# plot a combined figure
# color according to season
pred_names = [c.name for c in prediction.terminals]
tree_utils.erase_color(combined_data.T)
for c in combined_data.T.get_terminals():
if c.name in pred_names:
c.color = (178, 34, 34)
else:
c.color = (0, 255, 255)
prediction.interpolate_color(combined_data.T)
tree_utils.label_nodes(combined_data.T, seq_labels)
fig = plt.figure(figsize=(10, 6))
ax = plt.subplot(121)
plt.title('until Feb ' + str(year))
tree_utils.draw_tree(prediction.T, axes=ax, cb=True)
ax = plt.subplot(122)
plt.title('before Mar ' + str(year) + ' (red) + season ' + str(year) +
"/" + str(year + 1) + " (cyan)")
tree_utils.draw_tree(combined_data.T, axes=ax, cb=False)
plt.tight_layout()
plt.savefig(tree_figure_folder + base_name + '_' + method[0] + method[1] +
'_' + name_mod + '.pdf')
# perform additional analysis on the tree and the prediction
if params.analysis:
# calculate the fitness differentials for each internal branch and associate with
# different types of mutations that happen on these branches
dfit = []
for node in prediction.non_terminals:
for child in node.clades:
method='polarizer',
axes=ax,
cb=True,
offset=0.0005,
internal=False)
#subplot 2: prediction data and test data
ax = plt.subplot(122)
#add panel label
plt.text(-0.06, 0.95, 'B', transform=plt.gca().transAxes, fontsize=36)
plt.title('until Feb ' + str(year) + ' + season ' + str(year) + "/" +
str(year + 1) + " (grey)")
pred_names = [c.name for c in prediction.terminals]
tree_utils.erase_color(combined_data.T)
prediction.color_other_tree(combined_data.T.get_terminals(),
method='polarizer',
offset=0.0005)
for c in combined_data.T.get_terminals():
if c.name in pred_names:
pass
# c.color = (178, 34, 34 )
else:
c.color = (0, 255, 255)
c.color = (100, 100, 100)
prediction.interpolate_color(combined_data.T)
tree_utils.draw_tree(combined_data.T, axes=ax, cb=False)
for ff in file_formats:
plt.savefig('../figures_ms/Fig4AB_' + base_name + '_combined_polarizer' +
ff)
fig = plt.figure(figsize = (12,6))
#subplot 1: only the prediction data
ax = plt.subplot(121)
#add panel label
plt.text(-0.06,0.95,'A', transform = plt.gca().transAxes, fontsize = 36)
plt.title('until Feb '+str(year))
plt.tight_layout()
tree_utils.plot_prediction_tree(prediction, axes=ax, cb=True, offset = 0.0005, internal=False)
#subplot 2: prediction data and test data
ax = plt.subplot(122)
#add panel label
plt.text(-0.06,0.95,'B', transform = plt.gca().transAxes, fontsize = 36)
plt.title('until Feb '+str(year)+' + season '+str(year)+"/"+str(year+1)+" (grey)")
pred_names = [c.name for c in prediction.terminals]
tree_utils.erase_color(combined_data.T)
prediction.color_other_tree(combined_data.T.get_terminals(), offset = 0.0005)
for c in combined_data.T.get_terminals():
if c.name in pred_names:
pass
# c.color = (178, 34, 34 )
else:
c.color = (0,255,255)
c.color = (100,100,100)
prediction.interpolate_color(combined_data.T)
tree_utils.draw_tree(combined_data.T, axes=ax, cb=False)
plt.savefig('../figures/Fig3AB_'+base_name+'_combined_'+name_mod+'.svg')
# plot a combined figure
# color according to season
pred_names = [c.name for c in prediction.terminals]
tree_utils.erase_color(combined_data.T)
for c in combined_data.T.get_terminals():
if c.name in pred_names:
c.color = (178, 34, 34 )
else:
c.color = (0,255,255)
prediction.interpolate_color(combined_data.T)
tree_utils.label_nodes(combined_data.T, seq_labels)
fig = plt.figure(figsize = (10,6))
ax = plt.subplot(121)
plt.title('until Feb '+str(year))
tree_utils.draw_tree(prediction.T, axes=ax, cb=True)
ax = plt.subplot(122)
plt.title('before Mar '+str(year)+' (red) + season '+str(year)+"/"+str(year+1)+" (cyan)")
tree_utils.draw_tree(combined_data.T, axes=ax, cb=False)
plt.tight_layout()
plt.savefig(tree_figure_folder+base_name+'_'+method[0]+method[1]+'_'+name_mod+'.pdf')
# perform additional analysis on the tree and the prediction
if params.analysis:
# calculate the fitness differentials for each internal branch and associate with
# different types of mutations that happen on these branches
dfit = []
for node in prediction.non_terminals:
for child in node.clades:
delta_fitness = child.mean_fitness - node.mean_fitness
