

In [3]:

    
%%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')

Build `pair_is` from `det_df`

I need a function for selecting the detector pairs within a given theta range. I will add the option of including or discluding the detector pairs that include a channel directly next to the fission chamber. (There were triggering issues in these channels)

Patricia Schuster
Jan 2018
UM NERS



In [4]:

    
import pandas as pd
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_palette('spectral')
sns.set_style(style='white')



In [5]:

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



In [6]:

    
%load_ext autoreload
%autoreload 2

Load `det_df`



In [7]:

    
det_df = bicorr.load_det_df(plot_flag=True)



In [8]:

    
det_df.head()

Select pairs within a given angle range



In [9]:

    
th_min = 0
th_max = 20



In [19]:

    
ind_mask = (det_df['angle'] > th_min) & (det_df['angle'] <= th_max)



In [20]:

    
det_df[ind_mask].head()



In [21]:

    
det_df[ind_mask].index.values









    Out[21]:





array([  0,   8,  44,  87, 129, 170, 210, 249, 287, 332, 360, 368, 395,
       429, 462, 494, 525, 555, 584, 620, 639, 665, 690, 714, 737, 759,
       780, 800, 837, 845, 854, 870, 885, 899, 912, 924, 935, 953, 954,
       962, 969, 975, 980, 984, 987, 989], dtype=int64)



In [22]:

    
pair_is = det_df[ind_mask].index.values

Select pairs that are not next to the fission chamber channel



In [69]:

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



In [70]:

    
chIgnore = [1,17,33]

Ignore indices in det_df that include one of these channels.



In [77]:

    
det_df.head()



In [85]:

    
det_df_ig = det_df[~det_df['d1'].isin(chIgnore) & ~det_df['d2'].isin(chIgnore)]
det_df_ig









    Out[85]:






  
    
      
      d1
      d2
      d1d2
      angle
    
  
  
    
      44
      2
      3
      203
      15.000000
    
    
      45
      2
      4
      204
      30.000000
    
    
      46
      2
      5
      205
      45.000000
    
    
      47
      2
      6
      206
      60.000000
    
    
      48
      2
      7
      207
      75.000000
    
    
      49
      2
      8
      208
      90.000000
    
    
      50
      2
      9
      209
      105.000000
    
    
      51
      2
      10
      210
      24.647971
    
    
      52
      2
      11
      211
      23.170902
    
    
      53
      2
      12
      212
      29.872934
    
    
      54
      2
      13
      213
      40.901585
    
    
      55
      2
      14
      214
      53.627703
    
    
      56
      2
      15
      215
      67.057326
    
    
      58
      2
      18
      218
      94.626775
    
    
      59
      2
      19
      219
      108.412595
    
    
      60
      2
      20
      220
      40.871807
    
    
      61
      2
      21
      221
      45.302126
    
    
      62
      2
      22
      222
      52.941675
    
    
      63
      2
      23
      223
      62.560032
    
    
      64
      2
      24
      224
      73.285301
    
    
      65
      2
      25
      225
      84.560560
    
    
      66
      2
      26
      226
      95.997155
    
    
      67
      2
      27
      227
      107.255406
    
    
      68
      2
      28
      228
      117.941468
    
    
      69
      2
      29
      229
      75.000000
    
    
      70
      2
      30
      230
      90.000000
    
    
      71
      2
      31
      231
      105.000000
    
    
      73
      2
      34
      234
      135.000000
    
    
      74
      2
      35
      235
      150.000000
    
    
      75
      2
      36
      236
      165.000000
    
    
      ...
      ...
      ...
      ...
      ...
    
    
      960
      39
      46
      3946
      105.000000
    
    
      961
      39
      47
      3947
      120.000000
    
    
      962
      40
      41
      4041
      15.000000
    
    
      963
      40
      42
      4042
      30.000000
    
    
      964
      40
      43
      4043
      45.000000
    
    
      965
      40
      44
      4044
      60.000000
    
    
      966
      40
      45
      4045
      75.000000
    
    
      967
      40
      46
      4046
      90.000000
    
    
      968
      40
      47
      4047
      105.000000
    
    
      969
      41
      42
      4142
      15.000000
    
    
      970
      41
      43
      4143
      30.000000
    
    
      971
      41
      44
      4144
      45.000000
    
    
      972
      41
      45
      4145
      60.000000
    
    
      973
      41
      46
      4146
      75.000000
    
    
      974
      41
      47
      4147
      90.000000
    
    
      975
      42
      43
      4243
      15.000000
    
    
      976
      42
      44
      4244
      30.000000
    
    
      977
      42
      45
      4245
      45.000000
    
    
      978
      42
      46
      4246
      60.000000
    
    
      979
      42
      47
      4247
      75.000000
    
    
      980
      43
      44
      4344
      15.000000
    
    
      981
      43
      45
      4345
      30.000000
    
    
      982
      43
      46
      4346
      45.000000
    
    
      983
      43
      47
      4347
      60.000000
    
    
      984
      44
      45
      4445
      15.000000
    
    
      985
      44
      46
      4446
      30.000000
    
    
      986
      44
      47
      4447
      45.000000
    
    
      987
      45
      46
      4546
      15.000000
    
    
      988
      45
      47
      4547
      30.000000
    
    
      989
      46
      47
      4647
      15.000000
    
  

861 rows × 4 columns

Make sure that we don't have any of those channels.



In [86]:

    
plt.plot(det_df_ig['d1'],det_df_ig['d2'],'.k')
plt.axvline( 1); plt.axhline( 1)
plt.axvline(17); plt.axhline(17)
plt.axvline(33); plt.axhline(33)
plt.xlabel('d1'); plt.ylabel('d2'); plt.title('Channels in det_df_ig')
plt.show()

Combine those options to `bicorr.generate_pair_is`



In [87]:

    
import inspect



In [88]:

    
print(inspect.getsource(bicorr.generate_pair_is))









    



def generate_pair_is(det_df, th_min = None, th_max = None, i_bin = None, ignore_fc_neighbors_flag = False):
    """
    Generate list of indices of pairs within a given angle range (th_min,th_max] for bicorr_hist_master.
    
    Parameters
    ----------
    det_df : pandas dataFrame
        dataFrame of detector pair indices and angles 
    th_min : int, optional
        Exclusive lower limit (th > th_min)
    th_max : int, optional
        Inclusive upper limit (th <= th_max)  
    i_bin : int, optional
        Index of desired bin in det_df['bin']
    ignore_fc_neighbors_flag : bool, optional
        Whether to ignore channels next to fission chamber [1,17,33]
    
    Return
    ------
    pair_is : list
        Indices of detector pairs in range in bicorr_hist_master
    """
    
    # What are the conditions?    
    by_th_flag = np.logical_and(th_min is not None,th_max is not None) # True: by th range. False: by bins
    by_bin_flag = (i_bin is not None) # True: by bin range. False: by th range
    
    # If you want to ignore the channels next to the fission chamber [1,17,33], remove those lines from det_df
    if ignore_fc_neighbors_flag:
        chIgnore = [1,17,33]
        det_df = det_df[~det_df['d1'].isin(chIgnore) & ~det_df['d2'].isin(chIgnore)]
    
    if by_th_flag:
        ind_mask = (det_df['angle'] > th_min) & (det_df['angle'] <= th_max)
        pair_is = det_df.index[ind_mask].values
    elif by_bin_flag:
        pair_is = det_df[det_df['bin']==i_bin].index.values
    else: # Select all pairs, no constraints
        pair_is = det_df.index.values
            
    return pair_is



In [89]:

    
bicorr.generate_pair_is(det_df,0,20)









    Out[89]:





array([  0,   8,  44,  87, 129, 170, 210, 249, 287, 332, 360, 368, 395,
       429, 462, 494, 525, 555, 584, 620, 639, 665, 690, 714, 737, 759,
       780, 800, 837, 845, 854, 870, 885, 899, 912, 924, 935, 953, 954,
       962, 969, 975, 980, 984, 987, 989], dtype=int64)



In [90]:

    
det_df.iloc[bicorr.generate_pair_is(det_df,0,20)]



In [91]:

    
det_df.iloc[bicorr.generate_pair_is(det_df,0,20,ignore_fc_neighbors_flag=True)]



In [ ]:

	d1	d2	d1d2	angle
0	1	2	102	15.000000
8	1	10	110	16.328077
44	2	3	203	15.000000
87	3	4	304	15.000000
129	4	5	405	15.000000

	d1	d2	d1d2	angle
44	2	3	203	15.000000
45	2	4	204	30.000000
46	2	5	205	45.000000
47	2	6	206	60.000000
48	2	7	207	75.000000
49	2	8	208	90.000000
50	2	9	209	105.000000
51	2	10	210	24.647971
52	2	11	211	23.170902
53	2	12	212	29.872934
54	2	13	213	40.901585
55	2	14	214	53.627703
56	2	15	215	67.057326
58	2	18	218	94.626775
59	2	19	219	108.412595
60	2	20	220	40.871807
61	2	21	221	45.302126
62	2	22	222	52.941675
63	2	23	223	62.560032
64	2	24	224	73.285301
65	2	25	225	84.560560
66	2	26	226	95.997155
67	2	27	227	107.255406
68	2	28	228	117.941468
69	2	29	229	75.000000
70	2	30	230	90.000000
71	2	31	231	105.000000
73	2	34	234	135.000000
74	2	35	235	150.000000
75	2	36	236	165.000000
...	...	...	...	...
960	39	46	3946	105.000000
961	39	47	3947	120.000000
962	40	41	4041	15.000000
963	40	42	4042	30.000000
964	40	43	4043	45.000000
965	40	44	4044	60.000000
966	40	45	4045	75.000000
967	40	46	4046	90.000000
968	40	47	4047	105.000000
969	41	42	4142	15.000000
970	41	43	4143	30.000000
971	41	44	4144	45.000000
972	41	45	4145	60.000000
973	41	46	4146	75.000000
974	41	47	4147	90.000000
975	42	43	4243	15.000000
976	42	44	4244	30.000000
977	42	45	4245	45.000000
978	42	46	4246	60.000000
979	42	47	4247	75.000000
980	43	44	4344	15.000000
981	43	45	4345	30.000000
982	43	46	4346	45.000000
983	43	47	4347	60.000000
984	44	45	4445	15.000000
985	44	46	4446	30.000000
986	44	47	4447	45.000000
987	45	46	4546	15.000000
988	45	47	4547	30.000000
989	46	47	4647	15.000000

Table of Contents

Build pair_is from det_df

Load det_df

Select pairs within a given angle range

Select pairs that are not next to the fission chamber channel

Combine those options to bicorr.generate_pair_is

Build `pair_is` from `det_df`

Load `det_df`

Combine those options to `bicorr.generate_pair_is`