periodicity_calculation.py

r = rois.all[38]
test_mask = copy.copy(r.metrics.flash_mask) #+ [108, 109, 111]
print test_mask
# Calculate periodicity approximation
# Method 1: Select max drop for each cluster
#         |"```"|`     
#         -*****-*   -> 5 points
working_mask_m1 = copy.copy(test_mask)
working_mask_m1_grouping = [map(operator.itemgetter(1), g) 
                           for k, g in groupby(enumerate(working_mask_m1), 
                                               lambda (i,x):i-x)]
working_mask_m1_clusters = [s for i, s in enumerate(working_mask_m1_grouping)
                            if len(s) > 1]
for i, cluster in enumerate(working_mask_m1_clusters):
    vals = [r.metrics.normalized_gfp[x] for x in cluster]
    cluster.pop(vals.index(max(vals)))
    working_mask_m1_clusters[i] = cluster
working_mask_m1_final = sorted(set(working_mask_m1) \
                               - set([c for cluster in working_mask_m1_clusters 
                                                           for c in cluster]))

working_mask_m1_distances = [i-j for i, j in zip(working_mask_m1_final[1:], 
                                                 working_mask_m1_final[:-1])]

# Method 2: Non-drop points between detectable drops
#         |"```"|`
#         --***--*   -> 3 points
working_mask_m2 = copy.copy(test_mask)
working_mask_m2_distances = [i-j for i, j in zip(working_mask_m2[1:], 
                                                 working_mask_m2[:-1])
                             if i-j != 1]