{ "info": { "author": "Tom O'Connell", "author_email": "toconnel@caltech.edu", "bugtrack_url": null, "classifiers": [], "description": ".. image:: https://travis-ci.org/tom-f-oconnell/drosolf.svg?branch=master\n :target: https://travis-ci.org/tom-f-oconnell/drosolf\n\nInstallation\n~~~~~~~~~~~~\n\n::\n\n pip install drosolf\n\nIf you need elevated permissions to install (if the ``pip install`` line\nfails with some sort of permissions error), you can try:\n\n::\n\n sudo -H pip install drosolf\n\nExamples\n~~~~~~~~\n\nTo get Hallem and Carlson ORN responses, with the baseline added back\nin. Returned as a pandas DataFrame with columns of receptor and row\nindices of odor. The transpose (i.e. ``orn_responses.T``) will have\nodors as the columns.\n\n::\n\n from drosolf import orns\n orn_responses = orns.orns()\n\nTo get simulated projection neuron responses, using the Olsen input gain\ncontrol model and the ORN responses.\n\n::\n\n from drosolf import pns\n pn_responses = pns.pns()\n\nGet correlation matrices at the ORN and (simulated) PN levels for a list\nof odors, named as the columns of the previous DataFrames.\n\n::\n\n from drosolf import corrs\n orn_correlations, pn_correlations = corrs.get_corrs(list_of_odors)\n\nGenerate plots of the same ORN and PN correlation matrices (uses\nseaborn).\n\n::\n\n import matplotlib.pyplot as plt\n from drosolf import corrs\n\n corrs.plot_corrs(list_of_odors)\n plt.show()\n\nTodo\n~~~~\n\n- DoOR integration\n- KC model(s)\n- sympy description of transformations applied to ORN data\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, 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