左心室靶心

该示例演示了如何为美国心脏协会（AHA）推荐的左心室创建17段模型。

左心室靶心示例

  1. import numpy as np
  2. import matplotlib as mpl
  3. import matplotlib.pyplot as plt
  6. def bullseye_plot(ax, data, segBold=None, cmap=None, norm=None):
  7.     """
  8.     Bullseye representation for the left ventricle.
  10.     Parameters
  11.     ----------
  12.     ax : axes
  13.     data : list of int and float
  14.         The intensity values for each of the 17 segments
  15.     segBold: list of int, optional
  16.         A list with the segments to highlight
  17.     cmap : ColorMap or None, optional
  18.         Optional argument to set the desired colormap
  19.     norm : Normalize or None, optional
  20.         Optional argument to normalize data into the [0.0, 1.0] range
  23.     Notes
  24.     -----
  25.     This function create the 17 segment model for the left ventricle according
  26.     to the American Heart Association (AHA) [1]_
  28.     References
  29.     ----------
  30.     .. [1] M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs,
  31.         S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan,
  32.         and M. S. Verani, "Standardized myocardial segmentation and
  33.         nomenclature for tomographic imaging of the heart",
  34.         Circulation, vol. 105, no. 4, pp. 539-542, 2002.
  35.     """
  36.     if segBold is None:
  37.         segBold = []
  39.     linewidth = 2
  40.     data = np.array(data).ravel()
  42.     if cmap is None:
  43.         cmap = plt.cm.viridis
  45.     if norm is None:
  46.         norm = mpl.colors.Normalize(vmin=data.min(), vmax=data.max())
  48.     theta = np.linspace(0, 2 * np.pi, 768)
  49.     r = np.linspace(0.2, 1, 4)
  51.     # Create the bound for the segment 17
  52.     for i in range(r.shape[0]):
  53.         ax.plot(theta, np.repeat(r[i], theta.shape), '-k', lw=linewidth)
  55.     # Create the bounds for the segments 1-12
  56.     for i in range(6):
  57.         theta_i = np.deg2rad(i * 60)
  58.         ax.plot([theta_i, theta_i], [r[1], 1], '-k', lw=linewidth)
  60.     # Create the bounds for the segments 13-16
  61.     for i in range(4):
  62.         theta_i = np.deg2rad(i * 90 - 45)
  63.         ax.plot([theta_i, theta_i], [r[0], r[1]], '-k', lw=linewidth)
  65.     # Fill the segments 1-6
  66.     r0 = r[2:4]
  67.     r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T
  68.     for i in range(6):
  69.         # First segment start at 60 degrees
  70.         theta0 = theta[i * 128:i * 128 + 128] + np.deg2rad(60)
  71.         theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
  72.         z = np.ones((128, 2)) * data[i]
  73.         ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
  74.         if i + 1 in segBold:
  75.             ax.plot(theta0, r0, '-k', lw=linewidth + 2)
  76.             ax.plot(theta0[0], [r[2], r[3]], '-k', lw=linewidth + 1)
  77.             ax.plot(theta0[-1], [r[2], r[3]], '-k', lw=linewidth + 1)
  79.     # Fill the segments 7-12
  80.     r0 = r[1:3]
  81.     r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T
  82.     for i in range(6):
  83.         # First segment start at 60 degrees
  84.         theta0 = theta[i * 128:i * 128 + 128] + np.deg2rad(60)
  85.         theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
  86.         z = np.ones((128, 2)) * data[i + 6]
  87.         ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
  88.         if i + 7 in segBold:
  89.             ax.plot(theta0, r0, '-k', lw=linewidth + 2)
  90.             ax.plot(theta0[0], [r[1], r[2]], '-k', lw=linewidth + 1)
  91.             ax.plot(theta0[-1], [r[1], r[2]], '-k', lw=linewidth + 1)
  93.     # Fill the segments 13-16
  94.     r0 = r[0:2]
  95.     r0 = np.repeat(r0[:, np.newaxis], 192, axis=1).T
  96.     for i in range(4):
  97.         # First segment start at 45 degrees
  98.         theta0 = theta[i * 192:i * 192 + 192] + np.deg2rad(45)
  99.         theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
  100.         z = np.ones((192, 2)) * data[i + 12]
  101.         ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
  102.         if i + 13 in segBold:
  103.             ax.plot(theta0, r0, '-k', lw=linewidth + 2)
  104.             ax.plot(theta0[0], [r[0], r[1]], '-k', lw=linewidth + 1)
  105.             ax.plot(theta0[-1], [r[0], r[1]], '-k', lw=linewidth + 1)
  107.     # Fill the segments 17
  108.     if data.size == 17:
  109.         r0 = np.array([0, r[0]])
  110.         r0 = np.repeat(r0[:, np.newaxis], theta.size, axis=1).T
  111.         theta0 = np.repeat(theta[:, np.newaxis], 2, axis=1)
  112.         z = np.ones((theta.size, 2)) * data[16]
  113.         ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
  114.         if 17 in segBold:
  115.             ax.plot(theta0, r0, '-k', lw=linewidth + 2)
  117.     ax.set_ylim([0, 1])
  118.     ax.set_yticklabels([])
  119.     ax.set_xticklabels([])
  122. # Create the fake data
  123. data = np.array(range(17)) + 1
  126. # Make a figure and axes with dimensions as desired.
  127. fig, ax = plt.subplots(figsize=(12, 8), nrows=1, ncols=3,
  128.                        subplot_kw=dict(projection='polar'))
  129. fig.canvas.set_window_title('Left Ventricle Bulls Eyes (AHA)')
  131. # Create the axis for the colorbars
  132. axl = fig.add_axes([0.14, 0.15, 0.2, 0.05])
  133. axl2 = fig.add_axes([0.41, 0.15, 0.2, 0.05])
  134. axl3 = fig.add_axes([0.69, 0.15, 0.2, 0.05])
  137. # Set the colormap and norm to correspond to the data for which
  138. # the colorbar will be used.
  139. cmap = mpl.cm.viridis
  140. norm = mpl.colors.Normalize(vmin=1, vmax=17)
  142. # ColorbarBase derives from ScalarMappable and puts a colorbar
  143. # in a specified axes, so it has everything needed for a
  144. # standalone colorbar.  There are many more kwargs, but the
  145. # following gives a basic continuous colorbar with ticks
  146. # and labels.
  147. cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmap, norm=norm,
  148.                                 orientation='horizontal')
  149. cb1.set_label('Some Units')
  152. # Set the colormap and norm to correspond to the data for which
  153. # the colorbar will be used.
  154. cmap2 = mpl.cm.cool
  155. norm2 = mpl.colors.Normalize(vmin=1, vmax=17)
  157. # ColorbarBase derives from ScalarMappable and puts a colorbar
  158. # in a specified axes, so it has everything needed for a
  159. # standalone colorbar.  There are many more kwargs, but the
  160. # following gives a basic continuous colorbar with ticks
  161. # and labels.
  162. cb2 = mpl.colorbar.ColorbarBase(axl2, cmap=cmap2, norm=norm2,
  163.                                 orientation='horizontal')
  164. cb2.set_label('Some other units')
  167. # The second example illustrates the use of a ListedColormap, a
  168. # BoundaryNorm, and extended ends to show the "over" and "under"
  169. # value colors.
  170. cmap3 = mpl.colors.ListedColormap(['r', 'g', 'b', 'c'])
  171. cmap3.set_over('0.35')
  172. cmap3.set_under('0.75')
  174. # If a ListedColormap is used, the length of the bounds array must be
  175. # one greater than the length of the color list.  The bounds must be
  176. # monotonically increasing.
  177. bounds = [2, 3, 7, 9, 15]
  178. norm3 = mpl.colors.BoundaryNorm(bounds, cmap3.N)
  179. cb3 = mpl.colorbar.ColorbarBase(axl3, cmap=cmap3, norm=norm3,
  180.                                 # to use 'extend', you must
  181.                                 # specify two extra boundaries:
  182.                                 boundaries=[0] + bounds + [18],
  183.                                 extend='both',
  184.                                 ticks=bounds,  # optional
  185.                                 spacing='proportional',
  186.                                 orientation='horizontal')
  187. cb3.set_label('Discrete intervals, some other units')
  190. # Create the 17 segment model
  191. bullseye_plot(ax[0], data, cmap=cmap, norm=norm)
  192. ax[0].set_title('Bulls Eye (AHA)')
  194. bullseye_plot(ax[1], data, cmap=cmap2, norm=norm2)
  195. ax[1].set_title('Bulls Eye (AHA)')
  197. bullseye_plot(ax[2], data, segBold=[3, 5, 6, 11, 12, 16],
  198.               cmap=cmap3, norm=norm3)
  199. ax[2].set_title('Segments [3,5,6,11,12,16] in bold')
  201. plt.show()

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