用python绘制一个3D条形直方图

Question

我有一些 x 和 y 数据，我想用它们生成一个带有颜色渐变（bwr 或其他）的 3D 直方图。

我编写了一个脚本，它绘制了 x 和 y 脓肿的有趣值，介于 -2 和 2 之间：

import numpy as np
import numpy.random
import matplotlib.pyplot as plt

# To generate some test data
x = np.random.randn(500)
y = np.random.randn(500)

XY = np.stack((x,y),axis=-1)

def selection(XY, limitXY=[[-2,+2],[-2,+2]]):
        XY_select = []
        for elt in XY:
            if elt[0] > limitXY[0][0] and elt[0] < limitXY[0][1] and elt[1] > limitXY[1][0] and elt[1] < limitXY[1][1]:
                XY_select.append(elt)

        return np.array(XY_select)

XY_select = selection(XY, limitXY=[[-2,+2],[-2,+2]])

heatmap, xedges, yedges = np.histogram2d(XY_select[:,0], XY_select[:,1], bins = 7, range = [[-2,2],[-2,2]])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]


plt.figure("Histogram")
#plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()

并给出正确的结果：

现在，我想将其转换为 3D 直方图。不幸的是，我没有成功地用bar3d 正确绘制它，因为默认情况下它采用横坐标的 x 和 y 的长度。

我很确定有一种非常简单的方法可以使用 imshow 以 3D 形式进行绘制。就像一个未知的选项...

Answer 1

我终于成功了。我几乎可以肯定有更好的方法来做到这一点，但至少它有效：

import numpy as np
import numpy.random
import matplotlib.pyplot as plt

# To generate some test data
x = np.random.randn(500)
y = np.random.randn(500)

XY = np.stack((x,y),axis=-1)

def selection(XY, limitXY=[[-2,+2],[-2,+2]]):
        XY_select = []
        for elt in XY:
            if elt[0] > limitXY[0][0] and elt[0] < limitXY[0][1] and elt[1] > limitXY[1][0] and elt[1] < limitXY[1][1]:
                XY_select.append(elt)

        return np.array(XY_select)

XY_select = selection(XY, limitXY=[[-2,+2],[-2,+2]])


xAmplitudes = np.array(XY_select)[:,0]#your data here
yAmplitudes = np.array(XY_select)[:,1]#your other data here


fig = plt.figure() #create a canvas, tell matplotlib it's 3d
ax = fig.add_subplot(111, projection='3d')


hist, xedges, yedges = np.histogram2d(x, y, bins=(7,7), range = [[-2,+2],[-2,+2]]) # you can change your bins, and the range on which to take data
# hist is a 7X7 matrix, with the populations for each of the subspace parts.
xpos, ypos = np.meshgrid(xedges[:-1]+xedges[1:], yedges[:-1]+yedges[1:]) -(xedges[1]-xedges[0])


xpos = xpos.flatten()*1./2
ypos = ypos.flatten()*1./2
zpos = np.zeros_like (xpos)

dx = xedges [1] - xedges [0]
dy = yedges [1] - yedges [0]
dz = hist.flatten()

cmap = cm.get_cmap('jet') # Get desired colormap - you can change this!
max_height = np.max(dz)   # get range of colorbars so we can normalize
min_height = np.min(dz)
# scale each z to [0,1], and get their rgb values
rgba = [cmap((k-min_height)/max_height) for k in dz] 

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=rgba, zsort='average')
plt.title("X vs. Y Amplitudes for ____ Data")
plt.xlabel("My X data source")
plt.ylabel("My Y data source")
plt.savefig("Your_title_goes_here")
plt.show()

我使用这个example，但我修改了它，因为它引入了一个偏移量。结果是这样的：

Answer 2

您可以使用以下简单的方法生成相同的结果：

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-2, 2, 7)
y = np.linspace(-2, 2, 7)

xx, yy = np.meshgrid(x, y)

z = xx*0+yy*0+ np.random.random(size=[7,7])

plt.imshow(z, interpolation='nearest', cmap=plt.cm.viridis, extent=[-2,2,2,2])
plt.show()

from mpl_toolkits.mplot3d import Axes3D
ax = Axes3D(plt.figure())

ax.plot_surface(xx, yy, z, cmap=plt.cm.viridis, cstride=1, rstride=1)
plt.show()

结果如下：