def __call__(self,
labels=(2, 3),
filename='trajectory_plots.pdf',
exclude_labels=(0, )):
cmap = unsupervised_cmap(self._nclusters)
try:
pdf = PdfPages(join(self._odir, filename))
for title_, tracks in self._data.iteritems():
title = '%s (%s)' % (self._position, title_.lower())
# checking for binary matrix
if np.unique(tracks).size == 2:
is_binary = True
labels_ = (1, )
else:
is_binary = False
labels_ = labels
# trajectory plots
fig = plots.trajectories(tracks,
labels_,
title=title,
cmap=cmap)
pdf.savefig(fig)
delchars = NameValidator.defaultdeletechars.copy()
delchars.remove('-')
validator = NameValidator(case_sensitive='lower',
deletechars=delchars)
fname = validator.validate(title)[0] + '.csv'
np.savetxt(join(self._odir, fname),
tracks,
fmt='%d',
delimiter=',')
# boxplots
if not is_binary:
ylabel = "dwell time (%s)" % self.timeunit
xlabel = "class labels"
dwell_times = self.dwell_times(tracks)
fig1 = plots.dwell_boxplot(dwell_times,
title,
ylabel=ylabel,
xlabel=xlabel,
exclude_labels=exclude_labels,
cmap=cmap)
fig2 = plots.barplot(dwell_times,
title,
xlabel=xlabel,
ylabel=ylabel,
exclude_labels=exclude_labels,
cmap=cmap)
pdf.savefig(fig1)
pdf.savefig(fig2)
finally:
pdf.close()
def __init__(self, nclusters, *args, **kw):
super(ClassDefinitionUnsup, self).__init__(*args, **kw)
self.nclusters = nclusters
self.colormap = unsupervised_cmap(self.nclusters)
# dummy attribute to recyle export function in timeholder
self.classifier = GaussianMixtureModel()
for i in xrange(self.nclusters):
name = "cluster-%d" % i
self.class_labels[name] = i
self.class_names[i] = name
self.hexcolors[name] = rgb2hex(self.colormap(i))
def __init__(self, nclusters, *args, **kw):
super(ClassDefinitionUnsup, self).__init__(*args, **kw)
self.nclusters = nclusters
self.colormap = unsupervised_cmap(self.nclusters)
# dummy attribute to recyle export function in timeholder
self.classifier = GaussianMixtureModel()
for i in xrange(self.nclusters):
name = "cluster-%d" % i
self.class_labels[name] = i
self.class_names[i] = name
self.hexcolors[name] = rgb2hex(self.colormap(i))
def __call__(self, labels=(2,3), filename='trajectory_plots.pdf',
exclude_labels=(0, )):
cmap = unsupervised_cmap(self._nclusters)
try:
pdf = PdfPages(join(self._odir, filename))
for title_, tracks in self._data.iteritems():
title = '%s (%s)' %(self._position, title_.lower())
# checking for binary matrix
if np.unique(tracks).size == 2:
is_binary = True
labels_ = (1, )
else:
is_binary = False
labels_ = labels
# trajectory plots
fig = plots.trajectories(tracks, labels_, title=title, cmap=cmap)
pdf.savefig(fig)
delchars = NameValidator.defaultdeletechars.copy()
delchars.remove('-')
validator = NameValidator(case_sensitive='lower',
deletechars=delchars)
fname = validator.validate(title)[0]+'.csv'
np.savetxt(join(self._odir, fname), tracks, fmt='%d',
delimiter=',')
# boxplots
if not is_binary:
ylabel = "dwell time (%s)" %self.timeunit
xlabel = "class labels"
dwell_times = self.dwell_times(tracks)
fig1 = plots.dwell_boxplot(dwell_times, title,
ylabel=ylabel, xlabel=xlabel,
exclude_labels=exclude_labels,
cmap=cmap)
fig2 = plots.barplot(dwell_times, title, xlabel=xlabel,
ylabel=ylabel,
exclude_labels=exclude_labels,
cmap=cmap)
pdf.savefig(fig1)
pdf.savefig(fig2)
finally:
pdf.close()
