| --- |
| license: other |
| license_name: sdrbench |
| license_link: https://sdrbench.github.io/ |
| pretty_name: "SDRBench: SCALE-LETKF" |
| tags: |
| - scientific-data |
| - lossy-compression |
| - sdrbench |
| - hpc |
| - sz3 |
| configs: |
| - config_name: files |
| data_files: files.jsonl |
| default: true |
| --- |
| |
| # SDRBench — SCALE-LETKF |
|
|
| Weather simulation from the SCALE-RM model with LETKF data assimilation (RIKEN): 12 variables on 98 x 1200 x 1200 (z, y, x), single precision, little-endian. |
|
|
| > **Raw binary arrays, not an Arrow/Parquet dataset.** `datasets.load_dataset("sdrbench/scale-letkf")` returns the table of files (`files.jsonl`: path, dtype, shape, sha256). Read the arrays with the `sdrbench` package or `hf_hub_download` + numpy as shown below. |
| |
| This repository is an **unmodified mirror** of the SCALE-LETKF data of [SDRBench](https://sdrbench.github.io/datasets.html), the |
| Scientific Data Reduction Benchmark. The originals are hosted by Argonne National Laboratory on Globus; |
| every archive was unpacked and its files uploaded byte-for-byte (sha256 verified) (revision `18665ddfb9`). `metadata/` holds |
| the original SDRBench property and template files. Kept in sync automatically by |
| [szcompressor/sdrbench](https://github.com/szcompressor/sdrbench). |
| |
| - **Data provider:** RIKEN; contact Guo-Yuan Lien (guoyuan.lien@gmail.com) |
| - **Size:** 13.5 GB in 2 variant(s) |
| - **Format:** raw little-endian binary, C order (slowest dimension first); dtype and shape per field below |
| |
| ## Usage |
| |
| ```bash |
| pip install "sdrbench[sz3]" # sdrbench + pysz (SZ3) |
| ``` |
| |
| ```python |
| import numpy as np |
| import sdrbench |
| from pysz import sz, szConfig, szErrorBoundMode |
| |
| ds = sdrbench.dataset("scale-letkf") # default variant "original" |
| print(ds.fields) |
| x = ds["PRES"] # numpy array, dtype <f4, shape (98, 1200, 1200) |
| |
| # compress with SZ3 (pysz) at a 1e-3 value-range-relative error bound |
| conf = szConfig() |
| conf.errorBoundMode = szErrorBoundMode.REL |
| conf.relErrorBound = 1e-3 |
| compressed, ratio = sz.compress(np.ascontiguousarray(x), conf) |
| y, _ = sz.decompress(compressed, x.dtype.type, x.shape) |
| max_err, psnr, nrmse = sz.verify(np.asarray(x), y) |
| print(f"ratio {ratio:.1f}x, PSNR {psnr:.1f} dB, max error {max_err:.3g}") |
| ``` |
| |
| Files are fetched on first use (in parallel with `ds.download("data/")`) and cached, pinned to the revision |
| of the installed `sdrbench` release; if Hugging Face is unreachable the package falls back to the original |
| archive on Globus. Existing local SDRBench copies can be used with `sdrbench.dataset("scale-letkf", root="/path")`. |
| |
| Without the package, with `huggingface_hub` and numpy only: |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import numpy as np |
| p = hf_hub_download("sdrbench/scale-letkf", "98x1200x1200/PRES-98x1200x1200.f32", repo_type="dataset", revision="18665ddfb989801761218b81e665072f998a23ff") |
| x = np.fromfile(p, dtype="<f4").reshape((98, 1200, 1200)) |
| ``` |
| |
| ## Variants and fields |
| |
| |
| ### `original` (default) |
| |
| `sdrbench.dataset("scale-letkf", "original")` — folder `98x1200x1200/` — 12 files, 6.8 GB, from [SDRBENCH-SCALE-98x1200x1200.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/SCALE_LETKF/SDRBENCH-SCALE-98x1200x1200.tar.gz). |
| |
| All variables. |
| |
| | Field | dtype | Shape (C order) | File | |
| |---|---|---|---| |
| | `PRES` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/PRES-98x1200x1200.f32` | |
| | `QC` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QC-98x1200x1200.f32` | |
| | `QG` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QG-98x1200x1200.f32` | |
| | `QI` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QI-98x1200x1200.f32` | |
| | `QR` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QR-98x1200x1200.f32` | |
| | `QS` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QS-98x1200x1200.f32` | |
| | `QV` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QV-98x1200x1200.f32` | |
| | `RH` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/RH-98x1200x1200.f32` | |
| | `T` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/T-98x1200x1200.f32` | |
| | `U` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/U-98x1200x1200.f32` | |
| | `V` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/V-98x1200x1200.f32` | |
| | `W` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/W-98x1200x1200.f32` | |
| |
| ### `log` |
| |
| `sdrbench.dataset("scale-letkf", "log")` — folder `98x1200x1200_log/` — 12 files, 6.8 GB, from [SDRBENCH-SCALE-98x1200x1200_log.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/SCALE_LETKF/SDRBENCH-SCALE-98x1200x1200_log.tar.gz). |
| |
| The hydrometeor and vapour mixing ratios log10-transformed; the other variables unchanged. |
| |
| | Field | dtype | Shape (C order) | File | |
| |---|---|---|---| |
| | `PRES` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/PRES-98x1200x1200.f32` | |
| | `QC_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QC-98x1200x1200.log10.f32` | |
| | `QG_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QG-98x1200x1200.log10.f32` | |
| | `QI_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QI-98x1200x1200.log10.f32` | |
| | `QR_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QR-98x1200x1200.log10.f32` | |
| | `QS_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QS-98x1200x1200.log10.f32` | |
| | `QV_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QV-98x1200x1200.log10.f32` | |
| | `RH` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/RH-98x1200x1200.f32` | |
| | `T` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/T-98x1200x1200.f32` | |
| | `U` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/U-98x1200x1200.f32` | |
| | `V` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/V-98x1200x1200.f32` | |
| | `W` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/W-98x1200x1200.f32` | |
| |
| ## Citation |
| |
| Please cite SDRBench; `sdrbench cite scale-letkf` prints it: |
| |
| ```bibtex |
| @inproceedings{zhao2020sdrbench, |
| title = {{SDRBench}: Scientific Data Reduction Benchmark for Lossy Compressors}, |
| author = {Zhao, Kai and Di, Sheng and Liang, Xin and Li, Sihuan and Tao, Dingwen and Chen, Zizhong and Cappello, Franck}, |
| booktitle = {2020 IEEE International Conference on Big Data (Big Data)}, |
| pages = {2716--2724}, |
| year = {2020} |
| } |
| ``` |
| |
| ## License and terms |
| |
| The data are distributed by the SDRBench team for research use under the terms of the original |
| data providers listed above (see the [SDRBench website](https://sdrbench.github.io/datasets.html)); please acknowledge them as |
| requested. Rights remain with the data providers. |
| |
