Ovation Graphics Translation Layer(s) - Pro Analysis

Date: 2025 09-September 17

CB 0: Load OVMAN90 and OVMAN91, request translation layers

Hi

  

I need you to help create translation layers between modern web graphics (html, javascript, css) to proprietary Ovation source and diagram graphics. A python library for doing the translation would be great, or another language can be recommended.

  

Let's use this session effectively. Ask clarifying questions before diving in.

GG 0: Initial Analysis and Clarifying Questions

Of course. I can help with that. To ensure I provide the most relevant guidance, I have a few clarifying questions. In the meantime, here is a breakdown of the core concepts of the Ovation Graphics system based on OVMAN90 and OVMAN91.

Understanding Ovation Graphics

Ovation process diagrams are defined using a proprietary language detailed in the "Ovation™™ Graphics Language Reference Manual" (OVMAN91.pdf). Here's a summary of the key concepts:

Clarifying Questions:

To better assist you, could you please provide more information on the following:

  1. What is the primary goal of the translation?

    • Are you aiming to convert existing HTML/JS/CSS graphics into Ovation format?

    • Or are you looking to create a tool that allows for the dynamic generation of Ovation graphics from web-based inputs?

  2. What is the nature of the web graphics you are working with?

    • Are they static displays, or do they involve complex animations and user interactions?

    • Are they based on a specific framework like React, Angular, or Vue?

  3. What level of fidelity is required in the translation?

    • Are you looking for a pixel-perfect conversion, or is an approximation of the original graphic acceptable?

    • How important is it to maintain complex animations and interactions?

  4. What is your development environment?

    • What operating system are you using?

    • Do you have access to an Ovation system for testing and validation?

Once I have a better understanding of your requirements, I can provide more specific recommendations for a suitable programming language and a potential library structure.

CB 1: Clarifying Question Answers

I do have access to an Ovation system

I do not like the proprietary graphics editor. I think it is outdated, and it does not enable the user to quickly and elegantly generate beautiful and useful graphics. All the graphics we have appear outdated. I would like to be able to generate beautiful graphics, which can be informed by assets like CAD, P&ID, and GIS - these are abstract and can be first converted to web. I do not have any existing web graphics - rather, I am trying to design a pipeline and graphics creation that exceeds the capacity of the current one. I want to create cool graphics that are immersive, with a key goal being isometric images. I am sure this can be accomplished using modern matric algebra based tools. I am primarily a python developer, i use powershell in windows 11. The OVation system i have is Ovation 3.8 on windows 10.

Pixel perfect is not necessary. What appears on the final Ovation diagram can stylistically be a low fi version of an initial web version (which does not exist yet, and can be formed to be stylistically approximate).

Complex animations and interactions can be determined by the limitations of the Ovation system - there is no need to generate more complex animation than this in the original. Please provide information about these constraints.

I do not yet have a specific web framework in mind. Please recommend.

GG 1: Framework Recommendations

Excellent. Thank you for the detailed clarification. This provides a clear vision of your goal: to create a modern, sophisticated graphics generation pipeline that treats Ovation's proprietary format as a final "compilation target" rather than a primary design environment. This is an ambitious but very achievable project.

Based on your requirements, here is a recommended approach, including a suggested toolchain, a breakdown of Ovation's constraints, and a proposed structure for a Python library.

Language: Python

Your preference for Python is a perfect fit for this task. It is exceptionally well-suited for building this kind of translation layer due to its:

Web Format: SVG (Scalable Vector Graphics)

I strongly recommend using SVG as your intermediate web graphics format. Here’s why:

JavaScript Library: D3.js

For the initial step of converting your abstract data (from CAD, P&ID, etc.) into the SVG format, D3.js is an excellent choice. It is a powerful JavaScript library for creating data-driven documents. It would allow you to write scripts that:

Proposed Graphics Pipeline

Here is a high-level workflow based on this toolchain:

  1. Asset Conversion (Data -> SVG):

    • Write scripts (Python or another language) to parse your source assets (CAD, P&ID, GIS).

    • This script will output a structured data format (like JSON) that describes the shapes, positions, and any associated process data.

    • Use a web page with a D3.js script to read this JSON data and render a dynamic, interactive SVG. This is the stage where you implement your "beautiful" and "immersive" designs, including isometric projections.

    • Crucially, embed Ovation-specific metadata into the SVG elements. For example:

      XML

      <rect id="pump1" x="100" y="150" width="50" height="70" 
      data-ovation-point="PUMP-01.AV" 
      data-ovation-dynamic="fill-color" />
<g id="valve-control" data-ovation-poke-type="7" data-ovation-program="6" 
   data-ovation-point1="VLV-101.ID" data-ovation-point2="VLV-101.ID">
   <path d="..." /> 
   <text>V-101</text>
</g>

  2. Translation (SVG -> Ovation .src file):

    • This is the core of your Python library. The library will take the SVG file as input.

    • It will parse the SVG's XML structure.

    • It will iterate through each element and translate it into one or more Ovation Graphics Language commands.

    • It will transform SVG coordinates to fit Ovation's 16,384 x 16,384 virtual coordinate system2.

    • It will read the data-* attributes to create dynamic elements, poke fields, and conditionals.

    • Finally, it will assemble all the commands into a valid

      .src file, complete with DIAGRAM, BACKGROUND, and FOREGROUND sections333333333.

  3. Compilation & Deployment (Ovation System):

    • Use the Ovation Developer Studio to import the generated .src file.

    • Compile the graphic, which creates the .diag file for use on the Operator Station.

    • Download the new graphic to the operator drops.

Ovation System Constraints

As you correctly noted, the source web graphic should be designed with Ovation's limitations in mind. Here are the key constraints on animation and interaction:

Animation Constraints

Ovation does not support general keyframe animations like you find in CSS. Instead, it offers a set of specific animation commands that are tied to process data. Your web "source" should model animation as state changes that map to these commands:

Recommendation: In your SVG/JS environment, avoid CSS transitions or complex timeline animations. Instead, define "states" for your graphical elements. For example, a valve could have an "open" state (green color) and a "closed" state (red color). Your Python translator would then convert these states into Ovation conditional logic.

Interaction Constraints

User interaction is not as freeform as a web page onclick event. All interactions are handled by rectangular, invisible "poke fields" (

POKE_FLD) or stylized BUTTON elements10101010.

Recommendation: In your SVG, any element that should be interactive must be tagged with metadata specifying the Ovation poke_type and the necessary parameters for the corresponding Application Program. Your Python translator will then generate a POKE_FLD command with the correct size and position to overlay that SVG element.

Proposed Python Library Structure

Here is a possible structure for your ovation_translator Python library:

ovation_translator/
├── __init__.py
├── main.py                 # Command-line interface for the translator
├── svg_parser.py           # Logic to read and parse the SVG file
├── ovation_elements.py     # Classes for each Ovation graphic element (Line, Rect, PokeField, etc.)
├── ovation_diagram.py      # A class to represent the entire diagram and generate the final .src file
└── utils.py                # Helper functions for coordinate mapping, color conversion, etc.

ovation_elements.py Example:

Python

# ovation_elements.py

class OvationElement:
    def to_src(self):
        raise NotImplementedError

class OvationRectangle(OvationElement):
    def __init__(self, x, y, w, h, line_width, line_pat, fill_pat):
        self.x = x
        self.y = y
        # ... other properties

    def to_src(self):
        # Returns the RECTANGLE command string
        return f"RECTANGLE {self.x} {self.y} {self.w} {self.h} {self.line_width} {self.line_pat} {self.fill_pat}"

class OvationPokeField(OvationElement):
    def __init__(self, x, y, w, h, poke_type, program_id, args):
        # ... properties
    
    def to_src(self):
        # Returns the POKE_FLD command string with all its arguments
        # ...

svg_parser.py Logic:

Python

# svg_parser.py (pseudocode)
import xml.etree.ElementTree as ET
from . import ovation_elements, ovation_diagram

def parse_svg(filepath):
    tree = ET.parse(filepath)
    root = tree.getroot()
    
    diagram = ovation_diagram.OvationDiagram(name="TranslatedGraphic")

    for element in root.iter():
        if element.tag.endswith('rect'):
            # Extract attributes from SVG <rect>
            x = float(element.get('x'))
            # ... transform coordinates, map colors, etc.
            
            # Create an OvationRectangle object
            rect = ovation_elements.OvationRectangle(...)
            diagram.add_foreground_element(rect)
            
        elif element.tag.endswith('g') and 'data-ovation-poke-type' in element.attrib:
            # This is an interactive group
            # Extract bounding box for poke field dimensions
            # Extract data-* attributes for poke parameters
            
            poke_field = ovation_elements.OvationPokeField(...)
            diagram.add_keyboard_element(poke_field)

    return diagram

This approach provides a robust and extensible framework for your project. You can start by implementing translation for simple shapes and gradually add support for more complex elements, dynamics, and interactions as you refine your pipeline.

CB 2: Design Affect and Pipeline Tip to Tail

Excellent

  

I would like to generate views of the plant. These can be top-down aerial views of the entire plant, zoomed in to different subsystems, or views can be angled and isometric. Each view should be it's own graphic. These graphics can have navigation buttons that allow movement around the plant, almost like google street view, and could harken back to dungeon CLI games ("Walk north, pick up rock"). As I collect CAD data, GIS, P&ID assets, etc, what would you recommend for organizing those files for successful translation using 3D?

  

I do not yet have a geotag/coordinate location assigned to every sensor/point in the Ovation DCS system, but this is a goal. In this way, each relevant sensor can be made available in each view, with a distance veil / level of detail (LOD) setting as necessary. COlor coding of sensors can be based on current quality, with blinking LED's representing rate, etc.

  

Describe more about the level of animation I could possibly achieve.

GG 2: Specific complete architecture

Excellent. Your vision is to essentially create a lightweight "digital twin" interface within the Ovation ecosystem. By using modern 3D/geospatial data as a single source of truth and web tech as a rendering pipeline, you can generate sophisticated, data-rich 2D views that are far beyond what could be created efficiently with the native builder.

Here is a recommended strategy for organizing your files and a deeper dive into the animation and interaction capabilities of the Ovation system.

Strategy for Organizing 3D and Asset Files

The key to success is creating a single source of truth that links your physical assets to their digital representations and their DCS points. I recommend a centralized data hub approach.

Proposed File Structure:

/plant_graphics_project/
|-- /source_assets/
|   |-- /cad_models/       # Plant 3D, Revit, STEP files, etc.
|   |-- /gis_data/         # Shapefiles, GeoJSON for site layout
|   |-- /p&ids/            # Original P&ID drawings (PDF, DWG)
|
|-- /data_hub/
|   |-- master_asset_list.csv    # The core linking file
|   |-- view_definitions.json    # "Camera" positions for each graphic
|
|-- /generation_pipeline/
|   |-- 1_asset_extractor.py     # Python script to parse source assets
|   |-- 2_svg_renderer.js        # D3.js script to generate SVG views
|   |-- 3_ovation_translator.py  # Your main Python translation library
|
|-- /output/
|   |-- /intermediate_svgs/  # SVG files generated for each view
|   |-- /ovation_src/        # Final .src files ready for import
  1. The Data Hub - Your "Single Source of Truth":

This is the most critical part. Your master_asset_list.csv (or a simple database like SQLite) will be a master table that connects everything. Each row could represent a single piece of equipment or sensor with columns like:

AssetID AssetType P&ID_Tag Ovation_Point_Name CAD_Object_ID GIS_X GIS_Y GIS_Z
PMP-101A Pump P-101A PUMP-101A-SPEED uuid_... 30.45 -90.01 15.2
VLV-205 Valve FCV-205 VLV-205-STATUS uuid_... 30.51 -90.03 15.5
TANK-300 Tank TK-300 TANK-300-LEVEL uuid_... 31.00 -89.95 18.0
  1. View Definitions:

The view_definitions.json file will define each Ovation graphic you want to create. Each entry will act as a "virtual camera" in your 3D space.

JSON

{
  "views": [
    {
      "name": "Overall_Plant_View",
      "type": "top-down",
      "camera_pos": [500, 500, 1000],
      "zoom_level": 0.1
    },
    {
      "name": "Boiler_Area_Isometric",
      "type": "isometric",
      "camera_pos": [30, -90, 45],
      "camera_angle": [45, 35.264, 0]
    },
    {
      "name": "Boiler_Feedpump_Detail",
      "type": "isometric",
      "camera_pos": [30.4, -90.0, 16],
      "zoom_level": 5.0
    }
  ]
}

Implementing LOD and Geotagging

Your plan to geotag every sensor is the correct approach. This allows for a procedural, data-driven generation of graphics.

Your Python script (1_asset_extractor.py) will perform the following logic for each view defined in view_definitions.json:

  1. Set the Camera: Read the camera position, angle, and type for the view.

  2. Cull the Assets: Query your master_asset_list.csv to find all assets within the camera's view frustum or a defined radius. This is your distance veiling.

  3. Apply Level of Detail (LOD): For each visible asset, calculate its distance from the camera.

    • If the asset is very far, represent it as a simple dot or icon.

    • If it is at a medium distance, use a simplified 3D model or symbol.

    • If it is close, use a detailed 3D model.

  4. Project to 2D: For each visible asset's geometry, apply a 3D-to-2D projection matrix based on the camera's position and angle (this is where you'd implement the isometric view). The output of this step is a set of 2D coordinates and paths.

  5. Generate Data for SVG: Package this 2D information and the associated Ovation point data into a structured format (like JSON) to be consumed by your SVG renderer.

Deep Dive: Ovation Animation Capabilities

This is where you translate the "state" of your web graphic into what Ovation can actually render. Ovation's animation is state-driven, not timeline-based. An action happens because a process point changes its value or quality.

Here are the primary animation types you can achieve, with citations from the provided manuals:

Summary of Animation Feasibility: Your vision of color-coding for quality and blinking for rate is fully achievable with Ovation's graphics language. The key is to map these visual states to the appropriate conditional commands in your Python translator.

Putting It All Together: An Example Workflow for One View

Let's say you want to generate the "Boiler_Area_Isometric" graphic:

  1. Define the View: Your Python pipeline reads the "Boiler_Area_Isometric" definition from view_definitions.json.

  2. Query Assets: It queries master_asset_list.csv for all assets within the boiler area, applying LOD rules. Let's say this includes a feedpump PMP-101A and a valve VLV-205.

  3. Generate SVG: Your D3.js script takes this data and:

    • Draws the static background geometry (pipes, platforms) using isometric projection.

    • Draws a symbol for PMP-101A. It embeds <g id="PMP-101A" data-ovation-point="PUMP-101A-SPEED" data-ovation-animation="blinking-rate">...</g>.

    • Draws a symbol for VLV-205. It embeds <g id="VLV-205" data-ovation-point="VLV-205-STATUS" data-ovation-states='{"open": "green", "closed": "red"}'>...</g>.

    • Draws a "Walk North" button with <rect id="nav-north" data-ovation-poke-type="1" data-ovation-diag-name="Boiler_Top_View">...</rect>.

  4. Translate to .src: Your ovation_translator.py library reads this SVG:

    • The static geometry is converted to POLYGON and LINE commands in the BACKGROUND section.

    • The PMP-101A group is translated into a BLINKING_SHAPE command inside an IF_DISPLAY (PUMP-101A-SPEED.AV > 0) block. The speed parameter of the command is set directly to the PUMP-101A-SPEED.AV point.

    • The VLV-205 group is translated into a SHAPE command with a COLOR command before it that uses a conditional: COLOR FG green (VLV-205-STATUS.AV = 1) red.

    • The nav-north rectangle is translated into a POKE_FLD command in the KEYBOARD section that calls CHGDIAG (1) with the diagram name "Boiler_Top_View".

  5. Deploy: The final .src file is created, ready for you to compile and use in Ovation.

This pipeline allows you to separate the complex work of geometric projection and visual design (in the web environment) from the rule-based task of generating the proprietary Ovation source code (in Python), giving you the modern workflow and high-quality results you're looking for.

CB 3: Code Request:

excellent

  

show safe but rigorous and functional initial versions of 1_ovation_translator.py, 2_svg_renderer.js, and 1_asset_extractor.py . Create python classes as necessary. include a __name__ ==__main__ section in each python file at the bottom as a basic demo for your prescribed workflow, assuming the necessary other files exist.

  

Recommend python tests, without falling into the typical trap of over-definition.

GG: Code, initial versions

Of course. This is an excellent project, and I will provide you with a set of initial, functional scripts that follow your prescribed workflow.

These scripts are designed as a robust starting point. They are safe, using standard libraries and file operations, and demonstrate the core logic of each step in the pipeline. I have structured the Python code with classes to make it rigorous and extensible.

The 3-Step Graphics Pipeline

Here is the code for the three main components of your workflow:

  1. 1_asset_extractor.py: Reads asset data, applies a 3D isometric projection, and exports the data for web rendering.

  2. svg_renderer.html: A simple HTML/JavaScript file that ingests the data from step 1 and renders an SVG, embedding Ovation-specific metadata.

  3. 3_ovation_translator.py: The core Python library that parses the SVG from step 2 and translates it into a valid Ovation .src file.

File 1: 1_asset_extractor.py

This script simulates reading a master list of assets, defines a "camera" for an isometric view, projects the 3D coordinates into 2D, and saves the result as a JSON file for the next step.

Python

# 1_asset_extractor.py
import csv
import json
import numpy as np
from pathlib import Path

class Asset:
    """A simple data class to hold asset information."""
    def __init__(self, asset_id, asset_type, ovation_point, x, y, z):
        self.id = asset_id
        self.type = asset_type
        self.ovation_point = ovation_point
        self.coords_3d = np.array([float(x), float(y), float(z)])

class ViewCamera:
    """Represents a virtual camera and handles 3D-to-2D projection."""
    def __init__(self, elevation_deg=35.264, azimuth_deg=45):
        """
        Initializes the camera with standard isometric projection angles.
        """
        # Convert degrees to radians for numpy trigonometric functions
        elevation_rad = np.deg2rad(elevation_deg)
        azimuth_rad = np.deg2rad(azimuth_deg)

        # Create rotation matrices
        rot_x = np.array([
            [1, 0, 0],
            [0, np.cos(elevation_rad), -np.sin(elevation_rad)],
            [0, np.sin(elevation_rad), np.cos(elevation_rad)]
        ])
        rot_z = np.array([
            [np.cos(azimuth_rad), -np.sin(azimuth_rad), 0],
            [np.sin(azimuth_rad), np.cos(azimuth_rad), 0],
            [0, 0, 1]
        ])

        # Isometric projection matrix (drop the z-axis for 2D)
        projection_2d = np.array([
            [1, 0, 0],
            [0, 1, 0]
        ])
        
        # Combined transformation matrix
        self.transform_matrix = projection_2d @ rot_x @ rot_z

    def project(self, coords_3d):
        """Projects a 3D numpy array to a 2D numpy array."""
        return self.transform_matrix @ coords_3d

class AssetExtractor:
    """Loads asset data and generates 2D view data for rendering."""
    def __init__(self, asset_csv_path):
        self.assets = self._load_assets(asset_csv_path)

    def _load_assets(self, path):
        assets = []
        with open(path, 'r') as f:
            reader = csv.DictReader(f)
            for row in reader:
                assets.append(Asset(
                    asset_id=row['AssetID'],
                    asset_type=row['AssetType'],
                    ovation_point=row['Ovation_Point_Name'],
                    x=row['X'], y=row['Y'], z=row['Z']
                ))
        return assets

    def generate_view_data(self, camera, output_path):
        view_data = {'assets': []}
        for asset in self.assets:
            # Project 3D coordinates to 2D screen coordinates
            coords_2d = camera.project(asset.coords_3d)
            
            # Here you would implement Level of Detail (LOD) logic
            # For this demo, we'll include all assets
            
            view_data['assets'].append({
                'id': asset.id,
                'type': asset.type,
                'ovation_point': asset.ovation_point,
                'screen_x': coords_2d[0],
                'screen_y': coords_2d[1]
            })
            
        with open(output_path, 'w') as f:
            json.dump(view_data, f, indent=2)
        print(f"Successfully extracted asset data to '{output_path}'")

# --- Demo Section ---
if __name__ == "__main__":
    print("--- Running Asset Extractor Demo ---")
    
    # 1. Create a dummy CSV file for demonstration purposes
    demo_csv_content = """AssetID,AssetType,Ovation_Point_Name,X,Y,Z
PMP-101A,Pump,PUMP-101A-SPEED,0,0,0
VLV-205,Valve,VLV-205-STATUS,100,0,0
TANK-300,Tank,TANK-300-LEVEL,0,100,0
NAV-BTN-NORTH,NavButton,Boiler_Top_View,50,200,0
"""
    asset_file = Path("master_asset_list.csv")
    asset_file.write_text(demo_csv_content)
    print(f"Created demo asset file: '{asset_file}'")

    # 2. Instantiate the extractor and the camera
    extractor = AssetExtractor(asset_file)
    isometric_camera = ViewCamera()

    # 3. Generate the view data and save to JSON
    json_output_path = Path("boiler_area.json")
    extractor.generate_view_data(isometric_camera, json_output_path)

    # 4. Clean up dummy file
    asset_file.unlink()

File 2: svg_renderer.html

This file is opened in a web browser. It will automatically fetch boiler_area.json and render an SVG. A button is provided to download the generated SVG, which becomes the input for the next step.

HTML

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>SVG Renderer for Ovation</title>
    <style>
        body { font-family: sans-serif; margin: 2em; }
        svg { border: 1px solid #ccc; background-color: #f0f0f0; }
        .pump { fill: #8888dd; }
        .valve { fill: #dd8888; }
        .tank { fill: #88dd88; }
        .nav-button { fill: #dddd88; cursor: pointer; }
    </style>
</head>
<body>
    <h1>Ovation Graphic Preview (SVG)</h1>
    <p>This page reads `boiler_area.json` and generates an SVG. The SVG contains embedded metadata for the Ovation translator.</p>
    
    <svg id="canvas" width="800" height="600" viewBox="-200 -200 400 400"></svg>
    <br>
    <button id="downloadBtn">Download SVG</button>

    <script>
        // In a real application, you would use a library like D3.js here for more power.
        // This is a simple vanilla JS implementation for clarity.

        const svgNS = "http://www.w3.org/2000/svg";
        const canvas = document.getElementById('canvas');

        // Function to create and add an SVG element
        function createSvgElement(asset) {
            let elem;
            // A simple factory to create different shapes based on asset type
            switch (asset.type) {
                case 'Pump':
                    elem = document.createElementNS(svgNS, 'circle');
                    elem.setAttribute('cx', asset.screen_x);
                    elem.setAttribute('cy', asset.screen_y);
                    elem.setAttribute('r', 20);
                    elem.setAttribute('class', 'pump');
                    // Embed Ovation metadata
                    elem.setAttribute('data-ovation-point', asset.ovation_point);
                    elem.setAttribute('data-ovation-dynamic-type', 'blinking-rate');
                    break;
                case 'Valve':
                    elem = document.createElementNS(svgNS, 'rect');
                    elem.setAttribute('x', asset.screen_x - 15);
                    elem.setAttribute('y', asset.screen_y - 15);
                    elem.setAttribute('width', 30);
                    elem.setAttribute('height', 30);
                    elem.setAttribute('class', 'valve');
                    elem.setAttribute('data-ovation-point', asset.ovation_point);
                    elem.setAttribute('data-ovation-dynamic-type', 'conditional-color');
                    break;
                case 'Tank':
                    elem = document.createElementNS(svgNS, 'rect');
                    elem.setAttribute('x', asset.screen_x - 25);
                    elem.setAttribute('y', asset.screen_y - 40);
                    elem.setAttribute('width', 50);
                    elem.setAttribute('height', 80);
                    elem.setAttribute('class', 'tank');
                    elem.setAttribute('data-ovation-point', asset.ovation_point);
                    elem.setAttribute('data-ovation-dynamic-type', 'variable-fill');
                    break;
                case 'NavButton':
                     elem = document.createElementNS(svgNS, 'rect');
                    elem.setAttribute('x', asset.screen_x - 40);
                    elem.setAttribute('y', asset.screen_y - 15);
                    elem.setAttribute('width', 80);
                    elem.setAttribute('height', 30);
                    elem.setAttribute('class', 'nav-button');
                    // Embed poke field metadata
                    elem.setAttribute('data-ovation-poke-type', '2'); // Diagram & Group
                    elem.setAttribute('data-ovation-diag-name', asset.ovation_point); // Using point field for diag name
                    elem.setAttribute('data-ovation-group-num', '0');
                    break;
            }
            if (elem) {
                canvas.appendChild(elem);
            }
        }

        // Fetch the data and render the SVG
        fetch('boiler_area.json')
            .then(response => response.json())
            .then(data => {
                data.assets.forEach(asset => createSvgElement(asset));
            })
            .catch(error => console.error('Error loading asset data:', error));
            
        // Download handler
        document.getElementById('downloadBtn').addEventListener('click', () => {
            const svgData = new XMLSerializer().serializeToString(canvas);
            const blob = new Blob([svgData], { type: 'image/svg+xml' });
            const url = URL.createObjectURL(blob);
            const link = document.createElement('a');
            link.href = url;
            link.download = 'boiler_area.svg';
            document.body.appendChild(link);
            link.click();
            document.body.removeChild(link);
        });
    </script>
</body>
</html>

File 3: 3_ovation_translator.py

This script contains the core translation logic, structured into classes as requested. It parses the SVG, maps elements and metadata to Ovation commands, and generates the final .src file.

Python

# 3_ovation_translator.py
import xml.etree.ElementTree as ET
from pathlib import Path
import abc

# --- Ovation Element Classes ---

class OvationElement(abc.ABC):
    """Abstract base class for all Ovation graphic elements."""
    @abc.abstractmethod
    def to_src(self) -> str:
        pass

class OvationColor(OvationElement):
    """Represents a COLOR command."""
    def __init__(self, fg='white', bg='black', er='black', ol=0):
        self.fg = fg
        self.bg = bg
        self.er = er
        self.ol = ol
    
    def to_src(self):
        return f"COLOR FG {self.fg} BG {self.bg} ER {self.er} OL {self.ol} BLINK FG OFF BG OFF"

class OvationRectangle(OvationElement):
    """Represents a RECTANGLE command."""
    def __init__(self, x, y, w, h, line_width=1, line_pat='solid', fill_pat='unfilled'):
        self.x, self.y, self.w, self.h = x, y, w, h
        self.line_width = line_width
        self.line_pat = line_pat
        self.fill_pat = fill_pat

    def to_src(self):
        return f"RECTANGLE {self.x} {self.y} {self.w} {self.h} {self.line_width} {self.line_pat} {self.fill_pat}"

class OvationCircle(OvationElement):
    """Represents a CIRCLE command."""
    def __init__(self, cx, cy, r, line_width=1, line_pat='solid', fill_pat='unfilled'):
        self.cx, self.cy, self.r = cx, cy, r
        self.line_width = line_width
        self.line_pat = line_pat
        self.fill_pat = fill_pat
        
    def to_src(self):
        return f"CIRCLE {self.cx} {self.cy} {self.r} {self.line_width} {self.line_pat} {self.fill_pat}"

class OvationPokeField(OvationElement):
    """Represents a POKE_FLD command."""
    def __init__(self, x, y, w, h, poke_type, diag_name, group_num):
        self.x, self.y, self.w, self.h = x, y, w, h
        self.poke_type = poke_type
        self.diag_name = diag_name
        self.group_num = group_num
    
    def to_src(self):
        # This is a simplified POKE_FLD for poke_type 2
        return f"POKE_FLD {self.x} {self.y} {self.w} {self.h} ON {self.poke_type} {self.diag_name} {self.group_num}"

class OvationDiagram:
    """Represents a complete Ovation diagram and generates the final .src file."""
    def __init__(self, name="Default", width=16384, height=16384):
        self.name = name
        self.width = width
        self.height = height
        self._background_elements = []
        self._foreground_elements = []
        self._keyboard_elements = []

    def add_element(self, element, section='background'):
        if section == 'background':
            self._background_elements.append(element)
        elif section == 'foreground':
            self._foreground_elements.append(element)
        elif section == 'keyboard':
            self._keyboard_elements.append(element)

    def to_src(self):
        src_lines = []
        # Header
        src_lines.append(f'DIAGRAM MAIN 0 0 0 1920 1080 gray50 ZOOMABLE 1 0 0 {self.width} {self.height} 1 DEFAULT_POSITION DEFAULT_SIZE "{self.name}"')
        
        # Background Section
        if self._background_elements:
            src_lines.append("\nBACKGROUND")
            for elem in self._background_elements:
                src_lines.append(elem.to_src())
        
        # Foreground Section
        if self._foreground_elements:
            src_lines.append("\nFOREGROUND")
            for elem in self._foreground_elements:
                src_lines.append(elem.to_src())

        # Keyboard Section (for poke fields)
        if self._keyboard_elements:
            src_lines.append("\nKEYBOARD")
            for elem in self._keyboard_elements:
                src_lines.append(elem.to_src())
                
        return "\n".join(src_lines)

class SVGTranslator:
    """Translates an SVG file into an OvationDiagram object."""
    OVATION_WIDTH = 16384
    OVATION_HEIGHT = 16384

    def __init__(self, svg_filepath):
        self.tree = ET.parse(svg_filepath)
        self.root = self.tree.getroot()
        self.diagram = OvationDiagram(name=Path(svg_filepath).stem)

    def _transform_coords(self, svg_x, svg_y, svg_w, svg_h):
        """Scales SVG coordinates to the Ovation virtual grid. This is a simple linear scale."""
        svg_viewbox = [float(v) for v in self.root.get('viewBox').split()]
        svg_vb_w = svg_viewbox[2]
        svg_vb_h = svg_viewbox[3]
        
        scale_x = self.OVATION_WIDTH / svg_vb_w
        scale_y = self.OVATION_HEIGHT / svg_vb_h

        ov_x = int((svg_x - svg_viewbox[0]) * scale_x)
        ov_y = int((svg_y - svg_viewbox[1]) * scale_y)
        ov_w = int(svg_w * scale_x)
        ov_h = int(svg_h * scale_y)

        return ov_x, ov_y, ov_w, ov_h

    def translate(self):
        self.diagram.add_element(OvationColor(), 'background')

        for elem in self.root.iter():
            tag = elem.tag.split('}')[-1] # Remove namespace if present

            if tag == 'rect':
                svg_x, svg_y = float(elem.get('x', 0)), float(elem.get('y', 0))
                svg_w, svg_h = float(elem.get('width', 0)), float(elem.get('height', 0))
                ov_x, ov_y, ov_w, ov_h = self._transform_coords(svg_x, svg_y, svg_w, svg_h)

                # Check for poke field metadata
                if 'data-ovation-poke-type' in elem.attrib:
                    poke = OvationPokeField(
                        x=ov_x, y=ov_y, w=ov_w, h=ov_h,
                        poke_type=elem.get('data-ovation-poke-type'),
                        diag_name=elem.get('data-ovation-diag-name'),
                        group_num=elem.get('data-ovation-group-num')
                    )
                    self.diagram.add_element(poke, section='keyboard')
                else:
                    # It's a regular or dynamic rectangle
                    rect = OvationRectangle(x=ov_x, y=ov_y, w=ov_w, h=ov_h, fill_pat='solid')
                    self.diagram.add_element(rect, section='background')
            
            elif tag == 'circle':
                svg_cx, svg_cy = float(elem.get('cx', 0)), float(elem.get('cy', 0))
                svg_r = float(elem.get('r', 0))
                # For circles, width and height of transform are based on radius
                _, _, ov_rx, ov_ry = self._transform_coords(0, 0, svg_r * 2, svg_r * 2)
                ov_cx, ov_cy, _, _ = self._transform_coords(svg_cx, svg_cy, 0, 0)
                
                # Ovation uses radius, avg of scaled rx and ry is a good start
                ov_r_avg = int((ov_rx + ov_ry) / 4) 
                
                circle = OvationCircle(cx=ov_cx, cy=ov_cy, r=ov_r_avg, fill_pat='solid')
                self.diagram.add_element(circle, section='background')
        
        return self.diagram

# --- Demo Section ---
if __name__ == "__main__":
    print("\n--- Running Ovation Translator Demo ---")

    # 1. Create a dummy SVG file for demonstration
    demo_svg_content = """<?xml version="1.0" encoding="UTF-8"?>
<svg id="canvas" width="800" height="600" viewBox="-200 -200 400 400" xmlns="http://www.w3.org/2000/svg">
  <circle cx="-70.71" cy="70.71" r="20" class="pump" data-ovation-point="PUMP-101A-SPEED" data-ovation-dynamic-type="blinking-rate"/>
  <rect x="-15.0" y="-85.71" width="30" height="30" class="valve" data-ovation-point="VLV-205-STATUS" data-ovation-dynamic-type="conditional-color"/>
  <rect x="53.03" y="-12.32" width="50" height="80" class="tank" data-ovation-point="TANK-300-LEVEL" data-ovation-dynamic-type="variable-fill"/>
  <rect x="-42.68" y="129.9" width="80" height="30" class="nav-button" data-ovation-poke-type="2" data-ovation-diag-name="Boiler_Top_View" data-ovation-group-num="0"/>
</svg>
"""
    svg_file = Path("boiler_area.svg")
    svg_file.write_text(demo_svg_content)
    print(f"Created demo SVG file: '{svg_file}'")

    # 2. Instantiate the translator
    translator = SVGTranslator(svg_file)
    
    # 3. Translate the SVG to an OvationDiagram object
    ovation_diagram_obj = translator.translate()
    
    # 4. Generate the .src file content
    src_content = ovation_diagram_obj.to_src()
    
    # 5. Save the final .src file
    src_file = Path("BOILER_AREA_ISO.src")
    src_file.write_text(src_content)
    print(f"Successfully translated SVG to Ovation source file: '{src_file}'")
    
    print("\n--- Generated .src Content ---")
    print(src_content)
    
    # 6. Clean up dummy file
    svg_file.unlink()

Python Test Recommendations (A Practical Approach)

Over-defining tests can slow down initial development. Here’s a pragmatic strategy using the pytest framework.

  1. Unit Test Your Ovation Elements:

These are the easiest and most valuable tests. Ensure each element class produces the correct source string.

Python

# In a file like `tests/test_ovation_elements.py`
from ovation_translator.ovation_elements import OvationRectangle

def test_rectangle_to_src():
    rect = OvationRectangle(x=100, y=200, w=300, h=400, line_width=2, line_pat='dashed', fill_pat='solid')
    expected_src = "RECTANGLE 100 200 300 400 2 dashed solid"
    assert rect.to_src() == expected_src
  1. Unit Test Key Utilities:

Your coordinate transformation logic is critical. Test it with known values.

Python

# In a file like `tests/test_translator.py`
from ovation_translator.ovation_translator import SVGTranslator

def test_coordinate_transform():
    # Create a dummy SVG translator instance to access the method
    # (Requires a simple dummy SVG file in your test fixtures)
    translator = SVGTranslator('tests/fixtures/dummy_800x600.svg')
    svg_x, svg_y, svg_w, svg_h = 100, 50, 20, 30
    # Ovation grid is 16384x16384, SVG is 800x600 (viewBox)
    # Expected ov_x = 100/800 * 16384 = 2048
    # Expected ov_y = 50/600 * 16384 = 1365 (approx)
    ov_x, ov_y, ov_w, ov_h = translator._transform_coords(svg_x, svg_y, svg_w, svg_h)
    assert ov_x == 2048
    assert abs(ov_y - 1365) < 2 # Allow for float rounding
  1. Integration Test the Full Translation:

This test ensures the parser and element generators work together. Instead of comparing the entire output string (which is brittle), check the structure of the result.

Python

# In `tests/test_translator.py`
from ovation_translator.ovation_translator import SVGTranslator
from ovation_translator.ovation_elements import OvationCircle, OvationPokeField

def test_full_translation_structure():
    # Use the SVG from the __main__ demo as a test fixture
    translator = SVGTranslator('boiler_area.svg')
    diagram = translator.translate()

    # Assert that the right number of elements were created in the right sections
    assert len(diagram._background_elements) == 4 # 1 Color + 3 shapes
    assert len(diagram._keyboard_elements) == 1
    
    # Assert that the types are correct
    assert isinstance(diagram._background_elements[1], OvationCircle)
    assert isinstance(diagram._keyboard_elements[0], OvationPokeField)

    # Spot-check a key property
    poke_field = diagram._keyboard_elements[0]
    assert poke_field.poke_type == '2'
    assert poke_field.diag_name == 'Boiler_Top_View'

This testing strategy focuses on validating your core logic without getting bogged down in minute details, allowing you to build and refactor your translation library with confidence.