groupby apply函数中缺少值

如何解决groupby apply函数中缺少值

我有一个大数据集[time，lat，lon]。我想针对网格中的每个点针对时间对变量进行回归，但是使用for循环需要永远的时间。我发现按纬度/经度对数据数组进行分组并应用函数可以大大减少计算时间。我正在将一个函数应用于经纬度分组的数据数组[time，lat，lon]。我的最终结果将是[lat，lon]。某些网格点没有全部数据（np.nan）。我希望函数为此类网格点返回np.nan，但是出现以下错误： SVD不能在线性最小二乘中收敛

这是代码：

hus = ifile.hus

#stack lat and lon into a single dimension called allpoints 
stacked = hus.stack(allpoints=['lat','lon'])

# define a function to compute a linear trend of a timeseries
def linear_trend(x):
    x = x.dropna(dim='time')
    if len(x)==0:
        return xr.DataArray(np.nan)
    else:
        time = np.arange(len(x))
        pf = np.polyfit(time,x,1)
        return xr.DataArray(pf[0])
    

trend = stacked.groupby('allpoints').apply(linear_trend)
trend_unstacked = trend.unstack('allpoints')
trend_unstacked

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-4e885325e9c4> in <module>
     13 
     14 # apply the function over allpoints to calculate the trend at each point
---> 15 trend = t.groupby('allpoints').apply(linear_trend)
     16 trend_unstacked = trend.unstack('allpoints')
     17 

~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in apply(self,func,shortcut,args,**kwargs)
    824             stacklevel=2,825         )
--> 826         return self.map(func,shortcut=shortcut,args=args,**kwargs)
    827 
    828     def _combine(self,applied,restore_coord_dims=False,shortcut=False):

~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in map(self,**kwargs)
    809             grouped = self._iter_grouped()
    810         applied = (maybe_wrap_array(arr,func(arr,*args,**kwargs)) for arr in grouped)
--> 811         return self._combine(applied,shortcut=shortcut)
    812 
    813     def apply(self,shortcut=False,args=(),**kwargs):

~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _combine(self,restore_coord_dims,shortcut)
    833             combined = self._concat_shortcut(applied,dim,positions)
    834         else:
--> 835             combined = concat(applied,dim)
    836             combined = _maybe_reorder(combined,positions)
    837 

~/anaconda3/lib/python3.7/site-packages/xarray/core/concat.py in concat(objs,data_vars,coords,compat,positions,fill_value,join)
    133             "objects,got %s" % type(first_obj)
    134         )
--> 135     return f(objs,join)
    136 
    137 

~/anaconda3/lib/python3.7/site-packages/xarray/core/concat.py in _dataarray_concat(arrays,join)
    427     join="outer",428 ):
--> 429     arrays = list(arrays)
    430 
    431     if data_vars != "all":

~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in <genexpr>(.0)
    808         else:
    809             grouped = self._iter_grouped()
--> 810         applied = (maybe_wrap_array(arr,**kwargs)) for arr in grouped)
    811         return self._combine(applied,shortcut=shortcut)
    812 

<ipython-input-9-4e885325e9c4> in linear_trend(x)
      8     else:
      9         time = np.arange(len(y))
---> 10         pf = np.polyfit(time,1)
     11         # we need to return a dataarray or else xarray's groupby won't be happy
     12         return xr.DataArray(pf[0])

<__array_function__ internals> in polyfit(*args,**kwargs)

~/anaconda3/lib/python3.7/site-packages/numpy/lib/polynomial.py in polyfit(x,y,deg,rcond,full,w,cov)
    603         raise TypeError("expected 1D or 2D array for y")
    604     if x.shape[0] != y.shape[0]:
--> 605         raise TypeError("expected x and y to have same length")
    606 
    607     # set rcond

TypeError: expected x and y to have same length