def recalc_M(S, D_cba, Y, nr_sectors):
""" Calculate Multipliers based on footprints.
Parameters
----------
D_cba : pandas.DataFrame or numpy array
Footprint per sector and country
Y : pandas.DataFrame or numpy array
Final demand: aggregated across categories or just one category, one
column per country. This will be diagonalized per country block.
The diagonolized form must be invertable for this method to work.
nr_sectors : int
Number of sectors in the MRIO
Returns
-------
pandas.DataFrame or numpy.array
Multipliers M
The type is determined by the type of D_cba.
If DataFrame index/columns as D_cba
"""
Y_diag = ioutil.diagonalize_blocks(Y.values, blocksize=nr_sectors)
Y_inv = np.linalg.inv(Y_diag)
M = D_cba.dot(Y_inv)
if type(D_cba) is pd.DataFrame:
M.columns = D_cba.columns
M.index = D_cba.index
return M
def recalc_M(S, D_cba, Y, nr_sectors):
""" Calculate Multipliers based on footprints.
Parameters
----------
D_cba : pandas.DataFrame or numpy array
Footprint per sector and country
Y : pandas.DataFrame or numpy array
Final demand: aggregated across categories or just one category, one
column per country. This will be diagonalized per country block.
The diagonolized form must be invertable for this method to work.
nr_sectors : int
Number of sectors in the MRIO
Returns
-------
pandas.DataFrame or numpy.array
Multipliers M
The type is determined by the type of D_cba.
If DataFrame index/columns as D_cba
"""
Y_diag = ioutil.diagonalize_blocks(Y.values, blocksize=nr_sectors)
Y_inv = np.linalg.inv(Y_diag)
M = D_cba.dot(Y_inv)
if type(D_cba) is pd.DataFrame:
M.columns = D_cba.columns
M.index = D_cba.index
return M
def recalc_N(S, D_iba, V, nr_sectors):
"""Calculate Nultipliers based on footprints.
Parameters
----------
D_iba : pandas.DataFrame or numpy array
Footprint per sector and country
V : pandas.DataFrame or numpy array
Final demand: aggregated across categories or just one category, one
column per country. This will be diagonalized per country block.
The diagonolized form must be invertable for this method to work.
nr_sectors : int
Number of sectors in the NRIO
Returns
-------
pandas.DataFrame or numpy.array
Nultipliers N
The type is determined by the type of D_iba.
If DataFrame index/columns as D_iba
"""
V_diag = ioutil.diagonalize_blocks(V.values, blocksize=nr_sectors)
#V_inv = np.linalg.inv(V_diag)
#problem there are zeros in V_diag: this prohibits the inversion
diag = V_diag.diagonal()
#masking where 0
mdiag = np.ma.masked_array(diag, mask=(diag == 0), fill_value=0)
inv_diag = 1 / mdiag
inv_diag[inv_diag.mask] = 0
inv_diag.data
V_inv = np.diag(inv_diag)
N = D_iba.dot(V_inv)
if type(D_iba) is pd.DataFrame:
N.columns = D_iba.columns
N.index = D_iba.index
return N
def calc_D_iba(S, G, V, nr_sectors):
"""Calculate sector specific cba and pba based accounts, imp and exp accounts
The total industry output x for the calculation
is recalculated from L and y
Parameters
----------
G : pandas.DataFrame
Ghosh input output table L
S : pandas.DataFrame
Direct impact coefficients
V : pandas.DataFrame
Value added: aggregated across categories or just one category, one
column per country
nr_sectors : int
Number of sectors in the MRIO
Returns
-------
Value
D_iba
Format: D_row x L_col (=nr_countries*nr_sectors)
- D_iba Income-based footprints per sector and country
"""
# diagonalize each sector block per country
# this results in a disaggregated y with final demand per country per
# sector in one column
V_diag = ioutil.diagonalize_blocks(V.values, blocksize=nr_sectors)
x_diag = G.dot(V_diag)
x_tot = x_diag.values.sum(1)
del V_diag
D_iba = pd.DataFrame(S.values.dot(x_diag),
index=S.index,
columns=S.columns)
return D_iba
def calc_accounts(S, L, Y, nr_sectors):
""" Calculate sector specific cba and pba based accounts, imp and exp accounts
The total industry output x for the calculation
is recalculated from L and y
Parameters
----------
L : pandas.DataFrame
Leontief input output table L
S : pandas.DataFrame
Direct impact coefficients
Y : pandas.DataFrame
Final demand: aggregated across categories or just one category, one
column per country
nr_sectors : int
Number of sectors in the MRIO
Returns
-------
Tuple
(D_cba, D_pba, D_imp, D_exp)
Format: D_row x L_col (=nr_countries*nr_sectors)
- D_cba Footprint per sector and country
- D_pba Total factur use per sector and country
- D_imp Total global factor use to satisfy total final demand in
the country per sector
- D_exp Total factor use in one country to satisfy final demand
in all other countries (per sector)
"""
# diagonalize each sector block per country
# this results in a disaggregated y with final demand per country per
# sector in one column
Y_diag = ioutil.diagonalize_blocks(Y.values, blocksize=nr_sectors)
x_diag = L.dot(Y_diag)
x_tot = x_diag.values.sum(1)
del Y_diag
D_cba = pd.DataFrame(S.values.dot(x_diag),
index=S.index,
columns=S.columns)
# D_pba = S.dot(np.diagflat(x_tot))
# faster broadcasted calculation:
D_pba = pd.DataFrame(S.values * x_tot.reshape((1, -1)),
index=S.index,
columns=S.columns)
# for the traded accounts set the domestic industry output to zero
dom_block = np.zeros((nr_sectors, nr_sectors))
x_trade = ioutil.set_block(x_diag.values, dom_block)
D_imp = pd.DataFrame(S.values.dot(x_trade),
index=S.index,
columns=S.columns)
x_exp = x_trade.sum(1)
# D_exp = S.dot(np.diagflat(x_exp))
# faster broadcasted version:
D_exp = pd.DataFrame(S.values * x_exp.reshape((1, -1)),
index=S.index,
columns=S.columns)
return (D_cba, D_pba, D_imp, D_exp)
def calc_accounts(S, L, Y, nr_sectors):
""" Calculate sector specific cba and pba based accounts, imp and exp accounts
The total industry output x for the calculation
is recalculated from L and y
Parameters
----------
L : pandas.DataFrame
Leontief input output table L
S : pandas.DataFrame
Direct impact coefficients
Y : pandas.DataFrame
Final demand: aggregated across categories or just one category, one
column per country
nr_sectors : int
Number of sectors in the MRIO
Returns
-------
Tuple
(D_cba, D_pba, D_imp, D_exp)
Format: D_row x L_col (=nr_countries*nr_sectors)
- D_cba Footprint per sector and country
- D_pba Total factur use per sector and country
- D_imp Total global factor use to satisfy total final demand in
the country per sector
- D_exp Total factor use in one country to satisfy final demand
in all other countries (per sector)
"""
# diagonalize each sector block per country
# this results in a disaggregated y with final demand per country per
# sector in one column
Y_diag = ioutil.diagonalize_blocks(Y.values, blocksize=nr_sectors)
x_diag = L.dot(Y_diag)
x_tot = x_diag.values.sum(1)
del Y_diag
D_cba = pd.DataFrame(S.values.dot(x_diag),
index=S.index,
columns=S.columns)
# D_pba = S.dot(np.diagflat(x_tot))
# faster broadcasted calculation:
D_pba = pd.DataFrame(S.values*x_tot.reshape((1, -1)),
index=S.index,
columns=S.columns)
# for the traded accounts set the domestic industry output to zero
dom_block = np.zeros((nr_sectors, nr_sectors))
x_trade = ioutil.set_block(x_diag.values, dom_block)
D_imp = pd.DataFrame(S.values.dot(x_trade),
index=S.index,
columns=S.columns)
x_exp = x_trade.sum(1)
# D_exp = S.dot(np.diagflat(x_exp))
# faster broadcasted version:
D_exp = pd.DataFrame(S.values * x_exp.reshape((1, -1)),
index=S.index,
columns=S.columns)
return (D_cba, D_pba, D_imp, D_exp)
