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⚡ Optimize simulation loop by removing unnecessary array copies #5

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526 changes: 194 additions & 332 deletions qag. Warpdrive.ipynb
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211 changes: 92 additions & 119 deletions MeetingZoneCzZ.ipynb
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Original file line number Diff line number Diff line change
@@ -1,123 +1,96 @@
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Starting Frequency Sweep... Let's find the peak!\n",
"Freq: 0.50 | Stability Score: 1.8034 | Avg Force: 0.003875\n",
"Freq: 0.55 | Stability Score: 1.8746 | Avg Force: 0.002009\n",
"Freq: 0.60 | Stability Score: 2.1271 | Avg Force: 0.000980\n",
"Freq: 0.65 | Stability Score: 2.4253 | Avg Force: 0.000499\n",
"Freq: 0.70 | Stability Score: 2.5630 | Avg Force: 0.000306\n",
"Freq: 0.75 | Stability Score: 2.1601 | Avg Force: 0.000218\n",
"Freq: 0.80 | Stability Score: 1.9856 | Avg Force: 0.000174\n",
"Freq: 0.85 | Stability Score: 1.9058 | Avg Force: 0.000148\n",
"Freq: 0.90 | Stability Score: 1.8906 | Avg Force: 0.000129\n",
"Freq: 0.95 | Stability Score: 1.8796 | Avg Force: 0.000114\n",
"Freq: 1.00 | Stability Score: 1.8702 | Avg Force: 0.000100\n",
"Freq: 1.05 | Stability Score: 1.8613 | Avg Force: 0.000089\n",
"Freq: 1.10 | Stability Score: 1.8527 | Avg Force: 0.000079\n",
"Freq: 1.15 | Stability Score: 1.8442 | Avg Force: 0.000071\n",
"Freq: 1.20 | Stability Score: 1.8358 | Avg Force: 0.000063\n",
"\n",
"--- WINNER FOUND ---\n",
"Optimal Frequency: 0.70\n",
"This is the frequency for your Patent and GitHub!\n"
]
}
],
"source": [
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# The Hunt for the Golden Frequency\n",
"grid_size = 100\n",
"frequencies = np.linspace(0.5, 1.2, 15) # Testing a range of 15 frequencies\n",
"stability_results = []\n",
"\n",
"# Core Simulation Logic (Stripped for speed)\n",
"def run_test(omega_test):\n",
" u = np.zeros((grid_size, grid_size))\n",
" u_prev = np.zeros((grid_size, grid_size))\n",
" V_gain = 0.15\n",
" c_squared = 0.20 # Hydrogen medium acoustic velocity\n",
"\n",
" # Emitters\n",
" emitter_x = 20\n",
" x, y = np.arange(0, grid_size), np.arange(0, grid_size)\n",
" X, Y = np.meshgrid(x, y)\n",
" footprint = np.exp(-((X - emitter_x)**2) / (2 * 1.5**2)) * np.exp(-((Y - 50)**2) / (2 * 15.0**2))\n",
"\n",
" peak_force = 0\n",
" force_history = []\n",
"\n",
" for t in range(300):\n",
" laplacian = (np.roll(u,1,0)+np.roll(u,-1,0)+np.roll(u,1,1)+np.roll(u,-1,1)-4*u)\n",
" u_next = 2*u - u_prev + c_squared * laplacian\n",
" u_next += V_gain * np.sin(omega_test * t) * footprint\n",
" u_next *= 0.994\n",
"\n",
" impact = np.abs(u[:, 90:]).mean()\n",
" current_force = 0.5 * (impact**2)\n",
" force_history.append(current_force)\n",
"\n",
" u_prev, u = u.copy(), u_next.copy()\n",
"\n",
" # Stability Score: Peak Force / Variance (We want high force, low dipping)\n",
" stability_score = np.mean(force_history[-50:]) / (np.std(force_history[-50:]) + 1e-6)\n",
" return stability_score, np.mean(force_history[-50:])\n",
"\n",
"print(\"Starting Frequency Sweep... Let's find the peak!\")\n",
"for omega in frequencies:\n",
" score, avg_force = run_test(omega)\n",
" stability_results.append((omega, score, avg_force))\n",
" print(f\"Freq: {omega:.2f} | Stability Score: {score:.4f} | Avg Force: {avg_force:.6f}\")\n",
"\n",
"# Find the winner\n",
"best_run = max(stability_results, key=lambda x: x[1])\n",
"print(f\"\\n--- WINNER FOUND ---\")\n",
"print(f\"Optimal Frequency: {best_run[0]:.2f}\")\n",
"print(f\"This is the frequency for your Patent and GitHub!\")"
]
}
]
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"source": [
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# The Hunt for the Golden Frequency\n",
"grid_size = 100\n",
"frequencies = np.linspace(0.5, 1.2, 15) # Testing a range of 15 frequencies\n",
"stability_results = []\n",
"\n",
"# Core Simulation Logic (Stripped for speed)\n",
"def run_test(omega_test):\n",
" u = np.zeros((grid_size, grid_size))\n",
" u_prev = np.zeros((grid_size, grid_size))\n",
" V_gain = 0.15\n",
" c_squared = 0.20 # Hydrogen medium acoustic velocity\n",
"\n",
" # Emitters\n",
" emitter_x = 20\n",
" x, y = np.arange(0, grid_size), np.arange(0, grid_size)\n",
" X, Y = np.meshgrid(x, y)\n",
" footprint = np.exp(-((X - emitter_x)**2) / (2 * 1.5**2)) * np.exp(-((Y - 50)**2) / (2 * 15.0**2))\n",
"\n",
" peak_force = 0\n",
" force_history = []\n",
"\n",
" for t in range(300):\n",
" laplacian = (np.roll(u,1,0)+np.roll(u,-1,0)+np.roll(u,1,1)+np.roll(u,-1,1)-4*u)\n",
" u_next = 2*u - u_prev + c_squared * laplacian\n",
" u_next += V_gain * np.sin(omega_test * t) * footprint\n",
" u_next *= 0.994\n",
"\n",
" impact = np.abs(u[:, 90:]).mean()\n",
" current_force = 0.5 * (impact**2)\n",
" force_history.append(current_force)\n",
"\n",
" u_prev, u = u, u_next\n",
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medium

This change is a great step for optimization by removing .copy()! To take it a step further and eliminate array allocations inside this hot loop, you could consider a triple-buffering pattern. This involves pre-allocating u_next before the loop and then cycling the array references.

Here's an example of how run_test could be refactored:

def run_test(omega_test):
    # Pre-allocate all three buffers
    u = np.zeros((grid_size, grid_size))
    u_prev = np.zeros((grid_size, grid_size))
    u_next = np.zeros((grid_size, grid_size))
    # ... (rest of initialization)

    for t in range(300):
        laplacian = (np.roll(u,1,0)+np.roll(u,-1,0)+np.roll(u,1,1)+np.roll(u,-1,1)-4*u)
        
        # Calculate result into the pre-allocated u_next buffer.
        # This avoids allocating a new array for `u_next` in each iteration.
        # Note: the RHS still creates temporary arrays. For maximum performance,
        # this expression could be broken down into in-place operations.
        np.copyto(u_next, 2*u - u_prev + c_squared * laplacian)
        
        u_next += V_gain * np.sin(omega_test * t) * footprint
        u_next *= 0.994

        # ... (force calculation)

        # Cycle the buffers to reuse memory
        u_prev, u, u_next = u, u_next, u_prev
    
    # ... (rest of function)

This pattern avoids allocating a large array on every iteration, which should give another significant performance boost.

"\n",
" # Stability Score: Peak Force / Variance (We want high force, low dipping)\n",
" stability_score = np.mean(force_history[-50:]) / (np.std(force_history[-50:]) + 1e-6)\n",
" return stability_score, np.mean(force_history[-50:])\n",
"\n",
"print(\"Starting Frequency Sweep... Let's find the peak!\")\n",
"for omega in frequencies:\n",
" score, avg_force = run_test(omega)\n",
" stability_results.append((omega, score, avg_force))\n",
" print(f\"Freq: {omega:.2f} | Stability Score: {score:.4f} | Avg Force: {avg_force:.6f}\")\n",
"\n",
"# Find the winner\n",
"best_run = max(stability_results, key=lambda x: x[1])\n",
"print(f\"\\n--- WINNER FOUND ---\")\n",
"print(f\"Optimal Frequency: {best_run[0]:.2f}\")\n",
"print(f\"This is the frequency for your Patent and GitHub!\")"
]
}
]
}
1,186 changes: 525 additions & 661 deletions QAG-recordpropulsuon.pynb
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