def __init__(self, info_path):
self.info_path = expand_path(info_path)
self.results_dir = os.path.dirname(self.info_path)
self.output_dir = os.path.join(self.results_dir, 'pymsbayes-output')
self.observed_index_to_path = {}
self.observed_path_to_index = {}
self.observed_index_to_config = {}
self.observed_config_to_index = {}
self.observed_index_to_prior_index = {}
self.prior_index_to_config = {}
self.prior_config_to_index = {}
self.num_taxon_pairs = None
self.num_sim_reps = None
self.sort_index = None
self._parse_info_file()
self.combined_prior_index = '{0}-combined'.format(''.join(
[str(i) for i in sorted(self.prior_index_to_config.keys())]))
self.combined_prior_results = True
for i in self.observed_index_to_path.iterkeys():
prefix = self.get_result_path_prefix(i, self.combined_prior_index,
1)
pattern = prefix + '*-posterior-sample*'
files = glob.glob(pattern)
if not files:
self.combined_prior_results = False
break
self.prior_configs = {}
for k, v in self.prior_index_to_config.iteritems():
self.prior_configs[k] = config.MsBayesConfig(v)
self.compresslevel = 9
def __init__(self, info_path):
self.info_path = expand_path(info_path)
self.results_dir = os.path.dirname(self.info_path)
self.output_dir = os.path.join(self.results_dir, 'pymsbayes-output')
self.observed_index_to_path = {}
self.observed_path_to_index = {}
self.observed_index_to_config = {}
self.observed_config_to_index = {}
self.observed_index_to_prior_index = {}
self.prior_index_to_config = {}
self.prior_config_to_index = {}
self.num_taxon_pairs = None
self.num_sim_reps = None
self.sort_index = None
self._parse_info_file()
self.combined_prior_index = '{0}-combined'.format(''.join(
[str(i) for i in sorted(self.prior_index_to_config.keys())]))
self.combined_prior_results = True
for i in self.observed_index_to_path.iterkeys():
prefix = self.get_result_path_prefix(i, self.combined_prior_index,
1)
pattern = prefix + '*-posterior-sample*'
files = glob.glob(pattern)
if not files:
self.combined_prior_results = False
break
self.prior_configs = {}
for k, v in self.prior_index_to_config.iteritems():
self.prior_configs[k] = config.MsBayesConfig(v)
self.compresslevel = 9
def parse_sim_results(info_path):
_LOG.info('Parsing and writing results...')
info_path = expand_path(info_path)
sim_results = DMCSimulationResults(info_path)
sim_results.write_result_summaries(
prior_indices = [sim_results.combined_prior_index],
include_tau_exclusion_info = True)
def arg_is_dir(path):
try:
if not is_dir(path):
raise
except:
msg = '{0!r} is not a directory'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def arg_is_config(path):
try:
if not MsBayesConfig.is_config(path):
raise
except:
msg = '{0!r} is not an msBayes config file'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def arg_is_path(path):
try:
if not os.path.exists(path):
raise
except:
msg = 'path {0!r} does not exist'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def arg_is_dir(path):
try:
if not is_dir(path):
raise
except:
msg = '{0!r} is not a directory'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def arg_is_config(path):
try:
if not MsBayesConfig.is_config(path):
raise
except:
msg = '{0!r} is not an msBayes config file'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def arg_is_path(path):
try:
if not os.path.exists(path):
raise
except:
msg = 'path {0!r} does not exist'.format(path)
raise argparse.ArgumentTypeError(msg)
return expand_path(path)
def summarize_sim_results(info_path):
info_path = expand_path(info_path)
sim_results = DMCSimulationResults(info_path)
out_dir = os.path.dirname(info_path)
summary_path = os.path.join(out_dir, 'results-summary.txt')
result_path = sim_results.get_result_summary_path(observed_index = 1,
prior_index = sim_results.combined_prior_index)
d = get_dict_from_spreadsheets([result_path])
num_excluded = [int(x) for x in d['num_excluded']]
num_excluded_glm = [int(x) for x in d['num_excluded_glm']]
bf_num_excluded = [int(x) for x in d['bf_num_excluded']]
bf_num_excluded_glm = [int(x) for x in d['bf_num_excluded_glm']]
prob_of_exclusion = [float(x) for x in d['prob_of_exclusion']]
prob_of_exclusion_glm = [float(x) for x in d['prob_of_exclusion_glm']]
prior_prob_of_exclusion = [float(x) for x in d['prior_prob_of_exclusion']]
bf_of_exclusion = [float(x) for x in d['bf_of_exclusion']]
bf_of_exclusion_glm = [float(x) for x in d['bf_of_exclusion_glm']]
num_sims = sim_results.num_sim_reps
assert len(num_excluded) == num_sims
assert len(num_excluded_glm) == num_sims
assert len(prob_of_exclusion) == num_sims
assert len(prob_of_exclusion_glm) == num_sims
summary_stream, close = process_file_arg(summary_path, 'w')
summary_stream.write('Proportion of simulations excluding truth: {0}'
'\n'.format(
len([1 for x in bf_num_excluded if x > 0]) / float(num_sims)))
summary_stream.write('Proportion of simulations excluding truth with GLM-'
'adjustment: {0}\n'.format(
len([1 for x in bf_num_excluded_glm if x > 0]) / float(num_sims)))
summary_stream.write('Average number of tau parameters excluded: {0}'
'\n'.format(
sum(bf_num_excluded) / float(num_sims)))
summary_stream.write('Average number of tau parameters excluded with GLM: '
'{0}\n'.format(sum(bf_num_excluded_glm) / float(num_sims)))
summary_stream.write('Mode number of tau parameters excluded: {0}\n'.format(
mode_list(bf_num_excluded)))
summary_stream.write('Mode number of tau parameters excluded with GLM: '
'{0}\n'.format(mode_list(bf_num_excluded_glm)))
summary_stream.write('Max number of tau parameters excluded: {0}\n'.format(
max(bf_num_excluded)))
summary_stream.write('Max number of tau parameters excluded with GLM: '
'{0}\n'.format(max(bf_num_excluded_glm)))
summary_stream.write('Average probability of exclusion: {0}\n'.format(
sum(prob_of_exclusion) / float(num_sims)))
summary_stream.write('Average probability of exclusion with GLM: {0}\n'.format(
sum(prob_of_exclusion_glm) / float(num_sims)))
summary_stream.write('Median probability of exclusion: {0}\n'.format(
median(prob_of_exclusion)))
summary_stream.write('Median probability of exclusion with GLM: {0}\n'.format(
median(prob_of_exclusion_glm)))
summary_stream.write('Average Bayes factor of exclusion: {0}\n'.format(
sum(bf_of_exclusion) / float(num_sims)))
summary_stream.write('Average Bayes factor of exclusion with GLM: {0}\n'.format(
sum(bf_of_exclusion_glm) / float(num_sims)))
summary_stream.write('Median Bayes factor of exclusion: {0}\n'.format(
median(bf_of_exclusion)))
summary_stream.write('Median Bayes factor of exclusion with GLM: {0}\n'.format(
median(bf_of_exclusion_glm)))
summary_stream.write('Max Bayes factor of exclusion: {0}\n'.format(
max(bf_of_exclusion)))
summary_stream.write('Max Bayes factor of exclusion with GLM: {0}\n'.format(
max(bf_of_exclusion_glm)))
prob_of_bf_exclusion = (len([1 for x in bf_of_exclusion if x > 10.0]) /
float(num_sims))
prob_of_bf_exclusion_glm = (len([
1 for x in bf_of_exclusion_glm if x > 10.0]) /
float(num_sims))
summary_stream.write('Estimated probability Bayes factor of exclusion '
'> 10: {0}\n'.format(prob_of_bf_exclusion))
summary_stream.write('Estimated probability Bayes factor of exclusion '
'> 10 with GLM: {0}\n'.format(prob_of_bf_exclusion_glm))
summary_stream.close()
if plotting.MATPLOTLIB_AVAILABLE:
approx_prior_exclusion = 0.39184
prior_odds = approx_prior_exclusion / (1.0 - approx_prior_exclusion)
post_odds = prior_odds * 10
post = post_odds / (1.0 + post_odds)
observed_config1 = MsBayesConfig(sim_results.observed_index_to_config[1])
observed_config2 = MsBayesConfig(sim_results.observed_index_to_config[1])
cfg_to_num_ex = {observed_config1: bf_num_excluded,
observed_config2: bf_num_excluded_glm}
cfg_to_prob_exclusion = {observed_config1: prob_of_exclusion,
observed_config2: prob_of_exclusion_glm}
cfg_to_prob_of_bf_exclusion = {observed_config1: prob_of_bf_exclusion,
observed_config2: prob_of_bf_exclusion_glm}
ex_prob_plot = plotting.ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_prob_exclusion,
variable = 'tau_exclusion',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = post,
cfg_to_prob_of_bf_exclusion = cfg_to_prob_of_bf_exclusion,
height = 3.7,
margin_left = 0.03,
margin_bottom = 0.06,
margin_right = 1,
margin_top = 0.96,
padding_between_horizontal = 0.5,
padding_between_vertical = 1.0,
num_columns = 2)
fig = ex_prob_plot.create_grid()
fig.savefig(os.path.join(out_dir, 'prob_of_exclusion.pdf'))
ex_plot = plotting.PowerPlotGrid(
observed_config_to_estimates = cfg_to_num_ex,
variable = 'tau_exclusion',
num_columns = 2,
height = 3.7,
margin_left = 0.03,
margin_bottom = 0.06,
margin_right = 1,
margin_top = 0.95,
padding_between_horizontal = 0.5,
padding_between_vertical = 1.0)
fig = ex_plot.create_grid()
fig.savefig(os.path.join(out_dir, 'num_tau_excluded.pdf'))
