[gnuastro-commits] master 584fbd3: Cartouche on --convolved in large object detection tutuorial

[Mohammad Akhlaghi]

branch: master
commit 584fbd3235fee10af683b0932d009cbd64791335
Author: Mohammad Akhlaghi <address@hidden>
Commit: Mohammad Akhlaghi <address@hidden>

    Cartouche on --convolved in large object detection tutuorial
    
    Unlike the demo image used in the tutorial, images with such extended
    objects are usually very large and the processing can be very slow. To
    speed things up, a small cartouche (box) was added to the tutoral, briefly
    describing `--convolved' option to avoid convolution.
---
 doc/gnuastro.texi | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 80d8186..9dadb37 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -3700,14 +3700,13 @@ detecting such targets using @ref{NoiseChisel}.
 
 @cartouche
 @noindent
address@hidden't start with this tutorial:} If you haven't already done the
address@hidden't start with this tutorial:} If you haven't already completed
 @ref{General program usage tutorial}, we strongly recommend going through
 that tutorial before starting this one. Basic features like access to this
 book on the command-line, the configuration files of Gnuastro's programs,
-benefiting from the modular nature of the programs, or viewing
-multi-extension FITS files easily, are discussed in much better detail
-there. Doing that tutorial first will thus help you better understand and
-benefit from this tutorial.
+benefiting from the modular nature of the programs, viewing multi-extension
+FITS files, or using NoiseChisel's outputs are discussed in more detail
+there.
 @end cartouche
 
 @cindex M51
@@ -3865,6 +3864,18 @@ values. Finally in the last extension 
(@code{QTHRESH-APPLIED}), you can see
 the effect of applying @code{QTHRESH_ERODE} on @code{CONVOLVED} (pixels
 with a value of 0 were below the threshold).
 
address@hidden
address@hidden
address@hidden convolution for faster tests:} The slowest step of
+NoiseChisel is the convolution of the input dataset. Therefore when your
+dataset is large (unlike the one in this test), and you are not changing
+the input dataset or kernel in multiple runs (as in the tests of this
+tutorial), it is faster to do the convolution separately once (using
address@hidden) and use NoiseChisel's @option{--convolved} option to
+directly feed the convolved image and avoid convolution. For more on
address@hidden, see @ref{NoiseChisel input}.
address@hidden cartouche
+
 Fortunately this image is large and has a nice and clean region also
 (filled with very small/distant stars and galaxies). So our first solution
 is to increase the tile size. To identify the skewness caused by NGC 5195