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Merge pull request #1244 from zm711/master
Fixed Documentation Typos
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doc/source/core.rst

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@@ -191,7 +191,7 @@ Relationship:
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:download:`Click here for a better quality SVG diagram <./images/simple_generated_diagram.svg>`
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.. note:: This figure do not include :class:`ChannelView` and :class:`RegionOfInterest`.
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.. note:: This figure does not include :class:`ChannelView` and :class:`RegionOfInterest`.
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For more details, see the :doc:`api_reference`.
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doc/source/developers_guide.rst

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@@ -146,7 +146,7 @@ example can be copied and pasted as-is.
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The documentation is written in `reStructuredText`_, using the `Sphinx`_
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documentation system. Any mention of another Neo module, class, attribute,
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method, or function should be properly marked up so automatic
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links can be generated. The same goes for quantities or numpy.
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links can be generated. The same goes for quantities or NumPy.
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To build the documentation::
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.. _pep8: https://pypi.org/project/pep8/
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.. _flake8: https://pypi.org/project/flake8/
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.. _pyflakes: https://pypi.org/project/pyflakes/
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.. _`maintainers team`: https://github.com/orgs/NeuralEnsemble/teams/neo-maintainers
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.. _`maintainers team`: https://github.com/orgs/NeuralEnsemble/teams/neo-maintainers

doc/source/io.rst

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@@ -114,7 +114,7 @@ Read a time slice of 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 everything.
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By default ``time_slice=None`` will load everything.
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This reads everything::
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Lazy option and proxy objects
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=============================
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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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In some cases you may not want to load everything in memory (RAM) 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 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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but they do not contain any array-like data.
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All proxy objects contain a ``load()`` method to postpone the real load of array like data.
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Further more the ``load()`` method has a ``time_slice`` argument to load only a slice
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Furthermore the ``load()`` method has a ``time_slice`` argument to load only a slice
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from the file. In this way the consumption of memory can be finely controlled.
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Here are two examples that read a dataset, extract sections of the signal based on recorded events,
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and averages the sections.
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and average the sections.
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The first example is without lazy mode, so it consumes more memory::
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@@ -178,7 +178,7 @@ The second example uses lazy mode, so it consumes less memory::
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apply_my_fancy_average(all_sig_chunks)
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In addition to ``time_slice``, AnalogSignalProxy supports the ``channel_indexes`` argument.
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This allows loading only a subset of channels. This is useful where the channel count is very high.
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This allows loading only a subset of channels. This is useful when the channel count is very high.
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.. TODO: add something about magnitude mode when implemented for all objects.
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@@ -295,7 +295,7 @@ For more complex logging, please see the documentation for the logging_ module.
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.. note:: If you wish to implement more advanced logging as describe in the documentation for the logging_ module or elsewhere on the internet, please do so before calling any :mod:`neo` functions or initializing any :mod:`neo` classes.
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This is because the default handler is created when :mod:`neo` is imported, but it is not attached to the :mod:`neo` logger until a class that uses logging is initialized or a function that uses logging is called.
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Further, the handler is only attached if there are no handlers already attached to the root logger or the :mod:`neo` logger, so adding your own logger will override the default one.
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Furthermore, the handler is only attached if there are no handlers already attached to the root logger or the :mod:`neo` logger, so adding your own logger will override the default one.
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Additional functions and/or classes may get logging during bugfix releases, so code relying on particular modules not having logging may break at any time without warning.
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.. _`logging`: https://docs.python.org/3/library/logging.html

doc/source/io_developers_guide.rst

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@@ -22,9 +22,9 @@ For read/write classes you can mix the two levels neo.rawio for reading and neo.
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Recipe to develop an IO module for a new data format:
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1. Fully understand the object model. See :doc:`core`. If in doubt ask the `mailing list`_.
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2. Fully understand :mod:`neo.rawio.examplerawio`, It is a fake IO to explain the API. If in doubt ask the list.
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2. Fully understand :mod:`neo.rawio.examplerawio`. It is a fake IO to explain the API. If in doubt ask the list.
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3. Copy/paste ``examplerawio.py`` and choose clear file and class names for your IO.
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4. implement all methods that **raise(NotImplementedError)** in :mod:`neo.rawio.baserawio`. Return None when the object is not supported (spike/waveform)
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4. Implement all methods that **raise(NotImplementedError)** in :mod:`neo.rawio.baserawio`. Return None when the object is not supported (spike/waveform)
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5. Write good docstrings. List dependencies, including minimum version numbers.
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6. Add your class to :mod:`neo.rawio.__init__`. Keep imports inside ``try/except`` for dependency reasons.
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7. Create a class in :file:`neo/io/`
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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: 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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* check for compliance with the schema: hierarchy, attribute types, ...
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* For IO modules confirm they able to both write and read data; they compare 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/``.
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The test scripts download all files from `gin-gnode`_ and store them locally in ``<home>/ 'ephy_testing_data'``.
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Subsequent test runs use the previously downloaded files, rather than trying to download them each time.
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Each test must have at least one class that inherits ``BaseTestRawIO`` and that has 3 attributes:
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Logging
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=======
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All IO classes by default have logging using the standard :mod:`logging` module: already set up.
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All IO classes by default have logging using the standard :mod:`logging` module already set up.
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The logger name is the same as the fully qualified class name, e.g. :class:`neo.io.nixio.NixIO`.
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The :attr:`class.logger` attribute holds the logger for easy access.
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doc/source/rawio.rst

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@@ -15,7 +15,7 @@ For performance and memory consumption reasons a new layer has been added to Neo
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In brief:
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* **neo.io** is the user-oriented read/write layer. Reading consists of getting a tree
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of Neo objects from a data source (file, url, or directory).
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When reading, all Neo objects are correctly scaled to the correct units.
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When reading, all Neo objects are scaled to the correct units.
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Writing consists of making a set of Neo objects persistent in a file format.
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* **neo.rawio** is a low-level layer for reading data only. Reading consists of getting
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NumPy buffers (often int16/int64) of signals/spikes/events.
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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 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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on a GPU while scaling the zoom. This should improve bandwidth from HD/SSD to RAM
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and from RAM to GPU memory.
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* load only a small chunk of data for heavy computations. For instance
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the spike sorting module tridesclous_ does this.
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There are 3 ways to select a subset of channels: by index (0 based), by id or by name.
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By index is unambiguous 0 to n-1 (included), whereas for some IOs channel_names
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By index is unambiguous 0 to n-1 (inclusive), whereas for some IOs channel_names
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(and sometimes channel_ids) have no guarantees to
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be unique. In such cases, using names or ids may raise an error.
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