def articles_metrics(scopus_id, metric='PublicationsInTopJournalPercentiles'):
query = {'metricTypes':'%s'%(metric),
'yearRange':'5yrsAndCurrent',
'includeSelfCitations':'true',
'byYear':'false',
'includedDocs':'ArticlesOnly',
'journalImpactType':'SJR',
'showAsFieldWeighted':'true',
'indexType':'hIndex',
'authors':'%s' %(scopus_id)}
if metric== 'PublicationsInTopJournalPercentiles':
response = api_query(query)
data = response['results'][0]['metrics'][0]['values']
return data[-1]['value']
else:
query = {'metricTypes':'%s'%(metric),
'yearRange':'5yrsAndCurrent',
'includeSelfCitations':'true',
'byYear':'false',
'includedDocs':'ArticlesOnly',
'journalImpactType':'CiteScore',
'showAsFieldWeighted':'true',
'indexType':'hIndex',
'authors':'%s' %(scopus_id)}
response = api_query(query)
data = response['results'][0]['metrics'][0]['values']
return data[2]['value']
def scival_citescore_percentile_retrive(author_id):
''' This function is used for SciVal matric CiteScore percentile retrive
It takes API file, author id(SCOPUS_ID) as inputs and return
1st,5th, and 10th percentile pulications results as list. '''
import pandas as pd
from scival_author_metrics import api_query
pd.options.display.float_format = '{:.2f}'.format
assert type(author_id) is str
query = {
'metricTypes':
'FieldWeightedCitationImpact,PublicationsInTopJournalPercentiles',
'byYear': 'false',
'yearRange': '5yrsAndCurrent',
'authors': '%s' % (author_id)
}
#HTTP request response object
response = api_query(query)
# Retriving only 1,5 and 10th percentile publication values
*data, _ = response['results'][0]['metrics'][1]['values']
#Data frame from data dictioanry and only values are return
df = pd.DataFrame(data)
percentile = df['percentage'].values
return percentile
def benchmarking_metrics(scopus_id):
"""Retriving SciVal publication metrics using Scival API
input scopus_id for which return all metrics defined in the query
Parameter
----------
scopus_id : str
Author Scopus id
Returns
-------
metrics : obj
pandas dataframe metricType as index
value as column"""
query = {'metricTypes': """ScholarlyOutput,CitationCount,
FieldWeightedCitationImpact""",
'byYear': 'false',
'yearRange': '5yrsAndCurrent',
'includedDocs':'ArticlesOnly',
'journalImpactType':'CiteScore',
'authors': '%s' %(scopus_id)}
response = api_query(query)
response_data = response['results'][0]['metrics']
metrics = {data['metricType']: data['value'] for data in response_data}
return metrics
def scival_metrics(scopus_id):
"""Retriving SciVal default metrics using Scival API
input scopus_id for which return all default metrics
displayed in Scival.https://www.scival.com/overview/summary
Parameter
----------
scopus_id : str
Author Scopus id
Returns
-------
metrics : obj
pandas dataframe metricType as index
value as column"""
import pandas as pd
from scival_author_metrics import api_query
pd.set_option('display.float_format', lambda x: '%.3f' % x)
assert isinstance(scopus_id, (str, int))
#query for http request
query = {'metricTypes': """ScholarlyOutput,CitationCount,hIndices,
FieldWeightedCitationImpact,CitationsPerPublication,
OutputsInTopCitationPercentiles,PublicationsInTopJournalPercentiles""",
'byYear': 'false',
'yearRange': '5yrsAndCurrent',
'authors': '%s' %(scopus_id) }
response = api_query(query)
response_data = response['results'][0]['metrics']
#Making a pandas dataframe from data dictionary
df = pd.DataFrame.from_records(response_data)
# Publication metrics are CiteScore Percentile default of SciVal metrics
metrics = df[['metricType', 'value']]
#metrics = metrics.fillna(0.0)
metrics = metrics.set_index('metricType')
return metrics
def get_scival_fwci(author_id):
''' This function is used for retriving SciVal
Field weighted citation impact exclude selfcitations
input is author id (SCOPUS_ID).
Return mean FWCI.
Parameter
----------
author_id : str or int
Author Scopus Id
Return
-------
mean_fwci : float
mean FWCI for 5 years and current
'''
import pandas as pd
from scival_author_metrics import api_query
assert isinstance(author_id, (str, int))
#FWCI for last years and 5 years and current year exclude selfcitations and journal impact type SNIP
query = {
'metricTypes': 'FieldWeightedCitationImpact',
'yearRange': '5yrsAndCurrent',
'includeSelfCitations': 'false',
'byYear': 'true',
'includedDocs': 'AllPublicationTypes',
'journalImpactType': 'SNIP',
'showAsFieldWeighted': 'false',
'indexType': 'hIndex',
'authors': '%s' % (author_id)
}
#HTTP response object from request
response = api_query(query)
data = response['results'][0]['metrics'][0]
#Dataframe from the data dictionary and calculating mean from the values
fwci = pd.DataFrame.from_dict(data)
fwci = fwci.drop(['metricType'], axis=1)
mean_fwci = fwci.mean()
return mean_fwci
def scival_SJR_percentile_retrive(author_id):
''' This function retrives SciVal matric SJR top ten percentile.
input is author id(SCOPUS_ID) and return ten percentile
exclude selfcitations pulications in SJR journal.
Parameter
----------
author_id : str or int
Author Scopus Id
Return
-------
top_10_percentile : float
ten percentile publication in SJR journal
'''
import pandas as pd
from scival_author_metrics import api_query
pd.options.display.float_format = '{:.2f}'.format
assert isinstance(author_id, (str, int))
query = {
'metricTypes': 'PublicationsInTopJournalPercentiles',
'yearRange': '5yrs',
'includeSelfCitations': 'false',
'byYear': 'false',
'includedDocs': 'AllPublicationTypes',
'journalImpactType': 'SJR',
'showAsFieldWeighted': 'false',
'indexType': 'hIndex',
'authors': '%s' % (author_id)
}
#HTTP request response object
response = api_query(query)
data = response['results'][0]['metrics'][0]['values']
#Dataframe from the SJR percentile data dictionary
df = pd.DataFrame.from_dict(data)
# Get the publication percentile from the data frmae
# If the nubmer is needed change dataframe column name to value
top_10_percentile = df.iloc[2]['percentage']
return top_10_percentile
def get_scival_fwci_selfcite(author_id):
''' This function is used for retriving SciVal
Field weighted citation impact with selfcitations
input is author id (SCOPUS_ID).
Return FWCI Journal impact type is CiteScore.
Parameter
----------
author_id : str or int
Author Scopus Id
Return
-------
mean_fwci : float
FWCI for 5 years and current
'''
from scival_author_metrics import api_query
assert isinstance(author_id, (str, int))
query = {'metricTypes' :'FieldWeightedCitationImpact',
'yearRange' : '5yrsAndCurrent',
'includeSelfCitations':'true',
'byYear':'false',
'includedDocs':'AllPublicationTypes',
'journalImpactType':'CiteScore',
'showAsFieldWeighted':'true',
'indexType':'hIndex',
'authors': '%s' %(author_id)}
#HTTP request response object
response = api_query(query)
#FWCI data frame from data dictionary
fwci = response['results'][0]['metrics'][0]['value']
return fwci
def scival_top_citation_percentiles_retrive(author_id):
''' This function retrive SciVal matric outputs in top
percentile. Input is author id(SCOPUS_ID)
and return output in top 10th percentile cited.
Parameter
----------
author_id : str or int
Author Scopus Id
Return
-------
percentile : float
Top 10th percentile cited
'''
import pandas as pd
from scival_author_metrics import api_query
pd.options.display.float_format = '{:.2f}'.format
assert isinstance(author_id, (str, int))
query = {
'metricTypes': 'OutputsInTopCitationPercentiles',
'showAsFieldWeighted': 'true',
'byYear': 'false',
'yearRange': '5yrsAndCurrent',
'authors': '%s' % (author_id)
}
#HTTP response from request
response = api_query(query)
data = response['results'][0]['metrics'][0]['values']
#Dataframe from the top citiation percentiles data
df = pd.DataFrame(data)
percentile = df['percentage'].values
return percentile[2]