Adds possibility to select multiple ROIs from a source

Load.py

@@ -172,7 +172,7 @@ mnemonics = {
 
 def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False,
     validate=False, runpath='/gpfs/exfel/exp/{}/{}/{}/raw/r{:04d}/',
-    subset=by_index[:]):
+    subset=by_index[:], rois={}):
     """ Load a run and extract the data. Output is an xarray with aligned trainIds
 
         Inputs:
@@ -188,7 +188,11 @@ def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False,
             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.
+                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
@@ -234,10 +238,16 @@ def load(fields, runNB, proposalNB, semesterNB, topic='SCS', display=False,
             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)