{"id":19705848,"url":"https://github.com/llnl/ddd","last_synced_at":"2026-01-28T18:32:52.224Z","repository":{"id":63050294,"uuid":"473692036","full_name":"LLNL/ddd","owner":"LLNL","description":"Delaunay density diagnostic","archived":false,"fork":false,"pushed_at":"2024-09-06T22:58:10.000Z","size":2588,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-12T05:03:19.896Z","etag":null,"topics":["math-physics"],"latest_commit_sha":null,"homepage":"","language":"Fortran","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LLNL.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-24T16:43:30.000Z","updated_at":"2024-09-06T22:58:14.000Z","dependencies_parsed_at":"2023-12-01T00:27:05.761Z","dependency_job_id":"86ad7c7d-6758-4cf9-99df-93e668dd2239","html_url":"https://github.com/LLNL/ddd","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/LLNL/ddd","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fddd","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fddd/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fddd/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fddd/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LLNL","download_url":"https://codeload.github.com/LLNL/ddd/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fddd/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28849160,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-28T15:15:36.453Z","status":"ssl_error","status_checked_at":"2026-01-28T15:15:13.020Z","response_time":57,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["math-physics"],"created_at":"2024-11-11T21:30:55.233Z","updated_at":"2026-01-28T18:32:52.207Z","avatar_url":"https://github.com/LLNL.png","language":"Fortran","funding_links":[],"categories":[],"sub_categories":[],"readme":"Delaunay Density Diagnostic\n----------------\n   Version 2.1, September 2024.\n\n   This code implements algorithms described in:\\\n   **Algorithm XXXX: The Delaunay Density Diagnostic**\\\n   under review at ACM Transactions on Mathematical Software\\\n   original title: ``Data-driven geometric scale detection via Delaunay interpolation''\n   Andrew Gillette and Eugene Kur, 2022 \\\n   https://arxiv.org/abs/2203.05685\n\n\nUsage\n----------------\n\n1. Activate a python environment that includes the packages listed in the REQUIREMENTS.txt file.  \n\n2. Ensure that the `gfortran` compiler is installed.\n\n3. Run the driver script for the Griewank and/or static data examples:\n   ~~~~\n   python run_ddd_griewank.py\n   ~~~~\n   The above script will run a total of 100 trials of the `delaunay_density_diagnostic.py` script,\n      using data from the 2D Griewank function.  The results are saved as `.csv` files.  Then the script `generate_ddd_figures.py` is called to generate a `.png` figure called `ddd-figure-griewank.png`.  The figure should match the file `ddd-figure-griewank-repo.png` that is contained in the repository.  More details can be found in the header of  `run_ddd_griewank.py`.\n\n   A typical run time for a single trial is a few seconds, so the whole script should complete\n      in 5-10 minutes.\n\n   ~~~~\n   python run_ddd_static.py\n   ~~~~\n   The above script will run a total of 100 trials of the `delaunay_density_diagnostic.py` script,\n      using data from the static topography dataset described in the paper (and stored in the subfolder `staticdata/`).   The results are saved as `.csv` files.  Then the script `generate_ddd_figures.py` is called to generate a `.png` figure called `ddd-figure-static.png`.  The figure should match the file `ddd-figure-static-repo.png` that is contained in the repository.  More details can be found in the header of  `run_ddd_static.py`.\n\n\n4. If the figures generates correctly, run\n   ~~~~\n   python delaunay_density_diagnostic.py --help\n   ~~~~\n   to see the command line options that can be added to the driver scripts for\n   user-specified experiments.\n\nDebugging notes\n----------------\n\nThe package includes source files in Fortran that impmlement a version of TOMS Algorithm 1012:\nDELAUNAYSPARSE.  This version that has been updated from the original submission to more easily allow python wrapping.  Running the script `delaunay_density_diagnostic.py` will compile the relevant files using `gfortran`.  \n\nDuring compiling, this type of warning may occur:\n~~~~\nWarning: Rank mismatch between actual argument at (1) and actual argument at (2)\n~~~~\nThis warning is issued by the `slatec` library that is included with the DELAUNAYSPARSE source code and is not easily suppressed.  However, this warning is only due to a change in Fortran conventions since the original publication of TOMS 1012 and does not cause any issues in regards to the results.\n\nAuthors\n----------------\nThe Delaunay density diagnostic code was created by Andrew Gillette, gillette7@llnl.gov, with input from Eugene Kur, kur1@llnl.gov.\n\nCitation information\n----------------\nIf you are referring to this code in a publication, please cite the following paper:\n\nAndrew Gillette and Eugene Kur.  *Data-driven geometric scale detection via Delaunay interpolation*.  Submitted.  2022.  LLNL-JRNL-832470.\n\n~~~~\n@article{GK2022,\n  author = Gillette, Andrew and Kur, Eugene},\n  title = {Data-driven geometric scale detection via Delaunay interpolation},\n  journal = {Submitted. Preprint at arXiv:2203.05685},\n  year = {2022},\n}\n~~~~\n\nIf you wish to cite the code specifically, please use:\n\n~~~~\n@misc{ doecode_72093,\ntitle = {Delaunay density diagnostic},\nauthor = {Gillette, Andrew K.},\nurl = {https://doi.org/10.11578/dc.20220324.3},\nhowpublished = {[Computer Software] \\url{https://doi.org/10.11578/dc.20220324.3}},\nyear = {2022},\nmonth = {mar}\n}\n~~~~\n\nThe DOI for this repository is:  https://doi.org/10.11578/dc.20220324.3\n\n\nLicense\n----------------\n\nDelaunay density diagnostic is distributed under the terms of the MIT license.\n\nAll new contributions must be made under the MIT license.\n\nSee [LICENSE](https://github.com/ddd/blob/main/LICENSE) and\n[NOTICE](https://github.com/ddd/blob/main/NOTICE) for details.\n\nSPDX-License-Identifier: (MIT)\n\nLLNL-CODE-833036\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fddd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fllnl%2Fddd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fddd/lists"}