From c0259d3243830ccbe992f817b6c64107b0c34f45 Mon Sep 17 00:00:00 2001
From: Johannes Niskanen <niskanen@max-exfl099.desy.de>
Date: Tue, 31 May 2022 10:28:22 +0200
Subject: [PATCH] Modified and commented dark_load Centroid function modified
to always output spectra
---
src/toolbox_scs/detectors/hrixs.py | 55 ++++++++++++++++++++++++------
1 file changed, 44 insertions(+), 11 deletions(-)
diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py
index 28d3d88..d8f94ad 100644
--- a/src/toolbox_scs/detectors/hrixs.py
+++ b/src/toolbox_scs/detectors/hrixs.py
@@ -328,30 +328,56 @@ class hRIXS:
return data
+
def load_dark(self, runNB, proposal=None, use_dark=True, mask=True,
mask_threshold=None):
+ #*************************************************************
+ # Loads a dark image and assigns it to the hRIXS instance
+ # In addition sets attributes whether or not
+ # - hot pixels are identified and masked out
+ # - the dark image is to be used in background subtraction
+ # In addition a threshold value for hot pixel mask generation
+ # can be given.
+ #*************************************************************
if mask_threshold == None:
mask_threshold = self.DARK_MASK_THRESHOLD
- try:
- # Checks to see if runNB can be iterated over (is list-like)
+ #*************************************************************
+ # If given a list of runs, iterate over them.
+ # Otherwise read one. Give an exception if neither is the case.
+ #*************************************************************
+ if type(runNB) == type([]):
data_list = []
for run in runNB:
data_list.append(self.from_run(run, proposal))
- data = xr.concat(data_list, dim='trainId')
- except TypeError:
- # If runNB cannot be iterated over, we assume it's a single run
+ data = xr.concat(data_list, dim='trainId')
+ elif type(runNB) == type(1):
data = self.from_run(runNB, proposal)
+ else:
+ raise Exception('load_dark() expects a list of indeces or an integer.')
+ #*************************************************************
+ # Store the dark image (mean over aqs.) in two formats
+ #*************************************************************
self.dark_image = data['hRIXS_det'].mean(dim='trainId')
self.dark_im_array = self.dark_image.to_numpy()
+ #*************************************************************
+ # Set a flag whether the dark image is to be used later
+ #*************************************************************
if use_dark:
self.USE_DARK = True
+ #*************************************************************
+ # If hot/dead pixel masking is requested, find the mask and
+ # set a flag in the instance. Set the masked dark values to
+ # mean intensity.
+ #*************************************************************
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_mask = 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
+ return
+
def find_curvature(self, runNB, proposal=None, plot=True, args=None, **kwargs):
data = self.from_run(runNB, proposal)
@@ -367,6 +393,7 @@ class hRIXS:
return self.CURVE_A, self.CURVE_B
def centroid(self, data, bins=None, return_hits=False):
+ print('jee')
if bins is None:
bins = self.BINS
hit_x = []
@@ -406,12 +433,18 @@ class hRIXS:
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:
- return data.assign(hits=(("trainId"), hits),
- xhits=(("trainId"), hit_x),
- yhits=(("trainId"), hit_y))
- else:
- return data.assign(spectrum=(("trainId", "energy"), ret))
+ 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 integrate(self, data):
bins = self.Y_RANGE.stop - self.Y_RANGE.start
--
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