def test_array_shift(self, array: np.ndarray) -> None:
for shift in (-1, 1):
for wrap in (True, False):
tests = []
post1 = util.array_shift(array=array,
shift=shift,
axis=0,
wrap=wrap)
tests.append(post1)
if array.ndim == 2:
post2 = util.array_shift(array=array,
shift=shift,
axis=1,
wrap=wrap)
tests.append(post2)
for post in tests:
self.assertTrue(array.shape == post.shape)
# type is only always maintained if we are wrapping
if wrap:
self.assertTrue(array.dtype == post.dtype)
def roll(self,
shift: int,
include_index: bool = False) -> 'Series':
'''Return a Series with values rotated forward and wrapped around the index (with a postive shift) or backward and wrapped around the index (with a negative shift).
Args:
shift: Postive or negative integer shift.
include_index: Determine if the Index is shifted with the underlying data.
'''
if shift % len(self.values):
values = array_shift(self.values,
shift,
axis=0,
wrap=True)
values.flags.writeable = False
else:
values = self.values
if include_index:
index = self._index.roll(shift=shift)
own_index = True
else:
index = self._index
own_index = False
return self.__class__(values,
index=index,
name=self._name,
own_index=own_index)
def roll(self, shift: int) -> 'Index':
'''Return an Index with values rotated forward and wrapped around (with a postive shift) or backward and wrapped around (with a negative shift).
'''
values = self.values # force usage of property for cache update
if shift % len(values):
values = array_shift(values, shift, axis=0, wrap=True)
values.flags.writeable = False
return self.__class__(values)
def roll(self, shift: int) -> 'IndexHierarchy':
'''Return an Index with values rotated forward and wrapped around (with a postive shift) or backward and wrapped around (with a negative shift).
'''
if self._recache:
self._update_array_cache()
values = self._labels
if shift % len(values):
values = array_shift(array=values, shift=shift, axis=0, wrap=True)
values.flags.writeable = False
return self.__class__.from_labels(values)
def test_array_shift_b(self):
a1 = np.array([('a', 'b', 'e', 'd'), ('c', 'd', 'f', 'w'),
('e', 'f', 's', 'q')])
self.assertEqual(
array_shift(a1, 2, axis=0, wrap=True).tolist(),
[['c', 'd', 'f', 'w'], ['e', 'f', 's', 'q'], ['a', 'b', 'e', 'd']])
self.assertEqual(
array_shift(a1, -2, axis=0, wrap=True).tolist(),
[['e', 'f', 's', 'q'], ['a', 'b', 'e', 'd'], ['c', 'd', 'f', 'w']])
self.assertEqual(
array_shift(a1, -2, axis=0, wrap=False, fill_value='XX').dtype,
np.dtype('<U2'))
self.assertEqual(
array_shift(a1, -2, axis=0, wrap=False, fill_value='XX').tolist(),
[['e', 'f', 's', 'q'], ['XX', 'XX', 'XX', 'XX'],
['XX', 'XX', 'XX', 'XX']])
self.assertEqual(
array_shift(a1, 2, axis=1, wrap=False, fill_value='XX').tolist(),
[['XX', 'XX', 'a', 'b'], ['XX', 'XX', 'c', 'd'],
['XX', 'XX', 'e', 'f']])
self.assertEqual(
array_shift(a1, -2, axis=1, wrap=False, fill_value='XX').tolist(),
[['e', 'd', 'XX', 'XX'], ['f', 'w', 'XX', 'XX'],
['s', 'q', 'XX', 'XX']])
def shift(self, shift: int, fill_value=np.nan) -> 'Series':
'''Return a Series with values shifted forward on the index (with a postive shift) or backward on the index (with a negative shift).
Args:
shift: Postive or negative integer shift.
fill_value: Value to be used to fill data missing after the shift.
'''
if shift:
values = array_shift(self.values,
shift,
axis=0,
wrap=False,
fill_value=fill_value)
values.flags.writeable = False
else:
values = self.values
return self.__class__(values, index=self._index)
def test_array_shift_a(self):
a1 = np.arange(6)
# import ipdb; ipdb.set_trace()
self.assertEqual(
array_shift(a1, 2, axis=0, wrap=True).tolist(), [4, 5, 0, 1, 2, 3])
self.assertEqual(
array_shift(a1, -2, axis=0, wrap=True).tolist(),
[2, 3, 4, 5, 0, 1])
self.assertEqual(
array_shift(a1, 5, axis=0, wrap=True).tolist(), [1, 2, 3, 4, 5, 0])
self.assertEqual(
array_shift(a1, 2, axis=0, wrap=False, fill_value=-1).tolist(),
[-1, -1, 0, 1, 2, 3])
self.assertEqual(
array_shift(a1, 2, axis=0, wrap=False, fill_value=1.5).tolist(),
[1.5, 1.5, 0, 1, 2, 3])
self.assertEqual(
array_shift(a1, -2, axis=0, wrap=False, fill_value=1.5).tolist(),
[2, 3, 4, 5, 1.5, 1.5])
def test_array_shift_c(self):
a1 = np.arange(6)
post = array_shift(a1, 0, axis=0, wrap=False)
self.assertEqual(a1.tolist(), post.tolist())
