def topic_compare_subtopic_top_words(topics_id):
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
selected_focal_sets_id = request.args['focal_sets_id']
word_count = request.args['word_count'] if 'word_count' in request.args else 20
# first we need to figure out which timespan they are working on
selected_snapshot_timespans = apicache.cached_topic_timespan_list(user_mediacloud_key(), topics_id,
snapshots_id=snapshots_id)
selected_timespan = None
for t in selected_snapshot_timespans:
if t['timespans_id'] == int(timespans_id):
selected_timespan = t
try:
focal_set = apicache.topic_focal_set(user_mediacloud_key(), topics_id, snapshots_id, selected_focal_sets_id)
except ValueError:
return json_error_response('Invalid Focal Set Id')
timespans = apicache.matching_timespans_in_foci(topics_id, selected_timespan, focal_set['foci'])
for idx in range(0, len(timespans)):
data = apicache.topic_word_counts(user_mediacloud_key(), topics_id,
timespans_id=timespans[idx]['timespans_id'])
focal_set['foci'][idx]['top_words'] = data
# stitch together the counts to download now
data = []
headers = [f['name'] for f in focal_set['foci']]
for idx in range(0, word_count):
row = {f['name']: "{} ({})".format(f['top_words'][idx]['term'], f['top_words'][idx]['count'])
for f in focal_set['foci']}
data.append(row)
return csv.stream_response(data, headers,
'topic-{}-subtopic-{}-{}-top-words-comparison'.format(
topics_id, focal_set['name'], selected_focal_sets_id))
def collection_update(collection_id):
user_mc = user_admin_mediacloud_client()
label = '{}'.format(request.form['name'])
description = request.form['description']
static = request.form['static'] if 'static' in request.form else None
show_on_stories = request.form['showOnStories'] if 'showOnStories' in request.form else None
show_on_media = request.form['showOnMedia'] if 'showOnMedia' in request.form else None
formatted_name = format_name_from_label(label)
source_ids = []
if len(request.form['sources[]']) > 0:
source_ids = [int(sid) for sid in request.form['sources[]'].split(',')]
# first update the collection
updated_collection = user_mc.updateTag(collection_id, formatted_name, label, description,
is_static=(static == 'true'),
show_on_stories=(show_on_stories == 'true'),
show_on_media=(show_on_media == 'true'))
# get the sources in the collection first, then remove and add as needed
existing_source_ids = [int(m['media_id']) for m in media_with_tag(user_mediacloud_key(), collection_id)]
source_ids_to_remove = list(set(existing_source_ids) - set(source_ids))
source_ids_to_add = [sid for sid in source_ids if sid not in existing_source_ids]
# logger.debug(existing_source_ids)
# logger.debug(source_ids_to_add)
# logger.debug(source_ids_to_remove)
# then go through and tag all the sources specified with the new collection id
tags_to_add = [MediaTag(sid, tags_id=collection_id, action=TAG_ACTION_ADD) for sid in source_ids_to_add]
tags_to_remove = [MediaTag(sid, tags_id=collection_id, action=TAG_ACTION_REMOVE) for sid in source_ids_to_remove]
tags = tags_to_add + tags_to_remove
if len(tags) > 0:
user_mc.tagMedia(tags)
apicache.invalidate_collection_source_representation_cache(user_mediacloud_key(), collection_id)
return jsonify(updated_collection['tag'])
def get_top_themes_by_story_tag_counts(topics_id, num_themes):
user_mc_key = user_mediacloud_key()
nyt_counts = []
#get overall timespan
timespans = cached_topic_timespan_list(user_mediacloud_key(), topics_id)
overall_timespan = [t for t in timespans if t['period'] == "overall"]
overall_timespan = next(iter(overall_timespan))
timespan_query = "timespans_id:{}".format(overall_timespan['timespans_id'])
# get the top themes by the story counts iwth overall timespan
top_nyt_tags = _cached_topic_tag_counts(user_mediacloud_key(), topics_id, NYT_LABELS_TAG_SET_ID,
TAG_COUNT_SAMPLE_SIZE, timespan_query)
# get the total stories for a topic
total_stories = topic_story_count(user_mediacloud_key(), topics_id)['count']
top_nyt_tags = top_nyt_tags[:num_themes]
# for each country, set up the requisite info for UI
for tag in top_nyt_tags:
nyt_counts.append({
'label': tag['label'],
'geo_tag': tag['tag'],
'tags_id': tag['tags_id'],
'count': tag['count'],
'pct': float(tag['count']) / float(total_stories), #story_tag_count / total story per topic count
})
return nyt_counts
def topic_words(topics_id):
sample_size = request.args['sample_size'] if 'sample_size' in request.args else WORD_COUNT_SAMPLE_SIZE
if access_public_topic(topics_id):
results = apicache.topic_word_counts(TOOL_API_KEY, topics_id, sample_size=sample_size,
snapshots_id=None, timespans_id=None, foci_id=None, q=None)
elif is_user_logged_in():
# grab the top words, respecting all the filters
results = apicache.topic_word_counts(user_mediacloud_key(), topics_id, sample_size=sample_size)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
totals = [] # important so that these get reset on the client when they aren't requested
logger.debug(request.args)
if (is_user_logged_in()) and ('withTotals' in request.args) and (request.args['withTotals'] == "true"):
# return along with the results for the overall timespan, to facilitate comparison
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
overall_timespan = _find_overall_timespan(topics_id, snapshots_id)
totals = apicache.topic_word_counts(user_mediacloud_key(), topics_id, sample_size=sample_size,
timespans_id=overall_timespan['timespans_id'], foci_id=None, q=None)
response = {
'list': results[:WORD_COUNT_UI_NUM_WORDS],
'totals': totals[:WORD_COUNT_UI_NUM_WORDS],
'sample_size': str(sample_size)
}
return jsonify(response)
def get_top_countries_by_story_tag_counts(topics_id, num_countries):
tag_country_counts = []
# get the total stories for a topic
total_stories = topic_story_count(user_mediacloud_key(), topics_id)['count']
# get the top countries by the story tag counts iwth overall timespan
timespans = cached_topic_timespan_list(user_mediacloud_key(), topics_id)
overall_timespan = [t for t in timespans if t['period'] == "overall"]
overall_timespan = next(iter(overall_timespan))
timespan_query = "timespans_id:{}".format(overall_timespan['timespans_id'])
top_geo_tags = _cached_topic_tag_counts(user_mediacloud_key(), topics_id, GEO_TAG_SET, GEO_SAMPLE_SIZE, timespan_query)
# make sure the geo tag is in the geo_tags whitelist (is a country)
country_tag_counts = [r for r in top_geo_tags if
int(r['tag'].split('_')[1]) in list(COUNTRY_GEONAMES_ID_TO_APLHA3.keys())]
country_tag_counts = country_tag_counts[:num_countries]
# for each country, set up the requisite info for UI
for tag in country_tag_counts:
tag_country_counts.append({
'label': tag['label'],
'geo_tag': tag['tag'],
'tags_id': tag['tags_id'],
'count': tag['count'],
'pct': float(tag['count']) / float(total_stories), # story_tag_count / total story per topic count
})
return tag_country_counts
def media_type_coverage(topics_id):
media_type_tags = tags_in_tag_set(TOOL_API_KEY, TAG_SETS_ID_MEDIA_TYPE)
# grab the total stories
total_stories = topic_story_count(user_mediacloud_key(), topics_id)['count']
# count the stories in any media in tagged as media_type
tags_ids = " ".join(str(tag['tags_id']) for tag in media_type_tags)
query_clause = "tags_id_media:({})".format(tags_ids)
tagged_story_count = topic_story_count(user_mediacloud_key(), topics_id, q=query_clause)['count']
return jsonify({'counts': {'count': tagged_story_count, 'total': total_stories}})
def _public_safe_topic_story_count(topics_id, q):
if access_public_topic(topics_id):
total = apicache.topic_story_count(TOOL_API_KEY, topics_id, q=apicache.add_to_user_query(None))
# force a count with just the query
matching = apicache.topic_story_count(TOOL_API_KEY, topics_id, q=apicache.add_to_user_query(q))
elif is_user_logged_in():
total = apicache.topic_story_count(user_mediacloud_key(), topics_id, q=apicache.add_to_user_query(None))
# force a count with just the query
matching = apicache.topic_story_count(user_mediacloud_key(), topics_id, q=apicache.add_to_user_query(q))
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
return jsonify({'counts': {'count': matching['count'], 'total': total['count']}})
def api_collection_details(collection_id):
add_in_sources = False
if ('getSources' in request.args) and (request.args['getSources'] == 'true'):
add_in_sources = True
user_mc = user_mediacloud_client()
info = user_mc.tag(collection_id)
add_user_favorite_flag_to_collections([info])
info['id'] = collection_id
info['tag_set'] = _tag_set_info(user_mediacloud_key(), info['tag_sets_id'])
if add_in_sources:
media_in_collection = media_with_tag(user_mediacloud_key(), collection_id)
info['sources'] = media_in_collection
analytics_db.increment_count(analytics_db.TYPE_COLLECTION, collection_id, analytics_db.ACTION_SOURCE_MGR_VIEW)
return jsonify({'results': info})
def api_collection_source_representation_csv(collection_id):
user_mc = user_mediacloud_client()
info = user_mc.tag(collection_id)
source_representation = apicache.collection_source_representation(user_mediacloud_key(), collection_id)
props = ['media_id', 'media_name', 'media_url', 'stories', 'sample_size', 'story_pct']
filename = info['label'] + "-source sentence counts.csv"
return csv.stream_response(source_representation, props, filename)
def collection_wordcount_csv(collection_id):
solr_q = 'tags_id_media:' + str(collection_id)
solr_fq = None
# add in the publish_date clause if there is one
if ('q' in request.args) and (len(request.args['q']) > 0):
solr_fq = request.args['q']
return stream_wordcount_csv(user_mediacloud_key(), 'wordcounts-Collection-' + collection_id, solr_q, solr_fq)
def api_collection_sources_csv(collection_id):
user_mc = user_mediacloud_client()
collection = user_mc.tag(collection_id) # not cached because props can change often
all_media = media_with_tag(user_mediacloud_key(), collection_id)
file_prefix = "Collection {} ({}) - sources ".format(collection_id, collection['tag'])
properties_to_include = SOURCE_LIST_CSV_EDIT_PROPS
return csv.download_media_csv(all_media, file_prefix, properties_to_include)
def api_metadata_values(tag_sets_id):
'''
Source metadata is encoded in various tag sets - this returns the set and the list of
available tags you can use
'''
data = tags_in_tag_set(user_mediacloud_key(), tag_sets_id, False, True) # use the file-based cache here
return jsonify(data)
def _find_overall_timespan(topics_id, snapshots_id):
selected_snapshot_timespans = apicache.cached_topic_timespan_list(user_mediacloud_key(), topics_id,
snapshots_id=snapshots_id)
for timespan in selected_snapshot_timespans:
if timespan['period'] == 'overall':
return timespan
raise RuntimeError('Missing overall timespan in snapshot {} (topic {})!'.format(snapshots_id, topics_id))
def media(topics_id, media_id):
user_mc = user_admin_mediacloud_client()
combined_media_info = apicache.topic_media_list(user_mediacloud_key(), topics_id, media_id=media_id)['media'][0]
media_info = user_mc.media(media_id)
for key in list(media_info.keys()):
if key not in list(combined_media_info.keys()):
combined_media_info[key] = media_info[key]
return jsonify(combined_media_info)
def media_words_csv(topics_id, media_id):
query = apicache.add_to_user_query('media_id:'+media_id)
ngram_size = request.args['ngram_size'] if 'ngram_size' in request.args else 1 # default to word count
word_counts = apicache.topic_ngram_counts(user_mediacloud_key(), topics_id, ngram_size=ngram_size, q=query,
num_words=WORD_COUNT_DOWNLOAD_NUM_WORDS,
sample_size=WORD_COUNT_DOWNLOAD_SAMPLE_SIZE)
return csv.stream_response(word_counts, apicache.WORD_COUNT_DOWNLOAD_COLUMNS,
'topic-{}-media-{}-sampled-ngrams-{}-word'.format(topics_id, media_id, ngram_size))
def media_type_story_counts(topics_id):
tag_story_counts = []
media_type_tags = tags_in_tag_set(TOOL_API_KEY, TAG_SETS_ID_MEDIA_TYPE)
# grab the total stories
total_stories = topic_story_count(user_mediacloud_key(), topics_id)['count']
# make a count for each tag based on media_id
for tag in media_type_tags:
query_clause = "tags_id_media:{}".format(tag['tags_id'])
tagged_story_count = topic_story_count(user_mediacloud_key(), topics_id, q=query_clause)['count']
tag_story_counts.append({
'label': tag['label'],
'tags_id': tag['tags_id'],
'count': tagged_story_count,
'pct': float(tagged_story_count)/float(total_stories)
})
return jsonify({'story_counts': tag_story_counts})
def _collection_source_story_split_historical_counts(collection_id):
media_list = media_with_tag(user_mediacloud_key(), collection_id)
jobs = [{'media': m} for m in media_list]
# fetch in parallel to make things faster
pool = Pool(processes=HISTORICAL_COUNT_POOL_SIZE)
results = pool.map(_source_story_split_count_worker, jobs) # blocks until they are all done
pool.terminate() # extra safe garbage collection
return results
def topic_media(topics_id):
if access_public_topic(topics_id):
media_list = topic_media_list(TOOL_API_KEY, topics_id, snapshots_id=None, timespans_id=None, foci_id=None, sort=None, limit=None, link_id=None)
elif is_user_logged_in():
media_list = topic_media_list(user_mediacloud_key(), topics_id)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
return jsonify(media_list)
def cached_media_tags(tag_sets_id):
partisanship_tags = cached_tags_in_tag_set(tag_sets_id)
for tag in partisanship_tags:
media = media_with_tag(user_mediacloud_key(), tag['tags_id'], True) # cache this list
media_ids = [str(m['media_id']) for m in media] # as strs so we can concat into a query str later with .join call
tag['media'] = media
tag['media_ids'] = media_ids
tag['media_query'] = "media_id:({})".format(" ".join(media_ids))
return partisanship_tags
def _find_overall_timespan(topics_id, snapshots_id):
selected_snapshot_timespans = apicache.cached_topic_timespan_list(
user_mediacloud_key(), topics_id, snapshots_id=snapshots_id)
for timespan in selected_snapshot_timespans:
if timespan['period'] == 'overall':
return timespan
raise RuntimeError(
'Missing overall timespan in snapshot {} (topic {})!'.format(
snapshots_id, topics_id))
def media_source_words(media_id):
solr_q = 'media_id:'+str(media_id)
solr_fq = None
if ('q' in request.args) and (len(request.args['q']) > 0):
solr_fq = request.args['q']
info = {
'wordcounts': word_count(user_mediacloud_key(), solr_q, solr_fq)
}
return jsonify({'results': info})
def remove_sources_from_collection(collection_id):
source_ids_to_remove = request.form['sources[]'].split(',')
source_ids_to_remove = [int(s) for s in source_ids_to_remove]
user_mc = user_admin_mediacloud_client()
# get the sources in the collection first, then remove and add as needed
existing_source_ids = [int(m['media_id']) for m in media_with_tag(user_mediacloud_key(), collection_id)]
source_ids_to_remain = list(set(existing_source_ids) - set(source_ids_to_remove))
media_to_remove = [MediaTag(sid, tags_id=collection_id, action=TAG_ACTION_REMOVE) for sid in source_ids_to_remove]
media_to_remain = [MediaTag(sid, tags_id=collection_id, action=TAG_ACTION_ADD) for sid in
source_ids_to_remain] # do I need to run similar or TAG_ACTION_REMOVE?
current_media = media_to_remove + media_to_remain
if len(current_media) > 0:
results = user_mc.tagMedia(current_media)
apicache.invalidate_collection_source_representation_cache(user_mediacloud_key(), collection_id)
return jsonify(results)
def topic_split_story_count(topics_id):
if access_public_topic(topics_id):
results = apicache.topic_split_story_counts(TOOL_API_KEY, topics_id, snapshots_id=None, timespans_id=None, foci_id=None,q=None)
elif is_user_logged_in():
results = apicache.topic_split_story_counts(user_mediacloud_key(), topics_id)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
return jsonify({'results': results})
def topic_split_story_count(topics_id):
if access_public_topic(topics_id):
results = apicache.topic_split_story_counts(TOOL_API_KEY, topics_id, snapshots_id=None, timespans_id=None, foci_id=None,q=None)
elif is_user_logged_in():
results = apicache.topic_split_story_counts(user_mediacloud_key(), topics_id)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
return jsonify({'results': results})
def media(topics_id, media_id):
user_mc = user_admin_mediacloud_client()
combined_media_info = apicache.topic_media_list(
user_mediacloud_key(), topics_id, media_id=media_id)['media'][0]
media_info = user_mc.media(media_id)
for key in media_info.keys():
if key not in combined_media_info.keys():
combined_media_info[key] = media_info[key]
return jsonify(combined_media_info)
def api_metadata_values(tag_sets_id):
'''
Source metadata is encoded in various tag sets - this returns the set and the list of
available tags you can use
'''
data = tags_in_tag_set(user_mediacloud_key(), tag_sets_id, False,
True) # use the file-based cache here
data['short_list'] = get_metadata_defaults(tag_sets_id)
return jsonify(data)
def _collection_source_story_split_historical_counts(collection_id):
media_list = media_with_tag(user_mediacloud_key(), collection_id)
jobs = [{'media': m} for m in media_list]
# fetch in parallel to make things faster
pool = Pool(processes=HISTORICAL_COUNT_POOL_SIZE)
results = pool.map(_source_story_split_count_worker,
jobs) # blocks until they are all done
pool.terminate() # extra safe garbage collection
return results
def api_collection_sources(collection_id):
int(collection_id)
results = {
'tags_id': collection_id
}
media_in_collection = media_with_tag(user_mediacloud_key(), collection_id)
add_user_favorite_flag_to_sources(media_in_collection)
results['sources'] = media_in_collection
return jsonify(results)
def _cached_tag_coverage_pct(query, tag_sets_id):
user_mc = user_mediacloud_client()
story_count = source_story_count(user_mediacloud_key(), query)
tagged_story_counts = user_mc.storyTagCount(solr_query=query, tag_sets_id=tag_sets_id)
# sum tagged articles because there are different versions
tagged_sum = sum([tag_info['count'] for tag_info in tagged_story_counts])
# compute coverage ratio (protect against div by zero)
ratio = float(tagged_sum) / float(story_count) if story_count > 0 else 0
return ratio
def collection_wordcount_csv(collection_id):
solr_q = 'tags_id_media:' + str(collection_id)
solr_fq = None
# add in the publish_date clause if there is one
if ('q' in request.args) and (len(request.args['q']) > 0):
solr_fq = request.args['q']
return stream_wordcount_csv(user_mediacloud_key(),
'wordcounts-Collection-' + collection_id,
solr_q, solr_fq)
def media_stories(topics_id, media_id):
sort = validated_sort(request.args.get('sort'))
limit = request.args.get('limit')
stories = apicache.topic_story_list(user_mediacloud_key(),
topics_id,
media_id=media_id,
sort=sort,
limit=limit)
return jsonify(stories)
def topic_timespan_list(topics_id, snapshots_id):
ignored_snapshots_id, timespans_id, foci_id, q = filters_from_args(
request.args)
timespans = apicache.cached_topic_timespan_list(user_mediacloud_key(),
topics_id, snapshots_id,
foci_id)
# add the focal_set type to the timespan so we can use that in the client (ie. decide what to show or not
# based on what type of focal_set this timespan is part of)
focal_sets = apicache.topic_focal_sets_list(user_mediacloud_key(),
topics_id, snapshots_id)
for t in timespans:
for fs in focal_sets:
for f in fs['foci']:
if f['foci_id'] == t['foci_id']:
t['focal_set'] = fs
t['focus'] = f
break
return jsonify({'list': timespans})
def api_collection_set(tag_sets_id):
'''
Return a list of all the (public only or public and private, depending on user role) collections in a tag set. Not cached because this can change, and load time isn't terrible.
:param tag_sets_id: the tag set to query for public collections
:return: dict of info and list of collections in
'''
if user_has_auth_role(ROLE_MEDIA_EDIT):
info = apicache.tag_set_with_private_collections(
user_mediacloud_key(), tag_sets_id)
else:
info = apicache.tag_set_with_public_collections(
user_mediacloud_key(), tag_sets_id)
add_user_favorite_flag_to_collections(info['tags'])
# rename to make more sense here
info['collections'] = info['tags']
del info['tags']
return jsonify(info)
def topic_words(topics_id):
sample_size = request.args[
'sample_size'] if 'sample_size' in request.args else WORD_COUNT_SAMPLE_SIZE
if access_public_topic(topics_id):
results = apicache.topic_word_counts(TOOL_API_KEY,
topics_id,
sample_size=sample_size,
snapshots_id=None,
timespans_id=None,
foci_id=None,
q=None)
elif is_user_logged_in():
# grab the top words, respecting all the filters
results = apicache.topic_word_counts(user_mediacloud_key(),
topics_id,
sample_size=sample_size)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
totals = [
] # important so that these get reset on the client when they aren't requested
logger.debug(request.args)
if (is_user_logged_in()) and ('withTotals' in request.args) and (
request.args['withTotals'] == "true"):
# return along with the results for the overall timespan, to facilitate comparison
snapshots_id, timespans_id, foci_id, q = filters_from_args(
request.args)
overall_timespan = _find_overall_timespan(topics_id, snapshots_id)
totals = apicache.topic_word_counts(
user_mediacloud_key(),
topics_id,
sample_size=sample_size,
timespans_id=overall_timespan['timespans_id'],
foci_id=None,
q=None)
response = {
'list': results[:WORD_COUNT_UI_NUM_WORDS],
'totals': totals[:WORD_COUNT_UI_NUM_WORDS],
'sample_size': str(sample_size)
}
return jsonify(response)
def collection_source_split_stories(collection_id):
collections_query = "tags_id_media:{}".format(collection_id)
exclude_spidered_stories = " tags_id_media:{} AND NOT tags_id_stories:{}".format(str(collection_id), 8875452)\
if 'separate_spidered' in request.args else collections_query
interval = 'day' # default, and not currently passed to the calls above
all_results = apicache.last_year_split_story_count(user_mediacloud_key(), collections_query)
# same if request.args doesn't ask to exclude_spidered
non_spidered_results = apicache.last_year_split_story_count(user_mediacloud_key(), exclude_spidered_stories)
all_stories = {
'total_story_count': all_results['total_story_count'],
'list': all_results['counts'],
}
partial_stories = {
'total_story_count': non_spidered_results['total_story_count'],
'list': non_spidered_results['counts'],
}
return jsonify({'results': {'all_stories': all_stories, 'partial_stories': partial_stories, 'interval': interval}})
def api_collection_set(tag_sets_id):
"""
Return a list of all the (public only or public and private, depending on user role) collections in a tag set.
Not cached because this can change, and load time isn't terrible.
:param tag_sets_id: the tag set to query for public collections
:return: dict of info and list of collections in
"""
if user_has_auth_role(ROLE_MEDIA_EDIT):
info = apicache.tag_set_with_private_collections(user_mediacloud_key(), tag_sets_id)
else:
info = apicache.tag_set_with_public_collections(user_mediacloud_key(), tag_sets_id)
add_user_favorite_flag_to_collections(info['tags'])
# rename to make more sense here
for t in info['tags']:
t['sort_key'] = t['label'] if t['label'] else t['tag']
info['collections'] = sorted(info['tags'], key=itemgetter('sort_key'))
del info['tags']
return jsonify(info)
def api_collections_by_ids():
collection_ids = request.args['coll[]'].split(',')
sources_list = []
for tags_id in collection_ids:
all_media = media_with_tag(user_mediacloud_key(), tags_id)
info = [{'media_id': m['media_id'], 'name': m['name'], 'url': m['url'], 'public_notes': m['public_notes']} for m
in all_media]
add_user_favorite_flag_to_sources(info)
sources_list += info
return jsonify({'results': sources_list})
def topic_stories(topics_id):
local_mc = None
if access_public_topic(topics_id):
stories = topic_story_list(TOOL_API_KEY, topics_id, snapshots_id=None, timespans_id=None, foci_id=None, q=None)
elif is_user_logged_in():
stories = topic_story_list(user_mediacloud_key(), topics_id)
else:
return jsonify({'status':'Error', 'message': 'Invalid attempt'})
return jsonify(stories)
def topic_stories_csv(topics_id):
as_attachment = True
fb_data = False
if ('attach' in request.args):
as_attachment = request.args['attach'] == 1
if ('fbData' in request.args):
fb_data = int(request.args['fbData']) == 1
user_mc = user_admin_mediacloud_client()
topic = user_mc.topic(topics_id)
return stream_story_list_csv(user_mediacloud_key(), topic['name']+'-stories', topics_id, as_attachment=as_attachment, fb_data=fb_data)
def _cached_tag_coverage_pct(query, tag_sets_id):
user_mc = user_mediacloud_client()
story_count = source_story_count(user_mediacloud_key(), query)
tagged_story_counts = user_mc.storyTagCount(solr_query=query,
tag_sets_id=tag_sets_id)
# sum tagged articles because there are different versions
tagged_sum = sum([tag_info['count'] for tag_info in tagged_story_counts])
# compute coverage ratio (protect against div by zero)
ratio = float(tagged_sum) / float(story_count) if story_count > 0 else 0
return ratio
def retweet_partisanship_story_counts(topics_id):
# TODO: add in overall timespan id here so it works in different snapshots
tag_story_counts = []
partisanship_tags = _cached_media_tags(
TAG_SETS_ID_RETWEET_PARTISANSHIP_2016)
# grab the total stories
try:
total_stories = topic_story_count(user_mediacloud_key(),
topics_id)['count']
except mediacloud.error.MCException:
total_stories = 0
# make a count for each tag
for tag in partisanship_tags:
try:
tagged_story_count = topic_story_count(user_mediacloud_key(),
topics_id,
q=tag['query'])['count']
pct = float(tagged_story_count) / float(total_stories)
except ZeroDivisionError:
tagged_story_count = 0
pct = 0
except mediacloud.error.MCException:
tagged_story_count = 0
pct = 0
tag_story_counts.append({
'label': tag['label'],
'tags_id': tag['tags_id'],
'count': tagged_story_count,
'pct': pct
})
# order them in the way a person would expect ( left to center to right)
ordered_tag_story_counts = list()
ordered_tag_story_counts.append(
[t for t in tag_story_counts if t['tags_id'] == 9360520][0])
ordered_tag_story_counts.append(
[t for t in tag_story_counts if t['tags_id'] == 9360521][0])
ordered_tag_story_counts.append(
[t for t in tag_story_counts if t['tags_id'] == 9360522][0])
ordered_tag_story_counts.append(
[t for t in tag_story_counts if t['tags_id'] == 9360523][0])
ordered_tag_story_counts.append(
[t for t in tag_story_counts if t['tags_id'] == 9360524][0])
return jsonify({'story_counts': ordered_tag_story_counts})
def base_snapshot_timespan(topics_id):
# find the timespan matching this one in the base snapshot (ie. with no foci_id)
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
base_snapshot_timespans = apicache.cached_topic_timespan_list(user_mediacloud_key(), topics_id,
snapshots_id=snapshots_id, foci_id=None)
timespan = apicache.topic_timespan(topics_id, snapshots_id, foci_id, timespans_id) # the selected timespan
for t in base_snapshot_timespans:
if apicache.is_timespans_match(timespan, t):
return t
raise ValueError("Can't find a timespan in the base snapshot matching the one specified")
def topic_media(topics_id):
if access_public_topic(topics_id):
media_list = apicache.topic_media_list(TOOL_API_KEY, topics_id, snapshots_id=None, timespans_id=None,
foci_id=None, sort=None, limit=None, link_id=None)
elif is_user_logged_in():
media_list = apicache.topic_media_list(user_mediacloud_key(), topics_id)
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
return jsonify(media_list)
def api_collection_sources_csv(collection_id):
user_mc = user_mediacloud_client()
collection = user_mc.tag(
collection_id) # not cached because props can change often
all_media = media_with_tag(user_mediacloud_key(), collection_id)
file_prefix = "Collection {} ({}) - sources ".format(
collection_id, collection['tag'])
properties_to_include = SOURCE_LIST_CSV_EDIT_PROPS
return csv.download_media_csv(all_media, file_prefix,
properties_to_include)
def api_collections_by_ids():
collection_ids = request.args['coll[]'].split(',')
sources_list = []
for tags_id in collection_ids:
all_media = media_with_tag(user_mediacloud_key(), tags_id)
info = [{'media_id': m['media_id'], 'name': m['name'], 'url': m['url'], 'public_notes': m['public_notes']} for m
in all_media]
add_user_favorite_flag_to_sources(info)
sources_list += info
return jsonify({'results': sources_list})
def story_counts(topics_id):
if access_public_topic(topics_id):
local_key = TOOL_API_KEY
elif is_user_logged_in():
local_key = user_mediacloud_key()
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
total = topic_story_count(local_key, topics_id, timespans_id=None, q=None)
filtered = topic_story_count(local_key, topics_id) # force a count with just the query
return jsonify({'counts': {'count': filtered['count'], 'total': total['count']}})
def url_sharing_focal_set(topics_id, snapshots_id) -> bool:
"""
Return the focal_set that is marked as the auto-generated "URL Sharing" one.
:param topics_id:
:param snapshots_id:
:return: a focal set, or None if the topic doesn't have one
"""
focal_sets = topic_focal_sets_list(user_mediacloud_key(), topics_id, snapshots_id)
url_sharing_focal_sets = [fs for fs in focal_sets if is_url_sharing_focal_set(fs)]
return url_sharing_focal_sets[0] if len(url_sharing_focal_sets) > 0 else None
def story_counts(topics_id):
if access_public_topic(topics_id):
local_key = TOOL_API_KEY
elif is_user_logged_in():
local_key = user_mediacloud_key()
else:
return jsonify({'status': 'Error', 'message': 'Invalid attempt'})
total = apicache.topic_story_count(local_key, topics_id, timespans_id=None, snapshots_id=None, q=None, foci_id=None)
filtered = apicache.topic_story_count(local_key, topics_id)
return jsonify({'counts': {'count': filtered['count'], 'total': total['count']}})
def api_collection_sources_csv(collection_id):
user_mc = user_admin_mediacloud_client()
# info = user_mc.tag(int(collection_id))
all_media = media_with_tag(user_mediacloud_key(), collection_id)
for src in all_media:
for tag in src['media_source_tags']:
if is_metadata_tag_set(tag['tag_sets_id']):
format_metadata_fields(src, tag['tag_sets_id'], tag['tag'])
file_prefix = "Collection_Sourcelist_Template_for_" + collection_id + "_"
what_type_download = COLLECTIONS_TEMPLATE_PROPS_EDIT
return csv.download_media_csv(all_media, file_prefix, what_type_download)
def api_media_source_split_stories(media_id):
media_query = 'media_id:' + str(media_id)
exclude_spidered_stories = " media_id:{} AND NOT tags_id_stories:{}".format(str(media_id), 8875452) if 'separate_spidered' in request.args else media_query
health = _cached_media_source_health(user_mediacloud_key(), media_id)
all_results = apicache.last_year_split_story_count(user_mediacloud_key(), media_query)
non_spidered_results = apicache.last_year_split_story_count(user_mediacloud_key(), exclude_spidered_stories) #same if request.args doesn't ask to exclude_spidered
all_stories = {
'total_story_count' : all_results['total_story_count'],
'health': health,
'list': all_results['counts'],
}
partial_stories = {
'total_story_count': non_spidered_results['total_story_count'],
'health': health,
'list': non_spidered_results['counts'],
}
return jsonify({'results': {'all_stories':all_stories, 'partial_stories': partial_stories}})
def topic_media_csv(topics_id):
sort = validated_sort(request.args.get('sort'))
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
return _stream_media_list_csv(user_mediacloud_key(),
'media-for-topic-' + topics_id,
topics_id,
sort=sort,
snapshots_id=snapshots_id,
timespans_id=timespans_id,
foci_id=foci_id,
q=q)
def collection_source_split_stories(collection_id):
q = "tags_id_media:{}".format(collection_id)
results = apicache.last_year_split_story_count(user_mediacloud_key(), q)
interval = 'day' # default, and not currently passed to the calls above
return jsonify({
'results': {
'list': results['counts'],
'total_story_count': results['total_story_count'],
'interval': interval
}
})
def topic_focal_set_list(topics_id):
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
include_story_counts = request.args.get('includeStoryCounts')
focal_sets = apicache.topic_focal_sets_list(user_mediacloud_key(),
topics_id, snapshots_id)
# now mark the ones that are the magically added URL sharing platform ones
for fs in focal_sets:
fs['is_url_sharing'] = is_url_sharing_focal_set(fs)
if include_story_counts and (include_story_counts == u'1'):
_add_story_counts_to_foci(topics_id, focal_sets)
return jsonify(focal_sets)
def _generate_network_of_frames(topics_id, timespans_id, num_of_sources, out_name, top_media_sort,
remove_media_list=None, remove_word_list=[], generate_word_lists=False,
include_media_list=None, media_attribs=None, num_words=None):
if remove_media_list is None:
remove_media_list = []
# use this specify attributes on the media source that should be added to the node as attributes
# if(media_attribs == None):
# media_attribs = {}
if include_media_list is None:
media_sources_md = topic_media_list(user_mediacloud_key(), topics_id, timespans_id=timespans_id,
limit=num_of_sources + len(remove_media_list), sort=top_media_sort)['media']
else:
media_sources_md = include_media_list
if remove_media_list is not None:
for r in remove_media_list:
media_sources_md = _remove_media_source(r, media_sources_md)
top_words = _build_top_words(media_sources_md, topics_id, timespans_id, remove_word_list, num_words)
if remove_word_list is not None:
top_words = _clean_top_words(top_words, remove_word_list)
frame_network = _build_network(top_words, media_sources_md, media_attribs)
_export_gexf_network(frame_network, '%s.gexf' % out_name)
_export_d3_network(frame_network, '%s' % out_name)
if generate_word_lists:
with open('%s.txt' % out_name, 'w', encoding="utf-8") as wl:
all_words = []
media_sources = {ms['media_id']: ms['name'] for ms in media_sources_md}
# counts = {}
for ms in top_words:
# wl.write("\n\n%s (media id: %d):\n" % (media_sources[ms].encode('ascii', 'ignore'), ms))
wl.write("\n\n{} (media id: {}):\n".format(media_sources[ms], ms))
for w in top_words[ms]:
all_words.append(w['term'])
# increment count to see how many media source include each word
# counts[ms]
# wl.write("- %s (%d)\n" % (w['term'].encode('ascii', 'ignore'), w['count']))
wl.write("- {} ({})\n".format(w['term'], w['count']))
wl.write("\n")
linefeed = chr(10) # linefeed=\n
s = linefeed.join(nx.generate_gexf(frame_network)) # doctest: +SKIP
# for line in nx.generate_gexf(frame_network): # doctest: +SKIP
# logger.debug line
return s
def topic_tag_coverage(topics_id, tags_id):
'''
Useful for seeing how many stories in the topic are tagged with a specific tag
'''
if isinstance(tags_id, list): # doesn't repect duck-typing, but quick fix
tags_id_str = "({})".format(" ".join([str(tid) for tid in tags_id]))
else:
tags_id_str = str(tags_id)
# respect any query filter the user has set
query_with_tag = add_to_user_query("tags_id_stories:{}".format(tags_id_str))
# now get the counts
if access_public_topic(topics_id):
total = topic_story_count(TOOL_API_KEY, topics_id)
tagged = topic_story_count(TOOL_API_KEY, topics_id, q=query_with_tag) # force a count with just the query
elif is_user_logged_in():
total = topic_story_count(user_mediacloud_key(), topics_id)
tagged = topic_story_count(user_mediacloud_key(), topics_id, q=query_with_tag) # force a count with just the query
else:
return None
return {'counts': {'count': tagged['count'], 'total': total['count']}}
def topic_focal_set_split_stories_compare(topics_id, focal_sets_id):
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
# need the timespan info, to find the appropriate timespan with each focus
try:
base_timespan = base_snapshot_timespan(topics_id)
focal_set = apicache.topic_focal_set(user_mediacloud_key(), topics_id,
snapshots_id, focal_sets_id)
except ValueError as e:
return json_error_response(e.message)
# collect the story split counts for each foci
timespans = apicache.matching_timespans_in_foci(topics_id, base_timespan,
focal_set['foci'])
for idx in range(0, len(timespans)):
data = apicache.topic_split_story_counts(
user_mediacloud_key(),
topics_id,
snapshots_id=snapshots_id,
timespans_id=timespans[idx]['timespans_id'])
focal_set['foci'][idx]['split_story_counts'] = data
return jsonify(focal_set)
def api_collection_source_representation_csv(collection_id):
user_mc = user_mediacloud_client()
info = user_mc.tag(collection_id)
source_representation = apicache.collection_source_representation(
user_mediacloud_key(), collection_id)
props = [
'media_id', 'media_name', 'media_url', 'stories', 'sample_size',
'story_pct'
]
filename = info['label'] + "-source sentence counts.csv"
return csv.stream_response(source_representation, props, filename)
def topic_tag_coverage(topics_id, tags_id):
'''
Useful for seeing how many stories in the topic are tagged with a specific tag
'''
# respect any query filter the user has set
snapshots_id, timespans_id, foci_id, q = filters_from_args(request.args)
query_with_tag = add_to_user_query("tags_id_stories:{}".format(tags_id))
# now get the counts
if access_public_topic(topics_id):
total = topic_story_count(TOOL_API_KEY, topics_id)
tagged = topic_story_count(
TOOL_API_KEY, topics_id,
q=query_with_tag) # force a count with just the query
elif is_user_logged_in():
total = topic_story_count(user_mediacloud_key(), topics_id)
tagged = topic_story_count(
user_mediacloud_key(), topics_id,
q=query_with_tag) # force a count with just the query
else:
return None
return {'counts': {'count': tagged['count'], 'total': total['count']}}
def _source_story_split_count_job(info):
source = info['media']
q = "media_id:{}".format(source['media_id'])
split_stories = apicache.split_story_count(user_mediacloud_key(), q, 360)
source_data = {
'media_id': source['media_id'],
'media_name': source['name'],
'media_url': source['url'],
'total_story_count': split_stories['total_story_count'],
'splits_over_time': split_stories['counts'],
}
return source_data
def get_topic_story_links_csv(topics_id):
user_mc = user_mediacloud_client()
topic = user_mc.topic(topics_id)
#page through results for timespand
props = [
'stories_id', 'publish_date', 'title', 'url', 'language', 'ap_syndicated',
'themes', 'subtopics',
'inlink_count', 'facebook_share_count', 'outlink_count', 'media_inlink_count',
'media_id', 'media_name', 'media_url',
# 'media_pub_country', 'media_pub_state', 'media_language', 'media_about_country', 'media_media_type'
]
return stream_story_link_list_csv(user_mediacloud_key(), topic['name'] + '-stories', topics_id)
