Rewrite bullseye example to use bar() instead of pcolormesh().

anntzer · anntzer · commit 04f12e59e215 · 2023-01-13T17:35:17.000+01:00
It is much simpler to take advantage of bar()'s support for polar plots.
Also improve z-ordering of various elements so that edges don't get
overdrawn (or clipped).
diff --git a/examples/pie_and_polar_charts/nested_pie.py b/examples/pie_and_polar_charts/nested_pie.py
@@ -6,6 +6,7 @@
 The following examples show two ways to build a nested pie chart
 in Matplotlib. Such charts are often referred to as donut charts.
 
+See also the :doc:`/gallery/specialty_plots/leftventricle_bullseye` example.
 """
 
 import matplotlib.pyplot as plt
diff --git a/examples/specialty_plots/leftventricle_bullseye.py b/examples/specialty_plots/leftventricle_bullseye.py
@@ -7,28 +7,30 @@
 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 numpy as np
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 
 
-def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
+def bullseye_plot(ax, data, seg_bold=None, cmap="viridis", 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
-    seg_bold : list of int, optional
-        A list with the segments to highlight
-    cmap : ColorMap or None, optional
-        Optional argument to set the desired colormap
+    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
-        Optional argument to normalize data into the [0.0, 1.0] range
+        Normalizer for the data.
 
     Notes
     -----
@@ -43,93 +45,49 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
         nomenclature for tomographic imaging of the heart",
         Circulation, vol. 105, no. 4, pp. 539-542, 2002.
     """
-    if seg_bold is None:
-        seg_bold = []
 
-    linewidth = 2
     data = np.ravel(data)
-
-    if cmap is None:
-        cmap = plt.cm.viridis
-
+    if seg_bold is None:
+        seg_bold = []
     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)
 
-    # Remove grid
-    ax.grid(False)
-
-    # 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 - 1, 2 - 1)) * data[i]
-        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm, shading='auto')
-        if i + 1 in seg_bold:
-            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 - 1, 2 - 1)) * data[i + 6]
-        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm, shading='auto')
-        if i + 7 in seg_bold:
-            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 - 1, 2 - 1)) * data[i + 12]
-        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm, shading='auto')
-        if i + 13 in seg_bold:
-            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 - 1, 2 - 1)) * data[16]
-        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm, shading='auto')
-        if 17 in seg_bold:
-            ax.plot(theta0, r0, '-k', lw=linewidth + 2)
-
-    ax.set_ylim([0, 1])
-    ax.set_yticklabels([])
-    ax.set_xticklabels([])
+    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
@@ -151,7 +109,7 @@ def bullseye_plot(ax, data, seg_bold=None, cmap=None, norm=None):
 # 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')
+             orientation='horizontal', label='Some units')
 
 
 # And again for the second colorbar.
