Linear utility functions


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get_linear_model

 get_linear_model (X:numpy.ndarray, X_prime:numpy.ndarray,
                   sorted:bool=True)

Returns the linear model A such that X’ = AX

Type Default Details
X ndarray data matrix (state_vars, time_steps)
X_prime ndarray shifted data matrix (state_vars, time_steps)
sorted bool True sort eigenvalues and eigenvectors
Returns Tuple (eigenvalues, eigenvectors)

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sort_eigvals_eigvecs

 sort_eigvals_eigvecs (eigvals:numpy.ndarray, eigvecs:numpy.ndarray)
Type Details
eigvals ndarray eigenvalues
eigvecs ndarray eigenvectors
Returns Tuple (eigenvalues, eigenvectors)

DMD Utils


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fast_predict

 fast_predict (y:jax.Array, inv_modes:jax.Array, fwd_modes:jax.Array,
               eigs:jax.Array, lenght:int=None)
Type Default Details
y Array gridsize
inv_modes Array
fwd_modes Array
eigs Array
lenght int None

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fit_dmd_to_sample

 fit_dmd_to_sample (x:jax.Array, r:int=50)
Type Default Details
x Array
r int 50
Returns DMD (timesteps, gridpoints)

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hankelize

 hankelize (u:jax.Array, d:int=2)

Utilities to replace weights


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set_params_from_linear

 set_params_from_linear (params:Dict, single_eigvals:jax.Array,
                         single_lstvecs:jax.Array, model:str)
Type Details
params Dict
single_eigvals Array
single_lstvecs Array
model str “lru” or “koopman”
Returns Dict

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get_linear_approximation

 get_linear_approximation (y:jax.Array, r:int=50, method:str='dmd')
Type Default Details
y Array (time_steps, grid_size)
r int 50
method str dmd “dmd”, “full”, “analytical”
Returns Tuple