Research Investigation of Timeseries with Multiday Oscillations
Project description
Rhythmic 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:
>>> 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()
To run individual modules:
>>> ritmo.run_edm() # Empirical dynamical modelling module
>>> ritmo.run_plv() # Phase locking value module
>>> ritmo.run_mutual_information() # Mutual information module
Releasing
Releases are published automatically when a tag is pushed to GitHub.
export RELEASE=x.x.x
git commit --allow-empty -m "Release $RELEASE"
git tag -a $RELEASE -m "Version $RELEASE"
git push upstream --tags
Project details
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