nanodent 0.1.0

Analysis package for nanoindentation experimental data.

nanodent

Installation

The Python package nanodent can be installed from PyPI:

python -m pip install nanodent

Development installation

If you want to contribute to the development of nanodent, we recommend the following editable installation from this repository:

git clone https://github.com/thomasisensee/nanodent
cd nanodent
python -m pip install --editable .[dev,docs,lint,tests]

Having done so, the test suite can be run using pytest:

python -m pytest

Quick Start

nanodent loads .hld as the canonical source.

from datetime import datetime, timedelta

import matplotlib.pyplot as plt

from nanodent import (
    load_folder,
    plot_experiments,
    plot_group_timeline,
    save_experiment_plots,
)

study = load_folder("path/to/experiment-folder")
filtered_study = study.classify_quality()
filtered_study = filtered_study.analyze_oliver_pharr()
filtered_study = filtered_study.analyze_hertzian()
hardness_rows = filtered_study.scalar_series("hardness")
pop_in_rows = filtered_study.detect_force_peaks().scalar_series("pop_in_load")
radius_rows = filtered_study.scalar_series("hertzian_radius")
tau_rows = filtered_study.scalar_series("tau_max")
manual_groups = filtered_study.group_by_datetime_ranges(
    [
        (
            datetime(2026, 3, 4, 13, 0, 0),
            datetime(2026, 3, 4, 15, 0, 0),
        ),
    ]
)

timeline_fig, timeline_ax = plot_group_timeline(
    filtered_study,
    max_gap=timedelta(minutes=30),
)

fig, ax = plt.subplots()
plot_experiments(
    ax,
    filtered_study,
    fit_kwargs={"color": "gray", "linestyle": "solid", "linewidth": 2},
    zero_onset=False,
    cmap="rainbow",
)

ax.set_xlabel("Displacement h / nm")
ax.set_ylabel("Force P / μN")

saved = save_experiment_plots(
    filtered_study, "plots/", zero_onset=False
)

filtered_study.save_session("analysis-session.pkl")
resumed_study = load_folder("path/to/experiment-folder").load_session(
    "analysis-session.pkl"
)

Study.classify_quality() keeps all experiments loaded but marks heuristically bad runs as enabled=False with a short disabled_reason, currently including gradual_onset, flat_force, and local-jump outliers such as outlier_disp or outlier_force. Grouping and plotting ignore disabled experiments by default; group summaries include them by default so quality decisions stay visible. Pass include_disabled=True when you want disabled runs included in plots or grouping output. plot_experiments(...) and save_experiment_plots(...) always visualize the test force-displacement curve.

The public API also exposes:

• load_experiment(path) -> Experiment
• load_folder(path) -> Study
• Study.analyze_oliver_pharr(...) -> Study
• Study.analyze_hertzian(...) -> Study
• Study.scalar_series(...) -> list[dict[str, Any]]
• Study.save_session(path) -> Path
• Study.load_session(path) -> Study
• Study.group_by_datetime_ranges(...) -> list[ExperimentGroup]
• Study.group_by_time_gap(...) -> list[ExperimentGroup]
• plot_group_timeline(...) -> tuple[Figure, Axes]
• plot_experiments(...) -> Axes
• save_experiment_plots(...) -> list[Path]

Demo notebook

Use the demo notebook to test the functionality of nanodent and see how it can be used to analyze and visualize nanoindentation experiments.

Acknowledgments

This repository was set up using the SSC Cookiecutter for Python Packages.