def plotXcorr(qdata,save=False,fname='hmmSortingUnits.pdf'):
unitTimePoints = qdata['unitTimePoints']
samplingRate = qdata.get('samplingRate',30000.0)
units = unitTimePoints.keys()
nunits = len(units)
xsize = max(10,nunits*2)
fig = plt.figure(figsize=(xsize,(6.0/8)*xsize) )
fig.subplots_adjust(left=0.03,right=0.97,bottom=0.03,top=0.97)
i = 1
if not 'XCorr' in qdata:
if isinstance(qdata,dict):
qdata['XCorr'] = {}
else:
qdata.create_group('XCorr')
for k1 in xrange(len(units)-1) :
if not units[k1] in qdata['XCorr']:
qdata['XCorr'].create_group(units[k1])
for k2 in xrange(k1+1,len(units)):
if not units[k2] in qdata['XCorr'][units[k1]]:
T1 = unitTimePoints[units[k1]][:]/(samplingRate/1000)
T2 = unitTimePoints[units[k2]][:]/(samplingRate/1000)
#compute differences less than 50 ms
C = pdist_threshold2(T1,T2,50)
qdata['XCorr'][units[k1]].create_dataset(units[k2],data=C,compression=2,fletcher32=True,shuffle=True)
else:
C = qdata['XCorr'][units[k1]][units[k2]][:]
n,b = np.histogram(C,np.arange(-50,50),normed=True)
ax = Subplot(fig,nunits-1,nunits,k1*nunits+k2)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
ax.fill_betweenx([0,n.max()],-1.0,1.0,color='r',alpha=0.3)
if not (k1 == len(units)-2 and k2 == len(units)-1):
ax.set_xticklabels('')
ax.set_yticklabels('')
if save:
fig.savefig(os.path.expanduser('~/Documents/research/figures/SpikeSorting/hmm/%s' %( fname,)),bbox='tight')
else:
plt.draw()
def plotSpikes(qdata,save=False,fname='hmmSorting.pdf',tuning=False,figsize=(10,6)):
allSpikes = qdata['allSpikes']
unitSpikes = qdata['unitSpikes']
spikeIdx = qdata['spikeIdx']
spikeIdx = qdata['unitTimePoints']
units = qdata['unitTimePoints']
spikeForms = qdata['spikeForms']
channels = qdata['channels']
uniqueIdx = qdata['uniqueIdx']
samplingRate = qdata.get('samplingRate',30000.0)
"""
mustClose = False
if isinstance(dataFile,str):
dataFile = h5py.File(dataFile,'r')
mustClose = True
data = dataFile['data'][:]
"""
keys = np.array(units.keys())
x = np.arange(32)[None,:] + 42*np.arange(spikeForms.shape[1])[:,None]
xt = np.linspace(0,31,spikeForms.shape[-1])[None,:] + 42*np.arange(spikeForms.shape[1])[:,None]
xch = 10 + 42*np.arange(len(channels))
for c in units.keys():
ymin,ymax = (5000,-5000)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(hspace=0.3)
print "Unit: %s " %(str(c),)
print "\t Plotting waveforms..."
sys.stdout.flush()
#allspikes = data[units[c][:,None]+np.arange(-10,22)[None,:],:]
#allspikes = allSpikes[spikeIdx[c]]
allspikes = qdata['unitSpikes'][c]
otherunits = keys[keys!=c]
#nonOverlapIdx = np.prod(np.array([~np.lib.arraysetops.in1d(spikeIdx[c],spikeIdx[c1]) for c1 in otherunits]),axis=0).astype(np.bool)
#nonOverlapIdx = np.prod(np.array([pdist_threshold(spikeIdx[c],spikeIdx[c1],3) for c1 in otherunits]),axis=0).astype(np.bool)
#nonOverlapIdx = uniqueIdx[c]
nonOverlapIdx = qdata['nonOverlapIdx'][c]
overlapIdx = np.lib.arraysetops.setdiff1d(np.arange(qdata['unitTimePoints'][c].shape[0]),nonOverlapIdx)
#allspikes = allSpikes[np.lib.arraysetops.union1d(nonOverlapIdx,overlapIdx)]
ax = Subplot(fig,2,3,1)
fig.add_axes(ax)
formatAxis(ax)
#plt.plot(x.T,sp,'b')
m = allspikes[:].mean(0)
s = allspikes[:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
#find the minimum point for this template
ich = spikeForms[int(c)].min(1).argmin()
ix = spikeForms[int(c)][ich,:].argmin()
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
ax.set_title('All spikes (%d)' % (allspikes.shape[0],))
ax = Subplot(fig,2,3,2)
fig.add_axes(ax)
formatAxis(ax)
if len(nonOverlapIdx)>0:
m = allspikes[:][nonOverlapIdx,:,:].mean(0)
s = allspikes[:][nonOverlapIdx,:,:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
#for sp in allspikes[nonOverlapIdx,:,:]:
# plt.plot(x.T,sp,'r')
ax.set_title('Non-overlap spikes (%d)' %(nonOverlapIdx.shape[0],))
ax = Subplot(fig,2,3,3)
fig.add_axes(ax)
formatAxis(ax)
if len(overlapIdx)>0:
m = allspikes[:][overlapIdx,:,:].mean(0)
s = allspikes[:][overlapIdx,:,:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
#for sp in allspikes[~nonOverlapIdx,:,:]:
# plt.plot(x.T,sp,'g')
ax.set_title('Overlap spikes (%d)' % ((overlapIdx).shape[0],))
for a in fig.axes:
a.set_ylim((ymin,ymax))
a.set_xticks(xch)
a.set_xticklabels(map(str,channels))
a.set_xlabel('Channels')
for a in fig.axes[1:]:
a.set_yticklabels([])
fig.axes[0].set_ylabel('Amplitude')
"""
isi distribution
"""
print "\t ISI distribution..."
sys.stdout.flush()
timepoints = qdata['unitTimePoints'][c][:]/(samplingRate/1000)
if len(timepoints)<2:
print "Too few spikes. Aborting..."
continue
isi = np.log(np.diff(timepoints))
n,b = np.histogram(isi,100)
ax = Subplot(fig,2,3,4)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
yl = ax.get_ylim()
ax.vlines(0.0,0,yl[1],'r',lw=1.5)
ax.set_xlabel('ISI [ms]')
#get xticklabels
xl,xh = int(np.round((b[0]-0.5)*2))/2,int(np.round((b[-1]+0.5)*2))/2
xl = -0.5
dx = np.round(10.0*(xh-xl)/5.0)/10
xt_ = np.arange(xl,xh+1,dx)
ax.set_xticks(xt_)
ax.set_xticklabels(map(lambda s: r'$10^{%.1f}$' % (s,),xt_))
"""
auto-correlogram
"""
print "\t auto-correllogram..."
sys.stdout.flush()
if not 'autoCorr' in qdata:
if isinstance(qdata,dict):
qdata['autoCorr'] = {}
else:
qdata.create_group('autoCorr')
if not c in qdata['autoCorr']:
C = pdist_threshold2(timepoints,timepoints,50)
qdata['autoCorr'][c] = C
if not isinstance(qdata,dict):
qdata.flush()
else:
C = qdata['autoCorr'][c][:]
n,b = np.histogram(C[C!=0],np.arange(-50,50))
ax = Subplot(fig,2,3,5)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
ax.fill_betweenx([0,n.max()],-1.0,1.0,color='r',alpha=0.3)
ax.set_xlabel('Lag [ms]')
if tuning:
print "\tPlotting tuning..."
sys.stdout.flush()
#attempt to get tuning for the current session, based on PWD
stimCounts,isiCounts,angles,spikedata = gt.getTuning(sptrain=timepoints)
#reshape to number of orientations X number of reps, collapsing
#across everything else
#angles = np.append(angles,[angles[0]])
C = stimCounts['0'].transpose((1,0,2,3))
C = C.reshape(C.shape[0],C.size/C.shape[0])
ax = plt.subplot(2,3,6,polar=True)
ax.errorbar(angles*np.pi/180,C.mean(1),C.std(1))
if save:
if not os.path.isabs(fname):
fn = os.path.expanduser('~/Documents/research/figures/SpikeSorting/hmm/%s' % (fname.replace('.pdf','Unit%s.pdf' %(str(c),)),))
else:
fn = fname.replace('.pdf','Unit%s.pdf' %(str(c),))
fig.savefig(fn,bbox='tight')
if not save:
plt.draw()
"""
