def radar_to_cappi(radar1_filename, radar2_filename,**kwargs):
ini_fname=kwargs.get('ini_fname', os.getenv('HOME')+'/bom_mds/bom_mds.ini')
loud=kwargs.get('loud', False)
ini_dict=parse_ini.parse_ini(ini_fname)
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=pyradar.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=pyradar.load_radar(radar2_filename)
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)
if loud: print "Cappi-ing radar 2"
radar2_cube=radar_to_cart.make_cube(radar2, xar, yar, levs, displacement=displace)
cube_fname=ini_dict['cube_path']+'cappi_'+std_datestr(radar1_cube['date'])+'.nc'
netcdf_utis.save_data_cube(radar1_cube, radar2_cube, cube_fname)
def process_scan(scan, **kwargs):
ini_fname=kwargs.get('ini_fname', os.getenv('HOME')+'/bom_mds/bom_mds.ini')
ini_dict=parse_ini.parse_ini(ini_fname)
gated_vars=['VE', 'VR', 'CZ', 'RH', 'PH', 'ZD', 'SW','KD']
filtered_scan={}
for item in set(scan.keys())&set(gated_vars):
if ini_dict[item+'_bins'] !=0:
rec_array=numpy.zeros(scan[item].shape, dtype=float)
for az_num in range(scan[item].shape[0]):
rec_array[az_num, :]=filter_ray(scan[item][az_num,:], scan['RH'][az_num,:], npts=ini_dict[item+'_bins'], ftype=ini_dict[item+'_window'], RH_thresh=ini_dict['RH_thresh'])
filtered_scan.update({item:rec_array})
else:
filtered_scan.update({item:scan[item]})
rec_array=numpy.zeros(scan[item].shape, dtype=float)
for az_num in range(scan['PH'].shape[0]):
rec_array[az_num, :]=generate_kdp(scan['PH'][az_num,:], scan['range'][az_num,:] ,scan['RH'][az_num,:] ,npts=ini_dict['KD_bins'], ftype=ini_dict['KD_window'], RH_thresh=ini_dict['RH_thresh'])
filtered_scan.update({'KD':rec_array})
return filtered_scan
def pres_winds(datestr, **kwargs): #latstr, lonstr,
ini_fname=kwargs.get('ini_fname', os.getenv('HOME')+'/bom_mds/bom_mds.ini')
ini_dict=parse_ini.parse_ini(ini_fname)
parm=kwargs.get('parm','CZ')
dateobj=num2date(datestr2num(datestr))
tim_date=num2date(datestr2num(datestr))
radar1, radar2=netcdf_utis.load_cube('/data/cube_data/'+std_datestr(tim_date)+'_winds.nc')
print radar1['radar_name']
print radar2['radar_name']
lvl=kwargs.get('lvl',3000.0)
lvl_num=argsort(abs(radar1['levs']-lvl))[0]
print "Level_num=", lvl_num
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(radar1['radar_loc'][0]*pi/180.0))
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)
ber_loc=[-12.457, 130.925]
gp_loc=[-12.2492, 131.0444]
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)
if ini_dict['cross'][0]=='max' or ini_dict['cross'][1]=='max':
#maskedcz=radar1['CZ'][:,:,lvl_num]*mywts[:,:,lvl_num]
#i,j=mathematics.where_closest_2d(maskedcz.max(), maskedcz)
maskedw=radar1['w_array'][:,:,7]*mywts[:,:,7]
i,j=mathematics.where_closest_2d(maskedw.max(), maskedw)
print i,j
lat=lats[j[0]]
lon=lons[i[0]]
print "Max w at ", lat, lon
else:
lon=ini_dict['cross'][1]#float(lonstr)
lat=ini_dict['cross'][0]#float(latstr)
f=figure()
alat, alon, alvl=pres.plot_slices(lat, lon, lvl, radar1, lats, lons, radar1['levs'], radar1['u_array'], radar1['v_array'], radar1['w_array'], angs, mywts, par=parm, w_mag=1.0,box=ini_dict['pres_box'], bquiver=[0.05, 0.75], ksp=0.05,qscale=ini_dict['qscale'])
t1='Gunn Point reflectivity (dBZ) and reconstructed winds (m/s)\n sliced at %(alat)2.2fS and %(alon)3.2fE and %(alvl)d Metres on %(day)02d/%(mon)02d/%(yr)04d at ' %{'day':tim_date.day, 'mon':tim_date.month, 'yr':tim_date.year,'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}
ff=os.getenv('HOME')+'/bom_mds/output/tests/slicer_'+std_datestr(tim_date)+'_%(alat)2.02f_%(alon)3.02f_%(alvl)05d_' %{'alat':abs(alat), 'alon':alon, 'alvl':alvl}
print ff
savefig(ff+t2+'.png', dpi=200)
close(f)
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')
def read_class(date_obj, **kwargs):
hydro_filename = "/data/cpol_hyrdo_class/" + dt + "_" + tm + ".ascii"
hydro_file = open(hydro_filename, "r")
hydro_ascii = hydro_file.readlines()
hydro_file.close()
hydro_dateobj = num2date(datestr2num(hydro_ascii[0]))
data_list = [float(item) for item in hydro_ascii[1].split()]
data_dict = {
"zero_loc": [data_list[0], data_list[1]],
"xrng": [data_list[2], data_list[3]],
"yrng": [data_list[5], data_list[6]],
"zrng": [data_list[8], data_list[9]],
"res": [data_list[4], data_list[7], data_list[10]],
}
float_list = []
for item in hydro_ascii[2 : len(hydro_ascii)]:
for number in item.split():
float_list.append(float(number))
classifyT = zeros(data_dict["res"], dtype="float")
reflT = zeros(data_dict["res"], dtype="float")
# i+nx*(j-1)+nx*ny*(k-1)
classify = zeros(data_dict["res"], dtype="float")
refl = zeros(data_dict["res"], dtype="float")
for i in range(data_dict["res"][0]):
for j in range(data_dict["res"][1]):
for k in range(data_dict["res"][2]):
classifyT[i, j, k] = float_list[
int(i + j * data_dict["res"][0] + data_dict["res"][1] * data_dict["res"][0] * k) * 2 + 1
]
reflT[i, j, k] = float_list[
int(i + j * data_dict["res"][0] + data_dict["res"][1] * data_dict["res"][0] * k) * 2
]
for k in range(data_dict["res"][2]):
classify[:, :, k] = transpose(classifyT[:, :, k])
refl[:, :, k] = transpose(reflT[:, :, k])
xar = linspace(data_dict["xrng"][0], data_dict["xrng"][1], data_dict["res"][0]) * 1000.0
yar = linspace(data_dict["yrng"][0], data_dict["yrng"][1], data_dict["res"][1]) * 1000.0
zar = linspace(data_dict["zrng"][0], data_dict["zrng"][1], data_dict["res"][2]) * 1000.0
Re = 6371.0 * 1000.0
rad_at_radar = Re * sin(
pi / 2.0 - abs(data_dict["zero_loc"][0] * pi / 180.0)
) # ax_radius(float(lat_cpol), units='degrees')
lons = data_dict["zero_loc"][1] + 360.0 * xar / (rad_at_radar * 2.0 * pi)
lats = data_dict["zero_loc"][0] + 360.0 * yar / (Re * 2.0 * pi)
data_dict.update({"xar": xar, "yar": yar, "zar": zar, "CZ": refl, "classify": classify, "lats": lats, "lons": lons})
datestr = dt + " " + tm
kwargs = {}
ini_fname = kwargs.get("ini_fname", os.getenv("HOME") + "/bom_mds/bom_mds.ini")
ini_dict = parse_ini.parse_ini(ini_fname)
dateobj = num2date(datestr2num(datestr))
tim_date = num2date(datestr2num(datestr))
radar1, radar2 = netcdf_utis.load_cube("/data/cube_data/" + std_datestr(tim_date) + "_winds.nc")
Re = 6371.0 * 1000.0
rad_at_radar = Re * sin(pi / 2.0 - abs(radar1["radar_loc"][0] * pi / 180.0))
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)
ber_loc = [-12.457, 130.925]
gp_loc = [-12.2492, 131.0444]
date_dict={'y':date_obj.year,'m':date_obj.month,'d':date_obj.day,'HH':date_obj.hour, 'MM':date_obj.minute}
return "%(y)04d%(m)02d%(d)02d%(HH)02d%(MM)02d" %date_dict
kwargs={}
datestr='200601220700'
#check to see if we have the radar files
#check to see if there are deailased files
#kwargs={}
loud=kwargs.get('loud', False)
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"