def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
dep_vars = list(set([y for x in vars if x in VARS for y in VARS[x]]))
d = ds.from_netcdf(
filename,
dep_vars,
)
dx = {}
n, m = d['beta_raw'].shape
if altitude is None:
altitude = d['altitude']
if 'time' in vars:
dx['time'] = d['time'] / (24.0 * 60.0 * 60.0) + 2416480.5
dx['time_bnds'] = misc.time_bnds(dx['time'],
dx['time'][1] - dx['time'][0])
if 'backscatter' in vars:
dx['backscatter'] = d['beta_raw'] * 1e-11 * CALIBRATION_COEFF
if 'zfull' in vars:
zfull1 = d['range'] + altitude
dx['zfull'] = np.tile(zfull1, (n, 1))
if 'altitude' in vars:
dx['altitude'] = np.full(n, altitude, np.float64)
if 'lon' in vars:
dx['lon'] = np.full(n, lon, np.float64)
if 'lat' in vars:
dx['lat'] = np.full(n, lat, np.float64)
dx['.'] = META
dx['.'] = {x: dx['.'][x] for x in vars if x in VARS}
return dx
def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
dep_vars = list(set([y for x in vars if x in VARS for y in VARS[x]]))
d = ds.read(filename, dep_vars)
dx = {}
if 'time' in vars:
n = len(d['time'])
dx['time'] = d['time']/86400. + 2440587.5
dx['time_bnds'] = misc.time_bnds(dx['time'], dx['time'][1] - dx['time'][0])
if 'altitude' in vars:
dx['altitude'] = d['altitude'][:,0].astype(np.float64).filled(np.nan)
if 'zfull' in vars:
range_ = d['range'].astype(np.float64).filled(np.nan)
dx['zfull'] = np.tile(range_, (n, 1))
dx['zfull'] = (dx['zfull'].T + dx['altitude']).T
if 'lon' in vars:
dx['lon'] = np.full(n, lon, np.float64)
if 'lat' in vars:
dx['lat'] = np.full(n, lat, np.float64)
if 'backscatter' in vars:
profile_data = d['profile_data'].astype(np.float64).filled(np.nan)
dx['backscatter'] = profile_data*CALIBRATION_COEFF
n, m = dx['backscatter'].shape
j = np.max(np.nonzero(np.any(np.isfinite(dx['backscatter']), axis=0)))
dx['backscatter'] = dx['backscatter'][:,:(j + 1)]
if 'zfull' in vars:
dx['zfull'] = dx['zfull'][:,:(j + 1)]
dx['.'] = META
dx['.'] = {
x: dx['.'][x]
for x in vars
if x in VARS
}
return dx
def read(dirname, track, warnings=[], step=1. / 24.):
dd_index = ds.readdir(dirname,
variables=['time0', 'latitude', 'longitude'],
jd=True)
start_time = track['time'][0]
end_time = track['time'][1]
dd = []
for d_index in dd_index:
time = d_index['time0']
lon = d_index['longitude']
lon = np.where(lon < 0., 360. + lon, lon)
lat = d_index['latitude']
filename = d_index['filename']
ii = np.where((time >= start_time - GRACE_TIME)
& (time <= end_time + GRACE_TIME))[0]
for i in ii:
i2 = np.argmin(np.abs(track['time'] - time[i]))
lon0 = track['lon'][i2]
lat0 = track['lat'][i2]
l = np.argmin((lon - lon0)**2 + (lat - lat0)**2)
j, k = np.unravel_index(l, lon.shape)
# print('<- %s' % filename)
d = ds.read(filename,
variables=VARIABLES,
sel={
'time0': i,
'rlat': j,
'rlon': k
})
if not set(VARIABLES).issubset(d.keys()):
continue
clw = d['model_qcl']
cli = d['model_qcf']
cl = 100. * np.ones(len(clw), dtype=np.float64)
ps = 2 * d['model_press'][0] - d['model_press'][1]
orog = max(0., 2 * d['hybridt32'][0] - d['hybridt32'][1])
pfull = d['model_press']
zfull = d['hybridt32']
ta = d['theta_lev_temp']
newshape4 = (1, len(clw))
newshape3 = (1, )
d_new = {
'clw': clw.reshape(newshape4),
'cli': cli.reshape(newshape4),
'ta': ta.reshape(newshape4),
'cl': cl.reshape(newshape4),
'pfull': pfull.reshape(newshape4),
'zfull': zfull.reshape(newshape4),
'ps': [ps],
'orog': [orog],
'lon': np.array([lon[j, k]]),
'lat': np.array([lat[j, k]]),
'time': np.array([time[i]]),
'.': META,
}
dd.append(d_new)
d = ds.op.merge(dd, 'time')
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step)
return d
def read(dirname, track, warnings=[], step=3. / 24.):
dd_index = ds.readdir(dirname, variables=['time', 'lat', 'lon'], jd=True)
start_time = track['time'][0]
end_time = track['time'][-1]
dd = []
for d_index in dd_index:
time = d_index['time']
lat = d_index['lat']
lon = d_index['lon']
lon = np.where(lon < 0., 360. + lon, lon)
filename = d_index['filename']
ii = np.nonzero((time >= start_time) & (time < end_time))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
d = ds.read(filename,
variables=VARIABLES,
sel={
'time': i,
'lat': j,
'lon': k
})
clw = d['QL'][::-1]
cli = d['QI'][::-1]
cl = d['CLOUD'][::-1] * 100.
ps = d['PS']
orog = d['PHIS'] / 9.80665
pfull = d['PL'][::-1]
zfull = d['H'][::-1]
ta = d['T'][::-1]
nlev = len(clw)
newshape4 = (1, nlev)
newshape3 = (1, )
d_new = {
'clw': clw.reshape(newshape4),
'cli': cli.reshape(newshape4),
'ta': ta.reshape(newshape4),
'cl': cl.reshape(newshape4),
'pfull': pfull.reshape(newshape4),
'zfull': zfull.reshape(newshape4),
'ps': ps.reshape(newshape3),
'orog': orog.reshape(newshape3),
'lat': np.array([lat[j]]),
'lon': np.array([lon[k]]),
'time': np.array([t]),
'.': META,
}
dd.append(d_new)
d = ds.op.merge(dd, 'time')
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step)
d['.'] = META
return d
def read(dirname, track, warnings=[], step=3. / 24.):
dd_index = ds.readdir(dirname, variables=['XTIME'], jd=True)
start_time = track['time'][0]
end_time = track['time'][-1]
dd = []
for d_index in dd_index:
time = d_index['XTIME'][0]
time0 = d_index['.']['.']['SIMULATION_START_DATE']
time0 = aq.from_iso(time0.replace('_', 'T'))
if time < 2000000.:
time = time0 + time / (24. * 60.)
filename = d_index['filename']
if (time >= start_time - step * 0.5) & (time < end_time + step * 0.5):
k = np.argmin(np.abs(track['time'] - time))
lon0 = track['lon'][k]
lat0 = track['lat'][k]
d = ds.read(filename, variables=VARS, sel={'Time': 0})
lon = np.where(d['XLONG'] < 0., 360. + d['XLONG'], d['XLONG'])
lat = d['XLAT']
l = np.argmin((lon - lon0)**2 + (lat - lat0)**2)
i, j = np.unravel_index(l, lon.shape)
clw = d['QCLOUD'][:, i, j]
cli = d['QICE'][:, i, j]
cl = 100. * np.ones(len(clw), dtype=np.float64)
ps = d['PSFC'][i, j]
orog = d['HGT'][i, j]
pfull = d['PB'][:, i, j] + d['P'][:, i, j]
zfull = (d['PHB'][:, i, j] + d['PH'][:, i, j]) / 9.81
zfull = 0.5 * (zfull[1:] + zfull[:-1])
theta = d['T'][:, i, j] + d['T00']
ta = theta * (pfull / ps)**KAPPA
newshape3 = [1] + list(clw.shape)
newshape2 = [1] + list(ps.shape)
d_new = {
'clw': clw.reshape(newshape3),
'cli': cli.reshape(newshape3),
'ta': ta.reshape(newshape3),
'cl': cl.reshape(newshape3),
'pfull': pfull.reshape(newshape3),
'zfull': zfull.reshape(newshape3),
'ps': ps.reshape(newshape2),
'orog': orog.reshape(newshape2),
'lon': np.array([lon[i, j]]),
'lat': np.array([lat[i, j]]),
'time': np.array([time]),
'.': META,
}
dd.append(d_new)
d = ds.op.merge(dd, 'time')
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step, start_time, end_time)
d['time'] = np.mean(d['time_bnds'], axis=1)
d['.'] = META
return d
def read(filename, vars,
altitude=None,
lon=None,
lat=None,
calibration_coeff=CALIBRATION_COEFF,
fix_cl_range=False,
cl_crit_range=6000,
**kwargs
):
dep_vars = list(set([y for x in vars if x in VARS for y in VARS[x]]))
required_vars = dep_vars + DEFAULT_VARS
d = ds.from_netcdf(
filename,
required_vars
)
dx = {}
dx['time'] = d['time']/(24.0*60.0*60.0) + 2440587.5
dx['time_bnds'] = misc.time_bnds(dx['time'], dx['time'][1] - dx['time'][0])
n = len(dx['time'])
range_ = d['vertical_resolution'][0]*d['level']
if 'zfull' in vars:
zfull1 = range_
dx['zfull'] = np.tile(zfull1, (n, 1))
if altitude is not None:
dx['zfull'] += altitude
if 'backscatter' in vars:
dx['backscatter'] = d['backscatter']*calibration_coeff
mask = range_ > 6000
if fix_cl_range is True:
for i in range(n):
if d['detection_status'][i] == b'0':
dx['backscatter'][i,mask] *= (range_[mask]/6000)**2
if 'altitude' in vars:
dx['altitude'] = np.full(n, altitude, np.float64)
if 'lon' in vars:
dx['lon'] = np.full(n, lon, np.float64)
if 'lat' in vars:
dx['lat'] = np.full(n, lat, np.float64)
dx['.'] = META
dx['.'] = {
x: dx['.'][x]
for x in vars
if x in dx['.']
}
return dx
def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
dep_vars = list(set([y for x in vars if x in VARS for y in VARS[x]]))
d = ds.read(filename, dep_vars)
dx = {}
if 'time' in vars:
n = len(d['time'])
dx['time'] = d['time'] / 86400. + 2440587.5
dx['time_bnds'] = misc.time_bnds(dx['time'],
dx['time'][1] - dx['time'][0])
if 'altitude' in vars:
dx['altitude'] = d['elevation']
if 'zfull' in vars:
dx['zfull'] = np.tile(d['range'], (n, 1))
dx['zfull'] = (dx['zfull'].T + dx['altitude']).T
if 'lon' in vars:
dx['lon'] = np.full(n, lon, np.float64)
if 'lat' in vars:
dx['lat'] = np.full(n, lat, np.float64)
if 'backscatter' in vars:
dx['backscatter'] = d['beta_att'] * CALIBRATION_COEFF
dx['.'] = META
dx['.'] = {x: dx['.'][x] for x in vars if x in VARS}
return dx
def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
d = ds.read(filename, VARIABLES, jd=True)
mask = d['elevation_angle'] == 0.0
dx = {}
n, m = d['nrb_copol'].shape
altitude = d['gps_altitude'] if altitude is None else \
np.full(n, altitude, np.float64)
lon = d['gps_longitude'] if lon is None else \
np.full(n, lon, np.float64)
lat = d['gps_latitude'] if lat is None else \
np.full(n, lat, np.float64)
if 'time' in vars:
dx['time'] = d['time']
dx['time_bnds'] = misc.time_bnds(dx['time'],
dx['time'][1] - dx['time'][0])
if 'zfull' in vars:
dx['zfull'] = np.full((n, m), np.nan, np.float64)
for i in range(n):
range_ = 0.5 * d['bin_time'][i] * d['c'] * (np.arange(m) + 0.5)
dx['zfull'][i, :] = range_ * np.sin(
d['elevation_angle'][i] / 180.0 * np.pi)
dx['zfull'][i, :] += altitude[i]
if 'backscatter' in vars:
dx['backscatter'] = (d['nrb_copol'] +
2. * d['nrb_crosspol']) * CALIBRATION_COEFF
if 'altitude' in vars:
dx['altitude'] = altitude
if 'lon' in vars:
dx['lon'] = lon
if 'lat' in vars:
dx['lat'] = lat
dx['.'] = META
dx['.'] = {x: dx['.'][x] for x in vars if x in dx['.']}
return dx
def read(dirname, track, warnings=[], step=1./24.):
d_orog = ds.read(os.path.join(dirname, 'qrparm.orog.nc'), [
'latitude',
'longitude',
'surface_altitude',
])
dd_idx = ds.readdir(dirname,
variables=['TALLTS', 'latitude_t', 'longitude_t', 'DALLTH_zsea_theta'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
dd = []
for d_idx in dd_idx:
if 'TALLTS' not in d_idx:
continue
time = d_idx['TALLTS']
lat = d_idx['latitude_t']
lon = d_idx['longitude_t']
filename = d_idx['filename']
ii = np.nonzero(
(time >= start_time - step*0.5) &
(time < end_time + step*0.5)
)[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
d = ds.read(filename, VARS,
sel={'TALLTS': [i], 'latitude_t': j, 'longitude_t': k},
jd=True,
)
for a, b in TRANS.items():
if a in d.keys():
ds.rename(d, a, b)
d['lat'] = np.array([d['lat']])
d['lon'] = np.array([d['lon']])
d['.']['lat']['.dims'] = ['time']
d['.']['lon']['.dims'] = ['time']
orog = d_orog['surface_altitude'][j,k]
d['zfull'] = d['eta']*85000. + orog*(1. - d['eta']/d['eta'][51])**2
d['zfull'] = d['zfull'].reshape([1, len(d['zfull'])])
d['.']['zfull'] = {'.dims': ['time', 'level']}
d['orog'] = np.array([orog], np.float64)
d['.']['orog'] = {'.dims': ['time']}
del d['eta']
dd.append(d)
d = ds.op.merge(dd, 'time')
d['cl'] *= 100.
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step, start_time, end_time)
d['time'] = np.mean(d['time_bnds'], axis=1)
d['.'] = META
return d
def read(dirname, track, warnings=[], step=6. / 24.):
dd_index = ds.readdir(
dirname,
variables=['time', 'latitude', 'longitude', 'level_height'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
d_var = {}
for var in VARIABLES:
dd = []
for d_index in dd_index:
if var not in d_index['.']:
continue
time = d_index['time']
time_half = misc.half(time)
lat = d_index['latitude']
lon = d_index['longitude']
level_height = d_index['level_height']
filename = d_index['filename']
ii = np.nonzero((time_half[1:] >= start_time)
& (time_half[:-1] < end_time))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
d = ds.read(filename,
variables=[var],
sel={
'time': i,
'latitude': j,
'longitude': k
})
d_new = {
'lat': np.array([lat[j]]),
'lon': np.array([lon[k]]),
'time': np.array([t]),
'level_height': np.array([level_height]),
'.': META,
}
d_new['.']['level_height'] = {'.dims': ['level']}
d_new[TRANS[var]] = d[var].reshape([1] + list(d[var].shape))
if TRANS[var] == 'cl':
d_new[TRANS[var]] *= 100.
dd.append(d_new)
if len(dd) > 0:
d_var[TRANS[var]] = ds.op.merge(dd, 'time')
time_list = [set(d_var[var]['time']) for var in d_var.keys()]
time = time_list[0].intersection(*time_list[1:]) \
if len(time_list) > 0 \
else set()
d = {}
d['.'] = {}
if len(time) == 0:
return None
for var in d_var.keys():
idx = [i for i, t in enumerate(d_var[var]['time']) if t in time]
ds.select(d_var[var], {'time': idx})
d[var] = d_var[var][var]
d['lat'] = d_var[var]['lat']
d['lon'] = d_var[var]['lon']
d['time'] = d_var[var]['time']
d['level_height'] = d_var[var]['level_height']
if 'ps' not in d:
n, m = d['pfull'].shape
d['ps'] = np.full(n, np.nan, dtype=np.float64)
for i in range(n):
d['ps'][i] = 2 * d['pfull'][i, 0] - d['pfull'][i, 1]
if 'zfull' not in d:
n, m = d['pfull'].shape
d['zfull'] = np.full((n, m), np.nan, dtype=np.float64)
for i in range(n):
d['zfull'][i, :] = d['level_height']
del d['level_height']
if 'orog' not in d:
n, m = d['zfull'].shape
d['orog'] = np.full(n, np.nan, dtype=np.float64)
for i in range(n):
d['orog'][i] = 2 * d['zfull'][i, 0] - d['zfull'][i, 1]
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step)
d['.'] = META
return d
def read0(type_, dirname, track, warnings=[], step=1. / 24.):
dd_idx = ds.readdir(
dirname,
variables=['time', 'latitude', 'longitude'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
vars = {
'surf': VARS_SURF,
'plev': VARS_PLEV,
}[type_]
trans = {
'surf': TRANS_SURF,
'plev': TRANS_PLEV,
}[type_]
dd = []
for d_idx in dd_idx:
time = d_idx['time']
lat = d_idx['latitude']
lon = d_idx['longitude']
filename = d_idx['filename']
ii = np.nonzero((time >= start_time - step * 0.5)
& (time < end_time + step * 0.5))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
d = ds.read(
filename,
vars,
sel={
'time': [i],
'latitude': j,
'longitude': k
},
jd=True,
)
for a, b in trans.items():
if a in d.keys():
ds.rename(d, a, b)
d['lat'] = np.array([d['lat']])
d['lon'] = np.array([d['lon']])
d['.']['lat']['.dims'] = ['time']
d['.']['lon']['.dims'] = ['time']
if type_ == 'plev':
d['pfull'] = d['pfull'].reshape([1, len(d['pfull'])])
d['.']['pfull']['.dims'] = ['time', 'level']
d['cl'] = d['cl'][:, ::-1]
d['clw'] = d['clw'][:, ::-1]
d['cli'] = d['cli'][:, ::-1]
d['ta'] = d['ta'][:, ::-1]
d['zfull'] = d['zfull'][:, ::-1]
d['pfull'] = d['pfull'][:, ::-1]
dd.append(d)
d = ds.op.merge(dd, 'time')
if 'time' in d:
d['time_bnds'] = misc.time_bnds(d['time'], step, start_time, end_time)
d['time'] = np.mean(d['time_bnds'], axis=1)
if 'pfull' in d:
d['pfull'] = 1e2 * d['pfull']
if 'zfull' in d:
d['zfull'] /= 9.80665
if 'orog' in d:
d['orog'] /= 9.80665
if 'cl' in d:
d['cl'] *= 100.
d['.'] = META
return d
def read(dirname, track, warnings=[], step=6. / 24.):
d_ll = ds.read(os.path.join(dirname, 'LL125.nc'),
['latitude', 'longitude', 'z'])
lat_ll = d_ll['latitude']
lon_ll = d_ll['longitude']
orog_ll = d_ll['z'][0, :, :] / 9.80665
dd_idx = ds.readdir(
dirname,
variables=['time', 'latitude', 'longitude'],
jd=True,
full=True,
warnings=warnings,
)
start_time = track['time'][0]
end_time = track['time'][-1]
d_out = {}
for var in VARS:
dd = []
var2 = TRANS[var]
for d_idx in dd_idx:
if var not in d_idx['.']:
continue
time = d_idx['time']
lat = d_idx['latitude']
lon = d_idx['longitude']
filename = d_idx['filename']
ii = np.nonzero((time >= start_time - step * 0.5)
& (time < end_time + step * 0.5))[0]
for i in ii:
t = time[i]
i2 = np.argmin(np.abs(track['time'] - time[i]))
lat0 = track['lat'][i2]
lon0 = track['lon'][i2]
j = np.argmin(np.abs(lat - lat0))
k = np.argmin(np.abs(lon - lon0))
j_ll = np.argmin(np.abs(lat_ll - lat0))
k_ll = np.argmin(np.abs(lon_ll - lon0))
d = ds.read(
filename,
VARS_AUX + [var],
sel={
'time': [i],
'latitude': j,
'longitude': k,
},
jd=True,
)
for a, b in TRANS.items():
if a in d.keys():
ds.rename(d, a, b)
d['lat'] = np.array([d['lat']])
d['lon'] = np.array([d['lon']])
d['orog'] = np.array([orog_ll[j_ll, k_ll]])
d['.']['lat']['.dims'] = ['time']
d['.']['lon']['.dims'] = ['time']
d['.']['orog'] = {'.dims': ['time']}
if 'pfull' in ds.get_vars(d):
d['pfull'] = d['pfull'].reshape([1, len(d['pfull'])])
d['.']['pfull']['.dims'] = ['time', 'pfull']
d['pfull'] = d['pfull'][:, ::-1]
d[var2] = d[var2][:, ::-1]
ds.select(d, {'pfull': np.arange(27)})
dd.append(d)
d = ds.op.merge(dd, 'time')
for var_aux in VARS_AUX:
if TRANS[var_aux] in ds.get_vars(d_out) \
and TRANS[var_aux] in ds.get_vars(d) \
and not np.all(d_out[TRANS[var_aux]] == d[TRANS[var_aux]]):
raise ValueError('%s: Field differs between input files' %
TRANS[var_aux])
d_out.update(d)
d_out['pfull'] = d_out['pfull'] * 1e2
if 'time' in d_out:
d_out['time_bnds'] = misc.time_bnds(d_out['time'], step, start_time,
end_time)
d_out['time'] = np.mean(d_out['time_bnds'], axis=1)
d_out['.'] = META
return d_out
def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
dep_vars = list(set([y for x in vars if x in VARS for y in VARS[x]]))
required_vars = dep_vars + DEFAULT_VARS
d = ds.from_netcdf(
filename,
required_vars
)
mask = d['elevation_angle'] == 0.0
dx = {}
n = len(d['year'])
altitude = d['altitude'] if altitude is None else \
np.full(n, altitude, np.float64)
lon = d['longitude'] if lon is None else \
np.full(n, lon, np.float64)
lat = d['latitude'] if lat is None else \
np.full(n, lat, np.float64)
if 'time' in vars:
dx['time'] = np.array([
(dt.datetime(y, m, day, H, M, S) - dt.datetime(1970, 1, 1)).total_seconds()/(24.0*60.0*60.0) + 2440587.5
for y, m, day, H, M, S
in zip(d['year'], d['month'], d['day'], d['hour'], d['minute'], d['second'])
], np.float64)
tres = parse_temporal_resolution(d['.']['.']['temporal_resolution'])
dx['time_bnds'] = misc.time_bnds(dx['time'], tres)
# dx['time'] += 13.0/24.0
if 'zfull' in vars:
zfull1 = np.outer(
np.sin(d['elevation_angle']/180.0*np.pi),
d['range_nrb']*1e3
)
dx['zfull'] = np.tile(zfull1, (n, 1))
for i in range(n):
dx['zfull'][i,:] += altitude[i]
if 'backscatter' in vars:
dx['backscatter'] = (d['copol_nrb'] + 2.*d['crosspol_nrb'])*CALIBRATION_COEFF
if 'altitude' in vars:
dx['altitude'] = altitude
if 'lon' in vars:
dx['lon'] = lon
if 'lat' in vars:
dx['lat'] = lat
# if 'backscatter_x' in vars:
# dx['backscatter_x'] = d['copol_nrb']*CALIBRATION_COEFF
# if 'backscatter_y' in vars:
# dx['backscatter_y'] = d['crosspol_nrb']*CALIBRATION_COEFF
dx['.'] = META
# 'backscatter_x': {
# '.dims': ['time', 'level'],
# 'long_name': 'copolarized_attenuated_backscatter_coefficient',
# 'units': 'm-1 sr-1',
# },
# 'backscatter_y': {
# '.dims': ['time', 'level'],
# 'long_name': 'crosspolarized_attenuated_backscatter_coefficient',
# 'units': 'm-1 sr-1',
# },
dx['.'] = {
x: dx['.'][x]
for x in vars
if x in dx['.']
}
return dx