visualization with multiple projections included and improvements on …

…the model/measurements collocation (#4)

Collocation scripts (model/buoy) have been improved and tested. WW3 field visualization is checked and fixed, now working with any grid type.

postproc/ww3fields.py

@@ -6,6 +6,7 @@
 
 VERSION AND LAST UPDATE:
  v1.0  04/04/2022
+ v1.1  04/27/2022
 
 PURPOSE:
  Wave Field Map plots of WAVEWATCHIII results using cartopy. 
@@ -33,6 +34,8 @@
 
 AUTHOR and DATE:
  04/04/2022: Ricardo M. Campos, first version.
+ 04/27/2022: Ricardo M. Campos, Ali Abdolali, Matthew Masarik, Saeideh Banihashemi:
+   new grids added, including tripolar and unstructured grids. New global and polar projections
 
 PERSON OF CONTACT:
  Ricardo M Campos: ricardo.campos@noaa.gov
@@ -42,13 +45,18 @@
 import matplotlib
 matplotlib.use('Agg')
 import xarray as xr
+import netCDF4 as nc
 import numpy as np
 from pylab import *
+from calendar import timegm
+from time import strptime
+from datetime import datetime, timezone
 import matplotlib.pyplot as plt
 import sys
 import pandas as pd
-import cartopy.crs as ccrs
 import cartopy
+import cartopy.crs as ccrs
+from cartopy.util import add_cyclic_point
 from matplotlib import ticker
 # import pickle
 import warnings
@@ -75,69 +83,169 @@
 	sys.exit(' Too many inputs')
 
 # ----- READ DATA ----- 
-if np.str(fname).split('.')[-1] == 'grib2' or np.str(fname).split('.')[-1] == 'grb2':
+if np.str(fname).split('.')[-1] == 'grib2' or np.str(fname).split('.')[-1] == 'grb2': 
 	# grib2 format
 	ds = xr.open_dataset(fname, engine='cfgrib')
 	wtime = np.array(ds.time.values + ds.step.values )
 	if wvar=='hs':
 		wvar=np.str('swh')
 
-	wdata = np.array(np.flip(ds[wvar].values[:,:,:],1))
+	if (wvar in list(ds.keys()))==False:
+		sys.exit(' Variable name not included in the file. You can use ncdump -h filename to see variable names.')
+
+	if size(ds[wvar].shape)==3:
+		# Structured
+		gstr=2
+		wdata = np.array(np.flip(ds[wvar].values[:,:,:],1))
+	elif size(ds[wvar].shape)==2:
+		# Unstructured
+		gstr=1
+		wdata = np.array(ds[wvar].values)
+	else:
+		sys.exit(' Unexpected file shape.')
+
+	units_wdata = np.str(ds[wvar].units)
+	lat = np.array(ds.latitude.values); lon = np.array(ds.longitude.values)
+	if gstr==2 and size(lat.shape)==1:
+		lat = np.sort(lat)
+
+	ds.close(); del ds
+	# -----------
 
 else:
 	# netcdf format
-	ds = xr.open_dataset(fname)
-	wtime = np.array(ds.time.values)
-	wdata = np.array(ds[wvar].values[:,:,:])
-
-units_wdata = np.str(ds[wvar].units)
-lat = np.sort(np.array(ds.latitude.values[:]))
-lon = np.array(ds.longitude.values[:])
-ds.close(); del ds
-# -----------
+	f=nc.Dataset(fname)
+	if (wvar in list(f.variables.keys()))==False:
+		sys.exit(' Variable name not included in the file. You can use ncdump -h filename to see variable names.')
+
+	wdata = np.array(f.variables[wvar])
+	units_wdata = np.str(f.variables[wvar].units)
+	lat = np.array(f.variables['latitude']); lon = np.array(f.variables['longitude'])
+	if size(wdata.shape)==3 and size(lat.shape)==1:
+		# Structured
+		gstr=2
+	elif size(wdata.shape)==2 or size(lat.shape)==2:
+		# Unstructured
+		gstr=1
+
+	# time
+	auxt = np.array(f.variables['time'][:]*24*3600 + timegm( strptime(np.str(f.variables['time'].units).split(' ')[2][0:4]+'01010000', '%Y%m%d%H%M') )).astype('double')
+	f.close(); del f
+
+	wtime=[]
+	for i in range(0,auxt.shape[0]):
+		wtime = np.append(wtime,datetime.fromtimestamp(auxt[i], timezone.utc))
+
+	del auxt
+
+	wdata[wdata>360]=np.nan
+
+if size(wdata.shape)==3 and size(lat.shape)==2:
+	lon[lon>180]=lon[lon>180]-360.
+
 if np.any(slat):
-	slat=np.sort(slat)
+	slat=np.array(np.sort(slat))
 else:
 	slat=np.array([np.nanmin(lat),np.nanmax(lat)])
 
 if np.any(slon):
-	slon=np.sort(slon)
-	if slon.min()<-180. :
-		sys.exit(' Longitude below -180. Keep the longitude standard: 0to360 or -180to180 degrees.')
-
+	slon=np.array(np.sort(slon))
 else:
 	slon=np.array([np.nanmin(lon),np.nanmax(lon)])
 
+
 # cbar levels
-levels = np.linspace(np.nanmin(wdata),np.nanpercentile(wdata,99.99),101)
+extdm=1
+if "dir" in wvar or "dp" in wvar or "wlv" in wvar or "u" in wvar or "v" in wvar:
+	levels = np.linspace(np.nanmin(wdata),np.nanmax(wdata),101)
+	extdm=0
+elif "fp" in wvar:
+	levels = np.linspace(0.,np.nanpercentile(wdata,99.995),101)
+else:
+	levels = np.linspace(np.nanmin(wdata),np.nanpercentile(wdata,99.995),101)
+
 print("ww3fields.py maps, file: "+fname+",  field: "+wvar)
 # loop time
-for t in range(wdata[::sk,:,:].shape[0]):
-	fig, ax = plt.subplots()
-	ax = plt.axes(projection=ccrs.PlateCarree()) 
-	ax.set_extent([slon.min(),slon.max(),slat.min(),slat.max()], crs=ccrs.PlateCarree())  
-	gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color='grey', alpha=0.5, linestyle='--')
-	gl.xlabel_style = {'size': 9, 'color': 'k','rotation':0}; gl.ylabel_style = {'size': 9, 'color': 'k','rotation':0}
-	plt.contourf(lon,lat,wdata[::sk,:,:][t,:,:],levels,cmap=palette,extend="max", zorder=2)
+for t in range(wtime[::sk].shape[0]):
+
+	# Robinson projection if global grid
+	if np.diff(slat)>150 and np.diff(slon)>350 and gstr==2:
+		# ax=plt.axes(projection=ccrs.Mollweide())
+		ax=plt.axes(projection=ccrs.Robinson())
+		gl = ax.gridlines(draw_labels=True,x_inline=False, y_inline=False, linewidth=0.5, color='grey', alpha=0.5, linestyle='--')
+		gl.ylocator = ticker.MultipleLocator(15)
+		gl.xlabel_style = {'size': 7, 'color': 'black', 'rotation': 0, 'rotation_mode': 'anchor'}
+		gl.ylabel_style = {'size': 7, 'color': 'black', 'rotation': 0, 'rotation_mode': 'anchor'}
+		data=wdata[::sk,:,:][t,:,:]
+		adata, alon = add_cyclic_point(data, coord=lon)
+		alat=lat 
+
+	# Polar projection
+	elif slat.max()>87 and slat.min()>30:
+		ax=plt.axes(projection=ccrs.NorthPolarStereo())
+		ax.set_extent([-180, 180, slat.min(), 90], crs=ccrs.PlateCarree())
+		# ax.tricontourf(lon[ind],lat[ind],wdata[::sk,:][t,:][ind],levels,transform=ccrs.PlateCarree())
+		gl = ax.gridlines(draw_labels=True,x_inline=False, y_inline=False, linewidth=0.5, color='grey', alpha=0.5, linestyle='--')
+		gl.ylocator = ticker.MultipleLocator(10)
+		gl.xlabel_style = {'size': 7, 'color': 'black', 'rotation': 0, 'rotation_mode': 'anchor'}
+		gl.ylabel_style = {'size': 7, 'color': 'black', 'rotation': 0, 'rotation_mode': 'anchor'}
+		alon=lon; alat=lat
+		adata=wdata[::sk,:][t,:]
+
+	# Miller projection
+	else:
+		ax = plt.axes(projection=ccrs.PlateCarree())
+		ax.set_extent([slon.min(),slon.max(),slat.min(),slat.max()], crs=ccrs.PlateCarree())
+		gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color='grey', alpha=0.5, linestyle='--', zorder=3)
+		gl.xlabel_style = {'size': 9, 'color': 'k','rotation':0}; gl.ylabel_style = {'size': 9, 'color': 'k','rotation':0}
+		alon=lon; alat=lat
+		adata=wdata[::sk,:][t,:]
+
+	if gstr==1:
+		# Unstructured
+		ind=np.where(np.isnan(wdata[::sk,:][t,:])==False)
+		if extdm==1:	
+			if slat.max()>87 and slat.min()>30:
+				cs=ax.tricontourf(lon[ind],lat[ind],wdata[::sk,:][t,:][ind],levels,cmap=palette,extend="max", zorder=1,transform=ccrs.PlateCarree())
+			else:
+				cs=ax.tricontourf(lon[ind],lat[ind],wdata[::sk,:][t,:][ind],levels,cmap=palette,extend="max", zorder=1)
+		else:
+			if slat.max()>87 and slat.min()>30:
+				cs=ax.tricontourf(lon[ind],lat[ind],wdata[::sk,:][t,ind],levels,cmap=palette,zorder=1,transform=ccrs.PlateCarree())
+			else:
+				cs=ax.tricontourf(lon[ind],lat[ind],wdata[::sk,:][t,ind],levels,cmap=palette,zorder=1)
+	else:
+		# Structured
+		if extdm==1:
+			cs=ax.contourf(alon,alat,adata,levels,cmap=palette,extend="max", zorder=1,transform = ccrs.PlateCarree())
+		else:
+			cs=ax.contourf(alon,alat,adata,levels,cmap=palette,zorder=1,transform = ccrs.PlateCarree())
+
 	ax.add_feature(cartopy.feature.OCEAN,facecolor=("white"))
-	ax.add_feature(cartopy.feature.LAND,facecolor=("lightgrey"), edgecolor='grey',linewidth=0.5)
-	ax.add_feature(cartopy.feature.BORDERS, edgecolor='grey', linestyle='-',linewidth=0.5, alpha=1)
-	ax.coastlines(resolution='110m', color='grey',linewidth=0.5, linestyle='-', alpha=1)
-	plt.title(wvar+' ('+units_wdata+')    '+pd.to_datetime(wtime[::sk][t]).strftime('%Y/%m/%d %H')+'Z') 
-	fig.tight_layout()
+	ax.add_feature(cartopy.feature.LAND,facecolor=("lightgrey"), edgecolor='grey',linewidth=0.5, zorder=2)
+	ax.add_feature(cartopy.feature.BORDERS, edgecolor='grey', linestyle='-',linewidth=0.5, alpha=1, zorder=3)
+	ax.coastlines(resolution='110m', color='grey',linewidth=0.5, linestyle='-', alpha=1, zorder=4)
+	plt.title(wvar+' ('+units_wdata+')    '+pd.to_datetime(wtime[::sk][t]).strftime('%Y/%m/%d %H:%M')+'Z') 
+	plt.tight_layout()
 	ax = plt.gca()
 	pos = ax.get_position()
 	l, b, w, h = pos.bounds
-	cax = plt.axes([l+0.07, b-0.07, w-0.15, 0.025]) # setup colorbar axes.
-	cbar=plt.colorbar(cax=cax, orientation='horizontal'); cbar.ax.tick_params(labelsize=10)
+	cax = plt.axes([l+0.07, b-0.07, w-0.12, 0.025]) # setup colorbar axes.
+	cbar=plt.colorbar(cs,cax=cax, orientation='horizontal'); cbar.ax.tick_params(labelsize=10)
 	tick_locator = ticker.MaxNLocator(nbins=7); cbar.locator = tick_locator; cbar.update_ticks()
 	plt.axes(ax)  # make the original axes current again
-	fig.tight_layout()
+	plt.tight_layout()
 	# plt.savefig('wfields_'+wvar+'_'+np.str(pd.to_datetime(wtime[::sk][t]).strftime('%Y%m%d%H'))+'.eps', format='eps', dpi=200)
-	plt.savefig('wfields_'+wvar+'_'+np.str(pd.to_datetime(wtime[::sk][t]).strftime('%Y%m%d%H'))+'.png', dpi=200, facecolor='w', edgecolor='w',
+	plt.savefig('wfields_'+wvar+'_'+np.str(pd.to_datetime(wtime[::sk][t]).strftime('%Y%m%d%H%M'))+'.png', dpi=200, facecolor='w', edgecolor='w',
 		orientation='portrait', papertype=None, format='png',transparent=False, bbox_inches='tight', pad_inches=0.1)
 
-	# pickle.dump(fig, open('wfields_'+wvar+'_'+np.str(pd.to_datetime(wtime[::sk][t]).strftime('%Y%m%d%H'))+'.pickle', 'wb'))
-	plt.close('all'); del ax, fig
+	# The pickle line below allow users to save the figure and edit later, using pickle. 
+	#   to use this option, uncomment the line below and the initial import pickle
+	# pickle.dump(ax, open('wfields_'+wvar+'_'+np.str(pd.to_datetime(wtime[::sk][t]).strftime('%Y%m%d%H'))+'.pickle', 'wb'))
+	plt.close('all'); del ax
+
+
 
+# For gif animation using .png figures:
+# convert -delay 15 -loop 0 wfields_*.png wfields.gif
 

validation/modelBuoy_collocation_GEFSforecast.py

@@ -92,7 +92,8 @@
 members=np.loadtxt('enslist.txt',dtype=str)
 # WW3 files inside each dir
 os.system("ls -1 "+mww3p+"/"+members[0]+"/*_tab.nc | xargs -n 1 basename > ww3list.txt")
-wlist=np.loadtxt('ww3list.txt',dtype=str)
+wlist=list(np.loadtxt('ww3list.txt',dtype=str))
+
 
 # READ DATA
 # Model

validation/modelBuoy_collocation_forecast.py

@@ -57,14 +57,12 @@
 fnetcdf="NETCDF4"
 
 # Paths
-# WW3 Model
-mpath="/ww3runs/c00"
 # NDBC buoys
 ndbcp="/data/buoys/NDBC/wparam"
 # Copernicus buoys
 copernp="/data/buoys/Copernicus/wtimeseries"
-# import os; os.system("ls "+mpath+"/*tab.nc > ww3list.txt &")
-wlist=np.loadtxt('ww3list.txt',dtype=str)
+# import os; os.system("ls /modelpath/*tab.nc > ww3list.txt &")
+wlist=list(np.loadtxt('ww3list.txt',dtype=str))
 
 # Read Data
 # Model

validation/modelBuoy_collocation_hindcast.py

@@ -57,15 +57,12 @@
 fnetcdf="NETCDF4"
 
 # Paths
-# WW3 Model
-mpath="/ww3runs/c00"
 # NDBC buoys
 ndbcp="/data/buoys/NDBC/wparam"
 # Copernicus buoys
 copernp="/data/buoys/Copernicus/wtimeseries"
-# read list of ww3 files to be included in the collocation
-# import os; os.system("ls "+mpath+"/*tab.nc > ww3list.txt &")
-wlist=np.loadtxt('ww3list.txt',dtype=str)
+# import os; os.system("ls /modelpath/*tab.nc > ww3list.txt &")
+wlist=list(np.loadtxt('ww3list.txt',dtype=str))
 
 # Read Data
 # Model