To help you get started, we've collected the most common ways that pynwb is being used within popular public projects.

NeurodataWithoutBorders / pynwb / tests / unit / test_epoch.py View on Github

def test_init(self):
        tstamps = np.arange(1.0, 100.0, 0.1, dtype=np.float)
        ts = TimeSeries("test_ts", list(range(len(tstamps))), 'unit', timestamps=tstamps)
        ept = TimeIntervals('epochs', "TimeIntervals unittest")
        self.assertEqual(ept.name, 'epochs')
        ept.add_interval(10.0, 20.0, ["test", "unittest", "pynwb"], ts)
        row = ept[0]
        self.assertEqual(row[1], 10.0)
        self.assertEqual(row[2], 20.0)
        self.assertEqual(row[3], ["test", "unittest", "pynwb"])
        self.assertEqual(row[4], [(90, 100, ts)])

View more ways to use pynwb

PyNWB is a Python package for working with NWB files. The PyNWB documentation can be found at

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NWB Format API

PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format.

Neurodata Without Borders (NWB) is a project to develop a unified data format for cellular-based neurophysiology data, focused on the dynamics of groups of neurons measured under a large range of experimental conditions.

The NWB team consists of neuroscientists and software developers who recognize that adoption of a unified data format is an important step toward breaking down the barriers to data sharing in neuroscience.

Installation

See the PyNWB documentation for details

Code of Conduct

This project and everyone participating in it is governed by our code of conduct guidelines. By participating, you are expected to uphold this code.

Contributing

For details on how to contribute to PyNWB see our contribution guidelines.

Citing NWB

• Manuscript: Oliver Rübel, Andrew Tritt, Ryan Ly, Benjamin K Dichter, Satrajit Ghosh, Lawrence Niu, Pamela Baker, Ivan Soltesz, Lydia Ng, Karel Svoboda, Loren Frank, Kristofer E Bouchard. (2022). The Neurodata Without Borders ecosystem for neurophysiological data science. eLife, 11:e78362. doi:

• RRID: (PyNWB, RRID:SCR_017452)

LICENSE

"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:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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"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 .

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.