DOT coretime demand vs inflation simulation

dot_coretime_inflation.py

# Update starting burn to 5 million DOT/year
initial_coretime_demand_5m = 5e6  # 5 million DOT/year

# Recalculate coretime burn with 60% growth from 5M start
coretime_burn_5m = initial_coretime_demand_5m * np.exp(faster_growth_rate * (short_years_updated - 2024))
burn_interp_5m = interp1d(short_years_updated, coretime_burn_5m, kind='linear')
fine_burn_5m = burn_interp_5m(fine_years_updated)
fine_net_inflation_5m = fine_inflation_updated - fine_burn_5m

# Find precise tipping point with 5M starting burn
tipping_idx_5m = np.argmax(fine_burn_5m > fine_inflation_updated)
tipping_year_5m = fine_years_updated[tipping_idx_5m]
tipping_value_5m = fine_burn_5m[tipping_idx_5m]

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(fine_years_updated, fine_inflation_updated, label='DOT Inflation (new DOT/year)', linestyle='--')
plt.plot(fine_years_updated, fine_burn_5m, label='Coretime DOT Burn (5M start, 60% growth)', linestyle='-')
plt.plot(fine_years_updated, fine_net_inflation_5m, label='Net Inflation (Inflation - Burn)', linestyle='-.', color='purple')

# Annotate new tipping point
plt.axvline(x=tipping_year_5m, color='gray', linestyle=':', label='Tipping Point')
plt.annotate(f'Tipping Point\n({tipping_year_5m:.2f})', xy=(tipping_year_5m, tipping_value_5m),
             xytext=(tipping_year_5m+0.2, tipping_value_5m*1.1),
             arrowprops=dict(facecolor='black', arrowstyle='->'))

plt.title('DOT Inflation vs Coretime Burn (2025–2030, 5M Start)')
plt.xlabel('Year')
plt.ylabel('DOT per Year')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()