def return_profile_OTF(self, fam):
"""
Returns profiles as binary vectors for use with optimisation pipelines
"""
if type(fam) is str:
fam = hashutils.hogid2fam(fam)
ortho_fam = self.READ_ORTHO(fam)
tp = self.HAM_PIPELINE([fam, ortho_fam])
losses = [
self.taxaIndex[n.name] for n in tp.traverse()
if n.lost and n.name in self.taxaIndex
]
dupl = [
self.taxaIndex[n.name] for n in tp.traverse()
if n.dupl and n.name in self.taxaIndex
]
presence = [
self.taxaIndex[n.name] for n in tp.traverse()
if n.nbr_genes > 0 and n.name in self.taxaIndex
]
indices = dict(
zip(['presence', 'loss', 'dup'], [presence, losses, dupl]))
hog_matrix_raw = np.zeros((1, 3 * len(self.taxaIndex)))
for i, event in enumerate(indices):
if len(indices[event]) > 0:
taxindex = np.asarray(indices[event])
hogindex = np.asarray(indices[event]) + i * len(self.taxaIndex)
hog_matrix_raw[:, hogindex] = 1
return {fam: {'mat': hog_matrix_raw, 'tree': tp}}
def return_profile_complements(self, fam):
"""
Returns profiles for each loss to search for complementary hogs
"""
if type(fam) is str:
fam = hashutils.hogid2fam(fam)
ortho_fam = self.READ_ORTHO(fam)
tp = self.HAM_PIPELINE([fam, ortho_fam])
losses = set([
n.name for n in tp.traverse()
if n.lost and n.name in self.taxaIndex
])
#these are the roots of the fams we are looking for
#we just assume no duplications or losses from this point
ancestral_nodes = ([
n for n in profiler.tree.traverse() if n.name in losses
])
losses = []
dupl = []
complements = {n.name + '_loss': []}
indices = dict(
zip(['presence', 'loss', 'dup'], [presence, losses, dupl]))
hog_matrix_raw = np.zeros((1, 3 * len(self.taxaIndex)))
for i, event in enumerate(indices):
if len(indices[event]) > 0:
taxindex = np.asarray(indices[event])
hogindex = np.asarray(indices[event]) + i * len(self.taxaIndex)
hog_matrix_raw[:, hogindex] = 1
return {fam: {'mat': hog_matrix_raw, 'hash': tp}}
def pull_hashes(self, hoglist):
"""
Given a list of hog_ids , returns a dictionary containing their hashes.
This uses the hdf5 file to get the hashvalues
:param hog_id: query hog id
:param fam_id: query fam id
:return: a dict containing the hash values of the hogs in hoglist
"""
return {
hog: hashutils.fam2hash_hdf5(hashutils.hogid2fam(str(hog)),
self.hashes_h5,
nsamples=self.nsamples)
for hog in hoglist
}
def hog_query(self, hog_id=None, fam_id=None, k=100):
"""
Given a hog_id or a fam_id as a query, returns a dictionary containing the results of the LSH.
:param hog_id: query hog id
:param fam_id: query fam id
:return: list containing the results of the LSH for the given query
"""
if hog_id is not None:
fam_id = hashutils.hogid2fam(hog_id)
query_hash = hashutils.fam2hash_hdf5(fam_id,
self.hashes_h5,
nsamples=self.nsamples)
#print(query_hash.hashvalues)
results = self.lshobj.query(query_hash, k)
return results
def return_profile_OTF_DCA(self, fam, lock=None):
"""
Returns profiles as strings for use with DCA pipelines
just concatenate the numpy arrays and use the tostring
function to generate an input "alignment"
"""
if type(fam) is str:
fam = hashutils.hogid2fam(fam)
if lock is not None:
lock.acquire()
ortho_fam = self.READ_ORTHO(fam)
if lock is not None:
lock.release()
tp = self.HAM_PIPELINE([fam, ortho_fam])
dcastr = hashutils.tree2str_DCA(tp, self.taxaIndex)
return {fam: {'dcastr': dcastr, 'tree': tp}}
def hog_query_sorted(self, hog_id=None, fam_id=None, k=100):
"""
Given a hog_id or a fam_id as a query, returns a dictionary containing the results of the LSH.
:param hog_id: query hog id
:param fam_id: query fam id
:return: list containing the results of the LSH for the given query
"""
if hog_id is not None:
fam_id = hashutils.hogid2fam(hog_id)
query_hash = hashutils.fam2hash_hdf5(fam_id,
self.hashes_h5,
nsamples=self.nsamples)
results = self.lshobj.query(query_hash, k)
hogdict = self.pull_hashes(results)
hogdict = {hog: hogdict[hog].jaccard(query_hash) for hog in hogdict}
sortedhogs = [(k, v) for k, v in hogdict.items()]
sortedhogs = sorted(student_tuples, key=lambda x: x[1])
sortedhogs = [h[0] for h in sortehogs.reverse()]
return hogdict