def estimate_pmc_coverage(query, start_year="1800", end_year="3000"): pubmed_query = query + ' AND ("' + start_year + '"[pdat] : "' + end_year + '"[pdat])' pubmed_ids = pubmed.search(pubmed_query) num_pubmed = len(pubmed_ids) pmc_query = query + ' AND ("' + start_year + '"[PubDate] : "' + end_year + '"[PubDate])' pmc_ids = pubmedcentral.search(pmc_query) num_pmc = len(pmc_ids) ratio = num_pmc / (num_pubmed + 0.0) return (num_pmc, num_pubmed, ratio)
def estimate_pmc_coverage(query, start_year="1800", end_year="3000"): pubmed_query = query + ' AND ("' + start_year + '"[pdat] : "' + end_year + '"[pdat])' pubmed_ids = pubmed.search(pubmed_query) num_pubmed = len(pubmed_ids) pmc_query = query + ' AND ("' + start_year + '"[PubDate] : "' + end_year + '"[PubDate])' pmc_ids = pubmedcentral.search(pmc_query) num_pmc = len(pmc_ids) ratio = num_pmc / (num_pubmed + 0.0) return(num_pmc, num_pubmed, ratio)
feature_sources = ["mesh_basic", "mesh_major", "mesh_qualifier", "article_title", "tiabs", "abstract"] #A = healthy_1991to2008_5or6 #B = healthy_2002to2008_not5or6 #A = healthy_1991to2001_3or4 #B = healthy_1991to2001_1or2 + healthy_1991to2001_5or6 # + healthy_1991to2001_7 #A = healthy_1991to2001_5or6 #B = healthy_1991to2001_1or2 + healthy_1991to2001_3or4 from data import neuroethicslike #A = neuroethicslike.pubmed_neurethicslike_query_results #A = neuroethicslike.pubmed_fmri_neuroethicslike_query_results base_query = """("fmri"[text] OR "magnetic resonance imaging"[mesh]) AND ((neurosciences[mesh] OR neuroscience[Title/Abstract] OR neurology[mesh]) AND (ethics[sh] OR ethical[Title/Abstract] OR "bioethical issues"[mesh] OR "ethics, medical"[mesh] OR "legislation and jurisprudence"[Subheading])) OR neuroethic*[Title/Abstract]""" base = pubmed.search(base_query) A = pubmed.filter_pmids(base, "Personal Autonomy") dist = coveyquery.get_mesh_frequency_distributions(A, getter=pubmed.mesh_basic) print "\n\nTop list for (A, getter=pubmed.mesh_basic):" coveyquery.print_frequency_proportion(A, dist, 150) if False: dist = coveyquery.get_text_frequency_distributions(A, getter=pubmed.article_title) print "\n\nTop list for (A, getter=pubmed.article_title):" coveyquery.print_frequency_proportion(A, dist, 30) dist = coveyquery.get_text_frequency_distributions(A, getter=pubmed.abstract) print "\n\nTop list for (A, getter=pubmed.abstract):" coveyquery.print_frequency_proportion(A, dist, 30)