modules/brain_mapper.py

import numpy as np
import plotly.graph_objects as go
import mne
from typing import Dict, Optional, Tuple
import plotly.express as px
import networkx as nx

class BrainMapper:
    def __init__(self):
        self.montage = mne.channels.make_standard_montage('standard_1020')
        self._initialize_coordinates()
    
    def _initialize_coordinates(self):
        """Initialize electrode coordinates from standard montage"""
        pos = self.montage.get_positions()
        self.coords = pos['ch_pos']
        
        # Extract x, y, z coordinates
        self.ch_names = list(self.coords.keys())
        self.x_coords = np.array([self.coords[ch][0] for ch in self.ch_names])
        self.y_coords = np.array([self.coords[ch][1] for ch in self.ch_names])
        self.z_coords = np.array([self.coords[ch][2] for ch in self.ch_names])
    
    def create_visualization(self, features: Dict, map_type: str = "2D Topographic") -> go.Figure:
        """Create brain visualization based on the specified type"""
        if map_type == "2D Topographic":
            return self._create_topographic_map(features)
        elif map_type == "3D Surface":
            return self._create_3d_surface(features)
        elif map_type == "Connectivity":
            return self._create_connectivity_map(features)
        else:
            raise ValueError(f"Unsupported map type: {map_type}")
    
    def _create_topographic_map(self, features: Dict) -> go.Figure:
        """Create 2D topographic map of brain activity"""
        # Extract band powers for visualization
        band_powers = features['band_powers']
        
        # Create figure with subplots for each frequency band
        fig = go.Figure()
        
        for band_name, powers in band_powers.items():
            # Create interpolated grid
            xi = np.linspace(min(self.x_coords), max(self.x_coords), 100)
            yi = np.linspace(min(self.y_coords), max(self.y_coords), 100)
            xi, yi = np.meshgrid(xi, yi)
            
            # Add contour plot for each band
            fig.add_trace(go.Contour(
                x=xi[0],
                y=yi[:, 0],
                z=powers.reshape(xi.shape),
                name=band_name,
                colorscale='Viridis',
                showscale=True,
                visible=(band_name == 'alpha')  # Show alpha band by default
            ))
            
            # Add scatter plot for electrode positions
            fig.add_trace(go.Scatter(
                x=self.x_coords,
                y=self.y_coords,
                mode='markers+text',
                text=self.ch_names,
                textposition="top center",
                name='Electrodes',
                marker=dict(size=10, color='black'),
                visible=(band_name == 'alpha')
            ))
        
        # Update layout
        fig.update_layout(
            title="Brain Activity Topographic Map",
            xaxis_title="X Position",
            yaxis_title="Y Position",
            showlegend=True,
            updatemenus=[{
                'buttons': [
                    {'label': band,
                     'method': 'update',
                     'args': [{'visible': [i == j for i in range(len(band_powers)*2) for _ in range(2)]}]}
                    for j, band in enumerate(band_powers.keys())
                ],
                'direction': 'down',
                'showactive': True,
            }]
        )
        
        return fig
    
    def _create_3d_surface(self, features: Dict) -> go.Figure:
        """Create 3D surface plot of brain activity"""
        # Create 3D surface using electrode positions
        fig = go.Figure()
        
        # Add surface plot
        fig.add_trace(go.Surface(
            x=self.x_coords.reshape(-1, 1),
            y=self.y_coords.reshape(-1, 1),
            z=features['statistics']['mean'].reshape(-1, 1),
            colorscale='Viridis',
            name='Brain Activity'
        ))
        
        # Add scatter plot for electrode positions
        fig.add_trace(go.Scatter3d(
            x=self.x_coords,
            y=self.y_coords,
            z=self.z_coords,
            mode='markers+text',
            text=self.ch_names,
            marker=dict(size=5, color='red'),
            name='Electrodes'
        ))
        
        # Update layout
        fig.update_layout(
            title="3D Brain Activity Surface",
            scene=dict(
                xaxis_title="X Position",
                yaxis_title="Y Position",
                zaxis_title="Activity Level",
                camera=dict(
                    up=dict(x=0, y=0, z=1),
                    center=dict(x=0, y=0, z=0),
                    eye=dict(x=1.5, y=1.5, z=1.5)
                )
            )
        )
        
        return fig
    
    def _create_connectivity_map(self, features: Dict) -> go.Figure:
        """Create brain connectivity visualization"""
        # Extract connectivity matrix
        connectivity = features['connectivity']['correlation']
        
        # Create graph
        G = nx.from_numpy_array(connectivity)
        pos = nx.spring_layout(G, k=1, iterations=50)
        
        # Create edge trace
        edge_x = []
        edge_y = []
        for edge in G.edges():
            x0, y0 = pos[edge[0]]
            x1, y1 = pos[edge[1]]
            edge_x.extend([x0, x1, None])
            edge_y.extend([y0, y1, None])
            
        edge_trace = go.Scatter(
            x=edge_x, y=edge_y,
            line=dict(width=0.5, color='#888'),
            hoverinfo='none',
            mode='lines')
        
        # Create node trace
        node_x = []
        node_y = []
        for node in G.nodes():
            x, y = pos[node]
            node_x.append(x)
            node_y.append(y)
            
        node_trace = go.Scatter(
            x=node_x, y=node_y,
            mode='markers+text',
            hoverinfo='text',
            text=self.ch_names,
            marker=dict(
                showscale=True,
                colorscale='YlOrRd',
                size=10,
                colorbar=dict(
                    thickness=15,
                    title='Node Connections',
                    xanchor='left',
                    titleside='right'
                )
            )
        )
        
        # Color node points by the number of connections
        node_adjacencies = []
        for node, adjacencies in enumerate(G.adjacency()):
            node_adjacencies.append(len(adjacencies[1]))
        node_trace.marker.color = node_adjacencies
        
        # Create figure
        fig = go.Figure(data=[edge_trace, node_trace],
                       layout=go.Layout(
                           title='Brain Connectivity Network',
                           showlegend=False,
                           hovermode='closest',
                           margin=dict(b=20,l=5,r=5,t=40),
                           xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                           yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
                       ))
        
        return fig