Plotting

spicelab ships interactive Plotly helpers for common visualization tasks. Each helper returns a VizFigure, which wraps the underlying Plotly figure and exposes show(), to_html(), and to_image() convenience methods.

Install the optional viz extra to pull in Plotly and Kaleido (for static image export):

pip install spicelab[viz]

Time-domain traces

from spicelab.viz import plot_traces

ds = handle.dataset()
fig = plot_traces(ds, ys=["V(R)", "I(R)"] , title="RC response")
fig.to_html("rc_response.html")  # standalone HTML with embedded Plotly figure

Bode plots

from spicelab.viz import plot_bode

# 'y' must reference a complex trace (AC/Small-signal analysis)
ac_ds = ac_handle.dataset()
bode_fig = plot_bode(ac_ds, y="V(vout)")
bode_fig.show()  # open an interactive browser window

Step responses

from spicelab.viz import plot_step_response

tran_ds = tran_handle.dataset()  # time-domain dataset
step_fig = plot_step_response(
    tran_ds,
    y="V(vout)",
    settle_tolerance=0.02,
    title="RC step response",
)
step_fig.to_html("rc_step_response.html")

plot_step_response annotates rise time, overshoot and settling bands automatically when enough data is available. Pass x="time" to force a specific coordinate or override steady_state/initial_value when the default heuristics are not sufficient.

Nyquist plots

from spicelab.viz import plot_nyquist

loop_ds = ac_loop_handle.dataset()  # AC dataset with loop gain trace
nyquist_fig = plot_nyquist(loop_ds, y="loop_gain", title="Loop Nyquist")
nyquist_fig.show()

The Nyquist helper draws the complex trajectory of the selected trace and highlights the last segment direction to simplify stability inspections. Provide xlabel/ylabel to rename axes or disable show_arrow when exporting static reports.

Parameter sweeps

from spicelab.viz import plot_sweep_df

fig = plot_sweep_df(df, x="time", y="v(vout)", hue="R", title="Parameter sweep")
fig.to_image("sweep.png")  # requires kaleido; falls back to HTML otherwise

Explore spicelab.viz for lower-level building blocks (time_series_view, bode_view, monte_carlo_histogram, monte_carlo_param_scatter, etc.) when you want to assemble custom dashboards or compose multiple views manually.