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Copy file name to clipboardExpand all lines: doc/get_started/install_sorters.rst
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@@ -27,7 +27,7 @@ sorters to retrieve installation instructions for other operating systems.
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We use **pip** to install packages, but **conda** should also work in many cases.
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Some novel spike sorting algorithms are implemented directly in SpikeInterface using the
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:py:mod:`spikeinterface.sortingcomponents` module. Checkout the :ref:`SpikeInterface-based spike sorters` section of this page
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:py:mod:`spikeinterface.sortingcomponents` module. Checkout the :ref:`get_started/install_sorters:SpikeInterface-based spike sorters` section of this page
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for more information!
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If you experience installation problems please directly contact the authors of these tools or write on the
Please refer to the `stable documentation <https://spikeinterface.readthedocs.io/en/0.100.4>`_.
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Learn how to :ref:`update your code here <tutorials/waveform_extractor_to_sorting_analyzer:From WaveformExtractor to SortingAnalyzer>` and read more about the
Copy file name to clipboardExpand all lines: doc/modules/core.rst
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* data on-demand (lazy loading)
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* multiple segments, where each segment is a contiguous piece of data (recording, sorting, events).
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.. _core-recording:
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Recording
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---------
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2. a unique numpy.array with structured dtype aka "spikes vector". This is useful for processing by small chunks of
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time, like for extracting amplitudes from a recording.
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.. _core-sorting-analyzer:
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SortingAnalyzer
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---------------
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Importantly, the :py:class:`~spikeinterface.core.SortingAnalyzer` handles the *sparsity* and the physical *scaling*.
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Sparsity defines the channels on which waveforms and templates are calculated using, for example, a
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physical distance from the channel with the largest peak amplitude (see the :ref:`Sparsity` section). Scaling, set by
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physical distance from the channel with the largest peak amplitude (see the :ref:`modules/core:Sparsity` section). Scaling, set by
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the :code:`return_scaled` argument, determines whether the data is converted from integer values to :math:`\mu V` or not.
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By default, :code:`return_scaled` is true and all processed data voltage values are in :math:`\mu V` (e.g., waveforms, templates, spike amplitudes, etc.).
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The :py:class:`~spikeinterface.core.SortingAnalyzer` by default is defined *in memory*, but it can be saved at any time
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(or upon instantiation) to one of the following backends:
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* | :code:`zarr`: the sorting analyzer is saved to a `Zarr <https://zarr.dev/>`_ folder, and each extension is a Zarr group. This is the recommended backend, since Zarr files can be written to/read from the cloud and compression is applied.
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* | :code:`zarr`: the sorting analyzer is saved to a `Zarr <https://zarr.dev/>`__ folder, and each extension is a Zarr group. This is the recommended backend, since Zarr files can be written to/read from the cloud and compression is applied.
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* | :code:`binary_folder`: the sorting analyzer is saved to a folder, and each extension creates a sub-folder. The extension data are saved to either :code:`npy` (for arrays), :code:`csv` (for dataframes), or :code:`pickle` (for everything else).
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If the sorting analyzer is in memory, the :code:`SortingAnalyzer.save_as` function can be used to save it
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The :code:`save()` function allows to easily store SI objects to a folder on disk.
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:py:class:`~spikeinterface.core.BaseRecording` objects are stored in binary (.raw) or
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`Zarr <https://zarr.readthedocs.io/en/stable/tutorial.html>`_ (.zarr) format and
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`Zarr <https://zarr.readthedocs.io/en/stable/tutorial.html>`__ (.zarr) format and
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:py:class:`~spikeinterface.core.BaseSorting` and :py:class:`~spikeinterface.core.BaseSnippets` object in numpy (.npz)
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format. With the actual data, the :code:`save()` function also stores the provenance dictionary and all the properties
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and annotations associated to the object.
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* extract waveforms
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* sub-sample spikes for waveform extraction
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* compute templates (i.e. average extracellular waveforms) with different modes
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* save waveforms in a folder (in numpy / `Zarr <https://zarr.readthedocs.io/en/stable/tutorial.html>`_) for easy retrieval
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* save waveforms in a folder (in numpy / `Zarr <https://zarr.readthedocs.io/en/stable/tutorial.html>`__) for easy retrieval
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* save sparse waveforms or *sparsify* dense waveforms
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