void-dwarf-analysis

Table of Contents

• About
• Getting Started
  • Prerequisites
  • Installation
• Contact
• Acknowledgements

About

This is a pipeline for producing maps of kinematic properties (velocity and velocity dispersion), emission line fluxes, and gas-phase metallicities from KCWI datacubes.

Before running this analysis pipeline, these datacubes must be first be reduced by the KCWI Data Reduction Pipeline. You may also want to stack multiple exposures; I used this code by Yuguang Chen to do this.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

Here are the main Python packages required and the versions used:

• astropy (4.0)
• cwitools (0.7)
• matplotlib (3.2.1)
• numpy (1.18.2)
• pandas (1.0.3)
• ppxf (7.0.1)
• vorbin (3.1.4)

Note that this is not an exhaustive list! The easiest way to install the full list of required packages is to create a conda environment using the enclosed kcwiredux_env.yml file:

conda env create -f kcwiredux_env.yml

Note that this file lists v0.6 of cwitools, but functions from v0.7 are needed. You may want to clone directly from Donal O'Sullivan's github repository.

Installation

Follow the usual steps to clone the repo:

git clone https://github.com/mdlreyes/void-dwarf-analysis.git

Usage

The redux folder includes the main reduction pipeline, which is run as follows:

• The params file contains reduction and plotting parameters for each of the galaxies.
  • The covariance parameters (alpha, beta, N) can be estimated using the CWITools framework when multiple exposures are stacked. This can also be done using the kcwiutils subfolder (in particular, the kcwialign.py script; you will need to create a list file in the lists subdirectory to do this).
• The script kcwiredux.py can be used to run the main reduction script and to make the final plots.
• If you would like to estimate errors on kinematic parameters using Monte Carlo error estimation (i.e., perturbing the spectra by errors, then running the reduction over many iterations), kcwiredux_mc_mpi.py does this, using multiprocessing to speed up the process.

The analysis folder includes all scripts made to make plots using the kinematic properties obtained from redux.

The Jupyter notebook example.ipynb shows how the plots in (de los Reyes et al., submitted) were made. (example.html and example.pdf provide alternative formats to view this notebook.)

Contact

Mia de los Reyes - @MiaDoesAstro - mdlreyes@stanford.edu

Project Link: https://github.com/mdlreyes/void-dwarf-analysis

Acknowledgements

README.md template