Vortex | 🌀 Discord Moderation Bot | Bot library

There isn't any problem handling any HTTP Method with HTTPS within a proxy.

All the requests with https://-protocol will be automatically received and sent to port 443 if not indicated otherwise.

Independently if you have a server where you deployed a HAProxy, NGINX, Apache Web Server or that you literally wrote a proxy like this one in JavaScript, only thing you have to do is to literally proxy the requests to the destination server address.

Regarding the encryption, precisely HTTPS ensures that there are no eavesdroppers between the client and the actual target, so the Proxy would act as initial target and then this would transparently intercept the connection.

1. Client starts HTTPS session to Proxy
2. Proxy intercepts and returns its certificate, signed by a CA trusted by the client.
3. Proxy starts HTTPS session to Target
4. Target returns its certificate, signed by a CA trusted by the Proxy.
5. Proxy streams content, decrypt and re-encrypt with its certificate.

Basically it's a concatenation of two HTTPS sessions, one between the client and the proxy and other between the proxy and the final destination.

I assume you're trying to send your sensor data from this device to another one while more or less accurately maintaining the 5-second sampling interval. You can create a simple asynchronous architecture yourself using 2 threads.

The existing thread (created by Arduino) runs your current loop() which reads the sensor every 5 seconds. You add a second thread which deals with transmitting the sample to other devices. The first thread posts the sample to the second thread through a FreeRTOS queue; second thread immediately goes to work transmitting. The first thread continues to mind its own business without waiting for transmission to complete.

Using the FreeRTOS documentation on creating tasks and queues:

Note: Thanks for providing a testable example. It made figuring out the answer to your question a lot simpler. Your problem was a bit subtle and I would not have been able to give a good answer without runnable code

Your first question: "Why are all the inputs 1"

"Short" Answer You have put the nonlinear solver to high in the model hierarchy, which then included a key precurser component that computed your input values. By moving the solver down to a lower level of the model, I was able to ensure that the precurser component (LTTGeometry) ran and had valid outputs before you got to the guess_nonlinear of implicit component.

Here is what you had (Notice the implicit solver included LTTGeometry even though the data cycle does not require that component:

I moved both the nonlinear solver and the linear solver down into the LTTCycle group, which then allows the LTTGeometry component to execute before getting to the nonlinear solver and guess_nonlinear step:

My fix is only partially correct, since there is a secondary cycle from the TestCL component that also needs a solver and does not have one. However, that cycle still does not involve the LTTGeometry group. So the fully correct fix is to restructure you model top run geometry first, and then put the LTTCycle and TestCL groups together so you can run a solver over just them. That was a bit more hacking than I wanted to do on your test problem, but you can see the general idea from the adjusted N2 above.

Long Answer The guess_nonlinear sequence in OpenMDAO does NOT run the compute method of explicit components or of groups. It follows the execution hierarchy, and calls any guess_nonlinear that it finds. So that means that any explicit components you have in your model will NOT get executed, their outputs will not get updated with computed values, and those computed values will not get passed to the inputs of downstream components.

Things get a little tricky when you have deep model hierarchies. The guess_nonlinear method is called as the first step in the nonlinear solver process. If you have a NonLinearRunOnce solver at the top level, it will follow the compute chain down the line calling compute or solve_nonlinear on each child and doing a data transfer after each one. If one of those children happens to be a group with a nonlinear solver, then that solver will call guess_nonlinear on its children (grandchildren of the top group with the NonLinearRunOnce solver) as the first step. So any outputs that were computed by the siblings of this group will be valid, but none of the outputs from the grandchild level will have been computed yet.

You may be wondering why not just have the guess_nonlinear method call the compute for any explicit components? There is a difficult to balance trade off here. If you assume that all explicit components are very cheap to run, then it might make sense to run the compute methods --- or it might not. A lot depends on the cyclic data structure. If some early component in the group needs guesses from the later one, then running its compute isn't going to help you much at all. Perhaps more importantly though, not all explicit components are cheap to run. You might have a very expensive computation, and calling compute as part of the guess process would be way too costly.

The compromise here, if you need some kind of top level guess process, is that you can implement guess_nonlinear at the group level. It's less common to do, but it gives you total control over what happens. You can call whatever you need to call in whatever sequence.

So the absolute key thing to remember is that the only data you have available to you when a guess_nonlinear is called is any data that was computed before your containing solver was executed. That means any thing that was computed before you got to the model scope of the containing solver (not the scope of the component with the guess_method itself).

Your second question: "How can I speed this up when the number of nodes gets large?"

This one not possible to give a generic answer to at all. I noticed that you have already specified sparse partial derivatives. That is a great start, but if its still not fast enough for you then it means you're reaching the limits of what you can do with a DirectSolver. You note that this solver is the only one that gets you through the optimization without issues, which I will take to mean that ScipyKryloventer link description here and PetscKrylov are not converging the linear system well for you --- at least not by themselves. Thats not surprising, as krylov solvers almost always require some kind of preconditioner... and this is why I can't offer a generic answer. Setting up efficient linear solvers for larger-scale compute is a tricky subject. If you look into the literature, you'll find some good suggestions. You can also study open source implementations like VSPAero for some tips.

effectively, you've reached the limit of what simple linear solvers can offer you. From this point forward, OpenMDAO can help a bit by making it easier to implement some preconditioning, but you'll have to suffer the math side yourself.

I can't speak to OpenSplice, but you can do this with CoreDX DDS. For example, given the IDL file "hello.idl":

You could use a plot of a trigonometric function to swirl down there:

First of all, Numba can perform parallel computations resulting in a faster code if you manually request it using mainly parallel=True and prange. This is useful for big arrays (but not for small ones).

Moreover, your computation is mainly memory bound. Thus, you should avoid creating big arrays when they are not reused multiple times, or more generally when they cannot be recomputed on the fly (in a relatively cheap way). This is the case for r_0 for example.

In addition, memory access pattern matters: vectorization is more efficient when accesses are contiguous in memory and the cache/RAM is use more efficiently. Consequently, arr[0, :, :] = 0 should be faster then arr[:, :, 0] = 0. Similarly, arr[:, :, 0] = arr[:, :, 1] = 0 should be mush slower than arr[:, :, 0:2] = 0 since the former performs to noncontinuous memory passes while the latter performs only one more contiguous memory pass. Sometimes, it can be beneficial to transpose your data so that the following calculations are much faster.

Moreover, Numpy tends to create many temporary arrays that are costly to allocate. This is a huge problem when the input arrays are small. The Numba jit can avoid that in most cases.

Finally, regarding your computation, it may be a good idea to use GPUs for big arrays (definitively not for small ones). You can give a look to cupy or clpy to do that quite easily.

Here is an optimized implementation working on the CPU:

That's because of the null safety update from Flutter 2.0

There are 2 solutions to solve this problem

First

You will have go migrate all your code for null safety

Second

Downgrade your sdk enviroment version in pubspec.yaml. Example : sdk: ">=2.10.0 <3.0.0"

You can choose which one you use, if you want to use null safety choose the first one, if you don't want to use null safety choose the second

This will hopefully help you.

IIUC: