def invertit(filein, fileout):
from isqg import isqg_data
from scipy.io import loadmat, savemat
d = isqg_data()
f = loadmat(filein,squeeze_me=True)
for key in f.keys():
if key[:2]!='__':
print "read ",key
setattr(d,key,f[key])
d.solve_sqg()
d.solve_psii()
outvar = ['us','vs','ui','vi','ut','vt','psis','psii','psit','ssh','ssd',
'rhos','rhoi','rho0','f0','z','zf','zc',
'lon','lat','vorticity']
dout = {}
for ov in outvar:
dout[ov] = getattr(d,ov)
savemat(fileout, dout)
def invertit(filein, fileout):
from isqg import isqg_data
from scipy.io import loadmat, savemat
d = isqg_data()
f = loadmat(filein, squeeze_me=True)
for key in f.keys():
if key[:2] != '__':
print "read ", key
setattr(d, key, f[key])
d.solve_sqg()
d.solve_psii()
outvar = [
'us', 'vs', 'ui', 'vi', 'ut', 'vt', 'psis', 'psii', 'psit', 'ssh',
'ssd', 'rhos', 'rhoi', 'rho0', 'f0', 'z', 'zf', 'zc', 'lon', 'lat',
'vorticity'
]
dout = {}
for ov in outvar:
dout[ov] = getattr(d, ov)
savemat(fileout, dout)
import numpy as np
import pylab as plt
import isqg
lon = np.linspace(0,10,50)
lat = np.linspace(30,40,50)
x,y = isqg.lonlat2xy(lon,lat)
radi = 1e5 # a gaussian eddy with 200km radius
gauss = lambda x,y,r: np.exp(- (
( x-x.mean() )**2 + ( y-y.mean() )**2
) /2./r**2 )
ssd = 0.1*gauss(x,y,radi)
z = np.linspace(0,-2000,30)
n2 = np.ones_like(z)* 1e-5
d = isqg.isqg_data(n2=n2,z=z,rhosorg=ssd-ssd.mean(),lon=lon,lat=lat,rho0=1035.)
ssda=ssd-ssd.mean()
ssdm = np.ones_like(ssd)*ssd.mean()
rhom = isqg.N2rho(ssdm, isqg.twopave(n2), np.diff(z))
d.bottomboundary='psi=0'
d.solve_sqg()
d.ssh = np.roll(1.2*d.psis[0,:,:]*d.f0/9.81,5,axis=1)
d.solve_psii()
ssda = ssda+ssdm
d.rhos = d.rhos+rhom
plt.figure()
plt.subplot(221)
plt.contourf(ssda,levels=np.arange(-0.06,0.11,0.02))
plt.colorbar()
if __name__ == '__main__':
import numpy as np
import pylab as plt
import isqg
lon = np.linspace(0, 10, 50)
lat = np.linspace(30, 40, 50)
x, y = isqg.lonlat2xy(lon, lat)
radi = 1e5 # a gaussian eddy with 200km radius
gauss = lambda x, y, r: np.exp(-((x - x.mean())**2 +
(y - y.mean())**2) / 2. / r**2)
ssd = 0.1 * gauss(x, y, radi)
z = np.linspace(0, -2000, 30)
n2 = np.ones_like(z) * 1e-5
d = isqg.isqg_data(n2=n2, z=z, lon=lon, lat=lat, rho0=1035.)
ssda = ssd - ssd.mean()
ssdm = np.ones_like(ssd) * ssd.mean()
rhom = isqg.N2rho(ssdm, isqg.twopave(n2), np.diff(z))
d.bottomboundary = 'psi=0'
d.solve_sqg()
d.ssh = np.roll(1.2 * d.psis[0, :, :] * d.f0 / 9.81, 5, axis=1)
d.solve_psii()
ssda = ssda + ssdm
d.rhos = d.rhos + rhom
plt.figure()
plt.subplot(221)
plt.contourf(ssda, levels=np.arange(-0.06, 0.11, 0.02))
plt.colorbar()
