AAMutPlot.scale_ranking

AAMutPlot.scale_ranking(df_impact, top_n=20, ax=None, figsize=(6, 5), color=None)

Plot the per-scale ranking of substitution sensitivity.

A horizontal bar chart of the top_n scales with the largest mean absolute substitution delta, ordered most-sensitive first.

Parameters:

• df_impact (pd.DataFrame) – Substitution-impact table produced by AAMut.run().

• top_n (int, default=20) – Number of most sensitive scales to show.

• ax (Axes, optional) – Pre-defined Axes object to plot on. If None, a new one is created.

• figsize (tuple, default=(6, 5)) – Figure dimensions (width, height) in inches (used when ax is None).

• color (str, optional) – Bar color. If None, the TMD color is used.

Returns:

• fig (Figure) – Figure object containing the plot.

• ax (Axes) – Axes object of the scale-ranking plot.

Notes

• Returned as a (fig, ax) pair (see AAMutPlot for the shared return contract).

Examples

:meth:AAMutPlot.scale_ranking plots the most substitution-sensitive scales.

import matplotlib.pyplot as plt
import aaanalysis as aa
aa.plot_settings()
df_impact = aa.AAMut().run(from_aa=["M", "L", "K"], to_aa=["V", "A", "D"])
aa.AAMutPlot().scale_ranking(df_impact=df_impact, top_n=10)
plt.tight_layout()
plt.show()