def flashgrating (datapath, logpath, modality, numbins, showflag):
'''analysis for grating stimuli including: orientation, spatial frequency or temporal frequency'''
print "loading data from:",datapath
numberofshanks = 8
'''get data'''
#sweeptiming = loadsweeptimes(datapath)
(spiketimes, cellnumber) = loadclu(datapath, numberofshanks)
nc = size(spiketimes,1)
'''get log'''
#(sweeporder, sweeptable, bgdimnames) = getSweepTimesEP(logpath)
(stimc, duration, constring) = getSweepTimesEP(logpath, datapath, modality)
tfreq = stimc[0,3]
if (modality.find("sf")+1):
tlabel = "Spatial frequency (Cyc/Deg)"
ticks = np.arange(0, 0.62, 0.1)
print tlabel
print constring
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
ostr = str(orivals[i])+"Deg"
constring = constring + " at " + ostr
print ostr
(tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem) = dotuning(stimuluscondition, spiketimes, cellnumber, 2, duration, tlabel, ticks, constring, ostr, numbins, showflag)
elif (modality.find("tf")+1):
tlabel = "Temporal frequency (Cyc/Sec)"
ticks = range(0,15,3)
print tlabel
print constring
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
ostr = str(orivals[i])+"Deg"
constring = constring + " at " + ostr
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
print ostr
(tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem) = dotuning(stimuluscondition, spiketimes, cellnumber, 3, duration, tlabel, ticks, constring, ostr, numbins, showflag)
elif (modality.find("ori")+1):
tlabel = "Orientation (Deg)"
ticks = range(0,361,90)
print tlabel
print constring
stimuluscondition = stimc
ostr = "allori"
(tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem) = dotuning(stimuluscondition, spiketimes, cellnumber, 1, duration, tlabel, ticks, constring, ostr, numbins, showflag)
else:
print "No modality specified"
fileout = datapath+'_cellnumber.dat'
np.savetxt(fileout, cellnumber,'%f')
return (cellnumber, tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem)
def sftf(datapath, logpath, numbins, showflag):
'''analysis for spatial frequency X temporal frequency X orientation stimulus'''
print "loading data from:",datapath
numberofshanks = 8
'''get data'''
(spiketimes, cellnumber) = loadclu(datapath, numberofshanks)
nc = size(spiketimes,1)
'''get log'''
(stimc, duration, constring) = getSweepTimesEP(logpath, datapath, 'sftf')
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
ostr = str(orivals[i])+"Deg"
constring = constring + " at " + ostr
(f0mean, f0sem, f1mean, f1sem, f2mean, f2sem) = sftftuning(stimuluscondition, spiketimes, cellnumber, 3, duration, constring, ostr, numbins, showflag)
return (cellnumber, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem)
def conrevEP(datapath, logpath, numbins, showflag):
'''analysis for contrast reversing e-phys data'''
print "loading data from:",datapath
numberofshanks = 8
'''get data'''
(spiketimes, cellnumber) = loadclu(datapath, numberofshanks)
nc = size(spiketimes,1)
'''get log'''
modality = 'conrev'
(stimc, duration, tfreq, constring) = getSweepTimesEPcr(logpath, datapath)
tlabel = "Phase (Deg)"
ticks = range(0,151,30)
print tlabel
print constring
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
ostr = str(orivals[i])+"Deg"
print ostr
constring = constring + " at " + ostr
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
(tuning, f0m, f0s, f1m, f1s, f2m, f2s) = dotuning(stimuluscondition, spiketimes, cellnumber, 2, duration, tlabel, ticks, constring, ostr, numbins, showflag)
plotconrev(tuning, f1m, f1s, f2m, f2s, ostr, constring)
if i == 0:
f0mean = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f0sem = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f1mean = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f1sem = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f2mean = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f2sem = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f0mean[:,:,i] = f0m[:,:]
f0sem[:,:,i] = f0s[:,:]
f1mean[:,:,i] = f1m[:,:]
f1sem[:,:,i] = f1s[:,:]
f2mean[:,:,i] = f2m[:,:]
f2sem[:,:,i] = f2s[:,:]
return (tuning, f0mean, f1mean, f2mean, f0sem, f1sem, f2sem)
'''plot data'''
def flashgrating (datapath, logpath, modality, numbins, showflag):
'''analysis for EPhys grating experiments including: orientation, spatial frequency or temporal frequency'''
print "loading data from:",datapath
numberofshanks = 8
'''get data'''
#sweeptiming = loadsweeptimes(datapath)
(spiketimes, cellnumber) = loadclu(datapath, numberofshanks)
nc = size(spiketimes,1)
'''get log'''
#(sweeporder, sweeptable, bgdimnames) = getSweepTimesEP(logpath)
(stimc, duration, constring) = getSweepTimesEP(logpath, datapath, modality)
tfreq = stimc[0,3]
if (modality.find("sf")+1):
tlabel = "Spatial frequency (Cyc/Deg)"
ticks = np.arange(0, 0.62, 0.1)
print tlabel
print constring
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
ostr = str(orivals[i])+"Deg"
constring = constring + " at " + ostr
print ostr
(tuning, f0m, f0s, f1m, f1s, f2m, f2s) = dotuning(stimuluscondition, spiketimes, cellnumber, 2, duration, tlabel, ticks, constring, ostr, numbins, showflag)
if i == 0:
f0mean = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f0sem = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f1mean = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f1sem = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f2mean = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f2sem = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f0mean[:,:,i] = f0m[:,:]
f0sem[:,:,i] = f0s[:,:]
f1mean[:,:,i] = f1m[:,:]
f1sem[:,:,i] = f1s[:,:]
f2mean[:,:,i] = f2m[:,:]
f2sem[:,:,i] = f2s[:,:]
elif (modality.find("tf")+1):
tlabel = "Temporal frequency (Cyc/Sec)"
ticks = range(0,16,3)
print tlabel
print constring
orivals = np.unique(stimc[:,1])
orivals = np.delete(orivals, 3, 0)
for i in range(len(orivals)):
ostr = str(orivals[i])+"Deg"
constring = constring + " at " + ostr
stimuluscondition = stimc[np.where(stimc[:,1] == orivals[i])]
print ostr
(tuning, f0m, f0s, f1m, f1s, f2m, f2s) = dotuning(stimuluscondition, spiketimes, cellnumber, 3, duration, tlabel, ticks, constring, ostr, numbins, showflag)
if i == 0:
f0mean = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f0sem = np.empty((size(f0m,0), size(f0m,1), len(orivals)))
f1mean = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f1sem = np.empty((size(f2m,0), size(f1m,1), len(orivals)))
f2mean = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f2sem = np.empty((size(f2m,0), size(f2m,1), len(orivals)))
f0mean[:,:,i] = f0m[:,:]
f0sem[:,:,i] = f0s[:,:]
f1mean[:,:,i] = f1m[:,:]
f1sem[:,:,i] = f1s[:,:]
f2mean[:,:,i] = f2m[:,:]
f2sem[:,:,i] = f2s[:,:]
elif (modality.find("ori")+1):
tlabel = "Orientation (Deg)"
ticks = range(0,361,90)
print tlabel
print constring
stimuluscondition = stimc
ostr = "allori"
(tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem) = dotuning(stimuluscondition, spiketimes, cellnumber, 1, duration, tlabel, ticks, constring, ostr, numbins, showflag)
else:
print "No modality specified"
'''plot data'''
for s in range(1,9):
firstcell = findlevelbuffer(cellnumber, s, 100)
lastcell = findlevelbuffer(cellnumber, (s+1), 100)
if lastcell > firstcell:
sn = lastcell - firstcell
print "shank #"+str(s)+" has "+str(sn)+" cells"
figure(s)
for c in range(sn):
sp = c + firstcell
ax1 = subplot(ceil(sqrt(sn)), round(sqrt(sn)), c+1)
if (modality.find("sf")+1) or (modality.find("tf")+1):
ax1.set_xscale("log", nonposx='clip')
ax1.errorbar(tuning, f1mean[:,sp,0], yerr=f1sem[:,sp,0], fmt = 'ro', capsize=2, linestyle='-')
ax1.errorbar(tuning, f1mean[:,sp,1], yerr=f1sem[:,sp,1], fmt = 'bo', capsize=2, linestyle='-')
ax1.errorbar(tuning, f1mean[:,sp,2], yerr=f1sem[:,sp,2], fmt = 'go', capsize=2, linestyle='-')
else:
ax1.errorbar(tuning, f1mean[:,sp], yerr=f1sem[:,sp], fmt = 'ro', capsize=2, linestyle='-')
ax1.set_ylabel('F1', fontsize=10)
ax1.set_ylim(bottom=0)
xticks(ticks)
xlabel(tlabel, fontsize=10)
text(0,0, str(c+1), fontsize=10)
tick_params(axis='both', which='major', labelsize=7)
tight_layout()
subplots_adjust(top=0.9)
suptitle("Shank #"+str(s)+':'+constring, fontsize=14)
if (modality.find("sf")+1) or (modality.find("tf")+1):
figtext(0.1, 0.92, '0 Deg', color='red')
figtext(0.2, 0.92, '120 Deg', color = 'blue')
figtext(0.3, 0.92, '240 Deg', color = 'green')
fname = datapath+'_'+modality+'_tuning'+str(s)+'.png'
savefig(fname)
show(False)
'''save data'''
fullfilename = datapath + "_" + modality + "Data.h5"
f = h5py.File(fullfilename, 'w')
dset = f.create_dataset("f0mean", f0mean.shape, 'f')
dset[...] = f0mean
if (modality.find("sf")+1) or (modality.find("tf")+1):
dset.attrs["orivalues"] = orivals
dset.attrs["datapath"] = datapath
dset.attrs["logpath"] = logpath
dset2 = f.create_dataset("f0sem", f0sem.shape, 'f')
dset2[...] = f0sem
dset3 = f.create_dataset("f1mean", f1mean.shape, 'f')
dset3[...] = f1mean
dset4 = f.create_dataset("f1sem", f1sem.shape, 'f')
dset4[...] = f1sem
dset5 = f.create_dataset("tuning", tuning.shape, 'f')
dset5[...] = tuning
dset6 = f.create_dataset("cellnumber", cellnumber.shape, 'f')
dset6[...] = cellnumber
f.close()
return (cellnumber, tuning, f0mean, f0sem, f1mean, f1sem, f2mean, f2sem)
