def test_dyn_height_shallower_pref():
"""
Check that we can handle a p_ref that is shallower than the top of the
cast. To make the results from bin 1 on down independent of whether
bin 0 has been deleted, we need to use linear interpolation.
"""
p = cv.p_chck_cast
CT = cv.CT_chck_cast
SA = cv.SA_chck_cast
strf0 = gsw.geo_strf_dyn_height(SA, CT, p, p_ref=0, interp_method='linear')
strf1 = gsw.geo_strf_dyn_height(SA[1:], CT[1:], p[1:], p_ref=0,
interp_method='linear')
found = strf1 - strf1[0]
expected = strf0[1:] - strf0[1]
assert_almost_equal(found, expected)
def test_geostrophy():
lon = cv.long_chck_cast
lat = cv.lat_chck_cast
p = cv.p_chck_cast
CT = cv.CT_chck_cast
SA = cv.SA_chck_cast
strf = gsw.geo_strf_dyn_height(SA, CT, p)
geovel, midlon, midlat = gsw.geostrophic_velocity(strf, lon, lat)
assert_almost_equal(geovel, cv.geo_strf_velocity)
assert_almost_equal(midlon, cv.geo_strf_velocity_mid_long[0])
assert_almost_equal(midlat, cv.geo_strf_velocity_mid_lat[0])
def test_strf_no_good():
# revised geo_strf_dyn_height requires 2 valid points
shape = (5,)
SA = np.ma.masked_all(shape, dtype=float)
CT = np.ma.masked_all(shape, dtype=float)
p = np.array([0.0, 10.0, 20.0, 30.0, 40.0])
# No valid points.
strf = gsw.geo_strf_dyn_height(SA, CT, p, p_ref=0, axis=0)
expected = np.zeros(shape, float) + np.nan
assert_array_equal(strf.filled(np.nan), expected)
# 1 valid point: not enough.
SA[:1] = 35
CT[:1] = 5
strf = gsw.geo_strf_dyn_height(SA, CT, p, p_ref=0, axis=0)
expected = np.zeros(shape, float) + np.nan
assert_array_equal(strf.filled(np.nan), expected)
# 2 valid points: enough for the calculation to proceed.
SA[:2] = 35
CT[:2] = [5, 4]
strf = gsw.geo_strf_dyn_height(SA, CT, p, p_ref=0, axis=0)
assert strf.count() == 2
def geo_strf_isopycnal(SA,
CT,
p,
p_ref,
Neutral_Density,
p_Neutral_Density,
A="s2"):
p = np.abs(np.asarray(p))
try:
dyn_height = gsw.geo_strf_dyn_height(SA, CT, p, p_ref)
except:
print("something wrong")
print(SA, CT, p, p_ref)
return np.nan
p_Neutral_Density, idxs = np.unique(p_Neutral_Density, return_index=True)
Neutral_Density = Neutral_Density[idxs]
filt = np.where(np.isin(p, p_Neutral_Density))
nsdyn_height = dyn_height[filt]
nstemps = CT[filt]
nssals = SA[filt]
db2Pa = 1e4
sa_iref_cast, ct_iref_cast, p_iref_cast = interp_ref_cast(
Neutral_Density, A)
cp0 = 3991.86795711963
part1 = 0.5 * db2Pa * (p_Neutral_Density - p_iref_cast) * (
gsw.specvol(nssals, nstemps, p_Neutral_Density) -
gsw.specvol(sa_iref_cast, ct_iref_cast, p_Neutral_Density))
part2 = 0
part3 = (-0.225e-15) * (db2Pa * db2Pa) * (nstemps - ct_iref_cast) * (
p_Neutral_Density - p_iref_cast) * (p_Neutral_Density - p_iref_cast)
part4 = nsdyn_height - enthalpy_SSO_0(p_Neutral_Density)
part5 = gsw.enthalpy(sa_iref_cast, ct_iref_cast,
p_Neutral_Density) - cp0 * ct_iref_cast
return part1 + part2 + part3 + part4 + part5
def calc_geostrophic_velocity(var_w_sa_ct_p_lon_lat):
import numpy as np
import xarray as xr
import gsw
# need geostrophic stream function first
temp = var_w_sa_ct_p_lon_lat.sortby('z', ascending=False)
temp['geo_height'] = xr.full_like(
temp.SA, fill_value=np.nan) # make empty dataarray
temp.geo_height.values = gsw.geo_strf_dyn_height(temp.SA,
temp.CT,
temp.Pressure,
p_ref=10.1325,
axis=1)
temp['geo_velocity'] = xr.full_like(
temp.geo_height, fill_value=np.nan) # make empty dataarray
temp.geo_velocity[0:-1, :] = np.transpose(
gsw.geostrophic_velocity(temp.geo_height.T.values, temp.lon,
temp.lat)[0])
var_w_sa_ct_p_lon_lat = temp.sortby('z', ascending=True)
return var_w_sa_ct_p_lon_lat
# DYNAMIC HEIGHT and depth
nanarray = np.empty((ctd['P'].shape[0], len(transects[nts])))
nanarray[:] = np.nan
SA, CT, g, depth, pt, nprof = nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy(), nanarray.copy()
for i, profile in enumerate(transects[nts]):
nprof[:, i] = profile
SA[:, i] = SA_from_SP(ctd['S'][:, profile], P, ctd['lon'][0, profile], ctd['lat'][0, profile])
CT[:, i] = CT_from_t(SA[:, i], ctd['T'][:, profile], P)
g[:, i] = grav(ctd['lat'][0, profile], P)
depth[:, i] = abs(z_from_p(P, ctd['lat'][0, profile]))
pt[:, i] = pt_from_t(SA[:, i], ctd['T'][:, profile], P, p_ref)
sig0 = sigma0(SA, CT)
# for i
deltaD = geo_strf_dyn_height(SA, CT, P, p_ref=p_ref) / g
deltaD500 = np.tile(deltaD[i500], (deltaD.shape[0],1))
# g_p = grav(ctd['lat'][0, profile], P)
# SA = SA_from_SP(ctd['S'][:, profile], P, ctd['lon'][0, profile], ctd['lat'][0, profile])
# CT = CT_from_t(SA, ctd['T'][:, profile], P)
# pt = pt_from_t(SA, ctd['T'][:, profile], P, 0)
# depth = abs(z_from_p(P, ctd['lat'][0, profile]))
# deltaD = geo_strf_dyn_height(SA, CT, P)
# fig, ax = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(20, 8))
# ct0 = ax[0].contourf(deltaD500, depth, CT, cmap=cmocean.cm.thermal)
# fig.colorbar(ct0, ax=ax[0])
# ax[0].set_title('Conservative Temperature')
# ax[0].set_ylabel('Depth in m')
# ax[0].invert_yaxis()
# change one dimension variables to two dimensions
p, lon, lat = nc['p'][:], nc['lon'][:, 0][:, np.newaxis], nc[
'lat'][:, 0][:, np.newaxis]
# convert in-situ variables to gsw variables
p_ref = 1494 # shallowest profile (4) except (34) which goes until 1004
SA = SA_from_SP(nc['SP'][:], p, lon, lat)
CT = CT_from_t(SA, nc['t'][:], p)
pt = pt_from_t(SA, nc['t'][:], p, p_ref)
deltaD = np.ma.masked_invalid(
geo_strf_dyn_height(SA.data,
CT.data,
p,
p_ref=p_ref,
axis=1))
# transect stations
transects = {
1: list(reversed(range(2, 10))),
2: list(range(10, 18)),
3: list(reversed(range(18, 27))),
4: list(range(26, 34)),
5: list(reversed(range(36, 46))),
6: list(range(46, 57)),
7: list(reversed(range(56, 65))),
8: list(reversed(range(68, 76))),
9: list(range(76, 84)),
10: list(reversed(range(84, 91))),
lat_array = np.zeros(pres.shape)
lat_array[:] = np.NaN
lon_array = np.zeros(pres.shape)
lon_array[:] = np.NaN
for l in np.arange(lat_array.shape[0]):
lat_array[l, :] = lat[l]
lon_array[l, :] = lon[l]
SA = gsw.SA_from_SP(sal, pres, lon_array, lat_array)
CT = gsw.CT_from_t(SA, temp, pres)
oxy_eq = gsw.O2sol(SA, CT, pres, lon_array, lat_array)
# calculate depth
geo_strf = gsw.geo_strf_dyn_height(SA=SA.T,
CT=CT.T,
p=pres.T)
geo_strf = geo_strf.T
z = gsw.z_from_p(p=pres,
lat=lat_array,
geo_strf_dyn_height=geo_strf)
z = -1 * z
oxy_sat = doxy / oxy_eq * 100
# Calculate density
density = np.zeros(temp.shape)
# Calculate MLD
mld = np.zeros(sal.shape[0])
mld[:] = np.NaN
dict_stations['P'])
dict_stations[station]['g'] = grav(
dict_stations[station]['lat'], dict_stations['P'])
dict_stations[station]['depth'] = abs(
z_from_p(dict_stations['P'],
dict_stations[station]['lat']))
dict_stations[station]['pt'] = pt_from_t(
dict_stations[station]['SA'], dict_stations[station]['T'],
dict_stations['P'], p_ref)
dict_stations[station]['sigma0'] = sigma0(
dict_stations[station]['SA'], dict_stations[station]['CT'])
dict_stations[station]['spiciness0'] = spiciness0(
dict_stations[station]['SA'], dict_stations[station]['CT'])
dict_stations[station]['deltaD'] = geo_strf_dyn_height(
dict_stations[station]['SA'],
dict_stations[station]['CT'],
dict_stations['P'],
p_ref=p_ref)
# save dictionary to file
write_dict(dict_stations, path, filename,
protocol=2) # protocol 2 allows python2
print('data stored in file')
# 2) CALCULATE NEUTRAL DENSITY IN PYTHON2
if sys.version_info[0] == 2: # python2
from pygamman import gamman as nds
if os.path.exists(os.path.join(path, filename + '.pkl')):
dict_stations = read_dict(path, filename + '.pkl')
Matlab functions. Therefore we have to make our own test
values, using the current test cast inputs.
This is a minimal script for that purpose, to be run in
the tests directory in which it lives. It should be run
only if we change to a different calculation algorithm,
or we update the cast input and general check value file.
"""
import numpy as np
import gsw
from gsw._utilities import Bunch
cv = Bunch(np.load('gsw_cv_v3_0.npz'))
dyn_height = gsw.geo_strf_dyn_height(cv.SA_chck_cast,
cv.CT_chck_cast,
cv.p_chck_cast,
cv.pr)
np.save('geo_strf_dyn_height.npy', dyn_height)
lon = cv.long_chck_cast
lat = cv.lat_chck_cast
p = cv.p_chck_cast
CT = cv.CT_chck_cast
SA = cv.SA_chck_cast
strf = gsw.geo_strf_dyn_height(SA, CT, p)
geovel, midlon, midlat = gsw.geostrophic_velocity(strf, lon, lat)
np.save('geo_strf_velocity.npy', geovel)
# midlon, midlat are OK