def test_rate_to_probability_vectorizability():
rate = 0.001
rate = np.array([rate] * 100)
prob = rate_to_probability(rate)
assert round(prob[10], 5) == round(0.00099950016662497809, 5)
assert round(np.sum(rate), 5) == round(np.sum(probability_to_rate(prob)),
5)
def on_time_step(self, event):
pop = self.population_view.get(event.index, query="alive =='alive' and sex != 'nan'")
pop['time_since_last_migration'] = event.time - pop['last_outmigration_time']
# only allow individuals that have not migrated internaly on the last year.
pop = pop[(pop['time_since_last_migration'] > pd.Timedelta("365 days")) | (pop['time_since_last_migration'].notnull() == False)]
prob_df = rate_to_probability(pd.DataFrame(self.int_outmigration_rate(pop.index)))
prob_df['No'] = 1-prob_df.sum(axis=1)
pop['internal_outmigration'] = self.random.choice(prob_df.index, prob_df.columns, prob_df)
int_outmigrated_pop = pop.query('internal_outmigration != "No"').copy()
if not int_outmigrated_pop.empty:
int_outmigrated_pop['internal_outmigration'] = pd.Series('Yes', index=int_outmigrated_pop.index)
int_outmigrated_pop['last_outmigration_time'] = event.time
int_outmigrated_pop['previous_LAD_locations'] += int_outmigrated_pop['location']
int_outmigrated_pop['previous_MSOA_locations'] += int_outmigrated_pop['MSOA']
new_MSOA, new_LAD = self.assign_internal_migration(int_outmigrated_pop)
int_outmigrated_pop['MSOA'] = new_MSOA
int_outmigrated_pop['location'] = new_LAD
self.population_view.update(int_outmigrated_pop[['last_outmigration_time',
'internal_outmigration',
'previous_LAD_locations',
'previous_MSOA_locations',
'MSOA',
'location']])
def on_time_step(self, event):
pop = self.population_view.get(event.index, query="alive =='alive'")
prob_df = rate_to_probability(pd.DataFrame(self.mortality_rate(pop.index)))
prob_df['no_death'] = 1-prob_df.sum(axis=1)
prob_df['cause_of_death'] = self.random.choice(prob_df.index, prob_df.columns, prob_df)
dead_pop = prob_df.query('cause_of_death != "no_death"').copy()
if not dead_pop.empty:
dead_pop['alive'] = pd.Series('dead', index=dead_pop.index)
dead_pop['exit_time'] = event.time
dead_pop['years_of_life_lost'] = self.life_expectancy(dead_pop.index)
self.population_view.update(dead_pop[['alive', 'exit_time', 'cause_of_death', 'years_of_life_lost']])
def on_time_step(self, event):
pop = self.population_view.get(
event.index, query="alive =='alive' and sex != 'nan'")
prob_df = rate_to_probability(
pd.DataFrame(self.emigration_rate(pop.index)))
prob_df['no_emigration'] = 1 - prob_df.sum(axis=1)
prob_df['emigrated'] = self.random.choice(prob_df.index,
prob_df.columns, prob_df)
emigrated_pop = prob_df.query('emigrated != "no_emigration"').copy()
if not emigrated_pop.empty:
emigrated_pop['alive'] = pd.Series('emigrated',
index=emigrated_pop.index)
emigrated_pop['emigrated'] = pd.Series('Yes',
index=emigrated_pop.index)
emigrated_pop['exit_time'] = event.time
self.population_view.update(
emigrated_pop[['alive', 'exit_time', 'emigrated']])
def test_rate_to_probability_symmetry():
rate = np.array([0.0001])
for _ in range(100):
prob = rate_to_probability(rate)
assert np.isclose(rate, probability_to_rate(prob))
rate += (1 - 0.0001) / 100.0
def test_rate_to_probability():
rate = np.array([0.001])
prob = rate_to_probability(rate)
assert np.isclose(prob, 0.00099950016662497809)
def _probability(self, index):
return rate_to_probability(self.transition_rate(index))
def _probability(self, index):
effective_rate = self.transition_rate(index)
return rate_to_probability(effective_rate)