# -*- coding: utf-8 -*-
"""
Statistical measures of spike trains (e.g., Fano factor) and functions to
estimate firing rates.
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
Complexity
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
References
----------
.. bibliography::
:keyprefix: statistics-
:copyright: Copyright 2014-2024 by the Elephant team, see `doc/authors.rst`.
:license: Modified BSD, see LICENSE.txt for details.
"""
from __future__ import division, print_function
import math
import warnings
import neo
import numpy as np
import quantities as pq
import scipy.stats
import scipy.signal
from numpy import ndarray
from scipy.special import erf
from typing import Union
import elephant.conversion as conv
import elephant.kernels as kernels
import elephant.trials
from elephant.conversion import BinnedSpikeTrain
from elephant.utils import deprecated_alias, check_neo_consistency, \
is_time_quantity, round_binning_errors
# do not import unicode_literals
# (quantities rescale does not work with unicodes)
__all__ = [
"isi",
"mean_firing_rate",
"fanofactor",
"cv",
"cv2",
"lv",
"lvr",
"instantaneous_rate",
"time_histogram",
"complexity_pdf",
"Complexity",
"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.
Examples
--------
>>> from elephant import statistics
>>> statistics.isi([0.3, 4.5, 6.7, 9.3])
array([4.2, 2.2, 2.6])
"""
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.
Examples
--------
>>> from elephant import statistics
>>> statistics.mean_firing_rate([0.3, 4.5, 6.7, 9.3])
0.4301075268817204
"""
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)
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
else:
raise TypeError("Invalid input spiketrain type: '{}'. Allowed: "
"neo.SpikeTrain, Quantity, ndarray".
format(type(spiketrain)))
return rates
[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.
Examples
--------
>>> import neo
>>> from elephant import statistics
>>> spiketrains = [
... neo.SpikeTrain([0.3, 4.5, 6.7, 9.3], t_stop=10, units='s'),
... neo.SpikeTrain([1.4, 3.3, 8.2], t_stop=10, units='s')
... ]
>>> statistics.fanofactor(spiketrains)
0.07142857142857142
"""
# 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
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 :cite:`statistics-Holt1996_1806`.
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.
Examples
--------
>>> from elephant import statistics
>>> statistics.cv2([0.3, 4.5, 6.7, 9.3])
0.8226190476190478
"""
# 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 :cite:`statistics-Shinomoto2003_2823`.
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.
Examples
--------
>>> from elephant import statistics
>>> statistics.lv([0.3, 4.5, 6.7, 9.3])
0.8306154336734695
"""
# 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 :cite:`statistics-Shinomoto2009_e1000433`.
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 fluctuations and sensory stimuli
:cite:`statistics-Shinomoto2009_e1000433`.
Parameters
----------
time_intervals : pq.Quantity or np.ndarray or list
Vector of consecutive time intervals. Must have time units, if not unit
is passed `ms` are assumed.
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.
Examples
--------
>>> from elephant import statistics
>>> statistics.lvr([0.3, 4.5, 6.7, 9.3], R=0.005)
0.833907445980624
"""
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')
# check units of intervals if available
if isinstance(time_intervals, pq.Quantity):
time_intervals = time_intervals.rescale('ms').magnitude
else:
warnings.warn('No units specified for time_intervals,'
' assuming milliseconds (ms)')
# 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, border_correction=False,
pool_trials=False, pool_spike_trains=False):
r"""
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, list of neo.SpikeTrain or elephant.trials.Trials # noqa
Input spike train(s) for which the instantaneous firing rate is
calculated. If a list of spike trains is supplied, the parameter
pool_spike_trains determines the behavior of the function. If a Trials
object is supplied, the behavior is determined by the parameters
pool_spike_trains (within a trial) and pool_trials (across trials).
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 :cite:`statistics-Shimazaki2010_171` and a
Gaussian kernel is constructed with this width. Automatized calculation
of the kernel width is not available for other than Gaussian kernel
shapes.
Note: The kernel width is not adaptive, i.e., it is calculated as
global optimum across the data.
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.
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 (ouput = input + kernel - 1) 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
border_correction : bool, optional
Apply a border correction to prevent underestimating the firing rates
at the borders of the spike trains, i.e., close to t_start and t_stop.
The correction is done by estimating the mass of the kernel outside
these spike train borders under the assumption that the rate does not
change strongly.
Only possible in the case of a Gaussian kernel.
Default: False
pool_trials: bool, optional
If true, calculate firing rates averaged over trials if spiketrains is
of type elephant.trials.Trials
Has no effect for single spike train or lists of spike trains.
Default: False
pool_spike_trains: bool, optional
If true, calculate firing rates averaged over spike trains. If the
input is a Trials object, spike trains are pooled across spike trains
within each trial, and pool_trials determines whether spike trains are
additionally pooled across trials.
Has no effect for a single spike train.
Default: False
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
may happen due to machine precision errors, are clipped to zero.
* The instantaneous firing rate estimate is calculated based on half-open
intervals ``[)``, except the last one e.g. if ``t_start = 0s``,
``t_stop = 4s`` and ``sampling_period = 1s``, the intervals are:
``[0, 1)`` ``[1, 2)`` ``[2, 3)`` ``[3, 4]``.
This induces a sampling bias, which can lead to a time shift of the
estimated rate, if the `sampling_period` is chosen large relative to the
duration ``(t_stop - t_start)``. One possibility to counteract this is
to choose a smaller `sampling_period`.
* The last interval of the given duration ``(t_stop - t_start)`` is
dropped if it is shorter than `sampling_period`,
e.g. if ``t_start = 0s``, ``t_stop = 4.5s`` and
``sampling_period = 1s``, the intervals considered are:
``[0, 1)`` ``[1, 2)`` ``[2, 3)`` ``[3, 4]``,
the last interval ``[4, 4.5]`` is excluded from all calculations.
Examples
--------
Example 1. Automatic kernel estimation.
>>> import neo
>>> import quantities as pq
>>> from elephant import statistics
>>> spiketrain = neo.SpikeTrain([0.3, 4.5, 6.7, 9.3], t_stop=10, units='s')
>>> rate = statistics.instantaneous_rate(spiketrain,
... sampling_period=10 * pq.ms,
... kernel='auto')
>>> rate.annotations['kernel']
{'type': 'GaussianKernel', 'sigma': '7.273225922958104 s', 'invert': False}
>>> print(rate.sampling_rate)
0.1 1/ms
Example 2. Manually set kernel.
>>> from elephant import kernels
>>> spiketrain = neo.SpikeTrain([0], t_stop=1, units='s')
>>> kernel = kernels.GaussianKernel(sigma=300 * pq.ms)
>>> rate = statistics.instantaneous_rate(spiketrain,
... sampling_period=200 * pq.ms, kernel=kernel, t_start=-1 * pq.s)
>>> rate
<AnalogSignal(array([[0.01007419],
[0.05842767],
[0.22928759],
[0.60883028],
[1.0938699 ],
[1.3298076 ],
[1.0938699 ],
[0.60883028],
[0.22928759],
[0.05842767]]) * Hz, [-1.0 s, 1.0 s], sampling rate: 0.005 1/ms)>
>>> rate.magnitude
array([[0.01007419],
[0.05842767],
[0.22928759],
[0.60883028],
[1.0938699 ],
[1.3298076 ],
[1.0938699 ],
[0.60883028],
[0.22928759],
[0.05842767]])
"""
if isinstance(spiketrains, elephant.trials.Trials):
kwargs = {
'kernel': kernel,
'cutoff': cutoff,
't_start': t_start,
't_stop': t_stop,
'trim': trim,
'center_kernel': center_kernel,
'border_correction': border_correction,
'pool_trials': False,
'pool_spike_trains': False,
}
if pool_trials:
list_of_lists_of_spiketrains = [
spiketrains.get_spiketrains_from_trial_as_list(
trial_id=trial_no)
for trial_no in range(spiketrains.n_trials)]
spiketrains_cross_trials = (
[list_of_lists_of_spiketrains[trial_no][spiketrain_idx]
for trial_no in range(spiketrains.n_trials)]
for spiketrain_idx, spiketrain in
enumerate(list_of_lists_of_spiketrains[0]))
rates_cross_trials = [instantaneous_rate(spiketrain,
sampling_period,
**kwargs)
for spiketrain in spiketrains_cross_trials]
average_rate_cross_trials = (
np.mean(rates, axis=1) for rates in rates_cross_trials)
if pool_spike_trains:
average_rate = np.mean(list(average_rate_cross_trials), axis=0)
analog_signal = rates_cross_trials[0]
return (neo.AnalogSignal(
signal=average_rate,
sampling_period=analog_signal.sampling_period,
units=analog_signal.units,
t_start=analog_signal.t_start,
t_stop=analog_signal.t_stop,
kernel=analog_signal.annotations)
)
list_of_average_rates_cross_trial = neo.AnalogSignal(
signal=np.array(list(average_rate_cross_trials)).transpose(),
sampling_period=rates_cross_trials[0].sampling_period,
units=rates_cross_trials[0].units,
t_start=rates_cross_trials[0].t_start,
t_stop=rates_cross_trials[0].t_stop,
kernel=rates_cross_trials[0].annotations)
return list_of_average_rates_cross_trial
if not pool_trials and not pool_spike_trains:
return [instantaneous_rate(
spiketrains.get_spiketrains_from_trial_as_list(
trial_id=trial_no), sampling_period, **kwargs)
for trial_no in range(spiketrains.n_trials)]
if not pool_trials and pool_spike_trains:
rates = [instantaneous_rate(
spiketrains.get_spiketrains_from_trial_as_list(
trial_id=trial_no), sampling_period, **kwargs)
for trial_no in range(spiketrains.n_trials)]
average_rates = (np.mean(rate, axis=1) for rate in rates)
list_of_average_rates_over_spiketrains = [
neo.AnalogSignal(signal=average_rate,
sampling_period=analog_signal.sampling_period,
units=analog_signal.units,
t_start=analog_signal.t_start,
t_stop=analog_signal.t_stop,
kernel=analog_signal.annotations)
for average_rate, analog_signal in zip(average_rates, rates)]
return list_of_average_rates_over_spiketrains
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 border_correction and not \
(kernel == 'auto' or isinstance(kernel, kernels.GaussianKernel)):
raise ValueError(
'The border correction is only implemented'
' for Gaussian kernels.')
if isinstance(spiketrains, neo.SpikeTrain):
if kernel == 'auto':
kernel = optimal_kernel(spiketrains)
spiketrains = [spiketrains]
if not all([isinstance(elem, neo.SpikeTrain) for elem in spiketrains]):
raise TypeError(f"'spiketrains' must be a list of neo.SpikeTrain's or "
f"a single neo.SpikeTrain. Found: {type(spiketrains)}")
if not is_time_quantity(sampling_period):
raise TypeError(f"The 'sampling_period' must be a time Quantity."
f"Found: {type(sampling_period)}")
if sampling_period.magnitude < 0:
raise ValueError(f"The 'sampling_period' ({sampling_period}) "
f"must be non-negative.")
if not (isinstance(kernel, kernels.Kernel) or kernel == 'auto'):
raise TypeError(f"'kernel' must be instance of class "
f"elephant.kernels.Kernel or string 'auto'. Found: "
f"{type(kernel)}, value {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
# Rescale units for consistent calculation
t_start = t_start.rescale(spiketrains[0].units)
t_stop = t_stop.rescale(spiketrains[0].units)
# Calculate parameters for np.histogram
n_bins = int(((t_stop - t_start) / sampling_period).simplified)
hist_range_end = t_start + n_bins * \
sampling_period.rescale(spiketrains[0].units)
hist_range = (t_start.item(), hist_range_end.item())
# Preallocation
histogram_arr = np.zeros((len(spiketrains), n_bins), dtype=np.float64)
for i, st in enumerate(spiketrains):
histogram_arr[i], _ = np.histogram(st.magnitude, bins=n_bins,
range=hist_range)
histogram_arr = histogram_arr.T # make it (time, units)
# Kernel
if cutoff < kernel.min_cutoff:
cutoff = kernel.min_cutoff
warnings.warn("The width of the kernel was adjusted to a minimally "
"allowed width.")
scaling_unit = pq.CompoundUnit(f"{sampling_period.rescale('s').item()}*s")
cutoff_sigma = cutoff * kernel.sigma.rescale(scaling_unit).magnitude
if center_kernel: # t_arr is centered on the kernel median.
median = kernel.icdf(0.5).rescale(scaling_unit).item()
else:
median = 0
# An odd number of points correctly resolves the median index of the
# kernel. This avoids a timeshift in the rate estimate for symmetric
# kernels. A number x given by 'x = 2 * n + 1' with n being an integer is
# always odd. Using `math.ceil` to calculate `t_arr_kernel_half` ensures an
# integer value, hence the number of points for the kernel (num) given by
# `num=2 * t_arr_kernel_half + 1` is always odd.
# (See Issue #360, https://github.com/NeuralEnsemble/elephant/issues/360)
t_arr_kernel_half = math.ceil(
cutoff * (kernel.sigma / sampling_period).simplified.item())
t_arr_kernel_length = 2 * t_arr_kernel_half + 1
# Shift kernel using the calculated median
t_arr_kernel = np.linspace(start=-cutoff_sigma + median,
stop=cutoff_sigma + median,
num=t_arr_kernel_length,
endpoint=True) * scaling_unit
# Calculate the kernel values with t_arr
kernel_arr = np.expand_dims(
kernel(t_arr_kernel).rescale(pq.Hz).magnitude, axis=1)
# Define mode for scipy.signal.fftconvolve
if trim:
fft_mode = 'valid'
else:
fft_mode = 'same'
rate = scipy.signal.fftconvolve(histogram_arr,
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)
# Adjust t_start and t_stop
if fft_mode == 'valid':
median_id = kernel.median_index(t_arr_kernel)
kernel_array_size = len(kernel_arr)
t_start = t_start + median_id * scaling_unit
t_stop = t_stop - (kernel_array_size - median_id) * scaling_unit
kernel_annotation = dict(type=type(kernel).__name__,
sigma=str(kernel.sigma),
invert=kernel.invert)
if isinstance(spiketrains, neo.core.spiketrainlist.SpikeTrainList) and (
pool_spike_trains):
rate = np.mean(rate, axis=1)
rate = neo.AnalogSignal(signal=rate,
sampling_period=sampling_period,
units=pq.Hz, t_start=t_start, t_stop=t_stop,
kernel=kernel_annotation)
if border_correction:
sigma = kernel.sigma.simplified.magnitude
times = rate.times.simplified.magnitude
correction_factor = 2 / (
erf((t_stop.simplified.magnitude - times) / (
np.sqrt(2.) * sigma))
- erf((t_start.simplified.magnitude - times) / (
np.sqrt(2.) * sigma)))
rate *= correction_factor[:, None]
duration = t_stop.simplified.magnitude - t_start.simplified.magnitude
# ensure integral over firing rate yield the exact number of spikes
for i, spiketrain in enumerate(spiketrains):
if len(spiketrain) > 0:
rate[:, i] *= len(spiketrain) /\
(np.mean(rate[:, i]).magnitude * duration)
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
Examples
--------
>>> import neo
>>> import quantities as pq
>>> from elephant import statistics
>>> spiketrains = [
... neo.SpikeTrain([0.3, 4.5, 6.7, 9.3], t_stop=10, units='s'),
... neo.SpikeTrain([0.7, 4.3, 8.2], t_stop=10, units='s')
... ]
>>> hist = statistics.time_histogram(spiketrains,
... bin_size=1 * pq.s)
>>> hist
<AnalogSignal(array([[2],
[0],
[0],
[0],
[2],
[0],
[1],
[0],
[1],
[1]]) * dimensionless, [0.0 s, 10.0 s], sampling rate: 1.0 1/s)>
>>> hist.magnitude.flatten()
array([2, 0, 0, 0, 2, 0, 1, 0, 1, 1])
"""
# Bin the spike trains and sum across columns
if binary:
binned_spiketrain = BinnedSpikeTrain(spiketrains,
t_start=t_start,
t_stop=t_stop, bin_size=bin_size
).binarize(copy=False)
else:
binned_spiketrain = BinnedSpikeTrain(spiketrains,
t_start=t_start,
t_stop=t_stop, bin_size=bin_size
)
bin_hist: Union[int, ndarray] = binned_spiketrain.get_num_of_spikes(axis=0)
# Flatten array
bin_hist.ravel()
# Re-normalise the histogram according to desired output
def _counts() -> pq.Quantity:
# 'counts': spike counts at each bin (as integer numbers).
return pq.Quantity(bin_hist, units=pq.dimensionless, copy=False)
def _mean() -> pq.Quantity:
# 'mean': mean spike counts per spike train.
return pq.Quantity(bin_hist / len(spiketrains),
units=pq.dimensionless, copy=False)
def _rate() -> pq.Quantity:
# 'rate': mean spike rate per spike train. Like 'mean', but the
# counts are additionally normalized by the bin width.
return bin_hist / (len(spiketrains) * bin_size)
output_mapping = {"counts": _counts, "mean": _mean, "rate": _rate}
try:
normalise_func = output_mapping.get(output)
normalised_bin_hist = normalise_func()
except TypeError:
raise ValueError(f'Parameter output ({output}) is not valid.')
return neo.AnalogSignal(signal=np.expand_dims(normalised_bin_hist, axis=1),
sampling_period=bin_size,
units=normalised_bin_hist.units,
t_start=binned_spiketrain.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
:cite:`statistics-Gruen2007_96`.
Deprecated in favor of :meth:`Complexity.pdf`.
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.
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
"""
warnings.warn("'complexity_pdf' is deprecated in favor of the Complexity "
"class which has a 'pdf' method", DeprecationWarning)
complexity = Complexity(spiketrains, bin_size=bin_size)
return complexity.pdf()
[docs]
class Complexity(object):
"""
Class for complexity distribution (i.e. number of synchronous spikes found)
:cite:`statistics-Gruen2007_96` of a list of `neo.SpikeTrain` objects.
Complexity is calculated by counting the number of spikes (i.e. non-empty
bins) that occur separated by `spread - 1` or less empty bins, within and
across spike trains in the `spiketrains` list.
Implementation (without spread) is based on the cited above paper.
Parameters
----------
spiketrains : list of neo.SpikeTrain
Spike trains with a common time axis (same `t_start` and `t_stop`)
sampling_rate : pq.Quantity or None, optional
Sampling rate of the spike trains with units of 1/time.
Used to shift the epoch edges in order to avoid rounding errors.
If None using the epoch to slice spike trains may introduce
rounding errors.
Default: None
bin_size : pq.Quantity or None, optional
Width of the histogram's time bins with units of time.
The user must specify the `bin_size` or the `sampling_rate`.
* If None and the `sampling_rate` is available
1/`sampling_rate` is used.
* If both are given then `bin_size` is used.
Default: None
binary : bool, optional
* If True then the time histograms will only count the number of
neurons which spike in each bin.
* If False the total number of spikes per bin is counted in the
time histogram.
Default: True
spread : int, optional
Number of bins in which to check for synchronous spikes.
Spikes that occur separated by `spread - 1` or less empty bins are
considered synchronous.
* ``spread = 0`` corresponds to a bincount accross spike trains.
* ``spread = 1`` corresponds to counting consecutive spikes.
* ``spread = 2`` corresponds to counting consecutive spikes and
spikes separated by exactly 1 empty bin.
* ``spread = n`` corresponds to counting spikes separated by exactly
or less than `n - 1` empty bins.
Default: 0
tolerance : float or None, optional
Tolerance for rounding errors in the binning process and in the input
data.
If None possible binning errors are not accounted for.
Default: 1e-8
Attributes
----------
epoch : neo.Epoch
An epoch object containing complexity values, left edges and durations
of all intervals with at least one spike.
* ``epoch.array_annotations['complexity']`` contains the
complexity values per spike.
* ``epoch.times`` contains the left edges.
* ``epoch.durations`` contains the durations.
time_histogram : neo.Analogsignal
A `neo.AnalogSignal` object containing the histogram values.
`neo.AnalogSignal[j]` is the histogram computed between
`t_start + j * binsize` and `t_start + (j + 1) * binsize`.
* If ``binary = True`` : Number of neurons that spiked in each bin,
regardless of the number of spikes.
* If ``binary = False`` : Number of neurons and spikes per neurons
in each bin.
complexity_histogram : np.ndarray
The number of occurrences of events of different complexities.
`complexity_hist[i]` corresponds to the number of events of
complexity `i` for `i > 0`.
Raises
------
ValueError
When `t_stop` is smaller than `t_start`.
When both `sampling_rate` and `bin_size` are not specified.
When `spread` is not a positive integer.
When `spiketrains` is an empty list.
When `t_start` is not the same for all spiketrains
When `t_stop` is not the same for all spiketrains
TypeError
When `spiketrains` is not a list.
When the elements in `spiketrains` are not instances of neo.SpikeTrain
Warns
-----
UserWarning
If no sampling rate is supplied which may lead to rounding errors
when using the epoch to slice spike trains.
Notes
-----
Note that with most common parameter combinations spike times can end up
on bin edges. This makes the binning susceptible to rounding errors which
is accounted for by moving spikes which are within tolerance of the next
bin edge into the following bin. This can be adjusted using the tolerance
parameter and turned off by setting `tolerance=None`.
See also
--------
elephant.conversion.BinnedSpikeTrain
elephant.spike_train_synchrony.Synchrotool
Examples
--------
>>> import neo
>>> import quantities as pq
>>> from elephant.statistics import Complexity
>>> sampling_rate = 1/pq.ms
>>> st1 = neo.SpikeTrain([1, 4, 6] * pq.ms, t_stop=10.0 * pq.ms)
>>> st2 = neo.SpikeTrain([1, 5, 8] * pq.ms, t_stop=10.0 * pq.ms)
>>> sts = [st1, st2]
>>> # spread = 0, a simple bincount
>>> cpx = Complexity(sts, sampling_rate=sampling_rate)
Complexity calculated at sampling rate precision
>>> print(cpx.complexity_histogram)
[5 4 1]
>>> print(cpx.time_histogram.flatten())
[0 2 0 0 1 1 1 0 1 0] dimensionless
>>> print(cpx.time_histogram.times)
[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.] ms
>>> # spread = 1, consecutive spikes
>>> cpx = Complexity(sts, sampling_rate=sampling_rate, spread=1)
Complexity calculated at sampling rate precision
>>> print(cpx.complexity_histogram) # doctest: +SKIP
[5 4 1]
>>> print(cpx.time_histogram.flatten())
[0 2 0 0 3 3 3 0 1 0] dimensionless
>>> # spread = 2, consecutive spikes and separated by 1 empty bin
>>> cpx = Complexity(sts, sampling_rate=sampling_rate, spread=2)
Complexity calculated at sampling rate precision
>>> print(cpx.complexity_histogram)
[4 0 1 0 1]
>>> print(cpx.time_histogram.flatten())
[0 2 0 0 4 4 4 4 4 0] dimensionless
>>> pdf1 = cpx.pdf()
>>> pdf1 # noqa
<AnalogSignal(array([[0.66666667],
[0. ],
[0.16666667],
[0. ],
[0.16666667]]) * dimensionless, [0.0 dimensionless, 5.0 dimensionless], sampling rate: 1.0 dimensionless)>
>>> pdf1.magnitude # doctest: +SKIP
array([[0.5],
[0.4],
[0.1]])
"""
[docs]
def __init__(self, spiketrains,
sampling_rate=None,
bin_size=None,
binary=True,
spread=0,
tolerance=1e-8):
check_neo_consistency(spiketrains, object_type=neo.SpikeTrain)
if bin_size is None and sampling_rate is None:
raise ValueError('No bin_size or sampling_rate was specified!')
if spread < 0:
raise ValueError('Spread must be >=0')
self.input_spiketrains = spiketrains
self.t_start = spiketrains[0].t_start
self.t_stop = spiketrains[0].t_stop
self.sampling_rate = sampling_rate
self.bin_size = bin_size
self.binary = binary
self.spread = spread
self.tolerance = tolerance
if bin_size is None and sampling_rate is not None:
self.bin_size = 1 / self.sampling_rate
if spread == 0:
self.time_histogram, self.complexity_histogram = \
self._histogram_no_spread()
self.epoch = self._epoch_no_spread()
else:
self.epoch = self._epoch_with_spread()
self.time_histogram, self.complexity_histogram = \
self._histogram_with_spread()
def pdf(self):
"""
Probability density computed from the complexity histogram.
Returns
-------
pdf : neo.AnalogSignal
A `neo.AnalogSignal` object containing the pdf values.
`neo.AnalogSignal[j]` is the histogram computed between
`t_start + j * binsize` and `t_start + (j + 1) * binsize`.
"""
norm_hist = self.complexity_histogram / self.complexity_histogram.sum()
# Convert the Complexity pdf to a neo.AnalogSignal
pdf = neo.AnalogSignal(
np.expand_dims(norm_hist, axis=1),
units=pq.dimensionless,
t_start=0 * pq.dimensionless,
sampling_period=1 * pq.dimensionless)
return pdf
def _histogram_no_spread(self):
"""
Calculate the complexity histogram and time histogram for `spread` = 0
"""
# Computing the population histogram with parameter binary=True to
# clip the spike trains before summing
time_hist = time_histogram(self.input_spiketrains,
self.bin_size,
binary=self.binary)
time_hist_magnitude = time_hist.magnitude.flatten()
# Computing the histogram of the entries of pophist
complexity_hist = np.bincount(time_hist_magnitude)
return time_hist, complexity_hist
def _histogram_with_spread(self):
"""
Calculate the complexity histogram and time histogram for `spread` > 0
"""
complexity_hist = np.bincount(
self.epoch.array_annotations['complexity'])
num_bins = (self.t_stop - self.t_start).rescale(
self.bin_size.units).item() / self.bin_size.item()
num_bins = round_binning_errors(num_bins, tolerance=self.tolerance)
time_hist = np.zeros(num_bins, dtype=int)
start_bins = (self.epoch.times - self.t_start).rescale(
self.bin_size.units).magnitude / self.bin_size.item()
stop_bins = (self.epoch.times + self.epoch.durations - self.t_start
).rescale(self.bin_size.units
).magnitude / self.bin_size.item()
if self.sampling_rate is not None:
shift = (.5 / self.sampling_rate / self.bin_size).simplified.item()
# account for the first bin not being shifted in the epoch creation
# if the shift would move it past t_start
if self.epoch.times[0] == self.t_start:
start_bins[1:] += shift
else:
start_bins += shift
stop_bins += shift
start_bins = round_binning_errors(start_bins, tolerance=self.tolerance)
stop_bins = round_binning_errors(stop_bins, tolerance=self.tolerance)
for idx, (start, stop) in enumerate(zip(start_bins, stop_bins)):
time_hist[start:stop] = \
self.epoch.array_annotations['complexity'][idx]
time_hist = neo.AnalogSignal(
signal=np.expand_dims(time_hist, axis=1),
sampling_period=self.bin_size, units=pq.dimensionless,
t_start=self.t_start)
empty_bins = (self.t_stop - self.t_start - self.epoch.durations.sum())
empty_bins = empty_bins.rescale(self.bin_size.units
).magnitude / self.bin_size.item()
empty_bins = round_binning_errors(empty_bins, tolerance=self.tolerance)
complexity_hist[0] = empty_bins
return time_hist, complexity_hist
def _epoch_no_spread(self):
"""
Get an epoch object of the complexity distribution with `spread` = 0
"""
left_edges = self.time_histogram.times
durations = self.bin_size * np.ones(self.time_histogram.shape)
if self.sampling_rate:
# ensure that spikes are not on the bin edges
bin_shift = .5 / self.sampling_rate
left_edges -= bin_shift
# Ensure that an epoch does not start before the minimum t_start.
# Note: all spike trains share the same t_start and t_stop.
if left_edges[0] < self.t_start:
left_edges[0] = self.t_start
durations[0] -= bin_shift
else:
warnings.warn('No sampling rate specified. '
'Note that using the complexity epoch to get '
'precise spike times can lead to rounding errors.')
complexity = self.time_histogram.magnitude.flatten()
complexity = complexity.astype(np.uint16)
epoch = neo.Epoch(left_edges,
durations=durations,
array_annotations={'complexity': complexity})
return epoch
def _epoch_with_spread(self):
"""
Get an epoch object of the complexity distribution with `spread` > 0
"""
bst = conv.BinnedSpikeTrain(self.input_spiketrains,
bin_size=self.bin_size,
tolerance=self.tolerance)
if self.binary:
bst = bst.binarize(copy=False)
bincount = bst.get_num_of_spikes(axis=0)
nonzero_indices = np.nonzero(bincount)[0]
left_diff = np.diff(nonzero_indices,
prepend=-self.spread - 1)
right_diff = np.diff(nonzero_indices,
append=len(bincount) + self.spread + 1)
# standalone bins (no merging required)
single_bin_indices = np.logical_and(left_diff > self.spread,
right_diff > self.spread)
single_bins = nonzero_indices[single_bin_indices]
# bins separated by fewer than spread bins form clusters
# that have to be merged
cluster_start_indices = np.logical_and(left_diff > self.spread,
right_diff <= self.spread)
cluster_starts = nonzero_indices[cluster_start_indices]
cluster_stop_indices = np.logical_and(left_diff <= self.spread,
right_diff > self.spread)
cluster_stops = nonzero_indices[cluster_stop_indices] + 1
single_bin_complexities = bincount[single_bins]
cluster_complexities = [bincount[start:stop].sum()
for start, stop in zip(cluster_starts,
cluster_stops)]
# merge standalone bins and clusters and sort them
combined_starts = np.concatenate((single_bins, cluster_starts))
combined_stops = np.concatenate((single_bins + 1, cluster_stops))
combined_complexities = np.concatenate((single_bin_complexities,
cluster_complexities))
sorting = np.argsort(combined_starts, kind='mergesort')
left_edges = bst.bin_edges[combined_starts[sorting]]
right_edges = bst.bin_edges[combined_stops[sorting]]
complexities = combined_complexities[sorting].astype(np.uint16)
if self.sampling_rate:
# ensure that spikes are not on the bin edges
bin_shift = .5 / self.sampling_rate
left_edges -= bin_shift
right_edges -= bin_shift
else:
warnings.warn('No sampling rate specified. '
'Note that using the complexity epoch to get '
'precise spike times can lead to rounding errors.')
# Ensure that an epoch does not start before the minimum t_start.
# Note: all spike trains share the same t_start and t_stop.
left_edges[0] = max(self.t_start, left_edges[0])
complexity_epoch = neo.Epoch(times=left_edges,
durations=right_edges - left_edges,
array_annotations={'complexity':
complexities})
return complexity_epoch
def nextpow2(x):
"""
Return the smallest integral power of 2 that is equal or larger than `x`.
"""
log2_n = math.ceil(math.log2(x))
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
:cite:`statistics-Shimazaki2010_171`, given as the standard deviation
sigma.
Original matlab code (sskernel.m)
http://2000.jukuin.keio.ac.jp/shimazaki/res/kernel.html has been ported to
Python by Subhasis Ray, NCBS.
Parameters
----------
spiketimes : np.ndarray
Sequence of spike times (sorted to be ascending).
times : np.ndarray or None, optional
Time points at which the kernel bandwidth is to be estimated.
If None, `spiketimes` is used.
Default: None
bandwidth : np.ndarray or None, 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.
"""
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)
