NormalModeAnalyzer

class pylimer_tools_cpp.NormalModeAnalyzer(self: pylimer_tools_cpp.NormalModeAnalyzer, spring_from: list[int], spring_to: list[int])

Bases: pybind11_object

Compute the normal modes and predict the loss/storage moduli.

Please cite Gusev and Bernhard [GB24] if you use this method in your work.

Initialize the NormalModeAnalyzer with the bonds (edges).

Constructs the connectivity matrix from the given edges.

Parameters:

• spring_from – Vector of starting node indices for springs/bonds

• spring_to – Vector of ending node indices for springs/bonds

Methods Summary

evaluate_loss_modulus(self, omega)	Evaluate the loss modulus \(G''(\omega)\).
evaluate_storage_modulus(self, omega)	Evaluate the storage modulus \(G'(\omega)\).
evaluate_stress_autocorrelation(self, t)	Evaluate stress autocorrelation \(C(t)\).
find_all_eigenvalues(self[, ...])	Find all eigenvalues using a dense solver.
find_sparse_eigenvalues(self, nr_of_eigenvalues)	Find the k smallest eigenvalues using a sparse solver.
get_eigenvalues(self)	Get the eigenvalues.
get_eigenvectors(self)	Get eigenvectors.
get_matrix(self)	Get the assembled connectivity matrix.
get_matrix_size(self)	Get the size of the matrix (the maximum number of eigenvalues that could be queried).
get_nr_of_soluble_clusters(self)	Get the number of soluble clusters (eigenvalues = 0).
set_eigenvalues(self, eigenvalues)	Set the eigenvalues, e.g. if you use an external solver.
set_eigenvectors(self, eigenvectors)	Set eigenvectors, e.g. if you use an external solver.

Methods Documentation

evaluate_loss_modulus(self: pylimer_tools_cpp.NormalModeAnalyzer, omega: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]

Evaluate the loss modulus \(G''(\omega)\). Yet misses the conversion factor.

Parameters:

omega – Angular frequencies

Returns:

Loss modulus values

evaluate_storage_modulus(self: pylimer_tools_cpp.NormalModeAnalyzer, omega: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]

Evaluate the storage modulus \(G'(\omega)\). Yet misses the conversion factor.

Parameters:

omega – Angular frequencies

Returns:

Storage modulus values

evaluate_stress_autocorrelation(self: pylimer_tools_cpp.NormalModeAnalyzer, t: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]

Evaluate stress autocorrelation \(C(t)\).

Parameters:

t – The time at which to evaluate the stress autocorrelation

Returns:

Stress autocorrelation values

find_all_eigenvalues(self: pylimer_tools_cpp.NormalModeAnalyzer, compute_eigenvectors: bool = False) → None

Find all eigenvalues using a dense solver.

Parameters:

compute_eigenvectors – Whether to also compute eigenvectors (default: False)

Returns:

True if computation was successful

find_sparse_eigenvalues(self: pylimer_tools_cpp.NormalModeAnalyzer, nr_of_eigenvalues: int, compute_eigenvectors: bool = False) → None

Find the k smallest eigenvalues using a sparse solver.

Parameters:

• nr_of_eigenvalues – Number of smallest eigenvalues to find

• compute_eigenvectors – Whether to also compute eigenvectors (default: False)

Returns:

True if computation was successful

get_eigenvalues(self: pylimer_tools_cpp.NormalModeAnalyzer) → numpy.ndarray[numpy.float64[m, 1]]

Get the eigenvalues.

Returns:

Vector of eigenvalues

get_eigenvectors(self: pylimer_tools_cpp.NormalModeAnalyzer) → numpy.ndarray[numpy.float64[m, n]]

Get eigenvectors.

Returns:

Matrix of eigenvectors

get_matrix(self: pylimer_tools_cpp.NormalModeAnalyzer) → scipy.sparse.csc_matrix[numpy.float64]

Get the assembled connectivity matrix.

Returns:

The connectivity matrix

get_matrix_size(self: pylimer_tools_cpp.NormalModeAnalyzer) → int

Get the size of the matrix (the maximum number of eigenvalues that could be queried).

Returns:

Size of the connectivity matrix

get_nr_of_soluble_clusters(self: pylimer_tools_cpp.NormalModeAnalyzer) → int

Get the number of soluble clusters (eigenvalues = 0).

Returns:

Number of soluble clusters

set_eigenvalues(self: pylimer_tools_cpp.NormalModeAnalyzer, eigenvalues: numpy.ndarray[numpy.float64[m, 1]]) → None

Set the eigenvalues, e.g. if you use an external solver.

Parameters:

eigenvalues – Vector of eigenvalues to set

set_eigenvectors(self: pylimer_tools_cpp.NormalModeAnalyzer, eigenvectors: numpy.ndarray[numpy.float64[m, n]]) → None

Set eigenvectors, e.g. if you use an external solver.

Parameters:

eigenvectors – Matrix of eigenvectors to set