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"""
=======================
Left ventricle bullseye
=======================
This example demonstrates how to create the 17 segment model for the left
ventricle recommended by the American Heart Association (AHA).
.. redirect-from:: /gallery/specialty_plots/leftventricle_bulleye
See also the :doc:`/gallery/pie_and_polar_charts/nested_pie` example.
"""
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
def bullseye_plot(ax, data, seg_bold=None, cmap="viridis", norm=None):
"""
Bullseye representation for the left ventricle.
Parameters
----------
ax : Axes
data : list[float]
The intensity values for each of the 17 segments.
seg_bold : list[int], optional
A list with the segments to highlight.
cmap : colormap, default: "viridis"
Colormap for the data.
norm : Normalize or None, optional
Normalizer for the data.
Notes
-----
This function creates the 17 segment model for the left ventricle according
to the American Heart Association (AHA) [1]_
References
----------
.. [1] M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs,
S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan,
and M. S. Verani, "Standardized myocardial segmentation and
nomenclature for tomographic imaging of the heart",
Circulation, vol. 105, no. 4, pp. 539-542, 2002.
"""
data = np.ravel(data)
if seg_bold is None:
seg_bold = []
if norm is None:
norm = mpl.colors.Normalize(vmin=data.min(), vmax=data.max())
r = np.linspace(0.2, 1, 4)
ax.set(ylim=[0, 1], xticklabels=[], yticklabels=[])
ax.grid(False) # Remove grid
# Fill segments 1-6, 7-12, 13-16.
for start, stop, r_in, r_out in [
(0, 6, r[2], r[3]),
(6, 12, r[1], r[2]),
(12, 16, r[0], r[1]),
(16, 17, 0, r[0]),
]:
n = stop - start
dtheta = 2*np.pi / n
ax.bar(np.arange(n) * dtheta + np.pi/2, r_out - r_in, dtheta, r_in,
color=cmap(norm(data[start:stop])))
# Now, draw the segment borders. In order for the outer bold borders not
# to be covered by inner segments, the borders are all drawn separately
# after the segments have all been filled. We also disable clipping, which
# would otherwise affect the outermost segment edges.
# Draw edges of segments 1-6, 7-12, 13-16.
for start, stop, r_in, r_out in [
(0, 6, r[2], r[3]),
(6, 12, r[1], r[2]),
(12, 16, r[0], r[1]),
]:
n = stop - start
dtheta = 2*np.pi / n
ax.bar(np.arange(n) * dtheta + np.pi/2, r_out - r_in, dtheta, r_in,
clip_on=False, color="none", edgecolor="k", linewidth=[
4 if i + 1 in seg_bold else 2 for i in range(start, stop)])
# Draw edge of segment 17 -- here; the edge needs to be drawn differently,
# using plot().
ax.plot(np.linspace(0, 2*np.pi), np.linspace(r[0], r[0]), "k",
linewidth=(4 if 17 in seg_bold else 2))
# Create the fake data
data = np.arange(17) + 1
# Make a figure and Axes with dimensions as desired.
fig = plt.figure(figsize=(10, 5), layout="constrained")
fig.get_layout_engine().set(wspace=.1, w_pad=.2)
axs = fig.subplots(1, 3, subplot_kw=dict(projection='polar'))
fig.canvas.manager.set_window_title('Left Ventricle Bulls Eyes (AHA)')
# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
cmap = mpl.cm.viridis
norm = mpl.colors.Normalize(vmin=1, vmax=17)
# Create an empty ScalarMappable to set the colorbar's colormap and norm.
# The following gives a basic continuous colorbar with ticks and labels.
fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap, norm=norm),
cax=axs[0].inset_axes([0, -.15, 1, .1]),
orientation='horizontal', label='Some units')
# And again for the second colorbar.
cmap2 = mpl.cm.cool
norm2 = mpl.colors.Normalize(vmin=1, vmax=17)
fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap2, norm=norm2),
cax=axs[1].inset_axes([0, -.15, 1, .1]),
orientation='horizontal', label='Some other units')
# The second example illustrates the use of a ListedColormap, a
# BoundaryNorm, and extended ends to show the "over" and "under"
# value colors.
cmap3 = (mpl.colors.ListedColormap(['r', 'g', 'b', 'c'])
.with_extremes(over='0.35', under='0.75'))
# If a ListedColormap is used, the length of the bounds array must be
# one greater than the length of the color list. The bounds must be
# monotonically increasing.
bounds = [2, 3, 7, 9, 15]
norm3 = mpl.colors.BoundaryNorm(bounds, cmap3.N)
fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap3, norm=norm3),
cax=axs[2].inset_axes([0, -.15, 1, .1]),
extend='both',
ticks=bounds, # optional
spacing='proportional',
orientation='horizontal',
label='Discrete intervals, some other units')
# Create the 17 segment model
bullseye_plot(axs[0], data, cmap=cmap, norm=norm)
axs[0].set_title('Bulls Eye (AHA)')
bullseye_plot(axs[1], data, cmap=cmap2, norm=norm2)
axs[1].set_title('Bulls Eye (AHA)')
bullseye_plot(axs[2], data, seg_bold=[3, 5, 6, 11, 12, 16],
cmap=cmap3, norm=norm3)
axs[2].set_title('Segments [3, 5, 6, 11, 12, 16] in bold')
plt.show()
|
