问题
I am working on a data set consisting of 'pulse length' values and 5 or 6 'voltage values' corresponding to each of the pulse lengths. The first value is pulse length followed by voltages. Find the table below.
15 -56V -47V -53V -50V -50V
16 -49V -46V -52V -47V -50V
17 -50V -51V -47V -50V -49V
18 -50V -51V -48V -48V -45V
19 -49V -51V -45V -47V -52V
20 -45V -47V -50V -47V -54V
21 -46V -52V -52V -49V -54V
22 -53V -51V -53V -56V -52V
23 -52V -45V -51V -56V -53V
24 -51V -52V -54V -58V -52V
25 -56V -53V -57V -55V -53V
26 -53V -52V -55V -52V
27 -54V -49V -56V -54V
28 -52V -52V -57V -56V -53V
29 -63V -60V -54V -58V -61V
30 -59V -70V -61V
I want the X and Y axis to be pulse length and voltage and I want the Z axis to be its probability distribution. I have a 2D plot for the same using the set of 'voltage values' and its probability. In the picture, the red graph corresponds to one pulse length and green graph corresponds to another pulse length. I tried doing the 3D plot the same way using an example of multivariate normal distribution example from stack overflow (Plot normal distribution in 3D). Since I have very little experience with 3D plots, I am unable to plot multiple surface plots on the same surface with different Y axis 'pulse length' values. The code that I tried is given below.
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.mlab import bivariate_normal
from mpl_toolkits.mplot3d import Axes3D
#Parameters to set
mu_x = -48.8
sigma_x = np.sqrt(6.5)
mu_y = 0
sigma_y = np.sqrt(16)
#Create grid and multivariate normal
x = range(-100,0)
y = range(15,30)
X, Y = np.meshgrid(x,y)
Z = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x,mu_y)
#Make a 3D plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z,cmap='Reds',linewidth=0, antialiased=True,
zorder = 0.5)
ax.set_xlabel('Voltage')
ax.set_ylabel('Pulse Length')
ax.set_zlabel('Normal Distribution')
plt.show()
I would be really thankful if someone could help me do the same for multiple pulse lengths. Thank you.
回答1:
I don't know what kind of plot you want to achieve exactly but from what I understood, you want something like the below figure. I am putting only the relevant/modified code below. It's also not clear what variable is your pulse length. Since you have many pulse lengths, you can put the function to define mu_x, 'mu_y', Z in a for loop and plot several 3d surfaces.
# Create grid and multivariate normal
x = np.linspace(-100, 0, 200) # Create a mesh of 200 x-points
y = np.linspace(-30, 30, 200) # Create a mesh of 200 y-points
X, Y = np.meshgrid(x,y)
Z = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x,mu_y)
Z2 = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x-20,mu_y+10)
fig = plt.figure(figsize=(10, 8))
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z,cmap='Reds',linewidth=0, antialiased=True, zorder = 0.5)
ax.plot_surface(X, Y, Z2,cmap='Blues',linewidth=0, alpha=0.5, antialiased=True, zorder = 0.5)
Output
来源：https://stackoverflow.com/questions/52247430/3d-probability-density-plots-in-python