def test_get_point():
db = WOA(dbname='WOA13')
t = db['sea_water_temperature'].extract(var='t_mn', doy=90,
depth=0, lat=17.5, lon=-37.5)
#depth=0, lat=10, lon=330)
assert np.allclose(t['t_mn'], [24.306862])
def test_lon_cyclic():
db = WOA()
t1 = db['TEMP'].extract(var='t_mn',
doy=136.875,
depth=0,
lat=17.5,
lon=182.5)
t2 = db['TEMP'].extract(var='t_mn',
doy=136.875,
depth=0,
lat=17.5,
lon=-177.5)
assert np.allclose(t1['t_mn'], t2['t_mn'])
t1 = db['TEMP'].extract(var='t_mn',
doy=136.875,
depth=0,
lat=17.5,
lon=[-37.5, -32.5])
t2 = db['TEMP'].extract(var='t_mn',
doy=136.875,
depth=0,
lat=17.5,
lon=[322.5, 327.5])
assert np.allclose(t1['t_mn'], t2['t_mn'])
def notest_get_track():
db = WOA()
db['TEMP'].get_track(doy=[datetime.now()], depth=0, lat=[10], lon=[330])
db['TEMP'].get_track(doy=2 * [datetime.now()],
depth=0,
lat=[10, 12],
lon=[330, -35])
def test_profile_maskedDepth():
"""Test BUG#10
"""
db = WOA()
depth = ma.masked_array([10, 100])
db['sea_water_temperature'].extract(var='mean', doy=10,
depth=depth, lat=10, lon=330)
def test_coincident_gridpoint():
db = WOA()
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.641016])
t = db['sea_water_temperature'].extract(var='t_mn', doy=[136.875, 228.125],
depth=0, lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.641016, 26.392792])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=[0, 10], lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.641016, 24.636028])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=[17.5, 12.5], lon=-37.5)
assert np.allclose(t['t_mn'], [25.167528, 24.641016])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=17.5, lon=[-37.5, -32.5])
assert np.allclose(t['t_mn'], [24.641016, 23.979605])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=[0, 10], lat=[17.5, 12.5], lon=-37.5)
assert np.allclose(t['t_mn'],
[[25.167528, 24.641016], [25.190489, 24.636028]])
def test_coincident_gridpoint_13():
db = WOA(dbname='WOA13')
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.60449791])
t = db['sea_water_temperature'].extract(var='t_mn', doy=[136.875, 228.125],
depth=0, lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.60449791, 26.38446426])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=[0, 10], lat=17.5, lon=-37.5)
assert np.allclose(t['t_mn'], [24.60449791, 24.62145996])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=[17.5, 12.5], lon=-37.5)
assert np.allclose(t['t_mn'], [25.17827606, 24.60449791])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=17.5, lon=[-37.5, -32.5])
assert np.allclose(t['t_mn'], [24.60449791, 23.98172188])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=[0, 10], lat=[17.5, 12.5], lon=-37.5)
assert np.allclose(t['t_mn'],
[[25.17827606, 24.60449791], [25.25433731, 24.62145996]])
def test_get_surface():
db = WOA()
t = db['sea_water_temperature'].extract(
var='t_mn',
doy=10,
depth=[0, 10],
lat=[21, 24, 28, 32],
lon=[-117, -114, -112, -105, -99, -93])
def test_get_section():
db = WOA()
t = db['sea_water_temperature'].extract(
var='t_mn',
doy=10,
depth=[0, 10],
lat=28,
lon=[-117, -114, -112, -105, -99, -93])
def test_horizontalSurface_coincidentLatLon():
""" Creates an horizontal surface with coincident Lat/Lon
"""
db = WOA()
t = db['sea_water_temperature'].extract(var='mean',
doy=136.875,
depth=43,
lon=np.arange(-180, -170, 1),
lat=np.arange(-50, -40, 1))
def test_interpolate_partially_insuficient_data():
""" Special case with unsificient data for some points
At 4700m depth the limited available data isin a plane that does not
contain the desired output. It should not fail, but return non
masked valid values where it is possible.
"""
db = WOA()
t = db['sea_water_temperature'].extract(var='mean', doy=108, lat=4, lon=-38)
assert not t['mean'].mask.all()
def test_ncs_size():
""" Check if loaded the 4 seasonal climatology files
The default for WOA is to load the seasonal climatology composed
of four files, one for each season. This test checks if all four
files were loaded.
"""
db = WOA()
assert len(db['sea_water_temperature'].ncs) == 4
assert len(db['sea_water_salinity'].ncs) == 4
def notest_get_track():
db = WOA()
db['sea_water_temperature'].get_track(doy=[datetime.now()],
depth=0,
lat=[10],
lon=[330])
db['sea_water_temperature'].get_track(doy=2 * [datetime.now()],
depth=0,
lat=[10, 12],
lon=[330, -35])
def test_get_point_inland():
db = WOA()
t = db['sea_water_temperature'].extract(var='t_mn',
doy=90,
depth=0,
lat=-19.9,
lon=-43.9)
for v in t:
assert t[v].mask.all()
def test_no_data_available():
""" This is a position without valid data """
db = WOA()
out = db['TEMP'].extract(doy=155,
lat=48.1953,
lon=-69.5855,
depth=[2.0, 5.0, 6.0, 21.0, 44.0, 79.0, 5000])
assert sorted(out.keys()) == [u't_dd', u't_mn', u't_sd', u't_se']
for v in out:
ma.getmaskarray(out[v]).all()
def notest_get_profile():
db = WOA()
t = db['TEMP'].extract(var='mn', doy=10, depth=[0, 10], lat=10, lon=330)
assert np.allclose(t['mn'], [28.09378815, 28.09343529])
t = db['TEMP'].extract(doy=10, depth=[0, 10], lat=10, lon=330)
assert np.allclose(t['t_se'], [0.01893404, 0.0176903])
assert np.allclose(t['t_sd'], [0.5348658, 0.4927946])
assert np.allclose(t['t_mn'], [28.09378815, 28.09343529])
assert np.allclose(t['t_dd'], [798, 776])
def test_no_data_available():
""" This is a position without valid data """
db = WOA()
out = db['sea_water_temperature'].extract(
doy=155, lat=48.1953, lon=-69.5855,
depth=[2.0, 5.0, 6.0, 21.0, 44.0, 79.0, 5000])
varnames = [u't_dd', u't_mn', u't_sd', u't_se']
for v in varnames:
assert v in out.keys()
for v in varnames:
assert ma.getmaskarray(out[v]).all()
def test_track_on_land():
"""A track on land should return masked NaN values
"""
db = WOA()
t = db["sea_water_temperature"].track(
var="mean",
doy=136.875,
depth=0,
lon=[1.4357, 1.4376],
lat=[43.5938, 43.5980]
)
for v in t:
assert ma.getmaskarray(t[v]).all()
def test_get_profile():
db = WOA(dbname='WOA13')
t = db['sea_water_temperature'].extract(var='mean', doy=10,
depth=[0,10], lat=10, lon=330)
assert np.allclose(t['mean'], [ 26.07524300, 26.12986183])
t = db['sea_water_temperature'].extract(doy=10,
depth=[0,10], lat=10, lon=330)
assert np.allclose(t['t_se'], [ 0.02941939, 0.0287159 ])
assert np.allclose(t['t_sd'], [ 0.8398821, 0.8142529])
assert np.allclose(t['t_mn'], [ 26.07524300, 26.12986183])
assert np.allclose(t['t_dd'], [ 813, 806])
def test_extract_overlimit():
""" Thest a request over the limits of the database """
db = WOA(dbname='WOA13')
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=5502, lat=17.5, lon=-37.5)
assert ma.is_masked(t['t_mn'])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=[10, 5502], lat=17.5, lon=-37.5)
assert np.all(t['t_mn'].mask == [False, True])
assert ma.allclose(t['t_mn'],
ma.masked_array([24.62145996, 0], mask=[False, True]))
def test_only_one_coincident_gridpoint():
"""Makes difference if only lat, or only lon, is coincident
Because lat/lon is interpolated together as a 2D space, if only
one coordinate is coincident it should be instead a 1D interpolation.
"""
db = WOA(dbname='WOA13')
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=-14.03, lon=62.5)
assert np.allclose(t['t_mn'], [27.41223907])
t = db['sea_water_temperature'].extract(var='t_mn', doy=136.875,
depth=0, lat=-12.5, lon=62.03)
assert np.allclose(t['t_mn'], [27.76179314])
def test_track():
db = WOA(dbname='WOA18')
params = [
[{"doy": 34, "depth": 0, "lat": 10, "lon": 330},
[25.51016]],
[{"doy": 120, "depth": 0, "lat": 10, "lon": 330},
[25.795078]],
[{"doy": 34, "depth": 300, "lat": 10, "lon": 330},
[10.52824]],
[{"doy": 34, "depth": 0, "lat": 12, "lon": -25},
[24.412867]],
[{"doy": [34, 120], "depth": 0, "lat": 10, "lon": 330},
[25.51016, 25.795078]],
[{"doy": 34, "depth": [0, 300], "lat": 10, "lon": 330},
[25.51016, 10.52824]],
[{"doy": 34, "depth": 0, "lat": [10, 12], "lon": [330, -25]},
[25.51016, 24.412867]],
[{"doy": [34, 120], "depth": 0, "lat": [10, 12], "lon": [330, -25]},
[25.51016, 24.953312]],
]
for p in params:
t = db['sea_water_temperature'].track(var='t_mn', **p[0])
assert np.allclose(t['t_mn'], p[1])
def test_available_vars():
db = WOA()
for v in ['sea_water_temperature', 'sea_water_salinity']:
assert v in db.keys()
def test_available_vars():
db = WOA()
for v in ['TEMP', 'PSAL']:
assert v in db.keys()
def test_oceansites_nomenclature():
db = WOA()
assert db['sea_water_temperature'] == db['TEMP']
def test_available_vars():
db = WOA()
for v in ['sea_water_temperature', 'sea_water_salinity']:
assert v in db.keys()
def test_dev():
db = WOA()
t = db['sea_water_temperature'].extract(doy=228.125, lat=12.5, lon=-37.5)
def notest_get_point():
db = WOA()
t = db['TEMP'].extract(var='t_mn', doy=90, depth=0, lat=17.5, lon=-37.5)
#depth=0, lat=10, lon=330)
assert np.allclose(t['mn'], [24.60449791])
def test_dev():
db = WOA()
t = db['TEMP'].extract(doy=228.125, lat=12.5, lon=-37.5)
def test_available_vars():
db = WOA()
for v in ['TEMP', 'PSAL']:
assert v in db.keys()
def test_import():
# A shortcut
from oceansdb import WOA
db = WOA()
