def eye(N, M, k):
return np.eye(
numba_basic.to_scalar(N),
numba_basic.to_scalar(M),
numba_basic.to_scalar(k),
dtype=dtype,
)
def arange(start, stop, step):
return np.arange(
numba_basic.to_scalar(start),
numba_basic.to_scalar(stop),
numba_basic.to_scalar(step),
dtype=dtype,
)
def clip(_x, _min, _max):
x = numba_basic.to_scalar(_x)
_min_scalar = numba_basic.to_scalar(_min)
_max_scalar = numba_basic.to_scalar(_max)
if x < _min_scalar:
return _min_scalar
elif x > _max_scalar:
return _max_scalar
else:
return x
def filldiagonaloffset(a, val, offset):
height, width = a.shape
if offset >= 0:
start = numba_basic.to_scalar(offset)
num_of_step = min(min(width, height), width - offset)
else:
start = -numba_basic.to_scalar(offset) * a.shape[1]
num_of_step = min(min(width, height), height + offset)
step = a.shape[1] + 1
end = start + step * num_of_step
b = a.ravel()
b[start:end:step] = val
# TODO: This isn't implemented in Numba
# a.flat[start:end:step] = val
# return a
return b.reshape(a.shape)
def broadcast_to(x, *shape):
scalars_shape = create_zeros_tuple()
i = 0
for s_i in literal_unroll(shape):
scalars_shape = numba_basic.tuple_setitem(
scalars_shape, i, numba_basic.to_scalar(s_i)
)
i += 1
return np.broadcast_to(x, scalars_shape)
def careduce_axis(x):
res_shape = res_shape_tuple_ctor(x.shape)
x_axis_first = x.transpose(reaxis_first)
res = np.full(res_shape,
numba_basic.to_scalar(identity),
dtype=dtype)
for m in range(x.shape[axis]):
reduce_fn(res, x_axis_first[m], res)
return set_out_dims(res)
def join(axis, *tensors):
return np.concatenate(tensors, numba_basic.to_scalar(axis))
def bartlett(x):
return np.bartlett(numba_basic.to_scalar(x))
def careduce_axis(x):
res = numba_basic.to_scalar(identity)
for val in x:
res = reduce_fn(res, val)
return set_out_dims(res)
