Colon
3 datasets, together covering 0.45–1.551 µm. Default: Giannios et al. 2017: Mucosa; n,k 0.45–1.551 µm.
Compare datasets
Tick datasets in the list to add them to the plot (up to 12).
Datasets
Use it in Lightsmith
Load this data in Python, simulate in one more line
pip install lightsmith
import lightsmith as ls
# Colon: Giannios et al. 2017: Mucosa; n,k 0.45–1.551 µm
colon = ls.fetch_refractiveindex("other", "colon", "Giannios-mucosa", ref="c5c2f188e848453def5970e347399d653df2ffc2")
print(colon.range_nm) # (450, 1551) nm
eps = colon.eps([633]) # complex permittivity
Downloads the original record once (pinned to the imported snapshot) and caches it.
import json, urllib.request
import lightsmith as ls
url = "https://materials.lightsmith.dev/v1/datasets/ds_e0450f63d4cf/lightsmith"
colon = ls.Tabulated.model_validate(json.load(urllib.request.urlopen(url)))
print(colon.source) # citation and provenance travel with the data
Any dataset here, as the library's Tabulated material (colon/giannios-2017-mucosa).
# ... load colon as above, then:
sim = ls.Simulation(
particles=[ls.Particle(shape=ls.Sphere(radius_nm=50), material=colon)],
medium_index=1.0,
wavelengths=ls.WavelengthRange(start_nm=450, stop_nm=1000, num=81),
)
res = ls.solve(sim) # exact Mie for a sphere
print(res.q_ext) # extinction efficiency at each wavelength
Same spec runs with DDA, FDTD, BEM or T-matrix: ls.compare(sim, ["mie", "dda"]).
Lightsmith is open source (MIT): Mie, T-matrix, BEM, DDA, FDTD, RCWA, TMM, mode solvers and inverse design behind one spec, on CPU or GPU. GitHub.
About Colon
Other names: Large intestine, Large bowel
API: /v1/materials/mat_558929c742b5 · slug colon