{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "id": "ps43xRsl9mzs",
        "outputId": "66ce5c65-44bb-4cf6-dd28-306ce0c9efe1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Noise=0.00, Coherence=0.097\n",
            "Noise=0.05, Coherence=0.267\n",
            "Noise=0.10, Coherence=0.114\n",
            "Noise=0.15, Coherence=0.070\n",
            "Noise=0.20, Coherence=0.125\n",
            "Noise=0.25, Coherence=0.150\n",
            "Noise=0.30, Coherence=0.122\n",
            "Noise=0.35, Coherence=0.096\n",
            "Noise=0.40, Coherence=0.105\n",
            "Noise=0.45, Coherence=0.106\n",
            "Noise=0.50, Coherence=0.105\n",
            "Noise=0.55, Coherence=0.127\n",
            "Noise=0.60, Coherence=0.112\n",
            "Noise=0.65, Coherence=0.117\n",
            "Noise=0.70, Coherence=0.120\n",
            "Noise=0.75, Coherence=0.113\n",
            "Noise=0.80, Coherence=0.116\n",
            "Noise=0.85, Coherence=0.111\n",
            "Noise=0.90, Coherence=0.112\n",
            "Noise=0.95, Coherence=0.115\n",
            "Noise=1.00, Coherence=0.110\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# ---------------------\n",
        "# SIMPLE BOIDS MODEL\n",
        "# ---------------------\n",
        "class Boids:\n",
        "    def __init__(self, n_boids=50, noise=0.0):\n",
        "        self.n = n_boids\n",
        "        self.noise = noise\n",
        "\n",
        "        # positions + velocities\n",
        "        self.pos = np.random.rand(self.n, 2)\n",
        "        angles = np.random.rand(self.n) * 2 * np.pi\n",
        "        self.vel = np.stack([np.cos(angles), np.sin(angles)], axis=1)\n",
        "\n",
        "    def step(self):\n",
        "        # Inject Gaussian noise into velocity\n",
        "        noise_vec = np.random.normal(0, self.noise, size=self.vel.shape)\n",
        "        self.vel += noise_vec\n",
        "\n",
        "        # Normalize velocity vectors\n",
        "        norms = np.linalg.norm(self.vel, axis=1, keepdims=True)\n",
        "        self.vel = self.vel / (norms + 1e-8)\n",
        "\n",
        "        # Update positions\n",
        "        self.pos = (self.pos + 0.01 * self.vel) % 1.0\n",
        "\n",
        "    def coherence(self):\n",
        "        # Mean alignment = magnitude of average velocity\n",
        "        mean_vec = np.mean(self.vel, axis=0)\n",
        "        return np.linalg.norm(mean_vec)\n",
        "\n",
        "\n",
        "# ---------------------\n",
        "# EXPERIMENT\n",
        "# ---------------------\n",
        "noise_levels = np.linspace(0, 1.0, 21) # 0.0 → 1.0\n",
        "results = []\n",
        "\n",
        "for noise in noise_levels:\n",
        "    boids = Boids(n_boids=60, noise=noise)\n",
        "\n",
        "    # Run simulation\n",
        "    coherence_vals = []\n",
        "    for _ in range(200):\n",
        "        boids.step()\n",
        "        coherence_vals.append(boids.coherence())\n",
        "\n",
        "    mean_coh = np.mean(coherence_vals)\n",
        "    results.append([noise, mean_coh])\n",
        "    print(f\"Noise={noise:.2f}, Coherence={mean_coh:.3f}\")\n",
        "\n",
        "# Convert to DataFrame\n",
        "df = pd.DataFrame(results, columns=[\"noise\", \"coherence\"])\n",
        "df.to_csv(\"coherence_vs_noise.csv\", index=False)\n",
        "\n",
        "# ---------------------\n",
        "# PLOT\n",
        "# ---------------------\n",
        "plt.figure(figsize=(7,4))\n",
        "plt.plot(df[\"noise\"], df[\"coherence\"], marker=\"o\")\n",
        "plt.title(\"Coherence vs Perturbation Noise (Boids Simulation)\")\n",
        "plt.xlabel(\"Noise Level\")\n",
        "plt.ylabel(\"Mean Coherence\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dVDdwjorBd-v",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving coherence_vs_noise.csv to coherence_vs_noise (2).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"coherence_vs_noise.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "2NtyMhFDB92Z",
        "outputId": "ff9c1803-6a6f-4cab-aabc-e7a6e7b523ec"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    noise  coherence\n",
              "0    0.00   0.097400\n",
              "1    0.05   0.266628\n",
              "2    0.10   0.114352\n",
              "3    0.15   0.069905\n",
              "4    0.20   0.124744\n",
              "5    0.25   0.150465\n",
              "6    0.30   0.122254\n",
              "7    0.35   0.096200\n",
              "8    0.40   0.105275\n",
              "9    0.45   0.106433\n",
              "10   0.50   0.105069\n",
              "11   0.55   0.127054\n",
              "12   0.60   0.112220\n",
              "13   0.65   0.117353\n",
              "14   0.70   0.119700\n",
              "15   0.75   0.113042\n",
              "16   0.80   0.116204\n",
              "17   0.85   0.111436\n",
              "18   0.90   0.111910\n",
              "19   0.95   0.115091\n",
              "20   1.00   0.109958"
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            "text/html": [
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              "      <td>0.50</td>\n",
              "      <td>0.105069</td>\n",
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              "      <td>0.55</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"noise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31024184114977144,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.0,\n          0.8500000000000001,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coherence\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03683691789057422,\n        \"min\": 0.0699048445535186,\n        \"max\": 0.2666275972497293,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.0973997501338056,\n          0.1114359084674484,\n          0.11304184798016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df[\"noise\"], df[\"coherence\"], marker=\"o\")\n",
        "plt.xlabel(\"Noise level\")\n",
        "plt.ylabel(\"Coherence\")\n",
        "plt.title(\"Coherence vs Noise (Your CSV Experiment)\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "t9F5FHojCQvw",
        "outputId": "f66a7cbb-feaa-492c-944e-beebc2032263"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}