Python TreeStyle.min_leaf_separation方法代码示例

# 需要导入模块: from ete2 import TreeStyle [as 别名]
# 或者: from ete2.TreeStyle import min_leaf_separation [as 别名]
def get_example_tree():
    t = Tree()
    ts = TreeStyle()
    ts.layout_fn = layout
    ts.mode = "c"
    ts.show_leaf_name = True
    ts.min_leaf_separation = 15
    t.populate(100)
    return t, ts

# 需要导入模块: from ete2 import TreeStyle [as 别名]
# 或者: from ete2.TreeStyle import min_leaf_separation [as 别名]
def give_tree_layout(t):

	# for all nodes give them the weight = score
	for n in t.traverse():
		n.add_features(weight=n.dist/20.0)

	# Create an empty TreeStyle
	ts = TreeStyle()

	# Set our custom layout function
	ts.layout_fn = layout

	ts.show_leaf_name = False
	# Draw a tree 
	ts.mode = "c"
	#ts.arc_start = -180
	#ts.arc_span = 180
	#ts.scale = 100
	ts.min_leaf_separation = 10
	ts.show_scale = False

	return ts

# 需要导入模块: from ete2 import TreeStyle [as 别名]
# 或者: from ete2.TreeStyle import min_leaf_separation [as 别名]

#.........这里部分代码省略.........
	for i in range(num_rows):
		matrix.append([.5*(1-.5*ckm30_norm[i,j]-.5*ckm30_norm[j,i])+.5*(1-.5*ckm50_norm[i,j]-.5*ckm50_norm[j,i]) for j in range(i+1)])
	
	#Construct the tree. Note I could use RapidNJ here, but a few tests have shown that the trees that RapidNJ creates are rubbish.
	dm = _DistanceMatrix(names, matrix)
	constructor = DistanceTreeConstructor()
	tree = constructor.nj(dm)
	t=Tree(tree.format('newick'),format=1)
	#tree.format('newick')
	#Phylo.draw_ascii(tree)
	
	#Now I will put internal nodes in a certain phylogenetic distance between the root and a given node.
	#Function to insert a node at a given distance
	def insert_node(t, name_to_insert, insert_above, dist_along):
		insert_at_node = t.search_nodes(name=insert_above)[0]
		parent = (t&insert_above).up
		orig_branch_length = t.get_distance(insert_at_node,parent)
		if orig_branch_length < dist_along:
			raise ValueError("error: dist_along larger than orig_branch_length")
		removed_node = insert_at_node.detach()
		removed_node.dist = orig_branch_length - dist_along
		added_node = parent.add_child(name=name_to_insert, dist=dist_along)
		added_node.add_child(removed_node)
	
	#Function to insert a node some % along a branch
	def insert_hyp_node(t, leaf_name, percent):
		total_dist = t.get_distance(t.name,leaf_name)
		percent_dist = percent*total_dist
		child_node = (t&leaf_name)
		ancestor_node = (t&child_node.name).up
		while t.get_distance(t.name, ancestor_node) > percent_dist:
			child_node = ancestor_node
			ancestor_node = (t&child_node.name).up
		insert_node(t, leaf_name+"_"+str(percent), child_node.name, percent_dist-t.get_distance(t.name, ancestor_node))
	
	#Insert hypothetical nodes
	hyp_node_names = dict()
	cutoffs = [.9,.8,.7,.6,.5,.4,.3,.2,.1]
	cutoffs = map(lambda y: y**1.5,cutoffs)
	for i in range(len(x_file_names)):
		xi = x[i:len(x):len(x_file_names)]
		for j in range(1,len(cutoffs)+1):
			if xi[j]>0:
				insert_hyp_node(t, x_file_names[i], cutoffs[j-1])
				hyp_node_names[x_file_names[i]+"_"+str(cutoffs[j-1])] = [x_file_names[i], cutoffs[j-1], j-1] #in case there are "_" in the file names
				#insert_hyp_node(t, x_file_names[i],.5/t.get_distance(t.name,t&x_file_names[i])*cutoffs[j])
	
	#Now put the bubbles on the nodes
	def layout(node):
		#print(node)
		if node.is_leaf():
			if node.name in x_file_names:
				#make reconstructed bubble
				size = x[x_file_names.index(node.name)]
				F = CircleFace(radius=500*math.sqrt(size), color="RoyalBlue", style="sphere")
				F.border.width = None
				F.opacity = 0.6
				faces.add_face_to_node(F,node, 0, position="branch-right")
				if taxonomic_names_on_leaves:
					nameFace = AttrFace("name", fsize=25, fgcolor='black',text_suffix="_"+taxonomy[x_file_names.index(node.name)])
					faces.add_face_to_node(nameFace, node, 0, position="branch-right")
				else:
					nameFace = AttrFace("name", fsize=25, fgcolor='black')
					faces.add_face_to_node(nameFace, node, 0, position="branch-right")
		elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
			node_base_name = hyp_node_names[node.name][0]
			percent = hyp_node_names[node.name][1]
			if node_base_name in x_file_names:
				idx = hyp_node_names[node.name][2]
				size = x[x_file_names.index(node_base_name)+(idx+1)*len(x_file_names)]
				F = CircleFace(radius=500*math.sqrt(size), color="RoyalBlue", style="sphere")
				F.border.width = None
				F.opacity = 0.6
				faces.add_face_to_node(F,node, 0, position="branch-right")
				#print node
				#print size
			else:
				size=0
		else:
			size=0
		#print(size)
	
	ts = TreeStyle()
	ts.layout_fn = layout
	if plot_rectangular:
		ts.mode = "r"
	else:
		ts.mode = "c"
	ts.show_leaf_name = False
	ts.min_leaf_separation = 50

	#Export the tree to a png image
	t.render(out_file, w=width, units="mm", tree_style=ts)

    #Export the xml file
	project = Phyloxml()
	phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
	phylo.phyloxml_phylogeny.set_name(title)
	project.add_phylogeny(phylo)
	project.export(open(out_file_xml,'w'))

# 需要导入模块: from ete2 import TreeStyle [as 别名]
# 或者: from ete2.TreeStyle import min_leaf_separation [as 别名]

#.........这里部分代码省略.........
		if LCA.name==t.name:
			percent_dist = percent*LCA_to_leaf_dist
			if mean_dist <= percent:
				child_node = (t&leaf_name)
			else:
				child_node = (t&nearby_names[0])#This means "go up from root" in the direction of the nearest guy
			ancestor_node = (t&child_node.name).up
		elif mean_dist <= percent:
			percent_dist = t.get_distance(LCA) + abs(percent-mean_dist)*(LCA_to_leaf_dist)/(1-mean_dist)
			child_node = (t&leaf_name)
			ancestor_node = (t&child_node.name).up
		else:
			percent_dist = t.get_distance(LCA) - abs(percent-mean_dist)*(t.get_distance(LCA))/(mean_dist)
			child_node = (t&leaf_name)
			ancestor_node = (t&child_node.name).up
		while t.get_distance(t.name, ancestor_node) > percent_dist:
			child_node = ancestor_node
			ancestor_node = (t&child_node.name).up
		insert_node(t, leaf_name+"_"+str(percent), child_node.name, percent_dist-t.get_distance(t.name, ancestor_node))

	#Set outgroup
	if outgroup in names:
		t.set_outgroup(t&outgroup) #I will need to check that this outgroup is actually one of the names...
	else:
		print("WARNING: the chosen outgroup " + outgroup + " is not in the given taxonomy: ")
		print(names)
		print("Proceeding without setting an outgroup. This may cause results to be uninterpretable.")

	#Insert hypothetical nodes
	hyp_node_names = dict()
	cutoffs = [.9,.8,.7,.6,.5,.4,.3,.2,.1]
	cutoffs = [-.5141*(val**3)+1.0932*(val**2)+0.3824*val for val in cutoffs]
	for i in range(len(org_names)):
		xi = x[i:len(x):len(org_names)]
		for j in range(1,len(cutoffs)+1):
			if xi[j]>0:
				insert_hyp_node(t, org_names[i], cutoffs[j-1],ckm_ave_train_dist, org_names)
				hyp_node_names[org_names[i]+"_"+str(cutoffs[j-1])] = [org_names[i], cutoffs[j-1], j-1] #in case there are "_" in the file names

	size_factor=250
	font_size=55

	#Now put the bubbles on the nodes
	def layout(node):
		node_style = NodeStyle()
		node_style["hz_line_width"] = 10
		node_style["vt_line_width"] = 10
		node.set_style(node_style)
		#print(node)
		if node.is_leaf():
			if node.name in org_names:
				#make reconstructed bubble
				size = x[org_names.index(node.name)]
				F = CircleFace(radius=size_factor*math.sqrt(size), color="RoyalBlue", style="sphere")
				F.border.width = None
				F.opacity = 0.6
				faces.add_face_to_node(F,node, 0, position="branch-right")
				#Denote that this was a training organism
				nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
				faces.add_face_to_node(nameFace, node, 0, position="branch-right")
		elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
			node_base_name = hyp_node_names[node.name][0]
			percent = hyp_node_names[node.name][1]
			if node_base_name in org_names:
				idx = hyp_node_names[node.name][2]
				size = x[org_names.index(node_base_name)+(idx+1)*len(org_names)]
				F = CircleFace(radius=size_factor*math.sqrt(size), color="RoyalBlue", style="sphere")
				F.border.width = None
				F.opacity = 0.6
				faces.add_face_to_node(F,node, 0, position="branch-right")
				#This is if I want the names of the hypothetical nodes to be printed as well
				#nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
				#faces.add_face_to_node(nameFace, node, 0, position="branch-right")
			else:
				size=0
		else:
			size=0
	
	ts = TreeStyle()
	ts.layout_fn = layout
	ts.mode = "r"
	#ts.mode = "c"
	ts.scale = 2*1000
	ts.show_leaf_name = False
	ts.min_leaf_separation = 50
	F = CircleFace(radius=.87*size_factor, color="RoyalBlue", style="sphere")
	F.border.width = None
	F.opacity = 0.6
	ts.legend.add_face(F,0)
	ts.legend.add_face(TextFace("  Inferred relative abundance",fsize=1.5*font_size,fgcolor="Blue"),1)
	ts.legend.add_face(TextFace("  Total absolute abundance depicted " + str(sum_x)[0:8], fsize=1.5*font_size,fgcolor="Black"),1)
	ts.legend_position=4
	#t.show(tree_style=ts)
	t.render(outfile, w=550, units="mm", tree_style=ts)
	
	#Redner the XML file
	project = Phyloxml()
	phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
	project.add_phylogeny(phylo)
	project.export(open(outfilexml,'w'))

# 需要导入模块: from ete2 import TreeStyle [as 别名]
# 或者: from ete2.TreeStyle import min_leaf_separation [as 别名]
def main(argv):
	input_file=''
	title='Title'
	label_internal_nodes = False
	label_leaves = False
	out_file=''
	width=750
	out_file_xml=''
	try:
		opts, args = getopt.getopt(argv,"h:i:t:lno:w:x:",["Help=","InputFile=","Title=","LabelLeaves=", "LabelInternalNodes=","OutFile=","Width=","OutFileXML="])
	except getopt.GetoptError:
		print 'Unknown option, call using: ./PlotTree.py -i <InputCAMIFile> -t <Title> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
		sys.exit(2)
	for opt, arg in opts:
		if opt == '-h':
			print './PlotTree.py -i <InputCAMIFile> -t <Title> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -o <OutFile> -x <OutFile.xml> -w <Width>'
			sys.exit(2)
		elif opt in ("-i", "--InputFile"):
			input_file = arg
		elif opt in ("-t", "--Title"):
			title = arg
		elif opt in ("-l", "--LabelLeaves"):
			label_leaves = True
		elif opt in ("-n","--LabelInternalNodes"):
			label_internal_nodes = True
		elif opt in ("-o", "--OutFile"):
			out_file = arg
		elif opt in ("-w", "--Width"):
			width = int(arg)
		elif opt in ("-x", "--OutFileXML"):
			out_file_xml = arg
			
	schema_names = COLOR_SCHEMES.keys()
	
	#Read the common kmer profile
	ckm_tax_paths = []
	ckm_name_to_perc = dict()
	fid = open(input_file,'r')
	file = fid.readlines()
	fid.close()
	
	#Put placeholders in for missing names like: "||" -> "|NA1|"
	file_noblank = list()
	i=0
	for line in file:
		while "||" in line:
			line = line.replace("||","|NONAME|",1)
			i = i+1
		file_noblank.append(line)
	
	#Get the names and weights
	for line in file_noblank:
		if line[0]!='#' and line[0]!='@' and line[0]!='\n': #Don't parse comments or blank lines
			temp = line.split()[3] #Get the names
			ckm_tax_paths.append(temp)
			ckm_name_to_perc[temp.split("|")[-1]] = line.split()[-1] #Get the weights
	
	#Create the tree
	t=Tree()
	names_to_nodes = dict()
	for i in range(0,len(ckm_tax_paths)):
		split_tax_path = ckm_tax_paths[i].split("|")
		if len(split_tax_path)==1: #If len==1, then it's a superkingdom
			names_to_nodes[split_tax_path[0]] = t.add_child(name=split_tax_path[0]) #connect directly to tree
		else:
			if split_tax_path[-2] in names_to_nodes: #If the parent is already in the tree, add to tree
				names_to_nodes[split_tax_path[-1]] = names_to_nodes[split_tax_path[-2]].add_child(name=split_tax_path[-1])
			else: #Otherwise iterate up until we have something that is in the tree
				j=2
				while split_tax_path[-j]=="NONAME":
					j = j + 1
				#This skips over the NONAMES
				names_to_nodes[split_tax_path[-1]] = names_to_nodes[split_tax_path[-j]].add_child(name=split_tax_path[-1])
	
	#Show the tree
	#print t.get_ascii(show_internal=True)
	
	#scheme = random.sample(schema_names, 1)[0] #'set2' is nice, 
	scheme = 'set2'

	def layout(node):
		if node.name in ckm_name_to_perc:
			ckm_perc = float(ckm_name_to_perc[node.name])
		else:
			ckm_perc = 0
		F = CircleFace(radius=3.14*math.sqrt(ckm_perc), color="RoyalBlue", style="sphere")
		F.border.width = None
		F.opacity = 0.6
		faces.add_face_to_node(F,node, 0, position="branch-right")
		if label_internal_nodes:
			faces.add_face_to_node(TextFace(node.name, fsize=7),node, 0, position="branch-top")
	
	ts = TreeStyle()
	ts.layout_fn = layout
	ts.mode = "r"
	ts.show_leaf_name = label_leaves
	ts.min_leaf_separation = 50
	ts.title.add_face(TextFace(title, fsize=20), column=0)
	
	#Export the tree to a png image
#.........这里部分代码省略.........