BornAgain provides MatPlotLib-based helpers for plotting simulation results,
measurements, and derived data. They are available directly from the Python
package through import bornagain as ba.
This reference explains which helper to choose and how to combine them.
Every plotting function has a docstring: use help(ba.plot_curve), for
example, for its signature, parameter descriptions, and defaults.
| Function | Use |
|---|---|
plot_datafield |
One Datafield, as a curve or heat map. |
plot_array |
An array with physical axes from a frame. |
plot_curve |
One 1D curve, optionally with measurements. |
plot_multicurve |
Several 1D curves with optional measurements. |
plot_heatmap |
One 2D image. |
plot_mask_overlay |
Mark excluded pixels on an existing image. |
plot2d_to_row |
Images in one row, with a shared color scale. |
plot2d_to_grid |
Images in a grid, with a shared color scale. |
plot_ff_to_row |
Form-factor images with a shared color scale. |
plot_material_profile |
One or more real SLD profiles. |
export |
Save the current figure to an image file. |
parse_commandline |
Read a script’s key=value options. |
For live fit progress, see Fit monitoring. For the
sample geometry rather than its scattering result, use
showSample3D.
import bornagain as ba
ba.plot_datafield(result)
ba.plt.show()
In a standalone script, call ba.plt.show() once after preparing all plots
to display the open figures, not after each plotting function. Short
snippets below may omit it. When only saving files, no show() is needed;
Jupyter notebooks normally display figures automatically.
ba.plt is MatPlotLib’s pyplot module. To put a BornAgain plot into your
own figure, create a subplot and pass it through ax:
fig, ax = ba.plt.subplots()
ba.plot_datafield(result, ax=ax)
ax.grid(True)
ba.export("result.svg")
For a heat map on a supplied subplot, request its colorbar with
with_cb=True. For several curve calls on one subplot, finish with
ax.legend().
You can also export data or convert them to NumPy arrays and use another plotting tool.