def test_set_block():
""" Set block util function """
full_arr = np.random.random((10, 10))
block_arr = np.zeros((2, 2))
mod_arr = set_block(full_arr, block_arr)
npt.assert_array_equal(mod_arr[0:2, 0:2], block_arr)
npt.assert_array_equal(mod_arr[2:4, 2:4], block_arr)
npt.assert_array_equal(mod_arr[0:2, 3:-1], full_arr[0:2, 3:-1])
with pytest.raises(ValueError):
block_arr = np.zeros((3, 3))
mod_arr = set_block(full_arr, block_arr)
with pytest.raises(ValueError):
full_arr = np.random.random((10, 12))
block_arr = np.zeros((2, 2))
mod_arr = set_block(full_arr, block_arr)
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)