def SPK_HFO_coupling(SPK,HFO,sample_rate,cluster = 'all',nbins = 32,color=None):
if color ==None:
colours = ['#000000','#0000FF','#FF0000','#8fbc8f','yellow']
ks = np.array([])
for ev in range(len(HFO.event)):
loc = [int((x- HFO.event[ev].start_sec)*sample_rate) for x in SPK.__getlist__('tstamp') if x > HFO.event[ev].start_sec and x < HFO.event[ev].end_sec]
if len(loc) > 0:
waveform = HFO.event[ev].waveform[HFO.event[ev].start_idx:HFO.event[ev].end_idx,1]
phs = np.angle(sig.hilbert(waveform))
k = phs[loc]
ks = np.append(ks,k)
clus = 1
plt.subplot(1,2,1,polar=True)
plt.hist(ks,bins=nbins,facecolor=colours[clus])
plt.yticks([])
ax = plt.subplot(1,2,2,polar=False)
nbin = np.concatenate((ks,ks+2*np.pi))
plt.hist(nbin,bins=2*nbins,facecolor=colours[clus])
plt.xticks(np.linspace(-np.pi,3*np.pi,9),[str(int(x)) for x in 180*np.linspace(-np.pi,3*np.pi,9)/np.pi])
plt.xlim([-np.pi,3*np.pi])
adjust_spines(ax, ['left','bottom'])
def rastergram(self, ax = None, spines = ['left','bottom'],time_edge = None, exclude = [],figure_size=(15,5),dpi=600, line = True):
"""
Plot rastergram
Parameters
----------
ax: matplotlib axes
None (default) - create a new figure
ax - axes of figure where figure should plot
spines: str
['left', 'bottom'] (default) - plot figure with left and bottom spines only
time_edge: tupple
Determine the x-axis limits.
exclude: list
Channels/Cluster to exclude from plot
figure_size: tuple
(5,5) (default) - Size of figure, tuple of integers with width, height in inches
dpi: int
600 - DPI resolution
"""
htype = self.event[0].htype
if ax == None:
# Creating the figure
f = plt.figure(figsize=figure_size,dpi=dpi)
# creating the axes
ax = f.add_subplot(111)
if htype == 'Spike':
cluster = self.__getlist__('cluster')
elif htype == 'HFO':
cluster = range(len(self.ch_labels))
if time_edge == None:
time_edge = self.time_edge
num_clus = int(np.max(cluster))+1
label = []
c_l = 0
for clus in [x for x in range(num_clus) if x not in exclude]:
label.append(self.ch_labels[clus])
if htype == 'Spike':
objs = [x for x in self.event if x.cluster == clus]
elif htype == 'HFO':
objs = [x for x in self.event if x.channel == clus]
for ev in objs:
rect = patches.Rectangle((ev.tstamp,c_l),0.001,.8, lw=0.5)
ax.add_patch(rect)
if line:
plt.hlines(c_l+.5,time_edge[0],time_edge[1],colors='c')
c_l += 1
ax.set_ylim(0,c_l)
ax.set_xlim(time_edge[0],time_edge[-1])
plt.yticks(np.arange(c_l)+0.5,label, size=16)
adjust_spines(ax, spines)
def ISI(self,ch=0,cl=1,t0=0,t1=1000,binsize=10,figure_size=(10,5)):
plt.figure(figsize=figure_size)
clus = np.round(self.times_cluster(ch,cl)*1000)
t_vector = np.diff(clus)
ax = plt.subplot(111)
n, bins, duu = plt.hist(t_vector,np.arange(t0,t1,binsize))
plt.xticks(np.arange(t0,t1,10*binsize),np.arange(t0,t1,10*binsize))
plt.xlabel('miliseconds')
plt.title('Inter-Spike Interval')
adjust_spines(ax,['left','bottom'])
def auto_corr(self,ch=0,cl=1,t0=0,t1=1000,binsize=10,figure_size=(10,5)):
plt.figure(figsize=figure_size)
clus = np.round(self.times_cluster(ch,cl)*1000)
t_matrix = numpy.matlib.repmat(clus, clus.shape[0], 1)
aux = t_matrix - t_matrix.T
t_vector = aux.reshape(-1)
ax = plt.subplot(111)
n, bins, duu = plt.hist(t_vector,np.arange(t0,t1,binsize))
plt.xticks(np.arange(t0,t1,10*binsize),np.arange(t0,t1,10*binsize))
plt.xlabel('miliseconds')
plt.title('Auto-correlogram')
adjust_spines(ax,['left','bottom'])
def cross_corr(self,ch1=0,cl1=1,ch2=0,cl2=2,t0=0,t1=1000,binsize=10,figure_size=(10,5)):
plt.figure(figsize=figure_size)
clus1 = np.round(self.times_cluster(ch1,cl1)*1000)
clus2 = np.round(self.times_cluster(ch2,cl2)*1000)
spk1matrix = numpy.matlib.repmat(clus1, clus2.shape[0], 1)
spk2matrix = numpy.matlib.repmat(clus2, clus1.shape[0], 1)
dif = spk1matrix - spk2matrix.T
t_vector = dif.reshape(-1)
ax = plt.subplot(111)
n, bins, duu = plt.hist(t_vector,np.arange(t0,t1,binsize))
plt.xticks(np.arange(t0,t1,10*binsize),np.arange(t0,t1,10*binsize))
plt.xlabel('miliseconds')
plt.title('Cross Correlogram')
adjust_spines(ax,['left','bottom'])
def rastergram(self, ax = None, spines = ['left','bottom'],time_edge = None, exclude_chan = [],exclude_clus = [],figure_size=(15,10),dpi=600, line = True,common=False):
"""
Plot rastergram
Parameters
----------
ax: matplotlib axes
None (default) - create a new figure
ax - axes of figure where figure should plot
spines: str
['left', 'bottom'] (default) - plot figure with left and bottom spines only
time_edge: tupple
Determine the x-axis limits.
exclude: list
Channels/Cluster to exclude from plot
figure_size: tuple
(5,5) (default) - Size of figure, tuple of integers with width, height in inches
dpi: int
600 - DPI resolution
"""
if ax == None:
# Creating the figure
f = plt.figure(figsize=figure_size,dpi=dpi)
# creating the axes
ax = f.add_subplot(111)
cluster = self.__getlist__('cluster')
channels = self.__getlist__('channel')
if time_edge == None:
time_edge = self.time_edge
num_clus = int(np.max(cluster))+1
num_chan = int(np.max(channels))+1
label = []
c_l = 0
if common:
label.append('Common_ref')
objs = [x for x in self.event if x.channel == 'common']
ax.scatter(objs,c_l*np.ones(len(objs)),c='k',marker='|')
if line:
plt.hlines(c_l,time_edge[0],time_edge[1],colors='c')
c_l += 1
else:
for chan in [x for x in range(num_chan) if x not in exclude_chan]:
for clus in [x for x in range(num_clus) if x not in exclude_clus]:
objs = [x.tstamp for x in self.event if x.channel == chan and x.cluster == clus]
if len(objs) > 0:
label.append(self.ch_labels[chan]+'_'+str(clus))
ax.scatter(objs,c_l*np.ones(len(objs)),c='k',marker='|')
if line:
plt.hlines(c_l,time_edge[0],time_edge[1],colors='c')
c_l += 1
ax.set_ylim(-.5,c_l)
ax.set_xlim(time_edge[0],time_edge[-1])
plt.yticks(np.arange(c_l),label, size=16)
plt.xticks(np.arange(self.time_edge[0],self.time_edge[1],600),np.arange(self.time_edge[0],self.time_edge[1],600)/60)
plt.xlim([self.time_edge[0],self.time_edge[1]])
adjust_spines(ax, spines)
def rastergram(self,
ax=None,
spines=['left', 'bottom'],
time_edge=None,
exclude=[],
figure_size=(15, 5),
dpi=600,
line=True):
"""
Plot rastergram
Parameters
----------
ax: matplotlib axes
None (default) - create a new figure
ax - axes of figure where figure should plot
spines: str
['left', 'bottom'] (default) - plot figure with left and bottom spines only
time_edge: tupple
Determine the x-axis limits.
exclude: list
Channels/Cluster to exclude from plot
figure_size: tuple
(5,5) (default) - Size of figure, tuple of integers with width, height in inches
dpi: int
600 - DPI resolution
"""
htype = self.event[0].htype
if ax == None:
# Creating the figure
f = plt.figure(figsize=figure_size, dpi=dpi)
# creating the axes
ax = f.add_subplot(111)
if htype == 'Spike':
cluster = self.__getlist__('cluster')
elif htype == 'HFO':
cluster = range(len(self.ch_labels))
if time_edge == None:
time_edge = self.time_edge
num_clus = int(np.max(cluster)) + 1
label = []
c_l = 0
for clus in [x for x in range(num_clus) if x not in exclude]:
label.append(self.ch_labels[clus])
if htype == 'Spike':
objs = [x for x in self.event if x.cluster == clus]
elif htype == 'HFO':
objs = [x for x in self.event if x.channel == clus]
for ev in objs:
rect = patches.Rectangle((ev.tstamp, c_l), 0.001, .8, lw=0.5)
ax.add_patch(rect)
if line:
plt.hlines(c_l + .5, time_edge[0], time_edge[1], colors='c')
c_l += 1
ax.set_ylim(0, c_l)
ax.set_xlim(time_edge[0], time_edge[-1])
plt.yticks(np.arange(c_l) + 0.5, label, size=16)
adjust_spines(ax, spines)
def phase_coupling(SPK,Data,low_cut,high_cut,cluster = 'all', ch=None,order = None,window = ('kaiser',0.5),nbins = 32,color=None, saveplot = None):
'''
Caulculate the phase coupling in certain frequency band
Parameters
----------
SPK: SpkObj
Data: DataObj
low_cut: int,
Low cut frequency
high_cut: int,
High cut frequency
'''
plt.figure(figsize=(10,10))
if len(Data.data.shape) == 1:
signal = Data
elif ch == None:
raise Exception('Choose a ch')
else:
signal = pop_channel(Data,ch)
filt = eegfilt(signal,low_cut,high_cut,order = order,window = window)
phs = np.angle(sig.hilbert(filt.data))
#ang = np.linspace(0.0,2*np.pi,nbins, endpoint=False)
if color ==None:
colours = ['#000000','#0000FF','#FF0000','#8fbc8f','yellow']
if cluster == 'all':
l = int(np.max(SPK.__getlist__('cluster'))+1)
elif cluster == None:
loc = [int(x*signal.sample_rate) for x in SPK.__getlist__('tstamp')]
l = 1
else:
idx = [int(i) for i,x in enumerate(SPK.__getlist__('cluster')) if x== cluster]
loc = [int(x*signal.sample_rate) for i,x in enumerate(SPK.__getlist__('tstamp')) if i in idx]
l = 1
c = 0
for clus in range(l):
if l == 1:
k = phs[loc]
else:
idx = [int(i) for i,x in enumerate(SPK.__getlist__('cluster')) if x== clus]
loc = [int(x*signal.sample_rate) for i,x in enumerate(SPK.__getlist__('tstamp')) if i in idx]
k = phs[loc]
c += 1
plt.subplot(l,2,c,polar=True)
plt.hist(k,bins=nbins,facecolor=colours[clus])
plt.yticks([])
c += 1
ax = plt.subplot(l,2,c,polar=False)
nbin = np.concatenate((k,k+2*np.pi))
plt.hist(nbin,bins=2*nbins,facecolor=colours[clus])
plt.xticks(np.linspace(-np.pi,3*np.pi,9),[str(int(x)) for x in 180*np.linspace(-np.pi,3*np.pi,9)/np.pi])
plt.xlim([-np.pi,3*np.pi])
adjust_spines(ax, ['left','bottom'])
r = np.sum(np.exp(1j*k))
r = np.abs(r)/k.shape[0]
print 'Cluster ' + str(clus) + ' r: ' + str(r)
if saveplot != None:
if type(saveplot) == str:
plt.savefig(saveplot, bbox_inches='tight')
else:
raise Exception('saveplot should be a string')
