removed all manual array.tolist() conversions (and similar) that were needed for json serializability

dap/algos/peakfind.py

@@ -54,8 +54,8 @@ def calc_peakfinder_analysis(results, data, pixel_mask):
     results["number_of_spots"] = number_of_spots
 
     # to coordinates where position of first pixel/point is (1, 1)
-    results["spot_x"] = [x + 0.5 for x in peak_list_x]
-    results["spot_y"] = [y + 0.5 for y in peak_list_y]
+    results["spot_x"] = peak_list_x + 0.5
+    results["spot_y"] = peak_list_y + 0.5
     results["spot_intensity"] = copy(peak_list_value) #TODO: why is this copy needed?
 
     npeaks_threshold_hit = results.get("npeaks_threshold_hit", 15)

dap/algos/radprof.py

@@ -33,7 +33,7 @@ def calc_radial_integration(results, data, pixel_mask):
             rp = rp / integral_silent_region
             results["radint_normalised"] = [silent_min, silent_max]
 
-    results["radint_I"] = rp[r_min:].tolist() #TODO: why not stop at r_max?
+    results["radint_I"] = rp[r_min:] #TODO: why not stop at r_max?
     results["radint_q"] = [r_min, r_max]
 
 

dap/algos/roi.py

@@ -40,7 +40,7 @@ def calc_roi(results, data):
             roi_sum_norm = np.nanmean(data_roi, dtype=float) # data_roi is np.float32, which cannot be json serialized
 
         roi_indices_x = [ix1, ix2]
-        roi_proj_x = np.nansum(data_roi, axis=0).tolist()
+        roi_proj_x = np.nansum(data_roi, axis=0)
 
         roi_intensities.append(roi_sum)
         roi_intensities_normalised.append(roi_sum_norm)

dap/algos/spiana.py

@@ -36,7 +36,7 @@ def calc_spi_analysis(results, data):
     hit = (photon_percentage > spi_threshold_hit_percentage)
 
     results["number_of_spots"] = photon_percentage
-    results["is_hit_frame"] = bool(hit) # json does not like numpy bool_ scalars
+    results["is_hit_frame"] = hit
 
 
 

dap/algos/streakfind.py

@@ -205,17 +205,16 @@ def _calc_streakfinder_analysis(results, snr, mask):
     streak_lengths = np.sqrt(
         np.power((streak_lines[..., 2] - streak_lines[..., 0]), 2) +
         np.power((streak_lines[..., 2] - streak_lines[..., 0]), 2)
-    ).tolist()
+    )
 
     streak_lines = streak_lines.T
     _, number_of_streaks = streak_lines.shape
 
-    list_result = streak_lines.tolist()  # arr(4, n_lines); coord x0, y0, x1, y1
     bragg_counts = [streak.total_mass() for streak in detected_streaks]
 
     results["number_of_streaks"] = number_of_streaks
     results["is_hit_frame"] = (number_of_streaks > min_hit_streaks)
-    results["streaks"] = list_result
+    results["streaks"] = streak_lines  # arr(4, n_lines); coord x0, y0, x1, y1
     results["streak_lengths"] = streak_lengths
     results["bragg_counts"] = bragg_counts
 

dap/worker.py

@@ -110,8 +110,8 @@ def work(backend_addr, accumulator_addr, visualisation_addr, fn_config, skip_fra
         if pixel_mask is not None:
             saturated_pixels_y, saturated_pixels_x = jfdata.get_saturated_pixels(raw_image, double_pixels)
             results["saturated_pixels"] = len(saturated_pixels_x)
-            results["saturated_pixels_x"] = saturated_pixels_x.tolist()
-            results["saturated_pixels_y"] = saturated_pixels_y.tolist()
+            results["saturated_pixels_x"] = saturated_pixels_x
+            results["saturated_pixels_y"] = saturated_pixels_y
 
 
         calc_radial_integration(results, image, pixel_mask)