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3d axes

Range of axes

3D figures have an attribute in layout called scene, which contains attributes such as xaxis, yaxis and zaxis parameters, in order to set the range, title, ticks, color etc. of the axes.

For creating 3D charts, see this page.

import plotly.graph_objects as go
import numpy as np
np.random.seed(1)

N = 70

fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)),
                   y=(55*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='rgba(244,22,100,0.6)'
                  )])

fig.update_layout(
    scene = dict(
        xaxis = dict(nticks=4, range=[-100,100],),
                     yaxis = dict(nticks=4, range=[-50,100],),
                     zaxis = dict(nticks=4, range=[-100,100],),),
    width=700,
    margin=dict(r=20, l=10, b=10, t=10))

fig.show()

Fixed Ratio Axes

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

N = 50

fig = make_subplots(rows=2, cols=2,
                    specs=[[{'is_3d': True}, {'is_3d': True}],
                           [{'is_3d': True}, {'is_3d': True}]],
                    print_grid=False)
for i in [1,2]:
    for j in [1,2]:
        fig.append_trace(
            go.Mesh3d(
                x=(60*np.random.randn(N)),
                y=(25*np.random.randn(N)),
                z=(40*np.random.randn(N)),
                opacity=0.5,
              ),
            row=i, col=j)

fig.update_layout(width=700, margin=dict(r=10, l=10, b=10, t=10))
# fix the ratio in the top left subplot to be a cube
fig.update_layout(scene_aspectmode='cube')
# manually force the z-axis to appear twice as big as the other two
fig.update_layout(scene2_aspectmode='manual',
                  scene2_aspectratio=dict(x=1, y=1, z=2))
# draw axes in proportion to the proportion of their ranges
fig.update_layout(scene3_aspectmode='data')
# automatically produce something that is well proportioned using 'data' as the default
fig.update_layout(scene4_aspectmode='auto')
fig.show()

Set Axes Title

import plotly.graph_objects as go
import numpy as np

# Define random surface
N = 50
fig = go.Figure()
fig.add_trace(go.Mesh3d(x=(60*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='yellow'
                  ))
fig.add_trace(go.Mesh3d(x=(70*np.random.randn(N)),
                   y=(55*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,
                   color='pink'
                  ))

fig.update_layout(scene = dict(
                    xaxis_title='X AXIS TITLE',
                    yaxis_title='Y AXIS TITLE',
                    zaxis_title='Z AXIS TITLE'),
                    width=700,
                    margin=dict(r=20, b=10, l=10, t=10))

fig.show()

Ticks Formatting

import plotly.graph_objects as go
import numpy as np

# Define random surface
N = 50
fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='rgba(100,22,200,0.5)'
                  )])

# Different types of customized ticks
fig.update_layout(scene = dict(
                    xaxis = dict(
                        ticktext= ['TICKS','MESH','PLOTLY','PYTHON'],
                        tickvals= [0,50,75,-50]),
                    yaxis = dict(
                        nticks=5, tickfont=dict(
                            color='green',
                            size=12,
                            family='Old Standard TT, serif',),
                        ticksuffix='#'),
                    zaxis = dict(
                        nticks=4, ticks='outside',
                        tick0=0, tickwidth=4),),
                    width=700,
                    margin=dict(r=10, l=10, b=10, t=10)
                  )

fig.show()

Background and Grid Color

import plotly.graph_objects as go
import numpy as np

N = 50
fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,)])


# xaxis.backgroundcolor is used to set background color
fig.update_layout(scene = dict(
                    xaxis = dict(
                         backgroundcolor="rgb(200, 200, 230)",
                         gridcolor="white",
                         showbackground=True,
                         zerolinecolor="white",),
                    yaxis = dict(
                        backgroundcolor="rgb(230, 200,230)",
                        gridcolor="white",
                        showbackground=True,
                        zerolinecolor="white"),
                    zaxis = dict(
                        backgroundcolor="rgb(230, 230,200)",
                        gridcolor="white",
                        showbackground=True,
                        zerolinecolor="white",),),
                    width=700,
                    margin=dict(
                    r=10, l=10,
                    b=10, t=10)
                  )
fig.show()

Disabling tooltip spikes

By default, guidelines originating from the tooltip point are drawn. It is possible to disable this behaviour with the showspikes parameter. In this example we only keep the z spikes (projection of the tooltip on the x-y plane). Hover on the data to show this behaviour.

import plotly.graph_objects as go
import numpy as np

N = 50
fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,)])
fig.update_layout(scene=dict(xaxis_showspikes=False,
                             yaxis_showspikes=False))
fig.show()
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