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 __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 overview(self, filename, figsize=(25, 20)):
pdf = PdfPages(filename)
sp_props = dict(top=0.95, bottom=0.05, hspace=0.2, wspace=0.2,
left=0.03, right=0.97)
try:
# nrows, _ = 5, len(self.data)
fig, axarr = plt.subplots(nrows=5, ncols=6, figsize=figsize)
fig.subplots_adjust(**sp_props)
for j, name in enumerate(sorted(self.data.keys())):
data = self.data[name]
i = j%6
if not i and j:
pdf.savefig(fig)
fig, axarr = plt.subplots(nrows=5, ncols=6, dpi=300,
figsize=figsize)
fig.subplots_adjust(**sp_props)
if data is None:
self._empty_figure(axarr, name, i, nrows=5)
continue
title = '%s, (%d tracks)' %(name, data.ntracks)
# hmm network
clcol = dict([(k, self.classdef.hexcolors[v])
for k, v in self.classdef.class_names.iteritems()
if k in data.states])
plots.hmm_network(data.transmat, clcol, title=title,
axes=axarr[0][i])
# trajectories
plots.trajectories(data.labels,
labels=self.ecopts.sorting_sequence,
cmap=self.classdef.colormap,
norm=self.classdef.normalize,
axes = axarr[1][i])
plots.trajectories(data.hmm_labels,
labels=self.ecopts.sorting_sequence,
cmap=self.classdef.colormap,
norm=self.classdef.normalize,
axes = axarr[2][i])
# dwell box/barplots
ylabel = "dwell time (%s)" %self.ecopts.timeunit
xlabel = "class labels"
plots.dwell_boxplot(data.dwell_times,
ylabel=ylabel,
xlabel=xlabel,
cmap=self.classdef.colormap,
ymax=self.ecopts.tmax,
axes=axarr[3][i])
plots.barplot(data.dwell_times, xlabel=xlabel, ylabel=ylabel,
cmap=self.classdef.colormap,
ymax=self.ecopts.tmax,
axes=axarr[4][i])
self._frame_off(axarr, i, 5)
pdf.savefig(fig)
finally:
pdf.close()
def overview(self, filename, figsize=(25, 20)):
pdf = PdfPages(filename)
sp_props = dict(top=0.95,
bottom=0.05,
hspace=0.2,
wspace=0.2,
left=0.03,
right=0.97)
try:
# nrows, _ = 5, len(self.data)
fig, axarr = plt.subplots(nrows=5, ncols=6, figsize=figsize)
fig.subplots_adjust(**sp_props)
for j, name in enumerate(sorted(self.data.keys())):
data = self.data[name]
i = j % 6
if not i and j:
pdf.savefig(fig)
fig, axarr = plt.subplots(nrows=5,
ncols=6,
dpi=300,
figsize=figsize)
fig.subplots_adjust(**sp_props)
if data is None:
self._empty_figure(axarr, name, i, nrows=5)
continue
title = '%s, (%d tracks)' % (name, data.ntracks)
# hmm network
clcol = dict([
(k, self.classdef.hexcolors[v])
for k, v in self.classdef.class_names.iteritems()
if k in data.states
])
plots.hmm_network(data.transmat,
clcol,
title=title,
axes=axarr[0][i])
# trajectories
plots.trajectories(data.labels,
labels=self.ecopts.sorting_sequence,
cmap=self.classdef.colormap,
norm=self.classdef.normalize,
axes=axarr[1][i],
stepwidth=data.stepwidth)
plots.trajectories(data.hmm_labels,
labels=self.ecopts.sorting_sequence,
cmap=self.classdef.colormap,
norm=self.classdef.normalize,
axes=axarr[2][i],
stepwidth=data.stepwidth)
# dwell box/barplots
ylabel = "dwell time (%s)" % self.ecopts.timeunit
xlabel = "class labels"
plots.dwell_boxplot(data.dwell_times,
ylabel=ylabel,
xlabel=xlabel,
cmap=self.classdef.colormap,
ymax=self.ecopts.tmax,
axes=axarr[3][i])
plots.barplot(data.dwell_times,
xlabel=xlabel,
ylabel=ylabel,
cmap=self.classdef.colormap,
ymax=self.ecopts.tmax,
axes=axarr[4][i])
self._frame_off(axarr, i, 5)
pdf.savefig(fig)
finally:
pdf.close()
