I'm trying to insert into a numpy matrix given a mask that defines a single cell per row. Effectively, it's inserting a value into each row but with a different column. I've tried to use
np.insert()
>>> x
array([[False, False, True, False, False],
[False, False, True, False, False],
[False, False, True, False, False],
[False, False, True, False, False],
[False, False, True, False, False]], dtype=bool)
>>> y = np.arange(25).reshape(5,5)
>>> y
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> np.insert(y, np.where(x)[1], 99, axis=1)
array([[ 0, 1, 99, 99, 99, 99, 99, 2, 3, 4],
[ 5, 6, 99, 99, 99, 99, 99, 7, 8, 9],
[10, 11, 99, 99, 99, 99, 99, 12, 13, 14],
[15, 16, 99, 99, 99, 99, 99, 17, 18, 19],
[20, 21, 99, 99, 99, 99, 99, 22, 23, 24]])
x
>>> x = np.zeros((5, 5), dtype=bool)
>>> x[1:, 2] = True
>>> x[0, 1] = True
>>> x
array([[False, True, False, False, False],
[False, False, True, False, False],
[False, False, True, False, False],
[False, False, True, False, False],
[False, False, True, False, False]], dtype=bool)
>>> np.insert(y, 2, [99, 99, 99, 99, 99], axis=1)
array([[ 0, 1, 99, 2, 3, 4],
[ 5, 6, 99, 7, 8, 9],
[10, 11, 99, 12, 13, 14],
[15, 16, 99, 17, 18, 19],
[20, 21, 99, 22, 23, 24]])
array([[ 0, 99, 1, 2, 3, 4],
[ 5, 6, 99, 7, 8, 9],
[10, 11, 99, 12, 13, 14],
[15, 16, 99, 17, 18, 19],
[20, 21, 99, 22, 23, 24]])
Approach #1 : Here's one way with booleanindexing

def insert_one_per_row(arr, mask, putval):
mask_ext = np.column_stack((mask, np.zeros((len(mask),1),dtype=bool)))
out = np.empty(mask_ext.shape, dtype=arr.dtype)
out[~mask_ext] = arr.ravel()
out[mask_ext] = putval
return out
Sample run 
In [88]: y
Out[88]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
In [89]: x
Out[89]:
array([[False, True, False, False, False],
[False, False, True, False, False],
[False, False, False, False, True],
[ True, False, False, False, False],
[False, False, True, False, False]], dtype=bool)
In [90]: insert_one_per_row(y, x, putval=99)
Out[90]:
array([[ 0, 99, 1, 2, 3, 4],
[ 5, 6, 99, 7, 8, 9],
[10, 11, 12, 13, 99, 14],
[99, 15, 16, 17, 18, 19],
[20, 21, 99, 22, 23, 24]])
We can also assign different values per row 
In [91]: insert_one_per_row(y, x, putval=[1,2,3,4,5])
Out[91]:
array([[ 0, 1, 1, 2, 3, 4],
[ 5, 6, 2, 7, 8, 9],
[10, 11, 12, 13, 3, 14],
[4, 15, 16, 17, 18, 19],
[20, 21, 5, 22, 23, 24]])
Approach #2 : We will get the flattened True
places on the mask and insert the new values with np.insert
on a flattened version of the input array at those places, like so 
def insert_one_per_row_v2(arr, mask, putval):
idx = np.flatnonzero(mask)
return np.insert(arr.ravel(), idx, putval).reshape(arr.shape[0],1)