I would like to plot a 2D kernel density estimation. I find the seaborn package very useful here. However, after searching for a long time, I couldn't figure out how to make the y-axis and x-axis non-transparent. Also, how to show the values of the density on the contour? I would be very appreciated if someone could help me out. Below please see my code and graph. 
我想绘制2D核密度估计。我发现seaborn包在这里非常有用。但是,经过长时间的搜索,我无法弄清楚如何使y轴和x轴不透明。另外,如何在轮廓上显示密度值?如果有人可以帮助我,我将非常感激。下面请看我的代码和图表。
import numpy as np
import seaborn as sns
import matplotlib.pyplot as pl
Y = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
ax = sns.kdeplot(Y, shade = True, cmap = "PuBu")
ax.patch.set_facecolor('white')
ax.collections[0].set_alpha(0)
ax.set_xlabel('$Y_1$', fontsize = 15)
ax.set_ylabel('$Y_0$', fontsize = 15)
pl.xlim(-3, 3)
pl.ylim(-3, 3)
pl.plot([-3, 3], [-3, 3], color = "black", linewidth = 1)
pl.show()
2 个解决方案
#1
Here is a solution using scipy and matplotlib only :
这是一个仅使用scipy和matplotlib的解决方案:
import numpy as np
import matplotlib.pyplot as pl
import scipy.stats as st
data = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
x = data[:, 0]
y = data[:, 1]
xmin, xmax = -3, 3
ymin, ymax = -3, 3
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
fig = pl.figure()
ax = fig.gca()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
# Contourf plot
cfset = ax.contourf(xx, yy, f, cmap='Blues')
## Or kernel density estimate plot instead of the contourf plot
#ax.imshow(np.rot90(f), cmap='Blues', extent=[xmin, xmax, ymin, ymax])
# Contour plot
cset = ax.contour(xx, yy, f, colors='k')
# Label plot
ax.clabel(cset, inline=1, fontsize=10)
ax.set_xlabel('Y1')
ax.set_ylabel('Y0')
pl.show()
The previous code gives the following result :
前面的代码给出了以下结果:
which has a non-transparent x-axis, a non-transparent y-axis and values of the density on the contour. Is this the expected result ?
它具有不透明的x轴,不透明的y轴和轮廓上的密度值。这是预期的结果吗?
#2
Did you check these examples?
你查过这些例子了吗?
http://matplotlib.org/examples/pylab_examples/contour_demo.html
http://matplotlib.org/examples/pylab_examples/contourf_demo.html
Scroll down to see more images.
向下滚动以查看更多图像。
#1
Here is a solution using scipy and matplotlib only :
这是一个仅使用scipy和matplotlib的解决方案:
import numpy as np
import matplotlib.pyplot as pl
import scipy.stats as st
data = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
x = data[:, 0]
y = data[:, 1]
xmin, xmax = -3, 3
ymin, ymax = -3, 3
# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
fig = pl.figure()
ax = fig.gca()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
# Contourf plot
cfset = ax.contourf(xx, yy, f, cmap='Blues')
## Or kernel density estimate plot instead of the contourf plot
#ax.imshow(np.rot90(f), cmap='Blues', extent=[xmin, xmax, ymin, ymax])
# Contour plot
cset = ax.contour(xx, yy, f, colors='k')
# Label plot
ax.clabel(cset, inline=1, fontsize=10)
ax.set_xlabel('Y1')
ax.set_ylabel('Y0')
pl.show()
The previous code gives the following result :
前面的代码给出了以下结果:
which has a non-transparent x-axis, a non-transparent y-axis and values of the density on the contour. Is this the expected result ?
它具有不透明的x轴,不透明的y轴和轮廓上的密度值。这是预期的结果吗?
#2
Did you check these examples?
你查过这些例子了吗?
http://matplotlib.org/examples/pylab_examples/contour_demo.html
http://matplotlib.org/examples/pylab_examples/contourf_demo.html
Scroll down to see more images.
向下滚动以查看更多图像。