Hi again, Erik.
To better illustrate (and hopefully make working with BaikalETK easier for you), I just performed the second step of the NRPy+ development process on BaikalETK: creating a separate Python module for generating BaikalETK.
Simply do a git pull then from within, e.g., iPython, type:
import BaikalETK.BaikalETK_Pymodule as BE BE.BaikalETK_codegen(outrootdir="BaikalETK-validate")
The full function call allows for many parameters to be adjusted as desired: def BaikalETK_codegen(outrootdir = "BaikalETK/",
FD_order=4, # Finite difference order: even numbers only, starting with 2. 12 is generally unstable LapseCondition = "OnePlusLog", # Set the standard 1+log lapse condition ShiftCondition = "GammaDriving2ndOrder_NoCovariant", # Set the standard, second-order advecting-shift, # Gamma-driving shift condition add_stress_energy_source_terms = False, # Enable stress-energy terms? default_KO_strength = 0.1 # default Kreiss-Oliger dissipation strength; adjustable within thorn's param.ccl. ):
Admittedly, the BaikalETK_Pymodule isn't very... modular at this point. Instead it calls on a number of largely infrastructure-agnostic NRPy+ modules. Once there are modules within NRPy+ for, e.g., setting param.ccl etc, modules like this one will shrink and simplify considerably. This is one of the goals of the proposed ETK/NRPy+ development.
If you wish, you are free to modify either the Python module (git friendly) or the Jupyter notebook (not as git-friendly, but where the documentation is housed), but are required to update both in the end. I think you'd agree that any code updates should require corresponding documentation updates.
I haven't yet updated the BaikalETK Jupyter notebook to validate that its output agrees perfectly with the separate BaikalETK_Pymodule Python module (we call this "self-validation"), but I did confirm independently that only a couple of harmless whitespace differences appear in the generated C code between the BaikalETK notebook & BaikalETK_Pymodule.
Thanks again for your feedback.
-Zach
* * * Prof. Zachariah Etienne Physics & Astronomy Dept. West Virginia University http://astro.phys.wvu.edu/zetienne/ http://blackholesathome.net https://blackholesathome.net
On Mon, Nov 4, 2019 at 12:15 PM Zach Etienne zachetie@gmail.com wrote:
Hi Erik,
Thanks for the feedback, and sorry to hear about the frustration. Indeed BaikalETK is only a proof-of-principle code at the moment, in which the second stage of development (copying the code blocks in the Jupyter notebook to a proper, callable, git-friendly Python module) hasn't been completed yet. I'm hoping that with more resources to combine ETK & NRPy+ development, the full modularization of all the ETK Jupyter notebooks can be completed.
The only way I see is to undo my changes ("git checkout"), and then
pull your changes. In this case that's fine because there are no major changes, but if I had made changes I'd be in trouble.
I generally do a `git diff` if `git pull` complains about conflicts, and it's usually easy to see if anything significant has changed (and then do `git checkout` if not). Maybe if I chose a standard way of prefixing lines that are ignorable comments (e.g., "COMMENT: Finished BSSN symbolic expressions in") a simple script could be written that checks for trivial differences like these. Removing all output isn't a good idea as it would wipe away the "standard" output displayed on nbviewer ( https://nbviewer.jupyter.org/github/zachetienne/nrpytutorial/blob/master/NRP...) in case the user wants only to learn from the Jupyter documentation.
Also if in doubt, one can simply compare the thorn output from the master Jupyter notebook with your own version. We're making it easier to redirect all NRPy+ output to whatever directory you like, so that such diffs can be made quickly and easily.
-Zach
Prof. Zachariah Etienne Physics & Astronomy Dept. West Virginia University http://astro.phys.wvu.edu/zetienne/ http://blackholesathome.net https://blackholesathome.net
On Mon, Nov 4, 2019 at 11:56 AM Erik Schnetter schnetter@cct.lsu.edu wrote:
On Mon, Nov 4, 2019 at 8:38 AM Zach Etienne < trac-noreply@einsteintoolkit.org> wrote:
#2064: Add GiRaFFE to the Einstein Toolkit Reporter: Zach Etienne Status: open Milestone: ET_2019_10 Version: development version Type: enhancement Priority: minor Component: EinsteinToolkit thorn
Comment (by Zach Etienne):
@Ian Hinder : Version control with Jupyter is only slightly more difficult than with plain source code, as its JSON is plaintext.
Is it a good idea to pull the whole code into notebooks? I don’t know how sustainable it is when you have multiple contributors; merging changes from different people can become very difficult.
Writing the Jupyter notebook is only the first step in NRPy+ development, but arguably the most important as it contains the documentation and some validation checks. I’d liken the Jupyter notebook phase more to collaboratively writing a journal article with a shared git repo than code development within such a repo.
I'd like to add a point to this, out of frustration:
A few weeks ago I downloaded and ran Tutorial-BaikalETK.ipynb. Today I try to "git pull", and it doesn't work, because there are differences in the output everywhere (e.g. "Finished BSSN symbolic expressions in 1.2987918853759766 seconds"). The only way I see is to undo my changes ("git checkout"), and then pull your changes. In this case that's fine because there are no major changes, but if I had made changes I'd be in trouble.
I think the problem is that the output is stored in the git repo. In the future, you might want to switch to a system where (a) notebooks automatically have all output removed before they are committed, and (b) upon commit, Travis or a similar system creates for-viewing-only notebooks with a standardized output.
-erik
After the notebook has been written, a separate Python module containing the code developed in the Jupyter notebook is then written. At the bottom of every NRPy+ Jupyter notebook it is required to include a self-validation check against the separate Python module (many NRPy+ Jupyter notebooks have additional validation checks). Further, all NRPy+ Python modules must include proper unit testing on all symbolic expressions generated, so that Travis CI can confirm agreement with the trusted version. This has the nice consequence of often requiring developers to update the documentation any time the module is updated in order for the Jupyter notebook to pass self-validation tests, though generally NRPy+ developers prefer updating the Jupyter notebooks as a first step, then propagating changes to the module & unit tests. Proper documentation makes our lives much easier and is the foundation of a sustainable codebase.
The above approach has worked fine with multiple contributors so far, as NRPy+ is extremely modular, with 70+ Python modules & about 100 Jupyter notebooks. Also I generally don’t put multiple students on exactly the same project (though they certainly do interact and benefit from each others' documentation). Thus
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