parser.add_argument(
'--outdir',
default='data/',
help='optional, directory to write TreeTime output files')
parser.add_argument('--ft2bin',
default='fasttree2',
help='optional, path to fasttree2 binary executable')
parser.add_argument('--ttbin',
default='treetime',
help='optional, path to treetime binary executable')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
cb = Callback()
cb.callback("Retrieving genomes")
fasta = retrieve_genomes(args.db, ref_file=args.ref, misstol=args.misstol)
cb.callback("Reconstructing tree with {}".format(args.ft2bin))
nwk = fasttree(fasta, binpath=args.ft2bin)
cb.callback("Reconstructing time-scaled tree with {}").format(args.ttbin)
nexus_file = treetime(nwk,
fasta,
outdir=args.outdir,
binpath=args.ttbin,
clock=args.clock)
cb.callback("")
parser.add_argument('--ft2bin', default='fasttree2',
help='optional, path to fasttree2 binary executable')
parser.add_argument('--ttbin', default='treetime',
help='optional, path to treetime binary executable')
parser.add_argument('--lineages', type=str,
default=os.path.join(covizu.__path__[0], "data/pango-designation/lineages.csv"),
help="optional, path to CSV file containing Pango lineage designations.")
parser.add_argument('--outfile', default='data/timetree.nwk',
help='output, path to write Newick tree string')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
cb = Callback()
cb.callback("Retrieving genomes")
with open(args.json) as handle:
by_lineage = json.load(handle)
cb.callback("Parsing Pango lineage designations")
handle = open(args.lineages)
header = next(handle)
if header != 'taxon,lineage\n':
cb.callback("Error: {} does not contain expected header row 'taxon,lineage'".format(args.lineages))
sys.exit()
lineages = {}
for line in handle:
taxon, lineage = line.strip().split(',')
lineages.update({taxon: lineage})
with open(args.ref) as handle:
reflen = len(seq_utils.convert_fasta(handle)[0][1])
loader = stream_local(args.infile, args.lineages, minlen=args.minlen,
mindate=args.mindate, callback=callback)
batcher = gisaid_utils.batch_fasta(loader, size=args.batchsize)
aligned = gisaid_utils.extract_features(batcher, ref_file=args.ref, binpath=args.mmbin,
nthread=args.mmthreads, minlen=args.minlen)
filtered = gisaid_utils.filter_problematic(aligned, vcf_file=args.vcf, cutoff=args.poisson_cutoff,
callback=callback)
return gisaid_utils.sort_by_lineage(filtered, callback=callback)
if __name__ == "__main__":
args = parse_args()
cb = Callback()
# check that user has loaded openmpi module
try:
subprocess.check_call(['mpirun', '-np', '2', 'ls'], stdout=subprocess.DEVNULL)
except FileNotFoundError:
cb.callback("mpirun not loaded - run `module load openmpi/gnu`", level='ERROR')
sys.exit()
by_lineage = process_local(args, cb.callback)
with open(args.bylineage, 'w') as handle:
# export to file to process large lineages with MPI
json.dump(by_lineage, handle)
# reconstruct time-scaled tree
timetree, residuals = build_timetree(by_lineage, args, cb.callback)
with open(args.ref) as handle:
reflen = len(seq_utils.convert_fasta(handle)[0][1])
loader = stream_local(args.infile, args.pangolineages, minlen=args.minlen,
mindate=args.mindate, callback=callback)
batcher = gisaid_utils.batch_fasta(loader, size=args.batchsize)
aligned = gisaid_utils.extract_features(batcher, ref_file=args.ref, binpath=args.mmbin,
nthread=args.mmthreads, minlen=args.minlen)
filtered = gisaid_utils.filter_problematic(aligned, vcf_file=args.vcf, cutoff=args.poisson_cutoff,
callback=callback)
return gisaid_utils.sort_by_lineage(filtered, callback=callback)
if __name__ == "__main__":
args = parse_args()
cb = Callback()
# check that user has loaded openmpi module
try:
subprocess.check_call(['mpirun', '-np', '2', 'ls'], stdout=subprocess.DEVNULL)
except FileNotFoundError:
cb.callback("mpirun not loaded - run `module load openmpi/gnu`", level='ERROR')
sys.exit()
# check that the user has included submodules
if (not os.path.exists(os.path.join(covizu.__path__[0], "data/pango-designation/lineages.csv")) or
not os.path.exists(os.path.join(covizu.__path__[0], "data/ProblematicSites_SARS-CoV2/problematic_sites_sarsCov2.vcf"))):
try:
subprocess.check_call("git submodule init; git submodule update", shell=True)
except:
cb.callback("Error adding the required submodules")
Called by batch.py via subprocess to handle lineages with excessive
numbers of genomes, to process via MPI
"""
try:
from mpi4py import MPI
except ModuleNotFoundError:
print("Script requires mpi4py - https://pypi.org/project/mpi4py/")
sys.exit()
comm = MPI.COMM_WORLD
my_rank = comm.Get_rank()
nprocs = comm.Get_size()
# command-line execution
args = parse_args()
cb = Callback(t0=args.timestamp, my_rank=my_rank, nprocs=nprocs)
# import lineage data from file
with open(args.json) as handle:
recoded = json.load(handle)
if args.mode == 'deep':
union, labels, indexed = unpack_recoded(recoded,
args.lineage,
callback=cb.callback)
# export map of sequence labels to tip indices
lineage_name = args.lineage.replace('/', '_') # issue #297
outfile = os.path.join(args.outdir, '{}.nwk'.format(lineage_name))
if len(indexed) == 1:
if args.url is None and "GISAID_URL" in os.environ:
args.url = os.environ["GISAID_URL"]
if args.user is None and "GISAID_USER" in os.environ:
args.user = os.environ["GISAID_USER"]
# otherwise download_feed() will prompt for username
if args.password is None and "GISAID_PSWD" in os.environ:
args.password = os.environ["GISAID_PSWD"]
# otherwise download_feed() will prompt for password
return args
if __name__ == '__main__':
args = parse_args()
cb = Callback()
cb.callback("Processing GISAID feed data")
# download xz file if not specified by user
if args.infile is None:
args.infile = download_feed(args.url, args.user, args.password)
loader = load_gisaid(args.infile,
minlen=args.minlen,
mindate=args.mindate,
debug=args.debug)
batcher = batch_fasta(loader, size=args.batchsize)
aligned = extract_features(batcher,
ref_file=args.ref,
binpath=args.binpath,
Called by batch.py via subprocess to handle lineages with excessive
numbers of genomes, to process via MPI
"""
try:
from mpi4py import MPI
except ModuleNotFoundError:
print("Script requires mpi4py - https://pypi.org/project/mpi4py/")
sys.exit()
comm = MPI.COMM_WORLD
my_rank = comm.Get_rank()
nprocs = comm.Get_size()
# command-line execution
args = parse_args()
cb = Callback(t0=args.timestamp, my_rank=my_rank, nprocs=nprocs)
# import lineage data from file
cb.callback('loading JSON')
with open(args.json) as handle:
by_lineage = json.load(handle)
records = by_lineage.get(args.lineage, None)
if records is None:
cb.callback("ERROR: JSON did not contain lineage {}".format(
args.lineage))
sys.exit()
# generate distance matrices from bootstrap samples [[ MPI ]]
union, labels, indexed = recode_features(records,
callback=cb.callback,
batcher = gisaid_utils.batch_fasta(loader, size=args.batchsize)
aligned = gisaid_utils.extract_features(batcher,
ref_file=args.ref,
binpath=args.mmbin,
nthread=args.mmthreads,
minlen=args.minlen)
filtered = gisaid_utils.filter_problematic(aligned,
vcf_file=args.vcf,
cutoff=args.poisson_cutoff,
callback=callback)
return gisaid_utils.sort_by_lineage(filtered, callback=callback)
if __name__ == "__main__":
args = parse_args()
cb = Callback()
# check that user has loaded openmpi module
try:
subprocess.check_call(['mpirun', '-np', '2', 'ls'],
stdout=subprocess.DEVNULL)
except FileNotFoundError:
cb.callback("mpirun not loaded - run `module load openmpi/gnu`",
level='ERROR')
sys.exit()
# check that the user has included submodules
if (not os.path.exists(
os.path.join(covizu.__path__[0],
"data/pango-designation/lineages.csv")
) or not os.path.exists(
help="Write data to disk for lineages above this "
"threshold; otherwise work in RAM. Override "
"with `--threads 1`.")
parser.add_argument(
"--cutoff",
type=float,
default=0.5,
help="Bootstrap cutoff for consensus tree (default 0.5). "
"Only used if --cons is specified.")
return parser.parse_args()
if __name__ == "__main__":
# command-line execution
args = parse_args()
cb = Callback()
cb.callback('loading lineage classifications from database')
lineages = db_utils.dump_lineages(args.db)
cb.callback('loading JSON')
features = import_json(args.json, vcf_file=args.vcf, callback=cb.callback)
by_lineage = split_by_lineage(features, lineages)
for lineage, lfeatures in by_lineage.items():
cb.callback('start {}, {} entries'.format(lineage, len(lfeatures)))
# calculate symmetric difference matrix and run NJ on bootstrap samples
filtered = seq_utils.filter_outliers(lfeatures)
trees, labels = build_trees(filtered,
nboot=args.nboot,
batcher = gisaid_utils.batch_fasta(loader, size=args.batchsize)
aligned = gisaid_utils.extract_features(batcher,
ref_file=args.ref,
binpath=args.mmbin,
nthread=args.mmthreads,
minlen=args.minlen)
filtered = gisaid_utils.filter_problematic(aligned,
vcf_file=args.vcf,
cutoff=args.poisson_cutoff,
callback=callback)
return gisaid_utils.sort_by_lineage(filtered, callback=callback)
if __name__ == "__main__":
args = parse_args()
cb = Callback()
# check that user has loaded openmpi module
try:
subprocess.check_call(['mpirun', '-np', '2', 'ls'],
stdout=subprocess.DEVNULL)
except FileNotFoundError:
cb.callback("mpirun not loaded - run `module load openmpi/gnu`",
level='ERROR')
sys.exit()
# download xz file if not specified by user
if args.infile is None:
cb.callback("No input specified, downloading data from GISAID feed...")
args.infile = gisaid_utils.download_feed(args.url, args.user,
args.password)
type=float,
default=0.5,
help="Bootstrap cutoff for consensus tree (default 0.5). "
"Only used if --cons is specified.")
parser.add_argument("outfile",
type=argparse.FileType('w'),
default='data/clusters.json',
help="output, dest for JSON beadplot file")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
cb = Callback()
# Generate time-scaled tree of Pangolin lineages
cb.callback("Retrieving lineage genomes")
fasta = treetime.retrieve_genomes(args.db,
nthread=args.mmthreads,
ref_file=args.ref,
misstol=args.misstol,
callback=cb.callback)
cb.callback("Reconstructing tree with {}".format(args.ft2bin))
nwk = treetime.fasttree(fasta, binpath=args.ft2bin)
cb.callback("Reconstructing time-scaled tree with {}".format(args.ttbin))
nexus_file = treetime.treetime(nwk,
fasta,
