JRP case test

JRP_test.py

import numpy as np
from pyunicorn.timeseries import RecurrencePlot, RecurrenceNetwork, CrossRecurrencePlot, JointRecurrencePlot


def logistic_map(x0, r, T):
    """
    Returns a time series of length T using the logistic map
    x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0.

    INPUT: x0 - Initial condition, 0 <= x0 <= 1
            r - Bifurcation parameter, 0 <= r <= 4
            T - length of the desired time series
    TODO: Cythonize
    """
    #  Initialize the time series array
    timeSeries = np.empty(T)

    timeSeries[0] = x0
    for i in range(1, len(timeSeries)):
        xn = timeSeries[i-1]
        timeSeries[i] = r * xn * (1 - xn)

    return timeSeries

#  Parameters of logistic map
r = 3.679  # Bifurcation parameter
x0 = 0.7   # Initial value

#  Length of the time series
T = 150

#  Settings for the embedding
DIM = 1  # Embedding dimension
TAU = 0  # Embedding delay

#  Settings for the recurrence plot
EPS = 0.05  # Fixed threshold
RR = 0.05   # Fixed recurrence rate
# Distance metric in phase space ->
# Possible choices ("manhattan","euclidean","supremum")
METRIC = "supremum"

#
#  Main script
#
#  Create a time series using the logistic map
x = logistic_map(x0, r, T)
y = logistic_map(0.9, 3.680, T)
time_points = range(0, T)

print(x.shape)
print(y.shape)


jrp = JointRecurrencePlot(x, y, threshold=(0.1,0.1))
print(jrp.recurrence_matrix())