"""Render scientific pilot figures using saved data; never reopen model weights."""
import argparse
import csv
import json
from pathlib import Path
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt


def main():
    p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);a=p.parse_args();root=a.root
    out=root/'figures';out.mkdir(exist_ok=True)
    plt.rcParams.update({'font.size':10,'savefig.bbox':'tight','pdf.fonttype':42,'svg.fonttype':'none'})
    def save(fig,name):
        for ext in ('png','pdf','svg'):fig.savefig(out/f'{name}.{ext}',dpi=180)
        plt.close(fig)
    status=json.loads((root/'functional-status.json').read_text())
    if (root/'candidate-summary.json').exists():
        c=json.loads((root/'candidate-summary.json').read_text())
        fig,ax=plt.subplots(figsize=(10,4.5))
        ax.bar(np.arange(len(c)),[max(v['maximum_symmetric_relative_error'],1e-8) for v in c],color=['#238b45' if v['passed'] else '#9e9e9e' for v in c])
        ax.axhline(.05,color='#b2182b',linestyle='--',label='Relative L2 screen = 0.05; other gates also apply')
        ax.set_yscale('log');ax.set_xticks(np.arange(len(c)),[v['id'] for v in c],rotation=45,ha='right')
        ax.set_ylabel('Worst calibration symmetric relative L2')
        ax.set_title('Fixed candidates; same-input function comparison\n4 calibration texts, 32 tokens; no training')
        ax.legend(loc='upper left',bbox_to_anchor=(0,1.01),fontsize=8);fig.tight_layout();save(fig,'candidate_function_errors')
    if (root/'interventions.json').exists():
        interventions=json.loads((root/'interventions.json').read_text())
        fig,ax=plt.subplots(figsize=(8,4.5))
        for idx,r in enumerate(interventions):
            values=r['sequence_nll_deltas'];x=np.arange(len(values))
            ax.plot(x,values,'o-',label=r['direction'])
        ax.axhline(0,color='gray',linewidth=.7);ax.axhline(.01,color='#b2182b',linestyle='--',label='Mean-delta budget (not per-sequence bound)')
        ax.set_xticks(np.arange(4),['heldout zh1','heldout zh2','heldout en1','heldout en2'])
        ax.set_ylabel('Token NLL change (nat/token)');ax.set_title('Single-site interventions; frozen candidate\n4 heldout texts, 32 tokens; limited pilot evidence')
        ax.legend(loc='upper left',bbox_to_anchor=(0,-.18),fontsize=8);fig.tight_layout();save(fig,'heldout_intervention_nll')
    text=['# CPU functional reuse pilot','',f'- State: {status["state"]}',f'- Positive bidirectional pilot: {status.get("positive_bidirectional_quality",False)}',
          f'- Elapsed analysis seconds: {status.get("elapsed_seconds")}',f'- Peak RSS KiB: {status.get("peak_rss_kib")}',
          '- Original BF16 weights decoded into FP32 CPU operators, no training.',
          '- Calibration and heldout samples are distinct but very small, short and experiment-authored.',
          '- A positive result is limited to these empirical tests, not global equivalence or GPU speedup.',
          '- Weight retention: keep until explicit user deletion request; no automatic cleanup.','']
    if status['state']!='completed':text+=['## Stop evidence','',f'{status.get("error_type")}: {status.get("error")}','']
    (root/'FUNCTIONAL_REPORT.md').write_text('\n'.join(text))


if __name__=='__main__':main()