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Source code for galaxy_ext.metadata.set_metadata

"""
Execute an external process to set_meta() on a provided list of pickled datasets.

This was formerly scripts/set_metadata.py and expects these arguments:

    %prog datatypes_conf.xml job_metadata_file metadata_in,metadata_kwds,metadata_out,metadata_results_code,output_filename_override,metadata_override... max_metadata_value_size

Galaxy should be importable on sys.path and output_filename_override should be
set to the path of the dataset on which metadata is being set
(output_filename_override could previously be left empty and the path would be
constructed automatically).
"""
import json
import logging
import os
import sys

# insert *this* galaxy before all others on sys.path
sys.path.insert(1, os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)))

from six.moves import cPickle
from sqlalchemy.orm import clear_mappers

import galaxy.model.mapping  # need to load this before we unpickle, in order to setup properties assigned by the mappers
from galaxy.model.custom_types import total_size
from galaxy.util import stringify_dictionary_keys

# ensure supported version
assert sys.version_info[:2] >= (2, 6) and sys.version_info[:2] <= (2, 7), 'Python version must be 2.6 or 2.7, this is: %s' % sys.version

logging.basicConfig()
log = logging.getLogger(__name__)

galaxy.model.Job()  # this looks REAL stupid, but it is REQUIRED in order for SA to insert parameters into the classes defined by the mappers --> it appears that instantiating ANY mapper'ed class would suffice here


[docs]def set_meta_with_tool_provided(dataset_instance, file_dict, set_meta_kwds, datatypes_registry): # This method is somewhat odd, in that we set the metadata attributes from tool, # then call set_meta, then set metadata attributes from tool again. # This is intentional due to interplay of overwrite kwd, the fact that some metadata # parameters may rely on the values of others, and that we are accepting the # values provided by the tool as Truth. extension = dataset_instance.extension if extension == "_sniff_": try: from galaxy.datatypes import sniff extension = sniff.handle_uploaded_dataset_file(dataset_instance.dataset.external_filename, datatypes_registry) # We need to both set the extension so it is available to set_meta # and record it in the metadata so it can be reloaded on the server # side and the model updated (see MetadataCollection.{from,to}_JSON_dict) dataset_instance.extension = extension # Set special metadata property that will reload this on server side. setattr(dataset_instance.metadata, "__extension__", extension) except Exception: # TODO: log this when metadata can log stuff... # https://trello.com/c/Nrwodu9d pass for metadata_name, metadata_value in file_dict.get('metadata', {}).items(): setattr(dataset_instance.metadata, metadata_name, metadata_value) dataset_instance.datatype.set_meta(dataset_instance, **set_meta_kwds) for metadata_name, metadata_value in file_dict.get('metadata', {}).items(): setattr(dataset_instance.metadata, metadata_name, metadata_value)
[docs]def set_metadata(): # locate galaxy_root for loading datatypes galaxy_root = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, os.pardir)) galaxy.datatypes.metadata.MetadataTempFile.tmp_dir = tool_job_working_directory = os.path.abspath(os.getcwd()) # This is ugly, but to transition from existing jobs without this parameter # to ones with, smoothly, it has to be the last optional parameter and we # have to sniff it. try: max_metadata_value_size = int(sys.argv[-1]) sys.argv = sys.argv[:-1] except ValueError: max_metadata_value_size = 0 # max_metadata_value_size is unspecified and should be 0 # Set up datatypes registry datatypes_config = sys.argv.pop(1) if not os.path.exists(datatypes_config): # This path should exist, except for jobs that started running on release 17.05, where a global # datatypes_config (instead of a datatypes_config per job) was used. For a while release 17.05 # would remove the global datatypes config on shutdown and toolbox reload, which would lead to # failed metadata jobs. To remedy this we scan jobs at startup for missing registry.xml files, # and if we detect such a job we write out the current registry.xml file. datatypes_config = os.path.join(tool_job_working_directory, "registry.xml") if not os.path.exists(datatypes_config): print("Metadata setting failed because registry.xml could not be found. You may retry setting metadata.") sys.exit(1) datatypes_registry = galaxy.datatypes.registry.Registry() datatypes_registry.load_datatypes(root_dir=galaxy_root, config=datatypes_config) galaxy.model.set_datatypes_registry(datatypes_registry) job_metadata = sys.argv.pop(1) existing_job_metadata_dict = {} new_job_metadata_dict = {} if job_metadata != "None" and os.path.exists(job_metadata): for line in open(job_metadata, 'r'): try: line = stringify_dictionary_keys(json.loads(line)) if line['type'] == 'dataset': existing_job_metadata_dict[line['dataset_id']] = line elif line['type'] == 'new_primary_dataset': new_job_metadata_dict[line['filename']] = line except: continue for filenames in sys.argv[1:]: fields = filenames.split(',') filename_in = fields.pop(0) filename_kwds = fields.pop(0) filename_out = fields.pop(0) filename_results_code = fields.pop(0) dataset_filename_override = fields.pop(0) # Need to be careful with the way that these parameters are populated from the filename splitting, # because if a job is running when the server is updated, any existing external metadata command-lines # will not have info about the newly added override_metadata file if fields: override_metadata = fields.pop(0) else: override_metadata = None set_meta_kwds = stringify_dictionary_keys(json.load(open(filename_kwds))) # load kwds; need to ensure our keywords are not unicode try: dataset = cPickle.load(open(filename_in)) # load DatasetInstance dataset.dataset.external_filename = dataset_filename_override files_path = os.path.abspath(os.path.join(tool_job_working_directory, "dataset_%s_files" % (dataset.dataset.id))) dataset.dataset.external_extra_files_path = files_path if dataset.dataset.id in existing_job_metadata_dict: dataset.extension = existing_job_metadata_dict[dataset.dataset.id].get('ext', dataset.extension) # Metadata FileParameter types may not be writable on a cluster node, and are therefore temporarily substituted with MetadataTempFiles if override_metadata: override_metadata = json.load(open(override_metadata)) for metadata_name, metadata_file_override in override_metadata: if galaxy.datatypes.metadata.MetadataTempFile.is_JSONified_value(metadata_file_override): metadata_file_override = galaxy.datatypes.metadata.MetadataTempFile.from_JSON(metadata_file_override) setattr(dataset.metadata, metadata_name, metadata_file_override) file_dict = existing_job_metadata_dict.get(dataset.dataset.id, {}) set_meta_with_tool_provided(dataset, file_dict, set_meta_kwds, datatypes_registry) if max_metadata_value_size: for k, v in list(dataset.metadata.items()): if total_size(v) > max_metadata_value_size: log.info("Key %s too large for metadata, discarding" % k) dataset.metadata.remove_key(k) dataset.metadata.to_JSON_dict(filename_out) # write out results of set_meta json.dump((True, 'Metadata has been set successfully'), open(filename_results_code, 'wb+')) # setting metadata has succeeded except Exception as e: json.dump((False, str(e)), open(filename_results_code, 'wb+')) # setting metadata has failed somehow for i, (filename, file_dict) in enumerate(new_job_metadata_dict.items(), start=1): new_dataset_filename = os.path.join(tool_job_working_directory, "working", file_dict['filename']) new_dataset = galaxy.model.Dataset(id=-i, external_filename=new_dataset_filename) extra_files = file_dict.get('extra_files', None) if extra_files is not None: new_dataset._extra_files_path = os.path.join(tool_job_working_directory, "working", extra_files) new_dataset.state = new_dataset.states.OK new_dataset_instance = galaxy.model.HistoryDatasetAssociation(id=-i, dataset=new_dataset, extension=file_dict.get('ext', 'data')) set_meta_with_tool_provided(new_dataset_instance, file_dict, set_meta_kwds, datatypes_registry) file_dict['metadata'] = json.loads(new_dataset_instance.metadata.to_JSON_dict()) # storing metadata in external form, need to turn back into dict, then later jsonify if existing_job_metadata_dict or new_job_metadata_dict: with open(job_metadata, 'wb') as job_metadata_fh: for value in list(existing_job_metadata_dict.values()) + list(new_job_metadata_dict.values()): job_metadata_fh.write("%s\n" % (json.dumps(value))) clear_mappers()