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This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
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,
text,
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.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
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):
if len(stream) > galaxy.util.DATABASE_MAX_STRING_SIZE:
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 galaxy.util.shrink_and_unicodify(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)
@property
def stdout(self):
stdout = self.tool_stdout or ''
if self.job_stdout:
stdout += "\n" + self.job_stdout
return stdout
@stdout.setter
def stdout(self, stdout):
raise NotImplementedError("Attempt to set stdout, must set tool_stdout or job_stdout")
@property
def stderr(self):
stderr = self.tool_stderr or ''
if self.job_stderr:
stderr += "\n" + self.job_stderr
return stderr
@stderr.setter
def stderr(self, stderr):
raise NotImplementedError("Attempt to set stdout, must set tool_stderr or job_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
db_session = object_session(self)
return self._calculate_or_set_disk_usage(db_session, dryrun=True)
[docs] def calculate_and_set_disk_usage(self):
"""
Calculates and sets user disk usage.
"""
db_session = object_session(self)
self._calculate_or_set_disk_usage(db_session, dryrun=False)
def _calculate_or_set_disk_usage(self, sa_session, dryrun=True):
"""
Utility to calculate and return the disk usage. If dryrun is False,
the new value is set immediately.
"""
sql_calc = """
WITH per_user_histories AS
(
SELECT history.id as id
FROM history
WHERE history.user_id = :id
AND history.purged = '0'
),
per_hist_hdas AS (
SELECT DISTINCT history_dataset_association.dataset_id as id
FROM history_dataset_association
WHERE history_dataset_association.purged = '0'
AND history_dataset_association.history_id in (SELECT id from per_user_histories)
)
SELECT sum(coalesce(dataset.total_size, coalesce(dataset.file_size, 0)))
FROM dataset
LEFT OUTER JOIN library_dataset_dataset_association ON dataset.id = library_dataset_dataset_association.dataset_id
WHERE dataset.id in (SELECT id from per_hist_hdas)
AND library_dataset_dataset_association.id IS NULL
"""
usage = sa_session.scalar(sql_calc, {'id': self.id})
if not dryrun:
self.set_disk_usage(usage)
sa_session.flush()
return usage
[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_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 Job(JobLike, UsesCreateAndUpdateTime, Dictifiable, RepresentById):
dict_collection_visible_keys = ['id', 'state', 'exit_code', 'update_time', 'create_time', 'galaxy_version']
dict_element_visible_keys = ['id', 'state', 'exit_code', 'update_time', 'create_time', 'galaxy_version']
"""
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_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_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_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 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 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))
@property
def all_entry_points_configured(self):
# consider an actual DB attribute for this.
all_configured = True
for ep in self.interactivetool_entry_points:
all_configured = ep.configured and all_configured
return all_configured
[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['galaxy_version'] = self.galaxy_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)
# 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_working_directory(self, working_directory):
self.working_directory = working_directory
[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_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]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 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 JobContainerAssociation(RepresentById):
def __init__(self, job=None, container_type=None, container_name=None, container_info=None):
self.job = job
self.container_type = container_type
self.container_name = container_name
self.container_info = container_info or {}
[docs]class InteractiveToolEntryPoint(Dictifiable, RepresentById):
dict_collection_visible_keys = ['id', 'name', 'active']
dict_element_visible_keys = ['id', 'name', 'active']
def __init__(self, job=None, name=None, token=None, tool_port=None, host=None, port=None, protocol=None,
entry_url=None, info=None, configured=False, deleted=False):
self.job = job
self.name = name
if not token:
token = uuid4().hex
self.token = token
self.tool_port = tool_port
self.host = host
self.port = port
self.protocol = protocol
self.entry_url = entry_url
self.info = info or {}
self.configured = configured
self.deleted = deleted
@property
def active(self):
if self.configured and not self.deleted:
# FIXME: don't included queued?
return not self.job.finished
return False
[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
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]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
dataset.history = self
dataset.history_id = cached_id(self)
if set_genome:
self.genome_build = genome_build
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 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 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
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 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))])
# 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 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
validated_states = Bunch(
UNKNOWN='unknown',
INVALID='invalid',
OK='ok',
)
[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, validated_state='unknown', validated_state_message=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
self.validated_state = validated_state
self.validated_state_message = validated_state_message
# 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
@property
def peek(self):
return self._peek
@peek.setter
def peek(self, peek):
self._peek = unicodify(peek, strip_null=True)
@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()
file_name = property(get_file_name, set_file_name)
[docs] def link_to(self, path):
self.file_name = os.path.abspath(path)
# Since we are not copying the file into Galaxy's managed
# default file location, the dataset should never be purgable.
self.dataset.purgable = False
@property
def extra_files_path(self):
return self.dataset.extra_files_path
@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 set_created_from_basename(self, created_from_basename):
if self.dataset.created_from_basename is not None:
raise Exception("Underlying dataset already has a created_from_basename set.")
self.dataset.created_from_basename = created_from_basename
created_from_basename = property(get_created_from_basename, set_created_from_basename)
[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 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 as_display_type(self, type, **kwd):
return self.datatype.as_display_type(self, type, **kwd)
[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 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)
@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_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 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,
validated_state=hda.validated_state,
validated_state_message=hda.validated_state_message,
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'
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'
name = property(get_name, set_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,
created_from_basename=ldda.created_from_basename,
uploaded_by=ldda.user.email,
message=ldda.message,
date_uploaded=ldda.create_time.isoformat(),
update_time=ldda.update_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 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,
created_from_basename=ldda.created_from_basename)
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] def update_parent_folder_update_times(self):
# sets the update_time for all continaing folders up the tree
ldda = self
sql = text(
'''
WITH RECURSIVE parent_folders_of(folder_id) AS
(SELECT folder_id
FROM library_dataset
WHERE id = :library_dataset_id
UNION ALL
SELECT library_folder.parent_id
FROM library_folder, parent_folders_of
WHERE library_folder.id = parent_folders_of.folder_id )
UPDATE library_folder
SET update_time =
(SELECT update_time
FROM library_dataset_dataset_association
WHERE id = :ldda_id)
WHERE exists (SELECT 1 FROM parent_folders_of
WHERE library_folder.id = parent_folders_of.folder_id)
''').execution_options(autocommit=True)
ret = object_session(self).execute(sql, {'library_dataset_id': ldda.library_dataset_id, 'ldda_id': ldda.id})
if ret.rowcount < 1:
log.warn('Attempt to updated parent folder times failed: {0} records updated.'.format(ret.rowcount))
[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, unicodify(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 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
@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
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)
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 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
[docs] def workflow_output_for(self, output_label):
target_output = None
for workflow_output in self.workflow_outputs:
if workflow_output.label == output_label:
target_output = workflow_output
break
return target_output
@property
def workflow_output_labels(self):
names = []
for workflow_output in self.workflow_outputs:
names.append(workflow_output.label)
return names
@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]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 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', 'create_time', 'workflow_id', 'history_id', 'uuid', 'state']
dict_element_visible_keys = ['id', 'update_time', 'create_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',
)
non_terminal_states = [states.NEW, states.READY]
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 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] def step_invocation_for_label(self, label):
target_invocation_step = None
for invocation_step in self.steps:
if label == invocation_step.workflow_step.label:
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 get_output_object(self, label):
for output_dataset_assoc in self.output_datasets:
if output_dataset_assoc.workflow_output.label == label:
return output_dataset_assoc.dataset
for output_dataset_collection_assoc in self.output_dataset_collections:
if output_dataset_collection_assoc.workflow_output.label == label:
return output_dataset_collection_assoc.dataset_collection
# That probably isn't good.
workflow_output = self.workflow.workflow_output_for(label)
if workflow_output:
raise Exception("Failed to find workflow output named [%s], one was defined but none registered during execution." % label)
else:
raise Exception("Failed to find workflow output named [%s], workflow doesn't define output by that name - valid names are %s." % (label, self.workflow.workflow_output_labels))
[docs] def get_input_object(self, label):
for input_dataset_assoc in self.input_datasets:
if input_dataset_assoc.workflow_step.label == label:
return input_dataset_assoc.dataset
for input_dataset_collection_assoc in self.input_dataset_collections:
if input_dataset_collection_assoc.workflow_step.label == label:
return input_dataset_collection_assoc.dataset_collection
raise Exception("Failed to find input with label %s" % label)
@property
def output_associations(self):
outputs = []
for output_dataset_assoc in self.output_datasets:
outputs.append(output_dataset_assoc)
for output_dataset_collection_assoc in self.output_dataset_collections:
outputs.append(output_dataset_collection_assoc)
return outputs
@property
def input_associations(self):
inputs = []
for input_dataset_assoc in self.input_datasets:
inputs.append(input_dataset_assoc)
for input_dataset_collection_assoc in self.input_dataset_collections:
inputs.append(input_dataset_collection_assoc)
return inputs
[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=None, step=None):
assert step_id is not None or step is not None
def attach_step(request_to_content):
if step_id is not None:
request_to_content.workflow_step_id = step_id
else:
request_to_content.workflow_step = step
history_content_type = getattr(content, "history_content_type", None)
if history_content_type == "dataset":
request_to_content = WorkflowRequestToInputDatasetAssociation()
request_to_content.dataset = content
attach_step(request_to_content)
self.input_datasets.append(request_to_content)
elif history_content_type == "dataset_collection":
request_to_content = WorkflowRequestToInputDatasetCollectionAssociation()
request_to_content.dataset_collection = content
attach_step(request_to_content)
self.input_dataset_collections.append(request_to_content)
else:
request_to_content = WorkflowRequestInputStepParameter()
request_to_content.parameter_value = content
attach_step(request_to_content)
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 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
)
@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.
"""
history_content_type = "dataset"
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.
"""
history_content_type = "dataset_collection"
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."""
history_content_type = "dataset"
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."""
history_content_type = "dataset_collection"
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] @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] @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] @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] @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] @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_exists(cls, *args, **kwargs):
return cls.user_query().filter_by(*args, **kwargs).count() > 0
[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_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 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 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 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.
# 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]class HistoryDatasetAssociationRatingAssociation(ItemRatingAssociation, RepresentById):
[docs] def set_item(self, history_dataset_association):
self.history_dataset_association = history_dataset_association
# 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