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 test_generation_extraction():
# Creating a fake cohort
n = 0.00
r = 0.00
g = 0.05
population = create_testing_population_dataframe(year_start=2001,
year_end=2061,
rate=n)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
cohort = DataCohorts(population)
# Applying projection methods
year_length = 199
method = 'stable'
cohort.population_project(year_length, method=method)
cohort.fill(profile)
typ = None
cohort.proj_tax(g, r, typ, method='per_capita')
cohort = AccountingCohorts(cohort)
cohort._types = ['tax']
#Extracting generations
start = 2030
age = 0
generation = cohort.extract_generation(start, typ='tax', age=age)
count = age
while count <= 100 & start + count <= array(
list(generation.index_sets['year'])).max():
assert abs((1 + g)**(count + (start - 2001)) +
generation.get_value((count, 1,
start + count), 'tax')) == 0.0
count += 1
def test_generation_extraction():
# Creating a fake cohort
n = 0.00
r = 0.00
g = 0.05
population = create_testing_population_dataframe(year_start=2001, year_end=2061, rate=n)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
cohort = DataCohorts(population)
# Applying projection methods
year_length = 199
method = 'stable'
cohort.population_project(year_length, method = method)
cohort._fill(profile)
typ = None
cohort.proj_tax(g, r, typ, method = 'per_capita')
cohort = AccountingCohorts(cohort)
cohort._types = ['tax']
#Extracting generations
start = 2030
age = 0
generation = cohort.extract_generation(start, typ = 'tax', age = age)
count = age
while count <= 100 & start + count <= array(list(generation.index_sets['year'])).max():
assert abs((1+g)**(count+(start-2001)) + generation.get_value((count, 1, start+count), 'tax')) == 0.0
count +=1
def test_filter_value():
"""
Testing the method to filter data from a given cohort
"""
#Generate a testing cohort with 5% population and economy growth
n = 0.05
population = create_testing_population_dataframe(year_start=2001,
year_end=2061,
rate=n)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
cohort = DataCohorts(population)
cohort._fill(profile)
r = 0.0
g = 0.05
column = None
cohort.proj_tax(g, r, column, method='per_capita')
#Testing restrictions
cohort_filtered = cohort.filter_value(age=list(range(0, 100, 1)),
year=list(range(2001, 2060, 5)),
typ='tax')
count = 2001
while count <= 2060:
assert abs(
cohort_filtered.get_value((0, 1, count), 'tax') +
(1 + g)**(count - 2001)) == 0.0
count += 5
def test_fill_cohort():
population = create_empty_population_dataframe(2001, 2061)
profiles = create_constant_profiles_dataframe(population, tax = -1, subsidies = 0.5)
cohorts_test = DataCohorts(data = population, columns = ['pop'])
cohorts_test.fill(profiles, year = None)
test_value = cohorts_test.get_value((0,0,2060), 'tax')
assert test_value == -1
def test_column_combination():
year_start = 2001
pop_dataframe = create_empty_population_dataframe(year_start, 2061)
# print pop_dataframe
profiles_dataframe = create_constant_profiles_dataframe(pop_dataframe, tax=-1.0, sub=0.5)
# print profiles_dataframe
profiles_dataframe['net'] = profiles_dataframe.sum(axis=1)
# print profiles_dataframe['net']
test_value = profiles_dataframe.get_value((0,0,2001), 'net')
# print test_value
assert test_value == -0.5
def test_column_combination():
year_start = 2001
pop_dataframe = create_empty_population_dataframe(year_start, 2061)
# print pop_dataframe
profiles_dataframe = create_constant_profiles_dataframe(pop_dataframe, tax=-1.0, sub=0.5)
# print profiles_dataframe
profiles_dataframe["net"] = profiles_dataframe.sum(axis=1)
# print profiles_dataframe['net']
test_value = profiles_dataframe.get_value((0, 0, 2001), "net")
# print test_value
assert test_value == -0.5
def test_comparison():
population_dataframe = create_testing_population_dataframe(year_start=2001,
year_end=2261,
population=2)
profiles_dataframe = create_constant_profiles_dataframe(
population_dataframe, tax=1)
r = 0.00
g = 0.00
n = 0.00
simulation = Simulation()
simulation.population = population_dataframe
simulation.population_alt = population_dataframe
simulation.profiles = profiles_dataframe
net_gov_wealth = -10
net_gov_spending = 0
taxes_list = ['tax']
payments_list = ['sub']
simulation.set_population_projection(year_length=simulation.year_length,
method="exp_growth")
simulation.set_tax_projection(method="per_capita", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.set_population_growth_rate(n)
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(net_gov_spending, default=True)
simulation.create_cohorts()
simulation.create_present_values(typ='tax')
simulation.cohorts_alt = simulation.cohorts
simulation.cohorts_alt.loc[
[x == 2102 for x in simulation.cohorts_alt.index.get_level_values(2)],
'tax'] = (-100)
simulation.create_present_values(typ='tax', default=False)
ipl_base = simulation.compute_ipl(typ='tax')
ipl_alt = simulation.compute_ipl(typ='tax', default=False, precision=False)
#Saving the decomposed ipl:
to_save = simulation.break_down_ipl(typ='tax', default=False, threshold=60)
# to_save = age_class_pv
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Carole_Bonnet/choc_test_alt.xlsx"
to_save.to_excel(xls, 'ipl')
print ipl_base
print ipl_alt
assert ipl_base == -105232
def test_compute_net_transfers():
population = create_empty_population_dataframe(2001, 2061)
profiles = create_constant_profiles_dataframe(population, tax = 1, subsidies = 0.5)
tax = ['tax']
subsidy = ['subsidies']
cohorts_test = DataCohorts(data = population, columns = ['pop'])
cohorts_test.fill(profiles, year = None)
cohorts_test.compute_net_transfers(taxes_list = tax, payments_list = subsidy)
test_value = cohorts_test.get_value((0,0,2060), 'net_transfers')
assert test_value == 0.5
pass
def test_tax_projection_aggregated():
n = 1
population = create_testing_population_dataframe(year_start=2001, year_end=2061, rate=n)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
g = 0.5
r = 0.0
cohort = DataCohorts(population)
cohort.fill(profile)
typ = None
cohort.proj_tax(g, r, typ, method = 'aggregate')
test_value = cohort.get_value((0,1,2002), 'tax')
test_value2 = cohort.get_value((0,1,2002), 'sub')
assert test_value2 < 0.5 and test_value > -1
def test_comparison():
population_dataframe = create_testing_population_dataframe(year_start=2001, year_end=2261, population=2)
profiles_dataframe = create_constant_profiles_dataframe(population_dataframe, tax=1)
r = 0.00
g = 0.00
n = 0.00
simulation = Simulation()
simulation.population = population_dataframe
simulation.population_alt = population_dataframe
simulation.profiles = profiles_dataframe
net_gov_wealth = -10
net_gov_spending = 0
taxes_list = ['tax']
payments_list = ['sub']
simulation.set_population_projection(year_length=simulation.year_length, method="exp_growth")
simulation.set_tax_projection(method="per_capita", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.set_population_growth_rate(n)
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(net_gov_spending, default=True)
simulation.create_cohorts()
simulation.create_present_values(typ='tax')
simulation.cohorts_alt = simulation.cohorts
simulation.cohorts_alt.loc[[x==2102 for x in simulation.cohorts_alt.index.get_level_values(2)], 'tax'] = (-100)
simulation.create_present_values(typ='tax', default=False)
ipl_base = simulation.compute_ipl(typ='tax')
ipl_alt = simulation.compute_ipl(typ='tax', default=False, precision=False)
#Saving the decomposed ipl:
to_save = simulation.break_down_ipl(typ='tax', default=False, threshold=60)
# to_save = age_class_pv
xls = os.path.join(SRC_PATH, 'test_comparison.xlsx')
to_save.to_excel(xls, 'ipl')
print ipl_base
print ipl_alt
assert ipl_base == -105232
def test_tax_projection():
population = create_empty_population_dataframe(2001, 2061)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
g = 0.05
r = 0
cohort = DataCohorts(population)
year_length = 200
method = 'stable'
cohort.population_project(year_length, method=method)
cohort._fill(profile)
typ = None
cohort.proj_tax(g, r, typ, method='per_capita')
# print cohort
test_value = cohort.get_value((0, 1, 2002), 'tax')
test_value2 = cohort.get_value((0, 1, 2002), 'sub')
# print test_value, test_value2
# TODO: I don't understand the following <- Just wanted to check if the for loop works by changing the value of typ in the test
assert test_value2 > 0.5 and test_value < -1
def test_create_age_class():
"""
Testing the method to regroup age classes
"""
population = create_testing_population_dataframe(2001, 2003)
profile = create_constant_profiles_dataframe(population, tax = 1.0, sub=-0.5)
cohort = DataCohorts(population)
cohort._fill(profile)
cohort2 = cohort.per_capita_generation_present_value('tax', discount_rate=0)
print cohort2.head()
print cohort2.tail()
step = 5.0
age_class = cohort2.create_age_class(step = step, typ='tax')
print age_class.tail(10)
count = 0
while count < 100:
assert age_class.get_value((count, 1, 2001), 'tax') == 2, 'valeur obtenue %f' %(age_class.get_value((count, 1, 2001), 'tax'))
count += step
def test_tax_projection():
population = create_empty_population_dataframe(2001, 2061)
profile = create_constant_profiles_dataframe(population, tax = -1, sub=0.5)
g = 0.05
r = 0
cohort = DataCohorts(population)
year_length = 200
method = 'stable'
cohort.population_project(year_length, method = method)
cohort.fill(profile)
typ = None
cohort.proj_tax(g, r, typ, method = 'per_capita')
# print cohort
test_value = cohort.get_value((0,1,2002), 'tax')
test_value2 = cohort.get_value((0,1,2002), 'sub')
# print test_value, test_value2
# TODO: I don't understand the following <- Just wanted to check if the for loop works by changing the value of typ in the test
assert test_value2 > 0.5 and test_value < -1
def test_create_age_class():
"""
Testing the method to regroup age classes
"""
population = create_testing_population_dataframe(2001, 2003)
profile = create_constant_profiles_dataframe(population, tax=1.0, sub=-0.5)
cohort = DataCohorts(population)
cohort.fill(profile)
cohort2 = cohort.per_capita_generation_present_value('tax',
discount_rate=0)
print cohort2.head()
print cohort2.tail()
step = 5.0
age_class = cohort2.create_age_class(step=step)
print age_class.tail(10)
count = 0
while count < 100:
assert age_class.get_value((count, 1, 2001), 'tax') == 2
count += step
def test_filter_value():
"""
Testing the method to filter data from a given cohort
"""
# Generate a testing cohort with 5% population and economy growth
n = 0.05
population = create_testing_population_dataframe(year_start=2001, year_end=2061, rate=n)
profile = create_constant_profiles_dataframe(population, tax=-1, sub=0.5)
cohort = DataCohorts(population)
cohort._fill(profile)
r = 0.0
g = 0.05
column = None
cohort.proj_tax(g, r, column, method="per_capita")
# Testing restrictions
cohort_filtered = cohort.filter_value(age=list(range(0, 100, 1)), year=list(range(2001, 2060, 5)), typ="tax")
count = 2001
while count <= 2060:
assert abs(cohort_filtered.get_value((0, 1, count), "tax") + (1 + g) ** (count - 2001)) == 0.0
count += 5