def mtfft(tl, **kwargs):
"""
Multitaper fourier transform from point process times
[J,Nsp,f] = mtfftpt(tl, **kwargs)
Input:
tl ragged array of event times
Optional keyword arguments:
NW time-bandwidth parameter for DPSS tapers (default 3)
k number of DPSS tapers to use (default nw*2-1)
time_range the range of times to analyze (default is to use range of tl)
Fs the frequency resolution (default 1)
fpass frequency band to be used in the calculation in the form
(fmin, fmax). Default (0, Fs/2)
nfft number of frequency bins for transform
Output:
J complex spectrum with dimensions (freq x chan x tapers)
Nsp total spike count in each trial, with dimension (chan,)
f frequency grid, with dimension (freq,)
Note on units: if the units of the data are T, the corresponding frequency units
are in 1/T (sec -> Hz; msec -> kHz)
"""
from libtfr import fgrid, dpss
from numpy import arange, sqrt
import toelis
NW = kwargs.get('NW', 3)
K = kwargs.get('k', NW*2-1)
Fs = kwargs.get('Fs', 1)
fpass = kwargs.get('fpass', (0, Fs/2.))
dt = 1./Fs
try:
mintime, maxtime = kwargs['time_range']
t = arange(mintime, maxtime, dt)
except KeyError:
mintime, maxtime = toelis.range(tl)
# pad around spike times to avoid edge effects
t = arange(mintime-dt, maxtime+2*dt, dt)
N = len(t)
nfft = kwargs.get('nfft', N)
f,findx = fgrid(Fs, nfft, fpass)
tapers = dpss(N, NW, K)[0].T * sqrt(Fs)
J, Nsp = _mtfft(tl, tapers, nfft, t, f, findx)
return J, Nsp, f
def mtfft(tl, **kwargs):
"""
Multitaper fourier transform from point process times
[J,Nsp,f] = mtfftpt(tl, **kwargs)
Input:
tl ragged array of event times
Optional keyword arguments:
NW time-bandwidth parameter for DPSS tapers (default 3)
k number of DPSS tapers to use (default nw*2-1)
time_range the range of times to analyze (default is to use range of tl)
Fs the frequency resolution (default 1)
fpass frequency band to be used in the calculation in the form
(fmin, fmax). Default (0, Fs/2)
nfft number of frequency bins for transform
Output:
J complex spectrum with dimensions (freq x chan x tapers)
Nsp total spike count in each trial, with dimension (chan,)
f frequency grid, with dimension (freq,)
Note on units: if the units of the data are T, the corresponding frequency units
are in 1/T (sec -> Hz; msec -> kHz)
"""
from libtfr import fgrid, dpss
from numpy import arange, sqrt
import toelis
NW = kwargs.get('NW', 3)
K = kwargs.get('k', NW * 2 - 1)
Fs = kwargs.get('Fs', 1)
fpass = kwargs.get('fpass', (0, Fs / 2.))
dt = 1. / Fs
try:
mintime, maxtime = kwargs['time_range']
t = arange(mintime, maxtime, dt)
except KeyError:
mintime, maxtime = toelis.range(tl)
# pad around spike times to avoid edge effects
t = arange(mintime - dt, maxtime + 2 * dt, dt)
N = len(t)
nfft = kwargs.get('nfft', N)
f, findx = fgrid(Fs, nfft, fpass)
tapers = dpss(N, NW, K)[0].T * sqrt(Fs)
J, Nsp = _mtfft(tl, tapers, nfft, t, f, findx)
return J, Nsp, f
def convolve(tl, kernel, kdt, start=None, stop=None, dt=None):
"""Convolve a multi-trial point process with a kernel.
R(x) = SUM W(s-x)
s in S
tl: a list of event time arrays
kernel: the convolution kernel
kdt: the temporal resolution of the kernel
dt: the resolution of the output. Defaults to kdt
start: the first time point in the output. This is *exclusive*
stop: the last time point in the output. Also exclusive.
Returns:
result of convolution (array, time x trial);
time grid (array, time)
The kernel function can be any 1D sequence of values. It's assumed to start
at tau=0 on an evenly spaced grid (kdt). To ensure that the integral of the
output is equal to the spike count, sum(kernel)*kdt should be equal to
1.0. See signal.kernel() for help in constructing kernels with a fixed
bandwidth.
For acausal kernels (i.e., with support at t<0), shift the output.
"""
from toelis import range
from numpy import arange, column_stack
from dlab._convolve import discreteconv
if dt is None:
dt = kdt
t1, t2 = range(tl)
onset = start if start is not None else t1
offset = stop if stop is not None else t2
grid = arange(onset, offset, dt)
rate = [discreteconv(x, kernel, kdt, onset, offset, dt) for x in tl]
return column_stack(rate), grid
def mtstft(tl, window, step, **kwargs):
"""
Multitaper short time fourier transform from point process times
[J,Nsp,f,t] = mtstft(tl, window, step**kwargs)
Input:
tl ragged array of event times
window duration of short time analysis window
step step size between windows
Optional keyword arguments:
NW time-bandwidth parameter for DPSS tapers (default 3)
k number of DPSS tapers to use (default nw*2-1)
time_range the range of times to analyze (default is to use range of tl)
Fs the frequency resolution (default 1)
fpass frequency band to be used in the calculation in the form
(fmin, fmax). Default (0, Fs/2)
nfft number of frequency bins for transform
Output:
J complex spectrum with dimensions (freq x time x tapers x chan)
Nsp total spike count in each trial, with dimension (time, chan)
f frequency grid, with dimension (freq,)
t time grid, with dimension (time,)
Note on units: if the units of the data are T, the corresponding frequency units
are in 1/T (sec -> Hz; msec -> kHz)
"""
from libtfr import fgrid, dpss
from numpy import arange, sqrt, zeros
import toelis
NW = kwargs.get('NW', 3)
K = kwargs.get('k', NW*2-1)
Fs = kwargs.get('Fs', 1)
dt = 1./ Fs
fpass = kwargs.get('fpass', (0, Fs/2.))
C = len(tl)
twin = arange(0, window, dt)
N = len(twin)
nfft = kwargs.get('nfft', N)
f,findx = fgrid(Fs, nfft, fpass)
tapers = dpss(N, NW, K)[0].T * sqrt(Fs)
try:
mintime, maxtime = kwargs['time_range']
except KeyError:
mintime, maxtime = toelis.range(tl)
# pad around spikes
mintime -= dt
maxtime += 2*dt
tgrid = arange(mintime, maxtime, step)
J = zeros((f.size, tgrid.size, K, C), dtype='D')
Nsp = zeros((tgrid.size, C), dtype='i')
for i, offset in enumerate(tgrid):
j,n = _mtfft(tl, tapers, nfft, twin + offset, f, findx)
J[:,i,:,:] = j
Nsp[i,:] = n
return J, Nsp, f, tgrid
def mtstft(tl, window, step, **kwargs):
"""
Multitaper short time fourier transform from point process times
[J,Nsp,f,t] = mtstft(tl, window, step**kwargs)
Input:
tl ragged array of event times
window duration of short time analysis window
step step size between windows
Optional keyword arguments:
NW time-bandwidth parameter for DPSS tapers (default 3)
k number of DPSS tapers to use (default nw*2-1)
time_range the range of times to analyze (default is to use range of tl)
Fs the frequency resolution (default 1)
fpass frequency band to be used in the calculation in the form
(fmin, fmax). Default (0, Fs/2)
nfft number of frequency bins for transform
Output:
J complex spectrum with dimensions (freq x time x tapers x chan)
Nsp total spike count in each trial, with dimension (time, chan)
f frequency grid, with dimension (freq,)
t time grid, with dimension (time,)
Note on units: if the units of the data are T, the corresponding frequency units
are in 1/T (sec -> Hz; msec -> kHz)
"""
from libtfr import fgrid, dpss
from numpy import arange, sqrt, zeros
import toelis
NW = kwargs.get('NW', 3)
K = int(kwargs.get('k', NW * 2 - 1))
Fs = kwargs.get('Fs', 1)
dt = 1. / Fs
fpass = kwargs.get('fpass', (0, Fs / 2.))
C = len(tl)
twin = arange(0, window, dt)
N = len(twin)
nfft = kwargs.get('nfft', N)
f, findx = fgrid(Fs, nfft, fpass)
tapers = dpss(N, NW, K)[0].T * sqrt(Fs)
try:
mintime, maxtime = kwargs['time_range']
except KeyError:
mintime, maxtime = toelis.range(tl)
# pad around spikes
mintime -= dt
maxtime += 2 * dt
tgrid = arange(mintime, maxtime, step)
J = zeros((f.size, tgrid.size, K, C), dtype='D')
Nsp = zeros((tgrid.size, C), dtype='i')
for i, offset in enumerate(tgrid):
j, n = _mtfft(tl, tapers, nfft, twin + offset, f, findx)
J[:, i, :, :] = j
Nsp[i, :] = n
return J, Nsp, f, tgrid
