def factor_inputs_coefficients_matrix(self):
q = self.q
return np.dot(self.factor_inputs_transaction_matrix(), tl.invdiag(q))
def ext_coefficients_matrix(self):
q = self.q
return np.dot(self.ext_transaction_matrix(), tl.invdiag(q))
def transformation_matrix(self):
make = np.transpose(self.V)
g = np.sum(self.V, axis=0)
return np.dot(tl.invdiag(g), make)
def io_coefficient_matrix(self):
q = self.q
return np.dot(self.io_transaction_matrix(), tl.invdiag(q))
def main(directory, IO_tables):
# SETTINGS
ghg_index = [
0, 1, 2, 27, 28, 29, 52, 53, 54, 55, 56, 57, 58, 59, 77, 78, 403, 404,
405, 406, 407, 410
]
va_index = [0, 1, 2, 3, 4, 5, 6, 7, 8]
material_index = range(419, 439, 1)
tolerance = 1E-3
prd_cnt = 200
fd_cnt = 7
cntr_cnt = 49
Z = IO_tables.io_transaction_matrix()
Y = IO_tables.final_demand()
W = IO_tables.factor_inputs_transaction_matrix()
extensions = IO_tables.ext_transaction_matrix()
indicators_dir = "data/auxiliary/indicators_v3.txt"
indicators = tools.csv_file_to_list(indicators_dir, delimiter='\t')
H = tools.list_to_numpy_array(indicators, 0, 0)
va = np.sum(W[va_index, :], axis=0, keepdims=True)
E = extensions[:417, :]
E = E[ghg_index, :] # select CO2, CH4 and N2O emissions
M = extensions[417:, :]
M = M[material_index, :] # "domestic extraction used" metals and minerals
o_coeff = np.zeros((1, prd_cnt * cntr_cnt)) # dummy place holder
M = np.vstack((o_coeff, W, E, M)) # stack all extensions
M = np.dot(np.transpose(H), M)
# CALCULATE TOTALS
to = np.sum(Z, axis=1, keepdims=True) + np.sum(
Y, axis=1, keepdims=True) # total output ($)
ti = np.transpose(np.sum(Z, axis=0, keepdims=True) +
va) # total outlays ($)
# CALCULATE COEFFICIENTS
A = np.dot(Z,
tools.invdiag(to[:,
0])) # input-output coefficients matrix ($/$)
B = np.dot(M, tools.invdiag(to[:, 0])) # extension coefficients (xx/$)
# FILL IN TOTAL OUTPUT COEFFICIENTS IN B MATRIX AND REPLACE DUMMY
o_coeff = copy.deepcopy(to)
o_coeff[o_coeff > 0] = 1
B[0, :] = np.transpose(o_coeff)
# LEONTIEF INVERSE
I = np.eye(prd_cnt * cntr_cnt) # unity matrix ($)
L = np.linalg.inv(I - A) # Leontief inverse matrix ($/$)
# CHECK
# balanced to start with ?
diff = np.abs(to - ti)
for index in np.ndindex(diff.shape):
if diff[index] > tolerance:
print(
'difference to and ti larger than {} million Euro. Difference is {} at index {}.'
.format(tolerance, diff[index], index))
# calculated total output equal to to initial total output
x = np.dot(L, np.sum(Y, axis=1, keepdims=True))
diff = np.abs(x - to)
for index in np.ndindex(diff.shape):
if diff[index] > tolerance:
print(
'difference x and to larger than {} million Euro. Difference is {} at index {}.'
.format(tolerance, diff[index], index))
# AGGREGATE FINALDEMAND
Y_new = np.zeros([9800, 49])
for cntr_idx in range(0, cntr_cnt):
for fd_idx in range(0, fd_cnt):
old_idx = cntr_idx * fd_cnt + fd_idx
new_idx = cntr_idx
Y_new[:, new_idx] = Y_new[:, new_idx] + Y[:, old_idx]
Y = Y_new
# SOME DELETING
del Z
del W
del va
del E
del IO_tables
del extensions
# CREATE CANONICAL FILENAMES
full_io_fn = os.path.join(directory, 'A_v4.npy')
full_leontief_fn = os.path.join(directory, 'L_v4.npy')
full_finaldemand_fn = os.path.join(directory, 'Y_v4.npy')
full_extensions_fn = os.path.join(directory, 'B_v4.npy')
# SAVING MULTIREGIONAL DATA AS BINARY NUMPY ARRAY OBJECTS
np.save(full_io_fn, A)
np.save(full_leontief_fn, L)
np.save(full_finaldemand_fn, Y)
np.save(full_extensions_fn, B)