# # 左心室靶心

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.viridis

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):
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, 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] + np.deg2rad(60)
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] + np.deg2rad(45)
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.viridis
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()