def create_neutral_profiles_cohort(population):
"""
Utility function which generates a DataCohort with population and transfers.
No projection is needed, the dataframe is delivered without "hole"
Parameters
----------
population : Int
The value of the constant population at each period and for each age
Returns
-------
cohort : a cohort dataframe such that :
Anybody below 50 years old (excluded) pays -1
Anybody between 50 and 99 years old (included) recieve 1
Anybody over 100 years old (included) pays 0
As such, the total net transfer is zero for newborns
"""
n = 0
population_dataframe = create_testing_population_dataframe(year_start=2001, year_end=2201, rate=n, population=population)
# print population.get_value((0,0,2011), 'pop')
profile = create_constant_profiles_dataframe(population_dataframe, tax=-1)
g = 0.0
r = 0.0
cohort = DataCohorts(population_dataframe)
cohort.fill(profile)
cohort.loc[cohort.index.get_level_values(0) >= 0,'tax'] = -1
cohort.loc[cohort.index.get_level_values(0) >= 50,'tax'] = 1
cohort.loc[cohort.index.get_level_values(0) == 100,'tax'] = 0
typ = 'tax'
cohort.proj_tax(g, r, typ, method = 'per_capita')
return cohort
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_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_population_projection():
# Create cohorts
start_data = 2001
end_data = 2061
population = create_empty_population_dataframe(start_data, end_data)
cohorts = DataCohorts(data = population, columns = ['pop'])
# Complete population projection
year_length = 100
end_project = start_data + year_length
method = 'exp_growth'
growth_rate = n = 0.05
cohorts.population_project(year_length, method = method, growth_rate = n)
year_control = 2082
control_value = (1+n)**(year_control - end_data - 1)
test_value = cohorts.get_value((0,0,2081), "pop")
assert test_value == control_value
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_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_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 create_neutral_profiles_cohort(population):
"""
Utility function which generates a DataCohort with population and transfers.
No projection is needed, the dataframe is delivered without "hole"
Parameters
----------
population : Int
The value of the constant population at each period and for each age
Returns
-------
cohort : a cohort dataframe such that :
Anybody below 50 years old (excluded) pays -1
Anybody between 50 and 99 years old (included) recieve 1
Anybody over 100 years old (included) pays 0
As such, the total net transfer is zero for newborns
"""
n = 0
population_dataframe = create_testing_population_dataframe(
year_start=2001, year_end=2201, rate=n, population=population)
# print population.get_value((0,0,2011), 'pop')
profile = create_constant_profiles_dataframe(population_dataframe, tax=-1)
g = 0.0
r = 0.0
cohort = DataCohorts(population_dataframe)
cohort.fill(profile)
cohort.loc[cohort.index.get_level_values(0) >= 0, 'tax'] = -1
cohort.loc[cohort.index.get_level_values(0) >= 50, 'tax'] = 1
cohort.loc[cohort.index.get_level_values(0) == 100, 'tax'] = 0
typ = 'tax'
cohort.proj_tax(g, r, typ, method='per_capita')
return cohort
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_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
