An extension to ceres.

# Ceres-plusplus

This package was written as an extension to the CERES reduction pipeline (https://github.com/rabrahm/ceres) in the sense that it takes spectra reduced by it and extracts some activity indicators (CCF FWHM, BIS, CONTRAST) and calculates others (S index, Ha, HeI, NaID1D2)

It's been tested to work on FEROS and FIDEOS spectra. Feel free to use it with other instruments and let me know if it works :)

## Installation

You can try running pip install cerespp

If that fails you can clone the repository with

$git clone https://github.com/jvines/Ceres-plusplus$ cd Ceres-plusplus
\$ python setup.py install


## Dependencies

Ceres-plusplus depends on the following packages:

## Usage

Usage is simple, start by importing cerespp, optionally you can use glob to fetch the files. After importing (and having ready your files) the get_activities function, grab a coffee, and wait for your indicators!.

Below there's an example script

import cerespp
import glob

files = glob.glob('path/to/fits/files/*.fits')


Here files is a list with the fits files to process, the output act and header are the activities and the header of the file (i.e. the names of the columns). cerespp automatically saves the output in a file in the desired location, but if you need the output on the session you're working in (in a jupyter notebook, for example), that's what act and header are for ;)

## Plotting

cerespp offers some plotting tools to visually check things! The most important ones are plots showing the activity lines and their surroundings. Creating these is easy:

import cerespp
import glob

# These are the available lines. You can choose which ones to plot here
lines = ['CaHK', 'Ha', 'HeI', 'NaID1D2']

files = glob.glob('path/to/fits/files/*.fits')

# This function creates the plots from a fits file directly
cerespp.line_plot_from_file(files[0], lines, 'output/path/', 'starname')


There's a notebook exemplifying the usage in the examples folder!

## How it works

cerespp first calculates a radial velocity to correct the spectrum to rest-frame, after this has been done it extracts the available data from the fits headers (CCF FWHM, BIS, CONTRAST) and finally it merges the echelle orders and computes the activity indicators!

The radial velocity is computed using the standard cross-correlation function method, and thus you can specify which mask to use with the mask keyword in get_activities. Available masks are G2, K0, K5, and M2.

## Project details

Uploaded source