Question

我希望从NxM到NxMx3映射numpy.array，其中三个元素的向量是原始条目的函数：

lambda x: [f1(x), f2(x), f3(x)]

但是，numpy.vectorize之类的内容不允许更改尺寸。当然，我可以创建一个零数组并创建一个循环（and it is what I am doing by now），但它既没有Pythonic也没有效率（如Python中的每个循环）。

有没有更好的方法在numpy.array上执行元素运算，为每个条目生成一个向量？

Answer 1

如果我理解您的问题，建议您使用np.dstack：

Docstring:
Stack arrays in sequence depth wise (along third axis).

Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.

    In [1]: a = np.arange(9).reshape(3, 3)

    In [2]: a
    Out[2]: 
    array([[0, 1, 2],
           [3, 4, 5],
           [6, 7, 8]])

    In [3]: x, y, z = a*1, a*2, a*3  # in your case f1(a), f2(a), f3(a) 

    In [4]: np.dstack((x, y, z))
    Out[4]: 
    array([[[ 0,  0,  0],
            [ 1,  2,  3],
            [ 2,  4,  6]],

           [[ 3,  6,  9],
            [ 4,  8, 12],
            [ 5, 10, 15]],

           [[ 6, 12, 18],
            [ 7, 14, 21],
            [ 8, 16, 24]]])

Answer 2

现在我看到了你的代码，对于大多数简单的数学运算你可以让numpy进行循环，通常被称为 vectorization ：

def complex_array_to_rgb(X, theme='dark', rmax=None):
    '''Takes an array of complex number and converts it to an array of [r, g, b],
    where phase gives hue and saturaton/value are given by the absolute value.
    Especially for use with imshow for complex plots.'''
    absmax = rmax or np.abs(X).max()
    Y = np.zeros(X.shape + (3,), dtype='float')
    Y[..., 0] = np.angle(X) / (2 * pi) % 1
    if theme == 'light':
        Y[..., 1] = np.clip(np.abs(X) / absmax, 0, 1)
        Y[..., 2] = 1
    elif theme == 'dark':
        Y[..., 1] = 1
        Y[..., 2] = np.clip(np.abs(X) / absmax, 0, 1)
    Y = matplotlib.colors.hsv_to_rgb(Y)
    return Y

此代码的运行速度应该比您的快。

将NumPy数组元素地映射到更多维度的数组中

2 个答案: