plot_external_resources.py

"""
.. _external_resources:

Linking to External Resources (HERD)
====================================

The :py:class:`~pynwb.resources.HERD` (HDMF External Resources Data Structure) class lets you map
terms used in your data to entities defined in external, web-accessible resources such as
ontologies. For example, you may store a species name ``"Mus musculus"`` on a
:py:class:`~pynwb.file.Subject` and want to link it to the corresponding NCBI Taxonomy term so that
the value is standardized and easy to query.

From a user's perspective, a HERD can be treated as a single table that associates a ``key`` (a term
used on an ``object``, i.e. a dataset or attribute in the file) with an ``entity`` (a term in an
external resource, identified by an ``entity_id`` and an ``entity_uri``). Internally, HERD stores
this in six interlinked tables (``keys``, ``files``, ``entities``, ``entity_keys``, ``objects``, and
``object_keys``) and provides convenience methods so you rarely need to interact with those tables
directly.

This tutorial shows how to create a HERD, annotate objects in an NWB file, store the HERD in the
file, and inspect the annotations after reading the file back. For the full HERD API (including
``add_ref_termset`` for validating terms against a :py:class:`~hdmf.term_set.TermSet`, ``get_key``,
and compound-data references), see the
`HDMF HERD tutorial <https://hdmf.readthedocs.io/en/stable/tutorials/plot_external_resources.html>`_.
"""

# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_external_resources.png'
from datetime import datetime
from uuid import uuid4

from dateutil.tz import tzlocal

from pynwb import NWBHDF5IO, NWBFile
from pynwb.file import Subject
from pynwb.resources import HERD

###############################################################################
# Create an NWB file
# ------------------
# Start with an :py:class:`~pynwb.file.NWBFile` that has a :py:class:`~pynwb.file.Subject`. The
# subject's species is the value we will annotate with an external resource.

nwbfile = NWBFile(
    session_description="a demonstration of external resources",
    identifier=str(uuid4()),
    session_start_time=datetime(2018, 4, 25, 2, 30, 3, tzinfo=tzlocal()),
    subject=Subject(subject_id="001", species="Mus musculus"),
)

###############################################################################
# Create a HERD and attach it to the file
# ---------------------------------------
# Create a :py:class:`~pynwb.resources.HERD` and assign it to the ``external_resources`` field of the
# :py:class:`~pynwb.file.NWBFile`.

nwbfile.external_resources = HERD()

###############################################################################
# Add references with ``add_ref``
# -------------------------------
# Use :py:meth:`~hdmf.common.resources.HERD.add_ref` to add a row that links a key on an object to an
# external entity. Here we link the subject's species to the NCBI Taxonomy entry for *Mus musculus*.
# The subject must be part of a file before a reference is added to it.
#
# An entity is identified by an ``entity_id`` and an ``entity_uri``. The ``entity_id`` is a compact
# URI (CURIE) of the form ``prefix:identifier`` whose prefix is registered with
# `bioregistry.io <https://bioregistry.io/>`_, such as ``NCBITaxon`` for the NCBI Taxonomy. The
# ``entity_uri`` is the persistent URL the CURIE resolves to, which you can look up at
# ``https://bioregistry.io/<entity_id>``.

nwbfile.external_resources.add_ref(
    container=nwbfile.subject,
    key=nwbfile.subject.species,
    entity_id="NCBITaxon:10090",
    entity_uri="http://purl.obolibrary.org/obo/NCBITaxon_10090",
)

###############################################################################
# References can also point to an attribute of an object, such as a column of a table. Here we record
# the brain region of a set of electrodes in the electrodes table and link the region to the
# corresponding structure in the
# `Allen Mouse Brain Atlas <https://atlas.brain-map.org/>`_. When the target is a column, pass the
# table as the ``container`` and the column name as the ``attribute``; HERD resolves the reference to
# the column object itself.

device = nwbfile.create_device(name="probe")
electrode_group = nwbfile.create_electrode_group(
    name="shank0",
    description="a shank of the recording probe",
    location="VISp",
    device=device,
)
for _ in range(4):
    nwbfile.add_electrode(location="VISp", group=electrode_group)

nwbfile.external_resources.add_ref(
    container=nwbfile.electrodes,
    attribute="location",
    key="VISp",
    entity_id="MBA:385",
    entity_uri="https://purl.brain-bican.org/ontology/mbao/MBA_385",
)

###############################################################################
# Inspect the HERD
# ----------------
# :py:meth:`~hdmf.common.resources.HERD.to_dataframe` flattens the interlinked tables into a single
# :py:class:`~pandas.DataFrame`, with one row per (object, key, entity) association.

nwbfile.external_resources.to_dataframe()

###############################################################################
# You can also view the individual tables. Each is a
# :py:class:`~hdmf.common.table.DynamicTable` and has its own ``to_dataframe`` method.

nwbfile.external_resources.keys.to_dataframe()

###############################################################################

nwbfile.external_resources.entities.to_dataframe()

###############################################################################
# :py:meth:`~hdmf.common.resources.HERD.get_object_type` returns all annotations for objects of a
# given type, for example every annotated :py:class:`~pynwb.file.Subject`.

nwbfile.external_resources.get_object_type(object_type="Subject")

###############################################################################
# Write and read the NWB file
# ---------------------------
# Writing the file stores the HERD inside it. Reading the file back makes the HERD available again
# through the ``external_resources`` field.

filename = "external_resources_tutorial.nwb"
with NWBHDF5IO(filename, mode="w") as io:
    io.write(nwbfile)

read_io = NWBHDF5IO(filename, mode="r")
read_nwbfile = read_io.read()
read_herd = read_nwbfile.external_resources

###############################################################################
# Access the loaded data
# -----------------------
# The loaded HERD provides the same accessors as before. In a Jupyter notebook, displaying the HERD
# renders the flattened references as a table, and
# :py:meth:`~hdmf.common.resources.HERD.to_dataframe` returns that same table as a
# :py:class:`~pandas.DataFrame`. The individual tables give a more focused view.

read_herd.to_dataframe()

###############################################################################
# View the individual tables, for example:

read_herd.keys.to_dataframe()

###############################################################################
# :py:meth:`~hdmf.common.resources.HERD.get_object_entities` returns the entities annotated on a
# single object as a :py:class:`~pandas.DataFrame`. Here we view the species annotation stored for
# the subject:

read_herd.get_object_entities(container=read_nwbfile.subject)

###############################################################################
# Close the file once you are done reading from it.

read_io.close()

###############################################################################
# Alternative: store a HERD outside an NWB file
# ---------------------------------------------
# A HERD can also be saved independently of an NWB file as a zip archive of the underlying tables
# using :py:meth:`~hdmf.common.resources.HERD.to_zip`, and read back with
# :py:meth:`~hdmf.common.resources.HERD.from_zip`. This is useful when external resources span
# multiple files; see :ref:`external_resources_streaming` for an example that annotates many NWB
# files with a single HERD. For the full HERD API, see the
# `HDMF HERD tutorial <https://hdmf.readthedocs.io/en/stable/tutorials/plot_external_resources.html>`_.