def spike_patterns(self, cell):
"""Show distribution and PSTH of spike patterns.
Requires selection of spike windows in the Event Selector.
"""
dataset, cl = self._get_patterns(cell)
ev = self._get_events(cell)
spt = dataset['spt']
stim = dataset['stim']
plt.figure()
plt.subplot(211)
bins = np.arange(cl.min(), cl.max()+2)
n, _ = np.histogram(cl, bins)
n = n*1./np.sum(n)
bins = bins[:-1]
w = 0.8
plt.bar(bins, n, width=w, fc='none')
ndigits = int(np.ceil(np.log2(cl.max())))
labels = map(lambda x: self._dec2binstr(x, ndigits), bins)
plt.xticks(bins+w/2, labels)
plt.xlabel('spike patterns')
plt.ylabel('frequency')
ax=plt.subplot(212)
for i in np.unique(cl):
basic.plotPSTH(spt, stim[cl==i], rate=True,
label=self._dec2binstr(i, ndigits))
trans = blended_transform_factory(ax.transData, ax.transAxes)
plt.vlines(ev, 0, 1, transform=trans)
plt.legend(prop=dict(size=8))
spike_src = "/Gollum/s44gollum07/el3/cell1"
background_src = "/Gollum/s44gollum01/el4"
sp_win = [-2, 2]
pow_frac = 5
out_dataset = "/TestSubject/sSession01/el1"
if __name__ == "__main__":
in_filter = filters.BakerlabFilter("../data/gollum.inf")
out_filter = filters.PyTablesFilter("simulated.h5")
sp_sim, stim_bg, spt_sim = eval.mix_cellbg(in_filter, spike_src,
background_src,
sp_win, pow_frac)
#export
out_filter.write_spt(spt_sim, out_dataset+"/cell1_orig",
overwrite=True)
out_filter.write_spt(stim_bg,out_dataset+ "stim",
overwrite=True)
out_filter.write_sp(sp_sim, out_dataset+"/raw",
overwrite=True)
#plotting
print "Total spikes: ", len(spt_sim['data'])
raw_bg, _ = eval.read_data(in_filter, background_src)
basic.plotPSTH(spt_sim['data'], stim_bg['data'], win=[0,300])
plt.figure()
plt.plot(raw_bg['data'][0,:25E3])
plt.plot(sp_sim['data'][0,:25E3])
plt.show()
