (990, 4, 800, 800)  to  (990, 4, 200, 200)

In [18]:

    

print(bhm.shape)
print(dt_bin_edges[1]-dt_bin_edges[0])


    

    




(990, 4, 200, 200)
1.0

In [19]:

    

pair_is = bicorr.generate_pair_is(det_df, ignore_fc_neighbors_flag=True)
len(pair_is)


    

    
Out[19]:





861

In [20]:

    

plt.figure(figsize=(6,6))
plt.plot(det_df.iloc[pair_is]['d1'],det_df.iloc[pair_is]['d2'],'sk')
for i in [1,17,33]:
    plt.axvline(i,c='r')
    plt.axhline(i,c='r')
plt.xlabel('Detector 1 channel')
plt.ylabel('Detector 2 channel')
plt.title('Included detector pairs')
sns.despine(right=False)
plt.show()


    

In [21]:

    

bhp_nn_shape = bhm[pair_is,0,:,:].shape
print(bhp_nn_shape)


    

    




(861, 200, 200)

In [22]:

    

bhp_nn = np.zeros(bhp_nn_shape)
bhp_nn.shape


    

    
Out[22]:





(861, 200, 200)

In [23]:

    

for i in np.arange(len(pair_is)):
    pair_i = pair_is[i]
    
    bhp_nn[i,:,:] = bicorr.build_bhp(bhm,dt_bin_edges,pair_is=[pair_i],type_is=[0])[0]
    
print(bhp_nn.shape)


    

    




(861, 200, 200)

In [24]:

    

bicorr_plot.bhp_plot(np.sum(bhp_nn[:,:,:],axis=0),dt_bin_edges,show_flag=True)


    

    













    
Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0xabf55c0>






    






<Figure size 576x396 with 0 Axes>

In [25]:

    

i = 50
bicorr_plot.bhp_plot(bhp_nn[i,:,:],dt_bin_edges,show_flag=True)


    

    













    
Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x54d41d0>






    






<Figure size 576x396 with 0 Axes>

In [26]:

    

note = "Stored from IPOL simulation file with 1 ns time binning. Pairs are without fc neighbors. -PFS, 7/10/18"


    

In [27]:

    

save_filename = 'datap/bhp_nn_1ns'


    

In [28]:

    

np.savez(save_filename,
         bhp_nn = bhp_nn,
         dt_bin_edges = dt_bin_edges, 
         pair_is = pair_is,
         note = note)


    

In [29]:

    

os.listdir('datap')


    

    
Out[29]:





['Asym_df.csv',
 'bhm_e.npz',
 'bhp_nn_1ns.npz',
 'by_angle_e_df.csv',
 'det_df_e_filled.csv',
 'det_df_e_ignorefc_filled.csv',
 'Esum_df.csv',
 'num_fissions.mat',
 'singles_e_df_filled.csv',
 'singles_hist.npz',
 'singles_hist_e_n.npz',
 'slices_analysis.npz',
 'sparse_bhm.npz']

In [30]:

    

os.getcwd()


    

    
Out[30]:





'C:\\Users\\pfschus\\Box Sync\\Projects\\fnpc\\analysis\\ipol'

In [31]:

    

load_filename = 'datap/bhp_nn_1ns.npz'


    

In [32]:

    

npzfile = np.load(load_filename)
print(npzfile.files)
print(npzfile['note'])


    

    




['note', 'bhp_nn', 'pair_is', 'dt_bin_edges']
Stored from IPOL simulation file with 1 ns time binning. Pairs are without fc neighbors. -PFS, 7/10/18

In [33]:

    

pair_is = npzfile['pair_is']
bhp_nn  = npzfile['bhp_nn']
dt_bin_edges = npzfile['dt_bin_edges']
pair_is = npzfile['pair_is']


    

In [ ]: