def test_wave_reader(n=10000):
#plt.interactive(True)
try:
update = 0
FIG = plt.figure()
rb = None
Q = Queue()
bs_reader = BS3Reader(WAVEProtocolHandler, Q, verbose=False,
ident='TestWAVE')
bs_reader.start()
for _ in xrange(n):
#print Q.qsize()
try:
item = Q.get(block=True, timeout=2)
for wave in item.event_lst:
gid, uid, tv, nc, ns, wf = wave
if rb is None:
rb = MxRingBuffer(dimension=(ns, nc), capacity=2000)
rb.append(wf)
#print 'rb:', len(rb)
update += 1
if update > 1000:
print 'plotting enter'
FIG.clear()
waveforms({0: rb[:]}, tf=ns, plot_handle=FIG,
plot_separate=False, show=False)
#plt.draw()
save_figure(FIG, 'wave', file_dir='E:\SpiDAQ')
update = 0
print 'plotting exit'
except Empty:
continue
except Exception, ex:
print ex
def plot(self, data, views=2, show=False, filename=None):
"""plot clustering"""
# get plotting tools
try:
from spikeplot import plt, cluster, save_figure
except ImportError:
return None
# init
views = min(views, int(data.shape[1] / 2))
fig = plt.figure()
fig.suptitle('clustering [%s]' % self.clus_type)
ax = [fig.add_subplot(2, views, v + 1) for v in xrange(views)]
axg = fig.add_subplot(212)
ncmp = int(self.labels.max() + 1)
cdata = dict(
zip(xrange(ncmp), [data[self.labels == c] for c in xrange(ncmp)]))
# plot clustering
for v in xrange(views):
cluster(cdata,
data_dim=(2 * v, 2 * v + 1),
plot_handle=ax[v],
plot_mean=sp.sqrt(self.sigma_factor),
xlabel='PC %d' % int(2 * v),
ylabel='PC %d' % int(2 * v + 1),
show=False)
# plot gof
axg.plot(self._gof, ls='steps')
for i in xrange(1, len(self.crange)):
axg.axvline(i * self.repeats - 0.5, c='y', ls='--')
axg.axvspan(self._winner - 0.5,
self._winner + 0.5,
fc='gray',
alpha=0.2)
labels = []
for k in self.crange:
labels += ['%d' % k]
labels += ['.'] * (self.repeats - 1)
axg.set_xticks(sp.arange(len(labels)))
axg.set_xticklabels(labels)
axg.set_xlabel('cluster count and repeats')
axg.set_ylabel(str(self.gof_type).upper())
axg.set_xlim(-1, len(labels))
# handle the resulting plot
if filename is not None:
save_figure(fig, filename, '')
if show is True:
plt.show()
return True
def plot(self, data, views=2, show=False, filename=None):
"""plot clustering"""
# get plotting tools
try:
from spikeplot import plt, cluster, save_figure
except ImportError:
return None
# init
views = min(views, int(data.shape[1] / 2))
fig = plt.figure()
fig.suptitle('clustering [%s]' % self.clus_type)
ax = [fig.add_subplot(2, views, v + 1) for v in xrange(views)]
axg = fig.add_subplot(212)
ncmp = int(self.labels.max() + 1)
cdata = dict(zip(xrange(ncmp),
[data[self.labels == c] for c in xrange(ncmp)]))
# plot clustering
for v in xrange(views):
cluster(
cdata,
data_dim=(2 * v, 2 * v + 1),
plot_handle=ax[v],
plot_mean=sp.sqrt(self.sigma_factor),
xlabel='PC %d' % int(2 * v),
ylabel='PC %d' % int(2 * v + 1),
show=False)
# plot gof
axg.plot(self._gof, ls='steps')
for i in xrange(1, len(self.crange)):
axg.axvline(i * self.repeats - 0.5, c='y', ls='--')
axg.axvspan(self._winner - 0.5, self._winner + 0.5, fc='gray',
alpha=0.2)
labels = []
for k in self.crange:
labels += ['%d' % k]
labels += ['.'] * (self.repeats - 1)
axg.set_xticks(sp.arange(len(labels)))
axg.set_xticklabels(labels)
axg.set_xlabel('cluster count and repeats')
axg.set_ylabel(str(self.gof_type).upper())
axg.set_xlim(-1, len(labels))
# handle the resulting plot
if filename is not None:
save_figure(fig, filename, '')
if show is True:
plt.show()
return True
