{ "info": { "author": "SCoT Development Team", "author_email": "scotdev@googlegroups.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering" ], "description": "SCoT\r\n====\r\n\r\nSCoT is a Python package for EEG/MEG source connectivity estimation.\r\n\r\n\r\nObtaining SCoT\r\n--------------\r\n\r\n##### From PyPi\r\n\r\nUse the following command to install SCoT from PyPi:\r\n\r\n pip install scot\r\n\r\n\r\n##### From Source\r\n\r\nUse the following command to fetch the sources:\r\n\r\n git clone --recursive https://github.com/scot-dev/scot.git scot\r\n\r\nThe flag `--recursive` tells git to check out the numpydoc submodule, which is required for building the documentation.\r\n\r\n\r\nDocumentation\r\n-------------\r\nDocumentation is available online at http://scot-dev.github.io/scot-doc/index.html.\r\n\r\n\r\nDependencies\r\n------------\r\nRequired: numpy, scipy\r\n\r\nOptional: matplotlib, scikit-learn\r\n\r\nThe lowest supported versions of these libraries are numpy 1.8.0, scipy 0.13.3, scikit-learn 0.15.0, and\r\nmatplotlib 1.4.0. Lower versions may work but are not tested.\r\n\r\n\r\nExamples\r\n--------\r\nTo run the examples on Linux, invoke the following commands inside the SCoT main directory:\r\n\r\n PYTHONPATH=. python examples/misc/connectivity.py\r\n\r\n PYTHONPATH=. python examples/misc/timefrequency.py\r\n\r\netc.\r\n\r\n\r\nNote that you need to obtain the example data from https://github.com/SCoT-dev/scot-data. The scot-data package must be on Python's search path.\r\n\r\nNote\r\n----\r\nAs of version 0.2, the data format in all SCoT routines has changed. It is now consistent with Scipy and MNE-Python. Specifically, epoched input data is now arranged in three-dimensional arrays of shape `(epochs, channels, samples)`. 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