mechanicsdsl-datasets 0.1.0

Reference physics datasets for MechanicsDSL parameter estimation and inverse problem benchmarking

mechanicsdsl-datasets

Reference datasets for physics parameter estimation and inverse problem benchmarking.

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Install

pip install mechanicsdsl-datasets

Or clone for direct access to raw CSV/HDF5 files:

git clone https://github.com/MechanicsDSL/mechanicsdsl-datasets

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Available Datasets

Dataset	System	Observations	Rate	Noise σ	Ground Truth
pendulum_synthetic	Simple pendulum	1,001	100 Hz	0.010 rad	m=1.0, l=0.5, g=9.81
double_pendulum_synthetic	Double pendulum (near-periodic)	501	100 Hz	0.005 rad	m=1.0, l=1.0, g=9.81
coupled_oscillators_synthetic	Coupled pendulums (3 beat periods)	2,001	100 Hz	0.005 rad	m=1.0, l=1.0, k=0.5

All datasets include clean trajectories, Gaussian-noisy observations, HDF5 with embedded metadata, and paired estimation/validation scripts.

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Quick Start (pip install)

import mechanicsdsl_datasets as mds

# Load a dataset
dataset = mds.load("pendulum_synthetic")
print(dataset.ground_truth)    # {'m': 1.0, 'l': 0.5, 'g': 9.81}
print(dataset.t.shape)         # (1001,)
print(dataset.theta_noisy[:5]) # noisy observations

# Run built-in validation
dataset.validate()             # confirms forward simulation matches clean data

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Quick Start (raw files)

import pandas as pd
import json

data = pd.read_csv("datasets/pendulum_synthetic/data.csv")
meta = json.load(open("datasets/pendulum_synthetic/metadata.json"))

t         = data["t_s"].values
theta_obs = data["theta_rad_noisy"].values
truth     = meta["ground_truth"]   # {'m': 1.0, 'l': 0.5, 'g': 9.81}

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Repository Structure

mechanicsdsl-datasets/
├── datasets/
│   ├── pendulum_synthetic/
│   │   ├── README.md, data.csv, data.hdf5, metadata.json
│   │   └── examples/  estimate_scipy.py, validate_forward.py
│   ├── double_pendulum_synthetic/
│   │   ├── README.md, data.csv, data.hdf5, metadata.json
│   │   └── examples/  estimate_scipy.py, validate_forward.py
│   └── coupled_oscillators_synthetic/
│       ├── README.md, data.csv, data.hdf5, metadata.json
│       └── examples/  estimate_normal_modes.py, validate_forward.py
├── docs/
│   ├── schema.md           # metadata.json schema reference
│   └── estimation_guide.md # least-squares, MCMC, FFT methods
├── scripts/
│   ├── generate_all.py     # regenerate all synthetic datasets
│   └── validate_all.py     # run all validate_forward.py scripts
└── tests/
    └── test_datasets.py    # schema, row count, NaN, noise std checks

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Estimation Examples

Each dataset ships with working estimation scripts:

# Estimate pendulum length from noisy observations
cd datasets/pendulum_synthetic
python examples/estimate_scipy.py
# Estimated: l = 0.4998 m  (ground truth: 0.5000 m)

# Identify normal mode frequencies via FFT
cd datasets/coupled_oscillators_synthetic
python examples/estimate_normal_modes.py
# omega1 = 3.1321 rad/s, omega2 = 3.2636 rad/s, k = 0.500 N/m

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Testing

pip install pytest h5py numpy scipy pandas
pytest tests/ -v

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Contributing

See CONTRIBUTING.md. Experimental datasets with real sensor data especially welcome.

Citation

@dataset{mechanicsdsl_datasets2026,
  author = {Parsons, Noah},
  title  = {{MechanicsDSL Physics Datasets}},
  year   = {2026},
  doi    = {10.5281/zenodo.17771040},
  url    = {https://github.com/MechanicsDSL/mechanicsdsl-datasets}
}

License

MIT License — see LICENSE.