This blog post summarizes the results from my Jupyter notebook, which can be viewed here. The notebook is hosted on Github.
The Jupyter notebook examines the general use of filter functions in the SciPy library which can be used to design analog filters. Filters are one of the basic building blocks of signal processing. In general terms, filters used for signal processing can be divided into two groups, analog and digital filters. Analog filters, the topic of this notebook, are filters which can be described with linear differential equations and implemented in circuits which operate on continuously varying signals. A digital filter is a system that performs operations on a sampled, discrete-time signal with numerical operations.
The first part of this notebook uses SciPy functions to calculate analog filter polynomials based on the classical Butterworth, Chebyshev, Elliptic and Bessel filters. The last section of this notebook walks through a Butterworth filter design and Cauer synthesis of an LC ladder type filter.
