Source code for galaxy.model

"""
Galaxy data model classes

Naming: try to use class names that have a distinct plural form so that
the relationship cardinalities are obvious (e.g. prefer Dataset to Data)
"""
import base64
import errno
import json
import logging
import numbers
import operator
import os
import pwd
import random
import string
import time
from datetime import datetime, timedelta
from string import Template
from uuid import UUID, uuid4

from boltons.iterutils import remap
from six import string_types
from social_core.storage import AssociationMixin, CodeMixin, NonceMixin, PartialMixin, UserMixin
from sqlalchemy import (
    alias,
    and_,
    func,
    inspect,
    join,
    not_,
    or_,
    select,
    true,
    type_coerce,
    types)
from sqlalchemy.ext import hybrid
from sqlalchemy.orm import (
    aliased,
    joinedload,
    object_session,
)
from sqlalchemy.schema import UniqueConstraint

import galaxy.exceptions
import galaxy.model.metadata
import galaxy.model.orm.now
import galaxy.model.tags
import galaxy.security.passwords
import galaxy.util
from galaxy.model.item_attrs import get_item_annotation_str, UsesAnnotations
from galaxy.model.util import pgcalc
from galaxy.security import get_permitted_actions
from galaxy.util import (directory_hash_id, ready_name_for_url,
                         unicodify, unique_id)
from galaxy.util.bunch import Bunch
from galaxy.util.dictifiable import dict_for, Dictifiable
from galaxy.util.form_builder import (AddressField, CheckboxField, HistoryField,
                                      PasswordField, SelectField, TextArea, TextField, WorkflowField,
                                      WorkflowMappingField)
from galaxy.util.hash_util import new_secure_hash
from galaxy.util.json import safe_loads
from galaxy.util.sanitize_html import sanitize_html

log = logging.getLogger(__name__)

_datatypes_registry = None

# When constructing filters with in for a fixed set of ids, maximum
# number of items to place in the IN statement. Different databases
# are going to have different limits so it is likely best to not let
# this be unlimited - filter in Python if over this limit.
MAX_IN_FILTER_LENGTH = 100

# The column sizes for job metrics. Note: changing these values does not change the column sizes, a migration must be
# performed to do that.
JOB_METRIC_MAX_LENGTH = 1023
JOB_METRIC_PRECISION = 26
JOB_METRIC_SCALE = 7
# Tags that get automatically propagated from inputs to outputs when running jobs.
AUTO_PROPAGATED_TAGS = ["name"]


[docs]class RepresentById(object): def __repr__(self): try: r = '<galaxy.model.%s(%s) at %s>' % (self.__class__.__name__, cached_id(self), hex(id(self))) except Exception: r = object.__repr__(self) log.exception("Caught exception attempting to generate repr for: %s", r) return r
[docs]class NoConverterException(Exception):
[docs] def __init__(self, value): self.value = value
def __str__(self): return repr(self.value)
[docs]class ConverterDependencyException(Exception):
[docs] def __init__(self, value): self.value = value
def __str__(self): return repr(self.value)
def _get_datatypes_registry(): if _datatypes_registry is None: raise Exception("galaxy.model.set_datatypes_registry must be called before performing certain DatasetInstance operations.") return _datatypes_registry
[docs]def set_datatypes_registry(d_registry): """ Set up datatypes_registry """ global _datatypes_registry _datatypes_registry = d_registry
[docs]class HasTags(object): dict_collection_visible_keys = ['tags'] dict_element_visible_keys = ['tags']
[docs] def to_dict(self, *args, **kwargs): rval = super(HasTags, self).to_dict(*args, **kwargs) rval['tags'] = self.make_tag_string_list() return rval
[docs] def make_tag_string_list(self): # add tags string list tags_str_list = [] for tag in self.tags: tag_str = tag.user_tname if tag.value is not None: tag_str += ":" + tag.user_value tags_str_list.append(tag_str) return tags_str_list
[docs] def copy_tags_from(self, target_user, source): for source_tag_assoc in source.tags: new_tag_assoc = source_tag_assoc.copy() new_tag_assoc.user = target_user self.tags.append(new_tag_assoc)
@property def auto_propagated_tags(self): return [t for t in self.tags if t.user_tname in AUTO_PROPAGATED_TAGS]
[docs]class SerializationOptions(object):
[docs] def __init__(self, for_edit, serialize_dataset_objects=None, serialize_files_handler=None, strip_metadata_files=None): self.for_edit = for_edit if serialize_dataset_objects is None: serialize_dataset_objects = for_edit self.serialize_dataset_objects = serialize_dataset_objects self.serialize_files_handler = serialize_files_handler if strip_metadata_files is None: # If we're editing datasets - keep MetadataFile(s) in tact. For pure export # expect metadata tool to be rerun. strip_metadata_files = not for_edit self.strip_metadata_files = strip_metadata_files
[docs] def attach_identifier(self, id_encoder, obj, ret_val): if self.for_edit and obj.id: ret_val["id"] = obj.id elif obj.id: ret_val["encoded_id"] = id_encoder.encode_id(obj.id, kind='model_export') else: if not hasattr(obj, "temp_id"): obj.temp_id = uuid4().hex ret_val["encoded_id"] = obj.temp_id
[docs] def get_identifier(self, id_encoder, obj): if self.for_edit and obj.id: return obj.id elif obj.id: return id_encoder.encode_id(obj.id, kind='model_export') else: if not hasattr(obj, "temp_id"): obj.temp_id = uuid4().hex return obj.temp_id
[docs] def get_identifier_for_id(self, id_encoder, obj_id): if self.for_edit and obj_id: return obj_id elif obj_id: return id_encoder.encode_id(obj_id, kind='model_export') else: raise NotImplementedError()
[docs] def serialize_files(self, dataset, as_dict): if self.serialize_files_handler is not None: self.serialize_files_handler.serialize_files(dataset, as_dict)
[docs]class HasName(object):
[docs] def get_display_name(self): """ These objects have a name attribute can be either a string or a unicode object. If string, convert to unicode object assuming 'utf-8' format. """ name = self.name name = unicodify(name, 'utf-8') return name
[docs]class UsesCreateAndUpdateTime(object): @property def seconds_since_updated(self): update_time = self.update_time or galaxy.model.orm.now.now() # In case not yet flushed return (galaxy.model.orm.now.now() - update_time).total_seconds() @property def seconds_since_created(self): create_time = self.create_time or galaxy.model.orm.now.now() # In case not yet flushed return (galaxy.model.orm.now.now() - create_time).total_seconds()
[docs]class WorkerProcess(UsesCreateAndUpdateTime): def __init__(self, server_name, hostname): self.server_name = server_name self.hostname = hostname
[docs]def cached_id(galaxy_model_object): """Get model object id attribute without a firing a database query. Useful to fetching the id of a typical Galaxy model object after a flush, where SA is going to mark the id attribute as unloaded but we know the id is immutable and so we can use the database identity to fetch. With Galaxy's default SA initialization - any flush marks all attributes as unloaded - even objects completely unrelated to the flushed changes and even attributes we know to be immutable like id. See test_galaxy_mapping.py for verification of this behavior. This method is a workaround that uses the fact that we know all Galaxy objects use the id attribute as identity and SA internals (_sa_instance_state) to infer the previously loaded ID value. I tried digging into the SA internals extensively and couldn't find a way to get the previously loaded values after a flush to allow a generalization of this for other attributes. """ if hasattr(galaxy_model_object, "_sa_instance_state"): identity = galaxy_model_object._sa_instance_state.identity if identity: assert len(identity) == 1 return identity[0] return galaxy_model_object.id
[docs]class JobLike(object): MAX_NUMERIC = 10**(JOB_METRIC_PRECISION - JOB_METRIC_SCALE) - 1 def _init_metrics(self): self.text_metrics = [] self.numeric_metrics = []
[docs] def add_metric(self, plugin, metric_name, metric_value): plugin = unicodify(plugin, 'utf-8') metric_name = unicodify(metric_name, 'utf-8') number = isinstance(metric_value, numbers.Number) if number and int(metric_value) <= JobLike.MAX_NUMERIC: metric = self._numeric_metric(plugin, metric_name, metric_value) self.numeric_metrics.append(metric) elif number: log.warning("Cannot store metric due to database column overflow (max: %s): %s: %s", JobLike.MAX_NUMERIC, metric_name, metric_value) else: metric_value = unicodify(metric_value, 'utf-8') if len(metric_value) > (JOB_METRIC_MAX_LENGTH - 1): # Truncate these values - not needed with sqlite # but other backends must need it. metric_value = metric_value[:(JOB_METRIC_MAX_LENGTH - 1)] metric = self._text_metric(plugin, metric_name, metric_value) self.text_metrics.append(metric)
@property def metrics(self): # TODO: Make iterable, concatenate with chain return self.text_metrics + self.numeric_metrics
[docs] def set_streams(self, tool_stdout, tool_stderr, job_stdout=None, job_stderr=None, job_messages=None): def shrink_and_unicodify(what, stream): stream = galaxy.util.unicodify(stream) or u'' if (len(stream) > galaxy.util.DATABASE_MAX_STRING_SIZE): stream = galaxy.util.shrink_string_by_size(tool_stdout, galaxy.util.DATABASE_MAX_STRING_SIZE, join_by="\n..\n", left_larger=True, beginning_on_size_error=True) log.info("%s for %s %d is greater than %s, only a portion will be logged to database", what, type(self), self.id, galaxy.util.DATABASE_MAX_STRING_SIZE_PRETTY) return stream self.tool_stdout = shrink_and_unicodify('tool_stdout', tool_stdout) self.tool_stderr = shrink_and_unicodify('tool_stderr', tool_stderr) if job_stdout is not None: self.job_stdout = shrink_and_unicodify('job_stdout', job_stdout) else: self.job_stdout = None if job_stderr is not None: self.job_stderr = shrink_and_unicodify('job_stderr', job_stderr) else: self.job_stderr = None if job_messages is not None: self.job_messages = job_messages
[docs] def log_str(self): extra = "" safe_id = getattr(self, "id", None) if safe_id is not None: extra += "id=%s" % safe_id else: extra += "unflushed" return "%s[%s,tool_id=%s]" % (self.__class__.__name__, extra, self.tool_id)
[docs] def get_stdout(self): stdout = self.tool_stdout if self.job_stdout: stdout += "\n" + self.job_stdout return stdout
[docs] def set_stdout(self, stdout): raise NotImplementedError("Attempt to set stdout, must set tool_stdout or job_stdout")
[docs] def get_stderr(self): stderr = self.tool_stderr if self.job_stderr: stderr += "\n" + self.job_stderr return stderr
[docs] def set_stderr(self, stderr): raise NotImplementedError("Attempt to set stdout, must set tool_stderr or job_stderr")
stdout = property(get_stdout, set_stdout) stderr = property(get_stderr, set_stderr)
[docs]class User(Dictifiable, RepresentById): use_pbkdf2 = True """ Data for a Galaxy user or admin and relations to their histories, credentials, and roles. """ # attributes that will be accessed and returned when calling to_dict( view='collection' ) dict_collection_visible_keys = ['id', 'email', 'username', 'deleted', 'active', 'last_password_change'] # attributes that will be accessed and returned when calling to_dict( view='element' ) dict_element_visible_keys = ['id', 'email', 'username', 'total_disk_usage', 'nice_total_disk_usage', 'deleted', 'active', 'last_password_change'] def __init__(self, email=None, password=None, username=None): self.email = email self.password = password self.external = False self.deleted = False self.purged = False self.active = False self.activation_token = None self.username = username self.last_password_change = None # Relationships self.histories = [] self.credentials = [] # ? self.roles = [] @property def extra_preferences(self): data = {} extra_user_preferences = self.preferences.get('extra_user_preferences') if extra_user_preferences: try: data = json.loads(extra_user_preferences) except Exception: pass return data
[docs] def set_password_cleartext(self, cleartext): """ Set user password to the digest of `cleartext`. """ if User.use_pbkdf2: self.password = galaxy.security.passwords.hash_password(cleartext) else: self.password = new_secure_hash(text_type=cleartext) self.last_password_change = datetime.now()
[docs] def set_random_password(self, length=16): """ Sets user password to a random string of the given length. :return: void """ self.set_password_cleartext( ''.join(random.SystemRandom().choice(string.ascii_letters + string.digits) for _ in range(length)))
[docs] def check_password(self, cleartext): """ Check if `cleartext` matches user password when hashed. """ return galaxy.security.passwords.check_password(cleartext, self.password)
[docs] def system_user_pwent(self, real_system_username): """ Gives the system user pwent entry based on e-mail or username depending on the value in real_system_username """ system_user_pwent = None if real_system_username == 'user_email': try: system_user_pwent = pwd.getpwnam(self.email.split('@')[0]) except KeyError: pass elif real_system_username == 'username': try: system_user_pwent = pwd.getpwnam(self.username) except KeyError: pass else: try: system_user_pwent = pwd.getpwnam(real_system_username) except KeyError: log.warning("invalid configuration of real_system_username") system_user_pwent = None pass return system_user_pwent
[docs] def all_roles(self): """ Return a unique list of Roles associated with this user or any of their groups. """ try: db_session = object_session(self) user = db_session.query( User ).filter_by( # don't use get, it will use session variant. id=self.id ).options( joinedload("roles"), joinedload("roles.role"), joinedload("groups"), joinedload("groups.group"), joinedload("groups.group.roles"), joinedload("groups.group.roles.role") ).one() except Exception: # If not persistent user, just use models normaly and # skip optimizations... user = self roles = [ura.role for ura in user.roles] for group in [uga.group for uga in user.groups]: for role in [gra.role for gra in group.roles]: if role not in roles: roles.append(role) return roles
[docs] def all_roles_exploiting_cache(self): """ """ roles = [ura.role for ura in self.roles] for group in [uga.group for uga in self.groups]: for role in [gra.role for gra in group.roles]: if role not in roles: roles.append(role) return roles
[docs] def get_disk_usage(self, nice_size=False): """ Return byte count of disk space used by user or a human-readable string if `nice_size` is `True`. """ rval = 0 if self.disk_usage is not None: rval = self.disk_usage if nice_size: rval = galaxy.util.nice_size(rval) return rval
[docs] def set_disk_usage(self, bytes): """ Manually set the disk space used by a user to `bytes`. """ self.disk_usage = bytes
total_disk_usage = property(get_disk_usage, set_disk_usage)
[docs] def adjust_total_disk_usage(self, amount): if amount != 0: self.disk_usage = func.coalesce(self.table.c.disk_usage, 0) + amount
@property def nice_total_disk_usage(self): """ Return byte count of disk space used in a human-readable string. """ return self.get_disk_usage(nice_size=True)
[docs] def calculate_disk_usage(self): """ Return byte count total of disk space used by all non-purged, non-library HDAs in non-purged histories. """ # maintain a list so that we don't double count dataset_ids = [] total = 0 # this can be a huge number and can run out of memory, so we avoid the mappers db_session = object_session(self) for history in db_session.query(History).enable_eagerloads(False).filter_by(user_id=self.id, purged=False).yield_per(1000): for hda in db_session.query(HistoryDatasetAssociation).enable_eagerloads(False).filter_by(history_id=history.id, purged=False).yield_per(1000): # TODO: def hda.counts_toward_disk_usage(): # return ( not self.dataset.purged and not self.dataset.library_associations ) if hda.dataset.id not in dataset_ids and not hda.dataset.purged and not hda.dataset.library_associations: dataset_ids.append(hda.dataset.id) total += hda.dataset.get_total_size() return total
[docs] def calculate_and_set_disk_usage(self): """ Calculates and sets user disk usage. """ new = None db_session = object_session(self) current = self.get_disk_usage() if db_session.get_bind().dialect.name not in ('postgres', 'postgresql'): done = False while not done: new = self.calculate_disk_usage() db_session.refresh(self) # make sure usage didn't change while calculating # set done if it has not, otherwise reset current and iterate again. if self.get_disk_usage() == current: done = True else: current = self.get_disk_usage() else: new = pgcalc(db_session, self.id) if new not in (current, None): self.set_disk_usage(new) db_session.add(self) db_session.flush()
[docs] @staticmethod def user_template_environment(user): """ >>> env = User.user_template_environment(None) >>> env['__user_email__'] 'Anonymous' >>> env['__user_id__'] 'Anonymous' >>> user = User('foo@example.com') >>> user.id = 6 >>> user.username = 'foo2' >>> env = User.user_template_environment(user) >>> env['__user_id__'] '6' >>> env['__user_name__'] 'foo2' """ if user: user_id = '%d' % user.id user_email = str(user.email) user_name = str(user.username) else: user = None user_id = 'Anonymous' user_email = 'Anonymous' user_name = 'Anonymous' environment = {} environment['__user__'] = user environment['__user_id__'] = environment['userId'] = user_id environment['__user_email__'] = environment['userEmail'] = user_email environment['__user_name__'] = user_name return environment
[docs] @staticmethod def expand_user_properties(user, in_string): """ """ environment = User.user_template_environment(user) return Template(in_string).safe_substitute(environment)
[docs] def is_active(self): return self.active
[docs] def is_authenticated(self): # TODO: is required for python social auth (PSA); however, a user authentication is relative to the backend. # For instance, a user who is authenticated with Google, is not necessarily authenticated # with Amazon. Therefore, this function should also receive the backend and check if this # user is already authenticated on that backend or not. For now, returning always True # seems reasonable. Besides, this is also how a PSA example is implemented: # https://github.com/python-social-auth/social-examples/blob/master/example-cherrypy/example/db/user.py return True
[docs]class PasswordResetToken(object): def __init__(self, user, token=None): if token: self.token = token else: self.token = unique_id() self.user = user self.expiration_time = galaxy.model.orm.now.now() + timedelta(hours=24)
[docs]class DynamicTool(Dictifiable): dict_collection_visible_keys = ('id', 'tool_id', 'tool_format', 'tool_version', 'uuid', 'active', 'hidden') dict_element_visible_keys = ('id', 'tool_id', 'tool_format', 'tool_version', 'uuid', 'active', 'hidden') def __init__(self, tool_format=None, tool_id=None, tool_version=None, uuid=None, active=True, hidden=True, value=None): self.tool_format = tool_format self.tool_id = tool_id self.tool_version = tool_version self.active = active self.hidden = hidden self.value = value if uuid is None: self.uuid = uuid4() else: self.uuid = UUID(str(uuid))
[docs]class BaseJobMetric(object):
[docs] def __init__(self, plugin, metric_name, metric_value): self.plugin = plugin self.metric_name = metric_name self.metric_value = metric_value
[docs]class JobMetricText(BaseJobMetric, RepresentById): pass
[docs]class JobMetricNumeric(BaseJobMetric, RepresentById): pass
[docs]class TaskMetricText(BaseJobMetric, RepresentById): pass
[docs]class TaskMetricNumeric(BaseJobMetric, RepresentById): pass
[docs]class Job(JobLike, UsesCreateAndUpdateTime, Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'state', 'exit_code', 'update_time', 'create_time'] dict_element_visible_keys = ['id', 'state', 'exit_code', 'update_time', 'create_time'] """ A job represents a request to run a tool given input datasets, tool parameters, and output datasets. """ _numeric_metric = JobMetricNumeric _text_metric = JobMetricText states = Bunch(NEW='new', RESUBMITTED='resubmitted', UPLOAD='upload', WAITING='waiting', QUEUED='queued', RUNNING='running', OK='ok', ERROR='error', FAILED='failed', PAUSED='paused', DELETED='deleted', DELETED_NEW='deleted_new') terminal_states = [states.OK, states.ERROR, states.DELETED] #: job states where the job hasn't finished and the model may still change non_ready_states = [ states.NEW, states.RESUBMITTED, states.UPLOAD, states.WAITING, states.QUEUED, states.RUNNING, ] # Please include an accessor (get/set pair) for any new columns/members. def __init__(self): self.session_id = None self.user_id = None self.tool_id = None self.tool_version = None self.copied_from_job_id = None self.command_line = None self.dependencies = [] self.param_filename = None self.parameters = [] self.input_datasets = [] self.output_datasets = [] self.input_dataset_collections = [] self.output_dataset_collection_instances = [] self.output_dataset_collections = [] self.input_library_datasets = [] self.output_library_datasets = [] self.state = Job.states.NEW self.info = None self.job_runner_name = None self.job_runner_external_id = None self.destination_id = None self.destination_params = None self.post_job_actions = [] self.state_history = [] self.imported = False self.handler = None self.exit_code = None self.job_messages = None self._init_metrics() self.state_history.append(JobStateHistory(self)) @property def finished(self): states = self.states return self.state in [ states.OK, states.ERROR, states.DELETED, states.DELETED_NEW, ]
[docs] def io_dicts(self): inp_data = dict([(da.name, da.dataset) for da in self.input_datasets]) out_data = dict([(da.name, da.dataset) for da in self.output_datasets]) inp_data.update([(da.name, da.dataset) for da in self.input_library_datasets]) out_data.update([(da.name, da.dataset) for da in self.output_library_datasets]) out_collections = dict([(obj.name, obj.dataset_collection_instance) for obj in self.output_dataset_collection_instances]) out_collections.update([(obj.name, obj.dataset_collection) for obj in self.output_dataset_collections]) return inp_data, out_data, out_collections
# TODO: Add accessors for members defined in SQL Alchemy for the Job table and # for the mapper defined to the Job table.
[docs] def get_external_output_metadata(self): """ The external_output_metadata is currently a reference from Job to JobExternalOutputMetadata. It exists for a job but not a task. """ return self.external_output_metadata
[docs] def get_session_id(self): return self.session_id
[docs] def get_user_id(self): return self.user_id
[docs] def get_tool_id(self): return self.tool_id
[docs] def get_tool_version(self): return self.tool_version
[docs] def get_command_line(self): return self.command_line
[docs] def get_dependencies(self): return self.dependencies
[docs] def get_param_filename(self): return self.param_filename
[docs] def get_parameters(self): return self.parameters
[docs] def get_copied_from_job_id(self): return self.copied_from_job_id
[docs] def get_input_datasets(self): return self.input_datasets
[docs] def get_output_datasets(self): return self.output_datasets
[docs] def get_input_library_datasets(self): return self.input_library_datasets
[docs] def get_output_library_datasets(self): return self.output_library_datasets
[docs] def get_state(self): return self.state
[docs] def get_info(self): return self.info
[docs] def get_job_runner_name(self): # This differs from the Task class in that job_runner_name is # accessed instead of task_runner_name. Note that the field # runner_name is not the same thing. return self.job_runner_name
[docs] def get_job_runner_external_id(self): # This is different from the Task just in the member accessed: return self.job_runner_external_id
[docs] def get_post_job_actions(self): return self.post_job_actions
[docs] def get_imported(self): return self.imported
[docs] def get_handler(self): return self.handler
[docs] def get_params(self): return self.params
[docs] def get_user(self): # This is defined in the SQL Alchemy mapper as a relation to the User. return self.user
[docs] def get_tasks(self): # The tasks member is pert of a reference in the SQL Alchemy schema: return self.tasks
[docs] def get_id_tag(self): """ Return a tag that can be useful in identifying a Job. This returns the Job's get_id """ return "%s" % self.id
[docs] def set_session_id(self, session_id): self.session_id = session_id
[docs] def set_user_id(self, user_id): self.user_id = user_id
[docs] def set_tool_id(self, tool_id): self.tool_id = tool_id
[docs] def set_tool_version(self, tool_version): self.tool_version = tool_version
[docs] def set_command_line(self, command_line): self.command_line = command_line
[docs] def set_dependencies(self, dependencies): self.dependencies = dependencies
[docs] def set_param_filename(self, param_filename): self.param_filename = param_filename
[docs] def set_parameters(self, parameters): self.parameters = parameters
[docs] def set_copied_from_job_id(self, job_id): self.copied_from_job_id = job_id
[docs] def set_input_datasets(self, input_datasets): self.input_datasets = input_datasets
[docs] def set_output_datasets(self, output_datasets): self.output_datasets = output_datasets
[docs] def set_input_library_datasets(self, input_library_datasets): self.input_library_datasets = input_library_datasets
[docs] def set_output_library_datasets(self, output_library_datasets): self.output_library_datasets = output_library_datasets
[docs] def set_info(self, info): self.info = info
[docs] def set_runner_name(self, job_runner_name): self.job_runner_name = job_runner_name
[docs] def get_job(self): # Added so job and task have same interface (.get_job() ) to get at # underlying job object. return self
[docs] def set_runner_external_id(self, job_runner_external_id): self.job_runner_external_id = job_runner_external_id
[docs] def set_post_job_actions(self, post_job_actions): self.post_job_actions = post_job_actions
[docs] def set_imported(self, imported): self.imported = imported
[docs] def set_handler(self, handler): self.handler = handler
[docs] def set_params(self, params): self.params = params
[docs] def add_parameter(self, name, value): self.parameters.append(JobParameter(name, value))
[docs] def add_input_dataset(self, name, dataset=None, dataset_id=None): assoc = JobToInputDatasetAssociation(name, dataset) if dataset is None and dataset_id is not None: assoc.dataset_id = dataset_id self.input_datasets.append(assoc)
[docs] def add_output_dataset(self, name, dataset): self.output_datasets.append(JobToOutputDatasetAssociation(name, dataset))
[docs] def add_input_dataset_collection(self, name, dataset_collection): self.input_dataset_collections.append(JobToInputDatasetCollectionAssociation(name, dataset_collection))
[docs] def add_output_dataset_collection(self, name, dataset_collection_instance): self.output_dataset_collection_instances.append(JobToOutputDatasetCollectionAssociation(name, dataset_collection_instance))
[docs] def add_implicit_output_dataset_collection(self, name, dataset_collection): self.output_dataset_collections.append(JobToImplicitOutputDatasetCollectionAssociation(name, dataset_collection))
[docs] def add_input_library_dataset(self, name, dataset): self.input_library_datasets.append(JobToInputLibraryDatasetAssociation(name, dataset))
[docs] def add_output_library_dataset(self, name, dataset): self.output_library_datasets.append(JobToOutputLibraryDatasetAssociation(name, dataset))
[docs] def add_post_job_action(self, pja): self.post_job_actions.append(PostJobActionAssociation(pja, self))
[docs] def set_state(self, state): """ Save state history """ self.state = state self.state_history.append(JobStateHistory(self))
[docs] def get_param_values(self, app, ignore_errors=False): """ Read encoded parameter values from the database and turn back into a dict of tool parameter values. """ param_dict = self.raw_param_dict() tool = app.toolbox.get_tool(self.tool_id, tool_version=self.tool_version) param_dict = tool.params_from_strings(param_dict, app, ignore_errors=ignore_errors) return param_dict
[docs] def raw_param_dict(self): param_dict = dict([(p.name, p.value) for p in self.parameters]) return param_dict
[docs] def check_if_output_datasets_deleted(self): """ Return true if all of the output datasets associated with this job are in the deleted state """ for dataset_assoc in self.output_datasets: dataset = dataset_assoc.dataset # only the originator of the job can delete a dataset to cause # cancellation of the job, no need to loop through history_associations if not dataset.deleted: return False return True
[docs] def mark_deleted(self, track_jobs_in_database=False): """ Mark this job as deleted, and mark any output datasets as discarded. """ if self.finished: # Do not modify the state/outputs of jobs that are already terminal return if track_jobs_in_database: self.state = Job.states.DELETED_NEW else: self.state = Job.states.DELETED self.info = "Job output deleted by user before job completed." for dataset_assoc in self.output_datasets: dataset = dataset_assoc.dataset dataset.deleted = True dataset.state = dataset.states.DISCARDED for dataset in dataset.dataset.history_associations: # propagate info across shared datasets dataset.deleted = True dataset.blurb = 'deleted' dataset.peek = 'Job deleted' dataset.info = 'Job output deleted by user before job completed'
[docs] def mark_failed(self, info="Job execution failed", blurb=None, peek=None): """ Mark this job as failed, and mark any output datasets as errored. """ self.state = self.states.FAILED self.info = info for jtod in self.output_datasets: jtod.dataset.state = jtod.dataset.states.ERROR for hda in jtod.dataset.dataset.history_associations: hda.state = hda.states.ERROR if blurb: hda.blurb = blurb if peek: hda.peek = peek hda.info = info
[docs] def resume(self, flush=True): if self.state == self.states.PAUSED: self.set_state(self.states.NEW) object_session(self).add(self) jobs_to_resume = set() for jtod in self.output_datasets: jobs_to_resume.update(jtod.dataset.unpause_dependent_jobs(jobs_to_resume)) for job in jobs_to_resume: job.resume(flush=False) if flush: object_session(self).flush()
[docs] def serialize(self, id_encoder, serialization_options): job_attrs = dict_for(self) serialization_options.attach_identifier(id_encoder, self, job_attrs) job_attrs['tool_id'] = self.tool_id job_attrs['tool_version'] = self.tool_version job_attrs['state'] = self.state job_attrs['info'] = self.info job_attrs['traceback'] = self.traceback job_attrs['command_line'] = self.command_line job_attrs['tool_stderr'] = self.tool_stderr job_attrs['job_stderr'] = self.job_stderr job_attrs['tool_stdout'] = self.tool_stdout job_attrs['job_stdout'] = self.job_stdout job_attrs['exit_code'] = self.exit_code job_attrs['create_time'] = self.create_time.isoformat() job_attrs['update_time'] = self.update_time.isoformat() # Get the job's parameters param_dict = self.raw_param_dict() params_objects = {} for key in param_dict: params_objects[key] = safe_loads(param_dict[key]) def remap_objects(p, k, obj): if isinstance(obj, dict) and "src" in obj and obj["src"] in ["hda", "hdca"]: new_id = serialization_options.get_identifier_for_id(id_encoder, obj["id"]) new_obj = obj.copy() new_obj["id"] = new_id return (k, new_obj) return (k, obj) params_objects = remap(params_objects, remap_objects) params_dict = {} for name, value in params_objects.items(): params_dict[name] = value job_attrs['params'] = params_dict return job_attrs
[docs] def to_dict(self, view='collection', system_details=False): rval = super(Job, self).to_dict(view=view) rval['tool_id'] = self.tool_id rval['history_id'] = self.history_id if system_details: # System level details that only admins should have. rval['external_id'] = self.job_runner_external_id rval['command_line'] = self.command_line if view == 'element': param_dict = dict([(p.name, p.value) for p in self.parameters]) rval['params'] = param_dict input_dict = {} for i in self.input_datasets: if i.dataset is not None: input_dict[i.name] = { "id" : i.dataset.id, "src" : "hda", "uuid" : str(i.dataset.dataset.uuid) if i.dataset.dataset.uuid is not None else None } for i in self.input_library_datasets: if i.dataset is not None: input_dict[i.name] = { "id" : i.dataset.id, "src" : "ldda", "uuid": str(i.dataset.dataset.uuid) if i.dataset.dataset.uuid is not None else None } for k in input_dict: if k in param_dict: del param_dict[k] rval['inputs'] = input_dict output_dict = {} for i in self.output_datasets: if i.dataset is not None: output_dict[i.name] = { "id" : i.dataset.id, "src" : "hda", "uuid" : str(i.dataset.dataset.uuid) if i.dataset.dataset.uuid is not None else None } for i in self.output_library_datasets: if i.dataset is not None: output_dict[i.name] = { "id" : i.dataset.id, "src" : "ldda", "uuid" : str(i.dataset.dataset.uuid) if i.dataset.dataset.uuid is not None else None } rval['outputs'] = output_dict return rval
[docs] def set_final_state(self, final_state): self.set_state(final_state) if self.workflow_invocation_step: self.workflow_invocation_step.update()
[docs] def get_destination_configuration(self, dest_params, config, key, default=None): """ Get a destination parameter that can be defaulted back in specified config if it needs to be applied globally. """ param_unspecified = object() config_value = (self.destination_params or {}).get(key, param_unspecified) if config_value is param_unspecified: config_value = dest_params.get(key, param_unspecified) if config_value is param_unspecified: config_value = getattr(config, key, param_unspecified) if config_value is param_unspecified: config_value = default return config_value
[docs]class Task(JobLike, RepresentById): """ A task represents a single component of a job. """ _numeric_metric = TaskMetricNumeric _text_metric = TaskMetricText states = Bunch(NEW='new', WAITING='waiting', QUEUED='queued', RUNNING='running', OK='ok', ERROR='error', DELETED='deleted') # Please include an accessor (get/set pair) for any new columns/members. def __init__(self, job, working_directory, prepare_files_cmd): self.command_line = None self.parameters = [] self.state = Task.states.NEW self.info = None self.working_directory = working_directory self.task_runner_name = None self.task_runner_external_id = None self.job = job self.exit_code = None self.prepare_input_files_cmd = prepare_files_cmd self._init_metrics()
[docs] def get_param_values(self, app): """ Read encoded parameter values from the database and turn back into a dict of tool parameter values. """ param_dict = dict([(p.name, p.value) for p in self.job.parameters]) tool = app.toolbox.get_tool(self.job.tool_id, tool_version=self.job.tool_version) param_dict = tool.params_from_strings(param_dict, app) return param_dict
[docs] def get_id_tag(self): """ Return an id tag suitable for identifying the task. This combines the task's job id and the task's own id. """ return "%s_%s" % (self.job.id, self.id)
[docs] def get_command_line(self): return self.command_line
[docs] def get_parameters(self): return self.parameters
[docs] def get_state(self): return self.state
[docs] def get_info(self): return self.info
[docs] def get_working_directory(self): return self.working_directory
[docs] def get_task_runner_name(self): return self.task_runner_name
[docs] def get_task_runner_external_id(self): return self.task_runner_external_id
[docs] def get_job(self): return self.job
[docs] def get_prepare_input_files_cmd(self): return self.prepare_input_files_cmd
# The following accessors are for members that are in the Job class but # not in the Task class. So they can either refer to the parent Job # or return None, depending on whether Tasks need to point to the parent # (e.g., for a session) or never use the member (e.g., external output # metdata). These can be filled in as needed.
[docs] def get_external_output_metadata(self): """ The external_output_metadata is currently a backref to JobExternalOutputMetadata. It exists for a job but not a task, and when a task is cancelled its corresponding parent Job will be cancelled. So None is returned now, but that could be changed to self.get_job().get_external_output_metadata(). """ return None
[docs] def get_job_runner_name(self): """ Since runners currently access Tasks the same way they access Jobs, this method just refers to *this* instance's runner. """ return self.task_runner_name
[docs] def get_job_runner_external_id(self): """ Runners will use the same methods to get information about the Task class as they will about the Job class, so this method just returns the task's external id. """ # TODO: Merge into get_runner_external_id. return self.task_runner_external_id
[docs] def get_session_id(self): # The Job's galaxy session is equal to the Job's session, so the # Job's session is the same as the Task's session. return self.get_job().get_session_id()
[docs] def set_id(self, id): # This is defined in the SQL Alchemy's mapper and not here. # This should never be called. self.id = id
[docs] def set_command_line(self, command_line): self.command_line = command_line
[docs] def set_parameters(self, parameters): self.parameters = parameters
[docs] def set_state(self, state): self.state = state
[docs] def set_info(self, info): self.info = info
[docs] def set_working_directory(self, working_directory): self.working_directory = working_directory
[docs] def set_task_runner_name(self, task_runner_name): self.task_runner_name = task_runner_name
[docs] def set_job_runner_external_id(self, task_runner_external_id): # This method is available for runners that do not want/need to # differentiate between the kinds of Runnable things (Jobs and Tasks) # that they're using. log.debug("Task %d: Set external id to %s" % (self.id, task_runner_external_id)) self.task_runner_external_id = task_runner_external_id
[docs] def set_task_runner_external_id(self, task_runner_external_id): self.task_runner_external_id = task_runner_external_id
[docs] def set_job(self, job): self.job = job
[docs] def set_prepare_input_files_cmd(self, prepare_input_files_cmd): self.prepare_input_files_cmd = prepare_input_files_cmd
[docs]class JobParameter(RepresentById): def __init__(self, name, value): self.name = name self.value = value
[docs] def copy(self): return JobParameter(name=self.name, value=self.value)
[docs]class JobToInputDatasetAssociation(RepresentById): def __init__(self, name, dataset): self.name = name self.dataset = dataset self.dataset_version = 0 # We start with version 0 and update once the job is ready
[docs]class JobToOutputDatasetAssociation(RepresentById): def __init__(self, name, dataset): self.name = name self.dataset = dataset
[docs]class JobToInputDatasetCollectionAssociation(RepresentById): def __init__(self, name, dataset_collection): self.name = name self.dataset_collection = dataset_collection
# Many jobs may map to one HistoryDatasetCollection using these for a given # tool output (if mapping over an input collection).
[docs]class JobToOutputDatasetCollectionAssociation(RepresentById): def __init__(self, name, dataset_collection_instance): self.name = name self.dataset_collection_instance = dataset_collection_instance
# A DatasetCollection will be mapped to at most one job per tool output # using these. (You can think of many of these models as going into the # creation of a JobToOutputDatasetCollectionAssociation.)
[docs]class JobToImplicitOutputDatasetCollectionAssociation(RepresentById): def __init__(self, name, dataset_collection): self.name = name self.dataset_collection = dataset_collection
[docs]class JobToInputLibraryDatasetAssociation(RepresentById): def __init__(self, name, dataset): self.name = name self.dataset = dataset
[docs]class JobToOutputLibraryDatasetAssociation(RepresentById): def __init__(self, name, dataset): self.name = name self.dataset = dataset
[docs]class JobStateHistory(RepresentById): def __init__(self, job): self.job = job self.state = job.state self.info = job.info
[docs]class ImplicitlyCreatedDatasetCollectionInput(RepresentById): def __init__(self, name, input_dataset_collection): self.name = name self.input_dataset_collection = input_dataset_collection
[docs]class ImplicitCollectionJobs(RepresentById): populated_states = Bunch( NEW='new', # New implicit jobs object, unpopulated job associations OK='ok', # Job associations are set and fixed. FAILED='failed', # There were issues populating job associations, object is in error. ) def __init__( self, id=None, populated_state=None, ): self.id = id self.populated_state = populated_state or ImplicitCollectionJobs.populated_states.NEW @property def job_list(self): return [icjja.job for icjja in self.jobs]
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, populated_state=self.populated_state, jobs=[serialization_options.get_identifier(id_encoder, j_a.job) for j_a in self.jobs] ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs]class ImplicitCollectionJobsJobAssociation(RepresentById): def __init__(self): pass
[docs]class PostJobAction(RepresentById): def __init__(self, action_type, workflow_step, output_name=None, action_arguments=None): self.action_type = action_type self.output_name = output_name self.action_arguments = action_arguments self.workflow_step = workflow_step
[docs]class PostJobActionAssociation(RepresentById): def __init__(self, pja, job=None, job_id=None): if job is not None: self.job = job elif job_id is not None: self.job_id = job_id else: raise Exception("PostJobActionAssociation must be created with a job or a job_id.") self.post_job_action = pja
[docs]class JobExternalOutputMetadata(RepresentById): def __init__(self, job=None, dataset=None): self.job = job if isinstance(dataset, galaxy.model.HistoryDatasetAssociation): self.history_dataset_association = dataset elif isinstance(dataset, galaxy.model.LibraryDatasetDatasetAssociation): self.library_dataset_dataset_association = dataset @property def dataset(self): if self.history_dataset_association: return self.history_dataset_association elif self.library_dataset_dataset_association: return self.library_dataset_dataset_association return None
[docs]class JobExportHistoryArchive(RepresentById): def __init__(self, job=None, history=None, dataset=None, compressed=False, history_attrs_filename=None): self.job = job self.history = history self.dataset = dataset self.compressed = compressed self.history_attrs_filename = history_attrs_filename @property def temp_directory(self): return os.path.split(self.history_attrs_filename)[0] @property def up_to_date(self): """ Return False, if a new export should be generated for corresponding history. """ job = self.job return job.state not in [Job.states.ERROR, Job.states.DELETED] \ and job.update_time > self.history.update_time @property def ready(self): return self.job.state == Job.states.OK @property def preparing(self): return self.job.state in [Job.states.RUNNING, Job.states.QUEUED, Job.states.WAITING] @property def export_name(self): # Stream archive. hname = ready_name_for_url(self.history.name) hname = "Galaxy-History-%s.tar" % (hname) if self.compressed: hname += ".gz" return hname
[docs]class JobImportHistoryArchive(RepresentById): def __init__(self, job=None, history=None, archive_dir=None): self.job = job self.history = history self.archive_dir = archive_dir
[docs]class GenomeIndexToolData(RepresentById): def __init__(self, job=None, params=None, dataset=None, deferred_job=None, transfer_job=None, fasta_path=None, created_time=None, modified_time=None, dbkey=None, user=None, indexer=None): self.job = job self.dataset = dataset self.fasta_path = fasta_path self.user = user self.indexer = indexer self.created_time = created_time self.modified_time = modified_time self.deferred = deferred_job self.transfer = transfer_job
[docs]class DeferredJob(RepresentById): states = Bunch(NEW='new', WAITING='waiting', QUEUED='queued', RUNNING='running', OK='ok', ERROR='error') def __init__(self, state=None, plugin=None, params=None): self.state = state self.plugin = plugin self.params = params
[docs] def get_check_interval(self): if not hasattr(self, '_check_interval'): self._check_interval = None return self._check_interval
[docs] def set_check_interval(self, seconds): self._check_interval = seconds
check_interval = property(get_check_interval, set_check_interval)
[docs] def get_last_check(self): if not hasattr(self, '_last_check'): self._last_check = 0 return self._last_check
[docs] def set_last_check(self, seconds): try: self._last_check = int(seconds) except ValueError: self._last_check = time.time()
last_check = property(get_last_check, set_last_check) @property def is_check_time(self): if self.check_interval is None: return True elif (int(time.time()) - self.last_check) > self.check_interval: return True else: return False
[docs]class Group(Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'name'] dict_element_visible_keys = ['id', 'name'] def __init__(self, name=None): self.name = name self.deleted = False
[docs]class UserGroupAssociation(RepresentById): def __init__(self, user, group): self.user = user self.group = group
[docs]def is_hda(d): return isinstance(d, HistoryDatasetAssociation)
[docs]class History(HasTags, Dictifiable, UsesAnnotations, HasName, RepresentById): dict_collection_visible_keys = ['id', 'name', 'published', 'deleted'] dict_element_visible_keys = ['id', 'name', 'genome_build', 'deleted', 'purged', 'update_time', 'published', 'importable', 'slug', 'empty'] default_name = 'Unnamed history' def __init__(self, id=None, name=None, user=None): self.id = id self.name = name or History.default_name self.deleted = False self.purged = False self.importing = False self.genome_build = None self.published = False # Relationships self.user = user self.datasets = [] self.galaxy_sessions = [] self.tags = [] @property def empty(self): return self.hid_counter == 1 def _next_hid(self, n=1): # this is overriden in mapping.py db_next_hid() method if len(self.datasets) == 0: return n else: last_hid = 0 for dataset in self.datasets: if dataset.hid > last_hid: last_hid = dataset.hid return last_hid + n
[docs] def add_galaxy_session(self, galaxy_session, association=None): if association is None: self.galaxy_sessions.append(GalaxySessionToHistoryAssociation(galaxy_session, self)) else: self.galaxy_sessions.append(association)
[docs] def add_dataset(self, dataset, parent_id=None, genome_build=None, set_hid=True, quota=True): if isinstance(dataset, Dataset): dataset = HistoryDatasetAssociation(dataset=dataset) object_session(self).add(dataset) object_session(self).flush() elif not isinstance(dataset, HistoryDatasetAssociation): raise TypeError("You can only add Dataset and HistoryDatasetAssociation instances to a history" + " ( you tried to add %s )." % str(dataset)) if parent_id: for data in self.datasets: if data.id == parent_id: dataset.hid = data.hid break else: if set_hid: dataset.hid = self._next_hid() else: if set_hid: dataset.hid = self._next_hid() if quota and self.user: self.user.adjust_total_disk_usage(dataset.quota_amount(self.user)) dataset.history = self if genome_build not in [None, '?']: self.genome_build = genome_build dataset.history_id = self.id return dataset
[docs] def add_datasets(self, sa_session, datasets, parent_id=None, genome_build=None, set_hid=True, quota=True, flush=False): """ Optimized version of add_dataset above that minimizes database interactions when adding many datasets to history at once. """ all_hdas = all(is_hda(_) for _ in datasets) optimize = len(datasets) > 1 and parent_id is None and all_hdas and set_hid if optimize: self.__add_datasets_optimized(datasets, genome_build=genome_build) if quota and self.user: disk_usage = sum([d.get_total_size() for d in datasets]) self.user.adjust_total_disk_usage(disk_usage) sa_session.add_all(datasets) if flush: sa_session.flush() else: for dataset in datasets: self.add_dataset(dataset, parent_id=parent_id, genome_build=genome_build, set_hid=set_hid, quota=quota) sa_session.add(dataset) if flush: sa_session.flush()
def __add_datasets_optimized(self, datasets, genome_build=None): """ Optimized version of add_dataset above that minimizes database interactions when adding many datasets to history at once under certain circumstances. """ n = len(datasets) base_hid = self._next_hid(n=n) set_genome = genome_build not in [None, '?'] for i, dataset in enumerate(datasets): dataset.hid = base_hid + i # Don't let SA manage this. delattr(dataset, "history") dataset.history_id = cached_id(self) if set_genome: self.genome_build = genome_build for dataset in datasets: dataset.history_id = cached_id(self) return datasets
[docs] def add_dataset_collection(self, history_dataset_collection, set_hid=True): if set_hid: history_dataset_collection.hid = self._next_hid() history_dataset_collection.history = self # TODO: quota? self.dataset_collections.append(history_dataset_collection) return history_dataset_collection
[docs] def copy(self, name=None, target_user=None, activatable=False, all_datasets=False): """ Return a copy of this history using the given `name` and `target_user`. If `activatable`, copy only non-deleted datasets. If `all_datasets`, copy non-deleted, deleted, and purged datasets. """ name = name or self.name applies_to_quota = target_user != self.user # Create new history. new_history = History(name=name, user=target_user) db_session = object_session(self) db_session.add(new_history) db_session.flush([new_history]) # copy history tags and annotations (if copying user is not anonymous) if target_user: self.copy_item_annotation(db_session, self.user, self, target_user, new_history) new_history.copy_tags_from(target_user=target_user, source=self) # Copy HDAs. if activatable: hdas = self.activatable_datasets elif all_datasets: hdas = self.datasets else: hdas = self.active_datasets for hda in hdas: # Copy HDA. new_hda = hda.copy(force_flush=False) new_history.add_dataset(new_hda, set_hid=False, quota=applies_to_quota) if target_user: new_hda.copy_item_annotation(db_session, self.user, hda, target_user, new_hda) new_hda.copy_tags_from(target_user, hda) # Copy history dataset collections if all_datasets: hdcas = self.dataset_collections else: hdcas = self.active_dataset_collections for hdca in hdcas: new_hdca = hdca.copy() new_history.add_dataset_collection(new_hdca, set_hid=False) db_session.add(new_hdca) db_session.flush() if target_user: new_hdca.copy_item_annotation(db_session, self.user, hdca, target_user, new_hdca) new_hdca.copy_tags_from(target_user, hdca) new_history.hid_counter = self.hid_counter db_session.flush() return new_history
@property def has_possible_members(self): return True @property def activatable_datasets(self): # This needs to be a list return [hda for hda in self.datasets if not hda.dataset.deleted]
[docs] def serialize(self, id_encoder, serialization_options): history_attrs = dict_for( self, create_time=self.create_time.__str__(), update_time=self.update_time.__str__(), name=unicodify(self.name), hid_counter=self.hid_counter, genome_build=self.genome_build, annotation=unicodify(get_item_annotation_str(object_session(self), self.user, self)), tags=self.make_tag_string_list(), ) serialization_options.attach_identifier(id_encoder, self, history_attrs) return history_attrs
[docs] def to_dict(self, view='collection', value_mapper=None): # Get basic value. rval = super(History, self).to_dict(view=view, value_mapper=value_mapper) if view == 'element': rval['size'] = int(self.disk_size) return rval
@property def latest_export(self): exports = self.exports return exports and exports[0]
[docs] def unhide_datasets(self): for dataset in self.datasets: dataset.mark_unhidden()
[docs] def resume_paused_jobs(self): job = None for job in self.paused_jobs: job.resume(flush=False) if job is not None: # We'll flush once if there was a paused job object_session(job).flush()
@property def paused_jobs(self): db_session = object_session(self) return db_session.query(Job).filter(Job.history_id == self.id, Job.state == Job.states.PAUSED).all() @hybrid.hybrid_property def disk_size(self): """ Return the size in bytes of this history by summing the 'total_size's of all non-purged, unique datasets within it. """ # non-.expression part of hybrid.hybrid_property: called when an instance is the namespace (not the class) db_session = object_session(self) rval = db_session.query( func.sum(db_session.query(HistoryDatasetAssociation.dataset_id, Dataset.total_size).join(Dataset) .filter(HistoryDatasetAssociation.table.c.history_id == self.id) .filter(HistoryDatasetAssociation.purged != true()) .filter(Dataset.purged != true()) # unique datasets only .distinct().subquery().c.total_size)).first()[0] if rval is None: rval = 0 return rval @disk_size.expression def disk_size(cls): """ Return a query scalar that will get any history's size in bytes by summing the 'total_size's of all non-purged, unique datasets within it. """ # .expression acts as a column_property and should return a scalar # first, get the distinct datasets within a history that are not purged hda_to_dataset_join = join(HistoryDatasetAssociation, Dataset, HistoryDatasetAssociation.table.c.dataset_id == Dataset.table.c.id) distinct_datasets = ( select([ # use labels here to better access from the query above HistoryDatasetAssociation.table.c.history_id.label('history_id'), Dataset.total_size.label('dataset_size'), Dataset.id.label('dataset_id') ]) .where(HistoryDatasetAssociation.table.c.purged != true()) .where(Dataset.table.c.purged != true()) .select_from(hda_to_dataset_join) # TODO: slow (in general) but most probably here - index total_size for easier sorting/distinct? .distinct() ) # postgres needs an alias on FROM distinct_datasets_alias = aliased(distinct_datasets, name="datasets") # then, bind as property of history using the cls.id size_query = ( select([ func.coalesce(func.sum(distinct_datasets_alias.c.dataset_size), 0) ]) .select_from(distinct_datasets_alias) .where(distinct_datasets_alias.c.history_id == cls.id) ) # label creates a scalar return size_query.label('disk_size') @property def disk_nice_size(self): """Returns human readable size of history on disk.""" return galaxy.util.nice_size(self.disk_size) @property def active_datasets_and_roles(self): if not hasattr(self, '_active_datasets_and_roles'): db_session = object_session(self) query = (db_session.query(HistoryDatasetAssociation) .filter(HistoryDatasetAssociation.table.c.history_id == self.id) .filter(not_(HistoryDatasetAssociation.deleted)) .order_by(HistoryDatasetAssociation.table.c.hid.asc()) .options(joinedload("dataset"), joinedload("dataset.actions"), joinedload("dataset.actions.role"), joinedload("tags"))) self._active_datasets_and_roles = query.all() return self._active_datasets_and_roles @property def active_visible_datasets_and_roles(self): if not hasattr(self, '_active_visible_datasets_and_roles'): db_session = object_session(self) query = (db_session.query(HistoryDatasetAssociation) .filter(HistoryDatasetAssociation.table.c.history_id == self.id) .filter(not_(HistoryDatasetAssociation.deleted)) .filter(HistoryDatasetAssociation.visible) .order_by(HistoryDatasetAssociation.table.c.hid.asc()) .options(joinedload("dataset"), joinedload("dataset.actions"), joinedload("dataset.actions.role"), joinedload("tags"))) self._active_visible_datasets_and_roles = query.all() return self._active_visible_datasets_and_roles @property def active_visible_dataset_collections(self): if not hasattr(self, '_active_visible_dataset_collections'): db_session = object_session(self) query = (db_session.query(HistoryDatasetCollectionAssociation) .filter(HistoryDatasetCollectionAssociation.table.c.history_id == self.id) .filter(not_(HistoryDatasetCollectionAssociation.deleted)) .filter(HistoryDatasetCollectionAssociation.visible) .order_by(HistoryDatasetCollectionAssociation.table.c.hid.asc()) .options(joinedload("collection"), joinedload("tags"))) self._active_visible_dataset_collections = query.all() return self._active_visible_dataset_collections @property def active_contents(self): """ Return all active contents ordered by hid. """ return self.contents_iter(types=["dataset", "dataset_collection"], deleted=False, visible=True)
[docs] def contents_iter(self, **kwds): """ Fetch filtered list of contents of history. """ default_contents_types = [ 'dataset', ] types = kwds.get('types', default_contents_types) iters = [] if 'dataset' in types: iters.append(self.__dataset_contents_iter(**kwds)) if 'dataset_collection' in types: iters.append(self.__collection_contents_iter(**kwds)) return galaxy.util.merge_sorted_iterables(operator.attrgetter("hid"), *iters)
def __dataset_contents_iter(self, **kwds): return self.__filter_contents(HistoryDatasetAssociation, **kwds) def __filter_contents(self, content_class, **kwds): db_session = object_session(self) assert db_session is not None query = db_session.query(content_class).filter(content_class.table.c.history_id == self.id) query = query.order_by(content_class.table.c.hid.asc()) deleted = galaxy.util.string_as_bool_or_none(kwds.get('deleted', None)) if deleted is not None: query = query.filter(content_class.deleted == deleted) visible = galaxy.util.string_as_bool_or_none(kwds.get('visible', None)) if visible is not None: query = query.filter(content_class.visible == visible) if 'ids' in kwds: ids = kwds['ids'] max_in_filter_length = kwds.get('max_in_filter_length', MAX_IN_FILTER_LENGTH) if len(ids) < max_in_filter_length: query = query.filter(content_class.id.in_(ids)) else: query = (content for content in query if content.id in ids) return query def __collection_contents_iter(self, **kwds): return self.__filter_contents(HistoryDatasetCollectionAssociation, **kwds)
[docs]class HistoryUserShareAssociation(RepresentById): def __init__(self): self.history = None self.user = None
[docs]class UserRoleAssociation(RepresentById): def __init__(self, user, role): self.user = user self.role = role
[docs]class GroupRoleAssociation(RepresentById): def __init__(self, group, role): self.group = group self.role = role
[docs]class Role(Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'name'] dict_element_visible_keys = ['id', 'name', 'description', 'type'] private_id = None types = Bunch( PRIVATE='private', SYSTEM='system', USER='user', ADMIN='admin', SHARING='sharing' ) def __init__(self, name="", description="", type="system", deleted=False): self.name = name self.description = description self.type = type self.deleted = deleted
[docs]class UserQuotaAssociation(Dictifiable, RepresentById): dict_element_visible_keys = ['user'] def __init__(self, user, quota): self.user = user self.quota = quota
[docs]class GroupQuotaAssociation(Dictifiable, RepresentById): dict_element_visible_keys = ['group'] def __init__(self, group, quota): self.group = group self.quota = quota
[docs]class Quota(Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'name'] dict_element_visible_keys = ['id', 'name', 'description', 'bytes', 'operation', 'display_amount', 'default', 'users', 'groups'] valid_operations = ('+', '-', '=') def __init__(self, name="", description="", amount=0, operation="="): self.name = name self.description = description if amount is None: self.bytes = -1 else: self.bytes = amount self.operation = operation
[docs] def get_amount(self): if self.bytes == -1: return None return self.bytes
[docs] def set_amount(self, amount): if amount is None: self.bytes = -1 else: self.bytes = amount
amount = property(get_amount, set_amount) @property def display_amount(self): if self.bytes == -1: return "unlimited" else: return galaxy.util.nice_size(self.bytes)
[docs]class DefaultQuotaAssociation(Quota, Dictifiable, RepresentById): dict_element_visible_keys = ['type'] types = Bunch( UNREGISTERED='unregistered', REGISTERED='registered' ) def __init__(self, type, quota): assert type in self.types.__dict__.values(), 'Invalid type' self.type = type self.quota = quota
[docs]class DatasetPermissions(RepresentById): def __init__(self, action, dataset, role=None, role_id=None): self.action = action self.dataset = dataset if role is not None: self.role = role else: self.role_id = role_id
[docs]class LibraryPermissions(RepresentById): def __init__(self, action, library_item, role): self.action = action if isinstance(library_item, Library): self.library = library_item else: raise Exception("Invalid Library specified: %s" % library_item.__class__.__name__) self.role = role
[docs]class LibraryFolderPermissions(RepresentById): def __init__(self, action, library_item, role): self.action = action if isinstance(library_item, LibraryFolder): self.folder = library_item else: raise Exception("Invalid LibraryFolder specified: %s" % library_item.__class__.__name__) self.role = role
[docs]class LibraryDatasetPermissions(RepresentById): def __init__(self, action, library_item, role): self.action = action if isinstance(library_item, LibraryDataset): self.library_dataset = library_item else: raise Exception("Invalid LibraryDataset specified: %s" % library_item.__class__.__name__) self.role = role
[docs]class LibraryDatasetDatasetAssociationPermissions(RepresentById): def __init__(self, action, library_item, role): self.action = action if isinstance(library_item, LibraryDatasetDatasetAssociation): self.library_dataset_dataset_association = library_item else: raise Exception("Invalid LibraryDatasetDatasetAssociation specified: %s" % library_item.__class__.__name__) self.role = role
[docs]class DefaultUserPermissions(RepresentById): def __init__(self, user, action, role): self.user = user self.action = action self.role = role
[docs]class DefaultHistoryPermissions(RepresentById): def __init__(self, history, action, role): self.history = history self.action = action self.role = role
[docs]class StorableObject(object):
[docs] def __init__(self, id, uuid): self.id = id if uuid is None: self.uuid = uuid4() else: self.uuid = UUID(str(uuid))
[docs]class Dataset(StorableObject, RepresentById): states = Bunch(NEW='new', UPLOAD='upload', QUEUED='queued', RUNNING='running', OK='ok', EMPTY='empty', ERROR='error', DISCARDED='discarded', PAUSED='paused', SETTING_METADATA='setting_metadata', FAILED_METADATA='failed_metadata') # failed_metadata is only valid as DatasetInstance state currently non_ready_states = ( states.NEW, states.UPLOAD, states.QUEUED, states.RUNNING, states.SETTING_METADATA ) ready_states = tuple(set(states.__dict__.values()) - set(non_ready_states)) valid_input_states = tuple( set(states.__dict__.values()) - set([states.ERROR, states.DISCARDED]) ) terminal_states = ( states.OK, states.EMPTY, states.ERROR, states.DISCARDED, states.FAILED_METADATA, ) conversion_messages = Bunch(PENDING="pending", NO_DATA="no data", NO_CHROMOSOME="no chromosome", NO_CONVERTER="no converter", NO_TOOL="no tool", DATA="data", ERROR="error", OK="ok") permitted_actions = get_permitted_actions(filter='DATASET') file_path = "/tmp/" object_store = None # This get initialized in mapping.py (method init) by app.py engine = None def __init__(self, id=None, state=None, external_filename=None, extra_files_path=None, file_size=None, purgable=True, uuid=None): super(Dataset, self).__init__(id=id, uuid=uuid) self.state = state self.deleted = False self.purged = False self.purgable = purgable self.external_filename = external_filename self.external_extra_files_path = None self._extra_files_path = extra_files_path self.file_size = file_size self.sources = [] self.hashes = []
[docs] def in_ready_state(self): return self.state in self.ready_states
[docs] def get_file_name(self): if not self.external_filename: assert self.object_store is not None, "Object Store has not been initialized for dataset %s" % self.id if self.object_store.exists(self): return self.object_store.get_filename(self) else: return '' else: filename = self.external_filename # Make filename absolute return os.path.abspath(filename)
[docs] def set_file_name(self, filename): if not filename: self.external_filename = None else: self.external_filename = filename
file_name = property(get_file_name, set_file_name)
[docs] def get_extra_files_path(self): # Unlike get_file_name - external_extra_files_path is not backed by an # actual database column so if SA instantiates this object - the # attribute won't exist yet. if not getattr(self, "external_extra_files_path", None): if self.object_store.exists(self, dir_only=True, extra_dir=self._extra_files_rel_path): return self.object_store.get_filename(self, dir_only=True, extra_dir=self._extra_files_rel_path) return '' else: return os.path.abspath(self.external_extra_files_path)
[docs] def create_extra_files_path(self): if not self.extra_files_path_exists(): self.object_store.create(self, dir_only=True, extra_dir=self._extra_files_rel_path)
[docs] def set_extra_files_path(self, extra_files_path): if not extra_files_path: self.external_extra_files_path = None else: self.external_extra_files_path = extra_files_path
extra_files_path = property(get_extra_files_path, set_extra_files_path)
[docs] def extra_files_path_exists(self): return self.object_store.exists(self, extra_dir=self._extra_files_rel_path, dir_only=True)
@property def _extra_files_rel_path(self): store_by = getattr(self.object_store, "store_by", "id") return self._extra_files_path or "dataset_%s_files" % getattr(self, store_by) def _calculate_size(self): if self.external_filename: try: return os.path.getsize(self.external_filename) except OSError: return 0 else: return self.object_store.size(self)
[docs] def get_size(self, nice_size=False): """Returns the size of the data on disk""" if self.file_size: if nice_size: return galaxy.util.nice_size(self.file_size) else: return self.file_size else: if nice_size: return galaxy.util.nice_size(self._calculate_size()) else: return self._calculate_size()
[docs] def set_size(self, no_extra_files=False): """Sets the size of the data on disk. If the caller is sure there are no extra files, pass no_extra_files as True to optimize subsequent calls to get_total_size or set_total_size - potentially avoiding both a database flush and check against the file system. """ if not self.file_size: self.file_size = self._calculate_size() if no_extra_files: self.total_size = self.file_size
[docs] def get_total_size(self): if self.total_size is not None: return self.total_size # for backwards compatibility, set if unset self.set_total_size() db_session = object_session(self) db_session.flush() return self.total_size
[docs] def set_total_size(self): if self.file_size is None: self.set_size() self.total_size = self.file_size or 0 if self.object_store.exists(self, extra_dir=self._extra_files_rel_path, dir_only=True): for root, dirs, files in os.walk(self.extra_files_path): self.total_size += sum([os.path.getsize(os.path.join(root, file)) for file in files if os.path.exists(os.path.join(root, file))])
[docs] def has_data(self): """Detects whether there is any data""" return self.get_size() > 0
[docs] def mark_deleted(self): self.deleted = True
# FIXME: sqlalchemy will replace this def _delete(self): """Remove the file that corresponds to this data""" self.object_store.delete(self) @property def user_can_purge(self): return self.purged is False \ and not bool(self.library_associations) \ and len(self.history_associations) == len(self.purged_history_associations)
[docs] def full_delete(self): """Remove the file and extra files, marks deleted and purged""" # os.unlink( self.file_name ) try: self.object_store.delete(self) except galaxy.exceptions.ObjectNotFound: pass if self.object_store.exists(self, extra_dir=self._extra_files_rel_path, dir_only=True): self.object_store.delete(self, entire_dir=True, extra_dir=self._extra_files_rel_path, dir_only=True) # TODO: purge metadata files self.deleted = True self.purged = True
[docs] def get_access_roles(self, trans): roles = [] for dp in self.actions: if dp.action == trans.app.security_agent.permitted_actions.DATASET_ACCESS.action: roles.append(dp.role) return roles
[docs] def get_manage_permissions_roles(self, trans): roles = [] for dp in self.actions: if dp.action == trans.app.security_agent.permitted_actions.DATASET_MANAGE_PERMISSIONS.action: roles.append(dp.role) return roles
[docs] def has_manage_permissions_roles(self, trans): for dp in self.actions: if dp.action == trans.app.security_agent.permitted_actions.DATASET_MANAGE_PERMISSIONS.action: return True return False
[docs] def serialize(self, id_encoder, serialization_options): # serialize Dataset objects only for jobs that can actually modify these models. assert serialization_options.serialize_dataset_objects def to_int(n): return int(n) if n is not None else 0 rval = dict_for( self, state=self.state, deleted=self.deleted, purged=self.purged, external_filename=self.external_filename, _extra_files_path=self._extra_files_path, file_size=to_int(self.file_size), object_store_id=self.object_store_id, total_size=to_int(self.total_size), uuid=str(self.uuid or '') or None, hashes=list(map(lambda h: h.serialize(id_encoder, serialization_options), self.hashes)) ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs]class DatasetSource(RepresentById): """ """
[docs]class DatasetSourceHash(RepresentById): """ """
[docs]class DatasetHash(RepresentById): """ """
[docs] def serialize(self, id_encoder, serialization_options): # serialize Dataset objects only for jobs that can actually modify these models. rval = dict_for( self, hash_function=self.hash_function, hash_value=self.hash_value, extra_files_path=self.extra_files_path, ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs]def datatype_for_extension(extension, datatypes_registry=None): if datatypes_registry is None: datatypes_registry = _get_datatypes_registry() if not extension or extension == 'auto' or extension == '_sniff_': extension = 'data' ret = datatypes_registry.get_datatype_by_extension(extension) if ret is None: log.warning("Datatype class not found for extension '%s'" % extension) return datatypes_registry.get_datatype_by_extension('data') return ret
[docs]class DatasetInstance(object): """A base class for all 'dataset instances', HDAs, LDAs, etc""" states = Dataset.states conversion_messages = Dataset.conversion_messages permitted_actions = Dataset.permitted_actions
[docs] def __init__(self, id=None, hid=None, name=None, info=None, blurb=None, peek=None, tool_version=None, extension=None, dbkey=None, metadata=None, history=None, dataset=None, deleted=False, designation=None, parent_id=None, validation_errors=None, visible=True, create_dataset=False, sa_session=None, extended_metadata=None, flush=True): self.name = name or "Unnamed dataset" self.id = id self.info = info self.blurb = blurb self.peek = peek self.tool_version = tool_version self.extension = extension self.designation = designation # set private variable to None here, since the attribute may be needed in by MetadataCollection.__init__ self._metadata = None self.metadata = metadata or dict() self.extended_metadata = extended_metadata if dbkey: # dbkey is stored in metadata, only set if non-zero, or else we could clobber one supplied by input 'metadata' self._metadata['dbkey'] = dbkey self.deleted = deleted self.visible = visible # Relationships if not dataset and create_dataset: # Had to pass the sqlalchemy session in order to create a new dataset dataset = Dataset(state=Dataset.states.NEW) if flush: sa_session.add(dataset) sa_session.flush() self.dataset = dataset self.parent_id = parent_id self.validation_errors = validation_errors
[docs] def update(self): self.update_time = galaxy.model.orm.now.now()
@property def ext(self): return self.extension
[docs] def get_dataset_state(self): # self._state is currently only used when setting metadata externally # leave setting the state as-is, we'll currently handle this specially in the external metadata code if self._state: return self._state return self.dataset.state
[docs] def raw_set_dataset_state(self, state): if state != self.dataset.state: self.dataset.state = state return True else: return False
[docs] def set_dataset_state(self, state): if self.raw_set_dataset_state(state): sa_session = object_session(self) if sa_session: object_session(self).add(self.dataset) object_session(self).flush() # flush here, because hda.flush() won't flush the Dataset object
state = property(get_dataset_state, set_dataset_state)
[docs] def get_file_name(self): if self.dataset.purged: return "" return self.dataset.get_file_name()
[docs] def set_file_name(self, filename): return self.dataset.set_file_name(filename)
file_name = property(get_file_name, set_file_name) @property def extra_files_path(self): return self.dataset.extra_files_path
[docs] def extra_files_path_exists(self): return self.dataset.extra_files_path_exists()
@property def datatype(self): return datatype_for_extension(self.extension)
[docs] def get_metadata(self): # using weakref to store parent (to prevent circ ref), # does a Session.clear() cause parent to be invalidated, while still copying over this non-database attribute? if not hasattr(self, '_metadata_collection') or self._metadata_collection.parent != self: self._metadata_collection = galaxy.model.metadata.MetadataCollection(self) return self._metadata_collection
@property def set_metadata_requires_flush(self): return self.metadata.requires_dataset_id
[docs] def set_metadata(self, bunch): # Needs to accept a MetadataCollection, a bunch, or a dict self._metadata = self.metadata.make_dict_copy(bunch)
metadata = property(get_metadata, set_metadata) @property def metadata_file_types(self): meta_types = [] for meta_type in self.metadata.spec.keys(): if isinstance(self.metadata.spec[meta_type].param, galaxy.model.metadata.FileParameter): meta_types.append(meta_type) return meta_types # This provide backwards compatibility with using the old dbkey # field in the database. That field now maps to "old_dbkey" (see mapping.py).
[docs] def get_dbkey(self): dbkey = self.metadata.dbkey if not isinstance(dbkey, list): dbkey = [dbkey] if dbkey in [[None], []]: return "?" return dbkey[0]
[docs] def set_dbkey(self, value): if "dbkey" in self.datatype.metadata_spec: if not isinstance(value, list): self.metadata.dbkey = [value]
dbkey = property(get_dbkey, set_dbkey)
[docs] def change_datatype(self, new_ext): self.clear_associated_files() _get_datatypes_registry().change_datatype(self, new_ext)
[docs] def get_size(self, nice_size=False): """Returns the size of the data on disk""" if nice_size: return galaxy.util.nice_size(self.dataset.get_size()) return self.dataset.get_size()
[docs] def set_size(self, **kwds): """Sets and gets the size of the data on disk""" return self.dataset.set_size(**kwds)
[docs] def get_total_size(self): return self.dataset.get_total_size()
[docs] def set_total_size(self): return self.dataset.set_total_size()
[docs] def has_data(self): """Detects whether there is any data""" return self.dataset.has_data()
[docs] def get_raw_data(self): """Returns the full data. To stream it open the file_name and read/write as needed""" return self.datatype.get_raw_data(self)
[docs] def get_mime(self): """Returns the mime type of the data""" try: return _get_datatypes_registry().get_mimetype_by_extension(self.extension.lower()) except AttributeError: # extension is None return 'data'
[docs] def set_peek(self): return self.datatype.set_peek(self)
[docs] def init_meta(self, copy_from=None): return self.datatype.init_meta(self, copy_from=copy_from)
[docs] def set_meta(self, **kwd): self.clear_associated_files(metadata_safe=True) return self.datatype.set_meta(self, **kwd)
[docs] def missing_meta(self, **kwd): return self.datatype.missing_meta(self, **kwd)
[docs] def as_display_type(self, type, **kwd): return self.datatype.as_display_type(self, type, **kwd)
[docs] def display_peek(self): return self.datatype.display_peek(self)
[docs] def display_name(self): return self.datatype.display_name(self)
[docs] def display_info(self): return self.datatype.display_info(self)
[docs] def get_converted_files_by_type(self, file_type): for assoc in self.implicitly_converted_datasets: if not assoc.deleted and assoc.type == file_type: if assoc.dataset: return assoc.dataset return assoc.dataset_ldda return None
[docs] def get_converted_dataset_deps(self, trans, target_ext): """ Returns dict of { "dependency" => HDA } """ # List of string of dependencies try: depends_list = trans.app.datatypes_registry.converter_deps[self.extension][target_ext] except KeyError: depends_list = [] return dict([(dep, self.get_converted_dataset(trans, dep)) for dep in depends_list])
[docs] def get_converted_dataset(self, trans, target_ext, target_context=None, history=None): """ Return converted dataset(s) if they exist, along with a dict of dependencies. If not converted yet, do so and return None (the first time). If unconvertible, raise exception. """ # See if we can convert the dataset if target_ext not in self.get_converter_types(): raise NoConverterException("Conversion from '%s' to '%s' not possible" % (self.extension, target_ext)) # See if converted dataset already exists, either in metadata in conversions. converted_dataset = self.get_metadata_dataset(target_ext) if converted_dataset: return converted_dataset converted_dataset = self.get_converted_files_by_type(target_ext) if converted_dataset: return converted_dataset deps = {} # List of string of dependencies try: depends_list = trans.app.datatypes_registry.converter_deps[self.extension][target_ext] except KeyError: depends_list = [] # Conversion is possible but hasn't been done yet, run converter. # Check if we have dependencies try: for dependency in depends_list: dep_dataset = self.get_converted_dataset(trans, dependency) if dep_dataset is None: # None means converter is running first time return None elif dep_dataset.state == Job.states.ERROR: raise ConverterDependencyException("A dependency (%s) was in an error state." % dependency) elif dep_dataset.state != Job.states.OK: # Pending return None deps[dependency] = dep_dataset except NoConverterException: raise NoConverterException("A dependency (%s) is missing a converter." % dependency) except KeyError: pass # No deps new_dataset = next(iter(self.datatype.convert_dataset(trans, self, target_ext, return_output=True, visible=False, deps=deps, target_context=target_context, history=history).values())) new_dataset.name = self.name self.copy_attributes(new_dataset) assoc = ImplicitlyConvertedDatasetAssociation(parent=self, file_type=target_ext, dataset=new_dataset, metadata_safe=False) session = trans.sa_session session.add(new_dataset) session.add(assoc) session.flush() return new_dataset
[docs] def copy_attributes(self, new_dataset): """ Copies attributes to a new datasets, used for implicit conversions """ pass
[docs] def get_metadata_dataset(self, dataset_ext): """ Returns an HDA that points to a metadata file which contains a converted data with the requested extension. """ for name, value in self.metadata.items(): # HACK: MetadataFile objects do not have a type/ext, so need to use metadata name # to determine type. if dataset_ext == 'bai' and name == 'bam_index' and isinstance(value, MetadataFile): # HACK: MetadataFile objects cannot be used by tools, so return # a fake HDA that points to metadata file. fake_dataset = Dataset(state=Dataset.states.OK, external_filename=value.file_name) fake_hda = HistoryDatasetAssociation(dataset=fake_dataset) return fake_hda
[docs] def clear_associated_files(self, metadata_safe=False, purge=False): raise Exception("Unimplemented")
[docs] def get_converter_types(self): return self.datatype.get_converter_types(self, _get_datatypes_registry())
[docs] def can_convert_to(self, format): return format in self.get_converter_types()
[docs] def find_conversion_destination(self, accepted_formats, **kwd): """Returns ( target_ext, existing converted dataset )""" return self.datatype.find_conversion_destination(self, accepted_formats, _get_datatypes_registry(), **kwd)
[docs] def add_validation_error(self, validation_error): self.validation_errors.append(validation_error)
[docs] def extend_validation_errors(self, validation_errors): self.validation_errors.extend(validation_errors)
[docs] def mark_deleted(self): self.deleted = True
[docs] def mark_undeleted(self): self.deleted = False
[docs] def mark_unhidden(self): self.visible = True
[docs] def undeletable(self): if self.purged: return False return True
@property def is_ok(self): return self.state == self.states.OK @property def is_pending(self): """ Return true if the dataset is neither ready nor in error """ return self.state in (self.states.NEW, self.states.UPLOAD, self.states.QUEUED, self.states.RUNNING, self.states.SETTING_METADATA) @property def source_library_dataset(self): def get_source(dataset): if isinstance(dataset, LibraryDatasetDatasetAssociation): if dataset.library_dataset: return (dataset, dataset.library_dataset) if dataset.copied_from_library_dataset_dataset_association: source = get_source(dataset.copied_from_library_dataset_dataset_association) if source: return source if dataset.copied_from_history_dataset_association: source = get_source(dataset.copied_from_history_dataset_association) if source: return source return (None, None) return get_source(self) @property def source_dataset_chain(self): def _source_dataset_chain(dataset, lst): try: cp_from_ldda = dataset.copied_from_library_dataset_dataset_association if cp_from_ldda: lst.append((cp_from_ldda, "(Data Library)")) return _source_dataset_chain(cp_from_ldda, lst) except Exception as e: log.warning(e) try: cp_from_hda = dataset.copied_from_history_dataset_association if cp_from_hda: lst.append((cp_from_hda, cp_from_hda.history.name)) return _source_dataset_chain(cp_from_hda, lst) except Exception as e: log.warning(e) return lst return _source_dataset_chain(self, []) @property def creating_job(self): creating_job_associations = None if self.creating_job_associations: creating_job_associations = self.creating_job_associations else: inherit_chain = self.source_dataset_chain if inherit_chain: creating_job_associations = inherit_chain[-1][0].creating_job_associations if creating_job_associations: return creating_job_associations[0].job return None
[docs] def get_display_applications(self, trans): return self.datatype.get_display_applications_by_dataset(self, trans)
[docs] def get_visualizations(self): return self.datatype.get_visualizations(self)
[docs] def get_datasources(self, trans): """ Returns datasources for dataset; if datasources are not available due to indexing, indexing is started. Return value is a dictionary with entries of type (<datasource_type> : {<datasource_name>, <indexing_message>}). """ data_sources_dict = {} msg = None for source_type, source_list in self.datatype.data_sources.items(): data_source = None if source_type == "data_standalone": # Nothing to do. msg = None data_source = source_list else: # Convert. if isinstance(source_list, string_types): source_list = [source_list] # Loop through sources until viable one is found. for source in source_list: msg = self.convert_dataset(trans, source) # No message or PENDING means that source is viable. No # message indicates conversion was done and is successful. if not msg or msg == self.conversion_messages.PENDING: data_source = source break # Store msg. data_sources_dict[source_type] = {"name": data_source, "message": msg} return data_sources_dict
[docs] def convert_dataset(self, trans, target_type): """ Converts a dataset to the target_type and returns a message indicating status of the conversion. None is returned to indicate that dataset was converted successfully. """ # Get converted dataset; this will start the conversion if necessary. try: converted_dataset = self.get_converted_dataset(trans, target_type) except NoConverterException: return self.conversion_messages.NO_CONVERTER except ConverterDependencyException as dep_error: return {'kind': self.conversion_messages.ERROR, 'message': dep_error.value} # Check dataset state and return any messages. msg = None if converted_dataset and converted_dataset.state == Dataset.states.ERROR: job_id = trans.sa_session.query(JobToOutputDatasetAssociation) \ .filter_by(dataset_id=converted_dataset.id).first().job_id job = trans.sa_session.query(Job).get(job_id) msg = {'kind': self.conversion_messages.ERROR, 'message': job.stderr} elif not converted_dataset or converted_dataset.state != Dataset.states.OK: msg = self.conversion_messages.PENDING return msg
[docs] def serialize(self, id_encoder, serialization_options, for_link=False): if for_link: rval = dict_for( self ) serialization_options.attach_identifier(id_encoder, self, rval) return rval metadata = _prepare_metadata_for_serialization(id_encoder, serialization_options, self.metadata) rval = dict_for( self, create_time=self.create_time.__str__(), update_time=self.update_time.__str__(), name=unicodify(self.name), info=unicodify(self.info), blurb=self.blurb, peek=self.peek, extension=self.extension, metadata=metadata, designation=self.designation, deleted=self.deleted, visible=self.visible, dataset_uuid=(lambda uuid: str(uuid) if uuid else None)(self.dataset.uuid), ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
def _handle_serialize_files(self, id_encoder, serialization_options, rval): if serialization_options.serialize_dataset_objects: rval["dataset"] = self.dataset.serialize(id_encoder, serialization_options) else: serialization_options.serialize_files(self, rval)
[docs]class HistoryDatasetAssociation(DatasetInstance, HasTags, Dictifiable, UsesAnnotations, HasName, RepresentById): """ Resource class that creates a relation between a dataset and a user history. """ def __init__(self, hid=None, history=None, copied_from_history_dataset_association=None, copied_from_library_dataset_dataset_association=None, sa_session=None, **kwd): """ Create a a new HDA and associate it with the given history. """ # FIXME: sa_session is must be passed to DataSetInstance if the create_dataset # parameter is True so that the new object can be flushed. Is there a better way? DatasetInstance.__init__(self, sa_session=sa_session, **kwd) self.hid = hid # Relationships self.history = history self.copied_from_history_dataset_association = copied_from_history_dataset_association self.copied_from_library_dataset_dataset_association = copied_from_library_dataset_dataset_association def __create_version__(self, session): state = inspect(self) changes = {} for attr in state.mapper.columns: # We only create a new version if columns of the HDA table have changed, and ignore relationships. hist = state.get_history(attr.key, True) if not hist.has_changes(): continue # hist.deleted holds old value(s) changes[attr.key] = hist.deleted if self.update_time and self.state == self.states.OK: # We only record changes to HDAs that exist in the database and have a update_time new_values = {} new_values['name'] = changes.get('name', self.name) new_values['dbkey'] = changes.get('dbkey', self.dbkey) new_values['extension'] = changes.get('extension', self.extension) new_values['extended_metadata_id'] = changes.get('extended_metadata_id', self.extended_metadata_id) for k, v in new_values.items(): if isinstance(v, list): new_values[k] = v[0] new_values['update_time'] = self.update_time new_values['version'] = self.version or 1 new_values['metadata'] = self._metadata past_hda = HistoryDatasetAssociationHistory(history_dataset_association_id=self.id, **new_values) self.version = self.version + 1 if self.version else 1 session.add(past_hda)
[docs] def copy(self, parent_id=None, copy_tags=None, force_flush=True, copy_hid=True, new_name=None): """ Create a copy of this HDA. """ hid = None if copy_hid: hid = self.hid hda = HistoryDatasetAssociation(hid=hid, name=new_name or self.name, info=self.info, blurb=self.blurb, peek=self.peek, tool_version=self.tool_version, extension=self.extension, dbkey=self.dbkey, dataset=self.dataset, visible=self.visible, deleted=self.deleted, parent_id=parent_id, copied_from_history_dataset_association=self, flush=False) # update init non-keywords as well hda.purged = self.purged hda.copy_tags_to(copy_tags) object_session(self).add(hda) flushed = False # May need to set after flushed, as MetadataFiles require dataset.id if hda.set_metadata_requires_flush: object_session(self).flush([self]) flushed = True hda.metadata = self.metadata # In some instances peek relies on dataset_id, i.e. gmaj.zip for viewing MAFs if not self.datatype.copy_safe_peek: if not flushed: object_session(self).flush([self]) hda.set_peek() if force_flush: object_session(self).flush() return hda
[docs] def copy_tags_to(self, copy_tags=None): if copy_tags is not None: for tag in copy_tags.values(): copied_tag = tag.copy(cls=HistoryDatasetAssociationTagAssociation) self.tags.append(copied_tag)
[docs] def copy_attributes(self, new_dataset): new_dataset.hid = self.hid
[docs] def to_library_dataset_dataset_association(self, trans, target_folder, replace_dataset=None, parent_id=None, user=None, roles=None, ldda_message='', element_identifier=None): """ Copy this HDA to a library optionally replacing an existing LDDA. """ if replace_dataset: # The replace_dataset param ( when not None ) refers to a LibraryDataset that # is being replaced with a new version. library_dataset = replace_dataset else: # If replace_dataset is None, the Library level permissions will be taken from the folder and # applied to the new LibraryDataset, and the current user's DefaultUserPermissions will be applied # to the associated Dataset. library_dataset = LibraryDataset(folder=target_folder, name=self.name, info=self.info) object_session(self).add(library_dataset) object_session(self).flush() if not user: # This should never happen since users must be authenticated to upload to a data library user = self.history.user ldda = LibraryDatasetDatasetAssociation(name=element_identifier or self.name, info=self.info, blurb=self.blurb, peek=self.peek, tool_version=self.tool_version, extension=self.extension, dbkey=self.dbkey, dataset=self.dataset, library_dataset=library_dataset, visible=self.visible, deleted=self.deleted, parent_id=parent_id, copied_from_history_dataset_association=self, user=user) object_session(self).add(ldda) object_session(self).flush() # If roles were selected on the upload form, restrict access to the Dataset to those roles roles = roles or [] for role in roles: dp = trans.model.DatasetPermissions(trans.app.security_agent.permitted_actions.DATASET_ACCESS.action, ldda.dataset, role) trans.sa_session.add(dp) trans.sa_session.flush() # Must set metadata after ldda flushed, as MetadataFiles require ldda.id ldda.metadata = self.metadata # TODO: copy #tags from history if ldda_message: ldda.message = ldda_message if not replace_dataset: target_folder.add_library_dataset(library_dataset, genome_build=ldda.dbkey) object_session(self).add(target_folder) object_session(self).flush() library_dataset.library_dataset_dataset_association_id = ldda.id object_session(self).add(library_dataset) object_session(self).flush() if not self.datatype.copy_safe_peek: # In some instances peek relies on dataset_id, i.e. gmaj.zip for viewing MAFs ldda.set_peek() object_session(self).flush() return ldda
[docs] def clear_associated_files(self, metadata_safe=False, purge=False): """ """ # metadata_safe = True means to only clear when assoc.metadata_safe == False for assoc in self.implicitly_converted_datasets: if not assoc.deleted and (not metadata_safe or not assoc.metadata_safe): assoc.clear(purge=purge) for assoc in self.implicitly_converted_parent_datasets: assoc.clear(purge=purge, delete_dataset=False)
[docs] def get_access_roles(self, trans): """ Return The access roles associated with this HDA's dataset. """ return self.dataset.get_access_roles(trans)
[docs] def quota_amount(self, user): """ Return the disk space used for this HDA relevant to user quotas. If the user has multiple instances of this dataset, it will not affect their disk usage statistic. """ rval = 0 # Anon users are handled just by their single history size. if not user: return rval # Gets an HDA disk usage, if the user does not already # have an association of the same dataset if not self.dataset.library_associations and not self.purged and not self.dataset.purged: for hda in self.dataset.history_associations: if hda.id == self.id: continue if not hda.purged and hda.history and hda.history.user and hda.history.user == user: break else: rval += self.get_total_size() return rval
[docs] def serialize(self, id_encoder, serialization_options, for_link=False): if for_link: rval = dict_for( self ) serialization_options.attach_identifier(id_encoder, self, rval) return rval rval = super(HistoryDatasetAssociation, self).serialize(id_encoder, serialization_options) rval["hid"] = self.hid rval["annotation"] = unicodify(getattr(self, 'annotation', '')) rval["tags"] = self.make_tag_string_list() if self.history: rval["history_encoded_id"] = serialization_options.get_identifier(id_encoder, self.history) # Handle copied_from_history_dataset_association information... copied_from_history_dataset_association_chain = [] src_hda = self while src_hda.copied_from_history_dataset_association: src_hda = src_hda.copied_from_history_dataset_association copied_from_history_dataset_association_chain.append(serialization_options.get_identifier(id_encoder, src_hda)) rval["copied_from_history_dataset_association_id_chain"] = copied_from_history_dataset_association_chain self._handle_serialize_files(id_encoder, serialization_options, rval) return rval
[docs] def to_dict(self, view='collection', expose_dataset_path=False): """ Return attributes of this HDA that are exposed using the API. """ # Since this class is a proxy to rather complex attributes we want to # display in other objects, we can't use the simpler method used by # other model classes. original_rval = super(HistoryDatasetAssociation, self).to_dict(view=view) hda = self rval = dict(id=hda.id, hda_ldda='hda', uuid=(lambda uuid: str(uuid) if uuid else None)(hda.dataset.uuid), hid=hda.hid, file_ext=hda.ext, peek=unicodify(hda.display_peek()) if hda.peek and hda.peek != 'no peek' else None, model_class=self.__class__.__name__, name=hda.name, deleted=hda.deleted, purged=hda.purged, visible=hda.visible, state=hda.state, history_content_type=hda.history_content_type, file_size=int(hda.get_size()), create_time=hda.create_time.isoformat(), update_time=hda.update_time.isoformat(), data_type=hda.datatype.__class__.__module__ + '.' + hda.datatype.__class__.__name__, genome_build=hda.dbkey, misc_info=hda.info.strip() if isinstance(hda.info, string_types) else hda.info, misc_blurb=hda.blurb) rval.update(original_rval) if hda.copied_from_library_dataset_dataset_association is not None: rval['copied_from_ldda_id'] = hda.copied_from_library_dataset_dataset_association.id if hda.history is not None: rval['history_id'] = hda.history.id if hda.extended_metadata is not None: rval['extended_metadata'] = hda.extended_metadata.data for name, spec in hda.metadata.spec.items(): val = hda.metadata.get(name) if isinstance(val, MetadataFile): # only when explicitly set: fetching filepaths can be expensive if not expose_dataset_path: continue val = val.file_name # If no value for metadata, look in datatype for metadata. elif val is None and hasattr(hda.datatype, name): val = getattr(hda.datatype, name) rval['metadata_' + name] = val return rval
[docs] def unpause_dependent_jobs(self, jobs=None): if self.state == self.states.PAUSED: self.state = self.states.NEW self.info = None jobs_to_unpause = jobs or set() for jtida in self.dependent_jobs: if jtida.job not in jobs_to_unpause: jobs_to_unpause.add(jtida.job) for jtoda in jtida.job.output_datasets: jobs_to_unpause.update( jtoda.dataset.unpause_dependent_jobs(jobs=jobs_to_unpause) ) return jobs_to_unpause
@property def history_content_type(self): return "dataset" # TODO: down into DatasetInstance content_type = u'dataset' @hybrid.hybrid_property def type_id(self): return u'-'.join([self.content_type, str(self.id)]) @type_id.expression def type_id(cls): return ((type_coerce(cls.content_type, types.Unicode) + u'-' + type_coerce(cls.id, types.Unicode)).label('type_id'))
[docs]class HistoryDatasetAssociationHistory(RepresentById): def __init__(self, history_dataset_association_id, name, dbkey, update_time, version, extension, extended_metadata_id, metadata, ): self.history_dataset_association_id = history_dataset_association_id self.name = name self.dbkey = dbkey self.update_time = update_time self.version = version self.extension = extension self.extended_metadata_id = extended_metadata_id self._metadata = metadata
[docs]class HistoryDatasetAssociationDisplayAtAuthorization(RepresentById): def __init__(self, hda=None, user=None, site=None): self.history_dataset_association = hda self.user = user self.site = site
[docs]class HistoryDatasetAssociationSubset(RepresentById): def __init__(self, hda, subset, location): self.hda = hda self.subset = subset self.location = location
[docs]class Library(Dictifiable, HasName, RepresentById): permitted_actions = get_permitted_actions(filter='LIBRARY') dict_collection_visible_keys = ['id', 'name'] dict_element_visible_keys = ['id', 'deleted', 'name', 'description', 'synopsis', 'root_folder_id', 'create_time'] def __init__(self, name=None, description=None, synopsis=None, root_folder=None): self.name = name or "Unnamed library" self.description = description self.synopsis = synopsis self.root_folder = root_folder
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, name=self.name, description=self.description, synopsis=self.synopsis, ) if self.root_folder: rval["root_folder"] = self.root_folder.serialize(id_encoder, serialization_options) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs] def to_dict(self, view='collection', value_mapper=None): """ We prepend an F to folders. """ rval = super(Library, self).to_dict(view=view, value_mapper=value_mapper) if 'root_folder_id' in rval: rval['root_folder_id'] = 'F' + str(rval['root_folder_id']) return rval
[docs] def get_active_folders(self, folder, folders=None): # TODO: should we make sure the library is not deleted? def sort_by_attr(seq, attr): """ Sort the sequence of objects by object's attribute Arguments: seq - the list or any sequence (including immutable one) of objects to sort. attr - the name of attribute to sort by """ # Use the "Schwartzian transform" # Create the auxiliary list of tuples where every i-th tuple has form # (seq[i].attr, i, seq[i]) and sort it. The second item of tuple is needed not # only to provide stable sorting, but mainly to eliminate comparison of objects # (which can be expensive or prohibited) in case of equal attribute values. intermed = [(getattr(v, attr), i, v) for i, v in enumerate(seq)] intermed.sort() return [_[-1] for _ in intermed] if folders is None: active_folders = [folder] for active_folder in folder.active_folders: active_folders.extend(self.get_active_folders(active_folder, folders)) return sort_by_attr(active_folders, 'id')
[docs] def get_access_roles(self, trans): roles = [] for lp in self.actions: if lp.action == trans.app.security_agent.permitted_actions.LIBRARY_ACCESS.action: roles.append(lp.role) return roles
[docs]class LibraryFolder(Dictifiable, HasName, RepresentById): dict_element_visible_keys = ['id', 'parent_id', 'name', 'description', 'item_count', 'genome_build', 'update_time', 'deleted'] def __init__(self, name=None, description=None, item_count=0, order_id=None, genome_build=None): self.name = name or "Unnamed folder" self.description = description self.item_count = item_count self.order_id = order_id self.genome_build = genome_build self.folders = [] self.datasets = []
[docs] def add_library_dataset(self, library_dataset, genome_build=None): library_dataset.folder_id = self.id library_dataset.order_id = self.item_count self.item_count += 1 if genome_build not in [None, '?']: self.genome_build = genome_build
[docs] def add_folder(self, folder): folder.parent_id = self.id folder.order_id = self.item_count self.item_count += 1
@property def activatable_library_datasets(self): # This needs to be a list return [ld for ld in self.datasets if ld.library_dataset_dataset_association and not ld.library_dataset_dataset_association.dataset.deleted]
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, name=self.name, description=self.description, genome_build=self.genome_build, item_count=self.item_count, order_id=self.order_id, # update_time=self.update_time, deleted=self.deleted, ) folders = [] for folder in self.folders: folders.append(folder.serialize(id_encoder, serialization_options)) rval["folders"] = folders datasets = [] for dataset in self.datasets: datasets.append(dataset.serialize(id_encoder, serialization_options)) rval['datasets'] = datasets serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs] def to_dict(self, view='collection', value_mapper=None): rval = super(LibraryFolder, self).to_dict(view=view, value_mapper=value_mapper) rval['library_path'] = self.library_path rval['parent_library_id'] = self.parent_library.id return rval
@property def library_path(self): l_path = [] f = self while f.parent: l_path.insert(0, f.name) f = f.parent return l_path @property def parent_library(self): f = self while f.parent: f = f.parent return f.library_root[0]
[docs]class LibraryDataset(RepresentById): # This class acts as a proxy to the currently selected LDDA upload_options = [('upload_file', 'Upload files'), ('upload_directory', 'Upload directory of files'), ('upload_paths', 'Upload files from filesystem paths'), ('import_from_history', 'Import datasets from your current history')] def __init__(self, folder=None, order_id=None, name=None, info=None, library_dataset_dataset_association=None, **kwd): self.folder = folder self.order_id = order_id self.name = name self.info = info self.library_dataset_dataset_association = library_dataset_dataset_association
[docs] def set_library_dataset_dataset_association(self, ldda): self.library_dataset_dataset_association = ldda ldda.library_dataset = self object_session(self).add_all((ldda, self)) object_session(self).flush()
[docs] def get_info(self): if self.library_dataset_dataset_association: return self.library_dataset_dataset_association.info elif self._info: return self._info else: return 'no info'
[docs] def set_info(self, info): self._info = info
info = property(get_info, set_info)
[docs] def get_name(self): if self.library_dataset_dataset_association: return self.library_dataset_dataset_association.name elif self._name: return self._name else: return 'Unnamed dataset'
[docs] def set_name(self, name): self._name = name
name = property(get_name, set_name)
[docs] def display_name(self): self.library_dataset_dataset_association.display_name()
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, name=self.name, info=self.info, order_id=self.order_id, ldda=self.library_dataset_dataset_association.serialize(id_encoder, serialization_options, for_link=True), ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs] def to_dict(self, view='collection'): # Since this class is a proxy to rather complex attributes we want to # display in other objects, we can't use the simpler method used by # other model classes. ldda = self.library_dataset_dataset_association rval = dict(id=self.id, ldda_id=ldda.id, parent_library_id=self.folder.parent_library.id, folder_id=self.folder_id, model_class=self.__class__.__name__, state=ldda.state, name=ldda.name, file_name=ldda.file_name, uploaded_by=ldda.user.email, message=ldda.message, date_uploaded=ldda.create_time.isoformat(), file_size=int(ldda.get_size()), file_ext=ldda.ext, data_type=ldda.datatype.__class__.__module__ + '.' + ldda.datatype.__class__.__name__, genome_build=ldda.dbkey, misc_info=ldda.info, misc_blurb=ldda.blurb, peek=(lambda ldda: ldda.display_peek() if ldda.peek and ldda.peek != 'no peek' else None)(ldda)) if ldda.dataset.uuid is None: rval['uuid'] = None else: rval['uuid'] = str(ldda.dataset.uuid) for name, spec in ldda.metadata.spec.items(): val = ldda.metadata.get(name) if isinstance(val, MetadataFile): val = val.file_name elif isinstance(val, list): val = ', '.join([str(v) for v in val]) rval['metadata_' + name] = val return rval
[docs]class LibraryDatasetDatasetAssociation(DatasetInstance, HasName, RepresentById): def __init__(self, copied_from_history_dataset_association=None, copied_from_library_dataset_dataset_association=None, library_dataset=None, user=None, sa_session=None, **kwd): # FIXME: sa_session is must be passed to DataSetInstance if the create_dataset # parameter in kwd is True so that the new object can be flushed. Is there a better way? DatasetInstance.__init__(self, sa_session=sa_session, **kwd) if copied_from_history_dataset_association: self.copied_from_history_dataset_association_id = copied_from_history_dataset_association.id if copied_from_library_dataset_dataset_association: self.copied_from_library_dataset_dataset_association_id = copied_from_library_dataset_dataset_association.id self.library_dataset = library_dataset self.user = user
[docs] def to_history_dataset_association(self, target_history, parent_id=None, add_to_history=False): sa_session = object_session(self) hda = HistoryDatasetAssociation(name=self.name, info=self.info, blurb=self.blurb, peek=self.peek, tool_version=self.tool_version, extension=self.extension, dbkey=self.dbkey, dataset=self.dataset, visible=self.visible, deleted=self.deleted, parent_id=parent_id, copied_from_library_dataset_dataset_association=self, history=target_history) tag_manager = galaxy.model.tags.GalaxyTagHandler(sa_session) src_ldda_tags = tag_manager.get_tags_str(self.tags) tag_manager.apply_item_tags(user=self.user, item=hda, tags_str=src_ldda_tags) sa_session.add(hda) sa_session.flush() hda.metadata = self.metadata # need to set after flushed, as MetadataFiles require dataset.id if add_to_history and target_history: target_history.add_dataset(hda) if not self.datatype.copy_safe_peek: hda.set_peek() # in some instances peek relies on dataset_id, i.e. gmaj.zip for viewing MAFs sa_session.flush() return hda
[docs] def copy(self, parent_id=None, target_folder=None): sa_session = object_session(self) ldda = LibraryDatasetDatasetAssociation(name=self.name, info=self.info, blurb=self.blurb, peek=self.peek, tool_version=self.tool_version, extension=self.extension, dbkey=self.dbkey, dataset=self.dataset, visible=self.visible, deleted=self.deleted, parent_id=parent_id, copied_from_library_dataset_dataset_association=self, folder=target_folder) tag_manager = galaxy.model.tags.GalaxyTagHandler(sa_session) src_ldda_tags = tag_manager.get_tags_str(self.tags) tag_manager.apply_item_tags(user=self.user, item=ldda, tags_str=src_ldda_tags) sa_session.add(ldda) sa_session.flush() # Need to set after flushed, as MetadataFiles require dataset.id ldda.metadata = self.metadata if not self.datatype.copy_safe_peek: # In some instances peek relies on dataset_id, i.e. gmaj.zip for viewing MAFs ldda.set_peek() sa_session.flush() return ldda
[docs] def clear_associated_files(self, metadata_safe=False, purge=False): return
[docs] def get_access_roles(self, trans): return self.dataset.get_access_roles(trans)
[docs] def get_manage_permissions_roles(self, trans): return self.dataset.get_manage_permissions_roles(trans)
[docs] def has_manage_permissions_roles(self, trans): return self.dataset.has_manage_permissions_roles(trans)
[docs] def serialize(self, id_encoder, serialization_options, for_link=False): if for_link: rval = dict_for( self ) serialization_options.attach_identifier(id_encoder, self, rval) return rval rval = super(LibraryDatasetDatasetAssociation, self).serialize(id_encoder, serialization_options) self._handle_serialize_files(id_encoder, serialization_options, rval) return rval
[docs] def to_dict(self, view='collection'): # Since this class is a proxy to rather complex attributes we want to # display in other objects, we can't use the simpler method used by # other model classes. ldda = self try: file_size = int(ldda.get_size()) except OSError: file_size = 0 # TODO: render tags here rval = dict(id=ldda.id, hda_ldda='ldda', model_class=self.__class__.__name__, name=ldda.name, deleted=ldda.deleted, visible=ldda.visible, state=ldda.state, library_dataset_id=ldda.library_dataset_id, file_size=file_size, file_name=ldda.file_name, update_time=ldda.update_time.isoformat(), file_ext=ldda.ext, data_type=ldda.datatype.__class__.__module__ + '.' + ldda.datatype.__class__.__name__, genome_build=ldda.dbkey, misc_info=ldda.info, misc_blurb=ldda.blurb) if ldda.dataset.uuid is None: rval['uuid'] = None else: rval['uuid'] = str(ldda.dataset.uuid) rval['parent_library_id'] = ldda.library_dataset.folder.parent_library.id if ldda.extended_metadata is not None: rval['extended_metadata'] = ldda.extended_metadata.data for name, spec in ldda.metadata.spec.items(): val = ldda.metadata.get(name) if isinstance(val, MetadataFile): val = val.file_name # If no value for metadata, look in datatype for metadata. elif val is None and hasattr(ldda.datatype, name): val = getattr(ldda.datatype, name) rval['metadata_' + name] = val return rval
[docs]class ExtendedMetadata(RepresentById): def __init__(self, data): self.data = data
[docs]class ExtendedMetadataIndex(RepresentById): def __init__(self, extended_metadata, path, value): self.extended_metadata = extended_metadata self.path = path self.value = value
[docs]class LibraryInfoAssociation(RepresentById): def __init__(self, library, form_definition, info, inheritable=False): self.library = library self.template = form_definition self.info = info self.inheritable = inheritable
[docs]class LibraryFolderInfoAssociation(RepresentById): def __init__(self, folder, form_definition, info, inheritable=False): self.folder = folder self.template = form_definition self.info = info self.inheritable = inheritable
[docs]class LibraryDatasetDatasetInfoAssociation(RepresentById): def __init__(self, library_dataset_dataset_association, form_definition, info): # TODO: need to figure out if this should be inheritable to the associated LibraryDataset self.library_dataset_dataset_association = library_dataset_dataset_association self.template = form_definition self.info = info @property def inheritable(self): return True # always allow inheriting, used for replacement
[docs]class ValidationError(RepresentById): def __init__(self, message=None, err_type=None, attributes=None): self.message = message self.err_type = err_type self.attributes = attributes
[docs]class DatasetToValidationErrorAssociation(object):
[docs] def __init__(self, dataset, validation_error): self.dataset = dataset self.validation_error = validation_error
[docs]class ImplicitlyConvertedDatasetAssociation(RepresentById): def __init__(self, id=None, parent=None, dataset=None, file_type=None, deleted=False, purged=False, metadata_safe=True): self.id = id if isinstance(dataset, HistoryDatasetAssociation): self.dataset = dataset elif isinstance(dataset, LibraryDatasetDatasetAssociation): self.dataset_ldda = dataset else: raise AttributeError('Unknown dataset type provided for dataset: %s' % type(dataset)) if isinstance(parent, HistoryDatasetAssociation): self.parent_hda = parent elif isinstance(parent, LibraryDatasetDatasetAssociation): self.parent_ldda = parent else: raise AttributeError('Unknown dataset type provided for parent: %s' % type(parent)) self.type = file_type self.deleted = deleted self.purged = purged self.metadata_safe = metadata_safe
[docs] def clear(self, purge=False, delete_dataset=True): self.deleted = True if self.dataset: if delete_dataset: self.dataset.deleted = True if purge: self.dataset.purged = True if purge and self.dataset.deleted: # do something with purging self.purged = True try: os.unlink(self.file_name) except Exception as e: log.error("Failed to purge associated file (%s) from disk: %s" % (self.file_name, e))
DEFAULT_COLLECTION_NAME = "Unnamed Collection"
[docs]class DatasetCollection(Dictifiable, UsesAnnotations, RepresentById): """ """ dict_collection_visible_keys = ['id', 'collection_type'] dict_element_visible_keys = ['id', 'collection_type'] populated_states = Bunch( NEW='new', # New dataset collection, unpopulated elements OK='ok', # Collection elements populated (HDAs may or may not have errors) FAILED='failed', # some problem populating state, won't be populated ) def __init__( self, id=None, collection_type=None, populated=True, element_count=None ): self.id = id self.collection_type = collection_type if not populated: self.populated_state = DatasetCollection.populated_states.NEW self.element_count = element_count @property def dataset_states_and_extensions_summary(self): if not hasattr(self, '_dataset_states_and_extensions_summary'): db_session = object_session(self) dc = alias(DatasetCollection.table) de = alias(DatasetCollectionElement.table) hda = alias(HistoryDatasetAssociation.table) dataset = alias(Dataset.table) select_from = dc.outerjoin(de, de.c.dataset_collection_id == dc.c.id) depth_collection_type = self.collection_type while ":" in depth_collection_type: child_collection = alias(DatasetCollection.table) child_collection_element = alias(DatasetCollectionElement.table) select_from = select_from.outerjoin(child_collection, child_collection.c.id == de.c.child_collection_id) select_from = select_from.outerjoin(child_collection_element, child_collection_element.c.dataset_collection_id == child_collection.c.id) de = child_collection_element depth_collection_type = depth_collection_type.split(":", 1)[1] select_from = select_from.outerjoin(hda, hda.c.id == de.c.hda_id).outerjoin(dataset, hda.c.dataset_id == dataset.c.id) select_stmt = select([hda.c.extension, dataset.c.state]).select_from(select_from).where(dc.c.id == self.id).distinct() extensions = set() states = set() for extension, state in db_session.execute(select_stmt).fetchall(): states.add(state) extensions.add(extension) self._dataset_states_and_extensions_summary = (states, extensions) return self._dataset_states_and_extensions_summary @property def populated_optimized(self): if not hasattr(self, '_populated_optimized'): _populated_optimized = True if ":" not in self.collection_type: _populated_optimized = self.populated_state == DatasetCollection.populated_states.OK else: db_session = object_session(self) dc = alias(DatasetCollection.table) de = alias(DatasetCollectionElement.table) select_from = dc.outerjoin(de, de.c.dataset_collection_id == dc.c.id) collection_depth_aliases = [dc] depth_collection_type = self.collection_type while ":" in depth_collection_type: child_collection = alias(DatasetCollection.table) child_collection_element = alias(DatasetCollectionElement.table) select_from = select_from.outerjoin(child_collection, child_collection.c.id == de.c.child_collection_id) select_from = select_from.outerjoin(child_collection_element, child_collection_element.c.dataset_collection_id == child_collection.c.id) collection_depth_aliases.append(child_collection) de = child_collection_element depth_collection_type = depth_collection_type.split(":", 1)[1] select_stmt = select(list(map(lambda dc: dc.c.populated_state, collection_depth_aliases))).select_from(select_from).where(dc.c.id == self.id).distinct() for populated_states in db_session.execute(select_stmt).fetchall(): for populated_state in populated_states: if populated_state != DatasetCollection.populated_states.OK: _populated_optimized = False self._populated_optimized = _populated_optimized return self._populated_optimized @property def populated(self): top_level_populated = self.populated_state == DatasetCollection.populated_states.OK if top_level_populated and self.has_subcollections: return all(e.child_collection.populated for e in self.elements) return top_level_populated @property def dataset_action_tuples(self): if not hasattr(self, '_dataset_action_tuples'): db_session = object_session(self) dc = alias(DatasetCollection.table) de = alias(DatasetCollectionElement.table) hda = alias(HistoryDatasetAssociation.table) dataset = alias(Dataset.table) dataset_permission = alias(DatasetPermissions.table) select_from = dc.outerjoin(de, de.c.dataset_collection_id == dc.c.id) depth_collection_type = self.collection_type while ":" in depth_collection_type: child_collection = alias(DatasetCollection.table) child_collection_element = alias(DatasetCollectionElement.table) select_from = select_from.outerjoin(child_collection, child_collection.c.id == de.c.child_collection_id) select_from = select_from.outerjoin(child_collection_element, child_collection_element.c.dataset_collection_id == child_collection.c.id) de = child_collection_element depth_collection_type = depth_collection_type.split(":", 1)[1] select_from = select_from.outerjoin(hda, hda.c.id == de.c.hda_id).outerjoin(dataset, hda.c.dataset_id == dataset.c.id) select_from = select_from.outerjoin(dataset_permission, dataset.c.id == dataset_permission.c.dataset_id) select_stmt = select([dataset_permission.c.action, dataset_permission.c.role_id]).select_from(select_from).where(dc.c.id == self.id).distinct() _dataset_action_tuples = [] for _dataset_action_tuple in db_session.execute(select_stmt).fetchall(): if _dataset_action_tuple[0] is None: continue _dataset_action_tuples.append(_dataset_action_tuple) self._dataset_action_tuples = _dataset_action_tuples return self._dataset_action_tuples @property def waiting_for_elements(self): top_level_waiting = self.populated_state == DatasetCollection.populated_states.NEW if not top_level_waiting and self.has_subcollections: return any(e.child_collection.waiting_for_elements for e in self.elements) return top_level_waiting
[docs] def mark_as_populated(self): self.populated_state = DatasetCollection.populated_states.OK
[docs] def handle_population_failed(self, message): self.populated_state = DatasetCollection.populated_states.FAILED self.populated_state_message = message
[docs] def finalize(self, collection_type_description): # All jobs have written out their elements - everything should be populated # but might not be - check that second case! (TODO) self.mark_as_populated() if self.has_subcollections and collection_type_description.has_subcollections(): for element in self.elements: element.child_collection.finalize(collection_type_description.child_collection_type_description())
@property def dataset_instances(self): instances = [] for element in self.elements: if element.is_collection: instances.extend(element.child_collection.dataset_instances) else: instance = element.dataset_instance instances.append(instance) return instances @property def dataset_elements(self): elements = [] for element in self.elements: if element.is_collection: elements.extend(element.child_collection.dataset_elements) else: elements.append(element) return elements @property def first_dataset_element(self): for element in self.elements: if element.is_collection: first_element = element.child_collection.first_dataset_element if first_element: return first_element else: return element return None @property def state(self): # TODO: DatasetCollection state handling... return 'ok'
[docs] def validate(self): if self.collection_type is None: raise Exception("Each dataset collection must define a collection type.")
def __getitem__(self, key): get_by_attribute = "element_index" if isinstance(key, int) else "element_identifier" for element in self.elements: if getattr(element, get_by_attribute) == key: return element error_message = "Dataset collection has no %s with key %s." % (get_by_attribute, key) raise KeyError(error_message)
[docs] def copy(self, destination=None, element_destination=None): new_collection = DatasetCollection( collection_type=self.collection_type, element_count=self.element_count ) for element in self.elements: element.copy_to_collection( new_collection, destination=destination, element_destination=element_destination, ) object_session(self).add(new_collection) object_session(self).flush() return new_collection
[docs] def replace_failed_elements(self, replacements): for element in self.elements: if element.element_object in replacements: if element.element_type == 'hda': element.hda = replacements[element.element_object] element.hda.visible = False
# TODO: handle the case where elements are collections
[docs] def set_from_dict(self, new_data): # Nothing currently editable in this class. return {}
@property def has_subcollections(self): return ":" in self.collection_type
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, type=self.collection_type, populated_state=self.populated_state, elements=list(map(lambda e: e.serialize(id_encoder, serialization_options), self.elements)) ) serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs]class DatasetCollectionInstance(HasName): """ """
[docs] def __init__( self, collection=None, deleted=False, ): # Relationships self.collection = collection # Since deleted property is shared between history and dataset collections, # it could be on either table - some places in the code however it is convient # it is on instance instead of collection. self.deleted = deleted
@property def state(self): return self.collection.state @property def populated(self): return self.collection.populated @property def dataset_instances(self): return self.collection.dataset_instances
[docs] def display_name(self): return self.get_display_name()
def _base_to_dict(self, view): return dict( id=self.id, name=self.name, collection_type=self.collection.collection_type, populated=self.populated, populated_state=self.collection.populated_state, populated_state_message=self.collection.populated_state_message, element_count=self.collection.element_count, type="collection", # contents type (distinguished from file or folder (in case of library)) )
[docs] def set_from_dict(self, new_data): """ Set object attributes to the values in dictionary new_data limiting to only those keys in dict_element_visible_keys. Returns a dictionary of the keys, values that have been changed. """ # precondition: keys are proper, values are parsed and validated changed = self.collection.set_from_dict(new_data) # unknown keys are ignored here for key in (k for k in new_data.keys() if k in self.editable_keys): new_val = new_data[key] old_val = self.__getattribute__(key) if new_val == old_val: continue self.__setattr__(key, new_val) changed[key] = new_val return changed
[docs]class HistoryDatasetCollectionAssociation(DatasetCollectionInstance, HasTags, Dictifiable, UsesAnnotations, RepresentById): """ Associates a DatasetCollection with a History. """ editable_keys = ('name', 'deleted', 'visible') def __init__( self, id=None, hid=None, collection=None, history=None, name=None, deleted=False, visible=True, copied_from_history_dataset_collection_association=None, implicit_output_name=None, implicit_input_collections=[], ): super(HistoryDatasetCollectionAssociation, self).__init__( collection=collection, deleted=deleted, ) self.id = id self.hid = hid self.history = history self.name = name self.visible = visible self.copied_from_history_dataset_collection_association = copied_from_history_dataset_collection_association self.implicit_output_name = implicit_output_name self.implicit_input_collections = implicit_input_collections @property def history_content_type(self): return "dataset_collection" # TODO: down into DatasetCollectionInstance content_type = u'dataset_collection' @hybrid.hybrid_property def type_id(self): return u'-'.join([self.content_type, str(self.id)]) @type_id.expression def type_id(cls): return ((type_coerce(cls.content_type, types.Unicode) + u'-' + type_coerce(cls.id, types.Unicode)).label('type_id')) @property def job_source_type(self): if self.implicit_collection_jobs_id: return "ImplicitCollectionJobs" elif self.job_id: return "Job" else: return None @property def job_source_id(self): return self.implicit_collection_jobs_id or self.job_id
[docs] def to_hda_representative(self, multiple=False): rval = [] for dataset in self.collection.dataset_elements: rval.append(dataset.dataset_instance) if multiple is False: break if len(rval) > 0: return rval if multiple else rval[0]
[docs] def serialize(self, id_encoder, serialization_options, for_link=False): if for_link: rval = dict_for( self ) serialization_options.attach_identifier(id_encoder, self, rval) return rval rval = dict_for( self, display_name=self.display_name(), state=self.state, hid=self.hid, collection=self.collection.serialize(id_encoder, serialization_options), implicit_output_name=self.implicit_output_name, ) if self.history: rval["history_encoded_id"] = serialization_options.get_identifier(id_encoder, self.history) implicit_input_collections = [] for implicit_input_collection in self.implicit_input_collections: input_hdca = implicit_input_collection.input_dataset_collection implicit_input_collections.append({ "name": implicit_input_collection.name, "input_dataset_collection": serialization_options.get_identifier(id_encoder, input_hdca) }) if implicit_input_collections: rval["implicit_input_collections"] = implicit_input_collections # Handle copied_from_history_dataset_association information... copied_from_history_dataset_collection_association_chain = [] src_hdca = self while src_hdca.copied_from_history_dataset_collection_association: src_hdca = src_hdca.copied_from_history_dataset_collection_association copied_from_history_dataset_collection_association_chain.append(serialization_options.get_identifier(id_encoder, src_hdca)) rval["copied_from_history_dataset_collection_association_id_chain"] = copied_from_history_dataset_collection_association_chain serialization_options.attach_identifier(id_encoder, self, rval) return rval
[docs] def to_dict(self, view='collection'): original_dict_value = super(HistoryDatasetCollectionAssociation, self).to_dict(view=view) dict_value = dict( hid=self.hid, history_id=self.history.id, history_content_type=self.history_content_type, visible=self.visible, deleted=self.deleted, job_source_id=self.job_source_id, job_source_type=self.job_source_type, **self._base_to_dict(view=view) ) dict_value.update(original_dict_value) return dict_value
[docs] def add_implicit_input_collection(self, name, history_dataset_collection): self.implicit_input_collections.append(ImplicitlyCreatedDatasetCollectionInput(name, history_dataset_collection))
[docs] def find_implicit_input_collection(self, name): matching_collection = None for implicit_input_collection in self.implicit_input_collections: if implicit_input_collection.name == name: matching_collection = implicit_input_collection.input_dataset_collection break return matching_collection
[docs] def copy(self, element_destination=None): """ Create a copy of this history dataset collection association. Copy underlying collection. """ hdca = HistoryDatasetCollectionAssociation( hid=self.hid, collection=None, visible=self.visible, deleted=self.deleted, name=self.name, copied_from_history_dataset_collection_association=self, ) if self.implicit_collection_jobs_id: hdca.implicit_collection_jobs_id = self.implicit_collection_jobs_id elif self.job_id: hdca.job_id = self.job_id collection_copy = self.collection.copy( destination=hdca, element_destination=element_destination, ) hdca.collection = collection_copy object_session(self).add(hdca) object_session(self).flush() return hdca
[docs]class LibraryDatasetCollectionAssociation(DatasetCollectionInstance, RepresentById): """ Associates a DatasetCollection with a library folder. """ editable_keys = ('name', 'deleted') def __init__( self, id=None, collection=None, name=None, deleted=False, folder=None, ): super(LibraryDatasetCollectionAssociation, self).__init__( collection=collection, deleted=deleted, ) self.id = id self.folder = folder self.name = name
[docs] def to_dict(self, view='collection'): dict_value = dict( folder_id=self.folder.id, **self._base_to_dict(view=view) ) return dict_value
[docs]class DatasetCollectionElement(Dictifiable, RepresentById): """ Associates a DatasetInstance (hda or ldda) with a DatasetCollection. """ # actionable dataset id needs to be available via API... dict_collection_visible_keys = ['id', 'element_type', 'element_index', 'element_identifier'] dict_element_visible_keys = ['id', 'element_type', 'element_index', 'element_identifier'] UNINITIALIZED_ELEMENT = object() def __init__( self, id=None, collection=None, element=None, element_index=None, element_identifier=None, ): if isinstance(element, HistoryDatasetAssociation): self.hda = element elif isinstance(element, LibraryDatasetDatasetAssociation): self.ldda = element elif isinstance(element, DatasetCollection): self.child_collection = element elif element != self.UNINITIALIZED_ELEMENT: raise AttributeError('Unknown element type provided: %s' % type(element)) self.id = id self.collection = collection self.element_index = element_index self.element_identifier = element_identifier or str(element_index) @property def element_type(self): if self.hda: return "hda" elif self.ldda: return "ldda" elif self.child_collection: # TOOD: Rename element_type to element_type. return "dataset_collection" else: return None @property def is_collection(self): return self.element_type == "dataset_collection" @property def element_object(self): if self.hda: return self.hda elif self.ldda: return self.ldda elif self.child_collection: return self.child_collection else: return None @property def dataset_instance(self): element_object = self.element_object if isinstance(element_object, DatasetCollection): raise AttributeError("Nested collection has no associated dataset_instance.") return element_object @property def dataset(self): return self.dataset_instance.dataset
[docs] def first_dataset_instance(self): element_object = self.element_object if isinstance(element_object, DatasetCollection): return element_object.dataset_instances[0] else: return element_object
@property def dataset_instances(self): element_object = self.element_object if isinstance(element_object, DatasetCollection): return element_object.dataset_instances else: return [element_object]
[docs] def copy_to_collection(self, collection, destination=None, element_destination=None): element_object = self.element_object if element_destination: if self.is_collection: element_object = element_object.copy( destination=destination, element_destination=element_destination ) else: new_element_object = element_object.copy() if destination is not None and element_object.hidden_beneath_collection_instance: new_element_object.hidden_beneath_collection_instance = destination # Ideally we would not need to give the following # element an HID and it would exist in the history only # as an element of the containing collection. element_destination.add_dataset(new_element_object) element_object = new_element_object new_element = DatasetCollectionElement( element=element_object, collection=collection, element_index=self.element_index, element_identifier=self.element_identifier, ) return new_element
[docs] def serialize(self, id_encoder, serialization_options): rval = dict_for( self, element_type=self.element_type, element_index=self.element_index, element_identifier=self.element_identifier ) serialization_options.attach_identifier(id_encoder, self, rval) element_obj = self.element_object if isinstance(element_obj, HistoryDatasetAssociation): rval["hda"] = element_obj.serialize(id_encoder, serialization_options, for_link=True) else: rval["child_collection"] = element_obj.serialize(id_encoder, serialization_options) return rval
[docs]class Event(RepresentById): def __init__(self, message=None, history=None, user=None, galaxy_session=None): self.history = history self.galaxy_session = galaxy_session self.user = user self.tool_id = None self.message = message
[docs]class GalaxySession(RepresentById): def __init__(self, id=None, user=None, remote_host=None, remote_addr=None, referer=None, current_history=None, session_key=None, is_valid=False, prev_session_id=None, last_action=None): self.id = id self.user = user self.remote_host = remote_host self.remote_addr = remote_addr self.referer = referer self.current_history = current_history self.session_key = session_key self.is_valid = is_valid self.prev_session_id = prev_session_id self.histories = [] self.last_action = last_action or datetime.now()
[docs] def add_history(self, history, association=None): if association is None: self.histories.append(GalaxySessionToHistoryAssociation(self, history)) else: self.histories.append(association)
[docs] def get_disk_usage(self): if self.disk_usage is None: return 0 return self.disk_usage
[docs] def set_disk_usage(self, bytes): self.disk_usage = bytes
total_disk_usage = property(get_disk_usage, set_disk_usage)
[docs]class GalaxySessionToHistoryAssociation(RepresentById): def __init__(self, galaxy_session, history): self.galaxy_session = galaxy_session self.history = history
[docs]class UCI(object):
[docs] def __init__(self): self.id = None self.user = None
[docs]class StoredWorkflow(HasTags, Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'name', 'published', 'deleted'] dict_element_visible_keys = ['id', 'name', 'published', 'deleted'] def __init__(self): self.id = None self.user = None self.name = None self.slug = None self.published = False self.latest_workflow_id = None self.workflows = []
[docs] def get_internal_version(self, version): if version is None: return self.latest_workflow if len(self.workflows) <= version: raise Exception("Version does not exist") return list(reversed(self.workflows))[version]
[docs] def show_in_tool_panel(self, user_id): sa_session = object_session(self) return bool(sa_session.query(StoredWorkflowMenuEntry).filter( StoredWorkflowMenuEntry.stored_workflow_id == self.id, StoredWorkflowMenuEntry.user_id == user_id, ).count())
[docs] def copy_tags_from(self, target_user, source_workflow): # Override to only copy owner tags. for src_swta in source_workflow.owner_tags: new_swta = src_swta.copy() new_swta.user = target_user self.tags.append(new_swta)
[docs] def to_dict(self, view='collection', value_mapper=None): rval = super(StoredWorkflow, self).to_dict(view=view, value_mapper=value_mapper) rval['latest_workflow_uuid'] = (lambda uuid: str(uuid) if self.latest_workflow.uuid else None)(self.latest_workflow.uuid) return rval
[docs]class Workflow(Dictifiable, RepresentById): dict_collection_visible_keys = ['name', 'has_cycles', 'has_errors'] dict_element_visible_keys = ['name', 'has_cycles', 'has_errors'] input_step_types = ['data_input', 'data_collection_input', 'parameter_input'] def __init__(self, uuid=None): self.user = None self.name = None self.has_cycles = None self.has_errors = None self.steps = [] if uuid is None: self.uuid = uuid4() else: self.uuid = UUID(str(uuid))
[docs] def has_outputs_defined(self): """ Returns true or false indicating whether or not a workflow has outputs defined. """ for step in self.steps: if step.workflow_outputs: return True return False
[docs] def to_dict(self, view='collection', value_mapper=None): rval = super(Workflow, self).to_dict(view=view, value_mapper=value_mapper) rval['uuid'] = (lambda uuid: str(uuid) if uuid else None)(self.uuid) return rval
@property def steps_by_id(self): steps = {} for step in self.steps: step_id = step.id steps[step_id] = step return steps
[docs] def step_by_index(self, order_index): for step in self.steps: if order_index == step.order_index: return step raise KeyError("Workflow has no step with order_index '%s'" % order_index)
@property def input_steps(self): for step in self.steps: if step.type in Workflow.input_step_types: yield step @property def workflow_outputs(self): for step in self.steps: for workflow_output in step.workflow_outputs: yield workflow_output @property def top_level_workflow(self): """ If this workflow is not attached to stored workflow directly, recursively grab its parents until it is the top level workflow which must have a stored workflow associated with it. """ top_level_workflow = self if self.stored_workflow is None: # TODO: enforce this at creation... assert len(set(w.uuid for w in self.parent_workflow_steps)) == 1 return self.parent_workflow_steps[0].workflow.top_level_workflow return top_level_workflow @property def top_level_stored_workflow(self): """ If this workflow is not attached to stored workflow directly, recursively grab its parents until it is the top level workflow which must have a stored workflow associated with it and then grab that stored workflow. """ return self.top_level_workflow.stored_workflow
[docs] def copy(self): """ Copy a workflow (without user information) for a new StoredWorkflow object. """ copied_workflow = Workflow() copied_workflow.name = self.name copied_workflow.has_cycles = self.has_cycles copied_workflow.has_errors = self.has_errors # Map old step ids to new steps step_mapping = {} copied_steps = [] for step in self.steps: copied_step = WorkflowStep() copied_steps.append(copied_step) step_mapping[step.id] = copied_step for old_step, new_step in zip(self.steps, copied_steps): old_step.copy_to(new_step, step_mapping) copied_workflow.steps = copied_steps return copied_workflow
[docs] def log_str(self): extra = "" if self.stored_workflow: extra = ",name=%s" % self.stored_workflow.name return "Workflow[id=%d%s]" % (self.id, extra)
[docs]class WorkflowStep(RepresentById): def __init__(self): self.id = None self.type = None self.tool_id = None self.tool_inputs = None self.tool_errors = None self.dynamic_tool = None self.position = None self.inputs = [] self.config = None self.label = None self.uuid = uuid4() self.workflow_outputs = [] self._input_connections_by_name = None @property def tool_uuid(self): return self.dynamic_tool and self.dynamic_tool.uuid
[docs] def get_input(self, input_name): for step_input in self.inputs: if step_input.name == input_name: return step_input return None
[docs] def get_or_add_input(self, input_name): step_input = self.get_input(input_name) if step_input is None: step_input = WorkflowStepInput(self) step_input.name = input_name return step_input
[docs] def add_connection(self, input_name, output_name, output_step, input_subworkflow_step_index=None): step_input = self.get_or_add_input(input_name) conn = WorkflowStepConnection() conn.input_step_input = step_input conn.output_name = output_name conn.output_step = output_step if input_subworkflow_step_index is not None: input_subworkflow_step = self.subworkflow.step_by_index(input_subworkflow_step_index) conn.input_subworkflow_step = input_subworkflow_step return conn
@property def input_connections(self): connections = [_ for step_input in self.inputs for _ in step_input.connections] return connections @property def unique_workflow_outputs(self): # Older Galaxy workflows may have multiple WorkflowOutputs # per "output_name", when serving these back to the editor # feed only a "best" output per "output_name."" outputs = {} for workflow_output in self.workflow_outputs: output_name = workflow_output.output_name if output_name in outputs: found_output = outputs[output_name] if found_output.label is None and workflow_output.label is not None: outputs[output_name] = workflow_output else: outputs[output_name] = workflow_output return list(outputs.values()) @property def content_id(self): content_id = None if self.type == "tool": content_id = self.tool_id elif self.type == "subworkflow": content_id = self.subworkflow.id else: content_id = None return content_id @property def input_connections_by_name(self): if self._input_connections_by_name is None: self.setup_input_connections_by_name() return self._input_connections_by_name
[docs] def setup_input_connections_by_name(self): # Ensure input_connections has already been set. # Make connection information available on each step by input name. input_connections_by_name = {} for conn in self.input_connections: input_name = conn.input_name if input_name not in input_connections_by_name: input_connections_by_name[input_name] = [] input_connections_by_name[input_name].append(conn) self._input_connections_by_name = input_connections_by_name
[docs] def create_or_update_workflow_output(self, output_name, label, uuid): output = self.workflow_output_for(output_name) if output is None: output = WorkflowOutput(workflow_step=self, output_name=output_name) if uuid is not None: output.uuid = uuid if label is not None: output.label = label return output
[docs] def workflow_output_for(self, output_name): target_output = None for workflow_output in self.workflow_outputs: if workflow_output.output_name == output_name: target_output = workflow_output break return target_output
[docs] def copy_to(self, copied_step, step_mapping): copied_step.order_index = self.order_index copied_step.type = self.type copied_step.tool_id = self.tool_id copied_step.tool_inputs = self.tool_inputs copied_step.tool_errors = self.tool_errors copied_step.position = self.position copied_step.config = self.config copied_step.label = self.label copied_step.inputs = copy_list(self.inputs, copied_step) subworkflow_step_mapping = {} subworkflow = self.subworkflow if subworkflow: copied_subworkflow = subworkflow.copy() copied_step.subworkflow = copied_subworkflow for subworkflow_step, copied_subworkflow_step in zip(subworkflow.steps, copied_subworkflow.steps): subworkflow_step_mapping[subworkflow_step.id] = copied_subworkflow_step for old_conn, new_conn in zip(self.input_connections, copied_step.input_connections): new_conn.input_step_input = copied_step.get_or_add_input(old_conn.input_name) new_conn.output_step = step_mapping[old_conn.output_step_id] if old_conn.input_subworkflow_step_id: new_conn.input_subworkflow_step = subworkflow_step_mapping[old_conn.input_subworkflow_step_id] for orig_pja in self.post_job_actions: PostJobAction(orig_pja.action_type, copied_step, output_name=orig_pja.output_name, action_arguments=orig_pja.action_arguments) copied_step.workflow_outputs = copy_list(self.workflow_outputs, copied_step)
[docs] def log_str(self): return "WorkflowStep[index=%d,type=%s]" % (self.order_index, self.type)
[docs]class WorkflowStepInput(RepresentById): default_merge_type = None default_scatter_type = None def __init__(self, workflow_step): self.workflow_step = workflow_step self.name = None self.default_value = None self.default_value_set = False self.merge_type = self.default_merge_type self.scatter_type = self.default_scatter_type
[docs] def copy(self, copied_step): copied_step_input = WorkflowStepInput(copied_step) copied_step_input.name = self.name copied_step_input.default_value = self.default_value copied_step_input.default_value_set = self.default_value_set copied_step_input.merge_type = self.merge_type copied_step_input.scatter_type = self.scatter_type copied_step_input.connections = copy_list(self.connections) return copied_step_input
[docs]class WorkflowStepConnection(RepresentById): # Constant used in lieu of output_name and input_name to indicate an # implicit connection between two steps that is not dependent on a dataset # or a dataset collection. Allowing for instance data manager steps to setup # index data before a normal tool runs or for workflows that manage data # outside of Galaxy. NON_DATA_CONNECTION = "__NO_INPUT_OUTPUT_NAME__" def __init__(self): self.output_step_id = None self.output_name = None self.input_step_input_id = None @property def non_data_connection(self): return (self.output_name == self.input_name == WorkflowStepConnection.NON_DATA_CONNECTION) @property def input_name(self): return self.input_step_input.name @property def input_step(self): return self.input_step_input and self.input_step_input.workflow_step @property def input_step_id(self): input_step = self.input_step return input_step and input_step.id
[docs] def copy(self): # TODO: handle subworkflow ids... copied_connection = WorkflowStepConnection() copied_connection.output_name = self.output_name return copied_connection
[docs]class WorkflowOutput(RepresentById): def __init__(self, workflow_step, output_name=None, label=None, uuid=None): self.workflow_step = workflow_step self.output_name = output_name self.label = label if uuid is None: self.uuid = uuid4() else: self.uuid = UUID(str(uuid))
[docs] def copy(self, copied_step): copied_output = WorkflowOutput(copied_step) copied_output.output_name = self.output_name copied_output.label = self.label return copied_output
[docs]class StoredWorkflowUserShareAssociation(RepresentById): def __init__(self): self.stored_workflow = None self.user = None
[docs]class StoredWorkflowMenuEntry(RepresentById): def __init__(self): self.stored_workflow = None self.user = None self.order_index = None
[docs]class WorkflowInvocation(UsesCreateAndUpdateTime, Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'update_time', 'workflow_id', 'history_id', 'uuid', 'state'] dict_element_visible_keys = ['id', 'update_time', 'workflow_id', 'history_id', 'uuid', 'state'] states = Bunch( NEW='new', # Brand new workflow invocation... maybe this should be same as READY READY='ready', # Workflow ready for another iteration of scheduling. SCHEDULED='scheduled', # Workflow has been scheduled. CANCELLED='cancelled', FAILED='failed', ) def __init__(self): self.subworkflow_invocations = [] self.step_states = [] self.steps = []
[docs] def create_subworkflow_invocation_for_step(self, step): assert step.type == "subworkflow" subworkflow_invocation = WorkflowInvocation() self.attach_subworkflow_invocation_for_step(step, subworkflow_invocation) return subworkflow_invocation
[docs] def attach_subworkflow_invocation_for_step(self, step, subworkflow_invocation): assert step.type == "subworkflow" assoc = WorkflowInvocationToSubworkflowInvocationAssociation() assoc.workflow_invocation = self assoc.workflow_step = step subworkflow_invocation.history = self.history subworkflow_invocation.workflow = step.subworkflow assoc.subworkflow_invocation = subworkflow_invocation self.subworkflow_invocations.append(assoc) return assoc
[docs] def get_subworkflow_invocation_for_step(self, step): assoc = self.get_subworkflow_invocation_association_for_step(step) return assoc.subworkflow_invocation
[docs] def get_subworkflow_invocation_association_for_step(self, step): assert step.type == "subworkflow" assoc = None for subworkflow_invocation in self.subworkflow_invocations: if subworkflow_invocation.workflow_step == step: assoc = subworkflow_invocation break return assoc
@property def active(self): """ Indicates the workflow invocation is somehow active - and in particular valid actions may be performed on its WorkflowInvocationSteps. """ states = WorkflowInvocation.states return self.state in [states.NEW, states.READY]
[docs] def cancel(self): if not self.active: return False else: self.state = WorkflowInvocation.states.CANCELLED return True
[docs] def fail(self): self.state = WorkflowInvocation.states.FAILED
[docs] def step_states_by_step_id(self): step_states = {} for step_state in self.step_states: step_id = step_state.workflow_step_id step_states[step_id] = step_state return step_states
[docs] def step_invocations_by_step_id(self): step_invocations = {} for invocation_step in self.steps: step_id = invocation_step.workflow_step_id assert step_id not in step_invocations step_invocations[step_id] = invocation_step return step_invocations
[docs] def step_invocation_for_step_id(self, step_id): target_invocation_step = None for invocation_step in self.steps: if step_id == invocation_step.workflow_step_id: target_invocation_step = invocation_step return target_invocation_step
[docs] @staticmethod def poll_unhandled_workflow_ids(sa_session): and_conditions = [ WorkflowInvocation.state == WorkflowInvocation.states.NEW, WorkflowInvocation.handler.is_(None) ] query = sa_session.query( WorkflowInvocation.id ).filter(and_(*and_conditions)).order_by(WorkflowInvocation.table.c.id.asc()) return [wid for wid in query.all()]
[docs] @staticmethod def poll_active_workflow_ids( sa_session, scheduler=None, handler=None ): and_conditions = [ or_( WorkflowInvocation.state == WorkflowInvocation.states.NEW, WorkflowInvocation.state == WorkflowInvocation.states.READY ), ] if scheduler is not None: and_conditions.append(WorkflowInvocation.scheduler == scheduler) if handler is not None: and_conditions.append(WorkflowInvocation.handler == handler) query = sa_session.query( WorkflowInvocation.id ).filter(and_(*and_conditions)).order_by(WorkflowInvocation.table.c.id.asc()) # Immediately just load all ids into memory so time slicing logic # is relatively intutitive. return [wid for wid in query.all()]
[docs] def add_output(self, workflow_output, step, output_object): if step.type == 'parameter_input': # TODO: these should be properly tracked. return if output_object.history_content_type == "dataset": output_assoc = WorkflowInvocationOutputDatasetAssociation() output_assoc.workflow_invocation = self output_assoc.workflow_output = workflow_output output_assoc.workflow_step = step output_assoc.dataset = output_object self.output_datasets.append(output_assoc) elif output_object.history_content_type == "dataset_collection": output_assoc = WorkflowInvocationOutputDatasetCollectionAssociation() output_assoc.workflow_invocation = self output_assoc.workflow_output = workflow_output output_assoc.workflow_step = step output_assoc.dataset_collection = output_object self.output_dataset_collections.append(output_assoc) else: raise Exception("Unknown output type encountered")
[docs] def to_dict(self, view='collection', value_mapper=None, step_details=False, legacy_job_state=False): rval = super(WorkflowInvocation, self).to_dict(view=view, value_mapper=value_mapper) if view == 'element': steps = [] for step in self.steps: if step_details: v = step.to_dict(view='element') else: v = step.to_dict(view='collection') if legacy_job_state: step_jobs = step.jobs if step_jobs: for step_job in step_jobs: v_clone = v.copy() v_clone["state"] = step_job.state v_clone["job_id"] = step_job.id steps.append(v_clone) else: v["state"] = None steps.append(v) else: steps.append(v) rval['steps'] = steps inputs = {} for step in self.steps: if step.workflow_step.type == 'tool': for job in step.jobs: for step_input in step.workflow_step.input_connections: output_step_type = step_input.output_step.type if output_step_type in ['data_input', 'data_collection_input']: src = "hda" if output_step_type == 'data_input' else 'hdca' for job_input in job.input_datasets: if job_input.name == step_input.input_name: inputs[str(step_input.output_step.order_index)] = { "id": job_input.dataset_id, "src": src, "uuid" : str(job_input.dataset.dataset.uuid) if job_input.dataset.dataset.uuid is not None else None } rval['inputs'] = inputs outputs = {} for output_assoc in self.output_datasets: label = output_assoc.workflow_output.label if not label: continue outputs[label] = { 'src': 'hda', 'id': output_assoc.dataset_id, } output_collections = {} for output_assoc in self.output_dataset_collections: label = output_assoc.workflow_output.label if not label: continue output_collections[label] = { 'src': 'hdca', 'id': output_assoc.dataset_collection_id, } rval['outputs'] = outputs rval['output_collections'] = output_collections return rval
def update(self): self.update_time = galaxy.model.orm.now.now()
[docs] def add_input(self, content, step_id): history_content_type = getattr(content, "history_content_type", None) if history_content_type == "dataset": request_to_content = WorkflowRequestToInputDatasetAssociation() request_to_content.dataset = content request_to_content.workflow_step_id = step_id self.input_datasets.append(request_to_content) elif history_content_type == "dataset_collection": request_to_content = WorkflowRequestToInputDatasetCollectionAssociation() request_to_content.dataset_collection = content request_to_content.workflow_step_id = step_id self.input_dataset_collections.append(request_to_content) else: request_to_content = WorkflowRequestInputStepParameter() request_to_content.parameter_value = content request_to_content.workflow_step_id = step_id self.input_step_parameters.append(request_to_content)
@property def resource_parameters(self): resource_type = WorkflowRequestInputParameter.types.RESOURCE_PARAMETERS _resource_parameters = {} for input_parameter in self.input_parameters: if input_parameter.type == resource_type: _resource_parameters[input_parameter.name] = input_parameter.value return _resource_parameters
[docs] def has_input_for_step(self, step_id): for content in self.input_datasets: if content.workflow_step_id == step_id: return True for content in self.input_dataset_collections: if content.workflow_step_id == step_id: return True return False
[docs] def set_handler(self, handler): self.handler = handler
[docs] def log_str(self): extra = "" safe_id = getattr(self, "id", None) if safe_id is not None: extra += "id=%s" % safe_id else: extra += "unflushed" return "%s[%s]" % (self.__class__.__name__, extra)
[docs]class WorkflowInvocationToSubworkflowInvocationAssociation(Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'workflow_step_id', 'workflow_invocation_id', 'subworkflow_invocation_id'] dict_element_visible_keys = ['id', 'workflow_step_id', 'workflow_invocation_id', 'subworkflow_invocation_id']
[docs]class WorkflowInvocationStep(Dictifiable, RepresentById): dict_collection_visible_keys = ['id', 'update_time', 'job_id', 'workflow_step_id', 'state', 'action'] dict_element_visible_keys = ['id', 'update_time', 'job_id', 'workflow_step_id', 'state', 'action'] states = Bunch( NEW='new', # Brand new workflow invocation step READY='ready', # Workflow invocation step ready for another iteration of scheduling. SCHEDULED='scheduled', # Workflow invocation step has been scheduled. # CANCELLED='cancelled', TODO: implement and expose # FAILED='failed', TODO: implement and expose )
[docs] def update(self): self.workflow_invocation.update()
@property def is_new(self): return self.state == self.states.NEW
[docs] def add_output(self, output_name, output_object): if output_object.history_content_type == "dataset": output_assoc = WorkflowInvocationStepOutputDatasetAssociation() output_assoc.workflow_invocation_step = self output_assoc.dataset = output_object output_assoc.output_name = output_name self.output_datasets.append(output_assoc) elif output_object.history_content_type == "dataset_collection": output_assoc = WorkflowInvocationStepOutputDatasetCollectionAssociation() output_assoc.workflow_invocation_step = self output_assoc.dataset_collection = output_object output_assoc.output_name = output_name self.output_dataset_collections.append(output_assoc) else: raise Exception("Unknown output type encountered")
@property def jobs(self): if self.job: return [self.job] elif self.implicit_collection_jobs: return self.implicit_collection_jobs.job_list else: return []
[docs] def to_dict(self, view='collection', value_mapper=None): rval = super(WorkflowInvocationStep, self).to_dict(view=view, value_mapper=value_mapper) rval['order_index'] = self.workflow_step.order_index rval['workflow_step_label'] = self.workflow_step.label rval['workflow_step_uuid'] = str(self.workflow_step.uuid) # Following no longer makes sense... # rval['state'] = self.job.state if self.job is not None else None if view == 'element': jobs = [] for job in self.jobs: jobs.append(job.to_dict()) outputs = {} for output_assoc in self.output_datasets: name = output_assoc.output_name outputs[name] = { 'src': 'hda', 'id': output_assoc.dataset.id, 'uuid': str(output_assoc.dataset.dataset.uuid) if output_assoc.dataset.dataset.uuid is not None else None } output_collections = {} for output_assoc in self.output_dataset_collections: name = output_assoc.output_name output_collections[name] = { 'src': 'hdca', 'id': output_assoc.dataset_collection.id, } rval['outputs'] = outputs rval['output_collections'] = output_collections rval['jobs'] = jobs return rval
[docs]class WorkflowRequestInputParameter(Dictifiable, RepresentById): """ Workflow-related parameters not tied to steps or inputs. """ dict_collection_visible_keys = ['id', 'name', 'value', 'type'] types = Bunch( REPLACEMENT_PARAMETERS='replacements', STEP_PARAMETERS='step', META_PARAMETERS='meta', RESOURCE_PARAMETERS='resource', ) def __init__(self, name=None, value=None, type=None): self.name = name self.value = value self.type = type
[docs]class WorkflowRequestStepState(Dictifiable, RepresentById): """ Workflow step value parameters. """ dict_collection_visible_keys = ['id', 'name', 'value', 'workflow_step_id'] def __init__(self, workflow_step=None, name=None, value=None): self.workflow_step = workflow_step self.name = name self.value = value self.type = type
[docs]class WorkflowRequestToInputDatasetAssociation(Dictifiable, RepresentById): """ Workflow step input dataset parameters. """ dict_collection_visible_keys = ['id', 'workflow_invocation_id', 'workflow_step_id', 'dataset_id', 'name']
[docs]class WorkflowRequestToInputDatasetCollectionAssociation(Dictifiable, RepresentById): """ Workflow step input dataset collection parameters. """ dict_collection_visible_keys = ['id', 'workflow_invocation_id', 'workflow_step_id', 'dataset_collection_id', 'name']
[docs]class WorkflowRequestInputStepParameter(Dictifiable, RepresentById): """ Workflow step parameter inputs. """ dict_collection_visible_keys = ['id', 'workflow_invocation_id', 'workflow_step_id', 'parameter_value']
[docs]class WorkflowInvocationOutputDatasetAssociation(Dictifiable, RepresentById): """Represents links to output datasets for the workflow.""" dict_collection_visible_keys = ['id', 'workflow_invocation_id', 'workflow_step_id', 'dataset_id', 'name']
[docs]class WorkflowInvocationOutputDatasetCollectionAssociation(Dictifiable, RepresentById): """Represents links to output dataset collections for the workflow.""" dict_collection_visible_keys = ['id', 'workflow_invocation_id', 'workflow_step_id', 'dataset_collection_id', 'name']
[docs]class WorkflowInvocationStepOutputDatasetAssociation(Dictifiable, RepresentById): """Represents links to output datasets for the workflow.""" dict_collection_visible_keys = ['id', 'workflow_invocation_step_id', 'dataset_id', 'output_name']
[docs]class WorkflowInvocationStepOutputDatasetCollectionAssociation(Dictifiable, RepresentById): """Represents links to output dataset collections for the workflow.""" dict_collection_visible_keys = ['id', 'workflow_invocation_step_id', 'dataset_collection_id', 'output_name']
[docs]class MetadataFile(StorableObject, RepresentById): def __init__(self, dataset=None, name=None, uuid=None): super(MetadataFile, self).__init__(id=None, uuid=uuid) if isinstance(dataset, HistoryDatasetAssociation): self.history_dataset = dataset elif isinstance(dataset, LibraryDatasetDatasetAssociation): self.library_dataset = dataset self.name = name @property def file_name(self): # Ensure the directory structure and the metadata file object exist try: da = self.history_dataset or self.library_dataset if self.object_store_id is None and da is not None: self.object_store_id = da.dataset.object_store_id object_store = da.dataset.object_store store_by = object_store.store_by identifier = getattr(self, store_by) alt_name = "metadata_%s.dat" % identifier if not object_store.exists(self, extra_dir='_metadata_files', extra_dir_at_root=True, alt_name=alt_name): object_store.create(self, extra_dir='_metadata_files', extra_dir_at_root=True, alt_name=alt_name) path = object_store.get_filename(self, extra_dir='_metadata_files', extra_dir_at_root=True, alt_name=alt_name) return path except AttributeError: assert self.id is not None, "ID must be set before MetadataFile used without an HDA/LDDA (commit the object)" # In case we're not working with the history_dataset path = os.path.join(Dataset.file_path, '_metadata_files', *directory_hash_id(self.id)) # Create directory if it does not exist try: os.makedirs(path) except OSError as e: # File Exists is okay, otherwise reraise if e.errno != errno.EEXIST: raise # Return filename inside hashed directory return os.path.abspath(os.path.join(path, "metadata_%d.dat" % self.id))
[docs] def serialize(self, id_encoder, serialization_options): as_dict = dict_for(self) serialization_options.attach_identifier(id_encoder, self, as_dict) as_dict["uuid"] = str(self.uuid or '') or None return as_dict
[docs]class FormDefinition(Dictifiable, RepresentById): # The following form_builder classes are supported by the FormDefinition class. supported_field_types = [AddressField, CheckboxField, PasswordField, SelectField, TextArea, TextField, WorkflowField, WorkflowMappingField, HistoryField] types = Bunch(USER_INFO='User Information') dict_collection_visible_keys = ['id', 'name'] dict_element_visible_keys = ['id', 'name', 'desc', 'form_definition_current_id', 'fields', 'layout'] def __init__(self, name=None, desc=None, fields=[], form_definition_current=None, form_type=None, layout=None): self.name = name self.desc = desc self.fields = fields self.form_definition_current = form_definition_current self.type = form_type self.layout = layout
[docs] def to_dict(self, user=None, values=None, security=None): values = values or {} form_def = {'id': security.encode_id(self.id) if security else self.id, 'name': self.name, 'inputs': []} for field in self.fields: FieldClass = ({'AddressField' : AddressField, 'CheckboxField' : CheckboxField, 'HistoryField' : HistoryField, 'PasswordField' : PasswordField, 'SelectField' : SelectField, 'TextArea' : TextArea, 'TextField' : TextField, 'WorkflowField' : WorkflowField}).get(field['type'], TextField) form_def['inputs'].append(FieldClass(user=user, value=values.get(field['name'], field['default']), security=security, **field).to_dict()) return form_def
[docs] def grid_fields(self, grid_index): # Returns a dictionary whose keys are integers corresponding to field positions # on the grid and whose values are the field. gridfields = {} for i, f in enumerate(self.fields): if str(f['layout']) == str(grid_index): gridfields[i] = f return gridfields
[docs]class FormDefinitionCurrent(RepresentById): def __init__(self, form_definition=None): self.latest_form = form_definition
[docs]class FormValues(RepresentById): def __init__(self, form_def=None, content=None): self.form_definition = form_def self.content = content
[docs]class UserAddress(RepresentById): def __init__(self, user=None, desc=None, name=None, institution=None, address=None, city=None, state=None, postal_code=None, country=None, phone=None): self.user = user self.desc = desc self.name = name self.institution = institution self.address = address self.city = city self.state = state self.postal_code = postal_code self.country = country self.phone = phone
[docs] def to_dict(self, trans): return {'id' : trans.security.encode_id(self.id), 'name' : sanitize_html(self.name), 'desc' : sanitize_html(self.desc), 'institution' : sanitize_html(self.institution), 'address' : sanitize_html(self.address), 'city' : sanitize_html(self.city), 'state' : sanitize_html(self.state), 'postal_code' : sanitize_html(self.postal_code), 'country' : sanitize_html(self.country), 'phone' : sanitize_html(self.phone)}
[docs]class PSAAssociation(AssociationMixin, RepresentById): # This static property is set at: galaxy.authnz.psa_authnz.PSAAuthnz sa_session = None def __init__(self, server_url=None, handle=None, secret=None, issued=None, lifetime=None, assoc_type=None): self.server_url = server_url self.handle = handle self.secret = secret self.issued = issued self.lifetime = lifetime self.assoc_type = assoc_type
[docs] def save(self): self.sa_session.add(self) self.sa_session.flush()
[docs] @classmethod def store(cls, server_url, association): try: assoc = cls.sa_session.query(cls).filter_by(server_url=server_url, handle=association.handle)[0] except IndexError: assoc = cls(server_url=server_url, handle=association.handle) assoc.secret = base64.encodestring(association.secret).decode() assoc.issued = association.issued assoc.lifetime = association.lifetime assoc.assoc_type = association.assoc_type cls.sa_session.add(assoc) cls.sa_session.flush()
[docs] @classmethod def get(cls, *args, **kwargs): return cls.sa_session.query(cls).filter_by(*args, **kwargs)
[docs] @classmethod def remove(cls, ids_to_delete): cls.sa_session.query(cls).filter(cls.id.in_(ids_to_delete)).delete(synchronize_session='fetch')
[docs]class PSACode(CodeMixin, RepresentById): __table_args__ = (UniqueConstraint('code', 'email'),) # This static property is set at: galaxy.authnz.psa_authnz.PSAAuthnz sa_session = None def __init__(self, email, code): self.email = email self.code = code
[docs] def save(self): self.sa_session.add(self) self.sa_session.flush()
[docs] @classmethod def get_code(cls, code): return cls.sa_session.query(cls).filter(cls.code == code).first()
[docs]class PSANonce(NonceMixin, RepresentById): # This static property is set at: galaxy.authnz.psa_authnz.PSAAuthnz sa_session = None def __init__(self, server_url, timestamp, salt): self.server_url = server_url self.timestamp = timestamp self.salt = salt
[docs] def save(self): self.sa_session.add(self) self.sa_session.flush()
[docs] @classmethod def use(cls, server_url, timestamp, salt): try: return cls.sa_session.query(cls).filter_by(server_url=server_url, timestamp=timestamp, salt=salt)[0] except IndexError: instance = cls(server_url=server_url, timestamp=timestamp, salt=salt) cls.sa_session.add(instance) cls.sa_session.flush() return instance
[docs]class PSAPartial(PartialMixin, RepresentById): # This static property is set at: galaxy.authnz.psa_authnz.PSAAuthnz sa_session = None def __init__(self, token, data, next_step, backend): self.token = token self.data = data self.next_step = next_step self.backend = backend
[docs] def save(self): self.sa_session.add(self) self.sa_session.flush()
[docs] @classmethod def load(cls, token): return cls.sa_session.query(cls).filter(cls.token == token).first()
[docs] @classmethod def destroy(cls, token): partial = cls.load(token) if partial: cls.sa_session.delete(partial)
[docs]class UserAuthnzToken(UserMixin, RepresentById): __table_args__ = (UniqueConstraint('provider', 'uid'),) # This static property is set at: galaxy.authnz.psa_authnz.PSAAuthnz sa_session = None def __init__(self, provider, uid, extra_data=None, lifetime=None, assoc_type=None, user=None): self.provider = provider self.uid = uid self.user_id = user.id self.extra_data = extra_data self.lifetime = lifetime self.assoc_type = assoc_type
[docs] def get_id_token(self, strategy): if self.access_token_expired(): # Access and ID tokens have same expiration time; # hence, if one is expired, the other is expired too. self.refresh_token(strategy) return self.extra_data.get('id_token', None) if self.extra_data is not None else None
[docs] def set_extra_data(self, extra_data=None): if super(UserAuthnzToken, self).set_extra_data(extra_data): self.sa_session.add(self) self.sa_session.flush()
[docs] def save(self): self.sa_session.add(self) self.sa_session.flush()
[docs] @classmethod def username_max_length(cls): # Note: This is the maximum field length set for the username column of the galaxy_user table. # A better alternative is to retrieve this number from the table, instead of this const value. return 255
[docs] @classmethod def user_model(cls): return User
[docs] @classmethod def changed(cls, user): cls.sa_session.add(user) cls.sa_session.flush()
[docs] @classmethod def user_query(cls): return cls.sa_session.query(cls.user_model())
[docs] @classmethod def user_exists(cls, *args, **kwargs): return cls.user_query().filter_by(*args, **kwargs).count() > 0
[docs] @classmethod def get_username(cls, user): return getattr(user, 'username', None)
[docs] @classmethod def create_user(cls, *args, **kwargs): model = cls.user_model() instance = model(*args, **kwargs) instance.set_random_password() cls.sa_session.add(instance) cls.sa_session.flush() return instance
[docs] @classmethod def get_user(cls, pk): return cls.user_query().get(pk)
[docs] @classmethod def get_users_by_email(cls, email): return cls.user_query().filter_by(email=email)
[docs] @classmethod def get_social_auth(cls, provider, uid): uid = str(uid) try: return cls.sa_session.query(cls).filter_by(provider=provider, uid=uid)[0] except IndexError: return None
[docs] @classmethod def get_social_auth_for_user(cls, user, provider=None, id=None): qs = cls.sa_session.query(cls).filter_by(user_id=user.id) if provider: qs = qs.filter_by(provider=provider) if id: qs = qs.filter_by(id=id) return qs
[docs] @classmethod def create_social_auth(cls, user, uid, provider): uid = str(uid) instance = cls(user=user, uid=uid, provider=provider) cls.sa_session.add(instance) cls.sa_session.flush() return instance
[docs]class CustosAuthnzToken(RepresentById): def __init__(self, user, external_user_id, provider, access_token, id_token, refresh_token, expiration_time, refresh_expiration_time): self.id = None # 'user' is a backref to model.User self.user = user self.external_user_id = external_user_id self.provider = provider self.access_token = access_token self.id_token = id_token self.refresh_token = refresh_token self.expiration_time = expiration_time self.refresh_expiration_time = refresh_expiration_time
[docs]class CloudAuthz(RepresentById): def __init__(self, user_id, provider, config, authn_id, description=""): self.id = None self.user_id = user_id self.provider = provider self.config = config self.authn_id = authn_id self.tokens = None self.last_update = datetime.now() self.last_activity = datetime.now() self.description = description def __eq__(self, other): if not isinstance(other, CloudAuthz): return False return self.equals(other.user_id, other.provider, other.authn_id, other.config) def __ne__(self, other): return not self.__eq__(other)
[docs] def equals(self, user_id, provider, authn_id, config): return (self.user_id == user_id and self.provider == provider and self.authn_id == authn_id and len({k: self.config[k] for k in self.config if k in config and self.config[k] == config[k]}) == len(self.config))
[docs]class Page(Dictifiable, RepresentById): dict_element_visible_keys = ['id', 'title', 'latest_revision_id', 'slug', 'published', 'importable', 'deleted'] def __init__(self): self.id = None self.user = None self.title = None self.slug = None self.latest_revision_id = None self.revisions = [] self.importable = None self.published = None
[docs] def to_dict(self, view='element'): rval = super(Page, self).to_dict(view=view) rev = [] for a in self.revisions: rev.append(a.id) rval['revision_ids'] = rev return rval
[docs]class PageRevision(Dictifiable, RepresentById): dict_element_visible_keys = ['id', 'page_id', 'title', 'content'] def __init__(self): self.user = None self.title = None self.content = None
[docs] def to_dict(self, view='element'): rval = super(PageRevision, self).to_dict(view=view) rval['create_time'] = str(self.create_time) rval['update_time'] = str(self.update_time) return rval
[docs]class PageUserShareAssociation(RepresentById): def __init__(self): self.page = None self.user = None
[docs]class Visualization(RepresentById): def __init__(self, id=None, user=None, type=None, title=None, dbkey=None, slug=None, latest_revision=None): self.id = id self.user = user self.type = type self.title = title self.dbkey = dbkey self.slug = slug self.latest_revision = latest_revision self.revisions = [] if self.latest_revision: self.revisions.append(latest_revision)
[docs] def copy(self, user=None, title=None): """ Provide copy of visualization with only its latest revision. """ # NOTE: a shallow copy is done: the config is copied as is but datasets # are not copied nor are the dataset ids changed. This means that the # user does not have a copy of the data in his/her history and the # user who owns the datasets may delete them, making them inaccessible # for the current user. # TODO: a deep copy option is needed. if not user: user = self.user if not title: title = self.title copy_viz = Visualization(user=user, type=self.type, title=title, dbkey=self.dbkey) copy_revision = self.latest_revision.copy(visualization=copy_viz) copy_viz.latest_revision = copy_revision return copy_viz
[docs]class VisualizationRevision(RepresentById): def __init__(self, visualization=None, title=None, dbkey=None, config=None): self.id = None self.visualization = visualization self.title = title self.dbkey = dbkey self.config = config
[docs] def copy(self, visualization=None): """ Returns a copy of this object. """ if not visualization: visualization = self.visualization return VisualizationRevision( visualization=visualization, title=self.title, dbkey=self.dbkey, config=self.config )
[docs]class VisualizationUserShareAssociation(RepresentById): def __init__(self): self.visualization = None self.user = None
[docs]class TransferJob(RepresentById): # These states are used both by the transfer manager's IPC and the object # state in the database. Not all states are used by both. states = Bunch(NEW='new', UNKNOWN='unknown', PROGRESS='progress', RUNNING='running', ERROR='error', DONE='done') terminal_states = [states.ERROR, states.DONE] def __init__(self, state=None, path=None, info=None, pid=None, socket=None, params=None): self.state = state self.path = path self.info = info self.pid = pid self.socket = socket self.params = params
[docs]class Tag(RepresentById): def __init__(self, id=None, type=None, parent_id=None, name=None): self.id = id self.type = type self.parent_id = parent_id self.name = name def __str__(self): return "Tag(id=%s, type=%i, parent_id=%s, name=%s)" % (self.id, self.type or -1, self.parent_id, self.name)
[docs]class ItemTagAssociation(Dictifiable): dict_collection_visible_keys = ['id', 'user_tname', 'user_value'] dict_element_visible_keys = dict_collection_visible_keys
[docs] def __init__(self, id=None, user=None, item_id=None, tag_id=None, user_tname=None, value=None): self.id = id self.user = user self.item_id = item_id self.tag_id = tag_id self.user_tname = user_tname self.value = None self.user_value = None
[docs] def copy(self, cls=None): if cls: new_ta = cls() else: new_ta = type(self)() new_ta.tag_id = self.tag_id new_ta.user_tname = self.user_tname new_ta.value = self.value new_ta.user_value = self.user_value return new_ta
[docs]class HistoryTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class DatasetTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class HistoryDatasetAssociationTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class LibraryDatasetDatasetAssociationTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class PageTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class WorkflowStepTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class StoredWorkflowTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class VisualizationTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class HistoryDatasetCollectionTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class LibraryDatasetCollectionTagAssociation(ItemTagAssociation, RepresentById): pass
[docs]class ToolTagAssociation(ItemTagAssociation, RepresentById): def __init__(self, id=None, user=None, tool_id=None, tag_id=None, user_tname=None, value=None): self.id = id self.user = user self.tool_id = tool_id self.tag_id = tag_id self.user_tname = user_tname self.value = None self.user_value = None
# Item annotation classes.
[docs]class HistoryAnnotationAssociation(RepresentById): pass
[docs]class HistoryDatasetAssociationAnnotationAssociation(RepresentById): pass
[docs]class StoredWorkflowAnnotationAssociation(RepresentById): pass
[docs]class WorkflowStepAnnotationAssociation(RepresentById): pass
[docs]class PageAnnotationAssociation(RepresentById): pass
[docs]class VisualizationAnnotationAssociation(RepresentById): pass
[docs]class HistoryDatasetCollectionAssociationAnnotationAssociation(RepresentById): pass
[docs]class LibraryDatasetCollectionAnnotationAssociation(RepresentById): pass
# Item rating classes.
[docs]class ItemRatingAssociation(object):
[docs] def __init__(self, id=None, user=None, item=None, rating=0): self.id = id self.user = user self.item = item self.rating = rating
[docs] def set_item(self, item): """ Set association's item. """ pass
[docs]class HistoryRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, history): self.history = history
[docs]class HistoryDatasetAssociationRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, history_dataset_association): self.history_dataset_association = history_dataset_association
[docs]class StoredWorkflowRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, stored_workflow): self.stored_workflow = stored_workflow
[docs]class PageRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, page): self.page = page
[docs]class VisualizationRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, visualization): self.visualization = visualization
[docs]class HistoryDatasetCollectionRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, dataset_collection): self.dataset_collection = dataset_collection
[docs]class LibraryDatasetCollectionRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, dataset_collection): self.dataset_collection = dataset_collection
# Data manager classes.
[docs]class DataManagerHistoryAssociation(RepresentById): def __init__(self, id=None, history=None, user=None): self.id = id self.history = history self.user = user
[docs]class DataManagerJobAssociation(RepresentById): def __init__(self, id=None, job=None, data_manager_id=None): self.id = id self.job = job self.data_manager_id = data_manager_id
[docs]class UserPreference(RepresentById): def __init__(self, name=None, value=None): self.name = name self.value = value
[docs]class UserAction(RepresentById): def __init__(self, id=None, create_time=None, user_id=None, session_id=None, action=None, params=None, context=None): self.id = id self.create_time = create_time self.user_id = user_id self.session_id = session_id self.action = action self.params = params self.context = context
[docs]class APIKeys(RepresentById): def __init__(self, id=None, user_id=None, key=None): self.id = id self.user_id = user_id self.key = key
[docs]def copy_list(lst, *args, **kwds): if lst is None: return lst else: return [el.copy(*args, **kwds) for el in lst]
def _prepare_metadata_for_serialization(id_encoder, serialization_options, metadata): """ Prepare metatdata for exporting. """ processed_metadata = {} for name, value in metadata.items(): # Metadata files are not needed for export because they can be # regenerated. if isinstance(value, MetadataFile): if serialization_options.strip_metadata_files: continue else: value = value.serialize(id_encoder, serialization_options) processed_metadata[name] = value return processed_metadata