var_par = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_partition_1000_full.txt')
var_comp = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_composition_1000_full.txt')
tl_pars_par = tl.get_tl_par_file('out_files/TL_form_partition.txt')
random.seed(4)
qn_sets = random.sample(range(len(var_par)), 3)
fig = plt.figure(figsize = (10.5, 3.5))
# Plot the 3 Q-N pairs
for i, qn_pair in enumerate(qn_sets):
ax_qn = plt.subplot(1, 3, i + 1)
dat_row_par = list(var_par[qn_pair])
study_comp = var_comp[var_comp['study'] == dat_row_par[0]]
dat_row_comp = study_comp[(study_comp['Q'] == dat_row_par[1]) * (study_comp['N'] == dat_row_par[2])]
dat_row_comp = list(dat_row_comp[0])
if i == 0:
tl.plot_dens_par_comp_single_obs(dat_row_par[4], dat_row_par[5:], dat_row_comp[5:], \
ax = ax_qn, legend = True, loc = 2)
else: tl.plot_dens_par_comp_single_obs(dat_row_par[4], dat_row_par[5:], dat_row_comp[5:], ax = ax_qn)
ax_qn.annotate('Q = ' + str(dat_row_par[1]), xy = (0.7, 0.92), xycoords = 'axes fraction', fontsize = 10)
ax_qn.annotate('N = ' + str(dat_row_par[2]), xy = (0.7, 0.82), xycoords = 'axes fraction', fontsize = 10)
plt.xlabel('Variance', fontsize = 10)
plt.ylabel('Density', fontsize = 10)
plt.subplots_adjust(wspace = 0.29)
plt.savefig('FigB1.pdf', dpi = 600)
# Figure B2 - results from 4000 samples
study_info = tl.get_study_info('study_taxon_type.txt')
tl_pars_par = tl.get_tl_par_file('out_files/TL_form_partition_4000.txt')
var_par_1000 = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_partition_1000_full.txt')
var_par = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_partition_4000_full.txt', sample_size = 4000)
var_comp = tl.get_var_sample_file('out_files/taylor_QN_var_predicted_composition_4000_full.txt', sample_size = 4000)
