Metadata-Version: 2.1
Name: xmca
Version: 0.1.0
Summary: Maximum Covariance Analysis in Python
Home-page: https://github.com/nicrie/xmca
Author: Niclas Rieger
Author-email: niclasrieger@gmail.com
License: GPL-3.0
Keywords: mca
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.19.2)
Requires-Dist: xarray (>=0.16.2)
Requires-Dist: matplotlib (>=3.3.2)
Requires-Dist: statsmodels (>=0.12.2)
Requires-Dist: tqdm
Requires-Dist: cartopy (>=0.18.0)

# Maximum Covariance Analysis in Python
Maximum Covariance Analysis (MCA) maximises the temporal covariance between two different 
data fields and is closely related to Principal Component Analysis (PCA) / Empirical 
Orthogonal Function (EOF) analysis, which maximises the variance within a single data 
field. MCA allows to extract the dominant co-varying patterns between two different data 
fields.


The module `xmca` works with `numpy.ndarray` and `xarray.DataArray` as input fields.

## Installation 
```
pip install xmca
```

## Testing
After cloning the repository
```
python -m unittest discover -v -s tests/
```

## Core Features
- Standard PCA/MCA
- Rotated PCA/MCA
	- Orthogonal Varimax rotation
	- Oblique Promax rotation
- Complex PCA/MCA (also known as Hilbert EOF analysis)
	- Optimised Theta model extension
- normalization of input data
- latitude correction to compensate for stretched areas in higher latitutes



