Public account: You and the cabin by: Peter Editor: Peter
Hello, I’m Peter
This article focuses on how to use Plotly to draw 3D graphics.
In 3D graphics, there are usually three axes: x, y and Z. In Plotly, when setting the layout, we usually set a parameter called scene. This parameter includes x, Y, z3 axis Settings, such as axis range, name, color, etc. It also introduces different types of 3D graphics, such as 3D scatter plots and 3D planar graphs
A sneak peek
First let’s take a look at some of the 3D graphics implemented by Plotly. Do you believe they are actually the same?
Plotly serial
The full list of Plotly’s current articles is as follows:
Import library, data
Many graphs in this paper are drawn based on iris data set, so we enter database and data set first:
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
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3D Scatter: Based on PlotLY_Express
Basic 3D graphics
The simplest 3D figure, drawn using px.scatter_3D:
fig = px.scatter_3d(
iris,
x="sepal_length",
y="sepal_width",
z="petal_width",
color="species"
)
fig.show()
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Set scatter to different shapes and sizes;
You can also add a different Markder tag to each scatter
fig = px.scatter_3d(
iris,
x="sepal_length",
y="sepal_width",
z="petal_length",
color="petal_width",
symbol="species"
)
fig.show()
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# set scatter size
fig = px.scatter_3d(
iris,
x="sepal_length",
y="sepal_width",
z="petal_width",
color="petal_length",
size="petal_length",
size_max=18.# Scatter maximum
opacity=0.7.# transparency
symbol="species"
)
fig.update_layout(margin=dict(l=0,r=0,b=0,t=0))
fig.show()
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3D Scatter: Based on go.scatter3DMe
Basic 3D Graphics
import plotly.graph_objects as go
import numpy as np
# Simulation data
t = np.linspace(0.10.50)
x, y, z = np.cos(t), np.sin(t), t
fig = go.Figure(data=[go.Scatter3d(
x=x, # Set 3 different coordinate data
y=y,
z=z,
mode='markers')]) # 'lines', 'markers', 'text
fig.show()
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Change the color of the scatter:
import plotly.graph_objects as go
import numpy as np
N = 88
t = np.linspace(0.20, N)
x, y, z = np.cos(t), np.sin(t), t
fig = go.Figure(data=[go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker=dict( # Set the tag
size=10,
color=z, # color Settings
colorscale='Viridis'.# Select color
opacity=0.86 # transparency)))# Set the marginal range of the graph
fig.update_layout(margin=dict(l=4, r=4, b=0, t=0))
fig.show()
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3D floor plan: Go.surface implementation
3D plan based on terrain
The data used are as follows:
import plotly.graph_objects as go
import pandas as pd
# Read the online CSV file
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv',
index_col=0) The # index_col parameter indicates that the data in the first column is used as the index
z_data.head()
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fig = go.Figure(data=[go.Surface(z=z_data.values)])
fig.update_layout(title="3D Surface Graphics Rendering".# titles
autosize=False.# Scale automatically
width=700.# width
height=600,
margin=dict(l=65,r=50,b=65,t=90) # distance of 4 positions
)
fig.show()
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3D graphics with outline lines
# Same data as above
# 1. Mr. Canvas
fig = go.Figure(data=[go.Surface(z=z_data.values)])
fig.update_traces(contours_z=dict( # Profile Settings
show=True.# Enable display or not
usecolormap=True.# color Settings
highlightcolor="mistyrose".# highlight
project_z=True))
fig.update_layout(
title='3D plan with outline',
autosize=False,
scene_camera_eye=dict(x=1.87, y=0.88, z=-0.64),
width=600,
height=500,
margin=dict(l=65, r=50, b=65, t=90)
)
fig.show()
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Multiple 3D graphics
Draw multiple figures on a Canvas in China:
z1 = np.array([ Numpy is an array form
[8.83.8.89.8.81.8.87.8.9.8.87],
[8.89.8.94.8.85.8.94.8.96.8.92],
[8.84.8.9.8.82.8.92.8.93.8.91],
[8.79.8.85.8.79.8.9.8.94.8.92],
[8.79.8.88.8.81.8.9.8.95.8.92],
[8.8.8.82.8.78.8.91.8.94.8.92],
[8.75.8.78.8.77.8.91.8.95.8.92],
[8.8.8.8.8.77.8.91.8.95.8.94],
[8.74.8.81.8.76.8.93.8.98.8.99]
])
z2 = z1 + 5
z3 = z1 - 5
fig = go.Figure(data=[
go.Surface(z=z1),
go.Surface(z=z2, showscale=False, opacity=0.9),
go.Surface(z=z3, showscale=False, opacity=0.9)
])
fig.show()
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6 3D Bubble diagram: 3D Bubble
6.1 Implementation based on Plotly_Express
data = px.data.gapminder() # GDP data set
fig = px.scatter_3d(data, # incoming data
x='year'.# Select 3 coordinates
y='continent',
z='pop',
size='gdpPercap'.# Bubble size
color='lifeExp'.# color
hover_data=['country']) # Hover data
# If the data is too large, you can use logarithms
fig.update_layout(scene_zaxis_type="log") Take the logarithm of the z axis
fig.show()
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6.2 Implementation based on go.scatter3D
Data section:
start, end = 800.1200 Select start and end indexes to filter data
fig = go.Figure(data=go.Scatter3d(
x=df1['year'][start:end], # Slice out some data by setting the value
y=df1['continent'][start:end],
z=df1['pop'][start:end],
text=df1['country'][start:end], # Text display data
mode='markers'.# Display form of bubbles: marker
marker=dict(
sizemode = 'diameter'.Size: 'diameter', 'area'
sizeref = 1500,
size = df1['gdpPercap'][start:end],
color = df1['lifeExp'][start:end],
colorscale = 'Viridis',
line_color='rgb(140, 140, 170)'
)
))
fig.update_layout(height=800,
width=800,
title='3D bubble Drawing ')
fig.show()
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7 3D Isosurface Diagram
Use go.isosurface to draw
7.1 Basic 3D Equivalent
import plotly.graph_objects as go
fig= go.Figure(data=go.Isosurface(
x=[0.0.0.0.1.1.1.1].# Set 3 coordinates
y=[1.0.1.0.1.0.1.0],
z=[1.1.0.0.1.1.0.0],
value=[1.2.3.4.5.6.7.8].# set the value
isomin=2.Color range
isomax=7,
))
fig.show()
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7.2 Changing the number of contour surfaces
Mgrid, a function from Numpy, is used in the following example:
- The np.mgrid function returns multidimensional structures, such as 2D graphics, 3D graphics.
- Compared to NP. meshGrid, it can process big data faster and can handle multiple dimensions (nP. MeshGrid can only handle two dimensions).
- Ret = np.mgrid[dimension 1, dimension 2, dimension 3…] : returns multiple values, as multiple matrices,
- The first return value is the distribution of the first dimension data in the final structure, the second return value is the distribution of the second dimension data in the final structure, and so on.
import plotly.graph_objects as go
import numpy as np
Use numpy's mgrid function to generate three pieces of data
X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
# values
values = X * X * 0.5 + Y * Y + Z * Z * 2
fig = go.Figure(data=go.Isosurface(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
isomin=10,
isomax=50,
surface_count=5,
colorbar_nticks=5,
caps=dict(x_show=False, y_show=False)
))
fig.show()
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Change color and transparency
import plotly.graph_objects as go
import numpy as np
Use numpy's mgrid function to generate three pieces of data
X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
# values
values = X * X * 0.5 + Y * Y + Z * Z * 2
fig = go.Figure(data=go.Isosurface(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
opacity=0.6.# Change the opacity of the graph
colorscale='plotly3'.# Change color
isomin=10,
isomax=50,
surface_count=5,
colorbar_nticks=5,
caps=dict(x_show=False, y_show=False)
))
fig.show()
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Advanced contour surface diagram
Generate 3 numpy arrays
X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
# the specific value
values = X * X * 0.5 + Y * Y + Z * Z * 2
fig = go.Figure(data=go.Isosurface(
x=X.flatten(), # 3 coordinates
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(), # data values
isomin=5.# Range of values
isomax=50,
surface_fill=0.7.# Surface fill chroma
caps=dict(x_show=False, y_show=False),
slices_z=dict(show=True, locations=[-1, -9, -5]),
slices_y=dict(show=True, locations=[1.8]),
))
fig.show()
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j.0:5:20j]
values = X * X * 0.5 + Y * Y + Z * Z * 2
fig = go.Figure(data=go.Isosurface(
x=X.flatten(), # 3 axes and values
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
isomin=30.# The size of an equal surface
isomax=50,
surface=dict(count=3, fill=0.7, pattern='odd'), # pattern values: 'all', 'odd', 'even'
caps=dict(x_show=True, y_show=True),
))
fig.show()
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