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feature-ba
| Author | SHA1 | Date | |
|---|---|---|---|
| 8098ab9329 |
51
src/pg_rad/background/background.py
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51
src/pg_rad/background/background.py
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@ -0,0 +1,51 @@
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from importlib.resources import files
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from typing import Tuple
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import numpy as np
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from pandas import read_csv
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from pg_rad.configs.defaults import FWHM_PARAMS
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from pg_rad.detector.detector import Detector
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def generate_background(
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cps_array: np.ndarray,
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) -> np.ndarray:
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"""
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Generate synthetic background cps for a given detector and energy.
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"""
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pass
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def fwhm(A: float, B: float, C: float, E: float) -> float:
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return np.sqrt(A + B * E + C * E**2)
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def get_roi_from_fwhm(
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detector: Detector,
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energy_keV: float
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) -> Tuple[float, float]:
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"""
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Get the region of interest for given primary gamma energy, using
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the 3*FWHM rule.
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"""
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A, B, C = FWHM_PARAMS.get(detector.type)
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delta = 3*fwhm(A, B, C, energy_keV)
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return (energy_keV-delta, energy_keV+delta)
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def get_cps_from_roi(
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detector: Detector,
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roi_lo: float,
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roi_hi: float
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) -> float:
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csv = files('pg_rad.data.backgrounds').joinpath(detector.name+'.csv')
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data = read_csv(csv)
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# get indices of nearest bins
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idx_min = (data["Energy"] - roi_lo).abs().idxmin()
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idx_max = (data["Energy"] - roi_hi).abs().idxmin()
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idx_start, idx_end = sorted([idx_min, idx_max])
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cps_sum = data.loc[idx_start:idx_end, "cps"].sum()
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return cps_sum
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@ -23,3 +23,10 @@ DETECTOR_EFFICIENCIES = {
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"NaIR": 0.0216,
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"NaIR": 0.0216,
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"NaIF": 0.0254
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"NaIF": 0.0254
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}
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}
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# A, B, C parameters for different detector types
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# for formula FWHM(E) = sqrt(A + B*E + C*E^2)
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FWHM_PARAMS = {
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"NaI": (-70.55, 2.678, 0.000602),
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"HPGe": (1.778, 0.001843, 0.000001)
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}
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257
src/pg_rad/data/backgrounds/LU_NaI_3inch.csv
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257
src/pg_rad/data/backgrounds/LU_NaI_3inch.csv
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@ -0,0 +1,257 @@
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Energy,Data,cps
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0,0,0
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11.6866,0,0
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23.3732,1653,1.181558256
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35.0598,3827,2.735525375
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46.7464,5010,3.581129378
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58.433,7880,5.632594711
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70.1196,11068,7.911365261
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81.8062,13414,9.588277341
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93.4928,14536,10.39027877
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105.179,14580,10.42172981
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116.866,14138,10.10578985
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128.553,13559,9.691922802
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140.239,12725,9.095782702
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151.926,11597,8.289492495
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163.612,10696,7.645461044
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175.299,9847,7.038598999
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186.986,9063,6.478198713
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198.672,8192,5.855611151
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210.359,7627,5.451751251
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222.045,6941,4.961401001
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233.732,6450,4.610436026
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245.419,6046,4.321658327
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257.105,5560,3.974267334
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268.792,4845,3.463187991
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280.478,4267,3.05003574
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292.165,4066,2.906361687
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303.852,3711,2.652609006
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315.538,3413,2.439599714
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327.225,3080,2.201572552
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338.911,3035,2.169406719
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350.598,2789,1.993566833
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362.285,2766,1.977126519
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373.971,2432,1.73838456
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385.658,2182,1.55968549
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397.344,2010,1.436740529
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409.031,1911,1.365975697
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420.718,1871,1.337383846
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432.404,1678,1.199428163
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444.091,1601,1.144388849
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455.777,1538,1.099356683
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467.464,1473,1.052894925
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479.151,1390,0.993566833
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490.837,1369,0.978556112
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502.524,1358,0.970693352
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514.21,1356,0.96926376
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525.897,1323,0.945675482
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537.584,1256,0.897784132
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549.27,1171,0.837026447
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560.957,1072,0.766261615
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572.643,1094,0.781987134
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584.33,1157,0.827019299
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596.017,1073,0.766976412
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607.703,1083,0.774124375
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619.39,1042,0.744817727
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631.076,995,0.711222302
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642.763,938,0.670478914
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654.45,770,0.550393138
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666.136,787,0.562544675
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677.823,761,0.543959971
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689.509,703,0.502501787
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701.196,671,0.479628306
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712.883,611,0.436740529
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724.569,599,0.428162974
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736.256,573,0.40957827
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747.942,623,0.445318084
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759.629,636,0.454610436
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771.316,634,0.453180843
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783.002,602,0.430307362
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794.689,525,0.375268049
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806.375,544,0.388849178
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818.062,557,0.39814153
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829.749,527,0.376697641
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841.435,508,0.363116512
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853.122,509,0.363831308
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864.808,478,0.341672623
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876.495,498,0.355968549
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888.182,515,0.368120086
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899.868,521,0.372408863
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911.555,539,0.385275197
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923.241,546,0.390278771
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934.928,545,0.389563974
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946.615,529,0.378127234
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958.301,489,0.349535382
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969.988,490,0.350250179
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981.674,447,0.319513939
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993.361,437,0.312365976
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1005.05,406,0.290207291
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1016.73,380,0.271622588
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1028.42,357,0.255182273
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1040.11,346,0.247319514
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1051.79,348,0.248749107
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1063.48,337,0.240886347
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1075.17,376,0.268763402
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1086.85,376,0.268763402
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1098.54,363,0.259471051
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1110.23,388,0.277340958
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1121.91,330,0.235882773
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1133.6,364,0.260185847
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1145.29,323,0.230879199
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1156.97,359,0.256611866
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1168.66,330,0.235882773
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1180.35,284,0.203002144
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1192.03,256,0.182987848
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1203.72,252,0.180128663
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1215.41,243,0.173695497
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1227.09,264,0.188706219
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1238.78,226,0.16154396
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1250.47,214,0.152966405
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1262.15,184,0.131522516
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1273.84,179,0.127948535
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1285.53,168,0.120085776
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1297.21,185,0.132237312
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1308.9,177,0.126518942
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1320.59,175,0.12508935
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1332.27,171,0.122230164
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1343.96,187,0.133666905
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1355.65,206,0.147248034
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1367.33,255,0.182273052
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1379.02,331,0.23659757
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1390.71,395,0.282344532
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1402.39,540,0.385989993
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1414.08,608,0.43459614
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1425.77,612,0.437455325
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1437.45,575,0.411007863
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1449.14,516,0.368834882
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1460.82,467,0.333809864
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1472.51,369,0.263759828
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1484.2,254,0.181558256
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1495.88,220,0.157255182
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1507.57,125,0.089349535
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1519.26,136,0.097212294
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1530.94,92,0.065761258
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1542.63,74,0.052894925
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1554.32,67,0.047891351
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1566,68,0.048606147
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1577.69,70,0.05003574
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1589.38,58,0.041458184
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1601.06,66,0.047176555
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1671.18,84,0.060042888
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1682.87,88,0.062902073
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1694.56,88,0.062902073
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1706.24,65,0.046461758
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1717.93,78,0.05575411
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1729.62,84,0.060042888
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1741.3,62,0.04431737
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1752.99,71,0.050750536
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1764.68,54,0.038598999
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1776.36,53,0.037884203
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1788.05,53,0.037884203
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1799.74,34,0.024303074
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1811.42,36,0.025732666
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1823.11,46,0.032880629
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1834.8,45,0.032165833
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1846.48,41,0.029306648
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1858.17,33,0.023588277
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1869.86,33,0.023588277
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1881.54,29,0.020729092
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1893.23,39,0.027877055
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1904.92,37,0.026447462
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1916.6,28,0.020014296
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1928.29,41,0.029306648
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1939.98,39,0.027877055
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1951.66,46,0.032880629
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1963.35,46,0.032880629
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1975.04,36,0.025732666
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1986.72,39,0.027877055
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1998.41,47,0.033595425
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2010.1,59,0.042172981
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2021.78,56,0.040028592
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2033.47,44,0.031451036
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2045.15,57,0.040743388
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2056.84,50,0.035739814
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2068.53,68,0.048606147
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2080.21,56,0.040028592
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2091.9,53,0.037884203
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2103.59,51,0.03645461
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2115.27,30,0.021443888
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2126.96,40,0.028591851
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2138.65,47,0.033595425
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2150.33,42,0.030021444
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2162.02,49,0.035025018
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2173.71,31,0.022158685
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2185.39,29,0.020729092
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2197.08,49,0.035025018
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2208.77,26,0.018584703
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2220.45,32,0.022873481
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2232.14,23,0.016440315
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2243.83,19,0.013581129
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2255.51,31,0.022158685
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2267.2,16,0.011436741
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2278.89,21,0.015010722
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2290.57,19,0.013581129
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2302.26,20,0.014295926
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2313.95,14,0.010007148
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2325.63,18,0.012866333
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2337.32,18,0.012866333
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2349.01,22,0.015725518
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2360.69,27,0.0192995
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2372.38,21,0.015010722
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2384.07,21,0.015010722
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2395.75,35,0.02501787
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2407.44,49,0.035025018
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2419.13,55,0.039313796
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2430.81,47,0.033595425
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2442.5,68,0.048606147
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2454.19,56,0.040028592
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2465.87,57,0.040743388
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2477.56,63,0.045032166
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2500.93,61,0.043602573
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2524.31,34,0.024303074
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2547.68,26,0.018584703
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2559.37,15,0.010721944
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2571.05,16,0.011436741
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2582.74,9,0.006433167
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2629.48,2,0.001429593
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2641.17,7,0.005003574
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2652.86,5,0.003573981
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2664.54,2,0.001429593
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2676.23,2,0.001429593
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2687.92,0,0
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2699.6,6,0.004288778
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2711.29,2,0.001429593
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2722.98,6,0.004288778
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2734.66,5,0.003573981
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2746.35,5,0.003573981
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2758.04,2,0.001429593
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2781.41,6,0.004288778
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2793.1,7,0.005003574
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2804.78,2,0.001429593
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2828.16,6,0.004288778
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2863.22,1,0.000714796
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2898.28,5,0.003573981
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2909.96,2,0.001429593
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2921.65,3,0.002144389
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2933.34,4,0.002859185
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42
tests/test_roi.py
Normal file
42
tests/test_roi.py
Normal file
@ -0,0 +1,42 @@
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|
import pytest
|
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|
|
||||||
|
from pg_rad.background.background import get_roi_from_fwhm, get_cps_from_roi
|
||||||
|
from pg_rad.detector.detector import load_detector
|
||||||
|
|
||||||
|
E_gamma = 661.7 # keV
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture
|
||||||
|
def detector():
|
||||||
|
return load_detector("LU_NaI_3inch")
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture
|
||||||
|
def roi_Cs137_NaI(detector):
|
||||||
|
return get_roi_from_fwhm(detector, E_gamma)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize("ref_roi_lo, ref_roi_hi", [
|
||||||
|
(537.58, 792.69)
|
||||||
|
])
|
||||||
|
def test_roi_acquisition_Cs137_NaI(ref_roi_lo, ref_roi_hi, roi_Cs137_NaI):
|
||||||
|
"""Test against ROI used in GammaVision"""
|
||||||
|
est_roi_lo, est_roi_hi = roi_Cs137_NaI
|
||||||
|
|
||||||
|
assert pytest.approx(est_roi_lo, rel=0.05) == ref_roi_lo
|
||||||
|
assert pytest.approx(est_roi_hi, rel=0.05) == ref_roi_hi
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize("ref_cps", [
|
||||||
|
13.61
|
||||||
|
])
|
||||||
|
def test_cps_acquisition_Cs137_NaI(
|
||||||
|
ref_cps,
|
||||||
|
roi_Cs137_NaI,
|
||||||
|
detector
|
||||||
|
):
|
||||||
|
"""Test against cps from GammaVision"""
|
||||||
|
est_roi_lo, est_roi_hi = roi_Cs137_NaI
|
||||||
|
est_cps = get_cps_from_roi(detector, est_roi_lo, est_roi_hi)
|
||||||
|
|
||||||
|
assert pytest.approx(est_cps, rel=0.1) == ref_cps
|
||||||
Reference in New Issue
Block a user