Merge branch 'FastCCD_calibration' into 'master'

Fast ccd calibration

See merge request !14

Load.py

@@ -6,7 +6,7 @@
     Copyright (2019) SCS Team.
 """
 import numpy as np
-from karabo_data import RunDirectory
+from karabo_data import RunDirectory, by_index
 import xarray as xr
 
 mnemonics = {
@@ -119,10 +119,37 @@ mnemonics = {
                'key':'actual_current.value',
                'dim':None},
 
-    # FastCCD
+    # FastCCD, if in raw folder, raw images
+    #          if in proc folder, dark substracted and relative gain corrected
     "fastccd": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
                 'key':'data.image.pixels',
                 'dim':['x', 'y']},
+    # FastCCD with common mode correction
+    "fastccd_cm": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+                'key':'data.image.pixels_cm',
+                'dim':['x', 'y']},
+    # FastCCD charge split correction in very low photon count regime
+    "fastccd_classified": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+                'key':'data.image.pixels_classified',
+                'dim':['x', 'y']},
+    # FastCCD event multiplicity from the charge split correction:
+    # 0: no events
+    # 100, 101: single events
+    # 200-203: charge split into two pixels in four different orientations
+    # 300-303: charge split into three pixels in four different orientations
+    # 400-403: charge split into four pixels in four different orientations
+    # 1000: charge in more than four neighboring pixels. Cannot be produced by a single photon alone.
+    "fastccd_patterns": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+                'key':'data.image.patterns',
+                'dim':['x', 'y']},
+    # FastCCD gain map, 0 high gain, 1 medium gain, 2 low gain
+    "fastccd_gain": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+                'key':'data.image.gain',
+                'dim':['x', 'y']},
+    # FastCCD mask, bad pixel map to be ignored if > 0
+    "fastccd_mask": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+                'key':'data.image.mask',
+                'dim':['x', 'y']},
 
     # TIM
     "MCP1apd": {'source':'SCS_UTC1_ADQ/ADC/1:network',
@@ -149,14 +176,16 @@ mnemonics = {
     "MCP4raw": {'source':'SCS_UTC1_ADQ/ADC/1:network',
                 'key':'digitizers.channel_1_D.raw.samples',
                 'dim': ['samplesId']},
-    
+
     # KARABACON
     "KARABACON": {'source':'SCS_DAQ_SCAN/MDL/KARABACON',
                     'key': 'actualStep.value',
                     'dim': None}
 }
 
-def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False, validate=False):
+def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False,
+    validate=False, runpath='/gpfs/exfel/exp/{}/{}/{}/raw/r{:04d}/',
+    subset=by_index[:], rois={}):
     """ Load a run and extract the data. Output is an xarray with aligned trainIds
 
         Inputs:
@@ -169,13 +198,21 @@ def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False, vali
             topic: string of the topic
             display: boolean, whether to show the run.info or not
             validate: boolean, whether to run karabo-data-validate or not
+            runpath: a string to fromat the run folder path with topic,
+                semesterNB, proposalNB and runNB
+            subset: a subset of train that can be load with by_index[:5] for the
+                first 5 trains
+            rois: a dictionnary of mnemonics with a list of rois definition and the desired
+                names, for example {'fastccd':{'ref':{'roi':by_index[730:890, 535:720],
+                'dim': ['ref_x', 'ref_y']}, 'sam':{'roi':by_index[1050:1210, 535:720],
+                'dim': ['sam_x', 'sam_y']}}}
 
         Outputs:
             res: an xarray DataSet with aligned trainIds
     """
 
-    runFolder = '/gpfs/exfel/exp/{}/{}/{}/raw/r{:04d}/'.format(topic, semesterNB, proposalNB, runNB)
-    run = RunDirectory(runFolder)
+    runFolder = runpath.format(topic, semesterNB, proposalNB, runNB)
+    run = RunDirectory(runFolder).select_trains(subset)
 
     if validate:
         get_ipython().system('karabo-data-validate ' + runFolder)
@@ -214,10 +251,16 @@ def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False, vali
             print('Source {} not found in run. Skipping!'.format(v['source']))
             continue
 
-        vals.append(run.get_array(v['source'], v['key'], extra_dims=v['dim']))
-
-        keys.append(k)
-
+        if k not in rois:
+            # no ROIs selection, we read everything
+            vals.append(run.get_array(v['source'], v['key'], extra_dims=v['dim']))
+            keys.append(k)
+        else:
+            # ROIs selection, for each ROI we select a region of the data and save it with new name and dimensions
+            for nk,nv in rois[k].items():
+                vals.append(run.get_array(v['source'], v['key'], extra_dims=nv['dim'], roi=nv['roi']))
+                keys.append(nk)
+ 
     aligned_vals = xr.align(*vals, join='inner')
     result = dict(zip(keys, aligned_vals))
     result = xr.Dataset(result)