def get_profile(data, z_col, z_start, p_col, P, z_min, z_max, nr, nc):
"""
Run the ambient.extract_profile function and test that the data are
correctly parsed per the inputs given above.
"""
# Apply the profile extraction function
prof_data = ambient.extract_profile(data, z_col=z_col, z_start=z_start,
p_col=p_col, P_atm=P)
# Check that the returned profile extends to the free surface
assert prof_data[0,z_col] == 0.0
# Check that the profile is clipped at the expected depths
assert_approx_equal(prof_data[1,z_col], z_min, significant = 6)
assert_approx_equal(prof_data[-1,z_col], z_max, significant = 6)
# Check that the returned profile is the right shape and data type
assert prof_data.shape[0] == nr
if nc is not None:
assert prof_data.shape[1] == nc
assert isinstance(prof_data, np.ndarray)
# Check the that returned profile is in ascending order
for i in range(1, prof_data.shape[0]):
assert prof_data[i,z_col] > prof_data[i-1,z_col]
# Send back the extracted profile
return prof_data
def get_profile(data, z_col, z_start, p_col, P, z_min, z_max, nr, nc):
"""
Run the ambient.extract_profile function and test that the data are
correctly parsed per the inputs given above.
"""
# Apply the profile extraction function
prof_data = ambient.extract_profile(data,
z_col=z_col,
z_start=z_start,
p_col=p_col,
P_atm=P)
# Check that the returned profile extends to the free surface
assert prof_data[0, z_col] == 0.0
# Check that the profile is clipped at the expected depths
assert_approx_equal(prof_data[1, z_col], z_min, significant=6)
assert_approx_equal(prof_data[-1, z_col], z_max, significant=6)
# Check that the returned profile is the right shape and data type
assert prof_data.shape[0] == nr
if nc is not None:
assert prof_data.shape[1] == nc
assert isinstance(prof_data, np.ndarray)
# Check the that returned profile is in ascending order
for i in range(1, prof_data.shape[0]):
assert prof_data[i, z_col] > prof_data[i - 1, z_col]
# Send back the extracted profile
return prof_data
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
if __name__ == '__main__':
# Get the path to the input file
__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'
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
if __name__ == '__main__':
# Get the path to the input file
__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'
# 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)
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)
