def distributions(self, records=None):
logging.info("Analysing %s ...", self.database)
try:
if records is not None and isinstance(records, pd.DataFrame):
max_mut = np.max(records['MUT'])
self.n_samples = records.shape[0]
else:
# load from file
max_mut, self.n_samples = io.get_max_mut(self.database)
lin = np.linspace(0, max_mut, min(self.n_samples / 15., 12))
sets = [(0, 0)] + zip(lin[:-1], lin[1:])
if len(sets) == 1:
# no correction needs to be applied
return None
out_muts = [self.intra_donor_distance(
records, i, j) for i, j in zip(sets, sets)]
except StandardError as msg:
logging.critical(msg)
out_muts = []
my_dict = dict()
for f, m in out_muts:
my_dict.setdefault(m, []).append(f)
return my_dict
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