PyMechanical architecture#

PyMechanical provides a Python interface to Ansys Mechanical. Mechanical is a polyglot [1] desktop app whose graphical user interface (GUI) runs on either the Windows or Linux operating system. Mechanical’s APIs are implemented in C# using .NET Framework 4.x. They are exposed to both C# and two implementations [2] of Python, namely IronPython [3] and CPython.

Within Mechanical, Python scripting enables you to automate repetitive GUI actions. This is not unlike other apps developed either by Ansys or other software companies. Python scripting leverages the Mechanical API.

Mechanical, like some other apps, is customizable. Using the same API that you would use for scripting, you can implement extensions that add to the capabilities of Mechanical. For example, buttons can be added to the GUI and custom objects can be added to the data model. Even third-party or in-house solvers can be integrated into Mechanical and can take advantage of the powerful meshing, generic CAD reader, and the intuitive pre- and post- processing experience of Mechanical.

Before discussing how Mechanical’s API is implemented, the software design pattern known as the command pattern is explored. This pattern can be used in many programming languages. For a general description (using the Java programming language) of the command pattern, see Command Design Pattern in the HowToDoInJava newsletter.

Command pattern#

Many interactive apps use the command pattern. It turns anything that the user does within the app into a command object, which is immediately executed. This approach carries some additional benefits [4].

More relevant to this discussion is how command patterns can be used to implement automation APIs. If every action is a command, then that Command can serve as the API. So long as the Commands can be encoded in text, a scripting language can be used to execute them. In fact, this approach is roughly how the Ansys Electronics Desktop, Ansys Mechanical APDL, and the Ansys Workbench apps implement automation APIs for scripting.

Using the command pattern as a scripting API has disadvantages. Most importantly, command APIs are not symmetric. The four fundamental operations on data within a software app are Create, Read, Update, and Delete. These are often abbreviated as CRUD. While you can conceptually update, create, and delete using commands, you can not read using a command. This makes it difficult to “visit” the app’s data model.

Mechanical API implementation#

Mechanical’s API serves the needs of both automation and customization. For customization, it is necessary to read the data model. For example, when integrating a third-party solver, you must access boundary conditions, geometry, material properties, mesh, and connections to properly input them to the solver. As discussed earlier, the command pattern does not allow this kind of access. Due to this fact, the Mechanical API exposes its data model directly to the user. This is how a hypothetical command-based API would look for renaming an object.

RenameCommand(id=100, name="New name")

Instead, an API based on a data model, like Mechanical’s API, looks like this:

obj = GetObject(id=100)
obj.Name = "New name"

Notice that you could print the name by Reading a property of the object. A command-based API can not provide the same experience.

PyMechanical remote interface#

One way to interact with the Mechanical API from Python is as a remote session. You can run Mechanical as a server and send a Remote Procedure Call (RPC). The server handles the RPC and returns the response to the client. Currently, sending commands as a string and getting the result as a string is the only option.

PyMechanical embedded instance#

The embedded instance used by PyMechanical embeds an entire instance of the Mechanical app in-memory inside of a Python program. There is no additional running process associated with it. Mechanical’s data model is directly available within Python, which means that the fully CRUD data model of the Mechanical API can be used.

Distributed systems#

This section contains a very basic explanation of distributed systems. It is not meant be exhaustive and rigorous, but it instead introduces just the topics necessary as simply as possible to help you understand the choices made by the designers of PyMechanical.

A distributed system is a software system that uses a network to distribute software across physical machines. With a distributed system, the individual pieces of that system do not share an address space and therefore cannot call functions of each other directly. Instead, they communicate with each other by sending messages to each other. Examples include email, multiplayer games, web apps, and high-performance computing, among other things.

Distributed systems have unique characteristics when compared to classical software systems that share an address space. For instance, in a distributed system, any call can fail because of a problem with the network, and the caller can not always know whether a call has failed. For this reason, features of interest to distributed system designers, such as fault tolerance, redundancy, and idempotency, are not emphasized by classical software systems.

In a distributed system, any computer can theoretically talk to any other computer. However, it is useful to divide them conceptually into clients and servers. Clients send requests to servers. Servers are expected return a response to the client.

There are protocols [5] that describe how information moves between computers. The next section begins with an explanation of remote procedure calls.

Remote procedure calls#

When one computer invokes a procedure on another computer using a network, it is said to have done an RPC. Unlike a normal procedure call, it can be unreliable and orders of magnitude slower. There also needs to be a handshake so that both sides understand how to interpret the bytes that move between computers [6]. This handshake can be negotiated byte-per-byte, but in practice, the internet protocols provide useful conventions and standards that have been tried and tested.

While standards are useful, it is not practical for every developer to craft packets to send over the wire by hand. In many programming languages, libraries, and tools wrap around the most popular conventions of internet protocols. These libraries and tools can be opinionated in how RPCs can and should be written. Some offer low-level method invocation facilities, like gRPC and zeroMQ. Others take a view on how RPCs should be done. Two such views are Representational State Transfer (REST) and Remote Method Invocation (RMI).


The design principles of REST are often credited as being responsible for the infinite scalability of the internet. It can be thought of as a set of styles or constraints that most web apps comply with or at least try to. Since REST is not an official standard, it can be implemented on any protocol. Since it was designed for HTTP, it is usually associated with it and its direct descendants [7]. However, the principles of REST can be applied on any protocol.

Applications that conform to REST are said to provide RESTful APIs. For a more detailed description, see What is a REST API <> on the Red Hat Technology Topics page.

Remote method invocation#

In the nineties, Object Oriented Programming (OOP) exploded in popularity. Among other things, OOP allows programmers to add abstractions on top of data in their code using objects. When done well, objects can reduce code complexity and makes large scale software easier to reason about. It was thought that even the difference between RPCs and calls made in a program’s address space could be abstracted. In effect, the user of an object does not need to know whether an object exists remotely or locally. Operations on that object could be done in the same way, regardless.

This approach is known as Remote Method Invocation (RMI). RMI was widely implemented using CORBA, DCOM, Remoting (.NET), and Java RMI. However, this approach has fallen out of favor with the rise of the internet, as it was observed that it does not scale in the same way that something like REST does. As it became less popular, tools, and library support fell away. For instance, recent versions of .NET do not implement the Remoting library, and COM/DCOM are no longer taken seriously by web apps.

For an illuminating discussion of the problems with RMI, see Microservices and the First Law of Distributed Objects on Martin Fowler’s website.

Remote mechanical#

Mechanical’s official API is that of an object model, and PyMechanical provides exactly that API to Python. Because object models are not suitable as remote APIs, PyMechanical does not provide that API in a remote fashion. This is why the remote session API is based on strings, while the embedded instance API can provide the Mechanical API directly to Python.

A different remote interface#

An alternative remote API for Mechanical is practical so long as it is not using RMI. In fact, Mechanical uses a REST API internally as part of its GUI. This is not the official Mechanical API and is not currently documented. A new remote interface for Mechanical is not an immediate goal of PyMechanical.

Using PyMechanical in a distributed system#

You can still build a distributed system where Mechanical is run remotely using the embedded instance of Mechanical in PyMechanical. For this to work, Python itself would run remotely, and therefore the embedded instance would run remotely. The communication across the network in that system would be done in Python.