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- # -*- coding: utf-8 -*-
- """
- Spikesorting with SpikeInterface
- =================================
- One of the most common processing steps for neuronal data is the identification
- of spiking activity of individual neurons. [SpikeInterface](https://github.com/Spikeinterface/Spikeinterface)
- is a python packages that provides a common interface to multiple different software tools designed
- for this task. Here we demonstrate how to preprocess a BIDS-ephys compatible dataset using SpikeInterface
- and visualize the result. This script is accessible at :download:`source Python script <plot_spikesorting.py>`."""
- # %%
- # First we import SpikeInterface and locate the dataset
- import matplotlib.pyplot as plt
- import spikeinterface.full as si
- session_folder = '../ephys_nix/sub-i/ses-140703/ephys'
- # %%
- # SpikeInterface provides a convenience function for loading a BIDS-ephys compatible dataset.
- # This dataset contains multiple streams (data recorded at different sampling rates).
- # SpikeInterface represents each of those streams in a separate `recording` object.
- recordings = si.read_bids(session_folder)
- # %%
- # We select the most `raw` data stream by selecting the one with the highest temporal resolution, i.e. sampling frequency.
- recording_raw = recordings[0]
- for rec in recordings[1:]:
- if rec.get_sampling_frequency() > recording_raw.get_sampling_frequency():
- recording_raw = rec
- # %%
- # By default the software packages to be used for spike sorting needs to be installed locally to
- # perform the sorting. However, the installation of these specialized software tools can be
- # complicated and inconvenient due to specific system requirement. To simplify the usage of
- # different spike sorting packages SpikeInterface provides a set of containers with preinstalled
- # sorting packages. The list of available containers and their versions is available on
- # [DockerHub](<TODO: Add link here>).
- #
- # Here we run the sorting package `ironclust` using the containerized version. To run the container we are using `singularity`, alternativly SpikeInterface also supports running contains
- # in `docker`.
- sorting = si.run_sorter_container(sorter_name='ironclust', recording=recording_raw,
- mode='singularity',
- container_image='spikeinterface/ironclust-compiled-base',
- output_folder='ironclust_output',
- with_output=True, fGpu=False)
- print(sorting)
- # %%
- # Automatic spike sorting always requires manual inspection the results to confirm the sorting
- # algorithm and the applied parameters were suited for the dataset. Here we visualize the extracted
- # spike times for each unit (neuron) in a raster plot.
- si.plot_rasters(sorting)
- plt.show()
- # %%
- # We can extract the waveforms on which the sorting is based. To align the waveforms we remove the
- # low frequency components before extracting the waveform snippets from the raw signal.
- recording_filtered = si.highpass_filter(recording_raw, freq_min=300.) # frequencies are provided in Hz
- recording_filtered.annotate(is_filtered=True)
- waveforms = si.extract_waveforms(recording_filtered, sorting, './ironclust_waveform_output',
- overwrite=True, ms_before=1, ms_after=2.)
- si.plot_unit_waveforms(waveforms)
- plt.show()
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