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 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 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
def test_simple_float(self):
a = numpy.matrix([[3.0, 4.0], [10.0, 2.0]])
b = numpy.matrix([[4.0, 7.0], [2.0, 6.0]])
# inv(b) = [[0.6, -0.7], [-0.2, 0.4]]
# [3 4 * [ 0.6 -0.7
# 10 2] -0.2 0.4]
d = matrix_divide(a, b)
numpy.testing.assert_allclose(d, [[1., -0.5], [5.6, -6.2]])
def test_type(self):
a = numpy.matrix([[3.0, 4.0], [10.0, 2.0]])
b = numpy.matrix([[4.0, 7.0], [2.0, 6.0]])
d = matrix_divide(a, b)
self.assertIs(type(d), numpy.matrix)
