plt.plot(t,
t * ebola.date2dist.clock_rate + ebola.date2dist.intercept,
label="y = %1.5f t%1.3f" %
(ebola.date2dist.clock_rate, ebola.date2dist.intercept))
plt.legend(loc=2)
# rescale branch length to years and plot in axis 0
from treetime.treetime import plot_vs_years
fig, axs = plt.subplots(2,
1,
sharex=True,
figsize=(onecolumn_figsize[0],
onecolumn_figsize[1] * 1.7))
plot_vs_years(ebola,
years=.5,
ax=axs[0],
confidence=(0.05, 0.95),
ticks=False,
label_func=lambda x: "")
# axs[0].tick_params(labelsize=tick_fs)
# axs[0].set_axis_off()
# reset branch length to time (in substitution rate units)
for n in ebola.tree.find_clades():
if n.up:
n.branch_length = n.clock_length
axs[1].fill_between(skyline.x - ebola.tree.root.numdate,
confidence[0],
confidence[1],
color=(0.8, 0.8, 0.8))
axs[1].plot(skyline.x - ebola.tree.root.numdate,
print("inferred skyline assuming 50 generations per year:")
for (x,y) in zip(skyline.x, skyline.y):
print("%1.3f\t%1.3f"%(x,y))
base_name = '.'.join(params.aln.split('/')[-1].split('.')[:-1])
# plot
if params.plot:
from treetime.treetime import plot_vs_years
import matplotlib.pyplot as plt
plt.ion()
leaf_count = myTree.tree.count_terminals()
label_func = lambda x: x.name[:20] if (leaf_count<30 & x.is_terminal()) else ''
branch_label_func = lambda x: (','.join([a+str(pos)+d for a,pos, d in x.mutations[:10]])
+('...' if len(x.mutations)>10 else '')) if leaf_count<30 else ''
plot_vs_years(myTree, show_confidence=False, label_func = label_func) #, branch_labels=branch_label_func)
plt.savefig(base_name+'_tree.pdf')
print("--- saved tree as pdf in \n\t %s\n"%(base_name+'_tree.pdf'))
else:
# convert branch length to years (this is implicit in the above plot)
myTree.branch_length_to_years()
# decorate tree with inferred mutations
outaln_name = base_name+'_ancestral.fasta'
AlignIO.write(myTree.get_reconstructed_alignment(), outaln_name, 'fasta')
print("--- alignment including ancestral nodes saved as \n\t %s\n"%outaln_name)
terminal_count = 0
for n in myTree.tree.find_clades():
if n.up is None:
continue
time_marginal="assign",
vary_rate=True)
if res == ttconf.ERROR:
sys.exit()
# scatter root to tip divergence vs sampling date
ebola.plot_root_to_tip(add_internal=True)
plt.legend(loc=2)
# rescale branch length to years and plot in axis 0
from treetime.treetime import plot_vs_years
fig, axs = plt.subplots(1, 2, sharey=True, figsize=(12, 8))
plot_vs_years(ebola,
step=1,
ax=axs[1],
confidence=(0.05, 0.95),
label_func=lambda x: "")
axs[1].set_xlim(0, 2.5)
axs[1].set_title("time tree")
# assign mutation length to branch length and plot the mutation tree in axs[1]
axs[0].set_title("mutation tree")
for n in ebola.tree.find_clades():
n.branch_length = n.mutation_length
Phylo.draw(ebola.tree, label_func=lambda x: "", axes=axs[0])
plt.tight_layout()
# reset branch length to time (in substitution rate units)
for n in ebola.tree.find_clades():
n.branch_length = n.clock_length
# rerooting can be done along with the tree time inference
tt.run(root="best", branch_length_mode='input')
# if more complicated models (relaxed clocks, coalescent models) are to be used
# or you want to resolve polytomies, treetime needs to be run for
# several iterations, for example as
# tt.run(root="best", resolve_polytomies=True, max_iter=2)
# each node is now at a position that correspond to the given or inferred date
# the units of branch length are still clock rate.
print("clock rate: %1.5f" % tt.date2dist.clock_rate)
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
Phylo.draw(tt.tree,
label_func=lambda x: '',
show_confidence=False,
axes=axs[0])
axs[0].set_title("Tree: units are substitutions", fontsize=18)
# we can convert the branch length to units in years and redraw
tt.branch_length_to_years()
Phylo.draw(tt.tree,
label_func=lambda x: '',
show_confidence=False,
axes=axs[1])
axs[1].set_title("Tree: units are years", fontsize=18)
axs[0].tick_params(labelsize=14)
axs[1].tick_params(labelsize=14)
fig.tight_layout()
# treetime implements a convenience function to plot timetrees
from treetime.treetime import plot_vs_years
plot_vs_years(tt, label_func=lambda x: "", show_confidence=False)