diff --git a/accounts/management/commands/get_payments_data.py b/accounts/management/commands/get_payments_data.py
new file mode 100644
index 0000000..d637bfa
--- /dev/null
+++ b/accounts/management/commands/get_payments_data.py
@@ -0,0 +1,312 @@
+# -*- coding:utf-8 -*-
+
+import codecs
+import datetime
+
+from django.contrib.auth.models import User
+from django.core.management.base import BaseCommand
+from django.db.models import Sum
+
+from accounts.models import RealizedLemma, RealizedPhraseology, RealizedPhraseologyBinding, \
+ RealizedSemantics
+from dictionary.ajax_user_stats import get_used_bindings
+from dictionary.models import Lemma
+
+
+USERNAME = 'JakubS'
+FUNCTION = 'Leksykograf'
+POS = 'noun'
+STARTDATE = datetime.datetime(2011, 1, 1, 00, 00)
+ENDDATE = (datetime.datetime.now() -
+ datetime.timedelta(days=1)).replace(hour=23, minute=59, second=59)
+
+
+class Command(BaseCommand):
+ args = 'none'
+
+ def handle(self, **options):
+ get_payments_data(FUNCTION)
+
+
+def get_payments_data(function):
+ start = STARTDATE.strftime('%Y%m%d')
+ end = ENDDATE.strftime('%Y%m%d')
+ payments_path = 'data/work_%s_%s_%s-%s.csv' % (USERNAME, FUNCTION, start, end)
+ payments_file = codecs.open(payments_path, 'wt', 'utf-8')
+ user = User.objects.get(username=USERNAME)
+
+ if function == 'Semantyk':
+ work_stats = write_semantic_stats(payments_file, user, POS)
+ elif function == 'Superfrazeolog':
+ work_stats = write_superphraseologic_stats(payments_file, user, POS)
+ elif function == 'Frazeolog':
+ work_stats = write_phraseologic_stats(payments_file, user, POS)
+ elif function == 'Leksykograf':
+ work_stats = write_lexicographic_stats(payments_file, user, POS)
+ elif function == 'Superleksykograf':
+ work_stats = write_superlexicographic_stats(payments_file, user, POS)
+ total_earned_cash = work_stats['earned_cash']
+ if total_earned_cash > 0.0:
+ payments_file.write(u'\n%s\t%.2f\n' % (user.username,
+ total_earned_cash))
+ payments_file.close()
+
+
+def write_superlexicographic_stats(payments_file, user, pos):
+ real_lemmas = RealizedLemma.objects.filter(user_stats__user=user,
+ lemma__entry_obj__pos__tag=pos,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE,
+ status__type__sym_name='checked',
+ bonus=False)
+
+ earned_cash = real_lemmas.aggregate(Sum('cash'))['cash__sum']
+ if earned_cash == None:
+ earned_cash = 0.0
+
+ payments_file.write(u'Sprawdzone:\n')
+ for done_lemma in real_lemmas.order_by('date'):
+ payments_file.write(u'%s\t%.2f\t%s\n' % (done_lemma.lemma.entry_obj.name,
+ done_lemma.cash,
+ done_lemma.date.strftime('%Y%m%d')))
+
+ lex_work_stats = {'earned_cash': round(earned_cash, 2)}
+ return lex_work_stats
+
+
+def write_semantic_stats(payments_file, user, pos):
+
+ real_semantics = RealizedSemantics.objects.filter(user_stats__user=user,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE,
+ entry__pos__tag=pos)
+
+ earned_cash = real_semantics.filter(user_stats__user=user).aggregate(Sum('cash'))['cash__sum']
+ if earned_cash == None:
+ earned_cash = 0.0
+
+ bonus_cash = real_semantics.filter(user_stats__user=user,
+ bonus=True).aggregate(Sum('cash'))['cash__sum']
+ if bonus_cash == None:
+ bonus_cash = 0.0
+ prop_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('prop_frames'))[
+ 'prop_frames__sum']
+ if prop_frames == None:
+ prop_frames = 0
+ part_prop_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('part_prop_frames'))[
+ 'part_prop_frames__sum']
+ if part_prop_frames == None:
+ part_prop_frames = 0
+ wrong_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('wrong_frames'))[
+ 'wrong_frames__sum']
+ if wrong_frames == None:
+ wrong_frames = 0
+ corr_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('corr_frames'))[
+ 'corr_frames__sum']
+ if corr_frames == None:
+ corr_frames = 0
+ part_corr_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('part_corr_frames'))[
+ 'part_corr_frames__sum']
+ if part_corr_frames == None:
+ part_corr_frames = 0
+ ncorr_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('ncorr_frames'))[
+ 'ncorr_frames__sum']
+ if ncorr_frames == None:
+ ncorr_frames = 0
+ made_frames = real_semantics.filter(user_stats__user=user).aggregate(Sum('made_frames'))[
+ 'made_frames__sum']
+ if made_frames == None:
+ made_frames = 0
+ added_connections = real_semantics.filter(user_stats__user=user).aggregate(Sum('added_connections'))[
+ 'added_connections__sum']
+ if added_connections == None:
+ added_connections = 0
+ efficacy = 0.0
+ if prop_frames + wrong_frames > 0:
+ efficacy = float(prop_frames) / float(prop_frames + wrong_frames) * 100.0
+
+ payments_file.write(u'Wykonane:\n')
+ done_semantics = real_semantics.filter(bonus=False).order_by('date')
+ for done_sem in done_semantics:
+ done_cash = done_sem.cash
+ try:
+ done_bonus = real_semantics.get(bonus=True, entry=done_sem.entry).cash
+ done_cash += done_bonus
+ except RealizedSemantics.DoesNotExist:
+ pass
+ payments_file.write(u'%s\t%.2f\t%s\n' % (done_sem.entry.name,
+ done_cash,
+ done_sem.date.strftime('%Y%m%d')))
+
+ sem_work_stats = {'earned_cash': round(earned_cash, 2),
+ 'bonus_cash': round(bonus_cash, 2),
+ 'prop_frames': prop_frames,
+ 'part_prop_frames': part_prop_frames,
+ 'wrong_frames': wrong_frames,
+ 'corr_frames': corr_frames,
+ 'part_corr_frames': part_corr_frames,
+ 'checked_frames': ncorr_frames + corr_frames + part_corr_frames,
+ 'made_frames': made_frames,
+ 'efficacy': round(efficacy, 2),
+ 'added_connections': added_connections}
+ return sem_work_stats
+
+
+def write_superphraseologic_stats(payments_file, user, pos):
+ added_bindings = RealizedPhraseologyBinding.objects.filter(user_stats__user=user,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE)
+ used_bindings = get_used_bindings(added_bindings)
+
+ checked_phraseology = RealizedPhraseology.objects.filter(user_stats__user=user,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE,
+ bonus=False,
+ status__type__sym_name='checked_f',
+ lemma__entry_obj__pos__tag=pos)
+
+ earned_cash_frames = checked_phraseology.aggregate(Sum('cash'))['cash__sum']
+ if earned_cash_frames == None:
+ earned_cash_frames = 0.0
+ earned_cash_bindings = used_bindings.aggregate(Sum('cash'))['cash__sum']
+ if earned_cash_bindings == None:
+ earned_cash_bindings = 0.0
+ earned_cash = earned_cash_frames + earned_cash_bindings
+
+ phraseologic_empty_frame_value = 1.0
+ empty_value = 0.0
+
+ payments_file.write(u'Sprawdzone:\n')
+ checked_phraseology = checked_phraseology.order_by('date')
+ for checked_phr in checked_phraseology:
+ cash = checked_phr.cash
+ if cash == 0.0:
+ cash = phraseologic_empty_frame_value
+ empty_value += phraseologic_empty_frame_value
+ payments_file.write(u'%s\t%.2f\t%s\n' % (checked_phr.lemma.entry_obj.name,
+ cash,
+ checked_phr.date.strftime('%Y%m%d')))
+ earned_cash += empty_value
+
+ payments_file.write(u'\n\nDodane powiazania frazeologiczne:\n')
+ for binding in used_bindings.order_by('date'):
+ payments_file.write(u'%s\t%.2f\t%s\n' % (binding.binded_entry.name,
+ binding.cash,
+ binding.date.strftime('%Y%m%d')))
+
+
+ phraseology_work_stats = {'earned_cash': round(earned_cash, 2),
+ 'added_bindings': added_bindings.count(),
+ 'used_bindings': used_bindings.count()}
+ return phraseology_work_stats
+
+
+def write_phraseologic_stats(payments_file, user, pos):
+ added_bindings = RealizedPhraseologyBinding.objects.filter(user_stats__user=user,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE)
+ used_bindings = get_used_bindings(added_bindings)
+
+ checked_and_done_phraseology = RealizedPhraseology.objects.filter(user_stats__user=user,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE,
+ lemma__entry_obj__pos__tag=pos)
+
+ done_phraseology = checked_and_done_phraseology.filter(status__type__sym_name='ready_f',
+ bonus=False)
+
+ earned_cash_frames = done_phraseology.aggregate(Sum('cash'))['cash__sum']
+ if earned_cash_frames == None:
+ earned_cash_frames = 0.0
+ earned_cash_bindings = used_bindings.aggregate(Sum('cash'))['cash__sum']
+ if earned_cash_bindings == None:
+ earned_cash_bindings = 0.0
+ earned_cash = earned_cash_frames + earned_cash_bindings
+
+ bonus_cash = checked_and_done_phraseology.filter(bonus=True).aggregate(Sum('cash'))['cash__sum']
+ if bonus_cash == None:
+ bonus_cash = 0.0
+ earned_cash += bonus_cash
+
+ phraseologic_empty_frame_value = 1.0
+ empty_value = 0.0
+
+ payments_file.write(u'Wykonane:\n')
+ for done_phr in done_phraseology.order_by('date'):
+ cash = done_phr.cash
+ if cash == 0.0:
+ cash = phraseologic_empty_frame_value
+ empty_value += phraseologic_empty_frame_value
+ try:
+ done_bonus = checked_and_done_phraseology.get(bonus=True, lemma__entry_obj=done_phr.lemma.entry_obj).cash
+ cash += done_bonus
+ except RealizedPhraseology.DoesNotExist:
+ pass
+ payments_file.write(u'%s\t%.2f\t%s\n' % (done_phr.lemma.entry_obj.name,
+ cash,
+ done_phr.date.strftime('%Y%m%d')))
+
+ payments_file.write(u'\n\nDodane powiazania frazeologiczne:\n')
+ for binding in used_bindings.order_by('date'):
+ payments_file.write(u'%s\t%.2f\t%s\n' % (binding.binded_entry.name,
+ binding.cash,
+ binding.date.strftime('%Y%m%d')))
+
+ earned_cash += empty_value
+
+ phraseology_work_stats = {'earned_cash': round(earned_cash, 2),
+ 'added_bindings': added_bindings.count(),
+ 'used_bindings': used_bindings.count(),}
+ return phraseology_work_stats
+
+
+def write_lexicographic_stats(payments_file, user, pos):
+
+ real_lemmas = RealizedLemma.objects.filter(user_stats__user=user,
+ lemma__entry_obj__pos__tag=pos,
+ date__gte=STARTDATE,
+ date__lte=ENDDATE)
+
+ earned_cash = real_lemmas.filter(status__type__sym_name='ready').aggregate(Sum('cash'))['cash__sum']
+ if earned_cash == None:
+ earned_cash = 0.0
+
+ lemmas_to_erase_cash = 0.0
+ lemmas_marked_to_erase = Lemma.objects.filter(owner=user,
+ old=False,
+ status__type__sym_name='erase',
+ entry_obj__pos__tag=pos)
+
+ payments_file.write(u'Zaznaczone do usunięcia:\n')
+ for lemma in lemmas_marked_to_erase:
+ erase_date = lemma.status_history.order_by('-date')[0].date
+ if erase_date >= STARTDATE and erase_date <= ENDDATE:
+ payments_file.write(u'%s\t%.2f\t%s\n' % (lemma.entry_obj.name,
+ 1.0,
+ erase_date.strftime('%Y%m%d')))
+ lemmas_to_erase_cash += 1.0
+ earned_cash += lemmas_to_erase_cash
+
+ bonus_cash = real_lemmas.filter(bonus=True).aggregate(Sum('cash'))['cash__sum']
+ if bonus_cash == None:
+ bonus_cash = 0.0
+ earned_cash += bonus_cash
+
+ payments_file.write(u'\n\nWykonane:\n')
+ done_lemmas = real_lemmas.filter(bonus=False,
+ status__type__sym_name='ready').order_by('date')
+ for done_lemma in done_lemmas:
+ cash = done_lemma.cash
+ try:
+ bonus = real_lemmas.get(bonus=True, lemma__entry_obj=done_lemma.lemma.entry_obj).cash
+ cash += bonus
+ except RealizedLemma.DoesNotExist:
+ pass
+ payments_file.write(u'%s\t%.2f\t%s\n' % (done_lemma.lemma.entry_obj.name,
+ cash,
+ done_lemma.date.strftime('%Y%m%d')))
+
+ lex_work_stats = {'earned_cash': round(earned_cash, 2),
+ 'bonus_cash': round(bonus_cash, 2),
+ 'lemmas_to_erase_cash': round(lemmas_to_erase_cash, 2)}
+ return lex_work_stats
diff --git a/dictionary/management/commands/create_TEI_walenty.py b/dictionary/management/commands/create_TEI_walenty.py
index 3b89749..a6fe739 100644
--- a/dictionary/management/commands/create_TEI_walenty.py
+++ b/dictionary/management/commands/create_TEI_walenty.py
@@ -5,38 +5,56 @@ import os
import tarfile
from django.core.management.base import BaseCommand
+from optparse import make_option
from dictionary.models import Lemma, Frame_Opinion_Value, \
- get_ready_statuses
+ get_statuses
from dictionary.teixml import createteixml, write_phrase_types_expansions_in_TEI
from settings import WALENTY_PATH
class Command(BaseCommand):
args = '<dict dict ...>'
help = 'Get Walenty in TEI format.'
+ option_list = BaseCommand.option_list + (
+ make_option('--min_status',
+ action='store',
+ type='string',
+ dest='min_status_type',
+ default='ready',
+ help='Minimum lemma status.'),
+ make_option('--start_date',
+ action='store',
+ type='string',
+ dest='start_date',
+ default='all',
+ help='Status change start date (format: YYYY-MM-DD).'),
+
+ )
def handle(self, *args, **options):
try:
now = datetime.datetime.now().strftime('%Y%m%d')
-
vocab_names = list(args)
vocab_names.sort()
- if vocab_names:
- filename_base = '%s_%s_%s' % ('walenty', '+'.join(vocab_names), now)
- else:
- filename_base = '%s_%s' % ('walenty', now)
+
+ filename_base = self.create_filename_base(vocab_names, options, now)
base_path = os.path.join(WALENTY_PATH, filename_base)
outpath = base_path + '.xml'
- ready_statuses = get_ready_statuses()
+ statuses = get_statuses(options['min_status_type'])
lemmas = Lemma.objects.filter(old=False)
if vocab_names:
lemmas = lemmas.filter(vocabulary__name__in=vocab_names)
- ready_lemmas = lemmas.filter(status__in=ready_statuses).order_by('entry_obj__name')
+ lemmas = lemmas.filter(status__in=statuses)
+ if options['start_date'] != 'all':
+ lemmas = self.filter_lemmas_by_status_change(lemmas, statuses, options['start_date'])
+ lemmas = lemmas.order_by('entry_obj__name')
+
+ self.print_statistics(lemmas)
frame_opinion_values = Frame_Opinion_Value.objects.all()
- createteixml(outpath, ready_lemmas, frame_opinion_values)
+ createteixml(outpath, lemmas, frame_opinion_values)
archive = tarfile.open(base_path + '-TEI.tar.gz', 'w:gz')
phrase_types_expand_path = os.path.join(WALENTY_PATH,
@@ -50,3 +68,49 @@ class Command(BaseCommand):
archive.close()
os.remove(outpath)
os.remove(phrase_types_expand_path)
+
+ def create_filename_base(self, vocab_names, options, now):
+ start_date = ''
+ if options['start_date'] != 'all':
+ start_date = '-' + options['start_date'].replace('-', '')
+
+ vocab_names_str = ''
+ if vocab_names:
+ vocab_names_str = '-' + '+'.join(vocab_names)
+
+ min_status = ''
+ if options['min_status_type'] != 'ready':
+ min_status = '-' + options['min_status_type']
+
+ filename_base = 'walenty%s%s%s_%s' % (min_status, vocab_names_str,
+ start_date, now)
+ return filename_base
+
+
+ def filter_lemmas_by_status_change(self, lemmas, statuses, start_date_str):
+ start_date = self.parse_date(start_date_str)
+ filtered_lemmas_pks = []
+ for lemma in lemmas:
+ if lemma.status_history.filter(status=statuses[0], date__gte=start_date).exists():
+ filtered_lemmas_pks.append(lemma.pk)
+ return lemmas.filter(pk__in=filtered_lemmas_pks)
+
+ def parse_date(self, date_str):
+ date_parts = date_str.split('-')
+ year = int(date_parts[0])
+ month = int(date_parts[1].lstrip('0'))
+ day = int(date_parts[2].lstrip('0'))
+ date = datetime.datetime(year, month, day, 00, 00)
+ return date
+
+ def print_statistics(self, lemmas):
+ count = {'frames': 0,
+ 'arguments': 0}
+ for lemma in lemmas:
+ frames = lemma.entry_obj.actual_frames()
+ count['frames'] += frames.count()
+ for frame in frames.all():
+ count['arguments'] += frame.complements.count()
+ print (u'Lemmas:\t%d' % lemmas.count())
+ print (u'Frames:\t%d' % count['frames'])
+ print (u'Arguments:\t%d' % count['arguments'])
diff --git a/dictionary/models.py b/dictionary/models.py
index d187207..1ea63bf 100644
--- a/dictionary/models.py
+++ b/dictionary/models.py
@@ -100,6 +100,10 @@ def get_checked_statuses():
def get_ready_statuses():
ready_type = LemmaStatusType.objects.get(sym_name='ready')
return Lemma_Status.objects.filter(type__priority__gte=ready_type.priority).distinct()
+
+def get_statuses(min_status_type):
+ min_type = LemmaStatusType.objects.get(sym_name=min_status_type)
+ return Lemma_Status.objects.filter(type__priority__gte=min_type.priority).distinct()
class LemmaStatusType(Model):