def create_time_plot(config_path,
info_path,
out_dir,
iteration_index = 249):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
cfg = MsBayesConfig(config_path)
dmc = DMCSimulationResults(info_path)
sum_path = (dmc.get_result_path_prefix(1, 1, 1) +
'{0}-posterior-summary.txt'.format(iteration_index))
labels = []
for t in cfg.taxa:
l = t.strip().split('.')
labels.append(r'\textit{{{0} {1}}}'.format(l[0], l[1]))
pg = plotting.get_marginal_divergence_time_plot(
config_path = config_path,
posterior_summary_path = sum_path,
labels = labels,
horizontal = True,
label_size = 12.0,
measure_tick_label_size = 12.0,
measure_axis_label = 'Divergence time ($4N_C$ generations)',
measure_axis_label_size = 16.0,
label_axis_label = 'Taxon pair',
label_axis_label_size = 16.0)
pg.savefig(os.path.join(out_dir, 'negros-panay-marginal-times.pdf'))
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description=description)
parser.add_argument('info_path',
metavar='PYMSBAYES-INFO-FILE',
type=argparse_utils.arg_is_file,
help=('Path to `pymsbayes-info.txt` file.'))
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.'))
parser.add_argument(
'-i',
'--sample-index',
action='store',
type=argparse_utils.arg_is_positive_int,
help=('The prior-sample index of results to be summarized. '
'Output files should have a consistent schema. For '
'example, a results file for divergence models might look '
'something like '
'`d1-m1-s1-1000000-div-model-results.txt`. In this example, '
'the prior-sample index is "1000000". The default is to '
'use the largest prior-sample index, which is probably '
'what you want.'))
parser.add_argument(
'-o',
'--output-dir',
action='store',
type=argparse_utils.arg_is_dir,
help=('The directory in which all output plots will be written. '
'The default is to use the directory of the pymsbayes info '
'file.'))
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. The '
'default is the number of CPUs available on the machine.'))
parser.add_argument(
'-m',
'--mu',
action='store',
type=argparse_utils.arg_is_positive_float,
default=None,
help=('The mutation rate with which to scale time to units of '
'generations. By default, time is not scaled to '
'generations.'))
parser.add_argument(
'--extension',
action='store',
type=str,
default='pdf',
help=('The file format extension of the plots (e.g., "pdf", '
'"png"). The default is pdf.'))
parser.add_argument('--seed',
action='store',
type=argparse_utils.arg_is_positive_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 import plotting
from pymsbayes.utils import sumresults
from pymsbayes.utils import GLOBAL_RNG
if not plotting.MATPLOTLIB_AVAILABLE:
log.error('`matplotlib` could not be imported, so plots can not be\n'
'produced. Please install `matplotlib` and try again.')
sys.exit(1)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
if not args.output_dir:
args.output_dir = os.path.dirname(args.info_path)
args.output_dir = os.path.join(args.output_dir, 'plots')
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
results = sumresults.DMCSimulationResults(args.info_path)
if results.num_sim_reps > 1:
log.error('Results appear to be from simulation-based analysis, '
'for which this plotting script is not appropriate.')
sys.exit(1)
observed_indices = sorted(results.observed_index_to_config.keys())
prior_indices = sorted(results.prior_index_to_config.keys())
for obs_idx in observed_indices:
for prior_idx in prior_indices:
result_indices = results.get_result_indices(obs_idx, prior_idx, 1)
result_idx = max(result_indices)
result_path_prefix = '{0}{1}-'.format(
results.get_result_path_prefix(obs_idx, prior_idx, 1),
result_idx)
result_dir = os.path.dirname(result_path_prefix)
out_prefix = os.path.join(args.output_dir,
os.path.basename(result_path_prefix))
prior_cfg = results.prior_configs[prior_idx]
posterior_summary_path = get_result_path(result_path_prefix,
'posterior-summary')
div_model_path = get_result_path(result_path_prefix,
'div-model-results')
config_path = results.prior_index_to_config[prior_idx]
time_multiplier = 1.0
if args.mu is not None:
if prior_cfg.time_in_subs_per_site:
time_multiplier = 1.0 / args.mu
else:
try:
mean_theta = prior_cfg.theta.mean
except:
mean_theta = prior_cfg.d_theta.mean
time_multiplier = mean_theta / args.mu
if results.sort_index == 0:
#plot marginal times
if not posterior_summary_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping marginal times plot...'.format(
result_path_prefix, 'posterior-summary'))
else:
label_dimension = (0.34 * (prior_cfg.npairs + 1)) + 0.56
marginal_times_plot = plotting.get_marginal_divergence_time_plot(
config_path=config_path,
posterior_summary_path=posterior_summary_path,
labels=None,
estimate='median',
interval='HPD_95_interval',
time_multiplier=time_multiplier,
horizontal=True,
label_dimension=label_dimension,
measure_dimension=8.0,
label_size=12.0,
measure_tick_label_size=12.0,
measure_axis_label='Divergence time',
measure_axis_label_size=14.0,
label_axis_label='Taxon pair',
label_axis_label_size=14.0,
usetex=False)
marginal_times_path = '{0}{1}'.format(
out_prefix,
'marginal-divergence-times.' + args.extension)
marginal_times_plot.savefig(marginal_times_path)
#plot top ordered models
if not div_model_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping ordered div model plot...'.format(
result_path_prefix, 'div-model-results'))
else:
height = 12.0
margin_top = 0.99
margin_left = 0.03
padding_between_vertical = 0.8
if prior_cfg.npairs < 4:
height *= 0.8
margin_top -= 0.01
margin_left += 0.05
padding_between_vertical += 0.3
width = (0.38 * prior_cfg.npairs) + 1.5
div_model_plot = plotting.OrderedDivergenceModelPlotGrid(
div_model_results_path=div_model_path,
config_path=config_path,
num_top_models=10,
time_multiplier=time_multiplier,
height=height,
width=width,
plot_label_schema='uppercase',
plot_label_offset=0,
plot_label_size=12.0,
y_title='Divergence time',
y_title_size=14.0,
y_tick_label_size=10.0,
right_text_size=10.0,
margin_left=margin_left,
margin_bottom=0.0,
margin_right=1,
margin_top=margin_top,
padding_between_vertical=padding_between_vertical,
tab=0.08)
plot = div_model_plot.create_grid()
div_model_plot_path = '{0}{1}'.format(
out_prefix, 'ordered-div-models.' + args.extension)
plot.savefig(div_model_plot_path)
else:
#plot top unordered models
if not div_model_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping unordered div model plot...'.format(
result_path_prefix, 'div-model-results'))
else:
width = (0.38 * prior_cfg.npairs) + 1.5
div_model_plot = plotting.UnorderedDivergenceModelPlotGrid(
div_model_results_path=div_model_path,
num_top_models=10,
time_multiplier=time_multiplier,
height=10.0,
width=width,
data_label_size=10.0,
plot_label_schema='uppercase',
plot_label_offset=0,
plot_label_size=12.0,
y_title='Divergence time',
y_title_size=14.0,
y_tick_label_size=10.0,
right_text_size=10.0,
margin_left=0.03,
margin_bottom=0.0,
margin_right=1,
margin_top=0.99,
padding_between_vertical=0.8,
tab=0.08)
plot = div_model_plot.create_grid()
div_model_plot_path = '{0}{1}'.format(
out_prefix, 'ordered-div-models.' + args.extension)
plot.savefig(div_model_plot_path)
#plot ndiv plot
psi_path = get_result_path(result_path_prefix, 'psi-results')
if not psi_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping number of divergences plot...'.format(
result_path_prefix, 'psi-results'))
else:
width = (0.25 * prior_cfg.npairs) + 0.55
if width < 2.8:
width = 2.8
num_div_summary = plotting.NumberOfDivergencesSummary(
config_path=results.prior_index_to_config[prior_idx],
psi_results_path=psi_path,
posterior_summary_path=posterior_summary_path,
num_prior_samples=args.num_prior_samples,
num_processors=args.np)
num_div_summary.create_plot(plot_label_size=10.0,
right_text_size=10.0,
x_label_size=10.0,
y_label_size=10.0,
xtick_label_size=10.0,
ytick_label_size=8.0,
height=6.0,
width=width,
margin_bottom=0.0,
margin_left=0.0,
margin_top=0.97,
margin_right=1.0,
padding_between_vertical=1.0)
num_div_plot_path = '{0}{1}'.format(
out_prefix, 'number-of-divergences.' + args.extension)
num_div_summary.save_plot(num_div_plot_path)
bf_plot_path = '{0}{1}'.format(
out_prefix, ('number-of-divergences-bayes-factors-only.' +
args.extension))
num_div_summary.save_bf_plot(bf_plot_path)
num_div_bf_path = '{0}{1}'.format(
out_prefix, 'number-of-divergences-bayes-factors.txt')
with open(num_div_bf_path, 'w') as out:
out.write('num_of_divs\t2ln(bf)\n')
for n in sorted(num_div_summary.psi_bayes_factors.keys()):
out.write('{0}\t{1}\n'.format(
n, num_div_summary.psi_bayes_factors[n]))
log.info('The plots are in: {0}'.format(args.output_dir))
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description = description)
parser.add_argument('info_path',
metavar='PYMSBAYES-INFO-FILE',
type=argparse_utils.arg_is_file,
help=('Path to `pymsbayes-info.txt` file.'))
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.'))
parser.add_argument('-i', '--sample-index',
action = 'store',
type = argparse_utils.arg_is_positive_int,
help = ('The prior-sample index of results to be summarized. '
'Output files should have a consistent schema. For '
'example, a results file for divergence models might look '
'something like '
'`d1-m1-s1-1000000-div-model-results.txt`. In this example, '
'the prior-sample index is "1000000". The default is to '
'use the largest prior-sample index, which is probably '
'what you want.'))
parser.add_argument('-o', '--output-dir',
action = 'store',
type = argparse_utils.arg_is_dir,
help = ('The directory in which all output plots will be written. '
'The default is to use the directory of the pymsbayes info '
'file.'))
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. The '
'default is the number of CPUs available on the machine.'))
parser.add_argument('-m', '--mu',
action = 'store',
type = argparse_utils.arg_is_positive_float,
default = None,
help = ('The mutation rate with which to scale time to units of '
'generations. By default, time is not scaled to '
'generations.'))
parser.add_argument('--extension',
action = 'store',
type = str,
default = 'pdf',
help = ('The file format extension of the plots (e.g., "pdf", '
'"png"). The default is pdf.'))
parser.add_argument('--seed',
action = 'store',
type = argparse_utils.arg_is_positive_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 import plotting
from pymsbayes.utils import sumresults
from pymsbayes.utils import GLOBAL_RNG
if not plotting.MATPLOTLIB_AVAILABLE:
log.error(
'`matplotlib` could not be imported, so plots can not be\n'
'produced. Please install `matplotlib` and try again.')
sys.exit(1)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
if not args.output_dir:
args.output_dir = os.path.dirname(args.info_path)
args.output_dir = os.path.join(args.output_dir, 'plots')
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
results = sumresults.DMCSimulationResults(args.info_path)
if results.num_sim_reps > 1:
log.error('Results appear to be from simulation-based analysis, '
'for which this plotting script is not appropriate.')
sys.exit(1)
observed_indices = sorted(results.observed_index_to_config.keys())
prior_indices = sorted(results.prior_index_to_config.keys())
for obs_idx in observed_indices:
for prior_idx in prior_indices:
result_indices = results.get_result_indices(obs_idx, prior_idx, 1)
result_idx = max(result_indices)
result_path_prefix = '{0}{1}-'.format(
results.get_result_path_prefix(obs_idx, prior_idx, 1),
result_idx)
result_dir = os.path.dirname(result_path_prefix)
out_prefix = os.path.join(args.output_dir, os.path.basename(
result_path_prefix))
prior_cfg = results.prior_configs[prior_idx]
posterior_summary_path = get_result_path(result_path_prefix,
'posterior-summary')
div_model_path = get_result_path(result_path_prefix,
'div-model-results')
config_path = results.prior_index_to_config[prior_idx]
time_multiplier = 1.0
if args.mu is not None:
if prior_cfg.time_in_subs_per_site:
time_multiplier = 1.0 / args.mu
else:
try:
mean_theta = prior_cfg.theta.mean
except:
mean_theta = prior_cfg.d_theta.mean
time_multiplier = mean_theta / args.mu
if results.sort_index == 0:
#plot marginal times
if not posterior_summary_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping marginal times plot...'.format(
result_path_prefix,
'posterior-summary'))
else:
label_dimension = (0.34 * (prior_cfg.npairs + 1)) + 0.56
marginal_times_plot = plotting.get_marginal_divergence_time_plot(
config_path = config_path,
posterior_summary_path = posterior_summary_path,
labels = None,
estimate = 'median',
interval = 'HPD_95_interval',
time_multiplier = time_multiplier,
horizontal = True,
label_dimension = label_dimension,
measure_dimension = 8.0,
label_size = 12.0,
measure_tick_label_size = 12.0,
measure_axis_label = 'Divergence time',
measure_axis_label_size = 14.0,
label_axis_label = 'Taxon pair',
label_axis_label_size = 14.0,
usetex = False)
marginal_times_path = '{0}{1}'.format(out_prefix,
'marginal-divergence-times.' + args.extension)
marginal_times_plot.savefig(marginal_times_path)
#plot top ordered models
if not div_model_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping ordered div model plot...'.format(
result_path_prefix,
'div-model-results'))
else:
height = 12.0
margin_top = 0.99
margin_left = 0.03
padding_between_vertical = 0.8
if prior_cfg.npairs < 4:
height *= 0.8
margin_top -= 0.01
margin_left += 0.05
padding_between_vertical += 0.3
width = (0.38 * prior_cfg.npairs) + 1.5
div_model_plot = plotting.OrderedDivergenceModelPlotGrid(
div_model_results_path = div_model_path,
config_path = config_path,
num_top_models = 10,
time_multiplier = time_multiplier,
height = height,
width = width,
plot_label_schema = 'uppercase',
plot_label_offset = 0,
plot_label_size = 12.0,
y_title = 'Divergence time',
y_title_size = 14.0,
y_tick_label_size = 10.0,
right_text_size = 10.0,
margin_left = margin_left,
margin_bottom = 0.0,
margin_right = 1,
margin_top = margin_top,
padding_between_vertical = padding_between_vertical,
tab = 0.08)
plot = div_model_plot.create_grid()
div_model_plot_path = '{0}{1}'.format(out_prefix,
'ordered-div-models.' + args.extension)
plot.savefig(div_model_plot_path)
else:
#plot top unordered models
if not div_model_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping unordered div model plot...'.format(
result_path_prefix,
'div-model-results'))
else:
width = (0.38 * prior_cfg.npairs) + 1.5
div_model_plot = plotting.UnorderedDivergenceModelPlotGrid(
div_model_results_path = div_model_path,
num_top_models = 10,
time_multiplier = time_multiplier,
height = 10.0,
width = width,
data_label_size = 10.0,
plot_label_schema = 'uppercase',
plot_label_offset = 0,
plot_label_size = 12.0,
y_title = 'Divergence time',
y_title_size = 14.0,
y_tick_label_size = 10.0,
right_text_size = 10.0,
margin_left = 0.03,
margin_bottom = 0.0,
margin_right = 1,
margin_top = 0.99,
padding_between_vertical = 0.8,
tab = 0.08)
plot = div_model_plot.create_grid()
div_model_plot_path = '{0}{1}'.format(out_prefix,
'ordered-div-models.' + args.extension)
plot.savefig(div_model_plot_path)
#plot ndiv plot
psi_path = get_result_path(result_path_prefix,
'psi-results')
if not psi_path:
log.warning('Could not find {0}{1}.txt(.gz); '
'Skipping number of divergences plot...'.format(
result_path_prefix,
'psi-results'))
else:
width = (0.25 * prior_cfg.npairs) + 0.55
if width < 2.8:
width = 2.8
num_div_summary = plotting.NumberOfDivergencesSummary(
config_path = results.prior_index_to_config[prior_idx],
psi_results_path = psi_path,
posterior_summary_path = posterior_summary_path,
num_prior_samples = args.num_prior_samples,
num_processors = args.np)
num_div_summary.create_plot(
plot_label_size = 10.0,
right_text_size = 10.0,
x_label_size = 10.0,
y_label_size = 10.0,
xtick_label_size = 10.0,
ytick_label_size = 8.0,
height = 6.0,
width = width,
margin_bottom = 0.0,
margin_left = 0.0,
margin_top = 0.97,
margin_right = 1.0,
padding_between_vertical = 1.0)
num_div_plot_path = '{0}{1}'.format(out_prefix,
'number-of-divergences.' + args.extension)
num_div_summary.save_plot(num_div_plot_path)
bf_plot_path = '{0}{1}'.format(out_prefix,
('number-of-divergences-bayes-factors-only.' +
args.extension))
num_div_summary.save_bf_plot(bf_plot_path)
num_div_bf_path = '{0}{1}'.format(out_prefix,
'number-of-divergences-bayes-factors.txt')
with open(num_div_bf_path, 'w') as out:
out.write('num_of_divs\t2ln(bf)\n')
for n in sorted(num_div_summary.psi_bayes_factors.keys()):
out.write('{0}\t{1}\n'.format(n,
num_div_summary.psi_bayes_factors[n]))
log.info('The plots are in: {0}'.format(args.output_dir))
