def func(s1, s2, sm1, sm2):
shapein = broadcast_shapes(s1 + sm2[1:], s2 + sm2[1:])
data1 = np.arange(product(s1 + sm1)).reshape(s1 + sm1)
data2 = np.arange(product(s2 + sm2)).reshape(s2 + sm2)
op1 = DenseBlockDiagonalOperator(data1)
op2 = DenseBlockDiagonalOperator(data2)
comp1 = op1 * op2
assert_is_instance(comp1, DenseBlockDiagonalOperator)
with rule_manager(none=True):
comp2 = op1 * op2
assert_equal(comp1.todense(shapein), comp2.todense(shapein))
def func(shapein, shapeout, extradata, extrainput):
datashape = extradata + shapeout + shapein
d = np.arange(product(datashape)).reshape(datashape)
b = DenseBlockDiagonalOperator(d,
naxesin=len(shapein),
naxesout=len(shapeout))
new_shape = broadcast_shapes(extradata, extrainput)
bdense = b.todense(shapein=new_shape + shapein)
d_ = reshape_broadcast(d, new_shape + shapeout + shapein)
d_ = d_.reshape(-1, product(shapeout), product(shapein))
expected = BlockDiagonalOperator(
[_ for _ in d_],
axisin=0).todense(shapein=product(new_shape + shapein))
assert_same(bdense, expected)
bTdense = b.T.todense(shapein=new_shape + shapeout)
assert_same(bTdense, expected.T)
def func(shape, expected):
assert_equal(broadcast_shapes(*shape), expected)
def func(shape, expected):
assert_equal(broadcast_shapes(*shape), expected)