Metadata-Version: 1.1
Name: pyfda
Version: 0.1.2
Summary: pyFDA is a python tool with a user-friendly GUI for designing and analysing discrete time filters.
Home-page: https://github.com/chipmuenk/pyFDA
Author: Christian Muenker
Author-email: mail07@chipmuenk.de
License: MIT
Description-Content-Type: UNKNOWN
Description: 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.
        
        .. image:: https://github.com/chipmuenk/pyFDA/raw/master/images/pyFDA_screenshot_3.PNG
            :width: 300px
        
        Prerequisites
        -------------
        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.
        
        **conda**
        
        If you use the Anaconda distribution, you can install / update pyfda from my Anaconda channel `Chipmuenk` (https://anaconda.org/Chipmuenk/pyfda) 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``
        
        **from setup.py**
        
        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**: 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 (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
        * **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
        
        
Keywords: digital,discrete time,filter design,IIR,FIR,GUI
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
