def flort_wfp(ds, grid=False):
"""
Takes FLORT data recorded by the Wire-Following Profilers (used by CGSN/EA
as part of the coastal and global arrays) and cleans up the data set to
make it more user-friendly. Primary task is renaming parameters and
dropping some that are of limited use. Additionally, re-organize some of
the variables to permit better assessments of the data.
:param ds: initial FLORT data set downloaded from OOI via the M2M system
:param grid: boolean flag for whether the data should be gridded
:return ds: cleaned up data set
"""
# drop some of the variables:
# internal_timestamp == superseded by time, redundant so can remove
# suspect_timestamp = not used
# measurement_wavelength_* == metadata, move into variable attributes.
# seawater_scattering_coefficient == not used
# raw_internal_temp == not available, NaN filled
ds = ds.reset_coords()
ds = ds.drop([
'internal_timestamp', 'suspect_timestamp',
'measurement_wavelength_beta', 'measurement_wavelength_cdom',
'measurement_wavelength_chl', 'raw_internal_temp'
])
# lots of renaming here to get a better defined data set with cleaner attributes
rename = {
'int_ctd_pressure': 'seawater_pressure',
'ctdpf_ckl_seawater_temperature': 'seawater_temperature',
'raw_signal_chl': 'raw_chlorophyll',
'fluorometric_chlorophyll_a': 'estimated_chlorophyll',
'fluorometric_chlorophyll_a_qc_executed':
'estimated_chlorophyll_qc_executed',
'fluorometric_chlorophyll_a_qc_results':
'estimated_chlorophyll_qc_results',
'raw_signal_cdom': 'raw_cdom',
'raw_signal_beta': 'raw_backscatter',
'total_volume_scattering_coefficient': 'beta_700',
'total_volume_scattering_coefficient_qc_executed':
'beta_700_qc_executed',
'total_volume_scattering_coefficient_qc_results':
'beta_700_qc_results',
'optical_backscatter': 'bback',
'optical_backscatter_qc_executed': 'bback_qc_executed',
'optical_backscatter_qc_results': 'bback_qc_results',
}
ds = ds.rename(rename)
# reset some attributes
for key, value in ATTRS.items():
for atk, atv in value.items():
if key in ds.variables:
ds[key].attrs[atk] = atv
# add the original variable name as an attribute, if renamed
for key, value in rename.items():
ds[value].attrs['ooinet_variable_name'] = key
# parse the OOI QC variables and add QARTOD style QC summary flags to the data, converting the
# bitmap represented flags into an integer value representing pass == 1, suspect or of high
# interest == 3, and fail == 4.
ds = parse_qc(ds)
# create qc flags for the data and add them to the OOI qc flags
beta_flag, cdom_flag, chl_flag = quality_checks(ds)
ds['beta_700_qc_summary_flag'] = ('time', (np.array(
[ds.beta_700_qc_summary_flag, beta_flag])).max(axis=0, initial=1))
ds['fluorometric_cdom_qc_summary_flag'] = ('time', (np.array(
[ds.fluorometric_cdom_qc_summary_flag, cdom_flag])).max(axis=0,
initial=1))
ds['estimated_chlorophyll_qc_summary_flag'] = ('time', (np.array(
[ds.estimated_chlorophyll_qc_summary_flag, chl_flag])).max(axis=0,
initial=1))
if grid:
# clear out any duplicate time stamps
_, index = np.unique(ds['time'], return_index=True)
ds = ds.isel(time=index)
# since the scipy griddata function cannot use the time values as is (get converted to nanoseconds, which
# is too large of a value), we need to temporarily convert them to a floating point number in days since
# the start of the data record; we can then use that temporary date/time array for the gridding.
base_time = ds['time'].min().values
dt = (ds['time'] - base_time).astype(float) / 1e9 / 60 / 60 / 24
# construct the new grid, using 1 m depth bins from 30 to 510 m, and daily intervals from the start of
# the record to the end (centered on noon UTC).
depth_range = np.arange(30, 511, 1)
time_range = np.arange(0.5, np.ceil(dt.max()) + 0.5, 1)
gridded_time = base_time.astype('M8[D]') + pd.to_timedelta(time_range,
unit='D')
# grid the data, adding the results to a list of data arrays
gridded = []
for v in ds.variables:
if v not in ['time', 'depth']:
# grid the data for each variable
gdata = griddata((dt.values, ds['depth'].values),
ds[v].values,
(time_range[None, :], depth_range[:, None]),
method='linear')
# add the data to a data array
da = xr.DataArray(name=v,
data=gdata,
coords=[("depth", depth_range),
("time", gridded_time)])
da.attrs = ds[v].attrs
# reset the data types and fill values for floats and ints
if ds[v].dtype == np.dtype(int):
da = da.where(np.isnan is True, FILL_INT)
da.attrs['_FillValue'] = FILL_INT
da = da.astype(int)
else:
da.attrs['_FillValue'] = np.nan
da = da.astype(float)
# add to the list
gridded.append(da)
# recombine the gridded data arrays into a single dataset
gridded = xr.merge(gridded)
gridded.attrs = ds.attrs
ds = gridded
return ds
def flort_instrument(ds):
"""
Takes flort data recorded by the Sea-Bird Electronics SBE16Plus used in the
CGSN/EA moorings and cleans up the data set to make it more user-friendly.
Primary task is renaming parameters and dropping some that are of limited
use. Additionally, re-organize some of the variables to permit better
assessments of the data.
:param ds: initial flort data set downloaded from OOI via the M2M system
:return ds: cleaned up data set
"""
# drop some of the variables:
# internal_timestamp == superseded by time, redundant so can remove
# suspect_timestamp = not used
# measurement_wavelength_* == metadata, move into variable attributes.
# pressure_depth == variable assigned if this was a FLORT on a CSPP, not with moorings
ds = ds.reset_coords()
ds = ds.drop([
'internal_timestamp', 'suspect_timestamp',
'measurement_wavelength_beta', 'measurement_wavelength_cdom',
'measurement_wavelength_chl'
])
# lots of renaming here to get a better defined data set with cleaner attributes
rename = {
'temp': 'seawater_temperature',
'raw_signal_chl': 'raw_chlorophyll',
'fluorometric_chlorophyll_a': 'estimated_chlorophyll',
'fluorometric_chlorophyll_a_qc_executed':
'estimated_chlorophyll_qc_executed',
'fluorometric_chlorophyll_a_qc_results':
'estimated_chlorophyll_qc_results',
'raw_signal_cdom': 'raw_cdom',
'raw_signal_beta': 'raw_backscatter',
'total_volume_scattering_coefficient': 'beta_700',
'total_volume_scattering_coefficient_qc_executed':
'beta_700_qc_executed',
'total_volume_scattering_coefficient_qc_results':
'beta_700_qc_results',
'optical_backscatter': 'bback',
'optical_backscatter_qc_executed': 'bback_qc_executed',
'optical_backscatter_qc_results': 'bback_qc_results',
}
ds = ds.rename(rename)
# reset some attributes
for key, value in ATTRS.items():
for atk, atv in value.items():
if key in ds.variables:
ds[key].attrs[atk] = atv
# add the original variable name as an attribute, if renamed
for key, value in rename.items():
ds[value].attrs['ooinet_variable_name'] = key
# check if the raw data for all three channels is 0, if so the FLORT wasn't talking to the CTD and these are
# all just fill values that can be removed.
ds = ds.where(
ds['raw_backscatter'] + ds['raw_cdom'] + ds['raw_chlorophyll'] > 0,
drop=True)
if len(ds.time) == 0:
# this was one of those deployments where the FLORT was never able to communicate with the CTD.
warnings.warn(
'Communication failure between the FLORT and the CTDBP. No data was recorded.'
)
return None
# parse the OOI QC variables and add QARTOD style QC summary flags to the data, converting the
# bitmap represented flags into an integer value representing pass == 1, suspect or of high
# interest == 3, and fail == 4.
ds = parse_qc(ds)
# create qc flags for the data and add them to the OOI qc flags
beta_flag, cdom_flag, chl_flag = quality_checks(ds)
ds['beta_700_qc_summary_flag'] = ('time', (np.array(
[ds.beta_700_qc_summary_flag, beta_flag])).max(axis=0, initial=1))
ds['fluorometric_cdom_qc_summary_flag'] = ('time', (np.array(
[ds.fluorometric_cdom_qc_summary_flag, cdom_flag])).max(axis=0,
initial=1))
ds['estimated_chlorophyll_qc_summary_flag'] = ('time', (np.array(
[ds.estimated_chlorophyll_qc_summary_flag, chl_flag])).max(axis=0,
initial=1))
return ds
def flort_cspp(ds):
"""
Takes FLORT data recorded by the CSPP loggers used by the Endurance Array
and cleans up the data set to make it more user-friendly. Primary task is
renaming parameters and dropping some that are of limited use. Additionally,
re-organize some of the variables to permit better assessments of the data.
:param ds: initial FLORT data set downloaded from OOI via the M2M system
:return ds: cleaned up data set
"""
# drop some of the variables:
# internal_timestamp == superseded by time, redundant so can remove
# suspect_timestamp = not used
# measurement_wavelength_* == metadata, move into variable attributes.
# seawater_scattering_coefficient == not used
ds = ds.reset_coords()
ds = ds.drop([
'internal_timestamp', 'suspect_timestamp',
'measurement_wavelength_beta', 'measurement_wavelength_cdom',
'measurement_wavelength_chl'
])
# lots of renaming here to get a better defined data set with cleaner attributes
rename = {
'pressure': 'seawater_pressure',
'pressure_qc_executed': 'seawater_pressure_qc_executed',
'pressure_qc_results': 'seawater_pressure_qc_results',
'temperature': 'seawater_temperature',
'salinity': 'practical_salinity',
'raw_signal_chl': 'raw_chlorophyll',
'fluorometric_chlorophyll_a': 'estimated_chlorophyll',
'fluorometric_chlorophyll_a_qc_executed':
'estimated_chlorophyll_qc_executed',
'fluorometric_chlorophyll_a_qc_results':
'estimated_chlorophyll_qc_results',
'raw_signal_cdom': 'raw_cdom',
'raw_signal_beta': 'raw_backscatter',
'total_volume_scattering_coefficient': 'beta_700',
'total_volume_scattering_coefficient_qc_executed':
'beta_700_qc_executed',
'total_volume_scattering_coefficient_qc_results':
'beta_700_qc_results',
'optical_backscatter': 'bback',
'optical_backscatter_qc_executed': 'bback_qc_executed',
'optical_backscatter_qc_results': 'bback_qc_results',
}
ds = ds.rename(rename)
# reset some attributes
for key, value in ATTRS.items():
for atk, atv in value.items():
if key in ds.variables:
ds[key].attrs[atk] = atv
# add the original variable name as an attribute, if renamed
for key, value in rename.items():
ds[value].attrs['ooinet_variable_name'] = key
# parse the OOI QC variables and add QARTOD style QC summary flags to the data, converting the
# bitmap represented flags into an integer value representing pass == 1, suspect or of high
# interest == 3, and fail == 4.
ds = parse_qc(ds)
# create qc flags for the data and add them to the OOI qc flags
beta_flag, cdom_flag, chl_flag = quality_checks(ds)
ds['beta_700_qc_summary_flag'] = ('time', (np.array(
[ds.beta_700_qc_summary_flag, beta_flag])).max(axis=0, initial=1))
ds['fluorometric_cdom_qc_summary_flag'] = ('time', (np.array(
[ds.fluorometric_cdom_qc_summary_flag, cdom_flag])).max(axis=0,
initial=1))
ds['estimated_chlorophyll_qc_summary_flag'] = ('time', (np.array(
[ds.estimated_chlorophyll_qc_summary_flag, chl_flag])).max(axis=0,
initial=1))
return ds
def flort_datalogger(ds, burst=False):
"""
Takes flort data recorded by the data loggers used in the CGSN/EA moorings
and cleans up the data set to make it more user-friendly. Primary task is
renaming parameters and dropping some that are of limited use. Additionally,
re-organize some of the variables to permit better assessments of the data.
:param ds: initial flort data set downloaded from OOI via the M2M system
:param burst: resample the data to the defined time interval
:return ds: cleaned up data set
"""
# drop some of the variables:
# internal_timestamp == superseded by time, redundant so can remove
# suspect_timestamp = not used
# measurement_wavelength_* == metadata, move into variable attributes.
# pressure_depth == variable assigned if this was a FLORT on a CSPP, not with moorings
ds = ds.drop([
'internal_timestamp', 'suspect_timestamp',
'measurement_wavelength_beta', 'measurement_wavelength_cdom',
'measurement_wavelength_chl'
])
# check for data from a co-located CTD, if not present add it and reset the fill value for the optical
# backscatter derived values
if 'temp' not in ds.variables:
ds['temp'] = ('time', ds['deployment'] * np.nan)
ds['practical_salinity'] = ('time', ds['deployment'] * np.nan)
ds['optical_backscatter'] = ds['optical_backscatter'] * np.nan
ds['seawater_scattering_coefficient'] = ds[
'seawater_scattering_coefficient'] * np.nan
# lots of renaming here to get a better defined data set with cleaner attributes
rename = {
'temp': 'seawater_temperature',
'raw_signal_chl': 'raw_chlorophyll',
'fluorometric_chlorophyll_a': 'estimated_chlorophyll',
'fluorometric_chlorophyll_a_qc_executed':
'estimated_chlorophyll_qc_executed',
'fluorometric_chlorophyll_a_qc_results':
'estimated_chlorophyll_qc_results',
'raw_signal_cdom': 'raw_cdom',
'raw_signal_beta': 'raw_backscatter',
'total_volume_scattering_coefficient': 'beta_700',
'total_volume_scattering_coefficient_qc_executed':
'beta_700_qc_executed',
'total_volume_scattering_coefficient_qc_results':
'beta_700_qc_results',
'optical_backscatter': 'bback',
'optical_backscatter_qc_executed': 'bback_qc_executed',
'optical_backscatter_qc_results': 'bback_qc_results',
}
ds = ds.rename(rename)
# reset some attributes
for key, value in ATTRS.items():
for atk, atv in value.items():
if key in ds.variables:
ds[key].attrs[atk] = atv
# add the original variable name as an attribute, if renamed
for key, value in rename.items():
ds[value].attrs['ooinet_variable_name'] = key
# parse the OOI QC variables and add QARTOD style QC summary flags to the data, converting the
# bitmap represented flags into an integer value representing pass == 1, suspect or of high
# interest == 3, and fail == 4.
ds = parse_qc(ds)
# create QC flags for the data and add them to the OOI QC summary flags
beta_flag, cdom_flag, chl_flag = quality_checks(ds)
ds['beta_700_qc_summary_flag'] = ('time', (np.array(
[ds.beta_700_qc_summary_flag, beta_flag])).max(axis=0, initial=1))
ds['fluorometric_cdom_qc_summary_flag'] = ('time', (np.array(
[ds.fluorometric_cdom_qc_summary_flag, cdom_flag])).max(axis=0,
initial=1))
ds['estimated_chlorophyll_qc_summary_flag'] = ('time', (np.array(
[ds.estimated_chlorophyll_qc_summary_flag, chl_flag])).max(axis=0,
initial=1))
if burst:
# re-sample the data collected in burst mode using a 15-minute median average
burst = ds.resample(time='900s', skipna=True).median(dim='time',
keep_attrs=True)
# for each of the three FLORT measurements, calculate stats (min, max, and the standard deviation)
# for each of the bursts
cdom = ds['fluorometric_cdom'].resample(time='900s', skipna=True)
cdom = np.array([
cdom.min('time').values,
cdom.max('time').values,
cdom.std('time').values
])
chl = ds['estimated_chlorophyll'].resample(time='900s', skipna=True)
chl = np.array([
chl.min('time').values,
chl.max('time').values,
chl.std('time').values
])
beta = ds['beta_700'].resample(time='900s', skipna=True)
beta = np.array([
beta.min('time').values,
beta.max('time').values,
beta.std('time').values
])
# create a data set with the burst statistics for the variables
stats = xr.Dataset(
{
'fluorometric_cdom_burst_stats': (['time', 'stats'], cdom.T),
'estimated_chlorophyll_burst_stats':
(['time', 'stats'], chl.T),
'beta_700_burst_stats': (['time', 'stats'], beta.T)
},
coords={
'time': burst['time'],
'stats': np.arange(0, 3).astype('int32')
})
# add the stats into the burst averaged data set, and then remove the missing rows
burst = burst.merge(stats)
burst = burst.where(~np.isnan(burst.deployment), drop=True)
# save the newly average data
ds = burst
return ds