petrofit 0.6.0
Python package for calculating Petrosian properties and fitting galaxy light profiles.
Requires Python: >=3.12
Keywords: fitting, galaxy, light, petrosian, photometry, profile


.. image:: https://github.com/PetroFit/petrofit/raw/main/docs/images/petrofit_full_logo.png
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PetroFit
--------
|repostatus tag| |CI tag| |rtd tag| |pyOpenSci| |PyPI tag| |AJ tag| |zonodo tag| |python version tag| |CodeCov| |astropy tag| |photutils tag| 

PetroFit is a package for calculating Petrosian properties, such as radii and concentration indices, as well as fitting
galaxy light profiles. In particular, PetroFit includes tools for performing accurate photometry, segmentations,
Petrosian profiling, and Sérsic fitting. Please see the `petrofit documentation <https://petrofit.readthedocs.io/en/latest/>`_
for installation instructions and a guide to the ``petrofit`` module.

.. image:: https://github.com/PetroFit/petrofit/raw/main/docs/images/multi_fit.png
    :width: 100%

Installation
------------

You can install PetroFit using pip or build a `jupyter lab <https://jupyter.org/>`_ environment using `hatch <https://hatch.pypa.io/latest/>`_ . Please see
the `petrofit documentation <https://petrofit.readthedocs.io/en/latest/>`_ for detailed installation instructions.

**pip Install**: 

.. code-block:: bash

    pip install petrofit

**hatch  Jupyter Lab**:

cd into the dir you want to launch jupyter lab in, and then run:

.. code-block:: bash
    
    hatch run jupyter:lab

Examples
--------

Please see the `petrofit documentation <https://petrofit.readthedocs.io/en/latest/>`_
for detailed examples, a quick stat guide, and instructions.

**Image Fitting**: 

PetroFit can be used with ``astropy`` models to fit psf convolved galaxy light profiles. Given a 2D image (``image``) and a psf (``PSF``), the following code snippet demonstrates how to fit a Sérsic model to the image:

.. code-block:: python

    import petrofit as pf
    from astropy.modeling import models

    sersic_model = models.Sersic2D(
            amplitude=1,
            r_eff=10,
            n=4,
            x_0=0, y_0=0,
            ellip=0.2,
            theta=25,
            bounds=pf.get_default_sersic_bounds(),
    )

    psf_sersic_model = pf.PSFConvolvedModel2D(
        sersic_model, psf=PSF, oversample=4, psf_oversample=1
    )

    fitted_model, fit_info = pf.fit_model(
        image, psf_sersic_model,
    )

**Photometry and Petrosian**: 

Given a 2D image (``image``) the following code snippet demonstrates how to create a Petrosian profile:

.. code-block:: python

    import petrofit as pf
    
    # Make a segmentation map and 
    # catalog using Photutils wrapper
    cat, segm, segm_deblend = pf.make_catalog(
        image,
        threshold=image.std()*3,
        wcs=None, deblend=True,
        npixels=4**2, nlevels=30, contrast=0.001,
    )

    # Photomerty on first source in catalog
    r_list = pf.make_radius_list(max_pix=50, n=50)
    flux_arr, area_arr, error_arr = pf.source_photometry(
        cat[0], # Source (`photutils.segmentation.catalog.SourceCatalog`)
        image, # Image as 2D array
        segm_deblend, # Deblended segmentation map of image
        r_list, # list of aperture radii
        cutout_size=max(r_list)*2, # Cutout out size, set to double the max radius
    )

    # Make a Petrosian profile 
    p = pf.Petrosian(r_list, area_arr, flux_arr)
    print("{:0.4f} pix".format(p.r_half_light))


Citation
--------

Please see the `PetroFit citation documentation <https://petrofit.readthedocs.io/en/latest/citing.html>`_
for citation instructions. This information is also available in the ``CITATION.rst`` 
file in the PetroFit repo.

License
-------

This project is Copyright (c) The PetroFit Team and licensed under
the terms of the BSD 3-Clause license. This package is based upon
the `Astropy package template <https://github.com/astropy/package-template>`_
which is licensed under the BSD 3-clause license. See the licenses folder for
more information.

Generative AI Policy
--------------------

To maintain scientific rigor and reproducibility, no generative AI-produced code 
is permitted within this repository. All code contributions must be manually authored 
or closely supervised to ensure our standards. Generative AI can be used, however, for 
documentation, issues, and PR descriptions (i.e ``git`` and GitHub workflow).

Contributing
------------

We love contributions! petrofit is open source,
built on open source, and we'd love to have you hang out in our community.

**Imposter syndrome disclaimer**: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not
ready to be an open source contributor; that your skills aren't nearly good
enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at
all, you can contribute code to open source. Contributing to open source
projects is a fantastic way to advance one's coding skills. Writing perfect code
isn't the measure of a good developer (that would disqualify all of us!); it's
trying to create something, making mistakes, and learning from those
mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can
help out by writing documentation, tests, or even giving feedback about the
project (and yes - that includes giving feedback about the contribution
process). Some of these contributions may be the most valuable to the project as
a whole, because you're coming to the project with fresh eyes, so you can see
the errors and assumptions that seasoned contributors have glossed over.

Note: This disclaimer was originally written by
`Adrienne Lowe <https://github.com/adriennefriend>`_ for a
`PyCon talk <https://www.youtube.com/watch?v=6Uj746j9Heo>`_, and was adapted by
petrofit based on its use in the README file for the
`MetPy project <https://github.com/Unidata/MetPy>`_.


Acknowledgments
---------------
We thank Dr. Gregory D. Wirth, Dr. D. J. Pisano, and Dr. Alan Kahn for their guidance and support throughout this project. 
We would also like to thank the developers of Photutils, as much of this work is built on top of the tools they provid. 
This work was made possible by NASA grant AR 15058. Lastly, we give thanks to the Space Telescope Science Institute and 
the Technical Staff Research Committee (TSRC), a group that facilitates matching research projects with interested technical staff.

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