Iain Sproat
312 lines
9.9 KiB
Plaintext
312 lines
9.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Speckle Automate notebook example\n",
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"\n",
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"This is an example jupyter notebook, that will be executed by speckle automate\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Parameters\n",
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"\n",
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"These values are supplied to the notebook, when its being run by automate.\n",
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"\n",
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"**WARNING**: You really shouldn't modify this block, unless its following upstream changes from automate.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"from speckle_automate import AutomationContext, AutomationRunData\n",
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"\n",
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"function_inputs = '{\"forbiddenSpeckleType\": \"Objects.Geometry.Brep\"}'\n",
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"token_env_var = \"SPECKLE_TOKEN\"\n",
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"automation_run_data = ''\n",
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"\n",
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"execute_function = False"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Local testing\n",
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"\n",
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"The cells below are here to make it easier to locally test the notebook.\n",
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"\n",
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"The cell has an option to register a new automation for the project on every run.\n",
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"\n",
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"With the local testing flag enabled, `local_testing = True`, the subsequent cell will override parameters from the cell above.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"local test registered\n"
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]
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}
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],
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"source": [
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"local_testing = False\n",
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"register_automation = True\n",
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"\n",
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"if local_testing:\n",
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" execute_function = True\n",
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" speckle_token = \"\"\n",
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" from speckle_automate.helpers import register_new_automation, crypto_random_string\n",
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"\n",
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" if not register_automation:\n",
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" automation_id = \"\"\n",
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" automation_revision_id = \"\"\n",
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" automation_run_id = \"\"\n",
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" function_id = \"\"\n",
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" function_release = \"\"\n",
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" else:\n",
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" automation_id = crypto_random_string(10)\n",
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" automation_revision_id = crypto_random_string(10)\n",
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" automation_run_id = crypto_random_string(11)\n",
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" function_id = crypto_random_string(10)\n",
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" function_release = crypto_random_string(10)\n",
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"\n",
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" automation_context = AutomationContext.initialize(\n",
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" AutomationRunData(\n",
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" project_id=\"3354d7be31\",\n",
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" model_id=\"724617c963\",\n",
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" branch_name=\"example.ifc\",\n",
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" version_id=\"2a514e194f\",\n",
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" speckle_server_url=\"https://latest.speckle.systems\",\n",
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" automation_id=automation_id,\n",
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" automation_revision_id=automation_revision_id,\n",
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" automation_run_id=automation_run_id,\n",
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" function_id=function_id,\n",
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" function_release=function_release,\n",
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" ),\n",
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" speckle_token,\n",
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" )\n",
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"\n",
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" register_new_automation(\n",
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" automation_context.speckle_client,\n",
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" automation_context.automation_run_data.project_id,\n",
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" automation_context.automation_run_data.model_id,\n",
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" automation_context.automation_run_data.automation_id,\n",
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" \"Jupyter local test\",\n",
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" automation_context.automation_run_data.automation_revision_id,\n",
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" )\n",
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" print(\"local test registered\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Function inputs\n",
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"\n",
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"The `FunctionInputs` class defines the schema for the values, this function requires to run.\n",
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"These values will be provided by the users of the function.\n",
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"\n",
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"Automate uses the Json Schema, generated from the class, to validate user provided values.\n",
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"\n",
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"The schema block is tagged with the `function_input` tag, do not remove that!\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {
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"tags": [
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"function_inputs"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\"description\": \"These are function author defined values.\\n\\nAutomate will make sure to supply them matching the types specified here.\\nPlease use the pydantic model schema to define your inputs:\\nhttps://docs.pydantic.dev/latest/usage/models/\", \"properties\": {\"forbiddenSpeckleType\": {\"description\": \"If a object has the following speckle_type, it will be marked with an error.\", \"title\": \"Forbidden speckle type\", \"type\": \"string\"}}, \"required\": [\"forbiddenSpeckleType\"], \"title\": \"FunctionInputs\", \"type\": \"object\"}\n"
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]
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}
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],
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"source": [
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"from speckle_automate import AutomateBase\n",
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"from pydantic import Field\n",
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"import json\n",
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"\n",
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"\n",
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"class FunctionInputs(AutomateBase):\n",
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" \"\"\"These are function author defined values.\n",
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"\n",
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" Automate will make sure to supply them matching the types specified here.\n",
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" Please use the pydantic model schema to define your inputs:\n",
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" https://docs.pydantic.dev/latest/usage/models/\n",
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" \"\"\"\n",
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"\n",
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" forbidden_speckle_type: str = Field(\n",
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" title=\"Forbidden speckle type\",\n",
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" description=(\n",
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" \"If a object has the following speckle_type,\"\n",
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" \" it will be marked with an error.\"\n",
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" ),\n",
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" )\n",
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"\n",
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"\n",
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"print(json.dumps(FunctionInputs.model_json_schema()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Function logic\n",
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"\n",
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"in this block we're defining the actual business logic of the function.\n",
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"\n",
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"By all means modify this block but do keep the function signature compatible with the executing main function.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
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"outputs": [],
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"source": [
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"from specklepy.objects import Base\n",
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"from typing import Iterable\n",
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"\n",
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"from speckle_automate import AutomationContext\n",
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"\n",
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"\n",
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"def flatten_base(base: Base) -> Iterable[Base]:\n",
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" \"\"\"Take a base and flatten it to an iterable of bases.\"\"\"\n",
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" if hasattr(base, \"elements\"):\n",
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" for element in base[\"elements\"]:\n",
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" yield from flatten_base(element)\n",
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" yield base\n",
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"\n",
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"\n",
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"def automate_function(\n",
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" automate_context: AutomationContext,\n",
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" function_inputs: FunctionInputs,\n",
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"):\n",
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" version_root_object = automate_context.receive_version()\n",
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"\n",
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" count = 0\n",
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" for b in flatten_base(version_root_object):\n",
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" if b.speckle_type == function_inputs.forbidden_speckle_type:\n",
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" if not b.id:\n",
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" raise ValueError(\"Cannot operate on objects without their id's.\")\n",
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" automate_context.add_object_error(\n",
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" b.id,\n",
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" \"This project should not contain the type: \" f\"{b.speckle_type}\",\n",
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" )\n",
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" count += 1\n",
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"\n",
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" if count > 0:\n",
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" # this is how a run is marked with a failure cause\n",
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" automate_context.mark_run_failed(\n",
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" \"Automation failed: \"\n",
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" f\"Found {count} object that have one of the forbidden speckle types: \"\n",
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" f\"{function_inputs.forbidden_speckle_type}\"\n",
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" )\n",
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"\n",
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" else:\n",
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" automate_context.mark_run_success(\"No forbidden types found.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Function execution\n",
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"\n",
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"This last step executes the actual function using the automation context. The calculation cell must be tagged with `function_execution` tag, this allows the output to be discovered and reported to Speckle Automate.\n",
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"Do not change the below unless you know what you are doing!\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {
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"tags": [
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"function_execution"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"It took 2.040990 seconds to receive the speckle version 2a514e194f\n",
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"Automation run SUCCEEDED after 2.044367 seconds.\n",
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"No forbidden types found.\n",
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"Run completed with status: AutomationStatus.SUCCEEDED, message: No forbidden types found.\n"
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]
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}
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],
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"source": [
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"import os\n",
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"from speckle_automate.runner import run_function\n",
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"\n",
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"if execute_function:\n",
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" automation_context = run_function(\n",
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" AutomationContext.initialize(automation_run_data, os.environ[token_env_var]),\n",
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" automate_function,\n",
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" FunctionInputs.model_validate_json(function_inputs),\n",
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" )\n",
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"\n",
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" # WARNING: Make sure the last print statement is this!\n",
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" # automate relies on this convention, to process the automation results\n",
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" print(automation_context._automation_result.model_dump_json())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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