'''
Load data
'''
#%% Housekeeping
import numpy as np
import sciris as sc
from . import country_age_data as cad
from . import state_age_data as sad
from . import household_size_data as hsd
__all__ = ['default_age_data', 'get_country_aliases', 'map_entries', 'show_locations', 'get_age_distribution', 'get_household_size']
# Default age data, based on Seattle 2018 census data -- used in population.py
default_age_data = np.array([
[ 0, 4, 0.0605],
[ 5, 9, 0.0607],
[10, 14, 0.0566],
[15, 19, 0.0557],
[20, 24, 0.0612],
[25, 29, 0.0843],
[30, 34, 0.0848],
[35, 39, 0.0764],
[40, 44, 0.0697],
[45, 49, 0.0701],
[50, 54, 0.0681],
[55, 59, 0.0653],
[60, 64, 0.0591],
[65, 69, 0.0453],
[70, 74, 0.0312],
[75, 79, 0.02016], # Calculated based on 0.0504 total for >=75
[80, 84, 0.01344],
[85, 89, 0.01008],
[90, 99, 0.00672],
])
[docs]def get_country_aliases():
''' Define aliases for countries with odd names in the data '''
country_mappings = {
'Bolivia': 'Bolivia (Plurinational State of)',
'Burkina': 'Burkina Faso',
'Cape Verde': 'Cabo Verdeo',
'Hong Kong': 'China, Hong Kong Special Administrative Region',
'Macao': 'China, Macao Special Administrative Region',
"Cote d'Ivore": 'Côte d’Ivoire',
"Ivory Coast": 'Côte d’Ivoire',
'DRC': 'Democratic Republic of the Congo',
'Iran': 'Iran (Islamic Republic of)',
'Laos': "Lao People's Democratic Republic",
'Micronesia': 'Micronesia (Federated States of)',
'Korea': 'Republic of Korea',
'South Korea': 'Republic of Korea',
'Moldova': 'Republic of Moldova',
'Russia': 'Russian Federation',
'Palestine': 'State of Palestine',
'Syria': 'Syrian Arab Republic',
'Taiwan': 'Taiwan Province of China',
'Macedonia': 'The former Yugoslav Republic of Macedonia',
'UK': 'United Kingdom of Great Britain and Northern Ireland',
'United Kingdom': 'United Kingdom of Great Britain and Northern Ireland',
'Tanzania': 'United Republic of Tanzania',
'USA': 'United States of America',
'United States': 'United States of America',
'Venezuela': 'Venezuela (Bolivarian Republic of)',
'Vietnam': 'Viet Nam',
}
return country_mappings # Convert to lowercase
[docs]def map_entries(json, location):
'''
Find a match between the JSON file and the provided location(s).
Args:
json (list or dict): the data being loaded
location (list or str): the list of locations to pull from
'''
# The data have slightly different formats: list of dicts or just a dict
countries = [key.lower() for key in json.keys()]
# Set parameters
if location is None:
location = countries
else:
location = sc.promotetolist(location)
# Define a mapping for common mistakes
mapping = get_country_aliases()
mapping = {key.lower(): val.lower() for key, val in mapping.items()}
entries = {}
for loc in location:
lloc = loc.lower()
if lloc not in countries and lloc in mapping:
lloc = mapping[lloc]
try:
ind = countries.index(lloc)
entry = list(json.values())[ind]
entries[loc] = entry
except ValueError as E:
suggestions = sc.suggest(loc, countries, n=4)
if suggestions:
errormsg = f'Location "{loc}" not recognized, did you mean {suggestions}? ({str(E)})'
else:
errormsg = f'Location "{loc}" not recognized ({str(E)})'
raise ValueError(errormsg)
return entries
[docs]def show_locations(location=None, output=False):
'''
Print a list of available locations.
Args:
location (str): if provided, only check if this location is in the list
output (bool): whether to return the list (else print)
**Examples**::
sp.people.show_locations() # Print a list of valid locations
sp.people.show_locations('lithuania') # Check if Lithuania is a valid location
sp.people.show_locations('Viet-Nam') # Check if Viet-Nam is a valid location
New in version 1.10.0.
'''
country_json = sc.dcp(cad.data)
state_json = sc.dcp(sad.data)
aliases = get_country_aliases()
age_data = sc.mergedicts(state_json, country_json, aliases) # Countries will overwrite states, e.g. Georgia
household_data = sc.dcp(hsd.data)
loclist = sc.objdict()
loclist.age_distributions = sorted(list(age_data.keys()))
loclist.household_size_distributions = sorted(list(household_data.keys()))
if location is not None:
age_available = location.lower() in [v.lower() for v in loclist.age_distributions]
hh_available = location.lower() in [v.lower() for v in loclist.household_size_distributions]
age_sugg = ''
hh_sugg = ''
age_sugg = f'(closest match: {sc.suggest(location, loclist.age_distributions)})' if not age_available else ''
hh_sugg = f'(closest match: {sc.suggest(location, loclist.household_size_distributions)})' if not hh_available else ''
print(f'For location "{location}":')
print(f' Population age distribution is available: {age_available} {age_sugg}')
print(f' Household size distribution is available: {hh_available} {hh_sugg}')
return
if output:
return loclist
else:
print(f'There are {len(loclist.age_distributions)} age distributions and {len(loclist.household_size_distributions)} household size distributions.')
print('\nList of available locations (case insensitive):\n')
sc.pp(loclist)
return
[docs]def get_age_distribution(location=None):
'''
Load age distribution for a given country or countries.
Args:
location (str or list): name of the country or countries to load the age distribution for
Returns:
age_data (array): Numpy array of age distributions, or dict if multiple locations
New in version 1.10.0.
'''
# Load the raw data
country_json = sc.dcp(cad.data)
state_json = sc.dcp(sad.data)
json = sc.mergedicts(state_json, country_json) # Countries will overwrite states, e.g. Georgia
entries = map_entries(json, location)
max_age = 99
result = {}
for loc,age_distribution in entries.items():
total_pop = sum(list(age_distribution.values()))
local_pop = []
for age, age_pop in age_distribution.items():
if age[-1] == '+':
val = [int(age[:-1]), max_age, age_pop/total_pop]
else:
ages = age.split('-')
val = [int(ages[0]), int(ages[1]), age_pop/total_pop]
local_pop.append(val)
result[loc] = np.array(local_pop)
if len(result) == 1:
result = list(result.values())[0]
return result
[docs]def get_household_size(location=None):
'''
Load average household size distribution for a given country or countries.
Args:
location (str or list): name of the country or countries to load the age distribution for
Returns:
house_size (float): Size of household, or dict if multiple locations
New in version 1.10.0.
'''
# Load the raw data
json = sc.dcp(hsd.data)
result = map_entries(json, location)
if len(result) == 1:
result = list(result.values())[0]
return result