def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
python类classifier()的实例源码
def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
states.py 文件源码
项目：tf_aws_ecs_instance_draining_on_scale_in
作者: terraform-community-modules
项目源码
文件源码
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def term(self, lines, lineno):
"""Return a definition_list's term and optional classifiers."""
assert len(lines) == 1
text_nodes, messages = self.inline_text(lines[0], lineno)
term_node = nodes.term()
(term_node.source,
term_node.line) = self.state_machine.get_source_and_line(lineno)
term_node.rawsource = unescape(lines[0])
node_list = [term_node]
for i in range(len(text_nodes)):
node = text_nodes[i]
if isinstance(node, nodes.Text):
parts = self.classifier_delimiter.split(node.rawsource)
if len(parts) == 1:
node_list[-1] += node
else:
node_list[-1] += nodes.Text(parts[0].rstrip())
for part in parts[1:]:
classifier_node = nodes.classifier('', part)
node_list.append(classifier_node)
else:
node_list[-1] += node
return node_list, messages
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def apply_source_workaround(node):
# workaround: nodes.term have wrong rawsource if classifier is specified.
# The behavior of docutils-0.11, 0.12 is:
# * when ``term text : classifier1 : classifier2`` is specified,
# * rawsource of term node will have: ``term text : classifier1 : classifier2``
# * rawsource of classifier node will be None
if isinstance(node, nodes.classifier) and not node.rawsource:
definition_list_item = node.parent
node.source = definition_list_item.source
node.line = definition_list_item.line - 1
node.rawsource = node.astext() # set 'classifier1' (or 'classifier2')
if isinstance(node, nodes.term):
# strip classifier from rawsource of term
for classifier in reversed(node.parent.traverse(nodes.classifier)):
node.rawsource = re.sub(
'\s*:\s*%s' % re.escape(classifier.astext()), '', node.rawsource)
# workaround: recommonmark-0.2.0 doesn't set rawsource attribute
if not node.rawsource:
node.rawsource = node.astext()
if node.source and node.rawsource:
return
# workaround: docutils-0.10.0 or older's nodes.caption for nodes.figure
# and nodes.title for nodes.admonition doesn't have source, line.
# this issue was filed to Docutils tracker:
# sf.net/tracker/?func=detail&aid=3599485&group_id=38414&atid=422032
# sourceforge.net/p/docutils/patches/108/
if (isinstance(node, (
nodes.caption,
nodes.title,
nodes.rubric,
nodes.line,
))):
node.source = find_source_node(node)
node.line = 0 # need fix docutils to get `node.line`
return
def visit_definition_list_item(self, node):
self._classifier_count_in_li = len(node.traverse(nodes.classifier))
def map_nested_definitions(nested_content):
if nested_content is None:
raise Exception('Nested content should be iterable, not null')
# build definition dictionary
definitions = {}
for item in nested_content:
if not isinstance(item, nodes.definition_list):
continue
for subitem in item:
if not isinstance(subitem, nodes.definition_list_item):
continue
if not len(subitem.children) > 0:
continue
classifier = '@after'
idx = subitem.first_child_matching_class(nodes.classifier)
if idx is not None:
ci = subitem[idx]
if len(ci.children) > 0:
classifier = ci.children[0].astext()
if classifier is not None and classifier not in (
'@replace', '@before', '@after'):
raise Exception('Unknown classifier: %s' % classifier)
idx = subitem.first_child_matching_class(nodes.term)
if idx is not None:
ch = subitem[idx]
if len(ch.children) > 0:
term = ch.children[0].astext()
idx = subitem.first_child_matching_class(nodes.definition)
if idx is not None:
def_node = subitem[idx]
def_node.attributes['classifier'] = classifier
definitions[term] = def_node
return definitions
def apply_definition(definitions, my_def, name):
if name in definitions:
definition = definitions[name]
classifier = definition['classifier']
if classifier == '@replace':
return definition.children
if classifier == '@after':
return my_def + definition.children
if classifier == '@before':
return definition.children + my_def
raise Exception('Unknown classifier: %s' % classifier)
return my_def
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
textwriter.py 文件源码
项目：tf_aws_ecs_instance_draining_on_scale_in
作者: terraform-community-modules
项目源码
文件源码
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def visit_definition_list_item(self, node):
self._li_has_classifier = len(node) >= 2 and \
isinstance(node[1], nodes.classifier)
def _parse_definition_list(
def_list_node: nodes.definition_list) -> ExtraContentDict:
"""Parse a definition list inside the directive.
Args:
def_list_node: A definition list node containing definitions for
extending the Sphinx output.
Raises:
ValueError: The given classifier was unrecognized.
Returns:
A dict where keys are item IDs and values contain the classifiers
and the content as lists of docutils nodes.
"""
definitions = collections.defaultdict(lambda: None)
for node in def_list_node:
if not isinstance(node, nodes.definition_list_item):
continue
term = _get_matching_child(node, nodes.term, last=False).astext()
classifiers = set()
for child_node in node.children:
if not isinstance(child_node, nodes.classifier):
continue
classifier = child_node.astext()
if classifier not in ALL_CLASSIFIERS:
raise ValueError("unknown classifier '{0}'".format(classifier))
classifiers.add(classifier)
if not classifiers & CONTENT_CLASSIFIERS:
classifiers.add("@after")
if not classifiers & MARKUP_CLASSIFIERS:
classifiers.add("@auto")
content = _get_matching_child(
node, nodes.definition, last=False).children
if not definitions[term]:
definitions[term] = []
definitions[term].append(ExtraContent(classifiers, content))
return definitions
