fastmob 0.1.2

High-performance mobility analysis with a Rust-accelerated core and backend-agnostic DataFrame API.

fastmob

High-performance mobility analysis with backend-agnostic dataframe support and Rust-accelerated compute kernels.

Key Features

• Backend-agnostic dataframes: use pandas, Polars, or other eager Narwhals-compatible dataframes as inputs.
• Rust-accelerated kernels: run compute-heavy mobility operations through a compiled PyO3 extension.
• Trajectory preprocessing: filter noisy trajectories, compress movement records, detect stay locations, cluster stops, and transform CDR records.
• Mobility measures: compute jump lengths, radius of gyration, waiting times, location frequencies, entropy, predictability, origin-destination matrices, and related individual or collective metrics.
• Modeling and generation: build gravity, radiation, EPR, DITRAS, Markov diary, GeoSim, and spatial-temporal social trajectory models.
• Privacy analysis: evaluate mobility-data privacy risk with location, frequency, dataframe, and attack-oriented utilities.
• Tessellation and IO helpers: work with spatial tiles, trajectory dataframes, flow dataframes, datasets, and file-based mobility inputs.
• Validation-focused development: maintain correctness tests, benchmark outputs, profiling support, and compatibility checks for performance-sensitive workflows.

Installation

pip install fastmob

For development from source:

uv sync --extra dev
env -u CONDA_PREFIX uv run maturin develop

Quick Example

import pandas as pd
from fastmob import jump_lengths

df = pd.DataFrame(
    {
        "uid": ["alice", "alice", "alice", "bob", "bob"],
        "datetime": pd.date_range("2020-01-01", periods=5, freq="h"),
        "lat": [41.9, 42.0, 42.1, 40.7, 40.8],
        "lng": [12.5, 12.5, 12.5, -74.0, -74.0],
    }
)

result = jump_lengths(df)
print(result)

Development Checks

uv run pytest
uv run ruff check