2个用户定义的地理参考网格之间的距离，以km为单位

Question

我有2个变量“根区域”和“树覆盖”都处于地理位置（NetCDF）（基本上是网格，每个网格都有特定的值）。 TC中的值从0到100不等。每个网格大小为0.25度（可能有助于理解距离）。

我的问题是“我想从最接近的TC范围介于70-100到30-70之间的距离（因此，每个纬度和经度的TC值均大于30）计算距离。 0-30（小于30）。“

我想做的是创建一个二维散点图，其中X轴表示“从0到30的值，距离70-100 TC（和30-70 TC）的公里数”，Y轴表示“那些70-100 TC点（和30-70 TC）的RZS'

#I read the files using xarray
deficit_annual = xr.open_dataset('Rootzone_CHIRPS_era5_2000-2015_annual_SA_masked.nc')
tc = xr.open_dataset('Treecover_MODIS_2000-2015_annual_SA_masked.nc')
fig, ax = plt.subplots(figsize = (8,8))
## year I am interested in
year = 2000
i = year - 2000
# Select the indices of the low- and high-valued points
# This will results in warnings here because of NaNs;
# the NaNs should be filtered out in the indices, since they will 
# compare to False in all the comparisons, and thus not be 
# indexed by 'low' and 'high'
low = (tc[i,:,:] <= 30)    # Savanna
moderate = (tc[i,:,:] > 30) & (tc[i,:,:] < 70)    #Transitional forest
high = (tc[i,:,:] >= 70)   #Forest
# Get the coordinates for the low- and high-valued points,
# combine and transpose them to be in the correct format
y, x = np.where(low)
low_coords = np.array([x, y]).T
y, x = np.where(high)
high_coords = np.array([x, y]).T
y, x = np.where(moderate)
moderate_coords = np.array([x, y]).T
# We now calculate the distances between *all* low-valued points, and *all* high-valued points.
# This calculation scales as O^2, as does the memory cost (of the output), 
# so be wary when using it with large input sizes.
from scipy.spatial.distance import cdist, pdist

distances = cdist(low_coords, moderate_coords, 'euclidean')
# Now find the minimum distance along the axis of the high-valued coords, 
# which here is the second axis.
# Since we also want to find values corresponding to those minimum distances, 
# we should use the `argmin` function instead of a normal `min` function.
indices = distances.argmin(axis=1)
mindistances = distances[np.arange(distances.shape[0]), indices]
minrzs = np.array(deficit_annual[i,:,:]).flatten()[indices]

plt.scatter(mindistances*25, minrzs, s = 60, alpha = 0.5, color = 'goldenrod', label = 'Trasitional Forest')

distances = cdist(low_coords, high_coords, 'euclidean')
# Now find the minimum distance along the axis of the high-valued coords, 
# which here is the second axis.
# Since we also want to find values corresponding to those minimum distances, 
# we should use the `argmin` function instead of a normal `min` function.
indices = distances.argmin(axis=1)
mindistances = distances[np.arange(distances.shape[0]), indices]
minrzs = np.array(deficit_annual[i,:,:]).flatten()[indices]

plt.scatter(mindistances*25, minrzs, s = 60, alpha = 1, color = 'green', label = 'Forest')


plt.xlabel('Distance from Savanna (km)', fontsize = '14')
plt.xticks(fontsize = '14')
plt.yticks(fontsize = '14')
plt.ylabel('Rootzone storage capacity (mm/year)', fontsize = '14') 
plt.legend(fontsize = '14')
#plt.ylim((-10, 1100))   
#plt.xlim((0, 30))   

我想知道代码是否似乎有错误（因为它现在正在工作，但是当我增加'high =（tc [i，:::]> = 70 `到2000年为80。这使我想知道代码是否正确。

第二，可以定义一个20 km的缓冲区“ low =（tc [i，:::] <= 30）”。我的意思是，仅当Tree覆盖值的群集低于30而不是单个像素时才定义“低”。

下面的链接中包含一些netCDF文件： https://www.dropbox.com/sh/unm96q7sfto8y53/AAA7e12bs07XtpMiVFdML_PIa?dl=0

我想要的图形是这样的（来自上面的代码）。

谢谢您的帮助。