j0y1 / pyfosc

PyFOSC is a pipeline toolbox for spectroscopic data reduction written in Python. It can be used for FOSC data from Xinglong/Lijiang 2-meter telescopes.

PyFOSC

PyFOSC is a pipeline toolbox for long-slit spectroscopy data reduction written in Python. It can be used for FOSC (Faint Object Spectrograph and Camera) data from Xinglong/Lijiang 2-meter telescopes.

BFOSC (Beijing-Faint Object Spectrograph and Camera) is an instrument of the 2.16-m Telescope in Xinglong Observatory, National Astronomical Observatories, Chinese Academy of Sciences (NAOC) (IAU code: 327, coordinates: 40°23′39″ N, 117°34′30″ E). For more information about BFOSC, please see http://www.xinglong-naoc.org/html/en/gcyq/216/detail-18.html.

YFOSC (Yunnan-Faint Object Spectrograph and Camera) is an instrument of the 2.4-m Telescope in Lijiang Observatory, Yunnan Observatories, Chinese Academy of Sciences (YNAO) (IAU code: O44, coordinates: 26°42′33.1″ N, 100°1′51.6″ E). For more information about YFOSC, please see http://wiki.gmg.org.cn/pages/viewpage.action?pageId=557106.

1. Installation

1.1. Dependencies

IRAF
PyRAF
pandas

This package depends on IRAF and PyRAF. Although these two softwares will no longer be supported by their developers, you can still download and install them from AstroConda using its 'Legacy Software Stack'.

1.2. Install IRAF and PyRAF from astroconda

For detailed description on how to install IRAF and PyRAF from astroconda, you can either go to the documentation of AstroConda's Legacy Software Stack with IRAF, or my blog post How to install and initiate IRAF in Ubuntu/Mac OS. The minimal installation can be done with:

conda config --add channels http://ssb.stsci.edu/astroconda
conda create -n iraf27 python=2.7 iraf-all pyraf-all stsci
conda install -n iraf27 pandas

Note that every time you want to use IRAF/PyRAF and PyFOSC, you need to activate the env 'iraf27' that you have created:

source activate iraf27

And to deactivate, run:

source deactivate

1.3. Download PyFOSC and set environment variable for it.

You can use git clone to download this package.

git clone https://github.com/rudolffu/pyfosc.git

In order to run PyFOSC commands in the terminal, you need to add the path of PyFOSC and its sub-directory src to $PATH, by editing ~/.bashrc (Linux, e.g., Ubuntu) or ~/.bash_profile (Mac OS). An example of this can be:

export PATH=/Your/Path/to/pyfosc:$PATH
export PATH=/Your/Path/to/pyfosc/src:$PATH

2. Usage

2.1 Preparation

2.1.1 Run pyfosc_init to begin

First go to the working directory which contains FOSC spectroscopic data, activate iraf27 env and mkiraf.

source activate iraf27 (#or conda activate iraf27)
mkiraf

Select xgterm when the terminal prompts. Then run pyfosc_init from iraf27 env:

pyfosc_init

2.1.2 Update headers for BFOSC data obtained with 2.16-m Telescope, Xinglong Station, NAOC (BAO).

Activate iraf27 env and run addheaderinfo.py.

source activate iraf27
addheaderinfo.py

2.1.3 Generate lists of files

As PyFOSC pipeline reduce data grouped by different types (bias, flat, object, etc), make sure you have lists of fits files as follows:

zero.list --------------- List of bias files (e.g. bias*.fits).
flat.list --------------- List of flat field files (e.g. flat*.fits).
objall.list ------------- List of 2d spectra images of all objects (science targets and standard stars).
lampall.list ------------ List of all 2d lamp spectra images.
flatnall.list ----------- List of 2d images that need zero correction (flat.list + lampall.list + objall.list).
specall.list ------------ List of all 2d spectra images (objall.list + lampall.list).

The pyfosc$iraf_scripts directory contains template cl scripts that can be executed in IRAF/PyRAF to generate these lists. Users can modify the scripts for specific cases.

2.2 Running the pipeline

You can run pyfosc_run.sh from iraf27 env:

pyfosc_run.sh

Alternatively, you can run the scripts step by step, following the order as:

makezero2m.py     # Combine zero(bias) frames.
ccdotz.py         # Do zero(bias), overscan correction and trimming.
makeflat2m.py     # Combine flat fields.
makereflat2m.py   # Do (illumination) normalization and get perfect flat.
divideflat2m.py   # Do flat correction.
removecr2m.py     # Remove cosmic rays in two-d images. Please go to 3. Credits for more information.
doapall.py        # Extract spectra.
reidentlamp2m.py  # Reidentify lamp spectra with previously stored ones.
#Can use identlamp2m.py instead to identify lamp by oneself.
wavecal2m.py      # Do wavelength calibration, flux calibration.
telluric_base2m.py # Telluric correction and one-d spectra extraction.

Plot all final 1d spectra with:

plotonedsp.py

Move some intermediate files into INTMD directory:

mvintmd.sh

3. Credits

This software uses BSD 3-Clause License.

Copyright (c) 2019, Yuming Fu
All rights reserved.

This software contains sources from third-party softwares.

The pyfosc$iraf_data/onedstds directory is from IRAF, and it contains standard calibration data for extinction and sensitivity calibration.

The pyfosc$iraf_task/lacos_im.cl script is the IRAF version for Laplacian Cosmic Ray Identification by Pieter G. van Dokkum (Yale) from http://www.astro.yale.edu/dokkum/lacosmic/. L.A.Cosmic is an algorithm for robust cosmic ray identification. It detects cosmic rays of arbitrary shapes and sizes, and distinguishes between undersampled point sources and cosmic rays. If you use this program please refer to P. G. van Dokkum, 2001, PASP, 113, 1420.

Please see COPYRIGHT file and pyfosc$doc/LICENSES directory for detailed copyright information.

4. How to cite

Yuming Fu. (2020, June 29). PyFOSC: a pipeline toolbox for BFOSC/YFOSC long-slit spectroscopy data reduction (Version v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.3915021

Cite the current version (v1.0.1) with bibtex:

@software{yuming_fu_2020_3915021,
  author       = {Yuming Fu},
  title        = {{PyFOSC: a pipeline toolbox for BFOSC/YFOSC long-slit spectroscopy data reduction}},
  month        = jun,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.3915021},
  url          = {https://doi.org/10.5281/zenodo.3915021}
}