Colour scales

There are two default colour scales in cf-plot:

1. A continuous scale ('viridis') that goes from blue to green and then yellow and suits data that has no zero in it. For example air temperature in Kelvin or geopotential height - see example 1 in the plot gallery.

2. A diverging scale ('scale1') that goes from blue to red and suits data with a zero in it. For example temperature in Celsius or zonal wind - see example 4 in the plot gallery. The colour scale is automatically adjusted so that blue hues are below zero and red hues above zero.

When no calls have been made to cfp.cscale cf-plot selects one of theses scales based on whether there is a zero in the data passed for contouring. If a call is made to cfp.cscale with just a colour scale name cfp.cscale('radar'), for example, then this colour scale is used for all subsequent plots. The colour scale is adjusted automatically to fit the number of contour levels in the plot.

If a call to cfp.cscale specifies additional parameters to the colour scale, then the automatic colour adjustment is turned off giving the user fine tuning of colours as below.

cfp.levs(min=-80, max=80, step=10)
cfp.scale('scale1')

To change the number of colours in a scale use the ncols parameters.

cfp.cscale('scale1', ncols=12)
cfp.levs(min=-5, max=5, step=1)

To change the number of colours above and below the mid-point of the scale use the above and below parameters. This is useful for fields where you have differing extents of data above and below the zero line.

cfp.cscale('scale1', below=4, above=7)
cfp.levs(min=-30, max=60, step=10)

For data where you need white to indicate that this data region is insignificant use the white=white parameter. This can take single or multiple values of the index of the colour scale where white is required in the colour scale.

cfp.cscale('scale1', ncols=11, white=5)
cfp.levs(manual=[-10,-8, -6, -4, -2, 2, 4, 6, 8, 10])

To reverse a colour scale use the reverse=1 option to cscale and specify the number of colours required.

cfp.cscale('scale1', reverse=1, ncols=10)

As a short example to show the flexibilty of the colour scale routines we will make a orography plot using the wiki_2_0.rgb orography/bathymetry colour scale. This has as many colours for bathymetry as for the oroggraphy but in this case we just need a blue ocean as we are really only interested in the orography. So in this case we will define a set of levels using levs and then match the colour scale to them. The wiki_2_0.rgb colour scale has as many colours for the ocean as for the land so we can use the above and below options

import cf
import cfplot as cfp
import numpy as np
f=cf.read('cfplot_data/12km_orog.nc')[0]
cfp.cscale('wiki_2_0', ncols=16, below=2, above=14)
cfp.levs(manual=np.arange(15)*150)
cfp.con(f, lines=False)

User defined colour scales

Store these as rgb values in a file with one rgb value per line. i.e.

255 0   0
255 255 255
0   0   255

will give a red white blue colour scale. If the file is saved as /home/swsheaps/rwb.txt it is read in using

cfp.cscale('/home/swsheaps/rwb.txt')

Selecting colours for graph lines

This can be done in several ways:

1. Select the colours from the Matplotlib colour names - Google 'Images for matplotlib color names'.

cfp.lineplot(g.subspace(pressure=925), color='plum')

2. Use the hexadecimal code for the colour.

cfp.lineplot(g.subspace(pressure=925), color = '#eeefff')

3. Shades of grey can be selected with cmap(shade), where shade go from 0 to 1.

cfp.lineplot(g.subspace(pressure=925), color=cmap(0.8))

Predefined colour scales

A lot of the following colour maps were downloaded from the NCAR Command Language web site. Users of the IDL guide colour maps can see these maps at the end of the colour scales.

Perceptually uniform colour scales

A selection of perceptually uniform colour scales for contouring data without a zero in. See The end of the rainbow and Matplotlib colour maps for a good discussion on colour scales, colour blindness and uniform colour scales.

Name	Scale
viridis
magma
inferno
plasma
parula
gray

NCAR Command Language - MeteoSwiss colour maps

Name	Scale
hotcold_18lev
hotcolr_19lev
mch_default
perc2_9lev
percent_11lev
precip2_15lev
precip2_17lev
precip3_16lev
precip4_11lev
precip4_diff_19lev
precip_11lev
precip_diff_12lev
precip_diff_1lev
rh_19lev
spread_15lev

NCAR Command Language - small color maps (<50 colours)

Name	Scale
amwg
amwg_blueyellowred
BlueDarkRed18
BlueDarkOrange18
BlueGreen14
BrownBlue12
Cat12
cmp_flux
cosam12
cosam
GHRSST_anomaly
GreenMagenta16
hotcold_18lev
hotcolr_19lev
mch_default
nrl_sirkes
nrl_sirkes_nowhite
perc2_9lev
percent_11lev
posneg_2
prcp_1
prcp_2
prcp_3
precip_11lev
precip_diff_12lev
precip_diff_1lev
precip2_15lev
precip2_17lev
precip3_16lev
precip4_11lev
precip4_diff_19lev
radar
radar_1
rh_19lev
seaice_1
seaice_2
so4_21
spread_15lev
StepSeq25
sunshine_9lev
sunshine_diff_12lev
temp_19lev
temp_diff_18lev
temp_diff_1lev
topo_15lev
wgne15
wind_17lev

NCAR Command Language - large colour maps (>50 colours)

Name	Scale
amwg256
BkBlAqGrYeOrReViWh200
BlAqGrYeOrRe
BlAqGrYeOrReVi200
BlGrYeOrReVi200
BlRe
BlueRed
BlueRedGray
BlueWhiteOrangeRed
BlueYellowRed
BlWhRe
cmp_b2r
cmp_haxby
detail
extrema
GrayWhiteGray
GreenYellow
helix
helix1
hotres
matlab_hot
matlab_hsv
matlab_jet
matlab_lines
ncl_default
ncview_default
OceanLakeLandSnow
rainbow
rainbow_white_gray
rainbow_white
rainbow_gray
tbr_240_300
tbr_stdev_0_30
tbr_var_0_500
tbrAvg1
tbrStd1
tbrVar1
thelix
ViBlGrWhYeOrRe
wh_bl_gr_ye_re
WhBlGrYeRe
WhBlReWh
WhiteBlue
WhiteBlueGreenYellowRed
WhiteGreen
WhiteYellowOrangeRed
WhViBlGrYeOrRe
WhViBlGrYeOrReWh
wxpEnIR
3gauss
3saw
BrBG

NCAR Command Language - Enhanced to help with colour blindness

Name	Scale
StepSeq25
posneg_2
posneg_1
BlueDarkOrange18
BlueDarkRed18
GreenMagenta16
BlueGreen14
BrownBlue12
Cat12

Orography/bathymetry colour scales

Name	Scale
os250kmetres
wiki_1_0_2
wiki_1_0_3
wiki_2_0
wiki_2_0_reduced
arctic

IDL guide scales

Name	Scale
scale1
scale2
scale3
scale4
scale5
scale6
scale7
scale8
scale9
scale10
scale11
scale12
scale13
scale14
scale15
scale16
scale17
scale18
scale19
scale20
scale21
scale22
scale23
scale24
scale25
scale26
scale27
scale28
scale29
scale30
scale31
scale32
scale33
scale34
scale35
scale36
scale37
scale38
scale39
scale40
scale41
scale42
scale43
scale44