for n in node.postiter():
c = n2coords[n]
if not n.isleaf:
children = n.children
v = [n2coords[children[0]].y, n2coords[children[-1]].y]
v.sort()
ymin, ymax = v
c.y = (ymax + ymin)/2.0
if not scaled:
c.x = min([ n2coords[ch].x for ch in children ]) - 1.0
else:
c.x = c.length_to_root
if not scaled:
for i in range(smooth):
smooth_xpos(node, n2coords)
return n2coords
if __name__ == "__main__":
import tree
node = tree.read("(a:3,(b:2,(c:4,d:5):1,(e:3,(f:1,g:1):2):2):2);")
for i, n in enumerate(node.iternodes()):
if not n.isleaf:
n.label = "node%s" % i
node.label = "root"
n2c = calc_node_positions(node, width=10, height=10, scaled=True)
from pprint import pprint
pprint(n2c)
## N = len(labels)
## var = [ [ 0 for x in labels ] for y in labels ]
## cov = [ [ None for x in labels ] for y in labels ]
## d = root.leaf_distances()
## for i in range(N):
## for j in range(i+1, N):
## li = labels[i]
## lj = labels[j]
## for n in root.postiter():
## l2d = d[n]
## if (not n.isleaf) and (li in l2d) and (lj in l2d):
## dist = l2d[li] + l2d[lj]
## var[i][j] = dist
## cov[i][j] = sum([ x.length for x in n.rootpath()
## if x.parent ])
## break
## return var, cov
if __name__ == "__main__":
import tree, ascii
from pprint import pprint
n = tree.read("(((a:1,b:2):3,(c:3,d:1):1,(e:0.5,f:3):2.5):1,g:4);")
var, covar = vcv(n)
for x in n:
if not x.label: x.label = str(x.length or "")
else: x.label = "%s %s" % (x.length, x.label)
print ascii.render(n, scaled=1)
for k, v in var.items():
print [ x.label.split()[-1] for x in k ], v, covar[k]
PIC(child, data, results)
child_results = results[child]
X.append(child_results[0])
v.append(child_results[1])
else:
X.append(data[child.label])
v.append(child.length)
Xi, Xj = X # Xi - Xj is the contrast value
vi, vj = v
# Xk is the reconstructed state at the node
Xk = ((1.0 / vi) * Xi + (1 / vj) * Xj) / (1.0 / vi + 1.0 / vj)
# vk is the variance
vk = node.length + (vi * vj) / (vi + vj)
results[node] = (Xk, vk, Xi - Xj, vi + vj)
return results
if __name__ == "__main__":
import tree
n = tree.read("((((H**o:0.21,Pongo:0.21)N1:0.28,Macaca:0.49)N2:0.13," "Ateles:0.62)N3:0.38,Galago:1.00)N4:0.0;")
char1 = {"H**o": 4.09434, "Pongo": 3.61092, "Macaca": 2.37024, "Ateles": 2.02815, "Galago": -1.46968}
for k, v in PIC(n, char1).items():
print k.label or k.id, v
if node.isleaf:
buf.putstr(nc.r, nc.c+1, " "+node.label)
else:
if node.label and show_internal_labels:
buf.putstr(nc.r, nc.c-len(node.label), node.label)
buf.putstr(nc.r, nc.c, "+")
return str(buf)
if __name__ == "__main__":
import random, tree
rand = random.Random()
t = tree.read(
"(foo,((bar,(dog,cat)dc)dcb,(shoe,(fly,(cow, bowwow)cowb)cbf)X)Y)Z;"
)
#t = tree.read("(((foo:4.6):5.6, (bar:6.5, baz:2.3):3.0):3.0);")
#t = tree.read("(foo:4.6, (bar:6.5, baz:2.3)X:3.0)Y:3.0;")
i = 1
print render(t, scaled=0, show_internal_labels=1)
r = t.get("cat").parent
tree.reroot(t, r)
tp = t.parent
tp.remove_child(t)
c = t.children[0]
t.remove_child(c)
tp.add_child(c)
print render(r, scaled=0, show_internal_labels=1)
Xi, Xj = X # Xi - Xj is the contrast value
vi, vj = v
# Xk is the reconstructed state at the node
Xk = ((1.0 / vi) * Xi + (1 / vj) * Xj) / (1.0 / vi + 1.0 / vj)
# vk is the variance
vk = node.length + (vi * vj) / (vi + vj)
results[node] = (Xk, vk, Xi - Xj, vi + vj)
return results
if __name__ == "__main__":
import tree
n = tree.read(
"((((H**o:0.21,Pongo:0.21)N1:0.28,Macaca:0.49)N2:0.13,"\
"Ateles:0.62)N3:0.38,Galago:1.00)N4:0.0;"
)
char1 = {
"H**o": 4.09434,
"Pongo": 3.61092,
"Macaca": 2.37024,
"Ateles": 2.02815,
"Galago": -1.46968
}
for k, v in PIC(n, char1).items():
print(k.label or k.id, v)
