compute.compute_ap_contiguos returns NaNs silently

[afermg]

When using map.run_pipeline(), if some samples are only present once in the dataset compute.ap_contiguos returns zeros. This is due to compute.to_cutoffs being applied to an array of length 1, so the line in

copairs/src/copairs/compute.py

Line 171 in 491ebaf

cutoffs[0], cutoffs[1:] = 0, counts.cumsum()[:-1]

always returns [0].

These 0s are propagated to num_pos

copairs/src/copairs/compute.py

Line 97 in 491ebaf

num_pos = np.add.reduceat(rel_k_list, cutoffs)

and thus things are divided by zero, as this vector is used as a divisor

copairs/src/copairs/compute.py

Line 105 in 491ebaf

ap_scores = np.add.reduceat(pr_k * rel_k_list, cutoffs) / num_pos

.

It was a bit puzzling to figure out why my test with a small number of plates was plagued with nans, so it is probably worth adding a warning when 1 (or maybe a fraction, i.e., 1/4) of all elements appear only once.