psychohistory.py

import plotly.graph_objects as go  # Import Plotly for interactive plots
from mpl_toolkits.mplot3d import Axes3D  # Not needed anymore, but you can keep it if you use it elsewhere
import networkx as nx
import numpy as np
import json
import sys
import random

def generate_tree(current_x, current_y, depth, max_depth, max_nodes, x_range, G, parent=None, node_count_per_depth=None):
    """Generates a tree of nodes with positions adjusted on the x-axis, y-axis, and number of nodes on the z-axis."""
    if node_count_per_depth is None:
        node_count_per_depth = {}

    if depth > max_depth:
        return node_count_per_depth

    if depth not in node_count_per_depth:
        node_count_per_depth[depth] = 0

    num_children = random.randint(1, max_nodes)
    x_positions = [current_x + i * x_range / (num_children + 1) for i in range(num_children)]

    for x in x_positions:
        node_id = len(G.nodes)
        node_count_per_depth[depth] += 1
        prob = random.uniform(0, 1)
        G.add_node(node_id, pos=(x, prob, depth))
        if parent is not None:
            G.add_edge(parent, node_id)
        generate_tree(x, current_y + 1, depth + 1, max_depth, max_nodes, x_range, G, parent=node_id, node_count_per_depth=node_count_per_depth)

    return node_count_per_depth


def build_graph_from_json(json_data, G):
    """Builds a graph from JSON data, handling subevents recursively."""

    def add_event(parent_id, event_data, depth):
        node_id = len(G.nodes)
        prob = event_data['probability'] / 100.0
        # Use event_number as the z-coordinate for better visualization
        pos = (depth, prob, event_data['event_number'])
        label = event_data['name']
        G.add_node(node_id, pos=pos, label=label)
        if parent_id is not None:
            G.add_edge(parent_id, node_id)  # Connect to parent

        subevents = event_data.get('subevents', {}).get('event', [])
        if not isinstance(subevents, list):
            subevents = [subevents]

        for subevent in subevents:
            add_event(node_id, subevent, depth + 1)  # Recursively add subevents

    # Iterate through all top-level events
    for event_data in json_data.get('events', {}).values():
        add_event(None, event_data, 0)  # Add each event as a root node


def find_paths(G):
    """Finds paths with highest/lowest probability and longest/shortest durations."""
    best_path, worst_path = None, None
    longest_path, shortest_path = None, None
    best_mean_prob, worst_mean_prob = -1, float('inf')
    max_duration, min_duration = -1, float('inf')

    # Use nx.all_pairs_shortest_path for efficiency
    all_paths_dict = dict(nx.all_pairs_shortest_path(G))

    for source, paths_from_source in all_paths_dict.items():
        for target, path in paths_from_source.items():
            if source != target and all('pos' in G.nodes[node] for node in path):
                probabilities = [G.nodes[node]['pos'][1] for node in path]
                mean_prob = np.mean(probabilities)

                if mean_prob > best_mean_prob:
                    best_mean_prob = mean_prob
                    best_path = path
                if mean_prob < worst_mean_prob:
                    worst_mean_prob = mean_prob
                    worst_path = path

                x_positions = [G.nodes[node]['pos'][0] for node in path]
                duration = max(x_positions) - min(x_positions)

                if duration > max_duration:
                    max_duration = duration
                    longest_path = path
                if duration < min_duration and duration > 0:  # Avoid paths with 0 duration
                    min_duration = duration
                    shortest_path = path

    return best_path, best_mean_prob, worst_path, worst_mean_prob, longest_path, shortest_path

def draw_graph_plotly(G, title="3D Event Tree", highlight_color='gray'):
    """Draws the graph in 3D using Plotly and returns the HTML string."""
    pos = nx.get_node_attributes(G, 'pos')
    labels = nx.get_node_attributes(G, 'label')

    if not pos:
        print("Graph is empty. No nodes to visualize.")
        return ""

    x_vals, y_vals, z_vals = zip(*pos.values())

    node_colors = ['red' if prob < 0.33 else 'blue' if prob < 0.67 else 'green' for _, prob, _ in pos.values()]
    node_trace = go.Scatter3d(x=x_vals, y=y_vals, z=z_vals, mode='markers+text',
                             marker=dict(size=10, color=node_colors, line=dict(width=1, color='black')),
                             text=list(labels.values()), textposition='top center', hoverinfo='text')

    edge_traces = []
    for edge in G.edges():
        x_start, y_start, z_start = pos[edge[0]]
        x_end, y_end, z_end = pos[edge[1]]
        edge_trace = go.Scatter3d(x=[x_start, x_end], y=[y_start, y_end], z=[z_start, z_end],
                                 mode='lines', line=dict(width=2, color=highlight_color), hoverinfo='none')
        edge_traces.append(edge_trace)

    layout = go.Layout(scene=dict(xaxis_title='Time', yaxis_title='Probability', zaxis_title='Event Number'),
                       title=title)
    fig = go.Figure(data=[node_trace] + edge_traces, layout=layout)

    # Convert Plotly figure to HTML string
    html_str = fig.to_html(full_html=False, include_plotlyjs='cdn')
    return html_str

def main(json_data):
    G = nx.DiGraph()
    build_graph_from_json(json_data, G)

    # Generate the HTML string for the Plotly graph
    html_graph = draw_graph_plotly(G)

    # ... (Rest of your code for finding paths)

    if best_path:
        best_path_graph = draw_graph_plotly(G.subgraph(best_path), title="Best Path", highlight_color='blue')
        html_graph += best_path_graph
    if worst_path:
        worst_path_graph = draw_graph_plotly(G.subgraph(worst_path), title="Worst Path", highlight_color='red')
        html_graph += worst_path_graph
    # ... (Similar for longest_path and shortest_path)

    return html_graph  # Return the HTML string