def visualize_temporal_activities(class_predictions, max_value=200, fps=1, title=None, legend=False):
normalize = Normalize(vmin=1, vmax=max_value)
normalize.clip=False
cmap = plt.cm.Reds
cmap.set_under('w')
nb_instances = len(class_predictions)
plt.figure(num=None, figsize=(18, 1), dpi=100)
to_plot = class_predictions.astype(np.float32)
to_plot[class_predictions==0.] = np.ma.masked
plt.imshow(np.broadcast_to(to_plot, (20, nb_instances)), norm=normalize, interpolation='nearest', aspect='auto', cmap=cmap)
if title:
plt.title(title)
ax = plt.gca()
ax.get_yaxis().set_visible(False)
if legend:
index = np.arange(0,200)
colors_index = np.unique(to_plot).astype(np.int64)
if 0 in colors_index:
colors_index = np.delete(colors_index, 0)
patches = []
for c in colors_index:
patches.append(mpatches.Patch(color=cmap(normalize(c)), label=dataset.labels[c][1]))
if patches:
plt.legend(handles=patches, loc='upper center', bbox_to_anchor=(0.5, -.2), ncol=len(patches), fancybox=True, shadow=True)
plt.show()
def compare_temporal_activities(ground_truth, class_predictions, max_value=200, fps=1, title=None, legend=False, save_file='./img/activity_detection_sample_{}.png'):
global count
normalize = Normalize(vmin=1, vmax=max_value)
normalize.clip=False
cmap = plt.cm.Reds
cmap.set_under('w')
nb_instances = len(class_predictions)
to_plot = np.zeros((20, nb_instances))
to_plot[:10,:] = np.broadcast_to(ground_truth, (10, nb_instances))
to_plot[10:,:] = np.broadcast_to(class_predictions, (10, nb_instances))
to_plot = to_plot.astype(np.float32)
to_plot[to_plot==0.] = np.ma.masked
# Normalize the values and give them the largest distance possible between them
unique_values = np.unique(to_plot).astype(np.int64)
if 0 in unique_values:
unique_values = np.delete(unique_values, 0)
nb_different_values = len(unique_values)
color_values = np.linspace(40, 190, nb_different_values)
for i in range(nb_different_values):
to_plot[to_plot == unique_values[i]] = color_values[i]
plt.figure(num=None, figsize=(18, 1), dpi=100)
plt.imshow(to_plot, norm=normalize, interpolation='nearest', aspect='auto', cmap=cmap)
#plt.grid(True)
plt.axhline(9, linestyle='-', color='k')
plt.xlim([0,nb_instances])
if title:
plt.title(title)
ax = plt.gca()
#ax.get_yaxis().set_visible(False)
ax.xaxis.grid(True, which='major')
labels=['Ground\nTruth', 'Prediction']
plt.yticks([5,15], labels, rotation="horizontal", size=13)
plt.xlabel('Time (s)', horizontalalignment='left', fontsize=13)
ax.xaxis.set_label_coords(0, -0.3)
if legend:
patches = []
for c, l in zip(color_values, unique_values):
patches.append(mpatches.Patch(color=cmap(normalize(c)), label=dataset.labels[l][1]))
if patches:
plt.legend(handles=patches, loc='upper center', bbox_to_anchor=(.5, -.2), ncol=len(patches), fancybox=True, shadow=True)
#plt.show()
plt.savefig(save_file.format(count), bbox_inches='tight')
count += 1
def dotplot(df,
column='Adjusted P-value',
title='',
cutoff=0.05,
top_term=10,
sizes=None,
norm=None,
legend=True,
figsize=(6, 5.5),
cmap='RdBu_r',
ofname=None,
**kwargs):
"""Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame to show. Default: Adjusted P-value
:param title: figure title
:param cutoff: terms with 'column' value < cut-off are shown.
:param top_term: number of enriched terms to show.
:param ascending: bool, the order of y axis.
:param sizes: tuple, (min, max) scatter size. Not functional for now
:param norm: maplotlib.colors.Normalize object.
:param legend: bool, whether to show legend.
:param figsize: tuple, figure size.
:param cmap: matplotlib colormap
:param ofname: output file name. If None, don't save figure
"""
colname = column
# sorting the dataframe for better visualization
if colname in ['Adjusted P-value', 'P-value']:
# check if any values in `df[colname]` can't be coerced to floats
can_be_coerced = df[colname].map(isfloat)
if np.sum(~can_be_coerced) > 0:
raise ValueError(
'some value in %s could not be typecast to `float`' % colname)
else:
df.loc[:, colname] = df[colname].map(float)
df = df[df[colname] <= cutoff]
if len(df) < 1:
msg = "Warning: No enrich terms when cutoff = %s" % cutoff
return msg
df = df.assign(logAP=lambda x: -x[colname].apply(np.log10))
colname = 'logAP'
df = df.sort_values(by=colname).iloc[-top_term:, :]
#
temp = df['Overlap'].str.split("/", expand=True).astype(int)
df = df.assign(Hits=temp.iloc[:, 0], Background=temp.iloc[:, 1])
df = df.assign(Hits_ratio=lambda x: x.Hits / x.Background)
# x axis values
x = df.loc[:, colname].values
combined_score = df['Combined Score'].round().astype('int')
# y axis index and values
y = [i for i in range(0, len(df))]
ylabels = df['Term'].values
# Normalise to [0,1]
# b = (df['Count'] - df['Count'].min())/ np.ptp(df['Count'])
# area = 100 * b
# control the size of scatter and legend marker
levels = numbers = np.sort(df.Hits.unique())
if norm is None:
norm = Normalize()
elif isinstance(norm, tuple):
norm = Normalize(*norm)
elif not isinstance(norm, Normalize):
err = ("``size_norm`` must be None, tuple, " "or Normalize object.")
raise ValueError(err)
min_width, max_width = np.r_[20, 100] * plt.rcParams["lines.linewidth"]
norm.clip = True
if not norm.scaled():
norm(np.asarray(numbers))
size_limits = norm.vmin, norm.vmax
scl = norm(numbers)
widths = np.asarray(min_width + scl * (max_width - min_width))
if scl.mask.any():
widths[scl.mask] = 0
sizes = dict(zip(levels, widths))
df['sizes'] = df.Hits.map(sizes)
area = df['sizes'].values
# create scatter plot
if hasattr(sys, 'ps1') and (ofname is None):
# working inside python console, show figure
fig, ax = plt.subplots(figsize=figsize)
else:
# If working on commandline, don't show figure
fig = Figure(figsize=figsize)
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
vmin = np.percentile(combined_score.min(), 2)
vmax = np.percentile(combined_score.max(), 98)
sc = ax.scatter(x=x,
y=y,
s=area,
edgecolors='face',
c=combined_score,
cmap=cmap,
vmin=vmin,
vmax=vmax)
if column in ['Adjusted P-value', 'P-value']:
xlabel = "-log$_{10}$(%s)" % column
else:
xlabel = column
ax.set_xlabel(xlabel, fontsize=14, fontweight='bold')
ax.yaxis.set_major_locator(plt.FixedLocator(y))
ax.yaxis.set_major_formatter(plt.FixedFormatter(ylabels))
ax.set_yticklabels(ylabels, fontsize=16)
# ax.set_ylim([-1, len(df)])
ax.grid()
# colorbar
cax = fig.add_axes([0.95, 0.20, 0.03, 0.22])
cbar = fig.colorbar(
sc,
cax=cax,
)
cbar.ax.tick_params(right=True)
cbar.ax.set_title('Combined\nScore', loc='left', fontsize=12)
# for terms less than 3
if len(df) >= 3:
# find the index of the closest value to the median
idx = [
area.argmax(),
np.abs(area - area.mean()).argmin(),
area.argmin()
]
idx = unique(idx)
else:
idx = range(len(df))
label = df.iloc[idx, df.columns.get_loc('Hits')]
if legend:
handles, _ = ax.get_legend_handles_labels()
legend_markers = []
for ix in idx:
legend_markers.append(ax.scatter([], [], s=area[ix], c='b'))
# artist = ax.scatter([], [], s=size_levels,)
ax.legend(legend_markers, label, title='Hits')
ax.set_title(title, fontsize=20, fontweight='bold')
if ofname is not None:
# canvas.print_figure(ofname, bbox_inches='tight', dpi=300)
fig.savefig(ofname, bbox_inches='tight', dpi=300)
return
return ax
