Images in BornAgain examples are generated using the Python library Matplotlib.
Default settings can be overridden by function arguments, Matplotlib ressources, or environment variables.
In the BornAgain example scripts, Matplotlib is invoked through the following functions:
plot_simulation_result(result, **plotargs) plots one 1D or 2D simulation result.
The argument result is of type Datafield,
as returned by the function Simulation.simulate().
The optional plotargs arguments are discussed below.
plot_multicurve(results, **plotargs) plots several 1D simulation results into one frame.
The argument results is a list of Datafields.
The optional plotargs arguments are discussed below.
make_plot(results, ncol, **plotargs) plots several simulation results into as many different frames.
The argument results is a list of Datafields.
The argument ncol is the number of columns of the grid that holds the plot frames.
The optional plotargs arguments are discussed below.
In the functions documented above, **plotargs stands for optional keyword arguments.
These are supplied either directly like in
result = simulation.simulate()
bp.plot_simulation_result(result, cmap='viridis', legendloc='lower right')
bp.plt.show()
or through a dictionary variable like in
result = simulation.simulate()
plotargs = {}
plotargs['aspect'] = 'auto'
plotargs['intensity_min'] = 1e-12
plotargs['intensity_max'] = 1e2
bp.plot_simulation_result(result, **plotargs)
bp.plt.show()
Support keyword arguments include
intensity_min,intensity_max,xlabel,ylabel,zlabel,legendloc: where a legend is placed.aspect: aspect ratio, default is ‘auto’, other possible values are ’equal’, or a number,cmap: heat map, see below.For other optional arguments that may or may not be respected, see the Matplotlib customizing tutorial.
The above documented functions are implemented in the Python module
ba_plot
that is typically imported with the line
from bornagain import ba_plot as bp
Matplotlib supports various color schemes for plotting color-coded pixel values in 2d:
Matplotlib color schemes tutorial.
It is generally recommended to use one of the five “perceptually uniform sequential” color schemes:
When plotting through ba_plot, then the color scheme is determined in this order:
plotargs arguments if there is one with keyword cmap, like cmap='jet';CMAP, if defined;Note that the variable image.cmap from
matplotlib.pyplot.rcParams
is ignored.
This is a concious choice - we expect an environment variable to be more convenient for
users that are not Matplotlib experts.
Text enclosed between dollar signs $...$ is interpreted as LaTeX markup.
It allows us to plot greek letters ($\alpha$), superscripts ($F^2$),
subscripts ($\phi_{\rm i}$), and special symbols ($90^\circ$).
By default, it is processed by Matplotlib’s own LaTeX processor.
However, if usetex mode is activated, then the original LaTeX text
processor is used, and labels are typeset in LaTeX’s computer modern fonts.
For usetex mode to work, it may be necessary to install extra software.
Under Debian, the required packages are texlive-latex-extra (and
dependencies), dvipng and cm-super-minimal. We have no clue how
to support usetex mode under Windows.
Original LaTeX fonts differ in many respects from Matplotlib’s default
Helvetica fonts. Fonts from these different families should not be mixed.
Therefore, in usetex mode it is important that all plot labels and titles
be enclosed in $...$, or for upright text ${\rm ...}$.
To activate usetex mode, do one of the following:
USETEX to True, orrc['text.usetex'] to True.