如何解决如何优化多维数据数组上的一维插值?
from scipy.interpolate import interp1d
#data array dims
da[time,plev,la,lon]
#array with vertical levels
lev = da.plev
#new temperatures ->dummy values
tem = np.arange(10,100,5)
#begin loop for interpolation
for time in range(da.time.size):
for lat in range(da.lat.size):
for lon in range(da.lon.size):
f = interp1d(da[time,:,lat,lon],lev,fill_value='extrapolate')
holder[time,lon] = f(tem)
代码有效,但需要一段时间才能运行。我仍在学习 apply_ufunc 和 dask,我看到了一些示例 here,我认为这有助于大大减少运行时间(至少与 for 循环相比)。
我试图运行类似的东西
# return a tuple of DataArrays
res = xr.apply_ufunc(interp1d,hus,input_core_dims=[['plev'],['plev']],output_core_dims=[[]],vectorize=True)
但是当我尝试使用插值函数时:
holder = res(tem)
更新
我尝试了以下代码,将内插器放入函数中。我知道它在工作,因为我在 return 语句之前打印了一些结果。但问题在于 return 语句。
def interp(x,y):
# Wrapper around scipy linregress to use in apply_ufunc
f = interp1d(x,y,fill_value='extrapolate')
new = f(tem)
return (new)
holder_new = xr.apply_ufunc(interp,check,p,vectorize=True)
错误信息:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<timed exec> in <module>
~/anaconda3/lib/python3.7/site-packages/xarray/core/computation.py in apply_ufunc(func,input_core_dims,output_core_dims,exclude_dims,vectorize,join,dataset_join,dataset_fill_value,keep_attrs,kwargs,dask,output_dtypes,output_sizes,Meta,dask_gufunc_kwargs,*args)
1108 join=join,1109 exclude_dims=exclude_dims,-> 1110 keep_attrs=keep_attrs,1111 )
1112 # Feed Variables directly through apply_variable_ufunc
~/anaconda3/lib/python3.7/site-packages/xarray/core/computation.py in apply_dataarray_vfunc(func,signature,*args)
260
261 data_vars = [getattr(a,"variable",a) for a in args]
--> 262 result_var = func(*data_vars)
263
264 if signature.num_outputs > 1:
~/anaconda3/lib/python3.7/site-packages/xarray/core/computation.py in apply_variable_ufunc(func,*args)
698 )
699
--> 700 result_data = func(*input_data)
701
702 if signature.num_outputs == 1:
~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in __call__(self,*args,**kwargs)
2106 vargs.extend([kwargs[_n] for _n in names])
2107
-> 2108 return self._vectorize_call(func=func,args=vargs)
2109
2110 def _get_ufunc_and_otypes(self,func,args):
~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call(self,args)
2180 """Vectorized call to `func` over positional `args`."""
2181 if self.signature is not None:
-> 2182 res = self._vectorize_call_with_signature(func,args)
2183 elif not args:
2184 res = func()
~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call_with_signature(self,args)
2244
2245 for output,result in zip(outputs,results):
-> 2246 output[index] = result
2247
2248 if outputs is None:
ValueError: setting an array element with a sequence.
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