gjedlicska/foobar

Speckle Automate Workshop: Python Function Template

This repository serves as a starting point for creating Speckle Automate functions in Python for the Automate workshops. It is based on the Speckle Automate Python Function Template which is the preferred starting point for creating new Speckle Automate functions in Python.

Getting started

This template will be available from the Speckle Automate New Function wizard. Select the Workshop icon to create a new repository from this template.

By default, the wizard will create a new repository in your GitHub account, and you will be able to start editing the code in the main.py file.

To create a new version of your Function, create a new GitHub release in your repository. This will trigger GitHub Action that builds, tests and deploys your function to Speckle Automate.

Managing Dependencies with Poetry

Poetry simplifies dependency management for Python projects, ensuring consistent environments and hassle-free dependency resolution. Here's why we use Poetry:

Dependency Resolution: Poetry ensures compatible library versions, preventing conflicts. Virtual Environments: It manages project dependencies in isolated virtual environments. Lockfile: Generates a lockfile (poetry.lock) for reproducible builds. Simplified Installation: Adding dependencies is as easy as poetry add < package-name>. Dependency Isolation: Ensures project dependencies are self-contained and portable. Project Metadata: Manages project configuration in a single pyproject.toml file.

Adding Dependencies

To add new dependencies, use:

$ poetry add <package-name>

Replace with the desired package. Poetry handles the rest, updating project files automatically.

Note: It's while it is a good practice to combine the use of Poetry with virtual environments to ensure a clean and isolated development environment for your Python projects, other tools like pipenv or venv can also be used. It is not mandatory to use a virtual environment for Speckle Automate functions.

Configuring Launch Variables (Visual Studio Code)

To edit launch variables in Visual Studio Code, follow these steps:

Open the project in Visual Studio Code. Navigate to the .vscode directory. Open the launch.json file. Edit the configurations as needed. Save the file. These configurations specify how your Python script will be run and debugged within Visual Studio Code.

GitHub Codespaces

Once you have created a clone of this template repo with the Automate wizard, you can use GitHub Codespaces to develop your function in the cloud. In the Codespaces environment, you can edit code, run tests, and debug your function. To open your repository in a Codespace, click the "Code" button in the GitHub UI and select "Open with Codespaces".

Using this Speckle Function

1. Create a new Speckle Automation.
2. Select your Speckle Project and Speckle Model.
3. Select the Speckle Function you created from this template.
4. Enter the requested inputs. For first run this will be a phrase to use in a comment.
5. Click Create Automation.

Developer Requirements

1. Install the following:
   • Python 3 (>= 3.10)
   • Poetry
2. Run poetry shell && poetry install to install the required Python packages.

Building and Testing

The code can be tested locally by running poetry run pytest. The tests are located in the tests directory. The tests also allow for testing the function locally by mocking the Speckle Automate environment or using the specklepy authentication token to connect to a real Speckle Server and use real data.

Building and running the Docker Container Image

Running and testing your code on your own machine is a great way to develop your Function; the following instructions are a bit more in-depth and only required if you are having issues with your Function in GitHub Actions or on Speckle Automate.

Building the Docker Container Image

Your code is packaged by the GitHub Action into the format required by Speckle Automate. This is done by building a Docker Image, which is then run by Speckle Automate. You can attempt to build the Docker Image yourself to test the building process locally.

To build the Docker Container Image, you will need to have Docker installed.

Once you have Docker running on your local machine:

1. Open a terminal

2. Navigate to the directory in which you cloned this repository

3. 3.Run the following command:

   docker build -f ./Dockerfile -t speckle_automate_python_example .

Running the Docker Container Image

Once the image has been built by the GitHub Action, it is sent to Speckle Automate. When Speckle Automate runs your Function as part of an Automation, it will run the Docker Container Image. You can test that your Docker Container Image runs correctly by running it locally.

1. To then run the Docker Container Image, run the following command:

   docker run --rm speckle_automate_python_example \
   python -u main.py run \
   '{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}' \
   '{}' \
   yourSpeckleServerAuthenticationToken

Let's explain this in more detail:

docker run --rm speckle_automate_python_example tells Docker to run the Docker Container Image that we built earlier. speckle_automate_python_example is the name of the Docker Container Image that we built earlier. The --rm flag tells docker to remove the container after it has finished running, this frees up space on your machine.

The line python -u main.py run is the command that is run inside the Docker Container Image. The rest of the command is the arguments that are passed to the command. The arguments are:

• '{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}' - the metadata that describes the automation and the function.
• {} - the input parameters for the function that the Automation creator is able to set. Here they are blank, but you can add your own parameters to test your function.
• yourSpeckleServerAuthenticationToken - the authentication token for the Speckle Server that the Automation can connect to. This is required to be able to interact with the Speckle Server, for example to get data from the Model.

Resources

• Learn more about specklepy, and interacting with Speckle from Python.