Add background counts for 3x3 inch NaI. Add auto ROI lookup function from the FWHM. Add ROI test.

src/pg_rad/background/background.py

@@ -0,0 +1,51 @@
+from importlib.resources import files
+from typing import Tuple
+import numpy as np
+from pandas import read_csv
+
+from pg_rad.configs.defaults import FWHM_PARAMS
+from pg_rad.detector.detector import Detector
+
+
+def generate_background(
+    cps_array: np.ndarray,
+) -> np.ndarray:
+    """
+    Generate synthetic background cps for a given detector and energy.
+    """
+    pass
+
+
+def fwhm(A: float, B: float, C: float, E: float) -> float:
+    return np.sqrt(A + B * E + C * E**2)
+
+
+def get_roi_from_fwhm(
+    detector: Detector,
+    energy_keV: float
+) -> Tuple[float, float]:
+    """
+    Get the region of interest for given primary gamma energy, using
+    the 3*FWHM rule.
+    """
+    A, B, C = FWHM_PARAMS.get(detector.type)
+    delta = 3*fwhm(A, B, C, energy_keV)
+    return (energy_keV-delta, energy_keV+delta)
+
+
+def get_cps_from_roi(
+    detector: Detector,
+    roi_lo: float,
+    roi_hi: float
+) -> float:
+
+    csv = files('pg_rad.data.backgrounds').joinpath(detector.name+'.csv')
+    data = read_csv(csv)
+
+    # get indices of nearest bins
+    idx_min = (data["Energy"] - roi_lo).abs().idxmin()
+    idx_max = (data["Energy"] - roi_hi).abs().idxmin()
+    idx_start, idx_end = sorted([idx_min, idx_max])
+
+    cps_sum = data.loc[idx_start:idx_end, "cps"].sum()
+    return cps_sum