GREOPy: A General Relativistic Emitter-Observer problem Python algorithm

[irideselby]

Submitting Author: Jan P. Hackstein (@irideselby) All current maintainers: (@irideselby) Package Name: GREOPy One-Line Description of Package: Calculate relativistic light rays sent by an emitter to a receiver in the presence of a gravitational field. Repository Link: https://codeberg.org/JPHackstein/GREOPy Version submitted: v0.2.1 EiC: @coatless Editor: @jonas-eschle
Reviewer 1: @lpsinger Reviewer 2: @Arya-AD Archive: https://zenodo.org/records/14537866 JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

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Description

GREOPy is a Python library for calculating relativistic light rays sent by an emitter to a receiver in the presence of a gravitational field. Finding a light ray connecting two events is sometimes called "Emitter-Observer" problem and is always present when it comes to communication between two observers, e.g. two satellites in orbit. GREOPy allows the emitter and receiver to move along arbitrary curves, making this an initial-value problem to solve from the emitter's perspective, and the gravitational field can be described by a rotating, non-accelerating central mass. Everything is being calculated in the general relativistic framework to include relativistic effects like light bending and the relativistic Doppler effect to be able to quantify their impact on error propagation. While only two spacetimes are implemented at the moment (even though further additions are planned), GREOPy is written in a way to allow the community to easily expand the number of spacetimes to suit their needs.

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Data processing/munging: GREOPy uses parametrised curves, e.g. orbit data, to simulate communication via relativistic light rays between them. This allows analysis of relativistic effects on light and by extension any corresponding observable in some chosen spacetime, giving insights into fundamental properties of the underlying spacetime.

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This package is mainly targeted for scientists working in geodesy; it can be used to simulate satellite-satellite or satellite-ground station communication and from this derive, e.g. how the Earth mass distribution changes over time due to for example climate change.

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Not the same thing. There exist of course Python packages that implement General Relativity, e.g. to be able to calculate light rays (lightlike/nulllike geodesics) as one can do with EinsteinPy for example. However there appear to be no packages that implement specifically the Emitter-Observer problem (initial-value problem with a variable target boundary) in terms of General Relativity.

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[coatless]

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Editor comments

Thanks for bearing with us as we hibernated over the holidays and assigned out a new EiC.

Regarding the package, I'm happy to work on assigning an editor.

However, I do want to bring to your attention a few concerns.

Specifically, the "How to find initial conditions" page linked on the documentation website is blank.

https://greopy.readthedocs.io/en/latest/about/initial_conditions.html

Also, the tutorial gallery only one tutorial is present ("Quick Start"). Do you envision additional tutorials being added during the review process?

https://greopy.readthedocs.io/en/latest/auto_tutorials/index.html

Lastly, the Quickstart example on the reduced grid also took about 15 minutes of compute time on Colab. Would it be possible to include a note on the documentation that computations may be long running?

[irideselby]

Hello! Thank you very much for your answer and comments. Concerning the blank page on the wiki. It has sadly been sitting there for a while while I was working on different aspects of the package and it has been slipping through until now, but I think now will be a good time to finally update it. I definitely want to fill the tutorial gallery with more tutorials over time, and I think I have a good idea about a second small tutorial already. Now that I got the package into shape I can also focus more on tutorials. Also thank your for the notice on computation times, I will add a note to the documentation.

I will begin working on the comments in the next couple of days and update everything accordingly, so thanks again! Maybe one question: Between submitting the package and now, I have found a bug concerning the light ray calculation for which I have worked out a fix. May I just update the package while the review process is going on? I imagine that the review process will be conducted with the exact version I submitted, so in case the bug will be found by the reviewers as well, do I then just link to the package version/PR that fixes the bug?

[coatless]

@irideselby Okay, let's hold off on sending it out for the review until the bug regarding light ray calculation is fixed as that may impact what SME reviewers of the software respond with.

Sound okay?

[irideselby]

@coatless Thank you for waiting, I fixed the bug and bumped the version accordingly. I also edited the initial message in this thread to reflect this change, so now the submitted version is 0.2.1 instead of 0.2.0, I hope that's alright.

[coatless]

@irideselby thanks for letting me know. I'll start the process of assigning an editor.

[irideselby]

I have now added the paper.md for JOSS in a paper/ directory and added a tick for the corresponding bullet point in the initial submission message.

[lwasser]

hi @irideselby just a quick note that we are still onboarding new editors! We had a large group of packages submitted to us and are onboarding a bunch of new people. Thank you for your patience and for pulling the JOSS part together!

[coatless]

@irideselby Thank you for your patience! We've secured an editor to further move the review along.

We're happy to announce that @jonas-eschle will be the editor for your submission.

For next step, we'll be working toward getting reviewers assigned. This step is detailed here:

https://www.pyopensci.org/software-peer-review/how-to/author-guide.html#the-review-begins

[jonas-eschle]

Hi @irideselby! I'm excited to be the editor leading your reviews! I'm going to review everything and start working toward assigning reviewers for the software and will keep you updated here.

[irideselby]

@coatless Thank you very much! And hi @jonas-eschle, I'm looking forward to be working with you through the process.

[jonas-eschle]

Hi @irideselby, to give you a heads-up, I am looking for reviewers currently. I think the package overall suits well and I didn' find any major obstacle. Just a few minor things, they're not critical but good to know:

• the package seems barely discoverable. The only way I was able to find the package is via the link provided, but google does not return any result (DuckDuckGo does). Not sure what can be done, but just FYI.
• You seem to be the only contributor so far. Do you have any chance of involving other people, maybe for the long-term?
• Do you have any idea about the user-base?

I'll let you know once I found two reviewers!

[irideselby]

@jonas-eschle Thank you for the feedback. Concerning the second point, this package arose from research I'm currently doing in my PhD, which is why I have started out on my own. However, involving other people in the package's development is definitely something I would like to do in the future; that's the reason why I have put so much effort into trying to make the package accessible in the first place. I have also already talked about maintaining and working on the package long-term with some colleagues and am currently working with them on papers that will cite the package, which in turn will hopefully help with discoverability. The papers are of course aimed at the user-base, and I shortly address it in the JOSS paper draft that I added to the repository as well. I'm looking forward to the reviews!

[jonas-eschle]

@irideselby makes sense! And I am glad that you put the effort into making it maintainable and vetting it here. The comment about the hosting was also about familiarity, it's, IMH experience, just easier to get people on Github to contribute, because everyone has a Github account. But that's your decision of course, nothing to hold up the review.

And after searching for a while, I've finally found two reviewers, welcome @lpsinger and @KandiSanjana! Thank you for volunteering to review!

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[irideselby]

@jonas-eschle Thank you for your time and effort! And welcome to the thread, @lpsinger and @Arya-AD, I'm very excited to hear from you!

[jonas-eschle]

Hi @lpsinger and @Arya-AD, what are the status of your reviews? We're already slightly overdue, do you have an estimate when you can provide them?

[lpsinger]

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Review Comments

[lpsinger]

@irideselby, please see https://codeberg.org/JPHackstein/GREOPy/issues/22, related to the Metadata item above.

[lpsinger]

@irideselby, please add the following status badges:

• Continuous integration and test coverage
• A repostatus.org badge
• Python versions supported (https://shields.io can provide this)

[lpsinger]

@irideselby, the commands in the Quickstart fail with this error:

---------------------------------------------------------------------------
PicklingError                             Traceback (most recent call last)
Cell In[10], line 3
      1 from greopy.emitter_observer_problem import eop_solver
----> 3 light_rays = eop_solver(
      4         config,
      5         emission_curve_reduced,
      6         receiver_curve_data,
      7         multiprocessing=True,
      8         hypersurface_approximation=True,
      9         euclidean_approximation=True,
     10 )

File [~/Library/Python/3.11/lib/python/site-packages/greopy/emitter_observer_problem.py:1374](http://localhost:8888/~/Library/Python/3.11/lib/python/site-packages/greopy/emitter_observer_problem.py#line=1373), in eop_solver(config, curve_emitter, curve_receiver, multiprocessing, hypersurface_approximation, hypersurface_angle_range, de_relative_tolerance, de_absolute_tolerance, de_popsize, de_max_iterations, de_seed, solve_ivp_absolute_tolerance, solve_ivp_relative_tolerance, root_tolerance, max_distance_measure, euclidean_approximation, affine_parameter_mesh_length, verbose)
   1361 processes = [Process(
   1362     target=light_ray_calculation_multiprocessing,
   1363     args=(orbit_indexed[i],
   (...)   1370           q)
   1371 ) for i in range(len(orbit_indexed))]
   1373 for process in processes:
-> 1374     process.start()
   1376 # call q.get as often as there are processes
   1377 for process in processes:

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/process.py:121](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/process.py#line=120), in BaseProcess.start(self)
    118 assert not _current_process._config.get('daemon'), \
    119        'daemonic processes are not allowed to have children'
    120 _cleanup()
--> 121 self._popen = self._Popen(self)
    122 self._sentinel = self._popen.sentinel
    123 # Avoid a refcycle if the target function holds an indirect
    124 # reference to the process object (see bpo-30775)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/context.py:224](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/context.py#line=223), in Process._Popen(process_obj)
    222 @staticmethod
    223 def _Popen(process_obj):
--> 224     return _default_context.get_context().Process._Popen(process_obj)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/context.py:288](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/context.py#line=287), in SpawnProcess._Popen(process_obj)
    285 @staticmethod
    286 def _Popen(process_obj):
    287     from .popen_spawn_posix import Popen
--> 288     return Popen(process_obj)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_spawn_posix.py:32](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_spawn_posix.py#line=31), in Popen.__init__(self, process_obj)
     30 def __init__(self, process_obj):
     31     self._fds = []
---> 32     super().__init__(process_obj)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_fork.py:19](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_fork.py#line=18), in Popen.__init__(self, process_obj)
     17 self.returncode = None
     18 self.finalizer = None
---> 19 self._launch(process_obj)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_spawn_posix.py:47](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/popen_spawn_posix.py#line=46), in Popen._launch(self, process_obj)
     45 try:
     46     reduction.dump(prep_data, fp)
---> 47     reduction.dump(process_obj, fp)
     48 finally:
     49     set_spawning_popen(None)

File [/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/reduction.py:60](http://localhost:8888/opt/local/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/reduction.py#line=59), in dump(obj, file, protocol)
     58 def dump(obj, file, protocol=None):
     59     '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60     ForkingPickler(file, protocol).dump(obj)

PicklingError: Can't pickle <function _lambdifygenerated at 0x1687e1a80>: attribute lookup _lambdifygenerated on __main__ failed

[irideselby]

Ah I see, maybe for a quick workaround could you try to run the quickstart guide again but this time when calling the eop_solver function put multiprocessing=False to disable the multiprocessing and see whether the code runs through. I had implemented multiprocessing as a convenience, but it is not necessary to run the calculations. But I'm taking note that multiprocessing (while making things faster if it does work) might not be suitable for a 'quickstart' script.

[lpsinger]

Ah I see, maybe for a quick workaround could you try to run the quickstart guide again but this time when calling the eop_solver function put multiprocessing=False to disable the multiprocessing and see whether the code runs through.

Yes, that works, but I can't sign off on this item unless the scripts int he Quickstart work as is:

Functionality: Any functional claims of the software been confirmed.

[irideselby]

That's understandable. Are you running this script on a Windows machine? Because I think multiprocessing behaves differently on Windows and Linux, and I implemented multiprocessing with Linux in mind. If it is indeed the case, I think the easiest solution for now would be to change the quickstart guide to disable multiprocessing, because debugging this might be tricky for now.

[lpsinger]

Are you running this script on a Windows machine?

No, I'm on a Mac.

[irideselby]

That's a good info, thanks! It appears that on both Windows and Mac child processes are spawned, where on Linux they are forked instead, meaning that functions that are passed onto a child process via forking don't need to be pickled, while they are pickled when the process is being spawned instead. That's why it gives the error about not being able to pickle the lambdifiy function (which is indeed not pickleable). Definitely already good feedback, thank you. I'll look further into this to see how to handle the problem. In the meantime I hope I'm not stalling the rest of the review, maybe you can continue without the multiprocessing for now, since as I said earlier it is not necessary (but for the item on your list I'll try and figure something out).

[irideselby]

@lpsinger I have created a new branch with a potential fix. Would you please install the package from the branch by using pip install git+https://codeberg.org/JPHackstein/GREOPy.git@bugfix/multiprocessing_method and just run the quickstart guide again. Once you verify that it works with the fix I will merge it with the main branch.

[lpsinger]

Once you verify that it works with the fix I will merge it with the main branch.

No, it didn't work. I had to add this command manually before I ran any of the example commands:

import multiprocessing
multiprocessing.set_start_method('fork')

[irideselby]

Alright thanks for the feedback. I reverted the changes I made in the branch and now decided to take out the multiprocessing flag in the quickstart script, but I additionally added a note to the quickstart documentation and eop_solver docstring that addresses the choice of start method for different operating systems and suggests to manually set the start method before calling the function.

You can find the new documentation for the bugfix branch with the updated quickstart script at https://greopy.readthedocs.io/en/bugfix-multiprocessing_method/ Since I have reverted the previous change, it would be good to verify now whether the quickstart guide still works when manually adding multiprocessing=True to the eop_solver function and setting the start method as is described in the documentation. I'd like to see whether the guide is now consistent and everything works.

[lpsinger]

I encountered a small issue when trying to run the tests locally: https://codeberg.org/JPHackstein/GREOPy/pulls/23

Also, here is a PR to fix a typo: https://codeberg.org/JPHackstein/GREOPy/pulls/21

[lpsinger]

Another issue when running the tests locally: https://codeberg.org/JPHackstein/GREOPy/issues/24

[lpsinger]

Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)

The CI pipeline appears to not run the unit tests. Would you please have your CI pipeline run your unit tests?