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Source code for galaxy.tools.parameters.basic
"""
Basic tool parameters.
"""
from __future__ import print_function
import json
import logging
import os
import os.path
import re
from xml.etree.ElementTree import XML
from six import string_types
from webob.compat import cgi_FieldStorage
import galaxy.model
from galaxy import util
from galaxy.tool_util.parser import get_input_source as ensure_input_source
from galaxy.util import (
sanitize_param,
string_as_bool,
unicodify
)
from galaxy.util.bunch import Bunch
from galaxy.util.dictifiable import Dictifiable
from galaxy.util.expressions import ExpressionContext
from galaxy.util.rules_dsl import RuleSet
from galaxy.web import url_for
from . import (
dynamic_options,
history_query,
validation
)
from .dataset_matcher import (
get_dataset_matcher_factory,
)
from .sanitize import ToolParameterSanitizer
log = logging.getLogger(__name__)
workflow_building_modes = Bunch(DISABLED=False, ENABLED=True, USE_HISTORY=1)
WORKFLOW_PARAMETER_REGULAR_EXPRESSION = re.compile(r'\$\{.+?\}')
[docs]def contains_workflow_parameter(value, search=False):
if not isinstance(value, string_types):
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, trans.app.model.HistoryDatasetAssociation) and \
((hasattr(v, 'state') and v.state != galaxy.model.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
[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.load_contents = int(input_source.get("load_contents", 0))
self.refresh_on_change = input_source.get_bool("refresh_on_change", False)
self.optional = input_source.parse_optional()
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={}):
"""
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):
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={}):
"""Called via __str__ when used in the Cheetah template"""
if value is None:
value = ""
elif not isinstance(value, string_types):
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={}):
""" to_dict tool parameter. This can be overridden by subclasses. """
tool_dict = super(ToolParameter, self).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("parameter '%s' requires a 'type'" % (param_name))
elif param_type not in parameter_types:
raise ValueError("parameter '%s' uses an unknown type '%s'" % (param_name, param_type))
else:
return parameter_types[param_type](tool, input_source)
[docs]class TextToolParameter(ToolParameter):
"""
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
>>> assert sorted(p.to_dict(trans).items()) == [('area', False), ('argument', None), ('datalist', []), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'TextToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'text'), ('value', u'default')]
"""
[docs] def __init__(self, tool, input_source):
input_source = ensure_input_source(input_source)
super(TextToolParameter, self).__init__(tool, input_source)
self.datalist = []
for (title, value, selected) 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 to_json(self, value, app, use_security):
"""Convert a value to a string representation suitable for persisting"""
if value is None:
rval = ''
else:
rval = unicodify(value)
return rval
[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(TextToolParameter, self).validate(value, trans)
[docs] def to_dict(self, trans, other_values={}):
d = super(TextToolParameter, self).to_dict(trans)
d['area'] = self.area
d['datalist'] = self.datalist
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
>>> type(p.from_json("_string", trans))
Traceback (most recent call last):
...
ValueError: parameter '_name': an integer or workflow parameter is required
"""
dict_collection_visible_keys = ToolParameter.dict_collection_visible_keys + ['min', 'max']
[docs] def __init__(self, tool, input_source):
super(IntegerToolParameter, self).__init__(tool, input_source)
if self.value:
try:
int(self.value)
except ValueError:
raise ValueError("parameter '%s': the attribute 'value' must be an integer" % self.name)
elif self.value is None and not self.optional:
raise ValueError("parameter '%s': the attribute 'value' must be set for non optional parameters" % self.name)
self.min = input_source.get('min')
self.max = input_source.get('max')
if self.min:
try:
self.min = int(self.min)
except ValueError:
raise ValueError("parameter '%s': attribute 'min' must be an integer" % self.name)
if self.max:
try:
self.max = int(self.max)
except ValueError:
raise ValueError("parameter '%s': attribute 'max' must be an integer" % self.name)
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={}):
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 ValueError("parameter '%s': an integer or workflow parameter is required" % self.name)
else:
raise ValueError("parameter '%s': the attribute 'value' must be set for non optional parameters" % self.name)
[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:
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
>>> type(p.from_json("_string", trans))
Traceback (most recent call last):
...
ValueError: parameter '_name': an integer or workflow parameter is required
"""
dict_collection_visible_keys = ToolParameter.dict_collection_visible_keys + ['min', 'max']
[docs] def __init__(self, tool, input_source):
super(FloatToolParameter, self).__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 ValueError("parameter '%s': the attribute 'value' must be a real number" % self.name)
elif self.value is None and not self.optional:
raise ValueError("parameter '%s': the attribute 'value' must be set for non optional parameters" % self.name)
if self.min:
try:
self.min = float(self.min)
except ValueError:
raise ValueError("parameter '%s': attribute 'min' must be a real number" % self.name)
if self.max:
try:
self.max = float(self.max)
except ValueError:
raise ValueError("parameter '%s': attribute 'max' must be a real number" % self.name)
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={}):
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 ValueError("parameter '%s': an integer or workflow parameter is required" % self.name)
else:
raise ValueError("parameter '%s': the attribute 'value' must be set for non optional parameters" % self.name)
[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):
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
"""
[docs] def __init__(self, tool, input_source):
input_source = ensure_input_source(input_source)
super(BooleanToolParameter, self).__init__(tool, input_source)
self.truevalue = input_source.get('truevalue', 'true')
self.falsevalue = input_source.get('falsevalue', 'false')
self.checked = input_source.get_bool('checked', False)
[docs] def to_json(self, value, app, use_security):
if self.to_python(value, app):
return 'true'
else:
return 'false'
[docs] def to_param_dict_string(self, value, other_values={}):
if self.to_python(value):
return self.truevalue
else:
return self.falsevalue
[docs] def to_dict(self, trans, other_values={}):
d = super(BooleanToolParameter, self).to_dict(trans)
d['truevalue'] = self.truevalue
d['falsevalue'] = self.falsevalue
return d
@property
def legal_values(self):
return [self.truevalue, self.falsevalue]
[docs]class FileToolParameter(ToolParameter):
"""
Parameter that takes an uploaded file as a value.
>>> from galaxy.util.bunch import Bunch
>>> trans = Bunch(app=None, history=Bunch())
>>> p = FileToolParameter(None, XML('<param name="_name" type="file"/>'))
>>> print(p.name)
_name
>>> sorted(p.to_dict(trans).items())
[('argument', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'FileToolParameter'), ('name', '_name'), ('optional', False), ('refresh_on_change', False), ('type', 'file'), ('value', None)]
"""
[docs] def __init__(self, tool, input_source):
super(FileToolParameter, self).__init__(tool, input_source)
[docs] def from_json(self, value, trans=None, other_values={}):
# Middleware or proxies may encode files in special ways (TODO: this
# should be pluggable)
if type(value) == dict:
if 'session_id' in value:
# handle api upload
session_id = value["session_id"]
upload_store = trans.app.config.new_file_path
if re.match(r'^[\w-]+$', session_id) is None:
raise ValueError("Invald session id format.")
local_filename = os.path.abspath(os.path.join(upload_store, 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), "Filename provided by nginx (%s) is not in correct directory (%s)." % (local_filename, 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, string_types):
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.filename
raise Exception("FileToolParameter cannot be persisted")
[docs] def to_python(self, value, app):
if value is None:
return None
elif isinstance(value, string_types):
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(FTPFileToolParameter, self).__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 = "%s/" % 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={}):
if value == '':
return 'None'
lst = ['%s%s' % (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={}):
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 = []
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(FTPFileToolParameter, self).to_dict(trans)
d['multiple'] = self.multiple
return d
[docs]class GenomespaceFileToolParameter(ToolParameter):
"""
Parameter that takes one of two values.
"""
[docs] def __init__(self, tool, input_source):
super(GenomespaceFileToolParameter, self).__init__(tool, input_source)
self.value = input_source.get('value')
[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(HiddenToolParameter, self).__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)
>>> print(p.to_param_dict_string(None))
Traceback (most recent call last):
...
ValueError: Failed to convert 'None' to RGB.
"""
[docs] def __init__(self, tool, input_source):
input_source = ensure_input_source(input_source)
super(ColorToolParameter, self).__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={}):
if self.rgb:
try:
return str(tuple(int(value.lstrip('#')[i : i + 2], 16) for i in (0, 2, 4)))
except Exception:
raise ValueError("Failed to convert \'%s\' to RGB." % value)
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(BaseURLToolParameter, self).__init__(tool, input_source)
self.value = input_source.get('value', '')
def _get_value(self):
try:
return url_for(self.value, qualified=True)
except Exception as e:
log.debug('Url creation failed for "%s": %s', self.name, unicodify(e))
return self.value
[docs] def to_dict(self, trans, other_values={}):
d = super(BaseURLToolParameter, self).to_dict(trans)
return d
[docs]class SelectToolParameter(ToolParameter):
"""
Parameter that takes on one (or many) or a specific set of values.
>>> from galaxy.util.bunch import Bunch
>>> trans = Bunch(app=None, history=Bunch(), workflow_building_mode=False)
>>> p = SelectToolParameter(None, XML(
... '''
... <param name="_name" type="select">
... <option value="x">x_label</option>
... <option value="y" selected="true">y_label</option>
... <option value="z">z_label</option>
... </param>
... '''))
>>> print(p.name)
_name
>>> sorted(p.to_dict(trans).items())
[('argument', None), ('display', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'SelectToolParameter'), ('multiple', False), ('name', '_name'), ('optional', False), ('options', [('x_label', 'x', False), ('y_label', 'y', True), ('z_label', 'z', False)]), ('refresh_on_change', False), ('textable', False), ('type', 'select'), ('value', 'y')]
>>> p = SelectToolParameter(None, XML(
... '''
... <param name="_name" type="select" multiple="true">
... <option value="x">x_label</option>
... <option value="y" selected="true">y_label</option>
... <option value="z" selected="true">z_label</option>
... </param>
... '''))
>>> print(p.name)
_name
>>> sorted(p.to_dict(trans).items())
[('argument', None), ('display', None), ('help', ''), ('hidden', False), ('is_dynamic', False), ('label', ''), ('model_class', 'SelectToolParameter'), ('multiple', True), ('name', '_name'), ('optional', True), ('options', [('x_label', 'x', False), ('y_label', 'y', True), ('z_label', 'z', True)]), ('refresh_on_change', False), ('textable', False), ('type', 'select'), ('value', ['y', 'z'])]
>>> print(p.to_param_dict_string(["y", "z"]))
y,z
"""
[docs] def __init__(self, tool, input_source, context=None):
input_source = ensure_input_source(input_source)
super(SelectToolParameter, self).__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 (title, value, selected) 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):
if self.options:
return set(v for _, v, _ in self.options.get_options(trans, other_values))
elif self.dynamic_options:
try:
call_other_values = self._get_dynamic_options_call_other_values(trans, other_values)
return set(v for _, v, _ in eval(self.dynamic_options, self.tool.code_namespace, call_other_values))
except Exception as e:
log.debug("Determining legal values failed for '%s': %s", self.name, unicodify(e))
return set()
else:
return self.legal_values
[docs] def from_json(self, value, trans, other_values={}, require_legal_value=True):
try:
legal_values = self.get_legal_values(trans, other_values)
except ImplicitConversionRequired:
return value
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, string_types):
# 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 ValueError("parameter '%s': an invalid option (None) was selected, please verify" % self.name)
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.
return None
raise ValueError("parameter '%s': requires a value, but no legal values defined" % self.name)
if isinstance(value, list):
if not self.multiple:
raise ValueError("parameter '%s': multiple values provided but parameter is not expecting multiple values" % (self.name))
rval = []
for v in value:
if v not in legal_values:
raise ValueError("parameter '%s': an invalid option (%r) was selected (valid options: %s)" % (self.name, v, ",".join(legal_values)))
rval.append(v)
return rval
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 ValueError("parameter '%s': no option was selected for non optional parameter" % (self.name))
if value not in legal_values and require_legal_value:
raise ValueError("parameter '%s': an invalid option (%r) was selected (valid options: %s)" % (self.name, value, ",".join(legal_values)))
return value
[docs] def to_param_dict_string(self, value, other_values={}):
if value in (None, []):
return "None"
if isinstance(value, list):
if not self.multiple:
raise ValueError("parameter '%s': multiple values provided but parameter is not expecting multiple values" % (self.name))
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)
value = options[0][1]
else:
value = None
elif len(value) == 1 or not self.multiple:
value = value[0]
return value
[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 = map(str, value)
else:
options = list(self.static_options)
rval = []
for t, v, s 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={}):
d = super(SelectToolParameter, self).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]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=util.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)]
"""
[docs] def __init__(self, *args, **kwds):
super(GenomeBuildParameter, self).__init__(*args, **kwds)
if self.tool:
self.static_options = [(value, key, False) for key, value in self._get_dbkey_names()]
[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):
return set(dbkey for dbkey, _ in self._get_dbkey_names(trans=trans))
[docs] def to_dict(self, trans, other_values={}):
# 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 util.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(SelectTagParameter, self).__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={}):
if self.multiple:
tag_list = []
# split on newline and ,
if isinstance(value, list) or isinstance(value, string_types):
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)
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(SelectTagParameter, self).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(('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):
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={}):
d = super(SelectTagParameter, self).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(ColumnListParameter, self).__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 from_json(self, value, trans, other_values={}):
"""
Label convention prepends column number with a 'c', but tool uses the integer. This
removes the 'c' when entered into a workflow.
"""
if self.multiple:
# split on newline and ,
if isinstance(value, list) or isinstance(value, string_types):
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)
value = list(map(ColumnListParameter._strip_c, column_list))
else:
value = []
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(ColumnListParameter, self).from_json(value, trans, other_values)
@staticmethod
def _strip_c(column):
if isinstance(column, string_types):
if column.startswith('c'):
column = column.strip().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 (a dataset)
dataset = other_values.get(self.data_ref)
# Check if a dataset is selected
if not dataset:
return []
column_list = None
for dataset in util.listify(dataset):
# Use representative dataset if a dataset collection is parsed
if isinstance(dataset, trans.app.model.HistoryDatasetCollectionAssociation):
dataset = dataset.to_hda_representative()
if isinstance(dataset, trans.app.model.HistoryDatasetAssociation) and self.ref_input and self.ref_input.formats:
target_ext, converted_dataset = dataset.find_conversion_destination(self.ref_input.formats)
if 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 = []
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(), 'r') as f:
head = f.readline()
cnames = head.rstrip().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(('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(ColumnListParameter, self).get_initial_value(trans, other_values)
[docs] def get_legal_values(self, trans, other_values):
if self.data_ref not in other_values:
raise ValueError("Value for associated data reference not found (data_ref).")
return set(self.get_column_list(trans, other_values))
[docs] def to_dict(self, trans, other_values={}):
d = super(ColumnListParameter, self).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=util.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("<root>%s</root>" % open(from_file).read())
self.dynamic_options = elem.get('dynamic_options', None)
if self.dynamic_options:
self.is_dynamic = True
self.options = []
self.filtered = {}
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={}):
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):
def recurse_options(legal_values, options):
for option in options:
legal_values.append(option['value'])
recurse_options(legal_values, option['options'])
legal_values = []
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={}):
legal_values = self.get_legal_values(trans, other_values)
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 ValueError("parameter '%s': an invalid option (%r) was selected" % (self.name, value))
elif not legal_values:
raise ValueError("parameter '%s': 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 ValueError("parameter '%s': multiple values provided but parameter is not expecting multiple values" % (self.name))
rval = []
for val in value:
if val not in legal_values:
raise ValueError("parameter '%s': an invalid option (%r) was selected (valid options: %s)" % (self.name, val, ",".join(legal_values)))
rval.append(val)
return rval
[docs] def to_param_dict_string(self, value, other_values={}):
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 = []
recurse_option(rval, get_base_option(value, self.get_options(other_values=other_values)))
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)
if len(rval) > 1 and not self.multiple:
raise ValueError("parameter '%s': 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 = []
recurse_options(initial_values, options)
if len(initial_values) == 0:
initial_values = 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={}):
# 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):
[docs] def __init__(self, tool, input_source, trans):
super(BaseDataToolParameter, self).__init__(tool, input_source)
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("Datatype class not found for extension '%s', which is used in the 'format' attribute of parameter '%s'" % (extension, 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, galaxy.model.DatasetCollectionElement):
src = 'dce'
elif isinstance(value, app.model.HistoryDatasetCollectionAssociation):
src = 'hdca'
elif isinstance(value, app.model.LibraryDatasetDatasetAssociation):
src = 'ldda'
elif isinstance(value, app.model.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 = galaxy.model.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(app.model.DatasetCollectionElement).get(id)
elif value['src'] == 'hdca':
return app.model.context.query(app.model.HistoryDatasetCollectionAssociation).get(id)
elif value['src'] == 'ldda':
return app.model.context.query(app.model.LibraryDatasetDatasetAssociation).get(id)
else:
return app.model.context.query(app.model.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, string_types) and value.find(',') > -1:
return [app.model.context.query(app.model.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(app.model.HistoryDatasetCollectionAssociation).get(int(decoded_id))
elif str(value).startswith("dce:"):
return app.model.context.query(app.model.DatasetCollectionElement).get(int(value[len("dce:"):]))
elif str(value).startswith("hdca:"):
return app.model.context.query(app.model.HistoryDatasetCollectionAssociation).get(int(value[len("hdca:"):]))
else:
return app.model.context.query(app.model.HistoryDatasetAssociation).get(int(value))
[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(DataToolParameter, self).__init__(tool, input_source, trans)
# Add metadata validator
if not input_source.get_bool('no_validation', False):
self.validators.append(validation.MetadataValidator())
self._parse_formats(trans, input_source)
self.multiple = input_source.get_bool('multiple', False)
self.min = input_source.get('min')
self.max = input_source.get('max')
if self.min:
try:
self.min = int(self.min)
except ValueError:
raise ValueError("parameter '%s': attribute 'min' must be an integer" % self.name)
if self.max:
try:
self.max = int(self.max)
except ValueError:
raise ValueError("parameter '%s': attribute 'max' must be an integer" % self.name)
if not self.multiple and (self.min is not None):
raise ValueError("parameter '%s': cannot specify 'min' property on single data parameter '%s'. Set multiple=\"true\" to enable this option" % self.name)
if not self.multiple and (self.max is not None):
raise ValueError("parameter '%s': cannot specify 'max' property on single data parameter '%s'. Set multiple=\"true\" to enable this option" % self.name)
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], 'A name (%s) and type (%s) are required for explicit conversion' % (name, conv_extension)
if self.datatypes_registry:
conv_type = self.datatypes_registry.get_datatype_by_extension(conv_extension.lower())
if conv_type is None:
raise ValueError("parameter '%s': datatype class not found for extension '%s', which is used as 'type' attribute in conversion of data parameter" % (self.name, conv_type))
self.conversions.append((name, conv_extension, [conv_type]))
[docs] def from_json(self, value, trans, other_values={}):
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 ValueError("parameter '%s': 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, string_types) and value.find(",") > 0:
value = [int(value_part) for value_part in value.split(",")]
if isinstance(value, list):
rval = []
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(trans.app.model.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(trans.app.model.HistoryDatasetCollectionAssociation).get(decoded_id))
elif single_value['src'] == 'ldda':
decoded_id = trans.security.decode_id(single_value['id'])
rval.append(trans.sa_session.query(trans.app.model.LibraryDatasetDatasetAssociation).get(decoded_id))
else:
raise ValueError("Unknown input source %s passed to job submission API." % single_value['src'])
elif isinstance(single_value, trans.app.model.HistoryDatasetCollectionAssociation):
rval.append(single_value)
elif isinstance(single_value, trans.app.model.DatasetCollectionElement):
rval.append(single_value)
elif isinstance(single_value, trans.app.model.HistoryDatasetAssociation):
rval.append(single_value)
elif isinstance(single_value, trans.app.model.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(trans.app.model.HistoryDatasetAssociation).get(single_value))
if found_hdca:
for val in rval:
if not isinstance(val, trans.app.model.HistoryDatasetCollectionAssociation):
raise ValueError("parameter '%s': if collections are supplied to multiple data input parameter, only collections may be used" % self.name)
elif isinstance(value, trans.app.model.HistoryDatasetAssociation):
rval = 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 = trans.sa_session.query(trans.app.model.HistoryDatasetAssociation).get(decoded_id)
elif value['src'] == 'hdca':
decoded_id = trans.security.decode_id(value['id'])
rval = trans.sa_session.query(trans.app.model.HistoryDatasetCollectionAssociation).get(decoded_id)
else:
raise ValueError("Unknown input source %s passed to job submission API." % value['src'])
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(trans.app.model.HistoryDatasetCollectionAssociation).get(decoded_id)
rval.append(hdca)
elif isinstance(value, trans.app.model.HistoryDatasetCollectionAssociation) or isinstance(value, trans.app.model.DatasetCollectionElement):
rval = value
else:
rval = trans.sa_session.query(trans.app.model.HistoryDatasetAssociation).get(value)
values = util.listify(rval)
dataset_matcher_factory = get_dataset_matcher_factory(trans)
dataset_matcher = dataset_matcher_factory.dataset_matcher(self, other_values)
for v in values:
if v:
if hasattr(v, "deleted") and v.deleted:
raise ValueError("parameter '%s': the previously selected dataset has been deleted." % self.name)
elif hasattr(v, "dataset") and v.dataset.state in [galaxy.model.Dataset.states.ERROR, galaxy.model.Dataset.states.DISCARDED]:
raise ValueError("parameter '%s': the previously selected dataset has entered an unusable state" % self.name)
elif hasattr(v, "dataset"):
match = dataset_matcher.hda_match(v)
if match and match.implicit_conversion:
v.implicit_conversion = True
if not self.multiple:
if len(values) > 1:
raise ValueError("parameter '%s': more than one dataset supplied to single input dataset parameter" % self.name)
if len(values) > 0:
rval = values[0]
else:
raise ValueError("parameter '%s': invalid dataset supplied to single input dataset parameter" % self.name)
return rval
[docs] def to_param_dict_string(self, value, other_values={}):
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(["%s: %s" % (item.hid, item.name) for item in value])
except Exception:
pass
return "No dataset."
[docs] def validate(self, value, trans=None):
dataset_count = 0
for validator in self.validators:
def do_validate(v):
if validator.requires_dataset_metadata and v and hasattr(v, 'dataset') and v.dataset.state != galaxy.model.Dataset.states.OK:
return
else:
validator.validate(v, trans)
if value and self.multiple:
if not isinstance(value, list):
value = [value]
for v in value:
if isinstance(v, galaxy.model.HistoryDatasetCollectionAssociation):
for dataset_instance in v.collection.dataset_instances:
dataset_count += 1
do_validate(dataset_instance)
elif isinstance(v, galaxy.model.DatasetCollectionElement):
for dataset_instance in v.child_collection.dataset_instances:
dataset_count += 1
do_validate(dataset_instance)
else:
dataset_count += 1
do_validate(v)
else:
if value:
dataset_count += 1
do_validate(value)
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] 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 ref
[docs] def to_dict(self, trans, other_values={}):
# create dictionary and fill default parameters
d = super(DataToolParameter, self).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': []}
# 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,
'name' : name,
'tags' : [t.user_tname if not t.value else "%s:%s" % (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)
# 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 = '%s (as %s)' % (match.original_hda.name, 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, '(%s) %s' % (hda_state, hda.name), 'hda', True)
# 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 = "%s (with implicit datatype conversion)" % name
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['hid'], reverse=True)
d['options']['hdca'] = sorted(d['options']['hdca'], key=lambda k: k['hid'], reverse=True)
# return final dictionary
return d
def _history_query(self, trans):
assert self.multiple
dataset_collection_type_descriptions = trans.app.dataset_collections_service.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(DataCollectionToolParameter, self).__init__(tool, input_source, trans)
self._parse_formats(trans, input_source)
collection_types = input_source.get("collection_type", None)
if collection_types:
collection_types = [t.strip() for t in collection_types.split(",")]
self._collection_types = collection_types
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_collections_service.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_collections_service.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={}):
rval = None
if trans.workflow_building_mode is workflow_building_modes.ENABLED:
return None
if not value and not self.optional:
raise ValueError("parameter '%s': 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, string_types) and value.find(",") > 0:
value = [int(value_part) for value_part in value.split(",")]
elif isinstance(value, trans.app.model.HistoryDatasetCollectionAssociation):
rval = value
elif isinstance(value, trans.app.model.DatasetCollectionElement):
# When mapping over nested collection - this paramter will recieve
# 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(trans.app.model.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(trans.app.model.HistoryDatasetCollectionAssociation).get(trans.security.decode_id(value['id']))
elif isinstance(value, string_types):
if value.startswith("dce:"):
rval = trans.sa_session.query(trans.app.model.DatasetCollectionElement).get(value[len("dce:"):])
elif value.startswith("hdca:"):
rval = trans.sa_session.query(trans.app.model.HistoryDatasetCollectionAssociation).get(value[len("hdca:"):])
else:
rval = trans.sa_session.query(trans.app.model.HistoryDatasetCollectionAssociation).get(value)
if rval and isinstance(rval, trans.app.model.HistoryDatasetCollectionAssociation):
if rval.deleted:
raise ValueError("parameter '%s': 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, galaxy.model.HistoryDatasetCollectionAssociation):
display_text = "%s: %s" % (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(DataCollectionToolParameter, self).to_dict(trans)
d['extensions'] = self.extensions
d['multiple'] = self.multiple
d['options'] = {'hda': [], 'hdca': []}
# 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 directly matched collections
for hdca, implicit_conversion in self.match_collections(trans, history, dataset_collection_matcher):
name = hdca.name
if implicit_conversion:
name = "%s (with implicit datatype conversion)" % name
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 "%s:%s" % (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 = "%s (with implicit datatype conversion)" % name
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 "%s:%s" % (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['hid'], 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(LibraryDatasetToolParameter, self).__init__(tool, input_source)
self.multiple = input_source.get_bool('multiple', True)
[docs] def from_json(self, value, trans, other_values={}):
return self.to_python(value, trans.app, other_values=other_values, validate=True)
[docs] def to_param_dict_string(self, value, other_values={}):
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 = []
for item in value:
lda_id = lda_name = None
if isinstance(item, app.model.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={}, validate=False):
if not isinstance(value, list):
value = [value]
lst = []
for item in value:
if isinstance(item, app.model.LibraryDatasetDatasetAssociation):
lst.append(item)
else:
lda_id = None
if isinstance(item, dict):
lda_id = item.get('id')
elif isinstance(item, string_types):
lda_id = item
else:
lst = []
break
lda = app.model.context.query(app.model.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 ValueError("parameter '%s': 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 ValueError("parameter '%s': invalid library dataset selected" % self.name)
return None
else:
return lst
[docs] def to_dict(self, trans, other_values=None):
d = super(LibraryDatasetToolParameter, self).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 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={}):
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(RulesListToolParameter, self).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,
genomespacefile=GenomespaceFileToolParameter,
data=DataToolParameter,
data_collection=DataCollectionToolParameter,
library_data=LibraryDatasetToolParameter,
rules=RulesListToolParameter,
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(object):
"""
Wrapper to note a value that is not yet set, but will be required at runtime.
"""
pass
[docs]class ConnectedValue(RuntimeValue):
"""
Wrapper to note a value that is not yet set, but will be inferred from a connection.
"""
pass