Adiv Paradise
21d07f53a7
T21.nc (or equivalent if using a higher resolution--provide your own though), and randomly generates continents according to the user's desired number of landmasses and land fraction. It's much better at getting the land fraction right than the number of landmasses, since as they grow landmasses often merge. Also generates template topography (higher elevation at landmass center, no mountains), and writes SRA boundary condition files for land-sea mask and orography.
306 KiB
306 KiB
In [2]:
import netCDF4 as nc
import numpy as np
import matplotlib; import pylab as plt;
%matplotlib inlineIn [3]:
from mpl_toolkits.basemap import BasemapIn [4]:
dims = nc.Dataset("T21.nc","r")In [5]:
print "Code\tDimensions"
for i in dims.variables:
print i,'\t',dims.variables[i][:].shapeCode Dimensions lat (32,) lon (64,)
In [6]:
lts = dims.variables['lat'][:]
lns = dims.variables['lon'][:]
lons, lats = np.meshgrid(lns,lts)In [7]:
print lts[ 85.76058712 80.26877907 74.74454037 69.21297617 63.67863556 58.14295405 52.60652603 47.06964206 41.53246125 35.99507841 30.45755396 24.91992863 19.38223135 13.84448373 8.30670286 2.76890301 -2.76890301 -8.30670286 -13.84448373 -19.38223135 -24.91992863 -30.45755396 -35.99507841 -41.53246125 -47.06964206 -52.60652603 -58.14295405 -63.67863556 -69.21297617 -74.74454037 -80.26877907 -85.76058712]
In [8]:
print lns[ 0. 5.625 11.25 16.875 22.5 28.125 33.75 39.375 45. 50.625 56.25 61.875 67.5 73.125 78.75 84.375 90. 95.625 101.25 106.875 112.5 118.125 123.75 129.375 135. 140.625 146.25 151.875 157.5 163.125 168.75 174.375 180. 185.625 191.25 196.875 202.5 208.125 213.75 219.375 225. 230.625 236.25 241.875 247.5 253.125 258.75 264.375 270. 275.625 281.25 286.875 292.5 298.125 303.75 309.375 315. 320.625 326.25 331.875 337.5 343.125 348.75 354.375]
In [9]:
darea = np.zeros((len(lts),len(lns)))
NLAT = len(lts)
lts1 = np.zeros(NLAT)
lts2 = np.zeros(NLAT)
lts1[0] = 0.5*np.pi
lts1[NLAT-1] = 0.5*(lts[NLAT-2]+lts[NLAT-1])*np.pi/180.0
lts2[0] = 0.5*(lts[0]+lts[1])*np.pi/180.0
lts2[NLAT-1] = -0.5*np.pi
for jlat in range(1,NLAT-1):
lts1[jlat] = 0.5*(lts[jlat-1]+lts[jlat])*np.pi/180.0
lts2[jlat] = 0.5*(lts[jlat+1]+lts[jlat])*np.pi/180.0
NLON = len(lns)
for jlat in range(0,NLAT):
darea[jlat,:] = 0.5/NLON*(np.sin(lts1[jlat])-np.sin(lts2[jlat]))
globalarea = np.sum(darea)
print globalarea1.0
In [10]:
def wrap2d(datd,vals):
modf=np.zeros(datd.ndim,dtype=int)
modf[-1]=1
dd=np.zeros(datd.shape+modf)
dd[:,0:datd.shape[-1]]=datd
dd[:,datd.shape[-1]]=vals
return ddIn [11]:
latsw=wrap2d(lats,lats[:,0])
lonsw=wrap2d(lons,360.0)In [12]:
ncontinents = 7 #There's no guarantee you actually get this many if it's >1, since continents can mergeIn [13]:
landfraction = 0.29
In [14]:
name="Example"In [32]:
grid = np.zeros((len(lts),len(lns)))In [33]:
landpts=[]In [34]:
landarea = 0.0
for i in range(0,ncontinents):
filled = False
if landfraction<=i*np.amin(darea):
filled=True
while not filled:
lt = np.random.rand()*180.0-90.0
ln = np.random.rand()*360.0
alts = abs(lts-lt)
alns = abs(lns-ln)
ilt = np.where(alts == np.amin(alts))[0][0]
iln = np.where(alns == np.amin(alns))[0][0]
if (ilt,iln) not in landpts:
landpts.append((ilt,iln))
grid[ilt,iln] = 1.0
landarea+=darea[ilt,iln]
filled=TrueIn [35]:
print landarea,landpts
plt.close('all')
t=plt.pcolormesh(lons,lats,grid,shading='Gouraud',cmap='gist_earth',vmin=-0.3,vmax=2.5)
plt.xlabel('Degrees Longitude')
plt.ylabel('Degrees Latitude')
plt.ylim(np.amin(lts),np.amax(lts))
plt.xlim(np.amin(lns),np.amax(lns))
plt.title("Continent Seeds")
#plt.imshow(grid,cmap='gist_earth',vmin=-0.3,vmax=2.5)Out [35]:
0.00274372133357 [(16, 25), (0, 32), (31, 8), (9, 29), (0, 37), (6, 55), (17, 8)]
<matplotlib.text.Text at 0x7fbdfc4a4ed0>
In [36]:
done=False
maxiters = int(max(5000.0/(1-landfraction+1.0e-7),5.0e4))
directions = [(1,0),(0,1),(-1,0),(0,-1),(-1,1),
(-1,-1),(1,1),(1,-1)]
for i in range(0,len(directions)):
directions[i] = np.array(directions[i])
iters = 0
if len(landpts)<1:
done=True
history=[]
history.append(landarea)
nseeds=len(landpts)
if not done:
for k in range(0,75):
jn = k%(len(landpts)-1) #int(round(np.random.rand()*(nseeds-1)))
accepted = False
n=0
while not accepted and n<len(directions):
dn = int(round(np.random.rand()*(len(directions)-1)))
check=False
ct=0
while not check:
ckpt = list(np.array(landpts[jn])+directions[dn])
check=True
if ckpt[0]<0 or ckpt[0]>NLAT-1:
check=False
dn = int(round(np.random.rand()*(len(directions)-1)))
if ckpt[1]<0:
ckpt[1]=NLON-1
if ckpt[1]==NLON:
ckpt[1]=0
nupt = ckpt
if nupt not in landpts:
accepted = True
ilt = nupt[0]
iln = nupt[1]
if landarea+darea[ilt,iln]>landfraction*1.1:
accepted = False
else:
landarea+=darea[ilt,iln]
grid[ilt,iln] = 1.0
landpts.append(nupt)
history.append(landarea)
n+=1
partialgrid=np.zeros(grid.shape)
partialgrid[:] = grid[:]
t=plt.pcolormesh(lons,lats,partialgrid,shading='Gouraud',cmap='gist_earth',vmin=-0.3,vmax=2.5)
plt.xlabel('Degrees Longitude')
plt.ylabel('Degrees Latitude')
plt.ylim(np.amin(lts),np.amax(lts))
plt.xlim(np.amin(lns),np.amax(lns))
plt.title("Cratons")
if landarea>landfraction:
done=True
while not done:
jn = int(round(np.random.rand()*(len(landpts)-1)))
accepted = False
n=0
while not accepted and n<len(directions):
dn = int(round(np.random.rand()*(len(directions)-1)))
check=False
ct=0
while not check:
ckpt = list(np.array(landpts[jn])+directions[dn])
check=True
if ckpt[0]<0 or ckpt[0]>NLAT-1:
check=False
dn = int(round(np.random.rand()*(len(directions)-1)))
if ckpt[1]<0:
ckpt[1]=NLON-1
if ckpt[1]==NLON:
ckpt[1]=0
nupt = ckpt
if nupt not in landpts:
accepted = True
ilt = nupt[0]
iln = nupt[1]
if landarea+darea[ilt,iln]>landfraction*1.1:
accepted = False
else:
landarea+=darea[ilt,iln]
grid[ilt,iln] = 1.0
landpts.append(nupt)
history.append(landarea)
n+=1
iters+=1
if iters==maxiters or landarea>landfraction:
done=True
In [37]:
t=plt.pcolormesh(lons,lats,grid,shading='Gouraud',cmap='gist_earth',vmin=-0.3,vmax=2.5)
plt.xlabel('Degrees Longitude')
plt.ylabel('Degrees Latitude')
plt.ylim(np.amin(lts),np.amax(lts))
plt.xlim(np.amin(lns),np.amax(lns))
#plt.imshow(grid,cmap='gist_earth',vmin=-0.3,vmax=2.5)Out [37]:
(0.0, 354.375)
In [38]:
plt.plot(history)
plt.xlabel("Step Number")
plt.ylabel("Land Fraction")
plt.title("Algorithmic Continent Assembly")Out [38]:
<matplotlib.text.Text at 0x7fbdfc40e090>
In [39]:
print landarea0.290085095664
In [40]:
dataw=wrap2d(grid,grid[:,0])
m = Basemap(projection='moll',lon_0=0,resolution='c')
tm = m.pcolormesh(lonsw,latsw,dataw,shading='Gouraud',cmap='gist_earth',latlon=True,vmin=-0.3,vmax=2.5)
pr=m.drawparallels(np.arange(-90.,120.,30.),labels=[1,0,0,1])
mr=m.drawmeridians(np.arange(-180,180.,60.))
plt.title("Planet Grid")Out [40]:
<matplotlib.text.Text at 0x7fbdfddc4690>
In [41]:
#Topography:
topo = np.zeros(grid.shape)
topo = grid*1.0e4
maxiters = 10
dl1 = NLON/2+2
dl2 = NLON/2
dl3 = NLON/2-2
for i in range(0,maxiters):
nutop = np.zeros(grid.shape)
for jlat in range(0,NLAT):
j1 = jlat-1
j2 = jlat+1
flip1=False
flip2=False
if j1<0:
j1=0
flip1=True
if j2==NLAT:
j2=NLAT-1
flip2=True
for jlon in range(0,NLON):
l1 = jlon-1
l2 = jlon+1
if l1<0:
l1=NLON-1
if l2==NLON:
l2=0
c1 = topo[j1,(l1 +flip1*dl1)%NLON]
c2 = topo[j1,(jlon+flip1*dl2)%NLON]
c3 = topo[j1,(l2 +flip1*dl3)%NLON]
c4 = topo[jlat,l1]
c5 = topo[jlat,l2]
c6 = topo[j2,(l1 +flip2*dl1)%NLON]
c7 = topo[j2,(jlon+flip2*dl2)%NLON]
c8 = topo[j2,(l2 +flip2*dl3)%NLON]
nutop[jlat,jlon] = (2*topo[jlat,jlon]+c1+c2+c3+c4+c5+c6+c7+c8)*0.1
topo[:] = nutop[:]
topo*=gridIn [42]:
antigrid = -(grid-1)
topo -= 3000*antigrid
print np.amin(topo)-3000.0
In [43]:
dataw=wrap2d(topo,topo[:,0])
m = Basemap(projection='moll',lon_0=0,resolution='c')
tm = m.pcolormesh(lonsw,latsw,dataw/9.8,shading='Gouraud',cmap='gist_earth',latlon=True,vmin=np.amin(topo/9.8)*1.4,vmax=np.amax(topo/9.8)*1.25)#,vmin=-0.1*np.amax(topo/9.8),vmax=np.amax(topo/9.8)*1.25)
pr=m.drawparallels(np.arange(-90.,120.,30.),labels=[1,0,0,1])
mr=m.drawmeridians(np.arange(-180,180.,60.))
cb = m.colorbar(tm,"bottom", size="5%", pad="2%",label="Elevation [m]")
plt.title("Planet Orography")
plt.savefig("sampleplanet.jpg",bbox_inches='tight')
In [44]:
t=plt.pcolormesh(lons,lats,topo/9.8,shading='Gouraud',cmap='gist_earth',vmin=np.amin(topo/9.8)*1.4,vmax=np.amax(topo/9.8)*1.25)#vmin=-0.1*np.amax(topo/9.8),vmax=np.amax(topo/9.8)*1.25)
plt.xlabel('Degrees Longitude')
plt.ylabel('Degrees Latitude')
plt.ylim(np.amin(lts),np.amax(lts))
plt.xlim(np.amin(lns),np.amax(lns))
#plt.imshow(grid,cmap='gist_earth',vmin=-0.3,vmax=2.5)Out [44]:
(0.0, 354.375)
In [45]:
topo += 3000*antigridIn [46]:
def writeSRA(name,kcode,field,NLAT,NLON):
label=name+'_surf_%04d.sra'%kcode
header=[kcode,0,20170927,0,NLON,NLAT,0,0]
fmap = field.reshape((NLAT*NLON/8,8))
sheader = ''
for h in header:
sheader+=" %11d"%h
lines=[]
i=0
while i<NLAT*NLON/8:
l=''
for n in fmap[i,:]:
l+=' %9.3f'%n
lines.append(l)
i+=1
text=sheader+'\n'+'\n'.join(lines)+'\n'
f=open(label,'w')
f.write(text)
f.close()
print labelIn [47]:
writeSRA(name,129,topo,NLAT,NLON)
writeSRA(name,172,grid,NLAT,NLON)Example_surf_0129.sra Example_surf_0172.sra
In [ ]: