.. summary-start
|logo| adaptive
===============
|PyPI| |Conda| |Downloads| |Pipeline status| |DOI| |Binder| |Gitter|
|Documentation| |GitHub|
**Tools for adaptive parallel sampling of mathematical functions.**
``adaptive`` is an open-source Python library designed to
make adaptive parallel function evaluation simple. With ``adaptive`` you
just supply a function with its bounds, and it will be evaluated at the
“best” points in parameter space. With just a few lines of code you can
evaluate functions on a computing cluster, live-plot the data as it
returns, and fine-tune the adaptive sampling algorithm.
Run the ``adaptive`` example notebook `live on
Binder <https://mybinder.org/v2/gh/python-adaptive/adaptive/master?filepath=learner.ipynb>`_
to see examples of how to use ``adaptive`` or visit the
`tutorial on Read the Docs <https://adaptive.readthedocs.io/en/latest/tutorial/tutorial.html>`__.
.. summary-end
**WARNING: adaptive is still in a beta development stage**
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Implemented algorithms
----------------------
The core concept in ``adaptive`` is that of a *learner*. A *learner*
samples a function at the best places in its parameter space to get
maximum “information” about the function. As it evaluates the function
at more and more points in the parameter space, it gets a better idea of
where the best places are to sample next.
Of course, what qualifies as the “best places” will depend on your
application domain! ``adaptive`` makes some reasonable default choices,
but the details of the adaptive sampling are completely customizable.
The following learners are implemented:
- ``Learner1D``, for 1D functions ``f: ℝ → ℝ^N``,
- ``Learner2D``, for 2D functions ``f: ℝ^2 → ℝ^N``,
- ``LearnerND``, for ND functions ``f: ℝ^N → ℝ^M``,
- ``AverageLearner``, For stochastic functions where you want to
average the result over many evaluations,
- ``IntegratorLearner``, for
when you want to intergrate a 1D function ``f: ℝ → ℝ``,
- ``BalancingLearner``, for when you want to run several learners at once,
selecting the “best” one each time you get more points.
In addition to the learners, ``adaptive`` also provides primitives for
running the sampling across several cores and even several machines,
with built-in support for
`concurrent.futures <https://docs.python.org/3/library/concurrent.futures.html>`_,
`ipyparallel <https://ipyparallel.readthedocs.io/en/latest/>`_ and
`distributed <https://distributed.readthedocs.io/en/latest/>`_.
Examples
--------
.. raw:: html
<img src="https://user-images.githubusercontent.com/6897215/38739170-6ac7c014-3f34-11e8-9e8f-93b3a3a3d61b.gif" width='20%'> </img> <img src="https://user-images.githubusercontent.com/6897215/35219611-ac8b2122-ff73-11e7-9332-adffab64a8ce.gif" width='40%'> </img> <img src="https://user-images.githubusercontent.com/6897215/47256441-d6d53700-d480-11e8-8224-d1cc49dbdcf5.gif" width='20%'> </img>
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Installation
------------
``adaptive`` works with Python 3.6 and higher on Linux, Windows, or Mac,
and provides optional extensions for working with the Jupyter/IPython
Notebook.
The recommended way to install adaptive is using ``conda``:
.. code:: bash
conda install -c conda-forge adaptive
``adaptive`` is also available on PyPI:
.. code:: bash
pip install adaptive[notebook]
The ``[notebook]`` above will also install the optional dependencies for
running ``adaptive`` inside a Jupyter notebook.
Development
-----------
Clone the repository and run ``setup.py develop`` to add a link to the
cloned repo into your Python path:
.. code:: bash
git clone git@github.com:python-adaptive/adaptive.git
cd adaptive
python3 setup.py develop
We highly recommend using a Conda environment or a virtualenv to manage
the versions of your installed packages while working on ``adaptive``.
In order to not pollute the history with the output of the notebooks,
please setup the git filter by executing
.. code:: bash
python ipynb_filter.py
in the repository.
Credits
-------
We would like to give credits to the following people:
- Pedro Gonnet for his implementation of `CQUAD <https://www.gnu.org/software/gsl/manual/html_node/CQUAD-doubly_002dadaptive-integration.html>`_,
“Algorithm 4” as described in “Increasing the Reliability of Adaptive
Quadrature Using Explicit Interpolants”, P. Gonnet, ACM Transactions on
Mathematical Software, 37 (3), art. no. 26, 2010.
- Pauli Virtanen for his ``AdaptiveTriSampling`` script (no longer
available online since SciPy Central went down) which served as
inspiration for the `~adaptive.Learner2D`.
.. credits-end
For general discussion, we have a `Gitter chat
channel <https://gitter.im/python-adaptive/adaptive>`_. If you find any
bugs or have any feature suggestions please file a GitHub
`issue <https://github.com/python-adaptive/adaptive/issues/new>`_
or submit a `pull
request <https://github.com/python-adaptive/adaptive/pulls>`_.
.. references-start
.. |logo| image:: https://adaptive.readthedocs.io/en/latest/_static/logo.png
.. |PyPI| image:: https://img.shields.io/pypi/v/adaptive.svg
:target: https://pypi.python.org/pypi/adaptive
.. |Conda| image:: https://img.shields.io/badge/install%20with-conda-green.svg
:target: https://anaconda.org/conda-forge/adaptive
.. |Downloads| image:: https://img.shields.io/conda/dn/conda-forge/adaptive.svg
:target: https://anaconda.org/conda-forge/adaptive
.. |Pipeline status| image:: https://dev.azure.com/python-adaptive/adaptive/_apis/build/status/python-adaptive.adaptive?branchName=master
:target: https://dev.azure.com/python-adaptive/adaptive/_build/latest?definitionId=6?branchName=master
.. |DOI| image:: https://img.shields.io/badge/doi-10.5281%2Fzenodo.1182437-blue.svg
:target: https://doi.org/10.5281/zenodo.1182437
.. |Binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/python-adaptive/adaptive/master?filepath=learner.ipynb
.. |Gitter| image:: https://img.shields.io/gitter/room/nwjs/nw.js.svg
:target: https://gitter.im/python-adaptive/adaptive
.. |Documentation| image:: https://readthedocs.org/projects/adaptive/badge/?version=latest
:target: https://adaptive.readthedocs.io/en/latest/?badge=latest
.. |GitHub| image:: https://img.shields.io/github/stars/python-adaptive/adaptive.svg?style=social
:target: https://github.com/python-adaptive/adaptive/stargazers
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