pyplnoise 1.4
Arbitrarily long streams of power law noise using NumPy and SciPy.
License: BSD
Requires Python: >=3.7,<3.99

# Warning
This package is no longer maintained. Compatibility with recent versions of
Python, NumPy or SciPy cannot be guaranteed.
# pyplnoise
**Py**thon **p**ower **l**aw **noise** generates arbitrarily long streams of power law noise
using NumPy and SciPy.

The noise is generated with shaping filters and the methodology closely follows
[Stephane Plaszczynski, Fluct. Noise Lett. 7: R&ndash;R13, 2007](https://doi.org/10.1142/S0219477507003635).
You can also find the article on [arXiv:astro-ph/0510081](https://arxiv.org/abs/astro-ph/0510081).

**pyplnoise** consists of a single module providing classes implementing the following
noise sources:
* general 1/f<sup>&alpha;</sup> power law noise with upper and lower frequency
  limits (class `AlphaNoise`),
* a fast red (Brownian) noise generator with a lower frequency limit (class `RedNoise`),
* a convenience alias for pink noise (aka 1/f noise; class `PinkNoise`),
* and of course white noise (class `WhiteNoise`).

## Quick example
The interface is very simple: just instantiate one of the above classes and run
`get_sample()` to retrieve a single sample or `get_series(npts)` to
retrieve an array of `npts` samples. Detailed interface documentation is available in the code.

```python
import pyplnoise
import numpy as np

fs = 10. # sampling frequency in Hz

# instantiate a noise source with lower frequency limit 1e-3 Hz,
# upper frequency limit 5 Hz and 1/f^1.5 power spectrum
noisegen = pyplnoise.AlphaNoise(fs, 1e-3, fs/2., alpha=1.5, seed=42)

one_sample = noisegen.get_sample()
many_samples = noisegen.get_series(100000)
```

## Detailed examples
Jupyter notebooks are provided in the [/examples](https://github.com/janwaldmann/pyplnoise/tree/master/examples) directory:
1. [Overview of the noise sources and their properties](https://github.com/janwaldmann/pyplnoise/tree/master/examples/overview_of_noise_sources.ipynb)
2. [Application example: modeling the random signal errors of a gyroscope (Allan variance
   of synthetic noise)](https://github.com/janwaldmann/pyplnoise/tree/master/examples/application_example_allan_variance.ipynb)

## Installation
### Dependencies
* NumPy &ge; 1.17 (see NEP 19)
* SciPy &ge; 1.3

### Installing from [PyPI](https://pypi.org/project/pyplnoise/)
```python
pip install pyplnoise
```

### Installing directly from GitHub
Download the release tarball and run
```python
python setup.py install
```

Because everything is contained in the module `pyplnoise`, you can alternatively just copy
the module and the LICENSE file into your project.

## You may find pyplnoise useful, if...
* ...you're looking to generate 1/f<sup>&alpha;</sup> noise with very long correlation
  times (frequencies &ll; 10<sup>-7</sup> Hz); particularly if your machine has limited
  memory resources.
* ...you like to superimpose many colored noise sources, possibly sampled at different
  frequencies and possessing different bandwidths.

## You may *not* find pyplnoise useful, if...
* ...you're looking for a pink noise source for your software synthesizer or other audio stuff.
  There are lots of interesting solutions for such applications, notably
  "[A New Shade of Pink](https://github.com/Stenzel/newshadeofpink)",
  the [Voss-McCartney Algorithm, which is also available in Python](https://www.dsprelated.com/showarticle/908.php)
  and [some highly specialized filters](http://www.firstpr.com.au/dsp/pink-noise/).
* ...you want to generate finite 1/f<sup>&alpha;</sup> noise streams with relatively short
  correlation times (frequencies &ge; 10<sup>-7</sup> Hz). In such a case [Fourier transform
  methods](https://github.com/felixpatzelt/colorednoise) are tractable and in some cases these
  methods deliver higher quality results than the shaping filters used by **pyplnoise**.