From f68b7f7528b7b97e61bd4215c9516ae87a66241d Mon Sep 17 00:00:00 2001
From: Hampus Wikmark Kreuger <hampus.wikmark@physics.uu.se>
Date: Mon, 30 May 2022 17:19:22 +0200
Subject: [PATCH] Load_dark optionally generates a mask to filter bad pixels
---
src/toolbox_scs/detectors/hrixs.py | 36 ++++++++++++++++++++++++------
1 file changed, 29 insertions(+), 7 deletions(-)
diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index 88aaeec..8af3c81 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -295,7 +295,13 @@ class hRIXS:
BINS = abs(np.subtract(*RANGE)) * FACTOR
METHOD = 'centroid' # ['centroid', 'integral']
+
+ # Dark image and mask treatment
USE_DARK = False
+ USE_DARK_MASK = False
+ DARK_MASK_THRESHOLD = 100
+ MASK_AVG_X = np.s_[1850:2000]
+ MASK_AVG_Y = np.s_[500:1500]
ENERGY_INTERCEPT = 0
ENERGY_SLOPE = 1
@@ -322,7 +328,10 @@ class hRIXS:
return data
- def load_dark(self, runNB, proposal=None):
+ def load_dark(self, runNB, proposal=None, use_dark=True, mask=True,
+ mask_threshold=None):
+ if mask_threshold == None:
+ mask_threshold = self.DARK_MASK_THRESHOLD
try:
# Checks to see if runNB can be iterated over (is list-like)
data_list = []
@@ -334,7 +343,15 @@ class hRIXS:
data = self.from_run(runNB, proposal)
self.dark_image = data['hRIXS_det'].mean(dim='trainId')
self.dark_im_array = self.dark_image.to_numpy()
- self.USE_DARK = True
+ if use_dark:
+ self.USE_DARK = True
+ if mask:
+ dark_avg = np.mean(self.dark_im_array[self.MASK_AVG_Y,
+ self.MASK_AVG_X], (0, 1))
+ self.dark_mask = np.abs(self.dark_im_array - dark_avg) > mask_threshold
+ self.dark_im_array_m = np.array(self.dark_im_array)
+ self.dark_im_array_m[self.dark_mask] = dark_avg
+ self.USE_DARK_MASK = True
def find_curvature(self, runNB, proposal=None, plot=True, args=None, **kwargs):
data = self.from_run(runNB, proposal)
@@ -356,10 +373,15 @@ class hRIXS:
hit_y = []
ret = np.zeros((len(data["hRIXS_det"]), bins))
for image, r in zip(data["hRIXS_det"], ret):
- if self.USE_DARK:
- use_image = image.to_numpy() - self.dark_im_array
+ use_image = image.to_numpy()
+ if self.USE_DARK_MASK:
+ use_image[self.dark_mask] = np.mean(use_image[self.MASK_AVG_Y,
+ self.MASK_AVG_X], (0, 1))
+ if self.USE_DARK:
+ use_image = use_image - self.dark_im_array_m
else:
- use_image = image.to_numpy()
+ if self.USE_DARK:
+ use_image = use_image - self.dark_im_array
c = centroid(
use_image[self.X_RANGE, self.Y_RANGE].T,
threshold=self.THRESHOLD,
@@ -371,8 +393,8 @@ class hRIXS:
rc = np.array(c)
if return_hits:
- hit_x.append(rc[:,0])
- hit_y.append(rc[:,1])
+ hit_x.append(rc[:, 0])
+ hit_y.append(rc[:, 1])
hy, hx = np.histogram(
rc[:, 0], bins=bins,
--
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