def traitTreeMinimal(traits, mapper):
### Take dict of traits and [R,G,B]-returning function
### Draw a tree with the continuous trait painted on via a colormapping function
def rgb2hex(r, g, b):
hex = "#{:02x}{:02x}{:02x}".format(r, g, b)
return hex
ts = TreeStyle()
ts.show_leaf_name = False
ts.rotation = 270
ts.complete_branch_lines_when_necessary = False
#ts.optimal_scale_level = "full"
ts.scale = 800
# default NodeStyle
nstyle = NodeStyle()
nstyle["size"] = 0
nstyle["hz_line_color"] = "grey"
nstyle["vt_line_color"] = "grey"
nstyle["hz_line_width"] = 3
nstyle["vt_line_width"] = 3
for n in tree.traverse():
chroma = rgb2hex(*[
int(val) for val in mapper(traits[n.ND], gamma=0.8, scaleMax=255)
]) # setup for wl2RGB
nf = CircleFace(radius=10, color='none', style='circle', label=None)
n.set_style(nstyle)
n.add_face(nf, column=0, position='branch-top')
outFile = args.output + "/test_trees/cont_trait.pdf"
tree.render(outFile, tree_style=ts)
print >> sys.stderr, outFile
def set_default_TreeStyle(tree, draw_nodes):
ts = TreeStyle()
ts.mode = "c"
ts.arc_start = -180
ts.arc_span = 180
ts.root_opening_factor = 1
ts.show_branch_length = False
ts.show_branch_support = True
ts.force_topology = False
ts.show_leaf_name = False
ts.min_leaf_separation = 10
ts.root_opening_factor = 1
ts.complete_branch_lines_when_necessary = True
return ts, tree
def get_tree_style(**kwargs):
style = TreeStyle()
style.layout_fn = layout
style.allow_face_overlap = True
style.branch_vertical_margin = 5
style.complete_branch_lines_when_necessary = False
style.draw_aligned_faces_as_table = False
style.scale = 1500
style.scale_length = 0.05
style.show_branch_support = False
style.show_leaf_name = False
for key, value in kwargs.items():
if value == "True":
value = True
else:
try:
value = float(value)
except ValueError:
pass
setattr(style, key, value)
return style
def set_default_TreeStyle(tree):
ts = TreeStyle()
ts.mode = "c"
ts.root_opening_factor = 1
ts.show_branch_length = True
ts.show_branch_support = True
ts.force_topology = True
ts.show_leaf_name = False
ts.min_leaf_separation = 10
ts.root_opening_factor = 1
ts.complete_branch_lines_when_necessary = True
ns = NodeStyle()
ns["size"] = 6
ns["fgcolor"] = "Black"
ns["hz_line_width"] = 8
ns["vt_line_width"] = 8
for node in tree.traverse("postorder"):
# if not node.is_leaf():
node.set_style(ns)
tree.set_style(ts)
return ts, tree
def testTreesMinimal(scenarios, traits, mapper):
def rgb2hex(r, g, b):
hex = "#{:02x}{:02x}{:02x}".format(r, g, b)
return hex
### Draw test trees. This is a modified version of the test routine in pcoc_num_tree.py, stuffed in a for loop
for cutoff in sorted(scenarios.keys()):
tree = init_tree(args.tree)
# not mucking with additive trees yet; ultrametrize the tree and normalize to length 1
tree.convert_to_ultrametric(tree_length=1)
# read scenario into a dict
manual_mode_nodes = {"T": [], "C": []}
p_events = scenarios[cutoff].strip().split("/")
for e in p_events:
l_e = map(int, e.split(","))
manual_mode_nodes["T"].append(l_e[0])
manual_mode_nodes["C"].extend(l_e[1:])
ts = TreeStyle()
# ts.allow_face_overlap = True
ts.show_leaf_name = False
ts.rotation = 270
ts.complete_branch_lines_when_necessary = False
# ts.optimal_scale_level = "full"
ts.scale = 800
for n in tree.traverse():
# default NodeStyle
nstyle = NodeStyle()
nstyle["size"] = 0
nstyle["hz_line_color"] = "none"
nstyle["vt_line_color"] = "none"
nstyle["hz_line_width"] = 3
nstyle["vt_line_width"] = 3
# colored faces
chroma = rgb2hex(*[
int(val)
for val in mapper(traits[n.ND], gamma=0.8, scaleMax=255)
]) # setup for wl2RGB
nf = CircleFace(radius=10,
color=chroma,
style='circle',
label=None)
# scenario-dependent features
if manual_mode_nodes:
# if transition node
if n.ND in manual_mode_nodes["T"]:
#nstyle["hz_line_color"] = "orange"
nf.inner_border.width = 4
nf.inner_border.color = 'red'
# if convergent node
elif n.ND in manual_mode_nodes["C"]:
#nstyle["hz_line_color"] = "violet"
nf.inner_border.width = 4
nf.inner_border.color = 'white'
# if ancestral
else:
nstyle["hz_line_color"] = "none"
n.set_style(nstyle)
n.add_face(nf, column=0, position='branch-top')
# limiting number of digits
outFile = args.output + "/test_trees/" + str(cutoff).replace(
'.', '_')[:np.min([5, len(str(cutoff))])] + ".pdf"
tree.render(outFile, tree_style=ts)
print >> sys.stderr, outFile
if keySeq[i] != "-": # meaning anything except gaps
mappedCols[
keyIndex] = i + 1 # map the alignment column to the key column. Gotta shift the index!
keyIndex += 1
return mappedCols
## ETE3 TREE-VIZ FUNCTIONS ##
# basic tree style
tree_style = TreeStyle()
tree_style.show_leaf_name = False
tree_style.show_branch_length = False
tree_style.draw_guiding_lines = True
tree_style.complete_branch_lines_when_necessary = True
# make tree grow upward
tree_style.rotation = 270
# and make it appear ultrametric (which it is!)
tree_style.optimal_scale_level = "full"
# internal node style
nstyle = NodeStyle()
nstyle["fgcolor"] = "black"
nstyle["size"] = 0
# terminal node style
nstyle_L = NodeStyle()
nstyle["fgcolor"] = "black"
nstyle["size"] = 0
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)
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)
tree_ete = Tree(tree_newick, format=1)
node_fh = open(sys.argv[2])
color = {}
size = {}
value = {}
node_info = node_fh.readlines()
for info in node_info:
info = info.rstrip('\n')
infos = list(info.split('\t'))
color[infos[0]] = infos[1]
size[infos[0]] = infos[2]
value[infos[0]] = "%.2f %%" % (float(infos[3]) * 100)
tstyle = TreeStyle()
tstyle.complete_branch_lines_when_necessary = False
tstyle.show_leaf_name = False
tstyle.scale = 1
tstyle.branch_vertical_margin = 40
tstyle.show_scale = False
tree_ete.set_style(tstyle)
for node in tree_ete.traverse():
if node.name != "root":
name_face = TextFace(node.name)
abun_face = TextFace(value[node.name])
node.add_face(name_face, column=0, position="branch-top")
node.add_face(abun_face, column=0, position="branch-bottom")
nstyle = NodeStyle()
nstyle["fgcolor"] = color[node.name]
nstyle["size"] = float(size[node.name])
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-t",
action="store",
dest="treef",
help="The tree file")
parser.add_argument("-s",
action="store",
dest="statf",
help="The statistics file")
parser.add_argument("-c",
action="store",
default=0,
type=float,
dest="scaling",
help="If this parameter is set "
"to more than 0, "
"the size of the pie charts "
"correlate with the total "
"number of events at a node "
"(and are scaled by the factor "
"given as a float).")
parser.add_argument(
"-e",
action="store",
type=str,
default="all",
dest="event",
help=
"If an event type is specified, just this event type is visualized. Per default all event types are shown on the tree.\n"
"all\n"
"fusions\n"
"fissions\n"
"termLosses\n"
"termEmergences\n"
"singleDomainLosses\n"
"singleDomainEmergences")
parser.add_argument(
"-p",
action="store",
dest="treeshape",
default="r",
choices=["c", "r"],
help="shape of the tree, circle (c) or tree format (r)")
parser.add_argument("-o", action="store", dest="outputname")
parser.add_argument(
"-y",
action="store",
type=str,
dest="NodeIDtreeName",
default=None,
help="Name for output file that shows a tree with all node IDs.")
parser.add_argument(
"-l",
dest="short_legend",
help=
"Writes the full legend for all events in two levels for short trees",
action="store_true")
params = parser.parse_args()
if (params.event not in ("all", "fusions", "fissions", "termLosses",
"termEmergences", "singleDomainLosses",
"singleDomainEmergences")):
print(
"Error: Please specify a valid event type. For a list of possible options use the --help parameter."
)
sys.exit(1)
if params.NodeIDtreeName != None:
id_tree = Tree(params.treef, format=0)
coun = 0
for node in id_tree.traverse('preorder'):
node.add_features(ID=coun)
coun += 1
# Create empty TreeStyle
ts = TreeStyle()
# Set custom layout function
ts.layout_fn = layout_idtree
# Draw tree
ts.mode = params.treeshape
ts.complete_branch_lines_when_necessary = True
ts.extra_branch_line_type = 0
ts.extra_branch_line_color = "black"
# ts.optimal_scale_level ="full"
ts.branch_vertical_margin = 40
ts.scale = 100
# We will add node names manually
ts.show_leaf_name = False
ts.draw_guiding_lines = True
if (params.NodeIDtreeName.endswith(".pdf")):
pathout = params.NodeIDtreeName
else:
pathout = params.NodeIDtreeName + ".pdf"
id_tree.render(pathout, dpi=1200, tree_style=ts)
pl.close()
else:
tree = Tree(params.treef, format=0)
# Read statistics file
node_stat_dict = {}
with open(params.statf, "r") as sf:
for line in sf:
# Stop the loop at the second part of statistics file
if line.startswith(
"# Number of events per domain."
) or line.startswith(
"# Events per domain arrangement for last common ancestor"
):
break
if line[0] not in ('#', '\n'):
vecline = line.strip().split()
id = vecline.pop(0)
stats = [int(i) for i in vecline]
node_stat_dict[int(id)] = stats
# determine max. number of events per node for scaling
fus_max = 0
fis_max = 0
termLoss_max = 0
termGain_max = 0
singLoss_max = 0
singGain_max = 0
tot_max = 0
# Assign rearrangement events to leaves
c = 0
for node in tree.traverse('preorder'):
node.add_features(fusion=node_stat_dict[c][0])
if (node_stat_dict[c][0] > fus_max):
fus_max = node_stat_dict[c][0]
node.add_features(fission=node_stat_dict[c][1])
if (node_stat_dict[c][1] > fis_max):
fis_max = node_stat_dict[c][1]
node.add_features(termLoss=node_stat_dict[c][2])
if (node_stat_dict[c][2] > termLoss_max):
termLoss_max = node_stat_dict[c][2]
node.add_features(termGain=node_stat_dict[c][3])
if (node_stat_dict[c][3] > termGain_max):
termGain_max = node_stat_dict[c][3]
node.add_features(singLoss=node_stat_dict[c][4])
if (node_stat_dict[c][4] > singLoss_max):
singLoss_max = node_stat_dict[c][4]
node.add_features(singGain=node_stat_dict[c][5])
if (node_stat_dict[c][5] > singGain_max):
singGain_max = node_stat_dict[c][5]
if (sum(node_stat_dict[c]) > tot_max):
tot_max = sum(node_stat_dict[c])
c += 1
global scal
scal = params.scaling
global eve
event_options = {
"all": 0,
"fusions": 1,
"fissions": 2,
"termLosses": 3,
"termEmergences": 4,
"singleDomainLosses": 5,
"singleDomainEmergences": 6
}
eve = event_options[params.event]
# Create empty TreeStyle
ts = TreeStyle()
# Set custom layout function
ts.layout_fn = layout_gen_events
# Draw tree
ts.mode = params.treeshape
ts.complete_branch_lines_when_necessary = True
ts.extra_branch_line_type = 0
ts.extra_branch_line_color = "black"
#ts.optimal_scale_level ="full"
ts.branch_vertical_margin = 40
ts.scale = 100
# We will add node names manually
ts.show_leaf_name = False
# legend creation
if (params.event == "all"):
ts.legend.add_face(CircleFace(10, "DimGray"), column=0)
ts.legend.add_face(TextFace(" Fusion ",
fsize=16,
fgcolor='DimGray'),
column=1)
ts.legend.add_face(CircleFace(10, "DeepPink"), column=2)
ts.legend.add_face(TextFace(' Fission ',
fsize=16,
fgcolor='DeepPink'),
column=3)
ts.legend.add_face(CircleFace(10, "YellowGreen"), column=4)
ts.legend.add_face(TextFace(' Terminal Loss ',
fsize=16,
fgcolor='YellowGreen'),
column=5)
if params.short_legend:
ts.legend.add_face(CircleFace(10, "DarkBlue"), column=0)
ts.legend.add_face(TextFace(' Terminal Emergence ',
fsize=16,
fgcolor='DarkBlue'),
column=1)
ts.legend.add_face(CircleFace(10, "Chocolate"), column=2)
ts.legend.add_face(TextFace(' Single Domain Loss ',
fsize=16,
fgcolor='Chocolate'),
column=3)
ts.legend.add_face(CircleFace(10, "DeepSkyBlue"), column=4)
ts.legend.add_face(TextFace(' Single Domain Emergence ',
fsize=16,
fgcolor='DeepSkyBlue'),
column=5)
else:
ts.legend.add_face(CircleFace(10, "DarkBlue"), column=6)
ts.legend.add_face(TextFace(' Terminal Emergence ',
fsize=16,
fgcolor='DarkBlue'),
column=7)
ts.legend.add_face(CircleFace(10, "Chocolate"), column=8)
ts.legend.add_face(TextFace(' Single Domain Loss ',
fsize=16,
fgcolor='Chocolate'),
column=9)
ts.legend.add_face(CircleFace(10, "DeepSkyBlue"), column=10)
ts.legend.add_face(TextFace(' Single Domain Emergence ',
fsize=16,
fgcolor='DeepSkyBlue'),
column=11)
elif (params.event == "fusions"):
ts.legend.add_face(CircleFace(10, "DimGray"), column=0)
ts.legend.add_face(TextFace(" Fusion ",
fsize=16,
fgcolor='DimGray'),
column=1)
elif (params.event == "fissions"):
ts.legend.add_face(CircleFace(10, "DeepPink"), column=0)
ts.legend.add_face(TextFace(' Fission ',
fsize=16,
fgcolor='DeepPink'),
column=1)
elif (params.event == "termLosses"):
ts.legend.add_face(CircleFace(10, "YellowGreen"), column=0)
ts.legend.add_face(TextFace(' Terminal Loss ',
fsize=16,
fgcolor='YellowGreen'),
column=1)
elif (params.event == "termEmergences"):
ts.legend.add_face(CircleFace(10, "DarkBlue"), column=0)
ts.legend.add_face(TextFace(' Terminal Emergence ',
fsize=16,
fgcolor='DarkBlue'),
column=1)
elif (params.event == "singleDomainLosses"):
ts.legend.add_face(CircleFace(10, "Chocolate"), column=0)
ts.legend.add_face(TextFace(' Single Domain Loss ',
fsize=16,
fgcolor='Chocolate'),
column=1)
elif (params.event == "singleDomainEmergences"):
ts.legend.add_face(CircleFace(10, "DeepSkyBlue"), column=0)
ts.legend.add_face(TextFace(' Single Domain Emergence ',
fsize=16,
fgcolor='DeepSkyBlue'),
column=1)
ts.legend_position = 1
ts.draw_guiding_lines = True
if (params.outputname.endswith(".pdf")):
pathout = params.outputname
else:
pathout = params.outputname + ".pdf"
tree.render(pathout, dpi=1200, tree_style=ts)
pl.close()
sys.exit(0)
import matplotlib.colors as colors
# only decorate a tree that has already PHYDLOG ids
for asrRun in ('1','2','3'):
inputGLF = os.path.join(basePath, 'analysis', 'tables', 'gainLossNormBrLen' + str(asrRun) + '.dat')
outP = os.path.join(basePath, 'analysis', 'figures', 'spTree' + str(asrRun))
if asrRun == '3':
analysisNumber = '3b'
else:
analysisNumber = asrRun
spTreeF = os.path.join(basePath, 'analysis', 'iesdb', 'speciesTree' + analysisNumber + '.nhx')
gl = pd.read_csv(inputGLF, sep = "\t")
t = Tree(spTreeF)
t.sort_descendants(attr='O')
ts = TreeStyle()
ts.complete_branch_lines_when_necessary = False
# calculate branch colors
gainL = [] # list with all rates of gain
lossL = [] # list with all rates of loss
gm = gl.rgain.min()
gM = gl.rgain.max()
lm = gl.rloss.min()
lM = gl.rloss.max()
#bcrg = scaleCol(gl.pgain.tolist()) # Branch Colors for Rates of Gain
#bcrl = scaleCol(gl.ploss.tolist()) # Branch Colors for Rates of Loss
# make a "gain" and a "loss" copy of the tree
tg = t.copy()
tl = t.copy()
gcm = cm.ScalarMappable(norm = colors.Normalize(vmin = gm, vmax = gM), cmap = "coolwarm")
lcm = cm.ScalarMappable(norm = colors.Normalize(vmin = lm, vmax = lM), cmap = "coolwarm")
for node in tg.iter_descendants(): # do not include root
