def normalized_and_story_count(q, fq, open_q):
results = {}
mc_api_key = base_cache.api_key()
results['total'] = _cached_total_story_count(mc_api_key, q, fq)['count']
results['normalized_total'] = _cached_total_story_count(
mc_api_key, open_q, fq)['count']
return results
def normalized_and_story_split_count(q, fq, open_q):
results = {}
counts = []
mc_api_key = base_cache.api_key()
data = cached_story_split_count(mc_api_key, q, fq)
all_stories = cached_story_split_count(mc_api_key, open_q, fq)
for day in all_stories['counts']:
day_info = {
'date': trimSolrDate(day['date']),
'total_count': day['count']
}
matching = [d for d in data['counts'] if d['date'] == day['date']]
if len(matching) == 0:
day_info['count'] = 0
else:
day_info['count'] = matching[0]['count']
if day_info['count'] == 0 or day['count'] == 0:
day_info['ratio'] = 0
else:
day_info['ratio'] = float(day_info['count']) / float(day['count'])
counts.append(day_info)
results['counts'] = sorted(counts, key=itemgetter('date'))
results['total'] = sum([day['count'] for day in data['counts']])
results['normalized_total'] = sum([day['count'] for day in all_stories['counts']])
return results
def tag_set_coverage(total_q, subset_q, fq):
api_key = base_cache.api_key()
coverage = {
'totals': _cached_total_story_count(api_key, total_q, fq)['count'],
'counts': _cached_total_story_count(api_key, subset_q, fq)['count'],
}
coverage['coverage_percentage'] = 0 if coverage['totals'] is 0 else float(coverage['counts'])/float(coverage['totals'])
return coverage
def story_list_page(q,
fq,
last_processed_stories_id=None,
stories_per_page=1000,
sort=mc.SORT_PROCESSED_STORIES_ID):
return _cached_story_list_page(base_cache.api_key(), q, fq,
last_processed_stories_id, stories_per_page,
sort)
def _most_used_tags(q, fq, tag_sets_id):
# top tags used in stories matching query (pass in None for no limit)
api_key = base_cache.api_key()
tags = _cached_most_used_tags(api_key, q, fq, tag_sets_id, 1000)
# extract bogus NYT tags
for t in tags:
if is_bad_theme(t['tags_id']):
tags.remove(t)
return tags
def tag_set_coverage(total_q, subset_q, fq):
api_key = base_cache.api_key()
coverage = {
'totals': _cached_total_story_count(api_key, total_q, fq)['count'],
'counts': _cached_total_story_count(api_key, subset_q, fq)['count'],
}
coverage['coverage_percentage'] = 0 if coverage['totals'] is 0 else float(
coverage['counts']) / float(coverage['totals'])
return coverage
def _most_used_tags(q, fq, tag_sets_id):
# top tags used in stories matching query (pass in None for no limit)
api_key = base_cache.api_key()
tags = _cached_most_used_tags(api_key, q, fq, tag_sets_id,
TAG_COUNT_SAMPLE_SIZE)
# extract bogus NYT tags
for t in tags:
if is_bad_theme(t['tags_id']):
tags.remove(t)
return tags
def normalized_and_story_split_count(q, open_q, start_date, end_date):
results = {}
fq = dates_as_filter_query(start_date.strftime("%Y-%m-%d"),
end_date.strftime("%Y-%m-%d"))
mc_api_key = base_cache.api_key()
matching = cached_story_split_count(mc_api_key, q, fq)
matching = add_missing_dates_to_split_story_counts(matching['counts'],
start_date, end_date)
total = cached_story_split_count(mc_api_key, open_q, fq)
total = add_missing_dates_to_split_story_counts(total['counts'],
start_date, end_date)
results['counts'] = combined_split_and_normalized_counts(matching, total)
results['total'] = sum([day['count'] for day in matching])
results['normalized_total'] = sum([day['count'] for day in total])
return results
def tag_set(tag_sets_id):
return _cached_tag_set(base_cache.api_key(), tag_sets_id)
def word_count(q, fq, ngram_size, num_words, sample_size):
api_key = base_cache.api_key()
return _cached_word_count(api_key, q, fq, ngram_size, num_words, sample_size)
def story_list_page(q, fq, last_processed_stories_id=None, stories_per_page=1000, sort=mc.SORT_PROCESSED_STORIES_ID):
return _cached_story_list_page(base_cache.api_key(), q, fq, last_processed_stories_id, stories_per_page, sort)
def normalized_and_story_count(q, fq, open_q):
results = {}
mc_api_key = base_cache.api_key()
results['total'] = _cached_total_story_count(mc_api_key, q, fq)['count']
results['normalized_total'] = _cached_total_story_count(mc_api_key, open_q, fq)['count']
return results
def story_count(q, fq):
api_key = base_cache.api_key()
return _cached_total_story_count(api_key, q, fq)
def tag_set(tag_sets_id):
return _cached_tag_set(base_cache.api_key(), tag_sets_id)
def word_count(q, fq, ngram_size, num_words, sample_size):
api_key = base_cache.api_key()
return _cached_word_count(api_key, q, fq, ngram_size, num_words,
sample_size)
def story_count(q, fq):
api_key = base_cache.api_key()
return _cached_total_story_count(api_key, q, fq)