def topo_subset(llcrnrlon=-42, urcrnrlon=-35, llcrnrlat=-23,
urcrnrlat=-14, tfile='topo30.grd'):
"""
Get a subset from an etopo1, etopo2 or Smith and Sandwell topography file.
OBS: Modified from oceans.datasets.etopo_subset() function by Filipe Fernandes ([email protected]).
"""
topo = Dataset(tfile, 'r')
if 'smith_sandwell' in tfile.lower() or 'etopo1' in tfile.lower():
lons = topo.variables["lon"][:]
lats = topo.variables["lat"][:]
lons = lon360to180(lons)
elif 'etopo2' in tfile.lower():
lons = topo.variables["x"][:]
lats = topo.variables["y"][:]
else:
np.disp('Unknown topography file.')
return
res = get_indices(llcrnrlat, urcrnrlat, llcrnrlon, urcrnrlon, lons, lats)
lon, lat = np.meshgrid(lons[res[0]:res[1]], lats[res[2]:res[3]])
bathy = topo.variables["z"][int(res[2]):int(res[3]),
int(res[0]):int(res[1])]
return lon, lat, bathy
##----
if CALC_AICE:
iceconc_thresh = 0.15 # Ice concentration threshold.
fnames = [fnamen.replace('ocn', 'ice') for fnamen in fnames]
fnames = [fnamen.replace('.pop.h.', '.cice.h.') for fnamen in fnames]
AICE = np.array([])
nfirst = True
nmo = 0
for fnamen in fnames:
yeari = fnamen.split('/')[-1].split('.')[-2]
yeari2 = yeari[:-3]
print(yeari)
nci = Dataset(fnamen)
if nfirst:
tarea = nci['tarea'][:].data * 1e-6 # [km2]
lon = lon360to180(nci['TLON'][:].data)
lat = nci['TLAT'][:].data
tmask = nci['tmask'][:]
nfirst = False
Aice = nci.variables['aice'][
0, :
fcap, :] / 100. # Convert to fractional sea ice concentration (0-1).
# Calculate total ice area for valid ice cells.
# iarea=aice(aice>=dc & aice<=1.0 & aice~=0).*tarea(aice>=dc & aice<=1.0 & aice~=0).*1e-6;
fice = np.logical_and(Aice >= iceconc_thresh, Aice <= 1.0)
aice = np.sum(Aice[fice] * tarea[fice])
t.append(yeari)
AICE = np.append(AICE, aice)
t = np.array([Timestamp(str(ti) + '-15').to_pydatetime() for ti in t])
savez(fname_out_aice, icearea=AICE, lon=lont, lat=latt, tarea=tarea, t=t)
def wind_subset(times=(datetime(2009,12,28), datetime(2010,1,10)),
dt='daily', llcrnrlon=-45, urcrnrlon=-35, llcrnrlat=-25, urcrnrlat=-18):
"""
USAGE
-----
u,v = wind_subset(...)
Gets wind vectors from the NCDC/NOAA Blended Seawinds L4 product,
given a given lon, lat, time bounding box.
Input
-----
* times: A tuple with 2 datetime objects marking the
start and end of the desired subset.
If dt=='clim', this should be an integer indicating the
desired month, from 1 (January) through 12 (December).
This climatology is built from the 1995-2005 data.
* dt: Time resolution wanted. Choose `six-hourly`,
`daily` (default) or `monthly`.
* llcrnrlon, urcrnrlon: Longitude band wanted.
* llcrnrlat, urcrnrlat: Latitude band wanted.
Returns
-------
u,v: pandas Panels containing the data subsets.
Example
-------
TODO.
"""
head = 'http://www.ncdc.noaa.gov/thredds/dodsC/oceanwinds'
tail = {'six-hourly':'6hr','daily':'dly','monthly':'mon','clim':'clm-agg'}
url = head + tail[dt]
nc = Dataset(url)
lon = nc.variables.pop('lon')[:]
lon = lon360to180(lon) # Longitude is in the [0,360] range, BB is in the [-180,+180] range.
lat = nc.variables.pop('lat')[:]
time = nc.variables.pop('time')
time = num2date(time[:],time.units,calendar='standard')
# Subsetting the data.
maskx = np.logical_and(lon >= llcrnrlon, lon <= urcrnrlon)
masky = np.logical_and(lat >= llcrnrlat, lat <= urcrnrlat)
# Ignoring more precise time units than
# the resolution in the desired dataset.
nt = time.size
if dt=='six-hourly':
for i in xrange(nt):
time[i] = time[i].replace(minute=0,second=0)
elif dt=='daily':
for i in xrange(nt):
time[i] = time[i].replace(hour=0,minute=0,second=0)
elif dt=='monthly':
for i in xrange(nt):
time[i] = time[i].replace(day=1,hour=0,minute=0,second=0)
elif dt=='clim':
time[1] = time[1].replace(month=2) # Fixing February.
aux = []; [aux.append(Time.month) for Time in time]
time = np.copy(aux); del aux
else:
pass
# If the time wanted is a single month/day/hour,
# get the nearest time available in the dataset.
if dt=='clim':
maskt=time==times
else:
if np.size(times)<2:
idxt = np.abs(time-times).argmin()
maskt=time==time[idxt]
else:
maskt = np.logical_and(time >= times[0], time <= times[1])
np.disp('Downloading subset...')
lon, lat, time = lon[maskx], lat[masky], time[maskt]
u = nc.variables['u'][maskt,0,masky,maskx]
v = nc.variables['v'][maskt,0,masky,maskx]
np.disp('Downloaded.')
lat,lon,time,u,v = map(np.atleast_1d, [lat,lon,time,u,v])
# Sets the panels.
U = Panel(data=u, items=time, major_axis=lat, minor_axis=lon)
V = Panel(data=v, items=time, major_axis=lat, minor_axis=lon)
# Sorting major axis (longitude) to fix the crossing of the 0º meridian.
U = U.sort_index(axis=2)
V = V.sort_index(axis=2)
# Retrieves the u,v arrays to fix masked values.
u,v,lon,lat = map(np.atleast_1d, (U.values,V.values,U.minor_axis.values,U.major_axis.values))
fill_value = -9999.
u = np.ma.masked_equal(u, fill_value)
v = np.ma.masked_equal(v, fill_value)
# Resets the Panels with the sorted and masked u,v arrays.
U = Panel(data=u, items=time, major_axis=lat, minor_axis=lon)
V = Panel(data=v, items=time, major_axis=lat, minor_axis=lon)
return U,V
## Get model topography and grid. # head = '/lustre/atlas1/cli115/proj-shared/apaloczy/POP_full_simulation_from_monthly_outputs/' fname_grd = '/home/andre/sio/phd/papers/pathways_antarctica/mkfigs/data_reproduce_figs/SOsubset_avg_2005-2009.nc' # fname_grd = '/home/andre/sio/phd/papers/pathways_antarctica/mkfigs/data_reproduce_figs/POP_topog.nc' ncg = Dataset(fname_grd) lont = ncg.variables['TLONG'][:] latt = ncg.variables['TLAT'][:] lonu = ncg.variables['ULONG'][:] latu = ncg.variables['ULAT'][:] HTE = ncg.variables['HTE'][:] * cm2m HTN = ncg.variables['HTN'][:] * cm2m HUW = ncg.variables['HUW'][:] * cm2m HUS = ncg.variables['HUS'][:] * cm2m ht = ncg.variables['HT'][:] * cm2m lont = lon360to180(lont) lonu = lon360to180(lonu) nx = lonu.shape[1] ny = np.sum(latu[:, 0] <= LATCROP) fSO = latu <= LATCROP # Mask to get the Southern Ocean part of the grid. sh = (ny, nx) ht = ht[fSO].reshape(sh) latt = latt[fSO].reshape(sh) lont = lont[fSO].reshape(sh) latu = latu[fSO].reshape(sh) lonu = lonu[fSO].reshape(sh) HTE = HTE[fSO].reshape(sh) HTN = HTN[fSO].reshape(sh) HUW = HUW[fSO].reshape(sh) HUS = HUS[fSO].reshape(sh) lonurad, laturad = lonu.ravel() * deg2rad, latu.ravel() * deg2rad
fcap = 501
cm2m = 1e-2
W2TW = 1e-12
rho_sw = 1026.0 # [kg/m3].
cp0 = 39960000.0 # [erg/g/K].
cp0 = cp0 * 1e-7 * 1e3 # [J/kg/degC].
T2Q = rho_sw * cp0 * W2TW
Tf0 = -2.6431782684654603 # [degC] use the minimum freezing temperature found on the wanted isobath during the entire run.
CALC_PSIMEAN_ONLY = False
# Calculate subsegment averages of h(x), pseudo-cross isobath distance
# and the south and north row indices for each subsegment.
nc0 = Dataset(fnames[0])
ht = nc0.variables['HT'][:fcap, :] * cm2m # [m].
lattx = nc0.variables['TLAT'][:fcap, :]
lontx = lon360to180(nc0.variables['TLONG'][:fcap, :])
lontx0 = lontx[100, :]
hshallow, hdeep = 100, 2000
fhshs = dict()
fhdeeps = dict()
xdists = dict()
hx_yavgs = dict()
loslices = dict()
laslices = dict()
Lattxs = dict()
Lontxs = dict()
Lys = dict()
nlat_stretch = 25
for subseg in segbrys_psi.keys():
lol, lor = segbrys_psi[subseg]
flol, flor = near(lontx0, lol, return_index=True), near(lontx0,