Merge pull request #138 from lsst/tickets/DM-39332

DM-39332: Fix the PF1 metric to be threshold relative to mean

python/lsst/analysis/tools/actions/plot/histPlot.py

@@ -160,6 +160,11 @@ class HistPanel(Config):
         default=None,
         optional=True,
     )
+    refRelativeToMedian = Field[bool](
+        doc="Is the referenceValue meant to be an offset from the median?",
+        default=False,
+        optional=True,
+    )
     histDensity = Field[bool](
         doc="Whether to plot the histogram as a normalized probability distribution. Must also "
         "provide a value for referenceValue",
@@ -452,7 +457,7 @@ def _makePanel(self, data, panel, ax, colors, label_font_size=9, legend_font_siz
             # If histDensity is True, also plot a reference PDF with
             # mean = referenceValue and sigma = 1 for reference.
             if self.panels[panel].referenceValue is not None:
-                ax = self._addReferenceLines(ax, panel, panel_range, legend_font_size=legend_font_size)
+                ax = self._addReferenceLines(ax, panel, panel_range, meds, legend_font_size=legend_font_size)
 
             # Check if we should use the default stats panel or if a custom one
             # has been created.
@@ -536,7 +541,7 @@ def _calcStats(self, data):
         mad = sigmaMad(data)
         return num, med, mad
 
-    def _addReferenceLines(self, ax, panel, panel_range, legend_font_size=7):
+    def _addReferenceLines(self, ax, panel, panel_range, meds, legend_font_size=7):
         """Draw the vertical reference line and density curve (if requested)
         on the panel.
         """
@@ -548,10 +553,13 @@ def _addReferenceLines(self, ax, panel, panel_range, legend_font_size=7):
         if self.panels[panel].histDensity:
             reference_label = None
         else:
-            reference_label = "${{\\mu_{{ref}}}}$: {}".format(self.panels[panel].referenceValue)
-        ax2.axvline(
-            self.panels[panel].referenceValue, ls="-", lw=1, c="black", zorder=0, label=reference_label
-        )
+            if self.panels[panel].refRelativeToMedian:
+                reference_value = self.panels[panel].referenceValue + meds[0]
+                reference_label = "${{\\mu_{{ref}}}}$: {}".format(reference_value)
+            else:
+                reference_value = self.panels[panel].referenceValue
+                reference_label = "${{\\mu_{{ref}}}}$: {}".format(reference_value)
+            ax2.axvline(reference_value, ls="-", lw=1, c="black", zorder=0, label=reference_label)
         if self.panels[panel].histDensity:
             ref_x = np.arange(panel_range[0], panel_range[1], (panel_range[1] - panel_range[0]) / 100.0)
             ref_mean = self.panels[panel].referenceValue

python/lsst/analysis/tools/actions/scalar/scalarActions.py

@@ -159,6 +159,7 @@ class FracThreshold(ScalarAction):
     threshold = Field[float](doc="Threshold to apply.")
     vectorKey = Field[str](doc="Name of column")
     percent = Field[bool](doc="Express result as percentage", default=False)
+    relative_to_median = Field[bool](doc="Calculate threshold relative to " "the median?", default=False)
 
     def getInputSchema(self) -> KeyedDataSchema:
         return ((self.vectorKey, Vector),)
@@ -168,9 +169,14 @@ def __call__(self, data: KeyedData, **kwargs) -> Scalar:
         values = data[self.vectorKey.format(**kwargs)]
         values = values[mask]  # type: ignore
         values = values[np.logical_not(np.isnan(values))]
+        # If relative_to_median is set, shift the threshold to be median+thresh
+        if self.relative_to_median:
+            threshold = self.threshold + np.median(values)
+        else:
+            threshold = self.threshold
         result = cast(
             Scalar,
-            float(np.sum(getattr(operator, self.op)(values, self.threshold)) / len(values)),  # type: ignore
+            float(np.sum(getattr(operator, self.op)(values, threshold)) / len(values)),  # type: ignore
         )
         if self.percent:
             return 100.0 * result

python/lsst/analysis/tools/atools/photometricRepeatability.py

@@ -111,6 +111,7 @@ def setDefaults(self):
             op="ge",
             threshold=self.PA2Value,
             percent=True,
+            relative_to_median=True,
         )
         self.process.calculateActions.photRepeatNsources = CountAction(vectorKey="perGroupStdevFiltered")
 
@@ -125,6 +126,8 @@ def setDefaults(self):
         self.produce.plot.panels["panel_rms"].statsPanel.stat3 = ["photRepeatOutlier"]
 
         self.produce.plot.panels["panel_rms"].referenceValue = self.PA2Value
+        self.produce.plot.panels["panel_rms"].refRelativeToMedian = True
+
         self.produce.plot.panels["panel_rms"].label = "rms (mmag)"
         self.produce.plot.panels["panel_rms"].hists = dict(perGroupStdevFiltered="Filtered per group rms")