Correct bug with selected sensors

lib/backend/gixd.py

@@ -103,6 +103,7 @@ def process_vineyard(nxs_name, path_to_nxs_dir,
         bin_size=10, is_force_gamma=False, fgamma=0.,
         is_gixd_logx=False, is_gixd_logy=False, is_gixd_logz=False,
         nb_levels=50, map_gixd='jet', roi_soller=[510, 350, 130, 692],
+        selected_sensors = None,
         is_print_stamps=False, is_plot=False, is_print_info=False, is_raster=False):
     """
     Extract the nexus file, plot and return the channel of the Vineyard's peak.
@@ -132,6 +133,8 @@ def process_vineyard(nxs_name, path_to_nxs_dir,
         Colormap of the image.
     roi_soller : list of int, optional
         ROI of the Soller's slits.
+    selected_sensors : list of str, optional
+        List of aliases of sensors, for their mean value to be added to the plot.
     is_print_stamps : bool, optional
         Print the list of sensors contained in the nexus file.
     is_plot : bool, optional
@@ -145,13 +148,14 @@ def process_vineyard(nxs_name, path_to_nxs_dir,
         Vertical channel corresponding to the Vineyard's peak.
 
     """
-    x, y, x_label, y_label, time_str, _,\
+    x, y, x_label, y_label, time_str, sensors_str, _,\
     integ_rod, integ_rod_top, integ_rod_bottom, integ_rod_first_quarter,\
     mat, mat_binned, _, mean_pi, _, mean_gamma, _, _ = extract_gixd_scan(
         nxs_name, path_to_nxs_dir,
         0., 1., 1., 1.,
         bin_size, False,
         is_force_gamma, fgamma, roi_soller,
+        selected_sensors,
         is_print_stamps, is_print_info
         )
 
@@ -161,7 +165,7 @@ def process_vineyard(nxs_name, path_to_nxs_dir,
 
     if is_plot:
         plot_gixd_scan(
-            x, y, x_label, y_label, time_str,
+            x, y, x_label, y_label, time_str, sensors_str,
             integ_rod, integ_rod_top, integ_rod_bottom, integ_rod_first_quarter,
             mat_binned, mean_pi, mean_gamma,
             nxs_name, '', is_gixd_logx, is_gixd_logy, is_gixd_logz,

lib/backend/xrr.py

@@ -294,8 +294,9 @@ def extract_direct_xrr_liquid(
 
     """
     # Extract the sum of all images in the time scan
-    _, image_direct, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                    direct_scan_name, path_to_nxs_dir, is_print_stamps=False, is_print_info=False)
+    _, image_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                    direct_scan_name, path_to_nxs_dir, selected_sensors=None,
+                    is_print_stamps=False, is_print_info=False)
 
     # Replace the intensity of the dead zones with a value of 0
     image_direct = np.where(image_direct<0., 0., image_direct)
@@ -569,8 +570,8 @@ def extract_xrr_liquid_scan(
                 time_end = epoch_time[-1]
 
         # Extract the sum of all images in the time scan
-        _, image, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                        nxs_name, path_to_nxs_dir,
+        _, image, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                        nxs_name, path_to_nxs_dir, selected_sensors=None,
                         is_print_stamps=False, is_print_info=False)
 
         # Replace the intensity of the dead zones with a value of 0
@@ -1350,14 +1351,14 @@ def extract_direct_xrr_solid(
 
     if sensor_list[0] == 'zs':
         # If the direct is a height scan, take only the five first points
-        images_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                direct_scan_name, path_to_nxs_dir,
+        images_direct, _, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                direct_scan_name, path_to_nxs_dir, selected_sensors=None,
                 is_print_stamps=False, is_print_info=False)
         image_direct = np.mean(images_direct[0:5], axis = 0)
     else:
         # Extract the sum of all images in the time scan
-        _, image_direct, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                        direct_scan_name, path_to_nxs_dir,
+        _, image_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                        direct_scan_name, path_to_nxs_dir,selected_sensors=None,
                         is_print_stamps=False, is_print_info=False)
         image_direct = image_direct/nbpts
 
@@ -1596,8 +1597,8 @@ def extract_xrr_solid_scan(
         ####################################################
 
         # Extract individual images
-        images, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                        nxs_name, path_to_nxs_dir,
+        images, _, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                        nxs_name, path_to_nxs_dir, selected_sensors=None,
                         is_print_stamps=False, is_print_info=is_print_info)
 
         for image in images:

lib/frontend/process_widgets/wxrf.py

@@ -401,6 +401,7 @@ def display_widgets_identify_peaks(expt, dw):
                     sdd_gain=expt.params['sdd_gain'],
                     sdd_ev0=expt.params['sdd_ev0'],
                     arr_peaks=expt.params['arr_peaks'],
+                    selected_sensors = None,
                     is_use_ev=expt.params['is_use_ev'],
                     is_xrf_log=expt.params['is_xrf_log'],
                     absorbers='',

lib/frontend/process_widgets/wxrr.py

@@ -187,8 +187,8 @@ def preview_indiv_scan_xrr(nxs_name, expt):
 
     """
     # Extract the sum of all images in the time scan
-    _, images_sum, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
-                nxs_name, expt.paths['dir_nxs'],
+    _, images_sum, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+                nxs_name, expt.paths['dir_nxs'], selected_sensors=None,
                 is_print_stamps=False, is_print_info=False)
 
     # Replace the intensity of the dead zones with a value of 0
@@ -269,23 +269,26 @@ def preview_direct_xrr(expt):
 
         if sensor_list[0] == 'zs':
             # Extract each individual image from the height scan
-            images_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+            images_direct, _, _, _, _, _, _, _ ,_ = area_detector.extract_area_detector_scan(
                             expt.params['direct_scan_name'], expt.paths['dir_nxs'],
+                            selected_sensors=None,
                             is_print_stamps=False, is_print_info=False)
 
             # Take the average over the first five images to get the direct
             image_direct = np.mean(images_direct[0:5], axis = 0)
         else:
             # Extract the sum of all images in the time scan
-            _, image_direct, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+            _, image_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
                         expt.params['direct_scan_name'], expt.paths['dir_nxs'],
+                        selected_sensors=None,
                         is_print_stamps=False, is_print_info=False)
 
     elif expt.params['type_xrr'] == 'liquid':
 
         # Extract the sum of all images in the time scan
-        _, image_direct, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
+        _, image_direct, _, _, _, _, _, _, _ = area_detector.extract_area_detector_scan(
                                 expt.params['direct_scan_name'], expt.paths['dir_nxs'],
+                                selected_sensors=None,
                                 is_print_stamps=False, is_print_info=False)
 
     # Replace the intensity of the dead zones with a value of 0