在 jupyter notebook 中，我對(duì)資源進(jìn)行 OO 建模，但在控制循環(huán)中需要聚合多個(gè)對(duì)象的數(shù)據(jù)，與 ufunc 和類似操作相比效率低下。為了打包功能，我選擇了面向?qū)ο?，但為了高效?jiǎn)潔的代碼，我可能必須將數(shù)據(jù)提取到存儲(chǔ)類中（可能）并將所有 ri[0] 行推送到二維數(shù)組中，在本例中為 (2,K)。該類不需要日志，只需要最后一個(gè)條目。K = 100class Resource: def __init__(self): self.log = np.random( (5,K) ) # log gets filled during simulationr0 = Resource()r1 = Resource()# while control loop: #aggregate control data for k in K: total_row_0 = r0.log[0][k] + r1.log[0][k] #do sth with the totals and loop again這將大大提高性能，但如果單獨(dú)存儲(chǔ)，我很難將數(shù)據(jù)鏈接到類。你會(huì)如何處理這個(gè)問(wèn)題？pandas DataFrames、np View 還是淺拷貝？[[...] #r0 [...] ]#r1 same data into one array, efficient but map back to class difficult
1 回答
海綿寶寶撒
TA貢獻(xiàn)1809條經(jīng)驗(yàn) 獲得超8個(gè)贊
這是我的看法：
import numpy as np
K = 3
class Res:
logs = 2
def __init__(self):
self.log = None
def set_log(self, view):
self.log = view
batteries = [Res(), Res()]
d = {'Res': np.random.random( (Res.logs * len(batteries), K) )}
for i in range(len(batteries)):
view = d['Res'].view()[i::len(batteries)][:]
batteries[i].set_log(view)
print(d)
batteries[1].log[1][2] = 1#test modifies view of last entry of second Res of second log
print(d)
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