fields.append('mutsprot')
annotate_tree(patient, tree,
fields=fields,
VERBOSE=VERBOSE)
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
fields.extend(['sequence', 'confidence'])
fn = patient.get_consensi_tree_filename(region, format='json')
tree_json = tree_to_json(tree.root, fields=fields)
write_json(tree_json, fn)
if use_plot:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(15, 12))
Phylo.draw(tree, do_show=False, axes=ax)
ax.set_title(pname+', '+region)
x_max = max(tree.depths().itervalues())
ax.set_xlim(0.995, 0.995 + (x_max - 0.995) * 1.4)
ax.grid(True)
plt.ion()
plt.show()
if VERBOSE >= 2:
print 'Annotate tree'
fields = ['DSI', 'muts', 'VL', 'ntemplates', 'CD4', 'subtype']
if region in regionsprot:
fields.append('mutsprot')
annotate_tree(patient, tree, fields=fields, VERBOSE=VERBOSE)
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
fields.extend(['sequence', 'confidence'])
fn = patient.get_consensi_tree_filename(region, format='json')
tree_json = tree_to_json(tree.root, fields=fields)
write_json(tree_json, fn)
if use_plot:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(15, 12))
Phylo.draw(tree, do_show=False, axes=ax)
ax.set_title(pname + ', ' + region)
x_max = max(tree.depths().itervalues())
ax.set_xlim(0.995, 0.995 + (x_max - 0.995) * 1.4)
ax.grid(True)
plt.ion()
plt.show()
