Python Lingo Interpreter: Struct Rendering

Hey there! Today, we're diving deep into the exciting world of the Lingo interpreter, specifically focusing on how we can supercharge its capabilities within the Python environment. If you're working with scripting languages embedded in JSON or YAML, you know how crucial it is to have robust interpreters that can handle various data structures. The Lingo script specification, a flexible language executed by interpreters in both JavaScript and Python, is no exception. We've been diligently documenting its intricacies in the README and other files within the ./docs directory, with plenty of practical examples scattered across src/mspec/data/lingo/pages and src/mspec/data/lingo/scripts for both page-beta-1 and script-beta-1 variations. For those of you tinkering with the Python side of things, the interpreter itself resides in src/mspec/lingo.py, and the rendering magic happens in src/mspec/browser2.py. We rigorously test the Python interpreter using the scripts in tests/test_markup.py, which, in turn, iterate over the *_test_data.json specs located in src/mspec/data/lingo/scripts to ensure that given sets of input parameters consistently yield the expected outcomes. The JavaScript interpreter, for your reference, is housed in browser2/js/src, and you can give it a whirl by launching the dev server with ./server.py and then running the Playwright tests within that folder.

Recently, we've rolled out a brand-new page: src/mspec/data/lingo/pages/structs.json. This addition introduces a novel element type that we need to equip our interpreter with the ability to render. For this particular ticket, our laser focus will be on enhancing the Python renderer. We're talking about bringing struct rendering to life, making it easier to visualize and work with structured data directly within your Lingo-powered applications.

Rendering Individual Structs: A New Dimension

Let's start by talking about how we're going to handle individual structs in the Python Lingo interpreter. Imagine you've got a piece of your Lingo script that defines a struct like this:

{
    "type": "struct",
    "fields": {
        "color": "red",
        "amount": 10,
        "in_stock": true
    }
}

This is a neat way to group related data, isn't it? Now, to make this truly useful, we need our Python renderer to be able to translate this structured data into something human-readable. The plan is to update the LingoPage.render_value function within mspec/browser2.py. When this function encounters an element whose type is set to struct, it should spring into action and generate a clean, easy-to-understand table. This table will elegantly display the key-value pairs defined in the fields of your struct.

The table structure will look something like this:

This tabular format provides a clear visual representation of your struct's data, making it much simpler to parse and digest. But what if you don't need those header columns? We've got you covered! If your struct definition includes a display object with "headers": false,

{
    "type": "struct",
    "display": {
        "headers": false
    },
    "fields": {
        "color": ...

the renderer will intelligently omit the default "Key" and "Value" column headers. This offers a more streamlined view when headers are unnecessary, giving you control over the presentation. You'll find three example structs under the "Individual Structs" heading in the structs.json file. As you examine these examples, you'll notice that we need to ensure our implementation supports both hardcoded values and scripted values within these structs. This means the renderer must be robust enough to handle values that are directly defined and those that are dynamically generated by Lingo scripts, ensuring consistent and accurate rendering regardless of the value's origin.

Unpacking Lists of Structs: Tables with Flair

Moving on, let's explore how we'll handle lists of structs, which is another exciting enhancement for our Python Lingo interpreter. In the "List of Structs" section of structs.json, you'll encounter a scenario where we transform a value element of type: list into a beautifully formatted table. This is where the display object truly shines, allowing us to define specific columns and map them directly to the fields within our structs.

Consider this example:

{
    "type": "list",
    "display": {
        "format": "table",
        "headers": [
            {"text": "Color", "field": "color"},
            {"text": "Amount", "field": "amount"},
            {"text": "In Stock", "field": "in_stock"}
        ]
    },
    "value": [
        {
            "type": "struct",
            "fields": {
                "color": "red",
                "amount": 10,
                "in_stock": true
            }
        },
        ...
    ]
}

In this setup, each object within the "headers" array dictates the structure of our table columns. For instance, {"text": "Color", "field": "color"} tells the renderer that the column header should display the text "Color", and the data for this column in each row should be pulled from the "color" field of the corresponding struct. So, for the first row in the table, the value under the "Color" header would indeed be "red".

Our task is to integrate this list-to-table rendering capability into the LingoPage.render_value function. You'll recall that we already have support for rendering lists as bulleted or numbered lists, managed by the opt.format field:

bullet_format = element.get('opt', {}).get('format', 'bullets')
match bullet_format:
    case 'bullets':
        bullet_char = lambda _: '• '
    case 'numbers':
        bullet_char = lambda n: f'{n}. '
    case _:
        raise ValueError(f'Unknown list opt.format: {bullet_format}')

Now, we need to extend this logic to accommodate element.display.format being set to "table". When this format is detected, the renderer should construct and output the table as described above, using the headers array to define the columns and map them to the struct fields. A crucial requirement during the row creation process is to ensure that each element within the list is indeed a struct type. If any element isn't a struct, the renderer should raise an exception, maintaining data integrity. It's important to note that we'll need to correct how we access the format field. Currently, it's accessed via element.get('opt', {}).get('format', 'bullets'), which is incorrect. This needs to be updated to element.get('display', {}).get('format', 'bullets') to align with the JSON page structure and ensure we're correctly reading the display configuration.

Key Requirements for Implementation

To successfully integrate these new rendering capabilities, we need to address the following key requirements:

• Support for Individual Structs: The Python renderer must be updated to recognize and correctly render elements of type: "struct". This involves parsing the fields and presenting them in a tabular format, optionally omitting headers based on the display.headers setting.
• Rendering Lists of Structs as Tables: The Python renderer needs to be enhanced to handle lists where elements are structs. When element.display.format is set to "table", the renderer should generate an HTML table, using the display.headers configuration to define column headers and map them to struct fields. This includes validating that all list items are indeed structs and handling potential errors gracefully.

By implementing these changes, we'll significantly improve the Lingo interpreter's ability to handle and display structured data, making it a more powerful and versatile tool for developers working with JSON and YAML-based configurations and scripts.

For further insights into Python's capabilities with data structures, you might find the official Python documentation helpful. And for a broader understanding of JSON structure and best practices, the JSON.org website is an excellent resource.