Deconvoluted makes performing integral transforms simple and pythonic!
Project description
Deconvoluted
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:
F, p = fourier_transform(f, x)
By default, Fourier transforms use Fourier coefficients a=0, b=-2pi. Using another convention is simple:
F, k = fourier_transform(f, x, convention=(-1, 1))
As a physicist myself, I therefore switch the labelling of the output from p for momentum, to k for wavevector.
Performing multidimensional transforms is just as easy. For example:
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:
F_py, p = fourier_transform(f_xy, x, None)
F_xq, q = fourier_transform(f_xy, None, y)
History
0.1.0 (2019-06-03)
First release on PyPI.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for deconvoluted-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b4a28a00ed34fe28c47fd3c09c9945d65de02dc1fa67e660aee5fb91e4a8052e |
|
MD5 | 9b75bf954d9582bac0c390b38be6dd5c |
|
BLAKE2b-256 | f218e5fa3ec60b0f93736ca326a0dc919baace25864fbb6a35bc22f844ef164c |