Confidence Interval 3 dimensional plot-CodePudding

The

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter interval = np.random.normal(0, 1, size=(100, 100)) x = np.arange(0.1,1.1,0.01) y = np.linspace(-np.pi,np.pi,100) X,Y = np.meshgrid(x,y) result = [] for i,j in zip(X,Y): result.append(np.log(i) np.sin(j)) upper_bound = np.array(result) interval lower_bound = np.array(result)-interval fig = plt.figure() fig.set_figwidth(20) fig.set_figheight(6) ax = fig.gca(projection='3d') surf = ax.plot_surface(X, Y, np.array(result)) ax.zaxis.set_major_locator(LinearLocator(10)) ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()

CodePudding user response：

Check out this 3d surface plot using plotly graph objects:

import plotly.graph_objects as go import numpy as np x = np.arange(0.1,1.1,0.01) y = np.linspace(-np.pi,np.pi,100) X,Y = np.meshgrid(x,y) result = [] for i,j in zip(X,Y): result.append(np.log(i) np.sin(j)) upper_bound = np.array(result) 1 lower_bound = np.array(result)-1 fig = go.Figure(data=[ go.Surface(z=result), go.Surface(z=upper_bound, showscale=False, opacity=0.3,colorscale='purp'), go.Surface(z=lower_bound, showscale=False, opacity=0.3,colorscale='purp'), ]) fig.show()

This plots 3 surfaces, the one for your results and the 2 bounds. However if you'd like something that looks more like a filled volume you'd have to add volume graphs with scaling opacity.