def test_nearest_interpolation_2d_string():
rect = odl.IntervalProd([0, 0], [1, 1])
part = odl.uniform_partition_fromintv(rect, [4, 2], nodes_on_bdry=False)
# Coordinate vectors are:
# [0.125, 0.375, 0.625, 0.875], [0.25, 0.75]
space = odl.FunctionSet(rect, odl.Strings(1))
dspace = odl.ntuples(part.size, dtype='U1')
interp_op = NearestInterpolation(space, part, dspace)
values = np.array([c for c in 'mystring'])
function = interp_op(values)
# Evaluate at single point
val = function([0.3, 0.6]) # closest to index (1, 1) -> 3
assert val == 't'
# Input array, with and without output array
pts = np.array([[0.3, 0.6], [1.0, 1.0]])
true_arr = ['t', 'g']
assert all_equal(function(pts.T), true_arr)
out = np.empty(2, dtype='U1')
function(pts.T, out=out)
assert all_equal(out, true_arr)
# Input meshgrid, with and without output array
mg = sparse_meshgrid([0.3, 1.0], [0.4, 1.0])
# Indices: (1, 3) x (0, 1)
true_mg = [['s', 't'], ['n', 'g']]
assert all_equal(function(mg), true_mg)
out = np.empty((2, 2), dtype='U1')
function(mg, out=out)
assert all_equal(out, true_mg)
def test_dspace_type_cuda():
# Plain function set -> Ntuples-like
fset = odl.FunctionSet(odl.Interval(0, 1), odl.Strings(2))
assert dspace_type(fset, 'cuda') == odl.CudaNtuples
assert dspace_type(fset, 'cuda', np.int) == odl.CudaNtuples
# Real space
rspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.RealNumbers())
assert dspace_type(rspc, 'cuda') == odl.CudaFn
assert dspace_type(rspc, 'cuda', np.float64) == odl.CudaFn
assert dspace_type(rspc, 'cuda', np.int) == odl.CudaFn
with pytest.raises(TypeError):
dspace_type(rspc, 'cuda', np.complex)
with pytest.raises(TypeError):
dspace_type(rspc, 'cuda', np.dtype('<U2'))
# Complex space (not implemented)
cspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.ComplexNumbers())
with pytest.raises(NotImplementedError):
dspace_type(cspc, 'cuda')
with pytest.raises(NotImplementedError):
dspace_type(cspc, 'cuda', np.complex64)
with pytest.raises(TypeError):
dspace_type(cspc, 'cuda', np.float)
with pytest.raises(TypeError):
assert dspace_type(cspc, 'cuda', np.int)
def test_dspace_type_numpy():
# Plain function set -> Ntuples-like
fset = odl.FunctionSet(odl.Interval(0, 1), odl.Strings(2))
assert dspace_type(fset, 'numpy') == odl.Ntuples, None
assert dspace_type(fset, 'numpy', np.int) == odl.Ntuples
# Real space
rspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.RealNumbers())
assert dspace_type(rspc, 'numpy') == odl.Fn
assert dspace_type(rspc, 'numpy', np.float32) == odl.Fn
assert dspace_type(rspc, 'numpy', np.int) == odl.Fn
with pytest.raises(TypeError):
dspace_type(rspc, 'numpy', np.complex)
with pytest.raises(TypeError):
dspace_type(rspc, 'numpy', np.dtype('<U2'))
# Complex space
cspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.ComplexNumbers())
assert dspace_type(cspc, 'numpy') == odl.Fn
assert dspace_type(cspc, 'numpy', np.complex64) == odl.Fn
with pytest.raises(TypeError):
dspace_type(cspc, 'numpy', np.float)
with pytest.raises(TypeError):
assert dspace_type(cspc, 'numpy', np.int)
with pytest.raises(TypeError):
dspace_type(cspc, 'numpy', np.dtype('<U2'))
def test_fspace_init():
"""Check if all initialization patterns work."""
intv = odl.IntervalProd(0, 1)
FunctionSpace(intv)
FunctionSpace(intv, out_dtype=float)
FunctionSpace(intv, out_dtype=complex)
FunctionSpace(intv, out_dtype=(float, (2, 3)))
str3 = odl.Strings(3)
FunctionSpace(str3, out_dtype=int)
# Make sure repr shows something
assert repr(FunctionSpace(intv, out_dtype=(float, (2, 3))))
def test_fset_vector_eval():
str3 = odl.Strings(3)
ints = odl.Integers()
fset = FunctionSet(str3, ints)
strings = np.array(['aa', 'b', 'cab', 'aba'])
out_vec = np.empty((4, ), dtype=int)
# Vectorized for arrays only
f_vec = fset.element(lambda s: np.array([str(si).count('a') for si in s]))
true_vec = [2, 0, 1, 2]
assert f_vec('abc') == 1
assert all_equal(f_vec(strings), true_vec)
f_vec(strings, out=out_vec)
assert all_equal(out_vec, true_vec)
def test_fspace_elem_eval_unusual_dtypes():
"""Check evaluation with unusual data types (int and string)."""
str3 = odl.Strings(3)
fspace = FunctionSpace(str3, out_dtype=int)
strings = np.array(['aa', 'b', 'cab', 'aba'])
out_vec = np.empty((4, ), dtype=int)
# Vectorized for arrays only
func_elem = fspace.element(
lambda s: np.array([str(si).count('a') for si in s]))
true_values = [2, 0, 1, 2]
assert func_elem('abc') == 1
assert all_equal(func_elem(strings), true_values)
func_elem(strings, out=out_vec)
assert all_equal(out_vec, true_values)
def test_fspace_elem_eval_unusual_dtypes():
"""Check evaluation with unusual data types (int and string)."""
domain = odl.Strings(3)
strings = np.array(['aa', 'b', 'cab', 'aba'])
out_vec = np.empty((4, ), dtype='int64')
# Can be vectorized for arrays only
sampl_func = sampling_function(
lambda s: np.array([str(si).count('a') for si in s]),
domain,
out_dtype='int64')
collocator = partial(point_collocation, sampl_func)
true_values = [2, 0, 1, 2]
assert collocator('abc') == 1
assert all_equal(collocator(strings), true_values)
collocator(strings, out=out_vec)
assert all_equal(out_vec, true_values)
def test_fset_init():
str3 = odl.Strings(3)
ints = odl.Integers()
FunctionSet(str3, ints)
