Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/stringertheory/traces

A Python library for unevenly-spaced time series analysis
https://github.com/stringertheory/traces

Last synced: 6 days ago
JSON representation

A Python library for unevenly-spaced time series analysis

Awesome Lists containing this project

README 

        



[![Build status](https://img.shields.io/github/actions/workflow/status/stringertheory/traces/main.yml?branch=main)](https://github.com/stringertheory/traces/actions/workflows/main.yml?query=branch%3Amain)

[![codecov](https://codecov.io/gh/stringertheory/traces/branch/main/graph/badge.svg)](https://codecov.io/gh/stringertheory/traces)

[![Commit activity](https://img.shields.io/github/commit-activity/y/stringertheory/traces)](https://img.shields.io/github/commit-activity/m/stringertheory/traces)



# traces



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 can be useful to model these as

unevenly-spaced.



Traces was designed by the team at

[Datascope](<[https://datascopeanalytics.com/](https://en.wikipedia.org/wiki/Datascope_Analytics)>) 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://traces.readthedocs.io/).



## Installation



To install traces, run this command in your terminal:



```shell

$ 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.



```pycon

>>> 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.



```pycon

>>> 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.



```pycon

>>> 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.



```pycon

>>> 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?



```pycon

>>> 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.



```pycon

>>> 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

```



## More info



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#).



## Contributing



Contributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)

for more info.