Skip to content

Commit

Permalink
exercise 0
Browse files Browse the repository at this point in the history
  • Loading branch information
jsdbroughton committed Nov 12, 2024
1 parent 1d92dfa commit 78b94da
Show file tree
Hide file tree
Showing 3 changed files with 70 additions and 3 deletions.
Empty file removed Exercises/__init__.py
Empty file.
Empty file removed Exercises/exercise-0/__init__.py
Empty file.
73 changes: 70 additions & 3 deletions main.py
Original file line number Diff line number Diff line change
@@ -1,13 +1,80 @@
"""
This main entry point is the command line interface for the Speckle Automate function.
"""
import random

from pydantic import Field
from speckle_automate import (
execute_automate_function,
execute_automate_function, AutomateBase, AutomationContext,
)

import Exercises.exercise_0.inputs.FunctionInputs as FunctionInputs
import Exercises.exercise_0.function.automate_function as automate_function
from Utilities.flatten import flatten_base


class FunctionInputs(AutomateBase):
"""These are function author defined values.
Automate will make sure to supply them matching the types specified here.
Please use the pydantic model schema to define your inputs:
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.",
)


def automate_function(
automate_context: AutomationContext,
function_inputs: FunctionInputs,
) -> None:
"""This is an example Speckle Automate function.
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.
"""

# the context provides a convenient way, to receive the triggering version
version_root_object = automate_context.receive_version()

flat_list_of_objects = 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 = [
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 len(displayable_objects) == 0:
automate_context.mark_run_failed(
"Automation failed: No displayable objects found."
)

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

automate_context.attach_info_to_objects(
category="Selected Object",
object_ids=[random_object.id],
message=function_inputs.comment_phrase,
)

automate_context.mark_run_success("Added a comment to a random object.")

# 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
# Pass in the function reference with the inputs schema to the executor.
Expand Down

0 comments on commit 78b94da

Please sign in to comment.