Skip to content

Latest commit

 

History

History
274 lines (233 loc) · 8.2 KB

3d-axes.md

File metadata and controls

274 lines (233 loc) · 8.2 KB
jupyter
jupytext kernelspec language_info plotly
notebook_metadata_filter text_representation
all
extension format_name format_version jupytext_version
.md
markdown
1.3
1.15.1
display_name language name
Python 3 (ipykernel)
python
python3
codemirror_mode file_extension mimetype name nbconvert_exporter pygments_lexer version
name version
ipython
3
.py
text/x-python
python
python
ipython3
3.10.4
description display_as language layout name order page_type permalink thumbnail
How to format axes of 3d plots in Python with Plotly.
3d_charts
python
base
3D Axes
1
example_index
python/3d-axes/
thumbnail/3d-axes.png

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.

Set range on an axis to manually configure a range for that axis. If you don't set range, it's automatically calculated. In this example, we set a range on xaxis, yaxis, and zaxis.

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()

Setting only a Lower or Upper Bound for Range

New in 5.17

You can also set just a lower or upper bound for range. In this case, autorange is used for the other bound. In this example, we apply autorange to the lower bound of the yaxis and the upper bound of zaxis by setting them to None.

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=[None, 100],),
                     zaxis = dict(nticks=4, range=[-100, None],),),
    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=dict(
                          title=dict(
                              text='X AXIS TITLE'
                          )
                      ),
                      yaxis=dict(
                          title=dict(
                              text='Y AXIS TITLE'
                          )
                      ),
                      zaxis=dict(
                          title=dict(
                              text='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()