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
def main():
TF, SNR, PCADIM = 65, 0.5, 8
NTRL = 10
LOAD = False
if LOAD is True:
spks, spks_info, ndet = load_data()
else:
# spks, spks_info, ndet = get_data(tf=TF, trials=NTRL, snr=SNR,
# mean_correct=False, save=True)
pass
# plot.waveforms(spks, tf=TF, show=False)
input_obs = pre_processing(spks, ndet, TF, pca_dim=PCADIM)
plot.cluster(input_obs, show=False)
# kmeans
labels_km = cluster_kmeans(input_obs)
obs_km = {}
wf_km = {}
for i in xrange(labels_km.max() + 1):
obs_km[i] = input_obs[labels_km == i]
wf_km[i] = spks[labels_km == i]
if WITH_PLOT:
plot.cluster(obs_km, title='kmeans', show=False)
plot.waveforms(obs_km, tf=TF, title='kmeans', show=False)
# gmm
labels_gmm = cluster_gmm(input_obs)
obs_gmm = {}
wf_gmm = {}
for i in xrange(labels_km.max() + 1):
obs_gmm[i] = input_obs[labels_gmm == i]
wf_gmm[i] = spks[labels_gmm == i]
if WITH_PLOT:
plot.cluster(obs_gmm, title='gmm', show=False)
plot.waveforms(wf_gmm, tf=TF, title='gmm', show=False)
# ward
labels_ward = cluster_ward(input_obs)
obs_ward = {}
wf_ward = {}
for i in xrange(labels_km.max() + 1):
obs_ward[i] = input_obs[labels_ward == i]
wf_ward[i] = spks[labels_ward == i]
if WITH_PLOT:
plot.cluster(obs_ward, title='ward', show=False)
plot.waveforms(wf_ward, tf=TF, title='ward', show=False)
# spectral
#cluster_spectral(spks)
if WITH_PLOT:
plot.plt.show()
def main():
TF, SNR, PCADIM = 65, 0.5, 8
NTRL = 10
LOAD = False
if LOAD is True:
spks, spks_info, ndet = load_data()
else:
# spks, spks_info, ndet = get_data(tf=TF, trials=NTRL, snr=SNR,
# mean_correct=False, save=True)
pass
# plot.waveforms(spks, tf=TF, show=False)
input_obs = pre_processing(spks, ndet, TF, pca_dim=PCADIM)
plot.cluster(input_obs, show=False)
# kmeans
labels_km = cluster_kmeans(input_obs)
obs_km = {}
wf_km = {}
for i in xrange(labels_km.max() + 1):
obs_km[i] = input_obs[labels_km == i]
wf_km[i] = spks[labels_km == i]
if WITH_PLOT:
plot.cluster(obs_km, title='kmeans', show=False)
plot.waveforms(obs_km, tf=TF, title='kmeans', show=False)
# gmm
labels_gmm = cluster_gmm(input_obs)
obs_gmm = {}
wf_gmm = {}
for i in xrange(labels_km.max() + 1):
obs_gmm[i] = input_obs[labels_gmm == i]
wf_gmm[i] = spks[labels_gmm == i]
if WITH_PLOT:
plot.cluster(obs_gmm, title='gmm', show=False)
plot.waveforms(wf_gmm, tf=TF, title='gmm', show=False)
# ward
labels_ward = cluster_ward(input_obs)
obs_ward = {}
wf_ward = {}
for i in xrange(labels_km.max() + 1):
obs_ward[i] = input_obs[labels_ward == i]
wf_ward[i] = spks[labels_ward == i]
if WITH_PLOT:
plot.cluster(obs_ward, title='ward', show=False)
plot.waveforms(wf_ward, tf=TF, title='ward', show=False)
# spectral
#cluster_spectral(spks)
if WITH_PLOT:
plot.plt.show()
import scipy as sp
from spikeplot import cluster
# get some data
my_data = {0:sp.randn(500, 2), 1:sp.randn(300, 2) + 2}
# call the plot function on the axes
cluster(
my_data,
title='Test Plot',
plot_mean=2)