AFM-tools 2.1.4

A Python package for visualizing and analyzing Atomic Force Microscopy(AFM) and Piezoelectric Force Microscopy(PFM) experimental data, offering tools to process, visualize, and extract meaningful insights from AFM images and measurements.

AFM-tools is a Python package for loading, processing, and visualizing Atomic Force Microscopy (AFM) and Piezoelectric Force Microscopy (PFM) data.

Project Links

• Source: https://github.com/yig319/AFM-tools

• Issues: https://github.com/yig319/AFM-tools/issues

• PyPI: https://pypi.org/project/AFM-tools/

Installation

Install from PyPI:

pip install AFM-tools

Most users should use this pip install. It includes all core AFM/PFM features. 3D utilities in afm_tools.drawing_3d require mayavi (VTK/Qt stack), which is recommended via Conda.

Install from source:

git clone https://github.com/yig319/AFM-tools.git
cd AFM-tools
pip install -e .

Clone On A New Desktop (Core Pip Environment)

From a fresh machine, this is the recommended setup for core AFM-tools usage:

git clone https://github.com/yig319/AFM-tools.git
cd AFM-tools
python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -r requirements-dev.txt
pip install -e .

Optional: 3D environment (Mayavi via Conda)

If you need drawing_3d/Mayavi features:

conda env create -f environment-mayavi.yml
conda activate afm-tools-3d

This Conda environment installs mayavi/vtk/pyqt plus AFM-tools dependencies. Use it when you need 3D visualization.

Quick Start

import numpy as np
from afm_tools.afm_viz import AFMVisualizer

# Example image array (replace with real AFM/PFM image data)
img = np.random.randn(256, 256)

viz = AFMVisualizer()
viz.viz(img=img, scan_size={"image_size": 256, "scale_size": 1, "units": "µm"})

Usage Guide

See USAGE.md for a practical guide to loading IBW files, plotting single channels, rendering multi-channel previews, and understanding what belongs in AFM-tools versus sci-viz-utils.

IBW Preview Style

The high-level IBW preview keeps the compact AFM/PFM plotting style as the default: percentile color limits, MAD-based outlier handling, inward colorbar ticks, small colorbar labels, the unit above the colorbar, and a bottom-right scale bar with its label offset from the bar.

from afm_tools.afm_viz import AfmPreviewOptions, load_afm_dataset, render_afm_preview

dataset = load_afm_dataset("tests/ibw_preview/sample.ibw")
rendered = render_afm_preview(
    dataset,
    AfmPreviewOptions(selected_channel_indices=[0], show_metric_overlay=True),
)
rendered.figure.savefig("sample_preview.png", dpi=180, bbox_inches="tight")

You can tune the restored defaults without rewriting the plotting function:

options = AfmPreviewOptions(
    selected_channel_indices=[0],
    colorbar_setting={
        "style": "compact",
        "tick_labelsize": 7,
        "unit_position": "top",
        "scale_image": True,
    },
    scalebar_setting={
        "text_offset": 0.55,
        "text_fontsize": 9,
    },
)

Use colorbar_setting={"style": "matplotlib"} for Matplotlib’s standard side-label colorbar. Use {"scale_image": False, "tick_unit": True} to keep raw meter-valued image data and scale only the colorbar tick labels, matching older AFM visualizer behavior.

To generate preview PNGs from the bundled sample IBW file:

python tests/ibw_preview/ibw_preview.py
python tests/ibw_preview/ibw_preview.py --channels all

The images are written to tests/ibw_preview/outputs by default.

Features

• Read and parse AFM-related wave/image formats.

• 2D/3D visualization utilities for AFM/PFM datasets.

• Domain and morphology analysis helpers.

• Video and plotting utilities for time/scan series.

Documentation

Sphinx documentation is provided in the docs directory.

Build docs locally:

pip install -r docs/requirements.txt
pip install -e .
sphinx-build -b html docs docs/_build/html

License

This project is licensed under the MIT License. See LICENSE.txt.