pyhomogeneity 1.1
A python package for homogeneity test.
License: MIT

# pyHomogeneity
[![Build Status](https://travis-ci.org/mmhs013/pyHomogeneity.svg?branch=master)](https://travis-ci.org/mmhs013/pyHomogeneity)
[![PyPI](https://img.shields.io/pypi/v/pyhomogeneity)](https://pypi.org/project/pyhomogeneity/)
[![PyPI - License](https://img.shields.io/pypi/l/pyhomogeneity)](https://pypi.org/project/pyhomogeneity/)
[![PyPI - Status](https://img.shields.io/pypi/status/pyhomogeneity)](https://pypi.org/project/pyhomogeneity/)
[![Downloads](https://pepy.tech/badge/pyhomogeneity)](https://pepy.tech/project/pyhomogeneity)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyhomogeneity)](https://pypi.org/project/pyhomogeneity/)
[![DOI](https://zenodo.org/badge/255952268.svg)](https://zenodo.org/badge/latestdoi/255952268)

## What is the Homogeneity Test ?
The homogeneity test is a statistical test method, that checks if two (or more) datasets come from the same distribution or not. In a time series, the homogeneity test is applied to detect one (or more) change/breakpoint in the series.  This breakpoint occurs where the data set changes its distribution. Lots of statistical analyses require a homogenous dataset. That's why it is an important test in statistical analysis.

`pyHomogeneity` is a pure Python implementation for the homogeneity test. There are several tests available to check the homogeneity of a time series. pyHomogeneity package can perform six commonly used Homogeneity test listed below:


1. **Pettitt test (*pettitt_test*)** 

2. **Standard Normal Homogeinity Test (SNHT) Test (*snht_test*)** 

3. **Buishand Q Test (*buishand_q_test*)**

4. **Buishand's Range Test (*buishand_range_test*):**

5. **Buishand's Likelihood Ration Test (*buishand_likelihood_ratio_test*)**

6. **Buishand U Test (*buishand_u_test*)** 

## Function details:

All Homogeneity test functions have almost similar input parameters. These are:

- **x**:   a vector (list, numpy array or pandas series) data
- **alpha**: significance level (0.05 default)
- **sim**: No. of monte carlo simulation for p-value calculation.

And all Homogeneity tests return a named tuple which contained:

- **h**: True (if data is nonhomogeneous) or False (if data is homogeneous)
- **cp**: probable change point location
- **p**: p value of the significance test
- **U/T/Q/R/V**: test statistics which depends on the test method
- **avg**: mean values at before and after the change point


## Dependencies

For the installation of `pyHomogeneity`, the following packages are required:
- [numpy](https://www.numpy.org/)
- [scipy](https://www.scipy.org/)

## Installation

You can install `pyHomogeneity` using pip. For Linux users

```python
sudo pip install pyhomogeneity
```

or, for Windows user

```python
pip install pyhomogeneity
```

Or you can clone the repo and install it:

```bash
git clone https://github.com/mmhs013/pyhomogeneity
cd pyhomogeneity
python setup.py install
```

## Tests

`pyHomogeneity` is automatically tested using `pytest` package on each commit [here](https://travis-ci.org/mmhs013/pyHomogeneity/), but the tests can be manually run:

```
pytest -v
```

## Usage

A quick example of `pyHomogeneity` usage is given below. Several more examples are provided [here](https://github.com/mmhs013/pyHomogeneity/blob/master/Examples/).

```python
import numpy as np
import pyhomogeneity as hg

# Data generation for analysis
data = np.random.rand(360,1)

result = hg.pettitt_test(data)
print(result)
```
Output are like this:
```python
Pettitt_Test(h=False, cp=89, p=0.1428, U=3811.0, avg=mean(mu1=0.5487521427805625, mu2=0.46884198890609463))
```
Whereas, the output is a named tuple, so user can call by name for specific result:
```python
print(result.cp)
print(result.avg.mu1)
```
or, user can directly unpack results like this:
```python
h, cp, p, U, mu = hg.pettitt_test(x, 0.05)
```

## Contributions

`pyHomogeneity` is a community project and welcomes contributions. Additional information can be found in the [contribution guidelines](https://github.com/mmhs013/pyHomogeneity/blob/master/CONTRIBUTING.md)


## Code of Conduct

`pyHomogeneity` wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/mmhs013/pyHomogeneity/blob/master/CODE_OF_CONDUCT.md)


## References

1. Alexandersson, H., 1986. A homogeneity test applied to precipitation data. Journal of climatology, 6(6), pp.661-675. doi:[10.1002/joc.3370060607](https://doi.org/10.1002/joc.3370060607)

2. Buishand, T.A., 1982. Some methods for testing the homogeneity of rainfall records. Journal of hydrology, 58(1-2), pp.11-27. doi:[10.1016/0022-1694(82)90066-X](https://doi.org/10.1016/0022-1694(82)90066-X)

3. Pettitt, A.N., 1979. A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), pp.126-135. doi:[10.2307/2346729](https://doi.org/10.2307/2346729)

4. Pohlert, T., 2016. Package 'trend'. Title Non-Parametric Trend Tests and Change-Point Detection.

5. Verstraeten, G., Poesen, J., Demaree, G. and Salles, C., 2006. Long-term (105 years) variability in rain erosivity as derived from 10-min rainfall depth data for Ukkel (Brussels, Belgium): Implications for assessing soil erosion rates. Journal of Geophysical Research: Atmospheres, 111(D22). doi:[10.1029/2006JD007169](https://doi.org/10.1029/2006JD007169)