import os
from bibliobanana import compute_yearly_citations, load_results_from_file, \
plot_yearly_count
search_term = "increased interest"
start_date = 1964
end_date = 2020
save_file = "{}_{}-{}".format(search_term, start_date, end_date)
# Get the results from PubMed.
if not os.path.isfile(save_file + ".csv"):
result_dict = compute_yearly_citations(search_term, start_date, end_date, \
comparison_terms="banana", database="pubmed", pause=0.5, verbose=True, \
save_to_file=save_file+".csv", plot_to_file=None)
# Load the results from a local file.
else:
result_dict = load_results_from_file(save_file + ".csv")
# Plot the results.
fig, ax = plot_yearly_count(result_dict, plot_ratio=False, \
plot_average_comparison=False, scale_to_max=False, \
figsize=(8.0,6.0), dpi=600.0)
fig.savefig(save_file + ".png")
"Thyroid Neoplasms", \
]
# Define the comperison terms.
comparison_terms = ["Endocrine Gland Neoplasms"]
# Define the search range.
start_date = 1945
end_date = 2019
# Construct the name of the file to which we should save the data.
save_file = "MeSH-neoplasms_{}-{}".format(start_date, end_date)
# Get the results from PubMed.
if not os.path.isfile(save_file+".csv"):
print("Getting data from PubMed...")
result = compute_yearly_citations(search_terms, start_date, end_date, \
comparison_terms=comparison_terms, database="pubmed", \
pubmed_field="mesh", exact_phrase=True, pause=0.5, verbose=True, \
save_to_file=save_file+".csv", plot_to_file=None)
# Load from an existing file.
else:
print("Loading data from file...")
result = load_results_from_file(save_file+".csv")
print("Plotting results...")
# Plot the results.
fig, ax = plot_yearly_count(result, plot_ratio=False, \
plot_average_comparison=False, scale_to_max=False, \
figsize=(8.0,6.0), dpi=600.0)
fig.savefig(save_file+".png")
# Plot the results as ratios of the comparison terms.