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tickets/DM-12431: re-Verify performance of matchPessimisticB with new distortions. #79

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merged 5 commits into from
Jan 30, 2018
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tickets/DM-12431: re-Verify performance of matchPessimisticB with new distortions. #79

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af07887
Added sigma clipping to final matches.
morriscb Dec 22, 2017
b85af8d
Separated softening of pattern and max dist softening loops.
morriscb Jan 12, 2018
9db913f
Responded to reviewer comments.
morriscb Jan 25, 2018
9301154
Add clarity to simg clipping comment.
morriscb Jan 29, 2018
bddc773
Fix bug in match softening loop.
morriscb Jan 30, 2018
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10 changes: 6 additions & 4 deletions python/lsst/meas/astrom/matchPessimisticB.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import numpy as np
from scipy.spatial import cKDTree
from scipy.stats import sigmaclip

import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
Expand Down Expand Up @@ -490,15 +491,16 @@ def _doMatch(self, refCat, sourceCat, wcs, refFluxField, numUsableSources,
# we need to remove as many false positives as possible before sending
# the matches off to the solver.
distances_arcsec = np.degrees(matcher_struct.distances_rad) * 3600
clip_max_dist = np.max(
(sigmaclip(distances_arcsec, low=100, high=2)[-1],
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What's the justification for these values of low and high? They just seem like magic numbers here.

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The are the number of sigma for the clip. Low is 100 because the lower bound is always a value of 0 and high is a 2 sigma cut. I will add a comment explaining this. I could also make the high value a configurable if that is useful.

self.config.minMatchDistPixels * wcs.pixelScale().asArcseconds())
)
# We pick the largest of the the unsoftened maxMatchDistArcSec or
# a user specified distance in pixels. This prevents the
# AstrometryTask._matchAndFitWCS from over-fitting to a small number of
# objects and also allows the WCS fitter to bring in more matches as
# the WCS fit improves.
dist_cut_arcsec = np.max(
(self.config.minMatchDistPixels * wcs.pixelScale().asArcseconds(),
maxMatchDistArcSec)
)
dist_cut_arcsec = np.min((clip_max_dist, maxMatchDistArcSec))
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Seems like this makes the comment above untrue, right? You are no longer picking the largest of anything, but the smallest of the sigma clipped cut and maxMatchDistArcSec.


# A match has been found, return our list of matches and
# return.
Expand Down