def create_indices(self):
self.skel_records = SkelIndex(self.skel_table)()
self.chunk_records = ChunkIndex(self.chunk_table)()
self.ncx_records = []
toc = self.oeb.toc
entries = []
is_periodical = self.opts.mobi_periodical
if toc.count() < 1:
self.log.warn('Document has no ToC, MOBI will have no NCX index')
return
# Flatten the ToC into a depth first list
fl = toc.iterdescendants()
for i, item in enumerate(fl):
entry = {
'id': id(item),
'index': i,
'label': (item.title or _('Unknown')),
'children': []
}
entry['depth'] = getattr(item, 'ncx_hlvl', 0)
p = getattr(item, 'ncx_parent', None)
if p is not None:
entry['parent_id'] = p
for child in item:
child.ncx_parent = entry['id']
child.ncx_hlvl = entry['depth'] + 1
entry['children'].append(id(child))
if is_periodical:
if item.author:
entry['author'] = item.author
if item.description:
entry['description'] = item.description
entries.append(entry)
href = item.href or ''
href, frag = href.partition('#')[0::2]
aid = self.id_map.get((href, frag), None)
if aid is None:
aid = self.id_map.get((href, ''), None)
if aid is None:
pos, fid = 0, 0
chunk = self.chunk_table[pos]
offset = chunk.insert_pos + fid
else:
pos, fid, offset = self.aid_offset_map[aid]
entry['pos_fid'] = (pos, fid)
entry['offset'] = offset
# The Kindle requires entries to be sorted by (depth, playorder)
# However, I cannot figure out how to deal with non linear ToCs, i.e.
# ToCs whose nth entry at depth d has an offset after its n+k entry at
# the same depth, so we sort on (depth, offset) instead. This re-orders
# the ToC to be linear. A non-linear ToC causes section to section
# jumping to not work. kindlegen somehow handles non-linear tocs, but I
# cannot figure out how.
original = sorted(entries,
key=lambda entry: (entry['depth'], entry['index']))
linearized = sorted(entries,
key=lambda entry:
(entry['depth'], entry['offset']))
is_non_linear = original != linearized
entries = linearized
is_non_linear = False # False as we are using the linearized entries
if is_non_linear:
for entry in entries:
entry['kind'] = 'chapter'
for i, entry in enumerate(entries):
entry['index'] = i
id_to_index = {entry['id']: entry['index'] for entry in entries}
# Write the hierarchical information
for entry in entries:
children = entry.pop('children')
if children:
entry['first_child'] = id_to_index[children[0]]
entry['last_child'] = id_to_index[children[-1]]
if 'parent_id' in entry:
entry['parent'] = id_to_index[entry.pop('parent_id')]
# Write the lengths
def get_next_start(entry):
enders = [
e['offset'] for e in entries if e['depth'] <= entry['depth']
and e['offset'] > entry['offset']
]
if enders:
return min(enders)
return len(self.flows[0])
for entry in entries:
entry['length'] = get_next_start(entry) - entry['offset']
self.has_tbs = apply_trailing_byte_sequences(
entries, self.records, self.uncompressed_record_lengths)
idx_type = NonLinearNCXIndex if is_non_linear else NCXIndex
self.ncx_records = idx_type(entries)()
def create_indices(self):
self.skel_records = SkelIndex(self.skel_table)()
self.chunk_records = ChunkIndex(self.chunk_table)()
self.ncx_records = []
toc = self.oeb.toc
entries = []
is_periodical = self.opts.mobi_periodical
if toc.count() < 2:
self.log.warn('Document has no ToC, MOBI will have no NCX index')
return
# Flatten the ToC into a depth first list
fl = toc.iterdescendants()
for i, item in enumerate(fl):
entry = {'id': id(item), 'index': i, 'label':(item.title or
_('Unknown')), 'children':[]}
entry['depth'] = getattr(item, 'ncx_hlvl', 0)
p = getattr(item, 'ncx_parent', None)
if p is not None:
entry['parent_id'] = p
for child in item:
child.ncx_parent = entry['id']
child.ncx_hlvl = entry['depth'] + 1
entry['children'].append(id(child))
if is_periodical:
if item.author:
entry['author'] = item.author
if item.description:
entry['description'] = item.description
entries.append(entry)
href = item.href or ''
href, frag = href.partition('#')[0::2]
aid = self.id_map.get((href, frag), None)
if aid is None:
aid = self.id_map.get((href, ''), None)
if aid is None:
pos, fid = 0, 0
chunk = self.chunk_table[pos]
offset = chunk.insert_pos + fid
else:
pos, fid, offset = self.aid_offset_map[aid]
entry['pos_fid'] = (pos, fid)
entry['offset'] = offset
# The Kindle requires entries to be sorted by (depth, playorder)
# However, I cannot figure out how to deal with non linear ToCs, i.e.
# ToCs whose nth entry at depth d has an offset after its n+k entry at
# the same depth, so we sort on (depth, offset) instead. This re-orders
# the ToC to be linear. A non-linear ToC causes section to section
# jumping to not work. kindlegen somehow handles non-linear tocs, but I
# cannot figure out how.
original = sorted(entries,
key=lambda entry: (entry['depth'], entry['index']))
linearized = sorted(entries,
key=lambda entry: (entry['depth'], entry['offset']))
is_non_linear = original != linearized
entries = linearized
is_non_linear = False # False as we are using the linearized entries
if is_non_linear:
for entry in entries:
entry['kind'] = 'chapter'
for i, entry in enumerate(entries):
entry['index'] = i
id_to_index = {entry['id']:entry['index'] for entry in entries}
# Write the hierarchical information
for entry in entries:
children = entry.pop('children')
if children:
entry['first_child'] = id_to_index[children[0]]
entry['last_child'] = id_to_index[children[-1]]
if 'parent_id' in entry:
entry['parent'] = id_to_index[entry.pop('parent_id')]
# Write the lengths
def get_next_start(entry):
enders = [e['offset'] for e in entries if e['depth'] <=
entry['depth'] and e['offset'] > entry['offset']]
if enders:
return min(enders)
return len(self.flows[0])
for entry in entries:
entry['length'] = get_next_start(entry) - entry['offset']
self.has_tbs = apply_trailing_byte_sequences(entries, self.records,
self.uncompressed_record_lengths)
idx_type = NonLinearNCXIndex if is_non_linear else NCXIndex
self.ncx_records = idx_type(entries)()