This is a simple Python library for converting .FIT files to .GPX files. It also includes tools to convert Strava data downloads in bulk to GPX.
- FIT is a GIS data file format used by Garmin GPS sport devices and Garmin software
- GPX is an XML based format for GPS tracks.
- You need to convert .FIT files to pandas dataframe (e.g. for data analysis)
- You need to convert .FIT files to .GPX
- You need to fix files downloaded from Strava by converting the raw .FIT files to their .GPX counterparts
I decided to create this package after spending a few hours searching for a simple solution to quickly convert hundreds of FIT files to GPX. I needed to do this as GIS applications do not parse FIT files. Whilst a few solutions existed, they are command line scripts which offered very little flexibility.
- Pre-GPDR, you could bulk export all your Strava activities as GPX files.
- Post-GDPR, you can export an archive of your account. Whilst this includes much more data, activity GPS files are now downloaded in their original file format (eg. GPX or FIT format, some gzipped, some not) and named like 2500155647.gpx, 2500155647.gpx.gz, 2500155647.fit, and 2500155647.fit.gz.
- How to bulk export you Strava Data
The fit2gpx module provides two converter classes:
- Converter: used to convert a single or multiple FIT files to pandas dataframes or GPX files
- StravaConverter: used to fix all the Strava Bulk Export problems in three steps:
- Unzip GPX and FIT files
- Add activity metadata to existing GPX files
- Convert FIT files to GPX including activity metadata from Strava)
Step 1: Import module and create converter object
from fit2gpx import Converter
conv = Converter()Step 2: Convert FIT file to 2 pd.DataFrame: fit_to_dataframes()
df_lap, df_point = conv.fit_to_dataframes(fname='3323369944.fit')- df_laps: information per lap: lap number, start time, total distance, total elapsed time, max speed, max heart rate, average heart rate
- df_points: information per track point: longitude, latitude, altitude, timestamp, heart rate, cadence, speed, power, temperature
- Note the 'enhanced_speed' and 'enhanced_altitude' are also extracted. Where overlap exists with their default counterparts, values are identical. However, the default or enhanced speed/altitude fields may be empty depending on the device used to record (detailed information).
Import module and create converter object
from fit2gpx import Converter
conv = Converter() # create standard converter objectUse case 2.1: convert a single FIT file: fit_to_gpx()
gpx = conv.fit_to_gpx(f_in='3323369944.fit', f_out='3323369944.gpx')Use case 2.2: convert many FIT files to GPX files: fit_to_gpx_bulk()
conv.fit_to_gpx_bulk(dir_in='./project/activities/', dir_out='./project/activities_convert/')Copy the below code, adjusting the input directory (DIR_STRAVA), to fix the Strava Bulk Export problems discussed in the overview.
from fit2gpx import StravaConverter
DIR_STRAVA = 'C:/Users/dorian-saba/Documents/Strava/'
# Step 1: Create StravaConverter object
# - Note: the dir_in must be the path to the central unzipped Strava bulk export folder
# - Note: You can specify the dir_out if you wish. By default it is set to 'activities_gpx', which will be created in main Strava folder specified.
strava_conv = StravaConverter(
dir_in=DIR_STRAVA
)
# Step 2: Unzip the zipped files
strava_conv.unzip_activities()
# Step 3: Add metadata to existing GPX files
strava_conv.add_metadata_to_gpx()
# Step 4: Convert FIT to GPX
strava_conv.strava_fit_to_gpx()pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive.
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