Numpy argmax with max less than some number

Question

I have a numpy array like:

myArray
array([[ 1.    ,     nan,     nan,     nan,     nan],
       [ 1.    ,     nan,     nan,     nan,     nan],
       [ 0.63  ,  0.79  ,  1.    ,     nan,     nan],
       [ 0.25  ,  0.4   ,  0.64  ,  0.84  ,     nan]])

I need to find for each row the number of columns for the maximum value, but max should be less than 1.

In the array above, row 0.1 should return Nan.

Line 2 should return 1.

Line 3 should return 3.

I am not sure how to do this on argmax.

Answer 1

Here is one approach with np.where-

m = a < 1  # Mask of elems < 1 and non-NaNs

# Set NaNs and elems > 1 to global minimum values minus 1, 
# so that when used with argmax those would be ignored
idx0 = np.where(m, a,np.nanmin(a)-1).argmax(1)

# Look for rows with no non-NaN and < 1 elems and set those in o/p as NaNs
idx = np.where(m.any(1), idx0, np.nan)

Run Example -

In [97]: a
Out[97]: 
array([[ 1.  ,   nan,   nan,   nan,   nan],
       [ 1.  ,   nan,   nan,   nan,   nan],
       [ 0.63,  0.79,  1.  ,   nan,   nan],
       [ 0.25,  0.4 ,  0.64,  0.84,   nan]])

In [98]: m = a < 1

In [99]: idx0 = np.where(m, a,np.nanmin(a)-1).argmax(1)

In [100]: idx0
Out[100]: array([0, 0, 1, 3])

In [101]: np.where(m.any(1), idx0, np.nan)
Out[101]: array([ nan,  nan,   1.,   3.])