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 test_simple_case(self):
output = run_ras(self.row_sums, self.column_sums)
r = get_row_sum(output)
c = get_col_sum(output)
self.assertTrue((get_row_sum(output) == self.row_sums).all())
self.assertTrue((get_col_sum(output) == self.column_sums).all())
def _get_row_and_column_sums(m: numpy.matrix):
just_numbers = TotalsOnlyDataSource._remove_cs_and_make_elements_float(m)
row_sums = matrix_functions.get_row_sum(just_numbers.elements.astype(float))
column_sums = matrix_functions.get_col_sum(just_numbers.elements.astype(float))
return row_sums, column_sums
def test_matrix_row_sum(self):
m = numpy.matrix([[1, 4], [2, 6]])
rs = get_row_sum(m)
self.assertTrue((rs == [[5], [8]]).all())
def test_vector_row_sum(self):
m = numpy.matrix([1, 2, 4])
rs = get_row_sum(m)
self.assertTrue((rs == [7]).all())
def test_single_element_row_sum(self):
m = numpy.matrix([1])
rs = get_row_sum(m)
self.assertTrue((rs == [1]).all())
def test_empty_matrix_row_sum(self):
m = numpy.matrix([])
rs = get_row_sum(m)
self.assertTrue((rs == []).all())
def test_type_row_sum(self):
m = numpy.matrix([1])
self.assertIs(type(get_row_sum(m)), numpy.matrix)
def setUp(self):
self.matrix = numpy.matrix([[1, 2], [3, 4]])
self.row_sums = get_row_sum(self.matrix)
self.column_sums = get_col_sum(self.matrix)
