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

Project description


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.

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The software runs under Python 2.7 and 3.3 ... 3.6. The following additional libraries are required:

* 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

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.


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

``>> conda install -c Chipmuenk pyfda``

``>> conda update -c Chipmuenk pyfda``


Otherwise, you can install from PyPI using

``>> pip install pyfda``

or upgrade using

``>> pip install pyfda -U``


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 install``

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

``>> python 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


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


* **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

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Filename, size & hash SHA256 hash help File type Python version Upload date
pyfda-0.1.3.tar.gz (231.2 kB) Copy SHA256 hash SHA256 Source None Apr 27, 2018

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