Visualization

Surfaces provides built-in visualization through the .plot accessor on every test function. All plots use Plotly and return interactive plotly.graph_objects.Figure objects.

from surfaces.test_functions.algebraic import AckleyFunction

func = AckleyFunction()
fig = func.plot.surface()
fig.show()

Plot Compatibility

Not every plot type works with every function. Availability depends primarily on the number of dimensions. The table below is auto-generated from the actual func.plot.available() output.

Available Plot Types

Surface

Interactive 3D surface showing the objective landscape. Requires exactly 2 plotted dimensions.

func = AckleyFunction()
fig = func.plot.surface()

Contour

2D contour plot with isolines of equal objective value. Requires exactly 2 plotted dimensions.

fig = func.plot.contour()

Heatmap

Color-coded grid of objective values. Requires exactly 2 plotted dimensions.

fig = func.plot.heatmap()

Multi-Slice

1D slices through each dimension while all others are fixed. Works with any number of dimensions.

from surfaces.test_functions.algebraic import SphereFunction

func = SphereFunction(n_dim=5)
fig = func.plot.multi_slice()

Fitness Distribution

Histogram of objective values from random sampling across the search space. Works with any number of dimensions.

fig = func.plot.fitness_distribution()

Convergence

Best-so-far objective value vs. evaluation number. Requires evaluation history (either from func.search_data or passed explicitly).

# After running evaluations:
fig = func.plot.convergence()

# Or with explicit history:
fig = func.plot.convergence(history=my_history)

# Or via chaining:
fig = func.plot.with_history(my_history).convergence()

LaTeX/PDF

Publication-quality output with pgfplots surface and formula. Only available for 2D algebraic functions that have a latex_formula attribute.

pdf_path = func.plot.latex()
pdf_path = func.plot.latex(output_path="my_plot.pdf")

Discovering Available Plots

Use func.plot.available() to check which plots work for a given function instance:

func = SphereFunction(n_dim=2)
func.plot.available()
# ['surface', 'contour', 'heatmap', 'multi_slice', 'fitness_distribution', 'latex']

func = SphereFunction(n_dim=5)
func.plot.available()
# ['multi_slice', 'fitness_distribution']

N-D Functions: Selecting Dimensions

For functions with more than 2 dimensions, use the params dict to select which dimensions to plot and fix the rest:

func = SphereFunction(n_dim=5)

# Plot x0 vs x2, fix others at defaults
fig = func.plot.surface(params={"x0": ..., "x2": ...})

# Custom ranges and fixed values
fig = func.plot.surface(params={
    "x0": (-2, 2),
    "x2": (-1, 1),
    "x1": 0.0,
    "x3": 0.5,
    "x4": -1.0,
})

Customization

Resolution

Control the grid resolution (higher = more detail, slower):

fig = func.plot.surface(resolution=200)
fig = func.plot.contour(resolution=30)

Plotly kwargs

Additional keyword arguments are forwarded to the underlying Plotly calls:

fig = func.plot.surface(colorscale="Plasma")
fig = func.plot.heatmap(colorscale="RdBu")

Saving Plots

# Interactive HTML
fig.write_html("function_surface.html")

# Static image (requires kaleido)
fig.write_image("function_surface.png")
fig.write_image("function_surface.svg")