def get_tori(identifiers, bibcodes, self_cits=None):
# Get additional data necessary for Tori calculation
data = get_tori_data(identifiers)
if len(data) == 0:
return 0, 0, 0, 0, []
# If we did not get self-citations, retrieve them
if not self_cits:
self_cits = get_selfcitations(identifiers, bibcodes)[1]
self_citations = set((itertools.chain(*[x[0] for x in self_cits])))
# Now we can calculate the Tori index
tori_data = [p for p in list(itertools.chain(
*[p.rn_citation_data for p in data if p.rn_citation_data])) if
p['bibcode'] not in self_citations and 'pubyear' in p]
tori_data_ref = [p for p in list(itertools.chain(
*[p.rn_citation_data for p in data if p.refereed and
p.rn_citation_data])) if p['bibcode'] not in self_citations]
try:
tori = np.sum(
np.array([r['auth_norm'] * r['ref_norm'] for r in tori_data]))
tori_ref = np.sum(
np.array([r['auth_norm'] * r['ref_norm'] for r in tori_data_ref]))
except:
return 0, 0, 0, 0, tori_data
# The riq index follows from the Tori index and the year range
yrange = datetime.now().year - min([int(b[:4]) for b in bibcodes]) + 1
yrange_ref = datetime.now().year - \
min([int(p.bibcode[:4]) for p in data]) + 1
riq = int(1000.0 * sqrt(float(tori)) / float(yrange))
riq_ref = int(1000.0 * sqrt(float(tori_ref)) / float(yrange))
# Send the results back
return tori, tori_ref, riq, riq_ref, tori_data
def test_get_tori_data(self):
'''Test getting tori data'''
from models import get_tori_data
data = get_tori_data(testset)
# The most important thing here is to test that it is a list
# of MetricsModel instances
self.assertEqual(isinstance(data, list), True)
self.assertTrue(False not in
[x.__class__.__name__ == 'MetricsModel' for x in data])
def test_get_tori_data(self):
'''Test getting tori data'''
from models import get_tori_data
data = get_tori_data(testset)
# The most important thing here is to test that it is a list
# of MetricsModel instances
self.assertEqual(isinstance(data, list), True)
self.assertTrue(
False not in [x.__class__.__name__ == 'MetricsModel' for
x in data])
def get_tori(identifiers, bibcodes, self_cits=None):
# Get additional data necessary for Tori calculation
data = get_tori_data(identifiers)
if len(data) == 0:
return 0, 0, 0, 0, []
# If we did not get self-citations, retrieve them
if not self_cits:
self_cits = get_selfcitations(identifiers, bibcodes)[1]
self_citations = set((itertools.chain(*[x[0] for x in self_cits])))
# Now we can calculate the Tori index
tori_data = [
p for p in list(
itertools.chain(
*[p.rn_citation_data for p in data if p.rn_citation_data]))
if p['bibcode'] not in self_citations and 'pubyear' in p
]
tori_data_ref = [
p for p in list(
itertools.chain(*[
p.rn_citation_data for p in data
if p.refereed and p.rn_citation_data
])) if p['bibcode'] not in self_citations
]
try:
tori = np.sum(
np.array([r['auth_norm'] * r['ref_norm'] for r in tori_data]))
tori_ref = np.sum(
np.array([r['auth_norm'] * r['ref_norm'] for r in tori_data_ref]))
except:
return 0, 0, 0, 0, tori_data
# The riq index follows from the Tori index and the year range
yrange = datetime.now().year - min([int(b[:4]) for b in bibcodes]) + 1
yrange_ref = datetime.now().year - \
min([int(p.bibcode[:4]) for p in data]) + 1
riq = int(1000.0 * sqrt(float(tori)) / float(yrange))
riq_ref = int(1000.0 * sqrt(float(tori_ref)) / float(yrange))
# Send the results back
return tori, tori_ref, riq, riq_ref, tori_data
def get_time_series(identifiers,
bibcodes,
data=None,
usagedata=None,
tori_data=None,
include_tori=True,
self_cits=None):
series = {}
i10 = {}
i100 = {}
h = {}
g = {}
r10 = {}
tori = {}
# Get data if nothing was supplied
if not data:
data = get_citations(identifiers)
if not usagedata:
usagedata = get_usage_data(identifiers)
if not self_cits and include_tori:
self_cits = get_selfcitations(identifiers, bibcodes)[1]
self_citations = set((itertools.chain(*[x[0] for x in self_cits])))
if not tori_data and include_tori:
tdata = get_tori_data(identifiers)
tori_data = [
p for p in list(
itertools.chain(
*[p.rn_citation_data for p in tdata
if p.rn_citation_data]))
if p['bibcode'] not in self_citations and 'pubyear' in p
]
# Determine the year range
Nentries = datetime.now().year - 1996 + 1
years = [int(b[:4]) for b in bibcodes]
yrange = range(min(years), datetime.now().year + 1)
d0 = date(datetime.now().year, 1, 1)
d1 = date(datetime.now().year, datetime.now().month, datetime.now().day)
d2 = date(datetime.now().year, 12, 31)
delta = (d1 - d0).days + 1
ndays = (d2 - d0).days + 1
try:
r10_corr = float(ndays) / float(delta)
except:
r10_corr = 1.0
for year in yrange:
biblist = [b for b in bibcodes if int(b[:4]) <= year]
citations = sorted([
len([int(c[:4]) for c in p.citations if int(c[:4]) <= year])
for p in data if p.bibcode in biblist
],
reverse=True)
if year < 1996:
r10[year] = 0.0
else:
idx = year - 1996
r10[year] = sum([
float(p.reads[idx]) / float(p.author_num) for p in usagedata
if p.bibcode in biblist and int(p.bibcode[:4]) > year -
10 and p.reads and len(p.reads) == Nentries
])
try:
h[year] = max([i for i, n in enumerate(citations) if i <= n])
g[year] = max([
i for i, n in enumerate(np.cumsum(citations, axis=0))
if i**2 <= n
])
except:
h[year] = 0
g[year] = 0
i10[year] = len([c for c in citations if c >= 10])
i100[year] = len([c for c in citations if c >= 100])
if include_tori:
tori[year] = np.sum(
np.array([
r['auth_norm'] * r['ref_norm'] for r in tori_data
if r['pubyear'] <= year and r['cityear'] <= year
]))
r10[datetime.now().year] = r10[datetime.now().year] * r10_corr
series['i10'] = i10
series['i100'] = i100
series['h'] = h
series['g'] = g
series['read10'] = r10
if include_tori:
series['tori'] = tori
return series
def get_time_series(identifiers, bibcodes, data=None, usagedata=None,
tori_data=None, include_tori=True, self_cits=None):
series = {}
i10 = {}
i100 = {}
h = {}
g = {}
r10 = {}
tori = {}
# Get data if nothing was supplied
if not data:
data = get_citations(identifiers)
if not usagedata:
usagedata = get_usage_data(identifiers)
if not self_cits and include_tori:
self_cits = get_selfcitations(identifiers, bibcodes)[1]
self_citations = set((itertools.chain(*[x[0] for x in self_cits])))
if not tori_data and include_tori:
tdata = get_tori_data(identifiers)
tori_data = [p for p in list(itertools.chain(
*[p.rn_citation_data for p in tdata if p.rn_citation_data])) if
p['bibcode'] not in self_citations and 'pubyear' in p]
# Determine the year range
Nentries = datetime.now().year - 1996 + 1
years = [int(b[:4]) for b in bibcodes]
yrange = range(min(years), datetime.now().year + 1)
d0 = date(datetime.now().year, 1, 1)
d1 = date(datetime.now().year, datetime.now().month, datetime.now().day)
d2 = date(datetime.now().year, 12, 31)
delta = (d1 - d0).days + 1
ndays = (d2 - d0).days + 1
try:
r10_corr = float(ndays)/float(delta)
except:
r10_corr = 1.0
for year in yrange:
biblist = [b for b in bibcodes if int(b[:4]) <= year]
citations = sorted([len([int(c[:4]) for c in p.citations if int(
c[:4]) <= year]) for p in data if
p.bibcode in biblist], reverse=True)
if year < 1996:
r10[year] = 0.0
else:
idx = year - 1996
r10[year] = sum([float(p.reads[idx]) / float(p.author_num) for
p in usagedata if p.bibcode in biblist and int(
p.bibcode[:4]) > year - 10 and p.reads and
len(p.reads) == Nentries])
try:
h[year] = max([i for i, n in enumerate(citations) if i <= n])
g[year] = max(
[i for i, n in enumerate(np.cumsum(citations, axis=0)) if
i**2 <= n])
except:
h[year] = 0
g[year] = 0
i10[year] = len([c for c in citations if c >= 10])
i100[year] = len([c for c in citations if c >= 100])
if include_tori:
tori[year] = np.sum(np.array([r['auth_norm'] * r['ref_norm'] for
r in tori_data if
r['pubyear'] <= year and
r['cityear'] <= year]))
r10[datetime.now().year] = r10[datetime.now().year] * r10_corr
series['i10'] = i10
series['i100'] = i100
series['h'] = h
series['g'] = g
series['read10'] = r10
if include_tori:
series['tori'] = tori
return series