def create_plots(info_path):
output_dir = os.path.join(os.path.dirname(info_path), 'plots')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
prior_prob_omega_less_than = 0.0887
psi_results, omega_results, tau_results = parse_results(info_path)
cfg_to_psi = {}
cfg_to_psi_prob = {}
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
cfg_to_omega = {}
cfg_to_omega_prob = {}
cfg_to_omega_true_ests = {}
cfg_to_omega_true_ests_glm = {}
for cfg, omega in omega_results.iteritems():
cfg_to_omega[cfg] = omega.median
cfg_to_omega_prob[cfg] = omega.prob
cfg_to_omega_true_ests[cfg] = {'x': omega.true, 'y': omega.median}
cfg_to_omega_true_ests_glm[cfg] = {'x': omega.true, 'y': omega.mode_glm}
cfg_to_num_excluded = {}
cfg_to_num_excluded_glm = {}
cfg_to_prob_of_exclusion = {}
cfg_to_prob_of_exclusion_glm = {}
cfg_to_prob_of_bf_ex = {}
cfg_to_prob_of_bf_ex_glm = {}
bf_10_exclusion_prob = None
for cfg, tau in tau_results.iteritems():
cfg_to_num_excluded[cfg] = tau.num_excluded
cfg_to_num_excluded_glm[cfg] = tau.num_excluded_glm
cfg_to_prob_of_exclusion[cfg] = tau.prob_of_exclusion
cfg_to_prob_of_exclusion_glm[cfg] = tau.prob_of_exclusion_glm
cfg_to_prob_of_bf_ex[cfg] = tau.prob_of_bf_exclusion
cfg_to_prob_of_bf_ex_glm[cfg] = tau.prob_of_bf_exclusion_glm
if not bf_10_exclusion_prob:
bf_10_exclusion_prob = tau.bf_10_exclusion_prob
psi_plot = PowerPlotGrid(observed_config_to_estimates = cfg_to_psi,
variable = 'psi',
num_columns = 2)
fig = psi_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_psi_mode.pdf'))
psi_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_prob,
variable = 'psi',
div_model_prior = 'psi',
bayes_factor = 10,
num_columns = 2)
fig = psi_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_psi_prob.pdf'))
omega_plot = PowerPlotGrid(observed_config_to_estimates = cfg_to_omega,
variable = 'omega',
num_columns = 2)
fig = omega_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_omega_median.pdf'))
omega_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_prob,
variable = 'omega',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = prior_prob_omega_less_than,
num_columns = 2)
fig = omega_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_omega_prob.pdf'))
omega_accuracy_plot = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests,
num_columns = 2)
fig = omega_accuracy_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_accuracy_omega_median.pdf'))
omega_accuracy_plot_glm = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests_glm,
num_columns = 2)
fig = omega_accuracy_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir, 'power_accuracy_omega_mode_glm.pdf'))
ex_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_prob_of_exclusion,
variable = 'tau_exclusion',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = bf_10_exclusion_prob,
cfg_to_prob_of_bf_exclusion = cfg_to_prob_of_bf_ex,
num_columns = 2)
fig = ex_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_prob_exclusion.pdf'))
ex_prob_plot_glm = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_prob_of_exclusion_glm,
variable = 'tau_exclusion',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = bf_10_exclusion_prob,
cfg_to_prob_of_bf_exclusion = cfg_to_prob_of_bf_ex_glm,
num_columns = 2)
fig = ex_prob_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir, 'power_prob_exclusion_glm.pdf'))
ex_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_num_excluded,
variable = 'tau_exclusion',
num_columns = 2)
fig = ex_plot.create_grid()
fig.savefig(os.path.join(output_dir, 'power_num_excluded.pdf'))
ex_plot_glm = PowerPlotGrid(
observed_config_to_estimates = cfg_to_num_excluded_glm,
variable = 'tau_exclusion',
num_columns = 2)
fig = ex_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir, 'power_num_excluded_glm.pdf'))
def create_plots(info_path,
observed_prefixes_to_include = None,
observed_suffixes_to_include = None,
prior_suffixes_to_include = None):
dmc_sim = DMCSimulationResults(info_path)
prior_configs = {}
for k, v in dmc_sim.prior_index_to_config.iteritems():
prior_configs[k] = MsBayesConfig(v)
# output_dir = os.path.join(os.path.dirname(info_path), 'plots')
output_dir = project_util.IMAGE_DIR
if not os.path.exists(output_dir):
os.mkdir(output_dir)
prior_prob_omega_less_than = {
1: 0.0025575,
2: 0.049283,
3: 0.002171,
4: 0.049191}
psi_res, omega_res, prior_index_to_name = parse_results(dmc_sim,
observed_prefixes_to_include = observed_prefixes_to_include,
observed_suffixes_to_include = observed_suffixes_to_include,
prior_suffixes_to_include = prior_suffixes_to_include)
for observed_name in psi_res.iterkeys():
for prior_index in psi_res[observed_name].iterkeys():
prior_name = prior_index_to_name[prior_index]
div_model_prior = prior_name
if prior_name in ['old', 'u-shaped']:
div_model_prior = 'psi'
dpp_concentration_mean = None
if div_model_prior == 'dpp':
dpp_concentration_mean = prior_configs[
prior_index].dpp_concentration.mean
prior_prob_omega = prior_prob_omega_less_than[prior_index]
psi_results = psi_res[observed_name][prior_index]
omega_results = omega_res[observed_name][prior_index]
prefix = '_'.join([observed_name, prior_name])
cfg_to_psi = {}
cfg_to_psi_prob = {}
cfg_to_psi_glm = {}
cfg_to_psi_prob_glm = {}
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
cfg_to_psi_glm[cfg] = psi.mode_glm
cfg_to_psi_prob_glm[cfg] = psi.prob_glm
cfg_to_omega = {}
cfg_to_omega_prob = {}
cfg_to_omega_glm = {}
cfg_to_omega_prob_glm = {}
cfg_to_omega_true_ests = {}
cfg_to_omega_true_ests_glm = {}
for cfg, omega in omega_results.iteritems():
cfg_to_omega[cfg] = omega.median
cfg_to_omega_prob[cfg] = omega.prob
cfg_to_omega_glm[cfg] = omega.mode_glm
cfg_to_omega_prob_glm[cfg] = omega.prob_glm
cfg_to_omega_true_ests[cfg] = {'x': omega.true, 'y': omega.median}
cfg_to_omega_true_ests_glm[cfg] = {'x': omega.true, 'y': omega.mode_glm}
num_columns = len(observed_suffixes_to_include)
width = 15
height = 3.5
column_label_offset = 0.14
margin_top = 0.83
margin_bottom = 0.1
margin_left = 0.025
margin_right = 0.95
column_label_size = 26.0
title_size = 22.0
y_title_size = 20.0
right_text_size = 14.0
row_labels = []
if prior_name == 'old':
row_labels.append(r'$M_{msBayes}$')
elif prior_name == 'u-shaped':
row_labels.append(r'$M_{Ushaped}$')
elif prior_name == 'uniform':
row_labels.append(r'$M_{Uniform}$')
elif prior_name == 'dpp':
row_labels.append(r'$M_{DPP}$')
else:
row_labels.append('')
row_label_size = 32.0
row_label_offset = 0.08
psi_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_psi,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
num_columns = num_columns,
width = width,
height = height,
x_title = r'Estimated number of divergence events (mode)',
y_title_size = y_title_size,
include_right_text = False,
text_size = right_text_size,
xtick_label_size = 14.0)
pg = psi_plot.create_grid()
pg.label_schema = None
column_labels = []
for cfg, sp in psi_plot.cfg_to_subplot.iteritems():
column_labels.append((cfg.tau.maximum, tau_prior_in_generations(cfg)))
sp.set_left_text('')
pg.column_labels = [t for (c, t) in sorted(column_labels, key = lambda x : x[0])]
pg.margin_bottom = margin_bottom
pg.margin_top = margin_top
pg.margin_left = margin_left
pg.margin_right = margin_right
pg.padding_between_horizontal = 2.0
pg.column_label_offset = column_label_offset
pg.column_label_size = column_label_size
pg.title_size = title_size
pg.row_labels = row_labels
pg.row_label_size = row_label_size
pg.row_label_offset = row_label_offset
pg.reset_figure()
pg.set_shared_x_limits()
pg.set_shared_y_limits()
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode.pdf'))
pg.column_labels = ['' for x in column_labels]
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode_headless.pdf'))
psi_plot.width = 5.0
psi_plot.height = 4.0
pg = psi_plot.create_column_grid(
subplot_indices_to_exclude = [0,1,2],
x_title_size = 16.0,
y_title_size = 15.0,
add_column_labels = True,
column_label_size = 18.0,
column_label_offset = 0.14,
plot_label_size = 12.0,
right_text_size = 10.0,
x_tick_label_size = 14.0,
share_x = False,
share_y = False)
pg.label_schema = None
pg.column_labels = [[t for (c, t) in sorted(column_labels, key = lambda x : x[0])][-1]]
pg.margin_bottom = 0.07
pg.margin_top = 0.88
pg.margin_left = 0.07
pg.margin_right = 0.99
pg.column_label_offset = 0.04
pg.column_label_size = column_label_size
# pg.title_size = title_size
pg.row_labels = None
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode_last.pdf'))
psi_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_prob,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
draw_bayes_factor_line = False,
x_title = r'Posterior probability of one divergence',
y_title_size = y_title_size,
width = width,
height = height,
num_columns = num_columns,
text_size = right_text_size,
xtick_label_size = 14.0,
pretty_xtick_labels = True)
pg = psi_prob_plot.create_grid()
pg.label_schema = None
column_labels = []
for cfg, sp in psi_prob_plot.cfg_to_subplot.iteritems():
column_labels.append((cfg.tau.maximum, tau_prior_in_generations(cfg)))
sp.set_left_text('')
sp.set_right_text('')
pg.column_labels = [t for (c, t) in sorted(column_labels, key = lambda x : x[0])]
pg.margin_bottom = margin_bottom
pg.margin_top = margin_top
pg.margin_left = margin_left
pg.margin_right = margin_right
pg.column_label_offset = column_label_offset
pg.column_label_size = column_label_size
pg.padding_between_horizontal = 1.0
pg.title_size = title_size
pg.row_labels = row_labels
pg.row_label_size = row_label_size
pg.row_label_offset = row_label_offset
pg.reset_figure()
pg.set_shared_x_limits()
pg.set_shared_y_limits()
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_psi_prob.pdf'))
pg.column_labels = ['' for x in column_labels]
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_psi_prob_headless.pdf'))
# psi_prob_plot_glm = ProbabilityPowerPlotGrid(
# observed_config_to_estimates = cfg_to_psi_prob_glm,
# variable = 'psi',
# variable_symbol = r'|\mathbf{\tau}|',
# div_model_prior = div_model_prior,
# dpp_concentration_mean = dpp_concentration_mean,
# bayes_factor = 10,
# num_columns = 2)
# fig = psi_prob_plot_glm.create_grid()
# fig.savefig(os.path.join(output_dir,
# prefix + '_power_psi_prob_glm.pdf'))
omega_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_omega,
variable = 'omega',
variable_symbol = r'D_T',
num_columns = num_columns,
width = width,
height = height,
x_title = r'Estimated variance in divergence times (median)',
y_title_size = y_title_size,
text_size = right_text_size)
pg = omega_plot.create_grid()
pg.label_schema = None
column_labels = []
for cfg, sp in omega_plot.cfg_to_subplot.iteritems():
column_labels.append((cfg.tau.maximum, tau_prior_in_generations(cfg)))
sp.set_left_text('')
pg.column_labels = [t for (c, t) in sorted(column_labels, key = lambda x : x[0])]
pg.margin_bottom = margin_bottom
pg.margin_top = margin_top
pg.margin_left = margin_left
pg.margin_right = margin_right
pg.column_label_offset = column_label_offset
pg.column_label_size = column_label_size
pg.title_size = title_size
pg.row_labels = row_labels
pg.row_label_size = row_label_size
pg.row_label_offset = row_label_offset
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_omega_median.pdf'))
pg.column_labels = ['' for x in column_labels]
pg.reset_figure()
pg.savefig(os.path.join(output_dir,
prefix + '_power_omega_median_headless.pdf'))
def create_plots(info_path):
dmc_sim = DMCSimulationResults(info_path)
prior_configs = {}
for k, v in dmc_sim.prior_index_to_config.iteritems():
prior_configs[k] = MsBayesConfig(v)
output_dir = os.path.join(os.path.dirname(info_path), 'plots')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
(psi_res, cv_res, prior_index_to_name,
prior_index_to_model_type) = parse_results(dmc_sim)
for prior_index, prior_name in prior_index_to_name.iteritems():
cfg_to_psi = {}
cfg_to_psi_prob = {}
cfg_to_cv = {}
cfg_to_cv_prob = {}
cfg_to_cv_true_ests = {}
prefix = prior_name
for observed_name in psi_res.iterkeys():
div_model_prior = prior_index_to_model_type[prior_index]
dpp_concentration_mean = None
if div_model_prior == 'dpp':
dpp_concentration_mean = prior_configs[
prior_index].dpp_concentration.mean
psi_results = psi_res[observed_name][prior_index]
cv_results = cv_res[observed_name][prior_index]
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
for cfg, cv in cv_results.iteritems():
cfg_to_cv[cfg] = cv.median
cfg_to_cv_prob[cfg] = cv.prob
cfg_to_cv_true_ests[cfg] = {'x': cv.true, 'y': cv.median}
psi_plot = PowerPlotGrid(observed_config_to_estimates=cfg_to_psi,
variable='psi',
variable_symbol=r'|\mathbf{\tau}|',
num_columns=2,
margin_top=0.975)
fig = psi_plot.create_grid()
fig.savefig(os.path.join(output_dir, prefix + '_power_psi_mode.pdf'))
psi_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates=cfg_to_psi_prob,
variable='psi',
variable_symbol=r'|\mathbf{\tau}|',
div_model_prior=div_model_prior,
dpp_concentration_mean=dpp_concentration_mean,
bayes_factor=10,
num_columns=2)
fig = psi_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir, prefix + '_power_psi_prob.pdf'))
cv_accuracy_plot = AccuracyPowerPlotGrid(
observed_config_to_estimates=cfg_to_cv_true_ests,
variable_symbol=r'CV_T',
num_columns=2,
padding_between_vertical=2.0,
margin_left=0.04,
margin_bottom=0.03)
fig = cv_accuracy_plot.create_grid()
fig.savefig(
os.path.join(output_dir, prefix + '_accuracy_cv_median.pdf'))
def create_plots(info_path):
output_dir = os.path.join(os.path.dirname(info_path), 'plots')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
prior_prob_omega_less_than = 0.0887
psi_results, omega_results, tau_results = parse_results(info_path)
cfg_to_psi = {}
cfg_to_psi_prob = {}
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
cfg_to_omega = {}
cfg_to_omega_prob = {}
cfg_to_omega_true_ests = {}
cfg_to_omega_true_ests_glm = {}
for cfg, omega in omega_results.iteritems():
cfg_to_omega[cfg] = omega.median
cfg_to_omega_prob[cfg] = omega.prob
cfg_to_omega_true_ests[cfg] = {'x': omega.true, 'y': omega.median}
cfg_to_omega_true_ests_glm[cfg] = {'x': omega.true, 'y': omega.mode_glm}
cfg_to_num_excluded = {}
cfg_to_num_excluded_glm = {}
cfg_to_prob_of_exclusion = {}
cfg_to_prob_of_exclusion_glm = {}
cfg_to_prob_of_bf_ex = {}
cfg_to_prob_of_bf_ex_glm = {}
bf_10_exclusion_prob = None
for cfg, tau in tau_results.iteritems():
cfg_to_num_excluded[cfg] = tau.num_excluded
cfg_to_num_excluded_glm[cfg] = tau.num_excluded_glm
cfg_to_prob_of_exclusion[cfg] = tau.prob_of_exclusion
cfg_to_prob_of_exclusion_glm[cfg] = tau.prob_of_exclusion_glm
cfg_to_prob_of_bf_ex[cfg] = tau.prob_of_bf_exclusion
cfg_to_prob_of_bf_ex_glm[cfg] = tau.prob_of_bf_exclusion_glm
if not bf_10_exclusion_prob:
bf_10_exclusion_prob = tau.bf_10_exclusion_prob
psi_plot = PowerPlotGrid(observed_config_to_estimates = cfg_to_psi,
variable = 'psi',
num_columns = 2,
x_title = r'Estimated number of divergence events, $\hat{\Psi}$',
y_title = 'Density',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.835,
padding_between_horizontal = 0.5)
fig = psi_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 8.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-psi-mode-6.pdf'))
psi_plot.width = 10.0
fig = psi_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 12.0,
x_tick_label_size = 8.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-psi-mode-4.pdf'))
omega_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_prob,
variable = 'omega',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = prior_prob_omega_less_than,
num_columns = 2,
x_title = (r'Posterior probability of one divergence, '
r'$p(\Omega < 0.01 \, | \, B_{{\epsilon}}(S*))$'),
y_title = 'Density',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.835,
padding_between_horizontal = 0.5)
fig = omega_prob_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-omega-prob-6.pdf'))
omega_prob_plot.width = 10.0
fig = omega_prob_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-omega-prob-4.pdf'))
omega_prob_plot.label_offset = 6
omega_prob_plot.width = 14.0
fig = omega_prob_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-omega-prob-6-panel.pdf'))
omega_prob_plot.label_offset = 4
omega_prob_plot.width = 10.0
fig = omega_prob_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-omega-prob-4-panel.pdf'))
omega_accuracy_plot = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests,
num_columns = 2,
x_title = r'True variance of divergence times, $\Omega$',
y_title = r'$\hat{\Omega}$',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.825,
padding_between_horizontal = 0.5)
fig = omega_accuracy_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-median-6.pdf'))
omega_accuracy_plot.width = 10.0
fig = omega_accuracy_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-median-4.pdf'))
omega_accuracy_plot_glm = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests_glm,
num_columns = 2,
x_title = r'True variance of divergence times, $\Omega$',
y_title = r'$\hat{\Omega}$',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.825,
padding_between_horizontal = 0.5)
fig = omega_accuracy_plot_glm.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-mode-glm-6.pdf'))
omega_accuracy_plot_glm.width = 10.0
fig = omega_accuracy_plot_glm.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-mode-glm-4.pdf'))
omega_accuracy_plot_glm.label_offset = 6
omega_accuracy_plot_glm.width = 14.0
fig = omega_accuracy_plot_glm.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-mode-glm-6-panel.pdf'))
omega_accuracy_plot_glm.label_offset = 4
omega_accuracy_plot_glm.width = 10.0
fig = omega_accuracy_plot_glm.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = False,
share_y = False)
fig.savefig(os.path.join(output_dir, 'power-accuracy-omega-mode-glm-4-panel.pdf'))
ex_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_prob_of_exclusion,
variable = 'tau_exclusion',
div_model_prior = 'psi',
bayes_factor = 10,
bayes_factor_prob = bf_10_exclusion_prob,
draw_bayes_factor_line = False,
cfg_to_prob_of_bf_exclusion = cfg_to_prob_of_bf_ex,
num_columns = 2,
x_title = (r'Posterior probability of excluding true parameters, $p(\mathbf{\tau} \, \notin \, '
r'M \, | \, B_{\epsilon}(S*))$'),
y_title = 'Density',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.835,
padding_between_horizontal = 0.5)
fig = ex_prob_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-prob-exclusion-6.pdf'))
ex_prob_plot.width = 10.0
fig = ex_prob_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-prob-exclusion-4.pdf'))
ex_prob_plot.label_offset = 6
ex_prob_plot.width = 14.0
fig = ex_prob_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-prob-exclusion-6-panel.pdf'))
ex_prob_plot.label_offset = 4
ex_prob_plot.width = 10.0
fig = ex_prob_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
add_column_labels = False,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 10.0,
x_tick_label_size = 10.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-prob-exclusion-4-panel.pdf'))
ex_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_num_excluded,
variable = 'tau_exclusion',
num_columns = 2,
x_title = r'Number of true $\tau$ excluded',
y_title = 'Density',
width = 14.0,
height = 2.8,
auto_height = False,
auto_adjust_margins = False,
margin_left = 0.025,
margin_bottom = 0.1,
margin_right = 1,
margin_top = 0.835,
padding_between_horizontal = 0.5)
fig = ex_plot.create_column_grid(
subplot_indices_to_exclude = [],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
x_tick_label_size = 8.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-num-excluded-6.pdf'))
ex_plot.width = 10.0
fig = ex_plot.create_column_grid(
subplot_indices_to_exclude = [1,3],
x_title_size = 14.0,
y_title_size = 14.0,
column_label_size = 16.0,
column_label_offset = 0.14,
right_text_size = 12.0,
x_tick_label_size = 8.0,
share_x = True,
share_y = True)
fig.savefig(os.path.join(output_dir, 'power-num-excluded-4.pdf'))
def create_plots(info_path):
dmc_sim = DMCSimulationResults(info_path)
prior_configs = {}
for k, v in dmc_sim.prior_index_to_config.iteritems():
prior_configs[k] = MsBayesConfig(v)
output_dir = os.path.join(os.path.dirname(info_path), 'plots')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
(psi_res, cv_res, prior_index_to_name,
prior_index_to_model_type) = parse_results(dmc_sim)
for prior_index, prior_name in prior_index_to_name.iteritems():
cfg_to_psi = {}
cfg_to_psi_prob = {}
cfg_to_cv = {}
cfg_to_cv_prob = {}
cfg_to_cv_true_ests = {}
prefix = prior_name
for observed_name in psi_res.iterkeys():
div_model_prior = prior_index_to_model_type[prior_index]
dpp_concentration_mean = None
if div_model_prior == 'dpp':
dpp_concentration_mean = prior_configs[
prior_index].dpp_concentration.mean
psi_results = psi_res[observed_name][prior_index]
cv_results = cv_res[observed_name][prior_index]
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
for cfg, cv in cv_results.iteritems():
cfg_to_cv[cfg] = cv.median
cfg_to_cv_prob[cfg] = cv.prob
cfg_to_cv_true_ests[cfg] = {'x': cv.true, 'y': cv.median}
psi_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_psi,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
num_columns = 2,
margin_top = 0.975)
fig = psi_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode.pdf'))
psi_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_prob,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
num_columns = 2)
fig = psi_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_prob.pdf'))
cv_accuracy_plot = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_cv_true_ests,
variable_symbol = r'CV_T',
num_columns = 2,
padding_between_vertical = 2.0,
margin_left = 0.04,
margin_bottom = 0.03)
fig = cv_accuracy_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_accuracy_cv_median.pdf'))
def create_plots(info_path):
dmc_sim = DMCSimulationResults(info_path)
prior_configs = {}
for k, v in dmc_sim.prior_index_to_config.iteritems():
prior_configs[k] = MsBayesConfig(v)
output_dir = os.path.join(os.path.dirname(info_path), 'plots')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
prior_prob_omega_less_than = {
1: 0.0025575,
2: 0.049283,
3: 0.002171,
4: 0.049191}
psi_res, omega_res, prior_index_to_name = parse_results(dmc_sim)
for observed_name in psi_res.iterkeys():
for prior_index in psi_res[observed_name].iterkeys():
prior_name = prior_index_to_name[prior_index]
div_model_prior = prior_name
if prior_name in ['old', 'u-shaped']:
div_model_prior = 'psi'
dpp_concentration_mean = None
if div_model_prior == 'dpp':
dpp_concentration_mean = prior_configs[
prior_index].dpp_concentration.mean
prior_prob_omega = prior_prob_omega_less_than[prior_index]
psi_results = psi_res[observed_name][prior_index]
omega_results = omega_res[observed_name][prior_index]
prefix = '_'.join([observed_name, prior_name])
cfg_to_psi = {}
cfg_to_psi_prob = {}
cfg_to_psi_glm = {}
cfg_to_psi_prob_glm = {}
for cfg, psi in psi_results.iteritems():
cfg_to_psi[cfg] = psi.mode
cfg_to_psi_prob[cfg] = psi.prob
cfg_to_psi_glm[cfg] = psi.mode_glm
cfg_to_psi_prob_glm[cfg] = psi.prob_glm
cfg_to_omega = {}
cfg_to_omega_prob = {}
cfg_to_omega_glm = {}
cfg_to_omega_prob_glm = {}
cfg_to_omega_true_ests = {}
cfg_to_omega_true_ests_glm = {}
for cfg, omega in omega_results.iteritems():
cfg_to_omega[cfg] = omega.median
cfg_to_omega_prob[cfg] = omega.prob
cfg_to_omega_glm[cfg] = omega.mode_glm
cfg_to_omega_prob_glm[cfg] = omega.prob_glm
cfg_to_omega_true_ests[cfg] = {'x': omega.true, 'y': omega.median}
cfg_to_omega_true_ests_glm[cfg] = {'x': omega.true, 'y': omega.mode_glm}
psi_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_psi,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
num_columns = 2,
margin_top = 0.975)
fig = psi_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode.pdf'))
psi_plot_glm = PowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_glm,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
num_columns = 2,
margin_top = 0.975)
fig = psi_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_mode_glm.pdf'))
psi_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_prob,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
num_columns = 2)
fig = psi_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_prob.pdf'))
psi_prob_plot_glm = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_psi_prob_glm,
variable = 'psi',
variable_symbol = r'|\mathbf{\tau}|',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
num_columns = 2)
fig = psi_prob_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_psi_prob_glm.pdf'))
omega_plot = PowerPlotGrid(
observed_config_to_estimates = cfg_to_omega,
variable = 'omega',
variable_symbol = r'D_T',
num_columns = 2)
fig = omega_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_omega_median.pdf'))
omega_plot_glm = PowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_glm,
variable = 'omega',
variable_symbol = r'D_T',
num_columns = 2)
fig = omega_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_omega_mode_glm.pdf'))
omega_prob_plot = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_prob,
variable = 'omega',
variable_symbol = r'D_T',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
bayes_factor_prob = prior_prob_omega,
num_columns = 2,
text_size = 10.0)
fig = omega_prob_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_omega_prob.pdf'))
omega_prob_plot_glm = ProbabilityPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_prob_glm,
variable = 'omega',
variable_symbol = r'D_T',
div_model_prior = div_model_prior,
dpp_concentration_mean = dpp_concentration_mean,
bayes_factor = 10,
bayes_factor_prob = prior_prob_omega,
num_columns = 2,
text_size = 10.0)
fig = omega_prob_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_omega_prob_glm.pdf'))
omega_accuracy_plot = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests,
variable_symbol = r'D_T',
num_columns = 2,
padding_between_vertical = 2.0,
margin_left = 0.04,
margin_bottom = 0.03)
fig = omega_accuracy_plot.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_accuracy_omega_median.pdf'))
omega_accuracy_plot_glm = AccuracyPowerPlotGrid(
observed_config_to_estimates = cfg_to_omega_true_ests_glm,
variable_symbol = r'D_T',
num_columns = 2,
padding_between_vertical = 2.0,
margin_left = 0.04,
margin_bottom = 0.03)
fig = omega_accuracy_plot_glm.create_grid()
fig.savefig(os.path.join(output_dir,
prefix + '_power_accuracy_omega_mode_glm.pdf'))