generate_graphs.py

import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import itertools
import time

os.makedirs('images', exist_ok=True)

def draw_candle(ax, x, O, H, L, C):
    if C > O: color = 'green'
    elif C < O: color = 'red'
    else: color = 'black'
    
    # Draw wick
    ax.plot([x, x], [L, H], color=color, linewidth=2)
    
    # Draw body
    top = max(O, C)
    bottom = min(O, C)
    
    # Ensure Dojis (O == C) have a slight visual thickness
    height = max(top - bottom, 0.2) if top == bottom else (top - bottom)
    rect_y = bottom if top != bottom else bottom - 0.1
    
    rect = patches.Rectangle((x - 0.3, rect_y), 0.6, height, linewidth=1, edgecolor=color, facecolor=color)
    ax.add_patch(rect)

def normalize(tup):
    """
    Normalizes the raw integer sequence into pure structural ranks.
    E.g., (0, 7, 1, 6) and (2, 5, 3, 4) both normalize to (0, 3, 1, 2)
    """
    sorted_unique = sorted(list(set(tup)))
    mapping = {val: i for i, val in enumerate(sorted_unique)}
    return tuple(mapping[x] for x in tup)

def get_logic_string(p):
    """
    Converts a normalized tuple into a pure relational logic string.
    """
    labels = ['O1', 'H1', 'L1', 'C1', 'O2', 'H2', 'L2', 'C2']
    groups = {}
    
    for i, val in enumerate(p):
        if val not in groups:
            groups[val] = []
        groups[val].append(labels[i])
        
    logic_parts = []
    # Sort descending so the highest points are on the left
    for val in sorted(groups.keys(), reverse=True):
        logic_parts.append("(" + " = ".join(groups[val]) + ")")
        
    return " > ".join(logic_parts)

print("Calculating the universe of pure topological patterns...")
start_time = time.time()

valid_patterns = set()

# Since there are 8 points total, there can be at most 8 distinct levels.
# Iterating 0 to 7 covers all possible strict and equal relationships.
for p in itertools.product(range(8), repeat=8):
    O1, H1, L1, C1, O2, H2, L2, C2 = p
    
    # Intrinsic Rule: A candle's High must be the max, and Low must be the min
    if H1 != max(O1, H1, L1, C1) or L1 != min(O1, H1, L1, C1):
        continue
    if H2 != max(O2, H2, L2, C2) or L2 != min(O2, H2, L2, C2):
        continue
        
    valid_patterns.add(normalize(p))

patterns = sorted(list(valid_patterns))
total_patterns = len(patterns)
print(f"Found {total_patterns} mathematically unique 2-candle patterns in {time.time() - start_time:.2f} seconds.")

patterns_per_img = 10
markdown_lines = []
markdown_lines.append("# Exhaustive Pure Topological 2-Candle Patterns")
markdown_lines.append(f"**Total unique combinations:** {total_patterns}")
markdown_lines.append("")
markdown_lines.append("| Pattern ID | Mathematical Logic | Image Reference |")
markdown_lines.append("|---|---|---|")

print(f"Generating {(total_patterns // patterns_per_img) + 1} images... This might take a few minutes.")

for i in range(0, total_patterns, patterns_per_img):
    batch = patterns[i:i+patterns_per_img]
    
    fig, axes = plt.subplots(2, 5, figsize=(20, 8))
    fig.subplots_adjust(hspace=0.5, wspace=0.3)
    axes = axes.flatten()
    
    for ax in axes:
        ax.set_visible(False)
        
    for j, p in enumerate(batch):
        ax = axes[j]
        ax.set_visible(True)
        
        # Scale the ranks (0 to 7) by 5 for cleaner visualization on the Y-axis
        scale = 5.0
        O1, H1, L1, C1 = p[0]*scale, p[1]*scale, p[2]*scale, p[3]*scale
        O2, H2, L2, C2 = p[4]*scale, p[5]*scale, p[6]*scale, p[7]*scale
        
        draw_candle(ax, 1, O1, H1, L1, C1)
        draw_candle(ax, 2, O2, H2, L2, C2)
        
        # Fix axis limits so every image has identical scaling
        ax.set_ylim(-5, 40)
        ax.set_xlim(0, 3)
        ax.set_xticks([])
        ax.set_yticks([])
        
        pattern_id = f"P_{i+j:05d}"
        logic_str = get_logic_string(p)
        
        ax.set_title(f"{pattern_id}", fontsize=10)
        
        img_name = f"plot_{i//patterns_per_img + 1}.png"
        markdown_lines.append(f"| {pattern_id} | {logic_str} | {img_name} |")
        
    img_path = os.path.join('images', img_name)
    plt.savefig(img_path, bbox_inches='tight')
    plt.close(fig)

    # Basic progress tracker
    if (i // patterns_per_img) % 100 == 0 and i > 0:
        print(f"Processed {i} / {total_patterns} patterns...")

with open('2C_patterns.md', 'w') as f:
    f.write("\n".join(markdown_lines))

print(f"Success! Generated {total_patterns} patterns. Saved MD to 2C_patterns.md")