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.tool_util.provided_metadata
import json
import logging
import os
import re
from galaxy.util import stringify_dictionary_keys
log = logging.getLogger(__name__)
[docs]def parse_tool_provided_metadata(meta_file, provided_metadata_style=None, job_wrapper=None):
"""Return a ToolProvidedMetadata object for specified file path.
If meta_file is absent, return a NullToolProvidedMetadata. If provided_metadata_style is None
attempt to guess tool provided metadata type.
"""
if not os.path.exists(meta_file):
return NullToolProvidedMetadata()
if provided_metadata_style is None:
provided_metadata_style = _guess_tool_provided_metadata_style(meta_file)
assert provided_metadata_style in ["legacy", "default"]
if provided_metadata_style == "legacy":
return LegacyToolProvidedMetadata(meta_file, job_wrapper=job_wrapper)
elif provided_metadata_style == "default":
return ToolProvidedMetadata(meta_file)
def _guess_tool_provided_metadata_style(path):
try:
with open(path, "r") as f:
metadata = json.load(f)
metadata_type = metadata.get("type", None)
return "legacy" if metadata_type in ["dataset", "new_primary_dataset"] else "default"
except ValueError:
# Either empty or multiple JSON lines, either way we can safely treat
# it as legacy style.
return "legacy"
[docs]class BaseToolProvidedMetadata(object):
[docs] def get_new_datasets(self, output_name):
"""Find new datasets for dataset discovery for specified output.
Return a list of such datasets.
Called only in the context of discovering datasets when
discover_via="tool_provided_metadata" is defined in the tool.
"""
return []
[docs] def has_failed_outputs(self):
"""Determine if generation of any of the outputs failed.
"""
return False
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename):
"""For a discovered dataset, get the corresponding metadata entry.
The discovery may have been from explicit listing in this file (returned
from get_new_datasets) or via file regex, either way the basename of the
file is used to index the fetching of the metadata entry.
"""
return {}
[docs] def get_unnamed_outputs(self):
"""Return unnamed outputs dataset introduced for upload 2.0.
Needs more formal specification but see output_collect for how destinations,
types, elements, etc... are consumed.
"""
return []
[docs] def get_dataset_meta(self, output_name, dataset_id):
"""Return primary dataset metadata for specified output.
"""
return {}
[docs] def rewrite(self):
"""Write metadata back to the file system.
If metadata has not changed via outputs specified as mutable, the
implementation class may opt to not re-write the file.
"""
return None
[docs] def get_new_datasets_for_metadata_collection(self):
"""Return all datasets tracked that are not explicit primary outputs.
"""
return []
[docs]class LegacyToolProvidedMetadata(BaseToolProvidedMetadata):
[docs] def __init__(self, meta_file, job_wrapper=None):
self.meta_file = meta_file
self.tool_provided_job_metadata = []
with open(meta_file, 'r') as f:
for line in f:
try:
line = stringify_dictionary_keys(json.loads(line))
assert 'type' in line
except Exception:
log.exception('(%s) Got JSON data from tool, but data is improperly formatted or no "type" key in data' % job_wrapper.job_id)
log.debug('Offending data was: %s' % line)
continue
# Set the dataset id if it's a dataset entry and isn't set.
# This isn't insecure. We loop the job's output datasets in
# the finish method, so if a tool writes out metadata for a
# dataset id that it doesn't own, it'll just be ignored.
dataset_id_not_specified = line['type'] == 'dataset' and 'dataset_id' not in line
if dataset_id_not_specified:
dataset_basename = line['dataset']
if job_wrapper:
try:
line['dataset_id'] = job_wrapper.get_output_file_id(dataset_basename)
except KeyError:
log.warning('(%s) Tool provided job dataset-specific metadata without specifying a dataset' % job_wrapper.job_id)
continue
else:
match = re.match(r'dataset_(\d+)\.dat', dataset_basename)
line['dataset_id'] = int(match.group(1))
self.tool_provided_job_metadata.append(line)
[docs] def get_dataset_meta(self, output_name, dataset_id):
for meta in self.tool_provided_job_metadata:
if meta['type'] == 'dataset' and int(meta['dataset_id']) == dataset_id:
return meta
return {}
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename):
for meta in self.tool_provided_job_metadata:
if meta['type'] == 'new_primary_dataset' and meta['filename'] == basename:
return meta
[docs] def get_new_datasets(self, output_name):
log.warning("Called get_new_datasets with legacy tool metadata provider - that is unimplemented.")
return []
[docs] def has_failed_outputs(self):
found_failed = False
for meta in self.tool_provided_job_metadata:
if meta.get("failed", False):
found_failed = True
return found_failed
[docs] def rewrite(self):
with open(self.meta_file, 'wt') as job_metadata_fh:
for meta in self.tool_provided_job_metadata:
job_metadata_fh.write("%s\n" % (json.dumps(meta)))
[docs] def get_new_datasets_for_metadata_collection(self):
for meta in self.tool_provided_job_metadata:
if meta['type'] == 'new_primary_dataset':
yield meta
[docs]class ToolProvidedMetadata(BaseToolProvidedMetadata):
[docs] def __init__(self, meta_file):
self.meta_file = meta_file
with open(meta_file, 'r') as f:
self.tool_provided_job_metadata = json.load(f)
[docs] def get_dataset_meta(self, output_name, dataset_id):
return self.tool_provided_job_metadata.get(output_name, {})
[docs] def get_new_dataset_meta_by_basename(self, output_name, basename):
datasets = self.tool_provided_job_metadata.get(output_name, {}).get("datasets", [])
for meta in datasets:
if meta['filename'] == basename:
return meta
[docs] def get_new_datasets(self, output_name):
datasets = self.tool_provided_job_metadata.get(output_name, {}).get("datasets", [])
if not datasets:
elements = self.tool_provided_job_metadata.get(output_name, {}).get("elements", [])
if elements:
datasets = self._elements_to_datasets(elements)
return datasets
def _elements_to_datasets(self, elements, level=0):
for element in elements:
extra_kwds = {"identifier_%d" % level: element["name"]}
if "elements" in element:
for inner_element in self._elements_to_datasets(element["elements"], level=level + 1):
dataset = extra_kwds.copy()
dataset.update(inner_element)
yield dataset
else:
dataset = extra_kwds
extra_kwds.update(element)
yield extra_kwds
[docs] def has_failed_outputs(self):
found_failed = False
for output_name, meta in self.tool_provided_job_metadata.items():
if output_name == "__unnamed_outputs":
continue
if meta.get("failed", False):
found_failed = True
return found_failed
[docs] def get_unnamed_outputs(self):
log.debug("unnamed outputs [%s]" % self.tool_provided_job_metadata)
return self.tool_provided_job_metadata.get("__unnamed_outputs", [])
[docs] def rewrite(self):
with open(self.meta_file, 'wt') as job_metadata_fh:
json.dump(self.tool_provided_job_metadata, job_metadata_fh)