def get_median(graph):
"""
Returns the median
:param graph <dict> - the input graph
:return random_median <dict> - the median
"""
median_graph, n_meds, median_roots = DTLMedian.get_median_graph(
graph, gene_tree, species_tree, gene_root, best_roots)
med_counts = DTLMedian.get_med_counts(median_graph, median_roots)
random_median = DTLMedian.choose_random_median_wrapper(
median_graph, median_roots, med_counts)
return random_median
def event_support_hist(species_tree, gene_tree, gene_root, graph):
"""
Compute the event support histogram (x-axis is event support, y-axis is fraction of events
with that support)
:param species_tree <tree>
:param gene_tree <tree>
:param gene_root <node>
:param graph <recon_graph>
:return hist <array float> - fraction of events for each bin
:return bins <array float> - RHS of each bin (see numpy.histogram)
"""
preorder_mapping_nodes = DTLMedian.mapping_node_sort(gene_tree, species_tree, list(graph.keys()))
event_support, count = \
DTLMedian.generate_scores(list(reversed(preorder_mapping_nodes)), graph, gene_root)
supports = list(event_support.values())
hist, bins = np.histogram(supports, bins=20, range=(0,1))
total = np.sum(hist)
return hist / float(total), bins
def avg_event_support(species_tree, gene_tree, g, gene_root):
"""
Compute the average event support for a graph.
:param species_tree <tree>
:param gene_tree <tree>
:param g <recon_graph>
:param gene_root <node>
:return <float> - the average event support in g
"""
# Compute the event support for each event
preorder_mapping_nodes = DTLMedian.mapping_node_sort(gene_tree, species_tree, list(g.keys()))
event_support, count = \
DTLMedian.generate_scores(list(reversed(preorder_mapping_nodes)), g, gene_root)
# Take the average over each event
total_support = 0
for support in event_support.values():
total_support += support
return total_support / len(event_support)