def inertial_period(lat):
r"""
Calculate the inertial period as:
.. math::
Ti = \\frac{2\\pi}{f} = \\frac{T_{sd}}{2\\sin\\phi}
Parameters
----------
lat : array_like
latitude in decimal degress north [-90..+90]
Returns
-------
Ti : array_like
period in seconds
Examples
--------
>>> import seawater.extras.sw_extras as swe
>>> lat = 30
>>> swe.inertial_period(lat)/3600
23.934472399219292
Modifications: Filipe Fernandes, 2010
10-01-26. Filipe Fernandes, first version.
"""
# Convert input to numpy arrays
lat = np.asarray(lat)
Ti = 2 * np.pi / sw.cor(lat)
return Ti
filename = "/home/rsoutelino/phd/fm/BC-NBUC_FM.mat"
fm = FeatureModel(filename)
depth = np.arange(0, 1520, 20)
# getting the rho profile in the core of the jets at 19 S
lon0, lat0 = -37.411, -19.015 # as result from a ginput
line, col = near2d(fm.lon, fm.lat, lon0, lat0)
temp = fm.temp[:, line, col]
salt = fm.salt[:, line, col]
rho = sw.dens(salt, temp, depth)
N2 = brunt_vaissala(rho, depth)
H = depth.max()
f = sw.cor(lat0)
R = 100000 # abrolhos bank: ~100km
Bu = burger(N2, H, f, R)
#plt.figure()
#plt.plot(Bu, -depth)
#plt.grid()
#plt.title("Burger Number Profile")
#plt.show()
fBC = near(depth, 200)[0][0]
BuBC = Bu[:fBC].mean()
BuNBUC = Bu[fBC:].mean()
print "\n The Burger Number for BC is: %s \n" %str(BuBC)
print "\n The Burger Number for NBUC is: %s \n" %str(BuNBUC)
vd = np.copy(ud)
k = 0
for i in range(0,time.size,24):
ud[:,:,k] = u[:,:,i:i+24].mean(axis=2)
vd[:,:,k] = v[:,:,i:i+24].mean(axis=2)
if i == 0:
timei = datetime(time[i].year,time[i].month,time[i].day)
else:
timei = np.append(timei,datetime(time[i].year,time[i].month,time[i].day))
k = k+1
umy,umx,ut = sp.gradient(ud,dx,dy,dt)
vmy,vmx,vt = sp.gradient(vd,dx,dy,dt)
f = sw.cor(lat)
ix,jx,kx = ut.shape
f = np.repeat(f,kx).reshape(ix,jx,kx)
zeta = (vmx-umy)/np.abs(f)
zeta = np.ma.masked_array(zeta,np.abs(zeta)>10.)
#
# plotting
#
xgm1,ygm1 = m(data['lon'],data['lat'])
dec=2
for i in range(timei.size):
filename = mainpath + "phd/fm/BC-NBUC_FM.mat" fm = FeatureModel(filename) # BURGER NUMBER ################ # getting the rho profile in the core of the jets at 19 S lon0, lat0 = -37.411, -19.015 # as result from a ginput line, col = romslab.near2d(fm.lon, fm.lat, lon0, lat0) temp = fm.temp[:, line, col] salt = fm.salt[:, line, col] depth = fm.depth rho = sw.dens(salt, temp, depth) N2 = brunt_vaissala(rho, depth) H = depth.max() f = sw.cor(lat0) R = 100000 # abrolhos bank: ~100km (the most wide one) Bu = compute_burger(N2, H, f, R) fBC = romslab.near(depth, 300)[0][0] BuBC = Bu[:fBC].mean() BuNBUC = Bu[fBC:].mean() # ROSSBY NUMBER ################ fNBUC = romslab.near(depth, 400)[0][0] f0 = sw.cor(fm.lat.mean()) L = 150000 Ubc = np.sqrt(fm.u[0, ...].max()**2 + fm.v[0, ...].max()**2) Unbuc = np.sqrt(fm.u[fNBUC, ...].max()**2 + fm.v[fNBUC, ...].max()**2)
fm = FeatureModel(filename) # BURGER NUMBER ################ # getting the rho profile in the core of the jets at 19 S lon0, lat0 = -37.411, -19.015 # as result from a ginput line, col = romslab.near2d(fm.lon, fm.lat, lon0, lat0) temp = fm.temp[:, line, col] salt = fm.salt[:, line, col] depth = fm.depth rho = sw.dens(salt, temp, depth) N2 = brunt_vaissala(rho, depth) H = depth.max() f = sw.cor(lat0) R = 100000 # abrolhos bank: ~100km (the most wide one) Bu = compute_burger(N2, H, f, R) fBC = romslab.near(depth, 300)[0][0] BuBC = Bu[:fBC].mean() BuNBUC = Bu[fBC:].mean() # ROSSBY NUMBER ################ fNBUC = romslab.near(depth, 400)[0][0] f0 = sw.cor(fm.lat.mean()) L = 150000 Ubc = np.sqrt(fm.u[0,...].max()**2 + fm.v[0,...].max()**2) Unbuc = np.sqrt(fm.u[fNBUC,...].max()**2 + fm.v[fNBUC,...].max()**2)
test_print("eta_SA_SA") #FIXME: diffs are not found in the original
test_print("eta_SA_CT") #FIXME: diffs are not found in the original
test_print("eta_CT_CT") #FIXME: diffs are not found in the original
#pt_SA, pt_CT = gsw.pt_first_derivatives(SA_chck_cast, CT_chck_cast)
#test_print("pt_SA")
#test_print("pt_CT")
#pt_SA_SA, pt_SA_CT, pt_CT_CT = gsw.pt_second_derivatives(SA_chck_cast, CT_chck_cast)
#test_print("pt_SA_SA")
#test_print("pt_SA_CT")
#test_print("pt_CT_CT")
""" planet earth properties """
from seawater.csiro import cor
f = cor(gsw_cv.lat_chck_cast)
test_print("f")
grav = gsw.grav(gsw_cv.lat_chck_cast, gsw_cv.p_chck_cast)
test_print("grav")
distance = gsw.distance(gsw_cv.long_chck_cast, gsw_cv.lat_chck_cast, gsw_cv.p_chck_cast)
test_print("distance") #FIXME: diffs are not found in the original
""" Absolute Salinity from direct density measurements:- a laboratory function """
SA_from_rho = gsw.SA_from_rho(rho, gsw_cv.t_chck_cast, gsw_cv.p_chck_cast)
test_print("SA_from_rho")
#sigma0_pt = gsw.sigma0_pt(SA_chck_cast, pt0)
#test_print("sigma0_pt")
dist,ang = sw.dist(loni[:,300],lati[:,300])
dy = dist.mean() # [km], about 1 km
files = sorted(glob.glob(data_path), key=os.path.getmtime)
Eetaw = np.zeros((lats.size,lons.size,np.array(files).size))
Eetawf = np.zeros((lats.size,lons.size,np.array(files).size))
Eugw = np.zeros((lats.size,lons.size,np.array(files).size))
Eugwf = np.zeros((lats.size,lons.size,np.array(files).size))
Evgw = np.zeros((lats.size,lons.size,np.array(files).size))
Evgwf = np.zeros((lats.size,lons.size,np.array(files).size))
# constant for geostrophic vel.
f = np.repeat(sw.cor(lats),lons.size).reshape(lats.size,lons.size)
C = 9.81/f
kk = 0
for file in sorted(files):
data = np.load(file)
print kk
ix,jx,kx = data['eta'].shape
etai = np.zeros((lats.size,lons.size,kx))
ugi = np.zeros((lats.size,lons.size,kx))
vgi = np.zeros((lats.size,lons.size,kx))
# mask bad data
ETA = data['eta']