def get_hyphy_debug_info(hyphy_output):
"""
@param hyphy_output: the string representing the hyphy output
@return: a string explaining how the output was interpreted
"""
ns = Hyphy.get_hyphy_namespace(StringIO(hyphy_output))
out = StringIO()
print >> out, 'raw hyphy output:'
print >> out, '---------------------------------------'
print >> out, hyphy_output
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
print >> out, 'processed hyphy output lines:'
print >> out, '---------------------------------------'
for i, line in enumerate(ns.get_processed_lines()):
print >> out, i, ':', line
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
print >> out, 'hyphy namespace object dictionary:'
print >> out, '---------------------------------------'
print >> out, ns.__dict__
print >> out, '---------------------------------------'
return out.getvalue().strip()
def get_hyphy_debug_info(hyphy_output):
"""
@param hyphy_output: the string representing the hyphy output
@return: a string explaining how the output was interpreted
"""
ns = Hyphy.get_hyphy_namespace(StringIO(hyphy_output))
out = StringIO()
print >> out, 'raw hyphy output:'
print >> out, '---------------------------------------'
print >> out, hyphy_output
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
print >> out, 'processed hyphy output lines:'
print >> out, '---------------------------------------'
for i, line in enumerate(ns.get_processed_lines()):
print >> out, i, ':', line
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
print >> out, 'hyphy namespace object dictionary:'
print >> out, '---------------------------------------'
print >> out, ns.__dict__
print >> out, '---------------------------------------'
return out.getvalue().strip()
def get_response_content(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
# read the nexus data
nexus = Nexus.Nexus()
try:
nexus.load(StringIO(fs.nexus))
except Nexus.NexusError as e:
raise HandlingError(e)
# do things in the working directory
with Util.remember_cwd():
os.chdir(Config.data_path)
# create the batch file
with open(hyphy_bf, 'wt') as fout:
print >> fout, hky_hyphy_model
# create the nexus file
with open(hyphy_nexus, 'wt') as fout:
print >> fout, nexus
# run hyphy
cmd = [Config.hyphy_exe_path, hyphy_bf]
p = subprocess.Popen(cmd, close_fds=True, stdout=subprocess.PIPE)
hyphy_output = p.stdout.read()
# read the hyphy output
ns = Hyphy.get_hyphy_namespace(StringIO(hyphy_output))
out = StringIO()
if fs.outdebug:
print >> out, get_hyphy_debug_info(hyphy_output)
print >> out, ''
print >> out, ''
if fs.outmodel:
print >> out, 'hyphy model:'
print >> out, '---------------------------------------'
print >> out, hky_hyphy_model
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
print >> out, 'tree :', ns.givenTree.get_newick_string()
print >> out, ''
print >> out, 'log likelihood :', ns.lnL
print >> out, ''
print >> out, 'kappa :', ns.kappa
print >> out, ''
print >> out, 'A :', ns.eqFreqA
print >> out, 'C :', ns.eqFreqC
print >> out, 'G :', ns.eqFreqG
print >> out, 'T :', ns.eqFreqT
# return the results
return out.getvalue()
def get_response_content(fs):
# read the nexus data
nexus = Nexus.Nexus()
try:
nexus.load(StringIO(fs.nexus))
except Nexus.NexusError as e:
raise HandlingError(e)
# read the hyphy variables
ns = Hyphy.get_hyphy_namespace(StringIO(fs.hyphy))
# get the mixture weights
mixture_weights = [ns.P, 1.0 - ns.P]
# get the nucleotide distributions
nucleotide_distributions = []
for suffix in ("", "2"):
distribution = {}
for nt in list("ACGT"):
var = "eqFreq" + nt + suffix
proportion = getattr(ns, var)
distribution[nt] = proportion
nucleotide_distributions.append(distribution)
# create the normalized nucleotide HKY rate matrix objects
rate_matrix_objects = []
for nt_distribution in nucleotide_distributions:
rate_matrix_object = RateMatrix.get_unscaled_hky85_rate_matrix(nt_distribution, ns.kappa)
rate_matrix_object.normalize()
rate_matrix_objects.append(rate_matrix_object)
# create the mixture proportions
weight_sum = sum(mixture_weights)
mixture_proportions = [weight / weight_sum for weight in mixture_weights]
# scale each rate matrix object by its branch length ratio
for rate_matrix_object, tree_name in zip(rate_matrix_objects, ("givenTree", "otherTree")):
nexus_tree = nexus.tree
hyphy_tree = getattr(ns, tree_name)
try:
nexus_human_node = nexus_tree.get_unique_node("Human")
except Newick.NewickSearchError as e:
raise HandlingError("nexus tree error: %s" % e)
try:
hyphy_human_node = hyphy_tree.get_unique_node("HUMAN")
except Newick.NewickSearchError as e:
raise HandlingError("hyphy tree error: %s" % e)
sf = hyphy_human_node.blen / nexus_human_node.blen
rate_matrix_object.rescale(sf)
# create the mixture model
mixture_model = SubModel.MixtureModel(mixture_proportions, rate_matrix_objects)
# return the results
return do_analysis(mixture_model, nexus.alignment, nexus.tree) + "\n"
def get_response(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
# read the nexus data
nexus = Nexus.Nexus()
try:
nexus.load(StringIO(fs.nexus))
except Nexus.NexusError as e:
raise HandlingError(e)
# move to the data directory
original_directory = os.getcwd()
os.chdir(Config.data_path)
# create the batch file
category_suffixes = [
str(category + 1) for category in range(fs.ncategories)
]
hky_hyphy_model = get_hyphy_model_string(hyphy_nexus, fs.ncategories)
with open(hyphy_bf, 'wt') as fout:
print >> fout, hky_hyphy_model
# create the nexus file
with open(hyphy_nexus, 'wt') as fout:
print >> fout, nexus
# run hyphy
p = subprocess.Popen([Config.hyphy_exe_path, hyphy_bf],
close_fds=True,
stdout=subprocess.PIPE)
hyphy_output = p.stdout.read()
# move back to the original directory
os.chdir(original_directory)
# read the hyphy output
ns = Hyphy.get_hyphy_namespace(StringIO(hyphy_output))
out = StringIO()
if fs.outdebug:
print >> out, get_hyphy_debug_info(hyphy_output)
print >> out, ''
print >> out, ''
if fs.outmodel:
print >> out, 'hyphy model:'
print >> out, '---------------------------------------'
print >> out, hky_hyphy_model
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
if True:
print >> out, 'reformatted hyphy output:'
print >> out, '---------------------------------------'
# show the log likelihood
print >> out, 'log likelihood :', ns.lnL
print >> out, ''
# show the kappa value
print >> out, 'kappa :', ns.kappa
print >> out, ''
category_blocks = []
for suffix in category_suffixes:
block = StringIO()
print >> block, 'mixing proportion :', getattr(
ns, 'catFreq' + suffix)
print >> block, 'tree :', getattr(ns, 'tree' +
suffix).get_newick_string()
for nt in list('ACGT'):
print >> block, nt, ':', getattr(ns, 'eqFreq' + nt + suffix)
category_blocks.append(block.getvalue().strip())
print >> out, '\n\n'.join(category_blocks)
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
if fs.outcheck:
# get the raw matrices
matrices = []
for suffix in category_suffixes:
nt_dict = {}
for nt in list('ACGT'):
nt_dict[nt] = getattr(ns, 'eqFreq' + nt + suffix)
total = float(sum(nt_dict.values()))
nt_dict = dict((k, v / total) for k, v in nt_dict.items())
matrix = RateMatrix.get_unscaled_hky85_rate_matrix(
nt_dict, ns.kappa)
matrices.append(matrix)
raw_matrix_rates = [matrix.get_expected_rate() for matrix in matrices]
category_weights = []
for suffix in category_suffixes:
category_weights.append(getattr(ns, 'catFreq' + suffix))
total = float(sum(category_weights))
category_distribution = [weight / total for weight in category_weights]
mixture_model = SubModel.MixtureModel(category_distribution, matrices)
raw_mixture_rate = mixture_model.get_expected_rate()
# rescale the mixture model
# 0.75 is the expected rate of the initial model
r1 = 0.75
scaling_factor = r1
mixture_model.rescale(scaling_factor)
recomputed_log_likelihood = PhyLikelihood.get_log_likelihood(
nexus.tree, nexus.alignment, mixture_model)
print >> out, 'recomputed likelihood and rates:'
print >> out, '---------------------------------------'
print >> out, 'log likelihood :', recomputed_log_likelihood
print >> out, ''
print >> out, 'rate :', raw_mixture_rate
print >> out, ''
for rate, suffix in zip(raw_matrix_rates, category_suffixes):
print >> out, 'rate%s : %s' % (suffix, rate)
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
# return the response
return out.getvalue()
def get_response(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
# read the nexus data
nexus = Nexus.Nexus()
try:
nexus.load(StringIO(fs.nexus))
except Nexus.NexusError as e:
raise HandlingError(e)
# move to the data directory
original_directory = os.getcwd()
os.chdir(Config.data_path)
# create the batch file
category_suffixes = [str(category+1) for category in range(fs.ncategories)]
hky_hyphy_model = get_hyphy_model_string(hyphy_nexus, fs.ncategories)
with open(hyphy_bf, 'wt') as fout:
print >> fout, hky_hyphy_model
# create the nexus file
with open(hyphy_nexus, 'wt') as fout:
print >> fout, nexus
# run hyphy
p = subprocess.Popen([Config.hyphy_exe_path, hyphy_bf],
close_fds=True, stdout=subprocess.PIPE)
hyphy_output = p.stdout.read()
# move back to the original directory
os.chdir(original_directory)
# read the hyphy output
ns = Hyphy.get_hyphy_namespace(StringIO(hyphy_output))
out = StringIO()
if fs.outdebug:
print >> out, get_hyphy_debug_info(hyphy_output)
print >> out, ''
print >> out, ''
if fs.outmodel:
print >> out, 'hyphy model:'
print >> out, '---------------------------------------'
print >> out, hky_hyphy_model
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
if True:
print >> out, 'reformatted hyphy output:'
print >> out, '---------------------------------------'
# show the log likelihood
print >> out, 'log likelihood :', ns.lnL
print >> out, ''
# show the kappa value
print >> out, 'kappa :', ns.kappa
print >> out, ''
category_blocks = []
for suffix in category_suffixes:
block = StringIO()
print >> block, 'mixing proportion :', getattr(ns, 'catFreq'+suffix)
print >> block, 'tree :', getattr(ns, 'tree'+suffix).get_newick_string()
for nt in list('ACGT'):
print >> block, nt, ':', getattr(ns, 'eqFreq'+nt+suffix)
category_blocks.append(block.getvalue().strip())
print >> out, '\n\n'.join(category_blocks)
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
if fs.outcheck:
# get the raw matrices
matrices = []
for suffix in category_suffixes:
nt_dict = {}
for nt in list('ACGT'):
nt_dict[nt] = getattr(ns, 'eqFreq'+nt+suffix)
total = float(sum(nt_dict.values()))
nt_dict = dict((k, v/total) for k, v in nt_dict.items())
matrix = RateMatrix.get_unscaled_hky85_rate_matrix(
nt_dict, ns.kappa)
matrices.append(matrix)
raw_matrix_rates = [matrix.get_expected_rate() for matrix in matrices]
category_weights = []
for suffix in category_suffixes:
category_weights.append(getattr(ns, 'catFreq'+suffix))
total = float(sum(category_weights))
category_distribution = [weight / total for weight in category_weights]
mixture_model = SubModel.MixtureModel(category_distribution, matrices)
raw_mixture_rate = mixture_model.get_expected_rate()
# rescale the mixture model
# 0.75 is the expected rate of the initial model
r1 = 0.75
scaling_factor = r1
mixture_model.rescale(scaling_factor)
recomputed_log_likelihood = PhyLikelihood.get_log_likelihood(
nexus.tree, nexus.alignment, mixture_model)
print >> out, 'recomputed likelihood and rates:'
print >> out, '---------------------------------------'
print >> out, 'log likelihood :', recomputed_log_likelihood
print >> out, ''
print >> out, 'rate :', raw_mixture_rate
print >> out, ''
for rate, suffix in zip(raw_matrix_rates, category_suffixes):
print >> out, 'rate%s : %s' % (suffix, rate)
print >> out, '---------------------------------------'
print >> out, ''
print >> out, ''
# return the response
return out.getvalue()
