diff --git a/README.md b/README.md index 74be808..2e4f295 100644 --- a/README.md +++ b/README.md @@ -1,71 +1,129 @@ -# Speckle Automate function template - Python +# Speckle Automate Workshop: Python Function Template -This is a template repository for a Speckle Automate functions written in python -using the [specklepy](https://pypi.org/project/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. +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](https://github.com/specklesystems/speckle_automate_python_example) +which +is the preferred starting point for creating new +Speckle Automate functions in Python. ## Getting started -1. Use this template repository to create a new repository in your own / organization's profile. +This template will be available from the Speckle Automate New Function wizard. +Select the Workshop icon to create a new repository from this template. -Register the function +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. -### Add new dependencies +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. -To add new python package dependencies to the project, use: -`$ poetry add pandas` +### Managing Dependencies with Poetry -### Change launch variables +Poetry simplifies dependency management for Python projects, ensuring consistent +environments and hassle-free dependency +resolution. Here's why we use Poetry: -describe how the launch.json should be edited +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. -### Github Codespaces +#### Adding Dependencies -Create a new repo from this template, and use the create new code. +To add new dependencies, use: -### Using this Speckle Function +`$ poetry add ` + +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](https://automate.speckle.dev/) a new Speckle Automation. -1. Select your Speckle Project and Speckle Model. -1. Select the existing Speckle Function named [`Random comment on IFC beam`](https://automate.speckle.dev/functions/e110be8fad). -1. Enter a phrase to use in the comment. -1. Click `Create Automation`. - -## Getting Started with creating your own Speckle Function - -1. [Register](https://automate.speckle.dev/) your Function with [Speckle Automate](https://automate.speckle.dev/) and select the Python template. -1. A new repository will be created in your GitHub account. -1. Make changes to your Function in `main.py`. See below for the Developer Requirements, and instructions on how to test. -1. To create a new version of your Function, create a new [GitHub release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) in your repository. +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](https://www.python.org/downloads/) + - [Python 3](https://www.python.org/downloads/) (>= 3.10) - [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) -1. Run `poetry shell && poetry install` to install the required Python packages. +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 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. +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. +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](https://docs.docker.com/get-docker/) installed. +To build the Docker Container Image, you will need to +have [Docker](https://docs.docker.com/get-docker/) installed. Once you have Docker running on your local machine: 1. Open a terminal -1. Navigate to the directory in which you cloned this repository -1. Run the following command: +2. Navigate to the directory in which you cloned this repository +3. 3.Run the following command: ```bash docker build -f ./Dockerfile -t speckle_automate_python_example . @@ -73,7 +131,11 @@ Once you have Docker running on your local machine: #### 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. +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: @@ -87,14 +149,28 @@ Once the image has been built by the GitHub Action, it is sent to Speckle Automa 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. +`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: +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. +- `'{"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](https://speckle.guide/dev/python.html) more about SpecklePy, and interacting with Speckle from Python. +- [Learn](https://speckle.guide/dev/python.html) more about `specklepy`, and + interacting with Speckle from Python. diff --git a/poetry.lock b/poetry.lock index 419740e..6ee6108 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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