README.rst

# Introduction

This is the SDSS-V Local Volume Mapper (LVM) Data Analysis Pipeline (DAP) official repository.

The main and only script, lvm-dap, implements the Resolved Stellar Population method (Mejia-Narvaez+, in prep.). Instructions on how to run this code below.

# Usage

``` usage: lvm-dap [-h] [--input-fmt INPUT_FMT] [--error-file ERROR_FILE] [--config-file CONFIG_FILE]

[--emission-lines-file EMISSION_LINES_FILE] [--mask-file MASK_FILE] [--sigma-gas SIGMA_GAS] [--ignore-gas] [--rsp-nl-file RSP_NL_FILE] [--plot PLOT] [--flux-scale min max] [--w-range wmin wmax] [--w-range-nl wmin2 wmax2] [--redshift input_redshift delta_redshift min_redshift max_redshift] [--sigma input_sigma delta_sigma min_sigma max_sigma] [--AV input_AV delta_AV min_AV max_AV] [--ext-curve {CCM,CAL}] [--RV RV] [--single-rsp] [--n-mc N_MC] [-o path] [-c] [-v] [-d] spectrum-file rsp-file sigma-inst label

Run the spectral fitting procedure for the LVM

positional arguments:

spectrum-file input spectrum to fit rsp-file the resolved stellar population basis sigma-inst the standard deviation in wavelength of the Gaussian kernel to downgrade the resolution of the models to

match the observed spectrum. This is: sigma_inst^2 = sigma_obs^2 - sigma_mod^2

label string to label the current run

optional arguments:

-h, --help	show this help message and exit
--input-fmt INPUT_FMT
	the format of the input file. It can be either 'single' or 'rss'. Defaults to 'single'
--error-file ERROR_FILE
	the error file
--config-file CONFIG_FILE
	the configuration file used to set the parameters for the emission line fitting
--emission-lines-file EMISSION_LINES_FILE
	file containing emission lines list
--mask-file MASK_FILE
	the file listing the wavelength ranges to exclude during the fitting
--sigma-gas SIGMA_GAS
	the guess velocity dispersion of the gas
--ignore-gas	whether to ignore gas during the fitting or not. Defaults to False
--rsp-nl-file RSP_NL_FILE
	the resolved stellar population reduced basis, for non-linear fitting
--plot PLOT	whether to plot (1) or not (0, default) the fitting procedure. If 2, a plot of the result is store in a file without display on screen

--flux-scale min max scale of the flux in the input spectrum --w-range wmin wmax the wavelength range for the fitting procedure --w-range-nl wmin2 wmax2

the wavelength range for the non-linear fitting procedure

--redshift input_redshift delta_redshift min_redshift max_redshift

the guess, step, minimum and maximum value for the redshift during the fitting

--sigma input_sigma delta_sigma min_sigma max_sigma

same as the redshift, but for the line-of-sight velocity dispersion

--AV input_AV delta_AV min_AV max_AV

same as the redshift, but for the dust extinction in the V-band

--ext-curve {CCM,CAL}

the extinction model to choose for the dust effects modelling. Choices are: ['CCM', 'CAL']

--RV RV	total to selective extinction defined as: A_V / E(B-V). Default to 3.1
--single-rsp	whether to fit a single stellar template to the target spectrum or not. Default to False
--n-mc N_MC	number of MC realisations for the spectral fitting
-o path, --output-path path
	path to the outputs. Defaults to '/disk-a/mejia/Research/UNAM/lvm-dap'
-c, --clear-outputs
	whether to remove or not a previous run with the same label (if present). Defaults to false
-v, --verbose	if given, shows information about the progress of the script. Defaults to false.
-d, --debug	debugging mode. Defaults to false.

```

# Installation

We recommend installing in a virtual environment to avoid dependencies crashing. Some popular options are [miniconda](https://docs.conda.io/en/latest/miniconda.html), [venv](https://docs.python.org/3.8/library/venv.html), [pipenv](https://pipenv.pypa.io/en/latest/). We recommend venv.

Once you have created a virtual environment (if you chose to do so), simply run the following commands:

git clone [email protected]:chemical-evolution/lvm-dap.git cd lvm-dap pip install .

If you want to run the notebooks in the [testing notebooks](https://gitlab.com/chemical-evolution/lvm-dap/-/tree/master/noteboooks/dap-testing) section, you will need also to download the required data stored in [google drive](https://drive.google.com/drive/folders/1FwEGhTxnAyM7ld6nsSorG15Dq3LVH1I9?usp=sharing) into the lvm-dap directory. Ask for access to [email protected].

If the installation went successfully (and you downloaded the data) your tree directory should look like:

├── dist ├── _fitting-data ├── lvmdap ├── noteboooks ├── poetry.lock ├── pyproject.toml ├── README.md ├── README.rst └── setup.py

and you should be able to run the following example:

lvm-dap _fitting-data/simulations/ssps/fsps-ssp-mist-miles-1p00000_0p00100gyr.txt _fitting-data/_basis_mastar_v2/stellar-basis-spectra-100.fits.gz 0.33283937056926377 1p00000_0p00100gyr --mask-file _fitting-data/_configs/MaNGA/mask_elines.txt --emission-lines-file _fitting-data/_configs/MaNGA/emission_lines_long_list.MaNGA --w-range 3600 10000 --w-range-nl 3600 4700 --redshift 0 0 0 0 --sigma 0 0 0 0 --AV 0 0 0 0

which will produce the following output files:

1p00000_0p00100gyr 1p00000_0p00100gyr.autodetect.8400_9999.conf coeffs_1p00000_0p00100gyr 1p00000_0p00100gyr.autodetect.3600_5199.conf 1p00000_0p00100gyr.autodetect.auto_ssp_several.config elines_1p00000_0p00100gyr 1p00000_0p00100gyr.autodetect.5200_6799.conf 1p00000_0p00100gyr.autodetect.emission_lines.txt output.1p00000_0p00100gyr.fits.gz 1p00000_0p00100gyr.autodetect.6800_8399.conf 1p00000_0p00100gyr.autodetect.mask_elines.txt

# Examples

You can get familiar with the full spectral analysis implemented in lvm-dap either running the notebooks in the notebooks folder or running the following example in the console:

lvm-dap CS.LMC_043.RSS.fits.gz _fitting-data/_basis_mastar_v2/stellar-basis-spectra-100.fits.gz 2.31 test --input-fmt rss --error-file e_CS.LMC_043.RSS.fits.gz --rsp-nl-file _fitting-data/_basis_mastar_v2/stellar-basis-spectra-5.fits.gz --w-range 4800 8000 --w-range-nl 4800 6000 --redshift 0.000875 0 -0.5 0.5 --sigma 0 0 0 350 --AV 0 0.01 0 1.6 --sigma-gas 3.7 --emission-lines-file _fitting-data/_configs/MaNGA/emission_lines_long_list.txt -c

This will analyse the MUSE-LMC pointing 43 in RSS format and produce the outputs in the same format as pyFIT3D.