NLSA
4c62807fe9
build and deploy Speckle functions / publish-automate-function-version (push) Has been cancelled
210 lines
7.1 KiB
Python
210 lines
7.1 KiB
Python
"""This module contains the function's business logic.
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Use the automation_context module to wrap your function in an Automate context helper.
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"""
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import pandas as pd
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from datetime import datetime
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from pydantic import Field, SecretStr
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from speckle_automate import (
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AutomateBase,
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AutomationContext,
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execute_automate_function,
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)
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from utils.flatten import flatten_base
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from utils.data_extraction import get_properties_for_list, create_pivot_tables
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from utils.excel_print import print_scheduled_excel
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class FunctionInputs(AutomateBase):
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"""These are function author-defined values.
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Automate will make sure to supply them matching the types specified here.
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Please use the pydantic model schema to define your inputs:
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https://docs.pydantic.dev/latest/usage/models/
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"""
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file_name: str = Field(
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title="File Name",
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description="The name of the Excel file.",
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)
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inlcude_areas: bool = Field(
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title="Calculate Areas",
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description="If CHECKED it will calculate the area of all Areas in the model.",
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default= True
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)
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inlcude_rooms: bool = Field(
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title="Calculate Rooms",
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description="If CHECKED it will calculate the area of all Rooms in the model.",
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default= False
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)
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nua_list: str = Field(
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title="NUA (Nett Usable Area)",
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strict= False
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)
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nia_list: str = Field(
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title="NIA (Nett Internal Area)",
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strict= False
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)
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nla_list: str = Field(
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title="NLA (Nett Leasable Area)",
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strict= False
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)
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gia_list: str = Field(
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title="GIA (Gross Internal Area)",
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strict= False
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)
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gea_list: str = Field(
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title="GEA (Gross External Area)",
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strict= False
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)
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gla_list: str = Field(
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title="GLA (Gross Leasable Area)",
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strict= False
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)
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gba_list: str = Field(
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title="GBA (Gross Building Area)",
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strict= False
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)
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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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) -> None:
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"""This is an example Speckle Automate function.
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Args:
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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 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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"""
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# The context provides a convenient way to receive the triggering version.
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version_root_object = automate_context.receive_version()
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file_name = function_inputs.file_name
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areas_status = function_inputs.inlcude_areas
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rooms_status = function_inputs.inlcude_rooms
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NUA = [prop.strip() for prop in function_inputs.nua_list.split(",")]
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NIA = [prop.strip() for prop in function_inputs.nia_list.split(",")]
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NLA = [prop.strip() for prop in function_inputs.nla_list.split(",")]
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GIA = [prop.strip() for prop in function_inputs.gia_list.split(",")]
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GEA = [prop.strip() for prop in function_inputs.gea_list.split(",")]
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GLA = [prop.strip() for prop in function_inputs.gla_list.split(",")]
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GBA = [prop.strip() for prop in function_inputs.gba_list.split(",")]
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all_objects = list(flatten_base(version_root_object))
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filter_categories = []
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if rooms_status:
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filter_categories.append("Rooms")
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if areas_status:
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filter_categories.append("Areas")
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if not filter_categories:
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print("To make calculations you need to select one of the two options or both. Areas or/and Rooms")
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automate_context.mark_run_failed("To make calculations you need to select one of the two options or both. Areas or/and Rooms")
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return
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items = []
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id_lists = []
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for i in all_objects:
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if hasattr(i, "category"): # Check if the object has the "category" attribute
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if i.category in filter_categories: # Check if the category matches the filter list
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items.append(i) # Append the whole object to the items list
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id_lists.append(i.id)
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else:
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continue # Skip if "category" does not exist
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# List of properties
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list_prop = [
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"category",
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"level.name",
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"properties.Parameters.Instance Parameters.Identity Data.Name.value",
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"properties.Parameters.Instance Parameters.Dimensions.Area.value",
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]
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try:
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final_properties = get_properties_for_list(items, list_prop)
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# Convert to a DataFrame
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df = pd.DataFrame(final_properties)
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df.columns = ["category","level","name","area"]
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df_pivot_tables = create_pivot_tables(filter_categories, df)
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#NUA = []
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#NIA = ["Café","Café Kitchen","Common"]
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#NLA = ["Café","Café Kitchen","Common","Elevator E1","Level 5 Gross","Live/Work Unit"]
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# Create new DataFrame with grouped sums
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df_grouped = pd.DataFrame({
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"level": df["level"].unique(), # Ensure all levels are present
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"NUA": sum_group(df, NUA).values, # Handles empty group
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"NIA": sum_group(df, NIA).values,
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"NLA": sum_group(df, NLA).values,
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"GIA": sum_group(df, GIA).values,
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"GEA": sum_group(df, GEA).values,
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"GLA": sum_group(df, GLA).values,
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"GBA": sum_group(df, GBA).values
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})
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# Add total row
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df_grouped.loc["Total"] = df_grouped.select_dtypes(include="number").sum()
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df_grouped.loc["Total", "level"] = "Total"
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df_pivot_tables.append(df_grouped)
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titles = filter_categories
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titles.append("KPIs")
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scheduled_titles = ["Schedule " + word for word in titles]
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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xlsx_filename = f"{file_name}_{timestamp}.xlsx"
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output_file = print_scheduled_excel(df_pivot_tables, scheduled_titles, xlsx_filename)
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automate_context.store_file_result(f"./{output_file}")
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automate_context.mark_run_success("All data sent successfully! Download your file below.")
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except:
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automate_context.mark_run_failed("An error occurred while writing to the file. Ensure that the parameters 'Level,' 'Name,' and 'Area' exist. Additionally, verify that the area/room names are correctly typed and separated by commas.")
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# Function to sum area for each group, ensuring missing levels return 0
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def sum_group(df, group):
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if not group: # If the group is empty, return a series of 0s
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return pd.Series(0, index=df["level"].unique())
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grouped = df[df["name"].isin(group)].groupby("level")["area"].sum()
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return grouped.reindex(df["level"].unique(), fill_value=0) # Ensure all levels exist, fill missing with 0
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# make sure to call the function with the executor
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if __name__ == "__main__":
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# NOTE: always pass in the automate function by its reference; do not invoke it!
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# Pass in the function reference with the inputs schema to the executor.
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execute_automate_function(automate_function, FunctionInputs)
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# If the function has no arguments, the executor can handle it like so
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# execute_automate_function(automate_function_without_inputs)
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