def assign_stat_object_to_data_tables():
"""go through each data table object and add info about who curated and when"""
dts = m.DataTable.objects.filter(datasource__ephysconceptmap__isnull=False)
num_dts = dts.count()
for i, dt in enumerate(dts):
print "updating data tables with summary objects"
prog(i, num_dts)
update_data_table_stat(dt)
def add_table_ob_to_article(table_html, article_ob, text_mine = True, uploading_user = None):
if uploading_user:
user_uploaded = True
else:
user_uploaded = False
table_soup = BeautifulSoup(table_html, 'lxml')
table_html_cleaned = str(table_soup)
table_html_cleaned = add_id_tags_to_table(table_html_cleaned)
table_text = table_soup.get_text()
table_text = table_text[0:min(9999,len(table_text))]
data_table_ob = m.DataTable.objects.get_or_create(article = article_ob,
table_html = table_html_cleaned,
table_text = table_text,
uploading_user = uploading_user,
user_uploaded = user_uploaded
)[0]
data_table_ob = remove_spurious_table_headers(data_table_ob) # takes care of weird header thing for elsevier xml tables
ds = m.DataSource.objects.get_or_create(data_table=data_table_ob)[0]
# apply initial text mining of ephys concepts to table
if text_mine:
assocDataTableEphysVal(data_table_ob)
# creates data table stat object, relevance is to count and store num of unique ecms that were TMed
data_table_stat = update_data_table_stat(data_table_ob)
return data_table_ob
