quickspikes
-----------
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This is a very basic but very fast window discriminator for detecting
and extracting spikes in a time series. It was developed for analyzing
extracellular neural recordings, but also works with intracellular data
and probably many other kinds of time series.
Here’s how it works:
.. figure:: algorithm.png
:alt: detection diagram
detection diagram
The algorithm iterates through the time series. When the signal crosses
the threshold (1) going away from zero, the algorithm then looks for a
peak (2) that occurs within some number of samples from the threshold
crossing. The number of samples can be adjusted to filter out broad
spikes that are likely to be artifacts. If a peak occurs, its sample
index is added to an array. These times can be used as-is, or they can
be used to extract samples to either side of the peak for further
analysis (e.g. spike sorting).
The algorithm uses a streaming pattern (i.e., it processes chunks of
data and keeps its state between chunks), so it’s suitable for realtime
operations. Many signals of interest will require highpass filtering to
remove slow variations.
Installation and Use
~~~~~~~~~~~~~~~~~~~~
The algorithm is written in cython. You can get a python package from
PyPI:
::
pip install quickspikes
Or to build from a copy of the repository:
::
pip install .
To detect peaks, you instantiate the detector with parameters that match
the events you want to detect, and then send the detector chunks of
data. For example, an extracellular recording at 20 kHz stored in 16-bit
integers may have a noise floor around 2000, and the spikes will be on
the order of 20 samples wide:
.. code:: python
import quickspikes as qs
det = qs.detector(1000, 30)
times = det.send(samples)
You can continue sending chunks of data by calling ``send()``. The
detector will keep its state between calls, so you can detect spikes
that occur on chunk boundaries. For example, if you’re receiving data
from some kind of generator, you could use a pattern like this:
.. code:: python
for chunk in my_data_generator():
times = det.send(chunk)
# process times
Conversely, if the data are not contiguous, you should reinitialize the
detector for each chunk.
You can adjust the detector’s threshold at any point, for example to
compensate for shifts in the mean and standard deviation of the signal:
.. code:: python
reldet = qs.detector(2.5, 30)
reldet.scale_thresh(samples.mean(), samples.std())
times = reldet.send(samples)
To detect negative-going events, you’ll need to invert the signal.
There are also some functions you can use to extract and align spike
waveforms. Given a list of times returned from the ``detector.send()``
method, to extract waveforms starting 30 samples before the peak and
ending 270 samples after:
.. code:: python
f_times = qs.filter_times(times, 30, samples.size - 270)
spikes = qs.peaks(samples, f_times, 30, 270)
times, aligned = qs.realign_spikes(f_times, spikes, upsample=3, jitter=4)
Note that the list of event times may need to be filtered to avoid
trying to access data points outside the bounds of the input time
series. If you care about these events, you’ll need to pad your input
signal. The ``realign_spikes`` function uses a sinc-based resampling to
more accurately locate the peak of the event.
There is also a reference copy of an ANSI C implementation and an
``f2py`` wrapper in ``f2py/``. This algorithm is slightly less efficient
and flexible, but may give better results if included directly in a C
codebase.
Intracellular spikes
~~~~~~~~~~~~~~~~~~~~
There are some specialized tools for working with intracellular data.
Intracellular recordings present some special challenges for detecting
spikes. The data are not centered, and spike waveforms can change over
the course of stimulation. The approach used here is to find the largest
spike in the recording, (typically the first) and locate the onset of
the spike based on when the derivative begins to rapidly increase. The
peak and onset are used to set a threshold for extracting subsequent
spikes.
The following is an example for a recording at 50 kHz of a neuron with
spikes that have a rise time of about 1 ms (50 samples). Spikes
waveforms will start 7 ms before the peak and end 40 ms after, and will
be trimmed to avoid any overlap with subsequent spikes.
.. code:: python
from quickspikes.intracellular import SpikeFinder
detector = SpikeFinder(n_rise=50, n_before=350, n_after=2000)
detector.calculate_threshold(samples)
times, spikes = zip(*detector.extract_spikes(samples, min_amplitude=10))
License
~~~~~~~
Free for use under the terms of the GNU General Public License. See
[[COPYING]] for details.
If you use this code in an academic work, citations are appreciated.
There is no methods paper describing the algorithm, but the most
relevant reference is:
C. D. Meliza and D. Margoliash (2012). Emergence of selectivity and
tolerance in the avian auditory cortex. Journal of Neuroscience,
doi:10.1523/JNEUROSCI.0845-12.2012
Raw data
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"description": "quickspikes\n-----------\n\n|ProjectStatus|_ |Version|_ |BuildStatus|_ |License|_ |PythonVersions|_ |DOI|\n\n.. |ProjectStatus| image:: https://www.repostatus.org/badges/latest/active.svg\n.. _ProjectStatus: https://www.repostatus.org/#active\n\n.. |Version| image:: https://img.shields.io/pypi/v/quickspikes.svg\n.. _Version: https://pypi.python.org/pypi/quickspikes/\n\n.. |BuildStatus| image:: https://github.com/melizalab/quickspikes/actions/workflows/python_tests.yml/badge.svg\n.. _BuildStatus: https://github.com/melizalab/quickspikes/actions/workflows/python_tests.yml\n\n.. |License| image:: https://img.shields.io/pypi/l/quickspikes.svg\n.. _License: https://opensource.org/license/gpl-3-0/\n\n.. |PythonVersions| image:: https://img.shields.io/pypi/pyversions/quickspikes.svg\n.. _PythonVersions: https://pypi.python.org/pypi/quickspikes/\n\n.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1246809.svg\n :target: https://doi.org/10.5281/zenodo.1246809\n\nThis is a very basic but very fast window discriminator for detecting\nand extracting spikes in a time series. It was developed for analyzing\nextracellular neural recordings, but also works with intracellular data\nand probably many other kinds of time series.\n\nHere\u2019s how it works:\n\n.. figure:: algorithm.png\n :alt: detection diagram\n\n detection diagram\n\nThe algorithm iterates through the time series. When the signal crosses\nthe threshold (1) going away from zero, the algorithm then looks for a\npeak (2) that occurs within some number of samples from the threshold\ncrossing. The number of samples can be adjusted to filter out broad\nspikes that are likely to be artifacts. If a peak occurs, its sample\nindex is added to an array. These times can be used as-is, or they can\nbe used to extract samples to either side of the peak for further\nanalysis (e.g.\u00a0spike sorting).\n\nThe algorithm uses a streaming pattern (i.e., it processes chunks of\ndata and keeps its state between chunks), so it\u2019s suitable for realtime\noperations. Many signals of interest will require highpass filtering to\nremove slow variations.\n\nInstallation and Use\n~~~~~~~~~~~~~~~~~~~~\n\nThe algorithm is written in cython. You can get a python package from\nPyPI:\n\n::\n\n pip install quickspikes\n\nOr to build from a copy of the repository:\n\n::\n\n pip install .\n\nTo detect peaks, you instantiate the detector with parameters that match\nthe events you want to detect, and then send the detector chunks of\ndata. For example, an extracellular recording at 20 kHz stored in 16-bit\nintegers may have a noise floor around 2000, and the spikes will be on\nthe order of 20 samples wide:\n\n.. code:: python\n\n import quickspikes as qs\n det = qs.detector(1000, 30)\n times = det.send(samples)\n\nYou can continue sending chunks of data by calling ``send()``. The\ndetector will keep its state between calls, so you can detect spikes\nthat occur on chunk boundaries. For example, if you\u2019re receiving data\nfrom some kind of generator, you could use a pattern like this:\n\n.. code:: python\n\n for chunk in my_data_generator():\n times = det.send(chunk)\n # process times\n\nConversely, if the data are not contiguous, you should reinitialize the\ndetector for each chunk.\n\nYou can adjust the detector\u2019s threshold at any point, for example to\ncompensate for shifts in the mean and standard deviation of the signal:\n\n.. code:: python\n\n reldet = qs.detector(2.5, 30)\n reldet.scale_thresh(samples.mean(), samples.std())\n times = reldet.send(samples)\n\nTo detect negative-going events, you\u2019ll need to invert the signal.\n\nThere are also some functions you can use to extract and align spike\nwaveforms. Given a list of times returned from the ``detector.send()``\nmethod, to extract waveforms starting 30 samples before the peak and\nending 270 samples after:\n\n.. code:: python\n\n f_times = qs.filter_times(times, 30, samples.size - 270)\n spikes = qs.peaks(samples, f_times, 30, 270)\n times, aligned = qs.realign_spikes(f_times, spikes, upsample=3, jitter=4)\n\nNote that the list of event times may need to be filtered to avoid\ntrying to access data points outside the bounds of the input time\nseries. If you care about these events, you\u2019ll need to pad your input\nsignal. The ``realign_spikes`` function uses a sinc-based resampling to\nmore accurately locate the peak of the event.\n\nThere is also a reference copy of an ANSI C implementation and an\n``f2py`` wrapper in ``f2py/``. This algorithm is slightly less efficient\nand flexible, but may give better results if included directly in a C\ncodebase.\n\nIntracellular spikes\n~~~~~~~~~~~~~~~~~~~~\n\nThere are some specialized tools for working with intracellular data.\nIntracellular recordings present some special challenges for detecting\nspikes. The data are not centered, and spike waveforms can change over\nthe course of stimulation. The approach used here is to find the largest\nspike in the recording, (typically the first) and locate the onset of\nthe spike based on when the derivative begins to rapidly increase. The\npeak and onset are used to set a threshold for extracting subsequent\nspikes.\n\nThe following is an example for a recording at 50 kHz of a neuron with\nspikes that have a rise time of about 1 ms (50 samples). Spikes\nwaveforms will start 7 ms before the peak and end 40 ms after, and will\nbe trimmed to avoid any overlap with subsequent spikes.\n\n.. code:: python\n\n from quickspikes.intracellular import SpikeFinder\n detector = SpikeFinder(n_rise=50, n_before=350, n_after=2000)\n detector.calculate_threshold(samples)\n times, spikes = zip(*detector.extract_spikes(samples, min_amplitude=10))\n\nLicense\n~~~~~~~\n\nFree for use under the terms of the GNU General Public License. See\n[[COPYING]] for details.\n\nIf you use this code in an academic work, citations are appreciated.\nThere is no methods paper describing the algorithm, but the most\nrelevant reference is:\n\nC. D. Meliza and D. Margoliash (2012). Emergence of selectivity and\ntolerance in the avian auditory cortex. Journal of Neuroscience,\ndoi:10.1523/JNEUROSCI.0845-12.2012\n",
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