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from typing import List, Tuple, Union # upip.install("pycopy-typing")
from ulab import numpy as np
_DType = int
_RClassKeyType = Union[slice, int, float, list, tuple, np.ndarray]
# this is a stripped down version of RClass (used by np.r_[...etc])
# it doesn't include support for string arguments as the first index element
class RClass:
def __getitem__(self, key: Union[_RClassKeyType, Tuple[_RClassKeyType, ...]]):
if not isinstance(key, tuple):
key = (key,)
objs: List[np.ndarray] = []
scalars: List[int] = []
arraytypes: List[_DType] = []
scalartypes: List[_DType] = []
# these may get overridden in following loop
axis = 0
for idx, item in enumerate(key):
scalar = False
try:
if isinstance(item, np.ndarray):
newobj = item
elif isinstance(item, slice):
step = item.step
start = item.start
stop = item.stop
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size = int(abs(step))
newobj: np.ndarray = np.linspace(start, stop, num=size)
else:
newobj = np.arange(start, stop, step)
# if is number
elif isinstance(item, (int, float, bool)):
newobj = np.array([item])
scalars.append(len(objs))
scalar = True
scalartypes.append(newobj.dtype())
else:
newobj = np.array(item)
except TypeError:
raise Exception("index object %s of type %s is not supported by r_[]" % (
str(item), type(item)))
objs.append(newobj)
if not scalar and isinstance(newobj, np.ndarray):
arraytypes.append(newobj.dtype())
# Ensure that scalars won't up-cast unless warranted
final_dtype = min(arraytypes + scalartypes)
for idx, obj in enumerate(objs):
if obj.dtype != final_dtype:
objs[idx] = np.array(objs[idx], dtype=final_dtype)
return np.concatenate(tuple(objs), axis=axis)
# this seems weird - not sure what it's for
def __len__(self):
return 0
r_ = RClass()
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