我想将我的xarray对象重新采样到较低的空间分辨率（LESS PIXELS）。
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
```
这是降低变量的空间分辨率。
我尝试过的是：
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
但是我得到了显示的错误。
如何完成x和y的空间重采样？
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.
关于我上面创建的测试数据。关于从netcdf文件读取的真实数据
<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...
```
我尝试了类似的事情：
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。
答案 0 :(得分：3)
正如piman314所建议的那样，groupby是在xarray中执行此操作的唯一方法。重新采样只能用于日期时间坐标。
由于xarray当前无法处理多维分组依据，因此必须分两个阶段完成：
viewHolder = new ViewHolder();
如果只是想对数据进行下采样，则可以使用位置切片：
# this results in bin centers on 100, 300, ...
reduced = (
output_ds
.groupby(((output_ds.x//200) + 0.5) * 200)
.mean(dim='x')
.groupby(((output_ds.y//200) + 0.5) * 200)
.mean(dim='y'))
或者，使用命名的dims：
output_ds[:, ::200, ::200]
最后，还有其他软件包专门设计用于与xarray兼容的快速重新网格化。 xESMF是个好人。
答案 1 :(得分：3)
最近coarsen方法已添加到xarray中，我认为这是进行空间下采样的最佳方法，即使无法使用它来设置所需的最终分辨率并自动进行计算也是如此。 Coarsen将在不重叠的窗口上执行操作（平均，最大，最小等），并且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
采用Coarsen方法将空间分辨率从1200x1200降低到200x200，我们需要6x6的窗口。
output_ds.coarsen(x=6).mean().coarsen(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
答案 2 :(得分：2)
要使用xarray来完成此操作，最明显的方法是使用groupby_bins，但是事实证明这非常慢。放入numpy并使用超快速索引（[:, :, frequency]）
nsamples = 200
bins = np.linspace(output_ds.x.min(),
output_ds.x.max(), nsamples).astype(int)
output_ds = output_ds.groupby_bins('x', bins).first()
答案 3 :(得分：0)
当您使用已经由CDO操作过的NetCDF文件时，您还可以使用CDO SAMPLEGRID函数或NCO bilinear_interp函数：
SAMPLEGRID（https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf）不进行插值，仅删除第n个网格点。
bilinear_interp（http://nco.sourceforge.net/nco.html#Bilinear-interpolation）进行插值。
您可能想要平均值，最大值，您可能更喜欢NCO bilinear_interp的反照率值。但是CDO SAMPLEGRID可以为您提供NOC grid_out所需的bilinear_interp。