Datasetpublic

als-rixs/latent-image-training

squiggles (metadata-fix) OC-map FEM rebuild at 35 pixels per wavelength, with corrected geometries, Helmholtz residuals, and the resolved JCMsuite .jcm / .jcmp files used for each solve. Configs metadata (default) One row per structure folder (sample_XXXX). Geometry comes from published optical-constant maps (not the old nested-interface metadata). validation One row per FEM incidence (theta in {0, 45}). Self-contained pixel map:… See the full description on the dataset page: https://huggingface.co/datasets/als-rixs/latent-image-training.

sourceHugging Facemitupdated 2mo agoView on Hugging Face

0likes573downloads

Dataset Card

squiggles (metadata-fix)

OC-map FEM rebuild at 35 pixels per wavelength, with corrected geometries, Helmholtz residuals, and the resolved JCMsuite `.jcm` / `.jcmp` files used for each solve.

Configs

`metadata` (default)

One row per structure folder (sample_XXXX). Geometry comes from published optical-constant maps (not the old nested-interface metadata).

`validation`

One row per FEM incidence (theta in {0, 45}). Self-contained pixel map:

text

pitch_nm = wavelength_nm / pixels_per_wavelength
x_nm = x0_nm + ix * pitch_nm
y_nm = y0_nm + iy * pitch_nm

Also includes nested OC/E arrays, Helmholtz scalar metrics (s-pol staggered FD), and binary blobs of the generated project files:

—layout <- layout.jcm
—materials <- materials.jcm
—sources <- sources.jcm
—project <- project.jcmp (JCMsuite expanded project)
—grid <- grid.jcm

Load

python

from datasets import load_dataset
meta = load_dataset("als-rixs/latent-image-training", "metadata", split="train", revision="metadata-fix")
val = load_dataset("als-rixs/latent-image-training", "validation", split="train", revision="metadata-fix")
row = val[0]
layout_jcm = row["layout"]  # bytes