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 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 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 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 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