spykeutils-0.4.1/ 0000755 0001750 0001750 00000000000 12262547774 012076 5 ustar rob rob spykeutils-0.4.1/CHANGELOG.rst 0000644 0001750 0001750 00000004023 12262547774 014116 0 ustar rob rob 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.
spykeutils-0.4.1/README.rst 0000644 0001750 0001750 00000001404 12262547774 013564 0 ustar rob rob Overview
========
.. image:: https://secure.travis-ci.org/rproepp/spykeutils.png?branch=develop
:target: https://travis-ci.org/rproepp/spykeutils
Based on the `Neo `_ framework, spykeutils
is a Python library for analyzing and plotting data from neurophysiological
recordings. It can be used by itself or in conjunction with Spyke Viewer,
a multi-platform GUI application for navigating electrophysiological datasets.
For more information, see the documentation at
http://spykeutils.readthedocs.org
spykeutils was created by Robert Pröpper at the Neural Information
Processing Group (TU Berlin), supported by the Research Training Group
GRK 1589/1.
Dependencies
============
* Python 2.7
* scipy
* neo
* Optional: guiqwt for plots spykeutils-0.4.1/LICENSE 0000644 0001750 0001750 00000002735 12262547774 013112 0 ustar rob rob Copyright (c) 2012, Robert Pröpper and conbtributors.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of the author nor the names of the contributors may be
used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. spykeutils-0.4.1/doc/ 0000755 0001750 0001750 00000000000 12262547774 012643 5 ustar rob rob spykeutils-0.4.1/doc/source/ 0000755 0001750 0001750 00000000000 12262547774 014143 5 ustar rob rob spykeutils-0.4.1/doc/source/requirements.txt 0000644 0001750 0001750 00000000023 12262547774 017422 0 ustar rob rob quantities
neo
mock spykeutils-0.4.1/doc/source/changelog.rst 0000644 0001750 0001750 00000000065 12262547774 016625 0 ustar rob rob Changelog
=========
.. include:: ../../CHANGELOG.rst spykeutils-0.4.1/doc/source/index.rst 0000644 0001750 0001750 00000001331 12262547774 016002 0 ustar rob rob 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:
.. toctree::
:maxdepth: 2
intro
examples
apidoc/spykeutils
changelog
acknowledgements
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
spykeutils-0.4.1/doc/source/conf.py 0000644 0001750 0001750 00000022077 12262547774 015452 0 ustar rob rob # -*- coding: utf-8 -*-
#
# spykeutils documentation build configuration file, created by
# sphinx-quickstart on Mon Jul 30 16:54:09 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath(
os.path.join(os.pardir, os.pardir)))
# -- Mocking modules for Read the Docs compatibility ---------------------------
try:
import neo
import scipy
import PyQt4
import guiqwt
except ImportError:
from mock import MagicMock
MOCK_MODULES = ['tables', 'guiqwt', 'guiqwt.builder',
'guiqwt.baseplot', 'guiqwt.plot', 'guiqwt.curve',
'guiqwt.image', 'guiqwt.tools', 'guiqwt.signals',
'guiqwt.config', 'guiqwt.events', 'guiqwt.shapes',
'PyQt4', 'PyQt4.QtCore', 'PyQt4.QtGui',
'guidata', 'guidata.configtools', 'guidata.qthelpers']
for mod_name in MOCK_MODULES:
sys.modules[mod_name] = MagicMock()
# Needed for spykeutils.plot.Dialog.PlotDialog
class QDialog:
pass
class PlotManager:
pass
sys.modules['PyQt4.QtGui'].QDialog = QDialog
sys.modules['guiqwt.plot'].PlotManager = PlotManager
# Needed for spykeutils.plot.guiqwt_tools
class CommandTool:
pass
class InteractiveTool:
pass
sys.modules['guiqwt.tools'].CommandTool = CommandTool
sys.modules['guiqwt.tools'].InteractiveTool = InteractiveTool
import spykeutils
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest',
'sphinx.ext.intersphinx', 'sphinx.ext.coverage',
'sphinx.ext.pngmath', 'sphinx.ext.pngmath',
'sphinx.ext.ifconfig', 'sphinx.ext.viewcode']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'spykeutils'
copyright = u'2012, Robert Pröpper'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = spykeutils.__version__.rsplit('.', 1)[0]
# The full version, including alpha/beta/rc tags.
release = spykeutils.__version__
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
add_module_names = False
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# " v documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
html_favicon = 'icon.ico'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'spykeutilsdoc'
# -- Options for LaTeX output --------------------------------------------------
# The paper size ('letter' or 'a4').
#latex_paper_size = 'letter'
# The font size ('10pt', '11pt' or '12pt').
#latex_font_size = '10pt'
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'spykeutils.tex', u'spykeutils Documentation',
u'Robert Pröpper', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Additional stuff for the LaTeX preamble.
#latex_preamble = ''
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Add additional features ---------------------------------------------------
def setup(app):
app.add_javascript('copybutton.js')
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'spykeutils', u'spykeutils Documentation',
[u'Robert Pröpper'], 1)
]
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'python': ('http://docs.python.org/', None),
'neo': ('http://neo.readthedocs.org/en/latest/', None),
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
'quantities': ('http://packages.python.org/quantities/', None),
'guiqwt': ('http://packages.python.org/guiqwt/', None),
'guidata': ('http://packages.python.org/guidata/', None),
'sklearn': ('http://scikit-learn.org/stable/', None)}
spykeutils-0.4.1/doc/source/acknowledgements.rst 0000644 0001750 0001750 00000000516 12262547774 020231 0 ustar rob rob 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
spykeutils-0.4.1/doc/source/static/ 0000755 0001750 0001750 00000000000 12262547774 015432 5 ustar rob rob spykeutils-0.4.1/doc/source/static/copybutton.js 0000644 0001750 0001750 00000004631 12262547774 020202 0 ustar rob rob $(document).ready(function() {
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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
: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:
>>> 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, ...
:func:`spykeutils.rate_estimation.psth` returns two values: A dictionary
with the resulting histograms and a Quantity 1D with the bin edges.
If :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):
>>> import spykeutils.plot
>>> spykeutils.plot.psth({units[2]: units[2].spiketrains}, bin_size=400 * pq.ms, bar_plot=True) # doctest: +SKIP
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:
.. image:: /img/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,
:func:`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] # doctest: +ELLIPSIS
{: 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) # doctest: +SKIP
The resulting plot will look like the following:
.. image:: /img/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 :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.
>>> spykeutils.plot.signals(segments[0].analogsignals, spike_trains=[segments[0].spiketrains[2]], show_waveforms=False) # doctest: +SKIP
.. image:: /img/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.
spykeutils-0.4.1/doc/source/img/ 0000755 0001750 0001750 00000000000 12262547774 014717 5 ustar rob rob spykeutils-0.4.1/doc/source/img/signal.png 0000644 0001750 0001750 00000133514 12262547774 016711 0 ustar rob rob PNG
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kk2 YLLLYLLLq$}cϞ={ I7=8 DIw+/ x!IW %tEXtdate:create 2013-02-21T14:58:43+01:00| %tEXtdate:modify 2013-02-21T14:58:43+01:00Y tEXttiff:alpha unassociated( tEXttiff:endian msbTu}t tEXttiff:photometric RGB I tEXttiff:rows-per-strip 45Lk" IENDB` spykeutils-0.4.1/doc/source/apidoc/ 0000755 0001750 0001750 00000000000 12262547774 015402 5 ustar rob rob spykeutils-0.4.1/doc/source/apidoc/spykeutils.plot.rst 0000644 0001750 0001750 00000000140 12262547774 021320 0 ustar rob rob spykeutils.plot package
=======================
.. automodule:: spykeutils.plot
:members:
spykeutils-0.4.1/doc/source/apidoc/spykeutils.plugin.rst 0000644 0001750 0001750 00000000117 12262547774 021644 0 ustar rob rob plugin Package
==============
.. automodule:: spykeutils.plugin
:members:
spykeutils-0.4.1/doc/source/apidoc/spykeutils.rst 0000644 0001750 0001750 00000000276 12262547774 020355 0 ustar rob rob .. _apiref:
API reference
=============
spykeutils package
------------------
.. automodule:: spykeutils
Subpackages
-----------
.. toctree::
spykeutils.plot
spykeutils.plugin
spykeutils-0.4.1/doc/source/intro.rst 0000644 0001750 0001750 00000004635 12262547774 016040 0 ustar rob rob 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 :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.
.. _`Python`: http://python.org
.. _`neo`: http://neo.readthedocs.org
.. _`guiqwt`: http://packages.python.org/guiqwt
.. _`tables`: http://www.pytables.org
.. _`quantities`: http://pypi.python.org/pypi/quantities
.. _`scikit-learn`: http://scikit-learn.org
.. _`pip`: http://pypi.python.org/pypi/pip
.. _`scipy`: http://scipy.org
.. _`setuptools`: http://pypi.python.org/pypi/setuptools
.. _`NeuroDebian`: http://neuro.debian.net spykeutils-0.4.1/doc/Makefile 0000644 0001750 0001750 00000011002 12262547774 014275 0 ustar rob rob # Makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = build
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest
help:
@echo "Please use \`make ' where is one of"
@echo " html to make standalone HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@echo " json to make JSON files"
@echo " htmlhelp to make HTML files and a HTML help project"
@echo " qthelp to make HTML files and a qthelp project"
@echo " devhelp to make HTML files and a Devhelp project"
@echo " epub to make an epub"
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
@echo " latexpdf to make LaTeX files and run them through pdflatex"
@echo " text to make text files"
@echo " man to make manual pages"
@echo " changes to make an overview of all changed/added/deprecated items"
@echo " linkcheck to check all external links for integrity"
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
clean:
-rm -rf $(BUILDDIR)/*
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
htmlhelp:
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
@echo
@echo "Build finished; now you can run HTML Help Workshop with the" \
".hhp project file in $(BUILDDIR)/htmlhelp."
qthelp:
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/spykeutils.qhcp"
@echo "To view the help file:"
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/spykeutils.qhc"
devhelp:
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
@echo
@echo "Build finished."
@echo "To view the help file:"
@echo "# mkdir -p $$HOME/.local/share/devhelp/spykeutils"
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/spykeutils"
@echo "# devhelp"
epub:
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
@echo
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
latex:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
@echo "Run \`make' in that directory to run these through (pdf)latex" \
"(use \`make latexpdf' here to do that automatically)."
latexpdf:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through pdflatex..."
make -C $(BUILDDIR)/latex all-pdf
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
text:
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
@echo
@echo "Build finished. The text files are in $(BUILDDIR)/text."
man:
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
@echo
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
changes:
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
@echo
@echo "The overview file is in $(BUILDDIR)/changes."
linkcheck:
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
@echo
@echo "Link check complete; look for any errors in the above output " \
"or in $(BUILDDIR)/linkcheck/output.txt."
doctest:
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
@echo "Testing of doctests in the sources finished, look at the " \
"results in $(BUILDDIR)/doctest/output.txt."
spykeutils-0.4.1/doc/make.bat 0000644 0001750 0001750 00000010026 12262547774 014247 0 ustar rob rob @ECHO OFF
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set BUILDDIR=build
set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
if NOT "%PAPER%" == "" (
set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
)
if "%1" == "" goto help
if "%1" == "help" (
:help
echo.Please use `make ^` where ^ is one of
echo. html to make standalone HTML files
echo. dirhtml to make HTML files named index.html in directories
echo. singlehtml to make a single large HTML file
echo. pickle to make pickle files
echo. json to make JSON files
echo. htmlhelp to make HTML files and a HTML help project
echo. qthelp to make HTML files and a qthelp project
echo. devhelp to make HTML files and a Devhelp project
echo. epub to make an epub
echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
echo. text to make text files
echo. man to make manual pages
echo. changes to make an overview over all changed/added/deprecated items
echo. linkcheck to check all external links for integrity
echo. doctest to run all doctests embedded in the documentation if enabled
goto end
)
if "%1" == "clean" (
for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
del /q /s %BUILDDIR%\*
goto end
)
if "%1" == "html" (
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/html.
goto end
)
if "%1" == "dirhtml" (
%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
goto end
)
if "%1" == "singlehtml" (
%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
goto end
)
if "%1" == "pickle" (
%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
echo.
echo.Build finished; now you can process the pickle files.
goto end
)
if "%1" == "json" (
%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
echo.
echo.Build finished; now you can process the JSON files.
goto end
)
if "%1" == "htmlhelp" (
%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
echo.
echo.Build finished; now you can run HTML Help Workshop with the ^
.hhp project file in %BUILDDIR%/htmlhelp.
goto end
)
if "%1" == "qthelp" (
%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
echo.
echo.Build finished; now you can run "qcollectiongenerator" with the ^
.qhcp project file in %BUILDDIR%/qthelp, like this:
echo.^> qcollectiongenerator %BUILDDIR%\qthelp\spykeutils.qhcp
echo.To view the help file:
echo.^> assistant -collectionFile %BUILDDIR%\qthelp\spykeutils.ghc
goto end
)
if "%1" == "devhelp" (
%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
echo.
echo.Build finished.
goto end
)
if "%1" == "epub" (
%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
echo.
echo.Build finished. The epub file is in %BUILDDIR%/epub.
goto end
)
if "%1" == "latex" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
echo.
echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "text" (
%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
echo.
echo.Build finished. The text files are in %BUILDDIR%/text.
goto end
)
if "%1" == "man" (
%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
echo.
echo.Build finished. The manual pages are in %BUILDDIR%/man.
goto end
)
if "%1" == "changes" (
%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
echo.
echo.The overview file is in %BUILDDIR%/changes.
goto end
)
if "%1" == "linkcheck" (
%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
echo.
echo.Link check complete; look for any errors in the above output ^
or in %BUILDDIR%/linkcheck/output.txt.
goto end
)
if "%1" == "doctest" (
%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
echo.
echo.Testing of doctests in the sources finished, look at the ^
results in %BUILDDIR%/doctest/output.txt.
goto end
)
:end
spykeutils-0.4.1/spykeutils/ 0000755 0001750 0001750 00000000000 12262547774 014312 5 ustar rob rob spykeutils-0.4.1/spykeutils/__init__.py 0000644 0001750 0001750 00000003554 12262547774 016432 0 ustar rob rob """
.. 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
spykeutils-0.4.1/spykeutils/__pycache__/ 0000755 0001750 0001750 00000000000 12262547774 016522 5 ustar rob rob spykeutils-0.4.1/spykeutils/plugin/ 0000755 0001750 0001750 00000000000 12262547774 015610 5 ustar rob rob spykeutils-0.4.1/spykeutils/plugin/__init__.py 0000644 0001750 0001750 00000001774 12262547774 017732 0 ustar rob rob """ 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.
:mod:`analysis_plugin` Module
-----------------------------
.. automodule:: spykeutils.plugin.analysis_plugin
:members:
:show-inheritance:
:mod:`data_provider` Module
---------------------------
.. automodule:: spykeutils.plugin.data_provider
:members:
:show-inheritance:
:mod:`gui_data` Module
----------------------
.. automodule:: spykeutils.plugin.gui_data
""" spykeutils-0.4.1/spykeutils/plugin/io_plugin.py 0000644 0001750 0001750 00000002425 12262547774 020152 0 ustar rob rob import traceback
import inspect
import neo
from neo.io.baseio import BaseIO
from .. import SpykeException
def load_from_file(path):
""" Load IO plugins from a Python file. Inserts
the loaded plugins into the neo.iolist.
:param str path: The path to the file to search for IO plugins.
"""
f = open(path)
load_from_string(f.read(), path)
def load_from_string(code, path=''):
""" Load IO plugins from Python code. Inserts
the loaded plugins into the neo.iolist.
:param str code: The IO plugin code.
:param str path: The path for the IO plugin.
"""
exc_globals = {}
try:
exec(code, exc_globals)
except Exception:
raise SpykeException('Error during execution of ' +
'potential Neo IO file ' + path + ':\n' +
traceback.format_exc() + '\n')
for cl in exc_globals.values():
if not inspect.isclass(cl):
continue
# Should be a subclass of BaseIO...
if not issubclass(cl, BaseIO):
continue
# but should not be BaseIO (can happen when directly imported)
if cl == BaseIO:
continue
cl._is_spyke_plugin = True
cl._python_file = path
neo.io.iolist.insert(0, cl) spykeutils-0.4.1/spykeutils/plugin/analysis_plugin.py 0000644 0001750 0001750 00000040056 12262547774 021370 0 ustar rob rob 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 ret spykeutils-0.4.1/spykeutils/plugin/data_provider.py 0000644 0001750 0001750 00000036264 12262547774 021020 0 ustar rob rob 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) spykeutils-0.4.1/spykeutils/plugin/data_provider_neo.py 0000644 0001750 0001750 00000117072 12262547774 021656 0 ustar rob rob import os
import sys
from copy import copy
from collections import OrderedDict
import traceback
import atexit
import neo
from data_provider import DataProvider
from .. import conversions as convert
class NeoDataProvider(DataProvider):
""" Base class for data providers using NEO"""
# Dictionary of block lists, indexed by (filename, block index) tuples
loaded_blocks = {}
# Dictionary of index in file, indexed by block object
block_indices = {}
# Dictionary of io, indexed by block object
block_ios = {}
# Dictionary of io (IO name, read paramters) tuples for loaded blocks
block_read_params = {}
# Mode for data lazy loading:
# 0 - Full load
# 1 - Lazy load
# 2 - Caching lazy load
data_lazy_mode = 0
# Mode for lazy cascade
cascade_lazy = False
# Forced IO class for all files. If None, determine by file extension.
forced_io = None
# Active IO read parameters (dictionary indexed by IO class)
io_params = {}
def __init__(self, name, progress):
super(NeoDataProvider, self).__init__(name, progress)
@classmethod
def clear(cls):
""" Clears cached blocks
"""
cls.loaded_blocks.clear()
cls.block_indices.clear()
cls.block_read_params.clear()
ios = set()
for io in cls.block_ios.itervalues():
if io in ios:
continue
if hasattr(io, 'close'):
io.close()
ios.add(io)
cls.block_ios.clear()
@classmethod
def get_block(cls, filename, index, lazy=None, force_io=None,
read_params=None):
""" Return the block at the given index in the specified file.
:param str filename: Path to the file from which to load the block.
:param int index: The index of the block in the file.
:param int lazy: Override global lazy setting if not ``None``:
0 regular load, 1 lazy load, 2 caching lazy load.
:param force_io: Override global forced_io for the Neo IO class
to use when loading the file. If ``None``, the global
forced_io is used.
:param dict read_params: Override read parameters for the IO that
will load the block. If ``None``, the global io_params are
used.
"""
if lazy is None:
lazy = cls.data_lazy_mode > 0
else:
lazy = lazy > 0
if force_io is None:
force_io = cls.forced_io
if filename in cls.loaded_blocks:
return cls.loaded_blocks[filename][index]
io, blocks = cls._load_neo_file(filename, lazy, force_io, read_params)
if io and not lazy and not cls.cascade_lazy and hasattr(io, 'close'):
io.close()
if blocks is None:
return None
return blocks[index]
@classmethod
def get_blocks(cls, filename, lazy=None, force_io=None,
read_params=None):
""" Return a list of blocks loaded from the specified file
:param str filename: Path to the file from which to load the blocks.
:param int lazy: Override global lazy setting if not ``None``:
0 regular load, 1 lazy load, 2 caching lazy load.
:param force_io: Override global forced_io for the Neo IO class
to use when loading the file. If ``None``, the global
forced_io is used.
:param dict read_params: Override read parameters for the IO that
will load the block. If ``None``, the global io_params are
used.
"""
if lazy is None:
lazy = cls.data_lazy_mode > 0
else:
lazy = lazy > 0
if force_io is None:
force_io = cls.forced_io
if filename in cls.loaded_blocks:
return cls.loaded_blocks[filename]
io, blocks = cls._load_neo_file(filename, lazy, force_io, read_params)
if io and not lazy and not cls.cascade_lazy and hasattr(io, 'close'):
io.close()
return blocks
@classmethod
def _load_neo_file(cls, filename, lazy, force_io, read_params):
""" Returns a NEO io object and a list of contained blocks for a
file name. This function also caches all loaded blocks
:param str filename: The full path of the file (relative or absolute).
:param bool lazy: Determines if lazy mode is used for Neo io.
:param force_io: IO class to use for loading. If None, determined
by file extension or through trial and error for directories.
:param dict read_params: Override read parameters for the IO that
will load the block. If ``None``, the global io_params are
used.
"""
cascade = 'lazy' if cls.cascade_lazy else True
if os.path.isdir(filename):
if force_io:
try:
n_io = force_io(filename)
if read_params is None:
rp = cls.io_params.get(force_io, {})
else:
rp = read_params
content = n_io.read(lazy=lazy, cascade=cascade, **rp)
if force_io == neo.TdtIO and \
isinstance(content, neo.Block) and \
not content.segments:
# TdtIO can produce empty blocks for invalid dirs
sys.stderr.write(
'Could not load any blocks from "%s"' % filename)
return None, None
return cls._content_loaded(
content, filename, lazy, n_io, rp)
except Exception, e:
sys.stderr.write(
'Load error for directory "%s":\n' % filename)
tb = sys.exc_info()[2]
while not ('self' in tb.tb_frame.f_locals and
tb.tb_frame.f_locals['self'] == n_io):
if tb.tb_next is not None:
tb = tb.tb_next
else:
break
traceback.print_exception(type(e), e, tb)
else:
for io in neo.io.iolist:
if io.mode == 'dir':
try:
n_io = io(filename)
if read_params is None:
rp = cls.io_params.get(force_io, {})
else:
rp = read_params
content = n_io.read(lazy=lazy, cascade=cascade, **rp)
if io == neo.TdtIO and \
isinstance(content, neo.Block) and \
not content.segments:
# TdtIO can produce empty blocks for invalid dirs
continue
return cls._content_loaded(
content, filename, lazy, n_io, rp)
except Exception, e:
sys.stderr.write(
'Load error for directory "%s":\n' % filename)
tb = sys.exc_info()[2]
while not ('self' in tb.tb_frame.f_locals and
tb.tb_frame.f_locals['self'] == n_io):
if tb.tb_next is not None:
tb = tb.tb_next
else:
break
traceback.print_exception(type(e), e, tb)
else:
if force_io:
if read_params is None:
rp = cls.io_params.get(force_io, {})
else:
rp = read_params
return cls._load_file_with_io(filename, force_io, lazy, rp)
extension = filename.split('.')[-1]
for io in neo.io.iolist:
if extension in io.extensions:
if read_params is None:
rp = cls.io_params.get(io, {})
else:
rp = read_params
return cls._load_file_with_io(filename, io, lazy, rp)
return None, None
@classmethod
def _content_loaded(cls, content, filename, lazy, n_io, read_params):
if isinstance(content, neo.Block): # Neo 0.2.1
cls.block_indices[content] = 0
cls.loaded_blocks[filename] = [content]
cls.block_read_params[content] = (type(n_io).__name__, read_params)
if lazy or cls.cascade_lazy:
cls.block_ios[content] = n_io
return n_io, [content]
# Neo >= 0.3.0, read() returns a list of blocks
blocks = content
for i, b in enumerate(blocks):
cls.block_indices[b] = i
cls.block_read_params[b] = (type(n_io).__name__, read_params)
if lazy or cls.cascade_lazy:
cls.block_ios[b] = n_io
cls.loaded_blocks[filename] = blocks
return n_io, blocks
@classmethod
def _load_file_with_io(cls, filename, io, lazy, read_params):
if io == neo.NeoHdf5IO:
# Fix unicode problem with pyinstaller
if hasattr(sys, 'frozen'):
filename = filename.encode('UTF-8')
n_io = io(filename=filename)
if read_params is None:
rp = cls.io_params.get(io, {})
else:
rp = read_params
try:
cascade = 'lazy' if cls.cascade_lazy else True
if hasattr(io, 'read_all_blocks'): # Neo 0.2.1
content = n_io.read_all_blocks(lazy=lazy, cascade=cascade, **rp)
else:
content = n_io.read(lazy=lazy, cascade=cascade, **rp)
return cls._content_loaded(content, filename, lazy, n_io, rp)
except Exception, e:
sys.stderr.write(
'Load error for file "%s":\n' % filename)
tb = sys.exc_info()[2]
while not ('self' in tb.tb_frame.f_locals and
tb.tb_frame.f_locals['self'] == n_io):
if tb.tb_next is not None:
tb = tb.tb_next
else:
break
traceback.print_exception(type(e), e, tb)
return None, None
@classmethod
def _get_data_from_viewer(cls, viewer):
""" Return a dictionary with selection information from viewer
"""
# The links in this data format are based list indices
data = {}
data['type'] = 'Neo'
# Block entry: (Index of block in file, file location of block,
# block IO class name, block IO read parameters)
block_list = []
block_indices = {}
selected_blocks = viewer.neo_blocks()
block_files = viewer.neo_block_file_names()
for b in selected_blocks:
block_indices[b] = len(block_list)
block_list.append([NeoDataProvider.block_indices[b],
block_files[b],
cls.block_read_params[b][0],
cls.block_read_params[b][1]])
data['blocks'] = block_list
# Recording channel group entry:
# (Index of rcg in block, index of block)
rcg_list = []
rcg_indices = {}
selected_rcg = viewer.neo_channel_groups()
for rcg in selected_rcg:
rcg_indices[rcg] = len(rcg_list)
idx = rcg.block.recordingchannelgroups.index(rcg)
rcg_list.append([idx, block_indices[rcg.block]])
data['channel_groups'] = rcg_list
# Recording channel entry: (Index of channel in rcg, index of rcg)
# There can be multiple channel entries for one channel object, if
# it is part of multiple channel groups
channel_list = []
selected_channels = viewer.neo_channels()
for c in selected_channels:
for rcg in c.recordingchannelgroups:
if rcg in rcg_indices:
idx = rcg.recordingchannels.index(c)
channel_list.append([idx, rcg_indices[rcg]])
data['channels'] = channel_list
# Segment entry: (Index of segment in block, index of block)
segment_list = []
segment_indices = {}
selected_segments = viewer.neo_segments()
for s in selected_segments:
segment_indices[s] = len(segment_list)
idx = s.block.segments.index(s)
segment_list.append([idx, block_indices[s.block]])
data['segments'] = segment_list
# Unit entry: (Index of unit in rcg, index of rcg)
unit_list = []
selected_units = viewer.neo_units()
for u in selected_units:
segment_indices[u] = len(segment_list)
rcg_id = None if u.recordingchannelgroup is None \
else u.recordingchannelgroup.units.index(u)
rcg = rcg_indices[u.recordingchannelgroup] \
if u.recordingchannelgroup else None
unit_list.append([rcg_id, rcg])
data['units'] = unit_list
return data
@staticmethod
def find_io_class(name):
""" Return the Neo IO class with a given name.
:param str name: Class name of the desired IO class.
"""
for io in neo.io.iolist:
if io.__name__ == name:
return io
return None
def _active_block(self, old):
""" Return a copy of all selected elements in the given block.
Only container objects are copied, data objects are linked.
Needs to load all lazily loaded objects and will cache them
regardless of current lazy_mode,
"""
block = copy(old)
block.segments = []
selected_segments = set(self.segments() + [None])
selected_rcgs = set(self.recording_channel_groups() + [None])
selected_channels = set(self.recording_channels() + [None])
selected_units = set(self.units() + [None])
for s in old.segments:
if s in selected_segments:
segment = copy(s)
segment.analogsignals = [self._load_lazy_object(sig, True)
for sig in s.analogsignals
if sig.recordingchannel
in selected_channels]
segment.analogsignalarrays = [
self._load_lazy_object(asa, True)
for asa in s.analogsignalarrays
if asa.recordingchannelgroup in selected_rcgs]
segment.irregularlysampledsignals = [
self._load_lazy_object(iss, True)
for iss in s.irregularlysampledsignals
if iss.recordingchannel in selected_channels]
segment.spikes = [self._load_lazy_object(sp, True)
for sp in s.spikes
if sp.unit in selected_units]
segment.spiketrains = [self._load_lazy_object(st, True)
for st in s.spiketrains
if st.unit in selected_units]
segment.block = block
block.segments.append(segment)
block.recordingchannelgroups = []
for old_rcg in old.recordingchannelgroups:
if old_rcg in selected_rcgs:
rcg = copy(old_rcg)
rcg.analogsignalarrays = [
self._load_lazy_object(asa, True)
for asa in old_rcg.analogsignalarrays
if asa.segment in selected_segments]
rcg.recordingchannels = []
for c in old_rcg.recordingchannels:
if not c in selected_channels:
continue
channel = copy(c)
channel.analogsignals = [
self._load_lazy_object(sig, True)
for sig in c.analogsignals
if sig.segment in selected_segments]
channel.irregularlysampledsignals = [
self._load_lazy_object(iss, True)
for iss in c.irregularlysampledsignals
if iss.segment in selected_segments]
channel.recordingchannelgroups = copy(
c.recordingchannelgroups)
channel.recordingchannelgroups.insert(
channel.recordingchannelgroups.index(old_rcg), rcg)
channel.recordingchannelgroups.remove(old_rcg)
rcg.recordingchannels.append(channel)
rcg.units = []
for u in old_rcg.units:
if not u in selected_units:
continue
unit = copy(u)
unit.spikes = [self._load_lazy_object(sp, True)
for sp in u.spikes
if sp.segment in selected_segments]
unit.spiketrains = [self._load_lazy_object(st, True)
for st in u.spiketrains
if st.segment in selected_segments]
unit.recordingchannelgroup = rcg
rcg.units.append(unit)
rcg.block = block
block.recordingchannelgroups.append(rcg)
return block
def _get_object_io(self, o):
""" Find the IO for an object. Return ``None`` if no IO exists.
"""
if o.segment:
return self.block_ios.get(o.segment.block, None)
if hasattr(object, 'recordingchannelgroups'):
if o.recordingchannelgroups:
return self.block_ios.get(
o.recordingchannelgroups[0].block, None)
if hasattr(object, 'recordingchannel'):
c = o.recordingchannel
if c.recordingchannelgroups:
return self.block_ios.get(
c.recordingchannelgroups[0].block, None)
return None
def _load_lazy_object(self, o, change_links=False):
""" Return a loaded version of a lazily loaded object. The IO
needs a ``read_lazy_object`` that takes a lazily loaded data object
as parameter method for this to work.
:param o: The object to load.
:param bool change_links: If ``True``, replace the old object
in the hierarchy.
"""
if not hasattr(o, 'lazy_shape'):
return o
io = self._get_object_io(o)
if io:
if hasattr(io, 'load_lazy_object'):
ret = io.load_lazy_object(o)
elif isinstance(io, neo.io.NeoHdf5IO):
ret = io.get(o.hdf5_path, cascade=False, lazy=False)
else:
return o
ret.segment = o.segment
if hasattr(o, 'recordingchannelgroup'):
ret.recordingchannelgroup = o.recordingchannelgroup
elif hasattr(o, 'recordingchannel'):
ret.recordingchannel = o.recordingchannel
elif hasattr(o, 'unit'):
ret.unit = o.unit
if change_links:
name = type(o).__name__.lower() + 's'
l = getattr(o.segment, name)
try:
l[l.index(o)] = ret
except ValueError:
l.append(ret)
l = None
if hasattr(o, 'recordingchannelgroup'):
l = getattr(o.recordingchannelgroup, name)
elif hasattr(o, 'recordingchannel'):
l = getattr(o.recordingchannel, name)
elif hasattr(o, 'unit'):
l = getattr(o.unit, name)
if l is not None:
try:
l[l.index(o)] = ret
except ValueError:
l.append(ret)
return ret
return o
def _load_object_list(self, objects):
""" Return a list of loaded objects for a list of (potentially)
lazily loaded objects.
"""
ret = []
for o in objects:
ret.append(self._load_lazy_object(o, self.data_lazy_mode > 1))
return ret
def _load_object_dict(self, objects):
""" Return a dictionary (without changing indices) of loaded
objects for a dictionary of (potentially) lazily loaded objects.
"""
for k, v in objects.items():
if isinstance(v, list):
objects[k] = self._load_object_list(v)
elif isinstance(v, dict):
for ik, iv in v.items():
v[ik] = self._load_lazy_object(iv, self.data_lazy_mode > 1)
else:
raise ValueError(
'Only dicts or lists are supported as dictionary values!')
return objects
def selection_blocks(self):
""" Return a list of selected blocks.
"""
return [self._active_block(b) for b in self.blocks()]
def spike_trains(self):
""" Return a list of :class:`neo.core.SpikeTrain` objects.
"""
trains = []
units = set(self.units())
for s in self.segments():
trains.extend([t for t in s.spiketrains if t.unit in units or
t.unit is None])
for u in self.units():
trains.extend([t for t in u.spiketrains if t.segment is None])
return self._load_object_list(trains)
def spike_trains_by_unit(self):
""" Return a dictionary (indexed by Unit) of lists of
:class:`neo.core.SpikeTrain` objects.
"""
trains = OrderedDict()
segments = set(self.segments())
for u in self.units():
st = [t for t in u.spiketrains if t.segment in segments or
t.segment is None]
if st:
trains[u] = st
nonetrains = []
for s in self.segments():
nonetrains.extend([t for t in s.spiketrains if t.unit is None])
if nonetrains:
trains[self.no_unit] = nonetrains
return self._load_object_dict(trains)
def spike_trains_by_segment(self):
""" Return a dictionary (indexed by Segment) of lists of
:class:`neo.core.SpikeTrain` objects.
"""
trains = OrderedDict()
units = self.units()
for s in self.segments():
st = [t for t in s.spiketrains if t.unit in units or
t.unit is None]
if st:
trains[s] = st
nonetrains = []
for u in self.units():
nonetrains.extend([t for t in u.spiketrains if t.segment is None])
if nonetrains:
trains[self.no_segment] = nonetrains
return self._load_object_dict(trains)
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.
"""
trains = OrderedDict()
segments = self.segments()
for u in self.units():
for s in segments:
segtrains = [t for t in u.spiketrains if t.segment == s]
if segtrains:
if u not in trains:
trains[u] = OrderedDict()
trains[u][s] = segtrains[0]
nonetrains = [t for t in u.spiketrains if t.segment is None]
if nonetrains:
if u not in trains:
trains[u] = OrderedDict()
trains[u][self.no_segment] = nonetrains[0]
nonetrains = OrderedDict()
for s in self.segments():
segtrains = [t for t in s.spiketrains if t.unit is None]
if segtrains:
nonetrains[s] = segtrains[0]
if nonetrains:
trains[self.no_unit] = nonetrains
return self._load_object_dict(trains)
def spikes(self):
""" Return a list of :class:`neo.core.Spike` objects.
"""
spikes = []
units = self.units()
for s in self.segments():
spikes.extend([t for t in s.spikes if t.unit in units or
t.unit is None])
for u in self.units():
spikes.extend([t for t in u.spikes if t.segment is None])
return self._load_object_list(spikes)
def spikes_by_unit(self):
""" Return a dictionary (indexed by Unit) of lists of
:class:`neo.core.Spike` objects.
"""
spikes = OrderedDict()
segments = self.segments()
for u in self.units():
sp = [t for t in u.spikes if t.segment in segments or
t.segment is None]
if sp:
spikes[u] = sp
nonespikes = []
for s in self.segments():
nonespikes.extend([t for t in s.spikes if t.unit is None])
if nonespikes:
spikes[self.no_unit] = nonespikes
return self._load_object_dict(spikes)
def spikes_by_segment(self):
""" Return a dictionary (indexed by Segment) of lists of
:class:`neo.core.Spike` objects.
"""
spikes = OrderedDict()
units = self.units()
for s in self.segments():
sp = [t for t in s.spikes if t.unit in units or
t.unit is None]
if sp:
spikes[s] = sp
nonespikes = []
for u in self.units():
nonespikes.extend([t for t in u.spikes if t.segment is None])
if nonespikes:
spikes[self.no_segment] = nonespikes
return self._load_object_dict(spikes)
def spikes_by_unit_and_segment(self):
""" Return a dictionary (indexed by Unit) of dictionaries
(indexed by Segment) of :class:`neo.core.Spike` lists.
"""
spikes = OrderedDict()
segments = self.segments()
for u in self.units():
for s in segments:
segtrains = [t for t in u.spikes if t.segment == s]
if segtrains:
if u not in spikes:
spikes[u] = OrderedDict()
spikes[u][s] = segtrains
nonespikes = [t for t in u.spikes if t.segment is None]
if nonespikes:
if u not in spikes:
spikes[u] = OrderedDict()
spikes[u][self.no_segment] = nonespikes
nonespikes = OrderedDict()
for s in self.segments():
segspikes = [t for t in s.spikes if t.unit is None]
if segspikes:
nonespikes[s] = segspikes
if nonespikes:
spikes[self.no_unit] = nonespikes
return self._load_object_dict(spikes)
def events(self, include_array_events=True):
""" Return a dictionary (indexed by Segment) of lists of
Event objects.
"""
ret = OrderedDict()
for s in self.segments():
if s.events:
ret[s] = s.events
if include_array_events:
for a in s.eventarrays:
if s not in ret:
ret[s] = []
ret[s].extend(convert.event_array_to_events(a))
return ret
def labeled_events(self, label, include_array_events=True):
""" Return a dictionary (indexed by Segment) of lists of Event
objects with the given label.
"""
ret = OrderedDict()
for s in self.segments():
events = [e for e in s.events if e.label == label]
if events:
ret[s] = events
if include_array_events:
for a in s.eventarrays:
if s not in ret:
ret[s] = []
events = convert.event_array_to_events(a)
ret[s].extend((e for e in events if e.label == label))
return ret
def event_arrays(self):
""" Return a dictionary (indexed by Segment) of lists of
EventArray objects.
"""
ret = OrderedDict()
for s in self.segments():
if s.eventarrays:
ret[s] = s.eventarrays
return self._load_object_dict(ret)
def epochs(self, include_array_epochs=True):
""" Return a dictionary (indexed by Segment) of lists of
Epoch objects.
"""
ret = OrderedDict()
for s in self.segments():
if s.epochs:
ret[s] = s.epochs
if include_array_epochs:
for a in s.epocharrays:
if s not in ret:
ret[s] = []
ret[s].extend(convert.epoch_array_to_epochs(a))
return ret
def labeled_epochs(self, label, include_array_epochs=True):
""" Return a dictionary (indexed by Segment) of lists of Epoch
objects with the given label.
"""
ret = OrderedDict()
for s in self.segments():
epochs = [e for e in s.epochs if e.label == label]
if epochs:
ret[s] = epochs
if include_array_epochs:
for a in s.epocharrays:
if s not in ret:
ret[s] = []
epochs = convert.epoch_array_to_epochs(a)
ret[s].extend((e for e in epochs if e.label == label))
return ret
def epoch_arrays(self):
""" Return a dictionary (indexed by Segment) of lists of
EpochArray objects.
"""
ret = OrderedDict()
for s in self.segments():
if s.epocharrays:
ret[s] = s.epocharrays
return self._load_object_dict(ret)
def analog_signals(self, conversion_mode=1):
""" Return a list of :class:`neo.core.AnalogSignal` objects.
"""
signals = []
channels = self.recording_channels()
if conversion_mode == 1 or conversion_mode == 3:
for s in self.segments():
signals.extend([t for t in s.analogsignals
if t.recordingchannel in channels or
t.recordingchannel is None])
for u in self.recording_channels():
signals.extend([t for t in u.analogsignals
if t.segment is None])
if conversion_mode > 1:
for sa in self.analog_signal_arrays():
for sig in convert.analog_signal_array_to_analog_signals(sa):
if (sig.recordingchannel is None or
sig.recordingchannel in channels):
signals.append(sig)
return self._load_object_list(signals)
def analog_signals_by_segment(self, conversion_mode=1):
""" Return a dictionary (indexed by Segment) of lists of
:class:`neo.core.AnalogSignal` objects.
"""
signals = OrderedDict()
channels = self.recording_channels()
if conversion_mode == 1 or conversion_mode == 3:
for s in self.segments():
sig = []
for c in channels:
sig.extend([t for t in c.analogsignals
if t.segment == s])
sig.extend([t for t in s.analogsignals
if t.recordingchannel is None])
if sig:
signals[s] = sig
nonesignals = []
for c in channels:
nonesignals.extend([t for t in c.analogsignals
if t.segment is None])
if nonesignals:
signals[self.no_segment] = nonesignals
if conversion_mode > 1:
for o, sa_list in \
self.analog_signal_arrays_by_segment().iteritems():
for sa in sa_list:
for sig in \
convert.analog_signal_array_to_analog_signals(sa):
if sig.recordingchannel is None or \
sig.recordingchannel in channels:
if o not in signals:
signals[o] = []
signals[o].append(sig)
return self._load_object_dict(signals)
def analog_signals_by_channel(self, conversion_mode=1):
""" Return a dictionary (indexed by RecordingChannel) of lists
of :class:`neo.core.AnalogSignal` objects.
"""
signals = OrderedDict()
channels = self.recording_channels()
if conversion_mode == 1 or conversion_mode == 3:
segments = self.segments()
for c in channels:
sig = [t for t in c.analogsignals
if t.segment in segments or
t.segment is None]
if sig:
signals[c] = sig
nonesignals = []
for s in segments:
nonesignals.extend([t for t in s.analogsignals
if t.recordingchannel is None])
if nonesignals:
signals[self.no_channel] = nonesignals
if conversion_mode > 1:
for o, sa_list in \
self.analog_signal_arrays_by_channelgroup().iteritems():
for sa in sa_list:
for sig in \
convert.analog_signal_array_to_analog_signals(sa):
if sig.recordingchannel is None:
if self.no_channel not in signals:
signals[self.no_channel] = [sig]
else:
signals[self.no_channel].append(sig)
elif sig.recordingchannel in channels:
if sig.recordingchannel not in signals:
signals[sig.recordingchannel] = [sig]
else:
signals[sig.recordingchannel].append(sig)
return self._load_object_dict(signals)
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.
"""
signals = OrderedDict()
channels = self.recording_channels()
if conversion_mode == 1 or conversion_mode == 3:
segments = self.segments()
for c in channels:
for s in segments:
segsignals = [t for t in c.analogsignals if t.segment == s]
if segsignals:
if c not in signals:
signals[c] = OrderedDict()
signals[c][s] = segsignals
nonesignals = [t for t in c.analogsignals if t.segment is None]
if nonesignals:
if c not in signals:
signals[c] = OrderedDict()
signals[c][self.no_segment] = nonesignals
nonesignals = OrderedDict()
for s in self.segments():
segsignals = [t for t in s.analogsignals
if t.recordingchannel is None]
if segsignals:
nonesignals[s] = segsignals
if nonesignals:
signals[self.no_channel] = nonesignals
if conversion_mode > 1:
sigs = self.analog_signal_arrays_by_channelgroup_and_segment()
for cg, inner in sigs.iteritems():
for seg, sa_list in inner.iteritems():
for sa in sa_list:
for sig in convert.analog_signal_array_to_analog_signals(sa):
chan = sig.recordingchannel
if chan not in channels:
continue
if chan not in signals:
signals[chan] = OrderedDict()
if seg not in signals[chan]:
signals[chan][seg] = []
signals[chan][seg].append(sig)
return self._load_object_dict(signals)
def analog_signal_arrays(self):
""" Return a list of :class:`neo.core.AnalogSignalArray` objects.
"""
signals = []
channelgroups = self.recording_channel_groups()
for s in self.segments():
signals.extend([t for t in s.analogsignalarrays
if t.recordingchannelgroup in channelgroups or
t.recordingchannelgroup is None])
for u in channelgroups:
signals.extend([t for t in u.analogsignalarrays
if t.segment is None])
return self._load_object_list(signals)
def analog_signal_arrays_by_segment(self):
""" Return a dictionary (indexed by Segment) of lists of
:class:`neo.core.AnalogSignalArray` objects.
"""
signals = OrderedDict()
channelgroups = self.recording_channel_groups()
for s in self.segments():
sa = []
for c in channelgroups:
sa.extend([t for t in c.analogsignalarrays
if t.segment == s])
sa.extend([t for t in s.analogsignalarrays
if t.recordingchannelgroup is None])
if sa:
signals[s] = sa
nonesignals = []
for c in channelgroups:
nonesignals.extend([t for t in c.analogsignalarrays
if t.segment is None])
if nonesignals:
signals[self.no_segment] = nonesignals
return self._load_object_dict(signals)
def analog_signal_arrays_by_channelgroup(self):
""" Return a dictionary (indexed by RecordingChannelGroup) of
lists of :class:`neo.core.AnalogSignalArray` objects.
"""
signals = OrderedDict()
segments = self.segments()
for c in self.recording_channel_groups():
sa = [t for t in c.analogsignalarrays
if t.segment in segments]
if sa:
signals[c] = sa
nonesignals = []
for s in segments:
nonesignals.extend([t for t in s.analogsignalarrays
if t.recordingchannelgroup is None])
if nonesignals:
signals[self.no_channelgroup] = nonesignals
return self._load_object_dict(signals)
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` lists.
"""
signals = OrderedDict()
segments = self.segments()
for c in self.recording_channel_groups():
for s in segments:
segsignals = [t for t in c.analogsignalarrays
if t.segment == s]
if segsignals:
if c not in signals:
signals[c] = OrderedDict()
signals[c][s] = segsignals
nonesignals = [t for t in c.analogsignalarrays
if t.segment is None]
if nonesignals:
if c not in signals:
signals[c] = OrderedDict()
signals[c][self.no_segment] = nonesignals
nonesignals = OrderedDict()
for s in self.segments():
segsignals = [t for t in s.analogsignalarrays
if t.recordingchannelgroup is None]
if segsignals:
nonesignals[s] = segsignals
if nonesignals:
signals[self.no_channelgroup] = nonesignals
return self._load_object_dict(signals)
atexit.register(NeoDataProvider.clear) spykeutils-0.4.1/spykeutils/plugin/data_provider_stored.py 0000644 0001750 0001750 00000005675 12262547774 022402 0 ustar rob rob import json
from data_provider import DataProvider
from data_provider_neo import NeoDataProvider
from ..progress_indicator import ProgressIndicator
class NeoStoredProvider(NeoDataProvider):
def __init__(self, data, progress=ProgressIndicator()):
super(NeoStoredProvider, self).__init__(data['name'], progress)
self.data = data
self.block_cache = None
@classmethod
def from_current_selection(cls, name, viewer):
""" Create new NeoStoredProvider from current viewer selection
"""
data = cls._get_data_from_viewer(viewer)
data['name'] = name
return cls(data, viewer.progress)
@classmethod
def from_file(cls, file_name, progress=ProgressIndicator()):
""" Create new DBStoredProvider from JSON file
"""
data = json.load(file_name)
return cls(data, progress)
def save(self, file_name):
""" Save selection to JSON file
"""
f = open(file_name, 'w')
json.dump(self.data, f, sort_keys=True, indent=4)
f.close()
def data_dict(self):
""" Return a dictionary with all information to serialize the object
"""
self.data['name'] = self.name
return self.data
def blocks(self):
""" Return a list of selected Block objects
"""
if self.block_cache is None:
self.block_cache = []
for b in self.data['blocks']:
cl = None
rp = None
if len(b) > 2:
cl = self.find_io_class(b[2])
if len(b) > 3:
rp = b[3]
self.block_cache.append(NeoDataProvider.get_block(
b[1], b[0], force_io=cl, read_params=rp))
return self.block_cache
def segments(self):
""" Return a list of selected Segment objects
"""
blocks = self.blocks()
segments = []
for s in self.data['segments']:
segments.append(blocks[s[1]].segments[s[0]])
return segments
def recording_channel_groups(self):
""" Return a list of selected
"""
blocks = self.blocks()
rcgs = []
for rcg in self.data['channel_groups']:
rcgs.append(blocks[rcg[1]].recordingchannelgroups[rcg[0]])
return rcgs
def recording_channels(self):
""" Return a list of selected recording channel indices
"""
rcgs = self.recording_channel_groups()
rcs = []
for rc in self.data['channels']:
rcs.append(rcgs[rc[1]].recordingchannels[rc[0]])
return rcs
def units(self):
""" Return a list of selected Unit objects
"""
rcgs = self.recording_channel_groups()
units = []
for u in self.data['units']:
units.append(rcgs[u[1]].units[u[0]])
return units
# Enable automatic creation of NeoStoredProvider objects
DataProvider._factories['Neo'] = NeoStoredProvider spykeutils-0.4.1/spykeutils/plugin/gui_data.py 0000644 0001750 0001750 00000023204 12262547774 017740 0 ustar rob rob """ This module gives access to all members of
:mod:`guidata.dataset.dataitems` and :mod:`guidata.dataset.datatypes`.
If :mod:`guidata` cannot be imported, the module offers suitable dummy
objects instead (e.g. for use on a server).
"""
try:
from guidata.dataset.dataitems import *
from guidata.dataset.datatypes import *
except ImportError, e:
import datetime
import scipy as sp
# datatypes dummies
class DataItem(object):
def __init__(self, *args, **kwargs):
self._default = None
if 'default' in kwargs:
self._default = kwargs['default']
def get_prop(self, realm, name, default=None):
pass
def get_prop_value(self, realm, instance, name, default=None):
pass
def set_prop(self, realm, **kwargs):
return self
def set_pos(self, col=0, colspan=None):
return self
def get_help(self, instance):
pass
def get_auto_help(self, instance):
pass
def format_string(self, instance, value, fmt, func):
pass
def get_string_value(self, instance):
pass
def set_name(self, new_name):
self._name = new_name
def set_from_string(self, instance, string_value):
pass
def set_default(self, instance):
self.__set__(instance, self._default)
def accept(self, visitor):
pass
def __set__(self, instance, value):
setattr(instance, "_"+self._name, value)
def __get__(self, instance, klass):
if instance is not None:
return getattr(instance, "_"+self._name, self._default)
else:
return self
def get_value(self, instance):
return self.__get__(instance, instance.__class__)
def check_item(self, instance):
pass
def check_value(self, instance, value):
pass
def from_string(self, instance, string_value):
pass
def bind(self, instance):
return DataItemVariable(self, instance)
def serialize(self, instance, writer):
pass
def deserialize(self, instance, reader):
pass
class DataSetMeta(type):
""" DataSet metaclass.
Create class attribute `_items`: list of the DataSet class attributes.
Also make sure that all data items have the correct `_name` attribute.
"""
def __new__(mcs, name, bases, dct):
items = []
for base in bases:
if getattr(base, "__metaclass__", None) is DataSetMeta:
for item in base._items:
items.append(item)
for attrname, value in dct.items():
if isinstance(value, DataItem):
value.set_name(attrname)
items.append(value)
dct["_items"] = items
return type.__new__(mcs, name, bases, dct)
class DataSet(object):
__metaclass__ = DataSetMeta
def __init__(self, title=None, comment=None, icon=''):
self.set_defaults()
def _get_translation(self):
pass
def _compute_title_and_comment(self):
pass
def get_title(self):
pass
def get_comment(self):
pass
def get_icon(self):
pass
def set_defaults(self):
for item in self._items:
item.set_default(self)
def check(self):
pass
def text_edit(self):
pass
def edit(self, parent=None, apply=None):
pass
def view(self, parent=None):
pass
def to_string(self, debug=False, indent=None, align=False):
pass
def accept(self, vis):
pass
def serialize(self, writer):
pass
def deserialize(self, reader):
pass
def read_config(self, conf, section, option):
pass
def write_config(self, conf, section, option):
pass
@classmethod
def set_global_prop(cls, realm, **kwargs):
pass
class ItemProperty(object):
def __init__(self, callable=None):
pass
def __call__(self, instance, item, value):
pass
def set(self, instance, item, value):
pass
class FormatProp(ItemProperty):
def __init__(self, fmt, ignore_error=True):
pass
class GetAttrProp(ItemProperty):
pass
class ValueProp(ItemProperty):
pass
class NotProp(ItemProperty):
pass
class DataItemVariable(object):
def __init__(self, item, instance):
self.item = item
self.instance = instance
def get_prop_value(self, realm, name, default=None):
pass
def get_prop(self, realm, name, default=None):
pass
def get_help(self):
pass
def get_auto_help(self):
pass
def get_string_value(self):
pass
def set_default(self):
return self.item.set_default(self.instance)
def get(self):
return self.item.get_value(self.instance)
def set(self, value):
return self.item.__set__(self.instance, value)
def set_from_string(self, string_value):
pass
def check_item(self):
pass
def check_value(self, value):
pass
def from_string(self, string_value):
pass
def label(self):
pass
class GroupItem(DataItem):
pass
class BeginGroup(DataItem):
pass
class EndGroup(DataItem):
pass
class TabGroupItem(GroupItem):
pass
class BeginTabGroup(BeginGroup):
def get_group(self):
pass
class EndTabGroup(EndGroup):
pass
# dataitems dummies
class NumericTypeItem(DataItem):
def __init__(self, *args, **kwargs):
super(NumericTypeItem, self).__init__(*args, **kwargs)
class FloatItem(NumericTypeItem):
def __init__(self, *args, **kwargs):
super(FloatItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = float()
class IntItem(NumericTypeItem):
def __init__(self, *args, **kwargs):
super(IntItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = int()
class StringItem(DataItem):
def __init__(self, *args, **kwargs):
super(StringItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = str()
class BoolItem(DataItem):
def __init__(self, *args, **kwargs):
super(BoolItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = bool()
class DateItem(DataItem):
def __init__(self, *args, **kwargs):
super(DateItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = datetime.date.today()
class DateTimeItem(DateItem):
def __init__(self, *args, **kwargs):
super(DateTimeItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = datetime.datetime.now()
class ColorItem(StringItem):
def __init__(self, *args, **kwargs):
super(ColorItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = str()
class FileSaveItem(StringItem):
def __init__(self, *args, **kwargs):
super(FileSaveItem, self).__init__(*args, **kwargs)
class FileOpenItem(FileSaveItem):
def __init__(self, *args, **kwargs):
super(FileOpenItem, self).__init__(*args, **kwargs)
class FilesOpenItem(FileSaveItem):
def __init__(self, *args, **kwargs):
super(FilesOpenItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = list()
class DirectoryItem(StringItem):
def __init__(self, *args, **kwargs):
super(DirectoryItem, self).__init__(*args, **kwargs)
class ChoiceItem(DataItem):
def __init__(self, *args, **kwargs):
super(ChoiceItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = int()
class MultipleChoiceItem(ChoiceItem):
def __init__(self, *args, **kwargs):
super(MultipleChoiceItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = list()
class ImageChoiceItem(ChoiceItem):
def __init__(self, *args, **kwargs):
super(ImageChoiceItem, self).__init__(*args, **kwargs)
class FloatArrayItem(DataItem):
def __init__(self, *args, **kwargs):
super(FloatArrayItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = sp.array([])
class ButtonItem(DataItem):
def __init__(self, *args, **kwargs):
super(ButtonItem, self).__init__(*args, **kwargs)
class DictItem(ButtonItem):
def __init__(self, *args, **kwargs):
super(DictItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = dict()
class FontFamilyItem(StringItem):
def __init__(self, *args, **kwargs):
super(FontFamilyItem, self).__init__(*args, **kwargs)
if self._default is None:
self._default = str() spykeutils-0.4.1/spykeutils/plugin/startplugin.py 0000644 0001750 0001750 00000013411 12262547774 020536 0 ustar rob rob #! /usr/bin/env python
import sys
import os
import argparse
import inspect
import json
import pickle
from spykeutils.plugin.analysis_plugin import AnalysisPlugin
from spykeutils.plugin.data_provider import DataProvider
from spykeutils.plugin import io_plugin
from spykeutils import progress_indicator
# Data provider implementations need to be imported so they can be loaded
import spykeutils.plugin.data_provider_stored
try:
from spykeutils.plot.helper import _needs_qt, ProgressIndicatorDialog
from PyQt4.QtGui import QApplication
from PyQt4.QtCore import QTimer
# Prepare matplotlib
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot
matplotlib.pyplot.ion()
# Prepare application and progress bar
app = QApplication([])
progress = ProgressIndicatorDialog(None)
has_qt = True
except ImportError:
has_qt = False
try: # Use command line progressbar if package is available
import progressbar
class ProgressIndicatorConsole(progress_indicator.ProgressIndicator):
""" Implements a progress indicator for the CLI """
def __init__(self):
widgets = ['', progressbar.Percentage(), ' ',
progressbar.Bar(), ' ', progressbar.ETA()]
self.bar = progressbar.ProgressBar(widgets=widgets)
self.tick = 0
self.maxval = 0
def set_ticks(self, ticks):
""" Set the required number of ticks before the operation is
done """
self.bar.maxval = ticks
self.maxval = ticks
self.tick = 0
def begin(self, title=''):
""" Signal that the operation starts """
self.tick = 0
self.bar.finished = False
self.bar.widgets[0] = title + ' '
self.bar.start()
def step(self, num_steps=1):
""" Signal that one or more steps of the operation were
completed """
self.tick += num_steps
if self.tick > self.bar.maxval:
self.bar.maxval = self.tick
self.bar.update(self.tick)
super(ProgressIndicatorConsole, self).step(num_steps)
def set_status(self, newStatus):
""" Set status description """
self.bar.widgets[0] = newStatus + ' '
def done(self):
""" Signal that the operation is done. """
self.bar.finish()
progress = ProgressIndicatorConsole()
except ImportError:
progress = progress_indicator.ProgressIndicator()
def main():
parser = argparse.ArgumentParser(description='Start an analysis plugin')
parser.add_argument('Name', type=str, help='Name of analysis class')
parser.add_argument('Code', type=str, help='Code of the analysis')
parser.add_argument('Selection', type=str, help='Serialized selection')
parser.add_argument(
'-c', '--config', dest='config', type=str,
help='Pickled configuration of analysis')
parser.add_argument(
'-cf', '--codefile', dest='codefile', action='store_const',
const=True, default=False,
help='Code represents a filename containing '
'code (default: Code is a string containing code')
parser.add_argument(
'-sf', '--selectionfile', dest='selectionfile',
action='store_const', const=True, default=False,
help='Selection represents a filename containing '
'the serialized selection (default: Selection is a string')
parser.add_argument(
'-dd', '--datadir', type=str, help='The data directory')
parser.add_argument(
'-io', type=str, default=[], nargs='+', help='IO plugin file paths')
parsed = parser.parse_known_args()
args = parsed[0]
if parsed[1]:
print >> sys.stderr, ('Warning: the following command options are '
'invalid and were ignored:'), parsed[1]
exc_globals = {}
if args.codefile:
execfile(args.Code, exc_globals)
else:
exec(args.Code, exc_globals)
# Load plugin
plugin = None
for cl in exc_globals.values():
if not inspect.isclass(cl):
continue
if not issubclass(cl, AnalysisPlugin):
continue
if not cl.__name__ == args.Name:
continue
plugin = cl()
break
# Load IO plugins
for io in args.io:
io_plugin.load_from_file(io)
if not plugin:
sys.stderr.write('Could not find plugin class, aborting...\n')
sys.exit(1)
# Load configuration
if args.config:
plugin.set_parameters(pickle.loads(args.config))
# Load selection
try:
if args.selectionfile:
f = open(args.Selection, 'r')
sel_string = '\n'.join(f.readlines())
else:
sel_string = args.Selection
sels = json.loads(sel_string)
except Exception:
sys.stderr.write('Could not load selection, aborting...\n')
sys.exit(1)
selections = []
for s in sels:
selection = DataProvider.from_data(s)
selection.progress = progress
selections.append(selection)
if args.datadir and os.path.isdir(args.datadir):
AnalysisPlugin.data_dir = args.datadir
try:
plugin.start(selections[0], selections[1:])
except progress_indicator.CancelException:
print 'User canceled.'
finally:
progress.done()
if has_qt: # Quit event loop if the plugin has not created a Qt Window
if app.topLevelWidgets() == [progress]:
app.exit(0)
return 0
if __name__ == '__main__':
if has_qt:
QTimer.singleShot(0, main)
app.exec_()
else:
sys.exit(main()) spykeutils-0.4.1/spykeutils/signal_processing.py 0000644 0001750 0001750 00000044550 12262547774 020405 0 ustar rob rob import 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. 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
spykeutils-0.4.1/spykeutils/spike_train_generation.py 0000644 0001750 0001750 00000011341 12262547774 021407 0 ustar rob rob import neo
import numpy.random
import quantities as pq
import scipy as sp
import _scipy_quantities as spq
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)
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)]
spykeutils-0.4.1/spykeutils/sorting_quality_assesment.py 0000644 0001750 0001750 00000040172 12262547774 022207 0 ustar rob rob """ 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 ret spykeutils-0.4.1/spykeutils/stationarity.py 0000644 0001750 0001750 00000007074 12262547774 017426 0 ustar rob rob import scipy as sp
import quantities as pq
from progress_indicator import ProgressIndicator
from . import SpykeException
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
spykeutils-0.4.1/spykeutils/tests/ 0000755 0001750 0001750 00000000000 12262547774 015454 5 ustar rob rob spykeutils-0.4.1/spykeutils/tests/__init__.py 0000644 0001750 0001750 00000000000 12262547774 017553 0 ustar rob rob spykeutils-0.4.1/spykeutils/tests/test_tools.py 0000644 0001750 0001750 00000044354 12262547774 020237 0 ustar rob rob try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from builders import arange_spikes
from numpy.testing import assert_array_equal, assert_array_almost_equal
from spykeutils import tools
import neo
import neo.io.tools
import neo.test.tools
import quantities as pq
import scipy as sp
class TestApplyToDict(ut.TestCase):
@staticmethod
def fn(train, multiplier=1):
return multiplier * train.size
def test_maps_function_to_each_spike_train(self):
st_dict = {'a': [arange_spikes(5 * pq.s), arange_spikes(4 * pq.s)],
'b': [arange_spikes(7 * pq.s)]}
expected = {'a': [4, 3], 'b': [6]}
actual = tools.apply_to_dict(self.fn, st_dict)
self.assertEqual(expected, actual)
def test_works_on_empty_lists(self):
st_dict = {'a': [], 'b': []}
expected = {'a': [], 'b': []}
actual = tools.apply_to_dict(self.fn, st_dict)
self.assertEqual(expected, actual)
def test_works_on_empty_dict(self):
st_dict = {}
expected = {}
actual = tools.apply_to_dict(self.fn, st_dict)
self.assertEqual(expected, actual)
def test_allows_to_pass_additional_args(self):
st_dict = {'a': [arange_spikes(5 * pq.s), arange_spikes(4 * pq.s)],
'b': [arange_spikes(7 * pq.s)]}
expected = {'a': [8, 6], 'b': [12]}
actual = tools.apply_to_dict(self.fn, st_dict, 2)
self.assertEqual(expected, actual)
class TestBinSpikeTrains(ut.TestCase):
def test_bins_spike_train_using_its_properties(self):
a = neo.SpikeTrain(
sp.array([1000.0]) * pq.ms, t_start=500.0 * pq.ms,
t_stop=1500.0 * pq.ms)
sampling_rate = 4.0 * pq.Hz
expected = {0: [sp.array([0, 0, 1, 0])]}
expectedBins = sp.array([0.5, 0.75, 1.0, 1.25, 1.5]) * pq.s
actual, actualBins = tools.bin_spike_trains({0: [a]}, sampling_rate)
self.assertEqual(len(expected), len(actual))
self.assertEqual(len(expected[0]), len(actual[0]))
assert_array_equal(expected[0][0], actual[0][0])
assert_array_almost_equal(
expectedBins, actualBins.rescale(expectedBins.units))
def test_bins_spike_train_using_passed_properties(self):
a = neo.SpikeTrain(
sp.array([1.0]) * pq.s, t_start=0.0 * pq.s, t_stop=5.0 * pq.s)
sampling_rate = 4.0 * pq.Hz
t_start = 0.5 * pq.s
t_stop = 1.5 * pq.s
expected = {0: [sp.array([0, 0, 1, 0])]}
expectedBins = sp.array([0.5, 0.75, 1.0, 1.25, 1.5]) * pq.s
actual, actualBins = tools.bin_spike_trains(
{0: [a]}, sampling_rate=sampling_rate, t_start=t_start,
t_stop=t_stop)
self.assertEqual(len(expected), len(actual))
self.assertEqual(len(expected[0]), len(actual[0]))
assert_array_equal(expected[0][0], actual[0][0])
assert_array_almost_equal(
expectedBins, actualBins.rescale(expectedBins.units))
def test_uses_max_spike_train_interval(self):
a = arange_spikes(5 * pq.s)
b = arange_spikes(7 * pq.s, 15 * pq.s)
sampling_rate = 4.0 * pq.Hz
expectedBins = sp.arange(0.0, 15.1, 0.25) * pq.s
actual, actualBins = tools.bin_spike_trains(
{0: [a, b]}, sampling_rate=sampling_rate)
assert_array_almost_equal(
expectedBins, actualBins.rescale(expectedBins.units))
def test_handles_bin_size_which_is_not_divisor_of_duration(self):
a = arange_spikes(5 * pq.s)
sampling_rate = 1.0 / 1.3 * pq.Hz
expected = {0: [sp.array([1, 1, 1, 1])]}
expectedBins = sp.array([0.0, 1.3, 2.6, 3.9, 5.2]) * pq.s
actual, actualBins = tools.bin_spike_trains({0: [a]}, sampling_rate)
self.assertEqual(len(expected), len(actual))
self.assertEqual(len(expected[0]), len(actual[0]))
assert_array_equal(expected[0][0], actual[0][0])
assert_array_almost_equal(
expectedBins, actualBins.rescale(expectedBins.units))
class TestConcatenateSpikeTrains(ut.TestCase):
def test_concatenates_spike_trains(self):
a = arange_spikes(3.0 * pq.s)
b = arange_spikes(2.0 * pq.s, 5.0 * pq.s)
expected = arange_spikes(5.0 * pq.s)
actual = tools.concatenate_spike_trains((a, b))
assert_array_almost_equal(expected, actual)
def test_t_start_is_min_of_all_trains(self):
a = arange_spikes(3.0 * pq.s, 5.0 * pq.s)
b = arange_spikes(1.0 * pq.s, 6.0 * pq.s)
expected = 1.0 * pq.s
actual = tools.concatenate_spike_trains((a, b)).t_start
self.assertAlmostEqual(expected, actual)
def test_t_stop_is_max_of_all_trains(self):
a = arange_spikes(3.0 * pq.s, 5.0 * pq.s)
b = arange_spikes(1.0 * pq.s, 6.0 * pq.s)
expected = 6.0 * pq.s
actual = tools.concatenate_spike_trains((a, b)).t_stop
self.assertAlmostEqual(expected, actual)
class TestRemoveFromHierarchy(ut.TestCase):
SEGMENTS = 5
CHANNEL_GROUPS = 4
UNITS = 3
CHANNELS = 4
@classmethod
def create_hierarchy(cls, many_to_many):
b = neo.Block()
for ns in range(cls.SEGMENTS):
b.segments.append(neo.Segment())
channels = []
if many_to_many:
channels = [neo.RecordingChannel(name='Shared %d' % i,
index=i + cls.CHANNELS)
for i in range(cls.CHANNELS / 2)]
for ng in range(cls.CHANNEL_GROUPS):
rcg = neo.RecordingChannelGroup()
for nu in range(cls.UNITS):
unit = neo.Unit()
for ns in range(cls.SEGMENTS):
spike = neo.Spike(0 * pq.s)
unit.spikes.append(spike)
b.segments[ns].spikes.append(spike)
st = neo.SpikeTrain([] * pq.s, 0 * pq.s)
unit.spiketrains.append(st)
b.segments[ns].spiketrains.append(st)
rcg.units.append(unit)
if not many_to_many:
for nc in range(cls.CHANNELS):
rc = neo.RecordingChannel(
name='Single %d' % nc, index=nc)
rc.recordingchannelgroups.append(rcg)
rcg.recordingchannels.append(rc)
else:
for nc in range(cls.CHANNELS):
if nc % 2 == 0:
rc = neo.RecordingChannel(
name='Single %d' % (nc / 2), index=nc / 2)
else:
rc = channels[nc / 2]
rc.recordingchannelgroups.append(rcg)
rcg.recordingchannels.append(rc)
rcg.channel_indexes = sp.array(
[c.index for c in rcg.recordingchannels])
rcg.channel_names = sp.array(
[c.name for c in rcg.recordingchannels])
b.recordingchannelgroups.append(rcg)
neo.io.tools.create_many_to_one_relationship(b)
return b
def test_remove_block(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
tools.remove_from_hierarchy(block)
neo.test.tools.assert_same_sub_schema(block, comp)
def test_remove_segment_no_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
seg = block.segments[1]
tools.remove_from_hierarchy(seg)
self.assertFalse(seg in block.segments)
self.assertEqual(len(block.list_units),
self.UNITS * self.CHANNEL_GROUPS)
for u in block.list_units:
self.assertEqual(len(u.spikes), self.SEGMENTS - 1)
self.assertEqual(len(u.spiketrains), self.SEGMENTS - 1)
neo.test.tools.assert_same_sub_schema(seg, comp.segments[1])
def test_remove_segment_keep_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
seg = block.segments[1]
tools.remove_from_hierarchy(seg, False)
self.assertFalse(seg in block.segments)
self.assertEqual(len(block.list_units),
self.UNITS * self.CHANNEL_GROUPS)
for u in block.list_units:
self.assertEqual(len(u.spikes), self.SEGMENTS)
self.assertEqual(len(u.spiketrains), self.SEGMENTS)
neo.test.tools.assert_same_sub_schema(seg, comp.segments[1])
def test_remove_channel_group_no_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
rcg = block.recordingchannelgroups[1]
tools.remove_from_hierarchy(rcg)
self.assertFalse(rcg in block.recordingchannelgroups)
self.assertEqual(len(block.segments), self.SEGMENTS)
for s in block.segments:
self.assertEqual(len(s.spikes),
self.UNITS * (self.CHANNEL_GROUPS - 1))
self.assertEqual(len(s.spiketrains),
self.UNITS * (self.CHANNEL_GROUPS - 1))
neo.test.tools.assert_same_sub_schema(rcg,
comp.recordingchannelgroups[1])
def test_remove_channel_group_keep_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
rcg = block.recordingchannelgroups[1]
tools.remove_from_hierarchy(rcg, False)
self.assertFalse(rcg in block.recordingchannelgroups)
self.assertEqual(len(block.segments), self.SEGMENTS)
for s in block.segments:
self.assertEqual(len(s.spikes),
self.UNITS * self.CHANNEL_GROUPS)
self.assertEqual(len(s.spiketrains),
self.UNITS * self.CHANNEL_GROUPS)
neo.test.tools.assert_same_sub_schema(rcg,
comp.recordingchannelgroups[1])
def test_remove_channel(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
rc = block.list_recordingchannels[5]
tools.remove_from_hierarchy(rc)
self.assertFalse(rc in block.list_recordingchannels)
neo.test.tools.assert_same_sub_schema(rc,
comp.list_recordingchannels[5])
self.assertEqual(len(block.segments), self.SEGMENTS)
self.assertEqual(len(block.recordingchannelgroups),
self.CHANNEL_GROUPS)
self.assertEqual(len(block.list_recordingchannels),
self.CHANNEL_GROUPS * self.CHANNELS - 1)
# Should be removed from its own channel group
rcg = rc.recordingchannelgroups[0]
self.assertEqual(len(rcg.recordingchannels), self.CHANNELS - 1)
self.assertEqual(rcg.channel_indexes.shape[0], self.CHANNELS - 1)
self.assertEqual(rcg.channel_names.shape[0], self.CHANNELS - 1)
self.assertFalse(rc.index in rcg.channel_indexes)
self.assertFalse(rc.name in rcg.channel_names)
def test_remove_unique_channel_many_to_many(self):
block = self.create_hierarchy(True)
comp = self.create_hierarchy(True)
self.assertEqual(
len(block.list_recordingchannels),
self.CHANNEL_GROUPS * (self.CHANNELS / 2) + (self.CHANNELS / 2))
rc = block.list_recordingchannels[0] # Unique channel
tools.remove_from_hierarchy(rc)
neo.test.tools.assert_same_sub_schema(rc,
comp.list_recordingchannels[0])
self.assertFalse(rc in block.list_recordingchannels)
self.assertEqual(len(block.segments), self.SEGMENTS)
self.assertEqual(len(block.recordingchannelgroups),
self.CHANNEL_GROUPS)
self.assertEqual(
len(block.list_recordingchannels),
self.CHANNEL_GROUPS * (self.CHANNELS / 2) + (self.CHANNELS / 2) - 1)
# Should be removed from its own channel group
rcg = rc.recordingchannelgroups[0]
self.assertEqual(len(rcg.recordingchannels), self.CHANNELS - 1)
self.assertEqual(rcg.channel_indexes.shape[0], self.CHANNELS - 1)
self.assertEqual(rcg.channel_names.shape[0], self.CHANNELS - 1)
self.assertFalse(rc.index in rcg.channel_indexes)
self.assertFalse(rc.name in rcg.channel_names)
def test_remove_shared_channel_many_to_many(self):
block = self.create_hierarchy(True)
comp = self.create_hierarchy(True)
self.assertEqual(
len(block.list_recordingchannels),
self.CHANNEL_GROUPS * (self.CHANNELS / 2) + (self.CHANNELS / 2))
rc = block.list_recordingchannels[1] # Shared channel
tools.remove_from_hierarchy(rc)
neo.test.tools.assert_same_sub_schema(rc,
comp.list_recordingchannels[1])
self.assertFalse(rc in block.list_recordingchannels)
self.assertEqual(len(block.segments), self.SEGMENTS)
self.assertEqual(len(block.recordingchannelgroups),
self.CHANNEL_GROUPS)
self.assertEqual(
len(block.list_recordingchannels),
self.CHANNEL_GROUPS * (self.CHANNELS / 2) + (self.CHANNELS / 2) - 1)
# Should be removed from all channel groups
for rcg in block.recordingchannelgroups:
self.assertEqual(len(rcg.recordingchannels), self.CHANNELS - 1)
self.assertEqual(rcg.channel_indexes.shape[0], self.CHANNELS - 1)
self.assertEqual(rcg.channel_names.shape[0], self.CHANNELS - 1)
self.assertFalse(rc.index in rcg.channel_indexes)
self.assertFalse(rc.name in rcg.channel_names)
def test_remove_unit_no_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
unit = block.list_units[5]
tools.remove_from_hierarchy(unit)
self.assertFalse(unit in block.list_units)
self.assertEqual(len(block.list_units),
self.UNITS * self.CHANNEL_GROUPS - 1)
self.assertEqual(len(block.segments), self.SEGMENTS)
self.assertEqual(len(block.recordingchannelgroups),
self.CHANNEL_GROUPS)
for seg in block.segments:
self.assertEqual(len(seg.spikes),
self.UNITS * self.CHANNEL_GROUPS - 1)
self.assertEqual(len(seg.spiketrains),
self.UNITS * self.CHANNEL_GROUPS - 1)
self.assertFalse(unit in [s.unit for s in seg.spikes])
self.assertFalse(unit in [st.unit for st in seg.spiketrains])
neo.test.tools.assert_same_sub_schema(unit, comp.list_units[5])
def test_remove_unit_keep_orphans(self):
block = self.create_hierarchy(False)
comp = self.create_hierarchy(False)
unit = block.list_units[5]
tools.remove_from_hierarchy(unit, False)
self.assertFalse(unit in block.list_units)
self.assertEqual(len(block.list_units),
self.UNITS * self.CHANNEL_GROUPS - 1)
self.assertEqual(len(block.segments), self.SEGMENTS)
self.assertEqual(len(block.recordingchannelgroups),
self.CHANNEL_GROUPS)
for seg in block.segments:
self.assertEqual(len(seg.spikes),
self.UNITS * self.CHANNEL_GROUPS)
self.assertEqual(len(seg.spiketrains),
self.UNITS * self.CHANNEL_GROUPS)
self.assertFalse(unit in [s.unit for s in seg.spikes])
self.assertFalse(unit in [st.unit for st in seg.spiketrains])
neo.test.tools.assert_same_sub_schema(unit, comp.list_units[5])
def test_remove_spike(self):
unit = neo.Unit()
segment = neo.Segment()
s = neo.Spike(0 * pq.s)
unit.spikes.append(s)
segment.spikes.append(s)
s.unit = unit
s.segment = segment
st = neo.SpikeTrain([] * pq.s, 0 * pq.s)
unit.spiketrains.append(st)
segment.spiketrains.append(st)
st.unit = unit
st.segment = segment
tools.remove_from_hierarchy(s)
self.assertTrue(st in unit.spiketrains)
self.assertTrue(st in segment.spiketrains)
self.assertFalse(s in unit.spikes)
self.assertFalse(s in segment.spikes)
def test_remove_spiketrain(self):
unit = neo.Unit()
segment = neo.Segment()
s = neo.Spike(0 * pq.s)
unit.spikes.append(s)
segment.spikes.append(s)
s.unit = unit
s.segment = segment
st = neo.SpikeTrain([] * pq.s, 0 * pq.s)
unit.spiketrains.append(st)
segment.spiketrains.append(st)
st.unit = unit
st.segment = segment
tools.remove_from_hierarchy(st)
self.assertTrue(s in unit.spikes)
self.assertTrue(s in segment.spikes)
self.assertFalse(st in unit.spiketrains)
self.assertFalse(st in segment.spiketrains)
def test_extract_spikes(self):
s1 = sp.zeros(10000)
s2 = sp.ones(10000)
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(
train, [sig1, sig2], 100 * pq.ms, 10 * pq.ms)
self.assertEqual(len(spikes), 9)
for s in spikes:
self.assertAlmostEqual(s.waveform[:, 0].mean(), 0.0)
self.assertAlmostEqual(s.waveform[:, 1].mean(), 1.0)
def test_extract_different_spikes(self):
s1 = sp.ones(10500)
s2 = -sp.ones(10500)
for i in xrange(10):
s1[i * 1000 + 500:i * 1000 + 1500] *= i
s2[i * 1000 + 500:i * 1000 + 1500] *= i
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(
train, [sig1, sig2], 100 * pq.ms, 10 * pq.ms)
self.assertEqual(len(spikes), 10)
for i, s in enumerate(spikes):
self.assertAlmostEqual(s.waveform[:, 0].mean(), i)
self.assertAlmostEqual(s.waveform[:, 1].mean(), -i)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/tests/test_spike_train_metrics.py 0000644 0001750 0001750 00000120653 12262547774 023132 0 ustar rob rob
try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from builders import create_empty_spike_train
import neo
from numpy.testing import assert_array_almost_equal, assert_array_equal
import quantities as pq
import scipy as sp
import spykeutils.signal_processing as sigproc
import spykeutils.spike_train_metrics as stm
import warnings
class CommonMetricTestCases(object):
""" Provides some common test cases which should work for all spike train
metrics.
"""
def calc_metric(self, trains):
""" Calculates and returns the metric under test.
:param SpikeTrain a:
:param SpikeTrain b:
:rtype: float
"""
raise NotImplementedError()
def test_is_zero_for_identical_spike_trains(self):
st = neo.SpikeTrain(
sp.array([1, 2, 3]) * pq.s, t_start=0 * pq.s, t_stop=4 * pq.s)
expected = sp.zeros((2, 2))
assert_array_almost_equal(expected, self.calc_metric([st, st.copy()]))
def test_works_with_empty_spike_trains(self):
st = neo.SpikeTrain(sp.array([]) * pq.s, t_stop=2.0 * pq.s)
expected = sp.zeros((2, 2))
assert_array_almost_equal(expected, self.calc_metric([st, st.copy()]))
def test_is_symmetric(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
b = neo.SpikeTrain(sp.array([
0.86096077, 3.54273148, 4.20476326, 6.02451599, 6.42851683,
6.5564268, 7.07864592, 7.2368936, 7.31784319, 8.15148958,
8.53540889
]) * pq.s, t_stop=10.0 * pq.s)
assert_array_almost_equal(
self.calc_metric([a, b]), self.calc_metric([b, a]))
class CommonSimilarityTestCases(object):
""" Provides some common test cases which should work for all spike train
similarity measures.
"""
def calc_similarity(self, trains):
""" Calculates and returns the similarity measure under test.
:param SpikeTrain a:
:param SpikeTrain b:
:rtype: float
"""
raise NotImplementedError()
def test_returns_one_for_equal_spike_trains(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
expected = sp.ones((2, 2))
actual = self.calc_similarity([a, a.copy()])
assert_array_almost_equal(expected, actual)
def test_is_symmetric(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
b = neo.SpikeTrain(sp.array([
0.86096077, 3.54273148, 4.20476326, 6.02451599, 6.42851683,
6.5564268, 7.07864592, 7.2368936, 7.31784319, 8.15148958,
8.53540889
]) * pq.s, t_stop=10.0 * pq.s)
assert_array_almost_equal(
self.calc_similarity([a, b]),
self.calc_similarity([b, a]))
class Test_cs_dist(ut.TestCase):
def test_returns_zero_for_equal_spike_trains(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s, sampling_rate=100 * pq.Hz)
f = sigproc.GaussianKernel()
expected = sp.array([[0.0, 0.0], [0.0, 0.0]])
assert_array_almost_equal(expected, stm.cs_dist(
[a, a.copy()], f, 1 * pq.Hz))
def test_returns_nan_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
sampling_rate = 100 * pq.Hz
smoothing_filter = sigproc.GaussianKernel()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
self.assertTrue(sp.all(sp.isnan(stm.cs_dist(
[empty, non_empty], smoothing_filter,
sampling_rate=sampling_rate))[(0, 0, 1), (0, 1, 0)]))
def test_returns_correct_spike_train_cauchy_schwarz_distance(self):
a = neo.SpikeTrain(
sp.array([1.0]) * pq.s, t_start=0.6 * pq.s, t_stop=1.4 * pq.s)
b = neo.SpikeTrain(
sp.array([0.5, 1.5]) * pq.s, t_stop=2.0 * pq.s)
c = neo.SpikeTrain(
sp.array([1.5]) * pq.s, t_start=0.6 * pq.s, t_stop=1.6 * pq.s)
smoothing_filter = sigproc.GaussianKernel(1.0 * pq.s)
expected = sp.array(
[[0.0, 0.12467574, 0.48965132],
[0.12467574, 0.0, 0.47476452],
[0.48965132, 0.47476452, 0.0]])
actual = stm.cs_dist(
[a, b, c], smoothing_filter, sampling_rate=200 * pq.Hz)
assert_array_almost_equal(expected, actual, decimal=3)
def test_is_symmetric(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
b = neo.SpikeTrain(sp.array([
0.86096077, 3.54273148, 4.20476326, 6.02451599, 6.42851683,
6.5564268, 7.07864592, 7.2368936, 7.31784319, 8.15148958,
8.53540889
]) * pq.s, t_stop=10.0 * pq.s)
f = sigproc.GaussianKernel()
sampling_rate = 350 * pq.Hz
assert_array_almost_equal(
stm.cs_dist([a, b], f, sampling_rate=sampling_rate),
stm.cs_dist([b, a], f, sampling_rate=sampling_rate), decimal=3)
class Test_event_synchronization(ut.TestCase, CommonSimilarityTestCases):
def calc_similarity(self, trains):
return stm.event_synchronization(trains)
def test_returns_correct_event_synchronization(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([5.7, 1.0]) * pq.s, t_stop=10.0 * pq.s)
c = neo.SpikeTrain(sp.array([2.0, 2.1, 5.0]) * pq.s, t_stop=10.0 * pq.s)
expected = sp.array(
[[1.0, 0.81649658092772615, 0.0],
[0.81649658092772615, 1.0, 0.4082482904638631],
[0.0, 0.4082482904638631, 1.0]])
actual = stm.event_synchronization([a, b, c])
assert_array_almost_equal(expected, actual)
def test_allows_to_set_constant_tau(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.0, 5.7]) * pq.s, t_stop=10.0 * pq.s)
tau = 0.5 * pq.s
expected = sp.array(
[[1.0, 0.40824829046386307],
[0.40824829046386307, 1.0]])
actual = stm.event_synchronization([a, b], tau)
assert_array_almost_equal(expected, actual)
def test_allows_use_of_different_kernel(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.0, 5.7]) * pq.s, t_stop=10.0 * pq.s)
tau = 1.0 * pq.s
kernel = sigproc.LaplacianKernel(1.0, normalize=False)
expected = sp.array(
[[1.0, 0.70480122722318095],
[0.70480122722318095, 1.0]])
actual = stm.event_synchronization([a, b], tau, kernel=kernel)
assert_array_almost_equal(expected, actual)
class Test_hunter_milton_similarity(ut.TestCase, CommonSimilarityTestCases):
def calc_similarity(self, trains):
return stm.hunter_milton_similarity(trains)
def test_returns_correct_hunter_milton_similarity(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(
sp.array([1.2, 5.7, 8.0, 9.0]) * pq.s, t_stop=10.0 * pq.s)
c = neo.SpikeTrain(sp.array([2.1, 2.0, 5.0]) * pq.s, t_stop=10.0 * pq.s)
tau = 2.0 * pq.s
expected = sp.array(
[[1.0, 0.64128747518120299, 0.661254342403672],
[0.64128747518120299, 1.0, 0.5521235786217787],
[0.661254342403672, 0.5521235786217787, 1.0]])
actual = stm.hunter_milton_similarity([a, b, c], tau)
assert_array_almost_equal(expected, actual)
def test_allows_use_of_different_kernel(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(
sp.array([1.2, 5.7, 8.0, 9.0]) * pq.s, t_stop=10.0 * pq.s)
kernel = sigproc.TriangularKernel(1.0 * pq.s, normalize=False)
expected = sp.array(
[[1.0, 0.29166666666666663], [0.29166666666666663, 1.0]])
actual = stm.hunter_milton_similarity([a, b], kernel=kernel)
assert_array_almost_equal(expected, actual)
def test_spike_trains_may_be_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=3.0 * pq.s)
expected = sp.array([[1.0, 0.0], [0.0, 1.0]])
actual = stm.hunter_milton_similarity([empty, non_empty])
assert_array_almost_equal(expected, actual)
class Test_norm_dist(ut.TestCase):
def test_returns_zero_for_equal_spike_trains(self):
st = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s, sampling_rate=100 * pq.Hz)
f = sigproc.GaussianKernel()
expected = sp.zeros((2, 2)) * pq.Hz ** 0.5
assert_array_almost_equal(expected, stm.norm_dist(
[st, st.copy()], f, 1 * pq.Hz))
def test_returns_norm_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
sampling_rate = 100 * pq.Hz
smoothing_filter = sigproc.GaussianKernel()
norm = stm.st_norm(
non_empty, smoothing_filter, sampling_rate=sampling_rate)
expected = sp.array([[0.0, norm], [norm, 0.0]]) * pq.Hz ** 0.5
actual = stm.norm_dist(
[empty, non_empty], smoothing_filter, sampling_rate=sampling_rate)
assert_array_almost_equal(expected, actual, decimal=3)
def test_returns_correct_spike_train_norm_distance(self):
a = neo.SpikeTrain(
sp.array([1.0]) * pq.s, t_start=0.6 * pq.s, t_stop=1.4 * pq.s)
b = neo.SpikeTrain(sp.array([0.5, 1.5]) * pq.s, t_stop=2.0 * pq.s)
c = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
smoothing_filter = sigproc.GaussianKernel(1.0 * pq.s)
expected = sp.array(
[[0.0, 0.475035, 0.531116],
[0.475035, 0.0, 0.309422],
[0.531116, 0.309422, 0.0]]) * pq.Hz ** 0.5
actual = stm.norm_dist(
[a, b, c], smoothing_filter, sampling_rate=200 * pq.Hz)
assert_array_almost_equal(
expected, actual.rescale(expected.units), decimal=3)
def test_is_symmetric(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
b = neo.SpikeTrain(sp.array([
0.86096077, 3.54273148, 4.20476326, 6.02451599, 6.42851683,
6.5564268, 7.07864592, 7.2368936, 7.31784319, 8.15148958,
8.53540889
]) * pq.s, t_stop=10.0 * pq.s)
f = sigproc.GaussianKernel()
sampling_rate = 350 * pq.Hz
assert_array_almost_equal(
stm.norm_dist([a, b], f, sampling_rate=sampling_rate),
stm.norm_dist([b, a], f, sampling_rate=sampling_rate), decimal=3)
class Test_schreiber_similarity(ut.TestCase, CommonSimilarityTestCases):
def calc_similarity(self, trains):
k = sigproc.GaussianKernel()
return stm.schreiber_similarity(trains, k)
def test_returns_nan_if_one_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
k = sigproc.GaussianKernel()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
actual = stm.schreiber_similarity((empty, non_empty), k)
self.assertTrue(sp.isnan(actual[0, 0]))
self.assertTrue(sp.isnan(actual[0, 1]))
self.assertTrue(sp.isnan(actual[1, 0]))
def test_returns_correct_spike_train_schreiber_similarity(self):
a = neo.SpikeTrain(
sp.array([1.0]) * pq.s, t_start=0.6 * pq.s, t_stop=1.4 * pq.s)
b = neo.SpikeTrain(
sp.array([0.5, 1.5]) * pq.s, t_stop=2.0 * pq.s)
c = neo.SpikeTrain(
sp.array([2.0, 1.0]) * pq.s, t_start=0.6 * pq.s, t_stop=2.4 * pq.s)
k = sigproc.GaussianKernel(sp.sqrt(2.0) * pq.s)
expected = sp.array([
[1.0, 0.9961114, 0.9430803],
[0.9961114, 1.0, 0.9523332],
[0.9430803, 0.9523332, 1.0]])
actual = stm.schreiber_similarity((a, b, c), k)
assert_array_almost_equal(expected, actual)
class Test_st_inner(ut.TestCase):
def test_returns_zero_if_any_spike_train_is_empty(self):
empty = create_empty_spike_train()
non_empty = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
smoothing_filter = sigproc.GaussianKernel()
sampling_rate = 1 * pq.Hz
expected = sp.array([0.0]) * pq.Hz
self.assertAlmostEqual(
expected, stm.st_inner(
[empty], [empty], smoothing_filter, sampling_rate))
self.assertAlmostEqual(
expected, stm.st_inner(
[empty], [non_empty], smoothing_filter, sampling_rate))
self.assertAlmostEqual(
expected, stm.st_inner(
[non_empty], [empty], smoothing_filter, sampling_rate))
def test_returns_correct_inner_spike_train_product(self):
a = neo.SpikeTrain(
sp.array([1.0]) * pq.s, t_start=0.6 * pq.s, t_stop=1.4 * pq.s)
b = neo.SpikeTrain(
sp.array([0.5, 1.5]) * pq.s, t_stop=2.0 * pq.s)
smoothing_filter = sigproc.GaussianKernel(1.0 * pq.s)
expected = 0.530007 * pq.Hz
actual = stm.st_inner(
[a], [b], smoothing_filter, sampling_rate=1000 * pq.Hz)
self.assertAlmostEqual(
expected, actual.rescale(expected.units), places=3)
def test_is_symmetric(self):
a = neo.SpikeTrain(sp.array([
1.1844519, 1.57346687, 2.52261998, 3.65824785, 5.38988771,
5.63178278, 6.70500182, 7.99562401, 9.21135176
]) * pq.s, t_stop=10.0 * pq.s)
b = neo.SpikeTrain(sp.array([
0.86096077, 3.54273148, 4.20476326, 6.02451599, 6.42851683,
6.5564268, 7.07864592, 7.2368936, 7.31784319, 8.15148958,
8.53540889
]) * pq.s, t_stop=10.0 * pq.s)
f = sigproc.GaussianKernel()
sampling_rate = 150 * pq.Hz
assert_array_almost_equal(
stm.st_inner([a], [b], f, sampling_rate=sampling_rate),
stm.st_inner([b], [a], f, sampling_rate=sampling_rate), decimal=3)
def test_accepts_sequences_of_spike_trains(self):
a = neo.SpikeTrain(
sp.array([1000.0]) * pq.ms, t_start=0.6 * pq.s, t_stop=1.4 * pq.s)
b = neo.SpikeTrain(
sp.array([0.5, 1.5]) * pq.s, t_stop=2.0 * pq.s)
f = sigproc.GaussianKernel()
sampling_rate = 150 * pq.Hz
expected = sp.array(
[[0.282094, 0.530072], [0.530072, 1.003787]]) * pq.Hz
actual = stm.st_inner([a, b], [a, b], f, sampling_rate=sampling_rate)
assert_array_almost_equal(expected, actual, decimal=3)
class Test_st_norm(ut.TestCase):
def test_returns_zero_if_spike_train_is_empty(self):
empty = create_empty_spike_train()
smoothing_filter = sigproc.GaussianKernel()
self.assertAlmostEqual(0.0, stm.st_norm(
empty, smoothing_filter, 1 * pq.Hz))
def test_returns_correct_spike_train_norm(self):
st = neo.SpikeTrain(
sp.array([0.5, 1.0, 1.5]) * pq.s, t_stop=2.0 * pq.s)
smoothing_filter = sigproc.GaussianKernel(1.0 * pq.s)
expected = (2.34569 * pq.Hz) ** 0.5
actual = stm.st_norm(st, smoothing_filter, sampling_rate=200 * pq.Hz)
self.assertAlmostEqual(
expected, actual.rescale(expected.units), places=3)
class Test_van_rossum_dist(ut.TestCase, CommonMetricTestCases):
def calc_metric(self, trains):
return stm.van_rossum_dist(trains)
def test_return_correct_distance(self):
a = neo.SpikeTrain(
sp.array([1.0, 4.0, 5.0, 6.0, 9.0, 11.0]) * pq.s,
t_stop=12.0 * pq.s)
b = neo.SpikeTrain(
sp.array([2.0, 4.0, 7.0, 10.0]) * pq.s, t_stop=12.0 * pq.s)
c = neo.SpikeTrain(sp.array([4.0, 3.0]) * pq.s, t_stop=12.0 * pq.s)
tau = 3.0 * pq.s
expected = sp.array([
[0.0, 1.895846644204, 2.878796160479],
[1.895846644204, 0.0, 1.760192079676],
[2.878796160479, 1.760192079676, 0.0]])
actual = stm.van_rossum_dist((a, b, c), tau)
assert_array_almost_equal(expected, actual)
def test_distance_of_empty_spiketrain_and_single_spike_equals_one(self):
a = neo.SpikeTrain(sp.array([]) * pq.s, t_stop=2.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2.0 * pq.s)
expected = sp.array([[0.0, 1.0], [1.0, 0.0]])
actual = stm.van_rossum_dist((a, b), 3.0 * pq.s)
assert_array_almost_equal(expected, actual)
def test_allows_use_of_different_kernel(self):
a = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.5]) * pq.s, t_stop=2.0 * pq.s)
k = sigproc.GaussianKernel(1.0 * pq.s, normalize=False)
expected = sp.array([
[0.0, 0.8264827],
[0.8264827, 0.0]])
actual = stm.van_rossum_dist((a, b), kernel=k)
assert_array_almost_equal(expected, actual)
def test_does_not_fail_with_kernel_not_allowing_spike_trains_as_argument(
self):
# Compare
a = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.5]) * pq.s, t_stop=2.0 * pq.s)
k = sigproc.TriangularKernel(1.0 * pq.s, normalize=False)
stm.van_rossum_dist((a, b), kernel=k)
def test_allows_tau_equal_to_infinity(self):
a = neo.SpikeTrain(sp.array([1.0, 1.9, 2.0]) * pq.s, t_stop=3.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.5]) * pq.s, t_stop=2.0 * pq.s)
tau = sp.inf * pq.s
expected = sp.array([
[0.0, 4.0],
[4.0, 0.0]])
actual = stm.van_rossum_dist((a, b), tau)
assert_array_almost_equal(expected, actual)
class Test_van_rossum_multiunit_dist(ut.TestCase, CommonMetricTestCases):
# With only one spike train each we should get the normal van Rossum
# distance.
def calc_metric(self, trains):
return stm.van_rossum_multiunit_dist({0: trains}, 1)
def test_returns_correct_distance_for_multiunits(self):
a0 = neo.SpikeTrain(sp.array([1.0, 5.0, 7.0]) * pq.s, t_stop=8.0 * pq.s)
a1 = neo.SpikeTrain(sp.array([1.0, 2.0, 5.0]) * pq.s, t_stop=8.0 * pq.s)
b0 = neo.SpikeTrain(sp.array([2.0, 4.0, 5.0]) * pq.s, t_stop=8.0 * pq.s)
b1 = neo.SpikeTrain(sp.array([3.0, 8.0]) * pq.s, t_stop=9.0 * pq.s)
units = {0: [a0, a1], 1: [b0, b1]}
weighting = 0.3
expected = sp.array([[0.0, 2.37006181], [2.37006181, 0.0]])
actual = stm.van_rossum_multiunit_dist(units, weighting)
assert_array_almost_equal(expected, actual)
def test_allows_tau_equal_to_infinity_with_multiunits(self):
a0 = neo.SpikeTrain(sp.array([1.0, 5.0, 7.0]) * pq.s, t_stop=8.0 * pq.s)
a1 = neo.SpikeTrain(sp.array([5.0]) * pq.s, t_stop=8.0 * pq.s)
b0 = neo.SpikeTrain(sp.array([2.0, 4.0, 5.0]) * pq.s, t_stop=8.0 * pq.s)
b1 = neo.SpikeTrain(sp.array([3.0, 8.0]) * pq.s, t_stop=9.0 * pq.s)
units = {0: [a0, a1], 1: [b0, b1]}
weighting = 0.3
tau = sp.inf * pq.s
dist = sp.sqrt(5.0 + weighting * 4.0)
expected = sp.array([[0.0, dist], [dist, 0.0]])
actual = stm.van_rossum_multiunit_dist(units, weighting, tau)
assert_array_almost_equal(expected, actual)
class Test_victor_purpura_dist(ut.TestCase, CommonMetricTestCases):
def calc_metric(self, trains):
return stm.victor_purpura_dist(trains)
def test_inserted_spikes_equal_cost_of_one(self):
num_spikes = 3
st = neo.SpikeTrain(sp.arange(3) * pq.s, t_stop=num_spikes * pq.s)
st_empty = create_empty_spike_train()
expected = sp.array([[0.0, num_spikes], [num_spikes, 0.0]])
assert_array_almost_equal(
expected, stm.victor_purpura_dist([st, st_empty]))
def test_returns_q_weighted_dist_for_close_spike_pair(self):
a = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=2 * pq.s)
b = neo.SpikeTrain(sp.array([1.5]) * pq.s, t_stop=2 * pq.s)
q = 2.0 / pq.s
expected = sp.array([[0.0, 0.5 * 2.0], [0.5 * 2.0, 0.0]])
assert_array_almost_equal(expected, stm.victor_purpura_dist([a, b], q))
def test_returns_two_for_distant_spike_pair(self):
a = neo.SpikeTrain(sp.array([1.0]) * pq.s, t_stop=6 * pq.s)
b = neo.SpikeTrain(sp.array([5.0]) * pq.s, t_stop=6 * pq.s)
q = 1.0 / pq.s
expected = sp.array([[0.0, 2.0], [2.0, 0.0]])
assert_array_almost_equal(expected, stm.victor_purpura_dist([a, b], q))
def test_returns_correct_distance_for_two_spike_trains(self):
q = 1.0 / pq.s
a = neo.SpikeTrain(
sp.array([1.0, 2.0, 4.1, 7.0, 7.1]) * pq.s, t_stop=8.0 * pq.s)
b = neo.SpikeTrain(
sp.array([1.2, 4.0, 4.3]) * pq.s, t_stop=8.0 * pq.s)
# From a to b:
# - shift 1.0 to 1.2 (cost 0.2)
# - delete 2.0 (cost 1.0)
# - shift 4.1 to 4.0 (cost 0.1)
# - insert 4.3 (cost 1.0)
# - delete 7.0 (cost 1.0)
# - delete 7.1 (cost 1.0)
expected = sp.array([[0.0, 4.3], [4.3, 0.0]])
assert_array_almost_equal(expected, stm.victor_purpura_dist([a, b], q))
def test_returns_correct_distance_for_complex_spike_trains(self):
# This is a regression test for a bug that certain spike trains where
# producing to large distances.
trains = [
neo.SpikeTrain(sp.array([
0.02675798, 0.03181146, 0.03341735, 0.03775562, 0.07791623,
0.08822388, 0.10770132, 0.12325048, 0.16989942, 0.2017788,
0.20671708, 0.21338806, 0.24211925, 0.25483266, 0.27496442,
0.27587779, 0.27987714, 0.29092447, 0.3126856, 0.31699044,
0.33125793, 0.38880785, 0.38881775, 0.44730422, 0.47123718,
0.47530894, 0.50035773, 0.5110994, 0.5406418, 0.55932289,
0.56299461, 0.61127646, 0.6669967, 0.6878365, 0.69095517,
0.71292938, 0.74403481, 0.79566084, 0.80520382, 0.87465267,
0.9233359, 0.97119188, 0.97221954, 0.98573419, 1.00598374,
1.08840599, 1.10346633, 1.11300801, 1.11736787, 1.17619865,
1.17750093, 1.18119904, 1.19001107, 1.23349135, 1.24515837,
1.31601168, 1.32058585, 1.3274779, 1.3304611, 1.39192936,
1.396939, 1.42214471, 1.43682422, 1.44056841, 1.44614004,
1.45396973, 1.48746414, 1.51381587, 1.52504075, 1.56534678,
1.56654466, 1.56932347, 1.62405807, 1.63243667, 1.64011958,
1.65348796, 1.67166925, 1.6899014, 1.70019229, 1.71489787,
1.7498802, 1.75506253, 1.77316786, 1.79721912, 1.80556803,
1.82943579, 1.8393378, 1.85571875, 1.86451301, 1.86915057,
1.93494862, 1.95227868, 1.95787129, 2.01151238, 2.05108779,
2.05622847, 2.07756536, 2.09751716, 2.11014462, 2.12756709,
2.1301002, 2.22850943, 2.23546736, 2.26357638, 2.32916089,
2.35222596, 2.36019072, 2.44110203, 2.48733729, 2.48868378,
2.49587805, 2.50484364, 2.52888902, 2.54460952, 2.55477246,
2.56718557, 2.57197204, 2.58715912, 2.62834212, 2.6607554,
2.71456005, 2.71990732, 2.73476721, 2.76560221, 2.79578411,
2.81420671, 2.82842414, 2.84323564, 2.86808335, 2.89346033,
2.89759722, 2.90250757, 2.92396906, 2.96089258, 2.99343156,
2.99513297, 3.00295214, 3.00404354, 3.01155098, 3.0220984,
3.06860675, 3.10776003, 3.11125211, 3.12200107, 3.13822244,
3.172325, 3.17359243, 3.17693368, 3.18779785, 3.1898421,
3.2027296, 3.20308197, 3.22950711, 3.23480067, 3.25230996,
3.26324005, 3.30303045, 3.3323502, 3.34200826, 3.38362587,
3.39374602, 3.40100303, 3.42572902, 3.43405842, 3.48714745,
3.48808569, 3.50765539, 3.51019425, 3.51654164, 3.53508831,
3.55091076, 3.55806575, 3.56160866, 3.64616879, 3.66336828,
3.70684962, 3.71508041, 3.71960502, 3.74088608, 3.7694215,
3.78687648, 3.78826898, 3.8032681, 3.80442445, 3.82610046,
3.83252045, 3.83375399, 3.83963007, 3.87070708, 3.89921058,
3.91373461, 3.98189025, 3.99281868, 3.99615101, 4.03866165,
4.06296107, 4.0664576, 4.10008341, 4.13249147, 4.14948245,
4.15544816, 4.18645968, 4.23956819, 4.24159763, 4.25682634,
4.29096996, 4.29801235, 4.30704865, 4.3364981, 4.34955189,
4.35691426, 4.37946289, 4.40449102, 4.41415224, 4.42969554,
4.43297123, 4.43672311, 4.46269914, 4.50611436, 4.54325245,
4.59867291, 4.6118659, 4.62881441, 4.64220816, 4.68635809,
4.6919799, 4.69224906, 4.71150593, 4.75981344, 4.76055566,
4.8129406, 4.83692968, 4.87459801, 4.8895905, 4.89048346,
4.90390866, 4.92131202, 4.95374717, 4.95658542, 4.9779478,
4.99927772, 5.00321623, 5.02279036, 5.02980636, 5.06479496,
5.07471904, 5.09194692, 5.1019829, 5.10353541, 5.10481109,
5.10639536, 5.13999128, 5.1501336, 5.15279668, 5.16864755,
5.18043833, 5.18738265, 5.19297201, 5.19496814, 5.19888859,
5.20398454, 5.24268102, 5.25293838, 5.25735102, 5.27904209,
5.32513061, 5.33412059, 5.35300406, 5.36359518, 5.38220169,
5.41380451, 5.44608516, 5.45664259, 5.46624451, 5.49995728,
5.52030155, 5.52986433, 5.53527111, 5.58813843, 5.5986904,
5.63867497, 5.64965832, 5.70854657, 5.77092465, 5.78018575,
5.80469618, 5.82611303, 5.84211921, 5.84769114, 5.85898366,
5.86174668, 5.86686434, 5.86807339, 5.88557362, 5.93531383,
5.94590946, 5.9535614, 5.96181496, 5.96211509, 5.96322495,
5.99951691, 6.02956462, 6.03071066, 6.11325118, 6.12068097,
6.13916618, 6.15618799, 6.17405661, 6.19074313, 6.20637448,
6.21144991, 6.22694995, 6.2504859, 6.29414487, 6.3132762,
6.37532399, 6.37625784, 6.41398007, 6.41816266, 6.42386713,
6.42767342, 6.43909112, 6.48312163, 6.50112821, 6.50284644,
6.52335736, 6.55053573, 6.55945474, 6.56113336, 6.58452909,
6.58510608, 6.59753607, 6.61954437, 6.64973018, 6.66495931,
6.66904812, 6.67276565, 6.73951848, 6.75443413, 6.75483586,
6.79528155, 6.81670372, 6.83292695, 6.84892368, 6.90221611,
6.94186031, 6.97372169, 6.98930105, 7.00503816, 7.01156979,
7.01622253, 7.04066381, 7.08116801, 7.1022431, 7.10534942,
7.12276162, 7.17072979, 7.1846351, 7.21250037, 7.23569895,
7.23759221, 7.26638189, 7.31573003, 7.39632157, 7.40696688,
7.42971144, 7.45062847, 7.4634739, 7.4718392, 7.49271328,
7.55204862, 7.59257437, 7.60799196, 7.61363934, 7.62867287,
7.64457945, 7.65194936, 7.66110909, 7.66676376, 7.67758238,
7.68405278, 7.69391715, 7.6990212, 7.72407479, 7.75592843,
7.77321337, 7.78914379, 7.80573035, 7.81001852, 7.81201576,
7.81761754, 7.822486, 7.88454532, 7.90159693, 7.92447452,
7.93032758, 7.95127432, 7.95471672, 7.95611181, 7.99765534,
8.00169997, 8.05611102, 8.06999799, 8.0877689, 8.11370158,
8.12326905, 8.19558094, 8.20785861, 8.22790536, 8.25096989,
8.29404755, 8.32625888, 8.38768653, 8.41293726, 8.44072146,
8.45655928, 8.46028366, 8.46062243, 8.47631889, 8.50685359,
8.539859, 8.55656747, 8.57298557, 8.60573667, 8.65462893,
8.67784071, 8.68571095, 8.71909035, 8.72206184, 8.7314385,
8.73608901, 8.74239948, 8.74416149, 8.75145957, 8.77516598,
8.88377333, 8.8848043, 8.89789711, 8.91243437, 8.91476806,
8.91492797, 8.92139551, 8.93704381, 8.96318634, 8.99623903,
9.00131449, 9.01417633, 9.01421952, 9.03203569, 9.03786051,
9.04157583, 9.09361684, 9.09610771, 9.10131371, 9.10609705,
9.12084572, 9.15575811, 9.15644013, 9.1691256, 9.18362837,
9.18595479, 9.21164258, 9.24095542, 9.24290778, 9.25767234,
9.26005027, 9.26048416, 9.28017441, 9.29182669, 9.30192562,
9.31486222, 9.35580549, 9.37514957, 9.43470264, 9.46401276,
9.48844607, 9.4945491, 9.50132042, 9.5133463, 9.51426077,
9.52668188, 9.52888838, 9.53854506, 9.54400945, 9.55057675,
9.57993589, 9.63604947, 9.64316243, 9.66791914, 9.70282942,
9.71906419, 9.72696098, 9.7422066, 9.74416635, 9.76302569,
9.77237119, 9.77808876, 9.78865054, 9.79208195, 9.82398648,
9.83977829, 9.85440184, 9.87001817, 9.91401035, 9.92363489,
9.9347058, 9.94121602, 9.95317336, 9.95549832, 9.95695226,
9.97754868, 9.98384015]) * pq.s, t_stop=10.0 * pq.s),
neo.SpikeTrain(sp.array([
0.0114491, 0.02651815, 0.02672949, 0.02712123, 0.03514833,
0.05414386, 0.07570339, 0.09427385, 0.10903071, 0.11588711,
0.11739125, 0.1285715, 0.14934368, 0.16684372, 0.21166201,
0.22235881, 0.23386214, 0.24181703, 0.25805984, 0.2654033,
0.27348522, 0.30116999, 0.31207604, 0.31553495, 0.32936142,
0.32953416, 0.35437639, 0.40074384, 0.41165687, 0.44861386,
0.49870305, 0.5265349, 0.53879183, 0.57395557, 0.62112778,
0.63952386, 0.65174804, 0.68523672, 0.72572932, 0.74491922,
0.77281653, 0.77533443, 0.83372669, 0.84671895, 0.87093241,
0.92558636, 0.94601541, 0.94777018, 0.94821996, 0.97271642,
1.0005331, 1.00257254, 1.00735428, 1.0198866, 1.04727644,
1.09182491, 1.09894488, 1.10078114, 1.10360265, 1.11904421,
1.12918186, 1.13765565, 1.18229212, 1.20436513, 1.21081849,
1.22066808, 1.22314962, 1.26854532, 1.30229203, 1.31703206,
1.32135388, 1.32907158, 1.33047318, 1.36227875, 1.39697511,
1.4242654, 1.4244518, 1.43681519, 1.4493789, 1.45152151,
1.46461455, 1.47733094, 1.48771515, 1.53536739, 1.54864524,
1.55283995, 1.5898638, 1.60887471, 1.64490284, 1.64502768,
1.66872741, 1.70025134, 1.71529419, 1.71851586, 1.75573609,
1.78231052, 1.8083983, 1.81541951, 1.81772587, 1.84818917,
1.85059323, 1.88875683, 1.90898902, 1.93557862, 1.9643203,
1.96710505, 1.98391057, 1.98527593, 2.03665079, 2.08708411,
2.08761721, 2.11103023, 2.12101666, 2.13992148, 2.17117369,
2.18684568, 2.22655021, 2.24875486, 2.24929527, 2.28056109,
2.28729401, 2.31258209, 2.32301025, 2.32477238, 2.32491974,
2.34173467, 2.35126611, 2.35149399, 2.38431406, 2.40687869,
2.42583741, 2.42797991, 2.42828893, 2.45838911, 2.46432188,
2.46473762, 2.47316229, 2.51085401, 2.5283335, 2.55848724,
2.56442768, 2.59182815, 2.60989243, 2.65008826, 2.67778032,
2.67781156, 2.68312729, 2.68929609, 2.70518959, 2.73459435,
2.78244226, 2.78290087, 2.79595168, 2.80616739, 2.80701334,
2.81042141, 2.85470512, 2.87509772, 2.88886327, 2.89375791,
2.97284058, 2.97512514, 2.98540772, 3.01458122, 3.03159057,
3.05350786, 3.05518717, 3.10446297, 3.13792582, 3.15204826,
3.17267234, 3.19586531, 3.19657011, 3.21282816, 3.25677248,
3.27720176, 3.28887985, 3.29735282, 3.2982325, 3.32269346,
3.32343112, 3.32637092, 3.34520261, 3.34914751, 3.4176678,
3.43099532, 3.48336162, 3.48518715, 3.52127749, 3.52151362,
3.5773688, 3.59222194, 3.6013162, 3.62748155, 3.63613575,
3.64713969, 3.65456465, 3.66853991, 3.73818958, 3.74375182,
3.80164474, 3.86614106, 3.89385381, 3.97585319, 3.98647681,
4.00558264, 4.0212778, 4.05202117, 4.0594387, 4.09760178,
4.11367539, 4.12070204, 4.12999226, 4.15656723, 4.20514307,
4.27451413, 4.27635573, 4.28445258, 4.28533623, 4.33012486,
4.35620149, 4.37670464, 4.37681744, 4.39893272, 4.44981225,
4.45885746, 4.47979453, 4.48028014, 4.51009319, 4.52546144,
4.57879502, 4.66509915, 4.71338549, 4.71713202, 4.73567885,
4.75441602, 4.79556635, 4.79582663, 4.82047298, 4.82055109,
4.83059559, 4.83590133, 4.86399401, 4.87413277, 4.87833755,
4.89208783, 4.9192821, 4.941063, 4.98772884, 5.01993596,
5.02465223, 5.06293715, 5.06939498, 5.07198031, 5.11089343,
5.14112836, 5.15388206, 5.18105507, 5.19314929, 5.19670734,
5.22545792, 5.23334406, 5.23459961, 5.2494979, 5.2573258,
5.25908266, 5.2840583, 5.2853253, 5.28590158, 5.32314432,
5.35959824, 5.36241399, 5.38921977, 5.40694111, 5.4313708,
5.46598325, 5.47254526, 5.49387086, 5.49886878, 5.56592236,
5.57180461, 5.58869339, 5.58984367, 5.59601824, 5.62938579,
5.64426059, 5.6476461, 5.67241871, 5.6771723, 5.67873946,
5.68074113, 5.72312447, 5.7271727, 5.76271693, 5.79335885,
5.80349046, 5.83560725, 5.84101573, 5.85666574, 5.8643614,
5.86509986, 5.86531037, 5.87744489, 5.90506991, 5.91776312,
5.96371983, 5.96613482, 5.98032448, 5.98608614, 6.00144331,
6.00838531, 6.00846468, 6.01048934, 6.02474142, 6.0335397,
6.05113466, 6.06459963, 6.06576204, 6.08503265, 6.10602749,
6.10606072, 6.22065498, 6.2532318, 6.29605114, 6.31945753,
6.35632236, 6.35896878, 6.36120413, 6.38709957, 6.39295197,
6.41809868, 6.42367352, 6.44628183, 6.47049815, 6.48133661,
6.49090302, 6.49289679, 6.50896993, 6.51693538, 6.54015486,
6.56308082, 6.568914, 6.57395747, 6.61319395, 6.63516058,
6.65665992, 6.66478415, 6.6710301, 6.67832287, 6.6987939,
6.69954116, 6.70655977, 6.72576878, 6.77771021, 6.77863482,
6.79102832, 6.81049338, 6.81235249, 6.81465697, 6.83783569,
6.84815101, 6.89710246, 6.98537525, 7.01954059, 7.02622255,
7.04976656, 7.07571722, 7.11728241, 7.13478378, 7.13478557,
7.16044495, 7.16456219, 7.19152888, 7.19978497, 7.22787642,
7.24906524, 7.25812186, 7.27034077, 7.30769391, 7.31820919,
7.35549295, 7.37285349, 7.37292834, 7.37424801, 7.3785301,
7.4196362, 7.42932103, 7.43036261, 7.45139091, 7.47555417,
7.50122532, 7.51360212, 7.51962212, 7.55560134, 7.58438748,
7.62698845, 7.64682633, 7.66868854, 7.6760022, 7.69020752,
7.7238978, 7.76340706, 7.76775711, 7.79077235, 7.79151683,
7.79383994, 7.80542945, 7.83695238, 7.85946794, 7.88079942,
7.96879553, 7.99422322, 7.99584892, 8.09873296, 8.17614594,
8.17763643, 8.18175172, 8.18778704, 8.22797549, 8.23708879,
8.28821888, 8.30281824, 8.30487238, 8.33078119, 8.33420872,
8.34305369, 8.38206152, 8.40403832, 8.41224886, 8.43463245,
8.44389971, 8.46044352, 8.48956655, 8.51149039, 8.51796916,
8.53329742, 8.53599617, 8.56068013, 8.56657166, 8.59814286,
8.61214071, 8.61498351, 8.64246675, 8.65762517, 8.66282683,
8.67384567, 8.71396613, 8.71416081, 8.73722558, 8.73767664,
8.74798782, 8.76129767, 8.76855011, 8.80085479, 8.86199255,
8.89862794, 8.93913818, 8.96782975, 8.9819441, 8.98865031,
9.00024566, 9.00610235, 9.01314955, 9.02095248, 9.03094763,
9.03668298, 9.04652449, 9.0490157, 9.05181691, 9.0646427,
9.1264005, 9.13361863, 9.14618518, 9.15534379, 9.16200272,
9.16524096, 9.19437442, 9.20198553, 9.20475517, 9.28953836,
9.32111331, 9.32181408, 9.32632133, 9.32969553, 9.4558735,
9.45868453, 9.47407654, 9.52846898, 9.54261744, 9.55992241,
9.58831097, 9.59403646, 9.5989721, 9.63828129, 9.66338416,
9.67033722, 9.68634843, 9.7151767, 9.72467937, 9.76497421,
9.77592078, 9.78303691, 9.79368995, 9.7944104, 9.80563761,
9.82690855, 9.82845111, 9.87802691, 9.90843101, 9.91777335,
9.97014496, 9.9763017]) * pq.s, t_stop=10.0 * pq.s)]
expected = sp.array([[0.0, 66.05735182], [66.05735182, 0.0]])
actual = stm.victor_purpura_dist(trains)
assert_array_almost_equal(expected, actual)
def test_allows_use_of_different_kernel(self):
k = sigproc.LaplacianKernel(1.0 * pq.s, normalize=False)
a = neo.SpikeTrain(
sp.array([1.0, 2.0, 4.1, 7.0, 7.1]) * pq.s, t_stop=8.0 * pq.s)
b = neo.SpikeTrain(
sp.array([4.0, 4.3, 1.2]) * pq.s, t_stop=8.0 * pq.s)
# From a to b:
# - shift 1.0 to 1.2 (cost 0.3625385)
# - delete 2.0 (cost 1.0)
# - shift 4.1 to 4.0 (cost 0.1903252)
# - shift 4.3 to 7.0 (cost 1.8655890)
# - delete 7.0 (cost 1.0)
# - delete 7.1 (cost 1.0)
expected = sp.array([[0.0, 4.4184526], [4.4184526, 0.0]])
assert_array_almost_equal(
expected, stm.victor_purpura_dist([a, b], kernel=k))
def test_allows_q_to_be_zero(self):
q = 0.0
a = neo.SpikeTrain(
sp.array([1.0, 2.0, 4.1, 7.0, 7.1]) * pq.s, t_stop=8.0 * pq.s)
b = neo.SpikeTrain(
sp.array([1.2, 4.0, 4.3]) * pq.s, t_stop=8.0 * pq.s)
# Pure rate code
expected = sp.array([[0.0, 2.0], [2.0, 0.0]])
assert_array_almost_equal(expected, stm.victor_purpura_dist([a, b], q))
class Test_victor_purpura_multiunit_dist(ut.TestCase, CommonMetricTestCases):
# With only one spike train each we should get the normal VP distance.
def calc_metric(self, trains):
return stm.victor_purpura_multiunit_dist({0: trains}, 1)
def test_returns_correct_distance_for_multiunits(self):
a0 = neo.SpikeTrain(sp.array([1.0, 5.0, 7.0]) * pq.s, t_stop=8.0 * pq.s)
a1 = neo.SpikeTrain(sp.array([1.0, 2.0, 5.0]) * pq.s, t_stop=8.0 * pq.s)
b0 = neo.SpikeTrain(sp.array([2.0, 4.0, 5.0]) * pq.s, t_stop=8.0 * pq.s)
b1 = neo.SpikeTrain(sp.array([3.0, 8.0]) * pq.s, t_stop=9.0 * pq.s)
units = {0: [a0, a1], 1: [b0, b1]}
reassignment_cost = 0.7
expected = sp.array([[0.0, 4.4], [4.4, 0.0]])
actual = stm.victor_purpura_multiunit_dist(units, reassignment_cost)
assert_array_almost_equal(expected, actual)
def test_returns_empty_array_if_empty_dict_is_passed(self):
expected = sp.zeros((0, 0))
actual = stm.victor_purpura_multiunit_dist({}, 1.0)
assert_array_equal(expected, actual)
def test_returns_empty_array_if_trials_are_empty(self):
expected = sp.zeros((0, 0))
actual = stm.victor_purpura_multiunit_dist({0: [], 1: []}, 1.0)
assert_array_equal(expected, actual)
def test_raises_exception_if_number_of_trials_differs(self):
st = create_empty_spike_train()
with self.assertRaises(ValueError):
stm.victor_purpura_multiunit_dist({0: [st], 1: [st, st]}, 1.0)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/tests/__pycache__/ 0000755 0001750 0001750 00000000000 12262547774 017664 5 ustar rob rob spykeutils-0.4.1/spykeutils/tests/test_signal_processing.py 0000644 0001750 0001750 00000036102 12262547774 022600 0 ustar rob rob
try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from builders import create_empty_spike_train
from mock import MagicMock
from numpy.testing import assert_array_almost_equal, assert_array_equal
import neo
import quantities as pq
import scipy as sp
import spykeutils.signal_processing as sigproc
class TestKernel(ut.TestCase):
def test_call_returns_result_of_evaluate(self):
kernel_size = 1.3
kernel = sigproc.Kernel(kernel_size, normalize=False)
kernel._evaluate = MagicMock(name='_evaluate')
kernel._evaluate.return_value = 42
t = 3.3
actual = kernel(t)
kernel._evaluate.assert_called_with(t, kernel_size)
self.assertEqual(kernel._evaluate.return_value, actual)
def test_call_can_normalize_evaluate(self):
kernel_size = 1.3
kernel = sigproc.Kernel(kernel_size, normalize=True)
kernel._evaluate = MagicMock(name='_evaluate')
kernel._evaluate.return_value = 42
kernel.normalization_factor = MagicMock(name='normalization_factor')
kernel.normalization_factor.return_value = 0.5
t = 3.3
actual = kernel(t)
kernel._evaluate.assert_called_with(t, kernel_size)
kernel.normalization_factor.assert_called_with(kernel_size)
self.assertEqual(
kernel.normalization_factor.return_value *
kernel._evaluate.return_value, actual)
def test_call_can_overwrite_kernel_size(self):
kernel_size = 1.3
kernel = sigproc.Kernel(5.6, normalize=True)
kernel._evaluate = MagicMock(name='_evaluate')
kernel._evaluate.return_value = 42
kernel.normalization_factor = MagicMock(name='normalization_factor')
kernel.normalization_factor.return_value = 0.5
t = 3.3
actual = kernel(t, kernel_size)
kernel._evaluate.assert_called_with(t, kernel_size)
kernel.normalization_factor.assert_called_with(kernel_size)
self.assertEqual(
kernel.normalization_factor.return_value *
kernel._evaluate.return_value, actual)
def test_summed_dist_matrix(self):
kernel = sigproc.Kernel(1.0, normalize=False)
kernel._evaluate = lambda t, _: t
vectors = [sp.array([2.0, 1.0, 3.0]), sp.array([1.5, 4.0])]
expected = sp.array([[0.0, -4.5], [4.5, 0.0]])
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual)
def test_summed_dist_matrix_with_units(self):
kernel = sigproc.Kernel(2000 * pq.ms, normalize=False)
kernel._evaluate = lambda t, size: 1.0 / size / size * t
vectors = [sp.array([2.0, 1.0, 3.0]) * pq.s,
sp.array([1500, 4000]) * pq.ms]
expected = sp.array([[0.0, -1.125], [1.125, 0.0]]) / pq.s
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual.rescale(1.0 / pq.s))
class TestSymmetricKernel(ut.TestCase):
def test_summed_dist_matrix(self):
kernel = sigproc.Kernel(1.0, normalize=False)
kernel._evaluate = lambda t, _: sp.absolute(t)
vectors = [sp.array([2.0, 1.0, 3.0]), sp.array([1.5, 4.0])]
expected = sp.array([[8.0, 8.5], [8.5, 5.0]])
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual)
def test_summed_dist_matrix_with_units(self):
kernel = sigproc.Kernel(2000 * pq.ms, normalize=False)
kernel._evaluate = lambda t, size: 1.0 / size / size * sp.absolute(t)
vectors = [sp.array([2.0, 1.0, 3.0]) * pq.s,
sp.array([1500, 4000]) * pq.ms]
expected = sp.array([[2.0, 2.125], [2.125, 1.25]]) / pq.s
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual.rescale(1.0 / pq.s))
class Test_discretize_kernel(ut.TestCase):
def test_discretizes_requested_area(self):
kernel = sigproc.Kernel(1.0, normalize=False)
kernel.boundary_enclosing_at_least = MagicMock(
name='boundary_enclosing_at_least')
kernel.boundary_enclosing_at_least.return_value = 2.0
kernel._evaluate = lambda x, _: sp.ones(len(x))
sampling_rate = 1.0
mock_discretization = sp.ones(5)
kernel_area_fraction = 0.5
actual = sigproc.discretize_kernel(
kernel, sampling_rate, kernel_area_fraction)
kernel.boundary_enclosing_at_least.assert_called_with(
kernel_area_fraction)
assert_array_equal(actual, mock_discretization)
def test_discretizes_requested_area_with_units(self):
kernel = sigproc.Kernel(100.0 * pq.ms, normalize=False)
kernel.boundary_enclosing_at_least = MagicMock(
name='boundary_enclosing_at_least')
kernel.boundary_enclosing_at_least.return_value = 100.0 * pq.ms
kernel._evaluate = lambda x, _: sp.ones(len(x))
sampling_rate = 10.0 * pq.Hz
mock_discretization = sp.ones(3)
kernel_area_fraction = 0.5
actual = sigproc.discretize_kernel(
kernel, sampling_rate, kernel_area_fraction)
kernel.boundary_enclosing_at_least.assert_called_with(
kernel_area_fraction)
assert_array_equal(actual, mock_discretization)
def test_discretizes_requested_number_of_bins(self):
kernel = sigproc.Kernel(1.0, normalize=False)
kernel._evaluate = lambda x, _: sp.ones(len(x))
sampling_rate = 1.0
num_bins = 23
mock_discretization = sp.ones(num_bins)
actual = sigproc.discretize_kernel(
kernel, sampling_rate, num_bins=num_bins)
assert_array_equal(actual, mock_discretization)
def test_can_normalize_to_unit_area(self):
kernel = sigproc.Kernel(1.0, normalize=False)
kernel._evaluate = lambda x, _: sp.ones(len(x))
sampling_rate = 2.0
num_bins = 20
mock_discretization = sp.ones(num_bins) / num_bins * sampling_rate
actual = sigproc.discretize_kernel(
kernel, sampling_rate, num_bins=num_bins, ensure_unit_area=True)
assert_array_equal(actual, mock_discretization)
class TestCausalDecayingExpKernel(ut.TestCase):
def setUp(self):
self.kernel_size = 500 * pq.ms
self.kernel = sigproc.CausalDecayingExpKernel(self.kernel_size)
def test_evaluates_to_correct_values(self):
t = sp.array([-0.1, 0, 0.6, 1]) * pq.s
expected = sp.array([0.0, 2.0, 0.60238842, 0.27067057]) / pq.s
actual = self.kernel(t)
assert_array_almost_equal(expected, actual.rescale(expected.units))
def test_boundary_enclosing_at_least_is_correct(self):
actual = self.kernel.boundary_enclosing_at_least(0.99)
self.assertAlmostEqual(actual.rescale(pq.s), 2.30258509 * pq.s)
class TestGaussianKernel(ut.TestCase):
def setUp(self):
self.kernel_size = 500 * pq.ms
self.kernel = sigproc.GaussianKernel(self.kernel_size)
def test_evaluates_to_correct_values(self):
t = sp.array([-0.1, 0, 0.6, 1]) * pq.s
expected = sp.array([0.78208539, 0.79788456, 0.38837211, 0.10798193]) /\
pq.s
actual = self.kernel(t)
assert_array_almost_equal(expected, actual.rescale(expected.units))
def test_boundary_enclosing_at_least_is_correct(self):
actual = self.kernel.boundary_enclosing_at_least(0.99)
self.assertAlmostEqual(actual.rescale(pq.s), 1.28791465 * pq.s)
class TestLaplacianKernel(ut.TestCase):
def setUp(self):
self.kernel_size = 500 * pq.ms
self.kernel = sigproc.LaplacianKernel(self.kernel_size)
def test_evaluates_to_correct_values(self):
t = sp.array([-0.1, 0, 0.6, 1]) * pq.s
expected = sp.array([0.81873075, 1.0, 0.30119421, 0.13533528]) /\
pq.s
actual = self.kernel(t)
assert_array_almost_equal(expected, actual.rescale(expected.units))
def test_boundary_enclosing_at_least_is_correct(self):
actual = self.kernel.boundary_enclosing_at_least(0.99)
self.assertAlmostEqual(actual.rescale(pq.s), 2.30258509 * pq.s)
def test_summed_dist_matrix(self):
kernel = sigproc.LaplacianKernel(1.0, normalize=False)
vectors = [sp.array([2.0, 1.0, 3.0]), sp.array([1.5, 4.0])]
expected = sp.array(
[[4.7421883311589941, 1.9891932723496157],
[1.9891932723496157, 2.1641699972477975]])
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual)
def test_summed_dist_matrix_with_units(self):
kernel = sigproc.LaplacianKernel(1000 * pq.ms, normalize=True)
vectors = [sp.array([2.0, 1.0, 3.0]) * pq.s,
sp.array([1500, 4000]) * pq.ms]
expected = sp.array(
[[4.7421883311589941, 1.9891932723496157],
[1.9891932723496157, 2.1641699972477975]]) / 2.0 / pq.s
actual = kernel.summed_dist_matrix(vectors)
assert_array_almost_equal(expected, actual.rescale(1.0 / pq.s))
class TestRectangularKernel(ut.TestCase):
def setUp(self):
self.kernel_size = 500 * pq.ms
self.kernel = sigproc.RectangularKernel(self.kernel_size)
def test_evaluates_to_correct_values(self):
t = sp.array([-0.1, 0, 0.6, 1]) * pq.s
expected = sp.array([1.0, 1.0, 0.0, 0.0]) / pq.s
actual = self.kernel(t)
assert_array_almost_equal(expected, actual.rescale(expected.units))
def test_boundary_enclosing_at_least_is_correct(self):
actual = self.kernel.boundary_enclosing_at_least(0.99)
self.assertAlmostEqual(
actual.rescale(self.kernel_size.units), self.kernel_size)
class TestTriangularKernel(ut.TestCase):
def setUp(self):
self.kernel_size = 500 * pq.ms
self.kernel = sigproc.TriangularKernel(self.kernel_size)
def test_evaluates_to_correct_values(self):
t = sp.array([-0.7, -0.1, 0, 0.3, 0.6, 1]) * pq.s
expected = 2 * sp.array([0.0, 0.8, 1.0, 0.4, 0.0, 0.0]) / pq.s
actual = self.kernel(t)
assert_array_almost_equal(expected, actual.rescale(expected.units))
def test_boundary_enclosing_at_least_is_correct(self):
actual = self.kernel.boundary_enclosing_at_least(0.99)
self.assertAlmostEqual(
actual.rescale(self.kernel_size.units), self.kernel_size)
class Test_smooth(ut.TestCase):
def test_convolution_with_empty_binned_array_returns_array_of_zeros(self):
binned = sp.zeros(10)
sampling_rate = 10 * pq.Hz
result = sigproc.smooth(binned, sigproc.GaussianKernel(), sampling_rate)
self.assertTrue(sp.all(result == 0.0))
def test_length_of_returned_array_equals_length_of_binned(self):
binned = sp.ones(10)
sampling_rate = 10 * pq.Hz
result = sigproc.smooth(binned, sigproc.GaussianKernel(), sampling_rate)
self.assertEqual(binned.size, result.size)
def test_returns_smoothed_representation(self):
binned = sp.array([0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0])
sampling_rate = 4 * pq.Hz
kernel = sigproc.RectangularKernel(0.3 * pq.s)
# Because of the low sampling rate the expected result is a bit off
# from the analytical result.
expected = sp.array(
[0.0, 0.0, 0.0, 1.6666666, 1.6666666, 1.6666666, 0.0,
1.6666666, 1.6666666, 1.6666666, 0.0, 0.0]) * pq.Hz
actual = sigproc.smooth(binned, kernel, sampling_rate=sampling_rate)
assert_array_almost_equal(expected, actual)
def test_mode_allows_full_convolution(self):
binned = sp.ones(10)
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
expected_length = 12
actual = sigproc.smooth(
binned, kernel, sampling_rate=sampling_rate, mode='full')
self.assertEqual(actual.size, expected_length)
def test_mode_allows_valid_convolution(self):
binned = sp.ones(10)
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
expected_length = 8
actual = sigproc.smooth(
binned, kernel, sampling_rate=sampling_rate, mode='valid')
self.assertEqual(actual.size, expected_length)
class Test_st_convolve(ut.TestCase):
def test_convolution_with_empty_spike_train_returns_array_of_zeros(self):
st = create_empty_spike_train()
result, _ = sigproc.st_convolve(st, sigproc.GaussianKernel(), 1 * pq.Hz)
self.assertTrue(sp.all(result == 0.0))
def test_length_of_returned_array_equals_sampling_rate_times_duration(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
duration = stop - start
sampling_rate = 12 * pq.Hz
expected_length = (sampling_rate * duration).simplified
st = create_empty_spike_train(start, stop)
result, _ = sigproc.st_convolve(
st, sigproc.GaussianKernel(), sampling_rate=sampling_rate)
self.assertEqual(expected_length, result.size)
def test_returns_convolved_spike_train(self):
st = neo.SpikeTrain(sp.array([1.0, 2.0]) * pq.s, t_stop=3.0 * pq.s)
kernel = sigproc.RectangularKernel(0.3 * pq.s)
# Because of the low sampling rate the expected result is a bit off
# from the analytical result.
expected = sp.array(
[0.0, 0.0, 0.0, 1.6666666, 1.6666666, 1.6666666, 0.0,
1.6666666, 1.6666666, 1.6666666, 0.0, 0.0]) * pq.Hz
actual, _ = sigproc.st_convolve(st, kernel, sampling_rate=4 * pq.Hz)
assert_array_almost_equal(expected, actual)
def test_returns_discretization_bins(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
expected = sp.array([2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]) * pq.s
st = create_empty_spike_train(start, stop)
_, bins = sigproc.st_convolve(
st, sigproc.GaussianKernel(), sampling_rate=sampling_rate)
assert_array_almost_equal(expected, bins)
def test_mode_allows_full_convolution(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
st = create_empty_spike_train(start, stop)
expected_length = (stop - start) * sampling_rate + 2
expected_bins = sp.array(
[1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5]) * pq.s
binned, bins = sigproc.st_convolve(
st, kernel, mode='full', sampling_rate=sampling_rate)
self.assertEqual(binned.size, expected_length)
assert_array_almost_equal(expected_bins, bins)
def test_mode_allows_valid_convolution(self):
start = 2.0 * pq.s
stop = 5.0 * pq.s
sampling_rate = 2.0 * pq.Hz
kernel = sigproc.RectangularKernel(0.6 * pq.s)
st = create_empty_spike_train(start, stop)
expected_length = (stop - start) * sampling_rate - 2
expected_bins = sp.array([2.5, 3.0, 3.5, 4.0, 4.5]) * pq.s
binned, bins = sigproc.st_convolve(
st, kernel, mode='valid', sampling_rate=sampling_rate)
self.assertEqual(binned.size, expected_length)
assert_array_almost_equal(expected_bins, bins)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/tests/builders.py 0000644 0001750 0001750 00000002203 12262547774 017634 0 ustar rob rob
import neo
import quantities as pq
import scipy as sp
def create_empty_spike_train(t_start=0.0 * pq.s, t_stop=10.0 * pq.s):
return neo.SpikeTrain(sp.array([]) * pq.s, t_start=t_start, t_stop=t_stop)
def arange_spikes(t_start, t_stop=None, t_step=1.0 * pq.s):
""" Arranges equally spaced spikes in a spike train over an interval. There
will be no spikes at the interval boundaries.
:param t_start: The time point of the interval if `t_stop` is not `None`.
Otherwise, it will be end point of the interval and the start point will
be set to 0s.
:type t_start: Quantity scalar
:param t_stop: The end point of the interval.
:type t_stop: Quantity scalar
:param t_step: Spacing between the spikes.
:type t_step: Quantity scalar
:returns The arranged spike train.
:rtype: :class:`neo.SpikeTrain`
"""
if t_stop is None:
t_stop = t_start
t_start = 0.0 * pq.s
t_start.units = t_step.units
t_stop.units = t_step.units
spike_times = sp.arange(t_start + t_step, t_stop, t_step) * t_step.units
return neo.SpikeTrain(spike_times, t_start=t_start, t_stop=t_stop)
spykeutils-0.4.1/spykeutils/tests/test_quality_assesment.py 0000644 0001750 0001750 00000031670 12262547774 022646 0 ustar rob rob try:
import unittest2 as ut
except ImportError:
import unittest as ut
import scipy as sp
import numpy as np
import quantities as pq
import neo
from neo.test.tools import assert_arrays_equal
import spykeutils.sorting_quality_assesment as qa
import sys
class TestRefractoryPeriod(ut.TestCase):
def test_refperiod_violations_empty(self):
num, d = qa.get_refperiod_violations({}, 1 * pq.ms)
self.assertEqual(num, 0)
self.assertEqual(d, {})
def test_refperiod_violations(self):
t1 = sp.array([0, 5, 10, 12, 17, 18])
t2 = sp.array([20000, 18000, 14000, 9000, 0, 5000])
t3 = sp.array([1, 2, 3])
st1 = neo.SpikeTrain(t1 * pq.s, 20 * pq.s)
st2 = neo.SpikeTrain(t2 * pq.ms, 20 * pq.s)
st3 = neo.SpikeTrain(t3 * pq.h, 3 * pq.h)
r1, r2 = qa.get_refperiod_violations({0: [st1], 1: [st2, st3]},
3.0 * pq.s)
self.assertEqual(r1, 3,
'Total number of refractory period violations incorrect')
assert_arrays_equal(r2[0][0],
neo.SpikeTrain(sp.array([10, 17]) * pq.s, 20 * pq.s))
assert_arrays_equal(r2[1][0],
neo.SpikeTrain(sp.array([18]) * pq.s, 20 * pq.s))
assert_arrays_equal(r2[1][1],
neo.SpikeTrain(sp.array([]) * pq.s, 10800 * pq.ms))
def test_refperiod_fp_empty(self):
self.assertEqual(
qa.calculate_refperiod_fp({}, 1 * pq.ms, {}, 10 * pq.ms), {})
def test_refperiod_fp(self):
r = qa.calculate_refperiod_fp({1: 100, 2: 100, 3: 100},
2 * pq.ms, {1: 19, 2: 100, 3: 200}, 100 * pq.ms)
self.assertAlmostEqual(r[1], 0.05)
self.assertAlmostEqual(r[2], 0.5)
self.assertAlmostEqual(r[3], 1.0)
class TestClusterOverlap(ut.TestCase):
def setUp(self):
# Have tests always use the same random seed
self.rand = np.random.RandomState()
self.rand.seed(1704)
def test_1_cluster(self):
# One cluster cannot have overlaps...
cluster1 = self.rand.randn(8, 100)
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
total, pair = qa.overlap_fp_fn({1: clusterList1})
self.assertEqual(total[1][0], 0.0)
self.assertEqual(total[1][1], 0.0)
self.assertEqual(pair, {})
def test_equal_clusters_white(self):
cluster1 = self.rand.randn(40, 1000)
cluster2 = self.rand.randn(40, 1000)
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2},
means={1: sp.zeros(40), 2: sp.zeros(40)},
covariances='white')
self.assertAlmostEqual(total[1][0], 0.5)
self.assertAlmostEqual(total[1][1], 0.5)
self.assertAlmostEqual(total[2][0], 0.5)
self.assertAlmostEqual(total[2][1], 0.5)
self.assertAlmostEqual(pair[1][2][0], 0.5)
self.assertAlmostEqual(pair[1][2][1], 0.5)
self.assertAlmostEqual(pair[2][1][0], 0.5)
self.assertAlmostEqual(pair[2][1][1], 0.5)
def test_equal_clusters_estimate_mean(self):
# Smaller dimensionality and more data for reliable estimates
cluster1 = self.rand.randn(8, 100000)
cluster2 = self.rand.randn(8, 100000)
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2},
means={1: sp.zeros(8), 2: sp.zeros(8)})
self.assertAlmostEqual(total[1][0], 0.5, 3)
self.assertAlmostEqual(total[1][1], 0.5, 3)
self.assertAlmostEqual(total[2][0], 0.5, 3)
self.assertAlmostEqual(total[2][1], 0.5, 3)
self.assertAlmostEqual(pair[1][2][0], 0.5, 3)
self.assertAlmostEqual(pair[1][2][1], 0.5, 3)
self.assertAlmostEqual(pair[2][1][0], 0.5, 3)
self.assertAlmostEqual(pair[2][1][1], 0.5, 3)
def test_equal_clusters_estimate_all(self):
# Smaller dimensionality and more data for reliable estimates
cluster1 = self.rand.randn(8, 100000)
cluster2 = self.rand.randn(8, 100000)
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2})
self.assertAlmostEqual(total[1][0], 0.5, 2)
self.assertAlmostEqual(total[1][1], 0.5, 2)
self.assertAlmostEqual(total[2][0], 0.5, 2)
self.assertAlmostEqual(total[2][1], 0.5, 2)
self.assertAlmostEqual(pair[1][2][0], 0.5, 2)
self.assertAlmostEqual(pair[1][2][1], 0.5, 2)
self.assertAlmostEqual(pair[2][1][0], 0.5, 2)
self.assertAlmostEqual(pair[2][1][1], 0.5, 2)
def test_unequal_clusters(self):
cluster1 = self.rand.randn(40, 1000)
cluster2 = self.rand.randn(40, 2000)
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2},
{1: sp.zeros(40), 2: sp.zeros(40)},
{1: sp.eye(40), 2: sp.eye(40)})
self.assertAlmostEqual(total[1][0], 2.0 / 3.0)
self.assertAlmostEqual(total[1][1], 2.0 / 3.0)
self.assertAlmostEqual(total[2][0], 1.0 / 3.0)
self.assertAlmostEqual(total[2][1], 1.0 / 3.0)
self.assertAlmostEqual(pair[1][2][0], 2.0 / 3.0)
self.assertAlmostEqual(pair[1][2][1], 2.0 / 3.0)
self.assertAlmostEqual(pair[2][1][0], 1.0 / 3.0)
self.assertAlmostEqual(pair[2][1][1], 1.0 / 3.0)
def test_far_apart_clusters_estimate_all(self):
cluster1 = self.rand.randn(40, 10000)
cluster2 = self.rand.randn(40, 10000) * 2
cluster2[0, :] += 10
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2})
self.assertLess(total[1][0], 1e-4)
self.assertLess(total[1][1], 1e-4)
self.assertLess(total[2][0], 1e-4)
self.assertLess(total[2][1], 1e-4)
self.assertLess(pair[1][2][0], 1e-4)
self.assertLess(pair[1][2][1], 1e-4)
self.assertLess(pair[2][1][0], 1e-4)
self.assertLess(pair[2][1][1], 1e-4)
self.assertGreater(total[1][0], 0.0)
self.assertGreater(total[1][1], 0.0)
self.assertGreater(total[2][0], 0.0)
self.assertGreater(total[2][1], 0.0)
self.assertGreater(pair[1][2][0], 0.0)
self.assertGreater(pair[1][2][1], 0.0)
self.assertGreater(pair[2][1][0], 0.0)
self.assertGreater(pair[2][1][1], 0.0)
def test_3_clusters_estimate_means(self):
cluster1 = self.rand.randn(20, 10000)
cluster2 = self.rand.randn(20, 20000)
cluster3 = self.rand.randn(20, 10000)
cluster3[5, :] += 11
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
clusterList3 = [cluster3[:, i]
for i in xrange(sp.size(cluster3, 1))]
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2, 3: clusterList3},
covariances={1: sp.eye(20), 2: sp.eye(20), 3: sp.eye(20) * 1.5})
self.assertAlmostEqual(total[1][0], 2.0 / 3.0, 2)
self.assertAlmostEqual(total[1][1], 2.0 / 3.0, 2)
self.assertAlmostEqual(total[2][0], 1.0 / 3.0, 2)
self.assertAlmostEqual(total[2][1], 1.0 / 3.0, 2)
self.assertLess(total[3][0], 1e-4)
self.assertLess(total[3][1], 1e-4)
self.assertGreater(total[3][0], 0.0)
self.assertGreater(total[3][1], 0.0)
self.assertAlmostEqual(pair[1][2][0], 2.0 / 3.0, 2)
self.assertAlmostEqual(pair[1][2][1], 2.0 / 3.0, 2)
self.assertLess(pair[1][3][0], 1e-4)
self.assertLess(pair[1][3][1], 1e-4)
self.assertGreater(pair[1][3][0], 0.0)
self.assertGreater(pair[1][3][1], 0.0)
self.assertAlmostEqual(pair[2][1][0], 1.0 / 3.0, 2)
self.assertAlmostEqual(pair[2][1][1], 1.0 / 3.0, 2)
self.assertLess(pair[2][3][0], 1e-4)
self.assertLess(pair[2][3][1], 1e-4)
self.assertGreater(pair[2][3][0], 0.0)
self.assertGreater(pair[2][3][1], 0.0)
self.assertLess(pair[3][1][0], 1e-4)
self.assertLess(pair[3][1][1], 1e-4)
self.assertGreater(pair[3][1][0], 0.0)
self.assertGreater(pair[3][1][1], 0.0)
self.assertLess(pair[3][2][0], 1e-4)
self.assertLess(pair[3][2][1], 1e-4)
self.assertGreater(pair[3][2][0], 0.0)
self.assertGreater(pair[3][2][1], 0.0)
def test_spike_objects(self):
dimension = 40
offset = sp.zeros((dimension, 1))
offset[0] = 4
cluster1 = self.rand.randn(dimension, 10)
cluster2 = self.rand.randn(dimension, 100) + offset
cluster3 = self.rand.randn(dimension, 500) - offset
clusterList1 = [cluster1[:, i]
for i in xrange(sp.size(cluster1, 1))]
clusterList2 = [cluster2[:, i]
for i in xrange(sp.size(cluster2, 1))]
clusterList3 = [cluster3[:, i]
for i in xrange(sp.size(cluster3, 1))]
mean1 = sp.zeros(dimension)
mean2 = offset.flatten()
mean3 = -mean2
total, pair = qa.overlap_fp_fn(
{1: clusterList1, 2: clusterList2, 3: clusterList3},
means={1: mean1, 2: mean2, 3: mean3},
covariances={1: sp.eye(dimension), 2: sp.eye(dimension),
3: sp.eye(dimension)})
# Replacing some arrays with Spike objects
mean2 = neo.Spike(waveform=mean2.reshape(-1, 4) / 1000.0 * pq.mV)
mean3 = neo.Spike(waveform=mean3.reshape(-1, 4) / 1e6 * pq.V)
newClusterList = []
for s in clusterList1:
newClusterList.append(
neo.Spike(waveform=s.reshape(-1, 4) / 1000.0 * pq.mV))
total_s, pair_s = qa.overlap_fp_fn(
{1: newClusterList, 2: clusterList2, 3: clusterList3},
means={1: mean1, 2: mean2, 3: mean3},
covariances='white')
# Results should be identical for arrays and Spike objects
for i in total.keys():
self.assertAlmostEqual(total[i][0], total_s[i][0])
self.assertAlmostEqual(total[i][1], total_s[i][1])
for j in pair[i].keys():
self.assertAlmostEqual(pair[i][j][0], pair_s[i][j][0])
self.assertAlmostEqual(pair[i][j][1], pair_s[i][j][1])
class TestVarianceExplained(ut.TestCase):
def test_trains(self):
trains = {}
trains[0] = neo.SpikeTrain(
sp.zeros(2) * pq.ms, 0 * pq.ms,
waveforms=[[[-1, -2], [1, 2]], [[-1, -2], [1, 2]]] * pq.mV)
trains[1] = trains[0]
means = {}
means[1] = neo.Spike(0 * pq.ms, waveform=[[-1, -1], [1, 1]] * pq.mV)
exp = qa.variance_explained(trains, means)
self.assertAlmostEqual(exp[0][0], 1)
self.assertAlmostEqual(exp[0][0], 1)
self.assertAlmostEqual(exp[1][0], 1)
self.assertAlmostEqual(exp[1][1], 0.75)
def test_spikes(self):
trains = {}
trains[0] = [neo.Spike(0 * pq.ms, waveform=[[-1, -2], [1, 2]] * pq.mV),
neo.Spike(0 * pq.ms, waveform=[[-1, -2], [1, 2]] * pq.mV)]
trains[1] = trains[0]
means = {}
means[0] = neo.Spike(0 * pq.ms, waveform=[[-1, -1], [1, 1]] * pq.mV)
exp = qa.variance_explained(trains, means)
self.assertAlmostEqual(exp[1][0], 1)
self.assertAlmostEqual(exp[1][0], 1)
self.assertAlmostEqual(exp[0][0], 1)
self.assertAlmostEqual(exp[0][1], 0.75)
def test_noise(self):
trains = {}
trains[0] = neo.SpikeTrain(
sp.zeros(2) * pq.ms, 0 * pq.ms,
waveforms=[[[-1, -2], [1, 2]], [[-1, -2], [1, 2]]] * pq.mV)
trains[1] = trains[0]
means = {}
means[1] = neo.Spike(0 * pq.ms, waveform=[[-1, -1], [1, 1]] * pq.mV)
noise = [0, 1] * pq.mV ** 2
exp = qa.variance_explained(trains, means, noise)
self.assertAlmostEqual(exp[0][0], 1)
self.assertAlmostEqual(exp[0][0], 1)
self.assertAlmostEqual(exp[1][0], 1)
self.assertAlmostEqual(exp[1][1], 1)
if __name__ == '__main__':
ut.main() spykeutils-0.4.1/spykeutils/tests/test_spike_train_generation.py 0000644 0001750 0001750 00000005754 12262547774 023623 0 ustar rob rob try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
import neo
import quantities as pq
import scipy as sp
import spykeutils.spike_train_generation as stg
class CommonSpikeTrainGeneratorTests(object):
""" Provides some common test cases which should work for all spike train
generation functions.
"""
defaultRate = 10 * pq.Hz
lowRate = 1 * pq.Hz
highRate = 10000 * pq.Hz
def invoke_gen_func(self, rate, **kwargs):
""" This function is called to generate a spike train by the test cases.
"""
raise NotImplementedError()
def test_returns_SpikeTrain_containing_spikes(self):
st = self.invoke_gen_func(self.defaultRate, t_stop=100 * pq.s)
self.assertIsInstance(st, neo.SpikeTrain)
self.assertTrue(st.size > 0)
def test_exception_without_end_condition(self):
with self.assertRaises(ValueError):
self.invoke_gen_func(self.defaultRate, t_stop=None)
def test_times_limited_by_t_start_and_t_stop(self):
t_start = 10 * pq.s
t_stop = 20 * pq.s
st = self.invoke_gen_func(
self.defaultRate, t_start=t_start, t_stop=t_stop)
self.assertTrue(sp.all(t_start < st))
self.assertTrue(sp.all(st <= t_stop))
self.assertEqual(t_start, st.t_start)
self.assertEqual(t_stop, st.t_stop)
def test_num_spikes_limited_by_max_spike(self):
max_spikes = 10
# Use a high rate to provoke more than `max_spike` spikes.
self.assertTrue(
max_spikes >= self.invoke_gen_func(
self.highRate, max_spikes=max_spikes).size)
# Use a long trial length to provoke more than `max_spike` spikes.
self.assertTrue(
max_spikes >= self.invoke_gen_func(
self.lowRate, t_stop=10000 * pq.s, max_spikes=max_spikes).size)
def test_respects_refractory_period(self):
refractory = 100 * pq.ms
st = self.invoke_gen_func(
self.highRate, max_spikes=1000, refractory=refractory)
self.assertGreater(
sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
refractory.rescale(pq.s).magnitude)
st = self.invoke_gen_func(
self.highRate, t_stop=10 * pq.s, refractory=refractory)
self.assertGreater(
sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
refractory.rescale(pq.s).magnitude)
class Test_gen_homogeneous_poisson(ut.TestCase, CommonSpikeTrainGeneratorTests):
def invoke_gen_func(self, rate, **kwargs):
return stg.gen_homogeneous_poisson(rate, **kwargs)
class Test_gen_inhomogeneous_poisson(
ut.TestCase, CommonSpikeTrainGeneratorTests):
def invoke_gen_func(self, max_rate, **kwargs):
modulation = lambda ts: sp.sin(ts / (5.0 * pq.s) * sp.pi)
return stg.gen_inhomogeneous_poisson(modulation, max_rate, **kwargs)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/tests/test_rate_estimation.py 0000644 0001750 0001750 00000000616 12262547774 022257 0 ustar rob rob try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from builders import arange_spikes
from numpy.testing import assert_array_equal, assert_array_almost_equal
import spykeutils.rate_estimation as re
import neo
import quantities as pq
import scipy as sp
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/tests/test_scipy_quantities.py 0000644 0001750 0001750 00000024560 12262547774 022471 0 ustar rob rob
try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from itertools import combinations
from numpy.testing import assert_array_equal, assert_array_almost_equal
import scipy as sp
from spykeutils import _scipy_quantities as spq
import quantities as pq
class TestScipyMaximum(ut.TestCase):
def test_works_with_normal_arrays(self):
a = sp.array([[4, 2], [2, 4]])
b = sp.array([[3, 3], [3, 3]])
expected = sp.array([[4, 3], [3, 4]])
actual = spq.maximum(a, b)
assert_array_equal(expected, actual)
def test_works_with_quantity_arrays(self):
a = sp.array([[4, 2], [2, 4]]) * pq.s
b = sp.array([[3000, 3000], [3000, 3000]]) * pq.ms
expected = sp.array([[4, 3], [3, 4]]) * pq.s
actual = spq.maximum(a, b)
assert_array_almost_equal(expected, actual)
actual = spq.maximum(b, a)
assert_array_almost_equal(expected, actual)
def test_works_with_normal_and_quantity_arrays_mixed(self):
a = sp.array([[4, 2], [2, 4]])
b = sp.array([[3, 3], [3, 3]]) * pq.dimensionless
expected = sp.array([[4, 3], [3, 4]]) * pq.dimensionless
actual = spq.maximum(a, b)
assert_array_almost_equal(expected, actual)
actual = spq.maximum(b, a)
assert_array_almost_equal(expected, actual)
def test_works_with_(self):
a = sp.array([[4, 2], [2, 4]]) * pq.dimensionless
b = sp.array([[3, 3], [3, 3]]) * pq.dimensionless
expected = sp.array([[4, 3], [3, 4]]) * pq.dimensionless
actual = spq.maximum(a, b)
assert_array_almost_equal(expected, actual)
actual = spq.maximum(b, a)
assert_array_almost_equal(expected, actual)
def test_uses_out_param(self):
a = sp.array([[4, 2], [2, 4]]) * pq.s
b = sp.array([[3000, 3000], [3000, 3000]]) * pq.ms
expected = sp.array([[4, 3], [3, 4]]) * pq.s
out = sp.zeros_like(expected)
assert out.units == pq.s
spq.maximum(a, b, out)
assert_array_almost_equal(expected, out)
spq.maximum(b, a, out)
assert_array_almost_equal(expected, out)
class TestScipyMinimum(ut.TestCase):
def test_works_with_normal_arrays(self):
a = sp.array([[4, 2], [2, 4]])
b = sp.array([[3, 3], [3, 3]])
expected = sp.array([[3, 2], [2, 3]])
actual = spq.minimum(a, b)
assert_array_equal(expected, actual)
def test_works_with_quantity_arrays(self):
a = sp.array([[4, 2], [2, 4]]) * pq.s
b = sp.array([[3000, 3000], [3000, 3000]]) * pq.ms
expected = sp.array([[3, 2], [2, 3]]) * pq.s
actual = spq.minimum(a, b)
assert_array_almost_equal(expected, actual)
actual = spq.minimum(b, a)
assert_array_almost_equal(expected, actual)
def test_works_with_normal_and_quantity_arrays_mixed(self):
a = sp.array([[4, 2], [2, 4]])
b = sp.array([[3, 3], [3, 3]]) * pq.dimensionless
expected = sp.array([[3, 2], [2, 3]]) * pq.dimensionless
actual = spq.minimum(a, b)
assert_array_almost_equal(expected, actual)
actual = spq.minimum(b, a)
assert_array_almost_equal(expected, actual)
def test_uses_out_param(self):
a = sp.array([[4, 2], [2, 4]]) * pq.s
b = sp.array([[3000, 3000], [3000, 3000]]) * pq.ms
expected = sp.array([[3, 2], [2, 3]]) * pq.s
out = sp.zeros_like(expected)
assert out.units == pq.s
spq.minimum(a, b, out)
assert_array_almost_equal(expected, out)
spq.minimum(b, a, out)
assert_array_almost_equal(expected, out)
class TestScipyMeshgrid(ut.TestCase):
def test_works_with_normal_arrays(self):
a = sp.array([1, 2, 3])
b = sp.array([4, 5])
expected_a = sp.array([[1, 2, 3], [1, 2, 3]])
expected_b = sp.array([[4, 4, 4], [5, 5, 5]])
actual_a, actual_b = spq.meshgrid(a, b)
assert_array_equal(expected_a, actual_a)
assert_array_equal(expected_b, actual_b)
def test_works_with_quantity_arrays(self):
a = sp.array([1, 2, 3]) * pq.s
b = sp.array([4, 5]) * pq.m
expected_a = sp.array([[1, 2, 3], [1, 2, 3]]) * pq.s
expected_b = sp.array([[4, 4, 4], [5, 5, 5]]) * pq.m
actual_a, actual_b = spq.meshgrid(a, b)
assert_array_equal(expected_a, actual_a)
assert_array_equal(expected_b, actual_b)
def test_works_with_normal_and_quantity_arrays_mixed(self):
a = sp.array([1, 2, 3])
b = sp.array([4, 5]) * pq.m
expected_a = sp.array([[1, 2, 3], [1, 2, 3]])
expected_b = sp.array([[4, 4, 4], [5, 5, 5]]) * pq.m
actual_a, actual_b = spq.meshgrid(a, b)
assert_array_equal(expected_a, actual_a)
assert_array_equal(expected_b, actual_b)
a = sp.array([1, 2, 3]) * pq.s
b = sp.array([4, 5])
expected_a = sp.array([[1, 2, 3], [1, 2, 3]]) * pq.s
expected_b = sp.array([[4, 4, 4], [5, 5, 5]])
actual_a, actual_b = spq.meshgrid(a, b)
assert_array_equal(expected_a, actual_a)
assert_array_equal(expected_b, actual_b)
class TestScipyConcatenate(ut.TestCase):
def test_works_with_normal_arrays(self):
a = sp.array([[1]])
b = sp.array([[2]])
c = sp.array([[3]])
axis = 1
expected = sp.array([[1, 2, 3]])
actual = spq.concatenate((a, b, c), axis=axis)
assert_array_equal(expected, actual)
def test_works_with_quantity_arrays(self):
a = sp.array([[1]]) * pq.s
b = sp.array([[2000]]) * pq.ms
c = sp.array([[3]]) * pq.s
axis = 1
expected = sp.array([[1, 2, 3]]) * pq.s
actual = spq.concatenate((a, b, c), axis=axis)
assert_array_equal(expected, actual)
def test_works_with_normal_and_quantity_arrays_mixed(self):
a = sp.array([[1]])
b = sp.array([[2]]) * pq.dimensionless
c = sp.array([[3]])
axis = 1
expected = sp.array([[1, 2, 3]])
actual = spq.concatenate((a, b, c), axis=axis)
assert_array_equal(expected, actual)
def test_raises_exception_if_mixing_incompatible_units(self):
a = sp.array([[1]])
b = sp.array([[2]]) * pq.dimensionless
c = sp.array([[3]]) * pq.s
d = sp.array([[4]]) * pq.m
for p in combinations((a, c, d), 2):
with self.assertRaises(Exception):
spq.concatenate(p)
with self.assertRaises(Exception):
spq.concatenate(p[::-1])
for p in combinations((b, c, d), 2):
with self.assertRaises(Exception):
spq.concatenate(p)
with self.assertRaises(Exception):
spq.concatenate(p[::-1])
class TestScipyInner(ut.TestCase):
def test_works_with_normal_arrays(self):
a = sp.array([0.0, 1.0, 2.0])
b = sp.array([2.0, 2.0, 3.0])
expected = sp.inner(a, b)
actual = spq.inner(a, b)
self.assertAlmostEqual(expected, actual)
def test_works_with_quantities_arrays(self):
a = sp.array([0.0, 1.0, 2.0]) * pq.s
b = sp.array([2000.0, 2000.0, 3000.0]) * pq.ms
expected = 8.0 * pq.s
actual = spq.inner(a, b)
self.assertAlmostEqual(expected, actual.rescale(expected.units))
def test_works_with_normal_and_quantity_arrays_mixed(self):
a = sp.array([0.0, 1.0, 2.0]) * pq.dimensionless
b = sp.array([2.0, 2.0, 3.0])
expected = sp.inner(a, b)
actual = spq.inner(a, b)
self.assertAlmostEqual(expected, actual)
def test_raises_exception_if_mixing_incompatible_units(self):
a = sp.array([1.0])
b = sp.array([2.0]) * pq.dimensionless
c = sp.array([3.0]) * pq.s
d = sp.array([4.0]) * pq.m
for p in combinations((a, c, d), 2):
with self.assertRaises(Exception):
spq.inner(p)
with self.assertRaises(Exception):
spq.inner(p[::-1])
for p in combinations((b, c, d), 2):
with self.assertRaises(Exception):
spq.inner(p)
with self.assertRaises(Exception):
spq.inner(p[::-1])
def test_works_with_multidimensional_arrays(self):
a = sp.array([[0.0, 1.0], [2.0, 3.0]]) * pq.s
b = sp.array([[2.0, 2.0], [3.0, 4.0]]) * pq.s
expected = sp.array([[2.0, 4.0], [10.0, 18.0]]) * pq.s
actual = spq.inner(a, b)
assert_array_almost_equal(expected, actual.rescale(expected.units))
class TestScipyDiag(ut.TestCase):
def test_diag_of_unitless_1d_array(self):
a = sp.array([1, 2])
expected = sp.array([[1, 0], [0, 2]])
actual = spq.diag(a)
assert_array_equal(expected, actual)
def test_diag_of_unitless_2d_array(self):
a = sp.array([[1, 0], [0, 2]])
expected = sp.array([1, 2])
actual = spq.diag(a)
assert_array_equal(expected, actual)
def test_diag_of_1d_quantity(self):
a = sp.array([1, 2]) * pq.s
expected = sp.array([[1, 0], [0, 2]]) * pq.s
actual = spq.diag(a)
self.assertEqual(expected.units, actual.units)
assert_array_equal(expected, actual)
def test_diag_of_2d_quantity(self):
a = sp.array([[1, 0], [0, 2]]) * pq.s
expected = sp.array([1, 2]) * pq.s
actual = spq.diag(a)
self.assertEqual(expected.units, actual.units)
assert_array_equal(expected, actual)
def test_respects_k_argument(self):
a = sp.array([[1, 5], [0, 2]]) * pq.s
expected = sp.array([5]) * pq.s
actual = spq.diag(a, k=1)
self.assertEqual(expected.units, actual.units)
assert_array_equal(expected, actual)
class Test_linspace(ut.TestCase):
def test_returns_correct_quantity_for_one_bin(self):
expected = sp.array([1.0]) * pq.s
actual = spq.linspace(1.0 * pq.s, 4.0 * pq.s, 1)
assert_array_almost_equal(expected, actual)
def test_returns_correct_quantity_for_one_bin_after_rounding(self):
expected = sp.array([1.0]) * pq.s
actual = spq.linspace(1.0 * pq.s, 4.0 * pq.s, 1.4)
assert_array_almost_equal(expected, actual)
def test_returns_correct_quantity_for_more_than_one_bin(self):
expected = sp.array([1.0, 2.0, 3.0, 4.0]) * pq.s
actual = spq.linspace(1.0 * pq.s, 4.0 * pq.s, 4)
assert_array_almost_equal(expected, actual)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/_scipy_quantities.py 0000644 0001750 0001750 00000006531 12262547774 020425 0 ustar rob rob import scipy as sp
import quantities as pq
# At least up to quantities 0.10.1 the scipy element-wise minimum and maximum
# function did not work.
# This has been reported upstream as issue #53:
#
def _fix_binary_scipy_function_with_out_param(f):
def _fixed(x1, x2, out=None):
if isinstance(x1, pq.Quantity) or isinstance(x2, pq.Quantity):
x1 = x1 * pq.dimensionless
x2 = x2 * pq.dimensionless
if out is None:
x2 = x2.rescale(x1.units)
return f(x1.magnitude, x2.magnitude) * x1.units
else:
x1 = x1.rescale(out.units)
x2 = x2.rescale(out.units)
f(x1.magnitude, x2.magnitude, out.magnitude)
return out
else:
return f(x1, x2, out)
return _fixed
minimum = _fix_binary_scipy_function_with_out_param(sp.minimum)
maximum = _fix_binary_scipy_function_with_out_param(sp.maximum)
# At least up to quantities 0.10.1 the scipy meshgrid and concatenate
# functions did lose units.
# This has been reported upstream as issue #47:
#
def _fix_scipy_meshgrid(f):
def _fixed(x, y):
rx, ry = f(x, y)
if isinstance(x, pq.Quantity) and not isinstance(rx, pq.Quantity):
rx = rx * x.units
if isinstance(y, pq.Quantity) and not isinstance(ry, pq.Quantity):
ry = ry * y.units
return rx, ry
return _fixed
meshgrid = _fix_scipy_meshgrid(sp.meshgrid)
def _fix_scipy_concatenate(f):
def _fixed(arrays, axis=0):
is_quantity = len(arrays) > 0 and isinstance(arrays[0], pq.Quantity)
if is_quantity:
arrays = [(a * pq.dimensionless).rescale(arrays[0].units)
for a in arrays]
else:
for a in arrays:
if (isinstance(a, pq.Quantity) and
a.units.simplified != pq.dimensionless):
raise ValueError(
'Cannot concatenate arrays of different units')
concatenated = f(arrays, axis=axis)
if is_quantity:
concatenated = concatenated * arrays[0].units
return concatenated
return _fixed
concatenate = _fix_scipy_concatenate(sp.concatenate)
# At least up to quantities 0.10.1 the scipy inner and diag functions did not
# respect units.
def _fix_binary_scipy_function(f):
def _fixed(x1, x2):
if isinstance(x1, pq.Quantity) or isinstance(x2, pq.Quantity):
x1 = x1 * pq.dimensionless
x2 = x2 * pq.dimensionless
x2 = x2.rescale(x1.units)
return f(x1.magnitude, x2.magnitude) * x1.units
else:
return f(x1, x2)
return _fixed
inner = _fix_binary_scipy_function(sp.inner)
def diag(v, k=0):
if isinstance(v, pq.Quantity):
return sp.diag(v, k) * v.units
else:
return sp.diag(v, k)
# linspace loses unit for only one bin, see bug report
#
def linspace(start, stop, num=50, endpoint=True, retstep=False):
if int(num) == 1 and isinstance(start, pq.Quantity):
return sp.linspace(start, stop, num, endpoint, retstep) * start.units
else:
return sp.linspace(start, stop, num, endpoint, retstep)
spykeutils-0.4.1/spykeutils/monkeypatch/ 0000755 0001750 0001750 00000000000 12262547774 016634 5 ustar rob rob spykeutils-0.4.1/spykeutils/monkeypatch/quantities_patch.py 0000644 0001750 0001750 00000001537 12262547774 022561 0 ustar rob rob from __future__ import absolute_import
import quantities as pq
# At least up to quantities 0.10.1 the additional arguments to the min and max
# function did not get passed along.
# A fix already exists:
#
# Also a pull request exists which has not been merged at the time of writing
# 01/18/2013:
#
def _Quanitity_max(self, axis=None, out=None):
return pq.Quantity(
self.magnitude.max(axis, out),
self.dimensionality,
copy=False
)
pq.Quantity.max = _Quanitity_max
def _Quanitity_min(self, axis=None, out=None):
return pq.Quantity(
self.magnitude.min(axis, out),
self.dimensionality,
copy=False
)
pq.Quantity.min = _Quanitity_min
spykeutils-0.4.1/spykeutils/monkeypatch/__init__.py 0000644 0001750 0001750 00000000000 12262547774 020733 0 ustar rob rob spykeutils-0.4.1/spykeutils/monkeypatch/test/ 0000755 0001750 0001750 00000000000 12262547774 017613 5 ustar rob rob spykeutils-0.4.1/spykeutils/monkeypatch/test/test_quantities_patch.py 0000644 0001750 0001750 00000003073 12262547774 024574 0 ustar rob rob
try:
import unittest2 as ut
assert ut # Suppress pyflakes warning about redefinition of unused ut
except ImportError:
import unittest as ut
from numpy.testing import assert_array_equal, assert_array_almost_equal
import scipy as sp
import quantities as pq
from spykeutils.monkeypatch import quantities_patch
assert quantities_patch # Suppress pyflakes warning, patch applied by loading
class TestQuantityMax(ut.TestCase):
def test_returns_global_max(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
self.assertEqual(4, a.max())
def test_returns_axiswise_max(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
axis = 1
expected = sp.array([2, 4]) * pq.s
assert_array_equal(expected, a.max(axis=axis))
def test_returns_result_in_out_array(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
out = 0 * pq.s
expected = sp.array([4])
a.max(out=out)
self.assertEqual(expected, out)
class TestQuantityMin(ut.TestCase):
def test_returns_global_min(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
self.assertEqual(1, a.min())
def test_returns_axiswise_min(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
axis = 1
expected = sp.array([1, 3]) * pq.s
assert_array_equal(expected, a.min(axis=axis))
def test_returns_result_in_out_array(self):
a = sp.array([[1, 2], [3, 4]]) * pq.s
out = 0 * pq.s
expected = 1 * pq.s
a.min(out=out)
self.assertEqual(expected, out)
if __name__ == '__main__':
ut.main()
spykeutils-0.4.1/spykeutils/plot/ 0000755 0001750 0001750 00000000000 12262547774 015270 5 ustar rob rob spykeutils-0.4.1/spykeutils/plot/__init__.py 0000644 0001750 0001750 00000002502 12262547774 017400 0 ustar rob rob """ This package contains various plotting functions for neo objects.
The plots are created using :mod:`guiqwt` - if it is not installed,
this package can not be used.
.. automodule:: spykeutils.plot.rasterplot
:members:
.. automodule:: spykeutils.plot.correlogram
:members:
.. automodule:: spykeutils.plot.interspike_intervals
:members:
.. automodule:: spykeutils.plot.peri_stimulus_histogram
:members:
.. automodule:: spykeutils.plot.sde
:members:
.. automodule:: spykeutils.plot.analog_signals
:members:
.. automodule:: spykeutils.plot.spike_amp_hist
:members:
.. automodule:: spykeutils.plot.spike_waveforms
:members:
:mod:`dialog` Module
--------------------
.. automodule:: spykeutils.plot.dialog
:members:
:show-inheritance:
:mod:`helper` Module
--------------------
.. automodule:: spykeutils.plot.helper
:members:
:mod:`guiqwt_tools` Module
--------------------------
.. automodule:: spykeutils.plot.guiqwt_tools
:members:
:show-inheritance:
"""
from interspike_intervals import isi
from dialog import PlotDialog
from rasterplot import raster
from correlogram import cross_correlogram
from analog_signals import signals
from peri_stimulus_histogram import psth
from sde import sde
from spike_waveforms import spikes
from spike_amp_hist import spike_amplitude_histogram
spykeutils-0.4.1/spykeutils/plot/guiqwt_tools.py 0000644 0001750 0001750 00000004507 12262547774 020410 0 ustar rob rob """ Additional guiqwt tools to facilitate plot navigation.
"""
from PyQt4 import QtGui
from PyQt4.QtCore import Qt
from PyQt4.QtGui import QMessageBox
try:
from PyQt4 import QtCore
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
_fromUtf8 = lambda s: s
from guiqwt.tools import CommandTool, DefaultToolbarID, InteractiveTool
from guidata.qthelpers import get_std_icon
from guiqwt.config import _
from guiqwt.events import (setup_standard_tool_filter, PanHandler)
import icons_rc
class HomeTool(CommandTool):
""" A command to show all elements in the plot (same as pressing the
middle mouse button).
"""
def __init__(self, manager, toolbar_id=DefaultToolbarID):
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap(_fromUtf8(':/Plottools/Home')),
QtGui.QIcon.Normal, QtGui.QIcon.Off)
super(HomeTool, self).__init__(manager, 'Home', icon,
toolbar_id=toolbar_id)
def activate_command(self, plot, checked):
""" Activate that command!
"""
plot.do_autoscale()
class PanTool(InteractiveTool):
""" Allows panning with the left mouse button.
"""
TITLE = _("Pan")
ICON = "move.png"
CURSOR = Qt.OpenHandCursor
def setup_filter(self, baseplot):
filter = baseplot.filter
start_state = filter.new_state()
PanHandler(filter, Qt.LeftButton, start_state=start_state)
return setup_standard_tool_filter(filter, start_state)
class HelpTool(CommandTool):
""" A help tool that includes a message what a single middle click
does, otherwise identical to the guiqwt tool with the same name.
"""
def __init__(self, manager, toolbar_id=DefaultToolbarID):
super(HelpTool,self).__init__(manager, "Help",
get_std_icon("DialogHelpButton", 16),
toolbar_id=toolbar_id)
def activate_command(self, plot, checked):
"""Activate tool"""
QMessageBox.information(plot, "Help",
"""Keyboard/mouse shortcuts:
- single left-click: item (curve, image, ...) selection
- single right-click: context-menu relative to selected item
- single middle click: home
- shift: on-active-curve (or image) cursor
- alt: free cursor
- left-click + mouse move: move item (when available)
- middle-click + mouse move: pan
- right-click + mouse move: zoom""") spykeutils-0.4.1/spykeutils/plot/spike_waveforms.py 0000644 0001750 0001750 00000036422 12262547774 021055 0 ustar rob rob import scipy as sp
import quantities as pq
from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
from PyQt4 import Qt
from ..progress_indicator import ProgressIndicator
from .. import SpykeException
from dialog import PlotDialog
import helper
@helper.needs_qt
def spikes(spikes, axes_style, strong=None, anti_alias=False, fade=1.0,
subplot_layout=0, time_unit=pq.ms, progress=None):
""" Create a plot dialog with spike waveforms. Assumes that all spikes
have waveforms with the same number of channels.
:param dict spikes: A dictionary of :class:`neo.core.Spike` lists.
:param int axes_style: Plotting mode. The following values are possible:
- 1: Show each channel in a seperate plot, split vertically.
- 2: Show each channel in a separate plot, split horizontally.
- 3: Show each key of ``spikes`` in a separate plot,
channels are split vertically.
- 4: Show each key of ``spikes`` in a separate plot,
channels are split horizontally.
- 5: Show all channels in the same plot, split vertically.
- 6: Show all channels in the same plot, split horizontally.
:param dict strong: A dictionary of :class:`neo.core.Spike` lists. When
given, these spikes are shown as thick lines on top of the regular
spikes in the respective plots.
:param bool anti_alias: Determines whether an antialiased plot is created.
:param float fade: Vary transparency by segment. For values > 0, the first
spike for each unit is displayed with the corresponding alpha
value and alpha is linearly interpolated until it is 1 for the
last spike. For values < 0, alpha is 1 for the first spike and
``fade`` for the last spike. Does not affect spikes from ``strong``.
:param bool subplot_layout: The way subplots are arranged on the window:
- 0: Linear - horizontally or vertically,
depending on ``axis_style``.
- 1: Square - this layout tries to have the same number of plots per
row and per column.
:param Quantity time_unit: Unit of X-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if (not spikes or sum((len(l) for l in spikes.itervalues())) < 1) and \
(not strong or sum((len(l) for l in strong.itervalues())) < 1):
raise SpykeException('No spikes for spike waveform plot!')
if not progress:
progress = ProgressIndicator()
if strong is None:
strong = {}
progress.begin('Creating waveform plot')
progress.set_ticks(
sum((len(l) for l in spikes.itervalues())) +
sum((len(l) for l in strong.itervalues())))
win_title = 'Spike waveforms'
win = PlotDialog(toolbar=True, wintitle=win_title)
try:
ref_spike = spikes[spikes.keys()[0]][0]
except IndexError:
ref_spike = strong[strong.keys()[0]][0]
if ref_spike.waveform is None:
raise SpykeException(
'Cannot create waveform plot: At least one spike '
'has no waveform or sampling rate!')
ref_units = ref_spike.waveform.units
channels = range(ref_spike.waveform.shape[1])
# Keys from spikes and strong without duplicates in original order
seen = set()
indices = [k for k in spikes.keys() + strong.keys()
if k not in seen and not seen.add(k)]
if axes_style <= 2: # Separate channel plots
for c in channels:
pw = BaseCurveWidget(win)
plot = pw.plot
plot.set_antialiasing(anti_alias)
for u in spikes:
color = helper.get_object_color(u)
qcol = Qt.QColor(color)
alpha = fade if fade > 0.0 else 1.0
alpha_step = 1.0 - fade if fade > 0.0 else -1.0 - fade
alpha_step /= len(spikes[u])
if len(spikes[u]) == 1:
alpha = 1.0
for s in spikes[u]:
if s.waveform is None or s.sampling_rate is None:
raise SpykeException(
'Cannot create waveform plot: '
'At least one spike has no '
'waveform or sampling rate!')
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
curve = make.curve(
x, s.waveform[:, c].rescale(ref_units),
title=u.name, color=color)
qcol.setAlphaF(alpha)
curve.setPen(Qt.QPen(qcol))
alpha += alpha_step
plot.add_item(curve)
progress.step()
for u in strong:
color = helper.get_object_color(u)
for s in strong[u]:
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
outline = make.curve(
x, s.waveform[:, c].rescale(ref_units),
color='#000000', linewidth=4)
curve = make.curve(
x, s.waveform[:, c].rescale(ref_units),
color=color, linewidth=2)
plot.add_item(outline)
plot.add_item(curve)
progress.step()
_add_plot(plot, pw, win, c, len(channels), subplot_layout,
axes_style, time_unit, ref_units)
helper.make_window_legend(win, indices, True)
elif axes_style > 4: # Only one plot needed
pw = BaseCurveWidget(win)
plot = pw.plot
plot.set_antialiasing(anti_alias)
if axes_style == 6: # Horizontal split
l = _split_plot_hor(channels, spikes, strong, fade, ref_units,
time_unit, progress, plot)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(
BasePlot.X_BOTTOM, time_unit.dimensionality.string)
else: # Vertical split
channels.reverse()
max_offset = _find_y_offset(channels, spikes, strong, ref_units)
l = _split_plot_ver(channels, spikes, strong, fade, ref_units,
time_unit, progress, max_offset, plot)
plot.set_axis_title(BasePlot.Y_LEFT, 'Voltage')
plot.set_axis_unit(
BasePlot.Y_LEFT, ref_units.dimensionality.string)
win.add_plot_widget(pw, 0)
win.add_legend_option([l], True)
else: # One plot per unit
if axes_style == 3:
channels.reverse()
max_offset = _find_y_offset(channels, spikes, strong, ref_units)
for i, u in enumerate(indices):
pw = BaseCurveWidget(win)
plot = pw.plot
plot.set_antialiasing(anti_alias)
spk = {}
if u in spikes:
spk[u] = spikes[u]
st = {}
if u in strong:
st[u] = strong[u]
if axes_style == 3: # Vertical split
_split_plot_ver(channels, spk, st, fade, ref_units,
time_unit, progress, max_offset, plot)
else: # Horizontal split
_split_plot_hor(channels, spk, st, fade, ref_units,
time_unit, progress, plot)
_add_plot(plot, pw, win, i, len(indices), subplot_layout,
axes_style, time_unit, ref_units)
win.add_custom_curve_tools()
progress.done()
win.show()
if axes_style <= 2:
if len(channels) > 1:
win.add_x_synchronization_option(True, channels)
win.add_y_synchronization_option(True, channels)
elif axes_style <= 4:
if len(spikes) > 1:
win.add_x_synchronization_option(True, range(len(spikes)))
win.add_y_synchronization_option(True, range(len(spikes)))
return win
def _add_plot(plot, widget, win, index, total, subplot_layout, axes_style,
time_unit, ref_units):
""" Add a plot to the window in the right position with the correct axis
labels.
"""
if subplot_layout == 0:
if axes_style == 1 or axes_style == 4 or index == 0:
plot.set_axis_title(BasePlot.Y_LEFT, 'Voltage')
plot.set_axis_unit(
BasePlot.Y_LEFT, ref_units.dimensionality.string)
if axes_style == 2 or axes_style == 3 or index == total - 1:
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(
BasePlot.X_BOTTOM, time_unit.dimensionality.string)
if axes_style == 1 or axes_style == 4:
win.add_plot_widget(widget, index)
else:
win.add_plot_widget(widget, index, 0, index)
else:
size = int(sp.sqrt(total) + 0.99)
if index % size == 0:
plot.set_axis_title(BasePlot.Y_LEFT, 'Voltage')
plot.set_axis_unit(
BasePlot.Y_LEFT, ref_units.dimensionality.string)
if index >= total - size:
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(
BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(widget, index, index / size, index % size)
def _find_y_offset(channels, spikes, strong, ref_units):
""" Find y offset needed when plotting spikes split vertically by channel.
"""
max_y = []
min_y = []
if spikes:
for i, c in enumerate(channels):
max_y.append(
max(max(s.waveform[:, c].max() for s in d)
for d in spikes.itervalues()))
min_y.append(
min(min(s.waveform[:, c].min() for s in d)
for d in spikes.itervalues()))
if strong:
for i, c in enumerate(channels):
max_y.append(
max(max(s.waveform[:, c].max() for s in d)
for d in strong.itervalues()))
min_y.append(
min(min(s.waveform[:, c].min() for s in d)
for d in strong.itervalues()))
max_offset = 0 * ref_units
for i in range(1, len(channels)):
offset = max_y[i - 1] - min_y[i]
if offset > max_offset:
max_offset = offset
return max_offset
def _split_plot_ver(channels, spikes, strong, fade, ref_units, time_unit,
progress, max_offset, plot):
""" Fill a plot with spikes vertically split by channel. Returns legend.
"""
offset = 0 * ref_units
for c in channels:
for u in spikes:
color = helper.get_object_color(u)
qcol = Qt.QColor(color)
alpha = fade if fade > 0.0 else 1.0
alpha_step = 1.0 - fade if fade > 0.0 else -1.0 - fade
alpha_step /= len(spikes[u])
if len(spikes[u]) == 1:
alpha = 1.0
for s in spikes[u]:
if s.waveform is None or s.sampling_rate is None:
raise SpykeException('Cannot create waveform plot: '
'At least one spike has no '
'waveform or sampling rate!')
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
curve = make.curve(
x,
s.waveform[:, c].rescale(ref_units) + offset,
u.name, color=color)
qcol.setAlphaF(alpha)
curve.setPen(Qt.QPen(qcol))
alpha += alpha_step
plot.add_item(curve)
progress.step()
for u in strong:
color = helper.get_object_color(u)
for s in strong[u]:
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
outline = make.curve(
x, s.waveform[:, c].rescale(ref_units) + offset,
color='#000000', linewidth=4)
curve = make.curve(
x, s.waveform[:, c].rescale(ref_units) + offset,
color=color, linewidth=2)
plot.add_item(outline)
plot.add_item(curve)
progress.step()
offset += max_offset
l = _add_legend(plot, spikes, strong)
return l
def _split_plot_hor(channels, spikes, strong, fade, ref_units, time_unit,
progress, plot):
""" Fill a plot with spikeshorizontally split by channel. Returns legend.
"""
offset = 0 * time_unit
for c in channels:
x_off = 0 * time_unit
for u in spikes:
color = helper.get_object_color(u)
qcol = Qt.QColor(color)
alpha = fade if fade > 0.0 else 1.0
alpha_step = 1.0 - fade if fade > 0.0 else -1.0 - fade
alpha_step /= len(spikes[u])
if len(spikes[u]) == 1:
alpha = 1.0
for s in spikes[u]:
if s.waveform is None or s.sampling_rate is None:
raise SpykeException(
'Cannot create waveform plot: '
'At least one spike has no '
'waveform or sampling rate!')
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
x_off = max(x_off, x[-1])
curve = make.curve(
x + offset,
s.waveform[:, c].rescale(ref_units), u.name,
color=color)
qcol.setAlphaF(alpha)
curve.setPen(Qt.QPen(qcol))
alpha += alpha_step
plot.add_item(curve)
progress.step()
for u in strong:
color = helper.get_object_color(u)
for s in strong[u]:
x = (sp.arange(s.waveform.shape[0]) /
s.sampling_rate).rescale(time_unit)
x_off = max(x_off, x[-1])
outline = make.curve(
x + offset, s.waveform[:, c].rescale(ref_units),
color='#000000', linewidth=4)
curve = make.curve(
x + offset, s.waveform[:, c].rescale(ref_units),
color=color, linewidth=2)
plot.add_item(outline)
plot.add_item(curve)
progress.step()
offset += x_off
if c != channels[-1]:
plot.add_item(
make.marker((offset, 0), lambda x, y: '',
movable=False, markerstyle='|',
color='k', linestyle='-', linewidth=1))
l = _add_legend(plot, spikes, strong)
return l
def _add_legend(plot, spikes, strong):
# Keys from spikes and strong without duplicates in original order
seen = set()
indices = [k for k in spikes.keys() + strong.keys()
if k not in seen and not seen.add(k)]
legend_items = []
for u in indices:
legend_curve = make.curve(
sp.array([0]), sp.array([0]), u.name,
color=helper.get_object_color(u), linewidth=2)
legend_items.append(legend_curve)
plot.add_item(legend_curve)
l = make.legend(restrict_items=legend_items)
plot.add_item(l)
return l spykeutils-0.4.1/spykeutils/plot/spike_amp_hist.py 0000644 0001750 0001750 00000004773 12262547774 020654 0 ustar rob rob import scipy as sp
import quantities as pq
from guiqwt.plot import BaseImageWidget
from guiqwt.builder import make
from ..progress_indicator import ProgressIndicator
from .. import SpykeException
from ..stationarity import spike_amplitude_histogram as sah
import helper
from dialog import PlotDialog
@helper.needs_qt
def spike_amplitude_histogram(trains, num_bins, uniform_y_scale=True,
x_unit=pq.uV, progress=None):
""" Create a spike amplitude histogram.
This plot 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 x_unit: Unit of Y-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
:return:
"""
if not trains:
raise SpykeException('No spikes trains for Spike Amplitude Histogram!')
if not progress:
progress = ProgressIndicator()
hist, down, up = sah(trains, num_bins, uniform_y_scale, x_unit, progress)
num_channels = len(down)
columns = int(round(sp.sqrt(num_channels)))
win = PlotDialog(toolbar=True, wintitle="Spike Amplitude Histogram")
for c in xrange(num_channels):
pW = BaseImageWidget(
win, yreverse=False, lock_aspect_ratio=False)
plot = pW.plot
img = make.image(sp.log(hist[:, :, c] + 1),
ydata=[down[c], up[c]],
interpolation='nearest')
plot.add_item(img)
plot.set_axis_title(plot.Y_LEFT, 'Amplitude')
plot.set_axis_unit(plot.Y_LEFT, x_unit.dimensionality.string)
win.add_plot_widget(pW, c, column=c % columns)
progress.done()
win.add_custom_image_tools()
win.add_x_synchronization_option(True, range(num_channels))
win.add_y_synchronization_option(uniform_y_scale,
range(num_channels))
win.show()
return win spykeutils-0.4.1/spykeutils/plot/analog_signals.py 0000644 0001750 0001750 00000020074 12262547774 020626 0 ustar rob rob from __future__ import division
import scipy as sp
import quantities as pq
from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
from ..progress_indicator import ProgressIndicator
from .. import conversions
from .. import SpykeException
from dialog import PlotDialog
import helper
@helper.needs_qt
def signals(signals, events=None, epochs=None, spike_trains=None,
spikes=None, show_waveforms=True, use_subplots=True,
subplot_names=True, time_unit=pq.s, y_unit=None, progress=None):
""" Create a plot from a list of analog signals.
:param list signals: The list of :class:`neo.core.AnalogSignal` objects
to plot.
:param sequence events: A list of Event objects to be included in the
plot.
:param sequence epochs: A list of Epoch objects to be included in the
plot.
:param list spike_trains: A list of :class:`neo.core.SpikeTrain` objects
to be included in the plot. The ``unit`` property (if it exists) is
used for color and legend entries.
:param list spikes: A list :class:`neo.core.Spike` objects to be included
in the plot. The ``unit`` property (if it exists) is used for color
and legend entries.
:param bool show_waveforms: Determines if spikes from
:class:`neo.core.Spike` and :class:`neo.core.SpikeTrain` objects are
shown as waveforms (if available) or vertical lines.
:param bool use_subplots: Determines if a separate subplot for is created
each signal.
:param bool subplot_names: Only valid if ``use_subplots`` is True.
Determines if signal (or channel) names are shown for subplots.
:param Quantity time_unit: The unit of the x axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not signals:
raise SpykeException(
'Cannot create signal plot: No signal data provided!')
if not progress:
progress = ProgressIndicator()
# Plot title
win_title = 'Analog Signal'
if len(set((s.recordingchannel for s in signals))) == 1:
if signals[0].recordingchannel and signals[0].recordingchannel.name:
win_title += ' | Recording Channel: %s' %\
signals[0].recordingchannel.name
if len(set((s.segment for s in signals))) == 1:
if signals[0].segment and signals[0].segment.name:
win_title += ' | Segment: %s' % signals[0].segment.name
win = PlotDialog(toolbar=True, wintitle=win_title)
if events is None:
events = []
if epochs is None:
epochs = []
if spike_trains is None:
spike_trains = []
if spikes is None:
spikes = []
if show_waveforms:
for st in spike_trains:
if st.waveforms is not None:
spikes.extend(conversions.spike_train_to_spikes(st))
spike_trains = []
else:
unit_spikes = {}
for s in spikes:
unit_spikes.setdefault(s.unit, []).append(s)
for sps in unit_spikes.itervalues():
spike_trains.append(conversions.spikes_to_spike_train(sps, False))
spikes = []
channels = range(len(signals))
channel_indices = []
for s in signals:
if not s.recordingchannel:
channel_indices.append(-1)
else:
channel_indices.append(s.recordingchannel.index)
# Heuristic: If multiple channels have the same index, use channel order
# as index for spike waveforms
nonindices = max(0, channel_indices.count(-1) - 1)
if len(set(channel_indices)) != len(channel_indices) - nonindices:
channel_indices = range(len(signals))
progress.set_ticks((len(spike_trains) + len(spikes) + 1) * len(channels))
offset = 0 * signals[0].units
if use_subplots:
plot = None
for c in channels:
pW = BaseCurveWidget(win)
plot = pW.plot
if subplot_names:
if signals[c].name:
win.set_plot_title(plot, signals[c].name)
elif signals[c].recordingchannel:
if signals[c].recordingchannel.name:
win.set_plot_title(
plot, signals[c].recordingchannel.name)
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
helper.add_epochs(plot, epochs, x.units)
if y_unit is not None:
plot.add_item(make.curve(x, signals[c].rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c]))
helper.add_events(plot, events, x.units)
_add_spike_waveforms(
plot, spikes, x.units, channel_indices[c], offset, progress)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, c)
plot.set_axis_unit(
BasePlot.Y_LEFT, signals[c].dimensionality.string)
progress.step()
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
else:
channels.reverse()
pW = BaseCurveWidget(win)
plot = pW.plot
helper.add_epochs(plot, epochs, time_unit)
# Find plot y offset
max_offset = 0 * signals[0].units
for i, c in enumerate(channels[1:], 1):
cur_offset = signals[channels[i - 1]].max() - signals[c].min()
if cur_offset > max_offset:
max_offset = cur_offset
offset -= signals[channels[0]].min()
for c in channels:
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
if y_unit is not None:
plot.add_item(
make.curve(x, (signals[c] + offset).rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c] + offset))
_add_spike_waveforms(
plot, spikes, x.units, channel_indices[c], offset, progress)
offset += max_offset
progress.step()
helper.add_events(plot, events, x.units)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, 0)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
plot.set_axis_unit(BasePlot.Y_LEFT, signals[0].dimensionality.string)
win.add_custom_curve_tools()
units = set([s.unit for s in spike_trains])
units = units.union([s.unit for s in spikes])
progress.done()
helper.make_window_legend(win, units, False)
win.show()
if use_subplots:
win.add_x_synchronization_option(True, channels)
win.add_y_synchronization_option(False, channels)
return win
def _add_spike_waveforms(plot, spikes, x_units, channel, offset, progress):
for spike in spikes:
if spike.waveform is None or not spike.sampling_rate:
continue
if channel < 0 or spike.waveform.shape[1] <= channel:
continue
color = helper.get_object_color(spike.unit)
if spike.left_sweep:
lsweep = spike.left_sweep
else:
lsweep = 0.0 * pq.ms
start = (spike.time - lsweep).rescale(x_units)
stop = (spike.waveform.shape[0] / spike.sampling_rate +
spike.time - lsweep).rescale(x_units)
spike_x = sp.arange(
start, stop,
(1.0 / spike.sampling_rate).rescale(x_units)) * x_units
plot.add_item(
make.curve(spike_x, spike.waveform[:, channel] + offset,
color=color, linewidth=2))
progress.step() spykeutils-0.4.1/spykeutils/plot/correlogram.py 0000644 0001750 0001750 00000010370 12262547774 020157 0 ustar rob rob from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
import quantities as pq
import scipy as sp
from .. import SpykeException
from ..progress_indicator import ProgressIndicator
from ..correlations import correlogram
from dialog import PlotDialog
import helper
@helper.needs_qt
def cross_correlogram(trains, bin_size, max_lag=500 * pq.ms,
border_correction=True, per_second=True,
square=False, time_unit=pq.ms, progress=None):
""" Create (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: Maximum time lag for which spikes are considered
(end time of calculated correlogram).
:type max_lag: Quantity scalar
:param bool border_correction: Apply correction for less data at higher
timelags.
:param bool per_second: If ``True``, the y-axis is count per second,
otherwise it is count per spike train.
:param bool square: If ``True``, the plot will include all
cross-correlograms, even if they are just mirrored versions of each
other. The autocorrelograms are displayed as the diagonal of a
square plot matrix. If ``False``, mirrored plots are omitted.
:param Quantity time_unit: Unit of X-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not trains:
raise SpykeException('No spike trains for correlogram')
if not progress:
progress = ProgressIndicator()
win_title = 'Correlogram | Bin size ' + str(bin_size)
progress.begin('Creating correlogram')
progress.set_status('Calculating...')
win = PlotDialog(toolbar=True, wintitle=win_title, min_plot_width=150,
min_plot_height=100)
correlograms, bins = correlogram(
trains, bin_size, max_lag, border_correction,
per_second, time_unit, progress)
x = bins[:-1] + bin_size / 2
crlgs = []
indices = correlograms.keys()
for i1 in xrange(len(indices)):
start_i = 0
if not square:
start_i = i1
for i2 in xrange(start_i, len(indices)):
crlgs.append(
(correlograms[indices[i1]][indices[i2]],
indices[i1], indices[i2]))
columns = int(sp.sqrt(len(crlgs)))
legends = []
for i, c in enumerate(crlgs):
legend_items = []
pW = BaseCurveWidget(win)
plot = pW.plot
plot.set_antialiasing(True)
plot.add_item(make.curve(x, c[0]))
# Create legend
color = helper.get_object_color(c[1])
color_curve = make.curve(
[], [], c[1].name,
color, 'NoPen', linewidth=1, marker='Rect',
markerfacecolor=color, markeredgecolor=color)
legend_items.append(color_curve)
plot.add_item(color_curve)
if c[1] != c[2]:
color = helper.get_object_color(c[2])
color_curve = make.curve(
[], [], c[2].name,
color, 'NoPen', linewidth=1, marker='Rect',
markerfacecolor=color, markeredgecolor=color)
legend_items.append(color_curve)
plot.add_item(color_curve)
legends.append(make.legend(restrict_items=legend_items))
plot.add_item(legends[-1])
if i >= len(crlgs) - columns:
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, time_unit.dimensionality.string)
if i % columns == 0:
plot.set_axis_title(BasePlot.Y_LEFT, 'Correlation')
if per_second:
plot.set_axis_unit(BasePlot.Y_LEFT, 'count/second')
else:
plot.set_axis_unit(BasePlot.Y_LEFT, 'count/segment')
win.add_plot_widget(pW, i, column=i % columns)
win.add_custom_curve_tools()
progress.done()
win.add_legend_option(legends, True)
win.show()
if len(crlgs) > 1:
win.add_x_synchronization_option(True, range(len(crlgs)))
win.add_y_synchronization_option(False, range(len(crlgs)))
return win spykeutils-0.4.1/spykeutils/plot/sde.py 0000644 0001750 0001750 00000012006 12262547774 016414 0 ustar rob rob import scipy as sp
from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
import quantities as pq
from .. import SpykeException
from .. import rate_estimation
from .. import signal_processing
from ..progress_indicator import ProgressIndicator
from dialog import PlotDialog
import helper
@helper.needs_qt
def sde(trains, events=None, start=0 * pq.ms, stop=None,
kernel_size=100 * pq.ms, optimize_steps=0,
minimum_kernel=10 * pq.ms, maximum_kernel=500 * pq.ms,
kernel=None, time_unit=pq.ms, progress=None):
""" Create a spike density estimation plot.
The spike density estimations give an estimate of the instantaneous
rate. Optionally finds optimal kernel size for given data.
:param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists.
:param dict events: A dictionary (with the same indices as ``trains``)
of Event objects or lists of Event objects. In case of lists,
the first event in the list will be used for alignment. The events
will be at time 0 on the plot. If None, spike trains are used
unmodified.
: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. 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 last bin. It will
be recalculated if there are spike trains which end earlier
than this time.
:type stop: Quantity scalar
:param kernel_size: A uniform kernel size for all spike trains.
Only used if optimization of kernel sizes is not used (i.e.
``optimize_steps`` is 0).
:type kernel_size: Quantity scalar
:param int optimize_steps: The number of different kernel sizes tried
between ``minimum_kernel`` and ``maximum_kernel``.
If 0, ``kernel_size`` will be used.
:param minimum_kernel: The minimum kernel size to try in optimization.
:type minimum_kernel: Quantity scalar
:param maximum_kernel: The maximum kernel size to try in optimization.
:type maximum_kernel: 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:`spykeutils.signal_processing.Kernel`
:param Quantity time_unit: Unit of X-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not progress:
progress = ProgressIndicator()
start.units = time_unit
if stop:
stop.units = time_unit
kernel_size.units = time_unit
minimum_kernel.units = time_unit
maximum_kernel.units = time_unit
if kernel is None:
kernel = signal_processing.GaussianKernel(100 * pq.ms)
# Align spike trains
for u in trains:
if events:
trains[u] = rate_estimation.aligned_spike_trains(
trains[u], events)
# Calculate spike density estimation
if optimize_steps:
steps = sp.logspace(sp.log10(minimum_kernel),
sp.log10(maximum_kernel),
optimize_steps) * time_unit
sde, kernel_size, eval_points = \
rate_estimation.spike_density_estimation(
trains, start, stop,
optimize_steps=steps, kernel=kernel,
progress=progress)
else:
sde, kernel_size, eval_points = \
rate_estimation.spike_density_estimation(
trains, start, stop,
kernel_size=kernel_size, kernel=kernel,
progress=progress)
progress.done()
if not sde:
raise SpykeException('No spike trains for SDE!')
# Plot
win_title = 'Kernel Density Estimation'
win = PlotDialog(toolbar=True, wintitle=win_title)
pW = BaseCurveWidget(win)
plot = pW.plot
plot.set_antialiasing(True)
for u in trains:
if u and u.name:
name = u.name
else:
name = 'Unknown'
curve = make.curve(
eval_points, sde[u],
title='%s, Kernel width %.2f %s' %
(name, kernel_size[u], time_unit.dimensionality.string),
color=helper.get_object_color(u))
plot.add_item(curve)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, eval_points.dimensionality.string)
plot.set_axis_title(BasePlot.Y_LEFT, 'Rate')
plot.set_axis_unit(BasePlot.Y_LEFT, 'Hz')
l = make.legend()
plot.add_item(l)
win.add_plot_widget(pW, 0)
win.add_custom_curve_tools()
win.add_legend_option([l], True)
win.show()
return win spykeutils-0.4.1/spykeutils/plot/dialog.py 0000644 0001750 0001750 00000051124 12262547774 017104 0 ustar rob rob """ Contains a helper class for creating windows containing guiqwt plots.
"""
from PyQt4 import QtGui
from PyQt4.QtCore import Qt
from PyQt4.QtGui import (QDialog, QGridLayout, QToolBar, QHBoxLayout,
QVBoxLayout, QFrame, QWidget)
try:
from PyQt4 import QtCore
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
_fromUtf8 = lambda s: s
from PyQt4.QtGui import QColor, QScrollArea, QWidget, QFrame
from guiqwt.baseplot import BasePlot
from guiqwt.curve import CurvePlot
from guiqwt.image import ImagePlot
from guiqwt.plot import PlotManager
from guiqwt.shapes import Marker
from guiqwt.curve import CurveItem
from guiqwt.tools import (SelectTool, RectZoomTool, BasePlotMenuTool,
DeleteItemTool, ItemListPanelTool,
AntiAliasingTool, AxisScaleTool, DisplayCoordsTool,
ExportItemDataTool, ItemCenterTool, SignalStatsTool,
ColormapTool, ReverseYAxisTool, AspectRatioTool,
ContrastPanelTool, XCSPanelTool, YCSPanelTool,
CrossSectionTool, AverageCrossSectionTool,
SaveAsTool, PrintTool, CopyToClipboardTool)
from guiqwt.signals import SIG_PLOT_AXIS_CHANGED
from guidata.configtools import get_icon
import icons_rc
import guiqwt_tools
# Monkeypatch curve and image plot so synchronizing axes works with all tools
def fixed_do_zoom_rect_view(self, *args, **kwargs):
CurvePlot.old_do_zoom_rect_view(self, *args, **kwargs)
self.emit(SIG_PLOT_AXIS_CHANGED, self)
CurvePlot.old_do_zoom_rect_view = CurvePlot.do_zoom_rect_view
CurvePlot.do_zoom_rect_view = fixed_do_zoom_rect_view
def fixed_do_autoscale(self, *args, **kwargs):
CurvePlot.old_do_autoscale(self, *args, **kwargs)
if not isinstance(self, ImagePlot):
self.emit(SIG_PLOT_AXIS_CHANGED, self)
CurvePlot.old_do_autoscale = CurvePlot.do_autoscale
CurvePlot.do_autoscale = fixed_do_autoscale
def fixed_do_autoscale_image(self, *args, **kwargs):
ImagePlot.old_do_autoscale(self, *args, **kwargs)
self.emit(SIG_PLOT_AXIS_CHANGED, self)
ImagePlot.old_do_autoscale = ImagePlot.do_autoscale
ImagePlot.do_autoscale = fixed_do_autoscale_image
class PlotDialog(QDialog, PlotManager):
""" Implements a dialog to which an arbitrary number of plots can be
added.
This class implements a `QDialog` with a number of plots on it. The
axes of the plots can be arbitrarily synchronized and option checkboxes
can be added which provide callbacks when the checkbox state changes.
:param str wintitle: Title of the window.
:param bool major_grid: Show major grid in plots.
:param bool minor_grid: Show minor grid in plots.
:param bool toolbar: Show toolbar.
:param parent: Parent window.
:param panels: A list of guiqwt panels to be added to the window.
:param int min_plot_width: Default minimum width for plots.
:param int min_plot_height: Default minimum height for plots.
"""
def __init__(self, wintitle='Plot window', major_grid=True,
minor_grid=False, toolbar=True, parent=None,
panels=None, min_plot_width=100, min_plot_height=75):
QDialog.__init__(self, parent)
self.setWindowFlags(Qt.Window)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap(_fromUtf8(':/Application/Main')),
QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.setWindowIcon(icon)
self.major_grid = major_grid
self.minor_grid = minor_grid
self.min_plot_width = min_plot_width
self.min_plot_height = min_plot_height
# WidgetMixin copy
PlotManager.__init__(self, main=self)
self.main_layout = QVBoxLayout(self)
self.color_layout = QHBoxLayout()
self.plot_layout = QGridLayout()
self.plot_layout.setMargin(0)
self.plot_scroll_widget = QWidget()
self.plot_scroll_area = QScrollArea()
self.plot_scroll_area.setFrameShape(QFrame.NoFrame)
self.plot_scroll_area.setWidgetResizable(True)
self.option_layout = QHBoxLayout()
self.plot_widget = None
if panels is not None:
for panel in panels:
self.add_panel(panel)
self.toolbar = QToolBar('Tools')
if not toolbar:
self.toolbar.hide()
# Configuring widget layout
self._setup_widget_properties(wintitle=wintitle, icon=icon)
self._setup_widget_layout()
# Options
self.option_callbacks = {}
self.legend = None
self.axis_syncplots = {}
def _setup_widget_properties(self, wintitle, icon):
self.setWindowTitle(wintitle)
if isinstance(icon, basestring):
icon = get_icon(icon)
self.setWindowIcon(icon)
self.setMinimumSize(320, 240)
self.resize(720, 540)
def _setup_widget_layout(self):
self.main_layout.addWidget(self.toolbar)
self.main_layout.addLayout(self.color_layout)
self.main_layout.addWidget(self.plot_scroll_area)
self.plot_scroll_area.setWidget(self.plot_scroll_widget)
self.plot_scroll_widget.setLayout(self.plot_layout)
self.main_layout.addLayout(self.option_layout)
self.setLayout(self.main_layout)
def add_custom_curve_tools(self, antialiasing=True,
activate_zoom=True,
signal_stats=False):
""" Adds typically needed curve tools to the window.
:param bool antialiasing: Determines if the antialiasing tool is
added.
:param bool activate_zoom: Determines if the zoom tool is activated
initially (otherwise, the selection tool will be activated).
:param bool signal_stats: Determines if the signal stats tool is
available.
"""
self.add_toolbar(self.toolbar)
t = self.add_tool(SelectTool)
if not activate_zoom:
self.set_default_tool(t)
self.add_tool(BasePlotMenuTool, "item")
self.add_tool(ExportItemDataTool)
try: # Old versions of guiqwt and spyderlib do not support this
import spyderlib.widgets.objecteditor
from guiqwt.tools import EditItemDataTool
self.add_tool(EditItemDataTool)
except ImportError:
pass
self.add_tool(ItemCenterTool)
self.add_tool(DeleteItemTool)
self.add_separator_tool()
t = self.add_tool(RectZoomTool)
if activate_zoom:
self.set_default_tool(t)
self.add_tool(guiqwt_tools.HomeTool)
self.add_tool(guiqwt_tools.PanTool)
self.add_separator_tool()
self.add_tool(BasePlotMenuTool, "grid")
self.add_tool(BasePlotMenuTool, "axes")
self.add_tool(DisplayCoordsTool)
if self.get_itemlist_panel():
self.add_tool(ItemListPanelTool)
if signal_stats:
self.add_separator_tool()
self.add_tool(SignalStatsTool)
if antialiasing:
self.add_tool(AntiAliasingTool)
self.add_tool(AxisScaleTool)
self.add_separator_tool()
self.add_tool(SaveAsTool)
self.add_tool(CopyToClipboardTool)
self.add_tool(PrintTool)
self.add_tool(guiqwt_tools.HelpTool)
self.add_separator_tool()
self.get_default_tool().activate()
def add_custom_image_tools(self, activate_zoom=True):
""" Adds typically needed image tools to the window.
"""
self.add_toolbar(self.toolbar)
t = self.add_tool(SelectTool)
if not activate_zoom:
self.set_default_tool(t)
self.add_tool(BasePlotMenuTool, "item")
self.add_tool(ExportItemDataTool)
try: # Old versions of guiqwt and spyderlib do not support this
import spyderlib.widgets.objecteditor
from guiqwt.tools import EditItemDataTool
self.add_tool(EditItemDataTool)
except ImportError:
pass
self.add_tool(ItemCenterTool)
self.add_tool(DeleteItemTool)
self.add_separator_tool()
t = self.add_tool(RectZoomTool)
if activate_zoom:
self.set_default_tool(t)
self.add_tool(guiqwt_tools.HomeTool)
self.add_tool(guiqwt_tools.PanTool)
self.add_separator_tool()
self.add_tool(BasePlotMenuTool, "grid")
self.add_tool(BasePlotMenuTool, "axes")
self.add_tool(DisplayCoordsTool)
if self.get_itemlist_panel():
self.add_tool(ItemListPanelTool)
self.add_separator_tool()
self.add_tool(ColormapTool)
self.add_tool(ReverseYAxisTool)
self.add_tool(AspectRatioTool)
if self.get_contrast_panel():
self.add_tool(ContrastPanelTool)
if self.get_xcs_panel() and self.get_ycs_panel():
self.add_tool(XCSPanelTool)
self.add_tool(YCSPanelTool)
self.add_tool(CrossSectionTool)
self.add_tool(AverageCrossSectionTool)
self.add_separator_tool()
self.add_tool(SaveAsTool)
self.add_tool(CopyToClipboardTool)
self.add_tool(PrintTool)
self.add_tool(guiqwt_tools.HelpTool)
self.add_separator_tool()
self.get_default_tool().activate()
def add_option(self, name, change_callback, active=False):
""" Add an option (using a checkbox) to the window.
:param str name: The name of the option.
:param func change_callback: A function accepting the new state as
a parameter. The function will be called whenever the state
of the option changes.
:param bool active: Determines if the option is activated initially.
"""
checkBox = QtGui.QCheckBox(self)
checkBox.setChecked(active)
checkBox.setText(name)
checkBox.stateChanged.connect(self._option_callback)
self.option_callbacks[checkBox] = change_callback
self.option_layout.addWidget(checkBox)
def add_x_synchronization_option(self, active, ids=None):
""" Offer an option for X axes synchronization. This method should
be called after show(), so that a proper initial synchronization
can be performed.
:param bool active: Determines whether the axes are synchronized
initially.
:param sequence ids: List of plot ids to synchronize.
"""
self.axis_syncplots[BasePlot.X_BOTTOM] = ids
if active and ids:
self.synchronize_axis(BasePlot.X_BOTTOM)
self.add_option('Synchronize X Axes',
PlotDialog._synchronization_option_x, active)
def add_y_synchronization_option(self, active, ids=None):
""" Offer an option for Y axes synchronization. This method should
be called after show(), so that a proper initial synchronization
can be performed.
:param bool active: Determines whether the axes are synchronized
initially
:param sequence ids: List of plot ids to synchronize.
"""
self.axis_syncplots[BasePlot.Y_LEFT] = ids
if active and ids:
self.synchronize_axis(BasePlot.Y_LEFT)
self.add_option('Synchronize Y Axes',
PlotDialog._synchronization_option_y, active)
def _synchronization_option_x(self, state):
""" Callback for x-axis synchronization
"""
if state:
self.synchronize_axis(BasePlot.X_BOTTOM)
else:
self.unsynchronize_axis(BasePlot.X_BOTTOM)
def _synchronization_option_y(self, state):
""" Callback for y-axis synchronization
"""
if state:
self.synchronize_axis(BasePlot.Y_LEFT)
else:
self.unsynchronize_axis(BasePlot.Y_LEFT)
def replace_colors(self, replace_list):
""" Replace colors of items in all plots.
This can be useful when changing the background color to black
and black items should be drawn in white:
``replace_colors([('#000000', '#ffffff']))``
:param list replace_list: A list of tuples of Qt color names. The
first color in each tuple is replaced by the second color.
"""
for plot in self.plots.itervalues():
for i in plot.get_items():
if isinstance(i, CurveItem):
pen = i.pen()
elif isinstance(i, Marker):
pen = i.linePen()
else:
continue
for color in replace_list:
c1 = QColor(color[0])
c2 = QColor(color[1])
if pen.color() != c1:
continue
pen.setColor(c2)
break
if isinstance(i, CurveItem):
i.setPen(pen)
elif isinstance(i, Marker):
i.setLinePen(pen)
plot.replot()
def set_background_color(self, color):
""" Set the background color for all plots.
:param str color: A Qt color name (e.g. '#ff0000')
"""
for p in self.plots.itervalues():
p.setCanvasBackground(QColor(color))
p.replot()
def add_unit_color(self, color, name='Unit color:'):
""" Create a small legend on top of the window with only one entry.
:param str color: A Qt color name (e.g. '#ff0000')
:param str name: The name of the legend item. It will be displayed
on the left of the color.
"""
label = QtGui.QLabel(self)
label.setText(name)
label.setAlignment(Qt.AlignRight)
self.color_layout.addWidget(label)
label = QtGui.QLabel(self)
label.setStyleSheet('background-color:' + str(color))
label.setFrameShape(QFrame.StyledPanel)
label.setMaximumWidth(80)
self.color_layout.addWidget(label)
def add_custom_label(self, legend_string):
""" Add a label on the right of the plots
:param str legend_string: An arbitrary string (which can contain
newlines) to display on the right of the plots
"""
label = QtGui.QLabel(self)
label.setText(legend_string)
self.plot_layout.addWidget(
label, 0, self.plot_layout.columnCount(), -1, 1)
def add_color_legend(self, legend, show_option=None):
""" Create a legend on the right of the plots with colors and names.
:param sequence legend: List of (color, text) tuples, where `color`
is a Qt color name (e.g. '#ff0000') and `text` is the
corresponding text to display in the legend.
:param bool show_option: Determines whether a toggle for the legend
will be shown (if the parameter is not ``None``) and if the legend
is visible initially (``True``/``False``).
"""
widget = QWidget(self)
layout = QGridLayout(widget)
widget.setLayout(layout)
for l in legend:
label = QtGui.QLabel(self)
label.setStyleSheet('background-color:' + str(l[0]))
label.setFrameShape(QFrame.StyledPanel)
label.setMaximumWidth(80)
label.setMaximumHeight(12)
layout.addWidget(label, layout.rowCount(), 0, 1, 1)
label = QtGui.QLabel(self)
label.setText(l[1])
layout.addWidget(label, layout.rowCount() - 1, 1, 1, 1)
self.plot_layout.addWidget(
widget, 0, self.plot_layout.columnCount(), -1, 1)
if show_option is not None:
widget.setVisible(show_option)
self.add_option(
'Show Legend Sidebar', lambda w, s: widget.setVisible(s),
show_option)
def add_legend_option(self, legends, active):
""" Create a user option to show or hide a list of legend objects.
:param sequence legends: The legend objects affected by the option.
:param bool active: Determines whether the legends will be visible
initially.
"""
self.legends = legends
self._set_legend_visibility(active)
self.add_option('Show legend', self._legend_callback, active)
if active:
self._set_legend_visibility(True)
def _option_callback(self, state):
self.option_callbacks[self.sender()](self, state)
#noinspection PyUnusedLocal
def _legend_callback(self, win, state):
self._set_legend_visibility(state > 0)
def _set_legend_visibility(self, visible):
for p in self.plots.itervalues():
for l in self.legends:
p.set_item_visible(l, visible)
def add_plot_widget(self, plot_widget, plot_id, row=-1, column=0,
min_plot_width=None, min_plot_height=None):
""" Adds a guiqwt plot to the window.
:param plot_widget: The plot to add.
:type plot_widget: guiqwt plot widget
:param int plot_id: The id of the new plot.
:param int row: The row of the new plot. If this is -1, the new plot
will be added in a new row (if `column` is 0) or
in the last row.
:param int column: The column of the new plot.
:param int min_plot_width: The minimum width of this plot. If
``None``, the default minimum width for this dialog
is used.
:param int max_plot_height: The minimum height of this plot. If
``None``, the default minimum height for this dialog
is used.
"""
if row == -1:
if column == 0:
row = self.plot_layout.rowCount()
else:
row = self.plot_layout.rowCount() - 1
pw = min_plot_width
if pw is None:
pw = self.min_plot_width
ph = min_plot_height
if ph is None:
ph = self.min_plot_height
plot_widget.setMinimumSize(pw, ph)
self.plot_layout.addWidget(plot_widget, row, column)
new_plot = plot_widget.plot
self.add_plot(new_plot, plot_id)
def synchronize_axis(self, axis, plots=None):
if plots is None:
if axis in self.axis_syncplots:
plots = self.axis_syncplots[axis]
else:
plots = self.plots.keys()
if len(plots) < 1:
return
PlotManager.synchronize_axis(self, axis, plots)
# Find interval that needs to be shown in order to include all
# currently shown parts in the synchronized plots
plot_objects = [self.plots[p] for p in plots]
lb = min((p.axisScaleDiv(axis).lowerBound() for p in plot_objects))
ub = max((p.axisScaleDiv(axis).upperBound() for p in plot_objects))
for p in plot_objects:
p.setAxisScale(axis, lb, ub)
p.replot()
def unsynchronize_axis(self, axis, plots=None):
if plots is None:
if axis in self.axis_syncplots:
plots = self.axis_syncplots[axis]
else:
plots = self.plots.keys()
for plot_id in plots:
if not plot_id in self.synchronized_plots:
continue
synclist = self.synchronized_plots[plot_id]
for plot2_id in plots:
if plot_id == plot2_id:
continue
item = (axis, plot2_id)
if item in synclist:
synclist.remove(item)
def plot_axis_changed(self, plot):
ids = [k for k, p in self.plots.iteritems() if p == plot]
if len(ids) < 1:
return
plot_id = ids[0]
if plot_id not in self.synchronized_plots:
return
for (axis, other_plot_id) in self.synchronized_plots[plot_id]:
scalediv = plot.axisScaleDiv(axis)
other = self.get_plot(other_plot_id)
lb = scalediv.lowerBound()
ub = scalediv.upperBound()
other.setAxisScale(axis, lb, ub)
other.replot()
def set_plot_title(self, plot, title):
""" Set the title of a guiqwt plot and use the same font as for the
rest of the window.
:param plot: The plot for which the title is set.
:param str title: The new title of the plot.
"""
plot.setTitle(title)
l = plot.titleLabel()
l.setFont(self.font())
plot.setTitle(l.text())
def show(self):
for p in self.plots.itervalues():
if not self.minor_grid:
p.grid.gridparam.min_xenabled = False
p.grid.gridparam.min_yenabled = False
if not self.major_grid:
p.grid.gridparam.maj_xenabled = False
p.grid.gridparam.maj_yenabled = False
p.grid.update_params()
super(PlotDialog, self).show() spykeutils-0.4.1/spykeutils/plot/interspike_intervals.py 0000644 0001750 0001750 00000010701 12262547774 022105 0 ustar rob rob import scipy as sp
from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
import quantities as pq
from .. import SpykeException
from dialog import PlotDialog
import helper
@helper.needs_qt
def isi(trains, bin_size, cut_off, bar_plot=False, time_unit=pq.ms):
""" Create a plot dialog with an interspike interval histogram.
:param dict trains: Dictionary with lists of spike trains indexed by
units for which to display ISI histograms
:param bin_size: Bin size (time)
:type bin_size: Quantity scalar
:param cut_off: End of histogram (time)
:type bin_size: Quantity scalar
:param bool bar_plot: If ``True``, create a bar ISI histogram for each
index in ``trains``. Else, create a line ISI histogram.
:param Quantity time_unit: Unit of X-Axis.
"""
if not trains:
raise SpykeException('No spike trains for ISI histogram')
win_title = 'ISI Histogram | Bin size: ' + str(bin_size)
win = PlotDialog(toolbar=True, wintitle=win_title, min_plot_width=150,
min_plot_height=100)
bin_size = bin_size.rescale(time_unit)
cut_off = cut_off.rescale(time_unit)
bins = sp.arange(0 * time_unit, cut_off, bin_size) * time_unit
legends = []
if bar_plot:
ind = 0
columns = int(sp.sqrt(len(trains)))
for i, train_list in trains.iteritems():
pW = BaseCurveWidget(win)
plot = pW.plot
intervals = []
for t in train_list:
t = t.rescale(time_unit)
sTrain = sp.asarray(t)
sTrain.sort()
intervals.extend(sp.diff(sTrain))
(isi, bins) = sp.histogram(intervals, bins)
if i and hasattr(i, 'name') and i.name:
name = i.name
else:
name = 'Unknown'
show_isi = list(isi)
show_isi.insert(0, show_isi[0])
curve = make.curve(
bins, show_isi, name, color='k',
curvestyle="Steps", shade=1.0)
plot.add_item(curve)
# Create legend
color = helper.get_object_color(i)
color_curve = make.curve(
[], [], name, color, 'NoPen', linewidth=1, marker='Rect',
markerfacecolor=color, markeredgecolor=color)
plot.add_item(color_curve)
legends.append(make.legend(restrict_items=[color_curve]))
plot.add_item(legends[-1])
# Prepare plot
plot.set_antialiasing(False)
scale = plot.axisScaleDiv(BasePlot.Y_LEFT)
plot.setAxisScale(BasePlot.Y_LEFT, 0, scale.upperBound())
if ind % columns == 0:
plot.set_axis_title(BasePlot.Y_LEFT, 'Number of intervals')
if ind >= len(trains) - columns:
plot.set_axis_title(BasePlot.X_BOTTOM, 'Interval length')
plot.set_axis_unit(
BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(pW, ind, column=ind % columns)
ind += 1
else:
pW = BaseCurveWidget(win)
plot = pW.plot
legend_items = []
for i, train_list in trains.iteritems():
intervals = []
for t in train_list:
t = t.rescale(time_unit)
sTrain = sp.asarray(t)
sTrain.sort()
intervals.extend(sp.diff(sTrain))
(isi, bins) = sp.histogram(intervals, bins)
if i and hasattr(i, 'name') and i.name:
name = i.name
else:
name = 'Unknown'
color = helper.get_object_color(i)
curve = make.curve(bins, isi, name, color=color)
legend_items.append(curve)
plot.add_item(curve)
win.add_plot_widget(pW, 0)
legends.append(make.legend(restrict_items=legend_items))
plot.add_item(legends[-1])
plot.set_antialiasing(True)
plot.set_axis_title(BasePlot.Y_LEFT, 'Number of intervals')
plot.set_axis_title(BasePlot.X_BOTTOM, 'Interval length')
plot.set_axis_unit(BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_custom_curve_tools()
win.add_legend_option(legends, True)
win.show()
if bar_plot and len(trains) > 1:
win.add_x_synchronization_option(True, range(len(trains)))
win.add_y_synchronization_option(False, range(len(trains)))
return win spykeutils-0.4.1/spykeutils/plot/peri_stimulus_histogram.py 0000644 0001750 0001750 00000013320 12262547774 022622 0 ustar rob rob import scipy as sp
import quantities as pq
from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
from .. import rate_estimation
from ..progress_indicator import ProgressIndicator
from .. import SpykeException
from dialog import PlotDialog
import helper
@helper.needs_qt
def psth(trains, events=None, start=0 * pq.ms, stop=None,
bin_size=100 * pq.ms, rate_correction=True, bar_plot=False,
time_unit=pq.ms, progress=None):
""" Create a peri stimulus time histogram.
The peri stimulus time histogram gives an estimate of the instantaneous
rate.
:param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists.
:param dict events: A dictionary of Event objects, indexed by segment.
The events will be at time 0 on the plot. If None, spike trains
are used unmodified.
: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. 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 last bin. It will
be recalculated if there are spike trains which end earlier
than this time.
:type stop: Quantity scalar
:param bin_size: The bin size for the histogram.
:type bin_size: Quantity scalar
:param bool rate_correction: Determines if a rates (``True``) or
counts (``False``) are shown.
:param bool bar_plot: Determines if a bar plot (``True``) or a line
plot (``False``) will be created. In case of a bar plot, one plot
for each index in ``trains`` will be created.
:param Quantity time_unit: Unit of X-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not trains:
raise SpykeException('No spike trains for PSTH!')
if not progress:
progress = ProgressIndicator()
# Align spike trains
for u in trains:
if events:
trains[u] = rate_estimation.aligned_spike_trains(
trains[u], events)
rates, bins = rate_estimation.psth(
trains, bin_size, start=start, stop=stop,
rate_correction=rate_correction)
bins = bins.rescale(time_unit)
if not psth:
raise SpykeException('No spike trains for PSTH!')
win_title = 'PSTH | Bin size %.2f %s' % (bin_size,
time_unit.dimensionality.string)
win = PlotDialog(toolbar=True, wintitle=win_title, min_plot_width=150,
min_plot_height=100)
legends = []
if bar_plot:
ind = 0
columns = int(sp.sqrt(len(rates)))
for i, r in rates.iteritems():
if i and hasattr(i, 'name') and i.name:
name = i.name
else:
name = 'Unknown'
pW = BaseCurveWidget(win)
plot = pW.plot
show_rates = list(r)
show_rates.insert(0, show_rates[0])
curve = make.curve(
bins, show_rates, name, color='k',
curvestyle="Steps", shade=1.0)
plot.add_item(curve)
# Create legend
color = helper.get_object_color(i)
color_curve = make.curve(
[], [], name, color, 'NoPen', linewidth=1, marker='Rect',
markerfacecolor=color, markeredgecolor=color)
plot.add_item(color_curve)
legends.append(make.legend(restrict_items=[color_curve]))
plot.add_item(legends[-1])
# Prepare plot
plot.set_antialiasing(False)
scale = plot.axisScaleDiv(BasePlot.Y_LEFT)
plot.setAxisScale(BasePlot.Y_LEFT, 0, scale.upperBound())
if ind % columns == 0:
if not rate_correction:
plot.set_axis_title(BasePlot.Y_LEFT, 'Spike Count')
else:
plot.set_axis_title(BasePlot.Y_LEFT, 'Rate')
plot.set_axis_unit(BasePlot.Y_LEFT, 'Hz')
if ind >= len(trains) - columns:
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(
BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(pW, ind, column=ind % columns)
ind += 1
else:
bins = 0.5 * sp.diff(bins) + bins[:-1]
pW = BaseCurveWidget(win)
plot = pW.plot
legend_items = []
for i, r in rates.iteritems():
if i and hasattr(i, 'name') and i.name:
name = i.name
else:
name = 'Unknown'
curve = make.curve(
bins, r, name,
color=helper.get_object_color(i))
legend_items.append(curve)
plot.add_item(curve)
win.add_plot_widget(pW, 0)
legends.append(make.legend(restrict_items=legend_items))
plot.add_item(legends[-1])
if not rate_correction:
plot.set_axis_title(BasePlot.Y_LEFT, 'Spike Count')
else:
plot.set_axis_title(BasePlot.Y_LEFT, 'Rate')
plot.set_axis_unit(BasePlot.Y_LEFT, 'Hz')
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, time_unit.dimensionality.string)
plot.set_antialiasing(True)
win.add_custom_curve_tools()
win.add_legend_option(legends, True)
progress.done()
win.show()
if bar_plot and len(rates) > 1:
win.add_x_synchronization_option(True, range(len(rates)))
win.add_y_synchronization_option(False, range(len(rates)))
return win spykeutils-0.4.1/spykeutils/plot/helper.py 0000644 0001750 0001750 00000020350 12262547774 017121 0 ustar rob rob """
This module contains some utility functions that are usefult in plot creation.
"""
import functools
import scipy as sp
from PyQt4.QtGui import QApplication
from PyQt4 import QtCore
from PyQt4.QtGui import QProgressDialog
from guiqwt.builder import make
from ..progress_indicator import (ProgressIndicator,
CancelException)
class _MarkerName:
""" Helper class to create marker name functions for different strings.
"""
def __init__(self, name):
self.name = name
#noinspection PyUnusedLocal
def get_name(self, x, y):
return self.name
def _needs_qt(function):
@functools.wraps(function)
def inner(*args, **kwargs):
if not QApplication.instance():
_needs_qt.app = QApplication([])
ret = function(*args, **kwargs)
if _needs_qt.app:
_needs_qt.app.exec_()
return ret
return inner
_needs_qt.app = None
# Make need_qt decorator preserve signature if decorator package is available
try:
from decorator import FunctionMaker
def decorator_apply(dec, func):
return FunctionMaker.create(
func, 'return decorated(%(signature)s)',
dict(decorated=dec(func)), __wrapped__=func)
def needs_qt(func):
""" Decorator for functions making sure that an initialized PyQt exists.
"""
return decorator_apply(_needs_qt, func)
except ImportError:
needs_qt = _needs_qt
# Optimum contrast color palette (without white and black), see
# http://web.media.mit.edu/~wad/color/palette.html
__default_colors = [
'#575757', # Dark Gray
'#ad2323', # Red
'#2a4bd7', # Blue
'#296914', # Green
'#614a19', # Brown (lower R to better distinguish from purple)
'#8126c0', # Purple
'#a0a0a0', # Light Gray
'#81c57a', # Light Green
'#9dafff', # Light Blue
'#29d0d0', # Cyan
'#ff9233', # Orange
'#ffee33', # Yellow
'#b6ab88', # Tan (darkened for better visibility on white background)
'#ff89d1'] # Pink (darkened for better visibility on white background)
__colors = __default_colors
def get_color(entity_id):
""" Return a color for an int.
"""
return __colors[entity_id % len(__colors)]
def get_object_color(unit):
""" Return a color for a Neo object, based on the 'unique_id'
annotation. If the annotation is not present, return a color based
on the hash of the object.
"""
try:
if 'unique_id' in unit.annotations:
return get_color(unit.annotations['unique_id'])
except Exception:
return get_color(hash(unit))
return get_color(hash(unit))
def set_color_scheme(colors):
""" Set the color scheme used in plots.
:param sequence colors: A list of strings with HTML-style color codes
(e.g. ``'#ffffff'`` for white). If this is ``None`` or empty,
the default color scheme will be set.
"""
global __colors
global __default_colors
if not colors:
__colors = __default_colors
else:
__colors = colors
def add_events(plot, events, units=None):
""" Add Event markers to a guiqwt plot.
:param plot: The plot object.
:type plot: :class:`guiqwt.baseplot.BasePlot`
:param sequence events: The events (neo :class:`neo.Event` objects).
:param Quantity units: The x-scale of the plot. If this is ``None``,
the time unit of the events will be use.
"""
for m in events:
nameObject = _MarkerName(m.label)
if units:
time = m.time.rescale(units)
else:
time = m.time
plot.add_item(
make.marker(
(time, 0), nameObject.get_name,
movable=False, markerstyle='|', color='k', linestyle=':',
linewidth=1))
def add_spikes(plot, train, color='k', spike_width=1, spike_height=20000,
y_offset=0, name='', units=None):
""" Add all spikes from a spike train to a guiqwt plot as vertical lines.
:param plot: The plot object.
:type plot: :class:`guiqwt.baseplot.BasePlot`
:param train: A spike train with the spike times to show.
:type train: :class:`neo.core.SpikeTrain`
:param str color: The color for the spikes.
:param int spike_width: The width of the shown spikes in pixels.
:param int spike_height: The height of the shown spikes in pixels.
:param float y_offset: An offset for the drawing position on the y-axis.
:param str name: The name of the spike train.
:param Quantity units: The x-scale of the plot. If this is ``None``,
the time unit of the events will be use.
:returns: The plot item added for the spike train
"""
if units:
train = train.rescale(units)
spikes = make.curve(
train, sp.zeros(len(train)) + y_offset,
name, 'k', 'NoPen', linewidth=0, marker='Rect',
markerfacecolor=color, markeredgecolor=color)
s = spikes.symbol()
s.setSize(spike_width - 1, spike_height)
spikes.setSymbol(s)
plot.add_item(spikes)
return spikes
def add_epochs(plot, epochs, units=None):
""" Add Epoch markers to a guiqwt plot.
:param plot: The plot object.
:type plot: :class:`guiqwt.baseplot.BasePlot`
:param sequence epochs: The epochs (neo :class:`neo.Epoch` objects).
:param units: The x-scale of the plot. If this is ``None``,
the time unit of the events will be use.
"""
for e in epochs:
if units:
start = e.time.rescale(units)
end = (e.time + e.duration).rescale(units)
else:
start = e.time
end = e.time + e.duration
time = (start + end) / 2.0
o = make.range(start, end)
o.setTitle(e.label)
o.set_readonly(True)
o.set_movable(False)
o.set_resizable(False)
o.set_selectable(False)
o.set_rotatable(False)
plot.add_item(o)
nameObject = _MarkerName(e.label)
plot.add_item(make.marker(
(time, 0), nameObject.get_name,
movable=False, markerstyle='|', color='k', linestyle='NoPen',
linewidth=1))
def make_window_legend(win, objects, show_option=None):
""" Create a legend in a PlotDialog for a given sequence of neo objects.
:param win: The window where the legend will be added.
:type win: :class:`spykeutils.plot.dialogs.PlotDialog`
:param sequence objects: A list of neo objects which will be included in
the legend.
:param bool show_option: Determines whether a toggle for the legend
will be shown (if the parameter is not ``None``) and if the legend
is visible initially (``True``/``False``).
"""
if not objects:
return
legend = []
for u in objects:
if u is not None:
name = u.name
else:
name = 'No identifier'
legend.append((get_object_color(u), name))
win.add_color_legend(legend, show_option)
class ProgressIndicatorDialog(ProgressIndicator, QProgressDialog):
""" This class implements
:class:`spykeutils.progress_indicator.ProgressIndicator` as a
``QProgressDialog``. It can be used to indicate progress in a graphical
user interface. Qt needs to be initialized in order to use it.
"""
def __init__(self, parent, title='Processing...'):
QProgressDialog.__init__(self, parent)
self.setWindowTitle(title)
self.setMinimumWidth(500)
self.setAutoReset(False)
def set_ticks(self, ticks):
self.setMaximum(ticks)
if self.isVisible():
self.setValue(0)
def begin(self, title='Processing...'):
self.setWindowTitle(title)
self.setLabelText('')
self.setValue(0)
if not self.isVisible():
self.reset()
self.open()
QtCore.QCoreApplication.instance().processEvents()
def step(self, num_steps=1):
if not self.isVisible():
return
self.setValue(self.value() + num_steps)
QtCore.QCoreApplication.instance().processEvents()
if self.wasCanceled():
self.done()
raise CancelException()
super(ProgressIndicatorDialog, self).step()
def set_status(self, status):
self.setLabelText(status)
QtCore.QCoreApplication.instance().processEvents()
def done(self):
self.reset()
spykeutils-0.4.1/spykeutils/plot/icons_rc.py 0000644 0001750 0001750 00000141175 12262547774 017452 0 ustar rob rob # -*- coding: utf-8 -*-
# Resource object code
#
# Created: Di. Nov 27 12:08:49 2012
# by: The Resource Compiler for PyQt (Qt v4.8.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
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def qInitResources():
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qInitResources()
spykeutils-0.4.1/spykeutils/plot/rasterplot.py 0000644 0001750 0001750 00000004172 12262547774 020045 0 ustar rob rob from guiqwt.builder import make
from guiqwt.baseplot import BasePlot
from guiqwt.plot import BaseCurveWidget
import quantities as pq
from .. import SpykeException
from dialog import PlotDialog
import helper
@helper.needs_qt
def raster(trains, time_unit=pq.ms, show_lines=True, events=None, epochs=None):
""" Create a new plotting window with a rasterplot of spiketrains.
:param dict trains: Dictionary of spike trains indexed by a
Neo object (Unit or Segment).
:param Quantity time_unit: Unit of X-Axis.
:param bool show_lines: Determines if a horizontal line will be shown
for each spike train.
:param sequence events: A sequence of neo `Event` objects that will
be marked on the plot.
"""
if not trains:
raise SpykeException('No spike trains for rasterplot')
if not time_unit:
time_unit = pq.ms
win_title = 'Spike Trains'
win = PlotDialog(toolbar=True, wintitle=win_title, major_grid=False)
pW = BaseCurveWidget(win)
plot = pW.plot
if events is None:
events = []
if epochs is None:
epochs = []
offset = len(trains)
legend_items = []
for u, t in trains.iteritems():
color = helper.get_object_color(u)
train = helper.add_spikes(
plot, t, color, 2, 21, offset, u.name, time_unit)
if u.name:
legend_items.append(train)
if show_lines:
plot.add_item(make.curve(
[t.t_start.rescale(time_unit), t.t_stop.rescale(time_unit)],
[offset, offset], color='k'))
offset -= 1
helper.add_epochs(plot, epochs, time_unit)
helper.add_events(plot, events, time_unit)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(pW, 0)
legend = make.legend(restrict_items=legend_items)
plot.add_item(legend)
win.add_legend_option([legend], True)
if len(trains) > 1:
plot.set_axis_limits(BasePlot.Y_LEFT, 0.5, len(trains) + 0.5)
win.add_custom_curve_tools()
win.show()
return win spykeutils-0.4.1/spykeutils/correlations.py 0000644 0001750 0001750 00000011424 12262547774 017372 0 ustar rob rob import scipy as sp
from collections import OrderedDict
import quantities as pq
from progress_indicator import ProgressIndicator
from . import SpykeException
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 spykeutils-0.4.1/spykeutils/spike_train_metrics.py 0000644 0001750 0001750 00000110421 12262547774 020721 0 ustar rob rob 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]
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]
spykeutils-0.4.1/spykeutils/conversions.py 0000644 0001750 0001750 00000017112 12262547774 017236 0 ustar rob rob import 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
spykeutils-0.4.1/spykeutils/rate_estimation.py 0000644 0001750 0001750 00000024573 12262547774 020066 0 ustar rob rob 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.units spykeutils-0.4.1/spykeutils/progress_indicator.py 0000644 0001750 0001750 00000003141 12262547774 020563 0 ustar rob rob import functools
class CancelException(Exception):
""" This is raised when a user cancels a progress process. It is used
by :class:`ProgressIndicator` and its descendants.
"""
pass
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
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.
"""
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
def begin(self, title=''):
""" Signal that the operation starts.
:param string title: The name of the whole operation.
"""
pass
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
def set_status(self, new_status):
""" Set status description.
:param string new_status: A description of the current status.
"""
pass
def done(self):
""" Signal that the operation is done. """
pass spykeutils-0.4.1/spykeutils/tools.py 0000644 0001750 0001750 00000026545 12262547774 016040 0 ustar rob rob 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 spikes spykeutils-0.4.1/setup.py 0000644 0001750 0001750 00000003702 12262547774 013612 0 ustar rob rob # -*- coding: utf-8 -*-
from setuptools import setup, find_packages
import os
def find_version():
try:
f = open(os.path.join('spykeutils', '__init__.py'), 'r')
try:
for line in f:
if line.startswith('__version__'):
rval = line.split()[-1][1:-1]
break
finally:
f.close()
except Exception:
rval = '0'
return rval
DESC = """Based on the `Neo `_ framework,
spykeutils is a Python library for analyzing and plotting data from
neurophysiological recordings or simulations. It can be used by itself or
in conjunction with Spyke Viewer, a multi-platform GUI application for
navigating electrophysiological datasets.
For more information, see the documentation at
http://spykeutils.readthedocs.org"""
if __name__ == "__main__":
setup(
name="spykeutils",
version=find_version(),
packages=find_packages(),
install_requires=['scipy', 'quantities', 'neo>=0.2.1'],
extras_require = {
'plot': ['guiqwt>=2.1.4'],
'plugin': ['tables'],
'quality_estimation': ['scikit-learn>=0.11']
},
entry_points = {
'console_scripts':
['spykeplugin = spykeutils.plugin.startplugin:main']
},
author='Robert Pröpper',
maintainer='Robert Pröpper',
description='Utilities for analyzing electrophysiological data',
long_description=DESC,
license='BSD',
url='https://github.com/rproepp/spykeutils',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Scientific/Engineering :: Bio-Informatics'])