Update function.py
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Jonathon Broughton
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@@ -1,116 +1,110 @@
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import random
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from speckle_automate import AutomationContext
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from Exercises.exercise_2.inputs import FunctionInputs
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from Exercises.exercise_2.rules import RevitRules
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from Utilities.flatten import flatten_base
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from Utilities.helpers import flatten_base, speckle_print
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from Workshop.Exercise_2.inputs import FunctionInputs
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from Workshop.Exercise_2.rules import RevitRules, filter_objects_by_category
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def automate_function(
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automate_context: AutomationContext,
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function_inputs: FunctionInputs,
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automate_context: AutomationContext,
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function_inputs: FunctionInputs,
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) -> None:
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"""This version of the function will add a check for the new provide inputs.
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"""Exercise 2: Property validation for categorized Revit objects.
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This completely new implementation replaces the random comment functionality
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from Exercise 1 with systematic property validation. The key differences include:
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1. Instead of adding random comments, it validates specific properties on objects
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2. Filters objects by Revit category instead of just displayable objects
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3. Implements property validation using the new RevitRules system
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4. Provides detailed reporting on property status with error/warning/info markers
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Architecture Note:
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This implementation demonstrates best practices by utilizing external Rules classes
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(Rules and RevitRules). These classes form part of a reusable code library that can be
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shared across multiple Speckle Automate functions. This approach:
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- Promotes code reuse and consistency across automation tasks
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- Reduces duplication of common validation logic
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- Creates a private library of tested, reliable utilities
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- Makes automation functions cleaner and more focused on their specific tasks
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- Allows for centralized updates to validation logic
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Args:
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automate_context: A context helper object, that carries relevant information
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automate_context: A context-helper object that carries relevant information
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about the runtime context of this function.
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It gives access to the Speckle project data, that triggered this run.
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It also has convenience methods attach result data to the Speckle model.
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It gives access to the Speckle project data that triggered this run.
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It also has convenient methods for attaching result data to the Speckle model.
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function_inputs: An instance object matching the defined schema.
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Now includes:
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- category: The Revit category to check
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- property: The property name to validate
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"""
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# the context provides a convenient way, to receive the triggering version
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# Get the version root object
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version_root_object = automate_context.receive_version()
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# We can continue to work with a flattened list of objects.
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# Get flattened list of objects
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flat_list_of_objects = list(flatten_base(version_root_object))
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# filter to only include objects that are in the specified category
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in_category_objects = [
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speckle_object
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for speckle_object in flat_list_of_objects
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if RevitRules.is_category(speckle_object, function_inputs.category)
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]
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# check if the property exists on the objects
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non_property_objects = [
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obj
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for obj in in_category_objects
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if not RevitRules.has_parameter(obj, function_inputs.property)
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]
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property_objects = [
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obj
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for obj in in_category_objects
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if RevitRules.has_parameter(obj, function_inputs.property)
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]
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# property_objects should be those where while the property is present,
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# is not an empty string or the default value
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valid_property_objects = [
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obj
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for obj in property_objects
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if RevitRules.get_parameter_value(obj, function_inputs.property) not in ["", "Default", None]
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]
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for obj in valid_property_objects:
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speckle_print(RevitRules.get_parameter_value(obj, function_inputs.property))
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# invalid_property_objects property_objects not in valid_property_objects
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invalid_property_objects = [
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obj for obj in property_objects if obj not in valid_property_objects
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]
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# mark all the non-property objects as failed
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(
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automate_context.attach_error_to_objects(
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category=f"Missing Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in non_property_objects],
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message=f"This {function_inputs.category} does not have the specified property {function_inputs.property}",
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)
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if non_property_objects
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else None
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# Use the new filter_objects_by_category function from our reusable rules library
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in_category_objects, non_category_objects = filter_objects_by_category(
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flat_list_of_objects,
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function_inputs.category
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)
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# mark all the invalid property objects as warning
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(
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automate_context.attach_warning_to_objects(
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category=f"Invalid Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in invalid_property_objects],
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message=f"This {function_inputs.category} has the specified property {function_inputs.property} but it is "
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f"empty or default",
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)
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if invalid_property_objects
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else None
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)
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# Categorize objects based on property existence and validity
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non_property_objects = []
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property_objects = []
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valid_property_objects = []
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invalid_property_objects = []
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# mark all the property objects as successful
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(
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automate_context.attach_info_to_objects(
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category=f"Valid Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in property_objects],
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message=f"This {function_inputs.category} has the specified property {function_inputs.property}",
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)
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if property_objects
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else None
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)
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# Process each object using rules from our reusable library
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for obj in in_category_objects:
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if not RevitRules.has_parameter(obj, function_inputs.property):
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non_property_objects.append(obj)
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else:
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property_objects.append(obj)
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param_value = RevitRules.get_parameter_value(obj, function_inputs.property)
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if param_value not in ["", "Default", None]:
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valid_property_objects.append(obj)
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speckle_print(param_value) # Print valid values
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else:
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invalid_property_objects.append(obj)
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# Attach results to objects
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if non_property_objects:
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automate_context.attach_error_to_objects(
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category=f"Missing Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in non_property_objects],
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message=f"This {function_inputs.category} does not have the specified property {function_inputs.property}",
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)
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if invalid_property_objects:
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automate_context.attach_warning_to_objects(
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category=f"Invalid Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in invalid_property_objects],
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message=f"This {function_inputs.category} has the specified property {function_inputs.property} but it is empty or default",
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)
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if valid_property_objects:
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automate_context.attach_info_to_objects(
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category=f"Valid Property {function_inputs.category} Objects",
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object_ids=[obj.id for obj in valid_property_objects],
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message=f"This {function_inputs.category} has valid values for the property {function_inputs.property}",
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)
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# Mark overall run status with detailed counts
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total_objects = len(in_category_objects)
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if len(non_property_objects) > 0:
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automate_context.mark_run_failed(
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"Some objects do not have the specified property."
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f"Found {len(non_property_objects)} objects without the required property out of {total_objects} total {function_inputs.category} objects."
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)
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elif len(invalid_property_objects) > 0:
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automate_context.mark_run_success(
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"Some objects have the specified property but it is empty or default.",
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f"Found {len(invalid_property_objects)} objects with empty/default values out of {total_objects} total {function_inputs.category} objects."
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)
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else:
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automate_context.mark_run_success(
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f"All {len(in_category_objects)} {function_inputs.category} objects have the {function_inputs.property} property."
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f"All {total_objects} {function_inputs.category} objects have valid {function_inputs.property} properties."
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)
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# set the automation context view, to the original model / version view
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# Reset view
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automate_context.set_context_view()
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