def test_wrap_consistent_names():
assert (sorted(ones(10, dtype='i4', chunks=(4,)).dask) ==
sorted(ones(10, dtype='i4', chunks=(4,)).dask))
assert (sorted(ones(10, dtype='i4', chunks=(4,)).dask) !=
sorted(ones(10, chunks=(4,)).dask))
assert (sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='f8').dask) ==
sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='f8').dask))
assert (sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='i2').dask) !=
sorted(da.full((3, 3), 100, chunks=(2, 2)).dask))
def test_wrap_consistent_names():
assert sorted(ones(10, dtype='i4', chunks=(4,)).dask) ==\
sorted(ones(10, dtype='i4', chunks=(4,)).dask)
assert sorted(ones(10, dtype='i4', chunks=(4,)).dask) !=\
sorted(ones(10, chunks=(4,)).dask)
assert sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='f8').dask) ==\
sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='f8').dask)
assert sorted(da.full((3, 3), 100, chunks=(2, 2), dtype='f8').dask) !=\
sorted(da.full((3, 3), 100, chunks=(2, 2)).dask)
def test_wrap_consistent_names():
assert sorted(ones(10, dtype="i4", chunks=(4, )).dask) == sorted(
ones(10, dtype="i4", chunks=(4, )).dask)
assert sorted(ones(10, dtype="i4", chunks=(4, )).dask) != sorted(
ones(10, chunks=(4, )).dask)
assert sorted(da.full(
(3, 3), 100, chunks=(2, 2), dtype="f8").dask) == sorted(
da.full((3, 3), 100, chunks=(2, 2), dtype="f8").dask)
assert sorted(da.full(
(3, 3), 100, chunks=(2, 2), dtype="i2").dask) != sorted(
da.full((3, 3), 100, chunks=(2, 2)).dask)
def ones_like(a, dtype=None, order="C", chunks=None, name=None, shape=None):
"""
Return an array of ones with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
chunks : sequence of ints
The number of samples on each block. Note that the last block will have
fewer samples if ``len(array) % chunks != 0``.
name : str, optional
An optional keyname for the array. Defaults to hashing the input
keyword arguments.
shape : int or sequence of ints, optional.
Overrides the shape of the result.
Returns
-------
out : ndarray
Array of ones with the same shape and type as `a`.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
empty_like : Return an empty array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
"""
a = asarray(a, name=False)
shape, chunks = _get_like_function_shapes_chunks(a, chunks, shape)
return ones(
shape,
dtype=(dtype or a.dtype),
order=order,
chunks=chunks,
name=name,
meta=a._meta,
)
def test_singleton_size():
a = ones(10, dtype='i4', chunks=(4,))
x = np.array(a)
assert (x == np.ones(10, dtype='i4')).all()
def test_size_as_list():
a = ones([10, 10], dtype='i4', chunks=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), dtype='i4')).all()
def test_can_make_really_big_array_of_ones():
ones((1000000, 1000000), chunks=(100000, 100000))
ones(shape=(1000000, 1000000), chunks=(100000, 100000))
def test_kwargs():
a = ones(10, dtype="i4", chunks=(4, ))
x = np.array(a)
assert (x == np.ones(10, dtype="i4")).all()
def test_ones():
a = ones((10, 10), dtype="i4", chunks=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), "i4")).all()
assert a.name.startswith("ones_like-")
def test_ones():
a = ones((10, 10), dtype='i4', blockshape=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), 'i4')).all()
def test_kwargs():
a = ones(10, dtype='i4', blockshape=(4,))
x = np.array(a)
assert (x == np.ones(10, dtype='i4')).all()
def test_singleton_size():
a = ones(10, dtype='i4', blockshape=(4,))
x = np.array(a)
assert (x == np.ones(10, dtype='i4')).all()
def test_size_as_list():
a = ones([10, 10], dtype='i4', blockshape=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), dtype='i4')).all()
def test_kwargs():
a = ones(10, dtype='i4', chunks=(4,))
x = np.array(a)
assert (x == np.ones(10, dtype='i4')).all()
def test_size_as_list():
a = ones([10, 10], dtype="i4", chunks=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), dtype="i4")).all()
def test_can_make_really_big_array_of_ones():
ones((1000000, 1000000), chunks=(100000, 100000))
ones(shape=(1000000, 1000000), chunks=(100000, 100000))
def test_singleton_size():
a = ones(10, dtype="i4", chunks=(4, ))
x = np.array(a)
assert (x == np.ones(10, dtype="i4")).all()
def test_ones():
a = ones((10, 10), dtype='i4', chunks=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), 'i4')).all()
def test_ones():
a = ones((10, 10), dtype='i4', chunks=(4, 4))
x = np.array(a)
assert (x == np.ones((10, 10), 'i4')).all()
assert a.name.startswith('ones-')
