In [1]:
import os
import sys
import glob
root_folder = os.path.dirname(os.getcwd())
sys.path.append(root_folder)
import pprint
import shutil
from ResoFit._utilities import Layer
from ResoFit.calibration import Calibration
from ResoFit.fitresonance import FitResonance

In [2]:
# Global parameters
energy_min = 7
energy_max = 250
energy_step = 0.01

In [3]:
# Input sample name or names as str, case sensitive
layer_1 = 'U'
thickness_1 = 0.085
density_1 = None
layer_2 = 'Gd'
thickness_2 = 0.085
density_2 = None
layer = Layer()
layer.add_layer(layer=layer_1, thickness_mm=thickness_1, density_gcm3=density_1)
layer.add_layer(layer=layer_2, thickness_mm=thickness_2, density_gcm3=density_2)

In [4]:
folder = 'data/IPTS_19558/reso_data_19558'
data_file = 'spheres.csv'
spectra_file = 'Image002_Spectra.txt'
image_start = None  # Can be omitted or =None
image_end = None  # Can be omitted or =None
norm_to_file = None  # 'sphere_background_1.csv'
baseline = True
each_step = False
before = False
table = True
grid = True
peak = 'indexed'
items_to_plot = None

repeat = 1
source_to_detector_m = 16.  # 16#16.445359069030175#16.447496101100739
offset_us = 0  # 0#2.7120797253959119#2.7355447625559037

In [10]:
# Calibrate source_to_detector and/or delay
calibration = Calibration(data_file=data_file,
                          spectra_file=spectra_file,
                          layer=layer,
                          energy_min=energy_min,
                          energy_max=energy_max,
                          energy_step=energy_step,
                          folder=folder,
                          baseline=baseline)

calibration.experiment.norm_to(norm_to_file)
calibration.experiment.slice(start=300, end=image_end)

calibrate_result = calibration.calibrate(source_to_detector_m=source_to_detector_m,
                                         offset_us=offset_us,
                                         vary='all',
                                         each_step=each_step)
# calibration.find_peak()
calibration.index_peak(thres=0.13, min_dist=21)


+----------------- Calibration -----------------+
Params before:
Name                     Value      Min      Max   Stderr     Vary     Expr Brute_Step
offset_us                    0     -inf      inf     None     True     None     None
source_to_detector_m        16     -inf      inf     None     True     None     None

Params after:
Name                     Value      Min      Max   Stderr     Vary     Expr Brute_Step
offset_us                2.705     -inf      inf  0.02859     True     None     None
source_to_detector_m     16.43     -inf      inf 0.003151     True     None     None
Calibration chi^2 : 159.5384091064498

/Users/Shawn/anaconda3/envs/py36/lib/python3.6/site-packages/peakutils/peak.py:221: UserWarning:

At least 3 points required for Gaussian fitting

Out[10]:
{'230-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '231-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '232-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '233-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '234-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '235-U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  102.627980  0.104566    410      710  0.000115  0.028230,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  209.621265  0.000415    191      491  0.000080  0.019753
  1  189.521432  0.000339    216      516  0.000084  0.020774
  2  102.921022  0.000344    410      710  0.000115  0.028190}, '236-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [],
  'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '237-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '238-U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  116.833546  0.049514    364      664  0.000107  0.026458
  3  102.627980  0.104566    410      710  0.000115  0.028230
  4   66.002401  0.112798    591      891  0.000144  0.035202
  5   36.681655  0.248735    903     1203  0.000193  0.047219
  6   20.878876  0.277571   1302     1602  0.000257  0.062588,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.537399  0.485987    191      491  0.000080  0.019804
  1  189.691072  0.877455    216      516  0.000084  0.020764
  2  116.898955  0.558420    364      664  0.000107  0.026451
  3  102.564215  0.913330    410      710  0.000115  0.028239
  4   66.034811  0.828329    591      891  0.000144  0.035193
  5   36.685441  0.995554    903     1203  0.000193  0.047217
  6   20.873321  0.981611   1302     1602  0.000257  0.062596}, '239-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [],
  'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '240-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '241-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, 'U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  116.833546  0.049514    364      664  0.000107  0.026458
  3  102.627980  0.104566    410      710  0.000115  0.028230
  4   66.002401  0.112798    591      891  0.000144  0.035202
  5   36.681655  0.248735    903     1203  0.000193  0.047219
  6   20.878876  0.277571   1302     1602  0.000257  0.062588,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.537399  0.486009    191      491  0.000080  0.019804
  1  189.691052  0.877488    216      516  0.000084  0.020764
  2  116.898954  0.558445    364      664  0.000107  0.026451
  3  102.564222  0.913337    410      710  0.000115  0.028239
  4   66.034809  0.828341    591      891  0.000144  0.035193
  5   36.685441  0.995554    903     1203  0.000193  0.047217
  6   20.873333  0.981616   1302     1602  0.000257  0.062596}, '152-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  36.681655  0.248735    903     1203  0.000193  0.047219,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  36.861229  0.011505    903     1203  0.000193  0.047104}, '153-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [],
  'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '154-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  22.367439  0.034954   1247     1547  0.000248  0.060469,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  22.330643  0.039709   1247     1547  0.000248  0.060519}, '155-Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  116.833546  0.049514    364      664  0.000107  0.026458,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  116.504685  0.014208    364      664  0.000107  0.026495}, '156-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [],
  'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, '157-Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2   16.754604  0.038395   1491     1791  0.000288  0.069867,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.514213  0.059186    191      491  0.000080  0.019805
  1  190.590535  0.012611    216      516  0.000084  0.020715
  2   16.770348  0.242009   1491     1791  0.000288  0.069835}, '158-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  22.367439  0.034954   1247     1547  0.000248  0.060469,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  22.300856  0.220061   1247     1547  0.000248  0.060559}, '160-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [],
  'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: []}, 'Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1   22.367439  0.034954   1247     1547  0.000248  0.060469
  2   16.754604  0.038395   1491     1791  0.000288  0.069867,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.513989  0.063119    191      491  0.000080  0.019805
  1   22.304966  0.252113   1247     1547  0.000248  0.060554
  2   16.770351  0.243168   1491     1791  0.000288  0.069835}}

In [11]:
calibration.analyze_peak()


/Users/Shawn/anaconda3/envs/py36/lib/python3.6/site-packages/lmfit/models.py:30: FutureWarning:


The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'
instead.
The behavior of 'argmin' will be corrected to return the positional
minimum in the future. For now, use 'series.values.argmin' or
'np.argmin(np.array(values))' to get the position of the minimum
row.

230-U
231-U
232-U
233-U
234-U
235-U
236-U
237-U
238-U
239-U
240-U
241-U
U
152-Gd
153-Gd
154-Gd
155-Gd
156-Gd
157-Gd
158-Gd
160-Gd
Gd
+------------ Peak analysis ------------+
Lorentzian peak fitting:
  ele_name   center_val   fwhm_val
0       Gd   200.438720  61.623589
1        U   200.555419  61.626143
2        U   206.001475   2.619626
3        U   371.404339  44.410071
4        U   417.551973  29.919237
5        U   583.616512  18.535030
6        U   912.901433  17.064536
7       Gd  1237.000000  99.977987
8        U  1308.342215   6.558220
9       Gd  1500.999999  16.095270

Out[11]:
{'230-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '231-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '232-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '233-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '234-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '235-U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  102.627980  0.104566    410      710  0.000115  0.028230,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  209.621265  0.000415    191      491  0.000080  0.019753
  1  189.521432  0.000339    216      516  0.000084  0.020774
  2  102.921022  0.000344    410      710  0.000115  0.028190,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '236-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '237-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '238-U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  116.833546  0.049514    364      664  0.000107  0.026458
  3  102.627980  0.104566    410      710  0.000115  0.028230
  4   66.002401  0.112798    591      891  0.000144  0.035202
  5   36.681655  0.248735    903     1203  0.000193  0.047219
  6   20.878876  0.277571   1302     1602  0.000257  0.062588,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.537399  0.485987    191      491  0.000080  0.019804
  1  189.691072  0.877455    216      516  0.000084  0.020764
  2  116.898955  0.558420    364      664  0.000107  0.026451
  3  102.564215  0.913330    410      710  0.000115  0.028239
  4   66.034811  0.828329    591      891  0.000144  0.035193
  5   36.685441  0.995554    903     1203  0.000193  0.047217
  6   20.873321  0.981611   1302     1602  0.000257  0.062596,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '239-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '240-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '241-U': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, 'U': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2  116.833546  0.049514    364      664  0.000107  0.026458
  3  102.627980  0.104566    410      710  0.000115  0.028230
  4   66.002401  0.112798    591      891  0.000144  0.035202
  5   36.681655  0.248735    903     1203  0.000193  0.047219
  6   20.878876  0.277571   1302     1602  0.000257  0.062588,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.537399  0.486009    191      491  0.000080  0.019804
  1  189.691052  0.877488    216      516  0.000084  0.020764
  2  116.898954  0.558445    364      664  0.000107  0.026451
  3  102.564222  0.913337    410      710  0.000115  0.028239
  4   66.034809  0.828341    591      891  0.000144  0.035193
  5   36.685441  0.995554    903     1203  0.000193  0.047217
  6   20.873333  0.981616   1302     1602  0.000257  0.062596,
  'peak_span':      img_num  time_s  energy_ev         y
  0        469     NaN        NaN  0.012684
  1        470     NaN        NaN  0.004289
  2        471     NaN        NaN  0.001291
  3        472     NaN        NaN  0.008873
  4        473     NaN        NaN  0.006188
  5        474     NaN        NaN  0.005130
  6        475     NaN        NaN  0.010383
  7        476     NaN        NaN  0.009706
  8        477     NaN        NaN  0.003763
  9        478     NaN        NaN  0.003917
  10       479     NaN        NaN  0.007447
  11       480     NaN        NaN  0.004197
  12       481     NaN        NaN  0.006877
  13       482     NaN        NaN  0.005485
  14       483     NaN        NaN  0.005972
  15       484     NaN        NaN  0.003941
  16       485     NaN        NaN  0.004490
  17       486     NaN        NaN  0.008827
  18       487     NaN        NaN  0.011401
  19       488     NaN        NaN  0.007508
  20       489     NaN        NaN  0.009062
  21       490     NaN        NaN  0.018400
  22       491     NaN        NaN  0.030893
  23       492     NaN        NaN  0.022086
  24       493     NaN        NaN  0.019742
  25       494     NaN        NaN  0.013376
  26       495     NaN        NaN  0.009352
  27       496     NaN        NaN  0.005296
  28       497     NaN        NaN  0.009517
  29       498     NaN        NaN  0.004586
  ..       ...     ...        ...       ...
  164      891     NaN        NaN  0.112798
  165      892     NaN        NaN  0.092392
  166      893     NaN        NaN  0.065829
  167     1204     NaN        NaN  0.240044
  168     1205     NaN        NaN  0.210111
  169     1206     NaN        NaN  0.168009
  170     1207     NaN        NaN  0.132390
  171     1208     NaN        NaN  0.098721
  172     1209     NaN        NaN  0.076901
  173     1210     NaN        NaN  0.056926
  174     1211     NaN        NaN  0.041041
  175     1212     NaN        NaN  0.032215
  176     1213     NaN        NaN  0.032299
  177     1214     NaN        NaN  0.026595
  178     1215     NaN        NaN  0.019350
  179     1216     NaN        NaN  0.017041
  180     1217     NaN        NaN  0.009515
  181     1218     NaN        NaN  0.015088
  182     1219     NaN        NaN  0.009389
  183     1220     NaN        NaN  0.012895
  184     1221     NaN        NaN  0.007194
  185     1222     NaN        NaN  0.012612
  186     1605     NaN        NaN  0.250244
  187     1606     NaN        NaN  0.227217
  188     1607     NaN        NaN  0.196309
  189     1608     NaN        NaN  0.166811
  190     1609     NaN        NaN  0.141601
  191     1610     NaN        NaN  0.113972
  192     1611     NaN        NaN  0.097309
  193     1612     NaN        NaN  0.082321
  
  [194 rows x 4 columns]}, '152-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  36.681655  0.248735    903     1203  0.000193  0.047219,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  36.861229  0.011505    903     1203  0.000193  0.047104,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '153-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '154-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  22.367439  0.034954   1247     1547  0.000248  0.060469,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  22.330643  0.039709   1247     1547  0.000248  0.060519,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '155-Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  116.833546  0.049514    364      664  0.000107  0.026458,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  116.504685  0.014208    364      664  0.000107  0.026495,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '156-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '157-Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1  189.817157  0.063543    216      516  0.000084  0.020757
  2   16.754604  0.038395   1491     1791  0.000288  0.069867,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.514213  0.059186    191      491  0.000080  0.019805
  1  190.590535  0.012611    216      516  0.000084  0.020715
  2   16.770348  0.242009   1491     1791  0.000288  0.069835,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '158-Gd': {'exp':            x         y  x_num  x_num_o       x_s       x_A
  0  22.367439  0.034954   1247     1547  0.000248  0.060469,
  'ideal':            x         y  x_num  x_num_o       x_s       x_A
  0  22.300856  0.220061   1247     1547  0.000248  0.060559,
  'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, '160-Gd': {'exp': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'ideal': Empty DataFrame
  Columns: [x, y, x_num, x_num_o, x_s, x_A]
  Index: [], 'peak_span': Empty DataFrame
  Columns: [img_num, time_s, energy_ev, y]
  Index: []}, 'Gd': {'exp':             x         y  x_num  x_num_o       x_s       x_A
  0  208.734373  0.030893    191      491  0.000080  0.019795
  1   22.367439  0.034954   1247     1547  0.000248  0.060469
  2   16.754604  0.038395   1491     1791  0.000288  0.069867,
  'ideal':             x         y  x_num  x_num_o       x_s       x_A
  0  208.513989  0.063119    191      491  0.000080  0.019805
  1   22.304966  0.252113   1247     1547  0.000248  0.060554
  2   16.770351  0.243168   1491     1791  0.000288  0.069835,
  'peak_span':      img_num  time_s  energy_ev         y
  0        469     NaN        NaN  0.012684
  1        470     NaN        NaN  0.004289
  2        471     NaN        NaN  0.001291
  3        472     NaN        NaN  0.008873
  4        473     NaN        NaN  0.006188
  5        474     NaN        NaN  0.005130
  6        475     NaN        NaN  0.010383
  7        476     NaN        NaN  0.009706
  8        477     NaN        NaN  0.003763
  9        478     NaN        NaN  0.003917
  10       479     NaN        NaN  0.007447
  11       480     NaN        NaN  0.004197
  12       481     NaN        NaN  0.006877
  13       482     NaN        NaN  0.005485
  14       483     NaN        NaN  0.005972
  15       484     NaN        NaN  0.003941
  16       485     NaN        NaN  0.004490
  17       486     NaN        NaN  0.008827
  18       487     NaN        NaN  0.011401
  19       488     NaN        NaN  0.007508
  20       489     NaN        NaN  0.009062
  21       490     NaN        NaN  0.018400
  22       491     NaN        NaN  0.030893
  23       492     NaN        NaN  0.022086
  24       493     NaN        NaN  0.019742
  25       494     NaN        NaN  0.013376
  26       495     NaN        NaN  0.009352
  27       496     NaN        NaN  0.005296
  28       497     NaN        NaN  0.009517
  29       498     NaN        NaN  0.004586
  ..       ...     ...        ...       ...
  154     1577     NaN        NaN  0.015922
  155     1578     NaN        NaN  0.008554
  156     1579     NaN        NaN  0.014686
  157     1580     NaN        NaN  0.012511
  158     1581     NaN        NaN  0.010052
  159     1582     NaN        NaN  0.015927
  160     1583     NaN        NaN  0.011623
  161     1584     NaN        NaN  0.020440
  162     1585     NaN        NaN  0.020760
  163     1586     NaN        NaN  0.021391
  164     1587     NaN        NaN  0.014890
  165     1792     NaN        NaN  0.036879
  166     1793     NaN        NaN  0.022115
  167     1794     NaN        NaN  0.025880
  168     1795     NaN        NaN  0.024397
  169     1796     NaN        NaN  0.025111
  170     1797     NaN        NaN  0.019267
  171     1798     NaN        NaN  0.021771
  172     1799     NaN        NaN  0.017297
  173     1800     NaN        NaN  0.020138
  174     1801     NaN        NaN  0.016521
  175     1802     NaN        NaN  0.009995
  176     1803     NaN        NaN  0.011747
  177     1804     NaN        NaN  0.012940
  178     1805     NaN        NaN  0.007316
  179     1806     NaN        NaN  0.008670
  180     1807     NaN        NaN  0.009237
  181     1808     NaN        NaN -0.001981
  182     1809     NaN        NaN  0.015731
  183     1810     NaN        NaN  0.009290
  
  [184 rows x 4 columns]}}

In [23]:
calibration.plot(before=before,
                 table=table,
                 peak_exp=peak,
                 grid=grid,
                 index_level='ele',
                 peak_height=False,
                 interp=False,)


/Users/Shawn/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/tight_layout.py:198: UserWarning:

tight_layout cannot make axes width small enough to accommodate all axes decorations

/Users/Shawn/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/tight_layout.py:209: UserWarning:

tight_layout cannot make axes height small enough to accommodate all axes decorations

Out[23]:
<matplotlib.axes._subplots.AxesSubplot at 0x114813b38>

In [17]:
calibration.experiment.o_peak.peak_map_indexed[layer_1]['peak_span']


Out[17]:
img_num time_s energy_ev y
0 469 NaN NaN 0.012684
1 470 NaN NaN 0.004289
2 471 NaN NaN 0.001291
3 472 NaN NaN 0.008873
4 473 NaN NaN 0.006188
5 474 NaN NaN 0.005130
6 475 NaN NaN 0.010383
7 476 NaN NaN 0.009706
8 477 NaN NaN 0.003763
9 478 NaN NaN 0.003917
10 479 NaN NaN 0.007447
11 480 NaN NaN 0.004197
12 481 NaN NaN 0.006877
13 482 NaN NaN 0.005485
14 483 NaN NaN 0.005972
15 484 NaN NaN 0.003941
16 485 NaN NaN 0.004490
17 486 NaN NaN 0.008827
18 487 NaN NaN 0.011401
19 488 NaN NaN 0.007508
20 489 NaN NaN 0.009062
21 490 NaN NaN 0.018400
22 491 NaN NaN 0.030893
23 492 NaN NaN 0.022086
24 493 NaN NaN 0.019742
25 494 NaN NaN 0.013376
26 495 NaN NaN 0.009352
27 496 NaN NaN 0.005296
28 497 NaN NaN 0.009517
29 498 NaN NaN 0.004586
... ... ... ... ...
164 891 NaN NaN 0.112798
165 892 NaN NaN 0.092392
166 893 NaN NaN 0.065829
167 1204 NaN NaN 0.240044
168 1205 NaN NaN 0.210111
169 1206 NaN NaN 0.168009
170 1207 NaN NaN 0.132390
171 1208 NaN NaN 0.098721
172 1209 NaN NaN 0.076901
173 1210 NaN NaN 0.056926
174 1211 NaN NaN 0.041041
175 1212 NaN NaN 0.032215
176 1213 NaN NaN 0.032299
177 1214 NaN NaN 0.026595
178 1215 NaN NaN 0.019350
179 1216 NaN NaN 0.017041
180 1217 NaN NaN 0.009515
181 1218 NaN NaN 0.015088
182 1219 NaN NaN 0.009389
183 1220 NaN NaN 0.012895
184 1221 NaN NaN 0.007194
185 1222 NaN NaN 0.012612
186 1605 NaN NaN 0.250244
187 1606 NaN NaN 0.227217
188 1607 NaN NaN 0.196309
189 1608 NaN NaN 0.166811
190 1609 NaN NaN 0.141601
191 1610 NaN NaN 0.113972
192 1611 NaN NaN 0.097309
193 1612 NaN NaN 0.082321

194 rows × 4 columns


In [18]:
calibration.experiment.o_peak.peak_map_indexed[layer_2]['peak_span']


Out[18]:
img_num time_s energy_ev y
0 469 NaN NaN 0.012684
1 470 NaN NaN 0.004289
2 471 NaN NaN 0.001291
3 472 NaN NaN 0.008873
4 473 NaN NaN 0.006188
5 474 NaN NaN 0.005130
6 475 NaN NaN 0.010383
7 476 NaN NaN 0.009706
8 477 NaN NaN 0.003763
9 478 NaN NaN 0.003917
10 479 NaN NaN 0.007447
11 480 NaN NaN 0.004197
12 481 NaN NaN 0.006877
13 482 NaN NaN 0.005485
14 483 NaN NaN 0.005972
15 484 NaN NaN 0.003941
16 485 NaN NaN 0.004490
17 486 NaN NaN 0.008827
18 487 NaN NaN 0.011401
19 488 NaN NaN 0.007508
20 489 NaN NaN 0.009062
21 490 NaN NaN 0.018400
22 491 NaN NaN 0.030893
23 492 NaN NaN 0.022086
24 493 NaN NaN 0.019742
25 494 NaN NaN 0.013376
26 495 NaN NaN 0.009352
27 496 NaN NaN 0.005296
28 497 NaN NaN 0.009517
29 498 NaN NaN 0.004586
... ... ... ... ...
154 1577 NaN NaN 0.015922
155 1578 NaN NaN 0.008554
156 1579 NaN NaN 0.014686
157 1580 NaN NaN 0.012511
158 1581 NaN NaN 0.010052
159 1582 NaN NaN 0.015927
160 1583 NaN NaN 0.011623
161 1584 NaN NaN 0.020440
162 1585 NaN NaN 0.020760
163 1586 NaN NaN 0.021391
164 1587 NaN NaN 0.014890
165 1792 NaN NaN 0.036879
166 1793 NaN NaN 0.022115
167 1794 NaN NaN 0.025880
168 1795 NaN NaN 0.024397
169 1796 NaN NaN 0.025111
170 1797 NaN NaN 0.019267
171 1798 NaN NaN 0.021771
172 1799 NaN NaN 0.017297
173 1800 NaN NaN 0.020138
174 1801 NaN NaN 0.016521
175 1802 NaN NaN 0.009995
176 1803 NaN NaN 0.011747
177 1804 NaN NaN 0.012940
178 1805 NaN NaN 0.007316
179 1806 NaN NaN 0.008670
180 1807 NaN NaN 0.009237
181 1808 NaN NaN -0.001981
182 1809 NaN NaN 0.015731
183 1810 NaN NaN 0.009290

184 rows × 4 columns


In [19]:
def fill_zeros_to_num_str(num_str, tot_digits=5):
    while len(num_str)<tot_digits:
        num_str = '0' + num_str
    return num_str

In [24]:
# fill '0' to match digits of nume surffix
peak_map_indexed = calibration.experiment.o_peak.peak_map_indexed
img_num_str_dict = {}
for each_ele in peak_map_indexed.keys():
    img_num_str_dict[each_ele] = {}
    img_num = list(peak_map_indexed[each_ele]['peak_span']['img_num'])
    img_num_str_dict[each_ele]['num'] = [fill_zeros_to_num_str(num_str) for num_str in list(map(str, img_num))]
    img_num_str_dict[each_ele]['len'] = len(img_num_str_dict[each_ele]['num'])
pprint.pprint(img_num_str_dict)


{'152-Gd': {'len': 0, 'num': []},
 '153-Gd': {'len': 0, 'num': []},
 '154-Gd': {'len': 0, 'num': []},
 '155-Gd': {'len': 0, 'num': []},
 '156-Gd': {'len': 0, 'num': []},
 '157-Gd': {'len': 0, 'num': []},
 '158-Gd': {'len': 0, 'num': []},
 '160-Gd': {'len': 0, 'num': []},
 '230-U': {'len': 0, 'num': []},
 '231-U': {'len': 0, 'num': []},
 '232-U': {'len': 0, 'num': []},
 '233-U': {'len': 0, 'num': []},
 '234-U': {'len': 0, 'num': []},
 '235-U': {'len': 0, 'num': []},
 '236-U': {'len': 0, 'num': []},
 '237-U': {'len': 0, 'num': []},
 '238-U': {'len': 0, 'num': []},
 '239-U': {'len': 0, 'num': []},
 '240-U': {'len': 0, 'num': []},
 '241-U': {'len': 0, 'num': []},
 'Gd': {'len': 184,
        'num': ['00469',
                '00470',
                '00471',
                '00472',
                '00473',
                '00474',
                '00475',
                '00476',
                '00477',
                '00478',
                '00479',
                '00480',
                '00481',
                '00482',
                '00483',
                '00484',
                '00485',
                '00486',
                '00487',
                '00488',
                '00489',
                '00490',
                '00491',
                '00492',
                '00493',
                '00494',
                '00495',
                '00496',
                '00497',
                '00498',
                '00499',
                '00500',
                '00501',
                '00502',
                '00503',
                '00504',
                '00505',
                '00506',
                '00507',
                '00508',
                '00509',
                '00510',
                '00511',
                '00512',
                '00513',
                '00514',
                '00515',
                '00516',
                '00517',
                '00518',
                '00519',
                '00520',
                '00521',
                '00522',
                '00523',
                '00524',
                '00525',
                '00526',
                '00527',
                '00528',
                '00529',
                '00530',
                '00531',
                '00532',
                '01487',
                '01488',
                '01489',
                '01490',
                '01491',
                '01492',
                '01493',
                '01494',
                '01495',
                '01496',
                '01497',
                '01498',
                '01499',
                '01500',
                '01501',
                '01502',
                '01503',
                '01504',
                '01505',
                '01506',
                '01507',
                '01508',
                '01509',
                '01510',
                '01511',
                '01512',
                '01513',
                '01514',
                '01515',
                '01516',
                '01517',
                '01518',
                '01519',
                '01520',
                '01521',
                '01522',
                '01523',
                '01524',
                '01525',
                '01526',
                '01527',
                '01528',
                '01529',
                '01530',
                '01531',
                '01532',
                '01533',
                '01534',
                '01535',
                '01536',
                '01537',
                '01538',
                '01539',
                '01540',
                '01541',
                '01542',
                '01543',
                '01544',
                '01545',
                '01546',
                '01547',
                '01548',
                '01549',
                '01550',
                '01551',
                '01552',
                '01553',
                '01554',
                '01555',
                '01556',
                '01557',
                '01558',
                '01559',
                '01560',
                '01561',
                '01562',
                '01563',
                '01564',
                '01565',
                '01566',
                '01567',
                '01568',
                '01569',
                '01570',
                '01571',
                '01572',
                '01573',
                '01574',
                '01575',
                '01576',
                '01577',
                '01578',
                '01579',
                '01580',
                '01581',
                '01582',
                '01583',
                '01584',
                '01585',
                '01586',
                '01587',
                '01792',
                '01793',
                '01794',
                '01795',
                '01796',
                '01797',
                '01798',
                '01799',
                '01800',
                '01801',
                '01802',
                '01803',
                '01804',
                '01805',
                '01806',
                '01807',
                '01808',
                '01809',
                '01810']},
 'U': {'len': 194,
       'num': ['00469',
               '00470',
               '00471',
               '00472',
               '00473',
               '00474',
               '00475',
               '00476',
               '00477',
               '00478',
               '00479',
               '00480',
               '00481',
               '00482',
               '00483',
               '00484',
               '00485',
               '00486',
               '00487',
               '00488',
               '00489',
               '00490',
               '00491',
               '00492',
               '00493',
               '00494',
               '00495',
               '00496',
               '00497',
               '00498',
               '00499',
               '00500',
               '00501',
               '00502',
               '00503',
               '00504',
               '00505',
               '00506',
               '00507',
               '00508',
               '00509',
               '00510',
               '00511',
               '00512',
               '00513',
               '00514',
               '00515',
               '00516',
               '00517',
               '00518',
               '00519',
               '00520',
               '00521',
               '00522',
               '00523',
               '00524',
               '00525',
               '00526',
               '00527',
               '00528',
               '00529',
               '00530',
               '00531',
               '00532',
               '00504',
               '00505',
               '00506',
               '00507',
               '00508',
               '00649',
               '00650',
               '00651',
               '00652',
               '00653',
               '00654',
               '00655',
               '00656',
               '00657',
               '00658',
               '00659',
               '00660',
               '00661',
               '00662',
               '00663',
               '00664',
               '00665',
               '00666',
               '00667',
               '00668',
               '00669',
               '00670',
               '00671',
               '00672',
               '00673',
               '00674',
               '00675',
               '00676',
               '00677',
               '00678',
               '00679',
               '00680',
               '00681',
               '00682',
               '00683',
               '00684',
               '00685',
               '00686',
               '00687',
               '00688',
               '00689',
               '00690',
               '00691',
               '00692',
               '00693',
               '00694',
               '00702',
               '00703',
               '00704',
               '00705',
               '00706',
               '00707',
               '00708',
               '00709',
               '00710',
               '00711',
               '00712',
               '00713',
               '00714',
               '00715',
               '00716',
               '00717',
               '00718',
               '00719',
               '00720',
               '00721',
               '00722',
               '00723',
               '00724',
               '00725',
               '00726',
               '00727',
               '00728',
               '00729',
               '00730',
               '00731',
               '00732',
               '00733',
               '00874',
               '00875',
               '00876',
               '00877',
               '00878',
               '00879',
               '00880',
               '00881',
               '00882',
               '00883',
               '00884',
               '00885',
               '00886',
               '00887',
               '00888',
               '00889',
               '00890',
               '00891',
               '00892',
               '00893',
               '01204',
               '01205',
               '01206',
               '01207',
               '01208',
               '01209',
               '01210',
               '01211',
               '01212',
               '01213',
               '01214',
               '01215',
               '01216',
               '01217',
               '01218',
               '01219',
               '01220',
               '01221',
               '01222',
               '01605',
               '01606',
               '01607',
               '01608',
               '01609',
               '01610',
               '01611',
               '01612']}}

In [11]:
# fill '0' to match digits of nume surffix
peak_map_indexed = calibration.experiment.o_peak.peak_map_indexed
img_num_str_dict = {}
for each_ele in peak_map_indexed.keys():
    img_num_str_dict[each_ele] = {}
    img_num = list(peak_map_indexed[each_ele]['peak_span']['img_num'])
    img_num_str_dict[each_ele]['num'] = [fill_zeros_to_num_str(num_str) for num_str in list(map(str, img_num))]
    img_num_str_dict[each_ele]['len'] = len(img_num_str_dict[each_ele]['num'])
pprint.pprint(img_num_str_dict)


{'Gd': {'len': 32,
        'num': ['00489',
                '00490',
                '00491',
                '00492',
                '00493',
                '00494',
                '01545',
                '01546',
                '01547',
                '01548',
                '01549',
                '01550',
                '01551',
                '01552',
                '01553',
                '01554',
                '01782',
                '01783',
                '01784',
                '01785',
                '01786',
                '01787',
                '01788',
                '01789',
                '01790',
                '01791',
                '01792',
                '01793',
                '01794',
                '01795',
                '01796',
                '01797']},
 'U': {'len': 46,
       'num': ['00514',
               '00515',
               '00516',
               '00517',
               '00518',
               '00661',
               '00662',
               '00663',
               '00664',
               '00665',
               '00666',
               '00708',
               '00709',
               '00710',
               '00711',
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In [12]:
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In [13]:
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Out[13]:
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 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00985.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00986.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00987.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00988.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00989.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00990.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00991.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00992.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00993.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00994.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00995.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00996.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00997.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00998.fits',
 '/Users/y9z/Documents/Neutron_data/IPTS_19558/resonance_data/CT_runs/angles_redo/image10/Image010_00999.fits',
 ...]

In [14]:
# find full name with img_num
loc_dict = {}
for ele in img_num_str_dict.keys():
    loc_dict[ele] = []
for _names in _list_files:
    _base_name = _names.split('.')[0]
    for _ele in img_num_str_dict.keys():
        if _base_name[-5:] in img_num_str_dict[_ele]['num']:
            loc_dict[_ele].append(_names)

In [16]:
out_main_dir = '/Users/y9z/Downloads/test/'

for ele in img_num_str_dict.keys():
    out_ele_dir = out_main_dir + ele
    if os.path.exists(out_ele_dir) is False:
        os.makedirs(out_ele_dir)
    img_num_str_dict[ele]['dir'] = out_ele_dir

assert os.path.isdir(out_main_dir)

In [17]:
pprint.pprint(img_num_str_dict)


{'Gd': {'dir': '/Users/y9z/Downloads/test/Gd',
        'len': 32,
        'num': ['00489',
                '00490',
                '00491',
                '00492',
                '00493',
                '00494',
                '01545',
                '01546',
                '01547',
                '01548',
                '01549',
                '01550',
                '01551',
                '01552',
                '01553',
                '01554',
                '01782',
                '01783',
                '01784',
                '01785',
                '01786',
                '01787',
                '01788',
                '01789',
                '01790',
                '01791',
                '01792',
                '01793',
                '01794',
                '01795',
                '01796',
                '01797']},
 'U': {'dir': '/Users/y9z/Downloads/test/U',
       'len': 46,
       'num': ['00514',
               '00515',
               '00516',
               '00517',
               '00518',
               '00661',
               '00662',
               '00663',
               '00664',
               '00665',
               '00666',
               '00708',
               '00709',
               '00710',
               '00711',
               '00712',
               '00713',
               '00888',
               '00889',
               '00890',
               '00891',
               '00892',
               '00893',
               '01199',
               '01200',
               '01201',
               '01202',
               '01203',
               '01204',
               '01205',
               '01206',
               '01207',
               '01596',
               '01597',
               '01598',
               '01599',
               '01600',
               '01601',
               '01602',
               '01603',
               '01604',
               '01605',
               '01606',
               '01607',
               '01608',
               '01609']}}

In [49]:
for ele in img_num_str_dict.keys():
    for each in loc_dict[ele]:
        shutil.copy(src=each, dst=img_num_str_dict[ele]['dir'])

In [ ]: