def _sample(arr, level, axis, mode): arr = dtype.as_float32(arr.copy()) dx, dy, dz = arr.shape # Determine the new size, dim, of the down-/up-sampled dimension if mode == 0: dim = int(arr.shape[axis] / np.power(2, level)) if mode == 1: dim = int(arr.shape[axis] * np.power(2, level)) out = _init_out(arr, axis, dim) return extern.c_sample(mode, arr, dx, dy, dz, level, axis, out)
def _sample(arr, level, axis, mode): arr = dtype.as_float32(arr) dx, dy, dz = arr.shape if mode == 0: dim = arr.shape[axis] / np.power(2, level) if mode == 1: dim = arr.shape[axis] * np.power(2, level) out = _init_out(arr, axis, dim) return extern.c_sample(mode, arr, dx, dy, dz, level, axis, out)
def _sample(arr, level, axis, mode): arr = dtype.as_float32(arr) dx, dy, dz = arr.shape if mode == 0: dim = arr.shape[axis] / np.power(2, level) if mode == 1: dim = arr.shape[axis] * np.power(2, level) out = _init_out(arr, axis, dim) return extern.c_sample(mode, arr, dx, dy, dz, level, axis, out)
def _sample(arr, level, axis, mode): arr = dtype.as_float32(arr.copy()) dx, dy, dz = arr.shape # Determine the new size, dim, of the down-/up-sampled dimension if mode == 0: dim = int(arr.shape[axis] / np.power(2, level)) if mode == 1: dim = int(arr.shape[axis] * np.power(2, level)) out = _init_out(arr, axis, dim) return extern.c_sample(mode, arr, dx, dy, dz, level, axis, out)