def _run_db2falcon_jobs(cwd, config, dry_run, db2falcon_json_fn=None):
"""
cwd --- current workding directory
dry_run --- if True, do not actually run the scripts
db2falcon_json_fn --- if not None, write dict{0: dict('script_fn':script_fn, 'script_dir':script_dir)}
"""
# Generate preads4falcon.fasta from preads.db
script_fn = os.path.join(cwd, "run_db2falcon.sh")
job_done = script_fn + "_done"
args = {
"config": config,
"job_done": job_done,
"script_fn": script_fn,
"preads4falcon_fn": "preads4falcon.fasta",
"preads_db": "preads.db",
}
support.run_db2falcon(**args)
json_fn = "db2falcon.json" if db2falcon_json_fn is None else db2falcon_json_fn
# add script_dir to args for scattered tasks to work in the correct dir
json_data = {0: {"script_fn": os.path.basename(script_fn), "script_dir": cwd}}
with open(json_fn, "w") as writer:
writer.write(json.dumps(json_data) + "\n")
mkdir(cwd)
with cd(cwd):
if dry_run is False:
run_cmd("bash %s" % os.path.basename(script_fn), sys.stdout, sys.stderr, shell=False)
assert_nonzero("preads4falcon.fasta")
def run_daligner_jobs(input_files, output_files, db_prefix='raw_reads'):
print('run_daligner_jobs: %s %s' % (repr(input_files), repr(output_files)))
i_json_config_fn, run_daligner_job_fn, = input_files
o_fofn_fn, = output_files
db_dir = os.path.dirname(run_daligner_job_fn)
cmds = ['pwd', 'ls -al']
fns = ['.{pre}.bps', '.{pre}.idx', '{pre}.db']
cmds += [r'\rm -f %s' % fn for fn in fns]
cmds += ['ln -sf {dir}/%s .' % fn for fn in fns]
cmd = ';'.join(cmds).format(dir=os.path.relpath(db_dir), pre=db_prefix)
run_cmd(cmd, sys.stdout, sys.stderr, shell=True)
cwd = os.getcwd()
config = _get_config_from_json_fileobj(open(i_json_config_fn))
tasks = create_daligner_tasks(run_daligner_job_fn, cwd, db_prefix,
db_prefix + '.db', config)
odirs = []
for jobd, args in tasks.items():
with cd(jobd):
support.run_daligner(**args)
script_fn = args['script_fn']
run_cmd('bash %s' % script_fn, sys.stdout, sys.stderr, shell=False)
odirs.append(os.path.dirname(script_fn))
write_fns(
o_fofn_fn,
itertools.chain.from_iterable(
glob.glob('%s/*.las' % d) for d in odirs))
return 0
def _run_consensus_jobs(tasks, dry_run=False, cons_json_fn=None):
"""dry_run --- if True, do not actually run the scripts
cons_json_fn --- if not None, write dict{p_id: dict{'script_fn':script_fn, 'script_dir':script_dir}} to it
"""
fns = list()
json_data = dict()
for p_id, (cons_args, fasta_bfn) in tasks.items():
run_dir = "preads"
job_done = "c_%05d_done" % p_id
script_fn = os.path.join(run_dir, "c_%05d.sh" % (p_id))
cons_args["job_done"] = job_done
cons_args["script_fn"] = script_fn
# cons_args <- dict{
# 'out_file_fn': abspath to preads/out.00001.fasta
# 'script_fn' : c_00001.sh
# 'job_done' : c_00001_done
# 'raw_reads' : raw_reads
# 'config' : config}
support.run_consensus(**cons_args)
mkdir(run_dir)
with cd(run_dir):
if dry_run is False:
run_cmd("bash %s" % os.path.basename(script_fn), sys.stdout, sys.stderr, shell=False)
fns.append(os.path.join(run_dir, fasta_bfn))
# add script_dir to args for scattered tasks to work in the correct dir
json_data[p_id] = {
"script_fn": os.path.basename(script_fn), # 'c_00001.sh'
"script_dir": os.path.join(os.getcwd(), run_dir),
} # '/pbi/.../tasks/falcon_ns.tasks.task_falcon0_run_merge_jobs/preads/'
json_fn = "cons_jobs.json" if cons_json_fn is None else cons_json_fn
with open(json_fn, "w") as writer:
writer.write(json.dumps(json_data) + "\n")
return fns # *.fasta ['preads/out.0001.fasta', 'preads/out.00002.fasta', 'preads/out.00003.fasta']
def run_falcon_config_get_fasta(input_files, output_files):
i_config_fn, = input_files
o_fofn_fn, = output_files
config = _get_config(i_config_fn)
i_fofn_fn = config['input_fofn']
if not os.path.isabs(i_fofn_fn):
i_fofn_fn = os.path.join(os.path.dirname(i_config_fn), i_fofn_fn)
msg = '%r -> %r' % (i_fofn_fn, o_fofn_fn)
say(msg)
with cd(os.path.dirname(i_fofn_fn)):
return support.make_fofn_abs(i_fofn_fn, o_fofn_fn)
return 0
def run_falcon_config_get_fasta(input_files, output_files):
i_config_fn, = input_files
o_fofn_fn, = output_files
config = _get_config(i_config_fn)
i_fofn_fn = config["input_fofn"]
if not os.path.isabs(i_fofn_fn):
i_fofn_fn = os.path.join(os.path.dirname(i_config_fn), i_fofn_fn)
msg = "%r -> %r" % (i_fofn_fn, o_fofn_fn)
say(msg)
with cd(os.path.dirname(i_fofn_fn)):
return support.make_fofn_abs(i_fofn_fn, o_fofn_fn)
return 0
def run_falcon_make_fofn_abs(input_files, output_files):
i_json_fn, = input_files
o_fofn_fn, = output_files
config = _get_config_from_json_fileobj(open(i_json_fn))
i_fofn_fn = config["input_fofn"]
if not i_fofn_fn.startswith("/"):
# i_fofn_fn can be relative to the location of the config file.
original_config_fn = config["original_self"]
i_fofn_fn = os.path.join(os.path.dirname(original_config_fn), i_fofn_fn)
msg = "run_falcon_make_fofn_abs(%r -> %r)" % (i_fofn_fn, o_fofn_fn)
say(msg)
with cd(os.path.dirname(i_fofn_fn)):
return support.make_fofn_abs(i_fofn_fn, o_fofn_fn)
return 0
def run_falcon_make_fofn_abs(input_files, output_files):
i_json_fn, = input_files
o_fofn_fn, = output_files
config = _get_config_from_json_fileobj(open(i_json_fn))
i_fofn_fn = config['input_fofn']
if not i_fofn_fn.startswith('/'):
# i_fofn_fn can be relative to the location of the config file.
original_config_fn = config['original_self']
i_fofn_fn = os.path.join(os.path.dirname(original_config_fn),
i_fofn_fn)
msg = 'run_falcon_make_fofn_abs(%r -> %r)' % (i_fofn_fn, o_fofn_fn)
say(msg)
with cd(os.path.dirname(i_fofn_fn)):
return support.make_fofn_abs(i_fofn_fn, o_fofn_fn)
return 0
def _run_merge_jobs(tasks, dry_run=False, merge_json_fn=None):
"""dry_run --- if True, do not actually run the scripts,
merge_json_fn --- if not None, write dict{p_id->mege_args} to it
"""
fns = list()
json_data = dict()
for p_id, (merge_args, las_bfn) in tasks.items():
run_dir = merge_args['merge_subdir']
job_done = "merge_%05d_done" % p_id
script_fn = os.path.join(run_dir, "merge_%05d.sh" % (p_id))
merge_args['job_done'] = job_done
merge_args['script_fn'] = script_fn
del merge_args['merge_subdir'] # was just a temporary hack
# merge_args <- dict{
# 'job_done' : 'merge_00001_done',
# 'script_fn' : 'merge_00001.sh',
# 'script' : 'LAmege -v ...',
# 'config' : config}
support.run_las_merge(**merge_args)
mkdir(run_dir)
with cd(run_dir):
if dry_run is False:
run_cmd('bash %s' % os.path.basename(script_fn),
sys.stdout,
sys.stderr,
shell=False)
fns.append(os.path.join(run_dir, las_bfn))
# add script_dir to args for scattered tasks to work in the correct dir
json_data[p_id] = {
'script_dir': os.path.join(os.getcwd(),
run_dir), # 'merge_00001.sh'
'script_fn': os.path.basename(script_fn)
} # '/pbi/.../tasks/falcon_ns.task.task_falcon0_run_merge_consensus_jobs/m_00001',
json_fn = 'merge_jobs.json' if merge_json_fn is None else merge_json_fn
# Write dict{p_id: dict{'script_fn':script_fn, 'script_dir':script_dir}} to a json file
with open(json_fn, 'w') as writer:
writer.write(json.dumps(json_data) + "\n")
return fns # *.las, e.g., ['m_00001/raw_reads.1.las', 'm_00002/raw_reads.2.las', 'm_00003/raw_reads.3.las']
def _run_consensus_jobs(tasks, dry_run=False, cons_json_fn=None):
"""dry_run --- if True, do not actually run the scripts
cons_json_fn --- if not None, write dict{p_id: dict{'script_fn':script_fn, 'script_dir':script_dir}} to it
"""
fns = list()
json_data = dict()
for p_id, (cons_args, fasta_bfn) in tasks.items():
run_dir = 'preads'
job_done = "c_%05d_done" % p_id
script_fn = os.path.join(run_dir, "c_%05d.sh" % (p_id))
cons_args['job_done'] = job_done
cons_args['script_fn'] = script_fn
# cons_args <- dict{
# 'out_file_fn': abspath to preads/out.00001.fasta
# 'script_fn' : c_00001.sh
# 'job_done' : c_00001_done
# 'raw_reads' : raw_reads
# 'config' : config}
support.run_consensus(**cons_args)
mkdir(run_dir)
with cd(run_dir):
if dry_run is False:
run_cmd('bash %s' % os.path.basename(script_fn),
sys.stdout,
sys.stderr,
shell=False)
fns.append(os.path.join(run_dir, fasta_bfn))
# add script_dir to args for scattered tasks to work in the correct dir
json_data[p_id] = {
'script_fn': os.path.basename(script_fn), # 'c_00001.sh'
'script_dir': os.path.join(os.getcwd(), run_dir)
} # '/pbi/.../tasks/falcon_ns.tasks.task_falcon0_run_merge_jobs/preads/'
json_fn = "cons_jobs.json" if cons_json_fn is None else cons_json_fn
with open(json_fn, 'w') as writer:
writer.write(json.dumps(json_data) + "\n")
return fns # *.fasta ['preads/out.0001.fasta', 'preads/out.00002.fasta', 'preads/out.00003.fasta']
def run_scripts_in_json(input_files, output_files):
"""
input_files = ['*.json'] (e.g., merge|cons|db2falcon.json), where
*.json <- dict(p_id: dict('script_fn':script_fn, 'script_dir':script_dir))
output_files = ['*_done.txt'] (e.g., merge_done.txt, cons_done.txt, db2falcon_done.txt)
execute all script files.
"""
json_fn = input_files[0]
txt_fn = output_files[0]
a = json.load(open(json_fn, "r"))
writer = open(txt_fn, "w")
for p_id, args in a.iteritems():
if "script_fn" not in args:
raise ValueError("Could not find 'script_fn' in json %r key %r" % (json_fn, p_id))
script_dir = str(args["script_dir"])
with cd(script_dir):
script_fn = str(args["script_fn"])
run_cmd("bash %s" % script_fn, sys.stdout, sys.stderr, shell=False)
writer.write(script_fn + "\n")
writer.close()
return 0
def run_daligner_jobs(input_files, output_files, db_prefix="raw_reads"):
print("run_daligner_jobs: %s %s" % (repr(input_files), repr(output_files)))
i_json_config_fn, run_daligner_job_fn, = input_files
o_fofn_fn, = output_files
db_dir = os.path.dirname(run_daligner_job_fn)
cmds = ["pwd", "ls -al"]
fns = [".{pre}.bps", ".{pre}.idx", "{pre}.db"]
cmds += [r"\rm -f %s" % fn for fn in fns]
cmds += ["ln -sf {dir}/%s ." % fn for fn in fns]
cmd = ";".join(cmds).format(dir=os.path.relpath(db_dir), pre=db_prefix)
run_cmd(cmd, sys.stdout, sys.stderr, shell=True)
cwd = os.getcwd()
config = _get_config_from_json_fileobj(open(i_json_config_fn))
tasks = create_daligner_tasks(run_daligner_job_fn, cwd, db_prefix, db_prefix + ".db", config)
odirs = []
for jobd, args in tasks.items():
with cd(jobd):
support.run_daligner(**args)
script_fn = args["script_fn"]
run_cmd("bash %s" % script_fn, sys.stdout, sys.stderr, shell=False)
odirs.append(os.path.dirname(script_fn))
write_fns(o_fofn_fn, itertools.chain.from_iterable(glob.glob("%s/*.las" % d) for d in odirs))
return 0
def _run_db2falcon_jobs(cwd, config, dry_run, db2falcon_json_fn=None):
"""
cwd --- current workding directory
dry_run --- if True, do not actually run the scripts
db2falcon_json_fn --- if not None, write dict{0: dict('script_fn':script_fn, 'script_dir':script_dir)}
"""
# Generate preads4falcon.fasta from preads.db
script_fn = os.path.join(cwd, "run_db2falcon.sh")
job_done = script_fn + '_done'
args = {
'config': config,
'job_done': job_done,
'script_fn': script_fn,
'preads4falcon_fn': 'preads4falcon.fasta',
'preads_db': 'preads.db',
}
support.run_db2falcon(**args)
json_fn = "db2falcon.json" if db2falcon_json_fn is None else db2falcon_json_fn
# add script_dir to args for scattered tasks to work in the correct dir
json_data = {
0: {
'script_fn': os.path.basename(script_fn),
'script_dir': cwd
}
}
with open(json_fn, 'w') as writer:
writer.write(json.dumps(json_data) + "\n")
mkdir(cwd)
with cd(cwd):
if dry_run is False:
run_cmd('bash %s' % os.path.basename(script_fn),
sys.stdout,
sys.stderr,
shell=False)
assert_nonzero('preads4falcon.fasta')
def run_scripts_in_json(input_files, output_files):
"""
input_files = ['*.json'] (e.g., merge|cons|db2falcon.json), where
*.json <- dict(p_id: dict('script_fn':script_fn, 'script_dir':script_dir))
output_files = ['*_done.txt'] (e.g., merge_done.txt, cons_done.txt, db2falcon_done.txt)
execute all script files.
"""
json_fn = input_files[0]
txt_fn = output_files[0]
a = json.load(open(json_fn, 'r'))
writer = open(txt_fn, 'w')
for p_id, args in a.iteritems():
if 'script_fn' not in args:
raise ValueError("Could not find 'script_fn' in json %r key %r" %
(json_fn, p_id))
script_dir = str(args['script_dir'])
with cd(script_dir):
script_fn = str(args['script_fn'])
run_cmd('bash %s' % script_fn, sys.stdout, sys.stderr, shell=False)
writer.write(script_fn + "\n")
writer.close()
return 0
def _run_merge_jobs(tasks, dry_run=False, merge_json_fn=None):
"""dry_run --- if True, do not actually run the scripts,
merge_json_fn --- if not None, write dict{p_id->mege_args} to it
"""
fns = list()
json_data = dict()
for p_id, (merge_args, las_bfn) in tasks.items():
run_dir = merge_args["merge_subdir"]
job_done = "merge_%05d_done" % p_id
script_fn = os.path.join(run_dir, "merge_%05d.sh" % (p_id))
merge_args["job_done"] = job_done
merge_args["script_fn"] = script_fn
del merge_args["merge_subdir"] # was just a temporary hack
# merge_args <- dict{
# 'job_done' : 'merge_00001_done',
# 'script_fn' : 'merge_00001.sh',
# 'script' : 'LAmege -v ...',
# 'config' : config}
support.run_las_merge(**merge_args)
mkdir(run_dir)
with cd(run_dir):
if dry_run is False:
run_cmd("bash %s" % os.path.basename(script_fn), sys.stdout, sys.stderr, shell=False)
fns.append(os.path.join(run_dir, las_bfn))
# add script_dir to args for scattered tasks to work in the correct dir
json_data[p_id] = {
"script_dir": os.path.join(os.getcwd(), run_dir), # 'merge_00001.sh'
"script_fn": os.path.basename(script_fn),
} # '/pbi/.../tasks/falcon_ns.task.task_falcon0_run_merge_consensus_jobs/m_00001',
json_fn = "merge_jobs.json" if merge_json_fn is None else merge_json_fn
# Write dict{p_id: dict{'script_fn':script_fn, 'script_dir':script_dir}} to a json file
with open(json_fn, "w") as writer:
writer.write(json.dumps(json_data) + "\n")
return fns # *.las, e.g., ['m_00001/raw_reads.1.las', 'm_00002/raw_reads.2.las', 'm_00003/raw_reads.3.las']
def create_merge_tasks(i_fofn_fn, run_jobs_fn, wd, db_prefix, config):
#merge_scripts = bash.scripts_merge(config, db_prefix, run_jobs_fn)
tasks = {} # pid -> (merge_params, cons_params)
mjob_data = {}
with open(run_jobs_fn) as f:
for l in f:
l = l.strip().split()
if l[0] not in ("LAsort", "LAmerge", "mv"):
continue
if l[0] == "LAsort":
# We now run this part w/ daligner, but we still need
# a small script for some book-keeping.
p_id = int(l[2].split(".")[1])
mjob_data.setdefault(p_id, [])
#mjob_data[p_id].append( " ".join(l) ) # Already done w/ daligner!
if l[0] == "LAmerge":
l2 = l[2].split(".")
if l2[1][0] == "L":
p_id = int(l[2].split(".")[2])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
else:
p_id = int(l[2].split(".")[1])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
if l[0] == "mv":
l2 = l[1].split(".")
if l2[1][0] == "L":
p_id = int(l[1].split(".")[2])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
else:
p_id = int(l[1].split(".")[1])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
# Could be L1.* or preads.*
re_las = re.compile(r'\.(\d*)(\.\d*)?\.las$')
for p_id in mjob_data:
s_data = mjob_data[p_id]
support.make_dirs("%s/preads" % (wd))
support.make_dirs("%s/las_files" % (wd))
merge_subdir = "m_%05d" % p_id
merge_dir = os.path.join(wd, merge_subdir)
support.make_dirs(merge_dir)
#merge_script_file = os.path.abspath( "%s/m_%05d/m_%05d.sh" % (wd, p_id, p_id) )
merge_script = StringIO.StringIO()
with cd(merge_dir):
print("i_fofn_fn=%r" % i_fofn_fn)
# Since we could be in the gather-task-dir, instead of globbing,
# we will read the fofn.
for fn in open(i_fofn_fn).read().splitlines():
basename = os.path.basename(fn)
mo = re_las.search(basename)
if not mo:
continue
left_block = int(mo.group(1))
if left_block != p_id:
# By convention, m_00005 merges L1.5.*.las, etc.
continue
symlink(fn)
for l in s_data:
print >> merge_script, l
las_bfn = '%s.%d.las' % (db_prefix, p_id)
#print >> merge_script, 'echo %s >| %s' %(las_bfn, merged_las_fofn_bfn)
#job_done = makePypeLocalFile(os.path.abspath( "%s/m_%05d/m_%05d_done" % (wd, p_id, p_id) ))
parameters = {
"script": merge_script.getvalue(),
"merge_subdir": merge_subdir,
"config": config
}
merge_task = parameters
fasta_bfn = "out.%05d.fasta" % p_id
out_file_fn = os.path.abspath("%s/preads/%s" % (wd, fasta_bfn))
#out_done = makePypeLocalFile(os.path.abspath( "%s/preads/c_%05d_done" % (wd, p_id) ))
parameters = {
"db_fn": '{}/{}'.format(os.getcwd(), db_prefix),
"las_fn":
'{}/{}/{}'.format(os.getcwd(), merge_subdir,
las_bfn), # assuming merge ran in merge_dir
"out_file_fn": out_file_fn,
#"out_done": out_done,
"config": config
}
cons_task = parameters
tasks[p_id] = (merge_task, cons_task, las_bfn, fasta_bfn)
return tasks
def create_merge_tasks(i_fofn_fn, run_jobs_fn, wd, db_prefix, config):
# merge_scripts = bash.scripts_merge(config, db_prefix, run_jobs_fn)
tasks = {} # pid -> (merge_params, cons_params)
mjob_data = {}
with open(run_jobs_fn) as f:
for l in f:
l = l.strip().split()
if l[0] not in ("LAsort", "LAmerge", "mv"):
continue
if l[0] == "LAsort":
# We now run this part w/ daligner, but we still need
# a small script for some book-keeping.
p_id = int(l[2].split(".")[1])
mjob_data.setdefault(p_id, [])
# mjob_data[p_id].append( " ".join(l) ) # Already done w/ daligner!
if l[0] == "LAmerge":
l2 = l[2].split(".")
if l2[1][0] == "L":
p_id = int(l[2].split(".")[2])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
else:
p_id = int(l[2].split(".")[1])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
if l[0] == "mv":
l2 = l[1].split(".")
if l2[1][0] == "L":
p_id = int(l[1].split(".")[2])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
else:
p_id = int(l[1].split(".")[1])
mjob_data.setdefault(p_id, [])
mjob_data[p_id].append(" ".join(l))
# Could be L1.* or preads.*
re_las = re.compile(r"\.(\d*)(\.\d*)?\.las$")
for p_id in mjob_data:
s_data = mjob_data[p_id]
support.make_dirs("%s/preads" % (wd))
support.make_dirs("%s/las_files" % (wd))
merge_subdir = "m_%05d" % p_id
merge_dir = os.path.join(wd, merge_subdir)
support.make_dirs(merge_dir)
# merge_script_file = os.path.abspath( "%s/m_%05d/m_%05d.sh" % (wd, p_id, p_id) )
merge_script = StringIO.StringIO()
with cd(merge_dir):
print("i_fofn_fn=%r" % i_fofn_fn)
# Since we could be in the gather-task-dir, instead of globbing,
# we will read the fofn.
for fn in open(i_fofn_fn).read().splitlines():
basename = os.path.basename(fn)
mo = re_las.search(basename)
if not mo:
continue
left_block = int(mo.group(1))
if left_block != p_id:
# By convention, m_00005 merges L1.5.*.las, etc.
continue
symlink(fn)
for l in s_data:
print >> merge_script, l
las_bfn = "%s.%d.las" % (db_prefix, p_id)
# print >> merge_script, 'echo %s >| %s' %(las_bfn, merged_las_fofn_bfn)
# job_done = makePypeLocalFile(os.path.abspath( "%s/m_%05d/m_%05d_done" % (wd, p_id, p_id) ))
parameters = {"script": merge_script.getvalue(), "merge_subdir": merge_subdir, "config": config}
merge_task = parameters
fasta_bfn = "out.%05d.fasta" % p_id
out_file_fn = os.path.abspath("%s/preads/%s" % (wd, fasta_bfn))
# out_done = makePypeLocalFile(os.path.abspath( "%s/preads/c_%05d_done" % (wd, p_id) ))
parameters = {
"db_fn": "{}/{}".format(os.getcwd(), db_prefix),
"las_fn": "{}/{}/{}".format(os.getcwd(), merge_subdir, las_bfn), # assuming merge ran in merge_dir
"out_file_fn": out_file_fn,
# "out_done": out_done,
"config": config,
}
cons_task = parameters
tasks[p_id] = (merge_task, cons_task, las_bfn, fasta_bfn)
# tasks <- dict{p_id: (merge_task, cons_task, las_bfn, fasta_bfn)}, where
# p_id is an integer, e.g., 1
# merge_task <- dict{'merge_dir': 'm_00001', 'script'='LAmerge -v raw_reads.1 L1.1.1 L1.1.2 L1.1.3', 'script_fn': x}
# cons_task <- dict{'db_fn':x, 'las_fn':x, 'out_file_fn':x, 'config':config}
# las_bfn, e.g. raw_reads.1.las
# fasta_bfn, e.g., out.00001.fasta
return tasks