将xarray与自定义函数一起使用并重新采样

Question

我正在尝试获取一个数组，并使用自定义函数对其重新采样。来自这篇文章：应用函数沿时间维度的XArray

def special_mean(x, drop_min=False):
s = np.sum(x)
n = len(x)
if drop_min:
s = s - x.min()
n -= 1
return s/n

是示例sample_mean。

我有一个数据集：

<xarray.Dataset>
Dimensions:  (lat: 100, lon: 130, time: 7305)
Coordinates:
* lon      (lon) float32 -99.375 -99.291664 -99.208336 ... -88.708336 -88.625
* lat      (lat) float32 49.78038 49.696426 49.61247 ... 41.552795 41.46884
lev      float32 1.0
* time     (time) datetime64[ns] 2040-01-01 2040-01-02 ... 2059-12-31
Data variables:
tmin     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
tmax     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
prec     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
relh     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
wspd     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
rads     (time, lat, lon) float32 dask.array<chunksize=(366, 100, 130), meta=np.ndarray>
Attributes:
history:  Fri Jun 14 10:32:22 2019: ncatted -a _FillValue,,o,d,9e+20 IBIS...

然后我应用一个重采样，它是：

data.resample(time='1MS').map(special_mean)

<xarray.Dataset>
Dimensions:  (time: 240)
Coordinates:
* time     (time) datetime64[ns] 2040-01-01 2040-02-01 ... 2059-12-01
lev      float32 1.0
Data variables:
tmin     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>
tmax     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>
prec     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>
relh     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>
wspd     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>
rads     (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>

如何执行此函数，以便像执行时一样保留"lon"one_answers"lat"坐标

data.resample(time='1MS').mean()

Answer 1

以下是如何使用xr.apply_ufunc()的一个示例。

import xarray as xr
data = xr.tutorial.open_dataset('air_temperature')
def special_mean(x, drop_min=False):
s = np.sum(x)
n = len(x)
if drop_min:
s = s - x.min()
n -= 1
return s/n
def special_func(data):
return xr.apply_ufunc(special_mean, data, input_core_dims=[["time"]], 
kwargs={'drop_min': True}, dask = 'allowed', vectorize = True)
data.resample(time='1MS').apply(special_func)
<xarray.Dataset>
Dimensions:  (lat: 25, lon: 53, time: 24)
Coordinates:
* time     (time) datetime64[ns] 2013-01-01 2013-02-01 ... 2014-12-01
* lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
* lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
Data variables:
air      (time, lat, lon) float64 244.6 244.7 244.7 ... 297.7 297.7 297.7

Answer 2

我怀疑您可以使用apply_ufunc方法来执行您想要的操作。

(尽管作为免责声明，我不太了解Xarray API。(