Live View Repeated#

This demo illustrates how to perform a repeated scan and display the variance between repetitions on a user interface. Central to this demo is understanding how RasterRepeatedScan organizes segments in memory for convenient post-processing. The variance is incrementally computed as the scan progresses.


All acquired segments grouped by repetition. Note that some segments prior to the leading edge have not yet finished repeating, producing a dark band behind the leading edge.#


Variance of acquired segments.#

Incremental Processing of Repeated Scans#

def _update_image(self, endpoint, raw_bscan_idxs):
    with endpoint.tensor as volume:

        updates = {}
        for idx in raw_bscan_idxs:
            # which repeated bscan was updated
            bscan_idx = idx // self._repeat_count
            # which repetition number
            repeat_idx = idx % self._repeat_count

            # accumulate
            updates.setdefault(bscan_idx, []).append(repeat_idx)

        # update appropriate repeated bscans
        for (bscan_idx, repeat_idxs) in updates.items():
            # update based on the most recent repeat
            repeat_idx = max(repeat_idxs)

            # compute index into volume
            start = bscan_idx * self._repeat_count
            end = start + repeat_idx + 1

            # check validity
            if bscan_idx * self._repeat_count + repeat_idx > volume.shape[0]:

            # compute variance SVP
            self._image[bscan_idx, ...] = cupy.asnumpy(cupy.max(cupy.std(volume[start:end, ...], axis=0, keepdims=True), axis=2))