def test_calculate_h_index():
citations_with_h_index_5 = {
'123311': 34,
'123113': 3,
'3424': 5,
'3423421': 7,
'3242346': 8,
'3426733': 12,
'1231432': 2
}
expected = 5
result = calculate_h_index(citations_with_h_index_5)
assert expected == result
def serialize(self, pid, record, links_factory=None):
"""Return a different metrics for a given author recid.
:param pid:
Persistent identifier instance.
:param record:
Record instance.
:param links_factory:
Factory function for the link generation, which are added to
the response.
"""
author_pid = pid.pid_value
fields = set()
keywords = []
statistics = {}
statistics['citations'] = 0
statistics['publications'] = 0
statistics['types'] = {}
statistics_citations = {}
search = LiteratureSearch().query({
"match": {
"authors.recid": author_pid
}
}).params(
_source=[
"citation_count",
"control_number",
"facet_inspire_doc_type",
"facet_inspire_subjects",
"thesaurus_terms",
]
)
for result in search.scan():
result_source = result.to_dict()
# Increment the count of the total number of publications.
statistics['publications'] += 1
# Increment the count of citations.
citation_count = result_source.get('citation_count', 0)
statistics['citations'] += citation_count
statistics_citations[int(result_source['control_number'])] = \
citation_count
# Count how many times certain type of publication was published.
try:
publication_type = result_source.get(
'facet_inspire_doc_type', [])[0]
except IndexError:
pass
if publication_type:
if publication_type in statistics['types']:
statistics['types'][publication_type] += 1
else:
statistics['types'][publication_type] = 1
# Get fields.
for field in result_source.get('facet_inspire_subjects', []):
fields.add(field)
# Get keywords.
keywords.extend([
k for k in force_force_list(
get_value(result_source, 'thesaurus_terms.keyword'))
if k != '* Automatic Keywords *'])
# Calculate h-index together with i10-index.
statistics['hindex'] = calculate_h_index(statistics_citations)
statistics['i10index'] = calculate_i10_index(statistics_citations)
if fields:
statistics['fields'] = list(fields)
# Return the top 25 keywords.
if keywords:
counter = Counter(keywords)
statistics['keywords'] = [{
'count': i[1],
'keyword': i[0]
} for i in counter.most_common(25)]
return json.dumps(statistics)
def serialize(self, pid, record, links_factory=None):
"""Return a different metrics for a given author recid.
:param pid:
Persistent identifier instance.
:param record:
Record instance.
:param links_factory:
Factory function for the link generation, which are added to
the response.
"""
author_pid = pid.pid_value
fields = set()
keywords = []
statistics = {}
statistics['citations'] = 0
statistics['publications'] = 0
statistics['types'] = {}
statistics_citations = {}
search = LiteratureSearch().query({
"match": {
"authors.recid": author_pid
}
}).params(_source=[
"citation_count",
"control_number",
"facet_inspire_doc_type",
"facet_inspire_subjects",
"keywords",
])
for result in search.scan():
result_source = result.to_dict()
# Increment the count of the total number of publications.
statistics['publications'] += 1
# Increment the count of citations.
citation_count = result_source.get('citation_count', 0)
statistics['citations'] += citation_count
statistics_citations[result_source['control_number']] = \
citation_count
# Count how many times certain type of publication was published.
try:
publication_type = result_source.get('facet_inspire_doc_type',
[])[0]
except IndexError:
pass
if publication_type:
if publication_type in statistics['types']:
statistics['types'][publication_type] += 1
else:
statistics['types'][publication_type] = 1
# Get fields.
for field in result_source.get('facet_inspire_subjects', []):
fields.add(field)
# Get keywords.
keywords.extend([
k
for k in force_list(get_value(result_source, 'keywords.value'))
if k != '* Automatic Keywords *'
])
# Calculate h-index together with i10-index.
statistics['hindex'] = calculate_h_index(statistics_citations)
statistics['i10index'] = calculate_i10_index(statistics_citations)
if fields:
statistics['fields'] = list(fields)
# Return the top 25 keywords.
if keywords:
counter = Counter(keywords)
statistics['keywords'] = [{
'count': i[1],
'keyword': i[0]
} for i in counter.most_common(25)]
return json.dumps(statistics)
def test_calculate_h_index_ignores_none_values(citations_with_none_values):
expected = 4
result = calculate_h_index(citations_with_none_values)
assert expected == result