Metadata-Version: 2.1
Name: cerespp
Version: 0.0.5
Summary: An extension to ceres.
Home-page: https://github.com/jvines/Ceres-plusplus
Author: Jose Vines
Author-email: jose.vines@ug.uchile.cl
Maintainer: Jose Vines
Maintainer-email: jose.vines@ug.uchile.cl
License: MIT
Description: # 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
        
        ```bash
        $ git clone https://github.com/jvines/Ceres-plusplus
        $ cd Ceres-plusplus
        $ python setup.py install
        ```
        
        ## Dependencies
        
        Ceres-plusplus depends on the following packages:
        
        - numpy ([https://numpy.org/](https://numpy.org/))
        - scipy ([https://www.scipy.org/](https://www.scipy.org/))
        - matplotlib (for plotting, but it's optional for the core functionality) ([https://matplotlib.org/](https://matplotlib.org/))
        - astropy ([https://www.astropy.org/](https://www.astropy.org/))
        - PyAstronomy ([https://pyastronomy.readthedocs.io/en/latest/](https://pyastronomy.readthedocs.io/en/latest/))
        - tqdm ([https://tqdm.github.io/](https://tqdm.github.io/))
        - termcolor ([https://pypi.org/project/termcolor/](https://pypi.org/project/termcolor/))
        
        ## 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
        
        ```python
        import cerespp
        import glob
        
        files = glob.glob('path/to/fits/files/*.fits')
        act, header = cerespp.get_activities(files, 'output/path/filename.dat')
        ```
        
        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:
        
        ```python
        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`.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Astronomy
Requires: numpy
Requires: scipy
Requires: matplotlib
Requires: astropy
Requires: PyAstronomy
Requires: tqdm
Requires: termcolor
Requires-Python: >=3.6
Description-Content-Type: text/markdown
