Datasets:
thisisanshgupta
/
Pycode

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
828
to contain UTF-8 encoded JSON data.
""""""
with open(filename, 'rb') as f:
json_graph = f.read().decode('utf-8')
return cls.from_json(json_graph)"
1197,"def to_dot(self):
""""""
Produce a graph in DOT format.
""""""
edge_labels = {
edge.id: edge.annotation
for edge in self._edges
}
edges = [self._format_edge(edge_labels, edge) for edge in self._edges]
vertices = [
DOT_VERTEX_TEMPLATE.format(
vertex=vertex.id,
label=dot_quote(vertex.annotation),
)
for vertex in self.vertices
]
return DOT_DIGRAPH_TEMPLATE.format(
edges="""".join(edges),
vertices="""".join(vertices),
)"
1198,"def export_image(self, filename='refcycle.png', format=None,
dot_executable='dot'):
""""""
Export graph as an image.
This requires that Graphviz is installed and that the ``dot``
executable is in your path.
The *filename* argument specifies the output filename.
The *format* argument lets you specify the output format. It may be
any format that ``dot`` understands, including extended format
specifications like ``png:cairo``. If omitted, the filename extension
will be used; if no filename extension is present, ``png`` will be
used.
The *dot_executable* argument lets you provide a full path to the
``dot`` executable if necessary.
""""""
# Figure out what output format to use.
if format is None:
_, extension = os.path.splitext(filename)
if extension.startswith('.') and len(extension) > 1:
format = extension[1:]
else:
format = 'png'
# Convert to 'dot' format.
dot_graph = self.to_dot()
# We'll send the graph directly to the process stdin.
cmd = [
dot_executable,
'-T{}'.format(format),
'-o{}'.format(filename),
]
dot = subprocess.Popen(cmd, stdin=subprocess.PIPE)
dot.communicate(dot_graph.encode('utf-8'))"
1199,"def serve_command(info, host, port, reload, debugger, eager_loading,
with_threads):
""""""Runs a local development server for the Flask application.
This local server is recommended for development purposes only but it
can also be used for simple intranet deployments. By default it will
not support any sort of concurrency at all to simplify debugging. This
can be changed with the --with-threads option which will enable basic
multithreading.
The reloader and debugger are by default enabled if the debug flag of
Flask is enabled and disabled otherwise.
""""""
from werkzeug.serving import run_simple
debug = get_debug_flag()
if reload is None:
reload = bool(debug)
if debugger is None:
debugger = bool(debug)
if eager_loading is None:
eager_loading = not reload
app = DispatchingApp(info.load_app, use_eager_loading=eager_loading)
# Extra startup messages. This depends a but on Werkzeug internals to
# not double execute when the reloader kicks in.
if os.environ.get('WERKZEUG_RUN_MAIN') != 'true':
# If we have an import path we can print it out now which can help
# people understand what's being served. If we do not have an
# import path because the app was loaded through a callback then
# we won't print anything.
if info.app_import_path is not None: