def despike(var, window_size, spike_method="median"):
"""
Return a smooth baseline of data and the anomalous spikes
This script is copied from Nathan Briggs' MATLAB script as described in
Briggs et al (2011). It returns the baseline of the data using either a
rolling window method and the residuals of [measurements - baseline].
Parameters
----------
arr: numpy.ndarray or pandas.Series
Array of data variable for cleaning to be performed on.
window_size: int
the length of the rolling window size
method: str
A string with `minmax` or `median`. 'minmax' first applies a rolling
minimum to the dataset thereafter a rolling maximum is applied. This
forms the baseline, where the spikes are the difference from the
baseline. 'median' first applies a rolling median to the dataset, which
forms the baseline. The spikes are the difference between median and
baseline, and thus are more likely to be negative.
Returns
-------
baseline: numpy.ndarray or pandas.Series
The baseline from which outliers are determined.
spikes: numpy.ndarray or pandas.Series
Spikes are the residual of [measurements - baseline].
"""
from numpy import array, isnan, nan, nanmax, nanmedian, nanmin, ndarray
# convert to array
arr = array(var)
# create empty array for baseline
baseline = ndarray(arr.shape) * nan
# mask with exisiting nans masked out
mask = ~isnan(arr)
# if min-max method then get the rolling minimum and
# then the rolling maximum
if spike_method.startswith("min"):
base_min = rolling_window(arr[mask], nanmin, window_size)
base = rolling_window(base_min, nanmax, window_size)
else:
base = rolling_window(arr[mask], nanmedian, window_size)
baseline[mask] = base
spikes = arr - baseline
baseline = transfer_nc_attrs(getframe(), var, baseline, "_baseline")
spikes = transfer_nc_attrs(getframe(), var, spikes, "_spikes")
return baseline, spikes
def par_scaling(par_uV, scale_factor_wet_uEm2s, sensor_output_mV):
"""
Scaling correction for par with factory calibration coefficients.
The function subtracts the sensor output from the raw counts and divides
with the scale factor. The factory calibrations are unique for each
deployment and should be taken from the calibration file for that
deployment.
Parameters
----------
par_uV: numpy.ndarray or pandas.Series
The raw par data with units uV.
scale_factor_wet_uEm2s: float
The scale factor from the factory calibration file in units uE/m2/sec.
sensor_output_mV: float
The sensor output in the dark from the factory calibration file in
units mV.
Returns
par_uEm2s: numpy.ndarray or pandas.Series
The par data corrected for the sensor output and scale factor from the
factory calibration file in units uE/m2/sec.
"""
sensor_output_uV = sensor_output_mV / 1000.0
par_uEm2s = (par_uV - sensor_output_uV) / scale_factor_wet_uEm2s
par_uEm2s = transfer_nc_attrs(getframe(), par_uV, par_uEm2s, 'par_uEm2s')
return par_uEm2s
def potential_density(salt_PSU, temp_C, pres_db, lat, lon, pres_ref=0):
"""
Calculate density from glider measurements of salinity and temperature.
The Basestation calculates density from absolute salinity and potential
temperature. This function is a wrapper for this functionality, where
potential temperature and absolute salinity are calculated first.
Note that a reference pressure of 0 is used by default.
Parameters
----------
salt_PSU : array, dtype=float, shape=[n, ]
practical salinty
temp_C : array, dtype=float, shape=[n, ]
temperature in deg C
pres_db : array, dtype=float, shape=[n, ]
pressure in decibar
lat : array, dtype=float, shape=[n, ]
latitude in degrees north
lon : array, dtype=float, shape=[n, ]
longitude in degrees east
Returns
-------
potential_density : array, dtype=float, shape=[n, ]
Note
----
Using seawater.dens does not yield the same results as this function. We
get very close results to what the SeaGlider Basestation returns with this
function. The difference of this function with the basestation is on
average ~ 0.003 kg/m3
"""
try:
import gsw
salt_abs = gsw.SA_from_SP(salt_PSU, pres_db, lon, lat)
temp_pot = gsw.t_from_CT(salt_abs, temp_C, pres_db)
pot_dens = gsw.pot_rho_t_exact(salt_abs, temp_pot, pres_db, pres_ref)
except ImportError:
import seawater as sw
pot_dens = sw.pden(salt_PSU, temp_C, pres_db, pres_ref)
pot_dens = transfer_nc_attrs(
getframe(),
temp_C,
pot_dens,
'potential_density',
units='kg/m3',
comment='',
standard_name='potential_density',
)
return pot_dens
def brunt_vaisala(salt, temp, pres, lat=None):
r"""
Calculate the square of the buoyancy frequency.
This is a copy from GSW package, with the exception that
the array maintains the same shape as the input. Note that
it only works on ungridded data at the moment.
.. math::
N^{2} = \frac{-g}{\sigma_{\theta}} \frac{d\sigma_{\theta}}{dz}
Parameters
----------
SA : array-like
Absolute Salinity, g/kg
CT : array-like
Conservative Temperature (ITS-90), degrees C
p : array-like
Sea pressure (absolute pressure minus 10.1325 dbar), dbar
lat : array-like, 1-D, optional
Latitude, degrees.
axis : int, optional
The dimension along which pressure increases.
Returns
-------
N2 : array
Buoyancy frequency-squared at pressure midpoints, 1/s.
The shape along the pressure axis dimension is one
less than that of the inputs.
"""
from gsw import Nsquared
from numpy import nan, r_
def pad_nan(a):
r_[a, nan]
n2 = pad_nan(Nsquared(salt, temp, pres)[0])
n2 = transfer_nc_attrs(
getframe(),
temp,
n2,
'N_squared',
units='1/s2',
comment='',
standard_name='brunt_vaisala_freq',
)
return n2
def find_signature(sign):
path = Path(inspect.getfile(
inspect.getframe())).parent / 'signatures.json'
with path.open('r') as data_file:
data = json.load(data_file)
list_name = [name for name, hexa in data.items() if hexa == sign]
if len(list_name) > 1:
logging.warning('function signatures collision: %s', list_name)
return '_or_'.join(list_name)
elif list_name:
return list_name[0]
else:
return None
def mask_bad_dive_fraction(mask, dives, var, mask_frac=0.2):
"""
Find bad dives - where more than a fraction of the dive is masked
Parameters
----------
mask : array, dtype=bool, shape=[n, ]
boolean 1D array with masked values
dives : array, dtype=float, shape=[n, ]
discrete dive numbers (down round, up n.5)
var : array, dtype=float, shape=[n, ]
series or array containing data that will be masked with NaNs
mask_frac : int=0.2
fraction of the dive that is masked for the whole dive to be bad
Returns
-------
var : array, dtype=float, shape=[n, ]
the same as the input, but has been masked
mask_dives : array, dtype=bool
a mask array that has full dives that are deemed "bad" masked out
"""
from numpy import NaN, array
from pandas import Series
# catch dives where the marjority of the data is masked
# and return a fully masked dive
dives = array(dives)
arr = array(var)
grp = Series(mask).groupby(dives)
masked_frac_per_dive = grp.sum() / grp.count() > mask_frac
majority_masked = masked_frac_per_dive[masked_frac_per_dive].index.values
# create a mask that masks ungridded data
mask_dives = mask.copy()
for d in majority_masked:
i = array(dives) == d
mask_dives[i] = True
arr[mask_dives] = NaN
baddive = arr
baddive = transfer_nc_attrs(getframe(), var, baddive, None)
return baddive, mask_dives
def spice0(salt_PSU, temp_C, pres_db, lat, lon):
"""
Calculate spiciness from glider measurements of salinity and temperature.
Parameters
----------
salt_PSU : array, dtype=float, shape=[n, ]
practical salinty
temp_C : array, dtype=float, shape=[n, ]
temperature in deg C
pres_db : array, dtype=float, shape=[n, ]
pressure in decibar
lat : array, dtype=float, shape=[n, ]
latitude in degrees north
lon : array, dtype=float, shape=[n, ]
longitude in degrees east
Returns
-------
potential_density : array, dtype=float, shape=[n, ]
Note
----
Using seawater.dens does not yield the same results as this function. We
get very close results to what the SeaGlider Basestation returns with this
function. The difference of this function with the basestation is on
average ~ 0.003 kg/m3
"""
import gsw
salt_abs = gsw.SA_from_SP(salt_PSU, pres_db, lon, lat)
cons_temp = gsw.CT_from_t(salt_abs, temp_C, pres_db)
spice0 = gsw.spiciness0(salt_abs, cons_temp)
spice0 = transfer_nc_attrs(
getframe(),
temp_C,
spice0,
"spiciness0",
units=" ",
comment="",
standard_name="spiciness0",
)
return spice0
def par_dark_count(par, dives, depth, time):
"""
Calculates an in situ dark count from the PAR sensor.
The in situ dark count for the PAR sensor is calculated from the median,
with masking applied for values before 23:01 and outside the 90th %
Parameters
----------
par: numpy.ndarray or pandas.Series
The par array after factory calibration in units uE/m2/sec.
dives: numpy.ndarray or pandas.Series
The dive count (round is down dives, 0.5 is up dives).
depth: numpy.ndarray or pandas.Series
The depth array in metres.
time: numpy.ndarray or pandas.Series
The date & time array in a numpy.datetime64 format.
Returns
-------
par_dark: numpy.ndarray or pandas.Series
The par data corrected for the in situ dark value in units uE/m2/sec.
"""
from numpy import array, ma, nanmedian, isnan, nanpercentile
par_arr = array(par)
dives = array(dives)
depth = array(depth)
time = array(time)
# DARK CORRECTION FOR PAR
hrs = time.astype('datetime64[h]') - time.astype('datetime64[D]')
xi = ma.masked_inside(hrs.astype(int), 22, 2) # find 23:01 hours
yi = ma.masked_outside(depth,
*nanpercentile(depth[~isnan(par)],
[90, 100])) # 90th pctl of depth
i = ~(xi.mask | yi.mask)
dark = nanmedian(par_arr[i])
par_dark = par_arr - dark
par_dark[par_dark < 0] = 0
par_dark = transfer_nc_attrs(getframe(), par, par_dark, '_dark')
return par_dark
def outlier_bounds_iqr(arr, multiplier=1.5):
r"""
Mask values outside the upper/lower outlier limits by interquartile range:
.. math::
lim_{low} = Q_1 - 1.5\cdot(Q_3 - Q_1)\\
lim_{up} = Q_3 + 1.5\cdot(Q_3 - Q_1)
the multiplier [1.5] can be adjusted by the user
returns the lower_limit, upper_limit
Parameters
----------
arr : np.array|xr.DataArray, dtype=float, shape=[n, ]
the full timeseries of the entire dataset
multiplier : float=1.5
sets the interquartile range
Returns
-------
arr : array | xarray.DataArray
A data object where values outside the limits are masked.
Metdata will be preserved if the original input array is xr.DataArray
"""
from numpy import array, nan, nanpercentile
var = arr.copy()
arr = array(arr)
q1, q3 = nanpercentile(arr, [25, 75])
iqr = q3 - q1
ll = q1 - iqr * multiplier
ul = q3 + iqr * multiplier
mask = (arr < ll) | (arr > ul)
arr[mask] = nan
attrs = dict(outlier_lims=[ll, ul])
out = transfer_nc_attrs(getframe(), var, arr, "_outlierIQR", **attrs)
return out
def outlier_bounds_std(arr, multiplier=3):
r"""
Mask values outside the upper and lower outlier limits by standard
deviation
:math:`\mu \pm 3\sigma`
the multiplier [3] can be adjusted by the user
returns the lower_limit, upper_limit
Parameters
----------
arr : np.array|xr.DataArray, dtype=float, shape=[n, ]
the full timeseries of the entire dataset
multiplier : float=1.5
sets the standard deviation multiplier
Returns
-------
arr : array | xarray.DataArray
A data object where values outside the limits are masked.
Metdata will be preserved if the original input array is xr.DataArray
"""
from numpy import array, nan, nanmean, nanstd
var = arr.copy()
arr = array(arr)
mean = nanmean(arr)
std = nanstd(arr)
ll = mean - std * multiplier
ul = mean + std * multiplier
mask = (arr < ll) | (arr > ul)
arr[mask] = nan
attrs = dict(outlier_lims=[ll, ul])
out = transfer_nc_attrs(getframe(), var, arr, "_outlierSTD", **attrs)
return out
def predict(self, x):
"""
A wrapper around the normal predict function that takes
nans into account. An extra dimension is also added if needed.
"""
from xarray import DataArray
var = x.copy()
x = _np.array(x)
out = _np.ndarray(x.size) * _np.NaN
i = ~_np.isnan(x)
x = x[i].reshape(-1, 1)
out[i.squeeze()] = self._predict(x).squeeze()
out = transfer_nc_attrs(getframe(), var, out, "_calibrated")
if hasattr(self, "info") & isinstance(out, DataArray):
out.attrs["model_info"] = str(self.info)
return out
def fluorescence_dark_count(flr, depth, percentile=5):
"""
Calculates an in situ dark count from the fluorescence sensor.
The in situ dark count for the fluorescence sensor is calculated from the
user-defined percentile between 300 and 400m.
Parameters
----------
flr: numpy.ndarray or pandas.Series
The fluorescence array after factory calibration.
depth: numpy.ndarray or pandas.Series
The depth array in metres.
Returns
-------
flr: numpy.ndarray or pandas.Series
The fluorescence data corrected for the in situ dark value.
"""
from numpy import array, isnan, nanpercentile
import warnings
mask = (depth > 300) & (depth < 400)
flr_dark = array(flr)
if (~isnan(flr_dark[mask])).sum() == 0:
warnings.warn(
"\nThere are no fluorescence measurements between "
"300 and 400 metres.\nThe dark count correction "
"cannot be made and fluorescence data can't be processed.",
UserWarning,
)
dark_pctl = nanpercentile(flr_dark[mask], percentile)
flr_dark -= dark_pctl
flr_dark[flr_dark < 0] = 0
flr_dark = transfer_nc_attrs(getframe(), flr, flr_dark, "_dark")
return flr_dark
def backscatter_dark_count(bbp, depth, percentile=5):
"""
Calculates an in situ dark count from the backscatter sensor.
The in situ dark count for the backscatter sensor is calculated from the
user-defined percentile between 200 and 400m.
Parameters
----------
bbp: numpy.ndarray or pandas.Series
The total backscatter array after factory calibration in m-1.
depth: numpy.ndarray or pandas.Series
The depth array in metres.
Returns
-------
bbp: numpy.ndarray or pandas.Series
The total backscatter data corrected for the in situ dark value.
"""
from numpy import array, isnan, nanpercentile
import warnings
bbp_dark = array(bbp)
mask = (depth > 200) & (depth < 400)
if (~isnan(bbp[mask])).sum() == 0:
warnings.warn(
"There are no backscatter measurements between 200 "
"and 400 metres.The dark count correction cannot be "
"made and backscatter data can't be processed.",
UserWarning,
)
dark_pctl = nanpercentile(bbp_dark[mask], percentile)
bbp_dark -= dark_pctl
bbp_dark[bbp_dark < 0] = 0
bbp_dark = transfer_nc_attrs(getframe(), bbp, bbp_dark, "_dark")
return bbp_dark
def rolling_window(var, func, window):
"""
A rolling window function that is nan-resiliant
Parameters
----------
arr:array, dtype=float, shape=[n, ]
array that you want to pass the rolling window over
func : callable
an aggregating function. e.g. mean, std, median
window : int
the size of the rolling window that will be applied
Returns
-------
arr : array, dtype=float, shape=[n, ]
the same as the input array, but the rolling window has been applied
"""
from numpy import array, nan, ndarray, r_
n = window
# create an empty 2D array with shape (window, len(arr))
arr = array(var)
mat = ndarray([n, len(arr) - n]) * nan
# create a vector for each window
for i in range(n):
mat[i, :] = arr[i:i - n]
# get the mean or meidan or any other function of the matrix
out = func(mat, axis=0)
# the array will be shorter than the original
# pad the output with the rolling average of the values left out
i0 = n // 2
i1 = n - i0
seg0 = array([func(arr[:i + 1]) for i in range(i0)])
seg1 = array([func(arr[-i - 1:]) for i in range(i1)])
rolwin = r_[seg0, out, seg1]
rolwin = transfer_nc_attrs(getframe(), var, rolwin, "_rollwin")
return rolwin
def fluorescence_dark_count(flr, depth):
"""
Calculates an in situ dark count from the fluorescence sensor.
The in situ dark count for the fluorescence sensor is calculated from the
95th percentile between 300 and 400m.
Parameters
----------
flr: numpy.ndarray or pandas.Series
The fluorescence array after factory calibration.
depth: numpy.ndarray or pandas.Series
The depth array in metres.
Returns
-------
flr: numpy.ndarray or pandas.Series
The fluorescence data corrected for the in situ dark value.
"""
from numpy import nanpercentile, isnan, array
mask = (depth > 300) & (depth < 400)
flr_dark = array(flr)
if (~isnan(flr_dark[mask])).sum() == 0:
raise UserWarning(
'\nThere are no fluorescence measurements between '
'300 and 400 metres.\nThe dark count correction '
"cannot be made and fluorescence data can't be processed.")
dark_pctl5 = nanpercentile(flr_dark[mask], 5)
flr_dark -= dark_pctl5
flr_dark[flr_dark < 0] = 0
flr_dark = transfer_nc_attrs(getframe(), flr, flr_dark, '_dark')
return flr_dark
def time_average_per_dive(dives, time):
"""
Gets the average time stamp per dive. This is used to create psuedo
discrete time steps per dive for plotting data (using time as x-axis
variable).
Parameters
----------
dives : np.array, dtype=float, shape=[n, ]
discrete dive numbers (down = d.0; up = d.5) that matches time length
time : np.array, dtype=datetime64, shape=[n, ]
time stamp for each observed measurement
Returns
-------
time_average_per_dive : np.array, dtype=datetime64, shape=[n, ]
each dive will have the average time stamp of that dive. Can be used
for plotting where time_average_per_dive is set as the x-axis.
"""
from numpy import array, datetime64, nanmean
from pandas import Series
atime = array(time)
dives = array(dives)
if isinstance(atime[0], datetime64):
t = atime.astype("datetime64[s]").astype(float)
else:
t = atime
t_grp = Series(t).groupby(dives)
t_mid = nanmean([t_grp.max(), t_grp.min()], axis=0)
t_ser = Series(t_mid, index=t_grp.mean().index.values)
diveavg = t_ser.reindex(index=dives).values
diveavg = diveavg.astype("datetime64[s]")
diveavg = transfer_nc_attrs(getframe(), time, diveavg, "_diveavg")
return diveavg
def backscatter_dark_count(bbp, depth):
"""
Calculates an in situ dark count from the backscatter sensor.
The in situ dark count for the backscatter sensor is calculated from the
95th percentile between 200 and 400m.
Parameters
----------
bbp: numpy.ndarray or pandas.Series
The total backscatter array after factory calibration in m-1.
depth: numpy.ndarray or pandas.Series
The depth array in metres.
Returns
-------
bbp: numpy.ndarray or pandas.Series
The total backscatter data corrected for the in situ dark value.
"""
from numpy import nanpercentile, isnan, array
bbp_dark = array(bbp)
mask = (depth > 200) & (depth < 400)
if (~isnan(bbp[mask])).sum() == 0:
raise UserWarning('There are no backscatter measurements between 200 '
'and 400 metres.The dark count correction cannot be '
"made and backscatter data can't be processed.")
dark_pctl5 = nanpercentile(bbp_dark[mask], 5)
bbp_dark -= dark_pctl5
bbp_dark[bbp_dark < 0] = 0
bbp_dark = transfer_nc_attrs(getframe(), bbp, bbp_dark, '_dark')
return bbp
def quenching_correction(
flr,
bbp,
dives,
depth,
time,
lat,
lon,
max_photic_depth=100,
night_day_group=True,
surface_layer=5,
sunrise_sunset_offset=1,
):
"""
Corrects the fluorescence data based upon Thomalla et al. (2017).
The function calculates the quenching depth and performs the quenching
correction based on the fluorescence to backscatter ratio. The quenching
depth is calculated based upon the different between night and daytime
fluorescence. The default setting is for the preceding night to be used to
correct the following day's quenching (`night_day_group=True`). This can
be changed so that the following night is used to correct the preceding
day. The quenching depth is then found from the difference between the
night and daytime fluorescence, using the steepest gradient of the {5
minimum differences and the points the difference changes sign (+ve/-ve)}.
The function gets the backscatter/fluorescence ratio between from the
quenching depth to the surface, and then calculates a mean nighttime
ratio for each night. The quenching ratio is calculated from the nighttime
ratio and the daytime ratio, which is then applied to fluorescence to
correct for quenching. If the corrected value is less than raw, then the
function will return the original raw data.
Parameters
----------
flr: numpy.ndarray or pandas.Series
fluorescence data after cleaning and factory calibration conversion
bbp: numpy.ndarray or pandas.Series
Total backscatter after cleaning and factory calibration conversion
dives: numpy.ndarray or pandas.Series
The dive count (round is down dives, 0.5 is up dives).
depth: numpy.ndarray or pandas.Series
The depth array in metres.
time: numpy.ndarray or pandas.Series
The date & time array in a numpy.datetime64 format.
lat: numpy.ndarray or pandas.Series
The latitude of the glider position.
lon: numpy.ndarray or pandas.Series
The longitude of the glider position.
max_photic_depth: int
Limit the quenching correction to depth less than a given value [100].
night_day_group: bool
If True, use preceding night otherwise use following night for
calculating the flr:bbp ratio.
surface_layer: int
The surface depth that is omitted from the correction calculations
(metres)
sunrise_sunset_offset: int
The delayed onset and recovery of quenching in hours [1]
(assumes symmetrical).
Returns
-------
flr_corrected: numpy.ndarray or pandas.Series
The fluorescence data corrected for quenching.
quenching layer: bool
A boolean mask of where the fluorescence is quenched.
"""
import numpy as np
import pandas as pd
from scipy.interpolate import Rbf
from .cleaning import rolling_window
def grad_min(depth, fluor_diff, surface_layer=5):
"""
TODO: need to refine this function. Doesn't always correct to
the deepest quenching point
Quenching depth for a day/night fluorescence difference
INPUT: depth and fluorescence as pd.Series or np.ndarray
surface_layer [5] is the depth to search for the
reference in the gradient
OUPUT: Quenching layer as a boolean mask
"""
if depth.size <= surface_layer:
return np.zeros(depth.size).astype(bool)
x = np.array(depth)
y = rolling_window(np.array(fluor_diff), np.nanmean, 5)
s = x < surface_layer # surface data to the top 5 metres
mask = np.zeros(depth.size).astype(bool)
# get the smallest 5 points and where the difference crosses 0
small5 = np.argsort(np.abs(y))[:5]
cross0 = np.where(np.r_[False, np.diff((y) > 0)])[0]
# combine the indicies
i = np.unique(np.r_[small5, cross0])
# the max in the surface as a reference
if not s.sum():
return mask
j = y[s].argmax()
# calculate the gradient of the selected points to the reference
grad = (y[s][j] - y[i]) / (x[s][j] - x[i])
# If there are only nans in the gradient return only nans
if np.isnan(grad).all():
return mask
# get the index of the steepest gradient (min)
grad_min_i = i[np.nanargmin(grad)]
# fill the mask with True values above the quenching depth
mask[0:grad_min_i] = True
# on up dives the array is backwards so reverse the mask
if x[-1] < x[0]:
mask = ~mask
# If the majority of the points in the selected region are
# negative (night < day) then return an empty mask
return mask
var = flr.copy() # create a copy for netCDF attrs preservation
flr = np.array(flr)
bbp = np.array(bbp)
dives = np.array(dives)
depth = np.array(depth)
time = np.array(time)
lat = np.array(lat)
lon = np.array(lon)
# ############################ #
# GENERATE DAY/NIGHT BATCHES #
# ############################ #
sunrise, sunset = sunset_sunrise(time, lat, lon)
offset = np.timedelta64(sunrise_sunset_offset, 'h')
# creating quenching correction batches, where a batch is a night and the
# following day
day = (time > (sunrise + offset)) & (time < (sunset + offset))
# find day and night transitions
daynight_transitions = np.abs(np.diff(day.astype(int)))
# get the cumulative sum of daynight to generate separate batches for day
# and night
daynight_batches = daynight_transitions.cumsum()
# now get the batches with padded 0 to account for the diff
# also add a bool that makes night_day or day_night batches
batch = np.r_[0, (daynight_batches + night_day_group) // 2]
isday = (np.r_[0, daynight_batches] / 2 % 1) == 0
# ######################## #
# GET NIGHTTIME AVERAGES #
# ######################## #
# blank arrays to be filled
flr_night, bbp_night = flr.copy(), bbp.copy()
# create a dataframe with fluorescence and backscatter
df = pd.DataFrame(np.c_[flr, bbp], columns=['flr', 'bbp'])
# get the binned averages for each batch and select the night
night_ave = df.groupby([day, batch, np.around(depth)]).mean()
night_ave = night_ave.dropna().loc[False]
# A second group where only batches are grouped
grp_batch = df.groupby(batch)
# GETTING NIGHTTIME AVERAGE FOR NONGRIDDED DATA - USE RBF INTERPOLATION
for b in np.unique(night_ave.index.labels[0]):
i = grp_batch.groups[b].values # batch index
j = i[~np.isnan(flr[i]) & (depth[i] < 400)] # index without nans
x = night_ave.loc[b].index.values # batch depth
y = night_ave.loc[b] # batch flr and bbp
if y.flr.isna().all() | y.bbp.isna().all():
continue
elif y.flr.size <= 2:
continue
# radial basis functions with a smoothing factor
f1 = Rbf(x, y.flr.values, function='linear', smooth=20)
f2 = Rbf(x, y.bbp.values, function='linear', smooth=20)
# interpolation function is used to find flr and bbp for all
# nighttime fluorescence
flr_night[j] = f1(depth[j])
bbp_night[j] = f2(depth[j])
# calculate the difference between average nighttime - and fluorescence
fluor_diff = flr_night - flr
# ################################ #
# FIND THE QUENCHING DEPTH LAYER #
# ################################ #
# create a "photic layer" mask to which calc will be limited daytime,
# shalower than [100m] and fluoresence is quenched relative to night
photic_layer = isday & (depth < max_photic_depth) & (fluor_diff > 0)
# blank array to be filled
quenching_layer = np.zeros(depth.size).astype(bool)
# create a grouped dataset by dives to find the depth of quenching
cols = np.c_[depth, fluor_diff, dives][photic_layer]
grp = pd.DataFrame(cols, columns=['depth', 'flr_dif', 'dives'])
grp = grp.groupby('dives')
# apply the minimum gradient algorithm to each dive
quench_mask = grp.apply(lambda df: grad_min(df.depth, df.flr_dif))
# fill the quench_layer subscripted to the photic layer
quenching_layer[photic_layer] = np.concatenate([l for l in quench_mask])
# ################################### #
# DO THE QUENCHING CORRECTION MAGIC #
# ################################### #
# a copy of fluorescence to be filled with quenching corrected data
flr_corrected = flr.copy()
# nighttime backscatter to fluorescence ratio
flr_bb_night = flr_night / bbp_night
# quenching ratio for nighttime
quench_ratio = flr_bb_night * bbp / flr
# apply the quenching ratio to the fluorescence
quench_corrected = flr * quench_ratio
# if unquenched data is corrected return the original data
mask = quench_corrected < flr
quench_corrected[mask] = flr[mask]
# fill the array with queching corrected data in the quenching layer only
flr_corrected[quenching_layer] = quench_corrected[quenching_layer]
flr_corrected = transfer_nc_attrs(getframe(),
var,
flr_corrected,
'flr_quench_corrected',
units='RFU')
quenching_layer = transfer_nc_attrs(getframe(),
var,
quenching_layer,
'quench_layer',
units='')
return flr_corrected, quenching_layer
def par_fill_surface(par, dives, depth, max_curve_depth=100):
"""
Algebraically calculates the top 5 metres of the par profile.
The function removes the top 5 metres of par data, and then using an
exponential equation calculates the complete profile.
Parameters
----------
par: numpy.ndarray or pandas.Series
The par data with units uE/m2/sec.
dives: numpy.ndarray or pandas.Series
The dive count (round is down dives, 0.5 is up dives).
depth: numpy.ndarray or pandas.Series
The depth array in metres.
max_curve_depth: int
The maximum depth of which to fit the exponential function.
Returns
-------
par_filled: numpy.ndarray or pandas.Series
The par data with the algebraically calculated top 5 metres.
"""
from scipy.optimize import curve_fit
import numpy as np
def dive_par_fit(depth, par):
def exp_func(x, a, b):
return a * np.exp(b * x)
xj, yj = depth, par
mask = ~(np.isnan(xj) | np.isnan(yj)) & (xj < max_curve_depth)
xm, ym = xj[mask], yj[mask]
if all(ym == 0) | (mask.sum() <= 2):
yj_hat = np.ones_like(depth) * np.nan
else:
try:
[a, b], _ = curve_fit(exp_func,
xm,
ym,
p0=(500, -0.03),
maxfev=1000)
yj_hat = exp_func(xj, a, b)
except RuntimeError:
yj_hat = np.ones_like(depth) * np.nan
return yj_hat
var = par.copy()
par = np.array(par)
dives = np.array(dives)
depth = np.array(depth)
par_filled = np.ones_like(depth) * np.nan
for d in np.unique(dives):
i = dives == d
par_fit = dive_par_fit(depth[i], par[i])
par_filled[i] = par_fit
par_filled = transfer_nc_attrs(getframe(), var, par_filled, 'par_expfill')
return par_filled
def bottle_matchup(
gld_dives,
gld_depth,
gld_time,
btl_depth,
btl_time,
btl_values,
min_depth_diff_metres=5,
min_time_diff_minutes=120,
):
"""
Performs a matchup between glider and bottle samples based on time and
depth (or density).
Parameters
----------
gld_depth : np.array, dtype=float
glider depth at time of measurement
gld_dives : np.array, dtype=float
dive index of the glider (given by glider toolbox)
gld_time : np.array, dtype=datetime64
glider time that will be used as primary indexing variable
btl_time: np.array, dtype=datetime64
in-situ bottle sample's time
btl_depth : np.array, dtype=float
depth of in-situ sample
btl_values : np.array, dtype=float
the value that will be interpolated onto the glider time and
depth coordinates (time, depth/dens)
min_depth_diff_metres : float, default=5
the minimum allowable depth difference
min_time_diff_minutes : float, default=120
the minimum allowable time difference between bottles and glider
Returns
-------
array : float
Returns the bottle values in the format of the glider
i.e. the length of the output will be the same as gld_*
"""
from pandas import Series
# metadata preservation
var = gld_depth.copy()
if isinstance(btl_values, Series):
var_name = btl_values.name + "_bottle_matchups"
else:
var_name = "bottle_matchups"
# make all input variables np.arrays
args = gld_time, gld_depth, gld_dives, btl_time, btl_depth, btl_values
gld_time, gld_depth, gld_dives, btl_time, btl_depth, btl_values = map(
_np.array, args
)
# create a blank array that matches glider data
# (placeholder for calibration bottle values)
gld_cal = _np.ones_like(gld_depth) * _np.nan
# loop through each ship based CTD station
stations = _np.unique(btl_time)
for c, t in enumerate(stations):
# index of station from ship CTD
btl_idx = t == btl_time
# number of samples per station
btl_num = btl_idx.sum()
# string representation of station time
t_str = str(t.astype("datetime64[m]")).replace("T", " ")
t_dif = abs(gld_time - t).astype("timedelta64[m]").astype(float)
# loop through depths for the station
if t_dif.min() < min_time_diff_minutes:
# index of dive where minimum difference occurs
i = _np.where(gld_dives[_np.nanargmin(t_dif)] == gld_dives)[0]
n_depths = 0
for depth in btl_depth[btl_idx]:
# an index for bottle where depth and station match
j = btl_idx & (depth == btl_depth)
# depth difference for glider profile
d_dif = abs(gld_depth - depth)[i]
# only match depth if diff is less than given threshold
if _np.nanmin(d_dif) < min_depth_diff_metres:
# index of min diff for this dive
k = i[_np.nanargmin(d_dif)]
# assign the bottle values to the calibration output
gld_cal[k] = btl_values[j]
n_depths += 1
print(
(
"[stn {}/{}] SUCCESS: {} ({} of {} samples) match-up "
"within {} minutes"
).format(c, stations.size, t_str, n_depths, btl_num, t_dif.min())
)
else:
print(
(
"[stn {}/{}] FAILED: {} Couldn't find samples within "
"constraints"
).format(c, stations.size, t_str)
)
attrs = dict(units="", positive="", comment="", standard_name="", axis="")
gld_cal = transfer_nc_attrs(getframe(), var, gld_cal, var_name, **attrs)
return gld_cal
def horizontal_diff_outliers(dives,
depth,
arr,
multiplier=1.5,
depth_threshold=450,
mask_frac=0.2):
"""
Find Z-score outliers (> 3) on the horizontal. Can be limited below a
certain depth.
The function uses the horizontal gradient as a threshold, below a defined
depth threshold to find outliers. Useful to identify when a variable at
depth is not the same as neighbouring values.
Parameters
----------
dives: numpy.ndarray or pandas.Series
The dive count (round is down dives, 0.5 is up dives)
depth: numpy.ndarray or pandas.Series
The depth array in metres
arr: numpy.ndarray or pandas.Series
Array of data variable for cleaning to be performed on.
multiplier: float
A z-score threshold
depth_threshold: int
Outliers will be identified below this depth value to the max depth
value of the dive.
mask_frac: float
When the ratio of bad values per dive is greater than this value, then
the dive will be masked.
Returns
-------
mask
A mask of dives where the bad values per dive ratio is greater than
mask_frac.
"""
from numpy import abs, arange, array, inf, nanmean, nanstd
from .mapping import grid_data
var = arr.copy()
dives = array(dives)
depth = array(depth)
arr = array(arr)
# grid data so that the horizontal rolling median can be calculated
# we use a window of 3 to find only "horizonal spikes"
gridded = grid_data(
dives,
depth,
array(arr),
bins=arange(0, depth.max(), 1),
verbose=False,
return_xarray=False,
)
median = gridded.rolling(3, axis=1, center=True, min_periods=2).median()
# get zscore of the difference between the median and the raw data
diff = gridded - median
zdiff = abs(diff - nanmean(diff)) / nanstd(diff)
# this finds the 99.7th percentile outliers
# note that this is based on the global horizonal diff
# but is only applied below the depth threshold
# this means that the surface data sets a higher limit
deep_outlier = zdiff.loc[depth_threshold:] >= multiplier
# get the ratio of bad values per dive and mask if it
# exceeds a user defined fraction
deep_outlier_count = deep_outlier.sum()
deep_obs_num = gridded.shape[0] - depth_threshold # assumes bin of 1m
deep_outlier_ratio = deep_outlier_count / deep_obs_num
# finds the index where dives exceed the mask_frac threshold
i = deep_outlier_ratio > mask_frac
deep_outlier_dives = i[i].index.values
mask = arr < -inf # create a dummy mask
for d in deep_outlier_dives:
i = dives == d
mask[i] = True
baddives = mask_bad_dive_fraction(mask, dives, arr, mask_frac=mask_frac)[0]
out = transfer_nc_attrs(getframe(), var, baddives, "_horzOutlierSTD")
return out
def calc_physics(
variable,
dives,
depth,
spike_window=3,
spike_method="minmax",
iqr=1.5,
depth_threshold=400,
mask_frac=0.2,
savitzky_golay_window=11,
savitzky_golay_order=2,
verbose=True,
name="Physics Variable",
):
"""
A standard setup for processing physics variables (temperature, salinity).
The function applies a neighbourhood interquartile range (IQR)
outlier filter, the Briggs et al. (2011) spike filter
followed by a Savitzky-Golay smoothing function.
The Savitzky-Golay filter is demonstrated well on wikipedia:
https://en.wikipedia.org/wiki/Savitzky-Golay_filter
"""
from numpy import array, isnan
from .cleaning import (
despike,
horizontal_diff_outliers,
outlier_bounds_iqr,
savitzky_golay,
)
# an interpolation step is added so that no nans are created.
# Note that this interpolates on the flattened series
var = variable.copy() # attribute preservation
x = array(dives)
y = array(depth)
z = array(variable)
printv(verbose, "\n" + "=" * 50 + "\n{}:".format(name))
if iqr:
nans_before = isnan(z).sum()
z = outlier_bounds_iqr(z, multiplier=iqr)
nans_after = isnan(z).sum()
n_masked = nans_after - nans_before
printv(
verbose,
"\tRemoving outliers with IQR * {}: {} obs".format(iqr, n_masked),
)
if spike_window:
z = despike(z, spike_window, spike_method)[0]
printv(
verbose,
"\tRemoving spikes with rolling median (spike window={})".format(
spike_window),
)
if depth_threshold:
z = horizontal_diff_outliers(x, y, z, iqr, depth_threshold, mask_frac)
printv(
verbose,
("\tRemoving horizontal outliers "
"(fraction={}, multiplier={})").format(mask_frac, iqr),
)
if savitzky_golay_window:
printv(
verbose,
("\tSmoothing with Savitzky-Golay filter "
"(window={}, order={})").format(savitzky_golay_window,
savitzky_golay_order),
)
z = savitzky_golay(z, savitzky_golay_window, savitzky_golay_order)
z = transfer_nc_attrs(getframe(), var, z, "_processed")
return z
def savitzky_golay(var, window_size, order, deriv=0, rate=1, interpolate=True):
"""
Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
The Savitzky-Golay filter removes high frequency noise from data [1]_.
It has the advantage of preserving the original shape and features of the
signal better than other types of filtering approaches, such as moving
averages techniques. By default, nans in the array are interpolated with a
limit set to the window size of the dataset before smoothing. The nans are
inserted back into the dataset after the convolution. This limits the loss
of data over blocks where there are nans. This can be switched off with the
`interpolate` keyword arguement.
Parameters
----------
var : array, dtype=float, shape=[n, ]
the values of the time history of the signal.
window_size : int
the length of the window. Must be an odd integer number.
order : int
the order of the polynomial used in the filtering.
Must be less then `window_size` - 1.
deriv : int
the order of the derivative to compute (default = 0 means only
smoothing)
interpolate : bool=True
By default, nans in the array are interpolated with a limit set to
the window size of the dataset before smoothing. The nans are
inserted back into the dataset after the convolution. This limits
the loss of data over blocks where there are nans. This can be
switched off with the `interpolate` keyword arguement.
Returns
-------
ys : ndarray, shape (N)
the smoothed signal (or it's n-th derivative).
Notes
-----
The Savitzky-Golay is a type of low-pass filter, particularly
suited for smoothing noisy data. The main idea behind this
approach is to make for each point a least-square fit with a
polynomial of high order over a odd-sized window centered at
the point [2]_.
Examples
--------
>>> t = linspace(-4, 4, 500)
y = exp( -t**2 ) + random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.show()
References
----------
.. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
Data by Simplified Least Squares Procedures. Analytical
Chemistry, 1964, 36 (8), pp 1627-1639.
.. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
Cambridge University Press ISBN-13: 9780521880688
"""
from math import factorial
from numpy import abs, array, concatenate, convolve, isnan, linalg, mat, nan
from pandas import Series
# sorting out window stuff
arr = array(var)
try:
window_size = abs(int(window_size))
order = abs(int(order))
except ValueError:
raise ValueError("window_size and order have to be of type int")
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window_size size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomial order")
order_range = range(order + 1)
half_window = (window_size - 1) // 2
# allow to interpolate for the window size
if interpolate:
ser = Series(arr).interpolate()
y = array(ser)
else:
y = array(arr)
# precompute coefficients
b = mat([[k**i for i in order_range]
for k in range(-half_window, half_window + 1)])
m = linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - abs(y[1:half_window + 1][::-1] - y[0])
lastvals = y[-1] + abs(y[-half_window - 1:-1][::-1] - y[-1])
y = concatenate((firstvals, y, lastvals))
savgol = convolve(m[::-1], y, mode="valid")
oldnans = isnan(arr)
savgol[oldnans] = nan
savgol = transfer_nc_attrs(getframe(), var, savgol, "_savgolay")
return savgol
def calc_backscatter(
bb_raw,
tempC,
salt,
dives,
depth,
wavelength,
dark_count,
scale_factor,
spike_window=7,
spike_method="median",
iqr=3,
profiles_ref_depth=300,
deep_multiplier=1,
deep_method="median",
return_figure=False,
verbose=True,
):
r"""
The function processes the raw backscattering data in counts into total
backscatter (bbp) in metres.
The function uses a series of steps to clean the data before applying the
Zhang et al. (2009) functions to convert the data into total backscatter
(bbp/m)). The function uses functions from the flo_functions toolkit [1]_.
The theta angle of sensors (124deg) and xfactor for theta 124 (1.076) are
set values that should be updated if you are not using a WetLabs ECO BB2FL
The following standard sequence is applied:
1. find IQR outliers (i.e. data values outside of the lower and upper
limits calculated by cleaning.outlier_bounds_iqr)
2. find_bad_profiles (e.g. high values below 300 m are counted as bad
profiles)
3. flo_scale_and_offset (factory scale and offset)
4. flo_bback_total (total backscatter based on Zhang et al. 2009) [2]_
5. backscatter_dark_count (based on Briggs et al. 2011) [3]_
6. despike (using Briggs et al. 2011 - rolling min--max) [3]_
Parameters
----------
bb_raw: np.array / pd.Series, dtype=float, shape=[n, ]
The raw output from the backscatter channel in counts.
tempC: np.array / pd.Series, dtype=float, shape=[n, ]
The QC'd temperature data in degC.
salt: np.array / pd.Series, dtype=float, shape=[n, ]
The QC'd salinity in PSU.
dives: np.array / pd.Series, dtype=float, shape=[n, ]
The dive count (round is down dives, 0.5 is up dives).
depth: np.array / pd.Series, dtype=float, shape=[n, ]
The depth array in metres.
wavelength: int
The wavelength of the backscatter channel, e.g. 700 nm.
dark_count: float
The dark count factory values from the calibration sheet.
scale_factor: float
The scale factor factory values from the calibration sheet.
spike_window: int
The window size over which to run the despiking method.
spike_method: str
Whether to use a rolling median or combination of min+max filter as
the despiking method.
iqr: int
Multiplier to determine the lower and upper limits of the
interquartile range for outlier detection.
profiles_ref_depth: int
The depth threshold for optics.find_bad_profiles below which the
median or mean is calculated for identifying outliers.
deep_multiplier: int=1
The standard deviation multiplier for calculating outliers,
i.e. :math:`\mu \pm \sigma \cdot[1]`.
deep_method: str
Whether to use the deep median or deep mean to determine bad profiles
for optics.find_bad_profiles.
return_figure: bool
If True, will return a figure object that shows before and after the
quenching correction was applied.
verbose: bool
If True, will print the progress of the processing function.
Returns
-------
baseline: numpy.ma.masked_array
The despiked + bad profile identified backscatter with the mask
denoting the filtered values of the backscatter baseline as
defined in Briggs et al. (2011).
quench_corrected: np.array / pd.Series, dtype=float, shape=[n, ]
The backscatter spikes as defined in Briggs et al. (2011).
figs: object
The figures reporting the despiking, bad profiles and quenching
correction.
References
----------
.. [1] https://github.com/ooici/ion-functions Copyright (c) 2010, 2011 The
Regents of the University of California
.. [2] Zhang, X., Hu, L., & He, M. (2009). Scattering by pure seawater:
Effect of salinity. Optics Express, 17(7), 5698.
https://doi.org/10.1364/OE.17.005698
.. [3] Briggs, N., Perry, M. J., Cetinic, I., Lee, C., D'Asaro, E., Gray,
A. M., & Rehm, E. (2011). High-resolution observations of aggregate
flux during a sub-polar North Atlantic spring bloom. Deep-Sea
Research Part I: Oceanographic Research Papers, 58(10), 1031–1039.
https://doi.org/10.1016/j.dsr.2011.07.007
"""
from numpy import array, count_nonzero, isnan, nan, unique
from pandas import Series
from . import flo_functions as ff
from . import optics as op
from .cleaning import despike, despiking_report, outlier_bounds_iqr
var = bb_raw.copy() # metadata preservation
bb_raw = Series(bb_raw.copy())
dives = array(dives)
depth = array(depth)
tempC = array(tempC)
salt = array(salt)
name = "bb{:.0f}".format(wavelength)
theta = 124 # factory set angle of optical sensors
xfactor = 1.076 # for theta 124
# Values taken from Sullivan et al. (2013) & Slade and Boss (2015)
ref_depth = profiles_ref_depth
stdev_multiplier = deep_multiplier
method = deep_method
dive_count = count_nonzero(unique(dives))
printv(verbose, "\n" + "=" * 50 + "\n{}:".format(name))
if iqr:
nans_before = isnan(bb_raw).sum()
bb_raw = outlier_bounds_iqr(bb_raw, multiplier=iqr)
nans_after = isnan(bb_raw).sum()
n_masked = nans_after - nans_before
printv(
verbose,
"\tRemoving outliers with IQR * {}: {} obs".format(iqr, n_masked),
)
printv(
verbose,
"\tMask bad profiles based on deep values (depth={}m)".format(
ref_depth),
)
bad_profiles = op.find_bad_profiles(dives, depth, bb_raw, ref_depth,
stdev_multiplier, method)
bb_raw[bad_profiles[0]] = nan
bad_count = count_nonzero(bad_profiles[1])
printv(
verbose,
"\tNumber of bad profiles = {}/{}".format(bad_count, dive_count),
)
printv(verbose, "\tZhang et al. (2009) correction")
beta = ff.flo_scale_and_offset(bb_raw, dark_count, scale_factor)
bbp = ff.flo_bback_total(beta, tempC, salt, theta, wavelength, xfactor)
# This is from .Briggs et al. (2011)
printv(verbose, "\tDark count correction")
bbp = op.backscatter_dark_count(bbp, depth)
printv(
verbose,
"\tSpike identification (spike window={})".format(spike_window),
)
baseline, spikes = despike(bbp, spike_window, spike_method="median")
baseline = Series(baseline, name="bb{:.0f}".format(wavelength))
baseline = transfer_nc_attrs(
getframe(),
var,
baseline,
name + "_baseline",
units="units",
standard_name="backscatter",
)
spikes = transfer_nc_attrs(
getframe(),
var,
spikes,
name + "_spikes",
units="units",
standard_name="backscatter",
)
if not return_figure:
return baseline, spikes
else:
printv(verbose, "\tGenerating figure for despiking report")
fig = despiking_report(dives, depth, bbp, baseline, spikes, name=name)
return baseline, spikes, fig
def calc_fluorescence(
flr_raw,
bbp,
dives,
depth,
time,
lat,
lon,
dark_count,
scale_factor,
spike_window=7,
spike_method="median",
night_day_group=True,
sunrise_sunset_offset=1,
profiles_ref_depth=300,
deep_multiplier=1,
deep_method="median",
return_figure=False,
verbose=True,
):
r"""
This function processes raw fluorescence and corrects for quenching using
the Thomalla et al. (2018) approach [1]_.
The following standard sequence is applied:
1. find_bad_profiles (e.g. high Fluorescence in > 300 m water signals
bad profile)
2. fluorescence_dark_count & scale factor (i.e. factory correction)
3. despike (using Briggs et al. 2011 - rolling min--max)
4. quenching_correction (corrects for quenching with Thomalla et al. 2017)
Parameters
----------
flr_raw: np.array / pd.Series, dtype=float, shape=[n, ]
The raw output of fluorescence data in instrument counts.
bbp: np.array / pd.Series, dtype=float, shape=[n, ]
The processed backscatter data from the less noisy channel, i.e. the
one dataset with less spikes or bad profiles.
dives: np.array / pd.Series, dtype=float, shape=[n, ]
The dive count (round is down dives, 0.5 is up dives).
depth: np.array / pd.Series, dtype=float, shape=[n, ]
The depth array in metres.
time: np.array / pd.Series, dtype=float, shape=[n, ]
The date & time array in a numpy.datetime64 format.
lat: np.array / pd.Series, dtype=float, shape=[n, ]
The latitude of the glider position.
lon: np.array / pd.Series, dtype=float, shape=[n, ]
The longitude of the glider position.
dark_count: float
The dark count factory values from the calibration sheet.
scale_factor: float
The scale factor factory values from the calibration sheet.
spike_window: int=7
The window size over which to run the despiking method.
spike_method: str=median
Whether to use a rolling median or combination of min+max filter as
the despiking method.
night_day_group: bool=True
If True, use preceding night otherwise use following night for
calculating the flr:bbp ratio.
sunrise_sunset_offset: int=1
The delayed onset and recovery of quenching in hours [1]
(assumes symmetrical).
profiles_ref_depth: int=300
The depth threshold for optics.find_bad_profiles below which the
median or mean is calculated for identifying outliers.
deep_multiplier: int=1
The standard deviation multiplier for calculating outliers,
i.e. mean ± std x [1].
deep_method: str='median'
Whether to use the deep median or deep mean to determine bad profiles
for optics.find_bad_profiles.
return_figure: bool=False
If True, will return a figure object that shows before and after the
quenching correction was applied.
verbose: bool=True
If True, will print the progress of the processing function.
Returns
-------
baseline: array, dtype=float, shape=[n, ]
The despiked + bad profile identified fluorescence that has not had
the quenching correction applied.
quench_corrected: array, dtype=float, shape=[n, ]
The fluorescence data corrected for quenching.
quench_layer: array, dtype=bool, shape=[n, ]
The quenching layer as a mask.
figs: object
The figures reporting the despiking, bad profiles and quenching
correction.
References
----------
.. [1] Thomalla, S. J., Moutier, W., Ryan-Keogh, T. J., Gregor, L.,
& Schutt, J. (2018). An optimized method for correcting fluorescence
quenching using optical backscattering on autonomous platforms.
Limnology and Oceanography: Methods, 16(2), 132–144.
https://doi.org/10.1002/lom3.10234
"""
from numpy import array, count_nonzero, nan, unique
from . import optics as op
from .cleaning import despike, despiking_report
var = flr_raw.copy() # metdata preservation
flr_raw = array(flr_raw)
bbp = array(bbp)
dives = array(dives)
depth = array(depth)
time = array(time)
lat = array(lat)
lon = array(lon)
ref_depth = profiles_ref_depth
stdev_multiplier = deep_multiplier
method = deep_method
printv(
verbose,
("\n" + "=" * 50 + "\nFluorescence\n\tMask bad profiles based on "
"deep values (ref depth={}m)").format(ref_depth),
)
bad_profiles = op.find_bad_profiles(dives, depth, flr_raw, ref_depth,
stdev_multiplier, method)
flr_raw[bad_profiles[0]] = nan
bad_count = count_nonzero(bad_profiles[1])
dive_count = count_nonzero(unique(dives))
printv(
verbose,
"\tNumber of bad profiles = {}/{}".format(bad_count, dive_count),
)
printv(verbose, "\tDark count correction")
flr_raw -= dark_count
flr_dark = op.fluorescence_dark_count(flr_raw, depth)
flr_dark[flr_dark < 0] = nan
baseline, spikes = despike(flr_dark, spike_window, spike_method="median")
printv(verbose, "\tQuenching correction")
quench_corrected, quench_layer = op.quenching_correction(
baseline,
bbp,
dives,
depth,
time,
lat,
lon,
sunrise_sunset_offset=1,
night_day_group=True,
)
printv(
verbose,
"\tSpike identification (spike window={})".format(spike_window),
)
baseline = transfer_nc_attrs(
getframe(),
var,
baseline,
"FLR_baseline",
units="RFU",
standard_name="",
)
quench_corrected = transfer_nc_attrs(
getframe(),
var,
quench_corrected,
"FLR_quench_corrected",
units="RFU",
standard_name="fluorescence",
)
quench_layer = transfer_nc_attrs(
getframe(),
var,
quench_layer,
"quenching_layer",
units="",
standard_name="",
comment="",
)
if return_figure:
printv(verbose,
"\tGenerating figures for despiking and quenching report")
figs = (despiking_report(
dives,
depth,
flr_raw,
baseline.data,
spikes,
name="Fluorescence",
), )
figs += (op.quenching_report(
baseline.data,
quench_corrected.data,
quench_layer,
dives,
depth,
), )
return baseline, quench_corrected, quench_layer, figs
else:
return baseline, quench_corrected, quench_layer
def calc_par(
par_raw,
dives,
depth,
time,
scale_factor_wet_uEm2s,
sensor_output_mV,
curve_max_depth=80,
verbose=True,
):
"""
Calculates the theoretical PAR based on an exponential curve fit.
The processing steps are:
1. par_scaling (factory cal sheet scaling)
2. par_dark_count (correct deep par values to 0 using 5th %)
3. par_fill_surface (return the theoretical curve of par based
exponential fit)
Parameters
----------
All inputs must be ungridded np.ndarray or pd.Series data
par_raw : array, dtype=float, shape=[n, ]
raw PAR
dives : array, dtype=float, shape=[n, ]
the dive count (round is down dives, 0.5 up dives)
depth : array, dtype=float, shape=[n, ]
in metres
time : array, dtype=float, shape=[n, ]
as a np.datetime64 array
Returns
-------
par_filled : array, dtype=float, shape=[n, ]
PAR with filled surface values.
"""
from numpy import array
from . import optics as op
var = par_raw.copy() # metdata presrevation
par_raw = array(par_raw)
dives = array(dives)
depth = array(depth)
time = array(time)
printv(verbose, "\n" + "=" * 50 + "\nPAR\n\tDark correction")
# dark correction for par
par_scaled = op.par_scaling(par_raw, scale_factor_wet_uEm2s,
sensor_output_mV)
par_dark = op.par_dark_count(par_scaled, dives, depth, time)
printv(verbose, "\tFitting exponential curve to data")
par_filled = op.par_fill_surface(par_dark,
dives,
depth,
max_curve_depth=curve_max_depth)
par_filled[par_filled < 0] = 0
attrs = dict(
standard_name="photosynthetically_available_radiation",
units="uE/m2/s2",
comment="",
)
par_filled = transfer_nc_attrs(getframe(), var, par_filled,
"PAR_processed", **attrs)
par_filled = par_filled.fillna(0)
return par_filled
def interp_obj( # noqa: C901
x,
y,
z,
xi,
yi,
partial_sill=0.1,
nugget=0.01,
lenscale_x=20,
lenscale_y=20,
detrend=True,
max_points_per_quad=55,
min_points_per_quad=8,
return_error=False,
n_cpus=None,
verbose=True,
parallel_chunk_size=512,
):
"""
Performs objective interpolation (or Kriging) of a 2D field.
The objective interpolation breaks the problem into smaller fields by
iteratively breaking the problem into quadrants. Each quadrant is then
interpolated (also using intformation from its neighbours).
The interpolation is inverse distance weighted using a gaussian kernel (or
radial basis function). The kernel has a width of 12 hours if the
x-dimension is time, otherwise scaled by the x-variable unit. The kernel
is in meters assuming that depth is the y-coord. This can be changed with
keyword arguements. An error estimate can also be calculated if requested.
The following link provides good background on the Kriging procedure:
http://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/how-kriging-works.htm
Parameters
----------
x : np.array | pd.series
horizontal coordinates of the input data (same length as y, z)
can be types float or datetime64
y : np.array | pd.series
vertical coordinates of the input data (same length as x, z)
z : np.array | pd.series
values to be interoplated (same length as x, y)
xi : np.array
horizontal coordinates of the interpolation grid (must be 1D)
can be types float or datetime64
yi : np.array | pd.series
vertical coordinates of the interpolation grid (must be 1D)
nugget : float [0.01]
the error estimate due to sampling inaccuracy also known as the nugget
in Kriging literature. This should be taken from the semivariogram
partial_sill : float [0.1]
represents the spatial covariance of the variable being interpolated.
Should be estimated from the semivariogram. See Kriging literature for
more information
lenscale_x : float [20]
horizontal length scale horizontal coordinate variable
If dtype(x) is np.datetime64 (any format) then lenscale units is in
hours. Otherwise if type(x).
lenscale_y : float [20]
horizontal length scale horizontal coordinate variable.
max_points_per_quad : int [55]
the data is divided into quadrants using a quadtree approach -
iteratively dividing data into smaller quadrants using x and y
coordinates. The algorithm stops splitting the data into quadrants
when there are no quadrants exceeding the limit set with
max_points_per_quad is. This is done to reduce the computational
cost of the function.
min_points_per_quad : int [8]
sets the minimum number of points allowed in a neighbouring quadrant
when creating the interpolation function for a particular quadrant. If
the number of points is less than specified, the algorithm looks for
neighbours of the neighbours to include more points in the
interpolation.
n_cpus : int [n - 1]
use parallel computing. The quadrant calculations are spread across
CPUs. Must be positive and > 0
parallel_chunk_size : int [512]
the number of leaves that will be processed in parallel in one go. This
is a memory saving feature. If your dataset is very large, parallel
processing will use up a lot of memmory. Increasing the chunk size
increases the memory requirements.
verbose : bool [True]
will print out information about the interpolation
Returns
-------
xr.Dataset
Contains the following arrays:
- z: interpolated values
- variance: error estimate of the interpolation
- weights: the quadtree weighting used to calculate the estimates
- nugget: the nugget used in the interpolation
- partial_sill: value used for the interpolation
Note
----
The data may have semi-discrete artifacts. This is also present in the
MATLAB output.
Example
-------
>>> xi = np.arange(time.values.min(), time.values.max(), 30,
dtype='datetime64[m]')
>>> yi = np.linspace(depth.min(), depth.max(), 1.)
>>> interpolated = gt.mapping.interp_obj(
time, depth, var, xi, yi,
nugget=.0035, partial_sill=0.02,
lenscale_x=80, lenscale_y=80,
detrend=True)
"""
def get_detrend_model(x, y, z):
model = linear_model.LinearRegression()
model.fit(np.c_[x, y], z)
return model
import multiprocessing as mp
from functools import partial
from time import perf_counter as timer
import xarray as xr
from sklearn import linear_model
if (n_cpus is None) | (n_cpus == 0):
n_cpus = mp.cpu_count() - 1
if verbose:
print("Starting Interpolation with quadtree optimal interpolation")
print("----------------------------------------------------------")
print("\nPreparing for interpolations:")
zvar = z.copy()
yvar = y.copy()
xvar = x.copy()
is_time_x = np.issubdtype(x.dtype, np.datetime64)
is_time_xi = np.issubdtype(xi.dtype, np.datetime64)
ymessage = "y-coordinates are not the same type (x={}, xi={})".format(
y.dtype, yi.dtype)
xmessage = "x-coordinates are not the same type (x={}, xi={})".format(
x.dtype, xi.dtype)
assert y.dtype == yi.dtype, ymessage
assert (is_time_x + is_time_xi) != 1, xmessage
if is_time_x: # convert data to hours
if verbose:
print("\tTime conversion")
x = np.array(x).astype("datetime64[s]").astype(float) / 3600
xi = np.array(xi).astype("datetime64[s]").astype(float) / 3600
units_x = "hrs"
else:
units_x = ""
if verbose:
print("\tFinding and removing nans")
nans = np.isnan(z) | np.isnan(x) | np.isnan(y)
x, y, z = [np.array(a)[~nans] for a in [x, y, z]]
# detrend data using linear regression
if detrend:
if verbose:
print("\tRemoving data trend with linear regression")
model = get_detrend_model(x, y, z)
z_hat = model.predict(np.c_[x, y])
z -= z_hat
else:
if verbose:
print("\tRemoving data mean")
z_avg = np.nanmean(z)
z -= z_avg
if verbose:
print("\tBuilding QuadTree")
quad_tree = QuadTree(np.c_[x, y], max_points_per_quad=max_points_per_quad)
xx, yy = np.array(np.meshgrid(xi, yi)).reshape(2, -1)
leaves = quad_tree.leaves
n = len(leaves)
interp_info = "\n".join([
"\nInterpolation information:",
"\tbasis points: {}".format(x.size),
"\tinterp grid: {}, {}".format(xi.size, yi.size),
"\tmax_points_per_quad: {}".format(max_points_per_quad),
"\tmin_points_per_quad: {}".format(min_points_per_quad),
"\tnumber of quads: {}".format(n),
"\tdetrend_method: {}".format(
"linear_regression" if detrend else "mean"),
"\tpartial_sill: {}".format(partial_sill),
"\tnugget: {}".format(nugget),
"\tlengthscales: X = {} {}".format(lenscale_x, units_x),
"\t Y = {} m".format(lenscale_y),
])
if verbose:
print(interp_info)
pool = mp.Pool(n_cpus)
props = dict(
z=z,
xi=xx,
yi=yy,
nugget=nugget,
partial_sill=partial_sill,
lenscale_x=lenscale_x,
lenscale_y=lenscale_y,
min_points_per_quad=min_points_per_quad,
return_error=return_error,
verbose=verbose,
)
func = partial(interp_leaf, **props)
# predifining matricies for interpolation
errors = np.ndarray(xx.size) * 0
weights = np.ndarray(xx.size) * 0
variable = np.ndarray(xx.size) * 0
# creating a timer to inform the user
t0 = timer()
# getting the index used to split the data up into chunks
chunk_idx = np.arange(0, n, parallel_chunk_size, dtype=int)
n_chunks = chunk_idx.size
if verbose:
print("\nProcessing interpolation in {} parts over {} CPUs:".format(
n_chunks, n_cpus))
for c, i0 in enumerate(chunk_idx):
i1 = i0 + parallel_chunk_size
chunk_leaves = leaves[i0:i1]
# do the parallel processing
chunk_output = pool.map(func, chunk_leaves)
# add the parallel chunk output to the output arrays
for w, zi, er, ii in chunk_output:
weights[ii] += w
variable[ii] += zi
errors[ii] += er
# create info for the user
t1 = timer()
if verbose:
print("\tchunk {}/{} completed in {:.0f}s".format(
c + 1, n_chunks, t1 - t0))
t0 = timer()
# completing the interpolation
if verbose:
print("\nFinishing off interoplation")
if detrend:
if verbose:
print("\tAdding back the trend")
zi = (variable / weights) + model.predict(np.c_[xx, yy])
else:
if verbose:
print("\tAdding back the average")
zi = (variable / weights) + z_avg
errors = errors / weights
if verbose & is_time_x:
print("\tTime conversion")
xi = (xi * 3600).astype("datetime64[s]") if is_time_x else xi
if verbose:
print("\tCreating xarray dataset for output")
xds = xr.Dataset(
attrs={
"description": (
"interpolation output from the GliderTools.interp_obj"
"function. Print out mapping_info for more details"),
"mapping_info":
interp_info,
})
props = dict(dims=["y", "x"], coords={"y": yi, "x": xi})
xds["z"] = xr.DataArray(zi.reshape(yi.size, xi.size), **props)
xds["weights"] = xr.DataArray(weights.reshape(yi.size, xi.size), **props)
xds["variance"] = xr.DataArray(errors.reshape(yi.size, xi.size), **props)
xds.attrs["nugget"] = nugget
xds.attrs["partial_sill"] = partial_sill
dummy = transfer_nc_attrs(getframe(), zvar, zvar, "_interp")
if isinstance(zvar, xr.DataArray):
xds["z"].attrs = dummy.attrs
# xds = xds.rename({'z': dummy.name})
if isinstance(yvar, xr.DataArray):
xds["y"].attrs = yvar.attrs
xds = xds.rename({"y": yvar.name})
if isinstance(xvar, xr.DataArray):
xds["x"].attrs = xvar.attrs
xds = xds.rename({"x": xvar.name})
return xds
def grid_data(
x,
y,
var,
bins=None,
how='mean',
interp_lim=6,
verbose=True,
return_xarray=True,
):
"""
Grids the input variable to bins for depth/dens (y) and time/dive (x).
The bins can be specified to be non-uniform to adapt to variable sampling
intervals of the profile. It is useful to use the ``gt.plot.bin_size``
function to identify the sampling intervals. The bins are averaged (mean)
by default but can also be the ``median, std, count``,
Parameters
----------
x : np.array, dtype=float, shape=[n, ]
The horizontal values by which to bin need to be in a psudeo discrete
format already. Dive number or ``time_average_per_dive`` are the
standard inputs for this variable. Has ``p`` unique values.
y : np.array, dtype=float, shape=[n, ]
The vertical values that will be binned; typically depth, but can also
be density or any other variable.
bins : np.array, dtype=float; shape=[q, ], default=[0 : 1 : max_depth ]
Define the bin edges for y with this function. If not defined, defaults
to one meter bins.
how : str, defualt='mean'
the string form of a function that can be applied to pandas.Groupby
objects. These include ``mean, median, std, count``.
interp_lim : int, default=6
sets the maximum extent to which NaNs will be filled.
Returns
-------
glider_section : xarray.DataArray, shape=[p, q]
A 2D section in the format specified by ``ax_xarray`` input.
Raises
------
Userwarning
Triggers when ``x`` does not have discrete values.
"""
from pandas import cut, Series
from xarray import DataArray
from numpy import array, c_, unique, diff
xvar, yvar = x.copy(), y.copy()
z = Series(var)
y = array(y)
x = array(x)
u = unique(x).size
s = x.size
if (u / s) > 0.2:
raise UserWarning(
'The x input array must be psuedo discrete (dives or dive_time). '
'{:.0f}% of x is unique (max 20% unique)'.format(u / s * 100))
chunk_depth = 50
optimal_bins, avg_sample_freq = get_optimal_bins(y, chunk_depth)
if bins is None:
bins = optimal_bins
# warning if bin average is smaller than average bin size
if verbose:
avg_bin_size = diff(bins).mean()
print(('Mean bin size = {:.2f}\n'
'Mean depth binned ({} m) vertical sampling frequency = {:.2f}'
).format(avg_bin_size, chunk_depth, avg_sample_freq))
labels = c_[bins[:-1], bins[1:]].mean(axis=1)
bins = cut(y, bins, labels=labels)
grp = Series(z).groupby([x, bins])
grp_agg = getattr(grp, how)()
gridded = grp_agg.unstack(level=0)
gridded = gridded.reindex(labels.astype(float))
if interp_lim > 0:
gridded = gridded.interpolate(limit=interp_lim).bfill(limit=interp_lim)
if not return_xarray:
return gridded
if return_xarray:
dummy = transfer_nc_attrs(getframe(), var, var, '_vert_binned')
xda = gridded.stack().to_xarray()
if isinstance(var, DataArray):
xda.attrs = dummy.attrs
xda.name = dummy.name
if isinstance(yvar, DataArray):
y = xda.dims[0]
xda[y].attrs = yvar.attrs
xda = xda.rename({y: yvar.name})
if isinstance(xvar, DataArray):
x = xda.dims[1]
xda[x].attrs = xvar.attrs
xda = xda.rename({x: xvar.name})
return xda
def grid_data(
x,
y,
var,
bins=None,
how="mean",
interp_lim=6,
verbose=True,
return_xarray=True,
):
"""
Grids the input variable to bins for depth/dens (y) and time/dive (x).
The bins can be specified to be non-uniform to adapt to variable sampling
intervals of the profile. It is useful to use the ``gt.plot.bin_size``
function to identify the sampling intervals. The bins are averaged (mean)
by default but can also be the ``median, std, count``,
Parameters
----------
x : np.array, dtype=float, shape=[n, ]
The horizontal values by which to bin need to be in a psudeo discrete
format already. Dive number or ``time_average_per_dive`` are the
standard inputs for this variable. Has ``p`` unique values.
y : np.array, dtype=float, shape=[n, ]
The vertical values that will be binned; typically depth, but can also
be density or any other variable.
bins : np.array, dtype=float; shape=[q, ], default=[0 : 1 : max_depth ]
Define the bin edges for y with this function. If not defined, defaults
to one meter bins.
how : str, defualt='mean'
the string form of a function that can be applied to pandas.Groupby
objects. These include ``mean, median, std, count``.
interp_lim : int, default=6
sets the maximum extent to which NaNs will be filled.
Returns
-------
glider_section : xarray.DataArray, shape=[p, q]
A 2D section in the format specified by ``ax_xarray`` input.
Raises
------
Userwarning
Triggers when ``x`` does not have discrete values.
"""
from numpy import array, c_, diff, unique
from pandas import Series, cut
from xarray import DataArray
xvar, yvar = x.copy(), y.copy()
z = Series(var)
y = array(y)
x = array(x)
u = unique(x).size
s = x.size
if (u / s) > 0.2:
raise UserWarning(
"The x input array must be psuedo discrete (dives or dive_time). "
"{:.0f}% of x is unique (max 20% unique)".format(u / s * 100))
chunk_depth = 50
# -DB this might not work if the user uses anything other than depth, example
# density. Chunk_depth would in that case apply to density, which will
# probably have a range that is much smaller than 50.
optimal_bins, avg_sample_freq = get_optimal_bins(y, chunk_depth)
if bins is None:
bins = optimal_bins
# warning if bin average is smaller than average bin size
# -DB this is not being raised as a warning. Instead just seems like useful
# information conveyed to user. Further none of this works out if y is not
# depth, since avg_sample freq will not make sense otherwise.
if verbose:
avg_bin_size = diff(bins).mean()
print(("Mean bin size = {:.2f}\n"
"Mean depth binned ({} m) vertical sampling frequency = {:.2f}"
).format(avg_bin_size, chunk_depth, avg_sample_freq))
labels = c_[bins[:-1],
bins[1:]].mean(axis=1) # -DB creates the mean bin values
bins = cut(y, bins, labels=labels)
# -DB creates a new variable where instead of variable the bin category
# is mentioned (sort of like a discretization)
grp = Series(z).groupby([x, bins
]) # -DB put z into the many bins (like 2D hist)
grp_agg = getattr(
grp, how)() # -DB basically does grp.how() or in this case grp.mean()
gridded = grp_agg.unstack(level=0)
gridded = gridded.reindex(labels.astype(float))
if interp_lim > 0:
gridded = gridded.interpolate(limit=interp_lim).bfill(limit=interp_lim)
if not return_xarray:
return gridded
if return_xarray:
dummy = transfer_nc_attrs(getframe(), var, var, "_vert_binned")
xda = DataArray(gridded)
if isinstance(var, DataArray):
xda.attrs = dummy.attrs
xda.name = dummy.name
if isinstance(yvar, DataArray):
y = xda.dims[0]
xda[y].attrs = yvar.attrs
xda = xda.rename({y: yvar.name})
if isinstance(xvar, DataArray):
x = xda.dims[1]
xda[x].attrs = xvar.attrs
xda = xda.rename({x: xvar.name})
return xda
def calc_oxygen(
o2raw,
pressure,
salinity,
temperature,
auto_conversion=True,
spike_window=7,
spike_method="median",
savitzky_golay_window=0,
savitzky_golay_order=2,
verbose=True,
):
"""
This function processes oxygen.
It is assumed that either mL/L or umol/kg are passed as input.
The units are automatically detected by looking at the mean ratio.
Below are some conversions to help with the Oxygen units:
>>> µmol/l > µmol/kg * 1.025
µmol/l > ml/l * 44.66
µmol/l > mg/l * 31.25
Parameters
----------
o2raw : array, dtype=float, shape=[n, ]
raw oxygen without unit conversion
pressure : array, dtype=float, shape=[n, ]
salinity : array, dtype=float, shape=[n, ]
temperature : array, dtype=float, shape=[n, ]
conversion : bool=True
tries to determine the unit of oxygen based on ``o2raw`` values.
The user needs to do a manual conversion if False
spike_window : int=7
rolling window size to apply for the ``cleaning.despike`` function.
spike_method : string='median'
can be 'median' or 'minmax'. see ``cleaning.despike`` for more info.
savitzky_golay_window : int=0
rolling window size for ``cleaning.savitzky_golay`` function
savitzky_golay_order : int=2
polynomial order for ``cleaning.savitzky_golay`` function
verbose : bool=True
Returns
-------
o2mll : array, dtype=float, shape=[n, ]
oxygen concentration in mL/L (if unit auto_conversion is set True)
o2pct : array, dtype=float, shape=[n, ]
theoretical oxygen saturation percentage
o2aou : array, dtype=float, shape=[n, ]
aparent oxygen utilisation based on measured oxygen and oxygen
saturation.
Note
----
To Do: Oxygen processing should have its own section to be consistent
"""
import seawater as sw
from numpy import abs, array, c_, isnan, median, ones
from pandas import Series
from .cleaning import despike, outlier_bounds_iqr, savitzky_golay
var = o2raw.copy() # metdata preservation
if isinstance(o2raw, Series):
name = o2raw.name
else:
name = "Oxygen"
o2raw = array(o2raw)
pressure = array(pressure)
temperature = array(temperature)
salinity = array(salinity)
if spike_window:
o2raw, _ = despike(o2raw, spike_window, spike_method)
printv(
verbose,
"\n" + "=" * 50 + "\n{}:\n"
"\tSmoothing data with despiking algorithm:\n\t"
" spike identification (spike window={})"
"".format(name, spike_window),
)
if savitzky_golay_window:
printv(
verbose,
("\tSmoothing with Savitzky-Golay filter "
"(window={}, order={})").format(savitzky_golay_window,
savitzky_golay_order),
)
o2raw = savitzky_golay(o2raw, savitzky_golay_window,
savitzky_golay_order)
o2sat = sw.satO2(salinity, temperature)
density = sw.dens(salinity, temperature, pressure)
if auto_conversion:
# use linear regression to determine the oxygen unit
# raw surface (<10m) O2 is regressed theoretical saturation
# the slope of the regression will be indicative of the
# units as theoretical saturation is always in mL/L
# Use the min difference between the slope and known
# conversion factors to estimate the appropriate conversion.
# clean the data first with basic cleaning
surf = (pressure < 20) & ~isnan(o2raw) & ~isnan(o2sat)
# prepare the data for linear regression
Y = o2raw[surf].copy()
X = c_[ones(surf.sum()), o2sat[surf]]
# removing outliers accodring to IQR
ll, ul = outlier_bounds_iqr(Y, multiplier=1.5)
m = (Y > ll) & (Y < ul)
ratios = Y[m] / X[m, 1]
# compare the slopes
observed_ratio = median(ratios)
# the theoretical values have been divided by 1.025 to account for
# the density of seawater
theoretic_ratio = array([1, 43.5])
ratio_diffs = abs(observed_ratio - theoretic_ratio)
# catch if the difference is too big
if ratio_diffs.min() > 10:
printv(
verbose,
("Oxygen unit could not be estimated automatically. "
"Do the unit conversion on the raw data before "
"passing it to the function. \n"
"Below is some info to help you\n"
" µmol/l > µmol/kg * 1.025\n"
" µmol/l > ml/l * 44.66\n"
" µmol/l > mg/l * 31.25"),
)
# otherwise do the conversion
else:
unit_idx = ratio_diffs.argmin()
if unit_idx == 0:
unit = "mL/L"
o2mll = array(o2raw)
elif unit_idx == 2:
unit = "mg/L"
o2mll = array(o2raw) / 31.25 * (density / 1000)
elif unit_idx == 1:
unit = "umol/kg"
o2mll = array(o2raw) / 44.66 * (density / 1000)
else:
printv(verbose, "Difference is {}".format(ratio_diffs))
printv(verbose, "\tUnits automatically detected {}".format(unit))
if ratio_diffs.min() > 5:
print("\tWARNING: Confirm units mannually as near the "
"confidence threshold")
o2aou = o2sat - o2mll
o2pct = o2mll / o2sat * 100
o2mll = transfer_nc_attrs(
getframe(),
var,
o2mll,
"o2mll",
units="mL/L",
comment="",
standard_name="dissolved_oxygen",
)
o2aou = transfer_nc_attrs(
getframe(),
var,
o2mll,
"o2aou",
units="mL/L",
comment="",
standard_name="aparent_oxygen_utilisation",
)
o2pct = transfer_nc_attrs(
getframe(),
var,
o2mll,
"o2pct",
units="percent",
comment="",
standard_name="theoretical_oxgen_saturation",
)
return o2mll, o2pct, o2aou
else:
print("No oxygen conversion applied - user "
"must impliment before or after running "
"the cleaning functions.")
