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| “pynwb” Copyright (c) 2017-2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
| “pynwb” Copyright (c) 2017-2024, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
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| If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation & Partnerships Office at [email protected]. | ||
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| LICENSE | ||
| ======= | ||
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| "pynwb" Copyright (c) 2017-2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
| "pynwb" Copyright (c) 2017-2024, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
| Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: | ||
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| (1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. | ||
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@@ -124,7 +124,7 @@ You are under no obligation whatsoever to provide any bug fixes, patches, or upg | |
| COPYRIGHT | ||
| ========= | ||
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| "pynwb" Copyright (c) 2017-2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
| "pynwb" Copyright (c) 2017-2024, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. | ||
| If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation & Partnerships Office at [email protected]. | ||
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| NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit other to do so. | ||
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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 | ||
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| nwbfile = NWBFile( | ||
| session_description="my first synthetic recording", | ||
| identifier="EXAMPLE_ID", | ||
| session_start_time=datetime.now(tzlocal()), | ||
| session_id="LONELYMTN", | ||
| ) | ||
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| nwbfile.add_acquisition( | ||
| TimeSeries( | ||
| name="synthetic_timeseries", | ||
| description="Random values", | ||
| data=np.random.randn(100, 100), | ||
| unit="m", | ||
| rate=10e3, | ||
| ) | ||
| ) | ||
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| with NWBHDF5IO("test_edit.nwb", "w") as io: | ||
| io.write(nwbfile) | ||
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| ############################################## | ||
| # Now, let's edit the values of the dataset | ||
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| with NWBHDF5IO("test_edit.nwb", "r+") as io: | ||
| nwbfile = io.read() | ||
| nwbfile.acquisition["synthetic_timeseries"].data[:10] = 0.0 | ||
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| ############################################## | ||
| # You can edit the attributes of that dataset through the ``attrs`` attribute: | ||
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| with NWBHDF5IO("test_edit.nwb", "r+") as io: | ||
| nwbfile = io.read() | ||
| nwbfile.acquisition["synthetic_timeseries"].data.attrs["unit"] = "volts" | ||
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| ############################################## | ||
| # 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: | ||
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| from hdmf.backends.hdf5.h5_utils import H5DataIO | ||
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| nwbfile = NWBFile( | ||
| session_description="my first synthetic recording", | ||
| identifier="EXAMPLE_ID", | ||
| session_start_time=datetime.now(tzlocal()), | ||
| session_id="LONELYMTN", | ||
| ) | ||
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| data_io = H5DataIO(data=np.random.randn(100, 100), maxshape=(None, 100)) | ||
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| nwbfile.add_acquisition( | ||
| TimeSeries( | ||
| name="synthetic_timeseries", | ||
| description="Random values", | ||
| data=data_io, | ||
| unit="m", | ||
| rate=10e3, | ||
| ) | ||
| ) | ||
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| with NWBHDF5IO("test_edit2.nwb", "w") as io: | ||
| io.write(nwbfile) | ||
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| ############################################## | ||
| # 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. | ||
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| with NWBHDF5IO("test_edit2.nwb", "r+") as io: | ||
| nwbfile = io.read() | ||
| nwbfile.acquisition["synthetic_timeseries"].data.resize((200, 100)) | ||
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| ############################################## | ||
| # 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``: | ||
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| import h5py | ||
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| with h5py.File("test_edit.nwb", "r+") as f: | ||
| f["acquisition"]["synthetic_timeseries"].attrs["description"] = "Random values in volts" | ||
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| ############################################## | ||
| # .. 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: | ||
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| with h5py.File("test_edit.nwb", "r+") as f: | ||
| f["acquisition"].move("synthetic_timeseries", "synthetic_timeseries_renamed") | ||
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| ############################################## | ||
| # 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: | ||
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| with h5py.File("test_edit.nwb", "r+") as f: | ||
| f["acquisition"].move( | ||
| "synthetic_timeseries_renamed", | ||
| "/analysis/synthetic_timeseries_renamed", | ||
| ) |
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