Initial commit

mldmtd.py

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+import numpy as np
+import pandas as pd
+
+import gsw
+import netCDF4
+
+from scipy.optimize import curve_fit
+from scipy.stats import linregress
+from sklearn.metrics import r2_score
+from netCDF4 import Dataset
+
+########################
+# Get data from netcdf
+########################
+def getProfileData(file):
+	try:
+		nc = Dataset(file)
+		JULD = nc.variables['JULD']
+		JULD = netCDF4.num2date(JULD[0],JULD.units)
+		LATITUDE = nc.variables['LATITUDE'][0]
+		LONGITUDE = nc.variables['LONGITUDE'][0]
+		PRES_ADJUSTED = nc.variables['PRES_ADJUSTED'][0]
+		TEMP_ADJUSTED = nc.variables['TEMP_ADJUSTED'][0]
+		PSAL_ADJUSTED = nc.variables['PSAL_ADJUSTED'][0]
+		nc.close()
+	except:
+		return pd.DataFrame([])
+
+	n = len(PRES_ADJUSTED)
+	df = pd.DataFrame({'date': np.repeat(JULD, n), 'latitude': np.repeat(LATITUDE, n), 'longitude': np.repeat(LONGITUDE, n), 'pres': PRES_ADJUSTED, 'psal': PSAL_ADJUSTED, 'temp': TEMP_ADJUSTED})
+	df = df.dropna()
+
+	########################
+	# Calculate absolute salinity and conservative temperature
+	########################
+	df['asal'] = gsw.SA_from_SP(df.psal, df.pres, df.longitude, df.latitude)
+	df['ctemp'] = gsw.CT_from_t(df.asal, df.temp, df.pres)
+	return df
+
+########################
+# Sigmoid function
+########################
+def fsigmoid(x, a, b):
+    return 1.0 / (1.0 + np.exp(-a*(x-b)))
+
+########################
+# Normalization
+########################
+def norm(y, y_min, y_max):
+	return (y - y_min)/(y_max-y_min)
+
+########################
+# Denormalization
+########################
+def unnorm(y, y_min, y_max):
+	return y*(y_max-y_min)+y_min
+
+########################
+# Calculate thermocline
+########################
+def thermocline(df, m_precision=0.01, threshold=0.2):
+	########################
+	# parameters:
+	# 	df: Dataset, columns=['pres','asal','ctemp']
+	# 	m_precision: Accuracy in meters
+	#	threshold: Threshold for determining MLD and MTD
+	########################
+	x_true = df.pres.to_numpy()
+	y_true = df.ctemp.to_numpy()
+
+	########################
+	# Calculates the buoyancy frequency squared (N2)
+	########################
+	N2, p_mid = gsw.Nsquared(df.asal, df.ctemp, df.pres)
+	N2 = np.vstack((N2, p_mid)).T
+
+	########################
+	# Obtain the pressure range where N2 is greater
+	########################
+	try:
+		index = np.argmax(N2[:,0])
+	except:
+		return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
+
+	max_pres = x_true[index]*2
+	index = x_true < max_pres
+	x_true = x_true[index] 
+	y_true = y_true[index]
+	N2 = N2[index[:-1]]
+
+	########################
+	# Normalize data
+	########################
+	sign = 1
+	if (np.mean(y_true[:2]) > np.mean(y_true[-2:])):
+		y_true = -y_true # It is inverted to resemble the function
+		sign = -1
+
+	y_min = np.min(y_true) # Min before normalizing
+	y_max = np.max(y_true) # Max before normalizing
+	y_true = norm(y_true, y_min, y_max) # Normalization
+
+	bounds = ([np.min(y_true), np.min(x_true)], # Lower and upper limits of the parameters
+			  [np.max(y_true), np.max(x_true)])
+
+	########################
+	# Fit data to sigmoid
+	########################
+	try:
+		popt, pcov = curve_fit(fsigmoid, x_true, y_true, method='dogbox', bounds=bounds)
+	except:
+		return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
+
+	x_pred = np.linspace(np.min(x_true), np.max(x_true), int(np.max(x_true)-np.min(x_true)))
+	y_pred = fsigmoid(x_pred, *popt)
+
+	########################
+	# Calculate coefficient of determination (R2)
+	########################	
+	r2 = r2_score(y_true, fsigmoid(x_true, *popt))
+
+	########################
+	# Denormalization data
+	########################
+	y_true = unnorm(y_true, y_min, y_max) # Denormalize real data
+	y_pred = unnorm(y_pred, y_min, y_max) # Denormalize predicted data
+
+	########################
+	#  Calculate MLD
+	######################### 
+	pres_10m = 10
+	temp_10m = unnorm(fsigmoid(pres_10m, *popt), y_min, y_max)
+
+	pres_mld = np.nan
+	temp_mld = np.nan
+	for k in np.arange(pres_10m + m_precision, x_true[-1], m_precision): # check every 10 cm
+		temp_mld = unnorm(fsigmoid(k, *popt), y_min, y_max)
+		if (temp_mld - temp_10m) >= threshold:
+			pres_mld = k
+			break
+
+	########################
+	#  Calculate MTD
+	######################### 
+	pres_deep = x_true[-1]
+	temp_deep = unnorm(fsigmoid(pres_deep, *popt), y_min, y_max)
+
+	pres_thermo = np.nan
+	temp_thermo = np.nan
+	if ~np.isnan(pres_mld):
+		for k in np.arange(pres_deep + m_precision, pres_mld, -m_precision): # verificación cada 10 cm
+			temp_thermo = unnorm(fsigmoid(k, *popt), y_min, y_max)
+			if (temp_deep - temp_thermo) >= threshold:
+				pres_thermo = k
+				break
+
+	if pres_mld > pres_thermo:
+		return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
+
+	return pres_thermo, temp_thermo*sign, pres_mld, temp_mld*sign, r2, N2, x_pred, y_pred*sign