def __and__(self, other):
"""
>>> a1 = ArrayInterval([True, True, False, False])
>>> a2 = ArrayInterval([True, False, True, False])
>>> print(a1 & a2, (a1 & a2)[:])
ArrayInterval("0:1", shape=(4,)) [ True False False False]
>>> a1 = ArrayInterval([True, True, False, False], inverse_mode=True)
>>> a2 = ArrayInterval([True, False, True, False], inverse_mode=True)
>>> print(a1 & a2, (a1 & a2)[:])
ArrayInterval("1:4", shape=(4,), inverse_mode=True) [ True False False False]
>>> np.logical_and(a1, a2)
array([ True, False, False, False])
"""
if not isinstance(other, ArrayInterval):
return NotImplemented
elif self.inverse_mode is True and other.inverse_mode is True:
# short circuit
return ~((~self) | (~other))
elif self.inverse_mode is False and other.inverse_mode is False:
if other.shape != self.shape:
raise ValueError(
f'Cannot broadcast together ArrayIntervals with shapes '
f'{self.shape} {other.shape}')
ai = zeros(shape=self.shape)
normalized_intervals = self.normalized_intervals
intervals = []
for (start, stop) in other.normalized_intervals:
intervals.extend(
cy_intersection((start, stop), normalized_intervals))
ai.intervals = intervals
return ai
else:
raise NotImplementedError(self.inverse_mode, other.inverse_mode)
def test_cy_intersection():
assert cy_intersection((0, 3), ((1, 2), )) == ((1, 2), )
assert cy_intersection((1, 4), ((0, 2), (3, 5))) == ((1, 2), (3, 4))
assert cy_intersection((1, 2), ((0, 3), )) == ((1, 2), )
assert cy_intersection((4, 5), ((0, 3), )) == ()
def __getitem__(self, item):
"""
>>> ai = zeros(50)
>>> ai[19:26]
array([False, False, False, False, False, False, False])
>>> ai[10:20] = 1
>>> ai[25:30] = 1
>>> ai
ArrayInterval("10:20, 25:30", shape=(50,))
>>> ai[19:26]
array([ True, False, False, False, False, False, True])
>>> ai[19]
True
>>> ai[5]
False
>>> ai[49]
False
>>> ai[29]
True
>>> ai[-25:-20]
array([ True, True, True, True, True])
Get a similar behavior to numpy when indexing outside of shape:
>>> ai[-1:1]
array([], dtype=bool)
>>> ai[-10:]
array([False, False, False, False, False, False, False, False, False,
False])
>>> ai[45:100]
array([False, False, False, False, False])
"""
if isinstance(item, (int, np.integer)):
index = item
if index < 0:
if self.shape is None:
raise ValueError(
f'Negative indices can only be used on ArrayIntervals '
f'with a shape! index={index}')
index = index + self.shape[-1]
if index < 0 or self.shape is not None and index > self.shape[-1]:
raise IndexError(
f'Index {item} is out of bounds for ArrayInterval with '
f'shape {self.shape}')
# Could be optimized
for s, e in self.normalized_intervals:
if e > index:
return (index >= s) ^ self.inverse_mode
return self.inverse_mode
start, stop = cy_parse_item(item, self.shape)
# This is numpy behavior
if stop <= start:
return np.zeros(0, dtype=np.bool)
intervals = cy_intersection((start, stop), self.normalized_intervals)
if self.inverse_mode:
arr = np.ones(stop - start, dtype=np.bool)
for i_start, i_end in intervals:
arr[i_start - start:i_end - start] = False
else:
arr = np.zeros(stop - start, dtype=np.bool)
for i_start, i_end in intervals:
arr[i_start - start:i_end - start] = True
return arr