def row_scaling(a: numpy.matrix,
row_totals: numpy.matrix,
e: numpy.matrix) -> numpy.matrix:
r_hat = matrix_divide(diagonal(row_totals),
diagonal(matrix_multiply(a, e)))
return r_hat
def run_model(consumption: numpy.matrix, production: numpy.matrix,
emissions: numpy.matrix, final_demand: numpy.matrix=None) -> numpy.matrix:
"""
:param consumption: U
:param production: V
:param emissions: e
:return:
"""
i = numpy.identity(len(consumption))
q = get_row_sum(production.source_data.elements)
g = get_col_sum(production.source_data.elements)
if final_demand is None:
y = get_row_sum(consumption.source_data.elements)
else:
y = final_demand
b = matrix_divide(consumption.source_data.elements, diagonal(g))
d = matrix_divide(production.source_data.elements, diagonal(q))
a = matrix_multiply(b, d)
money_part = matrix_multiply(inv(i - a), y)
return element_wise_divide(emissions.source_data.elements, money_part)
def column_scaling(a: numpy.matrix,
column_totals: numpy.matrix,
e: numpy.matrix) -> numpy.matrix:
s_hat = matrix_divide(diagonal(column_totals),
diagonal(matrix_multiply(e.T, a)))
return s_hat
