Live View Raster Aim#

This demo illustrates how do design a custom scan that interleaves an aiming scan (two perpendicular segments) periodically throughout a raster scan. Helper classess are used to generate the segments for each scan with different Flags so that they can be separated in subsequent processing. See Interleave Different Scans for a more detailed expalanation of the scan generation.


Dense raster scan in en face view.#


Aiming scan segment in cross-section.#

Building an Interleaved Scan#

pattern = []
idx = numpy.linspace(0, len(raster_segments), 10, dtype=int)
for (i, (a, b)) in enumerate(zip(idx[:-1], idx[1:])):
    if i > 0:
        markers = raster_segments[a].markers
        markers.insert(0, VolumeBoundary(0, 0, False))
        markers.insert(0, ScanBoundary(0, 0))

    pattern += raster_segments[a:b]
    pattern += aiming_segments

ffsc = FreeformScanConfig()
ffsc.pattern = pattern
ffsc.loop = True

Separating Segments by Flags#

fc = FormatPlannerConfig()
fc.adapt_shape = False

fc.mask = rsc.flags
fc.segments_per_volume = rsc.bscans_per_volume
fc.records_per_segment = rsc.ascans_per_bscan
raster_format = FormatPlanner(get_logger('format-raster', cfg.log_level))
self._raster_format = raster_format

fc.mask = asc.flags
fc.segments_per_volume = asc.bscans_per_volume
fc.records_per_segment = asc.ascans_per_bscan
aiming_format = FormatPlanner(get_logger('format-aiming', cfg.log_level))
self._aiming_format = aiming_format

# format executors
cfec = StackFormatExecutorConfig()
cfec.sample_slice = SimpleSlice(self._process.config.samples_per_ascan // 2)
samples_to_save = cfec.sample_slice.count()

sfe = StackFormatExecutor()

raster_tensor_endpoint = StackDeviceTensorEndpoint(sfe, (rsc.bscans_per_volume, rsc.ascans_per_bscan, samples_to_save), get_logger('endpoint-raster', cfg.log_level))
self._raster_tensor_endpoint = raster_tensor_endpoint

aiming_tensor_endpoint = StackDeviceTensorEndpoint(sfe, (asc.bscans_per_volume, asc.ascans_per_bscan, samples_to_save), get_logger('endpoint-aiming', cfg.log_level))
self._aiming_tensor_endpoint = aiming_tensor_endpoint