我正在使用interpolate_to_grid在俄克拉荷马州Mesonet数据上运行Barnes目标分析程序，该程序将从interpolate_to_points中读取计算出的测站间距。这是使用ave_spacing = cdist（points，points）.mean（）计算的，它似乎是在计算每个单独数据点与 all 数据点之间的平均距离。结果，当相邻站点之间的实际平均间隔为30-40 km时，我的平均站距为〜228 km。我希望能够在客观分析过程中更改此值，而不必筛选所有不同的依赖项。

spacing = cdist(list(zip(xloc,yloc)),list(zip(xloc,yloc)))
print(spacing)

Output:
[[     0.         245145.42398369 281067.71959647 ... 181889.14491027
  307129.27783772 193503.08897866]
 [245145.42398369      0.         242581.9939922  ... 426945.42853957
  204288.62028541 345728.95107532]
 [281067.71959647 242581.9939922       0.         ... 410049.17526377
   70655.02912353 212376.09473731]
 ...
 [181889.14491027 426945.42853957 410049.17526377 ...      0.
  455951.64830299 226710.02224577]
 [307129.27783772 204288.62028541  70655.02912353 ... 455951.64830299
       0.         275129.18406574]
 [193503.08897866 345728.95107532 212376.09473731 ... 226710.02224577
  275129.18406574      0.        ]]


avg_spacing = np.mean(cdist(list(zip(xloc,yloc))))
print(avg_spacing)

Output:
227725.7196359123