def jackknifing_tree(file_pattern, di_method):
"""
Given a pattern for the list of subsampled DI files
(each file should have per-line format <sample>,<size>,<comma-separated DI>
and have one of the sizes be 'real')
Run clustering and count the differences between subsampled and real trees
Returns: tree (size-->sample-->list of trees), symmetric_difference (size-->list of diffs),
robinson_foulds_distance (size-->list of diffs)
"""
from clustering import Cluster
import dendropy
dTree = lambda x: dendropy.Tree.get_from_string(x, "newick")
samples = None
sizes = None
trees = {}
for file in glob.iglob(file_pattern):
print >> sys.stderr, "reading subsampled DI file {0}....".format(file)
d = {}
with open(file) as f:
for line in f:
sample, size, di = line.strip().split(',', 2)
if size not in d:
d[size] = {}
d[size][sample] = np.array(map(float, di.split(',')))
if len(d) == 0: continue
if sizes is None:
sizes = d.keys()
sizes.sort()
samples = d[sizes[0]].keys()
samples.sort()
for size, di_dict in d.iteritems():
c = Cluster(None)
c.init_from_di_list(di_dict, method=di_method, threshold=0)
c.run_till_end()
try:
trees[size].append(dTree(str(c.trees[0])))
except KeyError:
trees[size] = [dTree(str(c.trees[0]))]
# tally (1) symmetric differences (edge weight ignored)
# (2) robinson_foulds_distance (edge weight considered)
# 'real' is the size that is the full pool that we compare all other trees to
sym_diff = {}
rob_diff = {}
for size in sizes:
if size == 'real': continue
t_real = trees['real'][0]
sym_diff[size] = [t_real.symmetric_difference(t) for t in trees[size]]
rob_diff[size] = [t_real.robinson_foulds_distance(t) for t in trees[size]]
return trees, sym_diff, rob_diff
def jackknifing_tree_DF(file_pattern, di_method, samples_to_exclude=['1412-1','1412-4']):
"""
Similar as jackknifing_tree but using DF files and
(probably improved clustering in clustering.py which I need manually turn on)
Run clustering and count the differences between subsampled and real trees
Returns: tree (size-->sample-->list of trees), symmetric_difference (size-->list of diffs),
robinson_foulds_distance (size-->list of diffs)
"""
from clustering import Cluster
import dendropy
dTree = lambda x: dendropy.Tree.get_from_string(x, "newick")
trees = {}
for file in glob.iglob(file_pattern):
print >> sys.stderr, "reading subsampled DF file {0}....".format(file)
d = {} # size --> list of dfs
with open(file) as f:
for df in DF.DFReader(f):
sample = df.name
if sample in samples_to_exclude:
print >> sys.stderr, "EXCLUDING SAMPLE {0}!".format(sample)
continue
size = df.annotations['size']
if size not in d:
d[size] = []
# need to change the mask for df!!!
# not a problem when we did with DI becuz it was already masked
df.change_vec_mask(valid_DI_pos)
d[size].append(df)
for size, df_list in d.iteritems():
c = Cluster(df_list, method=di_method, threshold=0)
c.run_till_end()
try:
trees[size].append(dTree(str(c.trees[0])))
except KeyError:
trees[size] = [dTree(str(c.trees[0]))]
print "size", size, "file", file
print c.trees[0]
# tally (1) symmetric differences (edge weight ignored)
# (2) robinson_foulds_distance (edge weight considered)
# 'real' is the size that is the full pool that we compare all other trees to
sym_diff = {}
rob_diff = {}
for size in trees:
if size == 'real': continue
t_real = trees['real'][0]
sym_diff[size] = [t_real.symmetric_difference(t) for t in trees[size]]
rob_diff[size] = [t_real.robinson_foulds_distance(t) for t in trees[size]]
return trees, sym_diff, rob_diff
