# pymor.models.iosys¶

## Module Contents¶

class pymor.models.iosys.BilinearModel(A, N, B, C, D, E=None, sampling_time=0, error_estimator=None, visualizer=None, name=None)[source]

Class for bilinear systems.

This class describes input-output systems given by

$\begin{split}E x'(t) & = A x(t) + \sum_{i = 1}^m{N_i x(t) u_i(t)} + B u(t), \\ y(t) & = C x(t) + D u(t),\end{split}$

if continuous-time, or

$\begin{split}E x(k + 1) & = A x(k) + \sum_{i = 1}^m{N_i x(k) u_i(k)} + B u(k), \\ y(k) & = C x(k) + D u(t),\end{split}$

if discrete-time, where $$E$$, $$A$$, $$N_i$$, $$B$$, $$C$$, and $$D$$ are linear operators and $$m$$ is the number of inputs.

Parameters

A

The Operator A.

N

The tuple of Operators N_i.

B

The Operator B.

C

The Operator C.

D

The Operator D or None (then D is assumed to be zero).

E

The Operator E or None (then E is assumed to be identity).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

order[source]

The order of the system (equal to A.source.dim).

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

A[source]

The Operator A.

N[source]

The tuple of Operators N_i.

B[source]

The Operator B.

C[source]

The Operator C.

D[source]

The Operator D.

E[source]

The Operator E.

class pymor.models.iosys.LTIModel(A, B, C, D=None, E=None, sampling_time=0, presets=None, solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Class for linear time-invariant systems.

This class describes input-state-output systems given by

$\begin{split}E(\mu) \dot{x}(t, \mu) & = A(\mu) x(t, \mu) + B(\mu) u(t), \\ y(t, \mu) & = C(\mu) x(t, \mu) + D(\mu) u(t),\end{split}$

if continuous-time, or

$\begin{split}E(\mu) x(k + 1, \mu) & = A(\mu) x(k, \mu) + B(\mu) u(k), \\ y(k, \mu) & = C(\mu) x(k, \mu) + D(\mu) u(k),\end{split}$

if discrete-time, where $$A$$, $$B$$, $$C$$, $$D$$, and $$E$$ are linear operators.

All methods related to the transfer function (e.g., frequency response calculation and Bode plots) are attached to the transfer_function attribute.

Parameters

A

The Operator A.

B

The Operator B.

C

The Operator C.

D

The Operator D or None (then D is assumed to be zero).

E

The Operator E or None (then E is assumed to be identity).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

presets

A dict of preset attributes or None. The dict must only contain keys that correspond to attributes of LTIModel such as poles, c_lrcf, o_lrcf, c_dense, o_dense, hsv, h2_norm, hinf_norm, l2_norm and linf_norm. Additionaly, the frequency at which the $$\mathcal{H}_\infty/\mathcal{L}_\infty$$ norm is attained can be preset with fpeak.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

order[source]

The order of the system.

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

A[source]

The Operator A.

B[source]

The Operator B.

C[source]

The Operator C.

D[source]

The Operator D.

E[source]

The Operator E.

transfer_function[source]

The transfer function.

Methods

 from_abcde_files Create LTIModel from matrices stored in .[ABCDE] files. from_files Create LTIModel from matrices stored in separate files. from_mat_file Create LTIModel from matrices stored in a .mat file. from_matrices Create LTIModel from matrices. get_ast_spectrum Compute anti-stable subset of the poles of the LTIModel. gramian Compute a Gramian. h2_norm Compute the $$\mathcal{H}_2$$-norm of the LTIModel. hankel_norm Compute the Hankel-norm of the LTIModel. hinf_norm Compute the $$\mathcal{H}_\infty$$-norm of the LTIModel. hsv Hankel singular values. l2_norm Compute the $$\mathcal{L}_2$$-norm of the LTIModel. linf_norm Compute the $$\mathcal{L}_\infty$$-norm of the LTIModel. moebius_substitution Create a transformed LTIModel by applying an arbitrary Moebius transformation. poles Compute system poles. to_abcde_files Save operators as matrices to .[ABCDE] files in Matrix Market format. to_continuous Converts a discrete-time LTIModel to a continuous-time LTIModel. to_discrete Converts a continuous-time LTIModel to a discrete-time LTIModel. to_files Write operators to files as matrices. to_mat_file Save operators as matrices to .mat file. to_matrices Return operators as matrices.
classmethod from_abcde_files(files_basename, sampling_time=0, presets=None, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create LTIModel from matrices stored in .[ABCDE] files.

Parameters

files_basename

The basename of files containing A, B, C, and optionally D and E.

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

presets

A dict of preset attributes or None. See __init__.

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

lti

The LTIModel with operators A, B, C, D, and E.

classmethod from_files(A_file, B_file, C_file, D_file=None, E_file=None, sampling_time=0, presets=None, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create LTIModel from matrices stored in separate files.

Parameters

A_file

The name of the file (with extension) containing A.

B_file

The name of the file (with extension) containing B.

C_file

The name of the file (with extension) containing C.

D_file

None or the name of the file (with extension) containing D.

E_file

None or the name of the file (with extension) containing E.

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

presets

A dict of preset attributes or None. See __init__.

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

lti

The LTIModel with operators A, B, C, D, and E.

classmethod from_mat_file(file_name, sampling_time=0, presets=None, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create LTIModel from matrices stored in a .mat file.

Supports the format used in the SLICOT benchmark collection.

Parameters

file_name

The name of the .mat file (extension .mat does not need to be included) containing A, B, and optionally C, D, and E.

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

presets

A dict of preset attributes or None. See __init__.

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

lti

The LTIModel with operators A, B, C, D, and E.

classmethod from_matrices(A, B, C, D=None, E=None, sampling_time=0, presets=None, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create LTIModel from matrices.

Parameters

A
B
C
D

The NumPy array or SciPy spmatrix D or None (then D is assumed to be zero).

E

The NumPy array or SciPy spmatrix E or None (then E is assumed to be identity).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

presets

A dict of preset attributes or None. See __init__.

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

lti

The LTIModel with operators A, B, C, D, and E.

get_ast_spectrum(ast_pole_data=None, mu=None)[source]

Compute anti-stable subset of the poles of the LTIModel.

Parameters

ast_pole_data

Can be:

• dictionary of parameters for eigs,

• list of anti-stable eigenvalues (scalars),

• tuple (lev, ew, rev) where ew contains the sorted anti-stable eigenvalues and lev and rev are VectorArrays representing the eigenvectors.

• None if anti-stable eigenvalues should be computed via dense methods.

mu

Returns

lev

VectorArray of left eigenvectors.

ew

One-dimensional NumPy array of anti-stable eigenvalues sorted from smallest to largest.

rev

VectorArray of right eigenvectors.

gramian(typ, mu=None)[source]

Compute a Gramian.

Parameters

typ

The type of the Gramian:

• 'c_lrcf': low-rank Cholesky factor of the controllability Gramian,

• 'o_lrcf': low-rank Cholesky factor of the observability Gramian,

• 'c_dense': dense controllability Gramian,

• 'o_dense': dense observability Gramian.

Note

For '*_lrcf' types, the method assumes the system is asymptotically stable. For '*_dense' types, the method assumes that the underlying Lyapunov equation has a unique solution, i.e. no pair of system poles adds to zero in the continuous-time case and no pair of system poles multiplies to one in the discrete-time case.

mu

Returns

If typ is 'c_lrcf' or 'o_lrcf', then the Gramian factor as a VectorArray from self.A.source. If typ is 'c_dense' or 'o_dense', then the Gramian as a NumPy array.

h2_norm(mu=None)[source]

Compute the $$\mathcal{H}_2$$-norm of the LTIModel.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

$$\mathcal{H}_2$$-norm.

hankel_norm(mu=None)[source]

Compute the Hankel-norm of the LTIModel.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

Hankel-norm.

hinf_norm(mu=None, return_fpeak=False, ab13dd_equilibrate=False)[source]

Compute the $$\mathcal{H}_\infty$$-norm of the LTIModel.

Note

Assumes the system is asymptotically stable. Under this is assumption the $$\mathcal{H}_\infty$$-norm is equal to the $$\mathcal{H}_\infty$$-norm. Accordingly, this method calls linf_norm.

Parameters

mu
return_fpeak

Whether to return the frequency at which the maximum is achieved.

ab13dd_equilibrate

Whether slycot.ab13dd should use equilibration.

Returns

norm

$$\mathcal{H}_\infty$$-norm.

fpeak

Frequency at which the maximum is achieved (if return_fpeak is True).

hsv(mu=None)[source]

Hankel singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

sv

One-dimensional NumPy array of singular values.

l2_norm(ast_pole_data=None, mu=None)[source]

Compute the $$\mathcal{L}_2$$-norm of the LTIModel.

The $$\mathcal{L}_2$$-norm of an LTIModel is defined via the integral

$\lVert H \rVert_{\mathcal{L}_2} = \left( \frac{1}{2 \pi} \int_{-\infty}^{\infty} \lVert H(\boldsymbol{\imath} \omega) \rVert_{\operatorname{F}}^2 \operatorname{d}\!\omega \right)^{\frac{1}{2}}.$

Parameters

ast_pole_data

Can be:

• dictionary of parameters for eigs,

• list of anti-stable eigenvalues (scalars),

• tuple (lev, ew, rev) where ew contains the anti-stable eigenvalues and lev and rev are VectorArrays representing the eigenvectors.

• None if anti-stable eigenvalues should be computed via dense methods.

mu

Returns

norm

$$\mathcal{L}_2$$-norm.

linf_norm(mu=None, return_fpeak=False, ab13dd_equilibrate=False)[source]

Compute the $$\mathcal{L}_\infty$$-norm of the LTIModel.

The $$\mathcal{L}_\infty$$-norm of an LTIModel is defined via

$\lVert H \rVert_{\mathcal{L}_\infty} = \sup_{\omega \in \mathbb{R}} \lVert H(\boldsymbol{\imath} \omega) \rVert_2.$

Parameters

mu
return_fpeak

Whether to return the frequency at which the maximum is achieved.

ab13dd_equilibrate

Whether slycot.ab13dd should use equilibration.

Returns

norm

$$\mathcal{L}_\infty$$-norm.

fpeak

Frequency at which the maximum is achieved (if return_fpeak is True).

moebius_substitution(M, sampling_time=0)[source]

Create a transformed LTIModel by applying an arbitrary Moebius transformation.

This method returns a transformed LTIModel such that the transfer function of the original and transformed LTIModel are related by a Moebius substitution of the frequency argument:

$H(s)=\tilde{H}(M(s)),$

where

$M(s) = \frac{as+b}{cs+d}$

is a Moebius transformation. See [CCA96] for details.

Parameters

M

The MoebiusTransformation that defines the frequency mapping.

sampling_time

The sampling time of the transformed system (in seconds). 0 if the system is continuous-time, otherwise a positive number. Defaults to zero.

Returns

sys

The transformed LTIModel.

poles(mu=None)[source]

Compute system poles.

Note

Assumes the systems is small enough to use a dense eigenvalue solver.

Parameters

mu

Parameter values for which to compute the systems poles.

Returns

One-dimensional NumPy array of system poles.

to_abcde_files(files_basename)[source]

Save operators as matrices to .[ABCDE] files in Matrix Market format.

Parameters

files_basename

The basename of files containing the operators.

to_continuous(method='Tustin', w0=0)[source]

Converts a discrete-time LTIModel to a continuous-time LTIModel.

Parameters

method

A string that defines the transformation method. At the moment only Tustin’s method is supported.

w0

If method=='Tustin', this parameter can be used to specify the prewarping-frequency. Defaults to zero.

Returns

sys

Continuous-time LTIModel.

to_discrete(sampling_time, method='Tustin', w0=0)[source]

Converts a continuous-time LTIModel to a discrete-time LTIModel.

Parameters

sampling_time

A positive number that denotes the sampling time of the resulting system (in seconds).

method

A string that defines the transformation method. At the moment only Tustin’s method is supported.

w0

If method=='Tustin', this parameter can be used to specify the prewarping-frequency. Defaults to zero.

Returns

sys

Discrete-time LTIModel.

to_files(A_file, B_file, C_file, D_file=None, E_file=None)[source]

Write operators to files as matrices.

Parameters

A_file

The name of the file (with extension) containing A.

B_file

The name of the file (with extension) containing B.

C_file

The name of the file (with extension) containing C.

D_file

The name of the file (with extension) containing D or None if D is a ZeroOperator.

E_file

The name of the file (with extension) containing E or None if E is an IdentityOperator.

to_mat_file(file_name)[source]

Save operators as matrices to .mat file.

Parameters

file_name

The name of the .mat file (extension .mat does not need to be included).

to_matrices()[source]

Return operators as matrices.

Returns

A
B
C
D

The NumPy array or SciPy spmatrix D or None (if D is a ZeroOperator).

E

The NumPy array or SciPy spmatrix E or None (if E is an IdentityOperator).

class pymor.models.iosys.LinearDelayModel(A, Ad, tau, B, C, D=None, E=None, sampling_time=0, error_estimator=None, visualizer=None, name=None)[source]

Class for linear delay systems.

This class describes input-state-output systems given by

$\begin{split}E x'(t) & = A x(t) + \sum_{i = 1}^q{A_i x(t - \tau_i)} + B u(t), \\ y(t) & = C x(t) + D u(t),\end{split}$

if continuous-time, or

$\begin{split}E x(k + 1) & = A x(k) + \sum_{i = 1}^q{A_i x(k - \tau_i)} + B u(k), \\ y(k) & = C x(k) + D u(k),\end{split}$

if discrete-time, where $$E$$, $$A$$, $$A_i$$, $$B$$, $$C$$, and $$D$$ are linear operators.

All methods related to the transfer function (e.g., frequency response calculation and Bode plots) are attached to the transfer_function attribute.

Parameters

A

The Operator A.

The tuple of Operators A_i.

tau

The tuple of delay times (positive floats or ints).

B

The Operator B.

C

The Operator C.

D

The Operator D or None (then D is assumed to be zero).

E

The Operator E or None (then E is assumed to be identity).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

order[source]

The order of the system (equal to A.source.dim).

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

q[source]

The number of delay terms.

tau[source]

The tuple of delay times.

A[source]

The Operator A.

The tuple of Operators A_i.

B[source]

The Operator B.

C[source]

The Operator C.

D[source]

The Operator D.

E[source]

The Operator E.

transfer_function[source]

The transfer function.

class pymor.models.iosys.LinearStochasticModel(A, As, B, C, D=None, E=None, sampling_time=0, error_estimator=None, visualizer=None, name=None)[source]

Class for linear stochastic systems.

This class describes input-state-output systems given by

$\begin{split}E \mathrm{d}x(t) & = A x(t) \mathrm{d}t + \sum_{i = 1}^q{A_i x(t) \mathrm{d}\omega_i(t)} + B u(t) \mathrm{d}t, \\ y(t) & = C x(t) + D u(t),\end{split}$

if continuous-time, or

$\begin{split}E x(k + 1) & = A x(k) + \sum_{i = 1}^q{A_i x(k) \omega_i(k)} + B u(k), \\ y(k) & = C x(k) + D u(t),\end{split}$

if discrete-time, where $$E$$, $$A$$, $$A_i$$, $$B$$, $$C$$, and $$D$$ are linear operators and $$\omega_i$$ are stochastic processes.

Parameters

A

The Operator A.

As

The tuple of Operators A_i.

B

The Operator B.

C

The Operator C.

D

The Operator D or None (then D is assumed to be zero).

E

The Operator E or None (then E is assumed to be identity).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

order[source]

The order of the system (equal to A.source.dim).

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

q[source]

The number of stochastic processes.

A[source]

The Operator A.

As[source]

The tuple of Operators A_i.

B[source]

The Operator B.

C[source]

The Operator C.

D[source]

The Operator D.

E[source]

The Operator E.

class pymor.models.iosys.PHLTIModel(J, R, G, P=None, S=None, N=None, E=None, solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Class for (continuous) port-Hamiltonian linear time-invariant systems.

This class describes input-state-output systems given by

$\begin{split}E(\mu) \dot{x}(t, \mu) & = (J(\mu) - R(\mu)) x(t, \mu) + (G(\mu) - P(\mu)) u(t), \\ y(t, \mu) & = (G(\mu) + P(\mu))^T x(t, \mu) + (S(\mu) - N(\mu)) u(t),\end{split}$

with $$E(\mu) \succeq 0$$, $$J(\mu) = -J(\mu)^T$$, $$N(\mu) = -N(\mu)^T$$ and

$\begin{split}\mathcal{R}(\mu) = \begin{bmatrix} R(\mu) & P(\mu) \\ P(\mu)^T & S(\mu) \end{bmatrix} \succeq 0.\end{split}$

All methods related to the transfer function (e.g., frequency response calculation and Bode plots) are attached to the transfer_function attribute.

Parameters

J

The Operator J.

R

The Operator R.

G

The Operator G.

P

The Operator P or None (then P is assumed to be zero).

S

The Operator S or None (then S is assumed to be zero).

N

The Operator N or None (then N is assumed to be zero).

E

The Operator E or None (then E is assumed to be identity).

solver_options

The solver options to use to solve the Lyapunov equations.

name

Name of the system.

order[source]

The order of the system.

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

J[source]

The Operator J.

R[source]

The Operator R.

G[source]

The Operator G.

P[source]

The Operator P.

S[source]

The Operator S.

N[source]

The Operator N.

E[source]

The Operator E.

transfer_function[source]

The transfer function.

Methods

 from_matrices Create PHLTIModel from matrices. gramian Compute a Gramian. h2_norm Compute the H2-norm. hankel_norm Compute the Hankel-norm. hinf_norm Compute the H_infinity-norm. hsv Hankel singular values. poles Compute system poles. to_lti Return a standard linear time-invariant system representation. to_matrices Return operators as matrices.
classmethod from_matrices(J, R, G, P=None, S=None, N=None, E=None, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create PHLTIModel from matrices.

Parameters

J
R
G
P

The NumPy array or SciPy spmatrix P or None (then P is assumed to be zero).

S

The NumPy array or SciPy spmatrix S or None (then S is assumed to be zero).

N

The NumPy array or SciPy spmatrix N or None (then N is assumed to be zero).

E

The NumPy array or SciPy spmatrix E or None (then E is assumed to be identity).

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

phlti

The PHLTIModel with operators J, R, G, P, S, N, and E.

gramian(typ, mu=None)[source]

Compute a Gramian.

Parameters

typ

The type of the Gramian:

• 'c_lrcf': low-rank Cholesky factor of the controllability Gramian,

• 'o_lrcf': low-rank Cholesky factor of the observability Gramian,

• 'c_dense': dense controllability Gramian,

• 'o_dense': dense observability Gramian.

Note

For '*_lrcf' types, the method assumes the system is asymptotically stable. For '*_dense' types, the method assumes that the underlying Lyapunov equation has a unique solution, i.e. no pair of system poles adds to zero in the continuous-time case and no pair of system poles multiplies to one in the discrete-time case.

mu

Returns

If typ is 'c_lrcf' or 'o_lrcf', then the Gramian factor as a VectorArray from self.A.source. If typ is 'c_dense' or 'o_dense', then the Gramian as a NumPy array.

h2_norm(mu=None)[source]

Compute the H2-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

H_2-norm.

hankel_norm(mu=None)[source]

Compute the Hankel-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

Hankel-norm.

hinf_norm(mu=None, return_fpeak=False, ab13dd_equilibrate=False)[source]

Compute the H_infinity-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu
return_fpeak

Should the frequency at which the maximum is achieved should be returned.

ab13dd_equilibrate

Should slycot.ab13dd use equilibration.

Returns

norm

H_infinity-norm.

fpeak

Frequency at which the maximum is achieved (if return_fpeak is True).

hsv(mu=None)[source]

Hankel singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

sv

One-dimensional NumPy array of singular values.

poles(mu=None)[source]

Compute system poles.

Note

Assumes the systems is small enough to use a dense eigenvalue solver.

Parameters

mu

Returns

One-dimensional NumPy array of system poles.

to_lti()[source]

Return a standard linear time-invariant system representation.

The representation

$A = J - R,\qquad B = G - P,\qquad C = (G + P)^T,\qquad D = S - N,\qquad E = E$

is returned.

Returns

lti

LTIModel equivalent to the port-Hamiltonian model.

to_matrices()[source]

Return operators as matrices.

Returns

J
R
G
P
S

The NumPy array or SciPy spmatrix S or None (if Cv is a ZeroOperator).

N

The NumPy array or SciPy spmatrix N or None (if Cv is a ZeroOperator).

E
class pymor.models.iosys.SecondOrderModel(M, E, K, B, Cp, Cv=None, D=None, sampling_time=0, solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Class for linear second order systems.

This class describes input-output systems given by

$\begin{split}M(\mu) \ddot{x}(t, \mu) + E(\mu) \dot{x}(t, \mu) + K(\mu) x(t, \mu) & = B(\mu) u(t), \\ y(t, \mu) & = C_p(\mu) x(t, \mu) + C_v(\mu) \dot{x}(t, \mu) + D(\mu) u(t),\end{split}$

if continuous-time, or

$\begin{split}M(\mu) x(k + 2, \mu) + E(\mu) x(k + 1, \mu) + K(\mu) x(k, \mu) & = B(\mu) u(k), \\ y(k, \mu) & = C_p(\mu) x(k, \mu) + C_v(\mu) x(k + 1, \mu) + D(\mu) u(k),\end{split}$

if discrete-time, where $$M$$, $$E$$, $$K$$, $$B$$, $$C_p$$, $$C_v$$, and $$D$$ are linear operators.

All methods related to the transfer function (e.g., frequency response calculation and Bode plots) are attached to the transfer_function attribute.

Parameters

M

The Operator M.

E

The Operator E.

K

The Operator K.

B

The Operator B.

Cp

The Operator Cp.

Cv

The Operator Cv or None (then Cv is assumed to be zero).

D

The Operator D or None (then D is assumed to be zero).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

order[source]

The order of the system (equal to M.source.dim).

dim_input[source]

The number of inputs.

dim_output[source]

The number of outputs.

M[source]

The Operator M.

E[source]

The Operator E.

K[source]

The Operator K.

B[source]

The Operator B.

Cp[source]

The Operator Cp.

Cv[source]

The Operator Cv.

D[source]

The Operator D.

transfer_function[source]

The transfer function.

Methods

 from_files Create LTIModel from matrices stored in separate files. from_matrices Create a second order system from matrices. gramian Compute a second-order Gramian. h2_norm Compute the $$\mathcal{H}_2$$-norm. hankel_norm Compute the Hankel-norm. hinf_norm Compute the $$\mathcal{H}_\infty$$-norm. poles Compute system poles. psv Position singular values. pvsv Position-velocity singular values. to_files Write operators to files as matrices. to_lti Return a first order representation. to_matrices Return operators as matrices. vpsv Velocity-position singular values. vsv Velocity singular values.
classmethod from_files(M_file, E_file, K_file, B_file, Cp_file, Cv_file=None, D_file=None, sampling_time=0, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create LTIModel from matrices stored in separate files.

Parameters

M_file

The name of the file (with extension) containing A.

E_file

The name of the file (with extension) containing E.

K_file

The name of the file (with extension) containing K.

B_file

The name of the file (with extension) containing B.

Cp_file

The name of the file (with extension) containing Cp.

Cv_file

None or the name of the file (with extension) containing Cv.

D_file

None or the name of the file (with extension) containing D.

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

state_id

Id of the state space.

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

some

The SecondOrderModel with operators M, E, K, B, Cp, Cv, and D.

classmethod from_matrices(M, E, K, B, Cp, Cv=None, D=None, sampling_time=0, state_id='STATE', solver_options=None, error_estimator=None, visualizer=None, name=None)[source]

Create a second order system from matrices.

Parameters

M
E
K
B
Cp
Cv

The NumPy array or SciPy spmatrix Cv or None (then Cv is assumed to be zero).

D

The NumPy array or SciPy spmatrix D or None (then D is assumed to be zero).

sampling_time

0 if the system is continuous-time, otherwise a positive number that denotes the sampling time (in seconds).

solver_options

The solver options to use to solve the Lyapunov equations.

error_estimator

An error estimator for the problem. This can be any object with an estimate_error(U, mu, model) method. If error_estimator is not None, an estimate_error(U, mu) method is added to the model which will call error_estimator.estimate_error(U, mu, self).

visualizer

A visualizer for the problem. This can be any object with a visualize(U, model, ...) method. If visualizer is not None, a visualize(U, *args, **kwargs) method is added to the model which forwards its arguments to the visualizer’s visualize method.

name

Name of the system.

Returns

lti

The SecondOrderModel with operators M, E, K, B, Cp, Cv, and D.

gramian(typ, mu=None)[source]

Compute a second-order Gramian.

Parameters

typ

The type of the Gramian:

• 'pc_lrcf': low-rank Cholesky factor of the position controllability Gramian,

• 'vc_lrcf': low-rank Cholesky factor of the velocity controllability Gramian,

• 'po_lrcf': low-rank Cholesky factor of the position observability Gramian,

• 'vo_lrcf': low-rank Cholesky factor of the velocity observability Gramian,

• 'pc_dense': dense position controllability Gramian,

• 'vc_dense': dense velocity controllability Gramian,

• 'po_dense': dense position observability Gramian,

• 'vo_dense': dense velocity observability Gramian.

Note

For '*_lrcf' types, the method assumes the system is asymptotically stable. For '*_dense' types, the method assumes that the underlying Lyapunov equation has a unique solution, i.e. no pair of system poles adds to zero in the continuous-time case and no pair of system poles multiplies to one in the discrete-time case.

mu

Returns

If typ is 'pc_lrcf', 'vc_lrcf', 'po_lrcf' or 'vo_lrcf', then the Gramian factor as a VectorArray from self.M.source. If typ is 'pc_dense', 'vc_dense', 'po_dense' or 'vo_dense', then the Gramian as a NumPy array.

h2_norm(mu=None)[source]

Compute the $$\mathcal{H}_2$$-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

$$\mathcal{H}_2$$-norm.

hankel_norm(mu=None)[source]

Compute the Hankel-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

norm

Hankel-norm.

hinf_norm(mu=None, return_fpeak=False, ab13dd_equilibrate=False)[source]

Compute the $$\mathcal{H}_\infty$$-norm.

Note

Assumes the system is asymptotically stable.

Parameters

mu
return_fpeak

Should the frequency at which the maximum is achieved should be returned.

ab13dd_equilibrate

Should slycot.ab13dd use equilibration.

Returns

norm

$$\mathcal{H}_\infty$$.

fpeak

Frequency at which the maximum is achieved (if return_fpeak is True).

poles(mu=None)[source]

Compute system poles.

Note

Assumes the systems is small enough to use a dense eigenvalue solver.

Parameters

mu

Returns

One-dimensional NumPy array of system poles.

psv(mu=None)[source]

Position singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

One-dimensional NumPy array of singular values.

pvsv(mu=None)[source]

Position-velocity singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

One-dimensional NumPy array of singular values.

to_files(M_file, E_file, K_file, B_file, Cp_file, Cv_file=None, D_file=None)[source]

Write operators to files as matrices.

Parameters

M_file

The name of the file (with extension) containing M.

E_file

The name of the file (with extension) containing E.

K_file

The name of the file (with extension) containing K.

B_file

The name of the file (with extension) containing B.

Cp_file

The name of the file (with extension) containing Cp.

Cv_file

The name of the file (with extension) containing Cv or None if D is a ZeroOperator.

D_file

The name of the file (with extension) containing D or None if D is a ZeroOperator.

to_lti()[source]

Return a first order representation.

The first order representation

$\begin{split}\begin{bmatrix} I & 0 \\ 0 & M \end{bmatrix} \frac{\mathrm{d}}{\mathrm{d}t}\! \begin{bmatrix} x(t) \\ \dot{x}(t) \end{bmatrix} & = \begin{bmatrix} 0 & I \\ -K & -E \end{bmatrix} \begin{bmatrix} x(t) \\ \dot{x}(t) \end{bmatrix} + \begin{bmatrix} 0 \\ B \end{bmatrix} u(t), \\ y(t) & = \begin{bmatrix} C_p & C_v \end{bmatrix} \begin{bmatrix} x(t) \\ \dot{x}(t) \end{bmatrix} + D u(t)\end{split}$

is returned.

Returns

lti

LTIModel equivalent to the second-order model.

to_matrices()[source]

Return operators as matrices.

Returns

M
E
K
B
Cp
Cv

The NumPy array or SciPy spmatrix Cv or None (if Cv is a ZeroOperator).

D

The NumPy array or SciPy spmatrix D or None (if D is a ZeroOperator).

vpsv(mu=None)[source]

Velocity-position singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

One-dimensional NumPy array of singular values.

vsv(mu=None)[source]

Velocity singular values.

Note

Assumes the system is asymptotically stable.

Parameters

mu

Returns

One-dimensional NumPy array of singular values.

pymor.models.iosys.sparse_min_size(value=1000)[source]

Return minimal sparse problem size for which to warn about converting to dense.