File:Suomen vuoden sademaara 1.png
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Summary
editDescriptionSuomen vuoden sademaara 1.png |
Suomi: Suomen vuoden sademäärä millimetriä. |
Date | |
Source | Own work |
Author | Merikanto |
Source of data is WoldClim Historical climate data 2.1
https://www.worldclim.org/data/worldclim21.html
"This is WorldClim version 2.1 climate data for 1970-2000. This version was released in January 2020. "
Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.
R script to cut data
library(raster)
library(viridis)
inname1="wc2.1_30s_bio_12.nc"
outname1="out1.nc"
plotname1="out.png"
- 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, format="CDF", overwrite=TRUE)
Python 3 code to dram map
- netcdf plotting program
- lat, lon, zvar!
- -*- 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
- preprocess with GMT 5.4
- gmt grdcut tk7_gauss1.nc -Gwest_europe_tk7.nc -R-10/50/30/60
- netcdf infile name, variable names
- lon, lat, z vars
infilename='out1.nc'
zvarlabel='z'
latvarlabel='latitude'
lonvarlabel='longitude'
- labels
- legtitle="T July avg.°C (LGM, CCSM4)"
legtitle="Sademäärä millimetriä/v"
plottitle="Vuoden sademäärä mm"
- output
outimage="outi.svg"
outpdf="outi.pdf"
outpng="outi.png"
- cmap min, max
zeta=300
zetb=1000
deltazc=100
deltazd=50
- lona=-15
- lonb=40
- lata=30
- latb=70
lona=20
lonb=32
lata=58
latb=72
latdx=2
londy=2
dpi1 = 70
figux=1000
figuy=1600
figure(figsize=(10, 16), dpi=dpi1)
- cmap
- kolormap='jet'
kolormap='jet'
- kolormap='Spectral_r'
- kolormap='gist_rainbow_r'
kolormap='BrBG'
- kolormap='rainbow'
kolormap='viridis'
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]
kolormap = ListedColormap(parula_data2)
- -- open netcdf file
nc = open_ncfile(infilename)
var = nc.variables[zvarlabel][:,:]
var2=np.flipud(var)
lat = nc.variables[latvarlabel][:]
lon = nc.variables[lonvarlabel][:]
- -- create figure and axes instances
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)
map = Basemap(projection='cyl',llcrnrlat=lata,urcrnrlat= latb,\
resolution='h', llcrnrlon=lona,urcrnrlon=lonb)
map.drawcoastlines(color='black')
map.drawrivers(color='black')
map.drawcountries(linewidth=3.0, linestyle='dashed', color='red')
- map.drawborders(color='red')
- map.drawstates()
- map.drawcountries()
- kolorado=('black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black')
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)
- xx, yy = np.meshgrid(lon, lat)
- map.pcolormesh(xx, yy, var)
maplot1=map.imshow(var2, cmap=kolormap, vmin=zeta, vmax=zetb)
plt.rc('lines', linewidth=0.5, color='#000000')
cnplot = map.contour(x,y,var,dlevs,colors='#00003f', alpha=0.6)
- cnplot2 = map.contour(x,y,var,clevs,colors='#00003f', alpha=0.2)
cbar = map.colorbar(maplot1,location='bottom',pad="10%", label='T avg July oC')
cbar.set_label(legtitle, fontsize=28)
cbar.ax.tick_params(labelsize=28)
plt.clabel(cnplot, fmt='%.0f',fontsize=30, inline=1)
plt.title(plottitle, fontsize=40)
- plt.show()
- plt.savefig(outimage, bbox_inches='tight', dpi=dpi1)
- plt.savefig(outpdf, bbox_inches='tight', dpi=dpi1)
plt.savefig(outpng, bbox_inches='tight', dpi=dpi1)
Licensing
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- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
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