pyfda 0.1

pyFDA is a python tool with a user-friendly GUI for designing and analysing discrete time filters.

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.

Prerequisites

Besides standard python libraries, the project requires the following libraries:

• numpy

• scipy

• matplotlib

• pyQt4 or pyQt5

• Optional libraries:

  • docutils for rendering info text as rich text

  • xlwt and / or XlsxWriter for exporting filter coefficients as *.xls(x) files

Installing and starting pyFDA

>> pip install pyfda

or 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 run it where you have installed the python source files using (for testing / development)

>> python setup.py develop

In both cases, start scripts pyfdax and pyfdax_no_term are created (with / without terminal).

For development, you can also run pyFDA using:

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

or run individual files from pyFDA 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.

Features

• Filter design

  • Design methods from scipy.signal: Equiripple, Firwin, Butterworth, Elliptic, Chebychev 1 and Chebychev 2

  • 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

• Clearly structured GUI

  • only widgets needed for the currently selected design method are visible

  • enhanced matplotlib NavigationToolbar (nicer icons, additional functions)

• 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 (https://pypi.python.org/pypi/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

  • Filter design files can be added and edited without changing or even restarting the program

• Saving and loading

  • Save and load filter designs in pickled and in numpy’s NPZ-format

  • Export coefficients and poles/zeros as comma-separated values (CSV), in numpy’s NPZ-format, in Excel (R) or in Matlab (R) workspace format

• Display help files (own / Python docstrings) as rich text

• Runs under Python 2.7 and Python 3.3 … 3.5