我想绘制图像的牛眼图。我用 matplotlib 在极坐标图中尝试了这些代码 Shade 'cells'
对于牛眼图，我想使用不同的颜色。有没有办法设置这种颜色？color = choice(['navy','maroon','lightgreen'])颜色根据 for 循环迭代重复。
有谁知道在 matplotlib 中是否有对应于 matlab 的函数bullseye()？
问问题
2292 次
2 回答
1
我不认为有一个特定的bullseye方法matplotlib，但是比较matlab函数的作用和这个答案中的正确图，我认为你应该能够得到你想要的半径坐标。例如（借用上述答案）
import numpy as np
import matplotlib.pyplot as plt
theta, r = np.mgrid[0:2*np.pi:20j, 0.2:1:10j]
z = np.random.random(theta.size).reshape(theta.shape)
fig, ax = plt.subplots(ncols=1, subplot_kw=dict(projection='polar'))
ax.pcolormesh(theta, r, z)
ax.set_yticklabels([])
ax.set_ylim([0, 1])
plt.show()
地块
我不明白你关于颜色的问题到底是什么意思。ax.pcolormesh接受colormap或 matplotlib color。如果您想要特定的颜色，您可以使用这两个参数和/或创建自己的颜色图。
于 2013-03-08T12:49:37.290 回答
1
为了提供类似于您提到的 matlab 函数的东西，我起草了一些代码（也在 git hub 上共享），用于为每个径向细分创建具有任意数量扇区的靶心。颜色由颜色图和扇区下方的值给出。此外，这允许修改径向细分的中心。
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as ticker
def bulls_eye(ax, data, cmap=None, norm=None, raidal_subdivisions=(2, 8, 8, 11),
centered=(True, False, False, True), add_nomenclatures=True, cell_resolution=128,
pfi_where_to_save=None, colors_bound='-k'):
"""
Clockwise, from smaller radius to bigger radius.
:param ax:
:param data:
:param cmap:
:param norm:
:param raidal_subdivisions:
:param centered:
:param add_nomenclatures:
:param cell_resolution:
:param pfi_where_to_save:
:return:
"""
line_width = 1.5
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, 1, len(raidal_subdivisions)+1)
nomenclatures = []
if isinstance(add_nomenclatures, bool):
if add_nomenclatures:
nomenclatures = range(1, sum(raidal_subdivisions)+1)
elif isinstance(add_nomenclatures, list) or isinstance(add_nomenclatures, tuple):
assert len(add_nomenclatures) == sum(raidal_subdivisions)
nomenclatures = add_nomenclatures[:]
add_nomenclatures = True
# Create the circular bounds
line_width_circular = line_width
for i in range(r.shape[0]):
if i == range(r.shape[0])[-1]:
line_width_circular = int(line_width / 2.)
ax.plot(theta, np.repeat(r[i], theta.shape), colors_bound, lw=line_width_circular)
# iterate over cells divided by radial subdivision
for rs_id, rs in enumerate(raidal_subdivisions):
for i in range(rs):
cell_id = sum(raidal_subdivisions[:rs_id]) + i
theta_i = - i * 2 * np.pi / rs + np.pi / 2
if not centered[rs_id]:
theta_i += (2 * np.pi / rs) / 2
theta_i_plus_one = theta_i - 2 * np.pi / rs # clockwise
# Create colour fillings for each cell:
theta_interval = np.linspace(theta_i, theta_i_plus_one, cell_resolution)
r_interval = np.array([r[rs_id], r[rs_id+1]])
angle = np.repeat(theta_interval[:, np.newaxis], 2, axis=1)
radius = np.repeat(r_interval[:, np.newaxis], cell_resolution, axis=1).T
z = np.ones((cell_resolution, 2)) * data[cell_id]
ax.pcolormesh(angle, radius, z, cmap=cmap, norm=norm)
# Create radial bounds
if rs > 1:
ax.plot([theta_i, theta_i], [r[rs_id], r[rs_id+1]], colors_bound, lw=line_width)
# Add centered nomenclatures if needed
if add_nomenclatures:
if rs == 1 and rs_id ==0:
cell_center = (0, 0)
else:
cell_center = ((theta_i + theta_i_plus_one) / 2., r[rs_id] + .5 * r[1] )
if isinstance(nomenclatures[0], (int, long, float, complex)):
ax.annotate(r"${:.3g}$".format(nomenclatures[cell_id]), xy=cell_center,
xytext=(cell_center[0], cell_center[1]),
horizontalalignment='center', verticalalignment='center', size=8)
else:
ax.annotate(nomenclatures[cell_id], xy=cell_center,
xytext=(cell_center[0], cell_center[1]),
horizontalalignment='center', verticalalignment='center', size=12)
ax.grid(False)
ax.set_ylim([0, 1])
ax.set_yticklabels([])
ax.set_xticklabels([])
if pfi_where_to_save is not None:
plt.savefig(pfi_where_to_save, format='pdf', dpi=200)
def multi_bull_eyes(multi_data, cbar=None, cmaps=None, normalisations=None,
global_title=None, canvas_title='title', titles=None, units=None, raidal_subdivisions=(2, 8, 8, 11),
centered=(True, False, False, True), add_nomenclatures=(True, True, True, True),
pfi_where_to_save=None, show=True):
plt.clf()
n_fig = len(multi_data)
if cbar is None:
cbar = [True] * n_fig
if cmaps is None:
cmaps = [mpl.cm.viridis] * n_fig
if normalisations is None:
normalisations = [mpl.colors.Normalize(vmin=np.min(multi_data[i]), vmax=np.max(multi_data[i]))
for i in range(n_fig)]
if titles is None:
titles = ['Title {}'.format(i) for i in range(n_fig)]
h_space = 0.15 / n_fig
h_dim_fig = .8
w_dim_fig = .8 / n_fig
def fmt(x, pos):
# a, b = '{:.2e}'.format(x).split('e')
# b = int(b)
# return r'${} \times 10^{{{}}}$'.format(a, b)
return r"${:.4g}$".format(x)
# Make a figure and axes with dimensions as desired.
fig = plt.figure(figsize=(3 * n_fig, 4))
fig.canvas.set_window_title(canvas_title)
if global_title is not None:
plt.suptitle(global_title)
for n in range(n_fig):
origin_fig = (h_space * (n + 1) + w_dim_fig * n, 0.15)
ax = fig.add_axes([origin_fig[0], origin_fig[1], w_dim_fig, h_dim_fig], polar=True)
bulls_eye(ax, multi_data[n], cmap=cmaps[n], norm=normalisations[n], raidal_subdivisions=raidal_subdivisions,
centered=centered, add_nomenclatures=add_nomenclatures[n])
ax.set_title(titles[n], size=10)
if cbar[n]:
origin_cbar = (h_space * (n + 1) + w_dim_fig * n, .15)
axl = fig.add_axes([origin_cbar[0], origin_cbar[1], w_dim_fig, .05])
cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmaps[n], norm=normalisations[n], orientation='horizontal',
format=ticker.FuncFormatter(fmt))
cb1.ax.tick_params(labelsize=8)
if units is not None:
cb1.set_label(units[n])
if pfi_where_to_save is not None:
plt.savefig(pfi_where_to_save, format='pdf', dpi=330)
if show:
plt.show()
if __name__ == '__main__':
# Very dummy data:
data = np.array(range(29)) + 1
# TEST bull-eye three-fold
if True:
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')
# First one:
cmap = mpl.cm.viridis
norm = mpl.colors.Normalize(vmin=1, vmax=29)
bulls_eye(ax[0], data, cmap=cmap, norm=norm)
ax[0].set_title('Bulls Eye ')
axl = fig.add_axes([0.14, 0.15, 0.2, 0.05])
cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmap, norm=norm, orientation='horizontal')
cb1.set_label('Some Units')
# Second one
cmap2 = mpl.cm.cool
norm2 = mpl.colors.Normalize(vmin=1, vmax=29)
bulls_eye(ax[1], data, cmap=cmap2, norm=norm2)
ax[1].set_title('Bulls Eye ')
axl2 = fig.add_axes([0.41, 0.15, 0.2, 0.05])
cb2 = mpl.colorbar.ColorbarBase(axl2, cmap=cmap2, norm=norm2, orientation='horizontal')
cb2.set_label('Some other units')
# Third one
cmap3 = mpl.cm.winter
norm3 = mpl.colors.Normalize(vmin=1, vmax=29)
bulls_eye(ax[2], data, cmap=cmap3, norm=norm3)
ax[2].set_title('Bulls Eye third')
axl3 = fig.add_axes([0.69, 0.15, 0.2, 0.05])
cb3 = mpl.colorbar.ColorbarBase(axl3, cmap=cmap3, norm=norm3, orientation='horizontal')
cb3.set_label('Some more units')
plt.show()
if True:
fig = plt.figure(figsize=(5, 7))
fig.canvas.set_window_title('Bulls Eyes - segmentation assessment')
# First and only:
cmap = mpl.cm.viridis
norm = mpl.colors.Normalize(vmin=1, vmax=29)
ax = fig.add_axes([0.1, 0.2, 0.8, 0.7], polar=True)
bulls_eye(ax, data, cmap=cmap, norm=norm)
ax.set_title('Bulls Eye')
axl = fig.add_axes([0.1, 0.15, 0.8, 0.05])
cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmap, norm=norm, orientation='horizontal')
cb1.set_label('Some Units')
plt.show()
if True:
multi_data = [range(1,17), list( 0.000000001 * np.array(range(1,17))), list( 0.001 * np.array(range(1,17)))]
print multi_data
multi_bull_eyes(multi_data, raidal_subdivisions=(3,3,4,6),
centered=(True, True, True, True), add_nomenclatures=[True]*3)
plt.show(block=True)
它是在我在 github 上的代码LabelsManager中提出的，欢迎您使用。
这是一个示例，radal_subdivisions=(2, 8, 8, 11) 和 centered=(True, False, False, True)：
于 2018-01-12T15:54:31.177 回答