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Merge pull request #4087 from SpikeInterface/prepare_release
Prepare release 0.103.0
2 parents 75ade07 + 3d6844c commit 58cae73

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doc/api.rst

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-------------------
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.. automodule:: spikeinterface.core
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.. autofunction:: load_extractor
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.. autofunction:: load
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.. autoclass:: BaseRecording
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:members:
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.. autoclass:: BaseSorting
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.. autoclass:: BinaryRecordingExtractor
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.. autoclass:: ZarrRecordingExtractor
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.. autoclass:: BinaryFolderRecording
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.. autoclass:: NpzFolderSorting
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.. autoclass:: NumpyFolderSorting
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.. autoclass:: NpyFolderSnippets
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.. autoclass:: NumpyRecording
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.. autoclass:: NumpySorting
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.. automodule:: spikeinterface.preprocessing
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.. autofunction:: apply_preprocessing_pipeline
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.. autofunction:: get_preprocessing_dict_from_analyzer
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.. autofunction:: get_preprocessing_dict_from_file
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.. autofunction:: astype
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.. autofunction:: average_across_direction
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.. autofunction:: bandpass_filter
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.. autofunction:: available_sorters
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.. autofunction:: installed_sorters
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.. autofunction:: archived_sorters
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.. autofunction:: get_default_sorter_params
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.. autofunction:: get_sorter_params_description
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.. autofunction:: print_sorter_versions

doc/conf.py

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# -- Project information -----------------------------------------------------
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project = 'SpikeInterface'
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copyright = '2022, Alessio Paolo Buccino, Samuel Garcia, Cole Hurwitz, Jeremy Magland, Matthias Hennig'
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author = 'Alessio Paolo Buccino, Samuel Garcia, Cole Hurwitz, Jeremy Magland, Matthias Hennig'
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copyright = '2022-2025, SpikeInterface Team'
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author = 'SpikeInterface Team'
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# -- General configuration ---------------------------------------------------

doc/how_to/auto_curation_prediction.rst

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How to use a trained model to predict the curation labels
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=========================================================
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Use a trained model to predict the curation labels
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==================================================
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For a more detailed guide to using trained models, `read our tutorial here
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<https://spikeinterface.readthedocs.io/en/latest/tutorials/curation/plot_1_automated_curation.html>`_).

doc/how_to/auto_curation_training.rst

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How to train a model to predict curation labels
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===============================================
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Train a model to predict curation labels
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========================================
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A full tutorial for model-based curation can be found `here <https://spikeinterface.readthedocs.io/en/latest/tutorials/curation/plot_2_train_a_model.html>`_.
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doc/how_to/build_pipeline_with_dicts.rst

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Build a full Sorting pipeline with dicts
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========================================
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Build a full spike sorting pipeline with dicts
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==============================================
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When using ``SpikeInterface`` there are two phases. First, you should
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play: try to figure out any special steps or parameters you need to play
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Margin in ms on border to avoid border effect
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dtype : dtype or None
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The dtype of the returned traces. If None, the dtype of the parent recording is used
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**filter_kwargs : dict
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\*\*filter_kwargs : dict
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Certain keyword arguments for `scipy.signal` filters:
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filter_order : order
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The order of the filter. Note as filtering is applied with scipy's

doc/how_to/combine_recordings.rst

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Combine recordings in SpikeInterface
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====================================
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Combine recordings (concatenate or append)
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==========================================
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In this tutorial we will walk through combining multiple recording objects. Sometimes this occurs due to hardware
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settings (e.g. Intan software has a default setting of new files every 1 minute) or the experimenter decides to

doc/how_to/handle_drift.rst

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%load_ext autoreload
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%autoreload 2
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Handle motion/drift with spikeinterface
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Handle motion/drift in your recording
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=====================================
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Spikeinterface offers a very flexible framework to handle drift as a
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preprocessing step. If you want to know more, please read the

doc/how_to/index.rst

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:maxdepth: 1
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viewers
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handle_drift
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analyze_neuropixels
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load_matlab_data
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customize_a_plot
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combine_recordings
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process_by_channel_group
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build_pipeline_with_dicts
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physical_units
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unsigned_to_signed
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../tutorials/forhowto/plot_1_working_with_tetrodes
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analyze_neuropixels
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handle_drift
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drift_with_lfp
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load_matlab_data
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load_your_data_into_sorting
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benchmark_with_hybrid_recordings
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drift_with_lfp
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auto_curation_training
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auto_curation_prediction
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physical_units
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unsigned_to_signed
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customize_a_plot
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../tutorials/forhowto/plot_1_working_with_tetrodes

doc/how_to/load_your_data_into_sorting.rst

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Load your own data into a Sorting object
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Load your own data into a ``Sorting`` object
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============================================
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Why make a :code:`Sorting`?
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doc/how_to/physical_units.rst

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.. _physical_units:
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Working with physical units in SpikeInterface recordings
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Work with physical units in SpikeInterface recordings
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=====================================================
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In neurophysiology recordings, data is often stored in raw ADC (Analog-to-Digital Converter) integer values but needs to be analyzed in physical units.
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For extracellular recordings, this is typically microvolts (µV), but some recording devices may use different physical units.

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