Comments (4)
Original comment by Chris Mutel (Bitbucket: cmutel, GitHub: cmutel).
Thanks for the bug report! This could be a serious issue, but I am not sure I see the problem. What difference could it make if the filepaths were sorted or not?
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Original comment by Pedro Anchieta (Bitbucket: phanchieta, GitHub: phanchieta).
Thanks for the reply! We implement a version of the multi-core Monte Carlo simulation and some LCA score results were incorrect when using 1.7.5 and multiple cores. Some cores were producing correct results while others were several orders of magnitude off. Making local changes to utils.py has solved the issue temporarily but other users might also come across this same problem.
I’m unsure if this also happens in the ‘out of the box’ Monte Carlo simulation. Also, this issue only happened on Windows. Tested on Linux and results were OK. Probably due to the different file system.
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2.5 switches to using bw_processing
, which will load things deterministically (not by sorting the filepaths, but by using the order provided in the metadata datapacakge.json
file).
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Fixed in 2.0 development branch.
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Related Issues (20)
- Empty characterization matrix arrays for MultiLCA HOT 7
- Proposal: Allow user to calculate non-diagonalized results HOT 1
- Proposal: Cache Impact Assessment items
- `MultiLCA` class returning identical results under iteration HOT 7
- `MultiLCA` gives zero scores for single demand/multiple impact categories HOT 2
- `MultiLCA` normalizes and weights in all combinations HOT 4
- Should be able to pass `MethodConfig` to `MultiLCA` the way the cheat sheet recommends
- Use the expected value to parametrize lognormal distributions HOT 2
- Prevent multiple ``remap_inventory_dicts``
- Allow for selective use of distributions or arrays depending on matrix label
- `MappedMatrixDict` should support `__mul__` HOT 1
- Dependency Dashboard
- MultiLCA: Check that `method_config` resources are present in data objects
- MultiLCA: Filter data objects to those referred to in `method_config`
- Add tests for `scikit-umfpack` and `sparselu` solvers HOT 5
- Prepare for switch to sparse arrays
- MultiLCA checklist
- Document/Clarify Removal of GraphTraversal Functionality HOT 6
- Characterization matrix ... rabbit hole HOT 3
- Factorizing matrices with pypardiso is not a no-op
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