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plot_sigma_surfaces.py
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plot_sigma_surfaces.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gsw
import importlib
import numpy.ma as ma
import cartopy.crs as ccrs
import pdb
#from mpl_toolkits.basemap import Basemap, cm
from matplotlib.colors import LinearSegmentedColormap
from IPython.display import Image
import matplotlib.colors as colors
from scipy.interpolate import griddata
#import geopandas as gpd
from shapely.geometry import Point
from scipy.stats import gaussian_kde
import xarray as xr
import matplotlib
import matplotlib.gridspec as gridspec
from matplotlib.colorbar import Colorbar
import sys
def find_topOfSigmaSurfaceDepth(gdf):
return gdf.loc[gdf.groupby("PROFILE_NUMBER").head(1).index].DEPTH.mean()
def find_topOfSigmaSurfaceDepth_corr_CTEMP(gdf):
return gdf.loc[gdf.groupby("PROFILE_NUMBER").head(1).index].CTEMP.mean()
def find_topOfSigmaSurfaceDepth_corr_sal(gdf):
return gdf.loc[gdf.groupby("PROFILE_NUMBER").head(1).index].PSAL_ADJUSTED.mean()
def plot_slope_sigma0_surfaces_CF_climatology(dfmg, sigma_surfaces=[27.67, 27.74, 27.86], surface_type=["equal_to", "equal_to", "greater_than"], save=False, savename="Untitled.png", tol=0.01, figno=1, wd=190/25.4, ht=230/25.4):
plt.close(figno)
plt.figure(figno, figsize=(wd, ht))
gs = gridspec.GridSpec(3, 5, width_ratios=[1,1,1,0.01, 0.1], wspace=0.01, hspace=0.01)
vmin = [-1500, -1.5, 34.4]
vmax= [0, 1.5, 34.8]
levs = [np.arange(vmin[0], vmax[0]+0.1, 10), np.arange(vmin[1], vmax[1]+0.01, 0.05) ,
np.arange(vmin[2], vmax[2]+0.01, 0.005)]
extend = ['min', 'both']
cmap = ['viridis', 'coolwarm', 'viridis']
CF = []
ax = []
subplot_titles = ["$\sigma_O=$"+str(sigma_surfaces[0]), "$\sigma_0=$"+str(sigma_surfaces[1]), "$\sigma_O=$"+str(sigma_surfaces[2])]
lonbins = np.arange(0, 361, 5)
topOfSigmaSurface = np.zeros((12, len(lonbins)-1 ))
topOfSigmaSurface_corr_CTEMP = np.zeros((12, len(lonbins)-1 ))
topOfSigmaSurface_corr_sal = np.zeros((12, len(lonbins)-1 ))
for i in range(len(sigma_surfaces)):
for j in range(12):
if(surface_type[i] == "equal_to"):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE) & (dfmg.JULD.dt.month == j+1)
elif(surface_type[i] == "greater_than"):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]) & (dfmg.SHELF_BREAK_PROFILE) & (dfmg.JULD.dt.month == j+1)
else:
raise Exception("surface_type can only be either equal_to or greater_than")
topOfSigmaSurface[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth)
topOfSigmaSurface_corr_CTEMP[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth_corr_CTEMP)
topOfSigmaSurface_corr_sal[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth_corr_sal)
ax.append(plt.subplot(gs[0, i]))
print(lonbins[:-1].shape, topOfSigmaSurface.T.shape)
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(0,13,1), topOfSigmaSurface, cmap=cmap[0], vmin=vmin[0], vmax=vmax[0], ))
ax[-1].set_xticklabels("")
ax[-1].set_xticks([])
ax[-1].set_title(subplot_titles[i])
ax[-1].set_xlim(0, 360)
ax[-1].set_yticks(np.arange(1,12, 2))
ax.append(plt.subplot(gs[1, i]))
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(0,13,1), topOfSigmaSurface_corr_CTEMP, cmap=cmap[1], vmin=vmin[1], vmax=vmax[1]))
ax[-1].set_xticklabels("")
ax[-1].set_xticks([])
ax[-1].set_xlim(0, 360)
ax[-1].set_yticks(np.arange(1,12, 2))
ax.append(plt.subplot(gs[2, i]))
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(0,13,1), topOfSigmaSurface_corr_sal, cmap=cmap[2], vmin=vmin[2], vmax=vmax[2], ))
ax[-1].set_xlim(0, 360)
ax[-1].set_xlabel("Longitude")
ax[-1].set_yticks(np.arange(1,12, 2))
for j in range(1,4,1):
if i >0:
ax[-j].set_yticklabels("")
#ax[-j].set_yticks([])
else:
ax[-j].set_ylabel("Months")
ax.append(plt.subplot(gs[0, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[0], orientation = 'vertical', extend='min')
cbr.ax.set_ylabel("Depth (m)")
ax.append(plt.subplot(gs[1, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[1], orientation = 'vertical', extend='both')
cbr.ax.set_ylabel("CT $^O$C")
ax.append(plt.subplot(gs[2, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[2], orientation = 'vertical', extend = 'both')
cbr.ax.set_ylabel("Salinity (PSU)")
if save:
plt.savefig(savename, dpi=300, bbox_inches="tight")
plt.show()
def plot_slope_sigma0_surfaces_variability_mean(dfmg, sigma_surfaces=[27.67, 27.74, 27.86], surface_type=["equal_to", "equal_to", "greater_than"], save=False, savename="Untitled.png", tol=0.01, figno=1, wd=190/25.4, ht=230/25.4, echodepth_gt=-3000, echodepth_lt = -1000, lonbins = np.arange(0, 361, 5)):
plt.close(figno)
plt.figure(figno, figsize=(wd, ht))
gs = gridspec.GridSpec(4, 5, width_ratios=[1,1,1,0.01, 0.1], height_ratios = [1,1,1,0.5], wspace=0.01, hspace=0.01)
vmin = [-1500, -1.5, 34.4]
vmax= [0, 1.5, 34.8]
levs = [np.arange(vmin[0], vmax[0]+0.1, 10), np.arange(vmin[1], vmax[1]+0.01, 0.05) ,
np.arange(vmin[2], vmax[2]+0.01, 0.005)]
extend = ['min', 'both']
cmap = ['viridis', 'coolwarm', 'viridis']
CF = []
ax = []
subplot_titles = ["$\sigma_O=$"+str(sigma_surfaces[0]), "$\sigma_0=$"+str(sigma_surfaces[1]), "$\sigma_O=$"+str(sigma_surfaces[2])]
topOfSigmaSurface_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_std = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_CTEMP_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_CTEMP_std = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_sal_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_sal_std = np.zeros(len(lonbins)-1 )
ylabel = ["Depth (m)", "CT$^\circ$C", "Salinity (PSU)", "Count"]
subplot_labels = [["(a)", "(e)", "(i)"], ["(b)", "(f)", "(j)"], ["(c)", "(g)", "(k)"], ["(d)", "(h)", "(l)"]]
for i in range(len(sigma_surfaces)):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE) & (dfmg.ECHODEPTH > echodepth_gt) & (dfmg.ECHODEPTH < echodepth_lt)
# if(surface_type[i] == "equal_to"):
# dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE)
# elif(surface_type[i] == "greater_than"):
# dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]) & (dfmg.SHELF_BREAK_PROFILE)
# else:
# raise Exception("surface_type can only be either equal_to or greater_than")
topOfSigmaSurface_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).DEPTH.mean()
topOfSigmaSurface_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).DEPTH.std()
topOfSigmaSurface_corr_CTEMP_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).CTEMP.mean()
topOfSigmaSurface_corr_CTEMP_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).CTEMP.std()
topOfSigmaSurface_corr_sal_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).PSAL_ADJUSTED.mean()
topOfSigmaSurface_corr_sal_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).PSAL_ADJUSTED.std()
ax.append(plt.subplot(gs[0, i]))
ax[-1].plot(lonbins[:-1], topOfSigmaSurface_mean.values, marker=".", color="k", linewidth=0)
yerr=topOfSigmaSurface_std.values
ax[-1].fill_between(lonbins[:-1], topOfSigmaSurface_mean.values-yerr, topOfSigmaSurface_mean.values+yerr, facecolor="0.25", edgecolor="0.5", alpha=0.5 )
ax[-1].set_xlim(0, 360)
if(i < 2):
ax[-1].set_ylim(-700, 0)
ax[-1].set_yticks(np.arange(-700, 0.1, 100))
ax[-1].grid(linestyle=":")
ax[-1].text(10, -650, subplot_labels[0][i])
else:
ax[-1].set_ylim(-3000, 0)
axr = ax[-1].twinx()
axr.plot(lonbins[:-1], topOfSigmaSurface_mean.values, marker=".", color="k", linewidth=0)
axr.set_ylim(-3000, 0)
axr.text(25, -650/700.*3e3, subplot_labels[0][i])
axr.set_ylabel("Depth (m)")
ax[-1].grid(linestyle=":")
#ax[-1].set_xticklabels("")
#ax[-1].set_xticks([])
ax[-1].set_title(subplot_titles[i])
ax.append(plt.subplot(gs[1, i]))
ax[-1].plot(lonbins[:-1], topOfSigmaSurface_corr_CTEMP_mean.values, marker=".", color="r", linewidth=0)
yerr= topOfSigmaSurface_corr_CTEMP_std.values
ax[-1].fill_between(lonbins[:-1], topOfSigmaSurface_corr_CTEMP_mean.values - yerr, topOfSigmaSurface_corr_CTEMP_mean.values + yerr, facecolor="coral", edgecolor="r", alpha=0.5)
#ax[-1].set_xticklabels("")
#ax[-1].set_xticks([])
ax[-1].set_xlim(0, 360)
ax[-1].set_ylim(-2, 2.2)
ax[-1].set_yticks(np.arange(-1.5, 2, 1), minor=True )
ax[-1].grid(linestyle=":", which="both")
ax[-1].text(10, 1.5, subplot_labels[1][i])
ax.append(plt.subplot(gs[2, i]))
ax[-1].plot(lonbins[:-1], topOfSigmaSurface_corr_sal_mean.values, marker=".", color="b", linewidth=0)
yerr=topOfSigmaSurface_corr_sal_std.values
ax[-1].fill_between(lonbins[:-1], topOfSigmaSurface_corr_sal_mean.values - yerr, topOfSigmaSurface_corr_sal_mean.values + yerr, facecolor="skyblue", edgecolor="b", alpha=0.5)
ax[-1].set_xlim(0, 360)
ax[-1].set_xlabel("Longitude")
ax[-1].set_ylim(34.3, 34.85)
ax[-1].grid(linestyle=":")
ax[-1].text(10, 34.8, subplot_labels[2][i])
#ax[-1].set_xticklabels("")
ax.append(plt.subplot(gs[3, i]) )
count = dfmg[dfsel].CTEMP.groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins) ).count()
ax[-1].fill_between(lonbins[:-1], 0, count, color="k", alpha=0.7)
ax[-1].plot(lonbins[:-1], count, linewidth=0, marker="o", color="k", markersize=1, zorder=3)
ax[-1].set_xlim(0, 360)
ax[-1].set_ylim(1e-1, 4e3)
ax[-1].set_yscale("log")
ax[-1].text(10, 1e3, subplot_labels[3][i])
ax[-1].set_yticks([ 1e0, 1e1, 1e2, 1e3])
ax[-1].grid(linestyle=":", zorder=1)
ax[-1].set_xlabel("Longitude")
for j in range(1,5,1):
if i>0:
#pass
ax[-j].set_yticklabels("")
ax[-j].set_yticks([])
else:
ax[-j].set_ylabel(ylabel[::-1][j-1])
if save:
plt.savefig(savename, dpi=300, bbox_inches="tight")
plt.show()
def plot_slope_sigma0_surfaces_CF(dfmg, sigma_surfaces=[27.67, 27.74, 27.86], surface_type=["equal_to", "equal_to", "greater_than"], save=False, savename="Untitled.png", tol=0.01, figno=1, wd=190/25.4, ht=230/25.4):
dfsel = (dfmg.loc[:, 'LONGITUDE'] < 0)
dfmg.loc[dfsel, 'LONGITUDE'] = dfmg.loc[dfsel, 'LONGITUDE'] + 360
plt.close(figno)
plt.figure(figno, figsize=(wd, ht))
gs = gridspec.GridSpec(3, 5, width_ratios=[1,1,1,0.01, 0.1], wspace=0.01, hspace=0.01)
vmin = [-1500, -1.5, 34.4]
vmax= [0, 1.5, 34.8]
levs = [np.arange(vmin[0], vmax[0]+0.1, 10), np.arange(vmin[1], vmax[1]+0.01, 0.05) ,
np.arange(vmin[2], vmax[2]+0.01, 0.005)]
extend = ['min', 'both']
cmap = ['viridis', 'RdBu_r', 'viridis']
CF = []
ax = []
subplot_titles = ["$\sigma_O=$"+str(sigma_surfaces[0]), "$\sigma_0=$"+str(sigma_surfaces[1]), "$\sigma_O=$"+str(sigma_surfaces[2])]
lonbins = np.arange(0, 361, 5)
topOfSigmaSurface = np.zeros((12, len(lonbins)-1 ))
topOfSigmaSurface_corr_CTEMP = np.zeros((12, len(lonbins)-1 ))
topOfSigmaSurface_corr_sal = np.zeros((12, len(lonbins)-1 ))
for i in range(len(sigma_surfaces)):
for j in range(12):
if(surface_type[i] == "equal_to"):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE) & (dfmg.JULD.dt.month == j+1)
elif(surface_type[i] == "greater_than"):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]) & (dfmg.SHELF_BREAK_PROFILE) & (dfmg.JULD.dt.month == j+1)
else:
raise Exception("surface_type can only be either equal_to or greater_than")
topOfSigmaSurface[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth)
topOfSigmaSurface_corr_CTEMP[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth_corr_CTEMP)
topOfSigmaSurface_corr_sal[j] = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).apply(find_topOfSigmaSurfaceDepth_corr_sal)
ax.append(plt.subplot(gs[0, i]))
print(lonbins[:-1].shape, topOfSigmaSurface.T.shape)
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(1,13,1), topOfSigmaSurface, cmap=cmap[0], vmin=vmin[0], vmax=vmax[0], ))
ax[-1].set_xticklabels("")
ax[-1].set_xticks([])
ax[-1].set_title(subplot_titles[i])
ax[-1].set_xlim(0, 360)
ax.append(plt.subplot(gs[1, i]))
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(1,13,1), topOfSigmaSurface_corr_CTEMP, cmap=cmap[1], vmin=vmin[1], vmax=vmax[1]))
ax[-1].set_xticklabels("")
ax[-1].set_xticks([])
ax[-1].set_xlim(0, 360)
ax.append(plt.subplot(gs[2, i]))
CF.append(ax[-1].pcolormesh(lonbins[:-1], np.arange(1,13,1), topOfSigmaSurface_corr_sal, cmap=cmap[2], vmin=vmin[2], vmax=vmax[2], ))
ax[-1].set_xlim(0, 360)
ax[-1].set_xlabel("Longitude")
for j in range(1,4,1):
if i >0:
ax[-j].set_yticklabels("")
ax[-j].set_yticks([])
else:
ax[-j].set_ylabel("Months")
ax.append(plt.subplot(gs[0, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[0], orientation = 'vertical', extend='min')
cbr.ax.set_ylabel("Depth (m)")
ax.append(plt.subplot(gs[1, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[1], orientation = 'vertical', extend='both')
cbr.ax.set_ylabel("CT $^O$C")
ax.append(plt.subplot(gs[2, 4]))
cbr = Colorbar(ax = ax[-1], mappable = CF[2], orientation = 'vertical', extend = 'both')
cbr.ax.set_ylabel("Salinity (PSU)")
if save:
plt.savefig(savename, dpi=300, bbox_inches="tight")
dfsel = (dfmg.loc[:, 'LONGITUDE'] > 180)
dfmg.loc[dfsel, 'LONGITUDE'] = dfmg.loc[dfsel, 'LONGITUDE'] - 360
plt.show()
def plot_slope_sigma0_surfaces_variability(dfmg, sigma_surfaces=[27.67, 27.74, 27.86], surface_type=["equal_to", "equal_to", "greater_than"], save=False, savename="Untitled.png", tol=0.01, figno=1, wd=190/25.4, ht=230/25.4, lonbins = np.arange(0,361,5) ):
plt.close(figno)
plt.figure(figno, figsize=(wd, ht))
gs = gridspec.GridSpec(3, 5, width_ratios=[1,1,1,0.01, 0.1], wspace=0.01, hspace=0.01)
vmin = [-1500, -1.5, 34.4]
vmax= [0, 1.5, 34.8]
levs = [np.arange(vmin[0], vmax[0]+0.1, 10), np.arange(vmin[1], vmax[1]+0.01, 0.05) ,
np.arange(vmin[2], vmax[2]+0.01, 0.005)]
extend = ['min', 'both']
cmap = ['viridis', 'coolwarm', 'viridis']
CF = []
ax = []
subplot_titles = ["$\sigma_O=$"+str(sigma_surfaces[0]), "$\sigma_0=$"+str(sigma_surfaces[1]), "$\sigma_O=$"+str(sigma_surfaces[2])]
topOfSigmaSurface_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_std = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_CTEMP_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_CTEMP_std = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_sal_mean = np.zeros(len(lonbins)-1 )
topOfSigmaSurface_corr_sal_std = np.zeros(len(lonbins)-1 )
ylabel = ["Depth (m)", "CT$^\circ$C", "Salinity (PSU)"]
subplot_labels = [["(a)", "(d)", "(g)"], ["(b)", "(e)", "(h)"], ["(c)", "(f)", "(i)"]]
for i in range(len(sigma_surfaces)):
dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE)
# if(surface_type[i] == "equal_to"):
# dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]-tol) & (dfmg.POT_DENSITY < sigma_surfaces[i]+tol) & (dfmg.SHELF_BREAK_PROFILE)
# elif(surface_type[i] == "greater_than"):
# dfsel = (dfmg.POT_DENSITY > sigma_surfaces[i]) & (dfmg.SHELF_BREAK_PROFILE)
# else:
# raise Exception("surface_type can only be either equal_to or greater_than")
topOfSigmaSurface_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).DEPTH.mean()
topOfSigmaSurface_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).DEPTH.std()
topOfSigmaSurface_corr_CTEMP_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).CTEMP.mean()
topOfSigmaSurface_corr_CTEMP_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).CTEMP.std()
topOfSigmaSurface_corr_sal_mean = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).PSAL_ADJUSTED.mean()
topOfSigmaSurface_corr_sal_std = dfmg.loc[dfsel].groupby(pd.cut(dfmg[dfsel].LONGITUDE, lonbins )).PSAL_ADJUSTED.std()
ax.append(plt.subplot(gs[0, i]))
yerr=topOfSigmaSurface_std.values
ax[-1].plot(lonbins[:-1], yerr, marker=".", color="k", linewidth=0.2)
ax[-1].set_xlim(0, 360)
ax[-1].grid(linestyle=":")
ax[-1].set_xticklabels("")
#ax[-1].set_xticks([])
ax[-1].set_title(subplot_titles[i])
ax.append(plt.subplot(gs[1, i]))
yerr= topOfSigmaSurface_corr_CTEMP_std.values
ax[-1].plot(lonbins[:-1], yerr, marker=".", color="r", linewidth=0.2)
ax[-1].set_xticklabels("")
#ax[-1].set_xticks([])
ax[-1].set_xlim(0, 360)
#ax[-1].set_ylim(-2, 2)
ax[-1].grid(linestyle=":")
#ax[-1].text(10, 1.5, subplot_labels[1][i])
ax.append(plt.subplot(gs[2, i]))
yerr=topOfSigmaSurface_corr_sal_std.values
ax[-1].plot(lonbins[:-1], yerr, marker=".", color="b", linewidth=0.2)
ax[-1].set_xlim(0, 360)
ax[-1].set_xlabel("Longitude")
ax[-1].set_xlim(0, 360)
#ax[-1].set_ylim(34.3, 34.85)
ax[-1].grid(linestyle=":")
#ax[-1].text(10, 34.8, subplot_labels[2][i])
for j in range(1,4,1):
if i>0:
pass
#ax[-j].set_yticklabels("")
#ax[-j].set_yticks([])
else:
ax[-j].set_ylabel(ylabel[::-1][j-1])
if save:
plt.savefig(savename, dpi=300, bbox_inches="tight")
plt.show()