Stingray Submission #201

matteobachetti · 2024-06-14T12:59:47Z

Submitting Author: Name @matteobachetti
All current maintainers: @dhuppenkothen @mgullik @jdswinbank @matteolucchini1
Package Name: Stingray
One-Line Description of Package: A spectral-timing software package for astrophysical X-ray (and other) data
Repository Link: https://github.com/stingraysoftware/stingray

Version submitted: 2.1
EiC: @cmarmo
Editor: @hamogu
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

Code of Conduct & Commitment to Maintain Package

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I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

Include a brief paragraph describing what your package does:

Stingray is a Python library for "spectral timing", i.e. time-series analysis techniques that can be used to study how variability changes or correlates between different energy bands/wavelengths. It is Astropy-affiliated, and with an ever growing user base now comprising hundreds of researchers around the globe.

Scope

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Check out our package scope page to learn more about our
scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):
- Data retrieval
- Data extraction
- Data processing/munging
- Data deposition
- Data validation and testing
- Data visualization¹
- Workflow automation
- Citation management and bibliometrics
- Scientific software wrappers
- Database interoperability

Domain Specific

Geospatial
Education

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Astropy:My package adheres to Astropy community standards
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This package is mostly focused on the analysis of new data from high-energy missions. We added data validation and testing because it can be used as a quick look tool to point out possible anomalies in observations (e.g. solar or other background flares).

Who is the target audience and what are scientific applications of this package?

The target audience is principally researchers and students of X-ray and multi-wavelength astronomy.
This package fills a void for a free and open source (spectral-)timing package for X-ray astronomy. XRONOS, formerly maintained by HEASARC, is currently unmaintained, and the analysis of high-energy timeseries is done mostly with proprietary software or mission-specific packages. We provide a Python package that eases the learning curve for newcomers, also thanks to extensive tutorials based on Jupyter notebooks, and provides experts with a powerful, robust library for their analysis. We provide software to analyze astronomical time series and do a number of things, including periodicity searches, time lag calculations, covariance spectra, power spectral modeling.

Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Our package is arguably the most well-known Python package for X-ray spectral timing.

If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

#195

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The text was updated successfully, but these errors were encountered:

cmarmo · 2024-07-01T21:03:45Z

Editor in Chief checks

Hi there! I'm Chiara and I am taking care of the Editor in chief role for the next three months.
Thank you for submitting your package for pyOpenSci review.
Below are the basic checks that your package needs to pass to begin our review.
If some of these are missing, we will ask you to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements
below.

Initial onboarding survey was filled out
We appreciate each maintainer of the package filling out this survey individually. 🙌
Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. 🙌

Editor comments

Stingray is in excellent form! 💪
Congratulations!
I am going to check the availability of our Astropy editors and we will be back to you shortly.

cmarmo · 2024-07-02T18:43:37Z

Hello @matteobachetti I am pleased to announce that @hamogu will take care of your submission as editor.
Thank you Moritz!
I'm wishing to all of you a nice review process! :)

hamogu · 2024-07-02T21:22:40Z

Editor response to review:

Editor comments

👋 Hi @taldcroft and @masonng-astro! Thank you for volunteering to review
for pyOpenSci!

Please fill out our pre-review survey

Before beginning your review, please fill out our pre-review survey. This helps us improve all aspects of our review and better understand our community. No personal data will be shared from this survey - it will only be used in an aggregated format by our Executive Director to improve our processes and programs.

reviewer 1 survey completed.
reviewer 2 survey completed.
reviewer 3 (if applicable)

The following resources will help you complete your review:

Here is the reviewers guide. This guide contains all of the steps and information needed to complete your review.
Here is the review template that you will need to fill out and submit
here as a comment, once your review is complete.

Please get in touch with any questions or concerns! Your review is due:

Reviewers: Tom Aldcroft and Mason Ng
Due date: Sept 6th, 2024

hamogu · 2024-07-02T21:36:41Z

We have a previous JOSS paper describing the package. Is it acceptable to have a new paper, including the many improvements made in the last five years?

It's up to JOSS, not us, how exactly they handle that. Yo save everyone time, JOSS will generally accept our review as theirs, because there is really no need for a second set of reviewers that do essentially the same checks that our reviewers just did. However the final decision to accept into JOSS is up to JOSS.

So, I think there are two ways this can be done, at out choice:

Either you contact JOSS and ask for their opinion. (In that case, I would pause this review until you hear back from them.)
Or you write the paper.md as you would like to have it, and we review the code and paper as we normally do and pass it to JOSS once our review is done. (In that case, I would pause this review until you've written the paper.md so that our reviewers can actually review the paper.)

Option 2 is faster (you don't have to wait for the JOSS response first), but carries the risk that the JOSS editor decides not to publish it later.

Note that JOSS says: "we also review submissions that are significant contributions made to existing packages".
Based on this, I suggest option 2 (you add a 'paper.md` here before we review) and I will specifically instruct our reviewers to comment on if they judge the development in the last 5 years to be a "significant contribution".

matteobachetti · 2024-07-04T14:09:48Z

@hamogu we are working on the JOSS paper draft here: StingraySoftware/stingray#829
The text will not change much, we are working mostly on the author list and acknowledgements.
The new paper is paper2.md, and the bibliography joss2.bib (because we have the 2019 paper in the same directory)

hamogu · 2024-07-08T10:51:39Z

Just let me know when the paper is ready and I'll let the reviewers know to start their review. In the meantime, I'm emailing reviewers to find people to do the review when you are ready.

matteobachetti · 2024-07-10T10:17:58Z

@hamogu the paper is ready, but I'm still missing a couple OrcIDs and affiliations. Does it have to be merged or the link to the PR is sufficient?

hamogu · 2024-07-17T08:29:07Z

Just letting you know that I'm in the process of looking for reviewers; both I and some potential reviewers are on summer travel, but I'm hoping to get you two reviewers soon to get this started.

hamogu · 2024-08-12T05:09:42Z

Just letting you know that I'm still looking for reviewers. Seems that everyone is on vacation right now...

matteobachetti · 2024-08-12T07:10:53Z

Understandable 😅
No worries!

hamogu · 2024-08-12T15:44:32Z

👋 Hi @taldcroft and @masonng-astro!
Thanks for agreeing to do a review of stingray for us. Generic instructions (pre-review survey, etc.) are above: #201 (comment)

In addition, note that this package is also submitting to JOSS and JOSS will accept our review and not do a separate review. However, stingray already had a JOSS publication a few years ago. So, it would be very helpful to note in your review if you deem the new features added since then as new "substantial contribution" that merits a new JOSS publication. You can find the paper draft for JOSS in StingraySoftware/stingray#829

Please reach out to me if you have any questions!

hamogu · 2024-09-06T16:19:02Z

👋 Hi @taldcroft and @masonng-astro! I'm hoping to get your reviews for this package soon! If you have any problems or need help or guidance in how to do a review, please reach out to me any time!

masonng-astro · 2024-09-06T22:37:17Z

(Apologies for any erroneous formatting)

Package Review

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Documentation

The package includes all the following forms of documentation:

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Function Documentation: for all user-facing functions.
Examples for all user-facing functions.
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References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 6

---

Review Comments

As a frequent user of Stingray, I have casually followed the development of Stingray over the years. It has evolved from its previous iteration and from past projects (Stingray, HENDRICS, etc.) into a full software suite of spectral timing tools applicable to event data. I have always found the documentation to be extensive and comprehensive, with pedagogical Jupyter notebook-style tutorials to get started. My comments are thus fairly minor. I also note the absence of several items from the checklist above, though please let me know if I missed something.

On the JOSS publication, I believe the addition of new features, the integration of additional data formats, and increased performance of the codebase merits a new JOSS publication. I just have very minor comments below.

Consistency between using “modelling” and “modeling”, except for the reference to the subpackage
“analyse” was written in the text instead of “analyze” (I assume the authors are adopting US English spelling throughout)
The hyperlinks to “our benchmarks” and “Lightkurve” point to wrong locations
I believe the authors will be adding the references before final approval

I have glanced through all of the notebooks, and I encountered an issue in the “Bexvar tutorial” notebook. This might be my set-up, but I get the error that

```python
ImportError: dlopen(/opt/homebrew/anaconda3/lib/python3.10/site-packages/ultranest/mlfriends.cpython-310-darwin.so, 0x0002): tried: '/Users/masonng/Documents/MIT/Research/software/heasoft-6.33/aarch64-apple-darwin22.1.0/mlfriends.cpython-310-darwin.so' (no such file), '/Users/masonng/Documents/MIT/Research/software/heasoft-6.33/aarch64-apple-darwin22.1.0/lib/mlfriends.cpython-310-darwin.so' (no such file), '/opt/homebrew/anaconda3/lib/python3.10/site-packages/ultranest/mlfriends.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64')), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/anaconda3/lib/python3.10/site-packages/ultranest/mlfriends.cpython-310-darwin.so' (no such file), '/opt/homebrew/anaconda3/lib/python3.10/site-packages/ultranest/mlfriends.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64'))
```

which I wonder if it is because there is some incompatibility with the arm64 architecture. I did install UltraNest with “arch -arm64” but the file “mlfriends.cpython-310-darwin.so” is still in the x86_64 architecture. Apparently UltraNest does not explicitly support arm64 architecture (PyPI project here)?

I appreciate the “dependencies” sub-section under “installation instructions.” I would just like to include “UltraNest” to the list. Have there been tests done with the Apple Silicon chip-equipped devices as well? I encountered the above error with UltraNest for example, with my MacBook Pro M1; or this could be a personal problem.

```
>>> stingray.test()
=========================================================================================== test session starts ============================================================================================
platform darwin -- Python 3.10.12, pytest-7.1.2, pluggy-1.5.0

Running tests with stingray version 2.1.
Running tests in /opt/homebrew/anaconda3/lib/python3.10/site-packages/stingray.

Date: 2024-09-05T15:05:00

Platform: macOS-13.0.1-arm64-arm-64bit

Executable: /opt/homebrew/anaconda3/bin/python

Full Python Version:
3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:41:52) [Clang 15.0.7 ]

encodings: sys: utf-8, locale: UTF-8, filesystem: utf-8
byteorder: little
float info: dig: 15, mant_dig: 15

Package versions:
Numpy: 1.23.5
Scipy: 1.10.0
Matplotlib: 3.7.0
h5py: 3.7.0
scikit-image: 0.19.3

Using Astropy options: remote_data: none.

rootdir: /opt/homebrew/anaconda3/lib/python3.10/site-packages/stingray
plugins: anyio-3.5.0, remotedata-0.4.1, doctestplus-1.2.1, astropy-header-0.2.2, asdf-3.4.0
collected 1650 items / 1 skipped

../deadtime/tests/test_fad.py ................                                                                                                                                                       [  0%]
../deadtime/tests/test_models.py ...............                                                                                                                                                     [  1%]
../mission_support/tests/test_mission_support.py ..                                                                                                                                                  [  2%]
../modeling/tests/test_parameterestimation.py ............s.........s...............................                                                                                                 [  5%]
../modeling/tests/test_posterior.py .....................................................                                                                                                            [  8%]
../modeling/tests/test_scripts.py .......                                                                                                                                                            [  8%]
../pulse/overlapandsave/test_ols.py ss...                                                                                                                                                            [  9%]
../pulse/tests/test_accelsearch.py ......                                                                                                                                                            [  9%]
../pulse/tests/test_modeling.py ........                                                                                                                                                             [ 10%]
../pulse/tests/test_pulse.py .sss...............................                                                                                                                                     [ 12%]
../pulse/tests/test_search.py ..............................                                                                                                                                         [ 14%]
../simulator/tests/test_base.py ..                                                                                                                                                                   [ 14%]
../simulator/tests/test_simulator.py ........................................................                                                                                                        [ 17%]
../simulator/tests/test_transfer.py ................                                                                                                                                                 [ 18%]
test_base.py .............................................................................................................................                                                           [ 26%]
test_bexvar.py sssss..s.s.                                                                                                                                                                           [ 26%]
test_bispectrum.py ............................                                                                                                                                                      [ 28%]
test_covariancespectrum.py ......................                                                                                                                                                    [ 29%]
test_crosscorrelation.py ...........................                                                                                                                                                 [ 31%]
test_crossspectrum.py .............................................................................................................................................................................. [ 41%]
...........................................                                                                                                                                                          [ 44%]
test_events.py ..............................................................                                                                                                                        [ 48%]
test_filters.py ..........                                                                                                                                                                           [ 48%]
test_fourier.py .................................................................................................................................................................................... [ 59%]
........                                                                                                                                                                                             [ 60%]
test_gti.py .........................................................                                                                                                                                [ 63%]
test_io.py ....F..............                                                                                                                                                                       [ 64%]
test_lightcurve.py .....s..............................................................................................ss........................................ss.......                           [ 74%]
test_lombscargle.py ...............................................                                                                                                                                  [ 76%]
test_multitaper.py ......................................                                                                                                                                            [ 79%]
test_power_colors.py .............                                                                                                                                                                   [ 80%]
test_powerspectrum.py .................................................................................................................................................................              [ 89%]
test_sampledata.py ....                                                                                                                                                                              [ 90%]
test_spectroscopy.py xX......                                                                                                                                                                        [ 90%]
test_stats.py .......................................................                                                                                                                                [ 93%]
test_utils.py ...................................................s..........                                                                                                                         [ 97%]
test_varenergyspectrum.py ........................................                                                                                                                                   [100%]

================================================================================================= FAILURES =================================================================================================
______________________________________________________________________________ TestIO.test_event_file_read_and_automatic_sort ______________________________________________________________________________

self = <stingray.tests.test_io.TestIO object at 0x167545360>

    def test_event_file_read_and_automatic_sort(self):
        """Test event file reading."""
        fname = os.path.join(datadir, "monol_testA_calib.evt")
        with pytest.warns(AstropyUserWarning, match="No valid GTI extensions"):
            evdata = load_events_and_gtis(fname)
        fname_unsrt = os.path.join(datadir, "monol_testA_calib_unsrt.evt")
>       with pytest.warns(UserWarning, match="not sorted. Sorting them for you"):
E       Failed: DID NOT WARN. No warnings of type (<class 'UserWarning'>,) were emitted. The list of emitted warnings is: [].

test_io.py:77: Failed
============================================================================================= warnings summary =============================================================================================
../../_pytest/config/__init__.py:1129
  /opt/homebrew/anaconda3/lib/python3.10/site-packages/_pytest/config/__init__.py:1129: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: asdf
    self._mark_plugins_for_rewrite(hook)

../deadtime/tests/test_fad.py:22: 1 warning
../deadtime/tests/test_models.py:14: 1 warning
../modeling/tests/test_parameterestimation.py:34: 1 warning
../pulse/overlapandsave/test_ols.py:88: 1 warning
../pulse/tests/test_accelsearch.py:6: 1 warning
../pulse/tests/test_pulse.py:32: 1 warning
../pulse/tests/test_pulse.py:55: 1 warning
../pulse/tests/test_search.py:14: 1 warning
test_bexvar.py:11: 1 warning
test_crossspectrum.py:331: 1 warning
test_crossspectrum.py:909: 1 warning
test_crossspectrum.py:921: 1 warning
test_crossspectrum.py:938: 1 warning
test_crossspectrum.py:958: 1 warning
test_lightcurve.py:1605: 1 warning
test_multitaper.py:13: 1 warning
test_powerspectrum.py:210: 1 warning
test_spectroscopy.py:93: 1 warning
test_varenergyspectrum.py:171: 1 warning
test_varenergyspectrum.py:358: 1 warning
  PytestUnknownMarkWarning: Unknown pytest.mark.slow - is this a typo?  You can register custom marks to avoid this warning - for details, see <https://docs.pytest.org/en/stable/how-to/mark.html>

../../pint/pulsar_mjd.py:57
  RuntimeWarning: This platform does not support extended precision floating-point, and PINT will run at reduced precision.

deadtime/tests/test_fad.py: 2 warnings
tests/test_base.py: 3 warnings
tests/test_crossspectrum.py: 1 warning
tests/test_events.py: 2 warnings
tests/test_lightcurve.py: 1 warning
tests/test_powerspectrum.py: 1 warning
tests/test_varenergyspectrum.py: 1 warning
  UserWarning: table path was not set via the path= argument; using default path __astropy_table__

deadtime/tests/test_models.py::test_checkA[True]
deadtime/tests/test_models.py::test_checkA[False]
  UserWarning: All values for SymLogScale are below linthresh, making it effectively linear. You likely should lower the value of linthresh.

modeling/tests/test_parameterestimation.py::TestParameterEstimation::test_sampler_runs
  DeprecationWarning: The py23 module has been deprecated and will be removed in a future release. Please update your code.

modeling/tests/test_parameterestimation.py::TestPSDParEst::test_fit_method_works_with_correct_parameter
  RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.

modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_crossspectrum
modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_powerspectrum
pulse/tests/test_accelsearch.py::TestAccelsearch::test_signal
pulse/tests/test_accelsearch.py::TestAccelsearch::test_signal_neg_fdot
pulse/tests/test_accelsearch.py::TestAccelsearch::test_noisy
pulse/tests/test_accelsearch.py::TestAccelsearch::test_noisy_neg_fdot
  RuntimeWarning: divide by zero encountered in log

modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_crossspectrum
modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_powerspectrum
  RuntimeWarning: divide by zero encountered in divide

modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_crossspectrum
modeling/tests/test_scripts.py::TestFitLorentzians::test_fit_powerspectrum
  RuntimeWarning: invalid value encountered in subtract

pulse/tests/test_modeling.py::test_gaussian_bounds
  RuntimeWarning: underflow encountered in exp

pulse/tests/test_modeling.py::test_gaussian_bounds
  RuntimeWarning: underflow encountered in multiply

pulse/tests/test_search.py: 1 warning
simulator/tests/test_simulator.py: 39 warnings
tests/test_crosscorrelation.py: 1 warning
tests/test_crossspectrum.py: 2 warnings
tests/test_lightcurve.py: 1 warning
tests/test_lombscargle.py: 3 warnings
tests/test_multitaper.py: 1 warning
tests/test_varenergyspectrum.py: 3 warnings
  UserWarning: SIMON says: Stingray only uses poisson err_dist at the moment. All analysis in the light curve will assume Poisson errors. Sorry for the inconvenience.

simulator/tests/test_simulator.py: 2 warnings
tests/test_crosscorrelation.py: 1 warning
tests/test_crossspectrum.py: 75 warnings
tests/test_spectroscopy.py: 32 warnings
tests/test_varenergyspectrum.py: 1 warning
  UserWarning: n_ave is below 30. Please note that the error bars on the quantities derived from the cross spectrum are only reliable for a large number of averaged powers.

simulator/tests/test_simulator.py: 2 warnings
tests/test_crosscorrelation.py: 1 warning
tests/test_crossspectrum.py: 97 warnings
tests/test_lombscargle.py: 10 warnings
tests/test_spectroscopy.py: 46 warnings
tests/test_varenergyspectrum.py: 2 warnings
  RuntimeWarning: invalid value encountered in sqrt

tests/test_bexvar.py::TestInternalFunctions::test_lscg_gen
  UnitsWarning: 'ratio' did not parse as fits unit: At col 0, Unit 'ratio' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see <https://docs.astropy.org/en/latest/units/combining_and_defining.html>

tests/test_crosscorrelation.py::TestCrossCorrelation::test_simple_plot
tests/test_crosscorrelation.py::TestCrossCorrelation::test_plot_axis
tests/test_crosscorrelation.py::TestCrossCorrelation::test_plot_title
  UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

tests/test_crossspectrum.py: 12 warnings
  UserWarning: Some error bars in the Averaged Crossspectrum are invalid.Defaulting to sqrt(2 / M) in Leahy norm, rescaled to the appropriate norm.

tests/test_crossspectrum.py::TestAveragedCrossspectrumEvents::test_internal_from_events_works_acs
tests/test_crossspectrum.py::TestAveragedCrossspectrumEvents::test_from_events_works_acs
tests/test_crossspectrum.py::TestAveragedCrossspectrumEvents::test_rebin
tests/test_crossspectrum.py::TestAveragedCrossspectrumEvents::test_rebin_factor
tests/test_crossspectrum.py::TestAveragedCrossspectrumEvents::test_rebin_log
tests/test_crossspectrum.py::TestAveragedCrossspectrum::test_rebin
tests/test_crossspectrum.py::TestAveragedCrossspectrum::test_rebin_factor
tests/test_crossspectrum.py::TestAveragedCrossspectrum::test_rebin_log
  UserWarning: SIMON says: Number of segments used in averaging is significantly low. The result might not follow the expected statistical distributions.

tests/test_crossspectrum.py: 9 warnings
tests/test_powerspectrum.py: 4 warnings
  RuntimeWarning: invalid value encountered in divide

tests/test_multitaper.py::TestMultitaper::test_make_multitaper_from_lightcurve[lc_gauss]
tests/test_multitaper.py::TestMultitaper::test_multitaper_lombscargle
  UserWarning: SIMON says: Looks like your lightcurve statistic is not poisson.The errors in the Powerspectrum will be incorrect.

tests/test_powerspectrum.py::TestPowerspectrum::test_classical_significances_trial_correction
tests/test_powerspectrum.py::TestPowerspectrum::test_pvals_is_numpy_array
  RuntimeWarning: underflow encountered in _logp_multitrial_from_single_logp

-- Docs: <https://docs.pytest.org/en/stable/how-to/capture-warnings.html>
========================================================================================= short test summary info ==========================================================================================
FAILED test_io.py::TestIO::test_event_file_read_and_automatic_sort - Failed: DID NOT WARN. No warnings of type (<class 'UserWarning'>,) were emitted. The list of emitted warnings is: [].
======================================================== 1 failed, 1627 passed, 21 skipped, 1 xfailed, 1 xpassed, 410 warnings in 143.24s (0:02:23) ========================================================
<ExitCode.TESTS_FAILED: 1>

```

taldcroft · 2024-09-07T10:10:16Z

@hamogu - sorry, this fell off my radar. Will do early next week.

matteobachetti · 2024-09-09T16:30:47Z

Thanks for the ongoing reviews!
FYI, I've started addressing comments on readme/docs at StingraySoftware/stingray#842

taldcroft · 2024-09-09T22:17:10Z

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  - Not included. I somewhat question the utility of this badge since it seems to rely
    on the package maintainers to keep it up to date. An abandoned project is not
    likely to be marked as such.
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  - Looks like Py 3.8 to 3.12 are tested, but I do not see this in the install docs.
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For packages also submitting to JOSS

The package has an obvious research application according to JOSS's definition in their submission requirements.

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Estimated hours spent reviewing: 4

Review Comments

I am reviewing this package as an (inactive) astronomer and active Astropy developer.

From a scientific perspective it appears that the stingray package fills a distinct
need for modern open source Python-based X-ray spectral timing tools. It is being widely
used in the X-ray timing community, with 170 citations to the 2019 ApJ paper. In recent
years it is seeing around 35 citations per year.

From a software perspective this checks all the boxes (literally) for a package that
meets current standards for a well-developed, documented, and maintained package. The
test coverage is excellent.

I ran all the notebooks and found a couple of problems (documented as issues). This
highlights the need for StingraySoftware/stingray#802 to be
worked, but overall things are looking OK and those notebook issues are not a blocker
for accepting the package.

I opened one issue to document how to download the tutorial notebooks that form most of
the package functional documentation. This is a simple docs update and I would like to
see this done for accepting the package.

matteobachetti · 2024-09-10T12:43:14Z

Update:

Responses to pyOpenSci review StingraySoftware/stingray#842 addresses issues with documentation and package infrastructure, including minimum Python versions, badges and so on (under review).
Address PyOpenSci comments StingraySoftware/notebooks#103 addresses the issues with notebooks (ongoing)
New JOSS paper draft StingraySoftware/stingray#829 addresses comments to the JOSS paper (ongoing)

matteobachetti · 2024-09-10T14:10:27Z

@masonng-astro I have a similar setup to yours (Macbook pro with M1 processor), but I couldn't reproduce your bugs 🆘

The failing test on unsorted data is particularly weird, as it does not seem to rely on any specific dependency other than the default ones. There is a vague chance that it is related to the numba installation, as I use numba to check for sortedness, but it should also revert to standard numpy if numba is not installed or it fails.

I could also install UltraNest without issues. I wonder if you have conflicting versions of some dependencies? From the list of libraries in your error, there seems to be some kind of clash between HEASOFT-installed libraries and the standard Python path. Is it possible?

taldcroft · 2024-09-10T16:16:35Z

FWIW as noted in my review, tests passed for me on Mac Silicon and Python 3.12. I created a fresh conda environment (using conda defaults channel) for Python 3.12 and then pip installed everything else.

matteobachetti added 0/pre-review-checks New Submission! labels Jun 14, 2024

Batalex mentioned this issue Jun 22, 2024

Presubmission inquiry for Stingray #195

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This was referenced Sep 9, 2024

New JOSS paper draft StingraySoftware/stingray#829

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From pyOpenSci review: better deal with UltraNest dependency StingraySoftware/stingray#841

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