Beispiel #1
0
def cube_stats_f(date_str):
	#date_str='20060123 1600'
	tim_date=num2date(datestr2num(date_str))
	radar1, radar2=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/'+std_datestr(tim_date, 'uf')+'_winds.nc')
	Re=6371.0*1000.0
	rad_at_radar=Re*sin(pi/2.0 -abs(radar1['radar_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
	lons=radar1['radar_loc'][1]+360.0*radar1['xar']/(rad_at_radar*2.0*pi)
	lats=radar1['radar_loc'][0] + 360.0*radar1['yar']/(Re*2.0*pi)	
	angs=array(propigation.make_lobe_grid(radar2['radar_loc'], radar1['radar_loc'], lats,lons))
	mywts=met.make_mask_bad1(radar2, radar1, angs, 1.0, 80.0)
	submask=met.make_submask(mywts)
	X=[radar1['u_array'], radar1['v_array'], radar1['w_array']]
	req=[ 'alt(m)',  'wspd(m/s)',  'wdir(degs)', 'tdry(degs)','press(hPa)' ]
	first_sonde,second_sonde = read_sounding.get_two_best_conc_sondes(date_str, req_vars=req)
	interp_sonde=read_sounding.interp_sonde_time(first_sonde, second_sonde, tim_date, radar1['levs'])
	costs=grad_conj_solver_3d.return_cost(X, radar2, radar1, mywts, interp_sonde, submask,loud=True)
	disag=costs[1]/(mywts.sum()*2.0)
	print disag
Beispiel #2
0
def radar_to_winds(datestr, **kwargs):
	#check to see if we have the radar files
	#check to see if there are deailased files
	#kwargs={}
	loud=kwargs.get('loud', False)
	
	#datestr='200601220700'
	ini_fname=kwargs.get('ini_fname', os.getenv('HOME')+'/bom_mds/bom_mds.ini')
	
	dateobj=num2date(datestr2num(datestr))
	ini_fname=kwargs.get('ini_fname', os.getenv('HOME')+'/bom_mds/bom_mds.ini')
	ini_dict=parse_ini.parse_ini(ini_fname)
	radar1_deal_list=os.listdir(ini_dict['radar1_path'])
	radar2_deal_list=os.listdir(ini_dict['radar2_path'])
	radar1_raw_list=os.listdir(ini_dict['radar1_raw_path'])
	radar2_raw_list=os.listdir(ini_dict['radar2_raw_path'])
	
	radar1_target=ini_dict['radar1_prefix']+std_datestr(dateobj, ini_dict['radar1_type'])
	radar2_target=ini_dict['radar2_prefix']+std_datestr(dateobj, ini_dict['radar2_type'])
	
	poss_deal_files1=[]
	for item in radar1_deal_list:
		if radar1_target in item: poss_deal_files1.append(item)
	
	if len(poss_deal_files1)==0:
		poss_raw_files1=[]
		print "No dealiased files found... Dealiasing"
		for item in radar1_raw_list:
			if radar1_target in item: poss_raw_files1.append(item)
		if len(poss_raw_files1)==0:
			#print "no files found"
			raise IOError, 'Radar 2 File not there'
			#return
		else:
			print "Dealiasing "+poss_raw_files1[0]
			radar1_filename=dealias.dealias_arb(poss_raw_files1[0], ini_dict['radar1_type'], ini_dict['radar1_raw_path'], ini_dict['radar1_path'], ini_dict['radar1_prefix'])
	else:
		radar1_filename=poss_deal_files1[0]
	
	poss_deal_files2=[]
	for item in radar2_deal_list:
		if radar2_target in item: poss_deal_files2.append(item)
	
	if len(poss_deal_files2)==0:
		poss_raw_files2=[]
		print "No dealiased files found... Dealiasing"
		for item in radar2_raw_list:
			if radar2_target in item: poss_raw_files2.append(item)
		if len(poss_raw_files2)==0:
			#print "no files found"
			raise IOError, 'Radar 2 File not there'
		else:
			radar2_filename=dealias.dealias_arb(poss_raw_files2[0], ini_dict['radar2_type'], ini_dict['radar2_raw_path'], ini_dict['radar2_path'], ini_dict['radar2_prefix'])
	else:
		radar2_filename=poss_deal_files2[0]
	
	if loud: print "Loading radar file 1"
	
	if 'radar1_path' in ini_dict.keys():
		radar1=read_radar.load_radar(ini_dict['radar1_path']+radar1_filename)
	else:
		radar1=read_radar.load_radar(radar1_filename)
	
	if loud: print "Loading radar file 2"
	
	if 'radar2_path' in ini_dict.keys():
		radar2=read_radar.load_radar(ini_dict['radar2_path']+radar2_filename)
	else:
		radar2=read_radar.load_radar(radar2_filename)
	pres.plot_ppi(radar2[2],'VE', fig_path='/scratch/bom_mds_dumps/', fig_name='radar2_ve.png')
	pres.plot_ppi(radar2[2],'CZ', fig_path='/scratch/bom_mds_dumps/', fig_name='radar2_cz.png')
	cappi_z_bounds=ini_dict.get('cappi_z_bounds', [500,15000])
	cappi_xy_bounds=ini_dict.get('cappi_xy_bounds', [-50000, 50000])
	cappi_resolution=ini_dict.get('cappi_resolution', [100, 40])
	levs=linspace(cappi_z_bounds[0], cappi_z_bounds[1], cappi_resolution[1])
	xar=linspace(cappi_xy_bounds[0], cappi_xy_bounds[1], cappi_resolution[0])
	yar=linspace(cappi_xy_bounds[0], cappi_xy_bounds[1], cappi_resolution[0])
	displace=mathematics.corner_to_point(radar1[0]['radar_loc'], radar2[0]['radar_loc'])
	if loud: print "Cappi-ing radar 1"
	
	#radar1_cube_=radar_to_cart.make_cube(radar1, xar, yar, levs)
	radar1_cube=cappi_v2.make_cube_all(radar1,xar, yar,levs)
	#max_el=array([scan['Elev'][0] for scan in radar1]).max()
	#radar1_cube=cappi_v2.blend(radar1_cube_v,radar1_cube_h, max_el,loud=True)
	
	if loud: print "Cappi-ing radar 2"
	
	#radar2_cube_v=radar_to_cart.make_cube(radar2, xar, yar, levs, displacement=displace)
	radar2_cube=cappi_v2.make_cube_all(radar2,xar, yar,levs, displacement=displace)
	#max_el=array([scan['Elev'][0] for scan in radar2]).max()
	#radar2_cube=cappi_v2.blend(radar2_cube_v,radar2_cube_h, max_el,loud=True)
	#radar2_cube_v=radar_to_cart.make_cube(radar2, xar, yar, levs, displacement=displace)
	cube_fname=ini_dict['cube_path']+'cappi_'+std_datestr(radar1_cube['date'], "uf")+'.nc'
	#netcdf_utis.save_data_cube(radar1_cube, radar2_cube, cube_fname)
	
	#Initial Guess
	req=[ 'alt(m)',  'wspd(m/s)',  'wdir(degs)', 'tdry(degs)','press(hPa)' ]
	first_sonde,second_sonde = read_sounding.get_two_best_conc_sondes(datestr, req_vars=req)
	interp_sonde=read_sounding.interp_sonde_time(first_sonde, second_sonde, dateobj, levs)
		
	if ini_dict['initial_guess']=='sonde':
		#using a sonde for out initial gues
		u_ig=ones(radar1_cube['CZ'].shape, dtype=float)
		v_ig=ones(radar1_cube['CZ'].shape, dtype=float)
		w_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
		for k in range(len(levs)):
			u_ig[:,:,k]=1.0*u_ig[:,:,k]*interp_sonde['wspd(m/s)'][k]*sin(pi*interp_sonde['wdir(degs)'][k]/180.0)
			v_ig[:,:,k]=1.0*v_ig[:,:,k]*interp_sonde['wspd(m/s)'][k]*cos(pi*interp_sonde['wdir(degs)'][k]/180.0)
	else:
		u_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
		v_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
		w_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
	
	Re=6371.0*1000.0
	rad_at_radar=Re*sin(pi/2.0 -abs(radar1[0]['radar_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
	lons=radar1[0]['radar_loc'][1]+360.0*xar/(rad_at_radar*2.0*pi)
	lats=radar1[0]['radar_loc'][0] + 360.0*yar/(Re*2.0*pi)	
	#Masking
	angs=array(propigation.make_lobe_grid(radar2[0]['radar_loc'], radar1[0]['radar_loc'], lats,lons))
	mywts=met.make_mask_bad1(radar2_cube, radar1_cube, angs, 1.0, 80.0)
	
	print "Mean gp masked Velocity ", (radar1_cube['VE']*mywts).mean()
	print "min gp masked Velocity ", (radar1_cube['VE']*mywts).min()
	print "max gp masked Velocity ", (radar1_cube['VE']*mywts).max()
	print "Mean Berrimah masked Velocity ", (radar2_cube['VE']*mywts).mean()
	print "min Berrimah masked Velocity ", (radar2_cube['VE']*mywts).min()
	print "max Berrimah masked Velocity ", (radar2_cube['VE']*mywts).max()
	print "Mean gp masked CZ ", (radar1_cube['CZ']*mywts).mean()
	print "min gp masked CZ ", (radar1_cube['CZ']*mywts).min()
	print "max gp masked CZ ", (radar1_cube['CZ']*mywts).max()
	print "Mean Berrimah masked CZ ", (radar2_cube['CZ']*mywts).mean()
	print "min Berrimah masked CZ ", (radar2_cube['CZ']*mywts).min()
	print "max Berrimah masked CZ ", (radar2_cube['CZ']*mywts).max()
	print "Number of masked points", (mywts.shape[0]*mywts.shape[1]*mywts.shape[2])-mywts.sum()
	print "Number of unmasked points ", mywts.sum()
	print "**********************FALLSPEED INFO****************************"
	#def terminal_velocity(refl, temps, levs, display=False):
	tdry=interp_sonde['tdry(degs)']
	pressure=interp_sonde['press(hPa)']
	dummy=met.terminal_velocity(radar1_cube['CZ']*mywts, tdry, radar1_cube['levs'], display=True)
	print "**********************FALLSPEED INFO****************************"
	f=0.0
	X=[u_ig,v_ig,w_ig]
	G,F,X=grad_conj_solver_3d.gracon_3d_packaged(X ,radar2_cube, radar1_cube, mywts, interp_sonde)
	u_array,v_array,w_array=X 
	radar1_cube.update({'u_array':u_array, 'v_array':v_array, 'w_array':w_array})
	netcdf_utis.save_data_cube(radar1_cube, radar2_cube,  '/data/cube_data/'+std_datestr(dateobj, "uf") +'_winds.nc')
Beispiel #3
0
def recon(date_str, latstr, lonstr):
     use_guess='sonde'
     sonde_file='/bm/gdata/scollis/twpice/darwin.txt'
     #tim='1350'
     tim_date=num2date(datestr2num(date_str)) 
     ber, gp=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/'+std_datestr(tim_date)+'_deal.nc')
     sonde_list=read_sounding.read_sounding_within_a_day(sonde_file, tim_date)
     #launch_dates=[sonde['date_list'][0] for sonde in sonde_list]
     #launch_date_offset=[date2num(sonde['date_list'][0])- date2num(tim_date)  for sonde in sonde_list]
     #best_sonde=sonde_list[argsort(abs(array(launch_date_offset)))[0]]
     #print 'Time of radar: ', tim_date, ' Time of sonde_launch: ', best_sonde['date_list'][0], ' Time of sonde_termination: ', best_sonde['date_list'][-1]
     req=[ 'alt(m)',  'wspd(m/s)', 'tdry(degs)',  'wdir(degs)']	
     first_sonde, second_sonde=read_sounding.get_two_best_conc_sondes(date_str, req_vars=req)
     interp_sonde=read_sounding.interp_sonde_time(first_sonde, second_sonde, tim_date, gp['levs'])
     u_sonde=ones(gp['CZ'].shape, dtype=float)
     v_sonde=ones(gp['CZ'].shape, dtype=float)
     w_sonde=zeros(gp['CZ'].shape, dtype=float)
     for k in range(len(gp['levs'])):
	     u_sonde[:,:,k]=-1.0*u_sonde[:,:,k]*interp_sonde['wspd(m/s)'][k]*sin(pi*interp_sonde['wdir(degs)'][k]/180.0)
	     v_sonde[:,:,k]=-1.0*v_sonde[:,:,k]*interp_sonde['wspd(m/s)'][k]*cos(pi*interp_sonde['wdir(degs)'][k]/180.0)
     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
	  igw=ones(ber['CZ'].shape, dtype=float)*0.0
     elif use_guess=='sonde':
	  igu=u_sonde
	  igv=v_sonde
	  igw=w_sonde
     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']
	  igw=ones(ber['CZ'].shape, dtype=float)*0.0
     
     #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	
     mywts=met.make_mask(ber, gp, angs, 1.0, 80.0)
     print "Mean gp masked Velocity ", (gp['VE']*mywts).mean()
     print "min gp masked Velocity ", (gp['VE']*mywts).min()
     print "max gp masked Velocity ", (gp['VE']*mywts).max()
     print "Mean Berrimah masked Velocity ", (ber['VE']*mywts).mean()
     print "min Berrimah masked Velocity ", (ber['VE']*mywts).min()
     print "max Berrimah masked Velocity ", (ber['VE']*mywts).max()
     print "Mean gp masked CZ ", (gp['CZ']*mywts).mean()
     print "min gp masked CZ ", (gp['CZ']*mywts).min()
     print "max gp masked CZ ", (gp['CZ']*mywts).max()
     print "Mean Berrimah masked CZ ", (ber['CZ']*mywts).mean()
     print "min Berrimah masked CZ ", (ber['CZ']*mywts).min()
     print "max Berrimah masked CZ ", (ber['CZ']*mywts).max()
    
     print "Number of masked points", (mywts.shape[0]*mywts.shape[1]*mywts.shape[2])-mywts.sum()
     print "Number of unmasked points ", mywts.sum()
     print "**********************FALLSPEED INFO****************************"
     #def terminal_velocity(refl, temps, levs, display=False):
     tdry=interp_sonde['tdry(degs)']
     pressure=interp_sonde['press(hPa)']
     dummy=met.terminal_velocity(gp['CZ']*mywts, tdry, gp['levs'], display=True)
     print "**********************FALLSPEED INFO****************************"
     #print 
     
     
     f=0.0
     X=[igu,igv,igw]
     G,F,X=grad_conj_solver_3d.gracon_3d_packaged(X ,ber, gp, mywts, interp_sonde)
     u_array,v_array,w_array=X 
     lvl=2500.0
     lvl_num=argsort(abs(gp['levs']-lvl))[0]
     print "Level_num=", lvl_num
     if latstr=='max' or lonstr=='max':
	     maskedcz=gp['CZ'][:,:,lvl_num]*mywts[:,:,lvl_num]
	     i,j=mathematics.where_closest_2d(maskedcz.max(), maskedcz)
	     print i,j
	     lat=lats[j[0]]
	     lon=lons[i[0]]
	     print "Max CZ at ", lat, lon 
     else:
	     lon=float(lonstr)
	     lat=float(latstr)
     f=figure()
     #(lat_sl, lon_sl, lvl, data_cube, lats, lons, levs, u, v, w, angs, mask, par='CZ', w_mag=2.0, **kwargs)
     alat, alon, alvl=pres.plot_slices(lat, lon, lvl, gp, lats, lons, gp['levs'], u_array, v_array, w_array, angs, mywts, par='CZ', w_mag=2.0,box=[130.5, 131.5, -12.7, -12.0], bquiver=[0.05, 0.75], ksp=0.05)
     t1='Gunn Point reflectivity (dBZ) and reconstructed winds (m/s, *2.0 for w)\n sliced at %(alat)2.2fS and %(alon)3.2fE and %(alvl)d Metres on 22/01/06 at ' %{'alat':abs(alat), 'alon':alon, 'alvl':alvl}
     t2=" %(HH)02d%(MM)02dZ" %{'HH':tim_date.hour, 'MM':tim_date.minute}
     f.text( .1, .92, t1+t2) 
     inte_part=1000*(float(int(lat))-lat)
     print  {'alat':abs(alat), 'alon':alon, 'alvl':alvl}
     ff=os.getenv('HOME')+'/bom_mds/output/recons_'+std_datestr(tim_date)[0:-5]+'/slicer3_%(alat)2.02f_%(alon)3.02f_%(alvl)05d_' %{'alat':abs(alat), 'alon':alon, 'alvl':alvl}
     print ff
     savefig(ff+t2+'.png', dpi=200)
     gp.update({'u_array':u_array, 'v_array':v_array, 'w_array':w_array})
     netcdf_utis.save_data_cube(ber, gp, '/bm/gdata/scollis/cube_data/'+std_datestr(tim_date)+'_winds.nc', gp_loc)
     close(f)
Beispiel #4
0
	#radar2_cube_v=radar_to_cart.make_cube(radar2, xar, yar, levs, displacement=displace)
radar2_cube=cappi_v2.make_cube_all(radar2,xar, yar,levs, displacement=displace)
	#max_el=array([scan['Elev'][0] for scan in radar2]).max()
	#radar2_cube=cappi_v2.blend(radar2_cube_v,radar2_cube_h, max_el,loud=True)
	#radar2_cube_v=radar_to_cart.make_cube(radar2, xar, yar, levs, displacement=displace)
cube_fname=ini_dict['cube_path']+'cappi_'+std_datestr(radar1_cube['date'], "uf")+'.nc'
	#netcdf_utis.save_data_cube(radar1_cube, radar2_cube, cube_fname)
	
	#Initial Guess



cube_fname=ini_dict['cube_path']+'cappi_'+std_datestr(radar1_cube['date'], "uf")+'.nc'
req=[ 'alt(m)',  'wspd(m/s)',  'wdir(degs)', 'tdry(degs)','press(hPa)' ]
first_sonde,second_sonde = read_sounding.get_two_best_conc_sondes(datestr, req_vars=req)
interp_sonde=read_sounding.interp_sonde_time(first_sonde, second_sonde, dateobj, levs)

if ini_dict['initial_guess']=='sonde':
	#using a sonde for out initial gues
	u_ig=ones(radar1_cube['CZ'].shape, dtype=float)
	v_ig=ones(radar1_cube['CZ'].shape, dtype=float)
	w_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
	for k in range(len(levs)):
		u_ig[:,:,k]=1.0*u_ig[:,:,k]*interp_sonde['wspd(m/s)'][k]*sin(pi*interp_sonde['wdir(degs)'][k]/180.0)
		v_ig[:,:,k]=1.0*v_ig[:,:,k]*interp_sonde['wspd(m/s)'][k]*cos(pi*interp_sonde['wdir(degs)'][k]/180.0)
else:
	u_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
	v_ig=zeros(radar1_cube['CZ'].shape, dtype=float)
	w_ig=zeros(radar1_cube['CZ'].shape, dtype=float)

Re=6371.0*1000.0