Metadata-Version: 2.1
Name: lsqfitgp
Version: 0.11
Summary: Gaussian processes in nonlinear least-squares fits
Home-page: https://github.com/Gattocrucco/lsqfitgp
Author: Giacomo Petrillo
Author-email: info@giacomopetrillo.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

[![PyPI](https://img.shields.io/pypi/v/lsqfitgp)](https://pypi.org/project/lsqfitgp/)

# lsqfitgp

Python module for manipulating Gaussian processes. Features:

  * Use [gvar](https://github.com/gplepage/gvar) to keep track transparently of
    correlations between prior, data and posterior.

  * Fit a latent gaussian process in a nonlinear model with
    [lsqfit](https://github.com/gplepage/lsqfit).
    
  * [JAX](https://github.com/google/jax)-friendly.
  
  * Supports multidimensional structured non-numerical input with named
    dimensions.
    
  * Apply arbitrary linear transformations to the process.
  
  * Use dictionaries to manipulate hyperparameters and hyperpriors. Use
    `gvar.BufferDict` to transparently apply transformations.
    
  * Get a covariance matrix for the optimized hyperparameters.
  
## Installation

Python >= 3.7 required. Then:

```
$ pip install lsqfitgp
```

## Documentation

The complete manual is available online at
[gattocrucco.github.io/lsqfitgp/docs](https://gattocrucco.github.io/lsqfitgp/docs).
All the code is documented with docstrings, so you can also use the Python help
system directly from the shell:

```python
>>> import lsqfitgp as lgp
>>> help(lgp)
>>> help(lgp.something)
```

or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark
shortcut:

```
In [1]: lgp?

In [2]: lgp.something?
```

To access the manual for older versions, use the index at
[gattocrucco.github.io/lsqfitgp](https://gattocrucco.github.io/lsqfitgp).

## Development

Clone the repository, create a virtual environment and install the requirements:

```sh
$ git clone https://github.com/Gattocrucco/lsqfitgp.git
$ cd lsqfitgp
$ python -m venv myenv
$ . myenv/bin/activate
(myenv) $ pip install -r requirements.txt
```

The `Makefile` in the root directory contains targets to build the
documentation, run the tests, and prepare a release. Run `make` without
arguments to show the available targets:

```sh
$ make
available targets: upload release tests examples docscode docs covreport
release = tests examples docscode docs (in order)
$ make tests # or make examples, or ...
```

The tests are run on each push and the resulting coverage report is published
online at
[gattocrucco.github.io/lsqfitgp/htmlcov](https://gattocrucco.github.io/lsqfitgp/htmlcov/).
To browse it locally after `make tests` etc., do `make covreport` and open
`htmlcov/index.html` in your browser.


