Faili:Mean temperature 240ma 2.png
Faili halisi (piseli 1,800 × 800, saizi ya faili: 944 KB, aina ya MIME: image/png)
Muhtasari
| Maelezo |
English: Mean temperature 240ma, degrees Celsius |
| Tarehe | |
| Chanzo | Kazi yangu |
| Mwandishi | 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()
Hatimiliki
- Uko huru:
- kushiriki – kunakili na kusambaza kazi hiyo
- kwa remix – kurekebisha kazi
- Chini ya masharti yafuatayo:
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Historia ya faili
Bonyeza tarehe/saa kuona faili kama ilivyoonekana wakati huo.
| Tarehe/Saa | Picha ndogo | Vipimo | Mtumiaji | Maelezo | |
|---|---|---|---|---|---|
| sasa hivi | 10:50, 19 Juni 2022 | | 1,800 × 800 (944 KB) | Merikanto | Colors |
| 10:27, 19 Juni 2022 |  | 1,800 × 800 (728 KB) | Merikanto | Uploaded own work with UploadWizard |
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