Warning
This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
Source code for galaxy.tools.evaluation
import io
import json
import logging
import os
import tempfile
from six import string_types
from galaxy import model
from galaxy.job_execution.setup import ensure_configs_directory
from galaxy.model.none_like import NoneDataset
from galaxy.tools import global_tool_errors
from galaxy.tools.parameters import (
visit_input_values,
wrapped_json,
)
from galaxy.tools.parameters.basic import (
DataCollectionToolParameter,
DataToolParameter,
SelectToolParameter,
)
from galaxy.tools.parameters.grouping import (
Conditional,
Repeat,
Section
)
from galaxy.tools.wrappers import (
DatasetCollectionWrapper,
DatasetFilenameWrapper,
DatasetListWrapper,
ElementIdentifierMapper,
InputValueWrapper,
RawObjectWrapper,
SelectToolParameterWrapper,
ToolParameterValueWrapper,
)
from galaxy.util import (
find_instance_nested,
listify,
safe_makedirs,
unicodify,
)
from galaxy.util.bunch import Bunch
from galaxy.util.object_wrapper import wrap_with_safe_string
from galaxy.util.template import fill_template
from galaxy.work.context import WorkRequestContext
log = logging.getLogger(__name__)
[docs]class ToolEvaluator(object):
""" An abstraction linking together a tool and a job runtime to evaluate
tool inputs in an isolated, testable manner.
"""
[docs] def __init__(self, app, tool, job, local_working_directory):
self.app = app
self.job = job
self.tool = tool
self.local_working_directory = local_working_directory
[docs] def set_compute_environment(self, compute_environment, get_special=None):
"""
Setup the compute environment and established the outline of the param_dict
for evaluating command and config cheetah templates.
"""
self.compute_environment = compute_environment
job = self.job
incoming = dict([(p.name, p.value) for p in job.parameters])
incoming = self.tool.params_from_strings(incoming, self.app)
# Full parameter validation
request_context = WorkRequestContext(app=self.app, user=self._user, history=self._history)
def validate_inputs(input, value, context, **kwargs):
value = input.from_json(value, request_context, context)
input.validate(value, request_context)
visit_input_values(self.tool.inputs, incoming, validate_inputs)
# Restore input / output data lists
inp_data, out_data, out_collections = job.io_dicts()
if get_special:
# Set up output dataset association for export history jobs. Because job
# uses a Dataset rather than an HDA or LDA, it's necessary to set up a
# fake dataset association that provides the needed attributes for
# preparing a job.
class FakeDatasetAssociation (object):
fake_dataset_association = True
def __init__(self, dataset=None):
self.dataset = dataset
self.file_name = dataset.file_name
self.metadata = dict()
special = get_special()
if special:
out_data["output_file"] = FakeDatasetAssociation(dataset=special.dataset)
# These can be passed on the command line if wanted as $__user_*__
incoming.update(model.User.user_template_environment(job.history and job.history.user))
# Build params, done before hook so hook can use
param_dict = self.build_param_dict(
incoming,
inp_data,
out_data,
output_collections=out_collections,
)
# Certain tools require tasks to be completed prior to job execution
# ( this used to be performed in the "exec_before_job" hook, but hooks are deprecated ).
self.tool.exec_before_job(self.app, inp_data, out_data, param_dict)
# Run the before queue ("exec_before_job") hook
self.tool.call_hook('exec_before_job', self.app, inp_data=inp_data,
out_data=out_data, tool=self.tool, param_dict=incoming)
self.param_dict = param_dict
[docs] def build_param_dict(self, incoming, input_datasets, output_datasets, output_collections):
"""
Build the dictionary of parameters for substituting into the command
line. Each value is wrapped in a `InputValueWrapper`, which allows
all the attributes of the value to be used in the template, *but*
when the __str__ method is called it actually calls the
`to_param_dict_string` method of the associated input.
"""
compute_environment = self.compute_environment
job_working_directory = compute_environment.working_directory()
param_dict = dict()
def input():
raise SyntaxError("Unbound variable input.") # Don't let $input hang Python evaluation process.
param_dict["input"] = input
param_dict['__datatypes_config__'] = param_dict['GALAXY_DATATYPES_CONF_FILE'] = os.path.join(job_working_directory, 'registry.xml')
if self._history:
param_dict['__history_id__'] = self.app.security.encode_id(self._history.id)
param_dict['__galaxy_url__'] = self.compute_environment.galaxy_url()
param_dict.update(self.tool.template_macro_params)
# All parameters go into the param_dict
param_dict.update(incoming)
self.__populate_wrappers(param_dict, input_datasets, job_working_directory)
self.__populate_input_dataset_wrappers(param_dict, input_datasets)
self.__populate_output_dataset_wrappers(param_dict, output_datasets, job_working_directory)
self.__populate_output_collection_wrappers(param_dict, output_collections, job_working_directory)
self.__populate_unstructured_path_rewrites(param_dict)
# Call param dict sanitizer, before non-job params are added, as we don't want to sanitize filenames.
self.__sanitize_param_dict(param_dict)
# Parameters added after this line are not sanitized
self.__populate_non_job_params(param_dict)
# Populate and store templated InteractiveTools values
self.__populate_interactivetools(param_dict)
# Return the dictionary of parameters
return param_dict
def __walk_inputs(self, inputs, input_values, func):
def do_walk(inputs, input_values):
"""
Wraps parameters as neccesary.
"""
for input in inputs.values():
if isinstance(input, Repeat):
for d in input_values[input.name]:
do_walk(input.inputs, d)
elif isinstance(input, Conditional):
values = input_values[input.name]
current = values["__current_case__"]
func(values, input.test_param)
do_walk(input.cases[current].inputs, values)
elif isinstance(input, Section):
values = input_values[input.name]
do_walk(input.inputs, values)
else:
func(input_values, input)
do_walk(inputs, input_values)
def __populate_wrappers(self, param_dict, input_datasets, job_working_directory):
def wrap_input(input_values, input):
value = input_values[input.name]
if isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = \
DatasetListWrapper(job_working_directory,
dataset_instances,
compute_environment=self.compute_environment,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name,
formats=input.formats)
elif isinstance(input, DataToolParameter):
# FIXME: We're populating param_dict with conversions when
# wrapping values, this should happen as a separate
# step before wrapping (or call this wrapping step
# something more generic) (but iterating this same
# list twice would be wasteful)
# Add explicit conversions by name to current parent
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
# If we are at building cmdline step, then converters
# have already executed
conv_ext, converted_dataset = input_values[input.name].find_conversion_destination(conversion_datatypes)
# When dealing with optional inputs, we'll provide a
# valid extension to be used for None converted dataset
if not conv_ext:
conv_ext = conversion_extensions[0]
# input_values[ input.name ] is None when optional
# dataset, 'conversion' of optional dataset should
# create wrapper around NoneDataset for converter output
if input_values[input.name] and not converted_dataset:
# Input that converter is based from has a value,
# but converted dataset does not exist
raise Exception('A path for explicit datatype conversion has not been found: %s --/--> %s'
% (input_values[input.name].extension, conversion_extensions))
else:
# Trick wrapper into using target conv ext (when
# None) without actually being a tool parameter
input_values[conversion_name] = \
DatasetFilenameWrapper(converted_dataset,
datatypes_registry=self.app.datatypes_registry,
tool=Bunch(conversion_name=Bunch(extensions=conv_ext)),
name=conversion_name)
# Wrap actual input dataset
dataset = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=input.name,
compute_environment=self.compute_environment
)
element_identifier = element_identifier_mapper.identifier(dataset, param_dict)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = \
DatasetFilenameWrapper(dataset, **wrapper_kwds)
elif isinstance(input, DataCollectionToolParameter):
dataset_collection = value
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
tool=self,
name=input.name
)
wrapper = DatasetCollectionWrapper(
job_working_directory,
dataset_collection,
**wrapper_kwds
)
input_values[input.name] = wrapper
elif isinstance(input, SelectToolParameter):
if input.multiple:
value = listify(value)
input_values[input.name] = SelectToolParameterWrapper(
input, value, other_values=param_dict, compute_environment=self.compute_environment)
else:
input_values[input.name] = InputValueWrapper(
input, value, param_dict)
# HACK: only wrap if check_values is not false, this deals with external
# tools where the inputs don't even get passed through. These
# tools (e.g. UCSC) should really be handled in a special way.
if self.tool.check_values:
element_identifier_mapper = ElementIdentifierMapper(input_datasets)
self.__walk_inputs(self.tool.inputs, param_dict, wrap_input)
def __populate_input_dataset_wrappers(self, param_dict, input_datasets):
# TODO: Update this method for dataset collections? Need to test. -John.
# FIXME: when self.check_values==True, input datasets are being wrapped
# twice (above and below, creating 2 separate
# DatasetFilenameWrapper objects - first is overwritten by
# second), is this necessary? - if we get rid of this way to
# access children, can we stop this redundancy, or is there
# another reason for this?
# - Only necessary when self.check_values is False (==external dataset
# tool?: can this be abstracted out as part of being a datasouce tool?)
# For now we try to not wrap unnecessarily, but this should be untangled at some point.
for name, data in input_datasets.items():
param_dict_value = param_dict.get(name, None)
if data and param_dict_value is None:
# We may have a nested parameter that is not fully prefixed.
# We try recovering from param_dict, but tool authors should really use fully-qualified
# variables
wrappers = find_instance_nested(param_dict,
instances=(DatasetFilenameWrapper, DatasetListWrapper),
match_key=name)
if len(wrappers) == 1:
wrapper = wrappers[0]
param_dict[name] = wrapper
continue
if not isinstance(param_dict_value, (DatasetFilenameWrapper, DatasetListWrapper)):
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=name,
compute_environment=self.compute_environment,
)
param_dict[name] = DatasetFilenameWrapper(data, **wrapper_kwds)
def __populate_output_collection_wrappers(self, param_dict, output_collections, job_working_directory):
tool = self.tool
for name, out_collection in output_collections.items():
if name not in tool.output_collections:
continue
# message_template = "Name [%s] not found in tool.output_collections %s"
# message = message_template % ( name, tool.output_collections )
# raise AssertionError( message )
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
io_type='output',
tool=tool,
name=name
)
wrapper = DatasetCollectionWrapper(
job_working_directory,
out_collection,
**wrapper_kwds
)
param_dict[name] = wrapper
# TODO: Handle nested collections...
output_def = tool.output_collections[name]
for element_identifier, output_def in output_def.outputs.items():
if not output_def.implicit:
dataset_wrapper = wrapper[element_identifier]
param_dict[output_def.name] = dataset_wrapper
log.info("Updating param_dict for %s with %s" % (output_def.name, dataset_wrapper))
def __populate_output_dataset_wrappers(self, param_dict, output_datasets, job_working_directory):
for name, hda in output_datasets.items():
# Write outputs to the working directory (for security purposes)
# if desired.
param_dict[name] = DatasetFilenameWrapper(hda, compute_environment=self.compute_environment, io_type="output")
output_path = str(param_dict[name])
# Conditionally create empty output:
# - may already exist (e.g. symlink output)
# - parent directory might not exist (e.g. Pulsar)
if not os.path.exists(output_path) and os.path.exists(os.path.dirname(output_path)):
open(output_path, 'w').close()
# Provide access to a path to store additional files
# TODO: move compute path logic into compute environment, move setting files_path
# logic into DatasetFilenameWrapper. Currently this sits in the middle and glues
# stuff together inconsistently with the way the rest of path rewriting works.
store_by = getattr(hda.dataset.object_store, "store_by", "id")
file_name = "dataset_%s_files" % getattr(hda.dataset, store_by)
param_dict[name].files_path = os.path.abspath(os.path.join(job_working_directory, "working", file_name))
for out_name, output in self.tool.outputs.items():
if out_name not in param_dict and output.filters:
# Assume the reason we lack this output is because a filter
# failed to pass; for tool writing convienence, provide a
# NoneDataset
ext = getattr(output, "format", None) # populate only for output datasets (not collections)
param_dict[out_name] = NoneDataset(datatypes_registry=self.app.datatypes_registry, ext=ext)
def __populate_non_job_params(self, param_dict):
# -- Add useful attributes/functions for use in creating command line.
# Function for querying a data table.
def get_data_table_entry(table_name, query_attr, query_val, return_attr):
"""
Queries and returns an entry in a data table.
"""
if table_name in self.app.tool_data_tables:
return self.app.tool_data_tables[table_name].get_entry(query_attr, query_val, return_attr)
param_dict['__tool_directory__'] = self.compute_environment.tool_directory()
param_dict['__get_data_table_entry__'] = get_data_table_entry
param_dict['__local_working_directory__'] = self.local_working_directory
# We add access to app here, this allows access to app.config, etc
param_dict['__app__'] = RawObjectWrapper(self.app)
# More convienent access to app.config.new_file_path; we don't need to
# wrap a string, but this method of generating additional datasets
# should be considered DEPRECATED
param_dict['__new_file_path__'] = self.compute_environment.new_file_path()
# The following points to location (xxx.loc) files which are pointers
# to locally cached data
param_dict['__tool_data_path__'] = param_dict['GALAXY_DATA_INDEX_DIR'] = self.app.config.tool_data_path
# For the upload tool, we need to know the root directory and the
# datatypes conf path, so we can load the datatypes registry
param_dict['__root_dir__'] = param_dict['GALAXY_ROOT_DIR'] = os.path.abspath(self.app.config.root)
param_dict['__admin_users__'] = self.app.config.admin_users
param_dict['__user__'] = RawObjectWrapper(param_dict.get('__user__', None))
def __populate_unstructured_path_rewrites(self, param_dict):
def rewrite_unstructured_paths(input_values, input):
if isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(
input, input_values[input.name], other_values=param_dict, compute_environment=self.compute_environment)
if not self.tool.check_values and self.compute_environment:
# The tools weren't "wrapped" yet, but need to be in order to get
# the paths rewritten.
self.__walk_inputs(self.tool.inputs, param_dict, rewrite_unstructured_paths)
def __populate_interactivetools(self, param_dict):
"""
Populate InteractiveTools templated values.
"""
it = []
for ep in getattr(self.tool, 'ports', []):
ep_dict = {}
for key in 'port', 'name', 'url':
val = ep.get(key, None)
if val is not None:
val = fill_template(val, context=param_dict, python_template_version=self.tool.python_template_version)
clean_val = []
for line in val.split('\n'):
clean_val.append(line.strip())
val = '\n'.join(clean_val)
val = val.replace("\n", " ").replace("\r", " ").strip()
ep_dict[key] = val
it.append(ep_dict)
self.interactivetools = it
it_man = getattr(self.app, "interactivetool_manager", None)
if it_man:
it_man.create_interactivetool(self.job, self.tool, it)
return it
def __sanitize_param_dict(self, param_dict):
"""
Sanitize all values that will be substituted on the command line, with the exception of ToolParameterValueWrappers,
which already have their own specific sanitization rules and also exclude special-cased named values.
We will only examine the first level for values to skip; the wrapping function will recurse as necessary.
Note: this method follows the style of the similar populate calls, in that param_dict is modified in-place.
"""
# chromInfo is a filename, do not sanitize it.
skip = ['chromInfo'] + list(self.tool.template_macro_params.keys())
if not self.tool or not self.tool.options or self.tool.options.sanitize:
for key, value in list(param_dict.items()):
if key not in skip:
# Remove key so that new wrapped object will occupy key slot
del param_dict[key]
# And replace with new wrapped key
param_dict[wrap_with_safe_string(key, no_wrap_classes=ToolParameterValueWrapper)] = wrap_with_safe_string(value, no_wrap_classes=ToolParameterValueWrapper)
[docs] def build(self):
"""
Build runtime description of job to execute, evaluate command and
config templates corresponding to this tool with these inputs on this
compute environment.
"""
self.extra_filenames = []
self.command_line = None
try:
self.__build_config_files()
except Exception as e:
# capture and log parsing errors
global_tool_errors.add_error(self.tool.config_file, "Building Config Files", e)
raise e
try:
self.__build_param_file()
except Exception as e:
# capture and log parsing errors
global_tool_errors.add_error(self.tool.config_file, "Building Param File", e)
raise e
try:
self.__build_command_line()
except Exception as e:
# capture and log parsing errors
global_tool_errors.add_error(self.tool.config_file, "Building Command Line", e)
raise e
try:
self.__build_environment_variables()
except Exception as e:
global_tool_errors.add_error(self.tool.config_file, "Building Environment Variables", e)
raise e
return self.command_line, self.extra_filenames, self.environment_variables
def __build_command_line(self):
"""
Build command line to invoke this tool given a populated param_dict
"""
command = self.tool.command
param_dict = self.param_dict
interpreter = self.tool.interpreter
command_line = None
if not command:
return
try:
# Substituting parameters into the command
command_line = fill_template(command, context=param_dict, python_template_version=self.tool.python_template_version)
cleaned_command_line = []
# Remove leading and trailing whitespace from each line for readability.
for line in command_line.split('\n'):
cleaned_command_line.append(line.strip())
command_line = '\n'.join(cleaned_command_line)
# Remove newlines from command line, and any leading/trailing white space
command_line = command_line.replace("\n", " ").replace("\r", " ").strip()
except Exception:
# Modify exception message to be more clear
# e.args = ( 'Error substituting into command line. Params: %r, Command: %s' % ( param_dict, self.command ), )
raise
if interpreter:
# TODO: path munging for cluster/dataset server relocatability
executable = command_line.split()[0]
tool_dir = os.path.abspath(self.tool.tool_dir)
abs_executable = os.path.join(tool_dir, executable)
command_line = command_line.replace(executable, abs_executable, 1)
command_line = interpreter + " " + command_line
self.command_line = command_line
def __build_config_files(self):
"""
Build temporary file for file based parameter transfer if needed
"""
param_dict = self.param_dict
config_filenames = []
for name, filename, content in self.tool.config_files:
config_text, is_template = self.__build_config_file_text(content)
# If a particular filename was forced by the config use it
directory = ensure_configs_directory(self.local_working_directory)
if filename is not None:
# Explicit filename was requested, needs to be placed in tool working directory
directory = os.path.join(self.local_working_directory, "working")
config_filename = os.path.join(directory, filename)
else:
fd, config_filename = tempfile.mkstemp(dir=directory)
os.close(fd)
self.__write_workdir_file(config_filename, config_text, param_dict, is_template=is_template)
self.__register_extra_file(name, config_filename)
config_filenames.append(config_filename)
return config_filenames
def __build_environment_variables(self):
param_dict = self.param_dict
environment_variables = []
for environment_variable_def in self.tool.environment_variables:
directory = self.local_working_directory
environment_variable = environment_variable_def.copy()
environment_variable_template = environment_variable_def["template"]
inject = environment_variable_def.get("inject")
if inject == "api_key":
if self._user:
from galaxy.managers import api_keys
environment_variable_template = api_keys.ApiKeyManager(self.app).get_or_create_api_key(self._user)
else:
environment_variable_template = ""
is_template = False
else:
is_template = True
fd, config_filename = tempfile.mkstemp(dir=directory)
os.close(fd)
self.__write_workdir_file(config_filename, environment_variable_template, param_dict, is_template=is_template, strip=environment_variable_def.get("strip", False))
config_file_basename = os.path.basename(config_filename)
# environment setup in job file template happens before `cd $working_directory`
environment_variable["value"] = '`cat "$_GALAXY_JOB_DIR/%s"`' % config_file_basename
environment_variable["raw"] = True
environment_variables.append(environment_variable)
home_dir = self.compute_environment.home_directory()
tmp_dir = self.compute_environment.tmp_directory()
if home_dir:
environment_variable = dict(name="HOME", value='"%s"' % home_dir, raw=True)
environment_variables.append(environment_variable)
if tmp_dir:
for tmp_directory_var in self.tool.tmp_directory_vars:
environment_variable = dict(name=tmp_directory_var, value='"%s"' % tmp_dir, raw=True)
environment_variables.append(environment_variable)
self.environment_variables = environment_variables
return environment_variables
def __build_param_file(self):
"""
Build temporary file for file based parameter transfer if needed
"""
param_dict = self.param_dict
directory = self.local_working_directory
command = self.tool.command
if self.tool.profile < 16.04 and command and "$param_file" in command:
fd, param_filename = tempfile.mkstemp(dir=directory)
os.close(fd)
with open(param_filename, "w") as f:
for key, value in param_dict.items():
# parameters can be strings or lists of strings, coerce to list
if not isinstance(value, list):
value = [value]
for elem in value:
f.write('%s=%s\n' % (key, elem))
self.__register_extra_file('param_file', param_filename)
return param_filename
else:
return None
def __build_config_file_text(self, content):
if isinstance(content, string_types):
return content, True
content_format = content["format"]
handle_files = content["handle_files"]
if content_format != "json":
template = "Galaxy can only currently convert inputs to json, format [%s] is unhandled"
message = template % content_format
raise Exception(message)
return json.dumps(wrapped_json.json_wrap(self.tool.inputs, self.param_dict, handle_files=handle_files)), False
def __write_workdir_file(self, config_filename, content, context, is_template=True, strip=False):
parent_dir = os.path.dirname(config_filename)
if not os.path.exists(parent_dir):
safe_makedirs(parent_dir)
if is_template:
value = fill_template(content, context=context, python_template_version=self.tool.python_template_version)
else:
value = unicodify(content)
if strip:
value = value.strip()
with io.open(config_filename, "w", encoding='utf-8') as f:
f.write(value)
# For running jobs as the actual user, ensure the config file is globally readable
os.chmod(config_filename, 0o644)
def __register_extra_file(self, name, local_config_path):
"""
Takes in the local path to a config file and registers the (potentially
remote) ultimate path of the config file with the parameter dict.
"""
self.extra_filenames.append(local_config_path)
config_basename = os.path.basename(local_config_path)
compute_config_path = self.__join_for_compute(self.compute_environment.config_directory(), config_basename)
self.param_dict[name] = compute_config_path
def __join_for_compute(self, *args):
"""
os.path.join but with compute_environment.sep for cross-platform
compat.
"""
return self.compute_environment.sep().join(args)
@property
def _history(self):
return self.job.history
@property
def _user(self):
history = self._history
return history and history.user