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Examples — spykeutils 0.4.1 documentation

Examples

These examples demonstrate the usage of some functions in spykeutils. This includes the creation of a small Neo object hierarchy with toy data.

Creating the sample data

The functions in spykeutils work on electrophysiological data that is represented in Neo object hierarchies. Usually, you would load these objects from a file, but for the purpose of this demonstration we will manually create an object hierarchy to illustrate their structure. Note that most functions in spykeutils will also work with separate Neo data objects that are not contained in a complete hierarchy. First, we import the modules we will use:

>>> import quantities as pq
>>> import neo
>>> import scipy as sp
>>> import spykeutils.spike_train_generation as stg

We start with some container objects: two segments that represent trials and three units (representing neurons) that produced the spike trains:

>>> segments = [neo.Segment('Trial 1'), neo.Segment('Trial 2')]
>>> units = []
>>> units.append(neo.Unit('Regular intervals'))
>>> units.append(neo.Unit('Homogeneous Poisson'))
>>> units.append(neo.Unit('Modulated Poisson'))

We create some spike trains from regular intervals, a homogeneous Poisson process and a modulated Poisson process:

>>> trains = []
>>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 40) * pq.s, 10 * pq.s))
>>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 60) * pq.s, 10 * pq.s))
>>> trains.append(stg.gen_homogeneous_poisson(5 * pq.Hz, t_stop=10 * pq.s))
>>> trains.append(stg.gen_homogeneous_poisson(7 * pq.Hz, t_stop=10 * pq.s))
>>> modulation = lambda t: sp.sin(3 * sp.pi * t / 10.0 / pq.s) / 2.0 + 0.5
>>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s))
>>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s))

Next, we create analog signals using the spike trains. First, we convolve all spike times with a mock spike waveform.

>>> spike = sp.sin(-sp.linspace(0, 2 * sp.pi, 16))
>>> binned_trains = (sp.histogram(trains[0], bins=160000, range=(0,10))[0] +
...                  sp.histogram(trains[2], bins=160000, range=(0,10))[0] +
...                  sp.histogram(trains[4], bins=160000, range=(0,10))[0])
>>> train_waves = [sp.convolve(binned_trains, spike)]
>>> binned_trains = (sp.histogram(trains[1], bins=160000, range=(0,10))[0] +
...                  sp.histogram(trains[3], bins=160000, range=(0,10))[0] +
...                  sp.histogram(trains[5], bins=160000, range=(0,10))[0])
>>> train_waves.append(sp.convolve(binned_trains, spike))

Now we add Gaussian noise and create four signals in each segment:

>>> for i in range(8):
...     sig = train_waves[i%2] + 0.2 * sp.randn(train_waves[i%2].shape[0])
...     signal = neo.AnalogSignal(sig * pq.uV, sampling_rate=16 * pq.kHz)
...     signal.segment = segments[i%2]
...     segments[i%2].analogsignals.append(signal)

Now we create the relationships between the spike trains and container objects. Each unit has two spike trains, one in each segment:

>>> segments[0].spiketrains = [trains[0], trains[2], trains[4]]
>>> segments[1].spiketrains = [trains[1], trains[3], trains[5]]
>>> units[0].spiketrains = trains[:2]
>>> units[1].spiketrains = trains[2:4]
>>> units[2].spiketrains = trains[4:6]
>>> for s in segments:
...     for st in s.spiketrains:
...         st.segment = s
>>> for u in units:
...     for st in u.spiketrains:
...         st.unit = u

Now that our sample data is ready, we will use some of the function from spykeutils to analyze it.

PSTH and ISI

To create a peri stimulus time histogram from our spike trains, we call spykeutils.rate_estimation.psth(). This function can create multiple PSTHs and takes a dicionary of lists of spike trains. Since our spike trains were generated by three units, we will create three histograms, one for each unit:

>>> import spykeutils.rate_estimation
>>> st_dict = {}
>>> st_dict[units[0]] = units[0].spiketrains
>>> st_dict[units[1]] = units[1].spiketrains
>>> st_dict[units[2]] = units[2].spiketrains
>>> spykeutils.rate_estimation.psth(st_dict, 400 * pq.ms)[0] 
{<neo.core.unit.Unit object at 0x...>: array([ 6.25,  5.  ,  5.  ,  5.  ,  3.75, ...

spykeutils.rate_estimation.psth() returns two values: A dictionary with the resulting histograms and a Quantity 1D with the bin edges.

If guiqwt is installed, we can also use the spykeutils.plot package to create a PSTH plot from our data (in this case we want a bar histogram and therefore only use spike trains from one unit):

>>> import spykeutils.plot
>>> spykeutils.plot.psth({units[2]: units[2].spiketrains}, bin_size=400 * pq.ms, bar_plot=True) 

Similiarily, we can create an interspike interval histogram plot with:

>>> spykeutils.plot.isi({units[2]: units[2].spiketrains}, bin_size=30 * pq.ms, cut_off=300 * pq.ms, bar_plot=True)

This will open a plot window like the following:

_images/isi.png

Spike Density Estimation

Similar to a PSTH, a spike density estimation gives an esimate of the instantaneous firing rate. Instead of binning, it is based on a kernel convolution which results in a smoother estimate. Creating and SDE with spykeutils works very similar to creating a PSTH. Instead of manually choosing the size of the Gaussian kernel, spykeutils.rate_estimation.spike_density_estimation() also supports finding the optimal kernel size automatically for each unit:

>>> kernel_sizes = sp.logspace(2, 3.3, 100) * pq.ms
>>> spykeutils.rate_estimation.spike_density_estimation(st_dict, optimize_steps=kernel_sizes)[0] 
{<neo.core.unit.Unit object at 0x...>: array([ ...

As with the PSTH, there is also a plot function for creating a spike density estimation. Here, we use both units because the function produces a line plot where both units can be shown at the same time:

>>> spykeutils.plot.sde(st_dict, maximum_kernel=3000*pq.ms, optimize_steps=100) 

The resulting plot will look like the following:

_images/sde.png

While spike density estimations are preferable to PSTHs in many cases, the picture also shows an important weakness: The estimation will generally be too low on margins. The areas where this happens become larger with kernel size, which is clearly visible from the rounded shape of the purple and pink curves (which should be flat because of the constant rate of the spike trains) with their very large kernel size.

Signal Plot

As a final example, we will again use the spykeutils.plot package to create a plot of the signals we created. This plot will also display the spike times from one of our spike trains.

>>> spykeutils.plot.signals(segments[0].analogsignals, spike_trains=[segments[0].spiketrains[2]], show_waveforms=False) 
_images/signal.png

The plot shows all four signals from the first segments as well as the spike times of the inhomogeneous poisson process in the same segment.

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PKqBDD.%spykeutils-0.4.1/.buildinfo# Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. config: f8355ccab226af11a9c60021154756e6 tags: efa25262f700e02b1777eb79ee109f5c PK]BDDO&&&spykeutils-0.4.1/acknowledgements.html Acknowledgements — spykeutils 0.4.1 documentation

Acknowledgements

spykeutils was created by Robert Pröpper [1], supported by the Research Training Group GRK 1589/1. Jan Gosmann [2] contributed and tested the spike train metric implementations.

[1]Neural Information Processing Group, TU Berlin
[2]Bernstein Center for Computational Neuroscience, Berlin
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PKpBDDC]o3o3spykeutils-0.4.1/intro.html Requirements — spykeutils 0.4.1 documentation

Requirements

Spykeutils is a pure Python package and therefore easy to install. It depends on the following additional packages:

  • Python >= 2.7
  • neo >= 0.2.1
  • scipy
  • guiqwt >= 2.1.4 (Optional, for plotting)
  • tables (Optional, for analysis results data management. Also known as PyTables.)
  • scikit-learn (Optional, for spike sorting quality analysis using Gaussian cluster overlap.)

Please see the respective websites for instructions on how to install them if they are not present on your computer. If you use Linux, you might not have access rights to your Python package installation directory, depending on your configuration. In this case, you will have to execute all shell commands in this section with administrator privileges, e.g. by using sudo.

Download and Installation

The easiest way to get spykeutils is from the Python Package Index. If you have pip installed:

$ pip install spykeutils

Alternatively, if you have setuptools:

$ easy_install spykeutils

Users of NeuroDebian or its repositories (available for Debian and Ubuntu) can also install spykeutils using the package manager instead of pip:

$ sudo apt-get install python-spykeutils

Alternatively, you can get the latest version directly from GitHub at https://github.com/rproepp/spykeutils.

The master branch always contains the current stable version. If you want the latest development version, use the develop branch (selected by default). You can download the repository from the GitHub page or clone it using git and then install from the resulting folder:

$ python setup.py install

Usage

For the most part, spykeutils is a collection of functions that work on Neo objects. Many functions also take quantities as parameters. Therefore, make sure to get an overview of neo and quantities before using spykeutils. Once you are familiar with these packages, have a look at the Examples or head to the API reference to browse the contents of spykeutils.

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PKpBDD//spykeutils-0.4.1/changelog.html Changelog — spykeutils 0.4.1 documentation

Changelog

Version 0.4.1

  • Faster caching for Neo lazy loading.
  • Faster correlogram calculation.

Version 0.4.0

  • Correlogram plot supports new square plot matrix mode and count per second in addition to per segment.
  • New options in spike waveform plot.
  • DataProvider objects support transparent lazy loading for compatible IOs (currently only Hdf5IO).
  • DataProvider can be forced to use a certain IO instead of automatically determining it by file extension.
  • Load parameters for IOs can be specified in DataProvider.
  • IO class, IO parameters and IO plugins are saved in selections and properly used in startplugin.py
  • Qt implementation of ProgressBar available in plot.helper (moved from Spyke Viewer).
  • Loading support for IO plugins (moved from Spyke Viewer).

Version 0.3.0

  • Added implementations for various spike train metrics.
  • Added generation functions for poisson spike trains
  • Added tools module with various utility functions, e.g. binning spike trains or removing objects from Neo hierarchies.
  • Added explained variance function to spike sorting quality assessment.
  • Improved legends for plots involving colored lines.
  • Plots now have a minimum size and scroll bars appear if the plots would become too small.
  • Renamed plot.ISI to plot.isi for consistency

Version 0.2.1

  • Added “Home” and “Pan” tools for plots (useful when no middle mouse button is available).
  • Changed default grid in plots to show only major grid.
  • Added a method to DataProvider for refreshing views after object hierarchy changed.
  • New parameter for DataProvider AnalogSignal methods: AnalogSignalArrays can be automatically converted and returned.
  • Significantly improved speed of spike density estimation and optimal kernel size calculation.
  • Spike sorting quality assessment using gaussian clusters is now possible without prewhitening spikes or providing prewhitened means.
  • Renamed “spyke-plugin” script to “spykeplugin”

Version 0.2.0

Initial documented public release.

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PKqBDDIUspykeutils-0.4.1/genindex.html Index — spykeutils 0.4.1 documentation

Index

A | B | C | D | E | F | G | H | I | K | L | M | N | O | P | R | S | T | U | V

A

aligned_spike_trains() (in module spykeutils.rate_estimation)
analog_signal_array_to_analog_signals() (in module spykeutils.conversions)
analog_signal_arrays() (DataProvider method)
analog_signal_arrays_by_channelgroup() (DataProvider method)
analog_signal_arrays_by_channelgroup_and_segment() (DataProvider method)
analog_signal_arrays_by_segment() (DataProvider method)
analog_signal_arrays_by_segment_and_channelgroup() (DataProvider method)
analog_signals() (DataProvider method)
analog_signals_by_channel() (DataProvider method)
analog_signals_by_channel_and_segment() (DataProvider method)
analog_signals_by_segment() (DataProvider method)
analog_signals_by_segment_and_channel() (DataProvider method)
AnalysisPlugin (class in spykeutils.plugin.analysis_plugin)
apply_to_dict() (in module spykeutils.tools)
as_kernel_of_size() (in module spykeutils.signal_processing)

B

begin() (ProgressIndicator method)
bin_spike_trains() (in module spykeutils.tools)
blocks() (DataProvider method)
boundary_enclosing_at_least() (CausalDecayingExpKernel method)
(GaussianKernel method)
(Kernel method)
(LaplacianKernel method)
(RectangularKernel method)
(TriangularKernel method)

C

calculate_refperiod_fp() (in module spykeutils.sorting_quality_assesment)
CancelException
CausalDecayingExpKernel (class in spykeutils.signal_processing)
collapsed_spike_trains() (in module spykeutils.rate_estimation)
concatenate_spike_trains() (in module spykeutils.tools)
configure() (AnalysisPlugin method)
correlogram() (in module spykeutils.correlations)
cs_dist() (in module spykeutils.spike_train_metrics)

D

data_dict() (DataProvider method)
DataProvider (class in spykeutils.plugin.data_provider)
discretize_kernel() (in module spykeutils.signal_processing)
done() (ProgressIndicator method)

E

epoch_array_to_epochs() (in module spykeutils.conversions)
epoch_arrays() (DataProvider method)
epochs() (DataProvider method)
evaluate() (CausalDecayingExpKernel static method)
(GaussianKernel static method)
(LaplacianKernel static method)
(RectangularKernel static method)
(TriangularKernel static method)
event_array_to_events() (in module spykeutils.conversions)
event_arrays() (DataProvider method)
event_synchronization() (in module spykeutils.spike_train_metrics)
events() (DataProvider method)
extract_spikes() (in module spykeutils.tools)

F

from_data() (spykeutils.plugin.data_provider.DataProvider class method)

G

GaussianKernel (class in spykeutils.signal_processing)
gen_homogeneous_poisson() (in module spykeutils.spike_train_generation)
gen_inhomogeneous_poisson() (in module spykeutils.spike_train_generation)
get_name() (AnalysisPlugin method)
get_parameters() (AnalysisPlugin method)
get_refperiod_violations() (in module spykeutils.sorting_quality_assesment)

H

hunter_milton_similarity() (in module spykeutils.spike_train_metrics)

I

ignores_cancel() (in module spykeutils.progress_indicator)
is_symmetric() (Kernel method)
(KernelFromFunction method)
(SymmetricKernel method)

K

Kernel (class in spykeutils.signal_processing)
KernelFromFunction (class in spykeutils.signal_processing)

L

labeled_epochs() (DataProvider method)
labeled_events() (DataProvider method)
LaplacianKernel (class in spykeutils.signal_processing)
load() (AnalysisPlugin method)

M

maximum_spike_train_interval() (in module spykeutils.tools)
minimum_spike_train_interval() (in module spykeutils.tools)

N

norm_dist() (in module spykeutils.spike_train_metrics)
normalization_factor() (CausalDecayingExpKernel method)
(GaussianKernel method)
(Kernel method)
(LaplacianKernel method)
(RectangularKernel method)
(TriangularKernel method)

O

optimal_gauss_kernel_size() (in module spykeutils.rate_estimation)
overlap_fp_fn() (in module spykeutils.sorting_quality_assesment)

P

ProgressIndicator (class in spykeutils.progress_indicator)
psth() (in module spykeutils.rate_estimation)

R

recording_channel_groups() (DataProvider method)
recording_channels() (DataProvider method)
RectangularKernel (class in spykeutils.signal_processing)
refresh_view() (DataProvider method)
remove_from_hierarchy() (in module spykeutils.tools)

S

save() (AnalysisPlugin method)
schreiber_similarity() (in module spykeutils.spike_train_metrics)
segments() (DataProvider method)
selection_blocks() (DataProvider method)
set_parameters() (AnalysisPlugin method)
set_status() (ProgressIndicator method)
set_ticks() (ProgressIndicator method)
smooth() (in module spykeutils.signal_processing)
spike_amplitude_histogram() (in module spykeutils.stationarity)
spike_density_estimation() (in module spykeutils.rate_estimation)
spike_train_to_spikes() (in module spykeutils.conversions)
spike_trains() (DataProvider method)
spike_trains_by_segment() (DataProvider method)
spike_trains_by_segment_and_unit() (DataProvider method)
spike_trains_by_unit() (DataProvider method)
spike_trains_by_unit_and_segment() (DataProvider method)
spikes() (DataProvider method)
spikes_by_segment() (DataProvider method)
spikes_by_segment_and_unit() (DataProvider method)
spikes_by_unit() (DataProvider method)
spikes_by_unit_and_segment() (DataProvider method)
spikes_to_spike_train() (in module spykeutils.conversions)
SpykeException (class in spykeutils)
spykeutils (module)
spykeutils.conversions (module)
spykeutils.correlations (module)
spykeutils.plugin (module)
spykeutils.plugin.analysis_plugin (module)
spykeutils.plugin.data_provider (module)
spykeutils.plugin.gui_data (module)
spykeutils.progress_indicator (module)
spykeutils.rate_estimation (module)
spykeutils.signal_processing (module)
spykeutils.sorting_quality_assesment (module)
spykeutils.spike_train_generation (module)
spykeutils.spike_train_metrics (module)
spykeutils.stationarity (module)
spykeutils.tools (module)
st_convolve() (in module spykeutils.signal_processing)
st_inner() (in module spykeutils.spike_train_metrics)
st_norm() (in module spykeutils.spike_train_metrics)
start() (AnalysisPlugin method)
step() (ProgressIndicator method)
summed_dist_matrix() (Kernel method)
(LaplacianKernel method)
(SymmetricKernel method)
SymmetricKernel (class in spykeutils.signal_processing)

T

TriangularKernel (class in spykeutils.signal_processing)

U

units() (DataProvider method)

V

van_rossum_dist() (in module spykeutils.spike_train_metrics)
van_rossum_multiunit_dist() (in module spykeutils.spike_train_metrics)
variance_explained() (in module spykeutils.sorting_quality_assesment)
victor_purpura_dist() (in module spykeutils.spike_train_metrics)
victor_purpura_multiunit_dist() (in module spykeutils.spike_train_metrics)
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PKqBDDeE_aaspykeutils-0.4.1/objects.inv# Sphinx inventory version 2 # Project: spykeutils # Version: 0.4 # The remainder of this file is compressed using zlib. xZn6) tq"h MĘKD(R%)ާPي#Y$Sg8 YB+6(W}\-ZV/A# nBk?҆?~Z}yz\hPq+Q3 \,a9%F=zC|7&Ơ'$:H5VT#bqBp֢Q$ٖB pw@3A{D. Zh&Bfש A/Ȝ9bz7BuiBjtFpD@,<ǻQxl^4+JG 'xP(PO0tP_D'czRd$` ,@)mIśzlkJrV~Q'ZX`*\!GVL|r(Nb;.W4>apGal 9[rȺ!wVV`%I&r>!zRԉ$ \;-Or-OV @byB k4ʭkDlh#`"|7[S!:|U}#-/\t٠vs]/(oҗz_0@ĩ]s0Ge.d=ߐÁM,>̪C{.g:h]r4;(ׇ=z0aw/18zV6s~hZG))` ոx^˚Bm飲I\sO^tʓMP[4(ϒb>ǚXJ':Wu2/~A;ѡB:x) (f ~"6cB., -e)ҍ*Yqtxn1[aJߥ(m`C#LbKmiD*M\4mxj#Gu͙a@K(CɗNd[蘇M5AACB q;6Rx@1m<--f2+y={C%1;0I^<_uwMԫD2bcJ^_6$iv>nл"\[ ZzpAJWTɂj(d9 h\([.i8(y қ>c5AҪwDo#Le$vg'$?ܓָMNnՁT-7MivrȜ*2G!2h$<.l}"tf}O 98n# Welcome to the documentation of spykeutils! — spykeutils 0.4.1 documentation

Welcome to the documentation of spykeutils!

Based on the Neo framework, spykeutils is a Python library for analyzing and plotting neurophysiological data. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets.

A mailinglist for discussion and support is available at https://groups.google.com/d/forum/spyke-viewer

Contents:

Indices and tables

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PKpBDD2121)spykeutils-0.4.1/_modules/spykeutils.html spykeutils — spykeutils 0.4.1 documentation

Source code for spykeutils

"""
.. autoclass:: spykeutils.SpykeException

:mod:`conversions` Module
-------------------------

.. automodule:: spykeutils.conversions
    :members:
    :undoc-members:
    :show-inheritance:

:mod:`correlations` Module
--------------------------

.. automodule:: spykeutils.correlations
    :members:

:mod:`progress_indicator` Module
--------------------------------

.. automodule:: spykeutils.progress_indicator
    :members:
    :undoc-members:
    :show-inheritance:

:mod:`rate_estimation` Module
-----------------------------

.. automodule:: spykeutils.rate_estimation
    :members:

:mod:`signal_processing` Module
-------------------------------

.. automodule:: spykeutils.signal_processing
    :members:
    :show-inheritance:
    :undoc-members:

:mod:`spike_train_generation` Module
------------------------------------

.. automodule:: spykeutils.spike_train_generation
    :members:
    :undoc-members:

:mod:`spike_train_metrics` Module
------------------------------------

.. automodule:: spykeutils.spike_train_metrics
    :members:
    :undoc-members:

:mod:`sorting_quality_assesment` Module
---------------------------------------

.. automodule:: spykeutils.sorting_quality_assesment
    :members:
    :undoc-members:
    :show-inheritance:

:mod:`stationarity` Module
--------------------------

.. automodule:: spykeutils.stationarity
    :members:

:mod:`tools` Module
------------------------

.. automodule:: spykeutils.tools
    :members:
"""

__version__ = '0.4.1'


[docs]class SpykeException(Exception): """ Exception thrown when a function in spykeutils encounters a problem that is not covered by standard exceptions. When using Spyke Viewer, these exceptions will be caught and shown in the GUI, while general exceptions will not be caught (and therefore be visible in the console) for easier debugging. """ pass
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PKpBDD8<Ƹ&&$spykeutils-0.4.1/_modules/index.html Overview: module code — spykeutils 0.4.1 documentation
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PKpBDDLc]c]@spykeutils-0.4.1/_modules/spykeutils/spike_train_generation.html spykeutils.spike_train_generation — spykeutils 0.4.1 documentation

Source code for spykeutils.spike_train_generation

import neo
import numpy.random
import quantities as pq
import scipy as sp
import _scipy_quantities as spq


[docs]def gen_homogeneous_poisson( rate, t_start=0 * pq.s, t_stop=None, max_spikes=None, refractory=0 * pq.s): """ Generate a homogeneous Poisson spike train. The length is controlled with `t_stop` and `max_spikes`. Either one or both of these arguments have to be given. :param rate: Average firing rate of the spike train to generate as frequency scalar. :type rate: Quantity scalar :param t_start: Time at which the spike train begins as time scalar. The first actual spike will be greater than this time. :type t_start: Quantity scalar :param t_stop: Time at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by `max_spikes` and `t_stop` will be equal to the last generated spike. :type t_stop: Quantity scalar :param max_spikes: Maximum number of spikes to generate. Fewer spikes might be generated in case `t_stop` is also set. :param refractory: Absolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to `rate` again. :type refractory: Quantity scalar :returns: The generated spike train. :rtype: :class:`neo.core.SpikeTrain` """ if t_stop is None and max_spikes is None: raise ValueError('Either t_stop or max_spikes has to be set.') if max_spikes is not None: spike_times = sp.cumsum(numpy.random.exponential( rate ** -1, max_spikes)) * (rate.units ** -1).simplified spike_times += t_start if refractory > 0: spike_times += sp.arange(spike_times.size) * refractory if t_stop is not None: spike_times = spike_times[spike_times <= t_stop] else: scale = (rate ** -1).rescale(t_stop.units) trains = [] last_spike = t_start.rescale(t_stop.units) while last_spike < t_stop: # Generate a bit more than the average number of expected spike to # be finished in most cases in one loop. The factor was determined # empirically. num_spikes = int(1.7 * ( (t_stop - last_spike) * rate).simplified) + 1 train = sp.cumsum(numpy.random.exponential(scale, num_spikes)) * \ scale.units + last_spike if refractory > 0: train += sp.arange(train.size) * refractory if train.size > 0: last_spike = train[-1] if last_spike >= t_stop: train = train[train < t_stop] trains.append(train) spike_times = spq.concatenate(trains) if t_stop is None: t_stop = spike_times[-1] return neo.SpikeTrain(spike_times, t_start=t_start, t_stop=t_stop)
[docs]def gen_inhomogeneous_poisson( modulation, max_rate, t_start=0 * pq.s, t_stop=None, max_spikes=None, refractory=0 * pq.s): """ Generate an inhomogeneous Poisson spike train. The length is controlled with `t_stop` and `max_spikes`. Either one or both of these arguments have to be given. :param function modulation: Function :math:`f((t_1, \\dots, t_n)): [\\text{t\\_start}, \\text{t\\_end}]^n \\rightarrow [0, 1]^n` giving the instantaneous firing rates at times :math:`(t_1, \\dots, t_n)` as proportion of `max_rate`. Thus, a 1-D array will be passed to the function and it should return an array of the same size. :param max_rate: Maximum firing rate of the spike train to generate as frequency scalar. :type max_rate: Quantity scalar :param t_start: Time at which the spike train begins as time scalar. The first actual spike will be greater than this time. :type t_start: Quantity scalar :param t_stop: Time at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by `max_spikes` and `t_stop` will be equal to the last generated spike. :type t_stop: Quantity scalar :param refractory: Absolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to `rate` again. :type refractory: Quantity scalar :returns: The generated spike train. :rtype: :class:`neo.core.SpikeTrain` """ st = gen_homogeneous_poisson( max_rate, t_start, t_stop, max_spikes, refractory) return st[numpy.random.rand(st.size) < modulation(st)]
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PKpBDDil;;<spykeutils-0.4.1/_modules/spykeutils/progress_indicator.html spykeutils.progress_indicator — spykeutils 0.4.1 documentation

Source code for spykeutils.progress_indicator

import functools

[docs]class CancelException(Exception): """ This is raised when a user cancels a progress process. It is used by :class:`ProgressIndicator` and its descendants. """ pass
[docs]def ignores_cancel(function): """ Decorator for functions that should ignore a raised :class:`CancelException` and just return nothing in this case """ @functools.wraps(function) def inner(*args, **kwargs): try: return function(*args, **kwargs) except CancelException: return return inner
[docs]class ProgressIndicator(object): """ Base class for classes indicating progress of a long operation. This class does not implement any of the methods and can be used as a dummy if no progress indication is needed. """
[docs] def set_ticks(self, ticks): """ Set the required number of ticks before the operation is done. :param int ticks: The number of steps that the operation will take. """ pass
[docs] def begin(self, title=''): """ Signal that the operation starts. :param string title: The name of the whole operation. """ pass
[docs] def step(self, num_steps=1): """ Signal that one or more steps of the operation were completed. :param int num_steps: The number of steps that have been completed. """ pass
[docs] def set_status(self, new_status): """ Set status description. :param string new_status: A description of the current status. """ pass
[docs] def done(self): """ Signal that the operation is done. """ pass
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PKpBDD@QÕ((Cspykeutils-0.4.1/_modules/spykeutils/sorting_quality_assesment.html spykeutils.sorting_quality_assesment — spykeutils 0.4.1 documentation

Source code for spykeutils.sorting_quality_assesment

""" Functions for estimating the quality of spike sorting results. These
functions estimate false positive and false negative fractions.
"""

from __future__ import division

import scipy as sp
from scipy.spatial.distance import cdist
import quantities as pq
import neo

from progress_indicator import ProgressIndicator
from . import SpykeException
from conversions import spikes_to_spike_train


[docs]def get_refperiod_violations(spike_trains, refperiod, progress=None): """ Return the refractory period violations in the given spike trains for the specified refractory period. :param dict spike_trains: Dictionary of lists of :class:`neo.core.SpikeTrain` objects. :param refperiod: The refractory period (time). :type refperiod: Quantity scalar :param progress: Set this parameter to report progress. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Two values: * The total number of violations. * A dictionary (with the same indices as ``spike_trains``) of arrays with violation times (Quantity 1D with the same unit as ``refperiod``) for each spike train. :rtype: int, dict """ if type(refperiod) != pq.Quantity or \ refperiod.simplified.dimensionality != pq.s.dimensionality: raise ValueError('refperiod must be a time quantity!') if not progress: progress = ProgressIndicator() total_violations = 0 violations = {} for u, tL in spike_trains.iteritems(): violations[u] = [] for i, t in enumerate(tL): st = t.copy() st.sort() isi = sp.diff(st) violations[u].append(st[isi < refperiod].rescale(refperiod.units)) total_violations += len(violations[u][i]) progress.step() return total_violations, violations
[docs]def calculate_refperiod_fp(num_spikes, refperiod, violations, total_time): """ Return the rate of false positives calculated from refractory period calculations for each unit. The equation used is described in (Hill et al. The Journal of Neuroscience. 2011). :param dict num_spikes: Dictionary of total number of spikes, indexed by unit. :param refperiod: The refractory period (time). If the spike sorting algorithm includes a censored period (a time after a spike during which no new spikes can be found), subtract it from the refractory period before passing it to this function. :type refperiod: Quantity scalar :param dict violations: Dictionary of total number of violations, indexed the same as num_spikes. :param total_time: The total time in which violations could have occured. :type total_time: Quantity scalar :returns: A dictionary of false positive rates indexed by unit. Note that values above 0.5 can not be directly interpreted as a false positive rate! These very high values can e.g. indicate that the generating processes are not independent. """ if type(refperiod) != pq.Quantity or \ refperiod.simplified.dimensionality != pq.s.dimensionality: raise ValueError('refperiod must be a time quantity!') fp = {} factor = total_time / (2 * refperiod) for u, n in num_spikes.iteritems(): if n == 0: fp[u] = 0 continue zw = (violations[u] * factor / n ** 2).simplified if zw > 0.25: fp[u] = 0.5 + sp.sqrt(0.25 - zw).imag continue fp[u] = 0.5 - sp.sqrt(0.25 - zw) return fp
def _multi_norm(x, mean): """ Evaluate pdf of multivariate normal distribution with a mean at rows of x with high precision. """ d = x.shape[1] fac = (2 * sp.pi) ** (-d / 2.0) y = cdist(x, sp.atleast_2d(mean), 'sqeuclidean') * -0.5 return fac * sp.exp(sp.longdouble(y)) def _fast_overlap_whitened(spike_arrays, means): units = spike_arrays.keys() spikes = {u: spike_arrays[u].shape[1] for u in spike_arrays.iterkeys()} prior = {} total_spikes = 0 for u, mean in means.iteritems(): total_spikes += spikes[u] if total_spikes < 1: return {u: (0.0, 0.0) for u in units}, {} # Arrays of unnormalized posteriors (likelihood times prior) # for all units posterior = {} false_positive = {} false_negative = {} for u in units: prior[u] = spikes[u] / total_spikes false_positive[u] = 0 false_negative[u] = 0 # Calculate posteriors for u1 in units[:]: if not spikes[u1]: units.remove(u1) continue posterior[u1] = {} for u2, mean in means.iteritems(): llh = _multi_norm(spike_arrays[u1].T, mean) posterior[u1][u2] = llh * prior[u2] # Calculate pairwise false positives/negatives singles = {u: {} for u in units} for i, u1 in enumerate(units): u1 = units[i] for u2 in units[i + 1:]: f1 = sp.sum(posterior[u1][u2] / (posterior[u1][u1] + posterior[u1][u2]), dtype=sp.double) f2 = sp.sum(posterior[u2][u1] / (posterior[u2][u1] + posterior[u2][u2]), dtype=sp.double) singles[u1][u2] = (f1 / spikes[u1] if spikes[u1] else 0, f2 / spikes[u1] if spikes[u1] else 0) singles[u2][u1] = (f2 / spikes[u2] if spikes[u2] else 0, f1 / spikes[u2] if spikes[u2] else 0) # Calculate complete false positives/negatives with extended bayes for u1 in units: numerator = posterior[u1][u1] normalizer = sum(posterior[u1][u2] for u2 in units) false_positive[u1] = sp.sum((normalizer - numerator) / normalizer) other_units = units[:] other_units.remove(u1) numerator = sp.vstack((posterior[u][u1] for u in other_units)) normalizer = sp.vstack(sum(posterior[u][u2] for u2 in units) for u in other_units) false_negative[u1] = sp.sum(numerator / normalizer) # Prepare return values, convert sums to means totals = {} for u, fp in false_positive.iteritems(): fn = false_negative[u] if not spikes[u]: totals[u] = (0, 0) else: num = spikes[u] totals[u] = (fp / num, fn / num) return totals, singles def _pair_overlap(waves1, waves2, mean1, mean2, cov1, cov2): """ Calculate FP/FN estimates for two gaussian clusters """ from sklearn import mixture means = sp.vstack([[mean1], [mean2]]) covars = sp.vstack([[cov1], [cov2]]) weights = sp.array([waves1.shape[1], waves2.shape[1]], dtype=float) weights /= weights.sum() # Create mixture of two Gaussians from the existing estimates mix = mixture.GMM(n_components=2, covariance_type='full', init_params='') mix.covars_ = covars mix.weights_ = weights mix.means_ = means posterior1 = mix.predict_proba(waves1.T)[:, 1] posterior2 = mix.predict_proba(waves2.T)[:, 0] return (posterior1.mean(), posterior2.sum() / len(posterior1), posterior2.mean(), posterior1.sum() / len(posterior2)) def _object_has_size(obj, size): """ Return if the object, which could be either a neo.Spike or ndarray, has the given size. """ if isinstance(obj, neo.Spike): return obj.waveform.size == size return obj.size == size
[docs]def overlap_fp_fn(spikes, means=None, covariances=None): """ Return dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs :mod:`sklearn` if ``covariances`` is not set to ``'white'``. This function estimates the pairwise and total false positive and false negative rates for a number of waveform clusters. The results can be interpreted as follows: False positives are the fraction of spikes in a cluster that is estimated to belong to a different cluster (a specific cluster for pairwise results or any other cluster for total results). False negatives are the number spikes from other clusters that are estimated to belong to a given cluster (also expressed as fraction, this number can be larger than 1 in extreme cases). Details for the calculation can be found in (Hill et al. The Journal of Neuroscience. 2011). The calculation for total false positive and false negative rates does not follow Hill et al., who propose a simple addition of pairwise probabilities. Instead, the total error probabilities are estimated using all clusters at once. :param dict spikes: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. If the waveforms have multiple channels, they will be flattened automatically. All waveforms need to have the same number of samples. :param dict means: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Note that if you pass ``'white'`` for ``covariances`` and you want to provide means, they have to be whitened in the same way as the spikes. Default: None, means will be estimated from data. :param covariances: Dictionary, indexed by unit, of lists of covariance matrices. Covariances for units that are not in this dictionary will be estimated using the spikes. It is useful to give a covariance matrix if few spikes are present - consider using the noise covariance. If you use prewhitened spikes (i.e. all clusters are normal distributed, so their covariance matrix is the identity), you can pass ``'white'`` here. The calculation will be much faster in this case and the sklearn package is not required. Default: None, covariances will estimated from data. :type covariances: dict or str :returns: Two values: * A dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples. * A dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples. :rtype: dict, dict """ units = spikes.keys() total_spikes = 0 for spks in spikes.itervalues(): total_spikes += len(spks) if total_spikes < 1: return {u: (0.0, 0.0) for u in units}, {} if means is None: means = {} white = False if covariances is None: covariances = {} elif covariances == 'white': white = True covariances = {} # Convert Spike objects to arrays dimensionality = None spike_arrays = {} for u, spks in spikes.iteritems(): spikelist = [] if not spks or (len(spks) < 2 and u not in covariances): units.remove(u) continue for s in spks: if isinstance(s, neo.Spike): spikelist.append( sp.asarray(s.waveform.rescale(pq.uV)).T.flatten()) else: spikelist.append(s) spike_arrays[u] = sp.array(spikelist).T if dimensionality is None: dimensionality = spike_arrays[u].shape[0] elif dimensionality != spike_arrays[u].shape[0]: raise SpykeException('All spikes need to have the same number' 'of samples!') if not units: return {}, {} if len(units) == 1: return {units[0]: (0.0, 0.0)}, {} # Convert or calculate means and covariances shaped_means = {} covs = {} if white: cov = sp.eye(dimensionality) covariances = {u: cov for u in units} for u in units: if u in means and _object_has_size(means[u], dimensionality): mean = means[u] if isinstance(mean, neo.Spike): shaped_means[u] = sp.asarray( mean.waveform.rescale(pq.uV)).T.flatten() else: shaped_means[u] = means[u].T.flatten() else: shaped_means[u] = spike_arrays[u].mean(axis=1) if white: return _fast_overlap_whitened(spike_arrays, shaped_means) for u in units: if u not in covariances: covs[u] = sp.cov(spike_arrays[u]) else: covs[u] = covariances[u] # Calculate pairwise false positives/negatives singles = {u: {} for u in units} for i, u1 in enumerate(units): u1 = units[i] for u2 in units[i + 1:]: error_rates = _pair_overlap( spike_arrays[u1], spike_arrays[u2], shaped_means[u1], shaped_means[u2], covs[u1], covs[u2]) singles[u1][u2] = error_rates[0:2] singles[u2][u1] = error_rates[2:4] # Calculate complete false positives/negatives import sklearn mix = sklearn.mixture.GMM(n_components=2, covariance_type='full') mix_means = [] mix_covars = [] mix_weights = [] for u in units: mix_means.append(shaped_means[u]) mix_covars.append([covs[u]]) mix_weights.append(spike_arrays[u].shape[1]) mix.means_ = sp.vstack(mix_means) mix.covars_ = sp.vstack(mix_covars) mix_weights = sp.array(mix_weights, dtype=float) mix_weights /= mix_weights.sum() mix.weights_ = mix_weights # P(spikes of unit[i] in correct cluster) post_mean = sp.zeros(len(units)) # sum(P(spikes of unit[i] in cluster[j]) post_sum = sp.zeros((len(units), len(units))) for i, u in enumerate(units): posterior = mix.predict_proba(spike_arrays[u].T) post_mean[i] = posterior[:, i].mean() post_sum[i, :] = posterior.sum(axis=0) totals = {} for i, u in enumerate(units): fp = 1.0 - post_mean[i] ind = range(len(units)) ind.remove(i) fn = post_sum[ind, i].sum() / float(spike_arrays[u].shape[1]) totals[u] = (fp, fn) return totals, singles
[docs]def variance_explained(spikes, means=None, noise=None): """ Returns the fraction of variance in each channel that is explained by the means. Values below 0 or above 1 for large data sizes indicate that some assumptions were incorrect (e.g. about channel noise) and the results should not be trusted. :param dict spikes: Dictionary, indexed by unit, of :class:`neo.core.SpikeTrain` objects (where the ``waveforms`` member includes the spike waveforms) or lists of :class:`neo.core.Spike` objects. :param dict means: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Default: None - means will be estimated from given spikes. :type noise: Quantity 1D :param noise: The known noise levels (as variance) per channel of the original data. This should be estimated from the signal periods that do not contain spikes, otherwise the explained variance could be overestimated. If None, the estimate of explained variance is done without regard for noise. Default: None :return dict: A dictionary of arrays, both indexed by unit. If ``noise`` is ``None``, the dictionary contains the fraction of explained variance per channel without taking noise into account. If ``noise`` is given, it contains the fraction of variance per channel explained by the means and given noise level together. """ ret = {} if means is None: means = {} for u, spks in spikes.iteritems(): train = spks if not isinstance(train, neo.SpikeTrain): train = spikes_to_spike_train(spks) if u in means and means[u].waveform.shape[0] == train.waveforms.shape[1]: spike = means[u] else: spike = neo.Spike(0) spike.waveform = sp.mean(train.waveforms, axis=0) orig = sp.mean(sp.var(train.waveforms, axis=1), axis=0) waves = train.waveforms - spike.waveform new = sp.mean(sp.var(waves, axis=1), axis=0) if noise is not None: ret[u] = sp.asarray(1 - (new - noise) / orig) else: ret[u] = sp.asarray(1 - new / orig) return ret
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PKpBDDZ{ SWqWq6spykeutils-0.4.1/_modules/spykeutils/correlations.html spykeutils.correlations — spykeutils 0.4.1 documentation

Source code for spykeutils.correlations

import scipy as sp
from collections import OrderedDict

import quantities as pq

from progress_indicator import ProgressIndicator
from . import SpykeException


[docs]def correlogram(trains, bin_size, max_lag=500 * pq.ms, border_correction=True, per_second=True, unit=pq.ms, progress=None): """ Return (cross-)correlograms from a dictionary of spike train lists for different units. :param dict trains: Dictionary of :class:`neo.core.SpikeTrain` lists. :param bin_size: Bin size (time). :type bin_size: Quantity scalar :param max_lag: Cut off (end time of calculated correlogram). :type max_lag: Quantity scalar :param bool border_correction: Apply correction for less data at higher timelags. Not perfect for bin_size != 1*``unit``, especially with large ``max_lag`` compared to length of spike trains. :param bool per_second: If ``True``, counts returned are per second. Otherwise, counts per spike train are returned. :param Quantity unit: Unit of X-Axis. :param progress: A ProgressIndicator object for the operation. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Two values: * An ordered dictionary indexed with the indices of ``trains`` of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: ``c[index1][index2]`` and ``c[index2][index1]``. * The bins used for the correlogram calculation. :rtype: dict, Quantity 1D """ if not progress: progress = ProgressIndicator() bin_size.rescale(unit) max_lag.rescale(unit) # Create bins, making sure that 0 is at the center of central bin half_bins = sp.arange(bin_size / 2, max_lag, bin_size) all_bins = list(reversed(-half_bins)) all_bins.extend(half_bins) bins = sp.array(all_bins) * unit middle_bin = len(bins) / 2 - 1 indices = trains.keys() num_trains = len(trains[indices[0]]) if not num_trains: raise SpykeException('Could not create correlogram: No spike trains!') for u in range(1, len(indices)): if len(trains[indices[u]]) != num_trains: raise SpykeException('Could not create correlogram: All units ' + 'need the same number of spike trains!') progress.set_ticks(sp.sum(range(len(trains) + 1) * num_trains)) corrector = 1 if border_correction: # Need safe min/max functions def safe_max(seq): if len(seq) < 1: return 0 return max(seq) def safe_min(seq): if len(seq) < 1: return 2 ** 22 # Some arbitrary large value return min(seq) max_w = max([max([safe_max(t) for t in l]) for l in trains.itervalues()]) min_w = min([min([safe_min(t) for t in l]) for l in trains.itervalues()]) train_length = (max_w - min_w) l = int(round(middle_bin)) + 1 cE = max(train_length - (l * bin_size) + 1 * unit, 1 * unit) corrector = (train_length / sp.concatenate( (sp.linspace(cE, train_length, l - 1, False), sp.linspace(train_length, cE, l)))).magnitude correlograms = OrderedDict() for i1 in xrange(len(indices)): # For each index # For all later indices, including itself for i2 in xrange(i1, len(indices)): histogram = sp.zeros(len(bins) - 1) for t in xrange(num_trains): train1 = trains[indices[i1]][t].rescale(unit).reshape((1, -1)) train2 = trains[indices[i2]][t].rescale(unit).reshape((-1, 1)) histogram += sp.histogram( sp.subtract(train1, train2), bins=bins)[0] if i1 == i2: # Correction for autocorrelogram histogram[middle_bin] -= len(train2) progress.step() if per_second: l = train1.t_stop - train1.t_start if train2.t_stop - train2.t_start != l: raise SpykeException( 'A spike train pair does not have equal length,' 'cannot calculate count per second.') histogram /= l.rescale(pq.s) crg = corrector * histogram / num_trains if indices[i1] not in correlograms: correlograms[indices[i1]] = OrderedDict() correlograms[indices[i1]][indices[i2]] = crg if i1 != i2: if indices[i2] not in correlograms: correlograms[indices[i2]] = OrderedDict() correlograms[indices[i2]][indices[i1]] = crg[::-1] return correlograms, bins
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PKpBDDIw5spykeutils-0.4.1/_modules/spykeutils/conversions.html spykeutils.conversions — spykeutils 0.4.1 documentation

Source code for spykeutils.conversions

import scipy as sp
import neo

from . import SpykeException


[docs]def spike_train_to_spikes(spike_train, include_waveforms=True): """ Return a list of spikes for a spike train. Note that while the created spikes have references to the same segment and unit as the spike train, the relationships in the other direction are not automatically created (the spikes are not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spikes. :param spike_train: A spike train from which the :class:`neo.core.Spike` objects are constructed. :type spike_train: :class:`neo.core.SpikeTrain` :param bool include_waveforms: Determines if the ``waveforms`` property is converted to the spike waveforms. If ``waveforms`` is None, this parameter has no effect. :returns: A list of :class:`neo.core.Spike` objects, one for every spike in ``spike_train``. :rtype: list """ waves = None if include_waveforms: waves = spike_train.waveforms spikes = [] for i, t in enumerate(spike_train): s = neo.Spike(t, sampling_rate=spike_train.sampling_rate, left_sweep=spike_train.left_sweep) if waves is not None: s.waveform = waves[i, :, :] s.unit = spike_train.unit s.segment = spike_train.segment spikes.append(s) return spikes
[docs]def spikes_to_spike_train(spikes, include_waveforms=True): """ Return a spike train for a list of spikes. All spikes must have an identical left sweep, the same unit and the same segment, otherwise a ``SpykeException`` is raised. Note that while the created spike train has references to the same segment and unit as the spikes, the relationships in the other direction are not automatically created (the spike train is not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spike train. :param sequence spikes: A sequence of :class:`neo.core.Spike` objects from which the spike train is constructed. :param bool include_waveforms: Determines if the waveforms from the spike objects are used to fill the ``waveforms`` property of the resulting spike train. If ``True``, all spikes need a ``waveform`` property with the same shape or a ``SpykeException`` is raised (or the ``waveform`` property needs to be ``None`` for all spikes). :return: All elements of ``spikes`` as spike train. :rtype: :class:`neo.core.SpikeTrain` """ if not spikes: raise SpykeException('No spikes to create spike train!') tu = spikes[0].time.units times = sp.zeros(len(spikes)) * tu s = spikes[0].segment u = spikes[0].unit ls = spikes[0].left_sweep if include_waveforms and spikes[0].waveform is not None: sh = spikes[0].waveform.shape wu = spikes[0].waveform.units waves = sp.zeros((len(spikes), sh[0], sh[1])) * wu else: waves = None sh = None for i, spike in enumerate(spikes): if (u != spike.unit or s != spike.segment or ls != spike.left_sweep): raise SpykeException('Cannot create spike train from spikes with ' 'nonuniform properties!') times[i] = spikes[i].time if include_waveforms: if spike.waveform is None: if waves is not None: raise SpykeException('Cannot create spike train from ' 'spikes where some waveforms are ' 'None') elif sh != spike.waveform.shape: raise SpykeException('Cannot create spike train from spikes ' 'with nonuniform waveform shapes!') if waves is not None: waves[i, :, :] = spike.waveform ret = neo.SpikeTrain(times, t_start=times.min(), t_stop=times.max(), waveforms=waves, left_sweep=ls) ret.unit = u ret.segment = s ret.left_sweep = ls return ret
[docs]def analog_signal_array_to_analog_signals(signal_array): """ Return a list of analog signals for an analog signal array. If ``signal_array`` is attached to a recording channel group with exactly is many channels as there are channels in ``signal_array``, each created signal will be assigned the corresponding channel. If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel. Note that while the created signals may have references to a segment and channels, the relationships in the other direction are not automatically created (the signals are not attached to the recording channel or segment). Other properties like annotations are not copied or referenced in the created analog signals. :param signal_array: An analog signal array from which the :class:`neo.core.AnalogSignal` objects are constructed. :type signal_array: :class:`neo.core.AnalogSignalArray` :return: A list of analog signals, one for every channel in ``signal_array``. :rtype: list """ signals = [] rcg = signal_array.recordingchannelgroup for i in xrange(signal_array.shape[1]): s = neo.AnalogSignal( signal_array[:, i], t_start=signal_array.t_start, sampling_rate=signal_array.sampling_rate) if len(rcg.recordingchannels) == 1: s.recordingchannel = rcg.recordingchannels[0] elif len(rcg.recordingchannels) == signal_array.shape[1]: s.recordingchannel = rcg.recordingchannels[i] s.segment = signal_array.segment signals.append(s) return signals
[docs]def event_array_to_events(event_array): """ Return a list of events for an event array. Note that while the created events may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created events. :param event_array: An event array from which the Event objects are constructed. :type event_array: :class:`neo.core.EventArray` :return: A list of events, one for of the events in ``event_array``. :rtype: list """ events = [] for i, t in enumerate(event_array.times): e = neo.Event( t, event_array.labels[i] if i < len(event_array.labels) else '') e.segment = event_array.segment events.append(e) return events
[docs]def epoch_array_to_epochs(epoch_array): """ Return a list of epochs for an epoch array. Note that while the created epochs may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created epochs. :param epoch_array: A period array from which the Epoch objects are constructed. :type epoch_array: :class:`neo.core.EpochArray` :return: A list of events, one for of the events in ``epoch_array``. :rtype: list """ periods = [] for i, t in enumerate(epoch_array.times): p = neo.Epoch( t, epoch_array.durations[i], epoch_array.labels[i] if i < len(epoch_array.labels) else '') p.segment = epoch_array.segment periods.append(p) return periods
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PKpBDD/spykeutils-0.4.1/_modules/spykeutils/tools.html spykeutils.tools — spykeutils 0.4.1 documentation

Source code for spykeutils.tools

import neo
import neo.description
import quantities as pq
import scipy as sp
import _scipy_quantities as spq


[docs]def apply_to_dict(fn, dictionary, *args): """ Applies a function to all spike trains in a dictionary of spike train sequences. :param function fn: Function to apply. Should take a :class:`neo.core.SpikeTrain` as first argument. :param dict dictionary: Dictionary of sequences of :class:`neo.core.SpikeTrain` objects to apply the function to. :param args: Additional arguments which will be passed to ``fn``. :returns: A new dictionary with the same keys as ``dictionary``. :rtype: dict """ applied = {} for k in dictionary: applied[k] = [fn(st, *args) for st in dictionary[k]] return applied
[docs]def bin_spike_trains(trains, sampling_rate, t_start=None, t_stop=None): """ Creates binned representations of spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :type t_start: The desired time for the start of the first bin as time scalar. It will be the minimum start time of all spike trains if ``None`` is passed. :type t_start: Quantity scalar :param t_stop: The desired time for the end of the last bin as time scalar. It will be the maximum stop time of all spike trains if ``None`` is passed. :type t_stop: Quantity scalar :returns: A dictionary (with the same indices as ``trains``) of lists of spike train counts and the bin borders. :rtype: dict, Quantity 1D with time units """ if t_start is None or t_stop is None: max_start, max_stop = maximum_spike_train_interval(trains) if t_start is None: t_start = max_start if t_stop is None: t_stop = max_stop t_start = t_start.rescale(t_stop.units) duration = t_stop - t_start num_bins = (sampling_rate * duration).simplified bins = sp.arange(num_bins + 1) * (duration / num_bins) + t_start return apply_to_dict(_bin_single_spike_train, trains, bins), bins
def _bin_single_spike_train(train, bins): """ Return a binned representation of SpikeTrain object. :param train: A spike train to bin. :type train: :class:`neo.core.SpikeTrain` :param bins: The bin edges, including the rightmost edge, with time units. :type bins: Quantity 1D :returns: The binned spike train. :rtype: 1-D array """ return sp.histogram(train.rescale(bins.units), bins)[0]
[docs]def concatenate_spike_trains(trains): """ Concatenates spike trains. :param sequence trains: :class:`neo.core.SpikeTrain` objects to concatenate. :returns: A spike train consisting of the concatenated spike trains. The spikes will be in the order of the given spike trains and ``t_start`` and ``t_stop`` will be set to the minimum and maximum value. :rtype: :class:`neo.core.SpikeTrain` """ t_start, t_stop = maximum_spike_train_interval({0: trains}) return neo.SpikeTrain( spq.concatenate([train.view(type=pq.Quantity) for train in trains]), t_start=t_start, t_stop=t_stop)
[docs]def minimum_spike_train_interval( trains, t_start=-sp.inf * pq.s, t_stop=sp.inf * pq.s): """ Computes the maximum starting time and minimum end time that all given spike trains share. This yields the shortest interval shared by all spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param t_start: Minimal starting time to return. :type t_start: Quantity scalar :param t_stop: Maximum end time to return. If ``None``, infinity is used. :type t_stop: Quantity scalar :returns: Maximum shared t_start time and minimum shared t_stop time as time scalars. :rtype: Quantity scalar, Quantity scalar """ if t_stop is None: t_stop = sp.inf * pq.s # Load data and find shortest spike train for st in trains.itervalues(): if len(st) > 0: # Minimum length of spike of all spike trains for this unit t_start = max(t_start, max((t.t_start for t in st))) t_stop = min(t_stop, min((t.t_stop for t in st))) return t_start, t_stop
[docs]def maximum_spike_train_interval( trains, t_start=sp.inf * pq.s, t_stop=-sp.inf * pq.s): """ Computes the minimum starting time and maximum end time of all given spike trains. This yields an interval containing the spikes of all spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param t_start: Maximum starting time to return. :type t_start: Quantity scalar :param t_stop: Minimum end time to return. If ``None``, infinity is used. :type t_stop: Quantity scalar :returns: Minimum t_start time and maximum t_stop time as time scalars. :rtype: Quantity scalar, Quantity scalar """ if t_stop is None: t_stop = sp.inf * pq.s for st in trains.itervalues(): if len(st) > 0: t_start = min(t_start, min((t.t_start for t in st))) t_stop = max(t_stop, max((t.t_stop for t in st))) return t_start, t_stop
def _handle_orphans(obj, remove): """ Removes half-orphaned Spikes and SpikeTrains that occur when removing an object upwards in the hierarchy. """ if isinstance(obj, neo.Segment): for s in obj.spikes: if s.unit: if not remove: s.segment = None else: try: s.unit.spikes.remove(s) except ValueError: pass for st in obj.spiketrains: if st.unit: if not remove: st.segment = None else: try: st.unit.spiketrains.remove(st) except ValueError: pass elif isinstance(obj, neo.Unit): for s in obj.spikes: if s.segment: if not remove: s.unit = None else: try: s.segment.spikes.remove(s) except ValueError: pass for st in obj.spiketrains: if st.segment: if not remove: st.unit = None else: try: st.segment.spiketrains.remove(st) except ValueError: pass elif isinstance(obj, neo.RecordingChannelGroup): for u in obj.units: _handle_orphans(u, remove)
[docs]def remove_from_hierarchy(obj, remove_half_orphans=True): """ Removes a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in :class:`neo.core.Spike` or :class:`neo.core.SpikeTrain` objects). For example, when ``obj`` is a :class:`neo.core.Segment`, the link from its parent :class:`neo.core.Block` will be severed. Also, all links to the segment from its spikes and spike trains will be severed. :param obj: The object to be removed. :type obj: Neo object :param bool remove_half_orphans: When True, :class:`neo.core.Spike` and :class:`neo.core.SpikeTrain` belonging to a :class:`neo.core.Segment` or :class:`neo.core.Unit` removed by this function will be removed from the hierarchy as well, even if they are still linked from a :class:`neo.core.Unit` or :class:`neo.core.Segment`, respectively. In this case, their links to the hierarchy defined by ``obj`` will be kept intact. """ classname = type(obj).__name__ # Parent for arbitrary object if classname in neo.description.many_to_one_relationship: for n in neo.description.many_to_one_relationship[classname]: p = getattr(obj, n.lower()) if p is None: continue l = getattr(p, classname.lower() + 's', ()) try: l.remove(obj) except ValueError: pass # Many-to-many relationships if isinstance(obj, neo.RecordingChannel): for rcg in obj.recordingchannelgroups: try: idx = rcg.recordingchannels.index(obj) if rcg.channel_indexes.shape[0] == len(rcg.recordingchannels): rcg.channel_indexes = sp.delete(rcg.channel_indexes, idx) if rcg.channel_names.shape[0] == len(rcg.recordingchannels): rcg.channel_names = sp.delete(rcg.channel_names, idx) rcg.recordingchannels.remove(obj) except ValueError: pass if isinstance(obj, neo.RecordingChannelGroup): for rc in obj.recordingchannels: try: rc.recordingchannelgroups.remove(obj) except ValueError: pass _handle_orphans(obj, remove_half_orphans)
[docs]def extract_spikes(train, signals, length, align_time): """ Extract spikes with waveforms from analog signals using a spike train. Spikes that are too close to the beginning or end of the shortest signal to be fully extracted are ignored. :type train: :class:`neo.core.SpikeTrain` :param train: The spike times. :param sequence signals: A sequence of :class:`neo.core.AnalogSignal` objects from which the spikes are extracted. The waveforms of the returned spikes are extracted from these signals in the same order they are given. :type length: Quantity scalar :param length: The length of the waveform to extract as time scalar. :type align_time: Quantity scalar :param align_time: The alignment time of the spike times as time scalar. This is the time delta from the start of the extracted waveform to the exact time of the spike. :returns: A list of :class:`neo.core.Spike` objects, one for each time point in ``train``. All returned spikes include their ``waveform`` property. :rtype: list """ if not signals: raise ValueError('No signals to extract spikes from') ref = signals[0] for s in signals[1:]: if ref.sampling_rate != s.sampling_rate: raise ValueError( 'All signals for spike extraction need the same sampling rate') wave_unit = signals[0].units srate = signals[0].sampling_rate end = min(s.shape[0] for s in signals) aligned_train = train - align_time cut_samples = int((length * srate).simplified) st = sp.asarray((aligned_train * srate).simplified) # Find extraction epochs st_ok = (st >= 0) * (st < end - cut_samples) epochs = sp.vstack((st[st_ok], st[st_ok] + cut_samples)).T nspikes = epochs.shape[0] if not nspikes: return [] # Create data data = sp.vstack([sp.asarray(s.rescale(wave_unit)) for s in signals]) nc = len(signals) spikes = [] for s in xrange(nspikes): waveform = sp.zeros((cut_samples, nc)) for c in xrange(nc): waveform[:, c] = \ data[c, epochs[s, 0]:epochs[s, 1]] spikes.append(neo.Spike(train[st_ok][s], waveform=waveform * wave_unit, sampling_rate=srate)) return spikes
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PKpBDDC=spykeutils-0.4.1/_modules/spykeutils/spike_train_metrics.html spykeutils.spike_train_metrics — spykeutils 0.4.1 documentation

Source code for spykeutils.spike_train_metrics

from monkeypatch import quantities_patch
import quantities as pq
import scipy as sp
import _scipy_quantities as spq
import signal_processing as sigproc
import tools

assert quantities_patch  # Suppress pyflakes warning, patch applied by loading


def _calc_multiunit_dist_matrix_from_single_trials(units, dist_func, **params):
    if len(units) <= 0:
        return sp.zeros((0, 0))

    num_trials = len(units.itervalues().next())
    if not all((len(v) == num_trials for v in units.itervalues())):
        raise ValueError("Number of trials differs among units.")

    D = sp.empty((num_trials, num_trials))
    for i in xrange(num_trials):
        D[i, i] = 0.0
        a = [units[k][i] for k in units.iterkeys()]
        for j in xrange(i + 1, num_trials):
            b = [units[k][j] for k in units.iterkeys()]
            D[i, j] = D[j, i] = dist_func(a, b, **params)
    return D


def _create_matrix_from_indexed_function(
        shape, func, symmetric_2d=False, **func_params):
    mat = sp.empty(shape)
    if symmetric_2d:
        for i in xrange(shape[0]):
            for j in xrange(i, shape[1]):
                mat[i, j] = mat[j, i] = func(i, j, **func_params)
    else:
        for idx in sp.ndindex(*shape):
            mat[idx] = func(*idx, **func_params)
    return mat


def _merge_trains_and_label_spikes(trains):
    labels = sp.concatenate(
        [sp.zeros(st.size, dtype=int) + i for i, st in enumerate(trains)])
    trains = spq.concatenate([st.view(dtype=pq.Quantity) for st in trains])
    sorted_indices = sp.argsort(trains)
    return trains[sorted_indices], labels[sorted_indices]


[docs]def cs_dist( trains, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the Cauchy-Schwarz distance between two spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \\int_{\\mathcal{T}} v_a(t) v_b(t) dt`. Then, the Cauchy-Schwarz distance of the spike trains is defined as :math:`d_{CS}(a, b) = \\arccos \\frac{V(a, b)^2}{V(a, a) V(b, b)}`. The Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure :math:`S_S` by :math:`d_{CS} = \\arccos S_S^2` This function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use :func:`schreiber_similarity` for a more efficient and precise calculation. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param smoothing_filter: Smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the Cauchy-Schwarz distance of all pairs of spike trains :rtype: 2-D array """ inner = st_inner( trains, trains, smoothing_filter, sampling_rate, filter_area_fraction) return sp.arccos( inner ** 2 / sp.diag(inner) / sp.atleast_2d(sp.diag(inner)).T)
[docs]def event_synchronization( trains, tau=None, kernel=sigproc.RectangularKernel(1.0, normalize=False), sort=True): """ event_synchronization(trains, tau=None, kernel=signal_processing.RectangularKernel(1.0, normalize=False), sort=True) Calculates the event synchronization. Let :math:`d(x|y)` be the count of spikes in :math:`y` which occur shortly before an event in :math:`x` with a time difference of less than :math:`\\tau`. Moreover, let :math:`n_x` and :math:`n_y` be the number of total spikes in the spike trains :math:`x` and :math:`y`. The event synchrony is then defined as :math:`Q_T = \\frac{d(x|y) + d(y|x)}{\\sqrt{n_x n_y}}`. The time maximum time lag :math:`\\tau` can be determined automatically for each pair of spikes :math:`t^x_i` and :math:`t^y_j` by the formula :math:`\\tau_{ij} = \\frac{1}{2} \\min\{t^x_{i+1} - t^x_i, t^x_i - t^x_{i-1}, t^y_{j+1} - t^y_j, t^y_j - t^y_{j-1}\}` Further and more detailed information can be found in *Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the van Rossum distance will be calculated pairwise. :param tau: The maximum time lag for two spikes to be considered coincident or synchronous as time scalar. To have it determined automatically by above formula set it to `None`. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times are be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the event synchronization for all pairs of spike trains. :rtype: 2-D array """ trains = [st.view(type=pq.Quantity) for st in trains] if sort: trains = [sp.sort(st) for st in trains] if tau is None: inf_array = sp.array([sp.inf]) isis = [spq.concatenate( (inf_array * st.units, sp.diff(st), inf_array * st.units)) for st in trains] auto_taus = [spq.minimum(t[:-1], t[1:]) for t in isis] def compute(i, j): if i == j: return 1.0 else: if tau is None: tau_mat = spq.minimum(*spq.meshgrid( auto_taus[i], auto_taus[j])) / 2.0 else: tau_mat = sp.tile(tau, (trains[j].size, trains[i].size)) coincidence = sp.sum(kernel( (trains[i] - sp.atleast_2d(trains[j]).T) / tau_mat)) normalization = 1.0 / sp.sqrt(trains[i].size * trains[j].size) return normalization * coincidence return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric())
[docs]def hunter_milton_similarity(trains, tau=1.0 * pq.s, kernel=None): """ Calculates the Hunter-Milton similarity measure. If the kernel function is denoted as :math:`K(t)`, a function :math:`d(x_k) = K(x_k - y_{k'})` can be defined with :math:`y_{k'}` being the closest spike in spike train :math:`y` to the spike :math:`x_k` in spike train :math:`x`. With this the Hunter-Milton similarity measure is :math:`S_H = \\frac{1}{2} \\left(\\frac{1}{n_x} \\sum_{k = 1}^{n_x} d(x_k) + \\frac{1}{n_y} \\sum_{k' = 1}^{n_y} d(y_{k'})\\right)`. This implementation returns 0 if one of the spike trains is empty, but 1 if both are empty. Further information can be found in - *Hunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.* - *Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the Hunter-Milton similarity will be calculated pairwise. :param tau: The time scale for determining the coincidence of two events as time scalar. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `None`, a unnormalized Laplacian kernel will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: Matrix containing the Hunter-Milton similarity for all pairs of spike trains. :rtype: 2-D array """ if kernel is None: kernel = sigproc.LaplacianKernel(tau, normalize=False) def compute(i, j): if i == j: return 1.0 elif trains[i].size <= 0 or trains[j].size <= 0: return 0.0 else: diff_matrix = sp.absolute(trains[i] - sp.atleast_2d(trains[j]).T) return 0.5 * ( sp.sum(kernel(sp.amin(diff_matrix, axis=0))) / trains[i].size + sp.sum(kernel(sp.amin(diff_matrix, axis=1))) / trains[j].size) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric())
[docs]def norm_dist( trains, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the norm distance between spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as :math:`d_{ND}(a, b) = \\sqrt{\\int_{\\mathcal{T}} (v_a(t) - v_b(t))^2 dt}`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param smoothing_filter: Smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the norm distance of all pairs of spike trains given the smoothing_filter. :rtype: Quantity 2D with units depending on the smoothing filter (usually temporal frequency units) """ inner = st_inner( trains, trains, smoothing_filter, sampling_rate, filter_area_fraction) return spq.maximum( 0.0 * pq.Hz, (spq.diag(inner) + sp.atleast_2d(spq.diag(inner)).T - 2 * inner)) ** 0.5
[docs]def schreiber_similarity(trains, kernel, sort=True): """ Calculates the Schreiber et al. similarity measure between spike trains given a kernel. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \\int_{\\mathcal{T}} v_a(t) v_b(t) dt`. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as :math:`S_{S}(a, b) = \\frac{V(a, b)}{\\sqrt{V(a, a) V(b, b)}}`. It is closely related to the Cauchy-Schwarz distance :math:`d_{CS}` by :math:`S_S = \\sqrt{\\cos d_{CS}}`. In opposite to :func:`cs_dist` which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter's autocorrelation. This allows a more accurate and faster calculation. Further information can be found in: - *Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.* - *Paiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param kernel: Kernel to use. It corresponds to a smoothing filter by being the autocorrelation of such a filter. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times will be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the Schreiber et al. similarity measure of all pairs of spike trains. :rtype: 2-D array """ k_dist = kernel.summed_dist_matrix(trains, not sort) def compute(i, j): return sp.sqrt( k_dist[i, j] * k_dist[j, i] / k_dist[i, i] / k_dist[j, j]) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric())
[docs]def st_inner( a, b, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the inner product of spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as :math:`\\int_{\\mathcal{T}} v_a(t)v_b(t) dt`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence a: Sequence of :class:`neo.core.SpikeTrain` objects. :param sequence b: Sequence of :class:`neo.core.SpikeTrain` objects. :param smoothing_filter: A smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the `smoothing_filter` will be discretized. At least the given fraction of the complete `smoothing_filter` area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the inner product for each pair of spike trains with one spike train from `a` and the other one from `b`. :rtype: Quantity 2D with units depending on the smoothing filter (usually temporal frequency units) """ if all((x is y for x, y in zip(a, b))): convolved, sampling_rate = _prepare_for_inner_prod( a, smoothing_filter, sampling_rate, filter_area_fraction) convolved = convolved + convolved else: convolved, sampling_rate = _prepare_for_inner_prod( a + b, smoothing_filter, sampling_rate, filter_area_fraction) return (sp.inner(convolved[:len(a)], convolved[len(a):]) * convolved[0].units * convolved[1].units / sampling_rate)
def _prepare_for_inner_prod( trains, smoothing_filter, sampling_rate, filter_area_fraction): t_start, t_stop = tools.maximum_spike_train_interval({0: trains}) padding = smoothing_filter.boundary_enclosing_at_least(filter_area_fraction) t_start -= 2 * padding t_stop += 2 * padding return [sigproc.st_convolve( st, smoothing_filter, sampling_rate, mode='full', binning_params={'t_start': t_start, 't_stop': t_stop}, kernel_discretization_params={'area_fraction': filter_area_fraction})[0] for st in trains], sampling_rate
[docs]def st_norm( train, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the spike train norm given a smoothing filter. Let :math:`v(t)` with :math:`t \\in \\mathcal{T}` be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as :math:`\\int_{\\mathcal{T}} v(t)^2 dt`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param train: Spike train of which to calculate the norm. :type train: :class:`neo.core.SpikeTrain` :param smoothing_filter: Smoothing filter to be convolved with the spike train. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: The norm of the spike train given the smoothing_filter. :rtype: Quantity scalar with units depending on the smoothing filter (usually temporal frequency units) """ return st_inner( [train], [train], smoothing_filter, sampling_rate, filter_area_fraction) ** 0.5
[docs]def van_rossum_dist(trains, tau=1.0 * pq.s, kernel=None, sort=True): """ Calculates the van Rossum distance. It is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper. Given :math:`N` spike trains with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)`. An implementation in :math:`O(N^2 n)` would be possible but has a high constant factor rendering it slower in practical cases. :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the van Rossum distance will be calculated pairwise. :param tau: Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if `kernel` is not `None`. May also be :const:`scipy.inf` which will lead to only measuring differences in spike count. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If `kernel` is `None`, an unnormalized Laplacian kernel with a size of `tau` will be used. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times might be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the van Rossum distances for all pairs of spike trains. :rtype: 2-D array """ if kernel is None: if tau == sp.inf: spike_counts = [st.size for st in trains] return (spike_counts - sp.atleast_2d(spike_counts).T) ** 2 kernel = sigproc.LaplacianKernel(tau, normalize=False) k_dist = kernel.summed_dist_matrix( [st.view(type=pq.Quantity) for st in trains], not sort) vr_dist = sp.empty_like(k_dist) for i, j in sp.ndindex(*k_dist.shape): vr_dist[i, j] = ( k_dist[i, i] + k_dist[j, j] - k_dist[i, j] - k_dist[j, i]) return sp.sqrt(vr_dist)
[docs]def van_rossum_multiunit_dist(units, weighting, tau=1.0 * pq.s, kernel=None): """ Calculates the van Rossum multi-unit distance. The single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper. Given the :math:`p`- and :math:`q`-th spike train of `a` and respectively `b` let :math:`R_{pq}` be the squared single-unit distance between these two spike trains. Then the multi-unit distance is :math:`\\sqrt{\\sum_p (R_{pp} + c \\cdot \\sum_{q \\neq p} R_{pq})}` with :math:`c` being equal to `weighting`. The weighting parameter controls the interpolation between a labeled line and a summed population coding. More information can be found in *Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.* Given :math:`N` spike trains in total with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + Nn^2)` memory will be needed. :param dict units: Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a :class:`neo.core.SpikeTrain` and all units should have the same number of trials. :param float weighting: Controls the interpolation between a labeled line and a summed population coding. :param tau: Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if `kernel` is not `None`. May also be :const:`scipy.inf` which will lead to only measuring differences in spike count. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If `kernel` is `None`, an unnormalized Laplacian kernel with a size of `tau` will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: A 2D array with the multi-unit distance for each pair of trials. :rtype: 2D arrary """ if kernel is None and tau != sp.inf: kernel = sigproc.LaplacianKernel(tau, normalize=False) return _calc_multiunit_dist_matrix_from_single_trials( units, _van_rossum_multiunit_dist_for_trial_pair, weighting=weighting, tau=tau, kernel=kernel)
def _van_rossum_multiunit_dist_for_trial_pair(a, b, weighting, tau, kernel): if kernel is None: spike_counts = sp.atleast_2d([st.size for st in a + b]) k_dist = spike_counts.T * (spike_counts - spike_counts.T) else: k_dist = kernel.summed_dist_matrix(a + b) non_diagonal = sp.logical_not(sp.eye(len(a))) summed_population = ( sp.trace(k_dist) - sp.trace(k_dist, len(a)) - sp.trace(k_dist, -len(a))) labeled_line = ( sp.sum(k_dist[:len(a), :len(a)][non_diagonal]) + sp.sum(k_dist[len(a):, len(a):][non_diagonal]) - sp.sum(k_dist[:len(a), len(a):][non_diagonal]) - sp.sum(k_dist[len(a):, :len(a)][non_diagonal])) return sp.sqrt(summed_population + weighting * labeled_line)
[docs]def victor_purpura_dist(trains, q=1.0 * pq.Hz, kernel=None, sort=True): """ Calculates the Victor-Purpura's (VP) distance. It is often denoted as :math:`D^{\\text{spike}}[q]`. It is defined as the minimal cost of transforming spike train `a` into spike train `b` by using the following operations: * Inserting or deleting a spike (cost 1.0). * Shifting a spike from :math:`t` to :math:`t'` (cost :math:`q \\cdot |t - t'|`). A detailed description can be found in *Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.* Given the average number of spikes :math:`n` in a spike train and :math:`N` spike trains the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + n^2)` memory will be needed. :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param q: Cost factor for spike shifts as inverse time scalar. If `kernel` is not `None`, `q` will be ignored. :type q: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `kernel` is `None`, an unnormalized triangular kernel with a half width of `2.0/q` will be used. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times will be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the VP distance of all pairs of spike trains. :rtype: 2-D array """ if kernel is None: if q == 0.0: num_spikes = sp.atleast_2d([st.size for st in trains]) return sp.absolute(num_spikes.T - num_spikes) else: kernel = sigproc.TriangularKernel(2.0 / q, normalize=False) if sort: trains = [sp.sort(st.view(type=pq.Quantity)) for st in trains] def compute(i, j): if i == j: return 0.0 else: return _victor_purpura_dist_for_trial_pair( trains[i], trains[j], kernel) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric())
def _victor_purpura_dist_for_trial_pair(a, b, kernel): if a.size <= 0 or b.size <= 0: return max(a.size, b.size) if a.size < b.size: a, b = b, a # The algorithm used is based on the one given in # # Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal # coding in visual cortex: a metric-space analysis. Journal of # Neurophysiology. # # It constructs a matrix G[i, j] containing the minimal cost when only # considering the first i and j spikes of the spike trains. However, one # never needs to store more than one row and one column at the same time # for calculating the VP distance. # cost[0, :cost.shape[1] - i] corresponds to G[i:, i]. In the same way # cost[1, :cost.shape[1] - i] corresponds to G[i, i:]. # # Moreover, the minimum operation on the costs of the three kind of actions # (delete, insert or move spike) can be split up in two operations. One # operation depends only on the already calculated costs and kernel # evaluation (insertion of spike vs moving a spike). The other minimum # depends on that result and the cost of deleting a spike. This operation # always depends on the last calculated element in the cost array and # corresponds to a recursive application of # f(accumulated_min[i]) = min(f(accumulated_min[i-1]), accumulated_min[i]) # + 1. That '+1' can be excluded from this function if the summed value for # all recursive applications is added upfront to accumulated_min. # Afterwards it has to be removed again except one for the currently # processed spike to get the real costs up to the evaluation of i. # # All currently calculated costs will be considered -1 because this saves # a number of additions as in most cases the cost would be increased by # exactly one (the only exception is shifting, but in that calculation is # already the addition of a constant involved, thus leaving the number of # operations the same). The increase by one will be added after calculating # all minima by shifting decreasing_sequence by one when removing it from # accumulated_min. min_dim, max_dim = b.size, a.size + 1 cost = sp.asfortranarray(sp.tile(sp.arange(float(max_dim)), (2, 1))) decreasing_sequence = sp.asfortranarray(cost[:, ::-1]) k = 1 - 2 * sp.asfortranarray(kernel( (sp.atleast_2d(a).T - b).view(type=pq.Quantity)).simplified) for i in xrange(min_dim): # determine G[i, i] == accumulated_min[:, 0] #accumulated_min = sp.empty((2, max_dim - i - 1)) accumulated_min = cost[:, :-i - 1] + k[i:, i] accumulated_min[1, :b.size - i] = cost[1, :b.size - i] + k[i, i:] accumulated_min = sp.minimum( accumulated_min, # shift cost[:, 1:max_dim - i]) # insert acc_dim = accumulated_min.shape[1] # delete vs min(insert, shift) accumulated_min[:, 0] = min(cost[1, 1], accumulated_min[0, 0]) # determine G[i, :] and G[:, i] by propagating minima. accumulated_min += decreasing_sequence[:, -acc_dim - 1:-1] accumulated_min = sp.minimum.accumulate(accumulated_min, axis=1) cost[:, :acc_dim] = accumulated_min - decreasing_sequence[:, -acc_dim:] return cost[0, -min_dim - 1]
[docs]def victor_purpura_multiunit_dist( units, reassignment_cost, q=1.0 * pq.Hz, kernel=None): """ Calculates the Victor-Purpura's (VP) multi-unit distance. It is defined as the minimal cost of transforming the spike trains `a` into spike trains `b` by using the following operations: * Inserting or deleting a spike (cost 1.0). * Shifting a spike from :math:`t` to :math:`t'` (cost :math:`q \\cdot |t - t'|`). * Moving a spike to another spike train (cost `reassignment_cost`). A detailed description can be found in *Aronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.* Given the average number of spikes :math:`N` in a spike train and :math:`L` units with :math:`n` spike trains each the run-time complexity is :math:`O(n^2 LN^{L+1})`. The space complexity is :math:`O(n^2 + LN^{L+1})`. For calculating the distance between only two units one should use :func:`victor_purpura_dist` which is more efficient. :param dict units: Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a :class:`neo.core.SpikeTrain` and all units should have the same number of trials. :param float reassignment_cost: Cost to reassign a spike from one train to another (sometimes denoted with :math:`k`). Should be between 0 and 2. For 0 spikes can be reassigned without any cost, for 2 and above it is cheaper to delete and reinsert a spike. :param q: Cost factor for spike shifts as inverse time scalar. If `kernel` is not `None`, `q` will be ignored. :type q: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `kernel` is `None`, an unnormalized triangular kernel with a half width of `2.0/q` will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: A 2D array with the multi-unit distance for each pair of trials. :rtype: 2D arrary """ if kernel is None: kernel = sigproc.TriangularKernel(2.0 / q, normalize=False) return _calc_multiunit_dist_matrix_from_single_trials( units, _victor_purpura_multiunit_dist_for_trial_pair, reassignment_cost=reassignment_cost, kernel=kernel)
def _victor_purpura_multiunit_dist_for_trial_pair( a, b, reassignment_cost, kernel): # The algorithm used is based on the one given in # # Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal # coding in visual cortex: a metric-space analysis. Journal of # Neurophysiology. # # It constructs a matrix cost[i, j_1, ... j_L] containing the minimal cost # when only considering the first i spikes of the merged spikes of a and # j_w spikes of the spike trains of b (the reference given above denotes # this matrix with G). In this implementation the only the one submatrix # for one specific i is stored as in each step only i-1 and i will be # accessed. That saves some memory. # Initialization of various variables needed by the algorithm. Also swap # a and b if it will save time as the algorithm is not symmetric. a_num_spikes = [st.size for st in a] b_num_spikes = [st.size for st in b] a_num_total_spikes = sp.sum(a_num_spikes) complexity_same = a_num_total_spikes * sp.prod(b_num_spikes) complexity_swapped = sp.prod(a_num_spikes) * sp.sum(b_num_spikes) if complexity_swapped < complexity_same: a, b = b, a a_num_spikes, b_num_spikes = b_num_spikes, a_num_spikes a_num_total_spikes = sp.sum(a_num_spikes) if a_num_total_spikes <= 0: return sp.sum(b_num_spikes) b_dims = tuple(sp.asarray(b_num_spikes) + 1) cost = sp.asfarray(sp.sum(sp.indices(b_dims), axis=0)) a_merged = _merge_trains_and_label_spikes(a) b_strides = sp.cumprod((b_dims + (1,))[::-1])[:-1] flat_b_indices = sp.arange(cost.size) b_indices = sp.vstack(sp.unravel_index(flat_b_indices, b_dims)) flat_neighbor_indices = sp.maximum( 0, sp.atleast_2d(flat_b_indices).T - b_strides[::-1]) invalid_neighbors = b_indices.T == 0 b_train_mat = sp.empty((len(b), sp.amax(b_num_spikes))) * b[0].units for i, st in enumerate(b): b_train_mat[i, :st.size] = st.rescale(b[0].units) b_train_mat[i, st.size:] = sp.nan * b[0].units reassignment_costs = sp.empty((a_merged[0].size,) + b_train_mat.shape) reassignment_costs.fill(reassignment_cost) reassignment_costs[sp.arange(a_merged[1].size), a_merged[1], :] = 0.0 k = 1 - 2 * kernel(sp.atleast_2d( a_merged[0]).T - b_train_mat.flatten()).simplified.reshape( (a_merged[0].size,) + b_train_mat.shape) + reassignment_costs decreasing_sequence = flat_b_indices[::-1] # Do the actual calculations. for a_idx in xrange(1, a_num_total_spikes + 1): base_costs = cost.flat[flat_neighbor_indices] base_costs[invalid_neighbors] = sp.inf min_base_cost_labels = sp.argmin(base_costs, axis=1) cost_all_possible_shifts = k[a_idx - 1, min_base_cost_labels, :] + \ sp.atleast_2d(base_costs[flat_b_indices, min_base_cost_labels]).T cost_shift = cost_all_possible_shifts[ sp.arange(cost_all_possible_shifts.shape[0]), b_indices[min_base_cost_labels, flat_b_indices] - 1] cost_delete_in_a = cost.flat[flat_b_indices] # cost_shift is dimensionless, but there is a bug in quantities with # the minimum function: # <https://github.com/python-quantities/python-quantities/issues/52> # The explicit request for the magnitude circumvents this problem. cost.flat = sp.minimum(cost_delete_in_a, cost_shift.magnitude) + 1 cost.flat[0] = sp.inf # Minimum with cost for deleting in b # The calculation order is somewhat different from the order one would # expect from the naive algorithm. This implementation, however, # optimizes the use of the CPU cache giving a considerable speed # improvement. # Basically this codes calculates the values of a row of elements for # each dimension of cost. for dim_size, stride in zip(b_dims[::-1], b_strides): for i in xrange(stride): segment_size = dim_size * stride for j in xrange(i, cost.size, segment_size): s = sp.s_[j:j + segment_size:stride] seq = decreasing_sequence[-cost.flat[s].size:] cost.flat[s] = sp.minimum.accumulate( cost.flat[s] + seq) - seq return cost.flat[-1]
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PKpBDD[||9spykeutils-0.4.1/_modules/spykeutils/rate_estimation.html spykeutils.rate_estimation — spykeutils 0.4.1 documentation

Source code for spykeutils.rate_estimation

from __future__ import division

import scipy as sp
import quantities as pq
import neo
from progress_indicator import ProgressIndicator
import signal_processing as sigproc
import tools
import copy as cp
from . import SpykeException


[docs]def psth( trains, bin_size, rate_correction=True, start=0 * pq.ms, stop=sp.inf * pq.s): """ Return dictionary of peri stimulus time histograms for a dictionary of spike train lists. :param dict trains: A dictionary of lists of :class:`neo.core.SpikeTrain` objects. :param bin_size: The desired bin size (as a time quantity). :type bin_size: Quantity scalar :param bool rate_correction: Determines if a rates (``True``) or counts (``False``) are returned. :param start: The desired time for the start of the first bin. It will be recalculated if there are spike trains which start later than this time. :type start: Quantity scalar :param stop: The desired time for the end of the last bin. It will be recalculated if there are spike trains which end earlier than this time. :type stop: Quantity scalar :returns: A dictionary (with the same indices as ``trains``) of arrays containing counts (or rates if ``rate_correction`` is ``True``) and the bin borders. :rtype: dict, Quantity 1D """ if not trains: raise SpykeException('No spike trains for PSTH!') start, stop = tools.minimum_spike_train_interval(trains, start, stop) binned, bins = tools.bin_spike_trains(trains, 1.0 / bin_size, start, stop) cumulative = {} time_multiplier = 1.0 / float(bin_size.rescale(pq.s)) for u in binned: if rate_correction: cumulative[u] = sp.mean(sp.array(binned[u]), 0) else: cumulative[u] = sp.sum(sp.array(binned[u]), 0) cumulative[u] *= time_multiplier return cumulative, bins
[docs]def aligned_spike_trains(trains, events, copy=True): """ Return a list of spike trains aligned to an event (the event will be time 0 on the returned trains). :param list trains: A list of :class:`neo.core.SpikeTrain` objects. :param dict events: A dictionary of Event objects, indexed by segment. These events will be used to align the spike trains and will be at time 0 for the aligned spike trains. :param bool copy: Determines if aligned copies of the original spike trains will be returned. If not, every spike train needs exactly one corresponding event, otherwise a ``ValueError`` will be raised. Otherwise, entries with no event will be ignored. """ ret = [] for t in trains: s = t.segment if s not in events: if not copy: raise ValueError( 'Cannot align spike trains: At least one segment does' + 'not have an align event.') continue e = events[s] if copy: st = neo.SpikeTrain( t, t.t_stop, units=t.units, sampling_rate=t.sampling_rate, t_start=t.t_start, waveforms=t.waveforms, left_sweep=t.left_sweep, name=t.name, file_origin=t.file_origin, description=t.description, **t.annotations) else: st = t st -= e.time st.t_stop -= e.time st.t_start -= e.time ret.append(st) return ret
[docs]def spike_density_estimation(trains, start=0 * pq.ms, stop=None, kernel=None, kernel_size=100 * pq.ms, optimize_steps=None, progress=None): """ Create a spike density estimation from a dictionary of lists of spike trains. The spike density estimations give an estimate of the instantaneous rate. The density estimation is evaluated at 1024 equally spaced points covering the range of the input spike trains. Optionally finds optimal kernel size for given data using the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010). :param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists. :param start: The desired time for the start of the estimation. It will be recalculated if there are spike trains which start later than this time. This parameter can be negative (which could be useful when aligning on events). :type start: Quantity scalar :param stop: The desired time for the end of the estimation. It will be recalculated if there are spike trains which end earlier than this time. :type stop: Quantity scalar :param kernel: The kernel function or instance to use, should accept two parameters: A ndarray of distances and a kernel size. The total area under the kernel function should be 1. Automatic optimization assumes a Gaussian kernel and will likely not produce optimal results for different kernels. Default: Gaussian kernel :type kernel: func or :class:`.signal_processing.Kernel` :param kernel_size: A uniform kernel size for all spike trains. Only used if optimization of kernel sizes is not used. :type kernel_size: Quantity scalar :param optimize_steps: An array of time lengths that will be considered in the kernel width optimization. Note that the optimization assumes a Gaussian kernel and will most likely not give the optimal kernel size if another kernel is used. If None, ``kernel_size`` will be used. :type optimize_steps: Quantity 1D :param progress: Set this parameter to report progress. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Three values: * A dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as ``trains``. * A dictionary of kernel sizes (Quantity scalars). Indexed the same as ``trains``. * The used evaluation points. :rtype: dict, dict, Quantity 1D """ if not progress: progress = ProgressIndicator() if optimize_steps is None or len(optimize_steps) < 1: units = kernel_size.units else: units = optimize_steps.units if kernel is None: kernel = sigproc.GaussianKernel(100 * pq.ms) # Prepare evaluation points max_start, max_stop = tools.minimum_spike_train_interval(trains) start = max(start, max_start) start.units = units if stop is not None: stop = min(stop, max_stop) else: stop = max_stop stop.units = units bins = sp.linspace(start, stop, 1025) eval_points = bins[:-1] + (bins[1] - bins[0]) / 2 if optimize_steps is None or len(optimize_steps) < 1: kernel_size = {u: kernel_size for u in trains} else: # Find optimal kernel size for all spike train sets progress.set_ticks(len(optimize_steps) * len(trains)) progress.set_status('Calculating optimal kernel size') kernel_size = {} for u, t in trains.iteritems(): c = collapsed_spike_trains(t) kernel_size[u] = optimal_gauss_kernel_size( c.time_slice(start, stop), optimize_steps, progress) progress.set_ticks(len(trains)) progress.set_status('Creating spike density plot') # Calculate KDEs kde = {} for u, t in trains.iteritems(): # Collapse spike trains collapsed = collapsed_spike_trains(t).rescale(units) scaled_kernel = sigproc.as_kernel_of_size(kernel, kernel_size[u]) # Create density estimation using convolution sliced = collapsed.time_slice(start, stop) sampling_rate = 1024.0 / (sliced.t_stop - sliced.t_start) kde[u] = sigproc.st_convolve( sliced, scaled_kernel, sampling_rate, kernel_discretization_params={ 'num_bins': 2048, 'ensure_unit_area': True})[0] / len(trains[u]) kde[u].units = pq.Hz return kde, kernel_size, eval_points
[docs]def collapsed_spike_trains(trains): """ Return a superposition of a list of spike trains. :param iterable trains: A list of :class:`neo.core.SpikeTrain` objects :returns: A spike train object containing all spikes of the given spike trains. :rtype: :class:`neo.core.SpikeTrain` """ if not trains: return neo.SpikeTrain([], 0 * pq.s) start = min((t.t_start for t in trains)) stop = max((t.t_stop for t in trains)) collapsed = [] for t in trains: collapsed.extend(sp.asarray(t.rescale(stop.units))) return neo.SpikeTrain(collapsed * stop.units, t_stop=stop, t_start=start)
[docs]def optimal_gauss_kernel_size(train, optimize_steps, progress=None): """ Return the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with :func:`collapsed_spike_trains`) that should be included in a spike density estimation. Implements the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010). :param train: The spike train for which the kernel size should be optimized. :type train: :class:`neo.core.SpikeTrain` :param optimize_steps: Array of kernel sizes to try (the best of these sizes will be returned). :type optimize_steps: Quantity 1D :param progress: Set this parameter to report progress. Will be advanced by len(`optimize_steps`) steps. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Best of the given kernel sizes :rtype: Quantity scalar """ if not progress: progress = ProgressIndicator() x = train.rescale(optimize_steps.units) N = len(train) C = {} sampling_rate = 1024.0 / (x.t_stop - x.t_start) dt = float(1.0 / sampling_rate) y_hist = tools.bin_spike_trains({0: [x]}, sampling_rate)[0][0][0] y_hist = sp.asfarray(y_hist) / N / dt for step in optimize_steps: s = float(step) yh = sigproc.smooth( y_hist, sigproc.GaussianKernel(2 * step), sampling_rate, num_bins=2048, ensure_unit_area=True) * optimize_steps.units # Equation from Matlab code, 7/2012 c = (sp.sum(yh ** 2) * dt - 2 * sp.sum(yh * y_hist) * dt + 2 * 1 / sp.sqrt(2 * sp.pi) / s / N) C[s] = c * N * N progress.step() # Return kernel size with smallest cost return min(C, key=C.get) * optimize_steps.units
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PKpBDD) VAA;spykeutils-0.4.1/_modules/spykeutils/signal_processing.html spykeutils.signal_processing — spykeutils 0.4.1 documentation

Source code for spykeutils.signal_processing

import copy
import quantities as pq
import scipy as sp
import scipy.signal
import scipy.special
import tools

default_kernel_area_fraction = 0.99999


[docs]class Kernel(object): """ Base class for kernels. """ def __init__(self, kernel_size, normalize): """ :param kernel_size: Parameter controlling the kernel size. :type kernel_size: Quantity 1D :param bool normalize: Whether to normalize the kernel to unit area. """ self.kernel_size = kernel_size self.normalize = normalize def __call__(self, t, kernel_size=None): """ Evaluates the kernel at all time points in the array `t`. :param t: Time points to evaluate the kernel at. :type t: Quantity 1D :param kernel_size: If not `None` this overwrites the kernel size of the `Kernel` instance. :type kernel_size: Quantity scalar :returns: The result of the kernel evaluations. :rtype: Quantity 1D """ if kernel_size is None: kernel_size = self.kernel_size if self.normalize: normalization = self.normalization_factor(kernel_size) else: normalization = 1.0 * pq.dimensionless return self._evaluate(t, kernel_size) * normalization def _evaluate(self, t, kernel_size): """ Evaluates the kernel. :param t: Time points to evaluate the kernel at. :type t: Quantity 1D :param kernel_size: Controls the width of the kernel. :type kernel_size: Quantity scalar :returns: The result of the kernel evaluations. :rtype: Quantity 1D """ raise NotImplementedError()
[docs] def normalization_factor(self, kernel_size): """ Returns the factor needed to normalize the kernel to unit area. :param kernel_size: Controls the width of the kernel. :type kernel_size: Quantity scalar :returns: Factor to normalize the kernel to unit width. :rtype: Quantity scalar """ raise NotImplementedError()
[docs] def boundary_enclosing_at_least(self, fraction): """ Calculates the boundary :math:`b` so that the integral from :math:`-b` to :math:`b` encloses at least a certain fraction of the integral over the complete kernel. :param float fraction: Fraction of the whole area which at least has to be enclosed. :returns: boundary :rtype: Quantity scalar """ raise NotImplementedError()
[docs] def is_symmetric(self): """ Should return `True` if the kernel is symmetric. """ return False
[docs] def summed_dist_matrix(self, vectors, presorted=False): """ Calculates the sum of all element pair distances for each pair of vectors. If :math:`(a_1, \\dots, a_n)` and :math:`(b_1, \\dots, b_m)` are the :math:`u`-th and :math:`v`-th vector from `vectors` and :math:`K` the kernel, the resulting entry in the 2D array will be :math:`D_{uv} = \\sum_{i=1}^{n} \\sum_{j=1}^{m} K(a_i - b_j)`. :param sequence vectors: A sequence of Quantity 1D to calculate the summed distances for each pair. The required units depend on the kernel. Usually it will be the inverse unit of the kernel size. :param bool presorted: Some optimized specializations of this function may need sorted vectors. Set `presorted` to `True` if you know that the passed vectors are already sorted to skip the sorting and thus increase performance. :rtype: Quantity 2D """ D = sp.empty((len(vectors), len(vectors))) if len(vectors) > 0: might_have_units = self(vectors[0]) if hasattr(might_have_units, 'units'): D = D * might_have_units.units else: D = D * pq.dimensionless for i, j in sp.ndindex(len(vectors), len(vectors)): D[i, j] = sp.sum(self( (vectors[i] - sp.atleast_2d(vectors[j]).T).flatten())) return D
[docs]class KernelFromFunction(Kernel): """ Creates a kernel form a function. Please note, that not all methods for such a kernel are implemented. """ def __init__(self, kernel_func, kernel_size): Kernel.__init__(self, kernel_size, normalize=False) self._evaluate = kernel_func
[docs] def is_symmetric(self): return False
[docs]def as_kernel_of_size(obj, kernel_size): """ Returns a kernel of desired size. :param obj: Either an existing kernel or a kernel function. A kernel function takes two arguments. First a `Quantity 1D` of evaluation time points and second a kernel size. :type obj: Kernel or func :param kernel_size: Desired size of the kernel. :type kernel_size: Quantity 1D :returns: A :class:`Kernel` with the desired kernel size. If `obj` is already a :class:`Kernel` instance, a shallow copy of this instance with changed kernel size will be returned. If `obj` is a function it will be wrapped in a :class:`Kernel` instance. :rtype: :class:`Kernel` """ if isinstance(obj, Kernel): obj = copy.copy(obj) obj.kernel_size = kernel_size else: obj = KernelFromFunction(obj, kernel_size) return obj
[docs]class SymmetricKernel(Kernel): """ Base class for symmetric kernels. """ def __init__(self, kernel_size, normalize): """ :param kernel_size: Parameter controlling the kernel size. :type kernel_size: Quantity 1D :param bool normalize: Whether to normalize the kernel to unit area. """ Kernel.__init__(self, kernel_size, normalize)
[docs] def is_symmetric(self): return True
[docs] def summed_dist_matrix(self, vectors, presorted=False): D = sp.empty((len(vectors), len(vectors))) if len(vectors) > 0: might_have_units = self(vectors[0]) if hasattr(might_have_units, 'units'): D = D * might_have_units.units for i in xrange(len(vectors)): for j in xrange(i, len(vectors)): D[i, j] = D[j, i] = sp.sum(self( (vectors[i] - sp.atleast_2d(vectors[j]).T).flatten())) return D
[docs]class CausalDecayingExpKernel(Kernel): r""" Unnormalized: :math:`K(t) = \exp(-\frac{t}{\tau}) \Theta(t)` with :math:`\Theta(t) = \left\{\begin{array}{ll}0, & x < 0\\ 1, & x \geq 0\end{array}\right.` and kernel size :math:`\tau`. Normalized to unit area: :math:`K'(t) = \frac{1}{\tau} K(t)` """ @staticmethod
[docs] def evaluate(t, kernel_size): return sp.piecewise( t, [t < 0, t >= 0], [ lambda t: 0, lambda t: sp.exp( (-t * pq.dimensionless / kernel_size).simplified)])
def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size)
[docs] def normalization_factor(self, kernel_size): return 1.0 / kernel_size
def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize)
[docs] def boundary_enclosing_at_least(self, fraction): return -self.kernel_size * sp.log(1.0 - fraction)
[docs]class GaussianKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \exp(-\frac{t^2}{2 \sigma^2})` with kernel size :math:`\sigma` (corresponds to the standard deviation of a Gaussian distribution). Normalized to unit area: :math:`K'(t) = \frac{1}{\sigma \sqrt{2 \pi}} K(t)` """ @staticmethod
[docs] def evaluate(t, kernel_size): return sp.exp( -0.5 * (t * pq.dimensionless / kernel_size).simplified ** 2)
def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size)
[docs] def normalization_factor(self, kernel_size): return 1.0 / (sp.sqrt(2.0 * sp.pi) * kernel_size)
def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize)
[docs] def boundary_enclosing_at_least(self, fraction): return self.kernel_size * sp.sqrt(2.0) * \ scipy.special.erfinv(fraction + scipy.special.erf(0.0))
[docs]class LaplacianKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \exp(-|\frac{t}{\tau}|)` with kernel size :math:`\tau`. Normalized to unit area: :math:`K'(t) = \frac{1}{2 \tau} K(t)` """ @staticmethod
[docs] def evaluate(t, kernel_size): return sp.exp( -(sp.absolute(t) * pq.dimensionless / kernel_size).simplified)
def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size)
[docs] def normalization_factor(self, kernel_size): return 0.5 / kernel_size
def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize)
[docs] def boundary_enclosing_at_least(self, fraction): return -self.kernel_size * sp.log(1.0 - fraction)
[docs] def summed_dist_matrix(self, vectors, presorted=False): # This implementation is based on # # Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van # Rossum distances. Network: Computation in Neural Systems, 23(1-2), # 48-58. # # Note that the cited paper contains some errors: In formula (9) the # left side of the equation should be divided by two and in the last # sum in this equation it should say `j|v_i >= u_i` instead of # `j|v_i > u_i`. Also, in equation (11) it should say `j|u_i >= v_i` # instead of `j|u_i > v_i`. # # Given N vectors with n entries on average the run-time complexity is # O(N^2 * n). O(N^2 + N * n) memory will be needed. if len(vectors) <= 0: return sp.zeros((0, 0)) if not presorted: vectors = [v.copy() for v in vectors] for v in vectors: v.sort() sizes = sp.asarray([v.size for v in vectors]) values = sp.empty((len(vectors), max(1, sizes.max()))) values.fill(sp.nan) for i, v in enumerate(vectors): if v.size > 0: values[i, :v.size] = \ (v / self.kernel_size * pq.dimensionless).simplified exp_diffs = sp.exp(values[:, :-1] - values[:, 1:]) markage = sp.zeros(values.shape) for u in xrange(len(vectors)): markage[u, 0] = 0 for i in xrange(sizes[u] - 1): markage[u, i + 1] = (markage[u, i] + 1.0) * exp_diffs[u, i] # Same vector terms D = sp.empty((len(vectors), len(vectors))) D[sp.diag_indices_from(D)] = sizes + 2.0 * sp.sum(markage, axis=1) # Cross vector terms for u in xrange(D.shape[0]): all_ks = sp.searchsorted(values[u], values, 'left') - 1 for v in xrange(u): js = sp.searchsorted(values[v], values[u], 'right') - 1 ks = all_ks[v] slice_j = sp.s_[sp.searchsorted(js, 0):sizes[u]] slice_k = sp.s_[sp.searchsorted(ks, 0):sizes[v]] D[u, v] = sp.sum( sp.exp(values[v][js[slice_j]] - values[u][slice_j]) * (1.0 + markage[v][js[slice_j]])) D[u, v] += sp.sum( sp.exp(values[u][ks[slice_k]] - values[v][slice_k]) * (1.0 + markage[u][ks[slice_k]])) D[v, u] = D[u, v] if self.normalize: normalization = self.normalization_factor(self.kernel_size) else: normalization = 1.0 return normalization * D
[docs]class RectangularKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \left\{\begin{array}{ll}1, & |t| < \tau \\ 0, & |t| \geq \tau\end{array} \right.` with kernel size :math:`\tau` corresponding to the half width. Normalized to unit area: :math:`K'(t) = \frac{1}{2 \tau} K(t)` """ @staticmethod
[docs] def evaluate(t, half_width): return (sp.absolute(t) < half_width)
def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size)
[docs] def normalization_factor(self, half_width): return 0.5 / half_width
def __init__(self, half_width=1.0 * pq.s, normalize=True): Kernel.__init__(self, half_width, normalize)
[docs] def boundary_enclosing_at_least(self, fraction): return self.kernel_size
[docs]class TriangularKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \left\{ \begin{array}{ll}1 - \frac{|t|}{\tau}, & |t| < \tau \\ 0, & |t| \geq \tau \end{array} \right.` with kernel size :math:`\tau` corresponding to the half width. Normalized to unit area: :math:`K'(t) = \frac{1}{\tau} K(t)` """ @staticmethod
[docs] def evaluate(t, half_width): return sp.maximum( 0.0, (1.0 - sp.absolute(t.rescale(half_width.units)) * pq.dimensionless / half_width).magnitude)
def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size)
[docs] def normalization_factor(self, half_width): return 1.0 / half_width
def __init__(self, half_width=1.0 * pq.s, normalize=True): Kernel.__init__(self, half_width, normalize)
[docs] def boundary_enclosing_at_least(self, fraction): return self.kernel_size
[docs]def discretize_kernel( kernel, sampling_rate, area_fraction=default_kernel_area_fraction, num_bins=None, ensure_unit_area=False): """ Discretizes a kernel. :param kernel: The kernel or kernel function. If a kernel function is used it should take exactly one 1-D array as argument. :type kernel: :class:`Kernel` or function :param float area_fraction: Fraction between 0 and 1 (exclusive) of the integral of the kernel which will be at least covered by the discretization. Will be ignored if `num_bins` is not `None`. If `area_fraction` is used, the kernel has to provide a method :meth:`boundary_enclosing_at_least` (see :meth:`.Kernel.boundary_enclosing_at_least`). :param sampling_rate: Sampling rate for the discretization. The unit will typically be a frequency unit. :type sampling_rate: Quantity scalar :param int num_bins: Number of bins to use for the discretization. :param bool ensure_unit_area: If `True`, the area of the discretized kernel will be normalized to 1.0. :rtype: Quantity 1D """ t_step = 1.0 / sampling_rate if num_bins is not None: start = -num_bins // 2 stop = num_bins // 2 elif area_fraction is not None: boundary = kernel.boundary_enclosing_at_least(area_fraction) if hasattr(boundary, 'rescale'): boundary = boundary.rescale(t_step.units) start = sp.ceil(-boundary / t_step) stop = sp.floor(boundary / t_step) + 1 else: raise ValueError( "One of area_fraction and num_bins must not be None.") k = kernel(sp.arange(start, stop) * t_step) if ensure_unit_area: k /= sp.sum(k) * t_step return k
[docs]def smooth( binned, kernel, sampling_rate, mode='same', **kernel_discretization_params): """ Smoothes a binned representation (e.g. of a spike train) by convolving with a kernel. :param binned: Bin array to smooth. :type binned: 1-D array :param kernel: The kernel instance to convolve with. :type kernel: :class:`Kernel` :param sampling_rate: The sampling rate which will be used to discretize the kernel. It should be equal to the sampling rate used to obtain `binned`. The unit will typically be a frequency unit. :type sampling_rate: Quantity scalar :param mode: * 'same': The default which returns an array of the same size as `binned` * 'full': Returns an array with a bin for each shift where `binned` and the discretized kernel overlap by at least one bin. * 'valid': Returns only the discretization bins where the discretized kernel and `binned` completely overlap. See also `numpy.convolve <http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html>`_. :type mode: {'same', 'full', 'valid'} :param dict kernel_discretization_params: Additional discretization arguments which will be passed to :func:`.discretize_kernel`. :returns: The smoothed representation of `binned`. :rtype: Quantity 1D """ k = discretize_kernel( kernel, sampling_rate=sampling_rate, **kernel_discretization_params) return scipy.signal.convolve(binned, k, mode) * k.units
[docs]def st_convolve( train, kernel, sampling_rate, mode='same', binning_params={}, kernel_discretization_params={}): """ Convolves a :class:`neo.core.SpikeTrain` with a kernel. :param train: Spike train to convolve. :type train: :class:`neo.core.SpikeTrain` :param kernel: The kernel instance to convolve with. :type kernel: :class:`Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train. The unit will typically be a frequency unit. :type sampling_rate: Quantity scalar :param mode: * 'same': The default which returns an array covering the whole duration of the spike train `train`. * 'full': Returns an array with additional discretization bins in the beginning and end so that for each spike the whole discretized kernel is included. * 'valid': Returns only the discretization bins where the discretized kernel and spike train completely overlap. See also :func:`scipy.signal.convolve`. :type mode: {'same', 'full', 'valid'} :param dict binning_params: Additional discretization arguments which will be passed to :func:`.tools.bin_spike_trains`. :param dict kernel_discretization_params: Additional discretization arguments which will be passed to :func:`.discretize_kernel`. :returns: The convolved spike train, the boundaries of the discretization bins :rtype: (Quantity 1D, Quantity 1D with the inverse units of `sampling_rate`) """ binned, bins = tools.bin_spike_trains( {0: [train]}, sampling_rate, **binning_params) binned = binned[0][0] #sampling_rate = binned.size / (bins[-1] - bins[0]) result = smooth( binned, kernel, sampling_rate, mode, **kernel_discretization_params) assert (result.size - binned.size) % 2 == 0 num_additional_bins = (result.size - binned.size) // 2 bins = sp.linspace( bins[0] - num_additional_bins / sampling_rate, bins[-1] + num_additional_bins / sampling_rate, result.size + 1) return result, bins
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PKpBDD1ZZ6spykeutils-0.4.1/_modules/spykeutils/stationarity.html spykeutils.stationarity — spykeutils 0.4.1 documentation

Source code for spykeutils.stationarity

import scipy as sp
import quantities as pq

from progress_indicator import ProgressIndicator
from . import SpykeException


[docs]def spike_amplitude_histogram(trains, num_bins, uniform_y_scale=True, unit=pq.uV, progress=None): """ Return a spike amplitude histogram. The resulting is useful to assess the drift in spike amplitude over a longer recording. It shows histograms (one for each ``trains`` entry, e.g. segment) of maximum and minimum spike amplitudes. :param list trains: A list of lists of :class:`neo.core.SpikeTrain` objects. Each entry of the outer list will be one point on the x-axis (they could correspond to segments), all amplitude occurences of spikes contained in the inner list will be added up. :param int num_bins: Number of bins for the histograms. :param bool uniform_y_scale: If True, the histogram for each channel will use the same bins. Otherwise, the minimum bin range is computed separately for each channel. :param Quantity unit: Unit of Y-Axis. :param progress: Set this parameter to report progress. :type progress: :class:`.progress_indicator.ProgressIndicator` :return: A tuple with three values: * A three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of ``trains`` (e.g. segments) and the third dimension to channels. * A list of the minimum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true). * A list of the maximum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true). :rtype: (ndarray, list, list) """ if not progress: progress = ProgressIndicator() num_channels = 1 for t in trains: if not t: continue num_channels = t[0].waveforms.shape[2] break progress.set_ticks(2*len(trains)) progress.set_status('Calculating Spike Amplitude Histogram') # Find maximum and minimum amplitudes on all channels up = [0] * num_channels down = [0] * num_channels for t in trains: for s in t: if s.waveforms is None: continue if s.waveforms.shape[2] != num_channels: raise SpykeException('All spikes need to have the same ' + 'numer of channels for Spike Amplitude Histogram!') a = sp.asarray(s.waveforms.rescale(unit)) u = a.max(1) d = a.min(1) for c in xrange(num_channels): up[c] = max(up[c], sp.stats.mstats.mquantiles( u[:,c], [0.999])[0]) down[c] = min(down[c], sp.stats.mstats.mquantiles( d[:,c], [0.001])[0]) progress.step() if uniform_y_scale: up = [max(up)] * num_channels down = [min(down)] * num_channels # Create histogram bins = [sp.linspace(down[c],up[c], num_bins+1) for c in xrange(num_channels)] hist = sp.zeros((num_bins, len(trains), num_channels)) for i, t in enumerate(trains): for s in t: if s.waveforms is None: continue a = sp.asarray(s.waveforms.rescale(unit)) upper = a.max(1) lower = a.min(1) for c in xrange(num_channels): hist[:,i,c] += sp.histogram(upper[:,c], bins[c])[0] hist[:,i,c] += sp.histogram(lower[:,c], bins[c])[0] progress.step() return hist, down, up
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PKpBDD9'tii>spykeutils-0.4.1/_modules/spykeutils/plugin/data_provider.html spykeutils.plugin.data_provider — spykeutils 0.4.1 documentation

Source code for spykeutils.plugin.data_provider

import neo

[docs]class DataProvider(object): """ Defines all methods that should be implemented by a selection/data provider class. A `DataProvider` encapsulates access to a selection of data. It can be used by plugins to acesss data currently selected in the GUI or in saved selections. It also contains an attribute `progress`, a :class:`spykeutils.progress_indicator.ProgressIndicator` that can be used to report the progress of an operation (and is used by methods of this class if they can lead to processing times of half a second or more). This class serves as an abstract base class and should not be instantiated.""" _factories = {} no_unit = neo.Unit(name='No Unit') no_segment = neo.Segment(name='No segment') no_channel = neo.RecordingChannel(name='No recording channel') no_channelgroup = neo.RecordingChannelGroup(name='No recording channel group') no_unit.annotate(unique_id=-1) no_segment.annotate(unique_id=-1) no_channel.annotate(unique_id=-1) no_channelgroup.annotate(unique_id=-1) def __init__(self, name, progress): self.name = name self.progress = progress def _invert_indices(self, dictionary): """ Invert the indices of a dictionary of dictionaries. """ dict_type = type(dictionary) ret = dict_type() for i1 in dictionary: for i2 in dictionary[i1]: if not i2 in ret: ret[i2] = dict_type() ret[i2][i1] = dictionary[i1][i2] return ret
[docs] def blocks(self): """ Return a list of selected Block objects. The returned objects will contain all regular references, not just to selected objects. """ return []
[docs] def segments(self): """ Return a list of selected Segment objects. The returned objects will contain all regular references, not just to selected objects. """ return []
[docs] def recording_channel_groups(self): """ Return a list of selected RecordingChannelGroup objects. The returned objects will contain all regular references, not just to selected objects. """ return []
[docs] def recording_channels(self): """ Return a list of selected RecordingChannel objects. The returned objects will contain all regular references, not just to selected objects. """ return []
[docs] def units(self): """ Return a list of selected Unit objects. The returned objects will contain all regular references, not just to selected objects. """ return []
[docs] def selection_blocks(self): """ Return a list of selected blocks. The returned blocks will contain references to all other selected elements further down in the object hierarchy, but no references to elements which are not selected. The returned hierarchy is a copy, so changes made to it will not persist. The main purpose of this function is to provide an object hierarchy that can be saved to a neo file. It is not recommended to use it for data processing, the respective functions that return objects lower in the hierarchy are better suited for that purpose. """ return []
[docs] def spike_trains(self): """ Return a list of :class:`neo.core.SpikeTrain` objects. """ return []
[docs] def spike_trains_by_unit(self): """ Return a dictionary (indexed by Unit) of lists of :class:`neo.core.SpikeTrain` objects. If spike trains not attached to a Unit are selected, their dicionary key will be ``DataProvider.no_unit``. """ return {}
[docs] def spike_trains_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.SpikeTrain` objects. If spike trains not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {}
[docs] def spike_trains_by_unit_and_segment(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.SpikeTrain` objects. If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return {}
[docs] def spike_trains_by_segment_and_unit(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.SpikeTrain` objects. If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return self._invert_indices(self.spike_trains_by_unit_and_segment())
[docs] def spikes(self): """ Return a list of :class:`neo.core.Spike` objects. """ return []
[docs] def spikes_by_unit(self): """ Return a dictionary (indexed by Unit) of lists of :class:`neo.core.Spike` objects. If spikes not attached to a Unit are selected, their dicionary key will be ``DataProvider.no_unit``. """ return {}
[docs] def spikes_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.Spike` objects. If spikes not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {}
[docs] def spikes_by_unit_and_segment(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.Spike` lists. If there are multiple spikes in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spikes not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return {}
[docs] def spikes_by_segment_and_unit(self): """ Return a dictionary (indexed by Segment) of dictionaries (indexed by Unit) of lists of :class:`neo.core.Spike` lists. If spikes not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return self._invert_indices(self.spikes_by_unit_and_segment())
[docs] def events(self, include_array_events = True): """ Return a dictionary (indexed by Segment) of lists of Event objects. :param bool include_array_events: Determines if EventArray objects should be converted to Event objects and included in the returned list. """ return {}
[docs] def labeled_events(self, label, include_array_events = True): """ Return a dictionary (indexed by Segment) of lists of Event objects with the given label. :param str label: The name of the Event objects to be returnded :param bool include_array_events: Determines if EventArray objects should be converted to Event objects and included in the returned list. """ return []
[docs] def event_arrays(self): """ Return a dictionary (indexed by Segment) of lists of EventArray objects. """ return {}
[docs] def epochs(self, include_array_epochs = True): """ Return a dictionary (indexed by Segment) of lists of Epoch objects. :param bool include_array_epochs: Determines if EpochArray objects should be converted to Epoch objects and included in the returned list. """ return {}
[docs] def labeled_epochs(self, label, include_array_epochs = True): """ Return a dictionary (indexed by Segment) of lists of Epoch objects with the given label. :param str label: The name of the Epoch objects to be returnded :param bool include_array_epochs: Determines if EpochArray objects should be converted to Epoch objects and included in the returned list. """ return []
[docs] def epoch_arrays(self): """ Return a dictionary (indexed by Segment) of lists of EpochArray objects. """ return {}
[docs] def analog_signals(self, conversion_mode=1): """ Return a list of :class:`neo.core.AnalogSignal` objects. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return []
[docs] def analog_signals_by_segment(self, conversion_mode=1): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.AnalogSignal` objects. If analog signals not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {}
[docs] def analog_signals_by_channel(self, conversion_mode=1): """ Return a dictionary (indexed by RecordingChannel) of lists of :class:`neo.core.AnalogSignal` objects. If analog signals not attached to a RecordingChannel are selected, their dictionary key will be ``DataProvider.no_channel``. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {}
[docs] def analog_signals_by_channel_and_segment(self, conversion_mode=1): """ Return a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignal` lists. If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {}
[docs] def analog_signals_by_segment_and_channel(self, conversion_mode=1): """ Return a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of :class:`neo.core.AnalogSignal` lists. If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return self._invert_indices( self.analog_signals_by_channel_and_segment(conversion_mode))
[docs] def analog_signal_arrays(self): """ Return a list of :class:`neo.core.AnalogSignalArray` objects. """ return []
[docs] def analog_signal_arrays_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.AnalogSignalArray` objects. If analog signals arrays not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {}
[docs] def analog_signal_arrays_by_channelgroup(self): """ Return a dictionary (indexed by RecordingChannelGroup) of lists of :class:`neo.core.AnalogSignalArray` objects. If analog signals arrays not attached to a RecordingChannel are selected, their dictionary key will be ``DataProvider.no_channelgroup``. """ return {}
[docs] def analog_signal_arrays_by_channelgroup_and_segment(self): """ Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignalArray` objects. If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channelgroup``, respectively. """ return {}
[docs] def analog_signal_arrays_by_segment_and_channelgroup(self): """ Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignalArray` objects. If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channelgroup``, respectively. """ return self._invert_indices( self.analog_signal_arrays_by_channelgroup_and_segment())
[docs] def refresh_view(self): """ Refresh associated views of the data. Use this method if when you change the neo hierarchy on which the selection is based (e.g. adding or removing objects). It will ensure that all current views on the data are updated, for example in Spyke Viewer. """ pass
[docs] def data_dict(self): """ Return a dictionary with all information to serialize the object. """ return {}
@classmethod
[docs] def from_data(cls, data, progress=None): """ Create a new `DataProvider` object from a dictionary. This method is mostly for internal use. The respective type of `DataProvider` (e.g. :class:`spykeviewer.plugin_framework.data_provider_neo.DataProviderNeo` has to be imported in the environment where this function is called. :param dict data: A dictionary containing data from a `DataProvider` object, as returned by :func:`data_dict`. :param ProgressIndicator progress: The object where loading progress will be indicated. """ if progress: return cls._factories[data['type']](data, progress) return cls._factories[data['type']](data)
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PKpBDD[~P~~@spykeutils-0.4.1/_modules/spykeutils/plugin/analysis_plugin.html spykeutils.plugin.analysis_plugin — spykeutils 0.4.1 documentation

Source code for spykeutils.plugin.analysis_plugin

import hashlib
import json
import os
import tables
import time

import gui_data


class HashEntry(tables.IsDescription):
    hash = tables.StringCol(32)
    filename = tables.StringCol(992)  # 1024-32 -> long filenames are possible


[docs]class AnalysisPlugin(gui_data.DataSet): """ Base class for Analysis plugins. Inherit this class to create a plugin. The two most important methods are :func:`get_name` and :func:`start`. Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results. The GUI configuration uses :mod:`guidata`. Because `AnalysisPlugin` inherits from `DataSet`, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console:: from spykeutils.plugin import analysis_plugin, gui_data class SamplePlugin(analysis_plugin.AnalysisPlugin): some_time = gui_data.FloatItem('Some time', default=2.0, unit='ms') print_more = gui_data.BoolItem('Print additional info', default=True) def start(self, current, selections): print 'The selected time is', self.some_time, 'milliseconds.' if self.print_more: print 'This is important additional information!' The class attribute ``data_dir`` contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory. """ data_dir = '' def __init__(self): super(AnalysisPlugin, self).__init__()
[docs] def get_name(self): """ Return the name of an analysis. Override to specify analysis name. :returns: The name of the plugin. :rtype: str """ return 'Prototype Plugin'
def get_title(self): # Override guidata.DataSet.get_title() return self.get_name() def get_comment(self): # Override guidata.DataSet.get_comment() ret = None if self.__doc__: string = self.__doc__ if not isinstance(string, basestring): string = unicode(string) if not isinstance(string, unicode): string = unicode(string, 'utf-8') doc_lines = string.splitlines() # Remove empty lines at the begining of comment while doc_lines and not doc_lines[0].strip(): del doc_lines[0] if doc_lines: ret = "\n".join([x.strip() for x in doc_lines]) return ret
[docs] def start(self, current, selections): """ Entry point for processing. Override with analysis code. :param current: This data provider is used if the analysis should be performed on the data currently selected in the GUI. :type current: :class:`spykeviewer.plugin_framework.data_provider.DataProvider` :param list selections: This parameter contains all saved selections. It is used if an analysis needs multiple data sets. """ pass
[docs] def configure(self): """ Configure the analysis. Override if a different or additional configuration apart from guidata is needed. """ if self._items: return self.edit()
[docs] def get_parameters(self): """ Return a dictionary of the configuration that can be read with :func:`deserialize_parameters`. Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation). :returns: A dictionary of all configuration parameters. :rtype: dict """ if not hasattr(self, '_items'): return {} ret = {} for i in self._items: v = i.get_value(self) if isinstance(v, str): ret[i._name] = unicode(v) else: ret[i._name] = v return ret
[docs] def set_parameters(self, parameters): """ Load configuration from a dictionary that has been created by :func:`serialize_parameters`. Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized. :param dict parameters: A dictionary of all configuration parameters. """ for n, v in parameters.iteritems(): if hasattr(self, '_' + n): setattr(self, '_' + n, v)
def _get_hash(self, selections, params, use_guiparams): """ Return hash and the three strings used for it (guidata,selections,params) """ if use_guiparams: guidata_string = repr(sorted(self.get_parameters().items())) else: guidata_string = '' selection_string = json.dumps([s.data_dict() for s in selections]) if params: param_string = repr(sorted(params.items())) else: param_string = '' md5 = hashlib.md5() hash_string = guidata_string + selection_string + param_string md5.update(hash_string) return md5.hexdigest(), guidata_string, selection_string, param_string
[docs] def save(self, name, selections, params=None, save_guiparams=True): """ Return a HDF5 file object with parameters already stored. Save analysis results to this file. :param str name: The name of the results to save. A folder with this name will be used (and created if necessary) to store the analysis result files. :param sequence selections: A list of :class:`DataProvider` objects that are relevant for the analysis results. :param dict params: A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types. :param bool save_guiparams: Determines if the guidata parameters of the class should be saved in the file. :returns: An open PyTables file object ready to be used to store data. Afterwards, the file has to be closed by calling the :func:`tables.File.close` method. :rtype: :class:`tables.File` """ if not selections: selections = [] if not os.path.exists(os.path.join(self.data_dir, name)): os.makedirs(os.path.join(self.data_dir, name)) if params is None: params = {} # Use unicode parameters for n, v in params.iteritems(): if isinstance(v, str): params[n] = unicode(v) # Create parameter hash hash_, guidata_string, selection_string, param_string = \ self._get_hash(selections, params, save_guiparams) # File name is current time stamp time_stamp = time.strftime("%Y%m%d-%H%M%S") file_name_base = os.path.join(self.data_dir, name, time_stamp) file_name = file_name_base # Make sure not to overwrite another file i = 2 while os.path.exists(file_name): file_name = file_name_base + '_%d' % i i += 1 file_name += '.h5' self._add_hash_lookup_entry(name, hash_, file_name) h5 = tables.openFile(file_name, 'w') # Save guidata parameters paramgroup = h5.createGroup('/', 'guiparams') if save_guiparams: guiparams = self.get_parameters() for p, v in guiparams.iteritems(): t = type(v) if t == int or t == float: h5.setNodeAttr(paramgroup, p, v) else: h5.setNodeAttr(paramgroup, p, json.dumps(v)) # Save selections the provided by plugin h5.setNodeAttr('/', 'selections', selection_string) # Save additional parameters provided by plugin paramgroup = h5.createGroup('/', 'userparams') for p, v in params.iteritems(): t = type(v) if t == int or t == float: h5.setNodeAttr(paramgroup, p, v) else: h5.setNodeAttr(paramgroup, p, json.dumps(v)) # Save hash and current time h5.setNodeAttr('/', '_hash', hash_) h5.setNodeAttr('/', 'time', time.time()) return h5
[docs] def load(self, name, selections, params=None, consider_guiparams=True): """ Return the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by :func:`save`. :param str name: The name of the results to load. :param sequence selections: A list of :class:`DataProvider` objects that are relevant for the analysis results. :param dict params: A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types. :param bool consider_guiparams: Determines if the guidata parameters of the class should be considered if they exist in the HDF5 file. This should be set to False if :func:`save` is used with ``save_guiparams`` set to ``False``. :returns: An open PyTables file object ready to be used to read data. Afterwards, the file has to be closed by calling the :func:`tables.File.close` method. If no appropriate file exists, None is returned. :rtype: :class:`tables.File` """ if not selections: selections = [] if not os.path.exists(os.path.join(self.data_dir, name)): return None if params is None: params = {} # Use unicode parameters for n, v in params.iteritems(): if isinstance(v, str): params[n] = unicode(v) hash_, guidata_string, selection_string, param_string =\ self._get_hash(selections, params, consider_guiparams) # Loop through files and find the most recent match file_names = self._get_hash_file_names(name, hash_) newest = 0.0 best = None for fn in file_names: with tables.openFile(fn, 'r') as h5: file_hash = h5.getNodeAttr('/', '_hash') if hash_ != file_hash: continue # Hash is correct, check guidata parameters gui_params = {} for pname in h5.root.guiparams._v_attrs._f_list('user'): v = h5.getNodeAttr('/guiparams', pname) if isinstance(v, str): gui_params[pname] = json.loads(v) else: gui_params[pname] = v if gui_params: gui_param_string = repr(sorted(gui_params.items())) else: gui_param_string = '' if gui_param_string != guidata_string: continue # Check selections file_selections = h5.getNodeAttr('/', 'selections') if file_selections != selection_string: continue # Check custom parameters file_params = {} for pname in h5.root.userparams._v_attrs._f_list('user'): v = h5.getNodeAttr('/userparams', pname) if isinstance(v, str): file_params[pname] = json.loads(v) else: file_params[pname] = v if file_params: file_param_string = repr(sorted(file_params.items())) else: file_param_string = '' if file_param_string != param_string: continue # Make sure the most recent file is used analysis_time = h5.getNodeAttr('/', 'time') if analysis_time < newest: continue best = fn newest = analysis_time if best: return tables.openFile(best, 'r') return None
@classmethod def _create_hash_lookup_file(cls, name): """ (Re)creates a hash lookup file for a results directory. This file contains all file hashes in the directory so that the correct file for a given parameter set can be found quickly. :param str name: The name of the results. """ name = os.path.join(cls.data_dir, name) hashfile_name = os.path.join(name, 'hash.h5') hash_file = tables.openFile(hashfile_name, mode='w') table = hash_file.createTable('/', 'lookup_table', HashEntry, title='Hash lookup') # Loop through files and write hashes file_names = [os.path.join(name, f) for f in os.listdir(name)] entry = table.row for fn in file_names: if not fn.endswith('.h5') or fn == 'hash.h5': continue try: with tables.openFile(fn, 'r') as h5: file_hash = h5.getNodeAttr('/', '_hash') entry['hash'] = file_hash entry['filename'] = fn entry.append() except: pass # Not a valid data file, no problem hash_file.close() @classmethod def _add_hash_lookup_entry(cls, name, hash_, file_name): """ Add a new entry to the hash lookup file. :param str name: The name of the results. :param str hash_: The hash of the parameters. :param str file_name: The file name of the results. """ hashfile_name = os.path.join(cls.data_dir, name, 'hash.h5') if not os.path.exists(hashfile_name): cls._create_hash_lookup_file(name) hash_file = tables.openFile(hashfile_name, mode='r+') table = hash_file.root.lookup_table # Add entry entry = table.row entry['hash'] = hash_ entry['filename'] = file_name entry.append() hash_file.close() @classmethod def _get_hash_file_names(cls, name, hash_, _recurse=False): """ Return a list of file names for a parameter hash. If no hash lookup file exists, it will be created. If it can not be created, a list HDF5 files in the directory will be returned. :param str name: The name of the results. :param str hash_: The hash of the parameters. :param bool _recurse: Internal guard against infinite recursion. """ dataname = name name = os.path.join(cls.data_dir, name) hashfile_name = os.path.join(name, 'hash.h5') if not os.path.exists(hashfile_name): try: cls._create_hash_lookup_file(name) except: return [os.path.join(name, f) for f in os.listdir(name) if f.endswith('.h5') and not f == 'hash.h5'] hash_file = tables.openFile(hashfile_name, mode='r') table = hash_file.root.lookup_table files = [row['filename'] for row in table.where('hash == "%s"' % hash_)] ret = [] for f in files: if os.path.exists(f): ret.append(f) elif not _recurse: hash_file.close() try: cls._create_hash_lookup_file(name) except: return [os.path.join(name, f) for f in os.listdir(name) if f.endswith('.h5') and not f == 'hash.h5'] return cls._get_hash_file_names(dataname, hash_, True) hash_file.close() return ret
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PKpBDD=gk.spykeutils-0.4.1/apidoc/spykeutils.plugin.html plugin Package — spykeutils 0.4.1 documentation

plugin Package

This package provides support for writing plugins for Spyke Viewer. It belongs to spykeutils so that plugins can be executed in an evironment where the spykeviewer package and its dependencies are not installed (e.g. servers).

spykeutils installs a script named “spykeplugin” that can be used to start plugins directly from the command line, supplying selection and plugin parameter information. It is also the default script that Spyke Viewer uses when starting plugins remotely. If you want to implement your own script for starting plugins remotely, e.g. on a server, you should conform to the interface of this script.

analysis_plugin Module

class AnalysisPlugin[source]

Bases: spykeutils.plugin.gui_data.DataSet

Base class for Analysis plugins. Inherit this class to create a plugin.

The two most important methods are get_name() and start(). Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results.

The GUI configuration uses guidata. Because AnalysisPlugin inherits from DataSet, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console:

from spykeutils.plugin import analysis_plugin, gui_data

class SamplePlugin(analysis_plugin.AnalysisPlugin):
    some_time = gui_data.FloatItem('Some time', default=2.0, unit='ms')
    print_more = gui_data.BoolItem('Print additional info', default=True)

    def start(self, current, selections):
        print 'The selected time is', self.some_time, 'milliseconds.'
        if self.print_more:
            print 'This is important additional information!'

The class attribute data_dir contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory.

configure()[source]

Configure the analysis. Override if a different or additional configuration apart from guidata is needed.

get_name()[source]

Return the name of an analysis. Override to specify analysis name.

Returns:The name of the plugin.
Return type:str
get_parameters()[source]

Return a dictionary of the configuration that can be read with deserialize_parameters(). Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation).

Returns:A dictionary of all configuration parameters.
Return type:dict
load(name, selections, params=None, consider_guiparams=True)[source]

Return the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by save().

Parameters:
  • name (str) – The name of the results to load.
  • selections (sequence) – A list of DataProvider objects that are relevant for the analysis results.
  • params (dict) – A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.
  • consider_guiparams (bool) – Determines if the guidata parameters of the class should be considered if they exist in the HDF5 file. This should be set to False if save() is used with save_guiparams set to False.
Returns:

An open PyTables file object ready to be used to read data. Afterwards, the file has to be closed by calling the tables.File.close() method. If no appropriate file exists, None is returned.

Return type:

tables.File

save(name, selections, params=None, save_guiparams=True)[source]

Return a HDF5 file object with parameters already stored. Save analysis results to this file.

Parameters:
  • name (str) – The name of the results to save. A folder with this name will be used (and created if necessary) to store the analysis result files.
  • selections (sequence) – A list of DataProvider objects that are relevant for the analysis results.
  • params (dict) – A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.
  • save_guiparams (bool) – Determines if the guidata parameters of the class should be saved in the file.
Returns:

An open PyTables file object ready to be used to store data. Afterwards, the file has to be closed by calling the tables.File.close() method.

Return type:

tables.File

set_parameters(parameters)[source]

Load configuration from a dictionary that has been created by serialize_parameters(). Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized.

Parameters:parameters (dict) – A dictionary of all configuration parameters.
start(current, selections)[source]

Entry point for processing. Override with analysis code.

Parameters:
  • current (spykeviewer.plugin_framework.data_provider.DataProvider) – This data provider is used if the analysis should be performed on the data currently selected in the GUI.
  • selections (list) – This parameter contains all saved selections. It is used if an analysis needs multiple data sets.

data_provider Module

class DataProvider(name, progress)[source]

Bases: object

Defines all methods that should be implemented by a selection/data provider class.

A DataProvider encapsulates access to a selection of data. It can be used by plugins to acesss data currently selected in the GUI or in saved selections. It also contains an attribute progress, a spykeutils.progress_indicator.ProgressIndicator that can be used to report the progress of an operation (and is used by methods of this class if they can lead to processing times of half a second or more).

This class serves as an abstract base class and should not be instantiated.

analog_signal_arrays()[source]

Return a list of neo.core.AnalogSignalArray objects.

analog_signal_arrays_by_channelgroup()[source]

Return a dictionary (indexed by RecordingChannelGroup) of lists of neo.core.AnalogSignalArray objects.

If analog signals arrays not attached to a RecordingChannel are selected, their dictionary key will be DataProvider.no_channelgroup.

analog_signal_arrays_by_channelgroup_and_segment()[source]

Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of neo.core.AnalogSignalArray objects.

If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be DataProvider.no_segment or DataProvider.no_channelgroup, respectively.

analog_signal_arrays_by_segment()[source]

Return a dictionary (indexed by Segment) of lists of neo.core.AnalogSignalArray objects.

If analog signals arrays not attached to a Segment are selected, their dictionary key will be DataProvider.no_segment.

analog_signal_arrays_by_segment_and_channelgroup()[source]

Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of neo.core.AnalogSignalArray objects.

If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be DataProvider.no_segment or DataProvider.no_channelgroup, respectively.

analog_signals(conversion_mode=1)[source]

Return a list of neo.core.AnalogSignal objects.

Parameters:conversion_mode (int) –

Determines what signals are returned:

  1. AnalogSignal objects only
  2. AnalogSignal objects extracted from AnalogSignalArrays only
  3. Both AnalogSignal objects and extracted AnalogSignalArrays
analog_signals_by_channel(conversion_mode=1)[source]

Return a dictionary (indexed by RecordingChannel) of lists of neo.core.AnalogSignal objects.

If analog signals not attached to a RecordingChannel are selected, their dictionary key will be DataProvider.no_channel.

Parameters:conversion_mode (int) –

Determines what signals are returned:

  1. AnalogSignal objects only
  2. AnalogSignal objects extracted from AnalogSignalArrays only
  3. Both AnalogSignal objects and extracted AnalogSignalArrays
analog_signals_by_channel_and_segment(conversion_mode=1)[source]

Return a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of neo.core.AnalogSignal lists.

If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be DataProvider.no_segment or DataProvider.no_channel, respectively.

Parameters:conversion_mode (int) –

Determines what signals are returned:

  1. AnalogSignal objects only
  2. AnalogSignal objects extracted from AnalogSignalArrays only
  3. Both AnalogSignal objects and extracted AnalogSignalArrays
analog_signals_by_segment(conversion_mode=1)[source]

Return a dictionary (indexed by Segment) of lists of neo.core.AnalogSignal objects.

If analog signals not attached to a Segment are selected, their dictionary key will be DataProvider.no_segment.

Parameters:conversion_mode (int) –

Determines what signals are returned:

  1. AnalogSignal objects only
  2. AnalogSignal objects extracted from AnalogSignalArrays only
  3. Both AnalogSignal objects and extracted AnalogSignalArrays
analog_signals_by_segment_and_channel(conversion_mode=1)[source]

Return a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of neo.core.AnalogSignal lists.

If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be DataProvider.no_segment or DataProvider.no_channel, respectively.

Parameters:conversion_mode (int) –

Determines what signals are returned:

  1. AnalogSignal objects only
  2. AnalogSignal objects extracted from AnalogSignalArrays only
  3. Both AnalogSignal objects and extracted AnalogSignalArrays
blocks()[source]

Return a list of selected Block objects.

The returned objects will contain all regular references, not just to selected objects.

data_dict()[source]

Return a dictionary with all information to serialize the object.

epoch_arrays()[source]

Return a dictionary (indexed by Segment) of lists of EpochArray objects.

epochs(include_array_epochs=True)[source]

Return a dictionary (indexed by Segment) of lists of Epoch objects.

Parameters:include_array_epochs (bool) – Determines if EpochArray objects should be converted to Epoch objects and included in the returned list.
event_arrays()[source]

Return a dictionary (indexed by Segment) of lists of EventArray objects.

events(include_array_events=True)[source]

Return a dictionary (indexed by Segment) of lists of Event objects.

Parameters:include_array_events (bool) – Determines if EventArray objects should be converted to Event objects and included in the returned list.
classmethod from_data(data, progress=None)[source]

Create a new DataProvider object from a dictionary. This method is mostly for internal use.

The respective type of DataProvider (e.g. spykeviewer.plugin_framework.data_provider_neo.DataProviderNeo has to be imported in the environment where this function is called.

Parameters:
  • data (dict) – A dictionary containing data from a DataProvider object, as returned by data_dict().
  • progress (ProgressIndicator) – The object where loading progress will be indicated.
labeled_epochs(label, include_array_epochs=True)[source]

Return a dictionary (indexed by Segment) of lists of Epoch objects with the given label.

Parameters:
  • label (str) – The name of the Epoch objects to be returnded
  • include_array_epochs (bool) – Determines if EpochArray objects should be converted to Epoch objects and included in the returned list.
labeled_events(label, include_array_events=True)[source]

Return a dictionary (indexed by Segment) of lists of Event objects with the given label.

Parameters:
  • label (str) – The name of the Event objects to be returnded
  • include_array_events (bool) – Determines if EventArray objects should be converted to Event objects and included in the returned list.
recording_channel_groups()[source]

Return a list of selected RecordingChannelGroup objects.

The returned objects will contain all regular references, not just to selected objects.

recording_channels()[source]

Return a list of selected RecordingChannel objects.

The returned objects will contain all regular references, not just to selected objects.

refresh_view()[source]

Refresh associated views of the data.

Use this method if when you change the neo hierarchy on which the selection is based (e.g. adding or removing objects). It will ensure that all current views on the data are updated, for example in Spyke Viewer.

segments()[source]

Return a list of selected Segment objects.

The returned objects will contain all regular references, not just to selected objects.

selection_blocks()[source]

Return a list of selected blocks.

The returned blocks will contain references to all other selected elements further down in the object hierarchy, but no references to elements which are not selected. The returned hierarchy is a copy, so changes made to it will not persist. The main purpose of this function is to provide an object hierarchy that can be saved to a neo file. It is not recommended to use it for data processing, the respective functions that return objects lower in the hierarchy are better suited for that purpose.

spike_trains()[source]

Return a list of neo.core.SpikeTrain objects.

spike_trains_by_segment()[source]

Return a dictionary (indexed by Segment) of lists of neo.core.SpikeTrain objects.

If spike trains not attached to a Segment are selected, their dictionary key will be DataProvider.no_segment.

spike_trains_by_segment_and_unit()[source]

Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of neo.core.SpikeTrain objects.

If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be DataProvider.no_unit or DataProvider.no_segment, respectively.

spike_trains_by_unit()[source]

Return a dictionary (indexed by Unit) of lists of neo.core.SpikeTrain objects.

If spike trains not attached to a Unit are selected, their dicionary key will be DataProvider.no_unit.

spike_trains_by_unit_and_segment()[source]

Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of neo.core.SpikeTrain objects.

If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be DataProvider.no_unit or DataProvider.no_segment, respectively.

spikes()[source]

Return a list of neo.core.Spike objects.

spikes_by_segment()[source]

Return a dictionary (indexed by Segment) of lists of neo.core.Spike objects.

If spikes not attached to a Segment are selected, their dictionary key will be DataProvider.no_segment.

spikes_by_segment_and_unit()[source]

Return a dictionary (indexed by Segment) of dictionaries (indexed by Unit) of lists of neo.core.Spike lists.

If spikes not attached to a Unit or Segment are selected, their dictionary key will be DataProvider.no_unit or DataProvider.no_segment, respectively.

spikes_by_unit()[source]

Return a dictionary (indexed by Unit) of lists of neo.core.Spike objects.

If spikes not attached to a Unit are selected, their dicionary key will be DataProvider.no_unit.

spikes_by_unit_and_segment()[source]

Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of neo.core.Spike lists.

If there are multiple spikes in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spikes not attached to a Unit or Segment are selected, their dictionary key will be DataProvider.no_unit or DataProvider.no_segment, respectively.

units()[source]

Return a list of selected Unit objects.

The returned objects will contain all regular references, not just to selected objects.

gui_data Module

This module gives access to all members of guidata.dataset.dataitems and guidata.dataset.datatypes. If guidata cannot be imported, the module offers suitable dummy objects instead (e.g. for use on a server).

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PKpBDD8'spykeutils-0.4.1/apidoc/spykeutils.html API reference — spykeutils 0.4.1 documentation

API reference

spykeutils package

class SpykeException[source]

Exception thrown when a function in spykeutils encounters a problem that is not covered by standard exceptions.

When using Spyke Viewer, these exceptions will be caught and shown in the GUI, while general exceptions will not be caught (and therefore be visible in the console) for easier debugging.

conversions Module

analog_signal_array_to_analog_signals(signal_array)[source]

Return a list of analog signals for an analog signal array.

If signal_array is attached to a recording channel group with exactly is many channels as there are channels in signal_array, each created signal will be assigned the corresponding channel. If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel.

Note that while the created signals may have references to a segment and channels, the relationships in the other direction are not automatically created (the signals are not attached to the recording channel or segment). Other properties like annotations are not copied or referenced in the created analog signals.

Parameters:signal_array (neo.core.AnalogSignalArray) – An analog signal array from which the neo.core.AnalogSignal objects are constructed.
Returns:A list of analog signals, one for every channel in signal_array.
Return type:list
epoch_array_to_epochs(epoch_array)[source]

Return a list of epochs for an epoch array.

Note that while the created epochs may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created epochs.

Parameters:epoch_array (neo.core.EpochArray) – A period array from which the Epoch objects are constructed.
Returns:A list of events, one for of the events in epoch_array.
Return type:list
event_array_to_events(event_array)[source]

Return a list of events for an event array.

Note that while the created events may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created events.

Parameters:event_array (neo.core.EventArray) – An event array from which the Event objects are constructed.
Returns:A list of events, one for of the events in event_array.
Return type:list
spike_train_to_spikes(spike_train, include_waveforms=True)[source]

Return a list of spikes for a spike train.

Note that while the created spikes have references to the same segment and unit as the spike train, the relationships in the other direction are not automatically created (the spikes are not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spikes.

Parameters:
  • spike_train (neo.core.SpikeTrain) – A spike train from which the neo.core.Spike objects are constructed.
  • include_waveforms (bool) – Determines if the waveforms property is converted to the spike waveforms. If waveforms is None, this parameter has no effect.
Returns:

A list of neo.core.Spike objects, one for every spike in spike_train.

Return type:

list

spikes_to_spike_train(spikes, include_waveforms=True)[source]

Return a spike train for a list of spikes.

All spikes must have an identical left sweep, the same unit and the same segment, otherwise a SpykeException is raised.

Note that while the created spike train has references to the same segment and unit as the spikes, the relationships in the other direction are not automatically created (the spike train is not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spike train.

Parameters:
  • spikes (sequence) – A sequence of neo.core.Spike objects from which the spike train is constructed.
  • include_waveforms (bool) – Determines if the waveforms from the spike objects are used to fill the waveforms property of the resulting spike train. If True, all spikes need a waveform property with the same shape or a SpykeException is raised (or the waveform property needs to be None for all spikes).
Returns:

All elements of spikes as spike train.

Return type:

neo.core.SpikeTrain

correlations Module

correlogram(trains, bin_size, max_lag=array(500.0) * ms, border_correction=True, per_second=True, unit=UnitTime('millisecond', 0.001 * s, 'ms'), progress=None)[source]

Return (cross-)correlograms from a dictionary of spike train lists for different units.

Parameters:
  • trains (dict) – Dictionary of neo.core.SpikeTrain lists.
  • bin_size (Quantity scalar) – Bin size (time).
  • max_lag (Quantity scalar) – Cut off (end time of calculated correlogram).
  • border_correction (bool) – Apply correction for less data at higher timelags. Not perfect for bin_size != 1*``unit``, especially with large max_lag compared to length of spike trains.
  • per_second (bool) – If True, counts returned are per second. Otherwise, counts per spike train are returned.
  • unit (Quantity) – Unit of X-Axis.
  • progress (progress_indicator.ProgressIndicator) – A ProgressIndicator object for the operation.
Returns:

Two values:

  • An ordered dictionary indexed with the indices of trains of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: c[index1][index2] and c[index2][index1].
  • The bins used for the correlogram calculation.

Return type:

dict, Quantity 1D

progress_indicator Module

exception CancelException[source]

Bases: exceptions.Exception

This is raised when a user cancels a progress process. It is used by ProgressIndicator and its descendants.

class ProgressIndicator[source]

Bases: object

Base class for classes indicating progress of a long operation.

This class does not implement any of the methods and can be used as a dummy if no progress indication is needed.

begin(title='')[source]

Signal that the operation starts.

Parameters:title (string) – The name of the whole operation.
done()[source]

Signal that the operation is done.

set_status(new_status)[source]

Set status description.

Parameters:new_status (string) – A description of the current status.
set_ticks(ticks)[source]

Set the required number of ticks before the operation is done.

Parameters:ticks (int) – The number of steps that the operation will take.
step(num_steps=1)[source]

Signal that one or more steps of the operation were completed.

Parameters:num_steps (int) – The number of steps that have been completed.
ignores_cancel(function)[source]

Decorator for functions that should ignore a raised CancelException and just return nothing in this case

rate_estimation Module

aligned_spike_trains(trains, events, copy=True)[source]

Return a list of spike trains aligned to an event (the event will be time 0 on the returned trains).

Parameters:
  • trains (list) – A list of neo.core.SpikeTrain objects.
  • events (dict) – A dictionary of Event objects, indexed by segment. These events will be used to align the spike trains and will be at time 0 for the aligned spike trains.
  • copy (bool) – Determines if aligned copies of the original spike trains will be returned. If not, every spike train needs exactly one corresponding event, otherwise a ValueError will be raised. Otherwise, entries with no event will be ignored.
collapsed_spike_trains(trains)[source]

Return a superposition of a list of spike trains.

Parameters:trains (iterable) – A list of neo.core.SpikeTrain objects
Returns:A spike train object containing all spikes of the given spike trains.
Return type:neo.core.SpikeTrain
optimal_gauss_kernel_size(train, optimize_steps, progress=None)[source]

Return the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with collapsed_spike_trains()) that should be included in a spike density estimation.

Implements the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).

Parameters:
  • train (neo.core.SpikeTrain) – The spike train for which the kernel size should be optimized.
  • optimize_steps (Quantity 1D) – Array of kernel sizes to try (the best of these sizes will be returned).
  • progress (progress_indicator.ProgressIndicator) – Set this parameter to report progress. Will be advanced by len(optimize_steps) steps.
Returns:

Best of the given kernel sizes

Return type:

Quantity scalar

psth(trains, bin_size, rate_correction=True, start=array(0.0) * ms, stop=array(inf) * s)[source]

Return dictionary of peri stimulus time histograms for a dictionary of spike train lists.

Parameters:
  • trains (dict) – A dictionary of lists of neo.core.SpikeTrain objects.
  • bin_size (Quantity scalar) – The desired bin size (as a time quantity).
  • rate_correction (bool) – Determines if a rates (True) or counts (False) are returned.
  • start (Quantity scalar) – The desired time for the start of the first bin. It will be recalculated if there are spike trains which start later than this time.
  • stop (Quantity scalar) – The desired time for the end of the last bin. It will be recalculated if there are spike trains which end earlier than this time.
Returns:

A dictionary (with the same indices as trains) of arrays containing counts (or rates if rate_correction is True) and the bin borders.

Return type:

dict, Quantity 1D

spike_density_estimation(trains, start=array(0.0) * ms, stop=None, kernel=None, kernel_size=array(100.0) * ms, optimize_steps=None, progress=None)[source]

Create a spike density estimation from a dictionary of lists of spike trains.

The spike density estimations give an estimate of the instantaneous rate. The density estimation is evaluated at 1024 equally spaced points covering the range of the input spike trains. Optionally finds optimal kernel size for given data using the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).

Parameters:
  • trains (dict) – A dictionary of neo.core.SpikeTrain lists.
  • start (Quantity scalar) – The desired time for the start of the estimation. It will be recalculated if there are spike trains which start later than this time. This parameter can be negative (which could be useful when aligning on events).
  • stop (Quantity scalar) – The desired time for the end of the estimation. It will be recalculated if there are spike trains which end earlier than this time.
  • kernel (func or signal_processing.Kernel) – The kernel function or instance to use, should accept two parameters: A ndarray of distances and a kernel size. The total area under the kernel function should be 1. Automatic optimization assumes a Gaussian kernel and will likely not produce optimal results for different kernels. Default: Gaussian kernel
  • kernel_size (Quantity scalar) – A uniform kernel size for all spike trains. Only used if optimization of kernel sizes is not used.
  • optimize_steps (Quantity 1D) – An array of time lengths that will be considered in the kernel width optimization. Note that the optimization assumes a Gaussian kernel and will most likely not give the optimal kernel size if another kernel is used. If None, kernel_size will be used.
  • progress (progress_indicator.ProgressIndicator) – Set this parameter to report progress.
Returns:

Three values:

  • A dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as trains.
  • A dictionary of kernel sizes (Quantity scalars). Indexed the same as trains.
  • The used evaluation points.

Return type:

dict, dict, Quantity 1D

signal_processing Module

class CausalDecayingExpKernel(kernel_size=array(1.0) * s, normalize=True)[source]

Bases: spykeutils.signal_processing.Kernel

Unnormalized: K(t) = \exp(-\frac{t}{\tau}) \Theta(t) with \Theta(t) = \left\{\begin{array}{ll}0, & x < 0\\ 1, & x \geq
0\end{array}\right. and kernel size \tau.

Normalized to unit area: K'(t) = \frac{1}{\tau} K(t)

boundary_enclosing_at_least(fraction)[source]
static evaluate(t, kernel_size)[source]
normalization_factor(kernel_size)[source]
class GaussianKernel(kernel_size=array(1.0) * s, normalize=True)[source]

Bases: spykeutils.signal_processing.SymmetricKernel

Unnormalized: K(t) = \exp(-\frac{t^2}{2 \sigma^2}) with kernel size \sigma (corresponds to the standard deviation of a Gaussian distribution).

Normalized to unit area: K'(t) = \frac{1}{\sigma \sqrt{2 \pi}} K(t)

boundary_enclosing_at_least(fraction)[source]
static evaluate(t, kernel_size)[source]
normalization_factor(kernel_size)[source]
class Kernel(kernel_size, normalize)[source]

Bases: object

Base class for kernels.

boundary_enclosing_at_least(fraction)[source]

Calculates the boundary b so that the integral from -b to b encloses at least a certain fraction of the integral over the complete kernel.

Parameters:fraction (float) – Fraction of the whole area which at least has to be enclosed.
Returns:boundary
Return type:Quantity scalar
is_symmetric()[source]

Should return True if the kernel is symmetric.

normalization_factor(kernel_size)[source]

Returns the factor needed to normalize the kernel to unit area.

Parameters:kernel_size (Quantity scalar) – Controls the width of the kernel.
Returns:Factor to normalize the kernel to unit width.
Return type:Quantity scalar
summed_dist_matrix(vectors, presorted=False)[source]

Calculates the sum of all element pair distances for each pair of vectors.

If (a_1, \dots, a_n) and (b_1, \dots, b_m) are the u-th and v-th vector from vectors and K the kernel, the resulting entry in the 2D array will be D_{uv}
= \sum_{i=1}^{n} \sum_{j=1}^{m} K(a_i - b_j).

Parameters:
  • vectors (sequence) – A sequence of Quantity 1D to calculate the summed distances for each pair. The required units depend on the kernel. Usually it will be the inverse unit of the kernel size.
  • presorted (bool) – Some optimized specializations of this function may need sorted vectors. Set presorted to True if you know that the passed vectors are already sorted to skip the sorting and thus increase performance.
Return type:

Quantity 2D

class KernelFromFunction(kernel_func, kernel_size)[source]

Bases: spykeutils.signal_processing.Kernel

Creates a kernel form a function. Please note, that not all methods for such a kernel are implemented.

is_symmetric()[source]
class LaplacianKernel(kernel_size=array(1.0) * s, normalize=True)[source]

Bases: spykeutils.signal_processing.SymmetricKernel

Unnormalized: K(t) = \exp(-|\frac{t}{\tau}|) with kernel size \tau.

Normalized to unit area: K'(t) = \frac{1}{2 \tau} K(t)

boundary_enclosing_at_least(fraction)[source]
static evaluate(t, kernel_size)[source]
normalization_factor(kernel_size)[source]
summed_dist_matrix(vectors, presorted=False)[source]
class RectangularKernel(half_width=array(1.0) * s, normalize=True)[source]

Bases: spykeutils.signal_processing.SymmetricKernel

Unnormalized: K(t) = \left\{\begin{array}{ll}1, & |t| < \tau \\
0, & |t| \geq \tau\end{array} \right. with kernel size \tau corresponding to the half width.

Normalized to unit area: K'(t) = \frac{1}{2 \tau} K(t)

boundary_enclosing_at_least(fraction)[source]
static evaluate(t, half_width)[source]
normalization_factor(half_width)[source]
class SymmetricKernel(kernel_size, normalize)[source]

Bases: spykeutils.signal_processing.Kernel

Base class for symmetric kernels.

is_symmetric()[source]
summed_dist_matrix(vectors, presorted=False)[source]
class TriangularKernel(half_width=array(1.0) * s, normalize=True)[source]

Bases: spykeutils.signal_processing.SymmetricKernel

Unnormalized: K(t) = \left\{ \begin{array}{ll}1
- \frac{|t|}{\tau}, & |t| < \tau \\ 0, & |t| \geq \tau \end{array} \right. with kernel size \tau corresponding to the half width.

Normalized to unit area: K'(t) = \frac{1}{\tau} K(t)

boundary_enclosing_at_least(fraction)[source]
static evaluate(t, half_width)[source]
normalization_factor(half_width)[source]
as_kernel_of_size(obj, kernel_size)[source]

Returns a kernel of desired size.

Parameters:
  • obj (Kernel or func) – Either an existing kernel or a kernel function. A kernel function takes two arguments. First a Quantity 1D of evaluation time points and second a kernel size.
  • kernel_size (Quantity 1D) – Desired size of the kernel.
Returns:

A Kernel with the desired kernel size. If obj is already a Kernel instance, a shallow copy of this instance with changed kernel size will be returned. If obj is a function it will be wrapped in a Kernel instance.

Return type:

Kernel

discretize_kernel(kernel, sampling_rate, area_fraction=0.99999, num_bins=None, ensure_unit_area=False)[source]

Discretizes a kernel.

Parameters:
  • kernel (Kernel or function) – The kernel or kernel function. If a kernel function is used it should take exactly one 1-D array as argument.
  • area_fraction (float) – Fraction between 0 and 1 (exclusive) of the integral of the kernel which will be at least covered by the discretization. Will be ignored if num_bins is not None. If area_fraction is used, the kernel has to provide a method boundary_enclosing_at_least() (see Kernel.boundary_enclosing_at_least()).
  • sampling_rate (Quantity scalar) – Sampling rate for the discretization. The unit will typically be a frequency unit.
  • num_bins (int) – Number of bins to use for the discretization.
  • ensure_unit_area (bool) – If True, the area of the discretized kernel will be normalized to 1.0.
Return type:

Quantity 1D

smooth(binned, kernel, sampling_rate, mode='same', **kernel_discretization_params)[source]

Smoothes a binned representation (e.g. of a spike train) by convolving with a kernel.

Parameters:
  • binned (1-D array) – Bin array to smooth.
  • kernel (Kernel) – The kernel instance to convolve with.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to discretize the kernel. It should be equal to the sampling rate used to obtain binned. The unit will typically be a frequency unit.
  • mode ({‘same’, ‘full’, ‘valid’}) –
    • ‘same’: The default which returns an array of the same size as binned
    • ‘full’: Returns an array with a bin for each shift where binned and the discretized kernel overlap by at least one bin.
    • ‘valid’: Returns only the discretization bins where the discretized kernel and binned completely overlap.

    See also numpy.convolve.

  • kernel_discretization_params (dict) – Additional discretization arguments which will be passed to discretize_kernel().
Returns:

The smoothed representation of binned.

Return type:

Quantity 1D

st_convolve(train, kernel, sampling_rate, mode='same', binning_params={}, kernel_discretization_params={})[source]

Convolves a neo.core.SpikeTrain with a kernel.

Parameters:
  • train (neo.core.SpikeTrain) – Spike train to convolve.
  • kernel (Kernel) – The kernel instance to convolve with.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike train. The unit will typically be a frequency unit.
  • mode ({‘same’, ‘full’, ‘valid’}) –
    • ‘same’: The default which returns an array covering the whole duration of the spike train train.
    • ‘full’: Returns an array with additional discretization bins in the beginning and end so that for each spike the whole discretized kernel is included.
    • ‘valid’: Returns only the discretization bins where the discretized kernel and spike train completely overlap.

    See also scipy.signal.convolve().

  • binning_params (dict) – Additional discretization arguments which will be passed to tools.bin_spike_trains().
  • kernel_discretization_params (dict) – Additional discretization arguments which will be passed to discretize_kernel().
Returns:

The convolved spike train, the boundaries of the discretization bins

Return type:

(Quantity 1D, Quantity 1D with the inverse units of sampling_rate)

spike_train_generation Module

gen_homogeneous_poisson(rate, t_start=array(0.0) * s, t_stop=None, max_spikes=None, refractory=array(0.0) * s)[source]

Generate a homogeneous Poisson spike train. The length is controlled with t_stop and max_spikes. Either one or both of these arguments have to be given.

Parameters:
  • rate (Quantity scalar) – Average firing rate of the spike train to generate as frequency scalar.
  • t_start (Quantity scalar) – Time at which the spike train begins as time scalar. The first actual spike will be greater than this time.
  • t_stop (Quantity scalar) – Time at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by max_spikes and t_stop will be equal to the last generated spike.
  • max_spikes – Maximum number of spikes to generate. Fewer spikes might be generated in case t_stop is also set.
  • refractory (Quantity scalar) – Absolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to rate again.
Returns:

The generated spike train.

Return type:

neo.core.SpikeTrain

gen_inhomogeneous_poisson(modulation, max_rate, t_start=array(0.0) * s, t_stop=None, max_spikes=None, refractory=array(0.0) * s)[source]

Generate an inhomogeneous Poisson spike train. The length is controlled with t_stop and max_spikes. Either one or both of these arguments have to be given.

Parameters:
  • modulation (function) – Function f((t_1, \dots, t_n)):
[\text{t\_start}, \text{t\_end}]^n \rightarrow [0, 1]^n giving the instantaneous firing rates at times (t_1, \dots, t_n) as proportion of max_rate. Thus, a 1-D array will be passed to the function and it should return an array of the same size.
  • max_rate (Quantity scalar) – Maximum firing rate of the spike train to generate as frequency scalar.
  • t_start (Quantity scalar) – Time at which the spike train begins as time scalar. The first actual spike will be greater than this time.
  • t_stop (Quantity scalar) – Time at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by max_spikes and t_stop will be equal to the last generated spike.
  • refractory (Quantity scalar) – Absolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to rate again.
Returns:

The generated spike train.

Return type:

neo.core.SpikeTrain

spike_train_metrics Module

cs_dist(trains, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)[source]

Calculates the Cauchy-Schwarz distance between two spike trains given a smoothing filter.

Let v_a(t) and v_b(t) with t \in \mathcal{T} be the spike trains convolved with some smoothing filter and V(a, b)
= \int_{\mathcal{T}} v_a(t) v_b(t) dt. Then, the Cauchy-Schwarz distance of the spike trains is defined as d_{CS}(a, b) = \arccos \frac{V(a,
b)^2}{V(a, a) V(b, b)}.

The Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure S_S by d_{CS} = \arccos S_S^2

This function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use schreiber_similarity() for a more efficient and precise calculation.

Further information can be found in Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.

Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the distance will be calculated pairwise.
  • smoothing_filter (signal_processing.Kernel) – Smoothing filter to be convolved with the spike trains.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike trains as inverse time scalar.
  • filter_area_fraction (float) – A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).
Returns:

Matrix containing the Cauchy-Schwarz distance of all pairs of spike trains

Return type:

2-D array

event_synchronization(trains, tau=None, kernel=signal_processing.RectangularKernel(1.0, normalize=False), sort=True)[source]

Calculates the event synchronization.

Let d(x|y) be the count of spikes in y which occur shortly before an event in x with a time difference of less than \tau. Moreover, let n_x and n_y be the number of total spikes in the spike trains x and y. The event synchrony is then defined as Q_T = \frac{d(x|y)
+ d(y|x)}{\sqrt{n_x n_y}}.

The time maximum time lag \tau can be determined automatically for each pair of spikes t^x_i and t^y_j by the formula \tau_{ij} = \frac{1}{2} \min\{t^x_{i+1} - t^x_i, t^x_i - t^x_{i-1},
t^y_{j+1} - t^y_j, t^y_j - t^y_{j-1}\}

Further and more detailed information can be found in Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.

Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the van Rossum distance will be calculated pairwise.
  • tau (Quantity scalar) – The maximum time lag for two spikes to be considered coincident or synchronous as time scalar. To have it determined automatically by above formula set it to None.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance.
  • sort (bool) – Spike trains with sorted spike times are be needed for the calculation. You can set sort to False if you know that your spike trains are already sorted to decrease calculation time.
Returns:

Matrix containing the event synchronization for all pairs of spike trains.

Return type:

2-D array

hunter_milton_similarity(trains, tau=array(1.0) * s, kernel=None)[source]

Calculates the Hunter-Milton similarity measure.

If the kernel function is denoted as K(t), a function d(x_k)
= K(x_k - y_{k'}) can be defined with y_{k'} being the closest spike in spike train y to the spike x_k in spike train x. With this the Hunter-Milton similarity measure is S_H =
\frac{1}{2} \left(\frac{1}{n_x} \sum_{k = 1}^{n_x} d(x_k)
+ \frac{1}{n_y} \sum_{k' = 1}^{n_y} d(y_{k'})\right).

This implementation returns 0 if one of the spike trains is empty, but 1 if both are empty.

Further information can be found in

  • Hunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.
  • Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.
Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the Hunter-Milton similarity will be calculated pairwise.
  • tau (Quantity scalar) – The time scale for determining the coincidence of two events as time scalar.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance. If None, a unnormalized Laplacian kernel will be used.
Returns:

Matrix containing the Hunter-Milton similarity for all pairs of spike trains.

Return type:

2-D array

norm_dist(trains, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)[source]

Calculates the norm distance between spike trains given a smoothing filter.

Let v_a(t) and v_b(t) with t \in \mathcal{T} be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as d_{ND}(a, b)
= \sqrt{\int_{\mathcal{T}} (v_a(t) - v_b(t))^2 dt}.

Further information can be found in Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.

Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the distance will be calculated pairwise.
  • smoothing_filter (signal_processing.Kernel) – Smoothing filter to be convolved with the spike trains.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike trains as inverse time scalar.
  • filter_area_fraction (float) – A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).
Returns:

Matrix containing the norm distance of all pairs of spike trains given the smoothing_filter.

Return type:

Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)

schreiber_similarity(trains, kernel, sort=True)[source]

Calculates the Schreiber et al. similarity measure between spike trains given a kernel.

Let v_a(t) and v_b(t) with t \in \mathcal{T} be the spike trains convolved with some smoothing filter and V(a, b)
= \int_{\mathcal{T}} v_a(t) v_b(t) dt. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as S_{S}(a,
b) = \frac{V(a, b)}{\sqrt{V(a, a) V(b, b)}}. It is closely related to the Cauchy-Schwarz distance d_{CS} by S_S = \sqrt{\cos
d_{CS}}.

In opposite to cs_dist() which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter’s autocorrelation. This allows a more accurate and faster calculation.

Further information can be found in:

  • Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.
  • Paiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6
Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the distance will be calculated pairwise.
  • kernel (signal_processing.Kernel) – Kernel to use. It corresponds to a smoothing filter by being the autocorrelation of such a filter.
  • sort (bool) – Spike trains with sorted spike times will be needed for the calculation. You can set sort to False if you know that your spike trains are already sorted to decrease calculation time.
Returns:

Matrix containing the Schreiber et al. similarity measure of all pairs of spike trains.

Return type:

2-D array

st_inner(a, b, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)[source]

Calculates the inner product of spike trains given a smoothing filter.

Let v_a(t) and v_b(t) with t \in \mathcal{T} be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as \int_{\mathcal{T}}
v_a(t)v_b(t) dt.

Further information can be found in Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.

Parameters:
  • a (sequence) – Sequence of neo.core.SpikeTrain objects.
  • b (sequence) – Sequence of neo.core.SpikeTrain objects.
  • smoothing_filter (signal_processing.Kernel) – A smoothing filter to be convolved with the spike trains.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike train as inverse time scalar.
  • filter_area_fraction (float) – A value between 0 and 1 which controls the interval over which the smoothing_filter will be discretized. At least the given fraction of the complete smoothing_filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).
Returns:

Matrix containing the inner product for each pair of spike trains with one spike train from a and the other one from b.

Return type:

Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)

st_norm(train, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)[source]

Calculates the spike train norm given a smoothing filter.

Let v(t) with t \in \mathcal{T} be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as \int_{\mathcal{T}} v(t)^2 dt.

Further information can be found in Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.

Parameters:
  • train (neo.core.SpikeTrain) – Spike train of which to calculate the norm.
  • smoothing_filter (signal_processing.Kernel) – Smoothing filter to be convolved with the spike train.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike train as inverse time scalar.
  • filter_area_fraction (float) – A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).
Returns:

The norm of the spike train given the smoothing_filter.

Return type:

Quantity scalar with units depending on the smoothing filter (usually temporal frequency units)

van_rossum_dist(trains, tau=array(1.0) * s, kernel=None, sort=True)[source]

Calculates the van Rossum distance.

It is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763. This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.

Given N spike trains with n spikes on average the run-time complexity of this function is O(N^2 n^2). An implementation in O(N^2 n) would be possible but has a high constant factor rendering it slower in practical cases.

Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the van Rossum distance will be calculated pairwise.
  • tau (Quantity scalar) – Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if kernel is not None. May also be scipy.inf which will lead to only measuring differences in spike count.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If kernel is None, an unnormalized Laplacian kernel with a size of tau will be used.
  • sort (bool) – Spike trains with sorted spike times might be needed for the calculation. You can set sort to False if you know that your spike trains are already sorted to decrease calculation time.
Returns:

Matrix containing the van Rossum distances for all pairs of spike trains.

Return type:

2-D array

van_rossum_multiunit_dist(units, weighting, tau=array(1.0) * s, kernel=None)[source]

Calculates the van Rossum multi-unit distance.

The single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763. This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.

Given the p- and q-th spike train of a and respectively b let R_{pq} be the squared single-unit distance between these two spike trains. Then the multi-unit distance is \sqrt{\sum_p
(R_{pp} + c \cdot \sum_{q \neq p} R_{pq})} with c being equal to weighting. The weighting parameter controls the interpolation between a labeled line and a summed population coding.

More information can be found in Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.

Given N spike trains in total with n spikes on average the run-time complexity of this function is O(N^2 n^2) and O(N^2
+ Nn^2) memory will be needed.

Parameters:
  • units (dict) – Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a neo.core.SpikeTrain and all units should have the same number of trials.
  • weighting (float) – Controls the interpolation between a labeled line and a summed population coding.
  • tau (Quantity scalar) – Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if kernel is not None. May also be scipy.inf which will lead to only measuring differences in spike count.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If kernel is None, an unnormalized Laplacian kernel with a size of tau will be used.
Returns:

A 2D array with the multi-unit distance for each pair of trials.

Return type:

2D arrary

victor_purpura_dist(trains, q=array(1.0) * Hz, kernel=None, sort=True)[source]

Calculates the Victor-Purpura’s (VP) distance. It is often denoted as D^{\text{spike}}[q].

It is defined as the minimal cost of transforming spike train a into spike train b by using the following operations:

  • Inserting or deleting a spike (cost 1.0).
  • Shifting a spike from t to t' (cost q \cdot |t
- t'|).

A detailed description can be found in Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.

Given the average number of spikes n in a spike train and N spike trains the run-time complexity of this function is O(N^2 n^2) and O(N^2 + n^2) memory will be needed.

Parameters:
  • trains (sequence) – Sequence of neo.core.SpikeTrain objects of which the distance will be calculated pairwise.
  • q (Quantity scalar) – Cost factor for spike shifts as inverse time scalar. If kernel is not None, q will be ignored.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance. If kernel is None, an unnormalized triangular kernel with a half width of 2.0/q will be used.
  • sort (bool) – Spike trains with sorted spike times will be needed for the calculation. You can set sort to False if you know that your spike trains are already sorted to decrease calculation time.
Returns:

Matrix containing the VP distance of all pairs of spike trains.

Return type:

2-D array

victor_purpura_multiunit_dist(units, reassignment_cost, q=array(1.0) * Hz, kernel=None)[source]

Calculates the Victor-Purpura’s (VP) multi-unit distance.

It is defined as the minimal cost of transforming the spike trains a into spike trains b by using the following operations:

  • Inserting or deleting a spike (cost 1.0).
  • Shifting a spike from t to t' (cost q \cdot |t
- t'|).
  • Moving a spike to another spike train (cost reassignment_cost).

A detailed description can be found in Aronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.

Given the average number of spikes N in a spike train and L units with n spike trains each the run-time complexity is O(n^2 LN^{L+1}). The space complexity is O(n^2 + LN^{L+1}).

For calculating the distance between only two units one should use victor_purpura_dist() which is more efficient.

Parameters:
  • units (dict) – Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a neo.core.SpikeTrain and all units should have the same number of trials.
  • reassignment_cost (float) – Cost to reassign a spike from one train to another (sometimes denoted with k). Should be between 0 and 2. For 0 spikes can be reassigned without any cost, for 2 and above it is cheaper to delete and reinsert a spike.
  • q (Quantity scalar) – Cost factor for spike shifts as inverse time scalar. If kernel is not None, q will be ignored.
  • kernel (signal_processing.Kernel) – Kernel to use in the calculation of the distance. If kernel is None, an unnormalized triangular kernel with a half width of 2.0/q will be used.
Returns:

A 2D array with the multi-unit distance for each pair of trials.

Return type:

2D arrary

sorting_quality_assesment Module

Functions for estimating the quality of spike sorting results. These functions estimate false positive and false negative fractions.

calculate_refperiod_fp(num_spikes, refperiod, violations, total_time)[source]

Return the rate of false positives calculated from refractory period calculations for each unit. The equation used is described in (Hill et al. The Journal of Neuroscience. 2011).

Parameters:
  • num_spikes (dict) – Dictionary of total number of spikes, indexed by unit.
  • refperiod (Quantity scalar) – The refractory period (time). If the spike sorting algorithm includes a censored period (a time after a spike during which no new spikes can be found), subtract it from the refractory period before passing it to this function.
  • violations (dict) – Dictionary of total number of violations, indexed the same as num_spikes.
  • total_time (Quantity scalar) – The total time in which violations could have occured.
Returns:

A dictionary of false positive rates indexed by unit. Note that values above 0.5 can not be directly interpreted as a false positive rate! These very high values can e.g. indicate that the generating processes are not independent.

get_refperiod_violations(spike_trains, refperiod, progress=None)[source]

Return the refractory period violations in the given spike trains for the specified refractory period.

Parameters:
Returns:

Two values:

  • The total number of violations.
  • A dictionary (with the same indices as spike_trains) of arrays with violation times (Quantity 1D with the same unit as refperiod) for each spike train.

Return type:

int, dict

overlap_fp_fn(spikes, means=None, covariances=None)[source]

Return dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs sklearn if covariances is not set to 'white'.

This function estimates the pairwise and total false positive and false negative rates for a number of waveform clusters. The results can be interpreted as follows: False positives are the fraction of spikes in a cluster that is estimated to belong to a different cluster (a specific cluster for pairwise results or any other cluster for total results). False negatives are the number spikes from other clusters that are estimated to belong to a given cluster (also expressed as fraction, this number can be larger than 1 in extreme cases).

Details for the calculation can be found in (Hill et al. The Journal of Neuroscience. 2011). The calculation for total false positive and false negative rates does not follow Hill et al., who propose a simple addition of pairwise probabilities. Instead, the total error probabilities are estimated using all clusters at once.

Parameters:
  • spikes (dict) – Dictionary, indexed by unit, of lists of spike waveforms as neo.core.Spike objects or numpy arrays. If the waveforms have multiple channels, they will be flattened automatically. All waveforms need to have the same number of samples.
  • means (dict) – Dictionary, indexed by unit, of lists of spike waveforms as neo.core.Spike objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Note that if you pass 'white' for covariances and you want to provide means, they have to be whitened in the same way as the spikes. Default: None, means will be estimated from data.
  • covariances (dict or str) – Dictionary, indexed by unit, of lists of covariance matrices. Covariances for units that are not in this dictionary will be estimated using the spikes. It is useful to give a covariance matrix if few spikes are present - consider using the noise covariance. If you use prewhitened spikes (i.e. all clusters are normal distributed, so their covariance matrix is the identity), you can pass 'white' here. The calculation will be much faster in this case and the sklearn package is not required. Default: None, covariances will estimated from data.
Returns:

Two values:

  • A dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples.
  • A dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples.

Return type:

dict, dict

variance_explained(spikes, means=None, noise=None)[source]

Returns the fraction of variance in each channel that is explained by the means.

Values below 0 or above 1 for large data sizes indicate that some assumptions were incorrect (e.g. about channel noise) and the results should not be trusted.

Parameters:
  • spikes (dict) – Dictionary, indexed by unit, of neo.core.SpikeTrain objects (where the waveforms member includes the spike waveforms) or lists of neo.core.Spike objects.
  • means (dict) – Dictionary, indexed by unit, of lists of spike waveforms as neo.core.Spike objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Default: None - means will be estimated from given spikes.
  • noise (Quantity 1D) – The known noise levels (as variance) per channel of the original data. This should be estimated from the signal periods that do not contain spikes, otherwise the explained variance could be overestimated. If None, the estimate of explained variance is done without regard for noise. Default: None
Return dict:

A dictionary of arrays, both indexed by unit. If noise is None, the dictionary contains the fraction of explained variance per channel without taking noise into account. If noise is given, it contains the fraction of variance per channel explained by the means and given noise level together.

stationarity Module

spike_amplitude_histogram(trains, num_bins, uniform_y_scale=True, unit=UnitQuantity('microvolt', 1e-06 * V, 'uV'), progress=None)[source]

Return a spike amplitude histogram.

The resulting is useful to assess the drift in spike amplitude over a longer recording. It shows histograms (one for each trains entry, e.g. segment) of maximum and minimum spike amplitudes.

Parameters:
  • trains (list) – A list of lists of neo.core.SpikeTrain objects. Each entry of the outer list will be one point on the x-axis (they could correspond to segments), all amplitude occurences of spikes contained in the inner list will be added up.
  • num_bins (int) – Number of bins for the histograms.
  • uniform_y_scale (bool) – If True, the histogram for each channel will use the same bins. Otherwise, the minimum bin range is computed separately for each channel.
  • unit (Quantity) – Unit of Y-Axis.
  • progress (progress_indicator.ProgressIndicator) – Set this parameter to report progress.
Returns:

A tuple with three values:

  • A three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of trains (e.g. segments) and the third dimension to channels.
  • A list of the minimum amplitude value for each channel (all values will be equal if uniform_y_scale is true).
  • A list of the maximum amplitude value for each channel (all values will be equal if uniform_y_scale is true).

Return type:

(ndarray, list, list)

tools Module

apply_to_dict(fn, dictionary, *args)[source]

Applies a function to all spike trains in a dictionary of spike train sequences.

Parameters:
  • fn (function) – Function to apply. Should take a neo.core.SpikeTrain as first argument.
  • dictionary (dict) – Dictionary of sequences of neo.core.SpikeTrain objects to apply the function to.
  • args – Additional arguments which will be passed to fn.
Returns:

A new dictionary with the same keys as dictionary.

Return type:

dict

bin_spike_trains(trains, sampling_rate, t_start=None, t_stop=None)[source]

Creates binned representations of spike trains.

Parameters:
  • trains (dict) – A dictionary of sequences of neo.core.SpikeTrain objects.
  • sampling_rate (Quantity scalar) – The sampling rate which will be used to bin the spike trains as inverse time scalar.
  • t_stop (Quantity scalar) – The desired time for the end of the last bin as time scalar. It will be the maximum stop time of all spike trains if None is passed.
Returns:

A dictionary (with the same indices as trains) of lists of spike train counts and the bin borders.

Return type:

dict, Quantity 1D with time units

concatenate_spike_trains(trains)[source]

Concatenates spike trains.

Parameters:trains (sequence) – neo.core.SpikeTrain objects to concatenate.
Returns:A spike train consisting of the concatenated spike trains. The spikes will be in the order of the given spike trains and t_start and t_stop will be set to the minimum and maximum value.
Return type:neo.core.SpikeTrain
extract_spikes(train, signals, length, align_time)[source]

Extract spikes with waveforms from analog signals using a spike train. Spikes that are too close to the beginning or end of the shortest signal to be fully extracted are ignored.

Parameters:
  • train (neo.core.SpikeTrain) – The spike times.
  • signals (sequence) – A sequence of neo.core.AnalogSignal objects from which the spikes are extracted. The waveforms of the returned spikes are extracted from these signals in the same order they are given.
  • length (Quantity scalar) – The length of the waveform to extract as time scalar.
  • align_time (Quantity scalar) – The alignment time of the spike times as time scalar. This is the time delta from the start of the extracted waveform to the exact time of the spike.
Returns:

A list of neo.core.Spike objects, one for each time point in train. All returned spikes include their waveform property.

Return type:

list

maximum_spike_train_interval(trains, t_start=array(inf) * s, t_stop=array(-inf) * s)[source]

Computes the minimum starting time and maximum end time of all given spike trains. This yields an interval containing the spikes of all spike trains.

Parameters:
  • trains (dict) – A dictionary of sequences of neo.core.SpikeTrain objects.
  • t_start (Quantity scalar) – Maximum starting time to return.
  • t_stop (Quantity scalar) – Minimum end time to return. If None, infinity is used.
Returns:

Minimum t_start time and maximum t_stop time as time scalars.

Return type:

Quantity scalar, Quantity scalar

minimum_spike_train_interval(trains, t_start=array(-inf) * s, t_stop=array(inf) * s)[source]

Computes the maximum starting time and minimum end time that all given spike trains share. This yields the shortest interval shared by all spike trains.

Parameters:
  • trains (dict) – A dictionary of sequences of neo.core.SpikeTrain objects.
  • t_start (Quantity scalar) – Minimal starting time to return.
  • t_stop (Quantity scalar) – Maximum end time to return. If None, infinity is used.
Returns:

Maximum shared t_start time and minimum shared t_stop time as time scalars.

Return type:

Quantity scalar, Quantity scalar

remove_from_hierarchy(obj, remove_half_orphans=True)[source]

Removes a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in neo.core.Spike or neo.core.SpikeTrain objects). For example, when obj is a neo.core.Segment, the link from its parent neo.core.Block will be severed. Also, all links to the segment from its spikes and spike trains will be severed.

Parameters:
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nerated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV.set_paramsX-trX"sklearn.cluster.MeanShift.__init__r(jRjSXrhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift.__init__X-trX.sklearn.linear_model.RidgeCV.decision_functionr²(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV.decision_functionX-tròX(sklearn.decomposition.SparsePCA.__init__rIJ(jRjSX~http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA.__init__X-trŲX1sklearn.ensemble.RandomForestRegressor.set_paramsrƲ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor.set_paramsX-trDzX"sklearn.svm.OneClassSVM.set_paramsrȲ(jRjSXphttp://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM.set_paramsX-trɲXsklearn.cluster.Ward.set_paramsrʲ(jRjSXjhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.Ward.html#sklearn.cluster.Ward.set_paramsX-tr˲X,sklearn.cross_decomposition.PLSSVD.transformr̲(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html#sklearn.cross_decomposition.PLSSVD.transformX-trͲX.sklearn.multiclass.OneVsOneClassifier.__init__rβ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier.__init__X-trϲX!sklearn.lda.LDA.predict_log_probarв(jRjSXghttp://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html#sklearn.lda.LDA.predict_log_probaX-trѲXAsklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graphrҲ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graphX-trӲXsklearn.mixture.VBGMM.predictrԲ(jRjSXihttp://scikit-learn.org/stable/modules/generated/sklearn.mixture.VBGMM.html#sklearn.mixture.VBGMM.predictX-trղX;sklearn.linear_model.OrthogonalMatchingPursuitCV.set_paramsrֲ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#sklearn.linear_model.OrthogonalMatchingPursuitCV.set_paramsX-trײX%sklearn.linear_model.Perceptron.scorerز(jRjSX{http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron.scoreX-trٲX4sklearn.cross_decomposition.PLSRegression.set_paramsrڲ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html#sklearn.cross_decomposition.PLSRegression.set_paramsX-tr۲X6sklearn.semi_supervised.LabelPropagation.predict_probarܲ(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation.predict_probaX-trݲX1sklearn.semi_supervised.LabelSpreading.get_paramsr޲(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelSpreading.html#sklearn.semi_supervised.LabelSpreading.get_paramsX-tr߲X-sklearn.cluster.SpectralClustering.set_paramsr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering.set_paramsX-trX,sklearn.linear_model.ElasticNetCV.set_paramsr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV.set_paramsX-trXsklearn.lda.LDA.fit_transformr(jRjSXchttp://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html#sklearn.lda.LDA.fit_transformX-trX%sklearn.linear_model.RidgeCV.__init__r(jRjSXxhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV.__init__X-trX,sklearn.linear_model.SGDClassifier.transformr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.transformX-trX3sklearn.semi_supervised.LabelPropagation.get_paramsr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation.get_paramsX-trX3sklearn.ensemble.ExtraTreesClassifier.predict_probar(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier.predict_probaX-trX8sklearn.cluster.bicluster.SpectralCoclustering.get_shaper(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.bicluster.SpectralCoclustering.html#sklearn.cluster.bicluster.SpectralCoclustering.get_shapeX-trX.sklearn.decomposition.RandomizedPCA.set_paramsr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.decomposition.RandomizedPCA.html#sklearn.decomposition.RandomizedPCA.set_paramsX-trX*sklearn.linear_model.Perceptron.get_paramsr(jRjSXhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html#sklearn.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max_lag=500 * pq.ms, border_correction=True, per_second=True, unit=pq.ms, progress=None): """ Return (cross-)correlograms from a dictionary of spike train lists for different units. :param dict trains: Dictionary of :class:`neo.core.SpikeTrain` lists. :param bin_size: Bin size (time). :type bin_size: Quantity scalar :param max_lag: Cut off (end time of calculated correlogram). :type max_lag: Quantity scalar :param bool border_correction: Apply correction for less data at higher timelags. Not perfect for bin_size != 1*``unit``, especially with large ``max_lag`` compared to length of spike trains. :param bool per_second: If ``True``, counts returned are per second. Otherwise, counts per spike train are returned. :param Quantity unit: Unit of X-Axis. :param progress: A ProgressIndicator object for the operation. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Two values: * An ordered dictionary indexed with the indices of ``trains`` of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: ``c[index1][index2]`` and ``c[index2][index1]``. * The bins used for the correlogram calculation. :rtype: dict, Quantity 1D """ if not progress: progress = ProgressIndicator() bin_size.rescale(unit) max_lag.rescale(unit) # Create bins, making sure that 0 is at the center of central bin half_bins = sp.arange(bin_size / 2, max_lag, bin_size) all_bins = list(reversed(-half_bins)) all_bins.extend(half_bins) bins = sp.array(all_bins) * unit middle_bin = len(bins) / 2 - 1 indices = trains.keys() num_trains = len(trains[indices[0]]) if not num_trains: raise SpykeException('Could not create correlogram: No spike trains!') for u in range(1, len(indices)): if len(trains[indices[u]]) != num_trains: raise SpykeException('Could not create correlogram: All units ' + 'need the same number of spike trains!') progress.set_ticks(sp.sum(range(len(trains) + 1) * num_trains)) corrector = 1 if border_correction: # Need safe min/max functions def safe_max(seq): if len(seq) < 1: return 0 return max(seq) def safe_min(seq): if len(seq) < 1: return 2 ** 22 # Some arbitrary large value return min(seq) max_w = max([max([safe_max(t) for t in l]) for l in trains.itervalues()]) min_w = min([min([safe_min(t) for t in l]) for l in trains.itervalues()]) train_length = (max_w - min_w) l = int(round(middle_bin)) + 1 cE = max(train_length - (l * bin_size) + 1 * unit, 1 * unit) corrector = (train_length / sp.concatenate( (sp.linspace(cE, train_length, l - 1, False), sp.linspace(train_length, cE, l)))).magnitude correlograms = OrderedDict() for i1 in xrange(len(indices)): # For each index # For all later indices, including itself for i2 in xrange(i1, len(indices)): histogram = sp.zeros(len(bins) - 1) for t in xrange(num_trains): train1 = trains[indices[i1]][t].rescale(unit).reshape((1, -1)) train2 = trains[indices[i2]][t].rescale(unit).reshape((-1, 1)) histogram += sp.histogram( sp.subtract(train1, train2), bins=bins)[0] if i1 == i2: # Correction for autocorrelogram histogram[middle_bin] -= len(train2) progress.step() if per_second: l = train1.t_stop - train1.t_start if train2.t_stop - train2.t_start != l: raise SpykeException( 'A spike train pair does not have equal length,' 'cannot calculate count per second.') histogram /= l.rescale(pq.s) crg = corrector * histogram / num_trains if indices[i1] not in correlograms: correlograms[indices[i1]] = OrderedDict() correlograms[indices[i1]][indices[i2]] = crg if i1 != i2: if indices[i2] not in correlograms: correlograms[indices[i2]] = OrderedDict() correlograms[indices[i2]][indices[i1]] = crg[::-1] return correlograms, binsr@}rA(Xcorrelogram.safe_maxXdefrBKAKFX correlogramrCXdefrDK KxXcorrelogram.safe_minXdefrEKFKKu}rFX correlogramrGjFsj<Xspykeutils.conversionsrHrI}rJbXJimport scipy as sp import neo from . import SpykeException def spike_train_to_spikes(spike_train, include_waveforms=True): """ Return a list of spikes for a spike train. Note that while the created spikes have references to the same segment and unit as the spike train, the relationships in the other direction are not automatically created (the spikes are not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spikes. :param spike_train: A spike train from which the :class:`neo.core.Spike` objects are constructed. :type spike_train: :class:`neo.core.SpikeTrain` :param bool include_waveforms: Determines if the ``waveforms`` property is converted to the spike waveforms. If ``waveforms`` is None, this parameter has no effect. :returns: A list of :class:`neo.core.Spike` objects, one for every spike in ``spike_train``. :rtype: list """ waves = None if include_waveforms: waves = spike_train.waveforms spikes = [] for i, t in enumerate(spike_train): s = neo.Spike(t, sampling_rate=spike_train.sampling_rate, left_sweep=spike_train.left_sweep) if waves is not None: s.waveform = waves[i, :, :] s.unit = spike_train.unit s.segment = spike_train.segment spikes.append(s) return spikes def spikes_to_spike_train(spikes, include_waveforms=True): """ Return a spike train for a list of spikes. All spikes must have an identical left sweep, the same unit and the same segment, otherwise a ``SpykeException`` is raised. Note that while the created spike train has references to the same segment and unit as the spikes, the relationships in the other direction are not automatically created (the spike train is not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spike train. :param sequence spikes: A sequence of :class:`neo.core.Spike` objects from which the spike train is constructed. :param bool include_waveforms: Determines if the waveforms from the spike objects are used to fill the ``waveforms`` property of the resulting spike train. If ``True``, all spikes need a ``waveform`` property with the same shape or a ``SpykeException`` is raised (or the ``waveform`` property needs to be ``None`` for all spikes). :return: All elements of ``spikes`` as spike train. :rtype: :class:`neo.core.SpikeTrain` """ if not spikes: raise SpykeException('No spikes to create spike train!') tu = spikes[0].time.units times = sp.zeros(len(spikes)) * tu s = spikes[0].segment u = spikes[0].unit ls = spikes[0].left_sweep if include_waveforms and spikes[0].waveform is not None: sh = spikes[0].waveform.shape wu = spikes[0].waveform.units waves = sp.zeros((len(spikes), sh[0], sh[1])) * wu else: waves = None sh = None for i, spike in enumerate(spikes): if (u != spike.unit or s != spike.segment or ls != spike.left_sweep): raise SpykeException('Cannot create spike train from spikes with ' 'nonuniform properties!') times[i] = spikes[i].time if include_waveforms: if spike.waveform is None: if waves is not None: raise SpykeException('Cannot create spike train from ' 'spikes where some waveforms are ' 'None') elif sh != spike.waveform.shape: raise SpykeException('Cannot create spike train from spikes ' 'with nonuniform waveform shapes!') if waves is not None: waves[i, :, :] = spike.waveform ret = neo.SpikeTrain(times, t_start=times.min(), t_stop=times.max(), waveforms=waves, left_sweep=ls) ret.unit = u ret.segment = s ret.left_sweep = ls return ret def analog_signal_array_to_analog_signals(signal_array): """ Return a list of analog signals for an analog signal array. If ``signal_array`` is attached to a recording channel group with exactly is many channels as there are channels in ``signal_array``, each created signal will be assigned the corresponding channel. If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel. Note that while the created signals may have references to a segment and channels, the relationships in the other direction are not automatically created (the signals are not attached to the recording channel or segment). Other properties like annotations are not copied or referenced in the created analog signals. :param signal_array: An analog signal array from which the :class:`neo.core.AnalogSignal` objects are constructed. :type signal_array: :class:`neo.core.AnalogSignalArray` :return: A list of analog signals, one for every channel in ``signal_array``. :rtype: list """ signals = [] rcg = signal_array.recordingchannelgroup for i in xrange(signal_array.shape[1]): s = neo.AnalogSignal( signal_array[:, i], t_start=signal_array.t_start, sampling_rate=signal_array.sampling_rate) if len(rcg.recordingchannels) == 1: s.recordingchannel = rcg.recordingchannels[0] elif len(rcg.recordingchannels) == signal_array.shape[1]: s.recordingchannel = rcg.recordingchannels[i] s.segment = signal_array.segment signals.append(s) return signals def event_array_to_events(event_array): """ Return a list of events for an event array. Note that while the created events may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created events. :param event_array: An event array from which the Event objects are constructed. :type event_array: :class:`neo.core.EventArray` :return: A list of events, one for of the events in ``event_array``. :rtype: list """ events = [] for i, t in enumerate(event_array.times): e = neo.Event( t, event_array.labels[i] if i < len(event_array.labels) else '') e.segment = event_array.segment events.append(e) return events def epoch_array_to_epochs(epoch_array): """ Return a list of epochs for an epoch array. Note that while the created epochs may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created epochs. :param epoch_array: A period array from which the Epoch objects are constructed. :type epoch_array: :class:`neo.core.EpochArray` :return: A list of events, one for of the events in ``epoch_array``. :rtype: list """ periods = [] for i, t in enumerate(epoch_array.times): p = neo.Epoch( t, epoch_array.durations[i], epoch_array.labels[i] if i < len(epoch_array.labels) else '') p.segment = epoch_array.segment periods.append(p) return periods rK}rL(X%analog_signal_array_to_analog_signalsrMXdefrNKnKXspikes_to_spike_trainrOXdefrPK+KnXepoch_array_to_epochsrQXdefrRKKÇXspike_train_to_spikesrSXdefrTKK+Xevent_array_to_eventsrUXdefrVKKu}rW(X%analog_signal_array_to_analog_signalsrXjFXspikes_to_spike_trainrYjFXepoch_array_to_epochsrZjFXspike_train_to_spikesr[jFXevent_array_to_eventsr\jFuj<X$spykeutils.sorting_quality_assesmentr]r^}r_bXz@""" Functions for estimating the quality of spike sorting results. These functions estimate false positive and false negative fractions. """ from __future__ import division import scipy as sp from scipy.spatial.distance import cdist import quantities as pq import neo from progress_indicator import ProgressIndicator from . import SpykeException from conversions import spikes_to_spike_train def get_refperiod_violations(spike_trains, refperiod, progress=None): """ Return the refractory period violations in the given spike trains for the specified refractory period. :param dict spike_trains: Dictionary of lists of :class:`neo.core.SpikeTrain` objects. :param refperiod: The refractory period (time). :type refperiod: Quantity scalar :param progress: Set this parameter to report progress. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Two values: * The total number of violations. * A dictionary (with the same indices as ``spike_trains``) of arrays with violation times (Quantity 1D with the same unit as ``refperiod``) for each spike train. :rtype: int, dict """ if type(refperiod) != pq.Quantity or \ refperiod.simplified.dimensionality != pq.s.dimensionality: raise ValueError('refperiod must be a time quantity!') if not progress: progress = ProgressIndicator() total_violations = 0 violations = {} for u, tL in spike_trains.iteritems(): violations[u] = [] for i, t in enumerate(tL): st = t.copy() st.sort() isi = sp.diff(st) violations[u].append(st[isi < refperiod].rescale(refperiod.units)) total_violations += len(violations[u][i]) progress.step() return total_violations, violations def calculate_refperiod_fp(num_spikes, refperiod, violations, total_time): """ Return the rate of false positives calculated from refractory period calculations for each unit. The equation used is described in (Hill et al. The Journal of Neuroscience. 2011). :param dict num_spikes: Dictionary of total number of spikes, indexed by unit. :param refperiod: The refractory period (time). If the spike sorting algorithm includes a censored period (a time after a spike during which no new spikes can be found), subtract it from the refractory period before passing it to this function. :type refperiod: Quantity scalar :param dict violations: Dictionary of total number of violations, indexed the same as num_spikes. :param total_time: The total time in which violations could have occured. :type total_time: Quantity scalar :returns: A dictionary of false positive rates indexed by unit. Note that values above 0.5 can not be directly interpreted as a false positive rate! These very high values can e.g. indicate that the generating processes are not independent. """ if type(refperiod) != pq.Quantity or \ refperiod.simplified.dimensionality != pq.s.dimensionality: raise ValueError('refperiod must be a time quantity!') fp = {} factor = total_time / (2 * refperiod) for u, n in num_spikes.iteritems(): if n == 0: fp[u] = 0 continue zw = (violations[u] * factor / n ** 2).simplified if zw > 0.25: fp[u] = 0.5 + sp.sqrt(0.25 - zw).imag continue fp[u] = 0.5 - sp.sqrt(0.25 - zw) return fp def _multi_norm(x, mean): """ Evaluate pdf of multivariate normal distribution with a mean at rows of x with high precision. """ d = x.shape[1] fac = (2 * sp.pi) ** (-d / 2.0) y = cdist(x, sp.atleast_2d(mean), 'sqeuclidean') * -0.5 return fac * sp.exp(sp.longdouble(y)) def _fast_overlap_whitened(spike_arrays, means): units = spike_arrays.keys() spikes = {u: spike_arrays[u].shape[1] for u in spike_arrays.iterkeys()} prior = {} total_spikes = 0 for u, mean in means.iteritems(): total_spikes += spikes[u] if total_spikes < 1: return {u: (0.0, 0.0) for u in units}, {} # Arrays of unnormalized posteriors (likelihood times prior) # for all units posterior = {} false_positive = {} false_negative = {} for u in units: prior[u] = spikes[u] / total_spikes false_positive[u] = 0 false_negative[u] = 0 # Calculate posteriors for u1 in units[:]: if not spikes[u1]: units.remove(u1) continue posterior[u1] = {} for u2, mean in means.iteritems(): llh = _multi_norm(spike_arrays[u1].T, mean) posterior[u1][u2] = llh * prior[u2] # Calculate pairwise false positives/negatives singles = {u: {} for u in units} for i, u1 in enumerate(units): u1 = units[i] for u2 in units[i + 1:]: f1 = sp.sum(posterior[u1][u2] / (posterior[u1][u1] + posterior[u1][u2]), dtype=sp.double) f2 = sp.sum(posterior[u2][u1] / (posterior[u2][u1] + posterior[u2][u2]), dtype=sp.double) singles[u1][u2] = (f1 / spikes[u1] if spikes[u1] else 0, f2 / spikes[u1] if spikes[u1] else 0) singles[u2][u1] = (f2 / spikes[u2] if spikes[u2] else 0, f1 / spikes[u2] if spikes[u2] else 0) # Calculate complete false positives/negatives with extended bayes for u1 in units: numerator = posterior[u1][u1] normalizer = sum(posterior[u1][u2] for u2 in units) false_positive[u1] = sp.sum((normalizer - numerator) / normalizer) other_units = units[:] other_units.remove(u1) numerator = sp.vstack((posterior[u][u1] for u in other_units)) normalizer = sp.vstack(sum(posterior[u][u2] for u2 in units) for u in other_units) false_negative[u1] = sp.sum(numerator / normalizer) # Prepare return values, convert sums to means totals = {} for u, fp in false_positive.iteritems(): fn = false_negative[u] if not spikes[u]: totals[u] = (0, 0) else: num = spikes[u] totals[u] = (fp / num, fn / num) return totals, singles def _pair_overlap(waves1, waves2, mean1, mean2, cov1, cov2): """ Calculate FP/FN estimates for two gaussian clusters """ from sklearn import mixture means = sp.vstack([[mean1], [mean2]]) covars = sp.vstack([[cov1], [cov2]]) weights = sp.array([waves1.shape[1], waves2.shape[1]], dtype=float) weights /= weights.sum() # Create mixture of two Gaussians from the existing estimates mix = mixture.GMM(n_components=2, covariance_type='full', init_params='') mix.covars_ = covars mix.weights_ = weights mix.means_ = means posterior1 = mix.predict_proba(waves1.T)[:, 1] posterior2 = mix.predict_proba(waves2.T)[:, 0] return (posterior1.mean(), posterior2.sum() / len(posterior1), posterior2.mean(), posterior1.sum() / len(posterior2)) def _object_has_size(obj, size): """ Return if the object, which could be either a neo.Spike or ndarray, has the given size. """ if isinstance(obj, neo.Spike): return obj.waveform.size == size return obj.size == size def overlap_fp_fn(spikes, means=None, covariances=None): """ Return dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs :mod:`sklearn` if ``covariances`` is not set to ``'white'``. This function estimates the pairwise and total false positive and false negative rates for a number of waveform clusters. The results can be interpreted as follows: False positives are the fraction of spikes in a cluster that is estimated to belong to a different cluster (a specific cluster for pairwise results or any other cluster for total results). False negatives are the number spikes from other clusters that are estimated to belong to a given cluster (also expressed as fraction, this number can be larger than 1 in extreme cases). Details for the calculation can be found in (Hill et al. The Journal of Neuroscience. 2011). The calculation for total false positive and false negative rates does not follow Hill et al., who propose a simple addition of pairwise probabilities. Instead, the total error probabilities are estimated using all clusters at once. :param dict spikes: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. If the waveforms have multiple channels, they will be flattened automatically. All waveforms need to have the same number of samples. :param dict means: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Note that if you pass ``'white'`` for ``covariances`` and you want to provide means, they have to be whitened in the same way as the spikes. Default: None, means will be estimated from data. :param covariances: Dictionary, indexed by unit, of lists of covariance matrices. Covariances for units that are not in this dictionary will be estimated using the spikes. It is useful to give a covariance matrix if few spikes are present - consider using the noise covariance. If you use prewhitened spikes (i.e. all clusters are normal distributed, so their covariance matrix is the identity), you can pass ``'white'`` here. The calculation will be much faster in this case and the sklearn package is not required. Default: None, covariances will estimated from data. :type covariances: dict or str :returns: Two values: * A dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples. * A dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples. :rtype: dict, dict """ units = spikes.keys() total_spikes = 0 for spks in spikes.itervalues(): total_spikes += len(spks) if total_spikes < 1: return {u: (0.0, 0.0) for u in units}, {} if means is None: means = {} white = False if covariances is None: covariances = {} elif covariances == 'white': white = True covariances = {} # Convert Spike objects to arrays dimensionality = None spike_arrays = {} for u, spks in spikes.iteritems(): spikelist = [] if not spks or (len(spks) < 2 and u not in covariances): units.remove(u) continue for s in spks: if isinstance(s, neo.Spike): spikelist.append( sp.asarray(s.waveform.rescale(pq.uV)).T.flatten()) else: spikelist.append(s) spike_arrays[u] = sp.array(spikelist).T if dimensionality is None: dimensionality = spike_arrays[u].shape[0] elif dimensionality != spike_arrays[u].shape[0]: raise SpykeException('All spikes need to have the same number' 'of samples!') if not units: return {}, {} if len(units) == 1: return {units[0]: (0.0, 0.0)}, {} # Convert or calculate means and covariances shaped_means = {} covs = {} if white: cov = sp.eye(dimensionality) covariances = {u: cov for u in units} for u in units: if u in means and _object_has_size(means[u], dimensionality): mean = means[u] if isinstance(mean, neo.Spike): shaped_means[u] = sp.asarray( mean.waveform.rescale(pq.uV)).T.flatten() else: shaped_means[u] = means[u].T.flatten() else: shaped_means[u] = spike_arrays[u].mean(axis=1) if white: return _fast_overlap_whitened(spike_arrays, shaped_means) for u in units: if u not in covariances: covs[u] = sp.cov(spike_arrays[u]) else: covs[u] = covariances[u] # Calculate pairwise false positives/negatives singles = {u: {} for u in units} for i, u1 in enumerate(units): u1 = units[i] for u2 in units[i + 1:]: error_rates = _pair_overlap( spike_arrays[u1], spike_arrays[u2], shaped_means[u1], shaped_means[u2], covs[u1], covs[u2]) singles[u1][u2] = error_rates[0:2] singles[u2][u1] = error_rates[2:4] # Calculate complete false positives/negatives import sklearn mix = sklearn.mixture.GMM(n_components=2, covariance_type='full') mix_means = [] mix_covars = [] mix_weights = [] for u in units: mix_means.append(shaped_means[u]) mix_covars.append([covs[u]]) mix_weights.append(spike_arrays[u].shape[1]) mix.means_ = sp.vstack(mix_means) mix.covars_ = sp.vstack(mix_covars) mix_weights = sp.array(mix_weights, dtype=float) mix_weights /= mix_weights.sum() mix.weights_ = mix_weights # P(spikes of unit[i] in correct cluster) post_mean = sp.zeros(len(units)) # sum(P(spikes of unit[i] in cluster[j]) post_sum = sp.zeros((len(units), len(units))) for i, u in enumerate(units): posterior = mix.predict_proba(spike_arrays[u].T) post_mean[i] = posterior[:, i].mean() post_sum[i, :] = posterior.sum(axis=0) totals = {} for i, u in enumerate(units): fp = 1.0 - post_mean[i] ind = range(len(units)) ind.remove(i) fn = post_sum[ind, i].sum() / float(spike_arrays[u].shape[1]) totals[u] = (fp, fn) return totals, singles def variance_explained(spikes, means=None, noise=None): """ Returns the fraction of variance in each channel that is explained by the means. Values below 0 or above 1 for large data sizes indicate that some assumptions were incorrect (e.g. about channel noise) and the results should not be trusted. :param dict spikes: Dictionary, indexed by unit, of :class:`neo.core.SpikeTrain` objects (where the ``waveforms`` member includes the spike waveforms) or lists of :class:`neo.core.Spike` objects. :param dict means: Dictionary, indexed by unit, of lists of spike waveforms as :class:`neo.core.Spike` objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Default: None - means will be estimated from given spikes. :type noise: Quantity 1D :param noise: The known noise levels (as variance) per channel of the original data. This should be estimated from the signal periods that do not contain spikes, otherwise the explained variance could be overestimated. If None, the estimate of explained variance is done without regard for noise. Default: None :return dict: A dictionary of arrays, both indexed by unit. If ``noise`` is ``None``, the dictionary contains the fraction of explained variance per channel without taking noise into account. If ``noise`` is given, it contains the fraction of variance per channel explained by the means and given noise level together. """ ret = {} if means is None: means = {} for u, spks in spikes.iteritems(): train = spks if not isinstance(train, neo.SpikeTrain): train = spikes_to_spike_train(spks) if u in means and means[u].waveform.shape[0] == train.waveforms.shape[1]: spike = means[u] else: spike = neo.Spike(0) spike.waveform = sp.mean(train.waveforms, axis=0) orig = sp.mean(sp.var(train.waveforms, axis=1), axis=0) waves = train.waveforms - spike.waveform new = sp.mean(sp.var(waves, axis=1), axis=0) if noise is not None: ret[u] = sp.asarray(1 - (new - noise) / orig) else: ret[u] = sp.asarray(1 - new / orig) return retr`}ra(Xget_refperiod_violationsrbXdefrcKK:X _pair_overlaprdXdefreKKχX _multi_normrfXdefrgKdKnX_object_has_sizerhXdefriKKׇX overlap_fp_fnrjXdefrkKMXvariance_explainedrlXdefrmMMXcalculate_refperiod_fprnXdefroK:KdX_fast_overlap_whitenedrpXdefrqKnKu}rr(Xget_refperiod_violationsrsjFX overlap_fp_fnrtjFXcalculate_refperiod_fprujFXvariance_explainedrvjFuj<Xspykeutils.plugin.data_providerrwrx}rybX<import neo class DataProvider(object): """ Defines all methods that should be implemented by a selection/data provider class. A `DataProvider` encapsulates access to a selection of data. It can be used by plugins to acesss data currently selected in the GUI or in saved selections. It also contains an attribute `progress`, a :class:`spykeutils.progress_indicator.ProgressIndicator` that can be used to report the progress of an operation (and is used by methods of this class if they can lead to processing times of half a second or more). This class serves as an abstract base class and should not be instantiated.""" _factories = {} no_unit = neo.Unit(name='No Unit') no_segment = neo.Segment(name='No segment') no_channel = neo.RecordingChannel(name='No recording channel') no_channelgroup = neo.RecordingChannelGroup(name='No recording channel group') no_unit.annotate(unique_id=-1) no_segment.annotate(unique_id=-1) no_channel.annotate(unique_id=-1) no_channelgroup.annotate(unique_id=-1) def __init__(self, name, progress): self.name = name self.progress = progress def _invert_indices(self, dictionary): """ Invert the indices of a dictionary of dictionaries. """ dict_type = type(dictionary) ret = dict_type() for i1 in dictionary: for i2 in dictionary[i1]: if not i2 in ret: ret[i2] = dict_type() ret[i2][i1] = dictionary[i1][i2] return ret def blocks(self): """ Return a list of selected Block objects. The returned objects will contain all regular references, not just to selected objects. """ return [] def segments(self): """ Return a list of selected Segment objects. The returned objects will contain all regular references, not just to selected objects. """ return [] def recording_channel_groups(self): """ Return a list of selected RecordingChannelGroup objects. The returned objects will contain all regular references, not just to selected objects. """ return [] def recording_channels(self): """ Return a list of selected RecordingChannel objects. The returned objects will contain all regular references, not just to selected objects. """ return [] def units(self): """ Return a list of selected Unit objects. The returned objects will contain all regular references, not just to selected objects. """ return [] def selection_blocks(self): """ Return a list of selected blocks. The returned blocks will contain references to all other selected elements further down in the object hierarchy, but no references to elements which are not selected. The returned hierarchy is a copy, so changes made to it will not persist. The main purpose of this function is to provide an object hierarchy that can be saved to a neo file. It is not recommended to use it for data processing, the respective functions that return objects lower in the hierarchy are better suited for that purpose. """ return [] def spike_trains(self): """ Return a list of :class:`neo.core.SpikeTrain` objects. """ return [] def spike_trains_by_unit(self): """ Return a dictionary (indexed by Unit) of lists of :class:`neo.core.SpikeTrain` objects. If spike trains not attached to a Unit are selected, their dicionary key will be ``DataProvider.no_unit``. """ return {} def spike_trains_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.SpikeTrain` objects. If spike trains not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {} def spike_trains_by_unit_and_segment(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.SpikeTrain` objects. If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return {} def spike_trains_by_segment_and_unit(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.SpikeTrain` objects. If there are multiple spike trains in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spike trains not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return self._invert_indices(self.spike_trains_by_unit_and_segment()) def spikes(self): """ Return a list of :class:`neo.core.Spike` objects. """ return [] def spikes_by_unit(self): """ Return a dictionary (indexed by Unit) of lists of :class:`neo.core.Spike` objects. If spikes not attached to a Unit are selected, their dicionary key will be ``DataProvider.no_unit``. """ return {} def spikes_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.Spike` objects. If spikes not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {} def spikes_by_unit_and_segment(self): """ Return a dictionary (indexed by Unit) of dictionaries (indexed by Segment) of :class:`neo.core.Spike` lists. If there are multiple spikes in one Segment for the same Unit, only the first will be contained in the returned dictionary. If spikes not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return {} def spikes_by_segment_and_unit(self): """ Return a dictionary (indexed by Segment) of dictionaries (indexed by Unit) of lists of :class:`neo.core.Spike` lists. If spikes not attached to a Unit or Segment are selected, their dictionary key will be ``DataProvider.no_unit`` or ``DataProvider.no_segment``, respectively. """ return self._invert_indices(self.spikes_by_unit_and_segment()) def events(self, include_array_events = True): """ Return a dictionary (indexed by Segment) of lists of Event objects. :param bool include_array_events: Determines if EventArray objects should be converted to Event objects and included in the returned list. """ return {} def labeled_events(self, label, include_array_events = True): """ Return a dictionary (indexed by Segment) of lists of Event objects with the given label. :param str label: The name of the Event objects to be returnded :param bool include_array_events: Determines if EventArray objects should be converted to Event objects and included in the returned list. """ return [] def event_arrays(self): """ Return a dictionary (indexed by Segment) of lists of EventArray objects. """ return {} def epochs(self, include_array_epochs = True): """ Return a dictionary (indexed by Segment) of lists of Epoch objects. :param bool include_array_epochs: Determines if EpochArray objects should be converted to Epoch objects and included in the returned list. """ return {} def labeled_epochs(self, label, include_array_epochs = True): """ Return a dictionary (indexed by Segment) of lists of Epoch objects with the given label. :param str label: The name of the Epoch objects to be returnded :param bool include_array_epochs: Determines if EpochArray objects should be converted to Epoch objects and included in the returned list. """ return [] def epoch_arrays(self): """ Return a dictionary (indexed by Segment) of lists of EpochArray objects. """ return {} def analog_signals(self, conversion_mode=1): """ Return a list of :class:`neo.core.AnalogSignal` objects. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return [] def analog_signals_by_segment(self, conversion_mode=1): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.AnalogSignal` objects. If analog signals not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {} def analog_signals_by_channel(self, conversion_mode=1): """ Return a dictionary (indexed by RecordingChannel) of lists of :class:`neo.core.AnalogSignal` objects. If analog signals not attached to a RecordingChannel are selected, their dictionary key will be ``DataProvider.no_channel``. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {} def analog_signals_by_channel_and_segment(self, conversion_mode=1): """ Return a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignal` lists. If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return {} def analog_signals_by_segment_and_channel(self, conversion_mode=1): """ Return a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of :class:`neo.core.AnalogSignal` lists. If analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively. :param int conversion_mode: Determines what signals are returned: 1. AnalogSignal objects only 2. AnalogSignal objects extracted from AnalogSignalArrays only 3. Both AnalogSignal objects and extracted AnalogSignalArrays """ return self._invert_indices( self.analog_signals_by_channel_and_segment(conversion_mode)) def analog_signal_arrays(self): """ Return a list of :class:`neo.core.AnalogSignalArray` objects. """ return [] def analog_signal_arrays_by_segment(self): """ Return a dictionary (indexed by Segment) of lists of :class:`neo.core.AnalogSignalArray` objects. If analog signals arrays not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``. """ return {} def analog_signal_arrays_by_channelgroup(self): """ Return a dictionary (indexed by RecordingChannelGroup) of lists of :class:`neo.core.AnalogSignalArray` objects. If analog signals arrays not attached to a RecordingChannel are selected, their dictionary key will be ``DataProvider.no_channelgroup``. """ return {} def analog_signal_arrays_by_channelgroup_and_segment(self): """ Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignalArray` objects. If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channelgroup``, respectively. """ return {} def analog_signal_arrays_by_segment_and_channelgroup(self): """ Return a dictionary (indexed by RecordingChannelGroup) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignalArray` objects. If there are multiple analog signals in one RecordingChannel for the same Segment, only the first will be contained in the returned dictionary. If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channelgroup``, respectively. """ return self._invert_indices( self.analog_signal_arrays_by_channelgroup_and_segment()) def refresh_view(self): """ Refresh associated views of the data. Use this method if when you change the neo hierarchy on which the selection is based (e.g. adding or removing objects). It will ensure that all current views on the data are updated, for example in Spyke Viewer. """ pass def data_dict(self): """ Return a dictionary with all information to serialize the object. """ return {} @classmethod def from_data(cls, data, progress=None): """ Create a new `DataProvider` object from a dictionary. This method is mostly for internal use. The respective type of `DataProvider` (e.g. :class:`spykeviewer.plugin_framework.data_provider_neo.DataProviderNeo` has to be imported in the environment where this function is called. :param dict data: A dictionary containing data from a `DataProvider` object, as returned by :func:`data_dict`. :param ProgressIndicator progress: The object where loading progress will be indicated. """ if progress: return cls._factories[data['type']](data, progress) return cls._factories[data['type']](data)rz}r{(XDataProvider.analog_signalsXdefr|KKXDataProvider.labeled_epochsXdefr}KKXDataProvider.spikes_by_segmentXdefr~KKXDataProvider.epoch_arraysXdefrKKXDataProvider.epochsXdefrKKX&DataProvider.analog_signals_by_segmentXdefrKMXDataProvider.data_dictXdefrMMX2DataProvider.analog_signals_by_segment_and_channelXdefrM/MBXDataProvider.labeled_eventsXdefrKKӇXDataProvider.from_dataXdefrMMX1DataProvider.analog_signal_arrays_by_channelgroupXdefrMPMZXDataProvider.__init__XdefrKK XDataProvider.eventsXdefrKKȇXDataProvider._invert_indicesXdefrK K,X'DataProvider.spikes_by_segment_and_unitXdefrKKXDataProvider.selection_blocksXdefrKTKbXDataProvider.event_arraysXdefrKKهX%DataProvider.recording_channel_groupsXdefrK 0: spike_times += sp.arange(spike_times.size) * refractory if t_stop is not None: spike_times = spike_times[spike_times <= t_stop] else: scale = (rate ** -1).rescale(t_stop.units) trains = [] last_spike = t_start.rescale(t_stop.units) while last_spike < t_stop: # Generate a bit more than the average number of expected spike to # be finished in most cases in one loop. The factor was determined # empirically. num_spikes = int(1.7 * ( (t_stop - last_spike) * rate).simplified) + 1 train = sp.cumsum(numpy.random.exponential(scale, num_spikes)) * \ scale.units + last_spike if refractory > 0: train += sp.arange(train.size) * refractory if train.size > 0: last_spike = train[-1] if last_spike >= t_stop: train = train[train < t_stop] trains.append(train) spike_times = spq.concatenate(trains) if t_stop is None: t_stop = spike_times[-1] return neo.SpikeTrain(spike_times, t_start=t_start, t_stop=t_stop) def gen_inhomogeneous_poisson( modulation, max_rate, t_start=0 * pq.s, t_stop=None, max_spikes=None, refractory=0 * pq.s): """ Generate an inhomogeneous Poisson spike train. The length is controlled with `t_stop` and `max_spikes`. Either one or both of these arguments have to be given. :param function modulation: Function :math:`f((t_1, \\dots, t_n)): [\\text{t\\_start}, \\text{t\\_end}]^n \\rightarrow [0, 1]^n` giving the instantaneous firing rates at times :math:`(t_1, \\dots, t_n)` as proportion of `max_rate`. Thus, a 1-D array will be passed to the function and it should return an array of the same size. :param max_rate: Maximum firing rate of the spike train to generate as frequency scalar. :type max_rate: Quantity scalar :param t_start: Time at which the spike train begins as time scalar. The first actual spike will be greater than this time. :type t_start: Quantity scalar :param t_stop: Time at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by `max_spikes` and `t_stop` will be equal to the last generated spike. :type t_stop: Quantity scalar :param refractory: Absolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to `rate` again. :type refractory: Quantity scalar :returns: The generated spike train. :rtype: :class:`neo.core.SpikeTrain` """ st = gen_homogeneous_poisson( max_rate, t_start, t_stop, max_spikes, refractory) return st[numpy.random.rand(st.size) < modulation(st)] r}r(Xgen_inhomogeneous_poissonrXdefrKJKmXgen_homogeneous_poissonrXdefrKKJu}r(Xgen_inhomogeneous_poissonrjFXgen_homogeneous_poissonrjFuj<X spykeutilsrr}rbXl""" .. autoclass:: spykeutils.SpykeException :mod:`conversions` Module ------------------------- .. automodule:: spykeutils.conversions :members: :undoc-members: :show-inheritance: :mod:`correlations` Module -------------------------- .. automodule:: spykeutils.correlations :members: :mod:`progress_indicator` Module -------------------------------- .. automodule:: spykeutils.progress_indicator :members: :undoc-members: :show-inheritance: :mod:`rate_estimation` Module ----------------------------- .. automodule:: spykeutils.rate_estimation :members: :mod:`signal_processing` Module ------------------------------- .. automodule:: spykeutils.signal_processing :members: :show-inheritance: :undoc-members: :mod:`spike_train_generation` Module ------------------------------------ .. automodule:: spykeutils.spike_train_generation :members: :undoc-members: :mod:`spike_train_metrics` Module ------------------------------------ .. automodule:: spykeutils.spike_train_metrics :members: :undoc-members: :mod:`sorting_quality_assesment` Module --------------------------------------- .. automodule:: spykeutils.sorting_quality_assesment :members: :undoc-members: :show-inheritance: :mod:`stationarity` Module -------------------------- .. automodule:: spykeutils.stationarity :members: :mod:`tools` Module ------------------------ .. automodule:: spykeutils.tools :members: """ __version__ = '0.4.1' class SpykeException(Exception): """ Exception thrown when a function in spykeutils encounters a problem that is not covered by standard exceptions. When using Spyke Viewer, these exceptions will be caught and shown in the GUI, while general exceptions will not be caught (and therefore be visible in the console) for easier debugging. """ pass r}rXSpykeExceptionrXclassrKNKXs}rXSpykeExceptionrjFsj<Xspykeutils.rate_estimationrr}rbX{)from __future__ import division import scipy as sp import quantities as pq import neo from progress_indicator import ProgressIndicator import signal_processing as sigproc import tools import copy as cp from . import SpykeException def psth( trains, bin_size, rate_correction=True, start=0 * pq.ms, stop=sp.inf * pq.s): """ Return dictionary of peri stimulus time histograms for a dictionary of spike train lists. :param dict trains: A dictionary of lists of :class:`neo.core.SpikeTrain` objects. :param bin_size: The desired bin size (as a time quantity). :type bin_size: Quantity scalar :param bool rate_correction: Determines if a rates (``True``) or counts (``False``) are returned. :param start: The desired time for the start of the first bin. It will be recalculated if there are spike trains which start later than this time. :type start: Quantity scalar :param stop: The desired time for the end of the last bin. It will be recalculated if there are spike trains which end earlier than this time. :type stop: Quantity scalar :returns: A dictionary (with the same indices as ``trains``) of arrays containing counts (or rates if ``rate_correction`` is ``True``) and the bin borders. :rtype: dict, Quantity 1D """ if not trains: raise SpykeException('No spike trains for PSTH!') start, stop = tools.minimum_spike_train_interval(trains, start, stop) binned, bins = tools.bin_spike_trains(trains, 1.0 / bin_size, start, stop) cumulative = {} time_multiplier = 1.0 / float(bin_size.rescale(pq.s)) for u in binned: if rate_correction: cumulative[u] = sp.mean(sp.array(binned[u]), 0) else: cumulative[u] = sp.sum(sp.array(binned[u]), 0) cumulative[u] *= time_multiplier return cumulative, bins def aligned_spike_trains(trains, events, copy=True): """ Return a list of spike trains aligned to an event (the event will be time 0 on the returned trains). :param list trains: A list of :class:`neo.core.SpikeTrain` objects. :param dict events: A dictionary of Event objects, indexed by segment. These events will be used to align the spike trains and will be at time 0 for the aligned spike trains. :param bool copy: Determines if aligned copies of the original spike trains will be returned. If not, every spike train needs exactly one corresponding event, otherwise a ``ValueError`` will be raised. Otherwise, entries with no event will be ignored. """ ret = [] for t in trains: s = t.segment if s not in events: if not copy: raise ValueError( 'Cannot align spike trains: At least one segment does' + 'not have an align event.') continue e = events[s] if copy: st = neo.SpikeTrain( t, t.t_stop, units=t.units, sampling_rate=t.sampling_rate, t_start=t.t_start, waveforms=t.waveforms, left_sweep=t.left_sweep, name=t.name, file_origin=t.file_origin, description=t.description, **t.annotations) else: st = t st -= e.time st.t_stop -= e.time st.t_start -= e.time ret.append(st) return ret def spike_density_estimation(trains, start=0 * pq.ms, stop=None, kernel=None, kernel_size=100 * pq.ms, optimize_steps=None, progress=None): """ Create a spike density estimation from a dictionary of lists of spike trains. The spike density estimations give an estimate of the instantaneous rate. The density estimation is evaluated at 1024 equally spaced points covering the range of the input spike trains. Optionally finds optimal kernel size for given data using the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010). :param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists. :param start: The desired time for the start of the estimation. It will be recalculated if there are spike trains which start later than this time. This parameter can be negative (which could be useful when aligning on events). :type start: Quantity scalar :param stop: The desired time for the end of the estimation. It will be recalculated if there are spike trains which end earlier than this time. :type stop: Quantity scalar :param kernel: The kernel function or instance to use, should accept two parameters: A ndarray of distances and a kernel size. The total area under the kernel function should be 1. Automatic optimization assumes a Gaussian kernel and will likely not produce optimal results for different kernels. Default: Gaussian kernel :type kernel: func or :class:`.signal_processing.Kernel` :param kernel_size: A uniform kernel size for all spike trains. Only used if optimization of kernel sizes is not used. :type kernel_size: Quantity scalar :param optimize_steps: An array of time lengths that will be considered in the kernel width optimization. Note that the optimization assumes a Gaussian kernel and will most likely not give the optimal kernel size if another kernel is used. If None, ``kernel_size`` will be used. :type optimize_steps: Quantity 1D :param progress: Set this parameter to report progress. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Three values: * A dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as ``trains``. * A dictionary of kernel sizes (Quantity scalars). Indexed the same as ``trains``. * The used evaluation points. :rtype: dict, dict, Quantity 1D """ if not progress: progress = ProgressIndicator() if optimize_steps is None or len(optimize_steps) < 1: units = kernel_size.units else: units = optimize_steps.units if kernel is None: kernel = sigproc.GaussianKernel(100 * pq.ms) # Prepare evaluation points max_start, max_stop = tools.minimum_spike_train_interval(trains) start = max(start, max_start) start.units = units if stop is not None: stop = min(stop, max_stop) else: stop = max_stop stop.units = units bins = sp.linspace(start, stop, 1025) eval_points = bins[:-1] + (bins[1] - bins[0]) / 2 if optimize_steps is None or len(optimize_steps) < 1: kernel_size = {u: kernel_size for u in trains} else: # Find optimal kernel size for all spike train sets progress.set_ticks(len(optimize_steps) * len(trains)) progress.set_status('Calculating optimal kernel size') kernel_size = {} for u, t in trains.iteritems(): c = collapsed_spike_trains(t) kernel_size[u] = optimal_gauss_kernel_size( c.time_slice(start, stop), optimize_steps, progress) progress.set_ticks(len(trains)) progress.set_status('Creating spike density plot') # Calculate KDEs kde = {} for u, t in trains.iteritems(): # Collapse spike trains collapsed = collapsed_spike_trains(t).rescale(units) scaled_kernel = sigproc.as_kernel_of_size(kernel, kernel_size[u]) # Create density estimation using convolution sliced = collapsed.time_slice(start, stop) sampling_rate = 1024.0 / (sliced.t_stop - sliced.t_start) kde[u] = sigproc.st_convolve( sliced, scaled_kernel, sampling_rate, kernel_discretization_params={ 'num_bins': 2048, 'ensure_unit_area': True})[0] / len(trains[u]) kde[u].units = pq.Hz return kde, kernel_size, eval_points def collapsed_spike_trains(trains): """ Return a superposition of a list of spike trains. :param iterable trains: A list of :class:`neo.core.SpikeTrain` objects :returns: A spike train object containing all spikes of the given spike trains. :rtype: :class:`neo.core.SpikeTrain` """ if not trains: return neo.SpikeTrain([], 0 * pq.s) start = min((t.t_start for t in trains)) stop = max((t.t_stop for t in trains)) collapsed = [] for t in trains: collapsed.extend(sp.asarray(t.rescale(stop.units))) return neo.SpikeTrain(collapsed * stop.units, t_stop=stop, t_start=start) def optimal_gauss_kernel_size(train, optimize_steps, progress=None): """ Return the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with :func:`collapsed_spike_trains`) that should be included in a spike density estimation. Implements the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010). :param train: The spike train for which the kernel size should be optimized. :type train: :class:`neo.core.SpikeTrain` :param optimize_steps: Array of kernel sizes to try (the best of these sizes will be returned). :type optimize_steps: Quantity 1D :param progress: Set this parameter to report progress. Will be advanced by len(`optimize_steps`) steps. :type progress: :class:`.progress_indicator.ProgressIndicator` :returns: Best of the given kernel sizes :rtype: Quantity scalar """ if not progress: progress = ProgressIndicator() x = train.rescale(optimize_steps.units) N = len(train) C = {} sampling_rate = 1024.0 / (x.t_stop - x.t_start) dt = float(1.0 / sampling_rate) y_hist = tools.bin_spike_trains({0: [x]}, sampling_rate)[0][0][0] y_hist = sp.asfarray(y_hist) / N / dt for step in optimize_steps: s = float(step) yh = sigproc.smooth( y_hist, sigproc.GaussianKernel(2 * step), sampling_rate, num_bins=2048, ensure_unit_area=True) * optimize_steps.units # Equation from Matlab code, 7/2012 c = (sp.sum(yh ** 2) * dt - 2 * sp.sum(yh * y_hist) * dt + 2 * 1 / sp.sqrt(2 * sp.pi) / s / N) C[s] = c * N * N progress.step() # Return kernel size with smallest cost return min(C, key=C.get) * optimize_steps.unitsr}r(Xspike_density_estimationrXdefrKcK·Xcollapsed_spike_trainsrXdefrKKXpsthr Xdefr K K8Xoptimal_gauss_kernel_sizer Xdefr KMXaligned_spike_trainsr XdefrK8Kcu}r(Xcollapsed_spike_trainsrjFXspike_density_estimationrjFXpsthrjFXoptimal_gauss_kernel_sizerjFXaligned_spike_trainsrjFuj<Xspykeutils.toolsrr}rbXe-import neo import neo.description import quantities as pq import scipy as sp import _scipy_quantities as spq def apply_to_dict(fn, dictionary, *args): """ Applies a function to all spike trains in a dictionary of spike train sequences. :param function fn: Function to apply. Should take a :class:`neo.core.SpikeTrain` as first argument. :param dict dictionary: Dictionary of sequences of :class:`neo.core.SpikeTrain` objects to apply the function to. :param args: Additional arguments which will be passed to ``fn``. :returns: A new dictionary with the same keys as ``dictionary``. :rtype: dict """ applied = {} for k in dictionary: applied[k] = [fn(st, *args) for st in dictionary[k]] return applied def bin_spike_trains(trains, sampling_rate, t_start=None, t_stop=None): """ Creates binned representations of spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :type t_start: The desired time for the start of the first bin as time scalar. It will be the minimum start time of all spike trains if ``None`` is passed. :type t_start: Quantity scalar :param t_stop: The desired time for the end of the last bin as time scalar. It will be the maximum stop time of all spike trains if ``None`` is passed. :type t_stop: Quantity scalar :returns: A dictionary (with the same indices as ``trains``) of lists of spike train counts and the bin borders. :rtype: dict, Quantity 1D with time units """ if t_start is None or t_stop is None: max_start, max_stop = maximum_spike_train_interval(trains) if t_start is None: t_start = max_start if t_stop is None: t_stop = max_stop t_start = t_start.rescale(t_stop.units) duration = t_stop - t_start num_bins = (sampling_rate * duration).simplified bins = sp.arange(num_bins + 1) * (duration / num_bins) + t_start return apply_to_dict(_bin_single_spike_train, trains, bins), bins def _bin_single_spike_train(train, bins): """ Return a binned representation of SpikeTrain object. :param train: A spike train to bin. :type train: :class:`neo.core.SpikeTrain` :param bins: The bin edges, including the rightmost edge, with time units. :type bins: Quantity 1D :returns: The binned spike train. :rtype: 1-D array """ return sp.histogram(train.rescale(bins.units), bins)[0] def concatenate_spike_trains(trains): """ Concatenates spike trains. :param sequence trains: :class:`neo.core.SpikeTrain` objects to concatenate. :returns: A spike train consisting of the concatenated spike trains. The spikes will be in the order of the given spike trains and ``t_start`` and ``t_stop`` will be set to the minimum and maximum value. :rtype: :class:`neo.core.SpikeTrain` """ t_start, t_stop = maximum_spike_train_interval({0: trains}) return neo.SpikeTrain( spq.concatenate([train.view(type=pq.Quantity) for train in trains]), t_start=t_start, t_stop=t_stop) def minimum_spike_train_interval( trains, t_start=-sp.inf * pq.s, t_stop=sp.inf * pq.s): """ Computes the maximum starting time and minimum end time that all given spike trains share. This yields the shortest interval shared by all spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param t_start: Minimal starting time to return. :type t_start: Quantity scalar :param t_stop: Maximum end time to return. If ``None``, infinity is used. :type t_stop: Quantity scalar :returns: Maximum shared t_start time and minimum shared t_stop time as time scalars. :rtype: Quantity scalar, Quantity scalar """ if t_stop is None: t_stop = sp.inf * pq.s # Load data and find shortest spike train for st in trains.itervalues(): if len(st) > 0: # Minimum length of spike of all spike trains for this unit t_start = max(t_start, max((t.t_start for t in st))) t_stop = min(t_stop, min((t.t_stop for t in st))) return t_start, t_stop def maximum_spike_train_interval( trains, t_start=sp.inf * pq.s, t_stop=-sp.inf * pq.s): """ Computes the minimum starting time and maximum end time of all given spike trains. This yields an interval containing the spikes of all spike trains. :param dict trains: A dictionary of sequences of :class:`neo.core.SpikeTrain` objects. :param t_start: Maximum starting time to return. :type t_start: Quantity scalar :param t_stop: Minimum end time to return. If ``None``, infinity is used. :type t_stop: Quantity scalar :returns: Minimum t_start time and maximum t_stop time as time scalars. :rtype: Quantity scalar, Quantity scalar """ if t_stop is None: t_stop = sp.inf * pq.s for st in trains.itervalues(): if len(st) > 0: t_start = min(t_start, min((t.t_start for t in st))) t_stop = max(t_stop, max((t.t_stop for t in st))) return t_start, t_stop def _handle_orphans(obj, remove): """ Removes half-orphaned Spikes and SpikeTrains that occur when removing an object upwards in the hierarchy. """ if isinstance(obj, neo.Segment): for s in obj.spikes: if s.unit: if not remove: s.segment = None else: try: s.unit.spikes.remove(s) except ValueError: pass for st in obj.spiketrains: if st.unit: if not remove: st.segment = None else: try: st.unit.spiketrains.remove(st) except ValueError: pass elif isinstance(obj, neo.Unit): for s in obj.spikes: if s.segment: if not remove: s.unit = None else: try: s.segment.spikes.remove(s) except ValueError: pass for st in obj.spiketrains: if st.segment: if not remove: st.unit = None else: try: st.segment.spiketrains.remove(st) except ValueError: pass elif isinstance(obj, neo.RecordingChannelGroup): for u in obj.units: _handle_orphans(u, remove) def remove_from_hierarchy(obj, remove_half_orphans=True): """ Removes a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in :class:`neo.core.Spike` or :class:`neo.core.SpikeTrain` objects). For example, when ``obj`` is a :class:`neo.core.Segment`, the link from its parent :class:`neo.core.Block` will be severed. Also, all links to the segment from its spikes and spike trains will be severed. :param obj: The object to be removed. :type obj: Neo object :param bool remove_half_orphans: When True, :class:`neo.core.Spike` and :class:`neo.core.SpikeTrain` belonging to a :class:`neo.core.Segment` or :class:`neo.core.Unit` removed by this function will be removed from the hierarchy as well, even if they are still linked from a :class:`neo.core.Unit` or :class:`neo.core.Segment`, respectively. In this case, their links to the hierarchy defined by ``obj`` will be kept intact. """ classname = type(obj).__name__ # Parent for arbitrary object if classname in neo.description.many_to_one_relationship: for n in neo.description.many_to_one_relationship[classname]: p = getattr(obj, n.lower()) if p is None: continue l = getattr(p, classname.lower() + 's', ()) try: l.remove(obj) except ValueError: pass # Many-to-many relationships if isinstance(obj, neo.RecordingChannel): for rcg in obj.recordingchannelgroups: try: idx = rcg.recordingchannels.index(obj) if rcg.channel_indexes.shape[0] == len(rcg.recordingchannels): rcg.channel_indexes = sp.delete(rcg.channel_indexes, idx) if rcg.channel_names.shape[0] == len(rcg.recordingchannels): rcg.channel_names = sp.delete(rcg.channel_names, idx) rcg.recordingchannels.remove(obj) except ValueError: pass if isinstance(obj, neo.RecordingChannelGroup): for rc in obj.recordingchannels: try: rc.recordingchannelgroups.remove(obj) except ValueError: pass _handle_orphans(obj, remove_half_orphans) def extract_spikes(train, signals, length, align_time): """ Extract spikes with waveforms from analog signals using a spike train. Spikes that are too close to the beginning or end of the shortest signal to be fully extracted are ignored. :type train: :class:`neo.core.SpikeTrain` :param train: The spike times. :param sequence signals: A sequence of :class:`neo.core.AnalogSignal` objects from which the spikes are extracted. The waveforms of the returned spikes are extracted from these signals in the same order they are given. :type length: Quantity scalar :param length: The length of the waveform to extract as time scalar. :type align_time: Quantity scalar :param align_time: The alignment time of the spike times as time scalar. This is the time delta from the start of the extracted waveform to the exact time of the spike. :returns: A list of :class:`neo.core.Spike` objects, one for each time point in ``train``. All returned spikes include their ``waveform`` property. :rtype: list """ if not signals: raise ValueError('No signals to extract spikes from') ref = signals[0] for s in signals[1:]: if ref.sampling_rate != s.sampling_rate: raise ValueError( 'All signals for spike extraction need the same sampling rate') wave_unit = signals[0].units srate = signals[0].sampling_rate end = min(s.shape[0] for s in signals) aligned_train = train - align_time cut_samples = int((length * srate).simplified) st = sp.asarray((aligned_train * srate).simplified) # Find extraction epochs st_ok = (st >= 0) * (st < end - cut_samples) epochs = sp.vstack((st[st_ok], st[st_ok] + cut_samples)).T nspikes = epochs.shape[0] if not nspikes: return [] # Create data data = sp.vstack([sp.asarray(s.rescale(wave_unit)) for s in signals]) nc = len(signals) spikes = [] for s in xrange(nspikes): waveform = sp.zeros((cut_samples, nc)) for c in xrange(nc): waveform[:, c] = \ data[c, epochs[s, 0]:epochs[s, 1]] spikes.append(neo.Spike(train[st_ok][s], waveform=waveform * wave_unit, sampling_rate=srate)) return spikesr}r(X apply_to_dictrXdefrKKXbin_spike_trainsrXdefrKK>X_bin_single_spike_trainrXdefrK>KKXmaximum_spike_train_intervalr Xdefr!KyKXremove_from_hierarchyr"Xdefr#KKXconcatenate_spike_trainsr$Xdefr%KKK\Xextract_spikesr&Xdefr'KM8Xminimum_spike_train_intervalr(Xdefr)K\KyX_handle_orphansr*Xdefr+KKću}r,(X apply_to_dictr-jFXbin_spike_trainsr.jFXmaximum_spike_train_intervalr/jFXremove_from_hierarchyr0jFXconcatenate_spike_trainsr1jFXextract_spikesr2jFXminimum_spike_train_intervalr3jFuj<Xspykeutils.spike_train_metricsr4r5}r6bXfrom monkeypatch import quantities_patch import quantities as pq import scipy as sp import _scipy_quantities as spq import signal_processing as sigproc import tools assert quantities_patch # Suppress pyflakes warning, patch applied by loading def _calc_multiunit_dist_matrix_from_single_trials(units, dist_func, **params): if len(units) <= 0: return sp.zeros((0, 0)) num_trials = len(units.itervalues().next()) if not all((len(v) == num_trials for v in units.itervalues())): raise ValueError("Number of trials differs among units.") D = sp.empty((num_trials, num_trials)) for i in xrange(num_trials): D[i, i] = 0.0 a = [units[k][i] for k in units.iterkeys()] for j in xrange(i + 1, num_trials): b = [units[k][j] for k in units.iterkeys()] D[i, j] = D[j, i] = dist_func(a, b, **params) return D def _create_matrix_from_indexed_function( shape, func, symmetric_2d=False, **func_params): mat = sp.empty(shape) if symmetric_2d: for i in xrange(shape[0]): for j in xrange(i, shape[1]): mat[i, j] = mat[j, i] = func(i, j, **func_params) else: for idx in sp.ndindex(*shape): mat[idx] = func(*idx, **func_params) return mat def _merge_trains_and_label_spikes(trains): labels = sp.concatenate( [sp.zeros(st.size, dtype=int) + i for i, st in enumerate(trains)]) trains = spq.concatenate([st.view(dtype=pq.Quantity) for st in trains]) sorted_indices = sp.argsort(trains) return trains[sorted_indices], labels[sorted_indices] def cs_dist( trains, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the Cauchy-Schwarz distance between two spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \\int_{\\mathcal{T}} v_a(t) v_b(t) dt`. Then, the Cauchy-Schwarz distance of the spike trains is defined as :math:`d_{CS}(a, b) = \\arccos \\frac{V(a, b)^2}{V(a, a) V(b, b)}`. The Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure :math:`S_S` by :math:`d_{CS} = \\arccos S_S^2` This function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use :func:`schreiber_similarity` for a more efficient and precise calculation. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param smoothing_filter: Smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the Cauchy-Schwarz distance of all pairs of spike trains :rtype: 2-D array """ inner = st_inner( trains, trains, smoothing_filter, sampling_rate, filter_area_fraction) return sp.arccos( inner ** 2 / sp.diag(inner) / sp.atleast_2d(sp.diag(inner)).T) def event_synchronization( trains, tau=None, kernel=sigproc.RectangularKernel(1.0, normalize=False), sort=True): """ event_synchronization(trains, tau=None, kernel=signal_processing.RectangularKernel(1.0, normalize=False), sort=True) Calculates the event synchronization. Let :math:`d(x|y)` be the count of spikes in :math:`y` which occur shortly before an event in :math:`x` with a time difference of less than :math:`\\tau`. Moreover, let :math:`n_x` and :math:`n_y` be the number of total spikes in the spike trains :math:`x` and :math:`y`. The event synchrony is then defined as :math:`Q_T = \\frac{d(x|y) + d(y|x)}{\\sqrt{n_x n_y}}`. The time maximum time lag :math:`\\tau` can be determined automatically for each pair of spikes :math:`t^x_i` and :math:`t^y_j` by the formula :math:`\\tau_{ij} = \\frac{1}{2} \\min\{t^x_{i+1} - t^x_i, t^x_i - t^x_{i-1}, t^y_{j+1} - t^y_j, t^y_j - t^y_{j-1}\}` Further and more detailed information can be found in *Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the van Rossum distance will be calculated pairwise. :param tau: The maximum time lag for two spikes to be considered coincident or synchronous as time scalar. To have it determined automatically by above formula set it to `None`. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times are be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the event synchronization for all pairs of spike trains. :rtype: 2-D array """ trains = [st.view(type=pq.Quantity) for st in trains] if sort: trains = [sp.sort(st) for st in trains] if tau is None: inf_array = sp.array([sp.inf]) isis = [spq.concatenate( (inf_array * st.units, sp.diff(st), inf_array * st.units)) for st in trains] auto_taus = [spq.minimum(t[:-1], t[1:]) for t in isis] def compute(i, j): if i == j: return 1.0 else: if tau is None: tau_mat = spq.minimum(*spq.meshgrid( auto_taus[i], auto_taus[j])) / 2.0 else: tau_mat = sp.tile(tau, (trains[j].size, trains[i].size)) coincidence = sp.sum(kernel( (trains[i] - sp.atleast_2d(trains[j]).T) / tau_mat)) normalization = 1.0 / sp.sqrt(trains[i].size * trains[j].size) return normalization * coincidence return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric()) def hunter_milton_similarity(trains, tau=1.0 * pq.s, kernel=None): """ Calculates the Hunter-Milton similarity measure. If the kernel function is denoted as :math:`K(t)`, a function :math:`d(x_k) = K(x_k - y_{k'})` can be defined with :math:`y_{k'}` being the closest spike in spike train :math:`y` to the spike :math:`x_k` in spike train :math:`x`. With this the Hunter-Milton similarity measure is :math:`S_H = \\frac{1}{2} \\left(\\frac{1}{n_x} \\sum_{k = 1}^{n_x} d(x_k) + \\frac{1}{n_y} \\sum_{k' = 1}^{n_y} d(y_{k'})\\right)`. This implementation returns 0 if one of the spike trains is empty, but 1 if both are empty. Further information can be found in - *Hunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.* - *Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the Hunter-Milton similarity will be calculated pairwise. :param tau: The time scale for determining the coincidence of two events as time scalar. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `None`, a unnormalized Laplacian kernel will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: Matrix containing the Hunter-Milton similarity for all pairs of spike trains. :rtype: 2-D array """ if kernel is None: kernel = sigproc.LaplacianKernel(tau, normalize=False) def compute(i, j): if i == j: return 1.0 elif trains[i].size <= 0 or trains[j].size <= 0: return 0.0 else: diff_matrix = sp.absolute(trains[i] - sp.atleast_2d(trains[j]).T) return 0.5 * ( sp.sum(kernel(sp.amin(diff_matrix, axis=0))) / trains[i].size + sp.sum(kernel(sp.amin(diff_matrix, axis=1))) / trains[j].size) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric()) def norm_dist( trains, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the norm distance between spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as :math:`d_{ND}(a, b) = \\sqrt{\\int_{\\mathcal{T}} (v_a(t) - v_b(t))^2 dt}`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param smoothing_filter: Smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike trains as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the norm distance of all pairs of spike trains given the smoothing_filter. :rtype: Quantity 2D with units depending on the smoothing filter (usually temporal frequency units) """ inner = st_inner( trains, trains, smoothing_filter, sampling_rate, filter_area_fraction) return spq.maximum( 0.0 * pq.Hz, (spq.diag(inner) + sp.atleast_2d(spq.diag(inner)).T - 2 * inner)) ** 0.5 def schreiber_similarity(trains, kernel, sort=True): """ Calculates the Schreiber et al. similarity measure between spike trains given a kernel. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \\int_{\\mathcal{T}} v_a(t) v_b(t) dt`. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as :math:`S_{S}(a, b) = \\frac{V(a, b)}{\\sqrt{V(a, a) V(b, b)}}`. It is closely related to the Cauchy-Schwarz distance :math:`d_{CS}` by :math:`S_S = \\sqrt{\\cos d_{CS}}`. In opposite to :func:`cs_dist` which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter's autocorrelation. This allows a more accurate and faster calculation. Further information can be found in: - *Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.* - *Paiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6* :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param kernel: Kernel to use. It corresponds to a smoothing filter by being the autocorrelation of such a filter. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times will be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the Schreiber et al. similarity measure of all pairs of spike trains. :rtype: 2-D array """ k_dist = kernel.summed_dist_matrix(trains, not sort) def compute(i, j): return sp.sqrt( k_dist[i, j] * k_dist[j, i] / k_dist[i, i] / k_dist[j, j]) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric()) def st_inner( a, b, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the inner product of spike trains given a smoothing filter. Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \\in \\mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as :math:`\\int_{\\mathcal{T}} v_a(t)v_b(t) dt`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param sequence a: Sequence of :class:`neo.core.SpikeTrain` objects. :param sequence b: Sequence of :class:`neo.core.SpikeTrain` objects. :param smoothing_filter: A smoothing filter to be convolved with the spike trains. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the `smoothing_filter` will be discretized. At least the given fraction of the complete `smoothing_filter` area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: Matrix containing the inner product for each pair of spike trains with one spike train from `a` and the other one from `b`. :rtype: Quantity 2D with units depending on the smoothing filter (usually temporal frequency units) """ if all((x is y for x, y in zip(a, b))): convolved, sampling_rate = _prepare_for_inner_prod( a, smoothing_filter, sampling_rate, filter_area_fraction) convolved = convolved + convolved else: convolved, sampling_rate = _prepare_for_inner_prod( a + b, smoothing_filter, sampling_rate, filter_area_fraction) return (sp.inner(convolved[:len(a)], convolved[len(a):]) * convolved[0].units * convolved[1].units / sampling_rate) def _prepare_for_inner_prod( trains, smoothing_filter, sampling_rate, filter_area_fraction): t_start, t_stop = tools.maximum_spike_train_interval({0: trains}) padding = smoothing_filter.boundary_enclosing_at_least(filter_area_fraction) t_start -= 2 * padding t_stop += 2 * padding return [sigproc.st_convolve( st, smoothing_filter, sampling_rate, mode='full', binning_params={'t_start': t_start, 't_stop': t_stop}, kernel_discretization_params={'area_fraction': filter_area_fraction})[0] for st in trains], sampling_rate def st_norm( train, smoothing_filter, sampling_rate, filter_area_fraction=sigproc.default_kernel_area_fraction): """ Calculates the spike train norm given a smoothing filter. Let :math:`v(t)` with :math:`t \\in \\mathcal{T}` be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as :math:`\\int_{\\mathcal{T}} v(t)^2 dt`. Further information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.* :param train: Spike train of which to calculate the norm. :type train: :class:`neo.core.SpikeTrain` :param smoothing_filter: Smoothing filter to be convolved with the spike train. :type smoothing_filter: :class:`.signal_processing.Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train as inverse time scalar. :type sampling_rate: Quantity scalar :param float filter_area_fraction: A value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate). :returns: The norm of the spike train given the smoothing_filter. :rtype: Quantity scalar with units depending on the smoothing filter (usually temporal frequency units) """ return st_inner( [train], [train], smoothing_filter, sampling_rate, filter_area_fraction) ** 0.5 def van_rossum_dist(trains, tau=1.0 * pq.s, kernel=None, sort=True): """ Calculates the van Rossum distance. It is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper. Given :math:`N` spike trains with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)`. An implementation in :math:`O(N^2 n)` would be possible but has a high constant factor rendering it slower in practical cases. :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the van Rossum distance will be calculated pairwise. :param tau: Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if `kernel` is not `None`. May also be :const:`scipy.inf` which will lead to only measuring differences in spike count. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If `kernel` is `None`, an unnormalized Laplacian kernel with a size of `tau` will be used. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times might be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the van Rossum distances for all pairs of spike trains. :rtype: 2-D array """ if kernel is None: if tau == sp.inf: spike_counts = [st.size for st in trains] return (spike_counts - sp.atleast_2d(spike_counts).T) ** 2 kernel = sigproc.LaplacianKernel(tau, normalize=False) k_dist = kernel.summed_dist_matrix( [st.view(type=pq.Quantity) for st in trains], not sort) vr_dist = sp.empty_like(k_dist) for i, j in sp.ndindex(*k_dist.shape): vr_dist[i, j] = ( k_dist[i, i] + k_dist[j, j] - k_dist[i, j] - k_dist[j, i]) return sp.sqrt(vr_dist) def van_rossum_multiunit_dist(units, weighting, tau=1.0 * pq.s, kernel=None): """ Calculates the van Rossum multi-unit distance. The single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper. Given the :math:`p`- and :math:`q`-th spike train of `a` and respectively `b` let :math:`R_{pq}` be the squared single-unit distance between these two spike trains. Then the multi-unit distance is :math:`\\sqrt{\\sum_p (R_{pp} + c \\cdot \\sum_{q \\neq p} R_{pq})}` with :math:`c` being equal to `weighting`. The weighting parameter controls the interpolation between a labeled line and a summed population coding. More information can be found in *Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.* Given :math:`N` spike trains in total with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + Nn^2)` memory will be needed. :param dict units: Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a :class:`neo.core.SpikeTrain` and all units should have the same number of trials. :param float weighting: Controls the interpolation between a labeled line and a summed population coding. :param tau: Decay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if `kernel` is not `None`. May also be :const:`scipy.inf` which will lead to only measuring differences in spike count. :type tau: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If `kernel` is `None`, an unnormalized Laplacian kernel with a size of `tau` will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: A 2D array with the multi-unit distance for each pair of trials. :rtype: 2D arrary """ if kernel is None and tau != sp.inf: kernel = sigproc.LaplacianKernel(tau, normalize=False) return _calc_multiunit_dist_matrix_from_single_trials( units, _van_rossum_multiunit_dist_for_trial_pair, weighting=weighting, tau=tau, kernel=kernel) def _van_rossum_multiunit_dist_for_trial_pair(a, b, weighting, tau, kernel): if kernel is None: spike_counts = sp.atleast_2d([st.size for st in a + b]) k_dist = spike_counts.T * (spike_counts - spike_counts.T) else: k_dist = kernel.summed_dist_matrix(a + b) non_diagonal = sp.logical_not(sp.eye(len(a))) summed_population = ( sp.trace(k_dist) - sp.trace(k_dist, len(a)) - sp.trace(k_dist, -len(a))) labeled_line = ( sp.sum(k_dist[:len(a), :len(a)][non_diagonal]) + sp.sum(k_dist[len(a):, len(a):][non_diagonal]) - sp.sum(k_dist[:len(a), len(a):][non_diagonal]) - sp.sum(k_dist[len(a):, :len(a)][non_diagonal])) return sp.sqrt(summed_population + weighting * labeled_line) def victor_purpura_dist(trains, q=1.0 * pq.Hz, kernel=None, sort=True): """ Calculates the Victor-Purpura's (VP) distance. It is often denoted as :math:`D^{\\text{spike}}[q]`. It is defined as the minimal cost of transforming spike train `a` into spike train `b` by using the following operations: * Inserting or deleting a spike (cost 1.0). * Shifting a spike from :math:`t` to :math:`t'` (cost :math:`q \\cdot |t - t'|`). A detailed description can be found in *Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.* Given the average number of spikes :math:`n` in a spike train and :math:`N` spike trains the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + n^2)` memory will be needed. :param sequence trains: Sequence of :class:`neo.core.SpikeTrain` objects of which the distance will be calculated pairwise. :param q: Cost factor for spike shifts as inverse time scalar. If `kernel` is not `None`, `q` will be ignored. :type q: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `kernel` is `None`, an unnormalized triangular kernel with a half width of `2.0/q` will be used. :type kernel: :class:`.signal_processing.Kernel` :param bool sort: Spike trains with sorted spike times will be needed for the calculation. You can set `sort` to `False` if you know that your spike trains are already sorted to decrease calculation time. :returns: Matrix containing the VP distance of all pairs of spike trains. :rtype: 2-D array """ if kernel is None: if q == 0.0: num_spikes = sp.atleast_2d([st.size for st in trains]) return sp.absolute(num_spikes.T - num_spikes) else: kernel = sigproc.TriangularKernel(2.0 / q, normalize=False) if sort: trains = [sp.sort(st.view(type=pq.Quantity)) for st in trains] def compute(i, j): if i == j: return 0.0 else: return _victor_purpura_dist_for_trial_pair( trains[i], trains[j], kernel) return _create_matrix_from_indexed_function( (len(trains), len(trains)), compute, kernel.is_symmetric()) def _victor_purpura_dist_for_trial_pair(a, b, kernel): if a.size <= 0 or b.size <= 0: return max(a.size, b.size) if a.size < b.size: a, b = b, a # The algorithm used is based on the one given in # # Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal # coding in visual cortex: a metric-space analysis. Journal of # Neurophysiology. # # It constructs a matrix G[i, j] containing the minimal cost when only # considering the first i and j spikes of the spike trains. However, one # never needs to store more than one row and one column at the same time # for calculating the VP distance. # cost[0, :cost.shape[1] - i] corresponds to G[i:, i]. In the same way # cost[1, :cost.shape[1] - i] corresponds to G[i, i:]. # # Moreover, the minimum operation on the costs of the three kind of actions # (delete, insert or move spike) can be split up in two operations. One # operation depends only on the already calculated costs and kernel # evaluation (insertion of spike vs moving a spike). The other minimum # depends on that result and the cost of deleting a spike. This operation # always depends on the last calculated element in the cost array and # corresponds to a recursive application of # f(accumulated_min[i]) = min(f(accumulated_min[i-1]), accumulated_min[i]) # + 1. That '+1' can be excluded from this function if the summed value for # all recursive applications is added upfront to accumulated_min. # Afterwards it has to be removed again except one for the currently # processed spike to get the real costs up to the evaluation of i. # # All currently calculated costs will be considered -1 because this saves # a number of additions as in most cases the cost would be increased by # exactly one (the only exception is shifting, but in that calculation is # already the addition of a constant involved, thus leaving the number of # operations the same). The increase by one will be added after calculating # all minima by shifting decreasing_sequence by one when removing it from # accumulated_min. min_dim, max_dim = b.size, a.size + 1 cost = sp.asfortranarray(sp.tile(sp.arange(float(max_dim)), (2, 1))) decreasing_sequence = sp.asfortranarray(cost[:, ::-1]) k = 1 - 2 * sp.asfortranarray(kernel( (sp.atleast_2d(a).T - b).view(type=pq.Quantity)).simplified) for i in xrange(min_dim): # determine G[i, i] == accumulated_min[:, 0] #accumulated_min = sp.empty((2, max_dim - i - 1)) accumulated_min = cost[:, :-i - 1] + k[i:, i] accumulated_min[1, :b.size - i] = cost[1, :b.size - i] + k[i, i:] accumulated_min = sp.minimum( accumulated_min, # shift cost[:, 1:max_dim - i]) # insert acc_dim = accumulated_min.shape[1] # delete vs min(insert, shift) accumulated_min[:, 0] = min(cost[1, 1], accumulated_min[0, 0]) # determine G[i, :] and G[:, i] by propagating minima. accumulated_min += decreasing_sequence[:, -acc_dim - 1:-1] accumulated_min = sp.minimum.accumulate(accumulated_min, axis=1) cost[:, :acc_dim] = accumulated_min - decreasing_sequence[:, -acc_dim:] return cost[0, -min_dim - 1] def victor_purpura_multiunit_dist( units, reassignment_cost, q=1.0 * pq.Hz, kernel=None): """ Calculates the Victor-Purpura's (VP) multi-unit distance. It is defined as the minimal cost of transforming the spike trains `a` into spike trains `b` by using the following operations: * Inserting or deleting a spike (cost 1.0). * Shifting a spike from :math:`t` to :math:`t'` (cost :math:`q \\cdot |t - t'|`). * Moving a spike to another spike train (cost `reassignment_cost`). A detailed description can be found in *Aronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.* Given the average number of spikes :math:`N` in a spike train and :math:`L` units with :math:`n` spike trains each the run-time complexity is :math:`O(n^2 LN^{L+1})`. The space complexity is :math:`O(n^2 + LN^{L+1})`. For calculating the distance between only two units one should use :func:`victor_purpura_dist` which is more efficient. :param dict units: Dictionary of sequences with each sequence containing the trials of one unit. Each trial should be a :class:`neo.core.SpikeTrain` and all units should have the same number of trials. :param float reassignment_cost: Cost to reassign a spike from one train to another (sometimes denoted with :math:`k`). Should be between 0 and 2. For 0 spikes can be reassigned without any cost, for 2 and above it is cheaper to delete and reinsert a spike. :param q: Cost factor for spike shifts as inverse time scalar. If `kernel` is not `None`, `q` will be ignored. :type q: Quantity scalar :param kernel: Kernel to use in the calculation of the distance. If `kernel` is `None`, an unnormalized triangular kernel with a half width of `2.0/q` will be used. :type kernel: :class:`.signal_processing.Kernel` :returns: A 2D array with the multi-unit distance for each pair of trials. :rtype: 2D arrary """ if kernel is None: kernel = sigproc.TriangularKernel(2.0 / q, normalize=False) return _calc_multiunit_dist_matrix_from_single_trials( units, _victor_purpura_multiunit_dist_for_trial_pair, reassignment_cost=reassignment_cost, kernel=kernel) def _victor_purpura_multiunit_dist_for_trial_pair( a, b, reassignment_cost, kernel): # The algorithm used is based on the one given in # # Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal # coding in visual cortex: a metric-space analysis. Journal of # Neurophysiology. # # It constructs a matrix cost[i, j_1, ... j_L] containing the minimal cost # when only considering the first i spikes of the merged spikes of a and # j_w spikes of the spike trains of b (the reference given above denotes # this matrix with G). In this implementation the only the one submatrix # for one specific i is stored as in each step only i-1 and i will be # accessed. That saves some memory. # Initialization of various variables needed by the algorithm. Also swap # a and b if it will save time as the algorithm is not symmetric. a_num_spikes = [st.size for st in a] b_num_spikes = [st.size for st in b] a_num_total_spikes = sp.sum(a_num_spikes) complexity_same = a_num_total_spikes * sp.prod(b_num_spikes) complexity_swapped = sp.prod(a_num_spikes) * sp.sum(b_num_spikes) if complexity_swapped < complexity_same: a, b = b, a a_num_spikes, b_num_spikes = b_num_spikes, a_num_spikes a_num_total_spikes = sp.sum(a_num_spikes) if a_num_total_spikes <= 0: return sp.sum(b_num_spikes) b_dims = tuple(sp.asarray(b_num_spikes) + 1) cost = sp.asfarray(sp.sum(sp.indices(b_dims), axis=0)) a_merged = _merge_trains_and_label_spikes(a) b_strides = sp.cumprod((b_dims + (1,))[::-1])[:-1] flat_b_indices = sp.arange(cost.size) b_indices = sp.vstack(sp.unravel_index(flat_b_indices, b_dims)) flat_neighbor_indices = sp.maximum( 0, sp.atleast_2d(flat_b_indices).T - b_strides[::-1]) invalid_neighbors = b_indices.T == 0 b_train_mat = sp.empty((len(b), sp.amax(b_num_spikes))) * b[0].units for i, st in enumerate(b): b_train_mat[i, :st.size] = st.rescale(b[0].units) b_train_mat[i, st.size:] = sp.nan * b[0].units reassignment_costs = sp.empty((a_merged[0].size,) + b_train_mat.shape) reassignment_costs.fill(reassignment_cost) reassignment_costs[sp.arange(a_merged[1].size), a_merged[1], :] = 0.0 k = 1 - 2 * kernel(sp.atleast_2d( a_merged[0]).T - b_train_mat.flatten()).simplified.reshape( (a_merged[0].size,) + b_train_mat.shape) + reassignment_costs decreasing_sequence = flat_b_indices[::-1] # Do the actual calculations. for a_idx in xrange(1, a_num_total_spikes + 1): base_costs = cost.flat[flat_neighbor_indices] base_costs[invalid_neighbors] = sp.inf min_base_cost_labels = sp.argmin(base_costs, axis=1) cost_all_possible_shifts = k[a_idx - 1, min_base_cost_labels, :] + \ sp.atleast_2d(base_costs[flat_b_indices, min_base_cost_labels]).T cost_shift = cost_all_possible_shifts[ sp.arange(cost_all_possible_shifts.shape[0]), b_indices[min_base_cost_labels, flat_b_indices] - 1] cost_delete_in_a = cost.flat[flat_b_indices] # cost_shift is dimensionless, but there is a bug in quantities with # the minimum function: # # The explicit request for the magnitude circumvents this problem. cost.flat = sp.minimum(cost_delete_in_a, cost_shift.magnitude) + 1 cost.flat[0] = sp.inf # Minimum with cost for deleting in b # The calculation order is somewhat different from the order one would # expect from the naive algorithm. This implementation, however, # optimizes the use of the CPU cache giving a considerable speed # improvement. # Basically this codes calculates the values of a row of elements for # each dimension of cost. for dim_size, stride in zip(b_dims[::-1], b_strides): for i in xrange(stride): segment_size = dim_size * stride for j in xrange(i, cost.size, segment_size): s = sp.s_[j:j + segment_size:stride] seq = decreasing_sequence[-cost.flat[s].size:] cost.flat[s] = sp.minimum.accumulate( cost.flat[s] + seq) - seq return cost.flat[-1] r7}r8(Xvictor_purpura_distr9Xdefr:MMLXschreiber_similarity.computeXdefr;M1M5X$_create_matrix_from_indexed_functionr<Xdefr=KK*Xschreiber_similarityr>Xdefr?MM9Xcs_distr@XdefrAK2KcX-_victor_purpura_multiunit_dist_for_trial_pairrBXdefrCMMX)_van_rossum_multiunit_dist_for_trial_pairrDXdefrEMMXvan_rossum_multiunit_distrFXdefrGMMX_merge_trains_and_label_spikesrHXdefrIK*K2Xst_innerrJXdefrKM9MgXst_normrLXdefrMMuMX._calc_multiunit_dist_matrix_from_single_trialsrNXdefrOK KXevent_synchronizationrPXdefrQKcKXvan_rossum_distrRXdefrSMMXhunter_milton_similarityrTXdefrUKK݇X#_victor_purpura_dist_for_trial_pairrVXdefrWMLMXevent_synchronization.computeXdefrXKKX_prepare_for_inner_prodrYXdefrZMgMuXvictor_purpura_dist.computeXdefr[MAMHX norm_distr\Xdefr]KMX hunter_milton_similarity.computeXdefr^KKهXvictor_purpura_multiunit_distr_Xdefr`MMu}ra(Xst_innerrbjFXvictor_purpura_distrcjFXst_normrdjFXschreiber_similarityrejFXcs_distrfjFX norm_distrgjFXvan_rossum_multiunit_distrhjFXevent_synchronizationrijFXvan_rossum_distrjjFXhunter_milton_similarityrkjFXvictor_purpura_multiunit_distrljFuj<X!spykeutils.plugin.analysis_pluginrmrn}robX.@import hashlib import json import os import tables import time import gui_data class HashEntry(tables.IsDescription): hash = tables.StringCol(32) filename = tables.StringCol(992) # 1024-32 -> long filenames are possible class AnalysisPlugin(gui_data.DataSet): """ Base class for Analysis plugins. Inherit this class to create a plugin. The two most important methods are :func:`get_name` and :func:`start`. Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results. The GUI configuration uses :mod:`guidata`. Because `AnalysisPlugin` inherits from `DataSet`, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console:: from spykeutils.plugin import analysis_plugin, gui_data class SamplePlugin(analysis_plugin.AnalysisPlugin): some_time = gui_data.FloatItem('Some time', default=2.0, unit='ms') print_more = gui_data.BoolItem('Print additional info', default=True) def start(self, current, selections): print 'The selected time is', self.some_time, 'milliseconds.' if self.print_more: print 'This is important additional information!' The class attribute ``data_dir`` contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory. """ data_dir = '' def __init__(self): super(AnalysisPlugin, self).__init__() def get_name(self): """ Return the name of an analysis. Override to specify analysis name. :returns: The name of the plugin. :rtype: str """ return 'Prototype Plugin' def get_title(self): # Override guidata.DataSet.get_title() return self.get_name() def get_comment(self): # Override guidata.DataSet.get_comment() ret = None if self.__doc__: string = self.__doc__ if not isinstance(string, basestring): string = unicode(string) if not isinstance(string, unicode): string = unicode(string, 'utf-8') doc_lines = string.splitlines() # Remove empty lines at the begining of comment while doc_lines and not doc_lines[0].strip(): del doc_lines[0] if doc_lines: ret = "\n".join([x.strip() for x in doc_lines]) return ret def start(self, current, selections): """ Entry point for processing. Override with analysis code. :param current: This data provider is used if the analysis should be performed on the data currently selected in the GUI. :type current: :class:`spykeviewer.plugin_framework.data_provider.DataProvider` :param list selections: This parameter contains all saved selections. It is used if an analysis needs multiple data sets. """ pass def configure(self): """ Configure the analysis. Override if a different or additional configuration apart from guidata is needed. """ if self._items: return self.edit() def get_parameters(self): """ Return a dictionary of the configuration that can be read with :func:`deserialize_parameters`. Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation). :returns: A dictionary of all configuration parameters. :rtype: dict """ if not hasattr(self, '_items'): return {} ret = {} for i in self._items: v = i.get_value(self) if isinstance(v, str): ret[i._name] = unicode(v) else: ret[i._name] = v return ret def set_parameters(self, parameters): """ Load configuration from a dictionary that has been created by :func:`serialize_parameters`. Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized. :param dict parameters: A dictionary of all configuration parameters. """ for n, v in parameters.iteritems(): if hasattr(self, '_' + n): setattr(self, '_' + n, v) def _get_hash(self, selections, params, use_guiparams): """ Return hash and the three strings used for it (guidata,selections,params) """ if use_guiparams: guidata_string = repr(sorted(self.get_parameters().items())) else: guidata_string = '' selection_string = json.dumps([s.data_dict() for s in selections]) if params: param_string = repr(sorted(params.items())) else: param_string = '' md5 = hashlib.md5() hash_string = guidata_string + selection_string + param_string md5.update(hash_string) return md5.hexdigest(), guidata_string, selection_string, param_string def save(self, name, selections, params=None, save_guiparams=True): """ Return a HDF5 file object with parameters already stored. Save analysis results to this file. :param str name: The name of the results to save. A folder with this name will be used (and created if necessary) to store the analysis result files. :param sequence selections: A list of :class:`DataProvider` objects that are relevant for the analysis results. :param dict params: A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types. :param bool save_guiparams: Determines if the guidata parameters of the class should be saved in the file. :returns: An open PyTables file object ready to be used to store data. Afterwards, the file has to be closed by calling the :func:`tables.File.close` method. :rtype: :class:`tables.File` """ if not selections: selections = [] if not os.path.exists(os.path.join(self.data_dir, name)): os.makedirs(os.path.join(self.data_dir, name)) if params is None: params = {} # Use unicode parameters for n, v in params.iteritems(): if isinstance(v, str): params[n] = unicode(v) # Create parameter hash hash_, guidata_string, selection_string, param_string = \ self._get_hash(selections, params, save_guiparams) # File name is current time stamp time_stamp = time.strftime("%Y%m%d-%H%M%S") file_name_base = os.path.join(self.data_dir, name, time_stamp) file_name = file_name_base # Make sure not to overwrite another file i = 2 while os.path.exists(file_name): file_name = file_name_base + '_%d' % i i += 1 file_name += '.h5' self._add_hash_lookup_entry(name, hash_, file_name) h5 = tables.openFile(file_name, 'w') # Save guidata parameters paramgroup = h5.createGroup('/', 'guiparams') if save_guiparams: guiparams = self.get_parameters() for p, v in guiparams.iteritems(): t = type(v) if t == int or t == float: h5.setNodeAttr(paramgroup, p, v) else: h5.setNodeAttr(paramgroup, p, json.dumps(v)) # Save selections the provided by plugin h5.setNodeAttr('/', 'selections', selection_string) # Save additional parameters provided by plugin paramgroup = h5.createGroup('/', 'userparams') for p, v in params.iteritems(): t = type(v) if t == int or t == float: h5.setNodeAttr(paramgroup, p, v) else: h5.setNodeAttr(paramgroup, p, json.dumps(v)) # Save hash and current time h5.setNodeAttr('/', '_hash', hash_) h5.setNodeAttr('/', 'time', time.time()) return h5 def load(self, name, selections, params=None, consider_guiparams=True): """ Return the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by :func:`save`. :param str name: The name of the results to load. :param sequence selections: A list of :class:`DataProvider` objects that are relevant for the analysis results. :param dict params: A dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types. :param bool consider_guiparams: Determines if the guidata parameters of the class should be considered if they exist in the HDF5 file. This should be set to False if :func:`save` is used with ``save_guiparams`` set to ``False``. :returns: An open PyTables file object ready to be used to read data. Afterwards, the file has to be closed by calling the :func:`tables.File.close` method. If no appropriate file exists, None is returned. :rtype: :class:`tables.File` """ if not selections: selections = [] if not os.path.exists(os.path.join(self.data_dir, name)): return None if params is None: params = {} # Use unicode parameters for n, v in params.iteritems(): if isinstance(v, str): params[n] = unicode(v) hash_, guidata_string, selection_string, param_string =\ self._get_hash(selections, params, consider_guiparams) # Loop through files and find the most recent match file_names = self._get_hash_file_names(name, hash_) newest = 0.0 best = None for fn in file_names: with tables.openFile(fn, 'r') as h5: file_hash = h5.getNodeAttr('/', '_hash') if hash_ != file_hash: continue # Hash is correct, check guidata parameters gui_params = {} for pname in h5.root.guiparams._v_attrs._f_list('user'): v = h5.getNodeAttr('/guiparams', pname) if isinstance(v, str): gui_params[pname] = json.loads(v) else: gui_params[pname] = v if gui_params: gui_param_string = repr(sorted(gui_params.items())) else: gui_param_string = '' if gui_param_string != guidata_string: continue # Check selections file_selections = h5.getNodeAttr('/', 'selections') if file_selections != selection_string: continue # Check custom parameters file_params = {} for pname in h5.root.userparams._v_attrs._f_list('user'): v = h5.getNodeAttr('/userparams', pname) if isinstance(v, str): file_params[pname] = json.loads(v) else: file_params[pname] = v if file_params: file_param_string = repr(sorted(file_params.items())) else: file_param_string = '' if file_param_string != param_string: continue # Make sure the most recent file is used analysis_time = h5.getNodeAttr('/', 'time') if analysis_time < newest: continue best = fn newest = analysis_time if best: return tables.openFile(best, 'r') return None @classmethod def _create_hash_lookup_file(cls, name): """ (Re)creates a hash lookup file for a results directory. This file contains all file hashes in the directory so that the correct file for a given parameter set can be found quickly. :param str name: The name of the results. """ name = os.path.join(cls.data_dir, name) hashfile_name = os.path.join(name, 'hash.h5') hash_file = tables.openFile(hashfile_name, mode='w') table = hash_file.createTable('/', 'lookup_table', HashEntry, title='Hash lookup') # Loop through files and write hashes file_names = [os.path.join(name, f) for f in os.listdir(name)] entry = table.row for fn in file_names: if not fn.endswith('.h5') or fn == 'hash.h5': continue try: with tables.openFile(fn, 'r') as h5: file_hash = h5.getNodeAttr('/', '_hash') entry['hash'] = file_hash entry['filename'] = fn entry.append() except: pass # Not a valid data file, no problem hash_file.close() @classmethod def _add_hash_lookup_entry(cls, name, hash_, file_name): """ Add a new entry to the hash lookup file. :param str name: The name of the results. :param str hash_: The hash of the parameters. :param str file_name: The file name of the results. """ hashfile_name = os.path.join(cls.data_dir, name, 'hash.h5') if not os.path.exists(hashfile_name): cls._create_hash_lookup_file(name) hash_file = tables.openFile(hashfile_name, mode='r+') table = hash_file.root.lookup_table # Add entry entry = table.row entry['hash'] = hash_ entry['filename'] = file_name entry.append() hash_file.close() @classmethod def _get_hash_file_names(cls, name, hash_, _recurse=False): """ Return a list of file names for a parameter hash. If no hash lookup file exists, it will be created. If it can not be created, a list HDF5 files in the directory will be returned. :param str name: The name of the results. :param str hash_: The hash of the parameters. :param bool _recurse: Internal guard against infinite recursion. """ dataname = name name = os.path.join(cls.data_dir, name) hashfile_name = os.path.join(name, 'hash.h5') if not os.path.exists(hashfile_name): try: cls._create_hash_lookup_file(name) except: return [os.path.join(name, f) for f in os.listdir(name) if f.endswith('.h5') and not f == 'hash.h5'] hash_file = tables.openFile(hashfile_name, mode='r') table = hash_file.root.lookup_table files = [row['filename'] for row in table.where('hash == "%s"' % hash_)] ret = [] for f in files: if os.path.exists(f): ret.append(f) elif not _recurse: hash_file.close() try: cls._create_hash_lookup_file(name) except: return [os.path.join(name, f) for f in os.listdir(name) if f.endswith('.h5') and not f == 'hash.h5'] return cls._get_hash_file_names(dataname, hash_, True) hash_file.close() return retrp}rq(XAnalysisPlugin.set_parametersXdefrrK|KXAnalysisPlugin.__init__XdefrsK3K6XAnalysisPlugin.loadXdefrtKMVXAnalysisPlugin.configureXdefruK_KfXAnalysisPlugin.get_titleXdefrvK?KBX%AnalysisPlugin._add_hash_lookup_entryXdefrwMwMXAnalysisPlugin.get_commentXdefrxKBKSXAnalysisPlugin.startXdefryKSK_X#AnalysisPlugin._get_hash_file_namesXdefrzMMXAnalysisPlugin.get_parametersXdefr{KfK|XAnalysisPluginr|Xclassr}KMX'AnalysisPlugin._create_hash_lookup_fileXdefr~MWMvX HashEntryrXclassrK KXAnalysisPlugin.saveXdefrKKXAnalysisPlugin._get_hashXdefrKKXAnalysisPlugin.get_nameXdefrK6K?u}r(XAnalysisPlugin.set_parametersrj=XAnalysisPlugin.loadrj=XAnalysisPlugin.configurerj=XAnalysisPlugin.get_parametersrj=XAnalysisPlugin.startrj=XAnalysisPluginrj=XAnalysisPlugin.saverj=XAnalysisPlugin.get_namerj=uj<Xspykeutils.signal_processingrr}rbXhIimport copy import quantities as pq import scipy as sp import scipy.signal import scipy.special import tools default_kernel_area_fraction = 0.99999 class Kernel(object): """ Base class for kernels. """ def __init__(self, kernel_size, normalize): """ :param kernel_size: Parameter controlling the kernel size. :type kernel_size: Quantity 1D :param bool normalize: Whether to normalize the kernel to unit area. """ self.kernel_size = kernel_size self.normalize = normalize def __call__(self, t, kernel_size=None): """ Evaluates the kernel at all time points in the array `t`. :param t: Time points to evaluate the kernel at. :type t: Quantity 1D :param kernel_size: If not `None` this overwrites the kernel size of the `Kernel` instance. :type kernel_size: Quantity scalar :returns: The result of the kernel evaluations. :rtype: Quantity 1D """ if kernel_size is None: kernel_size = self.kernel_size if self.normalize: normalization = self.normalization_factor(kernel_size) else: normalization = 1.0 * pq.dimensionless return self._evaluate(t, kernel_size) * normalization def _evaluate(self, t, kernel_size): """ Evaluates the kernel. :param t: Time points to evaluate the kernel at. :type t: Quantity 1D :param kernel_size: Controls the width of the kernel. :type kernel_size: Quantity scalar :returns: The result of the kernel evaluations. :rtype: Quantity 1D """ raise NotImplementedError() def normalization_factor(self, kernel_size): """ Returns the factor needed to normalize the kernel to unit area. :param kernel_size: Controls the width of the kernel. :type kernel_size: Quantity scalar :returns: Factor to normalize the kernel to unit width. :rtype: Quantity scalar """ raise NotImplementedError() def boundary_enclosing_at_least(self, fraction): """ Calculates the boundary :math:`b` so that the integral from :math:`-b` to :math:`b` encloses at least a certain fraction of the integral over the complete kernel. :param float fraction: Fraction of the whole area which at least has to be enclosed. :returns: boundary :rtype: Quantity scalar """ raise NotImplementedError() def is_symmetric(self): """ Should return `True` if the kernel is symmetric. """ return False def summed_dist_matrix(self, vectors, presorted=False): """ Calculates the sum of all element pair distances for each pair of vectors. If :math:`(a_1, \\dots, a_n)` and :math:`(b_1, \\dots, b_m)` are the :math:`u`-th and :math:`v`-th vector from `vectors` and :math:`K` the kernel, the resulting entry in the 2D array will be :math:`D_{uv} = \\sum_{i=1}^{n} \\sum_{j=1}^{m} K(a_i - b_j)`. :param sequence vectors: A sequence of Quantity 1D to calculate the summed distances for each pair. The required units depend on the kernel. Usually it will be the inverse unit of the kernel size. :param bool presorted: Some optimized specializations of this function may need sorted vectors. Set `presorted` to `True` if you know that the passed vectors are already sorted to skip the sorting and thus increase performance. :rtype: Quantity 2D """ D = sp.empty((len(vectors), len(vectors))) if len(vectors) > 0: might_have_units = self(vectors[0]) if hasattr(might_have_units, 'units'): D = D * might_have_units.units else: D = D * pq.dimensionless for i, j in sp.ndindex(len(vectors), len(vectors)): D[i, j] = sp.sum(self( (vectors[i] - sp.atleast_2d(vectors[j]).T).flatten())) return D class KernelFromFunction(Kernel): """ Creates a kernel form a function. Please note, that not all methods for such a kernel are implemented. """ def __init__(self, kernel_func, kernel_size): Kernel.__init__(self, kernel_size, normalize=False) self._evaluate = kernel_func def is_symmetric(self): return False def as_kernel_of_size(obj, kernel_size): """ Returns a kernel of desired size. :param obj: Either an existing kernel or a kernel function. A kernel function takes two arguments. First a `Quantity 1D` of evaluation time points and second a kernel size. :type obj: Kernel or func :param kernel_size: Desired size of the kernel. :type kernel_size: Quantity 1D :returns: A :class:`Kernel` with the desired kernel size. If `obj` is already a :class:`Kernel` instance, a shallow copy of this instance with changed kernel size will be returned. If `obj` is a function it will be wrapped in a :class:`Kernel` instance. :rtype: :class:`Kernel` """ if isinstance(obj, Kernel): obj = copy.copy(obj) obj.kernel_size = kernel_size else: obj = KernelFromFunction(obj, kernel_size) return obj class SymmetricKernel(Kernel): """ Base class for symmetric kernels. """ def __init__(self, kernel_size, normalize): """ :param kernel_size: Parameter controlling the kernel size. :type kernel_size: Quantity 1D :param bool normalize: Whether to normalize the kernel to unit area. """ Kernel.__init__(self, kernel_size, normalize) def is_symmetric(self): return True def summed_dist_matrix(self, vectors, presorted=False): D = sp.empty((len(vectors), len(vectors))) if len(vectors) > 0: might_have_units = self(vectors[0]) if hasattr(might_have_units, 'units'): D = D * might_have_units.units for i in xrange(len(vectors)): for j in xrange(i, len(vectors)): D[i, j] = D[j, i] = sp.sum(self( (vectors[i] - sp.atleast_2d(vectors[j]).T).flatten())) return D class CausalDecayingExpKernel(Kernel): r""" Unnormalized: :math:`K(t) = \exp(-\frac{t}{\tau}) \Theta(t)` with :math:`\Theta(t) = \left\{\begin{array}{ll}0, & x < 0\\ 1, & x \geq 0\end{array}\right.` and kernel size :math:`\tau`. Normalized to unit area: :math:`K'(t) = \frac{1}{\tau} K(t)` """ @staticmethod def evaluate(t, kernel_size): return sp.piecewise( t, [t < 0, t >= 0], [ lambda t: 0, lambda t: sp.exp( (-t * pq.dimensionless / kernel_size).simplified)]) def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size) def normalization_factor(self, kernel_size): return 1.0 / kernel_size def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize) def boundary_enclosing_at_least(self, fraction): return -self.kernel_size * sp.log(1.0 - fraction) class GaussianKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \exp(-\frac{t^2}{2 \sigma^2})` with kernel size :math:`\sigma` (corresponds to the standard deviation of a Gaussian distribution). Normalized to unit area: :math:`K'(t) = \frac{1}{\sigma \sqrt{2 \pi}} K(t)` """ @staticmethod def evaluate(t, kernel_size): return sp.exp( -0.5 * (t * pq.dimensionless / kernel_size).simplified ** 2) def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size) def normalization_factor(self, kernel_size): return 1.0 / (sp.sqrt(2.0 * sp.pi) * kernel_size) def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize) def boundary_enclosing_at_least(self, fraction): return self.kernel_size * sp.sqrt(2.0) * \ scipy.special.erfinv(fraction + scipy.special.erf(0.0)) class LaplacianKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \exp(-|\frac{t}{\tau}|)` with kernel size :math:`\tau`. Normalized to unit area: :math:`K'(t) = \frac{1}{2 \tau} K(t)` """ @staticmethod def evaluate(t, kernel_size): return sp.exp( -(sp.absolute(t) * pq.dimensionless / kernel_size).simplified) def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size) def normalization_factor(self, kernel_size): return 0.5 / kernel_size def __init__(self, kernel_size=1.0 * pq.s, normalize=True): Kernel.__init__(self, kernel_size, normalize) def boundary_enclosing_at_least(self, fraction): return -self.kernel_size * sp.log(1.0 - fraction) def summed_dist_matrix(self, vectors, presorted=False): # This implementation is based on # # Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van # Rossum distances. Network: Computation in Neural Systems, 23(1-2), # 48-58. # # Note that the cited paper contains some errors: In formula (9) the # left side of the equation should be divided by two and in the last # sum in this equation it should say `j|v_i >= u_i` instead of # `j|v_i > u_i`. Also, in equation (11) it should say `j|u_i >= v_i` # instead of `j|u_i > v_i`. # # Given N vectors with n entries on average the run-time complexity is # O(N^2 * n). O(N^2 + N * n) memory will be needed. if len(vectors) <= 0: return sp.zeros((0, 0)) if not presorted: vectors = [v.copy() for v in vectors] for v in vectors: v.sort() sizes = sp.asarray([v.size for v in vectors]) values = sp.empty((len(vectors), max(1, sizes.max()))) values.fill(sp.nan) for i, v in enumerate(vectors): if v.size > 0: values[i, :v.size] = \ (v / self.kernel_size * pq.dimensionless).simplified exp_diffs = sp.exp(values[:, :-1] - values[:, 1:]) markage = sp.zeros(values.shape) for u in xrange(len(vectors)): markage[u, 0] = 0 for i in xrange(sizes[u] - 1): markage[u, i + 1] = (markage[u, i] + 1.0) * exp_diffs[u, i] # Same vector terms D = sp.empty((len(vectors), len(vectors))) D[sp.diag_indices_from(D)] = sizes + 2.0 * sp.sum(markage, axis=1) # Cross vector terms for u in xrange(D.shape[0]): all_ks = sp.searchsorted(values[u], values, 'left') - 1 for v in xrange(u): js = sp.searchsorted(values[v], values[u], 'right') - 1 ks = all_ks[v] slice_j = sp.s_[sp.searchsorted(js, 0):sizes[u]] slice_k = sp.s_[sp.searchsorted(ks, 0):sizes[v]] D[u, v] = sp.sum( sp.exp(values[v][js[slice_j]] - values[u][slice_j]) * (1.0 + markage[v][js[slice_j]])) D[u, v] += sp.sum( sp.exp(values[u][ks[slice_k]] - values[v][slice_k]) * (1.0 + markage[u][ks[slice_k]])) D[v, u] = D[u, v] if self.normalize: normalization = self.normalization_factor(self.kernel_size) else: normalization = 1.0 return normalization * D class RectangularKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \left\{\begin{array}{ll}1, & |t| < \tau \\ 0, & |t| \geq \tau\end{array} \right.` with kernel size :math:`\tau` corresponding to the half width. Normalized to unit area: :math:`K'(t) = \frac{1}{2 \tau} K(t)` """ @staticmethod def evaluate(t, half_width): return (sp.absolute(t) < half_width) def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size) def normalization_factor(self, half_width): return 0.5 / half_width def __init__(self, half_width=1.0 * pq.s, normalize=True): Kernel.__init__(self, half_width, normalize) def boundary_enclosing_at_least(self, fraction): return self.kernel_size class TriangularKernel(SymmetricKernel): r""" Unnormalized: :math:`K(t) = \left\{ \begin{array}{ll}1 - \frac{|t|}{\tau}, & |t| < \tau \\ 0, & |t| \geq \tau \end{array} \right.` with kernel size :math:`\tau` corresponding to the half width. Normalized to unit area: :math:`K'(t) = \frac{1}{\tau} K(t)` """ @staticmethod def evaluate(t, half_width): return sp.maximum( 0.0, (1.0 - sp.absolute(t.rescale(half_width.units)) * pq.dimensionless / half_width).magnitude) def _evaluate(self, t, kernel_size): return self.evaluate(t, kernel_size) def normalization_factor(self, half_width): return 1.0 / half_width def __init__(self, half_width=1.0 * pq.s, normalize=True): Kernel.__init__(self, half_width, normalize) def boundary_enclosing_at_least(self, fraction): return self.kernel_size def discretize_kernel( kernel, sampling_rate, area_fraction=default_kernel_area_fraction, num_bins=None, ensure_unit_area=False): """ Discretizes a kernel. :param kernel: The kernel or kernel function. If a kernel function is used it should take exactly one 1-D array as argument. :type kernel: :class:`Kernel` or function :param float area_fraction: Fraction between 0 and 1 (exclusive) of the integral of the kernel which will be at least covered by the discretization. Will be ignored if `num_bins` is not `None`. If `area_fraction` is used, the kernel has to provide a method :meth:`boundary_enclosing_at_least` (see :meth:`.Kernel.boundary_enclosing_at_least`). :param sampling_rate: Sampling rate for the discretization. The unit will typically be a frequency unit. :type sampling_rate: Quantity scalar :param int num_bins: Number of bins to use for the discretization. :param bool ensure_unit_area: If `True`, the area of the discretized kernel will be normalized to 1.0. :rtype: Quantity 1D """ t_step = 1.0 / sampling_rate if num_bins is not None: start = -num_bins // 2 stop = num_bins // 2 elif area_fraction is not None: boundary = kernel.boundary_enclosing_at_least(area_fraction) if hasattr(boundary, 'rescale'): boundary = boundary.rescale(t_step.units) start = sp.ceil(-boundary / t_step) stop = sp.floor(boundary / t_step) + 1 else: raise ValueError( "One of area_fraction and num_bins must not be None.") k = kernel(sp.arange(start, stop) * t_step) if ensure_unit_area: k /= sp.sum(k) * t_step return k def smooth( binned, kernel, sampling_rate, mode='same', **kernel_discretization_params): """ Smoothes a binned representation (e.g. of a spike train) by convolving with a kernel. :param binned: Bin array to smooth. :type binned: 1-D array :param kernel: The kernel instance to convolve with. :type kernel: :class:`Kernel` :param sampling_rate: The sampling rate which will be used to discretize the kernel. It should be equal to the sampling rate used to obtain `binned`. The unit will typically be a frequency unit. :type sampling_rate: Quantity scalar :param mode: * 'same': The default which returns an array of the same size as `binned` * 'full': Returns an array with a bin for each shift where `binned` and the discretized kernel overlap by at least one bin. * 'valid': Returns only the discretization bins where the discretized kernel and `binned` completely overlap. See also `numpy.convolve `_. :type mode: {'same', 'full', 'valid'} :param dict kernel_discretization_params: Additional discretization arguments which will be passed to :func:`.discretize_kernel`. :returns: The smoothed representation of `binned`. :rtype: Quantity 1D """ k = discretize_kernel( kernel, sampling_rate=sampling_rate, **kernel_discretization_params) return scipy.signal.convolve(binned, k, mode) * k.units def st_convolve( train, kernel, sampling_rate, mode='same', binning_params={}, kernel_discretization_params={}): """ Convolves a :class:`neo.core.SpikeTrain` with a kernel. :param train: Spike train to convolve. :type train: :class:`neo.core.SpikeTrain` :param kernel: The kernel instance to convolve with. :type kernel: :class:`Kernel` :param sampling_rate: The sampling rate which will be used to bin the spike train. 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Also known as PyTables.)h/hubeubaubh_)q}q(h.X]scikit-learn_ (Optional, for spike sorting quality analysis using Gaussian cluster overlap.) h/hYh0h3h5hch7}q(h9]h:]h;]h<]h>]uh@NhAhh)]qhN)q}q(h.X\scikit-learn_ (Optional, for spike sorting quality analysis using Gaussian cluster overlap.)h/hh0h3h5hRh7}q(h9]h:]h;]h<]h>]uh@K h)]q(hj)q}q(h.X scikit-learn_hmKh/hh5hnh7}q(UnameX scikit-learnqhpXhttp://scikit-learn.orgqh<]h;]h9]h:]h>]uh)]qhJX scikit-learnqׅq}q(h.Uh/hubaubhJXO (Optional, for spike sorting quality analysis using Gaussian cluster overlap.)qڅq}q(h.XO (Optional, for spike sorting quality analysis using Gaussian cluster overlap.)h/hubeubaubeubhN)q}q(h.XxPlease see the respective websites for instructions on how to install them if they are not present on your computer. If you use Linux, you might not have access rights to your Python package installation directory, depending on your configuration. In this case, you will have to execute all shell commands in this section with administrator privileges, e.g. by using ``sudo``.h/h,h0h3h5hRh7}q(h9]h:]h;]h<]h>]uh@KhAhh)]q(hJXoPlease see the respective websites for instructions on how to install them if they are not present on your computer. If you use Linux, you might not have access rights to your Python package installation directory, depending on your configuration. In this case, you will have to execute all shell commands in this section with administrator privileges, e.g. by using qᅁq}q(h.XoPlease see the respective websites for instructions on how to install them if they are not present on your computer. If you use Linux, you might not have access rights to your Python package installation directory, depending on your configuration. In this case, you will have to execute all shell commands in this section with administrator privileges, e.g. by using h/hubcdocutils.nodes literal q)q}q(h.X``sudo``h7}q(h9]h:]h;]h<]h>]uh/hh)]qhJXsudoq酁q}q(h.Uh/hubah5UliteralqubhJX.q}q(h.X.h/hubeubeubh+)q}q(h.Uh/hh0h3h5h6h7}q(h9]h:]h;]h<]qh#ah>]qh auh@KhAhh)]q(hC)q}q(h.XDownload and Installationqh/hh0h3h5hGh7}q(h9]h:]h;]h<]h>]uh@KhAhh)]qhJXDownload and Installationqq}q(h.hh/hubaubhN)q}q(h.X`The easiest way to get spykeutils is from the Python Package Index. If you have pip_ installed::h/hh0h3h5hRh7}q(h9]h:]h;]h<]h>]uh@KhAhh)]r(hJXPThe easiest way to get spykeutils is from the Python Package Index. If you have rr}r(h.XPThe easiest way to get spykeutils is from the Python Package Index. If you have h/hubhj)r}r(h.Xpip_hmKh/hh5hnh7}r(UnameXpiprhpXhttp://pypi.python.org/pypi/piprh<]h;]h9]h:]h>]uh)]r hJXpipr r }r (h.Uh/jubaubhJX installed:r r}r(h.X installed:h/hubeubcdocutils.nodes literal_block r)r}r(h.X$ pip install spykeutilsrh/hh0h3h5U literal_blockrh7}r(U xml:spacerUpreserverh<]h;]h9]h:]h>]uh@KhAhh)]rhJX$ pip install spykeutilsrr}r(h.Uh/jubaubhN)r}r(h.X(Alternatively, if you have setuptools_::rh/hh0h3h5hRh7}r(h9]h:]h;]h<]h>]uh@KhAhh)]r (hJXAlternatively, if you have r!r"}r#(h.XAlternatively, if you have h/jubhj)r$}r%(h.X setuptools_hmKh/jh5hnh7}r&(UnameX setuptoolsr'hpX&http://pypi.python.org/pypi/setuptoolsr(h<]h;]h9]h:]h>]uh)]r)hJX setuptoolsr*r+}r,(h.Uh/j$ubaubhJX:r-}r.(h.X:h/jubeubj)r/}r0(h.X$ easy_install spykeutilsr1h/hh0h3h5jh7}r2(jjh<]h;]h9]h:]h>]uh@KhAhh)]r3hJX$ easy_install spykeutilsr4r5}r6(h.Uh/j/ubaubhN)r7}r8(h.XUsers of NeuroDebian_ or its repositories (available for Debian and Ubuntu) can also install spykeutils using the package manager instead of pip_::h/hh0h3h5hRh7}r9(h9]h:]h;]h<]h>]uh@K!hAhh)]r:(hJX Users of r;r<}r=(h.X Users of h/j7ubhj)r>}r?(h.X NeuroDebian_hmKh/j7h5hnh7}r@(UnameX NeuroDebianhpXhttp://neuro.debian.netrAh<]h;]h9]h:]h>]uh)]rBhJX NeuroDebianrCrD}rE(h.Uh/j>ubaubhJXx or its repositories (available for Debian and Ubuntu) can also install spykeutils using the package manager instead of rFrG}rH(h.Xx or its repositories (available for Debian and Ubuntu) can also install spykeutils using the package manager instead of h/j7ubhj)rI}rJ(h.Xpip_hmKh/j7h5hnh7}rK(UnameXpiphpjh<]h;]h9]h:]h>]uh)]rLhJXpiprMrN}rO(h.Uh/jIubaubhJX:rP}rQ(h.X:h/j7ubeubj)rR}rS(h.X($ sudo apt-get install python-spykeutilsrTh/hh0h3h5jh7}rU(jjh<]h;]h9]h:]h>]uh@K$hAhh)]rVhJX($ sudo apt-get install python-spykeutilsrWrX}rY(h.Uh/jRubaubhN)rZ}r[(h.XlAlternatively, you can get the latest version directly from GitHub at https://github.com/rproepp/spykeutils.h/hh0h3h5hRh7}r\(h9]h:]h;]h<]h>]uh@K&hAhh)]r](hJXFAlternatively, you can get the latest version directly from GitHub at r^r_}r`(h.XFAlternatively, you can get the latest version directly from GitHub at h/jZubhj)ra}rb(h.X%https://github.com/rproepp/spykeutilsrch7}rd(Urefurijch<]h;]h9]h:]h>]uh/jZh)]rehJX%https://github.com/rproepp/spykeutilsrfrg}rh(h.Uh/jaubah5hnubhJX.ri}rj(h.X.h/jZubeubhN)rk}rl(h.XThe master branch always contains the current stable version. If you want the latest development version, use the develop branch (selected by default). You can download the repository from the GitHub page or clone it using git and then install from the resulting folder::h/hh0h3h5hRh7}rm(h9]h:]h;]h<]h>]uh@K)hAhh)]rnhJXThe master branch always contains the current stable version. If you want the latest development version, use the develop branch (selected by default). You can download the repository from the GitHub page or clone it using git and then install from the resulting folder:rorp}rq(h.XThe master branch always contains the current stable version. If you want the latest development version, use the develop branch (selected by default). You can download the repository from the GitHub page or clone it using git and then install from the resulting folder:h/jkubaubj)rr}rs(h.X$ python setup.py installrth/hh0h3h5jh7}ru(jjh<]h;]h9]h:]h>]uh@K.hAhh)]rvhJX$ python setup.py installrwrx}ry(h.Uh/jrubaubeubh+)rz}r{(h.Uh/hh0h3h5h6h7}r|(h9]h:]h;]h<]r}h(ah>]r~hauh@K1hAhh)]r(hC)r}r(h.XUsagerh/jzh0h3h5hGh7}r(h9]h:]h;]h<]h>]uh@K1hAhh)]rhJXUsagerr}r(h.jh/jubaubhN)r}r(h.X}For the most part, spykeutils is a collection of functions that work on Neo objects. Many functions also take quantities as parameters. Therefore, make sure to get an overview of :mod:`neo` and :mod:`quantities` before using spykeutils. Once you are familiar with these packages, have a look at the :ref:`examples` or head to the :ref:`apiref` to browse the contents of spykeutils.h/jzh0h3h5hRh7}r(h9]h:]h;]h<]h>]uh@K2hAhh)]r(hJXFor the most part, spykeutils is a collection of functions that work on Neo objects. Many functions also take quantities as parameters. 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q#XJ/var/build/user_builds/spykeutils/checkouts/0.4.1/doc/source/changelog.rstq$q%}q&bUtagnameq'Usectionq(U attributesq)}q*(Udupnamesq+]Uclassesq,]Ubackrefsq-]Uidsq.]q/haUnamesq0]q1hauUlineq2KUdocumentq3hh]q4(cdocutils.nodes title q5)q6}q7(h X Changelogq8h!hh"h%h'Utitleq9h)}q:(h+]h,]h-]h.]h0]uh2Kh3hh]q;cdocutils.nodes Text q}q?(h h8h!h6ubaubh)q@}qA(h Uh!hh"h#X../../CHANGELOG.rstqBqC}qDbh'h(h)}qE(h+]h,]h-]h.]qFhah0]qGh auh2Kh3hh]qH(h5)qI}qJ(h X Version 0.4.1qKh!h@h"hCh'h9h)}qL(h+]h,]h-]h.]h0]uh2Kh3hh]qMh}r?(h j:h!j8ubaubaubhX)r@}rA(h X-Renamed plot.ISI to plot.isi for consistency h!hh"hCh'h\h)}rB(h+]h,]h-]h.]h0]uh2Nh3hh]rCh_)rD}rE(h X,Renamed plot.ISI to plot.isi for consistencyrFh!j@h"hCh'hbh)}rG(h+]h,]h-]h.]h0]uh2K h]rHhh$}q?(h(]h)]h']h&]h*]uh-Kh.hh/]q@cdocutils.nodes Text qAXExamplesqBqC}qD(hh=hh;ubaubcdocutils.nodes paragraph qE)qF}qG(hXThese examples demonstrate the usage of some functions in spykeutils. This includes the creation of a small Neo object hierarchy with toy data.qHhhhhh"U paragraphqIh$}qJ(h(]h)]h']h&]h*]uh-Kh.hh/]qKhAXThese examples demonstrate the usage of some functions in spykeutils. This includes the creation of a small Neo object hierarchy with toy data.qLqM}qN(hhHhhFubaubh)qO}qP(hUhhhhh"h0h$}qQ(h(]h)]h']h&]qRUcreating-the-sample-dataqSah*]qTh auh-K h.hh/]qU(h:)qV}qW(hXCreating the sample dataqXhhOhhh"h>h$}qY(h(]h)]h']h&]h*]uh-K h.hh/]qZhAXCreating the sample dataq[q\}q](hhXhhVubaubhE)q^}q_(hXThe functions in spykeutils work on electrophysiological data that is represented in Neo object hierarchies. Usually, you would load these objects from a file, but for the purpose of this demonstration we will manually create an object hierarchy to illustrate their structure. Note that most functions in spykeutils will also work with separate Neo data objects that are not contained in a complete hierarchy. First, we import the modules we will use:q`hhOhhh"hIh$}qa(h(]h)]h']h&]h*]uh-K h.hh/]qbhAXThe functions in spykeutils work on electrophysiological data that is represented in Neo object hierarchies. Usually, you would load these objects from a file, but for the purpose of this demonstration we will manually create an object hierarchy to illustrate their structure. Note that most functions in spykeutils will also work with separate Neo data objects that are not contained in a complete hierarchy. First, we import the modules we will use:qcqd}qe(hh`hh^ubaubcdocutils.nodes doctest_block qf)qg}qh(hXu>>> import quantities as pq >>> import neo >>> import scipy as sp >>> import spykeutils.spike_train_generation as stghhOhhh"U doctest_blockqih$}qj(U xml:spaceqkUpreserveqlh&]h']h(]h)]h*]uh-Kh.hh/]qmhAXu>>> import quantities as pq >>> import neo >>> import scipy as sp >>> import spykeutils.spike_train_generation as stgqnqo}qp(hUhhgubaubhE)qq}qr(hXWe start with some container objects: two segments that represent trials and three units (representing neurons) that produced the spike trains:qshhOhhh"hIh$}qt(h(]h)]h']h&]h*]uh-Kh.hh/]quhAXWe start with some container objects: two segments that represent trials and three units (representing neurons) that produced the spike trains:qvqw}qx(hhshhqubaubhf)qy}qz(hX>>> segments = [neo.Segment('Trial 1'), neo.Segment('Trial 2')] >>> units = [] >>> units.append(neo.Unit('Regular intervals')) >>> units.append(neo.Unit('Homogeneous Poisson')) >>> units.append(neo.Unit('Modulated Poisson'))hhOhhh"hih$}q{(hkhlh&]h']h(]h)]h*]uh-Kh.hh/]q|hAX>>> segments = [neo.Segment('Trial 1'), neo.Segment('Trial 2')] >>> units = [] >>> units.append(neo.Unit('Regular intervals')) >>> units.append(neo.Unit('Homogeneous Poisson')) >>> units.append(neo.Unit('Modulated Poisson'))q}q~}q(hUhhyubaubhE)q}q(hXrWe create some spike trains from regular intervals, a homogeneous Poisson process and a modulated Poisson process:qhhOhhh"hIh$}q(h(]h)]h']h&]h*]uh-Kh.hh/]qhAXrWe create some spike trains from regular intervals, a homogeneous Poisson process and a modulated Poisson process:qq}q(hhhhubaubhf)q}q(hX<>>> trains = [] >>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 40) * pq.s, 10 * pq.s)) >>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 60) * pq.s, 10 * pq.s)) >>> trains.append(stg.gen_homogeneous_poisson(5 * pq.Hz, t_stop=10 * pq.s)) >>> trains.append(stg.gen_homogeneous_poisson(7 * pq.Hz, t_stop=10 * pq.s)) >>> modulation = lambda t: sp.sin(3 * sp.pi * t / 10.0 / pq.s) / 2.0 + 0.5 >>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s)) >>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s))hhOhhh"hih$}q(hkhlh&]h']h(]h)]h*]uh-K)h.hh/]qhAX<>>> trains = [] >>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 40) * pq.s, 10 * pq.s)) >>> trains.append(neo.SpikeTrain(sp.linspace(0, 10, 60) * pq.s, 10 * pq.s)) >>> trains.append(stg.gen_homogeneous_poisson(5 * pq.Hz, t_stop=10 * pq.s)) >>> trains.append(stg.gen_homogeneous_poisson(7 * pq.Hz, t_stop=10 * pq.s)) >>> modulation = lambda t: sp.sin(3 * sp.pi * t / 10.0 / pq.s) / 2.0 + 0.5 >>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s)) >>> trains.append(stg.gen_inhomogeneous_poisson(modulation, 10 * pq.Hz, t_stop=10*pq.s))qq}q(hUhhubaubhE)q}q(hXuNext, we create analog signals using the spike trains. First, we convolve all spike times with a mock spike waveform.qhhOhhh"hIh$}q(h(]h)]h']h&]h*]uh-K+h.hh/]qhAXuNext, we create analog signals using the spike trains. First, we convolve all spike times with a mock spike waveform.qq}q(hhhhubaubhf)q}q(hXn>>> spike = sp.sin(-sp.linspace(0, 2 * sp.pi, 16)) >>> binned_trains = (sp.histogram(trains[0], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[2], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[4], bins=160000, range=(0,10))[0]) >>> train_waves = [sp.convolve(binned_trains, spike)] >>> binned_trains = (sp.histogram(trains[1], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[3], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[5], bins=160000, range=(0,10))[0]) >>> train_waves.append(sp.convolve(binned_trains, spike))hhOhhh"hih$}q(hkhlh&]h']h(]h)]h*]uh-K6h.hh/]qhAXn>>> spike = sp.sin(-sp.linspace(0, 2 * sp.pi, 16)) >>> binned_trains = (sp.histogram(trains[0], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[2], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[4], bins=160000, range=(0,10))[0]) >>> train_waves = [sp.convolve(binned_trains, spike)] >>> binned_trains = (sp.histogram(trains[1], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[3], bins=160000, range=(0,10))[0] + ... sp.histogram(trains[5], bins=160000, range=(0,10))[0]) >>> train_waves.append(sp.convolve(binned_trains, spike))qq}q(hUhhubaubhE)q}q(hXBNow we add Gaussian noise and create four signals in each segment:qhhOhhh"hIh$}q(h(]h)]h']h&]h*]uh-K8h.hh/]qhAXBNow we add Gaussian noise and create four signals in each segment:qq}q(hhhhubaubhf)q}q(hX>>> for i in range(8): ... sig = train_waves[i%2] + 0.2 * sp.randn(train_waves[i%2].shape[0]) ... signal = neo.AnalogSignal(sig * pq.uV, sampling_rate=16 * pq.kHz) ... signal.segment = segments[i%2] ... segments[i%2].analogsignals.append(signal)hhOhhh"hih$}q(hkhlh&]h']h(]h)]h*]uh-K>h.hh/]qhAX>>> for i in range(8): ... sig = train_waves[i%2] + 0.2 * sp.randn(train_waves[i%2].shape[0]) ... signal = neo.AnalogSignal(sig * pq.uV, sampling_rate=16 * pq.kHz) ... signal.segment = segments[i%2] ... segments[i%2].analogsignals.append(signal)qq}q(hUhhubaubhE)q}q(hXNow we create the relationships between the spike trains and container objects. Each unit has two spike trains, one in each segment:qhhOhhh"hIh$}q(h(]h)]h']h&]h*]uh-K@h.hh/]qhAXNow we create the relationships between the spike trains and container objects. Each unit has two spike trains, one in each segment:qq}q(hhhhubaubhf)q}q(hX>>> segments[0].spiketrains = [trains[0], trains[2], trains[4]] >>> segments[1].spiketrains = [trains[1], trains[3], trains[5]] >>> units[0].spiketrains = trains[:2] >>> units[1].spiketrains = trains[2:4] >>> units[2].spiketrains = trains[4:6] >>> for s in segments: ... for st in s.spiketrains: ... st.segment = s >>> for u in units: ... for st in u.spiketrains: ... st.unit = uhhOhhh"hih$}q(hkhlh&]h']h(]h)]h*]uh-KMh.hh/]qhAX>>> segments[0].spiketrains = [trains[0], trains[2], trains[4]] >>> segments[1].spiketrains = [trains[1], trains[3], trains[5]] >>> units[0].spiketrains = trains[:2] >>> units[1].spiketrains = trains[2:4] >>> units[2].spiketrains = trains[4:6] >>> for s in segments: ... for st in s.spiketrains: ... st.segment = s >>> for u in units: ... for st in u.spiketrains: ... st.unit = uqq}q(hUhhubaubhE)q}q(hXbNow that our sample data is ready, we will use some of the function from spykeutils to analyze it.qhhOhhh"hIh$}q(h(]h)]h']h&]h*]uh-KOh.hh/]qhAXbNow that our sample data is ready, we will use some of the function from spykeutils to analyze it.qq}q(hhhhubaubeubh)q}q(hUhhhhh"h0h$}q(h(]h)]h']h&]qU psth-and-isiqah*]qh auh-KSh.hh/]q(h:)q}q(hX PSTH and ISIqhhhhh"h>h$}q(h(]h)]h']h&]h*]uh-KSh.hh/]qhAX PSTH and ISIqЅq}q(hhhhubaubhE)q}q(hX2To create a peri stimulus time histogram from our spike trains, we call :func:`spykeutils.rate_estimation.psth`. This function can create multiple PSTHs and takes a dicionary of lists of spike trains. Since our spike trains were generated by three units, we will create three histograms, one for each unit:hhhhh"hIh$}q(h(]h)]h']h&]h*]uh-KTh.hh/]q(hAXHTo create a peri stimulus time histogram from our spike trains, we call qׅq}q(hXHTo create a peri stimulus time histogram from our spike trains, we call hhubcsphinx.addnodes pending_xref q)q}q(hX':func:`spykeutils.rate_estimation.psth`qhhhhh"U pending_xrefqh$}q(UreftypeXfuncUrefwarnqU reftargetqXspykeutils.rate_estimation.psthU refdomainXpyqh&]h']U refexplicith(]h)]h*]UrefdocqXexamplesqUpy:classqNU py:moduleqNuh-KTh/]qcdocutils.nodes literal q)q}q(hhh$}q(h(]h)]q(UxrefqhXpy-funcqeh']h&]h*]uhhh/]qhAX!spykeutils.rate_estimation.psth()qq}q(hUhhubah"UliteralqubaubhAX. This function can create multiple PSTHs and takes a dicionary of lists of spike trains. Since our spike trains were generated by three units, we will create three histograms, one for each unit:qq}q(hX. This function can create multiple PSTHs and takes a dicionary of lists of spike trains. Since our spike trains were generated by three units, we will create three histograms, one for each unit:hhubeubhf)q}q(hXd>>> import spykeutils.rate_estimation >>> st_dict = {} >>> st_dict[units[0]] = units[0].spiketrains >>> st_dict[units[1]] = units[1].spiketrains >>> st_dict[units[2]] = units[2].spiketrains >>> spykeutils.rate_estimation.psth(st_dict, 400 * pq.ms)[0] # doctest: +ELLIPSIS {: array([ 6.25, 5. , 5. , 5. , 3.75, ...hhhhh"hih$}q(hkhlh&]h']h(]h)]h*]uh-K`h.hh/]qhAXd>>> import spykeutils.rate_estimation >>> st_dict = {} >>> st_dict[units[0]] = units[0].spiketrains >>> st_dict[units[1]] = units[1].spiketrains >>> st_dict[units[2]] = units[2].spiketrains >>> spykeutils.rate_estimation.psth(st_dict, 400 * pq.ms)[0] # doctest: +ELLIPSIS {: array([ 6.25, 5. , 5. , 5. , 3.75, ...qq}q(hUhhubaubhE)q}q(hX:func:`spykeutils.rate_estimation.psth` returns two values: A dictionary with the resulting histograms and a Quantity 1D with the bin edges.hhhhh"hIh$}r(h(]h)]h']h&]h*]uh-Kbh.hh/]r(h)r}r(hX':func:`spykeutils.rate_estimation.psth`rhhhhh"hh$}r(UreftypeXfunchhXspykeutils.rate_estimation.psthU refdomainXpyrh&]h']U refexplicith(]h)]h*]hhhNhNuh-Kbh/]rh)r}r (hjh$}r (h(]h)]r (hjXpy-funcr eh']h&]h*]uhjh/]r hAX!spykeutils.rate_estimation.psth()rr}r(hUhjubah"hubaubhAXe returns two values: A dictionary with the resulting histograms and a Quantity 1D with the bin edges.rr}r(hXe returns two values: A dictionary with the resulting histograms and a Quantity 1D with the bin edges.hhubeubhE)r}r(hXIf :mod:`guiqwt` is installed, we can also use the :mod:`spykeutils.plot` package to create a PSTH plot from our data (in this case we want a bar histogram and therefore only use spike trains from one unit):hhhhh"hIh$}r(h(]h)]h']h&]h*]uh-Keh.hh/]r(hAXIf rr}r(hXIf hjubh)r}r(hX :mod:`guiqwt`rhjhhh"hh$}r(UreftypeXmodhhXguiqwtU refdomainXpyrh&]h']U refexplicith(]h)]h*]hhhNhNuh-Keh/]r h)r!}r"(hjh$}r#(h(]h)]r$(hjXpy-modr%eh']h&]h*]uhjh/]r&hAXguiqwtr'r(}r)(hUhj!ubah"hubaubhAX# is installed, we can also use the r*r+}r,(hX# is installed, we can also use the hjubh)r-}r.(hX:mod:`spykeutils.plot`r/hjhhh"hh$}r0(UreftypeXmodhhXspykeutils.plotU refdomainXpyr1h&]h']U refexplicith(]h)]h*]hhhNhNuh-Keh/]r2h)r3}r4(hj/h$}r5(h(]h)]r6(hj1Xpy-modr7eh']h&]h*]uhj-h/]r8hAXspykeutils.plotr9r:}r;(hUhj3ubah"hubaubhAX package to create a PSTH plot from our data (in this case we want a bar histogram and therefore only use spike trains from one unit):r<r=}r>(hX package to create a PSTH plot from our data (in this case we want a bar histogram and therefore only use spike trains from one unit):hjubeubhf)r?}r@(hX>>> import spykeutils.plot >>> spykeutils.plot.psth({units[2]: units[2].spiketrains}, bin_size=400 * pq.ms, bar_plot=True) # doctest: +SKIPhhhhh"hih$}rA(hkhlh&]h']h(]h)]h*]uh-Kjh.hh/]rBhAX>>> import spykeutils.plot >>> spykeutils.plot.psth({units[2]: units[2].spiketrains}, bin_size=400 * pq.ms, bar_plot=True) # doctest: +SKIPrCrD}rE(hUhj?ubaubhE)rF}rG(hXFSimiliarily, we can create an interspike interval histogram plot with:rHhhhhh"hIh$}rI(h(]h)]h']h&]h*]uh-Klh.hh/]rJhAXFSimiliarily, we can create an interspike interval histogram plot with:rKrL}rM(hjHhjFubaubhf)rN}rO(hXr>>> spykeutils.plot.isi({units[2]: units[2].spiketrains}, bin_size=30 * pq.ms, cut_off=300 * pq.ms, bar_plot=True)rPhhhhh"hih$}rQ(hkhlh&]h']h(]h)]h*]uh-Knh.hh/]rRhAXr>>> spykeutils.plot.isi({units[2]: units[2].spiketrains}, bin_size=30 * pq.ms, cut_off=300 * pq.ms, bar_plot=True)rSrT}rU(hUhjNubaubhE)rV}rW(hX0This will open a plot window like the following:rXhhhhh"hIh$}rY(h(]h)]h']h&]h*]uh-Kph.hh/]rZhAX0This will open a plot window like the following:r[r\}r](hjXhjVubaubcdocutils.nodes image r^)r_}r`(hX.. image:: /img/isi.png hhhhh"Uimagerah$}rb(UuriX img/isi.pngrch&]h']h(]h)]U candidatesrd}reU*jcsh*]uh-Ksh.hh/]ubeubh)rf}rg(hUhhhhh"h0h$}rh(h(]h)]h']h&]riUspike-density-estimationrjah*]rkhauh-Kuh.hh/]rl(h:)rm}rn(hXSpike Density Estimationrohjfhhh"h>h$}rp(h(]h)]h']h&]h*]uh-Kuh.hh/]rqhAXSpike Density Estimationrrrs}rt(hjohjmubaubhE)ru}rv(hXSimilar to a PSTH, a spike density estimation gives an esimate of the instantaneous firing rate. Instead of binning, it is based on a kernel convolution which results in a smoother estimate. Creating and SDE with spykeutils works very similar to creating a PSTH. Instead of manually choosing the size of the Gaussian kernel, :func:`spykeutils.rate_estimation.spike_density_estimation` also supports finding the optimal kernel size automatically for each unit:hjfhhh"hIh$}rw(h(]h)]h']h&]h*]uh-Kvh.hh/]rx(hAXESimilar to a PSTH, a spike density estimation gives an esimate of the instantaneous firing rate. Instead of binning, it is based on a kernel convolution which results in a smoother estimate. Creating and SDE with spykeutils works very similar to creating a PSTH. Instead of manually choosing the size of the Gaussian kernel, ryrz}r{(hXESimilar to a PSTH, a spike density estimation gives an esimate of the instantaneous firing rate. Instead of binning, it is based on a kernel convolution which results in a smoother estimate. Creating and SDE with spykeutils works very similar to creating a PSTH. Instead of manually choosing the size of the Gaussian kernel, hjuubh)r|}r}(hX;:func:`spykeutils.rate_estimation.spike_density_estimation`r~hjuhhh"hh$}r(UreftypeXfunchhX3spykeutils.rate_estimation.spike_density_estimationU refdomainXpyrh&]h']U refexplicith(]h)]h*]hhhNhNuh-Kvh/]rh)r}r(hj~h$}r(h(]h)]r(hjXpy-funcreh']h&]h*]uhj|h/]rhAX5spykeutils.rate_estimation.spike_density_estimation()rr}r(hUhjubah"hubaubhAXK also supports finding the optimal kernel size automatically for each unit:rr}r(hXK also supports finding the optimal kernel size automatically for each unit:hjuubeubhf)r}r(hX>>> kernel_sizes = sp.logspace(2, 3.3, 100) * pq.ms >>> spykeutils.rate_estimation.spike_density_estimation(st_dict, optimize_steps=kernel_sizes)[0] # doctest: +ELLIPSIS {: array([ ...hjfhhh"hih$}r(hkhlh&]h']h(]h)]h*]uh-Kh.hh/]rhAX>>> kernel_sizes = sp.logspace(2, 3.3, 100) * pq.ms >>> spykeutils.rate_estimation.spike_density_estimation(st_dict, optimize_steps=kernel_sizes)[0] # doctest: +ELLIPSIS {: array([ ...rr}r(hUhjubaubhE)r}r(hXAs with the PSTH, there is also a plot function for creating a spike density estimation. Here, we use both units because the function produces a line plot where both units can be shown at the same time:rhjfhhh"hIh$}r(h(]h)]h']h&]h*]uh-Kh.hh/]rhAXAs with the PSTH, there is also a plot function for creating a spike density estimation. Here, we use both units because the function produces a line plot where both units can be shown at the same time:rr}r(hjhjubaubhf)r}r(hX`>>> spykeutils.plot.sde(st_dict, maximum_kernel=3000*pq.ms, optimize_steps=100) # doctest: +SKIPrhjfhhh"hih$}r(hkhlh&]h']h(]h)]h*]uh-Kh.hh/]rhAX`>>> spykeutils.plot.sde(st_dict, maximum_kernel=3000*pq.ms, optimize_steps=100) # doctest: +SKIPrr}r(hUhjubaubhE)r}r(hX0The resulting plot will look like the following:rhjfhhh"hIh$}r(h(]h)]h']h&]h*]uh-Kh.hh/]rhAX0The resulting plot will look like the following:rr}r(hjhjubaubj^)r}r(hX.. image:: /img/sde.png hjfhhh"jah$}r(UuriX img/sde.pngrh&]h']h(]h)]jd}rU*jsh*]uh-Kh.hh/]ubhE)r}r(hXWhile spike density estimations are preferable to PSTHs in many cases, the picture also shows an important weakness: The estimation will generally be too low on margins. The areas where this happens become larger with kernel size, which is clearly visible from the rounded shape of the purple and pink curves (which should be flat because of the constant rate of the spike trains) with their very large kernel size.rhjfhhh"hIh$}r(h(]h)]h']h&]h*]uh-Kh.hh/]rhAXWhile spike density estimations are preferable to PSTHs in many cases, the picture also shows an important weakness: The estimation will generally be too low on margins. The areas where this happens become larger with kernel size, which is clearly visible from the rounded shape of the purple and pink curves (which should be flat because of the constant rate of the spike trains) with their very large kernel size.rr}r(hjhjubaubeubh)r}r(hUhhhhh"h0h$}r(h(]h)]h']h&]rU signal-plotrah*]rhauh-Kh.hh/]r(h:)r}r(hX Signal Plotrhjhhh"h>h$}r(h(]h)]h']h&]h*]uh-Kh.hh/]rhAX Signal Plotrr}r(hjhjubaubhE)r}r(hXAs a final example, we will again use the :mod:`spykeutils.plot` package to create a plot of the signals we created. This plot will also display the spike times from one of our spike trains.hjhhh"hIh$}r(h(]h)]h']h&]h*]uh-Kh.hh/]r(hAX*As a final example, we will again use the rr}r(hX*As a final example, we will again use the hjubh)r}r(hX:mod:`spykeutils.plot`rhjhhh"hh$}r(UreftypeXmodhhXspykeutils.plotU refdomainXpyrh&]h']U refexplicith(]h)]h*]hhhNhNuh-Kh/]rh)r}r(hjh$}r(h(]h)]r(hjXpy-modreh']h&]h*]uhjh/]rhAXspykeutils.plotrr}r(hUhjubah"hubaubhAX~ package to create a plot of the signals we created. This plot will also display the spike times from one of our spike trains.rr}r(hX~ package to create a plot of the signals we created. 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If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel.hjhjhjh}r(h]h]h]h]h]uhKhhh}]r(hXIf rr}r(hXIf hjubjD)r}r(hX``signal_array``h}r(h]h]h]h]h]uhjh}]rhX signal_arrayrr}r(hUhjubahjOubhXa is attached to a recording channel group with exactly is many channels as there are channels in rr}r(hXa is attached to a recording channel group with exactly is many channels as there are channels in hjubjD)r}r(hX``signal_array``h}r(h]h]h]h]h]uhjh}]rhX signal_arrayrr}r(hUhjubahjOubhX, each created signal will be assigned the corresponding channel. If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel.rr}r(hX, each created signal will be assigned the corresponding channel. If the attached recording channel group has only one recording channel, all created signals will be assigned to this channel. In all other cases, the created signal will not have a reference to a recording channel.hjubeubj)r}r(hX;Note that while the created signals may have references to a segment and channels, the relationships in the other direction are not automatically created (the signals are not attached to the recording channel or segment). Other properties like annotations are not copied or referenced in the created analog signals.rhjhjhjh}r(h]h]h]h]h]uhK hhh}]rhX;Note that while the created signals may have references to a segment and channels, the relationships in the other direction are not automatically created (the signals are not attached to the recording channel or segment). Other properties like annotations are not copied or referenced in the created analog signals.rr}r(hjhjubaubcdocutils.nodes field_list r)r}r(hUhjhNhU field_listrh}r(h]h]h]h]h]uhNhhh}]r(cdocutils.nodes field r)r}r(hUh}r(h]h]h]h]h]uhjh}]r(cdocutils.nodes field_name r)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Parametersrr}r(hUhjubahU field_namerubcdocutils.nodes field_body r)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(cdocutils.nodes strong r)r}r(hX signal_arrayh}r(h]h]h]h]h]uhjh}]rhX signal_arrayrr}r(hUhjubahUstrongrubhX (rr}r(hUhjubh)r}r(hX#:class:`neo.core.AnalogSignalArray`rhjhNhjh}r(UreftypeXclassj<j=Xneo.core.AnalogSignalArrayU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAXspykeutils.conversionsruhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXneo.core.AnalogSignalArrayrr}r(hUhjubahjOubaubhX)r}r(hUhjubhX -- rr}r(hUhjubhX&An analog signal array from which the rr}r(hX&An analog signal array from which the hjubh)r}r(hX:class:`neo.core.AnalogSignal`rhjhNhjh}r(UreftypeXclassj<j=Xneo.core.AnalogSignalU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]r jD)r }r (hjh}r (h]h]r (jIjXpy-classreh]h]h]uhjh}]rhXneo.core.AnalogSignalrr}r(hUhj ubahjOubaubhX objects are constructed.rr}r(hX objects are constructed.hjubehjubahU field_bodyrubehUfieldrubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsr r!}r"(hUhjubahjubj)r#}r$(hUh}r%(h]h]h]h]h]uhjh}]r&j)r'}r((hUh}r)(h]h]h]h]h]uhj#h}]r*(hX3A list of analog signals, one for every channel in r+r,}r-(hX3A list of analog signals, one for every channel in hj'ubjD)r.}r/(hX``signal_array``h}r0(h]h]h]h]h]uhj'h}]r1hX signal_arrayr2r3}r4(hUhj.ubahjOubhX.r5}r6(hX.hj'ubehjubahjubehjubj)r7}r8(hUh}r9(h]h]h]h]h]uhjh}]r:(j)r;}r<(hUh}r=(h]h]h]h]h]uhj7h}]r>hX Return typer?r@}rA(hUhj;ubahjubj)rB}rC(hUh}rD(h]h]h]h]h]uhj7h}]rEj)rF}rG(hUh}rH(h]h]h]h]h]uhjBh}]rIhXlistrJrK}rL(hXlisthjFubahjubahjubehjubeubeubeubh)rM}rN(hUhj+hNhhh}rO(h]h]h]h]h]Uentries]rP(hX:epoch_array_to_epochs() (in module spykeutils.conversions)hEUtrQauhNhhh}]ubh)rR}rS(hUhj+hNhhh}rT(hωhXpyh]h]h]h]h]hXfunctionrUhjUuhNhhh}]rV(h)rW}rX(hX"epoch_array_to_epochs(epoch_array)hjRhhhhh}rY(h]rZhEahhXspykeutils.conversionsr[r\}r]bh]h]h]h]r^hEahXepoch_array_to_epochsr_hUhuhNhhh}]r`(h)ra}rb(hj_hjWhhhhh}rc(h]h]h]h]h]uhNhhh}]rdhXepoch_array_to_epochsrerf}rg(hUhjaubaubjt)rh}ri(hUhjWhhhjwh}rj(h]h]h]h]h]uhNhhh}]rkjz)rl}rm(hX epoch_arrayh}rn(h]h]h]h]h]uhjhh}]rohX epoch_arrayrprq}rr(hUhjlubahjubaubh)rs}rt(hUhjWhNhhh}ru(Uexprhh]h]h]h]h]uhNhhh}]rvh)rw}rx(hUh}ry(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/conversionsUrefidj_uhjsh}]rzj)r{}r|(hUh}r}(h]h]r~j ah]h]h]uhjwh}]rhX[source]rr}r(hUhj{ubahjubahjubaubeubj)r}r(hUhjRhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX+Return a list of epochs for an epoch array.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/conversions.py:docstring of spykeutils.conversions.epoch_array_to_epochsrhjh}r(h]h]h]h]h]uhKhhh}]rhX+Return a list of epochs for an epoch array.rr}r(hjhjubaubj)r}r(hXNote that while the created epochs may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created epochs.rhjhjhjh}r(h]h]h]h]h]uhKhhh}]rhXNote that while the created epochs may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created epochs.rr}r(hjhjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Parametersrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hX epoch_arrayh}r(h]h]h]h]h]uhjh}]rhX epoch_arrayrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hX:class:`neo.core.EpochArray`rhjhNhjh}r(UreftypeXclassj<j=Xneo.core.EpochArrayU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXneo.core.EpochArrayrr}r(hUhjubahjOubaubhX)r}r(hUhjubhX -- rr}r(hUhjubhX<A period array from which the Epoch objects are constructed.rr}r(hX<A period array from which the Epoch objects are constructed.hjubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(hX+A list of events, one for of the events in rr}r(hX+A list of events, one for of the events in hjubjD)r}r(hX``epoch_array``h}r(h]h]h]h]h]uhjh}]rhX epoch_arrayrr}r(hUhjubahjOubhX.r}r(hX.hjubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXlistrr}r(hXlisthjubahjubahjubehjubeubeubeubh)r}r(hUhj+hNhhh}r(h]h]h]h]h]Uentries]r(hX:event_array_to_events() (in module spykeutils.conversions)hcUtr auhNhhh}]ubh)r }r (hUhj+hNhhh}r (hωhXpyh]h]h]h]h]hXfunctionr hj uhNhhh}]r(h)r}r(hX"event_array_to_events(event_array)hj hhhhh}r(h]rhcahhXspykeutils.conversionsrr}rbh]h]h]h]rhcahXevent_array_to_eventsrhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXevent_array_to_eventsrr}r(hUhjubaubjt)r }r!(hUhjhhhjwh}r"(h]h]h]h]h]uhNhhh}]r#jz)r$}r%(hX event_arrayh}r&(h]h]h]h]h]uhj h}]r'hX event_arrayr(r)}r*(hUhj$ubahjubaubh)r+}r,(hUhjhNhhh}r-(Uexprhh]h]h]h]h]uhNhhh}]r.h)r/}r0(hUh}r1(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/conversionsUrefidjuhj+h}]r2j)r3}r4(hUh}r5(h]h]r6j ah]h]h]uhj/h}]r7hX[source]r8r9}r:(hUhj3ubahjubahjubaubeubj)r;}r<(hUhj hhhjh}r=(h]h]h]h]h]uhNhhh}]r>(j)r?}r@(hX+Return a list of events for an event array.rAhj;hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/conversions.py:docstring of spykeutils.conversions.event_array_to_eventsrBhjh}rC(h]h]h]h]h]uhKhhh}]rDhX+Return a list of events for an event array.rErF}rG(hjAhj?ubaubj)rH}rI(hXNote that while the created events may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created events.rJhj;hjBhjh}rK(h]h]h]h]h]uhKhhh}]rLhXNote that while the created events may have references to a segment, the relationships in the other direction are not automatically created (the events are not attached to the segment). Other properties like annotations are not copied or referenced in the created events.rMrN}rO(hjJhjHubaubj)rP}rQ(hUhj;hNhjh}rR(h]h]h]h]h]uhNhhh}]rS(j)rT}rU(hUh}rV(h]h]h]h]h]uhjPh}]rW(j)rX}rY(hUh}rZ(h]h]h]h]h]uhjTh}]r[hX Parametersr\r]}r^(hUhjXubahjubj)r_}r`(hUh}ra(h]h]h]h]h]uhjTh}]rbj)rc}rd(hUh}re(h]h]h]h]h]uhj_h}]rf(j)rg}rh(hX event_arrayh}ri(h]h]h]h]h]uhjch}]rjhX event_arrayrkrl}rm(hUhjgubahjubhX (rnro}rp(hUhjcubh)rq}rr(hX:class:`neo.core.EventArray`rshjchNhjh}rt(UreftypeXclassj<j=Xneo.core.EventArrayU refdomainXpyruh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]rvjD)rw}rx(hjsh}ry(h]h]rz(jIjuXpy-classr{eh]h]h]uhjqh}]r|hXneo.core.EventArrayr}r~}r(hUhjwubahjOubaubhX)r}r(hUhjcubhX -- rr}r(hUhjcubhX<An event array from which the Event objects are constructed.rr}r(hX<An event array from which the Event objects are constructed.hjcubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjPh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(hX+A list of events, one for of the events in rr}r(hX+A list of events, one for of the events in hjubjD)r}r(hX``event_array``h}r(h]h]h]h]h]uhjh}]rhX event_arrayrr}r(hUhjubahjOubhX.r}r(hX.hjubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjPh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXlistrr}r(hXlisthjubahjubahjubehjubeubeubeubh)r}r(hUhj+hNhhh}r(h]h]h]h]h]Uentries]r(hX:spike_train_to_spikes() (in module spykeutils.conversions)h$UtrauhNhhh}]ubh)r}r(hUhj+hNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r}r(hX:spike_train_to_spikes(spike_train, include_waveforms=True)hjhhhhh}r(h]rh$ahhXspykeutils.conversionsrr}rbh]h]h]h]rh$ahXspike_train_to_spikesrhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXspike_train_to_spikesrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hX spike_trainh}r(h]h]h]h]h]uhjh}]rhX spike_trainrr}r(hUhjubahjubjz)r}r(hXinclude_waveforms=Trueh}r(h]h]h]h]h]uhjh}]rhXinclude_waveforms=Truerr}r(hUhjubahjubeubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/conversionsUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX*Return a list of spikes for a spike train.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/conversions.py:docstring of spykeutils.conversions.spike_train_to_spikesrhjh}r(h]h]h]h]h]uhKhhh}]rhX*Return a list of spikes for a spike train.rr}r(hjhjubaubj)r}r (hX6Note that while the created spikes have references to the same segment and unit as the spike train, the relationships in the other direction are not automatically created (the spikes are not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spikes.r hjhjhjh}r (h]h]h]h]h]uhKhhh}]r hX6Note that while the created spikes have references to the same segment and unit as the spike train, the relationships in the other direction are not automatically created (the spikes are not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spikes.r r}r(hj hjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Parametersrr}r(hUhjubahjubj)r}r (hUh}r!(h]h]h]h]h]uhjh}]r"cdocutils.nodes bullet_list r#)r$}r%(hUh}r&(h]h]h]h]h]uhjh}]r'(cdocutils.nodes list_item r()r)}r*(hUh}r+(h]h]h]h]h]uhj$h}]r,j)r-}r.(hUh}r/(h]h]h]h]h]uhj)h}]r0(j)r1}r2(hX spike_trainh}r3(h]h]h]h]h]uhj-h}]r4hX spike_trainr5r6}r7(hUhj1ubahjubhX (r8r9}r:(hUhj-ubh)r;}r<(hX:class:`neo.core.SpikeTrain`r=hj-hNhjh}r>(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr?h]h]U refexplicith]h]h]j?jj@NjAjuhNh}]r@jD)rA}rB(hj=h}rC(h]h]rD(jIj?Xpy-classrEeh]h]h]uhj;h}]rFhXneo.core.SpikeTrainrGrH}rI(hUhjAubahjOubaubhX)rJ}rK(hUhj-ubhX -- rLrM}rN(hUhj-ubhXA spike train from which the rOrP}rQ(hXA spike train from which the hj-ubh)rR}rS(hX:class:`neo.core.Spike`rThj-hNhjh}rU(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyrVh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]rWjD)rX}rY(hjTh}rZ(h]h]r[(jIjVXpy-classr\eh]h]h]uhjRh}]r]hXneo.core.Spiker^r_}r`(hUhjXubahjOubaubhX objects are constructed.rarb}rc(hX objects are constructed.hj-ubehjubahU list_itemrdubj()re}rf(hUh}rg(h]h]h]h]h]uhj$h}]rhj)ri}rj(hUh}rk(h]h]h]h]h]uhjeh}]rl(j)rm}rn(hXinclude_waveformsh}ro(h]h]h]h]h]uhjih}]rphXinclude_waveformsrqrr}rs(hUhjmubahjubhX (rtru}rv(hUhjiubh)rw}rx(hUh}ry(UreftypeUobjrzU reftargetXboolr{U refdomainjh]h]U refexplicith]h]h]uhjih}]r|cdocutils.nodes emphasis r})r~}r(hj{h}r(h]h]h]h]h]uhjwh}]rhXboolrr}r(hUhj~ubahUemphasisrubahjubhX)r}r(hUhjiubhX -- rr}r(hUhjiubhXDetermines if the rr}r(hXDetermines if the hjiubjD)r}r(hX ``waveforms``h}r(h]h]h]h]h]uhjih}]rhX waveformsrr}r(hUhjubahjOubhX2 property is converted to the spike waveforms. If rr}r(hX2 property is converted to the spike waveforms. If hjiubjD)r}r(hX ``waveforms``h}r(h]h]h]h]h]uhjih}]rhX waveformsrr}r(hUhjubahjOubhX' is None, this parameter has no effect.rr}r(hX' is None, this parameter has no effect.hjiubehjubahjdubehU bullet_listrubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(hX A list of rr}r(hX A list of hjubh)r}r(hX:class:`neo.core.Spike`rhjhNhjh}r(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXneo.core.Spikerr}r(hUhjubahjOubaubhX! objects, one for every spike in rr}r(hX! objects, one for every spike in hjubjD)r}r(hX``spike_train``h}r(h]h]h]h]h]uhjh}]rhX spike_trainrr}r(hUhjubahjOubhX.r}r(hX.hjubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXlistrr}r(hXlisthjubahjubahjubehjubeubeubeubh)r}r(hUhj+hNhhh}r(h]h]h]h]h]Uentries]r(hX:spikes_to_spike_train() (in module spykeutils.conversions)haUtrauhNhhh}]ubh)r}r(hUhj+hNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r}r(hX5spikes_to_spike_train(spikes, include_waveforms=True)hjhhhhh}r(h]rhaahhXspykeutils.conversionsrr}rbh]h]h]h]rhaahXspikes_to_spike_trainrhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXspikes_to_spike_trainrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]r (jz)r }r (hXspikesh}r (h]h]h]h]h]uhjh}]r hXspikesrr}r(hUhj ubahjubjz)r}r(hXinclude_waveforms=Trueh}r(h]h]h]h]h]uhjh}]rhXinclude_waveforms=Truerr}r(hUhjubahjubeubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/conversionsUrefidjuhjh}]rj)r }r!(hUh}r"(h]h]r#j ah]h]h]uhjh}]r$hX[source]r%r&}r'(hUhj ubahjubahjubaubeubj)r(}r)(hUhjhhhjh}r*(h]h]h]h]h]uhNhhh}]r+(j)r,}r-(hX*Return a spike train for a list of spikes.r.hj(hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/conversions.py:docstring of spykeutils.conversions.spikes_to_spike_trainr/hjh}r0(h]h]h]h]h]uhKhhh}]r1hX*Return a spike train for a list of spikes.r2r3}r4(hj.hj,ubaubj)r5}r6(hX{All spikes must have an identical left sweep, the same unit and the same segment, otherwise a ``SpykeException`` is raised.hj(hj/hjh}r7(h]h]h]h]h]uhKhhh}]r8(hX^All spikes must have an identical left sweep, the same unit and the same segment, otherwise a r9r:}r;(hX^All spikes must have an identical left sweep, the same unit and the same segment, otherwise a hj5ubjD)r<}r=(hX``SpykeException``h}r>(h]h]h]h]h]uhj5h}]r?hXSpykeExceptionr@rA}rB(hUhj<ubahjOubhX is raised.rCrD}rE(hX is raised.hj5ubeubj)rF}rG(hX>Note that while the created spike train has references to the same segment and unit as the spikes, the relationships in the other direction are not automatically created (the spike train is not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spike train.rHhj(hj/hjh}rI(h]h]h]h]h]uhKhhh}]rJhX>Note that while the created spike train has references to the same segment and unit as the spikes, the relationships in the other direction are not automatically created (the spike train is not attached to the unit or segment). Other properties like annotations are not copied or referenced in the created spike train.rKrL}rM(hjHhjFubaubj)rN}rO(hUhj(hNhjh}rP(h]h]h]h]h]uhNhhh}]rQ(j)rR}rS(hUh}rT(h]h]h]h]h]uhjNh}]rU(j)rV}rW(hUh}rX(h]h]h]h]h]uhjRh}]rYhX ParametersrZr[}r\(hUhjVubahjubj)r]}r^(hUh}r_(h]h]h]h]h]uhjRh}]r`j#)ra}rb(hUh}rc(h]h]h]h]h]uhj]h}]rd(j()re}rf(hUh}rg(h]h]h]h]h]uhjah}]rhj)ri}rj(hUh}rk(h]h]h]h]h]uhjeh}]rl(j)rm}rn(hXspikesh}ro(h]h]h]h]h]uhjih}]rphXspikesrqrr}rs(hUhjmubahjubhX (rtru}rv(hUhjiubh)rw}rx(hUh}ry(UreftypejzU reftargetXsequencerzU refdomainjh]h]U refexplicith]h]h]uhjih}]r{j})r|}r}(hjzh}r~(h]h]h]h]h]uhjwh}]rhXsequencerr}r(hUhj|ubahjubahjubhX)r}r(hUhjiubhX -- rr}r(hUhjiubhXA sequence of rr}r(hXA sequence of hjiubh)r}r(hX:class:`neo.core.Spike`rhjihNhjh}r(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAjuhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXneo.core.Spikerr}r(hUhjubahjOubaubhX3 objects from which the spike train is constructed.rr}r(hX3 objects from which the spike train is constructed.hjiubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjah}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXinclude_waveformsh}r(h]h]h]h]h]uhjh}]rhXinclude_waveformsrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXboolrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXboolrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXHDetermines if the waveforms from the spike objects are used to fill the rr}r(hXHDetermines if the waveforms from the spike objects are used to fill the hjubjD)r}r(hX ``waveforms``h}r(h]h]h]h]h]uhjh}]rhX waveformsrr}r(hUhjubahjOubhX+ property of the resulting spike train. If rr}r(hX+ property of the resulting spike train. If hjubjD)r}r(hX``True``h}r(h]h]h]h]h]uhjh}]rhXTruerr}r(hUhjubahjOubhX, all spikes need a rr}r(hX, all spikes need a hjubjD)r}r(hX ``waveform``h}r(h]h]h]h]h]uhjh}]rhXwaveformrr}r(hUhjubahjOubhX# property with the same shape or a rr}r(hX# property with the same shape or a hjubjD)r}r(hX``SpykeException``h}r(h]h]h]h]h]uhjh}]rhXSpykeExceptionrr}r(hUhjubahjOubhX is raised (or the rr}r(hX is raised (or the hjubjD)r}r(hX ``waveform``h}r(h]h]h]h]h]uhjh}]rhXwaveformrr}r(hUhjubahjOubhX property needs to be rr}r(hX property needs to be hjubjD)r}r(hX``None``h}r(h]h]h]h]h]uhjh}]rhXNonerr}r(hUhjubahjOubhX for all spikes).rr}r(hX for all spikes).hjubehjubahjdubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjNh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsrr}r (hUhjubahjubj)r }r (hUh}r (h]h]h]h]h]uhjh}]r j)r}r(hUh}r(h]h]h]h]h]uhj h}]r(hXAll elements of rr}r(hXAll elements of hjubjD)r}r(hX ``spikes``h}r(h]h]h]h]h]uhjh}]rhXspikesrr}r(hUhjubahjOubhX as spike train.rr}r(hX as spike train.hjubehjubahjubehjubj)r}r (hUh}r!(h]h]h]h]h]uhjNh}]r"(j)r#}r$(hUh}r%(h]h]h]h]h]uhjh}]r&hX Return typer'r(}r)(hUhj#ubahjubj)r*}r+(hUh}r,(h]h]h]h]h]uhjh}]r-j)r.}r/(hUh}r0(h]h]h]h]h]uhj*h}]r1h)r2}r3(hX:class:`neo.core.SpikeTrain`r4hj.hNhjh}r5(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr6h]h]U refexplicith]h]h]j?jj@NjAjuhNh}]r7jD)r8}r9(hj4h}r:(h]h]r;(jIj6Xpy-classr<eh]h]h]uhj2h}]r=hXneo.core.SpikeTrainr>r?}r@(hUhj8ubahjOubaubahjubahjubehjubeubeubeubeubh)rA}rB(hUhhhj-hhh}rC(h]h]h]h]rD(Xmodule-spykeutils.correlationsrEhpeh]rFh auhK hhh}]rG(h)rH}rI(hX:mod:`correlations` ModulerJhjAhj-hhh}rK(h]h]h]h]h]uhK hhh}]rL(h)rM}rN(hX:mod:`correlations`rOhjHhhhjh}rP(UreftypeXmodj<j=X correlationsU refdomainXpyrQh]h]U refexplicith]h]h]j?jj@NjAjuhKh}]rRjD)rS}rT(hjOh}rU(h]h]rV(jIjQXpy-modrWeh]h]h]uhjMh}]rXhX correlationsrYrZ}r[(hUhjSubahjOubaubhX Moduler\r]}r^(hX Moduler_hjHubeubh)r`}ra(hUhjAhhhhh}rb(h]h]h]h]h]Uentries]rc(hX spykeutils.correlations (module)Xmodule-spykeutils.correlationsUtrdauhKhhh}]ubh)re}rf(hUhjAhNhhh}rg(h]h]h]h]h]Uentries]rh(hX1correlogram() (in module spykeutils.correlations)h3UtriauhNhhh}]ubh)rj}rk(hUhjAhNhhh}rl(hωhXpyrmh]h]h]h]h]hXfunctionrnhjnuhNhhh}]ro(h)rp}rq(hXcorrelogram(trains, bin_size, max_lag=array(500.0) * ms, border_correction=True, per_second=True, unit=UnitTime('millisecond', 0.001 * s, 'ms'), progress=None)hjjhhhhh}rr(h]rsh3ahhXspykeutils.correlationsrtru}rvbh]h]h]h]rwh3ahX correlogramrxhUhuhNhhh}]ry(h)rz}r{(hjxhjphhhhh}r|(h]h]h]h]h]uhNhhh}]r}hX correlogramr~r}r(hUhjzubaubjt)r}r(hUhjphhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hXtrainsh}r(h]h]h]h]h]uhjh}]rhXtrainsrr}r(hUhjubahjubjz)r}r(hXbin_sizeh}r(h]h]h]h]h]uhjh}]rhXbin_sizerr}r(hUhjubahjubjz)r}r(hXmax_lag=array(500.0) * msh}r(h]h]h]h]h]uhjh}]rhXmax_lag=array(500.0) * msrr}r(hUhjubahjubjz)r}r(hXborder_correction=Trueh}r(h]h]h]h]h]uhjh}]rhXborder_correction=Truerr}r(hUhjubahjubjz)r}r(hXper_second=Trueh}r(h]h]h]h]h]uhjh}]rhXper_second=Truerr}r(hUhjubahjubjz)r}r(hXunit=UnitTime('millisecond'h}r(h]h]h]h]h]uhjh}]rhXunit=UnitTime('millisecond'rr}r(hUhjubahjubjz)r}r(hX 0.001 * sh}r(h]h]h]h]h]uhjh}]rhX 0.001 * srr}r(hUhjubahjubjz)r}r(hX'ms')h}r(h]h]h]h]h]uhjh}]rhX'ms')rr}r(hUhjubahjubjz)r}r(hX progress=Noneh}r(h]h]h]h]h]uhjh}]rhX progress=Nonerr}r(hUhjubahjubeubh)r}r(hUhjphNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX _modules/spykeutils/correlationsUrefidjxuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hXWReturn (cross-)correlograms from a dictionary of spike train lists for different units.rhjhX}/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/correlations.py:docstring of spykeutils.correlations.correlogramrhjh}r(h]h]h]h]h]uhKhhh}]rhXWReturn (cross-)correlograms from a dictionary of spike train lists for different units.rr}r(hjhjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Parametersrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj#)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j()r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXtrainsh}r(h]h]h]h]h]uhjh}]rhXtrainsrr}r(hUhjubahjubhX (rr}r (hUhjubh)r }r (hUh}r (UreftypejzU reftargetXdictr U refdomainjmh]h]U refexplicith]h]h]uhjh}]rj})r}r(hj h}r(h]h]h]h]h]uhj h}]rhXdictrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXDictionary of rr}r(hXDictionary of hjubh)r}r(hX:class:`neo.core.SpikeTrain`r hjhhhjh}r!(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr"h]h]U refexplicith]h]h]j?jj@NjAXspykeutils.correlationsr#uhK h}]r$jD)r%}r&(hj h}r'(h]h]r((jIj"Xpy-classr)eh]h]h]uhjh}]r*hXneo.core.SpikeTrainr+r,}r-(hUhj%ubahjOubaubhX lists.r.r/}r0(hX lists.hjubehjubahjdubj()r1}r2(hUh}r3(h]h]h]h]h]uhjh}]r4j)r5}r6(hUh}r7(h]h]h]h]h]uhj1h}]r8(j)r9}r:(hXbin_sizeh}r;(h]h]h]h]h]uhj5h}]r<hXbin_sizer=r>}r?(hUhj9ubahjubhX (r@rA}rB(hUhj5ubh)rC}rD(hUh}rE(UreftypejzU reftargetXQuantity scalarrFU refdomainjmh]h]U refexplicith]h]h]uhj5h}]rGj})rH}rI(hjFh}rJ(h]h]h]h]h]uhjCh}]rKhXQuantity scalarrLrM}rN(hUhjHubahjubahjubhX)rO}rP(hUhj5ubhX -- rQrR}rS(hUhj5ubhXBin size (time).rTrU}rV(hXBin size (time).hj5ubehjubahjdubj()rW}rX(hUh}rY(h]h]h]h]h]uhjh}]rZj)r[}r\(hUh}r](h]h]h]h]h]uhjWh}]r^(j)r_}r`(hXmax_lagh}ra(h]h]h]h]h]uhj[h}]rbhXmax_lagrcrd}re(hUhj_ubahjubhX (rfrg}rh(hUhj[ubh)ri}rj(hUh}rk(UreftypejzU reftargetXQuantity scalarrlU refdomainjmh]h]U refexplicith]h]h]uhj[h}]rmj})rn}ro(hjlh}rp(h]h]h]h]h]uhjih}]rqhXQuantity scalarrrrs}rt(hUhjnubahjubahjubhX)ru}rv(hUhj[ubhX -- rwrx}ry(hUhj[ubhX-Cut off (end time of calculated correlogram).rzr{}r|(hX-Cut off (end time of calculated correlogram).hj[ubehjubahjdubj()r}}r~(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhj}h}]r(j)r}r(hXborder_correctionh}r(h]h]h]h]h]uhjh}]rhXborder_correctionrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXboolrU refdomainjmh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXboolrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXqApply correction for less data at higher timelags. Not perfect for bin_size != 1*``unit``, especially with large rr}r(hXqApply correction for less data at higher timelags. Not perfect for bin_size != 1*``unit``, especially with large hjubjD)r}r(hX ``max_lag``h}r(h]h]h]h]h]uhjh}]rhXmax_lagrr}r(hUhjubahjOubhX$ compared to length of spike trains.rr}r(hX$ compared to length of spike trains.hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hX per_secondh}r(h]h]h]h]h]uhjh}]rhX per_secondrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXboolrU refdomainjmh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXboolrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXIf rr}r(hXIf hjubjD)r}r(hX``True``h}r(h]h]h]h]h]uhjh}]rhXTruerr}r(hUhjubahjOubhXQ, counts returned are per second. Otherwise, counts per spike train are returned.rr}r(hXQ, counts returned are per second. Otherwise, counts per spike train are returned.hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXunith}r(h]h]h]h]h]uhjh}]rhXunitrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXQuantityrU refdomainjmh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXQuantityrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXUnit of X-Axis.rr}r(hXUnit of X-Axis.hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r (h]h]h]h]h]uhjh}]r (j)r }r (hXprogressh}r (h]h]h]h]h]uhjh}]rhXprogressrr}r(hUhj ubahjubhX (rr}r(hUhjubh)r}r(hX.:class:`.progress_indicator.ProgressIndicator`rhjhNhjh}r(UreftypeXclassU refspecificrj<j=X$progress_indicator.ProgressIndicatorU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj#uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classr eh]h]h]uhjh}]r!hX$progress_indicator.ProgressIndicatorr"r#}r$(hUhjubahjOubaubhX)r%}r&(hUhjubhX -- r'r(}r)(hUhjubhX-A ProgressIndicator object for the operation.r*r+}r,(hX-A ProgressIndicator object for the operation.hjubehjubahjdubehjubahjubehjubj)r-}r.(hUh}r/(h]h]h]h]h]uhjh}]r0(j)r1}r2(hUh}r3(h]h]h]h]h]uhj-h}]r4hXReturnsr5r6}r7(hUhj1ubahjubj)r8}r9(hUh}r:(h]h]h]h]h]uhj-h}]r;j)r<}r=(hUh}r>(h]h]h]h]h]uhj8h}]r?(j)r@}rA(hX Two values:rBhj<hjhjh}rC(h]h]h]h]h]uhKh}]rDhX Two values:rErF}rG(hjBhj@ubaubj#)rH}rI(hUh}rJ(UbulletrKX*h]h]h]h]h]uhj<h}]rL(j()rM}rN(hX:An ordered dictionary indexed with the indices of ``trains`` of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: ``c[index1][index2]`` and ``c[index2][index1]``.h}rO(h]h]h]h]h]uhjHh}]rPj)rQ}rR(hX:An ordered dictionary indexed with the indices of ``trains`` of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: ``c[index1][index2]`` and ``c[index2][index1]``.hjMhjhjh}rS(h]h]h]h]h]uhKh}]rT(hX2An ordered dictionary indexed with the indices of rUrV}rW(hX2An ordered dictionary indexed with the indices of hjQubjD)rX}rY(hX ``trains``h}rZ(h]h]h]h]h]uhjQh}]r[hXtrainsr\r]}r^(hUhjXubahjOubhX of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: r_r`}ra(hX of ordered dictionaries indexed with the same indices. Entries of the inner dictionaries are the resulting (cross-)correlograms as numpy arrays. All crosscorrelograms can be indexed in two different ways: hjQubjD)rb}rc(hX``c[index1][index2]``h}rd(h]h]h]h]h]uhjQh}]rehXc[index1][index2]rfrg}rh(hUhjbubahjOubhX and rirj}rk(hX and hjQubjD)rl}rm(hX``c[index2][index1]``h}rn(h]h]h]h]h]uhjQh}]rohXc[index2][index1]rprq}rr(hUhjlubahjOubhX.rs}rt(hX.hjQubeubahjdubj()ru}rv(hX.The bins used for the correlogram calculation.rwh}rx(h]h]h]h]h]uhjHh}]ryj)rz}r{(hjwhjuhjhjh}r|(h]h]h]h]h]uhKh}]r}hX.The bins used for the correlogram calculation.r~r}r(hjwhjzubaubahjdubehjubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXdict, Quantity 1Drr}r(hXdict, Quantity 1Dhjubahjubahjubehjubeubeubeubeubh)r}r(hUhhhj-hhh}r(h]h]h]h]r(X$module-spykeutils.progress_indicatorrhweh]rh4auhKhhh}]r(h)r}r(hX :mod:`progress_indicator` Modulerhjhj-hhh}r(h]h]h]h]h]uhKhhh}]r(h)r}r(hX:mod:`progress_indicator`rhjhNhjh}r(UreftypeXmodj<j=Xprogress_indicatorU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj#uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-modreh]h]h]uhjh}]rhXprogress_indicatorrr}r(hUhjubahjOubaubhX Modulerr}r(hX Modulerhjubeubh)r}r(hUhjhhhhh}r(h]h]h]h]h]Uentries]r(hX&spykeutils.progress_indicator (module)X$module-spykeutils.progress_indicatorUtrauhKhhh}]ubh)r}r(hUhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.CancelExceptionrhhh}r(h]h]h]h]h]Uentries]r(hXCancelExceptionrh%UtrauhNhhh}]ubh)r}r(hUhjhjhhh}r(hωhXpyh]h]h]h]h]hX exceptionrhjuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]rh%ahhXspykeutils.progress_indicatorrr}rbh]h]h]h]rh%ahjhUhuhNhhh}]r(h)r}r(hX exception hjhhhhh}r(h]h]h]h]h]uhNhhh}]rhX exception rr}r(hUhjubaubh)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXCancelExceptionrr}r(hUhjubaubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX$Bases: :class:`exceptions.Exception`hjhU rhjh}r(h]h]h]h]h]uhKhhh}]r(hXBases: rr}r(hXBases: hjubh)r}r(hX:class:`exceptions.Exception`rhjhhhjh}r(UreftypeXclassj<j=Xexceptions.ExceptionU refdomainXpyrh]h]U refexplicith]h]h]j?jj@jjAXspykeutils.progress_indicatorruhKh}]r jD)r }r (hjh}r (h]h]r (jIjXpy-classr eh]h]h]uhjh}]r hXexceptions.Exceptionr r }r (hUhj ubahjOubaubeubj)r }r (hXtThis is raised when a user cancels a progress process. It is used by :class:`ProgressIndicator` and its descendants.hjhjhjh}r (h]h]h]h]h]uhKhhh}]r (hXEThis is raised when a user cancels a progress process. It is used by r r }r (hXEThis is raised when a user cancels a progress process. It is used by hj ubh)r }r (hX:class:`ProgressIndicator`r hj hhhjh}r (UreftypeXclassj<j=XProgressIndicatorU refdomainXpyr h]h]U refexplicith]h]h]j?jj@jjAjuhK h}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-classr eh]h]h]uhj h}]r hXProgressIndicatorr r }r (hUhj ubahjOubaubhX and its descendants.r r! }r" (hX and its descendants.hj ubeubeubeubh)r# }r$ (hUhjhNhhh}r% (h]h]h]h]h]Uentries]r& (hX:ProgressIndicator (class in spykeutils.progress_indicator)hUtr' auhNhhh}]ubh)r( }r) (hUhjhNhhh}r* (hωhXpyh]h]h]h]h]hXclassr+ hj+ uhNhhh}]r, (h)r- }r. (hXProgressIndicatorr/ hj( hhhhh}r0 (h]r1 hahhXspykeutils.progress_indicatorr2 r3 }r4 bh]h]h]h]r5 hahj/ hUhuhNhhh}]r6 (h)r7 }r8 (hXclass hj- hhhhh}r9 (h]h]h]h]h]uhNhhh}]r: hXclass r; r< }r= (hUhj7 ubaubh)r> }r? (hj/ hj- hhhhh}r@ (h]h]h]h]h]uhNhhh}]rA hXProgressIndicatorrB rC }rD (hUhj> ubaubh)rE }rF (hUhj- hNhhh}rG (Uexprhh]h]h]h]h]uhNhhh}]rH h)rI }rJ (hUh}rK (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj/ uhjE h}]rL j)rM }rN (hUh}rO (h]h]rP j ah]h]h]uhjI h}]rQ hX[source]rR rS }rT (hUhjM ubahjubahjubaubeubj)rU }rV (hUhj( hhhjh}rW (h]h]h]h]h]uhNhhh}]rX (j)rY }rZ (hXBases: :class:`object`hjU hjhjh}r[ (h]h]h]h]h]uhKhhh}]r\ (hXBases: r] r^ }r_ (hXBases: hjY ubh)r` }ra (hX:class:`object`rb hjY hhhjh}rc (UreftypeXclassj<j=XobjectU refdomainXpyrd h]h]U refexplicith]h]h]j?jj@j/ jAjuhKh}]re jD)rf }rg (hjb h}rh (h]h]ri (jIjd Xpy-classrj eh]h]h]uhj` h}]rk hXobjectrl rm }rn (hUhjf ubahjOubaubeubj)ro }rp (hX?Base class for classes indicating progress of a long operation.rq hjU hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicatorrr hjh}rs (h]h]h]h]h]uhKhhh}]rt hX?Base class for classes indicating progress of a long operation.ru rv }rw (hjq hjo ubaubj)rx }ry (hXpThis class does not implement any of the methods and can be used as a dummy if no progress indication is needed.rz hjU hjr hjh}r{ (h]h]h]h]h]uhKhhh}]r| hXpThis class does not implement any of the methods and can be used as a dummy if no progress indication is needed.r} r~ }r (hjz hjx ubaubh)r }r (hUhjU hNhhh}r (h]h]h]h]h]Uentries]r (hX"begin() (ProgressIndicator method)h_Utr auhNhhh}]ubh)r }r (hUhjU hNhhh}r (hωhXpyr h]h]h]h]h]hXmethodr hj uhNhhh}]r (h)r }r (hX!ProgressIndicator.begin(title='')hj hhhhh}r (h]r h_ahhXspykeutils.progress_indicatorr r }r bh]h]h]h]r h_ahXProgressIndicator.beginr hj/ huhNhhh}]r (h)r }r (hXbeginhj hhhhh}r (h]h]h]h]h]uhNhhh}]r hXbeginr r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]r jz)r }r (hXtitle=''h}r (h]h]h]h]h]uhj h}]r hXtitle=''r r }r (hUhj ubahjubaubh)r }r (hUhj hNhhh}r (Uexprhh]h]h]h]h]uhNhhh}]r h)r }r (hUh}r (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj uhj h}]r j)r }r (hUh}r (h]h]r j ah]h]h]uhj h}]r hX[source]r r }r (hUhj ubahjubahjubaubeubj)r }r (hUhj hhhjh}r (h]h]h]h]h]uhNhhh}]r (j)r }r (hX!Signal that the operation starts.r hj hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicator.beginhjh}r (h]h]h]h]h]uhKhhh}]r hX!Signal that the operation starts.r r }r (hj hj ubaubj)r }r (hUhj hNhjh}r (h]h]h]h]h]uhNhhh}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hX Parametersr r }r (hUhj ubahjubj)r }r (hUh}r (h]h]h]h]h]uhj h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hXtitleh}r (h]h]h]h]h]uhj h}]r hXtitler r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXstringr U refdomainj h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXstringr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhX The name of the whole operation.r r }r (hX The name of the whole operation.hj ubehjubahjubehjubaubeubeubh)r }r (hUhjU hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicator.doner hhh}r (h]h]h]h]h]Uentries]r (hX!done() (ProgressIndicator method)h*Utr auhNhhh}]ubh)r }r (hUhjU hj hhh}r (hωhXpyh]h]h]h]h]hXmethodr hj uhNhhh}]r (h)r }r (hXProgressIndicator.done()hj hhhhh}r (h]r h*ahhXspykeutils.progress_indicatorr r }r bh]h]h]h]r h*ahXProgressIndicator.doner hj/ huhNhhh}]r (h)r }r (hXdonehj hhhhh}r (h]h]h]h]h]uhNhhh}]r hXdoner r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]ubh)r }r (hUhj hNhhh}r (Uexprhh]h]h]h]h]uhNhhh}]r h)r }r (hUh}r (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj uhj h}]r j)r }r (hUh}r! (h]h]r" j ah]h]h]uhj h}]r# hX[source]r$ r% }r& (hUhj ubahjubahjubaubeubj)r' }r( (hUhj hhhjh}r) (h]h]h]h]h]uhNhhh}]r* j)r+ }r, (hX"Signal that the operation is done.r- hj' hj hjh}r. (h]h]h]h]h]uhKhhh}]r/ hX"Signal that the operation is done.r0 r1 }r2 (hj- hj+ ubaubaubeubh)r3 }r4 (hUhjU hNhhh}r5 (h]h]h]h]h]Uentries]r6 (hX'set_status() (ProgressIndicator method)h5Utr7 auhNhhh}]ubh)r8 }r9 (hUhjU hNhhh}r: (hωhXpyr; h]h]h]h]h]hXmethodr< hj< uhNhhh}]r= (h)r> }r? (hX(ProgressIndicator.set_status(new_status)hj8 hhhhh}r@ (h]rA h5ahhXspykeutils.progress_indicatorrB rC }rD bh]h]h]h]rE h5ahXProgressIndicator.set_statusrF hj/ huhNhhh}]rG (h)rH }rI (hX set_statushj> hhhhh}rJ (h]h]h]h]h]uhNhhh}]rK hX set_statusrL rM }rN (hUhjH ubaubjt)rO }rP (hUhj> hhhjwh}rQ (h]h]h]h]h]uhNhhh}]rR jz)rS }rT (hX new_statush}rU (h]h]h]h]h]uhjO h}]rV hX new_statusrW rX }rY (hUhjS ubahjubaubh)rZ }r[ (hUhj> hNhhh}r\ (Uexprhh]h]h]h]h]uhNhhh}]r] h)r^ }r_ (hUh}r` (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidjF uhjZ h}]ra j)rb }rc (hUh}rd (h]h]re j ah]h]h]uhj^ h}]rf hX[source]rg rh }ri (hUhjb ubahjubahjubaubeubj)rj }rk (hUhj8 hhhjh}rl (h]h]h]h]h]uhNhhh}]rm (j)rn }ro (hXSet status description.rp hjj hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicator.set_statushjh}rq (h]h]h]h]h]uhKhhh}]rr hXSet status description.rs rt }ru (hjp hjn ubaubj)rv }rw (hUhjj hNhjh}rx (h]h]h]h]h]uhNhhh}]ry j)rz }r{ (hUh}r| (h]h]h]h]h]uhjv h}]r} (j)r~ }r (hUh}r (h]h]h]h]h]uhjz h}]r hX Parametersr r }r (hUhj~ ubahjubj)r }r (hUh}r (h]h]h]h]h]uhjz h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hX new_statush}r (h]h]h]h]h]uhj h}]r hX new_statusr r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXstringr U refdomainj; h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXstringr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhX$A description of the current status.r r }r (hX$A description of the current status.hj ubehjubahjubehjubaubeubeubh)r }r (hUhjU hNhhh}r (h]h]h]h]h]Uentries]r (hX&set_ticks() (ProgressIndicator method)hZUtr auhNhhh}]ubh)r }r (hUhjU hNhhh}r (hωhXpyr h]h]h]h]h]hXmethodr hj uhNhhh}]r (h)r }r (hX"ProgressIndicator.set_ticks(ticks)hj hhhhh}r (h]r hZahhXspykeutils.progress_indicatorr r }r bh]h]h]h]r hZahXProgressIndicator.set_ticksr hj/ huhNhhh}]r (h)r }r (hX set_tickshj hhhhh}r (h]h]h]h]h]uhNhhh}]r hX set_ticksr r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]r jz)r }r (hXticksh}r (h]h]h]h]h]uhj h}]r hXticksr r }r (hUhj ubahjubaubh)r }r (hUhj hNhhh}r (Uexprhh]h]h]h]h]uhNhhh}]r h)r }r (hUh}r (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj uhj h}]r j)r }r (hUh}r (h]h]r j ah]h]h]uhj h}]r hX[source]r r }r (hUhj ubahjubahjubaubeubj)r }r (hUhj hhhjh}r (h]h]h]h]h]uhNhhh}]r (j)r }r (hX>Set the required number of ticks before the operation is done.r hj hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicator.set_tickshjh}r (h]h]h]h]h]uhKhhh}]r hX>Set the required number of ticks before the operation is done.r r }r (hj hj ubaubj)r }r (hUhj hNhjh}r (h]h]h]h]h]uhNhhh}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hX Parametersr r }r (hUhj ubahjubj)r }r (hUh}r (h]h]h]h]h]uhj h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hXticksh}r (h]h]h]h]h]uhj h}]r hXticksr r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXintr U refdomainj h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXintr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhX1The number of steps that the operation will take.r r! }r" (hX1The number of steps that the operation will take.hj ubehjubahjubehjubaubeubeubh)r# }r$ (hUhjU hNhhh}r% (h]h]h]h]h]Uentries]r& (hX!step() (ProgressIndicator method)h2Utr' auhNhhh}]ubh)r( }r) (hUhjU hNhhh}r* (hωhXpyr+ h]h]h]h]h]hXmethodr, hj, uhNhhh}]r- (h)r. }r/ (hX#ProgressIndicator.step(num_steps=1)hj( hhhhh}r0 (h]r1 h2ahhXspykeutils.progress_indicatorr2 r3 }r4 bh]h]h]h]r5 h2ahXProgressIndicator.stepr6 hj/ huhNhhh}]r7 (h)r8 }r9 (hXstephj. hhhhh}r: (h]h]h]h]h]uhNhhh}]r; hXstepr< r= }r> (hUhj8 ubaubjt)r? }r@ (hUhj. hhhjwh}rA (h]h]h]h]h]uhNhhh}]rB jz)rC }rD (hX num_steps=1h}rE (h]h]h]h]h]uhj? h}]rF hX num_steps=1rG rH }rI (hUhjC ubahjubaubh)rJ }rK (hUhj. hNhhh}rL (Uexprhh]h]h]h]h]uhNhhh}]rM h)rN }rO (hUh}rP (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj6 uhjJ h}]rQ j)rR }rS (hUh}rT (h]h]rU j ah]h]h]uhjN h}]rV hX[source]rW rX }rY (hUhjR ubahjubahjubaubeubj)rZ }r[ (hUhj( hhhjh}r\ (h]h]h]h]h]uhNhhh}]r] (j)r^ }r_ (hX>Signal that one or more steps of the operation were completed.r` hjZ hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ProgressIndicator.stephjh}ra (h]h]h]h]h]uhKhhh}]rb hX>Signal that one or more steps of the operation were completed.rc rd }re (hj` hj^ ubaubj)rf }rg (hUhjZ hNhjh}rh (h]h]h]h]h]uhNhhh}]ri j)rj }rk (hUh}rl (h]h]h]h]h]uhjf h}]rm (j)rn }ro (hUh}rp (h]h]h]h]h]uhjj h}]rq hX Parametersrr rs }rt (hUhjn ubahjubj)ru }rv (hUh}rw (h]h]h]h]h]uhjj h}]rx j)ry }rz (hUh}r{ (h]h]h]h]h]uhju h}]r| (j)r} }r~ (hX num_stepsh}r (h]h]h]h]h]uhjy h}]r hX num_stepsr r }r (hUhj} ubahjubhX (r r }r (hUhjy ubh)r }r (hUh}r (UreftypejzU reftargetXintr U refdomainj+ h]h]U refexplicith]h]h]uhjy h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXintr r }r (hUhj ubahjubahjubhX)r }r (hUhjy ubhX -- r r }r (hUhjy ubhX-The number of steps that have been completed.r r }r (hX-The number of steps that have been completed.hjy ubehjubahjubehjubaubeubeubeubeubh)r }r (hUhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/progress_indicator.py:docstring of spykeutils.progress_indicator.ignores_cancelr hhh}r (h]h]h]h]h]Uentries]r (hX:ignores_cancel() (in module spykeutils.progress_indicator)hUUtr auhNhhh}]ubh)r }r (hUhjhj hhh}r (hωhXpyh]h]h]h]h]hXfunctionr hj uhNhhh}]r (h)r }r (hXignores_cancel(function)hj hhhhh}r (h]r hUahhXspykeutils.progress_indicatorr r }r bh]h]h]h]r hUahXignores_cancelr hUhuhNhhh}]r (h)r }r (hj hj hhhhh}r (h]h]h]h]h]uhNhhh}]r hXignores_cancelr r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]r jz)r }r (hXfunctionh}r (h]h]h]h]h]uhj h}]r hXfunctionr r }r (hUhj ubahjubaubh)r }r (hUhj hNhhh}r (Uexprhh]h]h]h]h]uhNhhh}]r h)r }r (hUh}r (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX&_modules/spykeutils/progress_indicatorUrefidj uhj h}]r j)r }r (hUh}r (h]h]r j ah]h]h]uhj h}]r hX[source]r r }r (hUhj ubahjubahjubaubeubj)r }r (hUhj hhhjh}r (h]h]h]h]h]uhNhhh}]r j)r }r (hXqDecorator for functions that should ignore a raised :class:`CancelException` and just return nothing in this casehj hj hjh}r (h]h]h]h]h]uhKhhh}]r (hX4Decorator for functions that should ignore a raised r r }r (hX4Decorator for functions that should ignore a raised hj ubh)r }r (hX:class:`CancelException`r hj hNhjh}r (UreftypeXclassj<j=XCancelExceptionU refdomainXpyr h]h]U refexplicith]h]h]j?jj@NjAjuhNh}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-classr eh]h]h]uhj h}]r hXCancelExceptionr r }r (hUhj ubahjOubaubhX% and just return nothing in this caser r }r (hX% and just return nothing in this casehj ubeubaubeubeubh)r }r (hUhhhj-hhh}r (h]h]h]h]r (X!module-spykeutils.rate_estimationr h{eh]r hRauhKhhh}]r (h)r }r (hX:mod:`rate_estimation` Moduler hj hj-hhh}r (h]h]h]h]h]uhKhhh}]r (h)r }r (hX:mod:`rate_estimation`r hj hNhjh}r (UreftypeXmodj<j=Xrate_estimationU refdomainXpyr h]h]U refexplicith]h]h]j?jj@NjAjuhNh}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-modr eh]h]h]uhj h}]r hXrate_estimationr r }r (hUhj ubahjOubaubhX Moduler r }r (hX Moduler hj ubeubh)r }r (hUhj hhhhh}r (h]h]h]h]h]Uentries]r (hX#spykeutils.rate_estimation (module)X!module-spykeutils.rate_estimationUtr auhKhhh}]ubh)r }r (hUhj hNhhh}r (h]h]h]h]h]Uentries]r (hX=aligned_spike_trains() (in module spykeutils.rate_estimation)h^Utr auhNhhh}]ubh)r }r (hUhj hNhhh}r (hωhXpyr h]h]h]h]h]hXfunctionr hj uhNhhh}]r (h)r }r (hX/aligned_spike_trains(trains, events, copy=True)hj hhhhh}r (h]r! h^ahhXspykeutils.rate_estimationr" r# }r$ bh]h]h]h]r% h^ahXaligned_spike_trainsr& hUhuhNhhh}]r' (h)r( }r) (hj& hj hhhhh}r* (h]h]h]h]h]uhNhhh}]r+ hXaligned_spike_trainsr, r- }r. (hUhj( ubaubjt)r/ }r0 (hUhj hhhjwh}r1 (h]h]h]h]h]uhNhhh}]r2 (jz)r3 }r4 (hXtrainsh}r5 (h]h]h]h]h]uhj/ h}]r6 hXtrainsr7 r8 }r9 (hUhj3 ubahjubjz)r: }r; (hXeventsh}r< (h]h]h]h]h]uhj/ h}]r= hXeventsr> r? }r@ (hUhj: ubahjubjz)rA }rB (hX copy=Trueh}rC (h]h]h]h]h]uhj/ h}]rD hX copy=TruerE rF }rG (hUhjA ubahjubeubh)rH }rI (hUhj hNhhh}rJ (Uexprhh]h]h]h]h]uhNhhh}]rK h)rL }rM (hUh}rN (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX#_modules/spykeutils/rate_estimationUrefidj& uhjH h}]rO j)rP }rQ (hUh}rR (h]h]rS j ah]h]h]uhjL h}]rT hX[source]rU rV }rW (hUhjP ubahjubahjubaubeubj)rX }rY (hUhj hhhjh}rZ (h]h]h]h]h]uhNhhh}]r[ (j)r\ }r] (hXdReturn a list of spike trains aligned to an event (the event will be time 0 on the returned trains).r^ hjX hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/rate_estimation.py:docstring of spykeutils.rate_estimation.aligned_spike_trainshjh}r_ (h]h]h]h]h]uhKhhh}]r` hXdReturn a list of spike trains aligned to an event (the event will be time 0 on the returned trains).ra rb }rc (hj^ hj\ ubaubj)rd }re (hUhjX hNhjh}rf (h]h]h]h]h]uhNhhh}]rg j)rh }ri (hUh}rj (h]h]h]h]h]uhjd h}]rk (j)rl }rm (hUh}rn (h]h]h]h]h]uhjh h}]ro hX Parametersrp rq }rr (hUhjl ubahjubj)rs }rt (hUh}ru (h]h]h]h]h]uhjh h}]rv j#)rw }rx (hUh}ry (h]h]h]h]h]uhjs h}]rz (j()r{ }r| (hUh}r} (h]h]h]h]h]uhjw h}]r~ j)r }r (hUh}r (h]h]h]h]h]uhj{ h}]r (j)r }r (hXtrainsh}r (h]h]h]h]h]uhj h}]r hXtrainsr r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXlistr U refdomainj h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXlistr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhX A list of r r }r (hX A list of hj ubh)r }r (hX:class:`neo.core.SpikeTrain`r hj hhhjh}r (UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr h]h]U refexplicith]h]h]j?jj@NjAXspykeutils.rate_estimationr uhK h}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-classr eh]h]h]uhj h}]r hXneo.core.SpikeTrainr r }r (hUhj ubahjOubaubhX objects.r r }r (hX objects.hj ubehjubahjdubj()r }r (hUh}r (h]h]h]h]h]uhjw h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hXeventsh}r (h]h]h]h]h]uhj h}]r hXeventsr r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXdictr U refdomainj h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXdictr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhXA dictionary of Event objects, indexed by segment. These events will be used to align the spike trains and will be at time 0 for the aligned spike trains.r r }r (hXA dictionary of Event objects, indexed by segment. These events will be used to align the spike trains and will be at time 0 for the aligned spike trains.hj ubehjubahjdubj()r }r (hUh}r (h]h]h]h]h]uhjw h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hXcopyh}r (h]h]h]h]h]uhj h}]r hXcopyr r }r (hUhj ubahjubhX (r r }r (hUhj ubh)r }r (hUh}r (UreftypejzU reftargetXboolr U refdomainj h]h]U refexplicith]h]h]uhj h}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXboolr r }r (hUhj ubahjubahjubhX)r }r (hUhj ubhX -- r r }r (hUhj ubhXDetermines if aligned copies of the original spike trains will be returned. If not, every spike train needs exactly one corresponding event, otherwise a r r }r (hXDetermines if aligned copies of the original spike trains will be returned. If not, every spike train needs exactly one corresponding event, otherwise a hj ubjD)r }r (hX``ValueError``h}r (h]h]h]h]h]uhj h}]r hX ValueErrorr r }r (hUhj ubahjOubhXB will be raised. Otherwise, entries with no event will be ignored.r r }r (hXB will be raised. Otherwise, entries with no event will be ignored.hj ubehjubahjdubehjubahjubehjubaubeubeubh)r }r (hUhj hNhhh}r (h]h]h]h]h]Uentries]r (hX?collapsed_spike_trains() (in module spykeutils.rate_estimation)hUtr auhNhhh}]ubh)r }r (hUhj hNhhh}r (hωhXpyr h]h]h]h]h]hXfunctionr hj uhNhhh}]r (h)r }r (hXcollapsed_spike_trains(trains)hj hhhhh}r (h]r hahhXspykeutils.rate_estimationr r }r bh]h]h]h]r hahXcollapsed_spike_trainsr hUhuhNhhh}]r (h)r }r (hj hj hhhhh}r! (h]h]h]h]h]uhNhhh}]r" hXcollapsed_spike_trainsr# r$ }r% (hUhj ubaubjt)r& }r' (hUhj hhhjwh}r( (h]h]h]h]h]uhNhhh}]r) jz)r* }r+ (hXtrainsh}r, (h]h]h]h]h]uhj& h}]r- hXtrainsr. r/ }r0 (hUhj* ubahjubaubh)r1 }r2 (hUhj hNhhh}r3 (Uexprhh]h]h]h]h]uhNhhh}]r4 h)r5 }r6 (hUh}r7 (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX#_modules/spykeutils/rate_estimationUrefidj uhj1 h}]r8 j)r9 }r: (hUh}r; (h]h]r< j ah]h]h]uhj5 h}]r= hX[source]r> r? }r@ (hUhj9 ubahjubahjubaubeubj)rA }rB (hUhj hhhjh}rC (h]h]h]h]h]uhNhhh}]rD (j)rE }rF (hX1Return a superposition of a list of spike trains.rG hjA hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/rate_estimation.py:docstring of spykeutils.rate_estimation.collapsed_spike_trainshjh}rH (h]h]h]h]h]uhKhhh}]rI hX1Return a superposition of a list of spike trains.rJ rK }rL (hjG hjE ubaubj)rM }rN (hUhjA hNhjh}rO (h]h]h]h]h]uhNhhh}]rP (j)rQ }rR (hUh}rS (h]h]h]h]h]uhjM h}]rT (j)rU }rV (hUh}rW (h]h]h]h]h]uhjQ h}]rX hX ParametersrY rZ }r[ (hUhjU ubahjubj)r\ }r] (hUh}r^ (h]h]h]h]h]uhjQ h}]r_ j)r` }ra (hUh}rb (h]h]h]h]h]uhj\ h}]rc (j)rd }re (hXtrainsh}rf (h]h]h]h]h]uhj` h}]rg hXtrainsrh ri }rj (hUhjd ubahjubhX (rk rl }rm (hUhj` ubh)rn }ro (hUh}rp (UreftypejzU reftargetXiterablerq U refdomainj h]h]U refexplicith]h]h]uhj` h}]rr j})rs }rt (hjq h}ru (h]h]h]h]h]uhjn h}]rv hXiterablerw rx }ry (hUhjs ubahjubahjubhX)rz }r{ (hUhj` ubhX -- r| r} }r~ (hUhj` ubhX A list of r r }r (hX A list of hj` ubh)r }r (hX:class:`neo.core.SpikeTrain`r hj` hNhjh}r (UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr h]h]U refexplicith]h]h]j?jj@NjAj uhNh}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-classr eh]h]h]uhj h}]r hXneo.core.SpikeTrainr r }r (hUhj ubahjOubaubhX objectsr r }r (hX objectshj` ubehjubahjubehjubj)r }r (hUh}r (h]h]h]h]h]uhjM h}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hXReturnsr r }r (hUhj ubahjubj)r }r (hUh}r (h]h]h]h]h]uhj h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hXEA spike train object containing all spikes of the given spike trains.r r }r (hXEA spike train object containing all spikes of the given spike trains.hj ubahjubahjubehjubj)r }r (hUh}r (h]h]h]h]h]uhjM h}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hX Return typer r }r (hUhj ubahjubj)r }r (hUh}r (h]h]h]h]h]uhj h}]r j)r }r (hUh}r (h]h]h]h]h]uhj h}]r h)r }r (hX:class:`neo.core.SpikeTrain`r hj hNhjh}r (UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr h]h]U refexplicith]h]h]j?jj@NjAj uhNh}]r jD)r }r (hj h}r (h]h]r (jIj Xpy-classr eh]h]h]uhj h}]r hXneo.core.SpikeTrainr r }r (hUhj ubahjOubaubahjubahjubehjubeubeubeubh)r }r (hUhj hNhhh}r (h]h]h]h]h]Uentries]r (hXBoptimal_gauss_kernel_size() (in module spykeutils.rate_estimation)h+Utr auhNhhh}]ubh)r }r (hUhj hNhhh}r (hωhXpyr h]h]h]h]h]hXfunctionr hj uhNhhh}]r (h)r }r (hX?optimal_gauss_kernel_size(train, optimize_steps, progress=None)hj hhhhh}r (h]r h+ahhXspykeutils.rate_estimationr r }r bh]h]h]h]r h+ahXoptimal_gauss_kernel_sizer hUhuhNhhh}]r (h)r }r (hj hj hhhhh}r (h]h]h]h]h]uhNhhh}]r hXoptimal_gauss_kernel_sizer r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]r (jz)r }r (hXtrainh}r (h]h]h]h]h]uhj h}]r hXtrainr r }r (hUhj ubahjubjz)r }r (hXoptimize_stepsh}r (h]h]h]h]h]uhj h}]r hXoptimize_stepsr r }r (hUhj ubahjubjz)r }r (hX progress=Noneh}r (h]h]h]h]h]uhj h}]r hX progress=Noner r }r(hUhj ubahjubeubh)r}r(hUhj hNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX#_modules/spykeutils/rate_estimationUrefidj uhjh}]rj)r }r (hUh}r (h]h]r j ah]h]h]uhjh}]r hX[source]rr}r(hUhj ubahjubahjubaubeubj)r}r(hUhj hhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX*Return the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with :func:`collapsed_spike_trains`) that should be included in a spike density estimation.hjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/rate_estimation.py:docstring of spykeutils.rate_estimation.optimal_gauss_kernel_sizerhjh}r(h]h]h]h]h]uhKhhh}]r(hXReturn the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with rr}r(hXReturn the optimal kernel size for a spike density estimation of a spike train for a gaussian kernel. This function takes a single spike train, which can be a superposition of multiple spike trains (created with hjubh)r}r(hX:func:`collapsed_spike_trains`rhjhNhjh}r (UreftypeXfuncj<j=Xcollapsed_spike_trainsU refdomainXpyr!h]h]U refexplicith]h]h]j?jj@NjAj uhNh}]r"jD)r#}r$(hjh}r%(h]h]r&(jIj!Xpy-funcr'eh]h]h]uhjh}]r(hXcollapsed_spike_trains()r)r*}r+(hUhj#ubahjOubaubhX8) that should be included in a spike density estimation.r,r-}r.(hX8) that should be included in a spike density estimation.hjubeubj)r/}r0(hXbImplements the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).r1hjhjhjh}r2(h]h]h]h]h]uhKhhh}]r3hXbImplements the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).r4r5}r6(hj1hj/ubaubj)r7}r8(hUhjhNhjh}r9(h]h]h]h]h]uhNhhh}]r:(j)r;}r<(hUh}r=(h]h]h]h]h]uhj7h}]r>(j)r?}r@(hUh}rA(h]h]h]h]h]uhj;h}]rBhX ParametersrCrD}rE(hUhj?ubahjubj)rF}rG(hUh}rH(h]h]h]h]h]uhj;h}]rIj#)rJ}rK(hUh}rL(h]h]h]h]h]uhjFh}]rM(j()rN}rO(hUh}rP(h]h]h]h]h]uhjJh}]rQj)rR}rS(hUh}rT(h]h]h]h]h]uhjNh}]rU(j)rV}rW(hXtrainh}rX(h]h]h]h]h]uhjRh}]rYhXtrainrZr[}r\(hUhjVubahjubhX (r]r^}r_(hUhjRubh)r`}ra(hX:class:`neo.core.SpikeTrain`rbhjRhNhjh}rc(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrdh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rejD)rf}rg(hjbh}rh(h]h]ri(jIjdXpy-classrjeh]h]h]uhj`h}]rkhXneo.core.SpikeTrainrlrm}rn(hUhjfubahjOubaubhX)ro}rp(hUhjRubhX -- rqrr}rs(hUhjRubhX>The spike train for which the kernel size should be optimized.rtru}rv(hX>The spike train for which the kernel size should be optimized.hjRubehjubahjdubj()rw}rx(hUh}ry(h]h]h]h]h]uhjJh}]rzj)r{}r|(hUh}r}(h]h]h]h]h]uhjwh}]r~(j)r}r(hXoptimize_stepsh}r(h]h]h]h]h]uhj{h}]rhXoptimize_stepsrr}r(hUhjubahjubhX (rr}r(hUhj{ubh)r}r(hUh}r(UreftypejzU reftargetX Quantity 1DrU refdomainj h]h]U refexplicith]h]h]uhj{h}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhX Quantity 1Drr}r(hUhjubahjubahjubhX)r}r(hUhj{ubhX -- rr}r(hUhj{ubhXHArray of kernel sizes to try (the best of these sizes will be returned).rr}r(hXHArray of kernel sizes to try (the best of these sizes will be returned).hj{ubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjJh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXprogressh}r(h]h]h]h]h]uhjh}]rhXprogressrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hX.:class:`.progress_indicator.ProgressIndicator`rhjhNhjh}r(UreftypeXclassjj<j=X$progress_indicator.ProgressIndicatorU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhX$progress_indicator.ProgressIndicatorrr}r(hUhjubahjOubaubhX)r}r(hUhjubhX -- rr}r(hUhjubhX?Set this parameter to report progress. Will be advanced by len(rr}r(hX?Set this parameter to report progress. Will be advanced by len(hjubcdocutils.nodes title_reference r)r}r(hX`optimize_steps`h}r(h]h]h]h]h]uhjh}]rhXoptimize_stepsrr}r(hUhjubahUtitle_referencerubhX) steps.rr}r(hX) steps.hjubehjubahjdubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhj7h}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXReturnsrr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXBest of the given kernel sizesrr}r(hXBest of the given kernel sizeshjubahjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhj7h}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXQuantity scalarrr}r(hXQuantity scalarhjubahjubahjubehjubeubeubeubh)r}r(hUhj hNhhh}r(h]h]h]h]h]Uentries]r(hX-psth() (in module spykeutils.rate_estimation)hUtrauhNhhh}]ubh)r}r(hUhj hNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r }r (hXXpsth(trains, bin_size, rate_correction=True, start=array(0.0) * ms, stop=array(inf) * s)hjhhhhh}r (h]r hahhXspykeutils.rate_estimationr r}rbh]h]h]h]rhahXpsthrhUhuhNhhh}]r(h)r}r(hjhj hhhhh}r(h]h]h]h]h]uhNhhh}]rhXpsthrr}r(hUhjubaubjt)r}r(hUhj hhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hXtrainsh}r (h]h]h]h]h]uhjh}]r!hXtrainsr"r#}r$(hUhjubahjubjz)r%}r&(hXbin_sizeh}r'(h]h]h]h]h]uhjh}]r(hXbin_sizer)r*}r+(hUhj%ubahjubjz)r,}r-(hXrate_correction=Trueh}r.(h]h]h]h]h]uhjh}]r/hXrate_correction=Truer0r1}r2(hUhj,ubahjubjz)r3}r4(hXstart=array(0.0) * msh}r5(h]h]h]h]h]uhjh}]r6hXstart=array(0.0) * msr7r8}r9(hUhj3ubahjubjz)r:}r;(hXstop=array(inf) * sh}r<(h]h]h]h]h]uhjh}]r=hXstop=array(inf) * sr>r?}r@(hUhj:ubahjubeubh)rA}rB(hUhj hNhhh}rC(Uexprhh]h]h]h]h]uhNhhh}]rDh)rE}rF(hUh}rG(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX#_modules/spykeutils/rate_estimationUrefidjuhjAh}]rHj)rI}rJ(hUh}rK(h]h]rLj ah]h]h]uhjEh}]rMhX[source]rNrO}rP(hUhjIubahjubahjubaubeubj)rQ}rR(hUhjhhhjh}rS(h]h]h]h]h]uhNhhh}]rT(j)rU}rV(hXYReturn dictionary of peri stimulus time histograms for a dictionary of spike train lists.rWhjQhX|/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/rate_estimation.py:docstring of spykeutils.rate_estimation.psthhjh}rX(h]h]h]h]h]uhKhhh}]rYhXYReturn dictionary of peri stimulus time histograms for a dictionary of spike train lists.rZr[}r\(hjWhjUubaubj)r]}r^(hUhjQhNhjh}r_(h]h]h]h]h]uhNhhh}]r`(j)ra}rb(hUh}rc(h]h]h]h]h]uhj]h}]rd(j)re}rf(hUh}rg(h]h]h]h]h]uhjah}]rhhX Parametersrirj}rk(hUhjeubahjubj)rl}rm(hUh}rn(h]h]h]h]h]uhjah}]roj#)rp}rq(hUh}rr(h]h]h]h]h]uhjlh}]rs(j()rt}ru(hUh}rv(h]h]h]h]h]uhjph}]rwj)rx}ry(hUh}rz(h]h]h]h]h]uhjth}]r{(j)r|}r}(hXtrainsh}r~(h]h]h]h]h]uhjxh}]rhXtrainsrr}r(hUhj|ubahjubhX (rr}r(hUhjxubh)r}r(hUh}r(UreftypejzU reftargetXdictrU refdomainjh]h]U refexplicith]h]h]uhjxh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXdictrr}r(hUhjubahjubahjubhX)r}r(hUhjxubhX -- rr}r(hUhjxubhXA dictionary of lists of rr}r(hXA dictionary of lists of hjxubh)r}r(hX:class:`neo.core.SpikeTrain`rhjxhNhjh}r(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXneo.core.SpikeTrainrr}r(hUhjubahjOubaubhX objects.rr}r(hX objects.hjxubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjph}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXbin_sizeh}r(h]h]h]h]h]uhjh}]rhXbin_sizerr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXQuantity scalarrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXQuantity scalarrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhX*The desired bin size (as a time quantity).rr}r(hX*The desired bin size (as a time quantity).hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhjph}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXrate_correctionh}r(h]h]h]h]h]uhjh}]rhXrate_correctionrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXboolrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXboolrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXDetermines if a rates (rr}r(hXDetermines if a rates (hjubjD)r}r(hX``True``h}r(h]h]h]h]h]uhjh}]rhXTruerr}r(hUhjubahjOubhX ) or counts (rr}r(hX ) or counts (hjubjD)r}r(hX ``False``h}r(h]h]h]h]h]uhjh}]rhXFalserr}r(hUhjubahjOubhX) are returned.r r }r (hX) are returned.hjubehjubahjdubj()r }r (hUh}r(h]h]h]h]h]uhjph}]rj)r}r(hUh}r(h]h]h]h]h]uhj h}]r(j)r}r(hXstarth}r(h]h]h]h]h]uhjh}]rhXstartrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r (UreftypejzU reftargetXQuantity scalarr!U refdomainjh]h]U refexplicith]h]h]uhjh}]r"j})r#}r$(hj!h}r%(h]h]h]h]h]uhjh}]r&hXQuantity scalarr'r(}r)(hUhj#ubahjubahjubhX)r*}r+(hUhjubhX -- r,r-}r.(hUhjubhXThe desired time for the start of the first bin. It will be recalculated if there are spike trains which start later than this time.r/r0}r1(hXThe desired time for the start of the first bin. It will be recalculated if there are spike trains which start later than this time.hjubehjubahjdubj()r2}r3(hUh}r4(h]h]h]h]h]uhjph}]r5j)r6}r7(hUh}r8(h]h]h]h]h]uhj2h}]r9(j)r:}r;(hXstoph}r<(h]h]h]h]h]uhj6h}]r=hXstopr>r?}r@(hUhj:ubahjubhX (rArB}rC(hUhj6ubh)rD}rE(hUh}rF(UreftypejzU reftargetXQuantity scalarrGU refdomainjh]h]U refexplicith]h]h]uhj6h}]rHj})rI}rJ(hjGh}rK(h]h]h]h]h]uhjDh}]rLhXQuantity scalarrMrN}rO(hUhjIubahjubahjubhX)rP}rQ(hUhj6ubhX -- rRrS}rT(hUhj6ubhXThe desired time for the end of the last bin. It will be recalculated if there are spike trains which end earlier than this time.rUrV}rW(hXThe desired time for the end of the last bin. It will be recalculated if there are spike trains which end earlier than this time.hj6ubehjubahjdubehjubahjubehjubj)rX}rY(hUh}rZ(h]h]h]h]h]uhj]h}]r[(j)r\}r](hUh}r^(h]h]h]h]h]uhjXh}]r_hXReturnsr`ra}rb(hUhj\ubahjubj)rc}rd(hUh}re(h]h]h]h]h]uhjXh}]rfj)rg}rh(hUh}ri(h]h]h]h]h]uhjch}]rj(hX'A dictionary (with the same indices as rkrl}rm(hX'A dictionary (with the same indices as hjgubjD)rn}ro(hX ``trains``h}rp(h]h]h]h]h]uhjgh}]rqhXtrainsrrrs}rt(hUhjnubahjOubhX+) of arrays containing counts (or rates if rurv}rw(hX+) of arrays containing counts (or rates if hjgubjD)rx}ry(hX``rate_correction``h}rz(h]h]h]h]h]uhjgh}]r{hXrate_correctionr|r}}r~(hUhjxubahjOubhX is rr}r(hX is hjgubjD)r}r(hX``True``h}r(h]h]h]h]h]uhjgh}]rhXTruerr}r(hUhjubahjOubhX) and the bin borders.rr}r(hX) and the bin borders.hjgubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhj]h}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rhXdict, Quantity 1Drr}r(hXdict, Quantity 1Dhjubahjubahjubehjubeubeubeubh)r}r(hUhj hNhhh}r(h]h]h]h]h]Uentries]r(hXAspike_density_estimation() (in module spykeutils.rate_estimation)hSUtrauhNhhh}]ubh)r}r(hUhj hNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r}r(hXspike_density_estimation(trains, start=array(0.0) * ms, stop=None, kernel=None, kernel_size=array(100.0) * ms, optimize_steps=None, progress=None)hjhhhhh}r(h]rhSahhXspykeutils.rate_estimationrr}rbh]h]h]h]rhSahXspike_density_estimationrhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXspike_density_estimationrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hXtrainsh}r(h]h]h]h]h]uhjh}]rhXtrainsrr}r(hUhjubahjubjz)r}r(hXstart=array(0.0) * msh}r(h]h]h]h]h]uhjh}]rhXstart=array(0.0) * msrr}r(hUhjubahjubjz)r}r(hX stop=Noneh}r(h]h]h]h]h]uhjh}]rhX stop=Nonerr}r(hUhjubahjubjz)r}r(hX kernel=Noneh}r(h]h]h]h]h]uhjh}]rhX kernel=Nonerr}r(hUhjubahjubjz)r}r(hXkernel_size=array(100.0) * msh}r(h]h]h]h]h]uhjh}]rhXkernel_size=array(100.0) * msrr}r(hUhjubahjubjz)r}r(hXoptimize_steps=Noneh}r(h]h]h]h]h]uhjh}]rhXoptimize_steps=Nonerr}r(hUhjubahjubjz)r}r(hX progress=Noneh}r(h]h]h]h]h]uhjh}]rhX progress=Nonerr}r(hUhjubahjubeubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX#_modules/spykeutils/rate_estimationUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hXMCreate a spike density estimation from a dictionary of lists of spike trains.r hjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/rate_estimation.py:docstring of spykeutils.rate_estimation.spike_density_estimationr hjh}r (h]h]h]h]h]uhKhhh}]r hXMCreate a spike density estimation from a dictionary of lists of spike trains.r r}r(hj hjubaubj)r}r(hXKThe spike density estimations give an estimate of the instantaneous rate. The density estimation is evaluated at 1024 equally spaced points covering the range of the input spike trains. Optionally finds optimal kernel size for given data using the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).rhjhj hjh}r(h]h]h]h]h]uhKhhh}]rhXKThe spike density estimations give an estimate of the instantaneous rate. The density estimation is evaluated at 1024 equally spaced points covering the range of the input spike trains. Optionally finds optimal kernel size for given data using the algorithm from (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).rr}r(hjhjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r }r!(hUh}r"(h]h]h]h]h]uhjh}]r#hX Parametersr$r%}r&(hUhj ubahjubj)r'}r((hUh}r)(h]h]h]h]h]uhjh}]r*j#)r+}r,(hUh}r-(h]h]h]h]h]uhj'h}]r.(j()r/}r0(hUh}r1(h]h]h]h]h]uhj+h}]r2j)r3}r4(hUh}r5(h]h]h]h]h]uhj/h}]r6(j)r7}r8(hXtrainsh}r9(h]h]h]h]h]uhj3h}]r:hXtrainsr;r<}r=(hUhj7ubahjubhX (r>r?}r@(hUhj3ubh)rA}rB(hUh}rC(UreftypejzU reftargetXdictrDU refdomainjh]h]U refexplicith]h]h]uhj3h}]rEj})rF}rG(hjDh}rH(h]h]h]h]h]uhjAh}]rIhXdictrJrK}rL(hUhjFubahjubahjubhX)rM}rN(hUhj3ubhX -- rOrP}rQ(hUhj3ubhXA dictionary of rRrS}rT(hXA dictionary of hj3ubh)rU}rV(hX:class:`neo.core.SpikeTrain`rWhj3hNhjh}rX(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrYh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rZjD)r[}r\(hjWh}r](h]h]r^(jIjYXpy-classr_eh]h]h]uhjUh}]r`hXneo.core.SpikeTrainrarb}rc(hUhj[ubahjOubaubhX lists.rdre}rf(hX lists.hj3ubehjubahjdubj()rg}rh(hUh}ri(h]h]h]h]h]uhj+h}]rjj)rk}rl(hUh}rm(h]h]h]h]h]uhjgh}]rn(j)ro}rp(hXstarth}rq(h]h]h]h]h]uhjkh}]rrhXstartrsrt}ru(hUhjoubahjubhX (rvrw}rx(hUhjkubh)ry}rz(hUh}r{(UreftypejzU reftargetXQuantity scalarr|U refdomainjh]h]U refexplicith]h]h]uhjkh}]r}j})r~}r(hj|h}r(h]h]h]h]h]uhjyh}]rhXQuantity scalarrr}r(hUhj~ubahjubahjubhX)r}r(hUhjkubhX -- rr}r(hUhjkubhXThe desired time for the start of the estimation. It will be recalculated if there are spike trains which start later than this time. This parameter can be negative (which could be useful when aligning on events).rr}r(hXThe desired time for the start of the estimation. It will be recalculated if there are spike trains which start later than this time. This parameter can be negative (which could be useful when aligning on events).hjkubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj+h}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXstoph}r(h]h]h]h]h]uhjh}]rhXstoprr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXQuantity scalarrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXQuantity scalarrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXThe desired time for the end of the estimation. It will be recalculated if there are spike trains which end earlier than this time.rr}r(hXThe desired time for the end of the estimation. It will be recalculated if there are spike trains which end earlier than this time.hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj+h}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXkernelh}r(h]h]h]h]h]uhjh}]rhXkernelrr}r(hUhjubahjubhX (rr}r(hUhjubhXfunc or rr}r(hXfunc or hjubh)r}r(hX":class:`.signal_processing.Kernel`rhjhNhjh}r(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXsignal_processing.Kernelrr}r(hUhjubahjOubaubhX)r}r(hUhjubhX -- rr}r(hUhjubhX2The kernel function or instance to use, should accept two parameters: A ndarray of distances and a kernel size. The total area under the kernel function should be 1. Automatic optimization assumes a Gaussian kernel and will likely not produce optimal results for different kernels. Default: Gaussian kernelrr}r(hX2The kernel function or instance to use, should accept two parameters: A ndarray of distances and a kernel size. The total area under the kernel function should be 1. Automatic optimization assumes a Gaussian kernel and will likely not produce optimal results for different kernels. Default: Gaussian kernelhjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj+h}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhjh}]rhX kernel_sizerr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXQuantity scalarrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXQuantity scalarrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXbA uniform kernel size for all spike trains. Only used if optimization of kernel sizes is not used.rr}r(hXbA uniform kernel size for all spike trains. Only used if optimization of kernel sizes is not used.hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj+h}]rj)r }r (hUh}r (h]h]h]h]h]uhjh}]r (j)r }r(hXoptimize_stepsh}r(h]h]h]h]h]uhj h}]rhXoptimize_stepsrr}r(hUhj ubahjubhX (rr}r(hUhj ubh)r}r(hUh}r(UreftypejzU reftargetX Quantity 1DrU refdomainjh]h]U refexplicith]h]h]uhj h}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhX Quantity 1Dr r!}r"(hUhjubahjubahjubhX)r#}r$(hUhj ubhX -- r%r&}r'(hUhj ubhXAn array of time lengths that will be considered in the kernel width optimization. Note that the optimization assumes a Gaussian kernel and will most likely not give the optimal kernel size if another kernel is used. If None, r(r)}r*(hXAn array of time lengths that will be considered in the kernel width optimization. Note that the optimization assumes a Gaussian kernel and will most likely not give the optimal kernel size if another kernel is used. If None, hj ubjD)r+}r,(hX``kernel_size``h}r-(h]h]h]h]h]uhj h}]r.hX kernel_sizer/r0}r1(hUhj+ubahjOubhX will be used.r2r3}r4(hX will be used.hj ubehjubahjdubj()r5}r6(hUh}r7(h]h]h]h]h]uhj+h}]r8j)r9}r:(hUh}r;(h]h]h]h]h]uhj5h}]r<(j)r=}r>(hXprogressh}r?(h]h]h]h]h]uhj9h}]r@hXprogressrArB}rC(hUhj=ubahjubhX (rDrE}rF(hUhj9ubh)rG}rH(hX.:class:`.progress_indicator.ProgressIndicator`rIhj9hNhjh}rJ(UreftypeXclassjj<j=X$progress_indicator.ProgressIndicatorU refdomainXpyrKh]h]U refexplicith]h]h]j?jj@NjAj uhNh}]rLjD)rM}rN(hjIh}rO(h]h]rP(jIjKXpy-classrQeh]h]h]uhjGh}]rRhX$progress_indicator.ProgressIndicatorrSrT}rU(hUhjMubahjOubaubhX)rV}rW(hUhj9ubhX -- rXrY}rZ(hUhj9ubhX&Set this parameter to report progress.r[r\}r](hX&Set this parameter to report progress.hj9ubehjubahjdubehjubahjubehjubj)r^}r_(hUh}r`(h]h]h]h]h]uhjh}]ra(j)rb}rc(hUh}rd(h]h]h]h]h]uhj^h}]rehXReturnsrfrg}rh(hUhjbubahjubj)ri}rj(hUh}rk(h]h]h]h]h]uhj^h}]rlj)rm}rn(hUh}ro(h]h]h]h]h]uhjih}]rp(j)rq}rr(hX Three values:rshjmhj hjh}rt(h]h]h]h]h]uhK'h}]ruhX Three values:rvrw}rx(hjshjqubaubj#)ry}rz(hUh}r{(jKX*h]h]h]h]h]uhjmh}]r|(j()r}}r~(hXbA dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as ``trains``.h}r(h]h]h]h]h]uhjyh}]rj)r}r(hXbA dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as ``trains``.hj}hj hjh}r(h]h]h]h]h]uhK)h}]r(hXWA dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as rr}r(hXWA dictionary of the spike density estimations (Quantity 1D in Hz). Indexed the same as hjubjD)r}r(hX ``trains``h}r(h]h]h]h]h]uhjh}]rhXtrainsrr}r(hUhjubahjOubhX.r}r(hX.hjubeubahjdubj()r}r(hXPA dictionary of kernel sizes (Quantity scalars). Indexed the same as ``trains``.h}r(h]h]h]h]h]uhjyh}]rj)r}r(hXPA dictionary of kernel sizes (Quantity scalars). Indexed the same as ``trains``.hjhj hjh}r(h]h]h]h]h]uhK+h}]r(hXEA dictionary of kernel sizes (Quantity scalars). Indexed the same as rr}r(hXEA dictionary of kernel sizes (Quantity scalars). 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ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r }r!(hUhjhhhjh}r"(h]h]h]h]h]uhNhhh}]ubeubh)r#}r$(hUhjlhNhhh}r%(h]h]h]h]h]Uentries]r&(hX.normalization_factor() (GaussianKernel method)hCUtr'auhNhhh}]ubh)r(}r)(hUhjlhNhhh}r*(hωhXpyh]h]h]h]h]hXmethodr+hj+uhNhhh}]r,(h)r-}r.(hX0GaussianKernel.normalization_factor(kernel_size)hj(hhhhh}r/(h]r0hCahhXspykeutils.signal_processingr1r2}r3bh]h]h]h]r4hCahX#GaussianKernel.normalization_factorr5hj:huhNhhh}]r6(h)r7}r8(hXnormalization_factorhj-hhhhh}r9(h]h]h]h]h]uhNhhh}]r:hXnormalization_factorr;r<}r=(hUhj7ubaubjt)r>}r?(hUhj-hhhjwh}r@(h]h]h]h]h]uhNhhh}]rAjz)rB}rC(hX kernel_sizeh}rD(h]h]h]h]h]uhj>h}]rEhX kernel_sizerFrG}rH(hUhjBubahjubaubh)rI}rJ(hUhj-hNhhh}rK(Uexprhh]h]h]h]h]uhNhhh}]rLh)rM}rN(hUh}rO(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidj5uhjIh}]rPj)rQ}rR(hUh}rS(h]h]rTj ah]h]h]uhjMh}]rUhX[source]rVrW}rX(hUhjQubahjubahjubaubeubj)rY}rZ(hUhj(hhhjh}r[(h]h]h]h]h]uhNhhh}]ubeubeubeubh)r\}r](hUhjhNhhh}r^(h]h]h]h]h]Uentries]r_(hX.Kernel (class in spykeutils.signal_processing)h[Utr`auhNhhh}]ubh)ra}rb(hUhjhNhhh}rc(hωhXpyh]h]h]h]h]hXclassrdhjduhNhhh}]re(h)rf}rg(hXKernel(kernel_size, normalize)hjahhhhh}rh(h]rih[ahhXspykeutils.signal_processingrjrk}rlbh]h]h]h]rmh[ahXKernelrnhUhuhNhhh}]ro(h)rp}rq(hXclass hjfhhhhh}rr(h]h]h]h]h]uhNhhh}]rshXclass rtru}rv(hUhjpubaubh)rw}rx(hjnhjfhhhhh}ry(h]h]h]h]h]uhNhhh}]rzhXKernelr{r|}r}(hUhjwubaubjt)r~}r(hUhjfhhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhj~h}]rhX kernel_sizerr}r(hUhjubahjubjz)r}r(hX normalizeh}r(h]h]h]h]h]uhj~h}]rhX normalizerr}r(hUhjubahjubeubh)r}r(hUhjfhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjnuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjahhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hXBases: :class:`object`hjhjhjh}r(h]h]h]h]h]uhKhhh}]r(hXBases: rr}r(hXBases: hjubh)r}r(hX:class:`object`rhjhNhjh}r(UreftypeXclassj<j=XobjectU refdomainXpyrh]h]U refexplicith]h]h]j?jj@jnjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXobjectrr}r(hUhjubahjOubaubeubj)r}r(hXBase class for kernels.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.Kernelhjh}r(h]h]h]h]h]uhKhhh}]rhXBase class for kernels.rr}r(hjhjubaubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX-boundary_enclosing_at_least() (Kernel method)hUtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyrh]h]h]h]h]hXmethodrhjuhNhhh}]r(h)r}r(hX,Kernel.boundary_enclosing_at_least(fraction)hjhhhhh}r(h]rhahhXspykeutils.signal_processingrr}rbh]h]h]h]rhahX"Kernel.boundary_enclosing_at_leastrhjnhuhNhhh}]r(h)r}r(hXboundary_enclosing_at_leasthjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXboundary_enclosing_at_leastrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]rjz)r}r(hXfractionh}r(h]h]h]h]h]uhjh}]rhXfractionrr}r(hUhjubahjubaubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hXCalculates the boundary :math:`b` so that the integral from :math:`-b` to :math:`b` encloses at least a certain fraction of the integral over the complete kernel.hjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.Kernel.boundary_enclosing_at_leasthjh}r(h]h]h]h]h]uhKhhh}]r(hXCalculates the boundary rr}r(hXCalculates the boundary hjubjR)r}r(hUh}r(UlatexXbh]h]h]h]h]uhjh}]hjVubhX so that the integral from rr}r (hX so that the integral from hjubjR)r }r (hUh}r (UlatexX-bh]h]h]h]h]uhjh}]hjVubhX to r r}r(hX to hjubjR)r}r(hUh}r(UlatexXbh]h]h]h]h]uhjh}]hjVubhXO encloses at least a certain fraction of the integral over the complete kernel.rr}r(hXO encloses at least a certain fraction of the integral over the complete kernel.hjubeubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r (h]h]h]h]h]uhjh}]r!hX Parametersr"r#}r$(hUhjubahjubj)r%}r&(hUh}r'(h]h]h]h]h]uhjh}]r(j)r)}r*(hUh}r+(h]h]h]h]h]uhj%h}]r,(j)r-}r.(hXfractionh}r/(h]h]h]h]h]uhj)h}]r0hXfractionr1r2}r3(hUhj-ubahjubhX (r4r5}r6(hUhj)ubh)r7}r8(hUh}r9(UreftypejzU reftargetXfloatr:U refdomainjh]h]U refexplicith]h]h]uhj)h}]r;j})r<}r=(hj:h}r>(h]h]h]h]h]uhj7h}]r?hXfloatr@rA}rB(hUhj<ubahjubahjubhX)rC}rD(hUhj)ubhX -- rErF}rG(hUhj)ubhX=Fraction of the whole area which at least has to be enclosed.rHrI}rJ(hX=Fraction of the whole area which at least has to be enclosed.hj)ubehjubahjubehjubj)rK}rL(hUh}rM(h]h]h]h]h]uhjh}]rN(j)rO}rP(hUh}rQ(h]h]h]h]h]uhjKh}]rRhXReturnsrSrT}rU(hUhjOubahjubj)rV}rW(hUh}rX(h]h]h]h]h]uhjKh}]rYj)rZ}r[(hUh}r\(h]h]h]h]h]uhjVh}]r]hXboundaryr^r_}r`(hXboundaryhjZubahjubahjubehjubj)ra}rb(hUh}rc(h]h]h]h]h]uhjh}]rd(j)re}rf(hUh}rg(h]h]h]h]h]uhjah}]rhhX Return typerirj}rk(hUhjeubahjubj)rl}rm(hUh}rn(h]h]h]h]h]uhjah}]roj)rp}rq(hUh}rr(h]h]h]h]h]uhjlh}]rshXQuantity scalarrtru}rv(hXQuantity scalarhjpubahjubahjubehjubeubeubeubh)rw}rx(hUhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.Kernel.is_symmetricryhhh}rz(h]h]h]h]h]Uentries]r{(hXis_symmetric() (Kernel method)hDUtr|auhNhhh}]ubh)r}}r~(hUhjhjyhhh}r(hωhXpyh]h]h]h]h]hXmethodrhjuhNhhh}]r(h)r}r(hXKernel.is_symmetric()hj}hhhhh}r(h]rhDahhXspykeutils.signal_processingrr}rbh]h]h]h]rhDahXKernel.is_symmetricrhjnhuhNhhh}]r(h)r}r(hX is_symmetrichjhhhhh}r(h]h]h]h]h]uhNhhh}]rhX is_symmetricrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]ubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhj}hhhjh}r(h]h]h]h]h]uhNhhh}]rj)r}r(hX0Should return `True` if the kernel is symmetric.hjhjyhjh}r(h]h]h]h]h]uhKhhh}]r(hXShould return rr}r(hXShould return hjubj)r}r(hX`True`h}r(h]h]h]h]h]uhjh}]rhXTruerr}r(hUhjubahjubhX if the kernel is symmetric.rr}r(hX if the kernel is symmetric.hjubeubaubeubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX&normalization_factor() (Kernel method)h8UtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyrh]h]h]h]h]hXmethodrhjuhNhhh}]r(h)r}r(hX(Kernel.normalization_factor(kernel_size)hjhhhhh}r(h]rh8ahhXspykeutils.signal_processingrr}rbh]h]h]h]rh8ahXKernel.normalization_factorrhjnhuhNhhh}]r(h)r}r(hXnormalization_factorhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXnormalization_factorrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]rjz)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhjh}]rhX kernel_sizerr}r(hUhjubahjubaubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX?Returns the factor needed to normalize the kernel to unit area.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.Kernel.normalization_factorhjh}r(h]h]h]h]h]uhKhhh}]rhX?Returns the factor needed to normalize the kernel to unit area.rr}r(hjhjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r hX Parametersr r }r (hUhjubahjubj)r }r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhj h}]r(j)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhjh}]rhX kernel_sizerr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r (hUh}r!(UreftypejzU reftargetXQuantity scalarr"U refdomainjh]h]U refexplicith]h]h]uhjh}]r#j})r$}r%(hj"h}r&(h]h]h]h]h]uhjh}]r'hXQuantity scalarr(r)}r*(hUhj$ubahjubahjubhX)r+}r,(hUhjubhX -- r-r.}r/(hUhjubhX!Controls the width of the kernel.r0r1}r2(hX!Controls the width of the kernel.hjubehjubahjubehjubj)r3}r4(hUh}r5(h]h]h]h]h]uhjh}]r6(j)r7}r8(hUh}r9(h]h]h]h]h]uhj3h}]r:hXReturnsr;r<}r=(hUhj7ubahjubj)r>}r?(hUh}r@(h]h]h]h]h]uhj3h}]rAj)rB}rC(hUh}rD(h]h]h]h]h]uhj>h}]rEhX-Factor to normalize the kernel to unit width.rFrG}rH(hX-Factor to normalize the kernel to unit width.hjBubahjubahjubehjubj)rI}rJ(hUh}rK(h]h]h]h]h]uhjh}]rL(j)rM}rN(hUh}rO(h]h]h]h]h]uhjIh}]rPhX Return typerQrR}rS(hUhjMubahjubj)rT}rU(hUh}rV(h]h]h]h]h]uhjIh}]rWj)rX}rY(hUh}rZ(h]h]h]h]h]uhjTh}]r[hXQuantity scalarr\r]}r^(hXQuantity scalarhjXubahjubahjubehjubeubeubeubh)r_}r`(hUhjhNhhh}ra(h]h]h]h]h]Uentries]rb(hX$summed_dist_matrix() (Kernel method)hr?}r@(hXMSome optimized specializations of this function may need sorted vectors. Set hjubj)rA}rB(hX `presorted`h}rC(h]h]h]h]h]uhjh}]rDhX presortedrErF}rG(hUhjAubahjubhX to rHrI}rJ(hX to hjubj)rK}rL(hX`True`h}rM(h]h]h]h]h]uhjh}]rNhXTruerOrP}rQ(hUhjKubahjubhXj if you know that the passed vectors are already sorted to skip the sorting and thus increase performance.rRrS}rT(hXj if you know that the passed vectors are already sorted to skip the sorting and thus increase performance.hjubehjubahjdubehjubahjubehjubj)rU}rV(hUh}rW(h]h]h]h]h]uhjh}]rX(j)rY}rZ(hUh}r[(h]h]h]h]h]uhjUh}]r\hX Return typer]r^}r_(hUhjYubahjubj)r`}ra(hUh}rb(h]h]h]h]h]uhjUh}]rcj)rd}re(hUh}rf(h]h]h]h]h]uhj`h}]rghX Quantity 2Drhri}rj(hX Quantity 2Dhjdubahjubahjubehjubeubeubeubeubeubh)rk}rl(hUhjhNhhh}rm(h]h]h]h]h]Uentries]rn(hX:KernelFromFunction (class in spykeutils.signal_processing)h0UtroauhNhhh}]ubh)rp}rq(hUhjhNhhh}rr(hωhXpyh]h]h]h]h]hXclassrshjsuhNhhh}]rt(h)ru}rv(hX,KernelFromFunction(kernel_func, kernel_size)hjphhhhh}rw(h]rxh0ahhXspykeutils.signal_processingryrz}r{bh]h]h]h]r|h0ahXKernelFromFunctionr}hUhuhNhhh}]r~(h)r}r(hXclass hjuhhhhh}r(h]h]h]h]h]uhNhhh}]rhXclass rr}r(hUhjubaubh)r}r(hj}hjuhhhhh}r(h]h]h]h]h]uhNhhh}]rhXKernelFromFunctionrr}r(hUhjubaubjt)r}r(hUhjuhhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hX kernel_funch}r(h]h]h]h]h]uhjh}]rhX kernel_funcrr}r(hUhjubahjubjz)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhjh}]rhX kernel_sizerr}r(hUhjubahjubeubh)r}r(hUhjuhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidj}uhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjphhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX3Bases: :class:`spykeutils.signal_processing.Kernel`hjhjhjh}r(h]h]h]h]h]uhKhhh}]r(hXBases: rr}r(hXBases: hjubh)r}r(hX,:class:`spykeutils.signal_processing.Kernel`rhjhNhjh}r(UreftypeXclassj<j=X#spykeutils.signal_processing.KernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@j}jAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhX#spykeutils.signal_processing.Kernelrr}r(hUhjubahjOubaubeubj)r}r(hXfCreates a kernel form a function. Please note, that not all methods for such a kernel are implemented.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.KernelFromFunctionhjh}r(h]h]h]h]h]uhKhhh}]rhXfCreates a kernel form a function. Please note, that not all methods for such a kernel are implemented.rr}r(hjhjubaubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX*is_symmetric() (KernelFromFunction method)hGUtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyh]h]h]h]h]hXmethodrhjuhNhhh}]r(h)r}r(hX!KernelFromFunction.is_symmetric()hjhhhhh}r(h]rhGahhXspykeutils.signal_processingrr}rbh]h]h]h]rhGahXKernelFromFunction.is_symmetricrhj}huhNhhh}]r(h)r}r(hX is_symmetrichjhhhhh}r(h]h]h]h]h]uhNhhh}]rhX is_symmetricrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]ubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]ubeubeubeubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX7LaplacianKernel (class in spykeutils.signal_processing)h=UtrauhNhhh}]ubh)r}r(hUhjhNhhh}r (hωhXpyh]h]h]h]h]hXclassr hj uhNhhh}]r (h)r }r 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hjubh)r}r(hX5:class:`spykeutils.signal_processing.SymmetricKernel`rhjhNhjh}r(UreftypeXclassj<j=X,spykeutils.signal_processing.SymmetricKernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@jjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhX,spykeutils.signal_processing.SymmetricKernelrr}r(hUhjubahjOubaubeubj)r}r(hXUnnormalized: :math:`K(t) = \left\{ \begin{array}{ll}1 - \frac{|t|}{\tau}, & |t| < \tau \\ 0, & |t| \geq \tau \end{array} \right.` with kernel size :math:`\tau` corresponding to the half width.hjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.TriangularKernelrhjh}r(h]h]h]h]h]uhKhhh}]r(hXUnnormalized: rr}r(hXUnnormalized: hjubjR)r}r(hUh}r(UlatexXlK(t) = \left\{ \begin{array}{ll}1 - \frac{|t|}{\tau}, & |t| < \tau \\ 0, & |t| \geq \tau \end{array} \right.h]h]h]h]h]uhjh}]hjVubhX with kernel size rr}r(hX with kernel size hjubjR)r}r(hUh}r(UlatexX\tauh]h]h]h]h]uhjh}]hjVubhX! corresponding to the half 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reftargetX%_modules/spykeutils/signal_processingUrefidjGuhjhh}]roj)rp}rq(hUh}rr(h]h]rsj ah]h]h]uhjlh}]rthX[source]rurv}rw(hUhjpubahjubahjubaubeubj)rx}ry(hUhj:hhhjh}rz(h]h]h]h]h]uhNhhh}]ubeubh)r{}r|(hUhjhNhhh}r}(h]h]h]h]h]Uentries]r~(hX0normalization_factor() (TriangularKernel method)hHUtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyh]h]h]h]h]hXmethodrhjuhNhhh}]r(h)r}r(hX1TriangularKernel.normalization_factor(half_width)hjhhhhh}r(h]rhHahhXspykeutils.signal_processingrr}rbh]h]h]h]rhHahX%TriangularKernel.normalization_factorrhjhuhNhhh}]r(h)r}r(hXnormalization_factorhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXnormalization_factorrr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]rjz)r}r(hX half_widthh}r(h]h]h]h]h]uhjh}]rhX half_widthrr}r(hUhjubahjubaubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]ubeubeubeubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX<as_kernel_of_size() (in module spykeutils.signal_processing)h UtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r}r(hX#as_kernel_of_size(obj, kernel_size)hjhhhhh}r(h]rh ahhXspykeutils.signal_processingrr}rbh]h]h]h]rh ahXas_kernel_of_sizerhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXas_kernel_of_sizerr}r(hUhjubaubjt)r}r(hUhjhhhjwh}r(h]h]h]h]h]uhNhhh}]r(jz)r}r(hXobjh}r(h]h]h]h]h]uhjh}]rhXobjrr}r(hUhjubahjubjz)r}r(hX kernel_sizeh}r(h]h]h]h]h]uhjh}]rhX kernel_sizerr}r(hUhjubahjubeubh)r}r(hUhjhNhhh}r(Uexprhh]h]h]h]h]uhNhhh}]rh)r}r(hUh}r(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhjh}]rj)r}r(hUh}r(h]h]rj ah]h]h]uhjh}]rhX[source]rr}r(hUhjubahjubahjubaubeubj)r}r(hUhjhhhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hX!Returns a kernel of desired size.rhjhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.as_kernel_of_sizehjh}r(h]h]h]h]h]uhKhhh}]rhX!Returns a kernel of desired size.rr}r(hjhjubaubj)r}r(hUhjhNhjh}r(h]h]h]h]h]uhNhhh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]r hX Parametersr r }r (hUhjubahjubj)r }r(hUh}r(h]h]h]h]h]uhjh}]rj#)r}r(hUh}r(h]h]h]h]h]uhj h}]r(j()r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hXobjh}r(h]h]h]h]h]uhjh}]r hXobjr!r"}r#(hUhjubahjubhX (r$r%}r&(hUhjubh)r'}r((hUh}r)(UreftypejzU reftargetXKernel or funcr*U refdomainjh]h]U refexplicith]h]h]uhjh}]r+j})r,}r-(hj*h}r.(h]h]h]h]h]uhj'h}]r/hXKernel or funcr0r1}r2(hUhj,ubahjubahjubhX)r3}r4(hUhjubhX -- r5r6}r7(hUhjubhX_Either an existing kernel or a kernel function. A kernel function takes two arguments. First a r8r9}r:(hX_Either an existing kernel or a kernel function. A kernel function takes two arguments. First a hjubj)r;}r<(hX `Quantity 1D`h}r=(h]h]h]h]h]uhjh}]r>hX Quantity 1Dr?r@}rA(hUhj;ubahjubhX4 of evaluation time points and second a kernel size.rBrC}rD(hX4 of evaluation time points and second a kernel size.hjubehjubahjdubj()rE}rF(hUh}rG(h]h]h]h]h]uhjh}]rHj)rI}rJ(hUh}rK(h]h]h]h]h]uhjEh}]rL(j)rM}rN(hX kernel_sizeh}rO(h]h]h]h]h]uhjIh}]rPhX kernel_sizerQrR}rS(hUhjMubahjubhX (rTrU}rV(hUhjIubh)rW}rX(hUh}rY(UreftypejzU reftargetX Quantity 1DrZU refdomainjh]h]U refexplicith]h]h]uhjIh}]r[j})r\}r](hjZh}r^(h]h]h]h]h]uhjWh}]r_hX Quantity 1Dr`ra}rb(hUhj\ubahjubahjubhX)rc}rd(hUhjIubhX -- rerf}rg(hUhjIubhXDesired size of the kernel.rhri}rj(hXDesired size of the kernel.hjIubehjubahjdubehjubahjubehjubj)rk}rl(hUh}rm(h]h]h]h]h]uhjh}]rn(j)ro}rp(hUh}rq(h]h]h]h]h]uhjkh}]rrhXReturnsrsrt}ru(hUhjoubahjubj)rv}rw(hUh}rx(h]h]h]h]h]uhjkh}]ryj)rz}r{(hUh}r|(h]h]h]h]h]uhjvh}]r}(hXA r~r}r(hXA hjzubh)r}r(hX:class:`Kernel`rhjzhNhjh}r(UreftypeXclassj<j=XKernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXKernelrr}r(hUhjubahjOubaubhX" with the desired kernel size. If rr}r(hX" with the desired kernel size. If hjzubj)r}r(hX`obj`h}r(h]h]h]h]h]uhjzh}]rhXobjrr}r(hUhjubahjubhX is already a rr}r(hX is already a hjzubh)r}r(hX:class:`Kernel`rhjzhNhjh}r(UreftypeXclassj<j=XKernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXKernelrr}r(hUhjubahjOubaubhXY instance, a shallow copy of this instance with changed kernel size will be returned. If rr}r(hXY instance, a shallow copy of this instance with changed kernel size will be returned. If hjzubj)r}r(hX`obj`h}r(h]h]h]h]h]uhjzh}]rhXobjrr}r(hUhjubahjubhX' is a function it will be wrapped in a rr}r(hX' is a function it will be wrapped in a hjzubh)r}r(hX:class:`Kernel`rhjzhNhjh}r(UreftypeXclassj<j=XKernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXKernelrr}r(hUhjubahjOubaubhX instance.rr}r(hX instance.hjzubehjubahjubehjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hUh}r(h]h]h]h]h]uhjh}]rhX Return typerr}r(hUhjubahjubj)r}r(hUh}r(h]h]h]h]h]uhjh}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]rh)r}r(hX:class:`Kernel`rhjhNhjh}r(UreftypeXclassj<j=XKernelU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-classreh]h]h]uhjh}]rhXKernelrr}r(hUhjubahjOubaubahjubahjubehjubeubeubeubh)r}r(hUhjhNhhh}r(h]h]h]h]h]Uentries]r(hX<discretize_kernel() (in module spykeutils.signal_processing)h,UtrauhNhhh}]ubh)r}r(hUhjhNhhh}r(hωhXpyrh]h]h]h]h]hXfunctionrhjuhNhhh}]r(h)r}r(hXfdiscretize_kernel(kernel, sampling_rate, area_fraction=0.99999, num_bins=None, ensure_unit_area=False)hjhhhhh}r(h]rh,ahhXspykeutils.signal_processingrr}rbh]h]h]h]rh,ahXdiscretize_kernelrhUhuhNhhh}]r(h)r}r(hjhjhhhhh}r(h]h]h]h]h]uhNhhh}]rhXdiscretize_kernelrr}r(hUhjubaubjt)r }r (hUhjhhhjwh}r (h]h]h]h]h]uhNhhh}]r (jz)r }r(hXkernelh}r(h]h]h]h]h]uhj h}]rhXkernelrr}r(hUhj ubahjubjz)r}r(hX sampling_rateh}r(h]h]h]h]h]uhj h}]rhX sampling_raterr}r(hUhjubahjubjz)r}r(hXarea_fraction=0.99999h}r(h]h]h]h]h]uhj h}]rhXarea_fraction=0.99999rr }r!(hUhjubahjubjz)r"}r#(hX num_bins=Noneh}r$(h]h]h]h]h]uhj h}]r%hX num_bins=Noner&r'}r((hUhj"ubahjubjz)r)}r*(hXensure_unit_area=Falseh}r+(h]h]h]h]h]uhj h}]r,hXensure_unit_area=Falser-r.}r/(hUhj)ubahjubeubh)r0}r1(hUhjhNhhh}r2(Uexprhh]h]h]h]h]uhNhhh}]r3h)r4}r5(hUh}r6(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidjuhj0h}]r7j)r8}r9(hUh}r:(h]h]r;j ah]h]h]uhj4h}]r<hX[source]r=r>}r?(hUhj8ubahjubahjubaubeubj)r@}rA(hUhjhhhjh}rB(h]h]h]h]h]uhNhhh}]rC(j)rD}rE(hXDiscretizes a kernel.rFhj@hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.discretize_kernelhjh}rG(h]h]h]h]h]uhKhhh}]rHhXDiscretizes a kernel.rIrJ}rK(hjFhjDubaubj)rL}rM(hUhj@hNhjh}rN(h]h]h]h]h]uhNhhh}]rO(j)rP}rQ(hUh}rR(h]h]h]h]h]uhjLh}]rS(j)rT}rU(hUh}rV(h]h]h]h]h]uhjPh}]rWhX ParametersrXrY}rZ(hUhjTubahjubj)r[}r\(hUh}r](h]h]h]h]h]uhjPh}]r^j#)r_}r`(hUh}ra(h]h]h]h]h]uhj[h}]rb(j()rc}rd(hUh}re(h]h]h]h]h]uhj_h}]rfj)rg}rh(hUh}ri(h]h]h]h]h]uhjch}]rj(j)rk}rl(hXkernelh}rm(h]h]h]h]h]uhjgh}]rnhXkernelrorp}rq(hUhjkubahjubhX (rrrs}rt(hUhjgubh)ru}rv(hX:class:`Kernel`rwhjghNhjh}rx(UreftypeXclassj<j=XKernelU refdomainXpyryh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rzjD)r{}r|(hjwh}r}(h]h]r~(jIjyXpy-classreh]h]h]uhjuh}]rhXKernelrr}r(hUhj{ubahjOubaubhX or functionrr}r(hX or functionhjgubhX)r}r(hUhjgubhX -- rr}r(hUhjgubhXmThe kernel or kernel function. If a kernel function is used it should take exactly one 1-D array as argument.rr}r(hXmThe kernel or kernel function. If a kernel function is used it should take exactly one 1-D array as argument.hjgubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj_h}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r(hX area_fractionh}r(h]h]h]h]h]uhjh}]rhX area_fractionrr}r(hUhjubahjubhX (rr}r(hUhjubh)r}r(hUh}r(UreftypejzU reftargetXfloatrU refdomainjh]h]U refexplicith]h]h]uhjh}]rj})r}r(hjh}r(h]h]h]h]h]uhjh}]rhXfloatrr}r(hUhjubahjubahjubhX)r}r(hUhjubhX -- rr}r(hUhjubhXFraction between 0 and 1 (exclusive) of the integral of the kernel which will be at least covered by the discretization. Will be ignored if rr}r(hXFraction between 0 and 1 (exclusive) of the integral of the kernel which will be at least covered by the discretization. Will be ignored if hjubj)r}r(hX `num_bins`h}r(h]h]h]h]h]uhjh}]rhXnum_binsrr}r(hUhjubahjubhX is not rr}r(hX is not hjubj)r}r(hX`None`h}r(h]h]h]h]h]uhjh}]rhXNonerr}r(hUhjubahjubhX. If rr}r(hX. If hjubj)r}r(hX`area_fraction`h}r(h]h]h]h]h]uhjh}]rhX area_fractionrr}r(hUhjubahjubhX- is used, the kernel has to provide a method rr}r(hX- is used, the kernel has to provide a method hjubh)r}r(hX#:meth:`boundary_enclosing_at_least`rhjhNhjh}r(UreftypeXmethj<j=Xboundary_enclosing_at_leastU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-methreh]h]h]uhjh}]rhXboundary_enclosing_at_least()rr}r(hUhjubahjOubaubhX (see rr}r(hX (see hjubh)r}r(hX+:meth:`.Kernel.boundary_enclosing_at_least`rhjhNhjh}r(UreftypeXmethjj<j=X"Kernel.boundary_enclosing_at_leastU refdomainXpyrh]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]rjD)r}r(hjh}r(h]h]r(jIjXpy-methreh]h]h]uhjh}]rhX$Kernel.boundary_enclosing_at_least()rr}r(hUhjubahjOubaubhX).rr}r(hX).hjubehjubahjdubj()r}r(hUh}r(h]h]h]h]h]uhj_h}]rj)r}r(hUh}r(h]h]h]h]h]uhjh}]r(j)r}r (hX sampling_rateh}r (h]h]h]h]h]uhjh}]r hX sampling_rater r }r (hUhjubahjubhX (r r }r (hUhjubh)r }r (hUh}r (UreftypejzU reftargetXQuantity scalarr U refdomainjh]h]U refexplicith]h]h]uhjh}]r j})r }r (hj h}r (h]h]h]h]h]uhj h}]r hXQuantity scalarr r }r (hUhj ubahjubahjubhX)r }r (hUhjubhX -- r r }r (hUhjubhXRSampling rate for the discretization. The unit will typically be a frequency unit.r r }r (hXRSampling rate for the discretization. The unit will typically be a frequency unit.hjubehjubahjdubj()r }r (hUh}r (h]h]h]h]h]uhj_h}]r j)r! }r" (hUh}r# (h]h]h]h]h]uhj h}]r$ (j)r% }r& (hXnum_binsh}r' (h]h]h]h]h]uhj! h}]r( hXnum_binsr) r* }r+ (hUhj% ubahjubhX (r, r- }r. (hUhj! ubh)r/ }r0 (hUh}r1 (UreftypejzU reftargetXintr2 U refdomainjh]h]U refexplicith]h]h]uhj! h}]r3 j})r4 }r5 (hj2 h}r6 (h]h]h]h]h]uhj/ h}]r7 hXintr8 r9 }r: (hUhj4 ubahjubahjubhX)r; }r< (hUhj! ubhX -- r= r> }r? (hUhj! ubhX-Number of bins to use for the discretization.r@ rA }rB (hX-Number of bins to use for the discretization.hj! ubehjubahjdubj()rC }rD (hUh}rE (h]h]h]h]h]uhj_h}]rF j)rG }rH (hUh}rI (h]h]h]h]h]uhjC h}]rJ (j)rK }rL (hXensure_unit_areah}rM (h]h]h]h]h]uhjG h}]rN hXensure_unit_arearO rP }rQ (hUhjK ubahjubhX (rR rS }rT (hUhjG ubh)rU }rV (hUh}rW (UreftypejzU reftargetXboolrX U refdomainjh]h]U refexplicith]h]h]uhjG h}]rY j})rZ }r[ (hjX h}r\ (h]h]h]h]h]uhjU h}]r] hXboolr^ r_ }r` (hUhjZ ubahjubahjubhX)ra }rb (hUhjG ubhX -- rc rd }re (hUhjG ubhXIf rf rg }rh (hXIf hjG ubj)ri }rj (hX`True`h}rk (h]h]h]h]h]uhjG h}]rl hXTruerm rn }ro (hUhji ubahjubhX?, the area of the discretized kernel will be normalized to 1.0.rp rq }rr (hX?, the area of the discretized kernel will be normalized to 1.0.hjG ubehjubahjdubehjubahjubehjubj)rs }rt (hUh}ru (h]h]h]h]h]uhjLh}]rv (j)rw }rx (hUh}ry (h]h]h]h]h]uhjs h}]rz hX Return typer{ r| }r} (hUhjw ubahjubj)r~ }r (hUh}r (h]h]h]h]h]uhjs h}]r j)r }r (hUh}r (h]h]h]h]h]uhj~ h}]r hX Quantity 1Dr r }r (hX Quantity 1Dhj ubahjubahjubehjubeubeubeubh)r }r (hUhjhNhhh}r (h]h]h]h]h]Uentries]r (hX1smooth() (in module spykeutils.signal_processing)h;Utr auhNhhh}]ubh)r }r (hUhjhNhhh}r (hωhXpyr h]h]h]h]h]hXfunctionr hj uhNhhh}]r (h)r }r (hXRsmooth(binned, kernel, sampling_rate, mode='same', **kernel_discretization_params)hj hhhhh}r (h]r h;ahhXspykeutils.signal_processingr r }r bh]h]h]h]r h;ahXsmoothr hUhuhNhhh}]r (h)r }r (hj hj hhhhh}r (h]h]h]h]h]uhNhhh}]r hXsmoothr r }r (hUhj ubaubjt)r }r (hUhj hhhjwh}r (h]h]h]h]h]uhNhhh}]r (jz)r }r (hXbinnedh}r (h]h]h]h]h]uhj h}]r hXbinnedr r }r (hUhj ubahjubjz)r }r (hXkernelh}r (h]h]h]h]h]uhj h}]r hXkernelr r }r (hUhj ubahjubjz)r }r (hX sampling_rateh}r (h]h]h]h]h]uhj h}]r hX sampling_rater r }r (hUhj ubahjubjz)r }r (hX mode='same'h}r (h]h]h]h]h]uhj h}]r hX mode='same'r r }r (hUhj ubahjubjz)r }r (hX**kernel_discretization_paramsh}r (h]h]h]h]h]uhj h}]r hX**kernel_discretization_paramsr r }r (hUhj ubahjubeubh)r }r (hUhj hNhhh}r (Uexprhh]h]h]h]h]uhNhhh}]r h)r }r (hUh}r (UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX%_modules/spykeutils/signal_processingUrefidj uhj h}]r j)r }r (hUh}r (h]h]r j ah]h]h]uhj h}]r hX[source]r r }r (hUhj ubahjubahjubaubeubj)r }r (hUhj hhhjh}r (h]h]h]h]h]uhNhhh}]r (j)r }r (hXUSmoothes a binned representation (e.g. of a spike train) by convolving with a kernel.r hj hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/signal_processing.py:docstring of spykeutils.signal_processing.smoothr hjh}r (h]h]h]h]h]uhKhhh}]r hXUSmoothes a binned representation (e.g. of a spike train) by convolving with a kernel.r r }r (hj hj ubaubj)r }r (hUhj hNhjh}r (h]h]h]h]h]uhNhhh}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j)r }r (hUh}r (h]h]h]h]h]uhj h}]r hX Parametersr r }r (hUhj ubahjubj)r }r (hUh}r (h]h]h]h]h]uhj h}]r j#)r }r (hUh}r (h]h]h]h]h]uhj h}]r (j()r!}r!(hUh}r!(h]h]h]h]h]uhj h}]r!j)r!}r!(hUh}r!(h]h]h]h]h]uhj!h}]r!(j)r!}r !(hXbinnedh}r !(h]h]h]h]h]uhj!h}]r !hXbinnedr !r !}r!(hUhj!ubahjubhX (r!r!}r!(hUhj!ubh)r!}r!(hUh}r!(UreftypejzU reftargetX 1-D arrayr!U refdomainj h]h]U refexplicith]h]h]uhj!h}]r!j})r!}r!(hj!h}r!(h]h]h]h]h]uhj!h}]r!hX 1-D arrayr!r!}r!(hUhj!ubahjubahjubhX)r!}r!(hUhj!ubhX -- r !r!!}r"!(hUhj!ubhXBin array to smooth.r#!r$!}r%!(hXBin array to smooth.hj!ubehjubahjdubj()r&!}r'!(hUh}r(!(h]h]h]h]h]uhj h}]r)!j)r*!}r+!(hUh}r,!(h]h]h]h]h]uhj&!h}]r-!(j)r.!}r/!(hXkernelh}r0!(h]h]h]h]h]uhj*!h}]r1!hXkernelr2!r3!}r4!(hUhj.!ubahjubhX (r5!r6!}r7!(hUhj*!ubh)r8!}r9!(hX:class:`Kernel`r:!hj*!hNhjh}r;!(UreftypeXclassj<j=XKernelU refdomainXpyr!}r?!(hj:!h}r@!(h]h]rA!(jIj!ubahjOubaubhX)rG!}rH!(hUhj*!ubhX -- rI!rJ!}rK!(hUhj*!ubhX%The kernel instance to convolve with.rL!rM!}rN!(hX%The kernel instance to convolve with.hj*!ubehjubahjdubj()rO!}rP!(hUh}rQ!(h]h]h]h]h]uhj h}]rR!j)rS!}rT!(hUh}rU!(h]h]h]h]h]uhjO!h}]rV!(j)rW!}rX!(hX sampling_rateh}rY!(h]h]h]h]h]uhjS!h}]rZ!hX sampling_rater[!r\!}r]!(hUhjW!ubahjubhX (r^!r_!}r`!(hUhjS!ubh)ra!}rb!(hUh}rc!(UreftypejzU reftargetXQuantity scalarrd!U refdomainj h]h]U refexplicith]h]h]uhjS!h}]re!j})rf!}rg!(hjd!h}rh!(h]h]h]h]h]uhja!h}]ri!hXQuantity scalarrj!rk!}rl!(hUhjf!ubahjubahjubhX)rm!}rn!(hUhjS!ubhX -- ro!rp!}rq!(hUhjS!ubhXvThe sampling rate which will be used to discretize the kernel. It should be equal to the sampling rate used to obtain rr!rs!}rt!(hXvThe sampling rate which will be used to discretize the kernel. It should be equal to the sampling rate used to obtain hjS!ubj)ru!}rv!(hX`binned`h}rw!(h]h]h]h]h]uhjS!h}]rx!hXbinnedry!rz!}r{!(hUhju!ubahjubhX.. The unit will typically be a frequency unit.r|!r}!}r~!(hX.. The unit will typically be a frequency unit.hjS!ubehjubahjdubj()r!}r!(hUh}r!(h]h]h]h]h]uhj h}]r!j)r!}r!(hUh}r!(h]h]h]h]h]uhj!h}]r!(j)r!}r!(hXmodeh}r!(h]h]h]h]h]uhj!h}]r!hXmoder!r!}r!(hUhj!ubahjubhX (r!r!}r!(hUhj!ubh)r!}r!(hUh}r!(UreftypejzU reftargetX{'same', 'full', 'valid'}r!U refdomainj h]h]U refexplicith]h]h]uhj!h}]r!j})r!}r!(hj!h}r!(h]h]h]h]h]uhj!h}]r!hX{'same', 'full', 'valid'}r!r!}r!(hUhj!ubahjubahjubhX)r!}r!(hUhj!ubhX -- r!r!}r!(hUhj!ubj#)r!}r!(hUh}r!(jKX*h]h]h]h]h]uhj!h}]r!(j()r!}r!(hXG'same': The default which returns an array of the same size as `binned`h}r!(h]h]h]h]h]uhj!h}]r!j)r!}r!(hXG'same': The default which returns an array of the same size as `binned`hj!hj hjh}r!(h]h]h]h]h]uhK h}]r!(hX?'same': The default which returns an array of the same size as r!r!}r!(hX?'same': The default which returns an array of the same size as hj!ubj)r!}r!(hX`binned`h}r!(h]h]h]h]h]uhj!h}]r!hXbinnedr!r!}r!(hUhj!ubahjubeubahjdubj()r!}r!(hXy'full': Returns an array with a bin for each shift where `binned` and the discretized kernel overlap by at least one bin.h}r!(h]h]h]h]h]uhj!h}]r!j)r!}r!(hXy'full': Returns an array with a bin for each shift where `binned` and the discretized kernel overlap by at least one bin.hj!hj hjh}r!(h]h]h]h]h]uhKh}]r!(hX9'full': Returns an array with a bin for each shift where r!r!}r!(hX9'full': Returns an array with a bin for each shift where hj!ubj)r!}r!(hX`binned`h}r!(h]h]h]h]h]uhj!h}]r!hXbinnedr!r!}r!(hUhj!ubahjubhX8 and the discretized kernel overlap by at least one bin.r!r!}r!(hX8 and the discretized kernel overlap by at least one bin.hj!ubeubahjdubj()r!}r!(hXl'valid': Returns only the discretization bins where the discretized kernel and `binned` completely overlap. h}r!(h]h]h]h]h]uhj!h}]r!j)r!}r!(hXk'valid': Returns only the discretization bins where the discretized kernel and `binned` completely overlap.hj!hj hjh}r!(h]h]h]h]h]uhKh}]r!(hXO'valid': Returns only the discretization bins where the discretized kernel and r!r!}r!(hXO'valid': Returns only the discretization bins where the discretized kernel and hj!ubj)r!}r!(hX`binned`h}r!(h]h]h]h]h]uhj!h}]r!hXbinnedr!r!}r!(hUhj!ubahjubhX completely overlap.r!r!}r!(hX completely overlap.hj!ubeubahjdubehjubj)r!}r!(hXeSee also `numpy.convolve `_.hj!hj hjh}r!(h]h]h]h]h]uhKh}]r!(hX See also r!r!}r!(hX See also hj!ubcdocutils.nodes reference r!)r!}r!(hX[`numpy.convolve `_h}r!(Unameh"Urefurir!XGhttp://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.htmlr!h]h]h]h]h]uhj!h}]r!hXnumpy.convolver!r!}r!(hUhj!ubahU referencer!ubh)r!}r!(hXJ U referencedr!Khj!hhh}r!(Urefurij!h]r!htah]h]h]h]r!h"auh}]ubhX.r!}r!(hX.hj!ubeubehjubahjdubj()r!}r!(hUh}r!(h]h]h]h]h]uhj h}]r!j)r"}r"(hUh}r"(h]h]h]h]h]uhj!h}]r"(j)r"}r"(hXkernel_discretization_paramsh}r"(h]h]h]h]h]uhj"h}]r"hXkernel_discretization_paramsr"r "}r "(hUhj"ubahjubhX (r "r "}r "(hUhj"ubh)r"}r"(hUh}r"(UreftypejzU reftargetXdictr"U refdomainj h]h]U refexplicith]h]h]uhj"h}]r"j})r"}r"(hj"h}r"(h]h]h]h]h]uhj"h}]r"hXdictr"r"}r"(hUhj"ubahjubahjubhX)r"}r"(hUhj"ubhX -- r"r"}r"(hUhj"ubhX<Additional discretization arguments which will be passed to r"r "}r!"(hX<Additional discretization arguments which will be passed to hj"ubh)r""}r#"(hX:func:`.discretize_kernel`r$"hj"hNhjh}r%"(UreftypeXfuncjj<j=Xdiscretize_kernelU refdomainXpyr&"h]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]r'"jD)r("}r)"(hj$"h}r*"(h]h]r+"(jIj&"Xpy-funcr,"eh]h]h]uhj""h}]r-"hXdiscretize_kernel()r."r/"}r0"(hUhj("ubahjOubaubhX.r1"}r2"(hX.hj"ubehjubahjdubehjubahjubehjubj)r3"}r4"(hUh}r5"(h]h]h]h]h]uhj h}]r6"(j)r7"}r8"(hUh}r9"(h]h]h]h]h]uhj3"h}]r:"hXReturnsr;"r<"}r="(hUhj7"ubahjubj)r>"}r?"(hUh}r@"(h]h]h]h]h]uhj3"h}]rA"j)rB"}rC"(hUh}rD"(h]h]h]h]h]uhj>"h}]rE"(hXThe smoothed representation of rF"rG"}rH"(hXThe smoothed representation of hjB"ubj)rI"}rJ"(hX`binned`h}rK"(h]h]h]h]h]uhjB"h}]rL"hXbinnedrM"rN"}rO"(hUhjI"ubahjubhX.rP"}rQ"(hX.hjB"ubehjubahjubehjubj)rR"}rS"(hUh}rT"(h]h]h]h]h]uhj 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hj"ubh)r"}r"(hX:class:`neo.core.SpikeTrain`r"hj"hNhjh}r"(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr"h]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]r"jD)r"}r"(hj"h}r"(h]h]r"(jIj"Xpy-classr"eh]h]h]uhj"h}]r"hXneo.core.SpikeTrainr"r"}r"(hUhj"ubahjOubaubhX with a kernel.r"r"}r"(hX with a kernel.hj"ubeubj)r"}r"(hUhj"hNhjh}r"(h]h]h]h]h]uhNhhh}]r"(j)r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"(j)r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"hX Parametersr"r"}r"(hUhj"ubahjubj)r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"j#)r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"(j()r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"j)r"}r"(hUh}r"(h]h]h]h]h]uhj"h}]r"(j)r"}r#(hXtrainh}r#(h]h]h]h]h]uhj"h}]r#hXtrainr#r#}r#(hUhj"ubahjubhX (r#r#}r#(hUhj"ubh)r #}r #(hX:class:`neo.core.SpikeTrain`r #hj"hNhjh}r #(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr #h]h]U refexplicith]h]h]j?jj@NjAj?uhNh}]r#jD)r#}r#(hj #h}r#(h]h]r#(jIj #Xpy-classr#eh]h]h]uhj #h}]r#hXneo.core.SpikeTrainr#r#}r#(hUhj#ubahjOubaubhX)r#}r#(hUhj"ubhX -- r#r#}r#(hUhj"ubhXSpike train to 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ah]h]h]uhj$h}]r$hX[source]r$r$}r$(hUhj$ubahjubahjubaubeubj)r$}r$(hUhj$hhhjh}r$(h]h]h]h]h]uhNhhh}]r$(j)r$}r$(hXGenerate a homogeneous Poisson spike train. The length is controlled with `t_stop` and `max_spikes`. Either one or both of these arguments have to be given.hj$hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_generation.py:docstring of spykeutils.spike_train_generation.gen_homogeneous_poissonhjh}r$(h]h]h]h]h]uhKhhh}]r$(hXJGenerate a homogeneous Poisson spike train. The length is controlled with r$r$}r$(hXJGenerate a homogeneous Poisson spike train. The length is controlled with hj$ubj)r$}r%(hX`t_stop`h}r%(h]h]h]h]h]uhj$h}]r%hXt_stopr%r%}r%(hUhj$ubahjubhX and r%r%}r%(hX and hj$ubj)r %}r %(hX `max_spikes`h}r %(h]h]h]h]h]uhj$h}]r %hX max_spikesr %r%}r%(hUhj %ubahjubhX9. Either one or both of these arguments have to be given.r%r%}r%(hX9. Either one or both of these arguments have to be given.hj$ubeubj)r%}r%(hUhj$hNhjh}r%(h]h]h]h]h]uhNhhh}]r%(j)r%}r%(hUh}r%(h]h]h]h]h]uhj%h}]r%(j)r%}r%(hUh}r%(h]h]h]h]h]uhj%h}]r%hX Parametersr%r %}r!%(hUhj%ubahjubj)r"%}r#%(hUh}r$%(h]h]h]h]h]uhj%h}]r%%j#)r&%}r'%(hUh}r(%(h]h]h]h]h]uhj"%h}]r)%(j()r*%}r+%(hUh}r,%(h]h]h]h]h]uhj&%h}]r-%j)r.%}r/%(hUh}r0%(h]h]h]h]h]uhj*%h}]r1%(j)r2%}r3%(hXrateh}r4%(h]h]h]h]h]uhj.%h}]r5%hXrater6%r7%}r8%(hUhj2%ubahjubhX (r9%r:%}r;%(hUhj.%ubh)r<%}r=%(hUh}r>%(UreftypejzU reftargetXQuantity scalarr?%U refdomainj$h]h]U refexplicith]h]h]uhj.%h}]r@%j})rA%}rB%(hj?%h}rC%(h]h]h]h]h]uhj<%h}]rD%hXQuantity scalarrE%rF%}rG%(hUhjA%ubahjubahjubhX)rH%}rI%(hUhj.%ubhX -- rJ%rK%}rL%(hUhj.%ubhXGAverage firing rate of the spike train to generate as frequency scalar.rM%rN%}rO%(hXGAverage firing rate of the spike train to generate as frequency scalar.hj.%ubehjubahjdubj()rP%}rQ%(hUh}rR%(h]h]h]h]h]uhj&%h}]rS%j)rT%}rU%(hUh}rV%(h]h]h]h]h]uhjP%h}]rW%(j)rX%}rY%(hXt_starth}rZ%(h]h]h]h]h]uhjT%h}]r[%hXt_startr\%r]%}r^%(hUhjX%ubahjubhX (r_%r`%}ra%(hUhjT%ubh)rb%}rc%(hUh}rd%(UreftypejzU reftargetXQuantity scalarre%U refdomainj$h]h]U refexplicith]h]h]uhjT%h}]rf%j})rg%}rh%(hje%h}ri%(h]h]h]h]h]uhjb%h}]rj%hXQuantity scalarrk%rl%}rm%(hUhjg%ubahjubahjubhX)rn%}ro%(hUhjT%ubhX -- rp%rq%}rr%(hUhjT%ubhXkTime at which the spike train begins as time scalar. The first actual spike will be greater than this time.rs%rt%}ru%(hXkTime at which the spike train begins as time scalar. The first actual spike will be greater than this time.hjT%ubehjubahjdubj()rv%}rw%(hUh}rx%(h]h]h]h]h]uhj&%h}]ry%j)rz%}r{%(hUh}r|%(h]h]h]h]h]uhjv%h}]r}%(j)r~%}r%(hXt_stoph}r%(h]h]h]h]h]uhjz%h}]r%hXt_stopr%r%}r%(hUhj~%ubahjubhX (r%r%}r%(hUhjz%ubh)r%}r%(hUh}r%(UreftypejzU reftargetXQuantity scalarr%U refdomainj$h]h]U refexplicith]h]h]uhjz%h}]r%j})r%}r%(hj%h}r%(h]h]h]h]h]uhj%h}]r%hXQuantity scalarr%r%}r%(hUhj%ubahjubahjubhX)r%}r%(hUhjz%ubhX -- r%r%}r%(hUhjz%ubhXTime at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by r%r%}r%(hXTime at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by hjz%ubj)r%}r%(hX `max_spikes`h}r%(h]h]h]h]h]uhjz%h}]r%hX max_spikesr%r%}r%(hUhj%ubahjubhX and r%r%}r%(hX and hjz%ubj)r%}r%(hX`t_stop`h}r%(h]h]h]h]h]uhjz%h}]r%hXt_stopr%r%}r%(hUhj%ubahjubhX+ will be equal to the last generated spike.r%r%}r%(hX+ will be equal to the last generated spike.hjz%ubehjubahjdubj()r%}r%(hUh}r%(h]h]h]h]h]uhj&%h}]r%j)r%}r%(hUh}r%(h]h]h]h]h]uhj%h}]r%(j)r%}r%(hX max_spikesh}r%(h]h]h]h]h]uhj%h}]r%hX max_spikesr%r%}r%(hUhj%ubahjubhX -- r%r%}r%(hUhj%ubhXNMaximum number of spikes to generate. Fewer spikes might be generated in case r%r%}r%(hXNMaximum number of spikes to generate. Fewer spikes might be generated in case hj%ubj)r%}r%(hX`t_stop`h}r%(h]h]h]h]h]uhj%h}]r%hXt_stopr%r%}r%(hUhj%ubahjubhX is also set.r%r%}r%(hX is also set.hj%ubehjubahjdubj()r%}r%(hUh}r%(h]h]h]h]h]uhj&%h}]r%j)r%}r%(hUh}r%(h]h]h]h]h]uhj%h}]r%(j)r%}r%(hX refractoryh}r%(h]h]h]h]h]uhj%h}]r%hX refractoryr%r%}r%(hUhj%ubahjubhX (r%r%}r%(hUhj%ubh)r%}r%(hUh}r%(UreftypejzU reftargetXQuantity scalarr%U refdomainj$h]h]U refexplicith]h]h]uhj%h}]r%j})r%}r%(hj%h}r%(h]h]h]h]h]uhj%h}]r%hXQuantity scalarr%r%}r%(hUhj%ubahjubahjubhX)r%}r%(hUhj%ubhX -- r%r%}r%(hUhj%ubhXAbsolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to r%r%}r%(hXAbsolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to hj%ubj)r%}r%(hX`rate`h}r%(h]h]h]h]h]uhj%h}]r%hXrater%r%}r%(hUhj%ubahjubhX again.r%r%}r%(hX again.hj%ubehjubahjdubehjubahjubehjubj)r%}r&(hUh}r&(h]h]h]h]h]uhj%h}]r&(j)r&}r&(hUh}r&(h]h]h]h]h]uhj%h}]r&hXReturnsr&r&}r &(hUhj&ubahjubj)r &}r &(hUh}r &(h]h]h]h]h]uhj%h}]r &j)r&}r&(hUh}r&(h]h]h]h]h]uhj &h}]r&hXThe generated spike train.r&r&}r&(hXThe generated spike train.hj&ubahjubahjubehjubj)r&}r&(hUh}r&(h]h]h]h]h]uhj%h}]r&(j)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&hX Return typer&r&}r&(hUhj&ubahjubj)r &}r!&(hUh}r"&(h]h]h]h]h]uhj&h}]r#&j)r$&}r%&(hUh}r&&(h]h]h]h]h]uhj &h}]r'&h)r(&}r)&(hX:class:`neo.core.SpikeTrain`r*&hj$&hNhjh}r+&(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr,&h]h]U refexplicith]h]h]j?jj@NjAX!spykeutils.spike_train_generationr-&uhNh}]r.&jD)r/&}r0&(hj*&h}r1&(h]h]r2&(jIj,&Xpy-classr3&eh]h]h]uhj(&h}]r4&hXneo.core.SpikeTrainr5&r6&}r7&(hUhj/&ubahjOubaubahjubahjubehjubeubeubeubh)r8&}r9&(hUhj}$hNhhh}r:&(h]h]h]h]h]Uentries]r;&(hXIgen_inhomogeneous_poisson() (in module spykeutils.spike_train_generation)hUtr<&auhNhhh}]ubh)r=&}r>&(hUhj}$hNhhh}r?&(hωhXpyr@&h]h]h]h]h]hXfunctionrA&hjA&uhNhhh}]rB&(h)rC&}rD&(hXgen_inhomogeneous_poisson(modulation, max_rate, t_start=array(0.0) * s, t_stop=None, max_spikes=None, refractory=array(0.0) * s)hj=&hhhhh}rE&(h]rF&hahhX!spykeutils.spike_train_generationrG&rH&}rI&bh]h]h]h]rJ&hahXgen_inhomogeneous_poissonrK&hUhuhNhhh}]rL&(h)rM&}rN&(hjK&hjC&hhhhh}rO&(h]h]h]h]h]uhNhhh}]rP&hXgen_inhomogeneous_poissonrQ&rR&}rS&(hUhjM&ubaubjt)rT&}rU&(hUhjC&hhhjwh}rV&(h]h]h]h]h]uhNhhh}]rW&(jz)rX&}rY&(hX modulationh}rZ&(h]h]h]h]h]uhjT&h}]r[&hX modulationr\&r]&}r^&(hUhjX&ubahjubjz)r_&}r`&(hXmax_rateh}ra&(h]h]h]h]h]uhjT&h}]rb&hXmax_raterc&rd&}re&(hUhj_&ubahjubjz)rf&}rg&(hXt_start=array(0.0) * sh}rh&(h]h]h]h]h]uhjT&h}]ri&hXt_start=array(0.0) * srj&rk&}rl&(hUhjf&ubahjubjz)rm&}rn&(hX t_stop=Noneh}ro&(h]h]h]h]h]uhjT&h}]rp&hX t_stop=Nonerq&rr&}rs&(hUhjm&ubahjubjz)rt&}ru&(hXmax_spikes=Noneh}rv&(h]h]h]h]h]uhjT&h}]rw&hXmax_spikes=Nonerx&ry&}rz&(hUhjt&ubahjubjz)r{&}r|&(hXrefractory=array(0.0) * sh}r}&(h]h]h]h]h]uhjT&h}]r~&hXrefractory=array(0.0) * sr&r&}r&(hUhj{&ubahjubeubh)r&}r&(hUhjC&hNhhh}r&(Uexprhh]h]h]h]h]uhNhhh}]r&h)r&}r&(hUh}r&(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX*_modules/spykeutils/spike_train_generationUrefidjK&uhj&h}]r&j)r&}r&(hUh}r&(h]h]r&j ah]h]h]uhj&h}]r&hX[source]r&r&}r&(hUhj&ubahjubahjubaubeubj)r&}r&(hUhj=&hhhjh}r&(h]h]h]h]h]uhNhhh}]r&(j)r&}r&(hXGenerate an inhomogeneous Poisson spike train. The length is controlled with `t_stop` and `max_spikes`. Either one or both of these arguments have to be given.hj&hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_generation.py:docstring of spykeutils.spike_train_generation.gen_inhomogeneous_poissonhjh}r&(h]h]h]h]h]uhKhhh}]r&(hXMGenerate an inhomogeneous Poisson spike train. The length is controlled with r&r&}r&(hXMGenerate an inhomogeneous Poisson spike train. The length is controlled with hj&ubj)r&}r&(hX`t_stop`h}r&(h]h]h]h]h]uhj&h}]r&hXt_stopr&r&}r&(hUhj&ubahjubhX and r&r&}r&(hX and hj&ubj)r&}r&(hX `max_spikes`h}r&(h]h]h]h]h]uhj&h}]r&hX max_spikesr&r&}r&(hUhj&ubahjubhX9. Either one or both of these arguments have to be given.r&r&}r&(hX9. Either one or both of these arguments have to be given.hj&ubeubj)r&}r&(hUhj&hNhjh}r&(h]h]h]h]h]uhNhhh}]r&(j)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&(j)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&hX Parametersr&r&}r&(hUhj&ubahjubj)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&j#)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&(j()r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&j)r&}r&(hUh}r&(h]h]h]h]h]uhj&h}]r&(j)r&}r&(hX modulationh}r&(h]h]h]h]h]uhj&h}]r&hX modulationr&r&}r&(hUhj&ubahjubhX (r&r&}r&(hUhj&ubh)r&}r&(hUh}r&(UreftypejzU reftargetXfunctionr&U refdomainj@&h]h]U refexplicith]h]h]uhj&h}]r&j})r&}r&(hj&h}r&(h]h]h]h]h]uhj&h}]r&hXfunctionr&r&}r&(hUhj&ubahjubahjubhX)r&}r&(hUhj&ubhX -- r&r&}r&(hUhj&ubhX Function r&r&}r&(hX Function hj&ubjR)r&}r&(hUh}r&(UlatexXMf((t_1, \dots, t_n)): [\text{t\_start}, \text{t\_end}]^n \rightarrow [0, 1]^nh]h]h]h]h]uhj&h}]hjVubhX0 giving the instantaneous firing rates at times r&r&}r&(hX0 giving the instantaneous firing rates at times hj&ubjR)r&}r&(hUh}r&(UlatexX(t_1, \dots, t_n)h]h]h]h]h]uhj&h}]hjVubhX as proportion of r&r&}r&(hX as proportion of hj&ubj)r&}r&(hX `max_rate`h}r&(h]h]h]h]h]uhj&h}]r&hXmax_rater&r&}r'(hUhj&ubahjubhXb. Thus, a 1-D array will be passed to the function and it should return an array of the same size.r'r'}r'(hXb. Thus, a 1-D array will be passed to the function and it should return an array of the same size.hj&ubehjubahjdubj()r'}r'(hUh}r'(h]h]h]h]h]uhj&h}]r'j)r'}r '(hUh}r '(h]h]h]h]h]uhj'h}]r '(j)r '}r '(hXmax_rateh}r'(h]h]h]h]h]uhj'h}]r'hXmax_rater'r'}r'(hUhj 'ubahjubhX (r'r'}r'(hUhj'ubh)r'}r'(hUh}r'(UreftypejzU reftargetXQuantity scalarr'U refdomainj@&h]h]U refexplicith]h]h]uhj'h}]r'j})r'}r'(hj'h}r'(h]h]h]h]h]uhj'h}]r'hXQuantity scalarr'r '}r!'(hUhj'ubahjubahjubhX)r"'}r#'(hUhj'ubhX -- r$'r%'}r&'(hUhj'ubhXGMaximum firing rate of the spike train to generate as frequency scalar.r''r('}r)'(hXGMaximum firing rate of the spike train to generate as frequency scalar.hj'ubehjubahjdubj()r*'}r+'(hUh}r,'(h]h]h]h]h]uhj&h}]r-'j)r.'}r/'(hUh}r0'(h]h]h]h]h]uhj*'h}]r1'(j)r2'}r3'(hXt_starth}r4'(h]h]h]h]h]uhj.'h}]r5'hXt_startr6'r7'}r8'(hUhj2'ubahjubhX (r9'r:'}r;'(hUhj.'ubh)r<'}r='(hUh}r>'(UreftypejzU reftargetXQuantity scalarr?'U refdomainj@&h]h]U refexplicith]h]h]uhj.'h}]r@'j})rA'}rB'(hj?'h}rC'(h]h]h]h]h]uhj<'h}]rD'hXQuantity scalarrE'rF'}rG'(hUhjA'ubahjubahjubhX)rH'}rI'(hUhj.'ubhX -- rJ'rK'}rL'(hUhj.'ubhXkTime at which the spike train begins as time scalar. The first actual spike will be greater than this time.rM'rN'}rO'(hXkTime at which the spike train begins as time scalar. The first actual spike will be greater than this time.hj.'ubehjubahjdubj()rP'}rQ'(hUh}rR'(h]h]h]h]h]uhj&h}]rS'j)rT'}rU'(hUh}rV'(h]h]h]h]h]uhjP'h}]rW'(j)rX'}rY'(hXt_stoph}rZ'(h]h]h]h]h]uhjT'h}]r['hXt_stopr\'r]'}r^'(hUhjX'ubahjubhX (r_'r`'}ra'(hUhjT'ubh)rb'}rc'(hUh}rd'(UreftypejzU reftargetXQuantity scalarre'U refdomainj@&h]h]U refexplicith]h]h]uhjT'h}]rf'j})rg'}rh'(hje'h}ri'(h]h]h]h]h]uhjb'h}]rj'hXQuantity scalarrk'rl'}rm'(hUhjg'ubahjubahjubhX)rn'}ro'(hUhjT'ubhX -- rp'rq'}rr'(hUhjT'ubhXTime at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by rs'rt'}ru'(hXTime at which the spike train ends as time scalar. All generated spikes will be lower or equal than this time. If set to None, the number of generated spikes is controlled by hjT'ubj)rv'}rw'(hX `max_spikes`h}rx'(h]h]h]h]h]uhjT'h}]ry'hX max_spikesrz'r{'}r|'(hUhjv'ubahjubhX and r}'r~'}r'(hX and hjT'ubj)r'}r'(hX`t_stop`h}r'(h]h]h]h]h]uhjT'h}]r'hXt_stopr'r'}r'(hUhj'ubahjubhX+ will be equal to the last generated spike.r'r'}r'(hX+ will be equal to the last generated spike.hjT'ubehjubahjdubj()r'}r'(hUh}r'(h]h]h]h]h]uhj&h}]r'j)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'(j)r'}r'(hX refractoryh}r'(h]h]h]h]h]uhj'h}]r'hX refractoryr'r'}r'(hUhj'ubahjubhX (r'r'}r'(hUhj'ubh)r'}r'(hUh}r'(UreftypejzU reftargetXQuantity scalarr'U refdomainj@&h]h]U refexplicith]h]h]uhj'h}]r'j})r'}r'(hj'h}r'(h]h]h]h]h]uhj'h}]r'hXQuantity scalarr'r'}r'(hUhj'ubahjubahjubhX)r'}r'(hUhj'ubhX -- r'r'}r'(hUhj'ubhXAbsolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to r'r'}r'(hXAbsolute refractory period as time scalar. No spike will follow another spike for the given duration. Afterwards the firing rate will instantaneously be set to hj'ubj)r'}r'(hX`rate`h}r'(h]h]h]h]h]uhj'h}]r'hXrater'r'}r'(hUhj'ubahjubhX again.r'r'}r'(hX again.hj'ubehjubahjdubehjubahjubehjubj)r'}r'(hUh}r'(h]h]h]h]h]uhj&h}]r'(j)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'hXReturnsr'r'}r'(hUhj'ubahjubj)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'j)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'hXThe generated spike train.r'r'}r'(hXThe generated spike train.hj'ubahjubahjubehjubj)r'}r'(hUh}r'(h]h]h]h]h]uhj&h}]r'(j)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'hX Return typer'r'}r'(hUhj'ubahjubj)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'j)r'}r'(hUh}r'(h]h]h]h]h]uhj'h}]r'h)r'}r'(hX:class:`neo.core.SpikeTrain`r'hj'hNhjh}r'(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr'h]h]U refexplicith]h]h]j?jj@NjAj-&uhNh}]r'jD)r'}r'(hj'h}r'(h]h]r'(jIj'Xpy-classr'eh]h]h]uhj'h}]r'hXneo.core.SpikeTrainr'r'}r'(hUhj'ubahjOubaubahjubahjubehjubeubeubeubeubh)r'}r'(hUhhhj-hhh}r'(h]h]h]h]r'(X%module-spykeutils.spike_train_metricsr'hyeh]r'hIauhK/hhh}]r'(h)r'}r'(hX!:mod:`spike_train_metrics` Moduler'hj'hj-hhh}r'(h]h]h]h]h]uhK/hhh}]r'(h)r'}r'(hX:mod:`spike_train_metrics`r(hj'hNhjh}r((UreftypeXmodj<j=Xspike_train_metricsU refdomainXpyr(h]h]U refexplicith]h]h]j?jj@NjAj-&uhNh}]r(jD)r(}r((hj(h}r((h]h]r((jIj(Xpy-modr(eh]h]h]uhj'h}]r (hXspike_train_metricsr (r (}r ((hUhj(ubahjOubaubhX Moduler (r(}r((hX Moduler(hj'ubeubh)r(}r((hUhj'hhhhh}r((h]h]h]h]h]Uentries]r((hX'spykeutils.spike_train_metrics (module)X%module-spykeutils.spike_train_metricsUtr(auhKhhh}]ubh)r(}r((hUhj'hNhhh}r((h]h]h]h]h]Uentries]r((hX4cs_dist() (in module spykeutils.spike_train_metrics)hTUtr(auhNhhh}]ubh)r(}r((hUhj'hNhhh}r((hωhXpyr(h]h]h]h]h]hXfunctionr(hj(uhNhhh}]r ((h)r!(}r"((hXNcs_dist(trains, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)hj(hhhhh}r#((h]r$(hTahhXspykeutils.spike_train_metricsr%(r&(}r'(bh]h]h]h]r((hTahXcs_distr)(hUhuhNhhh}]r*((h)r+(}r,((hj)(hj!(hhhhh}r-((h]h]h]h]h]uhNhhh}]r.(hXcs_distr/(r0(}r1((hUhj+(ubaubjt)r2(}r3((hUhj!(hhhjwh}r4((h]h]h]h]h]uhNhhh}]r5((jz)r6(}r7((hXtrainsh}r8((h]h]h]h]h]uhj2(h}]r9(hXtrainsr:(r;(}r<((hUhj6(ubahjubjz)r=(}r>((hXsmoothing_filterh}r?((h]h]h]h]h]uhj2(h}]r@(hXsmoothing_filterrA(rB(}rC((hUhj=(ubahjubjz)rD(}rE((hX sampling_rateh}rF((h]h]h]h]h]uhj2(h}]rG(hX sampling_raterH(rI(}rJ((hUhjD(ubahjubjz)rK(}rL((hXfilter_area_fraction=0.99999h}rM((h]h]h]h]h]uhj2(h}]rN(hXfilter_area_fraction=0.99999rO(rP(}rQ((hUhjK(ubahjubeubh)rR(}rS((hUhj!(hNhhh}rT((Uexprhh]h]h]h]h]uhNhhh}]rU(h)rV(}rW((hUh}rX((UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj)(uhjR(h}]rY(j)rZ(}r[((hUh}r\((h]h]r](j ah]h]h]uhjV(h}]r^(hX[source]r_(r`(}ra((hUhjZ(ubahjubahjubaubeubj)rb(}rc((hUhj(hhhjh}rd((h]h]h]h]h]uhNhhh}]re((j)rf(}rg((hXYCalculates the Cauchy-Schwarz distance between two spike trains given a smoothing filter.rh(hjb(hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.cs_distri(hjh}rj((h]h]h]h]h]uhKhhh}]rk(hXYCalculates the Cauchy-Schwarz distance between two spike trains given a smoothing filter.rl(rm(}rn((hjh(hjf(ubaubj)ro(}rp((hX>Let :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \in \mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \int_{\mathcal{T}} v_a(t) v_b(t) dt`. Then, the Cauchy-Schwarz distance of the spike trains is defined as :math:`d_{CS}(a, b) = \arccos \frac{V(a, b)^2}{V(a, a) V(b, b)}`.hjb(hji(hjh}rq((h]h]h]h]h]uhKhhh}]rr((hXLet rs(rt(}ru((hXLet hjo(ubjR)rv(}rw((hUh}rx((UlatexXv_a(t)h]h]h]h]h]uhjo(h}]hjVubhX and ry(rz(}r{((hX and hjo(ubjR)r|(}r}((hUh}r~((UlatexXv_b(t)h]h]h]h]h]uhjo(h}]hjVubhX with r(r(}r((hX with hjo(ubjR)r(}r((hUh}r((UlatexXt \in \mathcal{T}h]h]h]h]h]uhjo(h}]hjVubhX> be the spike trains convolved with some smoothing filter and r(r(}r((hX> be the spike trains convolved with some smoothing filter and hjo(ubjR)r(}r((hUh}r((UlatexX-V(a, b) = \int_{\mathcal{T}} v_a(t) v_b(t) dth]h]h]h]h]uhjo(h}]hjVubhXF. Then, the Cauchy-Schwarz distance of the spike trains is defined as r(r(}r((hXF. Then, the Cauchy-Schwarz distance of the spike trains is defined as hjo(ubjR)r(}r((hUh}r((UlatexX8d_{CS}(a, b) = \arccos \frac{V(a, b)^2}{V(a, a) V(b, b)}h]h]h]h]h]uhjo(h}]hjVubhX.r(}r((hX.hjo(ubeubj)r(}r((hXThe Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure :math:`S_S` by :math:`d_{CS} = \arccos S_S^2`hjb(hji(hjh}r((h]h]h]h]h]uhK hhh}]r((hXZThe Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure r(r(}r((hXZThe Cauchy-Schwarz distance is closely related to the Schreiber et al. similarity measure hj(ubjR)r(}r((hUh}r((UlatexXS_Sh]h]h]h]h]uhj(h}]hjVubhX by r(r(}r((hX by hj(ubjR)r(}r((hUh}r((UlatexXd_{CS} = \arccos S_S^2h]h]h]h]h]uhj(h}]hjVubeubj)r(}r((hXThis function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use :func:`schreiber_similarity` for a more efficient and precise calculation.hjb(hji(hjh}r((h]h]h]h]h]uhK hhh}]r((hXThis function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use r(r(}r((hXThis function numerically convolves the spike trains with the smoothing filter which can be quite slow and inaccurate. If the analytical result of the autocorrelation of the smoothing filter is known, one can use hj(ubh)r(}r((hX:func:`schreiber_similarity`r(hj(hNhjh}r((UreftypeXfuncj<j=Xschreiber_similarityU refdomainXpyr(h]h]U refexplicith]h]h]j?jj@NjAXspykeutils.spike_train_metricsr(uhNh}]r(jD)r(}r((hj(h}r((h]h]r((jIj(Xpy-funcr(eh]h]h]uhj(h}]r(hXschreiber_similarity()r(r(}r((hUhj(ubahjOubaubhX. for a more efficient and precise calculation.r(r(}r((hX. for a more efficient and precise calculation.hj(ubeubj)r(}r((hXFurther information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*hjb(hji(hjh}r((h]h]h]h]h]uhKhhh}]r((hX$Further information can be found in r(r(}r((hX$Further information can be found in hj(ubj})r(}r((hX*Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*h}r((h]h]h]h]h]uhj(h}]r(hXPaiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.r(r(}r((hUhj(ubahjubeubj)r(}r((hUhjb(hNhjh}r((h]h]h]h]h]uhNhhh}]r((j)r(}r((hUh}r((h]h]h]h]h]uhj(h}]r((j)r(}r((hUh}r((h]h]h]h]h]uhj(h}]r(hX Parametersr(r(}r((hUhj(ubahjubj)r(}r((hUh}r((h]h]h]h]h]uhj(h}]r(j#)r(}r((hUh}r((h]h]h]h]h]uhj(h}]r((j()r(}r((hUh}r((h]h]h]h]h]uhj(h}]r(j)r(}r((hUh}r((h]h]h]h]h]uhj(h}]r((j)r(}r((hXtrainsh}r((h]h]h]h]h]uhj(h}]r(hXtrainsr(r(}r((hUhj(ubahjubhX (r(r(}r((hUhj(ubh)r(}r((hUh}r((UreftypejzU reftargetXsequencer(U refdomainj(h]h]U refexplicith]h]h]uhj(h}]r(j})r(}r((hj(h}r((h]h]h]h]h]uhj(h}]r(hXsequencer(r(}r((hUhj(ubahjubahjubhX)r)}r)(hUhj(ubhX -- r)r)}r)(hUhj(ubhX Sequence of r)r)}r)(hX Sequence of hj(ubh)r)}r )(hX:class:`neo.core.SpikeTrain`r )hj(hNhjh}r )(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr )h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r )jD)r)}r)(hj )h}r)(h]h]r)(jIj )Xpy-classr)eh]h]h]uhj)h}]r)hXneo.core.SpikeTrainr)r)}r)(hUhj)ubahjOubaubhX; objects of which the distance will be calculated pairwise.r)r)}r)(hX; objects of which the distance will be calculated pairwise.hj(ubehjubahjdubj()r)}r)(hUh}r)(h]h]h]h]h]uhj(h}]r)j)r)}r)(hUh}r )(h]h]h]h]h]uhj)h}]r!)(j)r")}r#)(hXsmoothing_filterh}r$)(h]h]h]h]h]uhj)h}]r%)hXsmoothing_filterr&)r')}r()(hUhj")ubahjubhX (r))r*)}r+)(hUhj)ubh)r,)}r-)(hX":class:`.signal_processing.Kernel`r.)hj)hNhjh}r/)(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr0)h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r1)jD)r2)}r3)(hj.)h}r4)(h]h]r5)(jIj0)Xpy-classr6)eh]h]h]uhj,)h}]r7)hXsignal_processing.Kernelr8)r9)}r:)(hUhj2)ubahjOubaubhX)r;)}r<)(hUhj)ubhX -- r=)r>)}r?)(hUhj)ubhX7Smoothing filter to be convolved with the spike trains.r@)rA)}rB)(hX7Smoothing filter to be convolved with the spike trains.hj)ubehjubahjdubj()rC)}rD)(hUh}rE)(h]h]h]h]h]uhj(h}]rF)j)rG)}rH)(hUh}rI)(h]h]h]h]h]uhjC)h}]rJ)(j)rK)}rL)(hX sampling_rateh}rM)(h]h]h]h]h]uhjG)h}]rN)hX sampling_raterO)rP)}rQ)(hUhjK)ubahjubhX (rR)rS)}rT)(hUhjG)ubh)rU)}rV)(hUh}rW)(UreftypejzU reftargetXQuantity scalarrX)U refdomainj(h]h]U refexplicith]h]h]uhjG)h}]rY)j})rZ)}r[)(hjX)h}r\)(h]h]h]h]h]uhjU)h}]r])hXQuantity scalarr^)r_)}r`)(hUhjZ)ubahjubahjubhX)ra)}rb)(hUhjG)ubhX -- rc)rd)}re)(hUhjG)ubhXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.rf)rg)}rh)(hXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.hjG)ubehjubahjdubj()ri)}rj)(hUh}rk)(h]h]h]h]h]uhj(h}]rl)j)rm)}rn)(hUh}ro)(h]h]h]h]h]uhji)h}]rp)(j)rq)}rr)(hXfilter_area_fractionh}rs)(h]h]h]h]h]uhjm)h}]rt)hXfilter_area_fractionru)rv)}rw)(hUhjq)ubahjubhX (rx)ry)}rz)(hUhjm)ubh)r{)}r|)(hUh}r})(UreftypejzU reftargetXfloatr~)U refdomainj(h]h]U refexplicith]h]h]uhjm)h}]r)j})r)}r)(hj~)h}r)(h]h]h]h]h]uhj{)h}]r)hXfloatr)r)}r)(hUhj)ubahjubahjubhX)r)}r)(hUhjm)ubhX -- r)r)}r)(hUhjm)ubhXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).r)r)}r)(hXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).hjm)ubehjubahjdubehjubahjubehjubj)r)}r)(hUh}r)(h]h]h]h]h]uhj(h}]r)(j)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)hXReturnsr)r)}r)(hUhj)ubahjubj)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)j)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)hXJMatrix containing the Cauchy-Schwarz distance of all pairs of spike trainsr)r)}r)(hXJMatrix containing the Cauchy-Schwarz distance of all pairs of spike trainshj)ubahjubahjubehjubj)r)}r)(hUh}r)(h]h]h]h]h]uhj(h}]r)(j)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)hX Return typer)r)}r)(hUhj)ubahjubj)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)j)r)}r)(hUh}r)(h]h]h]h]h]uhj)h}]r)hX 2-D arrayr)r)}r)(hX 2-D arrayhj)ubahjubahjubehjubeubeubeubh)r)}r)(hUhj'hNhhh}r)(h]h]h]h]h]Uentries]r)(hXBevent_synchronization() (in module spykeutils.spike_train_metrics)hFUtr)auhNhhh}]ubh)r)}r)(hUhj'hNhhh}r)(hωhXpyr)h]h]h]h]h]hXfunctionr)hj)uhNhhh}]r)(h)r)}r)(hXtevent_synchronization(trains, tau=None, kernel=signal_processing.RectangularKernel(1.0, normalize=False), sort=True)hj)hhhhh}r)(h]r)hFahhXspykeutils.spike_train_metricsr)r)}r)bh]h]h]h]r)hFahXevent_synchronizationr)hUhuhNhhh}]r)(h)r)}r)(hj)hj)hhhhh}r)(h]h]h]h]h]uhNhhh}]r)hXevent_synchronizationr)r)}r)(hUhj)ubaubjt)r)}r)(hUhj)hhhjwh}r)(h]h]h]h]h]uhNhhh}]r)(jz)r)}r)(hXtrainsh}r)(h]h]h]h]h]uhj)h}]r)hXtrainsr)r)}r)(hUhj)ubahjubjz)r)}r)(hXtau=Noneh}r)(h]h]h]h]h]uhj)h}]r)hXtau=Noner)r)}r)(hUhj)ubahjubjz)r)}r)(hX.kernel=signal_processing.RectangularKernel(1.0h}r)(h]h]h]h]h]uhj)h}]r)hX.kernel=signal_processing.RectangularKernel(1.0r)r)}r)(hUhj)ubahjubjz)r)}r)(hXnormalize=False)h}r)(h]h]h]h]h]uhj)h}]r)hXnormalize=False)r)r)}r)(hUhj)ubahjubjz)r)}r)(hX sort=Trueh}r)(h]h]h]h]h]uhj)h}]r)hX sort=Truer)r)}r)(hUhj)ubahjubeubh)r)}r)(hUhj)hNhhh}r*(Uexprhh]h]h]h]h]uhNhhh}]r*h)r*}r*(hUh}r*(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj)uhj)h}]r*j)r*}r*(hUh}r*(h]h]r *j ah]h]h]uhj*h}]r *hX[source]r *r *}r *(hUhj*ubahjubahjubaubeubj)r*}r*(hUhj)hhhjh}r*(h]h]h]h]h]uhNhhh}]r*(j)r*}r*(hX%Calculates the event synchronization.r*hj*hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.event_synchronizationr*hjh}r*(h]h]h]h]h]uhKhhh}]r*hX%Calculates the event synchronization.r*r*}r*(hj*hj*ubaubj)r*}r*(hXkLet :math:`d(x|y)` be the count of spikes in :math:`y` which occur shortly before an event in :math:`x` with a time difference of less than :math:`\tau`. Moreover, let :math:`n_x` and :math:`n_y` be the number of total spikes in the spike trains :math:`x` and :math:`y`. The event synchrony is then defined as :math:`Q_T = \frac{d(x|y) + d(y|x)}{\sqrt{n_x n_y}}`.hj*hj*hjh}r*(h]h]h]h]h]uhKhhh}]r*(hXLet r*r *}r!*(hXLet hj*ubjR)r"*}r#*(hUh}r$*(UlatexXd(x|y)h]h]h]h]h]uhj*h}]hjVubhX be the count of spikes in r%*r&*}r'*(hX be the count of spikes in hj*ubjR)r(*}r)*(hUh}r**(UlatexXyh]h]h]h]h]uhj*h}]hjVubhX( which occur shortly before an event in r+*r,*}r-*(hX( which occur shortly before an event in hj*ubjR)r.*}r/*(hUh}r0*(UlatexXxh]h]h]h]h]uhj*h}]hjVubhX% with a time difference of less than r1*r2*}r3*(hX% with a time difference of less than hj*ubjR)r4*}r5*(hUh}r6*(UlatexX\tauh]h]h]h]h]uhj*h}]hjVubhX. Moreover, let r7*r8*}r9*(hX. Moreover, let hj*ubjR)r:*}r;*(hUh}r<*(UlatexXn_xh]h]h]h]h]uhj*h}]hjVubhX and r=*r>*}r?*(hX and hj*ubjR)r@*}rA*(hUh}rB*(UlatexXn_yh]h]h]h]h]uhj*h}]hjVubhX3 be the number of total spikes in the spike trains rC*rD*}rE*(hX3 be the number of total spikes in the spike trains hj*ubjR)rF*}rG*(hUh}rH*(UlatexXxh]h]h]h]h]uhj*h}]hjVubhX and rI*rJ*}rK*(hX and hj*ubjR)rL*}rM*(hUh}rN*(UlatexXyh]h]h]h]h]uhj*h}]hjVubhX). The event synchrony is then defined as rO*rP*}rQ*(hX). The event synchrony is then defined as hj*ubjR)rR*}rS*(hUh}rT*(UlatexX,Q_T = \frac{d(x|y) + d(y|x)}{\sqrt{n_x n_y}}h]h]h]h]h]uhj*h}]hjVubhX.rU*}rV*(hX.hj*ubeubj)rW*}rX*(hXThe time maximum time lag :math:`\tau` can be determined automatically for each pair of spikes :math:`t^x_i` and :math:`t^y_j` by the formula :math:`\tau_{ij} = \frac{1}{2} \min\{t^x_{i+1} - t^x_i, t^x_i - t^x_{i-1}, t^y_{j+1} - t^y_j, t^y_j - t^y_{j-1}\}`hj*hj*hjh}rY*(h]h]h]h]h]uhK hhh}]rZ*(hXThe time maximum time lag r[*r\*}r]*(hXThe time maximum time lag hjW*ubjR)r^*}r_*(hUh}r`*(UlatexX\tauh]h]h]h]h]uhjW*h}]hjVubhX9 can be determined automatically for each pair of spikes ra*rb*}rc*(hX9 can be determined automatically for each pair of spikes hjW*ubjR)rd*}re*(hUh}rf*(UlatexXt^x_ih]h]h]h]h]uhjW*h}]hjVubhX and rg*rh*}ri*(hX and hjW*ubjR)rj*}rk*(hUh}rl*(UlatexXt^y_jh]h]h]h]h]uhjW*h}]hjVubhX by the formula rm*rn*}ro*(hX by the formula hjW*ubjR)rp*}rq*(hUh}rr*(UlatexXj\tau_{ij} = \frac{1}{2} \min\{t^x_{i+1} - t^x_i, t^x_i - t^x_{i-1}, t^y_{j+1} - t^y_j, t^y_j - t^y_{j-1}\}h]h]h]h]h]uhjW*h}]hjVubeubj)rs*}rt*(hXFurther and more detailed information can be found in *Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.*hj*hj*hjh}ru*(h]h]h]h]h]uhKhhh}]rv*(hX6Further and more detailed information can be found in rw*rx*}ry*(hX6Further and more detailed information can be found in hjs*ubj})rz*}r{*(hX*Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.*h}r|*(h]h]h]h]h]uhjs*h}]r}*hXQuiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Physical Review E, 66(4), 041904.r~*r*}r*(hUhjz*ubahjubeubj)r*}r*(hUhj*hNhjh}r*(h]h]h]h]h]uhNhhh}]r*(j)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*(j)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*hX Parametersr*r*}r*(hUhj*ubahjubj)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*j#)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*(j()r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*j)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*(j)r*}r*(hXtrainsh}r*(h]h]h]h]h]uhj*h}]r*hXtrainsr*r*}r*(hUhj*ubahjubhX (r*r*}r*(hUhj*ubh)r*}r*(hUh}r*(UreftypejzU reftargetXsequencer*U refdomainj)h]h]U refexplicith]h]h]uhj*h}]r*j})r*}r*(hj*h}r*(h]h]h]h]h]uhj*h}]r*hXsequencer*r*}r*(hUhj*ubahjubahjubhX)r*}r*(hUhj*ubhX -- r*r*}r*(hUhj*ubhX Sequence of r*r*}r*(hX Sequence of hj*ubh)r*}r*(hX:class:`neo.core.SpikeTrain`r*hj*hNhjh}r*(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr*h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r*jD)r*}r*(hj*h}r*(h]h]r*(jIj*Xpy-classr*eh]h]h]uhj*h}]r*hXneo.core.SpikeTrainr*r*}r*(hUhj*ubahjOubaubhXF objects of which the van Rossum distance will be calculated pairwise.r*r*}r*(hXF objects of which the van Rossum distance will be calculated pairwise.hj*ubehjubahjdubj()r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*j)r*}r*(hUh}r*(h]h]h]h]h]uhj*h}]r*(j)r*}r*(hXtauh}r*(h]h]h]h]h]uhj*h}]r*hXtaur*r*}r*(hUhj*ubahjubhX (r*r*}r*(hUhj*ubh)r*}r*(hUh}r*(UreftypejzU reftargetXQuantity scalarr*U refdomainj)h]h]U refexplicith]h]h]uhj*h}]r*j})r*}r*(hj*h}r*(h]h]h]h]h]uhj*h}]r*hXQuantity scalarr*r*}r*(hUhj*ubahjubahjubhX)r*}r*(hUhj*ubhX -- r*r*}r*(hUhj*ubhXThe maximum time lag for two spikes to be considered coincident or synchronous as time scalar. To have it determined automatically by above formula set it to r*r*}r*(hXThe maximum time lag for two spikes to be considered coincident or synchronous as time scalar. To have it determined automatically by above formula set it to hj*ubj)r*}r*(hX`None`h}r*(h]h]h]h]h]uhj*h}]r*hXNoner*r*}r*(hUhj*ubahjubhX.r*}r*(hX.hj*ubehjubahjdubj()r*}r+(hUh}r+(h]h]h]h]h]uhj*h}]r+j)r+}r+(hUh}r+(h]h]h]h]h]uhj*h}]r+(j)r+}r+(hXkernelh}r +(h]h]h]h]h]uhj+h}]r +hXkernelr +r +}r +(hUhj+ubahjubhX (r+r+}r+(hUhj+ubh)r+}r+(hX":class:`.signal_processing.Kernel`r+hj+hNhjh}r+(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr+h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r+jD)r+}r+(hj+h}r+(h]h]r+(jIj+Xpy-classr+eh]h]h]uhj+h}]r+hXsignal_processing.Kernelr+r+}r+(hUhj+ubahjOubaubhX)r +}r!+(hUhj+ubhX -- r"+r#+}r$+(hUhj+ubhX1Kernel to use in the calculation of the distance.r%+r&+}r'+(hX1Kernel to use in the calculation of the distance.hj+ubehjubahjdubj()r(+}r)+(hUh}r*+(h]h]h]h]h]uhj*h}]r++j)r,+}r-+(hUh}r.+(h]h]h]h]h]uhj(+h}]r/+(j)r0+}r1+(hXsorth}r2+(h]h]h]h]h]uhj,+h}]r3+hXsortr4+r5+}r6+(hUhj0+ubahjubhX (r7+r8+}r9+(hUhj,+ubh)r:+}r;+(hUh}r<+(UreftypejzU reftargetXboolr=+U refdomainj)h]h]U refexplicith]h]h]uhj,+h}]r>+j})r?+}r@+(hj=+h}rA+(h]h]h]h]h]uhj:+h}]rB+hXboolrC+rD+}rE+(hUhj?+ubahjubahjubhX)rF+}rG+(hUhj,+ubhX -- rH+rI+}rJ+(hUhj,+ubhXTSpike trains with sorted spike times are be needed for the calculation. You can set rK+rL+}rM+(hXTSpike trains with sorted spike times are be needed for the calculation. You can set hj,+ubj)rN+}rO+(hX`sort`h}rP+(h]h]h]h]h]uhj,+h}]rQ+hXsortrR+rS+}rT+(hUhjN+ubahjubhX to rU+rV+}rW+(hX to hj,+ubj)rX+}rY+(hX`False`h}rZ+(h]h]h]h]h]uhj,+h}]r[+hXFalser\+r]+}r^+(hUhjX+ubahjubhXT if you know that your spike trains are already sorted to decrease calculation time.r_+r`+}ra+(hXT if you know that your spike trains are already sorted to decrease calculation time.hj,+ubehjubahjdubehjubahjubehjubj)rb+}rc+(hUh}rd+(h]h]h]h]h]uhj*h}]re+(j)rf+}rg+(hUh}rh+(h]h]h]h]h]uhjb+h}]ri+hXReturnsrj+rk+}rl+(hUhjf+ubahjubj)rm+}rn+(hUh}ro+(h]h]h]h]h]uhjb+h}]rp+j)rq+}rr+(hUh}rs+(h]h]h]h]h]uhjm+h}]rt+hXJMatrix containing the event synchronization for all pairs of spike trains.ru+rv+}rw+(hXJMatrix containing the event synchronization for all pairs of spike trains.hjq+ubahjubahjubehjubj)rx+}ry+(hUh}rz+(h]h]h]h]h]uhj*h}]r{+(j)r|+}r}+(hUh}r~+(h]h]h]h]h]uhjx+h}]r+hX Return typer+r+}r+(hUhj|+ubahjubj)r+}r+(hUh}r+(h]h]h]h]h]uhjx+h}]r+j)r+}r+(hUh}r+(h]h]h]h]h]uhj+h}]r+hX 2-D arrayr+r+}r+(hX 2-D arrayhj+ubahjubahjubehjubeubeubeubh)r+}r+(hUhj'hNhhh}r+(h]h]h]h]h]Uentries]r+(hXEhunter_milton_similarity() (in module spykeutils.spike_train_metrics)hBUtr+auhNhhh}]ubh)r+}r+(hUhj'hNhhh}r+(hωhXpyr+h]h]h]h]h]hXfunctionr+hj+uhNhhh}]r+(h)r+}r+(hXAhunter_milton_similarity(trains, tau=array(1.0) * s, kernel=None)hj+hhhhh}r+(h]r+hBahhXspykeutils.spike_train_metricsr+r+}r+bh]h]h]h]r+hBahXhunter_milton_similarityr+hUhuhNhhh}]r+(h)r+}r+(hj+hj+hhhhh}r+(h]h]h]h]h]uhNhhh}]r+hXhunter_milton_similarityr+r+}r+(hUhj+ubaubjt)r+}r+(hUhj+hhhjwh}r+(h]h]h]h]h]uhNhhh}]r+(jz)r+}r+(hXtrainsh}r+(h]h]h]h]h]uhj+h}]r+hXtrainsr+r+}r+(hUhj+ubahjubjz)r+}r+(hXtau=array(1.0) * sh}r+(h]h]h]h]h]uhj+h}]r+hXtau=array(1.0) * sr+r+}r+(hUhj+ubahjubjz)r+}r+(hX kernel=Noneh}r+(h]h]h]h]h]uhj+h}]r+hX kernel=Noner+r+}r+(hUhj+ubahjubeubh)r+}r+(hUhj+hNhhh}r+(Uexprhh]h]h]h]h]uhNhhh}]r+h)r+}r+(hUh}r+(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj+uhj+h}]r+j)r+}r+(hUh}r+(h]h]r+j ah]h]h]uhj+h}]r+hX[source]r+r+}r+(hUhj+ubahjubahjubaubeubj)r+}r+(hUhj+hhhjh}r+(h]h]h]h]h]uhNhhh}]r+(j)r+}r+(hX0Calculates the Hunter-Milton similarity measure.r+hj+hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.hunter_milton_similarityr+hjh}r+(h]h]h]h]h]uhKhhh}]r+hX0Calculates the Hunter-Milton similarity measure.r+r+}r+(hj+hj+ubaubj)r+}r+(hXIf the kernel function is denoted as :math:`K(t)`, a function :math:`d(x_k) = K(x_k - y_{k'})` can be defined with :math:`y_{k'}` being the closest spike in spike train :math:`y` to the spike :math:`x_k` in spike train :math:`x`. With this the Hunter-Milton similarity measure is :math:`S_H = \frac{1}{2} \left(\frac{1}{n_x} \sum_{k = 1}^{n_x} d(x_k) + \frac{1}{n_y} \sum_{k' = 1}^{n_y} d(y_{k'})\right)`.hj+hj+hjh}r+(h]h]h]h]h]uhKhhh}]r+(hX%If the kernel function is denoted as r+r+}r+(hX%If the kernel function is denoted as hj+ubjR)r+}r+(hUh}r+(UlatexXK(t)h]h]h]h]h]uhj+h}]hjVubhX , a function r+r+}r+(hX , a function hj+ubjR)r+}r+(hUh}r+(UlatexXd(x_k) = K(x_k - y_{k'})h]h]h]h]h]uhj+h}]hjVubhX can be defined with r+r+}r+(hX can be defined with hj+ubjR)r+}r+(hUh}r+(UlatexXy_{k'}h]h]h]h]h]uhj+h}]hjVubhX( being the closest spike in spike train r+r+}r+(hX( being the closest spike in spike train hj+ubjR)r+}r+(hUh}r+(UlatexXyh]h]h]h]h]uhj+h}]hjVubhX to the spike r+r+}r+(hX to the spike hj+ubjR)r+}r,(hUh}r,(UlatexXx_kh]h]h]h]h]uhj+h}]hjVubhX in spike train r,r,}r,(hX in spike train hj+ubjR)r,}r,(hUh}r,(UlatexXxh]h]h]h]h]uhj+h}]hjVubhX4. With this the Hunter-Milton similarity measure is r,r ,}r ,(hX4. With this the Hunter-Milton similarity measure is hj+ubjR)r ,}r ,(hUh}r ,(UlatexXtS_H = \frac{1}{2} \left(\frac{1}{n_x} \sum_{k = 1}^{n_x} d(x_k) + \frac{1}{n_y} \sum_{k' = 1}^{n_y} d(y_{k'})\right)h]h]h]h]h]uhj+h}]hjVubhX.r,}r,(hX.hj+ubeubj)r,}r,(hX[This implementation returns 0 if one of the spike trains is empty, but 1 if both are empty.r,hj+hj+hjh}r,(h]h]h]h]h]uhK hhh}]r,hX[This implementation returns 0 if one of the spike trains is empty, but 1 if both are empty.r,r,}r,(hj,hj,ubaubj)r,}r,(hX#Further information can be found inr,hj+hj+hjh}r,(h]h]h]h]h]uhK hhh}]r,hX#Further information can be found inr,r,}r,(hj,hj,ubaubj#)r ,}r!,(hUhj+hj+hjh}r",(jKX-h]h]h]h]h]uhKhhh}]r#,(j()r$,}r%,(hX*Hunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.*hj ,hj+hjdh}r&,(h]h]h]h]h]uhNhhh}]r',j)r(,}r),(hX*Hunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.*r*,hj$,hj+hjh}r+,(h]h]h]h]h]uhKh}]r,,j})r-,}r.,(hj*,h}r/,(h]h]h]h]h]uhj(,h}]r0,hXHunter, J. D., & Milton, J. G. (2003). Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation. Journal of Neurophysiology.r1,r2,}r3,(hUhj-,ubahjubaubaubj()r4,}r5,(hX*Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.* hj ,hj+hjdh}r6,(h]h]h]h]h]uhNhhh}]r7,j)r8,}r9,(hX*Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.*r:,hj4,hj+hjh}r;,(h]h]h]h]h]uhKh}]r<,j})r=,}r>,(hj:,h}r?,(h]h]h]h]h]uhj8,h}]r@,hXDauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.rA,rB,}rC,(hUhj=,ubahjubaubaubeubj)rD,}rE,(hUhj+hNhjh}rF,(h]h]h]h]h]uhNhhh}]rG,(j)rH,}rI,(hUh}rJ,(h]h]h]h]h]uhjD,h}]rK,(j)rL,}rM,(hUh}rN,(h]h]h]h]h]uhjH,h}]rO,hX ParametersrP,rQ,}rR,(hUhjL,ubahjubj)rS,}rT,(hUh}rU,(h]h]h]h]h]uhjH,h}]rV,j#)rW,}rX,(hUh}rY,(h]h]h]h]h]uhjS,h}]rZ,(j()r[,}r\,(hUh}r],(h]h]h]h]h]uhjW,h}]r^,j)r_,}r`,(hUh}ra,(h]h]h]h]h]uhj[,h}]rb,(j)rc,}rd,(hXtrainsh}re,(h]h]h]h]h]uhj_,h}]rf,hXtrainsrg,rh,}ri,(hUhjc,ubahjubhX (rj,rk,}rl,(hUhj_,ubh)rm,}rn,(hUh}ro,(UreftypejzU reftargetXsequencerp,U refdomainj+h]h]U refexplicith]h]h]uhj_,h}]rq,j})rr,}rs,(hjp,h}rt,(h]h]h]h]h]uhjm,h}]ru,hXsequencerv,rw,}rx,(hUhjr,ubahjubahjubhX)ry,}rz,(hUhj_,ubhX -- r{,r|,}r},(hUhj_,ubhX Sequence of r~,r,}r,(hX Sequence of hj_,ubh)r,}r,(hX:class:`neo.core.SpikeTrain`r,hj_,hNhjh}r,(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr,h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r,jD)r,}r,(hj,h}r,(h]h]r,(jIj,Xpy-classr,eh]h]h]uhj,h}]r,hXneo.core.SpikeTrainr,r,}r,(hUhj,ubahjOubaubhXK objects of which the Hunter-Milton similarity will be calculated pairwise.r,r,}r,(hXK objects of which the Hunter-Milton similarity will be calculated pairwise.hj_,ubehjubahjdubj()r,}r,(hUh}r,(h]h]h]h]h]uhjW,h}]r,j)r,}r,(hUh}r,(h]h]h]h]h]uhj,h}]r,(j)r,}r,(hXtauh}r,(h]h]h]h]h]uhj,h}]r,hXtaur,r,}r,(hUhj,ubahjubhX (r,r,}r,(hUhj,ubh)r,}r,(hUh}r,(UreftypejzU reftargetXQuantity scalarr,U refdomainj+h]h]U refexplicith]h]h]uhj,h}]r,j})r,}r,(hj,h}r,(h]h]h]h]h]uhj,h}]r,hXQuantity scalarr,r,}r,(hUhj,ubahjubahjubhX)r,}r,(hUhj,ubhX -- r,r,}r,(hUhj,ubhXLThe time scale for determining the coincidence of two events as time scalar.r,r,}r,(hXLThe time scale for determining the coincidence of two events as time scalar.hj,ubehjubahjdubj()r,}r,(hUh}r,(h]h]h]h]h]uhjW,h}]r,j)r,}r,(hUh}r,(h]h]h]h]h]uhj,h}]r,(j)r,}r,(hXkernelh}r,(h]h]h]h]h]uhj,h}]r,hXkernelr,r,}r,(hUhj,ubahjubhX (r,r,}r,(hUhj,ubh)r,}r,(hX":class:`.signal_processing.Kernel`r,hj,hNhjh}r,(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr,h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r,jD)r,}r,(hj,h}r,(h]h]r,(jIj,Xpy-classr,eh]h]h]uhj,h}]r,hXsignal_processing.Kernelr,r,}r,(hUhj,ubahjOubaubhX)r,}r,(hUhj,ubhX -- r,r,}r,(hUhj,ubhX5Kernel to use in the calculation of the distance. If r,r,}r,(hX5Kernel to use in the calculation of the distance. If hj,ubj)r,}r,(hX`None`h}r,(h]h]h]h]h]uhj,h}]r,hXNoner,r,}r,(hUhj,ubahjubhX/, a unnormalized Laplacian kernel will be used.r,r,}r,(hX/, a unnormalized Laplacian kernel will be used.hj,ubehjubahjdubehjubahjubehjubj)r,}r,(hUh}r,(h]h]h]h]h]uhjD,h}]r,(j)r,}r,(hUh}r,(h]h]h]h]h]uhj,h}]r,hXReturnsr,r,}r,(hUhj,ubahjubj)r,}r,(hUh}r,(h]h]h]h]h]uhj,h}]r,j)r,}r,(hUh}r,(h]h]h]h]h]uhj,h}]r,hXMMatrix containing the Hunter-Milton similarity for all pairs of spike trains.r,r-}r-(hXMMatrix containing the Hunter-Milton similarity for all pairs of spike trains.hj,ubahjubahjubehjubj)r-}r-(hUh}r-(h]h]h]h]h]uhjD,h}]r-(j)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r -hX Return typer -r -}r -(hUhj-ubahjubj)r -}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-j)r-}r-(hUh}r-(h]h]h]h]h]uhj -h}]r-hX 2-D arrayr-r-}r-(hX 2-D arrayhj-ubahjubahjubehjubeubeubeubh)r-}r-(hUhj'hNhhh}r-(h]h]h]h]h]Uentries]r-(hX6norm_dist() (in module spykeutils.spike_train_metrics)hQUtr-auhNhhh}]ubh)r-}r-(hUhj'hNhhh}r-(hωhXpyr -h]h]h]h]h]hXfunctionr!-hj!-uhNhhh}]r"-(h)r#-}r$-(hXPnorm_dist(trains, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)hj-hhhhh}r%-(h]r&-hQahhXspykeutils.spike_train_metricsr'-r(-}r)-bh]h]h]h]r*-hQahX norm_distr+-hUhuhNhhh}]r,-(h)r--}r.-(hj+-hj#-hhhhh}r/-(h]h]h]h]h]uhNhhh}]r0-hX norm_distr1-r2-}r3-(hUhj--ubaubjt)r4-}r5-(hUhj#-hhhjwh}r6-(h]h]h]h]h]uhNhhh}]r7-(jz)r8-}r9-(hXtrainsh}r:-(h]h]h]h]h]uhj4-h}]r;-hXtrainsr<-r=-}r>-(hUhj8-ubahjubjz)r?-}r@-(hXsmoothing_filterh}rA-(h]h]h]h]h]uhj4-h}]rB-hXsmoothing_filterrC-rD-}rE-(hUhj?-ubahjubjz)rF-}rG-(hX sampling_rateh}rH-(h]h]h]h]h]uhj4-h}]rI-hX sampling_raterJ-rK-}rL-(hUhjF-ubahjubjz)rM-}rN-(hXfilter_area_fraction=0.99999h}rO-(h]h]h]h]h]uhj4-h}]rP-hXfilter_area_fraction=0.99999rQ-rR-}rS-(hUhjM-ubahjubeubh)rT-}rU-(hUhj#-hNhhh}rV-(Uexprhh]h]h]h]h]uhNhhh}]rW-h)rX-}rY-(hUh}rZ-(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj+-uhjT-h}]r[-j)r\-}r]-(hUh}r^-(h]h]r_-j ah]h]h]uhjX-h}]r`-hX[source]ra-rb-}rc-(hUhj\-ubahjubahjubaubeubj)rd-}re-(hUhj-hhhjh}rf-(h]h]h]h]h]uhNhhh}]rg-(j)rh-}ri-(hXKCalculates the norm distance between spike trains given a smoothing filter.rj-hjd-hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.norm_distrk-hjh}rl-(h]h]h]h]h]uhKhhh}]rm-hXKCalculates the norm distance between spike trains given a smoothing filter.rn-ro-}rp-(hjj-hjh-ubaubj)rq-}rr-(hXLet :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \in \mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as :math:`d_{ND}(a, b) = \sqrt{\int_{\mathcal{T}} (v_a(t) - v_b(t))^2 dt}`.hjd-hjk-hjh}rs-(h]h]h]h]h]uhKhhh}]rt-(hXLet ru-rv-}rw-(hXLet hjq-ubjR)rx-}ry-(hUh}rz-(UlatexXv_a(t)h]h]h]h]h]uhjq-h}]hjVubhX and r{-r|-}r}-(hX and hjq-ubjR)r~-}r-(hUh}r-(UlatexXv_b(t)h]h]h]h]h]uhjq-h}]hjVubhX with r-r-}r-(hX with hjq-ubjR)r-}r-(hUh}r-(UlatexXt \in \mathcal{T}h]h]h]h]h]uhjq-h}]hjVubhXu be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as r-r-}r-(hXu be the spike trains convolved with some smoothing filter. Then, the norm distance of the spike trains is defined as hjq-ubjR)r-}r-(hUh}r-(UlatexX?d_{ND}(a, b) = \sqrt{\int_{\mathcal{T}} (v_a(t) - v_b(t))^2 dt}h]h]h]h]h]uhjq-h}]hjVubhX.r-}r-(hX.hjq-ubeubj)r-}r-(hXFurther information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*hjd-hjk-hjh}r-(h]h]h]h]h]uhK hhh}]r-(hX$Further information can be found in r-r-}r-(hX$Further information can be found in hj-ubj})r-}r-(hX*Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*h}r-(h]h]h]h]h]uhj-h}]r-hXPaiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.r-r-}r-(hUhj-ubahjubeubj)r-}r-(hUhjd-hNhjh}r-(h]h]h]h]h]uhNhhh}]r-(j)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-(j)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-hX Parametersr-r-}r-(hUhj-ubahjubj)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-j#)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-(j()r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-j)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-(j)r-}r-(hXtrainsh}r-(h]h]h]h]h]uhj-h}]r-hXtrainsr-r-}r-(hUhj-ubahjubhX (r-r-}r-(hUhj-ubh)r-}r-(hUh}r-(UreftypejzU reftargetXsequencer-U refdomainj -h]h]U refexplicith]h]h]uhj-h}]r-j})r-}r-(hj-h}r-(h]h]h]h]h]uhj-h}]r-hXsequencer-r-}r-(hUhj-ubahjubahjubhX)r-}r-(hUhj-ubhX -- r-r-}r-(hUhj-ubhX Sequence of r-r-}r-(hX Sequence of hj-ubh)r-}r-(hX:class:`neo.core.SpikeTrain`r-hj-hNhjh}r-(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr-h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r-jD)r-}r-(hj-h}r-(h]h]r-(jIj-Xpy-classr-eh]h]h]uhj-h}]r-hXneo.core.SpikeTrainr-r-}r-(hUhj-ubahjOubaubhX; objects of which the distance will be calculated pairwise.r-r-}r-(hX; objects of which the distance will be calculated pairwise.hj-ubehjubahjdubj()r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-j)r-}r-(hUh}r-(h]h]h]h]h]uhj-h}]r-(j)r-}r-(hXsmoothing_filterh}r-(h]h]h]h]h]uhj-h}]r-hXsmoothing_filterr-r-}r-(hUhj-ubahjubhX (r-r-}r-(hUhj-ubh)r-}r-(hX":class:`.signal_processing.Kernel`r.hj-hNhjh}r.(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr.h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r.jD)r.}r.(hj.h}r.(h]h]r.(jIj.Xpy-classr.eh]h]h]uhj-h}]r .hXsignal_processing.Kernelr .r .}r .(hUhj.ubahjOubaubhX)r .}r.(hUhj-ubhX -- r.r.}r.(hUhj-ubhX7Smoothing filter to be convolved with the spike trains.r.r.}r.(hX7Smoothing filter to be convolved with the spike trains.hj-ubehjubahjdubj()r.}r.(hUh}r.(h]h]h]h]h]uhj-h}]r.j)r.}r.(hUh}r.(h]h]h]h]h]uhj.h}]r.(j)r.}r.(hX sampling_rateh}r.(h]h]h]h]h]uhj.h}]r .hX sampling_rater!.r".}r#.(hUhj.ubahjubhX (r$.r%.}r&.(hUhj.ubh)r'.}r(.(hUh}r).(UreftypejzU reftargetXQuantity scalarr*.U refdomainj -h]h]U refexplicith]h]h]uhj.h}]r+.j})r,.}r-.(hj*.h}r..(h]h]h]h]h]uhj'.h}]r/.hXQuantity scalarr0.r1.}r2.(hUhj,.ubahjubahjubhX)r3.}r4.(hUhj.ubhX -- r5.r6.}r7.(hUhj.ubhXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.r8.r9.}r:.(hXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.hj.ubehjubahjdubj()r;.}r<.(hUh}r=.(h]h]h]h]h]uhj-h}]r>.j)r?.}r@.(hUh}rA.(h]h]h]h]h]uhj;.h}]rB.(j)rC.}rD.(hXfilter_area_fractionh}rE.(h]h]h]h]h]uhj?.h}]rF.hXfilter_area_fractionrG.rH.}rI.(hUhjC.ubahjubhX (rJ.rK.}rL.(hUhj?.ubh)rM.}rN.(hUh}rO.(UreftypejzU reftargetXfloatrP.U refdomainj -h]h]U refexplicith]h]h]uhj?.h}]rQ.j})rR.}rS.(hjP.h}rT.(h]h]h]h]h]uhjM.h}]rU.hXfloatrV.rW.}rX.(hUhjR.ubahjubahjubhX)rY.}rZ.(hUhj?.ubhX -- r[.r\.}r].(hUhj?.ubhXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).r^.r_.}r`.(hXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).hj?.ubehjubahjdubehjubahjubehjubj)ra.}rb.(hUh}rc.(h]h]h]h]h]uhj-h}]rd.(j)re.}rf.(hUh}rg.(h]h]h]h]h]uhja.h}]rh.hXReturnsri.rj.}rk.(hUhje.ubahjubj)rl.}rm.(hUh}rn.(h]h]h]h]h]uhja.h}]ro.j)rp.}rq.(hUh}rr.(h]h]h]h]h]uhjl.h}]rs.hX\Matrix containing the norm distance of all pairs of spike trains given the smoothing_filter.rt.ru.}rv.(hX\Matrix containing the norm distance of all pairs of spike trains given the smoothing_filter.hjp.ubahjubahjubehjubj)rw.}rx.(hUh}ry.(h]h]h]h]h]uhj-h}]rz.(j)r{.}r|.(hUh}r}.(h]h]h]h]h]uhjw.h}]r~.hX Return typer.r.}r.(hUhj{.ubahjubj)r.}r.(hUh}r.(h]h]h]h]h]uhjw.h}]r.j)r.}r.(hUh}r.(h]h]h]h]h]uhj.h}]r.hX[Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)r.r.}r.(hX[Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)hj.ubahjubahjubehjubeubeubeubh)r.}r.(hUhj'hNhhh}r.(h]h]h]h]h]Uentries]r.(hXAschreiber_similarity() (in module spykeutils.spike_train_metrics)h6Utr.auhNhhh}]ubh)r.}r.(hUhj'hNhhh}r.(hωhXpyr.h]h]h]h]h]hXfunctionr.hj.uhNhhh}]r.(h)r.}r.(hX/schreiber_similarity(trains, kernel, sort=True)hj.hhhhh}r.(h]r.h6ahhXspykeutils.spike_train_metricsr.r.}r.bh]h]h]h]r.h6ahXschreiber_similarityr.hUhuhNhhh}]r.(h)r.}r.(hj.hj.hhhhh}r.(h]h]h]h]h]uhNhhh}]r.hXschreiber_similarityr.r.}r.(hUhj.ubaubjt)r.}r.(hUhj.hhhjwh}r.(h]h]h]h]h]uhNhhh}]r.(jz)r.}r.(hXtrainsh}r.(h]h]h]h]h]uhj.h}]r.hXtrainsr.r.}r.(hUhj.ubahjubjz)r.}r.(hXkernelh}r.(h]h]h]h]h]uhj.h}]r.hXkernelr.r.}r.(hUhj.ubahjubjz)r.}r.(hX sort=Trueh}r.(h]h]h]h]h]uhj.h}]r.hX sort=Truer.r.}r.(hUhj.ubahjubeubh)r.}r.(hUhj.hNhhh}r.(Uexprhh]h]h]h]h]uhNhhh}]r.h)r.}r.(hUh}r.(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj.uhj.h}]r.j)r.}r.(hUh}r.(h]h]r.j ah]h]h]uhj.h}]r.hX[source]r.r.}r.(hUhj.ubahjubahjubaubeubj)r.}r.(hUhj.hhhjh}r.(h]h]h]h]h]uhNhhh}]r.(j)r.}r.(hXWCalculates the Schreiber et al. similarity measure between spike trains given a kernel.r.hj.hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.schreiber_similarityr.hjh}r.(h]h]h]h]h]uhKhhh}]r.hXWCalculates the Schreiber et al. similarity measure between spike trains given a kernel.r.r.}r.(hj.hj.ubaubj)r.}r.(hXLet :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \in \mathcal{T}` be the spike trains convolved with some smoothing filter and :math:`V(a, b) = \int_{\mathcal{T}} v_a(t) v_b(t) dt`. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as :math:`S_{S}(a, b) = \frac{V(a, b)}{\sqrt{V(a, a) V(b, b)}}`. It is closely related to the Cauchy-Schwarz distance :math:`d_{CS}` by :math:`S_S = \sqrt{\cos d_{CS}}`.hj.hj.hjh}r.(h]h]h]h]h]uhKhhh}]r.(hXLet r.r.}r.(hXLet hj.ubjR)r.}r.(hUh}r.(UlatexXv_a(t)h]h]h]h]h]uhj.h}]hjVubhX and r.r.}r.(hX and hj.ubjR)r.}r.(hUh}r.(UlatexXv_b(t)h]h]h]h]h]uhj.h}]hjVubhX with r.r.}r.(hX with hj.ubjR)r.}r.(hUh}r.(UlatexXt \in \mathcal{T}h]h]h]h]h]uhj.h}]hjVubhX> be the spike trains convolved with some smoothing filter and r.r.}r.(hX> be the spike trains convolved with some smoothing filter and hj.ubjR)r.}r.(hUh}r.(UlatexX-V(a, b) = \int_{\mathcal{T}} v_a(t) v_b(t) dth]h]h]h]h]uhj.h}]hjVubhX. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as r.r.}r.(hX. The autocorrelation of the smoothing filter corresponds to the kernel used to analytically calculate the Schreiber et al. similarity measure. It is defined as hj.ubjR)r.}r.(hUh}r/(UlatexX4S_{S}(a, b) = \frac{V(a, b)}{\sqrt{V(a, a) V(b, b)}}h]h]h]h]h]uhj.h}]hjVubhX7. It is closely related to the Cauchy-Schwarz distance r/r/}r/(hX7. It is closely related to the Cauchy-Schwarz distance hj.ubjR)r/}r/(hUh}r/(UlatexXd_{CS}h]h]h]h]h]uhj.h}]hjVubhX by r/r/}r /(hX by hj.ubjR)r /}r /(hUh}r /(UlatexXS_S = \sqrt{\cos d_{CS}}h]h]h]h]h]uhj.h}]hjVubhX.r /}r/(hX.hj.ubeubj)r/}r/(hXIn opposite to :func:`cs_dist` which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter's autocorrelation. This allows a more accurate and faster calculation.hj.hj.hjh}r/(h]h]h]h]h]uhK hhh}]r/(hXIn opposite to r/r/}r/(hXIn opposite to hj/ubh)r/}r/(hX:func:`cs_dist`r/hj/hNhjh}r/(UreftypeXfuncj<j=Xcs_distU refdomainXpyr/h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r/jD)r/}r/(hj/h}r/(h]h]r/(jIj/Xpy-funcr /eh]h]h]uhj/h}]r!/hX cs_dist()r"/r#/}r$/(hUhj/ubahjOubaubhX which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter's autocorrelation. This allows a more accurate and faster calculation.r%/r&/}r'/(hX which numerically convolves the spike trains with a smoothing filter, this function directly uses the kernel resulting from the smoothing filter's autocorrelation. This allows a more accurate and faster calculation.hj/ubeubj)r(/}r)/(hX$Further information can be found in:r*/hj.hj.hjh}r+/(h]h]h]h]h]uhKhhh}]r,/hX$Further information can be found in:r-/r./}r//(hj*/hj(/ubaubj#)r0/}r1/(hUhj.hj.hjh}r2/(jKX-h]h]h]h]h]uhKhhh}]r3/(j()r4/}r5/(hX*Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.*hj0/hj.hjdh}r6/(h]h]h]h]h]uhNhhh}]r7/j)r8/}r9/(hX*Dauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.*r:/hj4/hj.hjh}r;/(h]h]h]h]h]uhKh}]r/(hj:/h}r?/(h]h]h]h]h]uhj8/h}]r@/hXDauwels, J., Vialatte, F., Weber, T., & Cichocki, A. (2009). On similarity measures for spike trains. Advances in Neuro-Information Processing, 177-185.rA/rB/}rC/(hUhj=/ubahjubaubaubj()rD/}rE/(hX*Paiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6* hj0/hj.hjdh}rF/(h]h]h]h]h]uhNhhh}]rG/j)rH/}rI/(hX*Paiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6*rJ/hjD/hj.hjh}rK/(h]h]h]h]h]uhKh}]rL/j})rM/}rN/(hjJ/h}rO/(h]h]h]h]h]uhjH/h}]rP/hXPaiva, A. R. C., Park, I., & Principe, J. C. (2009). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 405-419. doi:10.1007/s00521-009-0307-6rQ/rR/}rS/(hUhjM/ubahjubaubaubeubj)rT/}rU/(hUhj.hNhjh}rV/(h]h]h]h]h]uhNhhh}]rW/(j)rX/}rY/(hUh}rZ/(h]h]h]h]h]uhjT/h}]r[/(j)r\/}r]/(hUh}r^/(h]h]h]h]h]uhjX/h}]r_/hX Parametersr`/ra/}rb/(hUhj\/ubahjubj)rc/}rd/(hUh}re/(h]h]h]h]h]uhjX/h}]rf/j#)rg/}rh/(hUh}ri/(h]h]h]h]h]uhjc/h}]rj/(j()rk/}rl/(hUh}rm/(h]h]h]h]h]uhjg/h}]rn/j)ro/}rp/(hUh}rq/(h]h]h]h]h]uhjk/h}]rr/(j)rs/}rt/(hXtrainsh}ru/(h]h]h]h]h]uhjo/h}]rv/hXtrainsrw/rx/}ry/(hUhjs/ubahjubhX (rz/r{/}r|/(hUhjo/ubh)r}/}r~/(hUh}r/(UreftypejzU reftargetXsequencer/U refdomainj.h]h]U refexplicith]h]h]uhjo/h}]r/j})r/}r/(hj/h}r/(h]h]h]h]h]uhj}/h}]r/hXsequencer/r/}r/(hUhj/ubahjubahjubhX)r/}r/(hUhjo/ubhX -- r/r/}r/(hUhjo/ubhX Sequence of r/r/}r/(hX Sequence of hjo/ubh)r/}r/(hX:class:`neo.core.SpikeTrain`r/hjo/hNhjh}r/(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr/h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r/jD)r/}r/(hj/h}r/(h]h]r/(jIj/Xpy-classr/eh]h]h]uhj/h}]r/hXneo.core.SpikeTrainr/r/}r/(hUhj/ubahjOubaubhX; objects of which the distance will be calculated pairwise.r/r/}r/(hX; objects of which the distance will be calculated pairwise.hjo/ubehjubahjdubj()r/}r/(hUh}r/(h]h]h]h]h]uhjg/h}]r/j)r/}r/(hUh}r/(h]h]h]h]h]uhj/h}]r/(j)r/}r/(hXkernelh}r/(h]h]h]h]h]uhj/h}]r/hXkernelr/r/}r/(hUhj/ubahjubhX (r/r/}r/(hUhj/ubh)r/}r/(hX":class:`.signal_processing.Kernel`r/hj/hNhjh}r/(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr/h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r/jD)r/}r/(hj/h}r/(h]h]r/(jIj/Xpy-classr/eh]h]h]uhj/h}]r/hXsignal_processing.Kernelr/r/}r/(hUhj/ubahjOubaubhX)r/}r/(hUhj/ubhX -- r/r/}r/(hUhj/ubhXbKernel to use. It corresponds to a smoothing filter by being the autocorrelation of such a filter.r/r/}r/(hXbKernel to use. It corresponds to a smoothing filter by being the autocorrelation of such a filter.hj/ubehjubahjdubj()r/}r/(hUh}r/(h]h]h]h]h]uhjg/h}]r/j)r/}r/(hUh}r/(h]h]h]h]h]uhj/h}]r/(j)r/}r/(hXsorth}r/(h]h]h]h]h]uhj/h}]r/hXsortr/r/}r/(hUhj/ubahjubhX (r/r/}r/(hUhj/ubh)r/}r/(hUh}r/(UreftypejzU reftargetXboolr/U refdomainj.h]h]U refexplicith]h]h]uhj/h}]r/j})r/}r/(hj/h}r/(h]h]h]h]h]uhj/h}]r/hXboolr/r/}r/(hUhj/ubahjubahjubhX)r/}r/(hUhj/ubhX -- r/r/}r/(hUhj/ubhXUSpike trains with sorted spike times will be needed for the calculation. You can set r/r/}r/(hXUSpike trains with sorted spike times will be needed for the calculation. You can set hj/ubj)r/}r/(hX`sort`h}r/(h]h]h]h]h]uhj/h}]r/hXsortr/r/}r/(hUhj/ubahjubhX to r/r/}r/(hX to hj/ubj)r/}r/(hX`False`h}r/(h]h]h]h]h]uhj/h}]r/hXFalser0r0}r0(hUhj/ubahjubhXT if you know that your spike trains are already sorted to decrease calculation time.r0r0}r0(hXT if you know that your spike trains are already sorted to decrease calculation time.hj/ubehjubahjdubehjubahjubehjubj)r0}r0(hUh}r0(h]h]h]h]h]uhjT/h}]r 0(j)r 0}r 0(hUh}r 0(h]h]h]h]h]uhj0h}]r 0hXReturnsr0r0}r0(hUhj 0ubahjubj)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0j)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0hXWMatrix containing the Schreiber et al. similarity measure of all pairs of spike trains.r0r0}r0(hXWMatrix containing the Schreiber et al. similarity measure of all pairs of spike trains.hj0ubahjubahjubehjubj)r0}r0(hUh}r0(h]h]h]h]h]uhjT/h}]r0(j)r 0}r!0(hUh}r"0(h]h]h]h]h]uhj0h}]r#0hX Return typer$0r%0}r&0(hUhj 0ubahjubj)r'0}r(0(hUh}r)0(h]h]h]h]h]uhj0h}]r*0j)r+0}r,0(hUh}r-0(h]h]h]h]h]uhj'0h}]r.0hX 2-D arrayr/0r00}r10(hX 2-D arrayhj+0ubahjubahjubehjubeubeubeubh)r20}r30(hUhj'hNhhh}r40(h]h]h]h]h]Uentries]r50(hX5st_inner() (in module spykeutils.spike_train_metrics)h9Utr60auhNhhh}]ubh)r70}r80(hUhj'hNhhh}r90(hωhXpyr:0h]h]h]h]h]hXfunctionr;0hj;0uhNhhh}]r<0(h)r=0}r>0(hXMst_inner(a, b, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)hj70hhhhh}r?0(h]r@0h9ahhXspykeutils.spike_train_metricsrA0rB0}rC0bh]h]h]h]rD0h9ahXst_innerrE0hUhuhNhhh}]rF0(h)rG0}rH0(hjE0hj=0hhhhh}rI0(h]h]h]h]h]uhNhhh}]rJ0hXst_innerrK0rL0}rM0(hUhjG0ubaubjt)rN0}rO0(hUhj=0hhhjwh}rP0(h]h]h]h]h]uhNhhh}]rQ0(jz)rR0}rS0(hXah}rT0(h]h]h]h]h]uhjN0h}]rU0hXarV0}rW0(hUhjR0ubahjubjz)rX0}rY0(hXbh}rZ0(h]h]h]h]h]uhjN0h}]r[0hXbr\0}r]0(hUhjX0ubahjubjz)r^0}r_0(hXsmoothing_filterh}r`0(h]h]h]h]h]uhjN0h}]ra0hXsmoothing_filterrb0rc0}rd0(hUhj^0ubahjubjz)re0}rf0(hX sampling_rateh}rg0(h]h]h]h]h]uhjN0h}]rh0hX sampling_rateri0rj0}rk0(hUhje0ubahjubjz)rl0}rm0(hXfilter_area_fraction=0.99999h}rn0(h]h]h]h]h]uhjN0h}]ro0hXfilter_area_fraction=0.99999rp0rq0}rr0(hUhjl0ubahjubeubh)rs0}rt0(hUhj=0hNhhh}ru0(Uexprhh]h]h]h]h]uhNhhh}]rv0h)rw0}rx0(hUh}ry0(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidjE0uhjs0h}]rz0j)r{0}r|0(hUh}r}0(h]h]r~0j ah]h]h]uhjw0h}]r0hX[source]r0r0}r0(hUhj{0ubahjubahjubaubeubj)r0}r0(hUhj70hhhjh}r0(h]h]h]h]h]uhNhhh}]r0(j)r0}r0(hXFCalculates the inner product of spike trains given a smoothing filter.r0hj0hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.st_innerr0hjh}r0(h]h]h]h]h]uhKhhh}]r0hXFCalculates the inner product of spike trains given a smoothing filter.r0r0}r0(hj0hj0ubaubj)r0}r0(hXLet :math:`v_a(t)` and :math:`v_b(t)` with :math:`t \in \mathcal{T}` be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as :math:`\int_{\mathcal{T}} v_a(t)v_b(t) dt`.hj0hj0hjh}r0(h]h]h]h]h]uhKhhh}]r0(hXLet r0r0}r0(hXLet hj0ubjR)r0}r0(hUh}r0(UlatexXv_a(t)h]h]h]h]h]uhj0h}]hjVubhX and r0r0}r0(hX and hj0ubjR)r0}r0(hUh}r0(UlatexXv_b(t)h]h]h]h]h]uhj0h}]hjVubhX with r0r0}r0(hX with hj0ubjR)r0}r0(hUh}r0(UlatexXt \in \mathcal{T}h]h]h]h]h]uhj0h}]hjVubhXu be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as r0r0}r0(hXu be the spike trains convolved with some smoothing filter. Then, the inner product of the spike trains is defined as hj0ubjR)r0}r0(hUh}r0(UlatexX"\int_{\mathcal{T}} v_a(t)v_b(t) dth]h]h]h]h]uhj0h}]hjVubhX.r0}r0(hX.hj0ubeubj)r0}r0(hXFurther information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*hj0hj0hjh}r0(h]h]h]h]h]uhK hhh}]r0(hX$Further information can be found in r0r0}r0(hX$Further information can be found in hj0ubj})r0}r0(hX*Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*h}r0(h]h]h]h]h]uhj0h}]r0hXPaiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.r0r0}r0(hUhj0ubahjubeubj)r0}r0(hUhj0hNhjh}r0(h]h]h]h]h]uhNhhh}]r0(j)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0(j)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0hX Parametersr0r0}r0(hUhj0ubahjubj)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0j#)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0(j()r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0j)r0}r0(hUh}r0(h]h]h]h]h]uhj0h}]r0(j)r0}r0(hXah}r0(h]h]h]h]h]uhj0h}]r0hXar0}r0(hUhj0ubahjubhX (r0r0}r0(hUhj0ubh)r0}r0(hUh}r0(UreftypejzU reftargetXsequencer0U refdomainj:0h]h]U refexplicith]h]h]uhj0h}]r0j})r0}r0(hj0h}r0(h]h]h]h]h]uhj0h}]r0hXsequencer0r0}r0(hUhj0ubahjubahjubhX)r0}r0(hUhj0ubhX -- r0r0}r0(hUhj0ubhX Sequence of r0r0}r0(hX Sequence of hj0ubh)r0}r0(hX:class:`neo.core.SpikeTrain`r0hj0hNhjh}r0(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr0h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r0jD)r0}r0(hj0h}r1(h]h]r1(jIj0Xpy-classr1eh]h]h]uhj0h}]r1hXneo.core.SpikeTrainr1r1}r1(hUhj0ubahjOubaubhX objects.r1r1}r 1(hX objects.hj0ubehjubahjdubj()r 1}r 1(hUh}r 1(h]h]h]h]h]uhj0h}]r 1j)r1}r1(hUh}r1(h]h]h]h]h]uhj 1h}]r1(j)r1}r1(hXbh}r1(h]h]h]h]h]uhj1h}]r1hXbr1}r1(hUhj1ubahjubhX (r1r1}r1(hUhj1ubh)r1}r1(hUh}r1(UreftypejzU reftargetXsequencer1U refdomainj:0h]h]U refexplicith]h]h]uhj1h}]r1j})r 1}r!1(hj1h}r"1(h]h]h]h]h]uhj1h}]r#1hXsequencer$1r%1}r&1(hUhj 1ubahjubahjubhX)r'1}r(1(hUhj1ubhX -- r)1r*1}r+1(hUhj1ubhX Sequence of r,1r-1}r.1(hX Sequence of hj1ubh)r/1}r01(hX:class:`neo.core.SpikeTrain`r11hj1hNhjh}r21(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr31h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r41jD)r51}r61(hj11h}r71(h]h]r81(jIj31Xpy-classr91eh]h]h]uhj/1h}]r:1hXneo.core.SpikeTrainr;1r<1}r=1(hUhj51ubahjOubaubhX objects.r>1r?1}r@1(hX objects.hj1ubehjubahjdubj()rA1}rB1(hUh}rC1(h]h]h]h]h]uhj0h}]rD1j)rE1}rF1(hUh}rG1(h]h]h]h]h]uhjA1h}]rH1(j)rI1}rJ1(hXsmoothing_filterh}rK1(h]h]h]h]h]uhjE1h}]rL1hXsmoothing_filterrM1rN1}rO1(hUhjI1ubahjubhX (rP1rQ1}rR1(hUhjE1ubh)rS1}rT1(hX":class:`.signal_processing.Kernel`rU1hjE1hNhjh}rV1(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyrW1h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]rX1jD)rY1}rZ1(hjU1h}r[1(h]h]r\1(jIjW1Xpy-classr]1eh]h]h]uhjS1h}]r^1hXsignal_processing.Kernelr_1r`1}ra1(hUhjY1ubahjOubaubhX)rb1}rc1(hUhjE1ubhX -- rd1re1}rf1(hUhjE1ubhX9A smoothing filter to be convolved with the spike trains.rg1rh1}ri1(hX9A smoothing filter to be convolved with the spike trains.hjE1ubehjubahjdubj()rj1}rk1(hUh}rl1(h]h]h]h]h]uhj0h}]rm1j)rn1}ro1(hUh}rp1(h]h]h]h]h]uhjj1h}]rq1(j)rr1}rs1(hX sampling_rateh}rt1(h]h]h]h]h]uhjn1h}]ru1hX sampling_raterv1rw1}rx1(hUhjr1ubahjubhX (ry1rz1}r{1(hUhjn1ubh)r|1}r}1(hUh}r~1(UreftypejzU reftargetXQuantity scalarr1U refdomainj:0h]h]U refexplicith]h]h]uhjn1h}]r1j})r1}r1(hj1h}r1(h]h]h]h]h]uhj|1h}]r1hXQuantity scalarr1r1}r1(hUhj1ubahjubahjubhX)r1}r1(hUhjn1ubhX -- r1r1}r1(hUhjn1ubhXSThe sampling rate which will be used to bin the spike train as inverse time scalar.r1r1}r1(hXSThe sampling rate which will be used to bin the spike train as inverse time scalar.hjn1ubehjubahjdubj()r1}r1(hUh}r1(h]h]h]h]h]uhj0h}]r1j)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1(j)r1}r1(hXfilter_area_fractionh}r1(h]h]h]h]h]uhj1h}]r1hXfilter_area_fractionr1r1}r1(hUhj1ubahjubhX (r1r1}r1(hUhj1ubh)r1}r1(hUh}r1(UreftypejzU reftargetXfloatr1U refdomainj:0h]h]U refexplicith]h]h]uhj1h}]r1j})r1}r1(hj1h}r1(h]h]h]h]h]uhj1h}]r1hXfloatr1r1}r1(hUhj1ubahjubahjubhX)r1}r1(hUhj1ubhX -- r1r1}r1(hUhj1ubhXCA value between 0 and 1 which controls the interval over which the r1r1}r1(hXCA value between 0 and 1 which controls the interval over which the hj1ubj)r1}r1(hX`smoothing_filter`h}r1(h]h]h]h]h]uhj1h}]r1hXsmoothing_filterr1r1}r1(hUhj1ubahjubhXB will be discretized. At least the given fraction of the complete r1r1}r1(hXB will be discretized. At least the given fraction of the complete hj1ubj)r1}r1(hX`smoothing_filter`h}r1(h]h]h]h]h]uhj1h}]r1hXsmoothing_filterr1r1}r1(hUhj1ubahjubhXc area will be covered. Higher values can lead to more accurate results (besides the sampling rate).r1r1}r1(hXc area will be covered. Higher values can lead to more accurate results (besides the sampling rate).hj1ubehjubahjdubehjubahjubehjubj)r1}r1(hUh}r1(h]h]h]h]h]uhj0h}]r1(j)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1hXReturnsr1r1}r1(hUhj1ubahjubj)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1j)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1(hX\Matrix containing the inner product for each pair of spike trains with one spike train from r1r1}r1(hX\Matrix containing the inner product for each pair of spike trains with one spike train from hj1ubj)r1}r1(hX`a`h}r1(h]h]h]h]h]uhj1h}]r1hXar1}r1(hUhj1ubahjubhX and the other one from r1r1}r1(hX and the other one from hj1ubj)r1}r1(hX`b`h}r1(h]h]h]h]h]uhj1h}]r1hXbr1}r1(hUhj1ubahjubhX.r1}r1(hX.hj1ubehjubahjubehjubj)r1}r1(hUh}r1(h]h]h]h]h]uhj0h}]r1(j)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1hX Return typer1r1}r1(hUhj1ubahjubj)r1}r1(hUh}r1(h]h]h]h]h]uhj1h}]r1j)r2}r2(hUh}r2(h]h]h]h]h]uhj1h}]r2hX[Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)r2r2}r2(hX[Quantity 2D with units depending on the smoothing filter (usually temporal frequency units)hj2ubahjubahjubehjubeubeubeubh)r2}r2(hUhj'hNhhh}r 2(h]h]h]h]h]Uentries]r 2(hX4st_norm() (in module spykeutils.spike_train_metrics)hUtr 2auhNhhh}]ubh)r 2}r 2(hUhj'hNhhh}r2(hωhXpyr2h]h]h]h]h]hXfunctionr2hj2uhNhhh}]r2(h)r2}r2(hXMst_norm(train, smoothing_filter, sampling_rate, filter_area_fraction=0.99999)hj 2hhhhh}r2(h]r2hahhXspykeutils.spike_train_metricsr2r2}r2bh]h]h]h]r2hahXst_normr2hUhuhNhhh}]r2(h)r2}r2(hj2hj2hhhhh}r2(h]h]h]h]h]uhNhhh}]r2hXst_normr 2r!2}r"2(hUhj2ubaubjt)r#2}r$2(hUhj2hhhjwh}r%2(h]h]h]h]h]uhNhhh}]r&2(jz)r'2}r(2(hXtrainh}r)2(h]h]h]h]h]uhj#2h}]r*2hXtrainr+2r,2}r-2(hUhj'2ubahjubjz)r.2}r/2(hXsmoothing_filterh}r02(h]h]h]h]h]uhj#2h}]r12hXsmoothing_filterr22r32}r42(hUhj.2ubahjubjz)r52}r62(hX sampling_rateh}r72(h]h]h]h]h]uhj#2h}]r82hX sampling_rater92r:2}r;2(hUhj52ubahjubjz)r<2}r=2(hXfilter_area_fraction=0.99999h}r>2(h]h]h]h]h]uhj#2h}]r?2hXfilter_area_fraction=0.99999r@2rA2}rB2(hUhj<2ubahjubeubh)rC2}rD2(hUhj2hNhhh}rE2(Uexprhh]h]h]h]h]uhNhhh}]rF2h)rG2}rH2(hUh}rI2(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidj2uhjC2h}]rJ2j)rK2}rL2(hUh}rM2(h]h]rN2j ah]h]h]uhjG2h}]rO2hX[source]rP2rQ2}rR2(hUhjK2ubahjubahjubaubeubj)rS2}rT2(hUhj 2hhhjh}rU2(h]h]h]h]h]uhNhhh}]rV2(j)rW2}rX2(hX9Calculates the spike train norm given a smoothing filter.rY2hjS2hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.st_normrZ2hjh}r[2(h]h]h]h]h]uhKhhh}]r\2hX9Calculates the spike train norm given a smoothing filter.r]2r^2}r_2(hjY2hjW2ubaubj)r`2}ra2(hXLet :math:`v(t)` with :math:`t \in \mathcal{T}` be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as :math:`\int_{\mathcal{T}} v(t)^2 dt`.hjS2hjZ2hjh}rb2(h]h]h]h]h]uhKhhh}]rc2(hXLet rd2re2}rf2(hXLet hj`2ubjR)rg2}rh2(hUh}ri2(UlatexXv(t)h]h]h]h]h]uhj`2h}]hjVubhX with rj2rk2}rl2(hX with hj`2ubjR)rm2}rn2(hUh}ro2(UlatexXt \in \mathcal{T}h]h]h]h]h]uhj`2h}]hjVubhXh be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as rp2rq2}rr2(hXh be a spike train convolved with some smoothing filter. Then, the norm of the spike train is defined as hj`2ubjR)rs2}rt2(hUh}ru2(UlatexX\int_{\mathcal{T}} v(t)^2 dth]h]h]h]h]uhj`2h}]hjVubhX.rv2}rw2(hX.hj`2ubeubj)rx2}ry2(hXFurther information can be found in *Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*hjS2hjZ2hjh}rz2(h]h]h]h]h]uhKhhh}]r{2(hX$Further information can be found in r|2r}2}r~2(hX$Further information can be found in hjx2ubj})r2}r2(hX*Paiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.*h}r2(h]h]h]h]h]uhjx2h}]r2hXPaiva, A. R. C., Park, I., & Principe, J. (2010). Inner products for representation and learning in the spike train domain. Statistical Signal Processing for Neuroscience and Neurotechnology, Academic Press, New York.r2r2}r2(hUhj2ubahjubeubj)r2}r2(hUhjS2hNhjh}r2(h]h]h]h]h]uhNhhh}]r2(j)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2(j)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2hX Parametersr2r2}r2(hUhj2ubahjubj)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2j#)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2(j()r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2j)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2(j)r2}r2(hXtrainh}r2(h]h]h]h]h]uhj2h}]r2hXtrainr2r2}r2(hUhj2ubahjubhX (r2r2}r2(hUhj2ubh)r2}r2(hX:class:`neo.core.SpikeTrain`r2hj2hNhjh}r2(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr2h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r2jD)r2}r2(hj2h}r2(h]h]r2(jIj2Xpy-classr2eh]h]h]uhj2h}]r2hXneo.core.SpikeTrainr2r2}r2(hUhj2ubahjOubaubhX)r2}r2(hUhj2ubhX -- r2r2}r2(hUhj2ubhX+Spike train of which to calculate the norm.r2r2}r2(hX+Spike train of which to calculate the norm.hj2ubehjubahjdubj()r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2j)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2(j)r2}r2(hXsmoothing_filterh}r2(h]h]h]h]h]uhj2h}]r2hXsmoothing_filterr2r2}r2(hUhj2ubahjubhX (r2r2}r2(hUhj2ubh)r2}r2(hX":class:`.signal_processing.Kernel`r2hj2hNhjh}r2(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr2h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r2jD)r2}r2(hj2h}r2(h]h]r2(jIj2Xpy-classr2eh]h]h]uhj2h}]r2hXsignal_processing.Kernelr2r2}r2(hUhj2ubahjOubaubhX)r2}r2(hUhj2ubhX -- r2r2}r2(hUhj2ubhX6Smoothing filter to be convolved with the spike train.r2r2}r2(hX6Smoothing filter to be convolved with the spike train.hj2ubehjubahjdubj()r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2j)r2}r2(hUh}r2(h]h]h]h]h]uhj2h}]r2(j)r2}r2(hX sampling_rateh}r2(h]h]h]h]h]uhj2h}]r2hX sampling_rater2r2}r2(hUhj2ubahjubhX (r2r2}r3(hUhj2ubh)r3}r3(hUh}r3(UreftypejzU reftargetXQuantity scalarr3U refdomainj2h]h]U refexplicith]h]h]uhj2h}]r3j})r3}r3(hj3h}r3(h]h]h]h]h]uhj3h}]r 3hXQuantity scalarr 3r 3}r 3(hUhj3ubahjubahjubhX)r 3}r3(hUhj2ubhX -- r3r3}r3(hUhj2ubhXSThe sampling rate which will be used to bin the spike train as inverse time scalar.r3r3}r3(hXSThe sampling rate which will be used to bin the spike train as inverse time scalar.hj2ubehjubahjdubj()r3}r3(hUh}r3(h]h]h]h]h]uhj2h}]r3j)r3}r3(hUh}r3(h]h]h]h]h]uhj3h}]r3(j)r3}r3(hXfilter_area_fractionh}r3(h]h]h]h]h]uhj3h}]r 3hXfilter_area_fractionr!3r"3}r#3(hUhj3ubahjubhX (r$3r%3}r&3(hUhj3ubh)r'3}r(3(hUh}r)3(UreftypejzU reftargetXfloatr*3U refdomainj2h]h]U refexplicith]h]h]uhj3h}]r+3j})r,3}r-3(hj*3h}r.3(h]h]h]h]h]uhj'3h}]r/3hXfloatr03r13}r23(hUhj,3ubahjubahjubhX)r33}r43(hUhj3ubhX -- r53r63}r73(hUhj3ubhXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).r83r93}r:3(hXA value between 0 and 1 which controls the interval over which the smoothing filter will be discretized. At least the given fraction of the complete smoothing filter area will be covered. Higher values can lead to more accurate results (besides the sampling rate).hj3ubehjubahjdubehjubahjubehjubj)r;3}r<3(hUh}r=3(h]h]h]h]h]uhj2h}]r>3(j)r?3}r@3(hUh}rA3(h]h]h]h]h]uhj;3h}]rB3hXReturnsrC3rD3}rE3(hUhj?3ubahjubj)rF3}rG3(hUh}rH3(h]h]h]h]h]uhj;3h}]rI3j)rJ3}rK3(hUh}rL3(h]h]h]h]h]uhjF3h}]rM3hX7The norm of the spike train given the smoothing_filter.rN3rO3}rP3(hX7The norm of the spike train given the smoothing_filter.hjJ3ubahjubahjubehjubj)rQ3}rR3(hUh}rS3(h]h]h]h]h]uhj2h}]rT3(j)rU3}rV3(hUh}rW3(h]h]h]h]h]uhjQ3h}]rX3hX Return typerY3rZ3}r[3(hUhjU3ubahjubj)r\3}r]3(hUh}r^3(h]h]h]h]h]uhjQ3h}]r_3j)r`3}ra3(hUh}rb3(h]h]h]h]h]uhj\3h}]rc3hX_Quantity scalar with units depending on the smoothing filter (usually temporal frequency units)rd3re3}rf3(hX_Quantity scalar with units depending on the smoothing filter (usually temporal frequency units)hj`3ubahjubahjubehjubeubeubeubh)rg3}rh3(hUhj'hNhhh}ri3(h]h]h]h]h]Uentries]rj3(hX<van_rossum_dist() (in module spykeutils.spike_train_metrics)h:Utrk3auhNhhh}]ubh)rl3}rm3(hUhj'hNhhh}rn3(hωhXpyro3h]h]h]h]h]hXfunctionrp3hjp3uhNhhh}]rq3(h)rr3}rs3(hXCvan_rossum_dist(trains, tau=array(1.0) * s, kernel=None, sort=True)hjl3hhhhh}rt3(h]ru3h:ahhXspykeutils.spike_train_metricsrv3rw3}rx3bh]h]h]h]ry3h:ahXvan_rossum_distrz3hUhuhNhhh}]r{3(h)r|3}r}3(hjz3hjr3hhhhh}r~3(h]h]h]h]h]uhNhhh}]r3hXvan_rossum_distr3r3}r3(hUhj|3ubaubjt)r3}r3(hUhjr3hhhjwh}r3(h]h]h]h]h]uhNhhh}]r3(jz)r3}r3(hXtrainsh}r3(h]h]h]h]h]uhj3h}]r3hXtrainsr3r3}r3(hUhj3ubahjubjz)r3}r3(hXtau=array(1.0) * sh}r3(h]h]h]h]h]uhj3h}]r3hXtau=array(1.0) * sr3r3}r3(hUhj3ubahjubjz)r3}r3(hX kernel=Noneh}r3(h]h]h]h]h]uhj3h}]r3hX kernel=Noner3r3}r3(hUhj3ubahjubjz)r3}r3(hX sort=Trueh}r3(h]h]h]h]h]uhj3h}]r3hX sort=Truer3r3}r3(hUhj3ubahjubeubh)r3}r3(hUhjr3hNhhh}r3(Uexprhh]h]h]h]h]uhNhhh}]r3h)r3}r3(hUh}r3(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidjz3uhj3h}]r3j)r3}r3(hUh}r3(h]h]r3j ah]h]h]uhj3h}]r3hX[source]r3r3}r3(hUhj3ubahjubahjubaubeubj)r3}r3(hUhjl3hhhjh}r3(h]h]h]h]h]uhNhhh}]r3(j)r3}r3(hX#Calculates the van Rossum distance.r3hj3hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.van_rossum_distr3hjh}r3(h]h]h]h]h]uhKhhh}]r3hX#Calculates the van Rossum distance.r3r3}r3(hj3hj3ubaubj)r3}r3(hXIt is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.hj3hj3hjh}r3(h]h]h]h]h]uhKhhh}]r3(hXIt is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in r3r3}r3(hXIt is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in hj3ubj})r3}r3(hXV*Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.*h}r3(h]h]h]h]h]uhj3h}]r3hXTRossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.r3r3}r3(hUhj3ubahjubhX This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.r3r3}r3(hX This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.hj3ubeubj)r3}r3(hXGiven :math:`N` spike trains with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)`. An implementation in :math:`O(N^2 n)` would be possible but has a high constant factor rendering it slower in practical cases.hj3hj3hjh}r3(h]h]h]h]h]uhK hhh}]r3(hXGiven r3r3}r3(hXGiven hj3ubjR)r3}r3(hUh}r3(UlatexXNh]h]h]h]h]uhj3h}]hjVubhX spike trains with r3r3}r3(hX spike trains with hj3ubjR)r3}r3(hUh}r3(UlatexXnh]h]h]h]h]uhj3h}]hjVubhX? spikes on average the run-time complexity of this function is r3r3}r3(hX? spikes on average the run-time complexity of this function is hj3ubjR)r3}r3(hUh}r3(UlatexX O(N^2 n^2)h]h]h]h]h]uhj3h}]hjVubhX. An implementation in r3r3}r3(hX. An implementation in hj3ubjR)r3}r3(hUh}r3(UlatexXO(N^2 n)h]h]h]h]h]uhj3h}]hjVubhXY would be possible but has a high constant factor rendering it slower in practical cases.r3r3}r3(hXY would be possible but has a high constant factor rendering it slower in practical cases.hj3ubeubj)r3}r3(hUhj3hNhjh}r3(h]h]h]h]h]uhNhhh}]r3(j)r3}r3(hUh}r3(h]h]h]h]h]uhj3h}]r3(j)r3}r3(hUh}r3(h]h]h]h]h]uhj3h}]r3hX Parametersr3r3}r3(hUhj3ubahjubj)r3}r4(hUh}r4(h]h]h]h]h]uhj3h}]r4j#)r4}r4(hUh}r4(h]h]h]h]h]uhj3h}]r4(j()r4}r4(hUh}r 4(h]h]h]h]h]uhj4h}]r 4j)r 4}r 4(hUh}r 4(h]h]h]h]h]uhj4h}]r4(j)r4}r4(hXtrainsh}r4(h]h]h]h]h]uhj 4h}]r4hXtrainsr4r4}r4(hUhj4ubahjubhX (r4r4}r4(hUhj 4ubh)r4}r4(hUh}r4(UreftypejzU reftargetXsequencer4U refdomainjo3h]h]U refexplicith]h]h]uhj 4h}]r4j})r4}r4(hj4h}r 4(h]h]h]h]h]uhj4h}]r!4hXsequencer"4r#4}r$4(hUhj4ubahjubahjubhX)r%4}r&4(hUhj 4ubhX -- r'4r(4}r)4(hUhj 4ubhX Sequence of r*4r+4}r,4(hX Sequence of hj 4ubh)r-4}r.4(hX:class:`neo.core.SpikeTrain`r/4hj 4hNhjh}r04(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr14h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r24jD)r34}r44(hj/4h}r54(h]h]r64(jIj14Xpy-classr74eh]h]h]uhj-4h}]r84hXneo.core.SpikeTrainr94r:4}r;4(hUhj34ubahjOubaubhXF objects of which the van Rossum distance will be calculated pairwise.r<4r=4}r>4(hXF objects of which the van Rossum distance will be calculated pairwise.hj 4ubehjubahjdubj()r?4}r@4(hUh}rA4(h]h]h]h]h]uhj4h}]rB4j)rC4}rD4(hUh}rE4(h]h]h]h]h]uhj?4h}]rF4(j)rG4}rH4(hXtauh}rI4(h]h]h]h]h]uhjC4h}]rJ4hXtaurK4rL4}rM4(hUhjG4ubahjubhX (rN4rO4}rP4(hUhjC4ubh)rQ4}rR4(hUh}rS4(UreftypejzU reftargetXQuantity scalarrT4U refdomainjo3h]h]U refexplicith]h]h]uhjC4h}]rU4j})rV4}rW4(hjT4h}rX4(h]h]h]h]h]uhjQ4h}]rY4hXQuantity scalarrZ4r[4}r\4(hUhjV4ubahjubahjubhX)r]4}r^4(hUhjC4ubhX -- r_4r`4}ra4(hUhjC4ubhXDecay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if rb4rc4}rd4(hXDecay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if hjC4ubj)re4}rf4(hX`kernel`h}rg4(h]h]h]h]h]uhjC4h}]rh4hXkernelri4rj4}rk4(hUhje4ubahjubhX is not rl4rm4}rn4(hX is not hjC4ubj)ro4}rp4(hX`None`h}rq4(h]h]h]h]h]uhjC4h}]rr4hXNoners4rt4}ru4(hUhjo4ubahjubhX. May also be rv4rw4}rx4(hX. May also be hjC4ubh)ry4}rz4(hX:const:`scipy.inf`r{4hjC4hNhjh}r|4(UreftypeXconstj<j=X scipy.infU refdomainXpyr}4h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r~4jD)r4}r4(hj{4h}r4(h]h]r4(jIj}4Xpy-constr4eh]h]h]uhjy4h}]r4hX scipy.infr4r4}r4(hUhj4ubahjOubaubhX> which will lead to only measuring differences in spike count.r4r4}r4(hX> which will lead to only measuring differences in spike count.hjC4ubehjubahjdubj()r4}r4(hUh}r4(h]h]h]h]h]uhj4h}]r4j)r4}r4(hUh}r4(h]h]h]h]h]uhj4h}]r4(j)r4}r4(hXkernelh}r4(h]h]h]h]h]uhj4h}]r4hXkernelr4r4}r4(hUhj4ubahjubhX (r4r4}r4(hUhj4ubh)r4}r4(hX":class:`.signal_processing.Kernel`r4hj4hNhjh}r4(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr4h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r4jD)r4}r4(hj4h}r4(h]h]r4(jIj4Xpy-classr4eh]h]h]uhj4h}]r4hXsignal_processing.Kernelr4r4}r4(hUhj4ubahjOubaubhX)r4}r4(hUhj4ubhX -- r4r4}r4(hUhj4ubhXpKernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If r4r4}r4(hXpKernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If hj4ubj)r4}r4(hX`kernel`h}r4(h]h]h]h]h]uhj4h}]r4hXkernelr4r4}r4(hUhj4ubahjubhX is r4r4}r4(hX is hj4ubj)r4}r4(hX`None`h}r4(h]h]h]h]h]uhj4h}]r4hXNoner4r4}r4(hUhj4ubahjubhX2, an unnormalized Laplacian kernel with a size of r4r4}r4(hX2, an unnormalized Laplacian kernel with a size of hj4ubj)r4}r4(hX`tau`h}r4(h]h]h]h]h]uhj4h}]r4hXtaur4r4}r4(hUhj4ubahjubhX will be used.r4r4}r4(hX will be used.hj4ubehjubahjdubj()r4}r4(hUh}r4(h]h]h]h]h]uhj4h}]r4j)r4}r4(hUh}r4(h]h]h]h]h]uhj4h}]r4(j)r4}r4(hXsorth}r4(h]h]h]h]h]uhj4h}]r4hXsortr4r4}r4(hUhj4ubahjubhX (r4r4}r4(hUhj4ubh)r4}r4(hUh}r4(UreftypejzU reftargetXboolr4U refdomainjo3h]h]U refexplicith]h]h]uhj4h}]r4j})r4}r4(hj4h}r4(h]h]h]h]h]uhj4h}]r4hXboolr4r4}r4(hUhj4ubahjubahjubhX)r4}r4(hUhj4ubhX -- r4r4}r4(hUhj4ubhXVSpike trains with sorted spike times might be needed for the calculation. You can set r4r4}r4(hXVSpike trains with sorted spike times might be needed for the calculation. You can set hj4ubj)r4}r4(hX`sort`h}r4(h]h]h]h]h]uhj4h}]r4hXsortr4r4}r4(hUhj4ubahjubhX to r4r5}r5(hX to hj4ubj)r5}r5(hX`False`h}r5(h]h]h]h]h]uhj4h}]r5hXFalser5r5}r5(hUhj5ubahjubhXT if you know that your spike trains are already sorted to decrease calculation time.r 5r 5}r 5(hXT if you know that your spike trains are already sorted to decrease calculation time.hj4ubehjubahjdubehjubahjubehjubj)r 5}r 5(hUh}r5(h]h]h]h]h]uhj3h}]r5(j)r5}r5(hUh}r5(h]h]h]h]h]uhj 5h}]r5hXReturnsr5r5}r5(hUhj5ubahjubj)r5}r5(hUh}r5(h]h]h]h]h]uhj 5h}]r5j)r5}r5(hUh}r5(h]h]h]h]h]uhj5h}]r5hXIMatrix containing the van Rossum distances for all pairs of spike trains.r5r 5}r!5(hXIMatrix containing the van Rossum distances for all pairs of spike trains.hj5ubahjubahjubehjubj)r"5}r#5(hUh}r$5(h]h]h]h]h]uhj3h}]r%5(j)r&5}r'5(hUh}r(5(h]h]h]h]h]uhj"5h}]r)5hX Return typer*5r+5}r,5(hUhj&5ubahjubj)r-5}r.5(hUh}r/5(h]h]h]h]h]uhj"5h}]r05j)r15}r25(hUh}r35(h]h]h]h]h]uhj-5h}]r45hX 2-D arrayr55r65}r75(hX 2-D arrayhj15ubahjubahjubehjubeubeubeubh)r85}r95(hUhj'hNhhh}r:5(h]h]h]h]h]Uentries]r;5(hXFvan_rossum_multiunit_dist() (in module spykeutils.spike_train_metrics)h7Utr<5auhNhhh}]ubh)r=5}r>5(hUhj'hNhhh}r?5(hωhXpyr@5h]h]h]h]h]hXfunctionrA5hjA5uhNhhh}]rB5(h)rC5}rD5(hXLvan_rossum_multiunit_dist(units, weighting, tau=array(1.0) * s, kernel=None)hj=5hhhhh}rE5(h]rF5h7ahhXspykeutils.spike_train_metricsrG5rH5}rI5bh]h]h]h]rJ5h7ahXvan_rossum_multiunit_distrK5hUhuhNhhh}]rL5(h)rM5}rN5(hjK5hjC5hhhhh}rO5(h]h]h]h]h]uhNhhh}]rP5hXvan_rossum_multiunit_distrQ5rR5}rS5(hUhjM5ubaubjt)rT5}rU5(hUhjC5hhhjwh}rV5(h]h]h]h]h]uhNhhh}]rW5(jz)rX5}rY5(hXunitsh}rZ5(h]h]h]h]h]uhjT5h}]r[5hXunitsr\5r]5}r^5(hUhjX5ubahjubjz)r_5}r`5(hX weightingh}ra5(h]h]h]h]h]uhjT5h}]rb5hX weightingrc5rd5}re5(hUhj_5ubahjubjz)rf5}rg5(hXtau=array(1.0) * sh}rh5(h]h]h]h]h]uhjT5h}]ri5hXtau=array(1.0) * srj5rk5}rl5(hUhjf5ubahjubjz)rm5}rn5(hX kernel=Noneh}ro5(h]h]h]h]h]uhjT5h}]rp5hX kernel=Nonerq5rr5}rs5(hUhjm5ubahjubeubh)rt5}ru5(hUhjC5hNhhh}rv5(Uexprhh]h]h]h]h]uhNhhh}]rw5h)rx5}ry5(hUh}rz5(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidjK5uhjt5h}]r{5j)r|5}r}5(hUh}r~5(h]h]r5j ah]h]h]uhjx5h}]r5hX[source]r5r5}r5(hUhj|5ubahjubahjubaubeubj)r5}r5(hUhj=5hhhjh}r5(h]h]h]h]h]uhNhhh}]r5(j)r5}r5(hX.Calculates the van Rossum multi-unit distance.r5hj5hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.van_rossum_multiunit_distr5hjh}r5(h]h]h]h]h]uhKhhh}]r5hX.Calculates the van Rossum multi-unit distance.r5r5}r5(hj5hj5ubaubj)r5}r5(hXThe single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in *Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.* This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.hj5hj5hjh}r5(h]h]h]h]h]uhKhhh}]r5(hXThe single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in r5r5}r5(hXThe single-unit distance is defined as Euclidean distance of the spike trains convolved with a causal decaying exponential smoothing filter. A detailed description can be found in hj5ubj})r5}r5(hXV*Rossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.*h}r5(h]h]h]h]h]uhj5h}]r5hXTRossum, M. C. W. (2001). A novel spike distance. Neural Computation, 13(4), 751-763.r5r5}r5(hUhj5ubahjubhX This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.r5r5}r5(hX This implementation is normalized to yield a distance of 1.0 for the distance between an empty spike train and a spike train with a single spike. Divide the result by sqrt(2.0) to get the normalization used in the cited paper.hj5ubeubj)r5}r5(hXGiven the :math:`p`- and :math:`q`-th spike train of `a` and respectively `b` let :math:`R_{pq}` be the squared single-unit distance between these two spike trains. Then the multi-unit distance is :math:`\sqrt{\sum_p (R_{pp} + c \cdot \sum_{q \neq p} R_{pq})}` with :math:`c` being equal to `weighting`. The weighting parameter controls the interpolation between a labeled line and a summed population coding.hj5hj5hjh}r5(h]h]h]h]h]uhK hhh}]r5(hX Given the r5r5}r5(hX Given the hj5ubjR)r5}r5(hUh}r5(UlatexXph]h]h]h]h]uhj5h}]hjVubhX- and r5r5}r5(hX- and hj5ubjR)r5}r5(hUh}r5(UlatexXqh]h]h]h]h]uhj5h}]hjVubhX-th spike train of r5r5}r5(hX-th spike train of hj5ubj)r5}r5(hX`a`h}r5(h]h]h]h]h]uhj5h}]r5hXar5}r5(hUhj5ubahjubhX and respectively r5r5}r5(hX and respectively hj5ubj)r5}r5(hX`b`h}r5(h]h]h]h]h]uhj5h}]r5hXbr5}r5(hUhj5ubahjubhX let r5r5}r5(hX let hj5ubjR)r5}r5(hUh}r5(UlatexXR_{pq}h]h]h]h]h]uhj5h}]hjVubhXe be the squared single-unit distance between these two spike trains. Then the multi-unit distance is r5r5}r5(hXe be the squared single-unit distance between these two spike trains. Then the multi-unit distance is hj5ubjR)r5}r5(hUh}r5(UlatexX7\sqrt{\sum_p (R_{pp} + c \cdot \sum_{q \neq p} R_{pq})}h]h]h]h]h]uhj5h}]hjVubhX with r5r5}r5(hX with hj5ubjR)r5}r5(hUh}r5(UlatexXch]h]h]h]h]uhj5h}]hjVubhX being equal to r5r5}r5(hX being equal to hj5ubj)r5}r5(hX `weighting`h}r5(h]h]h]h]h]uhj5h}]r5hX weightingr5r5}r5(hUhj5ubahjubhXk. The weighting parameter controls the interpolation between a labeled line and a summed population coding.r5r5}r5(hXk. The weighting parameter controls the interpolation between a labeled line and a summed population coding.hj5ubeubj)r5}r5(hXMore information can be found in *Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.*hj5hj5hjh}r5(h]h]h]h]h]uhKhhh}]r5(hX!More information can be found in r5r5}r5(hX!More information can be found in hj5ubj})r5}r5(hX*Houghton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.*h}r5(h]h]h]h]h]uhj5h}]r5hXHoughton, C., & Kreuz, T. (2012). On the efficient calculation of van Rossum distances. Network: Computation in Neural Systems, 23(1-2), 48-58.r5r5}r5(hUhj5ubahjubeubj)r5}r5(hXGiven :math:`N` spike trains in total with :math:`n` spikes on average the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + Nn^2)` memory will be needed.hj5hj5hjh}r5(h]h]h]h]h]uhKhhh}]r5(hXGiven r5r5}r5(hXGiven hj5ubjR)r5}r5(hUh}r5(UlatexXNh]h]h]h]h]uhj5h}]hjVubhX spike trains in total with r5r5}r5(hX spike trains in total with hj5ubjR)r5}r5(hUh}r6(UlatexXnh]h]h]h]h]uhj5h}]hjVubhX? spikes on average the run-time complexity of this function is r6r6}r6(hX? spikes on average the run-time complexity of this function is hj5ubjR)r6}r6(hUh}r6(UlatexX O(N^2 n^2)h]h]h]h]h]uhj5h}]hjVubhX and r6r6}r 6(hX and hj5ubjR)r 6}r 6(hUh}r 6(UlatexX O(N^2 + Nn^2)h]h]h]h]h]uhj5h}]hjVubhX memory will be needed.r 6r6}r6(hX memory will be needed.hj5ubeubj)r6}r6(hUhj5hNhjh}r6(h]h]h]h]h]uhNhhh}]r6(j)r6}r6(hUh}r6(h]h]h]h]h]uhj6h}]r6(j)r6}r6(hUh}r6(h]h]h]h]h]uhj6h}]r6hX Parametersr6r6}r6(hUhj6ubahjubj)r6}r 6(hUh}r!6(h]h]h]h]h]uhj6h}]r"6j#)r#6}r$6(hUh}r%6(h]h]h]h]h]uhj6h}]r&6(j()r'6}r(6(hUh}r)6(h]h]h]h]h]uhj#6h}]r*6j)r+6}r,6(hUh}r-6(h]h]h]h]h]uhj'6h}]r.6(j)r/6}r06(hXunitsh}r16(h]h]h]h]h]uhj+6h}]r26hXunitsr36r46}r56(hUhj/6ubahjubhX (r66r76}r86(hUhj+6ubh)r96}r:6(hUh}r;6(UreftypejzU reftargetXdictr<6U refdomainj@5h]h]U refexplicith]h]h]uhj+6h}]r=6j})r>6}r?6(hj<6h}r@6(h]h]h]h]h]uhj96h}]rA6hXdictrB6rC6}rD6(hUhj>6ubahjubahjubhX)rE6}rF6(hUhj+6ubhX -- rG6rH6}rI6(hUhj+6ubhXeDictionary of sequences with each sequence containing the trials of one unit. Each trial should be a rJ6rK6}rL6(hXeDictionary of sequences with each sequence containing the trials of one unit. Each trial should be a hj+6ubh)rM6}rN6(hX:class:`neo.core.SpikeTrain`rO6hj+6hNhjh}rP6(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrQ6h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]rR6jD)rS6}rT6(hjO6h}rU6(h]h]rV6(jIjQ6Xpy-classrW6eh]h]h]uhjM6h}]rX6hXneo.core.SpikeTrainrY6rZ6}r[6(hUhjS6ubahjOubaubhX5 and all units should have the same number of trials.r\6r]6}r^6(hX5 and all units should have the same number of trials.hj+6ubehjubahjdubj()r_6}r`6(hUh}ra6(h]h]h]h]h]uhj#6h}]rb6j)rc6}rd6(hUh}re6(h]h]h]h]h]uhj_6h}]rf6(j)rg6}rh6(hX weightingh}ri6(h]h]h]h]h]uhjc6h}]rj6hX weightingrk6rl6}rm6(hUhjg6ubahjubhX (rn6ro6}rp6(hUhjc6ubh)rq6}rr6(hUh}rs6(UreftypejzU reftargetXfloatrt6U refdomainj@5h]h]U refexplicith]h]h]uhjc6h}]ru6j})rv6}rw6(hjt6h}rx6(h]h]h]h]h]uhjq6h}]ry6hXfloatrz6r{6}r|6(hUhjv6ubahjubahjubhX)r}6}r~6(hUhjc6ubhX -- r6r6}r6(hUhjc6ubhXQControls the interpolation between a labeled line and a summed population coding.r6r6}r6(hXQControls the interpolation between a labeled line and a summed population coding.hjc6ubehjubahjdubj()r6}r6(hUh}r6(h]h]h]h]h]uhj#6h}]r6j)r6}r6(hUh}r6(h]h]h]h]h]uhj6h}]r6(j)r6}r6(hXtauh}r6(h]h]h]h]h]uhj6h}]r6hXtaur6r6}r6(hUhj6ubahjubhX (r6r6}r6(hUhj6ubh)r6}r6(hUh}r6(UreftypejzU reftargetXQuantity scalarr6U refdomainj@5h]h]U refexplicith]h]h]uhj6h}]r6j})r6}r6(hj6h}r6(h]h]h]h]h]uhj6h}]r6hXQuantity scalarr6r6}r6(hUhj6ubahjubahjubhX)r6}r6(hUhj6ubhX -- r6r6}r6(hUhj6ubhXDecay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if r6r6}r6(hXDecay rate of the exponential function as time scalar. Controls for which time scale the metric will be sensitive. This parameter will be ignored if hj6ubj)r6}r6(hX`kernel`h}r6(h]h]h]h]h]uhj6h}]r6hXkernelr6r6}r6(hUhj6ubahjubhX is not r6r6}r6(hX is not hj6ubj)r6}r6(hX`None`h}r6(h]h]h]h]h]uhj6h}]r6hXNoner6r6}r6(hUhj6ubahjubhX. May also be r6r6}r6(hX. May also be hj6ubh)r6}r6(hX:const:`scipy.inf`r6hj6hNhjh}r6(UreftypeXconstj<j=X scipy.infU refdomainXpyr6h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r6jD)r6}r6(hj6h}r6(h]h]r6(jIj6Xpy-constr6eh]h]h]uhj6h}]r6hX scipy.infr6r6}r6(hUhj6ubahjOubaubhX> which will lead to only measuring differences in spike count.r6r6}r6(hX> which will lead to only measuring differences in spike count.hj6ubehjubahjdubj()r6}r6(hUh}r6(h]h]h]h]h]uhj#6h}]r6j)r6}r6(hUh}r6(h]h]h]h]h]uhj6h}]r6(j)r6}r6(hXkernelh}r6(h]h]h]h]h]uhj6h}]r6hXkernelr6r6}r6(hUhj6ubahjubhX (r6r6}r6(hUhj6ubh)r6}r6(hX":class:`.signal_processing.Kernel`r6hj6hNhjh}r6(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr6h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r6jD)r6}r6(hj6h}r6(h]h]r6(jIj6Xpy-classr6eh]h]h]uhj6h}]r6hXsignal_processing.Kernelr6r6}r6(hUhj6ubahjOubaubhX)r6}r6(hUhj6ubhX -- r6r6}r6(hUhj6ubhXpKernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If r6r6}r6(hXpKernel to use in the calculation of the distance. This is not the smoothing filter, but its autocorrelation. If hj6ubj)r6}r6(hX`kernel`h}r6(h]h]h]h]h]uhj6h}]r6hXkernelr6r6}r7(hUhj6ubahjubhX is r7r7}r7(hX is hj6ubj)r7}r7(hX`None`h}r7(h]h]h]h]h]uhj6h}]r7hXNoner7r 7}r 7(hUhj7ubahjubhX2, an unnormalized Laplacian kernel with a size of r 7r 7}r 7(hX2, an unnormalized Laplacian kernel with a size of hj6ubj)r7}r7(hX`tau`h}r7(h]h]h]h]h]uhj6h}]r7hXtaur7r7}r7(hUhj7ubahjubhX will be used.r7r7}r7(hX will be used.hj6ubehjubahjdubehjubahjubehjubj)r7}r7(hUh}r7(h]h]h]h]h]uhj6h}]r7(j)r7}r7(hUh}r7(h]h]h]h]h]uhj7h}]r7hXReturnsr 7r!7}r"7(hUhj7ubahjubj)r#7}r$7(hUh}r%7(h]h]h]h]h]uhj7h}]r&7j)r'7}r(7(hUh}r)7(h]h]h]h]h]uhj#7h}]r*7hX@A 2D array with the multi-unit distance for each pair of trials.r+7r,7}r-7(hX@A 2D array with the multi-unit distance for each pair of trials.hj'7ubahjubahjubehjubj)r.7}r/7(hUh}r07(h]h]h]h]h]uhj6h}]r17(j)r27}r37(hUh}r47(h]h]h]h]h]uhj.7h}]r57hX Return typer67r77}r87(hUhj27ubahjubj)r97}r:7(hUh}r;7(h]h]h]h]h]uhj.7h}]r<7j)r=7}r>7(hUh}r?7(h]h]h]h]h]uhj97h}]r@7hX 2D arraryrA7rB7}rC7(hX 2D arraryhj=7ubahjubahjubehjubeubeubeubh)rD7}rE7(hUhj'hNhhh}rF7(h]h]h]h]h]Uentries]rG7(hX@victor_purpura_dist() (in module spykeutils.spike_train_metrics)hXUtrH7auhNhhh}]ubh)rI7}rJ7(hUhj'hNhhh}rK7(hωhXpyrL7h]h]h]h]h]hXfunctionrM7hjM7uhNhhh}]rN7(h)rO7}rP7(hXFvictor_purpura_dist(trains, q=array(1.0) * Hz, kernel=None, sort=True)hjI7hhhhh}rQ7(h]rR7hXahhXspykeutils.spike_train_metricsrS7rT7}rU7bh]h]h]h]rV7hXahXvictor_purpura_distrW7hUhuhNhhh}]rX7(h)rY7}rZ7(hjW7hjO7hhhhh}r[7(h]h]h]h]h]uhNhhh}]r\7hXvictor_purpura_distr]7r^7}r_7(hUhjY7ubaubjt)r`7}ra7(hUhjO7hhhjwh}rb7(h]h]h]h]h]uhNhhh}]rc7(jz)rd7}re7(hXtrainsh}rf7(h]h]h]h]h]uhj`7h}]rg7hXtrainsrh7ri7}rj7(hUhjd7ubahjubjz)rk7}rl7(hXq=array(1.0) * Hzh}rm7(h]h]h]h]h]uhj`7h}]rn7hXq=array(1.0) * Hzro7rp7}rq7(hUhjk7ubahjubjz)rr7}rs7(hX kernel=Noneh}rt7(h]h]h]h]h]uhj`7h}]ru7hX kernel=Nonerv7rw7}rx7(hUhjr7ubahjubjz)ry7}rz7(hX sort=Trueh}r{7(h]h]h]h]h]uhj`7h}]r|7hX sort=Truer}7r~7}r7(hUhjy7ubahjubeubh)r7}r7(hUhjO7hNhhh}r7(Uexprhh]h]h]h]h]uhNhhh}]r7h)r7}r7(hUh}r7(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidjW7uhj7h}]r7j)r7}r7(hUh}r7(h]h]r7j ah]h]h]uhj7h}]r7hX[source]r7r7}r7(hUhj7ubahjubahjubaubeubj)r7}r7(hUhjI7hhhjh}r7(h]h]h]h]h]uhNhhh}]r7(j)r7}r7(hXbCalculates the Victor-Purpura's (VP) distance. It is often denoted as :math:`D^{\text{spike}}[q]`.hj7hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.victor_purpura_distr7hjh}r7(h]h]h]h]h]uhKhhh}]r7(hXFCalculates the Victor-Purpura's (VP) distance. It is often denoted as r7r7}r7(hXFCalculates the Victor-Purpura's (VP) distance. It is often denoted as hj7ubjR)r7}r7(hUh}r7(UlatexXD^{\text{spike}}[q]h]h]h]h]h]uhj7h}]hjVubhX.r7}r7(hX.hj7ubeubj)r7}r7(hXyIt is defined as the minimal cost of transforming spike train `a` into spike train `b` by using the following operations:hj7hj7hjh}r7(h]h]h]h]h]uhKhhh}]r7(hX>It is defined as the minimal cost of transforming spike train r7r7}r7(hX>It is defined as the minimal cost of transforming spike train hj7ubj)r7}r7(hX`a`h}r7(h]h]h]h]h]uhj7h}]r7hXar7}r7(hUhj7ubahjubhX into spike train r7r7}r7(hX into spike train hj7ubj)r7}r7(hX`b`h}r7(h]h]h]h]h]uhj7h}]r7hXbr7}r7(hUhj7ubahjubhX# by using the following operations:r7r7}r7(hX# by using the following operations:hj7ubeubcdocutils.nodes block_quote r7)r7}r7(hUhj7hNhU block_quoter7h}r7(h]h]h]h]h]uhNhhh}]r7j#)r7}r7(hUh}r7(jKX*h]h]h]h]h]uhj7h}]r7(j()r7}r7(hX)Inserting or deleting a spike (cost 1.0).r7h}r7(h]h]h]h]h]uhj7h}]r7j)r7}r7(hj7hj7hj7hjh}r7(h]h]h]h]h]uhKh}]r7hX)Inserting or deleting a spike (cost 1.0).r7r7}r7(hj7hj7ubaubahjdubj()r7}r7(hXOShifting a spike from :math:`t` to :math:`t'` (cost :math:`q \cdot |t - t'|`). h}r7(h]h]h]h]h]uhj7h}]r7j)r7}r7(hXNShifting a spike from :math:`t` to :math:`t'` (cost :math:`q \cdot |t - t'|`).hj7hj7hjh}r7(h]h]h]h]h]uhKh}]r7(hXShifting a spike from r7r7}r7(hXShifting a spike from hj7ubjR)r7}r7(hUh}r7(UlatexXth]h]h]h]h]uhj7h}]hjVubhX to r7r7}r7(hX to hj7ubjR)r7}r7(hUh}r7(UlatexXt'h]h]h]h]h]uhj7h}]hjVubhX (cost r7r7}r7(hX (cost hj7ubjR)r7}r7(hUh}r7(UlatexXq \cdot |t - t'|h]h]h]h]h]uhj7h}]hjVubhX).r7r7}r7(hX).hj7ubeubahjdubehjubaubj)r7}r7(hXA detailed description can be found in *Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.*hj7hj7hjh}r7(h]h]h]h]h]uhK hhh}]r7(hX'A detailed description can be found in r7r7}r7(hX'A detailed description can be found in hj7ubj})r7}r7(hX*Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.*h}r7(h]h]h]h]h]uhj7h}]r7hXVictor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology.r7r7}r7(hUhj7ubahjubeubj)r7}r7(hXGiven the average number of spikes :math:`n` in a spike train and :math:`N` spike trains the run-time complexity of this function is :math:`O(N^2 n^2)` and :math:`O(N^2 + n^2)` memory will be needed.hj7hj7hjh}r7(h]h]h]h]h]uhKhhh}]r7(hX#Given the average number of spikes r7r8}r8(hX#Given the average number of spikes hj7ubjR)r8}r8(hUh}r8(UlatexXnh]h]h]h]h]uhj7h}]hjVubhX in a spike train and r8r8}r8(hX in a spike train and hj7ubjR)r8}r 8(hUh}r 8(UlatexXNh]h]h]h]h]uhj7h}]hjVubhX: spike trains the run-time complexity of this function is r 8r 8}r 8(hX: spike trains the run-time complexity of this function is hj7ubjR)r8}r8(hUh}r8(UlatexX O(N^2 n^2)h]h]h]h]h]uhj7h}]hjVubhX and r8r8}r8(hX and hj7ubjR)r8}r8(hUh}r8(UlatexX O(N^2 + n^2)h]h]h]h]h]uhj7h}]hjVubhX memory will be needed.r8r8}r8(hX memory will be needed.hj7ubeubj)r8}r8(hUhj7hNhjh}r8(h]h]h]h]h]uhNhhh}]r8(j)r8}r8(hUh}r 8(h]h]h]h]h]uhj8h}]r!8(j)r"8}r#8(hUh}r$8(h]h]h]h]h]uhj8h}]r%8hX Parametersr&8r'8}r(8(hUhj"8ubahjubj)r)8}r*8(hUh}r+8(h]h]h]h]h]uhj8h}]r,8j#)r-8}r.8(hUh}r/8(h]h]h]h]h]uhj)8h}]r08(j()r18}r28(hUh}r38(h]h]h]h]h]uhj-8h}]r48j)r58}r68(hUh}r78(h]h]h]h]h]uhj18h}]r88(j)r98}r:8(hXtrainsh}r;8(h]h]h]h]h]uhj58h}]r<8hXtrainsr=8r>8}r?8(hUhj98ubahjubhX (r@8rA8}rB8(hUhj58ubh)rC8}rD8(hUh}rE8(UreftypejzU reftargetXsequencerF8U refdomainjL7h]h]U refexplicith]h]h]uhj58h}]rG8j})rH8}rI8(hjF8h}rJ8(h]h]h]h]h]uhjC8h}]rK8hXsequencerL8rM8}rN8(hUhjH8ubahjubahjubhX)rO8}rP8(hUhj58ubhX -- rQ8rR8}rS8(hUhj58ubhX Sequence of rT8rU8}rV8(hX Sequence of hj58ubh)rW8}rX8(hX:class:`neo.core.SpikeTrain`rY8hj58hNhjh}rZ8(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr[8h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r\8jD)r]8}r^8(hjY8h}r_8(h]h]r`8(jIj[8Xpy-classra8eh]h]h]uhjW8h}]rb8hXneo.core.SpikeTrainrc8rd8}re8(hUhj]8ubahjOubaubhX; objects of which the distance will be calculated pairwise.rf8rg8}rh8(hX; objects of which the distance will be calculated pairwise.hj58ubehjubahjdubj()ri8}rj8(hUh}rk8(h]h]h]h]h]uhj-8h}]rl8j)rm8}rn8(hUh}ro8(h]h]h]h]h]uhji8h}]rp8(j)rq8}rr8(hXqh}rs8(h]h]h]h]h]uhjm8h}]rt8hXqru8}rv8(hUhjq8ubahjubhX (rw8rx8}ry8(hUhjm8ubh)rz8}r{8(hUh}r|8(UreftypejzU reftargetXQuantity scalarr}8U refdomainjL7h]h]U refexplicith]h]h]uhjm8h}]r~8j})r8}r8(hj}8h}r8(h]h]h]h]h]uhjz8h}]r8hXQuantity scalarr8r8}r8(hUhj8ubahjubahjubhX)r8}r8(hUhjm8ubhX -- r8r8}r8(hUhjm8ubhX8Cost factor for spike shifts as inverse time scalar. If r8r8}r8(hX8Cost factor for spike shifts as inverse time scalar. If hjm8ubj)r8}r8(hX`kernel`h}r8(h]h]h]h]h]uhjm8h}]r8hXkernelr8r8}r8(hUhj8ubahjubhX is not r8r8}r8(hX is not hjm8ubj)r8}r8(hX`None`h}r8(h]h]h]h]h]uhjm8h}]r8hXNoner8r8}r8(hUhj8ubahjubhX, r8r8}r8(hX, hjm8ubj)r8}r8(hX`q`h}r8(h]h]h]h]h]uhjm8h}]r8hXqr8}r8(hUhj8ubahjubhX will be ignored.r8r8}r8(hX will be ignored.hjm8ubehjubahjdubj()r8}r8(hUh}r8(h]h]h]h]h]uhj-8h}]r8j)r8}r8(hUh}r8(h]h]h]h]h]uhj8h}]r8(j)r8}r8(hXkernelh}r8(h]h]h]h]h]uhj8h}]r8hXkernelr8r8}r8(hUhj8ubahjubhX (r8r8}r8(hUhj8ubh)r8}r8(hX":class:`.signal_processing.Kernel`r8hj8hNhjh}r8(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr8h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r8jD)r8}r8(hj8h}r8(h]h]r8(jIj8Xpy-classr8eh]h]h]uhj8h}]r8hXsignal_processing.Kernelr8r8}r8(hUhj8ubahjOubaubhX)r8}r8(hUhj8ubhX -- r8r8}r8(hUhj8ubhX5Kernel to use in the calculation of the distance. If r8r8}r8(hX5Kernel to use in the calculation of the distance. If hj8ubj)r8}r8(hX`kernel`h}r8(h]h]h]h]h]uhj8h}]r8hXkernelr8r8}r8(hUhj8ubahjubhX is r8r8}r8(hX is hj8ubj)r8}r8(hX`None`h}r8(h]h]h]h]h]uhj8h}]r8hXNoner8r8}r8(hUhj8ubahjubhX9, an unnormalized triangular kernel with a half width of r8r8}r8(hX9, an unnormalized triangular kernel with a half width of hj8ubj)r8}r8(hX`2.0/q`h}r8(h]h]h]h]h]uhj8h}]r8hX2.0/qr8r8}r8(hUhj8ubahjubhX will be used.r8r8}r8(hX will be used.hj8ubehjubahjdubj()r8}r8(hUh}r8(h]h]h]h]h]uhj-8h}]r8j)r8}r8(hUh}r8(h]h]h]h]h]uhj8h}]r8(j)r8}r8(hXsorth}r8(h]h]h]h]h]uhj8h}]r8hXsortr8r8}r9(hUhj8ubahjubhX (r9r9}r9(hUhj8ubh)r9}r9(hUh}r9(UreftypejzU reftargetXboolr9U refdomainjL7h]h]U refexplicith]h]h]uhj8h}]r9j})r 9}r 9(hj9h}r 9(h]h]h]h]h]uhj9h}]r 9hXboolr 9r9}r9(hUhj 9ubahjubahjubhX)r9}r9(hUhj8ubhX -- r9r9}r9(hUhj8ubhXUSpike trains with sorted spike times will be needed for the calculation. You can set r9r9}r9(hXUSpike trains with sorted spike times will be needed for the calculation. You can set hj8ubj)r9}r9(hX`sort`h}r9(h]h]h]h]h]uhj8h}]r9hXsortr9r9}r9(hUhj9ubahjubhX to r9r 9}r!9(hX to hj8ubj)r"9}r#9(hX`False`h}r$9(h]h]h]h]h]uhj8h}]r%9hXFalser&9r'9}r(9(hUhj"9ubahjubhXT if you know that your spike trains are already sorted to decrease calculation time.r)9r*9}r+9(hXT if you know that your spike trains are already sorted to decrease calculation time.hj8ubehjubahjdubehjubahjubehjubj)r,9}r-9(hUh}r.9(h]h]h]h]h]uhj8h}]r/9(j)r09}r19(hUh}r29(h]h]h]h]h]uhj,9h}]r39hXReturnsr49r59}r69(hUhj09ubahjubj)r79}r89(hUh}r99(h]h]h]h]h]uhj,9h}]r:9j)r;9}r<9(hUh}r=9(h]h]h]h]h]uhj79h}]r>9hX?Matrix containing the VP distance of all pairs of spike trains.r?9r@9}rA9(hX?Matrix containing the VP distance of all pairs of spike trains.hj;9ubahjubahjubehjubj)rB9}rC9(hUh}rD9(h]h]h]h]h]uhj8h}]rE9(j)rF9}rG9(hUh}rH9(h]h]h]h]h]uhjB9h}]rI9hX Return typerJ9rK9}rL9(hUhjF9ubahjubj)rM9}rN9(hUh}rO9(h]h]h]h]h]uhjB9h}]rP9j)rQ9}rR9(hUh}rS9(h]h]h]h]h]uhjM9h}]rT9hX 2-D arrayrU9rV9}rW9(hX 2-D arrayhjQ9ubahjubahjubehjubeubeubeubh)rX9}rY9(hUhj'hNhhh}rZ9(h]h]h]h]h]Uentries]r[9(hXJvictor_purpura_multiunit_dist() (in module spykeutils.spike_train_metrics)h&Utr\9auhNhhh}]ubh)r]9}r^9(hUhj'hNhhh}r_9(hωhXpyr`9h]h]h]h]h]hXfunctionra9hja9uhNhhh}]rb9(h)rc9}rd9(hXWvictor_purpura_multiunit_dist(units, reassignment_cost, q=array(1.0) * Hz, kernel=None)hj]9hhhhh}re9(h]rf9h&ahhXspykeutils.spike_train_metricsrg9rh9}ri9bh]h]h]h]rj9h&ahXvictor_purpura_multiunit_distrk9hUhuhNhhh}]rl9(h)rm9}rn9(hjk9hjc9hhhhh}ro9(h]h]h]h]h]uhNhhh}]rp9hXvictor_purpura_multiunit_distrq9rr9}rs9(hUhjm9ubaubjt)rt9}ru9(hUhjc9hhhjwh}rv9(h]h]h]h]h]uhNhhh}]rw9(jz)rx9}ry9(hXunitsh}rz9(h]h]h]h]h]uhjt9h}]r{9hXunitsr|9r}9}r~9(hUhjx9ubahjubjz)r9}r9(hXreassignment_costh}r9(h]h]h]h]h]uhjt9h}]r9hXreassignment_costr9r9}r9(hUhj9ubahjubjz)r9}r9(hXq=array(1.0) * Hzh}r9(h]h]h]h]h]uhjt9h}]r9hXq=array(1.0) * Hzr9r9}r9(hUhj9ubahjubjz)r9}r9(hX kernel=Noneh}r9(h]h]h]h]h]uhjt9h}]r9hX kernel=Noner9r9}r9(hUhj9ubahjubeubh)r9}r9(hUhjc9hNhhh}r9(Uexprhh]h]h]h]h]uhNhhh}]r9h)r9}r9(hUh}r9(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX'_modules/spykeutils/spike_train_metricsUrefidjk9uhj9h}]r9j)r9}r9(hUh}r9(h]h]r9j ah]h]h]uhj9h}]r9hX[source]r9r9}r9(hUhj9ubahjubahjubaubeubj)r9}r9(hUhj]9hhhjh}r9(h]h]h]h]h]uhNhhh}]r9(j)r9}r9(hX9Calculates the Victor-Purpura's (VP) multi-unit distance.r9hj9hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/spike_train_metrics.py:docstring of spykeutils.spike_train_metrics.victor_purpura_multiunit_distr9hjh}r9(h]h]h]h]h]uhKhhh}]r9hX9Calculates the Victor-Purpura's (VP) multi-unit distance.r9r9}r9(hj9hj9ubaubj)r9}r9(hXIt is defined as the minimal cost of transforming the spike trains `a` into spike trains `b` by using the following operations:hj9hj9hjh}r9(h]h]h]h]h]uhKhhh}]r9(hXCIt is defined as the minimal cost of transforming the spike trains r9r9}r9(hXCIt is defined as the minimal cost of transforming the spike trains hj9ubj)r9}r9(hX`a`h}r9(h]h]h]h]h]uhj9h}]r9hXar9}r9(hUhj9ubahjubhX into spike trains r9r9}r9(hX into spike trains hj9ubj)r9}r9(hX`b`h}r9(h]h]h]h]h]uhj9h}]r9hXbr9}r9(hUhj9ubahjubhX# by using the following operations:r9r9}r9(hX# by using the following operations:hj9ubeubj7)r9}r9(hUhj9hNhj7h}r9(h]h]h]h]h]uhNhhh}]r9j#)r9}r9(hUh}r9(jKX*h]h]h]h]h]uhj9h}]r9(j()r9}r9(hX)Inserting or deleting a spike (cost 1.0).r9h}r9(h]h]h]h]h]uhj9h}]r9j)r9}r9(hj9hj9hj9hjh}r9(h]h]h]h]h]uhKh}]r9hX)Inserting or deleting a spike (cost 1.0).r9r9}r9(hj9hj9ubaubahjdubj()r9}r9(hXNShifting a spike from :math:`t` to :math:`t'` (cost :math:`q \cdot |t - t'|`).h}r9(h]h]h]h]h]uhj9h}]r9j)r9}r9(hXNShifting a spike from :math:`t` to :math:`t'` (cost :math:`q \cdot |t - t'|`).hj9hj9hjh}r9(h]h]h]h]h]uhKh}]r9(hXShifting a spike from r9r9}r9(hXShifting a spike from hj9ubjR)r9}r9(hUh}r9(UlatexXth]h]h]h]h]uhj9h}]hjVubhX to r9r9}r9(hX to hj9ubjR)r9}r9(hUh}r9(UlatexXt'h]h]h]h]h]uhj9h}]hjVubhX (cost r9r9}r9(hX (cost hj9ubjR)r9}r9(hUh}r9(UlatexXq \cdot |t - t'|h]h]h]h]h]uhj9h}]hjVubhX).r9r9}r9(hX).hj9ubeubahjdubj()r9}r9(hXBMoving a spike to another spike train (cost `reassignment_cost`). h}r9(h]h]h]h]h]uhj9h}]r9j)r9}r:(hXAMoving a spike to another spike train (cost `reassignment_cost`).hj9hj9hjh}r:(h]h]h]h]h]uhK h}]r:(hX,Moving a spike to another spike train (cost r:r:}r:(hX,Moving a spike to another spike train (cost hj9ubj)r:}r:(hX`reassignment_cost`h}r:(h]h]h]h]h]uhj9h}]r :hXreassignment_costr :r :}r :(hUhj:ubahjubhX).r :r:}r:(hX).hj9ubeubahjdubehjubaubj)r:}r:(hXA detailed description can be found in *Aronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.*hj9hj9hjh}r:(h]h]h]h]h]uhK hhh}]r:(hX'A detailed description can be found in r:r:}r:(hX'A detailed description can be found in hj:ubj})r:}r:(hX*Aronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.*h}r:(h]h]h]h]h]uhj:h}]r:hXAronov, D. (2003). Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. Journal of Neuroscience Methods.r:r:}r:(hUhj:ubahjubeubj)r:}r:(hXGiven the average number of spikes :math:`N` in a spike train and :math:`L` units with :math:`n` spike trains each the run-time complexity is :math:`O(n^2 LN^{L+1})`. The space complexity is :math:`O(n^2 + LN^{L+1})`.hj9hj9hjh}r :(h]h]h]h]h]uhKhhh}]r!:(hX#Given the average number of spikes r":r#:}r$:(hX#Given the average number of spikes hj:ubjR)r%:}r&:(hUh}r':(UlatexXNh]h]h]h]h]uhj:h}]hjVubhX in a spike train and r(:r):}r*:(hX in a spike train and hj:ubjR)r+:}r,:(hUh}r-:(UlatexXLh]h]h]h]h]uhj:h}]hjVubhX units with r.:r/:}r0:(hX units with hj:ubjR)r1:}r2:(hUh}r3:(UlatexXnh]h]h]h]h]uhj:h}]hjVubhX. spike trains each the run-time complexity is r4:r5:}r6:(hX. spike trains each the run-time complexity is hj:ubjR)r7:}r8:(hUh}r9:(UlatexXO(n^2 LN^{L+1})h]h]h]h]h]uhj:h}]hjVubhX. The space complexity is r::r;:}r<:(hX. The space complexity is hj:ubjR)r=:}r>:(hUh}r?:(UlatexXO(n^2 + LN^{L+1})h]h]h]h]h]uhj:h}]hjVubhX.r@:}rA:(hX.hj:ubeubj)rB:}rC:(hXwFor calculating the distance between only two units one should use :func:`victor_purpura_dist` which is more efficient.hj9hj9hjh}rD:(h]h]h]h]h]uhKhhh}]rE:(hXCFor calculating the distance between only two units one should use rF:rG:}rH:(hXCFor calculating the distance between only two units one should use hjB:ubh)rI:}rJ:(hX:func:`victor_purpura_dist`rK:hjB:hNhjh}rL:(UreftypeXfuncj<j=Xvictor_purpura_distU refdomainXpyrM:h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]rN:jD)rO:}rP:(hjK:h}rQ:(h]h]rR:(jIjM:Xpy-funcrS:eh]h]h]uhjI:h}]rT:hXvictor_purpura_dist()rU:rV:}rW:(hUhjO:ubahjOubaubhX which is more efficient.rX:rY:}rZ:(hX which is more efficient.hjB:ubeubj)r[:}r\:(hUhj9hNhjh}r]:(h]h]h]h]h]uhNhhh}]r^:(j)r_:}r`:(hUh}ra:(h]h]h]h]h]uhj[:h}]rb:(j)rc:}rd:(hUh}re:(h]h]h]h]h]uhj_:h}]rf:hX Parametersrg:rh:}ri:(hUhjc:ubahjubj)rj:}rk:(hUh}rl:(h]h]h]h]h]uhj_:h}]rm:j#)rn:}ro:(hUh}rp:(h]h]h]h]h]uhjj:h}]rq:(j()rr:}rs:(hUh}rt:(h]h]h]h]h]uhjn:h}]ru:j)rv:}rw:(hUh}rx:(h]h]h]h]h]uhjr:h}]ry:(j)rz:}r{:(hXunitsh}r|:(h]h]h]h]h]uhjv:h}]r}:hXunitsr~:r:}r:(hUhjz:ubahjubhX (r:r:}r:(hUhjv:ubh)r:}r:(hUh}r:(UreftypejzU reftargetXdictr:U refdomainj`9h]h]U refexplicith]h]h]uhjv:h}]r:j})r:}r:(hj:h}r:(h]h]h]h]h]uhj:h}]r:hXdictr:r:}r:(hUhj:ubahjubahjubhX)r:}r:(hUhjv:ubhX -- r:r:}r:(hUhjv:ubhXeDictionary of sequences with each sequence containing the trials of one unit. Each trial should be a r:r:}r:(hXeDictionary of sequences with each sequence containing the trials of one unit. Each trial should be a hjv:ubh)r:}r:(hX:class:`neo.core.SpikeTrain`r:hjv:hNhjh}r:(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr:h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r:jD)r:}r:(hj:h}r:(h]h]r:(jIj:Xpy-classr:eh]h]h]uhj:h}]r:hXneo.core.SpikeTrainr:r:}r:(hUhj:ubahjOubaubhX5 and all units should have the same number of trials.r:r:}r:(hX5 and all units should have the same number of trials.hjv:ubehjubahjdubj()r:}r:(hUh}r:(h]h]h]h]h]uhjn:h}]r:j)r:}r:(hUh}r:(h]h]h]h]h]uhj:h}]r:(j)r:}r:(hXreassignment_costh}r:(h]h]h]h]h]uhj:h}]r:hXreassignment_costr:r:}r:(hUhj:ubahjubhX (r:r:}r:(hUhj:ubh)r:}r:(hUh}r:(UreftypejzU reftargetXfloatr:U refdomainj`9h]h]U refexplicith]h]h]uhj:h}]r:j})r:}r:(hj:h}r:(h]h]h]h]h]uhj:h}]r:hXfloatr:r:}r:(hUhj:ubahjubahjubhX)r:}r:(hUhj:ubhX -- r:r:}r:(hUhj:ubhXKCost to reassign a spike from one train to another (sometimes denoted with r:r:}r:(hXKCost to reassign a spike from one train to another (sometimes denoted with hj:ubjR)r:}r:(hUh}r:(UlatexXkh]h]h]h]h]uhj:h}]hjVubhX). Should be between 0 and 2. For 0 spikes can be reassigned without any cost, for 2 and above it is cheaper to delete and reinsert a spike.r:r:}r:(hX). Should be between 0 and 2. For 0 spikes can be reassigned without any cost, for 2 and above it is cheaper to delete and reinsert a spike.hj:ubehjubahjdubj()r:}r:(hUh}r:(h]h]h]h]h]uhjn:h}]r:j)r:}r:(hUh}r:(h]h]h]h]h]uhj:h}]r:(j)r:}r:(hXqh}r:(h]h]h]h]h]uhj:h}]r:hXqr:}r:(hUhj:ubahjubhX (r:r:}r:(hUhj:ubh)r:}r:(hUh}r:(UreftypejzU reftargetXQuantity scalarr:U refdomainj`9h]h]U refexplicith]h]h]uhj:h}]r:j})r:}r:(hj:h}r:(h]h]h]h]h]uhj:h}]r:hXQuantity scalarr:r:}r:(hUhj:ubahjubahjubhX)r:}r:(hUhj:ubhX -- r:r:}r:(hUhj:ubhX8Cost factor for spike shifts as inverse time scalar. If r:r:}r:(hX8Cost factor for spike shifts as inverse time scalar. If hj:ubj)r:}r:(hX`kernel`h}r:(h]h]h]h]h]uhj:h}]r:hXkernelr:r;}r;(hUhj:ubahjubhX is not r;r;}r;(hX is not hj:ubj)r;}r;(hX`None`h}r;(h]h]h]h]h]uhj:h}]r;hXNoner ;r ;}r ;(hUhj;ubahjubhX, r ;r ;}r;(hX, hj:ubj)r;}r;(hX`q`h}r;(h]h]h]h]h]uhj:h}]r;hXqr;}r;(hUhj;ubahjubhX will be ignored.r;r;}r;(hX will be ignored.hj:ubehjubahjdubj()r;}r;(hUh}r;(h]h]h]h]h]uhjn:h}]r;j)r;}r;(hUh}r;(h]h]h]h]h]uhj;h}]r;(j)r ;}r!;(hXkernelh}r";(h]h]h]h]h]uhj;h}]r#;hXkernelr$;r%;}r&;(hUhj ;ubahjubhX (r';r(;}r);(hUhj;ubh)r*;}r+;(hX":class:`.signal_processing.Kernel`r,;hj;hNhjh}r-;(UreftypeXclassjj<j=Xsignal_processing.KernelU refdomainXpyr.;h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r/;jD)r0;}r1;(hj,;h}r2;(h]h]r3;(jIj.;Xpy-classr4;eh]h]h]uhj*;h}]r5;hXsignal_processing.Kernelr6;r7;}r8;(hUhj0;ubahjOubaubhX)r9;}r:;(hUhj;ubhX -- r;;r<;}r=;(hUhj;ubhX5Kernel to use in the calculation of the distance. If r>;r?;}r@;(hX5Kernel to use in the calculation of the distance. If hj;ubj)rA;}rB;(hX`kernel`h}rC;(h]h]h]h]h]uhj;h}]rD;hXkernelrE;rF;}rG;(hUhjA;ubahjubhX is rH;rI;}rJ;(hX is hj;ubj)rK;}rL;(hX`None`h}rM;(h]h]h]h]h]uhj;h}]rN;hXNonerO;rP;}rQ;(hUhjK;ubahjubhX9, an unnormalized triangular kernel with a half width of rR;rS;}rT;(hX9, an unnormalized triangular kernel with a half width of hj;ubj)rU;}rV;(hX`2.0/q`h}rW;(h]h]h]h]h]uhj;h}]rX;hX2.0/qrY;rZ;}r[;(hUhjU;ubahjubhX will be used.r\;r];}r^;(hX will be used.hj;ubehjubahjdubehjubahjubehjubj)r_;}r`;(hUh}ra;(h]h]h]h]h]uhj[:h}]rb;(j)rc;}rd;(hUh}re;(h]h]h]h]h]uhj_;h}]rf;hXReturnsrg;rh;}ri;(hUhjc;ubahjubj)rj;}rk;(hUh}rl;(h]h]h]h]h]uhj_;h}]rm;j)rn;}ro;(hUh}rp;(h]h]h]h]h]uhjj;h}]rq;hX@A 2D array with the multi-unit distance for each pair of trials.rr;rs;}rt;(hX@A 2D array with the multi-unit distance for each pair of trials.ru;hjn;ubahjubahjubehjubj)rv;}rw;(hUh}rx;(h]h]h]h]h]uhj[:h}]ry;(j)rz;}r{;(hUh}r|;(h]h]h]h]h]uhjv;h}]r};hX Return typer~;r;}r;(hUhjz;ubahjubj)r;}r;(hUh}r;(h]h]h]h]h]uhjv;h}]r;j)r;}r;(hUh}r;(h]h]h]h]h]uhj;h}]r;hX 2D arraryr;r;}r;(hX 2D arraryr;hj;ubahjubahjubehjubeubeubeubeubh)r;}r;(hUhhhj-hhh}r;(h]h]h]h]r;(X+module-spykeutils.sorting_quality_assesmentr;hoeh]r;h auhK6hhh}]r;(h)r;}r;(hX':mod:`sorting_quality_assesment` Moduler;hj;hj-hhh}r;(h]h]h]h]h]uhK6hhh}]r;(h)r;}r;(hX :mod:`sorting_quality_assesment`r;hj;hNhjh}r;(UreftypeXmodj<j=Xsorting_quality_assesmentU refdomainXpyr;h]h]U refexplicith]h]h]j?jj@NjAj(uhNh}]r;jD)r;}r;(hj;h}r;(h]h]r;(jIj;Xpy-modr;eh]h]h]uhj;h}]r;hXsorting_quality_assesmentr;r;}r;(hUhj;ubahjOubaubhX Moduler;r;}r;(hX Moduler;hj;ubeubh)r;}r;(hUhj;hhhhh}r;(h]h]h]h]h]Uentries]r;(hX-spykeutils.sorting_quality_assesment (module)X+module-spykeutils.sorting_quality_assesmentUtr;auhKhhh}]ubj)r;}r;(hXFunctions for estimating the quality of spike sorting results. These functions estimate false positive and false negative fractions.r;hj;hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/sorting_quality_assesment.py:docstring of spykeutils.sorting_quality_assesmenthjh}r;(h]h]h]h]h]uhKhhh}]r;hXFunctions for estimating the quality of spike sorting results. These functions estimate false positive and false negative fractions.r;r;}r;(hj;hj;ubaubh)r;}r;(hUhj;hNhhh}r;(h]h]h]h]h]Uentries]r;(hXIcalculate_refperiod_fp() (in module spykeutils.sorting_quality_assesment)hLUtr;auhNhhh}]ubh)r;}r;(hUhj;hNhhh}r;(hωhXpyr;h]h]h]h]h]hXfunctionr;hj;uhNhhh}]r;(h)r;}r;(hXEcalculate_refperiod_fp(num_spikes, refperiod, violations, total_time)hj;hhhhh}r;(h]r;hLahhX$spykeutils.sorting_quality_assesmentr;r;}r;bh]h]h]h]r;hLahXcalculate_refperiod_fpr;hUhuhNhhh}]r;(h)r;}r;(hj;hj;hhhhh}r;(h]h]h]h]h]uhNhhh}]r;hXcalculate_refperiod_fpr;r;}r;(hUhj;ubaubjt)r;}r;(hUhj;hhhjwh}r;(h]h]h]h]h]uhNhhh}]r;(jz)r;}r;(hX num_spikesh}r;(h]h]h]h]h]uhj;h}]r;hX num_spikesr;r;}r;(hUhj;ubahjubjz)r;}r;(hX refperiodh}r;(h]h]h]h]h]uhj;h}]r;hX refperiodr;r;}r;(hUhj;ubahjubjz)r;}r;(hX violationsh}r;(h]h]h]h]h]uhj;h}]r;hX violationsr;r;}r;(hUhj;ubahjubjz)r;}r;(hX total_timeh}r;(h]h]h]h]h]uhj;h}]r;hX total_timer;r;}r;(hUhj;ubahjubeubh)r;}r;(hUhj;hNhhh}r;(Uexprhh]h]h]h]h]uhNhhh}]r;h)r;}r;(hUh}r;(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX-_modules/spykeutils/sorting_quality_assesmentUrefidj;uhj;h}]r;j)r;}r;(hUh}r;(h]h]r<j ah]h]h]uhj;h}]r<hX[source]r<r<}r<(hUhj;ubahjubahjubaubeubj)r<}r<(hUhj;hhhjh}r<(h]h]h]h]h]uhNhhh}]r<(j)r <}r <(hXReturn the rate of false positives calculated from refractory period calculations for each unit. The equation used is described in (Hill et al. The Journal of Neuroscience. 2011).r <hj<hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/sorting_quality_assesment.py:docstring of spykeutils.sorting_quality_assesment.calculate_refperiod_fphjh}r <(h]h]h]h]h]uhKhhh}]r <hXReturn the rate of false positives calculated from refractory period calculations for each unit. The equation used is described in (Hill et al. The Journal of Neuroscience. 2011).r<r<}r<(hj <hj <ubaubj)r<}r<(hUhj<hNhjh}r<(h]h]h]h]h]uhNhhh}]r<(j)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<(j)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<hX Parametersr<r<}r<(hUhj<ubahjubj)r <}r!<(hUh}r"<(h]h]h]h]h]uhj<h}]r#<j#)r$<}r%<(hUh}r&<(h]h]h]h]h]uhj <h}]r'<(j()r(<}r)<(hUh}r*<(h]h]h]h]h]uhj$<h}]r+<j)r,<}r-<(hUh}r.<(h]h]h]h]h]uhj(<h}]r/<(j)r0<}r1<(hX num_spikesh}r2<(h]h]h]h]h]uhj,<h}]r3<hX num_spikesr4<r5<}r6<(hUhj0<ubahjubhX (r7<r8<}r9<(hUhj,<ubh)r:<}r;<(hUh}r<<(UreftypejzU reftargetXdictr=<U refdomainj;h]h]U refexplicith]h]h]uhj,<h}]r><j})r?<}r@<(hj=<h}rA<(h]h]h]h]h]uhj:<h}]rB<hXdictrC<rD<}rE<(hUhj?<ubahjubahjubhX)rF<}rG<(hUhj,<ubhX -- rH<rI<}rJ<(hUhj,<ubhX6Dictionary of total number of spikes, indexed by unit.rK<rL<}rM<(hX6Dictionary of total number of spikes, indexed by unit.hj,<ubehjubahjdubj()rN<}rO<(hUh}rP<(h]h]h]h]h]uhj$<h}]rQ<j)rR<}rS<(hUh}rT<(h]h]h]h]h]uhjN<h}]rU<(j)rV<}rW<(hX refperiodh}rX<(h]h]h]h]h]uhjR<h}]rY<hX refperiodrZ<r[<}r\<(hUhjV<ubahjubhX (r]<r^<}r_<(hUhjR<ubh)r`<}ra<(hUh}rb<(UreftypejzU reftargetXQuantity scalarrc<U refdomainj;h]h]U refexplicith]h]h]uhjR<h}]rd<j})re<}rf<(hjc<h}rg<(h]h]h]h]h]uhj`<h}]rh<hXQuantity scalarri<rj<}rk<(hUhje<ubahjubahjubhX)rl<}rm<(hUhjR<ubhX -- rn<ro<}rp<(hUhjR<ubhXThe refractory period (time). If the spike sorting algorithm includes a censored period (a time after a spike during which no new spikes can be found), subtract it from the refractory period before passing it to this function.rq<rr<}rs<(hXThe refractory period (time). If the spike sorting algorithm includes a censored period (a time after a spike during which no new spikes can be found), subtract it from the refractory period before passing it to this function.hjR<ubehjubahjdubj()rt<}ru<(hUh}rv<(h]h]h]h]h]uhj$<h}]rw<j)rx<}ry<(hUh}rz<(h]h]h]h]h]uhjt<h}]r{<(j)r|<}r}<(hX violationsh}r~<(h]h]h]h]h]uhjx<h}]r<hX violationsr<r<}r<(hUhj|<ubahjubhX (r<r<}r<(hUhjx<ubh)r<}r<(hUh}r<(UreftypejzU reftargetXdictr<U refdomainj;h]h]U refexplicith]h]h]uhjx<h}]r<j})r<}r<(hj<h}r<(h]h]h]h]h]uhj<h}]r<hXdictr<r<}r<(hUhj<ubahjubahjubhX)r<}r<(hUhjx<ubhX -- r<r<}r<(hUhjx<ubhXIDictionary of total number of violations, indexed the same as num_spikes.r<r<}r<(hXIDictionary of total number of violations, indexed the same as num_spikes.hjx<ubehjubahjdubj()r<}r<(hUh}r<(h]h]h]h]h]uhj$<h}]r<j)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<(j)r<}r<(hX total_timeh}r<(h]h]h]h]h]uhj<h}]r<hX total_timer<r<}r<(hUhj<ubahjubhX (r<r<}r<(hUhj<ubh)r<}r<(hUh}r<(UreftypejzU reftargetXQuantity scalarr<U refdomainj;h]h]U refexplicith]h]h]uhj<h}]r<j})r<}r<(hj<h}r<(h]h]h]h]h]uhj<h}]r<hXQuantity scalarr<r<}r<(hUhj<ubahjubahjubhX)r<}r<(hUhj<ubhX -- r<r<}r<(hUhj<ubhX6The total time in which violations could have occured.r<r<}r<(hX6The total time in which violations could have occured.hj<ubehjubahjdubehjubahjubehjubj)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<(j)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<hXReturnsr<r<}r<(hUhj<ubahjubj)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<j)r<}r<(hUh}r<(h]h]h]h]h]uhj<h}]r<hXA dictionary of false positive rates indexed by unit. Note that values above 0.5 can not be directly interpreted as a false positive rate! These very high values can e.g. indicate that the generating processes are not independent.r<r<}r<(hXA dictionary of false positive rates indexed by unit. Note that values above 0.5 can not be directly interpreted as a false positive rate! These very high values can e.g. indicate that the generating processes are not independent.hj<ubahjubahjubehjubeubeubeubh)r<}r<(hUhj;hNhhh}r<(h]h]h]h]h]Uentries]r<(hXKget_refperiod_violations() (in module spykeutils.sorting_quality_assesment)hUtr<auhNhhh}]ubh)r<}r<(hUhj;hNhhh}r<(hωhXpyr<h]h]h]h]h]hXfunctionr<hj<uhNhhh}]r<(h)r<}r<(hX@get_refperiod_violations(spike_trains, refperiod, progress=None)hj<hhhhh}r<(h]r<hahhX$spykeutils.sorting_quality_assesmentr<r<}r<bh]h]h]h]r<hahXget_refperiod_violationsr<hUhuhNhhh}]r<(h)r<}r<(hj<hj<hhhhh}r<(h]h]h]h]h]uhNhhh}]r<hXget_refperiod_violationsr<r<}r<(hUhj<ubaubjt)r<}r<(hUhj<hhhjwh}r<(h]h]h]h]h]uhNhhh}]r<(jz)r<}r<(hX spike_trainsh}r<(h]h]h]h]h]uhj<h}]r<hX spike_trainsr<r<}r<(hUhj<ubahjubjz)r<}r<(hX refperiodh}r<(h]h]h]h]h]uhj<h}]r=hX refperiodr=r=}r=(hUhj<ubahjubjz)r=}r=(hX progress=Noneh}r=(h]h]h]h]h]uhj<h}]r=hX progress=Noner=r =}r =(hUhj=ubahjubeubh)r =}r =(hUhj<hNhhh}r =(Uexprhh]h]h]h]h]uhNhhh}]r=h)r=}r=(hUh}r=(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX-_modules/spykeutils/sorting_quality_assesmentUrefidj<uhj =h}]r=j)r=}r=(hUh}r=(h]h]r=j ah]h]h]uhj=h}]r=hX[source]r=r=}r=(hUhj=ubahjubahjubaubeubj)r=}r=(hUhj<hhhjh}r=(h]h]h]h]h]uhNhhh}]r=(j)r=}r =(hXfReturn the refractory period violations in the given spike trains for the specified refractory period.r!=hj=hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/sorting_quality_assesment.py:docstring of spykeutils.sorting_quality_assesment.get_refperiod_violationsr"=hjh}r#=(h]h]h]h]h]uhKhhh}]r$=hXfReturn the refractory period violations in the given spike trains for the specified refractory period.r%=r&=}r'=(hj!=hj=ubaubj)r(=}r)=(hUhj=hNhjh}r*=(h]h]h]h]h]uhNhhh}]r+=(j)r,=}r-=(hUh}r.=(h]h]h]h]h]uhj(=h}]r/=(j)r0=}r1=(hUh}r2=(h]h]h]h]h]uhj,=h}]r3=hX Parametersr4=r5=}r6=(hUhj0=ubahjubj)r7=}r8=(hUh}r9=(h]h]h]h]h]uhj,=h}]r:=j#)r;=}r<=(hUh}r==(h]h]h]h]h]uhj7=h}]r>=(j()r?=}r@=(hUh}rA=(h]h]h]h]h]uhj;=h}]rB=j)rC=}rD=(hUh}rE=(h]h]h]h]h]uhj?=h}]rF=(j)rG=}rH=(hX spike_trainsh}rI=(h]h]h]h]h]uhjC=h}]rJ=hX spike_trainsrK=rL=}rM=(hUhjG=ubahjubhX (rN=rO=}rP=(hUhjC=ubh)rQ=}rR=(hUh}rS=(UreftypejzU reftargetXdictrT=U refdomainj<h]h]U refexplicith]h]h]uhjC=h}]rU=j})rV=}rW=(hjT=h}rX=(h]h]h]h]h]uhjQ=h}]rY=hXdictrZ=r[=}r\=(hUhjV=ubahjubahjubhX)r]=}r^=(hUhjC=ubhX -- r_=r`=}ra=(hUhjC=ubhXDictionary of lists of rb=rc=}rd=(hXDictionary of lists of hjC=ubh)re=}rf=(hX:class:`neo.core.SpikeTrain`rg=hjC=hNhjh}rh=(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyri=h]h]U refexplicith]h]h]j?jj@NjAX$spykeutils.sorting_quality_assesmentrj=uhNh}]rk=jD)rl=}rm=(hjg=h}rn=(h]h]ro=(jIji=Xpy-classrp=eh]h]h]uhje=h}]rq=hXneo.core.SpikeTrainrr=rs=}rt=(hUhjl=ubahjOubaubhX objects.ru=rv=}rw=(hX objects.hjC=ubehjubahjdubj()rx=}ry=(hUh}rz=(h]h]h]h]h]uhj;=h}]r{=j)r|=}r}=(hUh}r~=(h]h]h]h]h]uhjx=h}]r=(j)r=}r=(hX refperiodh}r=(h]h]h]h]h]uhj|=h}]r=hX refperiodr=r=}r=(hUhj=ubahjubhX (r=r=}r=(hUhj|=ubh)r=}r=(hUh}r=(UreftypejzU reftargetXQuantity scalarr=U refdomainj<h]h]U refexplicith]h]h]uhj|=h}]r=j})r=}r=(hj=h}r=(h]h]h]h]h]uhj=h}]r=hXQuantity scalarr=r=}r=(hUhj=ubahjubahjubhX)r=}r=(hUhj|=ubhX -- r=r=}r=(hUhj|=ubhXThe refractory period (time).r=r=}r=(hXThe refractory period (time).hj|=ubehjubahjdubj()r=}r=(hUh}r=(h]h]h]h]h]uhj;=h}]r=j)r=}r=(hUh}r=(h]h]h]h]h]uhj=h}]r=(j)r=}r=(hXprogressh}r=(h]h]h]h]h]uhj=h}]r=hXprogressr=r=}r=(hUhj=ubahjubhX (r=r=}r=(hUhj=ubh)r=}r=(hX.:class:`.progress_indicator.ProgressIndicator`r=hj=hNhjh}r=(UreftypeXclassjj<j=X$progress_indicator.ProgressIndicatorU refdomainXpyr=h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r=jD)r=}r=(hj=h}r=(h]h]r=(jIj=Xpy-classr=eh]h]h]uhj=h}]r=hX$progress_indicator.ProgressIndicatorr=r=}r=(hUhj=ubahjOubaubhX)r=}r=(hUhj=ubhX -- r=r=}r=(hUhj=ubhX&Set this parameter to report progress.r=r=}r=(hX&Set this parameter to report progress.hj=ubehjubahjdubehjubahjubehjubj)r=}r=(hUh}r=(h]h]h]h]h]uhj(=h}]r=(j)r=}r=(hUh}r=(h]h]h]h]h]uhj=h}]r=hXReturnsr=r=}r=(hUhj=ubahjubj)r=}r=(hUh}r=(h]h]h]h]h]uhj=h}]r=j)r=}r=(hUh}r=(h]h]h]h]h]uhj=h}]r=(j)r=}r=(hX Two values:r=hj=hj"=hjh}r=(h]h]h]h]h]uhK h}]r=hX Two values:r=r=}r=(hj=hj=ubaubj#)r=}r=(hUh}r=(jKX*h]h]h]h]h]uhj=h}]r=(j()r=}r=(hXThe total number of violations.r=h}r=(h]h]h]h]h]uhj=h}]r=j)r=}r=(hj=hj=hj"=hjh}r=(h]h]h]h]h]uhK h}]r=hXThe total number of violations.r=r=}r=(hj=hj=ubaubahjdubj()r=}r=(hXA dictionary (with the same indices as ``spike_trains``) of arrays with violation times (Quantity 1D with the same unit as ``refperiod``) for each spike train.h}r=(h]h]h]h]h]uhj=h}]r=j)r=}r=(hXA dictionary (with the same indices as ``spike_trains``) of arrays with violation times (Quantity 1D with the same unit as ``refperiod``) for each spike train.hj=hj"=hjh}r=(h]h]h]h]h]uhK h}]r=(hX'A dictionary (with the same indices as r=r=}r=(hX'A dictionary (with the same indices as hj=ubjD)r=}r=(hX``spike_trains``h}r=(h]h]h]h]h]uhj=h}]r>hX spike_trainsr>r>}r>(hUhj=ubahjOubhXD) of arrays with violation times (Quantity 1D with the same unit as r>r>}r>(hXD) of arrays with violation times (Quantity 1D with the same unit as hj=ubjD)r>}r>(hX ``refperiod``h}r >(h]h]h]h]h]uhj=h}]r >hX refperiodr >r >}r >(hUhj>ubahjOubhX) for each spike train.r>r>}r>(hX) for each spike train.hj=ubeubahjdubehjubehjubahjubehjubj)r>}r>(hUh}r>(h]h]h]h]h]uhj(=h}]r>(j)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>hX Return typer>r>}r>(hUhj>ubahjubj)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>j)r >}r!>(hUh}r">(h]h]h]h]h]uhj>h}]r#>hX int, dictr$>r%>}r&>(hX int, dicthj >ubahjubahjubehjubeubeubeubh)r'>}r(>(hUhj;hNhhh}r)>(h]h]h]h]h]Uentries]r*>(hX@overlap_fp_fn() (in module spykeutils.sorting_quality_assesment)h#Utr+>auhNhhh}]ubh)r,>}r->(hUhj;hNhhh}r.>(hωhXpyr/>h]h]h]h]h]hXfunctionr0>hj0>uhNhhh}]r1>(h)r2>}r3>(hX3overlap_fp_fn(spikes, means=None, covariances=None)hj,>hhhhh}r4>(h]r5>h#ahhX$spykeutils.sorting_quality_assesmentr6>r7>}r8>bh]h]h]h]r9>h#ahX overlap_fp_fnr:>hUhuhNhhh}]r;>(h)r<>}r=>(hj:>hj2>hhhhh}r>>(h]h]h]h]h]uhNhhh}]r?>hX overlap_fp_fnr@>rA>}rB>(hUhj<>ubaubjt)rC>}rD>(hUhj2>hhhjwh}rE>(h]h]h]h]h]uhNhhh}]rF>(jz)rG>}rH>(hXspikesh}rI>(h]h]h]h]h]uhjC>h}]rJ>hXspikesrK>rL>}rM>(hUhjG>ubahjubjz)rN>}rO>(hX means=Noneh}rP>(h]h]h]h]h]uhjC>h}]rQ>hX means=NonerR>rS>}rT>(hUhjN>ubahjubjz)rU>}rV>(hXcovariances=Noneh}rW>(h]h]h]h]h]uhjC>h}]rX>hXcovariances=NonerY>rZ>}r[>(hUhjU>ubahjubeubh)r\>}r]>(hUhj2>hNhhh}r^>(Uexprhh]h]h]h]h]uhNhhh}]r_>h)r`>}ra>(hUh}rb>(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX-_modules/spykeutils/sorting_quality_assesmentUrefidj:>uhj\>h}]rc>j)rd>}re>(hUh}rf>(h]h]rg>j ah]h]h]uhj`>h}]rh>hX[source]ri>rj>}rk>(hUhjd>ubahjubahjubaubeubj)rl>}rm>(hUhj,>hhhjh}rn>(h]h]h]h]h]uhNhhh}]ro>(j)rp>}rq>(hXReturn dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs :mod:`sklearn` if ``covariances`` is not set to ``'white'``.hjl>hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/sorting_quality_assesment.py:docstring of spykeutils.sorting_quality_assesment.overlap_fp_fnrr>hjh}rs>(h]h]h]h]h]uhKhhh}]rt>(hXgReturn dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs ru>rv>}rw>(hXgReturn dicts of tuples (False positive rate, false negative rate) indexed by unit. This function needs hjp>ubh)rx>}ry>(hX:mod:`sklearn`rz>hjp>hNhjh}r{>(UreftypeXmodj<j=XsklearnU refdomainXpyr|>h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r}>jD)r~>}r>(hjz>h}r>(h]h]r>(jIj|>Xpy-modr>eh]h]h]uhjx>h}]r>hXsklearnr>r>}r>(hUhj~>ubahjOubaubhX if r>r>}r>(hX if hjp>ubjD)r>}r>(hX``covariances``h}r>(h]h]h]h]h]uhjp>h}]r>hX covariancesr>r>}r>(hUhj>ubahjOubhX is not set to r>r>}r>(hX is not set to hjp>ubjD)r>}r>(hX ``'white'``h}r>(h]h]h]h]h]uhjp>h}]r>hX'white'r>r>}r>(hUhj>ubahjOubhX.r>}r>(hX.hjp>ubeubj)r>}r>(hXThis function estimates the pairwise and total false positive and false negative rates for a number of waveform clusters. The results can be interpreted as follows: False positives are the fraction of spikes in a cluster that is estimated to belong to a different cluster (a specific cluster for pairwise results or any other cluster for total results). False negatives are the number spikes from other clusters that are estimated to belong to a given cluster (also expressed as fraction, this number can be larger than 1 in extreme cases).r>hjl>hjr>hjh}r>(h]h]h]h]h]uhKhhh}]r>hXThis function estimates the pairwise and total false positive and false negative rates for a number of waveform clusters. The results can be interpreted as follows: False positives are the fraction of spikes in a cluster that is estimated to belong to a different cluster (a specific cluster for pairwise results or any other cluster for total results). False negatives are the number spikes from other clusters that are estimated to belong to a given cluster (also expressed as fraction, this number can be larger than 1 in extreme cases).r>r>}r>(hj>hj>ubaubj)r>}r>(hXEDetails for the calculation can be found in (Hill et al. The Journal of Neuroscience. 2011). The calculation for total false positive and false negative rates does not follow Hill et al., who propose a simple addition of pairwise probabilities. Instead, the total error probabilities are estimated using all clusters at once.r>hjl>hjr>hjh}r>(h]h]h]h]h]uhKhhh}]r>hXEDetails for the calculation can be found in (Hill et al. The Journal of Neuroscience. 2011). The calculation for total false positive and false negative rates does not follow Hill et al., who propose a simple addition of pairwise probabilities. Instead, the total error probabilities are estimated using all clusters at once.r>r>}r>(hj>hj>ubaubj)r>}r>(hUhjl>hNhjh}r>(h]h]h]h]h]uhNhhh}]r>(j)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>(j)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>hX Parametersr>r>}r>(hUhj>ubahjubj)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>j#)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>(j()r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>j)r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>(j)r>}r>(hXspikesh}r>(h]h]h]h]h]uhj>h}]r>hXspikesr>r>}r>(hUhj>ubahjubhX (r>r>}r>(hUhj>ubh)r>}r>(hUh}r>(UreftypejzU reftargetXdictr>U refdomainj/>h]h]U refexplicith]h]h]uhj>h}]r>j})r>}r>(hj>h}r>(h]h]h]h]h]uhj>h}]r>hXdictr>r>}r>(hUhj>ubahjubahjubhX)r>}r>(hUhj>ubhX -- r>r>}r>(hUhj>ubhX<Dictionary, indexed by unit, of lists of spike waveforms as r>r>}r>(hX<Dictionary, indexed by unit, of lists of spike waveforms as hj>ubh)r>}r>(hX:class:`neo.core.Spike`r>hj>hNhjh}r>(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyr>h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r>jD)r>}r>(hj>h}r>(h]h]r>(jIj>Xpy-classr>eh]h]h]uhj>h}]r>hXneo.core.Spiker>r>}r>(hUhj>ubahjOubaubhX objects or numpy arrays. If the waveforms have multiple channels, they will be flattened automatically. All waveforms need to have the same number of samples.r>r>}r>(hX objects or numpy arrays. If the waveforms have multiple channels, they will be flattened automatically. All waveforms need to have the same number of samples.hj>ubehjubahjdubj()r>}r>(hUh}r>(h]h]h]h]h]uhj>h}]r>j)r?}r?(hUh}r?(h]h]h]h]h]uhj>h}]r?(j)r?}r?(hXmeansh}r?(h]h]h]h]h]uhj?h}]r?hXmeansr?r ?}r ?(hUhj?ubahjubhX (r ?r ?}r ?(hUhj?ubh)r?}r?(hUh}r?(UreftypejzU reftargetXdictr?U refdomainj/>h]h]U refexplicith]h]h]uhj?h}]r?j})r?}r?(hj?h}r?(h]h]h]h]h]uhj?h}]r?hXdictr?r?}r?(hUhj?ubahjubahjubhX)r?}r?(hUhj?ubhX -- r?r?}r?(hUhj?ubhX<Dictionary, indexed by unit, of lists of spike waveforms as r?r ?}r!?(hX<Dictionary, indexed by unit, of lists of spike waveforms as hj?ubh)r"?}r#?(hX:class:`neo.core.Spike`r$?hj?hNhjh}r%?(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyr&?h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r'?jD)r(?}r)?(hj$?h}r*?(h]h]r+?(jIj&?Xpy-classr,?eh]h]h]uhj"?h}]r-?hXneo.core.Spiker.?r/?}r0?(hUhj(?ubahjOubaubhX objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Note that if you pass r1?r2?}r3?(hX objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Note that if you pass hj?ubjD)r4?}r5?(hX ``'white'``h}r6?(h]h]h]h]h]uhj?h}]r7?hX'white'r8?r9?}r:?(hUhj4?ubahjOubhX for r;?r?}r??(hX``covariances``h}r@?(h]h]h]h]h]uhj?h}]rA?hX covariancesrB?rC?}rD?(hUhj>?ubahjOubhX and you want to provide means, they have to be whitened in the same way as the spikes. Default: None, means will be estimated from data.rE?rF?}rG?(hX and you want to provide means, they have to be whitened in the same way as the spikes. Default: None, means will be estimated from data.hj?ubehjubahjdubj()rH?}rI?(hUh}rJ?(h]h]h]h]h]uhj>h}]rK?j)rL?}rM?(hUh}rN?(h]h]h]h]h]uhjH?h}]rO?(j)rP?}rQ?(hX covariancesh}rR?(h]h]h]h]h]uhjL?h}]rS?hX covariancesrT?rU?}rV?(hUhjP?ubahjubhX (rW?rX?}rY?(hUhjL?ubh)rZ?}r[?(hUh}r\?(UreftypejzU reftargetX dict or strr]?U refdomainj/>h]h]U refexplicith]h]h]uhjL?h}]r^?j})r_?}r`?(hj]?h}ra?(h]h]h]h]h]uhjZ?h}]rb?hX dict or strrc?rd?}re?(hUhj_?ubahjubahjubhX)rf?}rg?(hUhjL?ubhX -- rh?ri?}rj?(hUhjL?ubhXDictionary, indexed by unit, of lists of covariance matrices. Covariances for units that are not in this dictionary will be estimated using the spikes. It is useful to give a covariance matrix if few spikes are present - consider using the noise covariance. If you use prewhitened spikes (i.e. all clusters are normal distributed, so their covariance matrix is the identity), you can pass rk?rl?}rm?(hXDictionary, indexed by unit, of lists of covariance matrices. Covariances for units that are not in this dictionary will be estimated using the spikes. It is useful to give a covariance matrix if few spikes are present - consider using the noise covariance. If you use prewhitened spikes (i.e. all clusters are normal distributed, so their covariance matrix is the identity), you can pass hjL?ubjD)rn?}ro?(hX ``'white'``h}rp?(h]h]h]h]h]uhjL?h}]rq?hX'white'rr?rs?}rt?(hUhjn?ubahjOubhX here. The calculation will be much faster in this case and the sklearn package is not required. Default: None, covariances will estimated from data.ru?rv?}rw?(hX here. The calculation will be much faster in this case and the sklearn package is not required. Default: None, covariances will estimated from data.hjL?ubehjubahjdubehjubahjubehjubj)rx?}ry?(hUh}rz?(h]h]h]h]h]uhj>h}]r{?(j)r|?}r}?(hUh}r~?(h]h]h]h]h]uhjx?h}]r?hXReturnsr?r?}r?(hUhj|?ubahjubj)r?}r?(hUh}r?(h]h]h]h]h]uhjx?h}]r?j)r?}r?(hUh}r?(h]h]h]h]h]uhj?h}]r?(j)r?}r?(hX Two values:r?hj?hjr>hjh}r?(h]h]h]h]h]uhK*h}]r?hX Two values:r?r?}r?(hj?hj?ubaubj#)r?}r?(hUh}r?(jKX*h]h]h]h]h]uhj?h}]r?(j()r?}r?(hXZA dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples.h}r?(h]h]h]h]h]uhj?h}]r?j)r?}r?(hXZA dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples.r?hj?hjr>hjh}r?(h]h]h]h]h]uhK,h}]r?hXZA dictionary (indexed by unit) of total (false positive rate, false negative rate) tuples.r?r?}r?(hj?hj?ubaubahjdubj()r?}r?(hXsA dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples.h}r?(h]h]h]h]h]uhj?h}]r?j)r?}r?(hXsA dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples.r?hj?hjr>hjh}r?(h]h]h]h]h]uhK.h}]r?hXsA dictionary of dictionaries, both indexed by units, of pairwise (false positive rate, false negative rate) tuples.r?r?}r?(hj?hj?ubaubahjdubehjubehjubahjubehjubj)r?}r?(hUh}r?(h]h]h]h]h]uhj>h}]r?(j)r?}r?(hUh}r?(h]h]h]h]h]uhj?h}]r?hX Return typer?r?}r?(hUhj?ubahjubj)r?}r?(hUh}r?(h]h]h]h]h]uhj?h}]r?j)r?}r?(hUh}r?(h]h]h]h]h]uhj?h}]r?hX dict, dictr?r?}r?(hX dict, dicthj?ubahjubahjubehjubeubeubeubh)r?}r?(hUhj;hNhhh}r?(h]h]h]h]h]Uentries]r?(hXEvariance_explained() (in module spykeutils.sorting_quality_assesment)hdUtr?auhNhhh}]ubh)r?}r?(hUhj;hNhhh}r?(hωhXpyr?h]h]h]h]h]hXfunctionr?hj?uhNhhh}]r?(h)r?}r?(hX2variance_explained(spikes, means=None, noise=None)hj?hhhhh}r?(h]r?hdahhX$spykeutils.sorting_quality_assesmentr?r?}r?bh]h]h]h]r?hdahXvariance_explainedr?hUhuhNhhh}]r?(h)r?}r?(hj?hj?hhhhh}r?(h]h]h]h]h]uhNhhh}]r?hXvariance_explainedr?r?}r?(hUhj?ubaubjt)r?}r?(hUhj?hhhjwh}r?(h]h]h]h]h]uhNhhh}]r?(jz)r?}r?(hXspikesh}r?(h]h]h]h]h]uhj?h}]r?hXspikesr?r?}r?(hUhj?ubahjubjz)r?}r?(hX means=Noneh}r?(h]h]h]h]h]uhj?h}]r?hX means=Noner?r?}r?(hUhj?ubahjubjz)r?}r?(hX noise=Noneh}r?(h]h]h]h]h]uhj?h}]r?hX noise=Noner?r?}r?(hUhj?ubahjubeubh)r?}r?(hUhj?hNhhh}r?(Uexprhh]h]h]h]h]uhNhhh}]r?h)r?}r?(hUh}r@(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX-_modules/spykeutils/sorting_quality_assesmentUrefidj?uhj?h}]r@j)r@}r@(hUh}r@(h]h]r@j ah]h]h]uhj?h}]r@hX[source]r@r@}r @(hUhj@ubahjubahjubaubeubj)r @}r @(hUhj?hhhjh}r @(h]h]h]h]h]uhNhhh}]r @(j)r@}r@(hXPReturns the fraction of variance in each channel that is explained by the means.r@hj @hX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/sorting_quality_assesment.py:docstring of spykeutils.sorting_quality_assesment.variance_explainedr@hjh}r@(h]h]h]h]h]uhKhhh}]r@hXPReturns the fraction of variance in each channel that is explained by the means.r@r@}r@(hj@hj@ubaubj)r@}r@(hXValues below 0 or above 1 for large data sizes indicate that some assumptions were incorrect (e.g. about channel noise) and the results should not be trusted.r@hj @hj@hjh}r@(h]h]h]h]h]uhKhhh}]r@hXValues below 0 or above 1 for large data sizes indicate that some assumptions were incorrect (e.g. about channel noise) and the results should not be trusted.r@r@}r@(hj@hj@ubaubj)r@}r @(hUhj @hNhjh}r!@(h]h]h]h]h]uhNhhh}]r"@(j)r#@}r$@(hUh}r%@(h]h]h]h]h]uhj@h}]r&@(j)r'@}r(@(hUh}r)@(h]h]h]h]h]uhj#@h}]r*@hX Parametersr+@r,@}r-@(hUhj'@ubahjubj)r.@}r/@(hUh}r0@(h]h]h]h]h]uhj#@h}]r1@j#)r2@}r3@(hUh}r4@(h]h]h]h]h]uhj.@h}]r5@(j()r6@}r7@(hUh}r8@(h]h]h]h]h]uhj2@h}]r9@j)r:@}r;@(hUh}r<@(h]h]h]h]h]uhj6@h}]r=@(j)r>@}r?@(hXspikesh}r@@(h]h]h]h]h]uhj:@h}]rA@hXspikesrB@rC@}rD@(hUhj>@ubahjubhX (rE@rF@}rG@(hUhj:@ubh)rH@}rI@(hUh}rJ@(UreftypejzU reftargetXdictrK@U refdomainj?h]h]U refexplicith]h]h]uhj:@h}]rL@j})rM@}rN@(hjK@h}rO@(h]h]h]h]h]uhjH@h}]rP@hXdictrQ@rR@}rS@(hUhjM@ubahjubahjubhX)rT@}rU@(hUhj:@ubhX -- rV@rW@}rX@(hUhj:@ubhX Dictionary, indexed by unit, of rY@rZ@}r[@(hX Dictionary, indexed by unit, of hj:@ubh)r\@}r]@(hX:class:`neo.core.SpikeTrain`r^@hj:@hNhjh}r_@(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr`@h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]ra@jD)rb@}rc@(hj^@h}rd@(h]h]re@(jIj`@Xpy-classrf@eh]h]h]uhj\@h}]rg@hXneo.core.SpikeTrainrh@ri@}rj@(hUhjb@ubahjOubaubhX objects (where the rk@rl@}rm@(hX objects (where the hj:@ubjD)rn@}ro@(hX ``waveforms``h}rp@(h]h]h]h]h]uhj:@h}]rq@hX waveformsrr@rs@}rt@(hUhjn@ubahjOubhX2 member includes the spike waveforms) or lists of ru@rv@}rw@(hX2 member includes the spike waveforms) or lists of hj:@ubh)rx@}ry@(hX:class:`neo.core.Spike`rz@hj:@hNhjh}r{@(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyr|@h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r}@jD)r~@}r@(hjz@h}r@(h]h]r@(jIj|@Xpy-classr@eh]h]h]uhjx@h}]r@hXneo.core.Spiker@r@}r@(hUhj~@ubahjOubaubhX objects.r@r@}r@(hX objects.hj:@ubehjubahjdubj()r@}r@(hUh}r@(h]h]h]h]h]uhj2@h}]r@j)r@}r@(hUh}r@(h]h]h]h]h]uhj@h}]r@(j)r@}r@(hXmeansh}r@(h]h]h]h]h]uhj@h}]r@hXmeansr@r@}r@(hUhj@ubahjubhX (r@r@}r@(hUhj@ubh)r@}r@(hUh}r@(UreftypejzU reftargetXdictr@U refdomainj?h]h]U refexplicith]h]h]uhj@h}]r@j})r@}r@(hj@h}r@(h]h]h]h]h]uhj@h}]r@hXdictr@r@}r@(hUhj@ubahjubahjubhX)r@}r@(hUhj@ubhX -- r@r@}r@(hUhj@ubhX<Dictionary, indexed by unit, of lists of spike waveforms as r@r@}r@(hX<Dictionary, indexed by unit, of lists of spike waveforms as hj@ubh)r@}r@(hX:class:`neo.core.Spike`r@hj@hNhjh}r@(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyr@h]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r@jD)r@}r@(hj@h}r@(h]h]r@(jIj@Xpy-classr@eh]h]h]uhj@h}]r@hXneo.core.Spiker@r@}r@(hUhj@ubahjOubaubhX objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Default: None - means will be estimated from given spikes.r@r@}r@(hX objects or numpy arrays. Means for units that are not in this dictionary will be estimated using the spikes. Default: None - means will be estimated from given spikes.hj@ubehjubahjdubj()r@}r@(hUh}r@(h]h]h]h]h]uhj2@h}]r@j)r@}r@(hUh}r@(h]h]h]h]h]uhj@h}]r@(j)r@}r@(hXnoiseh}r@(h]h]h]h]h]uhj@h}]r@hXnoiser@r@}r@(hUhj@ubahjubhX (r@r@}r@(hUhj@ubh)r@}r@(hUh}r@(UreftypejzU reftargetX Quantity 1Dr@U refdomainj?h]h]U refexplicith]h]h]uhj@h}]r@j})r@}r@(hj@h}r@(h]h]h]h]h]uhj@h}]r@hX Quantity 1Dr@r@}r@(hUhj@ubahjubahjubhX)r@}r@(hUhj@ubhX -- r@r@}r@(hUhj@ubhX(The known noise levels (as variance) per channel of the original data. This should be estimated from the signal periods that do not contain spikes, otherwise the explained variance could be overestimated. If None, the estimate of explained variance is done without regard for noise. Default: Noner@r@}r@(hX(The known noise levels (as variance) per channel of the original data. This should be estimated from the signal periods that do not contain spikes, otherwise the explained variance could be overestimated. If None, the estimate of explained variance is done without regard for noise. Default: Nonehj@ubehjubahjdubehjubahjubehjubj)r@}r@(hUhj@hj@hjh}r@(h]h]h]h]h]uhKhhh}]r@(j)r@}r@(hX return dicth}r@(h]h]h]h]h]uhj@h}]r@hX Return dictr@r@}r@(hUhj@ubahjubj)r@}r@(hX1A dictionary of arrays, both indexed by unit. If ``noise`` is ``None``, the dictionary contains the fraction of explained variance per channel without taking noise into account. If ``noise`` is given, it contains the fraction of variance per channel explained by the means and given noise level together.h}r@(h]h]h]h]h]uhj@h}]r@j)r@}r@(hX1A dictionary of arrays, both indexed by unit. If ``noise`` is ``None``, the dictionary contains the fraction of explained variance per channel without taking noise into account. If ``noise`` is given, it contains the fraction of variance per channel explained by the means and given noise level together.hj@hj@hjh}r@(h]h]h]h]h]uhKh}]r@(hX1A dictionary of arrays, both indexed by unit. If r@r@}r@(hX1A dictionary of arrays, both indexed by unit. If hj@ubjD)r@}r@(hX ``noise``h}rA(h]h]h]h]h]uhj@h}]rAhXnoiserArA}rA(hUhj@ubahjOubhX is rArA}rA(hX is hj@ubjD)rA}r A(hX``None``h}r A(h]h]h]h]h]uhj@h}]r AhXNoner Ar A}rA(hUhjAubahjOubhXp, the dictionary contains the fraction of explained variance per channel without taking noise into account. If rArA}rA(hXp, the dictionary contains the fraction of explained variance per channel without taking noise into account. If hj@ubjD)rA}rA(hX ``noise``h}rA(h]h]h]h]h]uhj@h}]rAhXnoiserArA}rA(hUhjAubahjOubhXr is given, it contains the fraction of variance per channel explained by the means and given noise level together.rArA}rA(hXr is given, it contains the fraction of variance per channel explained by the means and given noise level together.hj@ubeubahjubeubeubeubeubeubh)rA}rA(hUhhhj-hhh}rA(h]h]h]h]rA(Xmodule-spykeutils.stationarityr Ahveh]r!Ah1auhK>hhh}]r"A(h)r#A}r$A(hX:mod:`stationarity` Moduler%AhjAhj-hhh}r&A(h]h]h]h]h]uhK>hhh}]r'A(h)r(A}r)A(hX:mod:`stationarity`r*Ahj#AhNhjh}r+A(UreftypeXmodj<j=X stationarityU refdomainXpyr,Ah]h]U refexplicith]h]h]j?jj@NjAjj=uhNh}]r-AjD)r.A}r/A(hj*Ah}r0A(h]h]r1A(jIj,AXpy-modr2Aeh]h]h]uhj(Ah}]r3AhX stationarityr4Ar5A}r6A(hUhj.AubahjOubaubhX Moduler7Ar8A}r9A(hX Moduler:Ahj#Aubeubh)r;A}rA(hX spykeutils.stationarity (module)Xmodule-spykeutils.stationarityUtr?AauhKhhh}]ubh)r@A}rAA(hUhjAhNhhh}rBA(h]h]h]h]h]Uentries]rCA(hX?spike_amplitude_histogram() (in module spykeutils.stationarity)h?UtrDAauhNhhh}]ubh)rEA}rFA(hUhjAhNhhh}rGA(hωhXpyrHAh]h]h]h]h]hXfunctionrIAhjIAuhNhhh}]rJA(h)rKA}rLA(hXspike_amplitude_histogram(trains, num_bins, uniform_y_scale=True, unit=UnitQuantity('microvolt', 1e-06 * V, 'uV'), progress=None)hjEAhhhhh}rMA(h]rNAh?ahhXspykeutils.stationarityrOArPA}rQAbh]h]h]h]rRAh?ahXspike_amplitude_histogramrSAhUhuhNhhh}]rTA(h)rUA}rVA(hjSAhjKAhhhhh}rWA(h]h]h]h]h]uhNhhh}]rXAhXspike_amplitude_histogramrYArZA}r[A(hUhjUAubaubjt)r\A}r]A(hUhjKAhhhjwh}r^A(h]h]h]h]h]uhNhhh}]r_A(jz)r`A}raA(hXtrainsh}rbA(h]h]h]h]h]uhj\Ah}]rcAhXtrainsrdAreA}rfA(hUhj`Aubahjubjz)rgA}rhA(hXnum_binsh}riA(h]h]h]h]h]uhj\Ah}]rjAhXnum_binsrkArlA}rmA(hUhjgAubahjubjz)rnA}roA(hXuniform_y_scale=Trueh}rpA(h]h]h]h]h]uhj\Ah}]rqAhXuniform_y_scale=TruerrArsA}rtA(hUhjnAubahjubjz)ruA}rvA(hXunit=UnitQuantity('microvolt'h}rwA(h]h]h]h]h]uhj\Ah}]rxAhXunit=UnitQuantity('microvolt'ryArzA}r{A(hUhjuAubahjubjz)r|A}r}A(hX 1e-06 * Vh}r~A(h]h]h]h]h]uhj\Ah}]rAhX 1e-06 * VrArA}rA(hUhj|Aubahjubjz)rA}rA(hX'uV')h}rA(h]h]h]h]h]uhj\Ah}]rAhX'uV')rArA}rA(hUhjAubahjubjz)rA}rA(hX progress=Noneh}rA(h]h]h]h]h]uhj\Ah}]rAhX progress=NonerArA}rA(hUhjAubahjubeubh)rA}rA(hUhjKAhNhhh}rA(Uexprhh]h]h]h]h]uhNhhh}]rAh)rA}rA(hUh}rA(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX _modules/spykeutils/stationarityUrefidjSAuhjAh}]rAj)rA}rA(hUh}rA(h]h]rAj ah]h]h]uhjAh}]rAhX[source]rArA}rA(hUhjAubahjubahjubaubeubj)rA}rA(hUhjEAhhhjh}rA(h]h]h]h]h]uhNhhh}]rA(j)rA}rA(hX#Return a spike amplitude histogram.rAhjAhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/stationarity.py:docstring of spykeutils.stationarity.spike_amplitude_histogramrAhjh}rA(h]h]h]h]h]uhKhhh}]rAhX#Return a spike amplitude histogram.rArA}rA(hjAhjAubaubj)rA}rA(hXThe resulting is useful to assess the drift in spike amplitude over a longer recording. It shows histograms (one for each ``trains`` entry, e.g. segment) of maximum and minimum spike amplitudes.hjAhjAhjh}rA(h]h]h]h]h]uhKhhh}]rA(hXzThe resulting is useful to assess the drift in spike amplitude over a longer recording. It shows histograms (one for each rArA}rA(hXzThe resulting is useful to assess the drift in spike amplitude over a longer recording. It shows histograms (one for each hjAubjD)rA}rA(hX ``trains``h}rA(h]h]h]h]h]uhjAh}]rAhXtrainsrArA}rA(hUhjAubahjOubhX> entry, e.g. segment) of maximum and minimum spike amplitudes.rArA}rA(hX> entry, e.g. segment) of maximum and minimum spike amplitudes.hjAubeubj)rA}rA(hUhjAhNhjh}rA(h]h]h]h]h]uhNhhh}]rA(j)rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rA(j)rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rAhX ParametersrArA}rA(hUhjAubahjubj)rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rAj#)rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rA(j()rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rAj)rA}rA(hUh}rA(h]h]h]h]h]uhjAh}]rA(j)rA}rA(hXtrainsh}rA(h]h]h]h]h]uhjAh}]rAhXtrainsrArA}rA(hUhjAubahjubhX (rArA}rA(hUhjAubh)rA}rA(hUh}rA(UreftypejzU reftargetXlistrAU refdomainjHAh]h]U refexplicith]h]h]uhjAh}]rAj})rA}rA(hjAh}rA(h]h]h]h]h]uhjAh}]rAhXlistrArA}rA(hUhjAubahjubahjubhX)rA}rA(hUhjAubhX -- rArA}rA(hUhjAubhXA list of lists of rArA}rA(hXA list of lists of hjAubh)rA}rA(hX:class:`neo.core.SpikeTrain`rAhjAhhhjh}rA(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrBh]h]U refexplicith]h]h]j?jj@NjAXspykeutils.stationarityrBuhKh}]rBjD)rB}rB(hjAh}rB(h]h]rB(jIjBXpy-classrBeh]h]h]uhjAh}]rBhXneo.core.SpikeTrainr Br B}r B(hUhjBubahjOubaubhX objects. Each entry of the outer list will be one point on the x-axis (they could correspond to segments), all amplitude occurences of spikes contained in the inner list will be added up.r Br B}rB(hX objects. Each entry of the outer list will be one point on the x-axis (they could correspond to segments), all amplitude occurences of spikes contained in the inner list will be added up.hjAubehjubahjdubj()rB}rB(hUh}rB(h]h]h]h]h]uhjAh}]rBj)rB}rB(hUh}rB(h]h]h]h]h]uhjBh}]rB(j)rB}rB(hXnum_binsh}rB(h]h]h]h]h]uhjBh}]rBhXnum_binsrBrB}rB(hUhjBubahjubhX (rBrB}r B(hUhjBubh)r!B}r"B(hUh}r#B(UreftypejzU reftargetXintr$BU refdomainjHAh]h]U refexplicith]h]h]uhjBh}]r%Bj})r&B}r'B(hj$Bh}r(B(h]h]h]h]h]uhj!Bh}]r)BhXintr*Br+B}r,B(hUhj&BubahjubahjubhX)r-B}r.B(hUhjBubhX -- r/Br0B}r1B(hUhjBubhX"Number of bins for the histograms.r2Br3B}r4B(hX"Number of bins for the histograms.r5BhjBubehjubahjdubj()r6B}r7B(hUh}r8B(h]h]h]h]h]uhjAh}]r9Bj)r:B}r;B(hUh}rB}r?B(hXuniform_y_scaleh}r@B(h]h]h]h]h]uhj:Bh}]rABhXuniform_y_scalerBBrCB}rDB(hUhj>BubahjubhX (rEBrFB}rGB(hUhj:Bubh)rHB}rIB(hUh}rJB(UreftypejzU reftargetXboolrKBU refdomainjHAh]h]U refexplicith]h]h]uhj:Bh}]rLBj})rMB}rNB(hjKBh}rOB(h]h]h]h]h]uhjHBh}]rPBhXboolrQBrRB}rSB(hUhjMBubahjubahjubhX)rTB}rUB(hUhj:BubhX -- rVBrWB}rXB(hUhj:BubhXIf True, the histogram for each channel will use the same bins. Otherwise, the minimum bin range is computed separately for each channel.rYBrZB}r[B(hXIf True, the histogram for each channel will use the same bins. Otherwise, the minimum bin range is computed separately for each channel.r\Bhj:Bubehjubahjdubj()r]B}r^B(hUh}r_B(h]h]h]h]h]uhjAh}]r`Bj)raB}rbB(hUh}rcB(h]h]h]h]h]uhj]Bh}]rdB(j)reB}rfB(hXunith}rgB(h]h]h]h]h]uhjaBh}]rhBhXunitriBrjB}rkB(hUhjeBubahjubhX (rlBrmB}rnB(hUhjaBubh)roB}rpB(hUh}rqB(UreftypejzU reftargetXQuantityrrBU refdomainjHAh]h]U refexplicith]h]h]uhjaBh}]rsBj})rtB}ruB(hjrBh}rvB(h]h]h]h]h]uhjoBh}]rwBhXQuantityrxBryB}rzB(hUhjtBubahjubahjubhX)r{B}r|B(hUhjaBubhX -- r}Br~B}rB(hUhjaBubhXUnit of Y-Axis.rBrB}rB(hXUnit of Y-Axis.rBhjaBubehjubahjdubj()rB}rB(hUh}rB(h]h]h]h]h]uhjAh}]rBj)rB}rB(hUh}rB(h]h]h]h]h]uhjBh}]rB(j)rB}rB(hXprogressh}rB(h]h]h]h]h]uhjBh}]rBhXprogressrBrB}rB(hUhjBubahjubhX (rBrB}rB(hUhjBubh)rB}rB(hX.:class:`.progress_indicator.ProgressIndicator`rBhjBhNhjh}rB(UreftypeXclassjj<j=X$progress_indicator.ProgressIndicatorU refdomainXpyrBh]h]U refexplicith]h]h]j?jj@NjAjBuhNh}]rBjD)rB}rB(hjBh}rB(h]h]rB(jIjBXpy-classrBeh]h]h]uhjBh}]rBhX$progress_indicator.ProgressIndicatorrBrB}rB(hUhjBubahjOubaubhX)rB}rB(hUhjBubhX -- rBrB}rB(hUhjBubhX&Set this parameter to report progress.rBrB}rB(hX&Set this parameter to report progress.rBhjBubehjubahjdubehjubahjubehjubj)rB}rB(hUh}rB(h]h]h]h]h]uhjAh}]rB(j)rB}rB(hUh}rB(h]h]h]h]h]uhjBh}]rBhXReturnsrBrB}rB(hUhjBubahjubj)rB}rB(hUh}rB(h]h]h]h]h]uhjBh}]rBj)rB}rB(hUh}rB(h]h]h]h]h]uhjBh}]rB(j)rB}rB(hXA tuple with three values:rBhjBhjAhjh}rB(h]h]h]h]h]uhKh}]rBhXA tuple with three values:rBrB}rB(hjBhjBubaubj#)rB}rB(hUh}rB(jKX*h]h]h]h]h]uhjBh}]rB(j()rB}rB(hXA three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of ``trains`` (e.g. segments) and the third dimension to channels.h}rB(h]h]h]h]h]uhjBh}]rBj)rB}rB(hXA three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of ``trains`` (e.g. segments) and the third dimension to channels.hjBhjAhjh}rB(h]h]h]h]h]uhKh}]rB(hX|A three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of rBrB}rB(hX|A three-dimensional histogram matrix, where the first dimension corresponds to bins, the second dimension to the entries of hjBubjD)rB}rB(hX ``trains``h}rB(h]h]h]h]h]uhjBh}]rBhXtrainsrBrB}rB(hUhjBubahjOubhX5 (e.g. segments) and the third dimension to channels.rBrB}rB(hX5 (e.g. segments) and the third dimension to channels.hjBubeubahjdubj()rB}rB(hXqA list of the minimum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true).h}rB(h]h]h]h]h]uhjBh}]rBj)rB}rB(hXqA list of the minimum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true).hjBhjAhjh}rB(h]h]h]h]h]uhKh}]rB(hXTA list of the minimum amplitude value for each channel (all values will be equal if rBrB}rB(hXTA list of the minimum amplitude value for each channel (all values will be equal if hjBubjD)rB}rB(hX``uniform_y_scale``h}rB(h]h]h]h]h]uhjBh}]rBhXuniform_y_scalerBrB}rB(hUhjBubahjOubhX is true).rBrB}rB(hX is true).hjBubeubahjdubj()rB}rB(hXqA list of the maximum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true).h}rB(h]h]h]h]h]uhjBh}]rBj)rB}rB(hXqA list of the maximum amplitude value for each channel (all values will be equal if ``uniform_y_scale`` is true).hjBhjAhjh}rB(h]h]h]h]h]uhKh}]rB(hXTA list of the maximum amplitude value for each channel (all values will be equal if rBrC}rC(hXTA list of the maximum amplitude value for each channel (all values will be equal if hjBubjD)rC}rC(hX``uniform_y_scale``h}rC(h]h]h]h]h]uhjBh}]rChXuniform_y_scalerCrC}rC(hUhjCubahjOubhX is true).r Cr C}r C(hX is true).hjBubeubahjdubehjubehjubahjubehjubj)r C}r C(hUh}rC(h]h]h]h]h]uhjAh}]rC(j)rC}rC(hUh}rC(h]h]h]h]h]uhj Ch}]rChX Return typerCrC}rC(hUhjCubahjubj)rC}rC(hUh}rC(h]h]h]h]h]uhj Ch}]rCj)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rChX(ndarray, list, list)rCr C}r!C(hX(ndarray, list, list)r"ChjCubahjubahjubehjubeubeubeubeubh)r#C}r$C(hUhhhj-hhh}r%C(h]h]h]h]r&C(Xmodule-spykeutils.toolsr'Chneh]r(ChauhKDhhh}]r)C(h)r*C}r+C(hX:mod:`tools` Moduler,Chj#Chj-hhh}r-C(h]h]h]h]h]uhKDhhh}]r.C(h)r/C}r0C(hX :mod:`tools`r1Chj*ChNhjh}r2C(UreftypeXmodj<j=XtoolsU refdomainXpyr3Ch]h]U refexplicith]h]h]j?jj@NjAjBuhNh}]r4CjD)r5C}r6C(hj1Ch}r7C(h]h]r8C(jIj3CXpy-modr9Ceh]h]h]uhj/Ch}]r:ChXtoolsr;CrCr?C}r@C(hX ModulerAChj*Cubeubh)rBC}rCC(hUhj#Chhhhh}rDC(h]h]h]h]h]Uentries]rEC(hXspykeutils.tools (module)Xmodule-spykeutils.toolsUtrFCauhKhhh}]ubh)rGC}rHC(hUhj#ChNhhh}rIC(h]h]h]h]h]Uentries]rJC(hX,apply_to_dict() (in module spykeutils.tools)h-UtrKCauhNhhh}]ubh)rLC}rMC(hUhj#ChNhhh}rNC(hωhXpyrOCh]h]h]h]h]hXfunctionrPChjPCuhNhhh}]rQC(h)rRC}rSC(hX$apply_to_dict(fn, dictionary, *args)hjLChhhhh}rTC(h]rUCh-ahhXspykeutils.toolsrVCrWC}rXCbh]h]h]h]rYCh-ahX apply_to_dictrZChUhuhNhhh}]r[C(h)r\C}r]C(hjZChjRChhhhh}r^C(h]h]h]h]h]uhNhhh}]r_ChX apply_to_dictr`CraC}rbC(hUhj\Cubaubjt)rcC}rdC(hUhjRChhhjwh}reC(h]h]h]h]h]uhNhhh}]rfC(jz)rgC}rhC(hXfnh}riC(h]h]h]h]h]uhjcCh}]rjChXfnrkCrlC}rmC(hUhjgCubahjubjz)rnC}roC(hX dictionaryh}rpC(h]h]h]h]h]uhjcCh}]rqChX dictionaryrrCrsC}rtC(hUhjnCubahjubjz)ruC}rvC(hX*argsh}rwC(h]h]h]h]h]uhjcCh}]rxChX*argsryCrzC}r{C(hUhjuCubahjubeubh)r|C}r}C(hUhjRChNhhh}r~C(Uexprhh]h]h]h]h]uhNhhh}]rCh)rC}rC(hUh}rC(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjZCuhj|Ch}]rCj)rC}rC(hUh}rC(h]h]rCj ah]h]h]uhjCh}]rChX[source]rCrC}rC(hUhjCubahjubahjubaubeubj)rC}rC(hUhjLChhhjh}rC(h]h]h]h]h]uhNhhh}]rC(j)rC}rC(hXPApplies a function to all spike trains in a dictionary of spike train sequences.rChjChXq/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.apply_to_dictrChjh}rC(h]h]h]h]h]uhKhhh}]rChXPApplies a function to all spike trains in a dictionary of spike train sequences.rCrC}rC(hjChjCubaubj)rC}rC(hUhjChNhjh}rC(h]h]h]h]h]uhNhhh}]rC(j)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rC(j)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rChX ParametersrCrC}rC(hUhjCubahjubj)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rCj#)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rC(j()rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rCj)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rC(j)rC}rC(hXfnh}rC(h]h]h]h]h]uhjCh}]rChXfnrCrC}rC(hUhjCubahjubhX (rCrC}rC(hUhjCubh)rC}rC(hUh}rC(UreftypejzU reftargetXfunctionrCU refdomainjOCh]h]U refexplicith]h]h]uhjCh}]rCj})rC}rC(hjCh}rC(h]h]h]h]h]uhjCh}]rChXfunctionrCrC}rC(hUhjCubahjubahjubhX)rC}rC(hUhjCubhX -- rCrC}rC(hUhjCubhX!Function to apply. Should take a rCrC}rC(hX!Function to apply. Should take a hjCubh)rC}rC(hX:class:`neo.core.SpikeTrain`rChjChhhjh}rC(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrCh]h]U refexplicith]h]h]j?jj@NjAXspykeutils.toolsrCuhK h}]rCjD)rC}rC(hjCh}rC(h]h]rC(jIjCXpy-classrCeh]h]h]uhjCh}]rChXneo.core.SpikeTrainrCrC}rC(hUhjCubahjOubaubhX as first argument.rCrC}rC(hX as first argument.hjCubehjubahjdubj()rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rCj)rC}rC(hUh}rC(h]h]h]h]h]uhjCh}]rC(j)rC}rC(hX dictionaryh}rC(h]h]h]h]h]uhjCh}]rChX dictionaryrCrC}rC(hUhjCubahjubhX (rCrC}rC(hUhjCubh)rC}rC(hUh}rC(UreftypejzU reftargetXdictrCU refdomainjOCh]h]U refexplicith]h]h]uhjCh}]rCj})rD}rD(hjCh}rD(h]h]h]h]h]uhjCh}]rDhXdictrDrD}rD(hUhjDubahjubahjubhX)rD}rD(hUhjCubhX -- r Dr D}r D(hUhjCubhXDictionary of sequences of r Dr D}rD(hXDictionary of sequences of hjCubh)rD}rD(hX:class:`neo.core.SpikeTrain`rDhjChhhjh}rD(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrDh]h]U refexplicith]h]h]j?jj@NjAjCuhK h}]rDjD)rD}rD(hjDh}rD(h]h]rD(jIjDXpy-classrDeh]h]h]uhjDh}]rDhXneo.core.SpikeTrainrDrD}rD(hUhjDubahjOubaubhX" objects to apply the function to.rDrD}r D(hX" objects to apply the function to.hjCubehjubahjdubj()r!D}r"D(hUh}r#D(h]h]h]h]h]uhjCh}]r$Dj)r%D}r&D(hUh}r'D(h]h]h]h]h]uhj!Dh}]r(D(j)r)D}r*D(hXargsh}r+D(h]h]h]h]h]uhj%Dh}]r,DhXargsr-Dr.D}r/D(hUhj)DubahjubhX -- r0Dr1D}r2D(hUhj%DubhX-Additional arguments which will be passed to r3Dr4D}r5D(hX-Additional arguments which will be passed to hj%DubjD)r6D}r7D(hX``fn``h}r8D(h]h]h]h]h]uhj%Dh}]r9DhXfnr:Dr;D}rD(hX.hj%Dubehjubahjdubehjubahjubehjubj)r?D}r@D(hUh}rAD(h]h]h]h]h]uhjCh}]rBD(j)rCD}rDD(hUh}rED(h]h]h]h]h]uhj?Dh}]rFDhXReturnsrGDrHD}rID(hUhjCDubahjubj)rJD}rKD(hUh}rLD(h]h]h]h]h]uhj?Dh}]rMDj)rND}rOD(hUh}rPD(h]h]h]h]h]uhjJDh}]rQD(hX'A new dictionary with the same keys as rRDrSD}rTD(hX'A new dictionary with the same keys as hjNDubjD)rUD}rVD(hX``dictionary``h}rWD(h]h]h]h]h]uhjNDh}]rXDhX dictionaryrYDrZD}r[D(hUhjUDubahjOubhX.r\D}r]D(hX.hjNDubehjubahjubehjubj)r^D}r_D(hUh}r`D(h]h]h]h]h]uhjCh}]raD(j)rbD}rcD(hUh}rdD(h]h]h]h]h]uhj^Dh}]reDhX Return typerfDrgD}rhD(hUhjbDubahjubj)riD}rjD(hUh}rkD(h]h]h]h]h]uhj^Dh}]rlDj)rmD}rnD(hUh}roD(h]h]h]h]h]uhjiDh}]rpDhXdictrqDrrD}rsD(hXdicthjmDubahjubahjubehjubeubeubeubh)rtD}ruD(hUhj#ChNhhh}rvD(h]h]h]h]h]Uentries]rwD(hX/bin_spike_trains() (in module spykeutils.tools)h!UtrxDauhNhhh}]ubh)ryD}rzD(hUhj#ChNhhh}r{D(hωhXpyr|Dh]h]h]h]h]hXfunctionr}Dhj}DuhNhhh}]r~D(h)rD}rD(hXBbin_spike_trains(trains, sampling_rate, t_start=None, t_stop=None)hjyDhhhhh}rD(h]rDh!ahhXspykeutils.toolsrDrD}rDbh]h]h]h]rDh!ahXbin_spike_trainsrDhUhuhNhhh}]rD(h)rD}rD(hjDhjDhhhhh}rD(h]h]h]h]h]uhNhhh}]rDhXbin_spike_trainsrDrD}rD(hUhjDubaubjt)rD}rD(hUhjDhhhjwh}rD(h]h]h]h]h]uhNhhh}]rD(jz)rD}rD(hXtrainsh}rD(h]h]h]h]h]uhjDh}]rDhXtrainsrDrD}rD(hUhjDubahjubjz)rD}rD(hX sampling_rateh}rD(h]h]h]h]h]uhjDh}]rDhX sampling_raterDrD}rD(hUhjDubahjubjz)rD}rD(hX t_start=Noneh}rD(h]h]h]h]h]uhjDh}]rDhX t_start=NonerDrD}rD(hUhjDubahjubjz)rD}rD(hX t_stop=Noneh}rD(h]h]h]h]h]uhjDh}]rDhX t_stop=NonerDrD}rD(hUhjDubahjubeubh)rD}rD(hUhjDhNhhh}rD(Uexprhh]h]h]h]h]uhNhhh}]rDh)rD}rD(hUh}rD(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjDuhjDh}]rDj)rD}rD(hUh}rD(h]h]rDj ah]h]h]uhjDh}]rDhX[source]rDrD}rD(hUhjDubahjubahjubaubeubj)rD}rD(hUhjyDhhhjh}rD(h]h]h]h]h]uhNhhh}]rD(j)rD}rD(hX/Creates binned representations of spike trains.rDhjDhXt/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.bin_spike_trainsrDhjh}rD(h]h]h]h]h]uhKhhh}]rDhX/Creates binned representations of spike trains.rDrD}rD(hjDhjDubaubj)rD}rD(hUhjDhNhjh}rD(h]h]h]h]h]uhNhhh}]rD(j)rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rD(j)rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rDhX ParametersrDrD}rD(hUhjDubahjubj)rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rDj#)rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rD(j()rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rDj)rD}rD(hUh}rD(h]h]h]h]h]uhjDh}]rD(j)rD}rD(hXtrainsh}rD(h]h]h]h]h]uhjDh}]rDhXtrainsrDrD}rD(hUhjDubahjubhX (rDrD}rD(hUhjDubh)rD}rD(hUh}rD(UreftypejzU reftargetXdictrDU refdomainj|Dh]h]U refexplicith]h]h]uhjDh}]rDj})rD}rD(hjDh}rD(h]h]h]h]h]uhjDh}]rDhXdictrDrE}rE(hUhjDubahjubahjubhX)rE}rE(hUhjDubhX -- rErE}rE(hUhjDubhXA dictionary of sequences of rErE}r E(hXA dictionary of sequences of hjDubh)r E}r E(hX:class:`neo.core.SpikeTrain`r EhjDhNhjh}r E(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrEh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rEjD)rE}rE(hj Eh}rE(h]h]rE(jIjEXpy-classrEeh]h]h]uhj Eh}]rEhXneo.core.SpikeTrainrErE}rE(hUhjEubahjOubaubhX objects.rErE}rE(hX objects.hjDubehjubahjdubj()rE}rE(hUh}rE(h]h]h]h]h]uhjDh}]rEj)r E}r!E(hUh}r"E(h]h]h]h]h]uhjEh}]r#E(j)r$E}r%E(hX sampling_rateh}r&E(h]h]h]h]h]uhj Eh}]r'EhX sampling_rater(Er)E}r*E(hUhj$EubahjubhX (r+Er,E}r-E(hUhj Eubh)r.E}r/E(hUh}r0E(UreftypejzU reftargetXQuantity scalarr1EU refdomainj|Dh]h]U refexplicith]h]h]uhj Eh}]r2Ej})r3E}r4E(hj1Eh}r5E(h]h]h]h]h]uhj.Eh}]r6EhXQuantity scalarr7Er8E}r9E(hUhj3EubahjubahjubhX)r:E}r;E(hUhj EubhX -- rE(hUhj EubhXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.r?Er@E}rAE(hXTThe sampling rate which will be used to bin the spike trains as inverse time scalar.rBEhj Eubehjubahjdubj()rCE}rDE(hUh}rEE(h]h]h]h]h]uhjDh}]rFEj)rGE}rHE(hUh}rIE(h]h]h]h]h]uhjCEh}]rJE(j)rKE}rLE(hXt_stoph}rME(h]h]h]h]h]uhjGEh}]rNEhXt_stoprOErPE}rQE(hUhjKEubahjubhX (rRErSE}rTE(hUhjGEubh)rUE}rVE(hUh}rWE(UreftypejzU reftargetXQuantity scalarrXEU refdomainj|Dh]h]U refexplicith]h]h]uhjGEh}]rYEj})rZE}r[E(hjXEh}r\E(h]h]h]h]h]uhjUEh}]r]EhXQuantity scalarr^Er_E}r`E(hUhjZEubahjubahjubhX)raE}rbE(hUhjGEubhX -- rcErdE}reE(hUhjGEubhXuThe desired time for the end of the last bin as time scalar. It will be the maximum stop time of all spike trains if rfErgE}rhE(hXuThe desired time for the end of the last bin as time scalar. It will be the maximum stop time of all spike trains if hjGEubjD)riE}rjE(hX``None``h}rkE(h]h]h]h]h]uhjGEh}]rlEhXNonermErnE}roE(hUhjiEubahjOubhX is passed.rpErqE}rrE(hX is passed.hjGEubehjubahjdubehjubahjubehjubj)rsE}rtE(hUh}ruE(h]h]h]h]h]uhjDh}]rvE(j)rwE}rxE(hUh}ryE(h]h]h]h]h]uhjsEh}]rzEhXReturnsr{Er|E}r}E(hUhjwEubahjubj)r~E}rE(hUh}rE(h]h]h]h]h]uhjsEh}]rEj)rE}rE(hUh}rE(h]h]h]h]h]uhj~Eh}]rE(hX'A dictionary (with the same indices as rErE}rE(hX'A dictionary (with the same indices as hjEubjD)rE}rE(hX ``trains``h}rE(h]h]h]h]h]uhjEh}]rEhXtrainsrErE}rE(hUhjEubahjOubhX5) of lists of spike train counts and the bin borders.rErE}rE(hX5) of lists of spike train counts and the bin borders.hjEubehjubahjubehjubj)rE}rE(hUh}rE(h]h]h]h]h]uhjDh}]rE(j)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rEhX Return typerErE}rE(hUhjEubahjubj)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rEj)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rEhX!dict, Quantity 1D with time unitsrErE}rE(hX!dict, Quantity 1D with time unitsrEhjEubahjubahjubehjubeubeubeubh)rE}rE(hUhj#ChNhhh}rE(h]h]h]h]h]Uentries]rE(hX7concatenate_spike_trains() (in module spykeutils.tools)h]UtrEauhNhhh}]ubh)rE}rE(hUhj#ChNhhh}rE(hωhXpyrEh]h]h]h]h]hXfunctionrEhjEuhNhhh}]rE(h)rE}rE(hX concatenate_spike_trains(trains)hjEhhhhh}rE(h]rEh]ahhXspykeutils.toolsrErE}rEbh]h]h]h]rEh]ahXconcatenate_spike_trainsrEhUhuhNhhh}]rE(h)rE}rE(hjEhjEhhhhh}rE(h]h]h]h]h]uhNhhh}]rEhXconcatenate_spike_trainsrErE}rE(hUhjEubaubjt)rE}rE(hUhjEhhhjwh}rE(h]h]h]h]h]uhNhhh}]rEjz)rE}rE(hXtrainsh}rE(h]h]h]h]h]uhjEh}]rEhXtrainsrErE}rE(hUhjEubahjubaubh)rE}rE(hUhjEhNhhh}rE(Uexprhh]h]h]h]h]uhNhhh}]rEh)rE}rE(hUh}rE(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjEuhjEh}]rEj)rE}rE(hUh}rE(h]h]rEj ah]h]h]uhjEh}]rEhX[source]rErE}rE(hUhjEubahjubahjubaubeubj)rE}rE(hUhjEhhhjh}rE(h]h]h]h]h]uhNhhh}]rE(j)rE}rE(hXConcatenates spike trains.rEhjEhX|/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.concatenate_spike_trainsrEhjh}rE(h]h]h]h]h]uhKhhh}]rEhXConcatenates spike trains.rErE}rE(hjEhjEubaubj)rE}rE(hUhjEhNhjh}rE(h]h]h]h]h]uhNhhh}]rE(j)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rE(j)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rEhX ParametersrErE}rE(hUhjEubahjubj)rE}rE(hUh}rE(h]h]h]h]h]uhjEh}]rFj)rF}rF(hUh}rF(h]h]h]h]h]uhjEh}]rF(j)rF}rF(hXtrainsh}rF(h]h]h]h]h]uhjFh}]rFhXtrainsr Fr F}r F(hUhjFubahjubhX (r Fr F}rF(hUhjFubh)rF}rF(hUh}rF(UreftypejzU reftargetXsequencerFU refdomainjEh]h]U refexplicith]h]h]uhjFh}]rFj})rF}rF(hjFh}rF(h]h]h]h]h]uhjFh}]rFhXsequencerFrF}rF(hUhjFubahjubahjubhX)rF}rF(hUhjFubhX -- rFrF}rF(hUhjFubh)r F}r!F(hX:class:`neo.core.SpikeTrain`r"FhjFhNhjh}r#F(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyr$Fh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]r%FjD)r&F}r'F(hj"Fh}r(F(h]h]r)F(jIj$FXpy-classr*Feh]h]h]uhj Fh}]r+FhXneo.core.SpikeTrainr,Fr-F}r.F(hUhj&FubahjOubaubhX objects to concatenate.r/Fr0F}r1F(hX objects to concatenate.hjFubehjubahjubehjubj)r2F}r3F(hUh}r4F(h]h]h]h]h]uhjEh}]r5F(j)r6F}r7F(hUh}r8F(h]h]h]h]h]uhj2Fh}]r9FhXReturnsr:Fr;F}rF(hUh}r?F(h]h]h]h]h]uhj2Fh}]r@Fj)rAF}rBF(hUh}rCF(h]h]h]h]h]uhj=Fh}]rDF(hXyA spike train consisting of the concatenated spike trains. The spikes will be in the order of the given spike trains and rEFrFF}rGF(hXyA spike train consisting of the concatenated spike trains. The spikes will be in the order of the given spike trains and hjAFubjD)rHF}rIF(hX ``t_start``h}rJF(h]h]h]h]h]uhjAFh}]rKFhXt_startrLFrMF}rNF(hUhjHFubahjOubhX and rOFrPF}rQF(hX and hjAFubjD)rRF}rSF(hX ``t_stop``h}rTF(h]h]h]h]h]uhjAFh}]rUFhXt_stoprVFrWF}rXF(hUhjRFubahjOubhX. will be set to the minimum and maximum value.rYFrZF}r[F(hX. will be set to the minimum and maximum value.hjAFubehjubahjubehjubj)r\F}r]F(hUh}r^F(h]h]h]h]h]uhjEh}]r_F(j)r`F}raF(hUh}rbF(h]h]h]h]h]uhj\Fh}]rcFhX Return typerdFreF}rfF(hUhj`Fubahjubj)rgF}rhF(hUh}riF(h]h]h]h]h]uhj\Fh}]rjFj)rkF}rlF(hUh}rmF(h]h]h]h]h]uhjgFh}]rnFh)roF}rpF(hX:class:`neo.core.SpikeTrain`rqFhjkFhNhjh}rrF(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrsFh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rtFjD)ruF}rvF(hjqFh}rwF(h]h]rxF(jIjsFXpy-classryFeh]h]h]uhjoFh}]rzFhXneo.core.SpikeTrainr{Fr|F}r}F(hUhjuFubahjOubaubahjubahjubehjubeubeubeubh)r~F}rF(hUhj#ChNhhh}rF(h]h]h]h]h]Uentries]rF(hX-extract_spikes() (in module spykeutils.tools)hJUtrFauhNhhh}]ubh)rF}rF(hUhj#ChNhhh}rF(hωhXpyrFh]h]h]h]h]hXfunctionrFhjFuhNhhh}]rF(h)rF}rF(hX2extract_spikes(train, signals, length, align_time)hjFhhhhh}rF(h]rFhJahhXspykeutils.toolsrFrF}rFbh]h]h]h]rFhJahXextract_spikesrFhUhuhNhhh}]rF(h)rF}rF(hjFhjFhhhhh}rF(h]h]h]h]h]uhNhhh}]rFhXextract_spikesrFrF}rF(hUhjFubaubjt)rF}rF(hUhjFhhhjwh}rF(h]h]h]h]h]uhNhhh}]rF(jz)rF}rF(hXtrainh}rF(h]h]h]h]h]uhjFh}]rFhXtrainrFrF}rF(hUhjFubahjubjz)rF}rF(hXsignalsh}rF(h]h]h]h]h]uhjFh}]rFhXsignalsrFrF}rF(hUhjFubahjubjz)rF}rF(hXlengthh}rF(h]h]h]h]h]uhjFh}]rFhXlengthrFrF}rF(hUhjFubahjubjz)rF}rF(hX align_timeh}rF(h]h]h]h]h]uhjFh}]rFhX align_timerFrF}rF(hUhjFubahjubeubh)rF}rF(hUhjFhNhhh}rF(Uexprhh]h]h]h]h]uhNhhh}]rFh)rF}rF(hUh}rF(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjFuhjFh}]rFj)rF}rF(hUh}rF(h]h]rFj ah]h]h]uhjFh}]rFhX[source]rFrF}rF(hUhjFubahjubahjubaubeubj)rF}rF(hUhjFhhhjh}rF(h]h]h]h]h]uhNhhh}]rF(j)rF}rF(hXExtract spikes with waveforms from analog signals using a spike train. Spikes that are too close to the beginning or end of the shortest signal to be fully extracted are ignored.rFhjFhXr/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.extract_spikesrFhjh}rF(h]h]h]h]h]uhKhhh}]rFhXExtract spikes with waveforms from analog signals using a spike train. Spikes that are too close to the beginning or end of the shortest signal to be fully extracted are ignored.rFrF}rF(hjFhjFubaubj)rF}rF(hUhjFhNhjh}rF(h]h]h]h]h]uhNhhh}]rF(j)rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rF(j)rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rFhX ParametersrFrF}rF(hUhjFubahjubj)rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rFj#)rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rF(j()rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rFj)rF}rF(hUh}rF(h]h]h]h]h]uhjFh}]rF(j)rF}rF(hXtrainh}rF(h]h]h]h]h]uhjFh}]rFhXtrainrFrF}rF(hUhjFubahjubhX (rFrF}rF(hUhjFubh)rG}rG(hX:class:`neo.core.SpikeTrain`rGhjFhNhjh}rG(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrGh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rGjD)rG}rG(hjGh}rG(h]h]r G(jIjGXpy-classr Geh]h]h]uhjGh}]r GhXneo.core.SpikeTrainr Gr G}rG(hUhjGubahjOubaubhX)rG}rG(hUhjFubhX -- rGrG}rG(hUhjFubhXThe spike times.rGrG}rG(hXThe spike times.hjFubehjubahjdubj()rG}rG(hUh}rG(h]h]h]h]h]uhjFh}]rGj)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rG(j)rG}r G(hXsignalsh}r!G(h]h]h]h]h]uhjGh}]r"GhXsignalsr#Gr$G}r%G(hUhjGubahjubhX (r&Gr'G}r(G(hUhjGubh)r)G}r*G(hUh}r+G(UreftypejzU reftargetXsequencer,GU refdomainjFh]h]U refexplicith]h]h]uhjGh}]r-Gj})r.G}r/G(hj,Gh}r0G(h]h]h]h]h]uhj)Gh}]r1GhXsequencer2Gr3G}r4G(hUhj.GubahjubahjubhX)r5G}r6G(hUhjGubhX -- r7Gr8G}r9G(hUhjGubhXA sequence of r:Gr;G}rG(hX:class:`neo.core.AnalogSignal`r?GhjGhNhjh}r@G(UreftypeXclassj<j=Xneo.core.AnalogSignalU refdomainXpyrAGh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rBGjD)rCG}rDG(hj?Gh}rEG(h]h]rFG(jIjAGXpy-classrGGeh]h]h]uhj=Gh}]rHGhXneo.core.AnalogSignalrIGrJG}rKG(hUhjCGubahjOubaubhX objects from which the spikes are extracted. The waveforms of the returned spikes are extracted from these signals in the same order they are given.rLGrMG}rNG(hX objects from which the spikes are extracted. The waveforms of the returned spikes are extracted from these signals in the same order they are given.hjGubehjubahjdubj()rOG}rPG(hUh}rQG(h]h]h]h]h]uhjFh}]rRGj)rSG}rTG(hUh}rUG(h]h]h]h]h]uhjOGh}]rVG(j)rWG}rXG(hXlengthh}rYG(h]h]h]h]h]uhjSGh}]rZGhXlengthr[Gr\G}r]G(hUhjWGubahjubhX (r^Gr_G}r`G(hUhjSGubh)raG}rbG(hUh}rcG(UreftypejzU reftargetXQuantity scalarrdGU refdomainjFh]h]U refexplicith]h]h]uhjSGh}]reGj})rfG}rgG(hjdGh}rhG(h]h]h]h]h]uhjaGh}]riGhXQuantity scalarrjGrkG}rlG(hUhjfGubahjubahjubhX)rmG}rnG(hUhjSGubhX -- roGrpG}rqG(hUhjSGubhX5The length of the waveform to extract as time scalar.rrGrsG}rtG(hX5The length of the waveform to extract as time scalar.hjSGubehjubahjdubj()ruG}rvG(hUh}rwG(h]h]h]h]h]uhjFh}]rxGj)ryG}rzG(hUh}r{G(h]h]h]h]h]uhjuGh}]r|G(j)r}G}r~G(hX align_timeh}rG(h]h]h]h]h]uhjyGh}]rGhX align_timerGrG}rG(hUhj}GubahjubhX (rGrG}rG(hUhjyGubh)rG}rG(hUh}rG(UreftypejzU reftargetXQuantity scalarrGU refdomainjFh]h]U refexplicith]h]h]uhjyGh}]rGj})rG}rG(hjGh}rG(h]h]h]h]h]uhjGh}]rGhXQuantity scalarrGrG}rG(hUhjGubahjubahjubhX)rG}rG(hUhjyGubhX -- rGrG}rG(hUhjyGubhXThe alignment time of the spike times as time scalar. This is the time delta from the start of the extracted waveform to the exact time of the spike.rGrG}rG(hXThe alignment time of the spike times as time scalar. This is the time delta from the start of the extracted waveform to the exact time of the spike.hjyGubehjubahjdubehjubahjubehjubj)rG}rG(hUh}rG(h]h]h]h]h]uhjFh}]rG(j)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rGhXReturnsrGrG}rG(hUhjGubahjubj)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rGj)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rG(hX A list of rGrG}rG(hX A list of hjGubh)rG}rG(hX:class:`neo.core.Spike`rGhjGhNhjh}rG(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyrGh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rGjD)rG}rG(hjGh}rG(h]h]rG(jIjGXpy-classrGeh]h]h]uhjGh}]rGhXneo.core.SpikerGrG}rG(hUhjGubahjOubaubhX% objects, one for each time point in rGrG}rG(hX% objects, one for each time point in hjGubjD)rG}rG(hX ``train``h}rG(h]h]h]h]h]uhjGh}]rGhXtrainrGrG}rG(hUhjGubahjOubhX$. All returned spikes include their rGrG}rG(hX$. All returned spikes include their hjGubjD)rG}rG(hX ``waveform``h}rG(h]h]h]h]h]uhjGh}]rGhXwaveformrGrG}rG(hUhjGubahjOubhX property.rGrG}rG(hX property.hjGubehjubahjubehjubj)rG}rG(hUh}rG(h]h]h]h]h]uhjFh}]rG(j)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rGhX Return typerGrG}rG(hUhjGubahjubj)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rGj)rG}rG(hUh}rG(h]h]h]h]h]uhjGh}]rGhXlistrGrG}rG(hXlisthjGubahjubahjubehjubeubeubeubh)rG}rG(hUhj#ChNhhh}rG(h]h]h]h]h]Uentries]rG(hX;maximum_spike_train_interval() (in module spykeutils.tools)hbUtrGauhNhhh}]ubh)rG}rG(hUhj#ChNhhh}rG(hωhXpyrGh]h]h]h]h]hXfunctionrGhjGuhNhhh}]rG(h)rG}rG(hXTmaximum_spike_train_interval(trains, t_start=array(inf) * s, t_stop=array(-inf) * s)hjGhhhhh}rG(h]rGhbahhXspykeutils.toolsrGrG}rGbh]h]h]h]rGhbahXmaximum_spike_train_intervalrHhUhuhNhhh}]rH(h)rH}rH(hjHhjGhhhhh}rH(h]h]h]h]h]uhNhhh}]rHhXmaximum_spike_train_intervalrHrH}rH(hUhjHubaubjt)r H}r H(hUhjGhhhjwh}r H(h]h]h]h]h]uhNhhh}]r H(jz)r H}rH(hXtrainsh}rH(h]h]h]h]h]uhj Hh}]rHhXtrainsrHrH}rH(hUhj Hubahjubjz)rH}rH(hXt_start=array(inf) * sh}rH(h]h]h]h]h]uhj Hh}]rHhXt_start=array(inf) * srHrH}rH(hUhjHubahjubjz)rH}rH(hXt_stop=array(-inf) * sh}rH(h]h]h]h]h]uhj Hh}]rHhXt_stop=array(-inf) * srHr H}r!H(hUhjHubahjubeubh)r"H}r#H(hUhjGhNhhh}r$H(Uexprhh]h]h]h]h]uhNhhh}]r%Hh)r&H}r'H(hUh}r(H(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjHuhj"Hh}]r)Hj)r*H}r+H(hUh}r,H(h]h]r-Hj ah]h]h]uhj&Hh}]r.HhX[source]r/Hr0H}r1H(hUhj*Hubahjubahjubaubeubj)r2H}r3H(hUhjGhhhjh}r4H(h]h]h]h]h]uhNhhh}]r5H(j)r6H}r7H(hXComputes the minimum starting time and maximum end time of all given spike trains. This yields an interval containing the spikes of all spike trains.r8Hhj2HhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.maximum_spike_train_intervalr9Hhjh}r:H(h]h]h]h]h]uhKhhh}]r;HhXComputes the minimum starting time and maximum end time of all given spike trains. This yields an interval containing the spikes of all spike trains.rH(hj8Hhj6Hubaubj)r?H}r@H(hUhj2HhNhjh}rAH(h]h]h]h]h]uhNhhh}]rBH(j)rCH}rDH(hUh}rEH(h]h]h]h]h]uhj?Hh}]rFH(j)rGH}rHH(hUh}rIH(h]h]h]h]h]uhjCHh}]rJHhX ParametersrKHrLH}rMH(hUhjGHubahjubj)rNH}rOH(hUh}rPH(h]h]h]h]h]uhjCHh}]rQHj#)rRH}rSH(hUh}rTH(h]h]h]h]h]uhjNHh}]rUH(j()rVH}rWH(hUh}rXH(h]h]h]h]h]uhjRHh}]rYHj)rZH}r[H(hUh}r\H(h]h]h]h]h]uhjVHh}]r]H(j)r^H}r_H(hXtrainsh}r`H(h]h]h]h]h]uhjZHh}]raHhXtrainsrbHrcH}rdH(hUhj^HubahjubhX (reHrfH}rgH(hUhjZHubh)rhH}riH(hUh}rjH(UreftypejzU reftargetXdictrkHU refdomainjGh]h]U refexplicith]h]h]uhjZHh}]rlHj})rmH}rnH(hjkHh}roH(h]h]h]h]h]uhjhHh}]rpHhXdictrqHrrH}rsH(hUhjmHubahjubahjubhX)rtH}ruH(hUhjZHubhX -- rvHrwH}rxH(hUhjZHubhXA dictionary of sequences of ryHrzH}r{H(hXA dictionary of sequences of hjZHubh)r|H}r}H(hX:class:`neo.core.SpikeTrain`r~HhjZHhNhjh}rH(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrHh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rHjD)rH}rH(hj~Hh}rH(h]h]rH(jIjHXpy-classrHeh]h]h]uhj|Hh}]rHhXneo.core.SpikeTrainrHrH}rH(hUhjHubahjOubaubhX objects.rHrH}rH(hX objects.hjZHubehjubahjdubj()rH}rH(hUh}rH(h]h]h]h]h]uhjRHh}]rHj)rH}rH(hUh}rH(h]h]h]h]h]uhjHh}]rH(j)rH}rH(hXt_starth}rH(h]h]h]h]h]uhjHh}]rHhXt_startrHrH}rH(hUhjHubahjubhX (rHrH}rH(hUhjHubh)rH}rH(hUh}rH(UreftypejzU reftargetXQuantity scalarrHU refdomainjGh]h]U refexplicith]h]h]uhjHh}]rHj})rH}rH(hjHh}rH(h]h]h]h]h]uhjHh}]rHhXQuantity scalarrHrH}rH(hUhjHubahjubahjubhX)rH}rH(hUhjHubhX -- rHrH}rH(hUhjHubhX Maximum starting time to return.rHrH}rH(hX Maximum starting time to return.hjHubehjubahjdubj()rH}rH(hUh}rH(h]h]h]h]h]uhjRHh}]rHj)rH}rH(hUh}rH(h]h]h]h]h]uhjHh}]rH(j)rH}rH(hXt_stoph}rH(h]h]h]h]h]uhjHh}]rHhXt_stoprHrH}rH(hUhjHubahjubhX (rHrH}rH(hUhjHubh)rH}rH(hUh}rH(UreftypejzU reftargetXQuantity scalarrHU refdomainjGh]h]U refexplicith]h]h]uhjHh}]rHj})rH}rH(hjHh}rH(h]h]h]h]h]uhjHh}]rHhXQuantity scalarrHrH}rH(hUhjHubahjubahjubhX)rH}rH(hUhjHubhX -- rHrH}rH(hUhjHubhXMinimum end time to return. If rHrH}rH(hXMinimum end time to return. If hjHubjD)rH}rH(hX``None``h}rH(h]h]h]h]h]uhjHh}]rHhXNonerHrH}rH(hUhjHubahjOubhX, infinity is used.rHrH}rH(hX, infinity is used.hjHubehjubahjdubehjubahjubehjubj)rH}rH(hUh}rH(h]h]h]h]h]uhj?Hh}]rH(j)rH}rH(hUh}rH(h]h]h]h]h]uhjHh}]rHhXReturnsrHrH}rH(hUhjHubahjubj)rH}rH(hUh}rH(h]h]h]h]h]uhjHh}]rHj)rH}rH(hUh}rH(h]h]h]h]h]uhjHh}]rHhX=Minimum t_start time and maximum t_stop time as time scalars.rHrH}rH(hX=Minimum t_start time and maximum t_stop time as time scalars.hjHubahjubahjubehjubj)rH}rH(hUh}rH(h]h]h]h]h]uhj?Hh}]rH(j)rH}rH(hUh}rI(h]h]h]h]h]uhjHh}]rIhX Return typerIrI}rI(hUhjHubahjubj)rI}rI(hUh}rI(h]h]h]h]h]uhjHh}]rIj)r I}r I(hUh}r I(h]h]h]h]h]uhjIh}]r IhX Quantity scalar, Quantity scalarr IrI}rI(hX Quantity scalar, Quantity scalarhj Iubahjubahjubehjubeubeubeubh)rI}rI(hUhj#ChNhhh}rI(h]h]h]h]h]Uentries]rI(hX;minimum_spike_train_interval() (in module spykeutils.tools)hUtrIauhNhhh}]ubh)rI}rI(hUhj#ChNhhh}rI(hωhXpyrIh]h]h]h]h]hXfunctionrIhjIuhNhhh}]rI(h)rI}rI(hXTminimum_spike_train_interval(trains, t_start=array(-inf) * s, t_stop=array(inf) * s)hjIhhhhh}rI(h]rIhahhXspykeutils.toolsrIr I}r!Ibh]h]h]h]r"IhahXminimum_spike_train_intervalr#IhUhuhNhhh}]r$I(h)r%I}r&I(hj#IhjIhhhhh}r'I(h]h]h]h]h]uhNhhh}]r(IhXminimum_spike_train_intervalr)Ir*I}r+I(hUhj%Iubaubjt)r,I}r-I(hUhjIhhhjwh}r.I(h]h]h]h]h]uhNhhh}]r/I(jz)r0I}r1I(hXtrainsh}r2I(h]h]h]h]h]uhj,Ih}]r3IhXtrainsr4Ir5I}r6I(hUhj0Iubahjubjz)r7I}r8I(hXt_start=array(-inf) * sh}r9I(h]h]h]h]h]uhj,Ih}]r:IhXt_start=array(-inf) * sr;IrI}r?I(hXt_stop=array(inf) * sh}r@I(h]h]h]h]h]uhj,Ih}]rAIhXt_stop=array(inf) * srBIrCI}rDI(hUhj>Iubahjubeubh)rEI}rFI(hUhjIhNhhh}rGI(Uexprhh]h]h]h]h]uhNhhh}]rHIh)rII}rJI(hUh}rKI(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidj#IuhjEIh}]rLIj)rMI}rNI(hUh}rOI(h]h]rPIj ah]h]h]uhjIIh}]rQIhX[source]rRIrSI}rTI(hUhjMIubahjubahjubaubeubj)rUI}rVI(hUhjIhhhjh}rWI(h]h]h]h]h]uhNhhh}]rXI(j)rYI}rZI(hXComputes the maximum starting time and minimum end time that all given spike trains share. This yields the shortest interval shared by all spike trains.r[IhjUIhX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.minimum_spike_train_intervalr\Ihjh}r]I(h]h]h]h]h]uhKhhh}]r^IhXComputes the maximum starting time and minimum end time that all given spike trains share. This yields the shortest interval shared by all spike trains.r_Ir`I}raI(hj[IhjYIubaubj)rbI}rcI(hUhjUIhNhjh}rdI(h]h]h]h]h]uhNhhh}]reI(j)rfI}rgI(hUh}rhI(h]h]h]h]h]uhjbIh}]riI(j)rjI}rkI(hUh}rlI(h]h]h]h]h]uhjfIh}]rmIhX ParametersrnIroI}rpI(hUhjjIubahjubj)rqI}rrI(hUh}rsI(h]h]h]h]h]uhjfIh}]rtIj#)ruI}rvI(hUh}rwI(h]h]h]h]h]uhjqIh}]rxI(j()ryI}rzI(hUh}r{I(h]h]h]h]h]uhjuIh}]r|Ij)r}I}r~I(hUh}rI(h]h]h]h]h]uhjyIh}]rI(j)rI}rI(hXtrainsh}rI(h]h]h]h]h]uhj}Ih}]rIhXtrainsrIrI}rI(hUhjIubahjubhX (rIrI}rI(hUhj}Iubh)rI}rI(hUh}rI(UreftypejzU reftargetXdictrIU refdomainjIh]h]U refexplicith]h]h]uhj}Ih}]rIj})rI}rI(hjIh}rI(h]h]h]h]h]uhjIh}]rIhXdictrIrI}rI(hUhjIubahjubahjubhX)rI}rI(hUhj}IubhX -- rIrI}rI(hUhj}IubhXA dictionary of sequences of rIrI}rI(hXA dictionary of sequences of hj}Iubh)rI}rI(hX:class:`neo.core.SpikeTrain`rIhj}IhNhjh}rI(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrIh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rIjD)rI}rI(hjIh}rI(h]h]rI(jIjIXpy-classrIeh]h]h]uhjIh}]rIhXneo.core.SpikeTrainrIrI}rI(hUhjIubahjOubaubhX objects.rIrI}rI(hX objects.hj}Iubehjubahjdubj()rI}rI(hUh}rI(h]h]h]h]h]uhjuIh}]rIj)rI}rI(hUh}rI(h]h]h]h]h]uhjIh}]rI(j)rI}rI(hXt_starth}rI(h]h]h]h]h]uhjIh}]rIhXt_startrIrI}rI(hUhjIubahjubhX (rIrI}rI(hUhjIubh)rI}rI(hUh}rI(UreftypejzU reftargetXQuantity scalarrIU refdomainjIh]h]U refexplicith]h]h]uhjIh}]rIj})rI}rI(hjIh}rI(h]h]h]h]h]uhjIh}]rIhXQuantity scalarrIrI}rI(hUhjIubahjubahjubhX)rI}rI(hUhjIubhX -- rIrI}rI(hUhjIubhX Minimal starting time to return.rIrI}rI(hX Minimal starting time to return.hjIubehjubahjdubj()rI}rI(hUh}rI(h]h]h]h]h]uhjuIh}]rIj)rI}rI(hUh}rI(h]h]h]h]h]uhjIh}]rI(j)rI}rI(hXt_stoph}rI(h]h]h]h]h]uhjIh}]rIhXt_stoprIrI}rI(hUhjIubahjubhX (rIrI}rI(hUhjIubh)rI}rI(hUh}rI(UreftypejzU reftargetXQuantity scalarrIU refdomainjIh]h]U refexplicith]h]h]uhjIh}]rIj})rI}rI(hjIh}rI(h]h]h]h]h]uhjIh}]rIhXQuantity scalarrIrI}rI(hUhjIubahjubahjubhX)rI}rI(hUhjIubhX -- rIrI}rI(hUhjIubhXMaximum end time to return. If rIrI}rI(hXMaximum end time to return. If hjIubjD)rI}rI(hX``None``h}rI(h]h]h]h]h]uhjIh}]rJhXNonerJrJ}rJ(hUhjIubahjOubhX, infinity is used.rJrJ}rJ(hX, infinity is used.hjIubehjubahjdubehjubahjubehjubj)rJ}rJ(hUh}r J(h]h]h]h]h]uhjbIh}]r J(j)r J}r J(hUh}r J(h]h]h]h]h]uhjJh}]rJhXReturnsrJrJ}rJ(hUhj Jubahjubj)rJ}rJ(hUh}rJ(h]h]h]h]h]uhjJh}]rJj)rJ}rJ(hUh}rJ(h]h]h]h]h]uhjJh}]rJhXKMaximum shared t_start time and minimum shared t_stop time as time scalars.rJrJ}rJ(hXKMaximum shared t_start time and minimum shared t_stop time as time scalars.hjJubahjubahjubehjubj)rJ}rJ(hUh}rJ(h]h]h]h]h]uhjbIh}]r J(j)r!J}r"J(hUh}r#J(h]h]h]h]h]uhjJh}]r$JhX Return typer%Jr&J}r'J(hUhj!Jubahjubj)r(J}r)J(hUh}r*J(h]h]h]h]h]uhjJh}]r+Jj)r,J}r-J(hUh}r.J(h]h]h]h]h]uhj(Jh}]r/JhX Quantity scalar, Quantity scalarr0Jr1J}r2J(hX Quantity scalar, Quantity scalarhj,Jubahjubahjubehjubeubeubeubh)r3J}r4J(hUhj#ChNhhh}r5J(h]h]h]h]h]Uentries]r6J(hX4remove_from_hierarchy() (in module spykeutils.tools)hAUtr7JauhNhhh}]ubh)r8J}r9J(hUhj#ChNhhh}r:J(hωhXpyr;Jh]h]h]h]h]hXfunctionrJ}r?J(hX4remove_from_hierarchy(obj, remove_half_orphans=True)hj8Jhhhhh}r@J(h]rAJhAahhXspykeutils.toolsrBJrCJ}rDJbh]h]h]h]rEJhAahXremove_from_hierarchyrFJhUhuhNhhh}]rGJ(h)rHJ}rIJ(hjFJhj>Jhhhhh}rJJ(h]h]h]h]h]uhNhhh}]rKJhXremove_from_hierarchyrLJrMJ}rNJ(hUhjHJubaubjt)rOJ}rPJ(hUhj>Jhhhjwh}rQJ(h]h]h]h]h]uhNhhh}]rRJ(jz)rSJ}rTJ(hXobjh}rUJ(h]h]h]h]h]uhjOJh}]rVJhXobjrWJrXJ}rYJ(hUhjSJubahjubjz)rZJ}r[J(hXremove_half_orphans=Trueh}r\J(h]h]h]h]h]uhjOJh}]r]JhXremove_half_orphans=Truer^Jr_J}r`J(hUhjZJubahjubeubh)raJ}rbJ(hUhj>JhNhhh}rcJ(Uexprhh]h]h]h]h]uhNhhh}]rdJh)reJ}rfJ(hUh}rgJ(UreftypejUrefdocjU refdomainjh]h]U refexplicith]h]h]U reftargetX_modules/spykeutils/toolsUrefidjFJuhjaJh}]rhJj)riJ}rjJ(hUh}rkJ(h]h]rlJj ah]h]h]uhjeJh}]rmJhX[source]rnJroJ}rpJ(hUhjiJubahjubahjubaubeubj)rqJ}rrJ(hUhj8Jhhhjh}rsJ(h]h]h]h]h]uhNhhh}]rtJ(j)ruJ}rvJ(hXRemoves a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in :class:`neo.core.Spike` or :class:`neo.core.SpikeTrain` objects). For example, when ``obj`` is a :class:`neo.core.Segment`, the link from its parent :class:`neo.core.Block` will be severed. Also, all links to the segment from its spikes and spike trains will be severed.hjqJhXy/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/tools.py:docstring of spykeutils.tools.remove_from_hierarchyrwJhjh}rxJ(h]h]h]h]h]uhKhhh}]ryJ(hX{Removes a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in rzJr{J}r|J(hX{Removes a Neo object from the hierarchy it is embedded in. Mostly downward links are removed (except for possible links in hjuJubh)r}J}r~J(hX:class:`neo.core.Spike`rJhjuJhNhjh}rJ(UreftypeXclassj<j=Xneo.core.SpikeU refdomainXpyrJh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rJjD)rJ}rJ(hjJh}rJ(h]h]rJ(jIjJXpy-classrJeh]h]h]uhj}Jh}]rJhXneo.core.SpikerJrJ}rJ(hUhjJubahjOubaubhX or rJrJ}rJ(hX or hjuJubh)rJ}rJ(hX:class:`neo.core.SpikeTrain`rJhjuJhNhjh}rJ(UreftypeXclassj<j=Xneo.core.SpikeTrainU refdomainXpyrJh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rJjD)rJ}rJ(hjJh}rJ(h]h]rJ(jIjJXpy-classrJeh]h]h]uhjJh}]rJhXneo.core.SpikeTrainrJrJ}rJ(hUhjJubahjOubaubhX objects). For example, when rJrJ}rJ(hX objects). For example, when hjuJubjD)rJ}rJ(hX``obj``h}rJ(h]h]h]h]h]uhjuJh}]rJhXobjrJrJ}rJ(hUhjJubahjOubhX is a rJrJ}rJ(hX is a hjuJubh)rJ}rJ(hX:class:`neo.core.Segment`rJhjuJhNhjh}rJ(UreftypeXclassj<j=Xneo.core.SegmentU refdomainXpyrJh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rJjD)rJ}rJ(hjJh}rJ(h]h]rJ(jIjJXpy-classrJeh]h]h]uhjJh}]rJhXneo.core.SegmentrJrJ}rJ(hUhjJubahjOubaubhX, the link from its parent rJrJ}rJ(hX, the link from its parent hjuJubh)rJ}rJ(hX:class:`neo.core.Block`rJhjuJhNhjh}rJ(UreftypeXclassj<j=Xneo.core.BlockU refdomainXpyrJh]h]U refexplicith]h]h]j?jj@NjAjCuhNh}]rJjD)rJ}rJ(hjJh}rJ(h]h]rJ(jIjJXpy-classrJeh]h]h]uhjJh}]rJhXneo.core.BlockrJrJ}rJ(hUhjJubahjOubaubhXb will be severed. Also, all links to the segment from its spikes and spike trains will be severed.rJrJ}rJ(hXb will be severed. 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If you want to implement your own script for starting plugins remotely, e.g. on a server, you should conform to the interface of this script.hIhubeubhE)q}q(hHUhIhFhJhuhOhPhQ}q(hS]hT]hU]hV]q(X(module-spykeutils.plugin.analysis_pluginqh?ehY]qhauh[Kh\hhC]q(h^)q}q(hHX:mod:`analysis_plugin` ModuleqhIhhJhuhOhbhQ}q(hS]hT]hU]hV]hY]uh[Kh\hhC]q(csphinx.addnodes pending_xref q)q}q(hHX:mod:`analysis_plugin`qhIhhJNhOU pending_xrefqhQ}q(UreftypeXmodUrefwarnqU reftargetqXanalysis_pluginU refdomainXpyqhV]hU]U refexplicithS]hT]hY]UrefdocqXapidoc/spykeutils.pluginqUpy:classqNU py:moduleqXspykeutils.pluginquh[NhC]qcdocutils.nodes literal q)q}q(hHhhQ}q(hS]hT]q(UxrefqhXpy-modqehU]hV]hY]uhIhhC]qheXanalysis_pluginqÅq}q(hHUhIhubahOUliteralqubaubheX ModuleqDžq}q(hHX ModuleqhIhubeubhi)q}q(hHUhIhhJhlhOhmhQ}q(hV]hU]hS]hT]hY]Uentries]q(hpX*spykeutils.plugin.analysis_plugin (module)X(module-spykeutils.plugin.analysis_pluginUtqauh[Kh\hhC]ubhi)q}q(hHUhIhhJNhOhmhQ}q(hV]hU]hS]hT]hY]Uentries]q(hpX;AnalysisPlugin (class in spykeutils.plugin.analysis_plugin)h Utqauh[Nh\hhC]ubcsphinx.addnodes desc q)q}q(hHUhIhhJNhOUdescqhQ}q(UnoindexqډUdomainqXpyhV]hU]hS]hT]hY]UobjtypeqXclassqUdesctypeqhuh[Nh\hhC]q(csphinx.addnodes desc_signature q)q}q(hHXAnalysisPlugin()hIhhJU qhOUdesc_signatureqhQ}q(hV]qh aUmoduleqhKX!spykeutils.plugin.analysis_pluginq腁q}qbhU]hS]hT]hY]qh aUfullnameqXAnalysisPluginqUclassqUUfirstquh[Nh\hhC]q(csphinx.addnodes desc_annotation q)q}q(hHXclass hIhhJhhOUdesc_annotationqhQ}q(hS]hT]hU]hV]hY]uh[Nh\hhC]qheXclass qq}q(hHUhIhubaubcsphinx.addnodes desc_name q)q}q(hHhhIhhJhhOU desc_nameqhQ}q(hS]hT]hU]hV]hY]uh[Nh\hhC]qheXAnalysisPluginrr}r(hHUhIhubaubcsphinx.addnodes only r)r}r(hHUhIhhJNhOUonlyrhQ}r(UexprUhtmlrhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (UreftypeUviewcoder UrefdochU refdomainUstdrhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidhuhIjhC]rcdocutils.nodes inline r)r}r(hHUhQ}r(hS]hT]rU viewcode-linkrahU]hV]hY]uhIj hC]rheX[source]rr}r(hHUhIjubahOUinlinerubahOhubaubeubcsphinx.addnodes desc_content r)r}r(hHUhIhhJhhOU desc_contentrhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r!}r"(hHX2Bases: :class:`spykeutils.plugin.gui_data.DataSet`hIjhJU r#hOhvhQ}r$(hS]hT]hU]hV]hY]uh[Kh\hhC]r%(heXBases: r&r'}r((hHXBases: hIj!ubh)r)}r*(hHX+:class:`spykeutils.plugin.gui_data.DataSet`r+hIj!hJNhOhhQ}r,(UreftypeXclasshhX"spykeutils.plugin.gui_data.DataSetU refdomainXpyr-hV]hU]U refexplicithS]hT]hY]hhhhhX!spykeutils.plugin.analysis_pluginr.uh[NhC]r/h)r0}r1(hHj+hQ}r2(hS]hT]r3(hj-Xpy-classr4ehU]hV]hY]uhIj)hC]r5heX"spykeutils.plugin.gui_data.DataSetr6r7}r8(hHUhIj0ubahOhubaubeubhr)r9}r:(hHXGBase class for Analysis plugins. Inherit this class to create a plugin.r;hIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPluginr<hOhvhQ}r=(hS]hT]hU]hV]hY]uh[Kh\hhC]r>heXGBase class for Analysis plugins. Inherit this class to create a plugin.r?r@}rA(hHj;hIj9ubaubhr)rB}rC(hHXThe two most important methods are :func:`get_name` and :func:`start`. Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results.hIjhJj<hOhvhQ}rD(hS]hT]hU]hV]hY]uh[Kh\hhC]rE(heX#The two most important methods are rFrG}rH(hHX#The two most important methods are hIjBubh)rI}rJ(hHX:func:`get_name`rKhIjBhJNhOhhQ}rL(UreftypeXfunchhXget_nameU refdomainXpyrMhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rNh)rO}rP(hHjKhQ}rQ(hS]hT]rR(hjMXpy-funcrSehU]hV]hY]uhIjIhC]rTheX get_name()rUrV}rW(hHUhIjOubahOhubaubheX and rXrY}rZ(hHX and hIjBubh)r[}r\(hHX :func:`start`r]hIjBhJNhOhhQ}r^(UreftypeXfunchhXstartU refdomainXpyr_hV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]r`h)ra}rb(hHj]hQ}rc(hS]hT]rd(hj_Xpy-funcreehU]hV]hY]uhIj[hC]rfheXstart()rgrh}ri(hHUhIjaubahOhubaubheX. Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results.rjrk}rl(hHX. Both should be overridden by every plugin. The class also has functionality for GUI configuration and saving/restoring analysis results.hIjBubeubhr)rm}rn(hHX>The GUI configuration uses :mod:`guidata`. Because `AnalysisPlugin` inherits from `DataSet`, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console::hIjhJj<hOhvhQ}ro(hS]hT]hU]hV]hY]uh[K h\hhC]rp(heXThe GUI configuration uses rqrr}rs(hHXThe GUI configuration uses hIjmubh)rt}ru(hHX:mod:`guidata`rvhIjmhJNhOhhQ}rw(UreftypeXmodhhXguidataU refdomainXpyrxhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]ryh)rz}r{(hHjvhQ}r|(hS]hT]r}(hjxXpy-modr~ehU]hV]hY]uhIjthC]rheXguidatarr}r(hHUhIjzubahOhubaubheX . Because rr}r(hHX . Because hIjmubh|)r}r(hHX`AnalysisPlugin`hQ}r(hS]hT]hU]hV]hY]uhIjmhC]rheXAnalysisPluginrr}r(hHUhIjubahOhubheX inherits from rr}r(hHX inherits from hIjmubh|)r}r(hHX `DataSet`hQ}r(hS]hT]hU]hV]hY]uhIjmhC]rheXDataSetrr}r(hHUhIjubahOhubheX, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console:rr}r(hHX, configuration options can easily be added directly to the class definition. For example, the following code creates an analysis that has two configuration options which are used in the start() method to print to the console:hIjmubeubcdocutils.nodes literal_block r)r}r(hHXfrom spykeutils.plugin import analysis_plugin, gui_data class SamplePlugin(analysis_plugin.AnalysisPlugin): some_time = gui_data.FloatItem('Some time', default=2.0, unit='ms') print_more = gui_data.BoolItem('Print additional info', default=True) def start(self, current, selections): print 'The selected time is', self.some_time, 'milliseconds.' if self.print_more: print 'This is important additional information!'hIjhJj<hOU literal_blockrhQ}r(U xml:spacerUpreserverhV]hU]hS]hT]hY]uh[Kh\hhC]rheXfrom spykeutils.plugin import analysis_plugin, gui_data class SamplePlugin(analysis_plugin.AnalysisPlugin): some_time = gui_data.FloatItem('Some time', default=2.0, unit='ms') print_more = gui_data.BoolItem('Print additional info', default=True) def start(self, current, selections): print 'The selected time is', self.some_time, 'milliseconds.' if self.print_more: print 'This is important additional information!'rr}r(hHUhIjubaubhr)r}r(hHXUThe class attribute ``data_dir`` contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory.hIjhJj<hOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]r(heXThe class attribute rr}r(hHXThe class attribute hIjubh)r}r(hHX ``data_dir``hQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXdata_dirrr}r(hHUhIjubahOhubheX5 contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory.rr}r(hHX5 contains a base directory for saving and loading data. It is set by Spyke Viewer to the directory specified in the settings. When using an AnalysisPlugin without Spyke Viewer, the default value is an empty string (so the current directory will be used) and the attribute can be set to an arbitrary directory.hIjubeubhi)r}r(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.configurerhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX#configure() (AnalysisPlugin method)hUtrauh[Nh\hhC]ubh)r}r(hHUhIjhJjhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXAnalysisPlugin.configure()hIjhJhhOhhQ}r(hV]rhahhKX!spykeutils.plugin.analysis_pluginrr}rbhU]hS]hT]hY]rhahXAnalysisPlugin.configurerhhhuh[Nh\hhC]r(h)r}r(hHX configurehIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX configurerr}r(hHUhIjubaubcsphinx.addnodes desc_parameterlist r)r}r(hHUhIjhJhhOUdesc_parameterlistrhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rhr)r}r(hHXiConfigure the analysis. Override if a different or additional configuration apart from guidata is needed.rhIjhJjhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXiConfigure the analysis. Override if a different or additional configuration apart from guidata is needed.rr}r(hHjhIjubaubaubeubhi)r}r(hHUhIjhJNhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX"get_name() (AnalysisPlugin method)h)Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJNhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXAnalysisPlugin.get_name()hIjhJhhOhhQ}r(hV]rh)ahhKX!spykeutils.plugin.analysis_pluginrr}rbhU]hS]hT]hY]rh)ahXAnalysisPlugin.get_namerhhhuh[Nh\hhC]r(h)r}r(hHXget_namehIjhJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heXget_namer r }r (hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r (hHUhIjubahOjubahOhubaubeubj)r!}r"(hHUhIjhJhhOjhQ}r#(hS]hT]hU]hV]hY]uh[Nh\hhC]r$(hr)r%}r&(hHXBReturn the name of an analysis. Override to specify analysis name.r'hIj!hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.get_namehOhvhQ}r((hS]hT]hU]hV]hY]uh[Kh\hhC]r)heXBReturn the name of an analysis. Override to specify analysis name.r*r+}r,(hHj'hIj%ubaubcdocutils.nodes field_list r-)r.}r/(hHUhIj!hJNhOU field_listr0hQ}r1(hS]hT]hU]hV]hY]uh[Nh\hhC]r2(cdocutils.nodes field r3)r4}r5(hHUhQ}r6(hS]hT]hU]hV]hY]uhIj.hC]r7(cdocutils.nodes field_name r8)r9}r:(hHUhQ}r;(hS]hT]hU]hV]hY]uhIj4hC]r<heXReturnsr=r>}r?(hHUhIj9ubahOU field_namer@ubcdocutils.nodes field_body rA)rB}rC(hHUhQ}rD(hS]hT]hU]hV]hY]uhIj4hC]rEhr)rF}rG(hHUhQ}rH(hS]hT]hU]hV]hY]uhIjBhC]rIheXThe name of the plugin.rJrK}rL(hHXThe name of the plugin.hIjFubahOhvubahOU field_bodyrMubehOUfieldrNubj3)rO}rP(hHUhQ}rQ(hS]hT]hU]hV]hY]uhIj.hC]rR(j8)rS}rT(hHUhQ}rU(hS]hT]hU]hV]hY]uhIjOhC]rVheX Return typerWrX}rY(hHUhIjSubahOj@ubjA)rZ}r[(hHUhQ}r\(hS]hT]hU]hV]hY]uhIjOhC]r]hr)r^}r_(hHUhQ}r`(hS]hT]hU]hV]hY]uhIjZhC]raheXstrrbrc}rd(hHXstrhIj^ubahOhvubahOjMubehOjNubeubeubeubhi)re}rf(hHUhIjhJNhOhmhQ}rg(hV]hU]hS]hT]hY]Uentries]rh(hpX(get_parameters() (AnalysisPlugin method)h,Utriauh[Nh\hhC]ubh)rj}rk(hHUhIjhJNhOhhQ}rl(hډhXpyhV]hU]hS]hT]hY]hXmethodrmhjmuh[Nh\hhC]rn(h)ro}rp(hHXAnalysisPlugin.get_parameters()hIjjhJhhOhhQ}rq(hV]rrh,ahhKX!spykeutils.plugin.analysis_pluginrsrt}rubhU]hS]hT]hY]rvh,ahXAnalysisPlugin.get_parametersrwhhhuh[Nh\hhC]rx(h)ry}rz(hHXget_parametershIjohJhhOhhQ}r{(hS]hT]hU]hV]hY]uh[Nh\hhC]r|heXget_parametersr}r~}r(hHUhIjyubaubj)r}r(hHUhIjohJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjohJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjwuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHXReturn a dictionary of the configuration that can be read with :func:`deserialize_parameters`. Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation).hIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.get_parametershOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]r(heX?Return a dictionary of the configuration that can be read with rr}r(hHX?Return a dictionary of the configuration that can be read with hIjubh)r}r(hHX:func:`deserialize_parameters`rhIjhJNhOhhQ}r(UreftypeXfunchhXdeserialize_parametersU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-funcrehU]hV]hY]uhIjhC]rheXdeserialize_parameters()rr}r(hHUhIjubahOhubaubheX. Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation).rr}r(hHX. Override both if non-guidata attributes need to be serialized or if some guidata parameters should not be serialized (e.g. they only affect the visual presentation).hIjubeubj-)r}r(hHUhIjhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(j3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXReturnsrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX-A dictionary of all configuration parameters.rr}r(hHX-A dictionary of all configuration parameters.hIjubahOhvubahOjMubehOjNubj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Return typerr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXdictrr}r(hHXdicthIjubahOhvubahOjMubehOjNubeubeubeubhi)r}r(hHUhIjhJNhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpXload() (AnalysisPlugin method)hUtrauh[Nh\hhC]ubh)r}r(hHUhIjhJNhOhhQ}r(hډhXpyrhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXKAnalysisPlugin.load(name, selections, params=None, consider_guiparams=True)hIjhJhhOhhQ}r(hV]rhahhKX!spykeutils.plugin.analysis_pluginrr}rbhU]hS]hT]hY]rhahXAnalysisPlugin.loadrhhhuh[Nh\hhC]r(h)r}r(hHXloadhIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXloadrr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(csphinx.addnodes desc_parameter r)r}r(hHXnamehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXnamerr}r(hHUhIjubahOUdesc_parameterrubj)r }r (hHX selectionshQ}r (hS]hT]hU]hV]hY]uhIjhC]r heX selectionsr r}r(hHUhIj ubahOjubj)r}r(hHX params=NonehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX params=Nonerr}r(hHUhIjubahOjubj)r}r(hHXconsider_guiparams=TruehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXconsider_guiparams=Truerr}r(hHUhIjubahOjubeubj)r}r(hHUhIjhJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r!h)r"}r#(hHUhQ}r$(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIjhC]r%j)r&}r'(hHUhQ}r((hS]hT]r)jahU]hV]hY]uhIj"hC]r*heX[source]r+r,}r-(hHUhIj&ubahOjubahOhubaubeubj)r.}r/(hHUhIjhJhhOjhQ}r0(hS]hT]hU]hV]hY]uh[Nh\hhC]r1(hr)r2}r3(hHXReturn the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by :func:`save`.hIj.hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.loadhOhvhQ}r4(hS]hT]hU]hV]hY]uh[Kh\hhC]r5(heXReturn the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by r6r7}r8(hHXReturn the most recent HDF5 file for a certain parameter configuration. If no such file exists, return None. This function works with the files created by hIj2ubh)r9}r:(hHX :func:`save`r;hIj2hJNhOhhQ}r<(UreftypeXfunchhXsaveU refdomainXpyr=hV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]r>h)r?}r@(hHj;hQ}rA(hS]hT]rB(hj=Xpy-funcrCehU]hV]hY]uhIj9hC]rDheXsave()rErF}rG(hHUhIj?ubahOhubaubheX.rH}rI(hHX.hIj2ubeubj-)rJ}rK(hHUhIj.hJNhOj0hQ}rL(hS]hT]hU]hV]hY]uh[Nh\hhC]rM(j3)rN}rO(hHUhQ}rP(hS]hT]hU]hV]hY]uhIjJhC]rQ(j8)rR}rS(hHUhQ}rT(hS]hT]hU]hV]hY]uhIjNhC]rUheX ParametersrVrW}rX(hHUhIjRubahOj@ubjA)rY}rZ(hHUhQ}r[(hS]hT]hU]hV]hY]uhIjNhC]r\cdocutils.nodes bullet_list r])r^}r_(hHUhQ}r`(hS]hT]hU]hV]hY]uhIjYhC]ra(cdocutils.nodes list_item rb)rc}rd(hHUhQ}re(hS]hT]hU]hV]hY]uhIj^hC]rfhr)rg}rh(hHUhQ}ri(hS]hT]hU]hV]hY]uhIjchC]rj(cdocutils.nodes strong rk)rl}rm(hHXnamehQ}rn(hS]hT]hU]hV]hY]uhIjghC]roheXnamerprq}rr(hHUhIjlubahOUstrongrsubheX (rtru}rv(hHUhIjgubh)rw}rx(hHUhQ}ry(UreftypeUobjrzU reftargetXstrr{U refdomainjhV]hU]U refexplicithS]hT]hY]uhIjghC]r|cdocutils.nodes emphasis r})r~}r(hHj{hQ}r(hS]hT]hU]hV]hY]uhIjwhC]rheXstrrr}r(hHUhIj~ubahOUemphasisrubahOhubheX)r}r(hHUhIjgubheX -- rr}r(hHUhIjgubheX The name of the results to load.rr}r(hHX The name of the results to load.hIjgubehOhvubahOU list_itemrubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIj^hC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHX selectionshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX selectionsrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXsequencerU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXsequencerr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheX A list of rr}r(hHX A list of hIjubh)r}r(hHX:class:`DataProvider`rhIjhJNhOhhQ}r(UreftypeXclasshhX DataProviderU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-classrehU]hV]hY]uhIjhC]rheX DataProviderrr}r(hHUhIjubahOhubaubheX4 objects that are relevant for the analysis results.rr}r(hHX4 objects that are relevant for the analysis results.hIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIj^hC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXparamshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXparamsrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXdictrU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXdictrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXA dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.rr}r(hHXA dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.hIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIj^hC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXconsider_guiparamshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXconsider_guiparamsrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXboolrU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXboolrr }r (hHUhIjubahOjubahOhubheX)r }r (hHUhIjubheX -- r r}r(hHUhIjubheXDetermines if the guidata parameters of the class should be considered if they exist in the HDF5 file. This should be set to False if rr}r(hHXDetermines if the guidata parameters of the class should be considered if they exist in the HDF5 file. This should be set to False if hIjubh)r}r(hHX :func:`save`rhIjhJNhOhhQ}r(UreftypeXfunchhXsaveU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-funcrehU]hV]hY]uhIjhC]rheXsave()rr }r!(hHUhIjubahOhubaubheX is used with r"r#}r$(hHX is used with hIjubh)r%}r&(hHX``save_guiparams``hQ}r'(hS]hT]hU]hV]hY]uhIjhC]r(heXsave_guiparamsr)r*}r+(hHUhIj%ubahOhubheX set to r,r-}r.(hHX set to hIjubh)r/}r0(hHX ``False``hQ}r1(hS]hT]hU]hV]hY]uhIjhC]r2heXFalser3r4}r5(hHUhIj/ubahOhubheX.r6}r7(hHX.hIjubehOhvubahOjubehOU bullet_listr8ubahOjMubehOjNubj3)r9}r:(hHUhQ}r;(hS]hT]hU]hV]hY]uhIjJhC]r<(j8)r=}r>(hHUhQ}r?(hS]hT]hU]hV]hY]uhIj9hC]r@heXReturnsrArB}rC(hHUhIj=ubahOj@ubjA)rD}rE(hHUhQ}rF(hS]hT]hU]hV]hY]uhIj9hC]rGhr)rH}rI(hHUhQ}rJ(hS]hT]hU]hV]hY]uhIjDhC]rK(heXqAn open PyTables file object ready to be used to read data. Afterwards, the file has to be closed by calling the rLrM}rN(hHXqAn open PyTables file object ready to be used to read data. Afterwards, the file has to be closed by calling the hIjHubh)rO}rP(hHX:func:`tables.File.close`rQhIjHhJNhOhhQ}rR(UreftypeXfunchhXtables.File.closeU refdomainXpyrShV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rTh)rU}rV(hHjQhQ}rW(hS]hT]rX(hjSXpy-funcrYehU]hV]hY]uhIjOhC]rZheXtables.File.close()r[r\}r](hHUhIjUubahOhubaubheX9 method. If no appropriate file exists, None is returned.r^r_}r`(hHX9 method. If no appropriate file exists, None is returned.hIjHubehOhvubahOjMubehOjNubj3)ra}rb(hHUhQ}rc(hS]hT]hU]hV]hY]uhIjJhC]rd(j8)re}rf(hHUhQ}rg(hS]hT]hU]hV]hY]uhIjahC]rhheX Return typerirj}rk(hHUhIjeubahOj@ubjA)rl}rm(hHUhQ}rn(hS]hT]hU]hV]hY]uhIjahC]rohr)rp}rq(hHUhQ}rr(hS]hT]hU]hV]hY]uhIjlhC]rsh)rt}ru(hHX:class:`tables.File`rvhIjphJNhOhhQ}rw(UreftypeXclasshhX tables.FileU refdomainXpyrxhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]ryh)rz}r{(hHjvhQ}r|(hS]hT]r}(hjxXpy-classr~ehU]hV]hY]uhIjthC]rheX tables.Filerr}r(hHUhIjzubahOhubaubahOhvubahOjMubehOjNubeubeubeubhi)r}r(hHUhIjhJNhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpXsave() (AnalysisPlugin method)h'Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJNhOhhQ}r(hډhXpyrhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXGAnalysisPlugin.save(name, selections, params=None, save_guiparams=True)hIjhJhhOhhQ}r(hV]rh'ahhKX!spykeutils.plugin.analysis_pluginrr}rbhU]hS]hT]hY]rh'ahXAnalysisPlugin.saverhhhuh[Nh\hhC]r(h)r}r(hHXsavehIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXsaverr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(j)r}r(hHXnamehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXnamerr}r(hHUhIjubahOjubj)r}r(hHX selectionshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX selectionsrr}r(hHUhIjubahOjubj)r}r(hHX params=NonehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX params=Nonerr}r(hHUhIjubahOjubj)r}r(hHXsave_guiparams=TruehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXsave_guiparams=Truerr}r(hHUhIjubahOjubeubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHX]Return a HDF5 file object with parameters already stored. Save analysis results to this file.rhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.savehOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheX]Return a HDF5 file object with parameters already stored. Save analysis results to this file.rr}r(hHjhIjubaubj-)r}r(hHUhIjhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(j3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rj])r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXnamehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXnamerr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXstrrU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r }r (hHjhQ}r (hS]hT]hU]hV]hY]uhIjhC]r heXstrr r}r(hHUhIj ubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXThe name of the results to save. A folder with this name will be used (and created if necessary) to store the analysis result files.rr}r(hHXThe name of the results to save. A folder with this name will be used (and created if necessary) to store the analysis result files.hIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r }r!(hHX selectionshQ}r"(hS]hT]hU]hV]hY]uhIjhC]r#heX selectionsr$r%}r&(hHUhIj ubahOjsubheX (r'r(}r)(hHUhIjubh)r*}r+(hHUhQ}r,(UreftypejzU reftargetXsequencer-U refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]r.j})r/}r0(hHj-hQ}r1(hS]hT]hU]hV]hY]uhIj*hC]r2heXsequencer3r4}r5(hHUhIj/ubahOjubahOhubheX)r6}r7(hHUhIjubheX -- r8r9}r:(hHUhIjubheX A list of r;r<}r=(hHX A list of hIjubh)r>}r?(hHX:class:`DataProvider`r@hIjhJNhOhhQ}rA(UreftypeXclasshhX DataProviderU refdomainXpyrBhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rCh)rD}rE(hHj@hQ}rF(hS]hT]rG(hjBXpy-classrHehU]hV]hY]uhIj>hC]rIheX DataProviderrJrK}rL(hHUhIjDubahOhubaubheX4 objects that are relevant for the analysis results.rMrN}rO(hHX4 objects that are relevant for the analysis results.hIjubehOhvubahOjubjb)rP}rQ(hHUhQ}rR(hS]hT]hU]hV]hY]uhIjhC]rShr)rT}rU(hHUhQ}rV(hS]hT]hU]hV]hY]uhIjPhC]rW(jk)rX}rY(hHXparamshQ}rZ(hS]hT]hU]hV]hY]uhIjThC]r[heXparamsr\r]}r^(hHUhIjXubahOjsubheX (r_r`}ra(hHUhIjTubh)rb}rc(hHUhQ}rd(UreftypejzU reftargetXdictreU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjThC]rfj})rg}rh(hHjehQ}ri(hS]hT]hU]hV]hY]uhIjbhC]rjheXdictrkrl}rm(hHUhIjgubahOjubahOhubheX)rn}ro(hHUhIjTubheX -- rprq}rr(hHUhIjTubheXA dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.rsrt}ru(hHXA dictionary, indexed by strings (which should be valid as python identifiers), with parameters apart from GUI configuration used to obtain the results. All keys have to be integers, floats, strings or lists of these types.hIjTubehOhvubahOjubjb)rv}rw(hHUhQ}rx(hS]hT]hU]hV]hY]uhIjhC]ryhr)rz}r{(hHUhQ}r|(hS]hT]hU]hV]hY]uhIjvhC]r}(jk)r~}r(hHXsave_guiparamshQ}r(hS]hT]hU]hV]hY]uhIjzhC]rheXsave_guiparamsrr}r(hHUhIj~ubahOjsubheX (rr}r(hHUhIjzubh)r}r(hHUhQ}r(UreftypejzU reftargetXboolrU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjzhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXboolrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjzubheX -- rr}r(hHUhIjzubheXNDetermines if the guidata parameters of the class should be saved in the file.rr}r(hHXNDetermines if the guidata parameters of the class should be saved in the file.hIjzubehOhvubahOjubehOj8ubahOjMubehOjNubj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXReturnsrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(heXrAn open PyTables file object ready to be used to store data. Afterwards, the file has to be closed by calling the rr}r(hHXrAn open PyTables file object ready to be used to store data. Afterwards, the file has to be closed by calling the hIjubh)r}r(hHX:func:`tables.File.close`rhIjhJNhOhhQ}r(UreftypeXfunchhXtables.File.closeU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-funcrehU]hV]hY]uhIjhC]rheXtables.File.close()rr}r(hHUhIjubahOhubaubheX method.rr}r(hHX method.hIjubehOhvubahOjMubehOjNubj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Return typerr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rh)r}r(hHX:class:`tables.File`rhIjhJNhOhhQ}r(UreftypeXclasshhX tables.FileU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-classrehU]hV]hY]uhIjhC]rheX tables.Filerr}r(hHUhIjubahOhubaubahOhvubahOjMubehOjNubeubeubeubhi)r}r(hHUhIjhJNhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX(set_parameters() (AnalysisPlugin method)h(Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJNhOhhQ}r(hډhXpyrhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHX)AnalysisPlugin.set_parameters(parameters)hIjhJhhOhhQ}r(hV]rh(ahhKX!spykeutils.plugin.analysis_pluginrr}rbhU]hS]hT]hY]rh(ahXAnalysisPlugin.set_parametersrhhhuh[Nh\hhC]r(h)r}r(hHXset_parametershIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXset_parametersrr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rj)r}r(hHX parametershQ}r(hS]hT]hU]hV]hY]uhIjhC]r heX parametersr r }r (hHUhIjubahOjubaubj)r }r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIj hC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r!}r"(hHXLoad configuration from a dictionary that has been created by :func:`serialize_parameters`. Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized.hIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.set_parametershOhvhQ}r#(hS]hT]hU]hV]hY]uh[Kh\hhC]r$(heX>Load configuration from a dictionary that has been created by r%r&}r'(hHX>Load configuration from a dictionary that has been created by hIj!ubh)r(}r)(hHX:func:`serialize_parameters`r*hIj!hJNhOhhQ}r+(UreftypeXfunchhXserialize_parametersU refdomainXpyr,hV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]r-h)r.}r/(hHj*hQ}r0(hS]hT]r1(hj,Xpy-funcr2ehU]hV]hY]uhIj(hC]r3heXserialize_parameters()r4r5}r6(hHUhIj.ubahOhubaubheX. Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized.r7r8}r9(hHX. Parameters that are not part of the guidata attributes of the plugin are ignored. Override if non-guidata attributes need to be serialized.hIj!ubeubj-)r:}r;(hHUhIjhJNhOj0hQ}r<(hS]hT]hU]hV]hY]uh[Nh\hhC]r=j3)r>}r?(hHUhQ}r@(hS]hT]hU]hV]hY]uhIj:hC]rA(j8)rB}rC(hHUhQ}rD(hS]hT]hU]hV]hY]uhIj>hC]rEheX ParametersrFrG}rH(hHUhIjBubahOj@ubjA)rI}rJ(hHUhQ}rK(hS]hT]hU]hV]hY]uhIj>hC]rLhr)rM}rN(hHUhQ}rO(hS]hT]hU]hV]hY]uhIjIhC]rP(jk)rQ}rR(hHX parametershQ}rS(hS]hT]hU]hV]hY]uhIjMhC]rTheX parametersrUrV}rW(hHUhIjQubahOjsubheX (rXrY}rZ(hHUhIjMubh)r[}r\(hHUhQ}r](UreftypejzU reftargetXdictr^U refdomainjhV]hU]U refexplicithS]hT]hY]uhIjMhC]r_j})r`}ra(hHj^hQ}rb(hS]hT]hU]hV]hY]uhIj[hC]rcheXdictrdre}rf(hHUhIj`ubahOjubahOhubheX)rg}rh(hHUhIjMubheX -- rirj}rk(hHUhIjMubheX-A dictionary of all configuration parameters.rlrm}rn(hHX-A dictionary of all configuration parameters.hIjMubehOhvubahOjMubehOjNubaubeubeubhi)ro}rp(hHUhIjhJNhOhmhQ}rq(hV]hU]hS]hT]hY]Uentries]rr(hpXstart() (AnalysisPlugin method)hUtrsauh[Nh\hhC]ubh)rt}ru(hHUhIjhJNhOhhQ}rv(hډhXpyrwhV]hU]hS]hT]hY]hXmethodrxhjxuh[Nh\hhC]ry(h)rz}r{(hHX)AnalysisPlugin.start(current, selections)hIjthJhhOhhQ}r|(hV]r}hahhKX!spykeutils.plugin.analysis_pluginr~r}rbhU]hS]hT]hY]rhahXAnalysisPlugin.startrhhhuh[Nh\hhC]r(h)r}r(hHXstarthIjzhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXstartrr}r(hHUhIjubaubj)r}r(hHUhIjzhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(j)r}r(hHXcurrenthQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXcurrentrr}r(hHUhIjubahOjubj)r}r(hHX selectionshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX selectionsrr}r(hHUhIjubahOjubeubj)r}r(hHUhIjzhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX*_modules/spykeutils/plugin/analysis_pluginUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjthJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHX8Entry point for processing. Override with analysis code.rhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/analysis_plugin.py:docstring of spykeutils.plugin.analysis_plugin.AnalysisPlugin.startrhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheX8Entry point for processing. Override with analysis code.rr}r(hHjhIjubaubj-)r}r(hHUhIjhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rj])r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXcurrenthQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXcurrentrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHX@:class:`spykeviewer.plugin_framework.data_provider.DataProvider`rhIjhJNhOhhQ}r(UreftypeXclasshhX7spykeviewer.plugin_framework.data_provider.DataProviderU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhhhj.uh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-classrehU]hV]hY]uhIjhC]rheX7spykeviewer.plugin_framework.data_provider.DataProviderrr}r(hHUhIjubahOhubaubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXiThis data provider is used if the analysis should be performed on the data currently selected in the GUI.rr}r(hHXiThis data provider is used if the analysis should be performed on the data currently selected in the GUI.rhIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHX selectionshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX selectionsrr}r (hHUhIjubahOjsubheX (r r }r (hHUhIjubh)r }r(hHUhQ}r(UreftypejzU reftargetXlistrU refdomainjwhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIj hC]rheXlistrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXaThis parameter contains all saved selections. It is used if an analysis needs multiple data sets.rr}r (hHXaThis parameter contains all saved selections. It is used if an analysis needs multiple data sets.r!hIjubehOhvubahOjubehOj8ubahOjMubehOjNubaubeubeubeubeubeubhE)r"}r#(hHUhIhFhJhuhOhPhQ}r$(hS]hT]hU]hV]r%(X&module-spykeutils.plugin.data_providerr&h@ehY]r'hauh[Kh\hhC]r((h^)r)}r*(hHX:mod:`data_provider` Moduler+hIj"hJhuhOhbhQ}r,(hS]hT]hU]hV]hY]uh[Kh\hhC]r-(h)r.}r/(hHX:mod:`data_provider`r0hIj)hJNhOhhQ}r1(UreftypeXmodhhX data_providerU refdomainXpyr2hV]hU]U refexplicithS]hT]hY]hhhNhj.uh[NhC]r3h)r4}r5(hHj0hQ}r6(hS]hT]r7(hj2Xpy-modr8ehU]hV]hY]uhIj.hC]r9heX data_providerr:r;}r<(hHUhIj4ubahOhubaubheX Moduler=r>}r?(hHX Moduler@hIj)ubeubhi)rA}rB(hHUhIj"hJhlhOhmhQ}rC(hV]hU]hS]hT]hY]Uentries]rD(hpX(spykeutils.plugin.data_provider (module)X&module-spykeutils.plugin.data_providerUtrEauh[Kh\hhC]ubhi)rF}rG(hHUhIj"hJNhOhmhQ}rH(hV]hU]hS]hT]hY]Uentries]rI(hpX7DataProvider (class in spykeutils.plugin.data_provider)hUtrJauh[Nh\hhC]ubh)rK}rL(hHUhIj"hJNhOhhQ}rM(hډhXpyhV]hU]hS]hT]hY]hXclassrNhjNuh[Nh\hhC]rO(h)rP}rQ(hHXDataProvider(name, progress)hIjKhJhhOhhQ}rR(hV]rShahhKXspykeutils.plugin.data_providerrTrU}rVbhU]hS]hT]hY]rWhahX DataProviderrXhUhuh[Nh\hhC]rY(h)rZ}r[(hHXclass hIjPhJhhOhhQ}r\(hS]hT]hU]hV]hY]uh[Nh\hhC]r]heXclass r^r_}r`(hHUhIjZubaubh)ra}rb(hHjXhIjPhJhhOhhQ}rc(hS]hT]hU]hV]hY]uh[Nh\hhC]rdheX DataProviderrerf}rg(hHUhIjaubaubj)rh}ri(hHUhIjPhJhhOjhQ}rj(hS]hT]hU]hV]hY]uh[Nh\hhC]rk(j)rl}rm(hHXnamehQ}rn(hS]hT]hU]hV]hY]uhIjhhC]roheXnamerprq}rr(hHUhIjlubahOjubj)rs}rt(hHXprogresshQ}ru(hS]hT]hU]hV]hY]uhIjhhC]rvheXprogressrwrx}ry(hHUhIjsubahOjubeubj)rz}r{(hHUhIjPhJNhOjhQ}r|(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r}h)r~}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjXuhIjzhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIj~hC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjKhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHXBases: :class:`object`rhIjhJj#hOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]r(heXBases: rr}r(hHXBases: hIjubh)r}r(hHX:class:`object`rhIjhJNhOhhQ}r(UreftypeXclasshhXobjectU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhjXhXspykeutils.plugin.data_providerruh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-classrehU]hV]hY]uhIjhC]rheXobjectrr}r(hHUhIjubahOhubaubeubhr)r}r(hHXRDefines all methods that should be implemented by a selection/data provider class.rhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProviderrhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXRDefines all methods that should be implemented by a selection/data provider class.rr}r(hHjhIjubaubhr)r}r(hHXA `DataProvider` encapsulates access to a selection of data. 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It also contains an attribute hIjubh|)r}r(hHX `progress`hQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXprogressrr}r(hHUhIjubahOhubheX, a rr}r(hHX, a hIjubh)r}r(hHX8:class:`spykeutils.progress_indicator.ProgressIndicator`rhIjhJNhOhhQ}r(UreftypeXclasshhX/spykeutils.progress_indicator.ProgressIndicatorU refdomainXpyrhV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]rh)r}r(hHjhQ}r(hS]hT]r(hjXpy-classrehU]hV]hY]uhIjhC]rheX/spykeutils.progress_indicator.ProgressIndicatorrr}r(hHUhIjubahOhubaubheX that can be used to report the progress of an operation (and is used by methods of this class if they can lead to processing times of half a second or more).rr}r(hHX that can be used to report the progress of an operation (and is used by methods of this class if they can lead to processing times of half a second or more).hIjubeubhr)r}r(hHXKThis class serves as an abstract base class and should not be instantiated.rhIjhJjhOhvhQ}r(hS]hT]hU]hV]hY]uh[K h\hhC]rheXKThis class serves as an abstract base class and should not be 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If analog signal arrays not attached to a Segment or RecordingChannelGroup are selected, their dictionary key will be hIj ubh)r }r (hHX``DataProvider.no_segment``hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXDataProvider.no_segmentr r }r (hHUhIj ubahOhubheX or r r }r (hHX or hIj ubh)r }r (hHX ``DataProvider.no_channelgroup``hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXDataProvider.no_channelgroupr r }r (hHUhIj ubahOhubheX, respectively.r r }r (hHX, respectively.hIj ubeubeubeubhi)r }r (hHUhIjhJNhOhmhQ}r (hV]hU]hS]hT]hY]Uentries]r (hpX&analog_signals() (DataProvider method)hUtr auh[Nh\hhC]ubh)r }r (hHUhIjhJNhOhhQ}r (hډhXpyr hV]hU]hS]hT]hY]hXmethodr hj uh[Nh\hhC]r (h)r }r (hHX.DataProvider.analog_signals(conversion_mode=1)hIj hJhhOhhQ}r (hV]r hahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r hahXDataProvider.analog_signalsr hjXhuh[Nh\hhC]r (h)r }r (hHXanalog_signalshIj hJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heXanalog_signalsr r }r (hHUhIj ubaubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j)r }r (hHXconversion_mode=1hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_mode=1r r }r (hHUhIj ubahOjubaubj)r }r (hHUhIj hJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIj hC]r j)r }r (hHUhQ}r (hS]hT]r jahU]hV]hY]uhIj hC]r heX[source]r r }r (hHUhIj ubahOjubahOhubaubeubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r }r (hHX8Return a list of :class:`neo.core.AnalogSignal` objects.hIj hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.analog_signalsr hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r (heXReturn a list of r r }r (hHXReturn a list of hIj ubh)r }r (hHX:class:`neo.core.AnalogSignal`r hIj hJNhOhhQ}r (UreftypeXclasshhXneo.core.AnalogSignalU refdomainXpyr hV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]r h)r }r (hHj hQ}r (hS]hT]r (hj Xpy-classr ehU]hV]hY]uhIj hC]r heXneo.core.AnalogSignalr r }r (hHUhIj ubahOhubaubheX objects.r r }r (hHX objects.hIj ubeubj-)r }r (hHUhIj hJNhOj0hQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j3)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (j8)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r heX Parametersr r }r (hHUhIj ubahOj@ubjA)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r! (jk)r" }r# (hHXconversion_modehQ}r$ (hS]hT]hU]hV]hY]uhIj hC]r% heXconversion_moder& r' }r( (hHUhIj" ubahOjsubheX (r) r* }r+ (hHUhIj ubh)r, }r- (hHUhQ}r. (UreftypejzU reftargetXintr/ U refdomainj hV]hU]U refexplicithS]hT]hY]uhIj hC]r0 j})r1 }r2 (hHj/ hQ}r3 (hS]hT]hU]hV]hY]uhIj, hC]r4 heXintr5 r6 }r7 (hHUhIj1 ubahOjubahOhubheX)r8 }r9 (hHUhIj ubheX -- r: r; }r< (hHUhIj ubhr)r= }r> (hHX%Determines what signals are returned:r? hIj hJj hOhvhQ}r@ (hS]hT]hU]hV]hY]uh[KhC]rA heX%Determines what signals are returned:rB rC }rD (hHj? hIj= ubaubcdocutils.nodes enumerated_list rE )rF }rG (hHUhQ}rH (UsuffixrI U.hV]hU]hS]UprefixrJ UhT]hY]UenumtyperK UarabicrL uhIj hC]rM (jb)rN }rO (hHXAnalogSignal objects onlyrP hQ}rQ (hS]hT]hU]hV]hY]uhIjF hC]rR hr)rS }rT (hHjP hIjN hJj hOhvhQ}rU (hS]hT]hU]hV]hY]uh[KhC]rV heXAnalogSignal objects onlyrW rX }rY (hHjP hIjS ubaubahOjubjb)rZ }r[ (hHX;AnalogSignal objects extracted from AnalogSignalArrays onlyr\ hQ}r] (hS]hT]hU]hV]hY]uhIjF hC]r^ hr)r_ }r` (hHj\ hIjZ hJj hOhvhQ}ra (hS]hT]hU]hV]hY]uh[KhC]rb heX;AnalogSignal objects extracted from AnalogSignalArrays onlyrc rd }re (hHj\ hIj_ ubaubahOjubjb)rf }rg (hHX:Both AnalogSignal objects and extracted AnalogSignalArraysrh hQ}ri (hS]hT]hU]hV]hY]uhIjF hC]rj hr)rk }rl (hHjh hIjf hJj hOhvhQ}rm (hS]hT]hU]hV]hY]uh[KhC]rn heX:Both AnalogSignal objects and extracted AnalogSignalArraysro rp }rq (hHjh hIjk ubaubahOjubehOUenumerated_listrr ubehOhvubahOjMubehOjNubaubeubeubhi)rs }rt (hHUhIjhJNhOhmhQ}ru (hV]hU]hS]hT]hY]Uentries]rv (hpX1analog_signals_by_channel() (DataProvider method)hUtrw auh[Nh\hhC]ubh)rx }ry (hHUhIjhJNhOhhQ}rz (hډhXpyr{ hV]hU]hS]hT]hY]hXmethodr| hj| uh[Nh\hhC]r} (h)r~ }r (hHX9DataProvider.analog_signals_by_channel(conversion_mode=1)hIjx hJhhOhhQ}r (hV]r hahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r hahX&DataProvider.analog_signals_by_channelr hjXhuh[Nh\hhC]r (h)r }r (hHXanalog_signals_by_channelhIj~ hJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heXanalog_signals_by_channelr r }r (hHUhIj ubaubj)r }r (hHUhIj~ hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j)r }r (hHXconversion_mode=1hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_mode=1r r }r (hHUhIj ubahOjubaubj)r }r (hHUhIj~ hJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIj hC]r j)r }r (hHUhQ}r (hS]hT]r jahU]hV]hY]uhIj hC]r heX[source]r r }r (hHUhIj ubahOjubahOhubaubeubj)r }r (hHUhIjx hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r }r (hHXeReturn a dictionary (indexed by RecordingChannel) of lists of :class:`neo.core.AnalogSignal` objects.hIj hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.analog_signals_by_channelr hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r (heX>Return a dictionary (indexed by RecordingChannel) of lists of r r }r (hHX>Return a dictionary (indexed by RecordingChannel) of lists of hIj ubh)r }r (hHX:class:`neo.core.AnalogSignal`r hIj hJNhOhhQ}r (UreftypeXclasshhXneo.core.AnalogSignalU refdomainXpyr hV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]r h)r }r (hHj hQ}r (hS]hT]r (hj Xpy-classr ehU]hV]hY]uhIj hC]r heXneo.core.AnalogSignalr r }r (hHUhIj ubahOhubaubheX objects.r r }r (hHX objects.hIj ubeubhr)r }r (hHX|If analog signals not attached to a RecordingChannel are selected, their dictionary key will be ``DataProvider.no_channel``.hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r (heX`If analog signals not attached to a RecordingChannel are selected, their dictionary key will be r r }r (hHX`If analog signals not attached to a RecordingChannel are selected, their dictionary key will be hIj ubh)r }r (hHX``DataProvider.no_channel``hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXDataProvider.no_channelr r }r (hHUhIj ubahOhubheX.r }r (hHX.hIj ubeubj-)r }r (hHUhIj hJNhOj0hQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j3)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (j8)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r heX Parametersr r }r (hHUhIj ubahOj@ubjA)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (jk)r }r (hHXconversion_modehQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_moder r }r (hHUhIj ubahOjsubheX (r r }r (hHUhIj ubh)r }r (hHUhQ}r (UreftypejzU reftargetXintr U refdomainj{ hV]hU]U refexplicithS]hT]hY]uhIj hC]r j})r }r (hHj hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXintr r }r (hHUhIj ubahOjubahOhubheX)r }r (hHUhIj ubheX -- r r }r (hHUhIj ubhr)r }r (hHX%Determines what signals are returned:r hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[KhC]r heX%Determines what signals are returned:r r }r (hHj hIj ubaubjE )r }r (hHUhQ}r (jI U.hV]hU]hS]jJ UhT]hY]jK jL uhIj hC]r (jb)r }r (hHXAnalogSignal objects onlyr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heXAnalogSignal objects onlyr r }r! (hHj hIj ubaubahOjubjb)r" }r# (hHX;AnalogSignal objects extracted from AnalogSignalArrays onlyr$ hQ}r% (hS]hT]hU]hV]hY]uhIj hC]r& hr)r' }r( (hHj$ hIj" hJj hOhvhQ}r) (hS]hT]hU]hV]hY]uh[K hC]r* heX;AnalogSignal objects extracted from AnalogSignalArrays onlyr+ r, }r- (hHj$ hIj' ubaubahOjubjb)r. }r/ (hHX:Both AnalogSignal objects and extracted AnalogSignalArraysr0 hQ}r1 (hS]hT]hU]hV]hY]uhIj hC]r2 hr)r3 }r4 (hHj0 hIj. hJj hOhvhQ}r5 (hS]hT]hU]hV]hY]uh[K hC]r6 heX:Both AnalogSignal objects and extracted AnalogSignalArraysr7 r8 }r9 (hHj0 hIj3 ubaubahOjubehOjr ubehOhvubahOjMubehOjNubaubeubeubhi)r: }r; (hHUhIjhJNhOhmhQ}r< (hV]hU]hS]hT]hY]Uentries]r= (hpX=analog_signals_by_channel_and_segment() (DataProvider method)hUtr> auh[Nh\hhC]ubh)r? }r@ (hHUhIjhJNhOhhQ}rA (hډhXpyrB hV]hU]hS]hT]hY]hXmethodrC hjC uh[Nh\hhC]rD (h)rE }rF (hHXEDataProvider.analog_signals_by_channel_and_segment(conversion_mode=1)hIj? hJhhOhhQ}rG (hV]rH hahhKXspykeutils.plugin.data_providerrI rJ }rK bhU]hS]hT]hY]rL hahX2DataProvider.analog_signals_by_channel_and_segmentrM hjXhuh[Nh\hhC]rN (h)rO }rP (hHX%analog_signals_by_channel_and_segmenthIjE hJhhOhhQ}rQ (hS]hT]hU]hV]hY]uh[Nh\hhC]rR heX%analog_signals_by_channel_and_segmentrS rT }rU (hHUhIjO ubaubj)rV }rW (hHUhIjE hJhhOjhQ}rX (hS]hT]hU]hV]hY]uh[Nh\hhC]rY j)rZ }r[ (hHXconversion_mode=1hQ}r\ (hS]hT]hU]hV]hY]uhIjV hC]r] heXconversion_mode=1r^ r_ }r` (hHUhIjZ ubahOjubaubj)ra }rb (hHUhIjE hJNhOjhQ}rc (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rd h)re }rf (hHUhQ}rg (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjM uhIja hC]rh j)ri }rj (hHUhQ}rk (hS]hT]rl jahU]hV]hY]uhIje hC]rm heX[source]rn ro }rp (hHUhIji ubahOjubahOhubaubeubj)rq }rr (hHUhIj? hJhhOjhQ}rs (hS]hT]hU]hV]hY]uh[Nh\hhC]rt (hr)ru }rv (hHXReturn a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of :class:`neo.core.AnalogSignal` lists.hIjq hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.analog_signals_by_channel_and_segmentrw hOhvhQ}rx (hS]hT]hU]hV]hY]uh[Kh\hhC]ry (heXZReturn a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of rz r{ }r| (hHXZReturn a dictionary (indexed by RecordingChannel) of dictionaries (indexed by Segment) of hIju ubh)r} }r~ (hHX:class:`neo.core.AnalogSignal`r hIju hJNhOhhQ}r (UreftypeXclasshhXneo.core.AnalogSignalU refdomainXpyr hV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]r h)r }r (hHj hQ}r (hS]hT]r (hj Xpy-classr ehU]hV]hY]uhIj} hC]r heXneo.core.AnalogSignalr r }r (hHUhIj ubahOhubaubheX lists.r r }r (hHX lists.hIju ubeubhr)r }r (hHXIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively.hIjq hJjw hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r (heXkIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be r r }r (hHXkIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be hIj ubh)r }r (hHX``DataProvider.no_segment``hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXDataProvider.no_segmentr r }r (hHUhIj ubahOhubheX or r r }r (hHX or hIj ubh)r }r (hHX``DataProvider.no_channel``hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXDataProvider.no_channelr r }r (hHUhIj ubahOhubheX, respectively.r r }r (hHX, respectively.hIj ubeubj-)r }r (hHUhIjq hJNhOj0hQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j3)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (j8)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r heX Parametersr r }r (hHUhIj ubahOj@ubjA)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (jk)r }r (hHXconversion_modehQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_moder r }r (hHUhIj ubahOjsubheX (r r }r (hHUhIj ubh)r }r (hHUhQ}r (UreftypejzU reftargetXintr U refdomainjB hV]hU]U refexplicithS]hT]hY]uhIj hC]r j})r }r (hHj hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXintr r }r (hHUhIj ubahOjubahOhubheX)r }r (hHUhIj ubheX -- r r }r (hHUhIj ubhr)r }r (hHX%Determines what signals are returned:r hIj hJjw hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heX%Determines what signals are returned:r r }r (hHj hIj ubaubjE )r }r (hHUhQ}r (jI U.hV]hU]hS]jJ UhT]hY]jK jL uhIj hC]r (jb)r }r (hHXAnalogSignal objects onlyr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjw hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heXAnalogSignal objects onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX;AnalogSignal objects extracted from AnalogSignalArrays onlyr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjw hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heX;AnalogSignal objects extracted from AnalogSignalArrays onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX:Both AnalogSignal objects and extracted AnalogSignalArraysr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjw hOhvhQ}r (hS]hT]hU]hV]hY]uh[KhC]r heX:Both AnalogSignal objects and extracted AnalogSignalArraysr r }r (hHj hIj ubaubahOjubehOjr ubehOhvubahOjMubehOjNubaubeubeubhi)r }r (hHUhIjhJNhOhmhQ}r (hV]hU]hS]hT]hY]Uentries]r (hpX1analog_signals_by_segment() (DataProvider method)hUtr auh[Nh\hhC]ubh)r }r (hHUhIjhJNhOhhQ}r (hډhXpyr hV]hU]hS]hT]hY]hXmethodr hj uh[Nh\hhC]r (h)r }r (hHX9DataProvider.analog_signals_by_segment(conversion_mode=1)hIj hJhhOhhQ}r (hV]r hahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r hahX&DataProvider.analog_signals_by_segmentr hjXhuh[Nh\hhC]r (h)r! }r" (hHXanalog_signals_by_segmenthIj hJhhOhhQ}r# (hS]hT]hU]hV]hY]uh[Nh\hhC]r$ heXanalog_signals_by_segmentr% r& }r' (hHUhIj! ubaubj)r( }r) (hHUhIj hJhhOjhQ}r* (hS]hT]hU]hV]hY]uh[Nh\hhC]r+ j)r, }r- (hHXconversion_mode=1hQ}r. (hS]hT]hU]hV]hY]uhIj( hC]r/ heXconversion_mode=1r0 r1 }r2 (hHUhIj, ubahOjubaubj)r3 }r4 (hHUhIj hJNhOjhQ}r5 (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r6 h)r7 }r8 (hHUhQ}r9 (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIj3 hC]r: j)r; }r< (hHUhQ}r= (hS]hT]r> jahU]hV]hY]uhIj7 hC]r? heX[source]r@ rA }rB (hHUhIj; ubahOjubahOhubaubeubj)rC }rD (hHUhIj hJhhOjhQ}rE (hS]hT]hU]hV]hY]uh[Nh\hhC]rF (hr)rG }rH (hHX\Return a dictionary (indexed by Segment) of lists of :class:`neo.core.AnalogSignal` objects.hIjC hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.analog_signals_by_segmentrI hOhvhQ}rJ (hS]hT]hU]hV]hY]uh[Kh\hhC]rK (heX5Return a dictionary (indexed by Segment) of lists of rL rM }rN (hHX5Return a dictionary (indexed by Segment) of lists of hIjG ubh)rO }rP (hHX:class:`neo.core.AnalogSignal`rQ hIjG hJNhOhhQ}rR (UreftypeXclasshhXneo.core.AnalogSignalU refdomainXpyrS hV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]rT h)rU }rV (hHjQ hQ}rW (hS]hT]rX (hjS Xpy-classrY ehU]hV]hY]uhIjO hC]rZ heXneo.core.AnalogSignalr[ r\ }r] (hHUhIjU ubahOhubaubheX objects.r^ r_ }r` (hHX objects.hIjG ubeubhr)ra }rb (hHXsIf analog signals not attached to a Segment are selected, their dictionary key will be ``DataProvider.no_segment``.hIjC hJjI hOhvhQ}rc (hS]hT]hU]hV]hY]uh[Kh\hhC]rd (heXWIf analog signals not attached to a Segment are selected, their dictionary key will be re rf }rg (hHXWIf analog signals not attached to a Segment are selected, their dictionary key will be hIja ubh)rh }ri (hHX``DataProvider.no_segment``hQ}rj (hS]hT]hU]hV]hY]uhIja hC]rk heXDataProvider.no_segmentrl rm }rn (hHUhIjh ubahOhubheX.ro }rp (hHX.hIja ubeubj-)rq }rr (hHUhIjC hJNhOj0hQ}rs (hS]hT]hU]hV]hY]uh[Nh\hhC]rt j3)ru }rv (hHUhQ}rw (hS]hT]hU]hV]hY]uhIjq hC]rx (j8)ry }rz (hHUhQ}r{ (hS]hT]hU]hV]hY]uhIju hC]r| heX Parametersr} r~ }r (hHUhIjy ubahOj@ubjA)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIju hC]r hr)r }r (hHUhQ}r (hS]hT]hU]hV]hY]uhIj hC]r (jk)r }r (hHXconversion_modehQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_moder r }r (hHUhIj ubahOjsubheX (r r }r (hHUhIj ubh)r }r (hHUhQ}r (UreftypejzU reftargetXintr U refdomainj hV]hU]U refexplicithS]hT]hY]uhIj hC]r j})r }r (hHj hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXintr r }r (hHUhIj ubahOjubahOhubheX)r }r (hHUhIj ubheX -- r r }r (hHUhIj ubhr)r }r (hHX%Determines what signals are returned:r hIj hJjI hOhvhQ}r (hS]hT]hU]hV]hY]uh[KhC]r heX%Determines what signals are returned:r r }r (hHj hIj ubaubjE )r }r (hHUhQ}r (jI U.hV]hU]hS]jJ UhT]hY]jK jL uhIj hC]r (jb)r }r (hHXAnalogSignal objects onlyr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjI hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heXAnalogSignal objects onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX;AnalogSignal objects extracted from AnalogSignalArrays onlyr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjI hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heX;AnalogSignal objects extracted from AnalogSignalArrays onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX:Both AnalogSignal objects and extracted AnalogSignalArraysr hQ}r (hS]hT]hU]hV]hY]uhIj hC]r hr)r }r (hHj hIj hJjI hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heX:Both AnalogSignal objects and extracted AnalogSignalArraysr r }r (hHj hIj ubaubahOjubehOjr ubehOhvubahOjMubehOjNubaubeubeubhi)r }r (hHUhIjhJNhOhmhQ}r (hV]hU]hS]hT]hY]Uentries]r (hpX=analog_signals_by_segment_and_channel() (DataProvider method)h Utr auh[Nh\hhC]ubh)r }r (hHUhIjhJNhOhhQ}r (hډhXpyr hV]hU]hS]hT]hY]hXmethodr hj uh[Nh\hhC]r (h)r }r (hHXEDataProvider.analog_signals_by_segment_and_channel(conversion_mode=1)r hIj hJhhOhhQ}r (hV]r h ahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r h ahX2DataProvider.analog_signals_by_segment_and_channelr hjXhuh[Nh\hhC]r (h)r }r (hHX%analog_signals_by_segment_and_channelhIj hJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heX%analog_signals_by_segment_and_channelr r }r (hHUhIj ubaubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r j)r }r (hHXconversion_mode=1hQ}r (hS]hT]hU]hV]hY]uhIj hC]r heXconversion_mode=1r r }r (hHUhIj ubahOjubaubj)r }r (hHUhIj hJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIj hC]r j)r }r (hHUhQ}r (hS]hT]r jahU]hV]hY]uhIj hC]r heX[source]r r }r (hHUhIj ubahOjubahOhubaubeubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r }r (hHXReturn a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of :class:`neo.core.AnalogSignal` lists.hIj hJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.analog_signals_by_segment_and_channelr hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r (heXZReturn a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of r r }r (hHXZReturn a dictionary (indexed by Segment) of dictionaries (indexed by RecordingChannel) of hIj ubh)r }r (hHX:class:`neo.core.AnalogSignal`r hIj hJNhOhhQ}r (UreftypeXclasshhXneo.core.AnalogSignalU refdomainXpyr hV]hU]U refexplicithS]hT]hY]hhhjXhjuh[NhC]r h)r }r (hHj hQ}r (hS]hT]r (hj Xpy-classr! ehU]hV]hY]uhIj hC]r" heXneo.core.AnalogSignalr# r$ }r% (hHUhIj ubahOhubaubheX lists.r& r' }r( (hHX lists.hIj ubeubhr)r) }r* (hHXIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be ``DataProvider.no_segment`` or ``DataProvider.no_channel``, respectively.hIj hJj hOhvhQ}r+ (hS]hT]hU]hV]hY]uh[Kh\hhC]r, (heXkIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be r- r. }r/ (hHXkIf analog signals not attached to a Segment or RecordingChannel are selected, their dictionary key will be hIj) ubh)r0 }r1 (hHX``DataProvider.no_segment``hQ}r2 (hS]hT]hU]hV]hY]uhIj) hC]r3 heXDataProvider.no_segmentr4 r5 }r6 (hHUhIj0 ubahOhubheX or r7 r8 }r9 (hHX or hIj) ubh)r: }r; (hHX``DataProvider.no_channel``hQ}r< (hS]hT]hU]hV]hY]uhIj) hC]r= heXDataProvider.no_channelr> r? }r@ (hHUhIj: ubahOhubheX, respectively.rA rB }rC (hHX, respectively.hIj) ubeubj-)rD }rE (hHUhIj hJNhOj0hQ}rF (hS]hT]hU]hV]hY]uh[Nh\hhC]rG j3)rH }rI (hHUhQ}rJ (hS]hT]hU]hV]hY]uhIjD hC]rK (j8)rL }rM (hHUhQ}rN (hS]hT]hU]hV]hY]uhIjH hC]rO heX ParametersrP rQ }rR (hHUhIjL ubahOj@ubjA)rS }rT (hHUhQ}rU (hS]hT]hU]hV]hY]uhIjH hC]rV hr)rW }rX (hHUhQ}rY (hS]hT]hU]hV]hY]uhIjS hC]rZ (jk)r[ }r\ (hHXconversion_modehQ}r] (hS]hT]hU]hV]hY]uhIjW hC]r^ heXconversion_moder_ r` }ra (hHUhIj[ ubahOjsubheX (rb rc }rd (hHUhIjW ubh)re }rf (hHUhQ}rg (UreftypejzU reftargetXintrh U refdomainj hV]hU]U refexplicithS]hT]hY]uhIjW hC]ri j})rj }rk (hHjh hQ}rl (hS]hT]hU]hV]hY]uhIje hC]rm heXintrn ro }rp (hHUhIjj ubahOjubahOhubheX)rq }rr (hHUhIjW ubheX -- rs rt }ru (hHUhIjW ubhr)rv }rw (hHX%Determines what signals are returned:rx hIjW hJj hOhvhQ}ry (hS]hT]hU]hV]hY]uh[K hC]rz heX%Determines what signals are returned:r{ r| }r} (hHjx hIjv ubaubjE )r~ }r (hHUhQ}r (jI U.hV]hU]hS]jJ UhT]hY]jK jL uhIjW hC]r (jb)r }r (hHXAnalogSignal objects onlyr hQ}r (hS]hT]hU]hV]hY]uhIj~ hC]r hr)r }r (hHj hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heXAnalogSignal objects onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX;AnalogSignal objects extracted from AnalogSignalArrays onlyr hQ}r (hS]hT]hU]hV]hY]uhIj~ hC]r hr)r }r (hHj hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[K hC]r heX;AnalogSignal objects extracted from AnalogSignalArrays onlyr r }r (hHj hIj ubaubahOjubjb)r }r (hHX:Both AnalogSignal objects and extracted AnalogSignalArraysr hQ}r (hS]hT]hU]hV]hY]uhIj~ hC]r hr)r }r (hHj hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[KhC]r heX:Both AnalogSignal objects and extracted AnalogSignalArraysr r }r (hHj hIj ubaubahOjubehOjr ubehOhvubahOjMubehOjNubaubeubeubhi)r }r (hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.blocksr hOhmhQ}r (hV]hU]hS]hT]hY]Uentries]r (hpXblocks() (DataProvider method)h Utr auh[Nh\hhC]ubh)r }r (hHUhIjhJj hOhhQ}r (hډhXpyhV]hU]hS]hT]hY]hXmethodr hj uh[Nh\hhC]r (h)r }r (hHXDataProvider.blocks()hIj hJhhOhhQ}r (hV]r h ahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r h ahXDataProvider.blocksr hjXhuh[Nh\hhC]r (h)r }r (hHXblockshIj hJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heXblocksr r }r (hHUhIj ubaubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r }r (hHUhIj hJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIj hC]r j)r }r (hHUhQ}r (hS]hT]r jahU]hV]hY]uhIj hC]r heX[source]r r }r (hHUhIj ubahOjubahOhubaubeubj)r }r (hHUhIj hJhhOjhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r (hr)r }r (hHX(Return a list of selected Block objects.r hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r heX(Return a list of selected Block objects.r r }r (hHj hIj ubaubhr)r }r (hHXWThe returned objects will contain all regular references, not just to selected objects.r hIj hJj hOhvhQ}r (hS]hT]hU]hV]hY]uh[Kh\hhC]r heXWThe returned objects will contain all regular references, not just to selected objects.r r }r (hHj hIj ubaubeubeubhi)r }r (hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.data_dictr hOhmhQ}r (hV]hU]hS]hT]hY]Uentries]r (hpX!data_dict() (DataProvider method)h Utr auh[Nh\hhC]ubh)r }r (hHUhIjhJj hOhhQ}r (hډhXpyhV]hU]hS]hT]hY]hXmethodr hj uh[Nh\hhC]r (h)r }r (hHXDataProvider.data_dict()hIj hJhhOhhQ}r (hV]r h ahhKXspykeutils.plugin.data_providerr r }r bhU]hS]hT]hY]r h ahXDataProvider.data_dictr hjXhuh[Nh\hhC]r (h)r }r (hHX data_dicthIj hJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX data_dictrr}r(hHUhIj ubaubj)r}r(hHUhIj hJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r (hHUhIj hJNhOjhQ}r (UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj uhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIj hC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIj hJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rhr)r}r(hHXAReturn a dictionary with all information to serialize the object.rhIjhJj hOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]r heXAReturn a dictionary with all information to serialize the object.r!r"}r#(hHjhIjubaubaubeubhi)r$}r%(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.epoch_arraysr&hOhmhQ}r'(hV]hU]hS]hT]hY]Uentries]r((hpX$epoch_arrays() (DataProvider method)h%Utr)auh[Nh\hhC]ubh)r*}r+(hHUhIjhJj&hOhhQ}r,(hډhXpyhV]hU]hS]hT]hY]hXmethodr-hj-uh[Nh\hhC]r.(h)r/}r0(hHXDataProvider.epoch_arrays()hIj*hJhhOhhQ}r1(hV]r2h%ahhKXspykeutils.plugin.data_providerr3r4}r5bhU]hS]hT]hY]r6h%ahXDataProvider.epoch_arraysr7hjXhuh[Nh\hhC]r8(h)r9}r:(hHX epoch_arrayshIj/hJhhOhhQ}r;(hS]hT]hU]hV]hY]uh[Nh\hhC]r<heX epoch_arraysr=r>}r?(hHUhIj9ubaubj)r@}rA(hHUhIj/hJhhOjhQ}rB(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)rC}rD(hHUhIj/hJNhOjhQ}rE(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rFh)rG}rH(hHUhQ}rI(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj7uhIjChC]rJj)rK}rL(hHUhQ}rM(hS]hT]rNjahU]hV]hY]uhIjGhC]rOheX[source]rPrQ}rR(hHUhIjKubahOjubahOhubaubeubj)rS}rT(hHUhIj*hJhhOjhQ}rU(hS]hT]hU]hV]hY]uh[Nh\hhC]rVhr)rW}rX(hHXHReturn a dictionary (indexed by Segment) of lists of EpochArray objects.rYhIjShJj&hOhvhQ}rZ(hS]hT]hU]hV]hY]uh[Kh\hhC]r[heXHReturn a dictionary (indexed by Segment) of lists of EpochArray objects.r\r]}r^(hHjYhIjWubaubaubeubhi)r_}r`(hHUhIjhJNhOhmhQ}ra(hV]hU]hS]hT]hY]Uentries]rb(hpXepochs() (DataProvider method)h.Utrcauh[Nh\hhC]ubh)rd}re(hHUhIjhJNhOhhQ}rf(hډhXpyrghV]hU]hS]hT]hY]hXmethodrhhjhuh[Nh\hhC]ri(h)rj}rk(hHX.DataProvider.epochs(include_array_epochs=True)hIjdhJhhOhhQ}rl(hV]rmh.ahhKXspykeutils.plugin.data_providerrnro}rpbhU]hS]hT]hY]rqh.ahXDataProvider.epochsrrhjXhuh[Nh\hhC]rs(h)rt}ru(hHXepochshIjjhJhhOhhQ}rv(hS]hT]hU]hV]hY]uh[Nh\hhC]rwheXepochsrxry}rz(hHUhIjtubaubj)r{}r|(hHUhIjjhJhhOjhQ}r}(hS]hT]hU]hV]hY]uh[Nh\hhC]r~j)r}r(hHXinclude_array_epochs=TruehQ}r(hS]hT]hU]hV]hY]uhIj{hC]rheXinclude_array_epochs=Truerr}r(hHUhIjubahOjubaubj)r}r(hHUhIjjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjruhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjdhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHXCReturn a dictionary (indexed by Segment) of lists of Epoch objects.rhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.epochsrhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXCReturn a dictionary (indexed by Segment) of lists of Epoch objects.rr}r(hHjhIjubaubj-)r}r(hHUhIjhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXinclude_array_epochshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXinclude_array_epochsrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXboolrU refdomainjghV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXboolrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXhDetermines if EpochArray objects should be converted to Epoch objects and included in the returned list.rr}r(hHXhDetermines if EpochArray objects should be converted to Epoch objects and included in the returned list.hIjubehOhvubahOjMubehOjNubaubeubeubhi)r}r(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.event_arraysrhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX$event_arrays() (DataProvider method)hUtrauh[Nh\hhC]ubh)r}r(hHUhIjhJjhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXDataProvider.event_arrays()hIjhJhhOhhQ}r(hV]rhahhKXspykeutils.plugin.data_providerrr}rbhU]hS]hT]hY]rhahXDataProvider.event_arraysrhjXhuh[Nh\hhC]r(h)r}r(hHX event_arrayshIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX event_arraysrr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U 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returned list.hIjjubehOhvubahOjMubehOjNubaubeubeubhi)r}r(hHUhIjhJNhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpXGfrom_data() (spykeutils.plugin.data_provider.DataProvider class method)h+Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJNhOhhQ}r(hډhXpyrhV]hU]hS]hT]hY]hX classmethodrhjuh[Nh\hhC]r(h)r}r(hHX+DataProvider.from_data(data, progress=None)hIjhJhhOhhQ}r(hV]rh+ahhKXspykeutils.plugin.data_providerrr}rbhU]hS]hT]hY]rh+ahXDataProvider.from_datarhjXhuh[Nh\hhC]r(h)r}r(hHU classmethod rhIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX classmethod rr}r(hHUhIjubaubh)r}r(hHX from_datahIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX from_datarr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(j)r}r(hHXdatahQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXdatarr}r(hHUhIjubahOjubj)r}r(hHX progress=NonehQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX progress=Nonerr}r(hHUhIjubahOjubeubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U 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called.hIjubeubj-)r }r (hHUhIjhJNhOj0hQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]rj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIj hC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rj])r}r(hHUhQ}r (hS]hT]hU]hV]hY]uhIjhC]r!(jb)r"}r#(hHUhQ}r$(hS]hT]hU]hV]hY]uhIjhC]r%hr)r&}r'(hHUhQ}r((hS]hT]hU]hV]hY]uhIj"hC]r)(jk)r*}r+(hHXdatahQ}r,(hS]hT]hU]hV]hY]uhIj&hC]r-heXdatar.r/}r0(hHUhIj*ubahOjsubheX (r1r2}r3(hHUhIj&ubh)r4}r5(hHUhQ}r6(UreftypejzU reftargetXdictr7U refdomainjhV]hU]U refexplicithS]hT]hY]uhIj&hC]r8j})r9}r:(hHj7hQ}r;(hS]hT]hU]hV]hY]uhIj4hC]r<heXdictr=r>}r?(hHUhIj9ubahOjubahOhubheX)r@}rA(hHUhIj&ubheX -- rBrC}rD(hHUhIj&ubheX$A dictionary containing data from a rErF}rG(hHX$A dictionary containing data from a hIj&ubh|)rH}rI(hHX`DataProvider`hQ}rJ(hS]hT]hU]hV]hY]uhIj&hC]rKheX DataProviderrLrM}rN(hHUhIjHubahOhubheX object, as returned by rOrP}rQ(hHX object, as returned by hIj&ubh)rR}rS(hHX:func:`data_dict`rThIj&hJNhOhhQ}rU(UreftypeXfunchhX data_dictU 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lists of Epoch objects with the given label.rhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.labeled_epochsrhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXXReturn a dictionary (indexed by Segment) of lists of Epoch objects with the given label.rr}r(hHjhIjubaubj-)r}r(hHUhIjhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rj])r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXlabelhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXlabelrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXstrrU refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXstrrr}r(hHUhIjubahOjubahOhubheX)r }r (hHUhIjubheX -- r r }r (hHUhIjubheX-The name of the Epoch objects to be returndedrr}r(hHX-The name of the Epoch objects to be returndedhIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXinclude_array_epochshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXinclude_array_epochsrr}r(hHUhIjubahOjsubheX (r r!}r"(hHUhIjubh)r#}r$(hHUhQ}r%(UreftypejzU reftargetXboolr&U refdomainjhV]hU]U refexplicithS]hT]hY]uhIjhC]r'j})r(}r)(hHj&hQ}r*(hS]hT]hU]hV]hY]uhIj#hC]r+heXboolr,r-}r.(hHUhIj(ubahOjubahOhubheX)r/}r0(hHUhIjubheX -- r1r2}r3(hHUhIjubheXhDetermines if EpochArray objects should be converted to Epoch objects and included in the returned list.r4r5}r6(hHXhDetermines if EpochArray objects should be converted to Epoch objects and included in the returned list.hIjubehOhvubahOjubehOj8ubahOjMubehOjNubaubeubeubhi)r7}r8(hHUhIjhJNhOhmhQ}r9(hV]hU]hS]hT]hY]Uentries]r:(hpX&labeled_events() (DataProvider method)hUtr;auh[Nh\hhC]ubh)r<}r=(hHUhIjhJNhOhhQ}r>(hډhXpyr?hV]hU]hS]hT]hY]hXmethodr@hj@uh[Nh\hhC]rA(h)rB}rC(hHX=DataProvider.labeled_events(label, include_array_events=True)hIj<hJhhOhhQ}rD(hV]rEhahhKXspykeutils.plugin.data_providerrFrG}rHbhU]hS]hT]hY]rIhahXDataProvider.labeled_eventsrJhjXhuh[Nh\hhC]rK(h)rL}rM(hHXlabeled_eventshIjBhJhhOhhQ}rN(hS]hT]hU]hV]hY]uh[Nh\hhC]rOheXlabeled_eventsrPrQ}rR(hHUhIjLubaubj)rS}rT(hHUhIjBhJhhOjhQ}rU(hS]hT]hU]hV]hY]uh[Nh\hhC]rV(j)rW}rX(hHXlabelhQ}rY(hS]hT]hU]hV]hY]uhIjShC]rZheXlabelr[r\}r](hHUhIjWubahOjubj)r^}r_(hHXinclude_array_events=TruehQ}r`(hS]hT]hU]hV]hY]uhIjShC]raheXinclude_array_events=Truerbrc}rd(hHUhIj^ubahOjubeubj)re}rf(hHUhIjBhJNhOjhQ}rg(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rhh)ri}rj(hHUhQ}rk(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjJuhIjehC]rlj)rm}rn(hHUhQ}ro(hS]hT]rpjahU]hV]hY]uhIjihC]rqheX[source]rrrs}rt(hHUhIjmubahOjubahOhubaubeubj)ru}rv(hHUhIj<hJhhOjhQ}rw(hS]hT]hU]hV]hY]uh[Nh\hhC]rx(hr)ry}rz(hHXXReturn a dictionary (indexed by Segment) of lists of Event objects with the given label.r{hIjuhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.labeled_eventsr|hOhvhQ}r}(hS]hT]hU]hV]hY]uh[Kh\hhC]r~heXXReturn a dictionary (indexed by Segment) of lists of Event objects with the given label.rr}r(hHj{hIjyubaubj-)r}r(hHUhIjuhJNhOj0hQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rj3)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(j8)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheX Parametersrr}r(hHUhIjubahOj@ubjA)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rj])r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXlabelhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXlabelrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXstrrU refdomainj?hV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXstrrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheX-The name of the Event objects to be returndedrr}r(hHX-The name of the Event objects to be returndedrhIjubehOhvubahOjubjb)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]rhr)r}r(hHUhQ}r(hS]hT]hU]hV]hY]uhIjhC]r(jk)r}r(hHXinclude_array_eventshQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXinclude_array_eventsrr}r(hHUhIjubahOjsubheX (rr}r(hHUhIjubh)r}r(hHUhQ}r(UreftypejzU reftargetXboolrU refdomainj?hV]hU]U refexplicithS]hT]hY]uhIjhC]rj})r}r(hHjhQ}r(hS]hT]hU]hV]hY]uhIjhC]rheXboolrr}r(hHUhIjubahOjubahOhubheX)r}r(hHUhIjubheX -- rr}r(hHUhIjubheXhDetermines if EventArray objects should be converted to Event objects and included in the returned list.rr}r(hHXhDetermines if EventArray objects should be converted to Event objects and included in the returned list.rhIjubehOhvubahOjubehOj8ubahOjMubehOjNubaubeubeubhi)r}r(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.recording_channel_groupsrhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX0recording_channel_groups() (DataProvider method)h1Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJjhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHX'DataProvider.recording_channel_groups()hIjhJhhOhhQ}r(hV]rh1ahhKXspykeutils.plugin.data_providerrr}rbhU]hS]hT]hY]rh1ahX%DataProvider.recording_channel_groupsrhjXhuh[Nh\hhC]r(h)r}r(hHXrecording_channel_groupshIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXrecording_channel_groupsrr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]r h)r }r (hHUhQ}r (Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjuhIjhC]r j)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIj hC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHX8Return a list of selected RecordingChannelGroup objects.rhIjhJjhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheX8Return a list of selected RecordingChannelGroup objects.rr }r!(hHjhIjubaubhr)r"}r#(hHXWThe returned objects will contain all regular references, not just to selected objects.r$hIjhJjhOhvhQ}r%(hS]hT]hU]hV]hY]uh[Kh\hhC]r&heXWThe returned objects will contain all regular references, not just to selected objects.r'r(}r)(hHj$hIj"ubaubeubeubhi)r*}r+(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.recording_channelsr,hOhmhQ}r-(hV]hU]hS]hT]hY]Uentries]r.(hpX*recording_channels() (DataProvider method)hUtr/auh[Nh\hhC]ubh)r0}r1(hHUhIjhJj,hOhhQ}r2(hډhXpyhV]hU]hS]hT]hY]hXmethodr3hj3uh[Nh\hhC]r4(h)r5}r6(hHX!DataProvider.recording_channels()hIj0hJhhOhhQ}r7(hV]r8hahhKXspykeutils.plugin.data_providerr9r:}r;bhU]hS]hT]hY]r<hahXDataProvider.recording_channelsr=hjXhuh[Nh\hhC]r>(h)r?}r@(hHXrecording_channelshIj5hJhhOhhQ}rA(hS]hT]hU]hV]hY]uh[Nh\hhC]rBheXrecording_channelsrCrD}rE(hHUhIj?ubaubj)rF}rG(hHUhIj5hJhhOjhQ}rH(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)rI}rJ(hHUhIj5hJNhOjhQ}rK(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rLh)rM}rN(hHUhQ}rO(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidj=uhIjIhC]rPj)rQ}rR(hHUhQ}rS(hS]hT]rTjahU]hV]hY]uhIjMhC]rUheX[source]rVrW}rX(hHUhIjQubahOjubahOhubaubeubj)rY}rZ(hHUhIj0hJhhOjhQ}r[(hS]hT]hU]hV]hY]uh[Nh\hhC]r\(hr)r]}r^(hHX3Return a list of selected RecordingChannel objects.r_hIjYhJj,hOhvhQ}r`(hS]hT]hU]hV]hY]uh[Kh\hhC]raheX3Return a list of selected RecordingChannel objects.rbrc}rd(hHj_hIj]ubaubhr)re}rf(hHXWThe returned objects will contain all regular references, not just to selected objects.rghIjYhJj,hOhvhQ}rh(hS]hT]hU]hV]hY]uh[Kh\hhC]riheXWThe returned objects will contain all regular references, not just to selected objects.rjrk}rl(hHjghIjeubaubeubeubhi)rm}rn(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.refresh_viewrohOhmhQ}rp(hV]hU]hS]hT]hY]Uentries]rq(hpX$refresh_view() (DataProvider method)hUtrrauh[Nh\hhC]ubh)rs}rt(hHUhIjhJjohOhhQ}ru(hډhXpyhV]hU]hS]hT]hY]hXmethodrvhjvuh[Nh\hhC]rw(h)rx}ry(hHXDataProvider.refresh_view()hIjshJhhOhhQ}rz(hV]r{hahhKXspykeutils.plugin.data_providerr|r}}r~bhU]hS]hT]hY]rhahXDataProvider.refresh_viewrhjXhuh[Nh\hhC]r(h)r}r(hHX refresh_viewhIjxhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheX refresh_viewrr}r(hHUhIjubaubj)r}r(hHUhIjxhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjxhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjshJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHX%Refresh associated views of the data.rhIjhJjohOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheX%Refresh associated views of the data.rr}r(hHjhIjubaubhr)r}r(hHXUse this method if when you change the neo hierarchy on which the selection is based (e.g. adding or removing objects). It will ensure that all current views on the data are updated, for example in Spyke Viewer.rhIjhJjohOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXUse this method if when you change the neo hierarchy on which the selection is based (e.g. adding or removing objects). It will ensure that all current views on the data are updated, for example in Spyke Viewer.rr}r(hHjhIjubaubeubeubhi)r}r(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.segmentsrhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX segments() (DataProvider method)hUtrauh[Nh\hhC]ubh)r}r(hHUhIjhJjhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXDataProvider.segments()hIjhJhhOhhQ}r(hV]rhahhKXspykeutils.plugin.data_providerrr}rbhU]hS]hT]hY]rhahXDataProvider.segmentsrhjXhuh[Nh\hhC]r(h)r}r(hHXsegmentshIjhJhhOhhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]rheXsegmentsrr}r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr}r(hHUhIjubahOjubahOhubaubeubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]r(hr)r}r(hHX*Return a list of selected Segment objects.rhIjhJjhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheX*Return a list of selected Segment objects.rr}r(hHjhIjubaubhr)r}r(hHXWThe returned objects will contain all regular references, not just to selected objects.rhIjhJjhOhvhQ}r(hS]hT]hU]hV]hY]uh[Kh\hhC]rheXWThe returned objects will contain all regular references, not just to selected objects.rr}r(hHjhIjubaubeubeubhi)r}r(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.selection_blocksrhOhmhQ}r(hV]hU]hS]hT]hY]Uentries]r(hpX(selection_blocks() (DataProvider method)h*Utrauh[Nh\hhC]ubh)r}r(hHUhIjhJjhOhhQ}r(hډhXpyhV]hU]hS]hT]hY]hXmethodrhjuh[Nh\hhC]r(h)r}r(hHXDataProvider.selection_blocks()hIjhJhhOhhQ}r(hV]rh*ahhKXspykeutils.plugin.data_providerrr}rbhU]hS]hT]hY]rh*ahXDataProvider.selection_blocksrhjXhuh[Nh\hhC]r(h)r}r (hHXselection_blockshIjhJhhOhhQ}r (hS]hT]hU]hV]hY]uh[Nh\hhC]r heXselection_blocksr r }r(hHUhIjubaubj)r}r(hHUhIjhJhhOjhQ}r(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)r}r(hHUhIjhJNhOjhQ}r(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rh)r}r(hHUhQ}r(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U reftargetX(_modules/spykeutils/plugin/data_providerUrefidjuhIjhC]rj)r}r(hHUhQ}r(hS]hT]rjahU]hV]hY]uhIjhC]rheX[source]rr }r!(hHUhIjubahOjubahOhubaubeubj)r"}r#(hHUhIjhJhhOjhQ}r$(hS]hT]hU]hV]hY]uh[Nh\hhC]r%(hr)r&}r'(hHX!Return a list of selected blocks.r(hIj"hJjhOhvhQ}r)(hS]hT]hU]hV]hY]uh[Kh\hhC]r*heX!Return a list of selected blocks.r+r,}r-(hHj(hIj&ubaubhr)r.}r/(hHXThe returned blocks will contain references to all other selected elements further down in the object hierarchy, but no references to elements which are not selected. The returned hierarchy is a copy, so changes made to it will not persist. The main purpose of this function is to provide an object hierarchy that can be saved to a neo file. It is not recommended to use it for data processing, the respective functions that return objects lower in the hierarchy are better suited for that purpose.r0hIj"hJjhOhvhQ}r1(hS]hT]hU]hV]hY]uh[Kh\hhC]r2heXThe returned blocks will contain references to all other selected elements further down in the object hierarchy, but no references to elements which are not selected. The returned hierarchy is a copy, so changes made to it will not persist. The main purpose of this function is to provide an object hierarchy that can be saved to a neo file. It is not recommended to use it for data processing, the respective functions that return objects lower in the hierarchy are better suited for that purpose.r3r4}r5(hHj0hIj.ubaubeubeubhi)r6}r7(hHUhIjhJX/var/build/user_builds/spykeutils/checkouts/0.4.1/spykeutils/plugin/data_provider.py:docstring of spykeutils.plugin.data_provider.DataProvider.spike_trainsr8hOhmhQ}r9(hV]hU]hS]hT]hY]Uentries]r:(hpX$spike_trains() (DataProvider method)h0Utr;auh[Nh\hhC]ubh)r<}r=(hHUhIjhJj8hOhhQ}r>(hډhXpyhV]hU]hS]hT]hY]hXmethodr?hj?uh[Nh\hhC]r@(h)rA}rB(hHXDataProvider.spike_trains()hIj<hJhhOhhQ}rC(hV]rDh0ahhKXspykeutils.plugin.data_providerrErF}rGbhU]hS]hT]hY]rHh0ahXDataProvider.spike_trainsrIhjXhuh[Nh\hhC]rJ(h)rK}rL(hHX spike_trainshIjAhJhhOhhQ}rM(hS]hT]hU]hV]hY]uh[Nh\hhC]rNheX spike_trainsrOrP}rQ(hHUhIjKubaubj)rR}rS(hHUhIjAhJhhOjhQ}rT(hS]hT]hU]hV]hY]uh[Nh\hhC]ubj)rU}rV(hHUhIjAhJNhOjhQ}rW(UexprjhV]hU]hS]hT]hY]uh[Nh\hhC]rXh)rY}rZ(hHUhQ}r[(Ureftypej UrefdochU refdomainjhV]hU]U refexplicithS]hT]hY]U 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span.className = className; span.appendChild(document.createTextNode(val.substr(pos, text.length))); node.parentNode.insertBefore(span, node.parentNode.insertBefore( document.createTextNode(val.substr(pos + text.length)), node.nextSibling)); node.nodeValue = val.substr(0, pos); } } else if (!jQuery(node).is("button, select, textarea")) { jQuery.each(node.childNodes, function() { highlight(this); }); } } return this.each(function() { highlight(this); }); }; /** * Small JavaScript module for the documentation. */ var Documentation = { init : function() { this.fixFirefoxAnchorBug(); this.highlightSearchWords(); this.initIndexTable(); }, /** * i18n support */ TRANSLATIONS : {}, PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; }, LOCALE : 'unknown', // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) gettext : function(string) { var translated = Documentation.TRANSLATIONS[string]; if (typeof translated == 'undefined') return string; return (typeof translated == 'string') ? translated : translated[0]; }, ngettext : function(singular, plural, n) { var translated = Documentation.TRANSLATIONS[singular]; if (typeof translated == 'undefined') return (n == 1) ? singular : plural; return translated[Documentation.PLURALEXPR(n)]; }, addTranslations : function(catalog) { for (var key in catalog.messages) this.TRANSLATIONS[key] = catalog.messages[key]; this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); this.LOCALE = catalog.locale; }, /** * add context elements like header anchor links */ addContextElements : function() { $('div[id] > :header:first').each(function() { $('\u00B6'). attr('href', '#' + this.id). attr('title', _('Permalink to this headline')). appendTo(this); }); $('dt[id]').each(function() { $('\u00B6'). attr('href', '#' + this.id). attr('title', _('Permalink to this definition')). appendTo(this); }); }, /** * workaround a firefox stupidity */ fixFirefoxAnchorBug : function() { if (document.location.hash && $.browser.mozilla) window.setTimeout(function() { document.location.href += ''; }, 10); }, /** * highlight the search words provided in the url in the text */ highlightSearchWords : function() { var params = $.getQueryParameters(); var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; if (terms.length) { var body = $('div.body'); window.setTimeout(function() { $.each(terms, function() { body.highlightText(this.toLowerCase(), 'highlighted'); }); }, 10); $('') .appendTo($('#searchbox')); } }, /** * init the domain index toggle buttons */ initIndexTable : function() { var togglers = $('img.toggler').click(function() { var src = $(this).attr('src'); var idnum = $(this).attr('id').substr(7); $('tr.cg-' + idnum).toggle(); if (src.substr(-9) == 'minus.png') $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); else $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); }).css('display', ''); if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { togglers.click(); } }, /** * helper function to hide the search marks again */ hideSearchWords : function() { $('#searchbox .highlight-link').fadeOut(300); $('span.highlighted').removeClass('highlighted'); }, /** * make the url absolute */ makeURL : function(relativeURL) { return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; }, /** * get the current relative url */ getCurrentURL : function() { var path = document.location.pathname; var parts = path.split(/\//); $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { if (this == '..') parts.pop(); }); var url = parts.join('/'); return path.substring(url.lastIndexOf('/') + 1, path.length - 1); } }; // quick alias for translations _ = Documentation.gettext; $(document).ready(function() { Documentation.init(); }); PKqBDD(xEE'spykeutils-0.4.1/_static/searchtools.js/* * searchtools.js_t * ~~~~~~~~~~~~~~~~ * * Sphinx JavaScript utilties for the full-text search. * * :copyright: Copyright 2007-2013 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ /** * Porter Stemmer */ var Stemmer = function() { var step2list = { ational: 'ate', tional: 'tion', enci: 'ence', anci: 'ance', izer: 'ize', bli: 'ble', alli: 'al', entli: 'ent', eli: 'e', ousli: 'ous', ization: 'ize', ation: 'ate', ator: 'ate', alism: 'al', iveness: 'ive', fulness: 'ful', ousness: 'ous', aliti: 'al', iviti: 'ive', biliti: 'ble', logi: 'log' }; var step3list = { icate: 'ic', ative: '', alize: 'al', iciti: 'ic', ical: 'ic', ful: '', ness: '' }; var c = "[^aeiou]"; // consonant var v = "[aeiouy]"; // vowel var C = c + "[^aeiouy]*"; // consonant sequence var V = v + "[aeiou]*"; // vowel sequence var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 var s_v = "^(" + C + ")?" + v; // vowel in stem this.stemWord = function (w) { var stem; var suffix; var firstch; var origword = w; if (w.length < 3) return w; var re; var re2; var re3; var re4; firstch = w.substr(0,1); if (firstch == "y") w = firstch.toUpperCase() + w.substr(1); // Step 1a re = /^(.+?)(ss|i)es$/; re2 = /^(.+?)([^s])s$/; if (re.test(w)) w = w.replace(re,"$1$2"); else if (re2.test(w)) w = w.replace(re2,"$1$2"); // Step 1b re = /^(.+?)eed$/; re2 = /^(.+?)(ed|ing)$/; if (re.test(w)) { var fp = re.exec(w); re = new RegExp(mgr0); if (re.test(fp[1])) { re = /.$/; w = w.replace(re,""); } } else if (re2.test(w)) { var fp = re2.exec(w); stem = fp[1]; re2 = new RegExp(s_v); if (re2.test(stem)) { w = stem; re2 = /(at|bl|iz)$/; re3 = new RegExp("([^aeiouylsz])\\1$"); re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); if (re2.test(w)) w = w + "e"; else if (re3.test(w)) { re = /.$/; w = w.replace(re,""); } else if (re4.test(w)) w = w + "e"; } } // Step 1c re = /^(.+?)y$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(s_v); if (re.test(stem)) w = stem + "i"; } // Step 2 re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; suffix = fp[2]; re = new RegExp(mgr0); if (re.test(stem)) w = stem + step2list[suffix]; } // Step 3 re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; suffix = fp[2]; re = new RegExp(mgr0); if (re.test(stem)) w = stem + step3list[suffix]; } // Step 4 re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; re2 = /^(.+?)(s|t)(ion)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(mgr1); if (re.test(stem)) w = stem; } else if (re2.test(w)) { var fp = re2.exec(w); stem = fp[1] + fp[2]; re2 = new RegExp(mgr1); if (re2.test(stem)) w = stem; } // Step 5 re = /^(.+?)e$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(mgr1); re2 = new RegExp(meq1); re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) w = stem; } re = /ll$/; re2 = new RegExp(mgr1); if (re.test(w) && re2.test(w)) { re = /.$/; w = w.replace(re,""); } // and turn initial Y back to y if (firstch == "y") w = firstch.toLowerCase() + w.substr(1); return w; } } /** * Simple result scoring code. */ var Scorer = { // Implement the following function to further tweak the score for each result // The function takes a result array [filename, title, anchor, descr, score] // and returns the new score. /* score: function(result) { return result[4]; }, */ // query matches the full name of an object objNameMatch: 11, // or matches in the last dotted part of the object name objPartialMatch: 6, // Additive scores depending on the priority of the object objPrio: {0: 15, // used to be importantResults 1: 5, // used to be objectResults 2: -5}, // used to be unimportantResults // Used when the priority is not in the mapping. objPrioDefault: 0, // query found in title title: 15, // query found in terms term: 5 }; /** * Search Module */ var Search = { _index : null, _queued_query : null, _pulse_status : -1, init : function() { var params = $.getQueryParameters(); if (params.q) { var query = params.q[0]; $('input[name="q"]')[0].value = query; this.performSearch(query); } }, loadIndex : function(url) { $.ajax({type: "GET", url: url, data: null, dataType: "script", cache: true, complete: function(jqxhr, textstatus) { if (textstatus != "success") { document.getElementById("searchindexloader").src = url; } }}); }, setIndex : function(index) { var q; this._index = index; if ((q = this._queued_query) !== null) { this._queued_query = null; Search.query(q); } }, hasIndex : function() { return this._index !== null; }, deferQuery : function(query) { this._queued_query = query; }, stopPulse : function() { this._pulse_status = 0; }, startPulse : function() { if (this._pulse_status >= 0) return; function pulse() { var i; Search._pulse_status = (Search._pulse_status + 1) % 4; var dotString = ''; for (i = 0; i < Search._pulse_status; i++) dotString += '.'; Search.dots.text(dotString); if (Search._pulse_status > -1) window.setTimeout(pulse, 500); } pulse(); }, /** * perform a search for something (or wait until index is loaded) */ performSearch : function(query) { // create the required interface elements this.out = $('#search-results'); this.title = $('

' + _('Searching') + '

').appendTo(this.out); this.dots = $('').appendTo(this.title); this.status = $('

').appendTo(this.out); this.output = $('