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.. _tutorials:


Tutorials
=========


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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to stream NWB files stored in Zarr format from Amazon S3 cloud storage. Streaming from S3 allows you to access large datasets without downloading the entire file, which is particularly useful for exploring data, reading specific subsets, or working with datasets too large for local storage.">

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  :doc:`/tutorials/plot_s3_streaming`

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      <div class="sphx-glr-thumbnail-title">Streaming NWB Zarr files from S3</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="To customize data write settings on a per-dataset basis, HDMF supports wrapping of data arrays using :py~hdmf.data_utils.DataIO. To support defining settings specific to Zarr hdmf-zarr provides the corresponding :py~hdmf_zarr.utils.ZarrDataIO class.">

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  :doc:`/tutorials/plot_zarr_dataset_io`

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      <div class="sphx-glr-thumbnail-title">Zarr Dataset I/O</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial illustrates how to convert data between HDF5 and Zarr using a Neurodata Without Borders (NWB) file from the DANDI data archive as an example. In this tutorial we will convert our example file from HDF5 to Zarr and then back again to HDF5. The NWB standard is defined using HDMF &lt;&gt; and uses the :py~ hdmf.backends.hdf5.h5tools.HDF5IO  HDF5 backend from HDMF for storage.">

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  :doc:`/tutorials/plot_convert_nwb_hdf5`

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      <div class="sphx-glr-thumbnail-title">Converting NWB HDF5 files to/from Zarr</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The :py~hdmf_zarr.backend.ZarrIO backend behaves in general much like the standard :py~hdmf.backends.hdf5.HDF5IO available with HDMF and is an adaptation of that backend to use Zarr instead of HDF5">

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  .. image:: /tutorials/images/thumb/sphx_glr_plot_zarr_io_thumb.png
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  :doc:`/tutorials/plot_zarr_io`

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      <div class="sphx-glr-thumbnail-title">ZarrIO Overview</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Similar to :pypynwb.NWBHDF5IO, the :py~hdmf_zarr.nwb.NWBZarrIO extends the basic :py~hdmf_zarr.backend.ZarrIO to perform default setup for BuildManager, loading or namespaces etc., in the context of the NWB format, to simplify using hdmf-zarr with the NWB data standard.  With this we can use :py~hdmf_zarr.nwb.NWBZarrIO directly with the PyNWB API to read/write NWB files to/from Zarr. I.e., we can follow the standard PyNWB tutorials for using NWB files, and only need to replace :pypynwb.NWBHDF5IO with :py~hdmf_zarr.nwb.NWBZarrIO for read/write (and replace the use of pyH5DataIO  with :py~hdmf_zarr.utils.ZarrDataIO).">

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  :doc:`/tutorials/plot_nwb_zarrio`

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      <div class="sphx-glr-thumbnail-title">Creating NWB files using NWBZarrIO</div>
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.. toctree::
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   /tutorials/plot_s3_streaming
   /tutorials/plot_zarr_dataset_io
   /tutorials/plot_convert_nwb_hdf5
   /tutorials/plot_zarr_io
   /tutorials/plot_nwb_zarrio

Resources
=========

sub-healthy-simulated-beta_ses-162_ecephys.nwb
--------------------------------------------------------

This NWB file was downloaded from `Dandiset 001333 <https://dandiarchive.org/dandiset/001333/0.250327.2220>`_

.. code-block:: python
    :linenos:

    import os
    from dandi.dandiapi import DandiAPIClient

    dandiset_id = "001333"
    filepath = "sub-healthy-simulated-beta/sub-healthy-simulated-beta_ses-162_ecephys.nwb"   # 220 KiB file
    with DandiAPIClient() as client:
        asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(filepath)

    s3_path = asset.get_content_url(follow_redirects=1, strip_query=True)
    filename = os.path.basename(asset.path)
    asset.download(filename)


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   /tutorials/resources/index.rst


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      :download:`Download all examples in Python source code: tutorials_python.zip </tutorials/tutorials_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: tutorials_jupyter.zip </tutorials/tutorials_jupyter.zip>`


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_