File:Mean temperature 240ma 2.png
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Summary
[edit]
| Description |
English: Mean temperature 240ma, degrees Celsius |
| Date | |
| Source | Own work |
| Author | Merikanto |
This image is based data from exoplasim simulation and Scotese paleodem maps.
https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/
PaleoDEM Resource – Scotese and Wright (2018) 11 August, 2018 by Sabin Zahirovic
PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic
Scotese, Christopher R, & Wright, Nicky M. (2018). PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5460860
Scotese, Christopher R; Wright, Nicky M https://zenodo.org/record/5460860
Params from exoplasim
- triassic middle
a_eccentricity1=0.0167022
a_obliquity1=23.441
a_lonvernaleq1=102.7
a_pCO21=1400.0e-6
R code to deflate downscaled raster
-
- scale raster to new side
- R script
- 19.6.2022 v 0000.000
library(raster)
library(ncdf4)
library(rgdal)
library(png)
skale_raster<-function(infile1, outfile1)
{
rx1=1600
ry1=800
r1<-raster(infile1)
mtx1<-matrix(rnorm(rx1*ry1),nrow=ry1)
mtx1<-mtx1*0
ext1<-c(-180,180,-90,90)
rsabluna1<-raster(mtx1)
extent(rsabluna1)<-ext1
plot(rsabluna1)
rout1<-resample(r1, rsabluna1, method="bilinear")
extent(rout1)<-ext1
crs(rout1)<-"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
writeRaster(rout1, outfile1, overwrite=TRUE, format="CDF", varname="Band1", varunit="",
longname="Band1", xname="lon", yname="lat")
}
infile1="./origo/temp_dskaled1.nc"
outfile1="./origo/tempavg.nc"
skale_raster(infile1, outfile1)
infile1="./origo/pr_dskaled1.nc"
outfile1="./origo/pravg.nc"
skale_raster(infile1, outfile1)
Draw map with matplotlib
import netCDF4 as nc
import numpy as np
from scipy import interpolate
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
from matplotlib.pylab import *
import matplotlib.mlab as mlab
def ncread(fn1, var1):
ds2 = nc.Dataset(fn1)
varr1=ds2[var1]
return(varr1)
def savenetcdf_single_frommem(outfilename1, outvarname1, xoutvalue1,xoutlats1,xoutlons1):
nlat1=len(xoutlats1)
nlon1=len(xoutlons1)
#indata_set1=indata1
print(outfilename1)
ncout1 = nc.Dataset(outfilename1, 'w', format='NETCDF4')
outlat1 = ncout1.createDimension('lat', nlat1)
outlon1 = ncout1.createDimension('lon', nlon1)
outlats1 = ncout1.createVariable('lat', 'f4', ('lat',))
outlons1 = ncout1.createVariable('lon', 'f4', ('lon',))
outvalue1 = ncout1.createVariable(outvarname1, 'f4', ('lat', 'lon',))
outvalue1.units = 'Unknown'
outlats1[:] = xoutlats1
outlons1[:] = xoutlons1
outvalue1[:, :] =xoutvalue1[:]
ncout1.close()
return 0
def ncsave_T21(oname1, ovar1, ovals1):
newx=64
newy=32
X = np.arange(-180, 180, 360/newx)
Y = np.arange(-90, 90, 180/newy)
savenetcdf_single_frommem(oname1, ovar1,ovals1,Y, X)
infilename1="./origo/tempavg.nc"
varname1="Band1"
kaption1="Mean temperature °C, 240 Ma"
infilename2="./origo/dem.nc"
savename1="mean_temperature_240ma_2.png"
varr0=ncread(infilename1, varname1)
varr1=np.flipud(np.asarray(varr0))
mask1=np.flipud(np.asarray(ncread(infilename2, "z")))
- plt.imshow(mask1)
- plt.show()
- quit(-1)
- ncsave_T21("tas_min.nc", "tas", minitable1)
kmap2 = plt.cm.get_cmap('RdBu_r')
contourrange1=[-80,-70,-60,-50,-45,-40,-35,-30,-25,-20,-15,-10,-5,0,5,10,15,20,25,30,35,40,45,50,60,70,80]
contourrange2=[-80,0,80]
contourrange3=np.arange(-40,60,1)
- range3=np.arange(-50,50,0.1)
range3=np.arange(-20,35,0.1)
- dravar1=minitable1
dravar1=varr1
meanmean1=np.mean(dravar1)
print("Mean avg.", meanmean1)
plt.rcParams["figure.figsize"] = (18,8)
- plt.rcParams["figure.figsize"] = (8,4)
plt.title(kaption1, fontsize=24)
plt.xlabel("Longitude", fontsize=18)
plt.xticks(fontsize=18)
plt.ylabel("Latitude", fontsize=18)
plt.yticks(fontsize=18)
- plt.imshow( mask1, extent=(-180,180,-90,90) )
cs2 = plt.contourf( dravar1,extent=(-180,180,-90,90), levels=range3, cmap=plt.cm.hsv.reversed(), extend='both')
cs = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, cmap=plt.cm.binary, levels=contourrange1, alpha=0.65 )
cs0a = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, cmap=plt.cm.hsv.reversed(), linestyles=':', alpha=0.65, linewidths=0.4, levels=contourrange3)
plt.clabel(cs, fontsize=20, inline=1,fmt = '%3.0f')
- cs3 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[1],cmap=plt.cm.binary,alpha=0.8, extend='both')
cs3 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[0],color="Black", alpha=0.8, extend='both')
- cs4 = plt.contour( mask1,extent=(-180,180,-90,90), width=5, levels=[0,2000],color="Black", alpha=0.4, extend='both')
plt.savefig(savename1)
plt.show()
Licensing
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- 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.
File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 10:50, 19 June 2022 | | 1,800 × 800 (944 KB) | Merikanto (talk | contribs) | Colors |
| 10:27, 19 June 2022 |  | 1,800 × 800 (728 KB) | Merikanto (talk | contribs) | Uploaded own work with UploadWizard |
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