X数组选择多维坐标最近的lat/lon
X数组选择多维坐标最近的lat/lon
提问于 2019-11-07 23:47:59
我有一个x数组数据集，它有不规则的纬度和经度坐标。我的目标是在最接近某个lat/lon的点上找到变量的值。
由于x和y维度不是lat/lon值，因此在这种情况下，ds.sel()方法本身似乎无法使用。是否有以x数组为中心的方法，通过引用多维lat/lon维度来定位最近期望的lat/lon点？例如，我想取出最近的lat=21.2和lon=-122.68的速度值。
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'})ds值
<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)无法工作，因为纬度和经度不是数据集维度。
回答 6
Stack Overflow用户
回答已采纳
发布于 2020-10-19 16:48:56
这里的聚会有点晚了，但我已经多次提到这个问题了。如果您的x和y坐标位于地理空间坐标系中，则可以使用cartopy将lat/lon点转换为该坐标系。如果查看netcdf中的元数据，构建cartopy投影通常是简单的。
import cartopy.crs as ccrs
# Example - your x and y coordinates are in a Lambert Conformal projection
data_crs = ccrs.LambertConformal(central_longitude=-100)
# Transform the point - src_crs is always Plate Carree for lat/lon grid
x, y = data_crs.transform_point(-122.68, 21.2, src_crs=ccrs.PlateCarree())
# Now you can select data
ds.sel(x=x, y=y)Stack Overflow用户
发布于 2019-11-08 21:42:02
我想出了一个不纯粹使用xarray的方法。我首先手动查找最近邻的索引，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)
另一个潜在的解决方案(同样，不是xarray)是使用KDTree，甚至更好的是使用scikit--学习的BallTree (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html)或使用https://github.com/xarray-contrib/xoak。
Stack Overflow用户
发布于 2019-11-08 02:15:45
我认为您需要以不同的方式创建数据集，以确保latitude和longitude具有可解释的维度(请参阅xarray的基本数据结构文章)。
例如：
import numpy as np
import pandas as pd
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])
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))
idx = pd.MultiIndex.from_arrays(arrays=[lons, lats], names=["lon", "lat"])
s = pd.Series(data=speed, index=idx)
da = xarray.DataArray.from_series(s)
print(da)
da.plot(ax=ax1)
print('-'*80)
print(da.sel(lat=21.2, lon=-122.68, method='nearest'))
# define grid.
num_points = 100
lats_i = np.linspace(np.min(lats), np.max(lats), num_points)
lons_i = np.linspace(np.min(lons), np.max(lons), num_points)
# grid the data.
speed_i = griddata((lats, lons), speed,
(lats_i[None, :], lons_i[:, None]), method='cubic')
# contour the gridded data
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.
for i, (lat, lon) in enumerate(zip(lats, lons)):
label = f'{speed[i]:0.2f}'
ax2.annotate(label, (lat, lon))
ax2.scatter(lats, lons, marker='o', c='b', s=5)
ax2.set_title(f'griddata test {num_points} points')
plt.subplots_adjust(wspace=0.2)
plt.show()结果
<xarray.DataArray (lat: 25, lon: 25)>
array([[ nan, nan, nan, nan, nan, 10.934007,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 10.941321, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, 10.991583, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, 11.063932, nan, nan, nan,
nan],
[ nan, nan, nan, 10.98778 , nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
11.159435],
[ nan, nan, nan, nan, nan, nan,
nan, nan, 10.975482, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, 10.990983, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
11.042522, nan, nan, nan, nan, nan,
nan],
[ nan, nan, 11.013505, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 11.131154,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, 11.001573, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
10.997754, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 11.03566 ,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, 11.011163, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 11.123781, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
11.000227, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 11.010223,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 11.049 , nan,
nan, nan, nan, nan, nan, nan,
nan],
[11.015698, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, 11.1449 , nan, nan,
nan],
[ nan, nan, nan, nan, 11.026604, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, 11.030653, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, 11.076904, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan],
[ nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan,
nan, 11.201464, nan, nan, nan, nan,
nan]])
Coordinates:
* lat (lat) float64 21.14 21.14 21.15 21.16 ... 21.24 21.25 21.26 21.27
* lon (lon) float64 -122.8 -122.7 -122.7 -122.7 ... -122.6 -122.6 -122.6
--------------------------------------------------------------------------------
<xarray.DataArray ()>
array(10.997754)
Coordinates:
lat float64 21.2
lon float64 -122.7还有一个包括格栅的情节只是为了好玩
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/58758480
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