def _decile_net(nivvie_net, champm, wprm):
'''
Décile de niveau de vie net
'men'
'''
labels = arange(1, 11)
method = 2
decile, values = mark_weighted_percentiles(nivvie_net, labels, wprm * champm, method, return_quantiles = True)
return decile * champm
def _pauvre60(nivvie, champm, wprm):
'''
Indicatrice de pauvreté à 60% du niveau de vie median
'men'
'''
labels = arange(1, 3)
method = 2
percentile, values = mark_weighted_percentiles(nivvie, labels, wprm * champm, method, return_quantiles = True)
threshold = .6 * values[1]
return (nivvie <= threshold) * champm
def _decile_net(nivvie_net, champm, wprm):
'''
Décile de niveau de vie net
'men'
'''
labels = arange(1, 11)
method = 2
decile, values = mark_weighted_percentiles(nivvie_net,
labels,
wprm * champm,
method,
return_quantiles=True)
return decile * champm
def _pauvre60(nivvie, champm, wprm):
'''
Indicatrice de pauvreté à 60% du niveau de vie median
'men'
'''
labels = arange(1, 3)
method = 2
percentile, values = mark_weighted_percentiles(nivvie,
labels,
wprm * champm,
method,
return_quantiles=True)
threshold = .6 * values[1]
return (nivvie <= threshold) * champm
def _decile(nivvie, champm, wprm):
'''
Décile de niveau de vie disponible
'men'
'''
labels = arange(1, 11)
method = 2
decile, values = mark_weighted_percentiles(nivvie, labels, wprm * champm, method, return_quantiles = True)
# print values
# print len(values)
# print (nivvie*champm).min()
# print (nivvie*champm).max()
# print decile.min()
# print decile.max()
# print (nivvie*(decile==1)*champm*wprm).sum()/( ((decile==1)*champm*wprm).sum() )
del values
return decile * champm
def _decile(nivvie, champm, wprm):
'''
Décile de niveau de vie disponible
'men'
'''
labels = arange(1, 11)
method = 2
decile, values = mark_weighted_percentiles(nivvie,
labels,
wprm * champm,
method,
return_quantiles=True)
# print values
# print len(values)
# print (nivvie*champm).min()
# print (nivvie*champm).max()
# print decile.min()
# print decile.max()
# print (nivvie*(decile==1)*champm*wprm).sum()/( ((decile==1)*champm*wprm).sum() )
del values
return decile * champm
def compute(self):
"""
Compute inequality dataframe
"""
output = self.simulation.output_table
final_df = None
WEIGHT = model.WEIGHT
FILTERING_VARS = model.FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity in entities:
#idx = output.index[entity]
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var = output._inputs.get_value(filter_var_name, entity)
items = []
# Compute mean
moy = (weights * filter_var * val).sum() / (weights *
filter_var).sum()
items.append(("Moyenne", [moy]))
# Compute deciles
labels = range(1, 11)
method = 2
decile, values = mark_weighted_percentiles(val,
labels,
weights * filter_var,
method,
return_quantiles=True)
labels = ['D' + str(d) for d in range(1, 11)]
del decile
for l, v in zip(labels[:-1], values[1:-1]):
items.append((l, [v]))
# Compute Gini
gini_coeff = gini(val, weights * filter_var)
items.append((_("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient='index', columns=[varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (
final_df['nivvie_net'] -
final_df['nivvie_ini']) / final_df['nivvie_ini']
final_df[u"Net à disponible"] = (
final_df['nivvie'] -
final_df['nivvie_net']) / final_df['nivvie_net']
final_df = final_df[[
'index', 'nivvie_ini', u"Initial à net", 'nivvie_net',
u"Net à disponible", 'nivvie'
]]
self.inequality_dataframe = final_df
# poverty
poverty = dict()
entity = "men"
varname = "nivvie"
for percentage in [40, 50, 60]:
# idx = output.index[entity]
varname = "pauvre" + str(percentage)
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var = output._inputs.get_value(filter_var_name, entity)
poverty[percentage] = (weights * filter_var *
val).sum() / (weights * filter_var).sum()
self.poverty = poverty
year = 2009
erf = ErfsDataTable(year=year)
#erf.set_config()
vars = [ "zrstm", "zchom", "pfamm", "wprm", "pauvre50m", "pauvre60m", "nivviem", "champm"]
df = erf.get_values(variables=vars, table="erf_menage" )
labels = arange(1,11)
method = 2
nivvie = df["nivviem"].astype("float64").values
wprm = df["wprm"].astype("float64").values
decil, values = mark_weighted_percentiles(nivvie, labels, wprm, method, return_quantiles = True)
df2 = DataFrame({"decile" : decil})
df["decile"] = df2["decile"]
indexes = { "zrstm" : .01, "zchom": .01, "pfamm" : .01} # TODO change 1%
results = DataFrame(index =indexes.keys(), columns = ["total", "pauvre50", "pauvre60"] + ["decile>"+str(decile) for decile in range(0,10)] )
for var, index in indexes.iteritems():
total = df[var]*index*df["wprm"]*df["champm"]
pauvre50 = df[var]*index*df["wprm"]*(df["pauvre50m"]<=0)*df["champm"]
pauvre60 = df[var]*index*df["wprm"]*(df["pauvre60m"]<=0)*df["champm"]
results.set_value(var, "total", total.sum()/1e6)
def compute(self):
"""
Compute inequality dataframe
"""
output = self.simulation.output_table
final_df = None
WEIGHT = model.WEIGHT
FILTERING_VARS = model.FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity in entities:
#idx = output.index[entity]
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var= output._inputs.get_value(filter_var_name, entity)
items = []
# Compute mean
moy = (weights*filter_var*val).sum()/(weights*filter_var).sum()
items.append( ("Moyenne", [moy]))
# Compute deciles
labels = range(1,11)
method = 2
decile, values = mark_weighted_percentiles(val, labels, weights*filter_var, method, return_quantiles=True)
labels = [ 'D'+str(d) for d in range(1,11)]
del decile
for l, v in zip(labels[:-1],values[1:-1]):
items.append( (l, [v]))
# Compute Gini
gini_coeff = gini(val, weights*filter_var)
items.append( ( _("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient = 'index', columns = [varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (final_df['nivvie_net']-final_df['nivvie_ini'])/final_df['nivvie_ini']
final_df[u"Net à disponible"] = (final_df['nivvie']-final_df['nivvie_net'])/final_df['nivvie_net']
final_df = final_df[['index','nivvie_ini', u"Initial à net", 'nivvie_net',u"Net à disponible",'nivvie']]
self.inequality_dataframe = final_df
# poverty
poverty = dict()
entity = "men"
varname = "nivvie"
for percentage in [ 40, 50, 60]:
# idx = output.index[entity]
varname = "pauvre" + str(percentage)
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var= output._inputs.get_value(filter_var_name, entity)
poverty[percentage] = (weights*filter_var*val).sum()/(weights*filter_var).sum()
self.poverty = poverty
