def _geocoor(self):
r_data = list()
x_data = list()
y_data = list()
h_data = list()
for i in self.rl:
r, x, y = grid_2d(i.data, i.lon, i.lat)
r_data.append(r)
x_data.append(x)
y_data.append(y)
for radial, elev in zip(self.rl, self.el):
hgh = height(radial.dist, elev, 0)
hgh_radial = np.asarray(hgh.tolist() * radial.data.shape[0]).reshape(radial.data.shape)
hgh_grid, x, y = grid_2d(hgh_radial, radial.lon, radial.lat)
h_data.append(hgh_grid)
return x_data, y_data, h_data, r_data
def quick_cr(r_list: Volume_T, resolution: Optional[tuple] = None) -> Grid:
r"""
Calculate composite reflectivity
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
composite reflectivity
"""
r_data = list()
for i in r_list:
r, x, y = grid_2d(i.data, i.lon, i.lat, resolution=resolution)
r_data.append(r)
cr = np.max(r_data, axis=0)
x, y = np.meshgrid(x, y)
l2_obj = Grid(
np.ma.array(cr, mask=(cr <= 0)),
i.drange,
i.reso,
i.code,
i.name,
i.scantime,
"CR",
i.stp["lon"],
i.stp["lat"],
x,
y,
**i.scan_info
)
return l2_obj
def _geocoor(self) -> tuple:
r_data = list()
x_data = list()
y_data = list()
h_data = list()
for i in self.rl:
if i.dtype in ["VEL", "SW"]:
r, x, y = grid_2d(i.data[0], i.lon, i.lat)
else:
r, x, y = grid_2d(i.data, i.lon, i.lat)
r_data.append(r)
x_data.append(x)
y_data.append(y)
hgh_grid, x, y = grid_2d(i.height, i.lon, i.lat)
h_data.append(hgh_grid)
return x_data, y_data, h_data, r_data
def quick_cr(r_list: Volume_T, resolution: tuple = (1000, 1000)) -> Dataset:
r"""
Calculate composite reflectivity
Paramters
---------
r_list: list of xarray.Dataset
Returns
-------
ret: xarray.Dataset
composite reflectivity
"""
r_data = list()
for i in r_list:
r, x, y = grid_2d(
i["REF"].values,
i["longitude"].values,
i["latitude"].values,
resolution=resolution,
)
r_data.append(r)
cr = np.nanmax(r_data, axis=0)
ret = Dataset(
{"CR": DataArray(cr, coords=[x, y], dims=["longitude", "latitude"])})
ret.attrs = i.attrs
ret.attrs["elevation"] = 0
return ret
def _geocoor(self) -> Tuple[list]:
r_data = list()
x_data = list()
y_data = list()
h_data = list()
for i in self.rl:
_lon = i["longitude"].values
_lat = i["latitude"].values
r, x, y = grid_2d(i[self.dtype].values, _lon, _lat)
r, x, y = grid_2d(i[self.dtype].values, _lon, _lat)
r_data.append(r)
x_data.append(x)
y_data.append(y)
hgh_grid, x, y = grid_2d(i["height"].values, _lon, _lat)
h_data.append(hgh_grid)
return x_data, y_data, h_data, r_data
def merge(self):
lon = self.gen_longitude()
lat = self.gen_latitude()
data_tmp = list()
for i in self.data_list:
data, x, y = grid_2d(i.data, i.lon, i.lat, x_out=lon, y_out=lat)
data_tmp.append(np.ma.array(data, mask=(data == 0)))
out_data = np.ma.average(data_tmp, axis=0)
return x, y, out_data
def quick_cr(r_list: RList) -> Grid:
r'''
Calculate composite reflectivity
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
composite reflectivity
'''
r_data = list()
for i in r_list:
r, x, y = grid_2d(i.data, i.lon, i.lat)
r_data.append(r)
cr = np.max(r_data, axis=0)
x, y = np.meshgrid(x, y)
l2_obj = Grid(np.ma.array(cr, mask=(cr <= 0)), i.drange, 1, i.code, i.name,
i.scantime, 'CR', x, y)
return l2_obj
