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# Leftventricle Bulleye

This example demonstrates how to create the 17 segment model for the left ventricle recommended by the American Heart Association (AHA).

    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    def bullseye_plot(ax, data, segBold=None, cmap=None, norm=None):
        """
        Bullseye representation for the left ventricle.

        Parameters
        ----------
        ax : axes
        data : list of int and float
            The intensity values for each of the 17 segments
        segBold: list of int, optional
            A list with the segments to highlight
        cmap : ColorMap or None, optional
            Optional argument to set the desired colormap
        norm : Normalize or None, optional
            Optional argument to normalize data into the [0.0, 1.0] range

        Notes
        -----
        This function create 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.
        """
        if segBold is None:
            segBold = []

        linewidth = 2
        data = np.array(data).ravel()

        if cmap is None:
            cmap = plt.cm.viridis

        if norm is None:
            norm = mpl.colors.Normalize(vmin=data.min(), vmax=data.max())

        theta = np.linspace(0, 2*np.pi, 768)
        r = np.linspace(0.2, 1, 4)

        # Create the bound for the segment 17
        for i in range(r.shape[0]):
            ax.plot(theta, np.repeat(r[i], theta.shape), '-k', lw=linewidth)

        # Create the bounds for the segments  1-12
        for i in range(6):
            theta_i = np.deg2rad(i * 60)
            ax.plot([theta_i, theta_i], [r[1], 1], '-k', lw=linewidth)

        # Create the bounds for the segments 13-16
        for i in range(4):
            theta_i = np.deg2rad(i * 90 - 45)
            ax.plot([theta_i, theta_i], [r[0], r[1]], '-k', lw=linewidth)

        # Fill the segments 1-6
        r0 = r[2:4]
        r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T
        for i in range(6):
            # First segment start at 60 degrees
            theta0 = theta[i*128:i*128+128] + np.deg2rad(60)
            theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
            z = np.ones((128, 2))*data[i]
            ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
            if i+1 in segBold:
                ax.plot(theta0, r0, '-k', lw=linewidth+2)
                ax.plot(theta0[0], [r[2], r[3]], '-k', lw=linewidth+1)
                ax.plot(theta0[-1], [r[2], r[3]], '-k', lw=linewidth+1)

        # Fill the segments 7-12
        r0 = r[1:3]
        r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T
        for i in range(6):
            # First segment start at 60 degrees
            theta0 = theta[i*128:i*128+128] + np.deg2rad(60)
            theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
            z = np.ones((128, 2))*data[i+6]
            ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
            if i+7 in segBold:
                ax.plot(theta0, r0, '-k', lw=linewidth+2)
                ax.plot(theta0[0], [r[1], r[2]], '-k', lw=linewidth+1)
                ax.plot(theta0[-1], [r[1], r[2]], '-k', lw=linewidth+1)

        # Fill the segments 13-16
        r0 = r[0:2]
        r0 = np.repeat(r0[:, np.newaxis], 192, axis=1).T
        for i in range(4):
            # First segment start at 45 degrees
            theta0 = theta[i*192:i*192+192] + np.deg2rad(45)
            theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
            z = np.ones((192, 2))*data[i+12]
            ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
            if i+13 in segBold:
                ax.plot(theta0, r0, '-k', lw=linewidth+2)
                ax.plot(theta0[0], [r[0], r[1]], '-k', lw=linewidth+1)
                ax.plot(theta0[-1], [r[0], r[1]], '-k', lw=linewidth+1)

        # Fill the segments 17
        if data.size == 17:
            r0 = np.array([0, r[0]])
            r0 = np.repeat(r0[:, np.newaxis], theta.size, axis=1).T
            theta0 = np.repeat(theta[:, np.newaxis], 2, axis=1)
            z = np.ones((theta.size, 2))*data[16]
            ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
            if 17 in segBold:
                ax.plot(theta0, r0, '-k', lw=linewidth+2)

        ax.set_ylim([0, 1])
        ax.set_yticklabels([])
        ax.set_xticklabels([])

    # Create the fake data
    data = np.array(range(17)) + 1

    # Make a figure and axes with dimensions as desired.
    fig, ax = plt.subplots(figsize=(12, 8), nrows=1, ncols=3,
                           subplot_kw=dict(projection='polar'))
    fig.canvas.set_window_title('Left Ventricle Bulls Eyes (AHA)')

    # Create the axis for the colorbars
    axl = fig.add_axes([0.14, 0.15, 0.2, 0.05])
    axl2 = fig.add_axes([0.41, 0.15, 0.2, 0.05])
    axl3 = fig.add_axes([0.69, 0.15, 0.2, 0.05])

    # 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)

    # ColorbarBase derives from ScalarMappable and puts a colorbar
    # in a specified axes, so it has everything needed for a
    # standalone colorbar.  There are many more kwargs, but the
    # following gives a basic continuous colorbar with ticks
    # and labels.
    cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmap, norm=norm,
                                    orientation='horizontal')
    cb1.set_label('Some Units')

    # Set the colormap and norm to correspond to the data for which
    # the colorbar will be used.
    cmap2 = mpl.cm.cool
    norm2 = mpl.colors.Normalize(vmin=1, vmax=17)

    # ColorbarBase derives from ScalarMappable and puts a colorbar
    # in a specified axes, so it has everything needed for a
    # standalone colorbar.  There are many more kwargs, but the
    # following gives a basic continuous colorbar with ticks
    # and labels.
    cb2 = mpl.colorbar.ColorbarBase(axl2, cmap=cmap2, norm=norm2,
                                    orientation='horizontal')
    cb2.set_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'])
    cmap3.set_over('0.35')
    cmap3.set_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)
    cb3 = mpl.colorbar.ColorbarBase(axl3, cmap=cmap3, norm=norm3,
                                    # to use 'extend', you must
                                    # specify two extra boundaries:
                                    boundaries=[0]+bounds+[18],
                                    extend='both',
                                    ticks=bounds,  # optional
                                    spacing='proportional',
                                    orientation='horizontal')
    cb3.set_label('Discrete intervals, some other units')

    # Create the 17 segment model
    bullseye_plot(ax[0], data, cmap=cmap, norm=norm)
    ax[0].set_title('Bulls Eye (AHA)')

    bullseye_plot(ax[1], data, cmap=cmap2, norm=norm2)
    ax[1].set_title('Bulls Eye (AHA)')

    bullseye_plot(ax[2], data, segBold=[3, 5, 6, 11, 12, 16],
                  cmap=cmap3, norm=norm3)
    ax[2].set_title('Segments [3,5,6,11,12,16] in bold')

    plt.show()

Total running time of the script: ( 0 minutes 0.257 seconds)

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