def definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
python类term()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 definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
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 resolve_any_xref(self, env, fromdocname, builder, target,
node, contnode):
results = []
ltarget = target.lower() # :ref: lowercases its target automatically
for role in ('ref', 'option'): # do not try "keyword"
res = self.resolve_xref(env, fromdocname, builder, role,
ltarget if role == 'ref' else target,
node, contnode)
if res:
results.append(('std:' + role, res))
# all others
for objtype in self.object_types:
key = (objtype, target)
if objtype == 'term':
key = (objtype, ltarget)
if key in self.data['objects']:
docname, labelid = self.data['objects'][key]
results.append(('std:' + self.role_for_objtype(objtype),
make_refnode(builder, fromdocname, docname,
labelid, contnode)))
return results
def definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
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 make_termnodes_from_paragraph_node(env, node, new_id=None):
gloss_entries = env.temp_data.setdefault('gloss_entries', set())
objects = env.domaindata['std']['objects']
termtext = node.astext()
if new_id is None:
new_id = nodes.make_id('term-' + termtext)
if new_id in gloss_entries:
new_id = 'term-' + str(len(gloss_entries))
gloss_entries.add(new_id)
objects['term', termtext.lower()] = env.docname, new_id
# add an index entry too
indexnode = addnodes.index()
indexnode['entries'] = [('single', termtext, new_id, 'main')]
new_termnodes = []
new_termnodes.append(indexnode)
new_termnodes.extend(node.children)
new_termnodes.append(addnodes.termsep())
for termnode in new_termnodes:
termnode.source, termnode.line = node.source, node.line
return new_id, termtext, new_termnodes
def resolve_any_xref(self, env, fromdocname, builder, target,
node, contnode):
results = []
ltarget = target.lower() # :ref: lowercases its target automatically
for role in ('ref', 'option'): # do not try "keyword"
res = self.resolve_xref(env, fromdocname, builder, role,
ltarget if role == 'ref' else target,
node, contnode)
if res:
results.append(('std:' + role, res))
# all others
for objtype in self.object_types:
key = (objtype, target)
if objtype == 'term':
key = (objtype, ltarget)
if key in self.data['objects']:
docname, labelid = self.data['objects'][key]
results.append(('std:' + self.role_for_objtype(objtype),
make_refnode(builder, fromdocname, docname,
labelid, contnode)))
return results
def run(self):
self.assert_has_content()
title = self.arguments[0]
content = '\n'.join(self.content)
math_node = self.make_math_node(self.prepare_latex(content))
tid = nodes.make_id(title)
target = nodes.target('', '', ids=['inference-' + tid])
self.state.document.note_explicit_target(target)
term, desc = nodes.term('', title), nodes.description('', math_node)
dli = nodes.definition_list_item('', term, desc)
dl = nodes.definition_list(content, target, dli)
set_source_info(self, dl)
return [dl]
def _format_subcommands(self, parser_info):
assert 'children' in parser_info
items = []
for subcmd in parser_info['children']:
subcmd_items = []
if subcmd['help']:
subcmd_items.append(nodes.paragraph(text=subcmd['help']))
else:
subcmd_items.append(nodes.paragraph(text='Undocumented'))
items.append(
nodes.definition_list_item(
'',
nodes.term('', '', nodes.strong(
text=subcmd['bare_usage'])),
nodes.definition('', *subcmd_items)))
return nodes.definition_list('', *items)
def definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
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 definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
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
阅读 20
收藏 0
点赞 0
评论 0
def definition_list_item(self, termline):
indented, indent, line_offset, blank_finish = \
self.state_machine.get_indented()
itemnode = nodes.definition_list_item(
'\n'.join(termline + list(indented)))
lineno = self.state_machine.abs_line_number() - 1
(itemnode.source,
itemnode.line) = self.state_machine.get_source_and_line(lineno)
termlist, messages = self.term(termline, lineno)
itemnode += termlist
definition = nodes.definition('', *messages)
itemnode += definition
if termline[0][-2:] == '::':
definition += self.reporter.info(
'Blank line missing before literal block (after the "::")? '
'Interpreted as a definition list item.',
line=lineno+1)
self.nested_parse(indented, input_offset=line_offset, node=definition)
return itemnode, blank_finish
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
阅读 25
收藏 0
点赞 0
评论 0
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 split_term_classifiers(line):
# split line into a term and classifiers. if no classifier, None is used..
parts = re.split(' +: +', line) + [None]
return parts
def make_glossary_term(env, textnodes, index_key, source, lineno, new_id=None):
# get a text-only representation of the term and register it
# as a cross-reference target
term = nodes.term('', '', *textnodes)
term.source = source
term.line = lineno
gloss_entries = env.temp_data.setdefault('gloss_entries', set())
objects = env.domaindata['std']['objects']
termtext = term.astext()
if new_id is None:
new_id = nodes.make_id('term-' + termtext)
if new_id in gloss_entries:
new_id = 'term-' + str(len(gloss_entries))
gloss_entries.add(new_id)
objects['term', termtext.lower()] = env.docname, new_id
# add an index entry too
indexnode = addnodes.index()
indexnode['entries'] = [('single', termtext, new_id, 'main', index_key)]
indexnode.source, indexnode.line = term.source, term.line
term.append(indexnode)
term['ids'].append(new_id)
term['names'].append(new_id)
return term
def make_term_from_paragraph_node(termnodes, ids):
# make a single "term" node with all the terms, separated by termsep
# nodes (remove the dangling trailing separator)
term = nodes.term('', '', *termnodes[:-1])
term.source, term.line = termnodes[0].source, termnodes[0].line
term.rawsource = term.astext()
term['ids'].extend(ids)
term['names'].extend(ids)
return term
def add_coqtop_output(self):
"""Add coqtop's responses to a Sphinx AST
Finds nodes to process using is_coqtop_block."""
with CoqTop(color=True) as repl:
for node in self.document.traverse(CoqtopBlocksTransform.is_coqtop_block):
options = node['coqtop_options']
opt_undo, opt_reset, opt_input, opt_output = self.parse_options(options)
if opt_reset:
repl.sendone("Reset Initial.")
pairs = []
for sentence in self.split_sentences(node.rawsource):
pairs.append((sentence, repl.sendone(sentence)))
if opt_undo:
repl.sendone("Undo {}.".format(len(pairs)))
dli = nodes.definition_list_item()
for sentence, output in pairs:
# Use Coqdoq to highlight input
in_chunks = highlight_using_coqdoc(sentence)
dli += nodes.term(sentence, '', *in_chunks, classes=self.block_classes(opt_input))
# Parse ANSI sequences to highlight output
out_chunks = AnsiColorsParser().colorize_str(output)
dli += nodes.definition(output, *out_chunks, classes=self.block_classes(opt_output, output))
node.clear()
node.rawsource = self.make_rawsource(pairs, opt_input, opt_output)
node['classes'].extend(self.block_classes(opt_input or opt_output))
node += nodes.inline('', '', classes=['coqtop-reset'] * opt_reset)
node += nodes.definition_list(node.rawsource, dli)
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 print_command_args_and_opts(arg_list, opt_list, sub_list=None):
items = []
if arg_list:
items.append(nodes.definition_list_item(
'', nodes.term(text='Positional arguments:'),
nodes.definition('', arg_list)))
if opt_list:
items.append(nodes.definition_list_item(
'', nodes.term(text='Options:'),
nodes.definition('', opt_list)))
if sub_list and len(sub_list):
items.append(nodes.definition_list_item(
'', nodes.term(text='Sub-commands:'),
nodes.definition('', sub_list)))
return nodes.definition_list('', *items)
def print_subcommand_list(data, nested_content):
definitions = map_nested_definitions(nested_content)
items = []
if 'children' in data:
for child in data['children']:
my_def = [nodes.paragraph(
text=child['help'])] if child['help'] else []
name = child['name']
my_def = apply_definition(definitions, my_def, name)
if len(my_def) == 0:
my_def.append(nodes.paragraph(text='Undocumented'))
if 'description' in child:
my_def.append(nodes.paragraph(text=child['description']))
my_def.append(nodes.literal_block(text=child['usage']))
my_def.append(print_command_args_and_opts(
print_arg_list(child, nested_content),
print_opt_list(child, nested_content),
print_subcommand_list(child, nested_content)
))
items.append(
nodes.definition_list_item(
'',
nodes.term('', '', nodes.strong(text=name)),
nodes.definition('', *my_def)
)
)
return nodes.definition_list('', *items)
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
