def auth_route(
username=None, password=None,
shadow=False, crypted=False
):
ws.logger.info('/auth')
if not username or not password:
redirect('/?logerror=1')
mode = 'plain'
if shadow:
mode = 'shadow'
if crypted:
mode = 'crypted'
# Try to find user in database
user = rights.get_user(username)
# No such user, or it's an external one
if not user or user.get('external', False):
# Try to redirect authentication to the external backend
if mode == 'plain':
response.status = 307
response.set_header('Location', '/auth/external')
return 'username={0}&password={1}'.format(
urlencode(username),
urlencode(password)
)
else:
#return HTTPError(403, 'Plain authentication required')
redirect('/?logerror=3')
# Local authentication: check if account is activated
if not user.get('enable', False):
#return HTTPError(403, 'This account is not enabled')
redirect('/?logerror=2')
user = check(mode=mode, user=user, password=password)
if not user:
redirect('/?logerror=1')
#return HTTPError(403, 'Forbidden')
session.create(user)
redirect('/')
def search():
q = request.query.q or ''
print 'searching', q, 'in weibo'
result = client.weibo_search("", q, 0, 20)
pub_result = client.user_search("", q, 0, 20)
return dict(query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Weibo',
results=[
dict(id=e.id,
name=e.title,
url="http://weibo.com/%s" % e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(','),
imgurl=e.imgurl) for e in result.entity
],
extra_results_list=[
dict(title="Users",
items=[
dict(text=pub.title,
link="http://weibo.com/u/%s" %
pub.original_id) for pub in pub_result.entity
]),
])
def search():
q = request.query.q or ''
print 'searching', q, 'in patent'
result = client.group_search("", q, 0, 20)
pub_result = client.patent_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Companies',
results=[
dict(id=e.id,
name=e.title,
url="http://pminer.org/company.do?m=viewCompany&id=%s" %
e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(','),
imgurl=e.imgurl) for e in result.entity
],
extra_results_list=[
dict(
title="Patents",
items=[
dict(
text=pub.title,
link="http://pminer.org/patent.do?m=viewPatent&id=%s" %
pub.original_id) for pub in pub_result.entity
]),
])
def search(dataset):
q = request.query.q or ''
print 'searching', q, 'in academic'
result = client.author_search(dataset, q, 0, 20)
pub_result = client.pub_search(dataset, q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Experts',
results=[
dict(id=e.id,
name=e.title,
url="http://arnetminer.org/person/-%s.html" % e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(','),
imgurl=e.imgurl) for e in result.entity
],
extra_results_list=[
dict(title="Publications",
items=[
dict(text=pub.title,
link="http://arnetminer.org/publication/-%s.html" %
pub.original_id) for pub in pub_result.entity
]),
])
def search():
q = request.query.q or ''
print 'searching', q, 'in weibo'
return dict(query=q,
count=0,
results=[],
encoded_query=urlencode({"q": q}))
def search():
q = request.query.q or ''
print 'searching', q, 'in weibo'
return dict(
query=q,
count=0,
results=[],
encoded_query=urlencode({"q": q})
)
def search():
q = request.query.q or ''
offset = int(request.query.offset or '0')
count = int(request.query.count or '20')
print 'searching', q, 'in academic'
result = client.author_search("", q, offset, count)
pub_result = client.pub_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode('utf8')}),
hotqueries=["data mining", "deep learning"],
offset=offset,
count=count,
total_count=result.total_count,
trends_enabled=True,
influence_enabled=True,
results_title='Experts',
results=[
dict(
id=e.id,
name=e.title,
url="http://arnetminer.org/person/-%s.html" % e.original_id,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=[t.title() for t in e.topics.split(',') if t.strip()],
imgurl=e.imgurl,
integrated=network_integration.query(e.original_id)
) for e in result.entity
],
extra_results_list=[
dict(
title="Publications",
items=[
dict(
text=pub.title,
link="http://arnetminer.org/publication/-%s.html" % pub.original_id,
stats=dict(
(s.type, s.value) for s in pub.stat
),
authors=client.author_search_by_id("", list(pub.related_entity[0].id)).entity
) for pub in pub_result.entity
]
),
]
)
def search():
q = request.query.q or ''
offset = int(request.query.offset or '0')
count = int(request.query.count or '20')
print 'searching', q, 'in Enron'
result = client.person_search(q, offset, count)
mail_result = client.mail_search(q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode('utf8')}),
hotqueries=["data mining", "deep learning"],
offset=offset,
count=count,
total_count=result.total_count,
trends_enabled=False,
influence_enabled=False,
results_title='People',
results=[
dict(
id = e.original_id,
name="%s <%s>" % (e.title, e.url),
url="mailto:%s" % e.url,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=[t.title() for t in e.topics.split(',') if t.strip()],
imgurl="http://www.gravatar.com/avatar/" + gravatar(e.url) + "?d=identicon"
) for x,e in enumerate(result.entity)
],
extra_results_list=[
dict(
title="Mails",
items=[
dict(
id = "mail/%s" % e.original_id,
text=e.title,
link="mail/%s" % e.id,
stats=dict(
(s.type, s.value) for s in e.stat
),
authors=[]
) for e in mail_result.entity
]
),
]
)
def get_url(routename, **kwargs):
'''
customized get_url to allow additional prefix args
'''
redirect = kwargs.pop('redirect', False)
scriptname = bottle.request.environ.get('SCRIPT_NAME',
'').strip('/') + '/'
location = app.router.build(routename, **kwargs).lstrip('/')
url = bottle.urljoin(bottle.urljoin('/', scriptname), location)
if prefix and not redirect:
url = prefix + '?' + bottle.urlencode({'q': url})
append = '?'
if '?' in url:
append = '&'
url += '{}_={}'.format(append, time.time())
return url
def search():
q = request.query.q or ''
print 'searching', q, 'in academic'
result = client.searchAuthors(q)
return dict(
query=q,
count=result.total_count,
results=[
dict(
id=a.naid,
name=a.names[0],
email=a.email
) for a in result.authors
],
encoded_query=urlencode({"q": q})
)
def search():
q = request.query.q or ""
offset = int(request.query.offset or "0")
count = int(request.query.count or "20")
print "searching", q, "in patent"
result = client.group_search("", q, offset, count)
pub_result = client.patent_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode("utf8")}),
hotqueries=["data mining", "search engine", "mobile phone"],
offset=offset,
count=count,
total_count=result.total_count,
trends_enabled=True,
influence_enabled=True,
results_title="Companies",
results=[
dict(
id=e.id,
name=e.title,
url="http://pminer.org/company.do?m=viewCompany&id=%s" % e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(","),
imgurl=e.imgurl,
)
for e in result.entity
],
extra_results_list=[
dict(
title="Patents",
items=[
dict(
text=pub.title,
link=r"http://patft1.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=/netahtml/PTO/srchnum.htm&r=1&f=G&l=50&s1=%22+7627620+%22.PN.&OS=PN/%22+7627620+%22&RS=PN/%22+7627620".replace(
"7627620", pub.url
),
stats=dict((s.type, s.value) for s in pub.stat),
authors=client.inventor_search_by_id("", list(pub.related_entity[0].id)).entity,
)
for pub in pub_result.entity
],
)
],
)
def search():
q = request.query.q or ""
offset = int(request.query.offset or "0")
count = int(request.query.count or "20")
print "searching", q, "in weibo"
result = client.user_search("", q, 0, 20)
pub_result = client.weibo_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode("utf8")}),
hotqueries=[u"高考", u"网络", u"星座", u"天气"],
offset=offset,
count=count,
total_count=result.total_count,
results_title="Users",
results=[
dict(
id=e.id,
name=e.title,
url="http://weibo.com/u/%s" % e.url,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(","),
imgurl=e.imgurl,
)
for e in result.entity
],
extra_results_list=[
dict(
title="Weibo",
items=[
dict(
text=pub.title,
link="",
stats=dict((s.type, s.value) for s in pub.stat),
user=client.user_search_by_id("", list(pub.related_entity[0].id)).entity
if len(pub.related_entity)
else [],
)
for pub in pub_result.entity
],
)
],
)
def search():
q = request.query.q or ''
offset = int(request.query.offset or '0')
count = int(request.query.count or '20')
print 'searching', q, 'in patent'
result = client.group_search("", q, offset, count)
pub_result = client.patent_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode('utf8')}),
hotqueries=["data mining", "search engine", "mobile phone"],
offset=offset,
count=count,
total_count=result.total_count,
trends_enabled=True,
influence_enabled=True,
results_title='Companies',
results=[
dict(id=e.id,
name=e.title,
url="http://pminer.org/company.do?m=viewCompany&id=%s" %
e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(','),
imgurl=e.imgurl) for e in result.entity
],
extra_results_list=[
dict(
title="Patents",
items=[
dict(
text=pub.title,
link=
r'http://patft1.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=/netahtml/PTO/srchnum.htm&r=1&f=G&l=50&s1=%22+7627620+%22.PN.&OS=PN/%22+7627620+%22&RS=PN/%22+7627620'
.replace('7627620', pub.url),
stats=dict((s.type, s.value) for s in pub.stat),
authors=client.inventor_search_by_id(
"", list(pub.related_entity[0].id)).entity)
for pub in pub_result.entity
]),
])
def search():
q = request.query.q or ''
offset = int(request.query.offset or '0')
count = int(request.query.count or '20')
print 'searching', q, 'in academic'
result = client.author_search("", q, offset, count)
pub_result = client.pub_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode('utf8')}),
hotqueries=["data mining", "deep learning"],
offset=offset,
count=count,
total_count=result.total_count,
trends_enabled=True,
influence_enabled=True,
results_title='Experts',
results=[
dict(id=e.id,
name=e.title,
url="http://arnetminer.org/person/-%s.html" % e.original_id,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=[t.title() for t in e.topics.split(',') if t.strip()],
imgurl=e.imgurl,
integrated=network_integration.query(e.original_id))
for e in result.entity
],
extra_results_list=[
dict(title="Publications",
items=[
dict(text=pub.title,
link="http://arnetminer.org/publication/-%s.html" %
pub.original_id,
stats=dict((s.type, s.value) for s in pub.stat),
authors=client.author_search_by_id(
"", list(pub.related_entity[0].id)).entity)
for pub in pub_result.entity
]),
])
def search():
q = request.query.q or ''
print 'searching', q, 'in academic'
result = client.author_search("", q, 0, 20)
pub_result = client.pub_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Experts',
results=[
dict(
id=e.id,
name=e.title,
url="http://arnetminer.org/person/-%s.html" % e.original_id,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=e.topics.split(','),
imgurl=e.imgurl,
integrated=network_integration.query(e.original_id)
) for e in result.entity
],
extra_results_list=[
dict(
title="Publications",
items=[
dict(
text=pub.title,
link="http://arnetminer.org/publication/-%s.html" % pub.original_id
) for pub in pub_result.entity
]
),
]
)
def search():
q = request.query.q or ''
offset = int(request.query.offset or '0')
count = int(request.query.count or '20')
print 'searching', q, 'in weibo'
result = client.user_search("", q, 0, 20)
pub_result = client.weibo_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query.encode('utf8')}),
hotqueries=[u"高考", u"网络", u"星座", u"天气"],
offset=offset,
count=count,
total_count=result.total_count,
results_title='Users',
results=[
dict(id=e.id,
name=e.title,
url="http://weibo.com/u/%s" % e.url,
description=e.description,
stats=dict((s.type, s.value) for s in e.stat),
topics=e.topics.split(','),
imgurl=e.imgurl) for e in result.entity
],
extra_results_list=[
dict(title="Weibo",
items=[
dict(text=pub.title,
link="",
stats=dict((s.type, s.value) for s in pub.stat),
user=client.user_search_by_id(
"", list(pub.related_entity[0].id)).entity
if len(pub.related_entity) else [])
for pub in pub_result.entity
]),
])
def search():
q = request.query.q or ''
print 'searching', q, 'in patent'
result = client.group_search("", q, 0, 20)
pub_result = client.patent_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Companies',
results=[
dict(
id=e.id,
name=e.title,
url="http://pminer.org/company.do?m=viewCompany&id=%s" % e.original_id,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=e.topics.split(','),
imgurl=e.imgurl
) for e in result.entity
],
extra_results_list=[
dict(
title="Patents",
items=[
dict(
text=pub.title,
link="http://pminer.org/patent.do?m=viewPatent&id=%s" % pub.original_id
) for pub in pub_result.entity
]
),
]
)
def search():
q = request.query.q or ''
print 'searching', q, 'in weibo'
result = client.weibo_search("", q, 0, 20)
pub_result = client.user_search("", q, 0, 20)
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title='Weibo',
results=[
dict(
id=e.id,
name=e.title,
url="http://weibo.com/%s" % e.original_id,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=e.topics.split(','),
imgurl=e.imgurl
) for e in result.entity
],
extra_results_list=[
dict(
title="Users",
items=[
dict(
text=pub.title,
link="http://weibo.com/u/%s" % pub.original_id
) for pub in pub_result.entity
]
),
]
)
def search(dataset):
q = request.query.q or ''
print 'searching', q, 'in academic'
result = client.entity_search(dataset, q)
print result
return dict(
query=q,
encoded_query=urlencode({"q": result.query}),
count=result.total_count,
results_title=dataset,
results=[
dict(
id=e.id,
name=e.title,
url="link:entity/%s" % e.id,
description=e.description,
stats=dict(
(s.type, s.value) for s in e.stat
),
topics=e.topics,
imgurl=e.imgurl
) for e in result.entities
],
extra_results_list=[
{
"title": extra_list.title,
"items": [
{
"title": item.title,
"link": "link:%s/%s" % (extra_list.title, item.id)
} for item in extra_list.item
]
} for extra_list in result.extra_list
]
)
