def test_large_concat_stack():
data = np.array([1], dtype=np.uint8)
coords = np.array([[255]], dtype=np.uint8)
xs = COO(coords, data, shape=(256,), has_duplicates=False, sorted=True)
x = xs.todense()
assert_eq(np.stack([x, x]),
sparse.stack([xs, xs]))
assert_eq(np.concatenate((x, x)),
sparse.concatenate((xs, xs)))
def test_concatenate():
xx = sparse.random((2, 3, 4), density=0.5)
x = xx.todense()
yy = sparse.random((5, 3, 4), density=0.5)
y = yy.todense()
zz = sparse.random((4, 3, 4), density=0.5)
z = zz.todense()
assert_eq(np.concatenate([x, y, z], axis=0),
sparse.concatenate([xx, yy, zz], axis=0))
xx = sparse.random((5, 3, 1), density=0.5)
x = xx.todense()
yy = sparse.random((5, 3, 3), density=0.5)
y = yy.todense()
zz = sparse.random((5, 3, 2), density=0.5)
z = zz.todense()
assert_eq(np.concatenate([x, y, z], axis=2),
sparse.concatenate([xx, yy, zz], axis=2))
assert_eq(np.concatenate([x, y, z], axis=-1),
sparse.concatenate([xx, yy, zz], axis=-1))
def test_concatenate():
x = random_x((2, 3, 4))
xx = COO.from_numpy(x)
y = random_x((5, 3, 4))
yy = COO.from_numpy(y)
z = random_x((4, 3, 4))
zz = COO.from_numpy(z)
assert_eq(np.concatenate([x, y, z], axis=0),
sparse.concatenate([xx, yy, zz], axis=0))
x = random_x((5, 3, 1))
xx = COO.from_numpy(x)
y = random_x((5, 3, 3))
yy = COO.from_numpy(y)
z = random_x((5, 3, 2))
zz = COO.from_numpy(z)
assert_eq(np.concatenate([x, y, z], axis=2),
sparse.concatenate([xx, yy, zz], axis=2))
assert_eq(np.concatenate([x, y, z], axis=-1),
sparse.concatenate([xx, yy, zz], axis=-1))
def test_concatenate_noarrays():
with pytest.raises(ValueError):
sparse.concatenate([])
def test_inconsistent_fv(self):
xs = sparse.random((3, 4), density=0.5, fill_value=1)
ys = sparse.random((3, 4), density=0.5, fill_value=2)
with pytest.raises(ValueError):
sparse.concatenate([xs, ys])
def concatenate(arrays, axis=0, array_mode=None, array_default_mode='numpy',
array_homo_mode=None):
"""Join a sequence of arrays along an existing axis.
Args:
arrays (Seq[array]): Input arrays
axis (int): The axis along which the arrays will be joined. If
axis is None, arrays are flattened before use. Default is 0.
array_mode (str, NoneType): See :func:`validate_array_args`.
array_default_mode (str, NoneType): See :func:`validate_array_args`.
array_homo_mode (str, NoneType): See :func:`validate_array_args`.
Returns:
The concatenated array.
Examples:
>>> a = np.random.random((3, 4))
>>> b = np.random.random((3, 4))
>>> c1 = np.concatenate((a,b))
>>> c2 = concatenate((a,b))
>>> isinstance(c2, np.ndarray)
True
>>> np.all(c1 == c2)
True
>>> c2 = concatenate((a,b), array_mode='scipy-sparse')
>>> scipy.sparse.issparse(c2)
True
>>> c3 = concatenate((a,b), array_mode='pydata-sparse')
>>> import sparse
>>> isinstance(c3, sparse.SparseArray)
True
>>> np.all(c1 == c2.todense())
True
>>> np.all(c1 == c3.todense())
True
>>> c1 = np.concatenate((a,b), axis=1)
>>> c2 = concatenate((a,b), array_mode='scipy-sparse', axis=1)
>>> scipy.sparse.issparse(c2)
True
>>> np.all(c1 == c2.todense())
True
"""
arrays = _utils.validate_array_args(*arrays,
mode=array_mode,
default_mode=array_default_mode,
homo_mode=array_homo_mode)
if all(scipy.sparse.issparse(a) for a in arrays):
axis = axis+2 if axis < 0 else axis
if axis == 0:
c = scipy.sparse.vstack(arrays)
else:
assert axis == 1
c = scipy.sparse.hstack(arrays)
elif (_config.has_package('sparse') and
all(_utils.is_pydata_sparse(a) for a in arrays)):
import sparse
c = sparse.concatenate(arrays, axis)
else:
c = np.concatenate(arrays, axis)
return c
