Update rule_processor.py (#66 )

v3 (#65 )
Amend README.md to reflect shift of companion application (#64 )
2025-06-05 15:23:23 +01:00 · 2025-06-05 14:03:44 +01:00 · 2025-05-12 17:12:45 +01:00 · 2025-05-12 15:53:33 +01:00 · 2025-04-23 00:45:46 +01:00 · 2025-04-23 00:45:34 +01:00
39 changed files with 7030 additions and 2648 deletions
@@ -0,0 +1,30 @@
+# Use the official Python 3.13 slim image as the base
+FROM python:3.13-slim
+
+# Change to UK mirror for better reliability (robust for missing files)
+RUN find /etc/apt/ -name '*.list' -exec sed -i 's|http://deb.debian.org|http://ftp.uk.debian.org|g' {} + || true
+
+# Force apt to use IPv4 to avoid CDN/network issues
+RUN echo 'Acquire::ForceIPv4 "true";' > /etc/apt/apt.conf.d/99force-ipv4
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+  git \
+  && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory
+WORKDIR /home/speckle
+
+# Create a non-root user
+RUN useradd -ms /bin/bash vscode
+USER vscode
+
+# Set environment variables
+ENV PYTHONPATH=/home/speckle
+ENV PYTHONUNBUFFERED=1
+
+# Install Python dependencies
+COPY requirements.txt requirements-dev.txt pyproject.toml ./
+RUN pip install --no-cache-dir -r requirements.txt && \
+  pip install --no-cache-dir -r requirements-dev.txt && \
+  echo 'export PATH=$PATH:$HOME/.local/bin' >> ~/.bashrc 
@@ -1,43 +1,49 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/python
 {
-  "name": "Python 3",
-  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
-  "features": {
-    "ghcr.io/devcontainers-contrib/features/poetry:2": {}
-  },
-
-  "remoteEnv": {
-    "SPECKLE_TOKEN": "foobar"
-  },
-  "containerEnv": {
-    "SPECKLE_TOKEN": "asdfasdf"
-  },
-
-  // Features to add to the dev container. More info: https://containers.dev/features.
-  // "features": {},
-
-  // Use 'forwardPorts' to make a list of ports inside the container available locally.
-  // "forwardPorts": [],
-
-  // Use 'postCreateCommand' to run commands after the container is created.
-  "postCreateCommand": "cp .env.example .env && POETRY_VIRTUALENVS_IN_PROJECT=true poetry install --no-root",
-
-  // Configure tool-specific properties.
+  "name": "Model Checker - An Automate Function",
+  "dockerFile": "Dockerfile",
+  "context": "..",
+  "workspaceFolder": "/home/speckle",
+  "runArgs": [
+    "--network",
+    "host"
+  ],
+  "mounts": [
+    "source=${localWorkspaceFolder},target=/home/speckle,type=bind,consistency=cached"
+  ],
  "customizations": {
    "vscode": {
-      // Add the IDs of extensions you want installed when the container is created.
      "extensions": [
-        "ms-python.vscode-pylance",
        "ms-python.python",
+        "ms-python.vscode-pylance",
        "ms-python.black-formatter",
-        "streetsidesoftware.code-spell-checker",
-        "mikestead.dotenv"
-      ]
+        "ms-python.isort",
+        "ms-python.flake8",
+        "littlefoxteam.vscode-python-test-adapter",
+        "ms-azuretools.vscode-docker",
+        "charliermarsh.ruff"
+      ],
+      "settings": {
+        "python.defaultInterpreterPath": "/usr/local/bin/python",
+        "python.linting.enabled": true,
+        "python.linting.flake8Enabled": true,
+        "python.formatting.provider": "black",
+        "python.testing.pytestEnabled": true,
+        "python.testing.unittestEnabled": false,
+        "python.testing.nosetestsEnabled": false,
+        "python.testing.pytestArgs": [
+          "tests"
+        ],
+        "python.testing.autoTestDiscoverOnSaveEnabled": true,
+        "python.testing.cwd": "${workspaceFolder}",
+        "[python]": {
+          "editor.formatOnSave": true,
+          "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+          }
+        }
+      }
    }
-  }
-
-  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
-  // "remoteUser": "root"
-}
+  },
+  "postCreateCommand": "sh -c \"mkdir -p ~/.pip && echo '[global]\nprefer-ipv4 = true' > ~/.pip/pip.conf\"",
+  "postStartCommand": "echo 'Container started successfully!'"
+}
@@ -11,27 +11,37 @@ jobs:
      FUNCTION_SCHEMA_FILE_NAME: functionSchema.json
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4.2.2
-      - uses: actions/setup-python@v5
+      # Step 1: Checkout the repository
+      - name: Checkout Repository
+        uses: actions/checkout@v4.2.2
+
+      # Step 2: Set up Python
+      - name: Setup Python
+        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
-      - name: Install poetry
+
+      # Step 3: Install dependencies using pip
+      - name: Install Dependencies
        run: |
-          pip install poetry==1.8.4 &&
-          poetry config virtualenvs.create false &&
-          poetry config virtualenvs.in-project false &&
-          poetry config installer.parallel true
-      - name: Restore dependencies
-        run: poetry install --no-root
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      # Step 4: Generate the schema
      - name: Extract functionInputSchema
        id: extract_schema
        run: |
-          python main.py generate_schema ${HOME}/${{ env.FUNCTION_SCHEMA_FILE_NAME }}
+          python main.py generate_schema "${HOME}/${{ env.FUNCTION_SCHEMA_FILE_NAME }}"
+          echo "Checking if functionSchema.json exists after generation..."
+          ls -lah "${HOME}/${{ env.FUNCTION_SCHEMA_FILE_NAME }}"
+
+      # Step 5: Build and publish the Speckle function
      - name: Speckle Automate Function - Build and Publish
        uses: specklesystems/speckle-automate-github-composite-action@0.9.0
        with:
-          speckle_automate_url: ${{ env.SPECKLE_AUTOMATE_URL || vars.SPECKLE_AUTOMATE_URL || 'https://automate.speckle.dev' }}
+          speckle_automate_url: ${{ env.SPECKLE_AUTOMATE_URL || 'https://automate.speckle.dev' }}
          speckle_token: ${{ secrets.SPECKLE_FUNCTION_TOKEN }}
          speckle_function_id: ${{ secrets.SPECKLE_FUNCTION_ID }}
          speckle_function_input_schema_file_path: ${{ env.FUNCTION_SCHEMA_FILE_NAME }}
          speckle_function_command: 'python -u main.py run'
+          speckle_function_recommended_memory_mi: 5000
@@ -1,12 +1,19 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.11.4 WSL (UbuntuDev): (/home/jsdbroughton/.virtualenvs/speckle_automate-mesh_density_checker/bin/python)" jdkType="Python SDK" />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/tests" isTestSource="true" />
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="uv (Checker)" jdkType="Python SDK" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
  <component name="PyDocumentationSettings">
    <option name="format" value="GOOGLE" />
    <option name="myDocStringFormat" value="Google" />
  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
 </module>
@@ -1,7 +1,10 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="Black">
-    <option name="sdkName" value="Python 3.11.4 WSL (UbuntuDev): (/home/jsdbroughton/.virtualenvs/speckle_automate-mesh_density_checker/bin/python)" />
+    <option name="sdkName" value="WSL Checker" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="uv (Checker)" project-jdk-type="Python SDK" />
+  <component name="PythonCompatibilityInspectionAdvertiser">
+    <option name="version" value="3" />
  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11.4 WSL (UbuntuDev): (/home/jsdbroughton/.virtualenvs/speckle_automate-mesh_density_checker/bin/python)" project-jdk-type="Python SDK" />
 </project>
@@ -1,7 +1,9 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="RuffConfigService">
+    <option name="enableLsp" value="false" />
    <option name="runRuffOnSave" value="true" />
+    <option name="useRuffFormat" value="true" />
    <option name="useRuffServer" value="true" />
  </component>
 </project>
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="WebResourcesPaths">
+    <contentEntries>
+      <entry url="file://$PROJECT_DIR$">
+        <entryData>
+          <resourceRoots>
+            <path value="file://$PROJECT_DIR$/test_data" />
+          </resourceRoots>
+        </entryData>
+      </entry>
+    </contentEntries>
+  </component>
+</project>
@@ -0,0 +1 @@
+3.13
@@ -5,5 +5,27 @@
    "stringcase",
    "typer"
  ],
-  "python.defaultInterpreterPath": ".venv/bin/python"
-}
+  "python.defaultInterpreterPath": ".venv/bin/python",
+  "python.testing.pytestEnabled": true,
+  "python.testing.unittestEnabled": false,
+  "python.testing.nosetestsEnabled": false,
+  "python.testing.pytestArgs": [
+    "tests"
+  ],
+  "python.testing.autoTestDiscoverOnSaveEnabled": true,
+  "python.testing.cwd": "${workspaceFolder}",
+  "editor.formatOnSave": true,
+  "editor.rulers": [
+    79
+  ],
+  "editor.codeActionsOnSave": {
+    "source.organizeImports": "explicit"
+  },
+  "[python]": {
+    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.formatOnSave": true,
+    "editor.codeActionsOnSave": {
+      "source.organizeImports.ruff": "explicit"
+    }
+  }
+}
@@ -1,54 +1,377 @@
-# Checker Function Development Guide
+# Checker Function Developer Guide

-## Setup
+This document provides technical details for developers working on the Speckle Checker Automate function.
+
+## Project Overview
+
+The Checker function enables validation of Speckle objects against user-defined rules in a spreadsheet. It's designed to
+be flexible, supporting various object schemas including both v2 and v3 Speckle APIs.
+
+## Setup Development Environment
+
+### Prerequisites
+
+- Python 3.10+
+- Poetry for dependency management
+
+### Installation
+
+1. Clone the repository
+2. Install dependencies:
+   ```bash
+   poetry install
+   ```
+3. Activate the virtual environment:
+   ```bash
+   poetry shell
+   ```
+
+### Test Automation Environment
+
+The project uses Speckle's [Test Automation feature](https://speckle.guide/automate/function-testing.html) to run
+integration tests against real Speckle data. This provides a sandboxed environment to validate the function's business
+logic without triggering actual automations.
+
+#### Setting Up a Test Automation
+
+1. Navigate to your Speckle project
+2. Go to the **Automations** tab
+3. Click **New Automation**
+4. Select **Create Test Automation** in the bottom left
+5. Follow the configuration steps
+
+Note: To create a test automation, you must:
+
+- Be an owner of the Speckle project
+- Have published this function to the Function Library
+- Have at least one release for the function
+
+#### Environment Configuration
+
+For local integration testing, create a `.env` file in the project root with these variables:

-1. Install dependencies:
-```bash
-poetry shell && poetry install
-```
-
-2. Configure `.env`:
 ```
+# Your Personal Access Token from Speckle
 SPECKLE_TOKEN=your_speckle_token
-SPECKLE_SERVER_URL=app.speckle.systems
+
+# The Speckle server URL
+SPECKLE_SERVER_URL=https://app.speckle.systems
+
+# From the test automation URL: /projects/[project-id]/automations/[automation-id]
+SPECKLE_PROJECT_ID=your_project_id
+SPECKLE_AUTOMATION_ID=your_automation_id
 ```

-Get test automation details from app.speckle.systems
+This configuration allows the test suite to:
+
+1. Connect to your test automation via the Speckle API
+2. Run the function locally against real Speckle data
+3. Submit results to the test automation for validation
+
+For detailed instructions, refer to
+the [official documentation on function testing](https://speckle.guide/automate/function-testing.html#how-to-create-a-test-automation).
+
+#### Running Integration Tests
+
+With the `.env` file configured:
+
+```bash
+# Run the integration tests
+pytest test_function.py
+```
+
+The SDK utilities will automatically:
+
+- Connect to your test automation
+- Execute your function with the specified test data
+- Submit results back to Speckle
+
+Test results will be visible on the automation page in the Speckle UI.
+
+#### Unit Tests
+
+For unit tests that don't require a full Speckle connection, you can run:
+
+```bash
+# Run unit tests only
+pytest test_comparisons.py test_rule_processing.py
+```
+
+Note: The `.env` file should never be committed to version control (it's included in .gitignore)

 ## Project Structure

- `function.py`: Main business logic
- `rules.py`: Rule definitions and processing
- `inputs.py`: Function input schema
- `helpers.py`: Utility functions
- `spreadsheet.py`: TSV handling
+```
+├── main.py                  # Entry point for Automate
+├── src/
+│   ├── function.py          # Main function logic
+│   ├── inputs.py            # Function input schema
+│   ├── helpers.py           # Utility functions
+│   ├── filters.py           # Object filtering functions
+│   ├── rules.py             # Rule definitions and property handling
+│   ├── predicates.py        # Predicate mapping for spreadsheet values
+│   ├── rule_processor.py    # Rule application and result handling
+│   └── spreadsheet.py       # TSV file parsing
+├── tests/
+│   ├── conftest.py          # Test fixtures
+│   ├── test_function.py     # Main function tests
+│   ├── test_comparisons.py  # Value comparison tests
+│   ├── test_parameters.py   # Parameter handling tests
+│   └── test_rule_processing.py  # Rule processing tests
+├── pyproject.toml           # Project dependencies
+└── poetry.lock              # Locked dependencies
+```
+
+## Core Components
+
+### 1. Function Execution Flow
+
+The main execution flow is defined in `function.py`:
+
+1. `automate_function()` receives context and inputs from Automate
+2. Retrieves Speckle objects via `automate_context.receive_version()`
+3. Flattens the object tree using `flatten_base()`
+4. Loads rules from the spreadsheet URL via `read_rules_from_spreadsheet()`
+5. Applies rules to objects using `apply_rules_to_objects()`
+6. Reports results via the Automate context
+
+### 2. Rule Processing
+
+Rules are processed through several stages:
+
+1. **Spreadsheet Parsing** (`spreadsheet.py`):
+    - Reads TSV data
+    - Groups rules by rule number
+    - Validates rule structure
+
+2. **Rule Application** (`rule_processor.py`):
+    - Processes rule logic (WHERE, AND, CHECK)
+    - Evaluates conditions against objects
+    - Attaches results to objects in Automate context
+
+3. **Property Rules** (`rules.py`):
+    - Handles property lookups in objects
+    - Implements comparison logic
+    - Supports both v2 and v3 Speckle schemas
+
+### 3. Property Access System
+
+The system uses a flexible property access mechanism that works with different Speckle schemas:
+
+- **V2 Schema**: Properties in `parameters` dictionary with internal definition names
+- **V3 Schema**: Properties in nested `properties.Parameters` structure
+
+The `PropertyRules` class provides methods to:
+
+- Find properties by path or name
+- Extract values with appropriate type conversion
+- Perform comparisons with tolerance and type handling
+
+## Test-Driven Development for Rules
+
+The test infrastructure is designed to support Test-Driven Development (TDD) when creating new rules or extending
+functionality. This approach is especially powerful for rule development as it allows you to verify behavior against
+known test objects.
+
+### Using Test Fixtures for Rule Development
+
+The `conftest.py` file contains test fixtures that provide sample Speckle objects for testing:
+
+```python
+@pytest.fixture
+def v2_wall():
+    """Creates a v2-style Speckle wall object"""
+    wall = Base()
+    wall.id = "cdb18060dc48281909e94f0f1d8d3cc0"
+    wall.type = "W30(Fc24)"
+    wall.units = "mm"
+    wall.family = "Basic Wall"
+    wall.height = 1400
+    wall.flipped = False
+    wall.category = "Walls"
+    # ... more properties
+
+    return wall
+
+
+@pytest.fixture
+def v3_wall():
+    """Creates a v3-style Speckle wall object"""
+    wall = Base()
+    wall.id = "46f06fef727d64a0bbcbd7ced51e0cd2"
+    wall.name = "Walls - W30(Fc24)"
+    wall.type = "W30(Fc24)"
+    wall.units = "mm"
+    wall.family = "Basic Wall"
+    # ... more properties
+
+    return wall
+```
+
+These fixtures create standardized test objects that represent different Speckle schema versions, allowing you to test
+rule behavior consistently.
+
+### TDD Workflow for New Rules
+
+When developing a new rule or predicate, follow this TDD approach:
+
+1. **Add test fixtures**: First, expand `conftest.py` with representative objects that your rule will process
+
+2. **Write tests first**: Create test cases in a test file (e.g., `test_my_rule.py`):
+   ```python
+   def test_new_wall_rule(v2_wall, v3_wall):
+       """Test a new rule that checks wall thickness requirements"""
+       # Test with v2 schema
+       assert PropertyRules.is_new_wall_check(v2_wall, "width", "300")
+       
+       # Test with v3 schema
+       assert PropertyRules.is_new_wall_check(v3_wall, "Width", "300")
+       
+       # Test failure case
+       v2_wall.parameters["WALL_ATTR_WIDTH_PARAM"].value = 200
+       assert not PropertyRules.is_new_wall_check(v2_wall, "width", "300")
+   ```
+
+3. **Implement the rule**: Add the new rule method to the `PropertyRules` class in `rules.py`:
+   ```python
+   @staticmethod
+   def is_new_wall_check(speckle_object: Base, parameter_name: str, expected_value: str) -> bool:
+       """Checks if a wall meets specific thickness requirements"""
+       parameter_value = PropertyRules.get_parameter_value(speckle_object, parameter_name)
+       # Implement rule logic
+       return result
+   ```
+
+4. **Add to predicate mapping**: Register your new rule in `predicates.py`:
+   ```python
+   PREDICATE_METHOD_MAP = {
+       # Existing predicates...
+       "new_wall_check": PropertyRules.is_new_wall_check.__name__,
+   }
+   ```
+
+5. **Run tests to verify**:
+   ```bash
+   pytest test_my_rule.py -v
+   ```
+
+### Creating Comprehensive Test Objects
+
+For the most effective testing, your test objects in `conftest.py` should:
+
+1. **Include diverse objects**: Walls, columns, beams, etc.
+2. **Cover edge cases**: Null values, missing properties, special characters
+3. **Represent both schemas**: Include both v2 and v3 format objects
+4. **Include real-world examples**: Extract sample objects from actual projects
+
+You can extract real objects for testing using:
+
+```python
+# Example code to extract and save real objects for test fixtures
+from specklepy.api import operations
+from specklepy.api.client import SpeckleClient
+from specklepy.transports.server import ServerTransport
+
+client = SpeckleClient(host="app.speckle.systems")
+client.authenticate_with_token(token)
+
+transport = ServerTransport(client=client, stream_id="stream_id")
+obj = operations.receive("object_id", transport)
+
+# Print structure to help with fixture creation
+print(obj.get_member_names())
+print(obj.get_dynamic_member_names())
+```
+
+By following this TDD approach and maintaining comprehensive test fixtures, you can develop robust rules that work
+reliably across different object schemas and handle edge cases appropriately.

 ## Testing

+### Running Tests
+
 ```bash
-poetry run pytest
+# Run all tests
+pytest
+
+# Run specific test file
+pytest tests/test_parameters.py
+
+# Run with coverage
+pytest --cov=src
 ```

-## Extending Rules
+### Test Data

-1. Add new predicate to `input_predicate_mapping` in `rules.py`
-2. Create corresponding method in `RevitRules` class
-3. Update tests
+Test fixtures in `conftest.py` provide sample objects:

-## Building
+- `v2_wall`: Wall object in v2 schema
+- `v3_wall`: Wall object in v3 schema

-The function is packaged as a Docker container:
+### Manual Testing with Real Data

-```bash
-docker build -f ./Dockerfile -t checker .
-```
+For testing with real Speckle data:

-## Local Testing
+```python
+from specklepy.api import operations
+from specklepy.api.client import SpeckleClient
+from specklepy.api.credentials import get_account_from_token
+from specklepy.transports.server import ServerTransport

-```bash
-docker run --rm checker python -u main.py run [automation_data] [parameters] [token]
+client = SpeckleClient(host="app.speckle.systems")
+account = get_account_from_token(token, "app.speckle.systems")
+client.authenticate_with_account(account)
+
+transport = ServerTransport(client=client, stream_id="your_stream_id")
+commit = client.commit.get(stream_id="your_stream_id", commit_id="your_commit_id")
+obj = operations.receive(commit.referencedObject, transport)
 ```

 ## Deployment

-Create a GitHub release to trigger deployment to Speckle Automate.
+The function is deployed through GitHub Actions:
+
+1. Create a GitHub release to trigger the build workflow
+2. The workflow builds the necessary artifacts and pushes them to the Speckle Automate registry
+3. The function becomes available in the Speckle Automate UI
+
+## Performance Considerations
+
+- **Large Object Trees**: When processing large models, use aggressive filtering with WHERE clauses
+- **Rule Complexity**: Minimize the number of nested property lookups
+- **Memory Usage**: Be aware of object reference handling and avoid deep copies
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Rule not matching expected objects**:
+    - Check property paths for the specific object type
+    - Verify data types (strings vs. numbers)
+    - Enable debug logging
+
+2. **Slow performance**:
+    - Check for inefficient property lookups
+    - Add more specific WHERE filters to reduce object set
+
+3. **Docker build failures**:
+    - Check dependency compatibility
+    - Verify Python version requirements
+
+## Contributing
+
+1. Create a branch for your feature or fix
+2. Add tests for new functionality
+3. Update documentation
+4. Submit a pull request
+5. Ensure CI tests pass
+
+## Future Development
+
+Potential improvements:
+
+- Support for more complex rule logic (OR conditions)
+- UI-based rule editor
+- Result visualization tools
+- Performance optimizations for large models
+- Support for referencing other objects in rules
@@ -1,16 +1,15 @@
-# We use the official Python 3.11 image as our base image and will add our code to it. For more details, see https://hub.docker.com/_/python
+# Use the official Python 3.13 slim image as the base
 FROM python:3.13-slim

-# We install poetry to generate a list of dependencies which will be required by our application
-RUN pip install poetry==1.8.4
-
-# We set the working directory to be the /home/speckle directory; all of our files will be copied here.
+# Set the working directory inside the container
 WORKDIR /home/speckle

-# Copy all of our code and assets from the local directory into the /home/speckle directory of the container.
-# We also ensure that the user 'speckle' owns these files, so it can access them
-# This assumes that the Dockerfile is in the same directory as the rest of the code
+# Copy the application files to the working directory
 COPY . /home/speckle

-# Using poetry, we generate a list of requirements, save them to requirements.txt, and then use pip to install them
-RUN poetry export --format requirements.txt --output /home/speckle/requirements.txt && pip install --requirement /home/speckle/requirements.txt
+# Upgrade pip and install dependencies using requirements.txt
+RUN pip install --no-cache-dir --upgrade pip && \
+  pip install --no-cache-dir -r /home/speckle/requirements.txt
+
+# Set the entrypoint for running the Speckle function
+CMD ["python", "-u", "main.py", "run"]
@@ -1,36 +1,130 @@
-# Public Function: Checker
+# Model Checker

-Validate Speckle objects against configurable rules using spreadsheet definitions.
+Model Checker is an Automate function that validates Speckle objects against configurable rules. This approach provides
+a flexible way to implement quality checks and maintain consistent standards across projects.

-## Usage
+## Overview

-1. Access the template Google Sheet [link needed]
-2. Make a copy to your Google Drive using File > Make a copy
-3. Define your rules in your sheet
-4. Click "Speckle" menu > "Publish Rules" to get your TSV URL
-5. Create an Automation in Speckle Automate using the Checker function
-6. Paste your TSV URL into the function configuration
-7. Run your automation
+The Model Checker allows you to:

-## Rule Types
+- Define validation rules for your objects
+- Configure severity levels for issues
+- Check properties across different types of objects
+- Generate reports of validation results
+- Apply consistent standards across projects

- Property existence
- Value matching
- Numeric comparisons
- Range checks
- List membership
- Pattern matching
- Boolean checks
+## Getting Started

-## Severity Levels
+### 1. Access the Model Checker Application

- WARNING: Issues that should be reviewed
- ERROR: Critical issues requiring attention
+1. Go to the [Model Checker Application](https://model-checker.speckle.systems)
+2. Sign in with your Speckle account
+3. Create and manage your validation rules through the intuitive web interface
+
+### 2. Create an Automation
+
+1. Go to your workspace project in [Speckle](https://app.speckle.systems/)
+2. Create a new Automation
+3. Select the Model Checker function
+4. Configure the function:
+    - Set minimum severity level to report
+    - Configure other options as needed
+5. Save and run your automation
+
+## Rule Definition Format
+
+Rules are defined with the following components:
+
+| Logic | Property Name | Predicate    | Value     | Message              | Report Severity |
+|-------|---------------|--------------|-----------|----------------------|-----------------|
+| WHERE | category      | matches      | Walls     | Wall thickness check | ERROR           |
+| CHECK | Width         | greater than | 200       |                      |                 |
+| WHERE | category      | matches      | Columns   | Column height check  | WARNING         |
+| AND   | height        | in range     | 2500,4000 |                      |                 |
+
+### Component Explanation
+
+- **Logic**: Defines how conditions are combined (WHERE, AND, CHECK)
+- **Property Name**: The object property or parameter to check
+- **Predicate**: Comparison operation (equals, greater than, etc.)
+- **Value**: Reference value for comparison
+- **Message**: Description shown in validation results
+- **Report Severity**: ERROR, WARNING, or INFO
+
+### Supported Predicates
+
+| Predicate        | Description                 | Example                               |
+|------------------|-----------------------------|---------------------------------------|
+| exists           | Checks if a property exists | `height` exists                       |
+| equal to         | Exact value match           | `width` equal to `300`                |
+| not equal to     | Value doesn't match         | `material` not equal to `Concrete`    |
+| greater than     | Value exceeds threshold     | `height` greater than `3000`          |
+| less than        | Value below threshold       | `thickness` less than `50`            |
+| in range         | Value within bounds         | `elevation` in range `0,10000`        |
+| in list          | Value in allowed set        | `type` in list `W1,W2,W3`             |
+| contains         | Property contains substring | `name` contains `Beam`                |
+| does not contain | Property doesn't contain    | `name` does not contain `temp`        |
+| is true          | Boolean property is true    | `is_structural` is true               |
+| is false         | Boolean property is false   | `is_placeholder` is false             |
+| is like          | Loose text matching         | `name` is like `Wall` matches `Walls` |
+
+## Rule Logic
+
+- **WHERE**: Filters objects to check (like SELECT WHERE in SQL)
+- **AND**: Additional filter conditions
+- **CHECK**: Final check condition (optional, defaults to last AND)
+
+Objects pass a rule when they match all conditions. Objects that match WHERE/AND filters but fail the CHECK condition
+are reported as issues.
+
+## Working with Object Properties
+
+The Model Checker understands properties in Speckle objects regardless of schema:
+
+- Direct properties: `category`, `name`, `id`
+- Nested properties: `parameters.WIDTH.value`
+- Revit parameters: Use parameter names like `Mark`, `Width`, `Assembly Code`

 ## Example Rules

-[Screenshot or example table to be added]
+### Wall Thickness Check
+
+```
+Rule: WHERE category equals "Walls" AND width less than "200"
+Message: "Walls must have width of at least 200."
+Severity: ERROR
+```
+
+### Door Naming Convention
+
+```
+Rule: WHERE category equals "Doors" AND name is not like "^D\d{3}$"
+Message: "All doors must have a name that follows the format "D" followed by three digits."
+Severity: WARNING
+```
+
+### Structural Column Height Range
+
+```
+Rule: WHERE category equals "Columns" AND is_structural is true AND height not in range "2400,4000"
+Message: "Structural columns must have a height between 2400 and 4000."
+Severity: ERROR
+```

 ## Support

-For issues or questions, please open a GitHub issue.
+For issues or questions, please let us know on the [Speckle Community Forum](https://speckle.community/).
+
+### Alternative: TSV File Format
+
+While the Model Checker Application is the recommended way to create and manage rules, you can also create compatible
+TSV (Tab-Separated Values) files manually. This can be useful for:
+
+- Programmatically generating rules
+- Version controlling rules in a text format
+- Integrating with existing workflows
+- Creating rules in bulk
+
+The TSV file should follow the same structure as shown in the table above, with columns separated by tabs. The file will
+then need to be hosted somewhere and served with MIME-type of `text/tab-separated-values` and the URL used in the
+automation configuration.
@@ -1,20 +0,0 @@
-{
-  "speckleToken": "YOUR SPEKCLE TOKEN",
-  "functionInputs": {
-    "whisperMessage": "you are doing something weird",
-    "forbiddenSpeckleType": "wall"
-  },
-  "automationRunData": {
-    "project_id": "project id",
-    "speckle_server_url": "https://latest.speckle.systems",
-    "automation_id": "automation id",
-    "automation_run_id": "automation run id",
-    "function_run_id": "function run id",
-    "triggers": [
-      {
-        "payload": { "modelId": "model id", "versionId": "version id" },
-        "triggerType": "versionCreation"
-      }
-    ]
-  }
-}
@@ -1,27 +0,0 @@
-"""Helper module for a simple speckle object tree flattening."""
-
-from collections.abc import Iterable
-
-from specklepy.objects import Base
-
-
-def flatten_base(base: Base) -> Iterable[Base]:
-    """Flatten a base object into an iterable of bases.
-    
-    This function recursively traverses the `elements` or `@elements` attribute of the 
-    base object, yielding each nested base object.
-
-    Args:
-        base (Base): The base object to flatten.
-
-    Yields:
-        Base: Each nested base object in the hierarchy.
-    """
-    # Attempt to get the elements attribute, fallback to @elements if necessary
-    elements = getattr(base, "elements", getattr(base, "@elements", None))
-    
-    if elements is not None:
-        for element in elements:
-            yield from flatten_base(element)
-    
-    yield base
@@ -0,0 +1,13 @@
+#!/bin/bash
+echo "Running pre-commit hook..."
+
+# Ensure dependencies are installed with uv
+uv pip install --requirement requirements.txt
+
+# Export dependencies with uv (and overwrite requirements.txt)
+uv pip freeze > requirements.txt
+
+# Add generated requirements.txt to git
+git add requirements.txt
+
+echo "Pre-commit hook completed successfully!"
@@ -1,30 +1,28 @@
-[tool.poetry]
-name = "speckle-automate-py"
-version = "0.1.0"
+[project]
+name = "speckle-automate-checker"
+version = "3.0.0"
 description = "Allows for QAQC property checking with Speckle"
-authors = ["Jonathon Broughton <jonathon@speckle.systems>"]
+authors = [{ name = "Jonathon Broughton", email = "jonathon@speckle.systems" }]
 readme = "README.md"
-package-mode = false
+requires-python = ">=3.13"
+dependencies = [
+  "more-itertools>=10.6.0",
+  "pandas>=2.2.3",
+  "pydantic==2.10.6",
+  "python-dotenv>=1.0.1",
+  "python-levenshtein>=0.26.1",
+  "specklepy>=3.0.0",
+  "pydantic-settings>=2.7.1",
+]

-[tool.poetry.dependencies]
-more-itertools = "^10.6.0"
-pandas = "^2.2.3"
-python = "^3.11"
-python-dotenv = "^1.0.1"
-python-levenshtein = "^0.26.1"
-specklepy = "^2.21.0"

-[tool.poetry.group.dev.dependencies]
-black = "^25.0.0"
-mypy = "^1.3.0"
-pydantic-settings = "^2.3.0"
-pytest = "^8.0.0"
-ruff = "^0.9.5"
-# specklepy = { path = "../specklepy", develop = true }
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[project.optional-dependencies]
+dev = [
+  "mypy>=1.15.0",
+  "pytest>=8.3.4",
+  "pytest-assertcount>=1.0.0",
+  "ruff==0.11.12",
+]

 [tool.ruff]
 select = [
@@ -34,6 +32,26 @@ select = [
  "D",  # pydocstyle
  "I",  # isort
 ]
+ignore = ["F401", "F403", "E501"]
+exclude = [".venv", "**/*.yml"]
+line-length = 79

 [tool.ruff.pydocstyle]
 convention = "google"
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+line-ending = "auto"
+docstring-code-format = true
+docstring-code-line-length = 79
+
+[tool.ruff.isort]
+known-first-party = ["src"]
+
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.setuptools]
+py-modules = []
@@ -0,0 +1,23 @@
+argcomplete==3.6.2
+click==8.1.8
+colorama==0.4.6
+coverage==7.8.2
+flake8==7.2.0
+iniconfig==2.1.0
+isort==6.0.1
+mccabe==0.7.0
+mypy_extensions==1.1.0
+packaging==24.2
+pathspec==0.12.1
+pipx==1.7.1
+platformdirs==4.3.7
+pluggy==1.6.0
+pycodestyle==2.13.0
+pyflakes==3.3.2
+Pygments==2.19.1
+pytest>=8.3.4
+pytest-assertcount>=1.0.0
+pytest-cov==6.1.1
+ruff==0.11.12
+userpath==1.9.2
+mypy>=1.15.0
@@ -0,0 +1,7 @@
+more-itertools>=10.6.0
+pandas>=2.2.3
+pydantic==2.10.6
+python-dotenv>=1.0.1
+python-levenshtein>=0.26.1
+specklepy>=3.0.0
+pydantic-settings>=2.7.1
@@ -0,0 +1,22 @@
+#!/bin/sh
+
+# Store the current Python environment
+CURRENT_ENV=$(pip freeze)
+
+# Remove dev dependencies
+pip uninstall -y pytest pytest-cov isort flake8 ruff
+
+# Generate production requirements
+pip freeze > requirements.txt
+
+# Reinstall dev dependencies
+pip install pytest pytest-cov isort flake8 ruff
+
+# Generate dev requirements
+pip freeze > requirements-dev.txt
+
+# Restore the original environment
+pip uninstall -y pytest pytest-cov isort flake8 ruff
+echo "$CURRENT_ENV" | pip install -r /dev/stdin
+
+echo "Requirements files have been updated successfully!" 
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+# Copy the pre-commit hook to the .git/hooks/ directory
+cp hooks/pre-commit .git/hooks/pre-commit
+
+# Ensure the hook is executable
+chmod +x .git/hooks/pre-commit
+
+echo "Git hooks have been set up!"
@@ -0,0 +1,12 @@
+{
+  "folders": [
+    {
+      "path": "."
+    }
+  ],
+  "settings": {
+    "files.autoSave": "onFocusChange",
+    "editor.defaultFormatter": null,
+    "editor.formatOnSave": true
+  }
+}
@@ -0,0 +1,31 @@
+from specklepy.objects.base import Base
+
+from src.rules import PropertyRules
+
+
+def filter_objects_by_category(speckle_objects: list[Base], category_input: str) -> tuple[list[Base], list[Base]]:
+    """Filters objects by category value and test.
+
+    This function takes a list of Speckle objects, filters out the objects
+    with a matching category value and satisfies the test, and returns
+    both the matching and non-matching objects.
+
+    Args:
+        speckle_objects (List[Base]): The list of Speckle objects to filter.
+        category_input (str): The category value to match against.
+
+    Returns:
+        Tuple[List[Base], List[Base]]: A tuple containing two lists:
+                                        - The first list contains objects with matching category and test.
+                                        - The second list contains objects without matching category or test.
+    """
+    matching_objects = []
+    non_matching_objects = []
+
+    for obj in speckle_objects:
+        if PropertyRules.is_category(obj, category_input):
+            matching_objects.append(obj)
+        else:
+            non_matching_objects.append(obj)
+
+    return matching_objects, non_matching_objects
@@ -1,46 +1,126 @@
-"""This module contains the function's business logic.
+"""This is the main entry point for the Speckle Automate function.

-Use the automation_context module to wrap your function in an Automate context helper.
+The Speckle Automate system works as follows:
+1. When a model is committed to Speckle, it triggers automations associated with the project
+2. For each automation, Speckle Automate prepares a runtime environment and context
+3. The automation context includes the model data and function inputs
+4. This function is executed to process the model and provide results
+5. Results are attached to objects in the model, creating an annotated view
+
+This function implements a configurable rule-based validation system that:
+- Reads validation rules from an external spreadsheet
+- Applies these rules to objects in the Speckle model
+- Reports validation results back to the Speckle platform
+- Provides an annotated view of the model showing validation issues
 """

-from speckle_automate import AutomationContext, AutomateBase
+from speckle_automate import AutomationContext
+from specklepy.objects.base import Base

-from src.rules import apply_rules_to_objects
+from src.helpers import flatten_base, speckle_print
 from src.inputs import FunctionInputs
-from src.helpers import flatten_base
+from src.rule_processor import apply_rules_to_objects
 from src.spreadsheet import read_rules_from_spreadsheet

+VERSION: int = 2
+

 def automate_function(
-        automate_context: AutomationContext,
-        function_inputs: FunctionInputs,
+    automate_context: AutomationContext,
+    function_inputs: FunctionInputs,
 ) -> None:
-    """This version of the function will add a check for the new provide inputs.
+    """Main entry point for the Speckle Automate function.
+
+    This function is called by the Speckle Automate system when the automation is triggered.
+    It orchestrates the entire validation process:
+
+    1. Receiving and flattening the model data
+    2. Detecting the Speckle object schema version
+    3. Loading and grouping rules from the external spreadsheet
+    4. Applying rules to objects and collecting results
+    5. Reporting results back to the Speckle platform

    Args:
-        automate_context: A context helper object, that carries relevant information
-            about the runtime context of this function.
-            It gives access to the Speckle project data, that triggered this run.
-            It also has convenience methods attach result data to the Speckle model.
-        function_inputs: An instance object matching the defined schema.
+        automate_context: A context helper provided by Speckle Automate that:
+            - Provides access to the Speckle model data
+            - Handles result reporting and view management
+            - Manages run status (success, failure, exception)
+        function_inputs: User-provided inputs defined in the FunctionInputs schema,
+            particularly the URL to the rules spreadsheet
    """
+    # -------------------------------------------------------------------------
+    # Step 1: Receive and process the model data
+    # -------------------------------------------------------------------------

-    # the context provides a convenient way, to receive the triggering version
-    version_root_object = automate_context.receive_version()
+    # The AutomationContext provides a convenient way to access the model data
+    # that triggered this automation run
+    version_root_object: Base = automate_context.receive_version()

-    # We can continue to work with a flattened list of objects.
+    # Flatten the object tree into a list of objects
+    # The Speckle object model is hierarchical, but for validation purposes,
+    # it's easier to work with a flat list of objects
    flat_list_of_objects = list(flatten_base(version_root_object))

-    # read the rules from the spreadsheet
-    rules = read_rules_from_spreadsheet(function_inputs.spreadsheet_url)
+    # -------------------------------------------------------------------------
+    # Step 2: Detect Speckle object schema version
+    # -------------------------------------------------------------------------

-    # apply the rules to the objects
-    apply_rules_to_objects(flat_list_of_objects, rules, automate_context)
+    # The Speckle object schema has evolved over time
+    # In newer models, we can detect the version from the root object
+    # This version information helps our validation logic handle different schemas
+    global VERSION
+    VERSION = getattr(version_root_object, "version", 2)  # noqa: F841SION = getattr(version_root_object,"version", 2)  # noqa: F841  # noqa: F841

-    # set the automation context view, to the original model / version view
+    # In v2, parameters are stored in a 'parameters' dictionary on each object
+    # In v3, they are nested in 'properties.Parameters' with categorization
+    speckle_print(f"Detected Speckle object schema version: {VERSION}")
+
+    # -------------------------------------------------------------------------
+    # Step 3: Load and process rules from the spreadsheet
+    # -------------------------------------------------------------------------
+
+    # The rules are defined in an external spreadsheet (TSV format)
+    # This allows non-technical users to define and modify rules
+    # without changing the code
+    grouped_rules, messages = read_rules_from_spreadsheet(
+        function_inputs.spreadsheet_url
+    )
+
+    # Handle any validation messages from rule processing
+    for message in messages:
+        speckle_print(message)  # or log them appropriately
+
+    # If rule processing failed, mark the run as failed and exit
+    if grouped_rules is None:
+        automate_context.mark_run_exception("Failed to process rules")
+        return
+
+    # -------------------------------------------------------------------------
+    # Step 4: Apply rules to objects and collect results
+    # -------------------------------------------------------------------------
+
+    # This is where the actual validation happens
+    # Each rule is applied to relevant objects, and results are collected
+    # Results are attached to objects in the model to create an annotated view
+    apply_rules_to_objects(
+        flat_list_of_objects,
+        grouped_rules,
+        automate_context,
+        minimum_severity=function_inputs.minimum_severity,
+        hide_skipped=function_inputs.hide_skipped,
+    )
+
+    # -------------------------------------------------------------------------
+    # Step 5: Finalize the automation run
+    # -------------------------------------------------------------------------
+
+    # Set the context view to the original model/version view
+    # This ensures that the results are displayed in the correct context
    automate_context.set_context_view()

-    # report success
+    # Mark the run as successful and provide a summary message
+    # This message will be displayed to the user in the Speckle UI
    automate_context.mark_run_success(
-        f"Successfully applied rules to {len(flat_list_of_objects)} objects."
+        f"Successfully applied {len(grouped_rules)} rules to "
+        f"{len(flat_list_of_objects)} version {VERSION} objects."
    )
@@ -1,14 +1,14 @@
 """Helper module for a speckle object tree flattening."""

-from collections.abc import Iterable
-from typing import Optional, Tuple, List
+from collections.abc import Generator, Iterable
+from typing import Any

 from specklepy.objects import Base
-from specklepy.objects.other import Instance, Transform
+from specklepy.objects.proxies import InstanceProxy as Instance


 def speckle_print(log_string: str = "banana") -> None:
-
+    """Print a string to the console with a green color."""
    print("\033[92m" + str(log_string) + "\033[0m")


@@ -21,7 +21,28 @@ def flatten_base(base: Base) -> Iterable[Base]:
    yield base


-def flatten_base_thorough(base: Base, parent_type: str = None) -> Iterable[Base]:
+def get_item(obj: Base | dict[str, Any], key, default=None):
+    """Get an item from a dictionary or an object with a default value."""
+    if isinstance(obj, dict):  # If it's a dictionary
+        return obj.get(key, default)
+    elif hasattr(obj, key):  # If it's an object with the attribute
+        return getattr(obj, key, default)
+    return default  # Return default if it's neither a dict nor an object with
+    # the attribute
+
+
+def has_item(obj: Base | dict[str, Any], key: str) -> bool:
+    """Check if an object has a key or an attribute."""
+    if isinstance(obj, dict):
+        return key in obj
+    elif hasattr(obj, key):
+        return True
+    return False
+
+
+def flatten_base_thorough(
+    base: Base, parent_type: str | None = None
+) -> Iterable[Base]:
    """Take a base and flatten it to an iterable of bases.

    Args:
@@ -63,31 +84,44 @@ def flatten_base_thorough(base: Base, parent_type: str = None) -> Iterable[Base]

 def extract_base_and_transform(
    base: Base,
-    inherited_instance_id: Optional[str] = None,
-    transform_list: Optional[List[Transform]] = None,
-) -> Tuple[Base, str, Optional[List[Transform]]]:
-    """
-    Traverses Speckle object hierarchies to yield `Base` objects and their transformations.
-    Tailored to Speckle's AEC data structures, it covers the newer hierarchical structures
-    with Collections and also  with patterns found in older Revit specific data.
+    inherited_instance_id: str | None = None,
+    transform_list: list[list[float]] | None = None,
+) -> Generator[
+    Base
+    | str
+    | list[list[float]]
+    | None
+    | tuple[Base, Any | None, list[list[float]] | None | list[Any]],
+    Any | None,
+]:
+    """Traverses Speckle object hierarchies to yield `Base`s and transformas.
+
+    Tailored to Speckle's AEC data structures, it covers the newer
+    hierarchical structures with Collections and also  with patterns found in
+    older Revit specific data.

    Parameters:
    - base (Base): The starting point `Base` object for traversal.
-    - inherited_instance_id (str, optional): The inherited identifier for `Base` objects without a unique ID.
-    - transform_list (List[Transform], optional): Accumulated list of transformations from parent to child objects.
+    - inherited_instance_id (str, optional): The inherited identifier for
+      `Base` objects without a unique ID.
+    - transform_list (List[List[float]], optional): Accumulated list of
+      transformations from parent to child objects.

    Yields:
-    - tuple: A `Base` object, its identifier, and a list of applicable `Transform` objects or None.
+    - tuple: A `Base` object, its identifier, and a list of applicable
+      transformations or None.

-    The id of the `Base` object is either the inherited identifier for a definition from an instance
-    or the one defined in the object.
+    The id of the `Base` object is either the inherited identifier for a
+    definition from an instance or the one defined in the object.
    """
-    # Derive the identifier for the current `Base` object, defaulting to an inherited one if needed.
+    # Derive the identifier for the current `Base` object, defaulting to an
+    # inherited one if needed.
    current_id = getattr(base, "id", inherited_instance_id)
    transform_list = transform_list or []

    if isinstance(base, Instance):
-        # Append transformation data and dive into the definition of `Instance` objects.
+        # Append transformation data and dive into the definition of `Instance`
+        # objects.
        if base.transform:
            transform_list.append(base.transform)
        if base.definition:
@@ -98,22 +132,31 @@ def extract_base_and_transform(
        # Initial yield for the current `Base` object.
        yield base, current_id, transform_list

-        # Process 'elements' and '@elements', typical containers for `Base` objects in AEC models.
-        elements_attr = getattr(base, "elements", []) or getattr(base, "@elements", [])
+        # Process 'elements' and '@elements', typical containers for `Base`
+        # objects in AEC models.
+        elements_attr = getattr(base, "elements", []) or getattr(
+            base, "@elements", []
+        )
        for element in elements_attr:
            if isinstance(element, Base):
-                # Recurse into each `Base` object within 'elements' or '@elements'.
+                # Recurse into each `Base` object within 'elements' or
+                # '@elements'.
                yield from extract_base_and_transform(
                    element, current_id, transform_list.copy()
                )

-        # Recursively process '@'-prefixed properties that are Base objects with 'elements'.
-        # This is a common pattern in older Speckle data models, such as those used for Revit commits.
+        # Recursively process '@'-prefixed properties that are Base objects
+        # with 'elements'.
+        # This is a common pattern in older Speckle data models, such as those
+        # used for Revit commits.
        for attr_name in dir(base):
            if attr_name.startswith("@"):
                attr_value = getattr(base, attr_name)
-                # If the attribute is a Base object containing 'elements', recurse into it.
-                if isinstance(attr_value, Base) and hasattr(attr_value, "elements"):
+                # If the attribute is a Base object containing 'elements',
+                # recurse into it.
+                if isinstance(attr_value, Base) and hasattr(
+                    attr_value, "elements"
+                ):
                    yield from extract_base_and_transform(
                        attr_value, current_id, transform_list.copy()
                    )
@@ -1,7 +1,30 @@
+"""This file contains the inputs for the function.
+
+It is used to define the inputs for the function and to validate them.
+"""
+
+from enum import Enum
+
 from pydantic import Field
 from speckle_automate import AutomateBase


+class PropertyMatchMode(Enum):
+    """Controls how strictly parameter names must match."""
+
+    STRICT = "strict"  # Exact parameter path match
+    FUZZY = "fuzzy"  # Search all parameters ignoring hierarchy
+    MIXED = "mixed"  # Exact match first, fuzzy fallback
+
+
+class MinimumSeverity(str, Enum):
+    """Enum for minimum severity level to report."""
+
+    INFO = "Info"
+    WARNING = "Warning"
+    ERROR = "Error"
+
+
 class FunctionInputs(AutomateBase):
    """These are function author defined values.

@@ -15,3 +38,24 @@ class FunctionInputs(AutomateBase):
        title="Spreadsheet URL",
        description="This is the URL of the spreadsheet to check. It should be a TSV format data source.",
    )
+
+    minimum_severity: MinimumSeverity = Field(
+        default=MinimumSeverity.INFO,
+        title="Minimum Severity Level",
+        description="Only report test results with this severity level or higher. Info will show all results, Warning will show warnings and errors, Error will show only errors.",
+    )
+
+    hide_skipped: bool = Field(
+        default=False,
+        title="Hide Skipped Tests",
+        description="If enabled, tests that were skipped (no matching objects found) will not be reported.",
+    )
+
+    # property_match_mode: PropertyMatchMode = Field(
+    #     default=PropertyMatchMode.MIXED,
+    #     title="Property Match Mode",
+    #     description='Controls how strictly parameter names must match. ' +
+    #                 'STRICT will only match exact parameter paths, ' +
+    #                 'FUZZY will search all parameters ignoring hierarchy, ' +
+    #                 'MIXED will exact match first, fuzzy fallback.'
+    # )
@@ -0,0 +1,22 @@
+"""Defines mappings between spreadsheet predicates and rule methods."""
+
+from src.rules import PropertyRules
+
+# Mapping of input predicates to the corresponding methods in PropertyRules
+PREDICATE_METHOD_MAP = {
+    "exists": PropertyRules.has_parameter.__name__,
+    "greater than": PropertyRules.is_parameter_value_greater_than.__name__,
+    "less than": PropertyRules.is_parameter_value_less_than.__name__,
+    "in range": PropertyRules.is_parameter_value_in_range.__name__,
+    "in list": PropertyRules.is_parameter_value_in_list.__name__,
+    "equal to": PropertyRules.is_equal_value.__name__,
+    "not equal to": PropertyRules.is_not_equal_value.__name__,
+    "is true": PropertyRules.is_parameter_value_true.__name__,
+    "is false": PropertyRules.is_parameter_value_false.__name__,
+    "is like": PropertyRules.is_parameter_value_like.__name__,
+    "identical to": PropertyRules.is_identical_value.__name__,
+    "contains": PropertyRules.is_parameter_value_containing.__name__,
+    "does not contain": (
+        PropertyRules.is_parameter_value_not_containing.__name__
+    ),
+}
@@ -0,0 +1,524 @@
+"""Module for processing rules against Speckle objects and updating the automate context with the results.
+
+This module implements the core rule processing logic that:
+1. Validates rule structure and logic
+2. Evaluates rule conditions against Speckle objects
+3. Separates filtering conditions and final check conditions
+4. Processes rule groups and tracks results
+5. Reports results back to the Speckle Automate context
+
+The rule processing follows a "filter then validate" approach:
+- Filter conditions (WHERE, AND) narrow down which objects to check
+- The final check condition (CHECK or last AND) determines pass/fail
+"""
+
+import json
+from enum import Enum
+from typing import Any
+
+import pandas as pd
+from pandas.core.groupby import DataFrameGroupBy
+from speckle_automate import AutomationContext, ObjectResultLevel
+from specklepy.objects.base import Base
+
+from src.helpers import speckle_print
+from src.inputs import MinimumSeverity
+from src.predicates import PREDICATE_METHOD_MAP
+from src.rules import PropertyRules
+
+
+def validate_rule_structure(rule_group: pd.DataFrame) -> None:
+    """Validates the structure and logic of a rule group.
+
+    This ensures the rule follows the proper format:
+    - First condition must be WHERE
+    - Following conditions can be AND
+    - Only one CHECK condition is allowed (and must be last)
+
+    Args:
+        rule_group: DataFrame containing the rule conditions
+
+    Raises:
+        ValueError: If rule structure is invalid
+    """
+    if rule_group.empty:
+        return
+
+    # Validate Logic column exists
+    if "Logic" not in rule_group.columns:
+        raise ValueError("Rule must have a 'Logic' column")
+
+    # Get uppercase Logic values for case-insensitive comparison
+    logic_values = rule_group["Logic"].str.upper()
+
+    # Check if first condition is WHERE
+    if logic_values.iloc[0] != "WHERE":
+        raise ValueError(
+            f"Rule {rule_group.iloc[0]['Rule Number']} must start with WHERE"
+        )
+
+    # Count CHECK conditions
+    check_count = sum(1 for value in logic_values if value == "CHECK")
+    if check_count > 1:
+        raise ValueError(
+            f"Rule {rule_group.iloc[0]['Rule Number']} has multiple CHECK conditions"
+        )
+
+    # If CHECK exists, ensure it's the last condition
+    check_indices = logic_values[logic_values == "CHECK"].index
+    if check_count == 1 and check_indices[0] != rule_group.index[-1]:
+        raise ValueError(
+            f"CHECK must be the last condition in rule {rule_group.iloc[0]['Rule Number']}"
+        )
+
+    # Validate Logic values
+    valid_values = {"WHERE", "AND", "CHECK"}
+    invalid_values = set(logic_values.unique()) - valid_values
+    if invalid_values:
+        raise ValueError(f"Invalid Logic values found: {invalid_values}")
+
+
+def evaluate_condition(
+    speckle_object: Base,
+    condition: pd.Series,
+    rule_number: str | None = None,
+    case_number: int | None = None,
+) -> bool:
+    """Evaluates a single condition against a Speckle object.
+
+    This function is the bridge between the rules defined in the spreadsheet
+    and the property checking methods in PropertyRules. It:
+    1. Extracts the property name, predicate, and value from the condition
+    2. Maps the predicate to the corresponding method in PropertyRules
+    3. Calls the method with the object, property name, and value
+
+    Args:
+        speckle_object: The Speckle object to evaluate against
+        condition: A pandas Series containing the condition details
+            - 'Property Name': The name of the property to check
+            - 'Predicate': The comparison operation (like 'equals',
+                           'greater than')
+            - 'Value': The value to compare against
+        rule_number: For tracking, the rule number being evaluated
+        case_number: For tracking, the condition number within the rule
+
+    Returns:
+        True if the condition is met, False otherwise
+    """
+    property_name = condition.get(
+        "Property Name", condition.get("Property Path")
+    )
+    predicate_key = condition["Predicate"]
+    value = condition["Value"]
+
+    # Debugging info
+    _ = rule_number
+    _ = case_number
+
+    # Look up the method name in the predicate map
+    # This map connects spreadsheet predicates to PropertyRules methods
+    if predicate_key in PREDICATE_METHOD_MAP:
+        method_name = PREDICATE_METHOD_MAP[predicate_key]
+        method = getattr(PropertyRules, method_name, None)
+
+        if method:
+            # Call the method with the object, property name, and value
+            return method(speckle_object, property_name, value)
+
+    return False
+
+
+def get_filters_and_check(
+    rule_group: pd.DataFrame,
+) -> tuple[pd.DataFrame, pd.Series]:
+    """Separates rule conditions into filtering conditions and the final check condition.
+
+    This function handles two rule formats:
+    1. Explicit format: WHERE + AND... + CHECK
+    2. Legacy format: WHERE + AND... (last AND is implicitly the check)
+
+    This separation enables the "filter then validate" approach.
+
+    Args:
+        rule_group: DataFrame containing rule conditions
+
+    Returns:
+        Tuple containing (filter_conditions, final_check_condition)
+    """
+    if rule_group.empty:
+        return pd.DataFrame(), pd.Series()
+
+    # Get uppercase Logic values for case-insensitive comparison
+    logic_values = rule_group["Logic"].str.upper()
+
+    # Look for explicit CHECK
+    check_conditions = rule_group[logic_values == "CHECK"]
+    has_explicit_check = not check_conditions.empty
+
+    if has_explicit_check:
+        # Use first CHECK condition as final check
+        final_check = check_conditions.iloc[0]
+        # All other conditions are filters
+        filters = rule_group[logic_values != "CHECK"]
+    else:
+        # Legacy behavior: use last AND as check if present
+        and_conditions = rule_group[logic_values == "AND"]
+        if not and_conditions.empty:
+            # Get the last AND as the check
+            final_check = and_conditions.iloc[-1]
+            # All conditions up to the last AND are filters
+            last_and_idx = and_conditions.index[-1]
+            filters = rule_group[rule_group.index < last_and_idx]
+        else:
+            # No AND conditions found, just use WHERE as filter
+            filters = rule_group
+            final_check = rule_group.iloc[
+                0
+            ]  # Default to first condition as check
+
+    return filters, final_check
+
+
+def process_rule(
+    speckle_objects: list[Base], rule_group: pd.DataFrame
+) -> tuple[list[Any], list[Any]] | tuple[list[Base], list[Base]]:
+    """Processes a rule group against a list of Speckle objects.
+
+    This function implements the "filter then validate" approach:
+    1. Apply filter conditions sequentially to narrow down objects
+    2. Apply the final check condition to determine pass/fail
+
+    This approach is efficient for large models as it reduces the number
+    of objects that need full validation.
+
+    Args:
+        speckle_objects: List of Speckle objects to be processed
+        rule_group: DataFrame defining the filter and check conditions
+
+    Returns:
+        A tuple of lists (pass_objects, fail_objects)
+    """
+    if not speckle_objects or rule_group.empty:
+        return [], []
+
+    try:
+        validate_rule_structure(rule_group)
+    except ValueError as e:
+        speckle_print(f"Rule validation error: {str(e)}")
+        return [], []
+
+    # Get filters and final check
+    filters, final_check = get_filters_and_check(rule_group)
+
+    # Start with all objects
+    filtered_objects = speckle_objects.copy()
+    rule_number = rule_group.iloc[0]["Rule Number"]
+
+    #  Apply each filter condition sequentially
+    for index, (_, filter_condition) in enumerate(filters.iterrows()):
+        filtered_objects = [
+            obj
+            for obj in filtered_objects
+            if evaluate_condition(
+                speckle_object=obj,
+                condition=filter_condition,
+                rule_number=rule_number,
+                case_number=index,
+            )
+        ]
+
+        # Early exit if no objects pass filters
+        if not filtered_objects:
+            return [], []
+
+    # For remaining objects, evaluate the final check
+    # This separates objects into pass/fail groups
+    pass_objects = []
+    fail_objects = []
+
+    for obj in filtered_objects:
+        if evaluate_condition(
+            speckle_object=obj,
+            condition=final_check,
+            rule_number=rule_number,
+            case_number=len(filters),
+        ):
+            pass_objects.append(obj)
+        else:
+            fail_objects.append(obj)
+
+    return pass_objects, fail_objects
+
+
+def apply_rules_to_objects(
+    speckle_objects: list[Base],
+    grouped_rules: DataFrameGroupBy,
+    automate_context: AutomationContext,
+    minimum_severity: MinimumSeverity = MinimumSeverity.INFO,
+    hide_skipped: bool = False,
+) -> dict[str, tuple[list[Base], list[Base]]]:
+    """Applies rules to objects and updates the automate context results.
+
+    This is the main orchestration function that:
+    1. Processes each rule group against all objects
+    2. Filters results based on severity levels
+    3. Attaches results to objects in the Speckle Automate context
+    4. Reports skipped rules (where no objects matched filters)
+
+    Args:
+        speckle_objects: The list of objects to which rules are applied
+        grouped_rules: The rules grouped by rule number
+        automate_context: Context manager for attaching results to objects
+        minimum_severity: Minimum severity level to report
+        hide_skipped: Whether to hide skipped rules in results
+
+    Returns:
+        Dictionary mapping rule IDs to (pass_objects, fail_objects) tuples
+    """
+    grouped_results = {}
+    rules_processed = 0
+    severity_levels = {
+        MinimumSeverity.INFO: 0,
+        MinimumSeverity.WARNING: 1,
+        MinimumSeverity.ERROR: 2,
+    }
+    min_severity_level = severity_levels[minimum_severity]
+
+    for rule_id, rule_group in grouped_rules:
+        rule_id_str = str(rule_id)  # Convert rule_id to string
+        rules_processed += 1
+
+        # Ensure rule_group has necessary columns
+        if "Message" not in rule_group.columns or (
+            "Report Severity" not in rule_group.columns
+            and "Severity" not in rule_group.columns
+        ):
+            continue  # Or raise an exception if these columns are mandatory
+
+        # Get the severity level for this rule
+        rule_severity = get_severity(rule_group.iloc[-1])
+        rule_severity_level = severity_levels[
+            MinimumSeverity(rule_severity.value)
+        ]
+
+        # Check if the rule severity level meets the minimum severity level
+        # no point in processing lower severity rules
+        if rule_severity_level < min_severity_level:
+            continue
+
+        pass_objects, fail_objects = process_rule(speckle_objects, rule_group)
+
+        # For passing objects, only attach if we're showing all levels (INFO)
+        if minimum_severity == MinimumSeverity.INFO:
+            attach_results(
+                pass_objects,
+                rule_group.iloc[-1],
+                rule_id_str,
+                automate_context,
+                True,
+            )
+
+        # For failing objects, attach if they meet minimum severity threshold
+        if len(fail_objects) and rule_severity_level >= min_severity_level:
+            attach_results(
+                fail_objects,
+                rule_group.iloc[-1],
+                rule_id_str,
+                automate_context,
+                False,
+            )
+
+        if (
+            len(pass_objects) == 0
+            and len(fail_objects) == 0
+            and not hide_skipped
+        ):
+            speckle_print(f"Rule {rule_id_str} Skipped")
+
+            automate_context.attach_info_to_objects(
+                category=f"Rule {rule_id_str} Skipped",
+                affected_objects=[Base()],
+                # This is a hack to get a rule to report with no valid objects
+                message=f"No objects found for rule {rule_id_str}",
+                metadata={},
+            )
+
+        grouped_results[rule_id_str] = (pass_objects, fail_objects)
+
+    # return pass_objects, fail_objects for each rule
+    return grouped_results
+
+
+class SeverityLevel(Enum):
+    """Enumeration for severity levels of rule results.
+
+    These severity levels determine how rule failures are displayed:
+    - INFO: Informational, no action required
+    - WARNING: Potential issue that should be reviewed
+    - ERROR: Critical issue requiring attention
+    """
+
+    INFO = "Info"
+    WARNING = "Warning"
+    ERROR = "Error"
+
+
+def get_severity(rule_info: pd.Series) -> SeverityLevel:
+    """Convert a string severity to the corresponding SeverityLevel enum.
+
+    This function normalizes user input with robust handling for:
+    - Case insensitivity (e.g., "info", "WARNING" → "Info", "Warning")
+    - Shorthand mappings (e.g., "WARN" → "Warning")
+    - Whitespace handling
+    - Default fallback to ERROR for invalid input
+
+    Args:
+        rule_info: Series containing rule information with 'Report Severity'
+                   key
+
+    Returns:
+        Appropriate SeverityLevel enum value
+    """
+    severity = rule_info.get("Report Severity") or rule_info.get(
+        "Severity"
+    )  # Extract severity from input data
+
+    # If severity is None or not a string (e.g., numeric input),
+    # default to ERROR
+    if not isinstance(severity, str):
+        return SeverityLevel.ERROR
+
+    severity = (
+        severity.strip().upper()
+    )  # Remove leading/trailing spaces & normalize case
+
+    # Define a mapping for shorthand or alternate spellings
+    alias_map = {
+        "WARN": "WARNING",  # Treat "WARN" as "WARNING"
+    }
+
+    # Replace shorthand values if applicable
+    severity = alias_map.get(severity, severity)
+
+    # Attempt to match with an existing SeverityLevel enum value
+    # (case-insensitive)
+    return next(
+        (level for level in SeverityLevel if level.value.upper() == severity),
+        SeverityLevel.ERROR,  # Default to ERROR if no match is found
+    )
+
+
+def get_metadata(
+    rule_id: str,
+    rule_info: pd.Series,
+    passed: bool,
+    speckle_objects: list[Base],
+) -> dict[str, str | int | Any]:
+    """Generates structured metadata for rule results.
+
+    This metadata is attached to objects in the Speckle platform and is:
+    1. Validated for JSON serializability
+    2. Structured for consistent representation
+    3. Includes key information about the rule and results
+
+    Args:
+        rule_id: Identifier for the rule
+        rule_info: Series containing rule information
+        passed: Boolean indicating if the rule passed
+        speckle_objects: List of Speckle objects affected
+
+    Returns:
+        Dictionary containing metadata if valid JSON serializable,
+          empty dict otherwise
+    """
+    try:
+        metadata = {
+            "rule_id": rule_id,
+            "status": "PASS" if passed else "FAIL",
+            "severity": get_severity(rule_info).value,
+            "rule_message": format_message(rule_info),
+            "object_count": len(speckle_objects),
+        }
+
+        # Validate JSON serializability
+        json.dumps(metadata)
+        return metadata
+
+    except (TypeError, ValueError, json.JSONDecodeError) as e:
+        # Log the error for debugging purposes
+        print(f"Error creating metadata: {str(e)}")
+        return {}
+
+
+def attach_results(
+    speckle_objects: list[Base],
+    rule_info: pd.Series,
+    rule_id: str,
+    context: AutomationContext,
+    passed: bool,
+) -> None:
+    """Attaches rule results to objects in the Speckle Automate context.
+
+    This function is the interface to the Speckle platform for reporting
+    results:
+    - For failing objects, attaches results with appropriate severity levels
+    - For passing objects, attaches informational results
+    - Includes structured metadata for consistent reporting
+
+    Args:
+        speckle_objects: The list of objects affected by the rule
+        rule_info: Information about the rule
+        rule_id: Identifier for the rule
+        context: The Speckle Automate context for result attachment
+        passed: Whether the objects passed the rule
+    """
+    if not speckle_objects:
+        return
+
+    # Create structured metadata for onward data analysis uses
+
+    metadata = get_metadata(rule_id, rule_info, passed, speckle_objects)
+    message = format_message(rule_info)
+
+    if not passed:
+        speckle_print(rule_info["Report Severity"])
+
+        severity = (
+            ObjectResultLevel.WARNING
+            if rule_info["Report Severity"].capitalize() in ["Warning", "Warn"]
+            else ObjectResultLevel.ERROR
+        )
+        context.attach_result_to_objects(
+            category=f"Rule {rule_id}",
+            affected_objects=speckle_objects,
+            message=message,
+            level=severity,
+            metadata=metadata,
+        )
+    else:
+        context.attach_info_to_objects(
+            category=f"Rule {rule_id}",
+            affected_objects=speckle_objects,
+            message=message,
+            metadata=metadata,
+        )
+
+
+def format_message(rule_info):
+    """Format the message for the rule result.
+
+    Handles cases where the message might be None or NaN.
+
+    Args:
+        rule_info: Series containing rule information with 'Message' key
+
+    Returns:
+        Formatted message string
+    """
+    message = (
+        str(rule_info["Message"])
+        if rule_info["Message"] is not None
+        and not pd.isna(rule_info["Message"])
+        else "No Message"
+    )
+    return message
@@ -1,18 +1,203 @@
+"""Module for reading and processing rules from a cloud hosted TSV file.
+
+This module handles the loading and processing of validation rules from external
+spreadsheet data, enabling non-technical users to define and modify rules.
+
+Key features:
+1. Reading from hosted TSV files (e.g., from Google Sheets)
+2. Processing rule numbers for consistent grouping
+3. Handling mixed data types in spreadsheet columns
+4. Validating rule structure and providing feedback
+5. Grouping related rule conditions for execution
+
+The spreadsheet format used follows a specific structure:
+- Rule Number: Groups related conditions together
+- Logic: WHERE/AND/CHECK to define condition relationships
+- Property Name: The property path to check
+- Predicate: The comparison operation (equals, greater than, etc.)
+- Value: The value to compare against
+- Message: The message to display for rule results
+- Severity: INFO/WARNING/ERROR level for failures
+"""
+
+import traceback
+
 import pandas as pd
+from pandas import DataFrame
+from pandas.core.groupby import DataFrameGroupBy


-def read_rules_from_spreadsheet(url):
-    """Reads a TSV file from a provided URL and returns a DataFrame.
+def process_rule_numbers(df: DataFrame) -> DataFrame:
+    """Process rule numbers in a DataFrame while preserving original rule identifiers.
+
+    This function handles various rule numbering scenarios:
+    1. Preserves existing rule numbers exactly as provided
+    2. Generates sequential numbers for missing rule numbers
+    3. Ensures all rows in a logical rule group have the same rule number
+
+    This is important because rule numbers determine how conditions are grouped
+    and executed together.

    Args:
-        url (str): The URL to the TSV file.
+        df: DataFrame with columns including 'Rule Number' and 'Logic'

    Returns:
-        DataFrame: Pandas DataFrame containing the TSV data.
+        DataFrame with processed rule numbers, where all related conditions
+        have the same rule number
+    """
+    # Create a copy to avoid modifying original
+    df = df.copy()
+
+    # Initialize tracking variables
+    used_rule_nums = set()
+    processed_rule_nums = []
+    next_auto_num = 1  # For generating missing rule numbers only
+
+    # Find indices where Logic is 'WHERE' to identify rule group starts
+    where_indices = df[df["Logic"].str.upper() == "WHERE"].index
+
+    # Process each group
+    for i in range(len(where_indices)):
+        start_idx = where_indices[i]
+        end_idx = where_indices[i + 1] if i + 1 < len(where_indices) else len(df)
+
+        # Get slice of rows for this group
+        group_slice = df.iloc[start_idx:end_idx]
+
+        # Try to get rule number from first row, fall back to "Rule #"
+        group_rule_num = (
+            group_slice["Rule Number"].iloc[0] if not pd.isna(group_slice["Rule Number"].iloc[0]) else "Rule #"
+        )
+
+        if pd.isna(group_rule_num):
+            # If no rule number, generate next available number
+            while str(next_auto_num) in used_rule_nums:
+                next_auto_num += 1
+            group_rule_num = str(next_auto_num)
+            next_auto_num += 1
+        else:
+            # Keep the original rule number exactly as is
+            group_rule_num = str(group_rule_num)
+
+        # Update tracking
+        used_rule_nums.add(group_rule_num)
+
+        # Fill rule numbers for this group
+        processed_rule_nums.extend([group_rule_num] * len(group_slice))
+
+    # Update DataFrame with processed rule numbers
+    df["Rule Number"] = processed_rule_nums
+
+    return df
+
+
+def validate_rule_numbers(df: DataFrame) -> list[str]:
+    """Validate rule numbers and return any warnings or errors.
+
+    This checks for issues like:
+    1. Missing rule numbers
+    2. Non-integer rule numbers
+    3. Duplicate rule numbers
+
+    These validations help ensure rule integrity without being overly strict,
+    allowing for different user approaches to rule numbering.
+
+    Args:
+        df: DataFrame with processed rule numbers
+
+    Returns:
+        List of warning/error messages
+    """
+    messages = []
+
+    # Check for missing rule numbers
+    if df["Rule Number"].isna().any():
+        messages.append("Warning: Some rules are missing rule numbers")
+
+    # # Check for non-integer rule numbers
+    # non_int_mask = df["Rule Number"].apply(lambda x: not pd.isna(x) and not float(x).is_integer())
+    # if non_int_mask.any():
+    #     messages.append("Warning: Some rule numbers are not integers")
+
+    # Check for duplicate rule numbers in WHERE rows
+    where_rules = df[df["Logic"].str.upper() == "WHERE"]["Rule Number"]
+    duplicates = where_rules[where_rules.duplicated()]
+    if not duplicates.empty:
+        messages.append(f"Warning: Duplicate rule numbers found: {list(duplicates)}")
+
+    return messages
+
+
+def read_rules_from_spreadsheet(url: str) -> tuple[DataFrameGroupBy, list[str]] | tuple[None, list[str]]:
+    """Reads rules from a TSV file at the provided URL, processes them, and returns grouped rules.
+
+    This function is the main entry point for rule loading:
+    1. Reads the TSV file from the provided URL
+    2. Converts mixed type columns to appropriate types
+    3. Processes rule numbers for consistent grouping
+    4. Validates rule numbers and collects messages
+    5. Groups rules by rule number for execution
+
+    Args:
+        url: The URL to the TSV file containing rule definitions
+
+    Returns:
+        Tuple containing:
+        - DataFrameGroupBy object with rules grouped by rule number (or None if error)
+        - List of validation messages/warnings
    """
    try:
-        # Since the output is a TSV, we use `pd.read_csv` with `sep='\t'` to specify tab-separated values.
-        return pd.read_csv(url, sep="\t")
+        # Read the TSV file
+        # The TSV format is chosen for compatibility with Google Sheets
+        # and other spreadsheet applications
+        df = pd.read_csv(url, sep="\t")
+
+        # Convert mixed type columns
+        # This handles inconsistencies in spreadsheet data
+        df = convert_mixed_columns(df)
+
+        # Process rule numbers
+        # This ensures all related conditions have the same rule number
+        df = process_rule_numbers(df)
+
+        # Get validation messages
+        # These are warnings about potential issues with the rules
+        messages = validate_rule_numbers(df)
+
+        # Group by rule number
+        # This creates a DataFrameGroupBy object that groups related conditions
+        grouped_rules = df.groupby("Rule Number")
+
+        return grouped_rules, messages
+
    except Exception as e:
-        print(f"Failed to read the TSV from the URL: {e}")
-        return None
+        # Handle any errors in reading or processing the spreadsheet
+        traceback.print_exc()
+        return None, [f"Failed to read the TSV from the URL: {str(e)}:{e.with_traceback(None)}"]
+
+
+def convert_mixed_columns(df: DataFrame) -> DataFrame:
+    """Converts columns in a DataFrame to appropriate types based on their content.
+
+    This handles common issues with spreadsheet data:
+    1. Numeric columns that contain strings
+    2. Mixed type columns
+    3. Empty cells and NaN values
+
+    The approach is to convert each column appropriately:
+    - Numeric columns remain as numbers
+    - Other columns are converted to strings, with empty strings for missing values
+
+    Args:
+        df: The DataFrame whose columns are to be converted
+
+    Returns:
+        DataFrame with columns converted to appropriate types
+    """
+    df = df.apply(
+        lambda c: c
+        if c.dropna().apply(lambda x: str(x).replace(".", "", 1).isdigit()).any()
+        else c.map(lambda x: "" if pd.isna(x) else str(x))
+    )
+
+    return df
@@ -0,0 +1,696 @@
+[
+  {
+    "id": "46f06fef727d64a0bbcbd7ced51e0cd2",
+    "name": "Walls - W30(Fc24)",
+    "type": "W30(Fc24)",
+    "level": {
+      "name": "1FL",
+      "units": "mm",
+      "elevation": 0
+    },
+    "units": "mm",
+    "family": "Basic Wall",
+    "flipped": false,
+    "category": "Walls",
+    "elements": [],
+    "location": {
+      "id": "9c76b8de34382c9052965ee463f8374b",
+      "end": {
+        "x": 22400.000000000015,
+        "y": 20500,
+        "z": 0,
+        "id": "3455575bfd8939f264d295b61e74156f",
+        "units": "mm",
+        "speckle_type": "Objects.Geometry.Point",
+        "applicationId": null
+      },
+      "bbox": null,
+      "start": {
+        "x": 22400.000000000007,
+        "y": 15199.999999999998,
+        "z": 0,
+        "id": "d0c4fdb2e11cc825e7f05f9dc88a0be1",
+        "units": "mm",
+        "speckle_type": "Objects.Geometry.Point",
+        "applicationId": null
+      },
+      "units": "mm",
+      "domain": {
+        "id": "3b97feaad2dbcc2d894c9cec024a9bf2",
+        "end": 17.388451443569522,
+        "start": -3.552713668866051e-14,
+        "speckle_type": "Objects.Primitive.Interval",
+        "applicationId": null
+      },
+      "length": 5300.000000000002,
+      "speckle_type": "Objects.Geometry.Line",
+      "applicationId": null
+    },
+    "topLevel": {
+      "name": "1FL",
+      "units": "mm",
+      "elevation": 0
+    },
+    "__closure": {
+      "0ad4db1fe261a5b640ad9f315a46a6fc": 100,
+      "0d5518a7a3e63fe345e198ad5d6acc4e": 100,
+      "ec2040af4bd9c8619f9029a43df61a2e": 100
+    },
+    "elementId": "4479852",
+    "worksetId": "0",
+    "properties": {
+      "Parameters": {
+        "Type Parameters": {
+          "Text": {
+            "符号": {
+              "name": "符号",
+              "value": "W30",
+              "internalDefinitionName": "ee1f33e1-5506-4a64-b87b-7b98d30aea52"
+            }
+          },
+          "Other": {
+            "Family Name": {
+              "name": "Family Name",
+              "value": "Basic Wall",
+              "internalDefinitionName": "SYMBOL_FAMILY_NAME_PARAM"
+            },
+            "横筋ピッチ": {
+              "name": "横筋ピッチ",
+              "units": "General",
+              "value": 0,
+              "internalDefinitionName": "cd19c040-0788-4424-8a67-7127427e311f"
+            },
+            "縦筋ピッチ": {
+              "name": "縦筋ピッチ",
+              "units": "General",
+              "value": 0,
+              "internalDefinitionName": "6796d143-ef09-4441-9ecc-3411fe837a65"
+            }
+          },
+          "Rebar Set": {
+            "横筋 主筋径1": {
+              "name": "横筋 主筋径1",
+              "value": "D13",
+              "internalDefinitionName": "横筋 主筋径1"
+            },
+            "横筋 主筋径2": {
+              "name": "横筋 主筋径2",
+              "value": "D16",
+              "internalDefinitionName": "横筋 主筋径2"
+            },
+            "縦筋 主筋径1": {
+              "name": "縦筋 主筋径1",
+              "value": "D13",
+              "internalDefinitionName": "縦筋 主筋径1"
+            },
+            "縦筋 主筋径2": {
+              "name": "縦筋 主筋径2",
+              "value": "D16",
+              "internalDefinitionName": "縦筋 主筋径2"
+            },
+            "横筋 主筋ピッチ": {
+              "name": "横筋 主筋ピッチ",
+              "value": "200",
+              "internalDefinitionName": "横筋 主筋ピッチ"
+            },
+            "縦筋 主筋ピッチ": {
+              "name": "縦筋 主筋ピッチ",
+              "value": "200",
+              "internalDefinitionName": "縦筋 主筋ピッチ"
+            },
+            "開口補強筋 斜筋 本数": {
+              "name": "開口補強筋 斜筋 本数",
+              "value": "―",
+              "internalDefinitionName": "開口補強筋 斜筋 本数"
+            },
+            "開口補強筋 横筋 本数": {
+              "name": "開口補強筋 横筋 本数",
+              "value": "―",
+              "internalDefinitionName": "開口補強筋 横筋 本数"
+            },
+            "開口補強筋 縦筋 本数": {
+              "name": "開口補強筋 縦筋 本数",
+              "value": "―",
+              "internalDefinitionName": "開口補強筋 縦筋 本数"
+            }
+          },
+          "Structure": {
+            "Fc24 (0)": {
+              "units": "mm",
+              "function": "Structure",
+              "material": "Fc24",
+              "thickness": 300
+            }
+          },
+          "Construction": {
+            "Width": {
+              "name": "Width",
+              "units": "Millimeters",
+              "value": 300,
+              "internalDefinitionName": "WALL_ATTR_WIDTH_PARAM"
+            },
+            "Function": {
+              "name": "Function",
+              "value": "Interior",
+              "internalDefinitionName": "FUNCTION_PARAM"
+            },
+            "Wrapping at Ends": {
+              "name": "Wrapping at Ends",
+              "value": "None",
+              "internalDefinitionName": "WRAPPING_AT_ENDS_PARAM"
+            },
+            "Wrapping at Inserts": {
+              "name": "Wrapping at Inserts",
+              "value": "Do not wrap",
+              "internalDefinitionName": "WRAPPING_AT_INSERTS_PARAM"
+            }
+          },
+          "Identity Data": {
+            "Cost": {
+              "name": "Cost",
+              "units": "Currency",
+              "value": 0,
+              "internalDefinitionName": "ALL_MODEL_COST"
+            },
+            "Type Name": {
+              "name": "Type Name",
+              "value": "W30(Fc24)",
+              "internalDefinitionName": "SYMBOL_NAME_PARAM"
+            }
+          },
+          "IFC Parameters": {
+            "Type IfcGUID": {
+              "name": "Type IfcGUID",
+              "value": "0oqavz9$zDZxycDyfZdYh0",
+              "internalDefinitionName": "IFC_TYPE_GUID"
+            },
+            "Export Type to IFC": {
+              "name": "Export Type to IFC",
+              "value": "Default",
+              "internalDefinitionName": "IFC_EXPORT_ELEMENT_TYPE"
+            }
+          },
+          "Analytical Properties": {
+            "Roughness": {
+              "name": "Roughness",
+              "value": 1,
+              "internalDefinitionName": "ANALYTICAL_ROUGHNESS"
+            },
+            "Absorptance": {
+              "name": "Absorptance",
+              "units": "General",
+              "value": 0.1,
+              "internalDefinitionName": "ANALYTICAL_ABSORPTANCE"
+            },
+            "Thermal Mass": {
+              "name": "Thermal Mass",
+              "units": "Kilojoules per square meter Kelvin",
+              "value": 0,
+              "internalDefinitionName": "ANALYTICAL_THERMAL_MASS"
+            },
+            "Thermal Resistance (R)": {
+              "name": "Thermal Resistance (R)",
+              "units": "Square meter kelvins per watt",
+              "value": 0,
+              "internalDefinitionName": "ANALYTICAL_THERMAL_RESISTANCE"
+            },
+            "Heat Transfer Coefficient (U)": {
+              "name": "Heat Transfer Coefficient (U)",
+              "units": "Watts per square meter kelvin",
+              "value": 0,
+              "internalDefinitionName": "ANALYTICAL_HEAT_TRANSFER_COEFFICIENT"
+            }
+          },
+          "Materials and Finishes": {
+            "Structural Material": {
+              "name": "Structural Material",
+              "value": "Fc24",
+              "internalDefinitionName": "STRUCTURAL_MATERIAL_PARAM"
+            }
+          }
+        },
+        "Instance Parameters": {
+          "Other": {
+            "Type": {
+              "name": "Type",
+              "value": "W30(Fc24)",
+              "internalDefinitionName": "ELEM_TYPE_PARAM"
+            },
+            "Family": {
+              "name": "Family",
+              "value": "Basic Wall",
+              "internalDefinitionName": "ELEM_FAMILY_PARAM"
+            },
+            "Type Id": {
+              "name": "Type Id",
+              "value": "Basic Wall W30(Fc24)",
+              "internalDefinitionName": "SYMBOL_ID_PARAM"
+            },
+            "Family and Type": {
+              "name": "Family and Type",
+              "value": "Basic Wall W30(Fc24)",
+              "internalDefinitionName": "ELEM_FAMILY_AND_TYPE_PARAM"
+            }
+          },
+          "Phasing": {
+            "Phase Created": {
+              "name": "Phase Created",
+              "value": "フェーズ1",
+              "internalDefinitionName": "PHASE_CREATED"
+            }
+          },
+          "Dimensions": {
+            "Area": {
+              "name": "Area",
+              "units": "Square meters",
+              "value": 7.630000000000015,
+              "internalDefinitionName": "HOST_AREA_COMPUTED"
+            },
+            "Length": {
+              "name": "Length",
+              "units": "Millimeters",
+              "value": 5300.000000000001,
+              "internalDefinitionName": "CURVE_ELEM_LENGTH"
+            },
+            "Volume": {
+              "name": "Volume",
+              "units": "Cubic meters",
+              "value": 2.2890000000000135,
+              "internalDefinitionName": "HOST_VOLUME_COMPUTED"
+            }
+          },
+          "Structural": {
+            "Structural": {
+              "name": "Structural",
+              "value": "Yes",
+              "internalDefinitionName": "WALL_STRUCTURAL_SIGNIFICANT"
+            },
+            "Structural Usage": {
+              "name": "Structural Usage",
+              "value": "Bearing",
+              "internalDefinitionName": "WALL_STRUCTURAL_USAGE_PARAM"
+            },
+            "Rebar Cover - Other Faces": {
+              "name": "Rebar Cover - Other Faces",
+              "value": "Rebar Cover Settings 内壁（フレーム、柱、および耐力壁）",
+              "internalDefinitionName": "CLEAR_COVER_OTHER"
+            },
+            "Rebar Cover - Exterior Face": {
+              "name": "Rebar Cover - Exterior Face",
+              "value": "Rebar Cover Settings 内壁（フレーム、柱、および耐力壁）",
+              "internalDefinitionName": "CLEAR_COVER_EXTERIOR"
+            },
+            "Rebar Cover - Interior Face": {
+              "name": "Rebar Cover - Interior Face",
+              "value": "Rebar Cover Settings 内壁（フレーム、柱、および耐力壁）",
+              "internalDefinitionName": "CLEAR_COVER_INTERIOR"
+            },
+            "構造スリット　下端": {
+              "name": "構造スリット　下端",
+              "value": "No",
+              "internalDefinitionName": "0560d1bb-007b-45e4-b8c3-87f9c6a3d73c"
+            },
+            "構造スリット　始端": {
+              "name": "構造スリット　始端",
+              "value": "No",
+              "internalDefinitionName": "60795a90-8c91-4e11-90a0-b77b819c2403"
+            },
+            "構造スリット　終端": {
+              "name": "構造スリット　終端",
+              "value": "No",
+              "internalDefinitionName": "e3223f14-6158-4d4f-8697-0de2bcc7f737"
+            }
+          },
+          "Constraints": {
+            "Top Offset": {
+              "name": "Top Offset",
+              "units": "Millimeters",
+              "value": -600,
+              "internalDefinitionName": "WALL_TOP_OFFSET"
+            },
+            "Base Offset": {
+              "name": "Base Offset",
+              "units": "Millimeters",
+              "value": -2000,
+              "internalDefinitionName": "WALL_BASE_OFFSET"
+            },
+            "Location Line": {
+              "name": "Location Line",
+              "value": "Core Centerline",
+              "internalDefinitionName": "WALL_KEY_REF_PARAM"
+            },
+            "Room Bounding": {
+              "name": "Room Bounding",
+              "value": "Yes",
+              "internalDefinitionName": "WALL_ATTR_ROOM_BOUNDING"
+            },
+            "Top Constraint": {
+              "name": "Top Constraint",
+              "value": "1FL",
+              "internalDefinitionName": "WALL_HEIGHT_TYPE"
+            },
+            "Base Constraint": {
+              "name": "Base Constraint",
+              "value": "1FL",
+              "internalDefinitionName": "WALL_BASE_CONSTRAINT"
+            },
+            "Related to Mass": {
+              "name": "Related to Mass",
+              "value": "No",
+              "internalDefinitionName": "RELATED_TO_MASS"
+            },
+            "Top is Attached": {
+              "name": "Top is Attached",
+              "value": "No",
+              "internalDefinitionName": "WALL_TOP_IS_ATTACHED"
+            },
+            "Base is Attached": {
+              "name": "Base is Attached",
+              "value": "No",
+              "internalDefinitionName": "WALL_BOTTOM_IS_ATTACHED"
+            },
+            "Unconnected Height": {
+              "name": "Unconnected Height",
+              "units": "Millimeters",
+              "value": 1400,
+              "internalDefinitionName": "WALL_USER_HEIGHT_PARAM"
+            },
+            "Top Extension Distance": {
+              "name": "Top Extension Distance",
+              "units": "Millimeters",
+              "value": 0,
+              "internalDefinitionName": "WALL_TOP_EXTENSION_DIST_PARAM"
+            },
+            "Base Extension Distance": {
+              "name": "Base Extension Distance",
+              "units": "Millimeters",
+              "value": 0,
+              "internalDefinitionName": "WALL_BOTTOM_EXTENSION_DIST_PARAM"
+            }
+          },
+          "Identity Data": {
+            "Has Association": {
+              "name": "Has Association",
+              "value": "Yes",
+              "internalDefinitionName": "ANALYTICAL_ELEMENT_HAS_ASSOCIATION"
+            },
+            "SPECKLE_Classification": {
+              "name": "SPECKLE_Classification",
+              "value": "Wall",
+              "internalDefinitionName": "SPECKLE_Classification"
+            }
+          },
+          "IFC Parameters": {
+            "IfcGUID": {
+              "name": "IfcGUID",
+              "value": "0oqavz9$zDZxycDyfZdYsW",
+              "internalDefinitionName": "IFC_GUID"
+            },
+            "Export to IFC": {
+              "name": "Export to IFC",
+              "value": "By Type",
+              "internalDefinitionName": "IFC_EXPORT_ELEMENT"
+            }
+          },
+          "Cross-Section Definition": {
+            "Cross-Section": {
+              "name": "Cross-Section",
+              "value": "Vertical",
+              "internalDefinitionName": "WALL_CROSS_SECTION"
+            }
+          }
+        }
+      },
+      "Material Quantities": {
+        "Fc24": {
+          "area": 7630000.000000016,
+          "units": "mm",
+          "volume": 2289000000.0000134,
+          "materialName": "Fc24",
+          "materialClass": "コンクリート",
+          "materialCategory": "コンクリート"
+        }
+      }
+    },
+    "worksetName": "ワークセット1",
+    "displayValue": [
+      {
+        "__closure": null,
+        "referencedId": "ec2040af4bd9c8619f9029a43df61a2e",
+        "speckle_type": "reference"
+      }
+    ],
+    "isStructural": true,
+    "speckle_type": "Objects.Data.DataObject:Objects.Data.RevitObject",
+    "applicationId": "32d24e7d-27ff-4d8f-bf26-37ca63da76cc-00445b6c",
+    "builtInCategory": "OST_Walls",
+    "totalChildrenCount": null
+  },
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+      20499.999865722657,
+      -2000.000015258789,
+      22550.00031738281,
+      20499.999865722657,
+      -2000.000015258789,
+      22250.000024414065,
+      20499.999865722657,
+      -2000.000015258789,
+      22250.000024414065,
+      20499.999865722657,
+      -600.0000045776368,
+      22550.00031738281,
+      20499.999865722657,
+      -600.0000045776368,
+      22550.00031738281,
+      15049.999969482422,
+      -600.0000045776368,
+      22550.00031738281,
+      20274.999645996093,
+      -600.0000045776368,
+      22550.00031738281,
+      20499.999865722657,
+      -600.0000045776368,
+      22250.000024414065,
+      20499.999865722657,
+      -600.0000045776368,
+      22250.000024414065,
+      20274.999645996093,
+      -600.0000045776368,
+      22250.000024414065,
+      15350.000262451173,
+      -600.0000045776368,
+      22250.000024414065,
+      15049.999969482422,
+      -600.0000045776368,
+      22550.00031738281,
+      15049.999969482422,
+      -600.0000045776368,
+      22250.000024414065,
+      15049.999969482422,
+      -600.0000045776368,
+      22250.000024414065,
+      15049.999969482422,
+      -2000.000015258789,
+      22550.00031738281,
+      15049.999969482422,
+      -2000.000015258789
+    ],
+    "speckle_type": "Speckle.Core.Models.DataChunk",
+    "applicationId": null
+  },
+  {
+    "id": "ec2040af4bd9c8619f9029a43df61a2e",
+    "area": 0,
+    "bbox": null,
+    "faces": [
+      {
+        "__closure": null,
+        "referencedId": "0ad4db1fe261a5b640ad9f315a46a6fc",
+        "speckle_type": "reference"
+      }
+    ],
+    "units": "mm",
+    "colors": [],
+    "volume": 0,
+    "vertices": [
+      {
+        "__closure": null,
+        "referencedId": "0d5518a7a3e63fe345e198ad5d6acc4e",
+        "speckle_type": "reference"
+      }
+    ],
+    "__closure": {
+      "0ad4db1fe261a5b640ad9f315a46a6fc": 100,
+      "0d5518a7a3e63fe345e198ad5d6acc4e": 100
+    },
+    "speckle_type": "Objects.Geometry.Mesh",
+    "applicationId": "d2c33253-d0ed-4682-9633-e083daaa87db",
+    "textureCoordinates": []
+  }
+]
@@ -1,24 +1,95 @@
-import os
-
-from dotenv import load_dotenv
+import pytest
+from specklepy.objects.base import Base


-def pytest_configure(config):
-    load_dotenv(dotenv_path=".env")
+@pytest.fixture
+def v3_wall():
+    """Creates a v3-style Speckle wall object."""
+    wall = Base()
+    wall.id = "46f06fef727d64a0bbcbd7ced51e0cd2"
+    wall.name = "Walls - W30(Fc24)"
+    wall.type = "W30(Fc24)"
+    wall.units = "mm"
+    wall.family = "Basic Wall"
+    wall.flipped = False
+    wall.category = "Walls"
+    wall.elementId = "4479852"
+    wall.worksetId = "0"

-    token_var = "SPECKLE_TOKEN"
-    server_var = "SPECKLE_SERVER_URL"
-    token = os.getenv(token_var)
-    server = os.getenv(server_var)
+    # Create location geometry
+    wall.location = Base()
+    wall.location.id = "9c76b8de34382c9052965ee463f8374b"
+    wall.location.start = Base()
+    wall.location.start.x = 22400.000000000007
+    wall.location.start.y = 15199.999999999998
+    wall.location.start.z = 0
+    wall.location.start.id = "d0c4fdb2e11cc825e7f05f9dc88a0be1"
+    wall.location.start.units = "mm"
+    wall.location.start.speckle_type = "Objects.Geometry.Point"
+    wall.location.end = Base()
+    wall.location.end.x = 22400.000000000015
+    wall.location.end.y = 20500
+    wall.location.end.z = 0
+    wall.location.end.id = "3455575bfd8939f264d295b61e74156f"
+    wall.location.end.units = "mm"
+    wall.location.end.speckle_type = "Objects.Geometry.Point"
+    wall.location.units = "mm"
+    wall.location.domain = Base()
+    wall.location.domain.id = "3b97feaad2dbcc2d894c9cec024a9bf2"
+    wall.location.domain.end = 17.388451443569522
+    wall.location.domain.start = -3.552713668866051e-14
+    wall.location.domain.speckle_type = "Objects.Primitive.Interval"
+    wall.location.length = 5300.000000000002
+    wall.location.speckle_type = "Objects.Geometry.Line"

-    if not token:
-        raise ValueError(f"Cannot run tests without a {token_var} environment variable")
+    # Create level references
+    wall.level = Base()
+    wall.level.name = "1FL"
+    wall.level.units = "mm"
+    wall.level.elevation = 0

-    if not server:
-        raise ValueError(
-            f"Cannot run tests without a {server_var} environment variable"
-        )
+    wall.topLevel = Base()
+    wall.topLevel.name = "1FL"
+    wall.topLevel.units = "mm"
+    wall.topLevel.elevation = 0

-    # Set the token as an attribute on the config object
-    config.SPECKLE_TOKEN = token
-    config.SPECKLE_SERVER_URL = server
+    # Create properties structure
+    wall.properties = Base()
+    wall.properties.Parameters = Base()
+    wall.properties.Parameters["Type Parameters"] = Base()
+
+    # Add Text section
+    wall.properties.Parameters["Type Parameters"].Text = Base()
+    wall.properties.Parameters["Type Parameters"].Text["符号"] = {
+        "name": "符号",
+        "value": "W30",
+        "internalDefinitionName": "ee1f33e1-5506-4a64-b87b-7b98d30aea52",
+    }
+
+    # Add Structure section
+    wall.properties.Parameters["Type Parameters"].Structure = Base()
+    wall.properties.Parameters["Type Parameters"].Structure["Fc24 (0)"] = {
+        "units": "mm",
+        "function": "Structure",
+        "material": "Fc24",
+        "thickness": 300,
+    }
+
+    # Add Construction section
+    wall.properties.Parameters["Type Parameters"].Construction = Base()
+    wall.properties.Parameters["Type Parameters"].Construction.Width = {
+        "name": "Width",
+        "units": "Millimeters",
+        "value": 300,
+        "internalDefinitionName": "WALL_ATTR_WIDTH_PARAM",
+    }
+
+    # Add Instance Parameters
+    wall.properties.Parameters["Instance Parameters"] = Base()
+    wall.properties.Parameters["Instance Parameters"].Structural = Base()
+    wall.properties.Parameters["Instance Parameters"].Structural.Structural = {
+        "name": "Structural",
+        "value": "Yes",
+    }
+
+    return wall
@@ -1,32 +0,0 @@
-"""Run integration tests with a speckle server."""
-
-from speckle_automate import (
-    AutomationContext,
-    AutomationRunData,
-    AutomationStatus,
-    run_function
-)
-
-from src.inputs import FunctionInputs
-from src.function import automate_function
-
-
-def test_function_run(test_automation_run_data: AutomationRunData, test_automation_token: str):
-    """Run an integration test for the automate function."""
-    automation_context = AutomationContext.initialize(
-        test_automation_run_data, test_automation_token
-    )
-    default_url: str = (
-        "https://docs.google.com/spreadsheets/d/e/2PACX-1vSFmjLfqxPKXJHg-wEs1cp_nJEJJhESGVTLCvWLG_"
-        "IgIuRZ4CmMDCSceOYFvuo8IqcmT4sj9qPiLfCx/pub?gid=0&single=true&output=tsv"
-    )
-
-    automate_sdk = run_function(
-        automation_context,
-        automate_function,
-        FunctionInputs(spreadsheet_url=default_url),
-    )
-
-    assert automate_sdk.run_status == AutomationStatus.SUCCEEDED
-
-# cli command to run just this test with pytest: pytest tests/local_test_exercise2.py::test_function_run
@@ -0,0 +1,86 @@
+from typing import Any
+
+import pytest
+
+from src.rules import PropertyRules
+
+
+class TestValueComparison:
+    """Test suite for value comparison functionality."""
+
+    @pytest.mark.parametrize(
+        "value1, value2",
+        [
+            # Basic numeric strings
+            ("1400", 1400.0),
+            ("1400.0", 1400),
+            ("1400.00", 1400),
+            # Whitespace handling
+            (" 1400 ", 1400.0),
+            ("  1400  ", 1400.0),
+            ("\t1400\n", 1400.0),
+            # Negative numbers
+            ("-1400", -1400.0),
+            (" -1400 ", -1400.0),
+            ("-1400.0", -1400),
+            # Zero handling
+            ("0", 0.0),
+            ("-0", 0.0),
+            ("0.0", 0),
+            # Simple integers
+            ("1", 1),
+            ("1.0", 1),
+        ],
+    )
+    def test_numeric_string_comparison(self, value1: Any, value2: Any):
+        """Test comparison of numeric strings with numbers."""
+        assert PropertyRules.compare_values(value1, value2)
+        # Test reverse comparison
+        assert PropertyRules.compare_values(value2, value1)
+
+    @pytest.mark.parametrize(
+        "value1, value2, expected",
+        [
+            ("Yes", True, True),
+            ("No", False, True),
+            ("yes", True, True),
+            ("no", False, True),
+            ("YES", True, True),
+            ("NO", False, True),
+            ("true", True, True),
+            ("false", False, True),
+            ("True", True, True),
+            ("False", False, True),
+        ],
+    )
+    def test_boolean_string_comparison(self, value1: str, value2: bool, expected: bool):
+        """Test comparison of boolean strings with booleans."""
+        assert PropertyRules.compare_values(value1, value2) == expected
+        # Test reverse comparison
+        assert PropertyRules.compare_values(value2, value1) == expected
+
+    @pytest.mark.parametrize(
+        "value1, value2, case_sensitive, expected",
+        [
+            ("hello", "HELLO", False, True),
+            ("hello", "HELLO", True, False),
+            ("Hello", "hello", False, True),
+            ("Hello", "Hello", True, True),
+        ],
+    )
+    def test_string_comparison(self, value1: str, value2: str, case_sensitive: bool, expected: bool):
+        """Test string comparison with case sensitivity options."""
+        assert PropertyRules.compare_values(value1, value2, case_sensitive=case_sensitive) == expected
+
+    @pytest.mark.parametrize(
+        "value1, value2, tolerance, expected",
+        [
+            (1.0001, 1.0, 1e-3, True),
+            (1.0001, 1.0, 1e-6, False),
+            (1.00000001, 1.0, 1e-6, True),
+            (-1.0001, -1.0, 1e-3, True),
+        ],
+    )
+    def test_float_comparison_tolerance(self, value1: float, value2: float, tolerance: float, expected: bool):
+        """Test float comparison with different tolerance levels."""
+        assert PropertyRules.compare_values(value1, value2, tolerance=tolerance) == expected
@@ -1,31 +1,51 @@
 """Run integration tests with a speckle server."""

-from pydantic import SecretStr
-
 from speckle_automate import (
    AutomationContext,
    AutomationRunData,
    AutomationStatus,
-    run_function
+    run_function,
 )
+from speckle_automate.fixtures import *  # noqa: F401, F403

-from main import FunctionInputs, automate_function
-
-from speckle_automate.fixtures import *
+from src.function import automate_function
+from src.helpers import speckle_print
+from src.inputs import FunctionInputs, MinimumSeverity


-def test_function_run(test_automation_run_data: AutomationRunData, test_automation_token: str):
-    """Run an integration test for the automate function."""
-    automation_context = AutomationContext.initialize(
-        test_automation_run_data, test_automation_token
-    )
-    automate_sdk = run_function(
-        automation_context,
-        automate_function,
-        FunctionInputs(
-            forbidden_speckle_type="None",
-            whisper_message=SecretStr("testing automatically"),
-        ),
-    )
+class TestFunction:
+    """Test suite for the automate function."""

-    assert automate_sdk.run_status == AutomationStatus.SUCCEEDED
+    def test_function_run(
+        self,
+        test_automation_run_data: AutomationRunData,
+        test_automation_token: str,
+    ):
+        """Run an integration test for the automate function.
+
+        Args:
+            test_automation_run_data (AutomationRunData): The automation run
+            data provided by sdk.
+            test_automation_token (str): The automation token.
+
+        """
+        speckle_print(str(test_automation_run_data))
+        speckle_print(str(test_automation_token))
+
+        """Run an integration test for the automate function."""
+        automation_context = AutomationContext.initialize(
+            test_automation_run_data, test_automation_token
+        )
+        default_url: str = "https://model-checker.speckle.systems/r/7YhnQyQNP_Ydv97QCwHbj7BWHrNkG022bez_jVkxbYs/tsv"
+
+        automate_sdk = run_function(
+            automation_context,
+            automate_function,
+            FunctionInputs(
+                spreadsheet_url=default_url,
+                minimum_severity=MinimumSeverity.INFO,
+                hide_skipped=True,
+            ),
+        )
+
+        assert automate_sdk.run_status == AutomationStatus.SUCCEEDED
@@ -0,0 +1,524 @@
+"""Test suite for parameter handling functionality."""
+
+import pytest
+from specklepy.objects.base import Base
+
+from src.rules import PropertyRules
+
+
+class TestParameterHandling:
+    """Test suite for parameter handling functionality."""
+
+    @pytest.fixture
+    def test_objects(self) -> Base:
+        """Pytest fixture to provide test objects."""
+        # Create a mock Base object with the required structure
+        v3_obj = Base()
+        v3_obj.properties = {
+            "Parameters": {
+                "category": "Walls",
+                "Width": 300,
+                "Construction": {"Width": 300},
+                "Instance Parameters": {
+                    "Dimensions": {"Length": 5300.000000000001},
+                    "Structural": {"Structural": {"value": "Yes"}},
+                    "Room Bounding": {"value": "Yes"},
+                    "top is attached": {"value": "No"},
+                },
+                "Type Parameters": {
+                    "Structure": {"Fc24 (0)": {"thickness": 300}},
+                    "Text": {"符号": {"value": "W30"}},
+                },
+                "Type": "W30(Fc24)",
+            }
+        }
+        v3_obj.speckle_type = "Revit"
+        return v3_obj
+
+    def test_deserialization_structure(self, test_objects):
+        """Test that objects are properly deserialized with correct structure."""
+        v3_obj = test_objects
+
+        # Check base class type
+        assert isinstance(v3_obj, Base), f"Expected {v3_obj} to be an instance of Base"
+
+        # Check v3 structure
+        assert hasattr(v3_obj, "properties"), (
+            "v3_obj should have 'properties' attribute"
+        )
+        assert v3_obj.properties is not None, "v3_obj.properties should not be None"
+        assert "Parameters" in v3_obj.properties, (
+            "'Parameters' key should exist in v3_obj.properties"
+        )
+
+    @pytest.mark.parametrize(
+        "param_name, expected_result",
+        [
+            ("category", True),  # Test parameters that should exist
+            ("Width", True),  # Test nested parameters
+            ("non_existent_param", False),  # Test non-existent parameters
+        ],
+    )
+    def test_v3_parameter_exists(self, test_objects, param_name, expected_result):
+        """Test parameter existence checking in v3 objects."""
+        v3_obj = test_objects
+        assert PropertyRules.has_parameter(v3_obj, param_name) == expected_result
+
+    @pytest.mark.parametrize(
+        "param_name_1, param_name_2",
+        [
+            # Test direct value access
+            (
+                "location.length",
+                "location.length",
+            ),
+            # Test .value key access
+            (
+                "Type Parameters.Text.符号",
+                "Type Parameters.Text.符号.value",
+            ),
+        ],
+    )
+    def test_v3_parameter_search_equivalence(
+        self,
+        v3_wall,
+        param_name_1,
+        param_name_2,
+    ):
+        """Test parameter existence checking equivalence in v3 objects."""
+        assert PropertyRules.get_parameter_value(
+            v3_wall, param_name_1
+        ) == PropertyRules.get_parameter_value(v3_wall, param_name_2)
+
+    @pytest.mark.parametrize(
+        "param_name, expected_value, default_value",
+        [
+            # Test direct parameters
+            ("category", "Walls", None),
+            # Test nested parameters - using both internal and friendly names
+            ("Construction.Width", 300, None),
+            # Test parameters with units
+            (
+                "Instance Parameters.Dimensions.Length",
+                5300.000000000001,
+                None,
+            ),
+            # Test non-existent parameters with a default value
+            (
+                "properties.Parameters.non_existent",
+                "default",
+                "default",
+            ),
+        ],
+    )
+    def test_parameter_value_retrieval(
+        self,
+        test_objects,
+        param_name,
+        expected_value,
+        default_value,
+    ):
+        """Test parameter value retrieval from v3 objects."""
+        v3_obj = test_objects
+        result = PropertyRules.get_parameter_value(
+            v3_obj,
+            param_name,
+            default_value=default_value,
+        )
+        assert result == expected_value
+
+    @pytest.mark.parametrize(
+        "param_name, expected_value, expected_result",
+        [
+            ("category", "Walls", True),  # Test exact match
+            ("Width", 300, True),  # Test numeric match
+            ("category", "Windows", False),  # Test non-match
+        ],
+    )
+    def test_v3_parameter_value_matching(
+        self,
+        test_objects,
+        param_name,
+        expected_value,
+        expected_result,
+    ):
+        """Test parameter value matching in v3 objects."""
+        v3_obj = test_objects
+        assert (
+            PropertyRules.is_parameter_value(
+                v3_obj,
+                param_name,
+                expected_value,
+            )
+            == expected_result
+        )
+
+    @pytest.mark.parametrize(
+        "comparison_func, param_name, value, expected_result",
+        [
+            (
+                PropertyRules.is_parameter_value_greater_than,
+                "Width",
+                "200",
+                True,
+            ),  # Test greater than
+            (
+                PropertyRules.is_parameter_value_less_than,
+                "Width",
+                "400",
+                True,
+            ),  # Test less than
+            (
+                PropertyRules.is_parameter_value_in_range,
+                "Width",
+                "200,400",
+                True,
+            ),  # Test in range
+        ],
+    )
+    def test_v3_parameter_numeric_comparisons(
+        self, test_objects, comparison_func, param_name, value, expected_result
+    ):
+        """Test numeric parameter comparisons in v3 objects."""
+        v3_obj = test_objects
+        assert comparison_func(v3_obj, param_name, value) == expected_result
+
+    @pytest.mark.parametrize(
+        "param_name, pattern, fuzzy, expected_result",
+        [
+            ("category", "^Walls$", False, True),  # Test exact pattern matches
+            ("category", "Walls", True, True),  # Test fuzzy matches
+            ("category", "Wall", False, True),  # Test partial pattern matches
+            ("category", "^Windows$", False, False),  # Test non-matches
+        ],
+    )
+    def test_v3_parameter_value_like(
+        self, test_objects, param_name, pattern, fuzzy, expected_result
+    ):
+        """Test pattern matching on parameter values in v3 objects."""
+        v3_obj = test_objects
+        assert (
+            PropertyRules.is_parameter_value_like(
+                v3_obj, param_name, pattern, fuzzy=fuzzy
+            )
+            == expected_result
+        )
+
+    @pytest.mark.parametrize(
+        "param_name, valid_list, expected_result",
+        [
+            (
+                "category",
+                ["Walls", "Windows", "Doors"],
+                True,
+            ),  # Test value in list
+            (
+                "category",
+                "Walls,Windows,Doors",
+                True,
+            ),  # Test comma-separated string list
+            (
+                "category",
+                ["Windows", "Doors"],
+                False,
+            ),  # Test value not in list
+        ],
+    )
+    def test_v3_parameter_lists(
+        self,
+        test_objects,
+        param_name,
+        valid_list,
+        expected_result,
+    ):
+        """Test list-based parameter checks in v3 objects."""
+        v3_obj = test_objects
+        assert (
+            PropertyRules.is_parameter_value_in_list(
+                v3_obj,
+                param_name,
+                valid_list,
+            )
+            == expected_result
+        )
+
+    @pytest.mark.parametrize(
+        "param_name, expected_result",
+        [
+            ("Room Bounding", True),  # Test true values
+            ("top is attached", False),  # Test false values
+            ("Top is Attached", False),  # Case sensitivity test
+        ],
+    )
+    def test_v3_boolean_parameters(
+        self,
+        test_objects,
+        param_name,
+        expected_result,
+    ):
+        """Test boolean parameter checks in v3 objects."""
+        v3_obj = test_objects
+        if expected_result:
+            assert PropertyRules.is_parameter_value_true(v3_obj, param_name)
+        else:
+            assert PropertyRules.is_parameter_value_false(v3_obj, param_name)
+
+    @pytest.mark.parametrize(
+        "attribute, value, expected",
+        [
+            # Test numeric value comparisons
+            (
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                300,
+                True,
+            ),
+            (
+                "Instance Parameters.Dimensions.Length",
+                5300.000000000002,
+                True,
+            ),
+            (
+                "Instance Parameters.Dimensions.Length",
+                5300,
+                True,
+            ),
+            # Test string value comparisons
+            (
+                "Type Parameters.Text.符号.value",
+                "W30",
+                True,
+            ),
+            (
+                "Instance Parameters.Structural.Structural.value",
+                "Yes",
+                True,
+            ),
+            # Test non-matches
+            (
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                301,
+                False,
+            ),
+            (
+                "nonexistent_param",
+                "any_value",
+                False,
+            ),
+        ],
+    )
+    def test_v3_parameter_value_comparisons(
+        self,
+        test_objects,
+        attribute,
+        value,
+        expected,
+    ):
+        """Test value comparisons using v3 wall parameters."""
+        assert PropertyRules.is_equal_value(test_objects, attribute, value) == expected
+
+    @pytest.mark.parametrize(
+        "wall, attribute, value, expected",
+        [
+            # V3 wall tests
+            (
+                "v3_wall",
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                300,
+                True,
+            ),
+            ("v3_wall", "type", "W30(Fc24)", True),
+            (
+                "v3_wall",
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                300.0001,
+                False,
+            ),
+            (
+                "v3_wall",
+                "location.length",
+                5300.000000000002,
+                False,
+            ),
+            (
+                "v3_wall",
+                "location.length",
+                5300,
+                False,
+            ),
+        ],
+    )
+    def test_identical_comparisons(
+        self,
+        test_objects,
+        wall,
+        attribute,
+        value,
+        expected,
+    ):
+        """Test identical value comparisons on v3 wall."""
+        if attribute == "type":
+            # Use case-insensitive comparison for type parameter
+            assert (
+                PropertyRules.is_equal_value(
+                    test_objects,
+                    attribute,
+                    value,
+                )
+                == expected
+            )
+        else:
+            # Use strict comparison for other parameters
+            assert (
+                PropertyRules.is_identical_value(
+                    test_objects,
+                    attribute,
+                    value,
+                )
+                == expected
+            )
+
+    @pytest.mark.parametrize(
+        "wall, attribute, value",
+        [
+            # V3 wall tests
+            (
+                "v3_wall",
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                301,
+            ),
+            (
+                "v3_wall",
+                "Type Parameters.Text.符号.value",
+                "W31",
+            ),
+            (
+                "v3_wall",
+                "nonexistent_param",
+                "any_value",
+            ),
+        ],
+    )
+    def test_not_equal_comparisons(
+        self,
+        test_objects,
+        wall,
+        attribute,
+        value,
+    ):
+        """Test not equal comparisons on v3 wall."""
+        assert PropertyRules.is_not_equal_value(test_objects, attribute, value)
+
+    @pytest.mark.parametrize(
+        "attribute, value, expected_equal, expected_identical",
+        [
+            # Test Yes/No conversion in equals (should convert)
+            (
+                "Instance Parameters.Structural.Structural.value",
+                True,
+                True,
+                False,
+            ),  # Yes vs True
+            (
+                "Instance Parameters.Structural.Structural.value",
+                "Yes",
+                True,
+                True,
+            ),  # Yes vs "Yes"
+            (
+                "Instance Parameters.Structural.Structural.value",
+                "yes",
+                True,
+                False,
+            ),  # Yes vs "yes"
+        ],
+    )
+    def test_boolean_conversions(
+        self,
+        test_objects,
+        attribute,
+        value,
+        expected_equal,
+        expected_identical,
+    ):
+        """Test conversion of Yes/No strings to boolean values."""
+        assert (
+            PropertyRules.is_equal_value(test_objects, attribute, value)
+            == expected_equal
+        )
+        assert (
+            PropertyRules.is_identical_value(test_objects, attribute, value)
+            == expected_identical
+        )
+
+    @pytest.mark.parametrize(
+        "wall, attribute, expected_value",
+        [
+            # V3 wall tests
+            (
+                "v3_wall",
+                "Type Parameters.Structure.Fc24 (0).thickness",
+                "300",
+            ),
+            (
+                "v3_wall",
+                "Instance Parameters.Dimensions.Length",
+                "5300.000000000002",
+            ),
+        ],
+    )
+    def test_numeric_string_handling(
+        self,
+        test_objects,
+        wall,
+        attribute,
+        expected_value,
+    ):
+        """Test handling of numeric strings in v3 wall."""
+        assert PropertyRules.is_equal_value(
+            test_objects,
+            attribute,
+            expected_value,
+        )
+
+    @pytest.mark.parametrize(
+        "param_name, substring, expected_result",
+        [
+            (
+                "speckle_type",
+                "Revit",
+                True,
+            ),  # Should pass as it does not contain Revit
+            (
+                "speckle_type",
+                "NotPresent",
+                True,
+            ),  # Should pass as it doesn't contain
+            (
+                "speckle_type",
+                "",
+                False,
+            ),  # Should fail as empty string is contained in any string
+            (
+                "non_existent",
+                "anything",
+                True,
+            ),  # Should pass as non-existent can't contain
+        ],
+    )
+    def test_parameter_value_not_contains(
+        self,
+        test_objects,
+        param_name,
+        substring,
+        expected_result,
+    ):
+        """Test negative substring matching on parameter values."""
+        v3_obj = test_objects
+        assert (
+            PropertyRules.is_parameter_value_not_containing(
+                v3_obj,
+                param_name,
+                substring,
+            )
+            == expected_result
+        )
@@ -0,0 +1,36 @@
+"""Tests for rule processing functionality."""
+
+import pandas as pd
+import pytest
+
+
+@pytest.fixture
+def explicit_check_rule():
+    """Create a rule using explicit CHECK format."""
+    return pd.DataFrame(
+        {
+            "Rule Number": [1, 1, 1],
+            "Logic": ["WHERE", "AND", "CHECK"],
+            "Property Name": ["category", "width", "material"],
+            "Predicate": ["matches", "greater than", "matches"],
+            "Value": ["Walls", "200", "Concrete"],
+            "Message": ["Test message", "", ""],
+            "Severity": ["Error", "", ""],
+        }
+    )
+
+
+@pytest.fixture
+def legacy_rule():
+    """Create a rule using legacy format (last AND is implicit check)."""
+    return pd.DataFrame(
+        {
+            "Rule Number": [1, 1, 1],
+            "Logic": ["WHERE", "AND", "AND"],
+            "Property Name": ["category", "width", "material"],
+            "Predicate": ["matches", "greater than", "matches"],
+            "Value": ["Walls", "200", "Concrete"],
+            "Message": ["Test message", "", ""],
+            "Severity": ["Error", "", ""],
+        }
+    )
@@ -0,0 +1,33 @@
+import pandas as pd
+import pytest
+
+from src.rule_processor import SeverityLevel, get_severity
+
+
+@pytest.mark.parametrize(
+    "input_severity, expected_enum",
+    [
+        ("INFO", SeverityLevel.INFO),
+        ("info", SeverityLevel.INFO),
+        ("Info", SeverityLevel.INFO),
+        ("WARNING", SeverityLevel.WARNING),
+        ("warning", SeverityLevel.WARNING),
+        ("Warning", SeverityLevel.WARNING),
+        ("ERROR", SeverityLevel.ERROR),
+        ("error", SeverityLevel.ERROR),
+        ("Error", SeverityLevel.ERROR),
+        ("WARN", SeverityLevel.WARNING),  
+        ("warn", SeverityLevel.WARNING),  
+        ("Critical", SeverityLevel.ERROR),  # Invalid → Defaults to ERROR
+        ("Severe", SeverityLevel.ERROR),  # Invalid → Defaults to ERROR
+        ("", SeverityLevel.ERROR),  # Empty string → Defaults to ERROR
+        (None, SeverityLevel.ERROR),  # None → Defaults to ERROR
+        (1.0, SeverityLevel.ERROR),  # None → Defaults to ERROR
+    ],
+)
+def test_severity_conversion(input_severity, expected_enum):
+    """Test various user inputs for severity and check expected outputs."""
+    rule_info = pd.Series({"Report Severity": input_severity})
+    severity = get_severity(rule_info)
+
+    assert severity == expected_enum, f"Failed for input: {input_severity}"