def plot_uncorrected_phylogeny(tree, species, latin_names, species_history):
"""
Generates a PDF figure of the input tree with same length for all branches.
:param tree: input tree from configuration file
:param species: the current focal species
:param latin_names: a dictionary-like data structure that associates each informal species name to its latin name
:param species_history: the list of ancestor nodes of the focal species, including the focal species and going up to the root.
"""
label_leaves_with_latin_names(tree, latin_names)
node_and_branch_style(tree)
ts = TreeStyle()
# ts.title.add_face(TextFace(" Input phylogenetic tree", ftype="Arial", fsize=18), column=0)
ts.orientation = 1
ts.branch_vertical_margin = 14
ts.show_leaf_name = False # because there is a Face showing it
ts.show_branch_length = False
ts.margin_left = 25
ts.margin_right = 25
ts.margin_top = 25
ts.margin_bottom = 25
ts.scale = 200
ts.show_scale = False
tree.render(os.path.join("rate_adjustment", f"{species}",
f"{_TREE.format(species)}"),
w=4.5,
units="in",
tree_style=ts)
def get_tree_style():
ts = TreeStyle()
# ts.mode = 'c'
ts.margin_top = 10
ts.margin_bottom = 10
ts.margin_left = 10
ts.margin_right = 10
ts.show_leaf_name = False
ts.show_branch_length = False
ts.show_branch_support = False
ts.show_scale = False
title = TextFace(" Tax Assignment Tree", fsize=10)
title.hz_align = 2
title.vt_align = 2
ts.title.add_face(TextFace(" "), column=0)
ts.title.add_face(TextFace(" "), column=0)
ts.title.add_face(title, column=0)
return ts
def get_default_tree_style(color_dict):
ts = TreeStyle()
ts.mode = "c"
# ts.layout_fn = layout
ts.margin_top = 50
ts.margin_bottom = 0
ts.margin_left = 50
ts.margin_right = 50
ts.show_scale = False
ts.show_leaf_name = False
ts.show_branch_length = False
ts.show_branch_support = False
for p, c in color_dict.iteritems():
ts.legend.add_face(TextFace(" ", fsize=30), column=0)
ts.legend.add_face(CircleFace(10, c), column=1)
ts.legend.add_face(TextFace(" %s" % p, fsize=30), column=2)
legend_margin_line = 5
while legend_margin_line:
ts.legend.add_face(TextFace(" "), column=0)
ts.legend.add_face(TextFace(" "), column=1)
ts.legend.add_face(TextFace(" "), column=2)
legend_margin_line -= 1
ts.legend_position = 3
return ts
def get_default_tree_style(color_dict):
ts = TreeStyle()
ts.mode = 'c'
# ts.layout_fn = layout
ts.margin_top = 50
ts.margin_bottom = 0
ts.margin_left = 50
ts.margin_right = 50
ts.show_scale = False
ts.show_leaf_name = False
ts.show_branch_length = False
ts.show_branch_support = False
for p, c in color_dict.iteritems():
ts.legend.add_face(TextFace(" ", fsize=30), column=0)
ts.legend.add_face(CircleFace(10, c), column=1)
ts.legend.add_face(TextFace(" %s" % p, fsize=30), column=2)
legend_margin_line = 5
while legend_margin_line:
ts.legend.add_face(TextFace(" "), column=0)
ts.legend.add_face(TextFace(" "), column=1)
ts.legend.add_face(TextFace(" "), column=2)
legend_margin_line -= 1
ts.legend_position = 3
return ts
def plot_species_tree(tree_newick, tree_type, gene_name, tree_file_name,
name_list, tree_image_folder):
# set tree parameters
tree = Tree(tree_newick, format=2)
ts = TreeStyle()
ts.mode = "r" # tree model: 'r' for rectangular, 'c' for circular
ts.show_leaf_name = False
tree_title = tree_type + ' (' + gene_name + ')' # define tree title
# set tree title text parameters
ts.title.add_face(TextFace(tree_title,
fsize=8,
fgcolor='black',
ftype='Arial',
tight_text=False),
column=0) # tree title text setting
# set layout parameters
ts.rotation = 0 # from 0 to 360
ts.show_scale = False
ts.margin_top = 10 # top tree image margin
ts.margin_bottom = 10 # bottom tree image margin
ts.margin_left = 10 # left tree image margin
ts.margin_right = 10 # right tree image margin
ts.show_border = False # set tree image border
ts.branch_vertical_margin = 3 # 3 pixels between adjancent branches
# set tree node style
for each_node in tree.traverse():
# leaf node parameters
if each_node.is_leaf():
ns = NodeStyle()
ns['shape'] = 'circle' # dot shape: circle, square or sphere
ns['size'] = 0 # dot size
ns['hz_line_width'] = 0.5 # branch line width
ns['vt_line_width'] = 0.5 # branch line width
ns['hz_line_type'] = 0 # branch line type: 0 for solid, 1 for dashed, 2 for dotted
ns['vt_line_type'] = 0 # branch line type
if each_node.name in name_list:
ns['fgcolor'] = 'red' # the dot setting
each_node.add_face(
TextFace(each_node.name,
fsize=8,
fgcolor='red',
tight_text=False,
bold=False),
column=0,
position='branch-right') # the node name text setting
each_node.set_style(ns)
else:
ns['fgcolor'] = 'blue' # the dot setting
each_node.add_face(
TextFace(each_node.name,
fsize=8,
fgcolor='black',
tight_text=False,
bold=False),
column=0,
position='branch-right') # the node name text setting
each_node.set_style(ns)
# non-leaf node parameters
else:
nlns = NodeStyle()
nlns['size'] = 0 # dot size
each_node.add_face(
TextFace(each_node.name,
fsize=4,
fgcolor='black',
tight_text=False,
bold=False),
column=5,
position='branch-top') # non-leaf node name text setting)
each_node.set_style(nlns)
# set figures size
tree.render('%s/%s.png' % (tree_image_folder, tree_file_name),
w=900,
units='px',
tree_style=ts)
def plot_tree(tree, tree_title, tree_output):
# set tree parameters
ts = TreeStyle()
ts.mode = "r" # tree model: 'r' for rectangular, 'c' for circular
ts.show_leaf_name = 0
# set tree title text parameters
ts.title.add_face(TextFace(tree_title,
fsize=8,
fgcolor='black',
ftype='Arial',
tight_text=False),
column=0) # tree title text setting
# set layout parameters
ts.rotation = 0 # from 0 to 360
ts.show_scale = False
ts.margin_top = 10 # top tree image margin
ts.margin_bottom = 10 # bottom tree image margin
ts.margin_left = 10 # left tree image margin
ts.margin_right = 10 # right tree image margin
ts.show_border = False # set tree image border
ts.branch_vertical_margin = 3 # 3 pixels between adjancent branches
# set tree node style
for each_node in tree.traverse():
# leaf node parameters
if each_node.is_leaf():
ns = NodeStyle()
ns["shape"] = "circle" # dot shape: circle, square or sphere
ns["size"] = 0 # dot size
ns['hz_line_width'] = 0.5 # branch line width
ns['vt_line_width'] = 0.5 # branch line width
ns['hz_line_type'] = 0 # branch line type: 0 for solid, 1 for dashed, 2 for dotted
ns['vt_line_type'] = 0 # branch line type
ns["fgcolor"] = "blue" # the dot setting
each_node.add_face(TextFace(each_node.name,
fsize=5,
fgcolor='black',
tight_text=False,
bold=False),
column=0,
position='branch-right'
) # leaf node the node name text setting
each_node.set_style(ns)
# non-leaf node parameters
else:
nlns = NodeStyle()
nlns["size"] = 0 # dot size
#nlns["rotation"] = 45
each_node.add_face(
TextFace(each_node.name,
fsize=3,
fgcolor='black',
tight_text=False,
bold=False),
column=5,
position='branch-top') # non-leaf node name text setting)
each_node.set_style(nlns)
tree.render(tree_output, w=900, units="px",
tree_style=ts) # set figures size
def format_tree(tree,
alignment,
al_len_dict,
edpos,
codontable={},
colors=None,
codon_col={},
text="C-to-U RNA editing",
ic_contents=[]):
"""Format the rendering of tree data for alignment"""
t = tree.copy()
# alignment is ordered dict
# flip alignment dict from gene ==> species ==> seq
# to species ==> gene ==> seq
specSeq = ddict(str)
edposSeq = ddict(list)
cur_len = 0
limits = []
for gname, specdict in alignment.items():
for node in t:
# fill missing with gap
specSeq[node.name] += specdict.get(node.name,
al_len_dict[gname] * '-')
edposSeq[node.name] += [
x + cur_len for x in edpos[gname].get(node.name, [])
]
# if node.name == 'Y08501':
# print(gname)
# print( edposSeq[node.name])
cur_len += al_len_dict.get(gname, 0)
limits.append((gname, cur_len))
for node in t:
node.add_feature("sequence", specSeq[node.name])
node.add_feature('edlist', edposSeq[node.name])
ts = TreeStyle()
ts.branch_vertical_margin = 15
ts.scale = 15
ts.allow_face_overlap = False
ts.show_scale = False
ts.show_leaf_name = False
ns = NodeStyle()
ns['shape'] = 'square'
ns['fgcolor'] = 'black'
ns['size'] = 0
def layout(node):
node.img_style = ns
if node.is_leaf():
faces.add_face_to_node(AttrFace(
'fullname',
fsize=14,
fgcolor=(MARKED_NODE_COLOR if
(node.name in colors
or node.fullname in colors) else 'black')),
node,
0,
position="aligned")
if hasattr(node, "sequence") and node.sequence:
seqface = SequenceFace(node.sequence,
"codon",
fsize=13,
codontable=codontable,
col_w=RES_COL_WIDTH,
bg_colors=codon_col,
black_out=node.edlist)
faces.add_face_to_node(seqface, node, 1, position="aligned")
ts.layout_fn = layout
# ts.title.add_face(TextFace('(%s) - SP score : %.0f | IC = %.2f' % (codon, sum(SP_score), sum(ic_contents)),
# fsize=14, fgcolor='red'), 0)
# ts.aligned_header.add_face(
# faces.RectFace(14, 14, 'white', 'white'), 1)
# ts.aligned_foot.add_face(
# faces.RectFace(14, 14, 'white', 'white'), 1)
# for (cod, col) in codon_col.items():
# ts.legend.add_face(faces.RectFace(50, 25, col, col), column=0)
# ts.legend.add_face(TextFace(" %s " % cod, fsize=8), column=1)
ts.legend.add_face(TextFace(text, fsize=14), column=1)
ts.legend_position = 1
ind = 1
prev_gend = 0
for (gname, gend) in limits:
ts.aligned_foot.add_face(
List90Face(list(range(0, gend - prev_gend, 3)),
fsize=13,
ftype='Monospace',
col_w=RES_COL_WIDTH * 3), ind)
ts.aligned_foot.add_face(
faces.RectFace(RES_COL_WIDTH * (gend - prev_gend), 13, '#BBBBBB',
'#EEEEEE'), ind)
ts.aligned_foot.add_face(TextFace(gname, fsize=13), ind)
ts.aligned_foot.add_face(
faces.RectFace(RES_COL_WIDTH * (gend - prev_gend), 5, 'white',
'white'), ind)
prev_gend += gend
ind += 1
#t.dist = 0
ts.margin_left = 5
ts.margin_right = 5
ts.margin_bottom = 5
return t, ts
def matriline_tree(id, db):
offspring = id
central_ind = db.get_elephant(id = id)[1]
#Start upwards to the oldest existing maternal ancestor
direct_mothers = []
mother = int
while mother is not None:
mother = db.get_mother(id=offspring)
direct_mothers.append(mother)
offspring = mother
if direct_mothers[-1] is None:
direct_mothers.pop()
#Find the oldest known female in the line
if direct_mothers != []:
oldest_mother = direct_mothers.pop()
else:
oldest_mother = id
#Go back down. The criterion to stop is that no female of generation 'n'
#has any offspring.
mothers = [oldest_mother]
generation_n = [1]
oldest_mother_num = db.get_elephant(id = oldest_mother)[1]
newick="('"+str(oldest_mother_num)+"_\u2640')"
branch_length = [[oldest_mother_num,2]]
while generation_n.__len__() != 0:
generation_n = []
for m in mothers:
m_num = db.get_elephant(id = m)[1]
m_birth = db.get_elephant(id = m)[5]
o = db.get_offsprings(id = m)
if o is not None:
taxon = []
for i in o:
generation_n.append(i)
info = db.get_elephant(id = i)
num = info[1]
sex = info[4]
birth = info[5]
age_of_mother_at_birth = round((birth - m_birth).days / 365.25)
branch_length.append([num,age_of_mother_at_birth])
if sex == 'F':
u = '\u2640'
elif sex == 'M':
u = '\u2642'
else:
u = '?'
taxon.append(str(num)+'_'+u)
#Could be refined so that branch length equals age of mother at childbirth
newick = newick.replace(("'"+str(m_num)+"_\u2640'"), (str(taxon).replace('[','(').replace(']',')').replace(' ','')+str(m_num)+'_\u2640'))
mothers = generation_n
newick = newick.replace("'","")+';'
#Now formatting for the actual plotting in ete3:
t = Tree(newick , format=8)
# print(t.get_ascii(attributes=['name'], show_internal=True))
ts = TreeStyle()
ts.show_leaf_name = False
ts.rotation = 90
ts.show_scale = False
ts.min_leaf_separation = 50
def my_layout(node):
F = TextFace(node.name, tight_text=True)
F.fsize=6
F.margin_left=5
F.margin_right=5
F.margin_top=0
F.margin_bottom=15
F.rotation=-90
add_face_to_node(F, node, column=0, position="branch-right")
ts.layout_fn = my_layout
ts.margin_left=10
ts.margin_right=10
ts.margin_top=10
ts.margin_bottom=10
i = 0
for n in t.traverse():
if i == 0:
n.delete()
n.img_style["size"] = 0.
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
i += 1
else:
if str(n.name[:-2]) == str(central_ind):
n.img_style["size"] = 10
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
n.img_style["shape"] = "circle"
n.img_style["fgcolor"] = "#A30B37"
n.dist = int(branch_length[i-1][1])
else:
n.img_style["size"] = 0.
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
n.dist = int(branch_length[i-1][1])
i += 1
t.render('tree.png', w=600, units= 'px', tree_style=ts)
taxa = []
for n in t.traverse():
taxa.append(n.name)
return(t.write(format=1),taxa)
def draw_tree(the_tree, colour, back_color, label, out_file, the_scale, extend,
bootstrap, group_file, grid_options, the_table, pres_abs,
circular):
t = Tree(the_tree, quoted_node_names=True)
# t.ladderize()
font_size = 8
font_type = 'Heveltica'
font_gap = 3
font_buffer = 10
o = t.get_midpoint_outgroup()
t.set_outgroup(o)
the_leaves = []
for leaves in t.iter_leaves():
the_leaves.append(leaves)
groups = {}
num = 0
# set cutoff value for clades as 1/20th of the distance between the furthest two branches
# assign nodes to groups
last_node = None
ca_list = []
if not group_file is None:
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = '#000000'
style["hz_line_color"] = '#000000'
style["vt_line_width"] = 1
style["hz_line_width"] = 1
for n in t.traverse():
n.set_style(style)
with open(group_file) as f:
group_dict = {}
for line in f:
group_dict[line.split()[0]] = line.split()[1]
for node in the_leaves:
i = node.name
for j in group_dict:
if j in i:
if group_dict[j] in groups:
groups[group_dict[j]].append(i)
else:
groups[group_dict[j]] = [i]
coloured_nodes = []
for i in groups:
the_col = i
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = the_col
style["hz_line_color"] = the_col
style["vt_line_width"] = 2
style["hz_line_width"] = 2
if len(groups[i]) == 1:
ca = t.search_nodes(name=groups[i][0])[0]
ca.set_style(style)
coloured_nodes.append(ca)
else:
ca = t.get_common_ancestor(groups[i])
ca.set_style(style)
coloured_nodes.append(ca)
tocolor = []
for j in ca.children:
tocolor.append(j)
while len(tocolor) > 0:
x = tocolor.pop(0)
coloured_nodes.append(x)
x.set_style(style)
for j in x.children:
tocolor.append(j)
ca_list.append((ca, the_col))
if back_color:
# for each common ancestor node get it's closest common ancestor neighbour and find the common ancestor of those two nodes
# colour the common ancestor then add it to the group - continue until only the root node is left
while len(ca_list) > 1:
distance = float('inf')
for i, col1 in ca_list:
for j, col2 in ca_list:
if not i is j:
parent = t.get_common_ancestor(i, j)
getit = True
the_dist = t.get_distance(i, j)
if the_dist <= distance:
distance = the_dist
the_i = i
the_j = j
the_i_col = col1
the_j_col = col2
ca_list.remove((the_i, the_i_col))
ca_list.remove((the_j, the_j_col))
rgb1 = strtorgb(the_i_col)
rgb2 = strtorgb(the_j_col)
rgb3 = ((rgb1[0] + rgb2[0]) / 2, (rgb1[1] + rgb2[1]) / 2,
(rgb1[2] + rgb2[2]) / 2)
new_col = colorstr(rgb3)
new_node = t.get_common_ancestor(the_i, the_j)
the_col = new_col
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = the_col
style["hz_line_color"] = the_col
style["vt_line_width"] = 2
style["hz_line_width"] = 2
new_node.set_style(style)
coloured_nodes.append(new_node)
ca_list.append((new_node, new_col))
for j in new_node.children:
tocolor.append(j)
while len(tocolor) > 0:
x = tocolor.pop(0)
if not x in coloured_nodes:
coloured_nodes.append(x)
x.set_style(style)
for j in x.children:
tocolor.append(j)
elif colour:
distances = []
for node1 in the_leaves:
for node2 in the_leaves:
if node1 != node2:
distances.append(t.get_distance(node1, node2))
distances.sort()
clade_cutoff = distances[len(distances) / 4]
for node in the_leaves:
i = node.name
if not last_node is None:
if t.get_distance(node, last_node) <= clade_cutoff:
groups[group_num].append(i)
else:
groups[num] = [num, i]
group_num = num
num += 1
else:
groups[num] = [num, i]
group_num = num
num += 1
last_node = node
for i in groups:
num = groups[i][0]
h = num * 360 / len(groups)
the_col = hsl_to_str(h, 0.5, 0.5)
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = the_col
style["hz_line_color"] = the_col
style["vt_line_width"] = 2
style["hz_line_width"] = 2
if len(groups[i]) == 2:
ca = t.search_nodes(name=groups[i][1])[0]
ca.set_style(style)
else:
ca = t.get_common_ancestor(groups[i][1:])
ca.set_style(style)
tocolor = []
for j in ca.children:
tocolor.append(j)
while len(tocolor) > 0:
x = tocolor.pop(0)
x.set_style(style)
for j in x.children:
tocolor.append(j)
ca_list.append((ca, h))
# for each common ancestor node get it's closest common ancestor neighbour and find the common ancestor of those two nodes
# colour the common ancestor then add it to the group - continue until only the root node is left
while len(ca_list) > 1:
distance = float('inf')
got_one = False
for i, col1 in ca_list:
for j, col2 in ca_list:
if not i is j:
parent = t.get_common_ancestor(i, j)
getit = True
for children in parent.children:
if children != i and children != j:
getit = False
break
if getit:
the_dist = t.get_distance(i, j)
if the_dist <= distance:
distance = the_dist
the_i = i
the_j = j
the_i_col = col1
the_j_col = col2
got_one = True
if not got_one:
break
ca_list.remove((the_i, the_i_col))
ca_list.remove((the_j, the_j_col))
new_col = (the_i_col + the_j_col) / 2
new_node = t.get_common_ancestor(the_i, the_j)
the_col = hsl_to_str(new_col, 0.5, 0.3)
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = the_col
style["hz_line_color"] = the_col
style["vt_line_width"] = 2
style["hz_line_width"] = 2
new_node.set_style(style)
ca_list.append((new_node, new_col))
# if you just want a black tree
else:
style = NodeStyle()
style['size'] = 0
style["vt_line_color"] = '#000000'
style["hz_line_color"] = '#000000'
style["vt_line_width"] = 1
style["hz_line_width"] = 1
for n in t.traverse():
n.set_style(style)
color_list = [(240, 163, 255), (0, 117, 220), (153, 63, 0), (76, 0, 92),
(25, 25, 25), (0, 92, 49), (43, 206, 72), (255, 204, 153),
(128, 128, 128), (148, 255, 181), (143, 124, 0),
(157, 204, 0), (194, 0, 136), (0, 51, 128), (255, 164, 5),
(255, 168, 187), (66, 102, 0), (255, 0, 16), (94, 241, 242),
(0, 153, 143), (224, 255, 102), (116, 10, 255), (153, 0, 0),
(255, 255, 128), (255, 255, 0), (255, 80, 5), (0, 0, 0),
(50, 50, 50)]
up_to_colour = {}
ts = TreeStyle()
column_list = []
width_dict = {}
if not grid_options is None:
colour_dict = {}
type_dict = {}
min_val_dict = {}
max_val_dict = {}
leaf_name_dict = {}
header_count = 0
the_columns = {}
if grid_options == 'auto':
with open(the_table) as f:
headers = f.readline().rstrip().split('\t')[1:]
for i in headers:
the_columns[i] = [i]
type_dict[i] = 'colour'
colour_dict[i] = {'empty': '#FFFFFF'}
width_dict[i] = 20
up_to_colour[i] = 0
column_list.append(i)
else:
with open(grid_options) as g:
for line in g:
if line.startswith('H'):
name, type, width = line.rstrip().split('\t')[1:]
if name in the_columns:
the_columns[name].append(name + '_' +
str(header_count))
else:
the_columns[name] = [
name + '_' + str(header_count)
]
width = int(width)
name = name + '_' + str(header_count)
header_count += 1
colour_dict[name] = {'empty': '#FFFFFF'}
type_dict[name] = type
width_dict[name] = width
column_list.append(name)
up_to_colour[name] = 0
min_val_dict[name] = float('inf')
max_val_dict[name] = 0
elif line.startswith('C'):
c_name, c_col = line.rstrip().split('\t')[1:]
if not c_col.startswith('#'):
c_col = colorstr(map(int, c_col.split(',')))
colour_dict[name][c_name] = c_col
val_dict = {}
with open(the_table) as f:
headers = f.readline().rstrip().split('\t')[1:]
column_no = {}
for num, i in enumerate(headers):
if i in the_columns:
column_no[num] = i
for line in f:
name = line.split('\t')[0]
leaf_name = None
for n in t.traverse():
if n.is_leaf():
if name.split('.')[0] in n.name:
leaf_name = n.name
if leaf_name is None:
continue
else:
leaf_name_dict[leaf_name] = name
vals = line.rstrip().split('\t')[1:]
if name in val_dict:
sys.exit('Duplicate entry found in table.')
else:
val_dict[name] = {}
for num, val in enumerate(vals):
if num in column_no and val != '':
for q in the_columns[column_no[num]]:
column_name = q
if type_dict[column_name] == 'colour':
val_dict[name][column_name] = val
if not val in colour_dict[column_name]:
colour_dict[column_name][val] = colorstr(
color_list[up_to_colour[column_name] %
len(color_list)])
up_to_colour[column_name] += 1
elif type_dict[column_name] == 'text':
val_dict[name][column_name] = val
elif type_dict[column_name] == 'colour_scale_date':
year, month, day = val.split('-')
year, month, day = int(year), int(month), int(
day)
the_val = datetime.datetime(
year, month, day, 0, 0,
0) - datetime.datetime(
1970, 1, 1, 0, 0, 0)
val_dict[name][
column_name] = the_val.total_seconds()
if the_val.total_seconds(
) < min_val_dict[column_name]:
min_val_dict[
column_name] = the_val.total_seconds()
if the_val.total_seconds(
) > max_val_dict[column_name]:
max_val_dict[
column_name] = the_val.total_seconds()
elif type_dict[column_name] == 'colour_scale':
the_val = float(val)
val_dict[name][column_name] = the_val
if the_val < min_val_dict[column_name]:
min_val_dict[column_name] = the_val
if the_val > max_val_dict[column_name]:
max_val_dict[column_name] = the_val
else:
sys.exit('Unknown column type')
if not out_file is None:
new_desc = open(out_file + '.new_desc', 'w')
else:
new_desc = open('viridis.new_desc', 'w')
ts.legend_position = 3
leg_column = 0
for num, i in enumerate(column_list):
nameF = TextFace(font_gap * ' ' + i.rsplit('_', 1)[0] +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True)
nameF.rotation = -90
ts.aligned_header.add_face(nameF, column=num + 1)
new_desc.write('H\t' + i.rsplit('_', 1)[0] + '\t' + type_dict[i] +
'\t' + str(width_dict[i]) + '\n')
x = num * 200
if type_dict[i] == 'colour':
ts.legend.add_face(TextFace(
font_gap * ' ' + i.rsplit('_', 1)[0] + ' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20, '#FFFFFF',
'#FFFFFF'),
column=leg_column)
for num2, j in enumerate(colour_dict[i]):
new_desc.write('C\t' + j + '\t' + colour_dict[i][j] + '\n')
ts.legend.add_face(TextFace(font_gap * ' ' + j +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20,
colour_dict[i][j],
colour_dict[i][j]),
column=leg_column)
leg_column += 2
elif type_dict[i] == 'colour_scale':
ts.legend.add_face(TextFace(
font_gap * ' ' + i.rsplit('_', 1)[0] + ' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20, '#FFFFFF',
'#FFFFFF'),
column=leg_column)
for num2 in range(11):
y = num2 * 20 + 30
val = (max_val_dict[i] - min_val_dict[i]) * num2 / 10.0
h = val / (max_val_dict[i] - min_val_dict[i]) * 270
s = 0.5
l = 0.5
colour = hsl_to_str(h, s, l)
ts.legend.add_face(TextFace(font_gap * ' ' + str(val) +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20, colour,
colour),
column=leg_column)
leg_column += 2
elif type_dict[i] == 'colour_scale_date':
ts.legend.add_face(TextFace(
font_gap * ' ' + i.rsplit('_', 1)[0] + ' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20, '#FFFFFF',
'#FFFFFF'),
column=leg_column)
for num2 in range(11):
y = num2 * 20 + 30
val = (max_val_dict[i] - min_val_dict[i]) * num2 / 10.0
h = val / (max_val_dict[i] - min_val_dict[i]) * 360
s = 0.5
l = 0.5
colour = hsl_to_str(h, s, l)
days = str(int(val / 60 / 60 / 24)) + ' days'
ts.legend.add_face(TextFace(font_gap * ' ' + days +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=leg_column + 1)
ts.legend.add_face(RectFace(width_dict[i], 20, colour,
colour),
column=leg_column)
leg_column += 2
for n in t.traverse():
if n.is_leaf():
name = leaf_name_dict[n.name]
if i in val_dict[name]:
val = val_dict[name][i]
else:
val = 'empty'
if type_dict[i] == 'colour':
n.add_face(RectFace(width_dict[i], 20,
colour_dict[i][val],
colour_dict[i][val]),
column=num + 1,
position="aligned")
elif type_dict[i] == 'colour_scale' or type_dict[
i] == 'colour_scale_date':
if val == 'empty':
colour = '#FFFFFF'
else:
h = (val - min_val_dict[i]) / (
max_val_dict[i] - min_val_dict[i]) * 360
s = 0.5
l = 0.5
colour = hsl_to_str(h, s, l)
n.add_face(RectFace(width_dict[i], 20, colour, colour),
column=num + 1,
position="aligned")
elif type_dict[i] == 'text':
n.add_face(TextFace(font_gap * ' ' + val +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=num + 1,
position="aligned")
if not pres_abs is None:
starting_col = len(column_list) + 1
subprocess.Popen('makeblastdb -out tempdb -dbtype prot -in ' +
pres_abs[0],
shell=True).wait()
folder = pres_abs[1]
len_dict = {}
gene_list = []
ts.legend.add_face(TextFace(font_gap * ' ' + 'Gene present/absent' +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=starting_col + 1)
ts.legend.add_face(RectFace(20, 20, '#FFFFFF', '#FFFFFF'),
column=starting_col)
ts.legend.add_face(TextFace(font_gap * ' ' + 'Gene present/absent' +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=starting_col + 1)
ts.legend.add_face(RectFace(20, 20, "#5ba965", "#5ba965"),
column=starting_col)
ts.legend.add_face(TextFace(font_gap * ' ' + 'Gene present/absent' +
' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True),
column=starting_col + 1)
ts.legend.add_face(RectFace(20, 20, "#cb5b4c", "#cb5b4c"),
column=starting_col)
with open(pres_abs[0]) as f:
for line in f:
if line.startswith('>'):
name = line.split()[0][1:]
gene_list.append(name)
len_dict[name] = 0
nameF = TextFace(font_gap * ' ' + name + ' ' * font_buffer,
fsize=font_size,
ftype=font_type,
tight_text=True)
nameF.rotation = -90
ts.aligned_header.add_face(nameF,
column=starting_col +
len(gene_list) - 1)
else:
len_dict[name] += len(line.rstrip())
min_length = 0.9
min_ident = 90
for n in t.iter_leaves():
the_name = n.name
if the_name[0] == '"' and the_name[-1] == '"':
the_name = the_name[1:-1]
if the_name.endswith('.ref'):
the_name = the_name[:-4]
if not os.path.exists(folder + '/' + the_name):
for q in os.listdir(folder):
if q.startswith(the_name):
the_name = q
if not os.path.exists(the_name + '.blast'):
subprocess.Popen(
'blastx -query ' + folder + '/' + the_name +
' -db tempdb -outfmt 6 -num_threads 24 -out ' + the_name +
'.blast',
shell=True).wait()
gotit = set()
with open(the_name + '.blast') as b:
for line in b:
query, subject, ident, length = line.split()[:4]
ident = float(ident)
length = int(length)
if ident >= min_ident and length >= min_length * len_dict[
subject]:
gotit.add(subject)
for num, i in enumerate(gene_list):
if i in gotit:
colour = "#5ba965"
else:
colour = "#cb5b4c"
n.add_face(RectFace(20, 20, colour, colour),
column=num + starting_col,
position="aligned")
# for num, i in enumerate(gene_list):
# x = (starting_col + num) * 200
# svg.writeString(i, x+50, 20, 12)
# y = 30
# svg.drawOutRect(x + 50, y, 12, 12, strtorgb('#5ba965'), strtorgb('#5ba965'), lt=0)
# svg.writeString('present', x + 70, y + 12, 12)
# y = 50
# svg.drawOutRect(x + 50, y, 12, 12, strtorgb('#cb5b4c'), strtorgb('#cb5b4c'), lt=0)
# svg.writeString('absent', x + 70, y + 12, 12)
# Set these to False if you don't want bootstrap/distance values
ts.show_branch_length = label
ts.show_branch_support = bootstrap
ts.show_leaf_name = False
for node in t.traverse():
if node.is_leaf():
node.add_face(AttrFace("name",
fsize=font_size,
ftype=font_type,
tight_text=True,
fgcolor='black'),
column=0,
position="aligned")
ts.margin_left = 20
ts.margin_right = 100
ts.margin_top = 20
ts.margin_bottom = 20
if extend:
ts.draw_guiding_lines = True
ts.scale = the_scale
if not circular is None:
ts.mode = "c"
ts.arc_start = 0
ts.arc_span = 360
if out_file is None:
t.show(tree_style=ts)
else:
t.render(out_file, w=210, units='mm', tree_style=ts)
from ete3 import Tree, TreeStyle
with open('phylogeny_tree.nwk', 'r') as f:
newick = f.read()
style = TreeStyle()
style.margin_right = 200
t = Tree(newick)
t.render('test-etetools-tree.svg', tree_style=style)
def treeMaker(path_to_prokka, path_to_hmm, pwd_hmmsearch_exe, pwd_mafft_exe,
pwd_fasttree_exe, plot_tree):
# Tests for presence of the tmp folder and deletes it
tmp_folder = 'get_species_tree_wd'
if os.path.exists(tmp_folder):
os.system('rm -r ' + tmp_folder)
os.mkdir(tmp_folder)
# List all prokka dirs in the target folder
prokka_files = [
i for i in os.listdir(path_to_prokka)
if os.path.isdir(path_to_prokka + '/' + i)
]
print('Detected %i input genomes' % len(prokka_files))
# Running hmmsearch on each file
print('Running hmmsearch...')
for f in prokka_files:
# call hmmsearch
#os.system('hmmsearch -o /dev/null --domtblout %s/%s_hmmout.tbl %s %s/%s/%s.faa' % (tmp_folder, f, path_to_hmm, path_to_prokka, f, f))
os.system(
'%s -o /dev/null --domtblout %s/%s_hmmout.tbl %s %s/%s/%s.faa' %
(pwd_hmmsearch_exe, tmp_folder, f, path_to_hmm, path_to_prokka, f,
f))
# Reading the protein file in a dictionary
proteinSequence = {}
for seq_record in SeqIO.parse('%s/%s/%s.faa' % (path_to_prokka, f, f),
'fasta'):
proteinSequence[seq_record.id] = str(seq_record.seq)
# Reading the hmmersearch table/extracting the protein part found beu hmmsearch out of the protein/Writing each protein sequence that was extracted to a fasta file (one for each hmm in phylo.hmm
hmm_id = ''
hmm_name = ''
hmm_pos1 = 0
hmm_pos2 = 0
hmm_score = 0
with open(tmp_folder + '/' + f.replace('prokka/', '') + '_hmmout.tbl',
'r') as tbl:
for line in tbl:
if line[0] == "#": continue
line = re.sub('\s+', ' ', line)
splitLine = line.split(' ')
if (hmm_id == ''):
hmm_id = splitLine[4]
hmm_name = splitLine[0]
hmm_pos1 = int(splitLine[17]) - 1
hmm_pos2 = int(splitLine[18])
hmm_score = float(splitLine[13])
elif (hmm_id == splitLine[4]):
if (float(splitLine[13]) > hmm_score):
hmm_name = splitLine[0]
hmm_pos1 = int(splitLine[17]) - 1
hmm_pos2 = int(splitLine[18])
hmm_score = float(splitLine[13])
else:
file_out = open(tmp_folder + '/' + hmm_id + '.fasta', 'a+')
file_out.write('>' + f + '\n')
if hmm_name != '':
seq = str(proteinSequence[hmm_name][hmm_pos1:hmm_pos2])
file_out.write(str(seq) + '\n')
file_out.close()
hmm_id = splitLine[4]
hmm_name = splitLine[0]
hmm_pos1 = int(splitLine[17]) - 1
hmm_pos2 = int(splitLine[18])
hmm_score = float(splitLine[13])
else:
file_out = open(tmp_folder + '/' + hmm_id + '.fasta', 'a+')
file_out.write('>' + f + '\n')
if hmm_name != '':
seq = str(proteinSequence[hmm_name][hmm_pos1:hmm_pos2])
file_out.write(str(seq) + '\n')
file_out.close()
# Call mafft to align all single fasta files with hmms
files = os.listdir(tmp_folder)
fastaFiles = [i for i in files if i.endswith('.fasta')]
print('Running mafft...')
for f in fastaFiles:
fastaFile1 = '%s/%s' % (tmp_folder, f)
fastaFile2 = fastaFile1.replace('.fasta', '_aligned.fasta')
os.system(pwd_mafft_exe + ' --quiet --maxiterate 1000 --globalpair ' +
fastaFile1 + ' > ' + fastaFile2 + ' ; rm ' + fastaFile1)
# concatenating the single alignments
# create the dictionary
print('Concatenating alignments...')
concatAlignment = {}
for element in prokka_files:
concatAlignment[element] = ''
# Reading all single alignment files and append them to the concatenated alignment
files = os.listdir(tmp_folder)
fastaFiles = [i for i in files if i.endswith('.fasta')]
for f in fastaFiles:
fastaFile = tmp_folder + '/' + f
proteinSequence = {}
alignmentLength = 0
for seq_record_2 in SeqIO.parse(fastaFile, 'fasta'):
proteinName = seq_record_2.id
proteinSequence[proteinName] = str(seq_record_2.seq)
alignmentLength = len(proteinSequence[proteinName])
for element in prokka_files:
if element in proteinSequence.keys():
concatAlignment[element] += proteinSequence[element]
else:
concatAlignment[element] += '-' * alignmentLength
# writing alignment to file
file_out = open('./species_tree.aln', 'w')
for element in prokka_files:
file_out.write('>' + element + '\n' + concatAlignment[element] + '\n')
file_out.close()
# calling fasttree for tree calculation
print('Running fasttree...')
os.system('%s -quiet species_tree.aln > species_tree.newick' %
pwd_fasttree_exe)
# Decomment the two following lines if tree is rooted but should be unrooted
#phyloTree = dendropy.Tree.get(path='phylogenticTree.phy', schema='newick', rooting='force-unrooted')
#dendropy.Tree.write_to_path(phyloTree, 'phylogenticTree_unrooted.phy', 'newick')
# plot species tree
if plot_tree == 1:
print('Plot species tree')
tree = Tree('species_tree.newick', format=1)
# set tree parameters
ts = TreeStyle()
ts.mode = "r" # tree model: 'r' for rectangular, 'c' for circular
ts.show_leaf_name = 0
# set tree title text parameters
ts.title.add_face(TextFace('Species_Tree',
fsize=8,
fgcolor='black',
ftype='Arial',
tight_text=False),
column=0) # tree title text setting
# set layout parameters
ts.rotation = 0 # from 0 to 360
ts.show_scale = False
ts.margin_top = 10 # top tree image margin
ts.margin_bottom = 10 # bottom tree image margin
ts.margin_left = 10 # left tree image margin
ts.margin_right = 10 # right tree image margin
ts.show_border = False # set tree image border
ts.branch_vertical_margin = 3 # 3 pixels between adjancent branches
# set tree node style
for each_node in tree.traverse():
# leaf node parameters
if each_node.is_leaf():
ns = NodeStyle()
ns["shape"] = "circle" # dot shape: circle, square or sphere
ns["size"] = 0 # dot size
ns['hz_line_width'] = 0.5 # branch line width
ns['vt_line_width'] = 0.5 # branch line width
ns['hz_line_type'] = 0 # branch line type: 0 for solid, 1 for dashed, 2 for dotted
ns['vt_line_type'] = 0 # branch line type
ns["fgcolor"] = "blue" # the dot setting
each_node.add_face(TextFace(each_node.name,
fsize=5,
fgcolor='black',
tight_text=False,
bold=False),
column=0,
position='branch-right'
) # leaf node the node name text setting
each_node.set_style(ns)
# non-leaf node parameters
else:
nlns = NodeStyle()
nlns["size"] = 0 # dot size
# nlns["rotation"] = 45
each_node.add_face(
TextFace(each_node.name,
fsize=3,
fgcolor='black',
tight_text=False,
bold=False),
column=5,
position='branch-top') # non-leaf node name text setting)
each_node.set_style(nlns)
tree.render('species_tree' + '.png', w=900, units="px",
tree_style=ts) # set figures size
if plot_tree == 0:
print('The built species tree was exported to species_tree.newick')
else:
print(
'The built species tree was exported to species_tree.newick and species_tree.png'
)
def matriline_tree(id, db, as_list=False):
offspring = id
e = db.get_elephant(id=id)
if e:
central_ind = e[1]
else:
return(None)
# Start upwards to the oldest existing maternal ancestor
direct_mothers = []
mother = str
while mother is not None:
mother = db.get_mother(id=offspring)
direct_mothers.append(mother)
offspring = mother
if direct_mothers[-1] is None:
direct_mothers.pop()
# Find the oldest known female in the line
if direct_mothers != []:
oldest_mother = direct_mothers.pop()
else:
oldest_mother = id
# Go back down. The criterion to stop is that no female of generation 'n' has any offspring.
mothers = [oldest_mother]
generation_n = [1]
oldest_mother_num = db.get_elephant(id=oldest_mother)[1]
newick = "('" + str(oldest_mother_num) + "_\u2640')"
branch_length = [[oldest_mother_num, 2]]
############################
# Exporation in list form
if as_list is True:
# at each generation, we will make two objects: an unstructured list giving all individuals,
# and a structured list keeping track of paths
# We make a first pass to create the unstructured list:
tree_list_unstructured = [oldest_mother_num]
g = 0
generation_n = [0]
while generation_n.__len__() != 0:
generation_n = []
# these_off = None
if type(tree_list_unstructured[g]) is list:
for i in tree_list_unstructured[g]:
these_off = db.get_offsprings(num=i)
if these_off:
for o in these_off:
generation_n.append(o)
else:
these_off = db.get_offsprings(num=tree_list_unstructured[g])
if these_off:
for o in these_off:
generation_n.append(o)
g += 1
tree_list_unstructured.append(generation_n)
if tree_list_unstructured[-1] == []:
tree_list_unstructured.pop()
# Now the genealogy is explored, we go through it and structure it:
tree_list_structured = [oldest_mother_num]
for generation in tree_list_unstructured:
next_generation = []
these_off = None
if type(generation) is not list:
these_off = db.get_offsprings(num=generation)
if these_off:
next_generation = these_off
else:
next_generation = []
elif type(generation) is list and generation != []:
for g in generation:
these_off = db.get_offsprings(num=g)
if these_off:
next_generation.append(these_off)
else:
next_generation.append([])
if not all(x==[] for x in next_generation):
tree_list_structured.append(next_generation)
return([tree_list_structured, tree_list_unstructured])
############################
# Exploration in Newick form
while generation_n.__len__() != 0:
generation_n = []
for m in mothers:
m_num = db.get_elephant(id=m)[1]
m_birth = db.get_elephant(id=m)[5]
o = db.get_offsprings(id=m)
if o is not None:
taxon = []
for i in o:
generation_n.append(i)
info = db.get_elephant(id=i)
num = info[1]
if not num:
num = info[3]
sex = info[4]
birth = info[5]
age_of_mother_at_birth = round((birth - m_birth).days / 365.25)
branch_length.append([num,age_of_mother_at_birth])
if sex == 'F':
u = '\u2640'
elif sex == 'M':
u = '\u2642'
else:
u = '?'
taxon.append(str(num)+'_'+u)
newick = newick.replace(("'" + str(m_num) + "_\u2640'"),
(str(taxon).replace('[', '(').replace(']', ')').replace(' ', '')
+ str(m_num) + '_\u2640'))
mothers = generation_n
newick = newick.replace("'", "")+';'
# Now formatting for the actual plotting in ete3:
t = Tree(newick, format=8)
# print(t.get_ascii(attributes=['name'], show_internal=True))
ts = TreeStyle()
ts.show_leaf_name = False
ts.rotation = 90
ts.show_scale = False
ts.min_leaf_separation = 50
def my_layout(node):
F = TextFace(node.name, tight_text=True)
F.fsize = 6
F.margin_left = 5
F.margin_right = 5
F.margin_top = 0
F.margin_bottom = 15
F.rotation = -90
add_face_to_node(F, node, column=0, position="branch-right")
ts.layout_fn = my_layout
ts.margin_left = 10
ts.margin_right = 10
ts.margin_top = 10
ts.margin_bottom = 10
i = 0
for n in t.traverse():
if i == 0:
n.delete()
n.img_style["size"] = 0.
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
i += 1
else:
if str(n.name[:-2]) == str(central_ind):
n.img_style["size"] = 10
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
n.img_style["shape"] = "circle"
n.img_style["fgcolor"] = "#A30B37"
n.dist = int(branch_length[i-1][1])
else:
n.img_style["size"] = 0.
n.img_style["vt_line_width"] = 1
n.img_style["hz_line_width"] = 1
n.dist = int(branch_length[i-1][1])
i += 1
t.render('tree.png', w=600, units= 'px', tree_style=ts)
taxa = []
for n in t.traverse():
taxa.append(n.name)
return(t.write(format=1), taxa)
def plotting_tree(species, latin_names, original_tree, correction_table,
consensus_strategy_for_multi_outgroups, ortholog_db,
peak_stats, nextflow_flag):
"""
Generate a PDF figure of the input tree with branch lengths equal to Ks distances.
If it is not possible to compute the branch length for a branch, the branch line is dashed. This happens when some\\
ortholog data to compute the branch-specific Ks contribution are missing.
:param species: the current focal species
:param latin_names: a dictionary-like data structure that associates each informal species name to its latin name
:param original_tree: Newick tree format of the phylogenetic tree among the involved species
:param correction_table: adjustment results in DataFrame format (contains both possible types of consensus strategy for how to deal with multiple outgroups)
:param consensus_strategy_for_multi_outgroups: user choice about which consensus strategy to use when dealing with multiple outgroups
:para ortholog_db: ortholog peak database used to get ortholog data for the relative rate test; if not available, will be ignored
:param peak_stats: flag to specify whether the ortholog distribution peak is the mode or the median
:param nextflow_flag: boolean flag to state whether the script is run in the Nextflow pipeline or not
"""
# Get an equivalent tree where the focal species is the top leaf
tree = reorder_tree_leaves(original_tree, species)
node_and_branch_style(tree)
species_node = get_species_node(species, tree)
labeling_internal_nodes(species_node)
species_history = get_species_history(species_node)
rate_species_dict, rate_sister_dict = {}, {}
for ancestor_node in species_history[:-2]:
# NOTE: at the moment the following function is only used to fill in the dictionaries of branch-specific Ks contributions
average_peak_of_divergence_event, margin_error_box, error_text = get_branch_length_and_errorbox(
species, ancestor_node, correction_table,
consensus_strategy_for_multi_outgroups, latin_names,
rate_species_dict, rate_sister_dict)
# Adding the branch length to the focal species node, otherwise it lacks it
if ancestor_node.name == species:
ancestor_node.dist = rate_species_dict[species]
draw_branch_length_label(ancestor_node, known_distance=True)
# Adding as TextFaces both the divergent Ks of the node (as mean) and the error range (left-most and right-most boundaries)
divergence_node = ancestor_node.up # getting parent node, where the current divergence takes place
divergence_node.add_feature("rate_species", rate_species_dict[species])
divergence_node.add_feature("avg_peak",
round(average_peak_of_divergence_event, 2))
divergence_node.add_feature("margins",
f"({error_text[0]}, {error_text[1]})")
### divergence_node.add_face(AttrFace("margins", fsize=5), column=0, position="branch-right") [ NOT USED FOR NOW ]
# Setting the branch length of the nodes belonging to the speciation history of the focal species
for divergence_node in species_history[1:]:
parent_node = divergence_node.up
try:
divergence_node.dist = round(
parent_node.rate_species - divergence_node.rate_species, 3)
draw_branch_length_label(divergence_node, known_distance=True)
except Exception:
divergence_node.dist = 10 # impossible number to flag an unknown length
draw_branch_length_label(divergence_node, known_distance=False)
unknown_branch_len_style(divergence_node)
if ortholog_db.empty: # branch-specific Ks contributions can be obtained only from adjustment_tables
logging.info(
"Getting branch-specific Ks contributions from rate-adjustment table data"
)
else: # if the ortholog DB is available, we can try to compute the branch-specific Ks contributions from there too
logging.info(
"Getting branch-specific Ks contributions from rate-adjustment table data"
)
logging.info(
"Computing branch-specific Ks contributions from ortholog peak data in database by applying principles of the relative rate test"
)
rate_dict = {}
get_rates_from_current_analysis(rate_dict, correction_table, species,
species_history, latin_names)
# Setting the branch length of the other remaining nodes
missing_ortholog_data_from_database = False
missing_ortholog_data_from_correction_table = False
for node in species_history[:-1]:
sister_node = node.get_sisters(
) # is a list containing the sister NODE (it's only ONE node)
if not ortholog_db.empty: # if there is an ortholog database that can help with computing the missing branch lengths
if len(sister_node[0].get_leaves()) > 1:
missing_ortholog_data_from_database = get_rates_from_ortholog_peak_db(
rate_dict, sister_node, latin_names, ortholog_db,
peak_stats, missing_ortholog_data_from_database)
else:
if sister_node[0].name in rate_sister_dict.keys(
): # if leaf has known length
sister_node[0].dist = rate_sister_dict[sister_node[0].name]
draw_branch_length_label(sister_node[0],
known_distance=True)
else: # if the leaf has unknown length
sister_node[
0].dist = 10 # impossible number to flag an unknown length
draw_branch_length_label(sister_node[0],
known_distance=False)
unknown_branch_len_style(sister_node[0])
else: # if ortholog database not available (the variable was previously set as an empty dataframe)
if len(sister_node[0].get_leaves()) > 1:
missing_ortholog_data_from_correction_table = True # correction_tables is not enough to know all branch lengths!
sister_node[
0].dist = 10 # impossible number to flag an unknown length
draw_branch_length_label(sister_node[0], known_distance=False)
unknown_branch_len_style(sister_node[0])
for node in sister_node[0].get_descendants():
node.dist = 10 # impossible number to flag an unknown length
draw_branch_length_label(node, known_distance=False)
unknown_branch_len_style(node)
else:
leaf = sister_node[0].get_leaves()[0] # there is only one leaf
if leaf.name in rate_sister_dict.keys():
leaf.dist = rate_sister_dict[leaf.name]
draw_branch_length_label(leaf, known_distance=True)
else: # if the leaf has unknown length
leaf.dist = 10 # impossible number to flag an unknown length
draw_branch_length_label(leaf, known_distance=False)
unknown_branch_len_style(leaf)
# If the ortholog peak database is lacking some required data (must have been deleted by the user) or
# if the peak database has been deleted and only the correction_table has been used for the branch contributions, gives a warning
if missing_ortholog_data_from_database or missing_ortholog_data_from_correction_table:
logging.warning("")
logging.warning(
"One or more branch lengths are unknown (dashed line) due to missing ortholog distribution peak data"
)
# If in Nextflow mode, tell the user to wait until the pipeline is finished in order to have all branch lengths
if nextflow_flag:
if missing_ortholog_data_from_database:
logging.info(
f"As soon as new ortholog data will become available, the tree branch lengths will be updated"
)
# If manual mode, tell the user how to get a complete branch tree (probably they deleted some data in the peak database)
else:
if missing_ortholog_data_from_database or missing_ortholog_data_from_correction_table:
logging.warning(
f"It's necessary to run a new Nextflow (or manual) pipeline to complete the tree branch length information"
)
label_leaves_with_latin_names(tree, latin_names)
adapt_unknown_branch_length(tree)
ts = TreeStyle()
# ts.title.add_face(TextFace(" Input tree with branch length equal to Ks distances ", ftype="Arial", fsize=18), column=0)
ts.orientation = 1
ts.branch_vertical_margin = 14
ts.show_leaf_name = False # because there is a Face showing it
ts.show_branch_length = False
ts.margin_left = 25
ts.margin_right = 25
ts.margin_top = 25
ts.scale = 200
#ts.scale_length = # to set a fixed scale branch length
root_of_corrected_tree = species_history[-1]
root_of_corrected_tree.render(os.path.join(
"rate_adjustment", f"{species}",
f"{_TREE_BRANCH_DISTANCES.format(species)}"),
w=4.5,
units="in",
tree_style=ts)
mark2.margin_left = 1
mark2.margin_bottom = 0
mark2.opacity = 1 # from 0 to 1
mark2.border.width = 1
mark2.background.color = "#F5F5DC"
ts.legend.add_face(mark2, column=0)
mark3 = TextFace("Selected branches", fsize=10, fgcolor="black")
mark3.margin_top = 2
mark3.margin_right = 20
mark3.margin_left = 5
mark3.margin_bottom = 2
ts.legend.add_face(mark3, column=1)
ts.margin_left = 20
ts.margin_right = 20
ts.margin_top = 10
ts.margin_bottom = 10
if len(sys.argv) >= 3:
title = TextFace(target_name,
fsize=16,
fgcolor="SteelBlue",
fstyle="italic",
bold=True)
title.margin_top = 10
title.margin_right = 10
title.margin_left = 10
title.margin_bottom = 10
ts.title.add_face(title, column=0)
# T.write(format=1, outfile=tree_file_name+".multifurc")
ts = TreeStyle()
# ts.mode = "c"
ts.scale = 500
ts.optimal_scale_level = "full"
# ts.arc_start = 180 # -180
# ts.arc_span = 180 # 359
ts.show_leaf_name = False
ts.show_branch_length = False
ts.show_branch_support = False
# ts.root_opening_factor = 0.75
ts.draw_guiding_lines = False
ts.margin_left = 50
ts.margin_right = 50
ts.margin_top = 50
ts.margin_bottom = 50
ts.rotation = 0
path_to_sequence_string_uid_to_isotype_map = "/Users/lime/Dropbox/quake/Bcell/selection/figures/treePlots/v2/Bcell_flu_high_res.sequences.isotypeDict.V6_Full.csv"
sequence_string_uid_to_isotype = {}
with open(path_to_sequence_string_uid_to_isotype_map) as f:
for line in f:
vals = line.rstrip().split()
sequence_string_uid_to_isotype[vals[1]] = vals[2]
path_to_isotype_to_color_map = "/Users/lime/Dropbox/quake/Bcell/selection/figures/treePlots/v2/isotype_to_color_dict.json"
with open(path_to_isotype_to_color_map, 'rU') as f:
isotype_to_color = json.load(f)
f.margin_top = cmar
f.margin_left = cmar
f.margin_right = cmar
node.add_face(f, column=0)
#%% plot tree
ts = TreeStyle()
ts.show_leaf_name = True
ts.mode = "c"
ts.show_scale = False
margins = 10
ts.margin_left = margins
ts.margin_right = margins
ts.margin_top = margins
ts.margin_bottom = margins
colors_dict = dict(zip(horizons, hexlist))
def create_colorstyle(h, colors_dict):
c = colors_dict.get(h)
ns = NodeStyle(bgcolor=c)
return ns
styles_dict = {h: create_colorstyle(h, colors_dict) for h in horizons}
for h in horizons:
