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Merrypopins

Merrypopins

Merrypopins CI Tests codecov CodeQL 📘 Merrypopins Documentation Merrypopins Streamlit App PyPI Python Docker Pulls Downloads Issues Dependencies Dependabot Status Last commit Release Contributors License: MIT

merrypopins is a Python library to streamline the workflow of nano‑indentation experiment data processing, automated pop-in detection and analysis. It provides five core modules:

  • load_datasets: Load and parse .txt measurement files and .tdm/.tdx metadata files into structured pandas DataFrames. Automatically detects headers, timestamps, and measurement channels.
  • preprocess: Clean and normalize indentation data with filtering, baseline correction, and contact point detection.
  • locate: Identify and extract pop‑in events within indentation curves using advanced detection algorithms, including:
  • Isolation Forest anomaly detection
  • CNN Autoencoder reconstruction error
  • Fourier-based derivative outlier detection
  • Savitzky-Golay smoothed gradient thresholds
  • statistics: Perform statistical analysis and model fitting on located pop‑in events (e.g., frequency, magnitude, distribution). The statistics module allows you to compute detailed pop-in statistics, such as:
  • Pop-in statistics (e.g., load-depth and stress-strain metrics)
  • Stress-strain transformation using Kalidindi & Pathak. (2008)
  • Curve-level summary statistics (e.g., total pop-in duration, average time between pop-ins)
  • Pop-in shape statistics like depth jump, average velocity, and curvature
  • make_dataset: Construct enriched datasets by running the full merrypopins pipeline and exporting annotated results and visualizations.

🌐 Try Merrypopins Library Online

🚀 Live demo: explore Merrypopins in your browser! Open in Streamlit

The hosted app lets you:

  • upload raw .txt indentation files (and optional .tdm/.tdx metadata),
  • tune preprocessing, detection & statistics parameters,
  • visualise pop-ins interactively,
  • download annotated CSVs + plots.

🛠 Source Instrumentation

Merrypopins was developed using datasets generated by the Bruker Hysitron TI 990 TriboIndenter — a high-precision nanoindentation platform. The library natively supports .txt and .tdm/.tdx file formats exported by the Hysitron software suite.

Hysitron TI 990 Nanoindenter

Typical indentation experiments conducted with the TI 990 include:

  • Force-depth curve acquisition at nano/micro scale
  • High-resolution pop-in event detection
  • Automated test grid data export

The preprocessing and pop-in detection tools in Merrypopins are tuned to handle the structural patterns and noise profiles specific to these datasets.

Example: Nanoindentation Grain Selection and Deformation

Below are example visualizations from Electron Backscatter Diffraction (EBSD) maps used to select grain areas, followed by indentation marks after testing:

➤ Pre-indentation EBSD with Labeled Grains

Grain Selection Map

➤ Post-indentation Microstructure with Deformation (Area on Grain 5)

Grain 5 After Indentation

These images highlight the complex deformation behavior analyzed by the merrypopins toolset for robust pop-in detection.

For a quick overview, see the Quickstart.

Merrypopins is developed by Cahit Acar, Anna Marcelissen, Hugo van Schrojenstein Lantman, and John M. Aiken.