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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.jobs.datasets import dataset_path_rewrites
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,
unicodify,
)
from galaxy.util.bunch import Bunch
from galaxy.util.none_like import NoneDataset
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
self.unstructured_path_rewriter = compute_environment.unstructured_path_rewriter()
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=job.history and job.history.user, history=job.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):
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,
output_paths=compute_environment.output_paths(),
job_working_directory=compute_environment.working_directory(),
input_paths=compute_environment.input_paths()
)
# 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, output_paths, job_working_directory, input_paths=[]):
"""
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.
"""
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')
param_dict.update(self.tool.template_macro_params)
# All parameters go into the param_dict
param_dict.update(incoming)
input_dataset_paths = dataset_path_rewrites(input_paths)
self.__populate_wrappers(param_dict, input_datasets, input_dataset_paths, job_working_directory)
self.__populate_input_dataset_wrappers(param_dict, input_datasets, input_dataset_paths)
self.__populate_output_dataset_wrappers(param_dict, output_datasets, output_paths, job_working_directory)
self.__populate_output_collection_wrappers(param_dict, output_collections, output_paths, 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)
# 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, input_dataset_paths, 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,
dataset_paths=input_dataset_paths,
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
)
if dataset:
# A None dataset does not have a filename
real_path = dataset.file_name
if real_path in input_dataset_paths:
wrapper_kwds["dataset_path"] = input_dataset_paths[real_path]
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,
dataset_paths=input_dataset_paths,
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, path_rewriter=self.unstructured_path_rewriter)
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, input_dataset_paths):
# 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,
)
if data:
real_path = data.file_name
if real_path in input_dataset_paths:
dataset_path = input_dataset_paths[real_path]
wrapper_kwds['dataset_path'] = dataset_path
param_dict[name] = DatasetFilenameWrapper(data, **wrapper_kwds)
def __populate_output_collection_wrappers(self, param_dict, output_collections, output_paths, job_working_directory):
output_dataset_paths = dataset_path_rewrites(output_paths)
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,
dataset_paths=output_dataset_paths,
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, output_paths, job_working_directory):
output_dataset_paths = dataset_path_rewrites(output_paths)
for name, hda in output_datasets.items():
# Write outputs to the working directory (for security purposes)
# if desired.
real_path = hda.file_name
if real_path in output_dataset_paths:
dataset_path = output_dataset_paths[real_path]
param_dict[name] = DatasetFilenameWrapper(hda, dataset_path=dataset_path)
try:
open(dataset_path.false_path, 'w').close()
except EnvironmentError:
pass # May well not exist - e.g. Pulsar.
else:
param_dict[name] = DatasetFilenameWrapper(hda)
# Provide access to a path to store additional files
# TODO: path munging for cluster/dataset server relocatability
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, 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, path_rewriter=self.unstructured_path_rewriter)
if not self.tool.check_values and self.unstructured_path_rewriter:
# 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 __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 = self.local_working_directory
if filename is not None:
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"]
fd, config_filename = tempfile.mkstemp(dir=directory)
os.close(fd)
self.__write_workdir_file(config_filename, environment_variable_template, param_dict)
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)
f = open(param_filename, "w")
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))
f.close()
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):
if is_template:
value = fill_template(content, context=context, python_template_version=self.tool.python_template_version)
else:
value = unicodify(content)
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)