def _cs_cs_flows(self, country_names=None, sectors=None):
"""
Calculate country-sector to country-sector flows using
country-country flows, the import ratios and the technical
coefficients
y_ijrs = p_ijr * d_jr * a_jrs * x_js
"""
x = self.total_production()
a = self.technical_coefficients()
d = self.import_ratios()
p = self.import_propensities()
# Rename various index levels
x = dataframe.rename_index_level(x, {'country':'to_country', 'sector':'to_sector'})
a = dataframe.rename_index_level(a, {'country':'to_country'})
d = dataframe.rename_index_level(d, {'country':'to_country', 'sector':'from_sector'})
p = dataframe.rename_index_level(p, {'sector':'from_sector'})
# d * a * x is easy to deal with since it's all within country j
broadcast = dataframe.broadcast
dax = broadcast(broadcast(a, x), d)
dax.name = 'dax'
# pdax: this is a little more complicated since p doesn't know about s,
# and dax doesn't know about i
p.name = 'p'
pdax = pd.merge(dax.reset_index(), p.reset_index())
pdax = pdax.set_index(['from_country', 'to_country', 'from_sector', 'to_sector']).sortlevel()
y_ijrs = pdax.dax * pdax.p
y_ijrs.name = 'cs_cs_flow'
return y_ijrs
def _split_flows_by_import_propensities(self, flows,
country_field='country',
country_names=None,
with_RoW=False):
"""
Calculate country to country flows using
the given flows vector and the import propensities
"""
ip = self._import_propensities.astype(float)
flows = dataframe.rename_index_level(flows, {country_field:'to_country'})
return dataframe.broadcast(ip, flows)
