scikit-fda

Functional Data Analysis Python package

https://github.com/gaa-uam/scikit-fda

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Repository

Functional Data Analysis Python package

README.rst

.. image:: https://raw.githubusercontent.com/GAA-UAM/scikit-fda/develop/docs/logos/title_logo/title_logo.png
	:alt: scikit-fda: Functional Data Analysis in Python

scikit-fda: Functional Data Analysis in Python
==============================================

|build-status| |docs| |Codecov| |repostatus| |versions| |PyPIBadge| |conda| |license| |doi|

Functional Data Analysis, or FDA, is the field of Statistics that analyses
data that depend on a continuous parameter.

This package offers classes, methods and functions to give support to FDA
in Python. Includes a wide range of utils to work with functional data, and its
representation, exploratory analysis, or preprocessing, among other tasks
such as inference, classification, regression or clustering of functional data.
See documentation for further information on the features included in the
package.

Documentation
=============

The documentation is available at
`fda.readthedocs.io/en/stable/ `_, which
includes detailed information of the different modules, classes and methods of
the package, along with several examples_ showing different functionalities.

The documentation of the latest version, corresponding with the develop
version of the package, can be found at
`fda.readthedocs.io/en/latest/ `_.

How do I start?
===============

If you want a quick overview of the package, we recommend you to try the
new :doc:`tutorial `. For articles about specific
topics, feel free to explore the :doc:`examples `. Want
to check the documentation of a particular class or function? Try searching
for it in the :doc:`API list `.

Installation
============

Currently, *scikit-fda* is available in Python versions above 3.8, regardless of the
platform.
The stable version can be installed via PyPI_:

.. code::

    pip install scikit-fda

It is also available from conda-forge_:

.. code::

    conda install -c conda-forge scikit-fda

Installation from source
------------------------

It is possible to install the latest version of the package, available in the
develop branch,  by cloning this repository and doing a manual installation.

.. code:: bash

    git clone https://github.com/GAA-UAM/scikit-fda.git
    pip install ./scikit-fda

Make sure that your default Python version is currently supported, or change
the python and pip commands by specifying a version, such as ``python3.8``:

.. code:: bash

    git clone https://github.com/GAA-UAM/scikit-fda.git
    python3.8 -m pip install ./scikit-fda

Requirements
------------
*scikit-fda* depends on the following packages:

* `fdasrsf `_ - SRSF framework
* `findiff `_ - Finite differences
* `matplotlib `_ - Plotting with Python
* `multimethod `_ - Multiple dispatch
* `numpy `_ - The fundamental package for scientific computing with Python
* `pandas `_ - Powerful Python data analysis toolkit
* `rdata `_ - Reader of R datasets in .rda format in Python
* `scikit-datasets `_ - Scikit-learn compatible datasets
* `scikit-learn `_ - Machine learning in Python
* `scipy `_ - Scientific computation in Python
* `setuptools `_ - Python Packaging

The dependencies are automatically installed.

Citing scikit-fda
=================

Please, if you find this software useful in your work, reference it citing the following paper:

.. code-block::

  @article{ramos-carreno++_2024_scikit-fda,
    author = {Ramos-Carreño, Carlos and Torrecilla, José L. and Carbajo Berrocal, Miguel and Marcos Manchón, Pablo and Suárez, Alberto},
    doi = {10.18637/jss.v109.i02},
    journal = {Journal of Statistical Software},
    month = may,
    number = {2},
    pages = {1--37},
    title = {{scikit-fda: A Python Package for Functional Data Analysis}},
    url = {https://www.jstatsoft.org/article/view/v109i02},
    volume = {109},
    year = {2024}
  }


You can additionally cite the software repository itself using:

.. code-block::

  @misc{ramos-carreno++_2024_scikit-fda-repo,
    author = {The scikit-fda developers},
    doi = {10.5281/zenodo.3468127},
    month = feb,
    title = {scikit-fda: Functional Data Analysis in Python},
    url = {https://github.com/GAA-UAM/scikit-fda},
    year = {2024}
  }

If you want to reference a particular version for reproducibility, check the version-specific DOIs available in Zenodo.

Contributions
=============
All contributions are welcome. You can help this project grow in multiple ways,
from creating an issue, reporting an improvement or a bug, to doing a
repository fork and creating a pull request to the development branch.

The people involved at some point in the development of the package can be
found in the `contributors
file `_.

License
=======

The package is licensed under the BSD 3-Clause License. A copy of the
license_ can be found along with the code.

.. _examples: https://fda.readthedocs.io/en/latest/auto_examples/index.html
.. _PyPI: https://pypi.org/project/scikit-fda/
.. _conda-forge: https://anaconda.org/conda-forge/scikit-fda/

.. |build-status| image:: https://github.com/GAA-UAM/scikit-fda/actions/workflows/tests.yml/badge.svg?event=push
    :alt: Build status
    :target: https://github.com/GAA-UAM/scikit-fda/actions/workflows/tests.yml

.. |docs| image:: https://readthedocs.org/projects/fda/badge/?version=latest
    :alt: Documentation Status
    :target: http://fda.readthedocs.io/en/latest/?badge=latest

.. |Codecov| image:: https://codecov.io/gh/GAA-UAM/scikit-fda/branch/develop/graph/badge.svg
    :alt: Code coverage through Codecov
    :target: https://app.codecov.io/gh/GAA-UAM/scikit-fda

.. |repostatus| image:: https://www.repostatus.org/badges/latest/active.svg
   :alt: Project Status: Active - The project has reached a stable, usable state and is being actively developed.
   :target: https://www.repostatus.org/#active
   
.. |versions| image:: https://img.shields.io/pypi/pyversions/scikit-fda
   :alt: PyPI - Python versions supported

.. |PyPIBadge| image:: https://badge.fury.io/py/scikit-fda.svg
   :alt: Available in Pypi
   :target: https://pypi.org/project/scikit-fda

.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/scikit-fda
    :alt: Available in Conda
    :target: https://anaconda.org/conda-forge/scikit-fda

.. |license| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
    :alt: BSD 3-Clause license
    :target: https://github.com/GAA-UAM/scikit-fda/blob/develop/LICENSE.txt

.. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468127.svg
    :alt: Available in Zenodo
    :target: https://doi.org/10.5281/zenodo.3468127

Acknowledgements
================

The project has received financial support from projects PID2019-109387GB-I00,
PID2019-106827GB-I00, and PID2022-139856NB-I00, funded by
MCIN/ AEI / 10.13039/501100011033 / FEDER, UE,
project IDEA-CM (TEC-2024/COM-89) from the Autonomous Community of Madrid,
and from the ELLIS Unit Madrid.  The authors acknowledge computational support
from the Centro de Computación Científica-Universidad Autónoma de Madrid
(CCC-UAM).

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Ramos-Carreño"
    given-names: "Carlos"
    orcid: "https://orcid.org/0000-0003-2566-7058"
    affiliation: "Universidad Autónoma de Madrid"
    email: vnmabus@gmail.com
  - family-names: "Suárez"
    given-names: "Alberto"
    orcid: "https://orcid.org/0000-0003-4534-0909"
    affiliation: "Universidad Autónoma de Madrid"
  - family-names: "Torrecilla"
    given-names: "José Luis"
    orcid: "https://orcid.org/0000-0003-3719-5190"
    affiliation: "Universidad Autónoma de Madrid"
  - family-names: "Carbajo Berrocal"
    given-names: "Miguel"
  - family-names: "Marcos Manchón"
    given-names: "Pablo"
  - family-names: "Pérez Manso"
    given-names: "Pablo"
  - family-names: "Hernando Bernabé"
    given-names: "Amanda"
  - family-names: "García Fernández"
    given-names: "David"
  - family-names: "Hong"
    given-names: "Yujian"
  - family-names: "Rodríguez-Ponga Eyriès"
    given-names: "Pedro Martín"
  - family-names: "Sánchez Romero"
    given-names: "Álvaro"
  - family-names: "Petrunina"
    given-names: "Elena"
  - family-names: "Castillo"
    given-names: "Álvaro"
  - family-names: "Serna"
    given-names: "Diego"
  - family-names: "Hidalgo"
    given-names: "Rafael"
  - family-names: "del Val"
    given-names: "David"
  - family-names: "Sánchez Signorini"
    given-names: "Martín"
  - family-names: "Pinto"
    given-names: "Óscar"
  - family-names: "Cuesta Sierra"
    given-names: "Pablo"
  - family-names: "Soto Martín"
    given-names: "Pablo"
  - family-names: "Terres Caballero"
    given-names: "Eduardo"
title: "GAA-UAM/scikit-fda: Functional Data Analysis in Python"
doi: 10.5281/zenodo.3468127
date-released: 2019-10-01
url: "https://github.com/GAA-UAM/scikit-fda"
license: BSD-3-Clause
keywords:
  - functional data analysis
  - machine learning
  - Python
  - scikit
identifiers:
  - description: "This is the collection of archived snapshots of all versions of scikit-fda"
    type: doi
    value: 10.5281/zenodo.3468127
  - description: "This is the archived snapshot of version 0.3 of scikit-fda"
    type: doi
    value: 10.5281/zenodo.3468128
  - description: "This is the archived snapshot of version 0.4 of scikit-fda"
    type: doi
    value: 10.5281/zenodo.3957915
  - description: "This is the archived snapshot of version 0.5 of scikit-fda"
    type: doi
    value: 10.5281/zenodo.4406983
  - description: "This is the archived snapshot of version 0.6 of scikit-fda"
    type: doi
    value: 10.5281/zenodo.5502108
  - description: "This is the archived snapshot of version 0.7.1 of scikit-fda"
    type: doi
    value: 10.5281/zenodo.5903557
preferred-citation:
  type: article
  title: "scikit-fda: A Python Package for Functional Data Analysis"
  authors:
    - family-names: "Ramos-Carreño"
      given-names: "Carlos"
      orcid: "https://orcid.org/0000-0003-2566-7058"
      affiliation: "Universidad Autónoma de Madrid"
      email: vnmabus@gmail.com
    - family-names: "Torrecilla"
      given-names: "José L."
      orcid: "https://orcid.org/0000-0003-3719-5190"
      affiliation: "Universidad Autónoma de Madrid"
      email: joseluis.torrecilla@uam.es
    - family-names: "Carbajo Berrocal"
      given-names: "Miguel"
    - family-names: "Marcos Manchón"
      given-names: "Pablo"
    - family-names: "Suárez"
      given-names: "Alberto"
      orcid: "https://orcid.org/0000-0003-4534-0909"
      affiliation: "Universidad Autónoma de Madrid"
  date-published: 2024-05-08
  abstract: "The library scikit-fda is a Python package for functional data analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in Python's scientific ecosystem. In particular, it conforms to the scikit-learn application programming interface so as to take advantage of the functionality for machine learning provided by this package: Pipelines, model selection, and hyperparameter tuning, among others. The scikit-fda package has been released as free and open-source software under a 3-clause BSD license and is open to contributions from the FDA community. The library's extensive documentation includes step-by-step tutorials and detailed examples of use."
  doi: 10.18637/jss.v109.i02
  institution:
    name: "Universidad Autónoma de Madrid"
  issn: "1548-7660"
  issue-date: "2024-05-08"
  journal: "Journal of Statistical Software"
  keywords:
    - "functional data analysis"
    - "computational statistics"
    - "interactive data visualization"
    - "Python"
    - "scikit-learn"
  languages:
    - en
  license: CC-BY-3.0
  publisher:
    name: "Foundation for Open Access Statistics"
  url: "https://www.jstatsoft.org/article/view/v109i02"
  volume: 109
  issue: 2
  start: 1
  end: 37
  

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