patch_notebook.py

import json

with open("Captains Logs/Ideas/Balance Meter Simulator.ipynb", "r") as f:
    nb = json.load(f)

new_cell_source = """\
# --- DUAL-LAYER BALANCE METER MATH (THE MOON CONSTRAINT) ---
# Testing the separation of Symbolic Moment (fast movers + Moon) 
# and Structural Weather (slow movers, NO Moon).

OUTER_PLANETS = {'saturn', 'uranus', 'neptune', 'pluto'}
# Note: Jupiter sits on the boundary. We'll group it with Outer/Structural for this simulation as is standard, but the actual wovenReportCore splits top 4 outer vs top 6 inner.
INNER_PLANETS = {'sun', 'moon', 'mercury', 'venus', 'mars'} 

def bm_magnitude_tanh(raw_magnitudes):
    '''Standard Non-Linear Dominance Function for Magnitude.'''
    mags = sorted([m for m in raw_magnitudes if m > 0], reverse=True)
    if not mags:
        return 0.0
    peak = mags[0]
    rest_sum = sum(mags[1:])
    raw_score = peak + (0.25 * rest_sum)
    return round(5.0 * math.tanh(0.4 * raw_score), 2)

def calculate_dual_layer_metrics(aspects):
    # Differentiate the raw aspects based on timescale
    weather_xs = [] # Structural Weather (Slow + Outer)
    moment_xs = []  # Symbolic Moment (Fast + Inner + The Moon Constraint)
    
    for asp in aspects:
        x, _ = bm_raw_driver(asp)
        if x <= 0: continue
            
        t_planet = asp.get('transiting_planet', '').lower()
        
        # THE MOON CONSTRAINT:
        # Moon drives Symbolic Moment exclusively, acting as a somatic sensor.
        # It is filtered OUT of Structural Weather.
        if t_planet == 'moon':
            moment_xs.append(x)
        elif t_planet in INNER_PLANETS:
            # Inner planets drive day-level moment
            moment_xs.append(x)
        else:
            # Outer planets (and Jupiter, nodes, etc) drive chapter-level structural weather
            weather_xs.append(x)
            
    # Calculate magnitudes using the same deterministic tanh dominance function,
    # but partitioned into their specific timescales.
    structural_weather_score = bm_magnitude_tanh(weather_xs)
    symbolic_moment_score = bm_magnitude_tanh(moment_xs)
    
    # Combined legacy magnitude for compatibility verification
    all_xs = weather_xs + moment_xs
    combined_score = bm_magnitude_tanh(all_xs)
    
    return structural_weather_score, symbolic_moment_score, combined_score


print("=" * 100)
print("DUAL-LAYER BALANCE UX — SIMULATION")
print("=" * 100)
print(f"{'Name':<12} {'Window':<20} {'Legacy Mag':>11} | {'Struct Weather':>15} {'Symb Moment':>14}")
print("-" * 100)

# Simulate what these metrics look like on the sample rows
for r in rows:
    aspects = r.get('raw_events_json', [])
    structural, moment, combined = calculate_dual_layer_metrics(aspects)
    
    # Display
    print(f"{r['name']:<12} {r['window_label']:<20} {combined:>11.2f} | {structural:>15.2f} {moment:>14.2f}")

print("\n" + "-" * 100)
print("Struct Weather = Outer/Slow planets. Provides contextual chapter load.")
print("Symb Moment    = Inner/Fast planets + Moon constraint. Provides immediate somatic texture.")
print("Legacy Mag     = Combined pool (for backwards compatibility).")
"""

new_cell = {
   "cell_type": "code",
   "execution_count": None,
   "metadata": {},
   "outputs": [],
   "source": new_cell_source.splitlines(True)
}

nb['cells'].append(new_cell)

with open("Captains Logs/Ideas/Balance Meter Simulator.ipynb", "w") as f:
    json.dump(nb, f, indent=1)

print("Notebook patched successfully.")