https://github.com/jonathf/numpoly
Numpy compatible polynomial representation
https://github.com/jonathf/numpoly
Last synced: 3 months ago
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Numpy compatible polynomial representation
- Host: GitHub
- URL: https://github.com/jonathf/numpoly
- Owner: jonathf
- License: bsd-2-clause
- Created: 2019-08-18T19:08:06.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2026-01-02T12:22:10.000Z (5 months ago)
- Last Synced: 2026-01-08T15:18:41.516Z (5 months ago)
- Language: Python
- Homepage: https://numpoly.readthedocs.io
- Size: 1.42 MB
- Stars: 12
- Watchers: 1
- Forks: 8
- Open Issues: 6
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- License: LICENSE
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README
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Numpoly is a generic library for creating, manipulating and evaluating
arrays of polynomials based on ``numpy.ndarray`` objects.
* Intuitive interface for users experienced with ``numpy``, as the library
provides a high level of compatibility with the ``numpy.ndarray``, including
fancy indexing, broadcasting, ``numpy.dtype``, vectorized operations to name
a few.
* Computationally fast evaluations of lots of functionality inherent from
``numpy``.
* Vectorized polynomial evaluation.
* Support for arbitrary number of dimensions.
* Native support for lots of ``numpy.`` functions using ``numpy``'s
compatibility layer (which also exists as ``numpoly.``
equivalents).
* Support for polynomial division through the operators ``/``, ``%`` and
``divmod``.
* Extra polynomial specific attributes exposed on the polynomial objects like
``poly.exponents``, ``poly.coefficients``, ``poly.indeterminants`` etc.
* Polynomial derivation through functions like ``numpoly.derivative``,
``numpoly.gradient``, ``numpoly.hessian`` etc.
* Decompose polynomial sums into vector of addends using ``numpoly.decompose``.
* Variable substitution through ``numpoly.call``.
Installation
============
Installation should be straight forward:
.. code-block:: bash
pip install numpoly
Example Usage
=============
Constructing polynomial is typically done using one of the available
constructors:
.. code-block:: python
>>> import numpoly
>>> numpoly.monomial(start=0, stop=3, dimensions=2)
polynomial([1, q0, q0**2, q1, q0*q1, q1**2])
It is also possible to construct your own from symbols together with
`numpy `_:
.. code-block:: python
>>> import numpy
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.polynomial([1, q0**2-1, q0*q1, q1**2-1])
polynomial([1, q0**2-1, q0*q1, q1**2-1])
Or in combination with numpy objects using various arithmetics:
.. code-block:: python
>>> q0**numpy.arange(4)-q1**numpy.arange(3, -1, -1)
polynomial([-q1**3+1, -q1**2+q0, q0**2-q1, q0**3-1])
The constructed polynomials can be evaluated as needed:
.. code-block:: python
>>> poly = 3*q0+2*q1+1
>>> poly(q0=q1, q1=[1, 2, 3])
polynomial([3*q1+3, 3*q1+5, 3*q1+7])
Or manipulated using various numpy functions:
.. code-block:: python
>>> numpy.reshape(q0**numpy.arange(4), (2, 2))
polynomial([[1, q0],
[q0**2, q0**3]])
>>> numpy.sum(numpoly.monomial(13)[::3])
polynomial(q0**12+q0**9+q0**6+q0**3+1)
Installation
============
Installation should be straight forward from `pip `_:
.. code-block:: bash
pip install numpoly
Alternatively, to get the most current experimental version, the code can be
installed from `Github `_ as follows:
* First time around, download the repository:
.. code-block:: bash
git clone git@github.com:jonathf/numpoly.git
* Every time, move into the repository:
.. code-block:: bash
cd numpoly/
* After the first time, you want to update the branch to the most current
version of ``master``:
.. code-block:: bash
git checkout master
git pull
* Install the latest version of ``numpoly`` with:
.. code-block:: bash
pip install .
Development
-----------
Installing ``numpoly`` for development can
be done from the repository root with the command::
pip install -e .[dev]
The deployment of the code is done with Python 3.10 and uses ``uv``::
uv sync --extra dev
Testing
-------
To run test:
.. code-block:: bash
pytest --doctest-modules numpoly test docs/user_guide/*.rst README.rst
Documentation
-------------
To build documentation locally on your system, use ``make`` from the ``doc/``
folder:
.. code-block:: bash
cd doc/
make html
Run ``make`` without argument to get a list of build targets. All targets
stores output to the folder ``doc/.build/html``.
Note that the documentation build assumes that ``pandoc`` is installed on your
system and available in your path.