如何編寫一個行爲類似numpy.ndarray的類，而不需要創建numpy.ndarray子類？

Question

假設下給出的類定義：

class Numeric(object): 
    def __init__(self, signal): 
    self.signal = signal 

現在，與Numeric不從numpy.ndarray繼承的要求，我怎麼也得延長該定義，Numeric表現得像numpy.ndarray？

編輯：signal應該是np.ndarray（或類似的，像quantities.Quantity）。我心裏有以下scenarious：

import numpy as np 
import quantities as pq 

a = Numeric(pq.Quantity([1,2,3], 'mV')) 
b = Numeric(pq.Quantity([1,3,5], 's')) 
c = Numeric(np.array([10,20,30])) 

a = Numeric(np.array([1,2,3])) 
b = Numeric(np.array([1,3,5])) 
a * c 
a * b 
a * np.array([3,4,5]) 

和：

import matplotlib.pyplot as plt 
plt.plot(b) 

Answer 1

隨着裝飾爲適應numpy -functions和內Numeric的__array__的實現，我可以解決大部分問題：

def adapt_signal_functions(cls): 
    def generateAdjustedFunction(functionName): 
    print functionName 
    def foo(self, *args, **kwargs): 
     function = getattr(self.signal.__class__, functionName) 
     return function(self.signal, *args, **kwargs) 
    return foo 
    functionNames = [ 
     '_get_units', 
     '_set_units', 
     'rescale', 
     'ptp', 
     'clip', 
     'copy', 
     'compress', 
     'conj', 
     'cumprod', 
     'cumsum', 
     'diagonal', 
     'dot', 
     'flatten', 
     'getfield', 
     'round', 
     'trace', 
     'max', 
     'mean', 
     'min', 
     'newbyteorder', 
     'prod', 
     'ravel', 
     'reshape', 
     'resize', 
     'round', 
     'std', 
     'sum', 
     'trace', 
     'transpose', 
     'var', 
     '__getitem__', 
     '__getslice__', 
     '__abs__', 
     # 
     '__add__', 
     '__div__', 
     '__divmod__', 
     '__floordiv__' 
     '__mod__', 
     '__mul__', 
     '__pow__', 
     '__sub__', 
     # 
     '__radd__', 
     '__div__', 
     '__divmod__', 
     '__rfloordiv__', 
     '__rmod__', 
     '__imul__', 
     #'__rmul__', 
     '__rpow__', 
     '__rsub__', 
     ] 
    for functionName in functionNames: 
    foo = generateAdjustedFunction(functionName) 
    setattr(cls, functionName, foo) 
    return cls 


@adapt_signal_functions 
class Numeric(object): 
    def __init__(self, signal): 
    self.signal = signal 
    self.adapt_quantity() 

    def adapt_quantity(self): 
    if hasattr(self.signal, '_dimensionality'): 
     self._dimensionality = self.signal._dimensionality 
     self.dimensionality = self.signal.dimensionality 

    def __array__(self): 
    return self.signal 

我可以這樣做：

import numpy as np 
import quantities as pq 

a = Numeric(pq.Quantity([1,2,3], 'mV')) 
b = Numeric(pq.Quantity([1,3,5], 's')) 
c = Numeric(np.array([10,20,30])) 
n = np.array([1,2,3]) 

a * a 
a * c 
a * n 
a.max() 

print type(a * n) == type(a.signal * n) 
# >>> True 
print type(a * c) == type(a.signal * c.signal) 
# >>> True 

返回類型對應於等效返回類型Numeric.signal。

一個問題仍然存在：

print type(n * a) == type(n * a.signal) 
# >>> False 

任何想法，如何解決？