Metadata-Version: 2.1
Name: deconvoluted
Version: 0.1.1
Summary: Deconvoluted makes performing integral transforms simple and pythonic!
Home-page: https://github.com/tbuli/deconvoluted
Author: Martin Roelfs
Author-email: martin.roelfs@kuleuven.be
License: MIT license
Keywords: deconvoluted
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: numpy

============
Deconvoluted
============


.. image:: https://img.shields.io/pypi/v/deconvoluted.svg
        :target: https://pypi.python.org/pypi/deconvoluted

.. image:: https://img.shields.io/travis/tbuli/deconvoluted.svg
        :target: https://travis-ci.org/tbuli/deconvoluted

.. image:: https://readthedocs.org/projects/deconvoluted/badge/?version=latest
        :target: https://deconvoluted.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status




Deconvoluted makes performing numerical integral transforms simple and pythonic!


* Free software: MIT license
* Documentation: https://deconvoluted.readthedocs.io.


Features
--------

Fourier Transforms
~~~~~~~~~~~~~~~~~~

As a first example, let's perform a Fourier transform:

.. code-block:: python

    t = np.linspace(0, 10, 201)
    f = np.sin(3 * 2 * np.pi * t)
    F, nu = fourier_transform(f, t)

By default, Fourier transforms use Fourier coefficients `a=0`,
`b=-2\pi`. Using another convention is simple:

.. code-block:: python

    F, omega = fourier_transform(f, t, convention=(-1, 1))

As a physicist myself, I therefore switch the labelling of the output from
`\nu` for frequency, to `\omega` for angular frequency.

Performing multidimensional transforms is just as easy. For example:

.. code-block:: python

    F_pq, p, q = fourier_transform(f_xy, x, y)

transforms both `x` and `y` at the same time.
Transforming only one of the two variables can be done simply by setting those
that shouldn't transform to ``None``:

.. code-block:: python

    F_py, p = fourier_transform(f_xy, x, None)
    F_xq, q = fourier_transform(f_xy, None, y)

See the documentation for more examples!


=======
History
=======

0.1.1 (2019-06-05)
------------------

* Implemented support for different FT conventions.

0.1.0 (2019-06-03)
------------------

* First release on PyPI.


