SieveColorSpace

Colormaps

How they work

Each SCS colormap is a geodesic path on the simplex delta-2 with coupled luminance ramp. The construction has two steps:

Chromaticity path: multi-waypoint geodesic on delta-2, reparameterized by gamma-weighted Fisher arc length (equal chromatic steps).
Luminance ramp: arcsin parameterization (ell = sin^2(theta)) so that equal parameter steps produce equal Fisher-Bernoulli distances (equal luminance steps).

The two are then coupled: the total path (ell, pi) is reparameterized by combined Fisher arc length:

d^2_total = (3/4) * d_lum^2 + (1/4) * d_chrom^2

This ensures uniform perceptual steps along the entire colormap.

Available colormaps

Sequential

Name	Colors	Use case
`scs_spectrum`	purple -> blue -> cyan -> green -> yellow	General scientific (PT viridis)
`scs_turbo`	blue -> cyan -> green -> yellow -> red	Full rainbow, no false contours
`scs_magma`	black -> purple -> orange -> pale yellow	Dark-to-bright intensity
`scs_terrain`	deep blue -> teal -> green -> brown -> white	Elevation, bathymetry
`scs_vegetation`	red-brown -> orange -> yellow -> green -> dark green	NDVI, biomass, forest cover
`scs_medical`	dark blue-gray -> warm white	MRI, CT, X-ray (high lum range)
`scs_thermal`	blue -> red	Heat maps
`scs_cool`	blue -> green	Ocean depth
`scs_warm`	green -> red	Activation, intensity
`scs_full`	blue -> green -> red	Full spectrum

Diverging

Name	Colors	Use case
`scs_diverging`	blue <- neutral -> red	Anomalies, T-statistics
`scs_seismic`	strong blue <- white -> strong red	Seismic, gravity anomalies

Usage with matplotlib

from scs.colormaps import register_matplotlib
register_matplotlib()

import matplotlib.pyplot as plt

# Use by name
plt.imshow(data, cmap='scs_spectrum')

# Or get the array directly
from scs.colormaps import scs_vegetation
cmap_array = scs_vegetation(256)  # (256, 3) float sRGB

Comparison with standard colormaps

Perceptual uniformity measured by coefficient of variation (CV) of successive Fisher distances. Lower = more uniform.

Colormap	CV total	Dead zones	Parameters
scs_diverging	0.063	0.4%	0
scs_vegetation	0.101	0%	0
scs_spectrum	0.102	0%	0
scs_medical	0.145	0%	0
scs_seismic	0.175	0%	0
scs_turbo	0.198	0%	0
scs_terrain	0.216	0.4%	0
scs_magma	0.230	0%	0
viridis	0.286	0%	fitted
turbo	0.292	1.6%	empirical
inferno	0.634	2.4%	fitted
jet	0.661	1.6%	empirical

9 out of 12 SCS colormaps beat viridis in Fisher uniformity.

When to use SCS colormaps vs standard ones

Use SCS when:

You need a certificate of uniformity (diagnostic medical imaging)
You need gamut portability (same colormap on sRGB and P3 displays)
You need the GFT conservation guarantee (no dead zones)

Use standard when:

Maximum chromatic contrast matters more than uniformity
Users are already trained on viridis/RdYlGn conventions
The colormap is decorative rather than part of the measurement chain

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