def sta_to_sta_idw(sta0, station, effectR=1000, nearNum=16):
sta1 = station.copy()
xyz_sta0 = lon_lat_to_cartesian(sta0.ix[:, 0], sta0.ix[:, 1], R=bd.ER)
xyz_sta1 = lon_lat_to_cartesian(sta1.ix[:, 0], sta1.ix[:, 1], R=bd.ER)
tree = cKDTree(xyz_sta0)
d, inds = tree.query(xyz_sta1, k=nearNum)
d += 1e-6
w = 1.0 / d**2
input_dat = sta0.ix[:, 2]
dat = np.sum(w * input_dat[inds], axis=1) / np.sum(w, axis=1)
dat[:] = np.where(d[:, 0] > effectR, 0, dat[:])
sta1.ix[:, 2] = dat
return sta1
def get_nearby_sta_index_ensemble(sta, grid, nearNum=1):
grd_en = bd.grid_data_ensemble(grid, nearNum)
xyz_sta = lon_lat_to_cartesian(sta.ix[:, 0], sta.ix[:, 0], R=bd.ER)
lon = np.arange(grd_en.nlon) * grd_en.dlon + grd_en.slon
lat = np.arange(grd_en.nlat) * grd_en.dlat + grd_en.slat
grid_lon, grid_lat = np.meshgrid(lon, lat)
xyz_grid = lon_lat_to_cartesian(grid_lon.flatten(),
grid_lat.flatten(),
R=bd.ER)
tree = cKDTree(xyz_sta)
_, inds = tree.query(xyz_grid, k=nearNum)
grd_en.dat = inds.reshape((nearNum, grd_en.nlat, grd_en.nlon))
return grd_en
def get_nearby_sta_index_ensemble(sta_to, nearNum=100, sta_from=None):
if (sta_to is None):
return None
sta_ensemble = bd.sta_data_ensemble(sta_to, nearNum)
if (sta_from is None):
sta_from = sta_to.copy()
xyz_sta0 = lon_lat_to_cartesian(sta_to.ix[:, 0], sta_to.ix[:, 1], R=bd.ER)
xyz_sta1 = lon_lat_to_cartesian(sta_from.ix[:, 0],
sta_from.ix[:, 1],
R=bd.ER)
tree = cKDTree(xyz_sta0)
_, indexs = tree.query(xyz_sta1, k=nearNum)
sta_ensemble.ix[:, 2:] = indexs
return sta_ensemble
def sta_to_grid_idw(sta, grid, background=None, effectR=1000, nearNum=16):
grd = bd.grid_data(grid)
xyz_sta = lon_lat_to_cartesian(sta.ix[:, 0], sta.ix[:, 1], R=bd.ER)
lon = np.arange(grd.nlon) * grd.dlon + grd.slon
lat = np.arange(grd.nlat) * grd.dlat + grd.slat
grid_lon, grid_lat = np.meshgrid(lon, lat)
xyz_grid = lon_lat_to_cartesian(grid_lon.flatten(),
grid_lat.flatten(),
R=bd.ER)
tree = cKDTree(xyz_sta)
d, inds = tree.query(xyz_grid, k=nearNum)
d += 1e-6
w = 1.0 / d**2
input_dat = sta.ix[:, 2]
dat = np.sum(w * input_dat[inds], axis=1) / np.sum(w, axis=1)
bg = bd.grid_data(grid)
if (background is not None):
bg = ggf.linearInterpolation(background, grid)
bg_dat = bg.dat.flatten()
dat = np.where(d[:, 0] > effectR, bg_dat, dat)
grd.dat = dat.reshape((grd.nlat, grd.nlon))
return grd