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[Deps] CMake is needed. #150

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loiseaujc opened this issue Jan 13, 2022 · 6 comments
Closed

[Deps] CMake is needed. #150

loiseaujc opened this issue Jan 13, 2022 · 6 comments
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@loiseaujc
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Hej,

Some of my students have started to use pysindy for my dynamical system class and encounter an issue while installing the code from pip. CMake is needed to compile one of the deps.

At least, we should mention this in the README. Ideally, it should be explicitly added as a dependency in the pip stuff but I have no idea how to do so.

++

@akaptano
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akaptano commented Jan 13, 2022

Hey JC, good to hear. Any idea which dependency needs cmake, or if this is a Mac/Windows/Linux specific error? Could you post the error messages that students are seeing? I have not heard of this issue before so I want to make sure what the issue is before I add it as a dependency.

Also, I highly recommend that you have your students download the latest release (1.6.1 https://pypi.org/project/pysindy/).

@akaptano
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@loiseaujc any update on this? How goes your new class?

@dajoedic
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dajoedic commented Feb 11, 2022

Hi @akaptano
Same "issue" here: The package that needs CMake is qdldl.

Edit: Running on Windows 10.

@akaptano
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@briandesilva Any idea about this? qdldl is not in the requirements.txt file, but it is installed on my computers, so my guess is that qdldl gets installed when you install some of the other requirements. Why is isn't being installed automatically on Windows machines is not clear. Maybe adding cmake as a dependency is the easiest solution.

@briandesilva
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It looks like this may be an issue with qdldl. Apparently other Windows 10 users are having trouble installing it properly (see this issue). A possible solution, suggested here, is to require cvxpy-base instead of cvxpy. Though I'm not sure whether cvxpy-base has all the functionality we need.

@briandesilva briandesilva added the bug Something isn't working label Feb 13, 2022
@akaptano
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I found a similar cmake issue on my desktop Mac computer, so I added it to the requirements.

jpcurbelo pushed a commit to jpcurbelo/pysindy_fork that referenced this issue May 9, 2024
The one-hot basin encoding used a single tensor (i.e., a single location
in memory) for all encodings. Hence, when a minibatch was created and
__getitem__ was called several times, the one-hot encoding of the
previous __getitem__ call would be overwritten by the encoding of the
current call.

Now, we create a new tensor in each __getitem__ to avoid this problem.
jpcurbelo pushed a commit to jpcurbelo/pysindy_fork that referenced this issue May 9, 2024
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