Merge pull request #118 from lsst/tickets/DM-23971

DM-23971: Undo TE1 "regression"

python/lsst/validate/drp/astromerrmodel.py

@@ -99,7 +99,7 @@ def fitAstromErrModel(snr, dist):
     return params
 
 
-def build_astrometric_error_model(matchedMultiVisitDataset, brightSnr=100,
+def build_astrometric_error_model(matchedMultiVisitDataset, brightSnrMin=100,
                                   medianRef=100, matchRef=500):
     r"""Serializable model of astrometry errors across multiple visits.
 
@@ -112,7 +112,7 @@ def build_astrometric_error_model(matchedMultiVisitDataset, brightSnr=100,
     matchedMultiVisitDataset : `MatchedMultiVisitDataset`
         A dataset containing matched statistics for stars across multiple
         visits.
-    brightSnr : `float` or `astropy.unit.Quantity`, optional
+    brightSnrMin : `float` or `astropy.unit.Quantity`, optional
         Minimum SNR for a star to be considered "bright" (dimensionless).
     medianRef : `float` or `astropy.unit.Quantity`, optional
         Median reference astrometric scatter (default: milliarcsecond).
@@ -124,7 +124,7 @@ def build_astrometric_error_model(matchedMultiVisitDataset, brightSnr=100,
     blob : `lsst.verify.Blob`
         Blob with datums:
 
-        - ``brightSnr``: Threshold SNR for bright sources used in this model.
+        - ``brightSnrMin``: Threshold SNR for bright sources used in this model.
         - ``C``: Model scaling factor.
         - ``theta``: Seeing (milliarcsecond).
         - ``sigmaSys``: Systematic error floor (milliarcsecond).
@@ -144,29 +144,29 @@ def build_astrometric_error_model(matchedMultiVisitDataset, brightSnr=100,
     # _doc['doc'] \
     #     = "Astrometric astrometry model: mas = C*theta/SNR + sigmaSys"
 
-    if not isinstance(brightSnr, u.Quantity):
-        brightSnr = brightSnr * u.Unit('')
+    if not isinstance(brightSnrMin, u.Quantity):
+        brightSnrMin = brightSnrMin * u.Unit('')
     if not isinstance(medianRef, u.Quantity):
         medianRef = medianRef * u.marcsec
 
     _compute(blob,
              matchedMultiVisitDataset['snr'].quantity,
              matchedMultiVisitDataset['dist'].quantity,
-             len(matchedMultiVisitDataset.goodMatches),
-             brightSnr, medianRef, matchRef)
+             len(matchedMultiVisitDataset.matchesFaint),
+             brightSnrMin, medianRef, matchRef)
     return blob
 
 
-def _compute(blob, snr, dist, nMatch, brightSnr, medianRef, matchRef):
+def _compute(blob, snr, dist, nMatch, brightSnrMin, medianRef, matchRef):
     median_dist = np.median(dist)
     msg = 'Median value of the astrometric scatter - all magnitudes: ' \
           '{0:.3f}'
     print(msg.format(median_dist))
 
-    bright = np.where(snr > brightSnr)
+    bright = np.where(snr > brightSnrMin)
     astromScatter = np.median(dist[bright])
     msg = 'Astrometric scatter (median) - snr > {0:.1f} : {1:.1f}'
-    print(msg.format(brightSnr, astromScatter))
+    print(msg.format(brightSnrMin, astromScatter))
 
     fit_params = fitAstromErrModel(snr[bright], dist[bright])
 
@@ -179,10 +179,9 @@ def _compute(blob, snr, dist, nMatch, brightSnr, medianRef, matchRef):
               '(should be > {1:d})'
         print(msg.format(nMatch, matchRef))
 
-    blob['brightSnr'] = Datum(quantity=brightSnr,
-                              label='Bright SNR',
-                              description='Threshold in SNR for bright sources used in this '
-                                          'model')
+    blob['brightSnrMin'] = Datum(quantity=brightSnrMin,
+                                 label='Bright SNR',
+                                 description='Threshold in SNR for bright sources used in this model')
     blob['C'] = Datum(quantity=fit_params['C'],
                       description='Scaling factor')
     blob['theta'] = Datum(quantity=fit_params['theta'],

python/lsst/validate/drp/calcnonsrd/model_phot_rep.py

@@ -19,7 +19,7 @@
 # see <https://www.lsstcorp.org/LegalNotices/>.
 
 from lsst.validate.drp.repeatability import measurePhotRepeat
-from lsst.validate.drp.matchreduce import reduceSources
+from lsst.validate.drp.matchreduce import getKeysFilter, filterSources
 
 
 def measure_model_phot_rep(metrics, filterName, matchedDataset, snr_bins=None):
@@ -60,8 +60,10 @@ def measure_model_phot_rep(metrics, filterName, matchedDataset, snr_bins=None):
         snr_bins = [((5, 10), (10, 20)), ((20, 40), (40, 80))]
     name_flux_all = ["base_PsfFlux", 'slot_ModelFlux']
     measurements = []
+    schema = matchedDataset._matchedCatalog.schema
     for name_flux in name_flux_all:
-        key_model_mag = matchedDataset._matchedCatalog.schema.find(f"{name_flux}_mag").key
+        keys = getKeysFilter(schema, nameFluxKey=name_flux)
+        key_model_mag = schema.find(f"{name_flux}_mag").key
         if name_flux == 'slot_ModelFlux':
             name_sources = ['Gal', 'Star']
             prefix_metric = 'model'
@@ -71,14 +73,17 @@ def measure_model_phot_rep(metrics, filterName, matchedDataset, snr_bins=None):
         for idx_bins, ((snr_one_min, snr_one_max), (snr_two_min, snr_two_max)) in enumerate(snr_bins):
             bin_base = 1 + 2*idx_bins
             for source in name_sources:
-                reduceSources(matchedDataset, matchedDataset._matchedCatalog, extended=source == 'Gal',
-                              nameFluxKey=name_flux, goodSnr=snr_one_min, goodSnrMax=snr_one_max,
-                              safeSnr=snr_two_min, safeSnrMax=snr_two_max)
+                matches = filterSources(
+                    matchedDataset._matchedCatalog, keys=keys, extended=(source == 'Gal'),
+                    faintSnrMin=snr_one_min, faintSnrMax=snr_one_max,
+                    brightSnrMin=snr_two_min, brightSnrMax=snr_two_max,
+                )
                 for bin_offset in [0, 1]:
                     model_phot_rep = measurePhotRepeat(
                         metrics[f'validate_drp.{prefix_metric}PhotRep{source}{bin_base + bin_offset}'],
                         filterName,
-                        matchedDataset.goodMatches if bin_offset == 0 else matchedDataset.safeMatches,
+                        matches.matchesFaint if bin_offset == 0 else matches.matchesBright,
                         key_model_mag)
                     measurements.append(model_phot_rep)
+
     return measurements

python/lsst/validate/drp/calcsrd/amx.py

@@ -95,7 +95,7 @@ def measureAMx(metric, matchedDataset, D, width=2., magRange=None, verbose=False
     and to astrometric measurements performed in the r and i bands.
     """
 
-    matches = matchedDataset.safeMatches
+    matches = matchedDataset.matchesBright
 
     datums = {}
 

python/lsst/validate/drp/calcsrd/tex.py

@@ -93,7 +93,7 @@ def measureTEx(metric, matchedDataset, D, bin_range_operator, verbose=False):
     Table 27: These residual PSF ellipticity correlations apply to the r and i bands.
     """
 
-    matches = matchedDataset.safeMatches
+    matches = matchedDataset.matchesBright
 
     datums = {}
     datums['D'] = Datum(quantity=D, description="Separation distance")