Metadata-Version: 2.1
Name: spatialentropy
Version: 0.0.2
Summary: A python implementation of spatial entropy
Home-page: https://github.com/Mr-Milk/SpatialEntropy
Author: Mr-Milk
Author-email: zym.zym1220@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scikit-learn

# SpatialEntropy

![Test](https://github.com/Mr-Milk/SpatialEntropy/workflows/Test/badge.svg) [![PyPI version](https://badge.fury.io/py/spatialentropy.svg)](https://badge.fury.io/py/spatialentropy)

This is a python implementation of spatial entropy, inspired by the R package *spatentropy*. For now, two spatial entropy
methods have been implemented:

- Leibovici’s entropy
- Altieri's entropy



## Installation

It's available on PyPI

```shell
pip install spatialentropy
```


## Usage

Let's generated some fake data first:

```python
import numpy as np

points = 100 * np.random.randn(10000, 2) + 1000
types = np.random.choice(range(30), 10000)
```

Here we have 10,000 points and then we assigned each point with a category from 30 categories.



To calculate the leibovici entropy, we need to set up a distance to define the co-occurrences.

```python
from spatialentropy import leibovici_entropy

# here we set the distance d into 5
e = leibovici_entropy(points, types, 5)

e.entropy # to get the entropy value
e.adj_matrix # to get the adjacency matrix
e.pairs_counts # to get the counts for each pair of co-occurrences
```



To calculate the altieri entropy, we need to set up intervals to define the co-occurrences.

```python
from spatialentropy import altieri_entropy

# if the cut is set as a number, it means how many times to cut evenly from [0,max]
# there for it will generate cut + 1 intervals
# if the cut is an array, it lets you define your own intervals
# e = leibovici_entropy(points, types, cut=[0,4,10])
e = leibovici_entropy(points, types, cut=2)

e.entropy # to get the entropy value, e.entropy = e.mutual_info + e.residue
e.mutual_info # the spatial mutual information
e.residue # the spatial residue entropy
e.adj_matrix # to get the adjacency matrix
```


