Crates.io | sciport-rs |
lib.rs | sciport-rs |
version | 0.0.2 |
source | src |
created_at | 2023-07-13 16:21:07.329329 |
updated_at | 2023-07-22 15:59:02.218073 |
description | Rust port of scipy |
homepage | https://github.com/ChristianBelloni/sciport-rs |
repository | https://github.com/ChristianBelloni/sciport-rs |
max_upload_size | |
id | 915475 |
size | 74,699 |
Sciport is a collection of mathematical algorithms ported from the popular python package Scipy
The main philosophy behind sciport is to change the api surface of scipy to better utilize the
rich rust typesystem, when deciding between keeping the original function signature and
rewriting it to better represent the valid input space, more often than not we'll decide to
change it.
for example this is the scipy butter filter api:
scipy.signal.butter(N: int, Wn: array_like, btype: String, analog: bool, output: String, fs:
float)
Wn represents a single or a pair of frequencies and btype is the type of filter, however, a single frequency makes sense only for a subset of btypes and so does a pair, in our implementation we rewrite this function like:
fn filter<T>(order: u32, band_filter: BandFilter, analog: Analog) { .. }
where T represents the output representation of the filter (Zpk, Ba, Sos), band_filter encapsulates the original Wn and btype like this:
enum BandFilter
pub enum BandFilter {
Highpass(f64),
Lowpass(f64),
Bandpass { low: f64, high: f64 },
Bandstop { low: f64, high: f64 },
}
and analog encapsulates analog and fs (since a sampling rate makes sense only when talking about a digital filter) like this:
pub enum Analog {
True,
False {
fs: f64
}
}
The signal processing toolbox currently contains some filtering functions, a limited set of filter design tools, and a few B-spline interpolation algorithms for 1- and 2-D data. While the B-spline algorithms could technically be placed under the interpolation category, they are included here because they only work with equally-spaced data and make heavy use of filter-theory and transfer-function formalism to provide a fast B-spline transform.
The main feature of this module is the definition of numerous special functions of mathematical physics. Available functions include airy, elliptic, bessel, gamma, beta, hypergeometric, parabolic cylinder, mathieu, spheroidal wave, struve, and kelvin.
If there's a specific module or function that you'd like to see been worked on open a pr linking the scipy documentation for the module or function