def formatDf(file): # convert pressure to depth
lat, lon, dt = castMeta(file)
df = readCnv(file)
df['depth'] = sw.dpth(df.index,lat)
df = df.set_index(['cast'])
df['lat'] = lat
df['lon'] = lon
return df, lat, lon, dt, df.index.unique()[0]
def check_prof_depth(nprof, presargo, latargo, depthmin):
print('check if profile has a good depth : ')
#convert pressure to depth
depthargo = seawater.dpth(presargo, latargo)
#look for the last level
indzprof = np.max(np.where(np.isnan(depthargo) == False))
dmax = depthargo[indzprof]
print('profile max depth is ' + str(dmax) + ' m')
if dmax >= depthmin:
check = 0
else:
check = 1
return check
def get_MCOMS_Argo(filename, get_data=True, get_dives=None):
Argo_nc = Dataset(filename, 'r')
inds = np.arange(
Argo_nc.dimensions['N_PROF'].size) if get_dives is None else get_dives
inds = inds.astype('int')
tt = [
dt.datetime(1950, 1, 1) + dt.timedelta(days=t) -
dt.datetime(2018, 1, 1)
for t in Argo_nc.variables['JULD'][inds].filled(np.nan)
]
time = np.array([t.days + t.seconds / 86400 for t in tt])
# doesn't matter because no matches anyway
#bad_inds = time>END_DATE
#inds = inds[~bad_inds]
#time = time[~bad_inds]
lat = Argo_nc.variables['LATITUDE'][inds].filled(np.nan)
lon = Argo_nc.variables['LONGITUDE'][inds].filled(np.nan)
if get_data:
depth = sw.dpth(Argo_nc.variables['PRES_ADJUSTED'][inds].filled(
np.nan),
lat=lat[0])
temp = Argo_nc.variables['TEMP_ADJUSTED'][inds].filled(np.nan)
sal = Argo_nc.variables['PSAL_ADJUSTED'][inds].filled(np.nan)
VSF = Argo_nc.variables['BBP700_ADJUSTED'][inds].filled(
np.nan) / (1.142 * 2 * np.pi
) # was changed from dividing by 1.1, need to re-run
output = (
time,
lat,
lon,
inds,
)
if get_data:
output += (
temp,
sal,
depth,
VSF,
)
return output
def get_HS(files, get_data=True, get_dives=None):
SB_files = np.sort(glob.glob(files))
if get_dives is not None:
SB_files = SB_files[get_dives.astype('int')]
lat = ()
lon = ()
t = ()
sta = ()
if get_data:
T = ()
S = ()
z = ()
VSF = ()
VSF_sd = ()
for i in range(len(SB_files)):
SB = readSB(SB_files[i], no_warn=True)
lat += (get_startlat(SB), )
lon += (get_startlon(SB), )
t += (get_starttime(SB), )
sta += (i, )
if get_data:
T += (np.array(SB.data['wt']), )
S += (np.array(SB.data['sal']), )
p = np.array(SB.data['pressure'])
z += (sw.dpth(p, lat=50.5), )
# convert using chi factor for 142deg angle used here
VSF += (np.array(SB.data['bbp700']) / (2 * np.pi * 1.18), )
# convert using chi factor for 142deg angle used here
VSF_sd += (np.array(SB.data['bbp700_sd']) / (2 * np.pi * 1.18), )
output = (np.array(t, dtype='object'), np.array(lat, dtype='object'),
np.array(lon, dtype='object'), np.array(sta, dtype='object'))
if get_data:
output += (
np.array(T, dtype='object'),
np.array(S, dtype='object'),
np.array(z, dtype='object'),
np.array(VSF, dtype='object'),
np.array(VSF_sd, dtype='object'),
)
return output
def check_prof_depth(fileEN4, prof):
print('check if profile has a good depth : ')
#open the profile file and read the infos on pressure and latitude
tfileEN4 = diren4 + fileEN4
dsen4 = Dataset(tfileEN4, 'r')
presen4 = dsen4.variables['PRES_ADJUSTED'][prof]
laten4 = dsen4.variables['LATITUDE'][prof]
#convert pressure to depth
depthen4 = seawater.dpth(presen4, laten4)
#look for the last level
indzprof = np.max(np.where(np.isnan(depthen4) == False))
dmax = depthen4[indzprof - 1]
print('profile max depth is ' + str(dmax) + ' m')
if dmax >= depthmin:
check = 0
else:
check = 1
return check
def csv2table(dir): # produce ctd and key dataframes from QC'd ctd data tables
if os.path.isdir(dir) is True:
files=glob(dir+'*.csv')
else:
files = [dir]
dfCtd,dfCtdKey = [pd.DataFrame() for i in range(2)]
for file in files:
dfCur = pd.read_csv(file,skiprows=[1])
cast = []
for name in dfCur.profile_id.values:
cast.append(float(name.split('_')[0][-3:]))
dfCur['cast'] = cast
dfCur['depth'] = sw.dpth(dfCur.pressure,dfCur.latitude)
dfCur = dfCur.rename(columns={"T_28": "temp", "S_41": "sal","latitude":"lat","longitude":"lon"})
dfCtdCur = dfCur[["cast","temp","sal","depth","lat","lon"]]
dfCtdCur = dfCtdCur.set_index('cast')
dfCtd = dfCtd.append(dfCtdCur)
dfKeyCur = dfCur[["cast","lat","lon","time"]]
dfKeyCur.lon = dfKeyCur.lon-360
dfKeyCur.time = pd.to_datetime(dfKeyCur.time)
dfCtdKey = dfCtdKey.append(dfKeyCur.drop_duplicates())
return dfCtd, dfCtdKey
def adiabatic_level_sw(P,
S,
T,
lat,
bin_width=100.,
order=1,
ret_coefs=False,
cap=None):
"""Generate smooth buoyancy frequency profile by applying the adiabatic
levelling method of Bray and Fofonoff (1981). This function uses the older
theormodynamic toolbox, 'seawater'.
Parameters
----------
P : 1-D ndarray
Pressure [dbar]
S : 1-D ndarray
Practical salinity [-]
T : 1-D ndarray
Temperature [degrees C]
lat : float
Latitude [-90...+90]
bin_width : float, optional
Pressure bin width [dbar]
deg : int, optional
Degree of polynomial fit. (DEGREES HIGHER THAN 1 NOT PROPERLY TESTED)
ret_coefs : bool, optional
Flag to return additional argument pcoefs. False by default.
cap : optional
Flag to change proceedure at ends of array where bins may be partially
filled. None by default, meaning they are included. Can also specify
'left', 'right' or 'both' to cap method before partial bins.
Returns
-------
N2_ref : 1-D ndarray
Reference buoyancy frequency [s-2]
pcoefs : 2-D ndarray
Fitting coefficients, returned only when the flag ret_coefs is set
True.
"""
valid = np.isfinite(P) & np.isfinite(S) & np.isfinite(T)
valid = np.squeeze(np.argwhere(valid))
P_, S_, T_ = P[valid], S[valid], T[valid]
flip = False
if (np.diff(P_) < 0).all():
flip = True
P_ = np.flipud(P_)
S_ = np.flipud(S_)
T_ = np.flipud(T_)
elif (np.diff(P_) < 0).any():
raise ValueError('P must be monotonically increasing/decreasing.')
i1 = np.searchsorted(P_, P_ - bin_width / 2.)
i2 = np.searchsorted(P_, P_ + bin_width / 2.)
if cap is None:
Nd = P_.size
elif cap == 'both':
icapl = i2[0]
icapr = i1[-1]
elif cap == 'left':
icapl = i2[0]
icapr = i2[-1]
elif cap == 'right':
icapl = i1[0]
icapr = i1[-1]
else:
raise ValueError("The argument cap must be either None, 'both', 'left'"
" or 'right'")
if cap is not None:
i1 = i1[icapl:icapr]
i2 = i2[icapl:icapr]
valid = valid[icapl:icapr]
Nd = icapr - icapl
dimax = np.max(i2 - i1)
Pb = np.full((dimax, Nd), np.nan)
Sb = np.full((dimax, Nd), np.nan)
Tb = np.full((dimax, Nd), np.nan)
for i in range(Nd):
imax = i2[i] - i1[i]
Pb[:imax, i] = P_[i1[i]:i2[i]]
Sb[:imax, i] = S_[i1[i]:i2[i]]
Tb[:imax, i] = T_[i1[i]:i2[i]]
Pbar = np.nanmean(Pb, axis=0)
rho = sw.pden(Sb, Tb, Pb, Pbar)
sv = 1. / rho
rhobar = np.nanmean(rho, axis=0)
p = np.full((order + 1, Nd), np.nan)
for i in range(Nd):
imax = i2[i] - i1[i]
p[:, i] = np.polyfit(Pb[:imax, i], sv[:imax, i], order)
g = sw.g(lat, -sw.dpth(Pbar, lat))
# The factor 1e-4 is needed for conversion from dbar to Pa.
N2 = -1e-4 * rhobar**2 * g**2 * p[order - 1, :]
N2_ref = np.full_like(P, np.nan)
pcoef = np.full((order + 1, P.size), np.nan)
if flip:
N2_ref[valid] = np.flipud(N2)
pcoef[:, valid] = np.fliplr(p)
else:
N2_ref[valid] = N2
pcoef[:, valid] = p
if ret_coefs:
return N2_ref, pcoef
else:
return N2_ref
def main(uri: str, filename: str):
"""Import NAFC CTD
:param str uri: Database URI
:param str filename: NetCDF file, or directory of files
"""
data.observational.init_db(uri, echo=False)
data.observational.create_tables()
datatype_map = {}
if os.path.isdir(filename):
filenames = sorted(glob.glob(os.path.join(filename, "*.nc")))
else:
filenames = [filename]
for fname in filenames:
print(fname)
with xr.open_dataset(fname) as ds:
if len(datatype_map) == 0:
# Generate the DataTypes; only consider variables that have depth
for var in filter(
lambda x, dataset=ds: 'level' in dataset[x].coords,
[d for d in ds.data_vars]):
dt = DataType.query.get(ds[var].standard_name)
if dt is None:
dt = DataType(key=ds[var].standard_name,
name=ds[var].long_name,
unit=ds[var].units)
datatype_map[var] = dt
data.observational.db.session.add_all(datatype_map.values())
# Query or generate the platform
# The files I worked off of were not finalized -- in this case the
# trip id also included the cast number, so I strip off the last 3
# digits.
unique_id = f"nafc_ctd_{ds.trip_id[:-3]}"
p = Platform.query.filter(
Platform.unique_id == unique_id).one_or_none()
if p is None:
p = Platform(type=Platform.Type.mission, unique_id=unique_id)
p.attrs = {
'Institution': ds.institution,
'Trip ID': ds.trip_id[:-3],
'Ship Name': ds.shipname,
}
data.observational.db.session.add(p)
# Generate the station
s = Station(
latitude=ds.latitude.values[0],
longitude=ds.longitude.values[0],
time=pd.Timestamp(ds.time.values[0]),
)
p.stations.append(s)
data.observational.db.session.commit()
ds['level'] = seawater.dpth(ds.level.values, ds.latitude[0].values)
# Generate the samples
for var, dt in datatype_map.items():
da = ds[var].dropna('level')
samples = [
Sample(value=d.item(),
depth=d.level.item(),
datatype_key=dt.key,
station_id=s.id) for d in da
]
data.observational.db.session.bulk_save_objects(samples)
data.observational.db.session.commit()
depthmod2d = np.zeros([lx, ly])
for j in np.arange(ly):
for i in np.arange(lx):
depthmod2d[j, i] = depthmod[np.min(
np.where(maskmod[:, j, i].values < 1))].values
# Read the profile file
tfileargo = dirargo + file_prof
dsargo = xr.open_dataset(tfileargo)
latargo = dsargo['LATITUDE'][prof_prof]
lonargo = dsargo['LONGITUDE'][prof_prof]
dayargo = dsargo['JULD'][prof_prof]
tempargo = dsargo['TEMP_ADJUSTED'][prof_prof]
saltargo = dsargo['PSAL_ADJUSTED'][prof_prof]
presargo = dsargo['PRES_ADJUSTED'][prof_prof]
depthargo = seawater.dpth(presargo, latargo)
# Find the last level and reduce the profiles
indzprof = np.min(np.where(np.isnan(depthargo) == True))
dmax = depthargo[indzprof - 1]
obsred_dep = np.zeros(int(indzprof))
obsred_temp = np.zeros(int(indzprof))
obsred_salt = np.zeros(int(indzprof))
for z in np.arange(int(indzprof)):
obsred_dep[int(z)] = depthargo[int(z)]
obsred_temp[int(z)] = tempargo[int(z)]
obsred_salt[int(z)] = saltargo[int(z)]
# Remove the NaN in the profiles
indtempnan = np.where(np.isnan(obsred_temp) == True)
if len(indtempnan[0]) > 0:
def main(uri: str, filename: str):
"""Import Argo Profiles
:param str uri: Database URI
:param str filename: Argo NetCDF Filename, or directory of files
"""
data.observational.init_db(uri, echo=False)
data.observational.create_tables()
session = data.observational.db.session
if os.path.isdir(filename):
filenames = sorted(glob.glob(os.path.join(filename, "*.nc")))
else:
filenames = [filename]
for fname in filenames:
print(fname)
with xr.open_dataset(fname) as ds:
times = pd.to_datetime(ds.JULD.values)
for f in META_FIELDS:
META_FIELDS[f] = ds[f].values.astype(str)
for prof in ds.N_PROF.values:
plat_number = ds.PLATFORM_NUMBER.values.astype(str)[prof]
unique_id = f"argo_{plat_number}"
# Grab the platform from the db base on the unique id
platform = (session.query(Platform).filter(
Platform.unique_id == unique_id,
Platform.type == Platform.Type.argo,
).first())
if platform is None:
# ... or make a new platform
platform = Platform(type=Platform.Type.argo,
unique_id=unique_id)
attrs = {}
for f in META_FIELDS:
attrs[ds[f].long_name] = META_FIELDS[f][prof].strip()
platform.attrs = attrs
session.add(platform)
# Make a new Station
station = Station(
time=times[prof],
latitude=ds.LATITUDE.values[prof],
longitude=ds.LONGITUDE.values[prof],
)
platform.stations.append(station)
# We need to commit the station here so that it'll have an id
session.commit()
depth = seawater.dpth(ds.PRES[prof].dropna("N_LEVELS").values,
ds.LATITUDE.values[prof])
samples = []
for variable in VARIABLES:
# First check our local cache for the DataType object, if
# that comes up empty, check the db, and failing that,
# create a new one from the variable's attributes
if variable not in datatype_map:
dt = DataType.query.get(ds[variable].standard_name)
if dt is None:
dt = DataType(
key=ds[variable].standard_name,
name=ds[variable].long_name,
unit=ds[variable].units,
)
data.observational.db.session.add(dt)
# Commit the DataType right away. This might lead
# to a few extra commits on the first import, but
# reduces overall complexity in having to
# 'remember' if we added a new one later.
data.observational.db.session.commit()
datatype_map[variable] = dt
else:
dt = datatype_map[variable]
values = ds[variable][prof].dropna("N_LEVELS").values
# Using station_id and datatype_key here instead of the
# actual objects so that we can use bulk_save_objects--this
# is much faster, but it doesn't follow any relationships.
samples = [
Sample(
depth=pair[0],
datatype_key=dt.key,
value=pair[1],
station_id=station.id,
) for pair in zip(depth, values)
]
data.observational.db.session.bulk_save_objects(samples)
session.commit()
def main(uri: str, filename: str):
"""Import Glider NetCDF
:param str uri: Database URI
:param str filename: Glider Filename, or directory of NetCDF files
"""
data.observational.init_db(uri, echo=False)
data.observational.create_tables()
if os.path.isdir(filename):
filenames = sorted(glob.glob(os.path.join(filename, "*.nc")))
else:
filenames = [filename]
datatype_map = {}
for fname in filenames:
print(fname)
with xr.open_dataset(fname) as ds:
variables = [v for v in VARIABLES if v in ds.variables]
df = ds[['TIME', 'LATITUDE', 'LONGITUDE', 'PRES',
*variables]].to_dataframe().reset_index().dropna()
df['DEPTH'] = seawater.dpth(df.PRES, df.LATITUDE)
for variable in variables:
if variable not in datatype_map:
dt = DataType.query.get(ds[variable].standard_name)
if dt is None:
dt = DataType(key=ds[variable].standard_name,
name=ds[variable].long_name,
unit=ds[variable].units)
data.observational.db.session.add(dt)
datatype_map[variable] = dt
data.observational.db.session.commit()
p = Platform(type=Platform.Type.glider,
unique_id=f"glider_{ds.deployment_label}")
attrs = {
'Glider Platform': ds.platform_code,
'WMO': ds.wmo_platform_code,
'Deployment': ds.deployment_label,
'Institution': ds.institution,
'Contact': ds.contact,
}
p.attrs = attrs
data.observational.db.session.add(p)
data.observational.db.session.commit()
stations = [
Station(
platform_id=p.id,
time=row.TIME,
latitude=row.LATITUDE,
longitude=row.LONGITUDE,
) for idx, row in df.iterrows()
]
# Using return_defaults=True here so that the stations will get
# updated with id's. It's slower, but it means that we can just
# put all the station ids into a pandas series to use when
# constructing the samples.
data.observational.db.session.bulk_save_objects(
stations, return_defaults=True)
df["STATION_ID"] = [s.id for s in stations]
samples = [[
Sample(station_id=row.STATION_ID,
depth=row.DEPTH,
value=row[variable],
datatype_key=datatype_map[variable].key)
for variable in variables
] for idx, row in df.iterrows()]
data.observational.db.session.bulk_save_objects(
[item for sublist in samples for item in sublist])
data.observational.db.session.commit()
data.observational.db.session.commit()
def arrays_to_netcdf(ncfile, lon, lat, t, p, T, S):
"""
Write the arrays into a single netCDF file `ncfile`
with a structure similar to SOCIB files
Inputs:
lon, lat, time, pressure, T and S are numpy ndarrays
(arrays of arrays), one array per profile
"""
with netCDF4.Dataset(ncfile, "w", format="NETCDF4") as nc:
ndepth = len(p)
# Dimensions
time = nc.createDimension("time", None) # unlimited
depth = nc.createDimension("depth", ndepth)
# Variables and attributes
time = nc.createVariable("time", "f8",("time",), fill_value=np.nan)
time.standard_name = "time"
time.units = "days since 01-01-01 00:00:00"
time.axis = "T"
time.calendar = "gregorian"
DEPTH = nc.createVariable("DEPTH", "f8",("time", "depth"))
DEPTH.ancillary_variables = "QC_DEPTH"
DEPTH.axis = "Z"
DEPTH.long_name = "Depth coordinate"
DEPTH.positive = "down"
DEPTH.reference_datum = "geographical coordinates, WGS84 projection"
DEPTH.standard_name = "depth"
DEPTH.units = "m"
LON = nc.createVariable("LON", "f4",("time",))
LON.standard_name = "longitude"
LON.long_name = "Longitude"
LON.units = "degrees_east"
LON.ancillary_variables = "QC_LON"
LON.axis = "X"
LON.valid_min = -180.
LON.valid_max = 180.
LON.reference_datum = "geographical coordinates, WGS84 projection" ;
LAT = nc.createVariable("LAT", "f4",("time",))
LAT.standard_name = "latitude"
LAT.long_name = "Latitude"
LAT.units = "degrees_north"
LAT.ancillary_variables = "QC_LAT"
LAT.axis = "Y"
LAT.valid_min = -90.
LAT.valid_max = 90.
LAT.reference_datum = "geographical coordinates, WGS84 projection"
WTR_PRE = nc.createVariable("WTR_PRE", "f8",("time", "depth"))
WTR_PRE.ancillary_variables = "QC_WTR_PRE"
WTR_PRE.coordinates = "time LAT LON DEPTH"
WTR_PRE.long_name = "Sea water pressure"
WTR_PRE.observation_type = "measured"
WTR_PRE.original_units = "dbar"
WTR_PRE.precision = "0.1"
WTR_PRE.resolution = "0.1"
WTR_PRE.standard_name = "sea_water_pressure"
WTR_PRE.units = "dbar"
WTR_TEM = nc.createVariable("WTR_TEM", "f8",("time", "depth"))
WTR_TEM.ancillary_variables = "QC_WTR_TEM"
WTR_TEM.coordinates = "time LAT LON DEPTH"
WTR_TEM.long_name = "Sea water tempature"
WTR_TEM.observation_type = "measured"
WTR_TEM.original_units = "C"
WTR_TEM.precision = "0.001"
WTR_TEM.resolution = "0.001"
WTR_TEM.standard_name = "sea_water_temperature"
WTR_TEM.units = "C"
SALT = nc.createVariable("SALT", "f8",("time", "depth"))
SALT.ancillary_variables = "QC_SALT"
SALT.coordinates = "time LAT LON DEPTH"
SALT.long_name = "Sea water salinity"
SALT.observation_type = "derived"
SALT.original_units = "psu"
SALT.precision = "0.001"
SALT.resolution = "0.001"
SALT.standard_name = "sea_water_salinity"
SALT.units = "psu"
# Add values to the variables
LON[:] = lon
LAT[:] = lat
# Remove 365 days because of reference year
time[:] = t - 365
for i, Pprofile in enumerate(p):
npoints = len(Pprofile)
if npoints > 0:
WTR_PRE[i,:npoints] = Pprofile
# Convert pressure to depth
depth = seawater.dpth(Pprofile, lat[i])
DEPTH[i,:npoints] = depth
for i, Tprofile in enumerate(T):
npoints = len(Tprofile)
WTR_TEM[i,:npoints] = Tprofile
for i, Sprofile in enumerate(S):
npoints = len(Sprofile)
SALT[i,:npoints] = Sprofile
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack(
(S[:, icol], T[:, icol], P[:, icol], UN_ptmp[:, icol], PT[:,
icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array(
[2749.54, 2288.61, 3170.58, 3147.85, 0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array(
[0.498088, 0.654990, 0.506244, 0.662975, 1.000073, 1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500], [5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array(
[[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922, -2.212],
[-0.650, -0.919, -1.188, -1.460, -1.735, -2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack(
[S[irow, :], P[irow, :], UN_fp[irow, :], FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack(
[S[:, icol], T[:, icol], P[:, icol], UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([
S[:, icol], T[:, icol], P[:, icol], UN_svel[:, icol], SVEL[:, icol]
])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" % tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" % tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" % tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1], [10, 10], [20, 20], [40, 40]]) / 1.00024
S = np.array([[20, 40], [20, 40], [20, 40], [20, 40]])
lit_O2 = np.array([[9.162, 7.984], [6.950, 6.121], [5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01], [12.64, 11.01], [10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877], [0.3397, 0.2989], [0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack((S[:, icol], T[:, icol], P[:, icol],
UN_ptmp[:, icol], PT[:, icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array([2749.54, 2288.61, 3170.58, 3147.85,
0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array([0.498088, 0.654990, 0.506244, 0.662975, 1.000073,
1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500],
[5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array([[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922,
-2.212], [-0.650, -0.919, -1.188, -1.460, -1.735,
-2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack([S[irow, :], P[irow, :], UN_fp[irow, :],
FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_svel[:, icol], SVEL[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" %
tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" %
tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" %
tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1],
[10, 10],
[20, 20],
[40, 40]]) / 1.00024
S = np.array([[20, 40],
[20, 40],
[20, 40],
[20, 40]])
lit_O2 = np.array([[9.162, 7.984],
[6.950, 6.121],
[5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01],
[12.64, 11.01],
[10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877],
[0.3397, 0.2989],
[0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
def convert_ios_dict(dat):
'''
convert_ios_dict
Converts an ios_ctd.ios_read dictionary object to a standardized
dictionary object.
So far the stand
'''
global STANDARD_KEYS, IOS_DAT_CONV_KEYS
new_dat = {}
# initialize the new object
for key in STANDARD_KEYS:
new_dat[key] = None
# code to convert ios data here
# longitude/latitude are floats, no need to convert
new_dat['Longitude'] = dat['Longitude']
new_dat['Latitude'] = dat['Latitude']
# Day/Month/Year determined by parsing Date string
date_str = dat['Date']
dt_obj = dateutil.parser.parse(date_str)
new_dat['Day'] = dt_obj.day
new_dat['Month'] = dt_obj.month
new_dat['Year'] = dt_obj.year
# ID is the complicated one, need to compare to available NOAA data
# -- this is necessary to determine whether two casts are repeated
# -- 17 character unique identifier
# FMT: YR MNTH DAY LONG LAT
# 4chr 2chr 2chr 3chr.2chr 2chr.2chr
# e.g: 20100915125314850
# - cast from Sept 15, 2010
# - lon: 125.31, lat 48.50
# NOTE: doesn't differentiate between N/S or E/W for lat/lon
new_dat['ID'] = '%04d%02d%02d%03.2f%02.2f' % (
new_dat['Year'], new_dat['Month'], new_dat['Day'],
np.abs(new_dat['Longitude']), np.abs(new_dat['Latitude']))
# remove the decimal point from lat and lon
new_dat['ID'] = new_dat['ID'].replace('.', '')
for count, var in enumerate(dat['Variables']):
# convert from pressure to depth
if 'Pressure' in var['Name']:
# need to use Seawater package to convert
# from dbar to depth
new_dat['Depth'] = []
for ii in dat['DATA'][count]:
new_dat['Depth'].append(SW.dpth(ii, new_dat['Latitude']))
continue
# otherwise, use the conversion keys to set data
for key, val in IOS_DAT_CONV_KEYS.iteritems():
# make sure we haven't already written this variable
# -- happens on occasion when Temperature:Primary and
# Temperature:Secondary are both available
if var['Name'] == key and val in new_dat:
if new_dat[val] is None or len(new_dat[val]) == 0:
new_dat[val] = dat['DATA'][count]
break
# archaic error handling for now
if 'Temperature' not in new_dat:
print('> ERROR :: TEMP ENTRY NOT FOUND')
return None
if 'Salinity' not in new_dat:
print('> ERROR :: SALN ENTRY NOT FOUND')
return None
if 'Depth' not in new_dat:
print('> ERROR :: PRESS ENTRY NOT FOUND')
return None
return new_dat
def get_BB9_RR(files, get_data=True, get_dives=None, wavelength=700):
SB_files = np.sort(glob.glob(files))
sta = np.arange(len(SB_files))
if get_dives is not None:
SB_files = SB_files[get_dives.astype('int')]
sta = sta[get_dives.astype('int')]
lat = ()
lon = ()
t = ()
if get_data:
T = ()
S = ()
z = ()
VSF = ()
VSF_sd = ()
for i in range(len(SB_files)):
SB = readSB(SB_files[i], no_warn=True)
if 'bbp650' not in SB.data.keys():
# sometimes this isn't recorded for some reason...do not use these
continue
lat += (get_startlat(SB), )
lon += (get_startlon(SB), )
t += (get_starttime(SB), )
if get_data:
T += (np.array(SB.data['wt']), )
S += (np.array(SB.data['sal']), )
p = np.array(SB.data['pressure'])
z += (sw.dpth(p, lat=50.5), )
if wavelength == 470:
wvs = [440, 488]
elif wavelength == 700:
wvs = [650, 715]
else:
print(
'Error: cannot have wavelength other than 470 or 700 right now'
)
VSF1 = np.array(SB.data['bbp%03d' % wvs[0]]) / (2 * np.pi * 1.076)
VSF1_sd = np.array(
SB.data['bbp%03d_sd' % wvs[0]]) / (2 * np.pi * 1.076)
VSF2 = np.array(SB.data['bbp%03d' % wvs[1]]) / (2 * np.pi * 1.076)
VSF2_sd = np.array(
SB.data['bbp%03d_sd' % wvs[1]]) / (2 * np.pi * 1.076)
VSF += (((wvs[1] - wavelength) * VSF1 +
(wavelength - wvs[0]) * VSF2) / (wvs[1] - wvs[0]), )
VSF_sd += (((wvs[1] - wavelength) * VSF1_sd +
(wavelength - wvs[0]) * VSF2_sd) / (wvs[1] - wvs[0]), )
output = np.array(t, dtype='object'), np.array(
lat, dtype='object'), np.array(lon, dtype='object'), sta
if get_data:
output += (
np.array(T, dtype='object'),
np.array(S, dtype='object'),
np.array(z, dtype='object'),
np.array(VSF, dtype='object'),
np.array(VSF_sd, dtype='object'),
)
return output
def get_FLBB_LF(filename,
get_data=True,
get_dives=None,
remove_seawater=True,
VSF_dark=0,
remove_deep_vals=True):
LF = readSB(filename, no_warn=True)
LF_time = get_time(LF)
LF_lon = np.array(LF.data['lon'])
LF_lat = np.array(LF.data['lat'])
LF_dep = np.array(sw.dpth(np.array(LF.data['pressure']), lat=50.5))
if get_data:
LF_temp = np.array(LF.data['wt'])
LF_sal = np.array(LF.data['sal'])
LF_VSF = np.array(LF.data['vsf700'])
if remove_seawater:
VSF_sw = betasw_wetlabs(700, 20, 140, 41, LF_temp, LF_sal,
np.array(LF.data['pressure']))
LF_VSF -= VSF_sw
begin_dive = np.where((np.diff(LF_time) > .01)
& (np.diff(LF_time) < .4))[0]
end_dive = np.array([
np.where((np.diff(LF_dep[i:i + 1000]) < -1)
& (LF_dep[i + 1:i + 1000] > 150))[0][0] + i
for i in begin_dive
])
if get_dives is None:
inds = np.arange(len(begin_dive))
else:
inds = get_dives
LF_lon_dive = np.array(LF_lon[begin_dive][inds])
LF_lat_dive = np.array(LF_lat[begin_dive][inds])
LF_time_dive = np.array(LF_time[begin_dive][inds])
if get_data:
LF_temp_dive = np.array(
[LF_temp[b:e] for b, e in zip(begin_dive[inds], end_dive[inds])],
dtype='object')
LF_sal_dive = np.array(
[LF_sal[b:e] for b, e in zip(begin_dive[inds], end_dive[inds])],
dtype='object')
LF_VSF_dive = np.array(
[LF_VSF[b:e] for b, e in zip(begin_dive[inds], end_dive[inds])],
dtype='object')
LF_dep_dive = np.array(
[LF_dep[b:e] for b, e in zip(begin_dive[inds], end_dive[inds])],
dtype='object')
if remove_deep_vals:
z_inds = [(d < 100) for d in LF_dep_dive]
LF_temp_dive = np.array(
[T[i] for T, i in zip(LF_temp_dive, z_inds)], dtype='object')
LF_sal_dive = np.array([S[i] for S, i in zip(LF_sal_dive, z_inds)],
dtype='object')
LF_VSF_dive = np.array([V[i] for V, i in zip(LF_VSF_dive, z_inds)],
dtype='object')
LF_dep_dive = np.array([z[i] for z, i in zip(LF_dep_dive, z_inds)],
dtype='object')
bad_inds = (LF_time_dive > END_DATE
) # beyond time of the main EXPORTS experiment
LF_time_dive = LF_time_dive[~bad_inds]
LF_lat_dive = LF_lat_dive[~bad_inds]
LF_lon_dive = LF_lon_dive[~bad_inds]
inds = inds[~bad_inds]
if get_data:
LF_temp_dive = LF_temp_dive[~bad_inds]
LF_sal_dive = LF_sal_dive[~bad_inds]
LF_dep_dive = LF_dep_dive[~bad_inds]
LF_VSF_dive = LF_VSF_dive[~bad_inds]
output = (
LF_time_dive,
LF_lat_dive,
LF_lon_dive,
inds,
)
if get_data:
output += (
LF_temp_dive,
LF_sal_dive,
LF_dep_dive,
LF_VSF_dive,
)
return output
def main(uri: str, filename: str):
"""Import Seal Profiles
:param str uri: Database URI
:param str filename: Seal NetCDF Filename, or directory of files
"""
data.observational.init_db(uri, echo=False)
data.observational.create_tables()
if os.path.isdir(filename):
filenames = sorted(glob.glob(os.path.join(filename, "*.nc")))
else:
filenames = [filename]
for fname in filenames:
print(fname)
# We're only loading Temperature and Salinity from these files, so
# we'll just make sure the DataTypes are in the db now.
if DataType.query.get("sea_water_temperature") is None:
dt = DataType(
key="sea_water_temperature",
name="Water Temperature",
unit="degree_Celsius",
)
data.observational.db.session.add(dt)
if DataType.query.get("sea_water_temperature") is None:
dt = DataType(key="sea_water_salinity",
name="Water Salinity",
unit="PSU")
data.observational.db.session.add(dt)
data.observational.db.session.commit()
with xr.open_dataset(fname) as ds:
ds["TIME"] = ds.JULD.to_index().to_datetimeindex()
ds["TIME"] = ds.TIME.swap_dims({"TIME": "N_PROF"})
depth = seawater.dpth(
ds.PRES_ADJUSTED,
np.tile(ds.LATITUDE, (ds.PRES.shape[1], 1)).transpose(),
)
ds["DEPTH"] = (["N_PROF", "N_LEVELS"], depth)
# This is a single platform, so we can construct it here.
p = Platform(type=Platform.Type.animal,
unique_id=ds.reference_file_name)
p.attrs = {
"Principle Investigator": ds.pi_name,
"Platform Code": ds.platform_code,
"Species": ds.species,
}
data.observational.db.session.add(p)
data.observational.db.session.commit()
# Generate Stations
df = ds[["LATITUDE", "LONGITUDE", "TIME"]].to_dataframe()
stations = [
Station(
platform_id=p.id,
latitude=row.LATITUDE,
longitude=row.LONGITUDE,
time=row.TIME,
) for idx, row in df.iterrows()
]
# Using return_defaults=True here so that the stations will get
# updated with id's. It's slower, but it means that we can just
# put all the station ids into a pandas series to use when
# constructing the samples.
data.observational.db.session.bulk_save_objects(
stations, return_defaults=True)
df["STATION_ID"] = [s.id for s in stations]
# Generate Samples
df_samp = (ds[["TEMP_ADJUSTED", "PSAL_ADJUSTED",
"DEPTH"]].to_dataframe().reorder_levels(
["N_PROF", "N_LEVELS"]))
samples = [[
Sample(
station_id=df.STATION_ID[idx[0]],
datatype_key="sea_water_temperature",
value=row.TEMP_ADJUSTED,
depth=row.DEPTH,
),
Sample(
station_id=df.STATION_ID[idx[0]],
datatype_key="sea_water_salinity",
value=row.PSAL_ADJUSTED,
depth=row.DEPTH,
),
] for idx, row in df_samp.iterrows()]
samples = [item for sublist in samples for item in sublist]
samples = [s for s in samples if not pd.isna(s.value)]
data.observational.db.session.bulk_save_objects(samples)
data.observational.db.session.commit()
d, ly, lx = maskmod.shape
depthmod2d = np.zeros([lx, ly])
for j in np.arange(ly):
for i in np.arange(lx):
depthmod2d[j, i] = depthmod[np.min(np.where(maskmod[:, j, i] < 1))]
# Read the profile file
tfileEN4 = diren4 + file_prof
dsen4 = Dataset(tfileEN4)
laten4 = dsen4.variables['LATITUDE'][prof_prof]
lonen4 = dsen4.variables['LONGITUDE'][prof_prof]
dayen4 = dsen4.variables['JULD'][prof_prof]
tempen4 = dsen4.variables['TEMP_ADJUSTED'][prof_prof]
salten4 = dsen4.variables['PSAL_ADJUSTED'][prof_prof]
presen4 = dsen4.variables['PRES_ADJUSTED'][prof_prof]
depthen4 = seawater.dpth(presen4, laten4)
# Find the last level and reduce the profiles
indzprof = np.max(np.where(np.isnan(depthen4) == False))
dmax = depthen4[indzprof - 1]
obsred_dep = np.zeros(int(indzprof))
obsred_temp = np.zeros(int(indzprof))
obsred_salt = np.zeros(int(indzprof))
for z in np.arange(int(indzprof)):
obsred_dep[int(z)] = depthen4[int(z)]
obsred_temp[int(z)] = tempen4[int(z)]
obsred_salt[int(z)] = salten4[int(z)]
# Remove the NaN in the profiles
indtempnan = np.where(np.isnan(obsred_temp) == True)
if len(indtempnan[0]) > 0:
def depth_combined(self, CTDPRS, DEPTH, LATITUDE):
#depth = DEPTH
depth_from_pres = -1 * sw.dpth(CTDPRS, LATITUDE)
#depth[np.isnan(depth)] = depth_from_pres[np.isnan(depth)]
return depth_from_pres
o2_mlL = float((element_list_1[-1]))
mol_volume_o2 = 44.66
sw_density = float(element_list_1[11])
o2_µmol = (o2_mlL * mol_volume_o2 / sw_density * 1000)
mn_o2.append(o2_µmol)
#mn_o2.append(o2_µmol + (-9.196871644229978) + (-0.007655474201452)*o2_µmol +\
#(0.011910402128384)*m + (-0.000021367427539)*(m*m) +\
#(-0.000326456036203)*(m*o2_µmol)) # o2 conversion ml to µmol
mn_net.append(net)
mn_index.append(net)
mn_haul.append(mn_filename)
volume = float(element_list_1[3])
mn_volume.append(volume)
mn_salinity.append(float(element_list_1[9]))
mn_temp.append(float(element_list_1[7]))
depth = sw.dpth(m, float(lat)) # calculating depth
mn_depth.append(depth)
line_counter += 1
#creating dataframe with mean value
mn_dataframe=pd.DataFrame({"haul": mn_haul, "net": mn_net,\
"volume": mn_volume,"pressure": mn_pressure, "depth":mn_depth, "o2": mn_o2,\
"salinity": mn_salinity, "temp": mn_temp})
mn_dataframe.loc[mn_dataframe['o2'] < 0,
'o2'] = 0 # get rid of negative value
# creating columns net opening, closing and delta value to calculate the integrated biomass and abundance
mn_data_mins = mn_dataframe.groupby([
"haul",
