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:
Eink, E-paper, Think Ink – Collin shares six segments pondering the unusual low-power display technology that somehow still seems a bit sci-fi – http://adafruit.com/thinkink
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