From 215ec05f0f9dc89384e574c73a2ba5a9ec61ba6a Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Wed, 30 Jul 2025 12:26:06 -0400
Subject: [PATCH 01/32] change peak finding params for hpge

---
 .../activation_foils/compass.py               | 69 ++++++++++++++-----
 1 file changed, 51 insertions(+), 18 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 22e4e26..b9b733d 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -169,6 +169,7 @@ class Measurement:
     stop_time: Union[datetime.datetime, None]
     name: str
     detectors: List[Detector]
+    detector_type: str = "NaI"  # Default detector type, can be 'NaI' or 'HPGe'
 
     def __init__(self, name: str) -> None:
         """
@@ -497,26 +498,57 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
             the peak indices in ``hist``
         """
 
-        # peak finding parameters
-        start_index = 100
-        prominence = 0.10 * np.max(hist[start_index:])
-        height = 0.10 * np.max(hist[start_index:])
-        width = [10, 150]
-        distance = 30
-        if self.check_source.nuclide == na22:
+        if self.detector_type.lower() == 'nai':
+            # peak finding parameters
             start_index = 100
-            height = 0.1 * np.max(hist[start_index:])
-            prominence = 0.1 * np.max(hist[start_index:])
+            prominence = 0.10 * np.max(hist[start_index:])
+            height = 0.10 * np.max(hist[start_index:])
             width = [10, 150]
             distance = 30
-        elif self.check_source.nuclide == co60:
-            start_index = 400
-            height = 0.60 * np.max(hist[start_index:])
-            prominence = None
-        elif self.check_source.nuclide == ba133:
-            width = [10, 200]
-        elif self.check_source.nuclide == mn54:
-            height = 0.6 * np.max(hist[start_index:])
+            if self.check_source.nuclide == na22:
+                start_index = 100
+                height = 0.1 * np.max(hist[start_index:])
+                prominence = 0.1 * np.max(hist[start_index:])
+                width = [10, 150]
+                distance = 30
+            elif self.check_source.nuclide == co60:
+                start_index = 400
+                height = 0.60 * np.max(hist[start_index:])
+                prominence = None
+            elif self.check_source.nuclide == ba133:
+                start_index = 10
+                height = 0.10 * np.max(hist[start_index:])
+                prominence = 0.10 * np.max(hist[start_index:])
+            elif self.check_source.nuclide == mn54:
+                height = 0.6 * np.max(hist[start_index:])
+        elif self.detector_type.lower() == 'hpge':
+            # peak finding parameters for HPGe detectors
+            start_index = 10
+            prominence = 0.10 * np.max(hist[start_index:])
+            height = 0.10 * np.max(hist[start_index:])
+            width = [2, 30]
+            distance = 10
+            if self.check_source.nuclide == na22:
+                start_index = 100
+                height = 0.2 * np.max(hist[start_index:])
+                prominence = 0.2 * np.max(hist[start_index:])
+                distance = 100
+            elif self.check_source.nuclide == co60:
+                height = 0.2 * np.max(hist[start_index:])
+                prominence = 0.2 * np.max(hist[start_index:])
+            elif self.check_source.nuclide == ba133:
+                start_index = 10
+                height = 0.10 * np.max(hist[start_index:])
+                prominence = 0.10 * np.max(hist[start_index:])
+                distance = 10
+            elif self.check_source.nuclide == mn54:
+                height = 0.9 * np.max(hist[start_index:])
+                prominence = 0.9 * np.max(hist[start_index:])
+                distance = 100
+        else:
+            raise ValueError(
+                f"Unknown detector type: {self.detector_type}. Supported types are 'NaI' and 'HPGe'."
+            )
 
         # update the parameters if kwargs are provided
         if kwargs:
@@ -735,7 +767,8 @@ def get_calibration_data(
 
             if len(peaks) != len(measurement.check_source.nuclide.energy):
                 raise ValueError(
-                    f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(measurement.check_source.nuclide.energy)} were expected"
+                    f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(measurement.check_source.nuclide.energy)} were expected",
+                    f" peaks found: {peaks} for {measurement.check_source.nuclide.name}",
                 )
             calibration_channels += list(peaks)
             calibration_energies += measurement.check_source.nuclide.energy

From f07e010a1bcc7c876890e6822e15758cdf27513c Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Mon, 18 Aug 2025 13:12:19 -0400
Subject: [PATCH 02/32] hpge mostly works

---
 .../activation_foils/calibration.py           | 12 +++++--
 .../activation_foils/compass.py               | 33 +++++++++++++++----
 2 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py
index b98dfde..5d5956e 100644
--- a/libra_toolbox/neutron_detection/activation_foils/calibration.py
+++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py
@@ -31,6 +31,8 @@ class Nuclide:
     half_life: float = None
     atomic_mass: float = None
     abundance: float = 1.00
+    peak_widths: List[float] = None
+    calibrated_peak_widths: List[float] = None
 
     @property
     def decay_constant(self):
@@ -40,10 +42,16 @@ def decay_constant(self):
         return np.log(2) / self.half_life
 
 
+# ba133 = Nuclide(
+#     name="Ba133",
+#     energy=[80.9979, 276.3989, 302.8508, 356.0129, 383.8485],
+#     intensity=[0.329, 0.0716, 0.1834, 0.6205, 0.0894],
+#     half_life=10.551 * 365.25 * 24 * 3600,
+# )
 ba133 = Nuclide(
     name="Ba133",
-    energy=[80.9979, 276.3989, 302.8508, 356.0129, 383.8485],
-    intensity=[0.329, 0.0716, 0.1834, 0.6205, 0.0894],
+    energy=[276.3989, 302.8508, 356.0129, 383.8485],
+    intensity=[0.0716, 0.1834, 0.6205, 0.0894],
     half_life=10.551 * 365.25 * 24 * 3600,
 )
 co60 = Nuclide(
diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index f40cda5..285b51d 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -416,6 +416,7 @@ def compute_detection_efficiency(
         calibration_coeffs: np.ndarray,
         channel_nb: int,
         search_width: float = 800,
+        threshold_overlap: float = 200,
     ) -> Union[np.ndarray, float]:
         """
         Computes the detection efficiency of a check source given the
@@ -448,12 +449,17 @@ def compute_detection_efficiency(
         )
 
         calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges)
+        # width_calibration_coeffs = np.append(calibration_coeffs[:-1], 0)
+        # self.check_source.nuclide.calibrated_peak_widths = np.polyval(calibration_coeffs,
+        #                                                               self.check_source.nuclide.calibrated_peak_widths)
+        print('libra-toolbox search_width:', search_width, ' threshold_overlap:', threshold_overlap)
 
         nb_counts_measured = get_multipeak_area(
             hist,
             calibrated_bin_edges,
             self.check_source.nuclide.energy,
             search_width=search_width,
+            threshold_overlap=threshold_overlap,
         )
 
         nb_counts_measured = np.array(nb_counts_measured)
@@ -537,7 +543,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
                 height = 0.2 * np.max(hist[start_index:])
                 prominence = 0.2 * np.max(hist[start_index:])
             elif self.check_source.nuclide == ba133:
-                start_index = 10
+                start_index = 150
                 height = 0.10 * np.max(hist[start_index:])
                 prominence = 0.10 * np.max(hist[start_index:])
                 distance = 10
@@ -804,17 +810,18 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
             )
 
     search_start = np.argmin(
-        np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals)
+        np.abs((peak_ergs[0] - search_width / ( len(peak_ergs))) - xvals)
     )
     search_end = np.argmin(
-        np.abs((peak_ergs[-1] + search_width / (2 * len(peak_ergs))) - xvals)
+        np.abs((peak_ergs[-1] + search_width / (len(peak_ergs))) - xvals)
     )
 
     slope_guess = (hist[search_end] - hist[search_start]) / (
         xvals[search_end] - xvals[search_start]
     )
 
-    guess_parameters = [0, slope_guess]
+    # guess_parameters = [0, slope_guess]
+    guess_parameters = [0, 0]
 
     for i in range(len(peak_ergs)):
         peak_ind = np.argmin(np.abs((peak_ergs[i]) - xvals))
@@ -824,6 +831,8 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
             search_width / (3 * len(peak_ergs)),
         ]
 
+    print('Search start:', search_start, ' Search end:', search_end)
+
     parameters, covariance = curve_fit(
         gauss,
         xvals[search_start:search_end],
@@ -841,20 +850,32 @@ def get_multipeak_area(
     if len(peak_ergs) > 1:
         if np.max(peak_ergs) - np.min(peak_ergs) > threshold_overlap:
             areas = []
-            for peak in peak_ergs:
+            for p, peak in enumerate(peak_ergs):
+                if isinstance(search_width, (np.ndarray, list)):
+                    search_w = int(search_width[p])
+                else:
+                    search_w = int(search_width)
                 area = get_multipeak_area(
                     hist,
                     bins,
                     [peak],
-                    search_width=search_width,
+                    search_width=search_w,
                     threshold_overlap=threshold_overlap,
                 )
                 areas += area
             return areas
+    
+    if isinstance(search_width, (np.ndarray, list)):
+        search_width = int(search_width[0])
 
     # get midpoints of every bin
     xvals = np.diff(bins) / 2 + bins[:-1]
 
+    print("libra-toolbox bins length:", len(bins))
+    print("libra-toolbox xvals:", xvals)
+    print("libra-toolbox hist[0, -1]: ", hist[0], hist[-1])
+    print("libra-toolbox peak_ergs:", peak_ergs)
+
     parameters, covariance = fit_peak_gauss(
         hist, xvals, peak_ergs, search_width=search_width
     )

From 76f09fcfd0ad0f6aa01c4e726e7c204fcc701dcb Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 17:48:52 -0400
Subject: [PATCH 03/32] defined materials

---
 libra_toolbox/neutronics/materials.py | 345 ++++++++++++++++++++++++++
 1 file changed, 345 insertions(+)
 create mode 100644 libra_toolbox/neutronics/materials.py

diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py
new file mode 100644
index 0000000..e31cd06
--- /dev/null
+++ b/libra_toolbox/neutronics/materials.py
@@ -0,0 +1,345 @@
+import openmc
+
+
+def get_exp_cllif_density(temp, LiCl_frac=0.695):
+    """ Calculates density of ClLiF [g/cc] from temperature in Celsius
+    and molar concentration of LiCl. Valid for 660 C - 1000 C. 
+    Source: 
+    G. J. Janz, R. P. T. Tomkins, C. B. Allen; 
+    Molten Salts: Volume 4, Part 4 
+    Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data. 
+    J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302.
+    https://doi.org/10.1063/1.555590
+    """
+    temp = temp + 273.15  # Convert temperature from Celsius to Kelvin
+    C = LiCl_frac * 100  # Convert molar concentration to molar percent
+
+    a = 2.25621
+    b = -8.20475e-3
+    c = -4.09235e-4
+    d = 6.37250e-5
+    e = -2.52846e-7
+    f = 8.73570e-9
+    g = -5.11184e-10
+
+    rho = a + b * C + c * temp + d * C**2 \
+        + e * C**3 + f * temp * C**2 + g * C * temp**2
+
+    return rho
+
+
+# Define Materials
+# Source: PNNL Materials Compendium April 2021
+# PNNL-15870, Rev. 2
+Inconel625 = openmc.Material(name='Inconel 625')
+Inconel625.set_density('g/cm3', 8.44)
+Inconel625.add_element('C',  0.000990, 'wo')
+Inconel625.add_element('Al', 0.003960, 'wo')
+Inconel625.add_element('Si', 0.004950, 'wo')
+Inconel625.add_element('P',  0.000148, 'wo')
+Inconel625.add_element('S',  0.000148, 'wo')
+Inconel625.add_element('Ti', 0.003960, 'wo')
+Inconel625.add_element('Cr', 0.215000, 'wo')
+Inconel625.add_element('Mn', 0.004950, 'wo')
+Inconel625.add_element('Fe', 0.049495, 'wo')
+Inconel625.add_element('Co', 0.009899, 'wo')
+Inconel625.add_element('Ni', 0.580000, 'wo')
+Inconel625.add_element('Nb', 0.036500, 'wo')
+Inconel625.add_element('Mo', 0.090000, 'wo')
+
+# alumina insulation
+# data from https://precision-ceramics.com/materials/alumina/
+Alumina = openmc.Material(name='Alumina insulation')
+Alumina.add_element('O', 0.6, 'ao')
+Alumina.add_element('Al', 0.4, 'ao')
+Alumina.set_density('g/cm3', 3.98)
+
+# epoxy
+Epoxy = openmc.Material(name='Epoxy')
+Epoxy.add_element('C', 0.70, 'wo')
+Epoxy.add_element('H', 0.08, 'wo')
+Epoxy.add_element('O', 0.15, 'wo')
+Epoxy.add_element('N', 0.07, 'wo')
+Epoxy.set_density('g/cm3', 1.2)
+
+# helium @5psig
+pressure = 34473.8  # Pa ~ 5 psig
+temperature = 300  # K
+R_he = 2077  # J/(kg*K)
+density = pressure / (R_he * temperature)  # in kg/cm^3
+density *= 1 / 1000  # in g/cm^3
+Helium = openmc.Material(name="Helium")
+Helium.add_element('He', 1.0, 'ao')
+Helium.set_density('g/cm3', density)
+
+# PbLi - eutectic - natural - pure
+Pbli = openmc.Material(name="pbli")
+Pbli.add_element("Pb", 84.2, "ao")
+Pbli.add_element("Li", 15.2, "ao")
+Pbli.set_density("g/cm3", 11)
+
+# lif-licl - eutectic - natural - pure
+Cllif = openmc.Material(name='ClLiF')
+LiCl_frac = 0.695  # at.fr.
+
+Cllif.add_element('F', .5*(1 - LiCl_frac), 'ao')
+Cllif.add_element('Li', 1.0, 'ao')
+Cllif.add_element('Cl', .5*LiCl_frac, 'ao')
+Cllif.set_density('g/cm3', get_exp_cllif_density(650)
+                  )  # 69.5 at. % LiCL at 650 C
+
+# lif-licl - eutectic - natural - EuF3 spiced
+Spicyclif = openmc.Material(name="spicyclif")
+Spicyclif.add_element("F", .15935, "wo")
+Spicyclif.add_element("Li", .17857, "wo")
+Spicyclif.add_element("Cl", .6340, "wo")
+Spicyclif.add_element("Eu", .0279, "wo")
+
+# FLiNaK - eutectic - natural - pure
+Flinak = openmc.Material(name="flinak")
+Flinak.add_element("F", 50, "ao")
+Flinak.add_element("Li", 23.25, "ao")
+Flinak.add_element("Na", 5.75, "ao")
+Flinak.add_element("K", 21, "ao")
+Flinak.set_density("g/cm3", 2.020)
+
+
+# Aluminum : 2.6989 g/cm3
+Aluminum = openmc.Material()
+Aluminum.set_density("g/cm3", 2.6989)
+Aluminum.add_nuclide("Al27", 1.0, "ao")
+
+# Name: Air
+# Density : 0.001205 g/cm3
+# Reference: None
+# Describes: All atmospheric, non-object chambers
+Air = openmc.Material(name="Air")
+Air.set_density("g/cm3", 0.001205)
+Air.add_element("C", 0.00015, "ao")
+Air.add_nuclide("N14", 0.784431, "ao")
+Air.add_nuclide("O16", 0.210748, "ao")
+Air.add_nuclide("Ar40", 0.004671, "ao")
+
+# Name: Portland concrete
+# Density: 2.3 g/cm3
+# Reference: PNNL Report 15870 (Rev. 1)
+# Describes: facility foundation, floors, walls
+Concrete = openmc.Material()
+Concrete.set_density("g/cm3", 2.3)
+Concrete.add_nuclide("H1", 0.168759, "ao")
+Concrete.add_element("C", 0.001416, "ao")
+Concrete.add_nuclide("O16", 0.562524, "ao")
+Concrete.add_nuclide("Na23", 0.011838, "ao")
+Concrete.add_element("Mg", 0.0014, "ao")
+Concrete.add_nuclide("Al27", 0.021354, "ao")
+Concrete.add_element("Si", 0.204115, "ao")
+Concrete.add_element("K", 0.005656, "ao")
+Concrete.add_element("Ca", 0.018674, "ao")
+Concrete.add_element("Fe", 0.004264, "ao")
+
+# Name: Portland iron concrete
+# Density: 3.8 g/cm3 as roughly measured using scale and assuming rectangular prism
+# Reference: PNNL Report 15870 (Rev. 1)
+# Describes: Potential new walls, shielding doors
+IronConcrete = openmc.Material()
+IronConcrete.set_density("g/cm3", 3.8)
+IronConcrete.add_nuclide("H1", 0.135585, "ao")
+IronConcrete.add_nuclide("O16", 0.150644, "ao")
+IronConcrete.add_element("Mg", 0.002215, "ao")
+IronConcrete.add_nuclide("Al27", 0.005065, "ao")
+IronConcrete.add_element("Si", 0.013418, "ao")
+IronConcrete.add_element("S", 0.000646, "ao")
+IronConcrete.add_element("Ca", 0.040919, "ao")
+IronConcrete.add_nuclide("Mn55", 0.002638, "ao")
+IronConcrete.add_element("Fe", 0.648869, "ao")
+
+#
+# Lead : 11.34 g/cm3
+Lead = openmc.Material()
+Lead.set_density("g/cm3", 11.34)
+Lead.add_nuclide("Pb204", 0.014, "ao")
+Lead.add_nuclide("Pb206", 0.241, "ao")
+Lead.add_nuclide("Pb207", 0.221, "ao")
+Lead.add_nuclide("Pb208", 0.524, "ao")
+
+# Name: Borated Polyethylene (5% B in via B4C additive)
+# Density: 0.95 g/cm3
+# Reference: PNNL Report 15870 (Rev. 1) but revised to make it 5 wt.% B
+# Describes: General purpose neutron shielding
+BPE = openmc.Material()
+BPE.set_density("g/cm3", 0.95)
+BPE.add_nuclide("H1", 0.1345, "wo")
+BPE.add_element("B", 0.0500, "wo")
+BPE.add_element("C", 0.8155, "wo")
+
+# Name: Non-borated polyethylene
+# Density: 0.93 g/cm3
+# Reference: PNNL Report 15870 (Rev. 1)
+# Describes: General purpose neutron shielding
+Polyethylene = openmc.Material()
+Polyethylene.set_density("g/cm3", 0.93)
+Polyethylene.add_nuclide("H1", 0.666662, "ao")
+Polyethylene.add_element("C", 0.333338, "ao")
+
+# High Density Polyethylene
+# Reference:  PNNL Report 15870 (Rev. 1)
+HDPE = openmc.Material(name="HDPE")
+HDPE.set_density("g/cm3", 0.95)
+HDPE.add_element("H", 0.143724, "wo")
+HDPE.add_element("C", 0.856276, "wo")
+
+# Name: Zirconium dihydride
+# Density: 5.6 g/cm3
+# Reference: JNM 386-388 (2009) 119-121
+# Describes: General purpose neutron shielding
+Material_22 = openmc.Material()
+Material_22.set_density("g/cm3", 5.6)
+Material_22.add_nuclide("H1", 0.0216, "wo")
+Material_22.add_element("Zr", 0.9784, "wo")
+
+# Name: Zirconium borohydride
+# Density: 1.18 g/cm3
+# Reference: JNM 386-388 (2009) 119-121
+# Describes: General purpose neutron shielding
+Material_23 = openmc.Material()
+Material_23.set_density("g/cm3", 1.18)
+Material_23.add_nuclide("H1", 0.1073, "wo")
+Material_23.add_nuclide("B10", 0.0571, "wo")
+Material_23.add_nuclide("B11", 0.23, "wo")
+Material_23.add_element("Zr", 0.6056, "wo")
+
+# Density: 1.848 g/cm3
+# Reference: None
+# Describes: Highest intenstiy neutron production target
+# Notes: Uses ENDF-derived proton nuclear data libray
+Material_30 = openmc.Material()
+Material_30.set_density("g/cm3", 1.848)
+Material_30.add_nuclide("Be9", 1.0, "ao")
+
+# Name: Concrete (Regular)
+# Density: 2.3 g/cm3
+# Reference: Provided by Matthey Carey, MIT EHS/RPP (mgcarey@mit.edu)
+# Describes: Facility walls, foundation, floors for activation calculations
+Material_40 = openmc.Material()
+Material_40.set_density("g/cm3", 2.3)
+Material_40.add_nuclide("Fe54", 2.0138e-05, "ao")
+Material_40.add_nuclide("Fe56", 0.00031874, "ao")
+Material_40.add_nuclide("Fe57", 7.2915e-06, "ao")
+Material_40.add_nuclide("Fe58", 1.0416e-06, "ao")
+Material_40.add_nuclide("H1", 0.01374, "ao")
+Material_40.add_nuclide("H2", 2.0613e-06, "ao")
+Material_40.add_nuclide("O16", 0.045685, "ao")
+Material_40.add_nuclide("O17", 1.8318e-05, "ao")
+Material_40.add_nuclide("Mg24", 9.0027e-05, "ao")
+Material_40.add_nuclide("Mg25", 1.1397e-05, "ao")
+Material_40.add_nuclide("Mg26", 1.2548e-05, "ao")
+Material_40.add_nuclide("Ca40", 0.001474, "ao")
+Material_40.add_nuclide("Ca42", 9.8378e-06, "ao")
+Material_40.add_nuclide("Ca43", 2.0527e-06, "ao")
+Material_40.add_nuclide("Ca44", 3.1718e-05, "ao")
+Material_40.add_nuclide("Ca46", 6.0821e-08, "ao")
+Material_40.add_nuclide("Ca48", 2.8434e-06, "ao")
+Material_40.add_nuclide("Si28", 0.015328, "ao")
+Material_40.add_nuclide("Si29", 0.00077613, "ao")
+Material_40.add_nuclide("Si30", 0.0005152, "ao")
+Material_40.add_nuclide("Na23", 0.00096395, "ao")
+Material_40.add_nuclide("K39", 0.00042949, "ao")
+Material_40.add_nuclide("K40", 4.6053e-08, "ao")
+Material_40.add_nuclide("K41", 3.0993e-05, "ao")
+Material_40.add_nuclide("Al27", 0.0017453, "ao")
+Material_40.add_nuclide("C12", 0.00011404, "ao")
+Material_40.add_nuclide("C13", 1.28e-06, "ao")
+
+# Soil material taken from PNNL Materials Compendium for Earth, U.S. Average
+Soil = openmc.Material(name="Soil")
+Soil.set_density("g/cm3", 1.52)
+Soil.add_element("O", 0.670604, percent_type="ao")
+Soil.add_element("Na", 0.005578, percent_type="ao")
+Soil.add_element("Mg", 0.011432, percent_type="ao")
+Soil.add_element("Al", 0.053073, percent_type="ao")
+Soil.add_element("Si", 0.201665, percent_type="ao")
+Soil.add_element("K", 0.007653, percent_type="ao")
+Soil.add_element("Ca", 0.026664, percent_type="ao")
+Soil.add_element("Ti", 0.002009, percent_type="ao")
+Soil.add_element("Mn", 0.000272, percent_type="ao")
+Soil.add_element("Fe", 0.021050, percent_type="ao")
+
+# Brick material taken from "Brick, Common Silica" from the PNNL Materials Compendium
+# PNNL-15870, Rev. 2
+Brick = openmc.Material(name="Brick")
+Brick.set_density("g/cm3", 1.8)
+Brick.add_element("O", 0.663427, percent_type="ao")
+Brick.add_element("Al", 0.003747, percent_type="ao")
+Brick.add_element("Si", 0.323229, percent_type="ao")
+Brick.add_element("Ca", 0.007063, percent_type="ao")
+Brick.add_element("Fe", 0.002534, percent_type="ao")
+
+# Previous model uses 10% borated high density polyethylene, but
+# according to Melhus, et. al., RicoRad consists of "2.00% mass boron
+# in a polyethylene-based matrix having a mass density of 0.945 g/cm^3"
+# Source:
+# Melhus, Christopher, et al. ‘Storage Safe Shielding Assessment for a
+# HDR Californium-252 Brachytherapy Source’.
+# Monte Carlo 2005 Topical Meeting, 01 2005, pp. 219–229.
+
+RicoRad = openmc.Material(name="RicoRad")
+RicoRad.set_density("g/cm3", 0.945)
+RicoRad.add_element("H", 0.14, percent_type="wo")
+RicoRad.add_element("C", 0.84, percent_type="wo")
+RicoRad.add_element("B", 0.02, percent_type="wo")
+
+# LIBRA Materials
+Steel = openmc.Material(name="Steel")
+Steel.add_element("C", 0.005, "wo")
+Steel.add_element("Fe", 0.995, "wo")
+Steel.set_density("g/cm3", 7.82)
+
+# Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2)
+SS304 = openmc.Material(name="Stainless Steel 304")
+# SS304.temperature = 700 + 273
+SS304.add_element("C", 0.000800, "wo")
+SS304.add_element("Mn", 0.020000, "wo")
+SS304.add_element("P", 0.000450, "wo")
+SS304.add_element("S", 0.000300, "wo")
+SS304.add_element("Si", 0.010000, "wo")
+SS304.add_element("Cr", 0.190000, "wo")
+SS304.add_element("Ni", 0.095000, "wo")
+SS304.add_element("Fe", 0.683450, "wo")
+SS304.set_density("g/cm3", 8.00)
+
+# Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2
+# https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/
+Firebrick = openmc.Material(name="Firebrick")
+# Estimate average temperature of Firebrick to be around 300 C
+# Firebrick.temperature = 273 + 300
+Firebrick.add_element("Al", 0.004, "ao")
+Firebrick.add_element("O", 0.666, "ao")
+Firebrick.add_element("Si", 0.240, "ao")
+Firebrick.add_element("Zr", 0.090, "ao")
+Firebrick.set_density("g/cm3", 0.30)
+
+# Using 2:1 atom ratio of LiF to BeF2, similar to values in
+# Seifried, Jeffrey E., et al. ‘A General Approach for Determination of
+# Acceptable FLiBe Impurity Concentrations in Fluoride-Salt Cooled High
+# Temperature Reactors (FHRs)’. Nuclear Engineering and Design, vol. 343, 2019,
+# pp. 85–95, https://doi.org10.1016/j.nucengdes.2018.09.038.
+# Also using natural lithium enrichment (~7.5 a% Li6)
+Flibe_nat = openmc.Material(name="Flibe_nat")
+# Flibe_nat.temperature = 700 + 273
+Flibe_nat.add_element("Be", 0.142857, "ao")
+Flibe_nat.add_nuclide("Li6", 0.021685, "ao")
+Flibe_nat.add_nuclide("Li7", 0.264029, "ao")
+Flibe_nat.add_element("F", 0.571429, "ao")
+Flibe_nat.set_density("g/cm3", 1.94)
+
+Copper = openmc.Material(name="Copper")
+# Estimate copper temperature to be around 100 C
+# Copper.temperature = 100 + 273
+Copper.add_element("Cu", 1.0, "ao")
+Copper.set_density("g/cm3", 8.96)
+
+Beryllium = openmc.Material(name="Beryllium")
+# Estimate Be temperature to be around 100 C
+# Be.temperature = 100 + 273
+Beryllium.add_element("Be", 1.0, "ao")
+Beryllium.set_density("g/cm3", 1.848)

From d31517648827b6006f915faf340f3a5b7ca6adf5 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 17:52:13 -0400
Subject: [PATCH 04/32] adapted vault to materials

---
 libra_toolbox/neutronics/vault.py | 365 +++---------------------------
 1 file changed, 28 insertions(+), 337 deletions(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index c211090..323955c 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -4,7 +4,6 @@ def build_vault_model(
     added_cells=[],
     added_materials=[],
     overall_exclusion_region=None,
-    cross_sections_destination="cross_sections",
 ) -> "openmc.model.Model":
     """
     Builds a complete OpenMC model for a simulation setup representing a
@@ -56,36 +55,29 @@ def build_vault_model(
         import openmc.model
         import openmc_data_downloader as odd
     except ModuleNotFoundError:
-        raise ModuleNotFoundError("openmc and openmc_data_downloader are required.")
+        raise ModuleNotFoundError(
+            "openmc and openmc_data_downloader are required.")
+
+    import materials as mat
+
+    # Define materials
+    Aluminum = mat.Aluminum
+    Air = mat.Air
+    Concrete = mat.Concrete
+    IronConcrete = mat.IronConcrete
+    RicoRad = mat.RicoRad
+    SS304 = mat.SS304
+    Copper = mat.Copper
 
     materials = openmc.Materials(
         [
             Aluminum,
-            Material_2,
             Air,
             Concrete,
             IronConcrete,
-            Material_6,
-            Material_7,
-            Material_8,
-            Material_10,
-            Lead,
-            BPE,
-            Polyethylene,
-            HDPE,
-            Material_22,
-            Material_23,
-            Material_30,
-            Material_40,
-            Soil,
-            Brick,
             RicoRad,
-            Steel,
             SS304,
-            Firebrick,
-            Flibe_nat,
             Copper,
-            Be,
         ]
     )
 
@@ -96,11 +88,11 @@ def build_vault_model(
         libraries=["ENDFB-8.0-NNDC"],
         set_OPENMC_CROSS_SECTIONS=True,
         particles=["neutron"],
-        destination=cross_sections_destination,
     )
     #
     # Definition of the spherical void/blackhole boundary
-    Surface_95 = openmc.Sphere(x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
+    Surface_95 = openmc.Sphere(
+        x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
 
     # 24
     Surface_24 = openmc.model.RectangularParallelepiped(
@@ -183,7 +175,8 @@ def build_vault_model(
 
     # with an angle of 2.8 degrees. The positive vector points towards the
     # lower-right (Southeast) corner of the geometry
-    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885, c=0.0, d=964.9095439999999)
+    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885,
+                              c=0.0, d=964.9095439999999)
 
     # The CMU wall partially covering the north shield wall in Room III
     Vault_north_wall_ext_reg = -Surface_22 & -Surface_48 & +Surface_11
@@ -374,18 +367,22 @@ def build_vault_model(
         fill=Aluminum, region=DANTE_vault_mag_stand_reg
     )
     DANTE_vault_top_magnet_cell = openmc.Cell(
-        fill=Material_2, region=DANTE_vault_top_magnet_reg
+        fill=Copper, region=DANTE_vault_top_magnet_reg
     )
     DANTE_vault_bot_magnet_cell = openmc.Cell(
-        fill=Material_2, region=DANTE_vault_bot_magnet_reg
+        fill=Copper, region=DANTE_vault_bot_magnet_reg
     )
-    I_beam_cell = openmc.Cell(fill=Material_6, region=I_beam_reg)
-    North_vault_wall_cell = openmc.Cell(fill=Concrete, region=North_vault_wall_reg)
+    I_beam_cell = openmc.Cell(fill=SS304, region=I_beam_reg)
+    North_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=North_vault_wall_reg)
     Vault_floor_cell = openmc.Cell(fill=Concrete, region=Vault_floor_reg)
     Vault_ceiling_cell = openmc.Cell(fill=Concrete, region=Vault_ceiling_reg)
-    West_vault_wall_cell = openmc.Cell(fill=Concrete, region=West_vault_wall_reg)
-    East_vault_wall_cell = openmc.Cell(fill=Concrete, region=East_vault_wall_reg)
-    South_vault_wall_cell = openmc.Cell(fill=Concrete, region=South_vault_wall_reg)
+    West_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=West_vault_wall_reg)
+    East_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=East_vault_wall_reg)
+    South_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=South_vault_wall_reg)
     foundation = openmc.Cell(fill=Concrete, region=Region_21)
     Vault_north_wall_ext_cell = openmc.Cell(
         fill=Concrete, region=Vault_north_wall_ext_reg
@@ -465,309 +462,3 @@ def build_vault_model(
     )
 
     return vault_model
-
-
-try:
-    import openmc
-
-    #
-    # **** Natural elements ****
-    #
-    # Aluminum : 2.6989 g/cm3
-    Aluminum = openmc.Material()
-    Aluminum.set_density("g/cm3", 2.6989)
-    Aluminum.add_nuclide("Al27", 1.0, "ao")
-
-    # Copper : 8.96 g/cm3
-    Material_2 = openmc.Material()
-    Material_2.set_density("g/cm3", 8.96)
-    Material_2.add_nuclide("Cu63", 0.6917, "ao")
-    Material_2.add_nuclide("Cu65", 0.3083, "ao")
-
-    # Name: Air
-    # Density : 0.001205 g/cm3
-    # Reference: None
-    # Describes: All atmospheric, non-object chambers
-    Air = openmc.Material(name="Air")
-    Air.set_density("g/cm3", 0.001205)
-    Air.add_element("C", 0.00015, "ao")
-    Air.add_nuclide("N14", 0.784431, "ao")
-    Air.add_nuclide("O16", 0.210748, "ao")
-    Air.add_nuclide("Ar40", 0.004671, "ao")
-
-    # Name: Portland concrete
-    # Density: 2.3 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: facility foundation, floors, walls
-    Concrete = openmc.Material()
-    Concrete.set_density("g/cm3", 2.3)
-    Concrete.add_nuclide("H1", 0.168759, "ao")
-    Concrete.add_element("C", 0.001416, "ao")
-    Concrete.add_nuclide("O16", 0.562524, "ao")
-    Concrete.add_nuclide("Na23", 0.011838, "ao")
-    Concrete.add_element("Mg", 0.0014, "ao")
-    Concrete.add_nuclide("Al27", 0.021354, "ao")
-    Concrete.add_element("Si", 0.204115, "ao")
-    Concrete.add_element("K", 0.005656, "ao")
-    Concrete.add_element("Ca", 0.018674, "ao")
-    Concrete.add_element("Fe", 0.004264, "ao")
-
-    # Name: Portland iron concrete
-    # Density: 3.8 g/cm3 as roughly measured using scale and assuming rectangular prism
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: Potential new walls, shielding doors
-    IronConcrete = openmc.Material()
-    IronConcrete.set_density("g/cm3", 3.8)
-    IronConcrete.add_nuclide("H1", 0.135585, "ao")
-    IronConcrete.add_nuclide("O16", 0.150644, "ao")
-    IronConcrete.add_element("Mg", 0.002215, "ao")
-    IronConcrete.add_nuclide("Al27", 0.005065, "ao")
-    IronConcrete.add_element("Si", 0.013418, "ao")
-    IronConcrete.add_element("S", 0.000646, "ao")
-    IronConcrete.add_element("Ca", 0.040919, "ao")
-    IronConcrete.add_nuclide("Mn55", 0.002638, "ao")
-    IronConcrete.add_element("Fe", 0.648869, "ao")
-
-    # Name: Stainless steel 304
-    # Density: 8.0 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: vacuum pipes, flanges, general steel objects
-    Material_6 = openmc.Material()
-    Material_6.set_density("g/cm3", 8.0)
-    Material_6.add_element("C", 0.00183, "ao")
-    Material_6.add_element("Si", 0.009781, "ao")
-    Material_6.add_nuclide("P31", 0.000408, "ao")
-    Material_6.add_element("S", 0.000257, "ao")
-    Material_6.add_element("Cr", 0.200762, "ao")
-    Material_6.add_nuclide("Mn55", 0.010001, "ao")
-    Material_6.add_element("Fe", 0.690375, "ao")
-    Material_6.add_element("Ni", 0.086587, "ao")
-
-    # Name: Wood (Southern Pine)
-    # Density: 0.64 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: doors
-    Material_7 = openmc.Material()
-    Material_7.set_density("g/cm3", 0.64)
-    Material_7.add_nuclide("H1", 0.462423, "ao")
-    Material_7.add_element("C", 0.323389, "ao")
-    Material_7.add_nuclide("N14", 0.002773, "ao")
-    Material_7.add_nuclide("O16", 0.208779, "ao")
-    Material_7.add_element("Mg", 0.000639, "ao")
-    Material_7.add_element("S", 0.001211, "ao")
-    Material_7.add_element("K", 0.000397, "ao")
-    Material_7.add_element("Ca", 0.000388, "ao")
-
-    # Name: Gypsum (wallboard)
-    # Density: 2.32 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: drywall walls (GWB)
-    Material_8 = openmc.Material()
-    Material_8.set_density("g/cm3", 2.32)
-    Material_8.add_nuclide("H1", 0.333321, "ao")
-    Material_8.add_nuclide("O16", 0.500014, "ao")
-    Material_8.add_element("S", 0.083324, "ao")
-    Material_8.add_element("Ca", 0.083341, "ao")
-
-    # **** Gamma shielding materials ****
-    #
-    # Tungsten : 19.3 g/cm3
-    Material_10 = openmc.Material()
-    Material_10.set_density("g/cm3", 19.3)
-    Material_10.add_nuclide("W182", 0.265, "ao")
-    Material_10.add_nuclide("W183", 0.1431, "ao")
-    Material_10.add_nuclide("W184", 0.3064, "ao")
-    Material_10.add_nuclide("W186", 0.2855, "ao")
-
-    #
-    # Lead : 11.34 g/cm3
-    Lead = openmc.Material()
-    Lead.set_density("g/cm3", 11.34)
-    Lead.add_nuclide("Pb204", 0.014, "ao")
-    Lead.add_nuclide("Pb206", 0.241, "ao")
-    Lead.add_nuclide("Pb207", 0.221, "ao")
-    Lead.add_nuclide("Pb208", 0.524, "ao")
-
-    # Name: Borated Polyethylene (5% B in via B4C additive)
-    # Density: 0.95 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1) but revised to make it 5 wt.% B
-    # Describes: General purpose neutron shielding
-    BPE = openmc.Material()
-    BPE.set_density("g/cm3", 0.95)
-    BPE.add_nuclide("H1", 0.1345, "wo")
-    BPE.add_element("B", 0.0500, "wo")
-    BPE.add_element("C", 0.8155, "wo")
-
-    # Name: Non-borated polyethylene
-    # Density: 0.93 g/cm3
-    # Reference: PNNL Report 15870 (Rev. 1)
-    # Describes: General purpose neutron shielding
-    Polyethylene = openmc.Material()
-    Polyethylene.set_density("g/cm3", 0.93)
-    Polyethylene.add_nuclide("H1", 0.666662, "ao")
-    Polyethylene.add_element("C", 0.333338, "ao")
-
-    # High Density Polyethylene
-    # Reference:  PNNL Report 15870 (Rev. 1)
-    HDPE = openmc.Material(name="HDPE")
-    HDPE.set_density("g/cm3", 0.95)
-    HDPE.add_element("H", 0.143724, "wo")
-    HDPE.add_element("C", 0.856276, "wo")
-
-    # Name: Zirconium dihydride
-    # Density: 5.6 g/cm3
-    # Reference: JNM 386-388 (2009) 119-121
-    # Describes: General purpose neutron shielding
-    Material_22 = openmc.Material()
-    Material_22.set_density("g/cm3", 5.6)
-    Material_22.add_nuclide("H1", 0.0216, "wo")
-    Material_22.add_element("Zr", 0.9784, "wo")
-
-    # Name: Zirconium borohydride
-    # Density: 1.18 g/cm3
-    # Reference: JNM 386-388 (2009) 119-121
-    # Describes: General purpose neutron shielding
-    Material_23 = openmc.Material()
-    Material_23.set_density("g/cm3", 1.18)
-    Material_23.add_nuclide("H1", 0.1073, "wo")
-    Material_23.add_nuclide("B10", 0.0571, "wo")
-    Material_23.add_nuclide("B11", 0.23, "wo")
-    Material_23.add_element("Zr", 0.6056, "wo")
-
-    # Density: 1.848 g/cm3
-    # Reference: None
-    # Describes: Highest intenstiy neutron production target
-    # Notes: Uses ENDF-derived proton nuclear data libray
-    Material_30 = openmc.Material()
-    Material_30.set_density("g/cm3", 1.848)
-    Material_30.add_nuclide("Be9", 1.0, "ao")
-
-    # Name: Concrete (Regular)
-    # Density: 2.3 g/cm3
-    # Reference: Provided by Matthey Carey, MIT EHS/RPP (mgcarey@mit.edu)
-    # Describes: Facility walls, foundation, floors for activation calculations
-    Material_40 = openmc.Material()
-    Material_40.set_density("g/cm3", 2.3)
-    Material_40.add_nuclide("Fe54", 2.0138e-05, "ao")
-    Material_40.add_nuclide("Fe56", 0.00031874, "ao")
-    Material_40.add_nuclide("Fe57", 7.2915e-06, "ao")
-    Material_40.add_nuclide("Fe58", 1.0416e-06, "ao")
-    Material_40.add_nuclide("H1", 0.01374, "ao")
-    Material_40.add_nuclide("H2", 2.0613e-06, "ao")
-    Material_40.add_nuclide("O16", 0.045685, "ao")
-    Material_40.add_nuclide("O17", 1.8318e-05, "ao")
-    Material_40.add_nuclide("Mg24", 9.0027e-05, "ao")
-    Material_40.add_nuclide("Mg25", 1.1397e-05, "ao")
-    Material_40.add_nuclide("Mg26", 1.2548e-05, "ao")
-    Material_40.add_nuclide("Ca40", 0.001474, "ao")
-    Material_40.add_nuclide("Ca42", 9.8378e-06, "ao")
-    Material_40.add_nuclide("Ca43", 2.0527e-06, "ao")
-    Material_40.add_nuclide("Ca44", 3.1718e-05, "ao")
-    Material_40.add_nuclide("Ca46", 6.0821e-08, "ao")
-    Material_40.add_nuclide("Ca48", 2.8434e-06, "ao")
-    Material_40.add_nuclide("Si28", 0.015328, "ao")
-    Material_40.add_nuclide("Si29", 0.00077613, "ao")
-    Material_40.add_nuclide("Si30", 0.0005152, "ao")
-    Material_40.add_nuclide("Na23", 0.00096395, "ao")
-    Material_40.add_nuclide("K39", 0.00042949, "ao")
-    Material_40.add_nuclide("K40", 4.6053e-08, "ao")
-    Material_40.add_nuclide("K41", 3.0993e-05, "ao")
-    Material_40.add_nuclide("Al27", 0.0017453, "ao")
-    Material_40.add_nuclide("C12", 0.00011404, "ao")
-    Material_40.add_nuclide("C13", 1.28e-06, "ao")
-
-    # Soil material taken from PNNL Materials Compendium for Earth, U.S. Average
-    Soil = openmc.Material(name="Soil")
-    Soil.set_density("g/cm3", 1.52)
-    Soil.add_element("O", 0.670604, percent_type="ao")
-    Soil.add_element("Na", 0.005578, percent_type="ao")
-    Soil.add_element("Mg", 0.011432, percent_type="ao")
-    Soil.add_element("Al", 0.053073, percent_type="ao")
-    Soil.add_element("Si", 0.201665, percent_type="ao")
-    Soil.add_element("K", 0.007653, percent_type="ao")
-    Soil.add_element("Ca", 0.026664, percent_type="ao")
-    Soil.add_element("Ti", 0.002009, percent_type="ao")
-    Soil.add_element("Mn", 0.000272, percent_type="ao")
-    Soil.add_element("Fe", 0.021050, percent_type="ao")
-
-    # Brick material taken from "Brick, Common Silica" from the PNNL Materials Compendium
-    # PNNL-15870, Rev. 2
-    Brick = openmc.Material(name="Brick")
-    Brick.set_density("g/cm3", 1.8)
-    Brick.add_element("O", 0.663427, percent_type="ao")
-    Brick.add_element("Al", 0.003747, percent_type="ao")
-    Brick.add_element("Si", 0.323229, percent_type="ao")
-    Brick.add_element("Ca", 0.007063, percent_type="ao")
-    Brick.add_element("Fe", 0.002534, percent_type="ao")
-
-    # Previous model uses 10% borated high density polyethylene, but
-    # according to Melhus, et. al., RicoRad consists of "2.00% mass boron
-    # in a polyethylene-based matrix having a mass density of 0.945 g/cm^3"
-    # Source:
-    # Melhus, Christopher, et al. ‘Storage Safe Shielding Assessment for a
-    # HDR Californium-252 Brachytherapy Source’.
-    # Monte Carlo 2005 Topical Meeting, 01 2005, pp. 219–229.
-
-    RicoRad = openmc.Material(name="RicoRad")
-    RicoRad.set_density("g/cm3", 0.945)
-    RicoRad.add_element("H", 0.14, percent_type="wo")
-    RicoRad.add_element("C", 0.84, percent_type="wo")
-    RicoRad.add_element("B", 0.02, percent_type="wo")
-
-    ### LIBRA Materials
-    Steel = openmc.Material(name="Steel")
-    Steel.add_element("C", 0.005, "wo")
-    Steel.add_element("Fe", 0.995, "wo")
-    Steel.set_density("g/cm3", 7.82)
-
-    # Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2)
-    SS304 = openmc.Material(name="Stainless Steel 304")
-    # SS304.temperature = 700 + 273
-    SS304.add_element("C", 0.000800, "wo")
-    SS304.add_element("Mn", 0.020000, "wo")
-    SS304.add_element("P", 0.000450, "wo")
-    SS304.add_element("S", 0.000300, "wo")
-    SS304.add_element("Si", 0.010000, "wo")
-    SS304.add_element("Cr", 0.190000, "wo")
-    SS304.add_element("Ni", 0.095000, "wo")
-    SS304.add_element("Fe", 0.683450, "wo")
-    SS304.set_density("g/cm3", 8.00)
-
-    # Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2
-    # https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/
-    Firebrick = openmc.Material(name="Firebrick")
-    # Estimate average temperature of Firebrick to be around 300 C
-    # Firebrick.temperature = 273 + 300
-    Firebrick.add_element("Al", 0.004, "ao")
-    Firebrick.add_element("O", 0.666, "ao")
-    Firebrick.add_element("Si", 0.240, "ao")
-    Firebrick.add_element("Zr", 0.090, "ao")
-    Firebrick.set_density("g/cm3", 0.30)
-
-    # Using 2:1 atom ratio of LiF to BeF2, similar to values in
-    # Seifried, Jeffrey E., et al. ‘A General Approach for Determination of
-    # Acceptable FLiBe Impurity Concentrations in Fluoride-Salt Cooled High
-    # Temperature Reactors (FHRs)’. Nuclear Engineering and Design, vol. 343, 2019,
-    # pp. 85–95, https://doi.org10.1016/j.nucengdes.2018.09.038.
-    # Also using natural lithium enrichment (~7.5 a% Li6)
-    Flibe_nat = openmc.Material(name="Flibe_nat")
-    # Flibe_nat.temperature = 700 + 273
-    Flibe_nat.add_element("Be", 0.142857, "ao")
-    Flibe_nat.add_nuclide("Li6", 0.021685, "ao")
-    Flibe_nat.add_nuclide("Li7", 0.264029, "ao")
-    Flibe_nat.add_element("F", 0.571429, "ao")
-    Flibe_nat.set_density("g/cm3", 1.94)
-
-    Copper = openmc.Material(name="Copper")
-    # Estimate copper temperature to be around 100 C
-    # Copper.temperature = 100 + 273
-    Copper.add_element("Cu", 1.0, "ao")
-    Copper.set_density("g/cm3", 8.96)
-
-    Be = openmc.Material(name="Be")
-    # Estimate Be temperature to be around 100 C
-    # Be.temperature = 100 + 273
-    Be.add_element("Be", 1.0, "ao")
-    Be.set_density("g/cm3", 1.848)
-except ImportError:
-    pass

From 742097ac0383fb4192afb84d87357ef4fb28136b Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 17:59:24 -0400
Subject: [PATCH 05/32] cleaned materials

---
 libra_toolbox/neutronics/materials.py | 79 ---------------------------
 1 file changed, 79 deletions(-)

diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py
index e31cd06..64fbfe9 100644
--- a/libra_toolbox/neutronics/materials.py
+++ b/libra_toolbox/neutronics/materials.py
@@ -172,15 +172,6 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 BPE.add_element("B", 0.0500, "wo")
 BPE.add_element("C", 0.8155, "wo")
 
-# Name: Non-borated polyethylene
-# Density: 0.93 g/cm3
-# Reference: PNNL Report 15870 (Rev. 1)
-# Describes: General purpose neutron shielding
-Polyethylene = openmc.Material()
-Polyethylene.set_density("g/cm3", 0.93)
-Polyethylene.add_nuclide("H1", 0.666662, "ao")
-Polyethylene.add_element("C", 0.333338, "ao")
-
 # High Density Polyethylene
 # Reference:  PNNL Report 15870 (Rev. 1)
 HDPE = openmc.Material(name="HDPE")
@@ -188,68 +179,6 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 HDPE.add_element("H", 0.143724, "wo")
 HDPE.add_element("C", 0.856276, "wo")
 
-# Name: Zirconium dihydride
-# Density: 5.6 g/cm3
-# Reference: JNM 386-388 (2009) 119-121
-# Describes: General purpose neutron shielding
-Material_22 = openmc.Material()
-Material_22.set_density("g/cm3", 5.6)
-Material_22.add_nuclide("H1", 0.0216, "wo")
-Material_22.add_element("Zr", 0.9784, "wo")
-
-# Name: Zirconium borohydride
-# Density: 1.18 g/cm3
-# Reference: JNM 386-388 (2009) 119-121
-# Describes: General purpose neutron shielding
-Material_23 = openmc.Material()
-Material_23.set_density("g/cm3", 1.18)
-Material_23.add_nuclide("H1", 0.1073, "wo")
-Material_23.add_nuclide("B10", 0.0571, "wo")
-Material_23.add_nuclide("B11", 0.23, "wo")
-Material_23.add_element("Zr", 0.6056, "wo")
-
-# Density: 1.848 g/cm3
-# Reference: None
-# Describes: Highest intenstiy neutron production target
-# Notes: Uses ENDF-derived proton nuclear data libray
-Material_30 = openmc.Material()
-Material_30.set_density("g/cm3", 1.848)
-Material_30.add_nuclide("Be9", 1.0, "ao")
-
-# Name: Concrete (Regular)
-# Density: 2.3 g/cm3
-# Reference: Provided by Matthey Carey, MIT EHS/RPP (mgcarey@mit.edu)
-# Describes: Facility walls, foundation, floors for activation calculations
-Material_40 = openmc.Material()
-Material_40.set_density("g/cm3", 2.3)
-Material_40.add_nuclide("Fe54", 2.0138e-05, "ao")
-Material_40.add_nuclide("Fe56", 0.00031874, "ao")
-Material_40.add_nuclide("Fe57", 7.2915e-06, "ao")
-Material_40.add_nuclide("Fe58", 1.0416e-06, "ao")
-Material_40.add_nuclide("H1", 0.01374, "ao")
-Material_40.add_nuclide("H2", 2.0613e-06, "ao")
-Material_40.add_nuclide("O16", 0.045685, "ao")
-Material_40.add_nuclide("O17", 1.8318e-05, "ao")
-Material_40.add_nuclide("Mg24", 9.0027e-05, "ao")
-Material_40.add_nuclide("Mg25", 1.1397e-05, "ao")
-Material_40.add_nuclide("Mg26", 1.2548e-05, "ao")
-Material_40.add_nuclide("Ca40", 0.001474, "ao")
-Material_40.add_nuclide("Ca42", 9.8378e-06, "ao")
-Material_40.add_nuclide("Ca43", 2.0527e-06, "ao")
-Material_40.add_nuclide("Ca44", 3.1718e-05, "ao")
-Material_40.add_nuclide("Ca46", 6.0821e-08, "ao")
-Material_40.add_nuclide("Ca48", 2.8434e-06, "ao")
-Material_40.add_nuclide("Si28", 0.015328, "ao")
-Material_40.add_nuclide("Si29", 0.00077613, "ao")
-Material_40.add_nuclide("Si30", 0.0005152, "ao")
-Material_40.add_nuclide("Na23", 0.00096395, "ao")
-Material_40.add_nuclide("K39", 0.00042949, "ao")
-Material_40.add_nuclide("K40", 4.6053e-08, "ao")
-Material_40.add_nuclide("K41", 3.0993e-05, "ao")
-Material_40.add_nuclide("Al27", 0.0017453, "ao")
-Material_40.add_nuclide("C12", 0.00011404, "ao")
-Material_40.add_nuclide("C13", 1.28e-06, "ao")
-
 # Soil material taken from PNNL Materials Compendium for Earth, U.S. Average
 Soil = openmc.Material(name="Soil")
 Soil.set_density("g/cm3", 1.52)
@@ -274,14 +203,6 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 Brick.add_element("Ca", 0.007063, percent_type="ao")
 Brick.add_element("Fe", 0.002534, percent_type="ao")
 
-# Previous model uses 10% borated high density polyethylene, but
-# according to Melhus, et. al., RicoRad consists of "2.00% mass boron
-# in a polyethylene-based matrix having a mass density of 0.945 g/cm^3"
-# Source:
-# Melhus, Christopher, et al. ‘Storage Safe Shielding Assessment for a
-# HDR Californium-252 Brachytherapy Source’.
-# Monte Carlo 2005 Topical Meeting, 01 2005, pp. 219–229.
-
 RicoRad = openmc.Material(name="RicoRad")
 RicoRad.set_density("g/cm3", 0.945)
 RicoRad.add_element("H", 0.14, percent_type="wo")

From 441e5943fd530a4c051a907cb4c14502ca542492 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 18:05:45 -0400
Subject: [PATCH 06/32] + more materials

---
 libra_toolbox/neutronics/materials.py | 48 +++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py
index 64fbfe9..6f960bf 100644
--- a/libra_toolbox/neutronics/materials.py
+++ b/libra_toolbox/neutronics/materials.py
@@ -264,3 +264,51 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 # Be.temperature = 100 + 273
 Beryllium.add_element("Be", 1.0, "ao")
 Beryllium.set_density("g/cm3", 1.848)
+
+# Heater
+Heater_mat = openmc.Material(name="heater")
+Heater_mat.add_element("C", 0.000990, "wo")
+Heater_mat.add_element("Al", 0.003960, "wo")
+Heater_mat.add_element("Si", 0.004950, "wo")
+Heater_mat.add_element("P", 0.000148, "wo")
+Heater_mat.add_element("S", 0.000148, "wo")
+Heater_mat.add_element("Ti", 0.003960, "wo")
+Heater_mat.add_element("Cr", 0.215000, "wo")
+Heater_mat.add_element("Mn", 0.004950, "wo")
+Heater_mat.add_element("Fe", 0.049495, "wo")
+Heater_mat.add_element("Co", 0.009899, "wo")
+Heater_mat.add_element("Ni", 0.580000, "wo")
+Heater_mat.add_element("Nb", 0.036500, "wo")
+Heater_mat.add_element("Mo", 0.090000, "wo")
+Heater_mat.set_density("g/cm3", 2.44)
+
+# Lithium-Lead
+# Composition from certificate of analysis provided with Lithium-Lead from Camex
+Lithium_lead = openmc.Material(name="Lithium Lead")
+Lithium_lead.add_element("Pb", 0.993479, "wo")
+Lithium_lead.add_element("Li", 0.0064, "wo")
+Lithium_lead.add_element("Tl", 0.00002, "wo")
+Lithium_lead.add_element("Zn", 0.000002, "wo")
+Lithium_lead.add_element("Sn", 0.000002, "wo")
+Lithium_lead.add_element("Sb", 0.000002, "wo")
+Lithium_lead.add_element("Ni", 0.000001, "wo")
+Lithium_lead.add_element("Cu", 0.000002, "wo")
+Lithium_lead.add_element("Cd", 0.000002, "wo")
+Lithium_lead.add_element("Bi", 0.00008, "wo")
+Lithium_lead.add_element("As", 0.000002, "wo")
+Lithium_lead.add_element("Ag", 0.000008, "wo")
+Lithium_lead.set_density("g/cm3", 9.10411395)  # Density at 600C
+
+# 316L Stainless Steel
+# Data from https://www.thyssenkrupp-materials.co.uk/stainless-steel-316l-14404.html
+SS316L = openmc.Material(name="316L Steel")
+SS316L.add_element("C", 0.0003, "wo")
+SS316L.add_element("Si", 0.01, "wo")
+SS316L.add_element("Mn", 0.02, "wo")
+SS316L.add_element("P", 0.00045, "wo")
+SS316L.add_element("S", 0.000151, "wo")
+SS316L.add_element("Cr", 0.175, "wo")
+SS316L.add_element("Ni", 0.115, "wo")
+SS316L.add_element("N", 0.001, "wo")
+SS316L.add_element("Mo", 0.00225, "wo")
+SS316L.add_element("Fe", 0.655599, "wo")

From 1bbcfc54c49887392c626088119957fd9afe54c8 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 18:14:03 -0400
Subject: [PATCH 07/32] materials in init

---
 libra_toolbox/neutronics/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libra_toolbox/neutronics/__init__.py b/libra_toolbox/neutronics/__init__.py
index c116466..a6cad33 100644
--- a/libra_toolbox/neutronics/__init__.py
+++ b/libra_toolbox/neutronics/__init__.py
@@ -1,2 +1,3 @@
 from .neutron_source import *
 from .vault import *
+import materials

From b2b4a280a43169971c3eaa52dd6352eb4515f6c5 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 18:18:41 -0400
Subject: [PATCH 08/32] fix init

---
 libra_toolbox/neutronics/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libra_toolbox/neutronics/__init__.py b/libra_toolbox/neutronics/__init__.py
index a6cad33..7755235 100644
--- a/libra_toolbox/neutronics/__init__.py
+++ b/libra_toolbox/neutronics/__init__.py
@@ -1,3 +1,3 @@
 from .neutron_source import *
 from .vault import *
-import materials
+from . import materials

From 14ef6a1a2254e6ae089aa8d670a682926aec5d7a Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 18:23:07 -0400
Subject: [PATCH 09/32] fix vault

---
 libra_toolbox/neutronics/vault.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index 323955c..46ddc79 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -58,16 +58,16 @@ def build_vault_model(
         raise ModuleNotFoundError(
             "openmc and openmc_data_downloader are required.")
 
-    import materials as mat
+    from materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
 
     # Define materials
-    Aluminum = mat.Aluminum
-    Air = mat.Air
-    Concrete = mat.Concrete
-    IronConcrete = mat.IronConcrete
-    RicoRad = mat.RicoRad
-    SS304 = mat.SS304
-    Copper = mat.Copper
+    # Aluminum = mat.Aluminum
+    # Air = mat.Air
+    # Concrete = mat.Concrete
+    # IronConcrete = mat.IronConcrete
+    # RicoRad = mat.RicoRad
+    # SS304 = mat.SS304
+    # Copper = mat.Copper
 
     materials = openmc.Materials(
         [

From 8dec490008827e58119940f02dbc5f4706c99bd7 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Thu, 31 Jul 2025 18:29:24 -0400
Subject: [PATCH 10/32] fix vault

---
 libra_toolbox/neutronics/vault.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index 46ddc79..9d7a907 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -58,7 +58,7 @@ def build_vault_model(
         raise ModuleNotFoundError(
             "openmc and openmc_data_downloader are required.")
 
-    from materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
+    from .materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
 
     # Define materials
     # Aluminum = mat.Aluminum

From f57b44662253e7c951c8ca29d35676652d6cdbd0 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <92783079+SteSeg@users.noreply.github.com>
Date: Fri, 1 Aug 2025 09:38:50 -0400
Subject: [PATCH 11/32] Update libra_toolbox/neutronics/vault.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Rémi Delaporte-Mathurin <40028739+RemDelaporteMathurin@users.noreply.github.com>
---
 libra_toolbox/neutronics/vault.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index 9d7a907..96cac2b 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -60,14 +60,6 @@ def build_vault_model(
 
     from .materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
 
-    # Define materials
-    # Aluminum = mat.Aluminum
-    # Air = mat.Air
-    # Concrete = mat.Concrete
-    # IronConcrete = mat.IronConcrete
-    # RicoRad = mat.RicoRad
-    # SS304 = mat.SS304
-    # Copper = mat.Copper
 
     materials = openmc.Materials(
         [

From 72cacf24f8485c5c205e648f1a9c7856b3484a47 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Fri, 1 Aug 2025 09:41:32 -0400
Subject: [PATCH 12/32] black formatting

---
 libra_toolbox/neutronics/materials.py | 88 +++++++++++++--------------
 libra_toolbox/neutronics/vault.py     | 22 +++----
 2 files changed, 50 insertions(+), 60 deletions(-)

diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py
index 6f960bf..905f4f5 100644
--- a/libra_toolbox/neutronics/materials.py
+++ b/libra_toolbox/neutronics/materials.py
@@ -2,12 +2,12 @@
 
 
 def get_exp_cllif_density(temp, LiCl_frac=0.695):
-    """ Calculates density of ClLiF [g/cc] from temperature in Celsius
-    and molar concentration of LiCl. Valid for 660 C - 1000 C. 
-    Source: 
-    G. J. Janz, R. P. T. Tomkins, C. B. Allen; 
-    Molten Salts: Volume 4, Part 4 
-    Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data. 
+    """Calculates density of ClLiF [g/cc] from temperature in Celsius
+    and molar concentration of LiCl. Valid for 660 C - 1000 C.
+    Source:
+    G. J. Janz, R. P. T. Tomkins, C. B. Allen;
+    Molten Salts: Volume 4, Part 4
+    Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data.
     J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302.
     https://doi.org/10.1063/1.555590
     """
@@ -22,8 +22,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
     f = 8.73570e-9
     g = -5.11184e-10
 
-    rho = a + b * C + c * temp + d * C**2 \
-        + e * C**3 + f * temp * C**2 + g * C * temp**2
+    rho = a + b * C + c * temp + d * C**2 + e * C**3 + f * temp * C**2 + g * C * temp**2
 
     return rho
 
@@ -31,36 +30,36 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 # Define Materials
 # Source: PNNL Materials Compendium April 2021
 # PNNL-15870, Rev. 2
-Inconel625 = openmc.Material(name='Inconel 625')
-Inconel625.set_density('g/cm3', 8.44)
-Inconel625.add_element('C',  0.000990, 'wo')
-Inconel625.add_element('Al', 0.003960, 'wo')
-Inconel625.add_element('Si', 0.004950, 'wo')
-Inconel625.add_element('P',  0.000148, 'wo')
-Inconel625.add_element('S',  0.000148, 'wo')
-Inconel625.add_element('Ti', 0.003960, 'wo')
-Inconel625.add_element('Cr', 0.215000, 'wo')
-Inconel625.add_element('Mn', 0.004950, 'wo')
-Inconel625.add_element('Fe', 0.049495, 'wo')
-Inconel625.add_element('Co', 0.009899, 'wo')
-Inconel625.add_element('Ni', 0.580000, 'wo')
-Inconel625.add_element('Nb', 0.036500, 'wo')
-Inconel625.add_element('Mo', 0.090000, 'wo')
+Inconel625 = openmc.Material(name="Inconel 625")
+Inconel625.set_density("g/cm3", 8.44)
+Inconel625.add_element("C", 0.000990, "wo")
+Inconel625.add_element("Al", 0.003960, "wo")
+Inconel625.add_element("Si", 0.004950, "wo")
+Inconel625.add_element("P", 0.000148, "wo")
+Inconel625.add_element("S", 0.000148, "wo")
+Inconel625.add_element("Ti", 0.003960, "wo")
+Inconel625.add_element("Cr", 0.215000, "wo")
+Inconel625.add_element("Mn", 0.004950, "wo")
+Inconel625.add_element("Fe", 0.049495, "wo")
+Inconel625.add_element("Co", 0.009899, "wo")
+Inconel625.add_element("Ni", 0.580000, "wo")
+Inconel625.add_element("Nb", 0.036500, "wo")
+Inconel625.add_element("Mo", 0.090000, "wo")
 
 # alumina insulation
 # data from https://precision-ceramics.com/materials/alumina/
-Alumina = openmc.Material(name='Alumina insulation')
-Alumina.add_element('O', 0.6, 'ao')
-Alumina.add_element('Al', 0.4, 'ao')
-Alumina.set_density('g/cm3', 3.98)
+Alumina = openmc.Material(name="Alumina insulation")
+Alumina.add_element("O", 0.6, "ao")
+Alumina.add_element("Al", 0.4, "ao")
+Alumina.set_density("g/cm3", 3.98)
 
 # epoxy
-Epoxy = openmc.Material(name='Epoxy')
-Epoxy.add_element('C', 0.70, 'wo')
-Epoxy.add_element('H', 0.08, 'wo')
-Epoxy.add_element('O', 0.15, 'wo')
-Epoxy.add_element('N', 0.07, 'wo')
-Epoxy.set_density('g/cm3', 1.2)
+Epoxy = openmc.Material(name="Epoxy")
+Epoxy.add_element("C", 0.70, "wo")
+Epoxy.add_element("H", 0.08, "wo")
+Epoxy.add_element("O", 0.15, "wo")
+Epoxy.add_element("N", 0.07, "wo")
+Epoxy.set_density("g/cm3", 1.2)
 
 # helium @5psig
 pressure = 34473.8  # Pa ~ 5 psig
@@ -69,8 +68,8 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 density = pressure / (R_he * temperature)  # in kg/cm^3
 density *= 1 / 1000  # in g/cm^3
 Helium = openmc.Material(name="Helium")
-Helium.add_element('He', 1.0, 'ao')
-Helium.set_density('g/cm3', density)
+Helium.add_element("He", 1.0, "ao")
+Helium.set_density("g/cm3", density)
 
 # PbLi - eutectic - natural - pure
 Pbli = openmc.Material(name="pbli")
@@ -79,21 +78,20 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 Pbli.set_density("g/cm3", 11)
 
 # lif-licl - eutectic - natural - pure
-Cllif = openmc.Material(name='ClLiF')
+Cllif = openmc.Material(name="ClLiF")
 LiCl_frac = 0.695  # at.fr.
 
-Cllif.add_element('F', .5*(1 - LiCl_frac), 'ao')
-Cllif.add_element('Li', 1.0, 'ao')
-Cllif.add_element('Cl', .5*LiCl_frac, 'ao')
-Cllif.set_density('g/cm3', get_exp_cllif_density(650)
-                  )  # 69.5 at. % LiCL at 650 C
+Cllif.add_element("F", 0.5 * (1 - LiCl_frac), "ao")
+Cllif.add_element("Li", 1.0, "ao")
+Cllif.add_element("Cl", 0.5 * LiCl_frac, "ao")
+Cllif.set_density("g/cm3", get_exp_cllif_density(650))  # 69.5 at. % LiCL at 650 C
 
 # lif-licl - eutectic - natural - EuF3 spiced
 Spicyclif = openmc.Material(name="spicyclif")
-Spicyclif.add_element("F", .15935, "wo")
-Spicyclif.add_element("Li", .17857, "wo")
-Spicyclif.add_element("Cl", .6340, "wo")
-Spicyclif.add_element("Eu", .0279, "wo")
+Spicyclif.add_element("F", 0.15935, "wo")
+Spicyclif.add_element("Li", 0.17857, "wo")
+Spicyclif.add_element("Cl", 0.6340, "wo")
+Spicyclif.add_element("Eu", 0.0279, "wo")
 
 # FLiNaK - eutectic - natural - pure
 Flinak = openmc.Material(name="flinak")
diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index 96cac2b..f29366a 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -55,12 +55,10 @@ def build_vault_model(
         import openmc.model
         import openmc_data_downloader as odd
     except ModuleNotFoundError:
-        raise ModuleNotFoundError(
-            "openmc and openmc_data_downloader are required.")
+        raise ModuleNotFoundError("openmc and openmc_data_downloader are required.")
 
     from .materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
 
-
     materials = openmc.Materials(
         [
             Aluminum,
@@ -83,8 +81,7 @@ def build_vault_model(
     )
     #
     # Definition of the spherical void/blackhole boundary
-    Surface_95 = openmc.Sphere(
-        x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
+    Surface_95 = openmc.Sphere(x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
 
     # 24
     Surface_24 = openmc.model.RectangularParallelepiped(
@@ -167,8 +164,7 @@ def build_vault_model(
 
     # with an angle of 2.8 degrees. The positive vector points towards the
     # lower-right (Southeast) corner of the geometry
-    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885,
-                              c=0.0, d=964.9095439999999)
+    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885, c=0.0, d=964.9095439999999)
 
     # The CMU wall partially covering the north shield wall in Room III
     Vault_north_wall_ext_reg = -Surface_22 & -Surface_48 & +Surface_11
@@ -365,16 +361,12 @@ def build_vault_model(
         fill=Copper, region=DANTE_vault_bot_magnet_reg
     )
     I_beam_cell = openmc.Cell(fill=SS304, region=I_beam_reg)
-    North_vault_wall_cell = openmc.Cell(
-        fill=Concrete, region=North_vault_wall_reg)
+    North_vault_wall_cell = openmc.Cell(fill=Concrete, region=North_vault_wall_reg)
     Vault_floor_cell = openmc.Cell(fill=Concrete, region=Vault_floor_reg)
     Vault_ceiling_cell = openmc.Cell(fill=Concrete, region=Vault_ceiling_reg)
-    West_vault_wall_cell = openmc.Cell(
-        fill=Concrete, region=West_vault_wall_reg)
-    East_vault_wall_cell = openmc.Cell(
-        fill=Concrete, region=East_vault_wall_reg)
-    South_vault_wall_cell = openmc.Cell(
-        fill=Concrete, region=South_vault_wall_reg)
+    West_vault_wall_cell = openmc.Cell(fill=Concrete, region=West_vault_wall_reg)
+    East_vault_wall_cell = openmc.Cell(fill=Concrete, region=East_vault_wall_reg)
+    South_vault_wall_cell = openmc.Cell(fill=Concrete, region=South_vault_wall_reg)
     foundation = openmc.Cell(fill=Concrete, region=Region_21)
     Vault_north_wall_ext_cell = openmc.Cell(
         fill=Concrete, region=Vault_north_wall_ext_reg

From 289bde45d943e5e9810c2c486cd586c16d0588c0 Mon Sep 17 00:00:00 2001
From: Stefano Segantin <segantin@psfc.mit.edu>
Date: Wed, 6 Aug 2025 16:59:23 -0400
Subject: [PATCH 13/32] + xs destination arg

---
 libra_toolbox/neutronics/vault.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index f29366a..4d777f3 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -4,6 +4,7 @@ def build_vault_model(
     added_cells=[],
     added_materials=[],
     overall_exclusion_region=None,
+    cross_sections_destination="cross_sections",
 ) -> "openmc.model.Model":
     """
     Builds a complete OpenMC model for a simulation setup representing a
@@ -55,7 +56,8 @@ def build_vault_model(
         import openmc.model
         import openmc_data_downloader as odd
     except ModuleNotFoundError:
-        raise ModuleNotFoundError("openmc and openmc_data_downloader are required.")
+        raise ModuleNotFoundError(
+            "openmc and openmc_data_downloader are required.")
 
     from .materials import Aluminum, Air, Concrete, IronConcrete, RicoRad, SS304, Copper
 
@@ -78,10 +80,12 @@ def build_vault_model(
         libraries=["ENDFB-8.0-NNDC"],
         set_OPENMC_CROSS_SECTIONS=True,
         particles=["neutron"],
+        destination=cross_sections_destination,
     )
     #
     # Definition of the spherical void/blackhole boundary
-    Surface_95 = openmc.Sphere(x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
+    Surface_95 = openmc.Sphere(
+        x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum")
 
     # 24
     Surface_24 = openmc.model.RectangularParallelepiped(
@@ -164,7 +168,8 @@ def build_vault_model(
 
     # with an angle of 2.8 degrees. The positive vector points towards the
     # lower-right (Southeast) corner of the geometry
-    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885, c=0.0, d=964.9095439999999)
+    Surface_48 = openmc.Plane(a=0.99881, b=-0.04885,
+                              c=0.0, d=964.9095439999999)
 
     # The CMU wall partially covering the north shield wall in Room III
     Vault_north_wall_ext_reg = -Surface_22 & -Surface_48 & +Surface_11
@@ -361,12 +366,16 @@ def build_vault_model(
         fill=Copper, region=DANTE_vault_bot_magnet_reg
     )
     I_beam_cell = openmc.Cell(fill=SS304, region=I_beam_reg)
-    North_vault_wall_cell = openmc.Cell(fill=Concrete, region=North_vault_wall_reg)
+    North_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=North_vault_wall_reg)
     Vault_floor_cell = openmc.Cell(fill=Concrete, region=Vault_floor_reg)
     Vault_ceiling_cell = openmc.Cell(fill=Concrete, region=Vault_ceiling_reg)
-    West_vault_wall_cell = openmc.Cell(fill=Concrete, region=West_vault_wall_reg)
-    East_vault_wall_cell = openmc.Cell(fill=Concrete, region=East_vault_wall_reg)
-    South_vault_wall_cell = openmc.Cell(fill=Concrete, region=South_vault_wall_reg)
+    West_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=West_vault_wall_reg)
+    East_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=East_vault_wall_reg)
+    South_vault_wall_cell = openmc.Cell(
+        fill=Concrete, region=South_vault_wall_reg)
     foundation = openmc.Cell(fill=Concrete, region=Region_21)
     Vault_north_wall_ext_cell = openmc.Cell(
         fill=Concrete, region=Vault_north_wall_ext_reg

From f6184d97f4bb3ac11d0d56a13b65c2b0c238c323 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?R=C3=A9mi=20Delaporte-Mathurin?=
 <40028739+RemDelaporteMathurin@users.noreply.github.com>
Date: Thu, 14 Aug 2025 16:55:03 -0400
Subject: [PATCH 14/32] fix duplicate materials

---
 libra_toolbox/neutronics/vault.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libra_toolbox/neutronics/vault.py b/libra_toolbox/neutronics/vault.py
index 4d777f3..13e4ea9 100644
--- a/libra_toolbox/neutronics/vault.py
+++ b/libra_toolbox/neutronics/vault.py
@@ -74,7 +74,9 @@ def build_vault_model(
     )
 
     # Add materials from imported model
-    materials += added_materials
+    for mat in added_materials:
+        if mat not in materials:
+            materials.append(mat)
 
     materials.download_cross_section_data(
         libraries=["ENDFB-8.0-NNDC"],

From 646a1d095bd604318975c92a2c058b7a11d5e233 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Thu, 21 Aug 2025 23:59:52 -0400
Subject: [PATCH 15/32] Added start_index to get_peak kwargs

---
 libra_toolbox/neutron_detection/activation_foils/compass.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 285b51d..b2e8e21 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -558,6 +558,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
 
         # update the parameters if kwargs are provided
         if kwargs:
+            start_index = kwargs.get("start_index", start_index)
             prominence = kwargs.get("prominence", prominence)
             height = kwargs.get("height", height)
             width = kwargs.get("width", width)

From f91bb28db56ef77dff7a1473f9ecdb2d8cd07ce0 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Mon, 25 Aug 2025 17:34:26 -0400
Subject: [PATCH 16/32] Added test for get_peaks

---
 test/neutron_detection/test_compass.py | 54 ++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index d01f2e2..e46916f 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -575,6 +575,60 @@ def test_check_source_detection_efficiency(expected_efficiency):
     # TEST
     assert np.isclose(computed_efficiency, expected_efficiency, rtol=1e-2)
 
+@pytest.mark.parametrize(
+    "peak_energies, width, start_index, expected_peaks",
+    [
+        ([400, 800], 50, 0, [400, 800]),
+        ([400, 800], 50, 600, [800]),
+        ([200, 250, 400], 5, 230, [250, 400]),
+    ],
+)
+def test_get_peaks(peak_energies, width, start_index, expected_peaks):
+    nb_events_measured = 60000
+    channel_nb = 1
+
+    overall_energy_events = np.array([])
+
+    for energy_level in peak_energies:
+        energy_events = np.random.normal(
+            loc=energy_level, scale=width, size=int(nb_events_measured)
+        )
+        overall_energy_events = np.concatenate((overall_energy_events, energy_events))
+
+    random_noise = np.random.uniform(0, 3000, size=int(nb_events_measured))
+    overall_energy_events = np.concatenate((overall_energy_events, random_noise))
+
+    # make sure the min and max are in the range of the detector
+    overall_energy_events[0] = 1
+    overall_energy_events[-1] = 3000
+
+    time_events = np.random.uniform(0, 100, size=int(nb_events_measured * (len(peak_energies) + 1)))
+
+    test_nuclide = Nuclide(
+        name="TestNuclide",
+        energy=peak_energies,
+        intensity=[1.0]*len(peak_energies),
+        half_life=10000,
+    )
+    check_source = CheckSource(
+        nuclide=test_nuclide,
+        activity_date=datetime.datetime(2024, 11, 7),
+        activity=5000,
+    )
+
+    # create CheckSourceMeasurement
+    measurement = compass.CheckSourceMeasurement(name="test measurement")
+    measurement.check_source = check_source
+    measurement.start_time = datetime.datetime(2024, 11, 7)
+    detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None)
+    detector.events = np.column_stack((time_events, overall_energy_events))
+    hist, bins = detector.get_energy_hist(bins=None)
+    
+    peak_indices = measurement.get_peaks(hist, start_index=start_index)
+
+    assert len(peak_indices) == len(expected_peaks)
+    assert np.allclose(expected_peaks - bins[peak_indices], 0, atol=2*width)
+
 
 @pytest.mark.parametrize(
     "a, b",

From e88b442a41649c2ba12c68df8172feff53a9ca26 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Mon, 25 Aug 2025 17:34:43 -0400
Subject: [PATCH 17/32] Temporary plotting of test

---
 test/neutron_detection/temp.ipynb | 156 ++++++++++++++++++++++++++++++
 1 file changed, 156 insertions(+)
 create mode 100644 test/neutron_detection/temp.ipynb

diff --git a/test/neutron_detection/temp.ipynb b/test/neutron_detection/temp.ipynb
new file mode 100644
index 0000000..cf9ad18
--- /dev/null
+++ b/test/neutron_detection/temp.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80681a86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import libra_toolbox.neutron_detection.activation_foils.compass as compass\n",
+    "from libra_toolbox.neutron_detection.activation_foils.calibration import Nuclide, CheckSource\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3038d9b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (3,) (2,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 56\u001b[39m\n\u001b[32m     53\u001b[39m ax.vlines(bins[start_index], \u001b[32m0\u001b[39m, np.max(hist), colors=\u001b[33m\"\u001b[39m\u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m, linestyles=\u001b[33m\"\u001b[39m\u001b[33mdashed\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     54\u001b[39m ax.plot(bins[peak_indices], hist[peak_indices], \u001b[33m\"\u001b[39m\u001b[33mo\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33morange\u001b[39m\u001b[33m\"\u001b[39m, markersize=\u001b[32m10\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28mprint\u001b[39m(np.allclose(\u001b[43mpeak_energies\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpeak_indices\u001b[49m\u001b[43m]\u001b[49m, \u001b[32m0\u001b[39m, atol=\u001b[32m2\u001b[39m*width))\n",
+      "\u001b[31mValueError\u001b[39m: operands could not be broadcast together with shapes (3,) (2,) "
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nb_events_measured = 60000\n",
+    "channel_nb = 1\n",
+    "cases = [\n",
+    "        ([400, 800], 50, 0, [400, 800]),\n",
+    "        ([400, 800], 50, 600, [800]),\n",
+    "        ([200, 250, 400], 5, 230, [250, 400]),\n",
+    "    ]\n",
+    "\n",
+    "for peak_energies, width, start_index, expected_peaks in cases:\n",
+    "\n",
+    "    overall_energy_events = np.array([])\n",
+    "\n",
+    "    for energy_level in peak_energies:\n",
+    "        energy_events = np.random.normal(\n",
+    "            loc=energy_level, scale=width, size=int(nb_events_measured)\n",
+    "        )\n",
+    "        overall_energy_events = np.concatenate((overall_energy_events, energy_events))\n",
+    "\n",
+    "    random_noise = np.random.uniform(0, 3000, size=int(nb_events_measured))\n",
+    "    overall_energy_events = np.concatenate((overall_energy_events, random_noise))\n",
+    "\n",
+    "    # make sure the min and max are in the range of the detector\n",
+    "    overall_energy_events[0] = 1\n",
+    "    overall_energy_events[-1] = 3000\n",
+    "\n",
+    "    time_events = np.random.uniform(0, 100, size=int(nb_events_measured * (len(peak_energies) + 1)))\n",
+    "\n",
+    "    test_nuclide = Nuclide(\n",
+    "        name=\"TestNuclide\",\n",
+    "        energy=peak_energies,\n",
+    "        intensity=[1.0]*len(peak_energies),\n",
+    "        half_life=10000,\n",
+    "    )\n",
+    "    check_source = CheckSource(\n",
+    "        nuclide=test_nuclide,\n",
+    "        activity_date=datetime.datetime(2024, 11, 7),\n",
+    "        activity=5000,\n",
+    "    )\n",
+    "\n",
+    "    # create CheckSourceMeasurement\n",
+    "    measurement = compass.CheckSourceMeasurement(name=\"test measurement\")\n",
+    "    measurement.check_source = check_source\n",
+    "    # measurement.start_time = datetime.datetime(2024, 11, 7)\n",
+    "    detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None)\n",
+    "    detector.events = np.column_stack((time_events, overall_energy_events))\n",
+    "    hist, bins = detector.get_energy_hist(bins=None)\n",
+    "\n",
+    "    peak_indices = measurement.get_peaks(hist, start_index=start_index)\n",
+    "\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "    ax.stairs(hist, bins)\n",
+    "    ax.vlines(bins[start_index], 0, np.max(hist), colors=\"r\", linestyles=\"dashed\")\n",
+    "    ax.plot(bins[peak_indices], hist[peak_indices], \"o\", color=\"orange\", markersize=10)\n",
+    "\n",
+    "\n",
+    "    print(len(peak_indices) == len(expected_peaks))\n",
+    "    print(np.allclose(expected_peaks - bins[peak_indices], 0, atol=2*width))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 94c010aab9a0cd0cca2988b8df41db394a1d0c71 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Thu, 28 Aug 2025 10:35:46 -0400
Subject: [PATCH 18/32] Removed notebook

---
 test/neutron_detection/temp.ipynb | 156 ------------------------------
 1 file changed, 156 deletions(-)
 delete mode 100644 test/neutron_detection/temp.ipynb

diff --git a/test/neutron_detection/temp.ipynb b/test/neutron_detection/temp.ipynb
deleted file mode 100644
index cf9ad18..0000000
--- a/test/neutron_detection/temp.ipynb
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "80681a86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import libra_toolbox.neutron_detection.activation_foils.compass as compass\n",
-    "from libra_toolbox.neutron_detection.activation_foils.calibration import Nuclide, CheckSource\n",
-    "import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3038d9b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n",
-      "False\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (3,) (2,) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 56\u001b[39m\n\u001b[32m     53\u001b[39m ax.vlines(bins[start_index], \u001b[32m0\u001b[39m, np.max(hist), colors=\u001b[33m\"\u001b[39m\u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m, linestyles=\u001b[33m\"\u001b[39m\u001b[33mdashed\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     54\u001b[39m ax.plot(bins[peak_indices], hist[peak_indices], \u001b[33m\"\u001b[39m\u001b[33mo\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33morange\u001b[39m\u001b[33m\"\u001b[39m, markersize=\u001b[32m10\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28mprint\u001b[39m(np.allclose(\u001b[43mpeak_energies\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpeak_indices\u001b[49m\u001b[43m]\u001b[49m, \u001b[32m0\u001b[39m, atol=\u001b[32m2\u001b[39m*width))\n",
-      "\u001b[31mValueError\u001b[39m: operands could not be broadcast together with shapes (3,) (2,) "
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nb_events_measured = 60000\n",
-    "channel_nb = 1\n",
-    "cases = [\n",
-    "        ([400, 800], 50, 0, [400, 800]),\n",
-    "        ([400, 800], 50, 600, [800]),\n",
-    "        ([200, 250, 400], 5, 230, [250, 400]),\n",
-    "    ]\n",
-    "\n",
-    "for peak_energies, width, start_index, expected_peaks in cases:\n",
-    "\n",
-    "    overall_energy_events = np.array([])\n",
-    "\n",
-    "    for energy_level in peak_energies:\n",
-    "        energy_events = np.random.normal(\n",
-    "            loc=energy_level, scale=width, size=int(nb_events_measured)\n",
-    "        )\n",
-    "        overall_energy_events = np.concatenate((overall_energy_events, energy_events))\n",
-    "\n",
-    "    random_noise = np.random.uniform(0, 3000, size=int(nb_events_measured))\n",
-    "    overall_energy_events = np.concatenate((overall_energy_events, random_noise))\n",
-    "\n",
-    "    # make sure the min and max are in the range of the detector\n",
-    "    overall_energy_events[0] = 1\n",
-    "    overall_energy_events[-1] = 3000\n",
-    "\n",
-    "    time_events = np.random.uniform(0, 100, size=int(nb_events_measured * (len(peak_energies) + 1)))\n",
-    "\n",
-    "    test_nuclide = Nuclide(\n",
-    "        name=\"TestNuclide\",\n",
-    "        energy=peak_energies,\n",
-    "        intensity=[1.0]*len(peak_energies),\n",
-    "        half_life=10000,\n",
-    "    )\n",
-    "    check_source = CheckSource(\n",
-    "        nuclide=test_nuclide,\n",
-    "        activity_date=datetime.datetime(2024, 11, 7),\n",
-    "        activity=5000,\n",
-    "    )\n",
-    "\n",
-    "    # create CheckSourceMeasurement\n",
-    "    measurement = compass.CheckSourceMeasurement(name=\"test measurement\")\n",
-    "    measurement.check_source = check_source\n",
-    "    # measurement.start_time = datetime.datetime(2024, 11, 7)\n",
-    "    detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None)\n",
-    "    detector.events = np.column_stack((time_events, overall_energy_events))\n",
-    "    hist, bins = detector.get_energy_hist(bins=None)\n",
-    "\n",
-    "    peak_indices = measurement.get_peaks(hist, start_index=start_index)\n",
-    "\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
-    "    ax.stairs(hist, bins)\n",
-    "    ax.vlines(bins[start_index], 0, np.max(hist), colors=\"r\", linestyles=\"dashed\")\n",
-    "    ax.plot(bins[peak_indices], hist[peak_indices], \"o\", color=\"orange\", markersize=10)\n",
-    "\n",
-    "\n",
-    "    print(len(peak_indices) == len(expected_peaks))\n",
-    "    print(np.allclose(expected_peaks - bins[peak_indices], 0, atol=2*width))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From fa711fca3d1d0aa101cc5473002a2180e7c18f74 Mon Sep 17 00:00:00 2001
From: SteSeg <segantin@mit.edu>
Date: Wed, 17 Sep 2025 09:09:40 -0400
Subject: [PATCH 19/32] fix cllif composition

---
 libra_toolbox/neutronics/materials.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py
index 905f4f5..8e780a8 100644
--- a/libra_toolbox/neutronics/materials.py
+++ b/libra_toolbox/neutronics/materials.py
@@ -82,7 +82,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695):
 LiCl_frac = 0.695  # at.fr.
 
 Cllif.add_element("F", 0.5 * (1 - LiCl_frac), "ao")
-Cllif.add_element("Li", 1.0, "ao")
+Cllif.add_element("Li", 0.5 * (1 - LiCl_frac) + 0.5 * LiCl_frac, "ao")
 Cllif.add_element("Cl", 0.5 * LiCl_frac, "ao")
 Cllif.set_density("g/cm3", get_exp_cllif_density(650))  # 69.5 at. % LiCL at 650 C
 

From d64deb57b4a3de2921a30f39ff9b0fa69ac9869e Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Mon, 5 Jan 2026 15:55:46 -0500
Subject: [PATCH 20/32] added kwargs to get_calibration_data

---
 .../neutron_detection/activation_foils/compass.py     | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index b2e8e21..eddc5fc 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -751,6 +751,7 @@ def get_calibration_data(
     check_source_measurements: List[CheckSourceMeasurement],
     background_measurement: Measurement,
     channel_nb: int,
+    peak_kwargs: dict = None,
 ):
     background_detector = [
         detector
@@ -769,7 +770,15 @@ def get_calibration_data(
             hist, bin_edges = detector.get_energy_hist_background_substract(
                 background_detector, bins=None
             )
-            peaks_ind = measurement.get_peaks(hist)
+            if peak_kwargs is not None:
+                if measurement.check_source.nuclide in peak_kwargs.keys():
+                    kwargs = peak_kwargs[measurement.check_source.nuclide]
+                else:
+                    kwargs = {}
+            else:
+                kwargs = {}
+
+            peaks_ind = measurement.get_peaks(hist, **kwargs)
             peaks = bin_edges[peaks_ind]
 
             if len(peaks) != len(measurement.check_source.nuclide.energy):

From a16f6ad79b9e156f34de0bb909ffe8348611cfbc Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Fri, 9 Jan 2026 14:38:33 -0500
Subject: [PATCH 21/32] Search for peaks at measured energies

---
 .../activation_foils/calibration.py           | 28 ++++++---
 .../activation_foils/compass.py               | 60 ++++++++++++-------
 2 files changed, 58 insertions(+), 30 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py
index 5d5956e..7ea2878 100644
--- a/libra_toolbox/neutron_detection/activation_foils/calibration.py
+++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py
@@ -1,5 +1,5 @@
-from dataclasses import dataclass
-from typing import List
+from dataclasses import dataclass, field
+from typing import List, Dict, Optional
 import datetime
 import numpy as np
 
@@ -26,13 +26,12 @@ class Nuclide:
     """
 
     name: str
-    energy: List[float] = None
-    intensity: List[float] = None
-    half_life: float = None
-    atomic_mass: float = None
+    energy: Optional[List[float]] = None
+    intensity: Optional[List[float]] = None
+    half_life: Optional[float] = None
+    atomic_mass: Optional[float] = None
     abundance: float = 1.00
-    peak_widths: List[float] = None
-    calibrated_peak_widths: List[float] = None
+    _uncalibrated_measured_energies: Optional[Dict] = field(default=None, repr=False)
 
     @property
     def decay_constant(self):
@@ -40,6 +39,19 @@ def decay_constant(self):
         Returns the decay constant of the nuclide in 1/s.
         """
         return np.log(2) / self.half_life
+    
+    def calibrated_measured_energies(self, channel_nb, calibration_coeffs):
+        """
+        Returns the calibrated measured energies of the nuclide in keV.
+        """
+        if self._uncalibrated_measured_energies is None:
+            return None
+        else:
+            uncalibrated = np.array(
+                self._uncalibrated_measured_energies.get(channel_nb, []),
+                dtype=float
+            )
+            return np.polyval(calibration_coeffs, uncalibrated)
 
 
 # ba133 = Nuclide(
diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index eddc5fc..a82832d 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -417,6 +417,7 @@ def compute_detection_efficiency(
         channel_nb: int,
         search_width: float = 800,
         threshold_overlap: float = 200,
+        summing_method: str = 'sum_gaussian',
     ) -> Union[np.ndarray, float]:
         """
         Computes the detection efficiency of a check source given the
@@ -449,17 +450,17 @@ def compute_detection_efficiency(
         )
 
         calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges)
-        # width_calibration_coeffs = np.append(calibration_coeffs[:-1], 0)
-        # self.check_source.nuclide.calibrated_peak_widths = np.polyval(calibration_coeffs,
-        #                                                               self.check_source.nuclide.calibrated_peak_widths)
-        print('libra-toolbox search_width:', search_width, ' threshold_overlap:', threshold_overlap)
+        print('Uncalibrated measured energies:', self.check_source.nuclide._uncalibrated_measured_energies[channel_nb])
+        print('Calibration coefficients:', calibration_coeffs)
+        print('Calibrated measured energies:', self.check_source.nuclide.calibrated_measured_energies(channel_nb, calibration_coeffs))
 
         nb_counts_measured = get_multipeak_area(
             hist,
             calibrated_bin_edges,
-            self.check_source.nuclide.energy,
+            self.check_source.nuclide.calibrated_measured_energies(channel_nb, calibration_coeffs),
             search_width=search_width,
             threshold_overlap=threshold_overlap,
+            summing_method=summing_method,
         )
 
         nb_counts_measured = np.array(nb_counts_measured)
@@ -530,24 +531,25 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
         elif self.detector_type.lower() == 'hpge':
             # peak finding parameters for HPGe detectors
             start_index = 10
-            prominence = 0.10 * np.max(hist[start_index:])
-            height = 0.10 * np.max(hist[start_index:])
-            width = [2, 30]
-            distance = 10
+            prominence = 0.50 * np.max(hist[start_index:])
+            height = 0.50 * np.max(hist[start_index:])
+            width = [2, 50]
+            distance = 100
             if self.check_source.nuclide == na22:
                 start_index = 100
                 height = 0.2 * np.max(hist[start_index:])
                 prominence = 0.2 * np.max(hist[start_index:])
                 distance = 100
             elif self.check_source.nuclide == co60:
-                height = 0.2 * np.max(hist[start_index:])
-                prominence = 0.2 * np.max(hist[start_index:])
+                height = 0.5 * np.max(hist[start_index:])
+                prominence = 0.5 * np.max(hist[start_index:])
             elif self.check_source.nuclide == ba133:
                 start_index = 150
                 height = 0.10 * np.max(hist[start_index:])
                 prominence = 0.10 * np.max(hist[start_index:])
                 distance = 10
             elif self.check_source.nuclide == mn54:
+                start_index = 400
                 height = 0.9 * np.max(hist[start_index:])
                 prominence = 0.9 * np.max(hist[start_index:])
                 distance = 100
@@ -788,6 +790,10 @@ def get_calibration_data(
                 )
             calibration_channels += list(peaks)
             calibration_energies += measurement.check_source.nuclide.energy
+            # Store the uncalibrated measured energies in the measurement object for later use
+            if measurement.check_source.nuclide._uncalibrated_measured_energies is None:
+                measurement.check_source.nuclide._uncalibrated_measured_energies = {}
+            measurement.check_source.nuclide._uncalibrated_measured_energies[channel_nb] = list(peaks)
 
     inds = np.argsort(calibration_channels)
     calibration_channels = np.array(calibration_channels)[inds]
@@ -840,8 +846,9 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
             peak_ergs[i],
             search_width / (3 * len(peak_ergs)),
         ]
+    print("TOOLBOX: Guessed parameters: ", guess_parameters)
 
-    print('Search start:', search_start, ' Search end:', search_end)
+    # print('Search start:', search_start, ' Search end:', search_end)
 
     parameters, covariance = curve_fit(
         gauss,
@@ -854,7 +861,12 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
 
 
 def get_multipeak_area(
-    hist, bins, peak_ergs, search_width=600, threshold_overlap=200
+    hist, 
+    bins, 
+    peak_ergs, 
+    search_width=600, 
+    threshold_overlap=200,
+    summing_method='sum_gaussian'
 ) -> List[float]:
 
     if len(peak_ergs) > 1:
@@ -881,10 +893,6 @@ def get_multipeak_area(
     # get midpoints of every bin
     xvals = np.diff(bins) / 2 + bins[:-1]
 
-    print("libra-toolbox bins length:", len(bins))
-    print("libra-toolbox xvals:", xvals)
-    print("libra-toolbox hist[0, -1]: ", hist[0], hist[-1])
-    print("libra-toolbox peak_ergs:", peak_ergs)
 
     parameters, covariance = fit_peak_gauss(
         hist, xvals, peak_ergs, search_width=search_width
@@ -908,17 +916,25 @@ def get_multipeak_area(
         # Use unimodal gaussian to estimate counts from just one peak
         peak_params = [parameters[0], parameters[1], parameters[2 + 3 * i], mean, sigma]
         all_peak_params += [peak_params]
-        gross_area = np.trapz(
-            gauss(xvals[peak_start:peak_end], *peak_params),
-            x=xvals[peak_start:peak_end],
-        )
 
+        if summing_method == 'sum_gaussian':
+            gross_area = np.trapezoid(
+                gauss(xvals[peak_start:peak_end], *peak_params),
+                x=xvals[peak_start:peak_end],
+            )
+        elif summing_method == 'sum_histogram':
+            gross_area = np.trapezoid(
+                hist[peak_start:peak_end],
+                x=xvals[peak_start:peak_end],
+            )
         # Cut off trapezoidal area due to compton scattering and noise
-        trap_cutoff_area = np.trapz(
+        trap_cutoff_area = np.trapezoid(
             parameters[0] + parameters[1] * xvals[peak_start:peak_end],
             x=xvals[peak_start:peak_end],
         )
         area = gross_area - trap_cutoff_area
+
+        print("TOOLBOX: Peak energy:", peak_ergs[i], " Gross area:", gross_area, " Cutoff area:", trap_cutoff_area, " Net area:", area)
         areas += [area]
 
     return areas

From ead849a5b1b493cf17ef35b12f184c2bc77b1376 Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Fri, 9 Jan 2026 15:14:07 -0500
Subject: [PATCH 22/32] Removed debugging print statements

---
 .../neutron_detection/activation_foils/compass.py         | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index a82832d..95ff4ce 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -450,9 +450,6 @@ def compute_detection_efficiency(
         )
 
         calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges)
-        print('Uncalibrated measured energies:', self.check_source.nuclide._uncalibrated_measured_energies[channel_nb])
-        print('Calibration coefficients:', calibration_coeffs)
-        print('Calibrated measured energies:', self.check_source.nuclide.calibrated_measured_energies(channel_nb, calibration_coeffs))
 
         nb_counts_measured = get_multipeak_area(
             hist,
@@ -846,9 +843,7 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
             peak_ergs[i],
             search_width / (3 * len(peak_ergs)),
         ]
-    print("TOOLBOX: Guessed parameters: ", guess_parameters)
 
-    # print('Search start:', search_start, ' Search end:', search_end)
 
     parameters, covariance = curve_fit(
         gauss,
@@ -934,7 +929,6 @@ def get_multipeak_area(
         )
         area = gross_area - trap_cutoff_area
 
-        print("TOOLBOX: Peak energy:", peak_ergs[i], " Gross area:", gross_area, " Cutoff area:", trap_cutoff_area, " Net area:", area)
         areas += [area]
 
     return areas
@@ -1018,7 +1012,7 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar
         time_values[ch] = np.empty(0)
         energy_values[ch] = np.empty(0)
         for i, filename in enumerate(data_filenames[ch]):
-            print(f'Processing File {i}')
+            # print(f'Processing File {i}')
 
             csv_file_path = os.path.join(directory, filename)
 

From 5c3b9fa09b415456388779b156af35368f53a77e Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Fri, 9 Jan 2026 16:34:06 -0500
Subject: [PATCH 23/32] Fixed broken tests in test_compass

---
 .../activation_foils/compass.py               | 29 ++++++++++++-------
 test/neutron_detection/test_compass.py        |  8 ++++-
 2 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 95ff4ce..4bc41e2 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -451,10 +451,15 @@ def compute_detection_efficiency(
 
         calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges)
 
+        peak_energies = self.check_source.nuclide.calibrated_measured_energies(channel_nb, calibration_coeffs)
+        if peak_energies is None:
+            print("TOOLBOX: No calibrated measured energies found for the check source. Cannot compute detection efficiency.")
+            peak_energies = self.check_source.nuclide.energy
+
         nb_counts_measured = get_multipeak_area(
             hist,
             calibrated_bin_edges,
-            self.check_source.nuclide.calibrated_measured_energies(channel_nb, calibration_coeffs),
+            peak_energies,
             search_width=search_width,
             threshold_overlap=threshold_overlap,
             summing_method=summing_method,
@@ -814,7 +819,9 @@ def gauss(x, b, m, *args):
     return out
 
 
-def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=200):
+def fit_peak_gauss(hist, xvals, peak_ergs, 
+                   search_width=600, 
+                   threshold_overlap=200):
 
     if len(peak_ergs) > 1:
         if np.max(peak_ergs) - np.min(peak_ergs) > threshold_overlap:
@@ -852,6 +859,8 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=2
         p0=guess_parameters,
     )
 
+    print("Fitted parameters:", parameters)
+
     return parameters, covariance
 
 
@@ -861,31 +870,29 @@ def get_multipeak_area(
     peak_ergs, 
     search_width=600, 
     threshold_overlap=200,
-    summing_method='sum_gaussian'
+    summing_method='sum_gaussian',
+    ax=None,
 ) -> List[float]:
+    
+    print(peak_ergs)
 
     if len(peak_ergs) > 1:
         if np.max(peak_ergs) - np.min(peak_ergs) > threshold_overlap:
             areas = []
             for p, peak in enumerate(peak_ergs):
-                if isinstance(search_width, (np.ndarray, list)):
-                    search_w = int(search_width[p])
-                else:
-                    search_w = int(search_width)
                 area = get_multipeak_area(
                     hist,
                     bins,
                     [peak],
-                    search_width=search_w,
+                    search_width=search_width,
                     threshold_overlap=threshold_overlap,
+                    summing_method=summing_method
                 )
                 areas += area
             return areas
     
-    if isinstance(search_width, (np.ndarray, list)):
-        search_width = int(search_width[0])
 
-    # get midpoints of every bin
+    # get midpoints of every binß
     xvals = np.diff(bins) / 2 + bins[:-1]
 
 
diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index e46916f..4645bb7 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -791,13 +791,19 @@ def test_get_multipeak_area_two_close_peaks():
 
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
+
     # RUN
-    areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy1, energy2])
+    areas = compass.get_multipeak_area(hist, 
+                                       bin_edges, 
+                                       peak_ergs=[energy1, energy2],
+                                       search_width=200)
+    
 
     # TEST
     expected_area_peak_1 = nb_events_peak1
     expected_area_peak_2 = nb_events_peak2
     for i, expected_area in enumerate([expected_area_peak_1, expected_area_peak_2]):
+        print(f"Peak {i+1}: Computed area = {areas[i]}, Expected area = {expected_area}")
         assert np.isclose(areas[i], expected_area, rtol=1e-2)
 
 

From 6f9cc2836dce527d170d5939adebe616418b4ad4 Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Fri, 9 Jan 2026 16:51:11 -0500
Subject: [PATCH 24/32] Added test for sum_histogram

---
 test/neutron_detection/test_compass.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index 4645bb7..4a16b8f 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -707,7 +707,8 @@ def test_get_calibration_data(a, b):
     assert np.allclose(calibration_energies, real_energies, rtol=1e-2)
 
 
-def test_get_multipeak_area_single_peak():
+@pytest.mark.parametrize("summing_method", ["sum_gaussian", "sum_histogram"])
+def test_get_multipeak_area_single_peak(summing_method):
     """
     Test the get_multipeak_area function from the compass module.
     Checks that the area under the peaks is correctly computed.
@@ -723,14 +724,15 @@ def test_get_multipeak_area_single_peak():
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
     # RUN
-    areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy])
+    areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy],
+                                       summing_method=summing_method)
 
     # TEST
     expected_area = np.sum(hist)
     assert np.isclose(areas[0], expected_area, rtol=1e-2)
 
-
-def test_get_multipeak_area_two_separated_peaks():
+@pytest.mark.parametrize("summing_method", ["sum_gaussian", "sum_histogram"])
+def test_get_multipeak_area_two_separated_peaks(summing_method):
     """
     Test the get_multipeak_area function from the compass module.
     Checks that the area under the peaks is correctly computed.
@@ -756,7 +758,9 @@ def test_get_multipeak_area_two_separated_peaks():
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
     # RUN
-    areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy1, energy2])
+    areas = compass.get_multipeak_area(hist, bin_edges, 
+                                       peak_ergs=[energy1, energy2],
+                                       summing_method=summing_method)
 
     # TEST
 
@@ -796,7 +800,8 @@ def test_get_multipeak_area_two_close_peaks():
     areas = compass.get_multipeak_area(hist, 
                                        bin_edges, 
                                        peak_ergs=[energy1, energy2],
-                                       search_width=200)
+                                       search_width=200,
+                                       summing_method="sum_gaussian")
     
 
     # TEST

From bfaec5eb8ee2539f42e3b9c8fed088a0a13b4e54 Mon Sep 17 00:00:00 2001
From: cdunn314 <cdunn314@mit.edu>
Date: Fri, 9 Jan 2026 16:59:02 -0500
Subject: [PATCH 25/32] Added sum_histogram test for get_gamma_emitted

---
 .../activation_foils/compass.py               |  8 ++
 test/neutron_detection/test_compass.py        | 77 ++++++++++++-------
 2 files changed, 59 insertions(+), 26 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 4bc41e2..66504c2 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -436,6 +436,12 @@ def compute_detection_efficiency(
             calibration_coeffs: the calibration polynomial coefficients for the detector
             channel_nb: the channel number of the detector
             search_width: the search width for the peak fitting
+            threshold_overlap: the threshold width for considering two peaks as overlapping
+            summing_method: method to sum counts under the peak, either 'sum_gaussian' or 'sum_histogram'
+                            with 'sum_gaussian' fitting a Gaussian to the peak and integrating it, 
+                            and with 'sum_histogram' summing the histogram counts under the peak.
+                            'sum_histogram' SHOULD NOT BE USED for OVERLAPPING peaks as it will
+                            overestimate the counts.
 
         Returns:
             the detection efficiency
@@ -592,6 +598,7 @@ def get_gamma_emitted(
         calibration_coeffs,
         channel_nb: int,
         search_width: float = 800,
+        summing_method: str = 'sum_gaussian',
     ):
         # find right background detector
 
@@ -611,6 +618,7 @@ def get_gamma_emitted(
             calibrated_bin_edges,
             energy,
             search_width=search_width,
+            summing_method=summing_method,
         )
 
         nb_counts_measured = np.array(nb_counts_measured)
diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index 4a16b8f..89531fa 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -105,17 +105,24 @@ def test_sort_compass_files(tmpdir, base_name: str, expected_filenames: dict):
         ("no_waveforms", 535148093, 1237, -2, [5, 15], 5),
         ("waveforms", 80413091, 1727, 0, [4], 4),
         ("waveforms", 2850906749, 1539, 2, [4], 4),
-        ("waveforms", 14300873206559, 1700, 6, [4], 4)
+        ("waveforms", 14300873206559, 1700, 6, [4], 4),
     ],
 )
-def test_get_events(waveform_directory, expected_time, 
-                    expected_energy, expected_idx,
-                    expected_keys, test_ch):
+def test_get_events(
+    waveform_directory,
+    expected_time,
+    expected_energy,
+    expected_idx,
+    expected_keys,
+    test_ch,
+):
     """
     Test the get_events function from the compass module.
     Checks that specific time and energy values are returned for a given channel
     """
-    test_directory = Path(__file__).parent / "compass_test_data/events" / waveform_directory
+    test_directory = (
+        Path(__file__).parent / "compass_test_data/events" / waveform_directory
+    )
     times, energies = compass.get_events(test_directory)
     assert isinstance(times, dict)
     assert isinstance(energies, dict)
@@ -575,6 +582,7 @@ def test_check_source_detection_efficiency(expected_efficiency):
     # TEST
     assert np.isclose(computed_efficiency, expected_efficiency, rtol=1e-2)
 
+
 @pytest.mark.parametrize(
     "peak_energies, width, start_index, expected_peaks",
     [
@@ -602,12 +610,14 @@ def test_get_peaks(peak_energies, width, start_index, expected_peaks):
     overall_energy_events[0] = 1
     overall_energy_events[-1] = 3000
 
-    time_events = np.random.uniform(0, 100, size=int(nb_events_measured * (len(peak_energies) + 1)))
+    time_events = np.random.uniform(
+        0, 100, size=int(nb_events_measured * (len(peak_energies) + 1))
+    )
 
     test_nuclide = Nuclide(
         name="TestNuclide",
         energy=peak_energies,
-        intensity=[1.0]*len(peak_energies),
+        intensity=[1.0] * len(peak_energies),
         half_life=10000,
     )
     check_source = CheckSource(
@@ -623,11 +633,11 @@ def test_get_peaks(peak_energies, width, start_index, expected_peaks):
     detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None)
     detector.events = np.column_stack((time_events, overall_energy_events))
     hist, bins = detector.get_energy_hist(bins=None)
-    
+
     peak_indices = measurement.get_peaks(hist, start_index=start_index)
 
     assert len(peak_indices) == len(expected_peaks)
-    assert np.allclose(expected_peaks - bins[peak_indices], 0, atol=2*width)
+    assert np.allclose(expected_peaks - bins[peak_indices], 0, atol=2 * width)
 
 
 @pytest.mark.parametrize(
@@ -708,7 +718,7 @@ def test_get_calibration_data(a, b):
 
 
 @pytest.mark.parametrize("summing_method", ["sum_gaussian", "sum_histogram"])
-def test_get_multipeak_area_single_peak(summing_method):
+def test_get_multipeak_area_single_peak(summing_method: str):
     """
     Test the get_multipeak_area function from the compass module.
     Checks that the area under the peaks is correctly computed.
@@ -724,15 +734,17 @@ def test_get_multipeak_area_single_peak(summing_method):
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
     # RUN
-    areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy],
-                                       summing_method=summing_method)
+    areas = compass.get_multipeak_area(
+        hist, bin_edges, peak_ergs=[energy], summing_method=summing_method
+    )
 
     # TEST
     expected_area = np.sum(hist)
     assert np.isclose(areas[0], expected_area, rtol=1e-2)
 
+
 @pytest.mark.parametrize("summing_method", ["sum_gaussian", "sum_histogram"])
-def test_get_multipeak_area_two_separated_peaks(summing_method):
+def test_get_multipeak_area_two_separated_peaks(summing_method: str):
     """
     Test the get_multipeak_area function from the compass module.
     Checks that the area under the peaks is correctly computed.
@@ -758,9 +770,9 @@ def test_get_multipeak_area_two_separated_peaks(summing_method):
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
     # RUN
-    areas = compass.get_multipeak_area(hist, bin_edges, 
-                                       peak_ergs=[energy1, energy2],
-                                       summing_method=summing_method)
+    areas = compass.get_multipeak_area(
+        hist, bin_edges, peak_ergs=[energy1, energy2], summing_method=summing_method
+    )
 
     # TEST
 
@@ -795,25 +807,37 @@ def test_get_multipeak_area_two_close_peaks():
 
     hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000))
 
-
     # RUN
-    areas = compass.get_multipeak_area(hist, 
-                                       bin_edges, 
-                                       peak_ergs=[energy1, energy2],
-                                       search_width=200,
-                                       summing_method="sum_gaussian")
-    
+    areas = compass.get_multipeak_area(
+        hist,
+        bin_edges,
+        peak_ergs=[energy1, energy2],
+        search_width=200,
+        summing_method="sum_gaussian",
+    )
 
     # TEST
     expected_area_peak_1 = nb_events_peak1
     expected_area_peak_2 = nb_events_peak2
     for i, expected_area in enumerate([expected_area_peak_1, expected_area_peak_2]):
-        print(f"Peak {i+1}: Computed area = {areas[i]}, Expected area = {expected_area}")
+        print(
+            f"Peak {i+1}: Computed area = {areas[i]}, Expected area = {expected_area}"
+        )
         assert np.isclose(areas[i], expected_area, rtol=1e-2)
 
 
-@pytest.mark.parametrize("efficiency", [1e-2, 0.1, 0.5, 1.0])
-def test_get_gamma_emitted(efficiency: float):
+@pytest.mark.parametrize(
+    "efficiency, summing_method",
+    [
+        (1e-2, "sum_gaussian"),
+        (0.1, "sum_gaussian"),
+        (0.5, "sum_gaussian"),
+        (1.0, "sum_gaussian"),
+        (1e-2, "sum_histogram"),
+        (0.1, "sum_histogram"),
+    ],
+)
+def test_get_gamma_emitted(efficiency: float, summing_method: str):
     # BUILD
     nuclide_reactant = Nuclide(name="TestNuclide", atomic_mass=200)
     activated_nuclide = Nuclide(
@@ -865,6 +889,7 @@ def test_get_gamma_emitted(efficiency: float):
         calibration_coeffs=np.array([1.0, 0.0]),  # assume perfect calibration
         channel_nb=3,
         search_width=300,
+        summing_method=summing_method,
     )
     computed_value = gammas_emmitted[0]
 

From fade7d72238276232adcc3dd4170d5e615627852 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:00:53 -0500
Subject: [PATCH 26/32] Added test background measurements

---
 .../background_measurements.h5                | Bin 0 -> 215432 bytes
 .../calibration_coefficients.json             |  18 ++++++++++++++++++
 2 files changed, 18 insertions(+)
 create mode 100644 test/neutron_detection/compass_test_data/background_measurement/background_measurements.h5
 create mode 100644 test/neutron_detection/compass_test_data/background_measurement/calibration_coefficients.json

diff --git a/test/neutron_detection/compass_test_data/background_measurement/background_measurements.h5 b/test/neutron_detection/compass_test_data/background_measurement/background_measurements.h5
new file mode 100644
index 0000000000000000000000000000000000000000..23fb0bff8b7d85a6b2c526e993f6ebc2cd6dfeca
GIT binary patch
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zBR6p~w{R=BaXWW#CwFl-_i!)waX%06AP?~{kMJmu@i<TLBv0`)&+shI@jNf^A}{eW
zukb3b@j7qtCU5aJ@9-|~@jf5$As_KEpYSQ4@i|}cC13G1-|#Kp@jXBABR}yozwj%+
V@jHL;Cx7ub|L`vZg#Z8k{|~n!nmzyk

literal 0
HcmV?d00001

diff --git a/test/neutron_detection/compass_test_data/background_measurement/calibration_coefficients.json b/test/neutron_detection/compass_test_data/background_measurement/calibration_coefficients.json
new file mode 100644
index 0000000..18aad4e
--- /dev/null
+++ b/test/neutron_detection/compass_test_data/background_measurement/calibration_coefficients.json
@@ -0,0 +1,18 @@
+{
+  "HPGe": {
+    "0": [
+      1.0198403099123785,
+      2.689961639523998
+    ]
+  },
+  "NaI": {
+    "4": [
+      0.7959901704589225,
+      -38.917607406086006
+    ],
+    "5": [
+      1.0622115094394577,
+      -30.576666718985596
+    ]
+  }
+}
\ No newline at end of file

From 414da576f1c316a3643f77c9ee744a7a8384fc6e Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:01:13 -0500
Subject: [PATCH 27/32] Added get_peaks test

---
 .../activation_foils/compass.py               |   3 +-
 test/neutron_detection/test_compass.py        | 134 ++++++++++++++++++
 2 files changed, 136 insertions(+), 1 deletion(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 66504c2..4047492 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -531,7 +531,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
                 height = 0.60 * np.max(hist[start_index:])
                 prominence = None
             elif self.check_source.nuclide == ba133:
-                start_index = 10
+                start_index = 150
                 height = 0.10 * np.max(hist[start_index:])
                 prominence = 0.10 * np.max(hist[start_index:])
             elif self.check_source.nuclide == mn54:
@@ -552,6 +552,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
                 height = 0.5 * np.max(hist[start_index:])
                 prominence = 0.5 * np.max(hist[start_index:])
             elif self.check_source.nuclide == ba133:
+                print("ToOLBOX: Using default peak finding parameters for Ba-133 with HPGe detector.")
                 start_index = 150
                 height = 0.10 * np.max(hist[start_index:])
                 prominence = 0.10 * np.max(hist[start_index:])
diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index 89531fa..615b795 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -7,10 +7,16 @@
     CheckSource,
     ActivationFoil,
     Reaction,
+    ba133,
+    cs137,
+    co60,
+    na22,
+    mn54,
 )
 from pathlib import Path
 import datetime
 import h5py
+import json
 
 
 @pytest.mark.parametrize(
@@ -1577,3 +1583,131 @@ def test_measurement_h5_spectrum_only_analysis_capability(tmpdir):
     assert loaded_detector.spectrum.dtype in [np.int32, np.int64, np.uint32, np.uint64]
     assert loaded_detector.bin_edges.dtype in [np.int32, np.int64, np.uint32, np.uint64]
     assert len(loaded_detector.bin_edges) == len(loaded_detector.spectrum) + 1
+
+
+def create_nuclide_spectrum(
+    nuclide: Nuclide,
+    signal_to_background_ratio: float,
+    peak_standard_deviation: float,
+    background_detector: compass.Detector,
+    background_divider: float = 1.0,
+):
+    # In case background has many events, scale it down to speed up test
+    background_hist = background_detector._spectrum / background_divider
+
+    total_counts_background = np.sum(background_hist)
+    desired_signal_counts = signal_to_background_ratio * total_counts_background
+
+    nuclide_events = np.zeros((0,))
+    for energy, intensity in zip(nuclide.energy, nuclide.intensity):
+        compton_fraction = 0.2  # ~20% of events go to Compton scattering
+        # Full energy peak
+        nuclide_events = np.append(
+            nuclide_events,
+            np.random.normal(
+                loc=energy,
+                scale=peak_standard_deviation,
+                size=int(desired_signal_counts * intensity * (1 - compton_fraction)),
+            ),
+        )
+
+        # Compton edge
+        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))
+        compton_events = int(desired_signal_counts * intensity * compton_fraction)
+
+        # Generate Compton events below the edge with a smooth falloff
+        # Using exponential distribution peaked near the edge
+        compton_samples = np.random.exponential(
+            scale=compton_edge / 3, size=compton_events
+        )
+        # Shift to be centered below the edge
+        compton_samples = compton_edge - np.abs(compton_samples)
+        compton_samples = compton_samples[
+            compton_samples > 0
+        ]  # Remove negative energies
+        nuclide_events = np.append(nuclide_events, compton_samples)
+    nuclide_hist, _ = np.histogram(
+        nuclide_events, bins=background_detector._calibrated_bin_edges
+    )
+
+    noise_level = 1.0
+    gaussian_noise = np.random.normal(
+        0, noise_level * np.sqrt(nuclide_hist), size=nuclide_hist.shape
+    )
+
+    overall_hist = nuclide_hist + background_hist + gaussian_noise
+    return overall_hist
+
+
+@pytest.mark.parametrize(
+    "detector_type, nuclide, signal_to_background_ratio",
+    [
+        ["NaI", co60, 1.0],
+        ["NaI", cs137, 1.0],
+        ["NaI", mn54, 0.1],
+        ["NaI", na22, 1.0],
+        ["NaI", na22, 0.1],
+        ["HPGe", co60, 1.0],
+        ["HPGe", cs137, 1.0],
+        ["HPGe", mn54, 0.1],
+        ["HPGe", na22, 1.0],
+        ["HPGe", na22, 0.1],
+    ],
+)
+def test_get_peaks(detector_type, nuclide, signal_to_background_ratio):
+
+    # get real background measurement
+    background_measurements = compass.Measurement.from_h5(
+        "compass_test_data/background_measurement/background_measurements.h5"
+    )
+
+    # read in calibration coefficients from json
+    with open(
+        "compass_test_data/background_measurement/calibration_coefficients.json", "r"
+    ) as f:
+        calibration_coefficients = json.load(f)
+
+    if detector_type == "NaI":
+        coeffs = calibration_coefficients["NaI"]["4"]
+        peak_standard_deviation = 30.0  # keV
+        background_divider = 100.0
+        for background_measurement in background_measurements:
+            if background_measurement.name == "NaI Background":
+                background_detector = background_measurement.get_detector(4)
+                break
+    elif detector_type == "HPGe":
+        coeffs = calibration_coefficients["HPGe"]["0"]
+        peak_standard_deviation = 2.0  # keV
+        background_divider = 1.0
+        for background_measurement in background_measurements:
+            if background_measurement.name == "HPGe Background":
+                background_detector = background_measurement.get_detector(0)
+                break
+    else:
+        raise ValueError(f"Unknown detector type: {detector_type}")
+
+    background_detector._calibrated_bin_edges = np.polyval(
+        coeffs, background_detector._bin_edges
+    )
+
+    # Build simulated spectrum with nuclide peaks + background
+    overall_hist = create_nuclide_spectrum(
+        nuclide=nuclide,
+        signal_to_background_ratio=signal_to_background_ratio,
+        peak_standard_deviation=peak_standard_deviation,
+        background_detector=background_detector,
+        background_divider=background_divider,
+    )
+
+    # Create a check source measurement instance
+    check_source_detector = compass.Detector()
+    check_source_detector._spectrum = overall_hist
+    check_source_detector._bin_edges = background_detector._calibrated_bin_edges
+    check_source_meas = compass.CheckSourceMeasurement(nuclide=nuclide)
+    check_source_meas.detectors = [check_source_detector]
+    check_source_meas.detector_type = detector_type
+
+    test_peaks = check_source_meas.get_peaks(overall_hist)
+    assert len(test_peaks) == len(nuclide.energy)
+    for test_energy, expected_energy in zip(test_peaks, nuclide.energy):
+        assert np.isclose(test_energy, expected_energy, rtol=1e-2)

From 2f0380c73bb82c3cef718c52f395ec0efc727284 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:01:36 -0500
Subject: [PATCH 28/32] Temporary get_peaks test viz

---
 .../temp_create_spectra.ipynb                 | 307 ++++++++++++++++++
 1 file changed, 307 insertions(+)
 create mode 100644 test/neutron_detection/temp_create_spectra.ipynb

diff --git a/test/neutron_detection/temp_create_spectra.ipynb b/test/neutron_detection/temp_create_spectra.ipynb
new file mode 100644
index 0000000..6e0ce4f
--- /dev/null
+++ b/test/neutron_detection/temp_create_spectra.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "89b5b7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libra_toolbox.neutron_detection.activation_foils import compass\n",
+    "from libra_toolbox.neutron_detection.activation_foils.calibration import ba133, cs137, co60, na22, mn54\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "48b94010",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'HPGe': {'0': [1.0198403099123785, 2.689961639523998]}, 'NaI': {'4': [0.7959901704589225, -38.917607406086006], '5': [1.0622115094394577, -30.576666718985596]}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# read in background spectra\n",
+    "\n",
+    "background_measurements = compass.Measurement.from_h5('compass_test_data/background_measurement/background_measurements.h5')\n",
+    "\n",
+    "# read in calibration coefficients from json\n",
+    "import json\n",
+    "with open('compass_test_data/background_measurement/calibration_coefficients.json', 'r') as f:\n",
+    "    calibration_coefficients = json.load(f)\n",
+    "print(calibration_coefficients)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "709b7de3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for measurement in background_measurements:\n",
+    "    for det_type in calibration_coefficients:\n",
+    "        if det_type in measurement.name:\n",
+    "            for detector in measurement.detectors:\n",
+    "                coeffs = calibration_coefficients[det_type][str(detector.channel_nb)]\n",
+    "                detector._calibrated_bin_edges = np.polyval(coeffs, detector._bin_edges)\n",
+    "            \n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9700f4e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "for measurement in background_measurements:\n",
+    "    for detector in measurement.detectors:\n",
+    "        ax.step(detector._calibrated_bin_edges[:-1], detector._spectrum, where='post', label=f'{measurement.name} - Ch {detector.channel_nb}')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_xlabel('Energy (keV)')\n",
+    "        ax.set_ylabel('Counts')\n",
+    "        ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c241f05b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# HPGe spectra simulator\n",
+    "nb_events_measured = 1e6\n",
+    "for nuclide in [ba133, cs137, co60, na22, mn54]:\n",
+    "    fig, ax = plt.subplots(figsize=(10,6))\n",
+    "    nuclide_events = np.zeros((0,))\n",
+    "    for energy, intensity in zip(nuclide.energy, nuclide.intensity):\n",
+    "        compton_fraction = 0.3  # ~30% of events go to Compton scattering\n",
+    "        # Full energy peak\n",
+    "        nuclide_events = np.append(nuclide_events, np.random.normal(\n",
+    "            loc=energy, scale=2, size=int(nb_events_measured * intensity * (1 - compton_fraction))\n",
+    "        ))\n",
+    "        \n",
+    "        # Compton edge\n",
+    "        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))\n",
+    "        compton_events = int(nb_events_measured * intensity * compton_fraction)\n",
+    "        \n",
+    "        # Generate Compton events below the edge with a smooth falloff\n",
+    "        # Using exponential distribution peaked near the edge\n",
+    "        compton_samples = np.random.exponential(\n",
+    "            scale=compton_edge/3, \n",
+    "            size=compton_events\n",
+    "        )\n",
+    "        # Shift to be centered below the edge\n",
+    "        compton_samples = compton_edge - np.abs(compton_samples)\n",
+    "        compton_samples = compton_samples[compton_samples > 0]  # Remove negative energies\n",
+    "        nuclide_events = np.append(nuclide_events, compton_samples)\n",
+    "    nuclide_hist, bins = np.histogram(nuclide_events, bins=background_measurements[0].detectors[0]._calibrated_bin_edges)\n",
+    "    background_hist = background_measurements[0].detectors[0]._spectrum\n",
+    "    overall_hist = nuclide_hist + background_hist\n",
+    "    ax.stairs(overall_hist, bins, label=nuclide.name)\n",
+    "    ax.set_yscale('linear')\n",
+    "    ax.set_xlim(0, 2000)\n",
+    "    ax.set_title(nuclide.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7bade06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# NaI spectra simulator\n",
+    "nb_events_measured = 1e7\n",
+    "for nuclide in [cs137, co60, na22, mn54]:\n",
+    "    fig, ax = plt.subplots(figsize=(10,6))\n",
+    "    nuclide_events = np.zeros((0,))\n",
+    "    for energy, intensity in zip(nuclide.energy, nuclide.intensity):\n",
+    "        compton_fraction = 0.2  # ~20% of events go to Compton scattering\n",
+    "        # Full energy peak\n",
+    "        nuclide_events = np.append(nuclide_events, np.random.normal(\n",
+    "            loc=energy, scale=30, size=int(nb_events_measured * intensity * (1 - compton_fraction))\n",
+    "        ))\n",
+    "        \n",
+    "        # Compton edge\n",
+    "        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))\n",
+    "        compton_events = int(nb_events_measured * intensity * compton_fraction)\n",
+    "        \n",
+    "        # Generate Compton events below the edge with a smooth falloff\n",
+    "        # Using exponential distribution peaked near the edge\n",
+    "        compton_samples = np.random.exponential(\n",
+    "            scale=compton_edge/3, \n",
+    "            size=compton_events\n",
+    "        )\n",
+    "        # Shift to be centered below the edge\n",
+    "        compton_samples = compton_edge - np.abs(compton_samples)\n",
+    "        compton_samples = compton_samples[compton_samples > 0]  # Remove negative energies\n",
+    "        nuclide_events = np.append(nuclide_events, compton_samples)\n",
+    "    nuclide_hist, bins = np.histogram(nuclide_events, bins=background_measurements[1].detectors[0]._calibrated_bin_edges)\n",
+    "\n",
+    "    noise_level = 1.0  # 5% relative noise\n",
+    "    gaussian_noise = np.random.normal(0, noise_level * np.sqrt(nuclide_hist), size=nuclide_hist.shape)\n",
+    "\n",
+    "    background_hist = background_measurements[1].detectors[0]._spectrum\n",
+    "    overall_hist_no_noise = nuclide_hist + background_hist\n",
+    "    ax.stairs(overall_hist_no_noise, bins, label=nuclide.name)\n",
+    "    overall_hist = nuclide_hist + background_hist + gaussian_noise\n",
+    "\n",
+    "    ax.stairs(overall_hist, bins, label=nuclide.name + ' + gaussian noise', alpha=0.5)\n",
+    "\n",
+    "    from scipy.ndimage import gaussian_filter1d\n",
+    "\n",
+    "    # Energy-dependent broadening based on detector resolution\n",
+    "    # For NaI: FWHM ≈ 7-8% at 662 keV, improves with √E\n",
+    "    bin_centers = (bins[:-1] + bins[1:]) / 2\n",
+    "    fwhm_percent = 7.0  # FWHM as % at 662 keV\n",
+    "    fwhm_energy = (fwhm_percent / 100) * bin_centers * np.sqrt(662 / bin_centers)\n",
+    "    sigma_energy = fwhm_energy / 2.355  # Convert FWHM to sigma\n",
+    "    bin_width = np.mean(np.diff(bins))\n",
+    "    sigma_bins = np.mean(sigma_energy / bin_width)  # Average sigma in bins\n",
+    "\n",
+    "    overall_hist_broadened = gaussian_filter1d(overall_hist_no_noise, sigma=sigma_bins)\n",
+    "    ax.stairs(overall_hist_broadened, bins, label=nuclide.name + ' + broadening')\n",
+    "    ax.set_yscale('log')\n",
+    "    ax.set_xlim(0, 2000)\n",
+    "    ax.set_title(nuclide.name)\n",
+    "    ax.legend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5e9e14b97d2ce8f58fe3fea61dd885ec01ee31c3 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:25:46 -0500
Subject: [PATCH 29/32] test_get_peaks debug 1

---
 test/neutron_detection/test_compass.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index 615b795..1365ecc 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -18,6 +18,7 @@
 import h5py
 import json
 
+this_file_path = Path(__file__).parent
 
 @pytest.mark.parametrize(
     "filename, expected_channel",
@@ -1647,6 +1648,7 @@ def create_nuclide_spectrum(
         ["NaI", mn54, 0.1],
         ["NaI", na22, 1.0],
         ["NaI", na22, 0.1],
+        ["HPGe", ba133, 1.0],
         ["HPGe", co60, 1.0],
         ["HPGe", cs137, 1.0],
         ["HPGe", mn54, 0.1],
@@ -1658,12 +1660,12 @@ def test_get_peaks(detector_type, nuclide, signal_to_background_ratio):
 
     # get real background measurement
     background_measurements = compass.Measurement.from_h5(
-        "compass_test_data/background_measurement/background_measurements.h5"
+        this_file_path / "compass_test_data/background_measurement/background_measurements.h5"
     )
 
     # read in calibration coefficients from json
     with open(
-        "compass_test_data/background_measurement/calibration_coefficients.json", "r"
+        this_file_path / "compass_test_data/background_measurement/calibration_coefficients.json", "r"
     ) as f:
         calibration_coefficients = json.load(f)
 
@@ -1700,14 +1702,21 @@ def test_get_peaks(detector_type, nuclide, signal_to_background_ratio):
     )
 
     # Create a check source measurement instance
-    check_source_detector = compass.Detector()
+    check_source_detector = compass.Detector(channel_nb=0)
     check_source_detector._spectrum = overall_hist
     check_source_detector._bin_edges = background_detector._calibrated_bin_edges
-    check_source_meas = compass.CheckSourceMeasurement(nuclide=nuclide)
+
+    check_source = CheckSource(nuclide=nuclide,
+                               activity_date=datetime.datetime(2024, 1, 1),
+                               activity=1.0)
+    
+    check_source_meas = compass.CheckSourceMeasurement('test')
+    check_source_meas.check_source = check_source
     check_source_meas.detectors = [check_source_detector]
     check_source_meas.detector_type = detector_type
 
     test_peaks = check_source_meas.get_peaks(overall_hist)
     assert len(test_peaks) == len(nuclide.energy)
     for test_energy, expected_energy in zip(test_peaks, nuclide.energy):
-        assert np.isclose(test_energy, expected_energy, rtol=1e-2)
+        print(f"Detected peak at {test_energy:.2f} keV, expected at {expected_energy} keV")
+        assert np.isclose(test_energy, expected_energy, rtol=0.05)

From 034956c2337ef842eb8f18d48509745b9882d4a6 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:46:59 -0500
Subject: [PATCH 30/32] Removed print statement

---
 .../neutron_detection/activation_foils/compass.py          | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 4047492..586190a 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -552,7 +552,6 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
                 height = 0.5 * np.max(hist[start_index:])
                 prominence = 0.5 * np.max(hist[start_index:])
             elif self.check_source.nuclide == ba133:
-                print("ToOLBOX: Using default peak finding parameters for Ba-133 with HPGe detector.")
                 start_index = 150
                 height = 0.10 * np.max(hist[start_index:])
                 prominence = 0.10 * np.max(hist[start_index:])
@@ -929,17 +928,17 @@ def get_multipeak_area(
         all_peak_params += [peak_params]
 
         if summing_method == 'sum_gaussian':
-            gross_area = np.trapezoid(
+            gross_area = np.trapz(
                 gauss(xvals[peak_start:peak_end], *peak_params),
                 x=xvals[peak_start:peak_end],
             )
         elif summing_method == 'sum_histogram':
-            gross_area = np.trapezoid(
+            gross_area = np.trapz(
                 hist[peak_start:peak_end],
                 x=xvals[peak_start:peak_end],
             )
         # Cut off trapezoidal area due to compton scattering and noise
-        trap_cutoff_area = np.trapezoid(
+        trap_cutoff_area = np.trapz(
             parameters[0] + parameters[1] * xvals[peak_start:peak_end],
             x=xvals[peak_start:peak_end],
         )

From 8f60aa5ca21c7292e70f155550b0781f1266c578 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 00:54:22 -0500
Subject: [PATCH 31/32] Debugged for Na22 and Mn54 test cases

---
 .../activation_foils/compass.py                  | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py
index 586190a..dd07ced 100644
--- a/libra_toolbox/neutron_detection/activation_foils/compass.py
+++ b/libra_toolbox/neutron_detection/activation_foils/compass.py
@@ -522,8 +522,8 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
             distance = 30
             if self.check_source.nuclide == na22:
                 start_index = 100
-                height = 0.1 * np.max(hist[start_index:])
-                prominence = 0.1 * np.max(hist[start_index:])
+                height = 0.4 * np.max(hist[start_index:])
+                prominence = 0.4 * np.max(hist[start_index:])
                 width = [10, 150]
                 distance = 30
             elif self.check_source.nuclide == co60:
@@ -545,8 +545,8 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
             distance = 100
             if self.check_source.nuclide == na22:
                 start_index = 100
-                height = 0.2 * np.max(hist[start_index:])
-                prominence = 0.2 * np.max(hist[start_index:])
+                height = 0.4 * np.max(hist[start_index:])
+                prominence = 0.4 * np.max(hist[start_index:])
                 distance = 100
             elif self.check_source.nuclide == co60:
                 height = 0.5 * np.max(hist[start_index:])
@@ -558,8 +558,8 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
                 distance = 10
             elif self.check_source.nuclide == mn54:
                 start_index = 400
-                height = 0.9 * np.max(hist[start_index:])
-                prominence = 0.9 * np.max(hist[start_index:])
+                height = 0.7 * np.max(hist[start_index:])
+                prominence = 0.7 * np.max(hist[start_index:])
                 distance = 100
         else:
             raise ValueError(
@@ -585,6 +585,10 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray:
         )
         peaks = np.array(peaks) + start_index
 
+        # special case for Mn-54, only keep the first high count energy peak
+        if self.check_source.nuclide == mn54 and len(peaks) > 1:
+            peaks = np.array([peaks[0]])
+
         return peaks
 
 

From f808dc0d264128f3a84df27e06e2cdb1b88622f5 Mon Sep 17 00:00:00 2001
From: Collin Dunn <cdunn314@mit.edu>
Date: Sun, 11 Jan 2026 01:03:44 -0500
Subject: [PATCH 32/32] Reduced noise and compton fraction

---
 .../temp_create_spectra.ipynb                 | 387 ++++++++++--------
 test/neutron_detection/test_compass.py        |  22 +-
 2 files changed, 233 insertions(+), 176 deletions(-)

diff --git a/test/neutron_detection/temp_create_spectra.ipynb b/test/neutron_detection/temp_create_spectra.ipynb
index 6e0ce4f..8257bc1 100644
--- a/test/neutron_detection/temp_create_spectra.ipynb
+++ b/test/neutron_detection/temp_create_spectra.ipynb
@@ -10,91 +10,215 @@
     "from libra_toolbox.neutron_detection.activation_foils import compass\n",
     "from libra_toolbox.neutron_detection.activation_foils.calibration import ba133, cs137, co60, na22, mn54\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import json\n",
+    "import datetime\n",
+    "from pathlib import Path\n",
+    "from libra_toolbox.neutron_detection.activation_foils.calibration import CheckSource, Nuclide"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "48b94010",
+   "id": "8eba995a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'HPGe': {'0': [1.0198403099123785, 2.689961639523998]}, 'NaI': {'4': [0.7959901704589225, -38.917607406086006], '5': [1.0622115094394577, -30.576666718985596]}}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# read in background spectra\n",
+    "def create_nuclide_spectrum(\n",
+    "    nuclide: Nuclide,\n",
+    "    signal_to_background_ratio: float,\n",
+    "    peak_standard_deviation: float,\n",
+    "    background_detector: compass.Detector,\n",
+    "    background_divider: float = 1.0,\n",
+    "):\n",
+    "    # In case background has many events, scale it down to speed up test\n",
+    "    background_hist = background_detector._spectrum / background_divider\n",
     "\n",
-    "background_measurements = compass.Measurement.from_h5('compass_test_data/background_measurement/background_measurements.h5')\n",
+    "    total_counts_background = np.sum(background_hist)\n",
+    "    desired_signal_counts = signal_to_background_ratio * total_counts_background\n",
     "\n",
-    "# read in calibration coefficients from json\n",
-    "import json\n",
-    "with open('compass_test_data/background_measurement/calibration_coefficients.json', 'r') as f:\n",
-    "    calibration_coefficients = json.load(f)\n",
-    "print(calibration_coefficients)"
+    "    nuclide_events = np.zeros((0,))\n",
+    "    for energy, intensity in zip(nuclide.energy, nuclide.intensity):\n",
+    "        compton_fraction = 0.1  # ~20% of events go to Compton scattering\n",
+    "        # Full energy peak\n",
+    "        nuclide_events = np.append(\n",
+    "            nuclide_events,\n",
+    "            np.random.normal(\n",
+    "                loc=energy,\n",
+    "                scale=peak_standard_deviation,\n",
+    "                size=int(desired_signal_counts * intensity * (1 - compton_fraction)),\n",
+    "            ),\n",
+    "        )\n",
+    "\n",
+    "        # Compton edge\n",
+    "        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))\n",
+    "        compton_events = int(desired_signal_counts * intensity * compton_fraction)\n",
+    "\n",
+    "        # Generate Compton events below the edge with a smooth falloff\n",
+    "        # Using exponential distribution peaked near the edge\n",
+    "        compton_samples = np.random.exponential(\n",
+    "            scale=compton_edge / 3, size=compton_events\n",
+    "        )\n",
+    "        # Shift to be centered below the edge\n",
+    "        compton_samples = compton_edge - np.abs(compton_samples)\n",
+    "        compton_samples = compton_samples[\n",
+    "            compton_samples > 0\n",
+    "        ]  # Remove negative energies\n",
+    "        nuclide_events = np.append(nuclide_events, compton_samples)\n",
+    "    nuclide_hist, _ = np.histogram(\n",
+    "        nuclide_events, bins=background_detector._calibrated_bin_edges\n",
+    "    )\n",
+    "\n",
+    "    noise_level = 1.0\n",
+    "    gaussian_noise = np.random.normal(\n",
+    "        0, noise_level * np.sqrt(nuclide_hist), size=nuclide_hist.shape\n",
+    "    )\n",
+    "\n",
+    "    overall_hist = nuclide_hist + background_hist + gaussian_noise\n",
+    "    return overall_hist\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "709b7de3",
+   "execution_count": null,
+   "id": "09ebc118",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for measurement in background_measurements:\n",
-    "    for det_type in calibration_coefficients:\n",
-    "        if det_type in measurement.name:\n",
-    "            for detector in measurement.detectors:\n",
-    "                coeffs = calibration_coefficients[det_type][str(detector.channel_nb)]\n",
-    "                detector._calibrated_bin_edges = np.polyval(coeffs, detector._bin_edges)\n",
-    "            \n",
-    "                "
+    "def test_get_peaks(detector_type, nuclide, signal_to_background_ratio):\n",
+    "\n",
+    "    # get real background measurement\n",
+    "    background_measurements = compass.Measurement.from_h5(\n",
+    "         \"compass_test_data/background_measurement/background_measurements.h5\"\n",
+    "    )\n",
+    "\n",
+    "    # read in calibration coefficients from json\n",
+    "    with open(\n",
+    "         \"compass_test_data/background_measurement/calibration_coefficients.json\", \"r\"\n",
+    "    ) as f:\n",
+    "        calibration_coefficients = json.load(f)\n",
+    "\n",
+    "    if detector_type == \"NaI\":\n",
+    "        coeffs = calibration_coefficients[\"NaI\"][\"4\"]\n",
+    "        peak_standard_deviation = 30.0  # keV\n",
+    "        background_divider = 100.0\n",
+    "        for background_measurement in background_measurements:\n",
+    "            if background_measurement.name == \"NaI Background\":\n",
+    "                background_detector = background_measurement.get_detector(4)\n",
+    "                break\n",
+    "    elif detector_type == \"HPGe\":\n",
+    "        coeffs = calibration_coefficients[\"HPGe\"][\"0\"]\n",
+    "        peak_standard_deviation = 2.0  # keV\n",
+    "        background_divider = 1.0\n",
+    "        for background_measurement in background_measurements:\n",
+    "            if background_measurement.name == \"HPGe Background\":\n",
+    "                background_detector = background_measurement.get_detector(0)\n",
+    "                break\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unknown detector type: {detector_type}\")\n",
+    "\n",
+    "    background_detector._calibrated_bin_edges = np.polyval(\n",
+    "        coeffs, background_detector._bin_edges\n",
+    "    )\n",
+    "\n",
+    "    # Build simulated spectrum with nuclide peaks + background\n",
+    "    overall_hist = create_nuclide_spectrum(\n",
+    "        nuclide=nuclide,\n",
+    "        signal_to_background_ratio=signal_to_background_ratio,\n",
+    "        peak_standard_deviation=peak_standard_deviation,\n",
+    "        background_detector=background_detector,\n",
+    "        background_divider=background_divider,\n",
+    "    )\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.stairs(overall_hist, background_detector._calibrated_bin_edges, label=\"Simulated Spectrum\")\n",
+    "    ax.set_xlabel(\"Energy (keV)\")\n",
+    "    ax.set_ylabel(\"Counts\")\n",
+    "    ax.vlines(background_detector._calibrated_bin_edges[400], \n",
+    "              0, np.max(overall_hist), \n",
+    "              colors='lightgray', linestyles='dotted', linewidth=0.5)\n",
+    "    ax.set_title(nuclide.name)\n",
+    "    if detector_type == \"NaI\":\n",
+    "        ax.set_yscale(\"log\")\n",
+    "    ax.grid()\n",
+    "\n",
+    "    # Create a check source measurement instance\n",
+    "    check_source_detector = compass.Detector(channel_nb=0)\n",
+    "    check_source_detector._spectrum = overall_hist\n",
+    "    check_source_detector._bin_edges = background_detector._calibrated_bin_edges\n",
+    "\n",
+    "    check_source = CheckSource(nuclide=nuclide,\n",
+    "                               activity_date=datetime.datetime(2024, 1, 1),\n",
+    "                               activity=1.0)\n",
+    "    \n",
+    "    check_source_meas = compass.CheckSourceMeasurement('test')\n",
+    "    check_source_meas.check_source = check_source\n",
+    "    check_source_meas.detectors = [check_source_detector]\n",
+    "    check_source_meas.detector_type = detector_type\n",
+    "\n",
+    "    test_peak_inds = check_source_meas.get_peaks(overall_hist)\n",
+    "    test_peaks = background_detector._calibrated_bin_edges[\n",
+    "        test_peak_inds\n",
+    "    ]\n",
+    "    if len(test_peaks) != len(nuclide.energy):\n",
+    "        print(f\"test_peak length: {len(test_peaks)}\", f\"nuclide energy length: {len(nuclide.energy)}\")\n",
+    "        print(f\"Detected peaks at: {test_peaks}\")\n",
+    "    # assert len(test_peaks) == len(nuclide.energy)\n",
+    "    for test_energy, expected_energy in zip(test_peaks, nuclide.energy):\n",
+    "        if not np.isclose(test_energy, expected_energy, rtol=0.05):\n",
+    "            print(f\"Detected peak at {test_energy:.2f} keV, expected at {expected_energy} keV\")\n",
+    "        # assert np.isclose(test_energy, expected_energy, rtol=0.05)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "9700f4e6",
+   "execution_count": 6,
+   "id": "25566ef8",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Testing NaI with Co60 at S/B=1.0\n",
+      "\n",
+      "Testing NaI with Cs137 at S/B=10.0\n",
+      "\n",
+      "Testing NaI with Mn54 at S/B=0.01\n",
+      "\n",
+      "Testing NaI with Na22 at S/B=10.0\n",
+      "\n",
+      "Testing NaI with Na22 at S/B=0.1\n",
+      "\n",
+      "Testing HPGe with Ba133 at S/B=1.0\n",
+      "\n",
+      "Testing HPGe with Co60 at S/B=1.0\n",
+      "\n",
+      "Testing HPGe with Cs137 at S/B=10.0\n",
+      "\n",
+      "Testing HPGe with Mn54 at S/B=0.01\n",
+      "\n",
+      "Testing HPGe with Na22 at S/B=10.0\n",
+      "\n",
+      "Testing HPGe with Na22 at S/B=0.1\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(10,6))\n",
-    "for measurement in background_measurements:\n",
-    "    for detector in measurement.detectors:\n",
-    "        ax.step(detector._calibrated_bin_edges[:-1], detector._spectrum, where='post', label=f'{measurement.name} - Ch {detector.channel_nb}')\n",
-    "        ax.set_yscale('log')\n",
-    "        ax.set_xlabel('Energy (keV)')\n",
-    "        ax.set_ylabel('Counts')\n",
-    "        ax.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "c241f05b",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -102,9 +226,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -112,9 +236,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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Hxx47IvJmDIRs8Mz0eQ6O2fLHsSIM7mJ/bkl0iPFLuJIbfXrc1jxjgHPfwLY2z5sWxNx9usRTVSIichgDIRs8MX3ehB0ZV5RrawEASrn9QWK/JGOSLjdf9bzsg8Y1gmIj61/8slloIM6VcPVvIvJeDIQkwtnz1i6VawEASXVJtvYwbV7LQMjz9p0zbo7bMjyo3mtu6BiDMg5dEpEXYyAkMYFZQmamNYQGdW5h92t6tDbuQl9UF0SR50QGK9EqKrjBa/q0iQIAHLjADViJyDsxEJIIV5a2ZlpVWhVg/8cyMtiYI5R7jl+0niQIAg7ll+FvPeMbvK59izAAXPSSiLwXAyGJcGjM2qXyGgBAh7ovT3uYEnJNARF5xqlLxqFIRSP5XL3reoTk/MATkZdiICQx9ghdcSjf2KsT6ECPEGDczmFFzjl3VInqselYEQCge6vIBq+LCFKiWWgg/le3FQcRkbdhICQRGafPWzlSUI6YsECHX1dQVo2k5vYnWJPrTIntg+3I59JU6XAkv8zdVSIicgoDIYkxWfoKuQzQGxxvj9HJcdjCRfs8avOxIkSHKBGqanwrlM6x4Ry6JCKvxUDIBk8sqGheWZpxkNnxixWY0LuVw68rr6llzpWHFZZp7d7sdlzPeJwvrUZZNTfHJSLvw0DIBk8sqMjvbUsGgwC9QUBF3aKKjrixYwwEAThVN+uM3O9CabXVjvP1MSVUF1fUuLNKREROYSBEXqGkythb0LN1lMOvDVIaP8bcZsMzavUG1NQakJIYZdf1KXX/ptxqg4i8EQMhicg4lmPhxMVyAEBUiOO5JN0TjDOXdnCXc48oqlvmICyw8fwgAOgUa1wO4Y+6mWZERN6EgZDEmCNkpK01ALjSe+CIiLpEXG7j4Bmm7Uy6xkfYdX1MmHEvsvAg+wInIiJPYiAkMc4aMzpftzGnSun4RzI2IggyGfDzvgtiV4tsMPXexUbUv8fYtTq1DMPKHK4lRETeh4GQRDgyZulYofHLNVzl3DRrpUKOhEb2vSJxmHJ9OsfZvwK4QRDQvgXXeiIi78NASGIczTH641gRmocGIjhQ4dTrB7Zvzh4HDzH12qkC7P+3SuvQHNtPMoeLiLwPAyGJsEfIUkJUMC5XOj+9+uzlSiTFsMfBE9YeKDDvIWavM8VV7qkMEZGLGAhJjB1CRpuPFSG9R8M7mTckvXucOc+I3Kt5mMq86aq9xqckAAAulPLfiIi8i18EQpWVlWjbti2ef/55qatiZtprjDOdjBQyGY7X5Qk5o6rGgMoaPWrqZp+R++w7V4pbHVwBvFpnXOOJiyoSkbfxi0DorbfewsCBA6WuhgUOjVmSyYCbeyU4/fqEKOMMpsoax1emJvuZ9oK7WLfpqr1Mq1CfdrAniYjI3Zp8IHT06FEcOnQI6enpUlfFJvYHGXcy11TXIsiB5NtrdYoNBwDsOVsqVrXIhrOXjYHMyORYh15n2pz1SIHzvX5ERO7g1YHQxo0bMX78eCQkJEAmk2H58uVW16jVaiQlJSEoKAgDBgzAtm3bLM4///zzeOeddzxUY/uxQ+iKCq1x2MTgwjChKsD4UeZQo3sVlhl7gqoc3M6kZbixxy5fwxwhIvIuXh0IVVRUICUlBWq12ub5JUuWICsrCzNnzsSuXbuQkpKC0aNHo7CwEACwYsUKdO7cGZ07d/ZktR3C721g12njtOp+SfZt4mlLcoJxleM1BwpEqRPZdrLIuLHtwPbNHXqdQi5DWvvm5qCXiMhbePWa9+np6Q0Oac2dOxdTpkzB5MmTAQDz5s3DqlWrMH/+fEybNg1//fUXFi9ejB9++AHl5eXQ6XSIiIjAjBkzbJan1Wqh1V7JfdBoNAAAnU4HnU4n4jsDVApApRBg0F8p+9r/+wsZDFApBChlBpvv3Z52CZQJUCkExIYp/aL9pPqsnC0uh0ohIEghOHzvMCWw70yx2+rsrz8/DWGbWGObWGuKbeLIe5EJPjKWIJPJsGzZMkyYMAEAUFNTg5CQECxdutR8DAAyMjJQUlKCFStWWLx+4cKFyM3Nxfvvv1/vPWbNmoXZs2dbHV+0aBFCQkJEeR9ERETkXpWVlbjnnntQWlqKiIiG90X06h6hhhQVFUGv1yM21jJpMzY2FocOHXKqzOnTpyMrK8v8XKPRIDExEaNGjWq0IR01/uM/kHepAl8/1B+920QDMEawa9aswciRI6FUOrfVhC965+dD+HbbKeTOGm3zvL3tMvT9DYgIUmLF1BvcVVWvIdVn5f7/24qjBeX46+XhDr/2sw3Hod5wDLteHYnAAPFH5f3156chbBNrbBNrTbFNTCM69vDZQMhRDz74YKPXqFQqqFQqqNVqqNVq6PXGfAalUin6h6NGkEGrl0GuCLAq2x3382Y1AtAxNrLR99xYuwSrAhEdrvKrtvP0Z2XrqVJ0ahnm1D1LtQZo9TLUCnKEurHO/vbzYw+2iTW2ibWm1CaOvA+vTpZuSExMDBQKBQoKLJNjCwoKEBcX51LZmZmZOHDgALZv3+5SOfbwiXFJN/ttfwGClM5PnTfp0ToSJ4u4To07tQhTOd2bM6hzCwBAYVm1mFUiInKJzwZCgYGB6Nu3L7Kzs83HDAYDsrOzkZaWJmHN7GOaPu8bGVruFR8VbN593hWaKp1L+5VR46pq9BjXw7mFL03piGL8WxMRicWrh8bKy8tx7Ngx8/O8vDzk5OSgWbNmaNOmDbKyspCRkYF+/fqhf//++PDDD1FRUWGeReasa4fG3EHGpaXNSiprMNHBLRtsualTC2w8WiRCjciWCm0tyrS10NY693NhWvSSn30i8iZeHQjt2LEDQ4cONT83JTJnZGRg4cKFmDRpEi5evIgZM2YgPz8fvXr1wurVq60SqB2VmZmJzMxMaDQaREZGulRWY3xk0p5bnbpUiYtljm3ZYEuQUo6aWgMulFYhPjJYhJrR1Uy9bQlRzrVt89BAAMC6QwUY09214WsiIrF4dSA0ZMiQRgOFqVOnYurUqR6qkXj4N7FRrd64SWpqUrTLZbVpFgoAXLTPTUoqjetytIsJder1ptW/W4SrRKsTEZGrfDZHyJ3UajWSk5ORmprq9ns1lf6gcyVVOFJQ5vDrqut2i28W5vqXY3iQMa6/UMptHNxh/3njPm6Rwc7NKpHJZOgSG45T3HiViLwIAyEbPDFrTKkwNn1ple+v5FlTa8ANc9Zh1D834nKFY8nKB84b13oIFmHWmGk203Em47pFeV1PW5tmzi8uevZypblniYjIGzAQkohpeKBCWytxTVzX+dVfzI9r6oa67GXKm20X4/rK3aYvaDGm4pO1vWdLAFwZ4nLG6O5xTidbExG5AwMhGzw5NObr/nBxllbO6RIAQHCg6+lqQUoF5DJg/eFCl8sia9EhgQhUyF2a9RWkVCBfw3WEiMh7MBCywaMLKvpwktCJi+W47/+2ArjSG+Po+zHUvSA0UJxeHIMAxIiQb0TWlu0+h26tXNtqpnloIAo1rs8QJCISCwMhifj6Uiorcs5h2Ae/m5/PvqWbU+Xka6oRFaJEVEigKPXq0yYKNbWODc+RfTq0CMXZy64lorcMV0Fv8OHon4iaHAZCEvPVr4SnF+eYH++ffWWzVMHBd7TnTImogUuQUoFfcvNFK4+uqNYZMKaba+v/qJQK1BoEUdaNIiISAwMhifhyh9B7vx4yP35zQneEqgKcfj8twlVoFipObxBgXJyxdTQXU3SHAxc0kLv4wY2PDAIAFDs4u5CIyF0YCNnAZOn6bT9ZDPX64wCAl8Zch/sGtnWpvPMl1ejZWrzVu2/ulYALpUzGFZuhbjjL0VmB12oZbgyEcs5cdrlORERiYCBkg2eTpX1rcOyOeVvMjx8d1N7qvKNvp6hcizwRd4w/X1KF0iqd+YubxGHaXmNg++YulWNa9NLFeIqISDQMhCTiixtPbj9ZbH587K10KK4aJ3H2/ej0BozrId6+Uymto0Qri644VWwMVqtqXFsDKCEqGAq5DAcvaMSoFhGRyxgIScxX+i2W7z5n7g26Li4cAQpxPjpF5TVQBYi3AGKVzvhFvf88v2jFdLpuW4yeIgSaeoOAonImSxORd2AgJBFf6g8qKtfimSU5AIARXVti9TOD6r3WkcDuUt2XoZjJ0jenJAAANNXcxkFMJy4aty1p09z1FcA7tgzz+eUjiKjpYCBkA5Olr6ipNaDfm2vNz7/IsN0mznyvVdYNs8RGBDlTNZtM+41xLSFxhQUFIDwoAGEq11cAD1YqcLqYG68SkXdgIGSDJ5OlvX1s7Op9xI69lS5q2abd6oOU4n0MA+uG7MqawB5u3qS0SmfeKNhVsRFBqNRyvzEi8g4MhCTiC0MDh/PLzI/HdItrMC/I9H4cmQVnyueJixSvRygiWAkAKOAUelGtP3TRpc1Wr3ZdXLjL0/CJiMTiej83ucTRlZg9ZcvxS7j787/Mz+fd31f0e2h1xi/DFuHi7Q1mmslWWsUcITG1iwlFiEacpHZVgBxnL1dBpzeI1stEROQs/haSjHd3CV0dBL1zaw+33OPkpQoAV4azxLTwz5Oil+nPLlVoES1SUrupB7Bax+ExIpIee4TIysLNeRbPh3dt2ehrZHWBnSMLKmprDQgPChB9TaWRybHc2FNkF0qrRds1Prpug90qnR7hQUpRyiQichYDIRvUajXUajX0evf/xeptC0v3f2stCq/aEPP422MtFk4UU02tAQmR4u8LpgqQm1dCJnFodQbc3re1KGWZPk+7T5dgtIubuBIRuYpDYzZ4YtaYNyZLV2hrLYKghZNT3RYEAcZVpQMU4pevClBg87FLopfrrwwGAfmaaoSoxMkRMg2xBSnFW0iTiMhZ7BGSmLd0CH391ym8tjzX/Hxt1mB0bBlm9+udCexyz5WKtkL11WLCAxEVwiEXsZRVG5ciCBJpBfC2zYyLMrq6XQcRkRjYI0So0NZaBEEprSPRLibU7fet1hkQLOIaQiYtwlTQcUFF0VTXGgOWpBjXV5UGgOBAY0B1hosqEpEXYCBE+HLLSYvnK6be6PSQmCM5T4cLytAuxv5eJ3sFBshRUaOHtpY9DmIw7QsWrBSnA9m0HhEXvSQib8BASGJSJ0sfKyzDP1YfNj8/6uTq0Y6GTaaFFws14i98aJqWXVDKjT3FYFoBXCwymQxtm4dw+jwReQUGQn5uxNyN5sc/PXWjxxa4q65bTHFUt1jRy+7btpnxHuwREoUpN0isoTHAuN/YyaIK0cojInIWAyGJSbmy9G/7882Pv38sDd1bRTpfmGmLDTvfz+GCMigVMvOaMmIKrpuNtC2vWPSy/ZFpg1Qx/63iIoOgZR4XEXkBBkJ+SlOtw6Nf7wQAtGkWgv7tmnn0/nqDAJ1eQJIbkrI7tDSWGczp2aI4VlgOAKLtNQYAkcFK5nARkVdgIGSDWq1GcnIyUlNTpa6KWxwrLEfK7N/Mzyf0SvB4HUxfgu7YXkNVN5RzmrOSRKGtNSBMJe4K4IEKOf46wR47IpIe1xGyITMzE5mZmdBoNIiMdGG4yA5SJEuPmPu7+XHOjJGIEmHIw9EtNorKjSs/m6ZSuwM3XhXHxTLxk84jgpVuCYKJiBzF30R+xjQV2kSMIMgZlyuMgVBsRJBbyk9pHcmhF5HUGgyiJ7W3bR6CGj1zhIhIeuwRkpgnO4SSpq2yeP7v+/t68O6WtLV6hAe57+MXHqQUfTNXf1WjF6CUi/s3U2mlsbeuQFPttmCYiMge7BHyUz89daOoG17KzLPG7LPl+CUEuHEfsz+OFWHR1tNuK9+f1OoNUAaI+2/VNykaAFBWzeFLIpIWAyE/sP5woUVv0Ad3pLg2VV4EIYEBCHNjj9D9A9uiS2y428r3J/vPaxAgco9QmMr4b88p9EQkNQZCUnNztnS1To/JC7abnw+7riVu69ta9Ps42l9gEAS3bK9hEhKowOXKGreV7y9MK4C3jFCJWm5g3VT88yXiryxOROQIBkJN3IC3sy2eP3xjO7feT7AzsPslN98tG66aVOv0KHTDbCd/o6nbeT5O5DyeZqHGJP3zJVWilktE5CgGQk3Y1hOXzFPIB3VugQWTU9GztbRDYiYxYYHI17gvUNl+8jIAYM+ZErfdwx+YApVag7g9ly3CjD1Mzm7uS0QkFgZCEnPXwFhplQ6T/vMXAKB7qwh8cEcKhnZpifAgpVvuZ5qhZc/7qdbpUVRegwFuXM16RLJxuncVN/Z0ia5uinu3hAhRyzV9XvK43xgRSazJB0IlJSXo168fevXqhe7du+Pzzz+Xukpudzi/zGLl6P9NvREtwsXN8XBFVY0xOGnTTLxNPK91ex9jHpRe5J4MX2LvMGVDTIGQe1YAl6Oyplb0comIHNHk1xEKDw/Hxo0bERISgoqKCnTv3h233normjdvLnXVALgnVzr9oys7yo/rGe916+mYhlniI923foxpuveB8xrc0DHGbffxRv/ZeBxv/3wIyfER+Pnpm1wqS6c3/lsFuCEQSkmMQrWOs8aISFpNvkdIoVAgJMTY86DVaiEIgih/KXuzqztB3p7YwyP3NK8jZEfT1hqMX37uzA9R1n1xv/XzQbfdw1skTVuFN346gJV7zqOqRo+3fz4EADhwQeNy2SV1Cx+6Y80nVYAcJy9xaIyIpOX1gdDGjRsxfvx4JCQkQCaTYfny5VbXqNVqJCUlISgoCAMGDMC2bdsszpeUlCAlJQWtW7fGCy+8gJiYptlDYDAIFusFbXpxKCKD3ZMT5IpaUy+DyGvTXK153aykSf0S3XYPKZwprjQPVwHAwbpg5//+yMPfv9uNrjNWi3o/05YsraODRS0XAFqEq9gjRESS8/pAqKKiAikpKVCr1TbPL1myBFlZWZg5cyZ27dqFlJQUjB49GoWFheZroqKisGfPHuTl5WHRokUoKCjwVPUbJWbv1Iv/3WvxPNGNOTiuMOXtuLNHyDQc2NSSpe+YtwVPLdqNrXmXjM//vcWt96upNSBIKXfL8GpUcCBqud8YEUnM63OE0tPTkZ6eXu/5uXPnYsqUKZg8eTIAYN68eVi1ahXmz5+PadOmWVwbGxuLlJQUbNq0CbfffrtVWVqtFlrtlSndGo3xr22dTgedTtytAAJggEohAAa9uexr/++o/+WcgapuM/fUpGjR69wQQV8LlUKAvrbxtiqpqIJKIUAm6O2qo7Pt0q9NBAy1tR5tB3fIK6pAqCoAAXIZLldUYf2hKvx55Dze6Aeo5NaBdN+20SjUaKHTG1x+75fLKyEY7Pt3cpRKYbD7M2APV39+miK2iTW2ibWm2CaOvBeZ4EMJMzKZDMuWLcOECRMAADU1NQgJCcHSpUvNxwAgIyMDJSUlWLFiBQoKChASEoLw8HCUlpbihhtuwHfffYcePaxzZ2bNmoXZs2dbHV+0aJE5z4iIiIi8W2VlJe655x6UlpYiIqLh5T+8vkeoIUVFRdDr9YiNjbU4Hhsbi0OHjAmjp06dwqOPPmpOkn7qqadsBkEAMH36dGRlZZmfazQaJCYmYtSoUY02pKP+/t1urDtciGljrsN9A9sCMEawa9aswciRI6FU2p/bU1JZgxv/sd78PHfWaFHrao+cM5dx3/9tw8rMG9C+RcNbZ8z55RC+234ae2aMsqtsZ9vlue/3QFOtw+cP9LP7Nd5Aq9NjyY4zuKd/G/R6Y43Na1RyAW/0M+C1HXJoDTKszLwR7VuEms+//+sh/H6kCP976kaX6tJ91q8A3POZ+nzTCXyUfRR7ZowSZZjU2c9JU8Y2scY2sdYU28Q0omMPnw6E7NG/f3/k5OTYda1KpYJKZb3ejlKpFP3DUQs5tHoZIFdYle3I/TYfK8K9X2yFabevhZNTJfkgyxUB0OplkCsCGr2/IFMgITrM4Xo6+u8QpFJi5b4Cn/rBFgQBnV4zrgH1xs9H0NgubutfGA7IFWgeZvm5NcgUqDHIXH7vcVGhGNy5hVvasKBMB61eBq1ehkiVeOW74+fV17FNrLFNrDWlNnHkfXh9snRDYmJioFAorJKfCwoKEBcX53S5arUaycnJSE1NdbWKjXJ1YHLdoStJ4d1bRWBIl5Yu1sj95m/Ow7HCcrffJyTQGOfnl3rvxp4/7T2Pv3+3GyculuOzDccxYu7vDV7/7/v74vlRnc09NBHBSqsgCIBoyc21esFtMw83Hi0CAGw5UeSW8omI7OHTgVBgYCD69u2L7OwrG4saDAZkZ2cjLS3N6XIzMzNx4MABbN++vfGLJaKvmyr/f3/kmY99OKmXdBVyeP959wsPMgZC/9tzXuKaWHrnl4PIPVeKP48XYeqi3Vi55zyGffA73l19CMcv2l5XJzwoACfnjMPobnGYOqyTx+qqqdZBFeCeXxMf390bABCqavId00Tkxbz+N1B5eTmOHTtmfp6Xl4ecnBw0a9YMbdq0QVZWFjIyMtCvXz/0798fH374ISoqKsyzyJyhVquhVquh17t/6rWzHUJvrjpg8Tw0UIGOLcNdr5CL7Hk/fdpEoUMjeURi0NRtOHvCi/azEgQB//79BH7Zl4/TxZV2v26fBHlf1To9yqprERzonl8TMXU9WWJv6EpE5Aiv7xHasWMHevfujd69jX89ZmVloXfv3pgxYwYAYNKkSXj//fcxY8YM9OrVCzk5OVi9erVVArUjvL1HqEBTjQWbT1oc2//6GGkq46AKbS12nS7BDzvPuv1epnWUamqlXatm09GLMBgEXCitQrvpPwOAXUHQhF4JLt3X1fCiQmvcByzRDYspAoBSYexF1En870NE/s3re4SGDBnS6KKDU6dOxdSpUz1UI+nddNUMMQBoVreKspTs3WLDdDrMA8Mhj9zUHm+uOojoEM8n/2lr9TicX4bKGj3u/79tjb8AxgUm9QYBO18dYc77uaNfolP5VGIMVNbULXaoUipEKM2aaf+yP49fwqhuzuf0ERG5wusDISl4dGjMwWzpq7fQMPlr+nCxquMxb03s7pH7JMdHoLrW86tLbzl+CQ8u2I7UpGi7rt/+ygi0CLdOer6hY4xkm8YeulAGwD07zwNARFAA5DLYfN9ERJ7CQMiGzMxMZGZmQqPRIDIyUurqmH3550mL5yO6toT63j4IdFMyqyPs7YG4XFFjvN4NWzbYolLKseHwRY/cy+T0pUo8uMA4rLr95OUGr/3P/X1xQ8cYr0wYFur67xKigtxSvkwmQ2xEECprat1SPhGRPbzvty/Va+bK/RbPv8hw//R+RwmNZKb8ccw4VTqltWcCzIggJQweSsYtq9Zh8bYzDu14784hIVcXjTdtiBoV4r6hV22tAScv2Z80TkQkNum7EsguW45fsnjeNV7cla49pbza+Ne/rbVv3EEmA/acLcXFMm3jFzdCW6vHL/sumJ9vOnoR2lo9BEHAi0v3oMes3xoNgubd1wdTbmoHAJjUL9HlOtVLhA63c5erAAAhge7JEQKAYKUCWu5AT0QSYo+QDZ7MEbLX3Z//ZfH8ySEdJKqJbfYOdZVV66BUyDySLA0A9/Rvgw2HL+J8SZXLuSizVu7Hd9vOYGLvVrilV4J5+KsxN3aMQf92zRAfGYQx3eMxpns8XhmX7FJdPKGs2rj8gNJNOUKAcRFQLWeNEZGEGAjZ4MkcoUZnWQkCvvnrlMWx50Z2xvgU16ZWu0tj72fbyWJ4ctmY+Ejj1O/Z/9uPH5+8wakyBEHAP9ccwXfbzgAAlu0+h2W7z9n12l+evkmy3jtXm7lGL6Btc/duNqxUyFGuZY4QEUmHgZAXEwTBvO6MyfUdmuOp4Z5bWVhsggAoPJQoDQBRdVPnd50ucfi1By9oMP+PPMy6uRv+te5Y4y+oszZrEI4WlCO9R7zD9xSLTISxMZ3e4NbeIADIPVeKk5cqIQiCxxLoiYiuxkBIYg0lF1fprIfmJqW6Ma/EBfZ+hW3NK3ZrPa5lWr14YPtmdl1/qVyLTUeLEKoKwFurDuDkpUqHF3/s2DLcK1b5dlVlTS0CRNgVviGmROlzJVVoHe3e3iciIlsYCNkgdY5Qrd6A3w5eQOaiXRbH1z03GO09sDWFKxoaGqvVez4XJDhQgZTWkUiIsm915L5vrnWo/Mk3JKFVVDCOFJRh56nLGJHs/Irm3uZcSbXb9hkzyRrZGXPXHEGtnttsEJE0GAjZIPU6QhM//ROHCq2nFHt7ENSYShs9XJ5wsUyLPWdLYTAI+PCu3ubjVTV6BAbIkXOmBKEqBTYfu9RAKVecnDMOgDGwU8hl5iEdba3ebYsPOsPF2fPQ6vRo3cy9vTRpHZoDa4BaAxOmiUgaDIQkZuvLKu9SBa4dbFow2fvWDLqaPekdUu35db60GgCwPOe8ORDS6Q3oOmM1buwYY17byB43dGxufhxwTdCjCnDfNHNHiZFuU1mjR5ibNlw1MQ296dgjREQS8Z4/X6leXz3UH0O7tJS6GnZpKOdJVzc09vCN7TxVHStfbTmJs5cr0emVXwDAoSDovdt74ttHBrqral7n7OVKqJTu/RVhSsbm0BgRSYWBkMSu/vVfUlljcS5cFYATb4/FoM4tPFspJ9gzS2n/OQ0A4MZOnt07a959fcyPZ6zYjxvfXd/A1VeM6xGP76YYA5+1WYNwhzsXQPRCFVo9Et2cwBxUF2hdqnB9wUsiImdwaMwGqZKlf9p7AVfPbUrr0BxyN8/aEVtDeSm/HcgHACRG25e4LJYx3R2fxr708TT0SoxCgEJuzgnyNY1td2LP64Pc3CPUMsK4j1mF1nsWLyUi/8IeIRsyMzNx4MABbN9u38rBYglWXskxCZDLcGuf1h69vydEBAVIMrX8xNtj7b523XOD0S+pmVUOkC9xNXwWBAE6veD2dYSIiKTGHiGJXd2DEhGshK7u8TEHvri9gT3JubV6AdfFSbPKslwuw/T06/DOL4csjn92bx/zwodPL96NPm2ifX52nhhM214Eunn6vMmibacwrqd0C1ASkf/in3teJFBhjCbSnRjK8QU1egOUAdIN9T022Hp/tqtXf/7ort7IuD7JgzVyL1emz58pNi7f4Or+bPayd+kCIiKxMRCSmK08jhfHdJGgJu5XqxcQIOdHzhNcnT5v6hGKCg4UoTZERN6LQ2NexPQXvBj7RHmbAk01Vu/Pl7oaZimtI3Ghbn0hsrb/fCkAuH36vGnlak/1PBERXYuBkMS0OutFBn1578n6hmNW53pHELTsyeuRX1qNwV1a2Gx7Mnrpv/sAAGEq9/6KUAUo0LdtNNrFhLr1PkRE9eE4hQ1qtRrJyclITXX/as7FFTWNX+QDGgve5tQlKb8wWtphv95topHeIx4hgQGIDm3awz6u5Ajd3b8NANi9R5srAuQyVEu0/QoREQMhG6SaPt+UVdV90U3o3UrimvgHV4dXW0cHIybMM4FiYIAc50qqPHIvIqJrMRAiUZmSv4vKtRBsdEm4ezdz8j1xEUGS7UNHRMRvJYldPWvMl3dburYHot+ba/HWqoMAjLu0m7g754R8T6gqwLwPHRGRpzn1rbRr1y4olUr06NEDALBixQosWLAAycnJmDVrFgIDm3buhbv5cK40AMBgMIZ0K/ecx097LyBfY5ydNS39OgQpvWeHdqrfe78e9ti9VAFyaKpqPXY/IqKrOdUj9Nhjj+HIkSMAgBMnTuCuu+5CSEgIfvjhB7z44ouiVpB8iyAA+rohscIyrTkIAoCB7ZtLVS2/40szD4MDFcjXVNscSiUicjenAqEjR46gV69eAIAffvgBgwYNwqJFi7Bw4UL897//FbN+TV5T+d1v+uK9Rb0ZvWb/ZvMad2/gSb6pdd0O93pDE/lhICKf4tQ3kyAIMBiMY/pr167F2LHGfbESExNRVFQkXu3IJ1XU2J4KHVe30zh5hq/0sCjrtpbR6X2jvkTUtDgVCPXr1w9vvvkmvv76a/z+++8YN24cACAvLw+xsbGiVpB8Q6vohtebOfJmOqJCmDvmKa6MjHl6Bldg3Q73nDlGRFJwKhD65z//iV27dmHq1Kl45ZVX0LFjRwDA0qVLcf3114taQSl4ckFFweKx7/5FHBZYf979S2Ou89gu5uS6mroZXO/fkeKR+ynkxrCtUseEaSLyPKdmjaWkpGDfvn1Wx9977z0EBPj+9OjMzExkZmZCo9EgMjLS4/f3pURXE7m8/ko/Nqi9B2tCrtLXDVGFqTwzw880k/CzDcfx+i3dPXJPIiITp/5Mb9++PS5dumR1vLq6Gp07d3a5UuSbbk5JsDr25UP9GwySyH2c7V+srcv/U8g904tn2un+530XPHI/IqKrOfWb7uTJk9DrrRNitVotzp4963Kl/ImP5LPa5V9398bJOePMz4+/PRaDO7eQsEZ+zIVuxdq62VsBHgpgr4sLBwAM7dLSI/cjIrqaQ+NYK1euND/+9ddfLYaN9Ho9srOz0a5dO/FqRz5NwZ4gn1So0QIAyrSeydlJbGacPr8i5zze81BeEhGRiUOB0IQJEwAAMpkMGRkZFueUSiWSkpLwwQcfiFY5f9NUOodu6NgcseGcKi81Z3sbNxwuBADsP19qc7jTXWq4zQYRScChQMi0dlC7du2wfft2xMTEuKVS/sX628rX+1G+fWSg1FXwe658hgLqprM/fKPneneHdGmBoABuv0JEnufUFK+8vDyx60FEXuLd1YcAWG+k604Kmcycm0RE5ElOz3XPzs5GdnY2CgsLzT1FJvPnz3e5YkQkjS6x4ThcUIbwIM8thaGQMxAiImk49Ztu9uzZeP3119GvXz/Ex8dD5osL33iJpjRrjLyLswt0Du/aEhU1teb1fTxBIZehmitLE5EEnAqE5s2bh4ULF+L+++8Xuz6iO3PmDO6//34UFhYiICAAr732Gu644w6pq2UTgyISiyt/m5Rra1HhoRljJgq5DAb2CBGRBJwKhGpqanxmK42AgAB8+OGH6NWrF/Lz89G3b1+MHTsWoaGhUletXuxgIyl9teWUx++p0xtQWqXz+H2JiJxaUPGRRx7BokWLxK6LW8THx6NXr14AgLi4OMTExKC4uFjaSl2FvUBEwK/7C7DvXKnHe6KIiJzqEaqursZ//vMfrF27Fj179oRSqbQ4P3fuXFEqBwAbN27Ee++9h507d+LChQtYtmyZeT0jE7Vajffeew/5+flISUnBxx9/jP79+1uVtXPnTuj1eiQmJopWPyJv5WyQfUPH5ogOCRS3MnYqrdIhVOX7+xUSke9wqkdo79696NWrF+RyOXJzc7F7927zfzk5OaJWsKKiAikpKVCr1TbPL1myBFlZWZg5cyZ27dqFlJQUjB49GoWFhRbXFRcX44EHHsB//vMfUetH5I1cmfqu0wtQKjyzz5jJrPHJdfdmwjQReZZTf3qtX79e7HrUKz09Henp6fWenzt3LqZMmYLJkycDMCZyr1q1CvPnz8e0adMAGPdAmzBhAqZNm9ZgbpNWq4VWqzU/12g0AACdTgedTtz8hQAYoFIIkENvLlswGPdvq62tFf1+vszUFmyTKxprE5mgR6BccKrNZIZaqJx8rbOS40KhUgio1tZAp3OuN4qfE2tsE2tsE2tNsU0ceS8yQfCdLBWZTGYxNFZTU4OQkBAsXbrUYrgsIyMDJSUlWLFiBQRBwD333IMuXbpg1qxZDZY/a9YszJ492+r4okWLEBISIuI7ISIiIneprKzEPffcg9LSUkRERDR4rVM9QkOHDm1w7aB169Y5U6zDioqKoNfrERsba3E8NjYWhw4ZV8fdvHkzlixZgp49e2L58uUAgK+//ho9evSwKm/69OnIysoyP9doNEhMTMSoUaMabUhH/f273Vh3uBC39m6F12/pDgBYs/88dKdyMPCmoWgWHizq/XyZTqfDmjVrMHLkSKt8NH/VWJvM+/04Fm8/gw3PD3G47LR3snFXaiKeHtFZhJra59zlKoz+aCPemtADt/Rybn8zfk6ssU2ssU2sNcU2MY3o2MOpQMg0C8tEp9MhJycHubm5VpuxSu3GG2+0Wvm6PiqVCiqVCmq1Gmq1Gnq9cahKqVSK/uGohRxavQy1gtxctkxuXMAuICCgyXwYxeSOfwdfV1+bCDIFagwyp9qrFnKEBgd5tK3joo0/D8//Nxe3p7Z1qSx+TqyxTayxTaw1pTZx5H04FQj985//tHl81qxZKC8vd6ZIp8TExEChUKCgoMDieEFBAeLi4pwuNzMzE5mZmdBoNIiMjHS1mkQ+RW8QIPfwWlaqAM8mZxMRmYj62+e+++7z6D5jgYGB6Nu3L7Kzs83HDAYDsrOzkZaW5rF6EHkjZ7P/BMG40rMncZseIpKKqAt2bNmyBUFBQWIWifLychw7dsz8PC8vDzk5OWjWrBnatGmDrKwsZGRkoF+/fujfvz8+/PBDVFRUmGeROePaoTF3uvq7ynfS1snbuRJWGASBgQkR+Q2nAqFbb73V4rkgCLhw4QJ27NiB1157TZSKmezYsQNDhw41PzclM2dkZGDhwoWYNGkSLl68iBkzZiA/Px+9evXC6tWrrRKoHSH50Bi/g0giCzfnobJG7/GhMSIiqTgVCF0bHMjlcnTp0gWvv/46Ro0aJUrFTIYMGYLGZvhPnToVU6dOFfW+RP5o1v8OAPD80BgRkVScCoQWLFggdj28ikeHxjgcRm7DDxcRUWNcSpbeuXMnvvnmG3zzzTfYvXu3WHWSXGZmJg4cOIDt27dLXRUip7ia4pN9sLDxi4iImgCneoQKCwtx1113YcOGDYiKigIAlJSUYOjQoVi8eDFatGghZh39Bv9+J2+hN/DTSET+wakeoaeeegplZWXYv38/iouLUVxcjNzcXGg0Gvz9738Xu44ep1arkZycjNTUVLffS7AR/nDCDonBlWHX1tHSrWzuQ7v+EFET4FQgtHr1anz66afo2rWr+VhycjLUajV++eUX0SonFQ6Nka9zdfq7lMnSOj0DISLyHKcCIYPBYHP5aqVSafd2FkTkvbJGem6fMZO7+ycCAHR6/g4hIs9xKhAaNmwYnn76aZw/f9587Ny5c3j22WcxfPhw0SrnF/jHL3mR1KRo3NqnFZqHqTx+78GdjbmFpVU6j9+biPyXU4HQJ598Ao1Gg6SkJHTo0AEdOnRAu3btoNFo8PHHH4tdR7/B1AgSkzMfpzPFVZIlSidEGfOSXl62T5L7E5F/cmrWWGJiInbt2oW1a9fi0KFDAICuXbtixIgRolZOKp5cR8gW5kqTVPI11ViRcx4f3dXb4/eW1+U1bTh80eP3JiL/5VCP0Lp165CcnAyNRgOZTIaRI0fiqaeewlNPPYXU1FR069YNmzZtclddPcaTydLsBCIyquWUfSKSgEOB0IcffogpU6YgIiLC6lxkZCQee+wxzJ07V7TKEZHzHJ2Gvmz3WQDAy2Ovc0d1GtWzlQT7+hGR33MoENqzZw/GjBlT7/lRo0Zh586dLleKiFzjzOz5Z5fsAQB8uuG4yLWxj5z7mxGRBBwKhAoKCmxOmzcJCAjAxYsc33fE1X+121pckcjTwoOcSh0kIvJJDgVCrVq1Qm5ubr3n9+7di/j4eJcrJTVPrixti6uL4RG5olWUdKtKExF5mkOB0NixY/Haa6+hurra6lxVVRVmzpyJv/3tb6JVTipMlqamwNnP1viUBFHrQUTkzRzqA3/11Vfx448/onPnzpg6dSq6dOkCADh06JB5uvkrr7zilooSkf1kLizCcHdqGxFr4phbe7fCj7vPSXZ/IvI/DgVCsbGx+PPPP/HEE09g+vTp5vwWmUyG0aNHQ61WIzY21i0VJSLPkDJpuWfrSPy4+xy0tXqoAhSS1YOI/IfDWZFt27bFzz//jMuXL+PYsWMQBAGdOnVCdHS0O+rX5HE1aaIr4iKDAAAVWgZCROQZTk8PiY6OliyZuKliUERicuTzdL6kyn0VcUCA3Ji2aOAPAxF5iFN7jTV1ks8ak+Su1JQ4OvFw1+nL7qmIg+riIBi4yjQReQgDIRs4a4xIGqb9xhgHEZGnMBAiaqIc3WLDG1wJhHyv7kTkmxgIETVBjg6vekvcwUCIiDyNgZDELLfYIPJvppn7N767HgBQVaOXsDZE5A8YCHkhJkuTvzpdXGl+/PWWk0h5/TfU1BokrBERNXXcXZGoibK3h1G9/hgWbT0NAAiQeAf4wIArf5u99+th1NQasPv0ZbRpHgKDwH3QiEh8DIQkxuEwcgdHps+/9+th8+MAhbSBUJDyyiKKmupaAMDO05cx6T9/mY/nvTOWGxMTkWg4NEZEZtU6aYehbHVI/WP1YYvn7ab/jIWb8zxUIyJq6hgI2SDZgoqcKUMSO/H2WEnvL6+npyc1KRrv3NrD/HzW/w5g+8liT1WLiJowBkI2eHJBRVtjY67sHE5k5kRcLeWGqwAQEmg9Wt8lNhw/PH497u7fBmuzBiFcZbwmY/42T1ePiJogBkJETZCvBtM3dGxu8fyeAW2w8qkbzM87tgzHr88OAgBU1uix20u2BiEi38VAiIi8hkwmQ+82UQCALx/qj4duaGe1C31CVDA2TxsGAJj46Z+cXk9ELmEgJDGB88bITXz1k/XlQ/2x9PE0DO7cAh1bhtm8plVUMN69zZgzdOZypc1riIjswUCIqAmyd3b5L/suuLciTogIUqJfUrNGr+uWEAkAGP7B7+6uEhE1YVxHiKiJKtfWImP+NvRoFYlP1h8DALx3e0/0b9cMbZuHAgDWHy6Usoou6ZYQYX785/EipLaJlLA2ROSrGAhJzNaMea4VR65avP0MAOD3Ixfx+5GL5uMvLN1rfnxTpxhsOlrk8bqJRSaT4b6BbfDNX6fx+Nc7sfOVYVJXiYh8EIfGiJqg3olRjV5zbRCU0tr3elQeSEsCcGUVaiIiRzEQImqC3pjQHcszb8AtvRIAAAPbN8OB10ejXUxova95744UT1VPNJ1jwxGkNP4a++avUxLXhoh8kV8EQhMnTkR0dDRuv/12qatihYtJkzsEKRXolRiFj+7qjZNzxmHxo2kICQzA+ueHIO+dsdj12kh8N2WgxWs6x4ZLVFvX/K2nMdj7fNMJiWtCRL7ILwKhp59+Gl999ZXU1WgUgyLyBJlMhmahgUjr0Bwn54xDhxb19xL5gvfrerIuVdRIXBMi8kV+EQgNGTIE4eG++dcukbs1hQD81j6tpK4CEfkorw+ENm7ciPHjxyMhIQEymQzLly+3ukatViMpKQlBQUEYMGAAtm3znT2IuKAiSc3QBCKhNyd0Nz8WmsD7ISLP8fpAqKKiAikpKVCr1TbPL1myBFlZWZg5cyZ27dqFlJQUjB49GoWFvrs+CpEnff94GpY9eb3U1XBJSGAAYsODAAAPfemBzZKJqMnw+nWE0tPTkZ6eXu/5uXPnYsqUKZg8eTIAYN68eVi1ahXmz5+PadOmOXQvrVYLrVZrfq7RaAAAOp0OOp3OidrXLwAGqBQCAmC4UrZBDwCora0V/X6+zNQWbJMrxGyT6CAFouPDfL59v32oH3Zt+R17TxejpqYGMi7IxZ8dG9gm1ppimzjyXmSCD/Ujy2QyLFu2DBMmTAAA1NTUICQkBEuXLjUfA4CMjAyUlJRgxYoV5mMbNmzAJ598gqVLl9Zb/qxZszB79myr44sWLUJISIho74OIiIjcp7KyEvfccw9KS0sRERHR4LVe3yPUkKKiIuj1esTGxlocj42NxaFDh8zPR4wYgT179qCiogKtW7fGDz/8gLS0NKvypk+fjqysLPNzjUaDxMREjBo1qtGGdNTfv9uNdYcLMbxLS3x0d28AwE85ZyA/vw9Dhg1HSJBK1Pv5Mp1OhzVr1mDkyJFQKpVSV8crsE2smdrktR1yaA0y5M4aLXWVJMfPiTW2ibWm2CamER17+HQgZK+1a9fadZ1KpYJKZR2AKJVK0T8ctZBDq5ehFvIrZcsVAICAgIAm82EUkzv+HXwd28Sa1iCDVi/D8UtVuC5O3D9gfBU/J9bYJtaaUps48j68Plm6ITExMVAoFCgoKLA4XlBQgLi4OKfLVavVSE5ORmpqqqtVbJTPjEsS+ZgxH27C7tOXpa4GEXk5nw6EAgMD0bdvX2RnZ5uPGQwGZGdn2xz6sldmZiYOHDiA7ds5+4TI17x3+5WtQlbuOS9hTYjIF3h9IFReXo6cnBzk5OQAAPLy8pCTk4PTp08DALKysvD555/jyy+/xMGDB/HEE0+goqLCPIvMGZ7sEbqa76StE3mv9O5XeoMXbD6JCi03ZCWi+nl9ILRjxw707t0bvXsbE4qzsrLQu3dvzJgxAwAwadIkvP/++5gxYwZ69eqFnJwcrF692iqB2hGe7BEq0FTbOMppv0Su+FfdBAQAGPzeBukqQkRez+uTpYcMGdLoSrFTp07F1KlTPVQjcen07AYiEtvNKQmICArAgwu2o6hci6JyLWLCOBOTiKx5fY+QFDw5NMa+HyL3aB0dbH48d80RCWtCRN6MgZANnhwaY38QkXt0bBmOMJWx03vR1tO4+ZM/JK4REXkjBkIS86GFvYl8zrZXhpsf7z1bKmFNiMhbMRDyIoyJiMQVEhhgMYssadoqCWtDRN6IgZANHl1Q0Ubww70iicTz9sQeFs+zDxbUcyUR+SMGQjZ4NkeI3UBE7hQdGog1zw4yP3/pv3slrA0ReRsGQhLjcBiR+3WKDTf3DBWV1yBp2iocv1guca2IyBswEJIY4yAiz5jYu5XF8+Ef/I6aWoNEtSEib8FAyAbP5ghdCYUYFBG5T3CgArmzR1scK6vWSVQbIvIWDIRskHodIeZKE7lHmCoATw7pYH7e9821KCyztc0NEfkLBkJE5FeyRnZGq6grq07f+O56CWtDRFJjICQ1jocReVSAQo7N04aZn9fUGpC1JEe6ChGRpBgISYxxEJE0BrZvZn784+5zWLnnPLS1eglrRERSYCBkg2TJ0pxLT+Qxr45LttiR/u/f7caKnPOo0NaiWud4QJRzpgSCIKC0Sof8UuYdEfkKBkI2SJ4szaWlidyue6tI7Hh1hMWxF5fuRbeZv+Kxr3c6VNa5kipMUG9Gu+k/Y8pXOzDwnWwxq0pEbsRASGLsBCKS1t3921gd+/3IRew4WYyzlystjusNAgo01cg+WIBvt54yH79hzjrz4215xQCAB+Zvw+WKGjfVmojEEiB1Bfwdt9ggktbbE7tjyk3tEBGsRL8315qP3z5vi/nxibfHYvwnf2D/eY3Fa7MPFmLdoUKb5W48chG931iDeff1wZju8e6pPBG5jD1CEmOPEJG0ZDIZ2rcIQ0yYCk8N62jzmvYv/2wVBAGoNwi62uPf7ELStFUwGPjDTuSNGAhJ7OpAiL8miaT13KgueGF0F5fKWP3MTTaPf77pBHacLMaZ4kqb54lIGhwa80JMlSaSzuODO+C9Xw/bff11ceEY2L45nhzSAeXaWrRvEYbVz9yEH3edw382njBf984vhyxed3LOONHqTETOYyBkg1qthlqthl7v/jVFOGWeyLso5DKcnDMOu09fxsRP/8TkG5KwYPNJAMC4nvFoHxOKb/46hXXPDUF0aKDFa1vW/f+6uAi8PDbCIhAi71ZaqcPxonLERQTh1k//RL6mGm9O6I4TFyswY3yy1NUjN2IgZENmZiYyMzOh0WgQGRnp1nsxDCLyTimto3D4zTFQBSgQGCDHbX1ao3NsOARBwNPDOyFA0XhmwU9P3Yi/ffyHzXNbT1zCgPbNxa42OenF/+7Br/sLLI69ujwXADB/cx6eHt4J96e1tVh7ipoG5ghJjB1CRN5JLpdBFaAAAExP74rOseEAjMnV9gRBgHGtolV/vxGvjutqdW7Sf/7C2cuVqNbp8eOus/j6r1M4WVQh3hsgu50rqbIKgq71UfZR3Pv5Vg/ViDyJPUIS4/R5oqatW0IkuiVE4rttp3H8omWgc+O765HYLBhniqvMx5qFBuKLjH7o0ybaY3Ws1RsQoJDjTHElbvrHeiyYnIqhXVo2/sImYu2BhoMgk8MFZUiatgo/PJ6G1KRmjb+AfAJ7hCRm0SPEmIioyVqeeQN2XrOSNQCLIAgAiitqsHjbaWw6ehHvO5C0bUtNraHRa86VVKHjK79g4NvZuOkf6wEAv+3PN59/d/UhzLkm0bspEQQBM1fud+g1rv67kHdhj5DEbG+x4fFqEJGbhQcpAQCqADm0jQQo3+84i+93nAUALNp2GhteGIKIutfba+uJS3j0y614vU/912R+uwvnSoyBWL7myv5owUrjV8M/1xzBZxuOAwC6xIVhYu/WDtXBm9XUGrDnXDGKG1n9+/8y+uHhL3dYHNt9psSNNSNPY4+QxCq1tVJXgYg86PCb6bguLtzu64sravDPNUcgCAL0BgFfbTmJpGmrcPqScT2ik0UV+OavU9Bfs2DjxqMXUW0j4BIEARsOFyJp2iqs2ncBOTa+1OdvzkPStFX4KPuo+dizS/Zg1sr96PPGGpRra/HR2qPQ6RvvcfJWJ4oqcPu8LXi0nn3lbuoUg8NvjjEHsFerqTVgde4Fd1eRPIQ9QhKrqNGjptaAwADGpET+4ofH07Bk+xm8ueqgXdcv2HzSPIXfZNB76zHn1h6Y9uM+AMC4HvGIDg2EtlaPLzbloarmSpCyen8+hnaNx5Pf7kKrqGB8t+20U/Ve+KexDvd+/hf2nC1Fv6Ro3NAxBoAxwDIIxuUHavUGVGj1CAsKgELunV3cika63p8Y0gGqAAX6t2uG/8vohzbNQjDynxvN5x//ZhfXgmoiGAh5AZ2egRCRPwkPUuKRm9pjYu9WaB6mQk2tAZ1f/cXhckxBEABkLtqFd2/riSClwrwgpMo46Q1/HC3C00v2QqxdPvacLQUA3PvFVjw2uD0GtmuOX3IvoLiiBl9kpGLumiP4dMNxfDdlINI6eNcSAaa12+ausc7z+fyBfhjapQVOXqpEx5Zh5uPDu8YCANrFhCLvqpl9Ty/ejQ8n9YKM+Qw+jd++NqjVaiQnJyM1NdUj96ut++3EGWRE/qV53Zo0gQFyjOkW51JZfx6/hJv+sR5jPtxodW55zrl6g6BnR3R26b7//v0EJi/cju93nMXag4VYuee8OUm71uB9Q2dPL84BAGw6VmRx/B+398TI5FgEKOQWQdDVXr+lm8XzFTnnMfT9De6oJnkQAyEbMjMzceDAAWzfvt0j96u9Zpydf10Q+R/1vX2wf/Zo5L0zFn+8NBS9EqOcKudSI8m/13pscHuMqOvxAIDs5wbjr+nDMeNvzq2m/PfvduOLP/IAABuPXIQgCHhtea7NXCQprDtse6PcO/slNvramzq1QLuYUItjJy9x7zhfx0BIQqZ4p5a7UhP5PYVchlBVAGQyGVpHh+C/T1yP7x9LMy/GKJcBN9bl44jh78M6Ysv0YQhSKpDS2riC/vwH+6FDizDERQbhoRvb4a2J3c3XfzdlIBKbBTt0j8835eGj7KP4+q9TmKDeLFrdnTXlqx2NX9SIxY8OxM0pCRbHdp667HK5JB0GQhJSyo3N78szL4jIPRRyGfq3a4axPeIBAGO6x+GbRwagU8swdI4Nw49PXo83rhmqsdfLY6/DsyM7Iz7SGNjcmZqIm1MS0Let5SKB9w5oi5NzxuHYW+lI69Ac/7qrN27plWCryHp9uPbKzLM/jxehWqfHI1/uQNK0VUiatgpTvtphNePNXS6Va20ebx1tf4AXGxGEV//WFVfngN/22Z/YUE9PE3k/BkISigoxTsv8dutpJE1bZbGOBxERAIQGGmdeje9pDEDWZA3Gb88ORp820bg/LQm7Xxtp9ZrlmTfg5JxxuKOv9bo/M8cn49FBHSyG4GMjgvCvu3sjMtj2WkWmLUV6t4nGR3f1xsk54yz23Mp+brBd7+Wez7fiutdWY+3BKys5rzlQgLR3svHT3vN2leGMWr0Bh/PLsOt0ic3zPz55vUPltQwPwol3LGeMaaq5FIqvYiAkoTCVcdKeacGykkqdlNUhIi8UGaLE8bfHIr2uZ+ha0aGB5scLJ6diVHKsOb9o5njLHqO5d6Zg8g3tRKnXz0/faH4cFaxE/3bNMHVoR6fKKizTQr3+uCj1smXX6RKMtpFEDgAxYSq0DA9y+R6BCuZ2+ipOn/ciQUo5wFiIiBx09Xo2Q2zsERYeGIDnh3bGrX3EWxm6ZXgQtr08HDV6A5qHqfD9Y2kAgE/WH3OqvIMXNCjX1uLnvRdwZ6oxcXnGilx8teUUVj9zE44XViC1XTRahgeZ90arz9Xn950txbQf91pd883DA/D5H6dwz4A2TtUXABZMTsXkBcZJNS/8sBdpHWLq7VUj78UeIS8SaOeO1kREjtjwwhA8cH1b0cttGRGE1tEhFscOvTEGAPDIjVd6nkzJ2I3pPvNXvPjfvVCvP4YXftiDr7acAgDsOHkZmYt24fONJ3CpXIvUt9biz2umv5us3HMeN7y7DtpaPVbtvYDxn/yBE9dsdgsAEUFK/OeBfjYDR3slXvXey7S1SJn9m9NlkXT4zSuhmmuTpDltnojcQKVUQBWg8Mi9gpQK/P7CELxSN9sNADKuT3KojPd+PYwfdp41P391eS4A47o9+ZpqXK7U4bPfj6OoXItbP92MZbvPolZvwOJtp/HJuqMo0GjR5dXVyFy0q957tG8RWu85e3VsGea1K2eT/Tg0JqFqnd7i+W+5+Xi8g0SVISISSdvmxiDjzQndsf1kMW7t0xrdW0Vi1D9t5+nYq7BMi7qFobHpaBH6vbkWgDEH6Nkle1wq21lH30xH+5d/luTeJI4m3yP0008/oUuXLujUqRO++OILqatjoVpn2SN04pJ19y0Rka+6b2BbfHRXbwBA51j7N5ptyN8+/kOUcsQil8vQ/KqE9aRpqySsDTmjSQdCtbW1yMrKwrp167B792689957uHTpktTVMgvgLAMi8iPfPGxcB+mdW3sg752x5uOf3dvHI/ff/soIbH5pqOjlrntuiMXzyhpOpfclTToQ2rZtG7p164ZWrVohLCwM6enp+O0370lm69sm2uJ5fITrUziJiLzVjZ1isCZrMO7u38ZiHaOrlwZIcXJrkcZEhSjRIlyFyODAxi92UESwZZbJyLkbsfHIRdHvQ+7h1YHQxo0bMX78eCQkJEAmk2H58uVW16jVaiQlJSEoKAgDBgzAtm3bzOfOnz+PVq1amZ+3atUK586d80TVHSaXcasNIqIVdYtBii1nxijRyzS5dn/IcyVVeGZJjtvuR+Ly6kCooqICKSkpUKvVNs8vWbIEWVlZmDlzJnbt2oWUlBSMHj0ahYW+t9S5QQAu1rP8OxFRU2WKIeIjg/DUsCsLMj4+uAPmP9gPgzu3MB9b5uAK0CamKf2eVFxRg7wi5n36Aq+eNZaeno709PR6z8+dOxdTpkzB5MmTAQDz5s3DqlWrMH/+fEybNg0JCQkWPUDnzp1D//796y1Pq9VCq70SjGg0GgCATqeDTifuSocBMMAAAd893A/RIYG45dPNUMkF8/3oClN7sF2uYJtYY5tY8/Y2eW5EB/RrGw2dToff/n4DVEq5ua7PjTBOoS2vqsFfxwuRmhSN7vFh2PfacPR7e22D5S58MBUPLtxufq6AAbq6ySnuahOVwrpH/91V+/HxPb1FvY87ePvnxBmOvBeZIAg+MR4jk8mwbNkyTJgwAQBQU1ODkJAQLF261HwMADIyMlBSUoIVK1agtrYWXbt2xYYNGxAZGYm+ffvizz//RPPmzW3eY9asWZg9e7bV8UWLFiEkJMTGK4iIiMjbVFZW4p577kFpaSkiIiIavNare4QaUlRUBL1ej9jYWIvjsbGxOHToEAAgICAAH3zwAYYOHQqDwYAXX3yx3iAIAKZPn46srCzzc41Gg8TERIwaNarRhnTU37/bDYNBwCd1syW6z/oVKrmAN/oZMHLkSCiVXKbdRKfTYc2aNWyXq7BNrLFNrDXVNpn07y3Yf0GDmX/rhnE94xASaPwqe2rRLigD5Jh7Z696X+uuNrn9sy04VKCxOr5nxiivX3SxKX5OTCM69vDZQMheN998M26++Wa7rlWpVFCpVFCr1VCr1dDrjQseKpVK0T8ctZDDAMFcrgFyaA0Gt92vKWC7WGObWGObWGtqbfLj1EE2j398XypkQIP7kJmI3SYr/m6sU6dXfoZOf2Wg5env9+Lf9/cT7T7u1JQ+J468D69Olm5ITEwMFAoFCgoKLI4XFBQgLi7OpbIzMzNx4MABbN++vfGLRfL08E4euxcRUVOkVMjtCoLcKTzI8gv41/0F9VxJ3sJnA6HAwED07dsX2dnZ5mMGgwHZ2dlIS0uTsGbOuT8tSeoqEBGRi0JVntnTjcTj1UNj5eXlOHbsmPl5Xl4ecnJy0KxZM7Rp0wZZWVnIyMhAv3790L9/f3z44YeoqKgwzyJz1rVDYx7hEynrRETUkAC5df9CSWUNokLEX8iRxOHVgdCOHTswdOiV5dBNicwZGRlYuHAhJk2ahIsXL2LGjBnIz89Hr169sHr1aqsEakdlZmYiMzMTGo0GkZGRLpVlL5XSZzvniIiojq3E6GeW5GDh5PqXbiFpeXUgNGTIEDQ2u3/q1KmYOnWqh2rkPkFKBQIVcgAe7IUiIiJRNQ8NxLFrjm04fBHbTxYjNamZJHWihrEbwga1Wo3k5GSkpqZ69L4BXj7FkoiIGrbksTQkx1svt7Jst3du70QMhGySYtYYYLtLlYiIfIvexr6Ri7aeRtK0VXhg/jYbryApMRDyItd3iJG6CkRE5KKZ45MxqV+izXMbj1xEYVm1h2tEDWEgZINUQ2Nzbuvh0fsREZH4ru8Yg3dv74mB7W3nBO0/b/+qx+R+DIRskGpoTCnxQmBERCSexY+mISTQel2h8upajPlwI37Zd0GCWtG1+M1LRETkJm9O6G517KnvduNQfhne+vmgBDWiazEQIiIicpO4iKB6z529XIW8ogoP1oZsYSBERETkJtd3bHgSzLAPNuDfvx9HVQ3XkJMKAyEbpEqWJiKipic6pP6d0AUBeOeXQ/hi0wkP1oiuxkDIBqmSpYmIqOn54fHrcVuf1g1ec7Fc66Ha0LUYCBEREblRx5Zh+ODOlAav+WrLKRy/WO6hGtHVGAgRERF5gQINF1qUAgMhIiIiLxAg51eyFNjqNjBZmoiIPI37TUqDgZANTJYmIiJPu+2zPwEAOr0BSdNWIWnaKolr5B8CpK4AERGRv2kfE4oTNhZT/GLTCej01rvXk/swECIiIvKwdc8PgaZah56zfrM4/uYqbrvhaQyEiIiIPGDunSkwCMDIrrEAgIig+hdaNJm1cj9GdYtFcnwEHv16J3q2ikRRuRZzbuuJIKX1hq7kOAZCREREHnBrI4sq2rLwz5NY+OdJLHl0ILblFWNbXjEA4HRxJX588gaxq+iXmCxtA2eNERGRJzw+uINd1/17o+UWHLtOl9i8ThAEGAwCBIF5RvZiIGQDZ40REZEnTEu/zq7r1h0qtOu69387jPYv/4wtxy9ZHK/VGwAAOWdK8NjXO6A3MFAyYSBERETkg77actLqmEJmXItIf1WP0Nd/ncKYjzYBMOYc/bq/ABPUm7H+sH3BVVPHQIiIiEhCHVuGoVdilMOvm7FiP6pq9Obnpy9V4l/rjgGARY/POz8fxLFC4z5meXVT9vedK8Wn649h/h956D7rVxdqb5t6/TF0nyl+ue7AZGkiIiIJrc0ajC3HL+Huz/9y+LVdZ6zGxN6tMD4lHg8t3GE+bgqEDpzXoLIuWLpcUYPSKp35mu0nL2P7yctQNTL5TG8QcKSgDF3jI+q9Zv/5Uoz71x8AgBFdW2LtwcZ7m04WVWDI+xvwr7t74+aUhEavdxf2CBEREUksItj5follu89ZBEGAMXhJmrYKY/+1yXysoqa20bKSpq3Cy8v2QRAE7D59GQAwY0Uu0j/ahGrdld6n9tNXoffrxjWQvt9xxhwEAbAKgpKmrcIE9WarexWVawEAe8+UNFovd2IgREREJLGQQGMgNLF3K5ycMw5574x1qTy5zHrfssYmkpl6kRZtPY1vtp7GxE//RO65Uuw8ZQyIDFcVYBCAy5XG3qXTlyobrU/OmRLsO1uKk0UV5l6p2+dtMdZV4j3WGAgRERFJTG8wzuoyBTAyG4GMIx75aofVsZv+sb7B10z89E/z47OXjcHN3z7+wxwgJc+wnfMjwL4ZaOM/+QND3t+A2z/70+K4i2/VZQyEiIiIJFY3ux2Kq76VJ/Zu5dE6nCgqt3n8UkWN+XHStFXmRR1N1OuP11umrY1jjxaWWxyXgT1CXocLKhIRkSeZel0UVw0TjajbikMSV3XyFF8VCAHAnf/eYn78+v8OuHwriUfGGAjZwgUViYjIk8KDjDlCvROjzcfG9YxHfGSQVFWyy/zNeS6XYSufyZMYCBEREUkssVkITs4ZhztTEy2Or35mkEQ18hx7ZrO5EwMhIiIiLxUZrESLcJXH73vt3mbuVKFlIERERET16BwbJnUVmjQGQkRERF6sbma9WWhgI0tBk0MYCBEREXkxwzUrIQYHcncsMTEQIiIi8mLXrgitkAP/uK0nAGMOka8z2Lceo9swECIiIvJiw7q2tHiukMmgUhq/vv8+vJNDZcVFeN90/Fq9ofGL3IiBEBERkRd7fHAHnJwzDifnjAMAKBQy8xYcQmMbiF3jm0cGiF4/V9VK3CXkF4HQxIkTER0djdtvv13qqhARETlt8g1JePe2nubVmOuLg9rHhNo8HuyFidaJzUIkvb9fBEJPP/00vvrqK6mrQURE5JKZ47vh+g4x5v256tvwdN3zQ5y+R2yE59YtiggKkDzPyS8CoSFDhiA8PFzqahAREYnCtCuFO0aVnhjcQdTyUlpH1ntOIZdZzYrzNMkDoY0bN2L8+PFISEiATCbD8uXLra5Rq9VISkpCUFAQBgwYgG3btnm+okRERF6isaExV7RrYXsBx8RmwU6V9/kD/eo9J5fJ3PIeHCF5IFRRUYGUlBSo1Wqb55csWYKsrCzMnDkTu3btQkpKCkaPHo3CwkLzNb169UL37t2t/jt//ryn3gYREZEH2R4aG9CuGf59f18AwKYXh+LVcV0tzrdsYLuO1tHByH5ucL0J2Ne3j7G7du1bXMlRimhg6Esmk8EgcbK05KsypaenIz09vd7zc+fOxZQpUzB58mQAwLx587Bq1SrMnz8f06ZNAwDk5OSIUhetVgutVmt+rtFoAAA6nQ46nU6Ue5gEwAADBItyTY/FvpevY7tYY5tYY5tYY5tYazJtItRCpRAAg974/zr3D0jEsM7NodPpEBeuRPvmQRbnr70eAFRy4/P/PZkGpVKJ0xc1VtcAQIgSNo/bMr57S8yr269MX1tb7+uUMgO0NTWi/3s4Up5McHTunRvJZDIsW7YMEyZMAADU1NQgJCQES5cuNR8DgIyMDJSUlGDFihV2l71hwwZ88sknWLp0ab3XzJo1C7Nnz7Y6vmjRIoSESJvVTkRERPaprKzEPffcg9LSUkRERDR4reQ9Qg0pKiqCXq9HbGysxfHY2FgcOnTI7nJGjBiBPXv2oKKiAq1bt8YPP/yAtLQ0q+umT5+OrKws83ONRoPExESMGjWq0YZ01N+/2w2DQcAn9/YxH9PpdFizZg1GjhwJpdL3VwsVC9vFGtvEGtvEGtvEWlNtk4V/5uH9347gn3f2wsjkK9+Zfxy9iMe/3YUnh3TAk0M6AgC6z/rVfH7jC0Mx8oN1eKOfwdwmm45exBN1r3l8UAf0fP03AMB9A9rim62nLO77tx7x+GnfBfPz2PAgFJRVY9OLQ3HTP9YjOT4C3z+WhgJNNYbP/d2q3imtI9GxRThm39JN1PYwjejYw6sDIbGsXbvWrutUKhVUKuvxU6VSKfoPTC3kMECwWa477tcUsF2ssU2ssU2ssU2sNbU2MUABrV4GuUJh8b7kAUpo9TIYcOV4eLAKReU1AIDgoEBoDcZ8I1ObyBQB0OplEGTG12j1dflIMgUCFAGoqNGby2/VPBxafb75eY82zXB67wWolIF195VDqVQiWGUwlwMA4aoABAbIoRPk0NddIyZHypM8WbohMTExUCgUKCgosDheUFCAuLg4t91XrVYjOTkZqampbrsHERGRWK7kuMgsjsuuvRDAjldHmh9HBCmRO2t0fYVZkMuA/a+PsTj2zDVbfMwcn4xfnxmEoEBjeCGvm97WPEyFHa+OMF+X/dxg/PrsINs38jCvDoQCAwPRt29fZGdnm48ZDAZkZ2fbHNoSS2ZmJg4cOIDt27e77R5ERERiMWX7ymxFPqg3tqnnWuPVsmuDKhtlmwIdE6Vcji5xV9btU1z1opiwKyMuLSOCLJ5LSfKhsfLychw7dsz8PC8vDzk5OWjWrBnatGmDrKwsZGRkoF+/fujfvz8+/PBDVFRUmGeRuYNarYZarYZer2/8YiIiIi9xbazSsWUYWoSrMLRLC4vjD6S1RVJz29twdGoZjhbhKgy55jXyayKhx20svGjawsMUmF37Gm8keSC0Y8cODB061PzclKyckZGBhQsXYtKkSbh48SJmzJiB/Px89OrVC6tXr7ZKoBZTZmYmMjMzodFoEBlZ/4qYRERE3qC+rTYSooLx1/ThUFzTc/P6Ld3rLSuxWYj5NRZr/FwT07w0pgsAIFipQJVOj+NvjzXfxxwIefW4k5HkgdCQIUMa3T136tSpmDp1qodqRERE5FuuDI1Z98BcGwTZw9Zrru3dufZeV7/GtG2GM/f2NB+I1TyPydJEROSL3Bl2jEq2PRLz3KjOaBVluf2GUiFHcnwEJl/fzuL43f0T8cQQcfcyc5XkPULeiENjRETkS0wjK+5KyendJgq920TbPPfITe3xQFqSxbHAADl+fvomq2vfubWnO6rnEvYIERER+bjGZo05XW7d/xtLeg4M8N1wwndrTkRERACAIKVxtpZSIe7XuqmnKSKo6Q4gNd135gJOnyciIl8yZVB7TBnUXvRyAxRynJwzzuLYtc99HXuEbOCCikRERP6BgRARERH5LQZCRERE5LcYCNnAdYSIiIj8AwMhG5gjRERE5B8YCBEREZHfYiBEREREfouBEBEREfktBkI2MFmaiIjIPzAQsoHJ0kRERP6BgRARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfmtAKkr4I3UajXUajVqa2sBABqNRvR71FSVw2AQLMrW6XSorKyERqOBUqkU/Z6+iu1ijW1ijW1ijW1ijW1iTco20VVVQFspE/171lSeIAiNXisT7LnKT509exaJiYlSV4OIiIiccObMGbRu3brBaxgINcBgMOD8+fMIDw+HTCZz+/00Gg0SExNx5swZREREuP1+voLtYo1tYo1tYo1tYo1tYq0ptokgCCgrK0NCQgLk8oazgDg01gC5XN5oJOkOERERTebDKCa2izW2iTW2iTW2iTW2ibWm1iaRkZF2XcdkaSIiIvJbDISIiIjIbzEQ8iIqlQozZ86ESqWSuipehe1ijW1ijW1ijW1ijW1izd/bhMnSRERE5LfYI0RERER+i4EQERER+S0GQkREROS3GAgRERGR32Ig5EXUajWSkpIQFBSEAQMGYNu2bVJXyS1mzZoFmUxm8d91111nPl9dXY3MzEw0b94cYWFhuO2221BQUGBRxunTpzFu3DiEhISgZcuWeOGFF8x7w/mKjRs3Yvz48UhISIBMJsPy5cstzguCgBkzZiA+Ph7BwcEYMWIEjh49anFNcXEx7r33XkRERCAqKgoPP/wwysvLLa7Zu3cvbrrpJgQFBSExMRH/+Mc/3P3WnNZYmzz44INWn50xY8ZYXNOU2uSdd95BamoqwsPD0bJlS0yYMAGHDx+2uEasn5cNGzagT58+UKlU6NixIxYuXOjut+c0e9plyJAhVp+Vxx9/3OKaptQun332GXr27GleFDEtLQ2//PKL+bw/fk7sJpBXWLx4sRAYGCjMnz9f2L9/vzBlyhQhKipKKCgokLpqops5c6bQrVs34cKFC+b/Ll68aD7/+OOPC4mJiUJ2drawY8cOYeDAgcL1119vPl9bWyt0795dGDFihLB7927h559/FmJiYoTp06dL8Xac9vPPPwuvvPKK8OOPPwoAhGXLllmcnzNnjhAZGSksX75c2LNnj3DzzTcL7dq1E6qqqszXjBkzRkhJSRH++usvYdOmTULHjh2Fu+++23y+tLRUiI2NFe69914hNzdX+O6774Tg4GDh3//+t6fepkMaa5OMjAxhzJgxFp+d4uJii2uaUpuMHj1aWLBggZCbmyvk5OQIY8eOFdq0aSOUl5ebrxHj5+XEiRNCSEiIkJWVJRw4cED4+OOPBYVCIaxevdqj79de9rTL4MGDhSlTplh8VkpLS83nm1q7rFy5Uli1apVw5MgR4fDhw8LLL78sKJVKITc3VxAE//yc2IuBkJfo37+/kJmZaX6u1+uFhIQE4Z133pGwVu4xc+ZMISUlxea5kpISQalUCj/88IP52MGDBwUAwpYtWwRBMH5ZyuVyIT8/33zNZ599JkRERAhardatdXeXa7/0DQaDEBcXJ7z33nvmYyUlJYJKpRK+++47QRAE4cCBAwIAYfv27eZrfvnlF0Emkwnnzp0TBEEQPv30UyE6OtqiXV566SWhS5cubn5HrqsvELrlllvqfU1Tb5PCwkIBgPD7778LgiDez8uLL74odOvWzeJekyZNEkaPHu3utySKa9tFEIyB0NNPP13va/yhXaKjo4UvvviCn5NGcGjMC9TU1GDnzp0YMWKE+ZhcLseIESOwZcsWCWvmPkePHkVCQgLat2+Pe++9F6dPnwYA7Ny5EzqdzqItrrvuOrRp08bcFlu2bEGPHj0QGxtrvmb06NHQaDTYv3+/Z9+Im+Tl5SE/P9+iHSIjIzFgwACLdoiKikK/fv3M14wYMQJyuRxbt241XzNo0CAEBgaarxk9ejQOHz6My5cve+jdiGvDhg1o2bIlunTpgieeeAKXLl0yn2vqbVJaWgoAaNasGQDxfl62bNliUYbpGl/5/XNtu5h8++23iImJQffu3TF9+nRUVlaazzXldtHr9Vi8eDEqKiqQlpbGz0kjuOmqFygqKoJer7f4AAJAbGwsDh06JFGt3GfAgAFYuHAhunTpggsXLmD27Nm46aabkJubi/z8fAQGBiIqKsriNbGxscjPzwcA5Ofn22wr07mmwPQ+bL3Pq9uhZcuWFucDAgLQrFkzi2vatWtnVYbpXHR0tFvq7y5jxozBrbfeinbt2uH48eN4+eWXkZ6eji1btkChUDTpNjEYDHjmmWdwww03oHv37gAg2s9LfddoNBpUVVUhODjYHW9JFLbaBQDuuecetG3bFgkJCdi7dy9eeuklHD58GD/++COAptku+/btQ1paGqqrqxEWFoZly5YhOTkZOTk5fv85aQgDIfK49PR08+OePXtiwIABaNu2Lb7//nuf/UEiz7jrrrvMj3v06IGePXuiQ4cO2LBhA4YPHy5hzdwvMzMTubm5+OOPP6Suilepr10effRR8+MePXogPj4ew4cPx/Hjx9GhQwdPV9MjunTpgpycHJSWlmLp0qXIyMjA77//LnW1vB6HxrxATEwMFAqFVQZ/QUEB4uLiJKqV50RFRaFz5844duwY4uLiUFNTg5KSEotrrm6LuLg4m21lOtcUmN5HQ5+JuLg4FBYWWpyvra1FcXGx37RV+/btERMTg2PHjgFoum0ydepU/PTTT1i/fj1at25tPi7Wz0t910RERHj1Hyf1tYstAwYMAACLz0pTa5fAwEB07NgRffv2xTvvvIOUlBR89NFHfv85aQwDIS8QGBiIvn37Ijs723zMYDAgOzsbaWlpEtbMM8rLy3H8+HHEx8ejb9++UCqVFm1x+PBhnD592twWaWlp2Ldvn8UX3po1axAREYHk5GSP198d2rVrh7i4OIt20Gg02Lp1q0U7lJSUYOfOneZr1q1bB4PBYP6ln5aWho0bN0Kn05mvWbNmDbp06eK1Q0COOHv2LC5duoT4+HgATa9NBEHA1KlTsWzZMqxbt85qSE+sn5e0tDSLMkzXeOvvn8baxZacnBwAsPisNLV2uZbBYIBWq/Xbz4ndpM7WJqPFixcLKpVKWLhwoXDgwAHh0UcfFaKioiwy+JuK5557TtiwYYOQl5cnbN68WRgxYoQQExMjFBYWCoJgnObZpk0bYd26dcKOHTuEtLQ0IS0tzfx60zTPUaNGCTk5OcLq1auFFi1a+Nz0+bKyMmH37t3C7t27BQDC3Llzhd27dwunTp0SBME4fT4qKkpYsWKFsHfvXuGWW26xOX2+d+/ewtatW4U//vhD6NSpk8VU8ZKSEiE2Nla4//77hdzcXGHx4sVCSEiIV04VF4SG26SsrEx4/vnnhS1btgh5eXnC2rVrhT59+gidOnUSqqurzWU0pTZ54oknhMjISGHDhg0W08ArKyvN14jx82KaFv3CCy8IBw8eFNRqtVdPi26sXY4dOya8/vrrwo4dO4S8vDxhxYoVQvv27YVBgwaZy2hq7TJt2jTh999/F/Ly8oS9e/cK06ZNE2QymfDbb78JguCfnxN7MRDyIh9//LHQpk0bITAwUOjfv7/w119/SV0lt5g0aZIQHx8vBAYGCq1atRImTZokHDt2zHy+qqpKePLJJ4Xo6GghJCREmDhxonDhwgWLMk6ePCmkp6cLwcHBQkxMjPDcc88JOp3O02/FJevXrxcAWP2XkZEhCIJxCv1rr70mxMbGCiqVShg+fLhw+PBhizIuXbok3H333UJYWJgQEREhTJ48WSgrK7O4Zs+ePcKNN94oqFQqoVWrVsKcOXM89RYd1lCbVFZWCqNGjRJatGghKJVKoW3btsKUKVOs/lhoSm1iqy0ACAsWLDBfI9bPy/r164VevXoJgYGBQvv27S3u4W0aa5fTp08LgwYNEpo1ayaoVCqhY8eOwgsvvGCxjpAgNK12eeihh4S2bdsKgYGBQosWLYThw4ebgyBB8M/Pib1kgiAInut/IiIiIvIezBEiIiIiv8VAiIiIiPwWAyEiIiLyWwyEiIiIyG8xECIiIiK/xUCIiIiI/BYDISIiIvJbDISIiOxw6dIltGzZEidPngQAbNiwATKZzGr/JrFNmzYNTz31lFvvQeTPGAgRkagefPBByGQyq//GjBkjddVc8tZbb+GWW25BUlKSy2UVFBRAqVRi8eLFNs8//PDD6NOnDwDg+eefx5dffokTJ064fF8issZAiIhEN2bMGFy4cMHiv++++86t96ypqXFb2ZWVlfi///s/PPzww6KUFxsbi3HjxmH+/PlW5yoqKvD999+b7xUTE4PRo0fjs88+E+XeRGSJgRARiU6lUiEuLs7iv6t3dpfJZPjiiy8wceJEhISEoFOnTli5cqVFGbm5uUhPT0dYWBhiY2Nx//33o6ioyHx+yJAhmDp1Kp555hlzsAAAK1euRKdOnRAUFIShQ4fiyy+/NA9hVVRUICIiAkuXLrW41/LlyxEaGoqysjKb7+fnn3+GSqXCwIED633PlZWVSE9Pxw033GAeLvviiy/QtWtXBAUF4brrrsOnn35qvv7hhx9GdnY2Tp8+bVHODz/8gNraWtx7773mY+PHj6+394iIXMNAiIgkMXv2bNx5553Yu3cvxo4di3vvvRfFxcUAgJKSEgwbNgy9e/fGjh07sHr1ahQUFODOO++0KOPLL79EYGAgNm/ejHnz5iEvLw+33347JkyYgD179uCxxx7DK6+8Yr4+NDQUd911FxYsWGBRzoIFC3D77bcjPDzcZl03bdqEvn371vteSkpKMHLkSBgMBqxZswZRUVH49ttvMWPGDLz11ls4ePAg3n77bbz22mv48ssvAQBjx45FbGwsFi5caFWXW2+9FVFRUeZj/fv3x9mzZ835SUQkIql3fSWipiUjI0NQKBRCaGioxX9vvfWW+RoAwquvvmp+Xl5eLgAQfvnlF0EQBOGNN94QRo0aZVHumTNnBADC4cOHBUEQhMGDBwu9e/e2uOall14SunfvbnHslVdeEQAIly9fFgRBELZu3SooFArh/PnzgiAIQkFBgRAQECBs2LCh3vd0yy23CA899JDFsfXr1wsAhIMHDwo9e/YUbrvtNkGr1ZrPd+jQQVi0aJHFa9544w0hLS3N/HzatGlCu3btBIPBIAiCIBw7dkyQyWTC2rVrLV5XWloqAGiwjkTkHPYIEZHohg4dipycHIv/Hn/8cYtrevbsaX4cGhqKiIgIFBYWAgD27NmD9evXIywszPzfddddBwA4fvy4+XXX9tIcPnwYqampFsf69+9v9bxbt27mnplvvvkGbdu2xaBBg+p9P1VVVQgKCrJ5buTIkejYsSOWLFmCwMBAAMY8n+PHj+Phhx+2eA9vvvmmRf0feugh5OXlYf369QCMvUFJSUkYNmyYxT2Cg4MBGIffiEhcAVJXgIiantDQUHTs2LHBa5RKpcVzmUwGg8EAACgvL8f48ePx7rvvWr0uPj7e4j7OeOSRR6BWqzFt2jQsWLAAkydPhkwmq/f6mJgYXL582ea5cePG4b///S8OHDiAHj16mOsPAJ9//jkGDBhgcb1CoTA/7tSpE2666SYsWLAAQ4YMwVdffYUpU6ZY1cU0ZNiiRQvH3ywRNYiBEBF5nT59+uC///0vkpKSEBBg/6+pLl264Oeff7Y4tn37dqvr7rvvPrz44ov417/+hQMHDiAjI6PBcnv37o1vvvnG5rk5c+YgLCwMw4cPx4YNG5CcnIzY2FgkJCTgxIkTFknPtjz88MN44okncPPNN+PcuXN48MEHra7Jzc2FUqlEt27dGiyLiBzHoTEiEp1Wq0V+fr7Ff1fP+GpMZmYmiouLcffdd2P79u04fvw4fv31V0yePBl6vb7e1z322GM4dOgQXnrpJRw5cgTff/+9ORn56l6W6Oho3HrrrXjhhRcwatQotG7dusH6jB49Gvv376+3V+j999/Hvffei2HDhuHQoUMAjMng77zzDv71r3/hyJEj2LdvHxYsWIC5c+davPaOO+6AUqnEY489hlGjRiExMdGq/E2bNuGmm24yD5ERkXgYCBGR6FavXo34+HiL/2688Ua7X5+QkIDNmzdDr9dj1KhR6NGjB5555hlERUVBLq//11a7du2wdOlS/Pjjj+jZsyc+++wz86wxlUplce3DDz+MmpoaPPTQQ43Wp0ePHujTpw++//77eq/55z//iTvvvBPDhg3DkSNH8Mgjj+CLL77AggUL0KNHDwwePBgLFy5Eu3btLF4XEhKCu+66C5cvX663LosXL8aUKVMarScROU4mCIIgdSWIiNzlrbfewrx583DmzBmL419//TWeffZZnD9/3pzk3JBVq1bhhRdeQG5uboPBmNh++eUXPPfcc9i7d69Dw4REZB/+VBFRk/Lpp58iNTUVzZs3x+bNm/Hee+9h6tSp5vOVlZW4cOEC5syZg8cee8yuIAgwJkUfPXoU586dszl85S4VFRVYsGABgyAiN2GPEBE1Kc8++yyWLFmC4uJitGnTBvfffz+mT59uDiRmzZqFt956C4MGDcKKFSsQFhYmcY2JSEoMhIiIiMhvMVmaiIiI/BYDISIiIvJbDISIiIjIbzEQIiIiIr/FQIiIiIj8FgMhIiIi8lsMhIiIiMhvMRAiIiIiv8VAiIiIiPzW/wPep8RA65VokgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -122,9 +246,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -132,62 +256,19 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# HPGe spectra simulator\n",
-    "nb_events_measured = 1e6\n",
-    "for nuclide in [ba133, cs137, co60, na22, mn54]:\n",
-    "    fig, ax = plt.subplots(figsize=(10,6))\n",
-    "    nuclide_events = np.zeros((0,))\n",
-    "    for energy, intensity in zip(nuclide.energy, nuclide.intensity):\n",
-    "        compton_fraction = 0.3  # ~30% of events go to Compton scattering\n",
-    "        # Full energy peak\n",
-    "        nuclide_events = np.append(nuclide_events, np.random.normal(\n",
-    "            loc=energy, scale=2, size=int(nb_events_measured * intensity * (1 - compton_fraction))\n",
-    "        ))\n",
-    "        \n",
-    "        # Compton edge\n",
-    "        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))\n",
-    "        compton_events = int(nb_events_measured * intensity * compton_fraction)\n",
-    "        \n",
-    "        # Generate Compton events below the edge with a smooth falloff\n",
-    "        # Using exponential distribution peaked near the edge\n",
-    "        compton_samples = np.random.exponential(\n",
-    "            scale=compton_edge/3, \n",
-    "            size=compton_events\n",
-    "        )\n",
-    "        # Shift to be centered below the edge\n",
-    "        compton_samples = compton_edge - np.abs(compton_samples)\n",
-    "        compton_samples = compton_samples[compton_samples > 0]  # Remove negative energies\n",
-    "        nuclide_events = np.append(nuclide_events, compton_samples)\n",
-    "    nuclide_hist, bins = np.histogram(nuclide_events, bins=background_measurements[0].detectors[0]._calibrated_bin_edges)\n",
-    "    background_hist = background_measurements[0].detectors[0]._spectrum\n",
-    "    overall_hist = nuclide_hist + background_hist\n",
-    "    ax.stairs(overall_hist, bins, label=nuclide.name)\n",
-    "    ax.set_yscale('linear')\n",
-    "    ax.set_xlim(0, 2000)\n",
-    "    ax.set_title(nuclide.name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7bade06",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -195,9 +276,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -205,9 +286,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -215,9 +296,19 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -225,61 +316,23 @@
     }
    ],
    "source": [
-    "# NaI spectra simulator\n",
-    "nb_events_measured = 1e7\n",
-    "for nuclide in [cs137, co60, na22, mn54]:\n",
-    "    fig, ax = plt.subplots(figsize=(10,6))\n",
-    "    nuclide_events = np.zeros((0,))\n",
-    "    for energy, intensity in zip(nuclide.energy, nuclide.intensity):\n",
-    "        compton_fraction = 0.2  # ~20% of events go to Compton scattering\n",
-    "        # Full energy peak\n",
-    "        nuclide_events = np.append(nuclide_events, np.random.normal(\n",
-    "            loc=energy, scale=30, size=int(nb_events_measured * intensity * (1 - compton_fraction))\n",
-    "        ))\n",
-    "        \n",
-    "        # Compton edge\n",
-    "        compton_edge = energy * (1 - 1 / (1 + (2 * energy / 511)))\n",
-    "        compton_events = int(nb_events_measured * intensity * compton_fraction)\n",
-    "        \n",
-    "        # Generate Compton events below the edge with a smooth falloff\n",
-    "        # Using exponential distribution peaked near the edge\n",
-    "        compton_samples = np.random.exponential(\n",
-    "            scale=compton_edge/3, \n",
-    "            size=compton_events\n",
-    "        )\n",
-    "        # Shift to be centered below the edge\n",
-    "        compton_samples = compton_edge - np.abs(compton_samples)\n",
-    "        compton_samples = compton_samples[compton_samples > 0]  # Remove negative energies\n",
-    "        nuclide_events = np.append(nuclide_events, compton_samples)\n",
-    "    nuclide_hist, bins = np.histogram(nuclide_events, bins=background_measurements[1].detectors[0]._calibrated_bin_edges)\n",
-    "\n",
-    "    noise_level = 1.0  # 5% relative noise\n",
-    "    gaussian_noise = np.random.normal(0, noise_level * np.sqrt(nuclide_hist), size=nuclide_hist.shape)\n",
+    "test_cases = [\n",
+    "        [\"NaI\", co60, 1.0],\n",
+    "        [\"NaI\", cs137, 10.0],\n",
+    "        [\"NaI\", mn54, 0.01],\n",
+    "        [\"NaI\", na22, 10.0],\n",
+    "        [\"NaI\", na22, 0.1],\n",
+    "        [\"HPGe\", ba133, 1.0],\n",
+    "        [\"HPGe\", co60, 1.0],\n",
+    "        [\"HPGe\", cs137, 10.0],\n",
+    "        [\"HPGe\", mn54, 0.01],\n",
+    "        [\"HPGe\", na22, 10.0],\n",
+    "        [\"HPGe\", na22, 0.1],\n",
+    "    ]\n",
     "\n",
-    "    background_hist = background_measurements[1].detectors[0]._spectrum\n",
-    "    overall_hist_no_noise = nuclide_hist + background_hist\n",
-    "    ax.stairs(overall_hist_no_noise, bins, label=nuclide.name)\n",
-    "    overall_hist = nuclide_hist + background_hist + gaussian_noise\n",
-    "\n",
-    "    ax.stairs(overall_hist, bins, label=nuclide.name + ' + gaussian noise', alpha=0.5)\n",
-    "\n",
-    "    from scipy.ndimage import gaussian_filter1d\n",
-    "\n",
-    "    # Energy-dependent broadening based on detector resolution\n",
-    "    # For NaI: FWHM ≈ 7-8% at 662 keV, improves with √E\n",
-    "    bin_centers = (bins[:-1] + bins[1:]) / 2\n",
-    "    fwhm_percent = 7.0  # FWHM as % at 662 keV\n",
-    "    fwhm_energy = (fwhm_percent / 100) * bin_centers * np.sqrt(662 / bin_centers)\n",
-    "    sigma_energy = fwhm_energy / 2.355  # Convert FWHM to sigma\n",
-    "    bin_width = np.mean(np.diff(bins))\n",
-    "    sigma_bins = np.mean(sigma_energy / bin_width)  # Average sigma in bins\n",
-    "\n",
-    "    overall_hist_broadened = gaussian_filter1d(overall_hist_no_noise, sigma=sigma_bins)\n",
-    "    ax.stairs(overall_hist_broadened, bins, label=nuclide.name + ' + broadening')\n",
-    "    ax.set_yscale('log')\n",
-    "    ax.set_xlim(0, 2000)\n",
-    "    ax.set_title(nuclide.name)\n",
-    "    ax.legend()"
+    "for detector_type, nuclide, signal_to_background_ratio in test_cases:\n",
+    "    print(f\"\\nTesting {detector_type} with {nuclide.name} at S/B={signal_to_background_ratio}\")\n",
+    "    test_get_peaks(detector_type, nuclide, signal_to_background_ratio)"
    ]
   }
  ],
diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py
index 1365ecc..ee8ea4b 100644
--- a/test/neutron_detection/test_compass.py
+++ b/test/neutron_detection/test_compass.py
@@ -1601,7 +1601,7 @@ def create_nuclide_spectrum(
 
     nuclide_events = np.zeros((0,))
     for energy, intensity in zip(nuclide.energy, nuclide.intensity):
-        compton_fraction = 0.2  # ~20% of events go to Compton scattering
+        compton_fraction = 0.1  # ~10% of events go to Compton scattering
         # Full energy peak
         nuclide_events = np.append(
             nuclide_events,
@@ -1631,7 +1631,7 @@ def create_nuclide_spectrum(
         nuclide_events, bins=background_detector._calibrated_bin_edges
     )
 
-    noise_level = 1.0
+    noise_level = 0.2
     gaussian_noise = np.random.normal(
         0, noise_level * np.sqrt(nuclide_hist), size=nuclide_hist.shape
     )
@@ -1644,15 +1644,15 @@ def create_nuclide_spectrum(
     "detector_type, nuclide, signal_to_background_ratio",
     [
         ["NaI", co60, 1.0],
-        ["NaI", cs137, 1.0],
-        ["NaI", mn54, 0.1],
-        ["NaI", na22, 1.0],
+        ["NaI", cs137, 10.0],
+        ["NaI", mn54, 0.01],
+        ["NaI", na22, 10.0],
         ["NaI", na22, 0.1],
         ["HPGe", ba133, 1.0],
         ["HPGe", co60, 1.0],
-        ["HPGe", cs137, 1.0],
-        ["HPGe", mn54, 0.1],
-        ["HPGe", na22, 1.0],
+        ["HPGe", cs137, 10.0],
+        ["HPGe", mn54, 0.01],
+        ["HPGe", na22, 10.0],
         ["HPGe", na22, 0.1],
     ],
 )
@@ -1715,7 +1715,11 @@ def test_get_peaks(detector_type, nuclide, signal_to_background_ratio):
     check_source_meas.detectors = [check_source_detector]
     check_source_meas.detector_type = detector_type
 
-    test_peaks = check_source_meas.get_peaks(overall_hist)
+    test_peak_inds = check_source_meas.get_peaks(overall_hist)
+    test_peaks = background_detector._calibrated_bin_edges[
+        test_peak_inds
+    ]
+
     assert len(test_peaks) == len(nuclide.energy)
     for test_energy, expected_energy in zip(test_peaks, nuclide.energy):
         print(f"Detected peak at {test_energy:.2f} keV, expected at {expected_energy} keV")