def calc_TLU_livestock(is_farming):
"""
Calculates the livestock ownership (in Tropical Livestock Units) of this
household (based on analysis of 1996 CVFS data).
"""
if is_farming:
TLU = draw_from_prob_dist(rcParams['TLU_probs.farmer'])
else:
TLU = draw_from_prob_dist(rcParams['TLU_probs.nonfarmer'])
# Given the way this prob dist was defined in R, if TLU is less than zero,
# it means that the household has 0 livestock units.
if TLU < 0: TLU = 0
return TLU
def calc_first_birth_time(person):
"""
Calculates the time from marriage until first birth for this person (not
used if the Ghimire and Axinn 2010 model is selected in rcparams.
"""
first_birth_prob_dist = rcParams['prob.firstbirth.times']
return int(draw_from_prob_dist(first_birth_prob_dist))
def calc_des_num_children():
"Calculates the desired number of children for this person."
des_num_children_prob_dist = rcParams['prob.num.children.desired']
# Use np.floor as the last number in the des_num_children prob dist (10) is
# not actually seen in the Chitwan data. It is included only as the
# right-hand bound of the distribution.
return np.floor(draw_from_prob_dist(des_num_children_prob_dist))
def calc_spouse_age_diff(person):
"""
This function draws the age difference between this person and their
spouse based on the observed probability distribution. Note that the age
difference is defined as male's age - woman's age, so positive age
differences should be subtracted from men's ages to get their spouse age, and
added to women's.
"""
return draw_from_prob_dist(rcParams['spousechoice.male.agediff'])
def calc_inmigrant_household_ethnicity(as_integer=False):
ethnicity = int(draw_from_prob_dist(rcParams['inmigrant_HH.prob.ethnicity']))
if not as_integer:
if ethnicity == 1:
ethnicity = "HighHindu"
elif ethnicity == 2:
ethnicity = "HillTibeto"
elif ethnicity == 3:
ethnicity = "LowHindu"
elif ethnicity == 4:
ethnicity = "Newar"
elif ethnicity == 5:
ethnicity = "TeraiTibeto"
else:
logger.critical("Undefined ethnicity %s drawn for new inmigrant household"%ethnicity)
return ethnicity
def calc_migration_length(person, BURN_IN):
"""
Calculated the length of a migration from a probability distribution.
Note: The BURN_IN variable is used in the initialization of a model run to
model migrations that occurred prior to the beginning of the data
collection. Permanent migrations are not allowed during this burn-in
period.
"""
# First decide if it is permanent, according to the
# "prob.migration.length.permanent" parameter:
if not BURN_IN and np.random.rand() < rcParams['prob.migration.length.permanent']:
# TODO: Instead of very long term in agent-store, just remove them from
# the model with the make_permanent_outmigration method.
return 99999999
mig_length_prob_dist = rcParams['prob.migration.lengths']
# Use ceil here so the minimum value is 1, and the maximum value is 36
return np.ceil(draw_from_prob_dist(mig_length_prob_dist))
def calc_hh_area():
"Calculates the area of this household."
hh_area_prob_dist = rcParams['lulc.area.hh']
return draw_from_prob_dist(hh_area_prob_dist)
def calc_total_possessions():
"""
Calculates the number of possessions this household owns (based on analysis
of 1996 CVFS data).
"""
return np.floor(draw_from_prob_dist(rcParams['total_possessions_probs']) + 1)
def calc_birth_interval():
"Calculates the birth interval for this person."
birth_interval_prob_dist = rcParams['prob.birth.intervals']
return np.floor(draw_from_prob_dist(birth_interval_prob_dist))
def calc_inmigrant_household_size():
return int(draw_from_prob_dist(rcParams['inmigrant_HH.prob.hh_size']))
def calc_num_inmigrant_households():
"""
Draws the number of in migrating households in a given month based on an
empirical probability distribution.
"""
return int(draw_from_prob_dist(rcParams['inmigrant_HH.prob.num_HHs']))
