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python - xarray选择具有多维坐标的最近纬度/经度

问题描述

我有一个带有不规则间隔纬度和经度坐标的 xarray 数据集。我的目标是在最接近某个纬度/经度的点处找到变量的值。

由于x和y尺寸不是纬度/经度值，因此在这种情况下似乎ds.sel()不能单独使用该方法。是否有以 xarray 为中心的方法通过参考多维纬度/经度维度来定位最接近所需纬度/经度的点？例如，我想提取最接近lat=21.2和的 SPEED 值lon=-122.68。

下面是一个示例数据集...

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':(('x', 'y'),speed)},
                    coords = {'latitude': (('x', 'y'), lats),
                              'longitude': (('x', 'y'), lons)},
                    attrs={'variable':'Wind Speed'})

的价值ds：

<xarray.Dataset>
Dimensions:    (x: 5, y: 5)
Coordinates:
    latitude   (x, y) float64 21.14 21.14 21.15 21.16 ... 21.25 21.26 21.27
    longitude  (x, y) float64 -122.7 -122.7 -122.7 ... -122.7 -122.7 -122.6
Dimensions without coordinates: x, y
Data variables:
SPEED      (x, y) float64 10.93 10.94 10.99 11.06 ... 11.03 11.03 11.08 11.2
Attributes:
    variable:  Wind Speed

同样，ds.sel(latitude=21.2, longitude=-122.68)因为纬度和经度不是数据集维度，所以不起作用。

标签： pythonpython-xarray

解决方案

我想出了一个不纯粹使用 xarray 的方法。我首先手动找到最近邻居的索引，然后使用该索引访问 xarray 维度。

# A 2D plot of the SPEED variable, assigning the coordinate values,
# and plot the verticies of each point
ds.SPEED.plot(x='longitude', y='latitude')
plt.scatter(ds.longitude, ds.latitude)

# I want to find the speed at a certain lat/lon point.
lat = 21.22
lon = -122.68

# First, find the index of the grid point nearest a specific lat/lon.   
abslat = np.abs(ds.latitude-lat)
abslon = np.abs(ds.longitude-lon)
c = np.maximum(abslon, abslat)

([xloc], [yloc]) = np.where(c == np.min(c))

# Now I can use that index location to get the values at the x/y diminsion
point_ds = ds.sel(x=xloc, y=yloc)

# Plot requested lat/lon point blue
plt.scatter(lon, lat, color='b')
plt.text(lon, lat, 'requested')

# Plot nearest point in the array red
plt.scatter(point_ds.longitude, point_ds.latitude, color='r')
plt.text(point_ds.longitude, point_ds.latitude, 'nearest')

plt.title('speed at nearest point: %s' % point_ds.SPEED.data)

示例数据集的二维风速网格

另一个潜在的解决方案（同样，不是 xarray）是使用 scipy 的 KDTree

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