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Merge pull request #1162 from apdavison/release-notes-0.11.0
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Release notes for 0.11.0, together with some docstring changes
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apdavison authored Sep 1, 2022
2 parents 89fd2f9 + 6d319e3 commit 08daa63
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48 changes: 48 additions & 0 deletions doc/source/releases/0.11.0.rst
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========================
Neo 0.11.0 release notes
========================

1st September 2022

.. currentmodule:: neo.io

Bug fixes and improvements in IO modules
----------------------------------------

Bug fixes and/or improvements have been made to :class:`NWBIO`, :class:`NeoMatlabIO`, :class:`NixIO`,
:class:`AxonIO`, :class:`SpikeGLXIO`, :class:`NeuroshareIO`, :class:`NeuroScopeIO`,
:class:`NeuralynxIO`, :class:`OpenEphysIO`, :class:`AsciiSignalIO`, :class:`AlphaOmegaIO`,
and :class:`TdtIO`.

New IO modules
--------------

Modules :class:`BiocamIO` and :class:`EDFIO` have been added.

Supported NumPy versions
------------------------

We no longer support versions of NumPy older than 1.18.5.


Other changes
-------------

.. currentmodule:: neo.io.spiketrainlist

- documentation fixes and updates
- improvements to :class:`SpikeTrainList`

See all `pull requests`_ included in this release and the `list of closed issues`_.

Acknowledgements
----------------

Thanks to Julia Sprenger, Samuel Garcia, Heberto Mayorquin, Andrew Davison, Tom Donoghue,
Alessio Buccino, Thomas Perret, Saksham Sharda, Ben Dichter and Elodie Legouée
for their contributions to this release.

.. generated with git shortlog --since=2022-03-08 -sne then checking Github for PRs merged since the last release but with commits before then
.. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.11.0+is%3Aclosed
.. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2022-03-08+milestone%3A0.11.0
1 change: 1 addition & 0 deletions doc/source/whatisnew.rst
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Expand Up @@ -6,6 +6,7 @@ Release notes
.. toctree::
:maxdepth: 1

releases/0.11.0.rst
releases/0.10.2.rst
releases/0.10.1.rst
releases/0.10.0.rst
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6 changes: 6 additions & 0 deletions neo/io/__init__.py
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* :attr:`BrainwareF32IO`
* :attr:`BrainwareSrcIO`
* :attr:`CedIO`
* :attr:`EDFIO`
* :attr:`ElanIO`
* :attr:`IgorIO`
* :attr:`IntanIO`
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.. autoattribute:: extensions
.. autoclass:: neo.io.EDFIO
.. autoattribute:: extensions
.. autoclass:: neo.io.ElanIO
.. autoattribute:: extensions
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AxonaIO,
AxonIO,
BCI2000IO,
BiocamIO,
BlackrockIO,
BlkIO,
BrainVisionIO,
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4 changes: 2 additions & 2 deletions neo/rawio/__init__.py
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* :attr:`BlackrockRawIO`
* :attr:`BrainVisionRawIO`
* :attr:`CedRawIO`
* :attr: `EdfRawIO`
* :attr: `EDFRawIO`
* :attr:`ElanRawIO`
* :attr:`IntanRawIO`
* :attr:`MaxwellRawIO`
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.. autoattribute:: extensions
.. autoclass:: neo.rawio.EdfRawIO
.. autoclass:: neo.rawio.EDFRawIO
.. autoattribute:: extensions
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15 changes: 8 additions & 7 deletions neo/rawio/axonarawio.py
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Expand Up @@ -30,22 +30,23 @@ class AxonaRawIO(BaseRawIO):
Class for reading raw, continuous data from the Axona dacqUSB system:
http://space-memory-navigation.org/DacqUSBFileFormats.pdf
The raw data is saved in .bin binary files with an accompanying
.set file about the recording setup (see the above manual for details).
The raw data is saved in .bin binary files with an accompanying .set
file about the recording setup (see the above manual for details).
Usage::
Usage:
import neo.rawio
r = neo.rawio.AxonaRawIO(
filename=os.path.join(dir_name, base_filename)
)
r = neo.rawio.AxonaRawIO(filename=os.path.join(dir_name, base_filename))
r.parse_header()
print(r)
raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0,
i_start=0, i_stop=1024, channel_names=channel_names)
i_start=0, i_stop=1024,
channel_names=channel_names)
float_chunk = reader.rescale_signal_raw_to_float(
raw_chunk, dtype='float64',
channel_indexes=[0, 3, 6]
)
"""

extensions = ['bin', 'set'] + [str(i) for i in range(1, 33)] # Never used?
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2 changes: 1 addition & 1 deletion neo/rawio/edfrawio.py
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Expand Up @@ -30,7 +30,7 @@ class EDFRawIO(BaseRawIO):
Usage:
>>> import neo.rawio
>>> r = neo.rawio.EdfRawIO(filename='file.edf')
>>> r = neo.rawio.EDFRawIO(filename='file.edf')
>>> r.parse_header()
>>> print(r)
>>> raw_chunk = r.get_analogsignal_chunk(block_index=0, seg_index=0,
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