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__init__.py
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__init__.py
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__author__ = "Johannes Köster"
__copyright__ = "Copyright 2022, Johannes Köster"
__email__ = "johannes.koester@uni-due.de"
__license__ = "MIT"
import os
import subprocess
import glob
from argparse import ArgumentError, ArgumentDefaultsHelpFormatter
import logging as _logging
import re
import sys
import inspect
import threading
import webbrowser
from functools import partial
import importlib
import shutil
import shlex
from importlib.machinery import SourceFileLoader
from snakemake.workflow import Workflow
from snakemake.dag import Batch
from snakemake.exceptions import print_exception, WorkflowError
from snakemake.logging import setup_logger, logger, SlackLogger, WMSLogger
from snakemake.io import load_configfile, wait_for_files
from snakemake.shell import shell
from snakemake.utils import update_config, available_cpu_count
from snakemake.common import Mode, __version__, MIN_PY_VERSION, get_appdirs
from snakemake.resources import parse_resources, DefaultResources
SNAKEFILE_CHOICES = [
"Snakefile",
"snakefile",
"workflow/Snakefile",
"workflow/snakefile",
]
def snakemake(
snakefile,
batch=None,
cache=None,
report=None,
report_stylesheet=None,
containerize=False,
lint=None,
generate_unit_tests=None,
listrules=False,
list_target_rules=False,
cores=1,
nodes=None,
local_cores=1,
max_threads=None,
resources=dict(),
overwrite_threads=None,
overwrite_scatter=None,
default_resources=None,
overwrite_resources=None,
config=dict(),
configfiles=None,
config_args=None,
workdir=None,
targets=None,
dryrun=False,
touch=False,
forcetargets=False,
forceall=False,
forcerun=[],
until=[],
omit_from=[],
prioritytargets=[],
stats=None,
printreason=False,
printshellcmds=False,
debug_dag=False,
printdag=False,
printrulegraph=False,
printfilegraph=False,
printd3dag=False,
nocolor=False,
quiet=False,
keepgoing=False,
cluster=None,
cluster_config=None,
cluster_sync=None,
drmaa=None,
drmaa_log_dir=None,
jobname="snakejob.{rulename}.{jobid}.sh",
immediate_submit=False,
standalone=False,
ignore_ambiguity=False,
snakemakepath=None,
lock=True,
unlock=False,
cleanup_metadata=None,
conda_cleanup_envs=False,
cleanup_shadow=False,
cleanup_scripts=True,
force_incomplete=False,
ignore_incomplete=False,
list_version_changes=False,
list_code_changes=False,
list_input_changes=False,
list_params_changes=False,
list_untracked=False,
list_resources=False,
summary=False,
archive=None,
delete_all_output=False,
delete_temp_output=False,
detailed_summary=False,
latency_wait=3,
wait_for_files=None,
print_compilation=False,
debug=False,
notemp=False,
all_temp=False,
keep_remote_local=False,
nodeps=False,
keep_target_files=False,
allowed_rules=None,
jobscript=None,
greediness=None,
no_hooks=False,
overwrite_shellcmd=None,
updated_files=None,
log_handler=[],
keep_logger=False,
max_jobs_per_second=None,
max_status_checks_per_second=100,
restart_times=0,
attempt=1,
verbose=False,
force_use_threads=False,
use_conda=False,
use_singularity=False,
use_env_modules=False,
singularity_args="",
conda_frontend="conda",
conda_prefix=None,
conda_cleanup_pkgs=None,
list_conda_envs=False,
singularity_prefix=None,
shadow_prefix=None,
scheduler="ilp",
scheduler_ilp_solver=None,
conda_create_envs_only=False,
mode=Mode.default,
wrapper_prefix=None,
kubernetes=None,
container_image=None,
tibanna=False,
tibanna_sfn=None,
google_lifesciences=False,
google_lifesciences_regions=None,
google_lifesciences_location=None,
google_lifesciences_cache=False,
tes=None,
preemption_default=None,
preemptible_rules=None,
precommand="",
default_remote_provider=None,
default_remote_prefix="",
tibanna_config=False,
assume_shared_fs=True,
cluster_status=None,
cluster_cancel=None,
cluster_cancel_nargs=None,
cluster_sidecar=None,
export_cwl=None,
show_failed_logs=False,
keep_incomplete=False,
keep_metadata=True,
messaging=None,
edit_notebook=None,
envvars=None,
overwrite_groups=None,
group_components=None,
max_inventory_wait_time=20,
execute_subworkflows=True,
conda_not_block_search_path_envvars=False,
scheduler_solver_path=None,
conda_base_path=None,
local_groupid="local",
):
"""Run snakemake on a given snakefile.
This function provides access to the whole snakemake functionality. It is not thread-safe.
Args:
snakefile (str): the path to the snakefile
batch (Batch): whether to compute only a partial DAG, defined by the given Batch object (default None)
report (str): create an HTML report for a previous run at the given path
lint (str): print lints instead of executing (None, "plain" or "json", default None)
listrules (bool): list rules (default False)
list_target_rules (bool): list target rules (default False)
cores (int): the number of provided cores (ignored when using cluster support) (default 1)
nodes (int): the number of provided cluster nodes (ignored without cluster support) (default 1)
local_cores (int): the number of provided local cores if in cluster mode (ignored without cluster support) (default 1)
resources (dict): provided resources, a dictionary assigning integers to resource names, e.g. {gpu=1, io=5} (default {})
default_resources (DefaultResources): default values for resources not defined in rules (default None)
config (dict): override values for workflow config
workdir (str): path to the working directory (default None)
targets (list): list of targets, e.g. rule or file names (default None)
dryrun (bool): only dry-run the workflow (default False)
touch (bool): only touch all output files if present (default False)
forcetargets (bool): force given targets to be re-created (default False)
forceall (bool): force all output files to be re-created (default False)
forcerun (list): list of files and rules that shall be re-created/re-executed (default [])
execute_subworkflows (bool): execute subworkflows if present (default True)
prioritytargets (list): list of targets that shall be run with maximum priority (default [])
stats (str): path to file that shall contain stats about the workflow execution (default None)
printreason (bool): print the reason for the execution of each job (default false)
printshellcmds (bool): print the shell command of each job (default False)
printdag (bool): print the dag in the graphviz dot language (default False)
printrulegraph (bool): print the graph of rules in the graphviz dot language (default False)
printfilegraph (bool): print the graph of rules with their input and output files in the graphviz dot language (default False)
printd3dag (bool): print a D3.js compatible JSON representation of the DAG (default False)
nocolor (bool): do not print colored output (default False)
quiet (bool): do not print any default job information (default False)
keepgoing (bool): keep going upon errors (default False)
cluster (str): submission command of a cluster or batch system to use, e.g. qsub (default None)
cluster_config (str,list): configuration file for cluster options, or list thereof (default None)
cluster_sync (str): blocking cluster submission command (like SGE 'qsub -sync y') (default None)
drmaa (str): if not None use DRMAA for cluster support, str specifies native args passed to the cluster when submitting a job
drmaa_log_dir (str): the path to stdout and stderr output of DRMAA jobs (default None)
jobname (str): naming scheme for cluster job scripts (default "snakejob.{rulename}.{jobid}.sh")
immediate_submit (bool): immediately submit all cluster jobs, regardless of dependencies (default False)
standalone (bool): kill all processes very rudely in case of failure (do not use this if you use this API) (default False) (deprecated)
ignore_ambiguity (bool): ignore ambiguous rules and always take the first possible one (default False)
snakemakepath (str): deprecated parameter whose value is ignored. Do not use.
lock (bool): lock the working directory when executing the workflow (default True)
unlock (bool): just unlock the working directory (default False)
cleanup_metadata (list): just cleanup metadata of given list of output files (default None)
drop_metadata (bool): drop metadata file tracking information after job finishes (--report and --list_x_changes information will be incomplete) (default False)
conda_cleanup_envs (bool): just cleanup unused conda environments (default False)
cleanup_shadow (bool): just cleanup old shadow directories (default False)
cleanup_scripts (bool): delete wrapper scripts used for execution (default True)
force_incomplete (bool): force the re-creation of incomplete files (default False)
ignore_incomplete (bool): ignore incomplete files (default False)
list_version_changes (bool): list output files with changed rule version (default False)
list_code_changes (bool): list output files with changed rule code (default False)
list_input_changes (bool): list output files with changed input files (default False)
list_params_changes (bool): list output files with changed params (default False)
list_untracked (bool): list files in the workdir that are not used in the workflow (default False)
summary (bool): list summary of all output files and their status (default False)
archive (str): archive workflow into the given tarball
delete_all_output (bool): remove all files generated by the workflow (default False)
delete_temp_output (bool): remove all temporary files generated by the workflow (default False)
latency_wait (int): how many seconds to wait for an output file to appear after the execution of a job, e.g. to handle filesystem latency (default 3)
wait_for_files (list): wait for given files to be present before executing the workflow
list_resources (bool): list resources used in the workflow (default False)
summary (bool): list summary of all output files and their status (default False). If no option is specified a basic summary will be output. If 'detailed' is added as an option e.g --summary detailed, extra info about the input and shell commands will be included
detailed_summary (bool): list summary of all input and output files and their status (default False)
print_compilation (bool): print the compilation of the snakefile (default False)
debug (bool): allow to use the debugger within rules
notemp (bool): ignore temp file flags, e.g. do not delete output files marked as a temp after use (default False)
keep_remote_local (bool): keep local copies of remote files (default False)
nodeps (bool): ignore dependencies (default False)
keep_target_files (bool): do not adjust the paths of given target files relative to the working directory.
allowed_rules (set): restrict allowed rules to the given set. If None or empty, all rules are used.
jobscript (str): path to a custom shell script template for cluster jobs (default None)
greediness (float): set the greediness of scheduling. This value between 0 and 1 determines how careful jobs are selected for execution. The default value (0.5 if prioritytargets are used, 1.0 else) provides the best speed and still acceptable scheduling quality.
overwrite_shellcmd (str): a shell command that shall be executed instead of those given in the workflow. This is for debugging purposes only.
updated_files(list): a list that will be filled with the files that are updated or created during the workflow execution
verbose (bool): show additional debug output (default False)
max_jobs_per_second (int): maximal number of cluster/drmaa jobs per second, None to impose no limit (default None)
restart_times (int): number of times to restart failing jobs (default 0)
attempt (int): initial value of Job.attempt. This is intended for internal use only (default 1).
force_use_threads: whether to force the use of threads over processes. helpful if shared memory is full or unavailable (default False)
use_conda (bool): use conda environments for each job (defined with conda directive of rules)
use_singularity (bool): run jobs in singularity containers (if defined with singularity directive)
use_env_modules (bool): load environment modules if defined in rules
singularity_args (str): additional arguments to pass to a singularity
conda_prefix (str): the directory in which conda environments will be created (default None)
conda_cleanup_pkgs (snakemake.deployment.conda.CondaCleanupMode):
whether to clean up conda tarballs after env creation (default None), valid values: "tarballs", "cache"
singularity_prefix (str): the directory to which singularity images will be pulled (default None)
shadow_prefix (str): prefix for shadow directories. The job-specific shadow directories will be created in $SHADOW_PREFIX/shadow/ (default None)
conda_create_envs_only (bool): if specified, only builds the conda environments specified for each job, then exits.
list_conda_envs (bool): list conda environments and their location on disk.
mode (snakemake.common.Mode): execution mode
wrapper_prefix (str): prefix for wrapper script URLs (default None)
kubernetes (str): submit jobs to Kubernetes, using the given namespace.
container_image (str): Docker image to use, e.g., for Kubernetes.
default_remote_provider (str): default remote provider to use instead of local files (e.g. S3, GS)
default_remote_prefix (str): prefix for default remote provider (e.g. name of the bucket).
tibanna (bool): submit jobs to AWS cloud using Tibanna.
tibanna_sfn (str): Step function (Unicorn) name of Tibanna (e.g. tibanna_unicorn_monty). This must be deployed first using tibanna cli.
google_lifesciences (bool): submit jobs to Google Cloud Life Sciences (pipelines API).
google_lifesciences_regions (list): a list of regions (e.g., us-east1)
google_lifesciences_location (str): Life Sciences API location (e.g., us-central1)
google_lifesciences_cache (bool): save a cache of the compressed working directories in Google Cloud Storage for later usage.
tes (str): Execute workflow tasks on GA4GH TES server given by URL.
precommand (str): commands to run on AWS cloud before the snakemake command (e.g. wget, git clone, unzip, etc). Use with --tibanna.
preemption_default (int): set a default number of preemptible instance retries (for Google Life Sciences executor only)
preemptible_rules (list): define custom preemptible instance retries for specific rules (for Google Life Sciences executor only)
tibanna_config (list): Additional tibanna config e.g. --tibanna-config spot_instance=true subnet=<subnet_id> security group=<security_group_id>
assume_shared_fs (bool): assume that cluster nodes share a common filesystem (default true).
cluster_status (str): status command for cluster execution. If None, Snakemake will rely on flag files. Otherwise, it expects the command to return "success", "failure" or "running" when executing with a cluster jobid as a single argument.
cluster_cancel (str): command to cancel multiple job IDs (like SLURM 'scancel') (default None)
cluster_cancel_nargs (int): maximal number of job ids to pass to cluster_cancel (default 1000)
cluster_sidecar (str): command that starts a sidecar process, see cluster documentation (default None)
export_cwl (str): Compile workflow to CWL and save to given file
log_handler (function): redirect snakemake output to this custom log handler, a function that takes a log message dictionary (see below) as its only argument (default None). The log message dictionary for the log handler has to following entries:
keep_incomplete (bool): keep incomplete output files of failed jobs
edit_notebook (object): "notebook.EditMode" object to configure notebook server for interactive editing of a rule notebook. If None, do not edit.
scheduler (str): Select scheduling algorithm (default ilp)
scheduler_ilp_solver (str): Set solver for ilp scheduler.
overwrite_groups (dict): Rule to group assignments (default None)
group_components (dict): Number of connected components given groups shall span before being split up (1 by default if empty)
conda_not_block_search_path_envvars (bool): Do not block search path envvars (R_LIBS, PYTHONPATH, ...) when using conda environments.
scheduler_solver_path (str): Path to Snakemake environment (this can be used to e.g. overwrite the search path for the ILP solver used during scheduling).
conda_base_path (str): Path to conda base environment (this can be used to overwrite the search path for conda, mamba, and activate).
local_groupid (str): Local groupid to use as a placeholder for groupid-referrring input functions of local jobs (internal use only, default: local).
log_handler (list): redirect snakemake output to this list of custom log handlers, each a function that takes a log message dictionary (see below) as its only argument (default []). The log message dictionary for the log handler has to following entries:
:level:
the log level ("info", "error", "debug", "progress", "job_info")
:level="info", "error" or "debug":
:msg:
the log message
:level="progress":
:done:
number of already executed jobs
:total:
number of total jobs
:level="job_info":
:input:
list of input files of a job
:output:
list of output files of a job
:log:
path to log file of a job
:local:
whether a job is executed locally (i.e. ignoring cluster)
:msg:
the job message
:reason:
the job reason
:priority:
the job priority
:threads:
the threads of the job
Returns:
bool: True if workflow execution was successful.
"""
assert not immediate_submit or (
immediate_submit and notemp
), "immediate_submit has to be combined with notemp (it does not support temp file handling)"
if tibanna:
assume_shared_fs = False
default_remote_provider = "S3"
default_remote_prefix = default_remote_prefix.rstrip("/")
assert (
default_remote_prefix
), "default_remote_prefix needed if tibanna is specified"
assert tibanna_sfn, "tibanna_sfn needed if tibanna is specified"
if tibanna_config:
tibanna_config_dict = dict()
for cf in tibanna_config:
k, v = cf.split("=")
if v == "true":
v = True
elif v == "false":
v = False
elif v.isnumeric():
v = int(v)
else:
try:
v = float(v)
except ValueError:
pass
tibanna_config_dict.update({k: v})
tibanna_config = tibanna_config_dict
# Google Cloud Life Sciences API uses compute engine and storage
if google_lifesciences:
assume_shared_fs = False
default_remote_provider = "GS"
default_remote_prefix = default_remote_prefix.rstrip("/")
if kubernetes:
assume_shared_fs = False
# Currently preemptible instances only supported for Google LifeSciences Executor
if preemption_default or preemptible_rules and not google_lifesciences:
logger.warning(
"Preemptible instances are only available for the Google Life Sciences Executor."
)
if updated_files is None:
updated_files = list()
if isinstance(cluster_config, str):
# Loading configuration from one file is still supported for
# backward compatibility
cluster_config = [cluster_config]
if cluster_config:
# Load all configuration files
configs = [load_configfile(f) for f in cluster_config]
# Merge in the order as specified, overriding earlier values with
# later ones
cluster_config_content = configs[0]
for other in configs[1:]:
update_config(cluster_config_content, other)
else:
cluster_config_content = dict()
run_local = not (
cluster
or cluster_sync
or drmaa
or kubernetes
or tibanna
or google_lifesciences
or tes
)
if run_local:
if not dryrun:
# clean up all previously recorded jobids.
shell.cleanup()
else:
if edit_notebook:
raise WorkflowError(
"Notebook edit mode is only allowed with local execution."
)
shell.conda_block_conflicting_envvars = not conda_not_block_search_path_envvars
# force thread use for any kind of cluster
use_threads = (
force_use_threads
or (os.name not in ["posix", "nt"])
or cluster
or cluster_sync
or drmaa
)
if not keep_logger:
stdout = (
(
dryrun
and not (printdag or printd3dag or printrulegraph or printfilegraph)
)
or listrules
or list_target_rules
or list_resources
)
setup_logger(
handler=log_handler,
quiet=quiet,
printreason=printreason,
printshellcmds=printshellcmds,
debug_dag=debug_dag,
nocolor=nocolor,
stdout=stdout,
debug=verbose,
use_threads=use_threads,
mode=mode,
show_failed_logs=show_failed_logs,
)
if greediness is None:
greediness = 0.5 if prioritytargets else 1.0
else:
if not (0 <= greediness <= 1.0):
logger.error("Error: greediness must be a float between 0 and 1.")
return False
if not os.path.exists(snakefile):
logger.error('Error: Snakefile "{}" not found.'.format(snakefile))
return False
snakefile = os.path.abspath(snakefile)
cluster_mode = (
(cluster is not None) + (cluster_sync is not None) + (drmaa is not None)
)
if cluster_mode > 1:
logger.error("Error: cluster and drmaa args are mutually exclusive")
return False
if debug and (cluster_mode or cores is not None and cores > 1):
logger.error(
"Error: debug mode cannot be used with more than one core or cluster execution."
)
return False
overwrite_config = dict()
if configfiles is None:
configfiles = []
for f in configfiles:
# get values to override. Later configfiles override earlier ones.
update_config(overwrite_config, load_configfile(f))
# convert provided paths to absolute paths
configfiles = list(map(os.path.abspath, configfiles))
# directly specified elements override any configfiles
if config:
update_config(overwrite_config, config)
if config_args is None:
config_args = unparse_config(config)
if workdir:
olddir = os.getcwd()
if not os.path.exists(workdir):
logger.info("Creating specified working directory {}.".format(workdir))
os.makedirs(workdir)
workdir = os.path.abspath(workdir)
os.chdir(workdir)
logger.setup_logfile()
try:
# handle default remote provider
_default_remote_provider = None
if default_remote_provider is not None:
try:
rmt = importlib.import_module(
"snakemake.remote." + default_remote_provider
)
except ImportError as e:
raise WorkflowError("Unknown default remote provider.")
if rmt.RemoteProvider.supports_default:
_default_remote_provider = rmt.RemoteProvider(
keep_local=True, is_default=True
)
else:
raise WorkflowError(
"Remote provider {} does not (yet) support to "
"be used as default provider."
)
workflow = Workflow(
snakefile=snakefile,
jobscript=jobscript,
overwrite_shellcmd=overwrite_shellcmd,
overwrite_config=overwrite_config,
overwrite_workdir=workdir,
overwrite_configfiles=configfiles,
overwrite_clusterconfig=cluster_config_content,
overwrite_threads=overwrite_threads,
max_threads=max_threads,
overwrite_scatter=overwrite_scatter,
overwrite_groups=overwrite_groups,
overwrite_resources=overwrite_resources,
group_components=group_components,
config_args=config_args,
debug=debug,
verbose=verbose,
use_conda=use_conda or list_conda_envs or conda_cleanup_envs,
use_singularity=use_singularity,
use_env_modules=use_env_modules,
conda_frontend=conda_frontend,
conda_prefix=conda_prefix,
conda_cleanup_pkgs=conda_cleanup_pkgs,
singularity_prefix=singularity_prefix,
shadow_prefix=shadow_prefix,
singularity_args=singularity_args,
scheduler_type=scheduler,
scheduler_ilp_solver=scheduler_ilp_solver,
mode=mode,
wrapper_prefix=wrapper_prefix,
printshellcmds=printshellcmds,
restart_times=restart_times,
attempt=attempt,
default_remote_provider=_default_remote_provider,
default_remote_prefix=default_remote_prefix,
run_local=run_local,
assume_shared_fs=assume_shared_fs,
default_resources=default_resources,
cache=cache,
cores=cores,
nodes=nodes,
resources=resources,
edit_notebook=edit_notebook,
envvars=envvars,
max_inventory_wait_time=max_inventory_wait_time,
conda_not_block_search_path_envvars=conda_not_block_search_path_envvars,
execute_subworkflows=execute_subworkflows,
scheduler_solver_path=scheduler_solver_path,
conda_base_path=conda_base_path,
check_envvars=not lint, # for linting, we do not need to check whether requested envvars exist
all_temp=all_temp,
local_groupid=local_groupid,
)
success = True
workflow.include(
snakefile,
overwrite_default_target=True,
print_compilation=print_compilation,
)
workflow.check()
if not print_compilation:
if lint:
success = not workflow.lint(json=lint == "json")
elif containerize:
workflow.containerize()
elif listrules:
workflow.list_rules()
elif list_target_rules:
workflow.list_rules(only_targets=True)
elif list_resources:
workflow.list_resources()
else:
# if not printdag and not printrulegraph:
# handle subworkflows
subsnakemake = partial(
snakemake,
local_cores=local_cores,
max_threads=max_threads,
cache=cache,
overwrite_threads=overwrite_threads,
overwrite_scatter=overwrite_scatter,
overwrite_resources=overwrite_resources,
default_resources=default_resources,
dryrun=dryrun,
touch=touch,
printreason=printreason,
printshellcmds=printshellcmds,
debug_dag=debug_dag,
nocolor=nocolor,
quiet=quiet,
keepgoing=keepgoing,
cluster=cluster,
cluster_sync=cluster_sync,
drmaa=drmaa,
drmaa_log_dir=drmaa_log_dir,
jobname=jobname,
immediate_submit=immediate_submit,
standalone=standalone,
ignore_ambiguity=ignore_ambiguity,
restart_times=restart_times,
attempt=attempt,
lock=lock,
unlock=unlock,
cleanup_metadata=cleanup_metadata,
conda_cleanup_envs=conda_cleanup_envs,
cleanup_shadow=cleanup_shadow,
cleanup_scripts=cleanup_scripts,
force_incomplete=force_incomplete,
ignore_incomplete=ignore_incomplete,
latency_wait=latency_wait,
verbose=verbose,
notemp=notemp,
all_temp=all_temp,
keep_remote_local=keep_remote_local,
nodeps=nodeps,
jobscript=jobscript,
greediness=greediness,
no_hooks=no_hooks,
overwrite_shellcmd=overwrite_shellcmd,
config=config,
config_args=config_args,
cluster_config=cluster_config,
keep_logger=True,
force_use_threads=use_threads,
use_conda=use_conda,
use_singularity=use_singularity,
use_env_modules=use_env_modules,
conda_prefix=conda_prefix,
conda_cleanup_pkgs=conda_cleanup_pkgs,
conda_frontend=conda_frontend,
singularity_prefix=singularity_prefix,
shadow_prefix=shadow_prefix,
singularity_args=singularity_args,
scheduler=scheduler,
scheduler_ilp_solver=scheduler_ilp_solver,
list_conda_envs=list_conda_envs,
kubernetes=kubernetes,
container_image=container_image,
conda_create_envs_only=conda_create_envs_only,
default_remote_provider=default_remote_provider,
default_remote_prefix=default_remote_prefix,
tibanna=tibanna,
tibanna_sfn=tibanna_sfn,
google_lifesciences=google_lifesciences,
google_lifesciences_regions=google_lifesciences_regions,
google_lifesciences_location=google_lifesciences_location,
google_lifesciences_cache=google_lifesciences_cache,
tes=tes,
precommand=precommand,
preemption_default=preemption_default,
preemptible_rules=preemptible_rules,
tibanna_config=tibanna_config,
assume_shared_fs=assume_shared_fs,
cluster_status=cluster_status,
cluster_cancel=cluster_cancel,
cluster_cancel_nargs=cluster_cancel_nargs,
cluster_sidecar=cluster_sidecar,
max_jobs_per_second=max_jobs_per_second,
max_status_checks_per_second=max_status_checks_per_second,
overwrite_groups=overwrite_groups,
group_components=group_components,
max_inventory_wait_time=max_inventory_wait_time,
conda_not_block_search_path_envvars=conda_not_block_search_path_envvars,
local_groupid=local_groupid,
)
success = workflow.execute(
targets=targets,
dryrun=dryrun,
generate_unit_tests=generate_unit_tests,
touch=touch,
scheduler_type=scheduler,
scheduler_ilp_solver=scheduler_ilp_solver,
local_cores=local_cores,
forcetargets=forcetargets,
forceall=forceall,
forcerun=forcerun,
prioritytargets=prioritytargets,
until=until,
omit_from=omit_from,
quiet=quiet,
keepgoing=keepgoing,
printshellcmds=printshellcmds,
printreason=printreason,
printrulegraph=printrulegraph,
printfilegraph=printfilegraph,
printdag=printdag,
cluster=cluster,
cluster_sync=cluster_sync,
jobname=jobname,
drmaa=drmaa,
drmaa_log_dir=drmaa_log_dir,
kubernetes=kubernetes,
container_image=container_image,
tibanna=tibanna,
tibanna_sfn=tibanna_sfn,
google_lifesciences=google_lifesciences,
google_lifesciences_regions=google_lifesciences_regions,
google_lifesciences_location=google_lifesciences_location,
google_lifesciences_cache=google_lifesciences_cache,
tes=tes,
precommand=precommand,
preemption_default=preemption_default,
preemptible_rules=preemptible_rules,
tibanna_config=tibanna_config,
max_jobs_per_second=max_jobs_per_second,
max_status_checks_per_second=max_status_checks_per_second,
printd3dag=printd3dag,
immediate_submit=immediate_submit,
ignore_ambiguity=ignore_ambiguity,
stats=stats,
force_incomplete=force_incomplete,
ignore_incomplete=ignore_incomplete,
list_version_changes=list_version_changes,
list_code_changes=list_code_changes,
list_input_changes=list_input_changes,
list_params_changes=list_params_changes,
list_untracked=list_untracked,
list_conda_envs=list_conda_envs,
summary=summary,
archive=archive,
delete_all_output=delete_all_output,
delete_temp_output=delete_temp_output,
latency_wait=latency_wait,
wait_for_files=wait_for_files,
detailed_summary=detailed_summary,
nolock=not lock,
unlock=unlock,
notemp=notemp,
keep_remote_local=keep_remote_local,
nodeps=nodeps,
keep_target_files=keep_target_files,
cleanup_metadata=cleanup_metadata,
conda_cleanup_envs=conda_cleanup_envs,
cleanup_shadow=cleanup_shadow,
cleanup_scripts=cleanup_scripts,
subsnakemake=subsnakemake,
updated_files=updated_files,
allowed_rules=allowed_rules,
greediness=greediness,
no_hooks=no_hooks,
force_use_threads=use_threads,
conda_create_envs_only=conda_create_envs_only,
cluster_status=cluster_status,
cluster_cancel=cluster_cancel,
cluster_cancel_nargs=cluster_cancel_nargs,
cluster_sidecar=cluster_sidecar,
report=report,
report_stylesheet=report_stylesheet,
export_cwl=export_cwl,
batch=batch,
keepincomplete=keep_incomplete,
keepmetadata=keep_metadata,
)
except BrokenPipeError:
# ignore this exception and stop. It occurs if snakemake output is piped into less and less quits before reading the whole output.
# in such a case, snakemake shall stop scheduling and quit with error 1
success = False
except (Exception, BaseException) as ex:
if "workflow" in locals():
print_exception(ex, workflow.linemaps)
else:
print_exception(ex, dict())
success = False
if workdir:
os.chdir(olddir)
if "workflow" in locals() and workflow.persistence:
workflow.persistence.unlock()
if not keep_logger:
logger.cleanup()
return success
def parse_set_threads(args):
return parse_set_ints(
args.set_threads,
"Invalid threads definition: entries have to be defined as RULE=THREADS pairs "
"(with THREADS being a positive integer).",
)
def parse_set_resources(args):
errmsg = (
"Invalid resource definition: entries have to be defined as RULE:RESOURCE=VALUE, with "
"VALUE being a positive integer or a string."
)
from collections import defaultdict
assignments = defaultdict(dict)
if args.set_resources is not None:
for entry in args.set_resources:
key, value = parse_key_value_arg(entry, errmsg=errmsg)
key = key.split(":")
if not len(key) == 2:
raise ValueError(errmsg)
rule, resource = key
try:
value = int(value)
except ValueError:
assignments[rule][resource] = value
continue
if value < 0:
raise ValueError(errmsg)
assignments[rule][resource] = value
return assignments
def parse_set_scatter(args):
return parse_set_ints(
args.set_scatter,
"Invalid scatter definition: entries have to be defined as NAME=SCATTERITEMS pairs "
"(with SCATTERITEMS being a positive integer).",
)
def parse_set_ints(arg, errmsg):
assignments = dict()
if arg is not None:
for entry in arg:
key, value = parse_key_value_arg(entry, errmsg=errmsg)
try:
value = int(value)
except ValueError:
raise ValueError(errmsg)
if value < 0:
raise ValueError(errmsg)
assignments[key] = value
return assignments
def parse_batch(args):
errmsg = "Invalid batch definition: batch entry has to be defined as RULE=BATCH/BATCHES (with integers BATCH <= BATCHES, BATCH >= 1)."
if args.batch is not None:
rule, batchdef = parse_key_value_arg(args.batch, errmsg=errmsg)
try:
batch, batches = batchdef.split("/")
batch = int(batch)
batches = int(batches)
except ValueError:
raise ValueError(errmsg)
if batch > batches or batch < 1:
raise ValueError(errmsg)
return Batch(rule, batch, batches)
return None
def parse_groups(args):
errmsg = "Invalid groups definition: entries have to be defined as RULE=GROUP pairs"
overwrite_groups = dict()
if args.groups is not None:
for entry in args.groups:
rule, group = parse_key_value_arg(entry, errmsg=errmsg)
overwrite_groups[rule] = group
return overwrite_groups
def parse_group_components(args):
errmsg = "Invalid group components definition: entries have to be defined as GROUP=COMPONENTS pairs (with COMPONENTS being a positive integer)"
group_components = dict()
if args.group_components is not None:
for entry in args.group_components:
group, count = parse_key_value_arg(entry, errmsg=errmsg)
try:
count = int(count)
except ValueError:
raise ValueError(errmsg)
if count <= 0:
raise ValueError(errmsg)
group_components[group] = count
return group_components
def parse_key_value_arg(arg, errmsg):
try:
key, val = arg.split("=", 1)
except ValueError:
raise ValueError(errmsg + " Unparseable value: %r." % arg)
return key, val
def _bool_parser(value):
if value == "True":
return True
elif value == "False":
return False
raise ValueError
def parse_config(args):
"""Parse config from args."""
import yaml
yaml_base_load = lambda s: yaml.load(s, Loader=yaml.loader.BaseLoader)
parsers = [int, float, _bool_parser, yaml_base_load, str]
config = dict()
if args.config is not None:
valid = re.compile(r"[a-zA-Z_]\w*$")
for entry in args.config:
key, val = parse_key_value_arg(
entry,
errmsg="Invalid config definition: Config entries have to be defined as name=value pairs.",
)
if not valid.match(key):
raise ValueError(
"Invalid config definition: Config entry must start with a valid identifier."
)
v = None
for parser in parsers:
try:
v = parser(val)
# avoid accidental interpretation as function
if not callable(v):
break
except:
pass
assert v is not None
update_config(config, {key: v})
return config
def unparse_config(config):
if not isinstance(config, dict):
raise ValueError("config is not a dict")
items = []
for key, value in config.items():
encoded = "'{}'".format(value) if isinstance(value, str) else value
items.append("{}={}".format(key, encoded))
return items
def get_profile_file(profile, file, return_default=False):
dirs = get_appdirs()
if os.path.exists(profile):
search_dirs = [os.path.dirname(profile)]
profile = os.path.basename(profile)
else:
search_dirs = [os.getcwd(), dirs.user_config_dir, dirs.site_config_dir]
get_path = lambda d: os.path.join(d, profile, file)
for d in search_dirs:
p = get_path(d)
# "file" can actually be a full command. If so, `p` won't exist as the
# below would check if e.g. '/path/to/profile/script --arg1 val --arg2'
# exists. To fix this, we use shlex.split() to get the path to the
# script. We check for both, in case the path contains spaces or some
# other thing that would cause shlex.split() to mangle the path
# inaccurately.
if os.path.exists(p) or os.path.exists(shlex.split(p)[0]):