Problem when running script

Good morning everyone,

I had an error when running a P4C script done by me. I do not know which is the problem as before, I was able to run the script without a problem, but now, I do not know the reason, when I do the Run As β†’ EASE script action, it is not running the script, it is trying to connect the Java socket but it does not connect. Does anyone have the same error as me?

When looking at the Error Log view, next messages are shown:

Regards,

Omar.

Errors you are showing are raised by EGit… Do you have any stacktrace that show some EASE code or Python4Capella code ?

I think the communication between Python and the JVM is done via a TCP connection. Maybe you changed some configurations in your OS or removed some rights from your Capella executable ?

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It is because I have done pip install matplotlib, pip install openpyxl and pip install scipy. For my script I only needed the openpyxl library, and when the others are installed, it creates a conflict with the numpy library integrated in the other two. When I uninstall the numpy library, there is not conflict!

OK there is a known issue when numpy is installed. It freeze the EASE script, that might also explain the connection issue with a timeout of instance. Can you try the patch provided in this issue and let me know if it helps.

Hi Yvan,

I have found the script that you have mentioned, but where do I have to attach the code provided by you?

Regards,

Omar.

The folder should exists in your Capella installation if the update site has been installed using p2 (the installation procedure from Python4Capella main page). Another way to install features and plugins can be via dropins. In this case the forlder might be in a dropins folder in the Capella installation folder.

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I have found the script, but where do I have to paste the code, at the end of the script?

Regards,

Omar

you need to comment the three lines by adding β€˜#’:

     #thread = _pyease_threading.Thread(target=_pyease_watchdog, args=(engine,))
     #thread.setDaemon(True)
     #thread.start()
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