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The new `--colormap' option was added to the call to Gnuastro's ConvertType
program. Since Gnuastro 0.8, ConvertType needs this option for converting a
single-channel dataset to a color-supporting format.
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Since the current implementation of this pipeline officially started in
2018, all the files only had 2018 in their copyright years. This has now
been corrected to 2018-2019.
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Until now, we were keeping the input file within the reproduction
pipeline's directories using the same name as the database/server. Now, we
are using a short/summarized filename convention for the input dataset.
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In most analysis situations (except for simulations), an input dataset is
necessary, but that part of the pipeline was just left out and a general
`SURVEY' variable was set and never used. So with this commit, we actually
use a sample FITS file from the FITS standard webpage, show it (as well as
its histogram) and do some basic calculations on it.
This preparation of the input datasets is done in a generic way to enable
easy addition of more datasets if necessary.
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To enable easy/proper reproduction of results, all the high-level
dependencies are now built within the pipeline and installed in a fixed
directory that is added to the PATH of the Makefile. This includes GNU Bash
and GNU Make, which are then used to run the pipeline.
The `./configure' script will first build Bash and Make within itself, then
it will build
All the dependencies are also built to be static. So after they are built,
changing of the system's low-level libraries (like C library) won't change
the tarballs.
Currently the C library and C compiler aren't built within the pipeline,
but we'll hopefully add them to the build process also.
With this change, we now have full control of the shell and Make that will
be used in the pipeline, so we can safely remove some of the generalities
we had before.
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Different implementations of AWK may use different random number
generators, so even setting the seed will not ensure a reproducible
result. Because of this, the random plot may be different when the
pipeline runs on different systems and this can confuse early users
(its contrary to the exact reproducibility that is the whole purpose
of this pipeline).
The plot is just a simple X^2 plot, showing the squared value of the X
axis on the Y axis. It is very simple, but atleast it will be
identical on all systems. Also, there may be too many complicated
things in the pipeline already for an early user, and its just a
demonstration, so the easier/simpler, the better.
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Making plots and including references are integral parts of a scientific
paper. Therefore to demonstrate how cleanly they can be used within the
pipeline, they are now used to produce the final PDF.
To use PGFPlots a random dataset is made (using AWK's random function) and
is plotted using PGFPlots. The minimum and maximum values of the dataset
are also included in the text to further show how such calculations can go
into the macros and text.
For the references, the NoiseChisel paper was added as a reference to cite
when using this pipeline along with the MUSE UDF paper I, which uses this
pipeline for two sections. Following this discussion, citation is also
discussed in `README.md` and the NoiseChisel paper is also added as a
published work with a reproduction pipeline.
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