def arxiv_plot_extract(obj, eng):
"""Extract plots from an arXiv archive.
:param obj: Workflow Object to process
:param eng: Workflow Engine processing the object
"""
arxiv_id = get_arxiv_id(obj.data)
filename = secure_filename('{0}.tar.gz'.format(arxiv_id))
tarball = obj.files[filename]
if tarball:
with TemporaryDirectory(prefix='plot_extract') as scratch_space:
tarball_file = retrieve_uri(tarball.file.uri, outdir=scratch_space)
try:
plots = process_tarball(
tarball_file,
output_directory=scratch_space,
)
except (InvalidTarball, NoTexFilesFound):
obj.log.info(
'Invalid tarball %s for arxiv_id %s',
tarball.file.uri,
arxiv_id,
)
return
except DelegateError as err:
obj.log.error(
'Error extracting plots for %s. Report and skip.',
arxiv_id,
)
current_app.logger.exception(err)
return
if 'figures' in obj.data:
for figure in obj.data['figures']:
if figure['key'] in obj.files:
del obj.files[figure['key']]
del obj.data['figures']
lb = LiteratureBuilder(source='arxiv', record=obj.data)
for index, plot in enumerate(plots):
plot_name = os.path.basename(plot.get('url'))
key = plot_name
if plot_name in obj.files.keys:
key = '{number}_{name}'.format(number=index,
name=plot_name)
with open(plot.get('url')) as plot_file:
obj.files[key] = plot_file
lb.add_figure(key=key,
caption=''.join(plot.get('captions', [])),
label=plot.get('label'),
material='preprint',
url='/api/files/{bucket}/{key}'.format(
bucket=obj.files[key].bucket_id,
key=key,
))
obj.data = lb.record
obj.log.info('Added {0} plots.'.format(len(plots)))
def add_document_or_figure(
self,
metadata,
stream=None,
is_document=True,
file_name=None,
key=None,
):
"""Add a document or figure to the record.
Args:
metadata(dict): metadata of the document or figure, see the schemas
for more details, will be validated.
stream(file like object): if passed, will extract the file contents
from it.
is_document(bool): if the given information is for a document,
set to ```False``` for a figure.
file_name(str): Name of the file, used as a basis of the key for
the files store.
key(str): if passed, will use this as the key for the files store
and ignore ``file_name``, use it to overwrite existing keys.
Returns:
dict: metadata of the added document or figure.
Raises:
TypeError: if not ``file_name`` nor ``key`` are passed (one of
them is required).
"""
if not key and not file_name:
raise TypeError(
'No file_name and no key passed, at least one of them is '
'needed.'
)
if not key:
key = self._get_unique_files_key(base_file_name=file_name)
if stream is not None:
self.files[key] = stream
builder = LiteratureBuilder(record=self.to_dict())
metadata['key'] = key
metadata['url'] = '/api/files/{bucket}/{key}'.format(
bucket=self.files[key].bucket_id,
key=key,
)
if is_document:
builder.add_document(**metadata)
else:
builder.add_figure(**metadata)
super(InspireRecord, self).update(builder.record)
return metadata
def add_document_or_figure(
self,
metadata,
stream=None,
is_document=True,
file_name=None,
key=None,
):
"""Add a document or figure to the record.
Args:
metadata(dict): metadata of the document or figure, see the schemas
for more details, will be validated.
stream(file like object): if passed, will extract the file contents
from it.
is_document(bool): if the given information is for a document,
set to ```False``` for a figure.
file_name(str): Name of the file, used as a basis of the key for
the files store.
key(str): if passed, will use this as the key for the files store
and ignore ``file_name``, use it to overwrite existing keys.
Returns:
dict: metadata of the added document or figure.
Raises:
TypeError: if not ``file_name`` nor ``key`` are passed (one of
them is required).
"""
if not key and not file_name:
raise TypeError(
'No file_name and no key passed, at least one of them is '
'needed.'
)
if not key:
key = self._get_unique_files_key(base_file_name=file_name)
if stream is not None:
self.files[key] = stream
builder = LiteratureBuilder(record=self.dumps())
metadata['key'] = key
metadata['url'] = '/api/files/{bucket}/{key}'.format(
bucket=self.files[key].bucket_id,
key=key,
)
if is_document:
builder.add_document(**metadata)
else:
builder.add_figure(**metadata)
super(InspireRecord, self).update(builder.record)
return metadata
def add_files(self, documents=None, figures=None):
"""Public method for adding documents and figures
Args:
documents (list[dict]): List of documents which should be added to this
record
figures (list[dict]): List of figures which should be added to this record
Documents and figures are lists of dicts.
Most obscure dict which whould be provided for each file is:
{
'url': 'http:// or /api/file/bucket_id/file_key'
'is_document': True or False(default)
}
Returns:
list: list of added keys
"""
if not documents and not figures:
raise TypeError("No files passed, at least one is needed")
if not current_app.config.get("FEATURE_FLAG_ENABLE_FILES", False):
if figures:
self.setdefault("figures", []).extend(figures)
if documents:
self.setdefault("documents", []).extend(documents)
return []
files = []
builder = LiteratureBuilder(record=self)
if documents:
doc_keys = [
doc_metadata["key"]
for doc_metadata in self.get("documents", [])
]
for doc in documents:
metadata = self._add_file(document=True, **doc)
if metadata["key"] not in doc_keys:
builder.add_document(**metadata)
files.append(metadata)
if figures:
fig_keys = [
fig_metadata["key"]
for fig_metadata in self.get("figures", [])
]
for fig in figures:
metadata = self._add_file(**fig)
if metadata["key"] not in fig_keys:
builder.add_figure(**metadata)
files.append(metadata)
# FIXME: this is wrong every time it goes to ``update``` function
# which means update refs, pidstore etc..
super().update(builder.record.dumps())
return files
def arxiv_plot_extract(obj, eng):
"""Extract plots from an arXiv archive.
:param obj: Workflow Object to process
:param eng: Workflow Engine processing the object
"""
arxiv_id = get_arxiv_id(obj.data)
filename = secure_filename('{0}.tar.gz'.format(arxiv_id))
tarball = obj.files[filename]
if tarball:
with TemporaryDirectory(prefix='plot_extract') as scratch_space, \
retrieve_uri(tarball.file.uri, outdir=scratch_space) as tarball_file:
try:
plots = process_tarball(
tarball_file,
output_directory=scratch_space,
)
except (InvalidTarball, NoTexFilesFound):
obj.log.info(
'Invalid tarball %s for arxiv_id %s',
tarball.file.uri,
arxiv_id,
)
return
except DelegateError as err:
obj.log.error(
'Error extracting plots for %s. Report and skip.',
arxiv_id,
)
current_app.logger.exception(err)
return
if 'figures' in obj.data:
for figure in obj.data['figures']:
if figure['key'] in obj.files:
del obj.files[figure['key']]
del obj.data['figures']
lb = LiteratureBuilder(source='arxiv', record=obj.data)
for index, plot in enumerate(plots):
plot_name = os.path.basename(plot.get('url'))
key = plot_name
if plot_name in obj.files.keys:
key = 'w{number}_{name}'.format(
number=index,
name=plot_name,
)
with open(plot.get('url')) as plot_file:
obj.files[key] = plot_file
lb.add_figure(
key=key,
caption=''.join(plot.get('captions', [])),
label=plot.get('label'),
material='preprint',
url='/api/files/{bucket}/{key}'.format(
bucket=obj.files[key].bucket_id,
key=key,
)
)
obj.data = lb.record
obj.log.info('Added {0} plots.'.format(len(plots)))
def arxiv_plot_extract(obj, eng):
"""Extract plots from an arXiv archive.
:param obj: Workflow Object to process
:param eng: Workflow Engine processing the object
"""
# Crude way to set memory limits for wand globally.
mem_limit = current_app.config.get("WAND_MEMORY_LIMIT")
if mem_limit and limits['memory'] != mem_limit:
limits['memory'] = mem_limit
# This sets disk limit, if not set it will swap data on disk
# instead of throwing exception
limits['disk'] = current_app.config.get("WAND_DISK_LIMIT", 0)
# It will throw an exception when memory and disk limit exceeds.
# At least workflow status will be saved.
arxiv_id = LiteratureReader(obj.data).arxiv_id
filename = secure_filename('{0}.tar.gz'.format(arxiv_id))
try:
tarball = obj.files[filename]
except KeyError:
obj.log.info('No file named=%s for arxiv_id %s', filename, arxiv_id)
return
with TemporaryDirectory(prefix='plot_extract') as scratch_space, \
retrieve_uri(tarball.file.uri, outdir=scratch_space) as tarball_file:
try:
plots = process_tarball(
tarball_file,
output_directory=scratch_space,
)
except (InvalidTarball, NoTexFilesFound):
obj.log.info(
'Invalid tarball %s for arxiv_id %s',
tarball.file.uri,
arxiv_id,
)
return
except DelegateError as err:
obj.log.error(
'Error extracting plots for %s. Report and skip.',
arxiv_id,
)
current_app.logger.exception(err)
return
if 'figures' in obj.data:
for figure in obj.data['figures']:
if figure['key'] in obj.files:
del obj.files[figure['key']]
del obj.data['figures']
lb = LiteratureBuilder(source='arxiv', record=obj.data)
for index, plot in enumerate(plots):
plot_name = os.path.basename(plot.get('url'))
key = plot_name
if plot_name in obj.files.keys:
key = 'w{number}_{name}'.format(
number=index,
name=plot_name,
)
with open(plot.get('url')) as plot_file:
obj.files[key] = plot_file
lb.add_figure(
key=key,
caption=''.join(plot.get('captions', [])),
label=plot.get('label'),
material='preprint',
url='/api/files/{bucket}/{key}'.format(
bucket=obj.files[key].bucket_id,
key=key,
)
)
obj.data = lb.record
obj.log.info('Added {0} plots.'.format(len(plots)))
