def create_negros_panay_plots(
config_path,
ordered_info_path,
unordered_info_path,
out_dir):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
dpp_num_div_values, dpp_div_model_values, dpp_ordered_model_values = get_dpp_prior_values(
config_path = config_path,
num_samples = 1000000)
ordered_hists = get_histograms(config_path = config_path,
info_path = ordered_info_path,
num_div_values = dpp_num_div_values,
div_model_values = dpp_div_model_values,
ordered_div_model_values = dpp_ordered_model_values,
iteration_index = 249,
y_limits = [0.25, 0.25, 0.15, 0.15],
xtick_label_size = 11.0)
unordered_hists = get_histograms(config_path = config_path,
info_path = unordered_info_path,
num_div_values = dpp_num_div_values,
div_model_values = dpp_div_model_values,
ordered_div_model_values = dpp_ordered_model_values,
iteration_index = 249,
y_limits = [0.25, 0.25, 0.15, 0.15],
xtick_label_size = 11.0)
hists = [unordered_hists[0]] + ordered_hists
row_labels = [r'Posterior $\mathbb{M}_{DPP}$', r'Posterior $\mathbb{M}^{\circ}_{DPP}$', 'Prior', r'Prior $\mathbb{E}(p(\mathbf{t}))$', r'Prior $\mathbb{E}(p(\mathbf{t^{\circ}}))$']
pg = PlotGrid(subplots = hists,
num_columns = 1,
share_x = True,
share_y = False,
height = 7.2,
width = 2.5,
auto_height = False,
title = r'Number of divergences, $|\tau|$',
title_top = False,
title_size = 10.0,
y_title = 'Probability',
row_labels = row_labels,
row_label_offset = 0.08,
row_label_size = 12.0)
pg.auto_adjust_margins = False
pg.margin_top = 0.98
pg.margin_bottom = 0.03
pg.margin_right = 0.88
pg.margin_left = 0.05
pg.padding_between_vertical = 1.0
pg.reset_figure()
pg.set_shared_x_limits()
pg.set_shared_y_limits(by_row = True)
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'negros-panay-psi.pdf'))
def create_plots(dpp_info_path, old_info_path, out_dir):
# matplotlib.rc('text',**{'usetex': True})
# old = ([1] * 992) + ([2] * 8)
if not os.path.exists(out_dir):
os.mkdir(out_dir)
dmc_sim = DMCSimulationResults(dpp_info_path)
dmc_sim_old = DMCSimulationResults(old_info_path)
psi_path = dmc_sim.get_result_path_prefix(1, 1, 1) + '99-psi-results.txt'
psi_path_old = dmc_sim_old.get_result_path_prefix(1, 1, 1) + '99-psi-results.txt'
psis = []
for d in spreadsheet_iter([psi_path]):
n = int(round(10000 * float(d['estimated_prob'])))
psis.extend([int(d['num_of_div_events'])] * n)
psis_old = []
for d in spreadsheet_iter([psi_path_old]):
n = int(round(10000 * float(d['estimated_prob'])))
psis_old.extend([int(d['num_of_div_events'])] * n)
bins = range(1, dmc_sim.num_taxon_pairs + 2)
hd = HistData(x = psis,
normed = True,
bins = bins,
histtype = 'bar',
align = 'mid',
orientation = 'vertical',
zorder = 0)
# hd_old= HistData(x = old,
hd_old= HistData(x = psis_old,
normed = True,
bins = bins,
histtype = 'bar',
align = 'mid',
orientation = 'vertical',
zorder = 0)
tick_labels = []
for x in bins[0:-1]:
if x % 2:
tick_labels.append(str(x))
else:
tick_labels.append('')
xticks_obj = Ticks(ticks = bins,
labels = tick_labels,
horizontalalignment = 'left')
hist = ScatterPlot(hist_data_list = [hd],
x_label = 'Number of divergence events',
y_label = 'Posterior probability',
xticks_obj = xticks_obj)
hist_old = ScatterPlot(hist_data_list = [hd_old],
x_label = 'Number of divergence events',
y_label = 'Posterior probability',
xticks_obj = xticks_obj)
hist.set_xlim(left = bins[0], right = bins[-1])
hist_old.set_xlim(left = bins[0], right = bins[-1])
hist.set_ylim(bottom = 0.0, top = 0.1)
pg = PlotGrid(subplots = [hist],
num_columns = 1,
height = 4.0,
width = 6.5,
label_schema = None,
auto_height = False)
pg.auto_adjust_margins = False
pg.margin_top = 1
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior.pdf'))
# hist.set_ylim(bottom = 0.0, top = 1.0)
hist.set_ylim(bottom = 0.0, top = 0.5)
hist.set_ylabel('')
# hist_old.set_ylim(bottom = 0.0, top = 1.0)
hist_old.set_ylim(bottom = 0.0, top = 0.5)
pg = PlotGrid(subplots = [hist_old, hist],
num_columns = 2,
height = 3.5,
width = 8.0,
share_x = True,
share_y = True,
label_schema = None,
auto_height = False,
# column_labels = [r'\texttt{msBayes}', r'\texttt{dpp-msbayes}'],
column_labels = [r'msBayes', r'dpp-msbayes'],
column_label_size = 18.0)
pg.auto_adjust_margins = False
pg.margin_top = 0.92
pg.padding_between_horizontal = 1.0
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior-old-vs-dpp.pdf'))
pg.label_schema = 'uppercase'
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior-old-vs-dpp-labels.pdf'))
prior_psis = get_dpp_psi_values(dmc_sim.num_taxon_pairs, 1.5, 18.099702, num_sims = 100000)
prior_hd = HistData(x = prior_psis,
normed = True,
bins = bins,
histtype = 'bar',
align = 'mid',
orientation = 'vertical',
zorder = 0)
prior_hist = ScatterPlot(hist_data_list = [prior_hd],
x_label = 'Number of divergence events',
y_label = 'Probability',
xticks_obj = xticks_obj)
prior_hist.set_xlim(left = bins[0], right = bins[-1])
prior_hist.set_ylim(bottom = 0.0, top = 0.12)
hist.set_ylim(bottom = 0.0, top = 0.12)
pg = PlotGrid(subplots = [prior_hist, hist],
num_columns = 2,
height = 3.5,
width = 8.0,
share_x = True,
share_y = True,
label_schema = None,
auto_height = False,
# column_labels = [r'\texttt{msBayes}', r'\texttt{dpp-msbayes}'],
column_labels = [r'Prior', r'Posterior'],
column_label_size = 18.0)
pg.auto_adjust_margins = False
pg.margin_top = 0.92
pg.padding_between_horizontal = 1.0
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior-prior.pdf'))
pg.label_schema = 'uppercase'
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior-prior-lablels.pdf'))
prior_psis_old = []
for i in range(22):
prior_psis_old.extend([i + 1] * 100)
prior_hd_old = HistData(x = prior_psis_old,
normed = True,
bins = bins,
histtype = 'bar',
align = 'mid',
orientation = 'vertical',
zorder = 0)
prior_hist_old = ScatterPlot(hist_data_list = [prior_hd_old],
x_label = 'Number of divergence events',
y_label = 'Prior probability',
xticks_obj = xticks_obj)
prior_hist.set_xlim(left = bins[0], right = bins[-1])
prior_hist.set_ylim(bottom = 0.0, top = 0.5)
hist.set_ylim(bottom = 0.0, top = 0.5)
prior_hist.set_ylim(bottom = 0.0, top = 0.5)
for h in [hist_old, hist, prior_hist_old, prior_hist]:
h.set_ylabel(ylabel = '')
h.set_xlabel(xlabel = '')
h.set_title_text('')
h.set_extra_y_label('')
pg = PlotGrid(subplots = [hist_old, hist, prior_hist_old, prior_hist],
num_columns = 2,
height = 6.0,
width = 8.0,
share_x = True,
share_y = False,
label_schema = None,
auto_height = False,
title = r'Number of divergence events',
title_top = False,
title_size = 16.0,
y_title = 'Probability',
y_title_size = 16.0,
column_labels = [r'msBayes', r'dpp-msbayes'],
row_labels = ['Posterior', 'Prior'],
column_label_offset = 0.07,
column_label_size = 22.0,
row_label_offset = 0.04,
row_label_size = 20.0)
pg.auto_adjust_margins = False
pg.margin_top = 0.94
pg.margin_bottom = 0.045
pg.margin_right = 0.95
pg.margin_left = 0.045
pg.padding_between_vertical = 0.5
pg.padding_between_horizontal = 1.0
pg.reset_figure()
pg.set_shared_x_limits()
pg.set_shared_y_limits(by_row = True)
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-dpp-psi-posterior-old-vs-dpp-with-prior.pdf'))
def create_plots(
dpp_config_path,
uniform_config_path,
old_config_path,
dpp_info_path,
dpp_simple_info_path,
dpp_inform_info_path,
uniform_info_path,
old_info_path,
out_dir):
# matplotlib.rc('text',**{'usetex': True})
if not os.path.exists(out_dir):
os.mkdir(out_dir)
dpp_num_div_values, dpp_div_model_values, dpp_ordered_model_values = get_dpp_prior_values(
config_path = dpp_config_path,
num_samples = 1000000)
dpp_hists = get_histograms(config_path = dpp_config_path,
info_path = dpp_info_path,
num_div_values = dpp_num_div_values,
div_model_values = dpp_div_model_values,
ordered_div_model_values = dpp_ordered_model_values)
dpp_simple_hists = get_histograms(config_path = dpp_config_path,
info_path = dpp_simple_info_path,
num_div_values = dpp_num_div_values,
div_model_values = dpp_div_model_values,
ordered_div_model_values = dpp_ordered_model_values)
dpp_inform_hists = get_histograms(config_path = dpp_config_path,
info_path = dpp_inform_info_path,
num_div_values = dpp_num_div_values,
div_model_values = dpp_div_model_values,
ordered_div_model_values = dpp_ordered_model_values)
uniform_hists = get_histograms(config_path = uniform_config_path,
info_path = uniform_info_path,
num_samples = 100000)
old_hists = get_histograms(config_path = old_config_path,
info_path = old_info_path,
num_samples = 100000)
hists = []
for i in range(len(dpp_hists)):
hists.append(old_hists[i])
hists.append(uniform_hists[i])
hists.append(dpp_hists[i])
hists.append(dpp_inform_hists[i])
hists.append(dpp_simple_hists[i])
column_labels = [r'$\mathbf{M}_{msBayes}$', r'$\mathbf{M}_{Uniform}$', r'$\mathbf{M}_{DPP_{ }}$', r'$\mathbf{M}^{inform}_{DPP}$', r'$\mathbf{M}^{simple}_{DPP}$']
row_labels = ['Posterior', 'Prior', r'Prior $\mathbb{E}(p(\mathbf{t}))$', r'Prior $\mathbb{E}(p(\mathbf{t^{\circ}}))$']
pg = PlotGrid(subplots = hists,
num_columns = 5,
share_x = True,
share_y = False,
height = 7.2,
width = 11.0,
auto_height = False,
title = r'Number of divergence events, $|\tau|$',
title_top = False,
y_title = 'Probability',
column_labels = column_labels,
column_label_offset = 0.17,
row_labels = row_labels,
row_label_offset = 0.08)
pg.auto_adjust_margins = False
pg.margin_top = 0.93
pg.margin_bottom = 0.04
pg.margin_right = 0.965
pg.padding_between_vertical = 1.0
pg.reset_figure()
pg.set_shared_x_limits()
pg.set_shared_y_limits(by_row = True)
pg.reset_figure()
pg.savefig(os.path.join(out_dir, 'philippines-psi.pdf'))