Reduce code duplication in LinOp tests

[Michael-T-McCann]

Many LinOps have tests for scalar multiplication, i.e., that a * (H @ x) = (a * H) @ x. Right now, this involves code duplication, e.g.,

scico/scico/test/linop/test_circconv.py

Lines 74 to 87 in 1a66887

	@pytest.mark.parametrize("operator", [op.mul, op.truediv])
	def test_scalar_left(self, axes_shape_spec, operator, jit):
	input_dtype = np.float32
	scalar = np.float32(3.141)
	x_shape, ndims, h_shape = axes_shape_spec
	h, key = randn(tuple(h_shape), dtype=input_dtype, key=self.key)
	A = CircularConvolve(h, x_shape, ndims, input_dtype, jit=jit)
	cA = operator(A, scalar)
	np.testing.assert_allclose(operator(A.h_dft.ravel(), scalar), cA.h_dft.ravel(), rtol=5e-5)

versus

scico/scico/test/linop/test_diag.py

Lines 146 to 158 in 1a66887

	@pytest.mark.parametrize("operator", [op.mul, op.truediv])
	def test_scalar_left(self, operator):
	diagonal_dtype = np.float32
	input_shape = (8,)
	diagonal1, key = randn(input_shape, dtype=diagonal_dtype, key=self.key)
	scalar = np.random.randn()
	x, key = randn(input_shape, dtype=diagonal_dtype, key=key)
	D = linop.Diagonal(diagonal=diagonal1)
	scaled_D = operator(D, scalar)
	np.testing.assert_allclose(scaled_D @ x, operator(D @ x, scalar), rtol=5e-5)

Can we create a standard function to test scaling (and other similar LinOp properties) rather than copy/pasting variants of this?