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)