Robust Error Handling with the fitdecode Library to Read FIT Files in Python

[Theodore]

Reading and analyzing FIT (Flexible and Interoperable Data Transfer) files is a common need for developers working with fitness data from devices like Garmin, Wahoo, or Fitbit. These files store detailed workout metrics and are typically compressed in a binary format, which makes them tricky to parse without a specialized library.

In Python, the fitdecode library offers an elegant way to read and process FIT files. However, developers often face challenges when encountering corrupted files, unexpected message types, or malformed data. In this blog post, we’ll explore how to implement robust error handling with fitdecode, ensuring your applications can gracefully handle real-world data inconsistencies.

What Is fitdecode?

fitdecode is a Python library designed to decode .fit files generated by fitness devices. It supports most FIT message types and provides access to records, timestamps, and metadata in a streamable format. It works by reading a FIT file as a stream of messages, each containing fields like GPS coordinates, heart rate, cadence, power, and more.

Basic usage looks like this:

import fitdecode

with fitdecode.FitReader('example.fit') as fit:
    for frame in fit:
        if frame.frame_type == fitdecode.FIT_FRAME_DATA:
            print(frame.name, frame.fields)

But things can go wrong — and when they do, error handling becomes crucial.

Common Issues When Reading FIT Files

Before diving into solutions, let’s look at typical problems that occur:

1. Corrupted files – Incomplete downloads or device write errors can corrupt FIT files.
2. Unexpected data types – Newer devices may include data types or fields that the library doesn’t recognize.
3. Large files – Some FIT files are massive, and memory issues can arise during reading.
4. Encoding problems – Although rare, some fields may contain improperly encoded characters.
5. Incorrect file extensions – Files named .fit but not in FIT format.

All of these scenarios can raise exceptions or silently fail if not handled correctly.

Best Practices for Error Handling with fitdecode

Wrap File Reading in Try-Except Blocks

Always wrap your reading logic in try-except to catch decoding errors:

import fitdecode

def read_fit_file(filepath):
    try:
        with fitdecode.FitReader(filepath) as fit:
            for frame in fit:
                if frame.frame_type == fitdecode.FIT_FRAME_DATA:
                    process_frame(frame)
    except fitdecode.FitHeaderError as e:
        print(f"Header error in {filepath}: {e}")
    except fitdecode.FitCRCError as e:
        print(f"CRC error in {filepath}: {e}")
    except Exception as e:
        print(f"Unknown error in {filepath}: {e}")

This ensures that a single corrupted file doesn’t crash your entire script.

Validate FIT File Before Processing

You can use a quick header check to validate the FIT file before decoding its contents:

from fitdecode import FitHeaderError

def is_valid_fit(filepath):
    try:
        with open(filepath, 'rb') as f:
            header = f.read(12)
            if header[:4] != b'\x0E\x10\x00\x00':
                return False
        return True
    except Exception:
        return False

This helps avoid unnecessary parsing of non-FIT files.

Use Logging Instead of Print

For scalable applications, avoid using print for errors. Instead, use the logging module for better control:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

try:
    # FIT parsing code
except fitdecode.FitHeaderError as e:
    logger.warning(f"Header error: {e}")
except fitdecode.FitCRCError as e:
    logger.warning(f"CRC check failed: {e}")

Logging enables you to track errors across thousands of files efficiently.

Implement Field-Level Error Checks

Sometimes individual fields may be missing or have unexpected values. Protect against that:

def process_frame(frame):
    try:
        timestamp = frame.get_value('timestamp')
        heart_rate = frame.get_value('heart_rate')
        if timestamp and heart_rate:
            print(f"{timestamp}: HR = {heart_rate}")
    except Exception as e:
        logger.error(f"Error processing frame {frame.name}: {e}")

frame.get_value(field_name) returns None if the field is not present, so you can handle missing data safely.

Graceful Degradation with Unknown Message Types

Some FIT files contain custom or future message types. fitdecode will still return them, but they may not be documented. You can catch and log these instead of skipping:

if frame.frame_type == fitdecode.FIT_FRAME_DATA:
    try:
        print(f"Data frame: {frame.name}")
    except AttributeError:
        logger.info(f"Unknown data frame encountered")

This allows your script to continue while informing you about potential compatibility issues.

Real-World Use Case: Batch Processing FIT Files

If you’re handling large datasets (e.g., user-uploaded fitness logs), error handling becomes even more essential. Here’s an example batch processing script:

import os
from pathlib import Path

def process_fit_folder(folder_path):
    for file in Path(folder_path).rglob("*.fit"):
        try:
            read_fit_file(file)
        except Exception as e:
            logger.error(f"Failed to process {file.name}: {e}")

process_fit_folder("/data/fitfiles/")

This approach ensures your pipeline is resilient, even if some files are malformed.

Debugging Tips

• Use a hex editor to inspect the binary structure of problematic files.
• Compare with a known-good FIT file to detect what’s missing or malformed.
• Enable debug logging in fitdecode if you’re modifying the library or diagnosing deep issues.
• Use fitdump (a CLI tool from fitdecode) for manual file inspection:

fitdump myfile.fit

Final thoughts:

Working with FIT files in Python using fitdecode is powerful, but real-world data is rarely perfect. Implementing thoughtful, structured error handling ensures your scripts and applications can gracefully manage corrupt files, unknown formats, or missing data — without crashing or giving incomplete results.

Whether you’re building an analytics platform, a data processing pipeline, or a one-off script, solid error handling is not just a best practice — it’s a necessity. Start small, handle the most common issues first, and refine your approach as you scale.

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