"""Functions linked directly to functionality called from API endpoints.
"""
from datetime import datetime
import bottle
import config
import db
import helpers
import models
def get_categories(connection):
"""Fetches a list of all known bioRxiv categories.
bioRxiv separates all papers into categories (or "collections"), such
as "bioinformatics", "genomics", etc. This function lists all the ones
we've pulled from the site so far. Rxivist uses the term "categories"
instead of "collections" to make this more broadly applicable in case
it's one day expanded to index more than just bioRxiv.
Arguments:
- connection: a Connection object with an active database session
Returns:
- a list of strings, one for each bioRxiv collection
"""
results = []
categories = connection.read("SELECT DISTINCT collection FROM articles WHERE collection IS NOT NULL ORDER BY collection;")
for cat in categories:
if len(cat) > 0:
results.append(cat[0])
return results
def paper_query(q, categories, timeframe, metric, page, page_size, connection):
"""Returns a list of the most downloaded papers that meet a given set of constraints.
Arguments:
- connection: a database Connection object.
- q: A search string to compare against article abstracts,
titles and author names. (Title matches are weighted more heavily.)
- categories: A list of bioRxiv categories the results can be in.
- timeframe: A description of the range of dates on which to
base the rankings (i.e. "alltime" or "lastmonth")
- metric: Which article-level statistic to use when sorting results
- page: Which page of the results to display (0-indexed)
- page_size: How many entries should be returned
Returns:
- An list of Article objects that meet the search criteria, sorted by the
specified metric in descending order.
"""
# We build two queries, 'select' and 'countselect': one to get the
# current page of results, and one to figure out the total number
# of results
select = "SELECT "
if metric == "downloads":
select += "r.downloads"
elif metric == "twitter":
select += "SUM(r.count)"
select += ", a.id, a.url, a.title, a.abstract, a.collection, a.posted, a.doi"
countselect = "SELECT COUNT(DISTINCT a.id)"
params = ()
query = ""
if q != "": # if there's a text search specified
params = (q,)
query += f' FROM {config.db["schema"]}.articles AS a INNER JOIN {config.db["schema"]}.'
if metric == "twitter":
query += "crossref_daily"
elif metric == "downloads":
query_times = {
"alltime": "alltime_ranks",
"ytd": "ytd_ranks",
"lastmonth": "month_ranks",
}
query += query_times[timeframe]
if metric == "twitter":
query += " AS r ON r.doi=a.doi"
elif metric == "downloads":
query += " AS r ON r.article=a.id"
if q != "":
query += """, plainto_tsquery(%s) query,
coalesce(setweight(a.title_vector, 'A') || setweight(a.abstract_vector, 'C') || setweight(a.author_vector, 'D')) totalvector
"""
# add a WHERE clause if we need one:
# (all-time twitter stats don't require it)
if metric == "downloads" or (metric == "twitter" and timeframe != "alltime") or len(categories) > 0:
query += " WHERE "
if metric == "downloads":
query += "r.downloads > 0"
if q != "" or len(categories) > 0:
query += " AND "
if q != "":
query += "query @@ totalvector "
if len(categories) > 0 or (metric == "twitter" and timeframe != "alltime"):
query += " AND "
if len(categories) > 0:
query += "collection=ANY(%s)"
if q != "":
params = (q,categories)
else:
params = (categories,)
if metric == "twitter" and timeframe != "alltime":
query += " AND "
if metric == "twitter" and timeframe != "alltime":
query += "r.source_date > now() - interval "
query_times = {
"day": 2,
"week": 7,
"month": 30,
"year": 365
}
query += f"'{query_times[timeframe]} days' "
# this is the last piece of the query we need for the one
# that counts the total number of results
countselect += query
resp = connection.read(countselect, params)
total = resp[0][0]
# continue building the query to get the full list of results:
if metric == "twitter":
query += " GROUP BY a.id"
query += " ORDER BY "
if metric == "downloads":
query += "r.rank ASC"
elif metric == "twitter":
query += "SUM(r.count) DESC"
query += f" LIMIT {page_size}"
if page > 0:
query += f" OFFSET {page * page_size}"
query += ";"
select += query
result = connection.read(select, params)
results = [models.SearchResultArticle(a, connection) for a in result]
return results, total
def author_rankings(connection, category=""):
"""Fetches a list of authors with the most cumulative downloads.
Arguments:
- connection: a database Connection object.
- category: (Optionally) a single bioRxiv collection to base download rankings on.
Returns:
- A list of Author objects that meet the search criteria.
"""
if category == "": # all time, all categories
table = "author_ranks"
where = ""
params = ()
else:
table = "author_ranks_category"
where = "WHERE r.category=%s"
params = (category,)
query = f"""
SELECT a.id, a.name, r.rank, r.downloads, r.tie
FROM authors AS a
INNER JOIN {config.db["schema"]}.{table} r ON a.id=r.author
{where}
ORDER BY r.rank
LIMIT {config.author_ranks_limit}
"""
authors = connection.read(query, params)
return [models.SearchResultAuthor(*a) for a in authors]
def author_details(author_id, connection):
"""Returns information about a single author, including a list of
all their papers.
Arguments:
- connection: a database Connection object.
- author_id: the Rxivist-issued ID of the author being queried.
Returns:
- An Author object containing information about that
author's publications and contact info.
"""
result = models.Author(author_id)
result.GetInfo(connection)
return result
def paper_details(article_id, connection):
"""Returns information about a single paper.
Arguments:
- connection: a database Connection object.
- article_id: the ID given to the author being queried.
Returns:
- A Paper object containing details about the paper and
its authors.
"""
result = models.ArticleDetails(article_id, connection)
return result
def paper_downloads(a_id, connection):
"""Returns time-series data from bioRxiv about how many
times a paper's webpage and PDF have been downloaded.
Arguments:
- connection: a database Connection object.
- a_id: the Rxivist-issued ID given to the paper being queried.
Returns:
- A list of months and the download stats for each month
"""
result = models.Article(a_id)
result.GetTraffic(connection)
return {
"query": {
"id": a_id
},
"results": [{"month": x.month, "year": x.year, "downloads": x.downloads, "views": x.views} for x in result.traffic]
}
def get_distribution(category, metric, connection):
"""Returns time-series data from bioRxiv about how many
times a paper's webpage and PDF have been downloaded.
Arguments:
- connection: a database Connection object.
- a_id: the Rxivist-issued ID given to the paper being queried.
Returns:
- A list of months and the download stats for each month
"""
# "category" param can be either "author" or "paper"
# "metric" param is (right now) limited to just "downloads"
if category == "paper":
category = "alltime"
data = connection.read("SELECT bucket, count FROM download_distribution WHERE category=%s ORDER BY bucket", (category,))
results = [(entry[0], entry[1]) for entry in data]
averages = {}
for avg in ["mean", "median"]:
cat = f"{category}_{avg}"
answer = connection.read("SELECT count FROM download_distribution WHERE category=%s", (cat,))
averages[avg] = answer[0][0]
return results, averages
def top_year(year, connection):
resp = connection.read("""
SELECT SUM(t.pdf) as downloads, t.article, a.url,
a.title, a.abstract, a.collection, a.posted, a.doi
FROM article_traffic t
INNER JOIN articles a ON t.article=a.id
WHERE t.year = %s
AND a.posted >= '%s-01-01'
AND a.posted <= '%s-12-31'
GROUP BY 2,3,4,5,6,7,8
ORDER BY 1 DESC
LIMIT 25
""", (year,year,year))
if len(resp) == 0:
return []
results = [models.SearchResultArticle(a, connection) for a in resp]
return results
def summary_stats(connection, category=None):
"""Returns time-series data reflecting how many submissions and downloads
were recorded in each month.
Arguments:
- connection: a database Connection object.
- category: *tk NOOP whether to restrict the query to only a single collection.
Returns:
- A dictionary containing collections, each of which stores either the submission
or download numbers for a single month.
"""
results = {
'submissions': [],
'downloads': [],
'submissions_categorical': []
}
# Submissions:
data = connection.read("""
SELECT EXTRACT(MONTH FROM posted)::int AS month,
EXTRACT(YEAR FROM posted)::int AS year, COUNT(id) AS submissions
FROM prod.articles
WHERE posted IS NOT NULL
GROUP BY year, month
ORDER BY year, month;
""")
for entry in data:
results['submissions'].append({
'month': entry[0],
'year': entry[1],
'count': entry[2],
})
# the reason this is so complicated is because not every category has submissions
# in every month, and we want an entry for each month.
catlist = get_categories(connection)
# figure out the most recent month, so we know when to stop:
data = connection.read(f"SELECT MAX(EXTRACT(YEAR FROM posted)) FROM {config.db['schema']}.articles")
maxyear = int(data[0][0])
data = connection.read(f"SELECT MAX(EXTRACT(MONTH FROM posted)) FROM {config.db['schema']}.articles WHERE EXTRACT(YEAR FROM posted) = %s", (maxyear,))
maxmonth = int(data[0][0])
for cat in catlist:
catdata = {}
for year in range(2013, maxyear + 1):
catdata[year] = {}
for month in range(1,13):
if year == 2013 and month < 11:
continue # nothing before nov 2013
if year == maxyear and month > maxmonth:
break
catdata[year][month] = 0
data = connection.read("""
SELECT EXTRACT(MONTH FROM posted)::int AS month,
EXTRACT(YEAR FROM posted)::int AS year, COUNT(id) AS submissions
FROM prod.articles
WHERE posted IS NOT NULL
AND collection=%s
GROUP BY year, month
ORDER BY year, month;
""",(cat,))
for entry in data:
catdata[entry[1]][entry[0]] = entry[2]
monthdata = []
for year, yeardata in catdata.items():
for month, count in yeardata.items():
monthdata.append({
'month': month,
'year': year,
'count': count,
})
results['submissions_categorical'].append({
'label': cat,
'data': monthdata
})
# Downloads:
# Don't show the previous month's download numbers until X days after the
# month ends, since those numbers won't be updated for every paper until
# after the month ends
current = datetime.now().day
adjust = 1 if current > config.summary_download_age else 2
if maxmonth > adjust:
maxmonth -= adjust
else:
maxmonth += 12 - adjust
maxyear -= 1
data = connection.read("""
SELECT month, year, sum(pdf) AS downloads
FROM prod.article_traffic
GROUP BY year, month
ORDER BY year, month
""")
for entry in data:
results['downloads'].append({
'month': entry[0],
'year': entry[1],
'count': entry[2]
})
if entry[1] == maxyear and entry[0] == maxmonth:
break
return results
def site_stats(connection):
"""Returns a (very) brief summary of the information indexed by Rxivist. More of
a data hygiene report than anything.
Arguments:
- connection: a database Connection object.
Returns:
- A dict with the total indexed papers and authors
"""
# Counting up how many of each entity we have
resp = connection.read("SELECT COUNT(id) FROM articles;")
if len(resp) != 1 or len(resp[0]) != 1:
paper_count = 0
else:
paper_count = resp[0][0]
resp = connection.read("SELECT COUNT(id) FROM authors;")
if len(resp) != 1 or len(resp[0]) != 1:
author_count = 0
else:
author_count = resp[0][0]
resp = connection.read("SELECT COUNT(id) FROM articles WHERE abstract IS NULL;")
if len(resp) != 1 or len(resp[0]) != 1:
no_abstract = 0
else:
no_abstract = resp[0][0]
resp = connection.read("SELECT COUNT(id) FROM articles WHERE posted IS NULL;")
if len(resp) != 1 or len(resp[0]) != 1:
no_posted = 0
else:
no_posted = resp[0][0]
outdated = {}
resp = connection.read("SELECT collection, COUNT(id) FROM articles WHERE last_crawled < now() - interval %s GROUP BY collection ORDER BY collection;", (config.outdated_limit,))
if len(resp) > 0:
for entry in resp:
if len(entry) < 2:
continue # something fishy with this entry
outdated[entry[0]] = entry[1]
resp = connection.read("SELECT COUNT(id) FROM articles WHERE collection IS NULL;")
if len(resp) != 1 or len(resp[0]) != 1:
no_category = 0
else:
no_category = resp[0][0]
resp = connection.read(f"""
SELECT COUNT(id)
FROM (
SELECT
a.id, COUNT(w.author) AS authors
FROM {config.db["schema"]}.articles a
LEFT JOIN {config.db["schema"]}.article_authors w ON w.article=a.id
GROUP BY a.id
ORDER BY authors
) AS authorcount
WHERE authors=0;
""")
if len(resp) != 1 or len(resp[0]) != 1:
no_authors = 0
else:
no_authors = resp[0][0]
resp = connection.read(f"""
SELECT COUNT(id)
FROM (
SELECT a.id, COUNT(z.article) AS num
FROM prod.authors a
LEFT JOIN prod.article_authors z ON a.id=z.author
GROUP BY 1
ORDER BY 2 DESC
) AS asdf
WHERE num = 0
""")
if len(resp) != 1 or len(resp[0]) != 1:
no_papers = 0
else:
no_papers = resp[0][0]
return {
"papers_indexed": paper_count,
"authors_indexed": author_count,
"missing_abstract": no_abstract,
"missing_date": no_posted,
"outdated_count": outdated,
"missing_authors": no_authors,
"missing_category": no_category,
"authors_no_papers": no_papers
}
