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        "description": "## Synopsis\n\nTools for the analysis of electrophysiological data collected primarily with Axona recording products using Python.\n\n## Code Example\n\nMain entry class is Trial contained in dacq2py_util i.e.\n\n```\nimport dacq2py_util\nT = dacq2py_util.Trial('/path/to/dataset/mytrial')\n```\n\nThe \"usual\" Axona dataset includes the following files:\n\n* mytrial.set\n* mytrial.1\n* mytrial.2\n* mytrial.3\n* mytrial.4\n* mytrial.pos\n* mytrial.eeg\n\nNote that you shouldn't specify a suffix when constructing the filename in the code example above.\n\nYou can now start analysing your data! i.e.\n\n```\nT.plotEEGPower()\nT.plotMap(tetrode=1, cluster=4)\n```\n\n## Motivation\n\nAnalysis using Axona's Tint cluster cutting program is great but limited. This extends that functionality.\n\n## Installation\n\nEasiest way is with pip (under Linux, don't know how this works under other OS's):\n\n> pip install dacq2py\n\nThis should install all the pre-requisites, which are as follows:\n\n* numpy\n* scipy\n* matplotlib\n* scikits-learn\n* [astropy](http://www.astropy.org/) (for NaN-friendly convolution)\n* skimage\n* [mahotas](http://mahotas.readthedocs.org/en/latest/)\n\nOptional packages include:\n\n* [klustakwik](https://github.com/klusta-team/klustakwik)\n\nDownload the files and extract to a folder and make sure it's on your Python path\n\n## API Reference\n\nMost classes/ methods have some explanatory text. The files in the docs folder are extracted from that using standard Python tools.\n\n## Tests\n\nTo be implemented.\n\n## Contributors\n\nRobin Hayman.\n\n## License\n\nDo what you want license.\n",
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