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[gnuastro-commits] master 06370a87 1/2: Book: fixing several minor typos
From: |
Mohammad Akhlaghi |
Subject: |
[gnuastro-commits] master 06370a87 1/2: Book: fixing several minor typos in the Clipping outliers section |
Date: |
Mon, 22 Jan 2024 04:17:48 -0500 (EST) |
branch: master
commit 06370a87a104d6fbe45a7daab1a97fa232552c63
Author: Raul Infante-Sainz <infantesainz@gmail.com>
Commit: Mohammad Akhlaghi <mohammad@akhlaghi.org>
Book: fixing several minor typos in the Clipping outliers section
Until this commit, there were some minor typos in the section of the Book
'Clipping outliers'.
With this commit, these typos have been corrected.
---
doc/gnuastro.texi | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 85fa7e31..1de64cf4 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -10937,12 +10937,14 @@ Let's look at them one by one (from the one that is
most affected to the least):
@item std.fits
The standard deviation (third image in DS9) is the most strongly affected
statistic by an outlier.
This is so strong that the edge of the circle is also clearly visible!
-The standard deviation is calculated by first finding th mean, and estimating
the difference of each element from the mean.
+The standard deviation is calculated by first finding the mean, and estimating
the difference of each element from the mean.
Those differences are then taken to the power of two and finally the square
root is taken (after a division by the number).
It is the power-of-two component that amplifies the effect of the single
outlier as you see here.
@item mean.fits
-The mean (first image in DS9) is also affected by the outlier such
+The mean (first image in DS9) is also affected by the outlier in such a way
that the circle footprint is clearly visible.
+This is because the nine images have the same importance in the combination
with a simple mean.
+Therefore, the outlier value pushes the result to higher values and the circle
is printed.
@item median.fits
The median (second image in DS9) is also affected by the outlier; although
much less significantly than the standard deviation or mean.
@@ -10958,7 +10960,7 @@ Therefore, using the 5th element (after sorting), we
are systematically choosing
With larger datasets, the difference between the central elements will be less.
However, the improved precision (in the elements without an outlier) will also
be more.
-A detailed analysis of the effect of a single outlier on the median based on
the number of inputs can be done as an excersize; but in general, as this
argument shows, the median is not immune to outliers; especially when you care
about low signal-to-noise signal (as we do in astronomy: low surface brightness
science).
+A detailed analysis of the effect of a single outlier on the median based on
the number of inputs can be done as an exercise; but in general, as this
argument shows, the median is not immune to outliers; especially when you care
about low signal-to-noise regimes (as we do in astronomy: low surface
brightness science).
@item mad.fits
The median absolute deviation (fourth image in DS9) is affected by outliers in
a similar fashion to the median.
@@ -10981,7 +10983,7 @@ $ astscript-fits-view build/collapsed-*.fits
@end example
The last command opens TOPCAT.
-In the ``Graphics'' menu, select plane plot and you will see all the values
fluctuating around zero (with a maximum/minimum around @mymath{\pm2}).
+In the ``Graphics'' menu, select plane plot and you will see all the values
fluctuating around 10 (with a maximum/minimum around @mymath{\pm2}).
Afterwards, click on the ``Layers'' menu and click on ``Add position control''.
In the opened tab at the bottom (where the scroll bar infront of ``Table'' is
empty), select the other table.
In the regions that there was no circle in any of the vertical axises, the two
match nicely (the noise level is the same).
@@ -11074,13 +11076,13 @@ When the outliers are as strong as above, the
outliers will be removed through t
@enumerate
@item
Calculate the standard deviation (@mymath{\sigma}) and median (@mymath{m}) of
a distribution.
-The median used because, as shown above, the mean is too significantly
affected by the presence of outliers.
+The median is used because, as shown above, the mean is too significantly
affected by the presence of outliers.
@item
Remove all points that are smaller or larger than @mymath{m\pm\alpha\sigma}.
@item
Go back to step 1, unless the selected exit criteria is reached.
There are commonly two types of exit criteria (to stop the
@mymath{\sigma}-clipping iteration).
-Within Gnuastro's programs that use sigma-clipping, the exit criteria is the
second value to the @option{--sclipparams} option (the first value is the
@mymath{m} above):
+Within Gnuastro's programs that use sigma-clipping, the exit criteria is the
second value to the @option{--sclipparams} option (the first value is the
@mymath{\alpha} above):
@itemize
@item