File:Suomen kasvukauden lamposumma 1.svg

1. 1. cut raster bu lon, lat area in "R"

library(raster) library(viridis)

inname1<-"./data2/CHELSA_gdd_5_1979-2013.tif"

outname1="out1.nc"

plotname1="out.png"

1. ext1 <- extent(-15,40,30 , 70)

x1=20 x2=32 y1=58 y2=72

pallength1=100

ext1 <- extent(x1,x2,y1,y2)

inras1<-raster(inname1) inshape1 <- getData("GADM", country="FI", level=0)

selras1<-crop(inras1,ext1)

png(plotname1)

image(selras1, col=rev(viridis(pallength1) ) , xlim=c(x1,x2), ylim=c(y1,y2) )

contour(selras1,add=TRUE) plot(inshape1, add=TRUE)

dev.off()

crs(selras1) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

writeRaster(selras1, filename=outname1, varname="z", format="CDF", overwrite=TRUE)

1. 3. plot netcdf mapped data with python
   5. 2.11.2021 0000.0001
   7. input must be cut netcdf area grid in many times!
2. 1. latitude, longitude, z

1. -*- coding: utf-8 -*-

from mpl_toolkits.basemap import Basemap, cm import matplotlib.pyplot as plt from netCDF4 import Dataset as open_ncfile import numpy as np from matplotlib.colors import ListedColormap, LinearSegmentedColormap from matplotlib.pyplot import figure

plottitle="Kasvukauden lämpösumma"

1. legtitle="T July avg.°C (LGM, CCSM4)"

legtitle="Yli 5 °C lämpösumma"

1. 1. output

outsvg="out_python.svg" outpng="out_python.png"

1. 1. contours and colormap min, max, step

zeta=0 ## z min zetb=2000 ## z max deltazd=100 ## z step, annotated contours

1. 1. a

deltazc=100 # z step, non-annotated contours

1. lona=-15
2. lonb=40
3. lata=30
4. latb=70

lona=20 lonb=32 lata=58 latb=72

1. 1. lon, lat lines in map

latdx=2 londy=2

1. 1. reso, figsize

dpi1 = 72

figux=1200 figuy=1200*2

figure(figsize=(10, 20), dpi=dpi1)

1. 1. palette

1. cmap
2. kolormap='jet'

kolormap='jet'

1. kolormap='Spectral_r'
2. kolormap='gist_rainbow_r'
3. kolormap='BrBG'
4. kolormap='rainbow'
5. kolormap='viridis'

1. kolormap = ListedColormap(parula_data2)

1. netcdf infile name, variable names
2. lon, lat, z vars

infilename='out1.nc' zvarlabel='z' latvarlabel='latitude' lonvarlabel='longitude'

1. load netcdf file

nc = open_ncfile(infilename)

var = nc.variables[zvarlabel][:,:]

var2=np.flipud(var)

sheippi=np.shape(var2)

xdim=sheippi[0] ydim=sheippi[1]

print (xdim) print (ydim)

1. 1. attempt to fill Nan

var2_filt=np.asarray(var2)

missing_values_indices = var2 < -55555 var2_filt[missing_values_indices] = np.nan

var3=var2_filt.reshape(xdim, ydim) var3_ud=np.flipud(var3)

1. plt.imshow(var3, cmap=kolormap, vmin=zeta, vmax=zetb)

1. plt.show()

1. quit(-1)

lat = nc.variables[latvarlabel][:] lon = nc.variables[lonvarlabel][:]

1. 1. plot

fig = plt.figure(figsize=(figux/dpi1, figuy/dpi1), dpi=dpi1)

ax = fig.add_axes([0.1,0.1,0.8,0.9])

map = Basemap(projection='cyl',llcrnrlat=lata,urcrnrlat= latb,\

             resolution='h',  llcrnrlon=lona,urcrnrlon=lonb)

1. map.fillcontinents(color='coral',lake_color='aqua')

map.drawcoastlines(color='black') map.drawrivers(color='black') map.drawcountries(linewidth=3.0, linestyle='dashed', color='red')

1. map.drawstates()

kolorado=( (0.0, 0.0, 0.5),(0.0, 0.0, 0.5),(0.0, 0.0, 0.5),(0.0, 0.0, 0.5), (0.0, 0.0, 0.5),(0.0, 0.0, 0.5),(0.0, 0.0, 0.5),(0.0, 0.0, 0.5))

map.drawparallels(np.arange(lata, latb,latdx),labels=[1,0,0,0],fontsize=26) map.drawmeridians(np.arange(lona,lonb,londy),labels=[0,0,0,1],fontsize=26)

x, y = map(*np.meshgrid(lon,lat))

clevs = np.arange(zeta,zetb,deltazc) dlevs = np.arange(zeta,zetb,deltazd)

maplot1=map.imshow(var3, cmap=kolormap, vmin=zeta, vmax=zetb)

plt.rc('lines', linewidth=0.5, color='#000000')

cnplot2 = map.contour(x,y,var3_ud,clevs,colors='#3f0000', alpha=0.2) cnplot = map.contour(x,y,var3_ud,dlevs,colors='#3f0000')

cbar = map.colorbar(maplot1,location='bottom',pad="10%", label=legtitle) cbar.set_label(legtitle, fontsize=28) cbar.ax.tick_params(labelsize=28)

plt.clabel(cnplot, fmt='%.0f',fontsize=34, inline=1)

plt.title(plottitle, fontsize=40)

1. plt.show()

plt.savefig(outsvg, bbox_inches='tight', dpi=dpi1) plt.savefig(outpng, bbox_inches='tight', dpi=dpi1)

parula_data = [[0.2422, 0.1504, 0.6603], [0.2444, 0.1534, 0.6728], [0.2464, 0.1569, 0.6847], [0.2484, 0.1607, 0.6961], [0.2503, 0.1648, 0.7071], [0.2522, 0.1689, 0.7179], [0.254, 0.1732, 0.7286], [0.2558, 0.1773, 0.7393], [0.2576, 0.1814, 0.7501], [0.2594, 0.1854, 0.761], [0.2611, 0.1893, 0.7719], [0.2628, 0.1932, 0.7828], [0.2645, 0.1972, 0.7937], [0.2661, 0.2011, 0.8043], [0.2676, 0.2052, 0.8148], [0.2691, 0.2094, 0.8249], [0.2704, 0.2138, 0.8346], [0.2717, 0.2184, 0.8439], [0.2729, 0.2231, 0.8528], [0.274, 0.228, 0.8612], [0.2749, 0.233, 0.8692], [0.2758, 0.2382, 0.8767], [0.2766, 0.2435, 0.884], [0.2774, 0.2489, 0.8908], [0.2781, 0.2543, 0.8973], [0.2788, 0.2598, 0.9035], [0.2794, 0.2653, 0.9094], [0.2798, 0.2708, 0.915], [0.2802, 0.2764, 0.9204], [0.2806, 0.2819, 0.9255], [0.2809, 0.2875, 0.9305], [0.2811, 0.293, 0.9352], [0.2813, 0.2985, 0.9397], [0.2814, 0.304, 0.9441], [0.2814, 0.3095, 0.9483], [0.2813, 0.315, 0.9524], [0.2811, 0.3204, 0.9563], [0.2809, 0.3259, 0.96], [0.2807, 0.3313, 0.9636], [0.2803, 0.3367, 0.967], [0.2798, 0.3421, 0.9702], [0.2791, 0.3475, 0.9733], [0.2784, 0.3529, 0.9763], [0.2776, 0.3583, 0.9791], [0.2766, 0.3638, 0.9817], [0.2754, 0.3693, 0.984], [0.2741, 0.3748, 0.9862], [0.2726, 0.3804, 0.9881], [0.271, 0.386, 0.9898], [0.2691, 0.3916, 0.9912], [0.267, 0.3973, 0.9924], [0.2647, 0.403, 0.9935], [0.2621, 0.4088, 0.9946], [0.2591, 0.4145, 0.9955], [0.2556, 0.4203, 0.9965], [0.2517, 0.4261, 0.9974], [0.2473, 0.4319, 0.9983], [0.2424, 0.4378, 0.9991], [0.2369, 0.4437, 0.9996], [0.2311, 0.4497, 0.9995], [0.225, 0.4559, 0.9985], [0.2189, 0.462, 0.9968], [0.2128, 0.4682, 0.9948], [0.2066, 0.4743, 0.9926], [0.2006, 0.4803, 0.9906], [0.195, 0.4861, 0.9887], [0.1903, 0.4919, 0.9867], [0.1869, 0.4975, 0.9844], [0.1847, 0.503, 0.9819], [0.1831, 0.5084, 0.9793], [0.1818, 0.5138, 0.9766], [0.1806, 0.5191, 0.9738], [0.1795, 0.5244, 0.9709], [0.1785, 0.5296, 0.9677], [0.1778, 0.5349, 0.9641], [0.1773, 0.5401, 0.9602], [0.1768, 0.5452, 0.956], [0.1764, 0.5504, 0.9516], [0.1755, 0.5554, 0.9473], [0.174, 0.5605, 0.9432], [0.1716, 0.5655, 0.9393], [0.1686, 0.5705, 0.9357], [0.1649, 0.5755, 0.9323], [0.161, 0.5805, 0.9289], [0.1573, 0.5854, 0.9254], [0.154, 0.5902, 0.9218], [0.1513, 0.595, 0.9182], [0.1492, 0.5997, 0.9147], [0.1475, 0.6043, 0.9113], [0.1461, 0.6089, 0.908], [0.1446, 0.6135, 0.905], [0.1429, 0.618, 0.9022], [0.1408, 0.6226, 0.8998], [0.1383, 0.6272, 0.8975], [0.1354, 0.6317, 0.8953], [0.1321, 0.6363, 0.8932], [0.1288, 0.6408, 0.891], [0.1253, 0.6453, 0.8887], [0.1219, 0.6497, 0.8862], [0.1185, 0.6541, 0.8834], [0.1152, 0.6584, 0.8804], [0.1119, 0.6627, 0.877], [0.1085, 0.6669, 0.8734], [0.1048, 0.671, 0.8695], [0.1009, 0.675, 0.8653], [0.0964, 0.6789, 0.8609], [0.0914, 0.6828, 0.8562], [0.0855, 0.6865, 0.8513], [0.0789, 0.6902, 0.8462], [0.0713, 0.6938, 0.8409], [0.0628, 0.6972, 0.8355], [0.0535, 0.7006, 0.8299], [0.0433, 0.7039, 0.8242], [0.0328, 0.7071, 0.8183], [0.0234, 0.7103, 0.8124], [0.0155, 0.7133, 0.8064], [0.0091, 0.7163, 0.8003], [0.0046, 0.7192, 0.7941], [0.0019, 0.722, 0.7878], [0.0009, 0.7248, 0.7815], [0.0018, 0.7275, 0.7752], [0.0046, 0.7301, 0.7688], [0.0094, 0.7327, 0.7623], [0.0162, 0.7352, 0.7558], [0.0253, 0.7376, 0.7492], [0.0369, 0.74, 0.7426], [0.0504, 0.7423, 0.7359], [0.0638, 0.7446, 0.7292], [0.077, 0.7468, 0.7224], [0.0899, 0.7489, 0.7156], [0.1023, 0.751, 0.7088], [0.1141, 0.7531, 0.7019], [0.1252, 0.7552, 0.695], [0.1354, 0.7572, 0.6881], [0.1448, 0.7593, 0.6812], [0.1532, 0.7614, 0.6741], [0.1609, 0.7635, 0.6671], [0.1678, 0.7656, 0.6599], [0.1741, 0.7678, 0.6527], [0.1799, 0.7699, 0.6454], [0.1853, 0.7721, 0.6379], [0.1905, 0.7743, 0.6303], [0.1954, 0.7765, 0.6225], [0.2003, 0.7787, 0.6146], [0.2061, 0.7808, 0.6065], [0.2118, 0.7828, 0.5983], [0.2178, 0.7849, 0.5899], [0.2244, 0.7869, 0.5813], [0.2318, 0.7887, 0.5725], [0.2401, 0.7905, 0.5636], [0.2491, 0.7922, 0.5546], [0.2589, 0.7937, 0.5454], [0.2695, 0.7951, 0.536], [0.2809, 0.7964, 0.5266], [0.2929, 0.7975, 0.517], [0.3052, 0.7985, 0.5074], [0.3176, 0.7994, 0.4975], [0.3301, 0.8002, 0.4876], [0.3424, 0.8009, 0.4774], [0.3548, 0.8016, 0.4669], [0.3671, 0.8021, 0.4563], [0.3795, 0.8026, 0.4454], [0.3921, 0.8029, 0.4344], [0.405, 0.8031, 0.4233], [0.4184, 0.803, 0.4122], [0.4322, 0.8028, 0.4013], [0.4463, 0.8024, 0.3904], [0.4608, 0.8018, 0.3797], [0.4753, 0.8011, 0.3691], [0.4899, 0.8002, 0.3586], [0.5044, 0.7993, 0.348], [0.5187, 0.7982, 0.3374], [0.5329, 0.797, 0.3267], [0.547, 0.7957, 0.3159], [0.5609, 0.7943, 0.305], [0.5748, 0.7929, 0.2941], [0.5886, 0.7913, 0.2833], [0.6024, 0.7896, 0.2726], [0.6161, 0.7878, 0.2622], [0.6297, 0.7859, 0.2521], [0.6433, 0.7839, 0.2423], [0.6567, 0.7818, 0.2329], [0.6701, 0.7796, 0.2239], [0.6833, 0.7773, 0.2155], [0.6963, 0.775, 0.2075], [0.7091, 0.7727, 0.1998], [0.7218, 0.7703, 0.1924], [0.7344, 0.7679, 0.1852], [0.7468, 0.7654, 0.1782], [0.759, 0.7629, 0.1717], [0.771, 0.7604, 0.1658], [0.7829, 0.7579, 0.1608], [0.7945, 0.7554, 0.157], [0.806, 0.7529, 0.1546], [0.8172, 0.7505, 0.1535], [0.8281, 0.7481, 0.1536], [0.8389, 0.7457, 0.1546], [0.8495, 0.7435, 0.1564], [0.86, 0.7413, 0.1587], [0.8703, 0.7392, 0.1615], [0.8804, 0.7372, 0.165], [0.8903, 0.7353, 0.1695], [0.9, 0.7336, 0.1749], [0.9093, 0.7321, 0.1815], [0.9184, 0.7308, 0.189], [0.9272, 0.7298, 0.1973], [0.9357, 0.729, 0.2061], [0.944, 0.7285, 0.2151], [0.9523, 0.7284, 0.2237], [0.9606, 0.7285, 0.2312], [0.9689, 0.7292, 0.2373], [0.977, 0.7304, 0.2418], [0.9842, 0.733, 0.2446], [0.99, 0.7365, 0.2429], [0.9946, 0.7407, 0.2394], [0.9966, 0.7458, 0.2351], [0.9971, 0.7513, 0.2309], [0.9972, 0.7569, 0.2267], [0.9971, 0.7626, 0.2224], [0.9969, 0.7683, 0.2181], [0.9966, 0.774, 0.2138], [0.9962, 0.7798, 0.2095], [0.9957, 0.7856, 0.2053], [0.9949, 0.7915, 0.2012], [0.9938, 0.7974, 0.1974], [0.9923, 0.8034, 0.1939], [0.9906, 0.8095, 0.1906], [0.9885, 0.8156, 0.1875], [0.9861, 0.8218, 0.1846], [0.9835, 0.828, 0.1817], [0.9807, 0.8342, 0.1787], [0.9778, 0.8404, 0.1757], [0.9748, 0.8467, 0.1726], [0.972, 0.8529, 0.1695], [0.9694, 0.8591, 0.1665], [0.9671, 0.8654, 0.1636], [0.9651, 0.8716, 0.1608], [0.9634, 0.8778, 0.1582], [0.9619, 0.884, 0.1557], [0.9608, 0.8902, 0.1532], [0.9601, 0.8963, 0.1507], [0.9596, 0.9023, 0.148], [0.9595, 0.9084, 0.145], [0.9597, 0.9143, 0.1418], [0.9601, 0.9203, 0.1382], [0.9608, 0.9262, 0.1344], [0.9618, 0.932, 0.1304], [0.9629, 0.9379, 0.1261], [0.9642, 0.9437, 0.1216], [0.9657, 0.9494, 0.1168], [0.9674, 0.9552, 0.1116], [0.9692, 0.9609, 0.1061], [0.9711, 0.9667, 0.1001], [0.973, 0.9724, 0.0938], [0.9749, 0.9782, 0.0872], [0.9769, 0.9839, 0.0805]]

parula_data2=parula_data[::-1]