Use xarray to resample to lower spatial resolution
我想把我的xarray对象重新取样到一个较低的空间分辨率（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
My question is, how to I resample the following by the x,y coordinates to a 200x200 grid?
这是在降低变量的空间分辨率。
我所尝试的是以下几点。
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))
-> 1200 freq = to_offset(freq)
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))
178 if delta is None:
ValueError: Invalid frequency: 200
But I get the error shown.
我怎样才能完成对x和y的这种空间重采样？
Ideally I want to do this:
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.
NOTE: Real data has different behaviour
这是我在上面创建的测试数据上的结果。在从netcdf文件读入的真实数据上
<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...
我也试过类似的事情。
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]
--> 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()
313 def __unicode__(self):
KeyError: 'x'
非常感谢任何帮助。
