def _set_axis_labels(self,
fig,
sub,
n_regions,
region_labels,
indices2emphasize,
color='k',
position='left'):
y_ticks = range(n_regions)
# region_labels = numpy.array(["%d. %s" % l for l in zip(y_ticks, region_labels)])
region_labels = generate_region_labels(len(y_ticks), region_labels,
". ",
self.print_regions_indices,
y_ticks)
big_ax = fig.add_subplot(sub, frameon=False)
if position == 'right':
big_ax.yaxis.tick_right()
big_ax.yaxis.set_label_position("right")
big_ax.set_yticks(y_ticks)
big_ax.set_yticklabels(region_labels, color='k')
if not (color == 'k'):
labels = big_ax.yaxis.get_ticklabels()
for idx in indices2emphasize:
labels[idx].set_color(color)
big_ax.yaxis.set_ticklabels(labels)
big_ax.invert_yaxis()
big_ax.axes.get_xaxis().set_visible(False)
def axYticks(labels, nTS, offsets=offset):
pyplot.gca().set_yticks((offset * numpy.array([list(range(nTS))]).flatten()).tolist())
try:
pyplot.gca().set_yticklabels(labels.flatten().tolist())
except:
labels = generate_region_labels(nTS, [], "", True)
self.logger.warning("Cannot convert region labels' strings for y axis ticks!")
def plot_vector(self,
vector,
labels,
subplot,
title,
show_y_labels=True,
indices_red=None,
sharey=None):
ax = pyplot.subplot(subplot, sharey=sharey)
pyplot.title(title)
n_vector = labels.shape[0]
y_ticks = numpy.array(range(n_vector), dtype=numpy.int32)
color = 'k'
colors = numpy.repeat([color], n_vector)
coldif = False
if indices_red is not None:
colors[indices_red] = 'r'
coldif = True
if len(vector.shape) == 1:
ax.barh(y_ticks, vector, color=colors, align='center')
else:
ax.barh(y_ticks, vector[0, :], color=colors, align='center')
# ax.invert_yaxis()
ax.grid(True, color='grey')
ax.set_yticks(y_ticks)
if show_y_labels:
region_labels = generate_region_labels(n_vector, labels, ". ",
self.print_regions_indices)
ax.set_yticklabels(region_labels)
if coldif:
labels = ax.yaxis.get_ticklabels()
for ids in indices_red:
labels[ids].set_color('r')
ax.yaxis.set_ticklabels(labels)
else:
ax.set_yticklabels([])
ax.autoscale(tight=True)
if sharey is None:
ax.invert_yaxis()
return ax
def _plot_matrix(self,
matrix,
xlabels,
ylabels,
subplot=111,
title="",
show_x_labels=True,
show_y_labels=True,
x_ticks=numpy.array([]),
y_ticks=numpy.array([]),
indices_red_x=None,
indices_red_y=None,
sharex=None,
sharey=None,
cmap='autumn_r',
vmin=None,
vmax=None):
ax = pyplot.subplot(subplot, sharex=sharex, sharey=sharey)
pyplot.title(title)
nx, ny = matrix.shape
indices_red = [indices_red_x, indices_red_y]
ticks = [x_ticks, y_ticks]
labels = [xlabels, ylabels]
nticks = []
for ii, (n, tick) in enumerate(zip([nx, ny], ticks)):
if len(tick) == 0:
ticks[ii] = numpy.array(range(n), dtype=numpy.int32)
nticks.append(len(ticks[ii]))
cmap = pyplot.set_cmap(cmap)
img = pyplot.imshow(matrix[ticks[0]][:, ticks[1]].T,
cmap=cmap,
vmin=vmin,
vmax=vmax,
interpolation='none')
pyplot.grid(True, color='black')
for ii, (xy, tick, ntick, ind_red, show, lbls, rot) in enumerate(
zip(["x", "y"], ticks, nticks, indices_red,
[show_x_labels, show_y_labels], labels, [90, 0])):
if show:
labels[ii] = \
generate_region_labels(len(tick), numpy.array(lbls)[tick], ". ", self.print_regions_indices, tick)
# labels[ii] = numpy.array(["%d. %s" % l for l in zip(tick, lbls[tick])])
getattr(pyplot, xy + "ticks")(numpy.array(range(ntick)),
labels[ii],
rotation=rot)
else:
labels[ii] = numpy.array(["%d." % l for l in tick])
getattr(pyplot, xy + "ticks")(numpy.array(range(ntick)),
labels[ii])
if ind_red is not None:
tck = tick.tolist()
ticklabels = getattr(ax, xy + "axis").get_ticklabels()
for iidx, indr in enumerate(ind_red):
try:
ticklabels[tck.index(indr)].set_color('r')
except:
pass
getattr(ax, xy + "axis").set_ticklabels(ticklabels)
ax.autoscale(tight=True)
divider = make_axes_locatable(ax)
cax1 = divider.append_axes("right", size="5%", pad=0.05)
pyplot.colorbar(
img,
cax=cax1) # fraction=0.046, pad=0.04) #fraction=0.15, shrink=1.0
return ax, cax1
def plot_vector_violin(self,
dataset,
vector=[],
lines=[],
labels=[],
subplot=111,
title="",
violin_flag=True,
colormap="YlOrRd",
show_y_labels=True,
indices_red=None,
sharey=None):
ax = pyplot.subplot(subplot, sharey=sharey)
pyplot.title(title)
n_violins = dataset.shape[1]
y_ticks = numpy.array(range(n_violins), dtype=numpy.int32)
# the vector plot
coldif = False
if indices_red is None:
indices_red = []
if violin_flag:
# the violin plot
colormap = matplotlib.cm.ScalarMappable(
cmap=pyplot.set_cmap(colormap))
colormap = colormap.to_rgba(numpy.mean(dataset, axis=0),
alpha=0.75)
violin_parts = ax.violinplot(dataset,
y_ticks,
vert=False,
widths=0.9,
showmeans=True,
showmedians=True,
showextrema=True)
violin_parts['cmeans'].set_color("k")
violin_parts['cmins'].set_color("b")
violin_parts['cmaxes'].set_color("b")
violin_parts['cbars'].set_color("b")
violin_parts['cmedians'].set_color("b")
for ii in range(len(violin_parts['bodies'])):
violin_parts['bodies'][ii].set_color(
numpy.reshape(colormap[ii], (1, 4)))
violin_parts['bodies'][ii]._alpha = 0.75
violin_parts['bodies'][ii]._edgecolors = numpy.reshape(
colormap[ii], (1, 4))
violin_parts['bodies'][ii]._facecolors = numpy.reshape(
colormap[ii], (1, 4))
else:
colorcycle = pyplot.rcParams['axes.prop_cycle'].by_key()['color']
n_samples = dataset.shape[0]
for ii in range(n_violins):
for jj in range(n_samples):
ax.plot(dataset[jj, ii],
y_ticks[ii],
"D",
mfc=colorcycle[jj % n_samples],
mec=colorcycle[jj % n_samples],
ms=20)
color = 'k'
colors = numpy.repeat([color], n_violins)
if indices_red is not None:
colors[indices_red] = 'r'
coldif = True
if len(vector) == n_violins:
for ii in range(n_violins):
ax.plot(vector[ii],
y_ticks[ii],
'*',
mfc=colors[ii],
mec=colors[ii],
ms=10)
if len(lines) == 2 and lines[0].shape[0] == n_violins and lines[
1].shape[0] == n_violins:
for ii in range(n_violins):
yy = (y_ticks[ii] - 0.45*lines[1][ii]/numpy.max(lines[1][ii]))\
* numpy.ones(numpy.array(lines[0][ii]).shape)
ax.plot(lines[0][ii], yy, '--', color=colors[ii])
ax.grid(True, color='grey')
ax.set_yticks(y_ticks)
if show_y_labels:
region_labels = generate_region_labels(n_violins, labels, ". ",
self.print_regions_indices)
ax.set_yticklabels(region_labels)
if coldif:
labels = ax.yaxis.get_ticklabels()
for ids in indices_red:
labels[ids].set_color('r')
ax.yaxis.set_ticklabels(labels)
else:
ax.set_yticklabels([])
if sharey is None:
ax.invert_yaxis()
ax.autoscale()
return ax
def plot_ts(self,
data,
time=None,
var_labels=[],
mode="ts",
subplots=None,
special_idx=[],
subtitles=[],
labels=[],
offset=0.5,
time_unit="ms",
title='Time series',
figure_name=None,
figsize=None):
if not isinstance(figsize, (list, tuple)):
figsize = self.config.figures.LARGE_SIZE
if isinstance(data, dict):
var_labels = data.keys()
data = data.values()
elif isinstance(data, numpy.ndarray):
if len(data.shape) < 3:
if len(data.shape) < 2:
data = numpy.expand_dims(data, 1)
data = numpy.expand_dims(data, 2)
data = [data]
else:
# Assuming a structure of Time X Space X Variables X Samples
data = [
data[:, :, iv].squeeze() for iv in range(data.shape[2])
]
elif isinstance(data, (list, tuple)):
data = ensure_list(data)
else:
raise_value_error("Input timeseries: %s \n"
"is not on of one of the following types: "
"[numpy.ndarray, dict, list, tuple]" % str(data))
n_vars = len(data)
data_lims = []
for id, d in enumerate(data):
if isequal_string(mode, "raster"):
data[id] = (d - d.mean(axis=0))
drange = numpy.max(data[id].max(axis=0) - data[id].min(axis=0))
data[id] = data[id] / drange # zscore(d, axis=None)
data_lims.append([d.min(), d.max()])
data_shape = data[0].shape
if len(data_shape) == 1:
n_times = data_shape[0]
nTS = 1
for iV in range(n_vars):
data[iV] = data[iV][:, numpy.newaxis]
else:
n_times, nTS = data_shape[:2]
if len(data_shape) > 2:
nSamples = data_shape[2]
else:
nSamples = 1
if special_idx is None:
special_idx = []
n_special_idx = len(special_idx)
if len(subtitles) == 0:
subtitles = var_labels
if isinstance(labels, list) and len(labels) == n_vars:
labels = [
generate_region_labels(nTS, label, ". ", self.print_ts_indices)
for label in labels
]
else:
labels = [
generate_region_labels(nTS, labels, ". ",
self.print_ts_indices)
for _ in range(n_vars)
]
if isequal_string(mode, "traj"):
data_fun, plot_lines, projection, n_rows, n_cols, def_alpha, loopfun, \
subtitle, subtitle_col, axlabels, axlimits = \
self._trajectories_plot(n_vars, nTS, nSamples, subplots)
else:
if isequal_string(mode, "raster"):
data_fun, time, plot_lines, projection, n_rows, n_cols, def_alpha, loopfun, \
subtitle, subtitle_col, axlabels, axlimits, axYticks = \
self._ts_plot(time, n_vars, nTS, n_times, time_unit, 0, offset, data_lims)
else:
data_fun, time, plot_lines, projection, n_rows, n_cols, def_alpha, loopfun, \
subtitle, subtitle_col, axlabels, axlimits, axYticks = \
self._ts_plot(time, n_vars, nTS, n_times, time_unit, ensure_list(subplots)[0])
alpha_ratio = 1.0 / nSamples
alphas = numpy.maximum(
numpy.array([def_alpha] * nTS) * alpha_ratio, 0.1)
alphas[special_idx] = numpy.maximum(alpha_ratio, 0.1)
if isequal_string(mode, "traj") and (n_cols * n_rows > 1):
colors = numpy.zeros((nTS, 4))
colors[special_idx] = \
numpy.array([numpy.array([1.0, 0, 0, 1.0]) for _ in range(n_special_idx)]).reshape((n_special_idx, 4))
else:
cmap = matplotlib.cm.get_cmap('jet')
colors = numpy.array([cmap(0.5 * iTS / nTS) for iTS in range(nTS)])
colors[special_idx] = \
numpy.array([cmap(1.0 - 0.25 * iTS / nTS) for iTS in range(n_special_idx)]).reshape((n_special_idx, 4))
colors[:, 3] = alphas
lines = []
pyplot.figure(title, figsize=figsize)
axes = []
for icol in range(n_cols):
if n_rows == 1:
# If there are no more rows, create axis, and set its limits, labels and possible subtitle
axes += ensure_list(
pyplot.subplot(n_rows,
n_cols,
icol + 1,
projection=projection))
axlimits(data_lims, time, n_vars, icol)
axlabels(labels[icol % n_vars], var_labels, n_vars, n_rows, 1,
0)
pyplot.gca().set_title(subtitles[icol])
for iTS in loopfun(nTS, n_rows, icol):
if n_rows > 1:
# If there are more rows, create axes, and set their limits, labels and possible subtitles
axes += ensure_list(
pyplot.subplot(n_rows,
n_cols,
iTS + 1,
projection=projection))
axlimits(data_lims, time, n_vars, icol)
subtitle(labels[icol % n_vars], iTS)
axlabels(labels[icol % n_vars], var_labels, n_vars, n_rows,
(iTS % n_rows) + 1, iTS)
lines += ensure_list(
plot_lines(data_fun(data, time, icol), iTS, colors,
labels[icol % n_vars]))
if isequal_string(
mode,
"raster"): # set yticks as labels if this is a raster plot
axYticks(labels[icol % n_vars], nTS)
yticklabels = pyplot.gca().yaxis.get_ticklabels()
self.tick_font_size = numpy.minimum(
self.tick_font_size,
int(numpy.round(self.tick_font_size * 100.0 / nTS)))
for iTS in range(nTS):
yticklabels[iTS].set_fontsize(self.tick_font_size)
if iTS in special_idx:
yticklabels[iTS].set_color(colors[iTS, :3].tolist() +
[1])
pyplot.gca().yaxis.set_ticklabels(yticklabels)
pyplot.gca().invert_yaxis()
if self.config.figures.MOUSE_HOOVER:
for line in lines:
self.HighlightingDataCursor(line,
formatter='{label}'.format,
bbox=dict(fc='white'),
arrowprops=dict(
arrowstyle='simple',
fc='white',
alpha=0.5))
self._save_figure(pyplot.gcf(), figure_name)
self._check_show()
return pyplot.gcf(), axes, lines
