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Commit a9fda5b2 authored by tomrink's avatar tomrink
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......@@ -130,26 +130,37 @@ def upsample_mean(grd):
def get_grid_cell_mean(grd_k):
grd_k = np.where(np.isnan(grd_k), 0, grd_k)
a = grd_k[:, 0::4, 0::4]
b = grd_k[:, 1::4, 0::4]
c = grd_k[:, 0::4, 1::4]
d = grd_k[:, 1::4, 1::4]
mean = np.nanmean([a, b, c, d], axis=0)
mean = np.nanmean([grd_k[:, 0::4, 0::4], grd_k[:, 1::4, 0::4], grd_k[:, 2::4, 0::4], grd_k[:, 3::4, 0::4],
grd_k[:, 0::4, 1::4], grd_k[:, 1::4, 1::4], grd_k[:, 2::4, 1::4], grd_k[:, 3::4, 1::4],
grd_k[:, 0::4, 2::4], grd_k[:, 1::4, 2::4], grd_k[:, 2::4, 2::4], grd_k[:, 3::4, 2::4],
grd_k[:, 0::4, 3::4], grd_k[:, 1::4, 3::4], grd_k[:, 2::4, 3::4], grd_k[:, 3::4, 3::4]], axis=0)
return mean
def get_min_max_std(grd_k):
grd_k = np.where(np.isnan(grd_k), 0, grd_k)
a = grd_k[:, 0::4, 0::4]
b = grd_k[:, 1::4, 0::4]
c = grd_k[:, 0::4, 1::4]
d = grd_k[:, 1::4, 1::4]
lo = np.nanmin([a, b, c, d], axis=0)
hi = np.nanmax([a, b, c, d], axis=0)
std = np.nanstd([a, b, c, d], axis=0)
avg = np.nanmean([a, b, c, d], axis=0)
lo = np.nanmin([grd_k[:, 0::4, 0::4], grd_k[:, 1::4, 0::4], grd_k[:, 2::4, 0::4], grd_k[:, 3::4, 0::4],
grd_k[:, 0::4, 1::4], grd_k[:, 1::4, 1::4], grd_k[:, 2::4, 1::4], grd_k[:, 3::4, 1::4],
grd_k[:, 0::4, 2::4], grd_k[:, 1::4, 2::4], grd_k[:, 2::4, 2::4], grd_k[:, 3::4, 2::4],
grd_k[:, 0::4, 3::4], grd_k[:, 1::4, 3::4], grd_k[:, 2::4, 3::4], grd_k[:, 3::4, 3::4]], axis=0)
hi = np.nanmax([grd_k[:, 0::4, 0::4], grd_k[:, 1::4, 0::4], grd_k[:, 2::4, 0::4], grd_k[:, 3::4, 0::4],
grd_k[:, 0::4, 1::4], grd_k[:, 1::4, 1::4], grd_k[:, 2::4, 1::4], grd_k[:, 3::4, 1::4],
grd_k[:, 0::4, 2::4], grd_k[:, 1::4, 2::4], grd_k[:, 2::4, 2::4], grd_k[:, 3::4, 2::4],
grd_k[:, 0::4, 3::4], grd_k[:, 1::4, 3::4], grd_k[:, 2::4, 3::4], grd_k[:, 3::4, 3::4]], axis=0)
std = np.nanstd([grd_k[:, 0::4, 0::4], grd_k[:, 1::4, 0::4], grd_k[:, 2::4, 0::4], grd_k[:, 3::4, 0::4],
grd_k[:, 0::4, 1::4], grd_k[:, 1::4, 1::4], grd_k[:, 2::4, 1::4], grd_k[:, 3::4, 1::4],
grd_k[:, 0::4, 2::4], grd_k[:, 1::4, 2::4], grd_k[:, 2::4, 2::4], grd_k[:, 3::4, 2::4],
grd_k[:, 0::4, 3::4], grd_k[:, 1::4, 3::4], grd_k[:, 2::4, 3::4], grd_k[:, 3::4, 3::4]], axis=0)
avg = np.nanmean([grd_k[:, 0::4, 0::4], grd_k[:, 1::4, 0::4], grd_k[:, 2::4, 0::4], grd_k[:, 3::4, 0::4],
grd_k[:, 0::4, 1::4], grd_k[:, 1::4, 1::4], grd_k[:, 2::4, 1::4], grd_k[:, 3::4, 1::4],
grd_k[:, 0::4, 2::4], grd_k[:, 1::4, 2::4], grd_k[:, 2::4, 2::4], grd_k[:, 3::4, 2::4],
grd_k[:, 0::4, 3::4], grd_k[:, 1::4, 3::4], grd_k[:, 2::4, 3::4], grd_k[:, 3::4, 3::4]], axis=0)
return lo, hi, std, avg
......
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