XanaduAI / thewalrus

A library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling.
https://the-walrus.readthedocs.io
Apache License 2.0
99 stars 54 forks source link

Seg fault test #391

Closed rachelchadwick closed 2 months ago

rachelchadwick commented 2 months ago

Just using this to try to track down the segmentation fault that often arises in the tests on Github

codecov[bot] commented 2 months ago

Codecov Report

Attention: Patch coverage is 98.36066% with 1 lines in your changes are missing coverage. Please review.

:exclamation: No coverage uploaded for pull request base (internal_modes@7c63905). Click here to learn what that means.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## internal_modes #391 +/- ## ================================================= Coverage ? 99.95% ================================================= Files ? 33 Lines ? 2227 Branches ? 0 ================================================= Hits ? 2226 Misses ? 1 Partials ? 0 ``` | [Files](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI) | Coverage Δ | | |---|---|---| | [thewalrus/\_hafnian.py](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?src=pr&el=tree&filepath=thewalrus%2F_hafnian.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI#diff-dGhld2FscnVzL19oYWZuaWFuLnB5) | `100.00% <ø> (ø)` | | | [thewalrus/internal\_modes/\_\_init\_\_.py](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?src=pr&el=tree&filepath=thewalrus%2Finternal_modes%2F__init__.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI#diff-dGhld2FscnVzL2ludGVybmFsX21vZGVzL19faW5pdF9fLnB5) | `100.00% <100.00%> (ø)` | | | [thewalrus/internal\_modes/pnr\_statistics.py](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?src=pr&el=tree&filepath=thewalrus%2Finternal_modes%2Fpnr_statistics.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI#diff-dGhld2FscnVzL2ludGVybmFsX21vZGVzL3Bucl9zdGF0aXN0aWNzLnB5) | `100.00% <100.00%> (ø)` | | | [thewalrus/internal\_modes/utils.py](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?src=pr&el=tree&filepath=thewalrus%2Finternal_modes%2Futils.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI#diff-dGhld2FscnVzL2ludGVybmFsX21vZGVzL3V0aWxzLnB5) | `100.00% <ø> (ø)` | | | [thewalrus/internal\_modes/fock\_density\_matrices.py](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?src=pr&el=tree&filepath=thewalrus%2Finternal_modes%2Ffock_density_matrices.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI#diff-dGhld2FscnVzL2ludGVybmFsX21vZGVzL2ZvY2tfZGVuc2l0eV9tYXRyaWNlcy5weQ==) | `98.57% <98.24%> (ø)` | | ------ [Continue to review full report in Codecov by Sentry](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?dropdown=coverage&src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI). > **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI) > `Δ = absolute (impact)`, `ø = not affected`, `? = missing data` > Powered by [Codecov](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?dropdown=coverage&src=pr&el=footer&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI). Last update [7c63905...121902c](https://app.codecov.io/gh/XanaduAI/thewalrus/pull/391?dropdown=coverage&src=pr&el=lastupdated&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=XanaduAI).