[英]Xarray rolling construct in multiple dimension
为多维滚动 window 设置 xarray 滚动构造的最佳方法是什么? 这是一个 numpy 示例:
import numpy as np
from numpy.lib.stride_tricks import as_strided
data = np.array(np.arange(6).reshape(2, 3),dtype="float64")
win_size = (
3 # Size of the window (e.g. 3*3)
)
win_size_half = int(np.floor(win_size / 2))
# pad with nan to get correct window for the edges
data = np.pad(
data,
(win_size_half, win_size_half),
"constant",
constant_values=(np.nan),
)
sub_shape = (win_size, win_size)
view_shape = tuple(np.subtract(data.shape, sub_shape) + 1) + sub_shape
data_view = as_strided(
data, view_shape, data.strides * 2
)
data_view = data_view.reshape((-1,) + sub_shape)
#Expected results
>>> data_view
array([[[nan, nan, nan],
[nan, 0., 1.],
[nan, 3., 4.]],
[[nan, nan, nan],
[ 0., 1., 2.],
[ 3., 4., 5.]],
[[nan, nan, nan],
[ 1., 2., nan],
[ 4., 5., nan]],
[[nan, 0., 1.],
[nan, 3., 4.],
[nan, nan, nan]],
[[ 0., 1., 2.],
[ 3., 4., 5.],
[nan, nan, nan]],
[[ 1., 2., nan],
[ 4., 5., nan],
[nan, nan, nan]]])
我想知道如何将 xarray 用于相同目的。 例如,使用 xarray 执行与上述相同的操作:
import xarray as xr
da =xr.DataArray(np.array(np.arange(6).reshape(2, 3),dtype="float64"),dims=("a","b"))
# And something like
rolling = da.rolling({"a":win_size,"b":win_size})
# producing same results as in numpy example
rolling.construct("window_dim")
据我了解, xr.rolling 不允许多维。 请让我知道是否有其他方法可以进行此类操作。
谢谢
xr.rolling 现在接受多个维度。 您必须为 rolling.construct 提供字典映射(或基于关键字)。
您的 numpy 示例采用 windows 中心,它不是 xr.rolling 的默认值,因此您必须明确提供center=True
以下代码给出与 numpy 代码相同的结果:
import xarray as xr
import numpy as np
da =xr.DataArray(np.array(np.arange(6).reshape(2, 3),dtype="float64"),dims=("a","b"))
rolling = da.rolling({"a":3,"b":3}, center=True)
# producing same results as in numpy example
da_roll = rolling.construct(a='ka',b='kb')
da_roll
Out[2]:
<xarray.DataArray (a: 2, b: 3, ka: 3, kb: 3)>
array([[[[nan, nan, nan],
[nan, 0., 1.],
[nan, 3., 4.]],
[[nan, nan, nan],
[ 0., 1., 2.],
[ 3., 4., 5.]],
[[nan, nan, nan],
[ 1., 2., nan],
[ 4., 5., nan]]],
[[[nan, 0., 1.],
[nan, 3., 4.],
[nan, nan, nan]],
[[ 0., 1., 2.],
[ 3., 4., 5.],
[nan, nan, nan]],
[[ 1., 2., nan],
[ 4., 5., nan],
[nan, nan, nan]]]])
Dimensions without coordinates: a, b, ka, kb
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