pytrackunit 2.3.0

Easy access for TrackUnit REST API

TrackUnitPython

Python API for Trackunit

What is this package

This packages contains some usefull functions for an easy interface to TrackUnit's REST API. For information to the data see here.

Features:

• caches requests for faster access
• for timespan's bigger than 30 days it sufficiantly devides it into allowed requests
• processing big datasets more efficient by preprocessing per dataslice
• makes use of asyncio
• if data is cached it can process 12000 cached requests with 20 million data points in under 5 minutes.

For more features write an issue here. Pull requests are welcome.

How to use

Install

Install the package via pip

pip install pytrackunit

Create a file in your execution directory with the name api.key which contains your TrackUnit API token. If that doesnt work for you, you can set the token in the constructor by calling tu = TrackUnit(api_key="<your key>").

Example

from pytrackunit.helper import *
from pytrackunit.TrackUnit import TrackUnit

# Create the API
# It loads the key from api.key by default
tu = TrackUnit()

# Get all vehicles from trackunit
# This is executing the 'Unit' request 
vehicles = tu.get_unitlist()

# Get history is executing 'Report/UnitHistory'
# Gets the history for the last 100 days
history = tu.get_history(vehicles[0]['id'],100)

# Get extended data 'Report/UnitExtendedInfo'
# Gets the history for the last 100 days
data = tu.get_candata(vehicles[0]['id'],100)

# Get faults 'Report/UnitActiveFaults'
# Gets the history for the last 100 days
data = tu.get_faults(vehicles[0]['id'],100)

# The library supports processing multiple vehicles too
# For memory intensive requests it supports preprocessing requests
# Preprocessing is done with a function taking a list and a dict and returning a list.
# By default it prints a progress bar when using this function.
# Besides the data wich was returned by trackunit, process_slice gets some meta-
# data about the query which produced the _slice data.
units = ["123456","456789"]
def process_slice(_slice,meta):
    return list(map(lambda x: (meta["id"],x["time"],x["externalPower"]),_slice))
data = tu.get_multi_history( units, 365, process_slice)