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exercise-3
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@jsdbroughton
jsdbroughton committed May 7, 2024
1 parent df2397b commit f1a2b97
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168 changes: 77 additions & 91 deletions main.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
from pydantic import Field
from speckle_automate import AutomationContext, AutomateBase, execute_automate_function
from Utilities.helpers import flatten_base, speckle_print
import random

from rules import RevitRules


class FunctionInputs(AutomateBase):
Expand All @@ -12,23 +13,21 @@ class FunctionInputs(AutomateBase):
https://docs.pydantic.dev/latest/usage/models/
"""

comment_phrase: str = Field(
title="Comment Phrase",
description="This phrase will be added to a random model element.",
# In this exercise, we will add two new input fields to the FunctionInputs class.
category: str = Field(
title="Revit Category",
description="This is the category objects to check.",
)

# We now want to specify the number of elements to which the comment phrase will be added.
number_of_elements: int = Field(
title="Number of Elements",
description="The number of elements to which the comment phrase will be added.",
property: str = Field(
title="Property Name",
description="This is the property to check.",
)


def automate_function(
automate_context: AutomationContext,
function_inputs: FunctionInputs,
) -> None:
"""This is an example Speckle Automate function.
"""This version of the function will add a check for the new provide inputs.
Args:
automate_context: A context helper object, that carries relevant information
Expand All @@ -41,107 +40,94 @@ def automate_function(
# the context provides a convenient way, to receive the triggering version
version_root_object = automate_context.receive_version()

# We can continue to work with a flattened list of objects.
flat_list_of_objects = list(flatten_base(version_root_object))

# filter the list to only include objects that are displayable.
# this is a simple example, that checks if the object has a displayValue
displayable_objects = [
# filter to only include objects that are in the specified category
in_category_objects = [
speckle_object
for speckle_object in flat_list_of_objects
if (
getattr(speckle_object, "displayValue", None)
or getattr(speckle_object, "@displayValue", None)
)
and getattr(speckle_object, "id", None) is not None
if RevitRules.is_category(speckle_object, function_inputs.category)
]

# a better displayable_objects should also include those instance objects that have a definition property
# that cross-references to a speckle id, that is in turn displayable, so we need to add those objects to the list
displayable_objects += [
instance_object
for instance_object in flat_list_of_objects
if (
getattr(instance_object, "definition", None)
and (
(
getattr(
getattr(instance_object, "definition"), "displayValue", None
)
or getattr(
getattr(instance_object, "definition"), "@displayValue", None
)
)
and getattr(getattr(instance_object, "definition"), "id", None)
is not None
)
)
# check if the property exists on the objects
non_property_objects = [
obj
for obj in in_category_objects
if not RevitRules.has_parameter(obj, function_inputs.property)
]

if len(displayable_objects) == 0:
automate_context.mark_run_failed(
"Automation failed: No displayable objects found."
)
property_objects = [
obj
for obj in in_category_objects
if RevitRules.has_parameter(obj, function_inputs.property)
]

else:
# select a random object from the list
# random_object = random.choice(displayable_objects)

# instead of a single object we will select a random subset of displayable objects from the provided dataset
real_number_of_elements = min(
# We cant take more elements than we have
function_inputs.number_of_elements,
len(displayable_objects),
)
# property_objects should be those where while the property is present,
# is not an empty string or the default value
valid_property_objects = [
obj
for obj in property_objects
if RevitRules.get_parameter_value(obj, function_inputs.property)
not in ["", "Default", None]
]

selected_objects = random.sample(
displayable_objects,
real_number_of_elements,
)
for obj in valid_property_objects:
speckle_print(RevitRules.get_parameter_value(obj, function_inputs.property))

# create a list of object ids for all selected objects
selected_object_ids = [obj.id for obj in selected_objects]
# invalid_property_objects property_objects not in valid_property_objects
invalid_property_objects = [
obj for obj in property_objects if obj not in valid_property_objects
]

# ACTIONS
# mark all the non-property objects as failed

# attach comment phrase to all selected objects
# it is possible to attach the same comment phrase to multiple objects
# the category "Selected Objects" is used to group the objects in the viewer
# grouping results in this way is a clean way to organize the objects in the viewer
comment_message = f"{function_inputs.comment_phrase}"
automate_context.attach_info_to_objects(
category="Selected Objects",
object_ids=selected_object_ids,
message=comment_message,
(
automate_context.attach_error_to_objects(
category=f"Missing Property {function_inputs.category} Objects",
object_ids=[obj.id for obj in non_property_objects],
message=f"This {function_inputs.category} does not have the specified property {function_inputs.property}",
)
if non_property_objects
else None
)

# attach index as gradient value for all selected objects. this will be used for visualisation purposes
# the category "Index Visualisation" is used to group the objects in the viewer
gradient_values = {
object_id: {"gradientValue": index + 1}
for index, object_id in enumerate(selected_object_ids)
}
# mark all the invalid property objects as warning
(
automate_context.attach_warning_to_objects(
category=f"Invalid Property {function_inputs.category} Objects",
object_ids=[obj.id for obj in invalid_property_objects],
message=f"This {function_inputs.category} has the specified property {function_inputs.property} but it is "
f"empty or default",
)
if invalid_property_objects
else None
)

# mark all the property objects as successful
(
automate_context.attach_info_to_objects(
category="Index Visualisation",
metadata={
"gradient": True,
"gradientValues": gradient_values,
},
message="Object Indexes",
object_ids=selected_object_ids,
category=f"Valid Property {function_inputs.category} Objects",
object_ids=[obj.id for obj in property_objects],
message=f"This {function_inputs.category} has the specified property {function_inputs.property}",
)
if property_objects
else None
)

if len(non_property_objects) > 0:
automate_context.mark_run_failed(
"Some objects do not have the specified property."
)
elif len(invalid_property_objects) > 0:
automate_context.mark_run_success(
f"Added comment to {real_number_of_elements} random objects."
"Some objects have the specified property but it is empty or default.",
)

else:
automate_context.mark_run_success(
f"All {function_inputs.category} objects have the {function_inputs.property} property."
)

# set the automation context view, to the original model / version view
automate_context.set_context_view()


# make sure to call the function with the executor
if __name__ == "__main__":
# NOTE: always pass in the automate function by its reference, do not invoke it!

# pass in the function reference with the inputs schema to the executor
execute_automate_function(automate_function, FunctionInputs)
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