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131 lines
5 KiB
Python
131 lines
5 KiB
Python
from typing import List, Dict, Any
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from collections import Counter
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from collections import defaultdict
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from model_data.utils import correct_spelling
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from model_data.epc_attributes.FloorAttributes import FloorAttributes
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from model_data.epc_attributes.HotWaterAttributes import HotWaterAttributes
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from model_data.epc_attributes.MainFuelAttributes import MainFuelAttributes
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from model_data.epc_attributes.MainheatAttributes import MainHeatAttributes
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from model_data.epc_attributes.MainheatControlAttributes import MainheatControlAttributes
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from model_data.epc_attributes.RoofAttributes import RoofAttributes
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from model_data.epc_attributes.WallAttributes import WallAttributes
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from model_data.epc_attributes.WindowAttributes import WindowAttributes
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from model_data.epc_attributes.LightingAttributes import LightingAttributes
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class EpcClean:
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"""
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Container for methods which we utilise for epc_attributes EPC data
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"""
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CLEANING_FIELDS: List[str] = [
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"floor-description",
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"hotwater-description",
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"main-fuel",
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"mainheat-description",
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"mainheatcont-description",
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"roof-description",
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"walls-description",
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"windows-description",
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"lighting-description"
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]
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def __init__(self, data: List[Dict[str, Any]],
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lighting_averages: List[Dict[str, str | float]] | None = None) -> None:
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"""
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EpcClean constructor.
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:param data: List of dictionaries containing EPC data.
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"""
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self.data: List[Dict[str, Any]] = data
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self.unique_vals: Dict[str, Any] = {}
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self.cleaned: Dict[str, List[Any]] = {}
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if not lighting_averages:
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self.lighting_averages = self._calculate_lighting_averages()
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else:
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self.lighting_averages = lighting_averages
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def _calculate_lighting_averages(self):
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"""
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This is a simple utility function that for few textual lighting descriptions, will calculate the average
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low energy lighting proportion. This is only valid for a very tiny number of cases and so a very simple
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methodology is applied
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This is done without pandas so we can utilise this inside of our lambdas
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:return: list of avergages for the corresponding descriptions
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"""
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data = self.data
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# Filter rows with the specified lighting descriptions
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filtered_data = [
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row for row in data if row["lighting-description"] in [
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'Below average lighting efficiency',
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'Good lighting efficiency',
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'Excelent lighting efficiency'
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]
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]
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# Convert low-energy-lighting to float
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for row in filtered_data:
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row["low-energy-lighting"] = float(row["low-energy-lighting"])
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# Calculate averages
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sums = defaultdict(float)
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counts = defaultdict(int)
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for row in filtered_data:
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description = row["lighting-description"]
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sums[description] += row["low-energy-lighting"]
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counts[description] += 1
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averages = [{
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"lighting-description": correct_spelling(description.lower()),
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"low-energy-lighting": total / counts[description]
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} for description, total in sums.items()]
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return averages
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def clean(self) -> None:
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"""
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Cleans the EPC data, mapping text fields to property epc_attributes.
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"""
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self._init_empty_cleaned_obj()
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for field in self.CLEANING_FIELDS:
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self.unique_vals[field] = Counter([v[field] for v in self.data])
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self.clean_wrapper(field="floor-description", cleaning_cls=FloorAttributes)
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self.clean_wrapper(field="hotwater-description", cleaning_cls=HotWaterAttributes)
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self.clean_wrapper(field="main-fuel", cleaning_cls=MainFuelAttributes)
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self.clean_wrapper(field="mainheat-description", cleaning_cls=MainHeatAttributes)
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self.clean_wrapper(field="mainheatcont-description", cleaning_cls=MainheatControlAttributes)
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self.clean_wrapper(field="roof-description", cleaning_cls=RoofAttributes)
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self.clean_wrapper(field="walls-description", cleaning_cls=WallAttributes)
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self.clean_wrapper(field="windows-description", cleaning_cls=WindowAttributes)
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self.clean_wrapper(
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field="lighting-description", cleaning_cls=LightingAttributes, averages=self.lighting_averages
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)
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def _init_empty_cleaned_obj(self) -> None:
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"""
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Initializes an empty object for cleaned data.
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"""
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self.cleaned = {field: [] for field in self.CLEANING_FIELDS}
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def clean_wrapper(self, field, cleaning_cls, **kwargs):
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for description in self.unique_vals[field].keys():
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cln = cleaning_cls(description, **kwargs)
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self.cleaned[field].append(
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{
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"original_description": description,
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"clean_description": cln.description.capitalize(),
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**cln.process()
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}
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)
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