I have a simple FMU file which contains a sine block that takes u as input and outputs y. In this case, u is set to equal to time. In my C++ code I have loaded the FMI library from FMILibrary and had done all the necessary steps up to a point where I want to give my input u a new value of pi(as 3.14). So I went:
fmistatus = fmi2_import_set_real(fmu, &uRef, 1, &pi);
while (timeCurrent < timeEnd){
fmistatus = fmi2_import_do_step(fmu, timeCurrent , stepSize, fmi2_true);
timeCurrent += stepSize;
}
u was still set to time even though I tried to give it a new value. Did I miss something?
PS. Is there anywhere I can find a more detailed description on the FMI library functions? Currently I can only find input output descriptions or did I miss something again.
UPDATE: After a few trials, I think this issue might be because I was trying to redefine my equation u = time. In other words when I change my u variable into RealInput block in openmodelica everything goes fine. So what if I really wants to redefine a certain equation? what do I have to do?
You shall not be able to set any variable in FMI - and especially not a variable with a binding equation - and I assume your Modelica model has "u=time;". Instead of having "u=time" you need to add a top-level input without any equation (so that the exported FMI has it as an input) - and then connect that to the sine-block.
Details:
For a co-simulation FMI the restriction on what you can set are in the state-diagram in section 4.2.4 of FMI2 specification.
Between fmi2DoStep you can only set Real variables that have causality="input" or causality="parameter" and variability="tunable" - and an input with an equation doesn't qualify.
Before starting the integration you could set it for other variables as well, but that is only guess-values for the initialization - and should not over-write the "u=time" equation.
Related
I want to find the symmetry group of an integer linear program. I think there is a function in skip called SCIPgetGeneratorsSymmetry . I how I can use this function?
You are right, to access symmetry information in SCIP, you have to call the function SCIPgetGeneratorsSymmetry() via C/C++. Note that you need to link SCIP against the external software bliss, because otherwise, SCIP is not able to compute symmetries of your (mixed-integer) linear program.
If you set up your (mixed-integer) linear program using a C/C++ project, you have several options for computing symmetries.
If you set the "recompute" parameter to FALSE, SCIP will return the currently available symmetry information - if symmetries have not been computed yet, SCIP will compute symmetries to give you access to this information.
If you set "recompute" to TRUE, SCIP will discard the available symmetry information and you get access to the generators of the current symmetry group. Moreover, you can control the kind of symmetries that are computed via the parameters "symspecrequire" and "symspecrequirefixed", e.g., to only compute symmetries of binary variables that fix continuous variables.
Edit:
If you have no experience with coding in C/C++ and you are only interested in printing the generators of the symmetry group, the easiest way is probably to modify SCIP's source code in presol_symmetry.c as follows:
Add two integer paramaters int i and int p at the very beginning of determineSymmetry().
Search within determineSymmetry() for the line in which computeSymmetryGroup() is called.
Add the following code snippet right after this function call:
for (p = 0; p < presoldata->nperms; ++p)
{
printf("permutation %d\n", p);
for (i = 0; i < presoldata->npermvars; ++i)
{
if ( TRUE )
printf("%d ", presoldata->perms[p][i]);
else
printf("%s ", SCIPvarGetName(presoldata->permvars[presoldata->perms[p][i]]));
}
printf("\n");
}
This code prints the generators of the symmetry group as a list of variable indices, e.g., 1 2 0 is the permutation that maps 0 -> 1, 1 -> 2, and 2 -> 0. If you change TRUE to FALSE, you get the same list but variable indices are replaced by their names.
Do not forget to recompile SCIP.
If you solve an instance with SCIP and symmetry handling is enabled, SCIP will print the generators in the above format whenever it computes the symmetry group. If you are interested in the symmetry group of the original problem, you should use the parameter setting presolving/symbreak/addconsstiming = 0 and propagating/orbitalfixing/symcomptiming = 0. If you are fine with symmetries of the presolved problem, change the zeros to ones.
I am new to Python, coming from MATLAB, and long ago from C. I have written a script in MATLAB which simulates sediment transport in rivers as a Markov Process. The code randomly places circles of a random diameter within a rectangular area of a specified dimension. The circles are non-uniform is size, drawn randomly from a specified range of sizes. I do not know how many times I will step through the circle placement operation so I use a while loop to complete the process. In an attempt to be more community oriented, I am translating the MATLAB script to Python. I used the online tool OMPC to get started, and have been working through it manually from the auto-translated version (was not that helpful, which is not surprising). To debug the code as I go, I use the
MATLAB generated results to generally compare and contrast against results in Python. It seems clear to me that I have declared variables in a way that introduces problems as calculations proceed in the script. Here are two examples of consistent problems between different instances of code execution. First, the code generated what I think are arrays within arrays because the script is returning results which look like:
array([[ True]
[False]], dtype=bool)
This result was generated for the following code snippet at the overlap_logix operation:
CenterCoord_Array = np.asarray(CenterCoordinates)
Diameter_Array = np.asarray(Diameter)
dist_check = ((CenterCoord_Array[:,0] - x_Center) ** 2 + (CenterCoord_Array[:,1] - y_Center) ** 2) ** 0.5
radius_check = (Diameter_Array / 2) + radius
radius_check_update = np.reshape(radius_check,(len(radius_check),1))
radius_overlap = (radius_check_update >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap), dtype=int)
overlap_update = np.reshape(overlap,(len(overlap),1))
overlap_logix = (radius_overlap == 1)
idx_true = overlap_update[overlap_logix]
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
A similar result for the same run was produced for variables:
radius_check_update
radius_overlap
overlap_update
Here is the same code snippet for the working MATLAB version (as requested):
distcheck = ((Circles.CenterCoordinates(1,:)-x_Center).^2 + (Circles.CenterCoordinates(2,:)-y_Center).^2).^0.5;
radius_check = (Circles.Diameter ./ 2) + radius;
radius_overlap = (radius_check >= distcheck);
% Now actually check the overalp condition.
if sum(radius_overlap) == 0
% The new circle does not overlap so proceed.
newCircle_Found = 1;
debug_value = 2;
elseif sum(radius_overlap) == 1
% The new circle overlaps with one other circle
temp = 1:size(radius_overlap,2);
idx_true = temp(radius_overlap == 1);
radius = distcheck(1,idx_true) - (Circles.Diameter(1,idx_true)/2);
In the Python version I have created arrays from lists to more easily operate on the contents (the first two lines of the code snippet). The array within array result and creating arrays to access data suggests to me that I have incorrectly declared variable types, but I am not sure. Furthermore, some variables have a size, for example, (2L,) (the numerical dimension will change as circles are placed) where there is no second dimension. This produces obvious problems when I try to use the array in an operation with another array with a size (2L,1L). Because of these problems I started reshaping arrays, and then I stopped because I decided these were hacks because I had declared one, or more than one variable incorrectly. Second, for the same run I encountered the following error:
TypeError: 'numpy.ndarray' object is not callable
for the operation:
radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
which occurs at the bottom of the above code snippet. I have posted the entire script at the following link because it is probably more useful to execute the script for oneself:
https://github.com/smchartrand/MarkovProcess_Bedload
I have set-up the code to run with some initial parameter values so decisions do not need to be made; these parameter values produce the expected results in the MATLAB-based script, which look something like this when plotted:
So, I seem to specifically be having issues with operations on lines 151-165, depending on the test value np.sum([radius_overlap]) and I think it is because I incorrectly declared variable types, but I am really not sure. I can say with confidence that the Python version and the MATLAB version are consistent in output through the first step of the while loop, and code line 127 which is entering the second step of the while loop. Below this point in the code the above documented issues eventually cause the script to crash. Sometimes the script executes to 15% complete, and sometimes it does not make it to 5% - this is due to the random nature of circle placement. I am preparing the code in the Spyder (Python 2.7) IDE and will share the working code publicly as a part of my research. I would greatly appreciate any help that can be offered to identify my mistakes and misapplications of python coding practice.
I believe I have answered my own question, and maybe it will be of use for someone down the road. The main sources of instruction for me can be found at the following three web pages:
Stackoverflow Question 176011
SciPy FAQ
SciPy NumPy for Matlab users
The third web page was very helpful for me coming from MATLAB. Here is the modified and working python code snippet which relates to the original snippet provided above:
dist_check = ((CenterCoordinates[0,:] - x_Center) ** 2 + (CenterCoordinates[1,:] - y_Center) ** 2) ** 0.5
radius_check = (Diameter / 2) + radius
radius_overlap = (radius_check >= dist_check)
# Now actually check the overalp condition.
if np.sum([radius_overlap]) == 0:
# The new circle does not overlap so proceed.
newCircle_Found = 1
debug_value = 2
elif np.sum([radius_overlap]) == 1:
# The new circle overlaps with one other circle
overlap = np.arange(0,len(radius_overlap[0]), dtype=int).reshape(1, len(radius_overlap[0]))
overlap_logix = (radius_overlap == 1)
idx_true = overlap[overlap_logix]
radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
In the end it was clear to me that it was more straightforward for this example to use numpy arrays vs. lists to store results for each iteration of filling the rectangular area. For the corrected code snippet this means I initialized the variables:
CenterCoordinates, and
Diameter
as numpy arrays whereas I initialized them as lists in the posted question. This made a few mathematical operations more straightforward. I was also incorrectly indexing into variables with parentheses () as opposed to the correct method using brackets []. Here is an example of a correction I made which helped the code execute as envisioned:
Incorrect: radius = dist_check(idx_true,1) - (Diameter(idx_true,1) / 2)
Correct: radius = dist_check[idx_true] - (Diameter[0,idx_true] / 2)
This example also shows that I had issues with array dimensions which I corrected variable by variable. I am still not sure if my working code is the most pythonic or most efficient way to fill a rectangular area in a random fashion, but I have tested it about 100 times with success. The revised and working code can be downloaded here:
Working Python Script to Randomly Fill Rectangular Area with Circles
Here is an image of a final results for a successful run of the working code:
The main lessons for me were (1) numpy arrays are more efficient for repetitive numerical calculations, and (2) dimensionality of arrays which I created were not always what I expected them to be and care must be practiced when establishing arrays. Thanks to those who looked at my question and asked for clarification.
I am using python wrappings for VTK. I want my script to let the user pick three arbitrary points and return a triangle with its normal information. In VTK VTK Triangle reference there is vtkTriangle::ComputeNormal (double v1[3], double v2[3],double v3[3],double n[3]).
I checked Cxx implementation examples about vtkTriangle but, I don't understand how to implement this in Python. Does n[3] stand for the normal? If so what it should be as an input parameter?
#g.stevo I understand that. However, when I give a random value the method ComputeNormal returns None. To be more clear you can find the snippet of related code below:
`p0 = trianglePolyData.GetPoints().GetPoint(0)
p1 = trianglePolyData.GetPoints().GetPoint(1)
p2 = trianglePolyData.GetPoints().GetPoint(2)
print vtk.vtkTriangle().TriangleArea(p0,p1,p2)
n=[0.0,0.0,0.0]
print vtk.vtkTriangle().ComputeNormal(p0,p1,p2,n)`
Your code is working. The result you are looking for is in the array n. The function ComputeNormal returns void, according to the documentation.
Try this:
n=[0.0,0.0,0.0]
vtk.vtkTriangle().ComputeNormal(p0,p1,p2,n)
print n
I want to modify some parameters of element's .ini file in OMNeT++, say a node's transmission rate, during the simulation run, e.g. when a node receives some control message.
I found information saying that it's possible to somehow loop the configuration stated as: some_variable = ${several values}, but there are no conditional clauses in .ini files and no way to pass to those files any data from C++ functions (as far as I'm concerned).
I use INET, but maybe some other models' users already bothered with such a problem.
I found information saying that it's possible to somehow loop the configuration stated as: some_variable = ${several values}, but there are no conditional clauses in .ini files and no way to pass to those files any data from C++ functions (as far as I'm concerned).
In fact you can use the built-in constraint expression in the INI file. This will allow you to create runs for the given configuration while respecting the specified constraint (condition).
However, this constraint will only apply to the parameters that are specified in the .ini file, i.e. this won't help you if the variable which you are trying to change is computed dynamically as part of the code
Below, I give you a rather complicated "code-snippet" from the .ini file which uses many of the built-in functions that you have mentioned (variable iteration, conditionals etc.)
# Parameter assignment using iteration loops and constrains #
# first define the static values on which the others depend #
scenario.node[*].application.ADVlowerBound = ${t0= 0.1}s
scenario.node[*].application.aggToServerUpperBound = ${t3= 0.9}s
#
## assign values to "dependent" parameters using variable names and loop iterations #
scenario.node[*].application.ADVupperBound = ${t1= ${t0}..${t3} step 0.1}s # ADVupperBound == t1; t1 will take values starting from t0 to t3 (0.1 - 0.9) iterating 0.1
scenario.node[*].application.CMtoCHupperBound = ${t2= ${t0}..${t3} step 0.1}s
#
## connect "dependent" parameters to their "copies" -- this part of the snippet is only variable assignment.
scenario.node[*].application.CMtoCHlowerBound = ${t11 = ${t1}}s
scenario.node[*].application.joinToServerLowerBound = ${t12 = ${t1}}s
#
scenario.node[*].application.aggToServerLowerBound = ${t21 = ${t2}}s
scenario.node[*].application.joinToServerUpperBound = ${t22 = ${t2}}s
#
constraint = ($t0) < ($t1) && ($t1) < ($t2) && ($t2) < ($t3)
# END END END #
The code above creates all the possible combinations of time values for t0 to t3, where they can take values between 0.1 and 0.9.
t0 and t3 are the beginning and the end points, respectively. t1 and t2 take values based on them.
t1 will take values between t0 and t3 each time being incremented by 0.1 (see the syntax above). The same is true for t2 too.
However, I want t0 to always be smaller than t1, t1 smaller than t2, and t2 smaller than t3. I specify these conditions in the constraint section.
I am sure, a thorough read through this section of the manual, will help you find the solution.
If you want to change some value during the simulation you can just do that in your C++ code. Something like:
handleMessage(cMessage *msg){
if(msg->getKind() == yourKind){ // replace yourKind with the one you are using for these messages
transmission_rate = new_value;
}
What you are refering to as some_variable = ${several values} can be used to perform multiple runs with different parameters. For example one run with a rate of 1s, one with 2s and one with 10s. That would then be:
transsmission_rate = ${1, 2, 10}s
For more detailed information how to use such values (like to do loops) see the relevant section in the OMNeT++ User Manual
While you can certainly manually change volatile parameters, OMNeT++ (as far as I am aware) offers no integrated support for automatic changing of parameters at runtime.
You can, however, write some model code that changes volatile parameters programatically.
I am using the IBM cplex optimizer to solve an optimization problem and I don't want all terminal prints that the optimizer does. Is there a member that turns this off in the IloCplex or IloModel class? These are the prints about cuts and iterations. Prints to the terminal are expensive and my problem will eventually be on the order of millions of variables and I don't want to waste time with these superfluous outputs. Thanks.
Using cplex/concert, you can completely turn off cplex's logging to the console with
cpx.setOut(env.getNullStream())
Where cpx is an IloCplex object. You can also use the setOut function to redirect logs to a file.
There are several cplex parameters to control what gets logged, for example MIPInterval will set the number of MIP nodes searched between lines. Turning MIPDisplay to 0 will turn off the display of cuts except when new solutions are found, while MIPDisplay of 5 will show detailed information about every lp-subproblem.
Logging-related parameters include MIPInterval MIPDisplay SimDisplay BarDisplay NetDisplay
You set parameters with the setParam function.
cpx.setParam(IloCplex::MIPInterval, 1000)