def _view_poi_histogram(self, event):
current_cluster = self.current_cluster()
if not current_cluster.points_of_interest:
plt.figure()
plt.figtext(0.1, 0.1, 'Sorry, no points of interest for this cluster. Try running explorer again with -poi poi_dataset.bed')
plt.show()
return
# Pandas tend to crash if subplots is True when only one dimension
number_poi_collections = len(current_cluster.points_of_interest_histogram.columns)
axes = current_cluster.points_of_interest_histogram.plot(kind='bar', subplots=(number_poi_collections > 1))
if number_poi_collections == 1:
axes = [axes]
for ax in axes:
ax.set_ylabel('Number of regions')
plt.xlabel('Bin')
plt.suptitle('Original points of interest')
axes = current_cluster.tracked_points_histogram.plot(kind='bar', subplots=(number_poi_collections > 1))
if number_poi_collections == 1:
axes = [axes]
for ax in axes:
ax.set_ylabel('Number of regions')
plt.xlabel('Warped bin')
plt.suptitle('Warped points of interest')
plt.show()
def create_figure(self, figsize=(12, 10), interactive=True):
plt.figure(num=None, figsize=figsize, facecolor='w', edgecolor='k')
self._gs_main = gridspec.GridSpec(2, 2, wspace=0, height_ratios=[1, 15])
self._figure = plt.gcf()
self._ax_dendrogram = plt.subplot(self.gs_dendrogram, rasterized=True)
if interactive:
self._figure.canvas.mpl_connect('button_press_event', self._onclick_listener)
self.draw_buttons()
def _plot_item_in_figure(self, index):
data = self.current_cluster().data.ix[index]
projected_data = self.current_cluster().projected_data.ix[index]
prototype = self.current_cluster().prototype
poi = self.current_cluster().points_of_interest.get(index, {})
tracked_poi = self.current_cluster().tracked_points_of_interest.get(index, {})
plt.figure()
ax1 = plt.subplot(3, 1, 1)
data.plot(ax=ax1, legend=False)
plt.figlegend(*ax1.get_legend_handles_labels(), loc='lower center')
if poi:
lim_min, lim_max = ax1.get_ylim()
height = (lim_max - lim_min) / 20
points_plotted_on = defaultdict(lambda: 0)
for key, value in poi.iteritems():
colour = self.highlight_colours[key]
for point in value:
items_on_current_point = points_plotted_on[point]
ax1.add_patch(Rectangle((point, lim_min + (height*items_on_current_point)), width=1, height=height, facecolor=colour, edgecolor='k'))
points_plotted_on[point] += 1
plt.title('Original')
ax2 = plt.subplot(3, 1, 2, sharey=ax1)
prototype.plot(ax=ax2, legend=False)
plt.title('Cluster Prototype')
ax3 = plt.subplot(3, 1, 3, sharey=ax1)
projected_data.plot(ax=ax3, legend=False)
if tracked_poi:
lim_min, lim_max = ax3.get_ylim()
height = (lim_max - lim_min) / 20
points_plotted_on = defaultdict(lambda: 0)
for key, value in tracked_poi.iteritems():
colour = self.highlight_colours[key]
for point in value:
items_on_current_point = points_plotted_on[point]
ax3.add_patch(Rectangle((point, lim_min + (height*items_on_current_point)), width=1, height=height,
facecolor=colour, edgecolor='k'))
points_plotted_on[point] += 1
plt.title('Warped')
plt.suptitle(index)
figure_dtw_mappings = visualise_dtw_mappings(data, prototype, dtw_function=self.dtw_function,
columns=data.columns, sequence_x_label=index,
sequence_y_label='Cluster Prototype')
def prototype_images_from_node_list(node_list, output_directory):
print '> Saving images to {0!r}'.format(output_directory)
ndims = node_list[0].prototype.shape[1]
plt.figure(figsize=(3 * ndims, 2))
for node in node_list:
filename = '{0}.png'.format(node.id)
full_filename = os.path.join(output_directory, filename)
print '> Saving {0}'.format(full_filename)
prototype_T = node.prototype.values.T
for i in xrange(ndims):
plt.subplot(1, ndims, i + 1)
plt.plot(prototype_T[i])
plt.savefig(full_filename)
plt.clf()
def prototype_images_from_node_list(node_list, output_directory):
print '> Saving images to {0!r}'.format(output_directory)
ndims = node_list[0].prototype.shape[1]
plt.figure(figsize=(3 * ndims, 2))
for node in node_list:
filename = '{0}.png'.format(node.id)
full_filename = os.path.join(output_directory, filename)
print '> Saving {0}'.format(full_filename)
prototype_T = node.prototype.values.T
for i in xrange(ndims):
plt.subplot(1, ndims, i+1)
plt.plot(prototype_T[i])
plt.savefig(full_filename)
plt.clf()
def _plot_regular_heatmap_on_figure(self, cluster):
f = plt.figure(figsize=(11.7, 8.3))
plt.subplots_adjust(left=0.05, bottom=0.05, top=0.95, right=0.95)
self._enlarged_heatmap_axis = plt.gca()
cluster.data.plot_heatmap(horizontal_grid=True,
sort_by=None, highlighted_points=cluster.points_of_interest, highlight_colours=self.highlight_colours)
f.canvas.mpl_connect('button_press_event', self._onclick_listener)
return f
def create_figure(self, figsize=(12, 10), interactive=True):
self._figure = plt.figure(num=None,
figsize=figsize,
facecolor='w', edgecolor='k')
self.title = plt.suptitle("") # Create text object for title
if interactive:
self.create_buttons()
self._figure.canvas.mpl_connect('button_press_event', self._onclick_listener)
self.draw()
def plot_dtw_sequences_dist_cost_and_path(sequence_x, sequence_y, dist,
cost_matrix, path):
"""
Generates a full DTW plot with both the cost matrix, path and both of the sequences
:param sequence_x:
:param sequence_y:
:param dist:
:param cost_matrix:
:param path:
:return:
"""
sequence_x = np.asarray(sequence_x)
sequence_y = np.asarray(sequence_y)
# Based on: http://matplotlib.org/examples/pylab_examples/scatter_hist.html
figure = plt.figure()
null_fmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
ax_width = 0.2
bottom_h = left_h = left + width + 0.02
rect_cost = [left, bottom, width, height]
rect_sequence_x = [left, bottom_h, width, ax_width]
rect_sequence_y = [left_h, bottom, ax_width, height]
text_pos_x = left_h
text_pos_y = bottom_h
ax_cost = plt.axes(rect_cost)
ax_sequence_x = plt.axes(rect_sequence_x)
ax_sequence_y = plt.axes(rect_sequence_y)
# Drop labels
ax_sequence_x.xaxis.set_major_formatter(null_fmt)
ax_sequence_y.yaxis.set_major_formatter(null_fmt)
# Plot main plot
plot_dtw_cost_and_path(cost_matrix, path, ax=ax_cost)
ax_sequence_x.plot(range(len(sequence_x)), sequence_x)
ax_sequence_y.plot(sequence_y, range(len(sequence_y)))
# Set limits based on cost
ax_sequence_x.set_xlim(ax_cost.get_xlim())
ax_sequence_y.set_ylim(ax_cost.get_ylim())
# Set Limits based on seq
ax_sequence_x.set_ylim(
min(sequence_x.min(), sequence_y.min()) - 1,
max(sequence_x.max(), sequence_y.max()) + 1)
ax_sequence_y.set_xlim(
min(sequence_x.min(), sequence_y.min()) - 1,
max(sequence_x.max(), sequence_y.max()) + 1)
# Add test showing the value of dist
plt.figtext(text_pos_x,
text_pos_y,
'Distance:\n{0:.5f}'.format(dist),
size='medium')
return figure
def visualise_dtw_mappings(sequence_x,
sequence_y,
dtw_function=dtw_std,
columns=None,
title=None,
sequence_x_label=None,
sequence_y_label=None):
def major_tick_step(ax, axis):
if axis == 'x':
ticks = ax.get_xticks()
else:
ticks = ax.get_yticks()
try:
step = ticks[1] - ticks[0]
except IndexError:
step = 0.2
return step
def expand_axes(ax):
x_increment = major_tick_step(ax, 'x') / 8.0
min_x, max_x = ax.get_xlim()
ax.set_xlim(min_x - x_increment, max_x + x_increment)
y_increment = major_tick_step(ax, 'y') / 8.0
min_y, max_y = ax.get_ylim()
ax.set_ylim(min_y - y_increment, max_y + y_increment)
def add_reversed_annotation(ax):
min_x, max_x = ax.get_xlim()
min_y, max_y = ax.get_ylim()
offset_x = major_tick_step(ax, 'x')
offset_y = major_tick_step(ax, 'y')
ax.text(min_x + offset_x / 8, max_y - offset_y / 2 - offset_y / 8,
'(reversed)')
dist, cost, path = dtw_function(sequence_x, sequence_y, dist_only=False)
sequence_x = np.asarray(sequence_x)
sequence_y = np.asarray(sequence_y)
try:
ndim = sequence_x.shape[1]
except IndexError:
ndim = 1
reversed = dtw_path_is_reversed(path)
if reversed:
sequence_x = reverse_sequence(sequence_x)
path_x = np.max(path[0]) - path[0]
path_y = path[1]
path = (path_x, path_y)
sequence_y_T = np.atleast_2d(sequence_y.T)
sequence_x_T = np.atleast_2d(sequence_x.T)
if columns is None and ndim > 1:
columns = ['Dimension #{0}'.format(i) for i in range(1, ndim + 1)]
elif ndim > 1:
if len(columns) != ndim:
raise ValueError(
'Number of column titles does not match the number of dimensions'
)
main_y_axis = None
xaxes_regular = [None] * ndim
xaxes_warped = [None] * ndim
figure = plt.figure()
figure.subplots_adjust(wspace=0.01, hspace=0.1)
for i in range(ndim):
x = sequence_x_T[i]
print x.shape
y = sequence_y_T[i]
ax2 = plt.subplot(2,
ndim,
ndim + i + 1,
sharey=main_y_axis,
sharex=xaxes_warped[i])
ax2.plot(y, color='g')
if i > 0:
ax2.yaxis.set_visible(False)
expand_axes(ax2)
if not main_y_axis:
main_y_axis = ax2
if not xaxes_warped[i]:
xaxes_warped[i] = ax2
ax1 = plt.subplot(2,
ndim,
i + 1,
sharey=main_y_axis,
sharex=xaxes_regular[i])
ax1.plot(x, color='b')
expand_axes(ax1)
if ndim > 1:
ax1.set_title(columns[i])
if i > 0:
ax1.yaxis.set_visible(False)
if not xaxes_regular[i]:
xaxes_regular[i] = ax1
if reversed:
add_reversed_annotation(ax1)
for p_i, p_j in zip(path[0], path[1]):
xy_a = (p_i, x[p_i])
xy_b = (p_j, y[p_j])
con = ConnectionPatch(xyA=xy_a,
xyB=xy_b,
coordsA="data",
coordsB="data",
axesA=ax1,
axesB=ax2,
arrowstyle="-",
shrinkB=2,
shrinkA=2,
alpha=0.2)
ax1.add_artist(con)
if title is not None:
plt.suptitle(title)
lines = xaxes_regular[0].get_lines()
lines.extend(xaxes_warped[0].get_lines())
if sequence_x_label and sequence_y_label:
plt.figlegend(lines, (sequence_x_label, sequence_y_label),
'lower center')
return figure
def plot_dtw_sequences_dist_cost_and_path(sequence_x, sequence_y, dist, cost_matrix, path):
"""
Generates a full DTW plot with both the cost matrix, path and both of the sequences
:param sequence_x:
:param sequence_y:
:param dist:
:param cost_matrix:
:param path:
:return:
"""
sequence_x = np.asarray(sequence_x)
sequence_y = np.asarray(sequence_y)
# Based on: http://matplotlib.org/examples/pylab_examples/scatter_hist.html
figure = plt.figure()
null_fmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
ax_width = 0.2
bottom_h = left_h = left + width + 0.02
rect_cost = [left, bottom, width, height]
rect_sequence_x = [left, bottom_h, width, ax_width]
rect_sequence_y = [left_h, bottom, ax_width, height]
text_pos_x = left_h
text_pos_y = bottom_h
ax_cost = plt.axes(rect_cost)
ax_sequence_x = plt.axes(rect_sequence_x)
ax_sequence_y = plt.axes(rect_sequence_y)
# Drop labels
ax_sequence_x.xaxis.set_major_formatter(null_fmt)
ax_sequence_y.yaxis.set_major_formatter(null_fmt)
# Plot main plot
plot_dtw_cost_and_path(cost_matrix, path, ax=ax_cost)
ax_sequence_x.plot(range(len(sequence_x)), sequence_x)
ax_sequence_y.plot(sequence_y, range(len(sequence_y)))
# Set limits based on cost
ax_sequence_x.set_xlim(ax_cost.get_xlim())
ax_sequence_y.set_ylim(ax_cost.get_ylim())
# Set Limits based on seq
ax_sequence_x.set_ylim(min(sequence_x.min(), sequence_y.min()) - 1,
max(sequence_x.max(), sequence_y.max()) + 1)
ax_sequence_y.set_xlim(min(sequence_x.min(), sequence_y.min()) - 1,
max(sequence_x.max(), sequence_y.max()) + 1)
# Add test showing the value of dist
plt.figtext(text_pos_x, text_pos_y, 'Distance:\n{0:.5f}'.format(dist), size='medium')
return figure
def visualise_dtw_mappings(sequence_x, sequence_y, dtw_function=dtw_std, columns=None, title=None, sequence_x_label=None,
sequence_y_label=None):
def major_tick_step(ax, axis):
if axis == 'x':
ticks = ax.get_xticks()
else:
ticks = ax.get_yticks()
try:
step = ticks[1] - ticks[0]
except IndexError:
step = 0.2
return step
def expand_axes(ax):
x_increment = major_tick_step(ax, 'x') / 8.0
min_x, max_x = ax.get_xlim()
ax.set_xlim(min_x - x_increment, max_x + x_increment)
y_increment = major_tick_step(ax, 'y') / 8.0
min_y, max_y = ax.get_ylim()
ax.set_ylim(min_y - y_increment, max_y + y_increment)
def add_reversed_annotation(ax):
min_x, max_x = ax.get_xlim()
min_y, max_y = ax.get_ylim()
offset_x = major_tick_step(ax, 'x')
offset_y = major_tick_step(ax, 'y')
ax.text(min_x + offset_x / 8, max_y - offset_y / 2 - offset_y / 8, '(reversed)')
dist, cost, path = dtw_function(sequence_x, sequence_y, dist_only=False)
sequence_x = np.asarray(sequence_x)
sequence_y = np.asarray(sequence_y)
try:
ndim = sequence_x.shape[1]
except IndexError:
ndim = 1
reversed = dtw_path_is_reversed(path)
if reversed:
sequence_x = reverse_sequence(sequence_x)
path_x = np.max(path[0]) - path[0]
path_y = path[1]
path = (path_x, path_y)
sequence_y_T = np.atleast_2d(sequence_y.T)
sequence_x_T = np.atleast_2d(sequence_x.T)
if columns is None and ndim > 1:
columns = ['Dimension #{0}'.format(i) for i in range(1, ndim+1)]
elif ndim > 1:
if len(columns) != ndim:
raise ValueError('Number of column titles does not match the number of dimensions')
main_y_axis = None
xaxes_regular = [None] * ndim
xaxes_warped = [None] * ndim
figure = plt.figure()
figure.subplots_adjust(wspace=0.01, hspace=0.1)
for i in range(ndim):
x = sequence_x_T[i]
print x.shape
y = sequence_y_T[i]
ax2 = plt.subplot(2, ndim, ndim + i + 1, sharey=main_y_axis, sharex=xaxes_warped[i])
ax2.plot(y, color='g')
if i > 0:
ax2.yaxis.set_visible(False)
expand_axes(ax2)
if not main_y_axis:
main_y_axis = ax2
if not xaxes_warped[i]:
xaxes_warped[i] = ax2
ax1 = plt.subplot(2, ndim, i + 1, sharey=main_y_axis, sharex=xaxes_regular[i])
ax1.plot(x, color='b')
expand_axes(ax1)
if ndim > 1:
ax1.set_title(columns[i])
if i > 0:
ax1.yaxis.set_visible(False)
if not xaxes_regular[i]:
xaxes_regular[i] = ax1
if reversed:
add_reversed_annotation(ax1)
for p_i, p_j in zip(path[0], path[1]):
xy_a = (p_i, x[p_i])
xy_b = (p_j, y[p_j])
con = ConnectionPatch(xyA=xy_a, xyB=xy_b, coordsA="data", coordsB="data",
axesA=ax1, axesB=ax2, arrowstyle="-", shrinkB=2, shrinkA=2, alpha=0.2)
ax1.add_artist(con)
if title is not None:
plt.suptitle(title)
lines = xaxes_regular[0].get_lines()
lines.extend(xaxes_warped[0].get_lines())
if sequence_x_label and sequence_y_label:
plt.figlegend(lines, (sequence_x_label, sequence_y_label), 'lower center')
return figure
def _plot_prototype_on_figure(self, cluster):
f = plt.figure()
plt.subplots_adjust(left=0.05, bottom=0.05, top=0.95, right=0.95)
cluster.prototype.plot(ax=f.gca())
return f