import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

hyp_rows = [{'id': 'h-b9c26a65', 'title': 'TREM2 Agonism with CX3CR1 Antagonism for Microglial Homeostasis', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '24250719', 'claim': 'TREM2 R47H variant increases AD risk and impairs microglial amyloid clearance'}; {'pmid': '25686174', 'claim': 'CX3CR1 deficiency reduces tau pathology in P301S mice'}; {'pmid': '31043486',", 'evidence_against': "{'pmid': '29328995', 'claim': 'TREM2 agonism worsens cerebral amyloid angiopathy (CAA) in aged APP/PS1 mice'}; {'claim': 'Dose-limiting liver enzyme elevations observed in AL002 Phase I', 'source': 'Clinical trial monito"}, {'id': 'h-dacf4657', 'title': 'NLRP3 Inflammasome Suppression via Selective Caspase-1 Inhibition', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '23974753', 'claim': 'NLRP3 inflammasome activation is documented in AD brains and correlates with cognitive decline'}; {'pmid': '23164578', 'claim': 'Caspase-1 deletion reduces amyloid pathology and improves co", 'evidence_against': "{'pmid': '31289364', 'claim': 'MCC950 and dapansutrile failed in gout and cardiovascular Phase II trials'}; {'pmid': 'GWAS catalog', 'claim': 'NLRP3 genetic variants show inconsistent associations with AD risk in GWAS'};"}, {'id': 'h-1ed9eec4', 'title': 'CD300f Immunoglobulin Receptor as Neuroinflammatory Brake', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '26928465', 'claim': 'CD300f negatively regulates neuroinflammation in mouse models of CNS injury'}; {'pmid': '31395389', 'claim': 'CD300f deficiency leads to increased microglial activation and neuronal damage'", 'evidence_against': "{'pmid': 'none', 'claim': 'No physiological ligand has been definitively identified—agonistic antibody development premature'}; {'pmid': 'GWAS catalog', 'claim': 'CD300f is not a GWAS-implicated AD risk gene'}; {'pmid': "}, {'id': 'h-f886036d', 'title': 'P2RX7-PANX1 Channel Blockade for Neuroinflammatory Cascade Interruption', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '26887441', 'year': '2015', 'claim': 'P2X7 receptor activation induces NLRP3 inflammasome and IL-1β release in cultured microglia', 'source': 'J Neuroinflammation'}; {'pmid': '30542063', 'year': '2018', 'claim':", 'evidence_against': "{'pmid': 'none', 'claim': 'CE-224,535 (Pfizer) showed no efficacy in rheumatoid arthritis Phase II'}; {'pmid': 'none', 'claim': 'GSK-1482160 terminated due to pharmacokinetic issues'}; {'pmid': 'none', 'claim': 'AZD9056 "}, {'id': 'h-5e0c4ddf', 'title': 'IL-33/ST2 Axis Augmentation for Synaptic Protection', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '25240225', 'year': '2014', 'claim': 'IL-33 administration reduces amyloid burden and improves cognitive performance in APP/PS1 mice', 'source': 'Brain'}; {'pmid': '29499312', 'year': '2018', 'claim': 'IL-33/ST2", 'evidence_against': "{'pmid': '31296952', 'claim': 'IL-33 administration delayed recovery and increased inflammation in spinal cord injury'}; {'pmid': 'none', 'claim': 'IL-33 promotes tumor growth in cancer models through ST2+ immune cell re"}, {'id': 'h-9e51501a', 'title': 'TYROBP Causal Network Inhibition for Microglial Repolarization', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '25609778', 'year': '2015', 'claim': 'TREM2 signaling through TYROBP is essential for microglial survival and amyloid containment in AD models', 'source': 'Neuron'}; {'pmid': '37952199', 'year': '2023', 'claim':", 'evidence_against': "{'pmid': 'none', 'claim': 'TYROBP lacks druggable pockets for selective negative allosteric modulation (scaffold protein)'}; {'pmid': '24250719', 'claim': 'Pharmacological TYROBP inhibition would recreate TREM2 R47H loss"}, {'id': 'h-887bddf5', 'title': 'AQP4 Water Channel Normalization as Surrogate Marker and Therapeutic Target', 'type': '', 'disease': 'neurodegeneration', 'composite_score': 0.105, 'confidence_score': 0.5, 'evidence_for': "{'pmid': '23164577', 'claim': 'AQP4 deletion accelerates amyloid plaque deposition in APP/PS1 mice'}; {'pmid': '36732336', 'claim': 'Perivascular AQP4 localization is impaired in AD brains and correlates with sleep disru", 'evidence_against': "{'pmid': 'none', 'claim': 'Multiple laboratories have failed to replicate the original glymphatic imaging findings'}; {'pmid': 'none', 'claim': 'AQP4 deletion does not consistently affect amyloid burden in all APP models"}]
hyp_df = pd.DataFrame(hyp_rows)
hyp_df

if len(hyp_df):
    ranked = hyp_df.sort_values('composite_score')
    fig, ax = plt.subplots(figsize=(9, max(4, len(ranked) * 0.45)))
    ax.barh(ranked['title'], ranked['composite_score'], color='#26a69a')
    ax.set_xlabel('Composite score')
    ax.set_title('Scored hypotheses')
    ax.grid(axis='x', alpha=0.25)
    plt.tight_layout(); plt.show()

dims = ['confidence_score', 'novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
labels = ['TREM2 Agonism with CX3CR1 Antagonism for Microgl', 'NLRP3 Inflammasome Suppression via Selective Cas', 'CD300f Immunoglobulin Receptor as Neuroinflammat', 'P2RX7-PANX1 Channel Blockade for Neuroinflammato', 'IL-33/ST2 Axis Augmentation for Synaptic Protect', 'TYROBP Causal Network Inhibition for Microglial ', 'AQP4 Water Channel Normalization as Surrogate Ma']
matrix = np.array([[0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.5]])
if matrix.size:
    fig, ax = plt.subplots(figsize=(10, max(4, len(labels) * 0.4)))
    im = ax.imshow(matrix, cmap='viridis', 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('Hypothesis scoring dimensions')
    plt.colorbar(im, ax=ax, shrink=0.8)
    plt.tight_layout(); plt.show()
else:
    print('No score matrix available')

debate_info = {'num_rounds': 4, 'quality_score': 0.5}
edge_data = []
print('Debate:', debate_info)
if edge_data:
    display(pd.DataFrame(edge_data))
else:
    print('No KG edges recorded for this analysis')