Fixed behaviour of TOF response reading due to change in API in PR483. (#506)

* Fixed behaviour of TOF response reading due to change in API in PR483.
* Change order of data reading and selection when estimating response to optimize for speed.

---------

Co-authored-by: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>

Author: daniloefl

docs/changelog.md

@@ -85,6 +85,8 @@ Fixed:
 - Fixed support for plotting images with zeros/negative numbers with
   [imshow2()][extra.utils.imshow2] and `lognorm=True` (!491).
 - Fixed counting threshold for `CookieboxCalibration` (!498).
+- [CookieboxCalibration][extra.applications.CookieboxCalibration] bug fix for
+  new `extra.applications.base.SerializableMixin` interface (!506).
 
 Changed:
 

src/extra/applications/cookiebox.py

@@ -453,8 +453,7 @@ def _fromdict(cls, all_data):
         if "_tof_response" in all_data.keys():
             self._tof_response = dict()
             for tof_id in sorted(all_data["_tof_response"].keys()):
-                self._tof_response[tof_id] = TOFAnalogResponse()
-                self._tof_response[tof_id]._fromdict(all_data["_tof_response"][tof_id])
+                self._tof_response[tof_id] = TOFAnalogResponse._fromdict(all_data["_tof_response"][tof_id])
         return self
 
     def setup(self,

src/extra/applications/cookiebox_deconvolve.py

@@ -457,13 +457,16 @@ def setup(self,
         data = list()
         h = list()
         bins = np.arange(0, self.n_samples+1)
-        if scan is not None:
-            for k, e in enumerate(scan.positions):
-                tof_data = tof.select_trains(by_id[scan.positions_train_ids[k]])
-                if self.count_threshold is None or self.count_threshold >= 0:
-                    this_tof_data = -tof_data.pulse_data(pulse_dim="pulseIndex").mean('pulse')
-                else:
-                    tof_data = tof_data.pulse_edges(pulse_dim='pulseIndex', threshold=self.count_threshold).reset_index()
+        if scan is not None: # use the scan
+            # if not counting photo-electrons -- ie: analog mode
+            if self.count_threshold is None or self.count_threshold >= 0:
+                this_tof_data = -tof.pulse_data(pulse_dim="pulseIndex").unstack("pulse")
+                for k, e in enumerate(scan.positions):
+                    data += [this_tof_data.sel(trainId=scan.positions_train_ids[k]).mean("trainId").mean("pulseIndex").to_numpy()]
+            else:
+                # count photo-electrons by histogramming peak positions
+                for k, e in enumerate(scan.positions):
+                    tof_data = tof.pulse_edges(pulse_dim='pulseIndex', threshold=self.count_threshold).reset_index()
                     this_tof_data, _ = np.histogram(tof_data.edge, bins=bins, weights=-tof_data.amplitude)
                     this_tof_data = xr.DataArray(this_tof_data, dims=('sample'), coords={'sample': bins[:-1]})
                 if self.roi is not None:

tests/test_applications_cookiebox.py

@@ -432,6 +432,13 @@ def test_deconvolve(mock_sqs_etof_calibration_run, tmp_path):
     tof_response.to_file(fpath)
     tof_response_read = TOFAnalogResponse.from_file(fpath)
 
+    # setup TOFAnalogResponse using counting of photo-electrons
+    tof_response_count = TOFAnalogResponse(roi=slice(75, None), n_samples=150, count_threshold=-20)
+    tof_response_count.setup(tof_channel[0], scan)
+
+    # setup TOFAnalogResponse without Scan
+    tof_response_noscan = TOFAnalogResponse(roi=slice(75, None), n_samples=150)
+    tof_response_noscan.setup(tof_channel[0])
 
     # create calibration object to read data in the appropriate format
     energy_axis = np.linspace(965, 1070, 160)