code stringlengths 1 1.72M | language stringclasses 1
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# Copyright (c) 2009-2012, Andrew McNabb
# Copyright (c) 2003-2008, Brent N. Chun
import optparse
import os
import shlex
import sys
import textwrap
from psshlib import version
_DEFAULT_PARALLELISM = 32
_DEFAULT_TIMEOUT = 0 # "infinity" by default
def common_parser():
"""
Create a basic OptionParser wit... | Python |
#!/usr/bin/python
# Copyright (c) 2009, Andrew McNabb
# Copyright (c) 2003-2008, Brent N. Chun
import os
import sys
import shutil
import tempfile
import time
import unittest
basedir, bin = os.path.split(os.path.dirname(os.path.abspath(sys.argv[0])))
sys.path.append("%s" % basedir)
if os.getenv("TEST_HOSTS") is Non... | Python |
from utils import *
Clean()
HgUpdate21()
PatchAll()
Build_XCode_64()
RunAll()
Clean()
| Python |
from utils import *
Clean()
HgUpdate33()
PatchAll()
Build_VC10_64()
RunAll()
Clean()
HgUpdate33()
| Python |
import fileinput
import os
import glob
import shutil
from PIL import Image
import subprocess
import sys
import math
CMakePath = r'C:\Program Files (x86)\CMake 2.8\bin\cmake.exe'
VisualStudio10Path = r'C:\Program Files (x86)\Microsoft Visual Studio 10.0\Common7\IDE\devenv.com'
def SetCMakePath(path):
glo... | Python |
from utils import *
HgUpdate33()
version = raw_input("Enter version number (ex : 0003) :")
Package("OpenGL-tutorial_v"+version+"_33.zip");
HgUpdate21()
Package("OpenGL-tutorial_v"+version+"_21.zip");
HgUpdate33()
| Python |
from utils import *
Clean()
HgUpdate21()
PatchAll()
Build_VC10_64()
OptimusForceIntel()
RunAll()
OptimusForceNVIDIA()
RunAll()
Clean()
HgUpdate33()
PatchAll()
Build_VC10_64()
OptimusForceNVIDIA()
RunAll()
Clean()
HgUpdate33()
| Python |
theLongDescription = """P4 does Bayesian and maximum likelihood phylogenetic analyses on
molecular sequences. It's specialty is that you can use heterogeneous
models, where the model parameters can differ in different parts of
the tree, or over different parts of the data.
"""
from distutils.core import setup, Extens... | Python |
import sys,string,os
#from utilities import fixCharsForLatex
#from Node import NodeGram
from DistanceMatrix import DistanceMatrix
from Glitch import Glitch
import func
from Var import var
def patristicDistanceMatrix(self):
"""Matrix of distances along tree path.
This method sums the branch lengths between eac... | Python |
from SequenceList import SequenceList
from NexusSets import NexusSets
from Glitch import Glitch
import string,copy, os
import func
from Var import var
if var.usePfAndNumpy:
from Part import Part
longMessage1 = """
You may want to do the alignment method
checkForDuplicateSequences(removeDupes=True, makeDict=... | Python |
import random,math
import pf,func
from Var import var
from Glitch import Glitch
import sys
#localCalls = 0
def proposeRoot3(self, theProposal):
"""For non-biRooted trees. Root on another internal node."""
internalsNoRoot = [n for n in self.propTree.iterInternalsNoRoot()]
if len(internalsNoRoot):
... | Python |
"""Peter's re-write of word completion for GNU readline
The foundation for this is the wonderful rlcompleter module that comes
with Python. It has been re-written to make it more informative.
When you import this module, it does this for you:
readline.parse_and_bind("tab: complete")
which makes the tab key the c... | Python |
import func
from Var import var
class NodeBranchPart(object):
def __init__(self):
self.rMatrixNum = -1
self.gdasrvNum = -1
#self.bigP = None
class NodeBranch(object):
def __init__(self):
self.len = 0.1
#self.textDrawSymbol = '-' # See var.modelSymbols for some alterna... | Python |
"""Various functions."""
import os
import sys
import re
import string
import math
import cStringIO
import random
import glob
import time
import types
from Var import var
from SequenceList import Sequence,SequenceList
from Alignment import Alignment
from Nexus import Nexus
from Tree import Tree
from Node import Node
fr... | Python |
from Glitch import Glitch
from Tree import Tree
import Nexus
from Var import var
import os,string,cStringIO,copy
class PosteriorSamples(object):
"""A container for mcmc samples from files.
This would be useful if you wanted to do eg posterior predictive
simulations based on the posterior distribution fro... | Python |
import sys
import os
import func
from Var import var
from Glitch import Glitch
from subprocess import Popen,PIPE
class DistanceMatrix:
"""A container for distances between sequences (usually).
The numbers are in self.matrix, a self.dim * self.dim list of lists.
There is also a self.names attribute, w... | Python |
from Glitch import Glitch
from TreePartitions import TreePartitions
from Tree import Tree
from Trees import Trees
from func import read, var
import sys, csv, random
from math import log, factorial, floor
ROOT_NODE_NAME = 'A_NAME_NOT_EASILY_FOUND_IN_A_TREE'
TAXON_SEPARATOR = ':'
class TreeSubsets(object):
... | Python |
import string
from Tree import Tree
from Node import Node
class Aho(object):
def isTripletCompatibleWithSet(self, set, triplet):
list = []
list.append(triplet)
for t in set:
list.append(t)
return self.isListCompatible(list)
def isSetCompatible(self, set):
... | Python |
import os,sys,math,string
import func
from Glitch import Glitch
from Var import var
if var.usePfAndNumpy:
import numpy
class Numbers(object):
"""Simple 1-dimensional data handling. Emphasis on 'simple'.
Feed this a list of floats, or the name of a file containing
floats. If it is a file, lines a... | Python |
import string,array
import func
from Var import var
from Alignment import Alignment
#from NexusToken import * # nextTok() et al
from NexusSets import NexusSets
from SequenceList import Sequence
from Glitch import Glitch
# Some definitions from the MadSwofMad Syst Biol Nexus format paper (MSM97).
#
# Punctuation: '\(\)... | Python |
#import string
import textwrap
import types
class Glitch(Exception):
"""A class for exceptions in p4.
You can raise this with a string, or a list of strings. If its
a single string, it gets wrapped. If its a list of 2 strings, the
first one is output flush and unwrapped, and the second is
indent... | Python |
import os
import func
import cPickle
import math
import numpy
import glob
from Glitch import Glitch
class McmcCheckPointReader(object):
"""Read in and display mcmc_checkPoint files.
Three options--
To read in a specific checkpoint file, specify the file name by
fName=whatever
To read in the ... | Python |
from Var import var
from DistanceMatrix import DistanceMatrix
from Glitch import Glitch
import numpy, numpy.linalg
import math,string
#import func # temp
import pf
def logDet(self, correction='TK02', doPInvarOfConstants=True, pInvar=None, pInvarOfConstants=None, missingCharacterStrategy='fudge', minCompCount=1, nonPo... | Python |
import func,pf
from Var import var
import math,random,copy,numpy
from Glitch import Glitch
import sys
class Chain(object):
# Import methods in other files
from Chain_propose1 import proposeRoot3, proposeBrLen, proposeLocal, proposeETBR_Blaise, proposeETBR, proposePolytomy, proposeAddEdge, _getCandidateNodes... | Python |
import types,string,cStringIO,sys,os
from Tree import Tree
from Node import Node,NodePart,NodeBranchPart
from Trees import Trees
from Nexus import Nexus,NexusData
from Glitch import Glitch
#from NexusToken import nextTok,safeNextTok,nexusSkipPastNextSemiColon
import NexusToken # needed for cStrings
#import NexusToken... | Python |
from Glitch import Glitch
import func
from func import read
from Var import var
from Tree import Tree
from Node import Node,NodeBranch
import sys
import random, copy
import types
from Numbers import Numbers
import math
# I suppose if there are input trees whose tax sets are equal to or subsets
# of the current bigT, ... | Python |
import os,string,sys,types
from Var import var
from Glitch import Glitch
############################################################################
#
# Token generation stuff.
#
# safeNextTok() # checks for None, and dies
# nextTok() # may return None
#
# Handling comments:
# - Behaviour is unde... | Python |
import string,os
from Var import var
from Glitch import Glitch
"""This class is used by Tree.draw(), Tree.eps(), and Tree.svg().
This week, there is no 'user-interface' for it, other than those two
methods."""
class TreePicture(object):
def __init__(self, theTree = None):
gm = ['TreePicture.__init__()']
... | Python |
import string
import pf
from Glitch import Glitch
from Var import var
from Part import Part
def _initParts(self):
gm = ['Alignment._initParts()']
if len(self.parts):
for p in self.parts:
del(p)
self.parts = []
if self.equates:
eqSymb = self.equates.keys()
eqSymb.sor... | Python |
import string,sys,os
import func
from Var import var
from SequenceList import Sequence
from Glitch import Glitch
def readOpenPhylipFile(self, flob, nTax, nChar):
"""Read flob to get data in phylip format.
The user would generally not need to call this method directly.
It is called by read() etc. """
... | Python |
from Glitch import Glitch
from Tree import Tree
import Nexus
from Var import var
import sys
import os,string,cStringIO,copy
class TreeFileLite(object):
"""Get trees in big files without reading the lot into memory.
P4 Tree objects are a little obese, and large tree files will
flood your RAM. This class ... | Python |
import time,os,string,glob,sys,math
import pf,func
from Glitch import Glitch
##Ignore
def _fixFileName(fName):
if fName.count('.') or fName.count(' '):
fName = list(fName)
for i in range(len(fName)):
theChar = fName[i]
if theChar == '.' or theChar == ' ':
fNa... | Python |
import time,os,string,sys
import pf,func
from Var import var
from Glitch import Glitch
import numpy
#def __del__(self, freeTree=pf.p4_freeTree, freeNode=pf.p4_freeNode):
#def __del__(self, freeTree=pf.p4_freeTree, dp_freeTree = pf.dp_freeTree, mysys=sys):
#def __del__(self, freeTree=pf.p4_freeTree, dp_freeTree = pf.... | Python |
"""Stuff to ignore when using p3rlcompleter.
The p3rlcompleter is meant to be for the user, to expose the user
interface. As such, it generally does not want to display all that
dir() can provide. This file is one way to tell the completer to
ignore. The other way is with ##Ignore comments in the method doc."""
co... | Python |
versionNumberString = '0.93' # a string that can be turned into a number
versionNumber = float(versionNumberString)
versionNumberModifier = ' [2015-04-08]' # a string
versionString = '%s%s' % (versionNumberString, versionNumberModifier)
dateString = "8 April, 2015"
| Python |
import func
from Glitch import Glitch
class Constraints(object):
"""A container for tree topology constraints.
taxNames
A list of taxNames in the same order as in
the data or alignment, and the same order as
in other tree or trees objects.
constraintTree
... | Python |
from Alignment import Alignment
import sys,time,os
import pf,func
from Var import var
from Glitch import Glitch
class Data:
"""All the alignments that you want to work with, in one place.
Initialize this with one of
- nothing (or None),
- a list of Alignment objects, or
- a single Alignment... | Python |
import pf,func
from Var import var
import math,random,string,sys,time,copy,os,cPickle,types
from Chain import Chain
from Glitch import Glitch
from TreePartitions import TreePartitions
from Constraints import Constraints
import datetime
# for proposal probs
fudgeFactor = {}
fudgeFactor['local'] = 1.0
fudgeFactor['brLen... | Python |
"""More Chain 'propose' methods."""
import func,pf
from Var import var
from Glitch import Glitch
import math,random
import numpy
def proposeCompWithSlider(self, theProposal):
gm = ['Chain.proposeCompWithSlider()']
mt = self.propTree.model.parts[theProposal.pNum].comps[theProposal.mtNum]
dim = self.propT... | Python |
# This is STMcmc, for super tree mcmc.
# Started 18 March 2011, first commit 22 March 2011.
import pf,func
from Var import var
import math,random,string,sys,time,copy,os,cPickle,types,glob
import numpy as np
from Glitch import Glitch
from TreePartitions import TreePartitions
from Constraints import Constraints
from Tr... | Python |
from Var import var
import string,math,random,copy,os
import types
import func
from Glitch import Glitch
from Node import Node,NodeBranch
if var.usePfAndNumpy:
import numpy
def node(self, specifier):
"""Get a node based on a specifier.
The *specifier* can be a nodeNum, name, or node object.
"""
n... | Python |
import os,sys,string,array,types
import copy
from Var import var
# Don't bother with NexusToken2, cuz sets blocks are small
from NexusToken import nexusSkipPastNextSemiColon,safeNextTok
import func
from Glitch import Glitch
## [Examples from the paup manual,
## but note the bad charpartition subset names '1' a... | Python |
import time,os,sys,random,string,math
from p4.Tree import Tree
from Glitch import Glitch
try:
from Tkinter import *
except ImportError:
raise Glitch, "TV and BTV need Tkinter, and it does not seem to be installed."
def randomColour():
# Colours are #xxyyzz, where each pair is a hex number. Colours
# ... | Python |
# -*- coding: latin-1 -*-
import sys,os
#import numpy
from Glitch import Glitch
## A Ala Alanine
## R Arg Arginine
## N Asn Asparagine
## D Asp Aspartic acid
## C Cys Cy... | Python |
# A couple of Trees methods. The first one works.
from Glitch import Glitch
def trackSplitsFromTree(self, theTree, windowSize=200, stride=100, fName='trackSplitsOut.py'):
"""See how slits from theTree changes over the trees in self.
This looks at how some splits change over the trees in self (self
is a ... | Python |
import pf, sys, random, math, types
import func
from Var import var
from Glitch import Glitch
import numpy
class BigQAndEig(object): # not used
def __init__(self, dim, comp, rMatrix):
self.dim = dim
self.comp = comp
self.rMatrix = rMatrix
self.bigR = numpy.zeros((dim,dim), numpy.fl... | Python |
import sys, csv, random
from Tree import Tree
from func import read
from Var import var
from Glitch import Glitch
from p4.ReducedStrictConsensus import Intersection, TreeBuilderFromSplits
class SuperTreeInputTrees(object):
def __init__(self, inputTree, distributionTrees=None):
"""
SuperTreeInputTr... | Python |
import sys,re,string,os,cStringIO
import func
import copy
from Var import var
from Glitch import Glitch
from subprocess import Popen,PIPE
class Sequence(object):
"""A container for a single molecular sequence.
We have
- **sequence** a string, the molecular sequence
- **name** a string, the name
... | Python |
from Glitch import Glitch
import pf,func
from Var import var
class Part:
def __del__(self, freePart=pf.freePart):
self.alignment = None
#print "Part.__del__() here. cPart=%s" % self.cPart
if self.cPart:
#print "Part.__del__() about to free part %i" % self.cPart
f... | Python |
"""Various Tree methods for defining models."""
from Data import Data
from Alignment import Part
from Node import NodeBranch,NodePart,NodeBranchPart
from Model import Model
from Glitch import Glitch
import random
import func,sys,math,pf
from Var import var
import numpy
def _setData(self, theData):
"""Sets self.da... | Python |
import re,sys,string,array,types,os
import func
from Var import var
from Alignment import ExcludeDelete
from DistanceMatrix import DistanceMatrix
from SequenceList import Sequence
from Glitch import Glitch
import numpy as np
from NexusSets import CharSet
import subprocess
cListPat = re.compile('(\d+)-?(.+)?')
cList2Pa... | Python |
import sys,os,math
import func
from Var import var
from Tree import Tree
from Glitch import Glitch
class Trees(object):
"""A bunch of trees, all with the same taxNames.
This class would be good for doing things that you might want to
do with a bunch of trees rather than just a single tree. For
examp... | Python |
"""Maximum likelihood and Bayesian phylogenetic analysis.
P4 is a phylogenetic toolkit that does Bayesian and maximum likelihood
phylogenetic analyses of molecular sequences. It's specialty is that
you can use heterogeneous models, where the model parameters can
differ in different parts of the tree, or over differen... | Python |
from Tree import Tree
from Node import Node
from func import read
from Var import var
from Glitch import Glitch
import sys, csv, operator, time
multiProcessing = False
try:
from multiprocessing import Process as Process
from multiprocessing import Queue, cpu_count
cpu_count = cpu_count()
multiPro... | Python |
import string
import sys
class GeneticCode:
"""A container for NCBI translation tables.
See the ncbi translation tables, which this week are at
http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi?mode=c
(If they move, poke around the 'taxonomy browser' area.)
This week we have
- **1** ... | Python |
import sys,string,types,cStringIO,math,copy
import func
from Var import var
from Glitch import Glitch
if var.usePfAndNumpy:
import numpy
from Model import Model
from Node import Node,NodePart,NodeBranchPart
import NexusToken
class Tree(object):
"""A phylogenetic tree.
**Some instance variables**
* ... | Python |
class QuartetSet(object):
def __init__(self):
self.quartetLogic = QuartetLogic()
self.quartetSet = DualIndexNoDupes()
self.closureSet = DualIndexNoDupes()
self.conflictingSet = DualIndexNoDupes()
self.compatibleSet = DualIndexNoDupes()
self.compatibl... | Python |
# Matrix representation / parsimony.
from Tree import Tree
from Alignment import Alignment
from Glitch import Glitch
import func
from NexusSets import CharSet
from Node import Node
from TreePartitions import TreePartitions
def mrpSlice(self, pos, zeroBasedNumbering=True):
"""Pretty-print a mrp site, with no '?'... | Python |
import pf
from Var import var
import numpy,string
from Glitch import Glitch
"""A faster version of nextTok(), using memory allocated (once only)
using numpy, and using functions written in C. The slow, pure
python module is NexusToken.py. This version is about twice as fast.
Which one is used is under the control o... | Python |
# Make the rst file scripts.rst
from p4.Glitch import Glitch
start = """
========================
Scripts for common tasks
========================
This is a cook book of reminders, suggestions, and points of departure
for customization. You can copy and paste these into your own files.
Or you can use the p4 recipes... | Python |
def hook(self, theTree):
"""Write stuff during the MCMC.
You get the stuff from the Mcmc (which is self) or from the
current tree. Write whatever you like, however you like it. You
will need to know how to get it, tho ...
In this demo, I write out the gen number+1, the composition, and
the ... | Python |
read("../d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=1)
t.setPInvar(free=0, val=0.0)
m = Mcmc(t, nChains=1, runNum=0, sampleInterval=10, checkPointInterval=2000)
# Set up a strong prior on polytomi... | Python |
tp = TreePartitions("mcmc_trees_0.nex", skip=200)
t = tp.consensus()
# put support on node.name's, for the text drawing
for n in t.iterInternalsNoRoot():
n.name = "%.0f" % (100. * n.br.support)
t.draw()
# Save it
t.writeNexus(fName='cons.nex')
| Python |
read("../../K_thermus/noTRuberNoGapsNoAmbiguities.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=0, val=0.0)
m = Mcmc(t, nChains=1, runNum=0, sampleInterv... | Python |
# Get the test quantity, X^2, from the original data.
read("../../K_thermus/noTRuberNoGapsNoAmbiguities.nex")
d = Data()
ret = d.compoChiSquaredTest()
#print ret
originalStat = ret[0][0]
# Get the sim stats
n = Numbers('mcmc_sims_0', col=1, skip=500)
# Evaluate the tail area probability
n.tailAreaProbability(original... | Python |
read("d.nex")
read('sets.nex')
a = var.alignments[0]
a.setCharPartition('p1')
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
pNum = 0
t.newComp(partNum=pNum, free=1, spec='empirical')
t.newRMatrix(partNum=pNum, free=0, spec='wag')
t.setNGammaCat(partNum=pNum, nGammaCat=4)
t.newGdasrv(partNum=pNum, free... | Python |
nTax = 5
taxNames = list(string.uppercase[:nTax])
a = func.newEmptyAlignment(dataType='protein', taxNames=taxNames, length=200)
b = func.newEmptyAlignment(dataType='protein', taxNames=taxNames, length=133)
d = Data([a,b])
t = func.randomTree(taxNames=taxNames)
t.data = d
pNum = 0
t.newComp(partNum=pNum, free=0, spec=... | Python |
from p4.PosteriorSamples import PosteriorSamples
read("d.nex")
read('sets.nex')
a = var.alignments[0]
a.setCharPartition('p1')
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
pNum=0
t.newComp(partNum=pNum, free=1, spec='wag')
t.newRMatrix(partNum=pNum, free=0, spec='wag')
t.setNGammaCat(partNum=pNum, n... | Python |
read("d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=0, val=0.0)
m = Mcmc(t, nChains=1, runNum=0, sampleInterval=100, checkPointInterval=None)
m.run(200... | Python |
nTax = 11
taxNames = list(string.uppercase[:nTax])
a = func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=200)
d = Data([a])
t = func.randomTree(taxNames=taxNames)
t.data = d
t.newComp(free=0, spec='specified', val=[0.4, 0.3, 0.0])
t.newRMatrix(free=0, spec='specified', val=[2., 3., 4., 5., 6., 7.])
t.s... | Python |
from p4.PosteriorSamples import PosteriorSamples
read("d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='specified', val=[2., 3., 4., 5., 6., 7.])
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=0, val=0.5)
t.setPInvar(free=0, val=0.0)
f... | Python |
os.system("rm -f mcmc*")
read("recoded.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=1, val=0.2)
m = Mcmc(t, nChains=4, runNum=0, sampleInterval=10, chec... | Python |
read('protein.nex')
a=var.alignments[0]
a.recodeDayhoff()
a.writeNexus('recoded.nex')
| Python |
read("../d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=1, val=0.2)
m = Mcmc(t, nChains=4, runNum=0, sampleInterval=10, checkPointInterval=2000)
m.run(4... | Python |
tp = TreePartitions("mcmc_trees_0.nex", skip=200)
t = tp.consensus()
# put support on node.name's, for the text drawing
for n in t.iterInternalsNoRoot():
n.name = "%.0f" % (100. * n.br.support)
t.draw()
# Save it
t.writeNexus(fName='cons.nex')
| Python |
read("mcmc_trees_0.nex")
tt = Trees()
var.trees = []
read('cons.nex')
t = var.trees[0]
tt.trackSplitsFromTree(t)
| Python |
theRunNum = int(var.argvAfterDoubleDash[0])
read("../d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=1)
#t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=0, val=0.0)
m = Mcmc(t, nChains=4, runNum=theRunNu... | Python |
skip=200
tp = TreePartitions("mcmc_trees_0.nex", skip=skip)
tp.read("mcmc_trees_1.nex", skip=skip)
tp.dump()
t = tp.consensus()
# put support on node.name's, for the text drawing
for n in t.iterInternalsNoRoot():
n.name = "%.0f" % (100. * n.br.support)
t.draw()
# Save it
t.writeNexus(fName='cons.nex')
| Python |
read("../d.nex")
d = Data()
m = func.unPickleMcmc(0, d)
m.run(2000)
m = func.unPickleMcmc(1, d)
m.run(2000)
#n = Numbers('mcmc_likes_0', col=1)
#n.plot()
| Python |
cpr = McmcCheckPointReader()
cpr.writeProposalAcceptances()
cpr.writeSwapMatrices()
#cpr.writeProposalProbs()
m = cpr.mm[0]
m.tunings.dump(advice=False)
cpr.compareSplits(2, 3)
print "\n\nComparing all splits from all pairs of checkPoints ..."
cpr.compareSplitsAll()
| Python |
read("../d.nex")
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=1, val=0.2)
m = Mcmc(t, nChains=4, runNum=0, sampleInterval=10, checkPointInterval=2000)
m.autoT... | Python |
tp = TreePartitions("mcmc_trees_0.nex", skip=200)
t = tp.consensus()
# put support on node.name's, for the text drawing
for n in t.iterInternalsNoRoot():
n.name = "%.0f" % (100. * n.br.support)
t.draw()
# Save it
t.writeNexus(fName='cons.nex')
| Python |
allTaxNames = ['t0', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 't8', 't9', 't10', 't11', 't12', 't13', 't14', 't15', 't16', 't17', 't18', 't19', 't20', 't21', 't22', 't23', 't24', 't25', 't26', 't27', 't28', 't29', 't30', 't31', 't32', 't33', 't34', 't35', 't36', 't37', 't38', 't39', 't40', 't41', 't42', 't43', 't44']
... | Python |
nTax = int(var.argvAfterDoubleDash[0])
nTrees = int(var.argvAfterDoubleDash[1])
#taxNames = list(string.uppercase[:20])
t = func.randomTree(nTax=nTax)
#t.draw()
a = func.newEmptyAlignment(dataType='dna', taxNames=t.taxNames, length=10)
t.data = Data([a])
t.newComp(spec='equal')
t.newRMatrix()
t.setPInvar()
t.setNGamma... | Python |
read('paupConTree.nex')
t = var.trees[0]
# The only point of the data is to get a taxNames list.
read('d.nex')
d = Data()
t.taxNames = d.taxNames
t.readBipartitionsFromPaupLogFile('paupLog')
# The support gets put in node.br.support, as a float from 0-1. To
# see it in a drawing or write it in newick format, we mov... | Python |
tp = TreePartitions('tt.nex')
tp.writeSplits() # If you like this sort of thing ...
t = tp.consensus()
t.draw()
| Python |
read(""" 2 2
one
ac
two
gt
""")
read('(one,two);')
t = var.trees[0]
t.data = Data()
t.newComp()
t.newRMatrix()
t.setPInvar()
t.calcLogLike()
| Python |
taxNames = list(string.uppercase[:7])
for i in range(6):
t = func.randomTree(taxNames)
t.name = 't%i' % (i + 1)
var.trees.append(t)
tt = Trees(taxNames=taxNames)
dm = tt.topologyDistanceMatrix('wrf')
dm.writeNexus()
| Python |
read('t.nex')
t1 = var.trees[0]
t2 = var.trees[1]
# See page 532 in Felsenstein
print "The 'symmetric distance' = ", t1.topologyDistance(t2, metric='sd')
print "The 'weighted Robinson Foulds distance' = ", t1.topologyDistance(t2, metric='wrf')
ret = t1.topologyDistance(t2, metric='bld')
print "The 'branch score' = %... | Python |
var.warnReadNoFile = 0
var.verboseRead = 0
read('d.nex')
d = Data()
taxNames = list(string.uppercase[:5])
read('((A, B), C, (D, E));')
read('((D, E), B, (C, A));')
read('((C, E), (B, D), A);')
for i in range(len(var.trees)):
var.trees[i].name = 't%i' % (i + 1)
for t in var.trees:
t.taxNames = taxNames
t.d... | Python |
var.warnReadNoFile = 0
var.verboseRead = 0
read('((D:0.4, E:0.3):0.03, B:0.5, (C:0.3, A:0.4):0.03);')
taxNames = list(string.uppercase[:5])
var.alignments.append(func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=500))
d = Data()
t = var.trees[0]
t.data = d
t.taxNames = taxNames
t.newComp(partNum=0, free... | Python |
read('d.nex')
d = Data()
for i in range(3):
read('t%i.p4_tPickle' % (i + 1))
tt = Trees()
tt.data = d
if 1:
tt.consel()
else:
tt.rell()
| Python |
var.verboseRead = 0
var.warnReadNoFile = 0
if 1:
read('((A, B), C, (D, E));')
read('((A, B), D, (E, C));')
read('((C, A), (D, B), E);')
taxNames = list(string.uppercase[:5])
theSplitTax = ['A', 'C']
else:
taxNames = list(string.uppercase[:7])
for i in range(30):
var.trees.append(fun... | Python |
var.warnReadNoFile = 0
var.verboseRead = 0
func.reseedCRandomizer(os.getpid())
nTax = 5
taxNames = list(string.uppercase[:nTax])
a = func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=400)
d = Data([a])
t = func.randomTree(taxNames=taxNames)
t.data = d
c1 = t.newComp(free=1, spec='specified', val=[0.1, ... | Python |
import sys
nTrees = 15
nSites = 400
fIn = file('siteLikes')
fOut = file('siteLikes.txt', 'w')
fIn.readline() # skip the first line
fOut.write('Tree\t-lnL\tSite\t-lnL\n')
for i in range(nTrees):
for j in range(nSites):
aLine = fIn.readline()
if not aLine:
print 'no workee! Ran out of l... | Python |
from p4.MRP import mrp
read('inTrees.phy')
a = mrp(var.trees)
a.writeNexus('mr.nex')
| Python |
tp = TreePartitions("mcmc_trees_0.nex", skip=500)
tp.read("mcmc_trees_1.nex", skip=500)
t = tp.consensus(minimumProportion=0.5)
for n in t.iterInternalsNoRoot():
n.name = "%.0f" % (100. * n.br.support)
t.draw()
t.name = 'stMcmc'
t.writeNexus('stMcmcCons.nex')
| Python |
n = Numbers('mcmc_likes_0', col=1)
n.plot()
if os.path.isfile('mcmc_prams_0'):
n = Numbers('mcmc_prams_0', col=1)
n.plot()
| Python |
read('inTrees.phy')
inTrees = var.trees
var.trees = []
read('stMcmcCons.nex')
read('mrpMajRuleConsTree.nex')
read('mrpStrictConsTree.nex')
var.trees[1].name = 'mrpMajRule'
var.trees[2].name = 'mrpStrict'
tt = Trees(taxNames=var.trees[0].taxNames)
tt.inputTreesToSuperTreeDistances(inTrees)
| Python |
#os.system("rm -f mcmc*")
# (self, inTrees, modelName='SR2008_rf_aZ', beta=1.0, stRFCalc='purePython1', runNum=0, sampleInterval=100, checkPointInterval=None)
# Choose one of these, the fastest available
myCalc='purePython1'
myCalc='bitarray'
#myCalc='fastReducedRF'
read('inTrees.phy')
stm = STMcmc(var.trees, modelN... | Python |
Subsets and Splits
SQL Console for ajibawa-2023/Python-Code-Large
Provides a useful breakdown of language distribution in the training data, showing which languages have the most samples and helping identify potential imbalances across different language groups.