def get_example_tree():
# sample sequence and a list of example motif types
seq = "LHGRISQQVEQSRSQVQAIGEKVSLAQAKIEKIKGSKKAIKVFSSAKYPAPERLQEYGSIFTDAQDPGLQRRPRHRIQSKQRPLDERALQEKLKDFPVCVSTKPEPEDDAEEGLGGLPSNISSVSSLLLFNTTENLYKKYVFLDPLAGAVTKTHVMLGAETEEKLFDAPLSISKREQLEQQVPENYFYVPDLGQVPEIDVPSYLPDLPGIANDLMYIADLGPGIAPSAPGTIPELPTFHTEVAEPLKVGELGSGMGAGPGTPAHTPSSLDTPHFVFQTYKMGAPPLPPSTAAPVGQGARQDDSSSSASPSVQGAPREVVDPSGGWATLLESIRQAGGIGKAKLRSMKERKLEKQQQKEQEQVRATSQGGHLMSDLFNKLVMRRKGISGKGPGAGDGPGGAFARVSDSIPPLPPPQQPQAEDEDDWES"
motifs = [
# seq.start, seq.end, shape, width, height, fgcolor, bgcolor
[10, 100, "[]", None, 10, "black", "rgradient:blue", "arial|8|white|domain Name"],
[110, 150, "o", None, 10, "blue", "pink", None],
[155, 180, "()", None, 10, "blue", "rgradient:purple", None],
[160, 170, "^", None, 14, "black", "yellow", None],
[172, 180, "v", None, 12, "black", "rgradient:orange", None],
[185, 190, "o", None, 12, "black", "brown", None],
[198, 200, "<>", None, 15, "black", "rgradient:gold", None],
[210, 240, "compactseq", 2, 10, None, None, None],
[300, 320, "seq", 10, 10, None, None, None],
[310, 345, "<>", None, 15, "black", "rgradient:black", None],
]
# Create a random tree and add to each leaf a random set of motifs
# from the original set
t = Tree()
t.populate(10)
for l in t.iter_leaves():
seq_motifs = [list(m) for m in motifs] #sample(motifs, randint(2, len(motifs)))
seqFace = SeqMotifFace(seq, seq_motifs, intermotif_format="line",
seqtail_format="compactseq", scale_factor=1)
seqFace.margin_bottom = 4
f = l.add_face(seqFace, 0, "aligned")
return t, TreeStyle()
def get_example_tree():
t = Tree()
t.populate(8)
# Node style handling is no longer limited to layout functions. You
# can now create fixed node styles and use them many times, save them
# or even add them to nodes before drawing (this allows to save and
# reproduce an tree image design)
# Set bold red branch to the root node
style = NodeStyle()
style["fgcolor"] = "#0f0f0f"
style["size"] = 0
style["vt_line_color"] = "#ff0000"
style["hz_line_color"] = "#ff0000"
style["vt_line_width"] = 8
style["hz_line_width"] = 8
style["vt_line_type"] = 0 # 0 solid, 1 dashed, 2 dotted
style["hz_line_type"] = 0
t.set_style(style)
#Set dotted red lines to the first two branches
style1 = NodeStyle()
style1["fgcolor"] = "#0f0f0f"
style1["size"] = 0
style1["vt_line_color"] = "#ff0000"
style1["hz_line_color"] = "#ff0000"
style1["vt_line_width"] = 2
style1["hz_line_width"] = 2
style1["vt_line_type"] = 2 # 0 solid, 1 dashed, 2 dotted
style1["hz_line_type"] = 2
t.children[0].img_style = style1
t.children[1].img_style = style1
# Set dashed blue lines in all leaves
style2 = NodeStyle()
style2["fgcolor"] = "#000000"
style2["shape"] = "circle"
style2["vt_line_color"] = "#0000aa"
style2["hz_line_color"] = "#0000aa"
style2["vt_line_width"] = 2
style2["hz_line_width"] = 2
style2["vt_line_type"] = 1 # 0 solid, 1 dashed, 2 dotted
style2["hz_line_type"] = 1
for l in t.iter_leaves():
l.img_style = style2
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
return t, ts
def get_example_tree():
t = Tree()
t.populate(8)
# Node style handling is no longer limited to layout functions. You
# can now create fixed node styles and use them many times, save them
# or even add them to nodes before drawing (this allows to save and
# reproduce an tree image design)
# Set bold red branch to the root node
style = NodeStyle()
style["fgcolor"] = "#0f0f0f"
style["size"] = 0
style["vt_line_color"] = "#ff0000"
style["hz_line_color"] = "#ff0000"
style["vt_line_width"] = 8
style["hz_line_width"] = 8
style["vt_line_type"] = 0 # 0 solid, 1 dashed, 2 dotted
style["hz_line_type"] = 0
t.set_style(style)
#Set dotted red lines to the first two branches
style1 = NodeStyle()
style1["fgcolor"] = "#0f0f0f"
style1["size"] = 0
style1["vt_line_color"] = "#ff0000"
style1["hz_line_color"] = "#ff0000"
style1["vt_line_width"] = 2
style1["hz_line_width"] = 2
style1["vt_line_type"] = 2 # 0 solid, 1 dashed, 2 dotted
style1["hz_line_type"] = 2
t.children[0].img_style = style1
t.children[1].img_style = style1
# Set dashed blue lines in all leaves
style2 = NodeStyle()
style2["fgcolor"] = "#000000"
style2["shape"] = "circle"
style2["vt_line_color"] = "#0000aa"
style2["hz_line_color"] = "#0000aa"
style2["vt_line_width"] = 2
style2["hz_line_width"] = 2
style2["vt_line_type"] = 1 # 0 solid, 1 dashed, 2 dotted
style2["hz_line_type"] = 1
for l in t.iter_leaves():
l.img_style = style2
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
return t, ts
def get_example_tree():
t = Tree("((a,b),c);")
right_c0_r0 = TextFace("right_col0_row0")
right_c0_r1 = TextFace("right_col0_row1")
right_c1_r0 = TextFace("right_col1_row0")
right_c1_r1 = TextFace("right_col1_row1")
right_c1_r2 = TextFace("right_col1_row2")
top_c0_r0 = TextFace("top_col0_row0")
top_c0_r1 = TextFace("top_col0_row1")
bottom_c0_r0 = TextFace("bottom_col0_row0")
bottom_c1_r0 = TextFace("bottom_col1_row0")
aligned_c0_r0 = TextFace("aligned_col0_row0")
aligned_c0_r1 = TextFace("aligned_col0_row1")
aligned_c1_r0 = TextFace("aligned_col1_row0")
aligned_c1_r1 = TextFace("aligned_col1_row1")
t.add_face(right_c0_r1, column=1, position="branch-right")
t.add_face(right_c0_r0, column=0, position="branch-right")
t.add_face(right_c1_r2, column=2, position="branch-right")
t.add_face(right_c1_r1, column=1, position="branch-right")
t.add_face(right_c1_r0, column=0, position="branch-right")
t.add_face(top_c0_r1, column=1, position="branch-top")
t.add_face(top_c0_r0, column=0, position="branch-top")
t.add_face(bottom_c0_r0, column=0, position="branch-bottom")
t.add_face(bottom_c1_r0, column=1, position="branch-bottom")
for leaf in t.iter_leaves():
leaf.add_face(aligned_c0_r1, 0, "aligned")
leaf.add_face(aligned_c0_r0, 0, "aligned")
leaf.add_face(aligned_c1_r1, 0, "aligned")
leaf.add_face(aligned_c1_r0, 0, "aligned")
return t, TreeStyle()
def get_example_tree():
t = Tree("((a,b),c);")
right_c0_r0 = TextFace("right_col0_row0")
right_c0_r1 = TextFace("right_col0_row1")
right_c1_r0 = TextFace("right_col1_row0")
right_c1_r1 = TextFace("right_col1_row1")
right_c1_r2 = TextFace("right_col1_row2")
top_c0_r0 = TextFace("top_col0_row0")
top_c0_r1 = TextFace("top_col0_row1")
bottom_c0_r0 = TextFace("bottom_col0_row0")
bottom_c1_r0 = TextFace("bottom_col1_row0")
aligned_c0_r0 = TextFace("aligned_col0_row0")
aligned_c0_r1 = TextFace("aligned_col0_row1")
aligned_c1_r0 = TextFace("aligned_col1_row0")
aligned_c1_r1 = TextFace("aligned_col1_row1")
t.add_face(right_c0_r1, column=1, position="branch-right")
t.add_face(right_c0_r0, column=0, position="branch-right")
t.add_face(right_c1_r2, column=2, position="branch-right")
t.add_face(right_c1_r1, column=1, position="branch-right")
t.add_face(right_c1_r0, column=0, position="branch-right")
t.add_face(top_c0_r1, column=1, position="branch-top")
t.add_face(top_c0_r0, column=0, position="branch-top")
t.add_face(bottom_c0_r0, column=0, position="branch-bottom")
t.add_face(bottom_c1_r0, column=1, position="branch-bottom")
for leaf in t.iter_leaves():
leaf.add_face(aligned_c0_r1, 0, "aligned")
leaf.add_face(aligned_c0_r0, 0, "aligned")
leaf.add_face(aligned_c1_r1, 0, "aligned")
leaf.add_face(aligned_c1_r0, 0, "aligned")
return t, TreeStyle()
def get_example_tree():
# sample sequence and a list of example motif types
seq = "LHGRISQQVEQSRSQVQAIGEKVSLAQAKIEKIKGSKKAIKVFSSAKYPAPERLQEYGSIFTDAQDPGLQRRPRHRIQSKQRPLDERALQEKLKDFPVCVSTKPEPEDDAEEGLGGLPSNISSVSSLLLFNTTENLYKKYVFLDPLAGAVTKTHVMLGAETEEKLFDAPLSISKREQLEQQVPENYFYVPDLGQVPEIDVPSYLPDLPGIANDLMYIADLGPGIAPSAPGTIPELPTFHTEVAEPLKVGELGSGMGAGPGTPAHTPSSLDTPHFVFQTYKMGAPPLPPSTAAPVGQGARQDDSSSSASPSVQGAPREVVDPSGGWATLLESIRQAGGIGKAKLRSMKERKLEKQQQKEQEQVRATSQGGHLMSDLFNKLVMRRKGISGKGPGAGDGPGGAFARVSDSIPPLPPPQQPQAEDEDDWES"
motifs = [
# seq.start, seq.end, shape, width, height, fgcolor, bgcolor
[
10, 100, "[]", None, 10, "black", "rgradient:blue",
"arial|8|white|domain Name"
],
[110, 150, "o", None, 10, "blue", "pink", None],
[155, 180, "()", None, 10, "blue", "rgradient:purple", None],
[160, 170, "^", None, 14, "black", "yellow", None],
[172, 180, "v", None, 12, "black", "rgradient:orange", None],
[185, 190, "o", None, 12, "black", "brown", None],
[198, 200, "<>", None, 15, "black", "rgradient:gold", None],
[210, 240, "compactseq", 2, 10, None, None, None],
[300, 320, "seq", 10, 10, None, None, None],
[310, 345, "<>", None, 15, "black", "rgradient:black", None],
]
# Create a random tree and add to each leaf a random set of motifs
# from the original set
t = Tree()
t.populate(10)
for l in t.iter_leaves():
seq_motifs = [list(m) for m in motifs
] #sample(motifs, randint(2, len(motifs)))
seqFace = SeqMotifFace(seq,
seq_motifs,
intermotif_format="line",
seqtail_format="compactseq",
scale_factor=1)
seqFace.margin_bottom = 4
f = l.add_face(seqFace, 0, "aligned")
return t, TreeStyle()
C.add_features(vowel=False, confidence=1.0)
A.add_features(vowel=True, confidence=0.5)
ancestor.add_features(nodetype="internal")
# Or, using the oneliner notation
(t & "H").add_features(vowel=False, confidence=0.2)
# But we can automatize this. (note that i will overwrite the previous
# values)
for leaf in t.traverse():
if leaf.name in "AEIOU":
leaf.add_features(vowel=True, confidence=random.random())
else:
leaf.add_features(vowel=False, confidence=random.random())
# Now we use these information to analyze the tree.
print "This tree has", len(t.search_nodes(vowel=True)), "vowel nodes"
print "Which are", [
leaf.name for leaf in t.iter_leaves() if leaf.vowel == True
]
# But features may refer to any kind of data, not only simple
# values. For example, we can calculate some values and store them
# within nodes.
#
# Let's detect leaf nodes under "ancestor" with distance higher thatn
# 1. Note that I'm traversing a subtree which starts from "ancestor"
matches = [leaf for leaf in ancestor.traverse() if leaf.dist > 1.0]
# And save this pre-computed information into the ancestor node
ancestor.add_feature("long_branch_nodes", matches)
# Prints the precomputed nodes
print "These are nodes under ancestor with long branches", \
[n.name for n in ancestor.long_branch_nodes]
# We can also use the add_feature() method to dynamically add new features.
label = raw_input("custom label:")
def main(argv):
parser = argparse.ArgumentParser(description=__DESCRIPTION__,
formatter_class=argparse.RawDescriptionHelpFormatter)
input_args = parser.add_argument_group("INPUT OPTIONS")
input_args.add_argument("source_trees", metavar='source_trees', type=str, nargs="*",
help='a list of source tree files')
input_args.add_argument("--source_file", dest="source_file",
type=str,
help="""path to a file containing many source trees, one per line""")
input_args.add_argument("-r", dest="reftree",
type=str, required=True,
help="""Reference tree""")
input_args.add_argument("--ref_tree_attr", dest="ref_tree_attr",
type=str, default="name",
help=("attribute in ref tree used as leaf name"))
input_args.add_argument("--src_tree_attr", dest="src_tree_attr",
type=str, default="name",
help=("attribute in source tree used as leaf name"))
input_args.add_argument("--min_support_ref",
type=float, default=0.0,
help=("min support for branches to be considered from the ref tree"))
input_args.add_argument("--min_support_src",
type=float, default=0.0,
help=("min support for branches to be considered from the source tree"))
output_args = parser.add_argument_group("OUTPUT OPTIONS")
output_args.add_argument("-o", dest="output",
type=str,
help="""Path to the tab delimited report file""")
opt_args = parser.add_argument_group("DISTANCE OPTIONS")
opt_args.add_argument("--outgroup", dest="outgroup",
nargs = "+",
help="""outgroup used to root reference and source trees before distance computation""")
opt_args.add_argument("--expand_polytomies", dest="polytomies",
action = "store_true",
help="""expand politomies if necessary""")
opt_args.add_argument("--unrooted", dest="unrooted",
action = "store_true",
help="""compare trees as unrooted""")
opt_args.add_argument("--min_support", dest="min_support",
type=float, default=0.0,
help=("min support value for branches to be counted in the distance computation (RF, treeko and refTree/targeGene compatibility)"))
opt_args = parser.add_argument_group("PHYLOGENETICS OPTIONS")
opt_args.add_argument("--extract_species",
action = "store_true",
help="When used, leaf names in the reference and source trees are assumed to represent species."
" If target trees are gene-trees whose species information is encoded as a part of the leaf sequence name,"
" it can be automatically extracted by providing a Perl regular expression that extract a "
" valid species code (see --sp_regexp). Such information will be also used to detect duplication"
" events. ")
opt_args.add_argument("--sp_regexp",
type=str,
help=("Specifies a Perl regular expression to automatically extract species names"
" from the name string in source trees. If not used, leaf names are assumed to represent species names."
" Example: use this expression '[^_]+_(.+)' to extract HUMAN from the string 'P53_HUMAN'."))
opt_args.add_argument("--collateral",
action='store_true',
help=(""))
args = parser.parse_args(argv)
print __DESCRIPTION__
reftree = args.reftree
if args.source_file and args.source_trees:
print >>sys.stderr, 'The use of targets_file and targets at the same time is not supported.'
sys.exit(1)
if args.source_file:
source_trees = tree_iterator(args.source_file)
else:
source_trees = args.source_trees
ref_tree = Tree(reftree)
if args.ref_tree_attr:
for lf in ref_tree.iter_leaves():
lf._origname = lf.name
if args.ref_tree_attr not in lf.features:
print lf
lf.name = getattr(lf, args.ref_tree_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = ref_tree.get_common_ancestor(args.outgroup)
else:
out = ref_tree.search_nodes(name=args.outgroup[0])[0]
ref_tree.set_outgroup(out)
HEADER = ("source tree", 'ref tree', 'common\ntips', 'normRF', 'RF', 'maxRF', "%reftree", "%genetree", "subtrees", "treeko\ndist")
if args.output:
OUT = open(args.output, "w")
print >>OUT, '# ' + ctime()
print >>OUT, '# ' + ' '.join(sys.argv)
print >>OUT, '#'+'\t'.join(HEADER)
else:
print '# ' + ctime()
print '# ' + ' '.join(sys.argv)
COL_WIDTHS = [20, 20] + [9] * 10
print_table([HEADER], fix_col_width=COL_WIDTHS, wrap_style='wrap')
prev_tree = None
ref_fname = os.path.basename(args.reftree)
for counter, tfile in enumerate(source_trees):
if args.source_file:
seedid, tfile = tfile
else:
seedid = None
if args.extract_species:
if args.sp_regexp:
SPMATCHER = re.compile(args.sp_regexp)
get_sp_name = lambda x: re.search(SPMATCHER, x).groups()[0]
else:
get_sp_name = lambda x: x
tt = PhyloTree(tfile, sp_naming_function = get_sp_name)
else:
tt = Tree(tfile)
if args.src_tree_attr:
for lf in tt.iter_leaves():
lf._origname = lf.name
lf.name = getattr(lf, args.src_tree_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = tt.get_common_ancestor(args.outgroup)
else:
out = tt.search_nodes(name=args.outgroup[0])[0]
tt.set_outgroup(out)
if args.source_trees:
fname = os.path.basename(tfile)
else:
fname = '%05d' %counter
r = tt.compare(ref_tree,
ref_tree_attr=args.ref_tree_attr,
source_tree_attr=args.src_tree_attr,
min_support_ref=args.min_support_ref,
min_support_source = args.min_support_src,
unrooted=args.unrooted,
has_duplications=args.extract_species)
print_table([map(istr, [fname[-30:], ref_fname[-30:], r['effective_tree_size'], r['norm_rf'],
r['rf'], r['max_rf'], r["source_edges_in_ref"],
r["ref_edges_in_source"], r['source_subtrees'], r['treeko_dist']])],
fix_col_width = COL_WIDTHS, wrap_style='cut')
if args.output:
OUT.close()
import time
from ete_dev import Tree
# Creates a random tree with 10,000 leaf nodes
tree = Tree()
tree.populate(10000)
# This code should be faster
t1 = time.time()
for leaf in tree.iter_leaves():
if "aw" in leaf.name:
print "found a match:", leaf.name,
break
print "Iterating: ellapsed time:", time.time()-t1
# This slower
t1 = time.time()
for leaf in tree.get_leaves():
if "aw" in leaf.name:
print "found a match:", leaf.name,
break
print "Getting: ellapsed time:", time.time()-t1
# Results in something like:
# found a match: guoaw Iterating: ellapsed time: 0.00436091423035 secs
# found a match: guoaw Getting: ellapsed time: 0.124316930771 secs
import time
from ete_dev import Tree
# Creates a random tree with 10,000 leaf nodes
tree = Tree()
tree.populate(10000)
# This code should be faster
t1 = time.time()
for leaf in tree.iter_leaves():
if "aw" in leaf.name:
print "found a match:", leaf.name,
break
print "Iterating: ellapsed time:", time.time() - t1
# This slower
t1 = time.time()
for leaf in tree.get_leaves():
if "aw" in leaf.name:
print "found a match:", leaf.name,
break
print "Getting: ellapsed time:", time.time() - t1
# Results in something like:
# found a match: guoaw Iterating: ellapsed time: 0.00436091423035 secs
# found a match: guoaw Getting: ellapsed time: 0.124316930771 secs
style1["fgcolor"] = "#0f0f0f"
style1["size"] = 0
style1["vt_line_color"] = "#ff0000"
style1["hz_line_color"] = "#ff0000"
style1["vt_line_width"] = 2
style1["hz_line_width"] = 2
style1["vt_line_type"] = 2 # 0 solid, 1 dashed, 2 dotted
style1["hz_line_type"] = 2
t.children[0].img_style = style1
t.children[1].img_style = style1
# Set dashed blue lines in all leaves
style2 = NodeStyle()
style2["fgcolor"] = "#000000"
style2["shape"] = "circle"
style2["vt_line_color"] = "#0000aa"
style2["hz_line_color"] = "#0000aa"
style2["vt_line_width"] = 2
style2["hz_line_width"] = 2
style2["vt_line_type"] = 1 # 0 solid, 1 dashed, 2 dotted
style2["hz_line_type"] = 1
for l in t.iter_leaves():
l.img_style = style2
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
t.render("node_style.png", w=400, tree_style=ts)
aligned_c0_r0 = TextFace("aligned_col0_row0")
aligned_c0_r1 = TextFace("aligned_col0_row1")
aligned_c1_r0 = TextFace("aligned_col1_row0")
aligned_c1_r1 = TextFace("aligned_col1_row1")
t.add_face(right_c0_r1, column=1, position="branch-right")
t.add_face(right_c0_r0, column=0, position="branch-right")
t.add_face(right_c1_r2, column=2, position="branch-right")
t.add_face(right_c1_r1, column=1, position="branch-right")
t.add_face(right_c1_r0, column=0, position="branch-right")
t.add_face(top_c0_r1, column=1, position="branch-top")
t.add_face(top_c0_r0, column=0, position="branch-top")
t.add_face(bottom_c0_r0, column=0, position="branch-bottom")
t.add_face(bottom_c1_r0, column=1, position="branch-bottom")
for leaf in t.iter_leaves():
leaf.add_face(aligned_c0_r1, 0, "aligned")
leaf.add_face(aligned_c0_r0, 0, "aligned")
leaf.add_face(aligned_c1_r1, 0, "aligned")
leaf.add_face(aligned_c1_r0, 0, "aligned")
t.show()
# used to add many features at the same time.
C.add_features(vowel=False, confidence=1.0)
A.add_features(vowel=True, confidence=0.5)
ancestor.add_features(nodetype="internal")
# Or, using the oneliner notation
(t&"H").add_features(vowel=False, confidence=0.2)
# But we can automatize this. (note that i will overwrite the previous
# values)
for leaf in t.traverse():
if leaf.name in "AEIOU":
leaf.add_features(vowel=True, confidence=random.random())
else:
leaf.add_features(vowel=False, confidence=random.random())
# Now we use these information to analyze the tree.
print "This tree has", len(t.search_nodes(vowel=True)), "vowel nodes"
print "Which are", [leaf.name for leaf in t.iter_leaves() if leaf.vowel==True]
# But features may refer to any kind of data, not only simple
# values. For example, we can calculate some values and store them
# within nodes.
#
# Let's detect leaf nodes under "ancestor" with distance higher thatn
# 1. Note that I'm traversing a subtree which starts from "ancestor"
matches = [leaf for leaf in ancestor.traverse() if leaf.dist>1.0]
# And save this pre-computed information into the ancestor node
ancestor.add_feature("long_branch_nodes", matches)
# Prints the precomputed nodes
print "These are nodes under ancestor with long branches", \
[n.name for n in ancestor.long_branch_nodes]
# We can also use the add_feature() method to dynamically add new features.
label = raw_input("custom label:")
value = raw_input("custom label value:")
def main(argv):
parser = argparse.ArgumentParser(
description=__DESCRIPTION__,
formatter_class=argparse.RawDescriptionHelpFormatter)
input_args = parser.add_argument_group("INPUT OPTIONS")
input_args.add_argument("source_trees",
metavar='source_trees',
type=str,
nargs="*",
help='a list of source tree files')
input_args.add_argument(
"--source_file",
dest="source_file",
type=str,
help="""path to a file containing many source trees, one per line""")
input_args.add_argument("-r",
dest="reftree",
type=str,
required=True,
help="""Reference tree""")
input_args.add_argument("--ref_tree_attr",
dest="ref_tree_attr",
type=str,
default="name",
help=("attribute in ref tree used as leaf name"))
input_args.add_argument(
"--src_tree_attr",
dest="src_tree_attr",
type=str,
default="name",
help=("attribute in source tree used as leaf name"))
input_args.add_argument(
"--min_support_ref",
type=float,
default=0.0,
help=("min support for branches to be considered from the ref tree"))
input_args.add_argument(
"--min_support_src",
type=float,
default=0.0,
help=(
"min support for branches to be considered from the source tree"))
output_args = parser.add_argument_group("OUTPUT OPTIONS")
output_args.add_argument("-o",
dest="output",
type=str,
help="""Path to the tab delimited report file""")
opt_args = parser.add_argument_group("DISTANCE OPTIONS")
opt_args.add_argument(
"--outgroup",
dest="outgroup",
nargs="+",
help=
"""outgroup used to root reference and source trees before distance computation"""
)
opt_args.add_argument("--expand_polytomies",
dest="polytomies",
action="store_true",
help="""expand politomies if necessary""")
opt_args.add_argument("--unrooted",
dest="unrooted",
action="store_true",
help="""compare trees as unrooted""")
opt_args.add_argument(
"--min_support",
dest="min_support",
type=float,
default=0.0,
help=
("min support value for branches to be counted in the distance computation (RF, treeko and refTree/targeGene compatibility)"
))
opt_args = parser.add_argument_group("PHYLOGENETICS OPTIONS")
opt_args.add_argument(
"--extract_species",
action="store_true",
help=
"When used, leaf names in the reference and source trees are assumed to represent species."
" If target trees are gene-trees whose species information is encoded as a part of the leaf sequence name,"
" it can be automatically extracted by providing a Perl regular expression that extract a "
" valid species code (see --sp_regexp). Such information will be also used to detect duplication"
" events. ")
opt_args.add_argument(
"--sp_regexp",
type=str,
help=
("Specifies a Perl regular expression to automatically extract species names"
" from the name string in source trees. If not used, leaf names are assumed to represent species names."
" Example: use this expression '[^_]+_(.+)' to extract HUMAN from the string 'P53_HUMAN'."
))
opt_args.add_argument("--collateral", action='store_true', help=(""))
args = parser.parse_args(argv)
print __DESCRIPTION__
reftree = args.reftree
if args.source_file and args.source_trees:
print >> sys.stderr, 'The use of targets_file and targets at the same time is not supported.'
sys.exit(1)
if args.source_file:
source_trees = tree_iterator(args.source_file)
else:
source_trees = args.source_trees
ref_tree = Tree(reftree)
if args.ref_tree_attr:
for lf in ref_tree.iter_leaves():
lf._origname = lf.name
if args.ref_tree_attr not in lf.features:
print lf
lf.name = getattr(lf, args.ref_tree_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = ref_tree.get_common_ancestor(args.outgroup)
else:
out = ref_tree.search_nodes(name=args.outgroup[0])[0]
ref_tree.set_outgroup(out)
HEADER = ("source tree", 'ref tree', 'common\ntips', 'normRF', 'RF',
'maxRF', "%reftree", "%genetree", "subtrees", "treeko\ndist")
if args.output:
OUT = open(args.output, "w")
print >> OUT, '# ' + ctime()
print >> OUT, '# ' + ' '.join(sys.argv)
print >> OUT, '#' + '\t'.join(HEADER)
else:
print '# ' + ctime()
print '# ' + ' '.join(sys.argv)
COL_WIDTHS = [20, 20] + [9] * 10
print_table([HEADER], fix_col_width=COL_WIDTHS, wrap_style='wrap')
prev_tree = None
ref_fname = os.path.basename(args.reftree)
for counter, tfile in enumerate(source_trees):
if args.source_file:
seedid, tfile = tfile
else:
seedid = None
if args.extract_species:
if args.sp_regexp:
SPMATCHER = re.compile(args.sp_regexp)
get_sp_name = lambda x: re.search(SPMATCHER, x).groups()[0]
else:
get_sp_name = lambda x: x
tt = PhyloTree(tfile, sp_naming_function=get_sp_name)
else:
tt = Tree(tfile)
if args.src_tree_attr:
for lf in tt.iter_leaves():
lf._origname = lf.name
lf.name = getattr(lf, args.src_tree_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = tt.get_common_ancestor(args.outgroup)
else:
out = tt.search_nodes(name=args.outgroup[0])[0]
tt.set_outgroup(out)
if args.source_trees:
fname = os.path.basename(tfile)
else:
fname = '%05d' % counter
r = tt.compare(ref_tree,
ref_tree_attr=args.ref_tree_attr,
source_tree_attr=args.src_tree_attr,
min_support_ref=args.min_support_ref,
min_support_source=args.min_support_src,
unrooted=args.unrooted,
has_duplications=args.extract_species)
print_table([
map(istr, [
fname[-30:], ref_fname[-30:], r['effective_tree_size'],
r['norm_rf'], r['rf'], r['max_rf'], r["source_edges_in_ref"],
r["ref_edges_in_source"], r['source_subtrees'],
r['treeko_dist']
])
],
fix_col_width=COL_WIDTHS,
wrap_style='cut')
if args.output:
OUT.close()
],
[160, 170, "^", 50, 14, "black", "yellow", None],
[172, 180, "v", 20, 12, "black", "rgradient:orange", None],
[185, 190, "o", 12, 12, "black", "brown", "Arial|12|white|Domain"],
[198, 200, "<>", 15, 15, "black", "rgradient:gold", None],
[210, 240, "compactseq", 2, 10, None, None, None],
[300, 320, "seq", 20, 20, None, None, "Courier|10|red|TestName"],
[
340, 350, "<>", 15, 15, "black", "rgradient:black",
"Arial|12|black|HOLASEQ"
],
]
# Create a random tree and add to each leaf a random set of motifs
# from the original set
t = Tree()
t.populate(20)
for l in t.iter_leaves():
# For each leaf, we create a random list of motifs from the original list.
seq_motifs = sample(motifs, randint(2, len(motifs)))
# And we add it as a Sequence Motif Face.
seqFace = SeqMotifFace(seq,
seq_motifs,
intermotif_format="line",
seqtail_format="compactseq")
seqFace.margin_bottom = 4
f = l.add_face(seqFace, 0, "aligned")
t.render("seq_motif_faces.png", w=1000)
#t.show()