LTs[ice and Numpy: a Fast convolution filter #Python #EE

The AcidBurbon blog takes their study of using the Python Numpy library with LTspice and ups the ante with faster processing.

In the previous post we discussed the possibility to use LTspice as a “plug in” into a Python/Numpy signal processing project. It works quite well: you send a numpy data vector to LTspice, let it run through the simulation and get back a numpy vector again. Everything is abstracted away nicely by the “apply_ltspice_filter.py” module. So far so good.

There is just one problem: It is slow. Every time you process a new signal it takes a few seconds to call up Spice again and to funnel the data through a csv file. Not good for looooong signals or many signals that you want to process with the same filter.

If there only was a way to speed things up… How about I show you a YouTube video with the end results right here, so you stay interested:

See the blog post for the full analysis.

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