Metadata-Version: 2.1
Name: domino-composite
Version: 0.31
Summary: A package for compositing atmospheric datasets
Home-page: https://github.com/joshdorrington/domino
Author: Josh Dorrington
Author-email: joshua.dorrington@kit.edu
License: bsd-3-clause
Description-Content-Type: text/markdown

# Domino is a package for analysing composites of atmospheric data.
## Based on xarray, Domino makes it easy to calculate lagged composites of fields and scalar indices around categorical event time series, and to compute bootstrapped confidence bounds.

## This is a beta release. While core functionality is stable, there may be some bugs: please contact me at joshua.dorrington@kit.edu if you encounter unexpected behaviour.

<img src="Imgs/domino_logo.png" alt="logo" width="180"/>


## Documentation

See our [API reference](https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf) for a full description of all functionality.

<!-- (Our preprint on Domino (under consideration at QJRMS), and its application to extreme rainfall prediction can be found [here](where)) -->

## Examples

See our Jupyter notebook examples for more detailed discussion of how to apply Domino to different use cases.

Our [basic](https://github.com/joshdorrington/domino/blob/master/examples/basic_compositing.ipynb) and [advanced](https://github.com/joshdorrington/domino/blob/master/examples/advanced_compositing.ipynb) compositing guides cover the use of Domino's flexible LaggedAnalyser class to easily compute time-lagged composites and apply bootstrap significance tests to them.

Producing filtered precursor patterns from composites, and computing precursor activity indices from those, is covered in our [Index_Computation guide](https://github.com/joshdorrington/domino/blob/master/examples/precursor_index_computation.ipynb), while an introduction to assessing the predictive power of indices is in the [Index_Predictability guide](https://github.com/joshdorrington/domino/blob/master/examples/Index_Predictability.ipynb).


## Install

domino can be installed using pip:
```
python -m pip install domino-composite
```
If you want to run the worked examples in the Jupyter notebooks you will need to download the [netcdf files containing example data](https://github.com/joshdorrington/domino/releases/tag/v1-data).
