def generate_intensity():
"""Generates an intensity data file related to some user action.
Clear: clear the old intensity mapping
Cluster: intensity mapping associated with a topic cluster
Word: intensity mapping associated with a particular word
Study: intensity mapping associated with a study cluster"""
clicked_on = request.args.get("options")
if clicked_on == "clear":
intensities_by_location = {}
elif clicked_on == "cluster" or clicked_on == "word":
if clicked_on == "cluster":
cluster = request.args.get("cluster")
word = TermCluster.get_words_in_cluster(cluster)
else:
word = request.args.get("word")
studies = StudyTerm.get_by_word(word)
# Create a dictionary of {pmid: frequency} values
frequencies_by_pmid, max_intensity = organize_frequencies_by_study(studies)
pmids = frequencies_by_pmid.keys()
# Get the activations for the keys of the dictionary
activations = Activation.get_activations_from_studies(pmids)
# Assemble the final dictionary of {location:intensity} values, scaling
# each value as we go
intensities_by_location = scale_frequencies_by_loc(activations, max_intensity, frequencies_by_pmid)
elif clicked_on == "study":
pmid = request.args.get("pmid")
study = Study.get_study_by_pmid(pmid)
# Look for cluster-mate studies
cluster_mates = study.get_cluster_mates()
# Get (location, study count) tuples from db
activations = Activation.get_location_count_from_studies(cluster_mates)
# Scale study counts in preparation for intensity mapping
intensities_by_location = scale_study_counts(activations)
print "Found intensities: ", intensities_by_location
# Assemble the intensity map
intensity_vals = generate_intensity_map(intensities_by_location)
return intensity_vals
def generate_topic_d3():
"""Returns JSON with a topic cluster as the root node."""
# TO DO Adding cluster ID validation and then extra tests to tests.py
cluster_id = request.args.get("cluster")
words = TermCluster.get_words_in_cluster(cluster_id)
root_dict = {"name": "", "children": []}
for word in words:
root_dict["children"].append({"name": "", "children": [{"name": word, "size": 40000}]})
return jsonify(root_dict)
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)
