Python “如何提取退回的”；“对象”；来自多处理？

我需要为numpy数组操作处理一个多处理函数。（我就是不能用numba加速功能） 它将一个数组作为输入，并将数据馈送到一个

多处理.pool

中，以在其上执行函数

func

。

func

执行一些操作并返回与原始数组中的编号位置（=与其位置）堆叠的子数组。 pool函数返回iterable对象

out

中的in-multiprocessing processed函数

func

的结果。 如何将iterable转换回numpy数组

可复制代码：

import numpy as np, time, multiprocessing as mp, pandas as pd; min_ = 0.7

def sub_sub_func(X,newmin,newmax):  
    if len(X) >1: 
        if  (X[0] == X[1:]).all():       X.fill(newmax)
        else: 
            X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
            return X_std * (newmax - newmin)+newmin    
    elif len(X) ==1:    
        X[0] = newmax    
    return X 

def sub_func(arr):   
    if np.min(arr)<=0:       
        arr[arr<=0] = sub_sub_func(arr[arr<=0],min_*0.65,min_)  
    elif np.min(arr)<min_:    
        arr[arr<=min_] = sub_sub_func(arr[arr<=min_],min_*0.80,min_)  
    return arr  

def func(mol_subrange, arr):
    result= np.array([slice_+slice for slice_ in  arr[mol_subrange] ]) 
    return np.column_stack((numberlocations, result)) # return it with its numberlocations

def mp_list_o_arr_comprehension(func, full_arr_to_process, numThreads=4, mpBatches=1):  
    molecule_subrange = np.array(range(len(full_arr_to_process))), 
    parts             = linParts(len(molecule_subrange),numThreads*mpBatches) 
    
    jobs=[]
    for i in range(1,len(parts)):
        job={'mol_subrange':molecule_subrange[parts[i-1]:parts[i]], 
             'arr':         full_arr_to_process[parts[i-1]:parts[i]], 
             'func':func}
        jobs.append(job)
       
    pool=mp.Pool(processes=numThreads) 
    outputs=pool.imap_unordered(expandCall,jobs) 
    
    out_list = []
    for out_ in outputs:
        out_list.append(out_.get())
    pool.close(); pool.join() # this is needed to prevent memory leaks return out 
    
    locs_arr, out_arr = np.array([]), np.array([])  
    for out_ in out_list: 
        out_locs = np.asarray(out_)[:,0]
        out_vals = np.asarray(out_)[:,1]
        
        out_arr  = np.concatenate((out_arr, out_vals))
        locs_arr = np.concatenate((locs_arr, out_locs))
           
    #sort order by converting it into a pandas series
    result = pd.series(out_arr, index=locs_arr).sort_index()  
    
    return np.array(result)

def linParts(numAtoms,numThreads):
    # partition of atoms with a single loop
    parts=np.linspace(0,numAtoms,min(numThreads,numAtoms)+1)
    parts=np.ceil(parts).astype(int)
    return parts

def expandCall(kargs): 
    # Expand the arguments of a callback function, kargs[’func’] 
    func=kargs['func'] 
    del kargs['func']  
    out=func(**kargs)  
    return out 

if __name__=='__main__': 
    LEN = 10000; temp = np.random.randint(1,high=100, size=LEN) 
    a  = [np.random.uniform(size=rand) for rand in temp]  
    result = mp_list_o_arr_comprehension(func, a, numThreads=4, mpBatches=10) 

将numpy作为np导入，将时间、多处理作为mp导入，将pandas作为pd导入；最小值=0.7
def sub_sub_func（X，newmin，newmax）：
如果len（X）>1：
如果（X[0]==X[1:]）.all（）：X.fill（newmax）
其他：
X_标准=（X-X.min（轴=0））/（X.max（轴=0）-X.min（轴=0））
返回X_标准*（newmax-newmin）+newmin
elif len（X）==1：
X[0]=newmax
返回X
def sub_func（arr）：
如果np.min（arr）
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "c:\...\pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "D:\... .py", line 67, in expandCall
    out=func(**kargs)
  File "D:\... .py", line 22, in func
    result= np.array([slice_+slice for slice_ in  arr[mol_subrange] ])
TypeError: list indices must be integers or slices, not tuple
"""


The above exception was the direct cause of the following exception:

Traceback (most recent call last):

  File "D:\... .py", line 73, in <module>
    result = mp_list_o_arr_comprehension(func, a, numThreads=4, mpBatches=10)

  File "D:\... ", line 40, in mp_list_o_arr_comprehension
    for out_ in outputs:

  File "c:\...\pool.py", line 868, in next
    raise value

TypeError: list indices must be integers or slices, not tuple