/
build_page.py
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/
build_page.py
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import sqlite3
import re
import argparse
import json
import datetime
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from dateutil.parser import parse
import tqdm
import GEOCacher
def combine_old_private(df_old, df_private):
df1 = df_old[["gse", "first_mentioned", "released"]]
df2 = df_private[["gse"]]
df2.loc[:, "first_mentioned"] = df_private["published_on"]
df2.loc[:, "released"] = [None] * (df_private.shape[0])
return pd.concat([df1, df2])
prepare_temp_tables = """
CREATE temp table first_mention as
select m.acc, p.paperid
from mentions m, papers p
where m.paperid = p.paperid and p.published_on =
(select min(published_on) from papers p, mentions m0 where p.paperid=m0.paperid and m0.acc = m.acc);
CREATE index temp.first_mention_acc_idx on first_mention(acc);
CREATE temp table gse_times AS
select ds.acc, ds.first_submitted_on as submitted, ds.first_public_on as released, p.published_on as first_mentioned, ds.title, m.paperid as first_paper
from datasets ds
left join first_mention m on m.acc = ds.acc
left join papers p on p.paperid = m.paperid where m.acc GLOB 'GSE*';
"""
def load_dataframes(maxlag):
print "Loading data..."
data_db = sqlite3.connect("data/odw.sqlite")
cache = GEOCacher.GEOCacher("data/cache.sqlite")
print "Preparing extra tables... ",
data_db.executescript(prepare_temp_tables)
print "done"
query_released = """
select acc as gse, doi, papers.title, submitted, first_mentioned, released, journal_nlm from gse_times, papers
where
papers.paperid = gse_times.first_paper
"""
df_released = pd.read_sql_query(query_released, data_db)
query_private = """select distinct acc as gse, published_on, journal_nlm as journal,
doi, title from mentions, papers
where gse not in (select acc from datasets) and mentions.paperid=papers.paperid
and mentions.acc GLOB 'GSE*'
order by published_on asc"""
df_missing = pd.read_sql_query(query_private, data_db)
skip_gses = map(lambda x: x.split()[0], open("whitelist.txt").readlines())
print "Double-checking missing GSE's using NCBI website..."
statuses_released = []
for (i, gse) in enumerate(tqdm.tqdm(df_released.gse)):
if df_released.released[i] is None:
status = cache.check_gse_cached(gse, maxlag=maxlag)
statuses_released.append(False)
if status == "private": # append it to df_missing then
# Index([u'gse', u'published_on', u'journal', u'doi', u'title'], dtype='object')
df_missing = df_missing.append({"gse": gse, "published_on": df_released.first_mentioned[i], "journal": df_released.journal_nlm[i],
"doi": df_released.doi[i], "title": df_released.title[i]}, ignore_index=True)
print "Weird GSE: ", gse
else:
statuses_released.append(True)
nonreleased = np.nonzero(np.invert(np.array(statuses_released)))[0]
# print "Missing GSEs that are mentioned in GEOMetadb :",
# df_released.gse[nonreleased]
df_released = df_released.ix[np.nonzero(statuses_released)[0]]
today_str = str(datetime.date.today())
statuses = []
for (i, gse) in enumerate(tqdm.tqdm(df_missing.gse)):
if gse in skip_gses:
statuses.append("skip")
else:
status = cache.check_gse_cached(gse, maxlag=maxlag)
if status == "present":
data = cache.get_geo_page(gse, maxlag=99999)
reldate = re.search("Public on (.*)<", data)
if reldate is None:
print "Failed to extract date for ", gse
reldate = today_str
else:
reldate = reldate.group(1)
df_released = df_released.append({"doi": df_missing.gse[i], "gse": gse, "submitted" : None, "title": df_missing.title[i], "journal_nlm": df_missing.journal[
i], "first_mentioned": df_missing.published_on[i], "released": reldate}, ignore_index=True)
statuses.append(status)
df_private = df_missing.ix[np.array(statuses) == "private"]
df_private = df_private.sort_values("published_on")
cur = data_db.execute(
"select value from metadata where name = 'GEOmetadb timestamp'")
meta_timestamp = cur.fetchone()[0]
return df_private, df_released, meta_timestamp
def get_hidden_df(df):
df = df.copy()
oneday = datetime.timedelta(1)
timestep = datetime.timedelta(3)
x = []
y = []
c = datetime.date.today() - oneday
mentioned = np.array(map(lambda x: parse(x).date(), df.first_mentioned))
filldate = (datetime.datetime.today() + datetime.timedelta(1)).date()
public = np.array(map(lambda x: parse(x).date(),
df.released.fillna(str(filldate))))
while c >= datetime.date(2008, 1, 1):
mask1 = mentioned < c
mask2 = public > c + oneday
x.append(c)
y.append(np.count_nonzero(mask1 & mask2))
c -= timestep
print "Current overdue: ", y[0]
return pd.DataFrame({"date": x, "overdue": y})
def update_graph(dff):
sns.set_style("white")
sns.set_style("ticks")
sns.set_context("talk")
dff.ix[::10].plot("date", "overdue", figsize=(7, 4), lw=3)
onemonth = datetime.timedelta(30)
plt.xlim(dff.date.min(), dff.date.max()+onemonth)
plt.ylabel("Overdue dataset")
plt.xlabel("Date")
plt.savefig("docs/graph.png")
def gse2url(gse):
return "http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=" + gse
def doi2url(doi):
return "http://dx.doi.org/" + doi
def format_gse(gse):
return """<a href="%s">%s</a>""" % (gse2url(gse), gse)
def format_doi(doi):
return """<a href="%s">%s</a>""" % (doi2url(doi), doi)
tracking_script = """
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-93388605-1', 'auto');
ga('send', 'pageview');
</script>
"""
def update_html(df, metadb_timestamp):
pd.set_option('display.max_colwidth', -1)
table_html = df.to_html(formatters={
"doi": format_doi, "gse": format_gse}, escape=False, index=False, justify="left", classes="table table-striped table-bordered")
html_template_str = unicode(open("output_template.html").read())
n_overdue = df.shape[0]
final_html = html_template_str.format(date_updated=datetime.date.today(), metageo_timestamp=metadb_timestamp,
n_overdue=n_overdue, table_html=table_html, tracking_script=tracking_script)
with open("docs/index.html", "w") as f:
f.write(final_html.encode("utf-8"))
def prepare_data_json(df_private, meta_timestamp, update_date):
result = dict()
result["meta_timestamp"] = meta_timestamp
result["update_date"] = update_date
result["data"] = [row[1].to_dict() for row in df_private.iterrows()]
json.dump(result, open("private_geo.json", "w"))
def main():
parser = argparse.ArgumentParser(description='Build a page for datawatch')
parser.add_argument("--maxlag", default=7)
parser.add_argument("--output", default="")
args = parser.parse_args()
df_private, df_released, meta_timestamp = load_dataframes(args.maxlag)
combined_df = combine_old_private(df_released, df_private)
print "Currently missing entries in GEOMetadb: ", df_private.shape[0]
graph_df = get_hidden_df(combined_df)
if args.output != "":
combined_df.to_csv(args.output + "_combined.csv", encoding='utf-8')
df_released.to_csv(args.output + "_released.csv", encoding='utf-8')
graph_df.to_csv(args.output + "_graph.csv", encoding='utf-8')
prepare_data_json(df_private, meta_timestamp, str(datetime.date.today()))
update_html(df_private, meta_timestamp)
update_graph(graph_df)
if __name__ == "__main__":
main()