python - 对 xarray 对象重新采样以降低空间分辨率

Question

使用 xarray 重新采样以降低空间分辨率

我想将我的 xarray 对象重新采样到较低的空间分辨率(更少的像素)。

import pandas as pd
import numpy as np
import xarray as xr

time = pd.date_range(np.datetime64('1998-01-02T00:00:00.000000000'), np.datetime64('2005-12-28T00:00:00.000000000'), freq='8D')
x = np.arange(1200)
y = np.arange(1200)

latitude = np.linspace(40,50,1200)
longitude = np.linspace(0,15.5572382,1200)

latitude, longitude = np.meshgrid(latitude, longitude)

BHR_SW = np.ones((365, 1200, 1200))

output_da = xr.DataArray(BHR_SW, coords=[time, y, x])
latitude_da = xr.DataArray(latitude, coords=[y, x])
longitude_da = xr.DataArray(longitude, coords=[y, x])

output_da = output_da.rename({'dim_0':'time','dim_1':'y','dim_2':'x'})
latitude_da = latitude_da.rename({'dim_0':'y','dim_1':'x'})
longitude_da = longitude_da.rename({'dim_0':'y','dim_1':'x'})

output_ds = output_da.to_dataset(name='BHR_SW')
output_ds = output_ds.assign({'latitude':latitude_da, 'longitude':longitude_da})

print(output_ds)

<xarray.Dataset>
Dimensions:    (time: 365, x: 1200, y: 1200)
Coordinates:
  * time       (time) datetime64[ns] 1998-01-02 1998-01-10 ... 2005-12-23
  * y          (y) int64 0 1 2 3 4 5 6 7 ... 1193 1194 1195 1196 1197 1198 1199
  * x          (x) int64 0 1 2 3 4 5 6 7 ... 1193 1194 1195 1196 1197 1198 1199
Data variables:
    BHR_SW     (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0
    latitude   (y, x) float64 40.0 40.01 40.02 40.03 ... 49.97 49.98 49.99 50.0
    longitude  (y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 15.56 15.56 15.56 15.56
```

我的问题是，如何通过 x,y 坐标将以下内容重新采样为 200x200 网格？

这是降低变量的空间分辨率。

我尝试过的方法如下:

output_ds.resample(x=200).mean()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-54-10fbdf855a5d> in <module>()
----> 1 output_ds.resample(x=200).mean()

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/common.pyc in resample(self, indexer, skipna, closed, label, base, keep_attrs, **indexer_kwargs)
    701         group = DataArray(dim_coord, coords=dim_coord.coords,
    702                           dims=dim_coord.dims, name=RESAMPLE_DIM)
--> 703         grouper = pd.Grouper(freq=freq, closed=closed, label=label, base=base)
    704         resampler = self._resample_cls(self, group=group, dim=dim_name,
    705                                        grouper=grouper,

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/pandas/core/resample.pyc in __init__(self, freq, closed, label, how, axis, fill_method, limit, loffset, kind, convention, base, **kwargs)
   1198                              .format(convention))
   1199
-> 1200         freq = to_offset(freq)
   1201
   1202         end_types = set(['M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'])

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in to_offset(freq)
    174                     delta = delta + offset
    175         except Exception:
--> 176             raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
    177
    178     if delta is None:

ValueError: Invalid frequency: 200

但是我收到了显示的错误。

如何完成 x 和 y 的空间重采样？

 理想情况下我想这样做:

output_ds.resample(x=200, y=200).mean()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-55-e0bfce19e037> in <module>()
----> 1 output_ds.resample(x=200, y=200).mean()

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/common.pyc in resample(self, indexer, skipna, closed, label, base, keep_attrs, **indexer_kwargs)
    679         if len(indexer) != 1:
    680             raise ValueError(
--> 681                 "Resampling only supported along single dimensions."
    682             )
    683         dim, freq = indexer.popitem()

ValueError: Resampling only supported along single dimensions.

注意:真实数据有不同的行为

这是我在上面创建的测试数据。关于从netcdf文件读入的真实数据

<xarray.Dataset>
Dimensions:    (time: 368, x: 1200, y: 1200)
Coordinates:
  * time       (time) datetime64[ns] 1998-01-02 1998-01-10 ... 2005-12-28
Dimensions without coordinates: x, y
Data variables:
    latitude   (y, x) float32 ...
    longitude  (y, x) float32 ...
    Data_Mask  (y, x) float32 ...
    BHR_SW     (time, y, x) float32 ...
Attributes:
    CDI:               Climate Data Interface version 1.9.5 (http://mpimet.mp...
    Conventions:       CF-1.4
    history:           Fri Dec 07 13:29:13 2018: cdo mergetime GLOBALBEDO/Glo...
    content:           extracted variabel BHR_SW of the original GlobAlbedo (...
    metadata_profile:  beam
    metadata_version:  0.5
    CDO:               Climate Data Operators version 1.9.5 (http://mpimet.mp...
```

我尝试过类似的事情:

ds.resample(x=200).mean()

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/common.pyc in resample(self, indexer, skipna, closed, label, base, keep_attrs, **indexer_kwargs)
    686         dim_coord = self[dim]
    687
--> 688         if isinstance(self.indexes[dim_name], CFTimeIndex):
    689             raise NotImplementedError(
    690                 'Resample is currently not supported along a dimension '

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/coordinates.pyc in __getitem__(self, key)
    309         if key not in self._sizes:
    310             raise KeyError(key)
--> 311         return self._variables[key].to_index()
    312
    313     def __unicode__(self):

KeyError: 'x'

非常感谢任何帮助。

Answer 1

最近coarsen方法已添加到 xarray 中，我认为这是空间下采样的最佳方法，即使不可能使用它设置所需的最终分辨率并自动计算它。Coarsen 将在非重叠窗口上执行操作(平均值、最大值、最小值等)，并根据您设置的窗口大小，您将获得所需的最终分辨率。

作者的原始输入数据:

import pandas as pd
import numpy as np
import xarray as xr

​

time = pd.date_range(np.datetime64('1998-01-02T00:00:00.000000000'), np.datetime64('2005-12-28T00:00:00.000000000'), freq='8D')
x = np.arange(1200)
y = np.arange(1200)


latitude = np.linspace(40,50,1200)
longitude = np.linspace(0,15.5572382,1200)
latitude, longitude = np.meshgrid(latitude, longitude)

BHR_SW = np.ones((365, 1200, 1200))

output_da = xr.DataArray(BHR_SW, coords=[time, y, x])
latitude_da = xr.DataArray(latitude, coords=[y, x])
longitude_da = xr.DataArray(longitude, coords=[y, x])
output_da = output_da.rename({'dim_0':'time','dim_1':'y','dim_2':'x'})
latitude_da = latitude_da.rename({'dim_0':'y','dim_1':'x'})
longitude_da = longitude_da.rename({'dim_0':'y','dim_1':'x'})

output_ds = output_da.to_dataset(name='BHR_SW')
output_ds = output_ds.assign({'latitude':latitude_da, 'longitude':longitude_da})
print(output_ds)

​

<xarray.Dataset>
Dimensions:    (time: 365, x: 1200, y: 1200)
Coordinates:
  * time       (time) datetime64[ns] 1998-01-02 1998-01-10 ... 2005-12-23
  * y          (y) int64 0 1 2 3 4 5 6 7 ... 1193 1194 1195 1196 1197 1198 1199
  * x          (x) int64 0 1 2 3 4 5 6 7 ... 1193 1194 1195 1196 1197 1198 1199
Data variables:
    BHR_SW     (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0
    latitude   (y, x) float64 40.0 40.01 40.02 40.03 ... 49.97 49.98 49.99 50.0
    longitude  (y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 15.56 15.56 15.56 15.56

Coarsen方法将空间分辨率从1200x1200降低到200x200，我们需要6x6窗口。

output_ds.coarsen(x=6).mean().coarsen(y=6).mean()
# or output_ds.coarsen(x=6,y=6).mean()
<xarray.Dataset>
Dimensions:    (time: 365, x: 200, y: 200)
Coordinates:
  * time       (time) datetime64[ns] 1998-01-02 1998-01-10 ... 2005-12-23
  * y          (y) float64 2.5 8.5 14.5 20.5 ... 1.184e+03 1.19e+03 1.196e+03
  * x          (x) float64 2.5 8.5 14.5 20.5 ... 1.184e+03 1.19e+03 1.196e+03
Data variables:
    BHR_SW     (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0
    latitude   (y, x) float64 40.02 40.07 40.12 40.17 ... 49.88 49.93 49.98
    longitude  (y, x) float64 0.03244 0.03244 0.03244 ... 15.52 15.52 15.52