Save 3D Matplot as interactive HTML-CodePudding

I´m trying to save my 3D trisurface Plot as an interactive HTML figure, so it should be possible to zoom in/ out and change the viewpoint. In the IDE the plot already exists and works so far, but I can`t save it in the HTML format because of the ValueError: "The fig parameter must be a dict or Figure. Received value of type <class 'matplotlib.figure.Figure'>: Figure(1600x900)". I don´t understand why the "<class 'matplotlib.figure.Figure'>" is not a Figure?

This was my approach: https://plotly.com/python/interactive-html-export/ And I tried it with go.Figure() (Export rotable 3D plots from Python to HTML) already but it didn´t work with the trisurf.

Is there a way to keep my Plot settings (use trisurf as it is) and get the interactive figure in HTML?

Thanks a lot for any answer

#Import libraries 
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt 
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pandas as pd
import plotly.express as px
import io
import plotly.io as pio
%matplotlib notebook


E = np.arange(225)
D = np.arange(225)
A = np.arange(225)

E = [10000.0, 10000.0, ...]
D = [500.0, 1000.0, ...]
A = [1.9495, 1.9644, ...]


#Create figure
fig = plt.figure(figsize =(16, 9)) 
ax = plt.axes(projection ='3d') 

# Creating color map
my_cmap = plt.get_cmap('hot')

# Data for three-dimensional scattered points
zdata = A
xdata = D
ydata = E

# Creating plot
trisurf = ax.plot_trisurf(xdata, ydata, zdata,
                         cmap = my_cmap,
                         linewidth = 0.2,
                         antialiased = True,
                         edgecolor = 'grey') 
fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 10)
ax.set_title('AIE_SIM0.003__lowE_10000_upE_460000_stepE_30000_lowD_500.0_upD_8000.0_stepD_500.0')

ax.set_xlabel('Damping Ns/m')
ax.set_ylabel('Stifness N/m')
ax.set_zlabel('Amplification')


A2 = np.arange(225)

A2.fill(20.757)


# Creating color map
my_cmap2 = plt.get_cmap('gray')

# Data for three-dimensional scattered points
zdata2 = A2
xdata = D
ydata = E

# Creating plot
trisurf2 = ax.plot_trisurf(xdata, ydata, zdata2,
                         cmap = my_cmap2,
                         linewidth = 0.2,
                         antialiased = False,
                         edgecolor = 'none', alpha = 0.2) 
fig.colorbar(trisurf2, ax = ax, shrink = 0.5, aspect = 10)
print(type(fig))
#fig.write_html("file.html")
plotly.io.to_html(fig=fig)

fig.savefig('3D_Plot_PNG_lowE_10000_upE_460000_stepE_30000_lowD_500.0_upD_8000.0_stepD_500.0.png')
fig.show()



------------------------------------------------------------------------------------------
Figure 1

printed: <class 'matplotlib.figure.Figure'>


ValueError: 
The fig parameter must be a dict or Figure.
    Received value of type <class 'matplotlib.figure.Figure'>: Figure(1600x900)

CodePudding user response：

As far as I'm aware, Matplotlib is not able to generate 3D html plot.

Moreover, what you tried above is wrong. That error message is telling you that Plotly's to_html only works with Plotly's Figure. So mixing Plotly and Matplotlib is not going to work. You need to create a Plotly figure.

Also, I don't think that Plotly exposes something similar to Matplotlib's plot_trisurf. However, it exposes go.Mesh that allows us to achieve the same result.

The recipe:

Generate your numerical data.
Create a triangulation. We will use Matplotlib's Triangulation class for this part.
Create the Plotly figure and add the surface.
Export the figure to html.

Here I'm going to post an example to guide you:

import numpy as np
import matplotlib.tri as mtri
import plotly.graph_objects as go

### DATA GENERATION
# Make parameter spaces radii and angles.
n_angles = 36
n_radii = 8
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)

angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2]  = np.pi/n_angles

# Map radius, angle pairs to x, y, z points.
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
z = (np.cos(radii)*np.cos(3*angles)).flatten()

### TRIANGULATION
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = mtri.Triangulation(x, y)

# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = xmid**2   ymid**2 < min_radius**2
triangles = triang.triangles[~mask]

### PLOT
fig = go.Figure(data=[
    # go.Mesh allows to provide the triangulation
    go.Mesh3d(
        x=x, y=y, z=z,
        colorbar_title='z',
        colorscale="aggrnyl",
        # Intensity of each vertex, which will be interpolated and color-coded
        intensity =z,
        # i, j and k give the vertices of triangles
        i = triangles[:, 0],
        j = triangles[:, 1],
        k = triangles[:, 2],
        showscale=True
    )
])

fig.show()

### EXPORT TO HTML
# Please, execute `help(fig.write_html)` to learn about all the
# available keyword arguments to control the output
fig.write_html("test.html", include_plotlyjs=True, full_html=True)