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search.py
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search.py
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from collections import namedtuple
import sys
from .logging import notify, error
from .signature import SourmashSignature
from .minhash import _get_max_hash_for_scaled
# generic SearchResult.
SearchResult = namedtuple('SearchResult',
'similarity, match, md5, filename, name')
def format_bp(bp):
"Pretty-print bp information."
bp = float(bp)
if bp < 500:
return '{:.0f} bp '.format(bp)
elif bp <= 500e3:
return '{:.1f} kbp'.format(round(bp / 1e3, 1))
elif bp < 500e6:
return '{:.1f} Mbp'.format(round(bp / 1e6, 1))
elif bp < 500e9:
return '{:.1f} Gbp'.format(round(bp / 1e9, 1))
return '???'
def search_databases(query, databases, threshold, do_containment, best_only,
ignore_abundance, unload_data=False):
results = []
found_md5 = set()
for (obj, filename, filetype) in databases:
search_iter = obj.search(query, threshold=threshold,
do_containment=do_containment,
ignore_abundance=ignore_abundance,
best_only=best_only,
unload_data=unload_data)
for (similarity, match, filename) in search_iter:
md5 = match.md5sum()
if md5 not in found_md5:
results.append((similarity, match, filename))
found_md5.add(md5)
# sort results on similarity (reverse)
results.sort(key=lambda x: -x[0])
x = []
for (similarity, match, filename) in results:
x.append(SearchResult(similarity=similarity,
match=match,
md5=match.md5sum(),
filename=filename,
name=match.name()))
return x
###
### gather code
###
GatherResult = namedtuple('GatherResult',
'intersect_bp, f_orig_query, f_match, f_unique_to_query, f_unique_weighted, average_abund, median_abund, std_abund, filename, name, md5, match,f_match_orig')
# build a new query object, subtracting found mins and downsampling
def _subtract_and_downsample(to_remove, old_query, scaled=None):
mh = old_query.minhash
mh = mh.downsample_scaled(scaled)
mh.remove_many(to_remove)
return SourmashSignature(mh)
def _find_best(dblist, query, threshold_bp):
"""
Search for the best containment, return precisely one match.
"""
best_cont = 0.0
best_match = None
best_filename = None
# quantize threshold_bp to be an integer multiple of scaled
query_scaled = query.minhash.scaled
threshold_bp = int(threshold_bp / query_scaled) * query_scaled
# search across all databases
for (obj, filename, filetype) in dblist:
for cont, match, fname in obj.gather(query, threshold_bp=threshold_bp):
assert cont # all matches should be nonzero.
# note, break ties based on name, to ensure consistent order.
if (cont == best_cont and match.name() < best_match.name()) or \
cont > best_cont:
# update best match.
best_cont = cont
best_match = match
# some objects may not have associated filename (e.g. SBTs)
best_filename = fname or filename
if not best_match:
return None, None, None
return best_cont, best_match, best_filename
def _filter_max_hash(values, max_hash):
for v in values:
if v < max_hash:
yield v
def gather_databases(query, databases, threshold_bp, ignore_abundance):
"""
Iteratively find the best containment of `query` in all the `databases`,
until we find fewer than `threshold_bp` (estimated) bp in common.
"""
# track original query information for later usage.
track_abundance = query.minhash.track_abundance and not ignore_abundance
orig_query_mh = query.minhash
orig_query_mins = orig_query_mh.get_hashes()
# do we pay attention to abundances?
orig_query_abunds = { k: 1 for k in orig_query_mins }
if track_abundance:
import numpy as np
orig_query_abunds = orig_query_mh.hashes
cmp_scaled = query.minhash.scaled # initialize with resolution of query
while query.minhash:
# find the best match!
best_cont, best_match, filename = _find_best(databases, query,
threshold_bp)
if not best_match: # no matches at all for this cutoff!
notify('found less than {} in common. => exiting',
format_bp(threshold_bp))
break
# subtract found hashes from search hashes, construct new search
query_mins = set(query.minhash.get_hashes())
found_mins = best_match.minhash.get_hashes()
# Is the best match computed with scaled? Die if not.
match_scaled = best_match.minhash.scaled
if not match_scaled:
error('Best match in gather is not scaled.')
error('Please prepare gather databases with --scaled')
raise Exception
# pick the highest scaled / lowest resolution
cmp_scaled = max(cmp_scaled, match_scaled)
# eliminate mins under this new resolution.
# (CTB note: this means that if a high scaled/low res signature is
# found early on, resolution will be low from then on.)
new_max_hash = _get_max_hash_for_scaled(cmp_scaled)
query_mins = set(_filter_max_hash(query_mins, new_max_hash))
found_mins = set(_filter_max_hash(found_mins, new_max_hash))
orig_query_mins = set(_filter_max_hash(orig_query_mins, new_max_hash))
sum_abunds = sum(( v for (k,v) in orig_query_abunds.items() if k < new_max_hash ))
# calculate intersection with query mins:
intersect_mins = query_mins.intersection(found_mins)
intersect_orig_query_mins = orig_query_mins.intersection(found_mins)
intersect_bp = cmp_scaled * len(intersect_orig_query_mins)
# calculate fractions wrt first denominator - genome size
genome_n_mins = len(found_mins)
f_match = len(intersect_mins) / float(genome_n_mins)
f_orig_query = len(intersect_orig_query_mins) / \
float(len(orig_query_mins))
# calculate fractions wrt second denominator - metagenome size
orig_query_mh = orig_query_mh.downsample_scaled(cmp_scaled)
query_n_mins = len(orig_query_mh)
f_unique_to_query = len(intersect_mins) / float(query_n_mins)
# calculate fraction of subject match with orig query
f_match_orig = best_match.minhash.contained_by(orig_query_mh,
downsample=True)
# calculate scores weighted by abundances
f_unique_weighted = sum((orig_query_abunds[k] for k in intersect_mins))
f_unique_weighted /= sum_abunds
# calculate stats on abundances, if desired.
average_abund, median_abund, std_abund = 0, 0, 0
if track_abundance:
intersect_abunds = (orig_query_abunds[k] for k in intersect_mins)
intersect_abunds = list(intersect_abunds)
average_abund = np.mean(intersect_abunds)
median_abund = np.median(intersect_abunds)
std_abund = np.std(intersect_abunds)
# build a result namedtuple
result = GatherResult(intersect_bp=intersect_bp,
f_orig_query=f_orig_query,
f_match=f_match,
f_match_orig=f_match_orig,
f_unique_to_query=f_unique_to_query,
f_unique_weighted=f_unique_weighted,
average_abund=average_abund,
median_abund=median_abund,
std_abund=std_abund,
filename=filename,
md5=best_match.md5sum(),
name=best_match.name(),
match=best_match)
# construct a new query, subtracting hashes found in previous one.
query = _subtract_and_downsample(found_mins, query, cmp_scaled)
# compute weighted_missed:
query_mins -= set(found_mins)
weighted_missed = sum((orig_query_abunds[k] for k in query_mins)) \
/ sum_abunds
yield result, weighted_missed, new_max_hash, query