make_dir(dirname)
#
# select a sub region that goes from -120 -> -115 deg lon and
# 20 - 25 deg lat
#
lon_hit = np.logical_and(lon_centers > -120, lon_centers < -115)
lon_indices = np.where(lon_hit)[0]
lat_hit = np.logical_and(lat_centers > 20, lat_centers < 25)
lat_indices = np.where(lat_hit)[0]
sub_lons = lon_centers[lon_indices]
sub_lats = lat_centers[lat_indices]
sub_chan1ref = chan1ref_grid[lat_indices[0] : lat_indices[-1], lon_indices[0] : lon_indices[-1]]
sub_chan31 = chan31_grid[lat_indices[0] : lat_indices[-1], lon_indices[0] : lon_indices[-1]]
bin_chan1ref = fastbin(0.05, 0.6, 50.0, -999, -888)
bin_chan31 = fastbin(2.0, 18.0, 50.0, -999, -888)
chan1_centers = bin_chan1ref.get_centers()
chan31_centers = bin_chan31.get_centers()
the_hist = fh.pyhist(sub_chan31, sub_chan1ref, bin_chan31, bin_chan1ref)
counts = the_hist.get_hist2d()
cmap = cm.RdBu_r
cmap.set_over("y")
cmap.set_under("k")
vmin = 10.0
vmax = 400.0
the_norm = Normalize(vmin=vmin, vmax=vmax, clip=False)
counts = counts.astype(np.float32)
# select none here to see the full image
#
max_rows= None
max_cols= None
partLats=fullLats[:max_rows,:max_cols]
partLons=fullLons[:max_rows,:max_cols]
partRads=chan31[:max_rows,:max_cols]
partMask=maskout[:max_rows,:max_cols]
partLand=landout[:max_rows,:max_cols]
partChan1rad=chan1[:max_rows,:max_cols]
partChan1ref=chan1ref[:max_rows,:max_cols]
numlatbins=800
numlonbins=600
bin_lats=fastbin(south,north,numlatbins,-999,-888)
bin_lons=fastbin(west,east,numlonbins,-999,-888)
lon_centers=bin_lons.get_centers()
lat_centers=bin_lats.get_centers()
new_hist=fh.pyhist(partLats,partLons,bin_lats,bin_lons)
chan31_grid=new_hist.get_mean(partRads)
mask_grid=new_hist.get_mean(partMask)
land_grid=new_hist.get_mean(partLand)
chan1rad_grid=new_hist.get_mean(partChan1rad)
chan1ref_grid=new_hist.get_mean(partChan1ref)
fig1=plt.figure(1)
fig1.clf()
crosspol = MPLfile.data[1]
copolvals = np.hstack(copol.values).astype('float32')
crosspolvals = np.hstack(crosspol.values).astype('float32')
depolMPL = crosspolvals/copolvals
depolvals = depolMPL/(depolMPL+1)
copol_mean = np.mean(copolvals)
copol_std = np.std(copolvals)
copol_min = copol_mean-copol_std
copol_max = copol_mean+copol_std
bin_copol=fastbin(0.,0.002,100.,-999,-888)
bin_depol=fastbin(0.,2.0,100.,-999,-888)
copol_centers=bin_copol.get_centers()
depol_centers=bin_depol.get_centers()
the_hist=fh.pyhist(depolvals,copolvals,bin_depol,bin_copol)
counts=the_hist.get_hist2d()
cmap=cm.RdBu_r
cmap.set_over('y')
cmap.set_under('k')
counts=counts.astype(np.float32)
vmin= 0.
vmax= 4
the_norm=Normalize(vmin=vmin,vmax=vmax,clip=False)
