cellmap_flow.serving.virtual_zarr
=================================

.. py:module:: cellmap_flow.serving.virtual_zarr

.. autoapi-nested-parse::

   The zarr v2 array an inference server makes up, as data.

   A server serves one model's output as ``<dataset>/.zattrs``,
   ``<dataset>/s0/.zarray`` and ``<dataset>/s0/<z>.<y>.<x>[.<c>]`` chunks, which
   neuroglancer reads as an OME-NGFF 0.4 group with one level. What those say,
   and which region each chunk key is, is plain arithmetic on the model's
   geometry and the raw data's; these functions are that arithmetic, with no
   Flask and no process-wide state, so the server's routes only call them.

   Every client reads this format, including dashboards older than the server,
   so it changes only additively.



Attributes
----------

.. autoapisummary::

   cellmap_flow.serving.virtual_zarr.logger
   cellmap_flow.serving.virtual_zarr.BLOSC


Functions
---------

.. autoapisummary::

   cellmap_flow.serving.virtual_zarr.zattrs
   cellmap_flow.serving.virtual_zarr.zarray
   cellmap_flow.serving.virtual_zarr.served_spatial_shape
   cellmap_flow.serving.virtual_zarr.chunk_roi
   cellmap_flow.serving.virtual_zarr.reorder_to_zarr_axes
   cellmap_flow.serving.virtual_zarr.chunk_encoder


Module Contents
---------------

.. py:data:: logger

.. py:data:: BLOSC

.. py:function:: zattrs(axes, output_voxel_size, origin, has_channel: bool, name: str) -> dict

   The group's OME-NGFF attributes: one level, s0, at the output voxel size.

   ``axes`` are the spatial axes, ``origin`` the lower corner of output
   voxel 0 in nm; OME writes voxel 0's centre, so neuroglancer draws voxel 0
   at the origin. A channel axis comes last, with scale 1 and translation 0.


.. py:function:: zarray(shape, chunks, dtype) -> dict

   The array's zarr v2 metadata. ``dtype`` is anything np.dtype takes;
   its typestr is written ("<f4", "|u1", ...).


.. py:function:: served_spatial_shape(raw_offset, raw_shape, raw_voxel_size, origin, output_voxel_size) -> list

   Output voxels from ``origin`` to the end of the raw data, rounded up.


.. py:function:: chunk_roi(index, spatial_block, output_voxel_size, origin) -> funlib.geometry.Roi

   The region (nm) chunk ``index`` covers: ``spatial_block`` output voxels
   per chunk, the grid starting at ``origin``.


.. py:function:: reorder_to_zarr_axes(data, model_axes, spatial_axes) -> numpy.ndarray

   ``data``, laid out as ``model_axes``, in zarr's order: the spatial axes, then "c".


.. py:function:: chunk_encoder() -> numcodecs.Blosc

   The codec for the chunks, as ``BLOSC`` describes it.