Added pipeline test for maxSelect in DonutStampSelector (#286)

* Added pipeline test for maxSelect in DonutStampSelector

doc/versionHistory.rst

@@ -6,13 +6,21 @@
 Version History
 ##################
 
+.. _lsst.ts.wep-12.6.1:
+
+-------------
+12.6.1
+-------------
+
+* Added a unit test for specifying DonutStampSelector.config.maxSelect in a pipeline config yaml.
+
 .. _lsst.ts.wep-12.6.0:
 
 -------------
 12.6.0
 -------------
 
-* Added maxSelect config to DonutStampsSelector
+* Added maxSelect config to DonutStampSelector
 
 .. _lsst.ts.wep-12.5.0:
 

tests/task/test_donutStampSelectorTask.py

@@ -60,7 +60,7 @@ def setUpClass(cls):
         instrument = "lsst.obs.lsst.LsstCam"
         cls.cameraName = "LSSTCam"
         pipelineYaml = os.path.join(
-            testPipelineConfigDir, "testCutoutsFamPipeline.yaml"
+            testPipelineConfigDir, "testDonutStampSelectorPipeline.yaml"
         )
 
         pipeCmd = writePipetaskCmd(
@@ -209,3 +209,11 @@ def testTaskRun(self):
         # Test that final selection numbers match
         self.assertEqual(len(donutStampsSelect), selected.sum())
         self.assertEqual(len(donutStampsSelect), donutsQuality["FINAL_SELECT"].sum())
+
+    def testPipelineRun(self):
+        # Config specifies maxSelect=1, so the Zernike table should only have
+        # 2 rows (average, and pair 1)
+        zernikes = self.butler.get(
+            "zernikes", dataId=self.dataIdExtra, collections=[self.runName]
+        )
+        self.assertEqual(len(zernikes), 2)

tests/testData/pipelineConfigs/testDonutStampSelectorPipeline.yaml

@@ -0,0 +1,47 @@
+# This yaml file is used to define the tasks and configuration of
+# a Gen 3 pipeline used for testing in ts_wep.
+description: wep basic processing test pipeline
+# Here we specify the corresponding instrument for the data we
+# will be using.
+instrument: lsst.obs.lsst.LsstCam
+# Then we can specify each task in our pipeline by a name
+# and then specify the class name corresponding to that task
+tasks:
+  isr:
+    class: lsst.ip.isr.isrTask.IsrTask
+    # Below we specify the configuration settings we want to use
+    # when running the task in this pipeline. Since our data doesn't
+    # include bias or flats we only want to use doApplyGains and
+    # doOverscan in our isr task.
+    config:
+      connections.outputExposure: "postISRCCD"
+      doBias: False
+      doVariance: True
+      doLinearize: False
+      doCrosstalk: False
+      doDefect: False
+      doNanMasking: False
+      doInterpolate: False
+      doBrighterFatter: False
+      doDark: False
+      doFlat: False
+      doApplyGains: True
+      doFringe: False
+      doOverscan: True
+      python: OverscanCorrectionTask.ConfigClass.fitType = 'MEDIAN'
+  generateDonutCatalogWcsTask:
+    class: lsst.ts.wep.task.generateDonutCatalogWcsTask.GenerateDonutCatalogWcsTask
+    config:
+      donutSelector.unblendedSeparation: 1
+  cutOutDonutsScienceSensorTask:
+    class: lsst.ts.wep.task.cutOutDonutsScienceSensorTask.CutOutDonutsScienceSensorTask
+    config:
+      # And here we specify the configuration settings originally defined in
+      # CutOutDonutsScienceSensorTaskConfig.
+      # Test with default instrument configuration parameters
+      donutStampSize: 160
+      initialCutoutPadding: 40
+  calcZernikesTask:
+    class: lsst.ts.wep.task.calcZernikesTask.CalcZernikesTask
+    config:
+      donutStampSelector.maxSelect: 1