Lammps Support #348
Lammps Support #348
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Add generation of u_nk and dHdl from linearly related dependencies
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This is really good! |
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Also would be good to add the docs with regard to this as well. |
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #348 +/- ##
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- Coverage 98.78% 82.82% -15.97%
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Files 28 29 +1
Lines 1982 2364 +382
Branches 354 462 +108
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Hits 1958 1958
- Misses 2 384 +382
Partials 22 22 ☔ View full report in Codecov by Sentry. |
* Add moving_average function for visualization and convergence testing * Update versionadded * Run Black * Bug fix bar_.py states * Update Changelog * Update the docs * Add tests * Formatting to align with Black * Update tests * Refactor moving_average to align with forward_backward_convergence function * Update tests * Update test_convergence and lambda tests in convergence.moving_average * Adjust convergence.py and tests for codecoverage * black * Update moving_average to block_average for more accurate descriptive name * Address reviewer comments * Update test to align with changed handling of dfs of different length in block_average * Remove incorrect popagation of error in BAR * Add tests and error catch for ill constructed BAR input, u_nk * black * Updated version comments --------- Co-authored-by: Oliver Beckstein <orbeckst@gmail.com>
- generated with https://bit.ly/cffinit - authors and order from AUTHORS - affiliations and ORCID from JOSS paper - emails from paper or individuals Co-authored-by: David L. Dotson <dotsdl@gmail.com>
- inferred DOI from proof - assume final acceptance in 2024...
- added David Mobley @davidlmobley (original conception and design of alchemlyb) - added Michael Shirts @mrshirts (original conception and design of alchemlyb)
- CHANGES: bump to 2.3.3 - add reminder to AUTHORS to also update CITATION.cff
Added all authors that were mentioned in the Acknowledgements of the JOSS paper but were not in AUTHORS - Wei-Tse Hsu @wehs7661 (2020) for code clean-up in a732380 - Jan Janssen @jan-janssen (2022) for creating the conda-forge package - Shujie Fan @VOD555 (2022) for initial code for fractional equilibration time - Helmut Carter @helmutcarter (2024) for doc fix in alchemistry#356
AUTHORS entry had been forgotten in PR alchemistry#381
* fix bug introduced in PR alchemistry#381: there was a change to creating the delta_f_ matrix, which resulted in the columns and indices being tuples that were in the wrong order for single lambda computations. * ensure that columns are in the correct order by explicitly sorting * add a test for the delta_f_ columns
- fix alchemistry#385 - use pyproject.toml instead of setup.py (note: may need to change README for PyPi to exclude banners) - remove versioneer and use versioningit (use alchemlyb.__version__ directly where the version is needed, e.g., for sphinx docs) - updated CHANGES for 2.4.0
- gitignore _version - ignore commits for blame that contain black-reformatting (in particular we forgot to include alchemistry#280)
- close alchemistry#402 - use same title in CITATION.cff for paper and project (required for the JOSS paper publication alchemistry#71) - added DOI for preferred citation in note because that is the only hint that shows up on zenodo)
replace `pandas.concat()` with `alchemlyb.concat()` in the tutorial (given that we explicitly tell users to use it)
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There are plenty of conflicts that need to be resolved. I'd say, ideally rebase this branch against the current main branch. I also opened alchemistry/alchemtest#95 to add data files to alchemlyb. I would actually prioritize tests over pretty much anything else as (1) the tests show how your code will be used (and that's important for review) and (2) without tests we cannot merge code. |
Thanks!
Yes, I've been putting together an example to add to alchemtest, but in the process I found a couple issues with the way I ran TI and BAR. At this point I'm 100% confident in the results and since alchemtest has a license associated with each simulation example I'm submitting my simulation data to our internal review process to associate the NIST license. |
This is a Draft I am actively using this and expect it's ready to go, but still needs tests.
Resolves: Issue #349
This module will allow for alchemlyb to be used with LAMMPS outputs. An independent package for easily generating the LAMMPS input files will shortly follow, although the functions have been designed to be flexible.
In addition to the
extract_dHdlandextract_u_nkfunctions, additional functions,extract_dHdl_from_u_nandextract_u_nk_from_u_nhave been added to allow these data frames to be generated from potential energy information that is separable from the target variable (e.g., epsilon in the LJ).