def run(target_folder, origins_path, destinations_path, mode):
"""
make a table that ranks from least total time to most
"""
# set up results paths
results_path = join(target_folder, 'results.csv')
rankings_path = join(target_folder, 'rankings.csv')
log_path = join(target_folder, 'log_results.txt')
geomap_path = join(target_folder, 'geomap.csv')
origins = load_unique_lines(origins_path)
destinations = load_unique_lines(destinations_path)
json = get_json(origins, destinations, mode)
# TODO: Check status of each output -> result.rows[i].elements[i].status
origins, destinations = (json['origin_addresses'],
json['destination_addresses'])
origin_to_destination_stats = zip(origins, json['rows'])
geomap_table = get_geotable(origins, destinations)
geomap_table.to_csv(geomap_path, index=False)
duration_results, rankings = get_results(origin_to_destination_stats,
destinations)
# Output results
log = [origin + ': ' + str(total_time) for
origin, total_time in rankings]
log = "\n".join(log)
with open(log_path, 'w') as f:
f.write(log)
columns = ['Lodging Name', 'Destination', 'Duration']
results_table = DataFrame(duration_results, columns=columns)
results_table.to_csv(results_path, index=False)
columns = ['Lodging Name', 'Total Time']
rankings_table = DataFrame(rankings, columns=columns)
rankings_table.to_csv(rankings_path, index=False)
# Required print statement for crosscompute
print("points_geotable_path = {0}".format(geomap_path))
print("rankings_table_path = {0}".format(rankings_path))
print("results_table_path = {0}".format(results_path))
print("results_text_path = {0}".format(log_path))
return (geomap_table, rankings_table)
def run(
target_folder, journals_path=None, authors_path=None,
keywords_path=None, mesh_terms_path=None,
from_date=None, to_date=None, date_interval_in_years=0):
search_count_path = join(target_folder, 'search_counts.csv')
first_name_path = join(target_folder, 'first_named_articles.csv')
image_path = join(target_folder, 'keyword_article_count.jpg')
# Input retrieval
journals = load_unique_lines(journals_path)
text_words = load_unique_lines(keywords_path)
mesh_terms = load_unique_lines(mesh_terms_path)
authors = load_unique_lines(authors_path)
# Get list containing intervals of date ranges based on interval specified
# by user
try:
date_ranges = get_date_ranges(
from_date, to_date, date_interval_in_years)
except ToolError as e:
exit('to_date.error = {0}'.format(e))
isAuthor, query_list = ((True, authors) if authors
else (False, journals))
# Tabulate keywords
results = tabulate(
query_list, date_ranges, text_words, mesh_terms, isAuthor)
author_articles = results['author_articles']
search_count = results['search_counts']
sc_df = DataFrame(search_count[1:], columns=search_count[0])
sc_df.to_csv(search_count_path, index=False)
# crosscompute print statement
if isAuthor:
cols = ['Author', 'No. first name articles']
first_name_articles = [(name, len(get_first_name_articles(
name, author_articles[name])))
for name in authors]
fa_df = DataFrame(first_name_articles, columns=cols)
fa_df.to_csv(first_name_path, index=False)
print("first_name_articles_table_path = " + first_name_path)
print("search_count_table_path = " + search_count_path)
def test_load_unique_lines():
lines = load_unique_lines(join(TEST_FOLDER, 'lines.txt'))
assert lines == ['one', 'two']
def test_load_unique_lines():
lines = load_unique_lines(join(TEST_FOLDER, 'lines.txt'))
assert lines == ['one', 'two']
