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Showing content from https://plotly.com/python/3d-mesh/ below:

3d mesh plots in Python

How to make 3D Mesh Plots

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go.Mesh3d draws a 3D set of triangles with vertices given by x, y and z. If only coordinates are given, an algorithm such as Delaunay triangulation is used to draw the triangles. Otherwise the triangles can be given using the i, j and k parameters (see examples below).

In [1]:

import plotly.graph_objects as go
import numpy as np

# Download data set from plotly repo
pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
x, y, z = pts.T

fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z, color='lightpink', opacity=0.50)])
fig.show()

In [2]:

import plotly.graph_objects as go
import numpy as np

pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
x, y, z = pts.T

fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z,
                   alphahull=5,
                   opacity=0.4,
                   color='cyan')])
fig.show()

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In this example we use the i, j and k parameters to specify manually the geometry of the triangles of the mesh.

In [4]:

import plotly.graph_objects as go

fig = go.Figure(data=[
    go.Mesh3d(
        x=[0, 1, 2, 0],
        y=[0, 0, 1, 2],
        z=[0, 2, 0, 1],
        colorbar=dict(title=dict(text='z')),
        colorscale=[[0, 'gold'],
                    [0.5, 'mediumturquoise'],
                    [1, 'magenta']],
        # Intensity of each vertex, which will be interpolated and color-coded
        intensity=[0, 0.33, 0.66, 1],
        # i, j and k give the vertices of triangles
        # here we represent the 4 triangles of the tetrahedron surface
        i=[0, 0, 0, 1],
        j=[1, 2, 3, 2],
        k=[2, 3, 1, 3],
        name='y',
        showscale=True
    )
])

fig.show()

In [5]:

import plotly.graph_objects as go
import numpy as np

fig = go.Figure(data=[
    go.Mesh3d(
        # 8 vertices of a cube
        x=[0, 0, 1, 1, 0, 0, 1, 1],
        y=[0, 1, 1, 0, 0, 1, 1, 0],
        z=[0, 0, 0, 0, 1, 1, 1, 1],
        colorbar=dict(title=dict(text='z')),
        colorscale=[[0, 'gold'],
                    [0.5, 'mediumturquoise'],
                    [1, 'magenta']],
        # Intensity of each vertex, which will be interpolated and color-coded
        intensity = np.linspace(0, 1, 8, endpoint=True),
        # i, j and k give the vertices of triangles
        i = [7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2],
        j = [3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3],
        k = [0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6],
        name='y',
        showscale=True
    )
])

fig.show()

The intensitymode attribute of go.Mesh3d can be set to vertex (default mode, in which case intensity values are interpolated between values defined on vertices), or to cell (value of the whole cell, no interpolation). Note that the intensity parameter should have the same length as the number of vertices or cells, depending on the intensitymode.

Whereas the previous example used the default intensitymode='vertex', we plot here the same mesh with intensitymode='cell'.

In [6]:

import plotly.graph_objects as go
fig = go.Figure(data=[
    go.Mesh3d(
        # 8 vertices of a cube
        x=[0, 0, 1, 1, 0, 0, 1, 1],
        y=[0, 1, 1, 0, 0, 1, 1, 0],
        z=[0, 0, 0, 0, 1, 1, 1, 1],
        colorbar=dict(title=dict(text='z')),
        colorscale=[[0, 'gold'],
                    [0.5, 'mediumturquoise'],
                    [1, 'magenta']],
        # Intensity of each vertex, which will be interpolated and color-coded
        intensity = np.linspace(0, 1, 12, endpoint=True),
        intensitymode='cell',
        # i, j and k give the vertices of triangles
        i = [7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2],
        j = [3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3],
        k = [0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6],
        name='y',
        showscale=True
    )
])

fig.show()

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

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