| """Minimal static figures from measured posterior outcomes.""" |
| import csv |
| import json |
| from pathlib import Path |
| import numpy as np |
| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
|
|
| HERE=Path(__file__).resolve().parent |
|
|
|
|
| def main(): |
| root=HERE/'results/posterior' |
| rows=[json.loads(p.read_text()) for p in root.glob('*__*.json')] |
| rows.sort(key=lambda r:(r['case'],r['sensitivity_axis'],r['axis_value'])) |
| keys=['case','setting','posterior_mean_deletions','posterior_mean_insertions', |
| 'posterior_mean_substitutions','posterior_retained_source_fraction', |
| 'ancestry_entropy_nats','known_correspondence_mean_probability', |
| 'moving_region_deletion_probability','emissions_seconds','forward_dp_seconds', |
| 'marginals_map_seconds','posterior_32_draw_seconds','process_peak_rss_MiB'] |
| with (HERE/'results/posterior_table.csv').open('w') as f: |
| w=csv.DictWriter(f,fieldnames=keys);w.writeheader() |
| for r in rows:w.writerow({k:r.get(k,'') for k in keys}) |
| plt.rcParams.update({'font.size':10, 'axes.spines.top':False,'axes.spines.right':False, |
| 'pdf.fonttype':42,'ps.fonttype':42}) |
| fig,ax=plt.subplots(1,3,figsize=(10,3.0),layout='constrained') |
| for case,label,color in [('identity_24_24','No motion','#666666'), |
| ('moving_domain_24_24','Moving region','#31688e')]: |
| r=sorted([x for x in rows if x['case']==case and x['sensitivity_axis']=='diffusion'], |
| key=lambda x:x['axis_value']) |
| ax[0].plot([x['axis_value'] for x in r], |
| [100*x['moving_region_correct_probability'] for x in r], |
| marker='o',label=label,color=color) |
| ax[1].plot([x['axis_value'] for x in r], |
| [100*x['moving_region_deletion_probability'] for x in r], |
| marker='o',label=label,color=color) |
| ax[0].set_ylabel('Correct correspondence (%)');ax[0].set_ylim(-2,102) |
| ax[1].set_ylabel('Region replaced (%)');ax[1].set_ylim(-2,102) |
| ax[0].legend(frameon=False) |
| r=sorted([x for x in rows if x['case']=='natural_282_203' and x['sensitivity_axis']=='diffusion'], |
| key=lambda x:x['axis_value']) |
| for key,label,color in [('posterior_mean_deletions','Deletions','#a6611a'), |
| ('posterior_mean_insertions','Insertions','#31688e')]: |
| ax[2].plot([x['axis_value'] for x in r],[x[key] for x in r],marker='o',label=label,color=color) |
| ax[2].set_ylabel('Expected residue edits');ax[2].legend(frameon=False) |
| for a in ax: |
| a.set_xscale('log',base=2);a.set_xticks([.5,1,2,4,8],[.5,1,2,4,8]) |
| a.set_xlabel('Diffusion SD (Å / √time)') |
| fig.savefig(HERE/'results/posterior_sensitivity.pdf') |
| fig.savefig(HERE/'results/posterior_sensitivity.png',dpi=180) |
| plt.close(fig) |
| p=root/'natural_282_203__diffusion_2.npz' |
| if p.exists(): |
| data=np.load(p) |
| fig,ax=plt.subplots(figsize=(5,4),layout='constrained') |
| im=ax.imshow(data['match'],origin='lower',aspect='auto',vmin=0,vmax=1,cmap='viridis') |
| ax.set_xlabel('Target residue');ax.set_ylabel('Source residue') |
| fig.colorbar(im,ax=ax,label='Persistence probability') |
| fig.savefig(HERE/'results/natural_correspondence.pdf') |
| fig.savefig(HERE/'results/natural_correspondence.png',dpi=180) |
| plt.close(fig) |
|
|
|
|
| if __name__=='__main__':main() |
|
|
