Named Colormaps

Named colormaps supported by LookupTable, Plotter, and other plotting-related methods.

The colormaps on this page are from multiple packages:

• mpl - matplotlib colormaps.

• cc - colorcet continuous and categorical colormaps.

• cmo - cmocean colormaps.

• cmc - cmcrameri colormaps.

The type of the colormap is indicated as:

• LSC if it is a LinearSegmentedColormap

• LC if it is a ListedColormap

Each colormap is also tagged as:

• - if it is perceptually uniform

• - if it is not perceptually uniform

A colormap is considered perceptually uniform if its color gradients are evenly spaced, meaning equal steps in data produce equal perceptual changes.

Some colormap libraries assess perceptual uniformity using only the lightness component (\(L^*\) in the Lab color space), while others also consider chromatic components (\(a^*\) and \(b^*\)). For the colormaps on this reference page, both lightness and color differences must meet the uniformity criteria to earn the label.

Specifically, a colormap is labeled as perceptually uniform if:

1. The cumulative change in lightness (\(L^*\) from CAM02-UCS) progresses linearly. See lightness of matplotlib colormaps.

2. The cumulative color difference (\(\Delta E\), using the CIEDE2000 metric) progresses linearly. See Fig. 3c: cumulative color lightness difference.

Linearity is defined as having a coefficient of determination (\(R^2\)) greater than 0.99 when fitted with linear regression. When choosing a colormap, those with the tag should be preferred over those with the tag.

Warning

Many of the matplotlib colormaps such as jet are not perceptually uniform and should be avoided where possible, since these colormaps can generate misleading visualizations. Colormaps from colorcet and cmocean, and cmcrameri are therefore generally recommended over those from matplotlib. See the misuse of colour in science communication and testing perceptual uniformity for more information.

Refer to the flowchart in the following dropdown for guidance on how to choose a colormap.

Guideline for choosing the right scientific colormap

Figure from Crameri, F., Shephard, G.E., & Heron, P.J. (2020). The misuse of colour in science communication. Nature Communications, 11, 5444. https://doi.org/10.1038/s41467-020-19160-7. Licensed under CC BY 4.0.

Note

Some colormap names such as gray and rainbow are duplicated across packages, but have slight variations in the mapped colors. Colormaps from colorcet have priority and are used first if available, followed by cmocean, followed by the stock colormaps from matplotlib.

Note

Install PyVista with pyvista[colormaps] or pyvista[all] to also install the colorcet, cmocean, and cmcrameri packages.

See also

Colormap Choices

Example using colormaps from different sources.

Named Colors

Similar reference for named colors.

Linear (Sequential)

Colormaps that progress in a single direction, ideal for representing ordered data such as intensities or magnitudes.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
mpl LC	viridis
cmo LSC	haline
cmc LC	imola
cmc LC	navia
cmc LC	davos
cmc LC	lapaz
mpl LC	cividis
cmc LC	nuuk
cmc LC	lipari
cmo LSC	thermal
mpl LC	plasma
cc LSC	bmy
mpl LC	inferno
mpl LC	magma
cmc LC	lajolla
mpl LSC	copper
mpl LSC	gist_heat
cc LSC	fire
mpl LSC	afmhot
mpl LSC	hot
cmo LSC	solar
cmc LC	bilbao
mpl LSC	pink
cmc LC	tokyo
cc LSC	dimgray
cmc LC	grayC
cc LSC	gray
cmo LSC	gray
mpl LSC	gray
mpl LSC	gist_gray
mpl LSC	bone
cmc LC	oslo
cmo LSC	ice
cmc LC	devon
cc LSC	kbc
mpl LSC	winter
cc LSC	bmw
cmc LC	acton
mpl LSC	cubehelix
cmc LC	batlowW
cmc LC	batlowK
cmc LC	batlow
cmc LC	turku
cmc LC	bamako
cc LSC	bgyw
cc LSC	kbgyw
cc LSC	gouldian
cc LSC	bgy
cc LSC	kgy
mpl LSC	summer
cmo LSC	oxy
cmc LC	hawaii
cmc LC	buda
mpl LSC	spring
mpl LSC	autumn
mpl LSC	Wistia
mpl LSC	Oranges
mpl LSC	YlOrBr
mpl LSC	OrRd
mpl LSC	YlOrRd
mpl LSC	Reds
cmo LSC	amp
mpl LSC	PuRd
mpl LSC	RdPu
cmo LSC	matter
mpl LSC	BuPu
mpl LSC	Purples
cmo LSC	dense
mpl LSC	Blues
mpl LSC	PuBu
mpl LSC	PuBuGn
cc LSC	blues
mpl LSC	GnBu
mpl LSC	YlGnBu
cmo LSC	tempo
cmo LSC	rain
cmo LSC	deep
mpl LSC	gist_yarg
mpl LSC	binary
mpl LSC	Grays
cmo LSC	turbid
cmo LSC	algae
cmo LSC	speed
mpl LSC	YlGn
mpl LSC	Greens
mpl LSC	BuGn
mpl LSC	cool
cmc LC	glasgow
cc LSC	kg
cc LSC	kb
cc LSC	kr

Diverging

Colormaps with two contrasting colors diverging from a central midpoint, useful for highlighting deviation from a reference value.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	coolwarm
mpl LSC	coolwarm
cc LSC	bkr
mpl LSC	bwr
mpl LSC	seismic
cmo LSC	balance
cmc LC	berlin
mpl LC	berlin
cmc LC	vik
cmo LSC	diff
cc LSC	bky
cc LSC	bwy
cmc LC	lisbon
cc LSC	bjy
cmc LC	broc
cmc LC	tofino
cmc LC	cork
cmo LSC	delta
mpl LSC	PRGn
cmc LC	vanimo
mpl LC	vanimo
cmc LC	bam
mpl LSC	PiYG
mpl LSC	RdYlGn
mpl LSC	Spectral
mpl LSC	BrBG
mpl LSC	RdYlBu
mpl LSC	RdBu
cmc LC	roma
mpl LSC	PuOr
cmc LC	managua
mpl LC	managua
cmo LSC	tarn
cc LSC	gwv
mpl LSC	RdGy
cmo LSC	curl
cc LSC	cwr

Multi-Sequential

Colormaps with multiple sequential gradients diverging from a midpoint, ideal for highlighting deviations on both sides of a reference value.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cmo LSC	topo
cmc LC	bukavu
cmc LC	oleron
cmc LC	fes

Cyclic

Colormaps designed to wrap around smoothly, best for data that is inherently circular such as angles and phase.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cmo LSC	phase
cc LSC	cyclic_isoluminant
cc LSC	colorwheel
mpl LSC	hsv
mpl LC	twilight
mpl LC	twilight_shifted
cmc LC	vikO
cmc LC	romaO
cmc LC	bamO
cmc LC	brocO
cmc LC	corkO

Categorical (Qualitative)

Colormaps using distinct colors for individual categories, suitable for labeling discrete classes or groups.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LC	glasbey
cc LC	glasbey_bw
cc LC	glasbey_cool
cc LC	glasbey_warm
cc LC	glasbey_dark
cc LC	glasbey_light
cc LC	glasbey_category10
cc LC	glasbey_hv
cmc LC	grayCS
cmc LC	bilbaoS
cmc LC	lajollaS
cmc LC	batlowWS
cmc LC	budaS
cmc LC	hawaiiS
cmc LC	tokyoS
cmc LC	nuukS
cmc LC	naviaS
cmc LC	davosS
cmc LC	lapazS
cmc LC	imolaS
cmc LC	devonS
cmc LC	osloS
cmc LC	lipariS
cmc LC	actonS
cmc LC	turkuS
cmc LC	batlowKS
cmc LC	batlowS
cmc LC	bamakoS
cmc LC	glasgowS
mpl LC	Accent
mpl LC	Dark2
mpl LC	Paired
mpl LC	Pastel1
mpl LC	Pastel2
mpl LC	Set1
mpl LC	Set2
mpl LC	Set3
mpl LC	tab10
mpl LC	tab20
mpl LC	tab20b
mpl LC	tab20c

Miscellaneous

Colormaps that don’t fit neatly into other categories, often used for artistic or specialized purposes.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	isolum
cc LSC	rainbow4
cc LSC	rainbow
mpl LSC	rainbow
mpl LSC	gist_rainbow
mpl LSC	jet
mpl LC	turbo
mpl LSC	nipy_spectral
mpl LSC	gist_ncar
mpl LSC	CMRmap
mpl LSC	brg
mpl LSC	gist_stern
mpl LSC	gnuplot
mpl LSC	gnuplot2
mpl LSC	ocean
mpl LSC	gist_earth
mpl LSC	terrain
mpl LSC	prism
mpl LSC	flag

CET Colormaps

This section includes all colorcet colormaps that have a named CET alias (originally from the Center for Exploration Targeting).

Colormap names have the form:

CET_[prefix]{type}{number}[suffix]

Where the prefix and suffix are optional ([]) and the type and number are required ({}).

Prefix :

Indicates additional information about color perception.

• CB: Colorblind—safe for red—green color vision deficiency (protanopia/deuteranopia)

• CBT: Colorblind—safe for blue—yellow color vision deficiency (tritanopia)

Type :

The type of colormap.

• C: Cyclic

• D: Diverging

• L: Linear

• R: Rainbow

• I: Isoluminant

Number :

A version number (starting at 1) for each unique [prefix]{type} combination.

Suffix :

Indicates a minor variation of the base colormap.

• A: Alternate version (such as adjusted contrast or hue emphasis)

• s: Shifted version (phase-shifted)

Note

Most of the colorcet colormaps presented above in Named Colors are duplicated here (using their CET aliases).

Linear (Sequential)

Colormaps that progress in a single direction, ideal for representing ordered data such as intensities or magnitudes.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	CET_CBL1
cc LSC	CET_CBL2
cc LSC	CET_CBL3
cc LSC	CET_CBL4
cc LSC	CET_CBTL1
cc LSC	CET_CBTL2
cc LSC	CET_CBTL3
cc LSC	CET_CBTL4
cc LSC	CET_L1
cc LSC	CET_L2
cc LSC	CET_L3
cc LSC	CET_L4
cc LSC	CET_L5
cc LSC	CET_L6
cc LSC	CET_L7
cc LSC	CET_L8
cc LSC	CET_L9
cc LSC	CET_L10
cc LSC	CET_L11
cc LSC	CET_L12
cc LSC	CET_L13
cc LSC	CET_L14
cc LSC	CET_L15
cc LSC	CET_L16
cc LSC	CET_L17
cc LSC	CET_L18
cc LSC	CET_L19
cc LSC	CET_L20

Diverging

Colormaps with two contrasting colors diverging from a central midpoint, useful for highlighting deviation from a reference value.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	CET_CBD1
cc LSC	CET_CBD2
cc LSC	CET_CBTD1
cc LSC	CET_D1
cc LSC	CET_D1A
cc LSC	CET_D2
cc LSC	CET_D3
cc LSC	CET_D4
cc LSC	CET_D6
cc LSC	CET_D7
cc LSC	CET_D8
cc LSC	CET_D9
cc LSC	CET_D10
cc LSC	CET_D11
cc LSC	CET_D12
cc LSC	CET_D13

Cyclic

Colormaps designed to wrap around smoothly, best for data that is inherently circular such as angles and phase.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	CET_C1
cc LSC	CET_C1s
cc LSC	CET_C2
cc LSC	CET_C2s
cc LSC	CET_C3
cc LSC	CET_C3s
cc LSC	CET_C4
cc LSC	CET_C4s
cc LSC	CET_C5
cc LSC	CET_C5s
cc LSC	CET_C6
cc LSC	CET_C6s
cc LSC	CET_C7
cc LSC	CET_C7s
cc LSC	CET_C8
cc LSC	CET_C8s
cc LSC	CET_C9
cc LSC	CET_C9s
cc LSC	CET_C10
cc LSC	CET_C10s
cc LSC	CET_C11
cc LSC	CET_C11s
cc LSC	CET_CBC1
cc LSC	CET_CBC2
cc LSC	CET_CBTC1
cc LSC	CET_CBTC2

Rainbow

Multi-hue colormaps that span the full visible spectrum, useful for highlighting local differences in sequential data.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	CET_R1
cc LSC	CET_R2
cc LSC	CET_R3
cc LSC	CET_R4

Isoluminant

Colormaps with constant perceived brightness, useful for emphasizing shape and structure without introducing false intensity cues.

Tags	Name	Swatch	Lightness \(L^*\)	Cumulative ΔE
cc LSC	CET_I1
cc LSC	CET_I2
cc LSC	CET_I3