Generate Data

skccm comes with function to generate test data. Quickly explore the behavior of different systems.

skccm.data.coupled_logistic(rx1, rx2, b12, b21, ts_length, random_start=False)

Coupled logistic map.

Parameters:
  • rx1 (float) – Parameter that determines chaotic behavior of the x1 series.
  • rx2 (float) – Parameter that determines chatotic behavior of the x2 series.
  • b12 (float) – Influence of x1 on x2.
  • b21 (float) – Influence of x2 on x1.
  • ts_length (int) – Length of the calculated time series.
  • random_start (bool) – Random initialization of starting conditions.
Returns:

  • x1 (1d array) – Array of length (ts_length,) that stores the values of the x series.
  • x2 (1d array) – Array of length (ts_length,) that stores the values of the y series.
skccm.data.driven_rand_logistic(rx2, b12, ts_length, random_start=False)

Logistic map with random forcing. x1 is the random array and x2 is the logistic map.

Parameters:
  • rx2 (float) – Parameter that determines chatotic behavior of the x2 series.
  • b12 (float) – Influence of x1 on x2.
  • ts_length (int) – Length of the calculated time series.
  • random_start (Boolean) – Random initialization of starting conditions.
Returns:

  • x1 (array) – Array of length (ts_length,)
  • x2 (array) – Array of length (ts_length,)
skccm.data.driving_sin(rx2, b12, ts_length, random_start=False)

Sine wave driving a logistic map.

Parameters:
  • rx2 (float) – Parameter that determines chatotic behavior of the x2 series.
  • b12 (float) – Influence of x1 on x2.
  • ts_length (int) – Length of the calculated time series.
  • random_start (Boolean) – Random initialization of starting conditions.
Returns:

  • x1 (array) – Array of length (ts_length,) that stores the values of the x series.
  • x2 (array) – Array of length (ts_length,) that stores the values of the y series.
skccm.data.lagged_coupled_logistic(rx1, rx2, b12, b21, ts_length, random_start=False)

Coupled logistic map. x1 is driven by random lags of x2.

Parameters:
  • rx1 (float) – Parameter that determines chaotic behavior of the x1 series.
  • rx2 (float) – Parameter that determines chatotic behavior of the x2 series.
  • b12 (float) – Influence of x1 on x2.
  • b21 (float) – Influence of x2 on x1.
  • ts_length (int) – Length of the calculated time series.
  • random_start (Boolean) – Random initialization of starting conditions.
Returns:

  • x1 (array) – Array of length (ts_length,) that stores the values of the x series.
  • x2 (array) – Array of length (ts_length,) that stores the values of the y series.
skccm.data.lorenz(sz=10000, noise=0, max_t=100.0)

Integrates the lorenz equation.

Parameters:
  • sz (int) – Length of the time series to be integrated.
  • noise (float) – Amplitude of noise to be added to the lorenz equation.
  • max_t (float) – Length of time to solve the lorenz equation over.
Returns:

X – Solutions to the Lorenz equations. Columns are X,Y,Z.
Return type:

2D array