"#d40000", # 4.00 - 5.00 inches
"#bc0000", # 5.00 - 6.00 inches
"#f800fd", # 6.00 - 8.00 inches
"#9854c6", # 8.00 - 10.00 inches
"#fdfdfd" # 10.00+
]
precip_colormap = matplotlib.colors.ListedColormap(nws_precip_colors)
# Precip bins to be used for precip
clevs = [0,1,2.5,5,7.5,10,15,20,30,40,50,70,100,150]
# More map configuration
# draw coastlines, country boundaries
m.drawcoastlines(linewidth=0.25)
m.drawcountries(linewidth=0.25)
# draw parallels.
parallels = np.arange(-40.,40,10.)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
# draw meridians
meridians = np.arange(80.,180.,10.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
# Make grid for TRMM data
# ny = trmm_precip.shape[0]
# nx = trmm_precip.shape[1]
# lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid.
# x, y = m(lons, lats) # compute map proj coordinates.
# Alternative grid
# Borders for Papua New Guinea (approximate for now) # East: 8.23.18.4.S 165.11.08.7.E # West: 5.50.46.7.S 139.47.10.4.E # North: 0.05'58.7"S 149.32'31.5"E # South: 12.26'14.3"S 151.49'38.0"E # Precip bins to be used for precip clevs = [0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 70, 100, 150] # More map configuration # draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40., 40, 10.) m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10) # draw meridians meridians = np.arange(80., 180., 10.) m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0] nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0]
colors = ("#F5A5FF", "#C4B7FF", "#78CAFF", "#00D9FF", "#00E3FF", "#00E9E8",
"#00EAC4", "#00E89A", "#26E46A", "#87DC25", "#B9D300", "#DEC900",
"#FCBC00", "#FFAF58", "#FFA28D", "#FF97BA")
print 'plt_diff_fc_abs_error'
fig, axarr = plt.subplots(1, 2, figsize=(7.87, 3))
for itim, tim in enumerate(timelist):
print itim, tim
dateobj = datetime.datetime.strptime(tim, '%Y%m%d')
ax = axarr[itim]
plt.sca(ax)
m = Basemap(projection='cyl', llcrnrlon=-40, \
urcrnrlon=30.,llcrnrlat=25,urcrnrlat=80, \
resolution='i', area_thresh=10000.)
m.drawcoastlines(False)
m.drawcountries()
m.drawcoastlines(linewidth=0.5)
x, y = m(lon, lat)
mylevels = np.arange(520, 590, 5)
# cs0 = m.contourf(x,y,((datadict_od_geopot_500[itim][timelist[itim]]/9.80665)/10.)-((datadict_od_geopot_1000[itim][timelist[itim]]/9.80665)/10.),
# shading='flat',latlon=True, levels=mylevels,
# cmap=plt.cm.gist_rainbow_r, extend = 'both',
# alpha=0.8)
# cs1 = m.contour(x,y,(datadict_od_geopot_500[itim][timelist[itim]]/9.80665)/10.,
# latlon = True, levels = np.arange(520,581,5),
# colors='k', linewidths=2,
# zorder=3)
cs1 = m.contourf(
x,
y, (datadict_od_geopot_500[itim][timelist[itim]] / 9.80665) / 10.,
latlon=True,