我编写了一个脚本,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 x 和 y 的长度作为横坐标。我非常确定有一种非常简单的方法可以使用 imshow 以 3D 形式绘制此图。就像一个未知的选项...
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
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:
dz = np.transpose(hist).flatten()
或
ypos, xpos = np.meshgrid(yedges[:-1] + yedges[1:], xedges[:-1] + xedges[1:])当你展平 np.histogram2d 的输出时,你会得到每个 x 堆叠在一起的一整行 y 值。 然后展平 meshgrid 的输出,得到沿数组堆叠的递增 x 和固定 y。因此,两者不再匹配