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Missing (deprecated) matrix_product from astropy in ecliptic.py #18

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TheWand3rer opened this issue Jan 1, 2025 · 0 comments
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@TheWand3rer
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TheWand3rer commented Jan 1, 2025

🐞 Ecliptic.py tries to import matrix_product from astropy but it has since been deprecated

I was trying to run the Multiple revolutions on Lambert’s problem and it failed on the imports because as the title says it cannot import that function from astropy.

Looking at their repo it seems that function has been deprecated.

File "C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\hapsira\frames\ecliptic.py", line 14, in <module>
    from astropy.coordinates.matrix_utilities import (
ImportError: cannot import name 'matrix_product' from 'astropy.coordinates.matrix_utilities' 
(C:\Users\user\AppData\Local\Programs\Python\Python311\Lib\site-packages\astropy\coordinates\matrix_utilities.py)

🖥 Please paste the output of following commands

  • conda info -a (only if you have conda)
  • conda list (only if you have conda)
  • pip freeze
# Paste your output here:
astropy==7.0.0
astropy-iers-data==0.2024.12.30.0.33.36
astroquery==0.4.7
backports.tarfile==1.2.0
beautifulsoup4==4.12.3
certifi==2024.12.14
charset-normalizer==3.4.1
contourpy==1.3.1
cycler==0.12.1
fonttools==4.55.3
hapsira==0.18.0
html5lib==1.1
idna==3.10
importlib_metadata==8.5.0
jaraco.classes==3.4.0
jaraco.context==6.0.1
jaraco.functools==4.1.0
jplephem==2.22
keyring==25.6.0
kiwisolver==1.4.8
llvmlite==0.43.0
matplotlib==3.7.5
more-itertools==10.5.0
numba==0.60.0
numpy==2.0.0
packaging==24.2
pandas==2.2.3
pillow==11.0.0
plotly==5.24.1
pyerfa==2.0.1.5
pyparsing==3.2.1
python-dateutil==2.9.0.post0
pytz==2024.2
pyvo==1.6
pywin32-ctypes==0.2.3
PyYAML==6.0.2
requests==2.32.3
scipy==1.14.1
six==1.17.0
soupsieve==2.6
tenacity==9.0.0
tzdata==2024.2
urllib3==2.3.0
webencodings==0.5.1
zipp==3.21.0

💡 Possible solutions

I'm not very knowledgeable in Python, so for the moment I have just readded function in the astropy package script. I'm sure it would be a simple solution otherwise. Perhaps calling the reduce(np.matmul, matrices) function directly?

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