Source code for snakemake.workflow

__author__ = "Johannes Köster"
__copyright__ = "Copyright 2021, Johannes Köster"
__email__ = ""
__license__ = "MIT"

import re
import os
import sys
import signal
import json
from tokenize import maybe
import urllib
from collections import OrderedDict, namedtuple
from itertools import filterfalse, chain
from functools import partial
from operator import attrgetter
import copy
import subprocess
from pathlib import Path
from urllib.request import pathname2url, url2pathname

from snakemake.logging import logger, format_resources, format_resource_names
from snakemake.rules import Rule, Ruleorder, RuleProxy
from snakemake.exceptions import (
from import shell
from snakemake.dag import DAG
from snakemake.scheduler import JobScheduler
from snakemake.parser import parse
from import (
from snakemake.persistence import Persistence
from snakemake.utils import update_config
from snakemake.script import script
from snakemake.notebook import notebook
from snakemake.wrapper import wrapper
from snakemake.cwl import cwl
import snakemake.wrapper
from snakemake.common import (
from snakemake.utils import simplify_path
from snakemake.checkpoints import Checkpoint, Checkpoints
from snakemake.resources import DefaultResources
from snakemake.caching.local import OutputFileCache as LocalOutputFileCache
from snakemake.caching.remote import OutputFileCache as RemoteOutputFileCache
from snakemake.modules import ModuleInfo, WorkflowModifier, get_name_modifier_func
from snakemake.ruleinfo import RuleInfo
from snakemake.sourcecache import (
from snakemake.deployment.conda import Conda
from snakemake import sourcecache

[docs]class Workflow: def __init__( self, snakefile=None, jobscript=None, overwrite_shellcmd=None, overwrite_config=None, overwrite_workdir=None, overwrite_configfiles=None, overwrite_clusterconfig=None, overwrite_threads=None, overwrite_scatter=None, overwrite_groups=None, overwrite_resources=None, group_components=None, config_args=None, debug=False, verbose=False, use_conda=False, conda_frontend=None, conda_prefix=None, use_singularity=False, use_env_modules=False, singularity_prefix=None, singularity_args="", shadow_prefix=None, scheduler_type="ilp", scheduler_ilp_solver=None, mode=Mode.default, wrapper_prefix=None, printshellcmds=False, restart_times=None, attempt=1, default_remote_provider=None, default_remote_prefix="", run_local=True, default_resources=None, cache=None, nodes=1, cores=1, resources=None, conda_cleanup_pkgs=None, edit_notebook=False, envvars=None, max_inventory_wait_time=20, conda_not_block_search_path_envvars=False, execute_subworkflows=True, scheduler_solver_path=None, conda_base_path=None, check_envvars=True, max_threads=None, all_temp=False, ): """ Create the controller. """ self.global_resources = dict() if resources is None else resources self.global_resources["_cores"] = cores self.global_resources["_nodes"] = nodes self._rules = OrderedDict() self.first_rule = None self._workdir = None self.overwrite_workdir = overwrite_workdir self.workdir_init = os.path.abspath(os.curdir) self._ruleorder = Ruleorder() self._localrules = set() self.linemaps = dict() self.rule_count = 0 self.basedir = os.path.dirname(snakefile) self.main_snakefile = os.path.abspath(snakefile) self.included = [] self.included_stack = [] self.jobscript = jobscript self.persistence = None self._subworkflows = dict() self.overwrite_shellcmd = overwrite_shellcmd self.overwrite_config = overwrite_config or dict() self.overwrite_configfiles = overwrite_configfiles self.overwrite_clusterconfig = overwrite_clusterconfig or dict() self.overwrite_threads = overwrite_threads or dict() self.overwrite_resources = overwrite_resources or dict() self.config_args = config_args self.immediate_submit = None self._onsuccess = lambda log: None self._onerror = lambda log: None self._onstart = lambda log: None self._wildcard_constraints = dict() self.debug = debug self.verbose = verbose self._rulecount = 0 self.use_conda = use_conda self.conda_frontend = conda_frontend self.conda_prefix = conda_prefix self.use_singularity = use_singularity self.use_env_modules = use_env_modules self.singularity_prefix = singularity_prefix self.singularity_args = singularity_args self.shadow_prefix = shadow_prefix self.scheduler_type = scheduler_type self.scheduler_ilp_solver = scheduler_ilp_solver self.global_container_img = None self.global_is_containerized = False self.mode = mode self.wrapper_prefix = wrapper_prefix self.printshellcmds = printshellcmds self.restart_times = restart_times self.attempt = attempt self.default_remote_provider = default_remote_provider self.default_remote_prefix = default_remote_prefix self.configfiles = list(overwrite_configfiles) or [] self.run_local = run_local self.report_text = None self.conda_cleanup_pkgs = conda_cleanup_pkgs self.edit_notebook = edit_notebook # environment variables to pass to jobs # These are defined via the "envvars:" syntax in the Snakefile itself self.envvars = set() self.overwrite_groups = overwrite_groups or dict() self.group_components = group_components or dict() self._scatter = dict(overwrite_scatter or dict()) self.overwrite_scatter = overwrite_scatter or dict() self.conda_not_block_search_path_envvars = conda_not_block_search_path_envvars self.execute_subworkflows = execute_subworkflows self.modules = dict() self.sourcecache = SourceCache() self.scheduler_solver_path = scheduler_solver_path self._conda_base_path = conda_base_path self.check_envvars = check_envvars self.max_threads = max_threads self.all_temp = all_temp self.scheduler = None _globals = globals() _globals["workflow"] = self _globals["cluster_config"] = copy.deepcopy(self.overwrite_clusterconfig) _globals["rules"] = Rules() _globals["checkpoints"] = Checkpoints() _globals["scatter"] = Scatter() _globals["gather"] = Gather() _globals["github"] = sourcecache.GithubFile _globals["gitlab"] = sourcecache.GitlabFile self.vanilla_globals = dict(_globals) self.modifier_stack = [WorkflowModifier(self, globals=_globals)] self.enable_cache = False if cache is not None: self.enable_cache = True self.cache_rules = set(cache) if self.default_remote_provider is not None: self.output_file_cache = RemoteOutputFileCache( self.default_remote_provider ) else: self.output_file_cache = LocalOutputFileCache() else: self.output_file_cache = None self.cache_rules = set() if default_resources is not None: self.default_resources = default_resources else: # only _cores, _nodes, and _tmpdir self.default_resources = DefaultResources(mode="bare") self.iocache = self.globals["config"] = copy.deepcopy(self.overwrite_config) if envvars is not None: self.register_envvars(*envvars) @property def conda_base_path(self): if self._conda_base_path: return self._conda_base_path if self.use_conda: try: return Conda().prefix_path except CreateCondaEnvironmentException as e: # Return no preset conda base path now and report error later in jobs. return None else: return None @property def modifier(self): return self.modifier_stack[-1] @property def globals(self): return self.modifier.globals
[docs] def lint(self, json=False): from snakemake.linting.rules import RuleLinter from snakemake.linting.snakefiles import SnakefileLinter json_snakefile_lints, snakefile_linted = SnakefileLinter( self, self.included ).lint(json=json) json_rule_lints, rules_linted = RuleLinter(self, self.rules).lint(json=json) linted = snakefile_linted or rules_linted if json: import json print( json.dumps( {"snakefiles": json_snakefile_lints, "rules": json_rule_lints}, indent=2, ) ) else: if not linted:"Congratulations, your workflow is in a good condition!") return linted
[docs] def is_cached_rule(self, rule: Rule): return in self.cache_rules
[docs] def get_sources(self): files = set() def local_path(f): if not isinstance(f, SourceFile) and is_local_file(f): return f if isinstance(f, LocalSourceFile): return f.get_path_or_uri() def norm_rule_relpath(f, rule): if not os.path.isabs(f): f = os.path.join(rule.basedir, f) return os.path.relpath(f) # get registered sources for f in self.included: f = local_path(f) if f: try: f = os.path.relpath(f) except ValueError: if ON_WINDOWS: pass # relpath doesn't work on win if files are on different drive else: raise files.add(f) for rule in self.rules: script_path = rule.script or rule.notebook if script_path: script_path = norm_rule_relpath(script_path, rule) files.add(script_path) script_dir = os.path.dirname(script_path) files.update( os.path.join(dirpath, f) for dirpath, _, files in os.walk(script_dir) for f in files ) if rule.conda_env: f = local_path(rule.conda_env) if f: # url points to a local env file env_path = norm_rule_relpath(f, rule) files.add(env_path) for f in self.configfiles: files.add(f) # get git-managed files # TODO allow a manifest file as alternative try: out = subprocess.check_output( ["git", "ls-files", "--recurse-submodules", "."], stderr=subprocess.PIPE ) for f in out.decode().split("\n"): if f: files.add(os.path.relpath(f)) except subprocess.CalledProcessError as e: if "fatal: not a git repository" in e.stderr.decode().lower(): logger.warning( "Unable to retrieve additional files from git. " "This is not a git repository." ) else: raise WorkflowError( "Error executing git:\n{}".format(e.stderr.decode()) ) return files
[docs] def check_source_sizes(self, filename, warning_size_gb=0.2): """A helper function to check the filesize, and return the file to the calling function Additionally, given that we encourage these packages to be small, we set a warning at 200MB (0.2GB). """ gb = bytesto(os.stat(filename).st_size, "g") if gb > warning_size_gb: logger.warning( "File {} (size {} GB) is greater than the {} GB suggested size " "Consider uploading larger files to storage first.".format( filename, gb, warning_size_gb ) ) return filename
@property def subworkflows(self): return self._subworkflows.values() @property def rules(self): return self._rules.values() @property def cores(self): if self._cores is None: raise WorkflowError( "Workflow requires a total number of cores to be defined (e.g. because a " "rule defines its number of threads as a fraction of a total number of cores). " "Please set it with --cores N with N being the desired number of cores. " "Consider to use this in combination with --max-threads to avoid " "jobs with too many threads for your setup. Also make sure to perform " "a dryrun first." ) return self._cores @property def _cores(self): return self.global_resources["_cores"] @property def nodes(self): return self.global_resources["_nodes"] @property def concrete_files(self): return ( file for rule in self.rules for file in chain(rule.input, rule.output) if not callable(file) and not file.contains_wildcard() )
[docs] def check(self): for clause in self._ruleorder: for rulename in clause: if not self.is_rule(rulename): raise UnknownRuleException( rulename, prefix="Error in ruleorder definition." )
[docs] def add_rule( self, name=None, lineno=None, snakefile=None, checkpoint=False, allow_overwrite=False, ): """ Add a rule. """ is_overwrite = self.is_rule(name) if not allow_overwrite and is_overwrite: raise CreateRuleException( "The name {} is already used by another rule".format(name) ) rule = Rule(name, self, lineno=lineno, snakefile=snakefile) self._rules[] = rule if not is_overwrite: self.rule_count += 1 if not self.first_rule: self.first_rule = return name
[docs] def is_rule(self, name): """ Return True if name is the name of a rule. Arguments name -- a name """ return name in self._rules
[docs] def get_rule(self, name): """ Get rule by name. Arguments name -- the name of the rule """ if not self._rules: raise NoRulesException() if not name in self._rules: raise UnknownRuleException(name) return self._rules[name]
[docs] def list_rules(self, only_targets=False): rules = self.rules if only_targets: rules = filterfalse(Rule.has_wildcards, rules) for rule in rules: logger.rule_info(, docstring=rule.docstring)
[docs] def list_resources(self): for resource in set( resource for rule in self.rules for resource in rule.resources ): if resource not in "_cores _nodes".split():
[docs] def is_local(self, rule): return is None and ( in self._localrules or rule.norun)
[docs] def check_localrules(self): undefined = self._localrules - set( for rule in self.rules) if undefined: logger.warning( "localrules directive specifies rules that are not " "present in the Snakefile:\n{}\n".format( "\n".join(map("\t{}".format, undefined)) ) )
[docs] def inputfile(self, path): """Mark file as being an input file of the workflow. This also means that eventual --default-remote-provider/prefix settings will be applied to this file. The file is returned as _IOFile object, such that it can e.g. be transparently opened with """ if isinstance(path, Path): path = str(path) if self.default_remote_provider is not None: path = self.modifier.modify_path(path) return IOFile(path)
[docs] def execute( self, targets=None, dryrun=False, generate_unit_tests=None, touch=False, scheduler_type=None, scheduler_ilp_solver=None, local_cores=1, forcetargets=False, forceall=False, forcerun=None, until=[], omit_from=[], prioritytargets=None, quiet=False, keepgoing=False, printshellcmds=False, printreason=False, printdag=False, cluster=None, cluster_sync=None, jobname=None, immediate_submit=False, ignore_ambiguity=False, printrulegraph=False, printfilegraph=False, printd3dag=False, drmaa=None, drmaa_log_dir=None, kubernetes=None, tibanna=None, tibanna_sfn=None, google_lifesciences=None, google_lifesciences_regions=None, google_lifesciences_location=None, google_lifesciences_cache=False, tes=None, precommand="", preemption_default=None, preemptible_rules=None, tibanna_config=False, container_image=None, stats=None, 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_conda_envs=False, summary=False, archive=None, delete_all_output=False, delete_temp_output=False, detailed_summary=False, latency_wait=3, wait_for_files=None, nolock=False, unlock=False, notemp=False, nodeps=False, cleanup_metadata=None, conda_cleanup_envs=False, cleanup_shadow=False, cleanup_scripts=True, subsnakemake=None, updated_files=None, keep_target_files=False, keep_shadow=False, keep_remote_local=False, allowed_rules=None, max_jobs_per_second=None, max_status_checks_per_second=None, greediness=1.0, no_hooks=False, force_use_threads=False, conda_create_envs_only=False, assume_shared_fs=True, cluster_status=None, report=None, report_stylesheet=None, export_cwl=False, batch=None, keepincomplete=False, keepmetadata=True, ): self.check_localrules() self.immediate_submit = immediate_submit self.cleanup_scripts = cleanup_scripts def rules(items): return map(self._rules.__getitem__, filter(self.is_rule, items)) if keep_target_files: def files(items): return filterfalse(self.is_rule, items) else: def files(items): relpath = ( lambda f: f if os.path.isabs(f) or f.startswith("root://") else os.path.relpath(f) ) return map(relpath, filterfalse(self.is_rule, items)) if not targets: targets = [self.first_rule] if self.first_rule is not None else list() if prioritytargets is None: prioritytargets = list() if forcerun is None: forcerun = list() if until is None: until = list() if omit_from is None: omit_from = list() priorityrules = set(rules(prioritytargets)) priorityfiles = set(files(prioritytargets)) forcerules = set(rules(forcerun)) forcefiles = set(files(forcerun)) untilrules = set(rules(until)) untilfiles = set(files(until)) omitrules = set(rules(omit_from)) omitfiles = set(files(omit_from)) targetrules = set( chain( rules(targets), filterfalse(Rule.has_wildcards, priorityrules), filterfalse(Rule.has_wildcards, forcerules), filterfalse(Rule.has_wildcards, untilrules), ) ) targetfiles = set(chain(files(targets), priorityfiles, forcefiles, untilfiles)) if ON_WINDOWS: targetfiles = set(tf.replace(os.sep, os.altsep) for tf in targetfiles) if forcetargets: forcefiles.update(targetfiles) forcerules.update(targetrules) rules = self.rules if allowed_rules: allowed_rules = set(allowed_rules) rules = [rule for rule in rules if in allowed_rules] if wait_for_files is not None: try:, latency_wait=latency_wait) except IOError as e: logger.error(str(e)) return False dag = DAG( self, rules, dryrun=dryrun, targetfiles=targetfiles, targetrules=targetrules, # when cleaning up conda, we should enforce all possible jobs # since their envs shall not be deleted forceall=forceall or conda_cleanup_envs, forcefiles=forcefiles, forcerules=forcerules, priorityfiles=priorityfiles, priorityrules=priorityrules, untilfiles=untilfiles, untilrules=untilrules, omitfiles=omitfiles, omitrules=omitrules, ignore_ambiguity=ignore_ambiguity, force_incomplete=force_incomplete, ignore_incomplete=ignore_incomplete or printdag or printrulegraph or printfilegraph, notemp=notemp, keep_remote_local=keep_remote_local, batch=batch, ) self.persistence = Persistence( nolock=nolock, dag=dag, conda_prefix=self.conda_prefix, singularity_prefix=self.singularity_prefix, shadow_prefix=self.shadow_prefix, warn_only=dryrun or printrulegraph or printfilegraph or printdag or summary or archive or list_version_changes or list_code_changes or list_input_changes or list_params_changes or list_untracked or delete_all_output or delete_temp_output, ) if self.mode in [Mode.subprocess, Mode.cluster]: self.persistence.deactivate_cache() if cleanup_metadata: for f in cleanup_metadata: self.persistence.cleanup_metadata(f) return True if unlock: try: self.persistence.cleanup_locks()"Unlocking working directory.") return True except IOError: logger.error( "Error: Unlocking the directory {} failed. Maybe " "you don't have the permissions?" ) return False"Building DAG of jobs...") dag.init() dag.update_checkpoint_dependencies() dag.check_dynamic() try: self.persistence.lock() except IOError: logger.error( "Error: Directory cannot be locked. Please make " "sure that no other Snakemake process is trying to create " "the same files in the following directory:\n{}\n" "If you are sure that no other " "instances of snakemake are running on this directory, " "the remaining lock was likely caused by a kill signal or " "a power loss. It can be removed with " "the --unlock argument.".format(os.getcwd()) ) return False if cleanup_shadow: self.persistence.cleanup_shadow() return True if ( self.subworkflows and self.execute_subworkflows and not printdag and not printrulegraph and not printfilegraph ): # backup globals globals_backup = dict(self.globals) # execute subworkflows for subworkflow in self.subworkflows: subworkflow_targets = subworkflow.targets(dag) logger.debug( "Files requested from subworkflow:\n {}".format( "\n ".join(subworkflow_targets) ) ) updated = list() if subworkflow_targets:"Executing subworkflow {}.".format( if not subsnakemake( subworkflow.snakefile, workdir=subworkflow.workdir, targets=subworkflow_targets, cores=self._cores, nodes=self.nodes, configfiles=[subworkflow.configfile] if subworkflow.configfile else None, updated_files=updated, ): return False dag.updated_subworkflow_files.update( for f in updated ) else: "Subworkflow {}: {}".format(, NOTHING_TO_BE_DONE_MSG ) ) if self.subworkflows:"Executing main workflow.") # rescue globals self.globals.update(globals_backup) dag.postprocess(update_needrun=False) if not dryrun: # deactivate IOCache such that from now on we always get updated # size, existence and mtime information # ATTENTION: this may never be removed without really good reason. # Otherwise weird things may happen. self.iocache.deactivate() # clear and deactivate persistence cache, from now on we want to see updates self.persistence.deactivate_cache() if nodeps: missing_input = [ f for job in dag.targetjobs for f in job.input if dag.needrun(job) and not os.path.exists(f) ] if missing_input: logger.error( "Dependency resolution disabled (--nodeps) " "but missing input " "files detected. If this happens on a cluster, please make sure " "that you handle the dependencies yourself or turn off " "--immediate-submit. Missing input files:\n{}".format( "\n".join(missing_input) ) ) return False updated_files.extend(f for job in dag.needrun_jobs for f in job.output) if generate_unit_tests: from snakemake import unit_tests path = generate_unit_tests deploy = [] if self.use_conda: deploy.append("conda") if self.use_singularity: deploy.append("singularity") unit_tests.generate( dag, path, deploy, configfiles=self.overwrite_configfiles ) return True elif export_cwl: from snakemake.cwl import dag_to_cwl import json with open(export_cwl, "w") as cwl: json.dump(dag_to_cwl(dag), cwl, indent=4) return True elif report: from import auto_report auto_report(dag, report, stylesheet=report_stylesheet) return True elif printd3dag: dag.d3dag() return True elif printdag: print(dag) return True elif printrulegraph: print(dag.rule_dot()) return True elif printfilegraph: print(dag.filegraph_dot()) return True elif summary: print("\n".join(dag.summary(detailed=False))) return True elif detailed_summary: print("\n".join(dag.summary(detailed=True))) return True elif archive: dag.archive(archive) return True elif delete_all_output: dag.clean(only_temp=False, dryrun=dryrun) return True elif delete_temp_output: dag.clean(only_temp=True, dryrun=dryrun) return True elif list_version_changes: items = list(chain(*map(self.persistence.version_changed, if items: print(*items, sep="\n") return True elif list_code_changes: items = list(chain(*map(self.persistence.code_changed, for j in items.extend(list(j.outputs_older_than_script_or_notebook())) if items: print(*items, sep="\n") return True elif list_input_changes: items = list(chain(*map(self.persistence.input_changed, if items: print(*items, sep="\n") return True elif list_params_changes: items = list(chain(*map(self.persistence.params_changed, if items: print(*items, sep="\n") return True elif list_untracked: dag.list_untracked() return True if self.use_singularity: if assume_shared_fs: dag.pull_container_imgs( dryrun=dryrun or list_conda_envs, quiet=list_conda_envs ) if self.use_conda: if assume_shared_fs: dag.create_conda_envs( dryrun=dryrun or list_conda_envs or conda_cleanup_envs, quiet=list_conda_envs, ) if conda_create_envs_only: return True if list_conda_envs: print("environment", "container", "location", sep="\t") for env in set(job.conda_env for job in if env: print( env.file.simplify_path(), env.container_img_url or "", simplify_path(env.path), sep="\t", ) return True if conda_cleanup_envs: self.persistence.conda_cleanup_envs() return True self.scheduler = JobScheduler( self, dag, local_cores=local_cores, dryrun=dryrun, touch=touch, cluster=cluster, cluster_status=cluster_status, cluster_config=cluster_config, cluster_sync=cluster_sync, jobname=jobname, max_jobs_per_second=max_jobs_per_second, max_status_checks_per_second=max_status_checks_per_second, quiet=quiet, keepgoing=keepgoing, drmaa=drmaa, drmaa_log_dir=drmaa_log_dir, kubernetes=kubernetes, 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, preemption_default=preemption_default, preemptible_rules=preemptible_rules, precommand=precommand, tibanna_config=tibanna_config, container_image=container_image, printreason=printreason, printshellcmds=printshellcmds, latency_wait=latency_wait, greediness=greediness, force_use_threads=force_use_threads, assume_shared_fs=assume_shared_fs, keepincomplete=keepincomplete, keepmetadata=keepmetadata, scheduler_type=scheduler_type, scheduler_ilp_solver=scheduler_ilp_solver, ) if not dryrun: if len(dag): shell_exec = shell.get_executable() if shell_exec is not None:"Using shell: {}".format(shell_exec)) if cluster or cluster_sync or drmaa: logger.resources_info( "Provided cluster nodes: {}".format(self.nodes) ) elif kubernetes or tibanna or google_lifesciences: logger.resources_info("Provided cloud nodes: {}".format(self.nodes)) else: if self._cores is not None: warning = ( "" if self._cores > 1 else " (use --cores to define parallelism)" ) logger.resources_info( "Provided cores: {}{}".format(self._cores, warning) ) logger.resources_info( "Rules claiming more threads " "will be scaled down." ) provided_resources = format_resources(self.global_resources) if provided_resources: logger.resources_info("Provided resources: " + provided_resources) if self.run_local and any( for rule in self.rules):"Group jobs: inactive (local execution)") if not self.use_conda and any(rule.conda_env for rule in self.rules):"Conda environments: ignored") if not self.use_singularity and any( rule.container_img for rule in self.rules ):"Singularity containers: ignored") if self.mode == Mode.default: logger.run_info("\n".join(dag.stats())) else: else: # the dryrun case if len(dag): logger.run_info("\n".join(dag.stats())) else: return True if quiet: # in case of dryrun and quiet, just print above info and exit return True if not dryrun and not no_hooks: self._onstart(logger.get_logfile()) success = self.scheduler.schedule() if not immediate_submit and not dryrun: dag.cleanup_workdir() if success: if dryrun: if len(dag): logger.run_info("\n".join(dag.stats())) "This was a dry-run (flag -n). The order of jobs " "does not reflect the order of execution." ) logger.remove_logfile() else: if stats: self.scheduler.stats.to_json(stats) logger.logfile_hint() if not dryrun and not no_hooks: self._onsuccess(logger.get_logfile()) return True else: if not dryrun and not no_hooks: self._onerror(logger.get_logfile()) logger.logfile_hint() return False
@property def current_basedir(self): """Basedir of currently parsed Snakefile.""" assert self.included_stack snakefile = self.included_stack[-1] basedir = snakefile.get_basedir() if isinstance(basedir, LocalSourceFile): return basedir.abspath() else: return basedir
[docs] def source_path(self, rel_path): """Return path to source file from work dir derived from given path relative to snakefile""" # TODO download to disk (use source cache) in case of remote file import inspect frame = inspect.currentframe().f_back calling_file = frame.f_code.co_filename calling_dir = os.path.dirname(calling_file) path = smart_join(calling_dir, rel_path) return self.sourcecache.get_path(infer_source_file(path))
@property def snakefile(self): import inspect frame = inspect.currentframe().f_back return frame.f_code.co_filename
[docs] def register_envvars(self, *envvars): """ Register environment variables that shall be passed to jobs. If used multiple times, union is taken. """ undefined = set(var for var in envvars if var not in os.environ) if self.check_envvars and undefined: raise WorkflowError( "The following environment variables are requested by the workflow but undefined. " "Please make sure that they are correctly defined before running Snakemake:\n" "{}".format("\n".join(undefined)) ) self.envvars.update(envvars)
[docs] def containerize(self): from snakemake.deployment.containerize import containerize containerize(self)
[docs] def include( self, snakefile, overwrite_first_rule=False, print_compilation=False, overwrite_shellcmd=None, ): """ Include a snakefile. """ basedir = self.current_basedir if self.included_stack else None snakefile = infer_source_file(snakefile, basedir) if not self.modifier.allow_rule_overwrite and snakefile in self.included:"Multiple includes of {} ignored".format(snakefile)) return self.included.append(snakefile) self.included_stack.append(snakefile) first_rule = self.first_rule code, linemap, rulecount = parse( snakefile, self, overwrite_shellcmd=self.overwrite_shellcmd, rulecount=self._rulecount, ) self._rulecount = rulecount if print_compilation: print(code) if isinstance(snakefile, LocalSourceFile): # insert the current directory into sys.path # this allows to import modules from the workflow directory sys.path.insert(0, snakefile.get_basedir().get_path_or_uri()) self.linemaps[snakefile] = linemap exec(compile(code, snakefile.get_path_or_uri(), "exec"), self.globals) if not overwrite_first_rule: self.first_rule = first_rule self.included_stack.pop()
[docs] def onstart(self, func): """Register onstart function.""" self._onstart = func
[docs] def onsuccess(self, func): """Register onsuccess function.""" self._onsuccess = func
[docs] def onerror(self, func): """Register onerror function.""" self._onerror = func
[docs] def global_wildcard_constraints(self, **content): """Register global wildcard constraints.""" self._wildcard_constraints.update(content) # update all rules so far for rule in self.rules: rule.update_wildcard_constraints()
[docs] def scattergather(self, **content): """Register scattergather defaults.""" self._scatter.update(content) self._scatter.update(self.overwrite_scatter) # add corresponding wildcard constraint self.global_wildcard_constraints(scatteritem="\d+-of-\d+") def func(*args, **wildcards): n = self._scatter[key] return expand( *args, scatteritem=map("{{}}-of-{}".format(n).format, range(1, n + 1)), **wildcards ) for key in content: setattr(self.globals["scatter"], key, func) setattr(self.globals["gather"], key, func)
[docs] def workdir(self, workdir): """Register workdir.""" if self.overwrite_workdir is None: os.makedirs(workdir, exist_ok=True) self._workdir = workdir os.chdir(workdir)
[docs] def configfile(self, fp): """Update the global config with data from the given file.""" global config if not self.modifier.skip_configfile: if os.path.exists(fp): self.configfiles.append(fp) c = update_config(config, c) if self.overwrite_config: "Config file {} is extended by additional config specified via the command line.".format( fp ) ) update_config(config, self.overwrite_config) elif not self.overwrite_configfiles: raise WorkflowError( "Workflow defines configfile {} but it is not present or accessible.".format( fp ) )
[docs] def pepfile(self, path): global pep try: import peppy except ImportError: raise WorkflowError("For PEP support, please install peppy.") self.pepfile = path pep = peppy.Project(self.pepfile)
[docs] def pepschema(self, schema): global pep try: import eido except ImportError: raise WorkflowError("For PEP schema support, please install eido.") if is_local_file(schema) and not os.path.isabs(schema): # schema is relative to current Snakefile schema = self.current_basedir.join(schema).get_path_or_uri() if self.pepfile is None: raise WorkflowError("Please specify a PEP with the pepfile directive.") eido.validate_project(project=pep, schema=schema, exclude_case=True)
[docs] def report(self, path): """Define a global report description in .rst format.""" self.report_text = self.current_basedir.join(path)
@property def config(self): return self.globals["config"]
[docs] def ruleorder(self, *rulenames): self._ruleorder.add(*map(self.modifier.modify_rulename, rulenames))
[docs] def subworkflow(self, name, snakefile=None, workdir=None, configfile=None): # Take absolute path of config file, because it is relative to current # workdir, which could be changed for the subworkflow. if configfile: configfile = os.path.abspath(configfile) sw = Subworkflow(self, name, snakefile, workdir, configfile) self._subworkflows[name] = sw self.globals[name] =
[docs] def localrules(self, *rulenames): self._localrules.update(rulenames)
[docs] def rule(self, name=None, lineno=None, snakefile=None, checkpoint=False): # choose a name for an unnamed rule if name is None: name = str(len(self._rules) + 1) if self.modifier.skip_rule(name): def decorate(ruleinfo): # do nothing, ignore rule return ruleinfo.func return decorate # Optionally let the modifier change the rulename. orig_name = name name = self.modifier.modify_rulename(name) name = self.add_rule( name, lineno, snakefile, checkpoint, allow_overwrite=self.modifier.allow_rule_overwrite, ) rule = self.get_rule(name) rule.is_checkpoint = checkpoint def decorate(ruleinfo): nonlocal name # If requested, modify ruleinfo via the modifier. ruleinfo.apply_modifier(self.modifier) if ruleinfo.wildcard_constraints: rule.set_wildcard_constraints( *ruleinfo.wildcard_constraints[0], **ruleinfo.wildcard_constraints[1] ) if = del self._rules[name] self._rules[] = rule name = rule.path_modifier = ruleinfo.path_modifier if ruleinfo.input: rule.set_input(*ruleinfo.input[0], **ruleinfo.input[1]) if ruleinfo.output: rule.set_output(*ruleinfo.output[0], **ruleinfo.output[1]) if ruleinfo.params: rule.set_params(*ruleinfo.params[0], **ruleinfo.params[1]) # handle default resources if self.default_resources is not None: rule.resources = copy.deepcopy(self.default_resources.parsed) if ruleinfo.threads is not None: if ( not isinstance(ruleinfo.threads, int) and not isinstance(ruleinfo.threads, float) and not callable(ruleinfo.threads) ): raise RuleException( "Threads value has to be an integer, float, or a callable.", rule=rule, ) if name in self.overwrite_threads: rule.resources["_cores"] = self.overwrite_threads[name] else: if isinstance(ruleinfo.threads, float): ruleinfo.threads = int(ruleinfo.threads) rule.resources["_cores"] = ruleinfo.threads if ruleinfo.shadow_depth: if ruleinfo.shadow_depth not in ( True, "shallow", "full", "minimal", "copy-minimal", ): raise RuleException( "Shadow must either be 'minimal', 'copy-minimal', 'shallow', 'full', " "or True (equivalent to 'full')", rule=rule, ) if ruleinfo.shadow_depth is True: rule.shadow_depth = "full" logger.warning( "Shadow is set to True in rule {} (equivalent to 'full'). It's encouraged to use the more explicit options 'minimal|copy-minimal|shallow|full' instead.".format( rule ) ) else: rule.shadow_depth = ruleinfo.shadow_depth if ruleinfo.resources: args, resources = ruleinfo.resources if args: raise RuleException("Resources have to be named.") if not all( map( lambda r: isinstance(r, int) or isinstance(r, str) or callable(r), resources.values(), ) ): raise RuleException( "Resources values have to be integers, strings, or callables (functions)", rule=rule, ) rule.resources.update(resources) if name in self.overwrite_resources: rule.resources.update(self.overwrite_resources[name]) if ruleinfo.priority: if not isinstance(ruleinfo.priority, int) and not isinstance( ruleinfo.priority, float ): raise RuleException( "Priority values have to be numeric.", rule=rule ) rule.priority = ruleinfo.priority if ruleinfo.version: rule.version = ruleinfo.version if ruleinfo.log: rule.set_log(*ruleinfo.log[0], **ruleinfo.log[1]) if ruleinfo.message: rule.message = ruleinfo.message if ruleinfo.benchmark: rule.benchmark = ruleinfo.benchmark if not self.run_local: group = self.overwrite_groups.get(name) or if group is not None: = group if ruleinfo.wrapper: rule.conda_env = snakemake.wrapper.get_conda_env( ruleinfo.wrapper, prefix=self.wrapper_prefix ) # TODO retrieve suitable singularity image if ruleinfo.env_modules: # If using environment modules and they are defined for the rule, # ignore conda and singularity directive below. # The reason is that this is likely intended in order to use # a software stack specifically compiled for a particular # HPC cluster. invalid_rule = not ( ruleinfo.script or ruleinfo.wrapper or ruleinfo.shellcmd or ruleinfo.notebook ) if invalid_rule: raise RuleException( "envmodules directive is only allowed with " "shell, script, notebook, or wrapper directives (not with run)", rule=rule, ) from snakemake.deployment.env_modules import EnvModules rule.env_modules = EnvModules(*ruleinfo.env_modules) if ruleinfo.conda_env: if not ( ruleinfo.script or ruleinfo.wrapper or ruleinfo.shellcmd or ruleinfo.notebook ): raise RuleException( "Conda environments are only allowed " "with shell, script, notebook, or wrapper directives " "(not with run).", rule=rule, ) if ( ruleinfo.conda_env is not None and is_local_file(ruleinfo.conda_env) and not os.path.isabs(ruleinfo.conda_env) ): ruleinfo.conda_env = self.current_basedir.join( ruleinfo.conda_env ).get_path_or_uri() rule.conda_env = ruleinfo.conda_env invalid_rule = not ( ruleinfo.script or ruleinfo.wrapper or ruleinfo.shellcmd or ruleinfo.notebook ) if ruleinfo.container_img: if invalid_rule: raise RuleException( "Singularity directive is only allowed " "with shell, script, notebook or wrapper directives " "(not with run).", rule=rule, ) rule.container_img = ruleinfo.container_img rule.is_containerized = ruleinfo.is_containerized elif self.global_container_img: if not invalid_rule and ruleinfo.container_img != False: # skip rules with run directive or empty image rule.container_img = self.global_container_img rule.is_containerized = self.global_is_containerized rule.norun = ruleinfo.norun if is not None: = rule.docstring = ruleinfo.docstring rule.run_func = ruleinfo.func rule.shellcmd = ruleinfo.shellcmd rule.script = ruleinfo.script rule.notebook = ruleinfo.notebook rule.wrapper = ruleinfo.wrapper rule.cwl = ruleinfo.cwl rule.restart_times = self.restart_times rule.basedir = self.current_basedir if ruleinfo.handover: if not ruleinfo.resources: # give all available resources to the rule rule.resources.update( { name: val for name, val in self.global_resources.items() if val is not None } ) # This becomes a local rule, which might spawn jobs to a cluster, # depending on its configuration (e.g. nextflow config). self._localrules.add( rule.is_handover = True if ruleinfo.cache is True: if not self.enable_cache: logger.warning( "Workflow defines that rule {} is eligible for caching between workflows " "(use the --cache argument to enable this).".format( ) else: self.cache_rules.add( elif not (ruleinfo.cache is False): raise WorkflowError( "Invalid argument for 'cache:' directive. Only true allowed. " "To deactivate caching, remove directive.", rule=rule, ) ruleinfo.func.__name__ = "__{}".format( self.globals[ruleinfo.func.__name__] = ruleinfo.func rule_proxy = RuleProxy(rule) if orig_name is not None: setattr(self.globals["rules"], orig_name, rule_proxy) setattr(self.globals["rules"],, rule_proxy) if checkpoint: self.globals["checkpoints"].register(rule, fallback_name=orig_name) rule.ruleinfo = ruleinfo return ruleinfo.func return decorate
[docs] def docstring(self, string): def decorate(ruleinfo): ruleinfo.docstring = string return ruleinfo return decorate
[docs] def input(self, *paths, **kwpaths): def decorate(ruleinfo): ruleinfo.input = (paths, kwpaths) return ruleinfo return decorate
[docs] def output(self, *paths, **kwpaths): def decorate(ruleinfo): ruleinfo.output = (paths, kwpaths) return ruleinfo return decorate
[docs] def params(self, *params, **kwparams): def decorate(ruleinfo): ruleinfo.params = (params, kwparams) return ruleinfo return decorate
[docs] def wildcard_constraints(self, *wildcard_constraints, **kwwildcard_constraints): def decorate(ruleinfo): ruleinfo.wildcard_constraints = ( wildcard_constraints, kwwildcard_constraints, ) return ruleinfo return decorate
[docs] def cache_rule(self, cache): def decorate(ruleinfo): ruleinfo.cache = cache return ruleinfo return decorate
[docs] def message(self, message): def decorate(ruleinfo): ruleinfo.message = message return ruleinfo return decorate
[docs] def benchmark(self, benchmark): def decorate(ruleinfo): ruleinfo.benchmark = benchmark return ruleinfo return decorate
[docs] def conda(self, conda_env): def decorate(ruleinfo): ruleinfo.conda_env = conda_env return ruleinfo return decorate
[docs] def container(self, container_img): def decorate(ruleinfo): # Explicitly set container_img to False if None is passed, indicating that # no container image shall be used, also not a global one. ruleinfo.container_img = ( container_img if container_img is not None else False ) ruleinfo.is_containerized = False return ruleinfo return decorate
[docs] def containerized(self, container_img): def decorate(ruleinfo): ruleinfo.container_img = container_img ruleinfo.is_containerized = True return ruleinfo return decorate
[docs] def envmodules(self, *env_modules): def decorate(ruleinfo): ruleinfo.env_modules = env_modules return ruleinfo return decorate
[docs] def global_container(self, container_img): self.global_container_img = container_img self.global_is_containerized = False
[docs] def global_containerized(self, container_img): self.global_container_img = container_img self.global_is_containerized = True
[docs] def threads(self, threads): def decorate(ruleinfo): ruleinfo.threads = threads return ruleinfo return decorate
[docs] def shadow(self, shadow_depth): def decorate(ruleinfo): ruleinfo.shadow_depth = shadow_depth return ruleinfo return decorate
[docs] def resources(self, *args, **resources): def decorate(ruleinfo): ruleinfo.resources = (args, resources) return ruleinfo return decorate
[docs] def priority(self, priority): def decorate(ruleinfo): ruleinfo.priority = priority return ruleinfo return decorate
[docs] def version(self, version): def decorate(ruleinfo): ruleinfo.version = version return ruleinfo return decorate
[docs] def group(self, group): def decorate(ruleinfo): = group return ruleinfo return decorate
[docs] def log(self, *logs, **kwlogs): def decorate(ruleinfo): ruleinfo.log = (logs, kwlogs) return ruleinfo return decorate
[docs] def handover(self, value): def decorate(ruleinfo): ruleinfo.handover = value return ruleinfo return decorate
[docs] def shellcmd(self, cmd): def decorate(ruleinfo): ruleinfo.shellcmd = cmd return ruleinfo return decorate
[docs] def script(self, script): def decorate(ruleinfo): ruleinfo.script = script return ruleinfo return decorate
[docs] def notebook(self, notebook): def decorate(ruleinfo): ruleinfo.notebook = notebook return ruleinfo return decorate
[docs] def wrapper(self, wrapper): def decorate(ruleinfo): ruleinfo.wrapper = wrapper return ruleinfo return decorate
[docs] def cwl(self, cwl): def decorate(ruleinfo): ruleinfo.cwl = cwl return ruleinfo return decorate
[docs] def norun(self): def decorate(ruleinfo): ruleinfo.norun = True return ruleinfo return decorate
[docs] def name(self, name): def decorate(ruleinfo): = name return ruleinfo return decorate
[docs] def run(self, func): return RuleInfo(func)
[docs] def module( self, name, snakefile=None, meta_wrapper=None, config=None, skip_validation=False, replace_prefix=None, ): self.modules[name] = ModuleInfo( self, name, snakefile=snakefile, meta_wrapper=meta_wrapper, config=config, skip_validation=skip_validation, replace_prefix=replace_prefix, )
[docs] def userule(self, rules=None, from_module=None, name_modifier=None, lineno=None): def decorate(maybe_ruleinfo): if from_module is not None: try: module = self.modules[from_module] except KeyError: raise WorkflowError( "Module {} has not been registered with 'module' statement before using it in 'use rule' statement.".format( from_module ) ) module.use_rules( rules, name_modifier, ruleinfo=None if callable(maybe_ruleinfo) else maybe_ruleinfo, ) else: # local inheritance if len(rules) > 1: raise WorkflowError( "'use rule' statement from rule in the same module must declare a single rule but multiple rules are declared." ) orig_rule = self._rules[self.modifier.modify_rulename(rules[0])] ruleinfo = maybe_ruleinfo if not callable(maybe_ruleinfo) else None with WorkflowModifier( self, rulename_modifier=get_name_modifier_func( rules, name_modifier, parent_modifier=self.modifier ), ruleinfo_overwrite=ruleinfo, ): self.rule( name=name_modifier, lineno=lineno, snakefile=self.included_stack[-1], )(orig_rule.ruleinfo) return decorate
@staticmethod def _empty_decorator(f): return f
[docs]class Subworkflow: def __init__(self, workflow, name, snakefile, workdir, configfile): self.workflow = workflow = name self._snakefile = snakefile self._workdir = workdir self.configfile = configfile @property def snakefile(self): if self._snakefile is None: return os.path.abspath(os.path.join(self.workdir, "Snakefile")) if not os.path.isabs(self._snakefile): return os.path.abspath(os.path.join(self.workflow.basedir, self._snakefile)) return self._snakefile @property def workdir(self): workdir = "." if self._workdir is None else self._workdir if not os.path.isabs(workdir): return os.path.abspath(os.path.join(self.workflow.basedir, workdir)) return workdir
[docs] def target(self, paths): if not_iterable(paths): path = paths path = ( path if os.path.isabs(path) or path.startswith("root://") else os.path.join(self.workdir, path) ) return flag(path, "subworkflow", self) return [ for path in paths]
[docs] def targets(self, dag): def relpath(f): if f.startswith(self.workdir): return os.path.relpath(f, start=self.workdir) # do not adjust absolute targets outside of workdir return f return [ relpath(f) for job in for f in job.subworkflow_input if job.subworkflow_input[f] is self ]
[docs]def srcdir(path): """Return the absolute path, relative to the source directory of the current Snakefile.""" if not workflow.included_stack: return None return workflow.current_basedir.join(path).get_path_or_uri()