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SDF to MJCF: Added light to links #36

Merged
merged 2 commits into from
May 19, 2022
Merged

SDF to MJCF: Added light to links #36

merged 2 commits into from
May 19, 2022

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ahcorde
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@ahcorde ahcorde commented May 19, 2022

🎉 New feature

Summary

SDF to MJCF: Added light to links

Checklist

  • Signed all commits for DCO
  • Added tests
  • Added example and/or tutorial
  • Updated documentation (as needed)
  • Updated migration guide (as needed)
  • Consider updating Python bindings (if the library has them)
  • codecheck passed (See contributing)
  • All tests passed (See test coverage)
  • While waiting for a review on your PR, please help review another open pull request to support the maintainers

Note to maintainers: Remember to use Squash-Merge and edit the commit message to match the pull request summary while retaining Signed-off-by messages.

@ahcorde ahcorde requested a review from azeey May 19, 2022 07:37
@ahcorde ahcorde self-assigned this May 19, 2022
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codecov bot commented May 19, 2022

Codecov Report

Merging #36 (b4be1b1) into main (5fd924b) will increase coverage by 0.08%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main      #36      +/-   ##
==========================================
+ Coverage   95.75%   95.83%   +0.08%     
==========================================
  Files           9        9              
  Lines         259      264       +5     
==========================================
+ Hits          248      253       +5     
  Misses         11       11              
Impacted Files Coverage Δ
...format_to_mjcf/sdformat_to_mjcf/converters/link.py 100.00% <100.00%> (ø)

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@ahcorde ahcorde mentioned this pull request May 19, 2022
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@ahcorde ahcorde merged commit c6c75ff into main May 19, 2022
@ahcorde ahcorde deleted the ahcorde/sdf2usd/lights branch May 19, 2022 14:27
light.linear_attenuation_factor(),
light.quadratic_attenuation_factor()],
body_mjcf.light[0].attenuation)
assert_allclose([0.864937, -0.549277, 0.013397],
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nit: it would be nice to compute this expected value based on the inputs (test_pose and [0.1, 0.2, 1])

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3 participants