def processFunc():
# remove old query tempfile if one exists
if closure["oldtmp"] != None:
os.remove(closure["oldtmp"])
elapse = util.toc()
closure["time"] += elapse
util.log("blasted %d of %d sequences (%.1f%%), elapse %.0f m, left %.0f m" % (
closure["index"], len(seqs.keys()),
100 * float(closure["index"]) / len(seqs.keys()),
closure["time"] / 60.0,
elapse / split * (len(seqs.keys()) - closure["index"]) / 60.0))
util.tic()
# find new subset of query sequences
i = closure["index"]
names = seqs.keys()[i:i+split]
# if no more sequences then quit
if len(names) == 0:
return False
# start blast
tmpfile = util.tempfile(".", "blastp", ".fasta")
seqs.write(tmpfile, names = names)
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 -e .1 %s" % \
(prog, databaseFile, tmpfile, options))
# update variables
closure["oldtmp"] = tmpfile
closure["index"] = i + split
return pipe
def processFunc():
# remove old query tempfile if one exists
if closure["oldtmp"] != None:
os.remove(closure["oldtmp"])
elapse = util.toc()
closure["time"] += elapse
util.log("blasted %d of %d sequences (%.1f%%), elapse %.0f m, left %.0f m" % (
closure["index"], len(seqs.keys()),
100 * float(closure["index"]) / len(seqs.keys()),
closure["time"] / 60.0,
elapse / split * (len(seqs.keys()) - closure["index"]) / 60.0))
util.tic()
# find new subset of query sequences
i = closure["index"]
names = seqs.keys()[i:i+split]
# if no more sequences then quit
if len(names) == 0:
return False
# start blast
tmpfile = util.tempfile(".", "blastp", ".fasta")
seqs.write(tmpfile, names = names)
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 -e .1 %s" % \
(prog, databaseFile, tmpfile, options))
# update variables
closure["oldtmp"] = tmpfile
closure["index"] = i + split
return pipe
def update(self, stream=None, msg="progress %2.0f%%"):
self.pos += 1
if (self.pos > self.prog):
self.prog += self.step * self.end
if stream is not None:
print>>stream, msg % (100 * self.pos / self.end)
else:
util.log(msg % (100 * self.pos / self.end))
def update(self, stream=None, msg="progress %2.0f%%"):
self.pos += 1
if (self.pos > self.prog):
self.prog += self.step * self.end
if stream != None:
print >> stream, msg % (100 * self.pos / self.end)
else:
util.log(msg % (100 * self.pos / self.end))
def __init__(self, *args, **dargs):
Progress.__init__(self, *args)
self.width = 60
self.step = 1 / self.width
self.bar = 0
if "title" in dargs:
title = dargs["title"]
else:
title = "progress"
util.log("+-" + title + ("-"*(self.width-len(title)-1)) + "+")
util.indent()
util.logExact("|")
self.printBar()
def __init__(self, *args, **dargs):
Progress.__init__(self, *args)
self.width = 60
self.step = 1 / self.width
self.bar = 0
if "title" in dargs:
title = dargs["title"]
else:
title = "progress"
util.log("+-" + title + ("-" * (self.width - len(title) - 1)) + "+")
util.indent()
util.logExact("|")
self.printBar()
def blast(prog, databaseFile, queryFile, options="", split=100, resume=None):
"""Executes blastp in several smaller batches"""
if not split:
# do blasting in one call
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 %s" % (prog, databaseFile, queryFile, options))
return BlastReader(pipe)
else:
# NOTE: split query file into about 100 sequences each
# this is a work around for ncbi blastall 2.2.10 problem with outputing
# in -m 8 mode. error was "BioseqFindFunc: couldn't uncache"
seqs = fasta.read_fasta(queryFile)
closure = {"index": 0, "oldtmp": None, "time": 0.0}
if resume:
try:
closure["index"] = seqs.keys().index(resume)
util.log("resuming with query '%s' (%d of %d)" % ((resume, closure["index"], len(seqs.keys()))))
except ValueError:
raise Exception("Could not resume from last query sequence '%s'" % resume)
def processFunc():
# remove old query tempfile if one exists
if closure["oldtmp"] is not None:
os.remove(closure["oldtmp"])
elapse = util.toc()
closure["time"] += elapse
util.log(
"blasted %d of %d sequences (%.1f%%), "
"elapse %.0f m, left %.0f m"
% (
closure["index"],
len(seqs.keys()),
100 * float(closure["index"]) / len(seqs.keys()),
closure["time"] / 60.0,
elapse / split * (len(seqs.keys()) - closure["index"]) / 60.0,
)
)
util.tic()
# find new subset of query sequences
i = closure["index"]
names = seqs.keys()[i : i + split]
# if no more sequences then quit
if len(names) == 0:
return False
# start blast
tmpfile = util.tempfile(".", "blastp", ".fasta")
seqs.write(tmpfile, names=names)
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 -e .1 %s" % (prog, databaseFile, tmpfile, options))
# update variables
closure["oldtmp"] = tmpfile
closure["index"] = i + split
return pipe
filename = processFunc()
if filename:
return BlastReader(filename, processFunc)
else:
return BlastReader(os.popen("less"))
def blast(prog, databaseFile, queryFile, options="", split=100, resume=None):
"""Executes blastp in several smaller batches"""
if not split:
# do blasting in one call
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 %s" %
(prog, databaseFile, queryFile, options))
return BlastReader(pipe)
else:
# NOTE: split query file into about 100 sequences each
# this is a work around for ncbi blastall 2.2.10 problem with outputing
# in -m 8 mode. error was "BioseqFindFunc: couldn't uncache"
seqs = fasta.read_fasta(queryFile)
closure = {
"index": 0,
"oldtmp": None,
"time": 0.0
}
if resume:
try:
closure["index"] = seqs.keys().index(resume)
util.log("resuming with query '%s' (%d of %d)" % (
(resume, closure["index"], len(seqs.keys()))))
except ValueError:
raise Exception(
"Could not resume from last query sequence '%s'" % resume)
def processFunc():
# remove old query tempfile if one exists
if closure["oldtmp"] is not None:
os.remove(closure["oldtmp"])
elapse = util.toc()
closure["time"] += elapse
util.log(
"blasted %d of %d sequences (%.1f%%), "
"elapse %.0f m, left %.0f m" % (
closure["index"], len(seqs.keys()),
100 * float(closure["index"]) / len(seqs.keys()),
closure["time"] / 60.0,
elapse / split *
(len(seqs.keys()) - closure["index"]) / 60.0))
util.tic()
# find new subset of query sequences
i = closure["index"]
names = seqs.keys()[i:i+split]
# if no more sequences then quit
if len(names) == 0:
return False
# start blast
tmpfile = util.tempfile(".", "blastp", ".fasta")
seqs.write(tmpfile, names=names)
pipe = os.popen("blastall -p %s -d %s -i %s -m 8 -e .1 %s" %
(prog, databaseFile, tmpfile, options))
# update variables
closure["oldtmp"] = tmpfile
closure["index"] = i + split
return pipe
filename = processFunc()
if filename:
return BlastReader(filename, processFunc)
else:
return BlastReader(os.popen("less"))
adef = raxml.new_analdef()
raxml.init_adef(adef)
tr = raxml.new_tree()
cmd = "raxmlHPC -t %s -s %s %s" % (treefile, seqfile, options.extra)
raxml.init_program(adef, tr, cmd.split(" "))
util.tic("Optimizing model...")
raxml.optimize_model(adef, tr)
util.toc()
# draw_raxml_tree(tr, adef)
util.tic("Getting parameters for LH...")
bestVector, bestLH, weightSum = raxml.compute_best_LH(tr)
util.log("bestLH: %.3f" % bestLH)
util.toc()
tree = treelib.read_tree(treefile)
for node in tree:
node.dist = 0
if "boot" in node.data:
del node.data["boot"]
treehash = phylo.hash_tree(treelib.unroot(tree, newCopy=True))
treehashes = set([treehash])
for i in xrange(options.niter):
while treehash in treehashes:
util.log("random spr")
node1, node2 = phylo.propose_random_spr(tree)
phylo.perform_spr(tree, node1, node2)
util.tic("Initializing RAXML and optimizing...")
module = raxml.RAxML()
module.optimize_model(treefile, seqfile, options.extra)
util.toc()
tree = treelib.read_tree(treefile)
for node in tree:
node.dist = 0
if "boot" in node.data:
del node.data["boot"]
treehash = phylo.hash_tree(treelib.unroot(tree, newCopy=True))
treehashes = set([treehash])
for i in xrange(options.niter):
while treehash in treehashes:
util.log("random spr")
node1, node2 = phylo.propose_random_spr(tree)
phylo.perform_spr(tree, node1, node2)
treehash = phylo.hash_tree(treelib.unroot(tree, newCopy=True))
treehashes.add(treehash)
tree.write(out, oneline=True); out.write('\n'); out.flush()
util.tic("Computing LH...")
p, Dlnl = module.compute_lik_test(tree)
util.log("pvalue: %.3f, Dlnl: %.3f" % (p, Dlnl))
util.toc()
if Dlnl <= 0:
util.log("worse likelihood?: %s" % False) # better topology (higher likelihood)
else:
def prob_gene_species_alignment_recon(alnfile,
partfile,
stree,
popsizes,
duprate,
lossrate,
subrate,
beta,
pretime,
premean,
coal_tree,
coal_recon,
nsamples_coal,
locus_tree,
locus_recon,
nsamples_locus,
daughters,
rates,
freqs,
alphas,
threads=1,
seed=ALIGNMENT_SEED,
eps=0.1,
info=None):
"""
Evaluate terms that depend on T^G and R^G.
That is, fix T^L, R^L, and daughters and evaluate the double integral:
int int P(t^L | T^L, R^L, S, theta) * P(T^G, R^G, t^G | t^L, T^L, daughters, R^L, theta) * P(A | T^G, t^G) dt^L dt^G
This is the probability we used in the searching process.
alnfile -- alignment file
partfile -- partition file
stree -- species tree
popsizes -- population sizes in species tree
duprate -- duplication rate
lossrate -- loss rate
subrate -- substitution rate
beta -- regularization parameter
pretime -- starting time before species tree
premean -- mean starting time before species tree
coal_tree -- coalescent tree
coal_recon -- reconciliation of coalescent tree to locus tree
nsamples_coal -- number of times to sample coal times t^G
locus_tree -- locus tree (has dup-loss)
locus_recon -- reconciliation of locus tree to species tree
nsamples_locus -- number of times to sample the locus tree times t^L
daughters -- daughter nodes
rates, freqs, alphas -- optimization parameters
"""
locus_events = phylo.label_events(locus_tree, locus_recon)
# optimize the parameters
# util.tic("optimize parameter")
# rates, freqs, alphas = pllprob.optimize_parameters(alnfile, partfile, coal_tree,
# threads=threads, seed=seed, eps=eps)
# util.toc()
# double integral
double_integral_list = []
double_integral = 0.0
util.tic("recon prob")
for i in xrange(nsamples_locus):
# sample t^L, the unit should be in myr
#util.tic("topo prob")
locus_times = duploss.sample_dup_times(locus_tree,
stree,
locus_recon,
duprate,
lossrate,
pretime,
premean,
events=locus_events)
treelib.set_dists_from_timestamps(locus_tree, locus_times)
# calculate P(T^G, R^G | T^L, t^L, daughters, theta)
topology_prob = prob_locus_coal_recon_topology(coal_tree, coal_recon,
locus_tree, popsizes,
daughters)
#util.toc()
# for a fixed t^L, compute coal_prob
# sample t^G for topology and compute the probabililty of observing the alignment using MonteCarlo integration
coal_prob = 0.0
alignment_prob_MonteCarlo = 0.0
alignment_prob_list = []
# check probability of lineage counts for this locus tree
zero_lineage_prob = False
#util.tic("set times")
for lnode in locus_tree:
lineages = coal.count_lineages_per_branch(coal_tree, coal_recon,
locus_tree)
bottom_num, top_num = lineages[lnode]
if lnode.parent:
T = lnode.dist
else:
T = util.INF
popsizes = popsizes
lineage_prob = prob_coal_counts(bottom_num, top_num, T, popsizes)
# set zero_lineage_prob = TRUE if one lineage returns zero probability
if (lineage_prob == 0.0):
zero_lineage_prob = True
#util.toc()
# if lineage_prob is zero, coal_prob is zero
if zero_lineage_prob:
coal_prob = -float("inf")
# otherwise, we calculate the coal_prob
else:
for j in xrange(nsamples_coal):
# sample coal times and set the coal_tree accordingly
# locus tree branch lengths are in myr
# make sure the input popsizes are scaled to fit the time unit (typically myr)
try:
sample_coal_times_topology(coal_tree, coal_recon,
locus_tree, popsizes)
except (ZeroDivisionError, ValueError):
# bad sample
util.log("bad sample")
alignment_prob = -util.INF
continue
#===============================================================================
# (log) probability of observing the alignment
#util.tic("alignment probability")
# convert branch lengths from myr to sub/site
for node in coal_tree:
node.dist *= subrate
#util.tic("alignment prob")
# set a regularization parameter beta
print beta
alignment_prob = beta * prob_alignment(alnfile,
partfile,
coal_tree,
rates,
freqs,
alphas,
threads=threads,
seed=seed,
eps=eps)
#util.toc()
### util.log("p = %.6f" % alignment_prob)
#util.toc()
#===============================================================================
### util.log(" log p = %.6g" % alignment_prob)
### util.log(" p = %.6g" % exp(alignment_prob))
alignment_prob_list.append(alignment_prob)
### util.log("p = %f" % alignment_prob_MonteCarlo)
# log_sum_exp function exponentiate the log probability of observing alignment,
# add them up, and take log again
if len(alignment_prob_list) == 0:
# all bad samples
alignment_prob_MonteCarlo = -util.INF
else:
alignment_prob_MonteCarlo = log_sum_exp(
alignment_prob_list) - log(nsamples_coal)
# P(T^G, R^G | T^L, t^L, daughters, theta) * $ P(t^G | ~) * P(A | T^G,t^G) dtG
# coal_prob is a log probability
coal_prob += topology_prob + alignment_prob_MonteCarlo
# add coal probability to a list for further processing
double_integral_list.append(coal_prob)
# log_sum_exp function exponentiate the log probability of observing alignment,
# add them up, and take log again
double_integral = log_sum_exp(double_integral_list) - log(
nsamples_locus)
# logging info
if info is not None:
info["topology_prob"] = topology_prob # one sample of t^L
info[
"alignment_prob"] = alignment_prob_MonteCarlo # one sample of t^L, averaged over t^G
info["coal_prob"] = double_integral
util.toc()
return double_integral
