def cluster_reps(reps, threshold=1.0, plot=1):
"""Do clustering based `reps`.
Returns a tuple with
- The indices of the largest cluster found
- The condensed distance matrix
- Cluster linkage
Keyword arguments:
`threshold` : chisq threshold to use in discrimination.
`plot` : Plot results, if True.
"""
cdm = chi2cdm(reps)
links = hc.linkage(cdm, method='complete')
clist = filter_with_linkage(links, threshold)
print("Clusters: %s" % str(clist))
if plot:
first = reps[0,...]
aver = mean_stack(reps)
filtered = mean_stack(reps[clist[0],...])
plot_clustering(filtered, first, aver, clist[0], cdm, links, threshold)
return (clist[0], cdm, links)
def filter_outliers(reps, threshold=1.0, plot=1):
"""Filter by removing repetitions having mutual chisq above `threshold`.
Returns a tuple containing the included indices and the condensed
distance matrix.
Repetitions are removed iteratively by checking which repetition
contributes the largest number of over the threshold chi-squared values
(outliers) in the chisq-distance matrix, and removing that point.
If two repetitions cause an equal number of outliers, the repetition
which has the highest chisq distance to a non-outlier distance matrix
point is removed.
"""
cdm = chi2cdm(reps)
dmat = squareform(cdm)
incinds = filter_distmat(dmat, threshold)
if plot:
first = reps[0,...]
aver = mean_stack(reps)
filtered = mean_stack(reps[incinds,...])
plot_outliers(filtered, first, aver, incinds, cdm, threshold)
return incinds, cdm
