Pyroparse

Fast and opinionated activity data parsing. Forged in Rust. Fired up in Python.

Pyroparse reads FIT files and gives you a typed PyArrow table with structured metadata. This Rust-backed parser loads a typical activity in 15 ms (see benchmark), which is roughly 20x faster than pure-Python FIT parsers. It standardizes the mess of manufacturer-specific field names into a clean, consistent schema. It round-trips to Parquet with metadata preserved. And it hands you Arrow memory that Polars, DuckDB, and pandas can consume with zero-copy.

Parse. Standardize. Serialize. Analyze. One library, no glue code.

---

Quick start

import pyroparse as pp

# One line to a DataFrame
df = pp.read_fit("ride.fit").to_pandas()

# Or zero-copy into Polars
import polars as pl
df = pl.from_arrow(pp.read_fit("ride.fit"))

With metadata

import pyroparse as pp

activity = pp.Activity.load_fit("ride.fit")

activity.metadata.sport         # "cycling" (open-sport-taxonomy code)
activity.metadata.start_time    # datetime(2024, 3, 19, 5, 30, tzinfo=UTC)
activity.metadata.duration      # 3842.7 (seconds)
activity.metadata.distance      # 45230.5 (meters)
activity.metadata.metrics       # {"heart_rate", "power", "speed", "cadence", "gps"}
activity.metadata.devices       # [Device(garmin edge_540 (creator), columns=[heart_rate,power])]

activity.data                   # pyarrow.Table — 21,666 rows × 11 typed columns

Lazy loading

open_fit() and open_parquet() read metadata immediately but defer data loading until you access .data. Useful when you need to inspect metadata before deciding whether to load the full timeseries.

activity = pp.Activity.open_fit("ride.fit")
activity.metadata.sport     # "cycling" — available immediately
activity.metadata.duration  # 3842.7         — no data parsed yet

activity.data               # pyarrow.Table — parsed on first access

FIT to Parquet

activity = pp.Activity.load_fit("ride.fit")
activity.to_parquet("ride.parquet")  # ZSTD compressed, metadata preserved

Load it back with data and metadata intact:

loaded = pp.Activity.load_parquet("ride.parquet")
loaded.metadata.sport      # "cycling"
loaded.metadata.distance   # 45230.5
loaded.data.num_rows       # 21,666

Batch conversion

Convert an entire directory tree of FIT files to Parquet, preserving the folder structure:

import pyroparse as pp

# In-place — parquet files appear next to fit files
pp.convert_fit_tree("~/garmin/activities")

# Mirror to a separate directory
pp.convert_fit_tree("~/garmin/activities", "~/parquet/activities")

# Use all CPU cores
result = pp.convert_fit_tree("~/garmin", "~/parquet", workers=-1, progress=True)
result.converted  # [Path("~/parquet/2024/ride.parquet"), ...]
result.errors     # [(Path("~/garmin/corrupt.fit"), FitParseError(...))]

Re-runs are idempotent — only new files are converted. Pass overwrite=True to force re-conversion.

CLI

Install the CLI tool:

curl -LsSf uvx.sh/pyroparse/install.sh | sh

# Single file
pyroparse convert morning_ride.fit
pyroparse convert morning_ride.fit -o /tmp/ride.parquet

# Directory tree, all cores, with progress bar
pyroparse convert ~/garmin/activities/ -o ~/parquet/ -w -1

# Dump raw FIT messages as JSON
pyroparse dump ride.fit
pyroparse dump ride.fit --kind event,hr_zone
pyroparse dump ride.fit --exclude record -o debug.json

Run pyroparse convert --help or pyroparse dump --help for all options.

---

Standardized schema

FIT files are a mess. enhanced_speed vs speed, semicircle-encoded GPS, manufacturer-specific field names. Pyroparse normalizes all of it into a single, opinionated schema with purpose-chosen Arrow types:

Column	Arrow Type	Notes
timestamp	Timestamp(us, UTC)	Microsecond, timezone-aware, always present
heart_rate	Int16	BPM
power	Int16	Watts
cadence	Int16	RPM (cycling), SPM (running), or strokes/min (swimming)
speed	Float32	m/s, normalized from enhanced_speed variants
latitude	Float64	Degrees, converted from semicircles
longitude	Float64	Degrees, converted from semicircles
altitude	Float32	Meters, normalized from enhanced_altitude
temperature	Int8	Celsius
distance	Float64	Cumulative meters
lap	Int16	0-based lap index, from FIT Lap messages

These 11 columns are the default output. Use columns="all" to get additional columns like core_temperature, smo2, form_power, and stance_time from CIQ apps and running dynamics, plus length and swim_stroke for pool swims (see Swimming).

Canonical columns and developer-field mappings

Manufacturer and Connect IQ developer fields are folded into canonical columns as a supported contract, so you don't have to special-case per-device naming:

Canonical column	Folded sources	Availability
power	native power; Stryd developer field Power	standard column
cadence	native cadence; Stryd developer field Cadence	standard column
smo2	saturated_hemoglobin_percent, SmO2, Current Saturated Hemoglobin Percent	columns="all"
core_temperature	core_temperature, Core Body Temperature	columns="all"
enhanced_respiration_rate	native enhanced_respiration_rate, already scaled to breaths/min	columns="all"

Sorted, de-duplicated by default

Every FIT load returns records sorted by timestamp. By default records that share a timestamp are collapsed to one row (deduplicate=True, keeping the last), giving a unique, index-ready series that absorbs device backward-corrections:

pp.read_fit("ride.fit")                      # sorted + deduplicated (default)
pp.read_fit("ride.fit", deduplicate=False)   # keep every row (still sorted)

Pass deduplicate=False for sub-second-sampled files — e.g. a 10 Hz sensor writing several records under one 1-second FIT timestamp — where collapsing to one row per second would discard real data.

These types are native across the ecosystem, no casting, no surprises:

# DuckDB: direct Arrow scan
import duckdb
duckdb.from_arrow(activity.data).filter("power > 300").fetchdf()

---

Laps

Pyroparse parses FIT Lap messages and assigns a lap index to every record row. The lap column is included by default — use it for per-lap analysis with any tool:

import polars as pl
import pyroparse as pp

activity = pp.Activity.load_fit("intervals.fit")
df = pl.from_arrow(activity.data)
df.group_by("lap").agg(pl.col("power").mean(), pl.col("heart_rate").mean())

The lap_trigger column tells you what ended each lap — useful for distinguishing manual presses from auto-laps:

activity = pp.Activity.load_fit("ride.fit", extra_columns=["lap_trigger"])
df = pl.from_arrow(activity.data)

# Find laps the user deliberately marked (ignoring auto-lap noise)
manual_laps = df.filter(pl.col("lap_trigger") == "manual")["lap"].unique()

Trigger values come directly from the FIT SDK: "manual", "distance", "time", "session_end", "fitness_equipment", "position_start", "position_lap", "position_waypoint", "position_marked". The trigger describes what ended the lap — so a lap closed by pressing the lap button has lap_trigger="manual".

Files without Lap messages get lap=0 for all rows. lap_trigger is omitted entirely when no laps are present.

---

Swimming

Pool ("lap") swimming is special: underwater there is no GPS or speed sensor, so the FIT Record stream carries only heart rate. The movement data lives in per-pool-length Length messages. Pyroparse reconstructs the missing distance, speed, and cadence columns from those lengths, so a pool swim behaves like any other activity:

import polars as pl
import pyroparse as pp

activity = pp.Activity.load_fit("pool-swim.fit")
df = pl.from_arrow(activity.data)

df["distance"].max()            # 1500.0 — reconstructed, reconciles with the session total

# Distance per lap (interval). `distance` is cumulative, so subtract within each lap:
df.group_by("lap").agg(
    (pl.col("distance").max() - pl.col("distance").min()).alias("lap_distance")
)

The three columns are stretched over each length's records differently. distance ramps smoothly within a length, so it's a genuine per-record cumulative curve, landing on the exact total at each wall. speed and cadence are the length's average held constant across all its records (there's no intra-length pace variation to recover — the file stores one average per length), and both are null while resting. Because these are reconstructed rather than measured, pyroparse says so — and exposes the pool length:

activity.metadata.extra["pool_length"]            # 25.0 (metres)
activity.metadata.extra["reconstructed_columns"]  # ["distance", "speed", "cadence"]

Two opt-in extra columns describe the pool-length structure (via columns="all" or extra_columns=[...]):

Column	Arrow Type	Notes
length	Int16	0-based pool-length index — the swim analogue of lap
swim_stroke	Utf8	FIT stroke name: freestyle, backstroke, breaststroke, butterfly, drill, mixed, im, … (null on rest lengths)

activity = pp.Activity.load_fit("pool-swim.fit", extra_columns=["length", "swim_stroke"])
df = pl.from_arrow(activity.data)

# Average speed per length (pace per 100 m = 100 / speed)
df.group_by("length").agg(pl.col("speed").first())
# Isolate the butterfly lengths
df.filter(pl.col("swim_stroke") == "butterfly")

Open-water swims carry GPS, distance, and speed in the Record stream like any outdoor activity, so nothing is reconstructed and neither length nor swim_stroke appears. The same is true of pool swims recorded without lap-swim mode. Reconstruction activates only when a file contains Length messages, and it never overwrites a measured value.

---

Structured metadata

Metadata is extracted from FIT Session and DeviceInfo messages, the same source Garmin Connect and Strava use. Sport, timestamps, duration, distance, device info, available metrics: all parsed into a typed dataclass, not left as raw dicts for you to dig through.

@dataclass
class ActivityMetadata:
    sport: str | None               # open-sport-taxonomy code, e.g. "cycling", "running.trail"
    name: str | None                # user-given activity name
    start_time: datetime | None     # UTC
    start_time_local: datetime | None  # naive, local wall-clock time
    duration: float | None          # seconds
    distance: float | None          # meters
    metrics: set[str]               # {"heart_rate", "power", "speed", "cadence", "gps"}
    devices: list[Device]           # head unit + connected sensors
    extra: dict                     # sub_sport, anything format-specific

start_time_local comes from the FIT Activity message, which records the activity's end in both UTC and local time; the difference is applied to the session start. It is None when a file has no Activity message (for example a truncated recording). Connect IQ apps that wrote data appear in devices with device_type="developer"; an installed app that never wrote a reading (only nulls or zeros) is not listed.

The extra dict holds format- or sport-specific fields that don't earn a top-level attribute: sub_sport (e.g. "lap_swimming"), and — for pool swims — pool_length (metres) and reconstructed_columns (which record columns were derived from Length messages rather than measured; see Swimming).

Manual overrides merge on top of file-native values. A sport override is validated against the taxonomy, so a typo fails loudly instead of silently entering your data:

activity = pp.Activity.load_fit("ride.fit", metadata={"sport": "cycling.gravel"})
activity.metadata.sport       # "cycling.gravel" (overridden)
activity.metadata.duration    # 3842.7           (preserved from FIT)

pp.Activity.load_fit("ride.fit", metadata={"sport": "gravel"})  # ValueError: invalid sport

Sport values

The sport field is an open-sport-taxonomy code, not a free-form string. The same vocabulary is used by pp.Sport (the taxonomy's Sport class, re-exported for convenience). Codes use a dotted hierarchy for disciplines and + for modifiers:

Example code	Meaning
cycling	cycling, discipline unspecified
cycling.road	road cycling
cycling.gravel	gravel cycling
cycling+stationary	indoor / trainer cycling
running.trail	trail running
running+stationary	treadmill running
generic	sport recorded but unrecognized

Specificity comes only from the FIT sport/sub_sport fields — pyroparse never guesses a discipline. A road ride saved without a sub_sport decodes to the bare cycling, and metadata.extra["sub_sport"] preserves the raw FIT sub-sport name when present.

---

Parquet with metadata

to_parquet() writes ZSTD-compressed Parquet with metadata embedded in the Arrow schema under the b"pyroparse" key. This means you can scan metadata across thousands of files without reading row data:

-- DuckDB: find all cycling activities
SELECT filename, json_extract_string(value, '$.sport') AS sport
FROM parquet_kv_metadata('activities/*.parquet')
WHERE key = 'pyroparse'
  AND json_extract_string(value, '$.sport') = 'cycling';

---

Batch operations

Scan a directory of .fit or .parquet files, filter by metadata, load only what you need:

import pyroparse as pp

# Scan: metadata only, no timeseries parsing (fast)
catalog = pp.scan_fit("~/data/activities/")
# file_path | sport | start_time | duration | distance | metrics | ...

# Same API for Parquet (reads schema footers only)
catalog = pp.scan_parquet("~/data/parquet/")

# Filter with PyArrow compute
import pyarrow.compute as pc
cycling = catalog.filter(pc.field("sport") == "cycling")

# Load only the files and columns you need
paths = cycling.column("file_path").to_pylist()
data = pp.load_fit_batch(paths, columns=["timestamp", "power", "heart_rate"])
# file_path | timestamp | power | heart_rate

Column selection

All loaders accept a columns parameter to keep only the data you need. For Parquet files, this pushes down to the reader and skips column chunks entirely. For FIT and CSV, it drops unwanted columns after parse.

# Single file: only timestamp and power
table = pp.read_fit("ride.fit", columns=["timestamp", "power"])

# Parquet: true column pushdown, skips unused data on disk
activity = pp.Activity.load_parquet("ride.parquet", columns=["timestamp", "speed"])

Polars

import polars as pl
import pyroparse.polars as ppl

ppl.scan_fit("~/data/")
  .filter(pl.col("sport") == "cycling")
  .fit.load_data(columns=["timestamp", "power"])
  .select("file_path", "timestamp", "power")

DuckDB

import pyroparse.duckdb as ppdb

catalog = ppdb.scan_fit("~/data/")
catalog.filter("sport = 'cycling'").fetchdf()

paths = catalog.filter("sport = 'cycling'").fetchnumpy()["file_path"].tolist()
data = ppdb.load_fit(paths, columns=["timestamp", "power"])
data.filter("power > 300").fetchdf()

Note: polars and duckdb are optional dependencies, install them separately.

---

Multi-activity FIT files

Triathlon and multisport files split cleanly by session:

session = pp.Session.load_fit("triathlon.fit")
session.activities[0].metadata.sport  # "swimming"
session.activities[1].metadata.sport  # "cycling"
session.activities[2].metadata.sport  # "running"

Activity.load_fit() raises MultipleActivitiesError for multi-activity files, no silent data loss.

---

Course files

Course FIT files (planned routes from Garmin Connect, Strava, race organizers) are a different file type from activities. Parse them with Course:

course = pp.Course.load_fit("stage3.fit")

course.track                          # PyArrow Table: latitude, longitude, altitude, distance
course.metadata.name                  # "Volta Ciclista a Catalunya 2026 - Stage 3"
course.metadata.distance              # 162110.4 (meters)
course.metadata.ascent                # 2358.0 (meters)
course.metadata.waypoints             # list[Waypoint] — turns, climbs, sprints, etc.
course.metadata.waypoints[0].name     # "km 0"
course.metadata.waypoints[0].type     # "generic"

course.to_parquet("stage3.parquet")   # single file, waypoints in schema metadata

Passing a course file to Activity.load_fit() raises FileTypeMismatchError with guidance to use Course instead.

---

Raw FIT messages

all_messages() is the escape hatch — every message in the FIT file, no pyroparse opinions applied. Field names, values, and units come straight from the FIT profile as decoded by fitparser. Use it for HR zones, workout steps, events, or anything the opinionated interface doesn't cover.

import pyroparse as pp

msgs = pp.all_messages("ride.fit")

# Each message has a kind and a list of fields
msgs[0]
# {"kind": "file_id", "fields": [{"name": "type", "number": 0, ...}, ...]}

# Get HR zones
zones = [m["fields"] for m in msgs if m["kind"] == "hr_zone"]

# Get all events in order
events = [m["fields"] for m in msgs if m["kind"] == "event"]

# Get workout interval definitions
steps = [m["fields"] for m in msgs if m["kind"] == "workout_step"]

# Access session fields that pyroparse doesn't model
sessions = [m for m in msgs if m["kind"] == "session"]
fields = {f["name"]: f["value"] for f in sessions[0]["fields"]}
fields["avg_stance_time"]  # not in ActivityMetadata, but here

Or from the command line:

pyroparse dump ride.fit --kind event,session --compact | jq '.'

---

CSV

activity = pp.Activity.load_csv("export.csv", metadata={"sport": "cycling"})
activity.to_parquet("ride.parquet")  # inferred + manual metadata preserved

Timestamps, duration, and available metrics are inferred automatically. Constant-value string columns (like sport=cycling in every row) are promoted to metadata.

---

Installation

uv add pyroparse

Or with pip:

pip install pyroparse

From source

Requires a Rust toolchain and maturin:

git clone <repo>
cd pyroparse
maturin develop --release

Releasing

Releases are automated via GitHub Actions. On tag push:

1. CI runs the full test suite
2. Wheels are built for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (x86_64)
3. All artifacts are published to PyPI via trusted publisher (OIDC)

# 1. Bump version in pyproject.toml and Cargo.toml
# 2. Commit and tag
git commit -am "Release v0.4.0"
git tag v0.4.0
git push && git push --tags

To build wheels locally for testing (requires Docker for Linux targets):

make wheels          # all targets
./build.sh macos     # macOS only
./build.sh linux     # Linux only (Docker)

Docker

A minimal HTTP server for FIT to Parquet/CSV conversion:

docker build -t pyroparse .
docker run -p 8000:8000 pyroparse
# Upload at http://localhost:8000