main_test.py

from pybliometrics.scopus import ScopusSearch,AbstractRetrieval,AuthorRetrieval
import pandas as pd 
import numpy as np 
import source_code
from source_code import functions,classes
from source_code.classes import research_topic

#--------------------------------------------------------------------------
#                                Case study
#--------------------------------------------------------------------------


#--------------------------------- Input data --------------------------------
# Define th name of research topic 
name='self_consumption' # also used for naming the xlsx and npy files  

# Define the eid of one reference paper 
reference_paper_eid="2-s2.0-85123755228"

# Define the keywords as a criterion
keywords=['collective self-consumption'
          ]

# Note: In this particular example, we intentionally used a long keyword to 
# reduce the number of corresponding papers and therefore get the results faster


# Find keyword variations 
keywords=functions.generate_variations(keywords)

#------------------------------ Doing the analysis --------------------------------
# Create an object of research topic 
self_consumption=research_topic(name,reference_paper_eid,keywords)

# Analyze the object "self_consumption"
results=self_consumption.analyze()

# Plot the interactive graph
results.plot_network_graph()

# -------------------------------- Output data ---------------------------------
# see the outputs xlsx - the main file with relevant publications 

# Additional files as publciations outside of scopus and with errors  
np.save(results.name+'_'+'publications_outside_scopus.npy',results.publications_outside_scopus)
np.save(results.name+'_'+'publications_with_errors.npy',results.publications_with_errors)
np.save(results.name+'_'+'paper_population.npy',results.paper_population)