Hello,
I developed a new package to analyze Einstein Toolkit simulations, kuibit [0,1]. kuibit is a Python3.6+ code that I built from scratch following the same design (and in various instances, implementation details too) of Wolfgang Kastaun's PostCactus.
kuibit provides high-level data types to easily work with grid functions, time and frequency series, gravitational waves, and so on. It also has readers to effortlessly access simulation data with full support for HDF5 and ASCII output (1D, 2D, 3D grid data, scalar data, reductions, horizon data, ...). You can find a reasonably comprehensive list of features in the documentation [2] or a high-level summary in the frontpage of the docs [3].
One of the main reasons I wrote this code is for other people to use it. Our group (University of Arizona) is a young one and we don't have any sophisticated toolchain to analyze simulation data. Without suitable tools, post-processing simulations can be a daunting task for those that are new to the Einstein Toolkit.
Given that I want other people to use kuibit, I made the effort to make the code user and developer-friendly. For users, there is documentation [4] with examples and small tutorials. Also, the package is on PyPI so it can be easily installed and updated. For developers, the entire codebase has unit tests and continuous integration [5], there are extensive comments, and the style of the code is rather verbose to help developers understand what is going on. The continuous integration also lints the code, performs static analysis, and generates the documentation, reducing the maintenance costs.
kuibit takes care of all the low-level details need to deal with simulation data, so it greatly lowers the entry barrier in using the Einstein Toolkit. I believe that this, along with the care I put in making the code accessible to other developers, makes kuibit a good candidate for inclusion in the Einstein Toolkit.
The main problem with kuibit is that it is a new code: regardless of all the tests I wrote, there will be bugs, unergonomic interfaces, and performance issues. kuibit needs to be tested with several real-world projects and cross-checked with other codes.
I am happy to give a short introduction to kuibit during a weekly call if there's interest. In the meantime, the code is available here: https://github.com/Sbozzolo/kuibit
Best regards, Gabriele Bozzola
[0] https://github.com/Sbozzolo/kuibit [1] https://github.com/Sbozzolo/kuibit#what-is-a-kuibit [2] https://sbozzolo.github.io/kuibit/features.html [3] https://sbozzolo.github.io/kuibit/#summary-of-features [4] https://sbozzolo.github.io/kuibit/ [5] https://github.com/Sbozzolo/kuibit/actions
Gabriele
This package looks quite interesting. Would you be interested in giving a show-and-tell presentation at some point, demonstrating how you use the package? I would be interested in attending.
-erik
On Thu, Jan 14, 2021 at 12:18 AM Gabriele Bozzola bozzola.gabriele@gmail.com wrote:
Hello,
I developed a new package to analyze Einstein Toolkit simulations, kuibit [0,1]. kuibit is a Python3.6+ code that I built from scratch following the same design (and in various instances, implementation details too) of Wolfgang Kastaun's PostCactus.
kuibit provides high-level data types to easily work with grid functions, time and frequency series, gravitational waves, and so on. It also has readers to effortlessly access simulation data with full support for HDF5 and ASCII output (1D, 2D, 3D grid data, scalar data, reductions, horizon data, ...). You can find a reasonably comprehensive list of features in the documentation [2] or a high-level summary in the frontpage of the docs [3].
One of the main reasons I wrote this code is for other people to use it. Our group (University of Arizona) is a young one and we don't have any sophisticated toolchain to analyze simulation data. Without suitable tools, post-processing simulations can be a daunting task for those that are new to the Einstein Toolkit.
Given that I want other people to use kuibit, I made the effort to make the code user and developer-friendly. For users, there is documentation [4] with examples and small tutorials. Also, the package is on PyPI so it can be easily installed and updated. For developers, the entire codebase has unit tests and continuous integration [5], there are extensive comments, and the style of the code is rather verbose to help developers understand what is going on. The continuous integration also lints the code, performs static analysis, and generates the documentation, reducing the maintenance costs.
kuibit takes care of all the low-level details need to deal with simulation data, so it greatly lowers the entry barrier in using the Einstein Toolkit. I believe that this, along with the care I put in making the code accessible to other developers, makes kuibit a good candidate for inclusion in the Einstein Toolkit.
The main problem with kuibit is that it is a new code: regardless of all the tests I wrote, there will be bugs, unergonomic interfaces, and performance issues. kuibit needs to be tested with several real-world projects and cross-checked with other codes.
I am happy to give a short introduction to kuibit during a weekly call if there's interest. In the meantime, the code is available here: https://github.com/Sbozzolo/kuibit
Best regards, Gabriele Bozzola
[0] https://github.com/Sbozzolo/kuibit [1] https://github.com/Sbozzolo/kuibit#what-is-a-kuibit [2] https://sbozzolo.github.io/kuibit/features.html [3] https://sbozzolo.github.io/kuibit/#summary-of-features [4] https://sbozzolo.github.io/kuibit/ [5] https://github.com/Sbozzolo/kuibit/actions _______________________________________________ Users mailing list Users@einsteintoolkit.org http://lists.einsteintoolkit.org/mailman/listinfo/users
Excellent work Gabriele! I am excited to try out the new package. I agree with Erik -- would be nice to see a demonstration of this in action!
-Zach
* * * Prof. Zachariah Etienne Physics & Astronomy Dept. West Virginia University http://astro.phys.wvu.edu/zetienne/ http://blackholesathome.net https://blackholesathome.net
On Thu, Jan 14, 2021 at 8:25 AM Erik Schnetter schnetter@cct.lsu.edu wrote:
Gabriele
This package looks quite interesting. Would you be interested in giving a show-and-tell presentation at some point, demonstrating how you use the package? I would be interested in attending.
-erik
On Thu, Jan 14, 2021 at 12:18 AM Gabriele Bozzola bozzola.gabriele@gmail.com wrote:
Hello,
I developed a new package to analyze Einstein Toolkit simulations,
kuibit [0,1].
kuibit is a Python3.6+ code that I built from scratch following the same
design
(and in various instances, implementation details too) of Wolfgang
Kastaun's
PostCactus.
kuibit provides high-level data types to easily work with grid
functions, time
and frequency series, gravitational waves, and so on. It also has
readers to
effortlessly access simulation data with full support for HDF5 and ASCII
output
(1D, 2D, 3D grid data, scalar data, reductions, horizon data, ...). You
can find
a reasonably comprehensive list of features in the documentation [2] or a high-level summary in the frontpage of the docs [3].
One of the main reasons I wrote this code is for other people to use it. Our group (University of Arizona) is a young one and we don't have any
sophisticated
toolchain to analyze simulation data. Without suitable tools,
post-processing
simulations can be a daunting task for those that are new to the
Einstein Toolkit.
Given that I want other people to use kuibit, I made the effort to make
the code user
and developer-friendly. For users, there is documentation [4] with
examples and
small tutorials. Also, the package is on PyPI so it can be easily
installed and updated.
For developers, the entire codebase has unit tests and continuous
integration [5],
there are extensive comments, and the style of the code is rather verbose to help developers understand what is going on. The continuous
integration also
lints the code, performs static analysis, and generates the
documentation,
reducing the maintenance costs.
kuibit takes care of all the low-level details need to deal with
simulation data, so
it greatly lowers the entry barrier in using the Einstein Toolkit. I
believe that this,
along with the care I put in making the code accessible to other
developers,
makes kuibit a good candidate for inclusion in the Einstein Toolkit.
The main problem with kuibit is that it is a new code: regardless of all
the
tests I wrote, there will be bugs, unergonomic interfaces, and
performance issues.
kuibit needs to be tested with several real-world projects and
cross-checked with
other codes.
I am happy to give a short introduction to kuibit during a weekly call
if there's
interest. In the meantime, the code is available here: https://github.com/Sbozzolo/kuibit
Best regards, Gabriele Bozzola
[0] https://github.com/Sbozzolo/kuibit [1] https://github.com/Sbozzolo/kuibit#what-is-a-kuibit [2] https://sbozzolo.github.io/kuibit/features.html [3] https://sbozzolo.github.io/kuibit/#summary-of-features [4] https://sbozzolo.github.io/kuibit/ [5] https://github.com/Sbozzolo/kuibit/actions _______________________________________________ Users mailing list Users@einsteintoolkit.org http://lists.einsteintoolkit.org/mailman/listinfo/users
-- Erik Schnetter schnetter@cct.lsu.edu http://www.perimeterinstitute.ca/personal/eschnetter/ _______________________________________________ Users mailing list Users@einsteintoolkit.org http://lists.einsteintoolkit.org/mailman/listinfo/users
Gabriele Bozzola, thank you very much for sharing this new and very interesting tool! I'm also interested in attending a presentation about it.
Cheers, Bruno
Il giorno gio 14 gen 2021 alle ore 06:18 Gabriele Bozzola < bozzola.gabriele@gmail.com> ha scritto:
Hello,
I developed a new package to analyze Einstein Toolkit simulations, kuibit [0,1]. kuibit is a Python3.6+ code that I built from scratch following the same design (and in various instances, implementation details too) of Wolfgang Kastaun's PostCactus.
kuibit provides high-level data types to easily work with grid functions, time and frequency series, gravitational waves, and so on. It also has readers to effortlessly access simulation data with full support for HDF5 and ASCII output (1D, 2D, 3D grid data, scalar data, reductions, horizon data, ...). You can find a reasonably comprehensive list of features in the documentation [2] or a high-level summary in the frontpage of the docs [3].
One of the main reasons I wrote this code is for other people to use it. Our group (University of Arizona) is a young one and we don't have any sophisticated toolchain to analyze simulation data. Without suitable tools, post-processing simulations can be a daunting task for those that are new to the Einstein Toolkit.
Given that I want other people to use kuibit, I made the effort to make the code user and developer-friendly. For users, there is documentation [4] with examples and small tutorials. Also, the package is on PyPI so it can be easily installed and updated. For developers, the entire codebase has unit tests and continuous integration [5], there are extensive comments, and the style of the code is rather verbose to help developers understand what is going on. The continuous integration also lints the code, performs static analysis, and generates the documentation, reducing the maintenance costs.
kuibit takes care of all the low-level details need to deal with simulation data, so it greatly lowers the entry barrier in using the Einstein Toolkit. I believe that this, along with the care I put in making the code accessible to other developers, makes kuibit a good candidate for inclusion in the Einstein Toolkit.
The main problem with kuibit is that it is a new code: regardless of all the tests I wrote, there will be bugs, unergonomic interfaces, and performance issues. kuibit needs to be tested with several real-world projects and cross-checked with other codes.
I am happy to give a short introduction to kuibit during a weekly call if there's interest. In the meantime, the code is available here: https://github.com/Sbozzolo/kuibit
Best regards, Gabriele Bozzola
[0] https://github.com/Sbozzolo/kuibit [1] https://github.com/Sbozzolo/kuibit#what-is-a-kuibit [2] https://sbozzolo.github.io/kuibit/features.html [3] https://sbozzolo.github.io/kuibit/#summary-of-features [4] https://sbozzolo.github.io/kuibit/ [5] https://github.com/Sbozzolo/kuibit/actions _______________________________________________ Users mailing list Users@einsteintoolkit.org http://lists.einsteintoolkit.org/mailman/listinfo/users
users@lists.einsteintoolkit.org
-
Bruno Giacomazzo -
Erik Schnetter -
Gabriele Bozzola -
Zach Etienne