[gnuastro-commits] master 5a7a254 1/2: No outlier rejection in NoiseChisel's Sky and Sky STD

[Mohammad Akhlaghi]

branch: master
commit 5a7a254a4365657bd4c980ccb448de7804c242ce
Author: Mohammad Akhlaghi <address@hidden>
Commit: Mohammad Akhlaghi <address@hidden>

    No outlier rejection in NoiseChisel's Sky and Sky STD
    
    Until now, we were doing outlier rejection on all three stages that
    NoiseChisel needs values over the whole tessellation (the quantile
    thresholds, the pseudo-detection Sky and its STD as well as the final Sky
    and its STD). But for the latter two it was too much and infact outliers
    are infact necessary (and must not be removed). Otherwise we won't be able
    to subtract gradients.
---
 NEWS                  | 14 +++++++-------
 bin/noisechisel/sky.c | 10 ++--------
 doc/gnuastro.texi     | 17 ++++-------------
 3 files changed, 13 insertions(+), 28 deletions(-)

diff --git a/NEWS b/NEWS
index 52f7c28..0ada736 100644
--- a/NEWS
+++ b/NEWS
@@ -13,15 +13,15 @@ GNU Astronomy Utilities NEWS                          -*- 
outline -*-
     --onedasimage: write output as an image if it has one dimension, not table.
 
   NoiseChisel:
-    - New outlier identification algorithm for quantile threshold and Sky
-      estimation. It is useful when there are extended and bright sources
-      in the dataset: the tiles containing very faint signal that pass the
-      general pixel-value distribution test due to the flatness of the
-      extended profiles (outliers), can be identified and removed in
-      comparison with the other passed tiles. The outlier finding algorithm
+    - New outlier identification algorithm for quantile thresholds. This is
+      very useful when there are extended and bright sources in the
+      dataset: the tiles containing very faint signal that pass the general
+      pixel-value distribution test due to the flatness of the extended
+      profiles, can be identified and removed as outliers in comparison
+      with the other passed tiles. The outlier finding algorithm
       (`gal_statistics_outlier_positive': a new library function) uses the
       distribution of distances between the sorted elements and is
-      configured with these three options.
+      configured with these options.
        --outliersclip: Sigma-clipping parameters for the process.
        --outliersigma: Multiple of sigma to define an outlier.
 
diff --git a/bin/noisechisel/sky.c b/bin/noisechisel/sky.c
index 0e23b44..582f0bb 100644
--- a/bin/noisechisel/sky.c
+++ b/bin/noisechisel/sky.c
@@ -214,14 +214,8 @@ sky_and_std(struct noisechiselparams *p, char *checkname)
     }
 
 
-  /* Remove the outliers. */
-  if(p->outliersigma!=0.0)
-    gal_tileinternal_no_outlier(p->sky, p->std, NULL, tl, p->outliersclip,
-                                p->outliersigma, checkname);
-
-
-  /* Set the blank checked bit of the ararys to zero, most probably there
-     are tiles with too much signal or outliers. */
+  /* Set the blank-checked bit of the arrays to zero so we are sure to
+     check for blanks. */
   p->sky->flag &= ~GAL_DATA_FLAG_BLANK_CH;
   p->std->flag &= ~GAL_DATA_FLAG_BLANK_CH;
 
diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index a6c87a1..05e4488 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -16146,25 +16146,16 @@ quantile is between 0.49 and 0.51 (recall that the 
median's quantile is
 @item --outliersclip=FLT,FLT
 Sigma-clipping parameters for the outlier rejection of the quantile
 threshold. The format of the given values is similar to
address@hidden below. In NoiseChisel, outlier rejection is used in
-three stages:
address@hidden
address@hidden
-Identifying the quantile thresholds (@option{--qthresh},
address@hidden below. In NoiseChisel, outlier rejection on tiles is
+used when identifying the quantile thresholds (@option{--qthresh},
 @option{--noerodequant}, and @option{detgrowquant}).
address@hidden
-Identifying the first estimate of the Sky and its standard deviation values
-for pseudo-detections (@option{--dthresh}).
address@hidden
-Identifying the final estimate of the Sky and its standard deviation.
address@hidden itemize
 
 Outlier rejection is useful when the dataset contains a large and diffuse
 (almost flat within each tile) signal. The flatness of the profile will
 cause it to successfully pass the mean-median quantile difference test, so
 we'll need to use the distribution of successful tiles for removing these
-false positive. For more, see the latter half of @ref{Quantifying signal in
-a tile}.
+false positives. For more, see the latter half of @ref{Quantifying signal
+in a tile}.
 
 @item --outliersigma=FLT
 Multiple of sigma to define an outlier. If this option is given a value of