ajax_lemma_status.py
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# -*- coding: utf-8 -*-
import operator
from django.db.models import Q
from django.db.models import Count, Max
from accounts.models import RealizedLemma, RealizedPhraseology, RealizedSemantics
from common.decorators import render, ajax, AjaxError
from dictionary.common_func import frame_structure_exists
from dictionary.models import Configuration, Frame, Lemma, Lemma_Status, StatusChange
from semantics.change_log import backup_lemma_and_get_frames
from semantics.models import SemanticFrame
from semantics.utils import get_frames_differences
@render('lemma_status.html')
@ajax(method='get', encode_result=False)
def get_lemma_status(request, id):
selected_lemma = Lemma.objects.get(id=id)
abort_status = None
next_statuses = []
pos = selected_lemma.entry_obj.pos
if ((selected_lemma.owner == request.user
and selected_lemma.status.type.sym_name != 'checked')
or request.user.has_perm('dictionary.confirm_lemma')
or (selected_lemma.vocabulary.editors.filter(id=request.user.id).exists()
and request.user.has_perm('dictionary.change_lemmas')
and selected_lemma.status.type.sym_name == 'initial')
or phraseologic_status_changes(request.user, selected_lemma)
or semantic_status_changes(request.user, selected_lemma)):
if request.user.groups.filter(group_settings__abort_statuses=selected_lemma.status.abort_status).exists():
abort_status = selected_lemma.status.abort_status
next_statuses = selected_lemma.status.next_statuses
if not lemma_can_be_temporary(selected_lemma):
next_statuses = next_statuses.exclude(status=u'zalążkowe')
next_statuses = next_statuses.filter(pk__in=request.user.groups.all()[0].group_settings.next_statuses.all())
next_statuses = next_statuses.all()
return {'lemma': selected_lemma,
'abort_status': abort_status,
'next_statuses': next_statuses,
'pos': pos,
'status_changes': selected_lemma.status_history.order_by('-date')}
def phraseologic_status_changes(user, selected_lemma):
phraseologic_change = False
if (user.has_perm('dictionary.add_phraseologic_frames') and
selected_lemma.status.type.sym_name != 'checked_f' and
selected_lemma.vocabulary.editors.filter(id=user.id).exists() and
(selected_lemma.phraseologist == user or selected_lemma.status.type.sym_name == 'checked')):
phraseologic_change = True
return phraseologic_change
def semantic_status_changes(user, selected_lemma):
semantic_change = False
if (user.has_perm('dictionary.add_semantic_frames') and
selected_lemma.status.type.sym_name != 'checked_s' and
selected_lemma.vocabulary.editors.filter(id=user.id).exists() and
(selected_lemma.semanticist == user or selected_lemma.status.type.sym_name == 'checked_f' or
selected_lemma.status.type.sym_name == 'checked')):
semantic_change = True
return semantic_change
def lemma_can_be_temporary(lemma):
can_be_temporary = False
system_conf = Configuration.objects.get(selected_conf=True)
if (lemma.frequency_1M < system_conf.min_1M_freq and
lemma.frequency_300M < system_conf.min_300M_freq and
lemma.skladnica_frames.exists()):
can_be_temporary = True
return can_be_temporary
@ajax(method='post')
def status_need_validation(request, status_id, lemma_id):
next_status = False
lemma_status = Lemma.objects.get(id=lemma_id).status
new_status = Lemma_Status.objects.get(id=status_id)
if(new_status.priority > lemma_status.priority):
next_status = True
need_validation = (new_status.validate or
new_status.check_examples or
new_status.check_semantics) and next_status
return {'need_validation': need_validation}
@ajax(method='post')
def lemma_status_change(request, status_id, lemma_id):
if not request.user.is_authenticated():
raise AjaxError('user logged out')
try:
lemma_obj = Lemma.objects.get(id=lemma_id, old=False)
except Lemma.DoesNotExist:
raise AjaxError('old version')
entry_obj = lemma_obj.entry_obj
# sprawdza czy uzytkownik moze modyfikowac hasla w danym slowniku
try:
lemma_obj.vocabulary.editors.get(username=request.user.username)
edit_vocabulary = True
except:
edit_vocabulary = False
changed = False
message = ''
new_status = None
try: # jesli zarezerowano haslo przyciskiem
new_status = Lemma_Status.objects.get(id=status_id)
except:
pass
if new_status and new_status == lemma_obj.status:
raise AjaxError('already changed')
if(new_status and
not new_status == lemma_obj.status.abort_status and
not lemma_obj.status.next_statuses.filter(pk=new_status.pk).exists()):
raise AjaxError('wrong change')
visible_semantic_frames = SemanticFrame.objects.none()
next_status = False
if(new_status):
visible_semantic_frames = backup_lemma_and_get_frames(lemma_obj)
if(new_status and new_status.priority > lemma_obj.status.priority):
next_status = True
# reserve lemma
if(lemma_obj.status.type.sym_name == 'initial' and edit_vocabulary and
request.user.has_perm('dictionary.change_lemmas')):
lemma_obj.owner = request.user
changed = True
if not new_status:
new_status = lemma_obj.status.next_statuses.order_by('priority')[0]
# resign from lemma
elif(not next_status and
new_status and new_status.type.sym_name == 'initial'):
lemma_obj.owner = None
changed = True
# lemma type changed to ready or temporary
elif(new_status and new_status.type.sym_name == 'ready' and next_status):
if entry_obj.pos.tag == 'verb':
flat_frames_value = 4.0
else:
flat_frames_value = 3.0
update_lemma_stats_ready(lemma_obj, lemma_obj.owner, new_status, flat_frames_value)
changed = True
# lemma type changed to checked
elif(new_status and new_status.type.sym_name == 'checked' and next_status):
if lemma_obj.owner == request.user:
if lemma_obj.status.abort_status != new_status:
message = u'Nie można zatwierdzać hasła, którego jest się właścicielem.'
else:
if lemma_obj.status.abort_status != new_status:
if entry_obj.pos.tag == 'verb':
checked_frame_value = 1.0
corrected_frame_value = 5.0
bonus_factor = 1.0
else:
checked_frame_value = 1.0
corrected_frame_value = 4.5
bonus_factor = 1.0
update_lemma_stats_conf(lemma_obj, lemma_obj.owner, request.user, new_status,
checked_frame_value, corrected_frame_value, bonus_factor)
changed = True
# zmiana statusu na w obrobce
elif(new_status and
(lemma_obj.status.type.sym_name == 'ready' or lemma_obj.status.type.sym_name == 'checked') and
lemma_obj.status.abort_status == new_status):
remove_lexicography_payments(lemma_obj)
changed = True
# pobieranie hasla do obrobki frazeologicznej
elif(new_status and
lemma_obj.status.type.sym_name == 'checked' and
edit_vocabulary and new_status.type.sym_name == 'edit_f'
and next_status):
lemma_obj.phraseologist = request.user
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# porzucanie obrobki frazeologicznej hasla
elif(new_status and
lemma_obj.status.type.sym_name == 'edit_f' and
lemma_obj.status.abort_status == new_status):
lemma_obj.phraseologist = None
changed = True
# zmiana statusu hasla na gotowe frazeologicznie
elif(new_status and new_status.type.sym_name == 'ready_f'
and next_status):
new_phraseologic_frame_value = 7.0
proposed_phraseologic_frame_value = 2.0
update_lemma_stats_ready_f(lemma_obj, lemma_obj.phraseologist,
new_status, new_phraseologic_frame_value,
proposed_phraseologic_frame_value)
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# zmiana statusu hasla na sprawdzone frazeologicznie
elif(new_status and new_status.type.sym_name == 'checked_f'
and next_status):
checked_frame_value = 4.0
corrected_frame_value = 7.0
bonus = 0.5
update_lemma_stats_conf_f(lemma_obj, lemma_obj.phraseologist, request.user, new_status,
checked_frame_value, corrected_frame_value, bonus)
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# pobieranie hasla do obrobki semantycznej
elif(new_status and
(lemma_obj.status.type.sym_name == 'checked_f' or lemma_obj.status.type.sym_name == 'checked') and
edit_vocabulary and new_status.type.sym_name == 'edit_s'
and next_status):
lemma_obj.semanticist = request.user
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# porzucanie obrobki semantycznej hasla
elif(new_status and
lemma_obj.status.type.sym_name == 'edit_s' and
lemma_obj.status.abort_status == new_status):
lemma_obj.semanticist = None
changed = True
# zmiana statusu hasla na gotowe semantycznie
elif(new_status and new_status.type.sym_name == 'ready_s'
and next_status):
### naliczanie oplat za gotowosc semantyczna
frame_value = 12.0
related_frame_value = 2.0
update_sem_stats_ready_s(lemma_obj.entry_obj, visible_semantic_frames,
lemma_obj.semanticist, new_status, frame_value,
related_frame_value)
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# zmiana statusu hasla na sprawdzone semantycznie
elif(new_status and new_status.type.sym_name == 'checked_s'
and next_status):
checked_frame_value = 0.0
corrected_frame_value = 0.0
bonus = 4.0
part_bonus = 2.0
connection_bonus = 0.1
### naliczanie oplat za sprawdzenie i bonusow
update_sem_stats_conf_s(entry=lemma_obj.entry_obj,
checked_sem_frames_backup=visible_semantic_frames,
semanticist=lemma_obj.semanticist,
supersemanticist=request.user,
status=new_status,
checked_frame_value=checked_frame_value,
corrected_frame_value=corrected_frame_value,
bonus_factor=bonus,
part_bonus_factor=part_bonus,
connection_bonus=connection_bonus)
add_new_frames_to_phraseologic_propositions(lemma_obj)
changed = True
# zmiana statusu na w obrobce semantycznej
elif(new_status and
(lemma_obj.status.type.sym_name == 'ready_s' or lemma_obj.status.type.sym_name == 'checked_s') and
lemma_obj.status.abort_status == new_status):
remove_semantic_payments(lemma_obj.entry_obj)
changed = True
# jak oznaczamy status jako "do usuniecia", to czyscimy oplaty za haslo
elif(new_status and new_status.type.sym_name == 'erase'
and next_status):
changed = True
erase_payments(lemma_obj)
elif(new_status):
changed = True
if changed and new_status != lemma_obj.status:
lemma_obj.status = new_status
lemma_obj.save()
status_change = StatusChange(act_owner=lemma_obj.owner,
changer=request.user,
lemma=lemma_obj,
status=new_status)
status_change.save()
status_change.semantic_frames.add(*visible_semantic_frames.all())
lemma_obj.status_history.add(status_change)
if new_status:
new_status_type = new_status.type.sym_name
else:
new_status_type = ''
return {'entry' : lemma_obj.entry,
'lemma_id' : lemma_obj.id,
'changed' : changed,
'message' : message,
'new_status_type': new_status_type,
'next_status' : next_status}
############# marking as 'to erase' #################
def erase_payments(lemma):
RealizedLemma.objects.filter(lemma__entry_obj=lemma.entry_obj).delete()
RealizedPhraseology.objects.filter(lemma__entry_obj=lemma.entry_obj).delete()
RealizedSemantics.objects.filter(entry=lemma.entry_obj).delete()
############## lexicography #######################
def update_lemma_stats_ready(lemma, lex, status, flat_frames_value):
lex_dict = {'made_frames': 0,
'cash': 0.0}
for frame in lemma.frames.all():
flat_frames = float(frame.positions.annotate(num_args=Count('arguments')).aggregate(Max('num_args'))['num_args__max'])
lex_dict['made_frames'] += flat_frames
lex_dict['cash'] += flat_frames_value*flat_frames
lex_real_lemma = RealizedLemma(lemma=lemma, cash=lex_dict['cash'],
made_frames=lex_dict['made_frames'],
paid=False, status=status, bonus=False,
counted=False)
lex_real_lemma.save()
lex.user_stats.lemma_real_history.add(lex_real_lemma)
def update_lemma_stats_conf(lemma, lex, superlex, status,
checked_frame_value, corrected_frame_value, bonus_factor):
ready_statuses = Lemma_Status.objects.filter(type__sym_name='ready')
q_ready_statuses = [Q(status=ready_status) for ready_status in ready_statuses.all()]
all_realized_lemmas = RealizedLemma.objects.filter(reduce(operator.or_, q_ready_statuses))
ready_lemma = all_realized_lemmas.get(lemma__entry_obj=lemma.entry_obj).lemma
lex_dict = {'same_frames': 0,
'wrong_frames': 0,
'cash': 0.0}
superlex_dict = {'same_frames': [],
'redo_frames': [],
'cash': 0.0}
q_same_frames = []
for frame in ready_lemma.frames.all():
flat_frames = float(frame.positions.annotate(num_args=Count('arguments')).aggregate(Max('num_args'))['num_args__max'])
try:
same_frame = lemma.frames.get(text_rep=frame.text_rep)
superlex_dict['same_frames'].append(frame)
superlex_dict['cash'] += checked_frame_value
lex_dict['same_frames'] += flat_frames
bonus = flat_frames * bonus_factor*flat_frames**(1.0/3.0)
lex_dict['cash'] += bonus
q_same_frames.append(Q(text_rep=same_frame.text_rep))
except Frame.DoesNotExist:
lex_dict['wrong_frames'] += flat_frames
continue
new_frames = lemma.frames
if len(q_same_frames) > 0:
new_frames = new_frames.exclude(reduce(operator.or_, q_same_frames))
superlex_dict['redo_frames'] = new_frames.all()
for frame in superlex_dict['redo_frames']:
superlex_dict['cash'] += corrected_frame_value
# superleksykograf nie zostal jeszcze oplacony za dane haslo, zmien wartosc
superlex_real_lemma = RealizedLemma(lemma=lemma, cash=superlex_dict['cash'],
corr_frames=len(superlex_dict['redo_frames']),
ncorr_frames=len(superlex_dict['same_frames']),
paid=False, status=status, bonus=False,
counted=False)
superlex_real_lemma.save()
superlex.user_stats.lemma_real_history.add(superlex_real_lemma)
# hasla pochodzace z cesara maja cash = 0.0 i nie nalezy ich bonusować
try:
RealizedLemma.objects.get(lemma__entry=lemma.entry,
status__type__sym_name='ready',
bonus=False, cash=0.0, paid=True,
counted=True)
except RealizedLemma.DoesNotExist:
lex_real_lemma = RealizedLemma(lemma=lemma,
cash=lex_dict['cash'],
prop_frames=lex_dict['same_frames'],
wrong_frames=lex_dict['wrong_frames'],
paid=False, status=status,
bonus=True, counted=False)
lex_real_lemma.save()
lex.user_stats.lemma_real_history.add(lex_real_lemma)
def remove_lexicography_payments(lemma):
RealizedLemma.objects.filter(lemma__entry_obj=lemma.entry_obj).delete()
######################## phraseology #############################
def add_new_frames_to_phraseologic_propositions(lemma):
entry_obj = lemma.entry_obj
phraseologic_frames = lemma.frames.filter(phraseologic=True).all()
phraseologic_propositions = entry_obj.phraseologic_propositions.all()
propositions_to_add = []
for frame in phraseologic_frames:
if not frame_structure_exists(frames=phraseologic_propositions,
searched_frame=frame):
propositions_to_add.append(frame)
entry_obj.phraseologic_propositions.add(*propositions_to_add)
def update_lemma_stats_ready_f(lemma, phraseologist, status,
new_phraseologic_frame_value, proposed_phraseologic_frame_value):
phraseologist_dict = {'new_frames': 0,
'reused_frames': 0,
'cash': 0.0}
for frame in lemma.frames.filter(phraseologic=True).all():
if frame_structure_exists(frames=lemma.entry_obj.phraseologic_propositions.all(),
searched_frame=frame):
phraseologist_dict['reused_frames'] += 1
phraseologist_dict['cash'] += proposed_phraseologic_frame_value
else:
phraseologist_dict['new_frames'] += 1
phraseologist_dict['cash'] += new_phraseologic_frame_value
phrase_real_lemma = RealizedPhraseology(lemma=lemma,
cash=phraseologist_dict['cash'],
new_frames=phraseologist_dict['new_frames'],
reused_frames=phraseologist_dict['reused_frames'],
paid=False,
status=status,
bonus=False,
counted=False)
phrase_real_lemma.save()
phraseologist.user_stats.phraseology_real_history.add(phrase_real_lemma)
def update_lemma_stats_conf_f(lemma, phraseologist, superphraseologist, status,
checked_frame_value, corrected_frame_value, bonus):
ready_statuses = Lemma_Status.objects.filter(type__sym_name='ready_f')
q_ready_statuses = [Q(status=ready_status) for ready_status in ready_statuses.all()]
ready_lemmas = RealizedPhraseology.objects.filter(reduce(operator.or_, q_ready_statuses))
ready_lemma = ready_lemmas.get(lemma__entry_obj=lemma.entry_obj).lemma
phraseologist_dict = {'same_frames': 0,
'wrong_frames': 0,
'cash': 0.0}
superphraseologist_dict = {'same_frames': [],
'redo_frames': [],
'cash': 0.0}
q_same_frames = []
for frame in ready_lemma.frames.filter(phraseologic=True).all():
try:
same_frame = lemma.frames.filter(phraseologic=True).get(text_rep=frame.text_rep)
superphraseologist_dict['same_frames'].append(frame)
superphraseologist_dict['cash'] += checked_frame_value
phraseologist_dict['same_frames'] += 1
phraseologist_dict['cash'] += bonus
q_same_frames.append(Q(text_rep=same_frame.text_rep))
except Frame.DoesNotExist:
phraseologist_dict['wrong_frames'] += 1
continue
new_frames = lemma.frames.filter(phraseologic=True)
if len(q_same_frames) > 0:
new_frames = new_frames.exclude(reduce(operator.or_, q_same_frames))
superphraseologist_dict['redo_frames'] = new_frames.all()
for frame in superphraseologist_dict['redo_frames']:
superphraseologist_dict['cash'] += corrected_frame_value
superphraseologist_real_lemma = RealizedPhraseology(lemma=lemma,
cash=superphraseologist_dict['cash'],
corr_frames=len(superphraseologist_dict['redo_frames']),
ncorr_frames=len(superphraseologist_dict['same_frames']),
paid=False,
status=status,
bonus=False,
counted=False)
superphraseologist_real_lemma.save()
superphraseologist.user_stats.phraseology_real_history.add(superphraseologist_real_lemma)
phraseologist_real_lemma = RealizedPhraseology(lemma=lemma,
cash=phraseologist_dict['cash'],
prop_frames=phraseologist_dict['same_frames'],
wrong_frames=phraseologist_dict['wrong_frames'],
paid=False,
status=status,
bonus=True,
counted=False)
phraseologist_real_lemma.save()
phraseologist.user_stats.phraseology_real_history.add(phraseologist_real_lemma)
####################### semantics #############################
def update_sem_stats_ready_s(entry, visible_semantic_frames, semanticist, status,
frame_value, related_frame_value):
actual_frames = entry.actual_frames()
actual_frames_count = actual_frames.count()
related_frames = entry.related_frames()
related_frames_count = related_frames.count()
cash = frame_value*float(actual_frames_count) + related_frame_value*float(related_frames_count)
realized_semantics = RealizedSemantics(entry=entry, cash=cash,
made_frames=actual_frames_count,
related_frames=related_frames_count,
status=status, bonus=False)
realized_semantics.save()
realized_semantics.frames.add(*visible_semantic_frames.all())
semanticist.user_stats.semantics_real_history.add(realized_semantics)
def update_sem_stats_conf_s(entry, checked_sem_frames_backup, semanticist, supersemanticist, status,
checked_frame_value, corrected_frame_value,
bonus_factor, part_bonus_factor, connection_bonus):
ready_statuses = Lemma_Status.objects.filter(type__sym_name='ready_s')
q_ready_statuses = [Q(status=ready_status) for ready_status in ready_statuses.all()]
ready_semantics = RealizedSemantics.objects.filter(reduce(operator.or_, q_ready_statuses))
ready_sem_visible_frames = ready_semantics.get(entry=entry).frames
ready_sem_actual_frames = entry.filter_local(ready_sem_visible_frames)
ready_sem_related_frames = entry.filter_related(ready_sem_visible_frames)
checked_sem_actual_frames = entry.actual_frames()
checked_sem_related_frames = entry.related_frames()
actual_ready_to_checked_diffs = get_frames_differences(ready_sem_actual_frames.all(), checked_sem_actual_frames.all())
actual_checked_to_ready_diffs = get_frames_differences(checked_sem_actual_frames.all(), ready_sem_actual_frames.all())
visible_ready_to_checked_diffs = get_frames_differences(ready_sem_visible_frames.all(), checked_sem_frames_backup.all())
connections_amount = count_connections(entry, visible_ready_to_checked_diffs)
sem_cash = (bonus_factor*float(len(actual_ready_to_checked_diffs['matching_frames'])) +
part_bonus_factor*float(len(actual_ready_to_checked_diffs['part_matching_frames'])) +
connection_bonus*float(connections_amount))
sem_dict = {'same_frames': len(actual_ready_to_checked_diffs['matching_frames']),
'part_same_frames': len(actual_ready_to_checked_diffs['part_matching_frames']),
'wrong_frames': len(actual_ready_to_checked_diffs['missing_frames']),
'added_connections': connections_amount,
'cash': sem_cash}
supersem_cash = (float(len(actual_checked_to_ready_diffs['missing_frames'])+len(actual_checked_to_ready_diffs['part_matching_frames']))*corrected_frame_value +
float(len(actual_ready_to_checked_diffs['matching_frames']))*checked_frame_value)
supersem_dict = {'same_frames': len(actual_checked_to_ready_diffs['matching_frames']),
'part_same_frames': len(actual_checked_to_ready_diffs['part_matching_frames']),
'redo_frames': len(actual_checked_to_ready_diffs['missing_frames']),
'cash': supersem_cash}
supersem_real_semantics = RealizedSemantics(entry=entry,
cash=supersem_dict['cash'],
corr_frames=supersem_dict['redo_frames'],
part_corr_frames=supersem_dict['part_same_frames'],
ncorr_frames=supersem_dict['same_frames'],
status=status,
bonus=False)
supersem_real_semantics.save()
supersem_real_semantics.frames.add(*checked_sem_frames_backup.all())
supersemanticist.user_stats.semantics_real_history.add(supersem_real_semantics)
sem_real_semantics = RealizedSemantics(entry=entry,
cash=sem_dict['cash'],
prop_frames=sem_dict['same_frames'],
part_prop_frames=sem_dict['part_same_frames'],
wrong_frames=sem_dict['wrong_frames'],
added_connections=sem_dict['added_connections'],
status=status,
bonus=True)
sem_real_semantics.save()
sem_real_semantics.frames.add(*checked_sem_frames_backup.all())
semanticist.user_stats.semantics_real_history.add(sem_real_semantics)
def count_connections(entry, differences):
amount = 0
schemata = entry.actual_schemata()
for frame in differences['matching_frames']:
amount += frame.connected_schemata().filter(pk__in=schemata).count()
for frame in differences['part_matching_frames']:
amount += frame.connected_schemata().filter(pk__in=schemata).count()
return amount
def remove_semantic_payments(entry):
RealizedSemantics.objects.filter(entry=entry).delete()