From 5b7c7ed60d695de719465091126628d979e8a8c2 Mon Sep 17 00:00:00 2001
From: Cammille Carinan <cammille.carinan@xfel.eu>
Date: Tue, 22 Feb 2022 02:02:48 +0100
Subject: [PATCH] Add new centroid algorithm by Martin
---
src/toolbox_scs/detectors/hrixs.py | 39 +++++++++++++++++++++++++++++-
1 file changed, 38 insertions(+), 1 deletion(-)
diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index 686db07..0f6ead7 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -156,7 +156,7 @@ CURVE_A = 2.19042931e-02 # curvature parameters as determined elsewhere
CURVE_B = -3.02191568e-07
-def centroid(image, threshold=THRESHOLD, curvature=(CURVE_A, CURVE_B)):
+def _esrf_centroid(image, threshold=THRESHOLD, curvature=(CURVE_A, CURVE_B)):
gs = 2
base = image.mean()
cp = np.argwhere(image[gs // 2: -gs // 2, gs // 2: -gs // 2] > threshold) + np.array([gs // 2, gs // 2])
@@ -175,6 +175,43 @@ def centroid(image, threshold=THRESHOLD, curvature=(CURVE_A, CURVE_B)):
return res
+def _new_centroid(image, threshold=THRESHOLD, curvature=(CURVE_A, CURVE_B)):
+ """find the position of photons with sub-pixel precision
+
+ A photon is supposed to have hit the detector if the intensity within a
+ 2-by-2 square exceeds a threshold. In this case the position of the photon
+ is calculated as the center-of-mass in a 4-by-4 square.
+
+ Return the list of x,y coordinate pairs, corrected by the curvature.
+ """
+ base = image.mean()
+ corners = image[1:, 1:] + image[:-1, 1:] + image[1:, :-1] + image[:-1, :-1]
+ threshold = corners.mean() + 3.5 * corners.std()
+ middle = corners[1:-1, 1:-1]
+ candidates = (
+ (middle > threshold)
+ * (middle >= corners[:-2, 1:-1]) * (middle > corners[2:, 1:-1])
+ * (middle >= corners[1:-1, :-2]) * (middle > corners[1:-1, 2:])
+ * (middle >= corners[:-2, :-2]) * (middle > corners[2:, :-2])
+ * (middle >= corners[:-2, 2:]) * (middle > corners[2:, 2:]))
+ cp = np.argwhere(candidates)
+ if len(cp) > 10000:
+ raise RuntimeError(
+ "too many peaks, threshold too low or acquisition time too high")
+
+ res = []
+ for cy, cx in cp:
+ spot = image[cy: cy + 4, cx: cx + 4] - base
+ mx = np.average(np.arange(cx, cx + 4), weights=spot.sum(axis=0))
+ my = np.average(np.arange(cy, cy + 4), weights=spot.sum(axis=1))
+ my -= (curvature[0] + curvature[1] * mx) * mx
+ res.append((my, mx))
+ return res
+
+
+centroid = _new_centroid
+
+
def decentroid(res):
res = np.array(res)
ret = np.zeros(shape=(res.max(axis=0) + 1).astype(int))
--
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