scripts.census_calibration

Census Calibration Module for Housing Data.

This module provides comprehensive calibration functionality for aligning housing and household data with census statistics. It implements Iterative Proportional Fitting (IPF) algorithms to adjust survey weights to match known population totals across multiple dimensions (building type, ownership, rooms, condition).

The calibration process handles vacancy adjustments and supports multi-year calibration with factor reuse for computational efficiency.

Typical usage example:

from scripts import census_calibration from scripts import misc

ip = misc.load_input_parameters() inh = load_inhabit_data() census_calibration.calibrate(ip, inh)

Functions

apply_calibration_factors(df, factor_lookup, ...)	Apply pre-computed calibration factors to a DataFrame.
calibrate_dataset(df, ip[, weight_col])	Main calibration method using Iterative Proportional Fitting (IPF).
calibrate_ds_hm(dwell_stock, year, ip[, ...])	Calibrate dwelling stock from housing model to census totals.
calibrate_inhabit(year, ip[, factor_lookup])	Calibrate inhabit (household-dwelling) data to Census 2022 targets.
calibrate_with_factor_reuse(years, ip)	Calibrate multiple years using factor reuse strategy.
census_no_vacated(ip)	Create census datasets with vacancy adjustments.
convert_back_to_dataframe(...)	Convert calibrated contingency table back to original DataFrame format.
get_dwell_stock_factors(ip, year, dwell_stock)	Calculate or load calibration factors for dwelling stock data.
get_row_wise_factor(calibrated_df, ...[, ...])	Calculate calibration factors from original and calibrated data.
inject_condition_to_census(inh, base_year, ...)	Inject building condition totals into census data.
ipf_calibrate_region(region_data, region, ...)	Apply Iterative Proportional Fitting (IPF) calibration for a region.
load_calibration_factors(ip, factors_file)	Load previously saved calibration factors from pickle file.
load_census_2022(ip, census_path)	Load and process raw census 2022 data from Excel files.
prepare_data_for_calibration(df, ip[, ...])	Prepare data for calibration by creating multi-dimensional contingency table.
save_calibrated_data(calibrated_df, ip, ...)	Save calibrated data to CSV file.
save_calibration_factors(factor_lookup, ip, ...)	Save calibration factors to pickle file for future reuse.