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Polynomials as a numpy datatype

Project description

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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.<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


Installation should be straight forward:

pip install numpoly

Example Usage

Constructing polynomial is typically done using one of the available constructors:

>>> 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:

>>> 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:

>>> 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:

>>> 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:

>>> numpy.reshape(q0**numpy.arange(4), (2, 2))
polynomial([[1, q0],
            [q0**2, q0**3]])
>>> numpy.sum(numpoly.monomial(13)[::3])


Installation should be straight forward from pip:

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:

    git clone
  • Every time, move into the repository:

    cd numpoly/
  • After the first time, you want to update the branch to the most current version of master:

    git checkout master
    git pull
  • Install the latest version of numpoly with:

    pip install .


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


To run test:

pytest --doctest-modules numpoly test docs/user_guide/*.rst README.rst


To build documentation locally on your system, use make from the doc/ folder:

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.

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