Quick Start

Introduction to opstool

opstool is a powerful and user-friendly library designed to simplify and enhance structural analysis workflows with OpenSees and OpenSeesPy. It provides advanced tools for preprocessing, postprocessing, and visualization, making structural simulations more efficient and accessible.

Key Features:

  1. Preprocessing Tools:

    • Fiber Section Meshing: Generate detailed fiber meshes for various geometries.

    • GMSH Integration: Import and convert Gmsh models, including geometry, mesh, and physical groups.

    • Unit System Management: Ensure consistency with automatic unit conversions.

    • Mass Generation: Automate lumped mass calculations.

  2. Postprocessing Capabilities:

    • Easy retrieval and interpretation of analysis results using xarray.

  3. Visualization:

    • Powered by PyVista (VTK-based) and Plotly (web-based).

    • Nearly identical APIs for flexible visualization of model geometry, modal analysis, and simulation results.

    • Supports most common OpenSees elements.

  4. Intelligent Analysis:

    • Features like automatic step size adjustment and algorithm switching to optimize simulation workflows.

    • Moment-Curvature Analysis: Generate moment-curvature curves for various sections.

Why Choose opstool?

  • Efficiency: Streamlines complex workflows, reducing time spent on repetitive tasks.

  • Flexibility: Provides nearly identical interfaces for different visualization engines.

  • Accessibility: Makes advanced structural analysis tools like OpenSeesPy more approachable to users of all levels.

opstool is actively evolving, with continuous additions of new features planned for the future. With opstool, you can focus on what matters most: understanding and solving your structural engineering challenges. Whether you are building models, visualizing results, or interpreting data, opstool is your go-to solution for OpenSeesPy workflows.

Quick Visualization Guide

Below is a quick guide to getting started with visualization in opstool:

Quick Model and Eigen Visualization.