np.set_printoptions(threshold=np.nan) # print entire matrices
import pandas as pd
from tqdm import *
# Import custom functions -------------------------------------
sys.path.append('../../scripts/')
import bicorr as bicorr
import bicorr_math as bicorr_math
import bicorr_plot as bicorr_plot
import bicorr_e as bicorr_e
import bicorr_sums as bicorr_sums
# Load data- experimental setup -------------------------------
det_df = bicorr.load_det_df('../../meas_info/det_df_pairs_angles.csv')
chList, fcList, detList, num_dets, num_det_pairs = bicorr.build_ch_lists()
dict_pair_to_index, dict_index_to_pair, dict_pair_to_angle = bicorr.build_dict_det_pair(det_df)
# Load data- singles and bicorr histograms
singles_hist_e_n, e_bin_edges, dict_det_to_index, dict_index_to_det = bicorr_e.load_singles_hist_both(filepath = 'datap/',plot_flag=True, save_flag=True)
bhm_e, e_bin_edges, note = bicorr_e.load_bhm_e('datap')
# Set up analysis parameters
e_slices = list(np.arange(1,6,.5))
e_slice_width = 0.1
print(e_slices)
th_bin_edges = np.arange(10.01,181,10)
th_bin_centers = bicorr_math.calc_centers(th_bin_edges)
print(th_bin_edges)
def build_bhm_both_sim(bicorr_path,
bicorr_filenames,
det_df=None,
dt_bin_edges=None,
dict_det_dist=None,
e_bin_edges=None,
checkpoint_flag=True,
save_flag=True,
sparse_filename='sparse_bhm',
bhm_e_filename='bhm_e',
disable_tqdm=False,
print_flag=True,
return_flag=False):
"""
Load bicorr_data specified by name. Built for e_bin_edges generated using default settings in bicorr.build_energy_bin_edges().
Parameters
----------
bicorr_path : str, optional
Full path of bicorr file, NOT including filename
bicorr_filenames : str, optional
Filenames
If just one, put in brackets: [filename]
det_df : pandas dataFrame, optional
dataFrame of detector pair indices and angles
Default is to look for the file in '../meas_info/det_df_pairs_angles.csv'
dt_bin_edges : ndarray, optional
Edges of time bin array in ns
If None, use default settings from build_dt_bin_edges()
dict_det_dist : dict
Dictionary of detector channel number : distance from fc
Default is to look for the file in '../meas_info/detector_distances.xlsx'
e_bin_edges : ndarray, optional
Edges of energy bin array in MeV
If None, use default settings from build_energy_bin_edges()
checkpoint_flag : bool, optional
Generate checkpoint plots?
save_flag : bool, optional
Save sparse matrix to disk?
sparse_filename : str, optional
Filename for sparse matrix
bhm_e_filename : str, optional
Filename for bhm_e array
disable_tqdm : bool, optional
Flag to disable tqdm progress bar
print_flag : bool, optional
Print status updates along the way?
return_flag : bool, optional
Option to return bhm_e and e_bin_edges. Otherwise, return nothing.
Returns
-------
bhm : ndarray
Master histogram of bicorrelation histograms across all detector pairs and interaction types.
Dimension 0: detector pair, use dictionary `dict_pair_to_index` where pair is (100*det1ch+det2ch)
Dimension 1: interaction type, length 4. (0=nn, 1=np, 2=pn, 3=pp)
Dimension 2: dt bin for detector 1
Dimension 3: dt bin for detector 2
dt_bin_edges : ndarray
One-dimensional array of time bin edges
bhm_e : ndarray
Master histogram of bicorrelation events in energy space.
Dimension 0: detector pair, use dictionary 'dict_pair_to_index', where pair is (100*det1ch+det2ch)
Dimension 1: interaction type, length 1. Only storing 0=nn.
Dimension 2: e bin for detector 1
Dimension 3: e bin for detector 2
e_bin_edges : ndarray
One-dimensional array of energy bin edges
"""
# Load det_df, dict_det_dist if not provided
if det_df is None: det_df = bicorr.load_det_df()
if dict_det_dist is None: dict_det_dist = bicorr.build_dict_det_dist()
# Handle dt_bin_edges
if dt_bin_edges is None: # Use default settings
dt_bin_edges, num_dt_bins = bicorr.build_dt_bin_edges(dt_min=0)
else:
num_dt_bins = len(dt_bin_edges) - 1
# Handle e_bin_edges
if e_bin_edges is None: # Use default settings
e_bin_edges, num_e_bins = bicorr_e.build_energy_bin_edges()
else:
num_e_bins = len(e_bin_edges) - 1
# Set up binning
num_det_pairs = len(det_df)
# Create bhm, empty- all event types
bhm = bicorr.alloc_bhm(num_det_pairs, 4, num_dt_bins)
# Create bhm_e, empty- nn only
bhm_e = bicorr_e.alloc_bhm_e(num_det_pairs, 1, num_e_bins)
for bicorr_filename in bicorr_filenames:
# Set up bicorr file full path
bicorr_path_full = os.path.join(bicorr_path, bicorr_filename)
# File to process (not going through folders)
if print_flag:
print('Generating bicorr histograms for bicorr data in file: ',
bicorr_path_full)
# Load files, fill the histograms
if print_flag: print('Loading data in file ', bicorr_path_full)
bicorr_data = bicorr.load_bicorr(bicorr_path=bicorr_path_full)
if checkpoint_flag:
fig_folder = os.path.join(bicorr_path, 'fig')
bicorr_plot.bicorr_checkpoint_plots(bicorr_data,
fig_folder=fig_folder,
show_flag=False)
if print_flag: print('Building bhms in ', bicorr_path)
bhm = bicorr.fill_bhm(bhm,
bicorr_data,
det_df,
dt_bin_edges,
disable_tqdm=disable_tqdm)
bhm_e = bicorr_e.fill_bhm_e(bhm_e,
bicorr_data,
det_df,
dict_det_dist,
e_bin_edges,
disable_tqdm=False)
if save_flag:
# bhm: Generate sparse matrix
if print_flag: print('Generating sparse matrix')
sparse_bhm = bicorr.generate_sparse_bhm(bhm, disable_tqdm=disable_tqdm)
if print_flag: print('Saving sparse matrix data to .npz file')
bicorr.save_sparse_bhm(sparse_bhm,
dt_bin_edges,
save_folder=bicorr_path)
# bhm_e: store npz
if print_flag: print('Saving bhm_e to .npz file')
note = 'bhm_e generated for file'.format(bicorr_path)
bicorr_e.save_bhm_e(bhm_e,
e_bin_edges,
save_folder=bicorr_path,
note='Original file: ' + bicorr_filename)
if print_flag: print('Bicorr hist master bhm, bhm_e build complete')
if return_flag: return bhm, dt_bin_edges, bhm_e, e_bin_edges
def build_bhm_both(folder_start=1,
folder_end=2,
det_df=None,
dt_bin_edges=None,
dict_det_dist=None,
e_bin_edges=None,
checkpoint_flag=True,
save_flag=True,
sparse_filename='sparse_bhm',
bhm_e_filename='bhm_e',
root_path=None,
disable_tqdm=False,
print_flag=True,
return_flag=False):
"""
Load bicorr_data from folder's bicorr# file and fill energy histogram. Loop through folders specified by `folder_start` and `folder_end`. Built for e_bin_edges generated using default settings in bicorr.build_energy_bin_edges().
Parameters
----------
folder_start : int, optional
First folder
folder_end : int, optional
Last folder + 1 (for example, folder_end = 2 will end at folder 1)
det_df : pandas dataFrame, optional
dataFrame of detector pair indices and angles
Default is to look for the file in '../meas_info/det_df_pairs_angles.csv'
dt_bin_edges : ndarray, optional
Edges of time bin array in ns
If None, use default settings from build_dt_bin_edges()
dict_det_dist : dict
Dictionary of detector channel number : distance from fc
Default is to look for the file in '../meas_info/detector_distances.xlsx'
e_bin_edges : ndarray, optional
Edges of energy bin array in MeV
If None, use default settings from build_energy_bin_edges()
checkpoint_flag : bool, optional
Generate checkpoint plots?
save_flag : bool, optional
Save sparse matrix to disk?
sparse_filename : str, optional
Filename for sparse matrix
bhm_e_filename : str, optional
Filename for bhm_e array
root_path : int, optional
Relative path to folder where data folders exist (1, 2, 3, etc.). default = cwd
disable_tqdm : bool, optional
Flag to disable tqdm progress bar
print_flag : bool, optional
Print status updates along the way?
return_flag : bool, optional
Option to return bhm_e and e_bin_edges. Otherwise, return nothing.
Returns
-------
bhm : ndarray
Master histogram of bicorrelation histograms across all detector pairs and interaction types.
Dimension 0: detector pair, use dictionary `dict_pair_to_index` where pair is (100*det1ch+det2ch)
Dimension 1: interaction type, length 4. (0=nn, 1=np, 2=pn, 3=pp)
Dimension 2: dt bin for detector 1
Dimension 3: dt bin for detector 2
dt_bin_edges : ndarray
One-dimensional array of time bin edges
bhm_e : ndarray
Master histogram of bicorrelation events in energy space.
Dimension 0: detector pair, use dictionary 'dict_pair_to_index', where pair is (100*det1ch+det2ch)
Dimension 1: interaction type, length 1. Only storing 0=nn.
Dimension 2: e bin for detector 1
Dimension 3: e bin for detector 2
e_bin_edges : ndarray
One-dimensional array of energy bin edges
"""
# Load det_df, dict_det_dist if not provided
if det_df is None: det_df = bicorr.load_det_df()
if dict_det_dist is None: dict_det_dist = bicorr.build_dict_det_dist()
# If no data path provided, look for data folders here
if root_path is None: root_path = os.getcwd()
# Folders to run
folders = np.arange(folder_start, folder_end, 1)
if print_flag:
print('Generating bicorr histograms for bicorr data in folders: ',
folders)
# Handle dt_bin_edges
if dt_bin_edges is None: # Use default settings
dt_bin_edges, num_dt_bins = bicorr.build_dt_bin_edges()
else:
num_dt_bins = len(dt_bin_edges) - 1
# Handle e_bin_edges
if e_bin_edges is None: # Use default settings
e_bin_edges, num_e_bins = build_energy_bin_edges()
else:
num_e_bins = len(e_bin_edges) - 1
# Set up binning
num_det_pairs = len(det_df)
# Create bhm, empty- all event types
bhm = bicorr.alloc_bhm(num_det_pairs, 4, num_dt_bins)
# Create bhm_e, empty- nn only
bhm_e = alloc_bhm_e(num_det_pairs, 1, num_e_bins)
# Loop through each folder and fill the histogram
for folder in folders:
if print_flag: print('Loading data in folder ', folder)
bicorr_data = bicorr.load_bicorr(folder, root_path=root_path)
if checkpoint_flag:
fig_folder = os.path.join(root_path + '/' + str(folder) + '/fig')
bicorr_plot.bicorr_checkpoint_plots(bicorr_data,
fig_folder=fig_folder,
show_flag=False)
if print_flag: print('Building bhms in folder ', folder)
bhm = bicorr.fill_bhm(bhm,
bicorr_data,
det_df,
dt_bin_edges,
disable_tqdm=disable_tqdm)
bhm_e = fill_bhm_e(bhm_e,
bicorr_data,
det_df,
dict_det_dist,
e_bin_edges,
disable_tqdm=False)
if save_flag:
# bhm: Generate sparse matrix
if print_flag: print('Generating sparse matrix')
sparse_bhm = bicorr.generate_sparse_bhm(bhm, disable_tqdm=disable_tqdm)
if print_flag: print('Saving sparse matrix data to .npz file')
bicorr.save_sparse_bhm(sparse_bhm,
dt_bin_edges,
save_folder=root_path,
sparse_filename=sparse_filename)
# bhm_e: store npz
if print_flag: print('Saving bhm_e to .npz file')
note = 'bhm_e generated from folder {} to {} in directory {}'.format(
folder_start, folder_end, os.getcwd())
save_bhm_e(bhm_e,
e_bin_edges,
bhm_e_filename=bhm_e_filename,
note=note)
if print_flag: print('Bicorr hist master bhm_e build complete')
if return_flag: return bhm, dt_bin_edges, bhm_e, e_bin_edges