def sbt_combine(args):
parser = argparse.ArgumentParser()
parser.add_argument('sbt_name', help='name to save SBT into')
parser.add_argument('sbts', nargs='+', help='SBTs to combine to a new SBT')
parser.add_argument('-x', '--bf-size', type=float, default=1e5)
sourmash_args.add_moltype_args(parser)
args = parser.parse_args(args)
moltype = sourmash_args.calculate_moltype(args)
inp_files = list(args.sbts)
notify('combining {} SBTs', len(inp_files))
tree = sourmash_lib.load_sbt_index(inp_files.pop(0))
for f in inp_files:
new_tree = sourmash_lib.load_sbt_index(f)
# TODO: check if parameters are the same for both trees!
tree.combine(new_tree)
notify('saving SBT under "{}".', args.sbt_name)
tree.save(args.sbt_name)
def main():
p = argparse.ArgumentParser()
p.add_argument('sbt')
p.add_argument('-o', '--output', type=argparse.FileType('wt'))
args = p.parse_args()
db = sourmash_lib.load_sbt_index(args.sbt)
for n, leaf in enumerate(db.leaves()):
if n % 1000 == 0:
print('... at leaf', n)
name = leaf.data.name()
# & output!
args.output.write('{}\n'.format(name))
print('got accessions from {} signatures'.format(n + 1))
def gather_main(args):
"""
"""
p = argparse.ArgumentParser()
p.add_argument('--debug', action='store_true')
p.add_argument('spreadsheet')
p.add_argument('species')
p.add_argument('--sbt')
p.add_argument('-o', '--output', type=argparse.FileType('wt'))
args = p.parse_args(args)
if args.debug:
set_debug(args.debug)
assignments, num_rows = load_taxonomy_assignments(args.spreadsheet)
found = False
for ident, lineage in assignments.items():
for vv in lineage:
if vv.rank == 'species' and vv.name == args.species:
found = True
found_lineage = lineage
break
if not found:
print('nothing found for {}; quitting'.format(args.species))
sys.exit(-1)
print('found:', ", ".join(lca_utils.zip_lineage(found_lineage)))
lineage_search = dict(found_lineage)
rank_found = defaultdict(list)
rank_idents = defaultdict(list)
taxlist = list(reversed(list(lca_utils.taxlist())))
for ident, lineage in assignments.items():
dd = dict(lineage)
for k in taxlist:
if dd.get(k) and dd.get(k) == lineage_search.get(k):
rank_found[k].append(lineage)
rank_idents[k].append(ident)
break
retrieve_idents = defaultdict(set)
gimme_idents = {}
for k in rank_found:
print('at', k, 'found', len(rank_found.get(k)))
num_to_extract = min(len(rank_idents[k]), 10)
gimme = random.sample(rank_idents[k], num_to_extract)
for g in gimme:
gimme_idents[g] = k
if not args.output or not args.sbt:
print('no output arg or SBT arg given; quitting without extracting')
sys.exit(-1)
print('looking for:', len(gimme_idents))
tree = sourmash_lib.load_sbt_index(args.sbt)
w = csv.writer(args.output)
for n, leaf in enumerate(tree.leaves()):
if n % 1000 == 0:
print('...', n)
name = leaf.data.name()
# hack for NCBI-style names, etc.
name = name.split(' ')[0].split('.')[0]
if name in gimme_idents:
level = gimme_idents[name]
level_n = taxlist.index(level)
filename = leaf.data.d['filename']
w.writerow([level, level_n, filename, leaf.data.name()])
print('FOUND!', leaf.data.name(), level)
def categorize(args):
parser = argparse.ArgumentParser()
parser.add_argument('sbt_name', help='name of SBT to load')
parser.add_argument('queries',
nargs='+',
help='list of signatures to categorize')
parser.add_argument('-q',
'--quiet',
action='store_true',
help='suppress non-error output')
parser.add_argument('-k', '--ksize', type=int, default=None)
parser.add_argument('--threshold', default=0.08, type=float)
parser.add_argument('--traverse-directory', action="store_true")
sourmash_args.add_moltype_args(parser)
parser.add_argument('--csv', type=argparse.FileType('at'))
parser.add_argument('--load-csv', default=None)
args = parser.parse_args(args)
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
already_names = set()
if args.load_csv:
with open(args.load_csv, 'rt') as fp:
r = csv.reader(fp)
for row in r:
already_names.add(row[0])
tree = sourmash_lib.load_sbt_index(args.sbt_name)
if args.traverse_directory:
inp_files = set(sourmash_args.traverse_find_sigs(args.queries))
else:
inp_files = set(args.queries) - already_names
inp_files = set(inp_files) - already_names
notify('found {} files to query', len(inp_files))
loader = sourmash_args.LoadSingleSignatures(inp_files, args.ksize, moltype)
for queryfile, query, query_moltype, query_ksize in loader:
notify('loaded query: {}... (k={}, {})',
query.name()[:30], query_ksize, query_moltype)
results = []
search_fn = sourmash_lib.sbtmh.SearchMinHashesFindBest().search
for leaf in tree.find(search_fn, query, args.threshold):
if leaf.data.md5sum() != query.md5sum(): # ignore self.
results.append((query.similarity(leaf.data), leaf.data))
best_hit_sim = 0.0
best_hit_query_name = ""
if results:
results.sort(key=lambda x: -x[0]) # reverse sort on similarity
best_hit_sim, best_hit_query = results[0]
notify('for {}, found: {:.2f} {}', query.name(), best_hit_sim,
best_hit_query.name())
best_hit_query_name = best_hit_query.name()
else:
notify('for {}, no match found', query.name())
if args.csv:
w = csv.writer(args.csv)
w.writerow([queryfile, best_hit_query_name, best_hit_sim])
if loader.skipped_ignore:
notify('skipped/ignore: {}', loader.skipped_ignore)
if loader.skipped_nosig:
notify('skipped/nosig: {}', loader.skipped_nosig)
def index(args):
"""
Build an Sequence Bloom Tree index of the given signatures.
"""
import sourmash_lib.sbtmh
parser = argparse.ArgumentParser()
parser.add_argument('sbt_name', help='name to save SBT into')
parser.add_argument('signatures',
nargs='+',
help='signatures to load into SBT')
parser.add_argument('-q',
'--quiet',
action='store_true',
help='suppress non-error output')
parser.add_argument('-k',
'--ksize',
type=int,
default=None,
help='k-mer size for which to build the SBT.')
parser.add_argument('-d',
'--n_children',
type=int,
default=2,
help='Number of children for internal nodes')
parser.add_argument('--traverse-directory',
action='store_true',
help='load all signatures underneath this directory.')
parser.add_argument('--append',
action='store_true',
default=False,
help='add signatures to an existing SBT.')
parser.add_argument('-x',
'--bf-size',
type=float,
default=1e5,
help='Bloom filter size used for internal nodes.')
parser.add_argument(
'-s',
'--sparseness',
type=float,
default=.0,
help='What percentage of internal nodes will not be saved. '
'Ranges from 0.0 (save all nodes) to 1.0 (no nodes saved)')
sourmash_args.add_moltype_args(parser)
args = parser.parse_args(args)
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
if args.append:
tree = sourmash_lib.load_sbt_index(args.sbt_name)
else:
tree = sourmash_lib.create_sbt_index(args.bf_size,
n_children=args.n_children)
if args.traverse_directory:
inp_files = list(sourmash_args.traverse_find_sigs(args.signatures))
else:
inp_files = list(args.signatures)
if args.sparseness < 0 or args.sparseness > 1.0:
error('sparseness must be in range [0.0, 1.0].')
notify('loading {} files into SBT', len(inp_files))
n = 0
ksizes = set()
moltypes = set()
nums = set()
scaleds = set()
for f in inp_files:
siglist = sig.load_signatures(f,
ksize=args.ksize,
select_moltype=moltype)
# load all matching signatures in this file
for ss in siglist:
ksizes.add(ss.minhash.ksize)
moltypes.add(sourmash_args.get_moltype(ss))
nums.add(ss.minhash.num)
scaleds.add(ss.minhash.scaled)
leaf = sourmash_lib.sbtmh.SigLeaf(ss.md5sum(), ss)
tree.add_node(leaf)
n += 1
# check to make sure we aren't loading incompatible signatures
if len(ksizes) > 1 or len(moltypes) > 1:
error('multiple k-mer sizes or molecule types present; fail.')
error('specify --dna/--protein and --ksize as necessary')
error('ksizes: {}; moltypes: {}', ", ".join(map(str, ksizes)),
", ".join(moltypes))
sys.exit(-1)
if nums == {0} and len(scaleds) == 1:
pass # good
elif scaleds == {0} and len(nums) == 1:
pass # also good
else:
error('trying to build an SBT with incompatible signatures.')
error('nums = {}; scaleds = {}', repr(nums), repr(scaleds))
sys.exit(-1)
# did we load any!?
if n == 0:
error('no signatures found to load into tree!? failing.')
sys.exit(-1)
notify('loaded {} sigs; saving SBT under "{}"', n, args.sbt_name)
tree.save(args.sbt_name, sparseness=args.sparseness)
def watch(args):
"Build a signature from raw FASTA/FASTQ coming in on stdin, search."
from sourmash_lib.sbtmh import SearchMinHashesFindBest
parser = argparse.ArgumentParser()
parser.add_argument('sbt_name', help='name of SBT to search')
parser.add_argument('inp_file', nargs='?', default='/dev/stdin')
parser.add_argument('-q',
'--quiet',
action='store_true',
help='suppress non-error output')
parser.add_argument('-o',
'--output',
type=argparse.FileType('wt'),
help='save signature generated from data here')
parser.add_argument('--threshold',
default=0.05,
type=float,
help='minimum threshold for matches')
parser.add_argument(
'--input-is-protein',
action='store_true',
help='Consume protein sequences - no translation needed')
sourmash_args.add_construct_moltype_args(parser)
parser.add_argument(
'-n',
'--num-hashes',
type=int,
default=DEFAULT_N,
help='number of hashes to use in each sketch (default: %(default)i)')
parser.add_argument('--name',
type=str,
default='stdin',
help='name to use for generated signature')
sourmash_args.add_ksize_arg(parser, DEFAULT_LOAD_K)
args = parser.parse_args(args)
set_quiet(args.quiet)
if args.input_is_protein and args.dna:
notify('WARNING: input is protein, turning off DNA hashing.')
args.dna = False
args.protein = True
if args.dna and args.protein:
notify('ERROR: cannot use "watch" with both DNA and protein.')
if args.dna:
moltype = 'DNA'
is_protein = False
else:
moltype = 'protein'
is_protein = True
tree = sourmash_lib.load_sbt_index(args.sbt_name)
# check ksize from the SBT we are loading
ksize = args.ksize
if ksize is None:
leaf = next(iter(tree.leaves()))
tree_mh = leaf.data.minhash
ksize = tree_mh.ksize
E = sourmash_lib.MinHash(ksize=ksize,
n=args.num_hashes,
is_protein=is_protein)
streamsig = sig.SourmashSignature(E, filename='stdin', name=args.name)
notify('Computing signature for k={}, {} from stdin', ksize, moltype)
def do_search():
search_fn = SearchMinHashesFindBest().search
results = []
for leaf in tree.find(search_fn, streamsig, args.threshold):
results.append((streamsig.similarity(leaf.data), leaf.data))
return results
notify('reading sequences from stdin')
screed_iter = screed.open(args.inp_file)
watermark = WATERMARK_SIZE
# iterate over input records
n = 0
for n, record in enumerate(screed_iter):
# at each watermark, print status & check cardinality
if n >= watermark:
notify('\r... read {} sequences', n, end='')
watermark += WATERMARK_SIZE
if do_search():
break
if args.input_is_protein:
E.add_protein(record.sequence)
else:
E.add_sequence(record.sequence, False)
results = do_search()
if not results:
notify('... read {} sequences, no matches found.', n)
else:
results.sort(key=lambda x: -x[0]) # take best
similarity, found_sig = results[0]
print_results('FOUND: {}, at {:.3f}', found_sig.name(), similarity)
if args.output:
notify('saving signature to {}', args.output.name)
sig.save_signatures([streamsig], args.output)
