Merge pull request #4550 from tacaswell/doc_aha_bullseye

tacaswell · tacaswell · commit 52595947e82d · 2015-06-22T22:07:06.000-04:00
Doc: AHA bullseye example
diff --git a/.gitignore b/.gitignore
@@ -67,3 +67,5 @@ examples/tests/*
 texput.log
 texput.aux
 result_images
+
+*.swp
diff --git a/examples/pylab_examples/leftventricle_bulleye.py b/examples/pylab_examples/leftventricle_bulleye.py
@@ -0,0 +1,207 @@
+#!/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()
