{
    "info": {
        "author": "Mike Stringer",
        "author_email": "mike.stringer@datascopeanalytics.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# traces\n\n[![Version](https://img.shields.io/pypi/v/traces.svg?)](https://pypi.python.org/pypi/traces) [![PyVersions](https://img.shields.io/pypi/pyversions/traces.svg)](https://pypi.python.org/pypi/traces) [![CircleCI](https://circleci.com/gh/datascopeanalytics/traces/tree/master.svg?style=shield)](https://circleci.com/gh/datascopeanalytics/traces/tree/master) [![Documentation Status](https://readthedocs.org/projects/traces/badge/?version=master)](https://traces.readthedocs.io/en/master/?badge=master) [![Coverage Status](https://coveralls.io/repos/github/datascopeanalytics/traces/badge.svg?branch=master)](https://coveralls.io/github/datascopeanalytics/traces?branch=master)\n\nA Python library for unevenly-spaced time series analysis.\n\n## Why?\n\nTaking measurements at irregular intervals is common, but most tools are\nprimarily designed for evenly-spaced measurements. Also, in the real\nworld, time series have missing observations or you may have multiple\nseries with different frequencies: it's can be useful to model these as\nunevenly-spaced.\n\nTraces was designed by the team at\n[Datascope](https://datascopeanalytics.com/) based on several practical\napplications in different domains, because it turns out [unevenly-spaced\ndata is actually pretty great, particularly for sensor data\nanalysis](https://datascopeanalytics.com/blog/unevenly-spaced-time-series/).\n\n## Installation\n\nTo install traces, run this command in your terminal:\n\n```bash\n$ pip install traces\n```\n\n## Quickstart: using traces\n\nTo see a basic use of traces, let's look at these data from a light\nswitch, also known as _Big Data from the Internet of Things_.\n\n![](docs/_static/img/trace.svg)\n\nThe main object in traces is a [TimeSeries](https://traces.readthedocs.io/en/master/api_reference.html#timeseries), which you\ncreate just like a dictionary, adding the five measurements at 6:00am,\n7:45:56am, etc.\n\n```python\n>>> time_series = traces.TimeSeries()\n>>> time_series[datetime(2042, 2, 1,  6,  0,  0)] = 0 #  6:00:00am\n>>> time_series[datetime(2042, 2, 1,  7, 45, 56)] = 1 #  7:45:56am\n>>> time_series[datetime(2042, 2, 1,  8, 51, 42)] = 0 #  8:51:42am\n>>> time_series[datetime(2042, 2, 1, 12,  3, 56)] = 1 # 12:03:56am\n>>> time_series[datetime(2042, 2, 1, 12,  7, 13)] = 0 # 12:07:13am\n```\n\nWhat if you want to know if the light was on at 11am? Unlike a python\ndictionary, you can look up the value at any time even if it's not one\nof the measurement times.\n\n```python\n>>> time_series[datetime(2042, 2, 1, 11,  0, 0)] # 11:00am\n0\n```\n\nThe `distribution` function gives you the fraction of time that the\n`TimeSeries` is in each state.\n\n```python\n>>> time_series.distribution(\n>>>   start=datetime(2042, 2, 1,  6,  0,  0), # 6:00am\n>>>   end=datetime(2042, 2, 1,  13,  0,  0)   # 1:00pm\n>>> )\nHistogram({0: 0.8355952380952381, 1: 0.16440476190476191})\n```\n\nThe light was on about 16% of the time between 6am and 1pm.\n\n### Adding more data...\n\nNow let's get a little more complicated and look at the sensor readings\nfrom forty lights in a house.\n\n![](docs/_static/img/traces.svg)\n\nHow many lights are on throughout the day? The merge function takes the\nforty individual `TimeSeries` and efficiently merges them into one\n`TimeSeries` where the each value is a list of all lights.\n\n```python\n>>> trace_list = [... list of forty traces.TimeSeries ...]\n>>> count = traces.TimeSeries.merge(trace_list, operation=sum)\n```\n\nWe also applied a `sum` operation to the list of states to get the\n`TimeSeries` of the number of lights that are on.\n\n![](docs/_static/img/count.svg)\n\nHow many lights are on in the building on average during business hours,\nfrom 8am to 6pm?\n\n```python\n>>> histogram = count.distribution(\n>>>   start=datetime(2042, 2, 1,  8,  0,  0),   # 8:00am\n>>>   end=datetime(2042, 2, 1,  12 + 6,  0,  0) # 6:00pm\n>>> )\n>>> histogram.median()\n17\n```\n\nThe `distribution` function returns a [Histogram](https://traces.readthedocs.io/en/master/api_reference.html#histogram) that\ncan be used to get summary metrics such as the mean or quantiles.\n\n### It's flexible\n\nThe measurements points (keys) in a `TimeSeries` can be in any units as\nlong as they can be ordered. The values can be anything.\n\nFor example, you can use a `TimeSeries` to keep track the contents of a\ngrocery basket by the number of minutes within a shopping trip.\n\n```python\n>>> time_series = traces.TimeSeries()\n>>> time_series[1.2] = {'broccoli'}\n>>> time_series[1.7] = {'broccoli', 'apple'}\n>>> time_series[2.2] = {'apple'}          # puts broccoli back\n>>> time_series[3.5] = {'apple', 'beets'} # mmm, beets\n```\n\nTo learn more, check the [examples](https://traces.readthedocs.io/en/master/examples.html) and the detailed [reference](https://traces.readthedocs.io/en/master/api_reference.html#).\n\n## More info\n\n## Contributing\n\nContributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)\nfor more info.\n\n\n",
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