def convert_oxygen(self):
if hasattr( self.data, 'sbe63_oxygen' ) & \
hasattr( self.data, 'prwl_salt' ) & \
hasattr( self.data, 'prwl_temp' ):
self.StatusBar.SetStatusText('Converting Oxygen')
# conversion from mL/L to umol/L
self.data.sbe63_oxygen = self.data.sbe63_oxygen * .7 * 44.661
# conversion to umol/kg
density = dens0(self.data.prwl_salt, self.data.prwl_temp)
self.data.sbe63_oxygen = self.data.sbe63_oxygen / density * 1000.
# calculating saturation potential
i = np.where(
[key.startswith('sbe63_oxygen')
for key in self.data.keys()])[0].max() + 1
sbe63_oxygen_sat = Series(None, index=self.data.index)
sbe63_oxygen_sat[:] = satO2(self.data.prwl_salt, self.data.prwl_temp)
sbe63_oxygen_sat = sbe63_oxygen_sat * 44.661
sbe63_oxygen_sat = sbe63_oxygen_sat / density * 1000.
self.data.insert(i, 'sbe63_oxygen_sat', sbe63_oxygen_sat)
# calculating saturation %
sbe63_oxygen_sat_precent = Series(None, index=self.data.index)
sbe63_oxygen_sat_precent = self.data.sbe63_oxygen / self.data.sbe63_oxygen_sat * 100.
self.data.insert(i + 1, 'sbe63_oxygen_sat_precent',
sbe63_oxygen_sat_precent)
else:
pass
def calculate_oxygen_saturation(self):
# Calculate the theoretical saturation
MEASURED_SALINITY = 0
MEASURED_TEMPERATURE = 21.5
max_saturation = sw.satO2(MEASURED_SALINITY, MEASURED_TEMPERATURE)
max_saturation_mol = max_saturation * 44.66
max_saturation_mol_1pct = max_saturation_mol * 0.01
def oxygensaturation(temperature: np.ndarray,
salinity: np.ndarray) -> np.ndarray:
"""
Calculate the solubility (saturation) of
Oxygen (O2) in seawater.
Required Arguments:
* temperature: temperature values in Celsius.
* salinity: salinity values.
"""
return seawater.satO2(salinity, temperature)
# In[22]:
sns.boxplot(atmospheric_all_df['Instrument'], atmospheric_all_df['O2µmol/L'])
plt.xlabel("Instrument", fontsize=16)
plt.ylabel("Concentration (uM)", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=12)
plt.title('Atmospheric Saturated Sample w/QC Bars', fontsize=18)
# Calculate the theoretical saturation
MEASURED_SALINITY = 0
MEASURED_TEMPERATURE = 21.5
max_saturation = sw.satO2(MEASURED_SALINITY, MEASURED_TEMPERATURE)
max_saturation_mol = max_saturation * 44.66
max_saturation_mol_1pct = max_saturation_mol * 0.01
# Plot the calculated saturation as a line on the chart
plt.plot([-0.5, 2.5], [max_saturation_mol, max_saturation_mol], color="#32a858")
# Plot the 1% upper and lower lines
plt.plot([-0.5, 2.5], [max_saturation_mol-max_saturation_mol_1pct, max_saturation_mol-max_saturation_mol_1pct], color="#2c5aa3")
plt.plot([-0.5, 2.5], [max_saturation_mol+max_saturation_mol_1pct, max_saturation_mol+max_saturation_mol_1pct], color="#2c5aa3")
# Plot the +- 1uM upper and lower lines
plt.plot([-0.5, 2.5], [max_saturation_mol-1, max_saturation_mol-1], color="#2c9fa3")
plt.plot([-0.5, 2.5], [max_saturation_mol+1, max_saturation_mol+1], color="#2c9fa3")
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack(
(S[:, icol], T[:, icol], P[:, icol], UN_ptmp[:, icol], PT[:,
icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array(
[2749.54, 2288.61, 3170.58, 3147.85, 0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array(
[0.498088, 0.654990, 0.506244, 0.662975, 1.000073, 1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500], [5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array(
[[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922, -2.212],
[-0.650, -0.919, -1.188, -1.460, -1.735, -2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack(
[S[irow, :], P[irow, :], UN_fp[irow, :], FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack(
[S[:, icol], T[:, icol], P[:, icol], UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0], [10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20], [30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35], [25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([
S[:, icol], T[:, icol], P[:, icol], UN_svel[:, icol], SVEL[:, icol]
])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" % tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" % tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" % tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1], [10, 10], [20, 20], [40, 40]]) / 1.00024
S = np.array([[20, 40], [20, 40], [20, 40], [20, 40]])
lit_O2 = np.array([[9.162, 7.984], [6.950, 6.121], [5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01], [12.64, 11.01], [10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877], [0.3397, 0.2989], [0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack(
[S[:, icol], T[:, icol], lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
def 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.")
conservative_temperature = gsw.conversions.CT_from_t(SA, Temp90, df['P'])
# SA= gsw.Sstar_from_SP(df['S'], df['P'], lat=df['Lat'], lon=df['Lon']) # SA=S* for Baltic
df['pt'] = gsw.pt_from_CT(SA, df['T'])
Solubility = gsw.O2sol_SP_pt(df.S, df['pt'])
# @-others
dat_file = '/mnt/D/workData/BalticSea/171003_ANS36/_doc/bottom_layer_data.xlsx'
print('reading {}'.format(dat_file))
df = pd.read_excel(dat_file, header=0)
# df.columns
# Index(['St', 'Date', 'Tim at Ship', 'Unnamed: 3', 'Time', 'Dist_sect_km', 'Т',
# 'S', 'Dens', 'О2%', 'area', 'Coord', 'P'],
dDist = np.diff(df.Dist_sect_km)
breakes = np.pad(
np.flatnonzero(np.logical_or(dDist == 0, np.abs(dDist) > 10)),
mode='constant', constant_values=(0, len(df.P)), pad_width=1
)
# array([36])
for st, en in zip(breakes[:-1], breakes[1:]):
df.iloc[st:en, df.columns == 'P'] = df.iloc[st:en].set_index('Dist_sect_km').P.interpolate().values
from seawater import satO2
Solubility = satO2(df.S.values, df['T'].values) # salinity [psu (PSS-78)], temperature [℃ (ITS-68)],
df['O2ppm'] = df['О2%'] * Solubility / 100
# @-others
# @-leo
def test(fileout='python-test.txt'):
r"""Copy of the Matlab test.
Modifications: Phil Morgan
03-12-12. Lindsay Pender, Converted to ITS-90.
"""
f = open(fileout, 'w')
asterisks = '*' * 76
f.write(asterisks)
f.write('\n TEST REPORT ')
f.write('\n')
f.write('\n SEA WATER LIBRARY %s' % sw.__version__)
f.write('\n')
# Show some info about this Python.
f.write('\npython version: %s' % sys.version)
f.write('\n on %s computer %s' % (uname()[0], uname()[-1]))
f.write('\n')
f.write('\n')
f.write(asctime(localtime()))
f.write('\n')
f.write('\n')
# Test main module ptmp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: ptmp')
f.write('\n** and SUB-MODULE: adtg')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
# Test 1 - data from Unesco 1983 p45.
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]])
T = T / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
Pr = np.array([0, 0, 0, 0, 0, 0])
UN_ptmp = np.array([[0, -0.3061, -0.9667, 0, -0.3856, -1.0974],
[10, 9.3531, 8.4684, 10, 9.2906, 8.3643],
[20, 19.0438, 17.9426, 20, 18.9985, 17.8654],
[30, 28.7512, 27.4353, 30, 28.7231, 27.3851],
[40, 38.4607, 36.9254, 40, 38.4498, 36.9023]])
PT = sw.ptmp(S, T, P, Pr) * 1.00024
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p45)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape # TODO: so many loops there must be a better way.
for icol in range(0, n):
f.write('\n Sal Temp Press PTMP ptmp')
f.write('\n (psu) (C) (db) (C) (C)\n')
result = np.vstack((S[:, icol], T[:, icol], P[:, icol],
UN_ptmp[:, icol], PT[:, icol]))
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.4f %11.5f\n" %
tuple(result[:, iline]))
# Test main module svan.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svan')
f.write('\n** and SUB-MODULE: dens dens0 smow seck pden ptmp')
f.write('\n%s' % asterisks)
# Test data FROM: Unesco Tech. Paper in Marine Sci. No. 44, p22.
s = np.array([0, 0, 0, 0, 35, 35, 35, 35])
p = np.array([0, 10000, 0, 10000, 0, 10000, 0, 10000])
t = np.array([0, 0, 30, 30, 0, 0, 30, 30]) / 1.00024
UN_svan = np.array([2749.54, 2288.61, 3170.58, 3147.85,
0.0, 0.00, 607.14, 916.34])
SVAN = sw.svan(s, t, p)
# DISPLAY RESULTS
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\nComparison of accepted values from UNESCO 1983')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p22)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Sal Temp Press SVAN svan')
f.write('\n (psu) (C) (db) (1e-8*m3/kg) (1e-8*m3/kg)\n')
result = np.vstack([s, t, p, UN_svan, 1e+8 * SVAN])
for iline in range(0, len(SVAN)):
f.write(" %4.0f %4.0f %5.0f %11.2f %11.3f\n" %
tuple(result[:, iline]))
# Test main module salt.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: salt')
f.write('\n** and SUB-MODULE: salrt salrp sals')
f.write('\n%s' % asterisks)
f.write('\n')
# Test 1 - data from Unesco 1983 p9.
R = np.array([1, 1.2, 0.65]) # cndr = R.
T = np.array([15, 20, 5]) / 1.00024
P = np.array([0, 2000, 1500])
#Rt = np.array([ 1, 1.0568875, 0.81705885])
UN_S = np.array([35, 37.245628, 27.995347])
S = sw.salt(R, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p9)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n Temp Press R S salt')
f.write('\n (C) (db) (no units) (psu) (psu)\n')
table = np.vstack([T, P, R, UN_S, S])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.2f %11.6f %14.7f\n" %
tuple(table[:, iline]))
# Test main module cndr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cndr')
f.write('\n** and SUB-MODULE: salds')
f.write('\n%s' % asterisks)
# Test 1 - data from Unesco 1983 p9.
T = np.array([0, 10, 0, 10, 10, 30]) / 1.00024
P = np.array([0, 0, 1000, 1000, 0, 0])
S = np.array([25, 25, 25, 25, 40, 40])
UN_R = np.array([0.498088, 0.654990, 0.506244, 0.662975, 1.000073,
1.529967])
R = sw.cndr(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p14)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Temp Press S cndr cndr')
f.write('\n (C) (db) (psu) (no units) (no units)\n')
table = np.vstack([T, P, S, UN_R, R])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %4.0f %8.6f %11.6f %14.8f\n" %
tuple(table[:, iline]))
# Test main module depth.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: depth')
f.write('\n%s' % asterisks)
# Test data - matrix "pressure", vector "lat" Unesco 1983 data p30.
lat = np.array([0, 30, 45, 90])
P = np.array([[500, 500, 500, 500],
[5000, 5000, 5000, 5000],
[10000, 10000, 10000, 10000]])
UN_dpth = np.array([[496.65, 496.00, 495.34, 494.03],
[4915.04, 4908.56, 4902.08, 4889.13],
[9725.47, 9712.65, 9699.84, 9674.23]])
dpth = sw.dpth(P, lat)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Unesco 1983 ')
f.write('\n(Unesco Tech. Paper in Marine Sci. No. 44, p28)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 3):
f.write('\n Lat Press DPTH dpth')
f.write('\n (degree) (db) (meter) (meter)\n')
table = np.vstack([lat, P[irow, :], UN_dpth[irow, :], dpth[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %6.3f %6.0f %8.2f %8.3f\n" %
tuple(table[:, iline]))
# Test main module fp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: fp')
f.write('\n%s' % asterisks)
# Test 1 - UNESCO data p.30.
S = np.array([[5, 10, 15, 20, 25, 30, 35, 40],
[5, 10, 15, 20, 25, 30, 35, 40]])
P = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[500, 500, 500, 500, 500, 500, 500, 500]])
UN_fp = np.array([[-0.274, -0.542, -0.812, -1.083, -1.358, -1.638, -1.922,
-2.212], [-0.650, -0.919, -1.188, -1.460, -1.735,
-2.014, -2.299, -2.589]])
FP = sw.fp(S, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p30)')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
for irow in range(0, 2):
f.write('\n Sal Press fp fp')
f.write('\n (psu) (db) (C) (C)\n')
table = np.vstack([S[irow, :], P[irow, :], UN_fp[irow, :],
FP[irow, :]])
m, n = table.shape
for iline in range(0, n):
f.write(" %4.0f %5.0f %8.3f %11.4f\n" %
tuple(table[:, iline]))
# Test main module cp.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: cp')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_cp = np.array([[4048.4, 3896.3, 3807.7, 3986.5, 3849.3, 3769.1],
[4041.8, 3919.6, 3842.3, 3986.3, 3874.7, 3804.4],
[4044.8, 3938.6, 3866.7, 3993.9, 3895.0, 3828.3],
[4049.1, 3952.0, 3883.0, 4000.7, 3909.2, 3844.3],
[4051.2, 3966.1, 3905.9, 4003.5, 3923.9, 3868.3]])
CP = sw.cp(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p37)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press Cp cp')
f.write('\n (psu) (C) (db) (J/kg.C) (J/kg.C)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_cp[:, icol], CP[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.2f\n" %
tuple(result[:, iline]))
# Test main module svel.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: svel')
f.write('\n%s' % asterisks)
# Test 1.
# Data from Pond and Pickard Intro. Dynamical Oceanography 2nd ed. 1986
T = np.array([[0, 0, 0, 0, 0, 0],
[10, 10, 10, 10, 10, 10],
[20, 20, 20, 20, 20, 20],
[30, 30, 30, 30, 30, 30],
[40, 40, 40, 40, 40, 40]]) / 1.00024
S = np.array([[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35],
[25, 25, 25, 35, 35, 35]])
P = np.array([[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000],
[0, 5000, 10000, 0, 5000, 10000]])
UN_svel = np.array([[1435.8, 1520.4, 1610.4, 1449.1, 1534.0, 1623.2],
[1477.7, 1561.3, 1647.4, 1489.8, 1573.4, 1659.0],
[1510.3, 1593.6, 1676.8, 1521.5, 1604.5, 1687.2],
[1535.2, 1619.0, 1700.6, 1545.6, 1629.0, 1710.1],
[1553.4, 1638.0, 1719.2, 1563.2, 1647.3, 1727.8]])
SVEL = sw.svel(S, T, P)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from UNESCO 1983 ')
f.write('\n (Unesco Tech. Paper in Marine Sci. No. 44, p50)')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = SVEL.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp Press SVEL svel')
f.write('\n (psu) (C) (db) (m/s) (m/s)\n')
result = np.vstack([S[:, icol], T[:, icol], P[:, icol],
UN_svel[:, icol], SVEL[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %5.0f %8.1f %11.3f\n" %
tuple(result[:, iline]))
# Test submodules alpha beta aonb.
f.write('\n%s' % asterisks)
f.write('\n** and SUB-MODULE: alpha beta aonb')
f.write('\n%s' % asterisks)
# Data from McDouogall 1987.
s = 40
PT = 10
p = 4000
beta_lit = 0.72088e-03
aonb_lit = 0.34763
alpha_lit = aonb_lit * beta_lit
BETA = sw.beta(s, PT, p, pt=True)
ALPHA = sw.alpha(s, PT, p, pt=True)
AONB = sw.aonb(s, PT, p, pt=True)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from MCDOUGALL 1987 ')
f.write('\n%s' % asterisks)
f.write('\n')
f.write('\n')
f.write('\n Sal Temp Press BETA beta')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, beta_lit, BETA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.5e\n" %
tuple(table))
f.write('\n Sal Temp Press AONB aonb')
f.write('\n (psu) (C) (db) (psu C^-1) (psu C^-1)\n')
table = np.hstack([s, PT, p, aonb_lit, AONB])
f.write(" %4.0f %4.0f %5.0f %8.5f %11.6f\n" %
tuple(table))
f.write('\n Sal Temp Press ALPHA alpha')
f.write('\n (psu) (C) (db) (psu^-1) (psu^-1)\n')
table = np.hstack([s, PT, p, alpha_lit, ALPHA])
f.write(" %4.0f %4.0f %5.0f %11.4e %11.4e\n" %
tuple(table))
# Test main moduleS satO2 satN2 satAr.
f.write('\n%s' % asterisks)
f.write('\n** TESTING MODULE: satO2 satN2 satAr')
f.write('\n%s' % asterisks)
f.write('\n')
# Data from Weiss 1970.
T = np.array([[-1, -1],
[10, 10],
[20, 20],
[40, 40]]) / 1.00024
S = np.array([[20, 40],
[20, 40],
[20, 40],
[20, 40]])
lit_O2 = np.array([[9.162, 7.984],
[6.950, 6.121],
[5.644, 5.015],
[4.050, 3.656]])
lit_N2 = np.array([[16.28, 14.01],
[12.64, 11.01],
[10.47, 9.21],
[7.78, 6.95]])
lit_Ar = np.array([[0.4456, 0.3877],
[0.3397, 0.2989],
[0.2766, 0.2457],
[0.1986, 0.1794]])
O2 = sw.satO2(S, T)
N2 = sw.satN2(S, T)
Ar = sw.satAr(S, T)
# Display results.
f.write('\n')
f.write('\n%s' % asterisks)
f.write('\nComparison of accepted values from Weiss, R.F. 1979 ')
f.write('\n"The solubility of nitrogen, oxygen and argon in water')
f.write('\n and seawater." Deep-Sea Research., 1970, Vol 17, pp721-735.')
f.write('\n%s' % asterisks)
f.write('\n')
m, n = S.shape
f.write('\n')
for icol in range(0, n):
f.write('\n Sal Temp O2 satO2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_O2[:, icol], O2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp N2 satN2')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_N2[:, icol], N2[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.2f %9.3f\n" %
tuple(result[:, iline]))
for icol in range(0, n):
f.write('\n Sal Temp Ar satAr')
f.write('\n (psu) (C) (ml/l) (ml/l)\n')
result = np.vstack([S[:, icol], T[:, icol],
lit_Ar[:, icol], Ar[:, icol]])
for iline in range(0, m):
f.write(" %4.0f %4.0f %8.4f %9.4f\n" %
tuple(result[:, iline]))
dfNL = dfNL.rename(columns={'Measured_TA_(micromol/kg)' : 'TA'})
dfNL = dfNL.rename(columns={'Measure_TCO2_(micromol/kg)' : 'TIC'})
dfNL = dfNL.rename(columns={'Density_(kg_m3)' : 'density'})
dfNL = dfNL.rename(columns={'TA_Data_Flag' : 'TAflag'})
dfNL = dfNL.rename(columns={'TCO2_Data_Flag' : 'TICflag'})
dfNL['Region'] = 'NL'
dfNL['temp_pH']= np.NaN
dfNL['pHflag']= np.NaN
dfNL = dfNL.loc[:,variables]
df = dfNL.copy(deep=True)
##############Make one df of all three Regions (datasets) of Original Data to plot (includes temperature and salinity data without associated carbonate data#######
df = df.reset_index(drop=True)
df['satO2'] = swx.satO2(df['salinity'], df['temperature'])
df['satO2_perc'] = df['O2']/df['satO2']*100 ######calculate percent oxygen saturation
df['AOU'] = df['O2']-df['satO2']
df['NO3']=df['NO3']/1.025 #####convert nutrient data from uM=mmol/m3/umol/L to umol/kgSW (/1.025)
df['SiO']=df['SiO']/1.025
df['PO4']=df['PO4']/1.025
#df.to_excel("C:\Users\gibbo\Documents\data\AZMP_OA\AZMP_OA_Alldata.xlsx")
dforig = df.copy(deep=True) ###all original data, since some is replaced/modified
#############data clean up for CO2sys = estimate TA from TA-S plot, remove empty rows, remove flagged data, replace nutrient nan with zeros################
#############drop flagged data####################
df.drop(df.loc[df['TAflag']>=3].index, inplace=True)
df.drop(df.loc[df['pHflag']>=3].index, inplace=True)
df.drop(df.loc[df['TICflag']>=3].index, inplace=True)
df.dropna(subset=['TA', 'TIC', 'pH_25'], axis=0, thresh=2, inplace=True)
