def noise_tree_groups(cd, nbs=20, noise=.1, mode='vdps', q=6000, N=14):
"""
cd : CellDoc other args for noise_tree
returns a document that contains a KeySet t of documents with "_" a tree2tup
of a clustering tree, "cell" a cell name from cd, "cond" a condition name.
These represent the result of running several sample noisy clustering trials
(as done by noise_tree), and labeling the resulting trees for cell and
condition
"""
cn = cd.keys(0, 'cell')
conds = ['cond1', 'cond2']
d = gd.Doc()
names = [cd[cn[0]]['stimclasses'][k]['file'] for k in
cd[cn[0]]['stimclasses'].keys(sort=True)]
i = 1
for cell in cn:
for cond in conds:
trees = noise_tree(cd[cell][cond], nbs, noise, mode, q,
len(names), False)
for t in trees:
d['t%i' % i] = {'_': clust.tree2tup(t), 'cell': cell,
'cond': cond}
i += 1
d['names'] = names
return d
def run(self, pars, out, messages):
dm = np.array(pars['dm'])
t = clust.dtree(dm)
out['tree'] = clust.tree2tup(t)