def simple_reconstruction_3d_pytest(tim, lvl_str, use_guess):
lvl=int(lvl_str)
ber, gp=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/20060122_'+tim+'_ver_hr_big.nc')
print gp['levs'][lvl]
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
ber_loc=[-12.457, 130.925]
gp_loc= [-12.2492, 131.0444]
if use_guess=='none':
igu=ones(ber['CZ'].shape, dtype=float)*0.0
igv=ones(ber['CZ'].shape, dtype=float)*0.0
else:
ber_ig, gp_ig=netcdf_utis.load_cube(use_guess)
print gp_ig.keys()
igu=gp_ig['u_array']
igv=gp_ig['v_array']
mywts=ones(ber['CZ'].shape, dtype=float)
angs=array(propigation.make_lobe_grid(ber_loc, gp_loc, lats,lons))
wts_ang=zeros(gp['CZ'][:,:,0].shape, dtype=float)
for i in range(angs.shape[0]):
for j in range(angs.shape[1]):
if (angs[i,j] < 150.0) and (angs[i,j] > 30.0): wts_ang[i,j]=1.0
for lvl_num in range(len(gp['levs'])):
#create a weighting grid
mask_reflect=10.0#dBZ
mask=(gp['CZ'][:,:,lvl_num]/mask_reflect).round().clip(min=0., max=1.0)
mask_vel_ber=(ber['VR'][:,:,lvl_num]+100.).clip(min=0., max=1.)
mywts[:,:,lvl_num]=mask*mask_vel_ber*wts_ang
f=0.0
gv_u=zeros(ber['CZ'].shape, dtype=float)
gv_v=zeros(ber['CZ'].shape, dtype=float)
wts=mask*mask_vel_ber*wts_ang
gu,gv,f= grad_conj_solver_plus_plus.meas_cost(gv_u, gv_v, f, igu, igv, ber['i_comp'], ber['j_comp'], gp['i_comp'], gp['j_comp'], ber['VR'], gp['VR'], mywts)
print "Mean U gradient", gu.mean(), "gv mean", gv.mean(), "F ", f
for i in range(len(gp['levs'])):
print "U,V ", (igu[:,:,i]).sum()/mywts[:,:,i].sum(), (igv[:,:,i]).sum()/mywts[:,:,i].sum()
#gv_u,gv_v,cost = vel_2d_cost(gv_u,gv_v,cost,u_array,v_array,i_cmpt_r1,j_cmpt_r1,i_cmpt_r2,j_cmpt_r2,vr1,vr2,weights,nx=shape(gv_u,0),ny=shape(gv_u,1))
print gracon_vel2d.vel_2d_cost(gv_u[:,:,i]*0.0,gv_v[:,:,i]*0.0,0.0,igu[:,:,i],igv[:,:,i],ber['i_comp'][:,:,i], ber['j_comp'][:,:,i], gp['i_comp'][:,:,i], gp['j_comp'][:,:,i], ber['VR'][:,:,i], gp['VR'][:,:,i],mywts[:,:,i])[2]
gv_u,gv_v,f,u_array,v_array = grad_conj_solver_plus_plus.gracon_vel2d_3d( gv_u, gv_v, f, igu, igv, ber['i_comp'], ber['j_comp'], gp['i_comp'], gp['j_comp'], ber['VR'], gp['VR'], mywts)#, nx=nx, ny=ny, nz=nz)
gp.update({'u_array': u_array, 'v_array':v_array})
netcdf_utis.save_data_cube(ber, gp, '/bm/gdata/scollis/cube_data/20060122_'+tim+'_winds_ver1.nc', gp['zero_loc'])
plotit=True
if plotit:
for lvl in range(len(gp['levs'])):
print lvl
f=figure()
mapobj=pres.generate_darwin_plot(box=[130.8, 131.2, -12.4, -12.0])
diff=gp['VR']-(u_array*gp['i_comp']+ v_array*gp['j_comp'])
gp.update({'diff':diff})
pres.reconstruction_plot(mapobj, lats, lons, gp, lvl, 'diff',u_array[:,:,lvl],v_array[:,:,lvl], angs, mywts[:,:,lvl])
#pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array).clip(min=-50, max=50),(wts*v_array).clip(min=-50, max=50))
t1='Gunn Point CAPPI (dBZ) and reconstructed winds (m/s) at %(lev)05dm \n 22/01/06 ' %{'lev':gp['levs'][lvl]}
title(t1+tim)
ff=os.getenv('HOME')+'/bom_mds/output/recons_22012006/real_%(lev)05d_' %{'lev':gp['levs'][lvl]}
savefig(ff+tim+'_2d_3d.png')
close(f)
def simple_reconstruction(tim, lvl_str):
#load data
srm=array([15.0, 5.0])/sqrt(2.0)
ber, gp=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/20060122_'+tim+'_ver1.nc')
lvl=int(lvl_str)
print gp['levs'][lvl]
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
ber_loc=[-12.457, 130.925]
gp_loc= [-12.2492, 131.0444]
angs=array(propigation.make_lobe_grid(ber_loc, gp_loc, lats,lons))
wts_ang=zeros(angs.shape, dtype=float)
for i in range(angs.shape[0]):
for j in range(angs.shape[1]):
if (angs[i,j] < 150.0) and (angs[i,j] > 30.0): wts_ang[i,j]=1.0
#create a weighting grid
mask_reflect=10.0#dBZ
mask=(gp['CZ'][:,:,lvl]/mask_reflect).round().clip(min=0., max=1.0)
mask_vel_ber=(ber['VR'][:,:,lvl]+100.).clip(min=0., max=1.)
#run gracon
print 'Into fortran'
nx,ny=ber['CZ'][:,:,lvl].shape
f=0.0
gv_u=zeros(ber['CZ'][:,:,lvl].shape, dtype=float)
gv_v=zeros(ber['CZ'][:,:,lvl].shape, dtype=float)
igu=ones(ber['CZ'][:,:,lvl].shape, dtype=float)*srm[0]
igv=ones(ber['CZ'][:,:,lvl].shape, dtype=float)*srm[1]
gv_u,gv_v,f,u_array,v_array = gracon_vel2d.gracon_vel2d(gv_u,gv_v,f,igu,igv,ber['i_comp'][:,:,lvl],ber['j_comp'][:,:,lvl],gp['i_comp'][:,:,lvl],gp['j_comp'][:,:,lvl], ber['VR'][:,:,lvl],gp['VR'][:,:,lvl],mask*mask_vel_ber*wts_ang, nx=nx,ny=ny)
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
wts=mask*mask_vel_ber*wts_ang
f=figure()
mapobj=pres.generate_darwin_plot(box=[130.8, 131.2, -12.4, -12.0])
pres.reconstruction_plot(mapobj, lats, lons, gp, lvl, 'CZ',u_array,v_array, angs, wts)
#pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array).clip(min=-50, max=50),(wts*v_array).clip(min=-50, max=50))
t1='Gunn Point CAPPI (dBZ) and reconstructed winds (m/s) at %(lev)05dm \n 22/01/06 ' %{'lev':gp['levs'][lvl]}
title(t1+tim)
ff=os.getenv('HOME')+'/bom_mds/output/recons_22012006/real_%(lev)05d_' %{'lev':gp['levs'][lvl]}
savefig(ff+tim+'_2d.png')
close(f)
def simple_reconstruction_3d(tim, lvl_str):
#load data
u=0
l=10
ui=zeros([l],dtype=float)
vi=zeros([l],dtype=float)
#srm=0.0*array([1.0, 5.0])/sqrt(2.0)
ber, gp=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/20060122_'+tim+'_ver1.nc')
for i in range(l):
ui[i], vi[i]=simple_reco(ber,gp,i)
lvl=int(lvl_str)
print gp['levs'][lvl]
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
ber_loc=[-12.457, 130.925]
gp_loc= [-12.2492, 131.0444]
angs=array(propigation.make_lobe_grid(ber_loc, gp_loc, lats,lons))
wts_ang=zeros(gp['CZ'][:,:,u:l].shape, dtype=float)
for i in range(wts_ang.shape[0]):
for j in range(wts_ang.shape[1]):
for k in range(wts_ang.shape[2]):
if (angs[i,j] < 150.0) and (angs[i,j] > 30.0): wts_ang[i,j,k]=1.0
#create a weighting grid
mask_reflect=12.0#dBZ
mask=(ber['CZ'][:,:,u:l]/mask_reflect).round().clip(min=0., max=1.0)
mask_vel_ber=(ber['VR'][:,:,u:l]+100.).clip(min=0., max=1.)
#run gracon
print 'Into fortran'
nx,ny,nz=ber['CZ'][:,:,u:l].shape
print nx,ny,nz
f=0.0
gv_u=zeros(ber['CZ'][:,:,u:l].shape, dtype=float)
gv_v=zeros(ber['CZ'][:,:,u:l].shape, dtype=float)
igu=ones(ber['CZ'][:,:,u:l].shape, dtype=float)
igv=ones(ber['CZ'][:,:,u:l].shape, dtype=float)
for i in range(len(ui)):
igu[:,:,i]=igu[:,:,i]*ui[i]
igv[:,:,i]=igu[:,:,i]*vi[i]
wts=mask*mask_vel_ber*wts_ang
#gv_u,gv_v,f,u_array,v_array = gracon_vel2d_3d(gv_u,gv_v,f,u_array,v_array,i_cmpt_r1,j_cmpt_r1,i_cmpt_r2,j_cmpt_r2,vr1,vr2,weights,nx=shape(gv_u,0),ny=shape(gv_u,1),nz=shape(gv_u,2))
gv_u,gv_v,f,u_array,v_array = gracon_vel2d_3d.gracon_vel2d_3d( gv_u, gv_v, f, igu, igv, ber['i_comp'][:,:,u:l], ber['j_comp'][:,:,u:l], gp['i_comp'][:,:,u:l], gp['j_comp'][:,:,u:l], ber['VR'][:,:,u:l], gp['VR'][:,:,u:l], mywts)#, nx=nx, ny=ny, nz=nz)
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
f=figure()
mapobj=pres.generate_darwin_plot(box=[130.8, 131.2, -12.4, -12.0])
diff=gp['VR'][:,:,u:l]-(u_array*gp['i_comp'][:,:,u:l]+ v_array*gp['j_comp'][:,:,u:l])
gp.update({'diff':diff})
pres.reconstruction_plot(mapobj, lats, lons, gp, lvl, 'diff',u_array[:,:,lvl],v_array[:,:,lvl], angs, wts[:,:,lvl])
#pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array).clip(min=-50, max=50),(wts*v_array).clip(min=-50, max=50))
t1='Gunn Point CAPPI (dBZ) and reconstructed winds (m/s) at %(lev)05dm \n 22/01/06 ' %{'lev':gp['levs'][lvl]}
title(t1+tim)
ff=os.getenv('HOME')+'/bom_mds/output/recons_22012006/real_%(lev)05d_' %{'lev':gp['levs'][lvl]}
savefig(ff+tim+'_2d_3d.png')
close(f)
