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"""
.. _editing:
Editing NWB files
=================
This tutorial demonstrates how to edit NWB files in-place to make small changes to
existing containers. To add or remove containers from an NWB file, see
:ref:`modifying_data`. How and whether it is possible to edit an NWB file depends on the
storage backend and the type of edit.
.. warning::
Manually editing an existing NWB file can make the file invalid if you are not
careful. We highly recommend making a copy before editing and running a validation
check on the file after editing it. See :ref:`validating`.
Editing datasets
----------------
When reading an HDF5 NWB file, PyNWB exposes :py:class:`h5py.Dataset` objects, which can
be edited in place. For this to work, you must open the file in read/write mode
(``"r+"`` or ``"a"``).
First, let's create an NWB file with data:
"""
from pynwb import NWBHDF5IO, NWBFile, TimeSeries
from datetime import datetime
from dateutil.tz import tzlocal
import numpy as np
nwbfile = NWBFile(
session_description="my first synthetic recording",
identifier="EXAMPLE_ID",
session_start_time=datetime.now(tzlocal()),
session_id="LONELYMTN",
)
nwbfile.add_acquisition(
TimeSeries(
name="synthetic_timeseries",
description="Random values",
data=np.random.randn(100, 100),
unit="m",
rate=10e3,
)
)
with NWBHDF5IO("test_edit.nwb", "w") as io:
io.write(nwbfile)
##############################################
# Now, let's edit the values of the dataset
with NWBHDF5IO("test_edit.nwb", "r+") as io:
nwbfile = io.read()
nwbfile.acquisition["synthetic_timeseries"].data[:10] = 0.0
##############################################
# You can edit the attributes of that dataset through the ``attrs`` attribute:
with NWBHDF5IO("test_edit.nwb", "r+") as io:
nwbfile = io.read()
nwbfile.acquisition["synthetic_timeseries"].data.attrs["unit"] = "volts"
##############################################
# Changing the shape of dataset
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Whether it is possible to change the shape of a dataset depends on how the dataset was
# created. If the dataset was created with a flexible shape, then it is possible to
# change in-place. Creating a dataset with a flexible shape is done by specifying the
# ``maxshape`` argument of the :py:class:`~hdmf.backends.hdf5.h5_utils.H5DataIO` class
# constructor. Using a ``None`` value for a component of the ``maxshape`` tuple allows
# the size of the corresponding dimension to grow, such that is can be be reset arbitrarily long
# in that dimension. Chunking is required for datasets with flexible shapes. Setting ``maxshape``,
# hence, automatically sets chunking to ``True``, if not specified.
#
# First, let's create an NWB file with a dataset with a flexible shape:
from hdmf.backends.hdf5.h5_utils import H5DataIO
nwbfile = NWBFile(
session_description="my first synthetic recording",
identifier="EXAMPLE_ID",
session_start_time=datetime.now(tzlocal()),
session_id="LONELYMTN",
)
data_io = H5DataIO(data=np.random.randn(100, 100), maxshape=(None, 100))
nwbfile.add_acquisition(
TimeSeries(
name="synthetic_timeseries",
description="Random values",
data=data_io,
unit="m",
rate=10e3,
)
)
with NWBHDF5IO("test_edit2.nwb", "w") as io:
io.write(nwbfile)
##############################################
# The ``None`` value in the first component of ``maxshape`` means that the
# the first dimension of the dataset is unlimited. By setting the second dimension
# of ``maxshape`` to ``100``, that dimension is fixed to be no larger than ``100``.
# If you do not specify a``maxshape``, then the shape of the dataset will be fixed
# to the shape that the dataset was created with. Here, you can change the shape of
# the first dimension of this dataset.
with NWBHDF5IO("test_edit2.nwb", "r+") as io:
nwbfile = io.read()
nwbfile.acquisition["synthetic_timeseries"].data.resize((200, 100))
##############################################
# This will change the shape of the dataset in-place. If you try to change the shape of
# a dataset with a fixed shape, you will get an error.
#
# .. note::
# There are several types of dataset edits that cannot be done in-place: changing the
# shape of a dataset with a fixed shape, or changing the datatype, compression,
# chunking, max-shape, or fill-value of a dataset. For any of these, we recommend using
# the :py:class:`pynwb.NWBHDF5IO.export` method to export the data to a new file. See
# :ref:`modifying_data` for more information.
#
# Editing groups
# --------------
# Editing of groups is not yet supported in PyNWB.
# To edit the attributes of a group, open the file and edit it using ``h5py``:
import h5py
with h5py.File("test_edit.nwb", "r+") as f:
f["acquisition"]["synthetic_timeseries"].attrs["description"] = "Random values in volts"
##############################################
# .. warning::
# Be careful not to edit values that will bring the file out of compliance with the
# NWB specification.
#
# Renaming groups and datasets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Rename groups and datasets in-place using the :py:meth:`~h5py.Group.move` method. For example, to rename
# the ``"synthetic_timeseries"`` group:
with h5py.File("test_edit.nwb", "r+") as f:
f["acquisition"].move("synthetic_timeseries", "synthetic_timeseries_renamed")
##############################################
# You can use this same technique to move a group or dataset to a different location in
# the file. For example, to move the ``"synthetic_timeseries_renamed"`` group to the
# ``"analysis"`` group:
with h5py.File("test_edit.nwb", "r+") as f:
f["acquisition"].move(
"synthetic_timeseries_renamed",
"/analysis/synthetic_timeseries_renamed",
)
|
