https://github.com/nunorc/astromlp

A framework for building deep learning models and pipelines for astrophysics applications.
https://github.com/nunorc/astromlp

astrophyics deep-learning

Last synced: 12 days ago
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A framework for building deep learning models and pipelines for astrophysics applications.

• Host: GitHub
• URL: https://github.com/nunorc/astromlp
• Owner: nunorc
• License: mit
• Created: 2021-11-12T09:00:08.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2023-03-11T13:35:08.000Z (almost 2 years ago)
• Last Synced: 2024-11-05T20:51:59.280Z (2 months ago)
• Topics: astrophyics, deep-learning
• Language: Python
• Homepage: https://nunorc.github.io/astromlp
• Size: 2.95 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

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README

        

astromlp

=====================================

**Experimental and under development!**

A framework for building deep learning models and pipelines for astrophysics applications.

You can explore the available pipelines and related models online from the `astromlp-app `_.

`Repository `_ | `Documentation `_

Installation

-------------------------------------

Install package from the git repository:

.. code-block:: bash

    $ pip install git+https://github.com/nunorc/astromlp@master

The collection of models available from the

`astromlp-models `_ repository is required,

quick clone:

.. code-block:: bash

    $ git clone https://github.com/nunorc/astromlp-models.git

And set the :code:`model_store` accordingly when necessary.

For development and exploring just clone **astromlp** recursively:

.. code-block:: bash

    $ git clone --recurse-submodules https://github.com/nunorc/astromlp.git

and run code from the repository root, **astromlp-models** is set as a `submodule` of the

**astromlp** repository, and this is the default location for :code:`model_store` when used.

Just make sure all the requirements are available in your environment, check the

`Installation `_ section for details.

Quick Start

-------------------------------------

Import pipelines for a specific topic, for example to import 

the :code:`One2One`, :code:`CherryPicked` and :code:`Universal` pipelines for galaxies characterization:

.. code-block:: python

    >>> from astromlp.galaxies import One2One, CherryPicked, Universal

Next, create an instance of the `One2One` pipeline, you may need to provide the location

of the `astromlp-models/model_store` directory where the actual models live,

for example:

.. code-block:: python

    >>> pipeline = One2One(model_store='./astromlp-models/model_store')

The galaxies pipelines are based on SDSS data, so the input to the pipeline

if an SDSS object identifier (`objid`), for example to process the object

`1237648720693755918 `_

using the selected pipeline run:

.. code-block:: python

    >>> result = pipeline.process(1237648720693755918)

The `result` object is an instance of :code:`PipelineResult`, the outputs of the pipeline

processing:

.. code-block:: python

    >>> result

    PipelineResult(redshift=0.0869317390024662, smass=23.44926865895589,

    subclass='STARFORMING', gz2c='ScR')

The :code:`PipelineResult` object implements other methods that provide extra data, namely:

- :code:`objid`: returns the SDSS object identifier;

- :code:`obj`: returns some information about the object from SDSS data;

- :code:`models`: returns the ensemble of models used;

- :code:`map`: returns the list of results of applying each individual model for each output.

You can easily create new ensembles of models using the :code:`MapReducPipeline` and passing the

list of outputs and corresponding models. For example, to create a pipeline that computes

the `redshift` using the `i2r` and `f2r` models:

.. code-block:: python

    >>> from astromlp.galaxies import MapReducePipeline

    >>> pipeline = MapReducePipeline({ 'redshift': ['i2r', 'f2r'] })

Acknowledgments

===============

Thank you to Dr. Andrew Humphrey for helping spawning this project and his contributions that helped improve this work.