優化Python / Numpy中的數組元素移位

Question

問題：

運行我編寫的數據分析代碼的行分析后，我發現大約70％的總運行時間集中在對兩個不同的數組操作例程的調用上。 我最終希望以實時方式分析數據，因此這里的任何優化都會有很大幫助。

這兩個函數采用左邊的矩陣並將其帶到右邊的表格（反之亦然）。

我感興趣的矩陣目前通過N 2d numpy數組存儲為N（其中N是偶數）。

碼：

我編寫了以下代碼來完成此任務：

# Shifts elements of a vector to the left by the given amount.
def Vec_shift_L(vec, shift=0):
    s = vec.size
    out = np.zeros(s, dtype=complex)
    out[:s-shift] = vec[shift:]
    out[s-shift:] = vec[:shift]
    return out

# Shifts elements of a vector to the right by the given amount.
def Vec_shift_R(vec,shift=0):
    s=vec.size
    out=np.zeros(s, dtype=complex)
    out[:shift] = vec[s-shift:]
    out[shift:] = vec[:s-shift]
    return out

# Shifts a matrix from the left form (above) to the right form.
def OP_Shift(Trace):
    s = Trace.shape
    Out = np.zeros(s, dtype=complex)

    for i in np.arange(s[0]):
        Out[i,:] = Vec_shift_L(Trace[i,:], (i+s[0]/2) % s[0])

    for i in np.arange(s[0]):
        Out[i,:] = np.flipud(Out[i,:])

    return Out

# Shifts a matrix from the right form (above) to the left form.
def iOP_Shift(Trace):
    s = Trace.shape
    Out = np.zeros(s, dtype=complex)
    for i in np.arange(s[0]):
        Out[i,:] = np.flipud(Trace[i,:])

    for i in np.arange(s[0]):
        Out[i,:] = Vec_shift_R(Out[i,:], (i+s[0]/2) % s[0])

    return Out

我最初寫這篇文章的時候並沒有意識到numpy的roll函數，所以我寫了vec_shift函數。 與使用當前系統上的roll相比，它們的性能似乎提高了約30％。

有沒有辦法進一步提高這段代碼的性能？

Answer 1

讓NumPy broadcasting為您提供矢量化解決方案！

# Store shape of input array
s = Trace.shape

# Store arrays corresponding to row and column indices
I = np.arange(s[0])
J = np.arange(s[1]-1,-1,-1)

# Store all iterating values in "(i+s[0]/2) % s[0]" as an array
shifts = (I + s[0]/2)%s[0]

# Calculate all 2D linear indices corresponding to 2D transformed array
linear_idx = (((shifts[:,None] + J)%s[1]) + I[:,None]*s[1])

# Finally index into input array with indices for final output
out = np.take(Trace,linear_idx)