2. Python environment for CAE integration

CAE integration work rarely lives in a single, clean Python installation. A typical project must talk to at least one commercial solver (Abaqus, ANSYS, COMSOL, OpenFOAM, or a custom in-house code), parse its output files, and often drive a pre/post-processor through an API. Each of these dependencies has its own version constraints. If you install everything into the system Python, a library upgrade required by one solver's Python API can silently break another. The practical answer is to treat every integration project as an isolated, reproducible environment.

Why Isolation Is Not Optional

Commercial CAE vendors ship their own embedded Python interpreters. Abaqus, for example, bundles a specific Python version with its own abaqus module and a fixed set of third-party packages. ANSYS exposes PyMAPDL and PyFluent through a separate Python installation. If you try to pip install a newer NumPy into the Abaqus interpreter, you risk breaking the solver's own scripts. The correct pattern is to keep the solver's interpreter untouched and create a client environment that communicates with the solver over a socket, file exchange, or a subprocess call.

This separation has three concrete benefits: dependency conflicts disappear, environments become reproducible across machines, and you can upgrade your analysis tooling without touching the solver installation.

Creating and Managing Virtual Environments

Python's built-in venv module is sufficient for most CAE integration work. The workflow below assumes Python 3.10 or later, which is the minimum for current PyMAPDL and most modern solver APIs.

# Create an isolated environment for a CAE integration project
python -m venv .venv-cae

# Activate it (Windows)
.venv-cae\Scripts\activate

# Activate it (Linux / macOS)
source .venv-cae/bin/activate

# Confirm the interpreter path points inside the environment
python -c "import sys; print(sys.executable)"

For projects that mix scientific computing with solver APIs, conda or mamba is often more convenient because it can install non-Python dependencies such as MPI, HDF5, and BLAS libraries that some solver clients require. A minimal conda environment for CAE integration looks like this:

conda create -n cae-integration python=3.11 numpy scipy pandas h5py matplotlib
conda activate cae-integration

Whichever tool you choose, pin the interpreter version in a file that travels with the project. A .python-version file (for pyenv) or an explicit python=3.11 in environment.yml prevents the "works on my machine" failure mode that plagues solver integration scripts.

Package Management and Reproducibility

Once the environment exists, dependencies must be recorded precisely. Two files matter: a human-readable requirements file and a lock file that captures exact versions and hashes.

# requirements.in — direct dependencies only
numpy>=1.26,<2.0
scipy>=1.11
pandas>=2.1
h5py>=3.10
ansys-mapdl-core>=0.68
pyvista>=0.43

# Generate a fully pinned lock file
pip install pip-tools
pip-compile requirements.in -o requirements.txt
pip-sync requirements.txt

The distinction between requirements.in and requirements.txt is important in CAE work because solver APIs frequently pin transitive dependencies (for example, a specific protobuf version required by a gRPC-based solver client). pip-compile resolves those constraints once and freezes them, so a colleague or a CI runner gets the identical set.

Essential Libraries for Solver Interaction

The library set depends on the solver, but a core stack appears repeatedly across projects. The table below summarizes the roles and typical use cases.

For Abaqus scripting, the abaqus and odbAccess modules are only available inside the Abaqus interpreter, so they cannot be installed via pip. The integration pattern is to run a small Abaqus-side script that writes results to a neutral format (CSV, HDF5, or JSON), then process that file in your client environment with the libraries above.

Verifying the Environment Before Real Work

A short smoke test catches most environment problems before they waste solver time. The script below checks imports, versions, and a basic numerical operation.

import sys, importlib

required = ["numpy", "scipy", "pandas", "h5py", "meshio"]
for name in required:
    mod = importlib.import_module(name)
    print(f"{name:12s} {getattr(mod, '__version__', 'n/a')}")

import numpy as np
a = np.array([[1.0, 2.0], [3.0, 4.0]])
print("det =", np.linalg.det(a))   # expected: -2.0

If an import fails, the traceback usually points to a missing binary dependency (for example, h5py needing HDF5) or a version conflict. In a conda environment, conda list shows the resolved versions; in a venv, pip check reports broken requirements. Run both after any dependency change.

Environment Layout for a Multi-Solver Project

The diagram below shows how a single integration project separates the client environment from multiple solver-side interpreters, with file exchange and network transport as the communication boundary.

The key takeaway from this topology is that the client environment is the only place where you install packages. The solver-side interpreters remain exactly as the vendor shipped them. Communication happens through a narrow, well-defined boundary: files, sockets, or subprocess pipes. This boundary is also where you should add logging, because failures at the transport layer are far easier to diagnose than failures inside a solver's opaque interpreter.

A Minimal Reproducible Setup Checklist

• Create a dedicated environment per project; never use the system Python.
• Pin the Python version in a version file or environment.yml.
• Separate direct dependencies (requirements.in) from the locked set (requirements.txt).
• Run pip check or conda list after every dependency change.
• Keep solver-side interpreters untouched; communicate over a defined transport.
• Run a smoke test that imports every required library and performs one numerical operation before launching a real solve.

With this foundation in place, the next step is to connect the client environment to an actual solver API and exchange real model data. The environment itself is not the goal; it is the precondition that makes solver interaction reliable and repeatable.