def __getitem__(self, index):
from numbers import Number
if isinstance(index, slice):
start, stop, step = index.indices(len(self))
sln = (stop - start) // step
sln = max(0, sln)
start += self._start
stop += self._start
if sln == 0:
return RangeIndex(0, None, self.name)
elif step == 1:
return RangeIndex(start, stop, self.name)
else:
return index_from_range(start, stop, step)
elif isinstance(index, Number):
index = utils.normalize_index(index, len(self))
index += self._start
return index
elif isinstance(index, (list, np.ndarray)):
index = np.asarray(index)
index = rmm.to_device(index)
else:
if is_scalar(index):
index = min_signed_type(index)(index)
index = column.as_column(index)
return as_index(self._values[index], name=self.name)
def __getitem__(self, index):
if isinstance(index, slice):
start, stop = utils.normalize_slice(index, len(self))
start += self._start
stop += self._start
if index.step is None:
return RangeIndex(start, stop)
else:
return index_from_range(start, stop, index.step)
elif isinstance(index, int):
index = utils.normalize_index(index, len(self))
index += self._start
return index
else:
raise ValueError(index)
def __getitem__(self, arg):
if isinstance(arg, slice):
sliced = self.mem[arg]
buf = Buffer(sliced)
buf.dtype = self.dtype # for np.datetime64 support
return buf
elif isinstance(arg, int):
arg = utils.normalize_index(arg, self.size)
# the dtype argument is necessary for datetime64 support
# because currently we can't pass datetime64 types into
# cuda dev arrays, so the type of the cuda dev array is
# an i64, and we view it as the dtype on the buffer
return self.mem[arg].view(self.dtype)
else:
raise NotImplementedError(type(arg))
def __getitem__(self, index):
if isinstance(index, slice):
start, stop, step, sln = utils.standard_python_slice(
len(self), index)
start += self._start
stop += self._start
if sln == 0:
return RangeIndex(0)
else:
return index_from_range(start, stop, step)
elif isinstance(index, int):
index = utils.normalize_index(index, len(self))
index += self._start
return index
elif isinstance(index, (list, np.ndarray)):
index = np.array(index)
index = rmm.to_device(index)
if isinstance(index, (DeviceNDArray)):
return self.take(index)
else:
raise ValueError(index)
