def upload_cbn_dir(dir_path, manager):
"""Uploads CBN data to edge store
:param str dir_path: Directory full of CBN JGIF files
:param pybel.Manager manager:
"""
t = time.time()
for jfg_path in os.listdir(dir_path):
if not jfg_path.endswith('.jgf'):
continue
path = os.path.join(dir_path, jfg_path)
log.info('opening %s', path)
with open(path) as f:
cbn_jgif_dict = json.load(f)
graph = pybel.from_cbn_jgif(cbn_jgif_dict)
out_path = os.path.join(dir_path, jfg_path.replace('.jgf', '.bel'))
with open(out_path, 'w') as o:
pybel.to_bel_script(graph, o)
strip_annotations(graph)
enrich_pubmed_citations(manager=manager, graph=graph)
pybel.to_database(graph, manager=manager)
log.info('')
log.info('done in %.2f', time.time() - t)
def main(directory: str):
"""Make hetionet exports."""
path = os.path.join(directory, 'hetionet.bel.nodelink.json.gz')
if not os.path.exists(path):
graph = get_hetionet()
to_nodelink_gz(graph, path)
else:
click.echo('loading pickle from {}'.format(path))
graph = from_nodelink_gz(path)
output_bel_gz_path = os.path.join(directory, 'hetionet.bel.gz')
if not os.path.exists(output_bel_gz_path):
click.echo('outputting whole hetionet as BEL GZ to {}'.format(output_bel_gz_path))
to_bel_script_gz(graph, output_bel_gz_path, use_identifiers=True)
output_graphdati_jsonl_gz_path = os.path.join(directory, 'hetionet.bel.graphdati.jsonl.gz')
if not os.path.exists(output_graphdati_jsonl_gz_path):
click.echo('outputting whole hetionet as BEL GraphDati JSONL GZ to {}'.format(output_graphdati_jsonl_gz_path))
to_graphdati_jsonl_gz(graph, output_graphdati_jsonl_gz_path, use_identifiers=True)
output_graphdati_gz_path = os.path.join(directory, 'hetionet.bel.graphdati.json.gz')
if not os.path.exists(output_graphdati_gz_path):
click.echo('outputting whole hetionet as BEL GraphDati JSON GZ to {}'.format(output_graphdati_gz_path))
to_graphdati_gz(graph, output_graphdati_gz_path, use_identifiers=True)
summary_tsv_path = os.path.join(directory, 'hetionet_summary.tsv')
if not os.path.exists(summary_tsv_path):
click.echo('getting metaedges')
rows = []
keep_keys = set()
for value in get_metaedge_to_key(graph).values():
u, v, key = choice(list(value))
keep_keys.add(key)
d = graph[u][v][key]
bel = edge_to_bel(u, v, d, use_identifiers=True)
rows.append((key[:8], bel))
df = pd.DataFrame(rows, columns=['key', 'bel'])
df.to_csv(summary_tsv_path, sep='\t', index=False)
non_sample_edges = [
(u, v, k, d)
for u, v, k, d in tqdm(graph.edges(keys=True, data=True), desc='Getting non-sample edges to remove')
if k not in keep_keys
]
click.echo('Removing non-sample edges')
graph.remove_edges_from(non_sample_edges)
graph.remove_nodes_from(list(nx.isolates(graph)))
sample_bel_path = os.path.join(directory, 'hetionet_sample.bel')
click.echo('outputting sample hetionet in BEL to {}'.format(sample_bel_path))
to_bel_script(graph, sample_bel_path, use_identifiers=True)
sample_graphdati_path = os.path.join(directory, 'hetionet_sample.bel.graphdati.json')
click.echo('outputting sample hetionet in BEL to {}'.format(sample_bel_path))
to_graphdati_file(graph, sample_graphdati_path, use_identifiers=True, indent=2)
def write_neurommsig_bel(
file: TextIO,
df: pd.DataFrame,
disease: str,
nift_values: Mapping[str, str],
) -> None:
"""Write the NeuroMMSigDB excel sheet to BEL.
:param file: a file or file-like that can be writen to
:param df:
:param disease:
:param nift_values: a dictionary of lower-cased to normal names in NIFT
"""
graph = get_neurommsig_bel(df, disease, nift_values)
pybel.to_bel_script(graph, file)
def save_model(self, path, output_format=None):
"""Save the :class:`pybel.BELGraph` using one of the outputs from
:py:mod:`pybel`
Parameters
----------
path : str
The path to output to
output_format : Optional[str]
Output format as ``cx``, ``pickle``, ``json`` or defaults to ``bel``
"""
if output_format == 'pickle':
pybel.to_pickle(self.model, path)
else:
with open(path, 'w') as fh:
if output_format == 'json':
pybel.to_nodelink_file(self.model, fh)
elif output_format == 'cx':
pybel.to_cx_file(self.model, fh)
else: # output_format == 'bel':
pybel.to_bel_script(self.model, fh)