def test_ifirst_is_not():
assert ifirst_is_not([1, 2, 2], 2) == 0
assert ifirst_is_not([2, 1, 1], 2) == 1
assert ifirst_is_not([{}, None, None], None) == 0
assert_raises(ValueError, ifirst_is_not, [], None)
assert_raises(ValueError, ifirst_is_not, [None, None], None)
assert ilast_is_not([1, 2, 2], 2) == 0
assert ilast_is_not([2, 1, 1], 2) == 2
assert ilast_is_not([{}, None, None], None) == 0
assert_raises(ValueError, ilast_is_not, [], None)
assert_raises(ValueError, ilast_is_not, [None, None], None)
def test_ifirst_is_not():
assert ifirst_is_not([1, 2, 2], 2) == 0
assert ifirst_is_not([2, 1, 1], 2) == 1
assert ifirst_is_not([{}, None, None], None) == 0
assert_raises(ValueError, ifirst_is_not, [], None)
assert_raises(ValueError, ifirst_is_not, [None, None], None,)
assert ilast_is_not([1, 2, 2], 2) == 0
assert ilast_is_not([2, 1, 1], 2) == 2
assert ilast_is_not([{}, None, None], None) == 0
assert_raises(ValueError, ilast_is_not, [], None)
assert_raises(ValueError, ilast_is_not, [None, None], None,)
def _normalize_selection(self, selection, shape):
# return the selection as an array of int or bool, a slice, an Ellipsis
# or a tuple.
if isinstance(selection, list) and len(selection) == 0:
return np.array([], self._dtype_index)
if selection is None or selection is Ellipsis or \
isinstance(selection, tuple) and len(selection) == 0:
return Ellipsis
if isscalarlike(selection):
return np.asarray([selection], self._dtype_index)
if isinstance(selection, slice):
return self._normalize_slice(selection, product(shape))
if isinstance(selection, (list, tuple)):
selection_ = np.asarray(selection)
if selection_.dtype == object:
if len(selection) > len(shape):
raise ValueError('Invalid selection dimensions.')
selection = tuple(self._normalize_slice(_, s)
if isinstance(_, slice) else _
for _, s in zip(selection, shape))
try:
return selection[:ilast_is_not(selection, Ellipsis)+1]
except ValueError:
return Ellipsis
else:
selection = selection_
elif not isinstance(selection, np.ndarray):
raise TypeError('Invalid selection.')
if selection.dtype not in (bool, int):
raise TypeError('Invalid selection.')
if selection.dtype == int:
if selection.ndim != 1:
raise ValueError('Index selection is not 1-dimensional.')
elif selection.shape != shape:
raise ValueError('Invalid boolean selection dimensions.')
return selection
def _normalize_selection(self, selection, shape):
# return the selection as an array of int or bool, a slice, an Ellipsis
# or a tuple.
if isinstance(selection, list) and len(selection) == 0:
return np.array([], self._dtype_index)
if selection is None or selection is Ellipsis or \
isinstance(selection, tuple) and len(selection) == 0:
return Ellipsis
if isscalarlike(selection):
return np.asarray([selection], self._dtype_index)
if isinstance(selection, slice):
return self._normalize_slice(selection, product(shape))
if isinstance(selection, (list, tuple)):
selection_ = np.asarray(selection)
if selection_.dtype == object:
if len(selection) > len(shape):
raise ValueError('Invalid selection dimensions.')
selection = tuple(
self._normalize_slice(_, s) if isinstance(_, slice) else _
for _, s in zip(selection, shape))
try:
return selection[:ilast_is_not(selection, Ellipsis) + 1]
except ValueError:
return Ellipsis
else:
selection = selection_
elif not isinstance(selection, np.ndarray):
raise TypeError('Invalid selection.')
if selection.dtype not in (bool, int):
raise TypeError('Invalid selection.')
if selection.dtype == int:
if selection.ndim != 1:
raise ValueError('Index selection is not 1-dimensional.')
elif selection.shape != shape:
raise ValueError('Invalid boolean selection dimensions.')
return selection