<jupyter_start><jupyter_text>Goal: Build and plot `bhp_e`<NAME>, University of Michigan  June 21, 2018* Load `bhm_e`* Build a function to sum across custom pairs for `bhp_e`* Plot it* Plot slices<jupyter_code>%%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')<jupyter_output><empty_output><jupyter_text><jupyter_code>%load_ext autoreload
%autoreload 2
import os
import sys
sys.path.append('../scripts/')
import numpy as np
import bicorr_e
import bicorr
import bicorr_plot as bicorr_plot
import bicorr_math as bicorr_math
import matplotlib.pyplot as plt
import matplotlib.colors
import seaborn as sns
sns.set(style='ticks')<jupyter_output><empty_output><jupyter_text>Step 1) Load `bhm_e`<jupyter_code>bhm_e, e_bin_edges, note = bicorr_e.load_bhm_e('../analysis/Cf072115_to_Cf072215b/datap')
print(bhm_e.shape)
print(e_bin_edges.shape)
print(note)<jupyter_output>(990, 1, 600, 600)
(601,)
Combined measurements from Cf072115 Cf072115b Cf072215a Cf072215b<jupyter_text>Load detector pair dictionaries.<jupyter_code>det_df = bicorr.load_det_df()
dict_pair_to_index, dict_index_to_pair, dict_pair_to_angle = bicorr.build_dict_det_pair(det_df)<jupyter_output><empty_output><jupyter_text>Step 2) Produce `bhp_e`<jupyter_code>help(bicorr_e.build_bhp_e)
bhp_e, norm_factor = bicorr_e.build_bhp_e(bhm_e,e_bin_edges)<jupyter_output><empty_output><jupyter_text>Look at subsets of pairs later. For now I'll assume it's going to work...<jupyter_code>bhp_e.shape<jupyter_output><empty_output><jupyter_text>Step 3) Plot it  I'm going to make a function called `bhp_e_plot` based off of `bhp_plot`.<jupyter_code>help(bicorr_plot.bhp_e_plot)
bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, title='Plot of bhp_e', show_flag=True)<jupyter_output><empty_output><jupyter_text>Try out the `vmin` and `vmax` input parameters.<jupyter_code>bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, vmin=10,vmax=1e4,title='Plot of bhp_e', show_flag=True)<jupyter_output><empty_output><jupyter_text>Try adding `num_fissions`.<jupyter_code>num_fissions = 2194651200.00
bhp_e, norm_factor = bicorr_e.build_bhp_e(bhm_e,e_bin_edges,num_fissions=num_fissions)
bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, title='Normalized plot of bhp_e', show_flag=True)<jupyter_output><empty_output><jupyter_text>Looks pretty good. Zoom in on the central area to see what's going on there.<jupyter_code>zoom_range = [0,6]

bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, zoom_range=zoom_range, title='Normalized plot of bhp_e', show_flag=True)
zoom_range = [0,0.5]

bicorr_plot.bhp_e_plot(bhp_e, e_bin_edges, zoom_range=zoom_range, title='Normalized plot of bhp_e', show_flag=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10813823857302118, 'fraction_numerical': 0.029728725380899292, 'mean_word_length': 9.768, 'pattern_counts': {'":': 0, '<': 41, '<?xml version=': 0, '>': 41, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '214308', 'license': 'MIT', 'n_tokens_mistral': 1199, 'n_tokens_neox': 1107, 'n_words': 189, 'path': 'methods/build_plot_bhp_e.ipynb', 'repo_name': 'pfschus/fission_bicorrelation', 'size': '2696'}