API map¶

One table per thing you might be trying to do, with what each entry point hands back. Everything here is imported as import complexplorer as cp, except engineering mode, which lives under cp.ee.

Draw a picture¶

You want	Call	You get back
A 2D phase portrait	`cp.plot(domain, func, cmap=...)`	a matplotlib `Axes`
Domain and codomain side by side	`cp.pair_plot(domain, func, cmap=...)`	a matplotlib `Figure`
A flat stereographic chart	`cp.riemann_chart(func)`	a matplotlib `Axes`
Both hemispheres as charts	`cp.riemann_hemispheres(func)`	a matplotlib `Figure`
A 3D analytic landscape	`cp.plot_landscape_pv(domain, func)`	a PyVista `Plotter`, or `None`
Two landscapes side by side	`cp.pair_plot_landscape_pv(domain, func)`	a PyVista `Plotter`, or `None`
The Riemann sphere	`cp.riemann_pv(func)`	a PyVista `Plotter`, or `None`
A Riemann surface	`cp.riemann_surface_pv("power", n=2)`	a PyVista `Plotter`, or `None`
Something, quickly	`cp.quick_plot(func, mode="2d")`	whichever of the above it dispatched to

Every 3D entry point takes filename= and interactive=False to write an image instead of opening a window, and return_plotter=True to hand back the Plotter for further composition. The 2D entry points take filename= too.

Choose what to draw¶

You want	Call	You get back
A region of the plane	`cp.Rectangle(4, 4)`, `cp.Disk(2)`, `cp.Annulus(0.2, 3)`	a `Domain`
A region built from others	`a \\| b`, `a & b`, `a - b`	a `CompositeDomain`
A colour convention	`cp.Phase(phase_sectors=6)` and the other families	a `Colormap`
A curated function	`cp.catalog.get("pole_flower_10")`	a `FunctionPreset`
Several of them	`cp.catalog.filter(tag="ornament")`	a list of `FunctionPreset`
Settings for a render	`cp.PlotPresets.publication_ready()`	a dict to spread as keyword arguments

PlotPresets configures a render; catalog supplies a function.

Engineering mode¶

You want	Call	You get back
A transfer function	`cp.ee.TransferFunction(num, den)`	a callable object with `poles`, `zeros`, `is_stable`
Its phase portrait	`cp.ee.transfer_portrait(H)`	a matplotlib `Axes`
Poles and zeros	`cp.ee.pole_zero_plot(H)`	a matplotlib `Axes`
Bode or Nyquist	`cp.ee.bode_plot(H)`, `cp.ee.nyquist_plot(H)`	a matplotlib `Figure`
The frequency response	`H.frequency_response()`	`(omega, response)`, both arrays

A TransferFunction is a plain callable, so every renderer above accepts it directly.

Produce a file¶

You want	Call	You get back
A printable ornament	`cp.create_ornament(func, "out.stl", size_mm=80)`	the path written
The same, with the mesh in hand	`cp.OrnamentGenerator(func).generate_and_save(...)`	the path written
A reproducible asset bundle	`cp.generate_gallery(out_dir, selection=...)`	the `index.json` manifest as a dict
An image from any renderer	pass `filename=`	the file is written; the return is unchanged

Scaling, and the pieces underneath¶

You want	Call	You get back
To compress `\\|f(z)\\|` into height	`modulus_mode="arctan"` on a 3D call	—
The scaling modes themselves	`cp.ModulusScaling`	a class of static methods
A bundled scaling	`cp.get_scaling_preset("balanced")`	a settings dict
The constant that fixes sea level	`cp.normalization_constant(zeros, poles)` or `cp.sampled_normalization_constant(...)`	a float
Phase or a sawtooth directly	`cp.phase(z)`, `cp.sawtooth(x)`	an array
Stereographic projection	`cp.stereographic_projection(z)`, `cp.inverse_stereographic(...)`	arrays

When something goes wrong¶

Every error the library raises derives from cp.ComplexplorerError, so one except catches all of them. cp.ValidationError covers bad arguments, and cp.ColormapError is a ValidationError for colormap configuration specifically.