Numpy 将scipy.stats.percentileofscore应用于xarray重采样减少函数

Numpy 将scipy.stats.percentileofscore应用于xarray重采样减少函数,numpy,scipy,python-xarray,Numpy,Scipy,Python Xarray,我有以下名为foo的xarray数据数组 <xarray.DataArray (time: 4, lat: 3, lon: 2)> array([[[0.061686, 0.434164], [0.642003, 0.78744 ], [0.068701, 0.526546]], [[0.53612 , 0.549919], [0.172044, 0.118106], [0.381638, 0.73658

我有以下名为
foo
的xarray数据数组

<xarray.DataArray (time: 4, lat: 3, lon: 2)>
array([[[0.061686, 0.434164],
        [0.642003, 0.78744 ],
        [0.068701, 0.526546]],

       [[0.53612 , 0.549919],
        [0.172044, 0.118106],
        [0.381638, 0.736584]],

       [[0.688589, 0.173351],
        [0.03593 , 0.833743],
        [0.667719, 0.890957]],

       [[0.712785, 0.04725 ],
        [0.132689, 0.938043],
        [0.681481, 0.67986 ]]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * lat      (lat) <U2 'IA' 'IL' 'IN'
  * lon      (lon) <U2 '00' '22'
我收到了以下错误:

\variable.py", line 1354, in reduce
    axis=axis, **kwargs)
TypeError: percentileofscore() got an unexpected keyword argument 'axis'
复制数据:

import xarray as xa

array = np.array([[[0.061686, 0.434164],
        [0.642003, 0.78744 ],
        [0.068701, 0.526546]],

       [[0.53612 , 0.549919],
        [0.172044, 0.118106],
        [0.381638, 0.736584]],

       [[0.688589, 0.173351],
        [0.03593 , 0.833743],
        [0.667719, 0.890957]],

       [[0.712785, 0.04725 ],
        [0.132689, 0.938043],
        [0.681481, 0.67986 ]]])

lat = ['IA','IL','IN']
lon = ['00','22']

times = pd.date_range('2000-01-01', periods=4, freq='H') #Hours

foo = xr.DataArray(array, coords=[times, lat, lon], dims=['time', 'lat', 'lon'])
您需要的功能:

from scipy import stats
import numpy as np

def func(x, axis, score):
    out = np.apply_along_axis(stats.percentileofscore, axis, x, *[score])
    return out

res = foo.resample(time='2H').reduce(func, **{'score':0.2}) #Each 2 hours
输出:

<xarray.DataArray (time: 2, lat: 3, lon: 2)>
array([[[ 50.,   0.],
        [ 50.,  50.],
        [ 50.,   0.]],

       [[  0., 100.],
        [100.,   0.],
        [  0.,   0.]]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T02:00:00
  * lat      (lat) <U2 'IA' 'IL' 'IN'
  * lon      (lon) <U2 '00' '22'
因此,您正在做的是:

stats.percentileofscore(a, score=0.2) #200  #Reduce over lon and lat
stats.percentileofscore(b, score=0.2) #200  #This raise another error
这就是为什么您需要一个通过axis运行的函数(例如,
np.mean(a,axis=None,…)
np。沿axis应用帮助我们完成此任务:

#reduce through hours 1-2 and hours 3-4 (axis=0)
np.apply_along_axis(stats.percentileofscore, 0, a, **{'score':0.2}) 
np.apply_along_axis(stats.percentileofscore, 0, b, **{'score':0.2}) 
stats.percentileofscore(a, score=0.2) #200  #Reduce over lon and lat
stats.percentileofscore(b, score=0.2) #200  #This raise another error
#reduce through hours 1-2 and hours 3-4 (axis=0)
np.apply_along_axis(stats.percentileofscore, 0, a, **{'score':0.2}) 
np.apply_along_axis(stats.percentileofscore, 0, b, **{'score':0.2})