@amurthy-sunysb I think the issue is that when cutoff=None, STUMPY tries its best to guess at the "best" rational cutoff value according to np.nanmax([np.nanmean(P) - 2.0 * np.nanstd(P), np.nanmin(P)]). However, in this case, np.nanmean(P) - 2.0 * np.nanstd(P) is negative and so the smallest possible cutoff is set to np.nanmin(P). Unfortunately, all of the matches have a distance that is greater than max_distance (also a guess by STUMPY). So, ultimately, you need to do something like:

stumpy.motifs(c, p[:,0], cutoff=0.053, max_distance=np.inf)

By increasing cutoff to an even larger value will give you more motifs while decreasing max_distance will reduce the number of possible matches. There is no magic here and it is up to you play around with the "best" cutoff and max_distance values. All of this is mentioned clearly in the stumpy.motifs docstring:

Note that, in the best case scenario, the returned arrays would have shape (max_motifs, max_matches) and contain all finite values. However, in reality, many conditions (see below) need to be satisfied in order for this to be true. Any truncation in the number of rows (i.e., motifs) may be the result of insufficient candidate motifs with matches greater than or equal to min_neighbors or that the matrix profile value for the candidate motif was larger than cutoff. Similarly, any truncation in the number of columns (i.e., matches) may be the result of insufficient matches being found with distances (to their corresponding candidate motif) that are equal to or less than max_distance. Only motifs and matches that satisfy all of these constraints will be returned.

If you must return a shape of (max_motifs, max_matches), then you may consider specifying a smaller min_neighbors, a larger max_distance, and/or a larger cutoff. For example, while it is ill advised, setting min_neighbors=1, max_distance=np.inf, and cutoff=np.inf will ensure that the shape of the output arrays will be (max_motifs, max_matches). However, given the lack of constraints, the quality of each motif and the quality of each match may be drastically different. Setting appropriate conditions will help ensure appropriately constrained results that may be easier to interpret.

I hope that helps