cmomy 1.0.3
Central (co)moment calculation/manipulation
Requires Python: >=3.10
Keywords: cmomy

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# cmomy

A Python package to calculate and manipulate Central (co)moments. The main
features of `cmomy` are as follows:

- [numba] accelerated computation of central moments and co-moments
- Routines to combine, and resample central moments.
- Supports [numpy] array and [xarray] DataArray or Dataset based data.
- Routines to convert between central and raw moments.

## Overview

`cmomy` is an open source package to calculate central moments and co-moments in
a numerical stable and direct way. Behind the scenes, `cmomy` makes use of
[numba] to rapidly calculate moments. A good introduction to the type of
[formulas used can be found here](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance).

## Features

- Fast calculation of central moments and central co-moments with weights
- Support for scalar or vector inputs
- numpy and xarray api's
- bootstrap resampling

## Status

This package is actively used by the author. Please feel free to create a pull
request for wanted features and suggestions!

## Example usage

```pycon
>>> import numpy as np
>>> import cmomy
>>> rng = cmomy.default_rng(seed=0)
>>> x = rng.random(100)
>>> m = x.mean()
>>> mom = np.array([((x - m) ** i).mean() for i in range(4)])
>>> c = cmomy.wrap_reduce_vals(x, mom=3, axis=0)

>>> np.testing.assert_allclose(c.cmom(), mom, atol=1e-8)
>>> c.cmom()
array([ 1.    ,  0.    ,  0.0919, -0.0061])

# break up into chunks
>>> c = cmomy.wrap_reduce_vals(x.reshape(-1, 2), mom=3, axis=0)

>>> c
<CentralMomentsArray(mom_ndim=1)>
array([[ 5.0000e+01,  5.3019e-01,  8.0115e-02, -4.3748e-03],
       [ 5.0000e+01,  5.6639e-01,  1.0297e-01, -8.9911e-03]])

# Reduce along an axis
>>> c.reduce(axis=0).cmom()
array([ 1.    ,  0.    ,  0.0919, -0.0061])

# unequal chunks
>>> x0, x1, x2 = x[:20], x[20:60], x[60:]

>>> cs = [cmomy.wrap_reduce_vals(_, mom=3, axis=0) for _ in (x0, x1, x2)]

>>> c = cs[0] + cs[1] + cs[2]

>>> np.testing.assert_allclose(c.cmom(), mom, atol=1e-8)
>>> c.cmom()
array([ 1.    ,  0.    ,  0.0919, -0.0061])

```

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## Installation

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Use one of the following

```bash
pip install cmomy
```

or

```bash
conda install -c conda-forge cmomy
```

### Note on caching

This code makes extensive use of the numba python package. This uses a jit
compiler to speed up vital code sections. This means that the first time a
function called, it has to compile the underlying code. However, caching has
been implemented. Therefore, the very first time you run a function, it may be
slow. But all subsequent uses (including other sessions) will be already
compiled. You can pre-compile the `cmomy` by running

```bash
python -m cmomy.compile
```

If you'll be using `cmomy` in parallel (e.g., using `multiprocessing`), make
sure to pre-compile `cmomy`, or to turn off caching by setting the environment
variable `CMOMY_NUMBA_CACHE=0`.

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## Documentation

See the [documentation][docs-link] for a look at `cmomy` in action.

## What's new?

See [changelog][changelog-link].

## License

This is free software. See [LICENSE][license-link].

## Related work

This package is used extensively in the newest version of `thermoextrap`.
[See here](https://github.com/usnistgov/thermo-extrap).

## Contact

The author can be reached at <wpk@nist.gov>.

## Credits

This package was created using
[Cookiecutter](https://github.com/audreyr/cookiecutter) with the
[usnistgov/cookiecutter-nist-python](https://github.com/usnistgov/cookiecutter-nist-python)
template.