From 0c6518707d66550ef7be2e8aa43f1082f2b0fd41 Mon Sep 17 00:00:00 2001
From: Martin Teichmann <martin.teichmann@xfel.eu>
Date: Thu, 8 Dec 2022 11:45:59 +0100
Subject: [PATCH] inline integrate into the hRIXS class
---
src/toolbox_scs/detectors/hrixs.py | 40 +++++++++++++-----------------
1 file changed, 17 insertions(+), 23 deletions(-)
diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index cb8c8f2..c143833 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -250,24 +250,6 @@ def parabola(x, a, b, c=0):
return (a * x + b) * x + c
-def integrate(image, factor=FACTOR, range=RANGE, curvature=(CURVE_A, CURVE_B)):
- x = np.arange(image.shape[1])[None, :]
- y = np.arange(image.shape[0])[:, None]
- ys = factor * (y - parabola(x, curvature[1], curvature[0]))
- ysf = np.floor(ys)
- rang = (factor * range[0], factor * range[1])
- bins = rang[1] - rang[0]
- lhy, lhx = np.histogram(ysf.ravel(), bins=bins, range=rang,
- weights=((ys - ysf) * image).ravel())
- rhy, rhx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
- weights=(((ysf + 1) - ys) * image).ravel())
- lvy, lvx = np.histogram(ysf.ravel(), bins=bins, range=rang,
- weights=(ys - ysf).ravel())
- rvy, rvx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
- weights=((ysf + 1) - ys).ravel())
- return (lhy + rhy) / (lvy + rvy)
-
-
class hRIXS:
"""The hRIXS analysis, especially curvature correction
@@ -489,14 +471,26 @@ class hRIXS:
data = h.integrate(data) # create spectrum by summing pixels
data.spectrum[0, :].plot() # plot the spectrum of the first image
"""
- bins = self.Y_RANGE.stop - self.Y_RANGE.start
- ret = np.zeros((len(data["hRIXS_det"]), bins - 20))
+ bins = self.Y_RANGE.stop - self.Y_RANGE.start - 20
+ ret = np.zeros((len(data["hRIXS_det"]), bins))
for image, r in zip(data["hRIXS_det"], ret):
if self.USE_DARK:
image = image - self.dark_image
- r[:] = integrate(image.values[self.X_RANGE, self.Y_RANGE].T,
- factor=1, range=(10, bins - 10),
- curvature=(self.CURVE_A, self.CURVE_B))
+ image = image.values[self.X_RANGE, self.Y_RANGE].T
+ x = np.arange(image.shape[1])[None, :]
+ y = np.arange(image.shape[0])[:, None]
+ ys = (y - parabola(x, self.CURVE_B, self.CURVE_A))
+ ysf = np.floor(ys)
+ rang = (10, bins + 10)
+ lhy, lhx = np.histogram(ysf.ravel(), bins=bins, range=rang,
+ weights=((ys - ysf) * image).ravel())
+ rhy, rhx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
+ weights=(((ysf + 1) - ys) * image).ravel())
+ lvy, lvx = np.histogram(ysf.ravel(), bins=bins, range=rang,
+ weights=(ys - ysf).ravel())
+ rvy, rvx = np.histogram((ysf + 1).ravel(), bins=bins, range=rang,
+ weights=((ysf + 1) - ys).ravel())
+ r[:] = (lhy + rhy) / (lvy + rvy)
data = data.assign_coords(
energy=np.arange(self.Y_RANGE.start + 10, self.Y_RANGE.stop - 10)
* self.ENERGY_SLOPE + self.ENERGY_INTERCEPT)
--
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