File:Fast rotating ocean planet like earth annual temperature 1 1 1 1.png

1. 1. python3 exoplasim netcf data visualize

1. 1. 30.10.2023 v 0000.0000

import matplotlib.mlab as mlab import matplotlib.pyplot as plt import netCDF4 from netCDF4 import Dataset from matplotlib.pylab import * import numpy as np from scipy import interpolate from scipy.interpolate import griddata

def savenetcdf_single_frommem(outfilename1, outvarname1, xoutvalue1,xoutlats1,xoutlons1): nlat1=len(xoutlats1) nlon1=len(xoutlons1) #indata_set1=indata1 print(outfilename1) ncout1 = netCDF4.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 loadnetcdf_single_tomem(infilename1, invarname1): global cache_lons1 global cache_lats1 print(infilename1) inc1 = netCDF4.Dataset(infilename1) inlatname1="lat" inlonname1="lon" inlats1=inc1[inlatname1][:] inlons1=inc1[inlonname1][:] cache_lons1=inlons1 cache_lats1=inlats1 indata1_set1 = inc1[invarname1][:] dim1=indata1_set1.shape nlat1=dim1[0] nlon1=dim1[1] inc1.close() return (indata1_set1)

num1=1 kk1=1

vari1="tas"

fintemp1 = "./earth_fastrot_ocean.nc"

1. fintemp1 = "./planet_run/MOST.0000"+str(num1)+".nc"
2. fintemp1="fields_biogem_2d.nc"

print(num1, kk1)

print(fintemp1)

ncinu1 = Dataset(fintemp1, "r+", format = "NETCDF4")

print(ncinu1)

1. quit(-1)

1. navalue=9.969209968386869e+36

lon0 = ncinu1.variables['lon'] lat0 = ncinu1.variables['lat']

kindex1=kk1-1

1. tsa00 = ncinu1.variables['tsa'][kindex1]-273.15

1. 1. tsa00 = ncinu1.variables[vari1][kindex1]

Kelvin=273.15

tsa00=np.mean(ncinu1.variables[vari1], axis=0)-Kelvin

1. mask00=ncinu1.variables['lsm']

1. print(mask00)

print(np.shape(tsa00))

1. quit(-1)

1. print(tsa00)

1. quit(-1)

1. print(type(lon1))

lon1=np.array(lon0) lat1=np.array(lat0)

1. print(lon1)

tsa=np.array(tsa00)

1. mask1=np.array(mask00)

1. print(mask1)

1. quit(-1)

avgtemp1=np.mean(tsa)

print("Tavg: ",avgtemp1)

1. quit(-1)

newx=36 newy=36

X = np.arange(-180, 180, 360/newx) Y = np.arange(-90, 90, 180/newy)

oname1="atm_temp.nc" ovar1="Band1"

1. savenetcdf_single_frommem(oname1, ovar1, tsa,Y, X)

kmap2 = plt.cm.get_cmap('RdBu_r') kmap1=plt.get_cmap('YlGnBu') mint1=round(np.min(tsa),1) maxt1=round(np.max(tsa),1)

title1="Fast rotating ocean planet like Earth" title2="Temperature deg C: min "+str(mint1)+" max "+str(maxt1)

plt.suptitle(title1, fontsize=22) plt.title(title2, fontsize=18)

plt.xlabel("Longitude", fontsize=18) plt.xticks(fontsize=18) plt.ylabel("Latitude", fontsize=18) plt.yticks(fontsize=18)

1. contourrange1=[-50,-30,-20,-10,0,10,20,30,40,50]

contourrange1=[-200,-150,-120,-100,-80,-50,-45,-40,-35,-30,-25,-20,-15,-10,-5,0,5,10,15,20,25,30,35,40,45,50,100,200,400]

from skimage.transform import resize

tsa2 = resize(tsa, (tsa.shape[0] *3, tsa.shape[1]*2),anti_aliasing=True)

plt.imshow( tsa2, origin="upper",extent=(-180,180,-90,90), cmap=plt.cm.coolwarm, vmin=-50, vmax=50, interpolation="bicubic" )

cs = plt.contour(tsa2, origin="upper", alpha=0.5, extent=(-180,180,-90,90), inline=True, colors=['#3f0000'],levels=contourrange1)

plt.clabel(cs, fontsize=20, inline=1,fmt = '%3.0f', colors=['#3f0000'])

plt.grid(alpha=0.3, linestyle="--")

plt.show()