Metadata-Version: 1.1
Name: SimpleSpectral
Version: 1.0.0
Summary: Heavily simplified scipy.signal.spectral module which only depends on NumPy and supports pyFFTW
Home-page: https://github.com/xmikos/simplespectral
Author: Michal Krenek (Mikos)
Author-email: m.krenek@gmail.com
License: MIT
Description: SimpleSpectral
        ==============
        
        Heavily simplified scipy.signal.spectral module which only depends on NumPy and supports pyFFTW
        
        Requirements
        ------------
        
        - `Python 3 <https://www.python.org>`_
        - `NumPy <http://www.numpy.org>`_
        - Optional: `pyFFTW <https://github.com/pyFFTW/pyFFTW>`_ (for fastest FFT calculations with FFTW library)
        - Optional: `SciPy <https://www.scipy.org>`_ (for faster FFT calculations with scipy.fftpack library)
        
        SimpleSpectral preferably uses pyfftw for FFT calculations, then scipy.fftpack
        and numpy.fft as a last resort.
        
        You should always install SciPy or pyFFTW, because numpy.fft has horrible
        memory usage and is also much slower.
        
        Differences
        -----------
        
        You can use scipy.signal `tutorial <https://scipy.github.io/devdocs/tutorial/signal.html#spectral-analysis>`_
        and `reference guide <https://scipy.github.io/devdocs/signal.html#spectral-analysis>`_ in most cases,
        but there are some important differences:
        
        - input data is assumed to be complex and two-sided spectrum is always returned (``return_onesided``
          argument is not implemented)
        - length of FFT is always same as length of segment (``nfft`` argument is not implemented)
        - functions work always over last axis of array (``axis`` argument is not implemented)
        - if you want to have best FFT performance with pyFFTW, you should create arrays with
          ``empty``, ``zeros`` or ``ones`` functions from SimpleSpectral instead of generic versions
          from NumPy (arrays will be byte aligned for your CPU)
        
        Implemented functions:
        **********************
        
        - empty
        - zeros
        - ones
        - fft
        - get_window
        - get_detrend
        - extend_boundaries
        - welch
        - periodogram
        - spectrogram
        - stft
        
        Supported windows:
        ******************
        
        - boxcar
        - hann
        - hamming
        - bartlett
        - blackman
        - kaiser
        - tukey
        
        Supported boundary extensions:
        ******************************
        
        - even
        - odd
        - constant
        - zeros
        
        Supported detrending functions:
        *******************************
        
        - constant
        
        Credits
        -------
        
        Based on code from excellent `SciPy <https://www.scipy.org>`_ project.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
