A common question from Ansys Rocky users is:
What is the difference between PyRocky and Ansys Rocky PrePost Scripting?
The most accurate answer is that PyRocky should not be understood as a complete replacement for Ansys Rocky’s native automation capabilities. Instead, PyRocky provides a Python client layer that enables users to interact remotely with Ansys Rocky via the Ansys Rocky PrePost Scripting.
In other words, PyRocky makes Ansys Rocky automation more accessible from external Python environments while still relying on Ansys Rocky’s established scripting capabilities.
Ansys Rocky PrePost Scripting is the core Python based automation mechanism for creating, modifying, running, analyzing, and exporting simulation data from Ansys Rocky projects. It is directly connected to Ansys Rocky’s internal simulation objects and is especially useful for users who already understand Ansys Rocky’s data structure.
PyRocky extends this workflow by allowing users to control Ansys Rocky from Python environments outside the Ansys Rocky application. This makes it easier to integrate Ansys Rocky with the broader Python ecosystem and with other PyAnsys libraries.
A practical comparison is shown below.
| Aspect | PyRocky | Ansys Rocky PrePost Scripting |
|---|---|---|
| Primary purpose | Control Ansys Rocky remotely from Python | Automate Ansys Rocky model setup, simulation execution, and post-processing |
| Execution context | Used from external Python environments | Typically used from Ansys Rocky scripting environments |
| Main interface | `RockyClient` and `RockyClient.api` | Ansys Rocky PrePost scripting objects and methods |
| Level of abstraction | Python client layer over Ansys Rocky scripting capabilities | Direct access to Ansys Rocky scripting capabilities |
| Integration with Python tools | More natural integration with NumPy, Pandas, Matplotlib, Jupyter, and other Python tools | Possible, but more tied to Ansys Rocky’s scripting environment |
| Integration with PyAnsys | Designed as part of the PyAnsys ecosystem | Not the focus |
| Best suited for | Workflow automation, batch execution, remote scripting, data pipelines, and reproducible studies | Direct Ansys Rocky automation and users familiar with Ansys Rocky scripting |
Therefore, the relationship between the two is complementary.
Ansys Rocky PrePost Scripting provides an underlying automation capability. PyRocky provides a Pythonic access layer that makes this capability easier to use in modern automation workflows.
This distinction is important because users should not expect every PyRocky command to behave like a completely new high-level API. In its current form, many operations are performed through the same Ansys Rocky PrePost Scripting methods that Ansys Rocky users may already know.
For experienced Ansys Rocky users, this is an advantage: existing scripting knowledge can be transferred to PyRocky workflows.
For Python users, PyRocky provides a more familiar, scalable environment for building scripts, notebooks, automation tools, and engineering data pipelines.
