try:
X = X/abs(X)
Y = Y/abs(Y)
except ArithmeticException, ae:
print "Error: Division by zero", e
except:
print "Anderer Fehler! (Vielleicht Index?)"
pd = n.exp(n.log(X)-n.log(Y))
return abs(pd.mean())
elif len(x.shape)==2:
X = n.zeros((x.shape),"D")
Y = n.zeros((y.shape),"D")
for i in range(x.shape[1]):
X[:,i] = hilbert(demean(x[:,i]))
Y[:,i] = hilbert(demean(y[:,i]))
try:
for i in range(X.shape[1]):
X[:,i] /= abs(X[:,i])
Y[:,i] /= abs(Y[:,i])
except ArithmeticException, ae:
print "Error: Division by zero", e
pd = n.exp(n.log(X)-n.log(Y))
return abs(pd.mean(axis=1))
#def phase_coherence_fetptrials
def phases(data,continuous=False):
def phase_coherence(x,y):
"""Diese Funktion berechnet aus den Arrays die mittlere Phasenkohärenz R,
ein Mass zwischen 0 und 1 (zirkuläre Statistik).
If the arrays are 1d, calculate the mean over time.
If they are 2d (second dimensions usually trials), calculate mean over this dimension."""
assert x.shape == y.shape
if len(x.shape)==1:
X = hilbert(demean(x))
Y = hilbert(demean(y))
try:
X = X/abs(X)
Y = Y/abs(Y)
except ArithmeticException, ae:
print "Error: Division by zero", e
except:
def phase_coherence(x, y):
"""Diese Funktion berechnet aus den Arrays die mittlere Phasenkohärenz R,
ein Mass zwischen 0 und 1 (zirkuläre Statistik).
If the arrays are 1d, calculate the mean over time.
If they are 2d (second dimensions usually trials), calculate mean over this dimension."""
assert x.shape == y.shape
if len(x.shape) == 1:
X = hilbert(demean(x))
Y = hilbert(demean(y))
try:
X = X / abs(X)
Y = Y / abs(Y)
except ArithmeticException, ae:
print "Error: Division by zero", e
except:
def wt_analyze(x,freqs=None,Fs=1000.0,wtSampleWidth=0.8):
"""
Analyzes a given dataset via wavelet-analyis.
:param x: input array
:type x: array-like
:param freqs: 1-d array of center frequencies
:type freqs: array-like
:param Fs: Sampling frequency
:type Fs: float
:param wtSampleWidth: time span for which the wavelets are sampled.
:type wtSampleWidth: float
.. hint::
.. plot::
:include-source:
import matplotlib.pyplot as p
import numpy as np
from eegpy.analysis.wavelet import wt_analyze
Fs = 100.
F_sin1 = 3.
F_sin2 = 7.
freqs = np.arange(1,10)
ts = np.arange(0,10,1/Fs)
ys = np.zeros((Fs*10))
ys[:Fs*10/2] = np.sin(2*np.pi*F_sin1*ts[:Fs*10/2])*np.hanning(Fs*10/2)
ys[Fs*10/2:] = np.sin(2*np.pi*F_sin2*ts[:Fs*10/2])*np.hanning(Fs*10/2) * 2
wts = wt_analyze(ys,freqs=freqs,Fs=Fs)
power = abs(wts)**2
p.subplot(211)
p.plot(ts,ys)
p.title(r"$F_1=%.1f, \; F_2=%.1f$"%(F_sin1,F_sin2))
p.subplot(212)
p.imshow(power.T,aspect="auto",interpolation="nearest",
origin="lower",
extent=[0,10,0.5,9.5])
"""
assert len(x)>0, "x must be an array containing the data"
if not freqs==None:
assert len(freqs)>0, "freqs must be an iterable object with the frequencies"
else:
freqs = n.logspace(0.4,2.1,80)
#tStart = time()
wts = make_wavelets(freqs,Fs=Fs,wtSampleWidth=wtSampleWidth)
#print "--", time()-tStart
x = taper(demean(x)) #preprocessing...
rv = n.zeros((wts.shape[1]+x.shape[0]-1,len(freqs)), "D")#wts.shape[1]),"D")
for fi in range(len(freqs)):
#print convolve(x,wts[fi,:]).shape, rv.shape
#sys.stdout.flush()
rv[:,fi] = convolve(x,wts[fi,:])#,"full")
rv = rv[(wts.shape[1]-1)/2 : rv.shape[0]-1-(wts.shape[1]-1)/2,:]
return rv
def wt_analyze(x, freqs=None, Fs=1000.0, wtSampleWidth=0.8):
"""
Analyzes a given dataset via wavelet-analyis.
:param x: input array
:type x: array-like
:param freqs: 1-d array of center frequencies
:type freqs: array-like
:param Fs: Sampling frequency
:type Fs: float
:param wtSampleWidth: time span for which the wavelets are sampled.
:type wtSampleWidth: float
.. hint::
.. plot::
:include-source:
import matplotlib.pyplot as p
import numpy as np
from eegpy.analysis.wavelet import wt_analyze
Fs = 100.
F_sin1 = 3.
F_sin2 = 7.
freqs = np.arange(1,10)
ts = np.arange(0,10,1/Fs)
ys = np.zeros((Fs*10))
ys[:Fs*10/2] = np.sin(2*np.pi*F_sin1*ts[:Fs*10/2])*np.hanning(Fs*10/2)
ys[Fs*10/2:] = np.sin(2*np.pi*F_sin2*ts[:Fs*10/2])*np.hanning(Fs*10/2) * 2
wts = wt_analyze(ys,freqs=freqs,Fs=Fs)
power = abs(wts)**2
p.subplot(211)
p.plot(ts,ys)
p.title(r"$F_1=%.1f, \; F_2=%.1f$"%(F_sin1,F_sin2))
p.subplot(212)
p.imshow(power.T,aspect="auto",interpolation="nearest",
origin="lower",
extent=[0,10,0.5,9.5])
"""
assert len(x) > 0, "x must be an array containing the data"
if not freqs == None:
assert len(
freqs) > 0, "freqs must be an iterable object with the frequencies"
else:
freqs = n.logspace(0.4, 2.1, 80)
#tStart = time()
wts = make_wavelets(freqs, Fs=Fs, wtSampleWidth=wtSampleWidth)
#print "--", time()-tStart
x = taper(demean(x)) #preprocessing...
rv = n.zeros((wts.shape[1] + x.shape[0] - 1, len(freqs)),
"D") #wts.shape[1]),"D")
for fi in range(len(freqs)):
#print convolve(x,wts[fi,:]).shape, rv.shape
#sys.stdout.flush()
rv[:, fi] = convolve(x, wts[fi, :]) #,"full")
rv = rv[(wts.shape[1] - 1) / 2:rv.shape[0] - 1 - (wts.shape[1] - 1) / 2, :]
return rv
try:
X = X / abs(X)
Y = Y / abs(Y)
except ArithmeticException, ae:
print "Error: Division by zero", e
except:
print "Anderer Fehler! (Vielleicht Index?)"
pd = n.exp(n.log(X) - n.log(Y))
return abs(pd.mean())
elif len(x.shape) == 2:
X = n.zeros((x.shape), "D")
Y = n.zeros((y.shape), "D")
for i in range(x.shape[1]):
X[:, i] = hilbert(demean(x[:, i]))
Y[:, i] = hilbert(demean(y[:, i]))
try:
for i in range(X.shape[1]):
X[:, i] /= abs(X[:, i])
Y[:, i] /= abs(Y[:, i])
except ArithmeticException, ae:
print "Error: Division by zero", e
pd = n.exp(n.log(X) - n.log(Y))
return abs(pd.mean(axis=1))
#def phase_coherence_fetptrials
