def temperature(self, lat, lon):
if not self._temperature:
self._temperature = load_data_interpolator(
'1510/v0_lat.npz', '1510/v0_lon.npz', '1510/v0_temp.npz',
bicubic_2D_interpolator)
return self._temperature(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def Pclw(self, lat, lon):
if not self._Pclw:
self._Pclw = load_data_interpolator(
'840/v6_lat.npz', '840/v6_lon.npz',
'840/v6_pclw.npz', bilinear_2D_interpolator, flip_ud=False)
return self._Pclw(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def sigma(self, lat, lon):
if not self._sigma:
self._sigma = load_data_interpolator(
'840/v7_lat.npz', '840/v7_lon.npz',
'840/v7_sigma.npz', bilinear_2D_interpolator, flip_ud=False)
return self._sigma(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def Beta(self, lat, lon):
if not self._Beta:
self._Beta = load_data_interpolator(
'837/esarain_lat_v5.npz', '837/esarain_lon_v5.npz',
'837/esarain_beta_v5.npz', bilinear_2D_interpolator,
flip_ud=False)
return self._Beta(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def temperature(self, lat, lon):
if not self._temperature:
self._temperature = load_data_interpolator(
'1510/v1_lat.npz', '1510/v1_lon.npz', '1510/v1_t_annual.npz',
bilinear_2D_interpolator)
lon[lon > 180] = lon[lon > 180] - 360
return self._temperature(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def wgs4_altitude(self, lat, lon):
if not self._wgs4_altitude:
self._wgs4_altitude = load_data_interpolator(
'1511/v2_lat.npz', '1511/v2_lon.npz', '1511/v2_egm2008.npz',
bicubic_2D_interpolator)
return self._wgs4_altitude(np.array([lat.ravel(),
lon.ravel()
]).T).reshape(lat.shape)
def altitude(self, lat, lon):
if not self._altitude:
self._altitude = load_data_interpolator('1511/v1_lat.npz',
'1511/v1_lon.npz',
'1511/v1_topo_0dot5.npz',
bicubic_2D_interpolator)
return self._altitude(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def topo_alt(self, lat, lon):
if self._topo_alt is None:
self._topo_alt = load_data_interpolator('836/v6_topolat.npz',
'836/v6_topolon.npz',
'836/v6_topo_0dot5.npz',
bicubic_2D_interpolator)
return self._topo_alt(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def M(self, lat, lon):
if not self._M:
self._M = load_data_interpolator(
'840/v6_lat.npz', '840/v6_lon.npz',
'840/v4_wred_lognormal_mean.npz', bilinear_2D_interpolator,
flip_ud=False)
return self._M(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def R001(self, lat, lon):
if not self._R001:
self._R001 = load_data_interpolator(
'837/v7_lat_r001.npz', '837/v7_lon_r001.npz',
'837/v7_r001.npz', bilinear_2D_interpolator)
# In this recommendation the longitude is encoded with format -180 to
# 180 whereas we always use 0 - 360 encoding
lon = np.array(lon)
lon[lon > 180] = lon[lon > 180] - 360
return self._R001(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def month_temperature(self, lat, lon, m):
if not self._month_temperature:
for _m in self.__months:
self._month_temperature[_m] = load_data_interpolator(
'1510/v1_lat.npz', '1510/v1_lon.npz',
'1510/v1_t_month{0:02d}.npz'.format(_m),
bilinear_2D_interpolator)
lon[lon > 180] = lon[lon > 180] - 360
return self._month_temperature[m](np.array([lat.ravel(),
lon.ravel()
]).T).reshape(lat.shape)
def isoterm_0(self, lat, lon):
if not self._zero_isoterm_data:
self._zero_isoterm_data = load_data_interpolator(
'839/v4_esalat.npz',
'839/v4_esalon.npz',
'839/v4_esa0height.npz',
bilinear_2D_interpolator,
flip_ud=False)
return self._zero_isoterm_data(np.array([lat.ravel(),
lon.ravel()
]).T).reshape(lat.shape)
def Lred(self, lat, lon, p):
if not self._Lred:
ps = [0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 30,
50, 60, 70, 80, 90, 95]
d_dir = os.path.join(dataset_dir, '840/v4_esawred_%s.npz')
for p_load in ps:
self._Lred[float(p_load)] = load_data_interpolator(
'840/v4_lat.npz', '840/v4_lon.npz',
d_dir % (str(p_load).replace('.', '')),
bilinear_2D_interpolator, flip_ud=False)
return self._Lred[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def Mt(self, lat, lon, m):
if not self._Mt:
for _m in self.months:
self._Mt[_m] = load_data_interpolator(
'837/v7_lat_mt.npz', '837/v7_lon_mt.npz',
'837/v7_mt_month{0:02d}.npz'.format(_m),
bilinear_2D_interpolator)
# In this recommendation the longitude is encoded with format -180 to
# 180 whereas we always use 0 - 360 encoding
lon = np.array(lon)
lon[lon > 180] = lon[lon > 180] - 360
return self._Mt[m](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def rho(self, lat, lon, p):
if not self._rho:
ps = [
0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 30, 50, 60, 70, 80, 90,
95, 99
]
d_dir = os.path.join(dataset_dir, '836/v5_rho_%s.npz')
for p_loads in ps:
self._rho[float(p_loads)] = load_data_interpolator(
'836/v5_lat.npz',
'836/v5_lon.npz',
d_dir % (str(p_loads).replace('.', '')),
bilinear_2D_interpolator,
flip_ud=False)
return self._rho[float(p)](np.array([lat.ravel(),
lon.ravel()
]).T).reshape(lat.shape)
