傅里叶反变换matlab代码-mrrt.nufft:CPU和GPU(1D,2D和3D)上的非均匀FFT

上传者: 38709312 | 上传时间: 2024-07-24 10:31:18 | 文件大小: 114KB | 文件类型: ZIP
傅里叶反变换matlab代码Python中的非均匀快速傅立叶变换 该库为Python提供了更高性能的CPU / GPU NUFFT。 该库最初是Jeff Fessler和他的学生所编写的Matlab NUFFT代码的移植端口,但是已经进行了全面的改进,并添加了GPU支持。 该库未实现所有NUFFT变体,仅实现了以下两种情况: 1.)从均匀的空间网格到非均匀采样的频域的转换。 2.)从非均匀傅立叶样本到均匀间隔的空间网格的逆变换。 那些对其他NUFFT类型感兴趣的人可能想考虑通过进行非官方python包装的。 转换以单精度和双精度变体实现。 基于低内存查找表的实现和完全预先计算的基于稀疏矩阵的实现都可用。 请参阅和以获取完整的许可证信息。 相关软件 软件包中提供了另一个具有CPU和GPU支持的基于Python的实现。 NUFFT的Sigpy实现非常紧凑,因为它用于从通用代码库为CPU和GPU变体提供及时的编译。 相反, mrrt.nufft将预编译的C代码用于CPU变体,并且GPU内核在运行时使用NVIDIA提供的NVIDIA运行时编译(NVRTC)进行编译。 该工具实现了更广泛的一组非

文件下载

资源详情

[{"title":"( 65 个子文件 114KB ) 傅里叶反变换matlab代码-mrrt.nufft:CPU和GPU(1D,2D和3D)上的非均匀FFT","children":[{"title":"mrrt.nufft-master","children":[{"title":"MANIFEST.in <span style='color:#111;'> 517B </span>","children":null,"spread":false},{"title":"requirements","children":[{"title":"default.txt <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":"test.txt <span style='color:#111;'> 6B </span>","children":null,"spread":false},{"title":"optional.txt <span style='color:#111;'> 27B </span>","children":null,"spread":false}],"spread":true},{"title":".gitattributes <span style='color:#111;'> 36B </span>","children":null,"spread":false},{"title":"LICENSE.txt <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false},{"title":"requirements-dev.txt <span style='color:#111;'> 55B </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 53B </span>","children":null,"spread":false},{"title":"doc","children":[{"title":"conf.py <span style='color:#111;'> 9.48KB </span>","children":null,"spread":false},{"title":"sphinxext","children":[{"title":"numpydoc.py <span style='color:#111;'> 6.49KB </span>","children":null,"spread":false},{"title":"docscrape.py <span style='color:#111;'> 17.46KB </span>","children":null,"spread":false},{"title":"math_dollar.py <span style='color:#111;'> 1.99KB </span>","children":null,"spread":false},{"title":"docscrape_sphinx.py <span style='color:#111;'> 7.64KB </span>","children":null,"spread":false}],"spread":true},{"title":"tools","children":[{"title":"apigen.py <span style='color:#111;'> 17.75KB </span>","children":null,"spread":false},{"title":"LICENSE.txt <span style='color:#111;'> 246B </span>","children":null,"spread":false},{"title":"buildmodref.py <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false}],"spread":true},{"title":"index.rst <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"Makefile <span style='color:#111;'> 6.64KB </span>","children":null,"spread":false},{"title":"related_implementations.rst <span style='color:#111;'> 500B </span>","children":null,"spread":false}],"spread":true},{"title":"setup_helpers.py <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false},{"title":"mrrt","children":[{"title":"__init__.py <span style='color:#111;'> 246B </span>","children":null,"spread":false},{"title":"nufft","children":[{"title":"_extensions","children":[{"title":"_nufft_table.pyx <span style='color:#111;'> 24.19KB </span>","children":null,"spread":false},{"title":"_nufft_table.pxd <span style='color:#111;'> 25.60KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"c","children":[{"title":"nufft_table.h <span style='color:#111;'> 827B </span>","children":null,"spread":false},{"title":"templating.h <span style='color:#111;'> 79B </span>","children":null,"spread":false},{"title":"nufft_table.template.h <span style='color:#111;'> 6.66KB </span>","children":null,"spread":false},{"title":"nufft_table.template.c <span style='color:#111;'> 35.55KB </span>","children":null,"spread":false},{"title":"nufft_table.c <span style='color:#111;'> 814B </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"_interp_table.py <span style='color:#111;'> 5.91KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 403B </span>","children":null,"spread":false},{"title":"cuda","children":[{"title":"cupy.py <span style='color:#111;'> 5.26KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"jinja","children":[{"title":"table_2d_forward.jinja <span style='color:#111;'> 4.41KB </span>","children":null,"spread":false},{"title":"table_3d_forward.jinja <span style='color:#111;'> 6.36KB </span>","children":null,"spread":false},{"title":"table_3d_adjoint.jinja <span style='color:#111;'> 6.25KB </span>","children":null,"spread":false},{"title":"table_1d_adjoint.jinja <span style='color:#111;'> 2.61KB </span>","children":null,"spread":false},{"title":"table_2d_adjoint.jinja <span style='color:#111;'> 4.38KB </span>","children":null,"spread":false},{"title":"table_1d_forward.jinja <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"gridding_kernel_includes.jinja <span style='color:#111;'> 1.38KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":"_kernels.py <span style='color:#111;'> 4.70KB </span>","children":null,"spread":false},{"title":"_kaiser_bessel.py <span style='color:#111;'> 7.02KB </span>","children":null,"spread":false},{"title":"tests","children":[{"title":"test_dtft.py <span style='color:#111;'> 6.26KB </span>","children":null,"spread":false},{"title":"test_kernels.py <span style='color:#111;'> 1.74KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"data","children":[{"title":"nufft_gauss.npz <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false},{"title":"nufft_interp_zn.npz <span style='color:#111;'> 40.81KB </span>","children":null,"spread":false}],"spread":false},{"title":"test_utils.py <span style='color:#111;'> 578B </span>","children":null,"spread":false},{"title":"test_kaiser.py <span style='color:#111;'> 3.88KB </span>","children":null,"spread":false},{"title":"test_nufft.py <span style='color:#111;'> 11.69KB </span>","children":null,"spread":false}],"spread":false},{"title":"_cupy.py <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"_nufft.py <span style='color:#111;'> 55.55KB </span>","children":null,"spread":false},{"title":"version.py <span style='color:#111;'> 2.77KB </span>","children":null,"spread":false},{"title":"_dtft.py <span style='color:#111;'> 6.37KB </span>","children":null,"spread":false},{"title":"_utils.py <span style='color:#111;'> 3.48KB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":".travis.yml <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"LICENSES_bundled.txt <span style='color:#111;'> 1.28KB </span>","children":null,"spread":false},{"title":"setup.cfg <span style='color:#111;'> 151B </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 7.75KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 3.86KB </span>","children":null,"spread":false},{"title":"Makefile <span style='color:#111;'> 432B </span>","children":null,"spread":false},{"title":".pre-commit-config.yaml <span style='color:#111;'> 646B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 753B </span>","children":null,"spread":false},{"title":"pyproject.toml <span style='color:#111;'> 675B </span>","children":null,"spread":false},{"title":".coveragerc <span style='color:#111;'> 63B </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明