| Name | FDApy JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | A Python package to analyze functional data. |
| upload_time | 2024-09-01 17:23:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.9 |
| license | MIT License Copyright (c) 2018 Steven Golovkine Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
functional
data
analysis
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
===================================================
FDApy: a Python package to analyze functional data
===================================================
.. image:: https://img.shields.io/pypi/pyversions/FDApy
:target: https://pypi.org/project/FDApy/
:alt: PyPI - Python Version
.. image:: https://img.shields.io/pypi/v/FDApy
:target: https://pypi.org/project/FDApy/
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.. image:: https://github.com/StevenGolovkine/FDApy/actions/workflows/python_package_ubuntu.yaml/badge.svg
:target: https://github.com/StevenGolovkine/FDApy/actions
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.. image:: https://img.shields.io/badge/License-MIT-blue.svg
:target: https://raw.githubusercontent.com/StevenGolovkine/FDApy/master/LICENSE
:alt: PyPI - License
.. image:: https://codecov.io/gh/StevenGolovkine/FDApy/branch/master/graph/badge.svg?token=S2H0D3QQMR
:target: https://codecov.io/gh/StevenGolovkine/FDApy
:alt: Coverage
.. image:: https://app.codacy.com/project/badge/Grade/3d9062cffc304ad4bb7c76bf97cc965c
:target: https://app.codacy.com/gh/StevenGolovkine/FDApy/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade
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.. image:: https://readthedocs.org/projects/fdapy/badge/?version=latest
:target: https://fdapy.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://zenodo.org/badge/155183454.svg
:target: https://zenodo.org/badge/latestdoi/155183454
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Description
===========
Functional Data Analysis, usually referred as FDA, concerns the field of Statistics that deals with discrete observations of continuous :math:`d`-dimensional functions.
This package provide modules for the analysis of such data. It includes methods for different dimensional data as well as irregularly sampled functional data. An implementation of (multivariate) functional principal component analysis is also given. Moreover, a simulation toolbox is provided. It might be used to simulate different clusters of functional data.
Check out the documentation for more complete information on the available features within the package.
Documentation
=============
The documentation is available `here <https://fdapy.readthedocs.io/en/stable/>`__, which included detailled information about API references and several examples presenting the different functionalities.
The documentation of the latest version can be found at `here <https://fdapy.readthedocs.io/en/latest/>`__
Installation
============
Up to now, *FDApy* is availlable in Python 3.9 on any Linux platforms. The stable version can be installed via `PyPI <https://pypi.org/project/FDApy/>`_:
.. code::
pip install FDApy
Installation from source
------------------------
It is possible to install the latest version of the package by cloning this repository and doing the manual installation.
.. code:: bash
git clone https://github.com/StevenGolovkine/FDApy.git
pip install ./FDApy
Requirements
------------
*FDApy* depends on the following packages:
* `matplotlib <https://github.com/matplotlib/matplotlib>`_ - Plotting with Python
* `numpy <https://github.com/numpy/numpy>`_ - The fundamental package for scientific computing with Python
* `pandas <https://github.com/pandas-dev/pandas>`_ - Powerful Python data analysis toolkit
* `scikit-learn <https://github.com/scikit-learn/scikit-learn>`_ - Machine learning in Python
* `scipy <https://github.com/scipy/scipy>`_ - Scientific computation in Python
Contributing
============
Contributions are welcome, and they are greatly appreciated! Every little bit
helps, and credit will always be given. Contributing guidelines are provided `here <https://github.com/StevenGolovkine/FDApy/blob/master/CONTRIBUTING.rst>`_.
License
=======
The package is licensed under the MIT License. A copy of the `license <https://github.com/StevenGolovkine/FDApy/blob/master/LICENSE>`_ can be found along with the code.
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