# -*- coding: utf-8 -*-
"""
Statistical measures of spike trains (e.g., Fano factor) and functions to
estimate firing rates.
Tutorial
--------
:doc:`View tutorial <../tutorials/statistics>`
Run tutorial interactively:
.. image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/NeuralEnsemble/elephant/master
?filepath=doc/tutorials/statistics.ipynb
.. current_module elephant.statistics
Functions overview
------------------
Rate estimation
~~~~~~~~~~~~~~~
.. autosummary::
:toctree: toctree/statistics/
mean_firing_rate
instantaneous_rate
time_histogram
optimal_kernel_bandwidth
Spike interval statistics
~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: toctree/statistics/
isi
cv
cv2
lv
lvr
Statistics across spike trains
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: toctree/statistics/
fanofactor
complexity_pdf
:copyright: Copyright 2014-2020 by the Elephant team, see `doc/authors.rst`.
:license: Modified BSD, see LICENSE.txt for details.
"""
from __future__ import division, print_function
# do not import unicode_literals
# (quantities rescale does not work with unicodes)
import warnings
import neo
import numpy as np
import math
import quantities as pq
import scipy.signal
import scipy.stats
from neo.core import SpikeTrain
import elephant.kernels as kernels
from elephant.conversion import BinnedSpikeTrain
from elephant.utils import deprecated_alias, get_common_start_stop_times, \
check_neo_consistency
from elephant.utils import is_time_quantity
__all__ = [
"isi",
"mean_firing_rate",
"fanofactor",
"cv",
"cv2",
"lv",
"lvr",
"instantaneous_rate",
"time_histogram",
"complexity_pdf",
"fftkernel",
"optimal_kernel_bandwidth"
]
cv = scipy.stats.variation
[docs]def isi(spiketrain, axis=-1):
"""
Return an array containing the inter-spike intervals of the spike train.
Accepts a `neo.SpikeTrain`, a `pq.Quantity` array, a `np.ndarray`, or a
list of time spikes. If either a `neo.SpikeTrain` or `pq.Quantity` is
provided, the return value will be `pq.Quantity`, otherwise `np.ndarray`.
The units of `pq.Quantity` will be the same as `spiketrain`.
Visualization of this function is covered in Viziphant:
:func:`viziphant.statistics.plot_isi_histogram`.
Parameters
----------
spiketrain : neo.SpikeTrain or pq.Quantity or array-like
The spike times.
axis : int, optional
The axis along which the difference is taken.
Default: the last axis.
Returns
-------
intervals : np.ndarray or pq.Quantity
The inter-spike intervals of the `spiketrain`.
Warns
-----
UserWarning
When the input array is not sorted, negative intervals are returned
with a warning.
"""
if isinstance(spiketrain, neo.SpikeTrain):
intervals = np.diff(spiketrain.magnitude, axis=axis)
# np.diff makes a copy
intervals = pq.Quantity(intervals, units=spiketrain.units, copy=False)
else:
intervals = np.diff(spiketrain, axis=axis)
if (intervals < 0).any():
warnings.warn("ISI evaluated to negative values. "
"Please sort the input array.")
return intervals
[docs]def mean_firing_rate(spiketrain, t_start=None, t_stop=None, axis=None):
"""
Return the firing rate of the spike train.
The firing rate is calculated as the number of spikes in the spike train
in the range `[t_start, t_stop]` divided by the time interval
`t_stop - t_start`. See the description below for cases when `t_start` or
`t_stop` is None.
Accepts a `neo.SpikeTrain`, a `pq.Quantity` array, or a plain
`np.ndarray`. If either a `neo.SpikeTrain` or `pq.Quantity` array is
provided, the return value will be a `pq.Quantity` array, otherwise a
plain `np.ndarray`. The units of the `pq.Quantity` array will be the
inverse of the `spiketrain`.
Parameters
----------
spiketrain : neo.SpikeTrain or pq.Quantity or np.ndarray
The spike times.
t_start : float or pq.Quantity, optional
The start time to use for the interval.
If None, retrieved from the `t_start` attribute of `spiketrain`. If
that is not present, default to 0. All spiketrain's spike times below
this value are ignored.
Default: None.
t_stop : float or pq.Quantity, optional
The stop time to use for the time points.
If not specified, retrieved from the `t_stop` attribute of
`spiketrain`. If that is not present, default to the maximum value of
`spiketrain`. All spiketrain's spike times above this value are
ignored.
Default: None.
axis : int, optional
The axis over which to do the calculation; has no effect when the
input is a neo.SpikeTrain, because a neo.SpikeTrain is always a 1-d
vector. If None, do the calculation over the flattened array.
Default: None.
Returns
-------
float or pq.Quantity or np.ndarray
The firing rate of the `spiketrain`
Raises
------
TypeError
If the input spiketrain is a `np.ndarray` but `t_start` or `t_stop` is
`pq.Quantity`.
If the input spiketrain is a `neo.SpikeTrain` or `pq.Quantity` but
`t_start` or `t_stop` is not `pq.Quantity`.
ValueError
If the input spiketrain is empty.
"""
if isinstance(spiketrain, neo.SpikeTrain) and t_start is None \
and t_stop is None and axis is None:
# a faster approach for a typical use case
n_spikes = len(spiketrain)
time_interval = spiketrain.t_stop - spiketrain.t_start
time_interval = time_interval.rescale(spiketrain.units)
rate = n_spikes / time_interval
return rate
if isinstance(spiketrain, pq.Quantity):
# Quantity or neo.SpikeTrain
if not is_time_quantity(t_start, allow_none=True):
raise TypeError("'t_start' must be a Quantity or None")
if not is_time_quantity(t_stop, allow_none=True):
raise TypeError("'t_stop' must be a Quantity or None")
units = spiketrain.units
if t_start is None:
t_start = getattr(spiketrain, 't_start', 0 * units)
t_start = t_start.rescale(units).magnitude
if t_stop is None:
t_stop = getattr(spiketrain, 't_stop',
np.max(spiketrain, axis=axis))
t_stop = t_stop.rescale(units).magnitude
# calculate as a numpy array
rates = mean_firing_rate(spiketrain.magnitude, t_start=t_start,
t_stop=t_stop, axis=axis)
rates = pq.Quantity(rates, units=1. / units)
return rates
elif isinstance(spiketrain, (np.ndarray, list, tuple)):
if isinstance(t_start, pq.Quantity) or isinstance(t_stop, pq.Quantity):
raise TypeError("'t_start' and 't_stop' cannot be quantities if "
"'spiketrain' is not a Quantity.")
spiketrain = np.asarray(spiketrain)
if len(spiketrain) == 0:
raise ValueError("Empty input spiketrain.")
if t_start is None:
t_start = 0
if t_stop is None:
t_stop = np.max(spiketrain, axis=axis)
time_interval = t_stop - t_start
if axis and isinstance(t_stop, np.ndarray):
t_stop = np.expand_dims(t_stop, axis)
rates = np.sum((spiketrain >= t_start) & (spiketrain <= t_stop),
axis=axis) / time_interval
return rates
else:
raise TypeError("Invalid input spiketrain type: '{}'. Allowed: "
"neo.SpikeTrain, Quantity, ndarray".
format(type(spiketrain)))
[docs]def fanofactor(spiketrains, warn_tolerance=0.1 * pq.ms):
r"""
Evaluates the empirical Fano factor F of the spike counts of
a list of `neo.SpikeTrain` objects.
Given the vector v containing the observed spike counts (one per
spike train) in the time window [t0, t1], F is defined as:
.. math::
F := \frac{var(v)}{mean(v)}
The Fano factor is typically computed for spike trains representing the
activity of the same neuron over different trials. The higher F, the
larger the cross-trial non-stationarity. In theory for a time-stationary
Poisson process, F=1.
Parameters
----------
spiketrains : list
List of `neo.SpikeTrain` or `pq.Quantity` or `np.ndarray` or list of
spike times for which to compute the Fano factor of spike counts.
warn_tolerance : pq.Quantity
In case of a list of input neo.SpikeTrains, if their durations vary by
more than `warn_tolerence` in their absolute values, throw a warning
(see Notes).
Default: 0.1 ms.
Returns
-------
fano : float
The Fano factor of the spike counts of the input spike trains.
Returns np.NaN if an empty list is specified, or if all spike trains
are empty.
Raises
------
TypeError
If the input spiketrains are neo.SpikeTrain objects, but
`warn_tolerance` is not a quantity.
Notes
-----
The check for the equal duration of the input spike trains is performed
only if the input is of type`neo.SpikeTrain`: if you pass a numpy array,
please make sure that they all have the same duration manually.
"""
# Build array of spike counts (one per spike train)
spike_counts = np.array([len(st) for st in spiketrains])
# Compute FF
if all(count == 0 for count in spike_counts):
# empty list of spiketrains reaches this branch, and NaN is returned
return np.nan
if all(isinstance(st, neo.SpikeTrain) for st in spiketrains):
if not is_time_quantity(warn_tolerance):
raise TypeError("'warn_tolerance' must be a time quantity.")
durations = [(st.t_stop - st.t_start).simplified.item()
for st in spiketrains]
durations_min = min(durations)
durations_max = max(durations)
if durations_max - durations_min > warn_tolerance.simplified.item():
warnings.warn("Fano factor calculated for spike trains of "
"different duration (minimum: {_min}s, maximum "
"{_max}s).".format(_min=durations_min,
_max=durations_max))
fano = spike_counts.var() / spike_counts.mean()
return fano
def __variation_check(v, with_nan):
# ensure the input ia a vector
if v.ndim != 1:
raise ValueError("The input must be a vector, not a {}-dim matrix.".
format(v.ndim))
# ensure we have enough entries
if v.size < 2:
if with_nan:
warnings.warn("The input size is too small. Please provide"
"an input with more than 1 entry. Returning `NaN`"
"since the argument `with_nan` is `True`")
return np.NaN
else:
raise ValueError("Input size is too small. Please provide "
"an input with more than 1 entry. Set 'with_nan' "
"to True to replace the error by a warning.")
return None
[docs]@deprecated_alias(v='time_intervals')
def cv2(time_intervals, with_nan=False):
r"""
Calculate the measure of Cv2 for a sequence of time intervals between
events.
Given a vector :math:`I` containing a sequence of intervals, the Cv2 is
defined as:
.. math::
Cv2 := \frac{1}{N} \sum_{i=1}^{N-1}
\frac{2|I_{i+1}-I_i|}
{|I_{i+1}+I_i|}
The Cv2 is typically computed as a substitute for the classical
coefficient of variation (Cv) for sequences of events which include some
(relatively slow) rate fluctuation. As with the Cv, Cv2=1 for a sequence
of intervals generated by a Poisson process.
Parameters
----------
time_intervals : pq.Quantity or np.ndarray or list
Vector of consecutive time intervals.
with_nan : bool, optional
If True, `cv2` of a spike train with less than two spikes results in a
np.NaN value and a warning is raised.
If False, `ValueError` exception is raised with a spike train with
less than two spikes.
Default: True.
Returns
-------
float
The Cv2 of the inter-spike interval of the input sequence.
Raises
------
ValueError
If an empty list is specified, or if the sequence has less than two
entries and `with_nan` is False.
If a matrix is passed to the function. Only vector inputs are
supported.
Warns
-----
UserWarning
If `with_nan` is True and `cv2` is calculated for a sequence with less
than two entries, generating a np.NaN.
References
----------
.. [1] G. R. Holt, W. R. Softky, C. Koch, & R. J. Douglas, "Comparison of
discharge variability in vitro and in vivo in cat visual cortex
neurons," Journal of Neurophysiology, vol. 75, no. 5, pp. 1806-1814,
1996.
"""
# convert to array, cast to float
time_intervals = np.asarray(time_intervals)
np_nan = __variation_check(time_intervals, with_nan)
if np_nan is not None:
return np_nan
# calculate Cv2 and return result
cv_i = np.diff(time_intervals) / (time_intervals[:-1] + time_intervals[1:])
return 2. * np.mean(np.abs(cv_i))
[docs]@deprecated_alias(v='time_intervals')
def lv(time_intervals, with_nan=False):
r"""
Calculate the measure of local variation Lv for a sequence of time
intervals between events.
Given a vector :math:`I` containing a sequence of intervals, the Lv is
defined as:
.. math::
Lv := \frac{1}{N} \sum_{i=1}^{N-1}
\frac{3(I_i-I_{i+1})^2}
{(I_i+I_{i+1})^2}
The Lv is typically computed as a substitute for the classical coefficient
of variation for sequences of events which include some (relatively slow)
rate fluctuation. As with the Cv, Lv=1 for a sequence of intervals
generated by a Poisson process.
Parameters
----------
time_intervals : pq.Quantity or np.ndarray or list
Vector of consecutive time intervals.
with_nan : bool, optional
If True, the Lv of a spike train with less than two spikes results in a
`np.NaN` value and a warning is raised.
If False, a `ValueError` exception is raised with a spike train with
less than two spikes.
Default: True.
Returns
-------
float
The Lv of the inter-spike interval of the input sequence.
Raises
------
ValueError
If an empty list is specified, or if the sequence has less than two
entries and `with_nan` is False.
If a matrix is passed to the function. Only vector inputs are
supported.
Warns
-----
UserWarning
If `with_nan` is True and the Lv is calculated for a spike train
with less than two spikes, generating a np.NaN.
References
----------
.. [1] S. Shinomoto, K. Shima, & J. Tanji, "Differences in spiking
patterns among cortical neurons," Neural Computation, vol. 15,
pp. 2823–2842, 2003.
"""
# convert to array, cast to float
time_intervals = np.asarray(time_intervals)
np_nan = __variation_check(time_intervals, with_nan)
if np_nan is not None:
return np_nan
cv_i = np.diff(time_intervals) / (time_intervals[:-1] + time_intervals[1:])
return 3. * np.mean(np.power(cv_i, 2))
[docs]def lvr(time_intervals, R=5*pq.ms, with_nan=False):
r"""
Calculate the measure of revised local variation LvR for a sequence of time
intervals between events.
Given a vector :math:`I` containing a sequence of intervals, the LvR is
defined as:
.. math::
LvR := \frac{3}{N-1} \sum_{i=1}^{N-1}
\left(1-\frac{4 I_i I_{i+1}}
{(I_i+I_{i+1})^2}\right)
\left(1+\frac{4 R}{I_i+I_{i+1}}\right)
The LvR is a revised version of the Lv, with enhanced invariance to firing
rate fluctuations by introducing a refractoriness constant R. The LvR with
`R=5ms` was shown to outperform other ISI variability measures in spike
trains with firing rate fluctuatins and sensory stimuli [1]_.
Parameters
----------
time_intervals : pq.Quantity or np.ndarray or list
Vector of consecutive time intervals.
R : pq.Quantity or int or float
Refractoriness constant (R >= 0). If no quantity is passed `ms` are
assumed.
Default: 5 ms.
with_nan : bool, optional
If True, LvR of a spike train with less than two spikes results in a
np.NaN value and a warning is raised.
If False, a `ValueError` exception is raised with a spike train with
less than two spikes.
Default: True.
Returns
-------
float
The LvR of the inter-spike interval of the input sequence.
Raises
------
ValueError
If an empty list is specified, or if the sequence has less than two
entries and `with_nan` is False.
If a matrix is passed to the function. Only vector inputs are
supported.
Warns
-----
UserWarning
If `with_nan` is True and the `lvr` is calculated for a spike train
with less than two spikes, generating a np.NaN.
If R is passed without any units attached milliseconds are assumed.
References
----------
.. [1] S. Shinomoto, H. Kim, T. Shimokawa et al. "Relating Neuronal Firing
Patterns to Functional Differentiation of Cerebral Cortex" PLOS
Computational Biology 5(7): e1000433, 2009.
"""
if isinstance(R, pq.Quantity):
R = R.rescale('ms').magnitude
else:
warnings.warn('No units specified for R, assuming milliseconds (ms)')
if R < 0:
raise ValueError('R must be >= 0')
# convert to array, cast to float
time_intervals = np.asarray(time_intervals)
np_nan = __variation_check(time_intervals, with_nan)
if np_nan is not None:
return np_nan
N = len(time_intervals)
t = time_intervals[:-1] + time_intervals[1:]
frac1 = 4 * time_intervals[:-1] * time_intervals[1:] / t**2
frac2 = 4 * R / t
lvr = (3 / (N-1)) * np.sum((1-frac1) * (1+frac2))
return lvr
[docs]@deprecated_alias(spiketrain='spiketrains')
def instantaneous_rate(spiketrains, sampling_period, kernel='auto',
cutoff=5.0, t_start=None, t_stop=None, trim=False,
center_kernel=True):
"""
Estimates instantaneous firing rate by kernel convolution.
Visualization of this function is covered in Viziphant:
:func:`viziphant.statistics.plot_instantaneous_rates_colormesh`.
Parameters
----------
spiketrains : neo.SpikeTrain or list of neo.SpikeTrain
Neo object(s) that contains spike times, the unit of the time stamps,
and `t_start` and `t_stop` of the spike train.
sampling_period : pq.Quantity
Time stamp resolution of the spike times. The same resolution will
be assumed for the kernel.
kernel : 'auto' or Kernel, optional
The string 'auto' or callable object of class `kernels.Kernel`.
The kernel is used for convolution with the spike train and its
standard deviation determines the time resolution of the instantaneous
rate estimation. Currently implemented kernel forms are rectangular,
triangular, epanechnikovlike, gaussian, laplacian, exponential, and
alpha function.
If 'auto', the optimized kernel width for the rate estimation is
calculated according to [1]_ and with this width a gaussian kernel is
constructed. Automatized calculation of the kernel width is not
available for other than gaussian kernel shapes.
Default: 'auto'.
cutoff : float, optional
This factor determines the cutoff of the probability distribution of
the kernel, i.e., the considered width of the kernel in terms of
multiples of the standard deviation sigma.
Default: 5.0.
t_start : pq.Quantity, optional
Start time of the interval used to compute the firing rate.
If None, `t_start` is assumed equal to `t_start` attribute of
`spiketrain`.
Default: None.
t_stop : pq.Quantity, optional
End time of the interval used to compute the firing rate (included).
If None, `t_stop` is assumed equal to `t_stop` attribute of
`spiketrain`.
Default: None.
trim : bool, optional
Accounts for the asymmetry of a kernel.
If False, the output of the Fast Fourier Transformation being a longer
vector than the input vector by the size of the kernel is reduced back
to the original size of the considered time interval of the
`spiketrain` using the median of the kernel. False (no trimming) is
equivalent to 'same' convolution mode for symmetrical kernels.
If True, only the region of the convolved signal is returned, where
there is complete overlap between kernel and spike train. This is
achieved by reducing the length of the output of the Fast Fourier
Transformation by a total of two times the size of the kernel, and
`t_start` and `t_stop` are adjusted. True (trimming) is equivalent to
'valid' convolution mode for symmetrical kernels.
Default: False.
center_kernel : bool, optional
If set to True, the kernel will be translated such that its median is
centered on the spike, thus putting equal weight before and after the
spike. If False, no adjustment is performed such that the spike sits at
the origin of the kernel.
Default: True
Returns
-------
rate : neo.AnalogSignal
2D matrix that contains the rate estimation in unit hertz (Hz) of shape
``(time, len(spiketrains))`` or ``(time, 1)`` in case of a single
input spiketrain. `rate.times` contains the time axis of the rate
estimate: the unit of this property is the same as the resolution that
is given via the argument `sampling_period` to the function.
Raises
------
TypeError
If `spiketrain` is not an instance of `neo.SpikeTrain`.
If `sampling_period` is not a `pq.Quantity`.
If `sampling_period` is not larger than zero.
If `kernel` is neither instance of `kernels.Kernel` nor string 'auto'.
If `cutoff` is neither `float` nor `int`.
If `t_start` and `t_stop` are neither None nor a `pq.Quantity`.
If `trim` is not `bool`.
ValueError
If `sampling_period` is smaller than zero.
If `kernel` is 'auto' and the function was unable to calculate optimal
kernel width for instantaneous rate from input data.
Warns
-----
UserWarning
If `cutoff` is less than `min_cutoff` attribute of `kernel`, the width
of the kernel is adjusted to a minimally allowed width.
Notes
-----
The resulting instantaneous firing rate values smaller than ``0``, which
can happen due to machine precision errors, are clipped to zero.
References
----------
.. [1] H. Shimazaki, & S. Shinomoto, "Kernel bandwidth optimization in
spike rate estimation," J Comput Neurosci, vol. 29, pp. 171–182,
2010.
Examples
--------
>>> import quantities as pq
>>> from elephant import kernels
>>> from elephant.spike_train_generation import homogeneous_poisson_process
>>> spiketrain = homogeneous_poisson_process(rate=10*pq.Hz, t_stop=5*pq.s)
>>> kernel = kernels.AlphaKernel(sigma=0.05*pq.s, invert=True)
>>> rate = instantaneous_rate(spiketrain, sampling_period=2*pq.ms,
... kernel=kernel)
"""
def optimal_kernel(st):
width_sigma = None
if len(st) > 0:
width_sigma = optimal_kernel_bandwidth(
st.magnitude, times=None, bootstrap=False)['optw']
if width_sigma is None:
raise ValueError("Unable to calculate optimal kernel width for "
"instantaneous rate from input data.")
return kernels.GaussianKernel(width_sigma * st.units)
if isinstance(spiketrains, neo.SpikeTrain):
if kernel == 'auto':
kernel = optimal_kernel(spiketrains)
spiketrains = [spiketrains]
elif not isinstance(spiketrains, (list, tuple)):
raise TypeError(
"'spiketrains' must be a list of neo.SpikeTrain's or a single "
"neo.SpikeTrain. Found: '{}'".format(type(spiketrains)))
if not is_time_quantity(sampling_period):
raise TypeError(
"The 'sampling_period' must be a time Quantity. \n"
"Found: {}".format(type(sampling_period)))
if sampling_period.magnitude < 0:
raise ValueError("The 'sampling_period' ({}) must be non-negative.".
format(sampling_period))
if not (isinstance(kernel, kernels.Kernel) or kernel == 'auto'):
raise TypeError(
"'kernel' must be either instance of class elephant.kernels.Kernel"
" or the string 'auto'. Found: %s, value %s" % (type(kernel),
str(kernel)))
if not isinstance(cutoff, (float, int)):
raise TypeError("'cutoff' must be float or integer")
if not is_time_quantity(t_start, allow_none=True):
raise TypeError("'t_start' must be a time Quantity")
if not is_time_quantity(t_stop, allow_none=True):
raise TypeError("'t_stop' must be a time Quantity")
if not isinstance(trim, bool):
raise TypeError("'trim' must be bool")
check_neo_consistency(spiketrains,
object_type=neo.SpikeTrain,
t_start=t_start, t_stop=t_stop)
if kernel == 'auto':
if len(spiketrains) == 1:
kernel = optimal_kernel(spiketrains[0])
else:
raise ValueError("Cannot estimate a kernel for a list of spike "
"trains. Please provide a kernel explicitly "
"rather than 'auto'.")
if t_start is None:
t_start = spiketrains[0].t_start
if t_stop is None:
t_stop = spiketrains[0].t_stop
units = pq.CompoundUnit(
"{}*s".format(sampling_period.rescale('s').item()))
t_start = t_start.rescale(spiketrains[0].units)
t_stop = t_stop.rescale(spiketrains[0].units)
n_bins = int(((t_stop - t_start) / sampling_period).simplified) + 1
time_vectors = np.zeros((len(spiketrains), n_bins), dtype=np.float64)
hist_range_end = t_stop + sampling_period.rescale(spiketrains[0].units)
hist_range = (t_start.item(), hist_range_end.item())
for i, st in enumerate(spiketrains):
time_vectors[i], _ = np.histogram(st.magnitude, bins=n_bins,
range=hist_range)
if cutoff < kernel.min_cutoff:
cutoff = kernel.min_cutoff
warnings.warn("The width of the kernel was adjusted to a minimally "
"allowed width.")
# An odd number of points correctly resolves the median index and the
# fact that the peak of an instantaneous rate should be centered at t=0
# for symmetric kernels applied on a single spike at t=0.
# See issue https://github.com/NeuralEnsemble/elephant/issues/360
n_half = math.ceil(cutoff * (
kernel.sigma / sampling_period).simplified.item())
cutoff_sigma = cutoff * kernel.sigma.rescale(units).magnitude
if center_kernel:
# t_arr must be centered at the kernel median.
# Not centering on the kernel median leads to underestimating the
# instantaneous rate in cases when sampling_period >> kernel.sigma.
median = kernel.icdf(0.5).rescale(units).item()
else:
median = 0
t_arr = np.linspace(-cutoff_sigma + median, stop=cutoff_sigma + median,
num=2 * n_half + 1, endpoint=True) * units
if center_kernel:
# keep the full convolve range and do the trimming afterwards;
# trimming is performed according to the kernel median index
fft_mode = 'full'
elif trim:
# no median index trimming is involved
fft_mode = 'valid'
else:
# no median index trimming is involved
fft_mode = 'same'
time_vectors = time_vectors.T # make it (time, units)
kernel_arr = np.expand_dims(kernel(t_arr).rescale(pq.Hz).magnitude, axis=1)
rate = scipy.signal.fftconvolve(time_vectors,
kernel_arr,
mode=fft_mode)
# the convolution of non-negative vectors is non-negative
rate = np.clip(rate, a_min=0, a_max=None, out=rate)
if center_kernel: # account for the kernel asymmetry
median_id = kernel.median_index(t_arr)
# the size of kernel() output matches the input size, len(t_arr)
kernel_array_size = len(t_arr)
if not trim:
rate = rate[median_id: -kernel_array_size + median_id]
else:
rate = rate[2 * median_id: -2 * (kernel_array_size - median_id)]
t_start = t_start + median_id * units
t_stop = t_stop - (kernel_array_size - median_id) * units
else:
# FIXME: don't shrink the output array
# (to be consistent with center_kernel=True)
# n points have n-1 intervals;
# instantaneous rate is a list of intervals;
# hence, the last element is excluded
rate = rate[:-1]
kernel_annotation = dict(type=type(kernel).__name__,
sigma=str(kernel.sigma),
invert=kernel.invert)
rate = neo.AnalogSignal(signal=rate,
sampling_period=sampling_period,
units=pq.Hz, t_start=t_start, t_stop=t_stop,
kernel=kernel_annotation)
return rate
[docs]@deprecated_alias(binsize='bin_size')
def time_histogram(spiketrains, bin_size, t_start=None, t_stop=None,
output='counts', binary=False):
"""
Time Histogram of a list of `neo.SpikeTrain` objects.
Visualization of this function is covered in Viziphant:
:func:`viziphant.statistics.plot_time_histogram`.
Parameters
----------
spiketrains : list of neo.SpikeTrain
`neo.SpikeTrain`s with a common time axis (same `t_start` and `t_stop`)
bin_size : pq.Quantity
Width of the histogram's time bins.
t_start : pq.Quantity, optional
Start time of the histogram. Only events in `spiketrains` falling
between `t_start` and `t_stop` (both included) are considered in the
histogram.
If None, the maximum `t_start` of all `neo.SpikeTrain`s is used as
`t_start`.
Default: None
t_stop : pq.Quantity, optional
Stop time of the histogram. Only events in `spiketrains` falling
between `t_start` and `t_stop` (both included) are considered in the
histogram.
If None, the minimum `t_stop` of all `neo.SpikeTrain`s is used as
`t_stop`.
Default: None
output : {'counts', 'mean', 'rate'}, optional
Normalization of the histogram. Can be one of:
* 'counts': spike counts at each bin (as integer numbers)
* 'mean': mean spike counts per spike train
* 'rate': mean spike rate per spike train. Like 'mean', but the
counts are additionally normalized by the bin width.
Default: 'counts'
binary : bool, optional
If True, indicates whether all `neo.SpikeTrain` objects should first
be binned to a binary representation (using the
`conversion.BinnedSpikeTrain` class) and the calculation of the
histogram is based on this representation.
Note that the output is not binary, but a histogram of the converted,
binary representation.
Default: False
Returns
-------
neo.AnalogSignal
A `neo.AnalogSignal` object containing the histogram values.
`neo.AnalogSignal[j]` is the histogram computed between
`t_start + j * bin_size` and `t_start + (j + 1) * bin_size`.
Raises
------
ValueError
If `output` is not 'counts', 'mean' or 'rate'.
Warns
-----
UserWarning
If `t_start` is None and the objects in `spiketrains` have different
`t_start` values.
If `t_stop` is None and the objects in `spiketrains` have different
`t_stop` values.
See also
--------
elephant.conversion.BinnedSpikeTrain
"""
# Bin the spike trains and sum across columns
bs = BinnedSpikeTrain(spiketrains, t_start=t_start, t_stop=t_stop,
bin_size=bin_size)
if binary:
bs = bs.binarize()
bin_hist = bs.get_num_of_spikes(axis=0)
# Flatten array
bin_hist = np.ravel(bin_hist)
# Renormalise the histogram
if output == 'counts':
# Raw
bin_hist = pq.Quantity(bin_hist, units=pq.dimensionless, copy=False)
elif output == 'mean':
# Divide by number of input spike trains
bin_hist = pq.Quantity(bin_hist / len(spiketrains),
units=pq.dimensionless, copy=False)
elif output == 'rate':
# Divide by number of input spike trains and bin width
bin_hist = bin_hist / (len(spiketrains) * bin_size)
else:
raise ValueError('Parameter output is not valid.')
return neo.AnalogSignal(signal=np.expand_dims(bin_hist, axis=1),
sampling_period=bin_size, units=bin_hist.units,
t_start=bs.t_start, normalization=output,
copy=False)
[docs]@deprecated_alias(binsize='bin_size')
def complexity_pdf(spiketrains, bin_size):
"""
Complexity Distribution of a list of `neo.SpikeTrain` objects.
Probability density computed from the complexity histogram which is the
histogram of the entries of the population histogram of clipped (binary)
spike trains computed with a bin width of `bin_size`.
It provides for each complexity (== number of active neurons per bin) the
number of occurrences. The normalization of that histogram to 1 is the
probability density.
Implementation is based on [1]_.
Parameters
----------
spiketrains : list of neo.SpikeTrain
Spike trains with a common time axis (same `t_start` and `t_stop`)
bin_size : pq.Quantity
Width of the histogram's time bins.
Returns
-------
complexity_distribution : neo.AnalogSignal
A `neo.AnalogSignal` object containing the histogram values.
`neo.AnalogSignal[j]` is the histogram computed between
`t_start + j * bin_size` and `t_start + (j + 1) * bin_size`.
See also
--------
elephant.conversion.BinnedSpikeTrain
References
----------
.. [1] S. Gruen, M. Abeles, & M. Diesmann, "Impact of higher-order
correlations on coincidence distributions of massively parallel
data," In "Dynamic Brain - from Neural Spikes to Behaviors",
pp. 96-114, Springer Berlin Heidelberg, 2008.
"""
# Computing the population histogram with parameter binary=True to clip the
# spike trains before summing
pophist = time_histogram(spiketrains, bin_size, binary=True)
# Computing the histogram of the entries of pophist (=Complexity histogram)
complexity_hist = np.histogram(
pophist.magnitude, bins=range(0, len(spiketrains) + 2))[0]
# Normalization of the Complexity Histogram to 1 (probabilty distribution)
complexity_hist = complexity_hist / complexity_hist.sum()
# Convert the Complexity pdf to an neo.AnalogSignal
complexity_distribution = neo.AnalogSignal(
np.expand_dims(complexity_hist, axis=1) *
pq.dimensionless, t_start=0 * pq.dimensionless,
sampling_period=1 * pq.dimensionless)
return complexity_distribution
"""
Kernel Bandwidth Optimization.
Python implementation by Subhasis Ray.
Original matlab code (sskernel.m) here:
http://2000.jukuin.keio.ac.jp/shimazaki/res/kernel.html
This was translated into Python by Subhasis Ray, NCBS. Tue Jun 10
23:01:43 IST 2014
"""
def nextpow2(x):
"""
Return the smallest integral power of 2 that is equal or larger than `x`.
"""
# PYTHON2: math.log2 does not exist
log2_n = int(math.ceil(math.log(x, 2)))
n = 2 ** log2_n
return n
def fftkernel(x, w):
"""
Applies the Gauss kernel smoother to an input signal using FFT algorithm.
Parameters
----------
x : np.ndarray
Vector with sample signal.
w : float
Kernel bandwidth (the standard deviation) in unit of the sampling
resolution of `x`.
Returns
-------
y : np.ndarray
The smoothed signal.
Notes
-----
1. MAY 5/23, 2012 Author Hideaki Shimazaki
RIKEN Brain Science Insitute
http://2000.jukuin.keio.ac.jp/shimazaki
2. Ported to Python: Subhasis Ray, NCBS. Tue Jun 10 10:42:38 IST 2014
"""
L = len(x)
Lmax = L + 3 * w
n = nextpow2(Lmax)
X = np.fft.fft(x, n)
f = np.arange(0, n, 1.0) / n
f = np.concatenate((-f[:int(n / 2)], f[int(n / 2):0:-1]))
K = np.exp(-0.5 * (w * 2 * np.pi * f) ** 2)
y = np.fft.ifft(X * K, n)
y = y[:L].copy()
return y
def logexp(x):
if x < 1e2:
y = np.log(1 + np.exp(x))
else:
y = x
return y
def ilogexp(x):
if x < 1e2:
y = np.log(np.exp(x) - 1)
else:
y = x
return y
def cost_function(x, N, w, dt):
"""
Computes the cost function for `sskernel`.
Cn(w) = sum_{i,j} int k(x - x_i) k(x - x_j) dx - 2 sum_{i~=j} k(x_i - x_j)
"""
yh = np.abs(fftkernel(x, w / dt)) # density
# formula for density
C = np.sum(yh ** 2) * dt - 2 * np.sum(yh * x) * \
dt + 2 / np.sqrt(2 * np.pi) / w / N
C = C * N * N
# formula for rate
# C = dt*sum( yh.^2 - 2*yh.*y_hist + 2/sqrt(2*pi)/w*y_hist )
return C, yh
[docs]@deprecated_alias(tin='times', w='bandwidth')
def optimal_kernel_bandwidth(spiketimes, times=None, bandwidth=None,
bootstrap=False):
"""
Calculates optimal fixed kernel bandwidth, given as the standard deviation
sigma.
Parameters
----------
spiketimes : np.ndarray
Sequence of spike times (sorted to be ascending).
times : np.ndarray, optional
Time points at which the kernel bandwidth is to be estimated.
If None, `spiketimes` is used.
Default: None.
bandwidth : np.ndarray, optional
Vector of kernel bandwidths (standard deviation sigma).
If specified, optimal bandwidth is selected from this.
If None, `bandwidth` is obtained through a golden-section search on a
log-exp scale.
Default: None.
bootstrap : bool, optional
If True, calculates the 95% confidence interval using Bootstrap.
Default: False.
Returns
-------
dict
'y' : np.ndarray
Estimated density.
't' : np.ndarray
Points at which estimation was computed.
'optw' : float
Optimal kernel bandwidth given as standard deviation sigma
'w' : np.ndarray
Kernel bandwidths examined (standard deviation sigma).
'C' : np.ndarray
Cost functions of `bandwidth`.
'confb95' : tuple of np.ndarray
Bootstrap 95% confidence interval: (lower level, upper level).
If `bootstrap` is False, `confb95` is None.
'yb' : np.ndarray
Bootstrap samples.
If `bootstrap` is False, `yb` is None.
If no optimal kernel could be found, all entries of the dictionary are
set to None.
References
----------
.. [1] H. Shimazaki, & S. Shinomoto, "Kernel bandwidth optimization in
spike rate estimation," Journal of Computational Neuroscience,
vol. 29, no. 1-2, pp. 171-82, 2010. doi:10.1007/s10827-009-0180-4.
"""
if times is None:
time = np.max(spiketimes) - np.min(spiketimes)
isi = np.diff(spiketimes)
isi = isi[isi > 0].copy()
dt = np.min(isi)
times = np.linspace(np.min(spiketimes),
np.max(spiketimes),
min(int(time / dt + 0.5),
1000)) # The 1000 seems somewhat arbitrary
t = times
else:
time = np.max(times) - np.min(times)
spiketimes = spiketimes[(spiketimes >= np.min(times)) &
(spiketimes <= np.max(times))].copy()
isi = np.diff(spiketimes)
isi = isi[isi > 0].copy()
dt = np.min(isi)
if dt > np.min(np.diff(times)):
t = np.linspace(np.min(times), np.max(times),
min(int(time / dt + 0.5), 1000))
else:
t = times
dt = np.min(np.diff(times))
yhist, bins = np.histogram(spiketimes, np.r_[t - dt / 2, t[-1] + dt / 2])
N = np.sum(yhist)
yhist = yhist / (N * dt) # density
optw = None
y = None
if bandwidth is not None:
C = np.zeros(len(bandwidth))
Cmin = np.inf
for k, w_ in enumerate(bandwidth):
C[k], yh = cost_function(yhist, N, w_, dt)
if C[k] < Cmin:
Cmin = C[k]
optw = w_
y = yh
else:
# Golden section search on a log-exp scale
wmin = 2 * dt
wmax = max(spiketimes) - min(spiketimes)
imax = 20 # max iterations
bandwidth = np.zeros(imax)
C = np.zeros(imax)
tolerance = 1e-5
phi = 0.5 * (np.sqrt(5) + 1) # The Golden ratio
a = ilogexp(wmin)
b = ilogexp(wmax)
c1 = (phi - 1) * a + (2 - phi) * b
c2 = (2 - phi) * a + (phi - 1) * b
f1, y1 = cost_function(yhist, N, logexp(c1), dt)
f2, y2 = cost_function(yhist, N, logexp(c2), dt)
k = 0
while (np.abs(b - a) > (tolerance * (np.abs(c1) + np.abs(c2)))) \
and (k < imax):
if f1 < f2:
b = c2
c2 = c1
c1 = (phi - 1) * a + (2 - phi) * b
f2 = f1
f1, y1 = cost_function(yhist, N, logexp(c1), dt)
bandwidth[k] = logexp(c1)
C[k] = f1
optw = logexp(c1)
y = y1 / (np.sum(y1 * dt))
else:
a = c1
c1 = c2
c2 = (2 - phi) * a + (phi - 1) * b
f1 = f2
f2, y2 = cost_function(yhist, N, logexp(c2), dt)
bandwidth[k] = logexp(c2)
C[k] = f2
optw = logexp(c2)
y = y2 / np.sum(y2 * dt)
k = k + 1
# Bootstrap confidence intervals
confb95 = None
yb = None
# If bootstrap is requested, and an optimal kernel was found
if bootstrap and optw:
nbs = 1000
yb = np.zeros((nbs, len(times)))
for ii in range(nbs):
idx = np.floor(np.random.rand(N) * N).astype(int)
xb = spiketimes[idx]
y_histb, bins = np.histogram(
xb, np.r_[t - dt / 2, t[-1] + dt / 2]) / dt / N
yb_buf = fftkernel(y_histb, optw / dt).real
yb_buf = yb_buf / np.sum(yb_buf * dt)
yb[ii, :] = np.interp(times, t, yb_buf)
ybsort = np.sort(yb, axis=0)
y95b = ybsort[np.floor(0.05 * nbs).astype(int), :]
y95u = ybsort[np.floor(0.95 * nbs).astype(int), :]
confb95 = (y95b, y95u)
# Only perform interpolation if y could be calculated
if y is not None:
y = np.interp(times, t, y)
return {'y': y,
't': times,
'optw': optw,
'w': bandwidth,
'C': C,
'confb95': confb95,
'yb': yb}
def sskernel(*args, **kwargs):
warnings.warn("'sskernel' function is deprecated; "
"use 'optimal_kernel_bandwidth'", DeprecationWarning)
return optimal_kernel_bandwidth(*args, **kwargs)
