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return vectorize(self.func)
def __call__(self, arr, window, min_count=None, axis=-1):
if min_count is None:
min_count = window
if not 0 < window < arr.shape[axis]:
raise ValueError("window not in valid range: {}".format(window))
if min_count < 0:
raise ValueError("min_count must be positive: {}".format(min_count))
axis = _validate_axis(axis, arr.ndim)
arr = np.moveaxis(arr, axis, -1)
result = self.gufunc(arr, window, min_count)
return np.moveaxis(result, -1, axis)
class NumbaNDMovingExp(NumbaNDMoving):
def __call__(self, arr, alpha, axis=-1):
if alpha < 0:
raise ValueError("alpha must be positive: {}".format(alpha))
axis = _validate_axis(axis, arr.ndim)
arr = np.moveaxis(arr, axis, -1)
result = self.gufunc(arr, alpha)
return np.moveaxis(result, -1, axis)
class NumbaGroupNDReduce(object):
def __init__(self, func, signature=DEFAULT_MOVING_SIGNATURE):
self.func = func
for sig in signature:
if not isinstance(sig, tuple):
raise TypeError(