gpxo 0.3.0

Load, analyze and plot GPS data from GPX files with numpy/pandas

About

Load GPS data from GPX files into Python as a pandas DataFrames. Initial parsing done using the gpxpy package. Trajectory plotting on a map available using folium or mplleaflet (optional).

Quick Start

Install

pip install gpxo

Load Track

import gpxo
track = gpxo.Track('ExampleTrack.gpx')

track.data is a pandas DataFrame containing time, position, elevation etc.; usual pandas methods can be used to analyze, manipulate and plot data.

Detailed Contents

Track class

Load, inspect and plot GPX data using the Track class, with the following methods and attributes.

Methods

• smooth(): smooth position and elevation data (see gpxo.smooth() below),
• plot(): plot trajectory data using a combination of shortnames (see shortnames below); also takes matplotlib.pyplot.plot() arguments/kwargs,
• closest_to(): find index of point in trajectory closest to a (lat, long) point.
• folium_map(): plot trajectory on a map, using the folium package,
• mplleaflet_map(): plot trajectory on a map, using mplleaflet.show(),

Attributes

• file: pathlib.Path from which data originates
• data (pandas DataFrame): containing at least the keys 'latitude (°)', 'longitude (°)', and if available 'elevation (m)', 'time'; by default, the table will also include 'distance (km)', 'compass (°)', 'duration (s)', 'velocity (km/h)', calculated if possible during instantiation.

Miscellaneous

Outside of the Track class, the following standalone function is also available:

• compass(pt1, pt2): compass bearing (°) between pt1 (lat1, long1) and pt2 (lat2, long2),
• closest_pt(pt, trajectory): index of closest pt in trajectory (latitudes, longitudes) to specified pt (lat, long),
• smooth(x, n, window): smooth 1-d array with a moving window of size n and type window.

Short names

Short name	Corresponding data
t	time
s	duration (s)
d	distance (km)
v	velocity (km/h)
z	elevation (m)
c	compass (°)
x	longitude (°)
y	latitude (°)

Examples

See Jupyter Notebook Examples.ipynb (https://github.com/ovinc/gpxo/blob/master/Examples.ipynb) for a detailed example using real GPX data.

Quick example 1: Plot distance versus time and velocity versus position for a given track:

import gpxo
track = gpxo.Track('ExampleTrack.gpx')
track.plot('td', '--k')    # matplotlib styles can be given
track.plot('dv', c='red')  # matplotlib kwargs can be passed
track.data  # pandas dataframe with all data

Quick example 2: show the path of a GPX file on a map using the folium package:

import gpxo
track = gpxo.Track('ExampleTrack.gpx')
mymap = track.folium_map(color='red', tiles='OpenTopoMap')
mymap.show_in_browser()

Quick example 3: show the path of a GPX file on a map with color-coding corresponding to elevation, using mplleaflet (see Troubleshooting section below in case of error):

import gpxo
track = gpxo.Track('ExampleTrack.gpx')
track.map(plot='scatter', c=track.elevation, cmap='plasma')

Troubleshooting

In case of the following error:

'XAxis' object has no attribute '_gridOnMajor

when using the mplleaflet_map() method, try downgrading Matplotlib to version <= 3.3.2 or install a forked version of mplleaflet (see https://github.com/jwass/mplleaflet/issues/75).

Information

Requirements

Python >= 3.6

Dependencies

(automatically installed by pip if necessary)

• numpy
• pandas
• matplotlib
• importlib-metadata
• gpxpy (https://github.com/tkrajina/gpxpy)
• vincenty (https://github.com/maurycyp/vincenty)

Optional:

• folium
• mplleaflet (https://github.com/jwass/mplleaflet)

Author

Olivier Vincent

([e-mail])

License

BSD 3-Clause (see LICENCE file)