使用 xarray 将数据重采样为较低的空间分辨率
我想将我的 xarray 对象重采样为较低的空间分辨率（即更少的像素）。
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，如何按照x,y坐标重新采样成200x200网格？
这是对变量空间分辨率的降低。
我尝试过以下方法：
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
但是我遇到了所示的错误。
我该如何完成x和y的空间重采样？
理想情况下，我想要这样做：
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：真实数据具有不同的行为
这是我上面创建的测试数据。在从netcdf文件中读取的真实数据中会有所不同。
<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。