/usr/lib/python2.7/dist-packages/cogent/core/tree.py is in python-cogent 1.9-9.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 | #!/usr/bin/env python
"""Classes for storing and manipulating a phylogenetic tree.
These trees can be either strictly binary, or have polytomies
(multiple children to a parent node).
Trees consist of Nodes (or branches) that connect two nodes. The Tree can
be created only from a newick formatted string read either from file or from a
string object. Other formats will be added as time permits.
Tree can:
- Deal with either rooted or unrooted tree's and can
convert between these types.
- Return a sub-tree given a list of tip-names
- Identify an edge given two tip names. This method facilitates the
statistical modelling by simplyifying the syntax for specifying
sub-regions of a tree.
- Assess whether two Tree instances represent the same topology.
Definition of relevant terms or abbreviations:
- edge: also known as a branch on a tree.
- node: the point at which two edges meet
- tip: a sequence or species
- clade: all and only the nodes (including tips) that descend
from a node
- stem: the edge immediately preceeding a clade
"""
from numpy import zeros, argsort, ceil, log
from copy import deepcopy
import re
from cogent.util.transform import comb
from cogent.maths.stats.test import correlation
from operator import or_
from cogent.util.misc import InverseDict
from random import shuffle, choice
__author__ = "Gavin Huttley, Peter Maxwell and Rob Knight"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Gavin Huttley", "Peter Maxwell", "Rob Knight",
"Andrew Butterfield", "Catherine Lozupone", "Micah Hamady",
"Jeremy Widmann", "Zongzhi Liu", "Daniel McDonald",
"Justin Kuczynski"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"
def distance_from_r_squared(m1, m2):
"""Estimates distance as 1-r^2: no correl = max distance"""
return 1 - (correlation(m1.flat, m2.flat)[0])**2
def distance_from_r(m1, m2):
"""Estimates distance as (1-r)/2: neg correl = max distance"""
return (1-correlation(m1.flat, m2.flat)[0])/2
class TreeError(Exception):
pass
class TreeNode(object):
"""Store information about a tree node. Mutable.
Parameters:
Name: label for the node, assumed to be unique.
Children: list of the node's children.
Params: dict containing arbitrary parameters for the node.
NameLoaded: ?
"""
_exclude_from_copy = dict.fromkeys(['_parent','Children'])
def __init__(self, Name=None, Children=None, Parent=None, Params=None, \
NameLoaded=True, **kwargs):
"""Returns new TreeNode object."""
self.Name = Name
self.NameLoaded = NameLoaded
if Params is None:
Params = {}
self.params = Params
self.Children = []
if Children is not None:
self.extend(Children)
self._parent = Parent
if (Parent is not None) and not (self in Parent.Children):
Parent.append(self)
### built-in methods and list interface support
def __repr__(self):
"""Returns reconstructable string representation of tree.
WARNING: Does not currently set the class to the right type.
"""
return 'Tree("%s")' % self.getNewick()
def __str__(self):
"""Returns Newick-format string representation of tree."""
return self.getNewick()
def compareName(self, other):
"""Compares TreeNode by name"""
if self is other:
return 0
try:
return cmp(self.Name, other.Name)
except AttributeError:
return cmp(type(self), type(other))
def compareByNames(self, other):
"""Equality test for trees by name"""
# if they are the same object then they must be the same tree...
if self is other:
return True
self_names = self.getNodeNames()
other_names = other.getNodeNames()
self_names.sort()
other_names.sort()
return self_names == other_names
def _to_self_child(self, i):
"""Converts i to self's type, with self as its parent.
Cleans up refs from i's original parent, but doesn't give self ref to i.
"""
c = self.__class__
if isinstance(i, c):
if i._parent not in (None, self):
i._parent.Children.remove(i)
else:
i = c(i)
i._parent = self
return i
def append(self, i):
"""Appends i to self.Children, in-place, cleaning up refs."""
self.Children.append(self._to_self_child(i))
def extend(self, items):
"""Extends self.Children by items, in-place, cleaning up refs."""
self.Children.extend(map(self._to_self_child, items))
def insert(self, index, i):
"""Inserts an item at specified position in self.Children."""
self.Children.insert(index, self._to_self_child(i))
def pop(self, index=-1):
"""Returns and deletes child of self at index (default: -1)"""
result = self.Children.pop(index)
result._parent = None
return result
def remove(self, target):
"""Removes node by name instead of identity.
Returns True if node was present, False otherwise.
"""
if isinstance(target, TreeNode):
target = target.Name
for (i, curr_node) in enumerate(self.Children):
if curr_node.Name == target:
self.removeNode(curr_node)
return True
return False
def __getitem__(self, i):
"""Node delegates slicing to Children; faster to access them
directly."""
return self.Children[i]
def __setitem__(self, i, val):
"""Node[i] = x sets the corresponding item in Children."""
curr = self.Children[i]
if isinstance(i, slice):
for c in curr:
c._parent = None
coerced_val = map(self._to_self_child, val)
self.Children[i] = coerced_val[:]
else: #assume we got a single index
curr._parent = None
coerced_val = self._to_self_child(val)
self.Children[i] = coerced_val
def __delitem__(self, i):
"""del node[i] deletes index or slice from self.Children."""
curr = self.Children[i]
if isinstance(i, slice):
for c in curr:
c._parent = None
else:
curr._parent = None
del self.Children[i]
def __iter__(self):
"""Node iter iterates over the Children."""
return iter(self.Children)
def __len__(self):
"""Node len returns number of children."""
return len(self.Children)
#support for copy module
def copyRecursive(self, memo=None, _nil=[], constructor='ignored'):
"""Returns copy of self's structure, including shallow copy of attrs.
constructor is ignored; required to support old tree unit tests.
"""
result = self.__class__()
efc = self._exclude_from_copy
for k, v in self.__dict__.items():
if k not in efc: #avoid infinite recursion
result.__dict__[k] = deepcopy(self.__dict__[k])
for c in self:
result.append(c.copy())
return result
def copy(self, memo=None, _nil=[], constructor='ignored'):
"""Returns a copy of self using an iterative approach"""
def __copy_node(n):
result = n.__class__()
efc = n._exclude_from_copy
for k,v in n.__dict__.items():
if k not in efc:
result.__dict__[k] = deepcopy(n.__dict__[k])
return result
root = __copy_node(self)
nodes_stack = [[root, self, len(self.Children)]]
while nodes_stack:
#check the top node, any children left unvisited?
top = nodes_stack[-1]
new_top_node, old_top_node, unvisited_children = top
if unvisited_children:
top[2] -= 1
old_child = old_top_node.Children[-unvisited_children]
new_child = __copy_node(old_child)
new_top_node.append(new_child)
nodes_stack.append([new_child, old_child, \
len(old_child.Children)])
else: #no unvisited children
nodes_stack.pop()
return root
__deepcopy__ = deepcopy = copy
def copyTopology(self, constructor=None):
"""Copies only the topology and labels of a tree, not any extra data.
Useful when you want another copy of the tree with the same structure
and labels, but want to e.g. assign different branch lengths and
environments. Does not use deepcopy from the copy module, so _much_
faster than the copy() method.
"""
if constructor is None:
constructor = self.__class__
children = [c.copyTopology(constructor) for c in self.Children]
return constructor(Name=self.Name[:], Children=children)
#support for basic tree operations -- finding objects and moving in the tree
def _get_parent(self):
"""Accessor for parent.
If using an algorithm that accesses Parent a lot, it will be much
faster to access self._parent directly, but don't do it if mutating
self._parent! (or, if you must, remember to clean up the refs).
"""
return self._parent
def _set_parent(self, Parent):
"""Mutator for parent: cleans up refs in old parent."""
if self._parent is not None:
self._parent.removeNode(self)
self._parent = Parent
if (Parent is not None) and (not self in Parent.Children):
Parent.Children.append(self)
Parent = property(_get_parent, _set_parent)
def indexInParent(self):
"""Returns index of self in parent."""
return self._parent.Children.index(self)
def isTip(self):
"""Returns True if the current node is a tip, i.e. has no children."""
return not self.Children
def isRoot(self):
"""Returns True if the current is a root, i.e. has no parent."""
return self._parent is None
def traverse(self, self_before=True, self_after=False, include_self=True):
"""Returns iterator over descendants. Iterative: safe for large trees.
self_before includes each node before its descendants if True.
self_after includes each node after its descendants if True.
include_self includes the initial node if True.
self_before and self_after are independent. If neither is True, only
terminal nodes will be returned.
Note that if self is terminal, it will only be included once even if
self_before and self_after are both True.
This is a depth-first traversal. Since the trees are not binary,
preorder and postorder traversals are possible, but inorder traversals
would depend on the data in the tree and are not handled here.
"""
if self_before:
if self_after:
return self.pre_and_postorder(include_self=include_self)
else:
return self.preorder(include_self=include_self)
else:
if self_after:
return self.postorder(include_self=include_self)
else:
return self.tips(include_self=include_self)
def levelorder(self, include_self=True):
"""Performs levelorder iteration over tree"""
queue = [self]
while queue:
curr = queue.pop(0)
if include_self or (curr is not self):
yield curr
if curr.Children:
queue.extend(curr.Children)
def preorder(self, include_self=True):
"""Performs preorder iteration over tree."""
stack = [self]
while stack:
curr = stack.pop()
if include_self or (curr is not self):
yield curr
if curr.Children:
stack.extend(curr.Children[::-1]) #20% faster than reversed
def postorder(self, include_self=True):
"""Performs postorder iteration over tree.
This is somewhat inelegant compared to saving the node and its index
on the stack, but is 30% faster in the average case and 3x faster in
the worst case (for a comb tree).
Zongzhi Liu's slower but more compact version is:
def postorder_zongzhi(self):
stack = [[self, 0]]
while stack:
curr, child_idx = stack[-1]
if child_idx < len(curr.Children):
stack[-1][1] += 1
stack.append([curr.Children[child_idx], 0])
else:
yield stack.pop()[0]
"""
child_index_stack = [0]
curr = self
curr_children = self.Children
curr_children_len = len(curr_children)
while 1:
curr_index = child_index_stack[-1]
#if there are children left, process them
if curr_index < curr_children_len:
curr_child = curr_children[curr_index]
#if the current child has children, go there
if curr_child.Children:
child_index_stack.append(0)
curr = curr_child
curr_children = curr.Children
curr_children_len = len(curr_children)
curr_index = 0
#otherwise, yield that child
else:
yield curr_child
child_index_stack[-1] += 1
#if there are no children left, return self, and move to
#self's parent
else:
if include_self or (curr is not self):
yield curr
if curr is self:
break
curr = curr.Parent
curr_children = curr.Children
curr_children_len = len(curr_children)
child_index_stack.pop()
child_index_stack[-1] += 1
def pre_and_postorder(self, include_self=True):
"""Performs iteration over tree, visiting node before and after."""
#handle simple case first
if not self.Children:
if include_self:
yield self
raise StopIteration
child_index_stack = [0]
curr = self
curr_children = self.Children
while 1:
curr_index = child_index_stack[-1]
if not curr_index:
if include_self or (curr is not self):
yield curr
#if there are children left, process them
if curr_index < len(curr_children):
curr_child = curr_children[curr_index]
#if the current child has children, go there
if curr_child.Children:
child_index_stack.append(0)
curr = curr_child
curr_children = curr.Children
curr_index = 0
#otherwise, yield that child
else:
yield curr_child
child_index_stack[-1] += 1
#if there are no children left, return self, and move to
#self's parent
else:
if include_self or (curr is not self):
yield curr
if curr is self:
break
curr = curr.Parent
curr_children = curr.Children
child_index_stack.pop()
child_index_stack[-1] += 1
def traverse_recursive(self, self_before=True, self_after=False, \
include_self=True):
"""Returns iterator over descendants. IMPORTANT: read notes below.
traverse_recursive is slower than traverse, and can lead to stack
errors. However, you _must_ use traverse_recursive if you plan to
modify the tree topology as you walk over it (e.g. in post-order),
because the iterative methods use their own stack that is not updated
if you alter the tree.
self_before includes each node before its descendants if True.
self_after includes each node after its descendants if True.
include_self includes the initial node if True.
self_before and self_after are independent. If neither is True, only
terminal nodes will be returned.
Note that if self is terminal, it will only be included once even if
self_before and self_after are both True.
This is a depth-first traversal. Since the trees are not binary,
preorder and postorder traversals are possible, but inorder traversals
would depend on the data in the tree and are not handled here.
"""
if self.Children:
if self_before and include_self:
yield self
for child in self.Children:
for i in child.traverse_recursive(self_before, self_after):
yield i
if self_after and include_self:
yield self
elif include_self:
yield self
def ancestors(self):
"""Returns all ancestors back to the root. Dynamically calculated."""
result = []
curr = self._parent
while curr is not None:
result.append(curr)
curr = curr._parent
return result
def root(self):
"""Returns root of the tree self is in. Dynamically calculated."""
curr = self
while curr._parent is not None:
curr = curr._parent
return curr
def isroot(self):
"""Returns True if root of a tree, i.e. no parent."""
return self._parent is None
def siblings(self):
"""Returns all nodes that are children of the same parent as self.
Note: excludes self from the list. Dynamically calculated.
"""
if self._parent is None:
return []
result = self._parent.Children[:]
result.remove(self)
return result
def iterTips(self, include_self=False):
"""Iterates over tips descended from self, [] if self is a tip."""
#bail out in easy case
if not self.Children:
if include_self:
yield self
raise StopIteration
#use stack-based method: robust to large trees
stack = [self]
while stack:
curr = stack.pop()
if curr.Children:
stack.extend(curr.Children[::-1]) #20% faster than reversed
else:
yield curr
def tips(self, include_self=False):
"""Returns tips descended from self, [] if self is a tip."""
return list(self.iterTips(include_self=include_self))
def iterNontips(self, include_self=False):
"""Iterates over nontips descended from self, [] if none.
include_self, if True (default is False), will return the current
node as part of the list of nontips if it is a nontip."""
for n in self.traverse(True, False, include_self):
if n.Children:
yield n
def nontips(self, include_self=False):
"""Returns nontips descended from self."""
return list(self.iterNontips(include_self=include_self))
def istip(self):
"""Returns True if is tip, i.e. no children."""
return not self.Children
def tipChildren(self):
"""Returns direct children of self that are tips."""
return [i for i in self.Children if not i.Children]
def nonTipChildren(self):
"""Returns direct children in self that have descendants."""
return [i for i in self.Children if i.Children]
def childGroups(self):
"""Returns list containing lists of children sharing a state.
In other words, returns runs of tip and nontip children.
"""
#bail out in trivial cases of 0 or 1 item
if not self.Children:
return []
if len(self.Children) == 1:
return [self.Children[0]]
#otherwise, have to do it properly...
result = []
curr = []
state = None
for i in self.Children:
curr_state = bool(i.Children)
if curr_state == state:
curr.append(i)
else:
if curr:
result.append(curr)
curr = []
curr.append(i)
state = curr_state
#handle last group
result.append(curr)
return result
def lastCommonAncestor(self, other):
"""Finds last common ancestor of self and other, or None.
Always tests by identity.
"""
my_lineage = set([id(node) for node in [self] + self.ancestors()])
curr = other
while curr is not None:
if id(curr) in my_lineage:
return curr
curr = curr._parent
return None
def lowestCommonAncestor(self, tipnames):
"""Lowest common ancestor for a list of tipnames
This should be around O(H sqrt(n)), where H is height and n is the
number of tips passed in.
"""
if len(tipnames) == 1:
return self.getNodeMatchingName(tipnames[0])
tipnames = set(tipnames)
tips = [tip for tip in self.tips() if tip.Name in tipnames]
if len(tips) == 0:
return None
# scrub tree
if hasattr(self, 'black'):
for n in self.traverse(include_self=True):
if hasattr(n, 'black'):
delattr(n, 'black')
for t in tips:
prev = t
curr = t.Parent
while curr and not hasattr(curr,'black'):
setattr(curr,'black',[prev])
prev = curr
curr = curr.Parent
# increase black count, multiple children lead to here
if curr:
curr.black.append(prev)
curr = self
while len(curr.black) == 1:
curr = curr.black[0]
return curr
lca = lastCommonAncestor #for convenience
#support for more advanced tree operations
def separation(self, other):
"""Returns number of edges separating self and other."""
#detect trivial case
if self is other:
return 0
#otherwise, check the list of ancestors
my_ancestors = dict.fromkeys(map(id, [self] + self.ancestors()))
count = 0
while other is not None:
if id(other) in my_ancestors:
#need to figure out how many steps there were back from self
curr = self
while not(curr is None or curr is other):
count += 1
curr = curr._parent
return count
else:
count += 1
other = other._parent
return None
def descendantArray(self, tip_list=None):
"""Returns numpy array with nodes in rows and descendants in columns.
A value of 1 indicates that the decendant is a descendant of that node/
A value of 0 indicates that it is not
Also returns a list of nodes in the same order as they are listed
in the array.
tip_list is a list of the names of the tips that will be considered,
in the order they will appear as columns in the final array. Internal
nodes will appear as rows in preorder traversal order.
"""
#get a list of internal nodes
node_list = [node for node in self.traverse() if node.Children]
node_list.sort()
#get a list of tip names if one is not supplied
if not tip_list:
tip_list = [n.Name for n in self.tips()]
tip_list.sort()
#make a blank array of the right dimensions to alter
result = zeros([len(node_list), len(tip_list)])
#put 1 in the column for each child of each node
for (i, node) in enumerate(node_list):
children = [n.Name for n in node.tips()]
for (j, dec) in enumerate(tip_list):
if dec in children:
result[i,j] = 1
return result, node_list
def _default_tree_constructor(self):
return TreeBuilder(constructor=self.__class__).edgeFromEdge
def nameUnnamedNodes(self):
"""sets the Data property of unnamed nodes to an arbitrary value
Internal nodes are often unnamed and so this function assigns a
value for referencing."""
#make a list of the names that are already in the tree
names_in_use = []
for node in self.traverse():
if node.Name:
names_in_use.append(node.Name)
#assign unique names to the Data property of nodes where Data = None
name_index = 1
for node in self.traverse():
if not node.Name:
new_name = 'node' + str(name_index)
#choose a new name if name is already in tree
while new_name in names_in_use:
name_index += 1
new_name = 'node' + str(name_index)
node.Name = new_name
names_in_use.append(new_name)
name_index += 1
def makeTreeArray(self, dec_list=None):
"""Makes an array with nodes in rows and descendants in columns.
A value of 1 indicates that the decendant is a descendant of that node/
A value of 0 indicates that it is not
also returns a list of nodes in the same order as they are listed
in the array"""
#get a list of internal nodes
node_list = [node for node in self.traverse() if node.Children]
node_list.sort()
#get a list of tips() Name if one is not supplied
if not dec_list:
dec_list = [dec.Name for dec in self.tips()]
dec_list.sort()
#make a blank array of the right dimensions to alter
result = zeros((len(node_list), len(dec_list)))
#put 1 in the column for each child of each node
for i, node in enumerate(node_list):
children = [dec.Name for dec in node.tips()]
for j, dec in enumerate(dec_list):
if dec in children:
result[i,j] = 1
return result, node_list
def removeDeleted(self,is_deleted):
"""Removes all nodes where is_deleted tests true.
Internal nodes that have no children as a result of removing deleted
are also removed.
"""
#Traverse tree
for node in list(self.traverse(self_before=False,self_after=True)):
#if node is deleted
if is_deleted(node):
#Store current parent
curr_parent=node.Parent
#Set current node's parent to None (this deletes node)
node.Parent=None
#While there are no chilren at node and not at root
while (curr_parent is not None) and (not curr_parent.Children):
#Save old parent
old_parent=curr_parent
#Get new parent
curr_parent=curr_parent.Parent
#remove old node from tree
old_parent.Parent=None
def prune(self):
"""Reconstructs correct topology after nodes have been removed.
Internal nodes with only one child will be removed and new connections
will be made to reflect change.
"""
#traverse tree to decide nodes to be removed.
nodes_to_remove = []
for node in self.traverse():
if (node.Parent is not None) and (len(node.Children)==1):
nodes_to_remove.append(node)
for node in nodes_to_remove:
#save current parent
curr_parent=node.Parent
#save child
child=node.Children[0]
#remove current node by setting parent to None
node.Parent=None
#Connect child to current node's parent
child.Parent=curr_parent
def sameShape(self, other):
"""Ignores lengths and order, so trees should be sorted first"""
if len(self.Children) != len(other.Children):
return False
if self.Children:
for (self_child, other_child) in zip(self.Children, other.Children):
if not self_child.sameShape(other_child):
return False
return True
else:
return self.Name == other.Name
def getNewickRecursive(self, with_distances=False, semicolon=True, \
escape_name=True):
"""Return the newick string for this edge.
Arguments:
- with_distances: whether branch lengths are included.
- semicolon: end tree string with a semicolon
- escape_name: if any of these characters []'"(),:;_ exist in a
nodes name, wrap the name in single quotes
"""
newick = []
subtrees = [child.getNewick(with_distances, semicolon=False)
for child in self.Children]
if subtrees:
newick.append("(%s)" % ",".join(subtrees))
if self.NameLoaded:
if self.Name is None:
name = ''
else:
name = str(self.Name)
if escape_name and not (name.startswith("'") and \
name.endswith("'")):
if re.search("""[]['"(),:;_]""", name):
name = "'%s'" % name.replace("'","''")
else:
name = name.replace(' ','_')
newick.append(name)
if isinstance(self, PhyloNode):
if with_distances and self.Length is not None:
newick.append(":%s" % self.Length)
if semicolon:
newick.append(";")
return ''.join(newick)
def getNewick(self, with_distances=False, semicolon=True, escape_name=True):
"""Return the newick string for this tree.
Arguments:
- with_distances: whether branch lengths are included.
- semicolon: end tree string with a semicolon
- escape_name: if any of these characters []'"(),:;_ exist in a
nodes name, wrap the name in single quotes
NOTE: This method returns the Newick representation of this node
and its descendents. This method is a modification of an implementation
by Zongzhi Liu
"""
result = ['(']
nodes_stack = [[self, len(self.Children)]]
node_count = 1
while nodes_stack:
node_count += 1
#check the top node, any children left unvisited?
top = nodes_stack[-1]
top_node, num_unvisited_children = top
if num_unvisited_children: #has any child unvisited
top[1] -= 1 #decrease the #of children unvisited
next_child = top_node.Children[-num_unvisited_children] # - for order
#pre-visit
if next_child.Children:
result.append('(')
nodes_stack.append([next_child, len(next_child.Children)])
else: #no unvisited children
nodes_stack.pop()
#post-visit
if top_node.Children:
result[-1] = ')'
if top_node.NameLoaded:
if top_node.Name is None:
name = ''
else:
name = str(top_node.Name)
if escape_name and not (name.startswith("'") and \
name.endswith("'")):
if re.search("""[]['"(),:;_]""", name):
name = "'%s'" % name.replace("'", "''")
else:
name = name.replace(' ','_')
result.append(name)
if isinstance(self, PhyloNode):
if with_distances and top_node.Length is not None:
#result.append(":%s" % top_node.Length)
result[-1] = "%s:%s" % (result[-1], top_node.Length)
result.append(',')
len_result = len(result)
if len_result == 2: # single node no name
if semicolon:
return ";"
else:
return ''
elif len_result == 3: # single node with name
if semicolon:
return "%s;" % result[1]
else:
return result[1]
else:
if semicolon:
result[-1] = ';'
else:
result.pop(-1)
return ''.join(result)
def removeNode(self, target):
"""Removes node by identity instead of value.
Returns True if node was present, False otherwise.
"""
to_delete = None
for i, curr_node in enumerate(self.Children):
if curr_node is target:
to_delete = i
break
if to_delete is None:
return False
else:
del self[to_delete]
return True
def getEdgeNames(self, tip1name, tip2name,
getclade, getstem, outgroup_name=None):
"""Return the list of stem and/or sub tree (clade) edge name(s).
This is done by finding the common intersection, and then getting
the list of names. If the clade traverses the root, then use the
outgroup_name argument to ensure valid specification.
Arguments:
- tip1/2name: edge 1/2 names
- getstem: whether the name of the clade stem edge is returned.
- getclade: whether the names of the edges within the clade are
returned
- outgroup_name: if provided the calculation is done on a version of
the tree re-rooted relative to the provided tip.
Usage:
The returned list can be used to specify subtrees for special
parameterisation. For instance, say you want to allow the primates
to have a different value of a particular parameter. In this case,
provide the results of this method to the parameter controller
method `setParamRule()` along with the parameter name etc..
"""
# If outgroup specified put it at the top of the tree so that clades are
# defined by their distance from it. This makes a temporary tree with
# a named edge at it's root, but it's only used here then discarded.
if outgroup_name is not None:
outgroup = self.getNodeMatchingName(outgroup_name)
if outgroup.Children:
raise TreeError('Outgroup (%s) must be a tip' % outgroup_name)
self = outgroup.unrootedDeepcopy()
join_edge = self.getConnectingNode(tip1name, tip2name)
edge_names = []
if getstem:
if join_edge.isroot():
raise TreeError('LCA(%s,%s) is the root and so has no stem' %
(tip1name, tip2name))
else:
edge_names.append(join_edge.Name)
if getclade:
#get the list of names contained by join_edge
for child in join_edge.Children:
branchnames = child.getNodeNames(includeself = 1)
edge_names.extend(branchnames)
return edge_names
def _getNeighboursExcept(self, parent=None):
# For walking the tree as if it was unrooted.
return [c for c in (tuple(self.Children) + (self.Parent,))
if c is not None and c is not parent]
def _getDistances(self, endpoints=None):
"""Iteratively calcluates all of the root-to-tip and tip-to-tip
distances, resulting in a tuple of:
- A list of (name, path length) pairs.
- A dictionary of (tip1,tip2):distance pairs
"""
## linearize the tips in postorder.
# .__start, .__stop compose the slice in tip_order.
if endpoints is None:
tip_order = list(self.tips())
else:
tip_order = []
for i,name in enumerate(endpoints):
node = self.getNodeMatchingName(name)
tip_order.append(node)
for i, node in enumerate(tip_order):
node.__start, node.__stop = i, i+1
num_tips = len(tip_order)
result = {}
tipdistances = zeros((num_tips), float) #distances from tip to curr node
def update_result():
# set tip_tip distance between tips of different child
for child1, child2 in comb(node.Children, 2):
for tip1 in range(child1.__start, child1.__stop):
for tip2 in range(child2.__start, child2.__stop):
name1 = tip_order[tip1].Name
name2 = tip_order[tip2].Name
result[(name1,name2)] = \
tipdistances[tip1] + tipdistances[tip2]
result[(name2,name1)] = \
tipdistances[tip1] + tipdistances[tip2]
for node in self.traverse(self_before=False, self_after=True):
if not node.Children:
continue
## subtree with solved child wedges
starts, stops = [], [] #to calc ._start and ._stop for curr node
for child in node.Children:
if hasattr(child, 'Length') and child.Length is not None:
child_len = child.Length
else:
child_len = 1 # default length
tipdistances[child.__start : child.__stop] += child_len
starts.append(child.__start); stops.append(child.__stop)
node.__start, node.__stop = min(starts), max(stops)
## update result if nessessary
if len(node.Children) > 1: #not single child
update_result()
from_root = []
for i,n in enumerate(tip_order):
from_root.append((n.Name, tipdistances[i]))
return from_root, result
def getDistances(self, endpoints=None):
"""The distance matrix as a dictionary.
Usage:
Grabs the branch lengths (evolutionary distances) as
a complete matrix (i.e. a,b and b,a).
"""
(root_dists, endpoint_dists) = self._getDistances(endpoints)
return endpoint_dists
def setMaxTipTipDistance(self):
"""Propagate tip distance information up the tree
This method was originally implemented by Julia Goodrich with the intent
of being able to determine max tip to tip distances between nodes on
large trees efficiently. The code has been modified to track the
specific tips the distance is between
"""
for n in self.postorder():
if n.isTip():
n.MaxDistTips = [[0.0, n.Name], [0.0, n.Name]]
else:
if len(n.Children) == 1:
tip_a, tip_b = n.Children[0].MaxDistTips
tip_a[0] += n.Children[0].Length or 0.0
tip_b[0] += n.Children[0].Length or 0.0
else:
tip_info = [(max(c.MaxDistTips), c) for c in n.Children]
dists = [i[0][0] for i in tip_info]
best_idx = argsort(dists)[-2:]
tip_a, child_a = tip_info[best_idx[0]]
tip_b, child_b = tip_info[best_idx[1]]
tip_a[0] += child_a.Length or 0.0
tip_b[0] += child_b.Length or 0.0
n.MaxDistTips = [tip_a, tip_b]
def getMaxTipTipDistance(self):
"""Returns the max tip tip distance between any pair of tips
Returns (dist, tip_names, internal_node)
"""
if not hasattr(self, 'MaxDistTips'):
self.setMaxTipTipDistance()
longest = 0.0
names = [None,None]
best_node = None
for n in self.nontips(include_self=True):
tip_a, tip_b = n.MaxDistTips
dist = (tip_a[0] + tip_b[0])
if dist > longest:
longest = dist
best_node = n
names = [tip_a[1], tip_b[1]]
return longest, names, best_node
def maxTipTipDistance(self):
"""returns the max distance between any pair of tips
Also returns the tip names that it is between as a tuple"""
distmtx, tip_order = self.tipToTipDistances()
idx_max = divmod(distmtx.argmax(),distmtx.shape[1])
max_pair = (tip_order[idx_max[0]].Name, tip_order[idx_max[1]].Name)
return distmtx[idx_max], max_pair
def _getSubTree(self, included_names, constructor=None, keep_root=False):
"""An equivalent node with possibly fewer children, or None"""
# Renumber autonamed edges
if constructor is None:
constructor = self._default_tree_constructor()
if self.Name in included_names:
return self.deepcopy(constructor=constructor)
else:
# don't need to pass keep_root to children, though
# internal nodes will be elminated this way
children = [child._getSubTree(included_names, constructor)
for child in self.Children]
children = [child for child in children if child is not None]
if len(children) == 0:
result = None
elif len(children) == 1 and not keep_root:
# Merge parameter dictionaries by adding lengths and making
# weighted averages of other parameters. This should probably
# be moved out of here into a ParameterSet class (Model?) or
# tree subclass.
params = {}
child = children[0]
if self.Length is not None and child.Length is not None:
shared_params = [n for (n,v) in self.params.items()
if v is not None
and child.params.get(n) is not None
and n is not "length"]
length = self.Length + child.Length
if length:
params = dict([(n,
(self.params[n]*self.Length +
child.params[n]*child.Length) / length)
for n in shared_params])
params['length'] = length
result = child
result.params = params
else:
result = constructor(self, tuple(children))
return result
def getSubTree(self, name_list, ignore_missing=False, keep_root=False):
"""A new instance of a sub tree that contains all the otus that are
listed in name_list.
ignore_missing: if False, getSubTree will raise a ValueError if
name_list contains names that aren't nodes in the tree
keep_root: if False, the root of the subtree will be the last common
ancestor of all nodes kept in the subtree. Root to tip distance is
then (possibly) different from the original tree
If True, the root to tip distance remains constant, but root may only
have one child node.
"""
edge_names = set(self.getNodeNames(includeself=1, tipsonly=False))
if not ignore_missing:
# this may take a long time
for name in name_list:
if name not in edge_names:
raise ValueError("edge %s not found in tree" % name)
new_tree = self._getSubTree(name_list, keep_root=keep_root)
if new_tree is None:
raise TreeError, "no tree created in make sub tree"
elif new_tree.istip():
raise TreeError, "only a tip was returned from selecting sub tree"
else:
new_tree.Name = "root"
# keep unrooted
if len(self.Children) > 2:
new_tree = new_tree.unrooted()
return new_tree
def _edgecount(self, parent, cache):
""""The number of edges beyond 'parent' in the direction of 'self',
unrooted"""
neighbours = self._getNeighboursExcept(parent)
key = (id(parent), id(self))
if key not in cache:
cache[key] = 1 + sum([child._edgecount(self, cache)
for child in neighbours])
return cache[key]
def _imbalance(self, parent, cache):
"""The edge count from here, (except via 'parent'), divided into that
from the heaviest neighbour, and that from the rest of them. 'cache'
should be a dictionary that can be shared by calls to self.edgecount,
it stores the edgecount for each node (from self) without having to
put it on the tree itself."""
max_weight = 0
total_weight = 0
for child in self._getNeighboursExcept(parent):
weight = child._edgecount(self, cache)
total_weight += weight
if weight > max_weight:
max_weight = weight
biggest_branch = child
return (max_weight, total_weight-max_weight, biggest_branch)
def _sorted(self, sort_order):
"""Score all the edges, sort them, and return minimum score and a
sorted tree.
"""
# Only need to duplicate whole tree because of .Parent pointers
constructor = self._default_tree_constructor()
if not self.Children:
tree = self.deepcopy(constructor)
score = sort_order.index(self.Name)
else:
scored_subtrees = [child._sorted(sort_order)
for child in self.Children]
scored_subtrees.sort()
children = tuple([child.deepcopy(constructor)
for (score, child) in scored_subtrees])
tree = constructor(self, children)
non_null_scores = [score
for (score, child) in scored_subtrees if score is not None]
score = (non_null_scores or [None])[0]
return (score, tree)
def sorted(self, sort_order=[]):
"""An equivalent tree sorted into a standard order. If this is not
specified then alphabetical order is used. At each node starting from
root, the algorithm will try to put the descendant which contains the
lowest scoring tip on the left.
"""
tip_names = self.getTipNames()
tip_names.sort()
full_sort_order = sort_order + tip_names
(score, tree) = self._sorted(full_sort_order)
return tree
def _asciiArt(self, char1='-', show_internal=True, compact=False):
LEN = 10
PAD = ' ' * LEN
PA = ' ' * (LEN-1)
namestr = self.Name or '' # prevents name of NoneType
if self.Children:
mids = []
result = []
for c in self.Children:
if c is self.Children[0]:
char2 = '/'
elif c is self.Children[-1]:
char2 = '\\'
else:
char2 = '-'
(clines, mid) = c._asciiArt(char2, show_internal, compact)
mids.append(mid+len(result))
result.extend(clines)
if not compact:
result.append('')
if not compact:
result.pop()
(lo, hi, end) = (mids[0], mids[-1], len(result))
prefixes = [PAD] * (lo+1) + [PA+'|'] * (hi-lo-1) + [PAD] * (end-hi)
mid = (lo + hi) / 2
prefixes[mid] = char1 + '-'*(LEN-2) + prefixes[mid][-1]
result = [p+l for (p,l) in zip(prefixes, result)]
if show_internal:
stem = result[mid]
result[mid] = stem[0] + namestr + stem[len(namestr)+1:]
return (result, mid)
else:
return ([char1 + '-' + namestr], 0)
def asciiArt(self, show_internal=True, compact=False):
"""Returns a string containing an ascii drawing of the tree.
Arguments:
- show_internal: includes internal edge names.
- compact: use exactly one line per tip.
"""
(lines, mid) = self._asciiArt(
show_internal=show_internal, compact=compact)
return '\n'.join(lines)
def _getXmlLines(self, indent=0, parent_params=None):
"""Return the xml strings for this edge.
"""
params = {}
if parent_params is not None:
params.update(parent_params)
pad = ' ' * indent
xml = ["%s<clade>" % pad]
if self.NameLoaded:
xml.append("%s <name>%s</name>" % (pad, self.Name))
for (n,v) in self.params.items():
if v == params.get(n, None):
continue
xml.append("%s <param><name>%s</name><value>%s</value></param>"
% (pad, n, v))
params[n] = v
for child in self.Children:
xml.extend(child._getXmlLines(indent + 1, params))
xml.append(pad + "</clade>")
return xml
def getXML(self):
"""Return XML formatted tree string."""
header = ['<?xml version="1.0"?>'] # <!DOCTYPE ...
return '\n'.join(header + self._getXmlLines())
def writeToFile(self, filename, with_distances=True, format=None):
"""Save the tree to filename
Arguments:
- filename: self-evident
- with_distances: whether branch lengths are included in string.
- format: default is newick, xml is alternate. Argument overrides
the filename suffix. All attributes are saved in the xml format.
"""
if format:
xml = format.lower() == 'xml'
else:
xml = filename.lower().endswith('xml')
if xml:
data = self.getXML()
else:
data = self.getNewick(with_distances=with_distances)
outf = open(filename, "w")
outf.writelines(data)
outf.close()
def getNodeNames(self, includeself=True, tipsonly=False):
"""Return a list of edges from this edge - may or may not include self.
This node (or first connection) will be the first, and then they will
be listed in the natural traverse order.
"""
if tipsonly:
nodes = self.traverse(self_before=False, self_after=False)
else:
nodes = list(self.traverse())
if not includeself:
nodes = nodes[:-1]
return [node.Name for node in nodes]
def getTipNames(self, includeself=False):
"""return the list of the names of all tips contained by this edge
"""
return self.getNodeNames(includeself, tipsonly=True)
def getEdgeVector(self, include_root=True):
"""Collect the list of edges in postfix order
Arguments:
- include_root: specifies whether root edge included"""
if include_root:
result = [n for n in self.traverse(False, True)]
else:
result = [n for n in self.traverse(False, True) if not n.isroot()]
return result
def _getNodeMatchingName(self, name):
"""
find the edge with the name, or return None
"""
for node in self.traverse(self_before=True, self_after=False):
if node.Name == name:
return node
return None
def getNodeMatchingName(self, name):
node = self._getNodeMatchingName(name)
if node is None:
raise TreeError("No node named '%s' in %s" %
(name, self.getTipNames()))
return node
def getConnectingNode(self, name1, name2):
"""Finds the last common ancestor of the two named edges."""
edge1 = self.getNodeMatchingName(name1)
edge2 = self.getNodeMatchingName(name2)
lca = edge1.lastCommonAncestor(edge2)
if lca is None:
raise TreeError("No LCA found for %s and %s" % (name1, name2))
return lca
def getConnectingEdges(self, name1, name2):
"""returns a list of edges connecting two nodes
includes self and other in the list"""
edge1 = self.getNodeMatchingName(name1)
edge2 = self.getNodeMatchingName(name2)
LCA = self.getConnectingNode(name1, name2)
node_path = [edge1]
node_path.extend(edge1.ancestors())
#remove nodes deeper than the LCA
LCA_ind = node_path.index(LCA)
node_path = node_path[:LCA_ind+1]
#remove LCA and deeper nodes from anc list of other
anc2 = edge2.ancestors()
LCA_ind = anc2.index(LCA)
anc2 = anc2[:LCA_ind]
anc2.reverse()
node_path.extend(anc2)
node_path.append(edge2)
return node_path
def getParamValue(self, param, edge):
"""returns the parameter value for named edge"""
return self.getNodeMatchingName(edge).params[param]
def setParamValue(self, param, edge, value):
"""set's the value for param at named edge"""
self.getNodeMatchingName(edge).params[param] = value
def reassignNames(self, mapping, nodes=None):
"""Reassigns node names based on a mapping dict
mapping : dict, old_name -> new_name
nodes : specific nodes for renaming (such as just tips, etc...)
"""
if nodes is None:
nodes = self.traverse()
for n in nodes:
if n.Name in mapping:
n.Name = mapping[n.Name]
def multifurcating(self, num, eps=None, constructor=None, \
name_unnamed=False):
"""Return a new tree with every node having num or few children
num : the number of children a node can have max
eps : default branch length to set if self or constructor is of
PhyloNode type
constructor : a TreeNode or subclass constructor. If None, uses self
"""
if num < 2:
raise TreeError, "Minimum number of children must be >= 2"
if eps is None:
eps = 0.0
if constructor is None:
constructor = self.__class__
if hasattr(constructor, 'Length'):
set_branchlength = True
else:
set_branchlength = False
new_tree = self.copy()
for n in new_tree.preorder(include_self=True):
while len(n.Children) > num:
new_node = constructor(Children=n.Children[-num:])
if set_branchlength:
new_node.Length = eps
n.append(new_node)
if name_unnamed:
alpha = 'abcdefghijklmnopqrstuvwxyz'
alpha += alpha.upper()
base = 'AUTOGENERATED_NAME_%s'
# scale the random names by tree size
s = int(ceil(log(len(new_tree.tips()))))
for n in new_tree.nontips():
if n.Name is None:
n.Name = base % ''.join([choice(alpha) for i in range(s)])
return new_tree
def bifurcating(self, eps=None, constructor=None, name_unnamed=False):
"""Wrap multifurcating with a num of 2"""
return self.multifurcating(2, eps, constructor, name_unnamed)
def getNodesDict(self):
"""Returns a dict keyed by node name, value is node
Will raise TreeError if non-unique names are encountered
"""
res = {}
for n in self.traverse():
if n.Name in res:
raise TreeError, "getNodesDict requires unique node names"
else:
res[n.Name] = n
return res
def subset(self):
"""Returns set of names that descend from specified node"""
return frozenset([i.Name for i in self.tips()])
def subsets(self):
"""Returns all sets of names that come from specified node and its kids"""
sets = []
for i in self.traverse(self_before=False, self_after=True, \
include_self=False):
if not i.Children:
i.__leaf_set = frozenset([i.Name])
else:
leaf_set = reduce(or_, [c.__leaf_set for c in i.Children])
if len(leaf_set) > 1:
sets.append(leaf_set)
i.__leaf_set = leaf_set
return frozenset(sets)
def compareBySubsets(self, other, exclude_absent_taxa=False):
"""Returns fraction of overlapping subsets where self and other differ.
Other is expected to be a tree object compatible with PhyloNode.
Note: names present in only one of the two trees will count as
mismatches: if you don't want this behavior, strip out the non-matching
tips first.
"""
self_sets, other_sets = self.subsets(), other.subsets()
if exclude_absent_taxa:
in_both = self.subset() & other.subset()
self_sets = [i & in_both for i in self_sets]
self_sets = frozenset([i for i in self_sets if len(i) > 1])
other_sets = [i & in_both for i in other_sets]
other_sets = frozenset([i for i in other_sets if len(i) > 1])
total_subsets = len(self_sets) + len(other_sets)
intersection_length = len(self_sets & other_sets)
if not total_subsets: #no common subsets after filtering, so max dist
return 1
return 1 - 2*intersection_length/float(total_subsets)
def tipToTipDistances(self, default_length=1):
"""Returns distance matrix between all pairs of tips, and a tip order.
Warning: .__start and .__stop added to self and its descendants.
tip_order contains the actual node objects, not their names (may be
confusing in some cases).
"""
## linearize the tips in postorder.
# .__start, .__stop compose the slice in tip_order.
tip_order = list(self.tips())
for i, tip in enumerate(tip_order):
tip.__start, tip.__stop = i, i+1
num_tips = len(tip_order)
result = zeros((num_tips, num_tips), float) #tip by tip matrix
tipdistances = zeros((num_tips), float) #distances from tip to curr node
def update_result():
# set tip_tip distance between tips of different child
for child1, child2 in comb(node.Children, 2):
for tip1 in range(child1.__start, child1.__stop):
for tip2 in range(child2.__start, child2.__stop):
result[tip1,tip2] = \
tipdistances[tip1] + tipdistances[tip2]
for node in self.traverse(self_before=False, self_after=True):
if not node.Children:
continue
## subtree with solved child wedges
starts, stops = [], [] #to calc ._start and ._stop for curr node
for child in node.Children:
if hasattr(child, 'Length') and child.Length is not None:
child_len = child.Length
else:
child_len = default_length
tipdistances[child.__start : child.__stop] += child_len
starts.append(child.__start); stops.append(child.__stop)
node.__start, node.__stop = min(starts), max(stops)
## update result if nessessary
if len(node.Children) > 1: #not single child
update_result()
return result+result.T, tip_order
def compareByTipDistances(self, other, dist_f=distance_from_r):
"""Compares self to other using tip-to-tip distance matrices.
Value returned is dist_f(m1, m2) for the two matrices. Default is
to use the Pearson correlation coefficient, with +1 giving a distance
of 0 and -1 giving a distance of +1 (the madimum possible value).
Depending on the application, you might instead want to use
distance_from_r_squared, which counts correlations of both +1 and -1
as identical (0 distance).
Note: automatically strips out the names that don't match (this is
necessary for this method because the distance between non-matching
names and matching names is undefined in the tree where they don't
match, and because we need to reorder the names in the two trees to
match up the distance matrices).
"""
self_names = [i.Name for i in self.tips()]
other_names = [i.Name for i in other.tips()]
common_names = frozenset(self_names) & frozenset(other_names)
if not common_names:
raise ValueError, "No names in common between the two trees."""
if len(common_names) <= 2:
return 1 #the two trees must match by definition in this case
#figure out correct order of the two name matrices
self_order = [self_names.index(i) for i in common_names]
other_order = [other_names.index(i) for i in common_names]
self_matrix = self.tipToTipDistances()[0][self_order][:,self_order]
other_matrix = other.tipToTipDistances()[0][other_order][:,other_order]
return dist_f(self_matrix, other_matrix)
class PhyloNode(TreeNode):
def __init__(self, *args, **kwargs):
length = kwargs.get('Length', None)
params = kwargs.get('Params', {})
if 'length' not in params:
params['length'] = length
kwargs['Params'] = params
super(PhyloNode, self).__init__(*args, **kwargs)
def _set_length(self, value):
if not hasattr(self, "params"):
self.params = {}
self.params["length"] = value
def _get_length(self):
return self.params.get("length", None)
Length = property(_get_length, _set_length)
def getNewick(self, with_distances=False, semicolon=True, escape_name=True):
return TreeNode.getNewick(self, with_distances, semicolon, escape_name)
def __str__(self):
"""Returns string version of self, with names and distances."""
return self.getNewick(with_distances=True)
def distance(self, other):
"""Returns branch length between self and other."""
#never any length between self and other
if self is other:
return 0
#otherwise, find self's ancestors and find the first ancestor of
#other that is in the list
self_anc = self.ancestors()
self_anc_dict = dict([(id(n),n) for n in self_anc])
self_anc_dict[id(self)] = self
count = 0
while other is not None:
if id(other) in self_anc_dict:
#found the first shared ancestor -- need to sum other branch
curr = self
while curr is not other:
if curr.Length:
count += curr.Length
curr = curr._parent
return count
else:
if other.Length:
count += other.Length
other = other._parent
return None
def totalDescendingBranchLength(self):
"""Returns total descending branch length from self"""
return sum([n.Length for n in self.traverse(include_self=False) \
if n.Length is not None])
def tipsWithinDistance(self, distance):
"""Returns tips within specified distance from self
Branch lengths of None will be interpreted as 0
"""
def get_distance(d1, d2):
if d2 is None:
return d1
else:
return d1 + d2
to_process = [(self, 0.0)]
tips_to_save = []
curr_node, curr_dist = to_process[0]
seen = set([id(self)])
while to_process:
curr_node, curr_dist = to_process.pop(0)
# have we've found a tip within distance?
if curr_node.isTip() and curr_node != self:
tips_to_save.append(curr_node)
continue
# add the parent node if it is within distance
parent_dist = get_distance(curr_dist, curr_node.Length)
if curr_node.Parent is not None and parent_dist <= distance and \
id(curr_node.Parent) not in seen:
to_process.append((curr_node.Parent, parent_dist))
seen.add(id(curr_node.Parent))
# add children if we haven't seen them and if they are in distance
for child in curr_node.Children:
if id(child) in seen:
continue
seen.add(id(child))
child_dist = get_distance(curr_dist, child.Length)
if child_dist <= distance:
to_process.append((child, child_dist))
return tips_to_save
def prune(self):
"""Reconstructs correct tree after nodes have been removed.
Internal nodes with only one child will be removed and new connections
and Branch lengths will be made to reflect change.
"""
#traverse tree to decide nodes to be removed.
nodes_to_remove = []
for node in self.traverse():
if (node.Parent is not None) and (len(node.Children)==1):
nodes_to_remove.append(node)
for node in nodes_to_remove:
#save current parent
curr_parent=node.Parent
#save child
child=node.Children[0]
#remove current node by setting parent to None
node.Parent=None
#Connect child to current node's parent
child.Parent=curr_parent
#Add the Length of the removed node to the Length of the Child
if child.Length is None or node.Length is None:
child.Length = child.Length or node.Length
else:
child.Length = child.Length + node.Length
def unrootedDeepcopy(self, constructor=None, parent=None):
# walks the tree unrooted-style, ie: treating self.Parent as just
# another child 'parent' is where we got here from, ie: the neighbour
# that we don't need to explore.
if constructor is None:
constructor = self._default_tree_constructor()
neighbours = self._getNeighboursExcept(parent)
children = []
for child in neighbours:
children.append(child.unrootedDeepcopy(constructor, parent=self))
# we might be walking UP the tree, so:
if parent is None:
# base edge
edge = None
elif parent.Parent is self:
# self's parent is becoming self's child, and edge params are stored
# by the child
edge = parent
else:
assert parent is self.Parent
edge = self
result = constructor(edge, tuple(children))
if parent is None:
result.Name = "root"
return result
def balanced(self):
"""Tree 'rooted' here with no neighbour having > 50% of the edges.
Usage:
Using a balanced tree can substantially improve performance of
the likelihood calculations. Note that the resulting tree has a
different orientation with the effect that specifying clades or
stems for model parameterisation should be done using the
'outgroup_name' argument.
"""
# this should work OK on ordinary 3-way trees, not so sure about
# other cases. Given 3 neighbours, if one has > 50% of edges it
# can only improve things to divide it up, worst case:
# (51),25,24 -> (50,1),49.
# If no neighbour has >50% we can't improve on where we are, eg:
# (49),25,26 -> (20,19),51
last_edge = None
edge = self
known_weight = 0
cache = {}
while 1:
(max_weight, remaining_weight, next_edge) = edge._imbalance(
last_edge, cache)
known_weight += remaining_weight
if max_weight <= known_weight+2:
break
last_edge = edge
edge = next_edge
known_weight += 1
return edge.unrootedDeepcopy()
def sameTopology(self, other):
"""Tests whether two trees have the same topology."""
tip_names = self.getTipNames()
root_at = tip_names[0]
me = self.rootedWithTip(root_at).sorted(tip_names)
them = other.rootedWithTip(root_at).sorted(tip_names)
return self is other or me.sameShape(them)
def unrooted(self):
"""A tree with at least 3 children at the root.
"""
constructor = self._default_tree_constructor()
need_to_expand = len(self.Children) < 3
new_children = []
for oldnode in self.Children:
if oldnode.Children and need_to_expand:
for sib in oldnode.Children:
sib = sib.deepcopy(constructor)
if sib.Length is not None and oldnode.Length is not None:
sib.Length += oldnode.Length
new_children.append(sib)
need_to_expand = False
else:
new_children.append(oldnode.deepcopy(constructor))
return constructor(self, new_children)
def rootedAt(self, edge_name):
"""Return a new tree rooted at the provided node.
Usage:
This can be useful for drawing unrooted trees with an orientation
that reflects knowledge of the true root location.
"""
newroot = self.getNodeMatchingName(edge_name)
if not newroot.Children:
raise TreeError("Can't use a tip (%s) as the root" %
repr(edge_name))
return newroot.unrootedDeepcopy()
def rootedWithTip(self, outgroup_name):
"""A new tree with the named tip as one of the root's children"""
tip = self.getNodeMatchingName(outgroup_name)
return tip.Parent.unrootedDeepcopy()
def rootAtMidpoint(self):
""" return a new tree rooted at midpoint of the two tips farthest apart
this fn doesn't preserve the internal node naming or structure,
but does keep tip to tip distances correct. uses unrootedDeepcopy()
"""
# max_dist, tip_names = tree.maxTipTipDistance()
# this is slow
max_dist, tip_names = self.maxTipTipDistance()
half_max_dist = max_dist/2.0
if max_dist == 0.0: # only pathological cases with no lengths
return self.unrootedDeepcopy()
# print tip_names
tip1 = self.getNodeMatchingName(tip_names[0])
tip2 = self.getNodeMatchingName(tip_names[1])
lca = self.getConnectingNode(tip_names[0],tip_names[1]) # last comm ancestor
if tip1.distance(lca) > half_max_dist:
climb_node = tip1
else:
climb_node = tip2
dist_climbed = 0.0
while dist_climbed + climb_node.Length < half_max_dist:
dist_climbed += climb_node.Length
climb_node = climb_node.Parent
# now midpt is either at on the branch to climb_node's parent
# or midpt is at climb_node's parent
# print dist_climbed, half_max_dist, 'dists cl hamax'
if dist_climbed + climb_node.Length == half_max_dist:
# climb to midpoint spot
climb_node = climb_node.Parent
if climb_node.isTip():
raise RuntimeError('error trying to root tree at tip')
else:
# print climb_node.Name, 'clmb node'
return climb_node.unrootedDeepcopy()
else:
# make a new node on climb_node's branch to its parent
old_br_len = climb_node.Length
new_root = type(self)()
new_root.Parent = climb_node.Parent
climb_node.Parent = new_root
climb_node.Length = half_max_dist - dist_climbed
new_root.Length = old_br_len - climb_node.Length
return new_root.unrootedDeepcopy()
def _find_midpoint_nodes(self, max_dist, tip_pair):
"""returns the nodes surrounding the maxTipTipDistance midpoint
WAS used for midpoint rooting. ORPHANED NOW
max_dist: The maximum distance between any 2 tips
tip_pair: Names of the two tips associated with max_dist
"""
half_max_dist = max_dist/2.0
#get a list of the nodes that separate the tip pair
node_path = self.getConnectingEdges(tip_pair[0], tip_pair[1])
tip1 = self.getNodeMatchingName(tip_pair[0])
for index, node in enumerate(node_path):
dist = tip1.distance(node)
if dist > half_max_dist:
return node, node_path[index-1]
def setTipDistances(self):
"""Sets distance from each node to the most distant tip."""
for node in self.traverse(self_before=False, self_after=True):
if node.Children:
node.TipDistance = max([c.Length + c.TipDistance for \
c in node.Children])
else:
node.TipDistance = 0
def scaleBranchLengths(self, max_length=100, ultrametric=False):
"""Scales BranchLengths in place to integers for ascii output.
Warning: tree might not be exactly the length you specify.
Set ultrametric=True if you want all the root-tip distances to end
up precisely the same.
"""
self.setTipDistances()
orig_max = max([n.TipDistance for n in self.traverse()])
if not ultrametric: #easy case -- just scale and round
for node in self.traverse():
curr = node.Length
if curr is not None:
node.ScaledBranchLength = \
max(1, int(round(1.0*curr/orig_max*max_length)))
else: #hard case -- need to make sure they all line up at the end
for node in self.traverse(self_before=False, self_after=True):
if not node.Children: #easy case: ignore tips
node.DistanceUsed = 0
continue
#if we get here, we know the node has children
#figure out what distance we want to set for this node
ideal_distance=int(round(node.TipDistance/orig_max*max_length))
min_distance = max([c.DistanceUsed for c in node.Children]) + 1
distance = max(min_distance, ideal_distance)
for c in node.Children:
c.ScaledBranchLength = distance - c.DistanceUsed
node.DistanceUsed = distance
#reset the BranchLengths
for node in self.traverse(self_before=True, self_after=False):
if node.Length is not None:
node.Length = node.ScaledBranchLength
if hasattr(node, 'ScaledBranchLength'):
del node.ScaledBranchLength
if hasattr(node, 'DistanceUsed'):
del node.DistanceUsed
if hasattr(node, 'TipDistance'):
del node.TipDistance
def _getDistances(self, endpoints=None):
"""Iteratively calcluates all of the root-to-tip and tip-to-tip
distances, resulting in a tuple of:
- A list of (name, path length) pairs.
- A dictionary of (tip1,tip2):distance pairs
"""
## linearize the tips in postorder.
# .__start, .__stop compose the slice in tip_order.
if endpoints is None:
tip_order = list(self.tips())
else:
tip_order = []
for i,name in enumerate(endpoints):
node = self.getNodeMatchingName(name)
tip_order.append(node)
for i, node in enumerate(tip_order):
node.__start, node.__stop = i, i+1
num_tips = len(tip_order)
result = {}
tipdistances = zeros((num_tips), float) #distances from tip to curr node
def update_result():
# set tip_tip distance between tips of different child
for child1, child2 in comb(node.Children, 2):
for tip1 in range(child1.__start, child1.__stop):
for tip2 in range(child2.__start, child2.__stop):
name1 = tip_order[tip1].Name
name2 = tip_order[tip2].Name
result[(name1,name2)] = \
tipdistances[tip1] + tipdistances[tip2]
result[(name2,name1)] = \
tipdistances[tip1] + tipdistances[tip2]
for node in self.traverse(self_before=False, self_after=True):
if not node.Children:
continue
## subtree with solved child wedges
starts, stops = [], [] #to calc ._start and ._stop for curr node
for child in node.Children:
if hasattr(child, 'Length') and child.Length is not None:
child_len = child.Length
else:
child_len = 1 # default length
tipdistances[child.__start : child.__stop] += child_len
starts.append(child.__start); stops.append(child.__stop)
node.__start, node.__stop = min(starts), max(stops)
## update result if nessessary
if len(node.Children) > 1: #not single child
update_result()
from_root = []
for i,n in enumerate(tip_order):
from_root.append((n.Name, tipdistances[i]))
return from_root, result
def getDistances(self, endpoints=None):
"""The distance matrix as a dictionary.
Usage:
Grabs the branch lengths (evolutionary distances) as
a complete matrix (i.e. a,b and b,a)."""
(root_dists, endpoint_dists) = self._getDistances(endpoints)
return endpoint_dists
def tipToTipDistances(self, endpoints=None, default_length=1):
"""Returns distance matrix between all pairs of tips, and a tip order.
Warning: .__start and .__stop added to self and its descendants.
tip_order contains the actual node objects, not their names (may be
confusing in some cases).
"""
all_tips = self.tips()
if endpoints is None:
tip_order = list(all_tips)
else:
if isinstance(endpoints[0], PhyloNode):
tip_order = endpoints
else:
tip_order = [self.getNodeMatchingName(n) for n in endpoints]
## linearize all tips in postorder
# .__start, .__stop compose the slice in tip_order.
for i, node in enumerate(all_tips):
node.__start, node.__stop = i, i+1
# the result map provides index in the result matrix
result_map = dict([(n.__start,i) for i,n in enumerate(tip_order)])
num_all_tips = len(all_tips) # total number of tips
num_tips = len(tip_order) # total number of tips in result
result = zeros((num_tips, num_tips), float) # tip by tip matrix
tipdistances = zeros((num_all_tips), float) # dist from tip to curr node
def update_result():
# set tip_tip distance between tips of different child
for child1, child2 in comb(node.Children, 2):
for tip1 in range(child1.__start, child1.__stop):
if tip1 not in result_map:
continue
res_tip1 = result_map[tip1]
for tip2 in range(child2.__start, child2.__stop):
if tip2 not in result_map:
continue
result[res_tip1,result_map[tip2]] = \
tipdistances[tip1] + tipdistances[tip2]
for node in self.traverse(self_before=False, self_after=True):
if not node.Children:
continue
## subtree with solved child wedges
starts, stops = [], [] #to calc ._start and ._stop for curr node
for child in node.Children:
if hasattr(child, 'Length') and child.Length is not None:
child_len = child.Length
else:
child_len = default_length
tipdistances[child.__start : child.__stop] += child_len
starts.append(child.__start); stops.append(child.__stop)
node.__start, node.__stop = min(starts), max(stops)
## update result if nessessary
if len(node.Children) > 1: #not single child
update_result()
return result+result.T, tip_order
def compareByTipDistances(self, other, sample=None, dist_f=distance_from_r,\
shuffle_f=shuffle):
"""Compares self to other using tip-to-tip distance matrices.
Value returned is dist_f(m1, m2) for the two matrices. Default is
to use the Pearson correlation coefficient, with +1 giving a distance
of 0 and -1 giving a distance of +1 (the madimum possible value).
Depending on the application, you might instead want to use
distance_from_r_squared, which counts correlations of both +1 and -1
as identical (0 distance).
Note: automatically strips out the names that don't match (this is
necessary for this method because the distance between non-matching
names and matching names is undefined in the tree where they don't
match, and because we need to reorder the names in the two trees to
match up the distance matrices).
"""
self_names = dict([(i.Name, i) for i in self.tips()])
other_names = dict([(i.Name, i) for i in other.tips()])
common_names = frozenset(self_names.keys()) & \
frozenset(other_names.keys())
common_names = list(common_names)
if not common_names:
raise ValueError, "No names in common between the two trees."""
if len(common_names) <= 2:
return 1 #the two trees must match by definition in this case
if sample is not None:
shuffle_f(common_names)
common_names = common_names[:sample]
self_nodes = [self_names[k] for k in common_names]
other_nodes = [other_names[k] for k in common_names]
self_matrix = self.tipToTipDistances(endpoints=self_nodes)[0]
other_matrix = other.tipToTipDistances(endpoints=other_nodes)[0]
return dist_f(self_matrix, other_matrix)
class TreeBuilder(object):
# Some tree code which isn't needed once the tree is finished.
# Mostly exists to give edges unique names
# Children must be created before their parents.
def __init__(self, mutable=False, constructor=PhyloNode):
self._used_names = {'edge':-1}
self._known_edges = {}
self.TreeNodeClass = constructor
def _unique_name(self, name):
# Unnamed edges become edge.0, edge.1 edge.2 ...
# Other duplicates go mouse mouse.2 mouse.3 ...
if not name:
name = 'edge'
if name in self._used_names:
self._used_names[name] += 1
name += '.' + str(self._used_names[name])
name = self._unique_name(name) # in case of names like 'edge.1.1'
else:
self._used_names[name] = 1
return name
def _params_for_edge(self, edge):
# default is just to keep it
return edge.params
def edgeFromEdge(self, edge, children, params=None):
"""Callback for tree-to-tree transforms like getSubTree"""
if edge is None:
assert not params
return self.createEdge(children, "root", {}, False)
else:
if params is None:
params = self._params_for_edge(edge)
return self.createEdge(
children, edge.Name, params, nameLoaded=edge.NameLoaded)
def createEdge(self, children, name, params, nameLoaded=True):
"""Callback for newick parser"""
if children is None:
children = []
node = self.TreeNodeClass(
Children = list(children),
Name = self._unique_name(name),
NameLoaded = nameLoaded and (name is not None),
Params = params,
)
self._known_edges[id(node)] = node
return node
|