### this is the merged table
orca.merge_tables('pets', tables=['pets', 'species'], columns=['pet_name', 'age', 'species_name', 'age_rate'])
# this is a common merge so might as well define it once here and use it
@orca.table()
def pets_merged(pets, species):
return orca.merge_tables(pets.name,
tables=[pets, species],
columns=['pet_name', 'age', 'species_name', 'age_rate'])
# this is the orca registered version of the merged table
orca.get_table('pets_merged').to_frame()
# eval_variable is a debugging tool to eval tables, functions and other injectables
with orca.injectables(x=1, y=2):
df = orca.eval_variable('pets_merged', pets=df_pet, species=df_species).to_frame()
df
##########################################################################
# pipelining
##########################################################################
# create a step to age pets at a rate appropriate to their species
@orca.step()
def age_simulate(pets):
new_age = pets.age + orca.get_table('pets_merged').age_rate
pets.update_col_from_series('age', new_age)
# create a second step to illustrate how pipelining works
@orca.step()
def summarize(pets, iter_var):
# this is the orca registered version of the merged table
orca.get_table('pets_merged').to_frame()
##########################################################################
# simple_simulate
##########################################################################
# register our spec table (customarily stored in configs dir)
# (configs_dir predefined as '.' by injectable in activitysim.defaults.misc.py)
@orca.injectable()
def pet_spec(configs_dir):
f = os.path.join(configs_dir, 'configs', "pet_activity.csv")
return asim.read_model_spec(f).fillna(0)
orca.eval_variable('pet_spec')
# step-by-step illustration of what simple_simulate does behind the scenes
@orca.step()
def pet_activity_simple_simulate(set_random_seed, pets_merged, pet_spec):
# choosers: the choice model will be applied to each row of the choosers table (a pandas.DataFrame)
choosers = pets_merged.to_frame()
# spec: table of variable specifications and coefficient values of alternatives (a pandas.DataFrame table
spec = pet_spec
# locals whose values will be accessible to the execution context when the expressions in spec are applied to choosers
locals_d=None
def test_read_settings():
settings = orca.eval_variable('settings')
assert settings.get('answer') == 42