def test_dsct():
population = create_empty_population_dataframe(2001, 2061)
cohorts = Cohorts(data=population, columns=['pop'])
cohorts.gen_dsct(0.05)
test_value = cohorts.get_value((0, 0, 2060), 'dsct')
# print test_value
assert test_value <= 1
def test_dsct():
population = create_empty_population_dataframe(2001, 2061)
cohorts = Cohorts(data=population, columns=["pop"])
cohorts.gen_dsct(0.05)
test_value = cohorts.get_value((0, 0, 2060), "dsct")
# print test_value
assert test_value <= 1
def population_project(self,
year_length=None,
method=None,
growth_rate=None):
"""
Continuation of population to provide convergent present values
Parameters
----------
year_length : int, default None
Duration to continue the population projection
method : str, default None
The value must be 'stable' or 'exp_growth'
"""
if 'pop' not in self.columns:
raise Exception('pop is not a column of cohort')
if year_length is None:
raise Exception('a duration in years should be provided')
if method is None:
raise Exception('a method should be specified')
years = self.index_sets['year']
first_year = min(years)
last_year = max(years)
if (first_year + year_length) > last_year:
new_last_year = first_year + year_length
else:
return
if method == 'stable':
last_pop = self.xs(last_year, level='year', axis=0)
pop = DataFrame(self['pop'])
years = range(last_year + 1, new_last_year + 1)
list_df = [last_pop] * len(years)
pop = concat(list_df, keys=years, names=['year'])
pop = pop.reorder_levels(['age', 'sex', 'year'], axis=0)
combined = self.combine_first(pop)
self.__init__(data=combined, columns=['pop'])
if method == 'exp_growth':
if growth_rate is None:
raise Exception(
'a growth rate must be provided for the method')
last_pop = self.xs(last_year, level='year', axis=0)
pop = DataFrame(self['pop'])
years = range(last_year + 1, new_last_year + 1)
list_df = [last_pop] * len(years)
pop = concat(list_df, keys=years, names=['year'])
pop = pop.reorder_levels(['age', 'sex', 'year'], axis=0)
pop = Cohorts(pop)
pop.gen_grth(growth_rate)
pop['pop'] *= pop['grth']
del pop['grth']
combined = self.combine_first(pop)
self.__init__(data=combined, columns=['pop'])
def extract_generation(self, year, typ, age = None):
"""
Returns a dataframe containning chosen data to follow the evolution of a given group over time.
Parameters
----------
year : Int
A year of reference contained in the cohort.
typ : Str
A column or a list of columns of the cohort that one wants to follow
age : Int
Default is zero. The age of reference of the group one is interested in.
Returns
-------
generation_cohort : a cohort dataframe with the data of the group of people
whose given references belong to.
"""
if year is None:
raise Exception('a year of reference is needed')
if year not in list(self.index_sets['year']):
raise Exception('The given year is not valid')
year_min = array(list(self.index_sets['year'])).min()
year_max = array(list(self.index_sets['year'])).max()
if typ not in self.columns:
raise Exception('the given column is not in the cohort')
#Normalizing the age if not given
if age is None:
age = 0
pvm = self.xs(0, level='sex')
pvf = self.xs(1, level='sex')
#Creating lower bounds for the filter generation
if age > 0:
if year-age >= year_min:
start_age = 0
year_start = year - age
else:
start_age = age - (year - year_min)
year_start = year_min
else:
start_age = age
year_start = year
#Creating upper bounds for filter_list
if year + (100-age) >= year_max:
year_end = year_max
end_age = age + (year_max - year)
else:
year_end = year + 100 - age
end_age = 100
#Creating the filtering list
filter_list = zip(range(start_age, end_age+1), range(year_start, year_end+1))
# Generation the generation DataFrame
generation_data_male = pvm.loc[filter_list, typ]
generation_data_female = pvf.loc[filter_list, typ]
pieces = [generation_data_male, generation_data_female]
res = concat(pieces, keys = [0,1], names = ["sex"] )
res = res.reorder_levels(['age','sex','year'])
generation_cohort = Cohorts(res)
generation_cohort.columns = [typ]
return generation_cohort
