models/visualizer_3d.py

"""
NEUROFLUX ULTIMATE - 3D Visualizer
Advanced 3D visualization with holographic effects
"""

import numpy as np
from typing import Dict, Any, Optional
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import logging

logger = logging.getLogger(__name__)

class NeuroVisualizer3D:
    """
    Advanced 3D visualization engine
    """
    
    def __init__(self, engine: str = "holographic_engine"):
        self.engine = engine
        self.default_colorscale = 'Viridis'
        
    def create_holographic_visualization(
        self,
        brain_analysis: Dict[str, Any],
        pathology_results: Dict[str, Any],
        interactive_mode: bool = True,
        quantum_rendering: bool = False
    ) -> Dict[str, Any]:
        """
        Create holographic 3D visualization
        
        Args:
            brain_analysis: Brain analysis results
            pathology_results: Pathology detection results
            interactive_mode: Enable interactive 3D controls
            quantum_rendering: Use quantum-inspired rendering
            
        Returns:
            Visualization data dictionary
        """
        # Create 3D brain visualization
        viz_3d = self._create_3d_brain_plot(brain_analysis, pathology_results)
        
        # Create anomaly heatmap
        anomaly_map = self._create_anomaly_heatmap(pathology_results)
        
        # Additional visualizations
        regional_plot = self._create_regional_analysis_plot(brain_analysis)
        
        return {
            'interactive_3d': viz_3d,
            'anomaly_map': anomaly_map,
            'regional_plot': regional_plot,
            'quantum_rendering': quantum_rendering
        }
    
    def _create_3d_brain_plot(
        self,
        brain_analysis: Dict[str, Any],
        pathology_results: Dict[str, Any]
    ) -> go.Figure:
        """Create interactive 3D brain visualization"""
        
        # Create synthetic 3D brain surface (demonstration)
        # In production, would use actual MRI volume data
        u = np.linspace(0, 2 * np.pi, 50)
        v = np.linspace(0, np.pi, 50)
        
        x = 10 * np.outer(np.cos(u), np.sin(v))
        y = 10 * np.outer(np.sin(u), np.sin(v))
        z = 8 * np.outer(np.ones(np.size(u)), np.cos(v))
        
        # Add anomalies if detected
        anomaly_locations = pathology_results.get('anomaly_locations', [])
        
        # Create brain surface
        brain_surface = go.Surface(
            x=x, y=y, z=z,
            colorscale='Blues',
            opacity=0.7,
            name='Brain Surface',
            showscale=False
        )
        
        # Add anomaly markers
        anomaly_traces = []
        if anomaly_locations:
            for idx, loc in enumerate(anomaly_locations[:5]):  # Limit to top 5
                # Map 2D coordinates to 3D (simplified)
                theta = np.random.uniform(0, 2 * np.pi)
                phi = np.random.uniform(0, np.pi)
                
                x_marker = 10 * np.cos(theta) * np.sin(phi)
                y_marker = 10 * np.sin(theta) * np.sin(phi)
                z_marker = 8 * np.cos(phi)
                
                anomaly_trace = go.Scatter3d(
                    x=[x_marker],
                    y=[y_marker],
                    z=[z_marker],
                    mode='markers',
                    marker=dict(
                        size=10,
                        color='red',
                        symbol='diamond',
                        opacity=0.8
                    ),
                    name=f'Anomaly {idx + 1}',
                    hovertext=f"Confidence: {loc.get('confidence', 0):.2f}"
                )
                anomaly_traces.append(anomaly_trace)
        
        # Create figure
        fig = go.Figure(data=[brain_surface] + anomaly_traces)
        
        # Update layout for holographic effect
        fig.update_layout(
            title={
                'text': '🧠 3D Brain Visualization - NEUROFLUX ULTIMATE',
                'font': {'size': 20, 'color': '#1E90FF'}
            },
            scene=dict(
                xaxis=dict(showbackground=False, showgrid=False, zeroline=False, visible=False),
                yaxis=dict(showbackground=False, showgrid=False, zeroline=False, visible=False),
                zaxis=dict(showbackground=False, showgrid=False, zeroline=False, visible=False),
                bgcolor='rgba(0, 0, 0, 0.9)',
                camera=dict(
                    eye=dict(x=1.5, y=1.5, z=1.2)
                )
            ),
            paper_bgcolor='rgba(10, 10, 30, 1)',
            plot_bgcolor='rgba(10, 10, 30, 1)',
            showlegend=True,
            height=600
        )
        
        return fig
    
    def _create_anomaly_heatmap(self, pathology_results: Dict[str, Any]) -> np.ndarray:
        """Create anomaly heatmap visualization"""
        anomaly_map = pathology_results.get('anomaly_map', np.zeros((224, 224)))
        
        # Ensure proper format
        if anomaly_map.max() <= 1.0:
            anomaly_map = (anomaly_map * 255).astype(np.uint8)
        
        # Apply color mapping for visualization
        import cv2
        heatmap = cv2.applyColorMap(anomaly_map.astype(np.uint8), cv2.COLORMAP_JET)
        
        return heatmap
    
    def _create_regional_analysis_plot(self, brain_analysis: Dict[str, Any]) -> go.Figure:
        """Create regional analysis bar plot"""
        regional_data = brain_analysis.get('regional_analysis', {})
        
        if not regional_data:
            # Create empty plot
            fig = go.Figure()
            fig.add_annotation(
                text="No regional analysis data available",
                xref="paper", yref="paper",
                x=0.5, y=0.5, showarrow=False,
                font=dict(size=16)
            )
            return fig
        
        # Extract regions and health scores
        regions = list(regional_data.keys())
        health_scores = [regional_data[r].get('health_score', 0) for r in regions]
        
        # Create bar plot
        fig = go.Figure(data=[
            go.Bar(
                x=regions,
                y=health_scores,
                marker=dict(
                    color=health_scores,
                    colorscale='RdYlGn',
                    cmin=0,
                    cmax=1,
                    colorbar=dict(title="Health Score")
                ),
                text=[f"{score:.2%}" for score in health_scores],
                textposition='outside',
                hovertemplate='<b>%{x}</b><br>Health Score: %{y:.2%}<extra></extra>'
            )
        ])
        
        fig.update_layout(
            title='Regional Brain Health Analysis',
            xaxis_title='Brain Region',
            yaxis_title='Health Score',
            yaxis=dict(range=[0, 1]),
            template='plotly_dark',
            height=400
        )
        
        return fig
    
    def create_timeline_plot(
        self,
        analysis_history: Optional[list] = None
    ) -> go.Figure:
        """Create timeline visualization"""
        # Demo timeline
        import datetime
        
        if analysis_history is None:
            # Create demo data
            dates = [datetime.datetime.now() - datetime.timedelta(days=x*30) for x in range(6, -1, -1)]
            scores = [0.92, 0.91, 0.89, 0.90, 0.88, 0.87, 0.86]
        else:
            dates = [entry['date'] for entry in analysis_history]
            scores = [entry['score'] for entry in analysis_history]
        
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(
            x=dates,
            y=scores,
            mode='lines+markers',
            name='Brain Health Score',
            line=dict(color='#1E90FF', width=3),
            marker=dict(size=10, color='#1E90FF'),
            hovertemplate='<b>Date:</b> %{x|%Y-%m-%d}<br><b>Score:</b> %{y:.2%}<extra></extra>'
        ))
        
        fig.update_layout(
            title='Brain Health Evolution Over Time',
            xaxis_title='Date',
            yaxis_title='Health Score',
            yaxis=dict(range=[0.8, 1.0]),
            template='plotly_dark',
            height=400,
            hovermode='x unified'
        )
        
        return fig