def main():
print("\nAUTOMATED LITERATURE SEARCH\n")
input('\n>> Press to see terms\n')
terms_brain = [['theta oscillation'], ['beta oscillation']]
terms_behav = [['memory'], ['motor']]
print('BRAIN TERMS')
for term in terms_brain:
print('\t' + term[0])
print('BEHAV TERMS')
for term in terms_behav:
print('\t' + term[0])
input('\n>> Press to run scrape\n')
# Initialize counts object
counts = Count()
counts.set_terms(terms_brain, 'A')
counts.set_terms(terms_behav, 'B')
counts.run_scrape(verbose=True)
input('\n>> Press to see results\n')
# Check the highest associations for each term
counts.check_cooc('A')
# Jump over to ERP-SCANR site
input('\n>> Press to jump to project site\n')
print("Opening ERP-SCANNER WebSite")
webbrowser.get('Safari').open('http://tomdonoghue.github.io/ERP_SCANR/')
def test_scrape():
"""Test that Count object successful scrapes data."""
counts = Count()
# Add ERPs and terms
counts.set_erps(['N400', 'P600'])
counts.set_terms(['language', 'memory'])
counts.set_exclusions(['protein', 'protein'])
counts.scrape_data(db='pubmed')
assert np.all(counts.dat_numbers)
assert np.all(counts.dat_percent)
check_funcs(counts)
def main():
"""Run scrape of counts data."""
counts = Count()
if TEST:
counts.set_terms([['language'], ['visual']])
else:
counts.terms['A'].set_terms_file(TERMS_FILE)
# Reduce number of terms
counts.terms['A'].terms = counts.terms['A'].terms[:40]
counts.terms['A'].labels = counts.terms['A'].labels[:40]
print('N terms:', len(counts.terms['A'].terms))
print('\n\nSTARTING COUNTS SCRAPE')
print('RUNNING TERMS TYPE: ', TERMS_FILE, '\n\n')
counts.run_scrape(verbose=True)
print('\n\nCOUNTS SCRAPE FINISHED\n\n')
save_pickle_obj(counts, S_NAME)
print('\n\nCOUNTS SCRAPE SAVED\n\n')
def test_save_pickle_obj():
"""Test the save_pickle_obj function."""
tdb = TDB()
count_obj = Count()
words_obj = Words()
save_pickle_obj(count_obj, 'test', db=tdb)
save_pickle_obj(words_obj, 'test', db=tdb)
assert True
# Test error checking
with raises(InconsistentDataError):
save_pickle_obj(['bad dat'], 'test_bad', db=tdb)
def test_scrape():
"""Test that Count object successful scrapes data."""
counts = Count()
counts.set_terms(['language', 'memory'])
counts.set_exclusions(['protein', 'protein'])
#counts.run_scrape(db='pubmed')
# check_funcs(counts)
# drop_data(counts)
assert True
def test_count():
assert Count()
################################################################################################### # # Count Object # ------------ # # There is also an OOP interface available in LISC, # to organize the terms and data, and run scrapes. # # Note that the underlying code is the same - the count object ultimately calls # the same scrape function as above. ################################################################################################### # Initialize counts object counts = Count() ################################################################################################### # Set terms to run counts.set_terms(terms_a) ################################################################################################### # Run scrape counts.run_scrape(verbose=True) ################################################################################################### # # # The Counts object also comes with some helper methods to check out the data.
def test_erpsc_count():
"""Test the Count object."""
# Check that ERPSCCount returns properly
assert Count()