├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── metdig ├── __init__.py ├── cal │ ├── __init__.py │ ├── base │ │ ├── __init__.py │ │ ├── arr.py │ │ ├── calculate.py │ │ ├── constants.py │ │ ├── css.py │ │ ├── cyclone.py │ │ ├── datetime.py │ │ ├── dynamic.py │ │ ├── filepath.py │ │ ├── geographical.py │ │ ├── geometry.py │ │ ├── grid.py │ │ ├── math.py │ │ ├── moisture.py │ │ ├── numeric.py │ │ ├── oban.py │ │ ├── psi_phi.py │ │ ├── regridding.py │ │ ├── stats.py │ │ ├── thermal.py │ │ └── utilities.py │ ├── classification.py │ ├── cross_sections_module.py │ ├── dynamic.py │ ├── elements.py │ ├── ensemble.py │ ├── extreme.py │ ├── identify.py │ ├── indexes.py │ ├── interpolate.py │ ├── lib │ │ ├── __init__.py │ │ └── utility.py │ ├── metpy_cal.py │ ├── moisture.py │ ├── other.py │ ├── qpf.py │ ├── resources │ │ ├── height_oy.nc │ │ ├── sysIdentify2023.jar │ │ └── sysIdentify2024.jar │ ├── sounding.py │ ├── thermal.py │ └── tools.py ├── graphics │ ├── __init__.py │ ├── bar_method.py │ ├── barbs_method.py │ ├── boxplot_method.py │ ├── cmap │ │ ├── __init__.py │ │ ├── cm.py │ │ └── cpt.py │ ├── contour_method.py │ ├── contourf_method.py │ ├── draw_compose.py │ ├── lib │ │ ├── __init__.py │ │ ├── utility.py │ │ └── utl_plotmap.py │ ├── other_method.py │ ├── pallete_set.py │ ├── pcolormesh_method.py │ ├── plot_method.py │ ├── quiver_method.py │ ├── resources │ │ ├── __init__.py │ │ ├── backgrounds │ │ │ ├── RenderData.png │ │ │ └── images.json │ │ ├── colormaps_guide │ │ │ ├── cs1.txt │ │ │ ├── cs10.txt │ │ │ ├── cs11.txt │ │ │ ├── cs12.txt │ │ │ ├── cs13.txt │ │ │ ├── cs14.txt │ │ │ ├── cs15.txt │ │ │ ├── cs16.txt │ │ │ ├── cs17.txt │ │ │ ├── cs18.txt │ │ │ ├── cs19.txt │ │ │ ├── cs2.txt │ │ │ ├── cs20.txt │ │ │ ├── cs21.txt │ │ │ ├── cs22.txt │ │ │ ├── cs23.txt │ │ │ ├── cs24.txt │ │ │ ├── cs25.txt │ │ │ ├── cs26.txt │ │ │ ├── cs27.txt │ │ │ ├── cs28.txt │ │ │ ├── cs29.txt │ │ │ ├── cs3.txt │ │ │ ├── cs30.txt │ │ │ ├── cs31.txt │ │ │ ├── cs32.txt │ │ │ ├── cs33.txt │ │ │ ├── cs34.txt │ │ │ ├── cs35.txt │ │ │ ├── cs36.txt │ │ │ ├── cs37.txt │ │ │ ├── cs38.txt │ │ │ ├── cs39.txt │ │ │ ├── cs4.txt │ │ │ ├── cs40.txt │ │ │ ├── cs41.txt │ │ │ ├── cs42.txt │ │ │ ├── cs43.txt │ │ │ ├── cs44.txt │ │ │ ├── cs45.txt │ │ │ ├── cs46.txt │ │ │ ├── cs5.txt │ │ │ ├── cs6.txt │ │ │ ├── cs7.txt │ │ │ ├── cs8.txt │ │ │ └── cs9.txt │ │ ├── colormaps_met │ │ │ ├── LU_MODIS20.rgb │ │ │ ├── LU_MODIS21.rgb │ │ │ ├── LU_NLCD_chris.rgb │ │ │ ├── LU_USGS24.rgb │ │ │ ├── ape_nws.rgb │ │ │ ├── dewpoint1.rgb │ │ │ ├── dpt.rgb │ │ │ ├── gust.rgb │ │ │ ├── height_nws.rgb │ │ │ ├── high_temperature_nws.rgb │ │ │ ├── high_thermal_temperature_nws.rgb │ │ │ ├── high_wind_speed_nws.rgb │ │ │ ├── ir_enhancement.rgb │ │ │ ├── ir_enhancement1.rgb │ │ │ ├── ir_enhancement2.rgb │ │ │ ├── irsat.rgb │ │ │ ├── loud_cover_nws.rgb │ │ │ ├── precip.rgb │ │ │ ├── precip1.rgb │ │ │ ├── precipitable_water_nws.rgb │ │ │ ├── precipitation_metpy.rgb │ │ │ ├── precipitation_nws.rgb │ │ │ ├── precipitation_type_nws.rgb │ │ │ ├── qpf_nws.rgb │ │ │ ├── qsf_nws.rgb │ │ │ ├── rain.rgb │ │ │ ├── rain_nws.rgb │ │ │ ├── reflect_ncd.rgb │ │ │ ├── relative_humidity_nws.rgb │ │ │ ├── rh.rgb │ │ │ ├── sftemp.rgb │ │ │ ├── sky.rgb │ │ │ ├── sleet_nws.rgb │ │ │ ├── slp_nws.rgb │ │ │ ├── snow2.rgb │ │ │ ├── snow_density_nws.rgb │ │ │ ├── snow_depth_nws.rgb │ │ │ ├── snow_nws.rgb │ │ │ ├── specific_humidity_nws.rgb │ │ │ ├── temp.rgb │ │ │ ├── temperature_nws.rgb │ │ │ ├── temperature_trend_nws.rgb │ │ │ ├── terrain_256.rgb │ │ │ ├── terrain_50.rgb │ │ │ ├── theta.rgb │ │ │ ├── vertical_velocity_nws.rgb │ │ │ ├── visibility_nws.rgb │ │ │ ├── wind.rgb │ │ │ ├── wind_speed_nws.rgb │ │ │ ├── wsp.rgb │ │ │ ├── wv_enhancement.rgb │ │ │ └── wvfl_ctable.rgb │ │ ├── colormaps_ncl │ │ │ ├── 3gauss.rgb │ │ │ ├── 3saw.rgb │ │ │ ├── BkBlAqGrYeOrReViWh200.rgb │ │ │ ├── BlAqGrWh2YeOrReVi22.rgb │ │ │ ├── BlAqGrYeOrRe.rgb │ │ │ ├── BlAqGrYeOrReVi200.rgb │ │ │ ├── BlGrYeOrReVi200.rgb │ │ │ ├── BlRe.rgb │ │ │ ├── BlWhRe.rgb │ │ │ ├── BlueDarkOrange18.rgb │ │ │ ├── BlueDarkRed18.rgb │ │ │ ├── BlueGreen14.rgb │ │ │ ├── BlueRed.rgb │ │ │ ├── BlueRedGray.rgb │ │ │ ├── BlueWhiteOrangeRed.rgb │ │ │ ├── BlueYellowRed.rgb │ │ │ ├── BrownBlue12.rgb │ │ │ ├── CBR_coldhot.rgb │ │ │ ├── CBR_drywet.rgb │ │ │ ├── CBR_set3.rgb │ │ │ ├── CBR_wet.rgb │ │ │ ├── Cat12.rgb │ │ │ ├── Copyright │ │ │ ├── GHRSST_anomaly.rgb │ │ │ ├── GMT_cool.rgb │ │ │ ├── GMT_copper.rgb │ │ │ ├── GMT_drywet.rgb │ │ │ ├── GMT_gebco.rgb │ │ │ ├── GMT_globe.rgb │ │ │ ├── GMT_gray.rgb │ │ │ ├── GMT_haxby.rgb │ │ │ ├── GMT_hot.rgb │ │ │ ├── GMT_jet.rgb │ │ │ ├── GMT_nighttime.rgb │ │ │ ├── GMT_no_green.rgb │ │ │ ├── GMT_ocean.rgb │ │ │ ├── GMT_paired.rgb │ │ │ ├── GMT_panoply.rgb │ │ │ ├── GMT_polar.rgb │ │ │ ├── GMT_red2green.rgb │ │ │ ├── GMT_relief.rgb │ │ │ ├── GMT_relief_oceanonly.rgb │ │ │ ├── GMT_seis.rgb │ │ │ ├── GMT_split.rgb │ │ │ ├── GMT_topo.rgb │ │ │ ├── GMT_wysiwyg.rgb │ │ │ ├── GMT_wysiwygcont.rgb │ │ │ ├── GSFC_landsat_udf_density.rgb │ │ │ ├── GrayWhiteGray.rgb │ │ │ ├── GreenMagenta16.rgb │ │ │ ├── GreenYellow.rgb │ │ │ ├── MPL_Accent.rgb │ │ │ ├── MPL_Blues.rgb │ │ │ ├── MPL_BrBG.rgb │ │ │ ├── MPL_BuGn.rgb │ │ │ ├── MPL_BuPu.rgb │ │ │ ├── MPL_Dark2.rgb │ │ │ ├── MPL_GnBu.rgb │ │ │ ├── MPL_Greens.rgb │ │ │ ├── MPL_Greys.rgb │ │ │ ├── MPL_OrRd.rgb │ │ │ ├── MPL_Oranges.rgb │ │ │ ├── MPL_PRGn.rgb │ │ │ ├── MPL_Paired.rgb │ │ │ ├── MPL_Pastel1.rgb │ │ │ ├── MPL_Pastel2.rgb │ │ │ ├── MPL_PiYG.rgb │ │ │ ├── MPL_PuBu.rgb │ │ │ ├── MPL_PuBuGn.rgb │ │ │ ├── MPL_PuOr.rgb │ │ │ ├── MPL_PuRd.rgb │ │ │ ├── MPL_Purples.rgb │ │ │ ├── MPL_RdBu.rgb │ │ │ ├── MPL_RdGy.rgb │ │ │ ├── MPL_RdPu.rgb │ │ │ ├── MPL_RdYlBu.rgb │ │ │ ├── MPL_RdYlGn.rgb │ │ │ ├── MPL_Reds.rgb │ │ │ ├── MPL_Set1.rgb │ │ │ ├── MPL_Set2.rgb │ │ │ ├── MPL_Set3.rgb │ │ │ ├── MPL_Spectral.rgb │ │ │ ├── MPL_StepSeq.rgb │ │ │ ├── MPL_YlGn.rgb │ │ │ ├── MPL_YlGnBu.rgb │ │ │ ├── MPL_YlOrBr.rgb │ │ │ ├── MPL_YlOrRd.rgb │ │ │ ├── MPL_afmhot.rgb │ │ │ ├── MPL_autumn.rgb │ │ │ ├── MPL_bone.rgb │ │ │ ├── MPL_brg.rgb │ │ │ ├── MPL_bwr.rgb │ │ │ ├── MPL_cool.rgb │ │ │ ├── MPL_coolwarm.rgb │ │ │ ├── MPL_copper.rgb │ │ │ ├── MPL_cubehelix.rgb │ │ │ ├── MPL_flag.rgb │ │ │ ├── MPL_gist_earth.rgb │ │ │ ├── MPL_gist_gray.rgb │ │ │ ├── MPL_gist_heat.rgb │ │ │ ├── MPL_gist_ncar.rgb │ │ │ ├── MPL_gist_rainbow.rgb │ │ │ ├── MPL_gist_stern.rgb │ │ │ ├── MPL_gist_yarg.rgb │ │ │ ├── MPL_gnuplot.rgb │ │ │ ├── MPL_gnuplot2.rgb │ │ │ ├── MPL_hot.rgb │ │ │ ├── MPL_hsv.rgb │ │ │ ├── MPL_jet.rgb │ │ │ ├── MPL_ocean.rgb │ │ │ ├── MPL_pink.rgb │ │ │ ├── MPL_prism.rgb │ │ │ ├── MPL_rainbow.rgb │ │ │ ├── MPL_s3pcpn.rgb │ │ │ ├── MPL_s3pcpn_l.rgb │ │ │ ├── MPL_seismic.rgb │ │ │ ├── MPL_spring.rgb │ │ │ ├── MPL_sstanom.rgb │ │ │ ├── MPL_summer.rgb │ │ │ ├── MPL_terrain.rgb │ │ │ ├── MPL_viridis.rgb │ │ │ ├── MPL_winter.rgb │ │ │ ├── NCV_banded.rgb │ │ │ ├── NCV_blu_red.rgb │ │ │ ├── NCV_blue_red.rgb │ │ │ ├── NCV_bright.rgb │ │ │ ├── NCV_gebco.rgb │ │ │ ├── NCV_jaisnd.rgb │ │ │ ├── NCV_jet.rgb │ │ │ ├── NCV_manga.rgb │ │ │ ├── NCV_rainbow2.rgb │ │ │ ├── NCV_roullet.rgb │ │ │ ├── NEO_div_vegetation_a.rgb │ │ │ ├── NEO_div_vegetation_b.rgb │ │ │ ├── NEO_div_vegetation_c.rgb │ │ │ ├── NEO_modis_ndvi.rgb │ │ │ ├── NMCRef.rgb │ │ │ ├── NMCVel.rgb │ │ │ ├── NOC_ndvi.rgb │ │ │ ├── OceanLakeLandSnow.rgb │ │ │ ├── SVG_Gallet13.rgb │ │ │ ├── SVG_Lindaa06.rgb │ │ │ ├── SVG_Lindaa07.rgb │ │ │ ├── SVG_bhw3_22.rgb │ │ │ ├── SVG_es_landscape_79.rgb │ │ │ ├── SVG_feb_sunrise.rgb │ │ │ ├── SVG_foggy_sunrise.rgb │ │ │ ├── SVG_fs2006.rgb │ │ │ ├── StepSeq25.rgb │ │ │ ├── UKM_hadcrut.rgb │ │ │ ├── ViBlGrWhYeOrRe.rgb │ │ │ ├── WhBlGrYeRe.rgb │ │ │ ├── WhBlReWh.rgb │ │ │ ├── WhViBlGrYeOrRe.rgb │ │ │ ├── WhViBlGrYeOrReWh.rgb │ │ │ ├── WhiteBlue.rgb │ │ │ ├── WhiteBlueGreenYellowRed.rgb │ │ │ ├── WhiteGreen.rgb │ │ │ ├── WhiteYellowOrangeRed.rgb │ │ │ ├── amwg.rgb │ │ │ ├── amwg256.rgb │ │ │ ├── amwg_blueyellowred.rgb │ │ │ ├── cb_9step.rgb │ │ │ ├── cb_rainbow.rgb │ │ │ ├── cb_rainbow_inv.rgb │ │ │ ├── circular_0.rgb │ │ │ ├── circular_1.rgb │ │ │ ├── circular_2.rgb │ │ │ ├── cividis.rgb │ │ │ ├── cmocean_algae.rgb │ │ │ ├── cmocean_amp.rgb │ │ │ ├── cmocean_balance.rgb │ │ │ ├── cmocean_curl.rgb │ │ │ ├── cmocean_deep.rgb │ │ │ ├── cmocean_delta.rgb │ │ │ ├── cmocean_dense.rgb │ │ │ ├── cmocean_gray.rgb │ │ │ ├── cmocean_haline.rgb │ │ │ ├── cmocean_ice.rgb │ │ │ ├── cmocean_matter.rgb │ │ │ ├── cmocean_oxy.rgb │ │ │ ├── cmocean_phase.rgb │ │ │ ├── cmocean_solar.rgb │ │ │ ├── cmocean_speed.rgb │ │ │ ├── cmocean_tempo.rgb │ │ │ ├── cmocean_thermal.rgb │ │ │ ├── cmocean_turbid.rgb │ │ │ ├── cmp_b2r.rgb │ │ │ ├── cmp_flux.rgb │ │ │ ├── cmp_haxby.rgb │ │ │ ├── cosam.rgb │ │ │ ├── cosam12.rgb │ │ │ ├── cyclic.rgb │ │ │ ├── default.rgb │ │ │ ├── detail.rgb │ │ │ ├── draw_cmap.ncl │ │ │ ├── drought_severity.rgb │ │ │ ├── example.rgb │ │ │ ├── extrema.rgb │ │ │ ├── grads_default.rgb │ │ │ ├── grads_rainbow.rgb │ │ │ ├── gscyclic.rgb │ │ │ ├── gsdtol.rgb │ │ │ ├── gsltod.rgb │ │ │ ├── gui_default.rgb │ │ │ ├── helix.rgb │ │ │ ├── helix1.rgb │ │ │ ├── hlu_default.rgb │ │ │ ├── hotcold_18lev.rgb │ │ │ ├── hotcolr_19lev.rgb │ │ │ ├── hotres.rgb │ │ │ ├── lithology.rgb │ │ │ ├── matlab_hot.rgb │ │ │ ├── matlab_hsv.rgb │ │ │ ├── matlab_jet.rgb │ │ │ ├── matlab_lines.rgb │ │ │ ├── mch_default.rgb │ │ │ ├── ncl_default.rgb │ │ │ ├── ncview_default.rgb │ │ │ ├── nice_gfdl.rgb │ │ │ ├── nrl_sirkes.rgb │ │ │ ├── nrl_sirkes_nowhite.rgb │ │ │ ├── perc2_9lev.rgb │ │ │ ├── percent_11lev.rgb │ │ │ ├── posneg_1.rgb │ │ │ ├── posneg_2.rgb │ │ │ ├── prcp_1.rgb │ │ │ ├── prcp_2.rgb │ │ │ ├── prcp_3.rgb │ │ │ ├── precip2_15lev.rgb │ │ │ ├── precip2_17lev.rgb │ │ │ ├── precip3_16lev.rgb │ │ │ ├── precip4_11lev.rgb │ │ │ ├── precip4_diff_19lev.rgb │ │ │ ├── precip_11lev.rgb │ │ │ ├── precip_diff_12lev.rgb │ │ │ ├── precip_diff_1lev.rgb │ │ │ ├── psgcap.rgb │ │ │ ├── radar.rgb │ │ │ ├── radar_1.rgb │ │ │ ├── rainbow+gray.rgb │ │ │ ├── rainbow+white+gray.rgb │ │ │ ├── rainbow+white.rgb │ │ │ ├── rainbow.rgb │ │ │ ├── rh_19lev.rgb │ │ │ ├── seaice_1.rgb │ │ │ ├── seaice_2.rgb │ │ │ ├── so4_21.rgb │ │ │ ├── so4_23.rgb │ │ │ ├── spread_15lev.rgb │ │ │ ├── srip_reanalysis.rgb │ │ │ ├── sunshine_9lev.rgb │ │ │ ├── sunshine_diff_12lev.rgb │ │ │ ├── t2m_29lev.rgb │ │ │ ├── tbrAvg1.rgb │ │ │ ├── tbrStd1.rgb │ │ │ ├── tbrVar1.rgb │ │ │ ├── tbr_240-300.rgb │ │ │ ├── tbr_stdev_0-30.rgb │ │ │ ├── tbr_var_0-500.rgb │ │ │ ├── temp1.rgb │ │ │ ├── temp_19lev.rgb │ │ │ ├── temp_diff_18lev.rgb │ │ │ ├── temp_diff_1lev.rgb │ │ │ ├── testcmap.rgb │ │ │ ├── thelix.rgb │ │ │ ├── topo_15lev.rgb │ │ │ ├── uniform.rgb │ │ │ ├── vegetation_ClarkU.rgb │ │ │ ├── vegetation_modis.rgb │ │ │ ├── wgne15.rgb │ │ │ ├── wh-bl-gr-ye-re.rgb │ │ │ ├── wind_17lev.rgb │ │ │ └── wxpEnIR.rgb │ │ ├── logo │ │ │ ├── __init__.py │ │ │ ├── cma.png │ │ │ ├── cma_Xlarge.png │ │ │ ├── cma_large.png │ │ │ ├── cma_medium.png │ │ │ ├── cma_small.png │ │ │ ├── nmc.png │ │ │ ├── nmc_Xlarge.png │ │ │ ├── nmc_large.png │ │ │ ├── nmc_medium.png │ │ │ ├── nmc_small.png │ │ │ ├── wmo.png │ │ │ ├── wmo_large.png │ │ │ ├── wmo_medium.png │ │ │ └── wmo_small.png │ │ ├── shapefile │ │ │ ├── NationalBorder.dbf │ │ │ ├── NationalBorder.prj │ │ │ ├── NationalBorder.qpj │ │ │ ├── NationalBorder.sbn │ │ │ ├── NationalBorder.sbx │ │ │ ├── NationalBorder.shp │ │ │ ├── NationalBorder.shp.xml │ │ │ ├── NationalBorder.shx │ │ │ ├── NationalBorder.xml │ │ │ ├── Province.dbf │ │ │ ├── Province.prj │ │ │ ├── Province.sbn │ │ │ ├── Province.sbx │ │ │ ├── Province.shp │ │ │ ├── Province.shp.xml │ │ │ ├── Province.shx │ │ │ ├── Province.xml │ │ │ ├── __init__.py │ │ │ ├── hyd1_4l.dbf │ │ │ ├── hyd1_4l.shp │ │ │ ├── hyd1_4l.shx │ │ │ ├── hyd1_4p.dbf │ │ │ ├── hyd1_4p.shp │ │ │ ├── hyd1_4p.shx │ │ │ ├── hyd2_4l.dbf │ │ │ ├── hyd2_4l.shp │ │ │ ├── hyd2_4l.shx │ │ │ ├── hyd2_4p.dbf │ │ │ ├── hyd2_4p.shp │ │ │ ├── hyd2_4p.shx │ │ │ ├── ne_10m_coastline.README.html │ │ │ ├── ne_10m_coastline.VERSION.txt │ │ │ ├── ne_10m_coastline.cpg │ │ │ ├── ne_10m_coastline.dbf │ │ │ ├── ne_10m_coastline.prj │ │ │ ├── ne_10m_coastline.shp │ │ │ ├── ne_10m_coastline.shx │ │ │ ├── worldmap.dbf │ │ │ ├── worldmap.prj │ │ │ ├── worldmap.shp │ │ │ └── worldmap.shx │ │ ├── south_china │ │ │ ├── RD.png │ │ │ ├── simple.png │ │ │ └── white.png │ │ └── stations │ │ │ ├── city_province.csv │ │ │ └── county.csv │ ├── scatter_method.py │ ├── streamplot_method.py │ └── text_method.py ├── hub │ ├── __init__.py │ ├── basic_anl.py │ ├── compare.py │ ├── evolution.py │ ├── lib │ │ ├── __init__.py │ │ ├── image_compose.py │ │ └── utility.py │ ├── stability.py │ └── ver_vs_anl.py ├── io │ ├── __init__.py │ ├── cassandra.py │ ├── cassandra_manual_download.py │ ├── cimiss.py │ ├── cmadaas.py │ ├── cmadass_manual_download.py │ ├── custom.py │ ├── era5.py │ ├── era5_manual_download.py │ ├── lib │ │ ├── __init__.py │ │ ├── config.py │ │ ├── package_config │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── cassandra_model_cfg.csv │ │ │ ├── cassandra_model_cfg.py │ │ │ ├── cassandra_obs_cfg.csv │ │ │ ├── cassandra_obs_cfg.py │ │ │ ├── cassandra_radar_cfg.csv │ │ │ ├── cassandra_radar_cfg.py │ │ │ ├── cassandra_sate_cfg.csv │ │ │ ├── cassandra_sate_cfg.py │ │ │ ├── cmadaas_datacode_cfg.csv │ │ │ ├── cmadaas_datacode_cfg.py │ │ │ ├── cmadaas_model_cfg.csv │ │ │ ├── cmadaas_model_cfg.py │ │ │ ├── cmadaas_obs_cfg.csv │ │ │ ├── cmadaas_obs_cfg.py │ │ │ ├── era5_cfg.csv │ │ │ ├── era5_cfg.py │ │ │ ├── thredds_model_cfg.csv │ │ │ └── thredds_model_cfg.py │ │ └── utility.py │ ├── nmc_cmadass_helper.py │ ├── nmc_micaps_helper.py │ └── thredds.py ├── onestep │ ├── __init__.py │ ├── complexgrid_var │ │ ├── __init__.py │ │ ├── div_uv.py │ │ ├── get_rain.py │ │ ├── get_snow.py │ │ ├── pv_div_uv.py │ │ ├── rh2m.py │ │ ├── spfh.py │ │ ├── theta.py │ │ ├── vort_uv.py │ │ ├── vvel.py │ │ ├── w.py │ │ ├── wsp.py │ │ └── wvfl.py │ ├── diag_crossection.py │ ├── diag_dynamic.py │ ├── diag_elements.py │ ├── diag_ensemble.py │ ├── diag_identify.py │ ├── diag_moisture.py │ ├── diag_qpf.py │ ├── diag_station.py │ ├── diag_synoptic.py │ ├── diag_theme_ne.py │ ├── diag_thermal.py │ ├── diag_trajectory.py │ ├── lib │ │ ├── __init__.py │ │ └── utility.py │ ├── observation_radar.py │ ├── observation_satellite.py │ ├── observation_station.py │ ├── observation_unusual.py │ └── veri_synop.py ├── package_tools.py ├── products │ ├── __init__.py │ ├── diag_crossection.py │ ├── diag_dynamic.py │ ├── diag_elements.py │ ├── diag_ensemble.py │ ├── diag_identify.py │ ├── diag_moisture.py │ ├── diag_qpf.py │ ├── diag_station.py │ ├── diag_synoptic.py │ ├── diag_theme_ne.py │ ├── diag_thermal.py │ ├── diag_trajectory.py │ ├── observation_radar.py │ ├── observation_satellite.py │ ├── observation_station.py │ ├── observation_unusual.py │ └── veri_synop.py └── utl │ ├── __init__.py │ ├── stda_attrs_cfg.csv │ ├── utl_stda_attrs.py │ ├── utl_stda_grid.py │ ├── utl_stda_station.py │ └── utl_units.py └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | .history/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | .vscode/settings.json 106 | .vscode/launch.json 107 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.md 2 | include LICENSE 3 | recursive-include metdig/metdig_graphics/resource * 4 | recursive-exclude * __pycache__ 5 | recursive-exclude * *.pyc 6 | recursive-exclude * *.pyo 7 | recursive-exclude * *.orig -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Meteorological Diagnostic Tools (metdig) 2 | ## Detailed documentation can be found at：https://www.showdoc.com.cn/metdig 3 | 4 | ## Dependencies 5 | Other required packages: 6 | 'matplotlib < 3.6', 7 | 'nmc_met_io <= 0.1.10.4', 8 | 'metpy >= 1.0', 9 | 'meteva > 1.3.*', 10 | 'xarray <= 0.19.0 ', 11 | 'cdsapi', 12 | 'numba', 13 | 'folium', 14 | 'shapely < 1.8.0', 15 | 'imageio', 16 | 'numpy < 1.21', 17 | 'protobuf<=3.20', 18 | 'ipython', 19 | 'pint < 0.20.0' 20 | ## Install 21 | please install metdig under anaconda enviroment. 22 | since Cartopy is hard to install, 23 | it is recommanded creating new env via conda and installing Cartopy first when the env is not complex yet. 24 | 25 | ``` install via pip 26 | conda install -c conda-forge cartopy=0.19.0 27 | pip install metdig 28 | ``` 29 | Using the following command to install packages: 30 | ``` 31 | pip install git+git://github.com/nmcdev/metdig.git 32 | ``` 33 | 34 | or download the package and install: 35 | ``` 36 | git clone --recursive https://github.com/nmcdev/metdig.git 37 | cd metdig 38 | python setup.py install 39 | ``` 40 | 41 | ## Welcome to discuss in Issues 42 | https://github.com/nmcdev/metdig/issues 43 | -------------------------------------------------------------------------------- /metdig/__init__.py: -------------------------------------------------------------------------------- 1 | __author__ = "The R & D Center for Weather Forecasting Technology in NMC, CMA" 2 | __version__ = '1.0.5' 3 | 4 | from . import cal 5 | from . import graphics 6 | from . import hub 7 | from . import io 8 | from . import onestep 9 | from . import products 10 | from . import utl 11 | from . import package_tools 12 | 13 | import logging 14 | _log = logging.getLogger(__name__) 15 | 16 | 17 | def _ensure_handler(): 18 | """ 19 | The first time this function is called, attach a `StreamHandler` using the 20 | same format as `logging.basicConfig` to the Matplotlib root logger. 21 | 22 | Return this handler every time this function is called. 23 | """ 24 | # BASIC_FORMAT = "%(levelname)s:%(name)s:%(message)s" 25 | BASIC_FORMAT = "%(levelname)s:%(name)s:%(lineno)d:%(message)s" 26 | 27 | handler = logging.StreamHandler() 28 | handler.setFormatter(logging.Formatter(BASIC_FORMAT)) 29 | _log.addHandler(handler) 30 | return handler 31 | 32 | 33 | def set_loglevel(level): 34 | """ 35 | Sets the Matplotlib's root logger and root logger handler level, creating 36 | the handler if it does not exist yet. 37 | 38 | Typically, one should call ``set_loglevel("info")`` or 39 | ``set_loglevel("debug")`` to get additional debugging information. 40 | 41 | Parameters 42 | ---------- 43 | level : {"notset", "debug", "info", "warning", "error", "critical"} 44 | The log level of the handler. 45 | 46 | Notes 47 | ----- 48 | The first time this function is called, an additional handler is attached 49 | to Matplotlib's root handler; this handler is reused every time and this 50 | function simply manipulates the logger and handler's level. 51 | """ 52 | _log.setLevel(level.upper()) 53 | _ensure_handler().setLevel(level.upper()) 54 | -------------------------------------------------------------------------------- /metdig/cal/__init__.py: -------------------------------------------------------------------------------- 1 | from .cross_sections_module import * # 廓线相关 2 | from .dynamic import * # 动力相关 3 | from .elements import * 4 | from .moisture import * # 水汽相关 5 | from .other import * 6 | from .qpf import * # 降水相关 7 | from .thermal import * # 热力相关 8 | from .sounding import * 9 | from .interpolate import * # 插值相关 10 | from .indexes import * 11 | from .identify import * # 天气系统识别相关 12 | from .ensemble import * # 集合预报相关 13 | from .extreme import * # 天气极端性相关 14 | from .classification import * # 聚类相关 15 | from .tools import * -------------------------------------------------------------------------------- /metdig/cal/base/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/cal/base/__init__.py -------------------------------------------------------------------------------- /metdig/cal/base/moisture.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2020 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Compute moisture parameters. 8 | """ 9 | 10 | import xarray as xr 11 | import numpy as np 12 | 13 | from metdig.cal.base.grid import interp_3D_to_surface 14 | 15 | 16 | def cal_ivt(q, u, v, lon, lat, lev, surf_pres=None): 17 | """ 18 | Calculate integrated water vapor transport. 19 | 20 | Args: 21 | q (numpy array): Specific humidity, g/kg, [nlev, nlat, nlon] 22 | u (numpy array): u component wind, m/s, [nlev, nlat, nlon] 23 | v (numpy array): v component wind, m/s, [nlev, nlat, nlon] 24 | lon (numpy array): vertical level, hPa, [nlev] 25 | lat (numpy array): longitude, [nlon] 26 | lev (numpy array): latitude, [nlat] 27 | surf_pres (numpy array, optional): surface pressure, hPa, [nlev, nlat, nlon]. Defaults to None. 28 | """ 29 | 30 | # compute water vapor transport 31 | qu = q * u 32 | qv = q * v 33 | 34 | # set up full grid levels 35 | pCoord, _, _ = np.meshgrid(lev, lat, lon, indexing='ij') 36 | 37 | # mask the grid points under the ground 38 | if surf_pres is not None: 39 | # 将三维格点场插值到地面上, 这里地面的高度使用地面气压来指示 40 | qus = interp_3D_to_surface(qu, lon, lat, lev, surf_pres) 41 | qvs = interp_3D_to_surface(qv, lon, lat, lev, surf_pres) 42 | 43 | # 判断三维格点是否在地面之下, 如果在地面之下, 其物理量用地面替代, 并且高度也设置为地面气压 44 | # 这样在后面积分过程中, 地面以下的积分为零值 45 | for ilevel, level in enumerate(lev): 46 | qu[ilevel, ...] = np.where(surf_pres >= level, qu[ilevel, ...], qus) 47 | qv[ilevel, ...] = np.where(surf_pres >= level, qv[ilevel, ...], qvs) 48 | pCoord[ilevel, ...] = np.where(surf_pres >= level, level, surf_pres) 49 | 50 | # compute the vertical integration, using trapezoid rule 51 | # 由于垂直坐标用的是百帕, 而比湿用的是g/kg, 因此转换单位100*0.001=0.1 52 | iqu = np.zeros((lat.size, lon.size)) 53 | iqv = np.zeros((lat.size, lon.size)) 54 | for ilevel, level in enumerate(lev[0:-1]): 55 | iqu += np.abs(pCoord[ilevel,...]-pCoord[ilevel+1,...])*(qu[ilevel,...]+qu[ilevel+1,...])*0.5*0.1/9.8 56 | iqv += np.abs(pCoord[ilevel,...]-pCoord[ilevel+1,...])*(qv[ilevel,...]+qv[ilevel+1,...])*0.5*0.1/9.8 57 | 58 | return iqu, iqv 59 | -------------------------------------------------------------------------------- /metdig/cal/base/numeric.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Numeric number manipulation. 8 | """ 9 | 10 | import math 11 | import numpy as np 12 | 13 | 14 | def ensure_numeric(A, typecode=None): 15 | """ 16 | Ensure that sequence is a numeric array. 17 | 18 | :param A: Sequence. If A is already a numeric array it will be returned 19 | unaltered. 20 | If not, an attempt is made to convert it to a 21 | numeric array. 22 | A: Scalar. Return 0-dimensional array containing that value. Note 23 | that a 0-dim array DOES NOT HAVE A LENGTH UNDER numpy. 24 | A: String. Array of ASCII values (numpy can't handle this) 25 | :param typecode: numeric type. If specified, use this in the conversion. 26 | If not, let numeric package decide. 27 | typecode will always be one of num.float, num.int, etc. 28 | :return: 29 | """ 30 | 31 | if isinstance(A, str): 32 | msg = 'Sorry, cannot handle strings in ensure_numeric()' 33 | raise Exception(msg) 34 | 35 | if typecode is None: 36 | if isinstance(A, np.ndarray): 37 | return A 38 | else: 39 | return np.array(A) 40 | else: 41 | return np.array(A, dtype=typecode, copy=False) 42 | 43 | 44 | def roundoff(a, digit=2): 45 | """ 46 | roundoff the number with specified digits. 47 | 48 | :param a: float 49 | :param digit: 50 | :return: 51 | 52 | :Examples: 53 | >>> roundoff(3.44e10, digit=2) 54 | 3.4e10 55 | >>> roundoff(3.49e-10, digit=2) 56 | 3.5e-10 57 | """ 58 | if a > 1: 59 | return round(a, -int(math.log10(a)) + digit - 1) 60 | else: 61 | return round(a, -int(math.log10(a)) + digit) 62 | -------------------------------------------------------------------------------- /metdig/cal/base/stats.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Statistic functions. 8 | """ 9 | 10 | import numpy as np 11 | 12 | 13 | def vcorrcoef(X, y, dim): 14 | """ 15 | Compute vectorized correlation coefficient. 16 | refer to: 17 | https://waterprogramming.wordpress.com/2014/06/13/numpy-vectorized-correlation-coefficient/ 18 | 19 | :param X: nD array. 20 | :param y: 1D array. 21 | :param dim: along dimension to compute correlation coefficient. 22 | :return: correlation coefficient array. 23 | """ 24 | 25 | X = np.array(X) 26 | ndim = X.ndim 27 | 28 | # roll lat dim axis to last 29 | X = np.rollaxis(X, dim, ndim) 30 | 31 | Xm = np.mean(X, axis=-1, keepdims=True) 32 | ym = np.mean(y) 33 | r_num = np.sum((X - Xm) * (y - ym), axis=-1) 34 | r_den = np.sqrt(np.sum((X - Xm) ** 2, axis=-1) * np.sum((y - ym) ** 2)) 35 | r = r_num / r_den 36 | 37 | return r 38 | -------------------------------------------------------------------------------- /metdig/cal/base/utilities.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Collections of utilities functions. 8 | """ 9 | 10 | 11 | def lon2txt(lon, fmt='%g'): 12 | """ 13 | Format the longitude number with degrees. 14 | 15 | :param lon: longitude 16 | :param fmt: 17 | :return: 18 | 19 | :Examples: 20 | >>> lon2txt(135) 21 | '135\N{DEGREE SIGN}E' 22 | >>> lon2txt(-30) 23 | '30\N{DEGREE SIGN}W' 24 | >>> lon2txt(250) 25 | '110\N{DEGREE SIGN}W' 26 | """ 27 | lon = (lon + 360) % 360 28 | if lon > 180: 29 | lonlabstr = u'%s\N{DEGREE SIGN}W' % fmt 30 | lonlab = lonlabstr % abs(lon - 360) 31 | elif lon < 180 and lon != 0: 32 | lonlabstr = u'%s\N{DEGREE SIGN}E' % fmt 33 | lonlab = lonlabstr % lon 34 | else: 35 | lonlabstr = u'%s\N{DEGREE SIGN}' % fmt 36 | lonlab = lonlabstr % lon 37 | return lonlab 38 | 39 | 40 | def lat2txt(lat, fmt='%g'): 41 | """ 42 | Format the latitude number with degrees. 43 | :param lat: 44 | :param fmt: 45 | :return: 46 | 47 | :Examples: 48 | >>> lat2txt(60) 49 | '60\N{DEGREE SIGN}N' 50 | >>> lat2txt(-30) 51 | '30\N{DEGREE SIGN}S' 52 | """ 53 | if lat < 0: 54 | latlabstr = u'%s\N{DEGREE SIGN}S' % fmt 55 | latlab = latlabstr % abs(lat) 56 | elif lat > 0: 57 | latlabstr = u'%s\N{DEGREE SIGN}N' % fmt 58 | latlab = latlabstr % lat 59 | else: 60 | latlabstr = u'%s\N{DEGREE SIGN}' % fmt 61 | latlab = latlabstr % lat 62 | return latlab 63 | -------------------------------------------------------------------------------- /metdig/cal/classification.py: -------------------------------------------------------------------------------- 1 | ''' 2 | 聚类 3 | ''' 4 | 5 | import numpy as np 6 | import math 7 | from datetime import datetime, timedelta 8 | 9 | 10 | from sklearn import preprocessing 11 | from sklearn.decomposition import PCA 12 | 13 | import xarray as xr 14 | 15 | from metdig.cal.lib.utility import unifydim_stda, check_stda 16 | 17 | try: 18 | from sklearn.cluster import KMeans 19 | except: 20 | pass 21 | 22 | 23 | __all__ = [ 24 | 'kmeans', 25 | ] 26 | 27 | @check_stda(['stda']) 28 | def kmeans(stda, axes, n_components, 29 | n_clusters=8, init='k-means++', n_init='warn', max_iter=300, 30 | tol=1e-4, verbose=0, random_state=None, copy_x=True, algorithm='lloyd'): 31 | """ 32 | stda 沿着 axes 进行聚类，并返回聚类结果。 33 | """ 34 | 35 | loop_axes = ['member', 'level', 'time', 'dtime'] 36 | loop_axes.remove(axes) 37 | for d in loop_axes: 38 | if stda[d].size != 1: 39 | raise Exception(f'{d} dimension size must be equal to 1') 40 | if stda[axes].size == 1: 41 | raise Exception(f'{axes} dimension size must be greater than 1') 42 | 43 | # 把要沿着的轴转移到第一维 44 | transpose_axes = ['member', 'level', 'time', 'dtime', 'lat', 'lon'] 45 | transpose_axes.remove(axes) 46 | transpose_axes.insert(0, axes) 47 | X = stda.transpose(*transpose_axes) 48 | 49 | # 6维变成2维(行是样本，列是特征) 50 | X = X.values 51 | X = X.reshape((X.shape[0], -1)) 52 | 53 | # 标准化 54 | X_scaled = preprocessing.scale(X, axis=1) # 这里应该按行标准化还是按列？ 55 | 56 | # pca降维 57 | pca = PCA(n_components=n_components) # 降至2维 58 | X_pca = pca.fit_transform(X_scaled) 59 | 60 | ret = KMeans(n_clusters=n_clusters, init=init, n_init=n_init, 61 | max_iter=max_iter, tol=tol, verbose=verbose, 62 | random_state=random_state, copy_x=copy_x, algorithm=algorithm).fit(X_pca) 63 | return ret 64 | 65 | # print(ret.inertia_) 66 | # print(values.shape) 67 | # for 68 | # print(loop_axes) 69 | pass -------------------------------------------------------------------------------- /metdig/cal/elements.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | ''' 4 | 5 | ''' 6 | 7 | 8 | -------------------------------------------------------------------------------- /metdig/cal/lib/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/cal/lib/__init__.py -------------------------------------------------------------------------------- /metdig/cal/qpf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import numpy as np 4 | 5 | import metpy.calc as mpcalc 6 | from metpy.units import units as mpunits 7 | 8 | from metdig.cal.lib import utility as utl 9 | import metdig.utl as mdgstda 10 | from metdig.cal.lib.utility import unifydim_stda, check_stda 11 | 12 | __all__ = [ 13 | 'cal_snow_sleet_rain', 14 | ] 15 | 16 | 17 | @check_stda(['rain_data', 'snow_data']) 18 | @unifydim_stda(['rain_data', 'snow_data']) 19 | def cal_snow_sleet_rain(rain_data, snow_data): 20 | sleet = rain_data.where(((rain_data - snow_data) > 0.1) & (snow_data > 0.1)) 21 | sleet.attrs = mdgstda.get_stda_attrs(var_name='sleet',valid_time=rain_data.attrs['valid_time'],data_source=snow_data.attrs['data_source'],level_type=snow_data.attrs['level_type']) 22 | 23 | snow = rain_data.where(((rain_data - snow_data) < 0.1) & (snow_data > 0.1)) 24 | snow.attrs = mdgstda.get_stda_attrs(var_name='snow',valid_time=snow_data.attrs['valid_time'],snow_data=snow.attrs['data_source'],level_type=snow_data.attrs['level_type']) 25 | 26 | rain = rain_data.where((rain_data > 0.1) & (snow_data < 0.1)) 27 | rain.attrs = mdgstda.get_stda_attrs(var_name='rain',valid_time=rain_data.attrs['valid_time'],data_source=rain_data.attrs['data_source'],level_type=rain_data.attrs['level_type']) 28 | 29 | return snow, sleet, rain 30 | -------------------------------------------------------------------------------- /metdig/cal/resources/height_oy.nc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/cal/resources/height_oy.nc -------------------------------------------------------------------------------- /metdig/cal/resources/sysIdentify2023.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/cal/resources/sysIdentify2023.jar -------------------------------------------------------------------------------- /metdig/cal/resources/sysIdentify2024.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/cal/resources/sysIdentify2024.jar -------------------------------------------------------------------------------- /metdig/graphics/__init__.py: -------------------------------------------------------------------------------- 1 | from . import boxplot_method 2 | from . import barbs_method 3 | from . import scatter_method 4 | from . import streamplot_method 5 | from . import contour_method 6 | from . import contourf_method 7 | from . import draw_compose 8 | from . import other_method 9 | from . import pallete_set 10 | from . import pcolormesh_method 11 | from . import quiver_method 12 | from . import text_method 13 | from . import plot_method 14 | 15 | from . import lib 16 | from . import cmap -------------------------------------------------------------------------------- /metdig/graphics/bar_method.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | import datetime 5 | import numpy as np 6 | import pandas as pd 7 | 8 | import matplotlib.pyplot as plt 9 | import matplotlib as mpl 10 | import matplotlib.lines as lines 11 | 12 | import metdig.graphics.pallete_set as pallete_set 13 | from metdig.graphics.lib.utility import save 14 | 15 | import metdig.cal as mdgcal 16 | import metpy.calc as mpcalc 17 | from metpy.units import units 18 | from metdig.graphics.lib.utility import kwargs_wrapper 19 | 20 | @kwargs_wrapper 21 | def bar_1d(ax, stda, xdim='fcst_time', color='#FF6600', width=0.1,**kwargs): 22 | 23 | x = stda.stda.get_dim_value(xdim) 24 | y = stda.stda.get_value(xdim) 25 | curve = ax.bar(x, y, color=color,width=width, **kwargs) 26 | 27 | return curve 28 | 29 | def bars_autolabel(ax, rects): 30 | for rect in rects: 31 | height = rect.get_height() 32 | if(height > 0): 33 | ax.annotate('%.2f' % height, 34 | xy=(rect.get_x() + rect.get_width() / 2, height), 35 | xytext=(0, 3), # 3 points vertical offset 36 | textcoords="offset points", 37 | ha='center', va='bottom') -------------------------------------------------------------------------------- /metdig/graphics/boxplot_method.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from metdig.graphics.lib.utility import kwargs_wrapper 4 | 5 | 6 | @kwargs_wrapper 7 | def boxplot_1D(ax, stda, medianline=False, label_gap=1, **kwargs): 8 | """[summary] 9 | 10 | Args: 11 | ax ([type]): [matplotlib 绘图对象] 12 | stda ([type]): [stda标准格式] 13 | medianline (bool, optional): [是否绘制中位数线]. Defaults to False. 14 | label_gap (int, optional): [横坐标的间隔]. Defaults to 1. 15 | 16 | """ 17 | # rain_x = list(map(lambda x: pd.to_datetime(x).strftime('%m月%d日%H时'), stda.stda.fcst_time.values)) 18 | rain_x=[' ']*len(stda.stda.fcst_time.values) 19 | for ix in range(1,len(rain_x),label_gap): 20 | rain_x[ix]=pd.to_datetime(stda.stda.fcst_time.values[ix]).strftime('%m月%d日%H时') 21 | 22 | rain_y = stda.stda.get_value() 23 | img = ax.boxplot(np.transpose(rain_y), labels=rain_x, meanline=False, showmeans=True, showfliers=False, whis=(0, 100), 24 | meanprops={'marker': None}, **kwargs) 25 | rain_mean = [] 26 | rain_median = [] 27 | for icurve_mean in img['means']: 28 | rain_mean.append(icurve_mean._y[0]) 29 | curve_rain_mean = ax.plot(np.arange(1, len(rain_mean)+1), rain_mean, c=img['means'][1]._color, 30 | linewidth=img['means'][1]._linewidth, linestyle='-', 31 | label='mean') 32 | 33 | if (medianline): 34 | for icurve_median in img['medians']: 35 | rain_median.append(icurve_median._y[0]) 36 | curve_rain_median = ax.plot(np.arange(1, len(rain_median)+1), rain_median, c=img['medians'][1]._color, 37 | linewidth=img['medians'][1]._linewidth, linestyle=img['medians'][1]._linestyle, 38 | label='median') 39 | 40 | rain_control = np.squeeze(rain_y[:, 0]) 41 | curve_rain_control = ax.plot(np.arange(1, len(rain_control)+1), rain_control, c='black', 42 | linewidth=img['medians'][1]._linewidth, linestyle=img['medians'][1]._linestyle, 43 | label='control') 44 | 45 | ax.legend(fontsize=15, loc='best') 46 | 47 | return img -------------------------------------------------------------------------------- /metdig/graphics/cmap/__init__.py: -------------------------------------------------------------------------------- 1 | from . import cm -------------------------------------------------------------------------------- /metdig/graphics/lib/__init__.py: -------------------------------------------------------------------------------- 1 | from . import utility 2 | from . import utl_plotmap -------------------------------------------------------------------------------- /metdig/graphics/quiver_method.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import cartopy.crs as ccrs 4 | import matplotlib as mpl 5 | import matplotlib.pyplot as plt 6 | import matplotlib.cm as cm 7 | from matplotlib.colors import BoundaryNorm, ListedColormap 8 | import matplotlib.patheffects as mpatheffects 9 | 10 | import metdig.graphics.lib.utility as utl 11 | import metdig.graphics.lib.utl_plotmap as utl_plotmap 12 | import metdig.graphics.cmap.cm as cm_collected 13 | from metdig.graphics.lib.utility import kwargs_wrapper 14 | 15 | @kwargs_wrapper 16 | def uv_quiver(ax, ustda, vstda,xdim='lon', ydim='lat', 17 | color='black',scale=1000, 18 | transform=ccrs.PlateCarree(), regrid_shape=30, 19 | **kwargs): 20 | # 数据准备 21 | x = ustda.stda.get_dim_value(xdim) 22 | y = ustda.stda.get_dim_value(ydim) 23 | u = ustda.stda.get_value(ydim, xdim) # 1/s 24 | v = vstda.stda.get_value(ydim, xdim) # 1/s 25 | # 绘制 26 | if regrid_shape is None or transform is None or (xdim != 'lon' and ydim != 'lat'): 27 | img = ax.quiver(x, y, u, v, color=color, scale=scale, **kwargs) 28 | else: 29 | img = ax.quiver(x, y, u, v, color=color, transform=transform, scale=scale, regrid_shape=regrid_shape, **kwargs) 30 | return img 31 | -------------------------------------------------------------------------------- /metdig/graphics/resources/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | nmc_met_map is a package that providing tools to proceed the diagnostic analysis. 3 | Including pyhsical parameter calculation and figure ploting. 4 | """ 5 | 6 | __author__ = "The R & D Center for Weather Forecasting Technology in NMC, CMA" 7 | __version__ = '0.1.0' -------------------------------------------------------------------------------- /metdig/graphics/resources/backgrounds/RenderData.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/graphics/resources/backgrounds/RenderData.png -------------------------------------------------------------------------------- /metdig/graphics/resources/backgrounds/images.json: -------------------------------------------------------------------------------- 1 | {"__comment__": "JSON file specifying the image to use for a given type/name and resolution. Read in by cartopy.mpl.geoaxes.read_user_background_images.", 2 | 3 | "RD": { 4 | "__comment__": "Natural Earth shaded relief", 5 | "__source__": "https://neo.sci.gsfc.nasa.gov/servlet/RenderData?si=196466&cs=rgb&format=PNG&width=3600&height=1800/", 6 | "__projection__": "PlateCarree", 7 | "high": "RenderData.png" } 8 | } 9 | 10 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs1.txt: -------------------------------------------------------------------------------- 1 | 10 50 120 2 | 15 75 165 3 | 30 110 200 4 | 60 160 240 5 | 80 180 250 6 | 130 210 255 7 | 160 240 255 8 | 200 250 255 9 | 230 255 255 10 | 255 250 220 11 | 255 232 120 12 | 255 192 60 13 | 255 160 0 14 | 255 96 0 15 | 255 50 0 16 | 225 20 0 17 | 192 0 0 18 | 165 0 0 19 | 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs10.txt: -------------------------------------------------------------------------------- 1 | 0 80 0 2 | 0 134 0 3 | 0 187 0 4 | 0 241 0 5 | 80 255 80 6 | 134 255 134 7 | 187 255 187 8 | 255 255 255 9 | 255 241 255 10 | 255 187 255 11 | 255 134 255 12 | 255 80 255 13 | 241 0 241 14 | 187 0 187 15 | 134 0 134 16 | 80 0 80 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs11.txt: -------------------------------------------------------------------------------- 1 | 41 10 216 2 | 38 77 255 3 | 63 160 255 4 | 114 217 255 5 | 170 247 255 6 | 224 255 255 7 | 255 255 191 8 | 255 224 153 9 | 255 173 114 10 | 247 109 94 11 | 216 38 50 12 | 165 0 33 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs12.txt: -------------------------------------------------------------------------------- 1 | 36 0 216 2 | 24 28 247 3 | 40 87 255 4 | 61 135 255 5 | 86 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-------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs17.txt: -------------------------------------------------------------------------------- 1 | 51 25 0 2 | 102 47 0 3 | 153 96 53 4 | 204 155 122 5 | 216 175 151 6 | 242 218 205 7 | 204 253 255 8 | 153 248 255 9 | 101 239 255 10 | 50 227 255 11 | 0 169 204 12 | 0 122 153 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs18.txt: -------------------------------------------------------------------------------- 1 | 0 153 204 2 | 102 229 255 3 | 153 255 255 4 | 204 255 255 5 | 229 229 229 6 | 153 153 153 7 | 102 102 102 8 | 51 51 51 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs19.txt: -------------------------------------------------------------------------------- 1 | 0 127 255 2 | 76 195 255 3 | 153 237 255 4 | 204 255 255 5 | 255 255 204 6 | 255 238 153 7 | 255 195 76 8 | 255 127 0 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs2.txt: -------------------------------------------------------------------------------- 1 | 0 84 255 2 | 0 199 255 3 | 0 255 255 4 | 0 255 178 5 | 66 255 0 6 | 184 255 0 7 | 245 255 0 8 | 255 209 0 9 | 255 135 0 10 | 255 28 0 11 | 230 0 0 12 | 191 0 0 13 | 148 0 0 14 | 105 0 20 15 | 161 0 135 16 | 184 0 186 17 | 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs20.txt: -------------------------------------------------------------------------------- 1 | 0 84 255 2 | 50 153 255 3 | 101 204 255 4 | 153 237 255 5 | 204 255 255 6 | 255 255 204 7 | 255 238 153 8 | 255 204 101 9 | 255 153 50 10 | 255 85 0 11 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs21.txt: -------------------------------------------------------------------------------- 1 | 0 42 255 2 | 25 101 255 3 | 50 153 255 4 | 101 204 255 5 | 153 237 255 6 | 204 255 255 7 | 255 255 204 8 | 255 238 153 9 | 255 204 101 10 | 255 153 50 11 | 255 102 25 12 | 255 42 0 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs22.txt: -------------------------------------------------------------------------------- 1 | 7 90 255 2 | 50 118 255 3 | 89 144 255 4 | 140 178 255 5 | 191 212 255 6 | 229 238 255 7 | 247 249 255 8 | 255 255 204 9 | 255 255 153 10 | 255 255 0 11 | 255 204 0 12 | 255 153 0 13 | 255 102 0 14 | 255 0 0 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs23.txt: -------------------------------------------------------------------------------- 1 | 165 0 33 2 | 216 38 50 3 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144 115 22 | 92 150 121 23 | 102 176 156 24 | 122 197 186 25 | 150 227 226 26 | 135 135 135 27 | 147 147 147 28 | 159 159 159 29 | 171 171 171 30 | 183 183 183 31 | 195 195 195 32 | 207 207 207 33 | 219 219 219 34 | 231 231 231 35 | 243 243 243 36 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs28.txt: -------------------------------------------------------------------------------- 1 | 255 0 0 2 | 0 255 0 3 | 0 0 255 4 | 255 153 0 5 | 54 144 192 6 | 153 0 0 7 | 0 102 0 8 | 255 153 255 9 | 153 0 153 10 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs29.txt: -------------------------------------------------------------------------------- 1 | 30 199 9 2 | 99 239 19 3 | 149 224 49 4 | 189 245 59 5 | 224 255 99 6 | 255 255 19 7 | 255 210 49 8 | 214 180 70 9 | 199 120 99 10 | 170 120 80 11 | 130 90 80 12 | 180 140 130 13 | 230 189 180 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/metdig/graphics/resources/colormaps_guide/cs33.txt: -------------------------------------------------------------------------------- 1 | 100 0 116 2 | 120 0 136 3 | 90 0 184 4 | 70 0 245 5 | 0 170 225 6 | 0 200 200 7 | 0 200 125 8 | 195 255 0 9 | 255 255 0 10 | 255 100 0 11 | 255 155 0 12 | 255 0 0 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs34.txt: -------------------------------------------------------------------------------- 1 | 255 255 255 2 | 170 255 255 3 | 85 160 255 4 | 29 0 255 5 | 126 229 91 6 | 78 204 67 7 | 46 178 57 8 | 30 153 61 9 | 255 255 102 10 | 255 204 102 11 | 255 136 76 12 | 255 25 25 13 | 204 61 61 14 | 165 49 49 15 | 237 0 237 16 | 137 103 205 17 | 250 240 230 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs35.txt: -------------------------------------------------------------------------------- 1 | 147 112 219 2 | 0 0 200 3 | 60 100 230 4 | 120 155 242 5 | 176 224 230 6 | 32 178 170 7 | 154 205 50 8 | 46 139 87 9 | 245 230 190 10 | 222 184 135 11 | 255 225 0 12 | 255 165 0 13 | 255 69 0 14 | 178 34 34 15 | 255 182 193 16 | 255 20 147 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs36.txt: -------------------------------------------------------------------------------- 1 | 130 32 240 2 | 0 0 150 3 | 0 0 205 4 | 65 105 225 5 | 30 144 255 6 | 0 191 255 7 | 160 210 255 8 | 210 245 255 9 | 255 255 200 10 | 255 225 50 11 | 255 170 0 12 | 255 110 0 13 | 255 0 0 14 | 200 0 0 15 | 160 35 35 16 | 255 105 180 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_guide/cs37.txt: -------------------------------------------------------------------------------- 1 | 255 255 255 2 | 233 204 249 3 | 207 128 223 4 | 131 51 147 5 | 58 0 176 6 | 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/metdig/graphics/resources/colormaps_met/qsf_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 187 187 187 3 | 148 148 148 4 | 109 109 109 5 | 79 79 82 6 | 151 208 246 7 | 118 181 250 8 | 80 165 241 9 | 64 151 236 10 | 47 127 228 11 | 37 106 229 12 | 28 100 202 13 | 21 91 187 14 | 64 10 128 15 | 79 6 135 16 | 90 8 136 17 | 106 7 133 18 | 134 12 131 19 | 159 15 129 20 | 201 17 124 21 | 201 17 124 22 | 227 27 115 23 | 227 27 115 24 | 243 62 150 25 | 252 93 173 26 | 253 108 177 27 | 248 131 186 28 | 237 142 191 29 | 236 147 197 30 | 234 154 202 31 | 215 168 209 32 | 211 176 211 33 | 191 198 220 34 | 179 212 232 35 | 165 228 233 36 | 155 239 240 37 | 146 249 247 38 | 144 242 240 39 | 126 217 216 40 | 118 181 198 41 | 111 187 195 42 | 125 181 196 43 | 127 178 198 44 | 137 177 203 45 | 136 171 200 46 | 140 168 203 47 | 145 168 211 48 | 146 168 207 49 | 149 160 219 50 | 152 163 212 51 | 161 157 222 52 | 163 156 217 53 | 169 156 210 54 | 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-------------------------------------------------------------------------------- 1 | # r g b 2 | 0.388235 0.266667 0.180392 3 | 0.490196 0.329412 0.211765 4 | 0.600000 0.384314 0.243137 5 | 0.658824 0.450980 0.309804 6 | 0.709804 0.537255 0.388235 7 | 0.807843 0.698039 0.580392 8 | 0.854902 0.776471 0.698039 9 | 0.866667 0.843137 0.776471 10 | 0.725490 0.780392 0.666667 11 | 0.666667 0.756863 0.611765 12 | 0.529412 0.733333 0.541176 13 | 0.423529 0.647059 0.568627 14 | 0.309804 0.411765 0.560784 15 | 0.309804 0.384314 0.560784 16 | 0.615686 0.094118 0.694118 17 | 0.474510 0.078431 0.380392 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/sleet_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 0.992157 0.847059 0.835294 3 | 0.984314 0.682353 0.725490 4 | 0.968627 0.427451 0.639216 5 | 0.827451 0.160784 0.572549 6 | 0.572549 0.003922 0.478431 7 | 0.317647 0.000000 0.423529 8 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/slp_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 253 144 235 3 | 235 120 229 4 | 239 83 224 5 | 241 31 211 6 | 241 31 211 7 | 162 14 155 8 | 136 5 118 9 | 109 2 88 10 | 95 8 83 11 | 42 13 168 12 | 47 26 167 13 | 61 39 180 14 | 63 60 182 15 | 109 92 222 16 | 162 140 249 17 | 193 179 255 18 | 221 220 254 19 | 24 97 219 20 | 32 108 229 21 | 36 132 244 22 | 82 165 238 23 | 145 212 255 24 | 178 239 248 25 | 222 254 255 26 | 201 253 189 27 | 145 247 139 28 | 83 237 84 29 | 29 179 30 30 | 12 161 4 31 | 255 249 164 32 | 255 226 127 33 | 250 194 53 34 | 255 157 4 35 | 255 94 0 36 | 248 51 2 37 | 224 19 4 38 | 162 2 0 39 | 96 51 41 40 | 140 102 83 41 | 177 137 129 42 | 221 192 179 43 | 248 163 162 44 | 221 102 99 45 | 202 60 59 46 | 161 36 29 47 | 108 111 109 48 | 138 138 138 49 | 170 170 170 50 | 197 197 197 51 | 213 213 213 52 | 231 227 228 53 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/snow_density_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 255 255 255 3 | 224 224 224 4 | 198 198 198 5 | 173 173 173 6 | 148 148 148 7 | 168 230 240 8 | 114 189 212 9 | 63 150 183 10 | 18 111 156 11 | 12 71 170 12 | 45 99 182 13 | 79 128 195 14 | 116 158 207 15 | 153 188 220 16 | 191 218 233 17 | 199 171 215 18 | 191 147 206 19 | 183 125 196 20 | 174 102 188 21 | 166 80 178 22 | 158 58 169 23 | 133 21 71 24 | 148 35 89 25 | 164 50 108 26 | 179 66 126 27 | 195 81 145 28 | 212 98 164 29 | 233 165 181 30 | 230 154 159 31 | 228 142 138 32 | 225 129 117 33 | 223 118 96 34 | 220 106 77 35 | 218 128 86 36 | 223 148 108 37 | 228 167 129 38 | 234 187 152 39 | 240 207 176 40 | 245 228 198 41 | 250 248 222 42 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/snow_depth_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 255 255 255 3 | 224 224 224 4 | 198 198 198 5 | 173 173 173 6 | 148 148 148 7 | 168 230 240 8 | 114 189 212 9 | 63 150 183 10 | 18 111 156 11 | 12 71 170 12 | 45 99 182 13 | 79 128 195 14 | 116 158 207 15 | 153 188 220 16 | 191 218 233 17 | 199 171 215 18 | 191 147 206 19 | 183 125 196 20 | 174 102 188 21 | 166 80 178 22 | 158 58 169 23 | 133 21 71 24 | 148 35 89 25 | 164 50 108 26 | 179 66 126 27 | 195 81 145 28 | 212 98 164 29 | 233 165 181 30 | 230 154 159 31 | 228 142 138 32 | 225 129 117 33 | 223 118 96 34 | 220 106 77 35 | 218 128 86 36 | 223 148 108 37 | 228 167 129 38 | 234 187 152 39 | 240 207 176 40 | 245 228 198 41 | 250 248 222 42 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/snow_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 0.917647 0.917647 0.917647 3 | 0.784314 0.784314 0.784314 4 | 0.603922 0.603922 0.603922 5 | 0.423529 0.423529 0.423529 6 | 0.227451 0.227451 0.227451 7 | 0.023529 0.023529 0.023529 8 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/temperature_trend_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 252 220 247 3 | 247 149 231 4 | 243 120 224 5 | 240 89 216 6 | 236 42 206 7 | 192 34 168 8 | 160 31 140 9 | 129 28 112 10 | 107 25 94 11 | 84 21 75 12 | 52 39 153 13 | 64 47 168 14 | 74 64 187 15 | 110 96 208 16 | 126 110 223 17 | 157 137 243 18 | 188 176 247 19 | 221 221 254 20 | 221 221 254 21 | 178 248 176 22 | 148 243 151 23 | 120 243 132 24 | 86 238 108 25 | 66 206 90 26 | 46 176 69 27 | 36 156 59 28 | 37 98 198 29 | 44 108 223 30 | 68 146 235 31 | 84 161 235 32 | 120 181 242 33 | 148 206 244 34 | 178 238 246 35 | 255 255 255 36 | 253 252 252 37 | 253 255 177 38 | 253 224 153 39 | 253 192 131 40 | 253 165 109 41 | 253 136 88 42 | 252 109 70 43 | 251 83 55 44 | 229 55 42 45 | 205 49 38 46 | 183 43 34 47 | 160 37 31 48 | 140 31 27 49 | 118 26 24 50 | 98 18 21 51 | 78 15 18 52 | 98 64 57 53 | 116 82 74 54 | 136 100 92 55 | 156 118 110 56 | 176 136 128 57 | 196 156 148 58 | 221 186 178 59 | 238 218 208 60 | 248 238 228 61 | 253 228 228 62 | 253 196 198 63 | 242 158 158 64 | 226 128 130 65 | 221 100 102 66 | 221 100 102 67 | 191 67 69 68 | 174 51 53 69 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/terrain_50.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 0.007843 0.380392 0.000000 3 | 0.023529 0.384314 0.000000 4 | 0.043137 0.384314 0.000000 5 | 0.062745 0.388235 0.000000 6 | 0.078431 0.392157 0.000000 7 | 0.098039 0.396078 0.000000 8 | 0.117647 0.400000 0.000000 9 | 0.133333 0.403922 0.000000 10 | 0.152941 0.407843 0.000000 11 | 0.172549 0.411765 0.000000 12 | 0.188235 0.415686 0.000000 13 | 0.207843 0.419608 0.000000 14 | 0.227451 0.423529 0.000000 15 | 0.247059 0.427451 0.007843 16 | 0.266667 0.431373 0.015686 17 | 0.286275 0.435294 0.027451 18 | 0.305882 0.439216 0.035294 19 | 0.325490 0.443137 0.047059 20 | 0.345098 0.447059 0.058824 21 | 0.364706 0.450980 0.066667 22 | 0.384314 0.454902 0.078431 23 | 0.403922 0.454902 0.086275 24 | 0.427451 0.458824 0.098039 25 | 0.447059 0.462745 0.105882 26 | 0.466667 0.466667 0.117647 27 | 0.486275 0.474510 0.125490 28 | 0.505882 0.478431 0.133333 29 | 0.525490 0.486275 0.145098 30 | 0.545098 0.494118 0.152941 31 | 0.564706 0.498039 0.160784 32 | 0.584314 0.505882 0.172549 33 | 0.607843 0.513725 0.180392 34 | 0.627451 0.521569 0.188235 35 | 0.647059 0.525490 0.200000 36 | 0.666667 0.533333 0.207843 37 | 0.686275 0.541176 0.215686 38 | 0.705882 0.545098 0.227451 39 | 0.725490 0.560784 0.250980 40 | 0.749020 0.596078 0.313725 41 | 0.772549 0.635294 0.376471 42 | 0.796078 0.670588 0.439216 43 | 0.819608 0.709804 0.501961 44 | 0.843137 0.745098 0.564706 45 | 0.866667 0.780392 0.627451 46 | 0.886275 0.819608 0.690196 47 | 0.909804 0.854902 0.752941 48 | 0.933333 0.894118 0.815686 49 | 0.956863 0.929412 0.878431 50 | 0.980392 0.964706 0.941176 51 | 1.000000 1.000000 1.000000 52 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/vertical_velocity_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 157 0 1 3 | 201 1 1 4 | 241 2 2 5 | 255 51 51 6 | 255 133 133 7 | 255 186 186 8 | 254 221 221 9 | 255 255 255 10 | 225 225 255 11 | 186 186 255 12 | 132 132 255 13 | 44 44 247 14 | 4 4 241 15 | 1 1 200 16 | 2 2 153 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/visibility_nws.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 49 0 126 3 | 0 50 179 4 | 0 125 255 5 | 0 189 255 6 | 255 34 144 7 | 255 174 215 8 | 255 255 0 9 | 255 152 0 10 | 23 215 139 11 | 42 169 42 12 | 83 255 0 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/wsp.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 255 245 157 3 | 255 238 88 4 | 255 202 40 5 | 255 193 7 6 | 255 152 0 7 | 251 140 0 8 | 230 74 25 9 | 191 54 12 10 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_met/wvfl_ctable.rgb: -------------------------------------------------------------------------------- 1 | # r g b 2 | 253 214 196 3 | 252 174 146 4 | 252 131 99 5 | 246 87 62 6 | 222 43 37 7 | 184 20 25 8 | 132 7 17 9 | 251 177 186 10 | 249 140 174 11 | 242 94 159 12 | 220 50 150 13 | 180 7 129 14 | 137 1 121 15 | 96 0 112 16 | 120 120 120 17 | 140 140 140 18 | 160 160 160 19 | 180 180 180 20 | 200 200 200 21 | 220 220 220 22 | 240 240 240 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlAqGrWh2YeOrReVi22.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 22 2 | # r g b 3 | 25 12 243 4 | 36 12 243 5 | 56 73 245 6 | 85 156 246 7 | 111 233 179 8 | 93 201 97 9 | 80 170 40 10 | 81 164 25 11 | 115 195 29 12 | 152 220 31 13 | 255 255 255 14 | 255 255 255 15 | 249 254 41 16 | 233 222 36 17 | 227 194 33 18 | 219 161 32 19 | 202 96 26 20 | 192 49 24 21 | 189 3 23 22 | 133 0 28 23 | 73 1 63 24 | 44 4 94 25 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlAqGrYeOrRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 100 2 | # r g b 3 | 0 0 255 4 | 0 21 255 5 | 0 43 255 6 | 0 64 255 7 | 0 85 255 8 | 0 106 255 9 | 0 128 255 10 | 0 149 255 11 | 0 170 255 12 | 0 191 255 13 | 0 213 255 14 | 0 234 255 15 | 0 255 255 16 | 12 255 255 17 | 24 255 255 18 | 35 255 255 19 | 47 255 255 20 | 59 255 255 21 | 71 255 255 22 | 82 255 255 23 | 94 255 255 24 | 106 255 255 25 | 118 255 255 26 | 129 255 255 27 | 141 255 255 28 | 153 255 255 29 | 140 255 234 30 | 128 255 213 31 | 115 255 191 32 | 102 255 170 33 | 89 255 149 34 | 77 255 128 35 | 64 255 106 36 | 51 255 85 37 | 38 255 64 38 | 26 255 43 39 | 13 255 21 40 | 0 255 0 41 | 16 255 0 42 | 31 255 0 43 | 47 255 0 44 | 63 255 0 45 | 78 255 0 46 | 94 255 0 47 | 110 255 0 48 | 126 255 0 49 | 141 255 0 50 | 157 255 0 51 | 173 255 0 52 | 188 255 0 53 | 204 255 0 54 | 207 255 0 55 | 210 255 0 56 | 214 255 0 57 | 217 255 0 58 | 220 255 0 59 | 223 255 0 60 | 226 255 0 61 | 230 255 0 62 | 233 255 0 63 | 236 255 0 64 | 239 255 0 65 | 242 255 0 66 | 245 255 0 67 | 249 255 0 68 | 252 255 0 69 | 255 255 0 70 | 255 248 0 71 | 255 240 0 72 | 255 233 0 73 | 255 225 0 74 | 255 218 0 75 | 255 210 0 76 | 255 203 0 77 | 255 195 0 78 | 255 188 0 79 | 255 180 0 80 | 255 173 0 81 | 255 165 0 82 | 255 158 0 83 | 255 150 0 84 | 255 143 0 85 | 255 135 0 86 | 255 128 0 87 | 255 120 0 88 | 255 112 0 89 | 255 104 0 90 | 255 96 0 91 | 255 88 0 92 | 255 80 0 93 | 255 72 0 94 | 255 64 0 95 | 255 56 0 96 | 255 48 0 97 | 255 40 0 98 | 255 32 0 99 | 255 24 0 100 | 255 16 0 101 | 255 8 0 102 | 255 0 0 103 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 96 2 | # r g b 3 | 0 0 128 4 | 0 0 133 5 | 0 0 138 6 | 0 0 143 7 | 0 0 148 8 | 0 0 153 9 | 0 0 158 10 | 0 0 164 11 | 0 0 169 12 | 0 0 174 13 | 0 0 179 14 | 0 0 184 15 | 0 0 189 16 | 0 0 194 17 | 0 0 199 18 | 0 0 204 19 | 0 0 209 20 | 0 0 214 21 | 0 0 219 22 | 0 0 225 23 | 0 0 230 24 | 0 0 235 25 | 0 0 240 26 | 0 0 245 27 | 0 0 250 28 | 0 0 255 29 | 10 10 255 30 | 20 20 255 31 | 31 31 255 32 | 41 41 255 33 | 51 51 255 34 | 61 61 255 35 | 71 71 255 36 | 82 82 255 37 | 92 92 255 38 | 102 102 255 39 | 112 112 255 40 | 122 122 255 41 | 133 133 255 42 | 143 143 255 43 | 153 153 255 44 | 163 163 255 45 | 173 173 255 46 | 184 184 255 47 | 194 194 255 48 | 204 204 255 49 | 214 214 255 50 | 224 224 255 51 | 255 224 224 52 | 255 214 214 53 | 255 204 204 54 | 255 194 194 55 | 255 184 184 56 | 255 173 173 57 | 255 163 163 58 | 255 153 153 59 | 255 143 143 60 | 255 133 133 61 | 255 122 122 62 | 255 112 112 63 | 255 102 102 64 | 255 92 92 65 | 255 82 82 66 | 255 71 71 67 | 255 61 61 68 | 255 51 51 69 | 255 41 41 70 | 255 31 31 71 | 255 20 20 72 | 255 10 10 73 | 255 0 0 74 | 250 0 0 75 | 245 0 0 76 | 240 0 0 77 | 235 0 0 78 | 230 0 0 79 | 225 0 0 80 | 219 0 0 81 | 214 0 0 82 | 209 0 0 83 | 204 0 0 84 | 199 0 0 85 | 194 0 0 86 | 189 0 0 87 | 184 0 0 88 | 179 0 0 89 | 174 0 0 90 | 169 0 0 91 | 164 0 0 92 | 158 0 0 93 | 153 0 0 94 | 148 0 0 95 | 143 0 0 96 | 138 0 0 97 | 133 0 0 98 | 128 0 0 99 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlWhRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 0 0 128 4 | 0 0 133 5 | 0 0 138 6 | 0 0 143 7 | 0 0 148 8 | 0 0 153 9 | 0 0 158 10 | 0 0 164 11 | 0 0 169 12 | 0 0 174 13 | 0 0 179 14 | 0 0 184 15 | 0 0 189 16 | 0 0 194 17 | 0 0 199 18 | 0 0 204 19 | 0 0 209 20 | 0 0 214 21 | 0 0 219 22 | 0 0 225 23 | 0 0 230 24 | 0 0 235 25 | 0 0 240 26 | 0 0 245 27 | 0 0 250 28 | 0 0 255 29 | 10 10 255 30 | 20 20 255 31 | 31 31 255 32 | 41 41 255 33 | 51 51 255 34 | 61 61 255 35 | 71 71 255 36 | 82 82 255 37 | 92 92 255 38 | 102 102 255 39 | 112 112 255 40 | 122 122 255 41 | 133 133 255 42 | 143 143 255 43 | 153 153 255 44 | 163 163 255 45 | 173 173 255 46 | 184 184 255 47 | 194 194 255 48 | 204 204 255 49 | 214 214 255 50 | 224 224 255 51 | 235 235 255 52 | 245 245 255 53 | 255 255 255 54 | 255 245 245 55 | 255 235 235 56 | 255 224 224 57 | 255 214 214 58 | 255 204 204 59 | 255 194 194 60 | 255 184 184 61 | 255 173 173 62 | 255 163 163 63 | 255 153 153 64 | 255 143 143 65 | 255 133 133 66 | 255 122 122 67 | 255 112 112 68 | 255 102 102 69 | 255 92 92 70 | 255 82 82 71 | 255 71 71 72 | 255 61 61 73 | 255 51 51 74 | 255 41 41 75 | 255 31 31 76 | 255 20 20 77 | 255 10 10 78 | 255 0 0 79 | 250 0 0 80 | 245 0 0 81 | 240 0 0 82 | 235 0 0 83 | 230 0 0 84 | 225 0 0 85 | 219 0 0 86 | 214 0 0 87 | 209 0 0 88 | 204 0 0 89 | 199 0 0 90 | 194 0 0 91 | 189 0 0 92 | 184 0 0 93 | 179 0 0 94 | 174 0 0 95 | 169 0 0 96 | 164 0 0 97 | 158 0 0 98 | 153 0 0 99 | 148 0 0 100 | 143 0 0 101 | 138 0 0 102 | 133 0 0 103 | 128 0 0 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlueDarkOrange18.rgb: -------------------------------------------------------------------------------- 1 | # Blue to Dark Orange, 18 steps 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 18 6 | 7 | # r g b 8 | 0 102 102 9 | 0 153 153 10 | 0 204 204 11 | 0 255 255 12 | 51 255 255 13 | 101 255 255 14 | 153 255 255 15 | 178 255 255 16 | 203 255 255 17 | 229 255 255 18 | 255 229 203 19 | 255 202 153 20 | 255 173 101 21 | 255 142 51 22 | 255 110 0 23 | 204 85 0 24 | 153 61 0 25 | 102 39 0 26 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlueDarkRed18.rgb: -------------------------------------------------------------------------------- 1 | # Blue to Dark Red, 18 steps, based on ColorBrewer 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 18 6 | 7 | # r g b 8 | 36 0 216 9 | 24 28 247 10 | 40 87 255 11 | 61 135 255 12 | 86 176 255 13 | 117 211 255 14 | 153 234 255 15 | 188 249 255 16 | 234 255 255 17 | 255 255 234 18 | 255 241 188 19 | 255 214 153 20 | 255 172 117 21 | 255 120 86 22 | 255 61 61 23 | 247 39 53 24 | 216 21 47 25 | 165 0 33 26 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BlueGreen14.rgb: -------------------------------------------------------------------------------- 1 | # Blue to Green 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 14 6 | 7 | # r g b 8 | 0 0 255 9 | 51 51 255 10 | 101 101 255 11 | 153 153 255 12 | 178 178 255 13 | 203 203 255 14 | 229 229 255 15 | 229 255 229 16 | 203 255 203 17 | 178 255 178 18 | 153 255 153 19 | 101 255 101 20 | 51 255 51 21 | 0 255 0 22 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/BrownBlue12.rgb: -------------------------------------------------------------------------------- 1 | # Brown to Blue, 12 steps 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 12 6 | 7 | # r g b 8 | 51 25 0 9 | 102 47 0 10 | 153 96 53 11 | 204 155 122 12 | 216 175 151 13 | 242 218 205 14 | 204 253 255 15 | 153 248 255 16 | 101 239 255 17 | 50 227 255 18 | 0 169 204 19 | 0 122 153 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/CBR_coldhot.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 11 2 | ;R G B 3 | 5 48 97 4 | 33 102 172 5 | 67 147 195 6 | 146 197 222 7 | 209 229 240 8 | 247 247 247 9 | 254 219 199 10 | 244 165 130 11 | 214 96 77 12 | 178 24 43 13 | 103 0 31 14 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/CBR_drywet.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 11 2 | ;R G B 3 | 84 48 5 4 | 140 81 10 5 | 191 129 45 6 | 223 194 125 7 | 246 232 195 8 | 245 245 245 9 | 199 234 229 10 | 128 205 193 11 | 53 151 143 12 | 1 102 95 13 | 0 60 48 14 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/CBR_set3.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 12 2 | ;R G B 3 | 141 211 199 4 | 255 255 179 5 | 190 186 218 6 | 251 128 114 7 | 128 177 211 8 | 253 180 98 9 | 179 222 105 10 | 252 205 229 11 | 217 217 217 12 | 188 128 189 13 | 204 235 197 14 | 255 237 111 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/CBR_wet.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 11 2 | ;R G B 3 | 255 255 255 4 | 247 252 240 5 | 224 243 219 6 | 204 235 197 7 | 168 221 181 8 | 123 204 196 9 | 78 179 211 10 | 43 140 190 11 | 8 104 172 12 | 8 64 129 13 | 0 32 62 14 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/Cat12.rgb: -------------------------------------------------------------------------------- 1 | # Categorical 12-step scheme, after ColorBrewer 11-step Paired Scheme 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 12 6 | 7 | # r g b 8 | 255 191 127 9 | 255 127 0 10 | 255 255 153 11 | 255 255 50 12 | 178 255 140 13 | 50 255 0 14 | 165 237 255 15 | 25 178 255 16 | 204 191 255 17 | 101 76 255 18 | 255 153 191 19 | 229 25 50 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/Copyright: -------------------------------------------------------------------------------- 1 | The contents of this directory are copyright protected: 2 | ------------------------------------------------------- 3 | NCL Version 6.3.0 4 | Copyright (C) 2015 5 | University Corporation for Atmospheric Research 6 | The use of this Software is governed by a License Agreement. 7 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GHRSST_anomaly.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 42 2 | # r g b 3 | 107 0 219 4 | 122 0 213 5 | 138 0 208 6 | 156 0 201 7 | 131 24 209 8 | 85 60 225 9 | 39 97 241 10 | 0 133 255 11 | 0 169 255 12 | 0 211 255 13 | 0 247 255 14 | 29 255 226 15 | 65 255 190 16 | 102 255 154 17 | 133 255 131 18 | 154 255 141 19 | 173 255 150 20 | 191 255 159 21 | 192 238 168 22 | 191 220 177 23 | 191 202 186 24 | 202 202 183 25 | 220 220 168 26 | 238 238 154 27 | 255 254 137 28 | 255 236 97 29 | 255 218 58 30 | 255 197 11 31 | 255 179 0 32 | 255 161 0 33 | 255 142 0 34 | 255 120 0 35 | 255 84 0 36 | 255 41 0 37 | 255 5 0 38 | 246 0 37 39 | 236 0 79 40 | 227 0 122 41 | 211 0 135 42 | 180 0 85 43 | 154 0 43 44 | 128 0 0 45 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_cool.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 10 2 | # r g b 3 | 0.050000 0.950000 1.000000 4 | 0.150000 0.850000 1.000000 5 | 0.250000 0.750000 1.000000 6 | 0.350000 0.650000 1.000000 7 | 0.450000 0.550000 1.000000 8 | 0.550000 0.450000 1.000000 9 | 0.650000 0.350000 1.000000 10 | 0.750000 0.250000 1.000000 11 | 0.850000 0.150000 1.000000 12 | 0.950000 0.050000 1.000000 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_copper.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 50 2 | # r g b 3 | 0.012549 0.007825 0.005020 4 | 0.037647 0.023474 0.015059 5 | 0.062745 0.039123 0.025098 6 | 0.087843 0.054773 0.035137 7 | 0.112941 0.070422 0.045176 8 | 0.138039 0.086072 0.055216 9 | 0.163137 0.101721 0.065255 10 | 0.188235 0.117370 0.075294 11 | 0.213333 0.133020 0.085333 12 | 0.238431 0.148669 0.095373 13 | 0.263529 0.164318 0.105412 14 | 0.288627 0.179968 0.115451 15 | 0.313725 0.195617 0.125490 16 | 0.338824 0.211266 0.135529 17 | 0.363922 0.226916 0.145569 18 | 0.389020 0.242565 0.155608 19 | 0.414118 0.258215 0.165647 20 | 0.439216 0.273864 0.175686 21 | 0.464314 0.289513 0.185725 22 | 0.489412 0.305163 0.195765 23 | 0.514510 0.320812 0.205804 24 | 0.539608 0.336461 0.215843 25 | 0.564706 0.352111 0.225882 26 | 0.589804 0.367760 0.235922 27 | 0.614902 0.383409 0.245961 28 | 0.640000 0.399059 0.256000 29 | 0.665098 0.414708 0.266039 30 | 0.690196 0.430358 0.276078 31 | 0.715294 0.446007 0.286118 32 | 0.740392 0.461656 0.296157 33 | 0.765490 0.477306 0.306196 34 | 0.790588 0.492955 0.316235 35 | 0.815686 0.508604 0.326275 36 | 0.840784 0.524254 0.336314 37 | 0.865882 0.539903 0.346353 38 | 0.890980 0.555552 0.356392 39 | 0.916078 0.571202 0.366431 40 | 0.941176 0.586851 0.376471 41 | 0.966275 0.602501 0.386510 42 | 0.991373 0.618150 0.396549 43 | 1.000000 0.633919 0.406588 44 | 1.000000 0.649750 0.416627 45 | 1.000000 0.665581 0.426667 46 | 1.000000 0.681412 0.436706 47 | 1.000000 0.697243 0.446745 48 | 1.000000 0.713074 0.456784 49 | 1.000000 0.728905 0.466824 50 | 1.000000 0.744736 0.476863 51 | 1.000000 0.760567 0.486902 52 | 1.000000 0.776398 0.496941 53 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_drywet.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 60 2 | # r g b 3 | 0.545882 0.400392 0.176078 4 | 0.586667 0.440392 0.198824 5 | 0.627451 0.480392 0.221569 6 | 0.668235 0.520392 0.244314 7 | 0.709020 0.560392 0.267059 8 | 0.749804 0.600392 0.289804 9 | 0.790588 0.640392 0.312549 10 | 0.831373 0.680392 0.335294 11 | 0.872157 0.720392 0.358039 12 | 0.912941 0.760392 0.380784 13 | 0.921961 0.788039 0.399020 14 | 0.899216 0.803333 0.412745 15 | 0.876471 0.818627 0.426471 16 | 0.853725 0.833922 0.440196 17 | 0.830980 0.849216 0.453922 18 | 0.808235 0.864510 0.467647 19 | 0.785490 0.879804 0.481373 20 | 0.762745 0.895098 0.495098 21 | 0.740000 0.910392 0.508824 22 | 0.717255 0.925686 0.522549 23 | 0.680392 0.933333 0.549020 24 | 0.629412 0.933333 0.588235 25 | 0.578431 0.933333 0.627451 26 | 0.527451 0.933333 0.666667 27 | 0.476471 0.933333 0.705882 28 | 0.425490 0.933333 0.745098 29 | 0.374510 0.933333 0.784314 30 | 0.323529 0.933333 0.823529 31 | 0.272549 0.933333 0.862745 32 | 0.221569 0.933333 0.901961 33 | 0.188627 0.910196 0.922157 34 | 0.173725 0.863922 0.923333 35 | 0.158824 0.817647 0.924510 36 | 0.143922 0.771373 0.925686 37 | 0.129020 0.725098 0.926863 38 | 0.114118 0.678824 0.928039 39 | 0.099216 0.632549 0.929216 40 | 0.084314 0.586275 0.930392 41 | 0.069412 0.540000 0.931569 42 | 0.054510 0.493725 0.932745 43 | 0.052157 0.447255 0.922549 44 | 0.062353 0.400588 0.900980 45 | 0.072549 0.353922 0.879412 46 | 0.082745 0.307255 0.857843 47 | 0.092941 0.260588 0.836275 48 | 0.103137 0.213922 0.814706 49 | 0.113333 0.167255 0.793137 50 | 0.123529 0.120588 0.771569 51 | 0.133725 0.073922 0.750000 52 | 0.143922 0.027255 0.728431 53 | 0.143137 0.013725 0.703922 54 | 0.131373 0.033333 0.676471 55 | 0.119608 0.052941 0.649020 56 | 0.107843 0.072549 0.621569 57 | 0.096078 0.092157 0.594118 58 | 0.084314 0.111765 0.566667 59 | 0.072549 0.131373 0.539216 60 | 0.060784 0.150980 0.511765 61 | 0.049020 0.170588 0.484314 62 | 0.037255 0.190196 0.456863 63 | 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1.000000 27 | 0.352941 1.000000 1.000000 28 | 0.352941 1.000000 1.000000 29 | 0.352941 1.000000 1.000000 30 | 0.352941 1.000000 1.000000 31 | 0.352941 1.000000 1.000000 32 | 0.352941 1.000000 1.000000 33 | 0.549020 1.000000 0.901961 34 | 0.549020 1.000000 0.901961 35 | 0.549020 1.000000 0.901961 36 | 0.549020 1.000000 0.901961 37 | 0.549020 1.000000 0.901961 38 | 0.549020 1.000000 0.901961 39 | 0.549020 1.000000 0.901961 40 | 0.549020 1.000000 0.901961 41 | 0.549020 1.000000 0.901961 42 | 0.549020 1.000000 0.901961 43 | 0.647059 1.000000 0.843137 44 | 0.647059 1.000000 0.843137 45 | 0.647059 1.000000 0.843137 46 | 0.647059 1.000000 0.843137 47 | 0.647059 1.000000 0.843137 48 | 0.647059 1.000000 0.843137 49 | 0.647059 1.000000 0.843137 50 | 0.647059 1.000000 0.843137 51 | 0.647059 1.000000 0.843137 52 | 0.647059 1.000000 0.843137 53 | 0.764706 1.000000 0.843137 54 | 0.764706 1.000000 0.843137 55 | 0.764706 1.000000 0.843137 56 | 0.764706 1.000000 0.843137 57 | 0.764706 1.000000 0.843137 58 | 0.764706 1.000000 0.843137 59 | 0.764706 1.000000 0.843137 60 | 0.764706 1.000000 0.843137 61 | 0.764706 1.000000 0.843137 62 | 0.764706 1.000000 0.843137 63 | 0.823529 1.000000 0.843137 64 | 0.823529 1.000000 0.843137 65 | 0.823529 1.000000 0.843137 66 | 0.823529 1.000000 0.843137 67 | 0.823529 1.000000 0.843137 68 | 0.901961 1.000000 0.941176 69 | 0.901961 1.000000 0.941176 70 | 0.901961 1.000000 0.941176 71 | 0.921569 1.000000 1.000000 72 | 0.921569 1.000000 1.000000 73 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_gray.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 10 2 | # r g b 3 | 0.050000 0.050000 0.050000 4 | 0.150000 0.150000 0.150000 5 | 0.250000 0.250000 0.250000 6 | 0.350000 0.350000 0.350000 7 | 0.450000 0.450000 0.450000 8 | 0.550000 0.550000 0.550000 9 | 0.650000 0.650000 0.650000 10 | 0.750000 0.750000 0.750000 11 | 0.850000 0.850000 0.850000 12 | 0.950000 0.950000 0.950000 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_haxby.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 32 2 | # r g b 3 | 0.039216 0.000000 0.474510 4 | 0.156863 0.000000 0.588235 5 | 0.078431 0.019608 0.686275 6 | 0.000000 0.039216 0.784314 7 | 0.000000 0.098039 0.831373 8 | 0.000000 0.156863 0.878431 9 | 0.101961 0.400000 0.941176 10 | 0.050980 0.505882 0.972549 11 | 0.098039 0.686275 1.000000 12 | 0.196078 0.745098 1.000000 13 | 0.266667 0.792157 1.000000 14 | 0.380392 0.882353 0.941176 15 | 0.415686 0.921569 0.882353 16 | 0.486275 0.921569 0.784314 17 | 0.541176 0.925490 0.682353 18 | 0.674510 0.960784 0.658824 19 | 0.803922 1.000000 0.635294 20 | 0.874510 0.960784 0.552941 21 | 0.941176 0.925490 0.474510 22 | 0.968627 0.843137 0.407843 23 | 1.000000 0.741176 0.341176 24 | 1.000000 0.627451 0.270588 25 | 0.956863 0.458824 0.294118 26 | 0.933333 0.313725 0.305882 27 | 1.000000 0.352941 0.352941 28 | 1.000000 0.486275 0.486275 29 | 1.000000 0.619608 0.619608 30 | 0.960784 0.701961 0.682353 31 | 1.000000 0.768627 0.768627 32 | 1.000000 0.843137 0.843137 33 | 1.000000 0.921569 0.921569 34 | 1.000000 1.000000 1.000000 35 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_nighttime.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 20 2 | # r g b 3 | 0.122500 0.002756 0.106335 4 | 0.167500 0.011306 0.166719 5 | 0.188926 0.023906 0.212500 6 | 0.202179 0.040556 0.257500 7 | 0.209621 0.061256 0.302500 8 | 0.212831 0.086006 0.347500 9 | 0.213388 0.114806 0.392500 10 | 0.212871 0.147656 0.437500 11 | 0.212861 0.184556 0.482500 12 | 0.225506 0.236076 0.527500 13 | 0.493403 0.568182 0.266012 14 | 0.578968 0.613636 0.315186 15 | 0.659091 0.654688 0.368492 16 | 0.704545 0.663738 0.425930 17 | 0.750000 0.677083 0.487500 18 | 0.795455 0.696351 0.553202 19 | 0.840909 0.723170 0.623037 20 | 0.886364 0.759168 0.697004 21 | 0.931818 0.805971 0.775103 22 | 0.977273 0.865209 0.857335 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_no_green.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 16 2 | # r g b 3 | 0.125490 0.376471 1.000000 4 | 0.125490 0.623529 1.000000 5 | 0.125490 0.749020 1.000000 6 | 0.000000 0.811765 1.000000 7 | 0.164706 1.000000 1.000000 8 | 0.333333 1.000000 1.000000 9 | 0.498039 1.000000 1.000000 10 | 0.666667 1.000000 1.000000 11 | 1.000000 1.000000 0.329412 12 | 1.000000 0.941176 0.000000 13 | 1.000000 0.749020 0.000000 14 | 1.000000 0.658824 0.000000 15 | 1.000000 0.541176 0.000000 16 | 1.000000 0.439216 0.000000 17 | 1.000000 0.301961 0.000000 18 | 1.000000 0.000000 0.000000 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_ocean.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 80 2 | # r g b 3 | 0.000000 0.000980 0.004902 4 | 0.000000 0.002941 0.014706 5 | 0.000000 0.004902 0.024510 6 | 0.000000 0.006863 0.034314 7 | 0.000000 0.008824 0.044118 8 | 0.000000 0.010784 0.053922 9 | 0.000000 0.012745 0.063725 10 | 0.000000 0.014706 0.073529 11 | 0.000000 0.016667 0.083333 12 | 0.000000 0.018627 0.093137 13 | 0.000000 0.020588 0.102941 14 | 0.000000 0.022549 0.112745 15 | 0.000000 0.024510 0.122549 16 | 0.000000 0.026471 0.132353 17 | 0.000000 0.028431 0.142157 18 | 0.000000 0.030392 0.151961 19 | 0.000000 0.032353 0.161765 20 | 0.000000 0.034314 0.171569 21 | 0.000000 0.036275 0.181373 22 | 0.000000 0.038235 0.191176 23 | 0.000000 0.052941 0.210784 24 | 0.000000 0.080392 0.240196 25 | 0.000000 0.107843 0.269608 26 | 0.000000 0.135294 0.299020 27 | 0.000000 0.162745 0.328431 28 | 0.000000 0.190196 0.357843 29 | 0.000000 0.217647 0.387255 30 | 0.000000 0.245098 0.416667 31 | 0.000000 0.272549 0.446078 32 | 0.000000 0.300000 0.475490 33 | 0.000000 0.327451 0.504902 34 | 0.000000 0.354902 0.534314 35 | 0.000000 0.382353 0.563725 36 | 0.000000 0.409804 0.593137 37 | 0.000000 0.437255 0.622549 38 | 0.000000 0.464706 0.651961 39 | 0.000000 0.492157 0.681373 40 | 0.000000 0.519608 0.710784 41 | 0.000000 0.547059 0.740196 42 | 0.000000 0.574510 0.769608 43 | 0.016863 0.597451 0.781176 44 | 0.050588 0.615882 0.774902 45 | 0.084314 0.634314 0.768627 46 | 0.118039 0.652745 0.762353 47 | 0.151765 0.671176 0.756078 48 | 0.185490 0.689608 0.749804 49 | 0.219216 0.708039 0.743529 50 | 0.252941 0.726471 0.737255 51 | 0.286667 0.744902 0.730980 52 | 0.320392 0.763333 0.724706 53 | 0.354118 0.781961 0.718431 54 | 0.387843 0.800784 0.712157 55 | 0.421569 0.819608 0.705882 56 | 0.455294 0.838431 0.699608 57 | 0.489020 0.857255 0.693333 58 | 0.522745 0.876078 0.687059 59 | 0.556471 0.894902 0.680784 60 | 0.590196 0.913725 0.674510 61 | 0.623922 0.932549 0.668235 62 | 0.657647 0.951373 0.661961 63 | 0.682157 0.961765 0.667255 64 | 0.697451 0.963725 0.684118 65 | 0.712745 0.965686 0.700980 66 | 0.728039 0.967647 0.717843 67 | 0.743333 0.969608 0.734706 68 | 0.758627 0.971569 0.751569 69 | 0.773922 0.973529 0.768431 70 | 0.789216 0.975490 0.785294 71 | 0.804510 0.977451 0.802157 72 | 0.819804 0.979412 0.819020 73 | 0.835098 0.981373 0.836078 74 | 0.850392 0.983333 0.853333 75 | 0.865686 0.985294 0.870588 76 | 0.880980 0.987255 0.887843 77 | 0.896275 0.989216 0.905098 78 | 0.911569 0.991176 0.922353 79 | 0.926863 0.993137 0.939608 80 | 0.942157 0.995098 0.956863 81 | 0.957451 0.997059 0.974118 82 | 0.972745 0.999020 0.991373 83 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_paired.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 12 2 | # r g b 3 | 0.650980 0.807843 0.890196 4 | 0.121569 0.470588 0.705882 5 | 0.698039 0.874510 0.541176 6 | 0.200000 0.627451 0.172549 7 | 0.984314 0.603922 0.600000 8 | 0.890196 0.101961 0.109804 9 | 0.992157 0.749020 0.435294 10 | 1.000000 0.498039 0.000000 11 | 0.792157 0.698039 0.839216 12 | 0.415686 0.239216 0.603922 13 | 1.000000 1.000000 0.600000 14 | 0.694118 0.349020 0.156863 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_panoply.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 16 2 | # r g b 3 | 0.015686 0.054902 0.847059 4 | 0.125490 0.313725 1.000000 5 | 0.254902 0.588235 1.000000 6 | 0.427451 0.756863 1.000000 7 | 0.525490 0.850980 1.000000 8 | 0.611765 0.933333 1.000000 9 | 0.686275 0.960784 1.000000 10 | 0.807843 1.000000 1.000000 11 | 1.000000 0.996078 0.278431 12 | 1.000000 0.921569 0.000000 13 | 1.000000 0.768627 0.000000 14 | 1.000000 0.564706 0.000000 15 | 1.000000 0.282353 0.000000 16 | 1.000000 0.000000 0.000000 17 | 0.835294 0.000000 0.000000 18 | 0.619608 0.000000 0.000000 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_polar.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 20 2 | # r g b 3 | 0.050000 0.050000 1.000000 4 | 0.150000 0.150000 1.000000 5 | 0.250000 0.250000 1.000000 6 | 0.350000 0.350000 1.000000 7 | 0.450000 0.450000 1.000000 8 | 0.550000 0.550000 1.000000 9 | 0.650000 0.650000 1.000000 10 | 0.750000 0.750000 1.000000 11 | 0.850000 0.850000 1.000000 12 | 0.950000 0.950000 1.000000 13 | 1.000000 0.950000 0.950000 14 | 1.000000 0.850000 0.850000 15 | 1.000000 0.750000 0.750000 16 | 1.000000 0.650000 0.650000 17 | 1.000000 0.550000 0.550000 18 | 1.000000 0.450000 0.450000 19 | 1.000000 0.350000 0.350000 20 | 1.000000 0.250000 0.250000 21 | 1.000000 0.150000 0.150000 22 | 1.000000 0.050000 0.050000 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_red2green.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 20 2 | # r g b 3 | 1.000000 0.050000 0.050000 4 | 1.000000 0.150000 0.150000 5 | 1.000000 0.250000 0.250000 6 | 1.000000 0.350000 0.350000 7 | 1.000000 0.450000 0.450000 8 | 1.000000 0.550000 0.550000 9 | 1.000000 0.650000 0.650000 10 | 1.000000 0.750000 0.750000 11 | 1.000000 0.850000 0.850000 12 | 1.000000 0.950000 0.950000 13 | 0.950000 1.000000 0.950000 14 | 0.850000 1.000000 0.850000 15 | 0.750000 1.000000 0.750000 16 | 0.650000 1.000000 0.650000 17 | 0.550000 1.000000 0.550000 18 | 0.450000 1.000000 0.450000 19 | 0.350000 1.000000 0.350000 20 | 0.250000 1.000000 0.250000 21 | 0.150000 1.000000 0.150000 22 | 0.050000 1.000000 0.050000 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_split.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 40 2 | # r g b 3 | 0.476863 0.476863 0.975098 4 | 0.426667 0.426667 0.925294 5 | 0.376471 0.376471 0.875490 6 | 0.326275 0.326275 0.825686 7 | 0.276078 0.276078 0.775882 8 | 0.225882 0.225882 0.726078 9 | 0.175686 0.175686 0.676275 10 | 0.125490 0.125490 0.626471 11 | 0.075294 0.075294 0.576667 12 | 0.025098 0.025098 0.526863 13 | 0.000000 0.000000 0.476863 14 | 0.000000 0.000000 0.426667 15 | 0.000000 0.000000 0.376471 16 | 0.000000 0.000000 0.326275 17 | 0.000000 0.000000 0.276078 18 | 0.000000 0.000000 0.225882 19 | 0.000000 0.000000 0.175686 20 | 0.000000 0.000000 0.125490 21 | 0.000000 0.000000 0.075294 22 | 0.000000 0.000000 0.025098 23 | 0.025098 0.000000 0.000000 24 | 0.075294 0.000000 0.000000 25 | 0.125490 0.000000 0.000000 26 | 0.175686 0.000000 0.000000 27 | 0.225882 0.000000 0.000000 28 | 0.276078 0.000000 0.000000 29 | 0.326275 0.000000 0.000000 30 | 0.376471 0.000000 0.000000 31 | 0.426667 0.000000 0.000000 32 | 0.476863 0.000000 0.000000 33 | 0.526863 0.025098 0.025098 34 | 0.576667 0.075294 0.075294 35 | 0.626471 0.125490 0.125490 36 | 0.676275 0.175686 0.175686 37 | 0.726078 0.225882 0.225882 38 | 0.775882 0.276078 0.276078 39 | 0.825686 0.326275 0.326275 40 | 0.875490 0.376471 0.376471 41 | 0.925294 0.426667 0.426667 42 | 0.975098 0.476863 0.476863 43 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GMT_wysiwyg.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 20 2 | # r g b 3 | 0.250980 0.000000 0.250980 4 | 0.250980 0.000000 0.752941 5 | 0.000000 0.250980 1.000000 6 | 0.000000 0.501961 1.000000 7 | 0.000000 0.627451 1.000000 8 | 0.250980 0.752941 1.000000 9 | 0.250980 0.878431 1.000000 10 | 0.250980 1.000000 1.000000 11 | 0.250980 1.000000 0.752941 12 | 0.250980 1.000000 0.250980 13 | 0.501961 1.000000 0.250980 14 | 0.752941 1.000000 0.250980 15 | 1.000000 1.000000 0.250980 16 | 1.000000 0.878431 0.250980 17 | 1.000000 0.627451 0.250980 18 | 1.000000 0.376471 0.250980 19 | 1.000000 0.125490 0.250980 20 | 1.000000 0.376471 0.752941 21 | 1.000000 0.627451 1.000000 22 | 1.000000 0.878431 1.000000 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GSFC_landsat_udf_density.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 11 2 | # r g b 3 | 233 255 190 4 | 153 255 119 5 | 56 224 9 6 | 61 204 66 7 | 61 184 104 8 | 51 166 137 9 | 26 147 171 10 | 33 110 158 11 | 32 75 145 12 | 27 46 133 13 | 12 16 120 14 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/GreenMagenta16.rgb: -------------------------------------------------------------------------------- 1 | # Green to Magenta, 16 steps 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 16 6 | 7 | # r g b 8 | 0 80 0 9 | 0 134 0 10 | 0 187 0 11 | 0 241 0 12 | 80 255 80 13 | 134 255 134 14 | 187 255 187 15 | 255 255 255 16 | 255 241 255 17 | 255 187 255 18 | 255 134 255 19 | 255 80 255 20 | 241 0 241 21 | 187 0 187 22 | 134 0 134 23 | 80 0 80 24 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/NCV_gebco.rgb: -------------------------------------------------------------------------------- 1 | ncolors=24 2 | # r g b 3 | 18 10 58 4 | 23 49 110 5 | 19 89 140 6 | 26 103 164 7 | 30 114 178 8 | 29 139 196 9 | 26 165 210 10 | 27 184 223 11 | 26 204 235 12 | 26 216 241 13 | 38 223 241 14 | 49 229 235 15 | 104 242 233 16 | 160 255 229 17 | 195 209 80 18 | 225 224 102 19 | 223 196 91 20 | 210 178 81 21 | 189 150 46 22 | 163 127 46 23 | 153 118 43 24 | 142 109 38 25 | 134 103 36 26 | 116 88 29 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/NMCRef.rgb: -------------------------------------------------------------------------------- 1 | ncolors=256 2 | # r g b 3 | 130 215 255 4 | 0 236 236 5 | 1 174 246 6 | 0 0 246 7 | 0 255 0 8 | 0 200 0 9 | 0 144 0 10 | 255 255 0 11 | 231 192 0 12 | 255 144 0 13 | 255 0 0 14 | 213 0 0 15 | 192 0 0 16 | 221 94 253 17 | 191 2 238 18 | 97 1 120 -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/NMCVel.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 17 2 | # r g b 3 | 0.539 0.066 0.559 4 | 0.293 0.121 0.621 5 | 0.461 0.363 0.887 6 | 0.437 0.684 0.809 7 | 0.066 0.543 0.066 8 | 0.125 0.652 0.125 9 | 0.430 0.770 0.430 10 | 0.633 0.816 0.633 11 | 0.906 0.902 0.906 12 | 0.984 0.859 0.050 13 | 0.984 0.750 0.113 14 | 0.879 0.641 0.211 15 | 0.750 0.535 0.289 16 | 0.629 0.441 0.344 17 | 0.871 0.375 0.469 18 | 0.945 0.277 0.418 19 | 0.809 0.156 0.254 -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/StepSeq25.rgb: -------------------------------------------------------------------------------- 1 | # Stepped sequential scheme, 5 steps 2 | # 3 | # Taken from Department of Geography, University of Oregon 4 | 5 | ncolors = 25 6 | 7 | # r g b 8 | 153 15 15 9 | 178 44 44 10 | 204 81 81 11 | 229 126 126 12 | 255 178 178 13 | 153 84 15 14 | 178 111 44 15 | 204 142 81 16 | 229 177 126 17 | 255 216 178 18 | 107 153 15 19 | 133 178 44 20 | 163 204 81 21 | 195 229 126 22 | 229 255 178 23 | 15 107 153 24 | 44 133 178 25 | 81 163 204 26 | 126 195 229 27 | 178 229 255 28 | 38 15 153 29 | 66 44 178 30 | 101 81 204 31 | 143 126 229 32 | 191 178 255 33 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/UKM_hadcrut.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 12 2 | # r g b 3 | 40 9 218 4 | 36 77 255 5 | 60 161 255 6 | 113 218 255 7 | 171 250 255 8 | 226 255 255 9 | 255 255 190 10 | 255 223 153 11 | 255 174 113 12 | 249 109 93 13 | 218 36 48 14 | 164 0 32 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/ViBlGrWhYeOrRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 87 0 136 4 | 81 0 144 5 | 75 0 152 6 | 70 0 160 7 | 64 0 168 8 | 58 0 176 9 | 52 0 184 10 | 46 0 192 11 | 41 0 199 12 | 35 0 207 13 | 29 0 215 14 | 23 0 223 15 | 17 0 231 16 | 12 0 239 17 | 6 0 247 18 | 0 0 255 19 | 1 11 240 20 | 1 22 225 21 | 2 34 210 22 | 2 45 195 23 | 3 56 180 24 | 3 67 165 25 | 4 78 150 26 | 4 90 135 27 | 5 101 120 28 | 5 112 105 29 | 6 123 90 30 | 6 134 75 31 | 7 145 60 32 | 7 157 45 33 | 8 168 30 34 | 8 179 15 35 | 24 184 31 36 | 41 189 47 37 | 57 194 63 38 | 74 199 79 39 | 90 204 95 40 | 107 209 111 41 | 123 214 127 42 | 140 220 143 43 | 156 225 159 44 | 173 230 175 45 | 189 235 191 46 | 206 240 207 47 | 222 245 223 48 | 239 250 239 49 | 255 255 255 50 | 255 255 255 51 | 255 255 255 52 | 255 255 255 53 | 255 255 255 54 | 255 255 255 55 | 255 255 255 56 | 255 255 255 57 | 255 255 255 58 | 255 255 238 59 | 255 255 221 60 | 255 255 204 61 | 255 255 187 62 | 255 255 170 63 | 255 255 153 64 | 255 255 136 65 | 255 255 119 66 | 255 255 102 67 | 255 255 85 68 | 255 255 68 69 | 255 255 51 70 | 255 255 34 71 | 255 255 17 72 | 255 255 0 73 | 255 249 0 74 | 255 244 0 75 | 255 238 0 76 | 255 233 0 77 | 255 227 0 78 | 255 221 0 79 | 255 216 0 80 | 255 210 0 81 | 255 204 0 82 | 255 199 0 83 | 255 193 0 84 | 255 188 0 85 | 255 182 0 86 | 255 176 0 87 | 255 171 0 88 | 255 165 0 89 | 255 154 0 90 | 255 143 0 91 | 255 132 0 92 | 255 121 0 93 | 255 110 0 94 | 255 99 0 95 | 255 88 0 96 | 255 77 0 97 | 255 66 0 98 | 255 55 0 99 | 255 44 0 100 | 255 33 0 101 | 255 22 0 102 | 255 11 0 103 | 255 0 0 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/WhBlGrYeRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 100 2 | # r g b 3 | 255 255 255 4 | 245 245 255 5 | 235 235 255 6 | 224 224 255 7 | 214 214 255 8 | 204 204 255 9 | 194 194 255 10 | 184 184 255 11 | 173 173 255 12 | 163 163 255 13 | 153 153 255 14 | 143 143 255 15 | 133 133 255 16 | 122 122 255 17 | 112 112 255 18 | 102 102 255 19 | 92 92 255 20 | 82 82 255 21 | 71 71 255 22 | 61 61 255 23 | 51 51 255 24 | 41 41 255 25 | 31 31 255 26 | 20 20 255 27 | 10 10 255 28 | 0 0 255 29 | 0 7 245 30 | 1 14 236 31 | 1 21 226 32 | 1 29 217 33 | 2 36 207 34 | 2 43 197 35 | 2 50 188 36 | 3 57 178 37 | 3 64 169 38 | 3 72 159 39 | 4 79 149 40 | 4 86 140 41 | 4 93 130 42 | 4 100 121 43 | 5 107 111 44 | 5 115 101 45 | 5 122 92 46 | 6 129 82 47 | 6 136 73 48 | 6 143 63 49 | 7 150 53 50 | 7 158 44 51 | 7 165 34 52 | 8 172 25 53 | 8 179 15 54 | 18 182 14 55 | 29 185 14 56 | 39 189 13 57 | 49 192 13 58 | 59 195 12 59 | 70 198 11 60 | 80 201 11 61 | 90 204 10 62 | 101 208 9 63 | 111 211 9 64 | 121 214 8 65 | 132 217 8 66 | 142 220 7 67 | 152 223 6 68 | 162 227 6 69 | 173 230 5 70 | 183 233 4 71 | 193 236 4 72 | 204 239 3 73 | 214 242 3 74 | 224 246 2 75 | 234 249 1 76 | 245 252 1 77 | 255 255 0 78 | 255 245 0 79 | 255 235 0 80 | 255 224 0 81 | 255 214 0 82 | 255 204 0 83 | 255 194 0 84 | 255 184 0 85 | 255 173 0 86 | 255 163 0 87 | 255 153 0 88 | 255 143 0 89 | 255 133 0 90 | 255 122 0 91 | 255 112 0 92 | 255 102 0 93 | 255 92 0 94 | 255 82 0 95 | 255 71 0 96 | 255 61 0 97 | 255 51 0 98 | 255 41 0 99 | 255 31 0 100 | 255 20 0 101 | 255 10 0 102 | 255 0 0 103 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/WhBlReWh.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 100 2 | 3 | # r g b 4 | 255 255 255 5 | 250 250 255 6 | 245 245 255 7 | 239 239 255 8 | 234 234 255 9 | 229 229 255 10 | 224 224 255 11 | 219 219 255 12 | 213 213 255 13 | 208 208 255 14 | 203 203 255 15 | 198 198 255 16 | 193 193 255 17 | 187 187 255 18 | 182 182 255 19 | 177 177 255 20 | 172 172 255 21 | 167 167 255 22 | 161 161 255 23 | 156 156 255 24 | 151 151 255 25 | 146 146 255 26 | 141 141 255 27 | 135 135 255 28 | 130 130 255 29 | 125 125 255 30 | 120 120 255 31 | 114 114 255 32 | 109 109 255 33 | 104 104 255 34 | 99 99 255 35 | 94 94 255 36 | 88 88 255 37 | 83 83 255 38 | 78 78 255 39 | 73 73 255 40 | 68 68 255 41 | 62 62 255 42 | 57 57 255 43 | 52 52 255 44 | 47 47 255 45 | 42 42 255 46 | 36 36 255 47 | 31 31 255 48 | 26 26 255 49 | 21 21 255 50 | 16 16 255 51 | 10 10 255 52 | 5 5 255 53 | 0 0 255 54 | 255 0 0 55 | 255 5 5 56 | 255 10 10 57 | 255 16 16 58 | 255 21 21 59 | 255 26 26 60 | 255 31 31 61 | 255 36 36 62 | 255 42 42 63 | 255 47 47 64 | 255 52 52 65 | 255 57 57 66 | 255 62 62 67 | 255 68 68 68 | 255 73 73 69 | 255 78 78 70 | 255 83 83 71 | 255 88 88 72 | 255 94 94 73 | 255 99 99 74 | 255 104 104 75 | 255 109 109 76 | 255 114 114 77 | 255 120 120 78 | 255 125 125 79 | 255 130 130 80 | 255 135 135 81 | 255 141 141 82 | 255 146 146 83 | 255 151 151 84 | 255 156 156 85 | 255 161 161 86 | 255 167 167 87 | 255 172 172 88 | 255 177 177 89 | 255 182 182 90 | 255 187 187 91 | 255 193 193 92 | 255 198 198 93 | 255 203 203 94 | 255 208 208 95 | 255 213 213 96 | 255 219 219 97 | 255 224 224 98 | 255 229 229 99 | 255 234 234 100 | 255 239 239 101 | 255 245 245 102 | 255 250 250 103 | 255 255 255 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/WhViBlGrYeOrRe.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 255 255 255 4 | 245 240 248 5 | 235 225 241 6 | 225 210 234 7 | 215 195 227 8 | 206 180 220 9 | 196 165 213 10 | 186 150 206 11 | 176 135 199 12 | 166 120 192 13 | 156 105 185 14 | 146 90 178 15 | 136 75 171 16 | 127 60 164 17 | 117 45 157 18 | 107 30 150 19 | 97 15 143 20 | 87 0 136 21 | 82 0 143 22 | 76 0 151 23 | 71 0 158 24 | 65 0 166 25 | 60 0 173 26 | 54 0 181 27 | 49 0 188 28 | 44 0 196 29 | 38 0 203 30 | 33 0 210 31 | 27 0 218 32 | 22 0 225 33 | 16 0 233 34 | 11 0 240 35 | 5 0 248 36 | 0 0 255 37 | 0 11 241 38 | 1 21 227 39 | 1 32 213 40 | 2 42 199 41 | 2 53 184 42 | 3 63 170 43 | 3 74 156 44 | 4 84 142 45 | 4 95 128 46 | 5 105 114 47 | 5 116 100 48 | 6 126 86 49 | 6 137 71 50 | 7 147 57 51 | 7 158 43 52 | 8 168 29 53 | 8 179 15 54 | 23 183 14 55 | 37 188 13 56 | 52 192 12 57 | 66 197 11 58 | 81 201 11 59 | 95 206 10 60 | 110 210 9 61 | 124 215 8 62 | 139 219 7 63 | 153 224 6 64 | 168 228 5 65 | 182 233 4 66 | 197 237 4 67 | 211 242 3 68 | 226 246 2 69 | 240 251 1 70 | 255 255 0 71 | 255 249 0 72 | 255 244 0 73 | 255 238 0 74 | 255 233 0 75 | 255 227 0 76 | 255 221 0 77 | 255 216 0 78 | 255 210 0 79 | 255 204 0 80 | 255 199 0 81 | 255 193 0 82 | 255 188 0 83 | 255 182 0 84 | 255 176 0 85 | 255 171 0 86 | 255 165 0 87 | 255 155 0 88 | 255 146 0 89 | 255 136 0 90 | 255 126 0 91 | 255 116 0 92 | 255 107 0 93 | 255 97 0 94 | 255 87 0 95 | 255 78 0 96 | 255 68 0 97 | 255 58 0 98 | 255 49 0 99 | 255 39 0 100 | 255 29 0 101 | 255 19 0 102 | 255 10 0 103 | 255 0 0 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/WhViBlGrYeOrReWh.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 255 255 255 4 | 240 232 244 5 | 224 209 233 6 | 209 185 223 7 | 194 162 212 8 | 179 139 201 9 | 163 116 190 10 | 148 93 179 11 | 133 70 168 12 | 118 46 158 13 | 102 23 147 14 | 87 0 136 15 | 79 0 135 16 | 71 0 135 17 | 63 0 134 18 | 55 0 133 19 | 47 0 132 20 | 40 0 132 21 | 32 0 131 22 | 24 0 130 23 | 16 0 129 24 | 8 0 129 25 | 0 0 128 26 | 0 0 140 27 | 0 0 151 28 | 0 0 163 29 | 0 0 174 30 | 0 0 186 31 | 0 0 197 32 | 0 0 209 33 | 0 0 220 34 | 0 0 232 35 | 0 0 243 36 | 0 0 255 37 | 0 23 232 38 | 0 46 209 39 | 0 70 185 40 | 0 93 162 41 | 0 116 139 42 | 0 139 116 43 | 0 162 93 44 | 0 185 70 45 | 0 209 46 46 | 0 232 23 47 | 0 255 0 48 | 21 255 0 49 | 43 255 0 50 | 64 255 0 51 | 85 255 0 52 | 106 255 0 53 | 128 255 0 54 | 149 255 0 55 | 170 255 0 56 | 191 255 0 57 | 213 255 0 58 | 234 255 0 59 | 255 255 0 60 | 255 247 0 61 | 255 239 0 62 | 255 230 0 63 | 255 222 0 64 | 255 214 0 65 | 255 206 0 66 | 255 198 0 67 | 255 190 0 68 | 255 181 0 69 | 255 173 0 70 | 255 165 0 71 | 243 150 0 72 | 232 135 0 73 | 220 120 0 74 | 209 105 0 75 | 197 90 0 76 | 186 75 0 77 | 174 60 0 78 | 163 45 0 79 | 151 30 0 80 | 140 15 0 81 | 128 0 0 82 | 140 0 0 83 | 151 0 0 84 | 163 0 0 85 | 174 0 0 86 | 186 0 0 87 | 197 0 0 88 | 209 0 0 89 | 220 0 0 90 | 232 0 0 91 | 243 0 0 92 | 255 0 0 93 | 255 23 23 94 | 255 46 46 95 | 255 70 70 96 | 255 93 93 97 | 255 116 116 98 | 255 139 139 99 | 255 162 162 100 | 255 185 185 101 | 255 209 209 102 | 255 232 232 103 | 255 255 255 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/amwg.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 16 2 | # r g b 3 | 147 112 219 4 | 0 0 200 5 | 60 100 230 6 | 120 155 242 7 | 176 224 230 8 | 32 178 170 9 | 154 205 50 10 | 46 139 87 11 | 245 230 190 12 | 222 184 135 13 | 255 225 0 14 | 255 165 0 15 | 255 69 0 16 | 178 34 34 17 | 255 182 193 18 | 255 20 147 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/amwg_blueyellowred.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 16 2 | ;R G B 3 | 130 32 240 4 | 0 0 150 5 | 0 0 205 6 | 65 105 225 7 | 30 144 255 8 | 0 191 255 9 | 160 210 255 10 | 210 245 255 11 | 255 255 200 12 | 255 225 50 13 | 255 170 0 14 | 255 110 0 15 | 255 0 0 16 | 200 0 0 17 | 160 35 35 18 | 255 105 180 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cb_9step.rgb: -------------------------------------------------------------------------------- 1 | ncolors=78 2 | # r g b 3 | 255 0 0 4 | 255 128 0 5 | 255 255 0 6 | 0 255 0 7 | 0 0 255 8 | 128 0 255 9 | 219 219 255 10 | 194 194 250 11 | 158 158 247 12 | 130 130 255 13 | 97 97 255 14 | 64 64 232 15 | 0 0 194 16 | 0 0 148 17 | 222 250 245 18 | 194 245 237 19 | 156 230 217 20 | 112 204 191 21 | 43 184 163 22 | 0 156 133 23 | 0 120 102 24 | 0 92 79 25 | 219 255 219 26 | 186 245 186 27 | 140 235 140 28 | 92 209 92 29 | 0 184 0 30 | 0 145 0 31 | 0 105 0 32 | 0 77 0 33 | 235 204 255 34 | 222 176 255 35 | 199 148 237 36 | 186 112 237 37 | 171 77 237 38 | 138 51 199 39 | 107 0 186 40 | 84 0 145 41 | 250 227 240 42 | 247 204 230 43 | 245 173 214 44 | 240 138 194 45 | 217 92 163 46 | 189 0 130 47 | 153 0 107 48 | 117 0 82 49 | 255 219 219 50 | 255 189 189 51 | 255 145 145 52 | 250 97 97 53 | 214 26 26 54 | 163 0 0 55 | 125 0 0 56 | 92 0 0 57 | 255 252 214 58 | 252 242 168 59 | 252 237 128 60 | 227 209 0 61 | 199 186 43 62 | 161 150 0 63 | 120 112 0 64 | 84 82 0 65 | 255 222 199 66 | 252 199 161 67 | 250 176 125 68 | 232 143 79 69 | 209 105 31 70 | 186 77 0 71 | 153 64 0 72 | 115 48 0 73 | 240 240 240 74 | 222 222 222 75 | 199 199 199 76 | 171 171 171 77 | 145 145 145 78 | 120 120 120 79 | 94 94 94 80 | 74 74 74 81 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/circular_0.rgb: -------------------------------------------------------------------------------- 1 | ncolors=18 2 | # R G B 3 | 61 100 226 4 | 235 110 110 5 | 240 128 128 6 | 114 200 242 7 | 184 225 244 8 | 207 235 249 9 | 255 255 255 10 | 255 255 255 11 | 255 255 255 12 | 255 255 255 13 | 255 255 255 14 | 255 255 255 15 | 207 235 249 16 | 184 225 244 17 | 114 200 242 18 | 240 128 128 19 | 235 110 110 20 | 61 100 226 21 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/circular_1.rgb: -------------------------------------------------------------------------------- 1 | ncolors=12 2 | # R G B 3 | 255 0 0 red 4 | 255 125 0 orange 5 | 255 255 0 yellow 6 | 125 255 0 spring green 7 | 102 204 0 green ; Zero TwoFiveFive Zero 8 | 102 255 178 turquoise ; Zero TwoFiveFive OneTwoFive 9 | 0 255 255 cyan 10 | 0 125 255 ocean 11 | 0 0 255 blue 12 | 125 0 255 violet 13 | 255 0 255 magenta 14 | 255 0 125 raspberry 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/circular_2.rgb: -------------------------------------------------------------------------------- 1 | ncolors=24 2 | # R G B 3 | 255 0 0 red 4 | 255 63 0 5 | 255 125 0 orange 6 | 255 190 0 7 | 255 225 0 8 | 255 255 0 yellow 9 | 125 255 0 spring green 10 | 125 204 0 11 | 0 150 0 12 | 0 190 0 GREEN-ish 13 | 0 255 90 14 | 0 255 190 15 | 0 255 255 cyan 16 | 0 190 255 17 | 0 125 255 ocean 18 | 0 63 255 19 | 0 0 255 blue 20 | 63 0 255 21 | 125 0 255 violet 22 | 190 0 255 23 | 255 0 255 magenta 24 | 255 0 190 25 | 255 0 125 rasperry 26 | 255 0 63 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cmp_b2r.rgb: -------------------------------------------------------------------------------- 1 | ncolors=64 2 | # r g b 3 | 49 54 149 4 | 52 63 153 5 | 55 72 158 6 | 57 81 162 7 | 60 89 166 8 | 63 98 171 9 | 66 107 175 10 | 69 116 180 11 | 75 124 184 12 | 81 132 188 13 | 88 140 192 14 | 95 148 196 15 | 101 156 200 16 | 108 163 204 17 | 115 171 208 18 | 122 178 212 19 | 130 184 215 20 | 137 190 218 21 | 145 196 222 22 | 153 203 225 23 | 161 209 228 24 | 168 215 232 25 | 176 219 234 26 | 183 223 237 27 | 191 227 239 28 | 198 230 241 29 | 206 234 243 30 | 213 238 245 31 | 221 241 247 32 | 226 242 240 33 | 231 239 225 34 | 235 236 211 35 | 239 234 196 36 | 243 231 181 37 | 247 228 167 38 | 252 225 152 39 | 254 221 141 40 | 254 214 134 41 | 254 207 128 42 | 254 200 121 43 | 253 193 115 44 | 253 186 108 45 | 253 179 101 46 | 253 171 96 47 | 251 162 91 48 | 250 153 87 49 | 249 144 83 50 | 248 134 79 51 | 246 125 74 52 | 245 116 70 53 | 243 107 66 54 | 239 99 62 55 | 235 90 58 56 | 231 81 54 57 | 227 73 50 58 | 223 64 46 59 | 219 56 43 60 | 214 47 39 61 | 207 40 39 62 | 200 34 39 63 | 193 27 39 64 | 186 20 38 65 | 179 13 38 66 | 172 7 38 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cmp_flux.rgb: -------------------------------------------------------------------------------- 1 | ncolors=22 2 | # r g b 3 | 0 253 253 4 | 8 222 253 5 | 16 189 253 6 | 24 157 253 7 | 32 125 253 8 | 40 93 253 9 | 48 60 253 10 | 85 85 253 11 | 133 133 253 12 | 181 181 253 13 | 230 230 253 14 | 253 230 230 15 | 253 181 181 16 | 253 133 133 17 | 253 85 85 18 | 253 60 48 19 | 253 93 40 20 | 253 125 32 21 | 253 157 24 22 | 253 189 16 23 | 253 224 8 24 | 253 253 0 25 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cmp_haxby.rgb: -------------------------------------------------------------------------------- 1 | ncolors=64 2 | # r g b 3 | 37 57 175 4 | 37 68 187 5 | 38 79 199 6 | 38 90 211 7 | 39 101 223 8 | 39 113 235 9 | 40 124 247 10 | 41 134 251 11 | 43 144 252 12 | 44 154 253 13 | 46 164 253 14 | 47 174 254 15 | 49 184 255 16 | 54 193 255 17 | 62 200 255 18 | 71 207 255 19 | 80 214 255 20 | 89 221 255 21 | 98 229 255 22 | 107 235 254 23 | 112 235 241 24 | 117 235 228 25 | 122 235 215 26 | 127 236 202 27 | 132 236 189 28 | 137 236 177 29 | 147 238 172 30 | 157 241 171 31 | 168 244 169 32 | 178 247 167 33 | 189 250 165 34 | 200 253 163 35 | 208 253 159 36 | 213 250 152 37 | 219 247 146 38 | 224 244 139 39 | 230 241 133 40 | 236 238 126 41 | 240 235 120 42 | 243 227 115 43 | 245 220 109 44 | 248 212 104 45 | 250 205 98 46 | 252 197 93 47 | 255 190 88 48 | 255 185 84 49 | 255 181 81 50 | 255 176 78 51 | 255 172 75 52 | 255 167 72 53 | 255 163 69 54 | 255 163 74 55 | 255 167 85 56 | 255 171 95 57 | 255 175 105 58 | 255 179 115 59 | 255 183 126 60 | 255 189 139 61 | 255 200 158 62 | 255 211 178 63 | 255 222 197 64 | 255 233 216 65 | 255 244 236 66 | 255 255 255 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cosam.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 10 2 | # r g b 3 | 120 0 136 4 | 90 0 184 5 | 70 0 245 6 | 0 170 225 7 | 0 200 200 8 | 0 200 125 9 | 195 255 0 10 | 255 255 0 11 | 255 155 0 12 | 255 0 0 13 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cosam12.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 12 2 | # r g b 3 | 100 0 116 4 | 120 0 136 5 | 90 0 184 6 | 70 0 245 7 | 0 170 225 8 | 0 200 200 9 | 0 200 125 10 | 195 255 0 11 | 255 255 0 12 | 255 100 0 13 | 255 155 0 14 | 255 0 0 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/cyclic.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 6 2 | # r g b 3 | 1.0 0.0 0.0 4 | 0.0 1.0 0.0 5 | 0.0 0.0 1.0 6 | 1.0 1.0 0.0 7 | 0.0 1.0 1.0 8 | 1.0 0.0 1.0 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/default.rgb: -------------------------------------------------------------------------------- 1 | # This is the old default NCL color table 2 | ncolors = 30 3 | 4 | # r g b 5 | -1.0 0.0 0.0 /* white/black */ 6 | -1.0 0.0 0.0 /* white/black */ 7 | 1.0 0.0 0.0 /* red */ 8 | 0.0 1.0 0.0 /* green */ 9 | 0.0 0.0 1.0 /* blue */ 10 | 1.0 1.0 0.0 /* yellow */ 11 | 0.0 1.0 1.0 /* cyan */ 12 | 1.0 0.0 1.0 /* magenta */ 13 | 0.5 0.0 0.0 14 | 0.5 1.0 1.0 15 | 0.0 0.0 0.5 16 | 1.0 1.0 0.5 17 | 0.5 0.0 1.0 18 | 1.0 0.5 0.0 19 | 0.0 0.5 1.0 20 | 0.5 1.0 0.0 21 | 0.5 0.0 0.5 22 | 0.5 1.0 0.5 23 | 1.0 0.5 1.0 24 | 0.0 0.5 0.0 25 | 0.5 0.5 1.0 26 | 1.0 0.0 0.5 27 | 0.5 0.5 0.0 28 | 0.0 0.5 0.5 29 | 1.0 0.5 0.5 30 | 0.0 1.0 0.5 31 | 0.5 0.5 0.5 32 | 0.125 0.125 0.125 33 | 0.75 0.75 0.75 34 | 0.25 0.25 0.25 35 | 0.625 0.625 0.625 36 | 0.375 0.375 0.375 37 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/draw_cmap.ncl: -------------------------------------------------------------------------------- 1 | ; 2 | ; This is a convenient script for quickly drawing a colormap so you 3 | ; can see what it looks like. 4 | ; 5 | ; You must run this script with: 6 | ; 7 | ; ncl 'cmap="xxx"' draw_cmap.ncl 8 | ; 9 | ; where "xxx" is the name of the colormap, like "uniform". 10 | ; 11 | 12 | load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl" 13 | 14 | begin 15 | if(.not.isvar("cmap")) then 16 | print("Error: you must indicate a color map with the 'cmap' variable.") 17 | end if 18 | 19 | wks = gsn_open_wks("x11","colormaps") 20 | 21 | gsn_define_colormap(wks,cmap) 22 | gsn_draw_colormap(wks) 23 | end 24 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/drought_severity.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 7 2 | # r g b 3 | 0 151 92 4 | 113 185 117 5 | 192 217 150 6 | 255 252 193 7 | 252 195 119 8 | 249 130 63 9 | 238 40 32 10 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/grads_default.rgb: -------------------------------------------------------------------------------- 1 | # GrADS color map from http://www.iges.org/grads/gadoc/colorcontrol.html 2 | 3 | ncolors = 14 4 | # r g b 5 | 250 60 60 6 | 0 220 0 7 | 30 60 255 8 | 0 200 200 9 | 240 0 130 10 | 230 220 50 11 | 240 130 40 12 | 160 0 200 13 | 160 230 50 14 | 0 160 255 15 | 230 175 45 16 | 0 210 140 17 | 130 0 220 18 | 170 170 170 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/grads_rainbow.rgb: -------------------------------------------------------------------------------- 1 | # GrADS color map from http://www.iges.org/grads/gadoc/colorcontrol.html 2 | 3 | ncolors = 13 4 | # r g b 5 | 160 0 200 6 | 130 0 220 7 | 30 60 255 8 | 0 160 255 9 | 0 200 200 10 | 0 210 140 11 | 0 220 0 12 | 160 230 50 13 | 230 220 50 14 | 230 175 45 15 | 240 130 40 16 | 250 60 60 17 | 240 0 130 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/gscyclic.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 6 2 | # r g b 3 | 0.5 0.5 0.5 4 | 0.125 0.125 0.125 5 | 0.75 0.75 0.75 6 | 0.25 0.25 0.25 7 | 0.625 0.625 0.625 8 | 0.375 0.375 0.375 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/gsdtol.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 31 2 | # r g b 3 | 0.03125 0.03125 0.03125 4 | 0.06250 0.06250 0.06250 5 | 0.09375 0.09375 0.09375 6 | 0.12500 0.12500 0.12500 7 | 0.15625 0.15625 0.15625 8 | 0.18750 0.18750 0.18750 9 | 0.21875 0.21875 0.21875 10 | 0.25000 0.25000 0.25000 11 | 0.28125 0.28125 0.28125 12 | 0.31250 0.31250 0.31250 13 | 0.34375 0.34375 0.34375 14 | 0.37500 0.37500 0.37500 15 | 0.40625 0.40625 0.40625 16 | 0.43750 0.43750 0.43750 17 | 0.46875 0.46875 0.46875 18 | 0.50000 0.50000 0.50000 19 | 0.53125 0.53125 0.53125 20 | 0.56250 0.56250 0.56250 21 | 0.59375 0.59375 0.59375 22 | 0.62500 0.62500 0.62500 23 | 0.65625 0.65625 0.65625 24 | 0.68750 0.68750 0.68750 25 | 0.71875 0.71875 0.71875 26 | 0.75000 0.75000 0.75000 27 | 0.78125 0.78125 0.78125 28 | 0.81250 0.81250 0.81250 29 | 0.84375 0.84375 0.84375 30 | 0.87500 0.87500 0.87500 31 | 0.90625 0.90625 0.90625 32 | 0.93750 0.93750 0.93750 33 | 0.96875 0.96875 0.96875 34 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/gsltod.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 31 2 | # r g b 3 | 0.96875 0.96875 0.96875 4 | 0.93750 0.93750 0.93750 5 | 0.90625 0.90625 0.90625 6 | 0.87500 0.87500 0.87500 7 | 0.84375 0.84375 0.84375 8 | 0.81250 0.81250 0.81250 9 | 0.78125 0.78125 0.78125 10 | 0.75000 0.75000 0.75000 11 | 0.71875 0.71875 0.71875 12 | 0.68750 0.68750 0.68750 13 | 0.65625 0.65625 0.65625 14 | 0.62500 0.62500 0.62500 15 | 0.59375 0.59375 0.59375 16 | 0.56250 0.56250 0.56250 17 | 0.53125 0.53125 0.53125 18 | 0.50000 0.50000 0.50000 19 | 0.46875 0.46875 0.46875 20 | 0.43750 0.43750 0.43750 21 | 0.40625 0.40625 0.40625 22 | 0.37500 0.37500 0.37500 23 | 0.34375 0.34375 0.34375 24 | 0.31250 0.31250 0.31250 25 | 0.28125 0.28125 0.28125 26 | 0.25000 0.25000 0.25000 27 | 0.21875 0.21875 0.21875 28 | 0.18750 0.18750 0.18750 29 | 0.15625 0.15625 0.15625 30 | 0.12500 0.12500 0.12500 31 | 0.09375 0.09375 0.09375 32 | 0.06250 0.06250 0.06250 33 | 0.03125 0.03125 0.03125 34 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/gui_default.rgb: -------------------------------------------------------------------------------- 1 | # number of colors in table 2 | ncolors = 22 3 | 4 | # r g b 5 | 0 0 1 6 | 0 0.4 1 7 | 0 0.835294 1 8 | 0.094118 1 1 9 | 0.4 1 1 10 | 0.6 1 1 11 | 0.4 1 0.6 12 | 0.101961 1 0.168627 13 | 0.2 1 0 14 | 0.494118 1 0 15 | 0.8 1 0 16 | 0.878431 0.964706 0.007843 17 | 1 1 0 18 | 1 1 0 19 | 1 0.909804 0 20 | 1 0.8 0 21 | 1 0.6 0 22 | 1 0.5 0 23 | 1 0.4 0 24 | 1 0.2 0 25 | 1 0.160784 0 26 | 1 0 0 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/hlu_default.rgb: -------------------------------------------------------------------------------- 1 | # this is the default hlu colormap 2 | ncolors = 30 3 | 4 | # r g b 5 | -1.0 0.0 0.0 /* white/black */ 6 | -1.0 0.0 0.0 /* white/black */ 7 | 1.0 0.0 0.0 /* red */ 8 | 0.0 1.0 0.0 /* green */ 9 | 0.0 0.0 1.0 /* blue */ 10 | 1.0 1.0 0.0 /* yellow */ 11 | 0.0 1.0 1.0 /* cyan */ 12 | 1.0 0.0 1.0 /* magenta */ 13 | 0.5 0.0 0.0 14 | 0.5 1.0 1.0 15 | 0.0 0.0 0.5 16 | 1.0 1.0 0.5 17 | 0.5 0.0 1.0 18 | 1.0 0.5 0.0 19 | 0.0 0.5 1.0 20 | 0.5 1.0 0.0 21 | 0.5 0.0 0.5 22 | 0.5 1.0 0.5 23 | 1.0 0.5 1.0 24 | 0.0 0.5 0.0 25 | 0.5 0.5 1.0 26 | 1.0 0.0 0.5 27 | 0.5 0.5 0.0 28 | 0.0 0.5 0.5 29 | 1.0 0.5 0.5 30 | 0.0 1.0 0.5 31 | 0.5 0.5 0.5 32 | 0.125 0.125 0.125 33 | 0.75 0.75 0.75 34 | 0.25 0.25 0.25 35 | 0.625 0.625 0.625 36 | 0.375 0.375 0.375 37 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/hotcold_18lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 19 5 | 6 | # r g b 7 | 24 24 112 8 | 16 78 139 9 | 23 116 205 10 | 72 118 255 11 | 91 172 237 12 | 173 215 230 13 | 209 237 237 14 | 229 239 249 15 | 242 255 255 16 | 255 255 255 17 | 253 245 230 18 | 255 228 180 19 | 243 164 96 20 | 237 118 0 21 | 205 102 29 22 | 224 49 15 23 | 237 0 0 24 | 205 0 0 25 | 139 0 0 26 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/hotcolr_19lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 20 5 | 6 | # r g b 7 | 0 0 50 8 | 24 24 112 9 | 16 78 139 10 | 23 116 205 11 | 72 118 255 12 | 91 172 237 13 | 173 215 230 14 | 209 237 237 15 | 229 239 249 16 | 242 255 255 17 | 253 245 230 18 | 255 228 180 19 | 243 164 96 20 | 237 118 0 21 | 205 102 29 22 | 224 49 15 23 | 237 0 0 24 | 205 0 0 25 | 139 0 0 26 | 50 0 0 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/matlab_hot.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 64 2 | # r g b 3 | 10 0 0 4 | 21 0 0 5 | 31 0 0 6 | 42 0 0 7 | 53 0 0 8 | 63 0 0 9 | 74 0 0 10 | 84 0 0 11 | 95 0 0 12 | 106 0 0 13 | 116 0 0 14 | 127 0 0 15 | 138 0 0 16 | 148 0 0 17 | 159 0 0 18 | 170 0 0 19 | 180 0 0 20 | 191 0 0 21 | 201 0 0 22 | 212 0 0 23 | 223 0 0 24 | 233 0 0 25 | 244 0 0 26 | 255 0 0 27 | 255 10 0 28 | 255 21 0 29 | 255 31 0 30 | 255 42 0 31 | 255 53 0 32 | 255 63 0 33 | 255 74 0 34 | 255 84 0 35 | 255 95 0 36 | 255 106 0 37 | 255 116 0 38 | 255 127 0 39 | 255 138 0 40 | 255 148 0 41 | 255 159 0 42 | 255 170 0 43 | 255 180 0 44 | 255 191 0 45 | 255 201 0 46 | 255 212 0 47 | 255 223 0 48 | 255 233 0 49 | 255 244 0 50 | 255 255 0 51 | 255 255 15 52 | 255 255 31 53 | 255 255 47 54 | 255 255 63 55 | 255 255 79 56 | 255 255 95 57 | 255 255 111 58 | 255 255 127 59 | 255 255 143 60 | 255 255 159 61 | 255 255 175 62 | 255 255 191 63 | 255 255 207 64 | 255 255 223 65 | 255 255 239 66 | 255 255 255 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/matlab_hsv.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 64 2 | # r g b 3 | 255 0 0 4 | 255 23 0 5 | 255 47 0 6 | 255 71 0 7 | 255 95 0 8 | 255 119 0 9 | 255 143 0 10 | 255 167 0 11 | 255 191 0 12 | 255 215 0 13 | 255 239 0 14 | 247 255 0 15 | 223 255 0 16 | 199 255 0 17 | 175 255 0 18 | 151 255 0 19 | 127 255 0 20 | 103 255 0 21 | 79 255 0 22 | 55 255 0 23 | 31 255 0 24 | 7 255 0 25 | 0 255 15 26 | 0 255 39 27 | 0 255 63 28 | 0 255 87 29 | 0 255 111 30 | 0 255 135 31 | 0 255 159 32 | 0 255 183 33 | 0 255 207 34 | 0 255 231 35 | 0 255 255 36 | 0 231 255 37 | 0 207 255 38 | 0 183 255 39 | 0 159 255 40 | 0 135 255 41 | 0 111 255 42 | 0 87 255 43 | 0 63 255 44 | 0 39 255 45 | 0 15 255 46 | 7 0 255 47 | 31 0 255 48 | 55 0 255 49 | 79 0 255 50 | 103 0 255 51 | 127 0 255 52 | 151 0 255 53 | 175 0 255 54 | 199 0 255 55 | 223 0 255 56 | 247 0 255 57 | 255 0 239 58 | 255 0 215 59 | 255 0 191 60 | 255 0 167 61 | 255 0 143 62 | 255 0 119 63 | 255 0 95 64 | 255 0 71 65 | 255 0 47 66 | 255 0 23 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/matlab_jet.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 64 2 | # r g b 3 | 0 0 143 4 | 0 0 159 5 | 0 0 175 6 | 0 0 191 7 | 0 0 207 8 | 0 0 223 9 | 0 0 239 10 | 0 0 255 11 | 0 15 255 12 | 0 31 255 13 | 0 47 255 14 | 0 63 255 15 | 0 79 255 16 | 0 95 255 17 | 0 111 255 18 | 0 127 255 19 | 0 143 255 20 | 0 159 255 21 | 0 175 255 22 | 0 191 255 23 | 0 207 255 24 | 0 223 255 25 | 0 239 255 26 | 0 255 255 27 | 15 255 239 28 | 31 255 223 29 | 47 255 207 30 | 63 255 191 31 | 79 255 175 32 | 95 255 159 33 | 111 255 143 34 | 127 255 127 35 | 143 255 111 36 | 159 255 95 37 | 175 255 79 38 | 191 255 63 39 | 207 255 47 40 | 223 255 31 41 | 239 255 15 42 | 255 255 0 43 | 255 239 0 44 | 255 223 0 45 | 255 207 0 46 | 255 191 0 47 | 255 175 0 48 | 255 159 0 49 | 255 143 0 50 | 255 127 0 51 | 255 111 0 52 | 255 95 0 53 | 255 79 0 54 | 255 63 0 55 | 255 47 0 56 | 255 31 0 57 | 255 15 0 58 | 255 0 0 59 | 239 0 0 60 | 223 0 0 61 | 207 0 0 62 | 191 0 0 63 | 175 0 0 64 | 159 0 0 65 | 143 0 0 66 | 127 0 0 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/matlab_lines.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 64 2 | # r g b 3 | 0 0 255 4 | 0 127 0 5 | 255 0 0 6 | 0 191 191 7 | 191 0 191 8 | 191 191 0 9 | 63 63 63 10 | 0 0 255 11 | 0 127 0 12 | 255 0 0 13 | 0 191 191 14 | 191 0 191 15 | 191 191 0 16 | 63 63 63 17 | 0 0 255 18 | 0 127 0 19 | 255 0 0 20 | 0 191 191 21 | 191 0 191 22 | 191 191 0 23 | 63 63 63 24 | 0 0 255 25 | 0 127 0 26 | 255 0 0 27 | 0 191 191 28 | 191 0 191 29 | 191 191 0 30 | 63 63 63 31 | 0 0 255 32 | 0 127 0 33 | 255 0 0 34 | 0 191 191 35 | 191 0 191 36 | 191 191 0 37 | 63 63 63 38 | 0 0 255 39 | 0 127 0 40 | 255 0 0 41 | 0 191 191 42 | 191 0 191 43 | 191 191 0 44 | 63 63 63 45 | 0 0 255 46 | 0 127 0 47 | 255 0 0 48 | 0 191 191 49 | 191 0 191 50 | 191 191 0 51 | 63 63 63 52 | 0 0 255 53 | 0 127 0 54 | 255 0 0 55 | 0 191 191 56 | 191 0 191 57 | 191 191 0 58 | 63 63 63 59 | 0 0 255 60 | 0 127 0 61 | 255 0 0 62 | 0 191 191 63 | 191 0 191 64 | 191 191 0 65 | 63 63 63 66 | 0 0 255 67 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/mch_default.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 15 5 | 6 | # r g b 7 | 255 255 255 8 | 0 0 0 9 | 32 32 32 10 | 64 64 64 11 | 96 96 96 12 | 128 128 128 13 | 160 160 160 14 | 192 192 192 15 | 224 224 224 16 | 255 0 0 17 | 0 255 0 18 | 0 0 255 19 | 255 255 0 20 | 0 255 255 21 | 255 0 255 22 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/nrl_sirkes.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 21 2 | 0 97 128 3 | 0 128 161 4 | 0 161 191 5 | 0 191 224 6 | 0 224 255 7 | 0 255 255 8 | 51 252 252 9 | 102 252 252 10 | 153 252 252 11 | 204 252 252 12 | 255 255 255 13 | 252 252 0 14 | 252 224 0 15 | 252 191 0 16 | 252 161 0 17 | 252 128 0 18 | 252 97 0 19 | 252 64 0 20 | 252 33 0 21 | 191 0 0 22 | 128 0 0 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/nrl_sirkes_nowhite.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 19 2 | 0 97 128 3 | 0 128 161 4 | 0 161 191 5 | 0 191 224 6 | 0 224 255 7 | 0 255 255 8 | 51 252 252 9 | 102 252 252 10 | 153 252 252 11 | 252 252 0 12 | 252 224 0 13 | 252 191 0 14 | 252 161 0 15 | 252 128 0 16 | 252 97 0 17 | 252 64 0 18 | 252 33 0 19 | 191 0 0 20 | 128 0 0 21 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/perc2_9lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 10 5 | 6 | # r g b 7 | 215 227 238 8 | 181 202 255 9 | 143 179 255 10 | 127 151 255 11 | 171 207 99 12 | 232 245 158 13 | 255 250 20 14 | 255 209 33 15 | 255 163 10 16 | 255 76 0 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/percent_11lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 12 5 | 6 | # r g b 7 | 100 0 150 8 | 39 48 215 9 | 89 141 252 10 | 139 239 217 11 | 96 207 145 12 | 26 152 80 13 | 145 207 96 14 | 217 239 139 15 | 254 224 139 16 | 252 141 89 17 | 215 48 39 18 | 150 0 100 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/posneg_1.rgb: -------------------------------------------------------------------------------- 1 | ncolors=19 2 | # r g b 3 | 255 255 255 4 | 0 0 0 5 | 0 0 0 6 | 24 24 112 7 | 16 78 139 8 | 23 116 205 9 | 72 118 255 10 | 91 172 237 11 | 173 215 230 12 | 209 237 237 13 | 230 230 250 14 | 255 228 180 15 | 243 164 96 16 | 237 118 0 17 | 210 105 30 18 | 255 0 0 19 | 237 0 0 20 | 205 0 0 21 | 139 0 0 22 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/posneg_2.rgb: -------------------------------------------------------------------------------- 1 | ncolors=20 2 | # r g b 3 | 255 255 255 4 | 0 0 0 5 | 24 24 112 6 | 16 78 139 7 | 23 116 205 8 | 72 118 255 9 | 91 172 237 10 | 173 215 230 11 | 209 237 237 12 | 229 239 249 13 | 242 255 255 14 | 253 245 230 15 | 255 228 180 16 | 243 164 96 17 | 237 118 0 18 | 205 102 29 19 | 224 49 15 20 | 237 0 0 21 | 205 0 0 22 | 139 0 0 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/prcp_1.rgb: -------------------------------------------------------------------------------- 1 | ncolors=17 2 | # r g b 3 | 255 255 255 4 | 170 255 255 5 | 85 160 255 6 | 29 0 255 7 | 126 229 91 8 | 78 204 67 9 | 46 178 57 10 | 30 153 61 11 | 255 255 102 12 | 255 204 102 13 | 255 136 76 14 | 255 25 25 15 | 204 61 61 16 | 165 49 49 17 | 237 0 237 18 | 137 103 205 19 | 250 240 230 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/prcp_2.rgb: -------------------------------------------------------------------------------- 1 | ncolors=12 2 | # r g b 3 | 245 245 245 4 | 175 237 237 5 | 152 251 152 6 | 67 205 128 7 | 59 179 113 8 | 250 250 210 9 | 255 255 0 10 | 255 164 0 11 | 255 0 0 12 | 205 55 0 13 | 199 20 133 14 | 237 130 237 15 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/prcp_3.rgb: -------------------------------------------------------------------------------- 1 | ncolors=23 2 | # r g b 3 | 255 255 255 4 | 0 0 0 5 | 255 255 255 6 | 233 204 249 7 | 207 128 223 8 | 131 51 147 9 | 58 0 176 10 | 29 0 215 11 | 0 0 255 12 | 3 60 175 13 | 5 119 95 14 | 8 179 15 15 | 132 217 8 16 | 255 255 0 17 | 255 170 0 18 | 255 85 0 19 | 255 0 0 20 | 179 0 0 21 | 102 0 0 22 | 51 0 0 23 | 0 0 0 24 | 250 197 250 25 | 255 255 255 26 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip2_15lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 16 5 | 6 | # r g b 7 | 255 255 255 8 | 100 0 100 9 | 175 0 175 10 | 220 0 220 11 | 50 50 200 12 | 0 100 255 13 | 0 150 150 14 | 0 200 50 15 | 100 255 0 16 | 150 255 0 17 | 200 255 50 18 | 255 255 0 19 | 255 200 0 20 | 255 160 0 21 | 255 125 0 22 | 225 25 0 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip2_17lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 18 5 | 6 | # r g b 7 | 255 255 255 8 | 235 246 255 9 | 214 226 255 10 | 181 201 255 11 | 142 178 255 12 | 127 150 255 13 | 114 133 248 14 | 99 112 248 15 | 0 158 30 16 | 60 188 61 17 | 179 209 110 18 | 185 249 110 19 | 255 249 19 20 | 255 163 9 21 | 229 0 0 22 | 189 0 0 23 | 129 0 0 24 | 0 0 0 25 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip3_16lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 17 5 | 6 | # r g b 7 | 255 255 255 8 | 214 226 255 9 | 181 201 255 10 | 142 178 255 11 | 127 150 255 12 | 99 112 247 13 | 0 99 255 14 | 0 150 150 15 | 0 198 51 16 | 99 255 0 17 | 150 255 0 18 | 198 255 51 19 | 255 255 0 20 | 255 198 0 21 | 255 160 0 22 | 255 124 0 23 | 255 25 0 24 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip4_11lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 12 5 | 6 | # r g b 7 | 254 254 254 8 | 223 255 249 9 | 154 217 202 10 | 103 194 163 11 | 64 173 117 12 | 50 166 150 13 | 90 160 205 14 | 66 146 199 15 | 76 141 196 16 | 7 47 107 17 | 7 30 70 18 | 76 0 115 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip4_diff_19lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 20 5 | 6 | # r g b 7 | 107 33 7 8 | 181 59 33 9 | 199 85 66 10 | 205 105 90 11 | 166 50 55 12 | 173 64 88 13 | 194 103 114 14 | 217 154 159 15 | 255 223 224 16 | 254 254 254 17 | 254 254 254 18 | 223 255 249 19 | 154 217 202 20 | 103 194 163 21 | 64 173 117 22 | 50 166 150 23 | 90 160 205 24 | 66 146 199 25 | 76 141 196 26 | 7 47 107 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip_11lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 12 5 | 6 | # r g b 7 | 255 255 255 8 | 237 250 194 9 | 205 255 205 10 | 153 240 178 11 | 83 189 159 12 | 50 166 150 13 | 50 150 180 14 | 5 112 176 15 | 5 80 140 16 | 10 31 150 17 | 44 2 70 18 | 106 44 90 19 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip_diff_12lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 13 5 | 6 | # r g b 7 | 182 106 40 8 | 205 133 63 9 | 225 165 100 10 | 245 205 132 11 | 245 224 158 12 | 255 245 186 13 | 255 255 255 14 | 205 255 205 15 | 153 240 178 16 | 83 189 159 17 | 110 170 200 18 | 5 112 176 19 | 2 56 88 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/precip_diff_1lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 2 5 | 6 | # r g b 7 | 83 189 159 8 | 225 165 100 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/radar.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 15 2 | # r g b 3 | 0 255 255 # cyan 4 | 0 157 255 # light.blue 5 | 0 0 255 # blue 6 | 9 130 175 # light.green 7 | 0 255 0 # green 8 | 8 175 20 # dark.green 9 | 255 214 0 # yellow 10 | 255 152 0 # light.orange 11 | 255 0 0 # red 12 | 221 0 27 # added 13 | 188 0 54 # med.red 14 | 121 0 109 # dark.red 15 | 121 51 160 # light.violet 16 | 195 163 212 # violet 17 | 255 255 255 # white 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/radar_1.rgb: -------------------------------------------------------------------------------- 1 | ncolors=24 2 | # r g b 3 | 178 248 255 4 | 178 184 255 5 | 125 37 205 6 | 84 26 139 7 | 237 230 133 8 | 205 198 115 9 | 150 150 150 10 | 255 255 255 11 | 170 255 255 12 | 85 160 255 13 | 29 0 255 14 | 126 229 91 15 | 78 204 67 16 | 46 178 57 17 | 30 153 61 18 | 255 255 102 19 | 255 204 102 20 | 255 136 76 21 | 255 25 25 22 | 204 61 61 23 | 165 49 49 24 | 237 0 237 25 | 137 103 205 26 | 250 240 230 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/rh_19lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 20 5 | 6 | # r g b 7 | 254 254 254 8 | 254 254 160 9 | 254 254 99 10 | 244 244 110 11 | 255 210 35 12 | 255 163 25 13 | 255 89 25 14 | 230 122 101 15 | 237 145 124 16 | 239 178 146 17 | 247 199 178 18 | 255 230 230 19 | 215 225 255 20 | 150 210 255 21 | 30 189 255 22 | 20 159 255 23 | 10 108 240 24 | 11 116 255 25 | 10 104 200 26 | 0 89 159 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/seaice_1.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 13 2 | # r g b 3 | 0 0 139 4 | 30 144 255 5 | 30 250 160 6 | 34 139 34 7 | 0 250 0 8 | 125 250 0 9 | 173 255 47 10 | 250 250 0 11 | 250 125 0 12 | 250 0 0 13 | 186 85 211 14 | 148 0 211 15 | 120 0 90 16 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/seaice_2.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 14 2 | # r g b 3 | 0 0 139 4 | 9 47 175 5 | 25 122 237 6 | 134 205 249 7 | 30 250 160 8 | 173 255 47 9 | 250 250 0 10 | 250 187 0 11 | 250 125 0 12 | 250 0 0 13 | 165 42 42 14 | 120 0 90 15 | 148 0 211 16 | 186 85 211 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/so4_21.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 21 2 | 3 | # r g b 4 | 0 0 0 5 | 29 0 45 6 | 58 0 91 7 | 87 0 136 8 | 58 0 176 9 | 29 0 215 10 | 0 0 255 11 | 3 60 175 12 | 5 119 95 13 | 8 179 15 14 | 132 217 8 15 | 255 255 0 16 | 255 170 0 17 | 255 85 0 18 | 255 0 0 19 | 250 27 80 20 | 245 53 160 21 | 240 80 240 22 | 245 138 245 23 | 250 197 250 24 | 255 255 255 25 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/so4_23.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 23 2 | 3 | # r g b 4 | 0 0 0 5 | 87 0 136 6 | 58 0 176 7 | 29 0 215 8 | 0 0 255 9 | 3 60 175 10 | 5 119 95 11 | 8 179 15 12 | 70 198 11 13 | 132 217 8 14 | 193 236 4 15 | 255 255 0 16 | 255 191 0 17 | 255 128 0 18 | 255 64 0 19 | 255 0 0 20 | 250 27 80 21 | 245 53 160 22 | 240 80 240 23 | 244 124 244 24 | 248 168 248 25 | 251 211 251 26 | 255 255 255 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/spread_15lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 16 5 | 6 | # r g b 7 | 255 255 255 8 | 255 225 225 9 | 255 210 210 10 | 255 165 165 11 | 255 120 120 12 | 255 75 75 13 | 255 0 0 14 | 255 100 0 15 | 255 150 0 16 | 255 200 0 17 | 255 255 0 18 | 140 255 0 19 | 0 255 0 20 | 0 205 95 21 | 0 145 200 22 | 0 0 255 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/srip_reanalysis.rgb: -------------------------------------------------------------------------------- 1 | ncolors=19 2 | #r g b 3 | 226 31 38 4 | 246 153 153 5 | 41 95 138 6 | 95 152 198 7 | 175 203 227 8 | 114 59 122 9 | 173 113 181 10 | 214 184 218 11 | 245 126 32 12 | 253 191 110 13 | 236 0 140 14 | 247 153 209 15 | 0 174 239 16 | 96 200 232 17 | 52 160 72 18 | 179 91 40 19 | 255 215 0 20 | 0 0 0 21 | 119 119 119 22 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/sunshine_9lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 10 5 | 6 | # r g b 7 | 255 255 255 8 | 255 245 204 9 | 255 230 112 10 | 255 204 51 11 | 255 175 51 12 | 255 153 51 13 | 255 111 51 14 | 255 85 0 15 | 230 40 30 16 | 200 30 20 17 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/sunshine_diff_12lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 13 5 | 6 | # r g b 7 | 81 9 121 8 | 149 15 223 9 | 183 75 243 10 | 203 126 246 11 | 225 180 250 12 | 236 208 252 13 | 255 255 255 14 | 255 245 204 15 | 255 230 112 16 | 255 204 51 17 | 255 175 51 18 | 255 111 0 19 | 230 40 30 20 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/t2m_29lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 30 5 | 6 | # r g b 7 | 109 227 255 8 | 175 240 255 9 | 255 196 226 10 | 255 153 204 11 | 255 0 255 12 | 128 0 128 13 | 0 0 128 14 | 70 70 255 15 | 51 102 255 16 | 133 162 255 17 | 255 255 255 18 | 204 204 204 19 | 179 179 179 20 | 153 153 153 21 | 96 96 96 22 | 128 128 0 23 | 0 92 0 24 | 0 128 0 25 | 51 153 102 26 | 157 213 0 27 | 212 255 91 28 | 255 255 0 29 | 255 184 112 30 | 255 153 0 31 | 255 102 0 32 | 255 0 0 33 | 188 75 0 34 | 171 0 56 35 | 128 0 0 36 | 163 112 255 37 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/tbrAvg1.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 100 2 | # r g b 3 | 0 0 0 4 | 0 0 0 5 | 0 0 0 6 | 0 0 0 7 | 0 0 0 8 | 0 0 0 9 | 0 0 0 10 | 17 0 27 11 | 35 0 54 12 | 52 0 82 13 | 70 0 109 14 | 87 0 136 15 | 87 0 136 16 | 87 0 136 17 | 87 0 136 18 | 87 0 136 19 | 87 0 136 20 | 87 0 136 21 | 87 0 136 22 | 87 0 136 23 | 87 0 136 24 | 87 0 136 25 | 87 0 136 26 | 83 10 129 27 | 78 20 123 28 | 74 30 116 29 | 69 40 109 30 | 65 50 102 31 | 61 60 96 32 | 56 70 89 33 | 52 80 82 34 | 48 90 76 35 | 43 99 69 36 | 39 109 62 37 | 34 119 55 38 | 30 129 49 39 | 26 139 42 40 | 21 149 35 41 | 17 159 28 42 | 12 169 22 43 | 8 179 15 44 | 8 179 15 45 | 8 179 15 46 | 8 179 15 47 | 8 179 15 48 | 8 179 15 49 | 8 179 15 50 | 22 183 14 51 | 35 187 13 52 | 49 192 13 53 | 63 196 12 54 | 77 200 11 55 | 90 204 10 56 | 104 209 9 57 | 118 213 8 58 | 132 217 8 59 | 145 221 7 60 | 159 225 6 61 | 173 230 5 62 | 186 234 4 63 | 200 238 3 64 | 214 242 3 65 | 228 247 2 66 | 241 251 1 67 | 255 255 0 68 | 255 255 0 69 | 255 237 0 70 | 255 219 0 71 | 255 200 0 72 | 255 182 0 73 | 255 164 0 74 | 255 146 0 75 | 255 128 0 76 | 255 109 0 77 | 255 91 0 78 | 255 73 0 79 | 255 55 0 80 | 255 36 0 81 | 255 18 0 82 | 255 0 0 83 | 254 7 20 84 | 253 13 40 85 | 251 20 60 86 | 250 27 80 87 | 249 33 100 88 | 248 40 120 89 | 246 47 140 90 | 245 53 160 91 | 244 60 180 92 | 243 67 200 93 | 241 73 220 94 | 240 80 240 95 | 242 102 242 96 | 244 124 244 97 | 246 146 246 98 | 248 168 248 99 | 249 189 249 100 | 251 211 251 101 | 253 233 253 102 | 255 255 255 103 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/tbrStd1.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 0 0 0 4 | 0 0 0 5 | 0 0 0 6 | 0 0 0 7 | 0 0 0 8 | 0 0 0 9 | 0 0 0 10 | 0 0 0 11 | 0 0 0 12 | 0 0 0 13 | 0 0 0 14 | 0 0 0 15 | 0 0 0 16 | 0 0 0 17 | 0 0 0 18 | 0 0 0 19 | 0 0 0 20 | 0 0 0 21 | 0 0 0 22 | 0 0 0 23 | 0 0 0 24 | 0 0 0 25 | 0 0 0 26 | 0 0 0 27 | 10 0 15 28 | 19 0 30 29 | 29 0 45 30 | 39 0 60 31 | 48 0 76 32 | 58 0 91 33 | 68 0 106 34 | 77 0 121 35 | 87 0 136 36 | 87 0 136 37 | 87 0 136 38 | 87 0 136 39 | 87 0 136 40 | 87 0 136 41 | 87 0 136 42 | 87 0 136 43 | 87 0 136 44 | 87 0 136 45 | 87 0 136 46 | 82 12 128 47 | 76 24 120 48 | 71 36 112 49 | 66 48 104 50 | 61 60 96 51 | 55 72 88 52 | 50 84 80 53 | 45 95 71 54 | 40 107 63 55 | 34 119 55 56 | 29 131 47 57 | 24 143 39 58 | 19 155 31 59 | 13 167 23 60 | 8 179 15 61 | 8 179 15 62 | 8 179 15 63 | 8 179 15 64 | 8 179 15 65 | 8 179 15 66 | 8 179 15 67 | 30 186 14 68 | 53 193 12 69 | 75 200 11 70 | 98 207 10 71 | 120 214 8 72 | 143 220 7 73 | 165 227 5 74 | 188 234 4 75 | 210 241 3 76 | 233 248 1 77 | 255 255 0 78 | 255 237 0 79 | 255 219 0 80 | 255 200 0 81 | 255 182 0 82 | 255 164 0 83 | 255 146 0 84 | 255 128 0 85 | 255 109 0 86 | 255 91 0 87 | 255 73 0 88 | 255 55 0 89 | 255 36 0 90 | 255 18 0 91 | 255 0 0 92 | 255 0 0 93 | 255 0 0 94 | 250 27 80 95 | 245 53 160 96 | 240 80 240 97 | 243 115 243 98 | 246 150 246 99 | 249 185 249 100 | 252 220 252 101 | 255 255 255 102 | 255 255 255 103 | 255 255 255 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/tbrVar1.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 101 2 | # r g b 3 | 0 0 0 4 | 0 0 0 5 | 0 0 0 6 | 0 0 0 7 | 0 0 0 8 | 0 0 0 9 | 17 0 27 10 | 35 0 54 11 | 52 0 82 12 | 70 0 109 13 | 87 0 136 14 | 87 0 136 15 | 87 0 136 16 | 87 0 136 17 | 87 0 136 18 | 87 0 136 19 | 87 0 136 20 | 87 0 136 21 | 87 0 136 22 | 87 0 136 23 | 87 0 136 24 | 87 0 136 25 | 80 16 125 26 | 73 33 114 27 | 65 49 103 28 | 58 65 92 29 | 51 81 81 30 | 44 98 70 31 | 37 114 59 32 | 30 130 48 33 | 22 146 37 34 | 15 163 26 35 | 8 179 15 36 | 8 179 15 37 | 8 179 15 38 | 8 179 15 39 | 8 179 15 40 | 8 179 15 41 | 35 187 13 42 | 63 196 12 43 | 90 204 10 44 | 118 213 8 45 | 145 221 7 46 | 173 230 5 47 | 200 238 3 48 | 228 247 2 49 | 255 255 0 50 | 255 255 0 51 | 255 255 0 52 | 255 255 0 53 | 255 255 0 54 | 255 243 0 55 | 255 231 0 56 | 255 219 0 57 | 255 206 0 58 | 255 194 0 59 | 255 182 0 60 | 255 170 0 61 | 255 158 0 62 | 255 146 0 63 | 255 134 0 64 | 255 121 0 65 | 255 109 0 66 | 255 97 0 67 | 255 85 0 68 | 255 73 0 69 | 255 61 0 70 | 255 49 0 71 | 255 36 0 72 | 255 24 0 73 | 255 12 0 74 | 255 0 0 75 | 255 0 0 76 | 255 0 0 77 | 255 0 0 78 | 255 0 0 79 | 255 0 0 80 | 255 0 0 81 | 255 0 0 82 | 255 0 0 83 | 251 20 60 84 | 248 40 120 85 | 244 60 180 86 | 240 80 240 87 | 240 80 240 88 | 240 80 240 89 | 240 80 240 90 | 240 80 240 91 | 240 80 240 92 | 241 95 241 93 | 243 109 243 94 | 244 124 244 95 | 245 138 245 96 | 246 153 246 97 | 248 168 248 98 | 249 182 249 99 | 250 197 250 100 | 251 211 251 101 | 253 226 253 102 | 254 240 254 103 | 255 255 255 104 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/temp1.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 61 2 | # r g b 3 | 0.700 0.700 0.700 4 | 0.650 0.650 0.700 5 | 0.610 0.600 0.700 6 | 0.550 0.550 0.700 7 | 0.560 0.500 0.700 8 | 0.450 0.450 0.700 9 | 0.420 0.400 0.700 10 | 0.350 0.350 0.700 11 | 0.300 0.300 0.700 12 | 0.250 0.250 0.700 13 | 0.200 0.200 0.700 14 | 0.150 0.150 0.700 15 | 0.100 0.100 0.700 16 | 0.050 0.050 0.700 17 | 0.000 0.000 0.700 18 | 0.000 0.050 0.700 19 | 0.000 0.100 0.700 20 | 0.000 0.150 0.700 21 | 0.000 0.200 0.700 22 | 0.000 0.250 0.700 23 | 0.000 0.300 0.700 24 | 0.000 0.350 0.700 25 | 0.000 0.400 0.700 26 | 0.000 0.450 0.600 27 | 0.000 0.500 0.500 28 | 0.000 0.550 0.400 29 | 0.000 0.600 0.300 30 | 0.000 0.650 0.200 31 | 0.000 0.700 0.100 32 | 0.000 0.725 0.000 33 | 0.000 0.690 0.000 34 | 0.030 0.685 0.000 35 | 0.060 0.680 0.000 36 | 0.100 0.575 0.000 37 | 0.130 0.570 0.000 38 | 0.160 0.565 0.000 39 | 0.550 0.550 0.000 40 | 0.555 0.545 0.000 41 | 0.560 0.530 0.000 42 | 0.565 0.485 0.000 43 | 0.570 0.420 0.000 44 | 0.675 0.375 0.000 45 | 0.680 0.330 0.000 46 | 0.690 0.300 0.000 47 | 0.700 0.285 0.000 48 | 0.700 0.270 0.000 49 | 0.700 0.260 0.000 50 | 0.700 0.240 0.000 51 | 0.700 0.180 0.000 52 | 0.700 0.130 0.000 53 | 0.700 0.120 0.000 54 | 0.700 0.100 0.000 55 | 0.700 0.090 0.000 56 | 0.750 0.090 0.000 57 | 0.800 0.090 0.000 58 | 0.830 0.070 0.000 59 | 0.870 0.050 0.000 60 | 0.900 0.030 0.000 61 | 0.950 0.010 0.000 62 | 0.990 0.000 0.000 63 | 1.000 0.000 0.000 64 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/temp_19lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 20 5 | 6 | # r g b 7 | 7 30 70 8 | 7 47 107 9 | 8 82 156 10 | 33 113 181 11 | 66 146 199 12 | 90 160 205 13 | 120 191 214 14 | 170 220 230 15 | 219 245 255 16 | 240 252 255 17 | 255 240 245 18 | 255 224 224 19 | 252 187 170 20 | 252 146 114 21 | 251 106 74 22 | 240 60 43 23 | 204 24 30 24 | 166 15 20 25 | 120 10 15 26 | 95 0 0 27 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/temp_diff_18lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 19 5 | 6 | # r g b 7 | 7 30 70 8 | 7 47 107 9 | 8 87 156 10 | 33 113 181 11 | 66 146 199 12 | 90 160 205 13 | 120 191 214 14 | 170 220 230 15 | 219 245 255 16 | 255 255 255 17 | 255 224 224 18 | 252 187 170 19 | 252 146 114 20 | 251 106 74 21 | 240 60 43 22 | 204 24 30 23 | 166 15 20 24 | 120 10 15 25 | 95 0 0 26 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/temp_diff_1lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 2 5 | 6 | # r g b 7 | 240 60 43 8 | 33 113 181 9 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/topo_15lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 16 5 | 6 | # r g b 7 | 40 54 154 8 | 0 201 50 9 | 30 211 104 10 | 94 224 116 11 | 162 235 130 12 | 223 248 146 13 | 246 229 149 14 | 200 178 118 15 | 162 126 94 16 | 143 97 84 17 | 162 125 116 18 | 178 150 139 19 | 199 176 170 20 | 219 205 202 21 | 236 228 226 22 | 255 255 255 23 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/vegetation_modis.rgb: -------------------------------------------------------------------------------- 1 | ncolors = 21 2 | # R G B 3 | 128 128 128 4 | 225 0 0 5 | 225 125 0 6 | 225 170 0 7 | 255 225 0 8 | 255 255 0 9 | 170 255 0 10 | 85 255 0 11 | 0 225 0 12 | 0 190 0 13 | 0 160 0 14 | 0 135 0 15 | 0 100 0 16 | 0 80 0 17 | 180 255 255 18 | 0 255 255 19 | 0 200 255 20 | 0 170 255 21 | 0 135 255 22 | 0 80 255 23 | 0 0 255 24 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/wgne15.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 15 2 | # r g b 3 | 160 32 240 4 | 0 0 180 5 | 60 100 230 6 | 120 155 242 7 | 176 224 230 8 | 46 139 87 9 | 100 225 0 10 | 210 255 47 11 | 245 230 190 12 | 222 184 135 13 | 255 225 0 14 | 255 165 0 15 | 255 69 0 16 | 150 34 34 17 | 255 105 180 18 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/wind_17lev.rgb: -------------------------------------------------------------------------------- 1 | # Converted from MeteoSwiss NCL library 2 | 3 | # number of colors in table 4 | ncolors = 18 5 | 6 | # r g b 7 | 255 255 255 8 | 239 244 209 9 | 232 244 158 10 | 170 206 99 11 | 226 237 22 12 | 255 237 0 13 | 255 237 130 14 | 244 209 127 15 | 237 165 73 16 | 229 140 61 17 | 219 124 61 18 | 239 7 61 19 | 232 86 163 20 | 155 112 168 21 | 99 112 247 22 | 127 150 255 23 | 142 178 255 24 | 181 201 255 25 | -------------------------------------------------------------------------------- /metdig/graphics/resources/colormaps_ncl/wxpEnIR.rgb: -------------------------------------------------------------------------------- 1 | ncolors= 100 2 | # r g b 3 | 255 255 0 4 | 255 255 0 5 | 255 255 0 6 | 255 255 0 7 | 255 255 0 8 | 255 255 0 9 | 255 255 0 10 | 255 255 0 11 | 100 50 25 12 | 100 50 25 13 | 100 50 25 14 | 100 50 25 15 | 0 255 0 16 | 0 255 0 17 | 0 255 0 18 | 0 255 0 19 | 0 255 0 20 | 0 128 0 21 | 0 128 0 22 | 0 128 0 23 | 0 128 0 24 | 0 128 0 25 | 0 0 128 26 | 0 0 128 27 | 0 0 128 28 | 0 0 128 29 | 0 0 255 30 | 0 0 255 31 | 0 0 255 32 | 0 0 255 33 | 0 0 255 34 | 0 255 255 35 | 0 255 255 36 | 0 255 255 37 | 0 255 255 38 | 0 255 255 39 | 0 255 255 40 | 0 255 255 41 | 0 255 255 42 | 0 255 255 43 | 0 128 128 44 | 0 128 128 45 | 0 128 128 46 | 0 128 128 47 | 0 128 128 48 | 0 128 128 49 | 0 128 128 50 | 0 128 128 51 | 0 128 128 52 | 205 205 205 53 | 201 201 201 54 | 197 197 197 55 | 193 193 193 56 | 189 189 189 57 | 185 185 185 58 | 180 180 180 59 | 176 176 176 60 | 172 172 172 61 | 168 168 168 62 | 164 164 164 63 | 160 160 160 64 | 156 156 156 65 | 152 152 152 66 | 148 148 148 67 | 144 144 144 68 | 139 139 139 69 | 135 135 135 70 | 131 131 131 71 | 127 127 127 72 | 123 123 123 73 | 119 119 119 74 | 115 115 115 75 | 111 111 111 76 | 107 107 107 77 | 103 103 103 78 | 98 98 98 79 | 94 94 94 80 | 90 90 90 81 | 86 86 86 82 | 82 82 82 83 | 78 78 78 84 | 74 74 74 85 | 70 70 70 86 | 66 66 66 87 | 62 62 62 88 | 57 57 57 89 | 53 53 53 90 | 49 49 49 91 | 45 45 45 92 | 41 41 41 93 | 37 37 37 94 | 33 33 33 95 | 29 29 29 96 | 25 25 25 97 | 21 21 21 98 | 16 16 16 99 | 12 12 12 100 | 8 8 8 101 | 4 4 4 102 | 0 0 0 103 | -------------------------------------------------------------------------------- /metdig/graphics/resources/logo/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | nmc_met_map is a package that providing tools to proceed the diagnostic analysis. 3 | Including pyhsical parameter calculation and figure ploting. 4 | """ 5 | 6 | __author__ = "The R & D Center for Weather Forecasting Technology in NMC, CMA" 7 | __version__ = '0.1.0' -------------------------------------------------------------------------------- /metdig/graphics/resources/logo/cma.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/graphics/resources/logo/cma.png -------------------------------------------------------------------------------- /metdig/graphics/resources/logo/cma_Xlarge.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | GEOGCS["Xian 1980",DATUM["Xian_1980",SPHEROID["IAG 1975",6378140,298.257,AUTHORITY["EPSG","7049"]],AUTHORITY["EPSG","6610"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4610"]] 2 | -------------------------------------------------------------------------------- /metdig/graphics/resources/shapefile/NationalBorder.sbn: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/graphics/resources/shapefile/NationalBorder.sbn -------------------------------------------------------------------------------- /metdig/graphics/resources/shapefile/NationalBorder.sbx: -------------------------------------------------------------------------------- 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/metdig/graphics/resources/shapefile/Province.dbf: -------------------------------------------------------------------------------- 1 | x"AFIDN 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 -------------------------------------------------------------------------------- /metdig/graphics/resources/shapefile/Province.prj: -------------------------------------------------------------------------------- 1 | GEOGCS["WGS84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.017453292519943295]] -------------------------------------------------------------------------------- /metdig/graphics/resources/shapefile/Province.sbn: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/graphics/resources/shapefile/Province.sbn 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-------------------------------------------------------------------------------- /metdig/graphics/resources/stations/county.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/graphics/resources/stations/county.csv -------------------------------------------------------------------------------- /metdig/graphics/streamplot_method.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import cartopy.crs as ccrs 4 | import matplotlib as mpl 5 | import matplotlib.pyplot as plt 6 | import matplotlib.cm as cm 7 | from matplotlib.colors import BoundaryNorm, ListedColormap 8 | import matplotlib.patheffects as mpatheffects 9 | 10 | import metdig.graphics.lib.utility as utl 11 | import metdig.graphics.lib.utl_plotmap as utl_plotmap 12 | import metdig.graphics.cmap.cm as cm_collected 13 | from metdig.graphics.lib.utility import kwargs_wrapper 14 | 15 | @kwargs_wrapper 16 | def uv_streamplot(ax, ustda, vstda,xdim='lon', ydim='lat', 17 | color='gray',density=2, 18 | transform=ccrs.PlateCarree(), 19 | **kwargs): 20 | # 数据准备 21 | x = ustda.stda.get_dim_value(xdim) 22 | y = ustda.stda.get_dim_value(ydim) 23 | u = ustda.stda.get_value(ydim, xdim) # 1/s 24 | v = vstda.stda.get_value(ydim, xdim) # 1/s 25 | # 绘制 26 | if transform is None or (xdim != 'lon' and ydim != 'lat'): 27 | img = ax.streamplot(x, y, u, v, color=color,density=density, **kwargs) 28 | else: 29 | img = ax.streamplot(x, y, u, v, color=color,density=density, transform=transform, **kwargs) 30 | return img 31 | -------------------------------------------------------------------------------- /metdig/hub/__init__.py: -------------------------------------------------------------------------------- 1 | from .basic_anl import * 2 | from .compare import * 3 | from .evolution import * 4 | from .ver_vs_anl import * 5 | from .stability import * -------------------------------------------------------------------------------- /metdig/hub/compare.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | ''' 4 | 多模式对比 5 | ''' 6 | import os 7 | import copy 8 | import datetime 9 | 10 | from metdig.hub.lib.utility import save_tab 11 | from metdig.hub.lib.utility import save_list 12 | from metdig.hub.lib.utility import mult_process 13 | from metdig.hub.lib.utility import get_onestep_ret_imgbufs 14 | from metdig.hub.lib.utility import get_onestep_ret_pngnames 15 | from metdig.hub.lib.utility import strparsetime 16 | 17 | __all__ = [ 18 | 'models_compare', 19 | ] 20 | 21 | 22 | def models_compare(init_time=None, fhour=24, data_names=['ecmwf', 'cma_gfs', 'ncep_gfs', 'cma_meso_3km'], 23 | func=None, func_other_args={}, max_workers=6, 24 | output_dir=None, show='tab', tab_size=(30, 18), list_size=(16, 9), 25 | is_clean_plt=False): 26 | ''' 27 | 28 | [多模式对比] 29 | 30 | Keyword Arguments: 31 | init_time {[datetime]} -- [起报时间] (default: {None}) 32 | fhour {[number]} -- [预报时效] (default: {None}) 33 | data_names {[list]} -- [多模式列表] (default: {None}) 34 | func {[function]} -- [函数名] (default: {None}) 35 | func_other_args {dict} -- [函数参数字典] (default: {{}}) 36 | max_workers {number} -- [最大进程数] (default: {6}) 37 | output_dir {[str]} -- [输出目录] (default: {None}) 38 | show {str} -- ['list', show all plots in one cell. 39 | 'tab', show one plot in each tab page. ] (default: {'tab'}) 40 | tab_size {tuple} -- [如果show='tab'时生效，输出图片分辨率] (default: {(30, 18)}) 41 | 42 | ''' 43 | init_time = strparsetime(init_time) 44 | 45 | # 参数准备 46 | func_args_all = [] 47 | for data_name in data_names: 48 | func_args = copy.deepcopy(func_other_args) 49 | func_args['init_time'] = init_time 50 | func_args['fhour'] = fhour 51 | func_args['data_name'] = data_name 52 | func_args['is_return_imgbuf'] = True 53 | func_args['is_return_pngname'] = True 54 | func_args_all.append(func_args) 55 | 56 | # 多进程绘图 57 | all_ret = mult_process(func=func, func_args_all=func_args_all, max_workers=max_workers) 58 | all_img_bufs = get_onestep_ret_imgbufs(all_ret) 59 | all_png_names = get_onestep_ret_pngnames(all_ret) 60 | 61 | # 输出 62 | ret = None 63 | if show == 'list': 64 | ret = save_list(all_img_bufs, output_dir, all_png_names, list_size=list_size, is_clean_plt=is_clean_plt) 65 | elif show == 'tab': 66 | png_name = 'compare_{}_{}_{:%Y%m%d%H}_{:03d}.png'.format(func.__name__, 'models', init_time, fhour) 67 | ret = save_tab(all_img_bufs, output_dir, png_name, tab_size=tab_size, is_clean_plt=is_clean_plt) 68 | 69 | if ret: 70 | return ret 71 | -------------------------------------------------------------------------------- /metdig/hub/lib/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/hub/lib/__init__.py -------------------------------------------------------------------------------- /metdig/io/cimiss.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- -------------------------------------------------------------------------------- /metdig/io/lib/__init__.py: -------------------------------------------------------------------------------- 1 | from .package_config.cassandra_model_cfg import cassandra_model_cfg 2 | from .package_config.cassandra_obs_cfg import cassandra_obs_cfg 3 | from .package_config.cassandra_radar_cfg import cassandra_radar_cfg 4 | from .package_config.cassandra_sate_cfg import cassandra_sate_cfg 5 | 6 | 7 | # from .package_config.cmadaas_datacode_cfg import cmadaas_datacode_cfg # 不对外使用 8 | from .package_config.cmadaas_model_cfg import cmadaas_model_cfg 9 | from .package_config.cmadaas_obs_cfg import cmadaas_obs_cfg 10 | 11 | from .package_config.era5_cfg import era5_cfg 12 | from .package_config.thredds_model_cfg import thredds_model_cfg -------------------------------------------------------------------------------- /metdig/io/lib/package_config/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/__init__.py -------------------------------------------------------------------------------- /metdig/io/lib/package_config/base.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | import threading 8 | 9 | from metpy.units import units 10 | 11 | 12 | def check_units(var_units): 13 | try: 14 | units(var_units) 15 | except Exception as e: 16 | raise e 17 | 18 | 19 | class SingletonMetaClass(type): 20 | _lock = threading.Lock() 21 | 22 | def __call__(cls, *args, **kwargs): 23 | if not hasattr(cls, '_instance'): 24 | with cls._lock: 25 | if not hasattr(cls, '_instance'): 26 | cls._instance = super(SingletonMetaClass, cls).__call__(*args, **kwargs) 27 | return cls._instance -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_obs_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cassandra_obs_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_obs_cfg.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | 8 | from metdig.io.lib.package_config.base import check_units, SingletonMetaClass 9 | 10 | 11 | class cassandra_obs_cfg(metaclass=SingletonMetaClass): 12 | def __init__(self): 13 | self.obs_cfg_csv = os.path.dirname(os.path.realpath(__file__)) + '/cassandra_obs_cfg.csv' 14 | self.obs_cfg = pd.read_csv(self.obs_cfg_csv, encoding='gbk', comment='#') 15 | self.obs_cfg = self.obs_cfg.fillna('') 16 | self.obs_cfg.apply(lambda row: check_units(row['var_units']), axis=1) # 检查是否满足units格式 17 | 18 | def obs_cassandra_dir(self, data_name=None, var_name=None): 19 | _obs_cfg = self.obs_cfg[(self.obs_cfg['data_name'] == data_name) & 20 | (self.obs_cfg['var_name'] == var_name)].copy(deep=True) 21 | 22 | if len(_obs_cfg) == 0: 23 | raise Exception('can not get data_name = {} var_name={} in {}!'.format(data_name, var_name, self.obs_cfg_csv)) 24 | 25 | return _obs_cfg['cassandra_path'].values[0] 26 | 27 | def obs_cassandra_units(self, data_name=None, var_name=None): 28 | _obs_cfg = self.obs_cfg[(self.obs_cfg['data_name'] == data_name) & 29 | (self.obs_cfg['var_name'] == var_name)].copy(deep=True) 30 | if len(_obs_cfg) == 0: 31 | return '' 32 | return _obs_cfg['var_units'].values[0] 33 | 34 | 35 | if __name__ == '__main__': 36 | 37 | x = cassandra_obs_cfg().obs_cassandra_dir(data_name='sfc_chn_hor', var_name='rain24') 38 | print(x) 39 | 40 | x = cassandra_obs_cfg().obs_cassandra_units(data_name='sfc_chn_hor', var_name='rain24') 41 | print(x) 42 | -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_radar_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cassandra_radar_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_radar_cfg.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | 8 | from metdig.io.lib.package_config.base import check_units, SingletonMetaClass 9 | 10 | 11 | class cassandra_radar_cfg(metaclass=SingletonMetaClass): 12 | def __init__(self): 13 | self.radar_cfg_csv = os.path.dirname(os.path.realpath(__file__)) + '/cassandra_radar_cfg.csv' 14 | self.radar_cfg = pd.read_csv(self.radar_cfg_csv, encoding='gbk', comment='#') 15 | self.radar_cfg = self.radar_cfg.fillna('') 16 | self.radar_cfg.apply(lambda row: check_units(row['var_units']), axis=1) # 检查是否满足units格式 17 | 18 | def get_radar_cfg(self, data_name=None, var_name=None): 19 | this_cfg = self.radar_cfg[(self.radar_cfg['data_name'] == data_name) & 20 | (self.radar_cfg['var_name'] == var_name)].copy(deep=True).reset_index(drop=True) 21 | 22 | if len(this_cfg) == 0: 23 | raise Exception('can not get data_name={} var_name={} in {}!'.format(data_name, var_name, self.radar_cfg_csv)) 24 | 25 | return this_cfg.to_dict('index')[0] 26 | 27 | def radar_cassandra_dir(self, data_name=None, var_name=None): 28 | return self.get_radar_cfg(data_name=data_name, var_name=var_name)['cassandra_path'] 29 | 30 | def radar_cassandra_units(self, data_name=None, var_name=None): 31 | return self.get_radar_cfg(data_name=data_name, var_name=var_name)['var_units'] 32 | 33 | if __name__ == '__main__': 34 | 35 | x = cassandra_radar_cfg().radar_cassandra_dir(data_name='achn', var_name='cref') 36 | print(x) 37 | 38 | x = cassandra_radar_cfg().radar_cassandra_dir(data_name='achn', var_name='cref') 39 | print(x) -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_sate_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cassandra_sate_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cassandra_sate_cfg.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | 8 | from metdig.io.lib.package_config.base import check_units, SingletonMetaClass 9 | 10 | 11 | class cassandra_sate_cfg(metaclass=SingletonMetaClass): 12 | def __init__(self): 13 | self.sate_cfg_csv = os.path.dirname(os.path.realpath(__file__)) + '/cassandra_sate_cfg.csv' 14 | self.sate_cfg = pd.read_csv(self.sate_cfg_csv, encoding='gbk', comment='#') 15 | self.sate_cfg = self.sate_cfg.fillna('') 16 | self.sate_cfg.apply(lambda row: check_units(row['var_units']), axis=1) # 检查是否满足units格式 17 | self.sate_cfg['channel'] = self.sate_cfg.apply(lambda row: row['channel'].strip('/').split('/'), axis=1) 18 | 19 | def get_sate_cfg(self, data_name=None, var_name=None, channel=None): 20 | this_cfg = self.sate_cfg[(self.sate_cfg['data_name'] == data_name) & 21 | (self.sate_cfg['var_name'] == var_name)].copy(deep=True).reset_index(drop=True) 22 | 23 | # channel 是list 24 | index = -1 25 | for idx, row in this_cfg.iterrows(): 26 | if 'any' in row['channel'] or str(channel) in row['channel']: 27 | index = idx 28 | break 29 | 30 | if index < 0: 31 | raise Exception('can not get data_name={} var_name={} channel={} in {}!'.format(data_name, var_name, channel, self.sate_cfg_csv)) 32 | 33 | return this_cfg.to_dict('index')[index] 34 | 35 | def sate_cassandra_dir(self, data_name=None, var_name=None, channel=None): 36 | return self.get_sate_cfg(data_name=data_name, var_name=var_name, channel=channel)['cassandra_path'] 37 | 38 | def sate_cassandra_units(self, data_name=None, var_name=None, channel=None): 39 | return self.get_sate_cfg(data_name=data_name, var_name=var_name, channel=channel)['var_units'] 40 | 41 | if __name__ == '__main__': 42 | 43 | x = cassandra_sate_cfg().sate_cassandra_dir(data_name='fy4al1', var_name='ref', channel=1) 44 | print(x) 45 | 46 | x = cassandra_sate_cfg().sate_cassandra_dir(data_name='fy4al1', var_name='ref', channel=1) 47 | print(x) -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cmadaas_datacode_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cmadaas_datacode_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cmadaas_datacode_cfg.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | 8 | from metdig.io.lib.package_config.base import check_units, SingletonMetaClass 9 | 10 | 11 | class cmadaas_datacode_cfg(metaclass=SingletonMetaClass): 12 | def __init__(self): 13 | self.datacode_cfg_csv = os.path.dirname(os.path.realpath(__file__)) + '/cmadaas_datacode_cfg.csv' 14 | self.datacode_cfg = pd.read_csv(self.datacode_cfg_csv, encoding='gbk', comment='#') 15 | self.datacode_cfg = self.datacode_cfg.fillna('') 16 | 17 | def get_datacode_cfg(self, data_name=None, fhour=0): 18 | 19 | if fhour == 0: 20 | fhour_flag = 0 21 | else: 22 | fhour_flag = 1 23 | this_cfg = self.datacode_cfg[(self.datacode_cfg['data_name'] == data_name) & 24 | (self.datacode_cfg['fhour_flag'] == fhour_flag)].copy(deep=True).reset_index(drop=True) 25 | 26 | if len(this_cfg) == 0: 27 | raise Exception('can not get data_name={} fhour_flag={} in {}!'.format(data_name, fhour_flag, self.datacode_cfg_csv)) 28 | 29 | if len(this_cfg) > 1: 30 | raise Exception('error: greater than 1 recode! data_name={} fhour_flag={} in {}!'.format(data_name, fhour_flag, self.datacode_cfg_csv)) 31 | 32 | return this_cfg['data_code'].values[0] 33 | 34 | -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cmadaas_model_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cmadaas_model_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cmadaas_obs_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/cmadaas_obs_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/cmadaas_obs_cfg.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | import os 4 | import pandas as pd 5 | 6 | import numpy as np 7 | 8 | from metdig.io.lib.package_config.base import check_units, SingletonMetaClass 9 | 10 | 11 | class cmadaas_obs_cfg(metaclass=SingletonMetaClass): 12 | def __init__(self): 13 | self.obs_cfg_csv = os.path.dirname(os.path.realpath(__file__)) + '/cmadaas_obs_cfg.csv' 14 | self.obs_cfg = pd.read_csv(self.obs_cfg_csv, encoding='gbk', comment='#') 15 | self.obs_cfg = self.obs_cfg.fillna('') 16 | self.obs_cfg.apply(lambda row: check_units(row['var_units']), axis=1) # 检查是否满足units格式 17 | 18 | def get_obs_cfg(self, data_name=None, var_name=None): 19 | this_cfg = self.obs_cfg[(self.obs_cfg['data_name'] == data_name) & 20 | (self.obs_cfg['var_name'] == var_name)].copy(deep=True).reset_index(drop=True) 21 | 22 | if len(this_cfg) == 0: 23 | raise Exception('can not get data_name={} var_name={} in {}!'.format(data_name, var_name, self.obs_cfg_csv)) 24 | 25 | return this_cfg.to_dict('index')[0] 26 | 27 | def obs_cmadaas_data_code(self, data_name=None, var_name=None): 28 | return self.get_obs_cfg(data_name=data_name, var_name=var_name)['cmadaas_data_code'] 29 | 30 | def obs_cmadaas_units(self, data_name=None, var_name=None): 31 | return self.get_obs_cfg(data_name=data_name, var_name=var_name)['var_units'] 32 | 33 | def obs_cmadaas_var_name(self, data_name=None, var_name=None): 34 | return self.get_obs_cfg(data_name=data_name, var_name=var_name)['cmadaas_var_name'] 35 | 36 | if __name__ == '__main__': 37 | x = cmadaas_obs_cfg().obs_cmadaas_data_code(data_name='sfc_chn_hor', var_name='rain24') 38 | print(x) 39 | 40 | x = cmadaas_obs_cfg().obs_cmadaas_units(data_name='sfc_chn_hor', var_name='rain24') 41 | print(x) 42 | 43 | x = cmadaas_obs_cfg().obs_cmadaas_var_name(data_name='sfc_chn_hor', var_name='rain24') 44 | print(x) -------------------------------------------------------------------------------- /metdig/io/lib/package_config/era5_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/era5_cfg.csv -------------------------------------------------------------------------------- /metdig/io/lib/package_config/thredds_model_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/io/lib/package_config/thredds_model_cfg.csv -------------------------------------------------------------------------------- /metdig/onestep/__init__.py: -------------------------------------------------------------------------------- 1 | from .diag_crossection import * 2 | from .diag_dynamic import * 3 | from .diag_elements import * 4 | from .diag_moisture import * 5 | from .diag_qpf import * 6 | from .diag_station import * 7 | from .diag_synoptic import * 8 | from .diag_thermal import * 9 | from .observation_radar import * 10 | from .observation_satellite import * 11 | from .observation_station import * 12 | from .observation_unusual import * 13 | from .veri_synop import * 14 | from .diag_ensemble import * 15 | from .lib import * 16 | from .diag_identify import * 17 | from .diag_trajectory import * 18 | from .diag_theme_ne import * 19 | -------------------------------------------------------------------------------- /metdig/onestep/complexgrid_var/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/onestep/complexgrid_var/__init__.py -------------------------------------------------------------------------------- /metdig/onestep/diag_ensemble.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import numpy as np 4 | 5 | from metdig.io import get_model_grid 6 | 7 | from metdig.onestep.lib.utility import get_map_area 8 | from metdig.onestep.lib.utility import mask_terrian 9 | from metdig.onestep.lib.utility import date_init 10 | 11 | from metdig.onestep.complexgrid_var.pv_div_uv import read_pv_div_uv 12 | from metdig.onestep.complexgrid_var.get_rain import read_rain 13 | from metdig.onestep.complexgrid_var.vort_uv import read_vort_uv 14 | from metdig.onestep.complexgrid_var.wsp import read_wsp 15 | 16 | from metdig.products import diag_ensemble as draw_ensemble 17 | 18 | import metdig.utl.utl_stda_grid as utl_stda_grid 19 | 20 | import metdig.cal as mdgcal 21 | 22 | __all__ = [ 23 | 'hgt_spaghetti', 24 | ] 25 | 26 | @date_init('init_time') 27 | def hgt_spaghetti(data_source='cassandra', data_name='ecmwf_ens', init_time=None, fhour=24, 28 | area='全国', is_return_data=False, is_draw=True, **products_kwargs): 29 | 30 | ret = {} 31 | map_extent = get_map_area(area) 32 | 33 | hgt = get_model_grid(data_source=data_source, init_time=init_time, fhour=fhour, 34 | data_name=data_name, var_name='hgt', level=500, extent=map_extent) 35 | 36 | if is_return_data: 37 | dataret = {'hgt': hgt} 38 | ret.update({'data': dataret}) 39 | 40 | if is_draw: 41 | draw_ensemble.draw_hgt_spaghetti(hgt,map_extent=map_extent,**products_kwargs) 42 | 43 | if __name__ == '__main__': 44 | import matplotlib.pyplot as plt 45 | hgt_spaghetti(init_time='21121008',fhour=84) 46 | plt.show() -------------------------------------------------------------------------------- /metdig/onestep/lib/__init__.py: -------------------------------------------------------------------------------- 1 | from .utility import * -------------------------------------------------------------------------------- /metdig/onestep/observation_unusual.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | from metdig.io.cassandra import get_wind_profiler_bytimerange 5 | 6 | from metdig.products import observation_unusual as draw_unusual 7 | 8 | import metdig.cal as mdgcal 9 | import metdig.utl as mdgstda 10 | 11 | __all__ = [ 12 | 'wind_profiler', 13 | ] 14 | 15 | 16 | def wind_profiler(obs_st_time=None, obs_ed_time=None, id_selected=53399, is_return_data=False, is_draw=True, **products_kwargs): 17 | ret = {} 18 | 19 | wsp = get_wind_profiler_bytimerange(obs_st_time, obs_ed_time, data_name='wind_profiler', var_name='wsp', id_selected=id_selected) 20 | wdir = get_wind_profiler_bytimerange(obs_st_time, obs_ed_time, data_name='wind_profiler', var_name='wdir', id_selected=id_selected) 21 | 22 | # 计算uv 23 | u, v = mdgcal.wind_components(wsp, wdir) 24 | 25 | # 转成格点stda 26 | u = mdgstda.stastda_to_gridstda(u, xdim='time', ydim='level') 27 | v = mdgstda.stastda_to_gridstda(v, xdim='time', ydim='level') 28 | 29 | # 时间维度简单稀疏化 30 | step = int(u.stda.time.size / 40) 31 | u = u.isel(time=slice(0, -1, step)) 32 | v = v.isel(time=slice(0, -1, step)) 33 | 34 | if is_return_data: 35 | dataret = {'u': u, 'v': v} 36 | ret.update({'data': dataret}) 37 | 38 | # plot 39 | if is_draw: 40 | drawret = draw_unusual.draw_wind_profiler(u, v, id_selected, obs_st_time, obs_ed_time, **products_kwargs) 41 | ret.update(drawret) 42 | 43 | if ret: 44 | return ret 45 | 46 | if __name__ == '__main__': 47 | import matplotlib.pyplot as plt 48 | from datetime import datetime 49 | ret=wind_profiler(obs_st_time=datetime(2022,6,22,10),obs_ed_time=datetime(2022,6,22,22),ylim=[0,5000]) 50 | # ret['ax'].set_ylim(0,5000) 51 | plt.show() -------------------------------------------------------------------------------- /metdig/package_tools.py: -------------------------------------------------------------------------------- 1 | import os 2 | import webbrowser 3 | 4 | 5 | def easy_sel_point(zoom_start=5, openinwebbrowser=False): 6 | ''' 7 | 选择获取经纬度 8 | ''' 9 | 10 | try: 11 | import folium 12 | except: 13 | raise Exception("folium not exists, please install folium first, such as: pip install folium") 14 | 15 | m = folium.Map( 16 | location=[35, 108], 17 | zoom_start=zoom_start, 18 | tiles='http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}', 19 | attr='default', 20 | control_scale=True, 21 | 22 | ) 23 | 24 | m.add_child(folium.LatLngPopup()) 25 | 26 | if openinwebbrowser: 27 | htmlpath = 'easy_sel_point.html' 28 | htmlpath = os.path.expanduser('~') + '/easy_sel_point.html' 29 | m.save(htmlpath) # 保存到本地 30 | webbrowser.open(htmlpath) # 在浏览器中打开 31 | 32 | return m 33 | 34 | 35 | if __name__ == '__main__': 36 | 37 | easy_sel_point() 38 | 39 | pass 40 | -------------------------------------------------------------------------------- /metdig/products/__init__.py: -------------------------------------------------------------------------------- 1 | from . import diag_crossection 2 | from . import diag_dynamic 3 | from . import diag_elements 4 | from . import diag_moisture 5 | from . import diag_qpf 6 | from . import diag_synoptic 7 | from . import diag_thermal 8 | from . import diag_station 9 | from . import observation_radar 10 | from . import observation_satellite 11 | from . import observation_station 12 | from . import observation_unusual 13 | from . import veri_synop 14 | from . import diag_identify 15 | from . import diag_trajectory -------------------------------------------------------------------------------- /metdig/products/diag_ensemble.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | import datetime 4 | import numpy as np 5 | import pandas as pd 6 | import matplotlib.pyplot as plt 7 | import matplotlib.lines as lines 8 | 9 | from metdig.graphics.barbs_method import * 10 | from metdig.graphics.contour_method import * 11 | from metdig.graphics.contourf_method import * 12 | from metdig.graphics.pcolormesh_method import * 13 | from metdig.graphics.quiver_method import * 14 | from metdig.graphics.streamplot_method import * 15 | from metdig.graphics.text_method import * 16 | from metdig.graphics.draw_compose import * 17 | 18 | 19 | def draw_hgt_spaghetti(hgt,hgt_contour_kwargs={}, map_extent=(60, 145, 15, 55), **pallete_kwargs): 20 | init_time = pd.to_datetime(hgt.coords['time'].values[0]).replace(tzinfo=None).to_pydatetime() 21 | fhour = int(hgt['dtime'].values[0]) 22 | fcst_time = init_time + datetime.timedelta(hours=fhour) 23 | 24 | data_name = str(hgt['member'].values[0]) 25 | title = '[{}] {}hPa 位势高度场 集合预报面条图'.format( 26 | data_name.upper(), 27 | hgt['level'].values[0]) 28 | 29 | forcast_info = hgt.stda.description() 30 | png_name = '{2}_位势高度场_集合预报面条图_预报_起报时间_{0:%Y}年{0:%m}月{0:%d}日{0:%H}时预报时效_{1:}小时.png'.format(init_time, fhour, data_name.upper()) 31 | 32 | obj = horizontal_compose(title=title, description=forcast_info, png_name=png_name, map_extent=map_extent, kwargs=pallete_kwargs) 33 | obj.img['hgt'] = hgt_spaghetti_contour(obj.ax, hgt, kwargs=hgt_contour_kwargs) 34 | 35 | control_line = lines.Line2D([], [], color='black',linestyle='dashed', label='控制预报') 36 | mean_line = lines.Line2D([], [], color='black', label='集合平均') 37 | leg = obj.ax.legend(handles=[control_line, mean_line], loc=1, framealpha=1) 38 | leg.set_zorder(100) 39 | obj.save() 40 | return obj.get_mpl() -------------------------------------------------------------------------------- /metdig/products/observation_radar.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | import datetime 5 | import numpy as np 6 | import pandas as pd 7 | 8 | import cartopy.crs as ccrs 9 | 10 | from metdig.graphics.barbs_method import * 11 | from metdig.graphics.contour_method import * 12 | from metdig.graphics.contourf_method import * 13 | from metdig.graphics.pcolormesh_method import * 14 | from metdig.graphics.draw_compose import * 15 | 16 | def draw_cref(cref,map_extent=(60, 145, 15, 55), 17 | ref_pcolormesh_kwargs={}, hgt_contour_kwargs={}, uv_barbs_kwargs={}, 18 | **pallete_kwargs): 19 | 20 | cref_time = cref.stda.time[0] 21 | 22 | title = '天气雷达组合反射率观测' 23 | forcast_info = '观测时间: {0:%m}月{0:%d}日{0:%H}时{0:%M}分（BJT）\nwww.nmc.cn'.format( 24 | cref_time) 25 | png_name = '天气雷达组合反射率_{0:%m}月{0:%d}日{0:%H}时{0:%M}分.png'.format(cref_time) 26 | 27 | obj = horizontal_compose(title=title, description=forcast_info, png_name=png_name, map_extent=map_extent, kwargs=pallete_kwargs) 28 | obj.img['cref'] = cref_contourf(obj.ax, cref, kwargs=ref_pcolormesh_kwargs) 29 | obj.save() 30 | return obj.get_mpl() 31 | 32 | 33 | def draw_cref_sounding_hgt(cref, hgt, sounding_u, sounding_v, map_extent=(60, 145, 15, 55), 34 | ref_pcolormesh_kwargs={}, hgt_contour_kwargs={}, uv_barbs_kwargs={}, 35 | **pallete_kwargs): 36 | 37 | cref_time = cref.stda.time[0] 38 | hgt_time = hgt.stda.fcst_time[0] 39 | sounding_time = sounding_u.stda.time[0] 40 | 41 | title = '天气雷达组合反射率观测 探空观测 高度场' 42 | forcast_info = '雷达观测时间: {0:%Y}年{0:%m}月{0:%d}日{0:%H}时\n探空观测时间: {1:%Y}年{1:%m}月{1:%d}日{1:%H}时\n高度场时间: {2:%Y}年{2:%m}月{2:%d}日{2:%H}时'.format( 43 | cref_time, sounding_time, hgt_time) 44 | png_name = '天气雷达组合反射率_位势高度_探空风_{0:%Y}年{0:%m}月{0:%d}日{0:%H}时.png'.format(cref_time) 45 | 46 | obj = horizontal_compose(title=title, description=forcast_info, png_name=png_name, map_extent=map_extent, kwargs=pallete_kwargs) 47 | obj.img['cref'] = cref_pcolormesh(obj.ax, cref, kwargs=ref_pcolormesh_kwargs) 48 | obj.img['uv'] = barbs_2d(obj.ax, sounding_u, sounding_v, length=7, lw=1.5, sizes=dict(emptybarb=0.0), regrid_shape=None, kwargs=uv_barbs_kwargs) 49 | obj.img['hgt'] = hgt_contour(obj.ax, hgt, kwargs=hgt_contour_kwargs) 50 | obj.img['hgt_588'] = hgt_contour(obj.ax, hgt, levels=[588], linewidths=4, kwargs=hgt_contour_kwargs) 51 | obj.save() 52 | return obj.get_mpl() 53 | -------------------------------------------------------------------------------- /metdig/products/observation_unusual.py: -------------------------------------------------------------------------------- 1 | 2 | import datetime 3 | import numpy as np 4 | 5 | import matplotlib.pyplot as plt 6 | import matplotlib as mpl 7 | import pandas as pd 8 | 9 | from metdig.graphics import pallete_set 10 | from metdig.graphics import draw_compose 11 | 12 | from metdig.graphics.lib.utility import save 13 | 14 | from metdig.graphics.barbs_method import * 15 | 16 | 17 | def draw_wind_profiler(u, v, id, st_time, ed_time, uv_barbs_kwargs={}, **pallete_kwargs): 18 | times = u.stda.time 19 | 20 | title = '风廓线雷达时间剖面图' 21 | forcast_info = '站号: {2}\n开始时间: {0:%Y}年{0:%m}月{0:%d}日{0:%H}时{0:%M}分\n结束时间: {1:%Y}年{1:%m}月{1:%d}日{1:%H}时{1:%M}分'.format( 22 | st_time, ed_time, id) 23 | png_name = '{2}_{0:%Y}年{0:%m}月{0:%d}日{0:%H}时{0:%M}分_{1:%Y}年{1:%m}月{1:%d}日{1:%H}时{1:%M}分风廓线雷达时间剖面图.png'.format(st_time, ed_time, id) 24 | 25 | obj = draw_compose.cross_timeheight_compose( times=times, title=title, description=forcast_info, png_name=png_name, kwargs=pallete_kwargs) 26 | obj.img['uv'] = barbs_2d(obj.ax, u, v, xdim='time', ydim='level', color='k', length=5, transform=None, regrid_shape=None, kwargs=uv_barbs_kwargs) 27 | 28 | obj.save() 29 | return obj.get_mpl() 30 | -------------------------------------------------------------------------------- /metdig/utl/__init__.py: -------------------------------------------------------------------------------- 1 | from .utl_stda_attrs import * 2 | from .utl_stda_grid import * 3 | from .utl_stda_station import * 4 | from .utl_units import * -------------------------------------------------------------------------------- /metdig/utl/stda_attrs_cfg.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/metdig/b7f8ab38f606d554bc14f2aaa3e824632bb12a9b/metdig/utl/stda_attrs_cfg.csv -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | from os import path 4 | from setuptools import setup, find_packages 5 | from codecs import open 6 | 7 | name = "metdig" 8 | author = __import__(name).__author__ 9 | version = __import__(name).__version__ 10 | 11 | here = path.abspath(path.dirname(__file__)) 12 | 13 | # Get the long description from the README file 14 | with open(path.join(here, 'README.md'), encoding='utf-8') as f: 15 | long_description = f.read() 16 | 17 | # setup 18 | setup( 19 | name=name, 20 | 21 | version=version, 22 | 23 | description='Weather analysis and diagnostic graphics for map discussion', 24 | long_description=long_description, 25 | 26 | # LICENSE 27 | license='GPL3', 28 | 29 | classifiers=[ 30 | 'Development Status :: 3 - Alpha', 31 | 'Intended Audience :: Developers', 32 | 'Programming Language :: Python :: 3', 33 | ], 34 | 35 | packages=find_packages(exclude=['metdig.egg-info']), 36 | include_package_data=True, 37 | exclude_package_data={'': ['.gitignore']}, 38 | 39 | install_requires=[ 40 | 'cartopy >= 0.22.0', 41 | 'nmc_met_io >= 0.1.13.0', 42 | 'metpy <= 1.5', 43 | 'meteva > 1.3', 44 | 'xarray <= 0.19.0 ', 45 | 'netcdf4', 46 | 'cdsapi >= 0.7.0', 47 | 'numba', 48 | 'folium', 49 | 'pandas<1.5', 50 | 'shapely', 51 | 'imageio', 52 | 'numpy < 2.0', 53 | 'protobuf<=3.20', 54 | 'ipython', 55 | 'pint < 0.20.0', 56 | 'scikit_learn', 57 | 'JPype1'], 58 | python_requires='>=3.9', 59 | zip_safe = False 60 | ) 61 | 62 | # development mode (DOS command): 63 | # python setup.py develop 64 | # python setup.py develop --uninstall 65 | 66 | # build mode： 67 | # python setup.py build --build-base=D:/test/python/build 68 | 69 | # distribution mode: 70 | # python setup.py sdist # create source tar.gz file in /dist 71 | # python setup.py bdist_wheel # create wheel binary in /dist --------------------------------------------------------------------------------