cmaps

NEW FEATURES ADDED BY PLUTOSET:

UPDATED 20241021

• Colormap generation codes and pdf outputs now available (see examples/plot_colormap.py and examples/colormap.pdf)

---

1. More colormaps from [Panoply colorbars[(https://www.giss.nasa.gov/tools/panoply/colorbars/) is now available.
2. Visualization of colormaps is now available. Just pull down the README file and you will see it.
3. changed README format from rst files to md files.

names	colormap
amwg
amwg256
amwg256_r
amwg_blueyellowred
amwg_blueyellowred_r
amwg_r
BkBlAqGrYeOrReViWh200
BkBlAqGrYeOrReViWh200_r
BlAqGrWh2YeOrReVi22
BlAqGrWh2YeOrReVi22_r
BlAqGrYeOrRe
BlAqGrYeOrReVi200
BlAqGrYeOrReVi200_r
BlAqGrYeOrRe_r
BlGrYeOrReVi200
BlGrYeOrReVi200_r
BlRe
BlRe_r
BlueDarkOrange18
BlueDarkOrange18_r
BlueDarkRed18
BlueDarkRed18_r
BlueGreen14
BlueGreen14_r
BlueRed
BlueRedGray
BlueRedGray_r
BlueRed_r
BlueWhiteOrangeRed
BlueWhiteOrangeRed_r
BlueYellowRed
BlueYellowRed_r
BlWhRe
BlWhRe_r
BrownBlue12
BrownBlue12_r
Carbone42
Carbone42_r
Cat12
Cat12_r
CBR_coldhot
CBR_coldhot_r
CBR_drywet
CBR_drywet_r
CBR_set3
CBR_set3_r
CBR_wet
CBR_wet_r
cb_9step
cb_9step_r
cb_rainbow
cb_rainbow_inv
cb_rainbow_inv_r
cb_rainbow_r
circular_0
circular_0_r
circular_1
circular_1_r
circular_2
circular_2_r
cividis
cividis_r
cmaps_rainbow_gray
cmaps_rainbow_gray_r
cmaps_rainbow_white
cmaps_rainbow_white_gray
cmaps_rainbow_white_gray_r
cmaps_rainbow_white_r
cmaps_tbr_240_300
cmaps_tbr_240_300_r
cmaps_tbr_stdev_0_30
cmaps_tbr_stdev_0_30_r
cmaps_tbr_var_0_500
cmaps_tbr_var_0_500_r
cmaps_wh_bl_gr_ye_re
cmaps_wh_bl_gr_ye_re_r
cmocean_algae
cmocean_algae_r
cmocean_amp
cmocean_amp_r
cmocean_balance
cmocean_balance_r
cmocean_curl
cmocean_curl_r
cmocean_deep
cmocean_deep_r
cmocean_delta
cmocean_delta_r
cmocean_dense
cmocean_dense_r
cmocean_gray
cmocean_gray_r
cmocean_haline
cmocean_haline_r
cmocean_ice
cmocean_ice_r
cmocean_matter
cmocean_matter_r
cmocean_oxy
cmocean_oxy_r
cmocean_phase
cmocean_phase_r
cmocean_solar
cmocean_solar_r
cmocean_speed
cmocean_speed_r
cmocean_tempo
cmocean_tempo_r
cmocean_thermal
cmocean_thermal_r
cmocean_turbid
cmocean_turbid_r
cmp_b2r
cmp_b2r_r
cmp_flux
cmp_flux_r
cmp_haxby
cmp_haxby_r
cosam
cosam12
cosam12_r
cosam_r
cyclic
cyclic_r
default
default_r
detail
detail_r
drought_severity
drought_severity_r
EO_aura_omi_formal
EO_aura_omi_formal_r
EO_carbon_density
EO_carbon_density_r
EO_lightning_lis_otd
EO_lightning_lis_otd_r
EO_sargassum_tamo
EO_sargassum_tamo_r
EO_soil_moist_div
EO_soil_moist_div_r
EO_temp_anom_4
EO_temp_anom_4_r
EVL_wind_anom
EVL_wind_anom_r
example
example_r
extrema
extrema_r
GHRSST_anomaly
GHRSST_anomaly_r
GISS_isccp_rainbow
GISS_isccp_rainbow_20
GISS_isccp_rainbow_20_r
GISS_isccp_rainbow_r
GMT_cool
GMT_cool_r
GMT_copper
GMT_copper_r
GMT_drywet
GMT_drywet_r
GMT_earth
GMT_earth_r
GMT_gebco
GMT_gebco_r
GMT_globe
GMT_globe_r
GMT_gray
GMT_gray_r
GMT_haxby
GMT_haxby_r
GMT_hot
GMT_hot_r
GMT_jet
GMT_jet_r
GMT_nighttime
GMT_nighttime_r
GMT_no_green
GMT_no_green_r
GMT_ocean
GMT_ocean_r
GMT_paired
GMT_paired_r
GMT_panoply
GMT_panoply_r
GMT_polar
GMT_polar_r
GMT_red2green
GMT_red2green_r
GMT_relief
GMT_relief_oceanonly
GMT_relief_oceanonly_r
GMT_relief_r
GMT_seis
GMT_seis_20
GMT_seis_20_r
GMT_seis_r
GMT_split
GMT_split_r
GMT_topo
GMT_topo_r
GMT_wysiwyg
GMT_wysiwygcont
GMT_wysiwygcont_r
GMT_wysiwyg_r
grads_default
grads_default_r
grads_rainbow
grads_rainbow_r
GrayWhiteGray
GrayWhiteGray_r
GreenMagenta16
GreenMagenta16_r
GreenYellow
GreenYellow_r
gscyclic
gscyclic_r
gsdtol
gsdtol_r
GSFC_landsat_udf_density
GSFC_landsat_udf_density_r
gsltod
gsltod_r
gui_default
gui_default_r
helix
helix1
helix1_r
helix_r
hlu_default
hlu_default_r
hotcold_18lev
hotcold_18lev_r
hotcolr_19lev
hotcolr_19lev_r
hotres
hotres_r
lithology
lithology_r
mask
mask_r
matlab_hot
matlab_hot_r
matlab_hsv
matlab_hsv_r
matlab_jet
matlab_jet_r
matlab_lines
matlab_lines_r
mch_default
mch_default_r
MPL_Accent
MPL_Accent_r
MPL_afmhot
MPL_afmhot_r
MPL_autumn
MPL_autumn_r
MPL_Blues
MPL_Blues_r
MPL_bone
MPL_bone_r
MPL_BrBG
MPL_BrBG_r
MPL_brg
MPL_brg_r
MPL_BuGn
MPL_BuGn_r
MPL_BuPu
MPL_BuPu_r
MPL_bwr
MPL_bwr_r
MPL_cool
MPL_coolwarm
MPL_coolwarm_r
MPL_cool_r
MPL_copper
MPL_copper_r
MPL_cubehelix
MPL_cubehelix_r
MPL_Dark2
MPL_Dark2_r
MPL_flag
MPL_flag_r
MPL_gist_earth
MPL_gist_earth_r
MPL_gist_gray
MPL_gist_gray_r
MPL_gist_heat
MPL_gist_heat_r
MPL_gist_ncar
MPL_gist_ncar_r
MPL_gist_rainbow
MPL_gist_rainbow_r
MPL_gist_stern
MPL_gist_stern_r
MPL_gist_yarg
MPL_gist_yarg_r
MPL_GnBu
MPL_GnBu_r
MPL_gnuplot
MPL_gnuplot2
MPL_gnuplot2_r
MPL_gnuplot_r
MPL_Greens
MPL_Greens_r
MPL_Greys
MPL_Greys_r
MPL_hot
MPL_hot_r
MPL_hsv
MPL_hsv_r
MPL_jet
MPL_jet_r
MPL_ocean
MPL_ocean_r
MPL_Oranges
MPL_Oranges_r
MPL_OrRd
MPL_OrRd_r
MPL_Paired
MPL_Paired_r
MPL_Pastel1
MPL_Pastel1_r
MPL_Pastel2
MPL_Pastel2_r
MPL_pink
MPL_pink_r
MPL_PiYG
MPL_PiYG_r
MPL_PRGn
MPL_PRGn_r
MPL_prism
MPL_prism_r
MPL_PuBu
MPL_PuBuGn
MPL_PuBuGn_r
MPL_PuBu_r
MPL_PuOr
MPL_PuOr_r
MPL_PuRd
MPL_PuRd_r
MPL_Purples
MPL_Purples_r
MPL_rainbow
MPL_rainbow_r
MPL_RdBu
MPL_RdBu_r
MPL_RdGy
MPL_RdGy_r
MPL_RdPu
MPL_RdPu_r
MPL_RdYlBu
MPL_RdYlBu_r
MPL_RdYlGn
MPL_RdYlGn_r
MPL_Reds
MPL_Reds_r
MPL_s3pcpn
MPL_s3pcpn_l
MPL_s3pcpn_l_r
MPL_s3pcpn_r
MPL_seismic
MPL_seismic_r
MPL_Set1
MPL_Set1_r
MPL_Set2
MPL_Set2_r
MPL_Set3
MPL_Set3_r
MPL_Spectral
MPL_Spectral_r
MPL_spring
MPL_spring_r
MPL_sstanom
MPL_sstanom_r
MPL_StepSeq
MPL_StepSeq_r
MPL_summer
MPL_summer_r
MPL_terrain
MPL_terrain_r
MPL_viridis
MPL_viridis_r
MPL_winter
MPL_winter_r
MPL_YlGn
MPL_YlGnBu
MPL_YlGnBu_r
MPL_YlGn_r
MPL_YlOrBr
MPL_YlOrBr_r
MPL_YlOrRd
MPL_YlOrRd_r
MRO_ice_freq
MRO_ice_freq_r
N3gauss
N3gauss_r
N3saw
N3saw_r
NCDC_snow_anom
NCDC_snow_anom_r
NCDC_temp_anom_f
NCDC_temp_anom_f_r
ncl_default
ncl_default_r
ncview_default
ncview_default_r
NCV_banded
NCV_banded_r
NCV_blue_red
NCV_blue_red_r
NCV_blu_red
NCV_blu_red_r
NCV_bright
NCV_bright_r
NCV_gebco
NCV_gebco_r
NCV_jaisnd
NCV_jaisnd_r
NCV_jet
NCV_jet_r
NCV_manga
NCV_manga_r
NCV_rainbow2
NCV_rainbow2_r
NCV_roullet
NCV_roullet_r
NEO_albedo_change
NEO_albedo_change_r
NEO_amsre_ss
NEO_amsre_sst_anom
NEO_amsre_sst_anom_r
NEO_amsre_ss_r
NEO_aod_diff
NEO_aod_diff_r
NEO_aquarius_sss
NEO_aquarius_sss_r
NEO_bright_temp
NEO_bright_temp_r
NEO_carb_emit_anom
NEO_carb_emit_anom_r
NEO_cdom
NEO_cdom_r
NEO_ceres_insol
NEO_ceres_insol_r
NEO_ceres_lw
NEO_ceres_lw_r
NEO_ceres_ne
NEO_ceres_ne_r
NEO_ceres_sw
NEO_ceres_sw_r
NEO_chlorophyll
NEO_chlorophyll_o
NEO_chlorophyll_o_r
NEO_chlorophyll_r
NEO_div_vegetation_
NEO_div_vegetation_a
NEO_div_vegetation_a_r
NEO_div_vegetation_b
NEO_div_vegetation_b_r
NEO_div_vegetation_c
NEO_div_vegetation_c_r
NEO_div_vegetation__r
NEO_evapstress
NEO_evapstress_r
NEO_gebco_bathymetry
NEO_gebco_bathymetry_r
NEO_ggmcf
NEO_ggmcf_r
NEO_giss_temp_anom
NEO_giss_temp_anom_r
NEO_grace_lwe_anom
NEO_grace_lwe_anom_r
NEO_grav_anom
NEO_grav_anom_r
NEO_imperv_surf
NEO_imperv_surf_r
NEO_meltseason_anom
NEO_meltseason_anom_r
NEO_modis_aer_od
NEO_modis_aer_od_r
NEO_modis_bs_albedo
NEO_modis_bs_albedo_r
NEO_modis_chlor
NEO_modis_chlor_r
NEO_modis_cld_ci
NEO_modis_cld_ci_r
NEO_modis_cld_fr
NEO_modis_cld_fr_r
NEO_modis_cld_o
NEO_modis_cld_o_r
NEO_modis_cld_rd
NEO_modis_cld_rd_r
NEO_modis_cld_wp
NEO_modis_cld_wp_r
NEO_modis_ls
NEO_modis_lst_anom
NEO_modis_lst_anom_r
NEO_modis_ls_r
NEO_modis_ndvi
NEO_modis_ndvi_r
NEO_modis_sky_wv
NEO_modis_sky_wv_r
NEO_modis_sst_45
NEO_modis_sst_45_r
NEO_mopitt_co
NEO_mopitt_co_r
NEO_ns_airtemp
NEO_ns_airtemp_r
NEO_omi_no2
NEO_omi_no2_r
NEO_omi_ozone_to3
NEO_omi_ozone_to3_r
NEO_omi_uvi
NEO_omi_uvi_r
NEO_pollution_conc_9
NEO_pollution_conc_9_r
NEO_pollution_mor
NEO_pollution_mor_r
NEO_rainfall_anom_9
NEO_rainfall_anom_9_r
NEO_seasurf_hgt_anom
NEO_seasurf_hgt_anom_r
NEO_sedac_pop
NEO_sedac_pop_r
NEO_snow_water
NEO_snow_water_r
NEO_soil_moisture
NEO_soil_moisture_anom
NEO_soil_moisture_anom_nl
NEO_soil_moisture_anom_nl_r
NEO_soil_moisture_anom_r
NEO_soil_moisture_pale
NEO_soil_moisture_pale_r
NEO_soil_moisture_r
NEO_srtm_topography
NEO_srtm_topography_r
NEO_tree_cover
NEO_tree_cover_r
NEO_trmm_rainfall
NEO_trmm_rainfall_r
NEO_wind_spd_anom
NEO_wind_spd_anomaly_pwg
NEO_wind_spd_anomaly_pwg_r
NEO_wind_spd_anom_r
nice_gfdl
nice_gfdl_r
NMCRef
NMCRef_r
NMCVel
NMCVel2
NMCVel2_r
NMCVel_r
NOC_ndvi
NOC_ndvi_r
nrl_sirkes
nrl_sirkes_nowhite
nrl_sirkes_nowhite_r
nrl_sirkes_r
NWSRef
NWSRef_r
NWSSPW
NWSSPW_r
NWSVel
NWSVel_r
NYT_drought
NYT_drought_r
OceanLakeLandSnow
OceanLakeLandSnow_r
perc2_9lev
perc2_9lev_r
percent_11lev
percent_11lev_r
posneg_1
posneg_1_r
posneg_2
posneg_2_r
prcp_1
prcp_1_r
prcp_2
prcp_2_r
prcp_3
prcp_3_r
precip2_15lev
precip2_15lev_r
precip2_17lev
precip2_17lev_r
precip3_16lev
precip3_16lev_r
precip4_11lev
precip4_11lev_r
precip4_diff_19lev
precip4_diff_19lev_r
precip_11lev
precip_11lev_r
precip_diff_12lev
precip_diff_12lev_r
precip_diff_1lev
precip_diff_1lev_r
psgcap
psgcap_r
radar
radar_1
radar_1_r
radar_r
rainbow
rainbow_r
rh_19lev
rh_19lev_r
seaice_1
seaice_1_r
seaice_2
seaice_2_r
so4_21
so4_21_r
so4_23
so4_23_r
spread_15lev
spread_15lev_r
srip_reanalysis
srip_reanalysis_r
StepSeq25
StepSeq25_r
sunshine_9lev
sunshine_9lev_r
sunshine_diff_12lev
sunshine_diff_12lev_r
SVG_bhw3_22
SVG_bhw3_22_r
SVG_es_landscape_79
SVG_es_landscape_79_r
SVG_feb_sunrise
SVG_feb_sunrise_r
SVG_foggy_sunrise
SVG_foggy_sunrise_r
SVG_fs2006
SVG_fs2006_r
SVG_Gallet13
SVG_Gallet13_r
SVG_Lindaa06
SVG_Lindaa06_r
SVG_Lindaa07
SVG_Lindaa07_r
SVS_soilmoisture
SVS_soilmoisture_r
SVS_tempanomaly
SVS_tempanomaly_r
t2m_29lev
t2m_29lev_r
tbrAvg1
tbrAvg1_r
tbrStd1
tbrStd1_r
tbrVar1
tbrVar1_r
temp1
temp1_r
temp_19lev
temp_19lev_r
temp_diff_18lev
temp_diff_18lev_r
temp_diff_1lev
temp_diff_1lev_r
testcmap
testcmap_r
thelix
thelix_r
TopoGray
TopoGray_r
topo_15lev
topo_15lev_r
TwoClass
TwoClass_r
UKM_hadcrut
UKM_hadcrut_10
UKM_hadcrut_10_r
UKM_hadcrut_r
uniform
uniform_r
vegetation_ClarkU
vegetation_ClarkU_r
vegetation_modis
vegetation_modis_r
ViBlGrWhYeOrRe
ViBlGrWhYeOrRe_r	![ViBlGrWhYeOrRe_r[(examples/ViBlGrWhYeOrRe_r.jpg)
wgne15
wgne15_r
WhBlGrYeRe
WhBlGrYeRe_r
WhBlReWh
WhBlReWh_r
WhiteBlue
WhiteBlueGreenYellowRed
WhiteBlueGreenYellowRed_r
WhiteBlue_r
WhiteGreen
WhiteGreen_r
WhiteYellowOrangeRed
WhiteYellowOrangeRed_r
WhViBlGrYeOrRe
WhViBlGrYeOrReWh
WhViBlGrYeOrReWh_r
WhViBlGrYeOrRe_r
wind_17lev
wind_17lev_r
wxpEnIR
wxpEnIR_r

---

original README

Make it easier to use user defined colormaps in matplotlib. Default colormaps are from NCL website.

Users can define a environmental variable CMAP_DIR pointing to the folder containing the self-defined rgb files.

Special thanks to Dr. Shen: for suggestions and the help of uploading this package to Pypi and anaconda cloud.

If you find my project helpful and would like to support my work with a small contribution (with Paypal), I would greatly appreciate it.

如果您觉得我的项目对您有所帮助，可以给我买杯奶茶！

see at .donation/sponsor.png

Installation:

pip install cmaps

or:

conda install -c conda-forge cmaps

or:

git clone https://github.com/hhuangwx/cmaps.git
cd cmaps
python setup.py install

Usage:

import matplotlib.pyplot as plt
import cmaps
import numpy as np

x = y = np.arange(-3.0, 3.01, 0.05)
X, Y = np.meshgrid(x, y)

sigmax = sigmay = 1.0
mux = muy = sigmaxy=0.0

Xmu = X-mux
Ymu = Y-muy

rho = sigmaxy/(sigmax*sigmay)
z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay)
denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2)
Z = np.exp(-z/(2*(1-rho**2))) / denom

plt.pcolormesh(X,Y,Z,cmap=cmaps.WhiteBlueGreenYellowRed)
plt.colorbar()

List the colormaps using the code in the examples:

import cmaps
import numpy as np
import inspect

import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('text', usetex=False)


def list_cmaps():
    attributes = inspect.getmembers(cmaps, lambda _: not (inspect.isroutine(_)))
    colors = [_[0] for _ in attributes if
              not (_[0].startswith('__') and _[0].endswith('__'))]
    return colors


if __name__ == '__main__':
    color = list_cmaps()

    a = np.outer(np.arange(0, 1, 0.001), np.ones(10))
    plt.figure(figsize=(20, 20))
    plt.subplots_adjust(top=0.95, bottom=0.05, left=0.01, right=0.99)
    ncmaps = len(color)
    nrows = 8
    for i, k in enumerate(color):
        plt.subplot(nrows, ncmaps // nrows + 1, i + 1)
        plt.axis('off')
        plt.imshow(a, aspect='auto', cmap=getattr(cmaps, k), origin='lower')
        plt.title(k, rotation=90, fontsize=10)
        plt.title(k, fontsize=10)
    plt.savefig('colormaps.png', dpi=300)
    plt.close()

New features:

1. "Slicing" function like list or numpy array is supported for cmaps:

   cmaps.amwg256[20:-20:2]
   cmaps.amwg256[-20:20:-2]
   

2. "add" function for the cmaps are supported now:

   cmaps.amwg256+WhiteBlueGreenYellowRed
   

3. a cmap can now be interpolated (different from the "resampled" function in the new version of matplotlib which only takes the nearest ones):

   cmaps.amwg256.interp(50)
   cmaps.amwg256.interp(1000)
   

4. a cmap can now be convert to LinearSegmentedColormap with different numbers of colors, with part of effect similar to interpolation:

   cmaps.amwg256.to_seg(N=100)