def main():
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
default_drm="local",
)
cosmos.initdb()
sp.check_call("mkdir -p analysis_output/ex1", shell=True)
os.chdir("analysis_output/ex1")
workflow = cosmos.start("Example1", restart=True, skip_confirm=True)
t = workflow.add_task(
func=say,
params=dict(text="Hello World", out_file="out.txt"),
uid="my_task",
time_req=None,
core_req=1,
mem_req=1024,
)
print(("task.params", t.params))
print(("task.input_map", t.input_map))
print(("task.output_map", t.output_map))
print(("task.core_req", t.core_req))
print(("task.time_req", t.time_req))
print(("task.drm", t.drm))
print(("task.uid", t.uid))
workflow.run()
sys.exit(0 if workflow.successful else 1)
def main():
# start cosmos engine
cosmos = Cosmos(
database_url="sqlite://",
default_drm="local",
# default_drm="ge",
default_queue="dev-short",
default_drm_options={},
get_submit_args=partial(default_get_submit_args, parallel_env="smp"),
)
cosmos.initdb()
# create cosmos workflow
workflow = cosmos.start(
# NOTE cosmos will make dirs in this path
# primary_log_path=os.path.join("logs", "cosmos.log"),
name="blah",
restart=True,
skip_confirm=True,
fail_fast=True,
)
for i in range(100):
print("add {}".format(i))
silly_recipe(workflow, i, 100)
workflow.make_output_dirs()
# run cosmos workflow
# with SGESignalHandler(workflow):
workflow.run()
def main():
cosmos = Cosmos("cosmos.sqlite").initdb()
workflow = cosmos.start("ex1", skip_confirm=True)
t = workflow.add_task(
func=say,
params=dict(text="Hello World", out_file="out.txt"),
uid="my_task",
time_req=None,
core_req=1,
mem_req=1024,
)
print(("task.params", t.params))
print(("task.input_map", t.input_map))
print(("task.output_map", t.output_map))
print(("task.core_req", t.core_req))
print(("task.time_req", t.time_req))
print(("task.drm", t.drm))
print(("task.uid", t.uid))
workflow.run(cmd_wrapper=py_call)
sys.exit(0 if workflow.successful else 1)
def main():
cosmos = Cosmos()
cosmos.initdb()
workflow = cosmos.start("env_variables", skip_confirm=True)
workflow.add_task(func=command_with_env_variables,
environment_variables=environment_variables_dict,
uid="special")
workflow.run(cmd_wrapper=py_call)
def test_zero_tasks():
cosmos = Cosmos()
cosmos.initdb()
temp_dir = tempfile.mkdtemp()
with cd(temp_dir):
workflow = cosmos.start('workflow', skip_confirm=True)
workflow.run(set_successful=False)
workflow.run(cmd_wrapper=py_call)
shutil.rmtree(temp_dir)
def main():
p = argparse.ArgumentParser()
p.add_argument("-drm",
default="local",
help="",
choices=("local", "drmaa:ge", "ge", "slurm"))
p.add_argument("-j",
"--job-class",
help="Submit to this job class if the DRM supports it")
p.add_argument("-q",
"--queue",
help="Submit to this queue if the DRM supports it")
args = p.parse_args()
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
# example of how to change arguments if you're not using default_drm='local'
get_submit_args=partial(default_get_submit_args, parallel_env="smp"),
default_drm=args.drm,
default_max_attempts=2,
default_job_class=args.job_class,
default_queue=args.queue,
)
cosmos.initdb()
sp.check_call("mkdir -p analysis_output/1000tasks/", shell=True)
os.chdir("analysis_output/1000tasks/")
workflow = cosmos.start("1000_tasks", restart=True, skip_confirm=True)
recipe(workflow)
workflow.make_output_dirs()
workflow.run(max_cores=100)
# Noting here that if you wanted to look at the outputs of any Tasks to decide how to generate the rest of a DAG
# you can do so here, proceed to add more tasks via workflow.add_task(), and then call workflow.run() again.
# Yes, it does require running all Tasks in the dag to get the outputs of any Task, and we hope to address
# that limitation at some point in the future.
if pygraphviz_available:
# These images can also be seen on the fly in the web-interface
draw_stage_graph(workflow.stage_graph(),
"/tmp/ex1_task_graph.png",
format="png")
draw_task_graph(workflow.task_graph(),
"/tmp/ex1_stage_graph.png",
format="png")
else:
print("Pygraphviz is not available :(")
sys.exit(0 if workflow.successful else 1)
def main():
cosmos = Cosmos()
cosmos.initdb()
workflow = cosmos.start('test', skip_confirm=True)
for i, num_gpus in enumerate([1, 1, 2, 2, 3]):
task = workflow.add_task(use_cuda_device,
dict(some_arg=i, num_gpus=num_gpus),
gpu_req=num_gpus,
uid=str(i))
workflow.run(max_gpus=len(
os.environ['COSMOS_LOCAL_GPU_DEVICES'].split(',')),
cmd_wrapper=py_call,
cleanup_at_exit=False)
def main():
cosmos = Cosmos().initdb()
workflow = cosmos.start("gpu", skip_confirm=True)
for i, num_gpus in enumerate([1, 1, 2, 2, 3]):
task = workflow.add_task(
use_cuda_device,
dict(some_arg=i, num_gpus=num_gpus),
gpu_req=num_gpus,
uid=str(i),
)
workflow.run(
max_gpus=len(os.environ["COSMOS_LOCAL_GPU_DEVICES"].split(",")),
cmd_wrapper=py_call,
)
def __init__(self, config_path, reference_version):
assert os.path.exists(config_path), '%s does not exist' % config_path
assert reference_version in [
'hg38', 'b37'
], 'bad reference_version: %s' % reference_version
self.config_path = config_path
self.config = ConfigParser(interpolation=ExtendedInterpolation())
self.config.read(config_path)
self.config.add_section('ref')
for k, v in self.config['ref_%s' % reference_version].iteritems():
self.config.set('ref', k, v)
assert len(self.config['ref'].items()) > 1
# set_env_aws_credentials()
os.environ['REQUESTS_CA_BUNDLE'] = '/etc/ssl/certs/ca-certificates.crt'
# export REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt
from cosmos.web.gemon.views import bprint as gemon_bprint
from cosmos.api import Cosmos, default_get_submit_args
from functools import partial
from flask import Flask
flask_app = Flask('genomekey',
template_folder=os.path.join(library_path,
'web/templates'))
flask_app.secret_key = '\x16\x89\xf5-\tK`\xf5FY.\xb9\x9c\xb4qX\xfdm\x19\xbd\xdd\xef\xa9\xe2'
flask_app.register_blueprint(gemon_bprint, url_prefix='/gemon')
self.flask_app = flask_app
self.cosmos_app = Cosmos(self.config['gk']['database_url'],
default_drm=self.config['gk']['default_drm'],
flask_app=flask_app,
get_submit_args=partial(
default_get_submit_args,
parallel_env='orte'))
def main():
p = ArgumentParser()
p.add_argument("--sleep", default=0, type=int)
args = p.parse_args()
cosmos = Cosmos("cosmos.sqlite").initdb()
workflow = cosmos.start("ex3", restart=True, skip_confirm=True)
t1 = workflow.add_task(func=say,
params=dict(text="Hello World", out_file="out.txt"),
uid="my_task")
t2 = workflow.add_task(func=sleep,
params=dict(secs=args.sleep),
uid="my_task")
workflow.make_output_dirs()
workflow.run(cmd_wrapper=py_call)
def main():
p = argparse.ArgumentParser()
p.add_argument("-drm",
default="local",
help="",
choices=("local", "awsbatch", "slurm", "drmaa:ge", "ge"))
p.add_argument("-q",
"--queue",
help="Submit to this queue if the DRM supports it")
args = p.parse_args()
cosmos = Cosmos("cosmos.sqlite",
default_drm=args.drm,
default_max_attempts=2,
default_queue=args.queue)
cosmos.initdb()
workflow = cosmos.start("Example2", skip_confirm=True)
recipe(workflow)
# any parameters that start with out_ are output directories, and will be created if
# the user calls workflow.make_output_dirs
workflow.make_output_dirs()
workflow.run(max_cores=10, cmd_wrapper=py_call)
# Noting here that if you wanted to look at the outputs of any Tasks to decide how to generate the rest of a DAG
# you can do so here, proceed to add more tasks via workflow.add_task(), and then call workflow.run() again.
# Yes, it does require running all Tasks in the dag to get the outputs of any Task, and we hope to address
# that limitation at some point in the future.
if pygraphviz_available:
# These images can also be seen on the fly in the web-interface
draw_stage_graph(workflow.stage_graph(),
"/tmp/ex1_task_graph.png",
format="png")
draw_task_graph(workflow.task_graph(),
"/tmp/ex1_stage_graph.png",
format="png")
else:
print("Pygraphviz is not available :(")
sys.exit(0 if workflow.successful else 1)
def main():
args = parse_args()
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
default_drm="awsbatch",
default_drm_options=dict(
container_image=args.container_image,
s3_prefix_for_command_script_temp_files=args.
s3_prefix_for_command_script_temp_files,
# only retry on spot instance death
retry_only_if_status_reason_matches="Host EC2 .+ terminated.",
),
default_queue=args.default_queue,
)
cosmos.initdb()
sp.check_call("mkdir -p analysis_output/ex1", shell=True)
os.chdir("analysis_output/ex1")
workflow = cosmos.start("Example1", restart=True, skip_confirm=True)
t = workflow.add_task(
func=get_instance_info,
params=dict(out_s3_uri=args.out_s3_uri, sleep=args.sleep),
uid="",
time_req=None,
max_attempts=args.max_attempts,
core_req=args.core_req,
mem_req=1024,
)
workflow.run()
print(("task.params", t.params))
print(("task.input_map", t.input_map))
print(("task.output_map", t.output_map))
print(("task.core_req", t.core_req))
print(("task.time_req", t.time_req))
print(("task.drm", t.drm))
print(("task.uid", t.uid))
print(("task.drm_options", t.drm_options))
print(("task.queue", t.queue))
sys.exit(0 if workflow.successful else 1)
def main(output_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
cosmos = Cosmos()
cosmos.initdb()
workflow = cosmos.start(
"test", skip_confirm=True, primary_log_path=os.path.join(output_dir, "workflow.log"),
)
for i, num_gpus in enumerate([1, 1, 2, 2, 3]):
task = workflow.add_task(
use_cuda_device, dict(some_arg=i, num_gpus=num_gpus), gpu_req=num_gpus, uid=str(i),
)
workflow.run(
max_gpus=len(os.environ["COSMOS_LOCAL_GPU_DEVICES"].split(",")),
cmd_wrapper=py_call_cmd_wrapper,
do_cleanup_atexit=False,
log_out_dir_func=partial(default_task_log_output_dir, prefix="%s" % output_dir),
)
def main():
p = ArgumentParser()
p.add_argument("--sleep", default=0, type=int)
args = p.parse_args()
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
default_drm="local",
)
cosmos.initdb()
workflow = cosmos.start("Example3", restart=True, skip_confirm=True)
t1 = workflow.add_task(func=say,
params=dict(text="Hello World", out_file="out.txt"),
uid="my_task")
t2 = workflow.add_task(func=sleep,
params=dict(secs=args.sleep),
uid="my_task")
workflow.make_output_dirs()
workflow.run(cmd_wrapper=py_call_cmd_wrapper)
def __init__(self, config, drm, restart):
self.config = config
self.cosmos = Cosmos(database_url='sqlite:///{}'.format(
self.config.db),
get_submit_args=default_get_submit_args,
default_drm=drm)
self.cosmos.initdb()
primary_logfile = os.path.join(
self.config.rootdir,
'{}.log'.format(self.config.project_name),
)
self.workflow = self.cosmos.start(
self.config.project_name,
primary_log_path=primary_logfile,
restart=restart,
)
self.setup_pipeline()
def main():
cosmos = Cosmos().initdb()
workflow = cosmos.start("duplicate_uids", skip_confirm=True)
task = workflow.add_task(func=prepare_data, params=dict(a=1), uid="x")
# normally you can't add a task with the same uid to the same stage
with pytest.raises(DuplicateUid):
workflow.add_task(func=prepare_data, params=dict(a=1), uid="x")
# normally you can't add a task with the same uid to the same stage
with pytest.raises(DuplicateUid):
workflow.add_task(func=prepare_data, params=dict(a=1), uid="x")
# set if_duplicate="return" to True to get the same task back that you added
task2 = workflow.add_task(func=prepare_data,
params=dict(a=1),
uid="x",
if_duplicate="return")
assert task == task2
# this can be especially useful in loops to avoid repeating computation
for _ in range(3):
task = workflow.add_task(func=prepare_data,
params=dict(a=1),
uid="x",
if_duplicate="return")
workflow.add_task(func=train_machine_learning_model,
params=dict(a=1),
uid="x",
if_duplicate="return",
parents=task)
# NOTE: parameters must be identical when using this feature
with pytest.raises(InvalidParams):
workflow.add_task(func=prepare_data,
params=dict(a=1000),
uid="x",
if_duplicate="return")
help='',
choices=('local', 'drmaa:ge', 'ge', 'slurm'))
p.add_argument('-j',
'--job-class',
help='Submit to this job class if the DRM supports it')
p.add_argument('-q',
'--queue',
help='Submit to this queue if the DRM supports it')
args = p.parse_args()
cosmos = Cosmos(
'sqlite:///%s/sqlite.db?check_same_thread=False' %
os.path.dirname(os.path.abspath(__file__)),
# example of how to change arguments if you're not using default_drm='local'
get_submit_args=partial(default_get_submit_args, parallel_env='smp'),
default_drm=args.drm,
default_max_attempts=2,
default_job_class=args.job_class,
default_queue=args.queue)
cosmos.initdb()
sp.check_call('mkdir -p analysis_output/ex2', shell=True)
os.chdir('analysis_output/ex2')
workflow = cosmos.start('Example2', restart=True, skip_confirm=True)
recipe(workflow)
workflow.make_output_dirs()
workflow.run(max_cores=10)
def make_app(database_url):
cosmos = Cosmos(database_url)
flask = cosmos.init_flask()
return flask
from cosmos.api import Execution, add_execution_args, Cosmos
from configparser import ConfigParser
from cosmos.util.helpers import mkdir
root_path = os.path.dirname(os.path.realpath(__file__))
config = ConfigParser()
config.read(os.path.join(root_path, 'settings.conf'))
settings = config['main']
if __name__ == '__main__':
import sys
#sys.path.append('./src')
#from src import testxx
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--growl', action='store_true',
help='sends a growl notification on execution status changes')
parser.add_argument('-d', '--debug', action='store_true',
help='launch ipdb on exception')
sps = parser.add_subparsers(title="Commands", metavar="<command>")
sp = sps.add_parser('resetdb', help=cosmos.resetdb.__doc__)
sp.set_defaults(func=cosmos.resetdb)
sp = sps.add_parser('initdb', help=cosmos.initdb.__doc__)
# all of the WordCounts (a many2one relationship).
summarize = execution.add_task(cat, dict(), word_counts, '',
'Summary_Analysis')
if pygraphviz_available:
# These images can also be seen on the fly in the web-interface
draw_stage_graph(execution.stage_graph(),
'/tmp/ex2_task_graph.png',
format='png')
draw_task_graph(execution.task_graph(),
'/tmp/ex2_stage_graph.png',
format='png')
else:
print 'Pygraphviz is not available :('
execution.run()
if __name__ == '__main__':
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start('Example2',
'analysis_output/ex2',
max_attempts=1,
restart=True,
skip_confirm=True,
max_cpus=10)
run_ex2(execution)
def make_app(database_url):
cosmos = Cosmos(database_url)
flask = cosmos.init_flask()
return flask
"""
Basic demonstration the structure of a Task instance
"""
import subprocess as sp
import os
import sys
from cosmos.api import Cosmos, default_get_submit_args
from functools import partial
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)),
get_submit_args=partial(default_get_submit_args,
parallel_env='smp'),
default_drm='local')
cosmos.initdb()
sp.check_call('mkdir -p analysis_output/ex1', shell=True)
os.chdir('analysis_output/ex1')
workflow = cosmos.start('Example1', restart=True, skip_confirm=True)
def say(text, out_file, core_req=1):
return r"""
echo "{text}" > {out_file}
""".format(text=text, out_file=out_file)
t = workflow.add_task(func=say,
params=dict(text='Hello World',
out_file='out.txt',
core_req=2),
"""
Basic demonstration the structure of a Task instance
"""
import subprocess as sp
import os
import sys
from cosmos.api import Cosmos
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)),
default_drm='local')
cosmos.initdb()
sp.check_call('mkdir -p analysis_output/ex1', shell=True)
os.chdir('analysis_output/ex1')
workflow = cosmos.start('Example1', restart=True, skip_confirm=True)
def say(text, out_file):
return r"""
echo "{text}" > {out_file}
""".format(text=text, out_file=out_file)
t = workflow.add_task(func=say,
params=dict(
text='Hello World',
out_file='out.txt',
),
uid='my_task',
time_req=None,
from cosmos.api import Cosmos, signal_execution_status_change, ExecutionStatus
from ex1 import run_ex1
import os
from cosmos.util.helpers import mkdir
def run_ex3(execution):
@signal_execution_status_change.connect
def sig(ex):
msg = "%s %s" % (ex, ex.status)
if ex.status in [ExecutionStatus.successful, ExecutionStatus.failed, ExecutionStatus.killed]:
text_message(msg)
ex.log.info('Sent a text message')
def text_message(message):
from twilio.rest import TwilioRestClient
account = "XYZ"
token = "XYZ"
client = TwilioRestClient(account, token)
message = client.messages.create(to="+1231231234", from_="+1231231234", body=message)
run_ex1(execution)
if __name__ == '__main__':
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start('Example_Email', 'analysis_output/ex3', max_attempts=2, restart=True, skip_confirm=True)
run_ex1(execution)
def variant_call(execution, bam_path, target_bed_path, max_complex_gap):
"""
Bioinformatics variant calling workflow
"""
contigs = sp.check_output("cat %s |cut -f1|uniq" % target_bed_path, shell=True).strip().split("\n")
bed_tasks = [execution.add_task(tools.filter_bed_by_contig, tags=dict(in_bam=bam_path, in_bed=target_bed_path, contig=contig), out_dir='work/{contig}')
for contig in contigs ]
freebayes_tasks = one2one(tools.freebayes, bed_tasks, dict(max_complex_gap=max_complex_gap))
merge_vcf_tasks = many2one(tools.vcf_concat_parts, freebayes_tasks)
execution.run()
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('bam_path')
p.add_argument('target_bed_path')
p.add_argument('--max_complex_gap', type=int, default=2)
add_execution_args(p)
start_kwargs, variant_call_args = pop_execution_args(vars(p.parse_args()))
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start(output_dir='../analysis_output/variant_calling', **start_kwargs)
variant_call(execution, **variant_call_args)
"""
Basic demonstration the structure of a Task instance
"""
import os
from cosmos.api import Cosmos
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start('Example1', 'analysis_output/ex3', restart=True, skip_confirm=True)
def cmd(in_files, out_file):
return r"""
echo "{in_files}" > {out_file}
""".format(**locals())
t = execution.add_task(cmd, tags=dict(in_files=[('a', 'b', 'in_file')], out_file='out.txt'))
print 'Task:', t
print 'task.tags', t.tags
print 'task.input_files', t.input_files
print 'task.output_files', t.output_files
#execution.run()
tags=dict(chars=True, **cat_task.tags),
parents=[cat_task],
out_dir='{word}/{n}')
for cat_task in cats]
# Cat the contents of all word_counts into one file. Only one node is being created who's parents are
# all of the WordCounts (a many2one relationship).
summarize = execution.add_task(cat,
tags=dict(),
parents=word_counts,
out_dir='',
stage_name='Summary_Analysis')
if pygraphviz_available:
# These images can also be seen on the fly in the web-interface
draw_stage_graph(execution.stage_graph(), '/tmp/ex1_task_graph.png', format='png')
draw_task_graph(execution.task_graph(), '/tmp/ex1_stage_graph.png', format='png')
else:
print 'Pygraphviz is not available :('
execution.run()
if __name__ == '__main__':
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start('Example1', 'analysis_output/ex1', max_attempts=1, restart=True, skip_confirm=True,
max_cpus=10)
run_ex1(execution)
with open(out_file) as fp:
i = int(fp.read())
else:
i = 0
with open(out_file, "w") as fp:
fp.write(str(i + 1))
if i < 2:
# fail the first 2 times
raise
if __name__ == "__main__":
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
default_drm="local",
)
cosmos.initdb()
workflow = cosmos.start("ExampleReattempt",
restart=True,
skip_confirm=True)
if os.path.exists("out.txt"):
os.unlink("out.txt")
t = workflow.add_task(func=add_one,
params=dict(out_file="out.txt"),
uid="my_task",
max_attempts=3)
workflow.make_output_dirs()
load_dotenv()
def set_env_variables():
return "\n".join([
f'export {variable}="{os.getenv(variable)}"'
for variable in env_variables
])
cosmos = Cosmos(
RDS_COSMOS_DATABASE,
default_drm="awsbatch",
default_drm_options=dict(
container_image=os.getenv("ECR_CONTAINER_IMAGE"),
s3_prefix_for_command_script_temp_files=os.path.join(
S3_BUCKET_PATH, "cosmos-tmp"),
shm_size=int(args.mem_req * 0.75),
retry_only_if_status_reason_matches=
"Host EC2 .+ terminated.", # only retry on spot instance death
),
default_queue=os.getenv("BATCH_QUEUE_NAME"),
)
cosmos.initdb()
workflow_name = f"{args.name}-{uuid1().hex}"
workflow = cosmos.start(workflow_name, restart=True, skip_confirm=True)
task_name = uuid1().hex
workflow.add_task(
func=pretrain,
class Pipeline(object):
def __init__(self, config, drm, restart, skip_confirm):
self.config = config
self.cosmos = Cosmos(database_url='sqlite:///{}'.format(
self.config.db),
get_submit_args=default_get_submit_args,
default_drm=drm)
self.cosmos.initdb()
primary_logfile = os.path.join(
self.config.rootdir,
'{}.log'.format(self.config.project_name),
)
self.workflow = self.cosmos.start(
self.config.project_name,
primary_log_path=primary_logfile,
restart=restart,
skip_confirm=skip_confirm,
)
self.setup_pipeline()
def setup_pipeline(self):
self.construct_pipeline()
self.workflow.make_output_dirs()
def run(self, task_flush):
# put set_successful to False if you intend to add more tasks to the
# pipeline later
custom_log_dir = lambda task: os.path.join(self.config.rootdir, 'logs',
task.stage.name, task.uid)
self.workflow.run(set_successful=False,
log_out_dir_func=custom_log_dir,
db_task_flush=task_flush)
def construct_pipeline(self):
# 1. remove unused alternates
remove_ac_0_tasks = self.create_remove_ac_0_tasks(1)
# 2. calculate sample missingness (counting phase)
count_sample_missingness_tasks = self.create_count_sample_missingness_tasks(
remove_ac_0_tasks, 2)
# 2.1 calculate sample missingness (merge and calculation phase)
calculate_sample_missingness_task = self.create_calculate_sample_missingness_task(
count_sample_missingness_tasks, 2.1)
# 3. denormalize, decompose, and uniq
dnu_tasks = self.create_decompose_normalize_unique_tasks(
remove_ac_0_tasks, 3)
# 4. remove symbolic alleles
rsa_tasks = self.create_remove_symbolic_deletion_tasks(dnu_tasks, 4)
# 5. filter missingness
filter_variant_missingness_tasks = self.create_filter_variant_missingness_tasks(
rsa_tasks, 5)
# 6. annotate allele balances
allele_balance_annotation_tasks = self.create_allele_balance_annotation_tasks(
filter_variant_missingness_tasks, 6)
# 7. annotate with 1000G
annotate_1000G_tasks = self.create_1000G_annotation_tasks(
allele_balance_annotation_tasks, 7)
# 8. annotate with ExAC
annotate_ExAC_tasks = self.create_ExAC_annotation_tasks(
annotate_1000G_tasks, 8)
# 9. VEP annotation
annotate_vep_cadd_tasks = self.create_vep_cadd_annotation_tasks(
annotate_ExAC_tasks, 9)
# 10. VCF concatenation
concatenated_vcfs = self.create_concatenate_vcfs_task(
annotate_vep_cadd_tasks, 10)
# 11. bcftools stats
bcftools_stats_tasks = self.create_bcftools_stats_tasks(
annotate_ExAC_tasks, 11)
# 11.1 Merge & Plot bcftools stats
bcftools_stats_summary_task = self.create_bcftools_stats_summary_task(
bcftools_stats_tasks, 11.1)
# 12. GATK VariantEval
variant_eval_tasks = self.create_variant_eval_tasks(
annotate_ExAC_tasks, 12)
# 12.1. Merge & Plot GATK VariantEval Stats
variant_eval_summary_task = self.create_variant_eval_summary_task(
variant_eval_tasks, 12.1)
def create_bcftools_stats_summary_task(self, parent_tasks, step_number):
stage = self._construct_task_name('bcftools-stats-summary',
step_number)
output_dir = os.path.join(self.config.rootdir, stage)
prior_stage_name = parent_tasks[0].stage.name
input_dir = os.path.join(self.config.rootdir, prior_stage_name)
lsf_params = get_lsf_params(bcftools_stats_summary_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
task = {
'func': bcftools_stats_summary,
'params': {
'in_dir': input_dir,
'out_dir': output_dir,
},
'stage_name': stage,
'uid': 'all-chroms',
'drm_params': lsf_params_json,
'parents': parent_tasks,
}
summary_task = self.workflow.add_task(**task)
return summary_task
def create_concatenate_vcfs_task(self, parent_tasks, step_number):
tasks = list()
stage = self._construct_task_name('concat-vcfs', step_number)
output_dir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(concatenate_vcfs_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
def region_key(task):
reference_fai = os.path.join(
'/gscmnt/ams1102/info/model_data/2869585698/build106942997',
'all_sequences.fa.fai')
return Region(all_sequences.fa.fai, task.params['in_chrom'])
def chromosome_key(task):
reference_fai = os.path.join(
'/gscmnt/ams1102/info/model_data/2869585698/build106942997',
'all_sequences.fa.fai')
return Region(reference_fai, task.params['in_chrom']).chrom
for ref_chrom, chrom_tasks in groupby(sorted(parent_tasks,
key=region_key),
key=chromosome_key):
ptasks = list(chrom_tasks)
input_vcfs = [x.params['out_vcf'] for x in ptasks]
output_vcf = 'concatenated.c{}.vcf.gz'.format(ref_chrom)
output_log = 'concatenate.{}.log'.format(ref_chrom)
task = {
'func': concatenate_vcfs,
'params': {
'in_vcfs': input_vcfs,
'in_chrom': ref_chrom,
'out_vcf': os.path.join(output_dir, ref_chrom, output_vcf),
'out_log': os.path.join(output_dir, ref_chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=ref_chrom),
'drm_params': lsf_params_json,
'parents': ptasks,
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_variant_eval_summary_task(self, parent_tasks, step_number):
stage = self._construct_task_name('gatk-variant-eval-summary',
step_number)
output_dir = os.path.join(self.config.rootdir, stage)
prior_stage_name = parent_tasks[0].stage.name
input_dir = os.path.join(self.config.rootdir, prior_stage_name)
lsf_params = get_lsf_params(variant_eval_summary_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
task = {
'func': variant_eval_summary,
'params': {
'in_dir': input_dir,
'out_dir': output_dir,
},
'stage_name': stage,
'uid': 'all-chroms',
'drm_params': lsf_params_json,
'parents': parent_tasks,
}
summary_task = self.workflow.add_task(**task)
return summary_task
def create_bcftools_stats_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('bcftools-stats', step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(bcftools_stats_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_stats = '{}.stats.out'.format(chrom)
task = {
'func': bcftools_stats,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_stats': os.path.join(basedir, chrom, output_stats),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_variant_eval_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('gatk-variant-eval', step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(gatk_variant_eval_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_stats = 'chrom-{}-variant-eval.out'.format(chrom)
output_log = 'chrom-{}-variant-eval.log'.format(chrom)
task = {
'func': gatk_variant_eval,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_stats': os.path.join(basedir, chrom, output_stats),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_vep_cadd_annotation_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('vep-cadd-annotation', step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(annotation_vep_cadd_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'annotated.vep.cadd.c{}.vcf.gz'.format(chrom)
output_log = 'vep.cadd.annotation.{}.log'.format(chrom)
task = {
'func': annotation_vep_cadd,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_ExAC_annotation_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('annotate-w-ExAC', step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(annotation_ExAC_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'ExAC-annotated.c{}.vcf.gz'.format(chrom)
output_log = 'ExAC-annotate.{}.log'.format(chrom)
task = {
'func': annotation_ExAC,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_1000G_annotation_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('annotate-w-1000G', step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(annotation_1000G_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = '1kg-annotated.c{}.vcf.gz'.format(chrom)
output_log = '1000G-annotate.{}.log'.format(chrom)
task = {
'func': annotation_1000G,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_allele_balance_annotation_tasks(self, parent_tasks,
step_number):
tasks = []
stage = self._construct_task_name('allele-balance-annotation',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(annotate_allele_balances_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'combined.c{chrom}.vcf.gz'.format(chrom=chrom)
output_log = 'allele-balance-{}.log'.format(chrom)
task = {
'func': annotate_allele_balances,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_filter_variant_missingness_tasks(self, parent_tasks,
step_number):
tasks = []
stage = self._construct_task_name('filter-variant-missingness',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(filter_variant_missingness_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'combined.c{chrom}.vcf.gz'.format(chrom=chrom)
output_log = 'filter-missingness-{}.log'.format(chrom)
task = {
'func': filter_variant_missingness,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_remove_symbolic_deletion_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('remove-symbolic-alleles',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(
remove_symbolic_deletion_alleles_lsf_params, self.config.email,
self.config.docker)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'combined.c{chrom}.vcf.gz'.format(chrom=chrom)
output_log = 'remove-symbolic-alleles-chrom-{}.log'.format(chrom)
task = {
'func': remove_symbolic_deletion_alleles,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_decompose_normalize_unique_tasks(self, parent_tasks,
step_number):
tasks = []
stage = self._construct_task_name('decompose-normalize-uniq',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(normalize_decompose_unique_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
output_vcf = 'combined.c{chrom}.vcf.gz'.format(chrom=chrom)
output_log = 'decompose-normalize-unique-{}.log'.format(chrom)
task = {
'func': normalize_decompose_unique,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_calculate_sample_missingness_task(self, parent_tasks,
step_number):
stage = self._construct_task_name('calculate-sample-missingness',
step_number)
output_dir = os.path.join(self.config.rootdir, stage)
prior_stage_name = parent_tasks[0].stage.name
input_dir = os.path.join(self.config.rootdir, prior_stage_name)
input_json_wildcard_path = os.path.join(input_dir, '*', '*.json')
lsf_params = get_lsf_params(calculate_sample_missingness_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
task = {
'func': calculate_sample_missingness,
'params': {
'in_json':
input_json_wildcard_path,
'out_stats':
os.path.join(output_dir, 'sample-missingness-pct.dat'),
'out_log':
os.path.join(output_dir, 'sample-missingness-pct.dat.log'),
},
'stage_name': stage,
'uid': '1-22',
'drm_params': lsf_params_json,
'parents': parent_tasks,
}
summary_task = self.workflow.add_task(**task)
return summary_task
def create_count_sample_missingness_tasks(self, parent_tasks, step_number):
tasks = []
stage = self._construct_task_name('count-sample-missingness',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(count_sample_missingness_lsf_params,
self.config.email, self.config.docker,
self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for ptask in parent_tasks:
chrom = ptask.params['in_chrom']
# only count missing genotypes on chromosomes 1-22 (not X, Y, or MT)
if not chrom[0].isdigit(): continue
output_json = '{chrom}-sample-missingness-counts.json'.format(
chrom=chrom)
output_log = '{}-sample-missingness-counts.log'.format(chrom)
task = {
'func': count_sample_missingness,
'params': {
'in_vcf': ptask.params['out_vcf'],
'in_chrom': chrom,
'out_json': os.path.join(basedir, chrom, output_json),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
'parents': [ptask],
}
tasks.append(self.workflow.add_task(**task))
return tasks
def create_remove_ac_0_tasks(self, step_number):
tasks = []
stage = self._construct_task_name('select-variants-ac-0-removal',
step_number)
basedir = os.path.join(self.config.rootdir, stage)
lsf_params = get_lsf_params(
gatk_select_variants_remove_ac_0_lsf_params, self.config.email,
self.config.docker, self.config.drm_queue)
lsf_params_json = to_json(lsf_params)
for chrom in self.config.chroms:
vcf = self.config.vcfs[chrom]
output_vcf = 'combined.c{chrom}.vcf.gz'.format(chrom=chrom)
output_log = 'select-variants-chrom-{}-gatk.log'.format(chrom)
task = {
'func': gatk_select_variants_remove_ac_0,
'params': {
'in_chrom': chrom,
'in_vcf': vcf,
'out_vcf': os.path.join(basedir, chrom, output_vcf),
'out_log': os.path.join(basedir, chrom, output_log),
},
'stage_name': stage,
'uid': '{chrom}'.format(chrom=chrom),
'drm_params': lsf_params_json,
}
tasks.append(self.workflow.add_task(**task))
return tasks
def _construct_task_name(self, name, number):
task_name = '{}-{}'.format(number, name)
return task_name
stage_name='Summary_Analysis',
uid='') # It's the only Task in this Stage, so doesn't need a specific uid
if __name__ == '__main__':
import argparse
p = argparse.ArgumentParser()
p.add_argument('-drm', default='local', help='', choices=('local', 'drmaa:ge', 'ge'))
p.add_argument('-q', '--queue', help='Submit to this queue of the DRM supports it')
args = p.parse_args()
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)),
# example of how to change arguments if you're NOT using default_drm='local'
get_submit_args=partial(default_get_submit_args, parallel_env='smp'),
default_drm=args.drm,
default_queue=args.queue)
cosmos.initdb()
sp.check_call('mkdir -p analysis_output/ex2', shell=True)
os.chdir('analysis_output/ex2')
workflow = cosmos.start('Example2', restart=True, skip_confirm=True)
recipe(workflow)
workflow.make_output_dirs()
workflow.run(max_attempts=1, max_cores=10)
if pygraphviz_available:
from cosmos.api import Cosmos, signal_workflow_status_change, WorkflowStatus
from ex1 import run_ex1
import os
def run_ex3(workflow):
@signal_workflow_status_change.connect
def sig(ex):
msg = "%s %s" % (ex, ex.status)
if ex.status in [WorkflowStatus.successful, WorkflowStatus.failed, WorkflowStatus.killed]:
text_message(msg)
ex.log.info('Sent a text message')
def text_message(message):
from twilio.rest import TwilioRestClient
account = "XYZ"
token = "XYZ"
client = TwilioRestClient(account, token)
message = client.messages.create(to="+1231231234", from_="+1231231234", body=message)
run_ex1(workflow)
if __name__ == '__main__':
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
workflow = cosmos.start('Example_Email', 'analysis_output/ex3', restart=True, skip_confirm=True)
run_ex1(workflow)
"""
Bioinformatics variant calling workflow
"""
contigs = sp.check_output("cat %s |cut -f1|uniq" % target_bed_path, shell=True).strip().split("\n")
freebayes_tasks = []
for contig in contigs:
bed_task = execution.add_task(tools.filter_bed_by_contig, tags=dict(in_bam=bam_path, in_bed=target_bed_path, contig=contig), out_dir='work/{contig}')
freebayes_task = execution.add_task(tools.freebayes, tags=dict(max_complex_gap=max_complex_gap), parents=bed_task, out_dir='work/{contig}')
freebayes_tasks.append(freebayes_task)
merge_vcf_tasks = many2one(tools.vcf_concat_parts, parents=freebayes_tasks)
execution.run(max_attempts=max_attempts, max_cores=max_cores)
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('bam_path')
p.add_argument('target_bed_path')
p.add_argument('--max_complex_gap', type=int, default=2)
add_execution_args(p)
args = p.parse_args()
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start(name=args.name, output_dir='../analysis_output/variant_calling',
restart=args.restart, skip_confirm=args.skip_confirm)
variant_call(execution, args.max_attempts, args.max_cores, args.bam_path, args.target_bed_path, args.max_complex_gap)
def main():
args = parse_args()
cosmos = Cosmos(
"sqlite:///%s/sqlite.db" % os.path.dirname(os.path.abspath(__file__)),
default_drm="awsbatch",
default_drm_options=dict(
container_image=args.container_image,
s3_prefix_for_command_script_temp_files=args.s3_prefix_for_command_script_temp_files,
# only retry on spot instance death
retry_only_if_status_reason_matches="Host EC2 .+ terminated.",
),
default_queue=args.default_queue,
)
cosmos.initdb()
# sp.check_call("mkdir -p analysis_output/ex1", shell=True)
# os.chdir("analysis_output/ex1")
workflow = cosmos.start(f"Evaluate_{args.id}", restart=True, skip_confirm=True)
parameters = np.load(f"optimize_awsbatch/parameters/{args.id}.npy")
for i, par in enumerate(parameters):
parameters_ = dict(
mean_weight=par[0],
c_w=par[1],
tau_pos=par[2],
tau_neg=par[3],
A_pos=par[4],
A_neg=par[5],
weight_decay=par[6],
n_filters=25,
time_max=250,
crop=20,
kernel_size=16,
stride=4,
intensity=127.5,
c_w_min=None,
c_l=True,
network_type="LC_SNN",
)
workflow.add_task(
func=evaluate,
params=dict(
parameters=parameters_,
out_s3_uri=f"{args.out_s3_uri}/scores/{args.id}/{i}.json",
sleep=args.sleep,
train=args.train,
calibrate=args.calibrate,
test=args.test
),
uid=str(i),
time_req=None,
max_attempts=args.max_attempts,
core_req=args.core_req,
mem_req=args.mem_req,
)
workflow.run()
sys.exit(0 if workflow.successful else 1)
import subprocess as sp
import os
import sys
from cosmos.api import Cosmos
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)),
default_drm='local')
cosmos.initdb()
sp.check_call('mkdir -p analysis_output/ex1', shell=True)
os.chdir('analysis_output/ex1')
workflow = cosmos.start('Example1', restart=True, skip_confirm=True)
def say(text, out_file):
return r"""
echo "{text}" > {out_file}
""".format(text=text, out_file=out_file)
t = workflow.add_task(func=say,
params=dict(text='Hello World', out_file='out.txt',),
uid='my_task', time_req=None, core_req=1, mem_req=1024)
print('task.params', t.params)
print('task.input_map', t.input_map)
print('task.output_map', t.output_map)
print('task.core_req', t.core_req)
print('task.time_req', t.time_req)
print('task.drm', t.drm)
print('task.uid', t.uid)
"""
Basic demonstration the structure of a Task instance
"""
import os
from cosmos.api import Cosmos
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start('Example1',
'analysis_output/ex3',
restart=True,
skip_confirm=True)
def cmd(in_files, out_file):
return r"""
echo "{in_files}" > {out_file}
""".format(**locals())
t = execution.add_task(cmd,
tags=dict(in_files=[('a', 'b', 'in_file')],
out_file='out.txt'))
print 'Task:', t
print 'task.tags', t.tags
print 'task.input_files', t.input_files
print 'task.output_files', t.output_files
#--------------------
# Generation des schemas du workflow (si pygraphviz installe)
if pygraphviz_available:
# These images can also be seen on the fly in the web-interface
draw_stage_graph(execution.stage_graph(), 'testing/workflow_info/test_task_graph.png', format='png')
draw_task_graph(execution.task_graph(), 'testing/workflow_info/test_stage_graph.png', format='png')
else:
print 'Pygraphviz is not available :('
execution.run(max_attempts=1, max_cores=10)
#------------------------
# EXECUTION DU WORKFLOW
if __name__ == '__main__':
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
subprocess.check_call('mkdir -p testing testing/data testing/results testing/workflow_info', shell=True)
#subprocess.check_call('cp extdata/4.fastq testing/data', shell=False)
execution = cosmos.start('Testx', 'testing',restart=True, skip_confirm=True)
run_test(execution)
"""else:
#--------------------
# Connexion aux services cosmos (BDD)
cosmos = Cosmos('sqlite:///sqlite.db')
cosmos.initdb()
#--------------------
# Creation des sous-dossiers resultats
subprocess.check_call('mkdir -p testing testing/data testing/results testing/workflow_info', shell=True)
from cosmos.api import Execution, add_execution_args, Cosmos
from configparser import ConfigParser
from cosmos.util.helpers import mkdir
root_path = os.path.dirname(os.path.realpath(__file__))
config = ConfigParser()
config.read(os.path.join(root_path, 'settings.conf'))
settings = config['main']
if __name__ == '__main__':
import ex1
import ex_fail
import ex_email
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'-g',
'--growl',
action='store_true',
help='sends a growl notification on execution status changes')
parser.add_argument('-d',
'--debug',
action='store_true',
help='launch ipdb on exception')
sps = parser.add_subparsers(title="Commands", metavar="<command>")
execution.add_task(tools.filter_bed_by_contig,
tags=dict(in_bam=bam_path,
in_bed=target_bed_path,
contig=contig),
out_dir='work/{contig}') for contig in contigs
]
freebayes_tasks = one2one(tools.freebayes, bed_tasks,
dict(max_complex_gap=max_complex_gap))
merge_vcf_tasks = many2one(tools.vcf_concat_parts, freebayes_tasks)
execution.run()
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('bam_path')
p.add_argument('target_bed_path')
p.add_argument('--max_complex_gap', type=int, default=2)
add_execution_args(p)
start_kwargs, variant_call_args = pop_execution_args(vars(p.parse_args()))
cosmos = Cosmos('sqlite:///%s/sqlite.db' %
os.path.dirname(os.path.abspath(__file__)))
cosmos.initdb()
execution = cosmos.start(output_dir='../analysis_output/variant_calling',
**start_kwargs)
variant_call(execution, **variant_call_args)
import os
from cosmos.api import Cosmos
cosmos = Cosmos('sqlite:///%s/sqlite.db' % os.path.dirname(os.path.abspath(__file__)))
cosmos.runweb('0.0.0.0', 5151)