Metadata-Version: 2.1
Name: traces
Version: 0.6.0
Summary: A library for unevenly-spaced time series analysis.
Home-page: https://github.com/datascopeanalytics/traces
Author: Mike Stringer
Author-email: mike.stringer@datascopeanalytics.com
License: MIT license
Description: # traces
        
        [![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)
        
        A Python library for unevenly-spaced time series analysis.
        
        ## Why?
        
        Taking measurements at irregular intervals is common, but most tools are
        primarily designed for evenly-spaced measurements. Also, in the real
        world, time series have missing observations or you may have multiple
        series with different frequencies: it's can be useful to model these as
        unevenly-spaced.
        
        Traces was designed by the team at
        [Datascope](https://datascopeanalytics.com/) based on several practical
        applications in different domains, because it turns out [unevenly-spaced
        data is actually pretty great, particularly for sensor data
        analysis](https://datascopeanalytics.com/blog/unevenly-spaced-time-series/).
        
        ## Installation
        
        To install traces, run this command in your terminal:
        
        ```bash
        $ pip install traces
        ```
        
        ## Quickstart: using traces
        
        To see a basic use of traces, let's look at these data from a light
        switch, also known as _Big Data from the Internet of Things_.
        
        ![](docs/_static/img/trace.svg)
        
        The main object in traces is a [TimeSeries](https://traces.readthedocs.io/en/master/api_reference.html#timeseries), which you
        create just like a dictionary, adding the five measurements at 6:00am,
        7:45:56am, etc.
        
        ```python
        >>> time_series = traces.TimeSeries()
        >>> time_series[datetime(2042, 2, 1,  6,  0,  0)] = 0 #  6:00:00am
        >>> time_series[datetime(2042, 2, 1,  7, 45, 56)] = 1 #  7:45:56am
        >>> time_series[datetime(2042, 2, 1,  8, 51, 42)] = 0 #  8:51:42am
        >>> time_series[datetime(2042, 2, 1, 12,  3, 56)] = 1 # 12:03:56am
        >>> time_series[datetime(2042, 2, 1, 12,  7, 13)] = 0 # 12:07:13am
        ```
        
        What if you want to know if the light was on at 11am? Unlike a python
        dictionary, you can look up the value at any time even if it's not one
        of the measurement times.
        
        ```python
        >>> time_series[datetime(2042, 2, 1, 11,  0, 0)] # 11:00am
        0
        ```
        
        The `distribution` function gives you the fraction of time that the
        `TimeSeries` is in each state.
        
        ```python
        >>> time_series.distribution(
        >>>   start=datetime(2042, 2, 1,  6,  0,  0), # 6:00am
        >>>   end=datetime(2042, 2, 1,  13,  0,  0)   # 1:00pm
        >>> )
        Histogram({0: 0.8355952380952381, 1: 0.16440476190476191})
        ```
        
        The light was on about 16% of the time between 6am and 1pm.
        
        ### Adding more data...
        
        Now let's get a little more complicated and look at the sensor readings
        from forty lights in a house.
        
        ![](docs/_static/img/traces.svg)
        
        How many lights are on throughout the day? The merge function takes the
        forty individual `TimeSeries` and efficiently merges them into one
        `TimeSeries` where the each value is a list of all lights.
        
        ```python
        >>> trace_list = [... list of forty traces.TimeSeries ...]
        >>> count = traces.TimeSeries.merge(trace_list, operation=sum)
        ```
        
        We also applied a `sum` operation to the list of states to get the
        `TimeSeries` of the number of lights that are on.
        
        ![](docs/_static/img/count.svg)
        
        How many lights are on in the building on average during business hours,
        from 8am to 6pm?
        
        ```python
        >>> histogram = count.distribution(
        >>>   start=datetime(2042, 2, 1,  8,  0,  0),   # 8:00am
        >>>   end=datetime(2042, 2, 1,  12 + 6,  0,  0) # 6:00pm
        >>> )
        >>> histogram.median()
        17
        ```
        
        The `distribution` function returns a [Histogram](https://traces.readthedocs.io/en/master/api_reference.html#histogram) that
        can be used to get summary metrics such as the mean or quantiles.
        
        ### It's flexible
        
        The measurements points (keys) in a `TimeSeries` can be in any units as
        long as they can be ordered. The values can be anything.
        
        For example, you can use a `TimeSeries` to keep track the contents of a
        grocery basket by the number of minutes within a shopping trip.
        
        ```python
        >>> time_series = traces.TimeSeries()
        >>> time_series[1.2] = {'broccoli'}
        >>> time_series[1.7] = {'broccoli', 'apple'}
        >>> time_series[2.2] = {'apple'}          # puts broccoli back
        >>> time_series[3.5] = {'apple', 'beets'} # mmm, beets
        ```
        
        To 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#).
        
        ## More info
        
        ## Contributing
        
        Contributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)
        for more info.
        
Keywords: traces
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: dev
Provides-Extra: pandas
