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curialecuriale
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Added the colormap and normalize as arguments
1 parent 45cef15 commit 0709202

2 files changed

Lines changed: 87 additions & 32 deletions

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‎.gitignore‎

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Original file line numberDiff line numberDiff line change
@@ -60,3 +60,5 @@ examples/tests/*
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!examples/tests/backend_driver.py
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texput.log
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result_images
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*.swp

‎examples/pylab_examples/leftventricle_bulleye.py‎

Lines changed: 85 additions & 32 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@
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import matplotlib.pyplot as plt
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def bullseye_plot(ax, data, vlim=None, segBold=[]):
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def bullseye_plot(ax, data, segBold=[], cmap=None, norm=None):
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"""
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Bullseye representation for the left ventricle.
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@@ -18,8 +18,10 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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ax : axes
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data : list of int and float
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The intensity values for each of the 17 segments
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vlim : [min, max] or None
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Optional argument to set the Intensity range
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cmap : ColorMap or None
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Optional argument to set the disaried colormap
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norm : Normalize or None
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Optional argument to normalize data into the [0.0, 1.0] range
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segBold: list of int
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A list with the segments to highlight
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@@ -33,16 +35,19 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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----------
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.. [1] M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs,
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S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan,
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and M. S. Verani, “Standardized myocardial segmentation and nomenclature
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for tomographic imaging of the heart,” Circulation, vol. 105, no. 4,
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pp. 539–542, 2002.
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and M. S. Verani, "Standardized myocardial segmentation and nomenclature
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for tomographic imaging of the heart", Circulation, vol. 105, no. 4,
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pp. 539-542, 2002.
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"""
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linewidth = 2
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data = np.array(data).ravel()
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if vlim is None:
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vlim = [data.min(), data.max()]
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if cmap is None:
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cmap = plt.cm.jet
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if norm is None:
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norm = mpl.colors.Normalize(vmin=data.min(), vmax=data.max())
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theta = np.linspace(0, 2*np.pi, 768)
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r = np.linspace(0.2, 1, 4)
@@ -69,7 +74,7 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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theta0 = theta[i*128:i*128+128] + 60*np.pi/180
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theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
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z = np.ones((128, 2))*data[i]
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ax.pcolormesh(theta0, r0, z, vmin=vlim[0], vmax=vlim[1])
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ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
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if i+1 in segBold:
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ax.plot(theta0, r0, '-k', lw=linewidth+2)
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ax.plot(theta0[0], [r[2], r[3]], '-k', lw=linewidth+1)
@@ -83,7 +88,7 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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theta0 = theta[i*128:i*128+128] + 60*np.pi/180
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theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
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z = np.ones((128, 2))*data[i+6]
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ax.pcolormesh(theta0, r0, z, vmin=vlim[0], vmax=vlim[1])
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ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
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if i+7 in segBold:
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ax.plot(theta0, r0, '-k', lw=linewidth+2)
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ax.plot(theta0[0], [r[1], r[2]], '-k', lw=linewidth+1)
@@ -97,7 +102,7 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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theta0 = theta[i*192:i*192+192] + 45*np.pi/180
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theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)
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z = np.ones((192, 2))*data[i+12]
100-
ax.pcolormesh(theta0, r0, z, vmin=vlim[0], vmax=vlim[1])
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ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
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if i+13 in segBold:
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ax.plot(theta0, r0, '-k', lw=linewidth+2)
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ax.plot(theta0[0], [r[0], r[1]], '-k', lw=linewidth+1)
@@ -109,7 +114,7 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
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r0 = np.repeat(r0[:, np.newaxis], theta.size, axis=1).T
110115
theta0 = np.repeat(theta[:, np.newaxis], 2, axis=1)
111116
z = np.ones((theta.size, 2))*data[16]
112-
ax.pcolormesh(theta0, r0, z, vmin=vlim[0], vmax=vlim[1])
117+
ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)
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if 17 in segBold:
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ax.plot(theta0, r0, '-k', lw=linewidth+2)
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@@ -120,32 +125,80 @@ def bullseye_plot(ax, data, vlim=None, segBold=[]):
120125

121126
# Create the fake data
122127
data = np.array(range(17)) + 1
123-
vlim = [data.min(), data.max()]
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125-
fig, ax = plt.subplots(figsize=(12, 8), nrows=1, ncols=2,
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130+
# Make a figure and axes with dimensions as desired.
131+
fig, ax = plt.subplots(figsize=(12, 8), nrows=1, ncols=3,
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subplot_kw=dict(projection='polar'))
127133
fig.canvas.set_window_title('Left Ventricle Bulls Eyes (AHA)')
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129-
bullseye_plot(ax[0], data, vlim=vlim)
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# Create the axis for the colorbars
136+
axl = fig.add_axes([0.14, 0.15, 0.2, 0.05])
137+
axl2 = fig.add_axes([0.41, 0.15, 0.2, 0.05])
138+
axl3 = fig.add_axes([0.69, 0.15, 0.2, 0.05])
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140+
141+
# Set the colormap and norm to correspond to the data for which
142+
# the colorbar will be used.
143+
cmap = mpl.cm.jet
144+
norm = mpl.colors.Normalize(vmin=1, vmax=17)
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146+
# ColorbarBase derives from ScalarMappable and puts a colorbar
147+
# in a specified axes, so it has everything needed for a
148+
# standalone colorbar. There are many more kwargs, but the
149+
# following gives a basic continuous colorbar with ticks
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# and labels.
151+
cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmap, norm=norm,
152+
orientation='horizontal')
153+
cb1.set_label('Some Units')
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155+
156+
# Set the colormap and norm to correspond to the data for which
157+
# the colorbar will be used.
158+
cmap2 = mpl.cm.cool
159+
norm2 = mpl.colors.Normalize(vmin=1, vmax=17)
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161+
# ColorbarBase derives from ScalarMappable and puts a colorbar
162+
# in a specified axes, so it has everything needed for a
163+
# standalone colorbar. There are many more kwargs, but the
164+
# following gives a basic continuous colorbar with ticks
165+
# and labels.
166+
cb2 = mpl.colorbar.ColorbarBase(axl2, cmap=cmap2, norm=norm2,
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orientation='horizontal')
168+
cb2.set_label('Some other units')
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170+
171+
# The second example illustrates the use of a ListedColormap, a
172+
# BoundaryNorm, and extended ends to show the "over" and "under"
173+
# value colors.
174+
cmap3 = mpl.colors.ListedColormap(['r', 'g', 'b', 'c'])
175+
cmap3.set_over('0.35')
176+
cmap3.set_under('0.75')
177+
178+
# If a ListedColormap is used, the length of the bounds array must be
179+
# one greater than the length of the color list. The bounds must be
180+
# monotonically increasing.
181+
bounds = [2, 3, 7, 9, 15]
182+
norm3 = mpl.colors.BoundaryNorm(bounds, cmap3.N)
183+
cb3 = mpl.colorbar.ColorbarBase(axl3, cmap=cmap3, norm=norm3,
184+
# to use 'extend', you must
185+
# specify two extra boundaries:
186+
boundaries=[0]+bounds+[18],
187+
extend='both',
188+
ticks=bounds, # optional
189+
spacing='proportional',
190+
orientation='horizontal')
191+
cb3.set_label('Discrete intervals, some other units')
192+
193+
194+
# Create the 17 segment model
195+
bullseye_plot(ax[0], data, cmap=cmap, norm=norm)
130196
ax[0].set_title('Bulls Eye (AHA)')
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132-
bullseye_plot(ax[1], data, segBold=[3, 5, 6, 11, 12, 16],
133-
vlim=vlim)
134-
ax[1].set_title('Segments [3,5,6,11,12,16] in bold')
135-
136-
137-
#Add legend
138-
cm = plt.cm.jet
139-
140-
#define the bins and normalize
141-
cNorm = mpl.colors.Normalize(vmin=vlim[0], vmax=vlim[1])
198+
bullseye_plot(ax[1], data, cmap=cmap2, norm=norm2)
199+
ax[1].set_title('Bulls Eye (AHA)')
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143-
ticks = [vlim[0], 0, vlim[1]]
144-
ax[0] = fig.add_axes([0.2, 0.15, 0.2, 0.05])
145-
cb = mpl.colorbar.ColorbarBase(ax[0], cmap=cm, norm=cNorm, ticks=ticks,
146-
orientation='horizontal')
147-
ax[1] = fig.add_axes([0.62, 0.15, 0.2, 0.05])
148-
cb = mpl.colorbar.ColorbarBase(ax[1], cmap=cm, norm=cNorm, ticks=ticks,
149-
orientation='horizontal')
201+
bullseye_plot(ax[2], data, segBold=[3, 5, 6, 11, 12, 16], cmap=cmap3, norm=norm3)
202+
ax[2].set_title('Segments [3,5,6,11,12,16] in bold')
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plt.show()

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