.. _pylab_examples-leftventricle_bulleye: pylab_examples example code: leftventricle_bulleye.py ===================================================== .. plot:: /home/tcaswell/source/my_source/matplotlib/doc/mpl_examples/pylab_examples/leftventricle_bulleye.py :: #!/usr/bin/env python """ 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.jet 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 = i*60*np.pi/180 ax.plot([theta_i, theta_i], [r[1], 1], '-k', lw=linewidth) # Create the bounds for the segmentss 13-16 for i in range(4): theta_i = i*90*np.pi/180 - 45*np.pi/180 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] + 60*np.pi/180 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] + 60*np.pi/180 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] + 45*np.pi/180 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.jet 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() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)