Vectorize population calibration to improve calculation speeds

[AndreasSahlberg]

onsset/onsset/onsset.py

Lines 1060 to 1153 in 5aab584

	def calibrate_pop_and_urban(self, pop_actual, pop_future_high, pop_future_low, urban_current, urban_future,
	start_year, end_year, intermediate_year):
	"""
	Calibrate the actual current population, the urban split and forecast the future population
	"""
	logging.info('Calibrate current population')
	project_life = end_year - start_year
	# Calculate the ratio between the actual population and the total population from the GIS layer
	pop_ratio = pop_actual / self.df[SET_POP].sum()
	# And use this ratio to calibrate the population in a new column
	self.df[SET_POP_CALIB] = self.df.apply(lambda row: row[SET_POP] * pop_ratio, axis=1)
	self.df[SET_ELEC_POP_CALIB] = self.df[SET_ELEC_POP] * pop_ratio
	if max(self.df[SET_URBAN]) == 3: # THIS OPTION IS CURRENTLY DISABLED
	calibrate = True if 'n' in input(
	'Use urban definition from GIS layer <y/n> (n=model calibration):') else False
	else:
	calibrate = True
	# RUN_PARAM: This is where manual calibration of urban/rural population takes place.
	# The model uses 0, 1, 2 as GHS population layer does.
	# As of this version, urban are only rows with value equal to 2
	if calibrate:
	urban_modelled = 2
	factor = 1
	while abs(urban_modelled - urban_current) > 0.01:
	self.df[SET_URBAN] = 0
	self.df.loc[(self.df[SET_POP_CALIB] > 5000 * factor) & (
	self.df[SET_POP_CALIB] / self.df[SET_GRID_CELL_AREA] > 350 * factor), SET_URBAN] = 1
	self.df.loc[(self.df[SET_POP_CALIB] > 50000 * factor) & (
	self.df[SET_POP_CALIB] / self.df[SET_GRID_CELL_AREA] > 1500 * factor), SET_URBAN] = 2
	pop_urb = self.df.loc[self.df[SET_URBAN] > 1, SET_POP_CALIB].sum()
	urban_modelled = pop_urb / pop_actual
	if urban_modelled > urban_current:
	factor *= 1.1
	else:
	factor *= 0.9
	# Get the calculated urban ratio, and limit it to within reasonable boundaries
	pop_urb = self.df.loc[self.df[SET_URBAN] > 1, SET_POP_CALIB].sum()
	urban_modelled = pop_urb / pop_actual
	if abs(urban_modelled - urban_current) > 0.01:
	print('The modelled urban ratio is {:.2f}. '
	'In case this is not acceptable please revise this part of the code'.format(urban_modelled))
	# Project future population, with separate growth rates for urban and rural
	logging.info('Project future population')
	if calibrate:
	urban_growth_high = (urban_future * pop_future_high) / (urban_modelled * pop_actual)
	rural_growth_high = ((1 - urban_future) * pop_future_high) / ((1 - urban_modelled) * pop_actual)
	yearly_urban_growth_rate_high = urban_growth_high ** (1 / project_life)
	yearly_rural_growth_rate_high = rural_growth_high ** (1 / project_life)
	urban_growth_low = (urban_future * pop_future_low) / (urban_modelled * pop_actual)
	rural_growth_low = ((1 - urban_future) * pop_future_low) / ((1 - urban_modelled) * pop_actual)
	yearly_urban_growth_rate_low = urban_growth_low ** (1 / project_life)
	yearly_rural_growth_rate_low = rural_growth_low ** (1 / project_life)
	else:
	urban_growth_high = pop_future_high / pop_actual
	rural_growth_high = pop_future_high / pop_actual
	yearly_urban_growth_rate_high = urban_growth_high ** (1 / project_life)
	yearly_rural_growth_rate_high = rural_growth_high ** (1 / project_life)
	urban_growth_low = pop_future_low / pop_actual
	rural_growth_low = pop_future_low / pop_actual
	yearly_urban_growth_rate_low = urban_growth_low ** (1 / project_life)
	yearly_rural_growth_rate_low = rural_growth_low ** (1 / project_life)
	# RUN_PARAM: Define here the years for which results should be provided in the output file.
	yearsofanalysis = [intermediate_year, end_year]
	for year in yearsofanalysis:
	self.df[SET_POP + "{}".format(year) + 'High'] = self.df.apply(lambda row: row[SET_POP_CALIB] *
	(yearly_urban_growth_rate_high **
	(year - start_year))
	if row[SET_URBAN] > 1
	else row[SET_POP_CALIB] *
	(yearly_rural_growth_rate_high ** (year - start_year)), axis=1)
	self.df[SET_POP + "{}".format(year) + 'Low'] = self.df.apply(lambda row: row[SET_POP_CALIB] *
	(yearly_urban_growth_rate_low **
	(year - start_year))
	if row[SET_URBAN] > 1
	else row[SET_POP_CALIB] *
	(yearly_rural_growth_rate_low ** (year - start_year)), axis=1)
	self.df[SET_POP + "{}".format(start_year)] = self.df.apply(lambda row: row[SET_POP_CALIB], axis=1)
	return urban_modelled

This issue is also related to issue 10

[AndreasSahlberg]

There are several case where using the "apply" method, which can be improved by working directly with the entires Series/DataFrame