def show(self):
self.win = pg.GraphicsWindow()
p5 = self.win.addPlot(title='stim')
p5.plot(self.stim['left'][0].time * 1000., self.stim['left'][0].sound)
p1 = self.win.addPlot(title='Bushy Vm', row=1, col=0)
for k, ear in enumerate(self.ears.keys()):
p1.plot(self['t'], self['vm_bu_%s' % ear], pen=(k, 15))
p2 = self.win.addPlot(title='SGC-BU xmtr left', row=0, col=1)
for i in range(30):
p2.plot(self['t'], self['xmtr%d_left'%i], pen=(i, 15))
p2.setXLink(p1)
p2r = self.win.addPlot(title='SGC-BU xmtr right', row=1, col=1)
for i in range(30):
p2r.plot(self['t'], self['xmtr%d_right'%i], pen=(i, 15))
p2r.setXLink(p1)
p3 = self.win.addPlot(title='MSO Vm', row=2, col=0)
p3.plot(self['t'], self['vm_mso'])
p3.setXLink(p1)
p4 = self.win.addPlot(title='BU-MSO xmtr', row=2, col=1)
for k, ear in enumerate(self.ears.keys()):
for i in range(30):
p2.plot(self['t'], self['mso_xmtr%d_%s'%(i, ear)], pen=(i, 15))
p4.setXLink(p1)
p4 = self.win.addPlot(title='AN spikes', row=3, col=0)
ntrain = len(self.sgc_cells['left'])
for k in range(ntrain):
yr = [k/float(ntrain), (k+0.8)/float(ntrain)]
vt = pg.VTickGroup(self.sgc_cells['left'][k]._spiketrain, yrange = yr, pen=(k, 15))
p4.addItem(vt)
p4.setXLink(p1)
p5.setXLink(p1)
# phaselocking calculations
phasewin = [self.stimdelay + 0.2*self.stimdur, self.stimdelay + self.stimdur]
msospk = PU.findspikes(self['t'], self['vm_mso'], -30.)
spkin = msospk[np.where(msospk > phasewin[0]*1e3)]
spikesinwin = spkin[np.where(spkin <= phasewin[1]*1e3)[0]]
# set freq for VS calculation
f0 = self.f0
fb = self.beatfreq
vs = PU.vector_strength(spikesinwin, f0)
print 'MSO Vector Strength at %.1f: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (f0, vs['r'], vs['d']*1e6, vs['R'], vs['p'], vs['n'])
if fb > 0:
vsb = PU.vector_strength(spikesinwin, fb)
print 'MSO Vector Strength to beat at %.1f: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (fb, vsb['r'], vsb['d']*1e6, vsb['R'], vsb['p'], vsb['n'])
(hist, binedges) = np.histogram(vs['ph'])
p6 = self.win.addPlot(title='VS', row=3, col=1)
p6.plot(binedges, hist, stepMode=True, fillBrush=(100, 100, 255, 150), fillLevel=0)
p6.setXRange(0., 2*np.pi)
self.win.show()
def showplots(name):
"""
Show traces from sound stimulation - without current injection
"""
f = open(name, 'r')
d = pickle.load(f)
f.close()
ncells = len(d['results'])
stiminfo = d['stim']
dur = stiminfo['rundur'] * 1000.
print 'dur: ', dur
print 'stim info: '
print ' fmod: ', stiminfo['fmod']
print ' dmod: ', stiminfo['dmod']
print ' f0: ', stiminfo['f0']
print ' cf: ', stiminfo['cf']
varsg = np.linspace(0.25, 2.0,
int((2.0 - 0.25) / 0.25) + 1) # was not stored...
fig, ax = mpl.subplots(ncells + 1, 2, figsize=(8.5, 11.))
spikelists = [[]] * ncells
prespikes = [[]] * ncells
xmin = 50.
for i in range(ncells):
vdat = d['results'][i]['v']
idat = d['results'][i]['i']
tdat = d['results'][i]['t']
pdat = d['results'][i]['pre']
PH.noaxes(ax[i, 0])
# if i == 0:
# PH.calbar(ax[0, 0], calbar=[120., -120., 25., 20.], axesoff=True, orient='left',
# unitNames={'x': 'ms', 'y': 'mV'}, fontsize=9, weight='normal', font='Arial')
for j in range(len(vdat)):
if j == 2:
ax[i, 0].plot(tdat - xmin, vdat[j], 'k', linewidth=0.5)
if j == 0:
ax[i, 0].annotate('%.2f' % varsg[i], (180., 20.))
ax[i, 0].set_xlim([0, dur - xmin])
ax[i, 0].set_ylim([-75, 50])
PH.referenceline(ax[i, 0],
reference=-62.0,
limits=None,
color='0.33',
linestyle='--',
linewidth=0.5,
dashes=[3, 3])
for j in range(len(vdat)):
detected = PU.findspikes(tdat, vdat[j], -20.)
detected = clean_spiketimes(detected)
spikelists[i].extend(detected)
if j == 0:
n, bins = np.histogram(detected,
np.linspace(0., dur, 201),
density=False)
else:
m, bins = np.histogram(detected,
np.linspace(0., dur, 201),
density=False)
n += m
prespikes[i].extend(pdat)
if j == 0:
n, bins = np.histogram(pdat,
np.linspace(0., dur, 201),
density=False)
else:
m, bins = np.histogram(pdat,
np.linspace(0., dur, 201),
density=False)
n += m
ax[i, 1].bar(bins[:-1] - xmin,
n,
width=bins[1],
facecolor='k',
alpha=0.75)
ax[i, 1].set_xlim([0, dur - xmin])
ax[i, 1].set_ylim([0, 30])
vs = PU.vector_strength(spikelists[i], stiminfo['fmod'])
pre_vs = PU.vector_strength(prespikes[i], stiminfo['fmod'])
# print 'pre: ', pre_vs
# print 'post: ', vs
# apos = ax[i,1].get_position()
# ax[i, 1].set_title('VS = %4.3f' % pre_vs['r'])
# # vector_plot(fig, vs['ph'], np.ones(len(vs['ph'])), yp = apos)
# phase_hist(fig, vs['ph'], yp=apos)
# phase_hist(fig, pre_vs['ph'], yp=apos)
prot = Variations(runtype, runname)
# stim_info = {'nreps': nrep, 'cf': cf, 'f0': f0, 'rundur': rundur, 'pipdur': pipdur, 'dbspl': dbspl, 'fmod': fmod, 'dmod': dmod}
if stiminfo['dmod'] > 0:
stimulus = 'SAM'
else:
stimulus = 'tone'
prot.make_stimulus(stimulus=stimulus,
cf=stiminfo['cf'],
f0=stiminfo['f0'],
simulator=None,
rundur=stiminfo['rundur'],
pipdur=stiminfo['pipdur'],
dbspl=stiminfo['dbspl'],
fmod=stiminfo['fmod'],
dmod=stiminfo['dmod'])
ax[-1, 1].plot(prot.stim.time * 1000. - xmin,
prot.stim.sound,
'k-',
linewidth=0.75)
ax[-1, 1].set_xlim([0, (dur - xmin)])
PH.noaxes(ax[-1, 0])
ax[-1, 0].set_xlim([0, dur - xmin])
ax[-1, 0].set_ylim([-75, 50])
# PH.referenceline(ax[-1, 0], reference=-62.0, limits=None, color='0.33', linestyle='--' ,linewidth=0.5, dashes=[3, 3])
PH.calbar(ax[-1, 0],
calbar=[20., 0., 25., 20.],
axesoff=True,
orient='left',
unitNames={
'x': 'ms',
'y': 'mV'
},
fontsize=9,
weight='normal',
font='Arial')
PH.cleanAxes(ax.ravel().tolist())
mpl.show()
def show(self):
self.win = pg.GraphicsWindow()
self.win.setBackground('w')
Fs = self.Fs
p1 = self.win.addPlot(title='Stimulus',
row=0,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'V'
})
p1.plot(self.stim.time * 1000,
self.stim.sound,
pen=pg.mkPen('k', width=0.75))
p1.setXLink(p1)
p2 = self.win.addPlot(title='AN spikes',
row=1,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'AN spikes (first trial)'
})
for nr in range(self.nrep):
xan = []
yan = []
for k in range(len(self.pre_cells[nr])):
r = self.pre_cells[nr][k]._spiketrain
xan.extend(r)
yr = k + np.zeros_like(r) + 0.2
yan.extend(yr)
c = pg.PlotCurveItem()
xp = np.repeat(np.array(xan), 2)
yp = np.repeat(np.array(yan), 2)
yp[1::2] = yp[::2] + 0.6
c.setData(xp.flatten(),
yp.flatten(),
connect='pairs',
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
p2.addItem(c)
p2.setXLink(p1)
p3 = self.win.addPlot(title='%s Spikes' % self.cell,
row=2,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'Trial #'
})
xcn = []
ycn = []
xspks = []
for k in range(self.nrep):
bspk = PU.findspikes(self.time[k], self.vms[k], -35.)
xcn.extend(bspk)
yr = k + np.zeros_like(bspk) + 0.2
ycn.extend(yr)
d = pg.PlotCurveItem()
xp = np.repeat(np.array(xcn), 2)
yp = np.repeat(np.array(ycn), 2)
yp[1::2] = yp[::2] + 0.6
d.setData(xp.flatten(),
yp.flatten(),
connect='pairs',
pen=pg.mkPen('k', width=1.5))
p3.addItem(d)
p3.setXLink(p1)
p4 = self.win.addPlot(title='%s Vm' % self.cell,
row=3,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'Vm (mV)'
})
for nr in range(self.nrep):
p4.plot(self.time[nr],
self.vms[nr],
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
p4.setXLink(p1)
p5 = self.win.addPlot(title='xmtr',
row=0,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'gSyn'
})
if synapseType == 'multisite':
for nr in [0]:
syn = self.synapses[nr]
j = 0
for k in range(self.n_sgc):
synapse = syn[k]
for i in range(synapse.terminal.n_rzones):
p5.plot(self.time[nr],
self.xmtrs[nr]['xmtr%04d' % j],
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
j = j + 1
p5.setXLink(p1)
p6 = self.win.addPlot(title='AN PSTH',
row=1,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'Sp/ms/trial'
})
bins = np.arange(0, 200, 1)
(hist, binedges) = np.histogram(xan, bins)
curve6 = p6.plot(binedges,
hist,
stepMode=True,
fillBrush=(0, 0, 0, 255),
brush=pg.mkBrush('k'),
fillLevel=0)
p7 = self.win.addPlot(title='%s PSTH' % self.cell,
row=2,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'Sp/ms/trial'
})
bins = np.arange(0, 200, 1)
(hist, binedges) = np.histogram(xcn, bins)
curve7 = p7.plot(binedges,
hist,
stepMode=True,
fillBrush=(0, 0, 0, 255),
brush=pg.mkBrush('k'),
fillLevel=0)
self.win.show()
def show(self):
self.win = pg.GraphicsWindow()
p1 = self.win.addPlot(title='stim', row=0, col=0)
p1.plot(self.stim.time * 1000, self.stim.sound)
p1.setXLink(p1)
p2 = self.win.addPlot(title='AN spikes', row=1, col=0)
vt = pg.VTickGroup(self.pre_cells[0]._spiketrain)
p2.addItem(vt)
p2.setXLink(p1)
p3 = self.win.addPlot(title='%s Spikes' % self.cell, row=2, col=0)
bspk = PU.findspikes(self['t'], self['vm'], -30.)
bspktick = pg.VTickGroup(bspk)
p3.addItem(bspktick)
p3.setXLink(p1)
p4 = self.win.addPlot(title='%s Vm' % self.cell, row=3, col=0)
p4.plot(self['t'], self['vm'])
p4.setXLink(p1)
p5 = self.win.addPlot(title='xmtr', row=0, col=1)
j = 0
for k in range(self.n_sgc):
synapse = self.synapses[k]
for i in range(synapse.terminal.n_rzones):
p5.plot(self['t'], self['xmtr%03d'%j], pen=(i, 15))
j = j + 1
p5.setXLink(p1)
p6 = self.win.addPlot(title='AN phase', row=1, col=1)
phasewin = [self.pip_start[0] + 0.25*self.pip_duration, self.pip_start[0] + self.pip_duration]
prespk = self.pre_cells[0]._spiketrain # just sample one...
spkin = prespk[np.where(prespk > phasewin[0]*1e3)]
spikesinwin = spkin[np.where(spkin <= phasewin[1]*1e3)]
print "\nCell type: %s" % self.cell
print "Stimulus: "
# set freq for VS calculation
if self.stimulus == 'tone':
f0 = self.f0
print "Tone: f0=%.3f at %3.1f dbSPL, cell CF=%.3f" % (self.f0, self.dbspl, self.cf)
if self.stimulus == 'SAM':
f0 = self.fMod
print ("SAM Tone: f0=%.3f at %3.1f dbSPL, fMod=%3.1f dMod=%5.2f, cell CF=%.3f" %
(self.f0, self.dbspl, self.fMod, self.dMod, self.cf))
if self.stimulus == 'clicks':
f0 = 1./self.click_rate
print "Clicks: interval %.3f at %3.1f dbSPL, cell CF=%.3f " % (self.click_rate, self.dbspl, self.cf)
vs = PU.vector_strength(spikesinwin, f0)
print 'AN Vector Strength at %.1f: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (f0, vs['r'], vs['d']*1e6, vs['R'], vs['p'], vs['n'])
(hist, binedges) = np.histogram(vs['ph'])
p6.plot(binedges, hist, stepMode=True, fillBrush=(100, 100, 255, 150), fillLevel=0)
p6.setXRange(0., 2*np.pi)
p7 = self.win.addPlot(title='%s phase' % self.cell, row=2, col=1)
spkin = bspk[np.where(bspk > phasewin[0]*1e3)]
spikesinwin = spkin[np.where(spkin <= phasewin[1]*1e3)]
vs = PU.vector_strength(spikesinwin, f0)
print '%s Vector Strength: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (self.cell, vs['r'], vs['d']*1e6, vs['R'], vs['p'], vs['n'])
(hist, binedges) = np.histogram(vs['ph'])
p7.plot(binedges, hist, stepMode=True, fillBrush=(100, 100, 255, 150), fillLevel=0)
p7.setXRange(0., 2*np.pi)
p7.setXLink(p6)
self.win.show()
def show(self):
self.win = pg.GraphicsWindow()
self.win.setBackground('w')
Fs = self.Fs
p1 = self.win.addPlot(title='Stimulus',
row=0,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'V'
})
p1.plot(self.stim.time * 1000,
self.stim.sound,
pen=pg.mkPen('k', width=0.75))
p1.setXLink(p1)
p2 = self.win.addPlot(title='AN spikes',
row=1,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'AN spikes (first trial)'
})
for nr in range(self.nrep):
xan = []
yan = []
for k in range(len(self.pre_cells[nr])):
r = self.pre_cells[nr][k]._spiketrain
xan.extend(r)
yr = k + np.zeros_like(r) + 0.2
yan.extend(yr)
c = pg.PlotCurveItem()
xp = np.repeat(np.array(xan), 2)
yp = np.repeat(np.array(yan), 2)
yp[1::2] = yp[::2] + 0.6
c.setData(xp.flatten(),
yp.flatten(),
connect='pairs',
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
p2.addItem(c)
p2.setXLink(p1)
p3 = self.win.addPlot(title='%s Spikes' % self.cell,
row=2,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'Trial #'
})
xcn = []
ycn = []
xspks = []
for k in range(self.nrep):
bspk = PU.findspikes(self.time[k], self.vms[k], -35.)
xcn.extend(bspk)
yr = k + np.zeros_like(bspk) + 0.2
ycn.extend(yr)
d = pg.PlotCurveItem()
xp = np.repeat(np.array(xcn), 2)
yp = np.repeat(np.array(ycn), 2)
yp[1::2] = yp[::2] + 0.6
d.setData(xp.flatten(),
yp.flatten(),
connect='pairs',
pen=pg.mkPen('k', width=1.5))
p3.addItem(d)
p3.setXLink(p1)
p4 = self.win.addPlot(title='%s Vm' % self.cell,
row=3,
col=0,
labels={
'bottom': 'T (ms)',
'left': 'Vm (mV)'
})
for nr in range(self.nrep):
p4.plot(self.time[nr],
self.vms[nr],
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
p4.setXLink(p1)
p5 = self.win.addPlot(title='xmtr',
row=0,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'gSyn'
})
if synapseType == 'multisite':
for nr in [0]:
syn = self.synapses[nr]
j = 0
for k in range(self.n_sgc):
synapse = syn[k]
for i in range(synapse.terminal.n_rzones):
p5.plot(self.time[nr],
self.xmtrs[nr]['xmtr%04d' % j],
pen=pg.mkPen(pg.intColor(nr, self.nrep),
hues=self.nrep,
width=1.0))
j = j + 1
p5.setXLink(p1)
p6 = self.win.addPlot(title='AN PSTH',
row=1,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'Sp/ms/trial'
})
bins = np.arange(0, 200, 1)
(hist, binedges) = np.histogram(xan, bins)
curve6 = p6.plot(binedges,
hist,
stepMode=True,
fillBrush=(0, 0, 0, 255),
brush=pg.mkBrush('k'),
fillLevel=0)
p7 = self.win.addPlot(title='%s PSTH' % self.cell,
row=2,
col=1,
labels={
'bottom': 'T (ms)',
'left': 'Sp/ms/trial'
})
bins = np.arange(0, 200, 1)
(hist, binedges) = np.histogram(xcn, bins)
curve7 = p7.plot(binedges,
hist,
stepMode=True,
fillBrush=(0, 0, 0, 255),
brush=pg.mkBrush('k'),
fillLevel=0)
# p6 = self.win.addPlot(title='AN phase', row=1, col=1)
# phasewin = [self.pip_start[0] + 0.25*self.pip_duration, self.pip_start[0] + self.pip_duration]
# prespk = self.pre_cells[0]._spiketrain # just sample one...
# spkin = prespk[np.where(prespk > phasewin[0]*1e3)]
# spikesinwin = spkin[np.where(spkin <= phasewin[1]*1e3)]
# print "\nCell type: %s" % self.cell
# print "Stimulus: "
#
# # set freq for VS calculation
# if self.stimulus == 'tone':
# f0 = self.f0
# print "Tone: f0=%.3f at %3.1f dbSPL, cell CF=%.3f" % (self.f0, self.dbspl, self.cf)
# if self.stimulus == 'SAM':
# f0 = self.fMod
# print ("SAM Tone: f0=%.3f at %3.1f dbSPL, fMod=%3.1f dMod=%5.2f, cell CF=%.3f" %
# (self.f0, self.dbspl, self.fMod, self.dMod, self.cf))
# if self.stimulus == 'clicks':
# f0 = 1./self.click_rate
# print "Clicks: interval %.3f at %3.1f dbSPL, cell CF=%.3f " % (self.click_rate, self.dbspl, self.cf)
# vs = PU.vector_strength(spikesinwin, f0)
#
# print 'AN Vector Strength: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (vs['r'], vs['d']*1e6, vs['R'], vs['p'], vs['n'])
# (hist, binedges) = np.histogram(vs['ph'])
# curve = p6.plot(binedges, hist, stepMode=True, fillBrush=(100, 100, 255, 150), fillLevel=0)
# p6.setXRange(0., 2*np.pi)
#
# p7 = self.win.addPlot(title='%s phase' % self.cell, row=2, col=1)
# spkin = bspk[np.where(bspk > phasewin[0]*1e3)]
# spikesinwin = spkin[np.where(spkin <= phasewin[1]*1e3)]
# vs = PU.vector_strength(spikesinwin, f0)
# print '%s Vector Strength: %7.3f, d=%.2f (us) Rayleigh: %7.3f p = %.3e n = %d' % (self.cell, vs['r'], vs['d']*1e6, vs['R'], vs['p'], vs['n'])
# (hist, binedges) = np.histogram(vs['ph'])
# curve = p7.plot(binedges, hist, stepMode=True, fillBrush=(100, 100, 255, 150), fillLevel=0)
# p7.setXRange(0., 2*np.pi)
# p7.setXLink(p6)
self.win.show()
