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commands.py
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"""
Functions implementing the main command-line subcommands.
"""
import csv
import os
import os.path
import sys
import shutil
import screed
from .compare import (compare_all_pairs, compare_serial_containment,
compare_serial_max_containment, compare_serial_avg_containment)
from . import MinHash
from .sbtmh import load_sbt_index, create_sbt_index
from . import signature as sig
from . import sourmash_args
from .logging import notify, error, print_results, set_quiet
from .sourmash_args import (FileOutput, FileOutputCSV,
SaveSignaturesToLocation)
from .search import prefetch_database, PrefetchResult
from .index import LazyLinearIndex
WATERMARK_SIZE = 10000
def _get_screen_width():
# default fallback is 80x24
(col, rows) = shutil.get_terminal_size()
return col
def compare(args):
"Compare multiple signature files and create a distance matrix."
import numpy
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
picklist = sourmash_args.load_picklist(args)
pattern_search = sourmash_args.load_include_exclude_db_patterns(args)
inp_files = list(args.signatures)
if args.from_file:
more_files = sourmash_args.load_pathlist_from_file(args.from_file)
inp_files.extend(more_files)
progress = sourmash_args.SignatureLoadingProgress()
# load in the various signatures
siglist = []
ksizes = set()
moltypes = set()
size_may_be_inaccurate = False
for filename in inp_files:
notify(f"loading '{filename}'", end='\r')
loaded = sourmash_args.load_file_as_signatures(filename,
ksize=args.ksize,
select_moltype=moltype,
picklist=picklist,
yield_all_files=args.force,
progress=progress,
pattern=pattern_search)
loaded = list(loaded)
if not loaded:
notify(f'\nwarning: no signatures loaded at given ksize/molecule type/picklist from {filename}')
siglist.extend(loaded)
# track ksizes/moltypes
for s in loaded:
ksizes.add(s.minhash.ksize)
moltypes.add(sourmash_args.get_moltype(s))
# error out while loading if we have more than one ksize/moltype
if len(ksizes) > 1 or len(moltypes) > 1:
break
if not siglist:
error('no signatures found! exiting.')
sys.exit(-1)
# check ksizes and type
if len(ksizes) > 1:
error('multiple k-mer sizes loaded; please specify one with -k.')
ksizes = sorted(ksizes)
error('(saw k-mer sizes {})'.format(', '.join(map(str, ksizes))))
sys.exit(-1)
if len(moltypes) > 1:
error('multiple molecule types loaded; please specify --dna, --protein')
sys.exit(-1)
notify(' '*79, end='\r')
notify(f'loaded {format(len(siglist))} signatures total.')
if picklist:
sourmash_args.report_picklist(args, picklist)
# check to make sure they're potentially compatible - either using
# scaled, or not.
scaled_sigs = [s.minhash.scaled for s in siglist]
is_scaled = all(scaled_sigs)
is_scaled_2 = any(scaled_sigs)
# complain if it's not all one or the other
if is_scaled != is_scaled_2:
error('cannot mix scaled signatures with bounded signatures')
sys.exit(-1)
is_containment = False
if args.containment or args.max_containment or args.avg_containment:
is_containment = True
containment_args = [args.containment, args.max_containment, args.avg_containment]
if sum(containment_args) > 1:
notify("ERROR: cannot specify more than one containment argument!")
sys.exit(-1)
# complain if --containment and not is_scaled
if is_containment and not is_scaled:
error('must use scaled signatures with --containment, --max-containment, and --avg-containment')
sys.exit(-1)
# complain if --ani and not is_scaled
return_ani = False
if args.estimate_ani:
return_ani = True
if return_ani and not is_scaled:
error('must use scaled signatures with --estimate-ani')
sys.exit(-1)
# notify about implicit --ignore-abundance:
if is_containment or return_ani:
track_abundances = any(( s.minhash.track_abundance for s in siglist ))
if track_abundances:
notify('NOTE: --containment, --max-containment, --avg-containment, and --estimate-ani ignore signature abundances.')
# if using --scaled, downsample appropriately
printed_scaled_msg = False
if is_scaled:
max_scaled = max(s.minhash.scaled for s in siglist)
new_siglist = []
for s in siglist:
if not size_may_be_inaccurate and not s.minhash.size_is_accurate():
size_may_be_inaccurate = True
if s.minhash.scaled != max_scaled:
if not printed_scaled_msg:
notify(f'downsampling to scaled value of {format(max_scaled)}')
printed_scaled_msg = True
with s.update() as s:
s.minhash = s.minhash.downsample(scaled=max_scaled)
new_siglist.append(s)
else:
new_siglist.append(s)
siglist = new_siglist
if len(siglist) == 0:
error('no signatures!')
sys.exit(-1)
notify('')
# build the distance matrix
numpy.set_printoptions(precision=3, suppress=True)
# do all-by-all calculation
labeltext = [str(item) for item in siglist]
if args.containment:
similarity = compare_serial_containment(siglist, return_ani=return_ani)
elif args.max_containment:
similarity = compare_serial_max_containment(siglist, return_ani=return_ani)
elif args.avg_containment:
similarity = compare_serial_avg_containment(siglist, return_ani=return_ani)
else:
similarity = compare_all_pairs(siglist, args.ignore_abundance,
n_jobs=args.processes, return_ani=return_ani)
# if distance matrix desired, switch to 1-similarity
if args.distance_matrix:
matrix = 1 - similarity
else:
matrix = similarity
if len(siglist) < 30:
for i, ss in enumerate(siglist):
# for small matrices, pretty-print some output
name_num = '{}-{}'.format(i, str(ss))
if len(name_num) > 20:
name_num = name_num[:17] + '...'
print_results('{:20s}\t{}'.format(name_num, matrix[i, :, ],))
if args.distance_matrix:
print_results('max distance in matrix: {:.3f}', numpy.max(matrix))
else:
print_results('min similarity in matrix: {:.3f}', numpy.min(matrix))
# shall we output a matrix to stdout?
if args.output:
labeloutname = args.output + '.labels.txt'
notify(f'saving labels to: {labeloutname}')
with open(labeloutname, 'w') as fp:
fp.write("\n".join(labeltext))
notify(f'saving comparison matrix to: {args.output}')
with open(args.output, 'wb') as fp:
numpy.save(fp, matrix)
# output CSV?
if args.csv:
with FileOutputCSV(args.csv) as csv_fp:
w = csv.writer(csv_fp)
w.writerow(labeltext)
for i in range(len(labeltext)):
y = []
for j in range(len(labeltext)):
y.append(str(matrix[i][j]))
w.writerow(y)
if size_may_be_inaccurate:
if args.distance_matrix:
notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI distances will be set to 1 for these comparisons.")
else:
notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will be set to 1 for these comparisons.")
def plot(args):
"Produce a clustering matrix and plot."
import matplotlib as mpl
mpl.use('Agg')
import numpy
import pylab
import scipy.cluster.hierarchy as sch
from . import fig as sourmash_fig
# load files
D_filename = args.distances
labelfilename = D_filename + '.labels.txt'
notify(f'loading comparison matrix from {D_filename}...')
D = numpy.load(open(D_filename, 'rb'))
# not sure how to change this to use f-strings
notify('...got {} x {} matrix.', *D.shape)
if args.labeltext:
labelfilename = args.labeltext
notify(f'loading labels from {labelfilename}')
labeltext = [ x.strip() for x in open(labelfilename) ]
if len(labeltext) != D.shape[0]:
error('{} labels != matrix size, exiting', len(labeltext))
sys.exit(-1)
# build filenames, decide on PDF/PNG output
dendrogram_out = os.path.basename(D_filename) + '.dendro'
if args.pdf:
dendrogram_out += '.pdf'
else:
dendrogram_out += '.png'
matrix_out = os.path.basename(D_filename) + '.matrix'
if args.pdf:
matrix_out += '.pdf'
else:
matrix_out += '.png'
hist_out = os.path.basename(D_filename) + '.hist'
if args.pdf:
hist_out += '.pdf'
else:
hist_out += '.png'
# output to a different directory?
if args.output_dir:
if not os.path.isdir(args.output_dir):
os.mkdir(args.output_dir)
dendrogram_out = os.path.join(args.output_dir, dendrogram_out)
matrix_out = os.path.join(args.output_dir, matrix_out)
hist_out = os.path.join(args.output_dir, hist_out)
# make the histogram
notify(f'saving histogram of matrix values => {hist_out}')
fig = pylab.figure(figsize=(8,5))
pylab.hist(numpy.array(D.flat), bins=100)
fig.savefig(hist_out)
### make the dendrogram:
fig = pylab.figure(figsize=(8,5))
ax1 = fig.add_axes([0.1, 0.1, 0.7, 0.8])
ax1.set_xticks([])
ax1.set_yticks([])
# subsample?
if args.subsample:
numpy.random.seed(args.subsample_seed)
sample_idx = list(range(len(labeltext)))
numpy.random.shuffle(sample_idx)
sample_idx = sample_idx[:args.subsample]
np_idx = numpy.array(sample_idx)
D = D[numpy.ix_(np_idx, np_idx)]
labeltext = [ labeltext[idx] for idx in sample_idx ]
### do clustering
Y = sch.linkage(D, method='single')
sch.dendrogram(Y, orientation='right', labels=labeltext)
fig.savefig(dendrogram_out)
notify(f'wrote dendrogram to: {dendrogram_out}')
### make the dendrogram+matrix:
(fig, rlabels, rmat) = sourmash_fig.plot_composite_matrix(D, labeltext,
show_labels=args.labels,
show_indices=args.indices,
vmin=args.vmin,
vmax=args.vmax,
force=args.force)
fig.savefig(matrix_out)
notify(f'wrote numpy distance matrix to: {matrix_out}')
if len(labeltext) < 30:
# for small matrices, print out sample numbering for FYI.
for i, name in enumerate(labeltext):
print_results('{}\t{}', i, name)
# write out re-ordered matrix and labels
if args.csv:
with FileOutputCSV(args.csv) as csv_fp:
w = csv.writer(csv_fp)
w.writerow(rlabels)
for i in range(len(rlabels)):
y = []
for j in range(len(rlabels)):
y.append('{}'.format(rmat[i][j]))
w.writerow(y)
notify(f'Wrote clustered matrix and labels out to {args.csv}')
def import_csv(args):
"Import a CSV file full of signatures/hashes."
with open(args.mash_csvfile, newline='') as fp:
reader = csv.reader(fp)
siglist = []
for row in reader:
hashfn = row[0]
hashseed = int(row[1])
# only support a limited import type, for now ;)
assert hashfn == 'murmur64'
assert hashseed == 42
_, _, ksize, name, hashes = row
ksize = int(ksize)
hashes = hashes.strip()
hashes = list(map(int, hashes.split(' ' )))
e = MinHash(len(hashes), ksize)
e.add_many(hashes)
s = sig.SourmashSignature(e, filename=name)
siglist.append(s)
notify(f'loaded signature: {name} {s.md5sum()[:8]}')
notify(f'saving {len(siglist)} signatures to JSON')
with SaveSignaturesToLocation(args.output) as save_sig:
save_sig.add_many(siglist)
def sbt_combine(args):
inp_files = list(args.sbts)
notify(f'combining {len(inp_files)} SBTs')
tree = load_sbt_index(inp_files.pop(0))
for f in inp_files:
new_tree = load_sbt_index(f)
# TODO: check if parameters are the same for both trees!
tree.combine(new_tree)
notify(f'saving SBT under "{args.sbt_name}".')
tree.save(args.sbt_name)
def index(args):
"""
Build a Sequence Bloom Tree index of the given signatures.
"""
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
picklist = sourmash_args.load_picklist(args)
if args.append:
tree = load_sbt_index(args.sbt_name)
else:
tree = create_sbt_index(args.bf_size, n_children=args.n_children)
if args.sparseness < 0 or args.sparseness > 1.0:
error('sparseness must be in range [0.0, 1.0].')
if args.scaled:
args.scaled = int(args.scaled)
notify(f'downsampling signatures to scaled={args.scaled}')
inp_files = list(args.signatures)
if args.from_file:
more_files = sourmash_args.load_pathlist_from_file(args.from_file)
inp_files.extend(more_files)
if not inp_files:
error("ERROR: no files to index!? Supply on command line or use --from-file")
sys.exit(-1)
notify(f'loading {len(inp_files)} files into SBT')
progress = sourmash_args.SignatureLoadingProgress()
n = 0
ksizes = set()
moltypes = set()
nums = set()
scaleds = set()
for f in inp_files:
siglist = sourmash_args.load_file_as_signatures(f,
ksize=args.ksize,
select_moltype=moltype,
yield_all_files=args.force,
picklist=picklist,
progress=progress)
# load all matching signatures in this file
ss = None
for ss in siglist:
ksizes.add(ss.minhash.ksize)
moltypes.add(sourmash_args.get_moltype(ss))
nums.add(ss.minhash.num)
with ss.update() as ss:
if args.scaled:
ss.minhash = ss.minhash.downsample(scaled=args.scaled)
if ss.minhash.track_abundance:
ss.minhash = ss.minhash.flatten()
scaleds.add(ss.minhash.scaled)
tree.insert(ss)
n += 1
if not ss:
continue
# 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)
notify('')
# did we load any!?
if n == 0:
error('no signatures found to load into tree!? failing.')
sys.exit(-1)
if picklist:
sourmash_args.report_picklist(args, picklist)
notify(f'loaded {n} sigs; saving SBT under "{args.sbt_name}"')
tree.save(args.sbt_name, sparseness=args.sparseness)
if tree.storage:
tree.storage.close()
def search(args):
from .search import (search_databases_with_flat_query,
search_databases_with_abund_query)
set_quiet(args.quiet, args.debug)
moltype = sourmash_args.calculate_moltype(args)
picklist = sourmash_args.load_picklist(args)
pattern_search = sourmash_args.load_include_exclude_db_patterns(args)
# set up the query.
query = sourmash_args.load_query_signature(args.query,
ksize=args.ksize,
select_moltype=moltype,
select_md5=args.md5)
notify(f'loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})')
if args.scaled:
if not query.minhash.scaled:
error('cannot downsample a signature not created with --scaled')
sys.exit(-1)
if args.scaled != query.minhash.scaled:
notify(f'downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}')
with query.update() as query:
query.minhash = query.minhash.downsample(scaled=args.scaled)
# set up the search databases
is_containment = args.containment or args.max_containment
if is_containment:
if args.containment and args.max_containment:
notify("ERROR: cannot specify both --containment and --max-containment!")
sys.exit(-1)
databases = sourmash_args.load_dbs_and_sigs(args.databases, query,
not is_containment,
picklist=picklist,
pattern=pattern_search,
fail_on_empty_database=args.fail_on_empty_database)
# handle signatures with abundance
if query.minhash.track_abundance:
if args.ignore_abundance:
if query.minhash.track_abundance:
# abund sketch + ignore abundance => flatten sketch.
with query.update() as query:
query.minhash = query.minhash.flatten()
elif args.containment or args.max_containment:
# abund sketch + keep abundance => no containment searches
notify("ERROR: cannot do containment searches on an abund signature; maybe specify --ignore-abundance?")
sys.exit(-1)
else:
# forcibly ignore abundances if query has no abundances
args.ignore_abundance = True
# do the actual search
if query.minhash.track_abundance:
try:
results = search_databases_with_abund_query(query, databases,
threshold=args.threshold,
do_containment=args.containment,
do_max_containment=args.max_containment,
best_only=args.best_only,
unload_data=True)
except TypeError as exc:
error(f"ERROR: {str(exc)}")
sys.exit(-1)
else:
results = search_databases_with_flat_query(query, databases,
threshold=args.threshold,
do_containment=args.containment,
do_max_containment=args.max_containment,
best_only=args.best_only,
unload_data=True,
estimate_ani_ci=args.estimate_ani_ci)
n_matches = len(results)
if args.best_only:
args.num_results = 1
if not args.num_results or n_matches <= args.num_results:
print_results(f'{len(results)} matches above threshold {args.threshold:0.3f}:')
else:
print_results(f'{len(results)} matches above threshold {args.threshold:0.3f}; showing first {args.num_results}:')
n_matches = args.num_results
size_may_be_inaccurate = False
jaccard_ani_untrustworthy = False
# output!
print_results("similarity match")
print_results("---------- -----")
for sr in results[:n_matches]:
pct = '{:.1f}%'.format(sr.similarity*100)
name = sr.match._display_name(60)
print_results('{:>6} {}', pct, name)
if sr.cmp_scaled is not None:
if not size_may_be_inaccurate and sr.size_may_be_inaccurate:
size_may_be_inaccurate = True
if not is_containment and sr.cmp.jaccard_ani_untrustworthy:
jaccard_ani_untrustworthy = True
if args.best_only:
notify("** reporting only one match because --best-only was set")
writer = None
if args.output:
with FileOutputCSV(args.output) as fp:
for sr in results:
# if this is the first result we're writing, initialize the csv, return writer
if writer is None:
writer = sr.init_dictwriter(fp)
sr.write(writer)
# save matching signatures upon request
if args.save_matches:
notify(f'saving all matched signatures to "{args.save_matches}"')
with SaveSignaturesToLocation(args.save_matches) as save_sig:
for sr in results:
save_sig.add(sr.match)
if picklist:
sourmash_args.report_picklist(args, picklist)
if size_may_be_inaccurate:
notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons.")
if jaccard_ani_untrustworthy:
notify("WARNING: Jaccard estimation for at least one of these comparisons is likely inaccurate. Could not estimate ANI for these comparisons.")
def categorize(args):
"Use a database to find the best match to many signatures."
from .index import MultiIndex
from .search import make_jaccard_search_query
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
# eliminate names we've already categorized
already_names = set()
if args.load_csv:
with open(args.load_csv, newline='') as fp:
r = csv.reader(fp)
for row in r:
already_names.add(row[0])
# load search database
db = sourmash_args.load_file_as_index(args.database)
if args.ksize or moltype:
db = db.select(ksize=args.ksize, moltype=moltype)
# utility function to load & select relevant signatures.
def _yield_all_sigs(queries, ksize, moltype):
for filename in queries:
mi = MultiIndex.load_from_path(filename, False)
mi = mi.select(ksize=ksize, moltype=moltype)
for ss, loc in mi.signatures_with_location():
yield ss, loc
csv_w = None
csv_fp = None
if args.csv:
csv_fp = open(args.csv, 'w', newline='')
csv_w = csv.writer(csv_fp)
search_obj = make_jaccard_search_query(threshold=args.threshold)
for orig_query, loc in _yield_all_sigs(args.queries, args.ksize, moltype):
# skip if we've already done signatures from this file.
if loc in already_names:
continue
notify(f'loaded query: {str(orig_query)[:30]}... (k={orig_query.minhash.ksize}, {orig_query.minhash.moltype})')
if args.ignore_abundance and orig_query.minhash.track_abundance:
query = orig_query.copy()
with query.update() as query:
query.minhash = query.minhash.flatten()
else:
if orig_query.minhash.track_abundance:
notify("ERROR: this search cannot be done on signatures calculated with abundance.")
notify("ERROR: please specify --ignore-abundance.")
sys.exit(-1)
query = orig_query.copy()
results = []
for sr in db.find(search_obj, query):
match = sr.signature
if match.md5sum() != query.md5sum(): # ignore self.
results.append((orig_query.similarity(match), match))
if results:
results.sort(key=lambda x: -x[0]) # reverse sort on similarity
best_hit_sim, best_hit_query = results[0]
notify(f'for {query}, found: {best_hit_sim:.2f} {best_hit_query}')
best_hit_query_name = best_hit_query.name
if csv_w:
csv_w.writerow([loc, query, best_hit_query_name,
best_hit_sim])
else:
notify(f'for {query}, no match found')
if csv_fp:
csv_fp.close()
def gather(args):
from .search import GatherDatabases, format_bp
set_quiet(args.quiet, args.debug)
moltype = sourmash_args.calculate_moltype(args)
picklist = sourmash_args.load_picklist(args)
pattern_search = sourmash_args.load_include_exclude_db_patterns(args)
# load the query signature & figure out all the things
query = sourmash_args.load_query_signature(args.query,
ksize=args.ksize,
select_moltype=moltype,
select_md5=args.md5)
notify(f'loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})')
# verify signature was computed right.
if not query.minhash.scaled:
error('query signature needs to be created with --scaled')
sys.exit(-1)
if args.scaled and args.scaled != query.minhash.scaled:
notify(f'downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}')
with query.update() as query:
query.minhash = query.minhash.downsample(scaled=args.scaled)
# empty?
if not len(query.minhash):
error('no query hashes!? exiting.')
sys.exit(-1)
# set up the search databases
cache_size = args.cache_size
if args.cache_size == 0:
cache_size = None
databases = sourmash_args.load_dbs_and_sigs(args.databases, query, False,
cache_size=cache_size,
picklist=picklist,
pattern=pattern_search,
fail_on_empty_database=args.fail_on_empty_database)
if args.linear: # force linear traversal?
databases = [ LazyLinearIndex(db) for db in databases ]
size_may_be_inaccurate = False
if args.prefetch: # note: on by default!
notify("Starting prefetch sweep across databases.")
prefetch_query = query.copy()
if prefetch_query.minhash.track_abundance:
with prefetch_query.update() as prefetch_query:
prefetch_query.minhash = prefetch_query.minhash.flatten()
noident_mh = prefetch_query.minhash.to_mutable()
save_prefetch = SaveSignaturesToLocation(args.save_prefetch)
save_prefetch.open()
# set up prefetch CSV output
prefetch_csvout_fp = None
prefetch_csvout_w = None
if args.save_prefetch_csv:
prefetch_csvout_fp = FileOutputCSV(args.save_prefetch_csv).open()
query_mh = prefetch_query.minhash
scaled = query_mh.scaled
counters = []
ident_mh = noident_mh.copy_and_clear()
for db in databases:
counter = None
try:
counter = db.counter_gather(prefetch_query, args.threshold_bp)
except ValueError:
# catch "no signatures to search" ValueError if empty db.
continue
save_prefetch.add_many(counter.signatures())
# update found/not found hashes from the union/intersection of
# found.
union_found = counter.union_found
ident_mh.add_many(union_found)
noident_mh.remove_many(union_found)
# optionally calculate and output prefetch info to csv
if prefetch_csvout_fp:
for found_sig in counter.signatures():
# calculate intersection stats and info
prefetch_result = PrefetchResult(prefetch_query, found_sig, cmp_scaled=scaled,
threshold_bp=args.threshold_bp, estimate_ani_ci=args.estimate_ani_ci)
if prefetch_csvout_w is None:
prefetch_csvout_w = prefetch_result.init_dictwriter(prefetch_csvout_fp)
prefetch_result.write(prefetch_csvout_w)
counters.append(counter)
# flush csvout so that things get saved progressively
if prefetch_csvout_fp:
prefetch_csvout_fp.flush()
notify(f"Found {len(save_prefetch)} signatures via prefetch; now doing gather.")
save_prefetch.close()
if prefetch_csvout_fp:
prefetch_csvout_fp.close()
else:
counters = databases
# we can't track unidentified hashes w/o prefetch
noident_mh = None
ident_mh = None
## ok! now do gather -
found = []
weighted_missed = 1
is_abundance = query.minhash.track_abundance and not args.ignore_abundance
orig_query_mh = query.minhash
if not orig_query_mh.size_is_accurate():
size_may_be_inaccurate = True
gather_iter = GatherDatabases(query, counters,
threshold_bp=args.threshold_bp,
ignore_abundance=args.ignore_abundance,
noident_mh=noident_mh,
ident_mh=ident_mh,
estimate_ani_ci=args.estimate_ani_ci)
screen_width = _get_screen_width()
sum_f_uniq_found = 0.
result = None
for result in gather_iter:
sum_f_uniq_found += result.f_unique_to_query
if not len(found): # first result? print header.
if is_abundance:
print_results("")
print_results("overlap p_query p_match avg_abund")
print_results("--------- ------- ------- ---------")
else:
print_results("")
print_results("overlap p_query p_match")
print_results("--------- ------- -------")
# print interim result & save in `found` list for later use
pct_query = '{:.1f}%'.format(result.f_unique_weighted*100)
pct_genome = '{:.1f}%'.format(result.f_match*100)
if is_abundance:
name = result.match._display_name(screen_width - 41)
average_abund ='{:.1f}'.format(result.average_abund)
print_results('{:9} {:>7} {:>7} {:>9} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
average_abund, name)
else:
name = result.match._display_name(screen_width - 31)
print_results('{:9} {:>7} {:>7} {}',
format_bp(result.intersect_bp), pct_query, pct_genome,
name)
found.append(result)
if args.num_results and len(found) >= args.num_results:
break
# report on thresholding -
if gather_iter.query:
# if still a query, then we failed the threshold.
notify(f'found less than {format_bp(args.threshold_bp)} in common. => exiting')
# basic reporting:
print_results(f'\nfound {len(found)} matches total;')
if args.num_results and len(found) == args.num_results:
print_results(f'(truncated gather because --num-results={args.num_results})')
if is_abundance and result:
p_covered = result.sum_weighted_found / result.total_weighted_hashes
p_covered *= 100
print_results(f'the recovered matches hit {p_covered:.1f}% of the abundance-weighted query.')
print_results(f'the recovered matches hit {sum_f_uniq_found*100:.1f}% of the query k-mers (unweighted).')
print_results('')
if gather_iter.scaled != query.minhash.scaled:
print_results(f'WARNING: final scaled was {gather_iter.scaled}, vs query scaled of {query.minhash.scaled}')
# save CSV?
w = None
if found and args.output:
with FileOutputCSV(args.output) as fp:
for result in found:
if w is None:
w = result.init_dictwriter(fp)
result.write(w)
# save matching signatures?
if found and args.save_matches:
notify(f"saving all matches to '{args.save_matches}'")
with SaveSignaturesToLocation(args.save_matches) as save_sig:
for sr in found:
save_sig.add(sr.match)
# save unassigned hashes?
if args.output_unassigned:
remaining_query = gather_iter.query
if not (remaining_query.minhash or noident_mh):
notify('no unassigned hashes to save with --output-unassigned!')
else:
notify(f"saving unassigned hashes to '{args.output_unassigned}'")
if noident_mh:
remaining_mh = remaining_query.minhash.to_mutable()
remaining_mh += noident_mh
remaining_query.minhash = remaining_mh
if is_abundance:
abund_query_mh = remaining_query.minhash.inflate(orig_query_mh)
remaining_query.minhash = abund_query_mh
with SaveSignaturesToLocation(args.output_unassigned) as save_sig:
save_sig.add(remaining_query)
if picklist:
sourmash_args.report_picklist(args, picklist)
if size_may_be_inaccurate:
notify("WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons.")
# DONE w/gather function.
def multigather(args):
"Gather many signatures against multiple databases."
from .search import GatherDatabases, format_bp
set_quiet(args.quiet)
moltype = sourmash_args.calculate_moltype(args)
if not args.db:
error('Error! must specify at least one database with --db')
sys.exit(-1)
if not args.query and not args.query_from_file:
error('Error! must specify at least one query signature with --query')
sys.exit(-1)
# flatten --db and --query
args.db = [item for sublist in args.db for item in sublist]
inp_files = [item for sublist in args.query for item in sublist]
if args.query_from_file:
more_files = sourmash_args.load_pathlist_from_file(args.query_from_file)
inp_files.extend(more_files)
# need a query to get ksize, moltype for db loading
query = next(iter(sourmash_args.load_file_as_signatures(inp_files[0], ksize=args.ksize, select_moltype=moltype)))
notify(f'loaded first query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})')
databases = sourmash_args.load_dbs_and_sigs(args.db, query, False,
fail_on_empty_database=args.fail_on_empty_database)
# run gather on all the queries.
n=0
size_may_be_inaccurate = False
for queryfile in inp_files:
# load the query signature(s) & figure out all the things
for query in sourmash_args.load_file_as_signatures(queryfile,
ksize=args.ksize,
select_moltype=moltype):
notify(f'loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})')
# verify signature was computed right.
if not query.minhash.scaled:
error('query signature needs to be created with --scaled; skipping')
continue
if args.scaled and args.scaled != query.minhash.scaled:
notify(f'downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}')
with query.update() as query:
query.minhash = query.minhash.downsample(scaled=args.scaled)
# empty?
if not len(query.minhash):
error('no query hashes!? skipping to next..')
continue
counters = []
prefetch_query = query.copy()
if prefetch_query.minhash.track_abundance:
with prefetch_query.update() as prefetch_query:
prefetch_query.minhash = prefetch_query.minhash.flatten()
ident_mh = prefetch_query.minhash.copy_and_clear()
noident_mh = prefetch_query.minhash.to_mutable()
counters = []
for db in databases:
try:
counter = db.counter_gather(prefetch_query, args.threshold_bp)
except ValueError:
# catch "no signatures to search" ValueError if empty db.
continue
counters.append(counter)
# track found/not found hashes
union_found = counter.union_found
noident_mh.remove_many(union_found)
ident_mh.add_many(union_found)
found = []
weighted_missed = 1
is_abundance = query.minhash.track_abundance and not args.ignore_abundance
orig_query_mh = query.minhash
gather_iter = GatherDatabases(query, counters,
threshold_bp=args.threshold_bp,
ignore_abundance=args.ignore_abundance,