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        "author": "Benjamin Doran",
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        "description": "# UniDip Python Port\n\nSee reference paper:  http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise\n\nUniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality.\n\n## Install\n\ncoming soon...\n```\npip3.6 install unidip\n```\n\n## Examples\n\n### Basic Usage\n\n```python\nfrom unidip import UniDip\n\n# create bi-modal distribution\ndat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3])\n\n# sort data so returned indices are meaningful\ndat = np.msort(dat)\n\n# get start and stop indices of peaks \nintervals = UniDip(dat).run()\n```\n\n### Advanced Options\n\n* **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise.\n* **mrg_dst**: Defines how close intervals must be before they are merged.\n* **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer.\n\n```python\nintervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run()\n```\n\n",
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