[gnuastro-commits] master d0cc2c1f: Book: pseudo changed to synthetic when talking about filters

[Mohammad Akhlaghi]

branch: master
commit d0cc2c1f24bef2d64214e708195dec09c096d62b
Author: Mohammad Akhlaghi <mohammad@akhlaghi.org>
Commit: Mohammad Akhlaghi <mohammad@akhlaghi.org>

    Book: pseudo changed to synthetic when talking about filters
    
    Until now, when we were talking about collapsing a cube into a narrow-band
    image, we used the term "pseudo". But the term "pseudo" usually has a
    negative/fake coonation.
    
    With this commit, this term has been replaced by "Synthetic" which fits the
    concept better.
---
 doc/gnuastro.texi | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/doc/gnuastro.texi b/doc/gnuastro.texi
index 8ac5e2cb..c425e0b5 100644
--- a/doc/gnuastro.texi
+++ b/doc/gnuastro.texi
@@ -334,7 +334,7 @@ Detecting lines and extracting spectra in 3D data
 * 3D measurements and spectra::  Measuring 3d properties including spectra.
 * Extracting a single spectrum and plotting it::  Extracting a single vector 
row.
 * Cubes with logarithmic third dimension::  When the wavelength/frequency is 
logarithmic.
-* Pseudo narrow-band images::   Collapsing the third dimension into a 2D image.
+* Synthetic narrow-band images::   Collapsing the third dimension into a 2D 
image.
 
 Color images with full dynamic range
 
@@ -2059,7 +2059,7 @@ Because all conditions are under control in a 
simulated/mock environment/dataset
 But they need to be as realistic as possible, so this tutorial is dedicated to 
this important step of an analysis (simulations).
 
 There are other tutorials also, on things that are commonly necessary in 
astronomical research:
-In @ref{Detecting lines and extracting spectra in 3D data}, we use MUSE cubes 
(an IFU dataset) to show how you can subtract the continuum, detect 
emission-line features, extract spectra and build pseudo narrow-band images.
+In @ref{Detecting lines and extracting spectra in 3D data}, we use MUSE cubes 
(an IFU dataset) to show how you can subtract the continuum, detect 
emission-line features, extract spectra and build synthetic narrow-band images.
 In @ref{Color channels in same pixel grid} we demonstrate how you can warp 
multiple images into a single pixel grid (often necessary with multi-wavelength 
data), and build a single color image.
 In @ref{Moire pattern in stacking and its correction} we show how you can 
avoid the unwanted Moir@'e pattern which happens when warping separate 
exposures to build a stacked/co-add deeper image.
 In @ref{Zero point of an image} we review the process of estimating the zero 
point of an image using a reference image or catalog.
@@ -7938,7 +7938,7 @@ $ wget http://akhlaghi.org/data/a370-crop.fits    # 
Downloads 287 MB
 
 In the sections below, we will first review how you can visually inspect a 3D 
data cube in DS9 and interactively see the spectra of any region.
 We will then subtract the continuum emission, detect the emission-lines within 
this cube and extract their spectra.
-We will finish with creating pseudo narrow-band images optimized for some of 
the emission lines.
+We will finish with creating synthetic narrow-band images optimized for some 
of the emission lines.
 
 @menu
 * Viewing spectra and redshifted lines::  Interactively see the spectra of an 
object
@@ -7948,7 +7948,7 @@ We will finish with creating pseudo narrow-band images 
optimized for some of the
 * 3D measurements and spectra::  Measuring 3d properties including spectra.
 * Extracting a single spectrum and plotting it::  Extracting a single vector 
row.
 * Cubes with logarithmic third dimension::  When the wavelength/frequency is 
logarithmic.
-* Pseudo narrow-band images::   Collapsing the third dimension into a 2D image.
+* Synthetic narrow-band images::   Collapsing the third dimension into a 2D 
image.
 @end menu
 
 @node Viewing spectra and redshifted lines, Sky lines in optical IFUs, 
Detecting lines and extracting spectra in 3D data, Detecting lines and 
extracting spectra in 3D data
@@ -8620,7 +8620,7 @@ Of course, the table in @file{spectrum-obj-198.fits} can 
be plotted using any ot
 In the next section (@ref{Cubes with logarithmic third dimension}), we'll 
review the necessary modifications to the recipes in this section for cubes 
where the third dimension is logarithmic, not linear (as in MUSE cubes).
 Finally, in @ref{Cubes with logarithmic third dimension}, you'll see how you 
can make narrow-band images of your desired target around your desired emission 
line.
 
-@node Cubes with logarithmic third dimension, Pseudo narrow-band images, 
Extracting a single spectrum and plotting it, Detecting lines and extracting 
spectra in 3D data
+@node Cubes with logarithmic third dimension, Synthetic narrow-band images, 
Extracting a single spectrum and plotting it, Detecting lines and extracting 
spectra in 3D data
 @subsection Cubes with logarithmic third dimension
 In @ref{Extracting a single spectrum and plotting it}, a single object's 
spectrum was extracted from the catalog and plotted.
 Extracting the wavelength of each slice was easy there because MUSE data cubes 
provide a linear third dimension.
@@ -8646,12 +8646,12 @@ $ asttable cat.fits --head=1 -csum-in-slice --transpose 
\
                                e p pow '$lr' x s'
 @end example
 
-@node Pseudo narrow-band images,  , Cubes with logarithmic third dimension, 
Detecting lines and extracting spectra in 3D data
-@subsection Pseudo narrow-band images
+@node Synthetic narrow-band images,  , Cubes with logarithmic third dimension, 
Detecting lines and extracting spectra in 3D data
+@subsection Synthetic narrow-band images
 
 In @ref{Continuum subtraction} we subtracted/separated the continuum from the 
emission/absorption lines of our galaxy in the MUSE cube.
 Let's visualize the morphology of the galaxy at some of the spectral lines to 
see how it looks.
-To do this, we will create pseudo narrow-band 2D images by collapsing the cube 
along the third dimension within a certain wavelength range that is optimized 
for that flux.
+To do this, we will create synthetic narrow-band 2D images by collapsing the 
cube along the third dimension within a certain wavelength range that is 
optimized for that flux.
 
 Let's find the wavelength range that corresponds to H-alpha emission we 
studied in @ref{Extracting a single spectrum and plotting it}.
 Fortunately MakeCatalog can calculate the minimum and maximum position of each 
label along each dimension like the command below.
@@ -8684,7 +8684,7 @@ $ astscript-fits-view crop-no-continuum.fits
 @end example
 
 Go through the slices and you will only see this particular region of the full 
cube.
-We can now collapse the third dimension of this image into a 2D pseudo-narrow 
band image with Arithmetic's @ref{Dimensionality changing operators}:
+We can now collapse the third dimension of this image into a 2D 
synthetic-narrow band image with Arithmetic's @ref{Dimensionality changing 
operators}:
 
 @example
 $ astarithmetic crop-no-continuum.fits 3 collapse-sum \
@@ -8694,7 +8694,7 @@ $ astscript-fits-view collapsed-all.fits
 @end example
 
 During the collapse, used all the pixels in each slice.
-This is not good for the faint outskirts in the peak of the emission line: the 
noise of the slices with less signal decreases the over-all signal-to-noise 
ratio in the pseudo-narrow band image.
+This is not good for the faint outskirts in the peak of the emission line: the 
noise of the slices with less signal decreases the over-all signal-to-noise 
ratio in the synthetic-narrow band image.
 So let's set all the pixels that aren't labeled with this object as NaN, then 
collapse.
 To do that, we first need to crop the @code{OBJECT} cube in @file{seg.fits}.
 With the second command, please have a look to confirm how the labels change 
as a function of wavelength.
@@ -8796,7 +8796,7 @@ In the ``Layers'' menu, select ``Add Position Control''.
 You will see that at the bottom half, a new scatter plot information is 
displayed.
 @item
 Click on the scroll-down menu in front of ``Table'' and select @file{2: 
collapsed-obj-rad.fits}.
-Afterwards, you will see the optimized pseudo-narrow-band image radial profile 
as blue points.
+Afterwards, you will see the optimized synthetic-narrow-band image radial 
profile as blue points.
 @end enumerate
 
 @node Color images with full dynamic range, Zero point of an image, Detecting 
lines and extracting spectra in 3D data, Tutorials
@@ -14604,7 +14604,7 @@ $ echo $my_std
 This @command{eval}-based solution has been tested in in GNU Bash, Dash and 
Zsh and it works nicely in them (is ``portable'').
 This is because the constructs used here are pretty low-level (and widely 
available).
 
-For examples usages of this technique, see the following sections: 
@ref{Extracting a single spectrum and plotting it} and @ref{Pseudo narrow-band 
images}.
+For examples usages of this technique, see the following sections: 
@ref{Extracting a single spectrum and plotting it} and @ref{Synthetic 
narrow-band images}.
 
 @node Truncating start of long string FITS keyword values,  , Separate shell 
variables for multiple outputs, Shell tips
 @subsubsection Truncating start of long string FITS keyword values
@@ -22793,7 +22793,7 @@ Therefore, when the WCS is important for later 
processing, be sure that the inpu
 @cindex Narrow-band image
 @cindex IFU: Integral Field Unit
 @cindex Integral field unit (IFU)
-One common application of this operator is the creation of pseudo broad-band 
or narrow-band 2D images from 3D data cubes.
+One common application of this operator is the creation of synthetic 
broad-band or narrow-band 2D images from 3D data cubes.
 For example, integral field unit (IFU) data products that have two spatial 
dimensions (first two FITS dimensions) and one spectral dimension (third FITS 
dimension).
 The command below will collapse the whole third dimension into a 2D array the 
size of the first two dimensions, and then convert the output to 
single-precision floating point (as discussed above).