multielectrode_grasp/code/python-neo/doc/source/releases/0.8.0.rst

=======================
Neo 0.8.0 release notes
=======================

30th September 2019

Lazy loading
------------

Neo 0.8 sees a major new feature, the ability to selectively load only parts of a data file
(for supported file formats) into memory, for example only a subset of the signals
in a segment, a subset of the channels in a signal, or even only a certain time slice of a given signal.

This can lead to major savings in time and memory consumption, or can allow files that are too
large to be loaded into memory in their entirety to be processed one section at a time.

Here is an example, loading only certain sections of a signal::

    lim0, lim1 = -500*pq.ms, +1500*pq.ms
    seg = reader.read_segment(lazy=True)    # this loads only the segment structure and metadata
                                            #    but all data objects are replaced by proxies
    triggers = seg.events[0].load()         # this loads all triggers in memory
    sigproxy = seg.analogsignals[0]         # this is a proxy object
    all_sig_chunks = []
    for t in triggers.times:
        t0, t1 = (t + lim0), (t + lim1)
        sig_chunk = sigproxy.load(time_slice=(t0, t1))  # here the actual data are loaded
        all_sig_chunks.append(sig_chunk)

Not all IO modules support lazy loading (but many do). To know whether a given IO class supports lazy mode,
use ``SomeIO.support_lazy``.

For more details,  see :ref:`section-lazy`.

Image sequence data
-------------------

Another new feature, although one that is more experimental, is support for image sequence data,
coming from calcium imaging of neuronal activity, voltage-sensitive dye imaging, etc.

The new :class:`ImageSequence` object contains a sequence of image frames as a 3D array.
As with other Neo data objects, the object also holds metadata such as the sampling rate/frame duration
and the spatial scale (physical size represented by one pixel).

Three new IO modules, :class:`TiffIO`, :class:`AsciiImageIO` and :class:`BlkIO`, allow
reading such data from file, e.g.::

    from quantities import Hz, mm, dimensionless
    from neo.io import TiffIO

    data = TiffIO(data_path).read(units=dimensionless, sampling_rate=25 * Hz,
                                  spatial_scale=0.05 * mm)
    images = data[0].segments[0].imagesequences[0]

:class:`ImageSequence` is a subclass of the NumPy :class:`ndarray`, and
so can be manipulated in the same ways, e.g.::

    images /= images.max()
    background = np.mean(images, axis=0)
    preprocessed_images = images - background

Since a common operation with image sequences is to extract time series from regions of interest,
Neo also provides various region-of-interest classes which perform this operation,
returning an :class:`AnalogSignal` object::

    roi = CircularRegionOfInterest(x=50, y=50, radius=10)
    signal = preprocessed_images.signal_from_region(roi)[0]


Other new features
------------------

  * new neo.utils module
  * Numpy 1.16+ compatibility
  * :meth:`time_shift()` method for :class:`Epoch`/:class:`Event`/:class:`AnalogSignal`
  * :meth:`time_slice()` method is now more robust
  * dropped support for Python 3.4

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

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

  * Blackrock
  * Neuroshare
  * NixIOFr
  * NixIO (array annotation + 1d coordinates)
  * AsciiSignal (fix + json metadata + IrregularlySampledSignals + write proxy)
  * Spike2 (group same sampling rate)
  * Brainvision
  * NeuralynxIO

.. Warning:: Some IOs (based on rawio) when loading can choose to split each
    channel into its own 1-channel :class:`AnalogSignal` or to group them
    in a multi-channel :class:`AnalogSignal`.
    The default behavior (either ``signal_group_mode='split-all'``
    or ``'group-same-units'``) is not the same for all IOs for backwards
    compatibility reasons. In the next release, all IOs will have the default
    ``signal_group_mode='group-same-units'``

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

Thanks to Achileas Koutsou, Chek Yin Choi, Richard C. Gerkin, Hugo van Kemenade,
Alexander Kleinjohann, Björn Müller, Jeffrey Gill, Christian Kothe,
Mike Sintsov, @rishidhingra, Michael Denker, Julia Sprenger, Corentin Fragnaud,
Andrew Davison and Samuel Garcia for their contributions to this release.


.. _`list of closed issues`: https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue+milestone%3A0.8.0+is%3Aclosed
.. _`pull requests`: https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+merged%3A%3E2018-11-27+milestone%3A0.8.0