if hasattr(n, "numdate_given"):
ofile.write("%s, %f, %f\n" %
(n.name, n.numdate_given, n.dist2root))
else:
ofile.write("%s, %f, %f\n" %
(n.name, d2d.numdate_from_dist2root(
n.dist2root), n.dist2root))
for n in myTree.tree.get_nonterminals(order='preorder'):
ofile.write(
"%s, %f, %f\n" %
(n.name, d2d.numdate_from_dist2root(n.dist2root), n.dist2root))
print("--- wrote dates and root-to-tip distances to \n\t %s\n" %
table_fname)
###########################################################################
### PLOT AND SAVE RESULT
###########################################################################
if params.plot:
import matplotlib.pyplot as plt
myTree.plot_root_to_tip(label=False)
t = np.array([np.min(dates.values()), np.max(dates.values())])
plt.plot(t,
t * d2d.clock_rate + d2d.intercept,
label='y=%1.4f+%1.5ft, r^2=%1.2f' %
(d2d.intercept, d2d.clock_rate, d2d.r_val**2))
plt.legend(loc=2)
plt.savefig(base_name + '_root_to_tip_regression.pdf')
print(
"--- root-to-tip plot saved to \n\t %s_root_to_tip_regression.pdf\n"
% base_name)
tt = TreeTime(gtr='Jukes-Cantor',
tree=base_name + '.nwk',
aln=base_name + '.fasta',
verbose=1,
dates=dates)
# inititally the root if the tree is a mess:
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
axs[0].set_title("Arbitrarily rooted tree", fontsize=18)
axs[1].set_title("Inverse divergence-time relationship", fontsize=18)
Phylo.draw(tt.tree,
show_confidence=False,
axes=axs[0],
label_func=lambda x: x.name.split('|')[0]
if x.is_terminal() else "")
tt.plot_root_to_tip(ax=axs[-1])
format_axes(fig, axs)
# lets reroot: we now have a positve correlation of root-to-tip distance with sampling date
tt.reroot(root="best")
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
axs[0].set_title("Tree rerooted by treetime", fontsize=18)
axs[1].set_title("Optimal divergence-time relationship", fontsize=18)
Phylo.draw(tt.tree,
show_confidence=False,
axes=axs[0],
label_func=lambda x: x.name.split('|')[0]
if x.is_terminal() else "")
tt.plot_root_to_tip(ax=axs[-1])
format_axes(fig, axs)
dates=dates)
# infer an ebola time tree while rerooting and resolving polytomies
ebola.run(root='best',
relaxed_clock=False,
max_iter=2,
resolve_polytomies=True,
Tc='skyline',
time_marginal="assign")
# get Skyline and 2-sigma confidence intervals
skyline, confidence = ebola.merger_model.skyline_inferred(gen=50,
confidence=2.0)
# scatter root to tip divergence vs sampling date
ebola.plot_root_to_tip(add_internal=True)
t = np.array([2014, 2016])
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,
axes=axs[0],
show_confidence=False,
label_func=lambda x: '')
offset = myTree.tree.root.time_before_present + myTree.tree.root.branch_length
cols = sns.color_palette()
depth = myTree.tree.depths()
x = np.linspace(-0.01, .2, 1000)
for ni, node in enumerate(myTree.tree.find_clades(order="postorder")):
axs[1].plot(offset - x,
node.marginal_pos_LH.prob_relative(x),
'-',
c=cols[ni % len(cols)])
if node.up is not None:
# add branch length distributions to tree
x_branch = np.linspace(
depth[node] - 2 * node.branch_length - 0.005, depth[node], 100)
axs[0].plot(
x_branch,
node.ypos - 0.7 *
node.branch_length_interpolator.prob_relative(depth[node] -
x_branch),
'-',
c=cols[ni % len(cols)])
axs[1].set_yscale('log')
axs[1].set_ylim([0.05, 1.2])
axs[0].set_xlabel('')
plt.tight_layout()
# make root-to-tip plot
myTree.plot_root_to_tip(add_internal=True, s=30)
# include internal nodes, set symbol size to 30
