Question

This post identifies a “feature” that I would like to disable.
Current numpy behavior:

>>> a = arange(10)
>>> a[a>5] = arange(10)
array([0, 1, 2, 3, 4, 5, 0, 1, 2, 3])

The reason it’s a problem: say I wanted an array to have two different sets of values on either side of a breakpoint (e.g., for making a “broken power-law” or some other simple piecewise function). I might accidentally do something like this:

>>> x = empty(10)
>>> a = arange(10)
>>> x[a<=5] = 0 # this is fine
>>> x[a>5] = a**2 # this is not
# but what I really meant is this
>>> x[a>5] = a[a>5]**2 

The first behavior, x[a>5] = a**2 yields something I would consider counterintuitive – the left side and right side shapes disagree and the right side is not scalar, but numpy lets me do this assignment. As pointed out on the other post, x[5:]=a**2 is not allowed.

So, my question: is there any way to make x[a>5] = a**2 raise an Exception instead of performing the assignment? I’m very worried that I have typos hiding in my code because I never before suspected this behavior.

Answer 1

I don’t know of a way offhand to disable a core numpy feature. Instead of disabling the behavior you could try using np.select:

http://docs.scipy.org/doc/numpy/reference/generated/numpy.select.html

In [110]: x = np.empty(10)
In [111]: a = np.arange(10)
In [112]: x[a<=5] = 0
In [113]: x[a>5] = a**2
In [114]: x
Out[114]: array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  4.,  9.])

In [117]: condlist = [a<=5,a>5]
In [119]: choicelist=[0,a**2]
In [120]: x = np.select(condlist,choicelist)
In [121]: x
Out[121]: array([ 0,  0,  0,  0,  0,  0, 36, 49, 64, 81])