def test_to_timedelta64_a(self):
timedelta = datetime.timedelta
self.assertEqual(to_timedelta64(timedelta(days=4)),
np.timedelta64(4, 'D'))
self.assertEqual(to_timedelta64(timedelta(seconds=4)),
np.timedelta64(4, 's'))
self.assertEqual(to_timedelta64(timedelta(minutes=4)),
np.timedelta64(240, 's'))
def _ufunc_binary_operator(self, *, operator: tp.Callable[..., tp.Any],
other: object) -> np.ndarray:
if self._recache:
self._update_array_cache()
if operator.__name__ == 'matmul' or operator.__name__ == 'rmatmul':
raise NotImplementedError('matrix multiplication not supported')
if isinstance(other, Index):
other = other.values # operate on labels to labels
elif isinstance(other, str):
# do not pass dtype, as want to coerce to this parsed type, not the type of sled
other = to_datetime64(other)
if isinstance(other, np.datetime64):
# convert labels to other's datetime64 type to enable matching on month, year, etc.
array = operator(self._labels.astype(other.dtype), other)
elif isinstance(other, datetime.timedelta):
array = operator(self._labels, to_timedelta64(other))
else:
# np.timedelta64 should work fine here
array = operator(self._labels, other)
array.flags.writeable = False
return array
def _ufunc_binary_operator(self, *,
operator: tp.Callable[..., tp.Any],
other: object,
fill_value: object = np.nan,
) -> np.ndarray:
if self._recache:
self._update_array_cache()
if operator.__name__ == 'matmul' or operator.__name__ == 'rmatmul':
raise NotImplementedError('matrix multiplication not supported')
if isinstance(other, Index):
other = other.values # operate on labels to labels
other_is_array = True
elif isinstance(other, str):
# do not pass dtype, as want to coerce to this parsed type, not the type of sled
other = to_datetime64(other)
other_is_array = False
elif other.__class__ is np.ndarray:
other_is_array = True
else:
other_is_array = False
if isinstance(other, np.datetime64):
# convert labels to other's datetime64 type to enable matching on month, year, etc.
result = operator(self._labels.astype(other.dtype), other)
elif isinstance(other, datetime.timedelta):
result = operator(self._labels, to_timedelta64(other))
else: # np.timedelta64 should work fine here
with WarningsSilent():
result = operator(self._labels, other)
# NOTE: similar branching as in container_util.apply_binary_operator
# NOTE: all string will have been converted to dt64, or raise ValueError; comparison to same sized iterables (list, tuple) will result in an array when they are the same size
if result is False: # will never be True
if not other_is_array and hasattr(other, '__len__') and len(other) == len(self):
# NOTE: equality comparisons of an array to same sized iterable normally return an array, but with dt64 types they just return False
result = np.full(self.shape, result, dtype=DTYPE_BOOL)
elif other_is_array and other.size == 1:
# elements in arrays of 0 or more dimensions are acceptable; this is what NP does for arithmetic operators when the types are compatible
result = np.full(self.shape, result, dtype=DTYPE_BOOL)
else:
raise ValueError('operands could not be broadcast together')
# raise on unaligned shapes as is done for arithmetic operators
result.flags.writeable = False
return result
def _ufunc_binary_operator(self, *, operator: tp.Callable, other) -> np.ndarray:
if self._recache:
self._update_array_cache()
if issubclass(other.__class__, Index):
other = other.values # operate on labels to labels
elif isinstance(other, str):
# do not pass dtype, as want to coerce to this parsed type, not the type of sled
other = to_datetime64(other)
if isinstance(other, np.datetime64):
# convert labels to other's datetime64 type to enable matching on month, year, etc.
array = operator(self._labels.astype(other.dtype), other)
elif isinstance(other, datetime.timedelta):
array = operator(self._labels, to_timedelta64(other))
else:
# np.timedelta64 should work fine here
array = operator(self._labels, other)
array.flags.writeable = False
return array