"""
Define defaults for use throughout FPsim
"""
import numpy as np
import sciris as sc
import starsim as ss
#%% Global defaults
useSI = True
mpy = 12 # Months per year, to avoid magic numbers
eps = 1e-9 # To avoid divide-by-zero
min_age = 15 # Minimum age to be considered eligible to use contraceptive methods
max_age = 99 # Maximum age (inclusive)
max_age_preg = 50 # Maximum age to become pregnant
max_parity = 20 # Maximum number of children to track - also applies to abortions, miscarriages, stillbirths
max_parity_spline = 20 # Used for parity splines
#%% Defaults when creating a new person
[docs]
class State:
def __init__(self, name, val=None, dtype=None, ncols=None):
"""
Initialize a state
Args:
name (str): name of state
val (list, array, float, or str): value(s) to populate array with
dtype (dtype): datatype. Inferred from val if not provided.
ncols (int): number of cols, needed for 2d states like birth_ages (n_agents * n_births)
"""
self.name = name
self.val = val
self.dtype = dtype
self.ncols = ncols
@property
def ndim(self):
return 1 if self.ncols is None else 2
[docs]
def new(self, n, vals=None):
"""
Define an empty array with the correct value and data type
"""
if vals is None: vals = self.val # Use default if none provided
if isinstance(vals, np.ndarray):
assert len(vals) == n
arr = vals
else:
if self.dtype is None: dtype = object if isinstance(vals, str) else None
else: dtype = self.dtype
shape = n if self.ncols is None else (n, self.ncols)
arr = np.full(shape=shape, fill_value=vals, dtype=dtype)
return arr
# Parse locations
[docs]
def get_location(location, printmsg=False):
default_location = 'senegal'
if not location:
if printmsg: print('Location not supplied: using parameters from Senegal')
location = default_location
location = location.lower() # Ensure it's lowercase
if location == 'test':
if printmsg: print('Running test simulation using parameters from Senegal')
location = default_location
if location == 'default':
if printmsg: print('Running default simulation using parameters from Senegal')
location = default_location
# Define valid locations
valid_country_locs = ['senegal', 'kenya', 'ethiopia']
if location not in valid_country_locs:
errormsg = f'Location "{location}" is not currently supported'
raise NotImplementedError(errormsg)
return location
# Defaults states and values of any new(born) agent unless initialized with data or other strategy
# or updated during the course of a simulation.
person_defaults = [
# Basic demographics
State('uid', -1, int),
State('age', 0, float),
State('age_by_group', 0, float),
State('sex', 0, bool),
State('alive', 1, bool),
# Contraception
State('on_contra', 0, bool), # whether she's on contraception
State('method', 0, int), # Which method to use. 0 used for those on no method
State('ti_contra', 0, int), # time point at which to set method
State('barrier', 0, int),
State('ever_used_contra', 0, bool), # Ever been on contraception. 0 for never having used
# Sexual and reproductive history
State('parity', 0, int),
State('pregnant', 0, bool),
State('fertile', 0, bool),
State('sexually_active', 0, bool),
State('sexual_debut', 0, bool),
State('sexual_debut_age', -1, float),
State('fated_debut', -1, float),
State('first_birth_age', -1, float),
State('lactating', 0, bool),
State('gestation', 0, int),
State('preg_dur', 0, int),
State('stillbirth', 0, int),
State('miscarriage', 0, int),
State('abortion', 0, int),
State('pregnancies', 0, int),
State('months_inactive', 0, int),
State('postpartum', 0, bool),
State('mothers', -1, int),
State('short_interval', 0, int),
State('secondary_birth', 0, int),
State('postpartum_dur', 0, int),
State('lam', 0, bool),
State('breastfeed_dur', 0, int),
State('breastfeed_dur_total', 0, int),
# Fecundity
State('remainder_months', 0, int),
State('personal_fecundity', 0, int),
# Empowerment - states will remain at these values if use_empowerment is False
# NOTE: to use empowerment metrics, please refer to the kenya_empowerment repo
# These states will be refactored into a separate module as part of the V3 release.
State('paid_employment', 0, bool),
State('decision_wages', 0, bool),
State('decision_health', 0, bool),
State('decision_purchase', 0, bool),
State('buy_decision_major', 0, bool), # whether she has decision making ability over major purchases
State('buy_decision_daily', 0, bool), # whether she has decision making over daily household purchases
State('buy_decision_clothes', 0, bool), # whether she has decision making over clothing purchases
State('decide_spending_partner', 0, bool), # whether she has decision makking over her partner's wages
State('has_savings', 0, bool), # whether she has savings
State('has_fin_knowl', 0, bool), # whether she knows where to get financial info
State('has_fin_goals', 0, bool), # whether she has financial goals
State('sexual_autonomy', 0, bool), # whether she has ability to refuse sex
# Composite empowerment attributes
State('financial_autonomy', 0, float),
State('decision_making', 0, float),
# Empowerment - fertility intent
State('fertility_intent', 0, bool),
State('categorical_intent', "cannot", "<U6"),
State('intent_to_use', 0, bool), # for women not on contraception, whether she has intent to use contraception
# Partnership information -- states will remain at these values if use_partnership is False
State('partnered', 0, bool),
State('partnership_age', -1, float),
# Socioeconomic
State('urban', 1, bool),
State('wealthquintile', 3, int), # her current wealth quintile, an indicator of the economic status of her household, 1: poorest quintile; 5: wealthiest quintile
# Education - states will remain at these values if use_education is False
State('edu_objective', 0, float),
State('edu_attainment', 0, float),
State('edu_dropout', 0, bool),
State('edu_interrupted', 0, bool),
State('edu_completed', 0, bool),
State('edu_started', 0, bool),
State('child_inds', -1, int, ncols=max_parity),
State('birth_ages', np.nan, float, ncols=max_parity), # Ages at time of live births
State('stillborn_ages', np.nan, float, ncols=max_parity), # Ages at time of stillbirths
State('miscarriage_ages', np.nan, float, ncols=max_parity), # Ages at time of miscarriages
State('abortion_ages', np.nan, float, ncols=max_parity), # Ages at time of abortions
# State('short_interval_ages', np.nan, float, ncols=max_parity) # Ages of agents at short birth interval
]
person_defaults = ss.ndict(person_defaults)
# Postpartum keys to months
postpartum_map = {
'pp0to5': [0, 6],
'pp6to11': [6, 12],
'pp12to23': [12, 24]
}
# Age bins for tracking age-specific fertility rate
age_bin_map = {
'10-14': [10, 15],
'15-19': [15, 20],
'20-24': [20, 25],
'25-29': [25, 30],
'30-34': [30, 35],
'35-39': [35, 40],
'40-44': [40, 45],
'45-49': [45, 50]
}
# Age and parity splines
spline_ages = np.arange(max_age + 1)
spline_preg_ages = np.arange(max_age_preg + 1)
spline_parities = np.arange(max_parity_spline + 1)
# Define allowable keys to select all (all ages, all methods, etc)
none_all_keys = [None, 'all', ':', [None], ['all'], [':']]
# Age bins for different method switching matrices -- can be overwritten by locations
method_age_map = {
'<18': [ 0, 18],
'18-20': [18, 20],
'20-25': [20, 25],
'25-35': [25, 35],
'>35': [35, max_age+1], # +1 since we're using < rather than <=
}
immutable_method_age_map = {
'<18': [ 0, 18],
'18-20': [18, 20],
'20-25': [20, 25],
'25-30': [25, 30],
'30-35': [30, 35],
'>35': [35, max_age+1], # +1 since we're using < rather than <=
}
method_youth_age_map = {
'<16': [10, 16],
'16-17': [16, 18],
'18-19': [18, 20],
'20-22': [20, 23],
'23-25': [23, 26],
'>25': [26, max_age+1]
}
array_results = sc.autolist(
't',
'pop_size_months',
'pregnancies',
'births',
'deaths',
'stillbirths',
'miscarriages',
'abortions',
'total_births',
'maternal_deaths',
'infant_deaths',
'cum_maternal_deaths',
'cum_infant_deaths',
'on_methods_mcpr',
'no_methods_mcpr',
'on_methods_cpr',
'no_methods_cpr',
'on_methods_acpr',
'no_methods_acpr',
'contra_access',
'new_users',
'mcpr',
'cpr',
'acpr',
'ever_used_contra',
'switchers',
'urban_women',
'pp0to5',
'pp6to11',
'pp12to23',
'parity0to1',
'parity2to3',
'parity4to5',
'parity6plus',
'wq1',
'wq2',
'wq3',
'wq4',
'wq5',
'nonpostpartum',
'total_women_fecund',
'method_failures',
'birthday_fraction',
'short_intervals',
'secondary_births',
'proportion_short_interval',
# Education
'edu_objective',
'edu_attainment',
# Empowerment and intent: all zero unless using an empowerment module
'perc_contra_intent',
'perc_fertil_intent',
'paid_employment',
'decision_wages',
'decide_spending_partner',
"buy_decision_major",
"buy_decision_daily",
"buy_decision_clothes",
"decision_health",
"has_savings",
"has_fin_knowl",
"has_fin_goals",
"financial_autonomy",
"decision_making",
)
for age_group in age_bin_map.keys():
array_results += 'total_births_' + age_group
array_results += 'total_women_' + age_group
list_results = sc.autolist(
'tfr_years',
'tfr_rates',
'pop_size',
'mcpr_by_year',
'cpr_by_year',
'contra_access_over_year',
'new_users_over_year',
'method_failures_over_year',
'infant_deaths_over_year',
'total_births_over_year',
'live_births_over_year',
'stillbirths_over_year',
'miscarriages_over_year',
'abortions_over_year',
'pregnancies_over_year',
'short_intervals_over_year',
'secondary_births_over_year',
'risky_pregs_over_year',
'maternal_deaths_over_year',
'proportion_short_interval_by_year',
'mmr',
'imr',
'birthday_fraction',
'method_usage',
)
# Map between key names in results and annualised results, some of them are different
to_annualize = {
'method_failures' :'method_failures',
'infant_deaths' :'infant_deaths',
'total_births' :'total_births',
'births' :'live_births', # X
'stillbirths' :'stillbirths',
'miscarriages' :'miscarriages',
'abortions' :'abortions',
'short_intervals' : 'short_intervals',
'secondary_births': 'secondary_births',
'maternal_deaths' : 'maternal_deaths',
'pregnancies' : 'pregnancies',
'contra_access' : 'contra_access',
'new_users' : 'new_users'}
# People's states for which we will need circular buffers
longitude_keys = [
'on_contra',
'intent_to_use',
'buy_decision_major',
'buy_decision_clothes',
'buy_decision_daily',
'has_fin_knowl',
'has_fin_goals',
'financial_autonomy',
'has_fin_goals',
'paid_employment',
'has_savings',
'decision_wages',
'decide_spending_partner',
'decision_health'
]
