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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__)
WORKFLOW_PARAMETER_REGULAR_EXPRESSION = re.compile(r"\$\{.+?\}")
[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]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]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 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_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 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_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]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 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]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 get_legal_values(self, trans, other_values, value):
"""
determine the set of values of legal options
"""
return {v for _, v, _ in self.get_options(trans, other_values)}
[docs] def get_legal_names(self, trans, other_values):
"""
determine a mapping from names to values for all legal options
"""
return {n: v for n, v, _ in self.get_options(trans, other_values)}
[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 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 get_legal_values(self, trans, other_values, value):
return {dbkey for dbkey, _ in self._get_dbkey_names(trans=trans)}
[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_legal_values(self, trans, other_values, value):
if self.data_ref not in other_values and not trans.workflow_building_mode:
raise ValueError("Value for associated data reference not found (data_ref).")
return set(self.get_tag_list(other_values))
[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 get_legal_values(self, trans, other_values, value):
if self.data_ref not in other_values:
raise ValueError("Value for associated data reference not found (data_ref).")
legal_values = self.get_column_list(trans, other_values)
if value is not None:
# There are cases where 'value' is a string of comma separated values. This ensures
# that it is converted into a list, with extra whitespace around items removed.
value = util.listify(value, do_strip=True)
if not set(value).issubset(set(legal_values)) and self.is_file_empty(trans, other_values):
legal_values.extend(value)
return set(legal_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 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 get_legal_values(self, trans, other_values, value):
def recurse_options(legal_values, options):
for option in options:
legal_values.append(option["value"])
recurse_options(legal_values, option["options"])
legal_values: List[str] = []
recurse_options(legal_values, self.get_options(trans=trans, other_values=other_values))
return legal_values
[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.
"""