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HLA-1350/1240: Update background determination algorithm used for pro…
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…ducing output source catalogs (#1904)
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@mdlpstsci
mdlpstsci authored Oct 28, 2024
1 parent 5bf7780 commit 52f14a7
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16 changes: 14 additions & 2 deletions CHANGELOG.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,18 @@ number of the code change for that issue. These PRs can be viewed at:
3.7.2 (unreleased)
==================

- Change to the algorithm which chooses which background determination algorithm to
use for processing when working on the output source catalogs. If the RMS from
the Photutils Background2D is greater than the RMS from the astropy.stats
sigma_clipped_stats, then the background algorithm is now set to use the
sigma_clipped_stats. [#1904]

- Properly account for illuminated and non-illuminated pixels through the use
of masks when determining the background algorithm to use when working on the
output source catalogs. Use of the proper masks is particularly important for
the ACS/SBC data where the pixel is in the illuminated zone, but its value may
be zero. SBC is a MAMA photo-counting detector. (#1894)

- Modifications to support an upgrade to Photutils v1.13.0. Changes were made
to accommodate new APIs, modified low-level functionality, and address columns
of a table in get_cutouts() by name rather than position to ensure the correct
Expand All @@ -40,8 +52,8 @@ number of the code change for that issue. These PRs can be viewed at:
defined by full paths rather than being in the current working directory. [#1835]


3.7.1 (1-Oct-2024)
===================
3.7.1.1 (1-Oct-2024)
====================

- Improved S_REGION using simplify-polygon, eorions, and dilation. [#1323]

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33 changes: 22 additions & 11 deletions drizzlepac/haputils/catalog_utils.py
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Expand Up @@ -288,7 +288,7 @@ def compute_background(self, box_size, win_size,
self.bkg_type = 'zero_background'

is_zero_background_defined = True
log.info("Input image contains excessive zero values in the background. Median: {} RMS: {}".format(self.bkg_median, self.bkg_rms_median))
log.info(f"Input image contains excessive zero values in the background. Median: {self.bkg_median:.6f} RMS: {self.bkg_rms_median:.6f}")

# BACKGROUND COMPUTATION 2 (sigma_clipped_stats)
# If the input data is not the unusual case of SBC "excessive zero background", compute
Expand Down Expand Up @@ -319,7 +319,7 @@ def compute_background(self, box_size, win_size,
# Use the "raw" values generated by sigma_clipped_stats()
# based on full unmasked image
bkg_skew = np.abs(3.0 * (bkg_mean_full - bkg_median_full) / bkg_rms_full)
log.info("Sigma-clipped computed skewness: {0:.2f}".format(bkg_skew))
log.info(f"Sigma-clipped computed skewness: {bkg_skew:.2f}")


# Refine background to better compute the median value
Expand All @@ -332,28 +332,28 @@ def compute_background(self, box_size, win_size,
sigma=nsigma_clip,
cenfunc='median')

log.info("Sigma-clipped Statistics - Background mean: {} median: {} rms: {}".format(bkg_mean, bkg_median, bkg_rms))
log.info(f"Sigma-clipped Statistics - Background mean: {bkg_mean:.6f} median: {bkg_median:.6f} rms: {bkg_rms:.6f}")
log.info("")

# Ensure the computed values are not negative
if bkg_mean < 0.0 or bkg_median < 0.0 or bkg_rms < 0.0:
bkg_mean = max(0, bkg_mean)
bkg_median = max(0, bkg_median)
bkg_rms = max(0, bkg_rms)
log.info("UPDATED Sigma-clipped Statistics - Background mean: {} median: {} rms: {}".format(bkg_mean, bkg_median, bkg_rms))
log.info(f"UPDATED Sigma-clipped Statistics - Background mean: {bkg_mean:.6f} median: {bkg_median:.6f} rms: {bkg_rms:.6f}")
log.info("")

# Compute a minimum rms value based upon information directly from the data
if self.keyword_dict["detector"].upper() not in ['IR', 'SBC']:
if self.keyword_dict["detector"].upper() != 'SBC':
minimum_rms = self.keyword_dict['atodgn'] * self.keyword_dict['readnse'] \
* self.keyword_dict['ndrizim'] / self.keyword_dict['texpo_time']

# Compare a minimum rms based upon input characteristics versus the one computed and use
# the larger of the two values.
if (bkg_rms < minimum_rms):
bkg_rms = minimum_rms
log.info("Mimimum RMS of input based upon the readnoise, gain, number of exposures, and total exposure time: {}".format(minimum_rms))
log.info("Sigma-clipped RMS has been updated - Background mean: {} median: {} rms: {}".format(bkg_mean, bkg_median, bkg_rms))
log.info(f"Mimimum RMS of input based upon the readnoise, gain, number of exposures, and total exposure time: {minimum_rms:.6f}")
log.info(f"Sigma-clipped RMS has been updated - Background mean: {bkg_mean:.6f} median: {bkg_median:.6f} rms: {bkg_rms:.6f}")
log.info("")

# Generate two-dimensional background and rms images with the attributes of
Expand Down Expand Up @@ -400,6 +400,9 @@ def compute_background(self, box_size, win_size,
negative_threshold = negative_sigma * bkg.background_rms_median
break

log.info(f"Background2D - Background median: {bkg_median:.6f} rms: {bkg_rms_median:.6f}")
log.info("")

# If computation of a two-dimensional background image were successful, compute the
# background-subtracted image and evaluate it for the number of negative values.
#
Expand All @@ -416,16 +419,24 @@ def compute_background(self, box_size, win_size,
del imgdata_bkgsub

# Report this information so the relative percentage and the threshold are known
log.info("Percentage of negative values in the background subtracted image {0:.2f} vs low threshold of {1:.2f}.".format(100.0 * negative_ratio, negative_percent))
log.info(f"Percentage of negative values in the background subtracted image {100.0*negative_ratio:.2f} vs low threshold of {negative_percent:.2f}.")

# If the background subtracted image has too many negative values which may be
# indicative of large negative regions, the two-dimensional computed background
# fit image should NOT be used. Use the sigma-clipped data instead.
if negative_ratio * 100.0 > negative_percent:
log.info("Percentage of negative values {0:.2f} in the background subtracted image exceeds the threshold of {1:.2f}.".format(100.0 * negative_ratio, negative_percent))
log.info(f"Percentage of negative values {100.0*negative_ratio:.2f} in the background subtracted image exceeds the threshold of {negative_percent:.2f}.")
log.info("")
log.info("*** Use the background image determined from the sigma_clip algorithm. ***")

# If the rms computed for the Background2D is larger than the rms computed
# for the sigma-clipped algorithm, use the sigma-clipped algorithm instead.
# The sigma-clipped values have already been set in BACKGROUND COMPUTATION 2
# as the class attributes.
elif bkg_rms_median > self.bkg_rms_median:
log.info(f"Background2D rms, {bkg_rms_median:.6f}, is greater than sigma-clipped rms, {self.bkg_rms_median:.6f}.")
log.info("*** Use the background image determined from the sigma_clip algorithm. ***")

# Update the class variables with the background fit data
else:
self.bkg_background_ra = bkg_background_ra.copy()
Expand All @@ -449,8 +460,8 @@ def compute_background(self, box_size, win_size,
log.info("")
log.info("Computation of image background complete")
log.info("Found: ")
log.info(" Median background: {}".format(self.bkg_median))
log.info(" Median RMS background: {}".format(self.bkg_rms_median))
log.info(f" Median background: {self.bkg_median:.6f}")
log.info(f" Median RMS background: {self.bkg_rms_median:.6f}")
log.info("")

del bkg, imgdata
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