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# Contributing Guidelines
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Thank you for your interest in contributing to `fooof`!
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Thank you for your interest in contributing to `specparam`!
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We welcome all contributions to the project that extend or improve code and/or documentation!
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All contributions must be within the scope of the module.
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`fooof` is a module for parameterizing neural power spectra. This includes model fitting, management and analysis of resulting parameters, and utilities to visualize power spectra and model results. This module also includes functionality to simulate power spectra based on the model.
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`specparam` is a module for parameterizing neural power spectra. This includes model fitting, management and analysis of resulting parameters, and utilities to visualize power spectra and model results. This module also includes functionality to simulate power spectra based on the model.
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Procedures and utilities that do not deal with operating upon power spectra or on model outputs will most likely be considered out of scope. Notably, this model does not include doing spectral estimation or time-domain analysis. For approaches such as these, the [neurodsp](https://github.com/neurodsp-tools/neurodsp/) module may be a more appropriate target.
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* If a new approach is added, a new tutorial or example may be appropriate
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* To build and check the documentation locally:
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* Install the requirements for the docsite (`pip install -r requirements-doc.txt`)
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* Move to the `fooof/doc` directory (`cd doc`)
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* Move to the `specparam/doc` directory (`cd doc`)
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* Run `make html` to create a local copy of the documentation website
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* The documentation can then be opened in a web browser by opening the file `fooof/doc/_build/html/index.html`
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* The documentation can then be opened in a web browser by opening the file `specparam/doc/_build/html/index.html`
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For more guidelines on how to write well formated and organized code, check out the [Python API Checklist](http://python.apichecklist.com).
FOOOF is a fast, efficient, and physiologically-informed tool to parameterize neural power spectra.
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Spectral parameterization is a fast, efficient, and physiologically-informed tool to parameterize neural power spectra.
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Overview
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--------
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Dependencies
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------------
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FOOOF is written in Python, and requires Python >= 3.6 to run.
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SpecParam is written in Python, and requires Python >= 3.6 to run.
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It has the following required dependencies:
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- `matplotlib <https://github.com/matplotlib/matplotlib>`_ is needed to visualize data and model fits
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- `tqdm <https://github.com/tqdm/tqdm>`_ is needed to print progress bars when fitting many models
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- `pandas <https://github.com/pandas-dev/pandas>`_ is needed to for exporting model fit results to dataframes
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- `pandas <https://github.com/pandas-dev/pandas>`_ is needed for exporting model fit results to dataframes
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- `pytest <https://github.com/pytest-dev/pytest>`_ is needed to run the test suite locally
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We recommend using the `Anaconda <https://www.anaconda.com/distribution/>`_ distribution to manage these requirements.
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Other Language Support
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----------------------
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The original implementation of FOOOF, available in this repository, is implemented in Python.
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The original implementation of `specparam`, available in this repository, is implemented in Python.
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If you wish to run FOOOF from another language, there are a couple potential options:
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If you wish to run specparam from another language, there are a couple potential options:
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- a `wrapper`, which allows for running the Python code from another language
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- a `reimplementation`, which reflects a new implementation of the fooof algorithm in another language
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- a `reimplementation`, which reflects a new implementation of the specparam algorithm in another language
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Below are listed some examples of wrappers and/or reimplementations in other languages (non-exhaustive).
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Matlab
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~~~~~~
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In Matlab, there is a reimplementation available in common toolboxes:
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- The `Brainstorm <https://neuroimage.usc.edu/brainstorm/Introduction>`_ toolbox has a reimplementation of fooof (see the `Brainstorm fooof tutorial <https://neuroimage.usc.edu/brainstorm/Tutorials/Fooof>`_)
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- The `Fieldtrip <https://www.fieldtriptoolbox.org/>`_ also uses the same reimplementation (see the `Fieldtrip fooof tutorial <https://www.fieldtriptoolbox.org/example/fooof/>`_)
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- The `Brainstorm <https://neuroimage.usc.edu/brainstorm/Introduction>`_ toolbox has a reimplementation of specparam (see the `Brainstorm fooof tutorial <https://neuroimage.usc.edu/brainstorm/Tutorials/Fooof>`_)
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- The `Fieldtrip <https://www.fieldtriptoolbox.org/>`_ toolbox also uses the same reimplementation (see the `Fieldtrip fooof tutorial <https://www.fieldtriptoolbox.org/example/fooof/>`_)
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There is also a Matlab wrapper in the `fooof_mat <http://github.com/fooof-tools/fooof_mat>`_ repository.
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**Fitting a Single Power Spectrum**
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With a power spectrum loaded (with 'freqs' storing frequency values, and 'spectrum' storing
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the power spectrum, both as 1D arrays in linear space) FOOOF can be used as follows:
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the power spectrum, both as 1D arrays in linear space) parameterization can be done as follows:
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.. code-block:: python
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# Import the FOOOF object
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fromfooofimportFOOOF
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# Import the model object
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fromspecparamimportSpectralModel
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# Initialize FOOOF object
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fm =FOOOF()
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# Initialize model object
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fm =SpectralModel()
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# Define frequency range across which to model the spectrum
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freq_range = [3, 40]
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#Model the power spectrum with FOOOF, and print out a report
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#Parameterize the power spectrum, and print out a report
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fm.report(freqs, spectrum, freq_range)
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FOOOF.report() fits the model, plots the original power spectrum with the associated FOOOF model fit,
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SpectralModel.report() fits the model, plots the original power spectrum with the associated model fit,
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and prints out the parameters of the model fit for both the aperiodic component, and parameters for
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any identified peaks, reflecting periodic components.
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Example output for the report of a FOOOF fit on an individual power spectrum:
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Example output for the report of a parameterized fit on an individual power spectrum:
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