def dataset_save():
"""
Save the dataset to the datalegend file cache
Note that this does not convert the dataset to RDF, nor does it upload it to the datalegend repository
---
tags:
- Dataset
parameters:
- name: dataset
in: body
description: The dataset definition that is to be saved to cache.
required: true
type: object
schema:
$ref: "#/definitions/DatasetSchema"
responses:
'200':
description: The dataset was succesfully saved to the file cache
schema:
$ref: "#/definitions/Message"
default:
description: Unexpected error
schema:
$ref: "#/definitions/Message"
"""
req_json = request.get_json(force=True)
dataset = req_json['dataset']
# dataset_path = os.path.join(config.TEMP_PATH, dataset['file'])
gc.write_cache(dataset['file'], {'dataset': dataset})
# fc.write_cache(dataset_path, {'dataset': dataset})
return jsonify({'code': 200, 'message': 'Success'})
def dataset_save():
"""
Save the dataset to the CSDH file cache
Note that this does not convert the dataset to RDF, nor does it upload it to the CSDH repository
---
tags:
- Dataset
parameters:
- name: dataset
in: body
description: The dataset definition that is to be saved to cache.
required: true
type: object
schema:
$ref: "#/definitions/DatasetSchema"
responses:
'200':
description: The dataset was succesfully saved to the file cache
schema:
$ref: "#/definitions/Message"
default:
description: Unexpected error
schema:
$ref: "#/definitions/Message"
"""
req_json = request.get_json(force=True)
dataset = req_json['dataset']
# dataset_path = os.path.join(config.TEMP_PATH, dataset['file'])
gc.write_cache(dataset['file'], {'dataset': dataset})
# fc.write_cache(dataset_path, {'dataset': dataset})
return jsonify({'code': 200, 'message': 'Success'})
def dataset_submit():
"""
Submit the dataset definition to the datalegend
Uses the DataCube converter to convert the JSON representation of variables to RDF DataCube and commits
the resulting RDF to the datalegend repository
---
tags:
- Dataset
parameters:
- name: dataset
in: body
description: The dataset definition that is to be converted and committed to the datalegend repository
required: true
schema:
type: object
properties:
dataset:
description: The dataset definition
$ref: "#/definitions/DatasetSchema"
user:
description: The Google user profile of the person uploading the dataset
type: object
responses:
'200':
description: The dataset was converted succesfully
schema:
$ref: "#/definitions/Message"
default:
description: Unexpected error
schema:
$ref: "#/definitions/Message"
"""
req_json = request.get_json(force=True)
dataset = req_json['dataset']
user = req_json['user']
log.debug("Writing cache to gitlab")
gc.write_cache(dataset['file'], {'dataset': dataset})
source_filename = gc.get_local_file_path(dataset['file'])
log.debug("Converter will be reading from {}".format(source_filename))
outfile = dataset['file'] + ".nq"
target_filename = gc.get_local_file_path(outfile)
log.debug("Converter will be writing to {}".format(target_filename))
log.debug("Starting conversion ...")
if 'path' in dataset:
# TODO: check when there's a path in dataset... where does this happen, and what is it for?
log.debug("There's a path in this dataset")
c = converter.Converter(dataset,
'/tmp/',
user,
source=dataset['path'],
target=target_filename)
else:
log.debug("There is no path in this dataset, filename is {}".format(
source_filename))
c = converter.Converter(dataset,
'/tmp/',
user,
source=source_filename,
target=target_filename)
c.setProcesses(1)
c.convert()
log.debug("Conversion successful")
with open(target_filename, "rb") as nquads_file:
data = nquads_file.read()
log.debug("Adding data to gitlab... ")
file_info = gc.add_file(outfile, data)
log.debug("Added to gitlab: {} ({})".format(file_info['url'],
file_info['commit_id']))
log.debug("Parsing dataset... ")
g = ConjunctiveGraph()
# TODO: This is really inefficient... why are we posting each graph separately?
g.parse(data=data, format="nquads")
log.debug("DataSet parsed")
for graph in g.contexts():
log.debug(g)
graph_uri = graph.identifier
log.debug("Posting {} ...".format(graph_uri))
sc.post_data(graph.serialize(format='turtle'), graph_uri=graph_uri)
log.debug("... done")
return jsonify({
'code': 200,
'message': 'Succesfully submitted converted data to datalegend',
'url': file_info['url']
})
def dataset_submit():
"""
Submit the dataset definition to the CSDH
Uses the DataCube converter to convert the JSON representation of variables to RDF DataCube and commits
the resulting RDF to the CSDH repository
---
tags:
- Dataset
parameters:
- name: dataset
in: body
description: The dataset definition that is to be converted and committed to the CSDH repository
required: true
schema:
type: object
properties:
dataset:
description: The dataset definition
$ref: "#/definitions/DatasetSchema"
user:
description: The Google user profile of the person uploading the dataset
type: object
responses:
'200':
description: The dataset was converted succesfully
schema:
$ref: "#/definitions/Message"
default:
description: Unexpected error
schema:
$ref: "#/definitions/Message"
"""
req_json = request.get_json(force=True)
dataset = req_json['dataset']
user = req_json['user']
log.debug("Writing cache to gitlab")
gc.write_cache(dataset['file'], {'dataset': dataset})
source_filename = gc.get_local_file_path(dataset['file'])
log.debug("Converter will be reading from {}".format(source_filename))
outfile = dataset['file'] + ".nq"
target_filename = gc.get_local_file_path(outfile)
log.debug("Converter will be writing to {}".format(target_filename))
log.debug("Starting conversion ...")
if 'path' in dataset:
# TODO: check when there's a path in dataset... where does this happen, and what is it for?
log.debug("There's a path in this dataset")
c = converter.Converter(dataset, '/tmp/', user, source=dataset['path'], target=target_filename)
else:
log.debug("There is no path in this dataset, filename is {}".format(source_filename))
c = converter.Converter(dataset, '/tmp/', user, source=source_filename, target=target_filename)
c.setProcesses(1)
c.convert()
log.debug("Conversion successful")
with open(target_filename, "rb") as nquads_file:
data = nquads_file.read()
log.debug("Adding data to gitlab... ")
file_info = gc.add_file(outfile, data)
log.debug("Added to gitlab: {} ({})".format(file_info['url'], file_info['commit_id']))
log.debug("Parsing dataset... ")
g = ConjunctiveGraph()
# TODO: This is really inefficient... why are we posting each graph separately?
g.parse(data=data, format="nquads")
log.debug("DataSet parsed")
for graph in g.contexts():
log.debug(g)
graph_uri = graph.identifier
log.debug("Posting {} ...".format(graph_uri))
sc.post_data(graph.serialize(format='turtle'), graph_uri=graph_uri)
log.debug("... done")
return jsonify({'code': 200,
'message': 'Succesfully submitted converted data to CSDH',
'url': file_info['url']})