def get_indicators(identifiers, data=None, usagedata=None):
ind = {}
ind_ref = {}
# Get the necessary data if we did not get any
if not data:
data = get_indicator_data(identifiers)
if not usagedata:
usagedata = get_usage_data(identifiers)
# Organize the citations with a running index (the citation
# data is already ordered from most to least cited)
citations = [(i + 1, p.citation_num) for i, p in enumerate(data)]
# First the Hirsch index
ind['h'] = max([x[0] for x in citations if x[1] >= x[0]] or [0])
# Next the g index
ind['g'] = max([i for (c, i) in zip(list(np.cumsum([x[1] for
x in citations], axis=0)), [x[0] for x in citations]) if
i**2 <= c] or [0])
# The number of paper with 10 or more citations (i10)
ind['i10'] = len([x for x in citations if x[1] >= 10])
# The number of paper with 100 or more citations (i100)
ind['i100'] = len([x for x in citations if x[1] >= 100])
# The m index is the g index divided by the range of publication years
yrange = datetime.now().year - \
min([int(p.bibcode[:4]) for p in usagedata]) + 1
ind['m'] = float(ind['h']) / float(yrange)
# The read10 index is calculated from current reads for papers published
# in the last 10 years, normalized by number of authors
year = datetime.now().year
Nentries = year - 1996 + 1
ind['read10'] = sum([float(p.reads[-2]) / float(p.author_num)
for p in usagedata if
int(p.bibcode[:4]) > year - 10 and p.reads and
len(p.reads) == Nentries])
# Now all the values for the refereed publications
citations = [(i + 1, n) for i, n in enumerate([p.citation_num for p in
data if p.refereed])]
# First the Hirsch index
ind_ref['h'] = max([x[0] for x in citations if x[1] >= x[0]] or [0])
# Next the g index
ind_ref['g'] = max([i for (c, i) in zip(list(np.cumsum(
[x[1] for x in citations], axis=0)), [x[0] for x in citations]) if
i**2 <= c] or [0])
# The number of paper with 10 or more citations (i10)
ind_ref['i10'] = len([x for x in citations if x[1] >= 10])
# The number of paper with 100 or more citations (i100)
ind_ref['i100'] = len([x for x in citations if x[1] >= 100])
# The m index is the g index divided by the range of publication years
yrange_ref = datetime.now().year - \
min([int(p.bibcode[:4]) for p in usagedata]) + 1
ind_ref['m'] = float(ind_ref['h']) / float(yrange_ref)
# The read10 index is calculated from current reads for papers published
# in the last 10 years, normalized by number of authors
year = datetime.now().year
Nentries = year - 1996 + 1
ind_ref['read10'] = sum([float(p.reads[-1]) / float(p.author_num)
for p in usagedata if p.refereed and
int(p.bibcode[:4]) > year - 10 and
p.reads and len(p.reads) == Nentries])
# Send results back
return ind, ind_ref
def test_get_indicator_data(self):
'''Test getting indicator data'''
from models import get_indicator_data
data = get_indicator_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_indicator_data(self):
'''Test getting indicator data'''
from models import get_indicator_data
data = get_indicator_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_indicators(identifiers, data=None, usagedata=None):
ind = {}
ind_ref = {}
# Get the necessary data if we did not get any
if not data:
data = get_indicator_data(identifiers)
if not usagedata:
usagedata = get_usage_data(identifiers)
# Organize the citations with a running index (the citation
# data is already ordered from most to least cited)
citations = [(i + 1, p.citation_num) for i, p in enumerate(data)]
# First the Hirsch index
ind['h'] = max([x[0] for x in citations if x[1] >= x[0]] or [0])
# Next the g index
ind['g'] = max([
i for (c, i) in zip(list(np.cumsum([x[1] for x in citations], axis=0)),
[x[0] for x in citations]) if i**2 <= c
] or [0])
# The number of paper with 10 or more citations (i10)
ind['i10'] = len([x for x in citations if x[1] >= 10])
# The number of paper with 100 or more citations (i100)
ind['i100'] = len([x for x in citations if x[1] >= 100])
# The m index is the g index divided by the range of publication years
yrange = datetime.now().year - \
min([int(p.bibcode[:4]) for p in usagedata]) + 1
ind['m'] = float(ind['h']) / float(yrange)
# The read10 index is calculated from current reads for papers published
# in the last 10 years, normalized by number of authors
year = datetime.now().year
Nentries = year - 1996 + 1
ind['read10'] = sum([
float(p.reads[-2]) / float(p.author_num) for p in usagedata
if int(p.bibcode[:4]) > year -
10 and p.reads and len(p.reads) == Nentries
])
# Now all the values for the refereed publications
citations = [
(i + 1, n)
for i, n in enumerate([p.citation_num for p in data if p.refereed])
]
# First the Hirsch index
ind_ref['h'] = max([x[0] for x in citations if x[1] >= x[0]] or [0])
# Next the g index
ind_ref['g'] = max([
i for (c, i) in zip(list(np.cumsum([x[1] for x in citations], axis=0)),
[x[0] for x in citations]) if i**2 <= c
] or [0])
# The number of paper with 10 or more citations (i10)
ind_ref['i10'] = len([x for x in citations if x[1] >= 10])
# The number of paper with 100 or more citations (i100)
ind_ref['i100'] = len([x for x in citations if x[1] >= 100])
# The m index is the g index divided by the range of publication years
yrange_ref = datetime.now().year - \
min([int(p.bibcode[:4]) for p in usagedata]) + 1
ind_ref['m'] = float(ind_ref['h']) / float(yrange_ref)
# The read10 index is calculated from current reads for papers published
# in the last 10 years, normalized by number of authors
year = datetime.now().year
Nentries = year - 1996 + 1
ind_ref['read10'] = sum([
float(p.reads[-1]) / float(p.author_num) for p in usagedata
if p.refereed and int(p.bibcode[:4]) > year -
10 and p.reads and len(p.reads) == Nentries
])
# Send results back
return ind, ind_ref