From 919a4e774ebe1a2a48bd1198eed33daee5a854f1 Mon Sep 17 00:00:00 2001
From: Johannes Niskanen <niskanen@max-exfl099.desy.de>
Date: Tue, 31 May 2022 14:07:13 +0200
Subject: [PATCH] jhns_centroid'.
---
src/toolbox_scs/detectors/hrixs.py | 54 +-----------------------------
1 file changed, 1 insertion(+), 53 deletions(-)
diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index 7615961..6b9f7ea 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -392,60 +392,8 @@ class hRIXS:
self.CURVE_B, self.CURVE_A, *_ = args
return self.CURVE_A, self.CURVE_B
+
def centroid(self, data, bins=None, return_hits=False):
- if bins is None:
- bins = self.BINS
- hit_x = []
- hit_y = []
- hits = []
- ret = np.zeros((len(data["hRIXS_det"]), bins))
- for image, r in zip(data["hRIXS_det"], ret):
- 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:
- 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,
- std_threshold=self.STD_THRESHOLD,
- curvature=(self.CURVE_A, self.CURVE_B))
- if not len(c):
- continue
-
- rc = np.array(c)
-
- if return_hits:
- hit_x.append(rc[:, 0])
- hit_y.append(rc[:, 1])
- hits.append(rc)
-
- hy, hx = np.histogram(
- rc[:, 0], bins=bins,
- range=(0, self.Y_RANGE.stop - self.Y_RANGE.start))
- r[:] = hy
-
- data = data.assign_coords(
- energy=np.linspace(self.Y_RANGE.start, self.Y_RANGE.stop, bins)
- * self.ENERGY_SLOPE + self.ENERGY_INTERCEPT)
- #**********************************************
- # If hits were requested, assign them to data
- #**********************************************
- if return_hits:
- data = data.assign(hits=(("trainId"), hits),
- xhits=(("trainId"), hit_x),
- yhits=(("trainId"), hit_y))
- #**********************************************
- # Always assign the spectrum to data
- #**********************************************
- data = data.assign(spectrum=(("trainId", "energy"), ret))
- return data
-
- def centroid_jhns(self, data, bins=None, return_hits=False):
#*************************************************************
# Carry out hit finding on data and bin them in grid
# Allows for
--
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