def generate_citations(radius=3):
"""Returns a list of text citations associated with some location, word
or topic (cluster)."""
clicked_on = request.args.get("options")
if clicked_on == "location":
x_coord = float(request.args.get("xcoord"))
y_coord = float(request.args.get("ycoord"))
z_coord = float(request.args.get("zcoord"))
pmids = Activation.get_pmids_from_xyz(x_coord, y_coord, z_coord, radius)
elif clicked_on == "word":
word = request.args.get("word")
# Get the pmids for a word
pmids = StudyTerm.get_pmid_by_term(word)
elif clicked_on == "cluster":
cluster = request.args.get("cluster")
# Get the words for a cluster
# Then get the top studies for the words
words = TermCluster.get_words_in_cluster(cluster)
pmids = StudyTerm.get_pmid_by_term(words)
elif clicked_on == "study":
pmid = request.args.get("pmid")
study = Study.get_study_by_pmid(pmid)
# Look for cluster-mate studies
pmids = study.get_cluster_mates()
citations = Study.get_references(pmids)
return jsonify(citations)
def generate_d3(radius=3):
""" Returns JSON with xyz at the root node.
Test with parameters: 40, -45, -25 (Fusiform face area)
"""
clicked_on = request.args.get("options")
if clicked_on == "location":
x_coord = float(request.args.get("xcoord"))
y_coord = float(request.args.get("ycoord"))
z_coord = float(request.args.get("zcoord"))
pmids = Activation.get_pmids_from_xyz(x_coord, y_coord, z_coord, radius)
scale = 70000
# Get [(wd, freq), ...] and [wd1, wd2] for most frequent words
elif clicked_on == "study":
pmid = request.args.get("pmid")
study = Study.get_study_by_pmid(pmid)
pmids = study.get_cluster_mates()
scale = 30000
terms_for_dict, words = StudyTerm.get_terms_by_pmid(pmids)
# Optional: transform the terms
# Get the top clusters
top_clusters = TermCluster.get_top_clusters(words)
# Get the cluster-word associations
associations = TermCluster.get_word_cluster_pairs(top_clusters, words)
# Make the root node:
root_dict = {"name": "", "children": []}
# Build the terminal nodes (leaves) first using (wd, freq) tuples
# Output: {word: {'name': word, 'size': freq}, word2: ... }
leaves = {}
for (word, freq) in terms_for_dict:
if word not in leaves:
leaves[word] = {"name": word, "size": freq * scale}
else:
leaves[word]["size"] += freq * scale
# Embed the leaves in the clusters:
# Output: {cluster_id: {'name': ID, 'children': [...]}, ... }
clusters = {}
for (cluster_id, word) in associations:
if cluster_id not in clusters:
clusters[cluster_id] = {"name": cluster_id, "children": [leaves[word]]}
else:
clusters[cluster_id]["children"].append(leaves[word])
# Put the clusters in the root dictionary
# Output: {'name': root, children: [{'name': id, 'children': []}, ...]
for cluster in top_clusters:
root_dict["children"].append(clusters[cluster])
return jsonify(root_dict)