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Merge pull request #1142 from TomDonoghue/master
[DOC] - Small fixes to documentation
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doc/source/core.rst

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There is a simple hierarchy of containers:
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* :py:class:`Segment`: A container for heterogeneous discrete or continous data sharing a common
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* :py:class:`Segment`: A container for heterogeneous discrete or continuous data sharing a common
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clock (time basis) but not necessarily the same sampling rate, start time or end time.
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A :py:class:`Segment` can be considered as equivalent to a "trial", "episode", "run",
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"recording", etc., depending on the experimental context.
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# ...
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Now each putative neuron is represented by a :class:`Group` containing the spiktrains of that neuron
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Now each putative neuron is represented by a :class:`Group` containing the spiketrains of that neuron
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and a view of the signal selecting only those channels from which the spikes were obtained.
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doc/source/developers_guide.rst

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Requirements
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------------
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* Python_ 3.5 or later
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* Python_ 3.7 or later
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* numpy_ >= 1.11.0
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* quantities_ >= 0.12.1
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* nose_ >= 1.1.2 (for running tests)
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Python version
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--------------
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Neo should work with Python 3.5 or newer. If you need support for Python 2.7,
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Neo should work with Python 3.7 or newer. If you need support for Python 2.7,
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use Neo v0.8.0 or earlier.
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Coding standards and style
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--------------------------
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All code should conform as much as possible to `PEP 8`_, and should run with
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Python 3.5 or newer.
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Python 3.7 or newer.
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You can use the `pep8`_ program to check the code for PEP 8 conformity.
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You can also use `flake8`_, which combines pep8 and pyflakes.

doc/source/grouping.rst

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@@ -149,5 +149,5 @@ Using :class:`ChannelView` and :class:`Group`::
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block.groups.extend(units)
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Now each putative neuron is represented by a :class:`Group` containing the spiktrains of that neuron
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Now each putative neuron is represented by a :class:`Group` containing the spiketrains of that neuron
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and a view of the signal selecting only those channels from which the spikes were obtained.

doc/source/index.rst

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The goal of Neo is to improve interoperability between Python tools for
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analyzing, visualizing and generating electrophysiology data, by providing a common,
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shared object model. In order to be as lightweight a dependency as possible,
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Neo is deliberately limited to represention of data, with no functions for data
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Neo is deliberately limited to representation of data, with no functions for data
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analysis or visualization.
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Neo is used by a number of other software tools, including
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License
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-------
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Neo is free software, distributed under a 3-clause Revised BSD licence (BSD-3-Clause).
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Neo is free software, distributed under a 3-clause Revised BSD license (BSD-3-Clause).
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Support

doc/source/io.rst

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Introduction
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============
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There is an intrinsic structure in the different Neo objects, that could be seen as a hierachy with cross-links. See :doc:`core`.
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There is an intrinsic structure in the different Neo objects, that could be seen as a hierarchy with cross-links. See :doc:`core`.
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The highest level object is the :class:`Block` object, which is the high level container able to encapsulate all the others.
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A :class:`Block` has therefore a list of :class:`Segment` objects, that can, in some file formats, be accessed individually.
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Depending on the file format, i.e. if it is streamable or not, the whole :class:`Block` may need to be loaded, but sometimes
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particular :class:`Segment` objects can be accessed individually.
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Within a :class:`Segment`, the same hierarchical organisation applies.
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Within a :class:`Segment`, the same hierarchical organization applies.
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A :class:`Segment` embeds several objects, such as :class:`SpikeTrain`,
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:class:`AnalogSignal`, :class:`IrregularlySampledSignal`, :class:`Epoch`, :class:`Event`
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(basically, all the different Neo objects).
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This is described in the :attr:`mode` attribute of the IO class.
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>>> from neo.io import MyFormatIO
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>>> print MyFormatIO.mode
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>>> print(MyFormatIO.mode)
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'file'
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All IOs have a read() method that returns a list of :class:`Block` objects (representing the whole content of the file)::
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>>> bl = reader.read()
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>>> print bl[0].segments[0]
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>>> print(bl[0].segments[0])
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neo.core.Segment
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Some objects support the ``time_slice`` argument in ``read_segment()``.
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This is useful to read only a subset of a dataset clipped in time.
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By default ``time_slice=None`` meaning load eveything.
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By default ``time_slice=None`` meaning load everything.
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This reads everything::
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In some cases you may not want to load everything in memory because it could be too big.
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For this scenario, some IOs implement ``lazy=True/False``.
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Since neo 0.7, a new lazy sytem have been added for some IO modules (all IO classes that inherit from rawio).
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Since neo 0.7, a new lazy system has been added for some IO modules (all IO classes that inherit from rawio).
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To know if a class supports lazy mode use ``ClassIO.support_lazy``.
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With ``lazy=True`` all data objects (AnalogSignal/SpikeTrain/Event/Epoch) are replaced by
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- each file format has an IO class (for example for Spike2 files you have a :class:`Spike2IO` class).
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- each IO class inherits from the :class:`BaseIO` class.
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- each IO class can read or write directly one or several Neo objects (for example :class:`Segment`, :class:`Block`, ...): see the :attr:`readable_objects` and :attr:`writable_objects` attributes of the IO class.
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- each IO class supports part of the :mod:`neo.core` hierachy, though not necessarily all of it (see :attr:`supported_objects`).
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- each IO class supports part of the :mod:`neo.core` hierarchy, though not necessarily all of it (see :attr:`supported_objects`).
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- each IO class has a :meth:`read()` method that returns a list of :class:`Block` objects. If the IO only supports :class:`Segment` reading, the list will contain one block with all segments from the file.
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- each IO class that supports writing has a :meth:`write()` method that takes as a parameter a list of blocks, a single block or a single segment, depending on the IO's :attr:`writable_objects`.
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- some IO are able to do a *lazy* load: all metadata (e.g. :attr:`sampling_rate`) are read, but not the actual numerical data.

doc/source/io_developers_guide.rst

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=============
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* If your IO supports several versions of a format (like ABF1, ABF2), upload to the gin.g-node.org test file repository all file versions possible. (for test coverage).
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* :py:func:`neo.core.Block.create_many_to_one_relationship` offers a utility to complete the hierachy when all one-to-many relationships have been created.
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* :py:func:`neo.core.Block.create_many_to_one_relationship` offers a utility to complete the hierarchy when all one-to-many relationships have been created.
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* In the docstring, explain where you obtained the file format specification if it is a closed one.
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* If your IO is based on a database mapper, keep in mind that the returned object MUST be detached,
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because this object can be written to another url for copying.
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To use these you need to upload some sample data files at `gin-gnode`_. They will be publicly accessible for testing Neo.
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These tests:
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* check the compliance with the schema: hierachy, attribute types, ...
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* check the compliance with the schema: hierarchy, attribute types, ...
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* For IO modules able to both write and read data, it compares a generated dataset with the same data after a write/read cycle.
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The test scripts download all files from `gin-gnode`_ and stores them locally in ``/tmp/files_for_tests/``.

doc/source/rawio.rst

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Possible uses of the :mod:`neo.rawio` API are:
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* fast reading chunks of signals in int16 and do the scaling of units (uV)
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on a GPU while scaling the zoom. This should improve bandwith HD to RAM
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on a GPU while scaling the zoom. This should improve bandwidth HD to RAM
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and RAM to GPU memory.
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* load only some small chunk of data for heavy computations. For instance
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the spike sorting module tridesclous_ does this.
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The :mod:`neo.rawio` API is less flexible than :mod:`neo.io` and has some limitations:
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* read-only
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* AnalogSignals must have the same characteristcs across all Blocks and Segments:
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* AnalogSignals must have the same characteristics across all Blocks and Segments:
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``sampling_rate``, ``shape[1]``, ``dtype``
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* AnalogSignals should all have the same value of ``sampling_rate``, otherwise they won't be read
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at the same time.

doc/source/usecases.rst

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for signal in seg.analogsignals:
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# use a simple threshhold detector
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# use a simple threshold detector
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spike_mask = np.where(np.min(signal.magnitude, axis=1) < -1.0)[0]
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# create a spike train

doc/source/whatisnew.rst

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* various bug fixes in neo.io
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* added ElphyIO
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* SpikeTrain performence improved
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* SpikeTrain performance improved
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* An IO class now can return a list of Block (see read_all_blocks in IOs)
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* python3 compatibility improved
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* Neuron is now Unit
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* use the quantities_ module for everything that can have units.
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* Some objects directly inherit from Quantity: SpikeTrain, AnalogSignal, AnalogSignalArray, instead of having an attribute for data.
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* Attributes are classifyed in 3 categories: necessary, recommended, free.
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* Attributes are classified in 3 categories: necessary, recommended, free.
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* lazy and cascade keywords are added to all IOs
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* Python 3 support
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* better tests

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