def mua_si_lfp_si(source, layers=False, ms=1, figsize=(7.5, 6.5)):
"""Pool recording.mua_si_lfp_si() results across recordings specified by source,
plot the result"""
uns = get_ipython().user_ns
recs, tracks = parse_source(source)
lfpsis, muasis = [], []
for rec in recs:
print(rec.absname)
lfpsi, muasi, t = rec.mua_si_lfp_si(ms=ms, layers=layers, plot=False, plotseries=False,
figsize=figsize)
lfpsis.append(lfpsi)
muasis.append(muasi)
lfpsi = np.hstack(lfpsis)
muasi = np.hstack(muasis)
# plot:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot([-1, 1], [-1, 1], 'e--') # underplot y=x line
a.plot(lfpsi, muasi[0], 'e.', ms=ms)
if layers:
a.plot(lfpsi, muasi[1], 'r.', ms=ms)
a.plot(lfpsi, muasi[2], 'g.', ms=ms)
a.plot(lfpsi, muasi[3], 'b.', ms=ms)
a.set_xlabel('LFP SI (%s)' % uns['LFPSIKIND'])
a.set_ylabel('MUA SI (%s)' % uns['MUASIKIND'])
a.set_xlim(-1, 1)
a.set_ylim(-1, 1)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def pospdf(self, neurons=None, dim='y', nbins=10, a=None, stats=False, figsize=(7.5, 6.5)):
"""Plot PDF of cell positions ('x' or 'y') along the polytrode
to get an idea of how cells are distributed in space"""
if neurons == 'all':
neurons = self.alln.values()
elif neurons == 'quiet':
neurons = self.qn.values()
else:
neurons = self.n.values()
dimi = {'x':0, 'y':1}[dim]
p = [ n.pos[dimi] for n in neurons ] # all position values
nbins = max(nbins, 2*intround(np.sqrt(self.nneurons)))
n, p = np.histogram(p, bins=nbins) # p includes rightmost bin edge
binwidth = p[1] - p[0] # take width of first bin in p
if stats:
mean = np.mean(p)
median = np.median(p)
argmode = n.argmax()
mode = p[argmode] + binwidth / 2 # middle of tallest bin
stdev = np.std(p)
if a == None:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
else: # add to existing axes
a.hold(True)
f = pl.gcf()
# use CLUSTERCOLOURDICT for familiarity with len 10 1-based id to colour mapping
#color = CLUSTERCOLOURDICT[int(self.id)]
color = 'k'
# exclude rightmost bin edge in p
a.bar(left=p[:-1], height=n, width=binwidth, bottom=0, color=color, ec=color,
yerr=None, xerr=None, capsize=3)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.set_xlabel('neuron %s position (um)' % dim)
a.set_ylabel('neuron count')
if stats:
# add stuff to top right of plot:
uns = get_ipython().user_ns
a.text(0.99, 0.99, 'mean = %.3f\n'
'median = %.3f\n'
'mode = %.3f\n'
'stdev = %.3f\n'
'minrate = %.2f Hz\n'
'nneurons = %d\n'
'dt = %d min'
% (mean, median, mode, stdev,
uns['MINRATE'], self.nneurons, intround(self.dtmin)),
transform = a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
f.canvas.draw() # this is needed if a != None when passed as arg
return a
def plot(self, nbins=None, rate=False, figsize=(7.5, 6.5)):
"""style can be 'rate', but defaults to count"""
if nbins == None:
nbins = intround(np.sqrt(len(self.dts))) # good heuristic
dts = self.dts / 1000 # in ms, converts to float64 array
trange = self.trange / 1000 # in ms, converts to float64 array
nbins = max(20, nbins) # enforce min nbins
nbins = min(200, nbins) # enforce max nbins
t = np.linspace(start=trange[0], stop=trange[1], num=nbins, endpoint=True)
n = np.histogram(dts, bins=t, density=False)[0]
binwidth = t[1] - t[0] # all should be equal width
if rate: # normalize by binwidth and convert to float:
n = n / float(binwidth)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=n, width=binwidth) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('ISI (ms)')
if rate:
a.set_ylabel('spike rate (Hz)')
else:
a.set_ylabel('count')
#a.set_title('n%d spikes relative to n%d spikes' % (self.n1.id, self.n0.id))
title = lastcmd() + ', binwidth: %.2f ms' % binwidth
a.set_title(title)
gcfm().window.setWindowTitle(title)
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
return self
def mua_si_lfp_si(source, layers=False, ms=1, figsize=(7.5, 6.5)):
"""Pool recording.mua_si_lfp_si() results across recordings specified by source,
plot the result"""
uns = get_ipython().user_ns
recs, tracks = parse_source(source)
lfpsis, muasis = [], []
for rec in recs:
print(rec.absname)
lfpsi, muasi, t = rec.mua_si_lfp_si(ms=ms,
layers=layers,
plot=False,
plotseries=False,
figsize=figsize)
lfpsis.append(lfpsi)
muasis.append(muasi)
lfpsi = np.hstack(lfpsis)
muasi = np.hstack(muasis)
# plot:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot([-1, 1], [-1, 1], 'e--') # underplot y=x line
a.plot(lfpsi, muasi[0], 'e.', ms=ms)
if layers:
a.plot(lfpsi, muasi[1], 'r.', ms=ms)
a.plot(lfpsi, muasi[2], 'g.', ms=ms)
a.plot(lfpsi, muasi[3], 'b.', ms=ms)
a.set_xlabel('LFP SI (%s)' % uns['LFPSIKIND'])
a.set_ylabel('MUA SI (%s)' % uns['MUASIKIND'])
a.set_xlim(-1, 1)
a.set_ylim(-1, 1)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self, normed=True, scale=2.0, MPL=False, margins=True):
win = RevCorrs.plot(self,
normed=normed,
title=lastcmd(),
scale=scale,
MPL=MPL,
margins=margins)
return win # necessary in IPython
def scstim(self, method='mean', width=None, tres=None, figsize=(7.5, 6.5)):
"""Scatter plot some summary statistic of spike correlations of each recording,
classified by the stimulus group each recording falls into. width and tres dictate
tranges to split recordings up into, if any"""
## TODO: for each pair of recordings, find common subset of active neurons and
## calculate pairwise corrs for each recording in that pair using just those neurons
## TODO: maybe limit to visually responsive cells
uns = get_ipython().user_ns
if width == None:
width = uns['SCWIDTH']
if tres == None:
tres = width
blankmseqrids = uns['BSRIDS'][self.absname] + uns['MSRIDS'][
self.absname]
movdriftrids = uns['NSRIDS'][self.absname] + uns['DBRIDS'][
self.absname]
blankmseqcorrs = []
movdriftcorrs = []
for rid in (blankmseqrids + movdriftrids):
r = self.r[rid]
print('%s: %s' % (r.absname, r.name))
spikecorr = r.sc(width=width, tres=tres)
sc = spikecorr.sct(method=method)[0]
sc = sc[
0] # pull out the spike correlation values that span all laminae
if rid in blankmseqrids:
blankmseqcorrs.append(sc)
else:
movdriftcorrs.append(sc)
blankmseqcorrs = np.hstack(blankmseqcorrs)
movdriftcorrs = np.hstack(movdriftcorrs)
# repeat each element in blankmseqcorrs len(movdriftcorrs) times:
x = np.repeat(blankmseqcorrs, len(movdriftcorrs))
# tile movdriftcorrs len(blankmseqcorrs) times:
y = np.tile(movdriftcorrs, len(blankmseqcorrs))
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
lim = min([x.min(), y.min(), 0]), max([x.max(), y.max()])
a.plot(lim, lim, c='e', ls='--', marker=None) # y=x line
a.plot(x, y, 'k.')
#a.set_xlim(lim)
#a.set_ylim(lim)
a.set_xlabel('%s spike correlations: blankscreen and mseq' % method)
a.set_ylabel('%s spike correlations: movie and drift bar' % method)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
f.show()
def scstim(self, method='mean', width=None, tres=None, figsize=(7.5, 6.5)):
"""Scatter plot some summary statistic of spike correlations of each recording,
classified by the stimulus group each recording falls into. width and tres dictate
tranges to split recordings up into, if any"""
## TODO: for each pair of recordings, find common subset of active neurons and calculate
## pairwise corrs for each recording in that pair using just those neurons
## TODO: maybe limit to visually responsive cells
uns = get_ipython().user_ns
if width == None:
width = uns['SCWIDTH']
if tres == None:
tres = width
blankmseqrids = uns['BSRIDS'][self.absname] + uns['MSRIDS'][self.absname]
movdriftrids = uns['NSRIDS'][self.absname] + uns['DBRIDS'][self.absname]
blankmseqcorrs = []
movdriftcorrs = []
for rid in (blankmseqrids + movdriftrids):
r = self.r[rid]
print('%s: %s' % (r.absname, r.name))
spikecorr = r.sc(width=width, tres=tres)
sc = spikecorr.sct(method=method)[0]
sc = sc[0] # pull out the spike correlation values that span all laminae
if rid in blankmseqrids:
blankmseqcorrs.append(sc)
else:
movdriftcorrs.append(sc)
blankmseqcorrs = np.hstack(blankmseqcorrs)
movdriftcorrs = np.hstack(movdriftcorrs)
# repeat each element in blankmseqcorrs len(movdriftcorrs) times:
x = np.repeat(blankmseqcorrs, len(movdriftcorrs))
# tile movdriftcorrs len(blankmseqcorrs) times:
y = np.tile(movdriftcorrs, len(blankmseqcorrs))
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
lim = min([x.min(), y.min(), 0]), max([x.max(), y.max()])
a.plot(lim, lim, c='e', ls='--', marker=None) # y=x line
a.plot(x, y, 'k.')
#a.set_xlim(lim)
#a.set_ylim(lim)
a.set_xlabel('%s spike correlations: blankscreen and mseq' % method)
a.set_ylabel('%s spike correlations: movie and drift bar' % method)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
f.show()
def meanratepdf(self, bins=None, figsize=(7.5, 6.5)):
"""Plot histogram of mean firing rates"""
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if bins == None:
bins = np.arange(0, 1, 0.05)
n, mr = np.histogram(self.meanrates, bins=bins, density=False)
binwidth = mr[1] - mr[0] # take width of first bin
a.bar(left=mr[:-1], height=n, width=binwidth, bottom=0, color='k', ec='k')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.set_xlabel('mean firing rate (Hz)')
a.set_ylabel('neuron count')
f.tight_layout(pad=0.3) # crop figure to contents
def sc_ising_vs_cch(source, ms=5, figsize=(7.5, 6.5)):
"""Scatter plot spike corrs calculated from Ising matrix against those calculated
from CCH. INCOMPLETE.
- find tracks in common, get allnids from each track
- how to deal with big time gaps between experiments in a single recording? I constrain
to the set of tranges of each experiment in rec.codes()
- maybe i can convert the core.SpikeCorr object to take a source argument instead of
recording/experiment objects
- do all the spikecorr analyses make sense for multiple recordings, or for recordings
from different tracks?
- for each track absname
"""
isingscs = {}
cchscs = {}
# init a dict
# for each rec, find out which track it's from
recs, tracks = parse_source(source)
isingscs, cchscs = [], []
for rec in recs:
print(rec.absname)
sc = rec.sc()
sc.calc()
isingscs.append(sc.corrs)
cchscs.append(rec.sc_cch())
# for something...
isingsc = np.hstack(isingscs)
cchsc = np.hstack(cchscs)
# plot:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot(isingsc, cchsc, 'e.', ms=ms)
a.set_xlabel('Ising spike corrs')
a.set_ylabel('CCH spike corrs')
a.set_xlim(-0.05, 0.2)
a.set_ylim(-0.5, 1)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def sc_ising_vs_cch(source, ms=5, figsize=(7.5, 6.5)):
"""Scatter plot spike corrs calculated from Ising matrix against those calculated
from CCH. INCOMPLETE.
- find tracks in common, get allnids from each track
- how to deal with big time gaps between experiments in a single recording? I constrain
to the set of tranges of each experiment in rec.codes()
- maybe i can convert the core.SpikeCorr object to take a source argument instead of
recording/experiment objects
- do all the spikecorr analyses make sense for multiple recordings, or for recordings
from different tracks?
- for each track absname
"""
isingscs = {}
cchscs = {}
# init a dict
# for each rec, find out which track it's from
recs, tracks = parse_source(source)
isingscs, cchscs = [], []
for rec in recs:
print(rec.absname)
sc = rec.sc()
sc.calc()
isingscs.append(sc.corrs)
cchscs.append(rec.sc_cch())
# for something...
isingsc = np.hstack(isingscs)
cchsc = np.hstack(cchscs)
# plot:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot(isingsc, cchsc, 'e.', ms=ms)
a.set_xlabel('Ising spike corrs')
a.set_ylabel('CCH spike corrs')
a.set_xlim(-0.05, 0.2)
a.set_ylim(-0.5, 1)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self, var='ori', fixed=None, figsize=(4, 4), title=False):
"""var: string name of variable you want to plot a tuning curve for
fixed: dict with keys containing names of vars to keep fixed when building tuning
curve, and values containing each var's value(s) to fix at
Ex: r71.n[1].tune().plot('phase0', fixed={'ori':138, 'sfreqCycDeg':[0.4, 0.8]})
"""
if var == 'ori':
theta, r, z, p = self.pref(var=var, fixed=fixed)
txt = ('pref=%.2f\n'
'r=%.2f\n'
'z=%.2f\n'
'p=%.6f' % (theta, r, z, p))
else:
self.calc(var=var, fixed=fixed)
ysum = self.y.sum()
if ysum != 0:
r = self.y.max() / ysum # fraction of spikes at max
else:
r = 0.0
txt = 'peak=%.2f\nr=%.2f' % (self.peak, r)
# create a new figure:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
fontsize = get_ipython().user_ns['fontsize'] # function
fs = fontsize() # save original font size
a.plot(self.x, self.y, c='e', ls='--', mew=0, mfc='k', ms=10)
a.set_ylim(ymin=0)
a.set_xlabel(var)
a.set_ylabel('spike count')
titlestr = lastcmd()
titlestr += ' nid%d' % self.neuron.id
if title:
a.set_title(titlestr)
f.canvas.window().setWindowTitle(titlestr)
a.text(0.99,
0.99,
txt,
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
return self
def std(self,
t0=None,
t1=None,
chani=-1,
width=None,
tres=None,
fmt='k-',
title=True,
figsize=(20, 3.5)):
"""Plot standard deviation of LFP signal from t0 to t1 on chani, using bins of width
and tres"""
uns = get_ipython().user_ns
self.get_data()
data = self.data[chani]
ts = self.get_tssec()
if t0 == None:
t0 = ts[0]
if t1 == None:
t1 = ts[-1]
if width == None:
width = uns['LFPSIWIDTH'] # sec
if tres == None:
tres = uns['LFPSITRES'] # sec
tranges = split_tranges([(t0, t1)], width, tres)
stds = []
for trange in tranges:
ti0, ti1 = ts.searchsorted(trange)
stds.append(data[ti0:ti1].std())
stds = np.hstack(stds)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot(tranges[:, 0], stds, fmt)
a.autoscale(enable=True, tight=True)
a.set_xlim(xmin=0) # ADC clock starts at t=0
a.set_xlabel('time (s)')
a.set_ylabel('LFP $\sigma$ ($\mu$V)')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
f.tight_layout(pad=0.3)
self.f = f
return stds
def meanratepdf(self, bins=None, figsize=(7.5, 6.5)):
"""Plot histogram of mean firing rates"""
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if bins == None:
bins = np.arange(0, 1, 0.05)
n, mr = np.histogram(self.meanrates, bins=bins, density=False)
binwidth = mr[1] - mr[0] # take width of first bin
a.bar(left=mr[:-1],
height=n,
width=binwidth,
bottom=0,
color='k',
ec='k')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.set_xlabel('mean firing rate (Hz)')
a.set_ylabel('neuron count')
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self, var='ori', fixed=None, figsize=(4, 4), title=False):
"""var: string name of variable you want to plot a tuning curve for
fixed: dict with keys containing names of vars to keep fixed when building tuning
curve, and values containing each var's value(s) to fix at
Ex: r71.n[1].tune().plot('phase0', fixed={'ori':138, 'sfreqCycDeg':[0.4, 0.8]})
"""
if var == 'ori':
theta, r, z, p = self.pref(var=var, fixed=fixed)
txt = ('pref=%.2f\n'
'r=%.2f\n'
'z=%.2f\n'
'p=%.6f' % (theta, r, z, p))
else:
self.calc(var=var, fixed=fixed)
ysum = self.y.sum()
if ysum != 0:
r = self.y.max() / ysum # fraction of spikes at max
else:
r = 0.0
txt = 'peak=%.2f\nr=%.2f' % (self.peak, r)
# create a new figure:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
fontsize = get_ipython().user_ns['fontsize'] # function
fs = fontsize() # save original font size
a.plot(self.x, self.y, c='e', ls='--', mew=0, mfc='k', ms=10)
a.set_ylim(ymin=0)
a.set_xlabel(var)
a.set_ylabel('spike count')
titlestr = lastcmd()
titlestr += ' nid%d' % self.neuron.id
if title:
a.set_title(titlestr)
f.canvas.window().setWindowTitle(titlestr)
a.text(0.99, 0.99, txt, transform=a.transAxes,
horizontalalignment='right', verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
return self
def std(self, t0=None, t1=None, chani=-1, width=None, tres=None, fmt='k-', title=True,
figsize=(20, 3.5)):
"""Plot standard deviation of LFP signal from t0 to t1 on chani, using bins of width
and tres"""
uns = get_ipython().user_ns
self.get_data()
data = self.data[chani]
ts = self.get_tssec()
if t0 == None:
t0 = ts[0]
if t1 == None:
t1 = ts[-1]
if width == None:
width = uns['LFPSIWIDTH'] # sec
if tres == None:
tres = uns['LFPSITRES'] # sec
tranges = split_tranges([(t0, t1)], width, tres)
stds = []
for trange in tranges:
ti0, ti1 = ts.searchsorted(trange)
stds.append(data[ti0:ti1].std())
stds = np.hstack(stds)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.plot(tranges[:, 0], stds, fmt)
a.autoscale(enable=True, tight=True)
a.set_xlim(xmin=0) # ADC clock starts at t=0
a.set_xlabel('time (s)')
a.set_ylabel('LFP $\sigma$ ($\mu$V)')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
f.tight_layout(pad=0.3)
self.f = f
return stds
def npos(self,
colour='active',
inchespermicron=0.007,
legend=False,
alpha=0.6):
"""Plot (x, y) cell positions over top of polytrode channel positions, to get an idea
of how cells are distributed in space. Colour cells by 'active', 'rftype',
'spiketype' or 'sigma'."""
uns = get_ipython().user_ns
npos = np.asarray([neuron.pos for neuron in self.alln.values()])
chanpos = self.chanpos
chanxs, chanys = chanpos[:, 0], chanpos[:, 1]
uchanxs = np.unique(chanxs)
xspace = np.diff(uchanxs).max(
) # max spacing of consecutive unique x chan positions
hsw = uns['PTSHANKWIDTHS'][self.pttype] / 2 # half shank width
xs = np.hstack((npos[:, 0], chanxs, [-hsw, hsw]))
ys = np.hstack((npos[:, 1], chanys))
ymin = min(min(ys), 0)
xlim = min(xs.min(),
uchanxs[0] - xspace / 2), max(xs.max(),
uchanxs[-1] + xspace / 2)
ylim = ys.max() + xspace, ymin # inverted y axis
figwidth = inchespermicron * np.ptp(
xlim) * 2 + 3 * legend # make space for y axis labels
figheight = inchespermicron * np.ptp(ylim)
f = pl.figure(figsize=(figwidth, figheight))
a = f.add_subplot(111, aspect='equal')
a.set_frame_on(False)
# plot rectangle representing shank width and length, excluding the tip:
sl = ylim[0]
# starting from bottom left, going clockwise:
shankxs = -hsw, -hsw, hsw, hsw
shankys = sl, ymin, ymin, sl
a.fill(shankxs, shankys, color='lightgrey', ec='none')
# plot electrode sites:
a.plot(chanpos[:, 0], chanpos[:, 1], 'k.', ms=5)
if colour == 'active':
# plot active and quiet cell positions in red and blue, respectively:
anpos = np.asarray([neuron.pos for neuron in self.n.values()])
qnpos = np.asarray([neuron.pos for neuron in self.qn.values()])
na = len(anpos)
nq = len(qnpos)
# layer in inverse order of importance:
if na:
a.plot(qnpos[:, 0],
qnpos[:, 1],
'b.',
ms=10,
alpha=alpha,
label='quiet')
if nq:
a.plot(anpos[:, 0],
anpos[:, 1],
'r.',
ms=10,
alpha=alpha,
label='active')
elif colour == 'rftype':
# plot simple, complex, LGN afferent and None in red, blue, green and grey:
spos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'simple'
])
cpos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'complex'
])
Lpos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'LGN'
])
Npos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.rftype == None
])
ns = len(spos)
nc = len(cpos)
nL = len(Lpos)
nN = len(Npos)
# layer in inverse order of importance:
if nN:
a.plot(Npos[:, 0],
Npos[:, 1],
'e.',
ms=10,
alpha=alpha,
label='unknown')
if nL:
a.plot(Lpos[:, 0],
Lpos[:, 1],
'g.',
ms=10,
alpha=alpha,
label='LGN afferent')
if nc:
a.plot(cpos[:, 0],
cpos[:, 1],
'b.',
ms=10,
alpha=alpha,
label='complex')
if ns:
a.plot(spos[:, 0],
spos[:, 1],
'r.',
ms=10,
alpha=alpha,
label='simple')
elif colour == 'spiketype':
# plot fast, slow, fastasym and slowasym in red, blue, green and grey:
fpos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'fast'
])
spos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'slow'
])
fapos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'fastasym'
])
sapos = np.asarray([
neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'slowasym'
])
nf = len(fpos)
ns = len(spos)
nfa = len(fapos)
nsa = len(sapos)
# layer in inverse order of frequency:
if nf:
a.plot(fpos[:, 0],
fpos[:, 1],
'r.',
ms=10,
alpha=alpha,
label='fast')
if ns:
a.plot(spos[:, 0],
spos[:, 1],
'b.',
ms=10,
alpha=alpha,
label='slow')
if nfa:
a.plot(fapos[:, 0],
fapos[:, 1],
'g.',
ms=10,
alpha=alpha,
label='fast asymmetric')
if nsa:
a.plot(sapos[:, 0],
sapos[:, 1],
'e.',
ms=10,
alpha=alpha,
label='slow asymmetric')
elif colour == 'sigma':
sigmas = np.asarray(
[neuron.sigma for neuron in self.alln.values()])
cmap = mpl.cm.hot_r
# best to fully saturate alpha because colour indicates value, not just class:
sc = a.scatter(npos[:, 0],
npos[:, 1],
edgecolor='none',
c=sigmas,
cmap=cmap,
alpha=1.0,
s=30,
zorder=10)
else:
raise RuntimeError("unknown colour kwarg %r" % colour)
a.set_xlim(xlim)
a.set_ylim(ylim)
a.set_xticks(uchanxs)
a.set_yticks(np.arange(0, ylim[0], 200))
#a.xaxis.set_ticks_position('bottom')
#a.yaxis.set_ticks_position('left')
# put legend to right of the axes:
if legend:
if colour == 'sigma':
f.colorbar(sc,
ax=a,
shrink=0.1,
pad=0.1,
aspect=10,
ticks=[min(sigmas), max(sigmas)],
format='%d',
label='sigma')
else:
a.legend(loc='center left',
bbox_to_anchor=(1.2, 0.5),
frameon=False)
bbox = a.get_position()
wh = bbox.width / bbox.height # w:h ratio of axes, includes all ticks and labels?
w, h = gcfm().canvas.get_width_height()
gcfm().resize(w * wh, h)
titlestr = lastcmd()
gcfm().set_window_title(titlestr)
a.set_title(self.absname)
def cch(self,
nid0,
nid1=None,
trange=50,
binw=None,
shift=None,
nshifts=10,
rate=False,
norm=False,
c='k',
title=True,
figsize=(7.5, 6.5)):
"""Copied from Recording.cch(). Plot cross-correlation histogram given nid0 and nid1.
If nid1 is None, calculate autocorrelogram. +/- trange and binw are in ms. If shift
(in ms) is set, calculate the average of +/- nshift CCHs shifted by shift, and then
subtract that from the unshifted CCH to get the shift corrected CCH"""
if nid1 == None:
nid1 = nid0
autocorr = nid0 == nid1
n0 = self.alln[nid0]
n1 = self.alln[nid1]
calctrange = trange * 1000 # calculation trange, in us
if shift:
assert nshifts > 0
shift *= 1000 # convert to us
maxshift = nshifts * shift
calctrange = trange + maxshift # expand calculated trange to encompass shifts
calctrange = np.array([-calctrange,
calctrange]) # convert to a +/- array, in us
dts = util.xcorr(n0.spikes, n1.spikes, calctrange) # in us
if autocorr:
dts = dts[dts != 0] # remove 0s for autocorr
if shift: # calculate dts for shift corrector
shiftis = range(-nshifts, nshifts + 1)
shiftis.remove(
0
) # don't shift by 0, that's the original which we'll subtract from
shifts = np.asarray(shiftis) * shift
shiftdts = np.hstack([dts + s for s in shifts]) # in us
print('shifts =', shifts / 1000)
if not binw:
nbins = intround(np.sqrt(len(dts))) # good heuristic
nbins = max(20, nbins) # enforce min nbins
nbins = min(200, nbins) # enforce max nbins
else:
nbins = intround(2 * trange / binw)
dts = dts / 1000 # in ms, converts to float64 array
t = np.linspace(start=-trange,
stop=trange,
num=nbins + 1,
endpoint=True) # ms
binw = t[1] - t[0] # all should be equal width, ms
n = np.histogram(dts, bins=t, density=False)[0]
if shift: # subtract shift corrector
shiftdts = shiftdts / 1000 # in ms, converts to float64 array
shiftn = np.histogram(shiftdts, bins=t,
density=False)[0] / (nshifts * 2)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=shiftn,
width=binw) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
n -= shiftn
if norm: # normalize and convert to float:
n = n / n.max()
elif rate: # normalize by binw and convert to float:
n = n / binw
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=n, width=binw, color=c,
ec=c) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
if norm:
a.set_ylabel('coincidence rate (AU)')
a.set_yticks([0, 1])
elif rate:
a.set_ylabel('coincidence rate (Hz)')
else:
a.set_ylabel('count')
if title:
a.set_title('spike times of n%d wrt n%d' %
(self.n1.id, self.n0.id))
wtitlestr = lastcmd() # + ', binw=%.1f ms' % binw
gcfm().window.setWindowTitle(wtitlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def specgram(self, t0=None, t1=None, f0=0.1, f1=100, p0=-60, p1=None, chanis=-1,
width=None, tres=None, cm='jet', colorbar=False,
showstates=False, lw=4, alpha=1, relative2t0=False, lim2stim=False,
title=True, reclabel=True, swapaxes=False, figsize=None):
"""Plot a spectrogram from t0 to t1 in sec, from f0 to f1 in Hz, and clip power values
from p0 to p1 in dB, based on channel index chani of LFP data. chanis=0 uses most
superficial channel, chanis=-1 uses deepest channel. If len(chanis) > 1, take mean of
specified chanis. width and tres are in sec. As an alternative to cm.jet (the
default), cm.gray, cm.hsv cm.terrain, and cm.cubehelix_r colormaps seem to bring out
the most structure in the spectrogram. showstates controls whether to plot lines
demarcating desynchronized and synchronized periods. relative2t0 controls whether to
plot relative to t0, or relative to start of ADC clock. lim2stim limits the time range
only to when a stimulus was on screen, i.e. to the outermost times of non-NULL din"""
uns = get_ipython().user_ns
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1] # full duration
if t1 == None:
t1 = t0 + 10 # 10 sec window
if lim2stim:
t0, t1 = self.apply_lim2stim(t0, t1)
dt = t1 - t0
if width == None:
width = uns['LFPSPECGRAMWIDTH'] # sec
if tres == None:
tres = uns['LFPSPECGRAMTRES'] # sec
assert tres <= width
NFFT = intround(width * self.sampfreq)
noverlap = intround(NFFT - tres * self.sampfreq)
t0i, t1i = ts.searchsorted((t0, t1))
#ts = ts[t0i:t1i] # constrained set of timestamps, in sec
data = self.data[:, t0i:t1i] # slice data
if figsize == None:
# convert from recording duration time to width in inches, 0.87 accommodates
# padding around the specgram:
figwidth = (dt / 1000) * 5 + 0.87
figheight = 2.5 # inches
figsize = figwidth, figheight
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if iterable(chanis):
data = data[chanis].mean(axis=0) # take mean of data on chanis
else:
data = data[chanis] # get single row of data at chanis
#data = filter.notch(data)[0] # remove 60 Hz mains noise
# convert data from uV to mV, returned t is midpoints of time bins in sec from
# start of data. I think P is in mV^2?:
P, freqs, t = mpl.mlab.specgram(data/1e3, NFFT=NFFT, Fs=self.sampfreq,
noverlap=noverlap)
if not relative2t0:
t += t0 # convert t to time from start of ADC clock:
# keep only freqs between f0 and f1:
if f0 == None:
f0 = freqs[0]
if f1 == None:
f1 = freqs[-1]
df = f1 - f0
lo, hi = freqs.searchsorted([f0, f1])
P, freqs = P[lo:hi], freqs[lo:hi]
# check for and replace zero power values (ostensibly due to gaps in recording)
# before attempting to convert to dB:
zis = np.where(P == 0.0) # row and column indices where P has zero power
if len(zis[0]) > 0: # at least one hit
P[zis] = np.finfo(np.float64).max # temporarily replace zeros with max float
minnzval = P.min() # get minimum nonzero value
P[zis] = minnzval # replace with min nonzero values
P = 10. * np.log10(P) # convert power to dB wrt 1 mV^2?
# for better visualization, clip power values to within (p0, p1) dB
if p0 != None:
P[P < p0] = p0
if p1 != None:
P[P > p1] = p1
#self.P = P
# plot horizontal bars over time demarcating different ranges of SI values,
# or manually defined desynched and synched periods:
statelinepos = f0 - df*0.015 # plot horizontal bars just below x axis
if showstates:
if showstates in [True, 'auto']:
print("TODO: there's an offset plotting bug for 'auto', compare with 'manual'")
si, t = self.si(plot=False)
stranges, states = self.si_split(si, t) # sec
STATECOLOURS = uns['LFPPRBINCOLOURS']
elif showstates == 'manual':
stranges, states = [], []
for state in uns['MANUALSTATES']:
for strange in uns['REC2STATE2TRANGES'][self.r.absname][state]:
stranges.append(strange)
states.append(state)
stranges = np.vstack(stranges) # 2D array
STATECOLOURS = uns['MANUALSTATECOLOURS']
else:
raise ValueError('invalid value showstates=%r' % showstates)
# clip stranges to t0, t1:
stranges[0, 0] = max(stranges[0, 0], t0)
stranges[-1, 1] = min(stranges[-1, 1], t1)
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
for strange, state in zip(stranges, states):
clr = STATECOLOURS[state]
lines(statelinepos, strange[0], strange[1], colors=clr, lw=lw, alpha=alpha,
clip_on=False)
# Label far left, right, top and bottom edges of imshow image. imshow interpolates
# between these to place the axes ticks. Time limits are
# set from midpoints of specgram time bins
extent = t[0], t[-1], freqs[0], freqs[-1]
#print('specgram extent: %r' % (extent,))
# flip P vertically for compatibility with imshow:
im = a.imshow(P[::-1], extent=extent, cmap=cm)
a.autoscale(enable=True, tight=True)
a.axis('tight')
# depending on relative2t0 above, x=0 represents either t0 or time ADC clock started:
a.set_xlim(xmin=0, xmax=t[-1])
a.set_ylim(ymin=freqs[0], ymax=freqs[-1])
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
a.set_xlabel("time (s)")
a.set_ylabel("frequency (Hz)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
if reclabel:
a.text(0.994, 0.95, '%s' % self.r.absname, color='w', transform=a.transAxes,
horizontalalignment='right', verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
if colorbar:
f.colorbar(im, pad=0) # creates big whitespace to the right for some reason
self.f = f
return P, freqs, t
def pospdf(self,
neurons='all',
dim='y',
edges=None,
nbins=10,
stats=False,
labels=True,
a=None,
figsize=(7.5, 6.5)):
"""Plot PDF of cell positions ('x' or 'y') along the polytrode
to get an idea of how cells are distributed in space"""
if neurons == 'all':
neurons = list(self.alln.values())
elif neurons == 'quiet':
neurons = list(self.qn.values())
elif neurons == 'active':
neurons = list(self.n.values())
dimi = {'x': 0, 'y': 1}[dim]
p = [n.pos[dimi] for n in neurons] # all position values
if edges != None:
nbins = len(edges) - 1
bins = edges # assume it includes rightmost bin edge
else:
nbins = max(nbins, 2 * intround(np.sqrt(self.nneurons)))
bins = nbins
n, p = np.histogram(p, bins=bins) # p includes rightmost bin edge
binwidth = p[1] - p[0] # take width of first bin in p
if stats:
mean = np.mean(p)
median = np.median(p)
argmode = n.argmax()
mode = p[argmode] + binwidth / 2 # middle of tallest bin
stdev = np.std(p)
if a == None:
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
else: # add to existing axes
a.hold(True)
f = pl.gcf()
# use CCWHITEDICT1 for familiarity with len 10 1-based id to colour mapping
#color = CCWHITEDICT1[int(self.id)]
color = 'k'
# exclude rightmost bin edge in p
a.bar(left=p[:-1],
height=n,
width=binwidth,
bottom=0,
color=color,
ec=color)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if labels:
a.set_title(titlestr)
a.set_xlabel('neuron %s position (um)' % dim)
a.set_ylabel('neuron count')
if stats:
# add stuff to top right of plot:
uns = get_ipython().user_ns
a.text(0.99,
0.99,
'mean = %.3f\n'
'median = %.3f\n'
'mode = %.3f\n'
'stdev = %.3f\n'
'minrate = %.2f Hz\n'
'nneurons = %d\n'
'dt = %d min' % (mean, median, mode, stdev, uns['MINRATE'],
self.nneurons, intround(self.dtmin)),
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
f.canvas.draw() # this is needed if a != None when passed as arg
return a
def si_plot(self,
si,
t,
t0=None,
t1=None,
xlim=None,
ylim=None,
yticks=None,
ylabel=None,
showxlabel=True,
showylabel=True,
showtitle=True,
title=None,
reclabel=True,
hlines=[0],
showstates=False,
statelinepos=None,
lw=4,
alpha=1,
swapaxes=False,
figsize=None):
"""Plot synchrony index as a function of time, with hopefully the same
temporal scale as some of the other plots in self"""
uns = get_ipython().user_ns
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
xlabel = "time (s)"
if ylabel == None:
ylabel = "synchrony index (AU?)"
if swapaxes:
t, si = si, t # swap t and si
xlim, ylim = ylim, xlim
ylim = ylim[1], ylim[0] # swap new ylimits so t=0 is at top
xlabel, ylabel = ylabel, xlabel # swap labels
showxlabel, showylabel = showylabel, showxlabel # swap flags
# underplot vertical lines:
for hline in hlines:
a.axvline(x=hline, c='e', ls='--', marker=None)
else:
# underplot horizontal lines:
for hline in hlines:
a.axhline(y=hline, c='e', ls='--', marker=None)
# plot horizontal bars over time demarcating different ranges of SI values,
# or manually defined desynched and synched periods:
if showstates in [True, 'auto']:
stranges, states = self.si_split(si, t)
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
slposs = statelinepos
if len(slposs) == 1: # use same statelinepos for all states
nstranges = len(stranges)
slposs = slposs * nstranges
for strange, state, slpos in zip(stranges, states, slposs):
clr = uns['LFPPRBINCOLOURS'][state]
lines(slpos,
strange[0],
strange[1],
colors=clr,
lw=lw,
alpha=alpha)
elif showstates == 'manual':
REC2STATETRANGES = uns['REC2STATETRANGES']
dtrange, strange = np.asarray(
REC2STATETRANGES[self.r.absname]) / 1e6
dtrange = max(dtrange[0],
t0), min(dtrange[1],
t1) # clip desynch trange to t0, t1
strange = max(strange[0],
t0), min(strange[1],
t1) # clip synch trange to t0, t1
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
slposs = statelinepos
if len(slposs) == 1: # use same statelinepos for both states
slposs = slposs * 2
lines(slposs[0],
dtrange[0],
dtrange[1],
colors='b',
lw=lw,
alpha=alpha)
lines(slposs[1],
strange[0],
strange[1],
colors='r',
lw=lw,
alpha=alpha)
a.plot(t, si, 'k-')
# depending on relative2t0 in si(), x=0 represents either t0 or time ADC clock started:
a.set_xlim(xlim) # low/high limits are unchanged if None
a.set_ylim(ylim)
if yticks != None:
a.set_yticks(yticks)
if showxlabel:
a.set_xlabel(xlabel)
if showylabel:
a.set_ylabel(ylabel)
#a.autoscale(axis='x', enable=True, tight=True)
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
if title == None:
title = lastcmd()
gcfm().window.setWindowTitle(title)
if showtitle:
a.set_title(title)
if reclabel:
a.text(0.994,
0.01,
'%s' % self.r.absname,
color='k',
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='bottom')
f.tight_layout(pad=0.3) # crop figure to contents
def cch(self, nid0, nid1=None, trange=50, binw=None, shift=None, nshifts=10,
rate=False, norm=False, c='k', title=True, figsize=(7.5, 6.5)):
"""Copied from Recording.cch(). Plot cross-correlation histogram given nid0 and nid1.
If nid1 is None, calculate autocorrelogram. +/- trange and binw are in ms. If shift
(in ms) is set, calculate the average of +/- nshift CCHs shifted by shift, and then
subtract that from the unshifted CCH to get the shift corrected CCH"""
if nid1 == None:
nid1 = nid0
autocorr = nid0 == nid1
n0 = self.alln[nid0]
n1 = self.alln[nid1]
calctrange = trange * 1000 # calculation trange, in us
if shift:
assert nshifts > 0
shift *= 1000 # convert to us
maxshift = nshifts * shift
calctrange = trange + maxshift # expand calculated trange to encompass shifts
calctrange = np.array([-calctrange, calctrange]) # convert to a +/- array, in us
dts = util.xcorr(n0.spikes, n1.spikes, calctrange) # in us
if autocorr:
dts = dts[dts != 0] # remove 0s for autocorr
if shift: # calculate dts for shift corrector
shiftis = range(-nshifts, nshifts+1)
shiftis.remove(0) # don't shift by 0, that's the original which we'll subtract from
shifts = np.asarray(shiftis) * shift
shiftdts = np.hstack([ dts+s for s in shifts ]) # in us
print('shifts =', shifts / 1000)
if not binw:
nbins = intround(np.sqrt(len(dts))) # good heuristic
nbins = max(20, nbins) # enforce min nbins
nbins = min(200, nbins) # enforce max nbins
else:
nbins = intround(2 * trange / binw)
dts = dts / 1000 # in ms, converts to float64 array
t = np.linspace(start=-trange, stop=trange, num=nbins+1, endpoint=True) # ms
binw = t[1] - t[0] # all should be equal width, ms
n = np.histogram(dts, bins=t, density=False)[0]
if shift: # subtract shift corrector
shiftdts = shiftdts / 1000 # in ms, converts to float64 array
shiftn = np.histogram(shiftdts, bins=t, density=False)[0] / (nshifts*2)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=shiftn, width=binw) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
n -= shiftn
if norm: # normalize and convert to float:
n = n / n.max()
elif rate: # normalize by binw and convert to float:
n = n / binw
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.bar(left=t[:-1], height=n, width=binw, color=c, ec=c) # omit last right edge in t
a.set_xlim(t[0], t[-1])
a.set_xlabel('spike interval (ms)')
if norm:
a.set_ylabel('coincidence rate (AU)')
a.set_yticks([0, 1])
elif rate:
a.set_ylabel('coincidence rate (Hz)')
else:
a.set_ylabel('count')
if title:
a.set_title('spike times of n%d wrt n%d' % (self.n1.id, self.n0.id))
wtitlestr = lastcmd()# + ', binw=%.1f ms' % binw
gcfm().window.setWindowTitle(wtitlestr)
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self, t0=None, t1=None, chanis=None, gain=1, c='k', alpha=1.0, yunits='um',
yticks=None, title=True, xlabel=True, relative2t0=False, lim2stim=False,
scalebar=True, lw=4, figsize=(20, 6.5)):
"""Plot chanis of LFP data between t0 and t1 in sec. Unfortunatley, setting an alpha <
1 doesn't seem to reveal detail when a line obscures itself, such as when plotting a
very long time series. relative2t0 controls whether to plot relative to t0, or
relative to start of ADC clock. lim2stim limits the time range only to when a stimulus
was on screen, i.e. to the outermost times of non-NULL din. If only one chan is
requested, it's plotted on a mV scale instead of a spatial scale."""
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1]
if t1 == None:
t1 = t0 + 10 # 10 sec window
if chanis == None:
chanis = range(len(self.chans)) # all chans
if lim2stim:
t0, t1 = self.apply_lim2stim(t0, t1)
t0i, t1i = ts.searchsorted((t0, t1))
ts = ts[t0i:t1i] # constrained set of timestamps, in sec
chanis = tolist(chanis)
nchans = len(chanis)
# grab desired channels and time range:
data = self.data[chanis][:, t0i:t1i]
if nchans > 1: # convert uV to um:
totalgain = self.UV2UM * gain
data = data * totalgain
else: # convert uV to mV:
data = data / 1000
yunits = 'mV'
nt = len(ts)
assert nt == data.shape[1]
if relative2t0:
# convert ts to time from t0, otherwise plot time from start of ADC clock:
ts -= t0
x = np.tile(ts, nchans)
x.shape = nchans, nt
segments = np.zeros((nchans, nt, 2)) # x vals in col 0, yvals in col 1
segments[:, :, 0] = x
if nchans > 1:
segments[:, :, 1] = -data # set to -ve here because of invert_yaxis() below
else:
segments[:, :, 1] = data
if nchans > 1: # add y offsets:
maxypos = 0
for chanii, chani in enumerate(chanis):
chan = self.chans[chani]
ypos = self.chanpos[chan][1] # in um
segments[chanii, :, 1] += ypos # vertical distance below top of probe
maxypos = max(maxypos, ypos)
if yunits == 'mm': # convert from um to mm
segments[:, :, 1] /= 1000
maxypos = maxypos / 1000 # convert from int to float
totalgain = totalgain / 1000
lc = LineCollection(segments, linewidth=1, linestyle='-', colors=c, alpha=alpha,
antialiased=True, visible=True)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.add_collection(lc) # add to axes' pool of LCs
if scalebar: # add vertical scale bar at end of last channel to represent 1 mV:
if nchans > 1:
ymin, ymax = maxypos-500*totalgain, maxypos+500*totalgain # +/- 0.5 mV
else:
ymin, ymax = -0.5, 0.5 # mV
a.vlines(ts.max()*0.99, ymin, ymax, lw=lw, colors='e')
a.autoscale(enable=True, tight=True)
# depending on relative2t0 above, x=0 represents either t0 or time ADC clock started:
a.set_xlim(xmin=0)
if nchans > 1:
a.invert_yaxis() # for spatial scale
if yticks != None:
a.set_yticks(yticks)
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
if xlabel:
a.set_xlabel("time (s)")
if yunits == 'um':
a.set_ylabel("depth ($\mu$m)")
elif yunits == 'mm':
a.set_ylabel("depth (mm)")
elif yunits == 'mV':
a.set_ylabel("LFP (mV)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
a.text(0.998, 0.99, '%s' % self.r.name, transform=a.transAxes,
horizontalalignment='right', verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
return self
def sc_si(source, method='mean', sisource='lfp', kind=None, chani=-1, sirange=None,
layers=False, ms=1, figsize=(7.5, 6.5)):
"""Pool recording.sc().si() results across recordings specified by source,
plot the result"""
uns = get_ipython().user_ns
if layers == False:
layers = ['all']
elif layers == True:
layers = ['sup', 'deep']
LAYER2I = {'all':0, 'sup':1, 'mid':2, 'deep':3, 'other':4}
layeris = [ LAYER2I[layer] for layer in layers ]
recs, tracks = parse_source(source)
if sisource not in ['lfp', 'mua']:
raise ValueError('unknown sisource %r' % sisource)
if kind == None:
if sisource == 'lfp':
kind = uns['LFPSIKIND']
else:
kind = uns['MUASIKIND']
# calculate
corrss, sis = [], []
for rec in recs:
print(rec.absname)
corrs, si, ylabel = rec.sc().si(method=method, sisource=sisource, kind=kind,
chani=chani, sirange=sirange, plot=False)
corrss.append(corrs)
sis.append(si)
corrs = np.hstack(corrss)
si = np.hstack(sis)
# plot
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
#ylim = corrs[layeris].min(), corrs[layeris].max()
#yrange = ylim[1] - ylim[0]
#extra = yrange*0.03 # 3 %
#ylim = ylim[0]-extra, ylim[1]+extra
ylim = uns['SCLIMITS']
# keep only those points whose synchrony index falls within sirange:
if sirange == None:
finitesi = si[np.isfinite(si)]
sirange = finitesi.min(), finitesi.max()
sirange = np.asarray(sirange)
keepis = (sirange[0] <= si[0]) * (si[0] <= sirange[1]) # boolean index array
si = si[:, keepis]
corrs = corrs[:, keepis]
# plot linear regressions of corrs vs si[0]:
if 'all' in layers:
m0, b0, r0, p0, stderr0 = linregress(si[0], corrs[0])
a.plot(sirange, m0*sirange+b0, 'e--')
if 'sup' in layers:
m1, b1, r1, p1, stderr1 = linregress(si[0], corrs[1])
a.plot(sirange, m1*sirange+b1, 'r--')
if 'mid' in layers:
m2, b2, r2, p2, stderr2 = linregress(si[0], corrs[2])
a.plot(sirange, m2*sirange+b2, 'g--')
if 'deep' in layers:
m3, b3, r3, p3, stderr3 = linregress(si[0], corrs[3])
a.plot(sirange, m3*sirange+b3, 'b--')
if 'other' in layers:
m4, b4, r4, p4, stderr4 = linregress(si[0], corrs[4])
a.plot(sirange, m4*sirange+b4, 'y--', zorder=0)
# scatter plot corrs vs si, one colour per laminarity:
if 'all' in layers:
a.plot(si[0], corrs[0], 'e.', ms=ms, label='all, m=%.3f, r=%.3f'
% (m0, r0))
if 'sup' in layers:
a.plot(si[0], corrs[1], 'r.', ms=ms, label='superficial, m=%.3f, r=%.3f'
% (m1, r1))
if 'mid' in layers:
a.plot(si[0], corrs[2], 'g.', ms=ms, label='middle, m=%.3f, r=%.3f'
% (m2, r2))
if 'deep' in layers:
a.plot(si[0], corrs[3], 'b.', ms=ms, label='deep, m=%.3f, r=%.3f'
% (m3, r3))
if 'other' in layers:
a.plot(si[0], corrs[4], 'y.', ms=ms, label='other, m=%.3f, r=%.3f'
% (m4, r4), zorder=0)
#a.set_xlim(sirange)
if kind[0] == 'n':
a.set_xlim(-1, 1)
a.set_ylim(ylim)
#a.autoscale(enable=True, axis='y', tight=True)
a.set_xlabel('%s SI (%s)' % (sisource.upper(), kind))
a.set_ylabel(ylabel)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.legend(loc='upper left', handlelength=1, handletextpad=0.5, labelspacing=0.1)
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self, normed=True, scale=2.0):
win = RevCorr.plot(self, normed=normed, title=lastcmd(), scale=scale)
return win # necessary in IPython
def si_plot(self, si, t, t0=None, t1=None, xlim=None, ylim=None, yticks=None,
ylabel=None, showxlabel=True, showylabel=True, showtitle=True,
title=None, reclabel=True, hlines=[0], showstates=False,
statelinepos=None, lw=4, alpha=1,
swapaxes=False, figsize=None):
"""Plot synchrony index as a function of time, with hopefully the same
temporal scale as some of the other plots in self"""
uns = get_ipython().user_ns
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
xlabel = "time (s)"
if ylabel == None:
ylabel = "synchrony index (AU?)"
if swapaxes:
t, si = si, t # swap t and si
xlim, ylim = ylim, xlim
ylim = ylim[1], ylim[0] # swap new ylimits so t=0 is at top
xlabel, ylabel = ylabel, xlabel # swap labels
showxlabel, showylabel = showylabel, showxlabel # swap flags
# underplot vertical lines:
for hline in hlines:
a.axvline(x=hline, c='e', ls='--', marker=None)
else:
# underplot horizontal lines:
for hline in hlines:
a.axhline(y=hline, c='e', ls='--', marker=None)
# plot horizontal bars over time demarcating different ranges of SI values,
# or manually defined desynched and synched periods:
if showstates in [True, 'auto']:
stranges, states = self.si_split(si, t)
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
slposs = statelinepos
if len(slposs) == 1: # use same statelinepos for all states
nstranges = len(stranges)
slposs = slposs * nstranges
for strange, state, slpos in zip(stranges, states, slposs):
clr = uns['LFPPRBINCOLOURS'][state]
lines(slpos, strange[0], strange[1], colors=clr, lw=lw, alpha=alpha)
elif showstates == 'manual':
REC2STATETRANGES = uns['REC2STATETRANGES']
dtrange, strange = np.asarray(REC2STATETRANGES[self.r.absname]) / 1e6
dtrange = max(dtrange[0], t0), min(dtrange[1], t1) # clip desynch trange to t0, t1
strange = max(strange[0], t0), min(strange[1], t1) # clip synch trange to t0, t1
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
slposs = statelinepos
if len(slposs) == 1: # use same statelinepos for both states
slposs = slposs * 2
lines(slposs[0], dtrange[0], dtrange[1], colors='b', lw=lw, alpha=alpha)
lines(slposs[1], strange[0], strange[1], colors='r', lw=lw, alpha=alpha)
a.plot(t, si, 'k-')
# depending on relative2t0 in si(), x=0 represents either t0 or time ADC clock started:
a.set_xlim(xlim) # low/high limits are unchanged if None
a.set_ylim(ylim)
if yticks != None:
a.set_yticks(yticks)
if showxlabel:
a.set_xlabel(xlabel)
if showylabel:
a.set_ylabel(ylabel)
#a.autoscale(axis='x', enable=True, tight=True)
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
if title == None:
title = lastcmd()
gcfm().window.setWindowTitle(title)
if showtitle:
a.set_title(title)
if reclabel:
a.text(0.994, 0.01, '%s' % self.r.absname, color='k', transform=a.transAxes,
horizontalalignment='right', verticalalignment='bottom')
f.tight_layout(pad=0.3) # crop figure to contents
def npos(self, colour='active', inchespermicron=0.007, legend=False, alpha=0.6):
"""Plot (x, y) cell positions over top of polytrode channel positions, to get an idea
of how cells are distributed in space. Colour cells by 'active', 'rftype',
'spiketype' or 'sigma'."""
uns = get_ipython().user_ns
npos = np.asarray([ neuron.pos for neuron in self.alln.values() ])
chanpos = self.chanpos
chanxs, chanys = chanpos[:, 0], chanpos[:, 1]
uchanxs = np.unique(chanxs)
xspace = np.diff(uchanxs).max() # max spacing of consecutive unique x chan positions
hsw = uns['PTSHANKWIDTHS'][self.pttype] / 2 # half shank width
xs = np.hstack((npos[:, 0], chanxs, [-hsw, hsw]))
ys = np.hstack((npos[:, 1], chanys))
ymin = min(min(ys), 0)
xlim = min(xs.min(), uchanxs[0]-xspace/2), max(xs.max(), uchanxs[-1]+xspace/2)
ylim = ys.max()+xspace, ymin # inverted y axis
figwidth = inchespermicron * np.ptp(xlim) * 2 + 3*legend # make space for y axis labels
figheight = inchespermicron * np.ptp(ylim)
f = pl.figure(figsize=(figwidth, figheight))
a = f.add_subplot(111, aspect='equal')
a.set_frame_on(False)
# plot rectangle representing shank width and length, excluding the tip:
sl = ylim[0]
# starting from bottom left, going clockwise:
shankxs = -hsw, -hsw, hsw, hsw
shankys = sl, ymin, ymin, sl
a.fill(shankxs, shankys, color='lightgrey', ec='none')
# plot electrode sites:
a.plot(chanpos[:, 0], chanpos[:, 1], 'k.', ms=5)
if colour == 'active':
# plot active and quiet cell positions in red and blue, respectively:
anpos = np.asarray([ neuron.pos for neuron in self.n.values() ])
qnpos = np.asarray([ neuron.pos for neuron in self.qn.values() ])
na = len(anpos)
nq = len(qnpos)
# layer in inverse order of importance:
if na: a.plot(qnpos[:, 0], qnpos[:, 1], 'b.', ms=10, alpha=alpha, label='quiet')
if nq: a.plot(anpos[:, 0], anpos[:, 1], 'r.', ms=10, alpha=alpha, label='active')
elif colour == 'rftype':
# plot simple, complex, LGN afferent and None in red, blue, green and grey:
spos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'simple' ])
cpos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'complex' ])
Lpos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.rftype == 'LGN' ])
Npos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.rftype == None ])
ns = len(spos)
nc = len(cpos)
nL = len(Lpos)
nN = len(Npos)
# layer in inverse order of importance:
if nN: a.plot(Npos[:, 0], Npos[:, 1], 'e.', ms=10, alpha=alpha, label='unknown')
if nL: a.plot(Lpos[:, 0], Lpos[:, 1], 'g.', ms=10, alpha=alpha, label='LGN afferent')
if nc: a.plot(cpos[:, 0], cpos[:, 1], 'b.', ms=10, alpha=alpha, label='complex')
if ns: a.plot(spos[:, 0], spos[:, 1], 'r.', ms=10, alpha=alpha, label='simple')
elif colour == 'spiketype':
# plot fast, slow, fastasym and slowasym in red, blue, green and grey:
fpos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'fast' ])
spos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'slow' ])
fapos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'fastasym' ])
sapos = np.asarray([ neuron.pos for neuron in self.alln.values()
if neuron.spiketype == 'slowasym' ])
nf = len(fpos)
ns = len(spos)
nfa = len(fapos)
nsa = len(sapos)
# layer in inverse order of frequency:
if nf: a.plot(fpos[:, 0], fpos[:, 1], 'r.', ms=10, alpha=alpha, label='fast')
if ns: a.plot(spos[:, 0], spos[:, 1], 'b.', ms=10, alpha=alpha, label='slow')
if nfa: a.plot(fapos[:, 0], fapos[:, 1], 'g.', ms=10, alpha=alpha,
label='fast asymmetric')
if nsa: a.plot(sapos[:, 0], sapos[:, 1], 'e.', ms=10, alpha=alpha,
label='slow asymmetric')
elif colour == 'sigma':
sigmas = np.asarray([ neuron.sigma for neuron in self.alln.values() ])
cmap = mpl.cm.hot_r
# best to fully saturate alpha because colour indicates value, not just class:
sc = a.scatter(npos[:, 0], npos[:, 1], edgecolor='none', c=sigmas, cmap=cmap,
alpha=1.0, s=30, zorder=10)
else:
raise RuntimeError("unknown colour kwarg %r" % colour)
a.set_xlim(xlim)
a.set_ylim(ylim)
a.set_xticks(uchanxs)
a.set_yticks(np.arange(0, ylim[0], 200))
#a.xaxis.set_ticks_position('bottom')
#a.yaxis.set_ticks_position('left')
# put legend to right of the axes:
if legend:
if colour == 'sigma':
f.colorbar(sc, ax=a, shrink=0.1, pad=0.1, aspect=10,
ticks=[min(sigmas), max(sigmas)], format='%d', label='sigma')
else:
a.legend(loc='center left', bbox_to_anchor=(1.2, 0.5), frameon=False)
bbox = a.get_position()
wh = bbox.width / bbox.height # w:h ratio of axes, includes all ticks and labels?
w, h = gcfm().canvas.get_width_height()
gcfm().resize(w*wh, h)
titlestr = lastcmd()
gcfm().set_window_title(titlestr)
a.set_title(self.absname)
def templates(self, chans='max', cindex='nidi'):
"""Plot cell templates in their polytrode layout. chans can be 'max', 'nneigh', 'all'.
cindex can be 'nidi' or 'nid', but best to colour cells by nidi to maximize
alternation."""
from colour import CCBLACKDICT0, CCBLACKDICT1
from matplotlib.collections import LineCollection
HUMPERINCH = 80 # for setting figure size in inches
VUMPERINCH = 160 # for setting figure size in inches
USPERUM = 15
UVPERUM = 3
HBORDERUS = 50 # us, horizontal border around chans
VBORDERUV = 150 # uV, vertical border around plots
HBORDER = HBORDERUS / USPERUM
VBORDER = VBORDERUV / UVPERUM
BG = 'black'
SCALE = 500, 100 # scalebar size in (us, uV)
SCALE = SCALE[0]/USPERUM, SCALE[1]/UVPERUM # um
SCALEXOFFSET = 2 # um
SCALEYOFFSET = 4 # um
if chans not in ['max', 'nneigh', 'all',]:
raise ValueError('unknown chans arg %r' % chans)
if cindex == 'nidi':
ccdict = CCBLACKDICT0 # use nidi to maximize colour alternation
elif cindex == 'nid':
ccdict = CCBLACKDICT1 # use nid to have colours that correspond to those in spyke
else:
raise ValueError('unknown cindex arg %r' % cindex)
# for mpl, convert probe chanpos to center bottom origin instead of center top,
# i.e. invert the y values:
chanpos = self.sort.chanpos.copy()
maxy = chanpos[:, 1].max()
for chan, (x, y) in enumerate(chanpos):
chanpos[chan, 1] = maxy - y
if chans == 'nneigh': # generate dict of nearest neighbours indexed by maxchan
dm = core.eucd(chanpos) # distance matrix
minspace = dm[dm!=0].min()
rincl = minspace * 1.1 # inclusion radius
nneighs = {}
for maxchan, pos in enumerate(chanpos):
d = dm[maxchan]
nnchans = np.where(d < rincl)[0]
nneighs[maxchan] = nnchans
colxs = np.unique(chanpos[:, 0]) # unique column x positions, sorted
rowys = np.unique(chanpos[:, 1]) # unique row y positions, sorted
ncols = len(colxs)
nrows = len(rowys)
hspace = (colxs[-1]-colxs[0]) / (ncols-1)
vspace = (rowys[-1]-rowys[0]) / (nrows-1)
# setting figure size actually sets window size, including toolbar and statusbar
figwidth = (ncols*hspace + 2*HBORDER) / HUMPERINCH # inches
figheight = (nrows*vspace + 2*VBORDER) / VUMPERINCH # inches
dpi = mpl.rcParams['figure.dpi']
#figwidth = (ncols*hspace) / HUMPERINCH # inches
#figheight = (nrows*vspace) / VUMPERINCH # inches
figwidth = intround(figwidth * dpi) / dpi # inches, rounded to nearest pixel
figheight = intround(figheight * dpi) / dpi # inches, rounded to nearest pixel
figsize = figwidth, figheight
f = pl.figure(figsize=figsize, facecolor=BG, edgecolor=BG)
a = f.add_subplot(111)
# plot chan lines? maybe just the vertical lines?
#for pos in chanpos:
tres = self.sort.tres # time resolution, in us
nts = np.unique([ neuron.nt for neuron in self.alln.values() ])
if len(nts) != 1:
raise RuntimeError("Not all neuron templates have the same number of timepoints. "
"That's probably bad.")
nt = nts[0]
ts = np.arange(0, neuron.nt*tres, tres) # time values in us
nids = sorted(self.alln)
for nidi, nid in enumerate(nids):
colour = ccdict[eval(cindex)]
neuron = self.alln[nid]
# ncs (neuron channels) should be 0-based channel IDs:
if chans == 'max':
ncs = [neuron.maxchan]
elif chans == 'nneigh':
ncs = nneighs[neuron.maxchan]
elif chans == 'all':
ncs = neuron.chans
# exclude channels of data within neigh that are missing from wavedata
ncs = [ nc for nc in ncs if nc in neuron.chans ]
# indices into neuron.chans, use to index into wavedata:
ncis = np.hstack([ np.where(neuron.chans == nc)[0] for nc in ncs ])
#import pdb; pdb.set_trace()
wavedata = neuron.wavedata[ncis]
# much less efficient, but much simpler than spyke code:
for c, wd in zip(ncs, wavedata):
x = chanpos[c, 0] + ts / USPERUM # um
y = chanpos[c, 1] + wd / UVPERUM # um
a.plot(x, y, ls='-', marker=None, lw=1, c=colour)
a.set_axis_bgcolor(BG)
a.set_xlabel('')
a.set_ylabel('')
a.xaxis.set_ticks([])
a.yaxis.set_ticks([]) # if displayed, y ticks would be distance from bottom chan
a.set_xlim(colxs[0]-HBORDER, colxs[-1]+nt*tres/USPERUM+HBORDER) # um
a.set_ylim(rowys[0]-VBORDER, rowys[-1]+VBORDER) # um
# add scale bars:
r, b = a.get_xlim()[1]-SCALEXOFFSET, a.get_ylim()[0]+SCALEYOFFSET # um
hbar = (r-SCALE[0], b), (r, b) # um
vbar = (r, b), (r, b+SCALE[1]) # um
scale = LineCollection([hbar, vbar], lw=1, colors='white', zorder=-1,
antialiased=True, visible=True)
a.add_collection(scale) # add to axes' pool of LCs
f.tight_layout(pad=0)
#f.canvas.toolbar.hide()
#f.canvas.window().statusBar().hide()
f.canvas.set_window_title(lastcmd())
def scsistim(self, method='mean', width=None, tres=None, timeaverage=False,
plottime=False, s=5, figsize=(7.5, 6.5)):
"""Scatter plot some summary statistic of spike correlations of each recording vs
LFP synchrony index SI. Colour each point according to stimulus type. width and tres
(sec) dictate tranges to split recordings up into. timeaverage averages across time
values of both sc and si for each recording. s is point size"""
## TODO: maybe limit to visually responsive cells
## TODO: add linear regression of si vs log(sc)
uns = get_ipython().user_ns
if width == None:
width = uns['LFPSIWIDTH']
if tres == None:
tres = width
bsrids = uns['BSRIDS'][self.absname]
msrids = uns['MSRIDS'][self.absname]
mvrids = uns['NSRIDS'][self.absname]
dbrids = uns['DBRIDS'][self.absname]
rids = sorted(bsrids + msrids + mvrids + dbrids) # do everything in rid order
print('blankscreen: %r' % [self.r[rid].name for rid in bsrids])
print('mseq: %r' % [self.r[rid].name for rid in msrids])
print('movie: %r' % [self.r[rid].name for rid in mvrids])
print('driftbar: %r' % [self.r[rid].name for rid in dbrids])
isect = core.intersect1d([msrids, bsrids, mvrids, dbrids])
if len(isect) != 0:
raise RuntimeError("some rids were classified into more than one type: %r" % isect)
scs, sis, c = [], [], []
for rid in rids:
r = self.r[rid]
print('%s: %s' % (r.absname, r.name))
spikecorr = r.sc(width=width, tres=tres)
"""
TODO: not sure if this is the right way to do this. A different set of neurons for
each recording are chosen, then mean sc(t) across all pairs for each recording is
found, and pooled across recordings. This pooling is maybe a bit dodgy. Is it
valid to pool sc(t) values across recordings when the included neurons are
different for each recording? The alternative is to deal only with neurons which
exceed MINTHRESH track-wide, but the problem with that is that for much of the
time, such neurons are completely silent, and therefore don't deserve to be
included in sc calculations for those durations.
"""
sc, si = spikecorr.si(method=method, plot=False) # calls sc.sct() and sc.si()
sc = sc[0] # pull out the spike correlation values that span all laminae
if timeaverage:
# average across all time values of sc and si to get a single coordinate
# per recording
sc = sc.mean()
si = si.mean()
scs.append(sc)
sis.append(si)
if rid in bsrids: color = 'e'
elif rid in msrids: color = 'k'
elif rid in mvrids: color = 'r'
elif rid in dbrids: color = 'b'
else: raise ValueError("unclassified recording: %r" % r.name)
c.append(np.tile(color, len(sc)))
scs = np.hstack(scs)
sis = np.hstack(sis)
c = np.hstack(c)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if plottime: # underplot lines connecting points adjacent in time
a.plot(scs, sis, 'e--')
a.scatter(scs, sis, c=c, edgecolors='none', s=s)
a.set_ylim(0, 1)
a.set_xlabel('%s spike correlations' % method)
a.set_ylabel('synchrony index')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
# make proxy line artists for legend:
bs = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='e', mec='e')
ms = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='k', mec='k')
mv = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='r', mec='r')
db = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='b', mec='b')
# add legend:
a.legend([bs, ms, mv, db],
['blank screen', 'mseq', 'movie', 'drift bar'],
numpoints=1, loc='lower right',
handlelength=1, handletextpad=0.5, labelspacing=0.1)
f.tight_layout(pad=0.3) # crop figure to contents
return scs, sis, c
def plot(self, normed=True, scale=2.0, MPL=False):
super(STCs, self).plot(normed=normed, title=lastcmd(), scale=scale, MPL=MPL)
def scsistim(self, method='weighted mean', width=None, tres=None, timeaverage=False,
plottime=False, s=5, figsize=(7.5, 6.5)):
"""Scatter plot some summary statistic of spike correlations of each recording vs
synchrony index SI. Colour each point according to stimulus type. width and tres
dictate tranges to split recordings up into. timeaverage means average across time
values of both sc and si for each recording"""
## TODO: maybe limit to visually responsive cells
## TODO: add linear regression of si vs log(sc)
uns = get_ipython().user_ns
if width == None:
width = uns['SIWIDTH'] # want powers of two for efficient FFT
if tres == None:
tres = width
rids = sorted(self.r) # do everything in rid order
recs = [ self.r[rid] for rid in rids ]
msrids, bsrids, mvrids, dbrids = [], [], [], []
for rid in rids:
r = self.r[rid]
rname = r.name
if 'mseq' in rname:
msrids.append(rid)
elif 'blank' in rname or 'spont' in rname:
bsrids.append(rid)
elif 'MVI' in rname:
mvrids.append(rid)
elif 'driftbar' in rname:
dbrids.append(rid)
print('mseq: %r' % [self.r[rid].name for rid in msrids])
print('blankscreen: %r' % [self.r[rid].name for rid in bsrids])
print('movie: %r' % [self.r[rid].name for rid in mvrids])
print('driftbar: %r' % [self.r[rid].name for rid in dbrids])
isect = core.intersect1d([msrids, bsrids, mvrids, dbrids])
if len(isect) != 0:
raise RuntimeError("some rids were classified into more than one type: %r" % isect)
rids = np.unique(np.hstack([msrids, bsrids, mvrids, dbrids]))
scs, sis, c = [], [], []
for rid in rids:
r = self.r[rid]
print('%s: %s' % (r.absname, r.name))
spikecorr = r.sc(width=width, tres=tres)
sc, si = spikecorr.si(method=method, plot=False) # calls sc.sct() and sc.si()
sc = sc[0] # pull out the spike correlation values that span all laminae
if timeaverage:
# average across all time values of sc and si to get a single coordinate
# per recording
sc = sc.mean()
si = si.mean()
scs.append(sc)
sis.append(si)
if rid in msrids: color = 'k'
elif rid in bsrids: color = 'e'
elif rid in mvrids: color = 'r'
elif rid in dbrids: color = 'b'
else: raise ValueError("unclassified recording: %r" % r.name)
c.append(np.tile(color, len(sc)))
scs = np.hstack(scs)
sis = np.hstack(sis)
c = np.hstack(c)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if plottime: # underplot lines connecting points adjacent in time
a.plot(scs, sis, 'e--')
a.scatter(scs, sis, c=c, edgecolors='none', s=s)
a.set_ylim(0, 1)
a.set_xlabel('%s spike correlations' % method)
a.set_ylabel('synchrony index')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
# make proxy line artists for legend:
ms = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='k', mec='k')
bs = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='e', mec='e')
mv = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='r', mec='r')
db = mpl.lines.Line2D([1], [1], color='white', marker='o', mfc='b', mec='b')
# add legend:
a.legend([ms, bs, mv, db],
['mseq', 'blank screen', 'movie', 'drift bar'],
numpoints=1, loc='lower right',
handlelength=1, handletextpad=0.5, labelspacing=0.1)
f.tight_layout(pad=0.3) # crop figure to contents
return scs, sis, c
def scsistim(self,
method='mean',
width=None,
tres=None,
timeaverage=False,
plottime=False,
s=5,
figsize=(7.5, 6.5)):
"""Scatter plot some summary statistic of spike correlations of each recording vs
LFP synchrony index SI. Colour each point according to stimulus type. width and tres
(sec) dictate tranges to split recordings up into. timeaverage averages across time
values of both sc and si for each recording. s is point size"""
## TODO: maybe limit to visually responsive cells
## TODO: add linear regression of si vs log(sc)
uns = get_ipython().user_ns
if width == None:
width = uns['LFPSIWIDTH']
if tres == None:
tres = width
bsrids = uns['BSRIDS'][self.absname]
msrids = uns['MSRIDS'][self.absname]
mvrids = uns['NSRIDS'][self.absname]
dbrids = uns['DBRIDS'][self.absname]
rids = sorted(bsrids + msrids + mvrids +
dbrids) # do everything in rid order
print('blankscreen: %r' % [self.r[rid].name for rid in bsrids])
print('mseq: %r' % [self.r[rid].name for rid in msrids])
print('movie: %r' % [self.r[rid].name for rid in mvrids])
print('driftbar: %r' % [self.r[rid].name for rid in dbrids])
isect = core.intersect1d([msrids, bsrids, mvrids, dbrids])
if len(isect) != 0:
raise RuntimeError(
"some rids were classified into more than one type: %r" %
isect)
scs, sis, c = [], [], []
for rid in rids:
r = self.r[rid]
print('%s: %s' % (r.absname, r.name))
spikecorr = r.sc(width=width, tres=tres)
"""
TODO: not sure if this is the right way to do this. A different set of neurons for
each recording are chosen, then mean sc(t) across all pairs for each recording is
found, and pooled across recordings. This pooling is maybe a bit dodgy. Is it
valid to pool sc(t) values across recordings when the included neurons are
different for each recording? The alternative is to deal only with neurons which
exceed MINTHRESH track-wide, but the problem with that is that for much of the
time, such neurons are completely silent, and therefore don't deserve to be
included in sc calculations for those durations.
"""
sc, si = spikecorr.si(method=method,
plot=False) # calls sc.sct() and sc.si()
sc = sc[
0] # pull out the spike correlation values that span all laminae
if timeaverage:
# average across all time values of sc and si to get a single coordinate
# per recording
sc = sc.mean()
si = si.mean()
scs.append(sc)
sis.append(si)
if rid in bsrids: color = 'e'
elif rid in msrids: color = 'k'
elif rid in mvrids: color = 'r'
elif rid in dbrids: color = 'b'
else: raise ValueError("unclassified recording: %r" % r.name)
c.append(np.tile(color, len(sc)))
scs = np.hstack(scs)
sis = np.hstack(sis)
c = np.hstack(c)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if plottime: # underplot lines connecting points adjacent in time
a.plot(scs, sis, 'e--')
a.scatter(scs, sis, c=c, edgecolors='none', s=s)
a.set_ylim(0, 1)
a.set_xlabel('%s spike correlations' % method)
a.set_ylabel('synchrony index')
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
# make proxy line artists for legend:
bs = mpl.lines.Line2D([1], [1],
color='white',
marker='o',
mfc='e',
mec='e')
ms = mpl.lines.Line2D([1], [1],
color='white',
marker='o',
mfc='k',
mec='k')
mv = mpl.lines.Line2D([1], [1],
color='white',
marker='o',
mfc='r',
mec='r')
db = mpl.lines.Line2D([1], [1],
color='white',
marker='o',
mfc='b',
mec='b')
# add legend:
a.legend([bs, ms, mv, db],
['blank screen', 'mseq', 'movie', 'drift bar'],
numpoints=1,
loc='lower right',
handlelength=1,
handletextpad=0.5,
labelspacing=0.1)
f.tight_layout(pad=0.3) # crop figure to contents
return scs, sis, c
def plot(self, interp='nearest', normed=True, scale=2.0):
win = RevCorr.plot(self, interp=interp, normed=normed, title=lastcmd(),
scale=scale)
return win # necessary in IPython
def plot(self, normed=True, scale=2.0, MPL=False, margins=True):
win = RevCorrs.plot(self, normed=normed, title=lastcmd(), scale=scale, MPL=MPL,
margins=margins)
return win # necessary in IPython
def psd(self, t0=None, t1=None, f0=0.2, f1=110, p0=None, p1=None, chanis=-1,
width=None, tres=None, xscale='log', figsize=(5, 5)):
"""Plot power spectral density from t0 to t1 in sec, from f0 to f1 in Hz, and clip
power values from p0 to p1 in dB, based on channel index chani of LFP data. chanis=0
uses most superficial channel, chanis=-1 uses deepest channel. If len(chanis) > 1,
take mean of specified chanis. width and tres are in sec."""
uns = get_ipython().user_ns
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1] # full duration
if t1 == None:
t1 = t0 + 10 # 10 sec window
if width == None:
width = uns['LFPSPECGRAMWIDTH'] # sec
if tres == None:
tres = uns['LFPSPECGRAMTRES'] # sec
assert tres <= width
NFFT = intround(width * self.sampfreq)
noverlap = intround(NFFT - tres * self.sampfreq)
t0i, t1i = ts.searchsorted((t0, t1))
#ts = ts[t0i:t1i] # constrained set of timestamps, in sec
data = self.data[:, t0i:t1i] # slice data
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if iterable(chanis):
data = data[chanis].mean(axis=0) # take mean of data on chanis
else:
data = data[chanis] # get single row of data at chanis
#data = filter.notch(data)[0] # remove 60 Hz mains noise
# convert data from uV to mV. I think P is in mV^2?:
P, freqs = mpl.mlab.psd(data/1e3, NFFT=NFFT, Fs=self.sampfreq, noverlap=noverlap)
# keep only freqs between f0 and f1:
if f0 == None:
f0 = freqs[0]
if f1 == None:
f1 = freqs[-1]
lo, hi = freqs.searchsorted([f0, f1])
P, freqs = P[lo:hi], freqs[lo:hi]
# check for and replace zero power values (ostensibly due to gaps in recording)
# before attempting to convert to dB:
zis = np.where(P == 0.0) # row and column indices where P has zero power
if len(zis[0]) > 0: # at least one hit
P[zis] = np.finfo(np.float64).max # temporarily replace zeros with max float
minnzval = P.min() # get minimum nonzero value
P[zis] = minnzval # replace with min nonzero values
P = 10. * np.log10(P) # convert power to dB wrt 1 mV^2?
# for better visualization, clip power values to within (p0, p1) dB
if p0 != None:
P[P < p0] = p0
if p1 != None:
P[P > p1] = p1
#self.P = P
a.plot(freqs, P, 'k-')
# add SI frequency band limits:
LFPPRLOBAND, LFPPRHIBAND = uns['LFPPRLOBAND'], uns['LFPPRHIBAND']
a.axvline(x=LFPPRLOBAND[0], c='r', ls='--')
a.axvline(x=LFPPRLOBAND[1], c='r', ls='--')
a.axvline(x=LFPPRHIBAND[0], c='b', ls='--')
a.axvline(x=LFPPRHIBAND[1], c='b', ls='--')
a.axis('tight')
a.set_xscale(xscale)
a.set_xlabel("frequency (Hz)")
a.set_ylabel("power (dB)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.text(0.998, 0.99, '%s' % self.r.name, color='k', transform=a.transAxes,
horizontalalignment='right', verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
return P, freqs
def specgram(self,
t0=None,
t1=None,
f0=0.1,
f1=100,
p0=-60,
p1=None,
chanis=-1,
width=None,
tres=None,
cm='jet',
colorbar=False,
showstates=False,
lw=4,
alpha=1,
relative2t0=False,
lim2stim=False,
title=True,
reclabel=True,
swapaxes=False,
figsize=None):
"""Plot a spectrogram from t0 to t1 in sec, from f0 to f1 in Hz, and clip power values
from p0 to p1 in dB, based on channel index chani of LFP data. chanis=0 uses most
superficial channel, chanis=-1 uses deepest channel. If len(chanis) > 1, take mean of
specified chanis. width and tres are in sec. As an alternative to cm.jet (the
default), cm.gray, cm.hsv cm.terrain, and cm.cubehelix_r colormaps seem to bring out
the most structure in the spectrogram. showstates controls whether to plot lines
demarcating desynchronized and synchronized periods. relative2t0 controls whether to
plot relative to t0, or relative to start of ADC clock. lim2stim limits the time range
only to when a stimulus was on screen, i.e. to the outermost times of non-NULL din"""
uns = get_ipython().user_ns
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1] # full duration
if t1 == None:
t1 = t0 + 10 # 10 sec window
if lim2stim:
t0, t1 = self.apply_lim2stim(t0, t1)
dt = t1 - t0
if width == None:
width = uns['LFPSPECGRAMWIDTH'] # sec
if tres == None:
tres = uns['LFPSPECGRAMTRES'] # sec
assert tres <= width
NFFT = intround(width * self.sampfreq)
noverlap = intround(NFFT - tres * self.sampfreq)
t0i, t1i = ts.searchsorted((t0, t1))
#ts = ts[t0i:t1i] # constrained set of timestamps, in sec
data = self.data[:, t0i:t1i] # slice data
if figsize == None:
# convert from recording duration time to width in inches, 0.87 accommodates
# padding around the specgram:
figwidth = (dt / 1000) * 5 + 0.87
figheight = 2.5 # inches
figsize = figwidth, figheight
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if iterable(chanis):
data = data[chanis].mean(axis=0) # take mean of data on chanis
else:
data = data[chanis] # get single row of data at chanis
#data = filter.notch(data)[0] # remove 60 Hz mains noise
# convert data from uV to mV, returned t is midpoints of time bins in sec from
# start of data. I think P is in mV^2?:
P, freqs, t = mpl.mlab.specgram(data / 1e3,
NFFT=NFFT,
Fs=self.sampfreq,
noverlap=noverlap)
if not relative2t0:
t += t0 # convert t to time from start of ADC clock:
# keep only freqs between f0 and f1:
if f0 == None:
f0 = freqs[0]
if f1 == None:
f1 = freqs[-1]
df = f1 - f0
lo, hi = freqs.searchsorted([f0, f1])
P, freqs = P[lo:hi], freqs[lo:hi]
# check for and replace zero power values (ostensibly due to gaps in recording)
# before attempting to convert to dB:
zis = np.where(
P == 0.0) # row and column indices where P has zero power
if len(zis[0]) > 0: # at least one hit
P[zis] = np.finfo(
np.float64).max # temporarily replace zeros with max float
minnzval = P.min() # get minimum nonzero value
P[zis] = minnzval # replace with min nonzero values
P = 10. * np.log10(P) # convert power to dB wrt 1 mV^2?
# for better visualization, clip power values to within (p0, p1) dB
if p0 != None:
P[P < p0] = p0
if p1 != None:
P[P > p1] = p1
#self.P = P
# plot horizontal bars over time demarcating different ranges of SI values,
# or manually defined desynched and synched periods:
statelinepos = f0 - df * 0.015 # plot horizontal bars just below x axis
if showstates:
if showstates in [True, 'auto']:
print(
"TODO: there's an offset plotting bug for 'auto', compare with 'manual'"
)
si, t = self.si(plot=False)
stranges, states = self.si_split(si, t) # sec
STATECOLOURS = uns['LFPPRBINCOLOURS']
elif showstates == 'manual':
stranges, states = [], []
for state in uns['MANUALSTATES']:
for strange in uns['REC2STATE2TRANGES'][
self.r.absname][state]:
stranges.append(strange)
states.append(state)
stranges = np.vstack(stranges) # 2D array
STATECOLOURS = uns['MANUALSTATECOLOURS']
else:
raise ValueError('invalid value showstates=%r' % showstates)
# clip stranges to t0, t1:
stranges[0, 0] = max(stranges[0, 0], t0)
stranges[-1, 1] = min(stranges[-1, 1], t1)
if swapaxes:
lines = a.vlines
else:
lines = a.hlines
for strange, state in zip(stranges, states):
clr = STATECOLOURS[state]
lines(statelinepos,
strange[0],
strange[1],
colors=clr,
lw=lw,
alpha=alpha,
clip_on=False)
# Label far left, right, top and bottom edges of imshow image. imshow interpolates
# between these to place the axes ticks. Time limits are
# set from midpoints of specgram time bins
extent = t[0], t[-1], freqs[0], freqs[-1]
#print('specgram extent: %r' % (extent,))
# flip P vertically for compatibility with imshow:
im = a.imshow(P[::-1], extent=extent, cmap=cm)
a.autoscale(enable=True, tight=True)
a.axis('tight')
# depending on relative2t0 above, x=0 represents either t0 or time ADC clock started:
a.set_xlim(xmin=0, xmax=t[-1])
a.set_ylim(ymin=freqs[0], ymax=freqs[-1])
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
a.set_xlabel("time (s)")
a.set_ylabel("frequency (Hz)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
if reclabel:
a.text(0.994,
0.95,
'%s' % self.r.absname,
color='w',
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
if colorbar:
f.colorbar(
im,
pad=0) # creates big whitespace to the right for some reason
self.f = f
return P, freqs, t
def plot(self, interp='nearest', normed=True, scale=2.0):
super(STCs, self).plot(interp=interp, normed=normed,
title=lastcmd(),
scale=scale)
def psd(self,
t0=None,
t1=None,
f0=0.2,
f1=110,
p0=None,
p1=None,
chanis=-1,
width=None,
tres=None,
xscale='log',
figsize=(5, 5)):
"""Plot power spectral density from t0 to t1 in sec, from f0 to f1 in Hz, and clip
power values from p0 to p1 in dB, based on channel index chani of LFP data. chanis=0
uses most superficial channel, chanis=-1 uses deepest channel. If len(chanis) > 1,
take mean of specified chanis. width and tres are in sec."""
uns = get_ipython().user_ns
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1] # full duration
if t1 == None:
t1 = t0 + 10 # 10 sec window
if width == None:
width = uns['LFPSPECGRAMWIDTH'] # sec
if tres == None:
tres = uns['LFPSPECGRAMTRES'] # sec
assert tres <= width
NFFT = intround(width * self.sampfreq)
noverlap = intround(NFFT - tres * self.sampfreq)
t0i, t1i = ts.searchsorted((t0, t1))
#ts = ts[t0i:t1i] # constrained set of timestamps, in sec
data = self.data[:, t0i:t1i] # slice data
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
if iterable(chanis):
data = data[chanis].mean(axis=0) # take mean of data on chanis
else:
data = data[chanis] # get single row of data at chanis
#data = filter.notch(data)[0] # remove 60 Hz mains noise
# convert data from uV to mV. I think P is in mV^2?:
P, freqs = mpl.mlab.psd(data / 1e3,
NFFT=NFFT,
Fs=self.sampfreq,
noverlap=noverlap)
# keep only freqs between f0 and f1:
if f0 == None:
f0 = freqs[0]
if f1 == None:
f1 = freqs[-1]
lo, hi = freqs.searchsorted([f0, f1])
P, freqs = P[lo:hi], freqs[lo:hi]
# check for and replace zero power values (ostensibly due to gaps in recording)
# before attempting to convert to dB:
zis = np.where(
P == 0.0) # row and column indices where P has zero power
if len(zis[0]) > 0: # at least one hit
P[zis] = np.finfo(
np.float64).max # temporarily replace zeros with max float
minnzval = P.min() # get minimum nonzero value
P[zis] = minnzval # replace with min nonzero values
P = 10. * np.log10(P) # convert power to dB wrt 1 mV^2?
# for better visualization, clip power values to within (p0, p1) dB
if p0 != None:
P[P < p0] = p0
if p1 != None:
P[P > p1] = p1
#self.P = P
a.plot(freqs, P, 'k-')
# add SI frequency band limits:
LFPPRLOBAND, LFPPRHIBAND = uns['LFPPRLOBAND'], uns['LFPPRHIBAND']
a.axvline(x=LFPPRLOBAND[0], c='r', ls='--')
a.axvline(x=LFPPRLOBAND[1], c='r', ls='--')
a.axvline(x=LFPPRHIBAND[0], c='b', ls='--')
a.axvline(x=LFPPRHIBAND[1], c='b', ls='--')
a.axis('tight')
a.set_xscale(xscale)
a.set_xlabel("frequency (Hz)")
a.set_ylabel("power (dB)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.text(0.998,
0.99,
'%s' % self.r.name,
color='k',
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
return P, freqs
def si(self, kind=None, chani=-1, width=None, tres=None,
lfpwidth=None, lfptres=None, lowband=None, highband=None, plot=True,
states=False, desynchsi=0.2, synchsi=0.2, lw=4, alpha=1, relative2t0=False,
lim2stim=False, showxlabel=True, showylabel=True, showtitle=True, showtext=True,
swapaxes=False, figsize=(20, 3.5)):
"""Calculate an LFP synchrony index, using potentially overlapping windows of width
and tres, in sec, from the LFP spectrogram, itself composed of bins of lfpwidth and
lfptres. relative2t0 controls whether to plot relative to t0, or relative to start of
ADC clock. lim2stim limits the time range only to when a stimulus was presented, i.e.
to the outermost times of non-NULL din.
Note that for power ratio methods (kind: L/(L+H) or L/H),
width and tres are not used, only lfpwidth and lfptres. Options for kind are:
'L/(L+H)': fraction of power in low band vs total power (Saleem2010)
'L/H': low to highband power ratio (Li, Poo, Dan 2009)
'cv': coefficient of variation (std / mean) of all power
'ncv': normalized CV: (std - mean) / (std + mean)
'nstdmed': normalized stdmed: (std - med) / (std + med)
'n2stdmean': normalized 2stdmean: (2*std - mean) / (2*std + mean)
'n3stdmean': normalized 3stdmean: (3*std - mean) / (3*std + mean)
"""
uns = get_ipython().user_ns
if kind == None:
kind = uns['LFPSIKIND']
if kind.startswith('L/'):
pratio = True
else:
pratio = False
data = self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
t0, t1 = ts[0], ts[-1]
if lim2stim:
t0, t1 = self.apply_lim2stim(t0, t1)
t0i, t1i = ts.searchsorted((t0, t1))
x = data[chani, t0i:t1i] / 1e3 # slice data, convert from uV to mV
x = filter.notch(x)[0] # remove 60 Hz mains noise
try:
rr = self.r.e0.I['REFRESHRATE']
except AttributeError: # probably a recording with no experiment
rr = 200 # assume 200 Hz refresh rate
if rr <= 100: # CRT was at low vertical refresh rate
print('filtering out %d Hz from LFP in %s' % (intround(rr), self.r.name))
x = filter.notch(x, freq=rr)[0] # remove CRT interference
if width == None:
width = uns['LFPSIWIDTH'] # sec
if tres == None:
tres = uns['LFPSITRES'] # sec
if lfpwidth == None:
lfpwidth = uns['LFPWIDTH'] # sec
if lfptres == None:
lfptres = uns['LFPTRES'] # sec
if lowband == None:
lowband = uns['LFPSILOWBAND']
f0, f1 = lowband
if highband == None:
highband = uns['LFPSIHIGHBAND']
f2, f3 = highband
assert lfptres <= lfpwidth
NFFT = intround(lfpwidth * self.sampfreq)
noverlap = intround(NFFT - lfptres * self.sampfreq)
#print('len(x), NFFT, noverlap: %d, %d, %d' % (len(x), NFFT, noverlap))
# t is midpoints of timebins in sec from start of data. P is in mV^2?:
P, freqs, Pt = mpl.mlab.specgram(x, NFFT=NFFT, Fs=self.sampfreq, noverlap=noverlap)
# don't convert power to dB, just washes out the signal in the ratio:
#P = 10. * np.log10(P)
if not relative2t0:
Pt += t0 # convert t to time from start of ADC clock:
nfreqs = len(freqs)
# keep only freqs between f0 and f1, and f2 and f3:
f0i, f1i, f2i, f3i = freqs.searchsorted([f0, f1, f2, f3])
lP = P[f0i:f1i] # nsubfreqs x nt
hP = P[f2i:f3i] # nsubfreqs x nt
lP = lP.sum(axis=0) # nt
hP = hP.sum(axis=0) # nt
if pratio:
t = Pt
ylim = 0, 1
ylabel = 'SI (%s)' % kind
else:
# potentially overlapping bin time ranges:
trange = Pt[0], Pt[-1]
tranges = split_tranges([trange], width, tres) # in sec
ntranges = len(tranges)
tis = Pt.searchsorted(tranges) # ntranges x 2 array
# number of timepoints to use for each trange, almost all will be the same width:
binnt = intround((tis[:, 1] - tis[:, 0]).mean())
binhP = np.zeros((ntranges, binnt)) # init appropriate array
for trangei, t0i in enumerate(tis[:, 0]):
binhP[trangei] = hP[t0i:t0i+binnt]
# get midpoint of each trange:
t = tranges.mean(axis=1)
#old_settings = np.seterr(all='ignore') # suppress div by 0 errors
# plot power signal to be analyzed
#self.si_plot(Pt, hP, t0=0, t1=t[-1], ylim=None, ylabel='highband power',
# title=lastcmd()+' highband power', text=self.r.name)
hlines = []
if kind[0] == 'n':
ylim = -1, 1
hlines = [0]
# calculate some metric of each column, ie each width:
if kind == 'L/(L+H)':
si = lP/(lP + hP)
elif kind == 'L/H':
si = lP/hP
elif kind == 'nLH':
t = Pt
si = (lP - hP) / (lP + hP)
ylabel = 'LFP (L - H) / (L + H)'
elif kind == 'cv':
si = binhP.std(axis=1) / binhP.mean(axis=1)
ylim = 0, 2
ylabel = 'LFP power CV'
elif kind == 'ncv':
s = binhP.std(axis=1)
mean = binhP.mean(axis=1)
si = (s - mean) / (s + mean)
ylabel = 'LFP power (std - mean) / (std + mean)'
#pl.plot(t, s)
#pl.plot(t, mean)
elif kind == 'n2stdmean':
s2 = 2 * binhP.std(axis=1)
mean = binhP.mean(axis=1)
si = (s2 - mean) / (s2 + mean)
ylabel = 'LFP power (2*std - mean) / (2*std + mean)'
hlines = [-0.1, 0, 0.1] # demarcate desynched and synched thresholds
#pl.plot(t, s2)
#pl.plot(t, mean)
elif kind == 'n3stdmean':
s3 = 3 * binhP.std(axis=1)
mean = binhP.mean(axis=1)
si = (s3 - mean) / (s3 + mean)
ylabel = 'LFP power (3*std - mean) / (3*std + mean)'
hlines = [-0.1, 0, 0.1] # demarcate desynched and synched thresholds
#pl.plot(t, s3)
#pl.plot(t, mean)
elif kind == 'n4stdmean':
s4 = 4 * binhP.std(axis=1)
mean = binhP.mean(axis=1)
si = (s4 - mean) / (s4 + mean)
ylabel = 'LFP power (4*std - mean) / (4*std + mean)'
#pl.plot(t, s4)
#pl.plot(t, mean)
elif kind == 'nstdmed':
s = binhP.std(axis=1)
med = np.median(binhP, axis=1)
si = (s - med) / (s + med)
ylabel = 'LFP power (std - med) / (std + med)'
hlines = [-0.1, 0, 0.1] # demarcate desynched and synched thresholds
#pl.plot(t, s)
#pl.plot(t, med)
elif kind == 'n2stdmed':
s2 = 2 * binhP.std(axis=1)
med = np.median(binhP, axis=1)
si = (s2 - med) / (s2 + med)
ylabel = 'LFP power (2*std - med) / (2*std + med)'
hlines = [-0.1, 0, 0.1] # demarcate desynched and synched thresholds
#pl.plot(t, s2)
#pl.plot(t, med)
elif kind == 'n3stdmed':
s3 = 3 * binhP.std(axis=1)
med = np.median(binhP, axis=1)
si = (s3 - med) / (s3 + med)
ylabel = 'LFP power (3*std - med) / (3*std + med)'
hlines = [-0.1, 0, 0.1] # demarcate desynched and synched thresholds
#pl.plot(t, s3)
#pl.plot(t, med)
elif kind == 'nstdmin':
s = binhP.std(axis=1)
min = binhP.min(axis=1)
si = (s - min) / (s + min)
ylabel = 'LFP power (std - min) / (std + min)'
#pl.plot(t, s)
#pl.plot(t, min)
elif kind == 'nmadmean':
mean = binhP.mean(axis=1)
mad = (np.abs(binhP - mean[:, None])).mean(axis=1)
si = (mad - mean) / (mad + mean)
ylabel = 'MUA (MAD - mean) / (MAD + mean)'
#pl.plot(t, mad)
#pl.plot(t, mean)
elif kind == 'nmadmed':
med = np.median(binhP, axis=1)
mad = (np.abs(binhP - med[:, None])).mean(axis=1)
si = (mad - med) / (mad + med)
ylabel = 'MUA (MAD - median) / (MAD + median)'
#pl.plot(t, mad)
#pl.plot(t, med)
elif kind == 'nvarmin':
v = binhP.var(axis=1)
min = binhP.min(axis=1)
si = (v - min) / (v + min)
ylabel = 'LFP power (std - min) / (std + min)'
#pl.plot(t, v)
#pl.plot(t, min)
elif kind == 'nptpmean':
ptp = binhP.ptp(axis=1)
mean = binhP.mean(axis=1)
si = (ptp - mean) / (ptp + mean)
ylabel = 'MUA (ptp - mean) / (ptp + mean)'
#pl.plot(t, ptp)
#pl.plot(t, mean)
elif kind == 'nptpmed':
ptp = binhP.ptp(axis=1)
med = np.median(binhP, axis=1)
si = (ptp - med) / (ptp + med)
ylabel = 'MUA (ptp - med) / (ptp + med)'
#pl.plot(t, ptp)
#pl.plot(t, med)
elif kind == 'nptpmin':
ptp = binhP.ptp(axis=1)
min = binhP.min(axis=1)
si = (ptp - min) / (ptp + min)
ylabel = 'MUA (ptp - min) / (ptp + min)'
#pl.plot(t, ptp)
#pl.plot(t, min)
elif kind == 'nmaxmin':
max = binhP.max(axis=1)
min = binhP.min(axis=1)
si = (max - min) / (max + min)
ylabel = 'MUA (max - min) / (max + min)'
#pl.plot(t, max)
#pl.plot(t, min)
else:
raise ValueError('unknown kind %r' % kind)
if plot:
# calculate xlim, always start from 0, add half a bin width to xmax:
if pratio:
xlim = (0, t[-1]+lfpwidth/2)
else:
xlim = (0, t[-1]+width/2)
self.si_plot(t, si, t0=t0, t1=t1, xlim=xlim, ylim=ylim, ylabel=ylabel,
showxlabel=showxlabel, showylabel=showylabel, showtitle=showtitle,
title=lastcmd(), showtext=showtext, text=self.r.name, hlines=hlines,
states=states, desynchsi=desynchsi, synchsi=synchsi, lw=lw,
alpha=alpha, relative2t0=relative2t0, swapaxes=swapaxes,
figsize=figsize)
#np.seterr(**old_settings) # restore old settings
return si, t # t are midpoints of bins, offset depends on relative2t0
def plot(self, var='ori', fixed=None):
"""var: string name of variable you want to plot a tuning curve for
fixed: dict with keys containing names of vars to keep fixed when building tuning
curve, and values containing each var's value(s) to fix at
Ex: r71.n[1].tune().plot('phase0', fixed={'ori':138, 'sfreqCycDeg':[0.4, 0.8]})
"""
if not self.done:
self.calc(tdelay=self.tdelay)
if fixed != None:
fixedsweepis = []
for fixedvar, fixedvals in fixed.items():
vals = self.experiment.sweeptable.data[fixedvar]
if fixedvar == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
sweepis = []
for fixedval in toiter(fixedvals):
sweepis.append(np.where(vals == fixedval)[0])
sweepis = np.concatenate(sweepis)
fixedsweepis.append(sweepis)
# intersect all fixedvar arrays in fixedsweepis:
fixedsweepis = core.intersect1d(fixedsweepis)
#print(fixedsweepis)
# get values for var at all unique sweep indices:
vals = self.experiment.sweeptable.data[var]
if var == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
x = np.unique(vals) # x axis
y = np.zeros(len(x), dtype=int) # spike counts for each variable value
for vali, val in enumerate(x):
sweepis = np.where(vals == val)[0]
if fixed != None:
sweepis = np.intersect1d(sweepis, fixedsweepis, assume_unique=True)
print sweepis
for sweepi in sweepis:
y[vali] += self.counts[sweepi].sum()
# create a new figure:
f = pl.figure()
a = f.add_subplot(111)
a.plot(x, y, 'k.-')
a.set_xlabel(var)
a.set_ylabel('spike count')
titlestr = lastcmd()
titlestr += ' nid%d' % self.neuron.id
a.set_title(titlestr)
f.canvas.window().setWindowTitle(titlestr)
a.text(0.99, 0.99, 'peak=(%s, %s)' % (x[y.argmax()], y.max()),
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
self.x, self.y = x, y
return self
def plot(self, normed=True, scale=2.0, MPL=False):
super(STCs, self).plot(normed=normed,
title=lastcmd(),
scale=scale,
MPL=MPL)
def sc_si(source,
method='mean',
sisource='lfp',
kind=None,
chani=-1,
sirange=None,
layers=False,
ms=1,
figsize=(7.5, 6.5)):
"""Pool recording.sc().si() results across recordings specified by source,
plot the result"""
uns = get_ipython().user_ns
if layers == False:
layers = ['all']
elif layers == True:
layers = ['sup', 'deep']
LAYER2I = {'all': 0, 'sup': 1, 'mid': 2, 'deep': 3, 'other': 4}
layeris = [LAYER2I[layer] for layer in layers]
recs, tracks = parse_source(source)
if sisource not in ['lfp', 'mua']:
raise ValueError('unknown sisource %r' % sisource)
if kind == None:
if sisource == 'lfp':
kind = uns['LFPSIKIND']
else:
kind = uns['MUASIKIND']
# calculate
corrss, sis = [], []
for rec in recs:
print(rec.absname)
corrs, si, ylabel = rec.sc().si(method=method,
sisource=sisource,
kind=kind,
chani=chani,
sirange=sirange,
plot=False)
corrss.append(corrs)
sis.append(si)
corrs = np.hstack(corrss)
si = np.hstack(sis)
# plot
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
#ylim = corrs[layeris].min(), corrs[layeris].max()
#yrange = ylim[1] - ylim[0]
#extra = yrange*0.03 # 3 %
#ylim = ylim[0]-extra, ylim[1]+extra
ylim = uns['SCLIMITS']
# keep only those points whose synchrony index falls within sirange:
if sirange == None:
finitesi = si[np.isfinite(si)]
sirange = finitesi.min(), finitesi.max()
sirange = np.asarray(sirange)
keepis = (sirange[0] <= si[0]) * (si[0] <= sirange[1]
) # boolean index array
si = si[:, keepis]
corrs = corrs[:, keepis]
# plot linear regressions of corrs vs si[0]:
if 'all' in layers:
m0, b0, r0, p0, stderr0 = linregress(si[0], corrs[0])
a.plot(sirange, m0 * sirange + b0, 'e--')
if 'sup' in layers:
m1, b1, r1, p1, stderr1 = linregress(si[0], corrs[1])
a.plot(sirange, m1 * sirange + b1, 'r--')
if 'mid' in layers:
m2, b2, r2, p2, stderr2 = linregress(si[0], corrs[2])
a.plot(sirange, m2 * sirange + b2, 'g--')
if 'deep' in layers:
m3, b3, r3, p3, stderr3 = linregress(si[0], corrs[3])
a.plot(sirange, m3 * sirange + b3, 'b--')
if 'other' in layers:
m4, b4, r4, p4, stderr4 = linregress(si[0], corrs[4])
a.plot(sirange, m4 * sirange + b4, 'y--', zorder=0)
# scatter plot corrs vs si, one colour per laminarity:
if 'all' in layers:
a.plot(si[0],
corrs[0],
'e.',
ms=ms,
label='all, m=%.3f, r=%.3f' % (m0, r0))
if 'sup' in layers:
a.plot(si[0],
corrs[1],
'r.',
ms=ms,
label='superficial, m=%.3f, r=%.3f' % (m1, r1))
if 'mid' in layers:
a.plot(si[0],
corrs[2],
'g.',
ms=ms,
label='middle, m=%.3f, r=%.3f' % (m2, r2))
if 'deep' in layers:
a.plot(si[0],
corrs[3],
'b.',
ms=ms,
label='deep, m=%.3f, r=%.3f' % (m3, r3))
if 'other' in layers:
a.plot(si[0],
corrs[4],
'y.',
ms=ms,
label='other, m=%.3f, r=%.3f' % (m4, r4),
zorder=0)
#a.set_xlim(sirange)
if kind[0] == 'n':
a.set_xlim(-1, 1)
a.set_ylim(ylim)
#a.autoscale(enable=True, axis='y', tight=True)
a.set_xlabel('%s SI (%s)' % (sisource.upper(), kind))
a.set_ylabel(ylabel)
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
a.set_title(titlestr)
a.legend(loc='upper left',
handlelength=1,
handletextpad=0.5,
labelspacing=0.1)
f.tight_layout(pad=0.3) # crop figure to contents
def plot(self,
t0=None,
t1=None,
chanis=None,
gain=1,
c='k',
alpha=1.0,
yunits='um',
yticks=None,
title=True,
xlabel=True,
relative2t0=False,
lim2stim=False,
scalebar=True,
lw=4,
figsize=(20, 6.5)):
"""Plot chanis of LFP data between t0 and t1 in sec. Unfortunatley, setting an alpha <
1 doesn't seem to reveal detail when a line obscures itself, such as when plotting a
very long time series. relative2t0 controls whether to plot relative to t0, or
relative to start of ADC clock. lim2stim limits the time range only to when a stimulus
was on screen, i.e. to the outermost times of non-NULL din. If only one chan is
requested, it's plotted on a mV scale instead of a spatial scale."""
self.get_data()
ts = self.get_tssec() # full set of timestamps, in sec
if t0 == None:
t0, t1 = ts[0], ts[-1]
if t1 == None:
t1 = t0 + 10 # 10 sec window
if chanis == None:
chanis = range(len(self.chans)) # all chans
if lim2stim:
t0, t1 = self.apply_lim2stim(t0, t1)
t0i, t1i = ts.searchsorted((t0, t1))
ts = ts[t0i:t1i] # constrained set of timestamps, in sec
chanis = tolist(chanis)
nchans = len(chanis)
# grab desired channels and time range:
data = self.data[chanis][:, t0i:t1i]
if nchans > 1: # convert uV to um:
totalgain = self.UV2UM * gain
data = data * totalgain
else: # convert uV to mV:
data = data / 1000
yunits = 'mV'
nt = len(ts)
assert nt == data.shape[1]
if relative2t0:
# convert ts to time from t0, otherwise plot time from start of ADC clock:
ts -= t0
x = np.tile(ts, nchans)
x.shape = nchans, nt
segments = np.zeros((nchans, nt, 2)) # x vals in col 0, yvals in col 1
segments[:, :, 0] = x
if nchans > 1:
segments[:, :,
1] = -data # set to -ve here because of invert_yaxis() below
else:
segments[:, :, 1] = data
if nchans > 1: # add y offsets:
maxypos = 0
for chanii, chani in enumerate(chanis):
chan = self.chans[chani]
ypos = self.chanpos[chan][1] # in um
segments[chanii, :,
1] += ypos # vertical distance below top of probe
maxypos = max(maxypos, ypos)
if yunits == 'mm': # convert from um to mm
segments[:, :, 1] /= 1000
maxypos = maxypos / 1000 # convert from int to float
totalgain = totalgain / 1000
lc = LineCollection(segments,
linewidth=1,
linestyle='-',
colors=c,
alpha=alpha,
antialiased=True,
visible=True)
f = pl.figure(figsize=figsize)
a = f.add_subplot(111)
a.add_collection(lc) # add to axes' pool of LCs
if scalebar: # add vertical scale bar at end of last channel to represent 1 mV:
if nchans > 1:
ymin, ymax = maxypos - 500 * totalgain, maxypos + 500 * totalgain # +/- 0.5 mV
else:
ymin, ymax = -0.5, 0.5 # mV
a.vlines(ts.max() * 0.99, ymin, ymax, lw=lw, colors='e')
a.autoscale(enable=True, tight=True)
# depending on relative2t0 above, x=0 represents either t0 or time ADC clock started:
a.set_xlim(xmin=0)
if nchans > 1:
a.invert_yaxis() # for spatial scale
if yticks != None:
a.set_yticks(yticks)
# turn off annoying "+2.41e3" type offset on x axis:
formatter = mpl.ticker.ScalarFormatter(useOffset=False)
a.xaxis.set_major_formatter(formatter)
if xlabel:
a.set_xlabel("time (s)")
if yunits == 'um':
a.set_ylabel("depth ($\mu$m)")
elif yunits == 'mm':
a.set_ylabel("depth (mm)")
elif yunits == 'mV':
a.set_ylabel("LFP (mV)")
titlestr = lastcmd()
gcfm().window.setWindowTitle(titlestr)
if title:
a.set_title(titlestr)
a.text(0.998,
0.99,
'%s' % self.r.name,
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
return self
