def get_ctd_profile():
"""
Load the ASCII CTD Data into an 'ambient.Profile' object.
This function performs the steps in ./profile_from_ctd.py to read in the
CTD data and create a Profile object. This is the data set that will be
used to demonstrate how to append data to a Profiile object.
"""
# Get the path to the input file
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__),
'../../tamoc/data'))
dat_file = os.path.join(__location__,'ctd_BM54.cnv')
# Load in the data using numpy.loadtxt
raw = np.loadtxt(dat_file, comments = '#', skiprows = 175,
usecols = (0, 1, 3, 8, 9, 10, 12))
# Describe the organization of the data in raw.
var_names = ['temperature', 'pressure', 'wetlab_fluorescence', 'z',
'salinity', 'density', 'oxygen']
var_units = ['deg C', 'db', 'mg/m^3', 'm', 'psu', 'kg/m^3', 'mg/l']
z_col = 3
# Clean the profile to remove reversals in the depth coordinate
data = ambient.extract_profile(raw, z_col, 50.0)
# Convert the profile data to standard units in TAMOC
profile, units = ambient.convert_units(data, var_units)
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__,'BM54.nc')
summary = 'Dataset created by profile_from_ctd in the ./bin directory' \
+ ' of TAMOC'
source = 'R/V Brooks McCall, station BM54'
sea_name = 'Gulf of Mexico'
p_lat = 28.0 + 43.945 / 60.0
p_lon = 360 - (88.0 + 22.607 / 60.0)
p_time = date2num(datetime(2010, 5, 30, 18, 22, 12),
units = 'seconds since 1970-01-01 00:00:00 0:00',
calendar = 'julian')
nc = ambient.create_nc_db(nc_file, summary, source, sea_name, p_lat,
p_lon, p_time)
# Insert the CTD data into the netCDF dataset
comments = ['measured'] * len(var_names)
nc = ambient.fill_nc_db(nc, profile, var_names, units, comments, z_col)
# Create an ambient.Profile object for this dataset
bm54 = ambient.Profile(nc, chem_names=['oxygen'])
# Return the Profile object
return bm54
def get_units(data, units, nr, nc, mks_units, ans=None):
"""
Run the ambient.convert_units function and test that the data are
correctly converted per the inputs given above.
"""
# Apply the units conversion function
data, units = ambient.convert_units(data, units)
# Check shape of output compared to input
assert np.atleast_2d(data).shape[0] == nr
assert np.atleast_2d(data).shape[1] == nc
# Check units converted as expected
for i in range(len(units)):
assert units[i] == mks_units[i]
# Check numerical result is correct if known
if ans is not None:
assert_array_almost_equal(data, ans, decimal=6)
# Send back the converted data
return (data, units)
def get_units(data, units, nr, nc, mks_units, ans=None):
"""
Run the ambient.convert_units function and test that the data are
correctly converted per the inputs given above.
"""
# Apply the units conversion function
data, units = ambient.convert_units(data, units)
# Check shape of output compared to input
assert np.atleast_2d(data).shape[0] == nr
assert np.atleast_2d(data).shape[1] == nc
# Check units converted as expected
for i in range(len(units)):
assert units[i] == mks_units[i]
# Check numerical result is correct if known
if ans is not None:
assert_array_almost_equal(data, ans, decimal = 6)
# Send back the converted data
return (data, units)
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__),
'../../tamoc/data'))
C_file = os.path.join(__location__, 'C.dat')
T_file = os.path.join(__location__, 'T.dat')
# Load in the data using numpy.loadtxt
C_raw = np.loadtxt(C_file, comments='%')
T_raw = np.loadtxt(T_file, comments='%')
# Clean the profiles to remove depth reversals
C_data = ambient.extract_profile(C_raw, 1, 25.0)
T_data = ambient.extract_profile(T_raw, 1, 25.0)
# Convert the data to standard units
C_profile, C_units = ambient.convert_units(C_data, ['psu', 'm'])
T_profile, T_units = ambient.convert_units(T_data, ['deg C', 'm'])
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__, 'DS.nc')
summary = 'Dataset created by profile_from_txt in the ./bin directory' \
+ ' of TAMOC'
source = 'Digitized data from the average CTD profile in the SINTEF ' + \
'DeepSpill Report'
sea_name = 'Norwegian Sea'
p_lat = 64.99066
p_lon = 4.84725
p_time = date2num(datetime(2000, 6, 27, 12, 0, 0),
def get_profile(self, nc_name, fname, u_a, v_a, w_a, depths):
"""
Read in the ambient CTD data
Read in the CTD data specified by API for all test cases. Append the
velocity information to the CTD file.
Parameters
----------
nc_name : str
Name to call the netCDF4 dataset.
u_a : float
Crossflow velocity for this test case (m/s).
Returns
-------
profile : `ambient.Profile` object
Returns an `ambient.Profile` object of the ambient CTD and velocity
information
"""
# Get the ambient CTD data
names = ['z', 'temperature', 'salinity', 'oxygen']
units = ['m', 'deg C', 'psu', 'mmol/m^3']
data = np.loadtxt(fname, comments='%')
# Convert the data to standard units
M_o2 = 31.9988 / 1000. # kg/mol
data[:, 3] = data[:, 3] / 1000. * M_o2
units[3] = 'kg/m^3'
data, units = ambient.convert_units(data, units)
# Create an empty netCDF4 dataset to store the CTD dat
summary = 'Global horizontal mean hydrographic and oxygen data'
source = 'Taken from page 226 of Sarmiento and Gruber'
sea_name = 'Global'
p_lat = 0.
p_lon = 0.
p_time = date2num(datetime(1998, 1, 1, 1, 0, 0),
units='seconds since 1970-01-01 00:00:00 0:00',
calendar='julian')
nc = ambient.create_nc_db(nc_name, summary, source, sea_name, p_lat,
p_lon, p_time)
# Insert the data into the netCDF dataset
comments = ['average', 'average', 'average', 'average']
nc = ambient.fill_nc_db(nc, data, names, units, comments, 0)
# Compute the pressure and insert into the netCDF dataset
P = ambient.compute_pressure(data[:, 0], data[:, 1], data[:, 2], 0)
P_data = np.vstack((data[:, 0], P)).transpose()
nc = ambient.fill_nc_db(nc, P_data, ['z', 'pressure'], ['m', 'Pa'],
['average', 'computed'], 0)
# Create an ambient.Profile object from this dataset
profile = ambient.Profile(nc, chem_names='all')
# Force the max depth to model
# depths[-1] = profile.z_max
# Add the crossflow velocity
print '******************'
print depths
print '******************'
u_crossflow = np.zeros((len(depths), 2))
u_crossflow[:, 0] = depths
if u_a.shape != depths.shape:
u_crossflow[:, 1] = np.linspace(u_a[0], u_a[-1], len(depths))
else:
u_crossflow[:, 1] = u_a
symbols = ['z', 'ua']
units = ['m', 'm/s']
comments = ['provided', 'provided']
profile.append(u_crossflow, symbols, units, comments, 0)
v_crossflow = np.zeros((len(depths), 2))
v_crossflow[:, 0] = depths
if v_a.shape != depths.shape:
v_crossflow[:, 1] = np.linspace(v_a[0], v_a[-1], len(depths))
else:
v_crossflow[:, 1] = v_a
symbols = ['z', 'va']
units = ['m', 'm/s']
comments = ['provided', 'provided']
profile.append(v_crossflow, symbols, units, comments, 0)
w_crossflow = np.zeros((len(depths), 2))
w_crossflow[:, 0] = depths
if w_a.shape != depths.shape:
w_crossflow[:, 1] = np.linspace(w_a[0], w_a[-1], len(depths))
else:
w_crossflow[:, 1] = w_a
symbols = ['z', 'wa']
units = ['m', 'm/s']
comments = ['provided', 'provided']
profile.append(w_crossflow, symbols, units, comments, 0)
# Finalize the profile (close the nc file)
profile.close_nc()
# Return the final profile
return profile
def get_lake_data():
"""
Create the netCDF dataset of CTD data for a lake simulation
Creates the ambient.Profile object and netCDF dataset of CTD data for
a lake simualtion from the `./data/lake.dat` text file, digitized from
the data in McGinnis et al. (2002) for Lake Hallwil.
"""
# Read in the lake CTD data
fname = '../../tamoc/data/lake.dat'
raw = np.loadtxt(fname, skiprows=9)
variables = ['z', 'temperature', 'salinity', 'oxygen']
units = ['m', 'deg C', 'psu', 'kg/m^3']
# Convert the units to mks
profile, units = ambient.convert_units(raw, units)
# Calculate the pressure data
P = ambient.compute_pressure(profile[:,0], profile[:,1], profile[:,2], 0)
profile = np.hstack((profile, np.atleast_2d(P).transpose()))
variables = variables + ['pressure']
units = units + ['Pa']
# Set up a netCDF dataset object
summary = 'Default lake.dat dataset provided by TAMOC'
source = 'Lake CTD data digitized from figures in McGinnis et al. 2004'
sea_name = 'Lake Hallwil, Switzerland'
lat = 47.277166666666666
lon = 8.217294444444445
date = datetime(2002, 7, 18)
t_units = 'seconds since 1970-01-01 00:00:00 0:00'
calendar = 'julian'
time = date2num(date, units=t_units, calendar=calendar)
nc = ambient.create_nc_db('../../test/output/lake.nc', summary, source,
sea_name, lat, lon, time)
# Insert the measured data
comments = ['digitized from measured data'] * 4
comments = comments + ['computed from z, T, S']
nc = ambient.fill_nc_db(nc, profile, variables, units, comments, 0)
# Insert an additional column with data for nitrogen and argon equal to
# their saturation concentrations at the free surface.
composition = ['nitrogen', 'oxygen', 'argon']
yk = np.array([0.78084, 0.209476, 0.009684])
air = dbm.FluidMixture(composition)
m = air.masses(yk)
Cs = air.solubility(m, profile[0,1], 101325., profile[0,2])
N2 = np.zeros((profile.shape[0], 1))
Ar = np.zeros((profile.shape[0], 1))
N2 = N2 + Cs[0,0]
Ar = Ar + Cs[0,2]
z = np.atleast_2d(profile[:,0]).transpose()
comments = ['calculated potential saturation value']*3
nc = ambient.fill_nc_db(nc, np.hstack((z, N2, Ar)),
['z', 'nitrogen', 'argon'],
['m', 'kg/m^3', 'kg/m^3'],
comments, 0)
# Create an ambient.Profile object
lake = ambient.Profile(nc, chem_names=['oxygen', 'nitrogen', 'argon'])
lake.close_nc()
# Return the Profile object
return lake
def create_ambient_profile(data,
labels,
units,
comments,
nc_name,
summary,
source,
sea_name,
p_lat,
p_lon,
p_time,
ca=[]):
"""
Create an ambient Profile object from given data
Create an ambient.Profile object using the given CTD and current data.
This function performs some standard operations to this data (unit
conversion, computation of pressure, insertion of concentrations for
dissolved gases, etc.) and returns the working ambient.Profile object.
The idea behind this function is to separate data manipulation and
creation of the ambient.Profile object from fetching of the data itself.
Parameters
----------
data : np.array
Array of the ambient ocean data to write to the CTD file. The
contents and dimensions of this data are specified in the labels
and units lists, below.
labels : list
List of string names of each variable in the data array.
units : list
List of units as strings for each variable in the data array.
comments : list
List of comments as strings that explain the types of data in the
data array. Typical comments include 'measured', 'modeled', or
'computed'.
nc_name : str
String containing the file path and file name to use when creating
the netCDF4 dataset that will contain this data.
summary : str
String describing the simulation for which this data will be used.
source : str
String documenting the source of the ambient ocean data provided.
sea_name : str
NC-compliant name for the ocean water body as a string.
p_lat : float
Latitude (deg)
p_lon : float
Longitude, negative is west of 0 (deg)
p_time : netCDF4 time format
Date and time of the CTD data using netCDF4.date2num().
ca : list, default=[]
List of gases for which to compute a standard dissolved gas profile;
choices are 'nitrogen', 'oxygen', 'argon', and 'carbon_dioxide'.
Returns
-------
profile : ambient.Profile
Returns an ambient.Profile object for manipulating ambient water
column data in TAMOC.
"""
# Convert the data to standard units
data, units = ambient.convert_units(data, units)
# Create an empty netCDF4-classic datast to store this CTD data
nc = ambient.create_nc_db(nc_name, summary, source, sea_name, p_lat, p_lon,
p_time)
# Put the CTD and current profile data into the ambient netCDF file
nc = ambient.fill_nc_db(nc, data, labels, units, comments, 0)
# Compute and insert the pressure data
z = nc.variables['z'][:]
T = nc.variables['temperature'][:]
S = nc.variables['salinity'][:]
P = ambient.compute_pressure(z, T, S, 0)
P_data = np.vstack((z, P)).transpose()
nc = ambient.fill_nc_db(nc, P_data, ['z', 'pressure'], ['m', 'Pa'],
['measured', 'computed'], 0)
# Use this netCDF file to create an ambient object
profile = ambient.Profile(
nc, ztsp=['z', 'temperature', 'salinity', 'pressure', 'ua', 'va'])
# Compute dissolved gas profiles to add to this dataset
if len(ca) > 0:
# Create a gas mixture object for air
gases = ['nitrogen', 'oxygen', 'argon', 'carbon_dioxide']
air = dbm.FluidMixture(gases)
yk = np.array([0.78084, 0.20946, 0.009340, 0.00036])
m = air.masses(yk)
# Set atmospheric conditions
Pa = 101325.
# Compute the desired concentrations
for i in range(len(ca)):
# Initialize a dataset of concentration data
conc = np.zeros(len(profile.z))
# Compute the concentrations at each depth
for j in range(len(conc)):
# Get the local water column properties
T, S, P = profile.get_values(
profile.z[j], ['temperature', 'salinity', 'pressure'])
# Compute the gas solubility at this temperature and salinity
# at the sea surface
Cs = air.solubility(m, T, Pa, S)[0, :]
# Adjust the solubility to the present depth
Cs = Cs * seawater.density(T, S, P) / \
seawater.density(T, S, 101325.)
# Extract the right chemical
conc[j] = Cs[gases.index(ca[i])]
# Add this computed dissolved gas to the Profile dataset
data = np.vstack((profile.z, conc)).transpose()
symbols = ['z', ca[i]]
units = ['m', 'kg/m^3']
comments = ['measured', 'computed from CTD data']
profile.append(data, symbols, units, comments, 0)
# Close the netCDF dataset
profile.close_nc()
# Return the profile object
return profile
def get_ctd_profile():
"""
Load the ASCII CTD Data into an `ambient.Profile` object.
This function performs the steps in ./profile_from_ctd.py to read in the
CTD data and create a Profile object. This is the data set that will be
used to demonstrate how to append data to a Profile object.
"""
# Get the path to the input file
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__),
'../../tamoc/data'))
dat_file = os.path.join(__location__, 'ctd_BM54.cnv')
# Load in the data using numpy.loadtxt
raw = np.loadtxt(dat_file,
comments='#',
skiprows=175,
usecols=(0, 1, 3, 8, 9, 10, 12))
# Describe the organization of the data in raw.
var_names = [
'temperature', 'pressure', 'wetlab_fluorescence', 'z', 'salinity',
'density', 'oxygen'
]
var_units = ['deg C', 'db', 'mg/m^3', 'm', 'psu', 'kg/m^3', 'mg/l']
z_col = 3
# Clean the profile to remove reversals in the depth coordinate
data = ambient.extract_profile(raw, z_col, 50.0)
# Convert the profile data to standard units in TAMOC
profile, units = ambient.convert_units(data, var_units)
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__, 'BM54.nc')
summary = 'Dataset created by profile_from_ctd in the ./bin directory' \
+ ' of TAMOC'
source = 'R/V Brooks McCall, station BM54'
sea_name = 'Gulf of Mexico'
p_lat = 28.0 + 43.945 / 60.0
p_lon = 360 - (88.0 + 22.607 / 60.0)
p_time = date2num(datetime(2010, 5, 30, 18, 22, 12),
units='seconds since 1970-01-01 00:00:00 0:00',
calendar='julian')
nc = ambient.create_nc_db(nc_file, summary, source, sea_name, p_lat, p_lon,
p_time)
# Insert the CTD data into the netCDF dataset
comments = ['measured'] * len(var_names)
nc = ambient.fill_nc_db(nc, profile, var_names, units, comments, z_col)
# Create an ambient.Profile object for this dataset
bm54 = ambient.Profile(nc, chem_names=['oxygen'])
# Return the Profile object
return bm54
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__),
'../../tamoc/data'))
C_file = os.path.join(__location__,'C.dat')
T_file = os.path.join(__location__,'T.dat')
# Load in the data using numpy.loadtxt
C_raw = np.loadtxt(C_file, comments = '%')
T_raw = np.loadtxt(T_file, comments = '%')
# Clean the profiles to remove depth reversals
C_data = ambient.extract_profile(C_raw, 1, 25.0)
T_data = ambient.extract_profile(T_raw, 1, 25.0)
# Convert the data to standard units
C_profile, C_units = ambient.convert_units(C_data, ['psu', 'm'])
T_profile, T_units = ambient.convert_units(T_data, ['deg C', 'm'])
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__,'DS.nc')
summary = 'Dataset created by profile_from_txt in the ./bin directory' \
+ ' of TAMOC'
source = 'Digitized data from the average CTD profile in the SINTEF ' + \
'DeepSpill Report'
sea_name = 'Norwegian Sea'
p_lat = 64.99066
p_lon = 4.84725
p_time = date2num(datetime(2000, 6, 27, 12, 0, 0),
skiprows=175,
usecols=(0, 1, 3, 8, 9, 10, 12))
# Describe the organization of the data in raw.
var_names = [
'temperature', 'pressure', 'wetlab_fluorescence', 'z', 'salinity',
'density', 'oxygen'
]
var_units = ['deg C', 'db', 'mg/m^3', 'm', 'psu', 'kg/m^3', 'mg/l']
z_col = 3
# Clean the profile to remove reversals in the depth coordinate
data = ambient.extract_profile(raw, z_col, 50.0)
# Convert the profile data to standard units in TAMOC
profile, units = ambient.convert_units(data, var_units)
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__, 'BM54.nc')
summary = 'Dataset created by profile_from_ctd in the ./bin directory' \
+ ' of TAMOC'
source = 'R/V Brooks McCall, station BM54'
sea_name = 'Gulf of Mexico'
p_lat = 28.0 + 43.945 / 60.0
p_lon = 360 - (88.0 + 22.607 / 60.0)
p_time = date2num(datetime(2010, 5, 30, 18, 22, 12),
units='seconds since 1970-01-01 00:00:00 0:00',
calendar='julian')
def get_lake_data():
"""
Create the netCDF dataset of CTD data for a lake simulation
Creates the ambient.Profile object and netCDF dataset of CTD data for
a lake simualtion from the `./data/lake.dat` text file, digitized from
the data in McGinnis et al. (2002) for Lake Hallwil.
"""
# Read in the lake CTD data
fname = '../../tamoc/data/lake.dat'
raw = np.loadtxt(fname, skiprows=9)
variables = ['z', 'temperature', 'salinity', 'oxygen']
units = ['m', 'deg C', 'psu', 'kg/m^3']
# Convert the units to mks
profile, units = ambient.convert_units(raw, units)
# Calculate the pressure data
P = ambient.compute_pressure(profile[:, 0], profile[:, 1], profile[:, 2],
0)
profile = np.hstack((profile, np.atleast_2d(P).transpose()))
variables = variables + ['pressure']
units = units + ['Pa']
# Set up a netCDF dataset object
summary = 'Default lake.dat dataset provided by TAMOC'
source = 'Lake CTD data digitized from figures in McGinnis et al. 2004'
sea_name = 'Lake Hallwil, Switzerland'
lat = 47.277166666666666
lon = 8.217294444444445
date = datetime(2002, 7, 18)
t_units = 'seconds since 1970-01-01 00:00:00 0:00'
calendar = 'julian'
time = date2num(date, units=t_units, calendar=calendar)
nc = ambient.create_nc_db('../../test/output/lake.nc', summary, source,
sea_name, lat, lon, time)
# Insert the measured data
comments = ['digitized from measured data'] * 4
comments = comments + ['computed from z, T, S']
nc = ambient.fill_nc_db(nc, profile, variables, units, comments, 0)
# Insert an additional column with data for nitrogen and argon equal to
# their saturation concentrations at the free surface.
composition = ['nitrogen', 'oxygen', 'argon']
yk = np.array([0.78084, 0.209476, 0.009684])
air = dbm.FluidMixture(composition)
m = air.masses(yk)
Cs = air.solubility(m, profile[0, 1], 101325., profile[0, 2])
N2 = np.zeros((profile.shape[0], 1))
Ar = np.zeros((profile.shape[0], 1))
N2 = N2 + Cs[0, 0]
Ar = Ar + Cs[0, 2]
z = np.atleast_2d(profile[:, 0]).transpose()
comments = ['calculated potential saturation value'] * 3
nc = ambient.fill_nc_db(nc, np.hstack(
(z, N2, Ar)), ['z', 'nitrogen', 'argon'], ['m', 'kg/m^3', 'kg/m^3'],
comments, 0)
# Create an ambient.Profile object
lake = ambient.Profile(nc, chem_names=['oxygen', 'nitrogen', 'argon'])
lake.close_nc()
# Return the Profile object
return lake
# Load in the data using numpy.loadtxt
raw = np.loadtxt(dat_file, comments = '#', skiprows = 175,
usecols = (0, 1, 3, 8, 9, 10, 12))
# Describe the organization of the data in raw.
var_names = ['temperature', 'pressure', 'wetlab_fluorescence', 'z',
'salinity', 'density', 'oxygen']
var_units = ['deg C', 'db', 'mg/m^3', 'm', 'psu', 'kg/m^3', 'mg/l']
z_col = 3
# Clean the profile to remove reversals in the depth coordinate
data = ambient.extract_profile(raw, z_col, 50.0)
# Convert the profile data to standard units in TAMOC
profile, units = ambient.convert_units(data, var_units)
# Create an empty netCDF4-classic dataset to store this CTD data
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__),
'../../test/output'))
nc_file = os.path.join(__location__,'BM54.nc')
summary = 'Dataset created by profile_from_ctd in the ./bin directory' \
+ ' of TAMOC'
source = 'R/V Brooks McCall, station BM54'
sea_name = 'Gulf of Mexico'
p_lat = 28.0 + 43.945 / 60.0
p_lon = 360 - (88.0 + 22.607 / 60.0)
p_time = date2num(datetime(2010, 5, 30, 18, 22, 12),
units = 'seconds since 1970-01-01 00:00:00 0:00',
calendar = 'julian')