Metadata-Version: 2.1
Name: cyclicityanalysis
Version: 1.0.1
Summary: Performs Cyclicity Analysis on A Collection of Time-Series
Author-email: Vivek Kaushik <vskaush2@illinois.edu>
License: MIT
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Provides-Extra: optional_dependencies
Requires-Dist: pytest ; extra == 'optional_dependencies'
Requires-Dist: jupyter ; extra == 'optional_dependencies'
Requires-Dist: requests ; extra == 'optional_dependencies'
Requires-Dist: scipy ; extra == 'optional_dependencies'

# Cyclicity Analysis of Time-Series
This repository contains a working implementation of Cyclicity Analysis, which is a pattern recognition technique for analyzing the leader follower dynamics of multiple time-series.

## Requirements
Download [Python >=3.7](https://www.python.org/downloads/)

## Installation

```bash
pip3 install cyclicityanalysis
```

## Usage

```python
from cyclicityanalysis.orientedarea import *
from cyclicityanalysis.coom import *

df = pd.DataFrame([[0, 1], [1, 0], [0, 0]], columns=['0', '1'])


oa = OrientedArea(df)
# Returns the lead lag matrix of df as a dataframe
lead_lag_df = oa.compute_lead_lag_df()

coom = COOM(lead_lag_df)
# Returns leading eigenvector of lead lag matrix as a numpy array
leading_eigenvector = coom.get_leading_eigenvector()
lead_lag_df , leading_eigenvector
 ```
