Warning

This document is for an old release 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.tools.parameters.basic

"""
Basic tool parameters.
"""

import contextlib
import json
import logging
import os
import os.path
import re
from typing import (
    Any,
    Dict,
    List,
    Optional,
    Tuple,
    Union,
)

from webob.compat import cgi_FieldStorage

from galaxy import util
from galaxy.files import ProvidesUserFileSourcesUserContext
from galaxy.model import (
    cached_id,
    Dataset,
    DatasetCollectionElement,
    DatasetInstance,
    HistoryDatasetAssociation,
    HistoryDatasetCollectionAssociation,
    LibraryDatasetDatasetAssociation,
)
from galaxy.schema.fetch_data import FilesPayload
from galaxy.tool_util.parser import get_input_source as ensure_input_source
from galaxy.util import (
    dbkeys,
    sanitize_param,
    string_as_bool,
    string_as_bool_or_none,
    unicodify,
    XML,
)
from galaxy.util.dictifiable import Dictifiable
from galaxy.util.expressions import ExpressionContext
from galaxy.util.rules_dsl import RuleSet
from . import (
    dynamic_options,
    history_query,
    validation,
)
from .dataset_matcher import get_dataset_matcher_factory
from .sanitize import ToolParameterSanitizer

log = logging.getLogger(__name__)


[docs]class workflow_building_modes: DISABLED = False ENABLED = True USE_HISTORY = 1
WORKFLOW_PARAMETER_REGULAR_EXPRESSION = re.compile(r"\$\{.+?\}")
[docs]class ImplicitConversionRequired(Exception): pass
[docs]def contains_workflow_parameter(value, search=False): if not isinstance(value, str): return False if search and WORKFLOW_PARAMETER_REGULAR_EXPRESSION.search(value): return True if not search and WORKFLOW_PARAMETER_REGULAR_EXPRESSION.match(value): return True return False
[docs]def is_runtime_value(value): return isinstance(value, RuntimeValue) or ( isinstance(value, dict) and value.get("__class__") in ["RuntimeValue", "ConnectedValue"] )
[docs]def is_runtime_context(trans, other_values): if trans.workflow_building_mode: return True for context_value in other_values.values(): if is_runtime_value(context_value): return True for v in util.listify(context_value): if isinstance(v, HistoryDatasetAssociation) and ( (hasattr(v, "state") and v.state != Dataset.states.OK) or hasattr(v, "implicit_conversion") ): return True return False
[docs]def parse_dynamic_options(param, input_source): options_elem = input_source.parse_dynamic_options_elem() if options_elem is not None: return dynamic_options.DynamicOptions(options_elem, param) return None
# Describe a parameter value error where there is no actual supplied # parameter - e.g. just a specification issue. NO_PARAMETER_VALUE = object()
[docs]@contextlib.contextmanager def assert_throws_param_value_error(message): exception_thrown = False try: yield except ParameterValueError as e: exception_thrown = True assert str(e) == message assert exception_thrown
[docs]class ParameterValueError(ValueError):
[docs] def __init__(self, message_suffix, parameter_name, parameter_value=NO_PARAMETER_VALUE, is_dynamic=None): message = f"parameter '{parameter_name}': {message_suffix}" super().__init__(message) self.message_suffix = message_suffix self.parameter_name = parameter_name self.parameter_value = parameter_value self.is_dynamic = is_dynamic
[docs] def to_dict(self): as_dict = {"message": unicodify(self)} as_dict["message_suffix"] = self.message_suffix as_dict["parameter_name"] = self.parameter_name if self.parameter_value is not NO_PARAMETER_VALUE: as_dict["parameter_value"] = self.parameter_value if self.is_dynamic is not None: as_dict["is_dynamic"] = self.is_dynamic return as_dict
[docs]class ToolParameter(Dictifiable): """ Describes a parameter accepted by a tool. This is just a simple stub at the moment but in the future should encapsulate more complex parameters (lists of valid choices, validation logic, ...) >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None) >>> p = ToolParameter(None, XML('<param argument="--parameter-name" type="text" value="default" />')) >>> assert p.name == 'parameter_name' >>> assert sorted(p.to_dict(trans).items()) == [('argument', '--parameter-name'), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'ToolParameter'), ('name', 'parameter_name'), ('optional', False), ('refresh_on_change', False), ('type', 'text'), ('value', None)] """ dict_collection_visible_keys = ["name", "argument", "type", "label", "help", "refresh_on_change"]
[docs] def __init__(self, tool, input_source, context=None): input_source = ensure_input_source(input_source) self.tool = tool self.argument = input_source.get("argument") self.name = self.__class__.parse_name(input_source) self.type = input_source.get("type") self.hidden = input_source.get_bool("hidden", False) self.refresh_on_change = input_source.get_bool("refresh_on_change", False) self.optional = input_source.parse_optional() self.optionality_inferred = False self.is_dynamic = False self.label = input_source.parse_label() self.help = input_source.parse_help() sanitizer_elem = input_source.parse_sanitizer_elem() if sanitizer_elem is not None: self.sanitizer = ToolParameterSanitizer.from_element(sanitizer_elem) else: self.sanitizer = None self.validators = [] for elem in input_source.parse_validator_elems(): self.validators.append(validation.Validator.from_element(self, elem))
@property def visible(self): """Return true if the parameter should be rendered on the form""" return True
[docs] def get_label(self): """Return user friendly name for the parameter""" return self.label if self.label else self.name
[docs] def from_json(self, value, trans=None, other_values=None): """ Convert a value from an HTML POST into the parameters preferred value format. """ return value
[docs] def get_initial_value(self, trans, other_values): """ Return the starting value of the parameter """ return None
[docs] def get_required_enctype(self): """ If this parameter needs the form to have a specific encoding return it, otherwise return None (indicating compatibility with any encoding) """ return None
[docs] def get_dependencies(self): """ Return the names of any other parameters this parameter depends on """ return []
[docs] def to_json(self, value, app, use_security): """Convert a value to a string representation suitable for persisting""" return unicodify(value)
[docs] def to_python(self, value, app): """Convert a value created with to_json back to an object representation""" return value
[docs] def value_to_basic(self, value, app, use_security=False): if is_runtime_value(value): return runtime_to_json(value) return self.to_json(value, app, use_security)
[docs] def value_from_basic(self, value, app, ignore_errors=False): # Handle Runtime and Unvalidated values if is_runtime_value(value): if isinstance(self, HiddenToolParameter): raise ParameterValueError(message_suffix="Runtime Parameter not valid", parameter_name=self.name) return runtime_to_object(value) elif isinstance(value, dict) and value.get("__class__") == "UnvalidatedValue": return value["value"] # Delegate to the 'to_python' method if ignore_errors: try: return self.to_python(value, app) except Exception: return value else: return self.to_python(value, app)
[docs] def value_to_display_text(self, value): if is_runtime_value(value): return "Not available." return self.to_text(value)
[docs] def to_text(self, value): """ Convert a value to a text representation suitable for displaying to the user >>> p = ToolParameter(None, XML('<param name="_name" />')) >>> print(p.to_text(None)) Not available. >>> print(p.to_text('')) Empty. >>> print(p.to_text('text')) text >>> print(p.to_text(True)) True >>> print(p.to_text(False)) False >>> print(p.to_text(0)) 0 """ if value is not None: str_value = unicodify(value) if not str_value: return "Empty." return str_value return "Not available."
[docs] def to_param_dict_string(self, value, other_values=None) -> str: """Called via __str__ when used in the Cheetah template""" if value is None: value = "" elif not isinstance(value, str): value = str(value) if self.tool is None or self.tool.options.sanitize: if self.sanitizer: value = self.sanitizer.sanitize_param(value) else: value = sanitize_param(value) return value
[docs] def validate(self, value, trans=None): if value in ["", None] and self.optional: return for validator in self.validators: validator.validate(value, trans)
[docs] def to_dict(self, trans, other_values=None): """to_dict tool parameter. This can be overridden by subclasses.""" other_values = other_values or {} tool_dict = super().to_dict() tool_dict["model_class"] = self.__class__.__name__ tool_dict["optional"] = self.optional tool_dict["hidden"] = self.hidden tool_dict["is_dynamic"] = self.is_dynamic tool_dict["value"] = self.value_to_basic( self.get_initial_value(trans, other_values), trans.app, use_security=True ) return tool_dict
[docs] @classmethod def build(cls, tool, input_source): """Factory method to create parameter of correct type""" input_source = ensure_input_source(input_source) param_name = cls.parse_name(input_source) param_type = input_source.get("type") if not param_type: raise ValueError(f"parameter '{param_name}' requires a 'type'") elif param_type not in parameter_types: raise ValueError(f"parameter '{param_name}' uses an unknown type '{param_type}'") else: return parameter_types[param_type](tool, input_source)
[docs] @staticmethod def parse_name(input_source): return input_source.parse_name()
[docs]class SimpleTextToolParameter(ToolParameter):
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) optional = input_source.get("optional", None) if optional is not None: optional = string_as_bool(optional) else: # Optionality not explicitly defined, default to False optional = False if self.type == "text": # A text parameter that doesn't raise a validation error on empty string # is considered to be optional try: for validator in self.validators: validator.validate("") optional = True self.optionality_inferred = True except ValueError: pass self.optional = optional if self.optional: self.value = None else: self.value = ""
[docs] def to_json(self, value, app, use_security): """Convert a value to a string representation suitable for persisting""" if value is None: rval = "" if not self.optional else None else: rval = unicodify(value) return rval
[docs] def get_initial_value(self, trans, other_values): return self.value
[docs]class TextToolParameter(SimpleTextToolParameter): """ Parameter that can take on any text value. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None) >>> p = TextToolParameter(None, XML('<param name="_name" type="text" value="default" />')) >>> print(p.name) _name >>> sorted(p.to_dict(trans).items()) [('area', False), ('argument', None), ('datalist', []), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'TextToolParameter'), ('name', '_name'), ('optional', True), ('refresh_on_change', False), ('type', 'text'), ('value', 'default')] """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.datalist = [] for title, value, _ in input_source.parse_static_options(): self.datalist.append({"label": title, "value": value}) self.value = input_source.get("value") self.area = input_source.get_bool("area", False)
[docs] def validate(self, value, trans=None): search = self.type == "text" if not ( trans and trans.workflow_building_mode is workflow_building_modes.ENABLED and contains_workflow_parameter(value, search=search) ): return super().validate(value, trans)
[docs] def to_dict(self, trans, other_values=None): d = super().to_dict(trans) other_values = other_values or {} d["area"] = self.area d["datalist"] = self.datalist d["optional"] = self.optional return d
[docs]class IntegerToolParameter(TextToolParameter): """ Parameter that takes an integer value. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(), workflow_building_mode=True) >>> p = IntegerToolParameter(None, XML('<param name="_name" type="integer" value="10" />')) >>> print(p.name) _name >>> assert sorted(p.to_dict(trans).items()) == [('area', False), ('argument', None), ('datalist', []), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('max', None), ('min', None), ('model_class', 'IntegerToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'integer'), ('value', u'10')] >>> assert type(p.from_json("10", trans)) == int >>> with assert_throws_param_value_error("parameter '_name': an integer or workflow parameter is required"): ... p.from_json("_string", trans) """ dict_collection_visible_keys = ToolParameter.dict_collection_visible_keys + ["min", "max"]
[docs] def __init__(self, tool, input_source): super().__init__(tool, input_source) if self.value: try: int(self.value) except ValueError: raise ParameterValueError("the attribute 'value' must be an integer", self.name) elif self.value is None and not self.optional: raise ParameterValueError("the attribute 'value' must be set for non optional parameters", self.name, None) self.min = input_source.get("min") self.max = input_source.get("max") if self.min: try: self.min = int(self.min) except ValueError: raise ParameterValueError("attribute 'min' must be an integer", self.name, self.min) if self.max: try: self.max = int(self.max) except ValueError: raise ParameterValueError("attribute 'max' must be an integer", self.name, self.max) if self.min is not None or self.max is not None: self.validators.append(validation.InRangeValidator(None, self.min, self.max))
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} try: return int(value) except (TypeError, ValueError): if contains_workflow_parameter(value) and trans.workflow_building_mode is workflow_building_modes.ENABLED: return value if not value and self.optional: return "" if trans.workflow_building_mode is workflow_building_modes.ENABLED: raise ParameterValueError("an integer or workflow parameter is required", self.name, value) else: raise ParameterValueError( "the attribute 'value' must be set for non optional parameters", self.name, value )
[docs] def to_python(self, value, app): try: return int(value) except (TypeError, ValueError) as err: if contains_workflow_parameter(value): return value if not value and self.optional: return None raise err
[docs] def get_initial_value(self, trans, other_values): if self.value is not None and self.value != "": return int(self.value) else: return None
[docs]class FloatToolParameter(TextToolParameter): """ Parameter that takes a real number value. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(), workflow_building_mode=True) >>> p = FloatToolParameter(None, XML('<param name="_name" type="float" value="3.141592" />')) >>> print(p.name) _name >>> assert sorted(p.to_dict(trans).items()) == [('area', False), ('argument', None), ('datalist', []), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('max', None), ('min', None), ('model_class', 'FloatToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'float'), ('value', u'3.141592')] >>> assert type(p.from_json("36.1", trans)) == float >>> with assert_throws_param_value_error("parameter '_name': an integer or workflow parameter is required"): ... p.from_json("_string", trans) """ dict_collection_visible_keys = ToolParameter.dict_collection_visible_keys + ["min", "max"]
[docs] def __init__(self, tool, input_source): super().__init__(tool, input_source) self.min = input_source.get("min") self.max = input_source.get("max") if self.value: try: float(self.value) except ValueError: raise ParameterValueError("the attribute 'value' must be a real number", self.name, self.value) elif self.value is None and not self.optional: raise ParameterValueError("the attribute 'value' must be set for non optional parameters", self.name, None) if self.min: try: self.min = float(self.min) except ValueError: raise ParameterValueError("attribute 'min' must be a real number", self.name, self.min) if self.max: try: self.max = float(self.max) except ValueError: raise ParameterValueError("attribute 'max' must be a real number", self.name, self.max) if self.min is not None or self.max is not None: self.validators.append(validation.InRangeValidator(None, self.min, self.max))
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} try: return float(value) except (TypeError, ValueError): if contains_workflow_parameter(value) and trans.workflow_building_mode is workflow_building_modes.ENABLED: return value if not value and self.optional: return "" if trans.workflow_building_mode is workflow_building_modes.ENABLED: raise ParameterValueError("an integer or workflow parameter is required", self.name, value) else: raise ParameterValueError( "the attribute 'value' must be set for non optional parameters", self.name, value )
[docs] def to_python(self, value, app): try: return float(value) except (TypeError, ValueError) as err: if contains_workflow_parameter(value): return value if not value and self.optional: return None raise err
[docs] def get_initial_value(self, trans, other_values): if self.value is None: return None try: return float(self.value) except Exception: return None
[docs]class BooleanToolParameter(ToolParameter): """ Parameter that takes one of two values. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch()) >>> p = BooleanToolParameter(None, XML('<param name="_name" type="boolean" checked="yes" truevalue="_truevalue" falsevalue="_falsevalue" />')) >>> print(p.name) _name >>> assert sorted(p.to_dict(trans).items()) == [('argument', None), ('falsevalue', '_falsevalue'), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'BooleanToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('truevalue', '_truevalue'), ('type', 'boolean'), ('value', True)] >>> print(p.from_json('true')) True >>> print(p.to_param_dict_string(True)) _truevalue >>> print(p.from_json('false')) False >>> print(p.to_param_dict_string(False)) _falsevalue >>> value = p.to_json('false', trans.app, use_security=False) >>> assert isinstance(value, bool) >>> assert value == False >>> value = p.to_json(True, trans.app, use_security=False) >>> assert isinstance(value, bool) >>> assert value == True """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.truevalue = input_source.get("truevalue", "true") self.falsevalue = input_source.get("falsevalue", "false") nullable = input_source.get_bool("optional", False) self.optional = nullable self.checked = input_source.get_bool("checked", None if nullable else False)
[docs] def from_json(self, value, trans=None, other_values=None): return self.to_python(value)
[docs] def to_python(self, value, app=None): if not self.optional: ret_val = string_as_bool(value) else: ret_val = string_as_bool_or_none(value) return ret_val
[docs] def to_json(self, value, app, use_security): return self.to_python(value, app)
[docs] def get_initial_value(self, trans, other_values): return self.checked
[docs] def to_param_dict_string(self, value, other_values=None): if self.to_python(value): return self.truevalue else: return self.falsevalue
[docs] def to_dict(self, trans, other_values=None): d = super().to_dict(trans) d["truevalue"] = self.truevalue d["falsevalue"] = self.falsevalue d["optional"] = self.optional return d
@property def legal_values(self): return [self.truevalue, self.falsevalue]
[docs]class FileToolParameter(ToolParameter): """ Parameter that takes an uploaded file as a value. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch()) >>> p = FileToolParameter(None, XML('<param name="_name" type="file"/>')) >>> print(p.name) _name >>> sorted(p.to_dict(trans).items()) [('argument', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'FileToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'file'), ('value', None)] """
[docs] def __init__(self, tool, input_source): super().__init__(tool, input_source)
[docs] def from_json(self, value, trans=None, other_values=None): # Middleware or proxies may encode files in special ways (TODO: this # should be pluggable) if isinstance(value, FilesPayload): # multi-part upload handled and persisted in service layer return value.dict() if type(value) == dict: if "session_id" in value: # handle api upload session_id = value["session_id"] upload_store = trans.app.config.tus_upload_store or trans.app.config.new_file_path if re.match(r"^[\w-]+$", session_id) is None: raise ValueError("Invalid session id format.") local_filename = os.path.abspath(os.path.join(upload_store, session_id)) if upload_store != trans.app.config.new_file_path and not os.path.exists(local_filename): # Fallback for old chunked API, remove in 22.05 local_filename = os.path.abspath(os.path.join(trans.app.config.new_file_path, session_id)) else: # handle nginx upload upload_store = trans.app.config.nginx_upload_store assert ( upload_store ), "Request appears to have been processed by nginx_upload_module but Galaxy is not configured to recognize it." local_filename = os.path.abspath(value["path"]) assert local_filename.startswith( upload_store ), f"Filename provided by nginx ({local_filename}) is not in correct directory ({upload_store})." value = dict(filename=value["name"], local_filename=local_filename) return value
[docs] def get_required_enctype(self): """ File upload elements require the multipart/form-data encoding """ return "multipart/form-data"
[docs] def to_json(self, value, app, use_security): if value in [None, ""]: return None elif isinstance(value, str): return value elif isinstance(value, dict): # or should we jsonify? try: return value["local_filename"] except KeyError: return None elif isinstance(value, cgi_FieldStorage): return value.file.name raise Exception("FileToolParameter cannot be persisted")
[docs] def to_python(self, value, app): if value is None: return None elif isinstance(value, str): return value else: raise Exception("FileToolParameter cannot be persisted")
[docs]class FTPFileToolParameter(ToolParameter): """ Parameter that takes a file uploaded via FTP as a value. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(), user=None) >>> p = FTPFileToolParameter(None, XML('<param name="_name" type="ftpfile"/>')) >>> print(p.name) _name >>> sorted(p.to_dict(trans).items()) [('argument', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'FTPFileToolParameter'), ('multiple', True), ('name', '_name'), ('optional', True), ('refresh_on_change', False), ('type', 'ftpfile'), ('value', None)] """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.multiple = input_source.get_bool("multiple", True) self.optional = input_source.parse_optional(True) self.user_ftp_dir = ""
[docs] def get_initial_value(self, trans, other_values): if trans is not None: if trans.user is not None: self.user_ftp_dir = f"{trans.user_ftp_dir}/" return None
@property def visible(self): if self.tool.app.config.ftp_upload_dir is None or self.tool.app.config.ftp_upload_site is None: return False return True
[docs] def to_param_dict_string(self, value, other_values=None): if value == "": return "None" lst = [f"{self.user_ftp_dir}{dataset}" for dataset in value] if self.multiple: return lst else: return lst[0]
[docs] def from_json(self, value, trans=None, other_values=None): return self.to_python(value, trans.app, validate=True)
[docs] def to_json(self, value, app, use_security): return self.to_python(value, app)
[docs] def to_python(self, value, app, validate=False): if not isinstance(value, list): value = [value] lst: List[str] = [] for val in value: if val in [None, ""]: lst = [] break if isinstance(val, dict): lst.append(val["name"]) else: lst.append(val) if len(lst) == 0: if not self.optional and validate: raise ValueError("Please select a valid FTP file.") return None if validate and self.tool.app.config.ftp_upload_dir is None: raise ValueError("The FTP directory is not configured.") return lst
[docs] def to_dict(self, trans, other_values=None): d = super().to_dict(trans) d["multiple"] = self.multiple return d
[docs]class HiddenToolParameter(ToolParameter): """ Parameter that takes one of two values. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch()) >>> p = HiddenToolParameter(None, XML('<param name="_name" type="hidden" value="_value"/>')) >>> print(p.name) _name >>> assert sorted(p.to_dict(trans).items()) == [('argument', None), ('help', ''), ('hidden', True), ('is_dynamic', False), ('label', ''), ('model_class', 'HiddenToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'hidden'), ('value', u'_value')] """
[docs] def __init__(self, tool, input_source): super().__init__(tool, input_source) self.value = input_source.get("value") self.hidden = True
[docs] def get_initial_value(self, trans, other_values): return self.value
[docs] def get_label(self): return None
[docs]class ColorToolParameter(ToolParameter): """ Parameter that stores a color. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch()) >>> p = ColorToolParameter(None, XML('<param name="_name" type="color" value="#ffffff"/>')) >>> print(p.name) _name >>> print(p.to_param_dict_string("#ffffff")) #ffffff >>> assert sorted(p.to_dict(trans).items()) == [('argument', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'ColorToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'color'), ('value', u'#ffffff')] >>> p = ColorToolParameter(None, XML('<param name="_name" type="color"/>')) >>> print(p.get_initial_value(trans, {})) #000000 >>> p = ColorToolParameter(None, XML('<param name="_name" type="color" value="#ffffff" rgb="True"/>')) >>> print(p.to_param_dict_string("#ffffff")) (255, 255, 255) >>> with assert_throws_param_value_error("parameter '_name': Failed to convert 'None' to RGB."): ... p.to_param_dict_string(None) """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.value = input_source.get("value", "#000000") self.rgb = input_source.get("rgb", False)
[docs] def get_initial_value(self, trans, other_values): if self.value is not None: return self.value.lower()
[docs] def to_param_dict_string(self, value, other_values=None): if self.rgb: try: return str(tuple(int(value.lstrip("#")[i : i + 2], 16) for i in (0, 2, 4))) except Exception: raise ParameterValueError(f"Failed to convert '{value}' to RGB.", self.name) return str(value)
[docs]class BaseURLToolParameter(HiddenToolParameter): """ Returns a parameter that contains its value prepended by the current server base url. Used in all redirects. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch()) >>> p = BaseURLToolParameter(None, XML('<param name="_name" type="base_url" value="_value"/>')) >>> print(p.name) _name >>> assert sorted(p.to_dict(trans).items()) == [('argument', None), ('help', ''), ('hidden', True), ('is_dynamic', False), ('label', ''), ('model_class', 'BaseURLToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'base_url'), ('value', u'_value')] """
[docs] def __init__(self, tool, input_source): super().__init__(tool, input_source) self.value = input_source.get("value", "")
[docs] def get_initial_value(self, trans, other_values): return self._get_value(trans)
[docs] def from_json(self, value=None, trans=None, other_values=None): return self._get_value(trans)
def _get_value(self, trans): try: if not self.value.startswith("/"): raise Exception("baseurl value must start with a /") return trans.url_builder(self.value, qualified=True) except Exception as e: log.debug('Url creation failed for "%s": %s', self.name, unicodify(e)) return self.value
[docs] def to_dict(self, trans, other_values=None): d = super().to_dict(trans) return d
[docs]class SelectToolParameter(ToolParameter): """ Parameter that takes on one (or many) or a specific set of values. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(), workflow_building_mode=False) >>> p = SelectToolParameter(None, XML( ... ''' ... <param name="_name" type="select"> ... <option value="x">x_label</option> ... <option value="y" selected="true">y_label</option> ... <option value="z">z_label</option> ... </param> ... ''')) >>> print(p.name) _name >>> sorted(p.to_dict(trans).items()) [('argument', None), ('display', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'SelectToolParameter'), ('multiple', False), ('name', '_name'), ('optional', False), ('options', [('x_label', 'x', False), ('y_label', 'y', True), ('z_label', 'z', False)]), ('refresh_on_change', False), ('textable', False), ('type', 'select'), ('value', 'y')] >>> p = SelectToolParameter(None, XML( ... ''' ... <param name="_name" type="select" multiple="true"> ... <option value="x">x_label</option> ... <option value="y" selected="true">y_label</option> ... <option value="z" selected="true">z_label</option> ... </param> ... ''')) >>> print(p.name) _name >>> sorted(p.to_dict(trans).items()) [('argument', None), ('display', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'SelectToolParameter'), ('multiple', True), ('name', '_name'), ('optional', True), ('options', [('x_label', 'x', False), ('y_label', 'y', True), ('z_label', 'z', True)]), ('refresh_on_change', False), ('textable', False), ('type', 'select'), ('value', ['y', 'z'])] >>> print(p.to_param_dict_string(["y", "z"])) y,z """ value_label: str
[docs] def __init__(self, tool, input_source, context=None): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.multiple = input_source.get_bool("multiple", False) # Multiple selects are optional by default, single selection is the inverse. self.optional = input_source.parse_optional(self.multiple) self.display = input_source.get("display", None) self.separator = input_source.get("separator", ",") self.legal_values = set() self.dynamic_options = input_source.get("dynamic_options", None) self.options = parse_dynamic_options(self, input_source) if self.options is not None: for validator in self.options.validators: self.validators.append(validator) if self.dynamic_options is None and self.options is None: self.static_options = input_source.parse_static_options() for _, value, _ in self.static_options: self.legal_values.add(value) self.is_dynamic = (self.dynamic_options is not None) or (self.options is not None)
def _get_dynamic_options_call_other_values(self, trans, other_values): call_other_values = ExpressionContext({"__trans__": trans}) if other_values: call_other_values.parent = other_values.parent call_other_values.update(other_values.dict) return call_other_values
[docs] def get_options(self, trans, other_values): if self.options: return self.options.get_options(trans, other_values) elif self.dynamic_options: call_other_values = self._get_dynamic_options_call_other_values(trans, other_values) try: return eval(self.dynamic_options, self.tool.code_namespace, call_other_values) except Exception as e: log.debug( "Error determining dynamic options for parameter '%s' in tool '%s':", self.name, self.tool.id, exc_info=e, ) return [] else: return self.static_options
[docs] def from_json(self, value, trans, other_values=None, require_legal_value=True): other_values = other_values or {} try: legal_values = self.get_legal_values(trans, other_values, value) except ImplicitConversionRequired: return value # if the given value is not found in the set of values of the legal # options we fall back to check if the value is in the set of names of # the legal options. this is done with the fallback_values dict which # allows to determine the corresponding legal values fallback_values = self.get_legal_names(trans, other_values) if (not legal_values or not require_legal_value) and is_runtime_context(trans, other_values): if self.multiple: # While it is generally allowed that a select value can be '', # we do not allow this to be the case in a dynamically # generated multiple select list being set in workflow building # mode we instead treat '' as 'No option Selected' (None) if value == "": value = None else: if isinstance(value, str): # Split on all whitespace. This not only provides flexibility # in interpreting values but also is needed because many browsers # use \r\n to separate lines. value = value.split() return value elif value is None: if self.optional: return None raise ParameterValueError( "an invalid option (None) was selected, please verify", self.name, None, is_dynamic=self.is_dynamic ) elif not legal_values: if self.optional and self.tool.profile < 18.09: # Covers optional parameters with default values that reference other optional parameters. # These will have a value but no legal_values. # See https://github.com/galaxyproject/tools-iuc/pull/1842#issuecomment-394083768 for context. return None raise ParameterValueError( "requires a value, but no legal values defined", self.name, is_dynamic=self.is_dynamic ) if isinstance(value, list): if not self.multiple: raise ParameterValueError( "multiple values provided but parameter is not expecting multiple values", self.name, is_dynamic=self.is_dynamic, ) if set(value).issubset(legal_values): return value elif set(value).issubset(set(fallback_values.keys())): return [fallback_values[v] for v in value] else: raise ParameterValueError( f"invalid options ({','.join(set(value) - set(legal_values))!r}) were selected (valid options: {','.join(legal_values)})", self.name, is_dynamic=self.is_dynamic, ) else: value_is_none = value == "None" and "None" not in legal_values if value_is_none or not value: if self.multiple: if self.optional: return [] else: raise ParameterValueError( "no option was selected for non optional parameter", self.name, is_dynamic=self.is_dynamic ) if is_runtime_value(value): return None if value in legal_values: return value elif value in fallback_values: return fallback_values[value] elif not require_legal_value: return value else: raise ParameterValueError( f"an invalid option ({value!r}) was selected (valid options: {','.join(legal_values)})", self.name, value, is_dynamic=self.is_dynamic, )
[docs] def to_param_dict_string(self, value, other_values=None): if value in (None, []): return "None" if isinstance(value, list): if not self.multiple: raise ParameterValueError( "multiple values provided but parameter is not expecting multiple values", self.name, is_dynamic=self.is_dynamic, ) value = list(map(str, value)) else: value = str(value) if self.tool is None or self.tool.options.sanitize: if self.sanitizer: value = self.sanitizer.sanitize_param(value) else: value = sanitize_param(value) if isinstance(value, list): value = self.separator.join(value) return value
[docs] def to_json(self, value, app, use_security): return value
[docs] def get_initial_value(self, trans, other_values): try: options = list(self.get_options(trans, other_values)) except ImplicitConversionRequired: return None if not options: return None value = [optval for _, optval, selected in options if selected] if len(value) == 0: if not self.optional and not self.multiple and options: # Nothing selected, but not optional and not a multiple select, with some values, # so we have to default to something (the HTML form will anyway) value2 = options[0][1] else: value2 = None elif len(value) == 1 or not self.multiple: value2 = value[0] else: value2 = value return value2
[docs] def to_text(self, value): if not isinstance(value, list): value = [value] # FIXME: Currently only translating values back to labels if they # are not dynamic if self.is_dynamic: rval = [str(_) for _ in value] else: options = list(self.static_options) rval = [] for t, v, _ in options: if v in value: rval.append(t) if rval: return "\n".join(rval) return "Nothing selected."
[docs] def get_dependencies(self): """ Get the *names* of the other params this param depends on. """ if self.options: return self.options.get_dependency_names() else: return []
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} d = super().to_dict(trans, other_values) # Get options, value. options = self.get_options(trans, other_values) d["options"] = options d["display"] = self.display d["multiple"] = self.multiple d["textable"] = is_runtime_context(trans, other_values) return d
[docs] def validate(self, value, trans=None): if not value: super().validate(value, trans) if self.multiple: if not isinstance(value, list): value = [value] else: value = [value] for v in value: super().validate(v, trans)
[docs]class GenomeBuildParameter(SelectToolParameter): """ Select list that sets the last used genome build for the current history as "selected". >>> # Create a mock transaction with 'hg17' as the current build >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(genome_build='hg17'), db_builds=dbkeys.read_dbnames(None)) >>> p = GenomeBuildParameter(None, XML('<param name="_name" type="genomebuild" value="hg17" />')) >>> print(p.name) _name >>> d = p.to_dict(trans) >>> o = d['options'] >>> [i for i in o if i[2] == True] [('Human May 2004 (NCBI35/hg17) (hg17)', 'hg17', True)] >>> [i for i in o if i[1] == 'hg18'] [('Human Mar. 2006 (NCBI36/hg18) (hg18)', 'hg18', False)] >>> p.is_dynamic True """
[docs] def __init__(self, *args, **kwds): super().__init__(*args, **kwds) if self.tool: self.static_options = [(value, key, False) for key, value in self._get_dbkey_names()] self.is_dynamic = True
[docs] def get_options(self, trans, other_values): last_used_build = object() if trans.history: last_used_build = trans.history.genome_build for dbkey, build_name in self._get_dbkey_names(trans=trans): yield build_name, dbkey, (dbkey == last_used_build)
[docs] def to_dict(self, trans, other_values=None): # skip SelectToolParameter (the immediate parent) bc we need to get options in a different way here d = ToolParameter.to_dict(self, trans) # Get options, value - options is a generator here, so compile to list options = list(self.get_options(trans, {})) value = options[0][1] for option in options: if option[2]: # Found selected option. value = option[1] d.update( { "options": options, "value": value, "display": self.display, "multiple": self.multiple, } ) return d
def _get_dbkey_names(self, trans=None): if not self.tool: # Hack for unit tests, since we have no tool return dbkeys.read_dbnames(None) return self.tool.app.genome_builds.get_genome_build_names(trans=trans)
[docs]class SelectTagParameter(SelectToolParameter): """ Select set that is composed of a set of tags available for an input. """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.tool = tool self.tag_key = input_source.get("group", False) self.optional = input_source.get("optional", False) self.multiple = input_source.get("multiple", False) self.accept_default = input_source.get_bool("accept_default", False) if self.accept_default: self.optional = True self.data_ref = input_source.get("data_ref", None) self.ref_input = None # Legacy style default value specification... self.default_value = input_source.get("default_value", None) if self.default_value is None: # Newer style... more in line with other parameters. self.default_value = input_source.get("value", None) self.is_dynamic = True
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} if self.multiple: tag_list = [] # split on newline and , if isinstance(value, list) or isinstance(value, str): if not isinstance(value, list): value = value.split("\n") for tag_str in value: for tag in str(tag_str).split(","): tag = tag.strip() if tag: tag_list.append(tag) if len(tag_list) == 0: value = None else: value = tag_list else: if not value: value = None # We skip requiring legal values -- this is similar to optional, but allows only subset of datasets to be positive # TODO: May not actually be required for (nested) collection input ? return super().from_json(value, trans, other_values, require_legal_value=False)
[docs] def get_tag_list(self, other_values): """ Generate a select list containing the tags of the associated dataset (if found). """ # Get the value of the associated data reference (a dataset) history_items = other_values.get(self.data_ref, None) # Check if a dataset is selected if is_runtime_value(history_items): return [] if not history_items: return [] tags = set() for history_item in util.listify(history_items): if hasattr(history_item, "dataset_instances"): for dataset in history_item.dataset_instances: for tag in dataset.tags: if tag.user_tname == "group": tags.add(tag.user_value) else: for tag in history_item.tags: if tag.user_tname == "group": tags.add(tag.user_value) return list(tags)
[docs] def get_options(self, trans, other_values): """ Show tags """ options = [] for tag in self.get_tag_list(other_values): options.append((f"Tags: {tag}", tag, False)) return options
[docs] def get_initial_value(self, trans, other_values): if self.default_value is not None: return self.default_value return SelectToolParameter.get_initial_value(self, trans, other_values)
[docs] def get_dependencies(self): return [self.data_ref]
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} d = super().to_dict(trans, other_values=other_values) d["data_ref"] = self.data_ref return d
[docs]class ColumnListParameter(SelectToolParameter): """ Select list that consists of either the total number of columns or only those columns that contain numerical values in the associated DataToolParameter. # TODO: we need better testing here, but not sure how to associate a DatatoolParameter with a ColumnListParameter # from a twill perspective... >>> # Mock up a history (not connected to database) >>> from galaxy.model import History, HistoryDatasetAssociation >>> from galaxy.util.bunch import Bunch >>> from galaxy.model.mapping import init >>> sa_session = init("/tmp", "sqlite:///:memory:", create_tables=True).session >>> hist = History() >>> sa_session.add(hist) >>> sa_session.flush() >>> hda = hist.add_dataset(HistoryDatasetAssociation(id=1, extension='interval', create_dataset=True, sa_session=sa_session)) >>> dtp = DataToolParameter(None, XML('<param name="blah" type="data" format="interval"/>')) >>> print(dtp.name) blah >>> clp = ColumnListParameter(None, XML('<param name="numerical_column" type="data_column" data_ref="blah" numerical="true"/>')) >>> print(clp.name) numerical_column """
[docs] def __init__(self, tool, input_source): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.numerical = input_source.get_bool("numerical", False) self.optional = input_source.parse_optional(False) self.accept_default = input_source.get_bool("accept_default", False) if self.accept_default: self.optional = True self.data_ref = input_source.get("data_ref", None) self.ref_input = None # Legacy style default value specification... self.default_value = input_source.get("default_value", None) if self.default_value is None: # Newer style... more in line with other parameters. self.default_value = input_source.get("value", None) if self.default_value is not None: self.default_value = ColumnListParameter._strip_c(self.default_value) self.is_dynamic = True self.usecolnames = input_source.get_bool("use_header_names", False)
[docs] def to_json(self, value, app, use_security): if isinstance(value, str): return value.strip() return value
[docs] def from_json(self, value, trans, other_values=None): """ Label convention prepends column number with a 'c', but tool uses the integer. This removes the 'c' when entered into a workflow. """ other_values = other_values or {} if self.multiple: # split on newline and , if isinstance(value, list) or isinstance(value, str): column_list = [] if not isinstance(value, list): value = value.split("\n") for column in value: for column2 in str(column).split(","): column2 = column2.strip() if column2: column_list.append(column2) if len(column_list) == 0: value = None else: value = list(map(ColumnListParameter._strip_c, column_list)) else: value = None else: if value: value = ColumnListParameter._strip_c(value) else: value = None if not value and self.accept_default: value = self.default_value or "1" return [value] if self.multiple else value return super().from_json(value, trans, other_values)
@staticmethod def _strip_c(column): if isinstance(column, str): column = column.strip() if column.startswith("c") and len(column) > 1 and all(c.isdigit() for c in column[1:]): column = column.lower()[1:] return column
[docs] def get_column_list(self, trans, other_values): """ Generate a select list containing the columns of the associated dataset (if found). """ # Get the value of the associated data reference (one or more datasets) datasets = other_values.get(self.data_ref) # Check if a dataset is selected if not datasets: return [] column_list = None for dataset in util.listify(datasets): # Use representative dataset if a dataset collection is parsed if isinstance(dataset, HistoryDatasetCollectionAssociation): dataset = dataset.to_hda_representative() if isinstance(dataset, DatasetCollectionElement) and dataset.hda: dataset = dataset.hda if isinstance(dataset, HistoryDatasetAssociation) and self.ref_input and self.ref_input.formats: direct_match, target_ext, converted_dataset = dataset.find_conversion_destination( self.ref_input.formats ) if not direct_match and target_ext: if not converted_dataset: raise ImplicitConversionRequired else: dataset = converted_dataset # Columns can only be identified if the dataset is ready and metadata is available if ( not hasattr(dataset, "metadata") or not hasattr(dataset.metadata, "columns") or not dataset.metadata.columns ): return [] # Build up possible columns for this dataset this_column_list = [] if self.numerical: # If numerical was requested, filter columns based on metadata for i, col in enumerate(dataset.metadata.column_types): if col == "int" or col == "float": this_column_list.append(str(i + 1)) else: this_column_list = [str(i) for i in range(1, dataset.metadata.columns + 1)] # Take the intersection of these columns with the other columns. if column_list is None: column_list = this_column_list else: column_list = [c for c in column_list if c in this_column_list] return column_list
[docs] def get_options(self, trans, other_values): """ Show column labels rather than c1..cn if use_header_names=True """ options: List[Tuple[str, Union[str, Tuple[str, str]], bool]] = [] if self.usecolnames: # read first row - assume is a header with metadata useful for making good choices dataset = other_values.get(self.data_ref, None) try: with open(dataset.get_file_name()) as f: head = f.readline() cnames = head.rstrip("\n\r ").split("\t") column_list = [("%d" % (i + 1), "c%d: %s" % (i + 1, x)) for i, x in enumerate(cnames)] if self.numerical: # If numerical was requested, filter columns based on metadata if hasattr(dataset, "metadata") and hasattr(dataset.metadata, "column_types"): if len(dataset.metadata.column_types) >= len(cnames): numerics = [i for i, x in enumerate(dataset.metadata.column_types) if x in ["int", "float"]] column_list = [column_list[i] for i in numerics] except Exception: column_list = self.get_column_list(trans, other_values) else: column_list = self.get_column_list(trans, other_values) for col in column_list: if isinstance(col, tuple) and len(col) == 2: options.append((col[1], col[0], False)) else: options.append((f"Column: {col}", col, False)) return options
[docs] def get_initial_value(self, trans, other_values): if self.default_value is not None: return self.default_value return super().get_initial_value(trans, other_values)
[docs] def is_file_empty(self, trans, other_values): for dataset in util.listify(other_values.get(self.data_ref)): # Use representative dataset if a dataset collection is parsed if isinstance(dataset, HistoryDatasetCollectionAssociation): dataset = dataset.to_hda_representative() if isinstance(dataset, DatasetCollectionElement): dataset = dataset.hda if isinstance(dataset, DatasetInstance): return not dataset.has_data() if is_runtime_value(dataset): return True else: msg = f"Dataset '{dataset}' for data_ref attribute '{self.data_ref}' of parameter '{self.name}' is not a DatasetInstance" log.debug(msg, exc_info=True) raise ParameterValueError(msg, self.name) return False
[docs] def get_dependencies(self): return [self.data_ref]
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} d = super().to_dict(trans, other_values=other_values) d["data_ref"] = self.data_ref d["numerical"] = self.numerical return d
[docs]class DrillDownSelectToolParameter(SelectToolParameter): """ Parameter that takes on one (or many) of a specific set of values. Creating a hierarchical select menu, which allows users to 'drill down' a tree-like set of options. >>> from galaxy.util.bunch import Bunch >>> trans = Bunch(app=None, history=Bunch(genome_build='hg17'), db_builds=dbkeys.read_dbnames(None)) >>> p = DrillDownSelectToolParameter(None, XML( ... ''' ... <param name="_name" type="drill_down" display="checkbox" hierarchy="recurse" multiple="true"> ... <options> ... <option name="Heading 1" value="heading1"> ... <option name="Option 1" value="option1"/> ... <option name="Option 2" value="option2"/> ... <option name="Heading 2" value="heading2"> ... <option name="Option 3" value="option3"/> ... <option name="Option 4" value="option4"/> ... </option> ... </option> ... <option name="Option 5" value="option5"/> ... </options> ... </param> ... ''')) >>> print(p.name) _name >>> d = p.to_dict(trans) >>> assert d['multiple'] == True >>> assert d['display'] == 'checkbox' >>> assert d['options'][0]['name'] == 'Heading 1' >>> assert d['options'][0]['value'] == 'heading1' >>> assert d['options'][0]['options'][0]['name'] == 'Option 1' >>> assert d['options'][0]['options'][0]['value'] == 'option1' >>> assert d['options'][0]['options'][1]['name'] == 'Option 2' >>> assert d['options'][0]['options'][1]['value'] == 'option2' >>> assert d['options'][0]['options'][2]['name'] == 'Heading 2' >>> assert d['options'][0]['options'][2]['value'] == 'heading2' >>> assert d['options'][0]['options'][2]['options'][0]['name'] == 'Option 3' >>> assert d['options'][0]['options'][2]['options'][0]['value'] == 'option3' >>> assert d['options'][0]['options'][2]['options'][1]['name'] == 'Option 4' >>> assert d['options'][0]['options'][2]['options'][1]['value'] == 'option4' >>> assert d['options'][1]['name'] == 'Option 5' >>> assert d['options'][1]['value'] == 'option5' """
[docs] def __init__(self, tool, input_source, context=None): def recurse_option_elems(cur_options, option_elems): for option_elem in option_elems: selected = string_as_bool(option_elem.get("selected", False)) cur_options.append( { "name": option_elem.get("name"), "value": option_elem.get("value"), "options": [], "selected": selected, } ) recurse_option_elems(cur_options[-1]["options"], option_elem.findall("option")) input_source = ensure_input_source(input_source) ToolParameter.__init__(self, tool, input_source) # TODO: abstract XML out of here - so non-XML InputSources can # specify DrillDown parameters. elem = input_source.elem() self.multiple = string_as_bool(elem.get("multiple", False)) self.display = elem.get("display", None) self.hierarchy = elem.get("hierarchy", "exact") # exact or recurse self.separator = elem.get("separator", ",") from_file = elem.get("from_file", None) if from_file: if not os.path.isabs(from_file): from_file = os.path.join(tool.app.config.tool_data_path, from_file) elem = XML(f"<root>{open(from_file).read()}</root>") self.dynamic_options = elem.get("dynamic_options", None) if self.dynamic_options: self.is_dynamic = True self.options = [] self.filtered: Dict[str, Any] = {} if elem.find("filter"): self.is_dynamic = True for filter in elem.findall("filter"): # currently only filtering by metadata key matching input file is allowed if filter.get("type") == "data_meta": if filter.get("data_ref") not in self.filtered: self.filtered[filter.get("data_ref")] = {} if filter.get("meta_key") not in self.filtered[filter.get("data_ref")]: self.filtered[filter.get("data_ref")][filter.get("meta_key")] = {} if filter.get("value") not in self.filtered[filter.get("data_ref")][filter.get("meta_key")]: self.filtered[filter.get("data_ref")][filter.get("meta_key")][filter.get("value")] = [] recurse_option_elems( self.filtered[filter.get("data_ref")][filter.get("meta_key")][filter.get("value")], filter.find("options").findall("option"), ) elif not self.dynamic_options: recurse_option_elems(self.options, elem.find("options").findall("option"))
def _get_options_from_code(self, trans=None, value=None, other_values=None): assert self.dynamic_options, Exception("dynamic_options was not specifed") call_other_values = ExpressionContext({"__trans__": trans, "__value__": value}) if other_values: call_other_values.parent = other_values.parent call_other_values.update(other_values.dict) try: return eval(self.dynamic_options, self.tool.code_namespace, call_other_values) except Exception: return []
[docs] def get_options(self, trans=None, value=None, other_values=None): other_values = other_values or {} if self.is_dynamic: if self.dynamic_options: options = self._get_options_from_code(trans=trans, value=value, other_values=other_values) else: options = [] for filter_key, filter_value in self.filtered.items(): dataset = other_values.get(filter_key) if dataset.__class__.__name__.endswith( "DatasetFilenameWrapper" ): # this is a bad way to check for this, but problems importing class (due to circular imports?) dataset = dataset.dataset if dataset: for meta_key, meta_dict in filter_value.items(): if hasattr(dataset, "metadata") and hasattr(dataset.metadata, "spec"): check_meta_val = dataset.metadata.spec[meta_key].param.to_string( dataset.metadata.get(meta_key) ) if check_meta_val in meta_dict: options.extend(meta_dict[check_meta_val]) return options return self.options
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} legal_values = self.get_legal_values(trans, other_values, value) if not legal_values and trans.workflow_building_mode: if self.multiple: if value == "": # No option selected value = None else: value = value.split("\n") return value elif value is None: if self.optional: return None raise ParameterValueError(f"an invalid option ({value!r}) was selected", self.name, value) elif not legal_values: raise ParameterValueError("requires a value, but no legal values defined", self.name) if not isinstance(value, list): value = [value] if len(value) > 1 and not self.multiple: raise ParameterValueError( "multiple values provided but parameter is not expecting multiple values", self.name ) rval = [] for val in value: if val not in legal_values: raise ParameterValueError( f"an invalid option ({val!r}) was selected (valid options: {','.join(legal_values)})", self.name, val, ) rval.append(val) return rval
[docs] def to_param_dict_string(self, value, other_values=None): other_values = other_values or {} def get_options_list(value): def get_base_option(value, options): for option in options: if value == option["value"]: return option rval = get_base_option(value, option["options"]) if rval: return rval return None # not found def recurse_option(option_list, option): if not option["options"]: option_list.append(option["value"]) else: for opt in option["options"]: recurse_option(option_list, opt) rval: List[str] = [] base_option = get_base_option(value, self.get_options(other_values=other_values)) recurse_option(rval, base_option) return rval or [value] if value is None: return "None" rval = [] if self.hierarchy == "exact": rval = value else: for val in value: options = get_options_list(val) rval.extend(options) rval = list(dict.fromkeys(rval)) if len(rval) > 1 and not self.multiple: raise ParameterValueError( "multiple values provided but parameter is not expecting multiple values", self.name ) rval = self.separator.join(rval) if self.tool is None or self.tool.options.sanitize: if self.sanitizer: rval = self.sanitizer.sanitize_param(rval) else: rval = sanitize_param(rval) return rval
[docs] def get_initial_value(self, trans, other_values): def recurse_options(initial_values, options): for option in options: if option["selected"]: initial_values.append(option["value"]) recurse_options(initial_values, option["options"]) # More working around dynamic options for workflow options = self.get_options(trans=trans, other_values=other_values) if not options: return None initial_values: List[str] = [] recurse_options(initial_values, options) if len(initial_values) == 0: return None return initial_values
[docs] def to_text(self, value): def get_option_display(value, options): for option in options: if value == option["value"]: return option["name"] rval = get_option_display(value, option["options"]) if rval: return rval return None # not found if not value: value = [] elif not isinstance(value, list): value = [value] # FIXME: Currently only translating values back to labels if they # are not dynamic if self.is_dynamic: if value: if isinstance(value, list): rval = value else: rval = [value] else: rval = [] else: rval = [] for val in value: rval.append(get_option_display(val, self.options) or val) if rval: return "\n".join(map(str, rval)) return "Nothing selected."
[docs] def get_dependencies(self): """ Get the *names* of the other params this param depends on. """ return list(self.filtered.keys())
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} # skip SelectToolParameter (the immediate parent) bc we need to get options in a different way here d = ToolParameter.to_dict(self, trans) d["options"] = self.get_options(trans=trans, other_values=other_values) d["display"] = self.display d["multiple"] = self.multiple return d
[docs]class BaseDataToolParameter(ToolParameter): multiple: bool
[docs] def __init__(self, tool, input_source, trans): super().__init__(tool, input_source) self.min = input_source.get("min") self.max = input_source.get("max") if self.min: try: self.min = int(self.min) except ValueError: raise ParameterValueError("attribute 'min' must be an integer", self.name) if self.max: try: self.max = int(self.max) except ValueError: raise ParameterValueError("attribute 'max' must be an integer", self.name) self.refresh_on_change = True # Find datatypes_registry if self.tool is None: if trans: # Must account for "Input Dataset" types, which while not a tool still need access to the real registry. # A handle to the transaction (and thus app) will be given by the module. self.datatypes_registry = trans.app.datatypes_registry else: # This occurs for things such as unit tests import galaxy.datatypes.registry self.datatypes_registry = galaxy.datatypes.registry.Registry() self.datatypes_registry.load_datatypes() else: self.datatypes_registry = ( self.tool.app.datatypes_registry ) # can be None if self.tool.app is a ValidationContext
def _parse_formats(self, trans, input_source): """ Build list of classes for supported data formats """ self.extensions = input_source.get("format", "data").split(",") formats = [] if self.datatypes_registry: # This may be None when self.tool.app is a ValidationContext normalized_extensions = [extension.strip().lower() for extension in self.extensions] for extension in normalized_extensions: datatype = self.datatypes_registry.get_datatype_by_extension(extension) if datatype is not None: formats.append(datatype) else: log.warning( f"Datatype class not found for extension '{extension}', which is used in the 'format' attribute of parameter '{self.name}'" ) self.formats = formats def _parse_options(self, input_source): # TODO: Enhance dynamic options for DataToolParameters. Currently, # only the special case key='build' of type='data_meta' is # a valid filter self.options_filter_attribute = None self.options = parse_dynamic_options(self, input_source) if self.options: # TODO: Abstract away XML handling here. options_elem = input_source.elem().find("options") self.options_filter_attribute = options_elem.get("options_filter_attribute", None) self.is_dynamic = self.options is not None
[docs] def get_initial_value(self, trans, other_values): if trans.workflow_building_mode is workflow_building_modes.ENABLED or trans.app.name == "tool_shed": return RuntimeValue() if self.optional: return None history = trans.history if history is not None: dataset_matcher_factory = get_dataset_matcher_factory(trans) dataset_matcher = dataset_matcher_factory.dataset_matcher(self, other_values) if isinstance(self, DataToolParameter): for hda in reversed(history.active_visible_datasets_and_roles): match = dataset_matcher.hda_match(hda) if match: return match.hda else: dataset_collection_matcher = dataset_matcher_factory.dataset_collection_matcher(dataset_matcher) for hdca in reversed(history.active_visible_dataset_collections): if dataset_collection_matcher.hdca_match(hdca): return hdca
[docs] def to_json(self, value, app, use_security): def single_to_json(value): src = None if isinstance(value, dict) and "src" in value and "id" in value: return value elif isinstance(value, DatasetCollectionElement): src = "dce" elif isinstance(value, HistoryDatasetCollectionAssociation): src = "hdca" elif isinstance(value, LibraryDatasetDatasetAssociation): src = "ldda" elif isinstance(value, HistoryDatasetAssociation) or hasattr(value, "id"): # hasattr 'id' fires a query on persistent objects after a flush so better # to do the isinstance check. Not sure we need the hasattr check anymore - it'd be # nice to drop it. src = "hda" if src is not None: object_id = cached_id(value) return {"id": app.security.encode_id(object_id) if use_security else object_id, "src": src} if value not in [None, "", "None"]: if isinstance(value, list) and len(value) > 0: values = [single_to_json(v) for v in value] else: values = [single_to_json(value)] return {"values": values} return None
[docs] def to_python(self, value, app): def single_to_python(value): if isinstance(value, dict) and "src" in value: id = value["id"] if isinstance(value["id"], int) else app.security.decode_id(value["id"]) if value["src"] == "dce": return app.model.context.query(DatasetCollectionElement).get(id) elif value["src"] == "hdca": return app.model.context.query(HistoryDatasetCollectionAssociation).get(id) elif value["src"] == "ldda": return app.model.context.query(LibraryDatasetDatasetAssociation).get(id) else: return app.model.context.query(HistoryDatasetAssociation).get(id) if isinstance(value, dict) and "values" in value: if hasattr(self, "multiple") and self.multiple is True: return [single_to_python(v) for v in value["values"]] elif len(value["values"]) > 0: return single_to_python(value["values"][0]) # Handle legacy string values potentially stored in databases none_values = [None, "", "None"] if value in none_values: return None if isinstance(value, str) and value.find(",") > -1: return [ app.model.context.query(HistoryDatasetAssociation).get(int(v)) for v in value.split(",") if v not in none_values ] elif str(value).startswith("__collection_reduce__|"): decoded_id = str(value)[len("__collection_reduce__|") :] if not decoded_id.isdigit(): decoded_id = app.security.decode_id(decoded_id) return app.model.context.query(HistoryDatasetCollectionAssociation).get(int(decoded_id)) elif str(value).startswith("dce:"): return app.model.context.query(DatasetCollectionElement).get(int(value[len("dce:") :])) elif str(value).startswith("hdca:"): return app.model.context.query(HistoryDatasetCollectionAssociation).get(int(value[len("hdca:") :])) else: return app.model.context.query(HistoryDatasetAssociation).get(int(value))
[docs] def validate(self, value, trans=None): def do_validate(v): for validator in self.validators: if ( validator.requires_dataset_metadata and v and hasattr(v, "dataset") and v.dataset.state != Dataset.states.OK ): return else: validator.validate(v, trans) dataset_count = 0 if value: if self.multiple: if not isinstance(value, list): value = [value] else: value = [value] for v in value: if isinstance(v, HistoryDatasetCollectionAssociation): for dataset_instance in v.collection.dataset_instances: dataset_count += 1 do_validate(dataset_instance) elif isinstance(v, DatasetCollectionElement): if v.hda: dataset_count += 1 do_validate(v.hda) else: for dataset_instance in v.child_collection.dataset_instances: dataset_count += 1 do_validate(dataset_instance) else: dataset_count += 1 do_validate(v) if self.min is not None: if self.min > dataset_count: raise ValueError("At least %d datasets are required for %s" % (self.min, self.name)) if self.max is not None: if self.max < dataset_count: raise ValueError("At most %d datasets are required for %s" % (self.max, self.name))
[docs]class DataToolParameter(BaseDataToolParameter): # TODO, Nate: Make sure the following unit tests appropriately test the dataset security # components. Add as many additional tests as necessary. """ Parameter that takes on one (or many) or a specific set of values. TODO: There should be an alternate display that allows single selects to be displayed as radio buttons and multiple selects as a set of checkboxes TODO: The following must be fixed to test correctly for the new security_check tag in the DataToolParameter (the last test below is broken) Nate's next pass at the dataset security stuff will dramatically alter this anyway. """
[docs] def __init__(self, tool, input_source, trans=None): input_source = ensure_input_source(input_source) super().__init__(tool, input_source, trans) self.load_contents = int(input_source.get("load_contents", 0)) # Add metadata validator if not input_source.get_bool("no_validation", False): self.validators.append(validation.MetadataValidator()) self._parse_formats(trans, input_source) tag = input_source.get("tag") self.multiple = input_source.get_bool("multiple", False) if not self.multiple and (self.min is not None): raise ParameterValueError( "cannot specify 'min' property on single data parameter. Set multiple=\"true\" to enable this option", self.name, ) if not self.multiple and (self.max is not None): raise ParameterValueError( "cannot specify 'max' property on single data parameter. Set multiple=\"true\" to enable this option", self.name, ) self.tag = tag self.is_dynamic = True self._parse_options(input_source) # Load conversions required for the dataset input self.conversions = [] for name, conv_extension in input_source.parse_conversion_tuples(): assert None not in [ name, conv_extension, ], f"A name ({name}) and type ({conv_extension}) are required for explicit conversion" if self.datatypes_registry: conv_type = self.datatypes_registry.get_datatype_by_extension(conv_extension.lower()) if conv_type is None: raise ParameterValueError( f"datatype class not found for extension '{conv_type}', which is used as 'type' attribute in conversion of data parameter", self.name, ) self.conversions.append((name, conv_extension, [conv_type]))
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} if trans.workflow_building_mode is workflow_building_modes.ENABLED or is_runtime_value(value): return None if not value and not self.optional: raise ParameterValueError("specify a dataset of the required format / build for parameter", self.name) if value in [None, "None", ""]: return None if isinstance(value, dict) and "values" in value: value = self.to_python(value, trans.app) if isinstance(value, str) and value.find(",") > 0: value = [int(value_part) for value_part in value.split(",")] rval = [] if isinstance(value, list): found_hdca = False for single_value in value: if isinstance(single_value, dict) and "src" in single_value and "id" in single_value: if single_value["src"] == "hda": decoded_id = trans.security.decode_id(single_value["id"]) rval.append(trans.sa_session.query(HistoryDatasetAssociation).get(decoded_id)) elif single_value["src"] == "hdca": found_hdca = True decoded_id = trans.security.decode_id(single_value["id"]) rval.append(trans.sa_session.query(HistoryDatasetCollectionAssociation).get(decoded_id)) elif single_value["src"] == "ldda": decoded_id = trans.security.decode_id(single_value["id"]) rval.append(trans.sa_session.query(LibraryDatasetDatasetAssociation).get(decoded_id)) elif single_value["src"] == "dce": decoded_id = trans.security.decode_id(single_value["id"]) rval.append(trans.sa_session.query(DatasetCollectionElement).get(decoded_id)) else: raise ValueError(f"Unknown input source {single_value['src']} passed to job submission API.") elif isinstance( single_value, ( HistoryDatasetCollectionAssociation, DatasetCollectionElement, HistoryDatasetAssociation, LibraryDatasetDatasetAssociation, ), ): rval.append(single_value) else: if len(str(single_value)) == 16: # Could never really have an ID this big anyway - postgres doesn't # support that for integer column types. log.warning("Encoded ID where unencoded ID expected.") single_value = trans.security.decode_id(single_value) rval.append(trans.sa_session.query(HistoryDatasetAssociation).get(single_value)) if found_hdca: for val in rval: if not isinstance(val, HistoryDatasetCollectionAssociation): raise ParameterValueError( "if collections are supplied to multiple data input parameter, only collections may be used", self.name, ) elif isinstance(value, (HistoryDatasetAssociation, LibraryDatasetDatasetAssociation)): rval.append(value) elif isinstance(value, dict) and "src" in value and "id" in value: if value["src"] == "hda": decoded_id = trans.security.decode_id(value["id"]) rval.append(trans.sa_session.query(HistoryDatasetAssociation).get(decoded_id)) elif value["src"] == "hdca": decoded_id = trans.security.decode_id(value["id"]) rval.append(trans.sa_session.query(HistoryDatasetCollectionAssociation).get(decoded_id)) elif value["src"] == "dce": decoded_id = trans.security.decode_id(value["id"]) rval.append(trans.sa_session.query(DatasetCollectionElement).get(decoded_id)) else: raise ValueError(f"Unknown input source {value['src']} passed to job submission API.") elif str(value).startswith("__collection_reduce__|"): encoded_ids = [v[len("__collection_reduce__|") :] for v in str(value).split(",")] decoded_ids = map(trans.security.decode_id, encoded_ids) rval = [] for decoded_id in decoded_ids: hdca = trans.sa_session.query(HistoryDatasetCollectionAssociation).get(decoded_id) rval.append(hdca) elif isinstance(value, HistoryDatasetCollectionAssociation) or isinstance(value, DatasetCollectionElement): rval.append(value) else: rval.append(trans.sa_session.query(HistoryDatasetAssociation).get(value)) dataset_matcher_factory = get_dataset_matcher_factory(trans) dataset_matcher = dataset_matcher_factory.dataset_matcher(self, other_values) for v in rval: if v: if hasattr(v, "deleted") and v.deleted: raise ParameterValueError("the previously selected dataset has been deleted.", self.name) elif hasattr(v, "dataset") and v.dataset.state in [Dataset.states.ERROR, Dataset.states.DISCARDED]: raise ParameterValueError( "the previously selected dataset has entered an unusable state", self.name ) elif hasattr(v, "dataset"): if isinstance(v, DatasetCollectionElement): v = v.hda match = dataset_matcher.hda_match(v) if match and match.implicit_conversion: v.implicit_conversion = True if not self.multiple: if len(rval) > 1: raise ParameterValueError("more than one dataset supplied to single input dataset parameter", self.name) if len(rval) > 0: rval = rval[0] else: raise ParameterValueError("invalid dataset supplied to single input dataset parameter", self.name) return rval
[docs] def to_param_dict_string(self, value, other_values=None): if value is None: return "None" return value.file_name
[docs] def to_text(self, value): if value and not isinstance(value, list): value = [value] if value: try: return ", ".join(f"{item.hid}: {item.name}" for item in value) except Exception: pass return "No dataset."
[docs] def get_dependencies(self): """ Get the *names* of the other params this param depends on. """ if self.options: return self.options.get_dependency_names() else: return []
[docs] def converter_safe(self, other_values, trans): if ( self.tool is None or self.tool.has_multiple_pages or not hasattr(trans, "workflow_building_mode") or trans.workflow_building_mode ): return False if other_values is None: return True # we don't know other values, so we can't check, assume ok converter_safe = [True] def visitor(prefix, input, value, parent=None): if isinstance(input, SelectToolParameter) and self.name in input.get_dependencies(): if input.is_dynamic and ( input.dynamic_options or (not input.dynamic_options and not input.options) or not input.options.converter_safe ): converter_safe[ 0 ] = False # This option does not allow for conversion, i.e. uses contents of dataset file to generate options self.tool.visit_inputs(other_values, visitor) return False not in converter_safe
[docs] def get_options_filter_attribute(self, value): # HACK to get around current hardcoded limitation of when a set of dynamic options is defined for a DataToolParameter # it always causes available datasets to be filtered by dbkey # this behavior needs to be entirely reworked (in a backwards compatible manner) options_filter_attribute = self.options_filter_attribute if options_filter_attribute is None: return value.get_dbkey() if options_filter_attribute.endswith("()"): call_attribute = True options_filter_attribute = options_filter_attribute[:-2] else: call_attribute = False ref = value for attribute in options_filter_attribute.split("."): ref = getattr(ref, attribute) if call_attribute: ref = ref() return str(ref)
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} # create dictionary and fill default parameters d = super().to_dict(trans) extensions = self.extensions all_edam_formats = ( self.datatypes_registry.edam_formats if hasattr(self.datatypes_registry, "edam_formats") else {} ) all_edam_data = self.datatypes_registry.edam_data if hasattr(self.datatypes_registry, "edam_formats") else {} edam_formats = [all_edam_formats.get(ext, None) for ext in extensions] edam_data = [all_edam_data.get(ext, None) for ext in extensions] d["extensions"] = extensions d["edam"] = {"edam_formats": edam_formats, "edam_data": edam_data} d["multiple"] = self.multiple if self.multiple: # For consistency, should these just always be in the dict? d["min"] = self.min d["max"] = self.max d["options"] = {"hda": [], "hdca": []} d["tag"] = self.tag # return dictionary without options if context is unavailable history = trans.history if history is None or trans.workflow_building_mode is workflow_building_modes.ENABLED: return d # prepare dataset/collection matching dataset_matcher_factory = get_dataset_matcher_factory(trans) dataset_matcher = dataset_matcher_factory.dataset_matcher(self, other_values) multiple = self.multiple # build and append a new select option def append(list, hda, name, src, keep=False, subcollection_type=None): value = { "id": trans.security.encode_id(hda.id), "hid": hda.hid if hda.hid is not None else -1, "name": name, "tags": [t.user_tname if not t.value else f"{t.user_tname}:{t.value}" for t in hda.tags], "src": src, "keep": keep, } if subcollection_type: value["map_over_type"] = subcollection_type return list.append(value) def append_dce(dce): if dce.hda: # well this isn't good, but what's the alternative ? # we should be precise about what we're (re-)running here. key = "hda" else: key = "hdca" d["options"][key].append( { "id": trans.security.encode_id(dce.id), "name": dce.element_identifier, "src": "dce", "tags": [], "keep": True, } ) # append DCE if isinstance(other_values.get(self.name), DatasetCollectionElement): dce = other_values[self.name] append_dce(dce) # add datasets hda_list = util.listify(other_values.get(self.name)) # Prefetch all at once, big list of visible, non-deleted datasets. for hda in history.active_visible_datasets_and_roles: match = dataset_matcher.hda_match(hda) if match: m = match.hda hda_list = [h for h in hda_list if h != m and h != hda] m_name = f"{match.original_hda.name} (as {match.target_ext})" if match.implicit_conversion else m.name append(d["options"]["hda"], m, m_name, "hda") for hda in hda_list: if hasattr(hda, "hid"): if hda.deleted: hda_state = "deleted" elif not hda.visible: hda_state = "hidden" else: hda_state = "unavailable" append(d["options"]["hda"], hda, f"({hda_state}) {hda.name}", "hda", True) elif isinstance(hda, DatasetCollectionElement): append_dce(hda) # add dataset collections dataset_collection_matcher = dataset_matcher_factory.dataset_collection_matcher(dataset_matcher) for hdca in history.active_visible_dataset_collections: match = dataset_collection_matcher.hdca_match(hdca) if match: subcollection_type = None if multiple and hdca.collection.collection_type != "list": collection_type_description = self._history_query(trans).can_map_over(hdca) if collection_type_description: subcollection_type = collection_type_description.collection_type else: continue name = hdca.name if match.implicit_conversion: name = f"{name} (with implicit datatype conversion)" append(d["options"]["hdca"], hdca, name, "hdca", subcollection_type=subcollection_type) continue # sort both lists d["options"]["hda"] = sorted(d["options"]["hda"], key=lambda k: k.get("hid", -1), reverse=True) d["options"]["hdca"] = sorted(d["options"]["hdca"], key=lambda k: k.get("hid", -1), reverse=True) # return final dictionary return d
def _history_query(self, trans): assert self.multiple dataset_collection_type_descriptions = trans.app.dataset_collection_manager.collection_type_descriptions # If multiple data parameter, treat like a list parameter. return history_query.HistoryQuery.from_collection_type("list", dataset_collection_type_descriptions)
[docs]class DataCollectionToolParameter(BaseDataToolParameter): """ """
[docs] def __init__(self, tool, input_source, trans=None): input_source = ensure_input_source(input_source) super().__init__(tool, input_source, trans) self._parse_formats(trans, input_source) collection_types = input_source.get("collection_type", None) tag = input_source.get("tag") if collection_types: collection_types = [t.strip() for t in collection_types.split(",")] self._collection_types = collection_types self.tag = tag self.multiple = False # Accessed on DataToolParameter a lot, may want in future self.is_dynamic = True self._parse_options(input_source) # TODO: Review and test.
@property def collection_types(self): return self._collection_types def _history_query(self, trans): dataset_collection_type_descriptions = trans.app.dataset_collection_manager.collection_type_descriptions return history_query.HistoryQuery.from_parameter(self, dataset_collection_type_descriptions)
[docs] def match_collections(self, trans, history, dataset_collection_matcher): dataset_collections = trans.app.dataset_collection_manager.history_dataset_collections( history, self._history_query(trans) ) for dataset_collection_instance in dataset_collections: match = dataset_collection_matcher.hdca_match(dataset_collection_instance) if not match: continue yield dataset_collection_instance, match.implicit_conversion
[docs] def match_multirun_collections(self, trans, history, dataset_collection_matcher): for history_dataset_collection in history.active_visible_dataset_collections: if not self._history_query(trans).can_map_over(history_dataset_collection): continue match = dataset_collection_matcher.hdca_match(history_dataset_collection) if match: yield history_dataset_collection, match.implicit_conversion
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} rval: Optional[Union[DatasetCollectionElement, HistoryDatasetCollectionAssociation]] = None if trans.workflow_building_mode is workflow_building_modes.ENABLED: return None if not value and not self.optional: raise ParameterValueError("specify a dataset collection of the correct type", self.name) if value in [None, "None"]: return None if isinstance(value, dict) and "values" in value: value = self.to_python(value, trans.app) if isinstance(value, str) and value.find(",") > 0: value = [int(value_part) for value_part in value.split(",")] elif isinstance(value, HistoryDatasetCollectionAssociation): rval = value elif isinstance(value, DatasetCollectionElement): # When mapping over nested collection - this parameter will receive # a DatasetCollectionElement instead of a # HistoryDatasetCollectionAssociation. rval = value elif isinstance(value, dict) and "src" in value and "id" in value: if value["src"] == "hdca": rval = trans.sa_session.query(HistoryDatasetCollectionAssociation).get( trans.security.decode_id(value["id"]) ) elif isinstance(value, list): if len(value) > 0: value = value[0] if isinstance(value, dict) and "src" in value and "id" in value: if value["src"] == "hdca": rval = trans.sa_session.query(HistoryDatasetCollectionAssociation).get( trans.security.decode_id(value["id"]) ) elif value["src"] == "dce": rval = trans.sa_session.query(DatasetCollectionElement).get( trans.security.decode_id(value["id"]) ) elif isinstance(value, str): if value.startswith("dce:"): rval = trans.sa_session.query(DatasetCollectionElement).get(value[len("dce:") :]) elif value.startswith("hdca:"): rval = trans.sa_session.query(HistoryDatasetCollectionAssociation).get(value[len("hdca:") :]) else: rval = trans.sa_session.query(HistoryDatasetCollectionAssociation).get(value) if rval and isinstance(rval, HistoryDatasetCollectionAssociation): if rval.deleted: raise ParameterValueError("the previously selected dataset collection has been deleted", self.name) # TODO: Handle error states, implement error states ... return rval
[docs] def to_text(self, value): try: if isinstance(value, HistoryDatasetCollectionAssociation): display_text = f"{value.hid}: {value.name}" else: display_text = "Element %d:%s" % (value.identifier_index, value.identifier_name) except AttributeError: display_text = "No dataset collection." return display_text
[docs] def to_dict(self, trans, other_values=None): # create dictionary and fill default parameters other_values = other_values or {} d = super().to_dict(trans) d["extensions"] = self.extensions d["multiple"] = self.multiple d["options"] = {"hda": [], "hdca": [], "dce": []} d["tag"] = self.tag # return dictionary without options if context is unavailable history = trans.history if history is None or trans.workflow_building_mode is workflow_building_modes.ENABLED: return d # prepare dataset/collection matching dataset_matcher_factory = get_dataset_matcher_factory(trans) dataset_matcher = dataset_matcher_factory.dataset_matcher(self, other_values) dataset_collection_matcher = dataset_matcher_factory.dataset_collection_matcher(dataset_matcher) # append DCE if isinstance(other_values.get(self.name), DatasetCollectionElement): dce = other_values[self.name] d["options"]["hdca"].append( { "id": trans.security.encode_id(dce.id), "hid": -1, "name": dce.element_identifier, "src": "dce", "tags": [], } ) # append directly matched collections for hdca, implicit_conversion in self.match_collections(trans, history, dataset_collection_matcher): name = hdca.name if implicit_conversion: name = f"{name} (with implicit datatype conversion)" d["options"]["hdca"].append( { "id": trans.security.encode_id(hdca.id), "hid": hdca.hid, "name": name, "src": "hdca", "tags": [t.user_tname if not t.value else f"{t.user_tname}:{t.value}" for t in hdca.tags], } ) # append matching subcollections for hdca, implicit_conversion in self.match_multirun_collections(trans, history, dataset_collection_matcher): subcollection_type = self._history_query(trans).can_map_over(hdca).collection_type name = hdca.name if implicit_conversion: name = f"{name} (with implicit datatype conversion)" d["options"]["hdca"].append( { "id": trans.security.encode_id(hdca.id), "hid": hdca.hid, "name": name, "src": "hdca", "tags": [t.user_tname if not t.value else f"{t.user_tname}:{t.value}" for t in hdca.tags], "map_over_type": subcollection_type, } ) # sort both lists d["options"]["hdca"] = sorted(d["options"]["hdca"], key=lambda k: k.get("hid", -1), reverse=True) # return final dictionary return d
[docs]class HiddenDataToolParameter(HiddenToolParameter, DataToolParameter): """ Hidden parameter that behaves as a DataToolParameter. As with all hidden parameters, this is a HACK. """
[docs] def __init__(self, tool, elem): DataToolParameter.__init__(self, tool, elem) self.value = "None" self.type = "hidden_data" self.hidden = True
[docs]class LibraryDatasetToolParameter(ToolParameter): """ Parameter that lets users select a LDDA from a modal window, then use it within the wrapper. """
[docs] def __init__(self, tool, input_source, context=None): input_source = ensure_input_source(input_source) super().__init__(tool, input_source) self.multiple = input_source.get_bool("multiple", True)
[docs] def from_json(self, value, trans, other_values=None): other_values = other_values or {} return self.to_python(value, trans.app, other_values=other_values, validate=True)
[docs] def to_param_dict_string(self, value, other_values=None): if value is None: return "None" elif self.multiple: return [dataset.get_file_name() for dataset in value] else: return value[0].get_file_name()
# converts values to json representation: # { id: LibraryDatasetDatasetAssociation.id, name: LibraryDatasetDatasetAssociation.name, src: 'lda' }
[docs] def to_json(self, value, app, use_security): if not isinstance(value, list): value = [value] lst: List[Dict[str, str]] = [] for item in value: lda_id = lda_name = None if isinstance(item, LibraryDatasetDatasetAssociation): lda_id = app.security.encode_id(item.id) if use_security else item.id lda_name = item.name elif isinstance(item, dict): lda_id = item.get("id") lda_name = item.get("name") else: lst = [] break if lda_id is not None: lst.append({"id": lda_id, "name": lda_name, "src": "ldda"}) if len(lst) == 0: return None else: return lst
# converts values into python representation: # LibraryDatasetDatasetAssociation # valid input values (incl. arrays of mixed sets) are: # 1. LibraryDatasetDatasetAssociation # 2. LibraryDatasetDatasetAssociation.id # 3. { id: LibraryDatasetDatasetAssociation.id, ... }
[docs] def to_python(self, value, app, other_values=None, validate=False): other_values = other_values or {} if not isinstance(value, list): value = [value] lst = [] for item in value: if isinstance(item, LibraryDatasetDatasetAssociation): lst.append(item) else: lda_id = None if isinstance(item, dict): lda_id = item.get("id") elif isinstance(item, str): lda_id = item else: lst = [] break lda = app.model.context.query(LibraryDatasetDatasetAssociation).get( lda_id if isinstance(lda_id, int) else app.security.decode_id(lda_id) ) if lda is not None: lst.append(lda) elif validate: raise ParameterValueError( "one of the selected library datasets is invalid or not available anymore", self.name ) if len(lst) == 0: if not self.optional and validate: raise ParameterValueError("invalid library dataset selected", self.name) return None else: return lst
[docs] def to_dict(self, trans, other_values=None): d = super().to_dict(trans) d["multiple"] = self.multiple return d
[docs]class BaseJsonToolParameter(ToolParameter): """ Class of parameter that tries to keep values as close to JSON as possible. In particular value_to_basic is overloaded to prevent params_to_strings from double encoding JSON and to_python using loads to produce values. """
[docs] def value_to_basic(self, value, app, use_security=False): if is_runtime_value(value): return runtime_to_json(value) return value
[docs] def to_json(self, value, app, use_security): """Convert a value to a string representation suitable for persisting""" return json.dumps(value)
[docs] def to_python(self, value, app): """Convert a value created with to_json back to an object representation""" return json.loads(value)
[docs]class DirectoryUriToolParameter(SimpleTextToolParameter): """galaxy.files URIs for directories."""
[docs] def __init__(self, tool, input_source, context=None): input_source = ensure_input_source(input_source) SimpleTextToolParameter.__init__(self, tool, input_source)
[docs] def validate(self, value, trans=None): super().validate(value, trans=trans) if not value: return # value is not set yet, do not validate file_source_path = trans.app.file_sources.get_file_source_path(value) file_source = file_source_path.file_source if file_source is None: raise ParameterValueError(f"'{value}' is not a valid file source uri.", self.name) user_context = ProvidesUserFileSourcesUserContext(trans) user_has_access = file_source.user_has_access(user_context) if not user_has_access: raise ParameterValueError(f"The user cannot access {value}.", self.name)
[docs]class RulesListToolParameter(BaseJsonToolParameter): """ Parameter that allows for the creation of a list of rules using the Galaxy rules DSL. """
[docs] def __init__(self, tool, input_source, context=None): input_source = ensure_input_source(input_source) BaseJsonToolParameter.__init__(self, tool, input_source) self.data_ref = input_source.get("data_ref", None)
[docs] def to_dict(self, trans, other_values=None): other_values = other_values or {} d = ToolParameter.to_dict(self, trans) target_name = self.data_ref if target_name in other_values: target = other_values[target_name] if not is_runtime_value(target): d["target"] = { "src": "hdca" if hasattr(target, "collection") else "hda", "id": trans.app.security.encode_id(target.id), } return d
[docs] def validate(self, value, trans=None): super().validate(value, trans=trans) if not isinstance(value, dict): raise ValueError("No rules specified for rules parameter.") if "rules" not in value: raise ValueError("No rules specified for rules parameter") mappings = value.get("mapping", None) if not mappings: raise ValueError("No column definitions defined for rules parameter.")
[docs] def to_text(self, value): if value: rule_set = RuleSet(value) return rule_set.display else: return ""
parameter_types = dict( text=TextToolParameter, integer=IntegerToolParameter, float=FloatToolParameter, boolean=BooleanToolParameter, genomebuild=GenomeBuildParameter, select=SelectToolParameter, color=ColorToolParameter, group_tag=SelectTagParameter, data_column=ColumnListParameter, hidden=HiddenToolParameter, hidden_data=HiddenDataToolParameter, baseurl=BaseURLToolParameter, file=FileToolParameter, ftpfile=FTPFileToolParameter, data=DataToolParameter, data_collection=DataCollectionToolParameter, library_data=LibraryDatasetToolParameter, rules=RulesListToolParameter, directory_uri=DirectoryUriToolParameter, drill_down=DrillDownSelectToolParameter, )
[docs]def runtime_to_json(runtime_value): if isinstance(runtime_value, ConnectedValue) or ( isinstance(runtime_value, dict) and runtime_value["__class__"] == "ConnectedValue" ): return {"__class__": "ConnectedValue"} else: return {"__class__": "RuntimeValue"}
[docs]def runtime_to_object(runtime_value): if isinstance(runtime_value, ConnectedValue) or ( isinstance(runtime_value, dict) and runtime_value["__class__"] == "ConnectedValue" ): return ConnectedValue() else: return RuntimeValue()
[docs]class RuntimeValue: """ Wrapper to note a value that is not yet set, but will be required at runtime. """
[docs]class ConnectedValue(RuntimeValue): """ Wrapper to note a value that is not yet set, but will be inferred from a connection. """