1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
|
# Authors: David Goodger, Ueli Schlaepfer
# Contact: goodger@users.sourceforge.net
# Revision: $Revision$
# Date: $Date$
# Copyright: This module has been placed in the public domain.
"""
This package contains modules for standard tree transforms available
to Docutils components. Tree transforms serve a variety of purposes:
- To tie up certain syntax-specific "loose ends" that remain after the
initial parsing of the input plaintext. These transforms are used to
supplement a limited syntax.
- To automate the internal linking of the document tree (hyperlink
references, footnote references, etc.).
- To extract useful information from the document tree. These
transforms may be used to construct (for example) indexes and tables
of contents.
Each transform is an optional step that a Docutils Reader may choose to
perform on the parsed document, depending on the input context. A Docutils
Reader may also perform Reader-specific transforms before or after performing
these standard transforms.
"""
__docformat__ = 'reStructuredText'
from docutils import languages, ApplicationError, TransformSpec
class TransformError(ApplicationError): pass
class Transform:
"""
Docutils transform component abstract base class.
"""
default_priority = None
"""Numerical priority of this transform, 0 through 999 (override)."""
def __init__(self, document, startnode=None):
"""
Initial setup for in-place document transforms.
"""
self.document = document
"""The document tree to transform."""
self.startnode = startnode
"""Node from which to begin the transform. For many transforms which
apply to the document as a whole, `startnode` is not set (i.e. its
value is `None`)."""
self.language = languages.get_language(
document.settings.language_code)
"""Language module local to this document."""
def apply(self, **kwargs):
"""Override to apply the transform to the document tree."""
raise NotImplementedError('subclass must override this method')
class Transformer(TransformSpec):
"""
Stores transforms (`Transform` classes) and applies them to document
trees. Also keeps track of components by component type name.
"""
def __init__(self, document):
self.transforms = []
"""List of transforms to apply. Each item is a 3-tuple:
``(priority string, transform class, pending node or None)``."""
self.unknown_reference_resolvers = []
"""List of hook functions which assist in resolving references"""
self.document = document
"""The `nodes.document` object this Transformer is attached to."""
self.applied = []
"""Transforms already applied, in order."""
self.sorted = 0
"""Boolean: is `self.tranforms` sorted?"""
self.components = {}
"""Mapping of component type name to component object. Set by
`self.populate_from_components()`."""
self.serialno = 0
"""Internal serial number to keep track of the add order of
transforms."""
def add_transform(self, transform_class, priority=None, **kwargs):
"""
Store a single transform. Use `priority` to override the default.
`kwargs` is a dictionary whose contents are passed as keyword
arguments to the `apply` method of the transform. This can be used to
pass application-specific data to the transform instance.
"""
if priority is None:
priority = transform_class.default_priority
priority_string = self.get_priority_string(priority)
self.transforms.append(
(priority_string, transform_class, None, kwargs))
self.sorted = 0
def add_transforms(self, transform_list):
"""Store multiple transforms, with default priorities."""
for transform_class in transform_list:
priority_string = self.get_priority_string(
transform_class.default_priority)
self.transforms.append(
(priority_string, transform_class, None, {}))
self.sorted = 0
def add_pending(self, pending, priority=None):
"""Store a transform with an associated `pending` node."""
transform_class = pending.transform
if priority is None:
priority = transform_class.default_priority
priority_string = self.get_priority_string(priority)
self.transforms.append(
(priority_string, transform_class, pending, {}))
self.sorted = 0
def get_priority_string(self, priority):
"""
Return a string, `priority` combined with `self.serialno`.
This ensures FIFO order on transforms with identical priority.
"""
self.serialno += 1
return '%03d-%03d' % (priority, self.serialno)
def populate_from_components(self, components):
"""
Store each component's default transforms, with default priorities.
Also, store components by type name in a mapping for later lookup.
"""
for component in components:
if component is None:
continue
self.add_transforms(component.get_transforms())
self.components[component.component_type] = component
self.sorted = 0
# Set up all of the reference resolvers for this transformer. Each
# component of this transformer is able to register its own helper
# functions to help resolve references.
unknown_reference_resolvers = []
for i in components:
unknown_reference_resolvers.extend(i.unknown_reference_resolvers)
decorated_list = [(f.priority, f) for f in unknown_reference_resolvers]
decorated_list.sort()
self.unknown_reference_resolvers.extend([f[1] for f in decorated_list])
def apply_transforms(self):
"""Apply all of the stored transforms, in priority order."""
self.document.reporter.attach_observer(
self.document.note_transform_message)
while self.transforms:
if not self.sorted:
# Unsorted initially, and whenever a transform is added.
self.transforms.sort()
self.transforms.reverse()
self.sorted = 1
priority, transform_class, pending, kwargs = self.transforms.pop()
transform = transform_class(self.document, startnode=pending)
transform.apply(**kwargs)
self.applied.append((priority, transform_class, pending, kwargs))
|
