I want map a numpy.array from NxM to NxMx3, where a vector of three elements is a function of the original entry:
lambda x: [f1(x), f2(x), f3(x)]
However, things like numpy.vectorize do not allow to change dimensions. Sure, I can create an array of zeros and make a loop (and it is what I am doing by now), but it does not sound neither Pythonic nor efficient (as every looping in Python).
Is there a better way to perform an elementwise operation on numpy.array, producing a vector for each entry?
Now that I see your code, for most simple mathematical operations you can let numpy do the looping, what is often referred to as 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
This code should run much faster than yours.
If I understand your problem correctly, I suggest you use 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]]])
翻译