Updating TODOs after discussion with Jola

src/calng/AgipdCorrection.py

@@ -31,6 +31,7 @@ class AgipdCorrection(BaseCorrection):
         ("badPixels", CorrectionFlags.BPMASK),
     )
     _gpu_runner_class = AgipdGpuRunner
+    _schema_cache_slots = BaseCorrection._schema_cache_slots | {"sendGainMap"}
 
     @staticmethod
     def expectedParameters(expected):
@@ -118,7 +119,14 @@ class AgipdCorrection(BaseCorrection):
             .assignmentOptional()
             .defaultValue("ADAPTIVE_GAIN")
             .options("ADAPTIVE_GAIN,FIXED_HIGH_GAIN,FIXED_MEDIUM_GAIN,FIXED_LOW_GAIN")
-            .commit()
+            .commit(),
+            BOOL_ELEMENT(expected)
+            .key("sendGainMap")
+            .displayedName("Send gain map on dataOutput")
+            .assignmentOptional()
+            .defaultValue(False)
+            .reconfigurable()
+            .commit(),
         )
         # TODO: hook this up to actual correction done
         # NOTE: wanted to configure this as table, could not make reaonly table with reconfigurable bools in rows

src/calng/agipd_gpu.py

@@ -249,6 +249,11 @@ class AgipdGpuRunner(base_gpu.BaseGpuRunner):
             ),
         )
 
+    def get_gain_map(self, out=None):
+        return cupy.ascontiguousarray(
+            cupy.transpose(self.gain_map_gpu, self.output_transpose)
+        ).get(out=out)
+
     def _init_kernels(self):
         kernel_source = self._kernel_template.render(
             {

src/calng/agipd_gpu_kernels.cpp

@@ -124,6 +124,7 @@ extern "C" {
 				if (corr_flags & REL_GAIN_XRAY) {
 					// TODO
 					//corrected *= rel_gain_xray_map[map_index];
+					// TODO: G_gain_value
 				}
 			}
 

src/calng/base_correction.py

@@ -510,6 +510,8 @@ class BaseCorrection(calibrationBase.CalibrationReceiverBaseDevice):
 
     def _write_combiner_preview(self, data_raw, data_corrected, train_id, source):
         # TODO: take into account updated pulse table after pulse filter
+        # TODO: send as ImageData (requires updated assembler)
+        # TODO: allow sending *all* frames for commissioning (request: Jola)
         preview_hash = Hash()
         preview_hash.set("image.trainId", train_id)
         preview_hash.set("image.pulseId", self._schema_cache["preview.pulse"])

src/calng/base_gpu.py

@@ -137,8 +137,8 @@ class BaseGpuRunner:
     def compute_preview(self, preview_index):
         """Generate single slice or reduction preview of raw and corrected data
 
-        Special values of preview_index are -1 for max (select max integrated intensity
-        frame), -2 for mean, -3 for sum, and -4 for stdev (across cells).
+        Special values of preview_index are -1 for max, -2 for mean, -3 for sum, and
+        -4 for stdev (across cells).
 
         Note that preview_index is taken from data without checking cell table.
         Caller has to figure out which index along memory cell dimension they
@@ -163,14 +163,13 @@ class BaseGpuRunner:
                 # TODO: change axis order when moving reshape to after correction
                 image_data[preview_index].astype(np.float32).get(out=output_buffer)
             elif preview_index == -1:
-                # TODO: select argmax independently for raw and corrected?
-                # TODO: send frame sums somewhere to compute global max frame
-                max_index = cupy.argmax(
-                    cupy.sum(image_data, axis=(1, 2), dtype=cupy.float32)
-                )
-                image_data[max_index].astype(np.float32).get(out=output_buffer)
+                # TODO: confirm that max is pixel and not integrated intensity
+                # separate from next case because dtype not applicable here
+                cupy.max(image_data, axis=0).astype(cupy.float32).get(out=output_buffer)
             elif preview_index in (-2, -3, -4):
-                stat_fun = {-2: cupy.mean, -3: cupy.sum, -4: cupy.std}[preview_index]
+                stat_fun = {-1: cupy.max, -2: cupy.mean, -3: cupy.sum, -4: cupy.std}[
+                    preview_index
+                ]
                 stat_fun(image_data, axis=0, dtype=cupy.float32).get(out=output_buffer)
         return self.preview_raw, self.preview_corrected
 

src/tests/test_dssc_kernels.py

@@ -92,22 +92,15 @@ def test_preview_slice():
 
 
 def test_preview_max():
-    # can it find max intensity frame?
     # note: in case correction failed, still test this separately
     kernel_runner.load_data(raw_data)
     kernel_runner.processed_data_gpu.set(corrected_data)
     preview_raw, preview_corrected = kernel_runner.compute_preview(-1)
     assert np.allclose(
-        preview_raw,
-        raw_data[np.argmax(np.sum(raw_data, axis=(1, 2), dtype=np.float32))]
-        .astype(np.float32)
-        .transpose(),
+        preview_raw, np.max(raw_data, axis=0).astype(np.float32).transpose()
     )
     assert np.allclose(
-        preview_corrected,
-        corrected_data[np.argmax(np.sum(corrected_data, axis=(1, 2), dtype=np.float32))]
-        .astype(np.float32)
-        .transpose(),
+        preview_corrected, np.max(corrected_data, axis=0).astype(np.float32).transpose()
     )