Parsing KMZ Track Data in Python

A few days back I stumbled across an interesting problem. I was asked to develop a solution that was doing some analysis work on geolocation data stored in KMZ format. Existing solutions like fastkml (64KB) and pykml (42KB) seemed nice at the first glance, proved to be unnecessary overhead, however. They’re mostly meant to manipulate and write data into KML format. I just needed to read the data for my later calculations. So I decided to build a solution using the Python Standard Library.

The first trick is that a KMZ file is nothing else but a zip-compressed KML file. Inside you’ll find a file called doc.kml. So let’s open and extract:

from zipfile import ZipFile

kmz = ZipFile(filename, 'r')
kml = kmz.open('doc.kml', 'r').read()

The KML data’s juicy part looks something like this:

<Folder>
    <name>11112222-XXYYZ-TESTTRACK</name>
    <Document>
        <name>11112222XXYYZTESTTRACK-track-20161214T105653+0100.kmz</name>
        <Placemark>
            <name>1111XXYYZ-track-20161214T105653+0100</name>
            <gx:Track>
                <when>2016-12-13T13:16:01.709+02:00</when>
                <when>2016-12-13T13:18:02.709+02:00</when>
                <when>2016-12-13T13:23:21.709+02:00</when>
                <when>2016-12-13T13:24:23.709+02:00</when>
                <!-- more timestamps -->
                <gx:coord>13.7111482XXXXXXX 51.0335960XXXXXXX 0</gx:coord>
                <gx:coord>13.7111577XXXXXXX 51.0337028XXXXXXX 0</gx:coord>
                <gx:coord>13.7113847XXXXXXX 51.0339241XXXXXXX 0</gx:coord>
                <gx:coord>13.7115764XXXXXXX 51.0341949XXXXXXX 0</gx:coord>
                <!-- more coordinates -->
                <ExtendedData>
                </ExtendedData>
            </gx:Track>
        </Placemark>
        <Placemark>
            <name>Reference Point #1</name>
            <Point>
                <coordinates>13.72467XXXXXXXXX,51.07873XXXXXXXXX,0</coordinates>
            </Point>
        </Placemark>
        <!-- more Placemarks -->
    </Document>
</Folder>

Now we can parse the resulting string using lxml :

from lxml import html

doc = html.fromstring(kml)
for pm in doc.cssselect('Folder Document Placemark'):
    tmp = pm.cssselect('track')
    name = pm.cssselect('name')[0].text_content()
    if len(tmp):
        # Track Placemark
        tmp = tmp[0]  # always one element by definition
        for desc in tmp.iterdescendants():
            content = desc.text_content()
            if desc.tag == 'when':
                do_timestamp_stuff(content)
            elif desc.tag == 'coord':
                do_coordinate_stuff(content)
            else:
                print("Skipping empty tag %s" % desc.tag)
    else:
        # Reference point Placemark
        coord = pm.cssselect('Point coordinates')[0].text_content()
        do_reference_stuff(coord)

Alright. Let’s see what’s going on here: First we regard the document as HTML and parse it using lxml.html. Then we iterate over all Placemarks in Folder > Document > Placemark. If a Placemark has a child track, it’s holding our timestamps and coordinate data. Otherwise it’s considered a reference point just holding some location data. With cssselect we can get the respective data and do stuff with it. Just keep in mind it returns a list, so you always have to access the first element. Then we call text_content()l to convert the tag content to a string for further manipulation and logging.

It’s also worth mentioning that lxml and by extension cssselect do not support the necessary pseudo elements for KML. So you won’t be able to address anything like gx:Track. It’s not a big deal here if you know that you can still address the element with cssselect('track'). For more info look it up in the docs.

I’m lazy, so I use cssselect. You might have to install this as a dependency with pip3 install cssselect. You can also use the selecting mechanism lxml provides, but previous experience has shown that it’s very tedious and hard to debug for such a quick and dirty hack.

The rest is just string magic, really. Just split the content you get, convert it to a float and insert it into your data structure of choice to continue working with it later.

Some info that helped me get a grip on the KML format: