simplify detector object. Add efficiency libraries and lookup interpolators

This commit is contained in:
Pim Nelissen
2026-03-20 09:21:40 +01:00
parent 1e81570cf4
commit 890570e148
11 changed files with 228 additions and 94 deletions

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@ -0,0 +1,37 @@
angle,662,1173,1332
0,0.015,0.030,0.033
10,0.011,0.021,0.024
20,0.086,0.127,0.146
30,0.294,0.356,0.397
40,0.661,0.700,0.734
50,1.054,1.057,1.057
60,1.154,1.140,1.137
70,1.186,1.152,1.138
80,1.151,1.114,1.097
90,1.000,1.000,1.000
100,1.020,1.040,1.047
110,1.074,1.093,1.103
120,1.113,1.092,1.102
130,1.139,1.122,1.113
140,1.146,1.152,1.140
150,1.113,1.118,1.104
160,1.113,1.096,1.099
170,1.091,1.076,1.083
180,1.076,1.066,1.078
-170,1.102,1.091,1.093
-160,1.122,1.100,1.102
-150,1.128,1.105,1.093
-140,1.144,1.112,1.123
-130,1.140,1.117,1.095
-120,1.146,1.127,1.098
-110,1.068,1.068,1.045
-100,1.013,1.025,1.016
-90,1.004,1.018,1.021
-80,1.150,1.137,1.132
-70,1.184,1.167,1.164
-60,1.158,1.140,1.138
-50,1.090,1.068,1.064
-40,0.595,0.620,0.631
-30,0.332,0.430,0.430
-20,0.055,0.081,0.096
-10,0.009,0.018,0.019
1 angle 662 1173 1332
2 0 0.015 0.030 0.033
3 10 0.011 0.021 0.024
4 20 0.086 0.127 0.146
5 30 0.294 0.356 0.397
6 40 0.661 0.700 0.734
7 50 1.054 1.057 1.057
8 60 1.154 1.140 1.137
9 70 1.186 1.152 1.138
10 80 1.151 1.114 1.097
11 90 1.000 1.000 1.000
12 100 1.020 1.040 1.047
13 110 1.074 1.093 1.103
14 120 1.113 1.092 1.102
15 130 1.139 1.122 1.113
16 140 1.146 1.152 1.140
17 150 1.113 1.118 1.104
18 160 1.113 1.096 1.099
19 170 1.091 1.076 1.083
20 180 1.076 1.066 1.078
21 -170 1.102 1.091 1.093
22 -160 1.122 1.100 1.102
23 -150 1.128 1.105 1.093
24 -140 1.144 1.112 1.123
25 -130 1.140 1.117 1.095
26 -120 1.146 1.127 1.098
27 -110 1.068 1.068 1.045
28 -100 1.013 1.025 1.016
29 -90 1.004 1.018 1.021
30 -80 1.150 1.137 1.132
31 -70 1.184 1.167 1.164
32 -60 1.158 1.140 1.138
33 -50 1.090 1.068 1.064
34 -40 0.595 0.620 0.631
35 -30 0.332 0.430 0.430
36 -20 0.055 0.081 0.096
37 -10 0.009 0.018 0.019

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@ -0,0 +1,4 @@
name,type,is_isotropic
dummy,NaI,true
LU_NaI_3inch,NaI,true
LU_HPGe_90,HPGe,false
1 name type is_isotropic
2 dummy NaI true
3 LU_NaI_3inch NaI true
4 LU_HPGe_90 HPGe false

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@ -0,0 +1,17 @@
energy_keV,field_efficiency_m2,uncertainty
59.5,0.00140,0.00005
81.0,0.00310,0.00010
122.1,0.00420,0.00013
136.5,0.00428,0.00017
160.6,0.00426,0.00030
223.2,0.00418,0.00024
276.4,0.00383,0.00012
302.9,0.00370,0.00012
356.0,0.00338,0.00010
383.8,0.00323,0.00010
511.0,0.00276,0.00008
661.7,0.00241,0.00007
834.8,0.00214,0.00007
1173.2,0.00179,0.00005
1274.5,0.00168,0.00005
1332.5,0.00166,0.00005
1 energy_keV field_efficiency_m2 uncertainty
2 59.5 0.00140 0.00005
3 81.0 0.00310 0.00010
4 122.1 0.00420 0.00013
5 136.5 0.00428 0.00017
6 160.6 0.00426 0.00030
7 223.2 0.00418 0.00024
8 276.4 0.00383 0.00012
9 302.9 0.00370 0.00012
10 356.0 0.00338 0.00010
11 383.8 0.00323 0.00010
12 511.0 0.00276 0.00008
13 661.7 0.00241 0.00007
14 834.8 0.00214 0.00007
15 1173.2 0.00179 0.00005
16 1274.5 0.00168 0.00005
17 1332.5 0.00166 0.00005

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@ -0,0 +1,64 @@
energy_keV,field_efficiency_m2
10,5.50129E-09
11.22018454,2.88553E-07
12.58925412,4.81878E-06
14.12537545,3.55112E-05
15.84893192,0.000146367
17.7827941,0.000397029
19.95262315,0.000803336
22.38721139,0.00131657
25.11886432,0.001862377
28.18382931,0.002373449
31.6227766,0.002811046
33.164,0.00269554
33.164,0.002698792
35.48133892,0.002509993
39.81071706,0.002801304
44.66835922,0.003015877
50.11872336,0.003227431
56.23413252,0.00341077
63.09573445,0.003562051
70.79457844,0.00368852
79.43282347,0.003788875
89,0.003867423
100,0.003925025
112.2018454,0.003967222
125.8925412,0.003991551
141.2537545,0.004000729
158.4893192,0.003993145
177.827941,0.003969163
199.5262315,0.003925289
223.8721139,0.003856247
251.1886432,0.00375596
281.8382931,0.003619634
316.227766,0.003446087
354.8133892,0.003242691
398.1071706,0.003021761
446.6835922,0.002791816
501.1872336,0.002568349
562.3413252,0.002350052
630.9573445,0.002147662
707.9457844,0.001957893
794.3282347,0.001785694
891,0.001626634
1000,0.001482571
1122.018454,0.00135047
1258.925412,0.001231358
1412.537545,0.001116695
1584.893192,0.001011833
1778.27941,0.000917017
1995.262315,0.000828435
2238.721139,0.000746854
2511.886432,0.000672573
2818.382931,0.00060493
3162.27766,0.000544458
3548.133892,0.000488446
3981.071706,0.000438438
4466.835922,0.000392416
5011.872336,0.00035092
5623.413252,0.000313959
6309.573445,0.000279409
7079.457844,0.000247794
7943.282347,0.000218768
8913,0.000190209
10000,0.000164309
1 energy_keV field_efficiency_m2
2 10 5.50129E-09
3 11.22018454 2.88553E-07
4 12.58925412 4.81878E-06
5 14.12537545 3.55112E-05
6 15.84893192 0.000146367
7 17.7827941 0.000397029
8 19.95262315 0.000803336
9 22.38721139 0.00131657
10 25.11886432 0.001862377
11 28.18382931 0.002373449
12 31.6227766 0.002811046
13 33.164 0.00269554
14 33.164 0.002698792
15 35.48133892 0.002509993
16 39.81071706 0.002801304
17 44.66835922 0.003015877
18 50.11872336 0.003227431
19 56.23413252 0.00341077
20 63.09573445 0.003562051
21 70.79457844 0.00368852
22 79.43282347 0.003788875
23 89 0.003867423
24 100 0.003925025
25 112.2018454 0.003967222
26 125.8925412 0.003991551
27 141.2537545 0.004000729
28 158.4893192 0.003993145
29 177.827941 0.003969163
30 199.5262315 0.003925289
31 223.8721139 0.003856247
32 251.1886432 0.00375596
33 281.8382931 0.003619634
34 316.227766 0.003446087
35 354.8133892 0.003242691
36 398.1071706 0.003021761
37 446.6835922 0.002791816
38 501.1872336 0.002568349
39 562.3413252 0.002350052
40 630.9573445 0.002147662
41 707.9457844 0.001957893
42 794.3282347 0.001785694
43 891 0.001626634
44 1000 0.001482571
45 1122.018454 0.00135047
46 1258.925412 0.001231358
47 1412.537545 0.001116695
48 1584.893192 0.001011833
49 1778.27941 0.000917017
50 1995.262315 0.000828435
51 2238.721139 0.000746854
52 2511.886432 0.000672573
53 2818.382931 0.00060493
54 3162.27766 0.000544458
55 3548.133892 0.000488446
56 3981.071706 0.000438438
57 4466.835922 0.000392416
58 5011.872336 0.00035092
59 5623.413252 0.000313959
60 6309.573445 0.000279409
61 7079.457844 0.000247794
62 7943.282347 0.000218768
63 8913 0.000190209
64 10000 0.000164309

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@ -0,0 +1,3 @@
energy_keV,field_efficiency_m2
0,1.0
10000,1.0
1 energy_keV field_efficiency_m2
2 0 1.0
3 10000 1.0

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@ -1,20 +0,0 @@
from pg_rad.inputparser.specs import DetectorSpec
from .detectors import IsotropicDetector, AngularDetector
class DetectorBuilder:
def __init__(
self,
detector_spec: DetectorSpec,
):
self.detector_spec = detector_spec
def build(self) -> IsotropicDetector | AngularDetector:
if self.detector_spec.is_isotropic:
return IsotropicDetector(
self.detector_spec.name,
self.detector_spec.efficiency
)
else:
raise NotImplementedError("Angular detector not supported yet.")

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@ -0,0 +1,43 @@
from importlib.resources import files
from pandas import read_csv
from pg_rad.utils.interpolators import (
get_field_efficiency, get_angular_efficiency
)
class Detector:
def __init__(
self,
name: str,
type: str,
is_isotropic: bool
):
self.name = name
self.type = type
self.is_isotropic = is_isotropic
def get_efficiency(self, energy_keV, angle=None):
f_eff = get_field_efficiency(self.name, energy_keV)
if self.is_isotropic or angle is None:
return f_eff
else:
f_eff = get_field_efficiency(self.name, energy_keV)
a_eff = get_angular_efficiency(self.name, energy_keV, *angle)
return f_eff * a_eff
def load_detector(name) -> Detector:
csv = files('pg_rad.data').joinpath('detectors.csv')
data = read_csv(csv)
dets = data['name'].values
if name in dets:
row = data[data['name'] == name].iloc[0]
return Detector(row['name'], row['type'], row['is_isotropic'])
else:
raise NotImplementedError(
f"Detector with name '{name}' not in detector library. Available:"
f"{', '.join(dets)}"
)

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@ -1,38 +0,0 @@
from abc import ABC
class BaseDetector(ABC):
def __init__(
self,
name: str,
efficiency: float
):
self.name = name
self.efficiency = efficiency
def get_efficiency(self):
pass
class IsotropicDetector(BaseDetector):
def __init__(
self,
name: str,
efficiency: float,
):
super().__init__(name, efficiency)
def get_efficiency(self, energy):
return self.efficiency
class AngularDetector(BaseDetector):
def __init__(
self,
name: str,
efficiency: float
):
super().__init__(name, efficiency)
def get_efficiency(self, angle, energy):
pass

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@ -353,41 +353,11 @@ class ConfigParser:
return specs return specs
def _parse_detector(self) -> DetectorSpec: def _parse_detector(self) -> DetectorSpec:
det_dict = self.config.get("detector") det_name = self.config.get("detector")
required = {"name", "is_isotropic"} if not det_name:
if not isinstance(det_dict, dict): raise MissingConfigKeyError("detector")
raise InvalidConfigValueError(
"detector is not specified correctly. Must contain at least"
f"the subkeys {required}"
)
missing = required - det_dict.keys() return DetectorSpec(name=det_name)
if missing:
raise MissingConfigKeyError("detector", missing)
name = det_dict.get("name")
is_isotropic = det_dict.get("is_isotropic")
eff = det_dict.get("efficiency")
default_detectors = defaults.DETECTOR_EFFICIENCIES
if name in default_detectors.keys() and not eff:
eff = default_detectors[name]
elif eff:
pass
else:
raise InvalidConfigValueError(
f"The detector {name} not found in library. Either "
f"specify {name}.efficiency or "
"choose a detector from the following list: "
f"{default_detectors.keys()}."
)
return DetectorSpec(
name=name,
efficiency=eff,
is_isotropic=is_isotropic
)
def _warn_unknown_keys(self, section: str, provided: set, allowed: set): def _warn_unknown_keys(self, section: str, provided: set, allowed: set):
unknown = provided - allowed unknown = provided - allowed

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@ -67,8 +67,6 @@ class RelativePointSourceSpec(PointSourceSpec):
@dataclass @dataclass
class DetectorSpec: class DetectorSpec:
name: str name: str
efficiency: float
is_isotropic: bool
@dataclass @dataclass

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@ -0,0 +1,56 @@
from importlib.resources import files
import numpy as np
from pandas import read_csv
from scipy.interpolate import interp1d, CubicSpline
from pg_rad.configs.filepaths import ATTENUATION_TABLE
def get_mass_attenuation_coeff(*args) -> float:
csv = files('pg_rad.data').joinpath(ATTENUATION_TABLE)
data = read_csv(csv)
x = data["energy_mev"].to_numpy()
y = data["mu"].to_numpy()
f = interp1d(x, y)
return f(*args)
def get_field_efficiency(name: str, energy_keV: float) -> float:
csv = files('pg_rad.data.field_efficiencies').joinpath(name+'.csv')
data = read_csv(csv)
data = data.groupby("energy_keV", as_index=False).mean()
x = data["energy_keV"].to_numpy()
y = data["field_efficiency_m2"].to_numpy()
f = CubicSpline(x, y)
return f(energy_keV)
def get_angular_efficiency(name: str, energy_keV: float, *angle: float):
csv = files('pg_rad.data.angular_efficiencies').joinpath(name+'.csv')
data = read_csv(csv)
# check all energies at which angular eff. is available for this detector.
# this is done within 1% tolerance
energy_cols = [col for col in data.columns if col != "angle"]
energies = np.array([float(col) for col in energy_cols])
rel_diff = np.abs(energies - energy_keV) / energies
match_idx = np.where(rel_diff <= 0.01)[0]
if len(match_idx) == 0:
raise NotImplementedError(
f"No angular efficiency defined for {energy_keV} keV "
f"in detector '{name}'. Available: {energies}"
)
best_idx = match_idx[np.argmin(rel_diff[match_idx])]
selected_energy_col = energy_cols[best_idx]
x = data["angle"].to_numpy()
y = data[selected_energy_col].to_numpy()
idx = np.argsort(x)
x = x[idx]
y = y[idx]
f = interp1d(x, y)
return f(angle)