use scipy.ndimage.binary_erosion

SIRF_data_preparation/evaluation_utilities.py

@@ -5,6 +5,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.ndimage import binary_erosion
 
 import sirf.STIR as STIR
 from petric import QualityMetrics
@@ -24,21 +25,18 @@ def get_metrics(qm: QualityMetrics, iters: Iterator[int], srcdir='.'):
         list(qm.evaluate(STIR.ImageData(str(Path(srcdir) / f'iter_{i:04d}.hv'))).values()) for i in iters])
 
 
-def pass_index(metrics: np.ndarray, thresh: np.ndarray, window: int = 1) -> int:
+def pass_index(metrics: np.ndarray, thresh: Iterator, window: int = 1) -> int:
     """
     Returns first index of `metrics` with value <= `thresh`.
-    The value must remain below the threshold for at least `window`.
-    Raises IndexError if doesn't pass.
+    The values must remain below the respective thresholds for at least `window` number of entries.
+    Otherwise raises IndexError.
     """
-    assert metrics.shape[1] == len(thresh)
+    thr_arr = np.asanyarray(thresh)
     assert metrics.ndim == 2
-    assert thresh.ndim == 1
-
-    m = (metrics <= thresh[None]).all(1)
-    res = m[:-window]
-    for i in range(1, window):
-        res &= m[i:-window+i]
-    res &= m[window:]
+    assert thr_arr.ndim == 1
+    assert metrics.shape[1] == thr_arr.shape[0]
+    passed = (metrics <= thr_arr[None]).all(axis=1)
+    res = binary_erosion(passed, structure=np.ones(window), origin=-(window // 2))
     return np.where(res)[0][0]