First steps with kuibit

kuibit is a powerful library to analyze simulations performed with the Einstein Toolkit, and other Cactus-based codes. The package has several features and it can appear intimidating at first. In this page, we introduce you to the most important concepts and recommend a possible path for you to learn how to use the tool. Some of these topics are covered in the YouTube series Using kuibit.

A basic knowledge of Python and the command-line is required to follow the content of this page. However, you don’t need to know much about the Einstein Toolkit to be able to get started.

What is kuibit?

kuibit is a Python library to support you in your science. You mostly interact with kuibit by importing its modules into Python scripts, Jupyter notebooks, REPLs, or anything that can interpret Python (at least version 3.6.1).

For example, you can check if you have kuibit correctly installed with the following piece of code:

import kuibit

You can save this snippet into a file and run it as python3, or execute it as a cell in a Jupyter notebook, or anything else that you might prefer.

kuibit is designed to support all the possible workflows.

How to install kuibit?

If you found that you did not have kuibit installed, you can easily fix the problem by running in your shell

pip3 install -U kuibit

This will ensure that you have latest stable version of kuibit available. Now, if you try to run again the code above, it should print the version of kuibit installed.

Test out kuibit with the examples

kuibit comes with a lot of examples that are ready to be used for science. Examples are provided as Python scripts that have to be executed from the command-line. If you want to take full advantage of the examples, you can install them system-wide following the recommendations on how to use the examples page. Alternatively, you can simply save them in the directory you want to analyze.

You can do a lot of things with the examples. The complete list with a short description is available on GitHub. Examples have also their space in the documentation (see, Scripts and Movies).

Here, we will assume you have a simulation in the folder my_sim (typically it will contain the subdirectories output-0000, output-0001, and so on). We will refer to this as the simulation directory, or the data directory (shortened as datadir).

There are two categories of examples: scripts, and movies.


Scripts are valid Python codes that use kuibit to achieve a goal: most of the scripts are targeted towards generating a plot. All the examples share some common features. In the following, we will consider to illustrate how examples work.

  • The examples are command-line scripts. If your examples are in your PATH, you can call them in your shell with

If you saved them in the folder,
  • The examples are designed to be as general and flexible as possible. For instance, they should work on any simulation data, and they provide a lot of options to configure them.

  • The examples have the --help flag. --help

This will print something like: plots a given grid function.

By default, no interpolation is performed so the image may look pixelated.
There are two available modes of interpolation. The first is activated
with --multilinear-interpolation. With this, the data from the simulation
is interpolated with a multilinear interpolation onto the plotting grid.
This is accurate and uses all the information available, but it is slow.
A second way to perform interpolation is passing a --interpolation-method
argument (e.g., bicubic). With this, the plotting data is interpolated.
This is much faster but it is not as accurate.

[-h] [-c CONFIGFILE] [-v] [--datadir DATADIR] [--outdir OUTDIR]
[--ignore-symlinks] [--pickle-file PICKLE_FILE] [--resolution RESOLUTION]
[-x0 ORIGIN ORIGIN] [-x1 CORNER CORNER] [--plane {xy,xz,yz}] [--figname
FIGNAME] [--fig-extension FIG_EXTENSION] [--tikz-clean-figure] [--ah-show]
[--ah-color AH_COLOR] [--ah-edge-color AH_EDGE_COLOR] [--ah-alpha AH_ALPHA]
[--ah-time-tolerance AH_TIME_TOLERANCE] --variable VARIABLE [--iteration
ITERATION] [--multilinear-interpolate] [--interpolation-method
INTERPOLATION_METHOD] [--colorbar] [--logscale] [--vmin VMIN] [--vmax VMAX]

optional arguments:
-h, --help            show this help message and exit
Config file path
-v, --verbose         Enable verbose output
--datadir DATADIR     Data directory
--outdir OUTDIR       Output directory
--ignore-symlinks     Ignore symlinks in the data directory
--pickle-file PICKLE_FILE   Read/write SimDir to this file
--resolution RESOLUTION     Resolution of the grid in number of points (default: 500)
--plane {xy,xz,yz}    Plane to plot (default: xy)
--figname FIGNAME     Name of the output figure (not including the extension).
--fig-extension FIG_EXTENSION
Extension of the output figure (default: png). [env var: KBIT_FIG_EXTENSION]
--tikz-clean-figure   Reduce the size of the figure when saving to a TikZ file.
--variable VARIABLE   Variable to plot.
--iteration ITERATION
Iteration to plot. If -1, the latest.
Whether to interpolate to smooth data with multilinear interpolation before plotting.
--interpolation-method INTERPOLATION_METHOD
Interpolation method for the plot. See docs of np.imshow. (default: none)
--colorbar            Whether to draw the color bar.
--logscale            Whether to use log scale.
--vmin VMIN           Minimum value of the variable. If logscale is True, this has to be the log.
--vmax VMAX           Maximum value of the variable. If logscale is True, this has to be the log.
--absolute            Whether to take the absolute value.

Horizon options:
--ah-show             Plot apparent horizons.
--ah-color AH_COLOR   Color name for horizons (default is 'k').
--ah-edge-color AH_EDGE_COLOR
Color name for horizons boundary (default is 'w').
--ah-alpha AH_ALPHA   Alpha (transparency) for apparent horizons (default: 1)
--ah-time-tolerance AH_TIME_TOLERANCE
Tolerance for matching horizon time [simulation units] (default is '0.1').

Args that start with '--' (eg. -v) can also be set in a config file
(specified via -c). Config file syntax allows: key=value, flag=true,
stuff=[a,b,c] (for details, see syntax at If an arg
is specified in more than one place, then commandline values override
environment variables which override config file values which override

This is a lot to digest, so let’s focus on the most important flags.

  1. The header describes briefly what the script is supposed to do and discusses some peculiarities.

  2. --datadir: Where the data lives. In our case, it is the folder my_sim. You can specify the top-level folder, or the specific subfolder (e.g., my_sim/output-0000), if you know exactly where the iteration you are interested in lives. Specifying the subfolder speeds up the discovery algorithm (organizing the data across the different subdirectories). This can be very significant for large simulations (alternatively, you can use pickles, see later).

  3. --outdir: Where to save the output. By default, this is the location where the script is run. If --figname is not specified, the output will have a default name

  4. --pickle-file: Scanning a simulation directory and all its subdirectories is an expensive operation. If the same simulation is analyzed with different scripts, it is convenient to save some information to disk. This is saved as Python pickle file. Passing this flag means that the pickle file is used and the directories are not scanned. If there’s a mismatch between what the pickle file contains and the actual directories, that would possibly lead to an error. Hence, it is best to use pickle file only on simulations that have completed, or the it is best to regenerate the pickle file every time the data changes. This can be done with the picklify utility.

  5. --fig-extension: By default, images are saves as pngs. If tikz is passed, the images are exported to LaTeX. This variable can be set with a environmental variable KBIT_FIG_EXTENSION.

  6. --iteration: This is which iteration you want to plot among the ones available in your data. Other examples will help you find which iterations are available. If you don’t specify this quantity, the latest iteration available will be used.

  7. --variable: This is the name of the grid function that you want to plot. This is the same name you would write in the par file or call in your thorn. Examples might be rho, or alp. In this case, 2D files are used (HDF5 preferred), so, you need to have output those variables for the plane you are interested in.

  8. Finally, the footer informs us that we can use configuration files. For instance, instead of passing variables via the command line, we can write a text file with the same information: e.g., instead of --vmin 1 , we would create a text file with content vmin=1 named, for instance plot.conf, and pass --configfile plot.conf. Configuration files and command-line arguments can be mixed, but command-line arguments will have the precedence.

Combining all the different flags, a possible invocation of the example would be: --datadir my_sim --variable rho -x0 -100 -100 -x1 100 100
                 --resolution 500 --logscale --colorbar --outdir plots
                 --vmin -10 --vmax 0 --iteration 0 --plane xy
                 --interpolation-method bicubic --verbose


The examples in kuibit use motionpicture to produce videos. motionpicture is a Python package that helps developers render movies from single frames. See A quick introduction to motionpicture for more details. motionpicture requires movie files to work, and kuibit provides some. For example, to make a 2D movie of any grid function, you can use the grid_var movie file. Then, the flags are similar to the ones discussed in the previous section, with the difference that movies are produced with the mopi binary:

mopi grid_var --datadir my_sim --variable rho -x0 -100 -100 -x1 100 100
              --resolution 500 --logscale --colorbar --outdir plots
               --vmin -10 --vmax 0 --iteration 0 --plane xy
               --interpolation-method bicubic --verbose --parallel
               --min-frame 0 --max-frame 10240 --fps 60

Here, we also added some options for mopi (which can be explored with mopi --help). In particular, --parallel ensures that all the cores on the machine are used to render the various frames.

Reproduce the examples

Now that you have run the examples, you can move to the next step, which is to start using the library. A useful pedagogical avenue to learn about kuibit is to consider the examples as “solved problems”. You can pick some of the examples and try to reproduce the same result. For instance, you may want to try to plot a 2D grid variable. To do that, you can read the relevant Tutorials and Usage pages. Those will instruct you about the details of kuibit as a library.


The examples aim to be general, so they contain some boilerplate and several if/else statements. These are not essential.


Let’s walk through one example: let’s try to reproduce Since we want to work with grid data, the relevant tutorials are the one on SimDir and the one one grid data.

First, we need import the relevant modules. In this case, we are only going to need simdir and visualize_matplotlib. We are also going to import matplotlib.

from kuibit import simdir sd
from kuibit import visualize_matplotlib as viz
import matplotlib.pyplot as plt

Next, we initialize a SimDir object. This is how all the codes start, since SimDir is how we interface with the simulation.

s = SimDir("my_sim")

The page Getting started with SimDir contain useful information about this object.

Now, we can specify some quantities of interest, like: what variable/iteration do we want to read, or what plane, and so on.

VAR = "rho"
PLANE = "xy"
X0 = -100, -100
X1 = 100, 100
SHAPE = 500, 500
VMIN, VMAX = -10, 1

X0 and X1 are respectively the lower and the topmost corner of the region we want to plot, in computational units (the same units of the simulation). LOGSCALE will specify if we want to use base-10 logarithm or not, and VMIN, VMAX define the range where we want to plot (in log). SHAPE will be discussed in the next paragraph.

We can finally read the variable as HierarchicalGridData. This is a complex object containing all the various components and refinement levels. This object cannot be plotted directly, but it needs to be resampled to a UniformGridData, which is a simpler object that contains a regular grid and data defined on this grid. The variable SHAPE controls the resolution of this grid.

reader = s.gridfunctions[PLANE][VAR]
var = reader[ITERATION]

You can plot this quantity directly with plot_color():


You can use everything you know about matplotlib. For example, you can add a title to the plot:

plt.plot("This is my first plot")

This is (almost) the minimum code possible to plot any given iteration of any given grid function. You should now try to run it and compare it with the output with the example. Next, you can have a look at the code of example to see what other options are available.


Let us use motionpicture to make a movie out of this. See A quick introduction to motionpicture for more details.

To use mopi, we first to write a movie file, which is just a regular Python file that defines a class MOPIMovie with three methods. The first is __init__(self, args)_, which takes a Namespace containing the command-line arguments passed (we are not going to use any here). The __init___ does all the preparatory work needed to generate frames. In this case, we want to initialize the SimDir and the reader, which are the common work needed to make a frame.

from kuibit.simdir import SimDir as sd
from kuibit import visualize_matplotlib as viz
import matplotlib.pyplot as plt

class MOPIMovie():
    def __init__(self, args):

        VAR = "rho"
        PLANE = "xy"

        self.X0 = -100, -100
        self.X1 = 100, 100
        self.SHAPE = 500, 500
        self.LOGSCALE = True
        self.VMIN, self.VMAX = -10, 1

        self.reader = sim("my_sim").gridfunctions[PLANE][VAR]

We made X0, X1, SHAPE, and reader attributes (with self.) because we want to access them in the other methods. The second method is get_frames(self) which defines the list of frames that compose the movie. In this case, we are going to use the iterations available

def get_frames(self):
    return self.reader.available_iterations

Finally, we need a method make_frame(self, path, iteration) that plots one frame. For that, we essentially copy what we have done with the previous script:

def make_frame(self, path, iteration):
    # We need to clear up pre-existing figures


    # Alternatively
    # plot_color(self.reader,
    #            iteration=iteration,
    #            shape=RESOLUTION,
    #            x0=X0,
    #            x1=X1)

    plt.title(f"Iteration = {iteration}")


This is it! Now you can save this as a file and run it with mopi -m file_name.

You can make this script more flexible by adding command-line arguments, so that you don’t have to modify the file when you want to change parameters.


Here we collect vocabulary that you might find used in kuibit.

  • Datadir: where the output of simulation lives.

  • Grid function: simulation data defined on the grid.

  • HierarchicalGridData: collection of components at possibly different refinements that form a grid with several levels.

  • motionpicture (mopi): external Python program to render movies.

  • Origin (corner): bottom left (top right) cell in a center-centered grid.

  • SimDir: fundamental interface to the data in the simulation.

  • Outdir: where to save the output of an example.

  • Pickle: binary file where a SimDir can be saved.

  • TikZ: package to render graphics in LaTeX. kuibit can optionally output in this format.

  • UniformGridData: data defined on a uniform grid.

Still confused?

Feel free to ask questions in the Telegram group or send an email to