def test_get_basic_stats_data(self):
'''Test getting basic stats data'''
from models import get_basic_stats_data
data = get_basic_stats_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_basic_stats_data(self):
'''Test getting basic stats data'''
from models import get_basic_stats_data
data = get_basic_stats_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_basic_stats(identifiers):
# basic stats for all publications
bs = {}
# basic stats for refereed publications`
bsr = {}
# Get the data to calculate the basic stats
data = get_basic_stats_data(identifiers)
# First get the number of (refereed) papers
bs['number of papers'] = len(identifiers)
bsr['number of papers'] = len([p for p in data if p.refereed])
# Next get the (refereed) normalized paper count
bs['normalized paper count'] = np.sum(
np.array([1.0 / float(p.author_num) for p in data]))
bsr['normalized paper count'] = np.sum(
np.array([1.0 / float(p.author_num) for p in data if p.refereed]))
# Get the total number of reads
year = datetime.now().year
Nentries = year - 1996 + 1
reads = [p.reads for p in data if p.reads and len(p.reads) == Nentries]
reads_ref = [
p.reads for p in data
if p.refereed and p.reads and len(p.reads) == Nentries
]
reads_totals = [sum(r) for r in reads]
reads_ref_totals = [sum(r) for r in reads_ref]
bs['total number of reads'] = np.sum(reads_totals or [0])
bsr['total number of reads'] = np.sum(reads_ref_totals or [0])
# Get the average number of reads
bs['average number of reads'] = np.mean(reads_totals or [0])
bsr['average number of reads'] = np.mean(reads_ref_totals or [0])
# Get the median number of reads
bs['median number of reads'] = np.median(reads_totals or [0])
bsr['median number of reads'] = np.median(reads_ref_totals or [0])
# Get the normalized number of reads
# bs['normalized number of reads'] = \
# np.sum([np.array(p.reads)/float(p.author_num)
# for p in data if p.reads and len(p.reads) == Nentries])
# bsr['normalized number of reads'] = \
# sum([p.reads[-1] for p in data if p.refereed and
# p.reads and len(p.reads) == Nentries])
# and finally, get the recent reads
bs['recent number of reads'] = sum(
[p.reads[-1] for p in data if p.reads and len(p.reads) == Nentries])
bsr['recent number of reads'] = sum([
p.reads[-1] for p in data
if p.refereed and p.reads and len(p.reads) == Nentries
])
# Do the same for the downloads
downloads = [
p.downloads for p in data
if p.downloads and len(p.downloads) == Nentries
]
downloads_ref = [
p.downloads for p in data
if p.refereed and p.downloads and len(p.downloads) == Nentries
]
downloads_totals = [sum(d) for d in downloads]
downloads_ref_totals = [sum(d) for d in downloads_ref]
bs['total number of downloads'] = np.sum(downloads_totals or [0])
bsr['total number of downloads'] = np.sum(downloads_ref_totals or [0])
# Get the average number of downloads
bs['average number of downloads'] = np.mean(downloads_totals or [0])
bsr['average number of downloads'] = np.mean(downloads_ref_totals or [0])
# Get the median number of downloads
bs['median number of downloads'] = np.median(downloads_totals or [0])
bsr['median number of downloads'] = np.median(downloads_ref_totals or [0])
# Get the normalized number of downloads
# bs['normalized number of downloads'] = \
# np.sum([np.array(p.downloads)/float(p.author_num) for p in data if
# p.downloads and len(p.downloads) == Nentries])
# bsr['normalized number of downloads'] = \
# np.sum([np.array(p.downloads)/float(p.author_num) for p in data if
# p.refereed and p.downloads and len(p.downloads) == Nentries])
# and finally, get the recent number of downloads
bs['recent number of downloads'] = sum([
p.downloads[-1] for p in data
if p.downloads and len(p.downloads) == Nentries
])
bsr['recent number of downloads'] = sum([
p.downloads[-1] for p in data
if p.refereed and p.downloads and len(p.downloads) == Nentries
])
# Return both results and the data (which will get used later on
# if the usage histograms are required)
return bs, bsr, data
def get_basic_stats(identifiers):
# basic stats for all publications
bs = {}
# basic stats for refereed publications`
bsr = {}
# Get the data to calculate the basic stats
data = get_basic_stats_data(identifiers)
# First get the number of (refereed) papers
bs['number of papers'] = len(identifiers)
bsr['number of papers'] = len([p for p in data if p.refereed])
# Next get the (refereed) normalized paper count
bs['normalized paper count'] = np.sum(
np.array([1.0 / float(p.author_num) for p in data]))
bsr['normalized paper count'] = np.sum(
np.array([1.0 / float(p.author_num) for p in data if p.refereed]))
# Get the total number of reads
year = datetime.now().year
Nentries = year - 1996 + 1
reads = [p.reads for p in data if p.reads and len(p.reads) == Nentries]
reads_ref = [
p.reads for p in data if p.refereed and p.reads and
len(p.reads) == Nentries]
reads_totals = [sum(r) for r in reads]
reads_ref_totals = [sum(r) for r in reads_ref]
bs['total number of reads'] = np.sum(reads_totals or [0])
bsr['total number of reads'] = np.sum(reads_ref_totals or [0])
# Get the average number of reads
bs['average number of reads'] = np.mean(reads_totals or [0])
bsr['average number of reads'] = np.mean(reads_ref_totals or [0])
# Get the median number of reads
bs['median number of reads'] = np.median(reads_totals or [0])
bsr['median number of reads'] = np.median(reads_ref_totals or [0])
# Get the normalized number of reads
# bs['normalized number of reads'] = \
# np.sum([np.array(p.reads)/float(p.author_num)
# for p in data if p.reads and len(p.reads) == Nentries])
# bsr['normalized number of reads'] = \
# sum([p.reads[-1] for p in data if p.refereed and
# p.reads and len(p.reads) == Nentries])
# and finally, get the recent reads
bs['recent number of reads'] = sum(
[p.reads[-1] for p in data if p.reads and len(p.reads) == Nentries])
bsr['recent number of reads'] = sum(
[p.reads[-1] for p in data if p.refereed and p.reads and
len(p.reads) == Nentries])
# Do the same for the downloads
downloads = [
p.downloads for p in data if p.downloads and
len(p.downloads) == Nentries]
downloads_ref = [p.downloads for p in data if p.refereed and
p.downloads and len(p.downloads) == Nentries]
downloads_totals = [sum(d) for d in downloads]
downloads_ref_totals = [sum(d) for d in downloads_ref]
bs['total number of downloads'] = np.sum(downloads_totals or [0])
bsr['total number of downloads'] = np.sum(downloads_ref_totals or [0])
# Get the average number of downloads
bs['average number of downloads'] = np.mean(downloads_totals or [0])
bsr['average number of downloads'] = np.mean(downloads_ref_totals or [0])
# Get the median number of downloads
bs['median number of downloads'] = np.median(downloads_totals or [0])
bsr['median number of downloads'] = np.median(downloads_ref_totals or [0])
# Get the normalized number of downloads
# bs['normalized number of downloads'] = \
# np.sum([np.array(p.downloads)/float(p.author_num) for p in data if
# p.downloads and len(p.downloads) == Nentries])
# bsr['normalized number of downloads'] = \
# np.sum([np.array(p.downloads)/float(p.author_num) for p in data if
# p.refereed and p.downloads and len(p.downloads) == Nentries])
# and finally, get the recent number of downloads
bs['recent number of downloads'] = sum(
[p.downloads[-1] for p in data if p.downloads and
len(p.downloads) == Nentries])
bsr['recent number of downloads'] = sum(
[p.downloads[-1] for p in data if p.refereed and p.downloads and
len(p.downloads) == Nentries])
# Return both results and the data (which will get used later on
# if the usage histograms are required)
return bs, bsr, data
