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Update to fooof-tools link
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setup.py

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of the FOOOF approach is that these peaks are characterized in terms of their specific center
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frequency, amplitude and bandwidth without requiring predefining specific bands of interest.
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In particular, it separates these peaks from a dynamic, and independently interesting 1/f
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background. This conception of the 1/f as potentially functional (and therefore worth carefully
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background. This conception of the 1/f as potenhttps://github.com/voytekresearch/fooof/releasestially functional (and therefore worth carefully
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modeling) is based on work from the Voytek lab and others that collectively shows that 1/f changes
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across task demands and participant demographics, and that it may index underlying
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excitation/inhibition (EI) balance.
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long_description = long_description,
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author = 'The Voytek Lab',
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author_email = 'voyteklab@gmail.com',
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url = 'https://github.com/voytekresearch/fooof',
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url = 'https://github.com/fooof-tools/fooof',
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packages = find_packages(),
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license = 'Apache License, 2.0',
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classifiers = [
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7'
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],
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download_url = 'https://github.com/voytekresearch/fooof/releases',
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download_url = '',
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keywords = ['neuroscience', 'neural oscillations', 'power spectra', '1/f', 'electrophysiology'],
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install_requires = ['numpy', 'scipy>=0.19.0'],
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tests_require = ['pytest'],
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'tests' : ['pytest'],
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'all' : ['matplotlib', 'tqdm', 'pytest']
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}
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)
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)

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