class TCSeqRec(SequenceRecord):
"""
A version of the SequenceRecord class with some extra functionality
for working with tree inference packages, notably TreeCollection
"""
def __init__(
self,
infile=None,
file_format='fasta',
name=None,
headers=[],
sequences=[],
dv=[],
datatype=None,
tree=None,
):
self.name = name
self.headers = headers
self.sequences = sequences
self.mapping = {}
self.datatype = datatype
self.length = 0
self.seqlength = 0
self.is_aligned = False
self.TCfiles = {}
self.dv = dv
if tree:
if isinstance(tree, Tree):
self.tree = tree
else:
self.tree = Tree()
if infile:
if file_format == 'fasta':
self.get_fasta_file(infile, name=name,
datatype=datatype)
elif file_format == 'phylip':
self.get_phylip_file(infile, name=name,
datatype=datatype)
self.index = -1
self._update()
def __add__(self, other):
"""
Allows Records to be added together, concatenating sequences
"""
self_set = set(self.headers)
other_set = set(other.headers)
if not self.datatype == other.datatype:
print 'Trying to add sequences of different datatypes'
return self
union = self_set | other_set
intersection = self_set & other_set
only_in_self = self_set - other_set
only_in_other = other_set - self_set
d = {}
for k in union:
if k in intersection:
d[k] = self.mapping[k] + other.mapping[k]
elif k in only_in_self:
d[k] = self.mapping[k] + 'N' * other.seqlength
elif k in only_in_other:
d[k] = 'N' * self.seqlength + other.mapping[k]
dvsum = self.dv + other.dv
return_object = TCSeqRec(headers=d.keys(),
sequences=d.values(),
datatype=self.datatype).sort_by_name(in_place=False)
return_object.dv = dvsum
return return_object
def sort_by_length(self, in_place=True):
"""
Sorts sequences by descending order of length
Uses zip as its own inverse [ zip(*zip(A,B)) == (A,B) ]
Gaps and 'N' characters are not counted
"""
(h, s) = zip(*sorted(zip(self.headers, self.sequences),
key=lambda item: len(item[1].replace('-', ''
).replace('N', '')), reverse=True))
if in_place:
self.headers = h
self.sequences = s
else:
return TCSeqRec(name=self.name, headers=h, sequences=s,
datatype=self.datatype)
def sort_by_name(self, in_place=True):
"""
Sorts sequences by name, treating numbers as integers (i.e.
sorting like this: 1, 2, 3, 10, 20 not 1, 10, 2, 20, 3).
If in_place = False the sorting doesn't mutate the underlying
object, and the output is returned
If in_place = True the sorting mutates the self object
"""
items = self.mapping.items()
if items == []:
return self
sort_key = lambda item: tuple((int(num) if num else alpha)
for (num, alpha) in re.findall(r'(\d+)|(\D+)', item[0]))
items = sorted(items, key=sort_key)
(h, s) = zip(*items)
if in_place:
self.headers = h
self.sequences = s
return self
else:
return TCSeqRec(name=self.name, headers=h, sequences=s,
datatype=self.datatype)
def sanitise(self):
self.sort_by_name()
l = []
for h in self.headers:
if '/' in h:
h = h[:h.index('/')]
while h.startswith(' '):
h = h[1:]
h = h.replace(' ', '_')
l.append(h)
self.headers = l
self.sequences = [seq.upper() for seq in self.sequences]
self._update()
def hashname(self):
H = hashlib.sha1()
H.update(self.name)
return H.hexdigest()
def _write_temp_phylip(self, tmpdir='/tmp', use_hashname=False):
if use_hashname:
filename = self.hashname()
else:
filename = self.name
self.write_phylip('{0}/{1}.phy'.format(tmpdir, filename))
return filename
def _write_temp_tc(
self,
tmpdir='/tmp',
make_guide_tree=True,
use_hashname=True,
):
if use_hashname:
filename = self.hashname()
else:
filename = self.name
num_matrices = len(self.dv)
if num_matrices == 0:
print 'No distance-variance matrix available'
return
all_labels = self.headers
len_all_labels = len(all_labels)
# Temporary files
dv_tmpfile = open('{0}/{1}_dv.txt'.format(tmpdir, filename), 'w'
)
labels_tmpfile = open('{0}/{1}_labels.txt'.format(tmpdir,
filename), 'w')
map_tmpfile = open('{0}/{1}_map.txt'.format(tmpdir, filename),
'w')
if make_guide_tree:
guidetree_tmpfile = open('{0}/{1}_tree.nwk'.format(tmpdir,
filename), 'w')
# Write headers to temp files
dv_tmpfile.write('{0}\n'.format(num_matrices))
map_tmpfile.write('{0} {1}\n'.format(num_matrices,
len_all_labels))
labels_tmpfile.write('''{0}
{1}
'''.format(len_all_labels,
' '.join(all_labels)))
labels_tmpfile.flush()
# Add info from self.dv to temp files
for (i, (matrix, labels)) in enumerate(self.dv):
labels = labels.split()
dim = len(labels)
index = i + 1
dv_tmpfile.write('''{0} {0} {1}
{2}
'''.format(dim, index,
matrix))
dv_tmpfile.flush()
for lab in all_labels:
if lab in labels:
map_tmpfile.write('{0} '.format(labels.index(lab)
+ 1))
else:
map_tmpfile.write('-1 ')
map_tmpfile.write('\n')
map_tmpfile.flush()
# Close finished temp files
map_tmpfile.close()
dv_tmpfile.close()
labels_tmpfile.close()
# Write the guidetree
if make_guide_tree:
guidetree = self.get_bionj_tree(ncat=1, tmpdir=tmpdir)
dpy_guidetree = dpy.Tree()
dpy_guidetree.read_from_string(guidetree.newick, 'newick')
dpy_guidetree.resolve_polytomies()
newick_string = dpy_guidetree.as_newick_string() + ';\n'
guidetree_tmpfile.write(newick_string)
guidetree_tmpfile.flush()
guidetree_tmpfile.close()
# Check it all worked
assert os.path.isfile('{0}/{1}_dv.txt'.format(tmpdir, filename))
assert os.path.isfile('{0}/{1}_labels.txt'.format(tmpdir,
filename))
assert os.path.isfile('{0}/{1}_map.txt'.format(tmpdir,
filename))
if make_guide_tree:
assert os.path.isfile('{0}/{1}_tree.nwk'.format(tmpdir,
filename))
return filename
def get_phyml_tree(
self,
model=None,
datatype=None,
ncat=4,
tmpdir='/tmp',
overwrite=True,
verbose=False,
):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(self.name)
return self.tree
self.tree = Tree()
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
print 'Running phyml on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if not model and not datatype: # quick-fix to allow specification of other
if self.datatype == 'dna': # models when calling phyml
model = 'GTR'
datatype = 'nt'
elif self.datatype == 'protein':
model = 'WAG'
datatype = 'aa'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = self.tree.run_phyml(
model,
input_file,
datatype,
self.name,
ncat=ncat,
overwrite=overwrite,
verbose=verbose,
)
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
return self.tree
def get_bionj_tree(
self,
model=None,
datatype=None,
ncat=1,
optimise='n',
tmpdir='/tmp',
overwrite=True,
verbose=False,
):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(self.name)
return self.tree
self.Tree = Tree()
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
print 'Running bionj on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if not model and not datatype: # quick-fix to allow specification of other
if self.datatype == 'dna': # models when calling phyml
model = 'GTR'
datatype = 'nt'
elif self.datatype == 'protein':
model = 'WAG'
datatype = 'aa'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = self.tree.run_bionj(
model,
input_file,
datatype,
ncat=ncat,
name=self.name,
optimise=optimise,
overwrite=overwrite,
verbose=verbose,
)
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
return self.tree
def get_raxml_tree(self, tmpdir='/tmp', overwrite=True):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(self.name)
return self.tree
self.tree = Tree()
self._write_temp_phylip(tmpdir=tmpdir)
print 'Running raxml on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, self.name)
if self.datatype == 'dna':
model = 'GTRGAMMA'
elif self.datatype == 'protein':
model = 'PROTGAMMAWAG'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
self.tree.run_raxml(model, input_file, self.name, tmpdir,
overwrite=overwrite)
os.remove('{0}/{1}.phy'.format(tmpdir, self.name))
if os.path.isfile('{0}/{1}.phy.reduced'.format(tmpdir,
self.name)):
os.remove('{0}/{1}.phy.reduced'.format(tmpdir, self.name))
return self.tree
def get_guide_tree(self, tmpdir='/tmp', overwrite=True):
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if self.datatype == 'dna':
model = 'GTRGAMMA'
elif self.datatype == 'protein':
model = 'PROTGAMMAWAG'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = Tree().run_raxml(model, input_file, self.name, tmpdir,
guide=True)
if os.path.isfile('{0}/{1}.phy'.format(tmpdir, filename)):
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
if os.path.isfile('{0}/{1}.phy.reduced'.format(tmpdir,
filename)):
os.remove('{0}/{1}.phy.reduced'.format(tmpdir, filename))
return t
def get_TC_tree(self, tmpdir='/tmp', overwrite=True):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(self.name)
return self.tree
filename = self._write_temp_tc(tmpdir=tmpdir, use_hashname=True)
print 'Running TreeCollection on ' + str(self.name) + '...'
self.tree.run_treecollection(
'{0}/{1}_dv.txt'.format(tmpdir, filename),
'{0}/{1}_map.txt'.format(tmpdir, filename),
'{0}/{1}_labels.txt'.format(tmpdir, filename),
'{0}/{1}_tree.nwk'.format(tmpdir, filename),
self.name,
overwrite=overwrite,
)
os.remove('{0}/{1}_dv.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_map.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_labels.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_tree.nwk'.format(tmpdir, filename))
return self.tree
def get_dv_matrix(
self,
tmpdir='/tmp',
helper='/Users/kgori/Projects/clustering_project/class_files/DV_wrapper.drw'
,
overwrite=True,
):
"""
Makes a call to the TC_wrapper.drw darwin helper script, which
calculates a distance-variance matrix from the sequence alignments,
and generates files needed by the treecollection binary
"""
if not overwrite and self.dv:
return self.dv[0]
if self.name:
fastafile = '{0}/{1}.fas'.format(tmpdir, self.name)
else:
fastafile = '{0}/fasta_tmp.fas'.format(tmpdir)
if self.datatype == 'dna':
datatype = 'DNA'
else:
datatype = 'AA'
if not os.path.isfile(helper):
print 'Can\'t find the darwin helper file at {0}'.format(helper)
return 0
self.write_fasta(fastafile)
print 'Running darwin on {0}, datatype = {1}'.format(fastafile,
datatype, helper)
command = \
'echo "fil := ReadFastaWithNames(\'{0}\'); seqtype := \'{1}\'; fpath := \'{2}/\'; ReadProgram(\'{3}\');" | darwin'.format(fastafile,
datatype, tmpdir, helper)
# print command
process = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)
(stdout, stderr) = process.communicate()
dv_string = \
open('{0}/temp_distvar.txt'.format(tmpdir)).read().rstrip()
labels = ' '.join(self.headers)
os.remove(fastafile)
os.remove('{0}/temp_distvar.txt'.format(tmpdir))
return (dv_string, labels)
def _pivot(self, lst):
new_lst = zip(*lst)
return [''.join(x) for x in new_lst]
def shuffle(self):
"""
Modifies in-place
"""
columns = self._pivot(self.sequences)
shf(columns)
self.sequences = self._pivot(columns)
self._update()
def split_by_lengths(self, lengths, names=None):
assert sum(lengths) == self.seqlength
columns = self._pivot(self.sequences)
newcols = []
for l in lengths:
newcols.append(columns[:l])
columns = columns[l:]
newrecs = []
for col in newcols:
newseqs = self._pivot(col)
newrec = TCSeqRec(headers=self.headers,
sequences=newseqs, datatype=self.datatype)
newrecs.append(newrec)
if names:
for i, newrec in enumerate(newrecs):
newrec.name = names[i]
else:
for i, newrec in enumerate(newrecs):
newrec.name = 'record_{0}'.format(i+1)
return newrecs
class TCSeqRec(SequenceRecord):
"""
A version of the SequenceRecord class with some extra functionality
for working with tree inference packages, notably TreeCollection
"""
def __init__(
self,
infile=None,
file_format='fasta',
name=None,
headers=[],
sequences=[],
dv=[],
datatype=None,
tree=None,
):
self.name = name
self.headers = headers
self.sequences = sequences
self.mapping = {}
self.datatype = datatype
self.length = 0
self.seqlength = 0
self.is_aligned = False
self.TCfiles = {}
self.dv = dv
if tree:
if isinstance(tree, Tree):
self.tree = tree
else:
self.tree = Tree()
if infile:
if file_format == 'fasta':
self.get_fasta_file(infile, name=name, datatype=datatype)
elif file_format == 'phylip':
self.get_phylip_file(infile, name=name, datatype=datatype)
self.index = -1
self._update()
def __add__(self, other):
"""
Allows Records to be added together, concatenating sequences
"""
self_set = set(self.headers)
other_set = set(other.headers)
if not self.datatype == other.datatype:
print 'Trying to add sequences of different datatypes'
return self
union = self_set | other_set
intersection = self_set & other_set
only_in_self = self_set - other_set
only_in_other = other_set - self_set
d = {}
for k in union:
if k in intersection:
d[k] = self.mapping[k] + other.mapping[k]
elif k in only_in_self:
d[k] = self.mapping[k] + 'N' * other.seqlength
elif k in only_in_other:
d[k] = 'N' * self.seqlength + other.mapping[k]
dvsum = self.dv + other.dv
return_object = TCSeqRec(
headers=d.keys(), sequences=d.values(),
datatype=self.datatype).sort_by_name(in_place=False)
return_object.dv = dvsum
return return_object
def sort_by_length(self, in_place=True):
"""
Sorts sequences by descending order of length
Uses zip as its own inverse [ zip(*zip(A,B)) == (A,B) ]
Gaps and 'N' characters are not counted
"""
(h, s) = zip(*sorted(
zip(self.headers, self.sequences),
key=lambda item: len(item[1].replace('-', '').replace('N', '')),
reverse=True))
if in_place:
self.headers = h
self.sequences = s
else:
return TCSeqRec(name=self.name,
headers=h,
sequences=s,
datatype=self.datatype)
def sort_by_name(self, in_place=True):
"""
Sorts sequences by name, treating numbers as integers (i.e.
sorting like this: 1, 2, 3, 10, 20 not 1, 10, 2, 20, 3).
If in_place = False the sorting doesn't mutate the underlying
object, and the output is returned
If in_place = True the sorting mutates the self object
"""
items = self.mapping.items()
if items == []:
return self
sort_key = lambda item: tuple(
(int(num) if num else alpha)
for (num, alpha) in re.findall(r'(\d+)|(\D+)', item[0]))
items = sorted(items, key=sort_key)
(h, s) = zip(*items)
if in_place:
self.headers = h
self.sequences = s
return self
else:
return TCSeqRec(name=self.name,
headers=h,
sequences=s,
datatype=self.datatype)
def sanitise(self):
self.sort_by_name()
l = []
for h in self.headers:
if '/' in h:
h = h[:h.index('/')]
while h.startswith(' '):
h = h[1:]
h = h.replace(' ', '_')
l.append(h)
self.headers = l
self.sequences = [seq.upper() for seq in self.sequences]
self._update()
def hashname(self):
H = hashlib.sha1()
H.update(self.name)
return H.hexdigest()
def _write_temp_phylip(self, tmpdir='/tmp', use_hashname=False):
if use_hashname:
filename = self.hashname()
else:
filename = self.name
self.write_phylip('{0}/{1}.phy'.format(tmpdir, filename))
return filename
def _write_temp_tc(
self,
tmpdir='/tmp',
make_guide_tree=True,
use_hashname=True,
):
if use_hashname:
filename = self.hashname()
else:
filename = self.name
num_matrices = len(self.dv)
if num_matrices == 0:
print 'No distance-variance matrix available'
return
all_labels = self.headers
len_all_labels = len(all_labels)
# Temporary files
dv_tmpfile = open('{0}/{1}_dv.txt'.format(tmpdir, filename), 'w')
labels_tmpfile = open('{0}/{1}_labels.txt'.format(tmpdir, filename),
'w')
map_tmpfile = open('{0}/{1}_map.txt'.format(tmpdir, filename), 'w')
if make_guide_tree:
guidetree_tmpfile = open(
'{0}/{1}_tree.nwk'.format(tmpdir, filename), 'w')
# Write headers to temp files
dv_tmpfile.write('{0}\n'.format(num_matrices))
map_tmpfile.write('{0} {1}\n'.format(num_matrices, len_all_labels))
labels_tmpfile.write('''{0}
{1}
'''.format(len_all_labels, ' '.join(all_labels)))
labels_tmpfile.flush()
# Add info from self.dv to temp files
for (i, (matrix, labels)) in enumerate(self.dv):
labels = labels.split()
dim = len(labels)
index = i + 1
dv_tmpfile.write('''{0} {0} {1}
{2}
'''.format(dim, index, matrix))
dv_tmpfile.flush()
for lab in all_labels:
if lab in labels:
map_tmpfile.write('{0} '.format(labels.index(lab) + 1))
else:
map_tmpfile.write('-1 ')
map_tmpfile.write('\n')
map_tmpfile.flush()
# Close finished temp files
map_tmpfile.close()
dv_tmpfile.close()
labels_tmpfile.close()
# Write the guidetree
if make_guide_tree:
guidetree = self.get_bionj_tree(ncat=1, tmpdir=tmpdir)
dpy_guidetree = dpy.Tree()
dpy_guidetree.read_from_string(guidetree.newick, 'newick')
dpy_guidetree.resolve_polytomies()
newick_string = dpy_guidetree.as_newick_string() + ';\n'
guidetree_tmpfile.write(newick_string)
guidetree_tmpfile.flush()
guidetree_tmpfile.close()
# Check it all worked
assert os.path.isfile('{0}/{1}_dv.txt'.format(tmpdir, filename))
assert os.path.isfile('{0}/{1}_labels.txt'.format(tmpdir, filename))
assert os.path.isfile('{0}/{1}_map.txt'.format(tmpdir, filename))
if make_guide_tree:
assert os.path.isfile('{0}/{1}_tree.nwk'.format(tmpdir, filename))
return filename
def get_phyml_tree(
self,
model=None,
datatype=None,
ncat=4,
tmpdir='/tmp',
overwrite=True,
verbose=False,
):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(
self.name)
return self.tree
self.tree = Tree()
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
print 'Running phyml on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if not model and not datatype: # quick-fix to allow specification of other
if self.datatype == 'dna': # models when calling phyml
model = 'GTR'
datatype = 'nt'
elif self.datatype == 'protein':
model = 'WAG'
datatype = 'aa'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = self.tree.run_phyml(
model,
input_file,
datatype,
self.name,
ncat=ncat,
overwrite=overwrite,
verbose=verbose,
)
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
return self.tree
def get_bionj_tree(
self,
model=None,
datatype=None,
ncat=1,
optimise='n',
tmpdir='/tmp',
overwrite=True,
verbose=False,
):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(
self.name)
return self.tree
self.Tree = Tree()
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
print 'Running bionj on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if not model and not datatype: # quick-fix to allow specification of other
if self.datatype == 'dna': # models when calling phyml
model = 'GTR'
datatype = 'nt'
elif self.datatype == 'protein':
model = 'WAG'
datatype = 'aa'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = self.tree.run_bionj(
model,
input_file,
datatype,
ncat=ncat,
name=self.name,
optimise=optimise,
overwrite=overwrite,
verbose=verbose,
)
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
return self.tree
def get_raxml_tree(self, tmpdir='/tmp', overwrite=True):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(
self.name)
return self.tree
self.tree = Tree()
self._write_temp_phylip(tmpdir=tmpdir)
print 'Running raxml on ' + str(self.name) + '...'
input_file = '{0}/{1}.phy'.format(tmpdir, self.name)
if self.datatype == 'dna':
model = 'GTRGAMMA'
elif self.datatype == 'protein':
model = 'PROTGAMMAWAG'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
self.tree.run_raxml(model,
input_file,
self.name,
tmpdir,
overwrite=overwrite)
os.remove('{0}/{1}.phy'.format(tmpdir, self.name))
if os.path.isfile('{0}/{1}.phy.reduced'.format(tmpdir, self.name)):
os.remove('{0}/{1}.phy.reduced'.format(tmpdir, self.name))
return self.tree
def get_guide_tree(self, tmpdir='/tmp', overwrite=True):
filename = self._write_temp_phylip(tmpdir=tmpdir, use_hashname=True)
input_file = '{0}/{1}.phy'.format(tmpdir, filename)
if self.datatype == 'dna':
model = 'GTRGAMMA'
elif self.datatype == 'protein':
model = 'PROTGAMMAWAG'
else:
print 'I don\'t know this datatype: {0}'.format(self.datatype)
return
t = Tree().run_raxml(model, input_file, self.name, tmpdir, guide=True)
if os.path.isfile('{0}/{1}.phy'.format(tmpdir, filename)):
os.remove('{0}/{1}.phy'.format(tmpdir, filename))
if os.path.isfile('{0}/{1}.phy.reduced'.format(tmpdir, filename)):
os.remove('{0}/{1}.phy.reduced'.format(tmpdir, filename))
return t
def get_TC_tree(self, tmpdir='/tmp', overwrite=True):
if not overwrite and self.tree.newick:
print '{0}: Tree exists and overwrite set to false'.format(
self.name)
return self.tree
filename = self._write_temp_tc(tmpdir=tmpdir, use_hashname=True)
print 'Running TreeCollection on ' + str(self.name) + '...'
self.tree.run_treecollection(
'{0}/{1}_dv.txt'.format(tmpdir, filename),
'{0}/{1}_map.txt'.format(tmpdir, filename),
'{0}/{1}_labels.txt'.format(tmpdir, filename),
'{0}/{1}_tree.nwk'.format(tmpdir, filename),
self.name,
overwrite=overwrite,
)
os.remove('{0}/{1}_dv.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_map.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_labels.txt'.format(tmpdir, filename))
os.remove('{0}/{1}_tree.nwk'.format(tmpdir, filename))
return self.tree
def get_dv_matrix(
self,
tmpdir='/tmp',
helper='/Users/kgori/Projects/clustering_project/class_files/DV_wrapper.drw',
overwrite=True,
):
"""
Makes a call to the TC_wrapper.drw darwin helper script, which
calculates a distance-variance matrix from the sequence alignments,
and generates files needed by the treecollection binary
"""
if not overwrite and self.dv:
return self.dv[0]
if self.name:
fastafile = '{0}/{1}.fas'.format(tmpdir, self.name)
else:
fastafile = '{0}/fasta_tmp.fas'.format(tmpdir)
if self.datatype == 'dna':
datatype = 'DNA'
else:
datatype = 'AA'
if not os.path.isfile(helper):
print 'Can\'t find the darwin helper file at {0}'.format(helper)
return 0
self.write_fasta(fastafile)
print 'Running darwin on {0}, datatype = {1}'.format(
fastafile, datatype, helper)
command = \
'echo "fil := ReadFastaWithNames(\'{0}\'); seqtype := \'{1}\'; fpath := \'{2}/\'; ReadProgram(\'{3}\');" | darwin'.format(fastafile,
datatype, tmpdir, helper)
# print command
process = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)
(stdout, stderr) = process.communicate()
dv_string = \
open('{0}/temp_distvar.txt'.format(tmpdir)).read().rstrip()
labels = ' '.join(self.headers)
os.remove(fastafile)
os.remove('{0}/temp_distvar.txt'.format(tmpdir))
return (dv_string, labels)
def _pivot(self, lst):
new_lst = zip(*lst)
return [''.join(x) for x in new_lst]
def shuffle(self):
"""
Modifies in-place
"""
columns = self._pivot(self.sequences)
shf(columns)
self.sequences = self._pivot(columns)
self._update()
def split_by_lengths(self, lengths, names=None):
assert sum(lengths) == self.seqlength
columns = self._pivot(self.sequences)
newcols = []
for l in lengths:
newcols.append(columns[:l])
columns = columns[l:]
newrecs = []
for col in newcols:
newseqs = self._pivot(col)
newrec = TCSeqRec(headers=self.headers,
sequences=newseqs,
datatype=self.datatype)
newrecs.append(newrec)
if names:
for i, newrec in enumerate(newrecs):
newrec.name = names[i]
else:
for i, newrec in enumerate(newrecs):
newrec.name = 'record_{0}'.format(i + 1)
return newrecs
