{
    "info": {
        "author": "Michal Krenek (Mikos)",
        "author_email": "m.krenek@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "SimpleSpectral\n==============\n\nHeavily simplified scipy.signal.spectral module which only depends on NumPy and supports pyFFTW\n\nRequirements\n------------\n\n- `Python 3 <https://www.python.org>`_\n- `NumPy <http://www.numpy.org>`_\n- Optional: `pyFFTW <https://github.com/pyFFTW/pyFFTW>`_ (for fastest FFT calculations with FFTW library)\n- Optional: `SciPy <https://www.scipy.org>`_ (for faster FFT calculations with scipy.fftpack library)\n\nSimpleSpectral preferably uses pyfftw for FFT calculations, then scipy.fftpack\nand numpy.fft as a last resort.\n\nYou should always install SciPy or pyFFTW, because numpy.fft has horrible\nmemory usage and is also much slower.\n\nDifferences\n-----------\n\nYou can use scipy.signal `tutorial <https://scipy.github.io/devdocs/tutorial/signal.html#spectral-analysis>`_\nand `reference guide <https://scipy.github.io/devdocs/signal.html#spectral-analysis>`_ in most cases,\nbut there are some important differences:\n\n- input data is assumed to be complex and two-sided spectrum is always returned (``return_onesided``\n  argument is not implemented)\n- length of FFT is always same as length of segment (``nfft`` argument is not implemented)\n- functions work always over last axis of array (``axis`` argument is not implemented)\n- if you want to have best FFT performance with pyFFTW, you should create arrays with\n  ``empty``, ``zeros`` or ``ones`` functions from SimpleSpectral instead of generic versions\n  from NumPy (arrays will be byte aligned for your CPU)\n\nImplemented functions:\n**********************\n\n- empty\n- zeros\n- ones\n- fft\n- get_window\n- get_detrend\n- extend_boundaries\n- welch\n- periodogram\n- spectrogram\n- stft\n\nSupported windows:\n******************\n\n- boxcar\n- hann\n- hamming\n- bartlett\n- blackman\n- kaiser\n- tukey\n\nSupported boundary extensions:\n******************************\n\n- even\n- odd\n- constant\n- zeros\n\nSupported detrending functions:\n*******************************\n\n- constant\n\nCredits\n-------\n\nBased on code from excellent `SciPy <https://www.scipy.org>`_ project.\n",
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        "keywords": "",
        "license": "MIT",
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        "name": "SimpleSpectral",
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        "summary": "Heavily simplified scipy.signal.spectral module which only depends on NumPy and supports pyFFTW",
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