DMD¶

Derived module from dmdbase.py for classic dmd.

`DMD.atilde`	Get the reduced Koopman operator A, called A tilde.
`DMD.dmd_timesteps`	Get the timesteps of the reconstructed states.
`DMD.dynamics`	Get the time evolution of each mode.
`DMD.eigs`	Get the eigenvalues of A tilde.
`DMD.fit`	Compute the Dynamic Modes Decomposition to the input data.
`DMD.modes`	Get the matrix containing the DMD modes, stored by column.
`DMD.original_timesteps`	Get the timesteps of the original snapshot.
`DMD.plot_eigs`	Plot the eigenvalues.
`DMD.plot_modes_2D`	Plot the DMD Modes.
`DMD.plot_snapshots_2D`	Plot the snapshots.
`DMD.reconstructed_data`	Get the reconstructed data.
`DMD.snapshots`	Get the original input data.

class DMD(svd_rank=0, tlsq_rank=0, exact=False, opt=False, rescale_mode=None, forward_backward=False, sorted_eigs=False, tikhonov_regularization=None)[source]

Bases: pydmd.dmdbase.DMDBase

Dynamic Mode Decomposition

Parameters

svd_rank (int or float) – the rank for the truncation; If 0, the method computes the optimal rank and uses it for truncation; if positive interger, the method uses the argument for the truncation; if float between 0 and 1, the rank is the number of the biggest singular values that are needed to reach the ‘energy’ specified by svd_rank; if -1, the method does not compute truncation.
tlsq_rank (int) – rank truncation computing Total Least Square. Default is 0, that means TLSQ is not applied.
exact (bool) – flag to compute either exact DMD or projected DMD. Default is False.
opt (bool or int) – argument to control the computation of DMD modes amplitudes. See DMDBase. Default is False.
rescale_mode ({'auto'} or None or numpy.ndarray) – Scale Atilde as shown in 10.1016/j.jneumeth.2015.10.010 (section 2.4) before computing its eigendecomposition. None means no rescaling, ‘auto’ means automatic rescaling using singular values, otherwise the scaling factors.
forward_backward (bool) – If True, the low-rank operator is computed like in fbDMD (reference: https://arxiv.org/abs/1507.02264). Default is False.
sorted_eigs ({'real', 'abs'} or False) – Sort eigenvalues (and modes/dynamics accordingly) by magnitude if sorted_eigs=’abs’, by real part (and then by imaginary part to break ties) if sorted_eigs=’real’. Default: False.
tikhonov_regularization (int or float) – Tikhonov parameter for the regularization. If None, no regularization is applied, if float, it is used as the \lambda tikhonov parameter.

fit(X)[source]

Compute the Dynamic Modes Decomposition to the input data.

Parameters: X (numpy.ndarray or iterable) – the input snapshots.

predict(X)[source]

Predict the output Y given the input X using the fitted DMD model.

Xnumpy array: Input data.

Ynumpy array: Predicted output.