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'))
Example #2
0
def main_cli():
    description = '{name} {version}'.format(**_program_info)
    parser = argparse.ArgumentParser(description = description,
            formatter_class = argparse_utils.SmartHelpFormatter)
    parser.add_argument('-c', '--config',
            type = argparse_utils.arg_is_config,
            required = True,
            help = ('msBayes config file to be used to generate saturation '
                    'plot.'))
    parser.add_argument('-n', '--num-prior-samples',
            action = 'store',
            type = int,
            default = 1000,
            help = ('The number of prior samples to simulate for the '
                    'saturation plot.'))
    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('-o', '--output-dir',
            action = 'store',
            type = argparse_utils.arg_is_dir,
            help = ('The directory in which all output files will be written. '
                    'The default is to use the directory of the first observed '
                    'config file.'))
    parser.add_argument('--temp-dir',
            action = 'store',
            type = argparse_utils.arg_is_dir,
            help = ('A directory to temporarily stage files. The default is to '
                    'use the output directory.'))
    parser.add_argument('-s', '--stat-prefixes',
            nargs = '*',
            type = str,
            default = ['pi', 'pi.net', 'wattTheta', 'tajD.denom'],
            help = ('Prefixes of summary statistics to use in the analyses. '
                    'The prefixes should be separated by spaces. '
                    'Default: `-s pi pi.net wattTheta tajD.denom`.'))
    parser.add_argument('--sort-index',
            action = 'store',
            type = int,
            default = 0,
            choices = range(12),
            help = argparse_utils.get_sort_index_help_message())
    parser.add_argument('--compress',
            action = 'store_true',
            help = 'Compress plot data file.')
    parser.add_argument('--keep-temps',
            action = 'store_true',
            help = 'Keep all temporary files.')
    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.workers import MsBayesWorker
    from pymsbayes.utils.parsing import (get_patterns_from_prefixes,
            DEFAULT_STAT_PATTERNS, get_dict_from_spreadsheets, dict_line_iter)
    from pymsbayes.manager import Manager
    from pymsbayes.utils.tempfs import TempFileSystem
    from pymsbayes.utils import probability, stats
    from pymsbayes.utils.functions import long_division
    from pymsbayes.config import MsBayesConfig
    from pymsbayes.utils import GLOBAL_RNG, MSBAYES_SORT_INDEX, ToolPathManager
    from pymsbayes.fileio import process_file_arg
    from pymsbayes import plotting

    MSBAYES_SORT_INDEX.set_index(args.sort_index)

    # get full paths to tools
    msbayes_path = ToolPathManager.get_tool_full_path('msbayes.pl')
    dpp_msbayes_path = ToolPathManager.get_tool_full_path('dpp-msbayes.pl')

    if not args.output_dir:
        args.output_dir = os.path.dirname(args.config)
    info = InfoLogger(os.path.join(args.output_dir, 'pymsbayes-info.txt'))

    sample_path = os.path.join(args.output_dir, 'prior-sample.txt')
    if args.compress:
        sample_path += '.gz'

    if not args.temp_dir:
        args.temp_dir = args.output_dir
    temp_fs = TempFileSystem(parent=args.temp_dir, prefix='temp-files-')
    args.stat_prefixes = [s.rstrip('.') for s in args.stat_prefixes]
    stat_patterns = get_patterns_from_prefixes(
            [s + '.' for s in args.stat_prefixes],
            ignore_case=True)
    if not args.seed:
        args.seed = random.randint(1, 999999999)
    GLOBAL_RNG.seed(args.seed)
    compress_level = None
    if args.compress:
        compress_level = 9

    cfg = MsBayesConfig(args.config)
    num_taxon_pairs = cfg.npairs

    info.write('[pymsbayes]', log.info)
    info.write('\tprogram_name = {name}'.format(**_program_info), log.info)
    info.write('\tversion = {version}'.format(**_program_info), log.info)
    info.write('\tinvocation = {0!r}'.format(' '.join(sys.argv)), log.info)
    info.write('\toutput_directory = {0!r}'.format(args.output_dir), log.info)
    info.write('\ttemp_directory = {0!r}'.format(temp_fs.base_dir), log.info)
    info.write('\tsort_index = {0}'.format(
            MSBAYES_SORT_INDEX.current_value()), log.info)
    info.write('\tstat_patterns = {0!r}'.format(
            ', '.join([p.pattern for p in stat_patterns])), log.info)
    info.write('\tseed = {0}'.format(args.seed), log.info)
    info.write('\tnum_prior_samples = {0}'.format(args.num_prior_samples),
            log.info)
    info.write('\tsample_path = {0!r}'.format(sample_path), log.info)
    info.write('\t[[tool_paths]]', log.info)
    info.write('\t\tdpp_msbayes = {0}'.format(dpp_msbayes_path), log.info)
    info.write('\t\tmsbayes = {0}'.format(msbayes_path), log.info)

    info.write('\t[[config]]', log.debug)
    info.write('{0}'.format(str(cfg)), log.debug)

    ##########################################################################
    ## begin analysis --- generate samples

    start_time = datetime.datetime.now()

    if args.np > args.num_prior_samples:
        args.np = args.num_prior_samples
    batch_size, remainder = long_division(args.num_prior_samples, args.np)
    schema = 'abctoolbox'
    workers = []
    for i in range(args.np):
        sample_size = batch_size
        if i == (args.np - 1):
            sample_size += remainder
        w = MsBayesWorker(
                temp_fs = temp_fs,
                sample_size = sample_size,
                config_path = args.config,
                report_parameters = True,
                schema = schema,
                include_header = True,
                stat_patterns = stat_patterns,
                write_stats_file = False)
        workers.append(w)

    log.info('Generating samples...')
    workers = Manager.run_workers(
            workers = workers,
            num_processors = args.np)
    log.info('Parsing samples...')
    sample = get_dict_from_spreadsheets([w.prior_path for w in workers])

    log.info('Writing prior samples...')
    out, close = process_file_arg(sample_path, 'w',
            compresslevel = compress_level)
    for row in dict_line_iter(sample, sep = '\t'):
        out.write(row)
    if close:
        out.close()

    log.info('Creating plots...')

    if not plotting.MATPLOTLIB_AVAILABLE:
        log.warning(
                '`matplotlib` could not be imported, so the plot can not be\n'
                'produced. The data to create the plot can be found in:\n\t'
                '{0!r}'.format(sample_path))
        sys.exit(1)

    for stat_pattern in stat_patterns:
        found = False
        for stat, values in sample.iteritems():
            if stat_pattern.match(stat):
                values = [float(v) for v in values]
                found = True
                plot_path = os.path.join(args.output_dir,
                        'plot-{0}.pdf'.format(stat))
                summary = stats.get_summary(values)
                s = r'mean = {0:.4f} ({1:.4f}-{2:.4f})'.format(
                        summary['mean'],
                        summary['qi_95'][0],
                        summary['qi_95'][1])
                hd = plotting.HistData(x = values,
                        normed = True,
                        bins = 20,
                        histtype = 'bar',
                        align = 'mid',
                        orientation = 'vertical',
                        zorder = 0)
                hist = plotting.ScatterPlot(hist_data_list = [hd],
                        right_text = s)
                hist.left_text_size = 12.0
                hist.right_text_size = 12.0
                xticks = [i for i in hist.ax.get_xticks()]
                xtick_labels = [i for i in xticks]
                yticks = [i for i in hist.ax.get_yticks()]
                ytick_labels = [i for i in yticks]
                if len(xtick_labels) >= 8:
                    for i in range(1, len(xtick_labels), 2):
                        xtick_labels[i] = ''
                if len(ytick_labels) >= 8:
                    for i in range(1, len(ytick_labels), 2):
                        ytick_labels[i] = ''
                xticks_obj = plotting.Ticks(ticks = xticks,
                        labels = xtick_labels,
                        horizontalalignment = 'center')
                yticks_obj = plotting.Ticks(ticks = yticks,
                        labels = ytick_labels)
                hist.xticks_obj = xticks_obj
                hist.yticks_obj = yticks_obj

                plot_grid = plotting.PlotGrid(subplots = [hist],
                        num_columns = 1,
                        label_schema = None,
                        title = stat,
                        title_size = 14.0,
                        title_top = False,
                        y_title = 'Density',
                        y_title_position = 0.001,
                        y_title_size = 14.0,
                        height = 4.0,
                        width = 6.0,
                        auto_height = False)
                plot_grid.auto_adjust_margins = False
                plot_grid.margin_left = 0.04
                plot_grid.margin_bottom = 0.04 
                plot_grid.margin_right = 1.0 
                plot_grid.margin_top = 0.97
                plot_grid.reset_figure()
                plot_grid.savefig(plot_path)

        if not found:
            raise Exception('stat pattern {0!r} not found in simulated stats:'
                    '\n\t{1}'.format(stat_pattern, ', '.join(sample.keys())))

    stop_time = datetime.datetime.now()
    log.info('Done!')
    info.write('\t[[run_stats]]', log.info)
    info.write('\t\tstart_time = {0}'.format(str(start_time)), log.info)
    info.write('\t\tstop_time = {0}'.format(str(stop_time)), log.info)
    info.write('\t\ttotal_duration = {0}'.format(str(stop_time - start_time)),
            log.info)

    if not args.keep_temps:
        log.debug('purging temps...')
        temp_fs.purge()
def main_cli():
    description = '{name} {version}'.format(**_program_info)
    parser = argparse.ArgumentParser(
        description=description,
        formatter_class=argparse_utils.SmartHelpFormatter)
    parser.add_argument(
        '-c',
        '--config',
        type=argparse_utils.arg_is_config,
        required=True,
        help=('msBayes config file to be used to generate saturation '
              'plot.'))
    parser.add_argument(
        '-n',
        '--num-prior-samples',
        action='store',
        type=int,
        default=1000,
        help=('The number of prior samples to simulate for the '
              'saturation plot.'))
    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(
        '-o',
        '--output-dir',
        action='store',
        type=argparse_utils.arg_is_dir,
        help=('The directory in which all output files will be written. '
              'The default is to use the directory of the first observed '
              'config file.'))
    parser.add_argument(
        '--temp-dir',
        action='store',
        type=argparse_utils.arg_is_dir,
        help=('A directory to temporarily stage files. The default is to '
              'use the output directory.'))
    parser.add_argument(
        '-s',
        '--stat-prefixes',
        nargs='*',
        type=str,
        default=['pi', 'pi.net', 'wattTheta', 'tajD.denom'],
        help=('Prefixes of summary statistics to use in the analyses. '
              'The prefixes should be separated by spaces. '
              'Default: `-s pi pi.net wattTheta tajD.denom`.'))
    parser.add_argument('--sort-index',
                        action='store',
                        type=int,
                        default=0,
                        choices=range(12),
                        help=argparse_utils.get_sort_index_help_message())
    parser.add_argument('--compress',
                        action='store_true',
                        help='Compress plot data file.')
    parser.add_argument('--keep-temps',
                        action='store_true',
                        help='Keep all temporary files.')
    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.workers import MsBayesWorker
    from pymsbayes.utils.parsing import (get_patterns_from_prefixes,
                                         DEFAULT_STAT_PATTERNS,
                                         get_dict_from_spreadsheets,
                                         dict_line_iter)
    from pymsbayes.manager import Manager
    from pymsbayes.utils.tempfs import TempFileSystem
    from pymsbayes.utils import probability, stats
    from pymsbayes.utils.functions import long_division
    from pymsbayes.config import MsBayesConfig
    from pymsbayes.utils import GLOBAL_RNG, MSBAYES_SORT_INDEX, ToolPathManager
    from pymsbayes.fileio import process_file_arg
    from pymsbayes import plotting

    MSBAYES_SORT_INDEX.set_index(args.sort_index)

    # get full paths to tools
    msbayes_path = ToolPathManager.get_tool_full_path('msbayes.pl')
    dpp_msbayes_path = ToolPathManager.get_tool_full_path('dpp-msbayes.pl')

    if not args.output_dir:
        args.output_dir = os.path.dirname(args.config)
    info = InfoLogger(os.path.join(args.output_dir, 'pymsbayes-info.txt'))

    sample_path = os.path.join(args.output_dir, 'prior-sample.txt')
    if args.compress:
        sample_path += '.gz'

    if not args.temp_dir:
        args.temp_dir = args.output_dir
    temp_fs = TempFileSystem(parent=args.temp_dir, prefix='temp-files-')
    args.stat_prefixes = [s.rstrip('.') for s in args.stat_prefixes]
    stat_patterns = get_patterns_from_prefixes(
        [s + '.' for s in args.stat_prefixes], ignore_case=True)
    if not args.seed:
        args.seed = random.randint(1, 999999999)
    GLOBAL_RNG.seed(args.seed)
    compress_level = None
    if args.compress:
        compress_level = 9

    cfg = MsBayesConfig(args.config)
    num_taxon_pairs = cfg.npairs

    info.write('[pymsbayes]', log.info)
    info.write('\tprogram_name = {name}'.format(**_program_info), log.info)
    info.write('\tversion = {version}'.format(**_program_info), log.info)
    info.write('\tinvocation = {0!r}'.format(' '.join(sys.argv)), log.info)
    info.write('\toutput_directory = {0!r}'.format(args.output_dir), log.info)
    info.write('\ttemp_directory = {0!r}'.format(temp_fs.base_dir), log.info)
    info.write('\tsort_index = {0}'.format(MSBAYES_SORT_INDEX.current_value()),
               log.info)
    info.write(
        '\tstat_patterns = {0!r}'.format(', '.join(
            [p.pattern for p in stat_patterns])), log.info)
    info.write('\tseed = {0}'.format(args.seed), log.info)
    info.write('\tnum_prior_samples = {0}'.format(args.num_prior_samples),
               log.info)
    info.write('\tsample_path = {0!r}'.format(sample_path), log.info)
    info.write('\t[[tool_paths]]', log.info)
    info.write('\t\tdpp_msbayes = {0}'.format(dpp_msbayes_path), log.info)
    info.write('\t\tmsbayes = {0}'.format(msbayes_path), log.info)

    info.write('\t[[config]]', log.debug)
    info.write('{0}'.format(str(cfg)), log.debug)

    ##########################################################################
    ## begin analysis --- generate samples

    start_time = datetime.datetime.now()

    if args.np > args.num_prior_samples:
        args.np = args.num_prior_samples
    batch_size, remainder = long_division(args.num_prior_samples, args.np)
    schema = 'abctoolbox'
    workers = []
    for i in range(args.np):
        sample_size = batch_size
        if i == (args.np - 1):
            sample_size += remainder
        w = MsBayesWorker(temp_fs=temp_fs,
                          sample_size=sample_size,
                          config_path=args.config,
                          report_parameters=True,
                          schema=schema,
                          include_header=True,
                          stat_patterns=stat_patterns,
                          write_stats_file=False)
        workers.append(w)

    log.info('Generating samples...')
    workers = Manager.run_workers(workers=workers, num_processors=args.np)
    log.info('Parsing samples...')
    sample = get_dict_from_spreadsheets([w.prior_path for w in workers])

    log.info('Writing prior samples...')
    out, close = process_file_arg(sample_path,
                                  'w',
                                  compresslevel=compress_level)
    for row in dict_line_iter(sample, sep='\t'):
        out.write(row)
    if close:
        out.close()

    log.info('Creating plots...')

    if not plotting.MATPLOTLIB_AVAILABLE:
        log.warning(
            '`matplotlib` could not be imported, so the plot can not be\n'
            'produced. The data to create the plot can be found in:\n\t'
            '{0!r}'.format(sample_path))
        sys.exit(1)

    for stat_pattern in stat_patterns:
        found = False
        for stat, values in sample.iteritems():
            if stat_pattern.match(stat):
                values = [float(v) for v in values]
                found = True
                plot_path = os.path.join(args.output_dir,
                                         'plot-{0}.pdf'.format(stat))
                summary = stats.get_summary(values)
                s = r'mean = {0:.4f} ({1:.4f}-{2:.4f})'.format(
                    summary['mean'], summary['qi_95'][0], summary['qi_95'][1])
                hd = plotting.HistData(x=values,
                                       normed=True,
                                       bins=20,
                                       histtype='bar',
                                       align='mid',
                                       orientation='vertical',
                                       zorder=0)
                hist = plotting.ScatterPlot(hist_data_list=[hd], right_text=s)
                hist.left_text_size = 12.0
                hist.right_text_size = 12.0
                xticks = [i for i in hist.ax.get_xticks()]
                xtick_labels = [i for i in xticks]
                yticks = [i for i in hist.ax.get_yticks()]
                ytick_labels = [i for i in yticks]
                if len(xtick_labels) >= 8:
                    for i in range(1, len(xtick_labels), 2):
                        xtick_labels[i] = ''
                if len(ytick_labels) >= 8:
                    for i in range(1, len(ytick_labels), 2):
                        ytick_labels[i] = ''
                xticks_obj = plotting.Ticks(ticks=xticks,
                                            labels=xtick_labels,
                                            horizontalalignment='center')
                yticks_obj = plotting.Ticks(ticks=yticks, labels=ytick_labels)
                hist.xticks_obj = xticks_obj
                hist.yticks_obj = yticks_obj

                plot_grid = plotting.PlotGrid(subplots=[hist],
                                              num_columns=1,
                                              label_schema=None,
                                              title=stat,
                                              title_size=14.0,
                                              title_top=False,
                                              y_title='Density',
                                              y_title_position=0.001,
                                              y_title_size=14.0,
                                              height=4.0,
                                              width=6.0,
                                              auto_height=False)
                plot_grid.auto_adjust_margins = False
                plot_grid.margin_left = 0.04
                plot_grid.margin_bottom = 0.04
                plot_grid.margin_right = 1.0
                plot_grid.margin_top = 0.97
                plot_grid.reset_figure()
                plot_grid.savefig(plot_path)

        if not found:
            raise Exception('stat pattern {0!r} not found in simulated stats:'
                            '\n\t{1}'.format(stat_pattern,
                                             ', '.join(sample.keys())))

    stop_time = datetime.datetime.now()
    log.info('Done!')
    info.write('\t[[run_stats]]', log.info)
    info.write('\t\tstart_time = {0}'.format(str(start_time)), log.info)
    info.write('\t\tstop_time = {0}'.format(str(stop_time)), log.info)
    info.write('\t\ttotal_duration = {0}'.format(str(stop_time - start_time)),
               log.info)

    if not args.keep_temps:
        log.debug('purging temps...')
        temp_fs.purge()