if VERBOSE >= 2:
print 'Annotate tree (for JSON format)'
from hivwholeseq.utils.nehercook.ancestral import ancestral_sequences
a = ancestral_sequences(tree, ali, alphabet='ACGT-N', copy_tree=False,
attrname='sequence', seqtype='str')
a.calc_ancestral_sequences()
del a
if VERBOSE >= 2:
print 'Save to file, JSON'
fn_out = get_subtype_reference_alignment_tree_filename(region,
subtype=subtype,
refname=refname,
type=alitype,
VERBOSE=VERBOSE,
format='json')
from hivwholeseq.utils.tree import tree_to_json
from hivwholeseq.utils.generic import write_json
tree_json = tree_to_json(tree.root, fields=('sequence', 'confidence'))
write_json(tree_json, fn_out)
else:
consrec = get_subtype_reference_alignment_tree(region,
subtype=subtype,
refname=refname,
type=alitype,
VERBOSE=VERBOSE)
print 'Annotate tree'
annotate_tree_time_freq_count(tree, ali)
annotate_tree(patient, tree, VERBOSE=VERBOSE)
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
rname = 'scan_'+str(win_start)+'-'+str(win_end)
fn = patient.get_local_tree_filename(rname, format='json')
mkdirs(os.path.dirname(fn))
tree_json = tree_to_json(tree.root,
fields=('DSI', 'sequence',
'muts',
'VL', 'CD4',
'frequency',
'count',
'confidence'),
)
write_json(tree_json, fn)
if use_plot:
if VERBOSE >= 2:
print 'Plot'
plot_tree(tree, title=patient.code+', '+str(win_start)+'-'+str(win_end))
win_start += gap
if VERBOSE >= 2:
print 'Annotate tree'
annotate_tree_time_freq_count(tree, ali)
annotate_tree(patient, tree, VERBOSE=VERBOSE)
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
rname = 'scan_' + str(win_start) + '-' + str(win_end)
fn = patient.get_local_tree_filename(rname, format='json')
mkdirs(os.path.dirname(fn))
tree_json = tree_to_json(
tree.root,
fields=('DSI', 'sequence', 'muts', 'VL', 'CD4',
'frequency', 'count', 'confidence'),
)
write_json(tree_json, fn)
if use_plot:
if VERBOSE >= 2:
print 'Plot'
plot_tree(tree,
title=patient.code + ', ' + str(win_start) + '-' +
str(win_end))
win_start += gap
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()
print 'Annotate tree'
annotate_tree(patient, tree, ali, VERBOSE=VERBOSE)
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
correct_minimal_branches(tree)
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
fn = patient.get_consensi_tree_filename(region, format='json')
tree_json = tree_to_json(
tree.root,
fields=('DSI', 'patient', 'sequence', 'muts', 'VL', 'CD4',
'subtype', 'confidence'),
)
write_json(tree_json, fn, indent=1)
if use_joint:
if VERBOSE >= 2:
print 'Align all patients',
ali_all = align_muscle(*seqs_all, sort=True)
if VERBOSE >= 2:
print 'OK'
if use_save:
if VERBOSE >= 2:
print 'Save all patients',
reg_tmp = '_'.join(pcodes) + '_' + region
if VERBOSE >= 2:
print 'Ladderize tree'
tree.ladderize()
correct_minimal_branches(tree)
if use_save:
if VERBOSE >= 2:
print 'Save tree (JSON)'
fn = patient.get_consensi_tree_filename(region, format='json')
tree_json = tree_to_json(tree.root,
fields=('DSI',
'patient',
'sequence',
'muts',
'VL',
'CD4',
'subtype',
'confidence'),
)
write_json(tree_json, fn, indent=1)
if use_joint:
if VERBOSE >= 2:
print 'Align all patients',
ali_all = align_muscle(*seqs_all, sort=True)
if VERBOSE >= 2:
print 'OK'
if use_save:
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()
