Fix index error when not specifying any requiredBands.

python/lsst/fgcmcal/fgcmBuildFromIsolatedStars.py

@@ -456,19 +456,33 @@ def _make_all_star_obs_from_isolated_stars(
                 self.log.info("No good sources found in tract %d", tract)
                 continue
 
-            # Need to re-sum numbers of observations of each star in the required bands.
+            # Need to count the observations of each star after cuts, per band.
+            # If we have requiredBands specified, we must make sure that each star
+            # has the minumum number of observations in each of thos bands.
+            # Otherwise, we must make sure that each star has at least the minimum
+            # number of observations in _any_ band.
             if len(self.config.requiredBands) > 0:
-                n_req = np.zeros((len(self.config.requiredBands), len(stars)), dtype=np.int32)
-                for i, band in enumerate(self.config.requiredBands):
-                    (band_use,) = (sources[good_sources]["band"] == band).nonzero()
-                    np.add.at(
-                        n_req,
-                        (i, sources[good_sources]["obj_index"][band_use]),
-                        1,
-                    )
+                loop_bands = self.config.requiredBands
+            else:
+                loop_bands = np.unique(sources['band'])
+
+            n_req = np.zeros((len(loop_bands), len(stars)), dtype=np.int32)
+            for i, band in enumerate(loop_bands):
+                (band_use,) = (sources[good_sources]["band"] == band).nonzero()
+                np.add.at(
+                    n_req,
+                    (i, sources[good_sources]["obj_index"][band_use]),
+                    1,
+                )
+
+            if len(self.config.requiredBands) > 0:
+                # The min gives us the band with the fewest observations, which must be
+                # above the limit.
                 (good_stars,) = (n_req.min(axis=0) >= self.config.minPerBand).nonzero()
             else:
-                good_stars = np.arange(len(stars))
+                # The max gives us the band with the most observations, which must be
+                # above the limit.
+                (good_stars,) = (n_req.max(axis=0) >= self.config.minPerBand).nonzero()
 
             # With the following matching:
             #   sources[good_sources][b] <-> stars[good_stars[a]]