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,
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
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)
dx['.'] = META
dx['.'] = {x: dx['.'][x] for x in vars if x in dx['.']}
return dx
def read(filename, vars, altitude=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'])
if altitude is None:
altitude = d['altitude']
else:
altitude = np.full(n, altitude, 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)
# 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 '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
def read(filename, vars, altitude=None, lon=None, lat=None, **kwargs):
d = ds.from_netcdf(filename, vars)
n = d['backscatter'].shape[0]
d['altitude'] = d['altitude'] if altitude is None and 'altitude' in d else \
np.full(n, altitude, np.float64)
d['lon'] = d['lon'] if lon is None and 'longitude' in d else \
np.full(n, lon, np.float64)
d['lat'] = d['lat'] if lat is None and 'latitude' in d else \
np.full(n, lat, np.float64)
d['.']['altitude'] = META['altitude']
d['.']['lon'] = META['lon']
d['.']['lat'] = META['lat']
d['.'] = {
x: d['.'][x]
for x in vars
if x in d['.']
}
return d
def get(*args, **opts):
if len(args) != 2:
raise TypeError('Usage: get <path> <input>')
path = args[0].split('/')
input_ = args[1]
d = ds.from_netcdf(input_, [])
if len(path) == 2 and path[0] == '' and path[1] == '':
j = json.dumps(d['.']['.'], sort_keys=True, indent=4, cls=NumpyEncoder)
print(j)
elif len(path) == 2 and path[0] == '':
attr = path[1]
print(d['.']['.'][attr])
elif len(path) == 2:
var = path[0]
attr = path[1]
print(d['.'][var][attr])
elif len(path) == 1:
var = path[0]
j = json.dumps(d['.'][var], sort_keys=True, indent=4, cls=NumpyEncoder)
print(j)
def read(filename, vars, **kwargs): d = ds.from_netcdf(filename, vars) return d
def read(filename):
return ds.from_netcdf(filename)