shnitsel.vis.plot

Submodules

Functions

biplot_kde(frames[, at1, at2, at3, at4, geo_filter, ...])

Generates a biplot that visualizes PCA projections and kernel density estimates (KDE)

plot_cdf_for_kde(z, level[, ax])

ski_plots(spectra)

Plot spectra for different times on top of each other,

pcm_plots(spectra)

Represent fosc as colour in a plot of fosc against time and energy.

Package Contents

biplot_kde(frames, at1=0, at2=1, at3=None, at4=None, geo_filter=None, levels=None, scatter_color='time', fill=True, nbins=4, mean=False)

Generates a biplot that visualizes PCA projections and kernel density estimates (KDE) of a property (distance, angle, dihedral angle) describing the geometry of specified atoms. The property is chosen based on the number of atoms specified:

  • 2 atoms => distance

  • 3 atoms => angle

  • 4 atoms => dihedral angle

Parameters:

  • frames – A dataset containing trajectory frames with atomic coordinates.

  • at1 (int) – Indices of the first, second, third and fourth atoms for geometric property calculation.

  • at2 (int) – Indices of the first, second, third and fourth atoms for geometric property calculation.

  • at3 (int | None) – Indices of the first, second, third and fourth atoms for geometric property calculation.

  • at4 (int | None) – Indices of the first, second, third and fourth atoms for geometric property calculation.

  • geo_filter (list[tuple[float, float]] | None) – A list of tuples representing ranges. A KDE is plotted for each range, indicating the distribution of points for which the value of the geometry feature falls in that range.

  • levels (int | list[float] | None) – Contour levels for the KDE plot. Defaults to [0.08, 1]. This parameter is passed to matplotlib.axes.Axes.contour.

  • scatter_color (Literal['time', 'geo']) – Must be one of ‘time’ or ‘geo’. If ‘time’, the scatter-points will be colored based on the time coordinate; if ‘geo’, the scatter-points will be colored based on the relevant geometry feature (see above).

  • fill (bool) – Whether to plot filled contours (fill=True, uses ax.contourf) or just contour lines (fill=False, uses ax.contour).

  • nbins – number of bins to be visualized, must be an integer between 1 and 4

Returns:

The computed KDE data for the atom-atom distance distribution.

Return type:

kde_dat

Notes

  • Computes a geometric property of the specified atoms across all frames.

  • Uses kernel density estimation (KDE) to analyze the distance distributions.

  • Performs PCA on trajectory pairwise distances and visualizes clustering of structural changes.

  • Produces a figure with PCA projection, cluster analysis, and KDE plots.

plot_cdf_for_kde(z, level, ax=None)
ski_plots(spectra)

Plot spectra for different times on top of each other, along with a dashed line that tracks the maximum. One plot per statecomb; plots stacked vertically. Expected to be used on data produced by spectra.spectra_all_times.

Parameters:

spectra (xarray.DataArray) – DataArray containing fosc values organized along ‘energy’, ‘time’ and ‘statecomb’ dimensions.

Return type:

Figure object corresponding to plot.

Examples

>>> import shnitsel as st
>>> from shnitsel.core.plot import spectra3d
>>> spectra_data = (
        st.io.read(path)
        .st.get_inter_state()
        .st.assign_fosc()
        .st.spectra_all_times())
>>> spectra3d.ski_plots(spectra_data)
pcm_plots(spectra)

Represent fosc as colour in a plot of fosc against time and energy. The colour scale is logarithmic. One plot per statecomb; plots stacked horizontally. Expected to be used on data produced by spectra.spectra_all_times.

Parameters:

spectra (xarray.DataArray) – DataArray containing fosc values organized along ‘energy’, ‘time’ and ‘statecomb’ dimensions.

Return type:

Figure object corresponding to plot.

Examples

>>> import shnitsel as st
>>> from shnitsel.core.plot import spectra3d
>>> spectra_data = (
        st.io.read(path)
        .st.get_inter_state()
        .st.assign_fosc()
        .st.spectra_all_times())
>>> spectra3d.pcm_plots(spectra_data)