Source code for snakemake.dag

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

import html
import os
import sys
import shutil
import textwrap
import time
import tarfile
from collections import defaultdict, Counter, deque, namedtuple
from itertools import chain, filterfalse, groupby
from functools import partial
from pathlib import Path
import uuid
import math

from import PeriodicityDetector, wait_for_files, is_flagged, IOFile
from import Reason, JobFactory, GroupJobFactory, Job
from snakemake.exceptions import MissingInputException
from snakemake.exceptions import MissingRuleException, AmbiguousRuleException
from snakemake.exceptions import CyclicGraphException, MissingOutputException
from snakemake.exceptions import IncompleteFilesException, ImproperOutputException
from snakemake.exceptions import PeriodicWildcardError
from snakemake.exceptions import RemoteFileException, WorkflowError, ChildIOException
from snakemake.exceptions import InputFunctionException
from snakemake.logging import logger
from snakemake.common import DYNAMIC_FILL, group_into_chunks
from snakemake.deployment import conda, singularity
from snakemake.output_index import OutputIndex
from snakemake import workflow

PotentialDependency = namedtuple("PotentialDependency", ["file", "jobs", "known"])

[docs]class Batch: """Definition of a batch for calculating only a partial DAG.""" def __init__(self, rulename: str, idx: int, batches: int): assert idx <= batches assert idx > 0 self.rulename = rulename self.idx = idx self.batches = batches
[docs] def get_batch(self, items: list): """Return the defined batch of the given items. Items are usually input files.""" # make sure that we always consider items in the same order if len(items) < self.batches: raise WorkflowError( "Batching rule {} has less input files than batches. " "Please choose a smaller number of batches.".format(self.rulename) ) items = sorted(items) batch_len = math.floor(len(items) / self.batches) # self.batch is one-based, hence we have to subtract 1 idx = self.idx - 1 i = idx * batch_len if self.is_final: # extend the last batch to cover rest of list return items[i:] else: return items[i : i + batch_len]
@property def is_final(self): return self.idx == self.batches def __str__(self): return "{}/{} (rule {})".format(self.idx, self.batches, self.rulename)
[docs]class DAG: """Directed acyclic graph of jobs.""" def __init__( self, workflow, rules=None, dryrun=False, targetfiles=None, targetrules=None, forceall=False, forcerules=None, forcefiles=None, priorityfiles=None, priorityrules=None, untilfiles=None, untilrules=None, omitfiles=None, omitrules=None, ignore_ambiguity=False, force_incomplete=False, ignore_incomplete=False, notemp=False, keep_remote_local=False, batch=None, ): self.dryrun = dryrun self.dependencies = defaultdict(partial(defaultdict, set)) self.depending = defaultdict(partial(defaultdict, set)) self._needrun = set() self._priority = dict() self._reason = defaultdict(Reason) self._finished = set() self._dynamic = set() self._len = 0 self.workflow = workflow self.rules = set(rules) self.ignore_ambiguity = ignore_ambiguity self.targetfiles = targetfiles self.targetrules = targetrules self.priorityfiles = priorityfiles self.priorityrules = priorityrules self.targetjobs = set() self.prioritytargetjobs = set() self._ready_jobs = set() self.notemp = notemp self.keep_remote_local = keep_remote_local self._jobid = dict() self.job_cache = dict() self.conda_envs = dict() self.container_imgs = dict() self._progress = 0 self._group = dict() self._n_until_ready = defaultdict(int) self._running = set() self.job_factory = JobFactory() self.group_job_factory = GroupJobFactory() self.forcerules = set() self.forcefiles = set() self.untilrules = set() self.untilfiles = set() self.omitrules = set() self.omitfiles = set() self.updated_subworkflow_files = set() if forceall: self.forcerules.update(self.rules) elif forcerules: self.forcerules.update(forcerules) if forcefiles: self.forcefiles.update(forcefiles) if untilrules: self.untilrules.update(set( for rule in untilrules)) if untilfiles: self.untilfiles.update(untilfiles) if omitrules: self.omitrules.update(set( for rule in omitrules)) if omitfiles: self.omitfiles.update(omitfiles) self.has_dynamic_rules = any(rule.dynamic_output for rule in self.rules) self.omitforce = set() self.batch = batch if batch is not None and not batch.is_final: # Since not all input files of a batching rule are considered, we cannot run # beyond that rule. # For the final batch, we do not need to omit anything. self.omitrules.add(batch.rulename) self.force_incomplete = force_incomplete self.ignore_incomplete = ignore_incomplete self.periodic_wildcard_detector = PeriodicityDetector() self.update_output_index()
[docs] def init(self, progress=False): """Initialise the DAG.""" for job in map(self.rule2job, self.targetrules): job = self.update([job], progress=progress, create_inventory=True) self.targetjobs.add(job) for file in self.targetfiles: job = self.update( self.file2jobs(file), file=file, progress=progress, create_inventory=True, ) self.targetjobs.add(job) self.cleanup() self.check_incomplete() self.update_needrun(create_inventory=True) self.set_until_jobs() self.delete_omitfrom_jobs() self.update_jobids() self.check_directory_outputs() # check if remaining jobs are valid for i, job in enumerate( job.is_valid()
[docs] def check_directory_outputs(self): """Check that no output file is contained in a directory output of the same or another rule.""" outputs = sorted( {(os.path.abspath(f), job) for job in for f in job.output} ) for i in range(len(outputs) - 1): (a, job_a), (b, job_b) = outputs[i : i + 2] try: common = os.path.commonpath([a, b]) except ValueError: # commonpath raises error if windows drives are different. continue if a != b and common == os.path.commonpath([a]) and job_a != job_b: raise ChildIOException(parent=outputs[i], child=outputs[i + 1])
@property def checkpoint_jobs(self): for job in self.needrun_jobs: if job.is_checkpoint: yield job
[docs] def update_checkpoint_outputs(self): workflow.checkpoints.future_output = set( f for job in self.checkpoint_jobs for f in job.output )
[docs] def update_jobids(self): for job in if job not in self._jobid: self._jobid[job] = len(self._jobid)
[docs] def cleanup_workdir(self): for job in if not self.is_edit_notebook_job(job): for io_dir in set( os.path.dirname(io_file) for io_file in chain(job.output, job.input) if not os.path.exists(io_file) ): if os.path.exists(io_dir) and not len(os.listdir(io_dir)): os.removedirs(io_dir)
[docs] def cleanup(self): self.job_cache.clear() final_jobs = set(self.bfs(self.dependencies, *self.targetjobs)) todelete = [job for job in self.dependencies if job not in final_jobs] for job in todelete: try: self._needrun.remove(job) except KeyError: pass # delete all pointers from dependencies to this job for dep in self.dependencies[job]: try: del self.depending[dep][job] except KeyError: # In case the pointer has been deleted before or # never created, we can simply continue. pass # delete all dependencies del self.dependencies[job] try: # delete all pointers to downstream dependencies del self.depending[job] except KeyError: pass
[docs] def create_conda_envs( self, dryrun=False, forceall=False, init_only=False, quiet=False ): # First deduplicate based on job.conda_env_file jobs = if forceall else self.needrun_jobs env_set = { (job.conda_env_file, job.container_img_url) for job in jobs if job.conda_env_file } # Then based on md5sum values self.conda_envs = dict() for (env_file, simg_url) in env_set: simg = None if simg_url and self.workflow.use_singularity: assert ( simg_url in self.container_imgs ), "bug: must first pull singularity images" simg = self.container_imgs[simg_url] env = conda.Env( env_file, self.workflow, container_img=simg, cleanup=self.workflow.conda_cleanup_pkgs, ) self.conda_envs[(env_file, simg_url)] = env if not init_only: for env in self.conda_envs.values(): if not dryrun or not quiet: env.create(dryrun)
[docs] def pull_container_imgs(self, dryrun=False, forceall=False, quiet=False): # First deduplicate based on job.conda_env_file jobs = if forceall else self.needrun_jobs img_set = { (job.container_img_url, job.is_containerized) for job in jobs if job.container_img_url } for img_url, is_containerized in img_set: img = singularity.Image(img_url, self, is_containerized) if not dryrun or not quiet: img.pull(dryrun) self.container_imgs[img_url] = img
[docs] def update_output_index(self): """Update the OutputIndex.""" self.output_index = OutputIndex(self.rules)
[docs] def check_incomplete(self): """Check if any output files are incomplete. This is done by looking up markers in the persistence module.""" if not self.ignore_incomplete: incomplete = self.incomplete_files if incomplete: if self.force_incomplete: logger.debug("Forcing incomplete files:") logger.debug("\t" + "\n\t".join(incomplete)) self.forcefiles.update(incomplete) else: raise IncompleteFilesException(incomplete)
[docs] def incomplete_external_jobid(self, job): """Return the external jobid of the job if it is marked as incomplete. Returns None, if job is not incomplete, or if no external jobid has been registered or if force_incomplete is True. """ if self.force_incomplete: return None jobids = self.workflow.persistence.external_jobids(job) if len(jobids) == 1: return jobids[0] elif len(jobids) > 1: raise WorkflowError( "Multiple different external jobids registered " "for output files of incomplete job {} ({}). This job " "cannot be resumed. Execute Snakemake with --rerun-incomplete " "to fix this issue.".format(job.jobid, jobids) )
[docs] def check_dynamic(self): """Check dynamic output and update downstream rules if necessary.""" if self.has_dynamic_rules: for job in filter( lambda job: (job.dynamic_output and not self.needrun(job)), list(, ): self.update_dynamic(job) self.postprocess()
[docs] def is_edit_notebook_job(self, job): return self.workflow.edit_notebook and job.targetfile in self.targetfiles
@property def dynamic_output_jobs(self): """Iterate over all jobs with dynamic output files.""" return (job for job in if job.dynamic_output) @property def jobs(self): """All jobs in the DAG.""" return self.dependencies.keys() @property def needrun_jobs(self): """Jobs that need to be executed.""" return filterfalse(self.finished, self._needrun) @property def local_needrun_jobs(self): """Iterate over all jobs that need to be run and are marked as local.""" return filter(lambda job: job.is_local, self.needrun_jobs) @property def finished_jobs(self): """Iterate over all jobs that have been finished.""" return filter(self.finished, @property def ready_jobs(self): """Jobs that are ready to execute.""" return self._ready_jobs
[docs] def needrun(self, job): """Return whether a given job needs to be executed.""" return job in self._needrun
[docs] def priority(self, job): """Return priority of given job.""" return self._priority[job]
[docs] def noneedrun_finished(self, job): """ Return whether a given job is finished or was not required to run at all. """ return not self.needrun(job) or self.finished(job)
[docs] def reason(self, job): """Return the reason of the job execution.""" return self._reason[job]
[docs] def finished(self, job): """Return whether a job is finished.""" return job in self._finished
[docs] def dynamic(self, job): """ Return whether a job is dynamic (i.e. it is only a placeholder for those that are created after the job with dynamic output has finished. """ if job.is_group(): for j in job: if j in self._dynamic: return True else: return job in self._dynamic
[docs] def requested_files(self, job): """Return the files a job requests.""" return set(*self.depending[job].values())
@property def incomplete_files(self): """Return list of incomplete files.""" return list( chain( *( job.output for job in filter( self.workflow.persistence.incomplete, filterfalse(self.needrun,, ) ) ) ) @property def newversion_files(self): """Return list of files where the current version is newer than the recorded version. """ return list( chain( *( job.output for job in filter(self.workflow.persistence.newversion, ) ) )
[docs] def missing_temp(self, job): """ Return whether a temp file that is input of the given job is missing. """ for job_, files in self.depending[job].items(): if self.needrun(job_) and any(not f.exists for f in files): return True return False
[docs] def check_and_touch_output( self, job, wait=3, ignore_missing_output=False, no_touch=False, force_stay_on_remote=False, ): """Raise exception if output files of job are missing.""" expanded_output = [job.shadowed_path(path) for path in job.expanded_output] if job.benchmark: expanded_output.append(job.benchmark) if not ignore_missing_output: try: wait_for_files( expanded_output, latency_wait=wait, force_stay_on_remote=force_stay_on_remote, ignore_pipe=True, ) except IOError as e: raise MissingOutputException( str(e) + "\nThis might be due to " "filesystem latency. If that is the case, consider to increase the " "wait time with --latency-wait." + "\nJob id: {jobid}".format(jobid=job.jobid), rule=job.rule, jobid=self.jobid(job), ) # Ensure that outputs are of the correct type (those flagged with directory() # are directories and not files and vice versa). We can't check for remote objects for f in expanded_output: if (f.is_directory and not f.remote_object and not os.path.isdir(f)) or ( not f.remote_object and os.path.isdir(f) and not f.is_directory ): raise ImproperOutputException(job.rule, [f]) # It is possible, due to archive expansion or cluster clock skew, that # the files appear older than the input. But we know they must be new, # so touch them to update timestamps. This also serves to touch outputs # when using the --touch flag. # Note that if the input files somehow have a future date then this will # not currently be spotted and the job will always be re-run. if not no_touch: for f in expanded_output: # This won't create normal files if missing, but will create # the flag file for directories. if f.exists_local: f.touch()
[docs] def unshadow_output(self, job, only_log=False): """Move files from shadow directory to real output paths.""" if not job.shadow_dir or not job.expanded_output: return files = job.log if only_log else chain(job.expanded_output, job.log) for real_output in files: shadow_output = job.shadowed_path(real_output).file # Remake absolute symlinks as relative if os.path.islink(shadow_output): dest = os.readlink(shadow_output) if os.path.isabs(dest): rel_dest = os.path.relpath(dest, job.shadow_dir) os.remove(shadow_output) os.symlink(rel_dest, shadow_output) if os.path.realpath(shadow_output) == os.path.realpath(real_output): continue logger.debug( "Moving shadow output {} to destination {}".format( shadow_output, real_output ) ) shutil.move(shadow_output, real_output) shutil.rmtree(job.shadow_dir)
[docs] def check_periodic_wildcards(self, job): """Raise an exception if a wildcard of the given job appears to be periodic, indicating a cyclic dependency.""" for wildcard, value in job.wildcards_dict.items(): periodic_substring = self.periodic_wildcard_detector.is_periodic(value) if periodic_substring is not None: raise PeriodicWildcardError( "The value {} in wildcard {} is periodically repeated ({}). " "This would lead to an infinite recursion. " "To avoid this, e.g. restrict the wildcards in this rule to certain values.".format( periodic_substring, wildcard, value ), rule=job.rule, )
[docs] def handle_protected(self, job): """Write-protect output files that are marked with protected().""" for f in job.expanded_output: if f in job.protected_output:"Write-protecting output file {}.".format(f)) f.protect()
[docs] def handle_touch(self, job): """Touches those output files that are marked for touching.""" for f in job.expanded_output: if f in job.touch_output: f = job.shadowed_path(f)"Touching output file {}.".format(f)) f.touch_or_create() assert os.path.exists(f)
[docs] def temp_input(self, job): for job_, files in self.dependencies[job].items(): for f in filter(job_.temp_output.__contains__, files): yield f
[docs] def temp_size(self, job): """Return the total size of temporary input files of the job. If none, return 0. """ return sum(f.size for f in self.temp_input(job))
[docs] def handle_temp(self, job): """Remove temp files if they are no longer needed. Update temp_mtimes.""" if self.notemp: return is_temp = lambda f: is_flagged(f, "temp") # handle temp input needed = lambda job_, f: any( f in files for j, files in self.depending[job_].items() if not self.finished(j) and self.needrun(j) and j != job ) def unneeded_files(): # temp input for job_, files in self.dependencies[job].items(): tempfiles = set(f for f in job_.expanded_output if is_temp(f)) yield from filterfalse(partial(needed, job_), tempfiles & files) # temp output if ( not job.dynamic_output and not job.is_checkpoint and ( job not in self.targetjobs or == self.workflow.first_rule ) ): tempfiles = ( f for f in job.expanded_output if is_temp(f) and f not in self.targetfiles ) yield from filterfalse(partial(needed, job), tempfiles) for f in unneeded_files():"Removing temporary output file {}.".format(f)) f.remove(remove_non_empty_dir=True)
[docs] def handle_log(self, job, upload_remote=True): for f in job.log: f = job.shadowed_path(f) if not f.exists_local: # If log file was not created during job, create an empty one. f.touch_or_create()
[docs] def handle_remote(self, job, upload=True): """Remove local files if they are no longer needed and upload.""" if upload: # handle output files files = job.expanded_output if job.benchmark: files = chain(job.expanded_output, (job.benchmark,)) if job.log: files = chain(files, job.log) for f in files: if f.is_remote and not f.should_stay_on_remote: f.upload_to_remote() remote_mtime = f.mtime.remote() # immediately force local mtime to match remote, # since conversions from S3 headers are not 100% reliable # without this, newness comparisons may fail down the line f.touch(times=(remote_mtime, remote_mtime)) if not f.exists_remote: raise RemoteFileException( "The file upload was attempted, but it does not " "exist on remote. Check that your credentials have " "read AND write permissions." ) if not self.keep_remote_local: if not any(f.is_remote for f in job.input): return # handle input files needed = lambda job_, f: any( f in files for j, files in self.depending[job_].items() if not self.finished(j) and self.needrun(j) and j != job ) def unneeded_files(): putative = ( lambda f: f.is_remote and not f.protected and not f.should_keep_local ) generated_input = set() for job_, files in self.dependencies[job].items(): generated_input |= files for f in filter(putative, files): if not needed(job_, f): yield f for f, f_ in zip(job.output, job.rule.output): if putative(f) and not needed(job, f) and not f in self.targetfiles: if f in job.dynamic_output: for f_ in job.expand_dynamic(f_): yield f_ else: yield f for f in filter(putative, job.input): # TODO what about remote inputs that are used by multiple jobs? if f not in generated_input: yield f for f in unneeded_files(): if f.exists_local:"Removing local copy of remote file: {}".format(f)) f.remove()
[docs] def jobid(self, job): """Return job id of given job.""" if job.is_group(): return job.jobid else: return self._jobid[job]
[docs] def update( self, jobs, file=None, visited=None, known_producers=None, skip_until_dynamic=False, progress=False, create_inventory=False, ): """Update the DAG by adding given jobs and their dependencies.""" if visited is None: visited = set() if known_producers is None: known_producers = dict() producers = [] exceptions = list() cycles = list() # check if all potential producers are strictly ordered jobs = sorted(jobs, reverse=True) discarded_jobs = set() def is_strictly_higher_ordered(pivot_job): return all( pivot_job > job for job in jobs if job is not pivot_job and job not in discarded_jobs ) for i, job in enumerate(jobs): logger.dag_debug(dict(status="candidate", job=job)) if file in job.input: cycles.append(job) continue if job in visited: cycles.append(job) continue try: self.check_periodic_wildcards(job) self.update_( job, visited=set(visited), known_producers=known_producers, skip_until_dynamic=skip_until_dynamic, progress=progress, create_inventory=create_inventory, ) producers.append(job) if is_strictly_higher_ordered(job): # All other jobs are either discarded or strictly less given # any defined ruleorder. break except ( MissingInputException, CyclicGraphException, PeriodicWildcardError, WorkflowError, ) as ex: exceptions.append(ex) discarded_jobs.add(job) except RecursionError as e: raise WorkflowError( e, "If building the DAG exceeds the recursion limit, " "this is likely due to a cyclic dependency." "E.g. you might have a sequence of rules that " "can generate their own input. Try to make " "the output files more specific. " "A common pattern is to have different prefixes " "in the output files of different rules." + "\nProblematic file pattern: {}".format(file) if file else "", ) if not producers: if cycles: job = cycles[0] raise CyclicGraphException(job.rule, file, rule=job.rule) if len(exceptions) > 1: raise WorkflowError(*exceptions) elif len(exceptions) == 1: raise exceptions[0] n = len(self.dependencies) if progress and n % 1000 == 0 and n and self._progress != n:"Processed {} potential jobs.".format(n)) self._progress = n producers.sort(reverse=True) producer = producers[0] ambiguities = list( filter(lambda x: not x < producer and not producer < x, producers[1:]) ) if ambiguities and not self.ignore_ambiguity: raise AmbiguousRuleException(file, producer, ambiguities[0]) logger.dag_debug(dict(status="selected", job=producer)) logger.dag_debug( dict( file=file, msg="Producer found, hence exceptions are ignored.", exception=WorkflowError(*exceptions), ) ) return producer
[docs] def update_( self, job, visited=None, known_producers=None, skip_until_dynamic=False, progress=False, create_inventory=False, ): """Update the DAG by adding the given job and its dependencies.""" if job in self.dependencies: return if visited is None: visited = set() if known_producers is None: known_producers = dict() visited.add(job) dependencies = self.dependencies[job] potential_dependencies = self.collect_potential_dependencies( job, known_producers=known_producers ) skip_until_dynamic = skip_until_dynamic and not job.dynamic_output missing_input = set() producer = dict() exceptions = dict() for res in potential_dependencies: if create_inventory: # If possible, obtain inventory information starting from # given file and store it in the IOCache. # This should provide faster access to existence and mtime information # than querying file by file. If the file type does not support inventory # information, this call is a no-op. res.file.inventory() if not # no producing job found if not res.file.exists: # file not found, hence missing input missing_input.add(res.file) known_producers[res.file] = None # file found, no problem continue if res.known: producer[res.file] =[0] else: try: selected_job = self.update(, file=res.file, visited=visited, known_producers=known_producers, skip_until_dynamic=skip_until_dynamic or res.file in job.dynamic_input, progress=progress, ) producer[res.file] = selected_job except ( MissingInputException, CyclicGraphException, PeriodicWildcardError, WorkflowError, ) as ex: if not res.file.exists: self.delete_job(job, recursive=False) # delete job from tree raise ex else: logger.dag_debug( dict( file=res.file, msg="No producers found, but file is present on disk.", exception=ex, ) ) known_producers[res.file] = None for file, job_ in producer.items(): dependencies[job_].add(file) self.depending[job_][job].add(file) if self.is_batch_rule(job.rule) and self.batch.is_final: # For the final batch, ensure that all input files from # previous batches are present on disk. if any((f not in producer and not f.exists) for f in job.input): raise WorkflowError( "Unable to execute batch {} because not all previous batches " "have been completed before or files have been deleted.".format( self.batch ) ) if missing_input: self.delete_job(job, recursive=False) # delete job from tree raise MissingInputException(job.rule, missing_input) if skip_until_dynamic: self._dynamic.add(job)
[docs] def update_needrun(self, create_inventory=False): """Update the information whether a job needs to be executed.""" if create_inventory: # Concurrently collect mtimes of all existing files. self.workflow.iocache.mtime_inventory( output_mintime = dict() def update_output_mintime(job): try: return output_mintime[job] except KeyError: for job_ in chain([job], self.depending[job]): try: t = output_mintime[job_] except KeyError: t = job_.output_mintime if t is not None: output_mintime[job] = t return output_mintime[job] = None def update_needrun(job): reason = self.reason(job) noinitreason = not reason updated_subworkflow_input = self.updated_subworkflow_files.intersection( job.input ) if ( job not in self.omitforce and job.rule in self.forcerules or not self.forcefiles.isdisjoint(job.output) ): reason.forced = True elif updated_subworkflow_input: reason.updated_input.update(updated_subworkflow_input) elif job in self.targetjobs: # TODO find a way to handle added/removed input files here? if not job.output and not job.benchmark: if job.input: if job.rule.norun: reason.updated_input_run.update( f for f in job.input if not f.exists ) else: reason.nooutput = True else: reason.noio = True else: if job.rule in self.targetrules: files = set(job.output) if job.benchmark: files.add(job.benchmark) else: files = set(chain(*self.depending[job].values())) if self.targetfiles: files.update( f for f in chain(job.output, job.log) if f in self.targetfiles ) if job.benchmark and job.benchmark in self.targetfiles: files.add(job.benchmark) reason.missing_output.update(job.missing_output(files)) if not reason: output_mintime_ = output_mintime.get(job) if output_mintime_: updated_input = [ f for f in job.input if f.exists and f.is_newer(output_mintime_) ] reason.updated_input.update(updated_input) if noinitreason and reason: reason.derived = False reason = self.reason _needrun = self._needrun dependencies = self.dependencies depending = self.depending _n_until_ready = self._n_until_ready _needrun.clear() _n_until_ready.clear() self._ready_jobs.clear() candidates = list( # Update the output mintime of all jobs. # We traverse them in BFS (level order) starting from target jobs. # Then, we check output mintime of job itself and all direct descendants, # which have already been visited in the level before. # This way, we achieve a linear runtime. for job in candidates: update_output_mintime(job) # update prior reason for all candidate jobs for job in candidates: update_needrun(job) queue = deque(filter(reason, candidates)) visited = set(queue) candidates_set = set(candidates) while queue: job = queue.popleft() _needrun.add(job) for job_, files in dependencies[job].items(): missing_output = list(job_.missing_output(files)) reason(job_).missing_output.update(missing_output) if missing_output and job_ not in visited: visited.add(job_) queue.append(job_) for job_, files in depending[job].items(): if job_ in candidates_set: if job_ not in visited: # TODO may it happen that order determines whether # _n_until_ready is incremented for this job? if all(f.is_ancient and f.exists for f in files): # No other reason to run job_. # Since all files are ancient, we do not trigger it. continue visited.add(job_) queue.append(job_) _n_until_ready[job_] += 1 reason(job_).updated_input_run.update(files) # update len including finished jobs (because they have already increased the job counter) self._len = len(self._finished | self._needrun)
[docs] def in_until(self, job): """Return whether given job has been specified via --until.""" return in self.untilrules or not self.untilfiles.isdisjoint( job.output )
[docs] def in_omitfrom(self, job): """Return whether given job has been specified via --omit-from.""" return in self.omitrules or not self.omitfiles.isdisjoint( job.output )
[docs] def until_jobs(self): """Returns a generator of jobs specified by untiljobs.""" return (job for job in if self.in_until(job))
[docs] def omitfrom_jobs(self): """Returns a generator of jobs specified by omitfromjobs.""" return (job for job in if self.in_omitfrom(job))
[docs] def downstream_of_omitfrom(self): """Returns the downstream of --omit-from rules or files and themselves.""" return self.bfs(self.depending, *self.omitfrom_jobs())
[docs] def delete_omitfrom_jobs(self): """Removes jobs downstream of jobs specified by --omit-from.""" if not self.omitrules and not self.omitfiles: return downstream_jobs = list( self.downstream_of_omitfrom() ) # need to cast as list before deleting jobs for job in downstream_jobs: self.delete_job(job, recursive=False, add_dependencies=True)
[docs] def set_until_jobs(self): """Removes jobs downstream of jobs specified by --omit-from.""" if not self.untilrules and not self.untilfiles: return self.targetjobs = set(self.until_jobs())
[docs] def update_priority(self): """Update job priorities.""" prioritized = ( lambda job: job.rule in self.priorityrules or not self.priorityfiles.isdisjoint(job.output) ) for job in self.needrun_jobs: self._priority[job] = job.rule.priority for job in self.bfs( self.dependencies, *filter(prioritized, self.needrun_jobs), stop=self.noneedrun_finished, ): self._priority[job] = Job.HIGHEST_PRIORITY
[docs] def update_groups(self): groups = dict() for job in self.needrun_jobs: if is None: continue stop = lambda j: != # BFS into depending needrun jobs if in same group # Note: never go up here (into depending), because it may contain # jobs that have been sorted out due to e.g. ruleorder. group =, ( job for job in self.bfs(self.dependencies, job, stop=stop) if self.needrun(job) ), ) # merge with previously determined groups if present for j in group: if j in groups: other = groups[j] other.merge(group) group = other # update assignment for j in group: # Since groups might have been merged, we need # to update each job j in group. groups[j] = group self._group = groups self._update_group_components()
def _update_group_components(self): # span connected components if requested groups_by_id = defaultdict(set) for group in self._group.values(): groups_by_id[group.groupid].add(group) for groupid, conn_components in groups_by_id.items(): n_components = self.workflow.group_components.get(groupid, 1) if n_components > 1: print(n_components) for chunk in group_into_chunks(n_components, conn_components): if len(chunk) > 1: primary = chunk[0] for secondary in chunk[1:]: primary.merge(secondary) for j in primary: self._group[j] = primary
[docs] def update_ready(self, jobs=None): """Update information whether a job is ready to execute. Given jobs must be needrun jobs! """ if jobs is None: jobs = self.needrun_jobs potential_new_ready_jobs = False candidate_groups = set() for job in jobs: if job in self._ready_jobs or job in self._running: # job has been seen before or is running, no need to process again continue if not self.finished(job) and self._ready(job): potential_new_ready_jobs = True if is None: self._ready_jobs.add(job) else: group = self._group[job] group.finalize() candidate_groups.add(group) self._ready_jobs.update( group for group in candidate_groups if all(self._ready(job) for job in group) ) return potential_new_ready_jobs
[docs] def get_jobs_or_groups(self): visited_groups = set() for job in if is None: yield job else: group = self._group[job] if group in visited_groups: continue visited_groups.add(group) yield group
[docs] def close_remote_objects(self): """Close all remote objects.""" for job in if not self.needrun(job): job.close_remote()
[docs] def postprocess(self, update_needrun=True): """Postprocess the DAG. This has to be invoked after any change to the DAG topology.""" self.cleanup() self.update_jobids() if update_needrun: self.update_needrun() self.update_priority() self.handle_pipes() self.update_groups() self.update_ready() self.close_remote_objects() self.update_checkpoint_outputs()
[docs] def handle_pipes(self): """Use pipes to determine job groups. Check if every pipe has exactly one consumer""" visited = set() for job in self.needrun_jobs: candidate_groups = set() if is not None: candidate_groups.add( all_depending = set() has_pipe = False for f in job.output: if is_flagged(f, "pipe"): if job.is_run: raise WorkflowError( "Rule defines pipe output but " "uses a 'run' directive. This is " "not possible for technical " "reasons. Consider using 'shell' or " "'script'.", rule=job.rule, ) has_pipe = True depending = [ j for j, files in self.depending[job].items() if f in files ] if len(depending) > 1: raise WorkflowError( "Output file {} is marked as pipe " "but more than one job depends on " "it. Make sure that any pipe " "output is only consumed by one " "job".format(f), rule=job.rule, ) elif len(depending) == 0: raise WorkflowError( "Output file {} is marked as pipe " "but it has no consumer. This is " "invalid because it can lead to " "a dead lock.".format(f), rule=job.rule, ) depending = depending[0] if depending.is_run: raise WorkflowError( "Rule consumes pipe input but " "uses a 'run' directive. This is " "not possible for technical " "reasons. Consider using 'shell' or " "'script'.", rule=job.rule, ) all_depending.add(depending) if is not None: candidate_groups.add( if not has_pipe: continue if len(candidate_groups) > 1: if all(isinstance(group, CandidateGroup) for group in candidate_groups): for g in candidate_groups: g.merge(group) else: raise WorkflowError( "An output file is marked as " "pipe, but consuming jobs " "are part of conflicting " "groups.", rule=job.rule, ) elif candidate_groups: # extend the candidate group to all involved jobs group = candidate_groups.pop() else: # generate a random unique group name group = CandidateGroup() # str(uuid.uuid4()) = group visited.add(job) for j in all_depending: = group visited.add(j) for job in visited: = if isinstance(group, CandidateGroup) else group
def _ready(self, job): """Return whether the given job is ready to execute.""" group = self._group.get(job, None) if group is None: return self._n_until_ready[job] == 0 else: n_internal_deps = lambda job: sum( self._group.get(dep) == group for dep in self.dependencies[job] ) return all( (self._n_until_ready[job] - n_internal_deps(job)) == 0 for job in group )
[docs] def update_checkpoint_dependencies(self, jobs=None): """Update dependencies of checkpoints.""" updated = False self.update_checkpoint_outputs() if jobs is None: jobs = [job for job in if not self.needrun(job)] for job in jobs: if job.is_checkpoint: depending = list(self.depending[job]) # re-evaluate depending jobs, replace and update DAG for j in depending:"Updating job {}.".format(j)) newjob = j.updated() self.replace_job(j, newjob, recursive=False) updated = True if updated: self.postprocess() return updated
[docs] def register_running(self, jobs): self._running.update(jobs) self._ready_jobs -= jobs for job in jobs: try: del self._n_until_ready[job] except KeyError: # already gone pass
[docs] def finish(self, job, update_dynamic=True): """Finish a given job (e.g. remove from ready jobs, mark depending jobs as ready).""" self._running.remove(job) # turn off this job's Reason if job.is_group(): for j in job: self.reason(j).mark_finished() else: self.reason(job).mark_finished() try: self._ready_jobs.remove(job) except KeyError: pass if job.is_group(): jobs = job else: jobs = [job] self._finished.update(jobs) updated_dag = False if update_dynamic: updated_dag = self.update_checkpoint_dependencies(jobs) depending = [ j for job in jobs for j in self.depending[job] if not self.in_until(job) and self.needrun(j) ] if not updated_dag: # Mark depending jobs as ready. # Skip jobs that are marked as until jobs. # This is not necessary if the DAG has been fully updated above. for job in depending: self._n_until_ready[job] -= 1 potential_new_ready_jobs = self.update_ready(depending) for job in jobs: if update_dynamic and job.dynamic_output:"Dynamically updating jobs") newjob = self.update_dynamic(job) if newjob: # simulate that this job ran and was finished before self.omitforce.add(newjob) self._needrun.add(newjob) self._finished.add(newjob) updated_dag = True self.postprocess() self.handle_protected(newjob) self.handle_touch(newjob) if updated_dag: # We might have new jobs, so we need to ensure that all conda envs # and singularity images are set up. if self.workflow.use_singularity: self.pull_container_imgs() if self.workflow.use_conda: self.create_conda_envs() potential_new_ready_jobs = True return potential_new_ready_jobs
[docs] def new_job(self, rule, targetfile=None, format_wildcards=None): """Create new job for given rule and (optional) targetfile. This will reuse existing jobs with the same wildcards.""" key = (rule, targetfile) if key in self.job_cache: assert targetfile is not None return self.job_cache[key] wildcards_dict = rule.get_wildcards(targetfile) job = rule, self, wildcards_dict=wildcards_dict, format_wildcards=format_wildcards, targetfile=targetfile, ) self.cache_job(job) return job
[docs] def cache_job(self, job): for f in job.products: self.job_cache[(job.rule, f)] = job
[docs] def update_dynamic(self, job): """Update the DAG by evaluating the output of the given job that contains dynamic output files.""" dynamic_wildcards = job.dynamic_wildcards if not dynamic_wildcards: # this happens e.g. in dryrun if output is not yet present return depending = list( filter(lambda job_: not self.finished(job_), self.bfs(self.depending, job)) ) newrule, non_dynamic_wildcards = job.rule.dynamic_branch( dynamic_wildcards, input=False ) self.specialize_rule(job.rule, newrule) # no targetfile needed for job newjob = self.new_job(newrule, format_wildcards=non_dynamic_wildcards) self.replace_job(job, newjob) for job_ in depending: needs_update = any( f.get_wildcard_names() & dynamic_wildcards.keys() for f in job_.rule.dynamic_input ) if needs_update: newrule_ = job_.rule.dynamic_branch(dynamic_wildcards) if newrule_ is not None: self.specialize_rule(job_.rule, newrule_) if not self.dynamic(job_): logger.debug("Updating job {}.".format(job_)) newjob_ = self.new_job( newrule_, targetfile=job_.output[0] if job_.output else None ) unexpected_output = self.reason( job_ ).missing_output.intersection(newjob.existing_output) if unexpected_output: logger.warning( "Warning: the following output files of rule {} were not " "present when the DAG was created:\n{}".format( newjob_.rule, unexpected_output ) ) self.replace_job(job_, newjob_) return newjob
[docs] def delete_job(self, job, recursive=True, add_dependencies=False): """Delete given job from DAG.""" if job in self.targetjobs: self.targetjobs.remove(job) if add_dependencies: for _job in self.dependencies[job]: self.targetjobs.add(_job) for job_ in self.depending[job]: del self.dependencies[job_][job] del self.depending[job] for job_ in self.dependencies[job]: depending = self.depending[job_] del depending[job] if not depending and recursive: self.delete_job(job_) del self.dependencies[job] if job in self._needrun: self._len -= 1 self._needrun.remove(job) del self._reason[job] if job in self._finished: self._finished.remove(job) if job in self._dynamic: self._dynamic.remove(job) if job in self._ready_jobs: self._ready_jobs.remove(job) if job in self._n_until_ready: del self._n_until_ready[job] # remove from cache for f in job.output: try: del self.job_cache[(job.rule, f)] except KeyError: pass
[docs] def replace_job(self, job, newjob, recursive=True): """Replace given job with new job.""" add_to_targetjobs = job in self.targetjobs try: jobid = self.jobid(job) except KeyError: # Job has been added while updating another checkpoint, # jobid is not yet known. jobid = None depending = list(self.depending[job].items()) if self.finished(job): self._finished.add(newjob) self.delete_job(job, recursive=recursive) if jobid is not None: self._jobid[newjob] = jobid if add_to_targetjobs: self.targetjobs.add(newjob) self.cache_job(newjob) self.update([newjob]) logger.debug("Replace {} with dynamic branch {}".format(job, newjob)) for job_, files in depending: # if not job_.dynamic_input: logger.debug("updating depending job {}".format(job_)) self.dependencies[job_][newjob].update(files) self.depending[newjob][job_].update(files)
[docs] def specialize_rule(self, rule, newrule): """Specialize the given rule by inserting newrule into the DAG.""" assert newrule is not None self.rules.add(newrule) self.update_output_index()
[docs] def is_batch_rule(self, rule): """Return True if the underlying rule is to be used for batching the DAG.""" return self.batch is not None and == self.batch.rulename
[docs] def collect_potential_dependencies(self, job, known_producers): """Collect all potential dependencies of a job. These might contain ambiguities. The keys of the returned dict represent the files to be considered.""" # use a set to circumvent multiple jobs for the same file # if user specified it twice file2jobs = self.file2jobs input_files = list(job.unique_input) if self.is_batch_rule(job.rule): # only consider the defined partition of the input files input_batch = self.batch.get_batch(input_files) if len(input_batch) != len(input_files): "Considering only batch {} for DAG computation.\n" "All jobs beyond the batching rule are omitted until the final batch.\n" "Don't forget to run the other batches too.".format(self.batch) ) input_files = input_batch for file in input_files: # omit the file if it comes from a subworkflow if file in job.subworkflow_input: continue try: yield PotentialDependency(file, known_producers[file], True) except KeyError: try: if file in job.dependencies: yield PotentialDependency( file, [self.new_job(job.dependencies[file], targetfile=file)], False, ) else: yield PotentialDependency(file, file2jobs(file), False) except MissingRuleException as ex: # no dependency found yield PotentialDependency(file, None, False)
[docs] def bfs(self, direction, *jobs, stop=lambda job: False): """Perform a breadth-first traversal of the DAG.""" queue = deque(jobs) visited = set(queue) while queue: job = queue.popleft() if stop(job): # stop criterion reached for this node continue yield job for job_ in direction[job].keys(): if not job_ in visited: queue.append(job_) visited.add(job_)
[docs] def level_bfs(self, direction, *jobs, stop=lambda job: False): """Perform a breadth-first traversal of the DAG, but also yield the level together with each job.""" queue = [(job, 0) for job in jobs] visited = set(jobs) while queue: job, level = queue.pop(0) if stop(job): # stop criterion reached for this node continue yield level, job level += 1 for job_, _ in direction[job].items(): if not job_ in visited: queue.append((job_, level)) visited.add(job_)
[docs] def dfs(self, direction, *jobs, stop=lambda job: False, post=True): """Perform depth-first traversal of the DAG.""" visited = set() def _dfs(job): """Inner function for DFS traversal.""" if stop(job): return if not post: yield job for job_ in direction[job]: if not job_ in visited: visited.add(job_) for j in _dfs(job_): yield j if post: yield job for job in jobs: for job_ in self._dfs(direction, job, visited, stop=stop, post=post): yield job_
[docs] def new_wildcards(self, job): """Return wildcards that are newly introduced in this job, compared to its ancestors.""" new_wildcards = set(job.wildcards.items()) for job_ in self.dependencies[job]: if not new_wildcards: return set() for wildcard in job_.wildcards.items(): new_wildcards.discard(wildcard) return new_wildcards
[docs] def rule2job(self, targetrule): """Generate a new job from a given rule.""" if targetrule.has_wildcards(): raise WorkflowError( "Target rules may not contain wildcards. " "Please specify concrete files or a rule without wildcards at the command line, " "or have a rule without wildcards at the very top of your workflow (e.g. the typical " '"rule all" which just collects all results you want to generate in the end).' ) return self.new_job(targetrule)
[docs] def file2jobs(self, targetfile): rules = self.output_index.match(targetfile) jobs = [] exceptions = list() for rule in rules: if rule.is_producer(targetfile): try: jobs.append(self.new_job(rule, targetfile=targetfile)) except InputFunctionException as e: exceptions.append(e) if not jobs: if exceptions: raise exceptions[0] raise MissingRuleException(targetfile) return jobs
[docs] def rule_dot2(self): dag = defaultdict(list) visited = set() preselect = set() def preselect_parents(job): for parent in self.depending[job]: if parent in preselect: continue preselect.add(parent) preselect_parents(parent) def build_ruledag(job, key=lambda job: if job in visited: return visited.add(job) deps = sorted(self.dependencies[job], key=key) deps = [ ( group[0] if preselect.isdisjoint(group) else preselect.intersection(group).pop() ) for group in (list(g) for _, g in groupby(deps, key)) ] dag[job].extend(deps) preselect_parents(job) for dep in deps: build_ruledag(dep) for job in self.targetjobs: build_ruledag(job) return self._dot(dag.keys(), print_wildcards=False, print_types=False, dag=dag)
[docs] def rule_dot(self): graph = defaultdict(set) for job in graph[job.rule].update(dep.rule for dep in self.dependencies[job]) return self._dot(graph)
[docs] def dot(self): def node2style(job): if not self.needrun(job): return "rounded,dashed" if self.dynamic(job) or job.dynamic_input: return "rounded,dotted" return "rounded" def format_wildcard(wildcard): name, value = wildcard if DYNAMIC_FILL in value: value = "..." return "{}: {}".format(name, value) node2rule = lambda job: job.rule node2label = lambda job: "\\n".join( chain( [], sorted(map(format_wildcard, self.new_wildcards(job))) ) ) dag = {job: self.dependencies[job] for job in} return self._dot( dag, node2rule=node2rule, node2style=node2style, node2label=node2label )
def _dot( self, graph, node2rule=lambda node: node, node2style=lambda node: "rounded", node2label=lambda node: node, ): # color rules huefactor = 2 / (3 * len(self.rules)) rulecolor = { rule: "{:.2f} 0.6 0.85".format(i * huefactor) for i, rule in enumerate(self.rules) } # markup node_markup = '\t{}[label = "{}", color = "{}", style="{}"];'.format edge_markup = "\t{} -> {}".format # node ids ids = {node: i for i, node in enumerate(graph)} # calculate nodes nodes = [ node_markup( ids[node], node2label(node), rulecolor[node2rule(node)], node2style(node), ) for node in graph ] # calculate edges edges = [ edge_markup(ids[dep], ids[node]) for node, deps in graph.items() for dep in deps ] return textwrap.dedent( """\ digraph snakemake_dag {{ graph[bgcolor=white, margin=0]; node[shape=box, style=rounded, fontname=sans, \ fontsize=10, penwidth=2]; edge[penwidth=2, color=grey]; {items} }}\ """ ).format(items="\n".join(nodes + edges))
[docs] def filegraph_dot( self, node2rule=lambda node: node, node2style=lambda node: "rounded", node2label=lambda node: node, ): # NOTE: This is code from the rule_dot method. # This method could be split like there as well, however, # it cannot easily reuse the _dot method due to the different node type graph = defaultdict(set) for job in graph[job.rule].update(dep.rule for dep in self.dependencies[job]) # node ids ids = {node: i for i, node in enumerate(graph)} # Compute colors for rules def hsv_to_htmlhexrgb(h, s, v): """Convert hsv colors to hex-encoded rgb colors usable by html.""" import colorsys hex_r, hex_g, hex_b = (round(255 * x) for x in colorsys.hsv_to_rgb(h, s, v)) return "#{hex_r:0>2X}{hex_g:0>2X}{hex_b:0>2X}".format( hex_r=hex_r, hex_g=hex_g, hex_b=hex_b ) huefactor = 2 / (3 * len(self.rules)) rulecolor = { rule: hsv_to_htmlhexrgb(i * huefactor, 0.6, 0.85) for i, rule in enumerate(self.rules) } def resolve_input_functions(input_files): """Iterate over all input files and replace input functions with a fixed string. """ files = [] for f in input_files: if callable(f): files.append("<input function>") # NOTE: This is a workaround. It would be more informative # to show the code of the input function here (if it is # short enough). This cannot be easily done with the inspect # module, since the line numbers in the Snakefile do not # behave as expected. One (complicated) solution for this # would be to find the Snakefile and directly extract the # code of the function. else: files.append(repr(f).strip("'")) return files def html_node(node_id, node, color): """Assemble a html style node for graphviz""" input_files = resolve_input_functions(node._input) output_files = [repr(f).strip("'") for f in node._output] input_header = ( '<b><font point-size="14">&#8618; input</font></b>' if input_files else "" ) output_header = ( '<b><font point-size="14">output &rarr;</font></b>' if output_files else "" ) html_node = [ '{node_id} [ shape=none, margin=0, label=<<table border="2" color="{color}" cellspacing="3" cellborder="0">'.format( node_id=node_id, color=color ), "<tr><td>", '<b><font point-size="18">{}</font></b>'.format(node=node), "</td></tr>", "<hr/>", '<tr><td align="left"> {input_header} </td></tr>'.format( input_header=input_header ), ] for filename in sorted(input_files): # Escape html relevant chars like '<' and '>' in filenames # These can be added by input functions etc. and cannot be # displayed in graphviz HTML nodes. in_file = html.escape(filename) html_node.extend( [ "<tr>", '<td align="left"><font face="monospace">{in_file}</font></td>'.format( in_file=in_file ), "</tr>", ] ) html_node.append("<hr/>") html_node.append( '<tr><td align="right"> {output_header} </td> </tr>'.format( output_header=output_header ) ) for filename in sorted(output_files): out_file = html.escape(filename) html_node.extend( [ "<tr>", '<td align="left"><font face="monospace">{out_file}</font></td>' "</tr>".format(out_file=out_file), ] ) html_node.append("</table>>]") return "\n".join(html_node) nodes = [ html_node(ids[node], node, rulecolor[node2rule(node)]) for node in graph ] # calculate edges edge_markup = "\t{} -> {}".format edges = [ edge_markup(ids[dep], ids[node], ids[dep], ids[node]) for node, deps in graph.items() for dep in deps ] return textwrap.dedent( """\ digraph snakemake_dag {{ graph[bgcolor=white, margin=0]; node[shape=box, style=rounded, fontname=sans, \ fontsize=10, penwidth=2]; edge[penwidth=2, color=grey]; {items} }}\ """ ).format(items="\n".join(nodes + edges))
[docs] def summary(self, detailed=False): if detailed: yield "output_file\tdate\trule\tversion\tlog-file(s)\tinput-file(s)\tshellcmd\tstatus\tplan" else: yield "output_file\tdate\trule\tversion\tlog-file(s)\tstatus\tplan" for job in output = job.rule.output if self.dynamic(job) else job.expanded_output for f in output: rule = self.workflow.persistence.rule(f) rule = "-" if rule is None else rule version = self.workflow.persistence.version(f) version = "-" if version is None else str(version) date = time.ctime(f.mtime.local_or_remote()) if f.exists else "-" pending = "update pending" if self.reason(job) else "no update" log = self.workflow.persistence.log(f) log = "-" if log is None else ",".join(log) input = self.workflow.persistence.input(f) input = "-" if input is None else ",".join(input) shellcmd = self.workflow.persistence.shellcmd(f) shellcmd = "-" if shellcmd is None else shellcmd # remove new line characters, leading and trailing whitespace shellcmd = shellcmd.strip().replace("\n", "; ") status = "ok" if not f.exists: status = "missing" elif self.reason(job).updated_input: status = "updated input files" elif self.workflow.persistence.version_changed(job, file=f): status = "version changed to {}".format(job.rule.version) elif self.workflow.persistence.code_changed(job, file=f): status = "rule implementation changed" elif self.workflow.persistence.input_changed(job, file=f): status = "set of input files changed" elif self.workflow.persistence.params_changed(job, file=f): status = "params changed" if detailed: yield "\t".join( (f, date, rule, version, log, input, shellcmd, status, pending) ) else: yield "\t".join((f, date, rule, version, log, status, pending))
[docs] def archive(self, path): """Archives workflow such that it can be re-run on a different system. Archiving includes git versioned files (i.e. Snakefiles, config files, ...), ancestral input files and conda environments. """ if path.endswith(".tar"): mode = "x" elif path.endswith("tar.bz2"): mode = "x:bz2" elif path.endswith("tar.xz"): mode = "x:xz" elif path.endswith("tar.gz"): mode = "x:gz" else: raise WorkflowError( "Unsupported archive format " "(supported: .tar, .tar.gz, .tar.bz2, .tar.xz)" ) if os.path.exists(path): raise WorkflowError("Archive already exists:\n" + path) self.create_conda_envs(forceall=True) try: workdir = Path(os.path.abspath(os.getcwd())) with, mode=mode, dereference=True) as archive: archived = set() def add(path): if workdir not in Path(os.path.abspath(path)).parents: logger.warning( "Path {} cannot be archived: " "not within working directory.".format(path) ) else: f = os.path.relpath(path) if f not in archived: archive.add(f) archived.add(f)"archived " + f) "Archiving snakefiles, scripts and files under " "version control..." ) for f in self.workflow.get_sources(): add(f)"Archiving external input files...") for job in # input files for f in job.input: if not any( f in files for files in self.dependencies[job].values() ): # this is an input file that is not created by any job add(f)"Archiving conda environments...") envs = set() for job in if job.conda_env_file: env_archive = job.archive_conda_env() envs.add(env_archive) for env in envs: add(env) except (Exception, BaseException) as e: os.remove(path) raise e
[docs] def clean(self, only_temp=False, dryrun=False): """Removes files generated by the workflow.""" for job in for f in job.output: if not only_temp or is_flagged(f, "temp"): # The reason for the second check is that dangling # symlinks fail f.exists. if f.exists or os.path.islink(f): if f.protected: logger.error("Skipping write-protected file {}.".format(f)) else: msg = "Deleting {}" if not dryrun else "Would delete {}" if not dryrun: # Remove non-empty dirs if flagged as temp() f.remove(remove_non_empty_dir=only_temp)
[docs] def list_untracked(self): """List files in the workdir that are not in the dag.""" used_files = set() files_in_cwd = set() for job in used_files.update( os.path.relpath(file) for file in chain(job.local_input, job.local_output, job.log) ) for root, dirs, files in os.walk(os.getcwd()): # Ignore hidden files and don't traverse into hidden dirs files_in_cwd.update( [ os.path.relpath(os.path.join(root, f)) for f in files if not f[0] == "." ] ) dirs[:] = [d for d in dirs if not d[0] == "."] for f in sorted(list(files_in_cwd - used_files)):
[docs] def d3dag(self, max_jobs=10000): def node(job): jobid = self.jobid(job) return { "id": jobid, "value": { "jobid": jobid, "label":, "rule":, }, } def edge(a, b): return {"u": self.jobid(a), "v": self.jobid(b)} jobs = list( if len(jobs) > max_jobs: "Job-DAG is too large for visualization (>{} jobs).".format(max_jobs) ) else: logger.d3dag( nodes=[node(job) for job in jobs], edges=[ edge(dep, job) for job in jobs for dep in self.dependencies[job] if self.needrun(dep) ], )
[docs] def stats(self): from tabulate import tabulate rules = Counter() rules.update(job.rule for job in self.needrun_jobs) rules.update(job.rule for job in self.finished_jobs) max_threads = defaultdict(int) min_threads = defaultdict(lambda: sys.maxsize) for job in chain(self.needrun_jobs, self.finished_jobs): max_threads[job.rule] = max(max_threads[job.rule], job.threads) min_threads[job.rule] = min(min_threads[job.rule], job.threads) rows = [ { "job": rule, "count": count, "min threads": min_threads[rule], "max threads": max_threads[rule], } for rule, count in sorted( rules.most_common(), key=lambda item: item[0].name ) ] rows.append( { "job": "total", "count": sum(rules.values()), "min threads": min(min_threads.values()), "max threads": max(max_threads.values()), } ) yield "Job stats:" yield tabulate(rows, headers="keys") yield ""
def __str__(self): return def __len__(self): return self._len
[docs]class CandidateGroup: def __init__(self): = str(uuid.uuid4()) def __eq__(self, other): return == def __hash__(self): return hash(
[docs] def merge(self, other): =