def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description=description)
parser.add_argument('div_model_path',
metavar='DIV-MODEL-RESULTS-FILE',
type=argparse_utils.arg_is_file,
help=('Path to divergence model results file (i.e., '
'`*-div-model-results.txt`).'))
parser.add_argument(
'-i',
'--taxon-indices',
nargs='+',
type=argparse_utils.arg_is_positive_int,
required=True,
help=('Two or more space-separated indices of taxa for which to '
'calculate the probability of them co-diverging. Indices '
'correspond to the line in the sample table of the config, '
'starting at 1 for the first line of the table. At least '
'two indices are required.'))
parser.add_argument(
'-c',
'--config',
type=argparse_utils.arg_is_config,
help=('msBayes config file to be used to estimate prior '
'probability via simulations. If provided, the '
'posterior and prior probability and bayes factor is '
'reported. If not provided, only the posterior '
'probability is reported.'))
parser.add_argument(
'-n',
'--num-prior-samples',
action='store',
type=argparse_utils.arg_is_positive_int,
default=100000,
help=('The number of prior samples to simulate for estimating'
'prior probabilities. Only used if a config file is '
'provided with the `-c` argument.'))
parser.add_argument(
'--np',
action='store',
type=argparse_utils.arg_is_positive_int,
default=multiprocessing.cpu_count(),
help=('The maximum number of processes to run in parallel for '
'prior simulations. The default is the number of CPUs '
'available on the machine. This option is only relevant '
'if a config file is provided using the `-c` argument.'))
parser.add_argument(
'--seed',
action='store',
type=argparse_utils.arg_is_positive_int,
help=('Random number seed to use for simulations. This option '
'is only relevant if a config file is provided using the '
'`-c` argument.'))
parser.add_argument('--version',
action='version',
version='%(prog)s ' + _program_info['version'],
help='Report version and exit.')
parser.add_argument('--quiet',
action='store_true',
help='Run without verbose messaging.')
parser.add_argument('--debug',
action='store_true',
help='Run in debugging mode.')
args = parser.parse_args()
##########################################################################
## handle args
from pymsbayes.utils.messaging import (LoggingControl, InfoLogger)
LoggingControl.set_logging_level("INFO")
if args.quiet:
LoggingControl.set_logging_level("WARNING")
if args.debug:
LoggingControl.set_logging_level("DEBUG")
log = LoggingControl.get_logger(__name__)
from pymsbayes import config
from pymsbayes.teams import ModelProbabilityEstimatorTeam
from pymsbayes.utils import sumresults, GLOBAL_RNG
if len(args.taxon_indices) < 2:
log.error('At least two taxon indices are required')
sys.exit(1)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
div_models = sumresults.OrderedDivergenceModelCollection(
div_model_results_path=args.div_model_path)
for i in args.taxon_indices:
if ((i < 1) or (i > div_models.npairs)):
log.error('taxon index {0} is out of bounds'.format(i))
sys.exit(1)
args.taxon_indices = [i - 1 for i in args.taxon_indices]
prob_shared_div = div_models.prob_of_shared_divergence(args.taxon_indices)
if args.config:
prob_estimator_team = ModelProbabilityEstimatorTeam(
config_paths=[args.config],
num_samples=args.num_prior_samples,
num_processors=args.np)
prob_estimator_team.start()
prior_prob = prob_estimator_team.shared_div_probs[args.config][len(
args.taxon_indices)]
bf = ((prob_shared_div / (1 - prob_shared_div)) / (prior_prob /
(1 - prior_prob)))
sys.stdout.write('posterior probability = {0}\n'.format(prob_shared_div))
if args.config:
sys.stdout.write('prior probability = {0}\n'.format(prior_prob))
sys.stdout.write('Bayes factor = {0}\n'.format(bf))
sys.stdout.write('2ln(Bayes factor) = {0}\n'.format(2 * math.log(bf)))
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description=description)
parser.add_argument(
'configs',
metavar='CONFIG-PATH',
type=arg_is_config,
nargs='+',
help=('msBayes config file paths for which to estimate prior '
'probabilities.'))
parser.add_argument('-n',
'--num-prior-samples',
action='store',
type=int,
default=1000,
help=('The number of prior samples to simulate for '
'proabability estimates.'))
parser.add_argument(
'--np',
action='store',
type=int,
default=multiprocessing.cpu_count(),
help=('The maximum number of processes to run in parallel. The '
'default is the number of CPUs available on the machine.'))
parser.add_argument(
'-d',
'--dispersion-threshold',
action='store',
type=float,
default=0.01,
help=('The threshold for the dispersion index of divegence '
'times. The estimated prior probability that the '
'dispersion index is less than this threshold will '
'be reported for each config.'))
parser.add_argument(
'-c',
'--cv-threshold',
action='store',
type=float,
default=0.01,
help=('The threshold for the coefficient of variation (CV) of '
'divegence times. The estimated prior probability that the '
'CV is less than this threshold will '
'be reported for each config.'))
parser.add_argument('--seed',
action='store',
type=int,
help='Random number seed to use for the analysis.')
parser.add_argument('--version',
action='version',
version='%(prog)s ' + _program_info['version'],
help='Report version and exit.')
parser.add_argument('--quiet',
action='store_true',
help='Run without verbose messaging.')
parser.add_argument('--debug',
action='store_true',
help='Run in debugging mode.')
args = parser.parse_args()
##########################################################################
## handle args
from pymsbayes.utils.messaging import (LoggingControl, InfoLogger)
LoggingControl.set_logging_level("INFO")
if args.quiet:
LoggingControl.set_logging_level("WARNING")
if args.debug:
LoggingControl.set_logging_level("DEBUG")
log = LoggingControl.get_logger(__name__)
from pymsbayes.teams import ModelProbabilityEstimatorTeam
from pymsbayes.utils import GLOBAL_RNG
if not args.seed:
args.seed = random.randint(1, 999999999)
log.info('Using seed {0}'.format(args.seed))
GLOBAL_RNG.seed(args.seed)
##########################################################################
## begin analysis --- generate samples
start_time = datetime.datetime.now()
prob_esimator_team = ModelProbabilityEstimatorTeam(
config_paths=args.configs,
num_samples=args.num_prior_samples,
omega_threshold=args.dispersion_threshold,
cv_threshold=args.cv_threshold,
num_processors=args.np)
prob_esimator_team.start()
for path in args.configs:
sys.stdout.write('Prior probabilities for model {0}:\n'.format(path))
for k, p in prob_esimator_team.psi_probs[path].iteritems():
sys.stdout.write('\tnum of divergence events = {0}: {1}\n'.format(
k, p))
sys.stdout.write('\tdispersion of div times < {0}: {1}\n'.format(
args.dispersion_threshold, prob_esimator_team.omega_probs[path]))
sys.stdout.write('\tCV of div times < {0}: {1}\n'.format(
args.cv_threshold, prob_esimator_team.cv_probs[path]))
stop_time = datetime.datetime.now()
log.info('[run_stats]')
log.info('\tstart_time = {0}'.format(str(start_time)))
log.info('\tstop_time = {0}'.format(str(stop_time)))
log.info('\ttotal_duration = {0}'.format(str(stop_time - start_time)))
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description = description)
parser.add_argument('div_model_path',
metavar='DIV-MODEL-RESULTS-FILE',
type=argparse_utils.arg_is_file,
help = ('Path to divergence model results file (i.e., '
'`*-div-model-results.txt`).'))
parser.add_argument('-i', '--taxon-indices',
nargs = '+',
type = argparse_utils.arg_is_positive_int,
required = True,
help = ('Two or more space-separated indices of taxa for which to '
'calculate the probability of them co-diverging. Indices '
'correspond to the line in the sample table of the config, '
'starting at 1 for the first line of the table. At least '
'two indices are required.'))
parser.add_argument('-c', '--config',
type = argparse_utils.arg_is_config,
help = ('msBayes config file to be used to estimate prior '
'probability via simulations. If provided, the '
'posterior and prior probability and bayes factor is '
'reported. If not provided, only the posterior '
'probability is reported.'))
parser.add_argument('-n', '--num-prior-samples',
action = 'store',
type = argparse_utils.arg_is_positive_int,
default = 100000,
help = ('The number of prior samples to simulate for estimating'
'prior probabilities. Only used if a config file is '
'provided with the `-c` argument.'))
parser.add_argument('--np',
action = 'store',
type = argparse_utils.arg_is_positive_int,
default = multiprocessing.cpu_count(),
help = ('The maximum number of processes to run in parallel for '
'prior simulations. The default is the number of CPUs '
'available on the machine. This option is only relevant '
'if a config file is provided using the `-c` argument.'))
parser.add_argument('--seed',
action = 'store',
type = argparse_utils.arg_is_positive_int,
help = ('Random number seed to use for simulations. This option '
'is only relevant if a config file is provided using the '
'`-c` argument.'))
parser.add_argument('--version',
action = 'version',
version = '%(prog)s ' + _program_info['version'],
help = 'Report version and exit.')
parser.add_argument('--quiet',
action = 'store_true',
help = 'Run without verbose messaging.')
parser.add_argument('--debug',
action = 'store_true',
help = 'Run in debugging mode.')
args = parser.parse_args()
##########################################################################
## handle args
from pymsbayes.utils.messaging import (LoggingControl,
InfoLogger)
LoggingControl.set_logging_level("INFO")
if args.quiet:
LoggingControl.set_logging_level("WARNING")
if args.debug:
LoggingControl.set_logging_level("DEBUG")
log = LoggingControl.get_logger(__name__)
from pymsbayes import config
from pymsbayes.teams import ModelProbabilityEstimatorTeam
from pymsbayes.utils import sumresults, GLOBAL_RNG
if len(args.taxon_indices) < 2:
log.error('At least two taxon indices are required')
sys.exit(1)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
div_models = sumresults.OrderedDivergenceModelCollection(
div_model_results_path = args.div_model_path)
for i in args.taxon_indices:
if ((i < 1) or (i > div_models.npairs)):
log.error('taxon index {0} is out of bounds'.format(i))
sys.exit(1)
args.taxon_indices = [i - 1 for i in args.taxon_indices]
prob_shared_div = div_models.prob_of_shared_divergence(args.taxon_indices)
if args.config:
prob_estimator_team = ModelProbabilityEstimatorTeam(
config_paths = [args.config],
num_samples = args.num_prior_samples,
num_processors = args.np)
prob_estimator_team.start()
prior_prob = prob_estimator_team.shared_div_probs[args.config][
len(args.taxon_indices)]
bf = ((prob_shared_div / (1 - prob_shared_div)) /
(prior_prob / (1 - prior_prob)))
sys.stdout.write('posterior probability = {0}\n'.format(prob_shared_div))
if args.config:
sys.stdout.write('prior probability = {0}\n'.format(prior_prob))
sys.stdout.write('Bayes factor = {0}\n'.format(bf))
sys.stdout.write('2ln(Bayes factor) = {0}\n'.format(2 * math.log(bf)))