如何用矢量化 numpy 替代方案替换嵌套 for 循环？

发布于 01-10 09:58 字数 932 浏览 2 评论 0原文

给定两个已知的 2D 数组（维度是时间和位置），dhdt_arr 和 dTsdt_arr，如何比使用嵌套 for 循环更快地生成其他 2D 数组？

我想要生成的数组的命名如下，并且已像已知数组一样进行设置：

dmdt_arr = np.zeros_like(dhdt_arr) #change in mass holdup
T_arr = np.zeros_like(dhdt_arr) # fluid temperature
m_hold_arr = np.zeros_like(dhdt_arr) # mass hold up
U_arr = np.zeros_like(dhdt_arr) # heat transfer coefficient array

我用来生成上述数组的嵌套 for 循环（假设代数是正确的，常量已知，并且我使用的函数是也已知）。我只是在寻找一种用更快的东西替换这个结构的方法，比如 numpy 矢量化方法）：

for i in range(time.shape[0]): #iteratung through 2D array time x #positions
    for j in range(n): #yep

        T = temperature(h_arr[j,i],P)

        dmdt_arr[j, i] = dhdt_arr[j, i] * dpdh(h_arr[j, i], P) * V
        T_arr[j,i] = T
        m_hold_arr[j, i] = mass(h_arr[j, i], P)
        U_arr[j,i] = mAlumina*CP_ALUMINA*dTsdt_arr[j,i]/(sa*(T_arr[j,i] - Ts_arr[j,i]))

如何以比嵌套 for 循环更快的方式生成这些相同的数组？

原文

Given two known 2D arrays (dimensions are time and position), dhdt_arr and dTsdt_arr, how can I generate some other 2D arrays from these faster than using nested for loops?

The arrays I would like to generate are named below, and have been setup like the known arrays:

dmdt_arr = np.zeros_like(dhdt_arr) #change in mass holdup
T_arr = np.zeros_like(dhdt_arr) # fluid temperature
m_hold_arr = np.zeros_like(dhdt_arr) # mass hold up
U_arr = np.zeros_like(dhdt_arr) # heat transfer coefficient array

The nested for loop I am using to generate the above arrays (assume the algebra is correct, the constants are known, and the functions I used are also known). I am just looking for a way to replace this structure with something faster, like a numpy vectorized approach):

for i in range(time.shape[0]): #iteratung through 2D array time x #positions
    for j in range(n): #yep

        T = temperature(h_arr[j,i],P)

        dmdt_arr[j, i] = dhdt_arr[j, i] * dpdh(h_arr[j, i], P) * V
        T_arr[j,i] = T
        m_hold_arr[j, i] = mass(h_arr[j, i], P)
        U_arr[j,i] = mAlumina*CP_ALUMINA*dTsdt_arr[j,i]/(sa*(T_arr[j,i] - Ts_arr[j,i]))

How can these same arrays be generated in a way that is faster than a nested for loop?

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