NumPy, the Python package for scientific computing, is an adolescent with prospects for a prolific maturity.

The Python extension for mathematical calculations is accelerated in version 2.0.0 with hardware support and offers many new functions.

Want to get better performance with Python? Here's how to use NumPy to toe the 'invisible line' of data and memory transfers and optimize efficiency.

[Zoltán] sends in his very interesting implementation of a NumPy-like library for micropython called ulab. He had a project in MicroPython that needed a very fast FFT on a micro controller, and ...

The vast majority of Cython functions are now exposed in pure Python mode, including functions for calling external C libraries. Another major area of improvement is NumPy support.

Posted in Software Hacks Tagged analog circuit, circuit, integration, LTSpice, modeling, numpy, programming, python, simulation, SPICE ← Simple Acrylic Plates Make Kirlian Photography A Breeze ...