{ "info": { "author": "Thomas A. Lasko, Jacek Bajor, John M Still", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "fast-intensity\n===============================\n\nauthors: Thomas A. Lasko, Jacek Bajor, John Still\n\n\nOverview\n--------\nFast density inference. Generates intensity curves from given events.\n\n\nInstallation\n------------\nWe only support Python 3 and above. We recommend using `fast_intensity` in a\n[virtual environment](https://docs.python.org/3/tutorial/venv.html); however,\nif you choose to install to a system-wide version of Python, be aware that some\ndistributions will alias Python 3's `pip` as `pip3`. You should be able to\nverify which Python `pip` is associated with by running `pip --version`.\n\nIf you prefer to install a precompiled binary, we provide wheels for OS X and\nLinux (via the [manylinux](https://github.com/pypa/manylinux) project). The\nbasic pip install command line\n\n $ pip install fast-intensity\n\nshould prefer one of our prebuilt binaries. Installation from source requires\nan environment with Cython, numpy, and scipy preinstalled.\n\n $ pip install cython numpy scipy\n\nThen you may install a release from source by specifying _not_ to use a binary:\n\n $ pip install fast-intensity --no-binary fast-intensity\n\n(Yes, it is necessary to specify `fast-intensity` twice.) Alternately, to\ninstall the bleeding edge version:\n\n $ git clone https://github.com/ComputationalMedicineLab/fast-intensity.git\n $ cd fast-intensity\n $ pip install -e .\n\n\nUsage\n-----\n```python\n%matplotlib inline\n%load_ext cython\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport datetime as dt\n# If working locally rather than from a wheel or other binary\nimport pyximport\npyximport.install(language_level=3)\nfrom fast_intensity import infer_intensity\n\nnp.random.seed(42)\n\n# Specify a series of 100 events spread over a year\ndays = np.arange(0.0, 365)\nnp.random.shuffle(days)\nevents = np.sort(days[:100])\n\n# Specify times (as reals) where we want to calculate the intensity of event occurrence\ngrid = np.linspace(1, 365, num=12)\n\n# Generate the intensity curve - the unit is events per time unit\ncurve = infer_intensity(events, grid)\nprint(curve)\n# array([0.38953 , 0.27764734, 0.33549508, 0.27285165, 0.22284481, 0.16997545,\n# 0.26651725, 0.23580527, 0.23351076, 0.25272662, 0.33146159, 0.28486727])\n\nplt.style.use('ggplot')\nfig, ax = plt.subplots(figsize=(9,9))\nax.scatter(events, np.zeros(len(events)), alpha='0.4', label='Events')\nax.scatter(grid, np.zeros(len(grid)) + 0.025, label='Grid')\nax.plot(grid, curve, label='Intensity')\nplt.legend()\nplt.show()\n```\n\nYou can see how the intensity graph dips in the middle, where events are more thinly spaced,\nand rises near the beginning (where we have a high density of events).\n\n![figure](https://github.com/ComputationalMedicineLab/fast_intensity/blob/master/intensity_figure.png)\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/ComputationalMedicineLab/fast_intensity", "keywords": "", "license": "BSD", "maintainer": "John M Still", "maintainer_email": "john.m.still@vumc.org", "name": "fast-intensity", "package_url": 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