#! /usr/bin/env python
"""
:Authors: David Goodger, Ueli Schlaepfer
:Contact: goodger@users.sourceforge.net
:Revision: $Revision: 1.1 $
:Date: $Date: 2002/08/07 12:43:18 $
:Copyright: This module has been placed in the public domain.

Transforms related to the front matter of a document (information
found before the main text):

- `DocTitle`: Used to transform a lone top level section's title to
  the document title, and promote a remaining lone top-level section's
  title to the document subtitle.

- `DocInfo`: Used to transform a bibliographic field list into docinfo
  elements.
"""

__docformat__ = 'reStructuredText'

import re
from docutils import nodes, utils
from docutils.transforms import TransformError, Transform


class DocTitle(Transform):

    """
    In reStructuredText_, there is no way to specify a document title
    and subtitle explicitly. Instead, we can supply the document title
    (and possibly the subtitle as well) implicitly, and use this
    two-step transform to "raise" or "promote" the title(s) (and their
    corresponding section contents) to the document level.

    1. If the document contains a single top-level section as its
       first non-comment element, the top-level section's title
       becomes the document's title, and the top-level section's
       contents become the document's immediate contents. The lone
       top-level section header must be the first non-comment element
       in the document.

       For example, take this input text::

           =================
            Top-Level Title
           =================

           A paragraph.

       Once parsed, it looks like this::

           <document>
               <section name="top-level title">
                   <title>
                       Top-Level Title
                   <paragraph>
                       A paragraph.

       After running the DocTitle transform, we have::

           <document name="top-level title">
               <title>
                   Top-Level Title
               <paragraph>
                   A paragraph.

    2. If step 1 successfully determines the document title, we
       continue by checking for a subtitle.

       If the lone top-level section itself contains a single
       second-level section as its first non-comment element, that
       section's title is promoted to the document's subtitle, and
       that section's contents become the document's immediate
       contents. Given this input text::

           =================
            Top-Level Title
           =================

           Second-Level Title
           ~~~~~~~~~~~~~~~~~~

           A paragraph.

       After parsing and running the Section Promotion transform, the
       result is::

           <document name="top-level title">
               <title>
                   Top-Level Title
               <subtitle name="second-level title">
                   Second-Level Title
               <paragraph>
                   A paragraph.

       (Note that the implicit hyperlink target generated by the
       "Second-Level Title" is preserved on the "subtitle" element
       itself.)

    Any comment elements occurring before the document title or
    subtitle are accumulated and inserted as the first body elements
    after the title(s).
    """

    def transform(self):
        if self.promote_document_title():
            self.promote_document_subtitle()

    def promote_document_title(self):
        section, index = self.candidate_index()
        if index is None:
            return None
        doctree = self.doctree
        # Transfer the section's attributes to the document element (at root):
        doctree.attributes.update(section.attributes)
        doctree[:] = (section[:1]       # section title
                      + doctree[:index] # everything that was in the document
                                        # before the section
                      + section[1:])    # everything that was in the section
        return 1

    def promote_document_subtitle(self):
        subsection, index = self.candidate_index()
        if index is None:
            return None
        subtitle = nodes.subtitle()
        # Transfer the subsection's attributes to the new subtitle:
        subtitle.attributes.update(subsection.attributes)
        # Transfer the contents of the subsection's title to the subtitle:
        subtitle[:] = subsection[0][:]
        doctree = self.doctree
        doctree[:] = (doctree[:1]       # document title
                      + [subtitle]
                      + doctree[1:index] # everything that was in the document
                                         # before the section
                      + subsection[1:]) # everything that was in the subsection
        return 1

    def candidate_index(self):
        """
        Find and return the promotion candidate and its index.

        Return (None, None) if no valid candidate was found.
        """
        doctree = self.doctree
        index = doctree.findnonclass(nodes.PreBibliographic)
        if index is None or len(doctree) > (index + 1) or \
              not isinstance(doctree[index], nodes.section):
            return None, None
        else:
            return doctree[index], index


class DocInfo(Transform):

    """
    This transform is specific to the reStructuredText_ markup syntax;
    see "Bibliographic Fields" in the `reStructuredText Markup
    Specification`_ for a high-level description. This transform
    should be run *after* the `DocTitle` transform.

    Given a field list as the first non-comment element after the
    document title and subtitle (if present), registered bibliographic
    field names are transformed to the corresponding DTD elements,
    becoming child elements of the "docinfo" element (except for the
    abstract, which becomes a "topic" element after "docinfo").

    For example, given this document fragment after parsing::

        <document>
            <title>
                Document Title
            <field_list>
                <field>
                    <field_name>
                        Author
                    <field_body>
                        <paragraph>
                            A. Name
                <field>
                    <field_name>
                        Status
                    <field_body>
                        <paragraph>
                            $RCSfile: frontmatter.py,v $
            ...

    After running the bibliographic field list transform, the
    resulting document tree would look like this::

        <document>
            <title>
                Document Title
            <docinfo>
                <author>
                    A. Name
                <status>
                    frontmatter.py
            ...

    The "Status" field contained an expanded RCS keyword, which is
    normally (but optionally) cleaned up by the transform. The sole
    contents of the field body must be a paragraph containing an
    expanded RCS keyword of the form "$keyword: expansion text $". Any
    RCS keyword can be processed in any bibliographic field. The
    dollar signs and leading RCS keyword name are removed. Extra
    processing is done for the following RCS keywords:

    - "RCSfile" expands to the name of the file in the RCS or CVS
      repository, which is the name of the source file with a ",v"
      suffix appended. The transform will remove the ",v" suffix.

    - "Date" expands to the format "YYYY/MM/DD hh:mm:ss" (in the UTC
      time zone). The RCS Keywords transform will extract just the
      date itself and transform it to an ISO 8601 format date, as in
      "2000-12-31".

      (Since the source file for this text is itself stored under CVS,
      we can't show an example of the "Date" RCS keyword because we
      can't prevent any RCS keywords used in this explanation from
      being expanded. Only the "RCSfile" keyword is stable; its
      expansion text changes only if the file name changes.)
    """

    def transform(self):
        doctree = self.doctree
        index = doctree.findnonclass(nodes.PreBibliographic)
        if index is None:
            return
        candidate = doctree[index]
        if isinstance(candidate, nodes.field_list):
            biblioindex = doctree.findnonclass(nodes.Titular)
            nodelist, remainder = self.extract_bibliographic(candidate)
            if remainder:
                doctree[index] = remainder
            else:
                del doctree[index]
            doctree[biblioindex:biblioindex] = nodelist
        return

    def extract_bibliographic(self, field_list):
        docinfo = nodes.docinfo()
        remainder = []
        bibliofields = self.language.bibliographic_fields
        abstract = None
        for field in field_list:
            try:
                name = field[0][0].astext()
                normedname = utils.normname(name)
                if not (len(field) == 2 and bibliofields.has_key(normedname)
                        and self.check_empty_biblio_field(field, name)):
                    raise TransformError
                biblioclass = bibliofields[normedname]
                if issubclass(biblioclass, nodes.TextElement):
                    if not self.check_compound_biblio_field(field, name):
                        raise TransformError
                    self.filter_rcs_keywords(field[1][0])
                    docinfo.append(biblioclass('', '', *field[1][0]))
                else:                   # multiple body elements possible
                    if issubclass(biblioclass, nodes.authors):
                        self.extract_authors(field, name, docinfo)
                    elif issubclass(biblioclass, nodes.topic):
                        if abstract:
                            field[-1] += self.doctree.reporter.warning(
                                  'There can only be one abstract.')
                            raise TransformError
                        title = nodes.title(
                              name, self.language.labels['abstract'])
                        abstract = nodes.topic('', title, CLASS='abstract',
                                               *field[1].children)
                    else:
                        docinfo.append(biblioclass('', *field[1].children))
            except TransformError:
                remainder.append(field)
                continue
        nodelist = []
        if len(docinfo) != 0:
            nodelist.append(docinfo)
        if abstract:
            nodelist.append(abstract)
        if remainder:
            field_list[:] = remainder
        else:
            field_list = None
        return nodelist, field_list

    def check_empty_biblio_field(self, field, name):
        if len(field[1]) < 1:
            field[-1] += self.doctree.reporter.warning(
                  'Cannot extract empty bibliographic field "%s".' % name)
            return None
        return 1

    def check_compound_biblio_field(self, field, name):
        if len(field[1]) > 1:
            field[-1] += self.doctree.reporter.warning(
                  'Cannot extract compound bibliographic field "%s".' % name)
            return None
        if not isinstance(field[1][0], nodes.paragraph):
            field[-1] += self.doctree.reporter.warning(
                  'Cannot extract bibliographic field "%s" containing anything '
                  'other than a single paragraph.'
                  % name)
            return None
        return 1

    rcs_keyword_substitutions = [
          (re.compile(r'\$' r'Date: (\d\d\d\d)/(\d\d)/(\d\d) [\d:]+ \$$',
                      re.IGNORECASE), r'\1-\2-\3'),
          (re.compile(r'\$' r'RCSfile: (.+),v \$$',
                      re.IGNORECASE), r'\1'),
          (re.compile(r'\$[a-zA-Z]+: (.+) \$$'), r'\1'),]

    def filter_rcs_keywords(self, paragraph):
        if len(paragraph) == 1 and isinstance(paragraph[0], nodes.Text):
            textnode = paragraph[0]
            for pattern, substitution in self.rcs_keyword_substitutions:
                match = pattern.match(textnode.data)
                if match:
                    textnode.data = pattern.sub(substitution, textnode.data)
                    return

    def extract_authors(self, field, name, docinfo):
        try:
            if len(field[1]) == 1:
                if isinstance(field[1][0], nodes.paragraph):
                    authors = self.authors_from_one_paragraph(field)
                elif isinstance(field[1][0], nodes.bullet_list):
                    authors = self.authors_from_bullet_list(field)
                else:
                    raise TransformError
            else:
                authors = self.authors_from_paragraphs(field)
            authornodes = [nodes.author('', '', *author)
                           for author in authors if author]
            docinfo.append(nodes.authors('', *authornodes))
        except TransformError:
            field[-1] += self.doctree.reporter.warning(
                  'Bibliographic field "%s" incompatible with extraction: '
                  'it must contain either a single paragraph (with authors '
                  'separated by one of "%s"), multiple paragraphs (one per '
                  'author), or a bullet list with one paragraph (one author) '
                  'per item.'
                  % (name, ''.join(self.language.author_separators)))
            raise

    def authors_from_one_paragraph(self, field):
        text = field[1][0].astext().strip()
        if not text:
            raise TransformError
        for authorsep in self.language.author_separators:
            authornames = text.split(authorsep)
            if len(authornames) > 1:
                break
        authornames = [author.strip() for author in authornames]
        authors = [[nodes.Text(author)] for author in authornames]
        return authors

    def authors_from_bullet_list(self, field):
        authors = []
        for item in field[1][0]:
            if len(item) != 1 or not isinstance(item[0], nodes.paragraph):
                raise TransformError
            authors.append(item[0].children)
        if not authors:
            raise TransformError
        return authors

    def authors_from_paragraphs(self, field):
        for item in field[1]:
            if not isinstance(item, nodes.paragraph):
                raise TransformError
        authors = [item.children for item in field[1]]
        return authors