Fixed comment wording and added missing test to matchPessimistcB.

matchPessimisticB.py: Added maxMatchDistArcsec to final cut on output
matches.

python/lsst/meas/astrom/matchPessimisticB.py

@@ -402,17 +402,16 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
         # the consensus mode of the matcher. If not default to Optimistic
         # pattern matcher behavior.
         numConsensus = self.config.numPatternConsensus
-        minRefAndSrcObjectsForConsensus = \
+        minObjectsForConsensus = \
             self.config.numBrightStars + self.config.numPointsForShapeAttempt
-        if ref_array.shape[0] < self.config.numBrightStars + \
-           self.config.numPointsForShapeAttempt or \
-           src_array.shape[0] < self.config.numBrightStars + \
-           self.config.numPointsForShapeAttempt:
+        if ref_array.shape[0] < minObjectsForConsensus or \
+           src_array.shape[0] < minObjectsForConsensus:
             numConsensus = 1
 
-        self.log.debug("Current tol maxDist: %.4f..." % maxMatchDistArcSec)
-        self.log.debug("Current shift: %.4f" % maxShiftArcseconds)
-        self.log.debug("Current rot: %.4f" % self.config.maxRotationDeg)
+        self.log.debug("Current tol maxDist: %.4f arcsec" %
+                       maxMatchDistArcSec)
+        self.log.debug("Current shift: %.4f arcsec" %
+                       maxShiftArcseconds)
 
         # Create our matcher object.
         pyPPMb = PessimisticPatternMatcherB(ref_array[:, :3], self.log)
@@ -459,7 +458,7 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
                         match_tolerance.failedPatternList)
                 )
             if try_idx == 0 and \
-               len(matcher_struct.matches) == 0 and \
+               len(matcher_struct.match_ids) == 0 and \
                match_tolerance.lastMatchedPattern is not None:
                 # If we found a pattern on a previous match-fit iteration and
                 # can't find an optimistic match on our first try with the
@@ -470,7 +469,7 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
                 match_tolerance.failedPatternList.append(
                     match_tolerance.lastMatchedPattern)
                 match_tolerance.lastMatchedPattern = None
-            elif len(matcher_struct.matches) > 0:
+            elif len(matcher_struct.match_ids) > 0:
                 # Match found, save a bit a state regarding this pattern
                 # in the struct and exit.
                 match_tolerance.maxShift = afwgeom.Angle(matcher_struct.shift,
@@ -483,7 +482,7 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
         # the reference and source catalog. For the currrent astrometry solver
         # we need to remove as many false positives as possible before sending
         # the matches off to the astronomery solver.
-        distances_arcsec = np.rad2deg(matcher_struct.distances_rad) * 3600
+        distances_arcsec = np.degrees(matcher_struct.distances_rad) * 3600
         # Rather than have a hard cut on the max distance we allow for
         # objects we preform a iterative sigma clipping. We input a
         # starting point for the clipping based on the largest of the
@@ -496,13 +495,17 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
             input_cut=np.max((10 * wcs.pixelScale().asArcseconds(),
                               maxMatchDistArcSec,
                               maxShiftArcseconds)))
-        # We pick the largest of the sigma clipping or 2 pixels. This pevents
+        print("len distances", len(distances_arcsec))
+        print("max_dist", distances_arcsec.max())
+        # We pick the largest of the sigma clipping, the unsofted
+        # maxMatchDistArcSec or 2 pixels. This pevents
         # the AstrometryTask._matchAndFitWCS from overfitting to a small
-        # number of objects and also allows the WCS fitter to use more
+        # number of objects and also allows the WCS fitter to bring in more
         # matches as the WCS fit improves.
         dist_cut_arcsec = np.max(
             (2 * wcs.pixelScale().asArcseconds(),
-             clipped_dist_arcsec))
+             clipped_dist_arcsec, maxMatchDistArcSec))
+        print("Dist cut", dist_cut_arcsec, "arcsec")
 
         # A match has been found, return our list of matches and
         # return.
@@ -514,10 +517,10 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
                 match.first = refCat[match_id_pair[1]]
                 match.second = sourceCat[match_id_pair[0]]
                 # We compute the true distance along and sphere instead
-                # and store it as an afw.geom.Angle object. The previous
+                # and store it in units of arcseconds. The previous
                 # distances we used were aproximate.
                 match.distance = match.first.getCoord().angularSeparation(
-                    match.second.getCoord())
+                    match.second.getCoord()).asArcseconds()
                 matches.append(match)
 
         return pipeBase.Struct(

python/lsst/meas/astrom/pessimistic_pattern_matcher_b_3D.py

@@ -20,7 +20,8 @@ def _rotation_matrix_chi_sq(flattened_rot_matrix, src_pattern, ref_pattern):
     ----------
     flattened_rot_matrix : float array
         A flattened array representing a 3x3 rotation matrix. The array is
-        flattened to comply with the API of scipy.optimize.leastsq
+        flattened to comply with the API of scipy.optimize.leastsq. Flattened
+        elements are [[0, 0], [0, 1], [0, 2], [1, 0]...]
     src_patterm : float array of 3 vectors
         A array containing N, 3 vectors
     ref_pattern : float array of 3 vectors
@@ -1073,7 +1074,7 @@ def _intermediate_verify(self, src_pattern, ref_pattern, shift_rot_matrix,
 
         Returns
         -------
-        float array
+        float array or None
            Return the fitted shift/rotation matrix if all of the points in our
            source pattern are within max_dist_rad of their matched reference
            objects. Returns None if this criteria is not satisfied.
@@ -1232,7 +1233,7 @@ def _match_sources(self, source_array, shift_rot_matrix):
 
         handshake_mask = self._handshake_match(ref_matches, src_matches)
         return pipeBase.Struct(
-            matches=ref_matches[handshake_mask],
+            match_ids=ref_matches[handshake_mask],
             distances_rad=tmp_src_dist[handshake_mask],)
 
     def _handshake_match(self, matches_ref, matches_src):

tests/test_PessimisticPatternMatcherB3D.py

@@ -119,10 +119,10 @@ def testMatchPerfect(self):
             source_array=self.source_obj_array, n_check=9, n_match=6,
             n_agree=2, max_n_patterns=100, max_shift=60., max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testOptimisticMatch(self):
         """ Test the optimistic mode of the pattern matcher. That is
@@ -137,10 +137,10 @@ def testOptimisticMatch(self):
             source_array=self.source_obj_array, n_check=9, n_match=6,
             n_agree=1, max_n_patterns=100, max_shift=60., max_rotation=6.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testMatchSkip(self):
         """ Test the ablity to skip specified patterns in the matching
@@ -154,10 +154,10 @@ def testMatchSkip(self):
             source_array=self.source_obj_array, n_check=9, n_match=6,
             n_agree=2, max_n_patterns=100, max_shift=60., max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=np.array([0]))
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testMatchMoreSources(self):
         """ Test the case where we have more sources than references
@@ -171,10 +171,10 @@ def testMatchMoreSources(self):
             source_array=self.source_obj_array, n_check=9, n_match=6,
             n_agree=2, max_n_patterns=100, max_shift=60.0, max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array[:500]))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testMatchMoreReferences(self):
         """ Test the case where we have more references than sources
@@ -188,10 +188,10 @@ def testMatchMoreReferences(self):
             source_array=self.source_obj_array[:500], n_check=9, n_match=6,
             n_agree=2, max_n_patterns=100, max_shift=60., max_rotation=1.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array[:500]))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testShift(self):
         """ Test the matcher when a shift is applied to the data.
@@ -217,10 +217,10 @@ def testShift(self):
             n_agree=2, max_n_patterns=100, max_shift=60, max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
 
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testRotation(self):
         """ Test the matcher for when a roation is applied to the data.
@@ -242,10 +242,10 @@ def testRotation(self):
             n_agree=2, max_n_patterns=100, max_shift=60, max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
 
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
     def testShiftRotation(self):
         """ Test both a shift and rotation being applied to the data.
@@ -276,10 +276,10 @@ def testShiftRotation(self):
             n_agree=2, max_n_patterns=100, max_shift=60., max_rotation=5.0,
             max_dist=5., min_matches=30, pattern_skip_array=None)
 
-        self.assertEqual(len(match_struct.matches),
+        self.assertEqual(len(match_struct.match_ids),
                          len(self.reference_obj_array))
         self.assertTrue(
-            np.all(match_struct.distances < 0.01/3600.0 * __deg_to_rad__))
+            np.all(match_struct.distances_rad < 0.01/3600.0 * __deg_to_rad__))
 
 
 if __name__ == '__main__':