numpy.searchsorted with 2D array

Question

I have this numpy array where the values in each row will always be sorted and monotonically increasing:

a = np.array([[1, 2, 3, 4, 8],
       [2, 5, 6, 7, 8],
       [5, 7, 11, 12, 13]])

and I want to search for the following values (which are NOT sorted or monotonic) for each row:

b = np.array([4.5, 2.3, 11.6])

so that I get an answer of:

[4, 1, 3]

However, searchsorted does not support this (it feels like it needs an axis keyword).

Is there an EFFICIENT way I can do this for a very large array? Obviously with a for loop I can index the array a and b like this:

for i in np.arange(np.alen(a)):
     print a[i].searchsorted(b[i])

but this is slow when a is large.

Is there a way to do this in numpy that is more efficient?

Answer 1

You can searchsorted on the ravel/flattened array:

In [11]: np.searchsorted(a.ravel(), b)
Out[11]: array([3, 6])

You can then use divmod on the result (which gets the row and column):

In [12]: divmod(np.searchsorted(a.ravel(), b), a.shape[1])
Out[12]: (array([0, 1]), array([3, 1]))