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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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slamicp library matlab wrappers with precompiled mex files  (MathWorks Inc)
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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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matlab mex  (MathWorks Inc)
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a – b , run times of Tiff readers and writers <t>for</t> <t>libtiff</t> <t>(MATLAB),</t> tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .
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Image Search Results


a – b , run times of Tiff readers and writers for libtiff (MATLAB), tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .

Journal: Nature Methods

Article Title: Image processing tools for petabyte-scale light sheet microscopy data

doi: 10.1038/s41592-024-02475-4

Figure Lengend Snippet: a – b , run times of Tiff readers and writers for libtiff (MATLAB), tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .

Article Snippet: Our Tiff reader/writer leverages the capabilities of the libtiff library in C++ with the MATLAB MEX interface.

Techniques:

a , Performance gains of our Cpp-Tiff reader versus the conventional Tiff reader in MATLAB and tifffile reader in Python versus the number of frames in 3D stacks. b , Performance gains of Cpp-Tiff writer versus the conventional Tiff writer in MATLAB and the tifffile writer in Python versus the number of frames in 3D stacks. c , Performance gains of our Cpp-Zarr reader versus the conventional Zarr reader (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. d , Performance gains of Cpp-Zarr writers versus the conventional Zarr writer (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. The images have a uint16 frame size of 512 × 1,800 ( xy ) in all cases. The benchmarks were run independently ten times on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). Data are shown as the mean ± s.d.

Journal: Nature Methods

Article Title: Image processing tools for petabyte-scale light sheet microscopy data

doi: 10.1038/s41592-024-02475-4

Figure Lengend Snippet: a , Performance gains of our Cpp-Tiff reader versus the conventional Tiff reader in MATLAB and tifffile reader in Python versus the number of frames in 3D stacks. b , Performance gains of Cpp-Tiff writer versus the conventional Tiff writer in MATLAB and the tifffile writer in Python versus the number of frames in 3D stacks. c , Performance gains of our Cpp-Zarr reader versus the conventional Zarr reader (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. d , Performance gains of Cpp-Zarr writers versus the conventional Zarr writer (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. The images have a uint16 frame size of 512 × 1,800 ( xy ) in all cases. The benchmarks were run independently ten times on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). Data are shown as the mean ± s.d.

Article Snippet: Our Tiff reader/writer leverages the capabilities of the libtiff library in C++ with the MATLAB MEX interface.

Techniques: