def test_simulation_creation():
# Building population and profiles dataframes
# with ones everywhere
population_dataframe = create_testing_population_dataframe(year_start=2001, year_end=2061)
profiles_dataframe = create_constant_profiles_dataframe(population_dataframe, tax=1)
r = 0
g = 0
simulation = Simulation()
simulation.set_population(population_dataframe)
simulation.set_profiles(profiles_dataframe)
simulation.set_population_projection(year_length=200, method="constant")
# simulation.set_tax_projection(rate = g, method="per_capita")
simulation.set_tax_projection(rate = g, method="aggregate")
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.create_cohorts()
cohorts = simulation.cohorts
pv = cohorts.aggregate_generation_present_value("tax")
assert pv.get_value((0,0,2007), "tax") == 54
assert pv.get_value((0,0,2008), "tax") == 53
def junk():
population = read_csv('C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\France\sources\data_fr\pop.csv', sep=',')
# print population.columns
population = population.set_index(['age', 'sex'])
population = population.stack()
population = population.reset_index()
population['level_2'] = population.level_2.convert_objects(convert_numeric=True)
population['year'] = population['level_2']
population['pop'] = population[0]
del population['level_2']
del population[0]
population = population.set_index(['age', 'sex', 'year'])
#Remove the years 2007 and beyond to ensure integrity when combined with INSEE data
year = list(range(1991, 2007, 1))
filter_year = array([x in year for x in population.index.get_level_values(2)])
population = population.iloc[filter_year, :]
#Loading insee data
projection = HDFStore('C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\France\sources\data_fr\proj_pop_insee\proj_pop.h5', 'r')
projection_dataframe = projection['/projpop0760_FECbasESPbasMIGbas'] # <-Do not know the precise meaning of this. For testing only
#Combining
concatened = concat([population, projection_dataframe], verify_integrity = True)
concatened = concatened.reset_index()
concatened['year'] = concatened.year.convert_objects(convert_numeric=True)
concatened = concatened.set_index(['age', 'sex', 'year'])
#Saving as HDF5 file
export = HDFStore('neo_population.h5')
export.append('pop', concatened, data_columns = concatened.columns)
export.close()
export = HDFStore('neo_population.h5', 'r')
print export
#Creating the simulation object
net_payments = Simulation()
net_payments.set_population(population)
France = 'France'
net_payments.set_country(France)
r = 0.0
g = 0.01
net_payments.set_discount_rate(r)
net_payments.set_growth_rate(g)
# print net_payments
# print net_payments.growth_rate, net_payments.discount_rate, net_payments.country
net_payments.load_population("neo_population.h5", 'pop')
net_payments.load_profiles("C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\profiles.h5", "profiles.h5")
year_length = 100
net_payments.set_population_projection(year_length = year_length, method = "exp_growth", rate = 0.02)
net_payments.set_tax_projection(method = "per_capita", typ = None, rate = g, discount_rate = r)
net_payments.create_cohorts()
#Creating a column with total taxes paid.
for typ in net_payments._types:
net_payments['total'] += hstack(net_payments[typ])
print net_payments['total']
concatened = concat([population, projection_dataframe], verify_integrity=True)
concatened = concatened.reset_index()
concatened["year"] = concatened.year.convert_objects(convert_numeric=True)
concatened = concatened.set_index(["age", "sex", "year"])
# Saving as HDF5 file
export = HDFStore("neo_population.h5")
export.append("pop", concatened, data_columns=concatened.columns)
export.close()
export = HDFStore("neo_population.h5", "r")
print export
# Creating the simulation object
net_payments = Simulation()
net_payments.set_population(population)
France = "France"
net_payments.set_country(France)
r = 0.0
g = 0.01
net_payments.set_discount_rate(r)
net_payments.set_growth_rate(g)
# print net_payments
# print net_payments.growth_rate, net_payments.discount_rate, net_payments.country
net_payments.load_population("neo_population.h5", "pop")
net_payments.load_profiles("C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\profiles.h5", "profiles.h5")
year_length = 100
net_payments.set_population_projection(year_length=year_length, method="exp_growth", rate=0.02)
net_payments.set_tax_projection(method="per_capita", typ=None, rate=g, discount_rate=r)
#Combining
concatened = concat([population, projection_dataframe], verify_integrity=True)
concatened = concatened.reset_index()
concatened['year'] = concatened.year.convert_objects(convert_numeric=True)
concatened = concatened.set_index(['age', 'sex', 'year'])
#Saving as HDF5 file
export = HDFStore('neo_population.h5')
export.append('pop', concatened, data_columns=concatened.columns)
export.close()
export = HDFStore('neo_population.h5', 'r')
print export
#Creating the simulation object
net_payments = Simulation()
net_payments.set_population(population)
France = 'France'
net_payments.set_country(France)
r = 0.0
g = 0.01
net_payments.set_discount_rate(r)
net_payments.set_growth_rate(g)
# print net_payments
# print net_payments.growth_rate, net_payments.discount_rate, net_payments.country
net_payments.load_population("neo_population.h5", 'pop')
net_payments.load_profiles(
"C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\profiles.h5",
"profiles.h5")
year_length = 100
def junk():
population = read_csv(
'C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\France\sources\data_fr\pop.csv',
sep=',')
# print population.columns
population = population.set_index(['age', 'sex'])
population = population.stack()
population = population.reset_index()
population['level_2'] = population.level_2.convert_objects(
convert_numeric=True)
population['year'] = population['level_2']
population['pop'] = population[0]
del population['level_2']
del population[0]
population = population.set_index(['age', 'sex', 'year'])
#Remove the years 2007 and beyond to ensure integrity when combined with INSEE data
year = list(range(1991, 2007, 1))
filter_year = array(
[x in year for x in population.index.get_level_values(2)])
population = population.iloc[filter_year, :]
#Loading insee data
projection = HDFStore(
'C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\France\sources\data_fr\proj_pop_insee\proj_pop.h5',
'r')
projection_dataframe = projection[
'/projpop0760_FECbasESPbasMIGbas'] # <-Do not know the precise meaning of this. For testing only
#Combining
concatened = concat([population, projection_dataframe],
verify_integrity=True)
concatened = concatened.reset_index()
concatened['year'] = concatened.year.convert_objects(convert_numeric=True)
concatened = concatened.set_index(['age', 'sex', 'year'])
#Saving as HDF5 file
export = HDFStore('neo_population.h5')
export.append('pop', concatened, data_columns=concatened.columns)
export.close()
export = HDFStore('neo_population.h5', 'r')
print export
#Creating the simulation object
net_payments = Simulation()
net_payments.set_population(population)
France = 'France'
net_payments.set_country(France)
r = 0.0
g = 0.01
net_payments.set_discount_rate(r)
net_payments.set_growth_rate(g)
# print net_payments
# print net_payments.growth_rate, net_payments.discount_rate, net_payments.country
net_payments.load_population("neo_population.h5", 'pop')
net_payments.load_profiles(
"C:\Users\Utilisateur\Documents\GitHub\ga\src\countries\profiles.h5",
"profiles.h5")
year_length = 100
net_payments.set_population_projection(year_length=year_length,
method="exp_growth",
rate=0.02)
net_payments.set_tax_projection(method="per_capita",
typ=None,
rate=g,
discount_rate=r)
net_payments.create_cohorts()
#Creating a column with total taxes paid.
for typ in net_payments._types:
net_payments['total'] += hstack(net_payments[typ])
print net_payments['total']
