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_fr():
#
#
#
############################################################
## load data
############################################################
# load population data
DIR = '../data_fr/proj_pop_insee'
store = HDFStore(os.path.join(DIR, 'proj_pop.h5'))
pop = store['projpop0760_FECcentESPcentMIGcent']
store.close()
profiles = Cohorts(data=pop, columns=['pop'])
DIR = '../data_fr'
store = HDFStore(os.path.join(DIR, 'profiles.h5'))
vars = store['profiles']
store.close()
profiles.fill(vars)
# set r and g
r = 0.060
g = 0.012
# create discount factor and growth rate
# print out.to_string()
# print coh.totaux(['year', 'age'], pivot = True).to_string()
profiles.gen_grth(g)
profiles.gen_dsct(r)
# profiles.to_percap()
# profiles.to_csv('out.csv')
# profiles['typ1'].unstack(level = 'year').to_csv('out.csv')
#print profiles['typ1'].unstack(level = 'year').head().to_string()
#print profiles['grth']
#print profiles['tva']
var = 'educ'
pv_g = profiles.pv_ga(var)
y = 2060
a = 4
# print (profiles.ix[a,1,y][var]*profiles.ix[a,1,y]['pop']*profiles.ix[a,1,y]['grth']*profiles.ix[a,1,y]['dsct'])
# print pvf[2060].head()
# print pvf.index
# print pv_g[2060].head()
p = profiles.pv_percap(var)
print p.index.names
print p.ix[:, 0, 2007].to_string()
print p
def test_fr():
#
#
#
############################################################
## load data
############################################################
# load population data
DIR= '../data_fr/proj_pop_insee'
store = HDFStore(os.path.join(DIR,'proj_pop.h5'))
pop = store['projpop0760_FECcentESPcentMIGcent']
store.close()
profiles = Cohorts(data = pop, columns = ['pop'])
DIR= '../data_fr'
store = HDFStore(os.path.join(DIR,'profiles.h5'))
vars = store['profiles']
store.close()
profiles.fill(vars)
# set r and g
r = 0.060
g = 0.012
# create discount factor and growth rate
# print out.to_string()
# print coh.totaux(['year', 'age'], pivot = True).to_string()
profiles.gen_grth(g)
profiles.gen_dsct(r)
# profiles.to_percap()
# profiles.to_csv('out.csv')
# profiles['typ1'].unstack(level = 'year').to_csv('out.csv')
#print profiles['typ1'].unstack(level = 'year').head().to_string()
#print profiles['grth']
#print profiles['tva']
var = 'educ'
pv_g = profiles.pv_ga(var)
y = 2060
a = 4
# print (profiles.ix[a,1,y][var]*profiles.ix[a,1,y]['pop']*profiles.ix[a,1,y]['grth']*profiles.ix[a,1,y]['dsct'])
# print pvf[2060].head()
# print pvf.index
# print pv_g[2060].head()
p = profiles.pv_percap(var)
print p.index.names
print p.ix[:,0,2007].to_string()
print p
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 = Cohorts(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_us():
############################################"
## set simulation parameters
############################################"
# set path to data
DIR= 'data_us'
# number of types (tax or transfer)
nb_type = 17
# define aggregate scenario
agg_scenar = 'aggmed' # can be in ('agglow', 'aggmed', 'agghigh')
# define population scenario
pop_scenar = 'popmed' # can be in ('popmed', 'pophigh', 'poplow')
############################################################
## load data
############################################################
# load population data
datafile = os.path.join(DIR, pop_scenar +'.csv')
profiles = Cohorts()
profiles.set_population_from_csv(datafile)
# load relative profiles and fill Cohorte
datafile = os.path.join(DIR, 'rp.csv')
data = read_csv(datafile, index_col = [0,1])
for i in range(1, nb_type + 1):
var = 'typ%i'%i
profiles.new_type(var)
for sex in profiles.index_sets['sex']:
profiles.fill(data.ix[sex, i], var, sex)
# load aggregate data (year x types)
datafile = os.path.join(DIR, agg_scenar+'.csv')
agg = read_csv(datafile, index_col = 0)
profiles.add_agg(agg)
# set r and g
r = 0.060
g = 0.012
# create discount factor and growth rate
# print out.to_string()
# print coh.totaux(['year', 'age'], pivot = True).to_string()
profiles.gen_grth(g)
profiles.gen_dsct(r)
profiles.to_percap()
# profiles['typ1'].unstack(level = 'year').to_csv('out.csv')
#print profiles['typ1'].unstack(level = 'year').head().to_string()
print profiles.pv_ga('typ1')
def test_us():
############################################"
## set simulation parameters
############################################"
# set path to data
DIR = 'data_us'
# number of types (tax or transfer)
nb_type = 17
# define aggregate scenario
agg_scenar = 'aggmed' # can be in ('agglow', 'aggmed', 'agghigh')
# define population scenario
pop_scenar = 'popmed' # can be in ('popmed', 'pophigh', 'poplow')
############################################################
## load data
############################################################
# load population data
datafile = os.path.join(DIR, pop_scenar + '.csv')
profiles = Cohorts()
profiles.set_population_from_csv(datafile)
# load relative profiles and fill Cohorte
datafile = os.path.join(DIR, 'rp.csv')
data = read_csv(datafile, index_col=[0, 1])
for i in range(1, nb_type + 1):
var = 'typ%i' % i
profiles.new_type(var)
for sex in profiles.index_sets['sex']:
profiles.fill(data.ix[sex, i], var, sex)
# load aggregate data (year x types)
datafile = os.path.join(DIR, agg_scenar + '.csv')
agg = read_csv(datafile, index_col=0)
profiles.add_agg(agg)
# set r and g
r = 0.060
g = 0.012
# create discount factor and growth rate
# print out.to_string()
# print coh.totaux(['year', 'age'], pivot = True).to_string()
profiles.gen_grth(g)
profiles.gen_dsct(r)
profiles.to_percap()
# profiles['typ1'].unstack(level = 'year').to_csv('out.csv')
#print profiles['typ1'].unstack(level = 'year').head().to_string()
print profiles.pv_ga('typ1')