"""
Define defaults for use throughout FPsim
"""
import numpy as np
import sciris as sc
from . import base as fpb
#%% 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
max_age_preg = 50 # Maximum age to become pregnant
max_parity = 20 # Maximum number of children
#%% Defaults when creating a new person
[docs]
class State:
def __init__(self, name, val=None, dtype=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.
"""
self.name = name
self.val = val
self.dtype = dtype
[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
elif isinstance(vals, list):
arr = [[] for _ in range(n)]
else:
if self.dtype is None: dtype = object if isinstance(vals, str) else None
else: dtype = self.dtype
arr = np.full(shape=n, fill_value=vals, dtype=dtype)
return arr
# 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('method', 0, int),
State('barrier', 0, int),
# 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),
# Indices of children -- list of lists
State('children', [], list),
# Dates
State('dobs', [], list), # Dates of birth of children
State('still_dates', [], list), # Dates of stillbirths -- list of lists
State('miscarriage_dates', [], list), # Dates of miscarriages -- list of lists
State('abortion_dates', [], list), # Dates of abortions -- list of lists
State('short_interval_dates',[], list), # age of agents at short birth interval -- list of lists
# Fecundity
State('remainder_months', 0, int),
State('personal_fecundity', 0, int),
# Empowerment - states will remain at these values if use_empowerment is False
State('paid_employment', 0, bool),
State('decision_wages', 0, float),
State('decision_health', 0, float),
State('sexual_autonomy', 0, float),
# Partnership information -- states will remain at these values if use_partnership is False
State('partnered', 0, bool),
State('partnership_age', -1, float),
# Urban (bsic demographics) -- state will remain at these values if use_urban is False
State('urban', 1, bool),
State('region', None, str),
# 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)
]
person_defaults = fpb.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 + 1)
# Define allowable keys to select all (all ages, all methods, etc)
none_all_keys = [None, 'all', ':', [None], ['all'], [':']]
# Definition of contraceptive methods and corresponding numbers -- can be overwritten by locations
method_map = {
'None' : 0,
'Pill' : 1,
'IUDs' : 2,
'Injectables' : 3,
'Condoms' : 4,
'BTL' : 5,
'Withdrawal' : 6,
'Implants' : 7,
'Other traditional' : 8,
'Other modern' : 9,
}
# Age bins for different method switching matrices -- can be overwritten by locations
method_age_map = {
'<18': [ 0, 18],
'18-20': [18, 20],
'21-25': [20, 25],
'26-35': [25, 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]
}
age_specific_channel_bins = method_youth_age_map
by_age_results = sc.autolist(
'acpr',
'cpr',
'mcpr',
'pregnancies',
'births',
'imr_numerator',
'imr_denominator',
'mmr_numerator',
'mmr_denominator',
'imr',
'mmr',
'as_stillbirths',
'stillbirths',
)
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',
'mcpr',
'cpr',
'acpr',
'pp0to5',
'pp6to11',
'pp12to23',
'nonpostpartum',
'total_women_fecund',
'unintended_pregs',
'birthday_fraction',
'short_intervals',
'secondary_births',
'proportion_short_interval'
)
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',
'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',
)
