问X数组选择多维坐标最近的lat/lon

EN

import numpy as np
import xarray
import matplotlib.pyplot as plt
from scipy.interpolate import griddata

lats = np.array([[21.138, 21.14499, 21.15197, 21.15894, 21.16591],
                 [21.16287, 21.16986, 21.17684, 21.18382, 21.19079],
                 [21.18775, 21.19474, 21.20172, 21.2087, 21.21568],
                 [21.21262, 21.21962, 21.22661, 21.23359, 21.24056],
                 [21.2375, 21.2445, 21.25149, 21.25848, 21.26545]])

lons = np.array([[-122.72, -122.69333, -122.66666, -122.63999, -122.61331],
                 [-122.7275, -122.70082, -122.67415, -122.64746, -122.62078],
                 [-122.735, -122.70832, -122.68163, -122.65494, -122.62825],
                 [-122.7425, -122.71582, -122.68912, -122.66243, -122.63573],
                 [-122.75001, -122.72332, -122.69662, -122.66992, -122.64321]])

speed = np.array([[10.934007, 10.941321, 10.991583, 11.063932, 11.159435],
                  [10.98778, 10.975482, 10.990983, 11.042522, 11.131154],
                  [11.013505, 11.001573, 10.997754, 11.03566, 11.123781],
                  [11.011163, 11.000227, 11.010223, 11.049, 11.1449],
                  [11.015698, 11.026604, 11.030653, 11.076904, 11.201464]])

ds = xarray.Dataset({'SPEED': (('i', 'j'), speed)},
                    coords={'latitude': (('i', 'j'), lats),
                            'longitude': (('i', 'j'), lons)},
                    attrs={'variable': 'Wind Speed'})

lat_min = float(np.min(ds.latitude))
lat_max = float(np.max(ds.latitude))
lon_min = float(np.min(ds.longitude))
lon_max = float(np.max(ds.longitude))
margin = 0.02

fig, ((ax1, ax2)) = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))

ax1.set_xlim(lat_min - margin, lat_max + margin)
ax1.set_ylim(lon_min - margin, lon_max + margin)
ax1.axis('equal')
ds.SPEED.plot(ax=ax1, x='latitude', y='longitude', cmap=plt.cm.jet)
ax1.scatter(ds.latitude, ds.longitude, color='black')

# find nearest_point for a requested lat/ lon
lat_requested = 21.22
lon_requested = -122.68

d_lat = ds.latitude - lat_requested
d_lon = ds.longitude - lon_requested
r2_requested = d_lat**2 + d_lon**2
i_j_loc = np.where(r2_requested == np.min(r2_requested))

nearest_point = ds.sel(i=i_j_loc[0], j=i_j_loc[1])

# Plot nearest point in the array red# Plot nearest point in the array red
ax1.scatter(lat_requested, lon_requested, color='green')
ax1.text(lat_requested, lon_requested, 'requested')
ax1.scatter(nearest_point.latitude, nearest_point.longitude, color='red')
ax1.text(nearest_point.latitude, nearest_point.longitude, 'nearest')
ax1.set_title(f'speed at nearest point: {float(nearest_point.SPEED.data):.2f}')

# define grid from the dataset
num_points = 100
lats_i = np.linspace(lat_min, lat_max, num_points)
lons_i = np.linspace(lon_min, lon_max, num_points)

# grid and contour the data.
speed_i = griddata((ds.latitude.values.flatten(), ds.longitude.values.flatten()),
                   ds.SPEED.values.flatten(),
                   (lats_i[None, :], lons_i[:, None]), method='cubic')

ax2.set_xlim(lat_min - margin, lat_max + margin)
ax2.set_ylim(lon_min - margin, lon_max + margin)
ax2.axis('equal')
ax2.set_title(f'griddata test {num_points} points')

ax2.contour(lats_i, lons_i, speed_i, 15, linewidths=0.5, colors='k')
contour = ax2.contourf(lats_i, lons_i, speed_i, 15, cmap=plt.cm.jet)
plt.colorbar(contour, ax=ax2)

# plot data points and labels
ax2.scatter(ds.latitude, ds.longitude, marker='o', c='b', s=5)

for i, (lat, lon) in enumerate(zip(ds.latitude.values.flatten(),
                                   ds.longitude.values.flatten())):
    text_label = f'{ds.SPEED.values.flatten()[i]:0.2f}'
    ax2.text(lat, lon, text_label)

# Plot nearest point in the array red
ax2.scatter(lat_requested, lon_requested, color='green')
ax2.text(lat_requested, lon_requested, 'requested')
ax2.scatter(nearest_point.latitude, nearest_point.longitude, color='red')

plt.subplots_adjust(wspace=0.2)
plt.show()