rob0tsunny / pyfda

Python Filter Design Analysis Tool

pyFDA

Python Filter Design Analysis Tool

pyFDA is a GUI based tool in Python / Qt for analysing and designing discrete time filters. The capability for generating Verilog and VHDL code for the designed and quantized filters will be added in the next release.

More screenshots from the current version:

Prerequisites

• Python versions: 2.7 or 3.3 ... 3.6
• All operating systems - there should be no OS specific requirements.
• Libraries:
  • (Py)Qt4 or (Py)Qt5. When both libraries are installed, PyQt5 is used.
  • numpy
  • scipy
  • matplotlib

Optional libraries:

• docutils for rich text in documentation
• xlwt and / or XlsxWriter for exporting filter coefficients as *.xls(x) files

Release History / Roadmap

Release 0.1 (Jan. 1st 2018)

Initial release

Release 0.2a1 (Apr. 5th 2019)

• Rework of signal-slot connections
  • Clearer structure: only one RX / TX signal connection per widget
  • More flexibility: transport dicts or lists via the signals
  • Much improved modularity - new functionality can be easily added
• HDL synthesis (very raw with LOTS of bugs)
  • Use myHDL to generate synthesizable VHDL / Verilog netlists for basic filter topologies and do fixpoint simulation (plots are displayed in pyFDA widgets)
  • When myHDL is missing on your system, pyFDA will start without the fixpoint tab but otherwise fully functional
  • Attention: When installing pyFDA with conda, myHDL needs to be installed separately with pip (not available via conda). myHDL 0.1.0 doesn't work with Python 3.7, you either need to go back to Python 3.6 or install bleeding edge myHDL.
• Didactic improvements
  • Improved display of transient response and FFT of transient response
  • Display poles / zeros in the magnitude frequency response to ease understanding the relationship
  • Apply filter on audio files (in the h[n] widget) to hear the filtering effect
• Documentation using Sphinx / ReadTheDocs Could be more and better ... but hey, it's a start!

Release 1.0 (planned for some time in the not so near future)

• Filter Manager
  • Store multiple designs in one filter dict
  • Compare multiple designs in plots
• Filter coefficients and poles / zeros
  • Display and edit second-order sections (SOS) in PZ editor

Following releases

• Add a tracking cursor
• Graphical modification of poles / zeros
• Export of filter properties as PDF / HTML files
• Design, analysis and export of filters as second-order sections
• Multiplier-free filter designs (CIC, GCIC, LDI, SigmaDelta-Filters, ...)
• Export of Python filter objects
• Analysis of different fixpoint filter topologies (direct form, cascaded form, parallel form, ...) concerning overflow and quantization noise

Installing and Starting pyFDA

There is only one version of pyfda for all supported operating systems, Python and Qt versions. As there are no binaries included, you can simply install from the source.

conda

If you use the Anaconda distribution, you can install / update pyfda from my Anaconda channel Chipmuenk using

conda install -c Chipmuenk pyfda

resp.

conda update  -c Chipmuenk pyfda

pip

Otherwise, you can install from PyPI using

pip install pyfda

or upgrade using

pip install pyfda -U

or install locally using

pip install -e <YOUR_PATH_TO_PYFDA>

where the specified path points to pyfda.setup.py but without including setup.py. In this case, you need to have a local copy of the pyfda project, preferrably using git.

setup.py

You could also download the zip file and extract it to a directory of your choice. Install it either to your <python>/Lib/site-packages subdirectory using

>> python setup.py install

or just create a link to where you have copied the python source files (for testing / development) using

>> python setup.py develop

Starting pyFDA

In any case, the start script pyfdax has been created in <python>/Scripts which should be in your path. So, simply start pyfda using

>> pyfdax

For development and debugging, you can also run pyFDA using

In [1]: %run -m pyfda.pyfdax # IPython or
>> python -m pyfda.pyfdax    # plain python interpreter

All individual files from pyFDA can be run using e.g.

In [2]: %run -m pyfda.input_widgets.input_pz    # IPython or 
>> python -m pyfda.input_widgets.input_pz       # plain python interpreter

Customization

The layout and some default paths can be customized using the file pyfda/pyfda_rc.py.

The following features are currently implemented:

• Filter design
  • Design methods: Equiripple, Firwin, Moving Average, Bessel, Butterworth, Elliptic, Chebychev 1 and 2 (from scipy.signal and custom methods)
  • Second-Order Sections are used in the filter design when available for more robust filter design and analysis
  • Remember all specifications when changing filter design methods
  • Fine-tune manually the filter order and corner frequencies calculated by minimum order algorithms
  • Compare filter designs for a given set of specifications and different design methods
  • Filter coefficients and poles / zeroes can be displayed, edited and quantized in various formats
• Clearly structured User Interface
  • only widgets needed for the currently selected design method are visible
  • enhanced matplotlib NavigationToolbar (nicer icons, additional functions)
  • display help files (own / Python docstrings) as rich text
  • tooltips for all control and entry widgets
• Common interface for all filter design methods:
  • specify frequencies as absolute values or normalized to sampling or Nyquist frequency
  • specify ripple and attenuations in dB, as voltage or as power ratios
  • enter expressions like exp(-pi/4 * 1j) with the help of the library simpleeval (included in source files)
• Graphical Analyses
  • Magnitude response (lin / power / log) with optional display of specification bands, phase and an inset plot
  • Phase response (wrapped / unwrapped)
  • Group delay
  • Pole / Zero plot
  • Impulse response and step response (lin / log)
  • 3D-Plots (|H(f)|, mesh, surface, contour) with optional pole / zero display
• Modular architecture, facilitating the implementation of new filter design and analysis methods
  • Filter design files not only contain the actual algorithm but also dictionaries specifying which parameters and standard widgets have to be displayed in the GUI.
  • Special widgets needed by design methods (e.g. for choosing the window type in Firwin) are included in the filter design file, not in the main program
• Saving and loading
  • Save and load filter designs in pickled and in numpy's NPZ-format
  • Export and import coefficients and poles/zeros as comma-separated values (CSV), in numpy's NPY- and NPZ-formats, in Excel (R) or in Matlab (R) workspace format
  • Export coefficients in FPGA vendor specific formats like Xilinx (R) COE-format

Why yet another filter design tool?

• Education: There is a very limited choice of user-friendly, license-free tools available to teach the influence of different filter design methods and specifications on time and frequency behaviour. It should be possible to run the tool without severe limitations also with the limited resolution of a beamer.
• Show-off: Demonstrate that Python is a potent tool for digital signal processing applications as well. The interfaces for textual filter design routines are a nightmare: linear vs. logarithmic specs, frequencies normalized w.r.t. to sampling or Nyquist frequency, -3 dB vs. -6 dB vs. band-edge frequencies ... (This is due to the different backgrounds and the history of filter design algorithms and not Python-specific.)
• Fixpoint filter design for uCs: Recursive filters have become a niche for experts. Convenient design and simulation support (round-off noise, stability under different quantization options and topologies) could attract more designers to these filters that are easier on hardware resources and much more suitable e.g. for uCs.
• Fixpoint filter design for FPGAs: Especially on low-budget FPGAs, multipliers are expensive. However, there are no good tools for designing and analyzing filters requiring a limited number of multipliers (or none at all) like CIC-, LDI- or Sigma-Delta based designs.
• HDL filter implementation: Implementing a fixpoint filter in VHDL / Verilog without errors requires some experience, verifying the correct performance in a digital design environment with very limited frequency domain simulation options is even harder. The Python module myHDL can automate both design and verification.