def get_cross_section(Alat, Alon, Blat, Blon):
# Now find cross-sections
Ax, Ay = gridded_data.getXY(lats,lons,Alat,Alon)
Bx, By = gridded_data.getXY(lats,lons,Blat,Blon)
# Number of points along cross-section
xspt = int(N.hypot(Bx-Ax,By-Ay))
xx = N.linspace(Ay,By,xspt).astype(int)
yy = N.linspace(Ax,Bx,xspt).astype(int)
# Get terrain heights along this transect
heights = topodata[xx,yy]
# Work out distance along this cross-section in m
xsdistance = gridded_data.xs_distance(Alat,Alon,Blat,Blon)
xvals = N.linspace(0,xsdistance,xspt)
xlabels = ['%3.1f' %(x/1000) for x in xvals]
# Now plot cross-sections
fig = plt.figure(figsize=(width,height))
plt.plot(xvals,heights)
delta = xspt/10
plt.xticks(xvals[::delta],xlabels[::delta])
plt.xlabel('Distance along cross-section (km)')
fname = 'test1.png'
plt.savefig(outdir+fname,bbox_inches='tight',pad_inches=0.3)
plt.clf()
def get_map(Nlim,Elim,Slim,Wlim):
ymax,xmax = gridded_data.getXY(lats,lons,Nlim,Elim) #Here, x is lat, y is lon
ymin,xmin = gridded_data.getXY(lats,lons,Slim,Wlim) # Not sure why!
terrain = topodata[xmin:xmax,ymin:ymax]
xlat = lats[xmin:xmax]
xlon = lons[ymin:ymax]
#pdb.set_trace()
return terrain,xlat,xlon
def return_shear(lev1,lev2,ncpath,ens,loc,timetuple,lats,lons,dom):
nc = Dataset(ncpath,'r')
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
u = nc.variables['U'][time,:,y,x][:]
v = nc.variables['V'][time,:,y,x][:]
#wind = N.sqrt(u**2 + v**2)
geopot = nc.variables['PH'][time,:,y,x][:] + nc.variables['PHB'][time,:,y,x][:]
Z = geopot/9.81
zAGL = Z - nc.variables['HGT'][time,y,x] # Height AGL
# Compute the height, AGL, of the u/v surfaces
zAGL_wind = N.array([(z0+z1)/2.0 for z0,z1 in zip(zAGL[:-1],zAGL[1:])])
# Find wind at lev2 via interpolation
lev2 *= 1000 # get into metres
u2 = N.interp(lev2,zAGL_wind,u)
v2 = N.interp(lev2,zAGL_wind,v)
if lev1 == 0:
u1 = nc.variables['U10'][time,y,x]
v1 = nc.variables['V10'][time,y,x]
else:
# Find wind at lev1 via interpolation
pass
shear = N.sqrt((u2-u1)**2 + (v2-v1)**2)
return shear
def plot_T2(loc,timetuple,lats,lons,dom):
fig = plt.figure()
for i,ens in enumerate(ensnames):
# Load data from nc file
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
ncpath = ncdir+ens+'/'+ncfname
nc = Dataset(ncpath,'r')
ens_T2 = nc.variables['T2'][time,y,x] - 273.15
plt.bar(i,ens_T2)
# if obs == 1:
# # Include observed shear
# D = load_sounding_data(obspath)
# obs_T2 = D['thta'][0]
# plt.bar(len(ensnames),obs_T2,color='black')
# xlabelnames = ensnames + ['OBS']
# xticklocs = [0.5+n for n in range(len(ensnames)+1)]
# else:
xlabelnames = ensnames
xticklocs = [0.5+n for n in range(len(ensnames))]
fname = '_'.join(('T2',nicetime))
plt.gca().autoscale(enable=True,axis='y',tight=True)
plt.xticks(xticklocs,xlabelnames)
plt.ylabel('2m potential temperature (C)')
fig.savefig(outdir+fname, bbox_inches='tight', pad_inches=0.5)
fig.clf()
def plot_Q2(loc,timetuple,lats,lons,dom):
fig = plt.figure()
for i,ens in enumerate(ensnames):
# Load data from nc file
time = convert_time(dom,timetuple)
x,y,exactlat,exactlon = gridded_data.getXY(lats,lons,loc[0],loc[1])
ncpath = ncdir+ens+'/'+ncfname
nc = Dataset(ncpath,'r')
ens_Q2 = nc.variables['Q2'][time,y,x]*1000
plt.bar(i,ens_Q2)
if obs == 1:
# Include observed shear
D = load_sounding_data(obspath)
obs_Q2 = D['mixr'][0]
plt.bar(len(ensnames),obs_Q2,color='black')
xlabelnames = ensnames + ['OBS']
xticklocs = [0.5+n for n in range(len(ensnames)+1)]
else:
xlabelnames = ensnames
xticklocs = [0.5+n for n in range(len(ensnames))]
fname = '_'.join(('Q2',nicetime))
plt.xticks(xticklocs,xlabelnames)
plt.ylabel('Mixing ratio at surface (g kg$^{-1}$)')
fig.savefig(outdir+fname, bbox_inches='tight', pad_inches=0.5)
fig.clf()
ax = plt.gca() handles, labels = ax.get_legend_handles_labels() obsArt = plt.Line2D((0, 1), (0, 0), color="black") wrfArt = plt.Line2D((0, 1), (0, 0), color="blue") plt.legend([obsArt, wrfArt], ["Observed", "WRF Output"]) # plt.savefig(outdir+'kslcSkewT.png',bbox_inches='tight') ### ADD MODEL DATA nc = openWRF(dom) # Pick domain here Nx, Ny, Nz, lons, lats, dx, dy, x, y = getDimensions(nc) del x, y # We want our own locations time = gettime() x, y, exactlat, exactlon = gridded_data.getXY(lats[:, Nx / 2], lons[Ny / 2, :], obs_latlon[0], obs_latlon[1]) P = nc.variables["P"][time, :, y, x] + nc.variables["PB"][time, :, y, x] elev = nc.variables["HGT"][0, y, x] thin_locs = gridded_data.thinned_barbs(P) ST._windbarbs(nc, time, y, x, P, thin_locs, n=45, color="blue") ST._temperature(nc, time, y, x, P, linestyle="solid", color="blue") ST._dewpoint(nc, time, y, x, P, linestyle="dashed", color="blue") plt.xticks(xticks, ["" if tick % 10 != 0 else str(tick) for tick in xticks]) plt.yticks(yticks, ytix) plt.savefig(outdirfull + "verifSkewT_" + ens + "_" + nicetime + ".png", bbox_inches="tight", pad_inches=0.1) plt.clf()
