Basic usage of tidynamics

This notebook demonstrates the use of the library tidynamics.

tidynamics is open-source and developed for research purposes. Consider citing the corresponding article if you use it in research or teaching material: status

In [ ]:
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import tidynamics

Computing the autocorrelation function

We generate random numbers uniformly in the range -1..1 and compute their autocorrelation function using tidynamics's acf routine.

Note: the numerical results for large lags contain fewer samples than for short lags and are not accurate. This is intrinsic to the computation and not a limitation of the algorithm.

In [ ]:
data = -1+2*np.random.random(size=1000)

plt.figure()
plt.plot(data)
plt.xlabel('time')
plt.title('data')

plt.figure()
plt.plot(tidynamics.acf(data))
plt.xlabel('lag')
plt.title('autocorrelation function of data')

Computing the mean-square displacement

We generate a random walk using +1/-1 random steps and compute the corresponding mean-square displacement using tidynamics' msd routine.

Note: the numerical results for large lags contain fewer samples than for short lags and are not accurate. This is intrinsic to the computation and not a limitation of the algorithm.

In [ ]:
# Generate random numbers of +1 or -1
steps = -1 + 2*np.random.randint(0, 2, size=1000)
# Add the steps to obtain the position
position = np.cumsum(steps)

plt.figure()
plt.plot(position)
plt.xlabel('time')
plt.title('Position as a function of time for a random walk')

plt.figure()
plt.plot(tidynamics.msd(position))
plt.xlabel('lag')
plt.title('Mean-square displacement for the random walk')