def _get_motif_tree(tree, data, circle=True, vmin=None, vmax=None):
try:
from ete3 import Tree, NodeStyle, TreeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
t = Tree(tree)
# Determine cutoff for color scale
if not(vmin and vmax):
for i in range(90, 101):
minmax = np.percentile(data.values, i)
if minmax > 0:
break
if not vmin:
vmin = -minmax
if not vmax:
vmax = minmax
norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap="RdBu_r")
m = 25 / data.values.max()
for node in t.traverse("levelorder"):
val = data[[l.name for l in node.get_leaves()]].values.mean()
style = NodeStyle()
style["size"] = 0
style["hz_line_color"] = to_hex(mapper.to_rgba(val))
style["vt_line_color"] = to_hex(mapper.to_rgba(val))
v = max(np.abs(m * val), 5)
style["vt_line_width"] = v
style["hz_line_width"] = v
node.set_style(style)
ts = TreeStyle()
ts.layout_fn = _tree_layout
ts.show_leaf_name= False
ts.show_scale = False
ts.branch_vertical_margin = 10
if circle:
ts.mode = "c"
ts.arc_start = 180 # 0 degrees = 3 o'clock
ts.arc_span = 180
return t, ts
def main(args):
if args.alignment:
t = PhyloTree(args.tree, alignment=args.alignment, alg_format='fasta')
else:
t = PhyloTree(args.tree)
if args.highlight_new:
runs = read_runs(args.highlight_new)
t.set_outgroup('EM_079422')
t.ladderize()
ts = TreeStyle()
ts.show_leaf_name = False
ts.show_branch_support = False
ts.layout_fn = layout
thick_hz_line = NodeStyle()
thick_hz_line["hz_line_width"] = 8
t.set_style(thick_hz_line)
#t.children[0].set_style(thick_hz_line)
#t.children[1].set_style(thick_hz_line)
thick_vt_line = NodeStyle()
thick_vt_line["vt_line_width"] = 4
t.set_style(thick_vt_line)
# header
if not args.hide_annotations:
ts.aligned_header.add_face(MyTextFace('Sample identifier', fstyle='Bold', fsize=8, tight_text=False), column = 1)
ts.aligned_header.add_face(MyTextFace('Prefecture', fstyle='Bold', fsize=8, tight_text=False), column = 2)
ts.aligned_header.add_face(MyTextFace('Sous-prefecture', fstyle='Bold', fsize=8, tight_text=False), column = 3)
ts.aligned_header.add_face(MyTextFace('Village', fstyle='Bold', fsize=8, tight_text=False), column = 4)
ts.aligned_header.add_face(MyTextFace('Sample received', fstyle='Bold', fsize=8, tight_text=False), column = 5)
if args.positions:
positions = read_positions(args.positions)
alg_header = RulerFace(positions,
col_width=11,
height=0, # set to 0 if dont want to use values
kind="stick",
hlines = [0],
hlines_col = ["white"], # trick to hide hz line
)
ts.aligned_header.add_face(alg_header, 6)
#legend
if args.legend:
legend = {}
for s in samples.values():
legend[s['prefec']] = s['prefec__colour']
for p in sorted(legend.keys()):
ts.legend.add_face(CircleFace(4, legend[p]), column=0)
ts.legend.add_face(MyTextFace(p, fsize=6, tight_text=False), column=1)
ts.legend_position=1
if args.circular:
ts.mode = "c"
ts.arc_start = -180 # 0 degrees = 3 o'clock
ts.arc_span = 180
# t.show(tree_style=ts)
t.render(args.output, tree_style=ts, w=1024)
else:
#We're at the root node
new_node.dist = 0
cur_node_id = str(current_bud_row.OrgID)
for idx, new_row in saved_pop_hosts[saved_pop_hosts.ParentID.eq(cur_node_id)].iterrows():
build_tree_recursive(new_row, new_node)
return new_node
root_node_row = saved_pop_hosts[saved_pop_hosts.ParentID == "(none)"].squeeze()
print("Building Tree")
build_tree_recursive(root_node_row, host_phylo)
print("Drawing Tree")
#Some drawing code
ts = TreeStyle()
ts.show_leaf_name = True
ts.mode = "c"
ts.arc_start = -180 # 0 degrees = 3 o'clock
ts.arc_span = 180
host_phylo.render("tree.png", tree_style=ts)
print("Saving Tree")
#Write the Newick Format Tree
host_phylo.write(format=1, outfile="avida_tree.nw")
kernel = gk.WeisfeilerLehman(base_kernel = gk.VertexHistogram, normalize= True) GK = pd.DataFrame(kernel.fit_transform(GL['graph'].values)) GK.columns = GK.index = label # Use 1-K as measure of Distance DM_GK = DistanceMatrix(1-GK.values) #make GK tree sktree = nj(DM_GK, result_constructor=str) GK_tree = Tree(sktree) GK_tree.name = 'AGORA network similarity tree' # style ts = TreeStyle() ts.show_leaf_name = True ts.mode = "c" ts.arc_start = -180 ts.arc_span = 360 #plot tree GK_tree.render(file_name='/home/acabbia/Documents/Muscle_Model/GSMM-distance/figures/GK_tree_AGORA.png', tree_style=ts) GK_tree.show(tree_style=ts) #%% #### # MAKE JACCARD TREE ### # make binary matrices (rxn, mets and gene matrices) ref_model = cobra.io.read_sbml_model(ref_model_file) reactions_matrix, metabolite_matrix, gene_matrix = make_binary_mat(model_library_folder, ref_model)
def plot_tree(self,
output_filepath,
time_series_info=None,
tree_style='horizontal_right',
leaf_size_map_to=None,
show_leaf_names=False,
color_branches_by=None,
line_width=1,
start_color='red',
end_color='purple',
ladderize=True):
"""
This method plots the phylogenetic tree as a dendogram. Uses the ete3 package to do this.
output_filepath - This is the path to the output image file.
time_series_info - This tells where the information of time-point can be found (if at all) in each of the element IDs. Acceptable values are:
None - (default) This means there is no time information in the tree
'start_of_id' - This means the time info is at the very beginning of each element ID, and is separated by a '_'. For example: '45.3_blahblahblah' would have a time point of 45.3.
tree_style - This give the style of tree plotting, i.e. circular, horizontal, etc. Acceptable values are:
'half_circle' - Tree is plotted as a half circle, where branches are radiating outward and upward.
'horizontal_right' - Tree is plotted as a normal dendogram where branching occurs from left to right.
leaf_size_map_to - If defined (default, None), this will inform what attribute of the node the leaf size maps to. Acceptable values are:
None - No leaf size info. All leaves the same size
'count' - leaf size proportional to the count attribute
'freq' - leaf size proportional to the freq attribute
show_leaf_names - If True (default, False), then will plot the names of each of the leafs of the tree.
color_branches_by - If defined (default, None) this will give how the leaf branches will be colored, if at all. Acceptable values are:
None - Default. No branch coloring
'time_point' - This means that the branches will be colored according to the 'time_point' attribute of each leaf node. There must be a 'time_point' attribute in the leaf nodes for this to work. So, one should run the 'add_time_info' method before doing this. Alternatively, one can define the 'time_series_info' parameter for this method, and this will be taken care of.
any other string - This will give the name of any other attribute of the leaf nodes for which to map the value to the leaf branch color.
then this will instruct to color the different time-points with different colors. This is ignored if 'time_series_info' is False
line_width - controls the line width. Default, 1
start_color - This gives the staring color. This should be a string the spells out the name of a color. Most simple colors should be fine. Uses the 'Color' module from 'colour'.
end_color - This gives the ending color. This should be a string the spells out the name of a color. Most simple colors should be fine. Uses the 'Color' module from 'colour'. The colors used for each unique attribute that gives the colors will span the spectrom from 'start_color' to 'end_color'.
ladderize - If True (default), this will ladderize the tree. That is it will sort the partitions of the internal nodes based upon number of decendant nodes in the child nodes.
"""
if not self.time_points and time_series_info:
self.add_time_info(time_series_info=time_series_info)
#set time-point colors, if desired
if color_branches_by:
start_color = Color(start_color)
end_color = Color(end_color)
if color_branches_by == 'time_point' and time_series_info:
colors = list(
start_color.range_to(end_color, len(self.time_points)))
hex_colors = [i.hex_l for i in colors]
tpoint_to_color_dic = {}
for index, i in enumerate(sorted(self.time_points)):
tpoint_to_color_dic[i] = hex_colors[index]
else:
#check if attribute already exists in the tree data. if not, add it
for leaf in self.tree:
if not color_branches_by in leaf.features:
self.add_attribute_to_leaves(
attribute_name=color_branches_by)
break
colors = list(
start_color.range_to(
end_color,
len(self.extra_leaf_features[color_branches_by])))
hex_colors = [i.hex_l for i in colors]
attribute_to_color_dic = {}
for index, i in enumerate(
sorted(self.extra_leaf_features[color_branches_by])):
attribute_to_color_dic[i] = hex_colors[index]
#set node styles
most_dist_leaf, size_to_tree_size_scaler = self.tree.get_farthest_leaf(
) #need to scale sizes by the length (divergence) of the tree
for node in self.tree.traverse():
node_style = NodeStyle()
#do stuff to leaf nodes
if node.is_leaf():
if color_branches_by:
if color_branches_by == 'time_point':
color = tpoint_to_color_dic[node.time_point]
else:
color = attribute_to_color_dic[getattr(
node, color_branches_by)]
else:
color = Color('black')
color = color.hex_l
node_style['hz_line_color'] = color
node_style['vt_line_color'] = color
if leaf_size_map_to:
if leaf_size_map_to == 'count':
radius = size_to_tree_size_scaler * 100 * math.log(
node.count)
elif leaf_size_map_to == 'freq':
radius = size_to_tree_size_scaler * 100 * node.freq
c = CircleFace(radius=radius, color=color, style='circle')
c.opacity = 0.3
node.add_face(c, 0, position='branch-right')
node_style['size'] = 0
node_style[
'hz_line_width'] = size_to_tree_size_scaler * 10 * line_width
node_style[
'vt_line_width'] = size_to_tree_size_scaler * 10 * line_width
node.set_style(node_style)
#set tree style
tree_steeze = TreeStyle()
if tree_style == 'half_circle':
tree_steeze.mode = 'c'
tree_steeze.arc_start = -180
tree_steeze.arc_span = 180
elif tree_style == 'horizontal_right':
pass
if show_leaf_names:
tree_steeze.show_leaf_name = True
else:
tree_steeze.show_leaf_name = False
self.tree.ladderize()
self.tree.render(output_filepath,
w=700,
h=700,
units='mm',
tree_style=tree_steeze)
return
def main(args):
if args.alignment:
t = PhyloTree(args.tree, alignment=args.alignment, alg_format='fasta')
else:
t = PhyloTree(args.tree)
if args.highlight_new:
runs = read_runs(args.highlight_new)
t.set_outgroup('EM_079422')
t.ladderize()
ts = TreeStyle()
ts.show_leaf_name = False
ts.show_branch_support = False
ts.layout_fn = layout
thick_hz_line = NodeStyle()
thick_hz_line["hz_line_width"] = 8
t.set_style(thick_hz_line)
#t.children[0].set_style(thick_hz_line)
#t.children[1].set_style(thick_hz_line)
thick_vt_line = NodeStyle()
thick_vt_line["vt_line_width"] = 4
t.set_style(thick_vt_line)
# header
if not args.hide_annotations:
ts.aligned_header.add_face(MyTextFace('Sample identifier',
fstyle='Bold',
fsize=8,
tight_text=False),
column=1)
ts.aligned_header.add_face(MyTextFace('Prefecture',
fstyle='Bold',
fsize=8,
tight_text=False),
column=2)
ts.aligned_header.add_face(MyTextFace('Sous-prefecture',
fstyle='Bold',
fsize=8,
tight_text=False),
column=3)
ts.aligned_header.add_face(MyTextFace('Village',
fstyle='Bold',
fsize=8,
tight_text=False),
column=4)
ts.aligned_header.add_face(MyTextFace('Sample received',
fstyle='Bold',
fsize=8,
tight_text=False),
column=5)
if args.positions:
positions = read_positions(args.positions)
alg_header = RulerFace(
positions,
col_width=11,
height=0, # set to 0 if dont want to use values
kind="stick",
hlines=[0],
hlines_col=["white"], # trick to hide hz line
)
ts.aligned_header.add_face(alg_header, 6)
#legend
if args.legend:
legend = {}
for s in samples.values():
legend[s['prefec']] = s['prefec__colour']
for p in sorted(legend.keys()):
ts.legend.add_face(CircleFace(4, legend[p]), column=0)
ts.legend.add_face(MyTextFace(p, fsize=6, tight_text=False),
column=1)
ts.legend_position = 1
if args.circular:
ts.mode = "c"
ts.arc_start = -180 # 0 degrees = 3 o'clock
ts.arc_span = 180
# t.show(tree_style=ts)
t.render(args.output, tree_style=ts, w=1024)
def deepbiome_draw_phylogenetic_tree(
log,
network_info,
path_info,
num_classes,
file_name="%%inline",
img_w=500,
branch_vertical_margin=20,
arc_start=0,
arc_span=360,
node_name_on=True,
name_fsize=10,
tree_weight_on=True,
tree_weight=None,
tree_level_list=['Genus', 'Family', 'Order', 'Class', 'Phylum'],
weight_opacity=0.4,
weight_max_radios=10,
phylum_background_color_on=True,
phylum_color=[],
phylum_color_legend=False,
show_covariates=True,
verbose=True):
"""
Draw phylogenetic tree
Parameters
----------
log (logging instance) :
python logging instance for logging
network_info (dictionary) :
python dictionary with network_information
path_info (dictionary):
python dictionary with path_information
num_classes (int):
number of classes for the network. 0 for regression, 1 for binary classificatin.
file_name (str):
name of the figure for save.
- "*.png", "*.jpg"
- "%%inline" for notebook inline output.
default="%%inline"
img_w (int):
image width (pt)
default=500
branch_vertical_margin (int):
vertical margin for branch
default=20
arc_start (int):
angle that arc start
default=0
arc_span (int):
total amount of angle for the arc span
default=360
node_name_on (boolean):
show the name of the last leaf node if True
default=False
name_fsize (int):
font size for the name of the last leaf node
default=10
tree_weight_on (boolean):
show the amount and the direction of the weight for each edge in the tree by circle size and color.
default=True
tree_weight (ndarray):
reference tree weights
default=None
tree_level_list (list):
name of each level of the given reference tree weights
default=['Genus', 'Family', 'Order', 'Class', 'Phylum']
weight_opacity (float):
opacity for weight circle
default= 0.4
weight_max_radios (int):
maximum radios for weight circle
default= 10
phylum_background_color_on (boolean):
show the background color for each phylum based on `phylumn_color`.
default= True
phylum_color (list):
specify the list of background colors for phylum level. If `phylumn_color` is empty, it will arbitrarily assign the color for each phylum.
default= []
phylum_color_legend (boolean):
show the legend for the background colors for phylum level
default= False
show_covariates (boolean):
show the effect of the covariates
default= True
verbose (boolean):
show the log if True
default=True
Returns
-------
Examples
--------
Draw phylogenetic tree
deepbiome_draw_phylogenetic_tree(log, network_info, path_info, num_classes, file_name = "%%inline")
"""
os.environ[
'QT_QPA_PLATFORM'] = 'offscreen' # for tree figure (https://github.com/etetoolkit/ete/issues/381)
reader_class = getattr(readers,
network_info['model_info']['reader_class'].strip())
reader = reader_class(log, path_info, verbose=verbose)
data_path = path_info['data_info']['data_path']
try:
count_path = path_info['data_info']['count_path']
x_list = np.array(
pd.read_csv(path_info['data_info']['count_list_path'],
header=None).iloc[:, 0])
x_path = np.array([
'%s/%s' % (count_path, x_list[fold])
for fold in range(x_list.shape[0]) if '.csv' in x_list[fold]
])
except:
x_path = np.array([
'%s/%s' % (data_path, path_info['data_info']['x_path'])
for fold in range(1)
])
reader.read_dataset(x_path[0], None, 0)
network_class = getattr(
build_network, network_info['model_info']['network_class'].strip())
network = network_class(network_info,
path_info,
log,
fold=0,
num_classes=num_classes,
tree_level_list=tree_level_list,
is_covariates=reader.is_covariates,
covariate_names=reader.covariate_names,
verbose=False)
if len(phylum_color) == 0:
colors = mcolors.CSS4_COLORS
colors_name = list(colors.keys())
if reader.is_covariates and show_covariates:
phylum_color = np.random.choice(
colors_name,
network.phylogenetic_tree_info['Phylum_with_covariates'].
unique().shape[0])
else:
phylum_color = np.random.choice(
colors_name,
network.phylogenetic_tree_info['Phylum'].unique().shape[0])
basic_st = NodeStyle()
basic_st['size'] = weight_max_radios * 0.5
basic_st['shape'] = 'circle'
basic_st['fgcolor'] = 'black'
t = Tree()
root_st = NodeStyle()
root_st["size"] = 0
t.set_style(root_st)
tree_node_dict = {}
tree_node_dict['root'] = t
upper_class = 'root'
lower_class = tree_level_list[-1]
lower_layer_names = tree_weight[-1].columns.to_list()
layer_tree_node_dict = {}
phylum_color_dict = {}
for j, val in enumerate(lower_layer_names):
t.add_child(name=val)
leaf_t = t.get_leaves_by_name(name=val)[0]
leaf_t.set_style(basic_st)
layer_tree_node_dict[val] = leaf_t
if lower_class == 'Phylum' and phylum_background_color_on:
phylum_st = copy.deepcopy(basic_st)
phylum_st["bgcolor"] = phylum_color[j]
phylum_color_dict[val] = phylum_color[j]
leaf_t.set_style(phylum_st)
tree_node_dict[lower_class] = layer_tree_node_dict
upper_class = lower_class
upper_layer_names = lower_layer_names
for i in range(len(tree_level_list) - 1):
lower_class = tree_level_list[-2 - i]
if upper_class == 'Disease' and show_covariates == False:
lower_layer_names = network.phylogenetic_tree_info[
lower_class].unique()
else:
lower_layer_names = tree_weight[-i - 1].index.to_list()
layer_tree_node_dict = {}
for j, val in enumerate(upper_layer_names):
parient_t = tree_node_dict[upper_class][val]
if upper_class == 'Disease':
child_class = lower_layer_names
else:
child_class = network.phylogenetic_tree_info[lower_class][
network.phylogenetic_tree_info[upper_class] ==
val].unique()
for k, child_val in enumerate(child_class):
parient_t.add_child(name=child_val)
leaf_t = parient_t.get_leaves_by_name(name=child_val)[0]
if lower_class == 'Phylum' and phylum_background_color_on:
phylum_st = copy.deepcopy(basic_st)
phylum_st["bgcolor"] = phylum_color[k]
phylum_color_dict[child_val] = phylum_color[k]
leaf_t.set_style(phylum_st)
else:
leaf_t.set_style(basic_st)
if tree_weight_on:
edge_weights = np.array(tree_weight[-1 - i])
edge_weights *= (weight_max_radios / np.max(edge_weights))
if upper_class == 'Disease':
upper_num = 0
else:
upper_num = network.phylogenetic_tree_dict[
upper_class][val]
if upper_class == 'Disease' and reader.is_covariates == True and show_covariates:
lower_num = network.phylogenetic_tree_dict[
'%s_with_covariates' % lower_class][child_val]
else:
lower_num = network.phylogenetic_tree_dict[
lower_class][child_val]
leaf_t.add_features(weight=edge_weights[lower_num,
upper_num])
layer_tree_node_dict[child_val] = leaf_t
tree_node_dict[lower_class] = layer_tree_node_dict
upper_class = lower_class
upper_layer_names = lower_layer_names
def layout(node):
if "weight" in node.features:
# Creates a sphere face whose size is proportional to node's
# feature "weight"
color = {1: "RoyalBlue", 0: "Red"}[int(node.weight > 0)]
C = CircleFace(radius=node.weight, color=color, style="circle")
# Let's make the sphere transparent
C.opacity = weight_opacity
# And place as a float face over the tree
faces.add_face_to_node(C, node, 0, position="float")
if node_name_on & node.is_leaf():
# Add node name to laef nodes
N = AttrFace("name", fsize=name_fsize, fgcolor="black")
faces.add_face_to_node(N, node, 0)
ts = TreeStyle()
ts.show_leaf_name = False
ts.mode = "c"
ts.arc_start = arc_start
ts.arc_span = arc_span
ts.layout_fn = layout
ts.branch_vertical_margin = branch_vertical_margin
ts.show_scale = False
if phylum_color_legend:
for phylum_name in np.sort(list(phylum_color_dict.keys())):
color_name = phylum_color_dict[phylum_name]
ts.legend.add_face(CircleFace(weight_max_radios * 1, color_name),
column=0)
ts.legend.add_face(TextFace(" %s" % phylum_name, fsize=name_fsize),
column=1)
return t.render(file_name=file_name, w=img_w, tree_style=ts)
# #########################################################################################################################
# if __name__ == "__main__":
# argdict = argv_parse(sys.argv)
# try: gpu_memory_fraction = float(argdict['gpu_memory_fraction'][0])
# except: gpu_memory_fraction = None
# try: max_queue_size=int(argdict['max_queue_size'][0])
# except: max_queue_size=10
# try: workers=int(argdict['workers'][0])
# except: workers=1
# try: use_multiprocessing=argdict['use_multiprocessing'][0]=='True'
# except: use_multiprocessing=False
# ### Logger ############################################################################################
# logger = logging_daily.logging_daily(argdict['log_info'][0])
# logger.reset_logging()
# log = logger.get_logging()
# log.setLevel(logging_daily.logging.INFO)
# log.info('Argument input')
# for argname, arg in argdict.items():
# log.info(' {}:{}'.format(argname,arg))
# ### Configuration #####################################################################################
# config_data = configuration.Configurator(argdict['path_info'][0], log)
# config_data.set_config_map(config_data.get_section_map())
# config_data.print_config_map()
# config_network = configuration.Configurator(argdict['network_info'][0], log)
# config_network.set_config_map(config_network.get_section_map())
# config_network.print_config_map()
# path_info = config_data.get_config_map()
# network_info = config_network.get_config_map()
# test_evaluation, train_evaluation, network = deepbiome_train(log, network_info, path_info, number_of_fold=20)
def plot_phylo(nw_tree,
out_name,
parenthesis_classif=True,
show_support=False,
radial_mode=False,
root=False):
from ete3 import Tree, AttrFace, TreeStyle, NodeStyle, TextFace
import orthogroup2phylogeny_best_refseq_uniprot_hity
ete2_tree = Tree(nw_tree, format=0)
print(root)
if root:
R = ete2_tree.get_midpoint_outgroup()
# and set it as tree outgroup
ete2_tree.set_outgroup(R)
#ete2_tree.set_outgroup('Bacillus subtilis')
ete2_tree.ladderize()
if parenthesis_classif:
#print ('parenthesis_classif!')
name2classif = {}
for lf in ete2_tree.iter_leaves():
#print (lf)
try:
classif = lf.name.split('_')[-2][0:-1]
#print ('classif', classif)
#lf.name = lf.name.split('(')[0]
name2classif[lf.name] = classif
except:
pass
classif_list = list(set(name2classif.values()))
classif2col = dict(
zip(
classif_list,
orthogroup2phylogeny_best_refseq_uniprot_hity.
get_spaced_colors(len(classif_list))))
for lf in ete2_tree.iter_leaves():
#try:
if parenthesis_classif:
try:
col = classif2col[name2classif[lf.name]]
except:
col = 'black'
else:
col = 'black'
#print col
#lf.name = '%s|%s-%s' % (lf.name, accession2name_and_phylum[lf.name][0],accession2name_and_phylum[lf.name][1])
if radial_mode:
ff = AttrFace("name", fsize=12, fstyle='italic')
else:
ff = AttrFace("name", fsize=12, fstyle='italic')
#ff.background.color = 'red'
ff.fgcolor = col
lf.add_face(ff, column=0)
if not show_support:
#print('support')
for n in ete2_tree.traverse():
#print (n.support)
nstyle = NodeStyle()
if float(n.support) < 1:
nstyle["fgcolor"] = "red"
nstyle["size"] = 4
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
else:
for n in ete2_tree.traverse():
nstyle = NodeStyle()
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
#nameFace = AttrFace(lf.name, fsize=30, fgcolor=phylum2col[accession2name_and_phylum[lf.name][1]])
#faces.add_face_to_node(nameFace, lf, 0, position="branch-right")
#
#nameFace.border.width = 1
'''
except:
col = 'red'
print col
lf.name = '%s| %s' % (lf.name, locus2organism[lf.name])
ff = AttrFace("name", fsize=12)
#ff.background.color = 'red'
ff.fgcolor = col
lf.add_face(ff, column=0)
'''
#n = TextFace(lf.name, fgcolor = "black", fsize = 12, fstyle = 'italic')
#lf.add_face(n, 0)
'''
for n in ete2_tree.traverse():
nstyle = NodeStyle()
if n.support < 90:
nstyle["fgcolor"] = "black"
nstyle["size"] = 4
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
'''
ts = TreeStyle()
ts.show_leaf_name = False
#ts.scale=2000
#ts.scale=20000
ts.show_branch_support = show_support
if radial_mode:
ts.mode = "c"
ts.arc_start = -90
ts.arc_span = 360
ts.tree_width = 370
ts.complete_branch_lines_when_necessary = True
ete2_tree.render(out_name, tree_style=ts, w=900)
