testbed/matplotlib__matplotlib/galleries/examples/specialty_plots/leftventricle_bullseye.py

"""
=======================
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()