.Surface()

The Surface class creates a 3D surface plot using Plotly’s graph_objects module. It allows for the visualization of data in three dimensions. This class enables the rendering of surfaces defined by x, y, and z coordinates, and can be customized with various parameters like colors, scales, and more to represent complex datasets.

Syntax

plotly.graph_objects.Surface(
    z=None,           # 2D array-like, required.
    x=None,           # 1D array-like, optional.
    y=None,           # 1D array-like, optional.
    colorscale=None,  # list of tuples or named color scale, optional.
    cmin=None,        # float, optional.
    cmax=None,        # float, optional.
    opacity=None,     # float between 0 and 1, optional.
    surfacecolor=None, # 2D array-like, optional.
    **kwargs
)

• z: It defines the surface’s Z coordinates (height/depth).
• x: It represents the X coordinates. If not provided, it defaults to the range of the number of columns in z.
• y: It represents the Y coordinates. If not provided, it defaults to the range of the number of rows in z.
• colorscale: Can be a named colour scale (e.g., 'Viridis') or a list of tuples that define custom colour mapping (optional).
• cmin: Minimum value of the colorscale range (optional).
• cmax: Maximum value of the colorscale range (optional).
• opacity: A float between 0 (completely transparent) and 1 (fully opaque), defining surface opacity (optional).
• surfacecolor: A 2D array of values used to colour the surface independent of the Z axis (optional).

Example

This code creates a 3D surface plot using Plotly’s Surface class from the graph_objects module. It defines a mathematical function $f(x, y) = x^2 + y^2$, where x and y form a mesh grid, and z represents the height of the surface:

import plotly.graph_objects as go
import numpy as np

x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
x, y = np.meshgrid(x, y)
z = x**2 + y**2

fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])

fig.update_layout(title='3D Surface Plot of f(x, y) = x^2 + y^2',
                  scene=dict(
                      xaxis_title='X',
                      yaxis_title='Y',
                      zaxis_title='f(x, y)'
                  ))
fig.show()

The above code generates the following output: