Speckle Automate function template - Python

This is a template repository for a Speckle Automate functions written in python using the specklepy SDK to interact with Speckle data.

This template contains the full scaffolding required to publish a function to the automate environment. Also has some sane defaults for a development environment setups.

Getting started

Use this template repository to create a new repository in your own / organization's profile.

Add new dependencies

To add new python package dependencies to the project, use: $ poetry add pandas

Change launch variables

describe how the launch.json should be edited

Github Codespaces

Create a new repo from this template, and use the create new code.

Using this Speckle Function

Create a new Speckle Automation.
Select your Speckle Project and Speckle Model.
Select the existing Speckle Function named Random comment on IFC beam.
Enter a phrase to use in the comment.
Click Create Automation.

Getting Started with creating your own Speckle Function

Register your Function with Speckle Automate and select the Python template.
A new repository will be created in your GitHub account.
Make changes to your Function in main.py. See below for the Developer Requirements, and instructions on how to test.
To create a new version of your Function, create a new GitHub release in your repository.

Developer Requirements

Install the following:
- Python 3
- Poetry
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.

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:

Open a terminal
Navigate to the directory in which you cloned this repository

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.

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.

gjedlicska/pytest