Metadata-Version: 2.1
Name: RITMO
Version: 1.2.0
Summary: Research Investigation of Timeseries with Multiday Oscillations
Home-page: https://github.com/rachel-stirling/RITMO
Author: Rachel E. Stirling
Author-email: rachelstirling1@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas (==1.3.5)
Requires-Dist: matplotlib (==3.5.3)
Requires-Dist: numpy (==1.21.6)
Requires-Dist: pyEDM (==1.14.0.0)
Requires-Dist: pymannkendall (==1.4.3)
Requires-Dist: pycwt (==0.3.0a22)
Requires-Dist: scikit-learn (==1.0.2)
Requires-Dist: scipy (==1.7.3)

## Research Investigation of Timeseries with Multiday Oscillations (RITMO)

---
This package provides a python toolbox for assessing the slow-drifting correlation and causation between two oscialltory timeseries' with multiday patterns. It includes three well-estabilised approaches:

* Empirical Dynamic Modelling (also known as EDM)
* Phase locking value
* Mutual information

---

## Installation

Command line using the Python pip module: `python -m pip install ritmo`

---

## Usage

Example usage at the python prompt:

```python
>>> from ritmo import Ritmo
>>> import numpy as np
>>> x = np.arange(0, 100*24*3.6e6, 3.6e6) # UNIX timestamps in milliseconds
>>> y1 = np.random.random(x.size) # first random timeseries
>>> y2 = np.random.random(x.size) # second random timeseries
>>> ritmo = Ritmo(y1 = y1, y2 = y2, x1 = x)
>>> ritmo.run()
```

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### References

Stirling et al. 2022. A methodology to assess cyclical correlates: case study of the heart and the epileptic brain.
