Fast Track to Designing FIR Filters with PythonStatus: Available Now
Thank you for your interest in the Fast Track to Designing FIR Filters with Python workshop! Below are the installation instructions as well as a Jupyter Notebook with the material that will be presented in the workshop.
Option 1: Easy path to Python: Install the Anaconda Individual Edition which will have all the tools we will be using: https://www.anaconda.
Option 2: Alternative manual path to Python: As a minimum install we will be using Python 3, Numpy, Matplotlib, and Scipy:
Install python: https://www.python.
From command window type:
pip install ipython
pip install numpy
pip install matplotlib
pip install scipy
(if you encounter any difficulty with installing the packages, see this page: https://packaging.
Being able to run Jupyter Notebooks is not necessary for the workshop but convenient as I am sharing the details of the workshop in the attached Jupyter Notebook for future reference. If you would like further basics on running the Notebook, please see this link:
Notebook: Fast Track FIR Filter Workshop.ipynb
Finite Impulse Response (FIR) filters are the more popular of the two main types of digital filter implementations used in DSP applications. In this workshop, we will go through best practice approaches for optimized FIR filter design and implementation using the free and open-source Python programming language. This will include the common techniques for going from filter requirements to practical implementation and demonstrate both creating FIR filter designs as well as evaluating filter frequency responses using the Python language and its signal processing library.
This workshop will include:
- Complete setup to get Python up and running for signal processing applications.
- Summary of the high-level approaches to FIR filter design – which are best and why?
- Fast track to using the signal processing library in Python for creating FIR designs.
- The complete design flow for FIR filters from specification through verification.
- Using Python for filter evaluation, including plotting magnitude and phase responses.