import pandas as pd
ann_rows = []
pd.DataFrame(ann_rows)

print('No GO:BP enrichment data available')

ppi = [{'protein1': '', 'protein2': '', 'score': 0, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0}]
ppi_df = pd.DataFrame(ppi).sort_values('score', ascending=False)
display_cols = [c for c in ['protein1','protein2','score','escore','tscore'] if c in ppi_df.columns]
print(f'{len(ppi_df)} STRING edges')
ppi_df[display_cols].head(20)

import math
ppi = [{'protein1': '', 'protein2': '', 'score': 0, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0}]
if ppi:
    nodes = sorted({p for e in ppi for p in (e['protein1'], e['protein2'])})
    n = len(nodes)
    pos = {n_: (math.cos(2*math.pi*i/n), math.sin(2*math.pi*i/n)) for i, n_ in enumerate(nodes)}
    fig, ax = plt.subplots(figsize=(7, 7))
    for e in ppi:
        x1,y1 = pos[e['protein1']]; x2,y2 = pos[e['protein2']]
        ax.plot([x1,x2],[y1,y2], color='#888', alpha=0.3+0.5*e.get('score',0))
    for name,(x,y) in pos.items():
        ax.scatter([x],[y], s=450, color='#ffd54f', edgecolors='#333', zorder=3)
        ax.annotate(name, (x,y), ha='center', va='center', fontsize=8, fontweight='bold', zorder=4)
    ax.set_aspect('equal'); ax.axis('off')
    ax.set_title(f'STRING PPI network ({len(ppi)} edges)')
    plt.tight_layout(); plt.show()

pw_rows = []
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('Ferroptosis as Disease-Modifying Amplifier', 0.538), ('Ferroptosis as Context-Dependent and Motor Neuron-Subty', 0.475), ('Ferroptosis as Epiphenomenon of Terminal Collapse', 0.363), ('Ferroptosis as Primary Driver of Motor Neuron Death', 0.339)]
titles = [h[0] for h in hyp_data][::-1]
scores = [h[1] for h in hyp_data][::-1]
fig, ax = plt.subplots(figsize=(10, max(8, len(titles)*0.4)))
colors = ['#ef5350' if s >= 0.6 else '#ffa726' if s >= 0.5 else '#66bb6a' for s in scores]
ax.barh(range(len(titles)), scores, color=colors)
ax.set_yticks(range(len(titles))); ax.set_yticklabels(titles, fontsize=7)
ax.set_xlabel('Composite Score'); ax.set_title('Is ferroptosis the primary driver of motor neuron death in ALS or an epiphenomenon of terminal cellular collapse?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['Ferroptosis as Disease-Modifying Amplifi', 'Ferroptosis as Context-Dependent and Mot', 'Ferroptosis as Epiphenomenon of Terminal', 'Ferroptosis as Primary Driver of Motor N']
matrix = np.array([[0.6, 0.45, 0.55, 0.75, 0.0, 0.4, 0.5, 0.5, 0.4], [0.8, 0.35, 0.45, 0.55, 0.0, 0.2, 0.35, 0.3, 0.3], [0.3, 0.4, 0.25, 0.5, 0.0, 0.5, 0.4, 0.45, 0.35], [0.5, 0.25, 0.2, 0.55, 0.0, 0.4, 0.3, 0.2, 0.2]])
dims = ['novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
if matrix.size:
    fig, ax = plt.subplots(figsize=(10, 5))
    im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
    ax.set_xticks(range(len(dims)))
    ax.set_xticklabels([d.replace('_score','').replace('_',' ').title() for d in dims],
                       rotation=45, ha='right', fontsize=8)
    ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=7)
    ax.set_title('Score dimensions — hypotheses')
    plt.colorbar(im, ax=ax, shrink=0.8)
    plt.tight_layout(); plt.show()
else:
    print('No score data available')

print('No PubMed results for hypothesis h-5669af0f')

print('No PubMed results for hypothesis h-d7b7189f')

print('No PubMed results for hypothesis h-8b4dd326')

print('No PubMed results for hypothesis h-b67ff2c9')

edge_data = []
if edge_data:
    pd.DataFrame(edge_data).head(25)
else:
    print('No KG edge data available')