Just because OpenSees is open source does not mean it is a fully transparent box. This is mostly because documentation has lagged behind development. So, pessimists would say the box is semi-opaque while optimists would characterize it as semi-transparent. But a few parts of OpenSees are definitely housed in an opaque box.

Take, for instance, the stiffness matrix. We received many requests for functionality to extract the stiffness matrix for post-processing and offline calculations, e.g., in MATLAB. As a result, the `printA`

function was added a few years ago. This function returns the left-hand side matrix in whatever linear system of equations, , OpenSees currently has in memory.

Note that the `printA`

function only works with the `FullGeneral`

system of equations, which stores the entire matrix. Getting the non-zeros out of sparse matrix storage and filling in zeros where needed is just not worth the effort.

With no input arguments, `printA`

prints the matrix to the standard output stream, which is useless for any model with more than two DOFs (). There is, however, an option to write the matrix to a file with `printA -file filename`

. You can then load this file into MATLAB. In OpenSeesPy, there is an option to return the matrix as a list to the interpreter, `printA('-ret')`

. More on this later.

For a static analysis, `printA`

gives the tangent stiffness matrix. For a dynamic analysis, `printA`

gives the effective tangent stiffness matrix, which is not physically meaningful because it is a linear combination of the mass, damping, and static stiffness matrices with scalar coefficients dictated by the integrator and the time step.

Let’s suppose you’re doing a dynamic analysis and want to get , , and from your model. You can get by first doing a static analysis, then calling `printA`

. Easy. And, I recently learned that you can get by calling `printA`

after switching to the `NewmarkExplicit`

integrator with . Also easy. If you are using Rayleigh damping where is a linear combination of and , you’re done.

But what if your model has viscous dampers and you want to get the non-classical damping matrix? What if you want to do state space calculations offline because OpenSees does not have state space functionality? You’ll be SOL on the damping matrix. As far as I can tell, there’s no integrator available that you can trick into forming only the damping matrix like you can with `NewmarkExplicit`

and the mass matrix.

So, after trying some arduous and non-scalable approaches to forming with imposed velocities and single point constraints, I realized I could just implement a new integrator to trick OpenSees into giving me what I want. This new integrator assembles any linear combination of mass, damping, and stiffness (tangent and initial) that you tell it. Then, you can grab that matrix with `printA`

. I named it `GimmeMCK`

(see PR #343) because better names just didn’t come to mind.

`Python`

`ops.integrator('GimmeMCK', m, c, kt, ki=0.0)`

Tcl`integrator GimmeMCK $m $c $kt <$ki>`

m = factor applied to mass matrix

c = factor applied to damping matrix

kt = factor applied to tangent stiffness matrix

ki = factor applied to initial stiffness matrix (optional, default=0.0)`Assembles A = m*M + c*C + kt*KT + ki*KI`

Because you don’t want to perform an actual analysis with this integrator, I removed the functionality to advance time in the domain and to update displacement, velocity, and acceleration like you see in the proper dynamic integrators. Although, maybe one would want this functionality to play around with new integrators without having to touch the source code. Let me know if that functionality appeals to you.

With your model defined, at any time you can switch to the `GimmeMCK`

integrator, dump out the matrices you want, then switch back to a real dynamic integrator and proceed as if nothing happened. Don’t worry if you see analysis failed warnings because the assembled matrix is singular, e.g., massless DOFs when assembling or small number of damping elements when assembling .

Below is a synopsis of using the `GimmeMCK`

integrator to get the mass, stiffness, and damping matrices from an OpenSees model defined in Python.

import openseespy.opensees as ops import numpy as np import scipy.linalg as slin # # Define your model # ops.wipeAnalysis() ops.system('FullGeneral') ops.analysis('Transient') # Mass ops.integrator('GimmeMCK',1.0,0.0,0.0) ops.analyze(1,0.0) # Number of equations in the model N = ops.systemSize() # Has to be done after analyze M = ops.printA('-ret') # Or use ops.printA('-file','M.out') M = np.array(M) # Convert the list to an array M.shape = (N,N) # Make the array an NxN matrix # Stiffness ops.integrator('GimmeMCK',0.0,0.0,1.0) ops.analyze(1,0.0) K = ops.printA('-ret') K = np.array(K) K.shape = (N,N) # Damping ops.integrator('GimmeMCK',0.0,1.0,0.0) ops.analyze(1,0.0) C = ops.printA('-ret') C = np.array(C) C.shape = (N,N)

Continue with the following code if you want to perform state space analysis on the model. You’ll notice code to separate the DOFs with mass from those without mass using the `nodeDOFs`

and `nodeMass`

commands. This separation is necessary for static condensation prior to the state space analysis. You may find the node utility commands useful for other stuff too.

# Determine number of DOFs with mass massDOFs = [] for nd in ops.getNodeTags(): for j in range(NDF): # NDF is number of DOFs/node if ops.nodeMass(nd,j+1) > 0.0: massDOFs.append(ops.nodeDOFs(nd)[j]) # Number of DOFs with mass Nmass = len(massDOFs) # DOFs without mass masslessDOFs = np.setdiff1d(range(N),massDOFs) Nmassless = len(masslessDOFs) # Form matrices for D*x = -lam*B*x B = np.zeros((2*Nmass,2*Nmass)) # = [ 0 M; M C] D = np.zeros((2*Nmass,2*Nmass)) # = [-M 0; 0 K] # Mass B[:Nmass,:][:,Nmass:2*Nmass] = M[massDOFs,:][:,massDOFs] B[Nmass:2*Nmass,:][:,:Nmass] = M[massDOFs,:][:,massDOFs] D[:Nmass,:][:,:Nmass] = -M[massDOFs,:][:,massDOFs] # Damping B[Nmass:2*Nmass,:][:,Nmass:2*Nmass] = C[massDOFs,:][:,massDOFs] # Static condensation Kmm = K[massDOFs,:][:,massDOFs]; Kmn = K[massDOFs,:][:,masslessDOFs] Knm = K[masslessDOFs,:][:,massDOFs]; Knn = K[masslessDOFs,:][:,masslessDOFs] # Kc = Kmm - Kmn*inv(Knn)*Knm if Nmassless > 0: Kc = Kmm - np.dot(Kmn,np.linalg.solve(Knn,Knm)) else: Kc = K # Stiffness at DOFs with mass D[Nmass:2*Nmass,:][:,Nmass:2*Nmass] = Kc # State space eigenvalue analysis [lam,x] = slin.eig(D,-B)

Let me know if you run into any problems getting the matrices out of OpenSees.

The title for this post was inspired by ZZ Top’s “Gimme All Your Lovin'”, which saw heavy play on MTV, back when MTV played music videos.

PD,

This is awesome! Just what I needed to perform a response spectrum analysis!

I am definitely going to try it and let you know!

As an extra comment, I just would say that saving a matrix to a text file is probably not the most efficient way. I am working in a model whose system size is 3000 and I found that saving it to a binary file using numpy.save() was the best approach.

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Hola Gustavo,

Thanks for the tip! Let me know how it goes.

PD

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