A python implementation of the Kuramoto model
Project description
Kuramoto Model
A python implementation of the kuramoto model.
Installation
pip install kuramoto_model
Usage
Import the model,
from kuramoto_model.kuramoto_model import Kuramoto
Initialise the model with the following,
- Number of neurons
n
- Coupling constant
k
- Timeseries
timeseries
: the timepoints to log results at - Intrinsic Frequencies
omega_n
: defaults to n random values from a normal distribution - Initial Phases
theta_n
: defaults to n random values between 0 and 2pi - Adjacency Matrix
adjacency_nxn
: defaults to all to all coupling (without self-coupling)
n = 100
k = 0.8
ts = np.linspace(0, 100, 1000)
model = Kuramoto(n, k, ts)
Find the phase, coherence and mean frequency timeseries,
phases = model.phase_timeseries()
coherences = model.coherence_timeseries()
mean_freq = model.mean_frequency_timeseries()
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