Juniper - Code Mockup to MVP Plan

Date: 2025 09-September 17

You are correct. The provided code snippets are designed to lay out the project's architecture and demonstrate the flow, but they contain placeholders for key functionality that needs to be implemented.

Here is a breakdown of the code status and a plan to button up a working version of the Juniper pipeline.

Status of the Codebase

1. 1_asset_extractor.py

• Status: Incomplete

• Details: The parse_cad_file and parse_ovation_points functions are currently placeholders. They return hardcoded data rather than parsing real .dxf files or text files of Ovation points. The logic for associating a CAD asset's geometry with an Ovation point is also a simplified placeholder.

2. 2_renderer_and_exporter.py

• Status: Incomplete

• Details: The script correctly outlines the process of rendering and exporting. However, the D3.js rendering logic embedded within the HTML string is a placeholder. It can only draw circles and is missing the ability to handle a full range of geometric shapes (polygons, lines, etc.) or apply the view-specific transformations (isometric, top, side). The code also assumes the playwright library is set up, which requires a separate installation step.

3. 3_svg_to_ovation_translator.py

• Status: Incomplete

• Details: The _parse_and_translate_svg method is the most critical placeholder. It has logic to handle a single type of SVG element (circles and text) and lacks the ability to parse the full range of SVG shapes (polygons, lines, etc.). The Ovation output commands (DEF_SYM, DEF_TEXT) are simplified strings and do not include all the attributes (color, line weight, blink behavior, data links) that a real Ovation graphic requires.

4. svg_renderer.html (Implied from discussions)

• Status: Incomplete

• Details: While not explicitly coded, the front-end rendering script that would be used by 2_renderer_and_exporter.py is a conceptual placeholder. The real svg_renderer.js needs to be a robust D3.js application that can handle different geometric shapes and apply the transformations for each view.

Plan to Button Up a Working Version

The plan is to turn these conceptual placeholders into a fully functional, end-to-end pipeline. The steps are sequential, with each one building on the success of the previous.

Step 1: Focus on the "Extraction" Layer (1-2 weeks)

• Task: Complete the 1_asset_extractor.py script.

• Sub-Tasks:

  • Implement a CAD Parser: Choose a Python library like ezdxf and write the code to parse a real .dxf file, extracting its polygons, lines, and text.

  • Implement a Point Parser: Write the code to accurately parse all the key-value pairs from the Ovation point text file.

  • Refine the JSON Schema: Define a final, comprehensive JSON schema. This schema will act as a contract for the entire pipeline. Ensure it can store all relevant information (coordinates, shape type, line style, point IDs, descriptions, etc.).

Step 2: Build a Robust Front-End Renderer (2-3 weeks)

• Task: Create a complete, well-structured D3.js script that can render all shapes from the JSON schema.

• Sub-Tasks:

  • Implement Shape Rendering: Write D3.js code to handle polygons, lines, circles, and text.

  • Add Transformation Logic: Create functions that, based on a configuration parameter ('top', 'side', 'isometric'), apply the correct geometric transformations.

  • Add Metadata-Driven Styling: Use the metadata in the JSON to style the graphics (e.g., set colors, stroke widths) and add interactive elements (e.g., tooltips for point descriptions).

Step 3: Complete the Ovation Translator (3-4 weeks)

• Task: Complete the 3_svg_to_ovation_translator.py script.

• Sub-Tasks:

  • Implement a Full SVG Parser: Use a Python library like xml.etree.ElementTree to fully parse the SVG file, extracting all shape attributes and metadata.

  • Build the Ovation Command Library: Define a set of Python classes or functions that represent each Ovation graphic command (DEF_LINE, DEF_SYM, DEF_FILL, etc.).

  • Add Conditional Logic: Implement the Ovation DEF_ACT and DEF_LINK commands to tie graphic elements to the point states (e.g., change color from green to red when a valve is closed).

  • Generate DEF_LINKs: Link the Ovation point IDs to the graphics for a real-time display.

Step 4: Integrate and Test End-to-End (1 week)

• Task: Connect all the components and perform a full pipeline test.

• Sub-Tasks:

  • Run a single command that takes a .dxf file and a point list as input and produces a .src file and a .png preview as output.

  • Import the generated .src file into an Ovation system to verify that it compiles and displays correctly.

  • Refine the code based on any compilation errors.

This plan focuses on solidifying one component at a time, minimizing complexity, and ensuring that each piece of the pipeline is fully functional before moving to the next.