def __getitem__(self, indexer):
result = super(LabelArray, self).__getitem__(indexer)
if result.ndim:
# Result is still a LabelArray, so we can just return it.
return result
# Result is a scalar value, which will be an instance of np.void.
# Map it back to one of our category entries.
index = result.view(int_dtype_with_size_in_bytes(self.itemsize))
return self.categories[index]
def as_int_array(self):
"""
Convert self into a regular ndarray of ints.
This is an O(1) operation. It does not copy the underlying data.
"""
return self.view(
type=ndarray,
dtype=int_dtype_with_size_in_bytes(self.itemsize),
)
def __getitem__(self, indexer):
result = super(LabelArray, self).__getitem__(indexer)
if result.ndim:
# Result is still a LabelArray, so we can just return it.
return result
# Result is a scalar value, which will be an instance of np.void.
# Map it back to one of our category entries.
index = result.view(int_dtype_with_size_in_bytes(self.itemsize))
return self.categories[index]
def as_int_array(self):
"""
Convert self into a regular ndarray of ints.
This is an O(1) operation. It does not copy the underlying data.
"""
return self.view(
type=ndarray,
dtype=int_dtype_with_size_in_bytes(self.itemsize),
)
def empty_like(self, shape):
"""
Make an empty LabelArray with the same categories as ``self``, filled
with ``self.missing_value``.
"""
return type(self)._from_codes_and_metadata(
codes=np.full(
shape,
self.reverse_categories[self.missing_value],
dtype=int_dtype_with_size_in_bytes(self.itemsize),
),
categories=self.categories,
reverse_categories=self.reverse_categories,
missing_value=self.missing_value,
)
def empty_like(self, shape):
"""
Make an empty LabelArray with the same categories as ``self``, filled
with ``self.missing_value``.
"""
return type(self)._from_codes_and_metadata(
codes=np.full(
shape,
self.reverse_categories[self.missing_value],
dtype=int_dtype_with_size_in_bytes(self.itemsize),
),
categories=self.categories,
reverse_categories=self.reverse_categories,
missing_value=self.missing_value,
)
