Visualizing data with matplotlib

The module visualize_matplotlib contains functions to easily visualize Einstein Toolkit data with matplotlib (Reference on kuibit.visualize_matplotlib).


Visualizations are a subjective matter: different people have different tastes, and it is impossible to have one-size-fits-all solutions. visualize_matplotlib comes with convenient functions to quickly visualize data, but the module has default values that are opinionated. Where possible, customization has been exposed for your convenience. However, some settings and choices cannot be changed.


Time and frequency series in kuibit can be plotted with matplotlib native functions. For example,

# Assuming rho_b is a TimeSeries

import matplotlib.pyplot as plt


Grid functions

The main way to visualize grid data is with the functions plot_color and plot_contourf. The first plots directly the values of the given data, the second plots contours (filled). The functions support a large variety of inputs:

  • NumPy arrays, with coordintes specified by the coordinates keyword.

  • UniformGridData: it will be plotted over the entire domain, unless the keywords x0, x1 and shape are provided. These specify the extent of the grid to be plotted. shape is essentially the resolution of the plot. See documentation on grid data for details.

  • HierarchicalGridData: it will be plotted over the entire domain, with resolution provided by shape. The keywords x0, x1 can change the grid extent.

  • BaseOneGridFunction: same as HierarchicalGridData, but you also have to provide the iteration with the iteration keyword.

A table at the end of this document sums up these options. shape, x0, x1 are all 2D lists or tuples.

The functions take additional arguments (for example, the labels for the axes). Details can be found in the reference material (Reference on kuibit.visualize_matplotlib).


Mask information will be lost upon resampling. If you want masks, you need to provide the UniformGridData you want to plot.

Plots can be enanched by adding a colorbar with plot_colorbar (or passing the colorbar option).

The function plot_contour() plots the contours without filling. It requires the number of levels, or an array that specify where the levels are.

With plot_components_boundaries(), you can plot the grid structure. The function takes a HierarchicalGridData as argument as plots its grid structure. By defaults, ghost zones are not included, but they can be passing remove_ghosts=False. The function plots the boundaries of the single components, but when multiple components can be merged into a single refinement level, then only the outer boundary is plotted (a notable case in which this is currently not possible is when there are multiple refinement centers).


Apparent horizons can be plotted with plot_horizon(). This function takes the 2D shape of an horizon as returned by shape_outline_at_iteration() or shape_outline_at_time(). There are two wrappers around this function to simplify plotting horizons on the usual planes xy, xz, and yz: plot_horizon_on_plane_at_iteration() and plot_horizon_on_plane_at_time(). These take an OneHorizon object, the iteration/time that has to be plotted, and the plane over which you are plotting. All these functions take also the color of the horizon, the color of its boundary, and the opacity (alpha).


When you take a cross section (an outline) of an horizon, kuibit finds points that are within a threshold to the plane that cuts the surface. However, the way points are distributed on apparent horizons is highly non-uniform. So, if you are cutting the horizon along an axis that is not one of the coordinate ones (for the horizon), it is likely that too few points will be close enough to the intersecting plane, resulting in a malformed or absent outline. In some distant future, kuibit will perform interpolations to solve this problem.

Other utilities


The default settings in matplotlib are not great (e.g., the text is typically too small). The function setup_matplotlib() sets some better defaults. Since “better” is relative, the function takes an optional argument params. This has to be dictionary with keys the parameters in matplotlib and values their new values. This can be used to override some of the defaults in setup_matplotlib().


The function setup_matplotlib() simply updates the settings in matplotlib. It does not have any other effect.


The function add_text_to_corner() annotates a figure adding a label. The location of the label can be specified with the node argument. This is identified with cardinal point (N,S,W, or E) or a combination of two of them. For example, the default behavior is to place the text in the bottom right corner (corresponding to South-East–SE). The distance from the border can also be customized by passing the offset argument.


The save() function saves the figure to file. The function takes the path of the output and saves the current figure. If the file has extension .tikz, then tikzplotlib instead of matplotlib is used to save the figure. This results in a PGFPlots/TikZ ASCII file ready to be compiled with LaTeX.

preprocess_plot and preprocess_plot_grid

preprocess_plot() and preprocess_plot_grid() are two decorators. The first one adds support for passing figure and axis to a function. Let us see how it works:

from kuibit.visualize_matplotlib import preprocess_plot

def my_plot(data, figure=None, axis=None):
    # My plotting, for example

What preprocess_plot() is the following: if the user provides figure and/or axis, then those are used. If the user does not provide those arguments, then the current one are used. This is roughly equivalent to checking if figure is None and if it is, then set figure = plt.gcf().

The second decorator is preprocess_plot_grid(). With this, you can forget about all the classes defined in kuibit and simply plot NumPy arrays. In more details: when you work with kuibit, you will typically work with HierarchicalGridData and UniformGridData. These are complex structures that cannot be plotted immediately. The decorator preprocess_plot_grid() takes care of all the boilerplate needed to work with those two classes so that the user can provide a OneGridFunction, UniformGridData, a HierarchicalGridData, or a NumPy array. Let us see how it works:

from kuibit.visualize_matplotlib import preprocess_plot_grid

def my_plot(data, coordinates=None, figure=None, axis=None):
    # My plotting, for example

# bob here is a H5OneGridFunction

# Some of the arguments are optional
my_plot(bob, shape=[500, 500], iteration=0, x0=[0, 0], x1=[1,1])

# rho_b here is a HierarchicalGridData

# Some of the arguments are optional
my_plot(rho_b, shape=[500, 500], x0=[0, 0], x1=[1,1])

# press here is a UniformGridData

# Some of the arguments are optional
my_plot(press, x0=[0,0])

# eps here is a NumPy array

Depending on the type of object passed, additional arguments might be needed. See table below for details.


Arguments needed

Arguments supported


iteration, shape

x0, x1, resample



x0, x1, resample


shape (if x0 or x1 are passed)

x0, x1, resample

2D NumPy array

coordinates (depending on the specific function)