def run_dump_provenances(args):
tree_sequence = tskit.load(args.tree_sequence)
if args.human:
for provenance in tree_sequence.provenances():
d = json.loads(provenance.record)
print("id={}, timestamp={}, record={}".format(
provenance.id, provenance.timestamp, json.dumps(d, indent=4)))
else:
tree_sequence.dump_text(provenances=sys.stdout)
def test_fileobj_multi(self, replicate_ts_fixture, fileobj):
file_offsets = []
for ts in replicate_ts_fixture:
ts.dump(fileobj)
file_offsets.append(fileobj.tell())
fileobj.close()
with open(fileobj.name, "rb") as f:
for ts, file_offset in zip(replicate_ts_fixture, file_offsets):
ts2 = tskit.load(f)
file_offset2 = f.tell()
assert ts.tables == ts2.tables
assert file_offset == file_offset2
def run_mutate(args):
tree_sequence = tskit.load(args.tree_sequence)
tree_sequence = msprime.mutate(
tree_sequence=tree_sequence,
rate=args.mutation_rate,
random_seed=args.random_seed,
keep=args.keep,
start_time=args.start_time,
end_time=args.end_time,
discrete=args.discrete,
)
tree_sequence.dump(args.output_tree_sequence)
def run_dump_provenances(args):
tree_sequence = tskit.load(args.tree_sequence)
if args.human:
for provenance in tree_sequence.provenances():
d = json.loads(provenance.record)
print(
"id={}, timestamp={}, record={}".format(
provenance.id, provenance.timestamp, json.dumps(d, indent=4)
)
)
else:
tree_sequence.dump_text(provenances=sys.stdout)
def run_date(args):
try:
ts = tskit.load(args.tree_sequence)
except tskit.FileFormatError as ffe:
exit("Error loading '{}: {}".format(args.tree_sequence, ffe))
dated_ts = tsdate.date(
ts, args.Ne, mutation_rate=args.mutation_rate,
recombination_rate=args.recombination_rate,
probability_space=args.probability_space, method=args.method,
eps=args.epsilon, num_threads=args.num_threads,
ignore_oldest_root=args.ignore_oldest, progress=args.progress)
dated_ts.dump(args.output)
def ts_to_stairway(self, ts_path, num_bootstraps=1, mask_file=None):
"""
Converts the specified tskit tree sequence to text files used by
stairway plot.
"""
derived_counts_all = [[] for _ in range(num_bootstraps + 1)]
total_length = 0
num_samples = 0
for i, ts_p in enumerate(ts_path):
ts = tskit.load(ts_p)
total_length += ts.sequence_length
num_samples = ts.num_samples
haps = ts.genotype_matrix()
SFSs = []
# Masking
retain = np.full(ts.get_num_mutations(), False)
if mask_file:
mask_table = pd.read_csv(mask_file, sep="\t", header=None)
chrom = ts_p.split("/")[-1].split(".")[0]
sub = mask_table[mask_table[0] == chrom]
mask_ints = pd.IntervalIndex.from_arrays(sub[1], sub[2])
snp_locs = [int(x.site.position) for x in ts.variants()]
tmp_bool = [mask_ints.contains(x) for x in snp_locs]
retain = np.logical_or(retain, tmp_bool)
total_length -= np.sum(mask_ints.length)
retain = np.logical_not(retain)
# append unmasked SFS
SFSs.append(allel.sfs(allel.HaplotypeArray(haps).count_alleles()[:, 1])[1:])
# get masked allele counts and append SFS
allele_counts = allel.HaplotypeArray(haps[retain, :]).count_alleles()
SFSs.append(allel.sfs(allele_counts[:, 1])[1:])
sfs_path = ts_p+".sfs.pdf"
plots.plot_sfs(SFSs, sfs_path)
# Bootstrap allele counts
derived_counts_all[0].extend(allele_counts[:, 1])
for j in range(1, num_bootstraps + 1):
nsites = np.shape(allele_counts)[0]
bootset = np.random.choice(np.arange(0, nsites, 1), nsites, replace=True)
bootac = allele_counts[bootset, :]
der_bootac = bootac[:, 1]
derived_counts_all[j].extend(der_bootac)
# Get the SFS minus the 0 bin and write output
stairway_files = []
for l in range(len(derived_counts_all)):
sfs = allel.sfs(derived_counts_all[l])[1:]
filename = self.workdir / "sfs_{}.txt".format(l)
write_stairway_sfs(total_length, num_samples, sfs, filename)
stairway_files.append(filename)
return stairway_files
def main():
description = """Simple CLI wrapper for tsinfer
tskit version: {}
tsinfer version: {}""".format(tskit.__version__, tsinfer.__version__)
parser = argparse.ArgumentParser(
description=description,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--verbosity', '-v', action='count', default=0)
parser.add_argument(
"samples",
help="The samples file name, as saved by tsinfer.SampleData.initialise()")
parser.add_argument(
"output",
help="The path to write the output file to")
parser.add_argument(
"-l", "--length", default=None, type=int,
help="The total sequence length")
parser.add_argument(
"-t", "--threads", default=1, type=int,
help="The number of worker threads to use")
parser.add_argument(
"-m", "--method", default="C", choices=['C','P'],
help="Which implementation to use, [C] (faster) or [P]ython (more debuggable)")
parser.add_argument(
"--inject-real-ancestors-from-ts", default=None,
help="Instead of inferring ancestors, construct known ones from this tree sequence file path")
parser.add_argument(
"-V", "--version", action='version', version=description)
args = parser.parse_args()
engine = tsinfer.PY_ENGINE if args.method == "P" else tsinfer.C_ENGINE
if not os.path.isfile(args.samples):
raise ValueError("No samples file")
sample_data = tsinfer.load(args.samples)
if all(False for _ in sample_data.genotypes(inference_sites=True)):
raise ValueError("No inference sites")
if args.inject_real_ancestors_from_ts is not None:
ancestor_data = tsinfer.AncestorData.initialise(sample_data, compressor=None)
orig_ts = tskit.load(args.inject_real_ancestors_from_ts)
eval_util.build_simulated_ancestors(sample_data, ancestor_data, orig_ts)
ancestor_data.finalise()
ancestors_ts = tsinfer.match_ancestors(
sample_data, ancestor_data, engine=engine)
ts = tsinfer.match_samples(
sample_data, ancestors_ts, engine=engine, simplify=True)
else:
ts = tsinfer.infer(
sample_data, num_threads=args.threads, engine=engine)
ts.dump(args.output)
def verify(self, cmd, num_samples):
with tempfile.TemporaryDirectory() as tmpdir:
filename = pathlib.Path(tmpdir) / "output.trees"
full_cmd = "python3 -m stdpopsim -q " + cmd + f" {filename}"
subprocess.run(full_cmd, shell=True, check=True)
# TODO converting to str isn't necessary in tskit 0.1.5. Remove.
ts = tskit.load(str(filename))
self.assertEqual(ts.num_samples, num_samples)
provenance = json.loads(ts.provenance(ts.num_provenances - 1).record)
tskit.validate_provenance(provenance)
stored_cmd = provenance["parameters"]["args"]
self.assertEqual(stored_cmd[0], "-q")
self.assertEqual(stored_cmd[1:-1], cmd.split())
def compare_python_api(self, input_ts, cmd, Ne, mutation_rate, method):
with tempfile.TemporaryDirectory() as tmpdir:
input_filename = pathlib.Path(tmpdir) / "input.trees"
input_ts.dump(input_filename)
output_filename = pathlib.Path(tmpdir) / "output.trees"
full_cmd = "date " + str(input_filename) + f" {output_filename} " + cmd
cli.tsdate_main(full_cmd.split())
output_ts = tskit.load(output_filename)
dated_ts = tsdate.date(input_ts, Ne=Ne, mutation_rate=mutation_rate,
method=method)
print(dated_ts.tables.nodes.time, output_ts.tables.nodes.time)
self.assertTrue(np.array_equal(dated_ts.tables.nodes.time,
output_ts.tables.nodes.time))
def run_mutate(args):
tree_sequence = tskit.load(args.tree_sequence)
tree_sequence = msprime.sim_mutations(
tree_sequence=tree_sequence,
rate=args.mutation_rate,
random_seed=args.random_seed,
keep=True,
start_time=args.start_time,
end_time=args.end_time,
discrete_genome=True,
model=args.model,
)
tree_sequence.dump(args.output_tree_sequence)
def combined_ts_constrained_samples(args):
high_cov_samples = tsinfer.load(args.high_cov)
dated_hgdp_1kg_sgdp_ts = tskit.load(args.dated_ts)
sites_time = tsdate.sites_time_from_ts(dated_hgdp_1kg_sgdp_ts)
dated_samples = tsdate.add_sampledata_times(high_cov_samples, sites_time)
# Record number of constrained sites
print("Total number of sites: ", sites_time.shape[0])
print("Number of ancient lower bounds: ",
np.sum(high_cov_samples.min_site_times(individuals_only=True) != 0))
print("Number of corrected times: ",
np.sum(dated_samples.sites_time[:] != sites_time))
high_cov_samples_copy = dated_samples.copy(args.output)
high_cov_samples_copy.finalise()
def run_infer(args):
setup_logging(args)
try:
sample_data = tsinfer.SampleData.load(args.samples)
except exceptions.FileFormatError as e:
# Check if the user has tried to infer a tree sequence, a common basic mistake
try:
tskit.load(args.samples)
except tskit.FileFormatError:
raise e # Re-raise the original error
raise exceptions.FileFormatError(
"Expecting a sample data file, not a tree sequence (you can create one "
"via the Python function `tsinfer.SampleData.from_tree_sequence()`)."
)
sample_data = tsinfer.SampleData.load(args.samples)
ts = tsinfer.infer(sample_data,
progress_monitor=args.progress,
num_threads=args.num_threads)
output_trees = get_output_trees_path(args.output_trees, args.samples)
logger.info("Writing output tree sequence to {}".format(output_trees))
ts.dump(output_trees)
summarise_usage()
def test_index_columns(self):
ts = migration_example()
ts.dump(self.temp_file)
with kastore.load(self.temp_file) as store:
all_data = dict(store)
edge_removal_order = "indexes/edge_removal_order"
edge_insertion_order = "indexes/edge_insertion_order"
data = dict(all_data)
del data[edge_removal_order]
del data[edge_insertion_order]
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.LibraryError):
tskit.load(self.temp_file)
data = dict(all_data)
del data[edge_removal_order]
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.LibraryError):
tskit.load(self.temp_file)
data = dict(all_data)
del data[edge_insertion_order]
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.LibraryError):
tskit.load(self.temp_file)
data = dict(all_data)
data[edge_insertion_order] = data[edge_insertion_order][:1]
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.FileFormatError):
tskit.load(self.temp_file)
data = dict(all_data)
data[edge_removal_order] = data[edge_removal_order][:1]
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.FileFormatError):
tskit.load(self.temp_file)
def verify(self, cmd, num_samples, seed=1):
with tempfile.TemporaryDirectory() as tmpdir:
filename = pathlib.Path(tmpdir) / "output.trees"
full_cmd = cmd + f" -q -o {filename} --seed={seed}"
with mock.patch("stdpopsim.cli.setup_logging"):
stdout, stderr = capture_output(cli.stdpopsim_main, full_cmd.split())
self.assertEqual(len(stderr), 0)
self.assertEqual(len(stdout), 0)
ts = tskit.load(str(filename))
self.assertEqual(ts.num_samples, num_samples)
provenance = json.loads(ts.provenance(0).record)
prov_seed = provenance["parameters"]["random_seed"]
self.assertEqual(prov_seed, seed)
def run_dump_macs(args):
"""
Write a macs formatted file so we can import into pbwt.
"""
tree_sequence = tskit.load(args.tree_sequence)
n = tree_sequence.get_sample_size()
m = tree_sequence.get_sequence_length()
print("COMMAND:\tnot_macs {} {}".format(n, m))
print("SEED:\tASEED")
for variant in tree_sequence.variants(as_bytes=True):
print(
"SITE:", variant.index, variant.position / m, 0.0,
"{}".format(variant.genotypes.decode()), sep="\t")
def compute_stats(ts_file):
st = dict()
ts = tskit.load(ts_file)
for key, func in stats_functions.items():
try:
res = func(ts)
except Exception:
# Print the filename so it's easier to trace problems.
warning(f"{ts_file} triggered exception")
raise
if res is not None:
st[key] = res
return st
def main():
"""Run main function."""
args = parse_args(sys.argv[1:])
# =========================================================================
# Gather args
# =========================================================================
tree = args.tree
outfile = args.outfile
if outfile is None:
outfile = path.split(tree)[-1]
ref_set = args.ref
tar_set = args.tar
gnn_win = args.gnn_windows
pop_ids = args.pop_ids[0]
# =========================================================================
# Loading and Checks
# =========================================================================
ts = tskit.load(tree) # load tree
print("tree loaded")
# set refernce for comparison
if ref_set: # custom ref sets for comparison
ref_nodes = []
with open(ref_set) as f:
for line in f:
x = line.split(",")
assert len(x) > 1, "recheck delimiter should be ,"
ref_nodes.append(list(map(int, x)))
else: # all populations
ref_nodes = [
ts.samples(population=i) for i in range(ts.num_populations)
]
# set target population
if tar_set is None:
tar_nodes = ts.samples()
elif tar_set.isnumeric():
tar_nodes = ts.samples(population=int(tar_set))
else:
tar_nodes = []
with open(tar_set) as f:
for line in f:
x = line.split(",")
assert len(x) > 1, "recheck delimiter should be ,"
tar_nodes.extend(list(map(int, x)))
# =========================================================================
# Main executions
# =========================================================================
if gnn_win:
gnn_windows_fx(outfile, ts, ref_nodes, tar_nodes, pop_ids)
else:
gnn_fx(outfile, ts, ref_nodes, tar_nodes, pop_ids)
def test_simulate(self):
saved_slim_env = os.environ.get("SLIM")
with tempfile.NamedTemporaryFile(mode="w") as f:
self.docmd(f"--slim-path slim HomSap -o {f.name}")
ts = tskit.load(f.name)
self.assertEqual(ts.num_samples, 10)
self.assertTrue(all(tree.num_roots == 1 for tree in ts.trees()))
if saved_slim_env is None:
del os.environ["SLIM"]
else:
os.environ["SLIM"] = saved_slim_env
with tempfile.NamedTemporaryFile(mode="w") as f:
self.docmd(f"--slim-no-recapitation HomSap -o {f.name}")
ts = tskit.load(f.name)
self.assertEqual(ts.num_samples, 10)
with tempfile.NamedTemporaryFile(mode="w") as f:
self.docmd(
f"--slim-no-recapitation --slim-no-burnin HomSap -o {f.name}")
ts = tskit.load(f.name)
self.assertEqual(ts.num_samples, 10)
# verify sample counts for a multipopulation demographic model
with tempfile.NamedTemporaryFile(mode="w") as f:
cmd = (f"-e slim HomSap -o {f.name} -l 0.00001 -c chr1 -s 1234 -q "
"-d OutOfAfrica_3G09 0 0 8").split()
capture_output(stdpopsim.cli.stdpopsim_main, cmd)
ts = tskit.load(f.name)
self.assertEqual(ts.num_populations, 3)
observed_counts = [0, 0, 0]
for sample in ts.samples():
observed_counts[ts.get_population(sample)] += 1
self.assertEqual(observed_counts[0], 0)
self.assertEqual(observed_counts[1], 0)
self.assertEqual(observed_counts[2], 8)
self.assertTrue(all(tree.num_roots == 1 for tree in ts.trees()))
def run_compress(args):
setup_logging(args)
for file_arg in args.files:
logger.info("Compressing {}".format(file_arg))
try:
ts = tskit.load(file_arg)
except tskit.FileFormatError as ffe:
exit("Error loading '{}': {}".format(file_arg, ffe))
logger.debug("Loaded tree sequence")
infile = pathlib.Path(file_arg)
outfile = pathlib.Path(file_arg + args.suffix)
check_output(outfile, args)
tszip.compress(ts, outfile, variants_only=args.variants_only)
remove_input(infile, args)
def test_accuracy(reps):
n = 100
Ne = 10000
mut_rate = 1e-8
rec_rate = 1e-8
theta = 4*10000*mut_rate
rho = 4*10000*rec_rate
length = 1e5
compare_df_master = pd.DataFrame(columns = ['truth', 'tsdate', 'tsdate_inferred', 'relate', 'geva'])
for rep in range(reps):
vanilla_ts = msprime.simulate(sample_size=n, Ne=Ne, mutation_rate=mut_rate, recombination_rate=rec_rate, length=length)
ts, dated_ts, dated_inferred_ts_mut = run_simulation(vanilla_ts, n, Ne, theta, rho)
# Run GEVA
samples = generate_samples(ts, 'testing')
ages = samplesdata_to_ages(samples, Ne=Ne, length=length, mut_rate=mut_rate, rec_rate=rec_rate, filename=str("test"))
# Run Relate on simulated data
relate_path = "/Users/anthonywohns/Documents/mcvean_group/software/relate_v1.0.13_MacOSX/"
def run_relate(ts, relate_path):
subprocess.check_output([relate_path + "bin/RelateFileFormats", "--mode", "ConvertFromVcf", "--haps", relate_path + "age_compare/compare.haps", "--sample", relate_path + "age_compare/compare.sample", "-i", path + "tmp/test"])
subprocess.check_output([relate_path + "bin/Relate", "--mode", "All", "-m", str(mut_rate), "-N", "20000", "--haps", relate_path + "age_compare/compare.haps", "--sample", relate_path + "age_compare/compare.sample", "--seed", "1", "-o", "compare", "--map", relate_path + "genetic_map.txt"])
subprocess.check_output(
[relate_path + "bin/RelateFileFormats", "--mode",
"ConvertToTreeSequence",
"-i", "compare", "-o", "compare"])
run_relate(ts, relate_path)
relate_ts = tskit.load('compare.trees')
table_collection = relate_ts.dump_tables()
table_collection.nodes.flags[0:n] = 1
table_collection = relate_ts.dump_tables()
table_collection.nodes.set_columns(
flags=np.array(np.concatenate(
[np.repeat(1, n),
np.repeat(0, relate_ts.num_nodes - n)]),
dtype='uint32'), time=relate_ts.tables.nodes.time)
relate_ts_fixed = table_collection.tree_sequence()
ts.dump('true_ts_' + str(rep) + '.trees')
dated_ts.dump('dated_ts_' + str(rep) + '.trees')
dated_inferred_ts_mut.dump('dated_inferred_ts_' + str(rep) + '.trees')
relate_ts_fixed.dump('relate_ts_' + str(rep) + '.trees')
compare_dict = compare_muts(n, ts, dated_ts,
dated_inferred_ts_mut, ages)
compare_df_master = pd.concat([compare_df_master, compare_dict])
compare_df_master.to_csv("compare_df")
def run_tsinfer_mismatch(
sample_fn,
length,
num_threads=1,
inject_real_ancestors_from_ts_fn=None,
rho=None,
error_probability=None,
):
with tempfile.NamedTemporaryFile("w+") as ts_out:
cmd = [tsinfer_executable, ts_out.name, "-s", "infer", sample_fn]
# cmd += ["--threads", str(num_threads), ts_out.name]
cpu_time, memory_use = time_cmd(cmd)
ts_simplified = tskit.load(ts_out.name)
return ts_simplified, cpu_time, memory_use
def test_duplicate_positions(self):
ts = msprime.simulate(10, mutation_rate=10)
for version in [2, 3]:
tskit.dump_legacy(ts, self.legacy_file_name, version=version)
root = h5py.File(self.legacy_file_name, "r+")
root['mutations/position'][:] = 0
root.close()
stdout, stderr = capture_output(
cli.tskit_main,
["upgrade", "-d", self.legacy_file_name, self.current_file_name])
self.assertEqual(stdout, "")
tsp = tskit.load(self.current_file_name)
self.assertEqual(tsp.sample_size, ts.sample_size)
self.assertEqual(tsp.num_sites, 1)
def verify(self, cmd, num_samples, seed=1):
with tempfile.TemporaryDirectory() as tmpdir:
filename = pathlib.Path(tmpdir) / "output.trees"
full_cmd = f"{sys.executable} -m stdpopsim -q {cmd} -o {filename} -s {seed}"
subprocess.run(full_cmd, shell=True, check=True)
ts = tskit.load(str(filename))
assert ts.num_samples == num_samples
provenance = json.loads(ts.provenance(ts.num_provenances - 1).record)
tskit.validate_provenance(provenance)
stored_cmd = provenance["parameters"]["args"]
assert stored_cmd[0] == "-q"
assert stored_cmd[-1] == str(seed)
assert stored_cmd[-2] == "-s"
assert stored_cmd[1:-4] == cmd.split()
def verify_equal_length_columns(self, ts, table):
ts.dump(self.temp_file)
with kastore.load(self.temp_file) as store:
all_data = dict(store)
table_cols = [
colname for colname in all_data.keys() if colname.startswith(table)
]
# Remove all the 'offset' columns
for col in list(table_cols):
if col.endswith("_offset"):
main_col = col[:col.index("_offset")]
table_cols.remove(main_col)
table_cols.remove(col)
if "metadata_schema" in col:
table_cols.remove(col)
# Remaining columns should all be the same length
for col in table_cols:
for bad_val in [[], all_data[col][:-1]]:
data = dict(all_data)
data[col] = bad_val
kastore.dump(data, self.temp_file)
with pytest.raises(exceptions.FileFormatError):
tskit.load(self.temp_file)
def load_tree(tree_file):
"""Reads tree sequence from disk.
Parameters
----------
tree : str
file path to tree sequence
Returns
-------
tskit tree sequencing object
"""
return tskit.load(tree_file)
def test_conversion(self):
ts1 = msprime.simulate(10)
for version in [2, 3]:
tskit.dump_legacy(ts1, self.legacy_file_name, version=version)
stdout, stderr = capture_output(
cli.tskit_main, [
"upgrade", self.legacy_file_name, self.current_file_name])
ts2 = tskit.load(self.current_file_name)
self.assertEqual(stdout, "")
self.assertEqual(stderr, "")
# Quick checks to ensure we have the right tree sequence.
# More thorough checks are done elsewhere.
self.assertEqual(ts1.get_sample_size(), ts2.get_sample_size())
self.assertEqual(ts1.num_edges, ts2.num_edges)
self.assertEqual(ts1.get_num_trees(), ts2.get_num_trees())
def ts_to_seg(path, n=None):
"""
Converts a tree sequence into a seg file for use by :code:`smcsmc.run_smcsmcs()`. This is especially
useful if you are simulating data from :code:`msprime` and would like to directly
use it in :code:`smcsmc`. For details of how to do this, please see the tutorial on simulation using :code:`msprime`.
Provide the path to the tree sequence, and the suffix will be replaced by :code:`.seg`. This code is adapted from PopSim.
:param str path: Full file path to the tree sequence created by :code:`ts.dump`.
:param list n: If more than one sample of haplotypes is being analysed simulateously, provide it here as a list. Otherwise, simply provide the number of haplotypes as a single-element list.
"""
if n is None:
ts = tskit.load(pathe)
dirr = os.path.dirname(path)
filen = os.path.basename(path)
sep = filen.split(".")
output = os.path.join(dirr, ".".join(sep) + ".seg")
fi = open(output, "w")
prev = 1
cur = 0
for var in ts.variants():
cur = int(var.site.position)
if cur > prev:
geno = ''.join(map(str, var.genotypes))
fi.write(f"{prev}\t{cur-prev}\t{geno}\n")
prev = cur
fi.close()
else:
for sample_size in n:
ts = smcsmc.utils.prune_tree_sequence(path, sample_size)
dirr = os.path.dirname(path)
filen = os.path.basename(path)
sep = filen.split(".")
chrom = sep[0]
sep.insert(0, str(sample_size))
output = os.path.join(dirr, ".".join(sep) + ".seg")
fi = open(output, "w")
prev = 1
cur = 0
for var in ts.variants():
cur = int(var.site.position)
if cur > prev:
geno = ''.join(map(str, var.genotypes))
fi.write(f"{prev}\t{cur-prev}\t{geno}\n")
prev = cur
fi.close()
return None
def run_list(args):
setup_logging(args)
# First try to load with tskit.
ts = None
try:
ts = tskit.load(args.path)
except tskit.FileFormatError:
pass
if ts is None:
tsinfer_file = tsinfer.load(args.path)
if args.storage:
print(tsinfer_file.info)
else:
print(tsinfer_file)
else:
summarise_tree_sequence(args.path, ts)
def run_tsdate(input_fn, Ne, mut_rate, timepoints, method):
with tempfile.NamedTemporaryFile("w+") as ts_out:
cmd = [
sys.executable,
tsdate_executable,
input_fn,
ts_out.name,
str(Ne),
"--mutation-rate",
str(mut_rate),
]
# cmd = ["tsdate", "date", input_fn, ts_out.name, str(Ne)]
# cmd += ["--mutation-rate", str(mut_rate), "--timepoints", str(timepoints), "--method", str(method)]
cpu_time, memory_use = time_cmd(cmd)
dated_ts = tskit.load(ts_out.name)
return dated_ts, cpu_time, memory_use
def load(cls, path):
'''
Load a :class:`SlimTreeSequence` from a .trees file on disk.
:param string path: The path to a .trees file.
:rtype SlimTreeSequence:
'''
ts = tskit.load(path)
# extract the reference sequence from the kastore
kas = kastore.load(path)
if 'reference_sequence/data' in kas:
int_rs = kas['reference_sequence/data']
reference_sequence = int_rs.tostring().decode('ascii')
else:
reference_sequence = None
return cls(ts, reference_sequence)
def run_dump_macs(args):
"""
Write a macs formatted file so we can import into pbwt.
"""
tree_sequence = tskit.load(args.tree_sequence)
n = tree_sequence.get_sample_size()
m = tree_sequence.get_sequence_length()
print("COMMAND:\tnot_macs {} {}".format(n, m))
print("SEED:\tASEED")
for variant in tree_sequence.variants(as_bytes=True):
print("SITE:",
variant.index,
variant.position / m,
0.0,
"{}".format(variant.genotypes.decode()),
sep="\t")
def load_from_stream(q_err, q_out, file_in):
"""
tskit.load() tree sequences from `file_in` and put them onto `q_out`.
Uncaught exceptions are placed onto the `q_err` queue.
"""
try:
with open(file_in, "rb") as f:
while True:
try:
ts = tskit.load(f)
except EOFError:
break
q_out.put(ts)
except Exception as exc:
tb = traceback.format_exc()
q_err.put((exc, tb))
def run_dump_mutations(args):
tree_sequence = tskit.load(args.tree_sequence)
tree_sequence.dump_text(mutations=sys.stdout, precision=args.precision)
def run_dump_vcf(args):
tree_sequence = tskit.load(args.tree_sequence)
tree_sequence.write_vcf(sys.stdout, args.ploidy)
def run_dump_variants(args):
tree_sequence = tskit.load(args.tree_sequence)
for variant in tree_sequence.variants(as_bytes=True):
print(variant.position, end="\t")
print("{}".format(variant.genotypes.decode()))
def run_dump_haplotypes(args):
tree_sequence = tskit.load(args.tree_sequence)
for h in tree_sequence.haplotypes():
print(h)
def run_dump_newick(args):
tree_sequence = tskit.load(args.tree_sequence)
for tree in tree_sequence.trees():
newick = tree.newick(precision=args.precision)
print(newick)
def run_dump_sites(args):
tree_sequence = tskit.load(args.tree_sequence)
tree_sequence.dump_text(sites=sys.stdout, precision=args.precision)
