{"id":15576826,"url":"https://github.com/tlambert03/pycudasirecon","last_synced_at":"2025-09-02T20:35:20.902Z","repository":{"id":44456967,"uuid":"375119011","full_name":"tlambert03/pycudasirecon","owner":"tlambert03","description":"CUDA-accelerated reconstruction for 3D-structured-illumination","archived":false,"fork":false,"pushed_at":"2024-07-25T12:24:42.000Z","size":53125,"stargazers_count":9,"open_issues_count":2,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-10-11T21:54:21.055Z","etag":null,"topics":["3d-sim","microscopy","structured-illumination","super-resolution"],"latest_commit_sha":null,"homepage":"https://www.talleylambert.com/pycudasirecon/","language":"Python","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/tlambert03.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2021-06-08T19:13:34.000Z","updated_at":"2024-09-24T07:07:40.000Z","dependencies_parsed_at":"2024-01-09T17:31:22.540Z","dependency_job_id":"e78b4005-a124-42d0-ade5-9d46fcfdd2df","html_url":"https://github.com/tlambert03/pycudasirecon","commit_stats":{"total_commits":42,"total_committers":3,"mean_commits":14.0,"dds":0.09523809523809523,"last_synced_commit":"c4318d7255a88aa6a2712d714e9df1097bba286a"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tlambert03%2Fpycudasirecon","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tlambert03%2Fpycudasirecon/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tlambert03%2Fpycudasirecon/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tlambert03%2Fpycudasirecon/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tlambert03","download_url":"https://codeload.github.com/tlambert03/pycudasirecon/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221809143,"owners_count":16883860,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["3d-sim","microscopy","structured-illumination","super-resolution"],"created_at":"2024-10-02T18:59:02.171Z","updated_at":"2024-10-28T08:45:44.478Z","avatar_url":"https://github.com/tlambert03.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pycudasirecon\n\nThis package provides a python wrapper and convenience functions for\n[cudasirecon](https://github.com/scopetools/cudasirecon), which is a CUDA/C++\nimplementation of Mats Gustafsson \u0026 Lin Shao's 3-beam SIM reconstruction software.\n\u003csup\u003e1\u003c/sup\u003e.  It also supports lattice-light-sheet SIM (with deskewing).\n\nAdditionally, it includes a GPU implementation of the Hessian denoising algorithm\ndescribed in Huang 2008 \u003csup\u003e2\u003c/sup\u003e.\n\n- 3D (3-beam) SIM reconstruction\n- 2D (2-beam) SIM reconstruction, including TIRF-SIM\n- Lattice-SIM reconstruction\n- GPU-backed Hessian denoising\n\n### Install\n\nThe conda package includes the required pre-compiled libraries for Windows and Linux. See GPU driver requirements [below](#gpu-requirements)\n\n```sh\nconda install -c conda-forge pycudasirecon\n```\n\n*macOS is not supported*\n\n### 📖   \u0026nbsp; [Documentation](http://www.talleylambert.com/pycudasirecon)\n\n\n### GPU requirements\n\nThis software requires a CUDA-compatible NVIDIA GPU. The underlying cudasirecon\nlibraries have been compiled against different versions of the CUDA toolkit.\nThe required CUDA libraries are bundled in the conda distributions so you don't\nneed to install the CUDA toolkit separately.  If desired, you can pick which\nversion of CUDA you'd like based on your needs, but please note that different\nversions of the CUDA toolkit have different GPU driver requirements:\n\nTo specify a specific cudatoolkit version, install as follows (for instance, to\nuse `cudatoolkit=10.2`)\n\n```sh\nconda install -c conda-forge pycudasirecon cudatoolkit=10.2\n```\n\n| CUDA | Linux driver | Win driver |\n| ---- | ------------ | ---------- |\n| 10.2 | ≥ 440.33     | ≥ 441.22   |\n| 11.0 | ≥ 450.36.06  | ≥ 451.22   |\n| 11.1 | ≥ 455.23     | ≥ 456.38   |\n| 11.2 | ≥ 460.27.03  | ≥ 460.82   |\n\n\nIf you run into trouble, feel free to [open an\nissue](https://github.com/tlambert03/pycudasirecon/issues) and describe your\nsetup.\n\n\n## Quickstart\n\nIf you have a raw SIM image volume and a PSF and you just want to get started, check\nout the `pycudasirecon.reconstruct` function, which should be able to handle most\nbasic applications.\n\n```python\nfrom pycudasirecon import reconstruct\n\nraw = tf.imread('path/to/raw_data.tif')\npsf = tf.imread('path/to/sim_psf.tif')\nmakeotf_kwargs = {}  # kwargs for pycudasirecon.make_otf\nrecon_params = {}  # kwargs for pycudasirecon.ReconParams\nresult = reconstruct(\n    raw,\n    psf=psf,\n    makeotf_kwargs=makeotf_kwargs,\n    **recon_params\n)\n```\n\nThis library is in development ... more details to follow.\n\n\n___\n\n#### References\n\n\u003csup\u003e1\u003c/sup\u003e Gustafsson MG, Shao L, Carlton PM, Wang CJ, Golubovskaya IN, Cande WZ, Agard DA, Sedat JW. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys J. 2008 Jun;94(12):4957-70. doi: [10.1529/biophysj.107.120345](https://dx.doi.org/10.1529/biophysj.107.120345). Epub 2008 Mar 7. PMID: 18326650; PMCID: PMC2397368.\n\n\u003csup\u003e2\u003c/sup\u003e Huang X, Fan J, Li L, Liu H, Wu R, Wu Y, Wei L, Mao H, Lal A, Xi P, Tang L, Zhang Y, Liu Y, Tan S, Chen L. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat Biotechnol. 2018 Jun;36(5):451-459. doi: [10.1038/nbt.4115](https://dx.doi.org/10.1038/nbt.4115). Epub 2018 Apr 11. PMID: 29644998.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftlambert03%2Fpycudasirecon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftlambert03%2Fpycudasirecon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftlambert03%2Fpycudasirecon/lists"}