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pars_data.py
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pars_data.py
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"""
Compilation of sexual behavior data and assumptions
"""
import numpy as np
debut = dict(
f=dict(dist='normal', par1=16.5, par2=2.),
m=dict(dist='normal', par1=17.0, par2=2.))
layer_probs = dict(
m=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0, 0.126, 0.599, 0.873, 0.936, 0.93, 0.90, 0.865, 0.55, 0.4, 0.4, 0.4, 0.4, 0.4], # Share of females of each age who are married
[0, 0, 0, 0.017, 0.205, 0.575, 0.835, 0.934, 0.952, 0.952, 0.5, 0.5, 0.5, 0.4, 0.4, 0.3]] # Share of males of each age who are married
),
c=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.10, 0.7, 0.8, 0.6, 0.6, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.05, 0.01], # Share of females of each age having casual relationships
[0, 0, 0.05, 0.7, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.01, 0.01]], # Share of males of each age having casual relationships
),
o=np.array([
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
[0, 0, 0.01, 0.05, 0.05, 0.04, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # Share of females of each age having one-off relationships
[0, 0, 0.01, 0.01, 0.01, 0.02, 0.03, 0.04, 0.05, 0.05, 0.03, 0.02, 0.01, 0.01, 0.01, 0.01]], # Share of males of each age having one-off relationships
),
)
mixing = dict(
m=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .1, .1, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, .5, .1, .5, .1, .1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 1, .5, .5, .5, .5, .1, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, .5, 1, 1, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, .1, 1, 1, 2, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, .1, 1, 1, 1, 1, 2, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, .1, .5, 1, 1, 1, 2, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
]),
c=np.array([
# 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, 1, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, 1, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, 1.0, 1.0, 0.5, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, .5, 1, 1, 1, .5, 0.5, 0.5, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, .5, 1, 1, 1, 1, .5, 0.5, 0.5, 0, 0, 0, 0, 0],
[40, 0, 0, 0, .5, .5, 1, 1, 1, 1, .5, 0.5, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0.5, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
o=np.array([
# 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, .5, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[30, 0, 0, 0, 0, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0, 0],
[35, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0, 0],
[40, 0, 0, 0, 0, 0, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0, 0],
[45, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0, 0],
[50, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0, 0],
[55, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0, 0],
[60, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5, 0, 0],
[65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, .5, 0],
[70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, .5],
[75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
]),
)