def entry_details(request, slug: str):
"""
Head word detail page. Will render a paradigm, if applicable. Fallback to search
page if no slug is not found.
:param slug: the stable unique ID of the lemma
"""
lemma = Wordform.objects.filter(slug=slug, is_lemma=True)
if lemma.count() != 1:
# The result is either empty or ambiguous; either way, do a search!
return redirect(url_for_query(slug.split("@")[0] or ""))
lemma = lemma.get()
paradigm_context: dict[str, Any] = {}
paradigm = lemma.paradigm
if paradigm is not None:
paradigm_manager = default_paradigm_manager()
sizes = list(paradigm_manager.sizes_of(paradigm))
if "basic" in sizes:
default_size = "basic"
else:
default_size = sizes[0]
if len(sizes) <= 1:
size = default_size
else:
size = request.GET.get("paradigm-size")
if size:
size = size.lower()
if size not in sizes:
size = default_size
paradigm = paradigm_for(lemma, size)
paradigm = get_recordings_from_paradigm(paradigm, request)
paradigm_context.update(paradigm=paradigm,
paradigm_size=size,
paradigm_sizes=sizes)
animate_emoji = AnimateEmoji.current_value_from_request(
request) # type: ignore
dict_source = get_dict_source(request) # type: ignore
context = create_context_for_index_template(
"word-detail",
# TODO: rename this to wordform ID
lemma_id=lemma.id,
# TODO: remove this parameter in favour of...
lemma=lemma,
# ...this parameter
wordform=presentation.serialize_wordform(lemma,
animate_emoji=animate_emoji,
dict_source=dict_source),
**paradigm_context,
)
return render(request, "CreeDictionary/index.html", context)
def __init__(
self,
importjson: list,
translate_wordforms: bool,
purge: bool,
incremental: bool,
atomic=True,
skip_building_vectors_because_testing=False,
):
"""
Create an Import process.
If atomic is False, this will use batch processing that still works when
not in a transaction.
"""
self.dictionary_source_cache = DictionarySourceCache()
self.data = importjson
self.translate_wordforms = translate_wordforms
self.incremental = incremental
self.purge = purge
self.skip_building_vectors_because_testing = (
skip_building_vectors_because_testing)
self._has_run = False
self.paradigm_manager = default_paradigm_manager()
self.translation_stats = TranslationStats()
trigger_deps = not atomic
self.wordform_buffer = InsertBuffer(Wordform.objects, assign_id=True)
self.definition_buffer = InsertBuffer(
Definition.objects,
assign_id=True,
trigger_deps=trigger_deps,
deps=[self.wordform_buffer],
)
self.citation_buffer = InsertBuffer(
Definition.citations.through.objects,
trigger_deps=trigger_deps,
deps=[self.definition_buffer],
)
self.source_language_keyword_buffer = InsertBuffer(
SourceLanguageKeyword.objects,
trigger_deps=trigger_deps,
deps=[self.wordform_buffer],
)
self.target_language_keyword_buffer = InsertBuffer(
TargetLanguageKeyword.objects,
trigger_deps=trigger_deps,
deps=[self.wordform_buffer],
)
def paradigm_for(wordform: Wordform, paradigm_size: str) -> Optional[Paradigm]:
"""
Returns a paradigm for the given wordform at the desired size.
If a paradigm cannot be found, None is returned
"""
manager = default_paradigm_manager()
if name := wordform.paradigm:
fst_lemma = wordform.lemma.text
if settings.MORPHODICT_ENABLE_FST_LEMMA_SUPPORT:
fst_lemma = wordform.lemma.fst_lemma
if paradigm := manager.paradigm_for(name, fst_lemma, paradigm_size):
return paradigm
if lemma.count() != 1:
# The result is either empty or ambiguous; either way, do a search!
return redirect(url_for_query(slug.split("@")[0] or ""))
lemma = lemma.get()
if rich_analysis := lemma.analysis:
morphemes = rich_analysis.generate_with_morphemes(lemma.text)
else:
morphemes = None
paradigm_context: dict[str, Any] = {}
paradigm = lemma.paradigm
if paradigm is not None:
paradigm_manager = default_paradigm_manager()
sizes = list(paradigm_manager.sizes_of(paradigm))
if "basic" in sizes:
default_size = "basic"
else:
default_size = sizes[0]
if len(sizes) <= 1:
size = default_size
else:
size = request.GET.get("paradigm-size")
if size:
size = size.lower()
if size not in sizes:
size = default_size
def paradigm_manager() -> ParadigmManager:
return default_paradigm_manager()