def produce_colour_arr(arr):
"""Function to produce a corresponding colour array for a dataset.
Args:
arr representing the data array you want to produce a colour array for
Returns:
array representing the colour array
"""
evenly_spaced_interval = np.linspace(0, 1, len(arr))
#cividis colour map used for plotting
colors = [cm.cividis(x) for x in evenly_spaced_interval]
return colors
def plot_graph(self, filename='loss.png'):
fig, ax = plt.subplots()
ax.set_xlabel('Iteration')
ax.set_ylabel('Loss')
ax.set_title('Training curve.')
for i, (key, value) in enumerate(self.logs.items()):
x = np.arange(len(value))
y = np.array(value)
ax.plot(x,
y,
label=key,
color=cm.cividis(i / len(self.logs.keys())))
ax.legend(loc='best')
save_path = os.path.join(self.output_dir, filename)
logger.info('Save {}'.format(save_path))
plt.savefig(save_path, transparent=True)
plt.clf()
plt.cla()
plt.close('all')
def colormap(cmin, cmax, c):
if cmin == cmax:
c_scal = 0.5
else:
c_scal = (c-cmin)/(cmax-cmin)
return color2hex(colormaps.cividis(c_scal))
cv2.imwrite("stripes_depth.png", stripes_depth)
progression = (stripes_depth.astype(np.float64) - clip_near)
progression[progression < 0] = 0
progression = progression / progression.max()
stripe_index = (progression * 62).astype(np.int64)
# For grayscale stripes:
# stripes_color = np.zeros((im_height,im_width,3),dtype=np.uint8)
# for i_channel in range(3):
# stripes_color[:,:,i_channel] = progression.astype(np.uint8)
stripe_colors = (np.fliplr(
np.array([cm.cividis(value)[0:3] for value in np.arange(0, 1.0, 1.0 / stripe_count)])) * 255).astype(np.uint8)
# For stripes with consecutively close colors:
# stripes_color = (cm.cividis(progression)[:,:,0:3] * 255).astype(np.uint8)
# For stripes with highly-varying colors:
new_stripe_colors = np.zeros((stripe_colors.shape[0] + 1, stripe_colors.shape[1]), dtype=np.uint8)
for i_color in range(stripe_count):
new_stripe_colors[i_color + 1] = stripe_colors[i_color // 2] if i_color % 2 == 0 else stripe_colors[
stripe_count // 2 + i_color // 2]
stripes_color = new_stripe_colors[stripe_index]
cv2.imwrite("stripes_color.png", stripes_color)
def plot_evaluation(values,
info,
measures=[
'Dice', 'Jaccard', 'TPR', 'TNR', '1-GCE', 'VS', 'RI',
'ARI', 'MI', '1-VOI', 'ICC', '1/(1+PBD)', 'KAP', 'AUC',
'1/(1+HD)', '1/(1+AVD)', 'MHD'
],
colourmap=None,
outfile='polar_results.png'):
"""Plot radial plot for all values and measures"""
_min = info['minimum']
_max = info['maximum']
if colourmap is None:
colourmap = [[86. / 255., 180. / 255., 233. / 255.]
for ii in range(values.shape[0])]
else:
# normalize colourmap values between 0 and 1
colourmap = (colourmap - _min) / (_max - _min)
# apply cividis, returns the RBG1 values for cividis, for dots
colourmap = [[cm.cividis(ii)] for ii in colourmap]
# elements of the circle
N = len(measures)
# evenly space measures around circle
x_as = [n / float(N) * 2 * pi for n in range(N)]
# Set color of axes
plt.rc('axes', linewidth=0.5, edgecolor="#888888")
# Create polar plot
fig = plt.figure(figsize=(11, 9.5))
gs = gridspec.GridSpec(1, 3, width_ratios=[17, 2, 1])
ax = plt.subplot(gs[0], polar=True)
# Set position of y-labels
ax.set_rlabel_position(0)
# Set color and linestyle of grid
ax.xaxis.grid(True, color="#888888", linestyle='solid', linewidth=0.5)
ax.yaxis.grid(True, color="#888888", linestyle='solid', linewidth=0.5)
# Set yticks
plt.yticks([0.2, 0.4, 0.6, 0.8, 1.0], ["0.2", "0.4", "0.6", "0.8", "1.0"],
fontsize=15)
pos = ax.get_rlabel_position()
ax.set_rlabel_position(pos + 0.4 * 360. / float(len(measures)))
# Plot data
for ii in np.arange(values.shape[0]):
xx = np.asarray(x_as) + np.random.randn(
len(x_as)) * np.diff(x_as)[0] / 15.
data_norm = None
if info['logplot']:
data_norm = matplotlib.colors.LogNorm(vmin=_min, vmax=_max)
sc = ax.scatter(xx,
values[ii, :],
23,
color=colourmap[ii] * len(xx),
norm=data_norm,
zorder=3)
# Fill area
# close the circle
median = list(np.median(values, axis=0))
median += median[:1]
upper = list(np.percentile(values, 75, axis=0))
upper += upper[:1]
lower = list(np.percentile(values, 25, axis=0))
lower += lower[:1]
x_as += x_as[:1]
ax.plot(x_as,
median,
color=[86. / 255., 180. / 255., 233. / 255.],
zorder=5)
ax.fill_between(x_as,
upper,
lower,
zorder=4,
color=[86. / 255., 180. / 255., 233. / 255.],
alpha=0.3)
# Set number of radial axes and remove labels
plt.xticks(x_as[:-1], [])
# Set axes limits
plt.ylim(0, 1)
# Draw ytick labels to make sure they fit properly
for i in range(N):
angle_rad = i / float(N) * 2 * pi - 0.05
text_size = 21
if i in {3, 8}:
ax.text(angle_rad,
1.15,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
elif i in {0}:
ax.text(angle_rad,
1.25,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
elif i in {1, 5, 7}:
ax.text(angle_rad,
1.29,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
elif i in {4}:
ax.text(angle_rad,
1.32,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="top")
elif i in {10}:
ax.text(angle_rad,
1.26,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
elif i in {6}:
ax.text(angle_rad,
1.25,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
elif i in {9}:
ax.text(angle_rad,
1.18,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
else:
ax.text(angle_rad,
1.22,
measures[i] + "\n(m=%0.2f)" % median[i],
size=text_size,
horizontalalignment='center',
verticalalignment="center")
# colorbar location on figure
cbaxes = plt.subplot(gs[2])
# log scaling option
norm = None
if info['logplot']:
norm = matplotlib.colors.LogNorm(vmin=_min, vmax=_max)
img = plt.imshow(np.array([[_min, _max]]),
aspect='auto',
cmap="cividis",
norm=norm)
img.set_visible(False)
# initialize colorbar
cbar = plt.colorbar(cax=cbaxes)
# ticks and label
c_values = cbar.get_ticks().tolist()
ticklabels = ["" for ii in c_values]
if _min < np.min(c_values):
c_values = [_min] + c_values
ticklabels = ["%0.1f %s" %
(np.min(c_values), info['unit'])] + ticklabels
else:
ticklabels[0] = "%0.1f %s" % (np.min(c_values), info['unit'])
if _max > np.max(c_values):
c_values = c_values + [_max]
ticklabels = ticklabels + [
"%0.1f %s" % (np.max(c_values), info['unit'])
]
else:
ticklabels[-1] = "%0.1f %s" % (np.max(c_values), info['unit'])
cbar.set_ticks(c_values)
cbar.set_ticklabels(ticklabels)
cbaxes.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
cbar.ax.set_ylabel(info["label"], labelpad=-20)
# font sizes for colorbar
cbar.ax.yaxis.label.set_size(19)
cbar.ax.tick_params(labelsize=14)
# Save and show polar plot
plt.savefig(outfile)
if info['display']:
plt.show()
plt.clf()
plt.close('all')
# v = np.linspace(0, np.pi, 20)
# radius = 4
# xx = radius * np.outer(np.sin(u), np.sin(v))
# yy = radius * np.outer(np.sin(u), np.cos(v))
# zzz = radius * np.outer(np.cos(u), np.ones_like(v))
# # https://pythonskills.co.uk/2020/01/22/plotting-a-sphere/
# # https://stackoverflow.com/questions/40460960/how-to-plot-a-sphere-when-we-are-given-a-central-point-and-a-radius-size
# # https://stackoverflow.com/questions/18897786/transparency-for-poly3dcollection-plot-in-matplotlib
# ax.plot_wireframe(xx, yy, zzz, rstride=1, cstride=1, color='w') #, shade=0, alpha=0.5)
norm = Normalize(vmin=np.min(z), vmax=np.max(z))
# cbar = fig.colorbar(cm.ScalarMappable(norm = norm), ax=ax)
# cbar.ax.set_ylabel('Density')
return ax
# In[123]:
x = np.random.normal(size=100000)
y = np.random.normal(size=100000) #x * 3 + np.random.normal(size=100000)
z = np.random.normal(size=100000)
density_scatter(x, y, z, bins=[20, 20, 20])
# In[122]:
# https://thomas-cokelaer.info/blog/2014/09/about-matplotlib-colormap-and-how-to-get-rgb-values-of-the-map/
# https://arxiv.org/pdf/1712.01662.pdf
from matplotlib import cm
cm.cividis(0)
