feat: commit processing and mass calcs

→ processes next-gen revit commit → logs any objects missing identified attributes → computes mass for objects with valid attributes
2025-02-09 12:48:08 +01:00
parent 0d066cee9a
commit a81dc28ed1
23 changed files with 377 additions and 220 deletions
@@ -1,4 +0,0 @@
 SPECKLE_TOKEN="mytoken"
 SPECKLE_SERVER_URL="http://127.0.0.1:3000"
 SPECKLE_PROJECT_ID=""
 SPECKLE_AUTOMATION_ID=""
@@ -0,0 +1,8 @@
 # Default ignored files
 /shelf/
 /workspace.xml
 # Editor-based HTTP Client requests
 /httpRequests/
 # Datasource local storage ignored files
 /dataSources/
 /dataSources.local.xml
@@ -0,0 +1,12 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
    <orderEntry type="inheritedJdk" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
  <component name="PyDocumentationSettings">
    <option name="format" value="PLAIN" />
    <option name="myDocStringFormat" value="Plain" />
  </component>
 </module>
@@ -0,0 +1,5 @@
 <component name="ProjectCodeStyleConfiguration">
  <state>
    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
  </state>
 </component>
@@ -0,0 +1,6 @@
 <component name="InspectionProjectProfileManager">
  <settings>
    <option name="USE_PROJECT_PROFILE" value="false" />
    <version value="1.0" />
  </settings>
 </component>
@@ -0,0 +1,10 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="Black">
    <option name="enabledOnReformat" value="true" />
    <option name="enabledOnSave" value="true" />
    <option name="pathToExecutable" value="C:\Users\stein\pipx\venvs\poetry\Scripts\black.exe" />
    <option name="sdkName" value="Poetry (Embodied-Carbon-Calculator)" />
  </component>
  <component name="ProjectRootManager" version="2" project-jdk-name="Poetry (Embodied-Carbon-Calculator)" project-jdk-type="Python SDK" />
 </project>
@@ -0,0 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="ProjectModuleManager">
    <modules>
      <module fileurl="file://$PROJECT_DIR$/.idea/Embodied-Carbon-Calculator.iml" filepath="$PROJECT_DIR$/.idea/Embodied-Carbon-Calculator.iml" />
    </modules>
  </component>
 </project>
@@ -0,0 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="VcsDirectoryMappings">
    <mapping directory="" vcs="Git" />
  </component>
 </project>
@@ -1,100 +1 @@
-# Speckle Automate function template - Python
+insert cool readme 😎 ...
 This template repository is for a Speckle Automate function written in Python
 using the [specklepy](https://pypi.org/project/specklepy/) SDK to interact with Speckle data.
 This template contains the full scaffolding required to publish a function to the Automate environment.
 It also has some sane defaults for development environment setups.
 ## Getting started
 1. Use this template repository to create a new repository in your own / organization's profile.
 Register the function 
 ### Add new dependencies
 To add new Python package dependencies to the project, use the following:
 `$ poetry add pandas`
 ### Change launch variables
 Describe how the launch.json should be edited.
 ### Github Codespaces
 Create a new repo from this template, and use the create new code.
 ### Using this Speckle Function
 1. [Create](https://automate.speckle.dev/) a new Speckle Automation.
 1. Select your Speckle Project and Speckle Model.
 1. Select the deployed Speckle Function.
 1. Enter a phrase to use in the comment.
 1. Click `Create Automation`.
 ## Getting Started with Creating Your Own Speckle Function
 1. [Register](https://automate.speckle.dev/) your Function with [Speckle Automate](https://automate.speckle.dev/) and select the Python template.
 1. A new repository will be created in your GitHub account.
 1. Make changes to your Function in `main.py`. See below for the Developer Requirements and instructions on how to test.
 1. To create a new version of your Function, create a new [GitHub release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) in your repository.
 ## Developer Requirements
 1. Install the following:
    - [Python 3](https://www.python.org/downloads/)
    - [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer)
 1. Run `poetry shell && poetry install` to install the required Python packages.
 ## Building and Testing
 The code can be tested locally by running `poetry run pytest`.
 ### Building and running the Docker Container Image
 Running and testing your code on your machine is a great way to develop your Function; the following instructions are a bit more in-depth and only required if you are having issues with your Function in GitHub Actions or on Speckle Automate.
 #### Building the Docker Container Image
 The GitHub Action packages your code into the format required by Speckle Automate. This is done by building a Docker Image, which Speckle Automate runs. You can attempt to build the Docker Image locally to test the building process.
 To build the Docker Container Image, you must have [Docker](https://docs.docker.com/get-docker/) installed.
 Once you have Docker running on your local machine:
 1. Open a terminal
 1. Navigate to the directory in which you cloned this repository
 1. Run the following command:
    ```bash
    docker build -f ./Dockerfile -t speckle_automate_python_example .
    ```
 #### Running the Docker Container Image
 Once the GitHub Action has built the image, it is sent to Speckle Automate. When Speckle Automate runs your Function as part of an Automation, it will run the Docker Container Image. You can test that your Docker Container Image runs correctly locally.
 1. To then run the Docker Container Image, run the following command:
    ```bash
    docker run --rm speckle_automate_python_example \
    python -u main.py run \
    '{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}' \
    '{}' \
    yourSpeckleServerAuthenticationToken
    ```
 Let's explain this in more detail:
 `docker run—-rm speckle_automate_python_example` tells Docker to run the Docker Container Image we built earlier. `speckle_automate_python_example` is the name of the Docker Container Image. The `--rm` flag tells Docker to remove the container after it has finished running, freeing up space on your machine.
 The line `python -u main.py run` is the command run inside the Docker Container Image. The rest of the command is the arguments passed to the command. The arguments are:
 - `'{"projectId": "1234", "modelId": "1234", "branchName": "myBranch", "versionId": "1234", "speckleServerUrl": "https://speckle.xyz", "automationId": "1234", "automationRevisionId": "1234", "automationRunId": "1234", "functionId": "1234", "functionName": "my function", "functionLogo": "base64EncodedPng"}'` - the metadata that describes the automation and the function.
 - `{}` - the input parameters for the function the Automation creator can set. Here, they are blank, but you can add your parameters to test your function.
 - `yourSpeckleServerAuthenticationToken`—the authentication token for the Speckle Server that the Automation can connect to. This is required to interact with the Speckle Server, for example, to get data from the Model.
 ## Resources
 - [Learn](https://speckle.guide/dev/python.html) more about SpecklePy and interacting with Speckle from Python.
@@ -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
@@ -1,8 +1,3 @@
 """This module contains the function's business logic.
 Use the automation_context module to wrap your function in an Automate context helper.
 """
 from pydantic import Field, SecretStr
 from speckle_automate import (
    AutomateBase,
@@ -10,17 +5,10 @@ from speckle_automate import (
    execute_automate_function,
 )
-from flatten import flatten_base
+from src.processors.commit_processory import CommitProcessor
 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/
    """
    # An example of how to use secret values.
    whisper_message: SecretStr = Field(title="This is a secret message")
    forbidden_speckle_type: str = Field(
@@ -36,68 +24,39 @@ def automate_function(
    automate_context: AutomationContext,
    function_inputs: FunctionInputs,
 ) -> None:
-    """This is an example Speckle Automate function.
+    # TODO: Add method to automation_context for sourceApplication
    version_id = automate_context.automation_run_data.triggers[0].payload.version_id
    commit_root = automate_context.speckle_client.commit.get(
        automate_context.automation_run_data.project_id, version_id
    )
-    Args:
+    # ℹ️ sourceApplication value for v2: AppName + Version => Revit2024, Revit2023 etc.
-        automate_context: A context-helper object that carries relevant information
+    # ℹ️ sourceApplication value for v3: slug => revit
-            about the runtime context of this function.
+    # ⚠️ We're just working with v3 data - adapt commit_processor for v2 data structure if you want
-            It gives access to the Speckle project data that triggered this run.
+    # ⚠️ Alternatively, write a model factory that injects the correct CommitProcessor()
-            It also has convenient methods for attaching result data to the Speckle model.
+    if commit_root.sourceApplication != "revit":
        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()
    objects_with_forbidden_speckle_type = [
        b
        for b in flatten_base(version_root_object)
        if b.speckle_type == function_inputs.forbidden_speckle_type
    ]
    count = len(objects_with_forbidden_speckle_type)
    if count > 0:
        # This is how a run is marked with a failure cause.
        automate_context.attach_error_to_objects(
            category="Forbidden speckle_type"
            f" ({function_inputs.forbidden_speckle_type})",
            object_ids=[o.id for o in objects_with_forbidden_speckle_type if o.id],
            message="This project should not contain the type: "
            f"{function_inputs.forbidden_speckle_type}",
        )
        automate_context.mark_run_failed(
-            "Automation failed: "
+            f"Automation built for v3 Revit commits. These are commits with a "
-            f"Found {count} object that have one of the forbidden speckle types: "
+            f"case-sensitive sourceApplication == 'revit', not {commit_root.sourceApplication})"
            f"{function_inputs.forbidden_speckle_type}"
        )
-        # Set the automation context view to the original model/version view
+    # Process elements
-        # to show the offending objects.
+    model_root = automate_context.receive_version()  # TODO: This is a waste!
-        automate_context.set_context_view()
+    processor = CommitProcessor()
    processor.process_elements(model_root)
-    else:
+    compliance_summary = processor.logger.get_summary()
-        automate_context.mark_run_success("No forbidden types found.")
+    for missing_property, elements in compliance_summary.items():
        automate_context.attach_warning_to_objects(
            category="Missing Revit Material Property",
            object_ids=elements,
            message=f"Missing {missing_property} on the object, preventing mass calculation. "
            f"Update Revit object to contain the necessary properties if element is critical. ",
        )
-    # If the function generates file results, this is how it can be
+    # TODO: create_new_version_in_project
-    # attached to the Speckle project/model
+    automate_context.mark_run_success("Under development.")
    # automate_context.store_file_result("./report.pdf")
 def automate_function_without_inputs(automate_context: AutomationContext) -> None:
    """A function example without inputs.
    If your function does not need any input variables,
     besides what the automation context provides,
     the inputs argument can be omitted.
    """
    pass
 # 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)
    # If the function has no arguments, the executor can handle it like so
    # execute_automate_function(automate_function_without_inputs)
@@ -42,6 +42,17 @@ files = [
    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
 ]
 [[package]]
 name = "astroid"
 version = "3.3.8"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
 python-versions = ">=3.9.0"
 files = [
    {file = "astroid-3.3.8-py3-none-any.whl", hash = "sha256:187ccc0c248bfbba564826c26f070494f7bc964fd286b6d9fff4420e55de828c"},
    {file = "astroid-3.3.8.tar.gz", hash = "sha256:a88c7994f914a4ea8572fac479459f4955eeccc877be3f2d959a33273b0cf40b"},
 ]
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -283,6 +294,21 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
 [[package]]
 name = "dill"
 version = "0.3.9"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
 files = [
    {file = "dill-0.3.9-py3-none-any.whl", hash = "sha256:468dff3b89520b474c0397703366b7b95eebe6303f108adf9b19da1f702be87a"},
    {file = "dill-0.3.9.tar.gz", hash = "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"},
 ]
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
 profile = ["gprof2dot (>=2022.7.29)"]
 [[package]]
 name = "gql"
 version = "3.5.0"
@@ -406,6 +432,32 @@ files = [
    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 [[package]]
 name = "isort"
 version = "6.0.0"
 description = "A Python utility / library to sort Python imports."
 optional = false
 python-versions = ">=3.9.0"
 files = [
    {file = "isort-6.0.0-py3-none-any.whl", hash = "sha256:567954102bb47bb12e0fae62606570faacddd441e45683968c8d1734fb1af892"},
    {file = "isort-6.0.0.tar.gz", hash = "sha256:75d9d8a1438a9432a7d7b54f2d3b45cad9a4a0fdba43617d9873379704a8bdf1"},
 ]
 [package.extras]
 colors = ["colorama"]
 plugins = ["setuptools"]
 [[package]]
 name = "mccabe"
 version = "0.7.0"
 description = "McCabe checker, plugin for flake8"
 optional = false
 python-versions = ">=3.6"
 files = [
    {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"},
    {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 [[package]]
 name = "multidict"
 version = "6.1.0"
@@ -882,6 +934,33 @@ azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 [[package]]
 name = "pylint"
 version = "3.3.4"
 description = "python code static checker"
 optional = false
 python-versions = ">=3.9.0"
 files = [
    {file = "pylint-3.3.4-py3-none-any.whl", hash = "sha256:289e6a1eb27b453b08436478391a48cd53bb0efb824873f949e709350f3de018"},
    {file = "pylint-3.3.4.tar.gz", hash = "sha256:74ae7a38b177e69a9b525d0794bd8183820bfa7eb68cc1bee6e8ed22a42be4ce"},
 ]
 [package.dependencies]
 astroid = ">=3.3.8,<=3.4.0-dev0"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
 dill = [
    {version = ">=0.3.7", markers = "python_version >= \"3.12\""},
    {version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
 isort = ">=4.2.5,<5.13.0 || >5.13.0,<7"
 mccabe = ">=0.6,<0.8"
 platformdirs = ">=2.2.0"
 tomlkit = ">=0.10.1"
 [package.extras]
 spelling = ["pyenchant (>=3.2,<4.0)"]
 testutils = ["gitpython (>3)"]
 [[package]]
 name = "pytest"
 version = "7.4.4"
@@ -1019,6 +1098,34 @@ files = [
    {file = "stringcase-1.2.0.tar.gz", hash = "sha256:48a06980661908efe8d9d34eab2b6c13aefa2163b3ced26972902e3bdfd87008"},
 ]
 [[package]]
 name = "structlog"
 version = "25.1.0"
 description = "Structured Logging for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
    {file = "structlog-25.1.0-py3-none-any.whl", hash = "sha256:843fe4f254540329f380812cbe612e1af5ec5b8172205ae634679cd35a6d6321"},
    {file = "structlog-25.1.0.tar.gz", hash = "sha256:2ef2a572e0e27f09664965d31a576afe64e46ac6084ef5cec3c2b8cd6e4e3ad3"},
 ]
 [package.extras]
 dev = ["freezegun (>=0.2.8)", "mypy (>=1.4)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "rich", "simplejson", "twisted"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "twisted"]
 tests = ["freezegun (>=0.2.8)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "simplejson"]
 typing = ["mypy (>=1.4)", "rich", "twisted"]
 [[package]]
 name = "tomlkit"
 version = "0.13.2"
 description = "Style preserving TOML library"
 optional = false
 python-versions = ">=3.8"
 files = [
    {file = "tomlkit-0.13.2-py3-none-any.whl", hash = "sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde"},
    {file = "tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"},
 ]
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1386,4 +1493,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "016af7c16ab48a41f716ccec40a277fee96b42bb15dc790ac1f6b2c0fc9920a4"
+content-hash = "1251201031b01adfe8b5f59d340deb29175d98c2d372a34dd1f883df16dec333"
@@ -9,6 +9,8 @@ package-mode = false
 [tool.poetry.dependencies]
 python = "^3.11"
 specklepy = "^2.21.0"
 pylint = "^3.3.4"
 structlog = "^25.1.0"
 [tool.poetry.group.dev.dependencies]
 black = "^23.3.0"
@@ -0,0 +1,32 @@
 from typing import Dict
 from collections import defaultdict
 class MassAggregator:
    def __init__(self):
        self.totals = defaultdict(lambda: defaultdict(lambda: defaultdict(float)))
    def add_mass(self, mass: float, level: str, type: str, material: str) -> None:
        if mass <= 1e-6:
            return
        self.totals[level][type][material] += mass
    def get_totals(self) -> Dict:
        return {
            "by_level": {
                level: {
                    "total": sum(
                        sum(material_masses.values())
                        for material_masses in types.values()
                    ),
                    "by_type": {
                        type_name: {
                            "total": sum(material_masses.values()),
                            "by_material": material_masses,
                        }
                        for type_name, material_masses in types.items()
                    },
                }
                for level, types in self.totals.items()
            },
        }
@@ -0,0 +1,28 @@
 import structlog
 from typing import Dict, DefaultDict
 from collections import defaultdict
 logger = structlog.get_logger()
 class ComplianceLogger:
    def __init__(self):
        self.missing_properties: DefaultDict[str, set] = defaultdict(set)
    def log_missing_properties(self, object_id: str, missing_property: str) -> None:
        # Log to our collection for automation results
        self.missing_properties[missing_property].add(object_id)
        # Still log individual cases for dev
        logger.warn(
            "non_compliant_element",
            object_id=object_id,
            property=missing_property,
            message=f"Missing: '{missing_property}' on object {object_id}. No computation on "
            f"for this object possible. Skipped.",
        )
    def get_summary(self) -> Dict[str, list]:
        return {
            prop: list(elements) for prop, elements in self.missing_properties.items()
        }
@@ -0,0 +1,109 @@
 from typing import Dict, Any
 from src.aggregators.carbon_totals import MassAggregator
 from src.logging.compliance_logger import ComplianceLogger
 from src.utils.constants import *  # wildcard is a little dangerous
 class CommitProcessor:
    def __init__(self):
        self.logger = ComplianceLogger()
        self.mass_aggregator = MassAggregator()
    def process_elements(
        self, model: "Base"
    ) -> None:  # No return needed, we're modifying in-place
        levels = getattr(model, ELEMENTS, None)
        if not levels:  # First nesting => levels
            raise ValueError("Invalid commit: missing elements at the model root.")
        for level in levels:
            type_groups = getattr(level, ELEMENTS, None)
            if not type_groups:
                raise ValueError(
                    f"Invalid level structure: missing elements in {getattr(level,NAME, '!Missing name attribute!')}"
                )
            for type_group in type_groups:
                revit_objects = getattr(type_group, ELEMENTS, None)
                if not revit_objects:
                    raise ValueError(
                        f"Invalid type structure: missing elements in "
                        f"{getattr(type_group, NAME, '!Missing name attribute!')}"
                    )
                level_name = getattr(level, NAME, None)
                type_name = getattr(type_group, NAME, None)
                if level_name is None or type_name is None:
                    raise ValueError(
                        f"Every object should be on a level and be of a type."
                    )
                for revit_object in revit_objects:
                    self.process_element(
                        level=level_name, type_name=type_name, revit_object=revit_object
                    )
    def process_element(
        self, level: str, type_name: str, revit_object: Dict[str, Any]
    ) -> None:  # Mutating in-place
        elements = getattr(revit_object, ELEMENTS, None)
        if not elements:
            self.logger.log_missing_properties(revit_object[ID], ELEMENTS)
        for element in elements:
            properties = getattr(element, PROPERTIES, None)
            if not properties:
                self.logger.log_missing_properties(
                    revit_object[ID], PROPERTIES
                )  # 🤔 revit_object/element?
                return
            material_quantities = properties.get(MATERIAL_QUANTITIES, None)
            if not material_quantities:
                self.logger.log_missing_properties(
                    revit_object[ID], MATERIAL_QUANTITIES
                )
                return
            for material_name, material_data in material_quantities.items():
                if VOLUME not in material_data:
                    self.logger.log_missing_properties(revit_object[ID], VOLUME)
                    return
                if STRUCTURAL_ASSET not in material_data:
                    self.logger.log_missing_properties(
                        revit_object[ID], STRUCTURAL_ASSET
                    )
                    return
                # ⚠️ This should never hit. No STRUCTURAL_ASSET → no DENSITY
                if DENSITY not in material_data:
                    self.logger.log_missing_properties(revit_object[ID], DENSITY)
                    return
                try:
                    # Dict structure for numerical properties(e.g.)
                    # {"name" : "volume", "value" : 100, "units" : "Cubic metres"}
                    # 🤫 Shouldn't change.
                    volume = material_data[VOLUME][VALUE]
                    density = material_data[DENSITY][VALUE]
                    mass = volume * density
                    material_data[MASS] = {
                        NAME: MASS,
                        VALUE: mass,
                        UNITS: material_data[DENSITY][UNITS].split()[0],
                        # TODO: 🫣 Units string operation is super sketchy.
                    }
                    self.mass_aggregator.add_mass(
                        mass, level, type_name, material_data[STRUCTURAL_ASSET]
                    )
                # ❗ We've validated everything. If the computation fails, there's a bug.
                # 🤾 Throw.
                except (ValueError, TypeError, KeyError) as e:
                    raise ValueError(
                        f"Computation failed for {material_name} despite having required properties: {str(e)}"
                    ) from e
@@ -0,0 +1,15 @@
 REQUIRED_PROPERTIES = ["volume", "density", "structuralAsset"]
 # Keys
 DENSITY = "density"
 ELEMENTS = "elements"
 ID = "id"
 MASS = "mass"
 MATERIAL_QUANTITIES = "Material Quantities"
 NAME = "name"
 PROPERTIES = "properties"
 SOURCE_APPLICATION = "sourceApplication"
 STRUCTURAL_ASSET = "structuralAsset"
 UNITS = "units"
 VALUE = "value"
 VOLUME = "volume"