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Fastkit-Mobility

Fastkit-Mobility (fastmob on PyPI) is a high-performance Python library for mobility analysis. It combines native Rust kernels with a Narwhals-based API, so the same code works unchanged against pandas, Polars, and other eager dataframe backends — no rewrites, no backend-specific branches.

Beyond individual/collective mobility measures, fastmob covers the full pipeline: raw position fixes → staypoints → triplegs → trips → tours, trajectory preprocessing (outlier filtering, simplification, segmentation, smoothing), mobility models (next-location prediction, gravity/radiation, synthetic diary generation), and network/social analysis (road/rail-matched distances, co-presence contact networks, mobility-law fitting).

Why fastmob

Backend-agnostic — pass a pandas, Polars, or any other Narwhals-compatible DataFrame; fastmob works without changes.
Rust-accelerated core — compute-heavy kernels run in parallel, zero-copy Rust instead of Python loops.
Full trajectory hierarchy — positionfixes, staypoints, triplegs, trips, tours, and locations, following the trackintel model.
Migration-friendly — API shapes mirror scikit-mobility where compatibility matters, with reproducible parity tests against it.
Measured, not claimed — correctness tests, coverage, and reproducible benchmarks back every performance and compatibility claim.

Install

pip install fastmob

Install optional capabilities only when needed:

pip install duckdb  # fetch road/rail networks from Overture
pip install statsmodels  # fit the Gravity Poisson-GLM
pip install "fastmob[vis]"  # visualization package

Quickstart

import pandas as pd
from fastmob import jump_lengths, radius_of_gyration

traj = pd.DataFrame(
    {
        "uid": ["alice", "alice", "alice"],
        "datetime": pd.date_range("2024-01-01", periods=3, freq="h"),
        "lat": [41.8902, 41.9028, 41.9109],
        "lng": [12.4922, 12.4964, 12.4818],
    }
)

print(jump_lengths(traj))
print(radius_of_gyration(traj))

Fastmob auto-detects common time, latitude, longitude, and user-ID column names. Pass explicit datetime_col, lat_col, lng_col, and uid_col arguments when your schema differs.

Documentation

Development

git clone https://github.com/gefgu/fastmob.git
cd fastmob
bash scripts/setup_env.sh
source .venv/bin/activate
pytest

To preview documentation locally:

uv sync --group docs
uv run --group docs zensical serve

See CONTRIBUTING.md for documentation conventions and the repository workflows.

Status and support

Fastmob is actively developed. Please include versions, dataframe backend, a minimal reproducible input, expected behavior, and the full error when opening an issue.

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fastmob 0.2.1

Fastkit-Mobility

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SHA256	`4263be4aeae0b3567c90ec1832b903ca9a103eb127619c8be5c6d5f988892161`
MD5	`1f762a55a6a8ccdafc140ed634413990`
BLAKE2b-256	`bbb482240a754fa312f2bde634569e6e75141b7154b2b34a0174380351087f59`

Algorithm	Hash digest
SHA256	`07d27f48dcc7b57f06636e46040182098d619536494fb10389988466389aa0cb`
MD5	`5c6ea634876b3153ba2329545dc96fdd`
BLAKE2b-256	`9c97f06bdee3850d0f3b40a0b777db4bd0a9c28b244ed6542eaf9f538fede957`