2. Python environment setup for d3plot processing

Before any d3plot file can be read, parsed, or visualized, a working Python environment must be assembled. LS-DYNA d3plot files are binary result databases written by the LS-DYNA solver; they are not plain text and cannot be inspected with a text editor. Reading them requires a dedicated parser library, and visualizing the extracted mesh, state, and time-history data requires a numerical and plotting stack. This chapter defines the concrete library set, explains why each dependency exists, and walks through a reproducible installation and verification procedure.

Core Library Stack

The de facto standard for reading LS-DYNA binary results in Python is lasso-python, developed and maintained by the LS-DYNA community. It exposes a Python API over the LS-PrePost reader core and supports d3plot, binout, and keyword files. Around it, three supporting layers are required:

• Numerical core: numpy for array storage of nodal coordinates, displacements, and element connectivity.
• Data handling: pandas for time-history tables and CSV export of extracted curves.
• Visualization: matplotlib for 2D plots and fringe/contour rendering; pyvista (optional but recommended) for interactive 3D mesh display and screenshot generation.

A minimal but complete environment therefore consists of lasso-python, numpy, pandas, and matplotlib. If you intend to render 3D geometry or export images for reports, add pyvista and its backend vtk. The dependency relationships are summarized in the diagram below.

Installation Procedure

Create an isolated environment first. Mixing lasso-python with an existing global site-packages directory frequently causes binary incompatibility with vtk or numpy versions. Using conda is recommended because lasso-python and vtk ship compiled extensions that are easier to resolve through conda channels than through pip wheels on some platforms.

# 1. Create and activate a dedicated environment
conda create -n d3plot python=3.11 -y
conda activate d3plot

# 2. Install the parser and numerical stack
conda install -c conda-forge lasso-python numpy pandas matplotlib -y

# 3. Optional: 3D visualization backend
conda install -c conda-forge pyvista vtk -y

# 4. Verify the installation
python -c "import lasso; print(lasso.__version__)"
python -c "import numpy, pandas, matplotlib; print('core stack OK')"

If you prefer pip, the equivalent is pip install lasso-python numpy pandas matplotlib. On Windows, ensure the Microsoft Visual C++ Redistributable is present, since the LS-PrePost reader core links against it. On Linux, libGL and libX11 are required for any rendering path; headless servers should install mesa-utils and use pyvista.start_xvfb() or set PYVISTA_OFF_SCREEN=true.

Verifying the Environment with a Real d3plot File

Installation success does not guarantee that the parser can open a given d3plot. The verification step should load an actual result file and confirm that the expected state count, node count, and time values are returned. The following script is a minimal smoke test. Replace the path with a d3plot from your own model; a small explicit-dynamics example with 10–20 states is sufficient.

import lasso
from lasso.dyna import D3plot, ArrayType

path = "example.d3plot"
db = D3plot(path)

# Inspect basic metadata
print("states:", db.header.n_states)
print("nodes :", db.header.n_nodes)
print("time   :", db.arrays[ArrayType.global_timesteps])

# Extract nodal displacement of the last state
disp = db.arrays[ArrayType.node_displacement]
print("displacement shape:", disp.shape)   # (n_states, n_nodes, 3)

Expected output for a model with 15 states and 8,000 nodes is a state count of 15, a node count of 8,000, a time array of length 15, and a displacement array shaped (15, 8000, 3). If n_states is zero or the displacement array is empty, the file is either truncated, was written with a non-standard output interval, or the path points to a d3plot01 continuation file rather than the root d3plot. Always open the root file; the parser follows the continuation chain automatically.

Common Installation and Runtime Failures

Environment Record for Reproducibility

Once the smoke test passes, export the environment specification so that the setup can be rebuilt on another machine or by a collaborator:

conda env export --no-builds > environment.yml
pip freeze > requirements.txt

Keep this file with your analysis scripts. When a result is later questioned, the environment file plus the d3plot path and the script form a complete, reproducible record. With the environment verified and recorded, you are ready to move from installation into actual data extraction and visualization in the following chapters.