Modan2 Documentation
Welcome to Modan2’s documentation!
Modan2 is a user-friendly desktop application that empowers researchers to explore and understand shape variations through geometric morphometrics. It streamlines the entire workflow from data acquisition (2D/3D) to statistical analysis and visualization.
Contents:
Features
Hierarchical Data Management: Organize data into nested datasets with a clear structure
2D & 3D Visualization: Integrated viewers for 2D images and 3D models with landmark plotting
Statistical Analysis: Perform Principal Component Analysis (PCA), Canonical Variate Analysis (CVA), and MANOVA
Missing Landmark Support: Advanced handling of incomplete landmark data with visual estimation
Data Import/Export: Supports various file types (TPS, NTS, OBJ, PLY, STL) with drag-and-drop
Persistent Storage: All data and analyses saved in a local SQLite database managed by Peewee ORM
Quick Start
Installation
Download the latest version from the releases page.
- For Windows:
Download and run the installer (
Modan2-Setup.exe
)- For Linux/macOS:
Download the appropriate package or run from source
Basic Usage
Create a New Dataset
Click “New Dataset” or press
Ctrl+N
to create a dataset for your morphometric study.Import Objects
Drag and drop 2D images or 3D models into your dataset, or use File → Import to load landmark files (TPS, NTS).
Place Landmarks
Double-click an object to open the Object Dialog, then click on the image/model to place landmarks.
Run Analysis
Select your dataset and click “Analyze Dataset” to perform:
Procrustes superimposition (aligns shapes)
Principal Component Analysis (PCA)
Canonical Variate Analysis (CVA)
MANOVA (multivariate analysis of variance)
Explore Results
View PC plots, shape variations, and statistical outputs in the Data Exploration dialog.
Keyboard Shortcuts:
Ctrl+N
- New DatasetCtrl+Shift+N
- New ObjectCtrl+S
- Save changesCtrl+O
- Open databaseDelete
- Delete selected items
For more detailed instructions, see the User Guide.
Technology Stack
Language: Python 3.11+
GUI Framework: PyQt5
- Core Libraries:
Database ORM: Peewee
Numerical/Scientific: NumPy, SciPy, Pandas, Statsmodels
3D Graphics & Image Processing: PyOpenGL, Trimesh, Pillow, OpenCV