STATCONT is a python-based tool designed to determine the continuum emission level in line-rich spectral data. The tool inspects the intensity distribution of a given spectrum and automatically determines the continuum level by using differeng statistical approaches. The different methods included in STATCONT have been tested against synthetic data. We conclude that the sigma-clipping algorithm provides the most accurate continuum level determination, together with information on the uncertainty in its determination. This uncertainty is used to correct the final continuum emission level, resulting in the here-called 'corrected sigma-clipping method' or cSCM. The cSCM has been tested against synthetic data cubes reproducing typical conditions found in astronomical line-rich sources. In general, we obtain accuracies of < 10 % in the continuum determination, and < 5 % in most cases. The main products of STATCONT are the continuum emission level, together with its uncertainty, and data cubes containing only spectral line emission, i.e. continuum-subtracted data cubes. STATCONT also includes the option to estimate the spectral index or variation of the continuum emission with frequency.
If you find STATCONT useful, please cite/refer to: Sanchez-Monge, Schilke, Ginsburg, Cesaroni and Schmiedeke 2018, A&A, 609, A101 Access to the article in PDF format.
More information about STATCONT can be found in http://www.astro.uni-koeln.de/~sanchez/statcont
This README document is intented to help you install and use STATCONT. In the following you will find:
- Installation instructions
- Required Python packages
- Main STATCONT commands and options
- Examples and test cases
- Real data examples
- Publications citing STATCONT
STATCONT uses the ASTROPY package-template and is fully compatible with the ASTROPY ecosystem. It is freely available for download at the GitHub repository Radio Astro Tools, as well as in this webpage. The only required software to use STATCONT is Python and Astropy, together with some basic Python packages that you can find listed here.
Installation method 1
You can clone STATCONT in your computer from the GitHub repository. For
this, create a directory and move there. Then type:
git init
git clone https://github.com/radio-astro-tools/statcont
cd statcont
python setup.py install
Installation method 2
You can also directly install STATCONT by typing in a terminal session in
your computer (you may need sudo permissions depending on the
configuration of your system):
pip install https://github.com/radio-astro-tools/statcont/archive/master.zip
Installation method 3
Alternatively, STATCONT can also be downloaded locally as a zip file
from: master.zip.
In order to install it, download the file to a directory in your computer:
unzip master.zip
cd statcont
python setup.py install
How to solve permission errors
If you get a permission error, this means that you do not have the required
administrative access to install STATCONT to your Python installation. In
this case you can use the option --user when installing the setup.py script.
You can follow these instructions:
python setup.py install --user --install-scripts="~/bin/"
export PATH=~/bin/:$PATH
Check your installation
Following the installation, you have immediate access to STATCONT in
your computer by typing "statcont" in a terminal session. For example,
inspect the help by doing:
statcont --help
At the beginning of the help message you will see the version of STATCONT.
The current stable release is version 1.5.
STATCONT uses the following Python packages:
- argparse : version >= 1.1
- astropy : version >= 4.2.1
- datetime
- math
- matplotlib : version >= 3.5.0
- numpy : version >= 1.21.4
- os
- scipy : version >= 1.7.3
The versions indicated above have been recently tested, older versions worked fine in previous tests and are expected to continue working.
The following table contains the main commands and options of STATCONT. They can be explored and executed by typing
statcont --help
| Command | Necessary? | Description |
|---|---|---|
--help |
Show the help message and all the STATCONT commands | |
-i / --iname |
NECESSARY (unless -f, -s or -l are used) |
Name of the FITS file to be processed (without extension, which should be .fits) Example: -i SYNTHETIC_cubeThe file SYNTHETIC_cube.fits has to be stored in the data directory |
-f / --ifile |
NECESSARY (unless -i, -s or -l are used) |
Text file containing a list of FITS files to be processed (files to be processed have to be listed without extension, which should be .fits) Example: -f file.txtThe file file.txt has to be stored in the running directory |
-s / --ispec |
NECESSARY (unless -i, -f or -l are used) |
Name of the ASCII file to be processed (without extension, which should be .dat) Example: -s my_emissionThe file my_emission.dat has to be stored in the data directory |
-l / --ilist |
NECESSARY (unless -i, -f or -s are used) |
Text file containing a list of ASCII files to be processed (files to be processed have to be listed without extension, which should be .dat) Example: -l file.txtThe file file.txt has to be stored in the running directory |
-p / --ipath |
OPTIONAL | Specify the path/directory there the files to be processed are stored. This path/directory has to inside the data directory |
-n / --noise |
NECESSARY | Typical RMS noise level of the observations A value of 1 can be used in most of the cases |
--continuum |
OPTIONAL | Determination of the continuum level and create of line-only cube or spectrum |
--plots |
OPTIONAL | Create plots on a pixel-by-pixel basis |
--cutout |
OPTIONAL | Create a cutout image of the original FITS cube Three parameters have to be specified ( xcen, ycen and size) in pixels |
--spindex |
OPTIONAL | Determine the spectral index (ALPHA) defined as flux = FACTOR * frequency^(ALPHA) |
In the following we explain how to execute the main tasks of STATCONT. A set of test cases is provided in this test_cases.tar.gz file. Download the file to your computer and follow these instructions:
gunzip test_cases.tar.gz
tar -xvf test_cases.tar
This creates a directory called statcont-tests. Inside, you will
find a directory called data that contains three other subdirectories
MAP_TESTS, SPEC_TESTS, and SPINDEX
STATCONT requires of a directory data where the files to be processed
are stored. By executing STATCONT, another directory called products
will be generated. The files to be processed, can be directly saved in
the data directory or in subdirectories within data. In the examples
provided here, we have a set of single-spectrum files (in ASCII format)
saved in the subdirectories SPEC_TESTS and SPINDEX, and a FITS cube
in the subdirectory MAP_TESTS.
statcont -p SPEC_TESTS -s my_emission -n 1
- The option -p indicates the subdirectory in data that contains the file to be analyzed
- The option -s indicates the name of the ASCII file to be analyzed, without the extension [.dat]
- The option -n indicates the rms noise level (in the units of the data) of the data to be analyzed. In this case, it is 1 K
If you want to determine the continuum level:
statcont -p SPEC_TESTS -s my_emission -n 1 --continuum
- The option --continuum makes use of the 'corrected sigma-clipping algorithm' described in Sanchez-Monge et al (2018), to determine the continuum level, the error in the continuum level, and to produce a file that contains only the line emission, i.e. a continuum-subtracted file
Using different methods to determine the continuum level. STATCONT contains a set of different statistical methods that can be used by the user at his/her convenience. You can select all them like this:
statcont -p SPEC_TESTS -s my_emission -n 1 --call
Or you can select individual methods like:
statcont -p SPEC_TESTS -s my_emission -n 1 --cmax
statcont -p SPEC_TESTS -s my_emission -n 1 --cmean
statcont -p SPEC_TESTS -s my_emission -n 1 --cmedian
statcont -p SPEC_TESTS -s my_emission -n 1 --cpercent
statcont -p SPEC_TESTS -s my_emission -n 1 --cGaussian
statcont -p SPEC_TESTS -s my_emission -n 1 --cKDEmax
statcont -p SPEC_TESTS -s my_emission -n 1 --csigmaclip
You can call several methods at once:
statcont -p SPEC_TESTS -s my_emission -n 1 --cmax --cGaussian --csigmaclip
The different methods are explained in Sanchez-Monge et al (2018)
If you want to remove the continuum from the original spectrum, in order to produce a line-only data file, you can use:
statcont -p SPEC_TESTS - s my_emission -n 1 --csigmaclip --cfree
- The option --cfree uses the 'corrected sigma-clipping algorithm' to determine the continuum, and removes it from the original datafile.
You can use other example files, like for example:
statcont -p SPEC_TESTS -s my_absorption -n 1 --continuum
And you can select multiple files simultaneously, as long as they are saved in the same subdirectory, and they are considered to have the same rms noise level (option -n ):
statcont -p SPEC_TESTS -s my_emission my_absorption my_broad-lines -n 1 --continuum
The products can be found in products/SPEC_TESTS You can produce plots of the spectrum analyzed with the continuum levels by using the option --plots. As an example:
statcont -p SPEC_TESTS -s my_emission -n 1 --continuum --plots
In this case the plot is saved in products/SPEC_TESTS/plots/my_emission_1_1.png You can use all the continuum methods and plot them all together, like:
statcont -p SPEC_TESTS -s my_emission -n 1 --call --plots
Have a look now at the plot products/SPEC_TESTS/plots/my_emission_1_1.png (see image below)
statcont -p MAP_TESTS -i SYNTHETIC_cube -n 1
- The option -p indicates the subdirectory in data that contains the file to be analyzed
- The option -i indicates the name of the FITS file to be analyzed, without the extension [.fits]
- The option -n indicates the rms noise level (in the units of the data) of the data to be analyzed. In this case, it is 1 K
If you want to determine the continuum level:
statcont -p MAP_TESTS -i SYNTHETIC_cube -n 1 --continuum
This process analyzes each individual pixel, determining the continuum level, and then combines all the pixels to produce a continuum FITS image with the label "_continuum". Simultaneously, the --csigmaclip method provides information on the error in the determination of the continuum that is saved as a FITS image with the label "_noise". Finally, a line-only FITS datacube is also produced. All these files are saved in the directory products/MAP_TESTS
All the other options applicable to the single-spectrum ASCII files are also available for the FITS images (e.g. different continuum methods, creation of plots).
If your original FITS file is too large and you just want to determine the continuum level of a small portion you can indicate it like this:
statcont -p MAP_TESTS -i SYNTHETIC_cube -n 1 --continuum --cutout 25 25 6
- The --cutout option allows to select a central pixel (in this case 25, 25) and the number of pixels in each direction of the final image (in this case 6). With this option, the products are saved with the label "_cutout"
If you have multiple ASCII or FITS files at different frequencies, you can use
the option --spindex to determine, first, the continuum level of every
single file, and then the spectral index, i.e. the variation of the
continuum emission with frequency.
The spectral index (ALPHA) is defined as flux = FACTOR * frequency^(ALPHA)
statcont -p SPINDEX -l list.txt -n 1 --continuum
statcont -p SPINDEX -l list.txt -n 1 --spindex
The first command determines the continuum level for all the files contained in the list.txt file, while the second command determines the spectral index.
If you have multiple ASCII or FITS files at different frequencies, you can use
 |
 |
|---|---|
| Example case SgrB2(N) (see Sanchez-Monge et al 2017) | Example case G29.96-0.02 (see Cesaroni et al 2017) |
In both examples: (Top-left) Continuum emission map as determined with STATCONT. (Top-right) Continuum emission map determined with the classical approach (search for line-free channels). (Middle-left) Noise map obtained with STATCONT. (Middle-right) Ratio of the STATCONT to classical-approach continuum maps. (Bottom panels) Continuum-subtracted spectra using STATCONT towards two selected positions A and B, shown in the top-left panel.
The following is a list with more than 30 publications using STATCONT in their analysis. The publications cover topics of low and high-mass star formation, extragalactic sources and astrochemical studies.
-
Is There Any Linkage between Interstellar Aldehyde and Alcohol?
by Mondal et al. 2021, ApJ, 922, 194 (link) -
A cold accretion flow onto one component of a multiple protostellar system
by Murillo et al. 2021, accepted for publication in A&A (link) -
Starburst Energy Feedback Seen Through HCO + /HOC + Emission in NGC 253 from ALCHEMI
by Harada et al. 2021, accepted for publication in ApJ (link) -
The GUAPOS project. II. A comprehensive study of peptide-like bond molecules
by Colzi et al. 2021, A&A, 653, A129 (link) -
ALCHEMI: an ALMA Comprehensive High-resolution Extragalactic Molecular Inventory. Survey presentation and first results from the ACA array
by Martin et al. 2021, accepted for publication in A&A (link) -
ALMA observations of doubly deuterated water: inheritance of water from the prestellar environment
by Jensen et al. 2021, A&A, 650, A172 (link) -
The ionized heart of a molecular disk. ALMA observations of the hyper-compact HII region G24.78+0.08 A1
by Moscadelli et al. 2021, A&A, 650, A142 (link) -
Star formation in 'the Brick': ALMA reveals an active protocluster in the Galactic centre cloud G0.253+0.016
by Walker et al. 2021, MNRAS, 503, 77 (link) -
Fragmentation in the massive G31.41+0.31 protocluster
by Beltran et al. 2021, A&A, 648, A100 (link) -
Digging into the Interior of Hot Cores with ALMA (DIHCA). I. Dissecting the High-mass Star-forming Core G335.579-0.292 MM1
by Olguin et al. 2021, ApJ, 909, 199 (link) -
The prebiotic molecular inventory of Serpens SMM1. I. An investigation of the isomers CH3NCO and HOCH2CN
by Ligterink et al. 2021, A&A, 647, A87 (link) -
Subarcsecond Imaging of the Complex Organic Chemistry in Massive Star-forming Region G10.6-0.4
by Law et al. 2021, ApJ, 909, 214 (link) -
The GUAPOS project: G31.41+0.31 Unbiased ALMA sPectral Observational Survey. I. Isomers of C2H4O2
by Mininni et al. 2020, A&A, 644, A84 (link) -
Multidirectional Mass Accretion and Collimated Outflows on Scales of 100-2000 au in Early Stages of High-mass Protostars
by Goddi et al. 2020, ApJ, 905, 25 (link) -
Evidence for Dense Gas Heated by the Explosion in Orion KL
by Li et al. 2020, ApJ, 901, 62 (link) -
Detection of hydroxyacetone in protostar IRAS 16293-2422 B
by Zhou et al. 2020, RAA, 20, 125 (link) -
Constraints of the Formation and Abundances of Methyl Carbamate, a Glycine Isomer, in Hot Corinos
by Sahu et al. 2020, ApJ, 899, 65 (link) -
Astrochemistry During the Formation of Stars
by Jorgensen et al. 2020, ARA&A, 58, 727 (link) -
Exploring the formation pathways of formamide. Near young O-type stars
by Allen et al. 2020, A&A, 636, A67 (link) -
The HI/OH/Recombination line survey of the inner Milky Way (THOR): data release 2 and H I overview
by Wang et al. 2020, A&A, 634, A83 (link) -
Survey Observation of CH 3 NH 2 and Its Formation Process
by Suzuki et al. 2019, submitted to ApJ (link) -
Molecular analysis of a high-mass prestellar core candidate in W43-MM1
by Molet et al. 2019, A&A, 626, A132 (link) -
The CARMA-NRO Orion Survey. Filamentary structure as seen in C18O emission
by Suri et al. 2019, A&A, 623, A142 (link) -
A 10-M⊙ YSO with a Keplerian disk and a nonthermal radio jet
by Moscadelli et al. 2019, A&A, 622, A206 (link) -
Evidence for the First Extragalactic Hydrogen Recombination Line Maser in NGC 253
by Baez-Rubio et al. 2018, ApJL, 867, L6 (link) -
Chasing discs around O-type (proto)stars. ALMA evidence for an SiO disc and disc wind from G17.64+0.16
by Maud et al. 2018, A&A, 620, A31 (link) -
The Extraordinary Outburst in the Massive Protostellar System NGC 6334I-MM1: Flaring of the Water Masers in a North-South Bipolar Outflow Driven by MM1B
by Brogan et al. 2018, ApJ, 866, 87 (link) -
The feedback of an HC HII region on its parental molecular core. The case of core A1 in the star-forming region G24.78+0.08
by Moscadelli et al. 2018, A&A, 616, A66 (link) -
Accelerating infall and rotational spin-up in the hot molecular core G31.41+0.31
by Beltran et al. 2018, A&A, 615, A141 (link) -
The physical and chemical structure of Sagittarius B2 - III. Radiative transfer simulations of the hot core Sgr B2(M) for methyl cyanide
by Pols et al. 2018, A&A, 614, A123 (link) -
Radio outburst from a massive (proto)star. When accretion turns into ejection
by Cesaroni et al. 2018, A&A, 612, A103 (link) -
Distributed Star Formation throughout the Galactic Center Cloud Sgr B2
by Ginsburg et al. 2018, ApJ, 853, 171 (link) -
The physical and chemical structure of Sagittarius B2 - II. Continuum millimeter emission of SgrB2(M) and SgrB2(N) with ALMA
by Sanchez-Monge et al. 2017, A&A, 604, A6 (link) -
Chasing disks around O-type (proto)stars: Evidence from ALMA observations
by Cesaroni et al. 2017, A&A, 602, A59 (link)