| Name | numpoly JSON |
| Version |
1.3.6
JSON |
| download |
| home_page | None |
| Summary | Polynomials as a numpy datatype |
| upload_time | 2024-12-06 12:27:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | BSD-2-Clause |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: https://github.com/jonathf/numpoly/raw/master/docs/.static/numpoly_logo.svg
:height: 200 px
:width: 200 px
:align: center
|circleci| |codecov| |readthedocs| |downloads| |pypi|
.. |circleci| image:: https://circleci.com/gh/jonathf/numpoly/tree/master.svg?style=shield
:target: https://circleci.com/gh/jonathf/numpoly/tree/master
.. |codecov| image:: https://codecov.io/gh/jonathf/numpoly/branch/master/graph/badge.svg
:target: https://codecov.io/gh/jonathf/numpoly
.. |readthedocs| image:: https://readthedocs.org/projects/numpoly/badge/?version=master
:target: http://numpoly.readthedocs.io/en/master/?badge=master
.. |downloads| image:: https://img.shields.io/pypi/dm/numpoly
:target: https://pypistats.org/packages/numpoly
.. |pypi| image:: https://badge.fury.io/py/numpoly.svg
:target: https://badge.fury.io/py/numpoly
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.<name>`` functions using ``numpy``'s
compatibility layer (which also exists as ``numpoly.<name>``
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 <https://python.org>`_:
.. 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 <https://pypi.org/>`_:
.. code-block:: bash
pip install numpoly
Alternatively, to get the most current experimental version, the code can be
installed from `Github <https://github.com/>`_ 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 dependencies are then
fixed using::
pip install -r requirements-dev.txt
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.
Raw data
{
"_id": null,
"home_page": null,
"name": "numpoly",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/b9/03/73b222c4341e02991ad97ce5b8facbc2d2f8430768f8ecaa20b749e9df9b/numpoly-1.3.6.tar.gz",
"platform": null,
"description": ".. image:: https://github.com/jonathf/numpoly/raw/master/docs/.static/numpoly_logo.svg\n :height: 200 px\n :width: 200 px\n :align: center\n\n|circleci| |codecov| |readthedocs| |downloads| |pypi|\n\n.. |circleci| image:: https://circleci.com/gh/jonathf/numpoly/tree/master.svg?style=shield\n :target: https://circleci.com/gh/jonathf/numpoly/tree/master\n.. |codecov| image:: https://codecov.io/gh/jonathf/numpoly/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/jonathf/numpoly\n.. |readthedocs| image:: https://readthedocs.org/projects/numpoly/badge/?version=master\n :target: http://numpoly.readthedocs.io/en/master/?badge=master\n.. |downloads| image:: https://img.shields.io/pypi/dm/numpoly\n :target: https://pypistats.org/packages/numpoly\n.. |pypi| image:: https://badge.fury.io/py/numpoly.svg\n :target: https://badge.fury.io/py/numpoly\n\nNumpoly is a generic library for creating, manipulating and evaluating\narrays of polynomials based on ``numpy.ndarray`` objects.\n\n* Intuitive interface for users experienced with ``numpy``, as the library\n provides a high level of compatibility with the ``numpy.ndarray``, including\n fancy indexing, broadcasting, ``numpy.dtype``, vectorized operations to name\n a few.\n* Computationally fast evaluations of lots of functionality inherent from\n ``numpy``.\n* Vectorized polynomial evaluation.\n* Support for arbitrary number of dimensions.\n* Native support for lots of ``numpy.<name>`` functions using ``numpy``'s\n compatibility layer (which also exists as ``numpoly.<name>``\n equivalents).\n* Support for polynomial division through the operators ``/``, ``%`` and\n ``divmod``.\n* Extra polynomial specific attributes exposed on the polynomial objects like\n ``poly.exponents``, ``poly.coefficients``, ``poly.indeterminants`` etc.\n* Polynomial derivation through functions like ``numpoly.derivative``,\n ``numpoly.gradient``, ``numpoly.hessian`` etc.\n* Decompose polynomial sums into vector of addends using ``numpoly.decompose``.\n* Variable substitution through ``numpoly.call``.\n\nInstallation\n============\n\nInstallation should be straight forward:\n\n.. code-block:: bash\n\n pip install numpoly\n\nExample Usage\n=============\n\nConstructing polynomial is typically done using one of the available\nconstructors:\n\n.. code-block:: python\n\n >>> import numpoly\n >>> numpoly.monomial(start=0, stop=3, dimensions=2)\n polynomial([1, q0, q0**2, q1, q0*q1, q1**2])\n\nIt is also possible to construct your own from symbols together with\n`numpy <https://python.org>`_:\n\n.. code-block:: python\n\n >>> import numpy\n >>> q0, q1 = numpoly.variable(2)\n >>> numpoly.polynomial([1, q0**2-1, q0*q1, q1**2-1])\n polynomial([1, q0**2-1, q0*q1, q1**2-1])\n\nOr in combination with numpy objects using various arithmetics:\n\n.. code-block:: python\n\n >>> q0**numpy.arange(4)-q1**numpy.arange(3, -1, -1)\n polynomial([-q1**3+1, -q1**2+q0, q0**2-q1, q0**3-1])\n\nThe constructed polynomials can be evaluated as needed:\n\n.. code-block:: python\n\n >>> poly = 3*q0+2*q1+1\n >>> poly(q0=q1, q1=[1, 2, 3])\n polynomial([3*q1+3, 3*q1+5, 3*q1+7])\n\nOr manipulated using various numpy functions:\n\n.. code-block:: python\n\n >>> numpy.reshape(q0**numpy.arange(4), (2, 2))\n polynomial([[1, q0],\n [q0**2, q0**3]])\n >>> numpy.sum(numpoly.monomial(13)[::3])\n polynomial(q0**12+q0**9+q0**6+q0**3+1)\n\nInstallation\n============\n\nInstallation should be straight forward from `pip <https://pypi.org/>`_:\n\n.. code-block:: bash\n\n pip install numpoly\n\nAlternatively, to get the most current experimental version, the code can be\ninstalled from `Github <https://github.com/>`_ as follows:\n\n* First time around, download the repository:\n\n .. code-block:: bash\n\n git clone git@github.com:jonathf/numpoly.git\n\n* Every time, move into the repository:\n\n .. code-block:: bash\n\n cd numpoly/\n\n* After the first time, you want to update the branch to the most current\n version of ``master``:\n\n .. code-block:: bash\n\n git checkout master\n git pull\n\n* Install the latest version of ``numpoly`` with:\n\n .. code-block:: bash\n\n pip install .\n\nDevelopment\n-----------\n\nInstalling ``numpoly`` for development can\nbe done from the repository root with the command::\n\n pip install -e .[dev]\n\nThe deployment of the code is done with Python 3.10 and dependencies are then\nfixed using::\n\n pip install -r requirements-dev.txt\n\nTesting\n-------\n\nTo run test:\n\n.. code-block:: bash\n\n pytest --doctest-modules numpoly test docs/user_guide/*.rst README.rst\n\nDocumentation\n-------------\n\nTo build documentation locally on your system, use ``make`` from the ``doc/``\nfolder:\n\n.. code-block:: bash\n\n cd doc/\n make html\n\nRun ``make`` without argument to get a list of build targets. All targets\nstores output to the folder ``doc/.build/html``.\n\nNote that the documentation build assumes that ``pandoc`` is installed on your\nsystem and available in your path.\n",
"bugtrack_url": null,
"license": "BSD-2-Clause",
"summary": "Polynomials as a numpy datatype",
"version": "1.3.6",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b90373b222c4341e02991ad97ce5b8facbc2d2f8430768f8ecaa20b749e9df9b",
"md5": "289510b99278a88e25ca6967793f1e39",
"sha256": "587e8459e9f43b10bafe84f15500382f75e031e2f0b06cb0f0c9d2fc7efbbe12"
},
"downloads": -1,
"filename": "numpoly-1.3.6.tar.gz",
"has_sig": false,
"md5_digest": "289510b99278a88e25ca6967793f1e39",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 369686,
"upload_time": "2024-12-06T12:27:40",
"upload_time_iso_8601": "2024-12-06T12:27:40.326515Z",
"url": "https://files.pythonhosted.org/packages/b9/03/73b222c4341e02991ad97ce5b8facbc2d2f8430768f8ecaa20b749e9df9b/numpoly-1.3.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-06 12:27:40",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "numpoly"
}
