"""
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='%{x}
Health Score: %{y:.2%}'
)
])
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='Date: %{x|%Y-%m-%d}
Score: %{y:.2%}'
))
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