ebm.model.bema module

BUILDING_CATEGORY_ORDER = mappingproxy({BuildingCategory.HOUSE: 101, BuildingCategory.APARTMENT_BLOCK: 102, 'residential': 199, 'holiday_home': 299, BuildingCategory.RETAIL: 301, BuildingCategory.OFFICE: 302, BuildingCategory.KINDERGARTEN: 303, BuildingCategory.SCHOOL: 304, BuildingCategory.UNIVERSITY: 305, BuildingCategory.HOSPITAL: 306, BuildingCategory.NURSING_HOME: 307, BuildingCategory.HOTEL: 308, BuildingCategory.SPORTS: 309, BuildingCategory.CULTURE: 310, BuildingCategory.STORAGE: 312, 'storage': 311, 'non_residential': 399}): An immutable dict of BeMa sorting order for building_category

BUILDING_GROUP_ORDER = mappingproxy({'residential': 199, 'holiday_home': 299, 'non_residential': 399}): An immutable dict of BeMa sorting order for building_group

TEK_ORDER = mappingproxy({'PRE_TEK49': 1814, 'TEK49': 1949, 'TEK69': 1969, 'TEK87': 1987, 'TEK97': 1997, 'TEK07': 2007, 'TEK10': 2010, 'TEK17': 2017, 'TEK21': 2021, 'default': 9998, 'all': 9999}): A dict of BeMa sorting order for TEK

PURPOSE_ORDER = mappingproxy({'heating_rv': 1, 'heating_dhw': 2, 'fans_and_pumps': 3, 'lighting': 4, 'electrical_equipment': 5, 'cooling': 6}): A dict of BeMa sorting order for purpose

BUILDING_CONDITION_ORDER = mappingproxy({BuildingCondition.ORIGINAL_CONDITION: 1, BuildingCondition.SMALL_MEASURE: 2, BuildingCondition.RENOVATION: 3, BuildingCondition.RENOVATION_AND_SMALL_MEASURE: 4, BuildingCondition.DEMOLITION: 5}): A dict of BeMa sorting order for building_condition

START_ROWS_CONSTRUCTION_BUILDING_CATEGORY = mappingproxy({BuildingCategory.HOUSE: 11, BuildingCategory.APARTMENT_BLOCK: 23, BuildingCategory.KINDERGARTEN: 41, BuildingCategory.SCHOOL: 55, BuildingCategory.UNIVERSITY: 69, BuildingCategory.OFFICE: 83, BuildingCategory.RETAIL: 97, BuildingCategory.HOTEL: 111, BuildingCategory.HOSPITAL: 125, BuildingCategory.NURSING_HOME: 139, BuildingCategory.CULTURE: 153, BuildingCategory.SPORTS: 167, BuildingCategory.STORAGE: 182}): A dict of BeMa sorting order for start row of each building category in the sheet nybygging

get_building_category_sheet(building_category: BuildingCategory, area_sheet: bool = True) → str[source]

Returns the appropriate sheet name based on the building category and area sheet type.

Parameters

building_category: An instance of BuildingCategory.
area_sheet (bool): Determines whether to return the area sheet (‘A’) or rates sheet (‘R’) name. Defaults to True for the area sheet.

Returns

sheet (str): The sheet name corresponding to the building category and sheet type.

map_sort_order(column)[source]

Map the sort order from bema to a DataFrame. The function is meant to be used as the key parameter for a pandas DataFrame methods sort_values and sort_index.

Example below.

Parameters

columnpandas.Series: A pandas Series whose name attribute determines which predefined mapping to apply to its values.

Returns

pandas.Series: A Series with values mapped to integers according to the corresponding sort order. If the column name does not match any predefined mapping, the original Series is returned unchanged.

Notes

The function supports the following mappings:

‘building_category’: uses _building_mix_order
‘building_group’: uses BUILDING_GROUP_ORDER
‘building_condition’: uses BUILDING_CONDITION_ORDER
‘purpose’: uses PURPOSE_ORDER
‘building_code’: uses TEK_ORDER

Examples

>>> import pandas as pd
>>> df = pd.DataFrame(
>>>    data=[('culture', 'PRE_TEK49', 'heating_rv', 2022, 'LAST'),
>>>          ('house', 'TEK07', 'heating_dhw', 2021, 'FIRST')],
>>>    columns=['building_category', 'building_code', 'purpose', 'year', 'value'])
>>> df.sort_values(by=['building_category', 'building_code', 'purpose', 'year'], key=map_sort_order)
    building_category        building_code     purpose  year  value
 1  house      TEK07  heating_dhw  2020  FIRST
 0  culture  PRE_TEK49   heating_rv  2021   LAST
…
>>> from ebm.model.bema import map_sort_order
>>> import pandas as pd
>>> df = pd.DataFrame(data=['3', '2', 'last', 'first'],
>>>                  index=pd.Index(['non_residential', 'holiday_home', 'all', 'residential'],
>>>                                 name='building_group'))
>>> df.sort_index(key=map_sort_order)
  building_group
  residential      first
  holiday_home         2
  non_residential      3
  all               last