yng87/sequential_ab_testing.ipynb Secret

## sequential_ab_testing.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/yng87/e400e5d871d23344a21789edee780d60/sequential_ab_testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Yml-r3LFEjws"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from scipy.stats import chi2_contingency\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "import seaborn.objects as so\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "kBUkR0WfFv7e"
      },
      "outputs": [],
      "source": [
        "# from scipy.special import loggamma, gammaln, xlogy\n",
        "# import numpy as np\n",
        "\n",
        "# def sequential_p_value(counts, assignment_probabilities, dirichlet_alpha=None):\n",
        "#     \"\"\"\n",
        "#     Compute the sequential p-value for given counts and assignment probabilities.\n",
        "#     Lindon, Michael, and Alan Malek.\n",
        "#     \"Anytime-Valid Inference For Multinomial Count Data.\"\n",
        "#     In Advances in Neural Information Processing Systems, 2022\n",
        "#     https://openreview.net/pdf?id=a4zg0jiuVi\n",
        "#     Parameters\n",
        "#     ----------\n",
        "#     counts : array like\n",
        "#         The observed counts in each treatment group.\n",
        "#     assignment_probabilities : array like\n",
        "#         The assignment probabilities to each treatment group.\n",
        "#     dirichlet_alpha : array like, optional\n",
        "#         The Dirichlet mixture parameter.\n",
        "#     Returns\n",
        "#     -------\n",
        "#     float\n",
        "#         The sequential p-value.\n",
        "#     \"\"\"\n",
        "#     counts = np.array(counts)\n",
        "#     assignment_probabilities = np.array(assignment_probabilities)\n",
        "#     if dirichlet_alpha is None:\n",
        "#         dirichlet_alpha = 100 * assignment_probabilities\n",
        "#     else:\n",
        "#         dirichlet_alpha = np.array(dirichlet_alpha)\n",
        "#     lm1 = (\n",
        "#         loggamma(counts.sum() + 1)\n",
        "#         - loggamma(counts + 1).sum()\n",
        "#         + loggamma(dirichlet_alpha.sum())\n",
        "#         - loggamma(dirichlet_alpha).sum()\n",
        "#         + loggamma(dirichlet_alpha + counts).sum()\n",
        "#         - loggamma((dirichlet_alpha + counts).sum())\n",
        "#     )\n",
        "#     lm0 = gammaln(counts.sum() + 1) + np.sum(\n",
        "#         xlogy(counts, assignment_probabilities) - gammaln(counts + 1), axis=-1\n",
        "#     )\n",
        "#     return min(1, np.exp(lm0 - lm1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.special import loggamma, gammaln, xlogy\n",
        "import numpy as np\n",
        "\n",
        "def sequential_p_value(counts, assignment_probabilities, dirichlet_alpha=None):\n",
        "    counts = np.array(counts)\n",
        "    assignment_probabilities = np.array(assignment_probabilities)\n",
        "    if dirichlet_alpha is None:\n",
        "        dirichlet_alpha = 100 * assignment_probabilities\n",
        "    else:\n",
        "        dirichlet_alpha = np.array(dirichlet_alpha)\n",
        "\n",
        "    log_bf = (\n",
        "        loggamma(dirichlet_alpha + counts).sum() - loggamma((dirichlet_alpha + counts).sum())\n",
        "        - loggamma(dirichlet_alpha).sum() + loggamma(dirichlet_alpha.sum())\n",
        "        - np.sum(xlogy(counts, assignment_probabilities))\n",
        "    )\n",
        "\n",
        "    return min(1, np.exp(-log_bf))"
      ],
      "metadata": {
        "id": "uKBtZbhkqdwn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "BDfT6BejSrT2"
      },
      "outputs": [],
      "source": [
        "def simulate_once(n_seq, prob_assignment, prob_conversion, rng):\n",
        "    n_groups = prob_assignment.shape[0]\n",
        "    counts = np.zeros((n_groups, n_seq + 1))\n",
        "    assignments = np.zeros((n_groups, n_seq + 1))\n",
        "    for i in range(n_seq):\n",
        "        group = rng.choice(n_groups, p = prob_assignment)\n",
        "        assignments[:, i + 1] = assignments[:, i]\n",
        "        assignments[group, i + 1] += 1\n",
        "\n",
        "        success = rng.binomial(1, prob_conversion[group])\n",
        "        counts[:, i + 1] = counts[:, i]\n",
        "        counts[group, i + 1] += int(success)\n",
        "\n",
        "    return assignments, counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "uNWBZOitAeSG"
      },
      "outputs": [],
      "source": [
        "def simulate_multi(n_exp, n_seq, prob_assignment, prob_conversion, rng):\n",
        "    n_groups = prob_assignment.shape[0]\n",
        "    counts = np.zeros((n_exp, n_groups, n_seq + 1))\n",
        "    assignments = np.zeros((n_exp, n_groups, n_seq + 1))\n",
        "    for i in range(n_exp):\n",
        "        assign, cnt = simulate_once(n_seq, prob_assignment, prob_conversion, rng)\n",
        "        counts[i] = cnt\n",
        "        assignments[i] = assign\n",
        "    return counts, assignments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "vBZ_JMNXA76T"
      },
      "outputs": [],
      "source": [
        "prob_assignment = np.array([0.5, 0.5])\n",
        "prob_conversion = np.array([0.5, 0.5])\n",
        "\n",
        "n_exp = 1000\n",
        "n_seq = 4000\n",
        "\n",
        "rng = np.random.default_rng(0)\n",
        "\n",
        "counts, assignments = simulate_multi(n_exp, n_seq, prob_assignment, prob_conversion, rng)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W32JlwElBIVK",
        "outputId": "146d2f8d-64e6-4b26-fb5f-33414469a6f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-3dcd56ae5909>:1: RuntimeWarning: invalid value encountered in divide\n",
            "  estimated_prob = counts[:, :, 10:] / assignments[:, :, 10:]\n"
          ]
        }
      ],
      "source": [
        "estimated_prob = counts[:, :, 10:] / assignments[:, :, 10:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "EbYzA0_PDHRY",
        "outputId": "61463d46-4554-4220-ffd8-2c6e829de0d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7c9663ca7130>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(estimated_prob[:, 0, :].mean(axis=0))\n",
        "plt.plot(estimated_prob[:, 1, :].mean(axis=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "GyAYQMKUTiZP"
      },
      "outputs": [],
      "source": [
        "def calc_sequential_pvalues(counts, prob_assignment):\n",
        "    ps = [1]\n",
        "    for i in range(counts.shape[1]):\n",
        "        p = sequential_p_value(counts[:, i], prob_assignment)\n",
        "        ps.append(min(ps[-1], p))\n",
        "    return ps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "eJ5UtBWuCPBH"
      },
      "outputs": [],
      "source": [
        "def chi2_pvalue(counts, assignments):\n",
        "    conversion = counts\n",
        "    non_conversion = assignments - conversion\n",
        "    table = np.vstack((conversion, non_conversion))\n",
        "    if (table.sum(axis=0)==0).any() or (table.sum(axis=1)==0).any():\n",
        "        return 1\n",
        "    _, p, _, _ = chi2_contingency(table)\n",
        "    return p\n",
        "\n",
        "def calc_chi2_pvalues(counts, assignments):\n",
        "    ps = [1]\n",
        "    for i in range(counts.shape[1]):\n",
        "        p = chi2_pvalue(counts[:, i], assignments[:, i])\n",
        "        ps.append(min(ps[-1], p))\n",
        "    return ps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "wDHOUV14IXp-",
        "outputId": "0248cdda-3390-4f64-c949-dc5ca2009d83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c9663d4acb0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ps_exp0 = calc_sequential_pvalues(counts[0], prob_assignment)\n",
        "ps_chi2_exp0 = calc_chi2_pvalues(counts[0], assignments[0])\n",
        "plt.plot(ps_exp0, label=\"Sequential p-value\")\n",
        "plt.plot(ps_chi2_exp0, label=\"Chi-square test\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "LWYJnxoMJzwT",
        "outputId": "9a1eb932-758c-405f-8ef9-ea4e4ebd2f3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c9663d4ad40>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ps_exp0 = calc_sequential_pvalues(counts[1], prob_assignment)\n",
        "ps_chi2_exp0 = calc_chi2_pvalues(counts[1], assignments[1])\n",
        "plt.plot(ps_exp0, label=\"Sequential p-value\")\n",
        "plt.plot(ps_chi2_exp0, label=\"Chi-square test\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "z-d79vf0GRvu"
      },
      "outputs": [],
      "source": [
        "ps = [calc_sequential_pvalues(counts[i], prob_assignment)[-1] for i in range(counts.shape[0])]\n",
        "ps_chi2 = [calc_chi2_pvalues(counts[i], assignments[i])[-1] for i in range(counts.shape[0])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "OnkDw2B2Glht",
        "outputId": "ff4a4297-7a0a-4c7d-afcc-f05e8bd5926b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c9661c948b0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(ps, alpha=0.7, label=\"Sequential p-value\")\n",
        "plt.hist(ps_chi2, alpha=0.7, label=\"Chi-square test\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfY3YN_4uHYX",
        "outputId": "5d60f163-4bb7-426b-b817-75913e227602"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.032, 0.41)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "(np.array(ps) < 0.05).mean(), (np.array(ps_chi2) < 0.05).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "kbR1M-4zG-E-"
      },
      "outputs": [],
      "source": [
        "prob_conversion = np.array([0.54, 0.5])\n",
        "rng = np.random.default_rng(0)\n",
        "\n",
        "counts, assignments = simulate_multi(n_exp, n_seq, prob_assignment, prob_conversion, rng)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHHDX_ZIHMzK",
        "outputId": "c9ac397f-d1f7-4676-ceb0-799751246b0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-3dcd56ae5909>:1: RuntimeWarning: invalid value encountered in divide\n",
            "  estimated_prob = counts[:, :, 10:] / assignments[:, :, 10:]\n"
          ]
        }
      ],
      "source": [
        "estimated_prob = counts[:, :, 10:] / assignments[:, :, 10:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "6kNFpStfHOy4",
        "outputId": "73415f1f-8d76-435a-a060-73b881e560ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7c9661a0a3e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(estimated_prob[:, 0, :].mean(axis=0))\n",
        "plt.plot(estimated_prob[:, 1, :].mean(axis=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "YtIcDSLMJRRQ",
        "outputId": "4abc647a-3f3a-4c3a-afd2-f724b4c3d315"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c9661a99ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ps_exp0 = calc_sequential_pvalues(counts[0], prob_assignment)\n",
        "ps_chi2_exp0 = calc_chi2_pvalues(counts[0], assignments[0])\n",
        "plt.plot(ps_exp0, label=\"Sequential p-value\")\n",
        "plt.plot(ps_chi2_exp0, label=\"Chi-square test\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "JTn5qw41KD-h",
        "outputId": "2e9c4c9a-6ec4-4d9f-e27e-4a7c071a81f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c966f4170d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3Hli27CVs2rQObdu2x9Gjh/DNN/+rtj1qvPZKMaRUwDIm5XxaDlB6+XTW6iMASu79nz6qI9q1aFDZ5kREbmnixEk4dOgrrFjxGjIzM6DXB6JlyzvwxhurrPOVAMCYMeMQEhKC9957B7t2vQetVosmTZqiV68+1jk/brrpJrz2WiJefXUp5s6dhSZNmmLGjJlYs2alzTF79uyFKVOmYufO97Bnz8fo2TMKTz89G08+OcVmvSef/D80a3YbPvjg39i0aR38/OqhWbPb0K/fAMV1TpqUgFdfXYJnn52JvLxc6zwpZc2fvwgvv7wYixY9D51OjxEjYpGfn4dt295WfMyS407ByZPfYO7cWZBlGcOHj8KkSQl2bXvXXYOwc+c2+PnVQ1SU7Uy5w4YNx6VLf2Lnzvfw7rtb0L17T8yfvwjx8eOr3Kcar71SknDjD6Yxmcy4erX2J1krLDbhxXdOIPfqVcwL2AoAeCZnPIqLBcxCYETflhjc07HX4RxJq5URFOSPjIxcl5+DoqY8vUbW5zqKi4tw5cpfaNCgMby8lPWyOmrgrKuoqL7x48faTObmzuw9f5bJ3BYuXIJ+/SoegOqqavozWtX7IjjY3+7J3Fx/Dl8V+Pt54c3/649Xn7iWVldOj0av9jcDKJmxj4iIiByLIaUq1w+6F4BcOrDI7L6dT0RERG6DY1KqZJtS5NJbBdmTQkRUM5s2vat2E5yuceNbcPDg12o3wy2xJ6VKZUJK6VNmFCIiIsdjSKmCzYyNQlgv97jxWGMiIiK3wZBSlbIhhZd7iIiInIYhRQFrSGFPChERkcMxpFRFKjsmpeS5iT0pREREDseQUqXrL/cAcumrJTx37iUiIiKXwZBiN8F5UoioTjh48EtMn56AmJj+6Nu3J0aOHIqlSxfh/Plz1nV69+6Kd98t/2Gy1/v44w/Ru3dXZGZmOrjFru/Eia/x1lsbHLb/9euT8MMP3zls/2phSKlKJXf3MKQQkadatSoRs2Y9BX//AMyc+Sxef30lxo9/BH/8cRbz589WtK+oqD5YvXqjzSf21lUnT36DLVs2Omz/GzeuxQ8/fO+w/auFk7kpIPHuHiLyYEeOHMQ772zG+PGP4JFHJluXR0R0xj33DMWhQ18p2l9QUBDq19dXv6IbKCoqglarhSzzb3tn4qtdlXIDZ0sesSeFiDzRtm3vIDi4AcaPf6TC70dF9bF5LoQZ69cn4d5778Y99wzA4sULkJ+fb/2+vZd7UlLO4Omnp2Lw4AEYMCAKY8YMxzvvbLZZ58MP38eIEfdiwIAoTJv2GH755Sf07t0Vu3d/ZF2noktQ27e/i969r32Cc35+PpYtewljxgzHgAFRGDHiXrz88mLk5OTYbDdixL1YtuwlvPPOZjzwwBAMGBAFg8EAANi9+yPExcUiOron7rsvBklJb8JkMlVa3/r1Sdi4cS3y8/PRu3dX9O7dFY8/Psn6/T/+OItZs2Zg0KA7cdddvfF//zcNf/550WYfH3/8AcaNG4X+/aMwePAAPPbYw/j551PWugFg5co3rPs/ccIzZrhlT0qVbC/3aEoTtJkDZ4moGkIIwFhUzToyhCM+BVnrbTsZpR2MRiN++OE73Hlnf2i19v3XsGvXdnTs2AnPPvs8Llw4j5Ur30BQUDAee+wJRceeOXMGgoODMWvWXAQEBODixQtIT0+zfv/Qoa+wdOkiDB58LwYMuBu//voz5s6dpegYFgUFBTCbzZg0aQoCA4OQlpaKt97agNmzn0JiYpLNul9++TmaNm2GadOehizL8PPzxbZtb2PVqkSMGjUWU6fOQEpKCtasWQmz2Vxp3ffeex/S09Owf/9evPHGagCAv78/AODPPy9i8uSJCA1tiTlznocsS3jrrQ2YNu0xvPvuLnh7e+Pbb09gyZKFGDPmn4iMjEJBQQF+/vkUcnKyAQCrV2/E5MkTMGLEaNx11z8AAC1atKjR6+NqGFIUsPSkcMZZIqqKEAJ5Hy6COfW0KsfXNLoDfkPnKAoqBkMWioqK0KjRzXZv06DBTZg//wUAQM+evfDbb7/gwIHPFIWUzMxM/PXXn5g27Sn07h0NAOjcuavNOps3r0fHjp0wZ858AECPHpEoKirCpk3r7D6ORVBQEJ5++trYGqPRiMaNb8GUKY/g/PlzaNbsNpvvvfLKcvj5+QEA8vJysX79Gowd+xDi4xOg1cro0qU7vLy0SEx8DWPH/hN6fWC5YzZs2AghIQ0hyzLatWtv872NG9dCp9PhtdfehI+PDwCgXbuOGDVqGD7++AMMHz4SP/10CjqdHgkJ06zb9erV2/rYss+GDW8ut393x8s9VSkzcFbiZG5EZCcJynoyXIWSYNOtWw+b582bt7DpASnLZDLBaDRa/wkhoNfrcfPNjZGUtAJ79nyMtLTUctv8+uvPiI7ua7O8b98BdrezrL17P8GECWMxcGAf9O3bE1OmlFzeunDhvM16nTp1sQYUAPjhh++Rn5+Hfv0G2NTRtWsPFBYWIiXljOK2HD9+FL17R0Oj0Vj3V79+fYSFheOXX34CAISHt4LBkIVFi57H8eNHUVBQUOPa3Q17Uqpk+2a13t3DgbNEVAVJkuA3dE61l3u0WhlGF7nco9Pp4e3tg9TUv+3eJiCgvs1zLy8vFBVVXvO0aY/h229PWJ8vX74anTt3xbJlK7BmzUosW/YS8vPzER7eGk88MR0REZ2RmZkBk8mEoKBgm30FBweX3b1dvvzyC7zwwnwMHXo/Jk2aAp0uEFeuXMacOU+jqKjQZt2goAY2z7OyMgEAEyeOq3DfZQOWPTIzM7F9+1Zs37613Pe0Wi8AQJcu3TB37r+wY8c2zJjxBLy9vdG37wBMm/YUdDrPGJhcGYaUqpR5j1+bFl+FthCRW5EkCfDyqXodrQxJco1BblqtFu3bd8Q33/wPRqPR7nEpSjzzzBzk5eVZn1surTRrdhteeOEl67iYNWvexMyZ0/H++3sQGBgEjUaDjIyrNvu6etX2OQB4e3vDaCy2WZadnW3z/IsvPsUdd4ThmWeetS47efKbCttbNufVr68DACxa9DIaNWoEjUaGyXTt/DVufEtlpVdKp9MjMjIKw4ePLPe9evXqWR8PGjQYgwYNRmZmJg4ePIDly1+DVqvF7NnzFB/TnfByj53E9Xf3MKUQkQeKjR2LK1euVDrp2JEjB29o/82aNUerVm2s/+rV87f5vlarRadOXfDgg+ORm5uLy5fTodFoEBbWCsnJB2zWPXDgs3L7DwlpiHPnztosO378mM3zwsJCaw+Fxb59e+1qf7t2HeDr64v09FS0atUGrVu3samnovEoFpX1MnXt2h1nz57BHXeE2+yrVas2aNasebn1AwMDMWTIfejWrTvOnfvDulyr1ZbrCfIE7EmpUiWXezgmhYg8UGRkb4wd+xA2bFiDP/44iwED7kZgYCAuXfoTn3zyIXJzcxAZ2bv6HSlw+vTvWLHiNQwYcDeaNGmKnJwcbNmyEY0b34ImTZoCAOLiJmLWrKewePEC6909//3v7nL76tt3AHbs2IpWrdqiWbPbsG/f7nJjZLp164Fly17Cpk3r0LZtexw9egjffPM/u9pav359PPzwZKxcmYi0tDR07doNgIRLly7iq6+SsWjRUvj6+la47W23tYDJZML27VvRvn0H+Pv7o1mz5nj44Xg88shDmDHjCQwdej+Cg4Nx9eoVnDx5Ah07RmDgwH9g/fokZGVlolOnLggKCsaZM6dx7NgRjB79oM3+Dx5MRseOneDn54dmzW4rFwLdEUOKAjIncyMiDzdlylS0b98Bu3Ztx4sv/gsFBfkICWmI7t17YsyYf9b68Ro0aIAGDRpgy5aNuHw5Hf7+AejYMQLz5i2ERqMBAPTufSeefno23nprAz79dB/atGmLBQsWY9Kk8Tb7Gj/+EWRkXMXGjWshyxKGDh2OkSPDsWLF69Z1hg0bjkuX/sTOne/h3Xe3oHv3npg/fxHi4233VZkxY8YhJCQE7733Dnbt2g6tVoMmTZqiV68+VV4ii4rqg/vvH4m3396EjIyr6NixE1asWIOmTW/F2rWbsXbtKixbtgT5+flo0OAmdOzYCS1b3gEAaNWqDbZv34rPP/8UeXm5CAlpiDFj/om4uIet+58xYybeeOMVPP30VBQWFlrH+7g7Sbjx/bQmkxlXr+bW+n61WhlBQf64ejUHmaviAAD+/1yOYyl5WPvRT2jTPAhPx3aq9eM6i6W+jIxcxwzacwGeXiPrcx3FxUW4cuUvNGjQGF5e3oq2ddjAWRfhyPqys7MRE9MPc+bMx+DB9zrkGNXx9PMH1LzGqt4XwcH+0GjsG23CMSlVKDs6nnf3EBEROQ9DigK8u4eIiMh5OCZFAX52DxGRa6hfvz4OHvSMz6ehyrEnRQHL5Z7TF7M4NT4REZGDMaQocP1An6M/KZ9ZkIiIiOzHkGIvIRB+a6D16VVD3fnsBCKqHntXia6prfcDQ0q1rt3h4+OtQXTHxgA4eJaISljm8vDE2T6JasryftBobmzoKwfOKmS9LZl/NRERAFnWwM8vADk5GQAAb28fuz/cz2yWYDJ57u8S1uf+lNYohEBRUSFycjLg5xcAWb6xvhCGFLuVnCTLLx9mFCKy0OlKPpHXElTsJcsyzGbPnQyM9bm/mtbo5xdgfV/cCIaU6kiw5BPrU4C3IRPRNZIkQa9vgPr1g2AyGe3aRqORoNfXQ1ZWnkf+Nc763F9Na9RotDfcg2LBkKKQnb24RFQHybIMWbZvanytVoavry/y800eObU663N/rlAjB84qJEmcdZaIiMgZGFKqJVX4jLcbEhERORZDikL2jtonIiKiG8OQYi9hubun5CkHzhIRETkWQ0q1ylzu4TQpRERETsGQotC1ydzUbQcREZGnY0ipTpkhKLzcQ0RE5BwMKQpJ4IyzREREzsCQYq8yA2d5CzIREZFjMaRUq+zA2dKeFDWaQkREVIcwpCjEydyIiIicgyHFbmUv96jYFCIiojqAIaU6Ze7ukXm5h4iIyCkYUpTiwFkiIiKnYEhRyDpwliGFiIjIoRhSqmV7vUfmmBQiIiKnYEipIYYUIiIix2JIsVdpKpF5uYeIiMgpGFKqxcnciIiI1KA4pJw5cwYTJkxAREQEoqKisHTpUhQVFVW7XUZGBubNm4e+ffsiIiICQ4YMwdatW2vUaFfAnhQiIiLH0ipZOSsrC3FxcWjevDkSExORmpqKJUuWoKCgAPPmzaty22nTpiElJQUzZsxA48aNkZycjOeffx4ajQajRo26oSKcw3K5p/QZMwoREZFDKQop27ZtQ25uLlasWIHAwEAAgMlkwoIFCxAfH49GjRpVuF16ejqOHTuGF198EcOHDwcAREZG4ocffsAnn3zi2iGlzGRuvNxDRETkHIou9yQnJyMyMtIaUAAgJiYGZrMZhw4dqnQ7o9EIAKhfv77N8oCAAPe5bGJpJidzIyIicgpFISUlJQWhoaE2y3Q6HUJCQpCSklLpdo0bN0bv3r2xevVqnD59Gjk5Odi9ezcOHTqEBx98sGYtd5qy86SUPDczoxARETmUoss9BoMBOp2u3HK9Xo+srKwqt01MTMT06dNxzz33AAA0Gg2ee+45DBo0SEkTytFqa/8GJY1GtvkKABqtXPJPUxJSJAcd2xkqqs/TeHqNrM/9eXqNrM/9uUKNikJKTQkhMHv2bPzxxx949dVXERISgsOHD2Px4sXQ6/XW4KKULEsICvKv5dZeo9P54YokQQDQ63zhFeQPf38fAIDWS+PQYzuDTuendhMcztNrZH3uz9NrZH3uT80aFYUUnU6H7OzscsuzsrKg1+sr3e7AgQPYu3cvPvzwQ4SHhwMAevTogStXrmDJkiU1Dilms4DBkFejbaui0cjQ6fxgMORbh6JkZeVDg1zk55Xcbl1UZERGRm6tH9sZrq/PZDKr3RyH8PQaWZ/78/QaWZ/7c1SNOp2f3b0zikJKaGhoubEn2dnZSE9PLzdW5XqnT5+GRqNBWFiYzfLWrVtjx44dyM/Ph59fzZKa0ei4H47rT4rJZIYwmq0DZk0m4dBjO4PJZHb7Gqrj6TWyPvfn6TWyPvenZo2KLjRFR0fj8OHDMBgM1mV79+6FLMuIioqqdLsmTZrAZDLh119/tVl+6tQpNGjQoMYBRQ2WW5CJiIjIsRSFlNjYWPj7+yMhIQEHDx7Erl27sHTpUsTGxtrMkRIXF4eBAwdan0dHR+OWW27B1KlT8cEHH+DIkSN4+eWX8f7772PcuHG1V40TWCKKmbcgExEROZSiyz16vR6bN2/GwoULkZCQAH9/f4wYMQLTp0+3Wc9sNsNkMlmfBwQEYNOmTXjttdfwyiuvIDs7G02bNsWsWbPcL6RYP2BQ5YYQERF5OMV397Rs2RKbNm2qcp0tW7aUW3bbbbfh9ddfV3o411GaSixXewTnnCUiInIoz73Bu9aU/RTkkq/sSSEiInIshhSFrl3uYUohIiJyJIYUu5Ve7rE8Y0YhIiJyKIaU6lT2KchMKURERA7FkKIQx6QQERE5B0OKvUpDibUnRcWmEBER1QUMKdWSKnzGyz1ERESOxZBiN8s8KZzMjYiIyBkYUqojVTZPClMKERGRIzGkKHRtxlkiIiJyJIYUO4lyl3sYU4iIiByJIUWha5+CrGoziIiIPB5DikISr/cQERE5BUOKvUpDiVyaUcy83ENERORQDCnVkMrNi1/yhRmFiIjIsRhSFLJe7uH1HiIiIodiSLEbPwWZiIjImRhSqlNuMjd+dg8REZEzMKQoxM/uISIicg6GFHtZPwW59CkzChERkUMxpCjEyz1ERETOwZCiED9gkIiIyDkYUuxmubuHM84SERE5A0NKdcrd3VPylTPOEhERORZDit0sI2fVbQUREVFdwZCikGwZOMueFCIiIodiSKkhRhQiIiLHYkixl/VTkC09KSq2hYiIqA5gSKlWxYNQeLmHiIjIsRhSFOKMs0RERM7BkGK30nlSOOMsERGRU2jVboDLq2SelPxCI7bs+1WFBt04WZLg46NFYaHRY+d78fQaWZ/78/QaWZ/7kyUJ0Z1vRYtG/qq1gSHFTqa/foWmQTPU8yl5yYqNZnxx4k+VW0VEROQ4v17IxOJJPVU7PkNKNYSpGABQ9N1ueLcbiGCdL6bc1w4X03NUblnNybIEPz9v5OcXwWz20L8APLxG1uf+PL1G1uf+ZFlCVKemqraBIaUaPt0eQOHBtwD52kvVtVVDdG3VUMVW3RitVkZQkD8yMnJhNJrVbo5DeHqNrM/9eXqNrM/9XV+jWjhwthqam24rfeSZSZmIiMhVMaRUi/ccExERqYEhhYiIiFwSQ0p1JH7sMRERkRoYUuzFyz1EREROxZBSLfakEBERqYEhpTrWjMKeFCIiImdiSKkW7+4hIiJSA0MKERERuSSGlOpI7EkhIiJSA0NKtSyDUhhSiIiInIkhpTq8uYeIiEgVDCnV4uUeIiIiNTCkVItdKURERGpgSCEiIiKXxJBSHeu4WV7uISIiciaGlGqVpBTBu3uIiIiciiGlOhySQkREpAqGlGpIvLuHiIhIFQwp1eJkbkRERGpgSKkOMwoREZEqFIeUM2fOYMKECYiIiEBUVBSWLl2KoqIiu7ZNTU3FzJkz0bNnT3To0AExMTH48MMPFTfauZhSiIiI1KBVsnJWVhbi4uLQvHlzJCYmIjU1FUuWLEFBQQHmzZtX5bZpaWkYPXo0WrRogYULFyIgIAC///673QFHNRJHzhIREalBUUjZtm0bcnNzsWLFCgQGBgIATCYTFixYgPj4eDRq1KjSbV9++WXcfPPNWLduHTQaDQAgMjKy5i0nIiIij6bock9ycjIiIyOtAQUAYmJiYDabcejQoUq3y8nJwZ49ezB27FhrQHE7vLuHiIjIqRT1pKSkpOCBBx6wWabT6RASEoKUlJRKtzt16hSKi4uh1Woxbtw4nDx5EoGBgbjvvvvw5JNPwsvLq2atB6DV1v7YX41Gtn7VaC2hSjjkWGq4vj5P5ek1sj735+k1sj735wo1KgopBoMBOp2u3HK9Xo+srKxKt7t8+TIA4LnnnsOoUaPw+OOP4/vvv8fy5cshyzKeeuophc0uIcsSgoL8a7StPXQ6PxSb68GAkuGzjjyWGnQ6P7Wb4HCeXiPrc3+eXiPrc39q1qgopNSU2WwGAPTq1QuzZs0CAPTs2RO5ubnYsGEDEhIS4OvrW4P9ChgMebXaVqAkNep0fjAY8lFkyAcACCGQkZFb68dSw/X1mUxmtZvjEJ5eI+tzf55eI+tzf46qUafzs7t3RlFI0el0yM7OLrc8KysLer2+yu2AkmByvcjISKxevRrnzp1DeHi4kqZYGY2O++EwmcwwmSxjUYRDj6UGk8nscTWV5ek1sj735+k1sj73p2aNii40hYaGlht7kp2djfT0dISGhla63e23317lfgsLC5U0Qx0mo9otICIiqlMUhZTo6GgcPnwYBoPBumzv3r2QZRlRUVGVbtekSROEhYXh8OHDNssPHz4MX1/fakOMqqRrL5Ep45KKDSEiIqpbFIWU2NhY+Pv7IyEhAQcPHsSuXbuwdOlSxMbG2syREhcXh4EDB9psO336dHz++edYtGgRDh06hNWrV2PDhg0YP3486tWrVzvVOIBU79plLHPmXyq2hIiIqG5RNCZFr9dj8+bNWLhwIRISEuDv748RI0Zg+vTpNuuZzWaYTCabZf3798eyZcuwcuVKbN26FQ0bNsQTTzyBSZMm3XgVDiRJMjSN7oAp9XdwanwiIiLnUXx3T8uWLbFp06Yq19myZUuFywcPHozBgwcrPaT6LFPjc0I3IiIip/HcWWhqEz+/h4iIyOkYUpRgTwoREZHTMKTYxdKTwpBCRETkLAwp9uCYFCIiIqdjSCEiIiKXxJBiD8uEbsKzpz4mIiJyJQwpRERE5JIYUuzBMSlEREROx5BCRERELokhxR4ck0JEROR0DClERETkkhhS7MExKURERE7HkKKA4IyzRERETsOQYhd+wCAREZGzMaTYQeLlHiIiIqdjSLEHQwoREZHTMaTYhZ+CTERE5GwMKUREROSSGFLsYb3cw8nciIiInIUhxR7WkKJuM4iIiOoShhS7cEwKERGRszGkEBERkUtiSLEHx6QQERE5HUOKPTgmhYiIyOkYUuzCMSlERETOxpCiCEMKERGRszCk2IPT4hMRETkdQ4o9SkMKMwoREZHzMKTYhWNSiIiInI0hxS4MKURERM7GkGIHiRmFiIjI6RhS7MLJ3IiIiJyNIcUe1q4UIiIichaGFLvweg8REZGzMaTYgxmFiIjI6RhS7MIxKURERM7GkGIPjkkhIiJyOoYUu/B6DxERkbMxpNjDmlEYUoiIiJyFIcUu/IBBIiIiZ2NIsQfHpBARETkdQ4pdOCaFiIjI2RhS7CHxcg8REZGzMaQoIBhSiIiInIYhxR78GGQiIiKnY0ixCwfOEhERORtDih0kjkkhIiJyOoYUJRhSiIiInIYhxR4ck0JEROR0DCl24ZgUIiIiZ2NIsQfHpBARETkdQ4oiDClERETOwpBiD/akEBEROR1Dil04JoWIiMjZGFLswZ4UIiIip2NIsQtvQSYiInI2xSHlzJkzmDBhAiIiIhAVFYWlS5eiqKhI0T42bdqE8PBwxMfHKz28OqwZhSGFiIjIWbRKVs7KykJcXByaN2+OxMREpKamYsmSJSgoKMC8efPs2kd6ejrefPNNNGjQoEYNVgd7UoiIiJxNUUjZtm0bcnNzsWLFCgQGBgIATCYTFixYgPj4eDRq1Kjafbz88svo378/Ll26VKMGq8I6JkXdZhAREdUlii73JCcnIzIy0hpQACAmJgZmsxmHDh2qdvuvv/4an376KZ566inFDVUXe1KIiIicTVFPSkpKCh544AGbZTqdDiEhIUhJSalyW5PJhIULF2Ly5Mlo2LCh8pZWQqut/bG/Go1s89WoKQkpkuSY4zlb2fo8kafXyPrcn6fXyPrcnyvUqCikGAwG6HS6csv1ej2ysrKq3Pbdd99Ffn4+xo8fr6iBVZFlCUFB/rW2v7J0Oj8AQKafD/IBeHtrHHo8Z7PU58k8vUbW5/48vUbW5/7UrFFRSKmpK1euYPny5XjppZfg7e1da/s1mwUMhrxa25+FRiNDp/ODwZAPk8mMgoJiAEBRYTEyMnJr/XjOVrY+T+TpNbI+9+fpNbI+9+eoGnU6P7t7ZxSFFJ1Oh+zs7HLLs7KyoNfrK93ujTfeQHh4OLp27QqDwQAAMBqNMBqNMBgMqFevHrTamuUlo9FxPxwmkxlGoxnm0kOYzcKhx3M2S32ezNNrZH3uz9NrZH3uT80aFSWD0NDQcmNPsrOzkZ6ejtDQ0Eq3O3v2LI4fP45u3bqV+163bt2wdu1aREdHK2mKc3GeFCIiIqdTFFKio6OxevVqm7Epe/fuhSzLiIqKqnS7OXPmWHtQLBYvXgxfX1/MmDED4eHhNWi6M/HuHiIiImdTFFJiY2OxZcsWJCQkID4+HqmpqVi6dCliY2Nt5kiJi4vDpUuXsH//fgBA69aty+1Lp9OhXr166NGjxw2W4AycJ4WIiMjZFN1XpNfrsXnzZmg0GiQkJODVV1/FiBEjMGvWLJv1zGYzTCZTrTZUVRJ7UoiIiJxN8WjVli1bYtOmTVWus2XLlmr3Y886LoOfgkxEROR0njsLjUMwpBARETkLQ4pd2JNCRETkbAwp9pCqX4WIiIhqF0OKPaTSl4k9KURERE7DkKIIQwoREZGzMKTYhWNSiIiInI0hxR4SB6UQERE5G0OKPUpDimBPChERkdMwpCjCkEJEROQsDCl2kDgmhYiIyOkYUuzBMSlEREROx5CiCHtSiIiInIUhxR6czI2IiMjpGFKUYEghIiJyGoYUe3BMChERkdMxpCjCnhQiIiJnYUixB8ekEBEROR1DihIMKURERE7DkGIP65gUhhQiIiJnYUixCwfOEhEROZtW7Qa4Bcus+EUFMF0+B0gS5KCmkGRmPCIiIkdhSLFH6cBZ85VzyPv3fACAtmUP+A14TM1WEREReTR2BdhBc3MY5JBQSP5BkHzrAwDMVy+q3CoiIiLPxp4UO8h+OvjfPw8AYPzrV+R/9CKE2ahyq4iIiDwbe1IUkjReJQ9MDClERESOxJCilKak80nkXIEwm1RuDBERkediSFFIrh9ifSwM6Sq2hIiIyLMxpCgkeftd602BWeXWEBEReS6GlBqQvPxKHnCafCIiIodhSKkJyzT5gj0pREREjsKQUiOWkMKeFCIiIkdhSKkJy3T4DClEREQOw5BSI7zcQ0RE5GgMKTUh8XIPERGRozGk1IRkudzDnhQiIiJHYUipidKQItiTQkRE5DAMKTXBW5CJiIgcjp+CXAOSJEEAMP39G1CUp3ZzFDNrZOSm+6AotxBmk2cGLXtqlINvhVz/Jie3jIiI7MWQUhNyyctWdHyXyg2puVy1G+AE1dboXQ8B416HpPV2RnOIiEghhpQa8I4YjKIfP3Xbu3skCdBqNTAaTe5aQrWqq9GcfhYoyoMoyIEUEOz8BhIRUbUYUmrA6/ZIeN0eqXYzakyrlREU5I+MjFwYjZ55uae6GrM3TSkJKcYCFVpHRET24MBZqpMkL9+SB8WF6jaEiIgqxZBCdZIlpIhi9qQQEbkqhhSqm7x8Sr6yJ4WIyGUxpFCddK0nJV/llhARUWUYUqhuKr3t2HTpF5UbQkRElWFIobrJMhbF20/ddhARUaUYUqhO0twcVvLAVKxuQ4iIqFIMKVQ3abwAAMWnPoMpLUXlxhARUUUYUqhOkurprY8L/7dDxZYQEVFlGFKoTvK6PRLa5l0AAMINPySSiKguYEihOknSesOr3V0lT4wcl0JE5IoYUqjOsnz6sTByQjciIlfEkEJ1l6YkpPAOHyIi18SQQnWWpC25w0cYi1RuCRERVUSrdIMzZ87ghRdewMmTJ+Hv749hw4bhySefhLe3d6XbpKWlYdOmTTh06BDOnz+P+vXro1u3bpgxYwaaNGlyQwUQ1Zi29PN7OCaFiMglKQopWVlZiIuLQ/PmzZGYmIjU1FQsWbIEBQUFmDdvXqXbnTp1Cvv378cDDzyAjh07IiMjA6tWrcLIkSPx8ccfIzg4+IYLIVJKKp0rBcIEYTZBkjXqNoiIiGwoCinbtm1Dbm4uVqxYgcDAQACAyWTCggULEB8fj0aNGlW4XZcuXbBnzx5otdcO17lzZ/Tt2xf/+c9/MHHixJpXQFRTlpACACYjwJBCRORSFI1JSU5ORmRkpDWgAEBMTAzMZjMOHTpU6XY6nc4moADAzTffjODgYKSlpSlrMVFt0Vz7mRQFBhUbQkREFVEUUlJSUhAaGmqzTKfTISQkBCkpyqYWP3v2LK5cuYKWLVsq2o6otlx/eYdT4xMRuR5Fl3sMBgN0Ol255Xq9HllZWXbvRwiBF154AQ0bNsQ999yjpAnlaLW1f4OSRiPbfPU0nl4fYH+NclATmDP+hCxMDvlZchRPP4eeXh/g+TWyPvfnCjUqvrunNiQmJuLo0aNYt24d6tWrV+P9yLKEoCD/WmyZLZ3Oz2H7dgWeXh9QfY2FIbcgL+NPaK6mIChokJNaVXs8/Rx6en2A59fI+tyfmjUqCik6nQ7Z2dnllmdlZUGv11ewRXnbt2/Hm2++iUWLFiEyMlLJ4csxmwUMhtr/3BWNRoZO5weDIR8mk7nW9682T68PsL/GYmPJ1+wfk6Ht9ZCTWnfjPP0cenp9gOfXyPrcn6Nq1On87O6dURRSQkNDy409yc7ORnp6ermxKhXZv38/nn/+eUydOhUjRoxQcuhKGY2O++EwmcwO3b/aPL0+oPoatWF9UJxyHJA1bvlaePo59PT6AM+vkfW5PzVrVHShKTo6GocPH4bBcO1OiL1790KWZURFRVW57bFjxzBjxgyMHDkSCQkJNWstUS2Tb2pW8qC4AEIIdRtDREQ2FIWU2NhY+Pv7IyEhAQcPHsSuXbuwdOlSxMbG2syREhcXh4EDB1qfnzlzBgkJCWjevDmGDRuGb7/91vrv/PnztVcNkUKSl2/JAyEATo9PRORSFF3u0ev12Lx5MxYuXIiEhAT4+/tjxIgRmD59us16ZrMZJpPJ+vy7775DdnY2srOzMWbMGJt177//fixZsuQGSiC6AVofABIAAVGcD8nLR+0WERFRKcV397Rs2RKbNm2qcp0tW7bYPB8+fDiGDx+u9FBEDidJEuDlCxTnA0UFQM1vNiMiolrmuTd4E9lJ8i65vc5s+FvllhAR0fUYUojMJZcmRWHt385OREQ1x5BCdZ4c0gIAIDhwlojIpTCkUJ0nab1LHpiK1W0IERHZYEghKg0pwliockOIiOh6DClU51l7UozsSSEiciUMKUTsSSEickkMKVTnsSeFiMg1MaQQaSw9Kby7h4jIlTCkUJ1n6UkxnjmG3B1zYLp8TuUWERERwJBCBDm4ackDUxHMGZdgPHNM3QYREREAhhQiaJu0gf+YV6Bt0RUAIAqyVW4REREBDClEAAC5/k3QNA4HAIhi3uVDROQKGFKILHgrMhGRS2FIISolaX1KHvAuHyIil8CQQlTKElJMl35G0Y/7VW4NERExpBCVknQNrY8L/7dDxZYQERHAkEJkpQluAr8hM0ueGIs4NoWISGUMKUTX0TRuBchaAIAoyFG5NUREdZtW7QYQuRJJkiD5BkDkZaL49yOQ6zcAJBnaJm0h+Qao3TwiojqFIYWoDMlPB5GXiaLjO63LtLd1gt+gaSq2ioio7mFIISrDp8v9KPrpM0AIiKI8mNPPwmxIVbtZRER1DkMKURna5p2gbd4JAGC6fA55/54PUZincquIiOoeDpwlqoLkUw8AIApzVW4JEVHdw5BCVAXJuySkwFQMYTKq2xgiojqGIYWoKl6+1oci57KKDSEiqnsYUoiqIMka62NT6hkVW0JEVPcwpBBVQ3NzGABAFBeo3BIiorqFIYWoGpKuEQBAFOWr3BIiorqFIYWoGpK3X8mDIt6GTETkTJwnhagalpAiCnOc86GDGi9IEv9+ICJiSCGqhmWulOJfklH8S7Ljj6drCP8HFkLy8nH4sYiIXBn/XCOqhqZxuM2tyI4mDGkwX73gtOMREbkq9qQQVUNzU3MExK0ATCaHHytv98swp56GOTcDmupXJyLyaAwpRHaQZC0gO/7tIvsHwwxA5F51+LGIiFwdL/cQuRDJPwgAUPj1f9RtCBGRC2BIIXIhsv7mkgfF+TBlXlK3MUREKmNIIXIhXq2irY8LPktSsSVEROpjSCFyIZKsgVfrfgAAkZ+lcmuIiNTFkELkYrw7DQFQElKE2ahya4iI1MOQQuRiJP9gQOMFCAGRw7t8iKjuYkghcjGSJEGufxMAwHjxlMqtISJSD0MKkQsSZjMAoPDgZpVbQkSkHoYUIhfk0/le62OnfKghEZELYkghckHaO6IAqWRi/JwN8TCe+1bdBhERqYAhhcgFSZIEbbMO1uf5/30dxvPfq9giIiLnY0ghclG+dz8B3+iJ1uf5XyRBCKFii4iInIshhchFSZIMbXhv+PQZX7KgMBci57KqbSIiciaGFCIXJkkyvFv3hdzgVgBA8c9fqtwiIiLnYUghcgNyg+YAgKJvP0benmUo+v0ozMYidRtFRORgWrUbQETV8+kyFMbfvgIAmC58j9wL38O7OAtoM0jllhEROQ57UojcgFw/BAETkkpuTS6VcypZxRYRETkee1KI3ITk5QO/fo/C1G4g8t5/HsXpF2DY+Tzkm8Pg0+V+QJLUbmKtEZBhLtZCGIsgjGa1m+MQnl4j63N/AjIAf1XbwJBC5GbkBs0g+dSDKMyDKS0FprQUFH+/V+1m1bpMtRvgBJlqN8DBMtVugINlqt0AJyhu3Qve/SardnyGFCI3I8kydA++Au/0n5DxXTKM538AwPlTiMgBhLq9RAwpRG5I9g1A/Q79YLy1O4oLClT/RVLbtFoZgYH1kJmZB6OHdqV7eo2sz/1ptTKCQoKRmZmnXhuUbnDmzBm88MILOHnyJPz9/TFs2DA8+eST8Pb2rnI7IQTWrl2Ld999F1evXkXr1q0xe/ZsRERE1LTtRARA0lb93nNHklaG7O0HycsMSfLM/wA8vUbW5/4krQxJ5bFuiu7uycrKQlxcHIqLi5GYmIjp06dj+/btWLJkSbXbrl27FsuXL8f48eORlJSEkJAQTJw4ERcuXKhx44mIiMhzKepJ2bZtG3Jzc7FixQoEBgYCAEwmExYsWID4+Hg0atSowu0KCwuRlJSEiRMnYvz48QCALl264B//+AfWr1+P559//kZqICIiIg+kqCclOTkZkZGR1oACADExMTCbzTh06FCl2504cQI5OTmIiYmxLvP29sbAgQORnMy5HoiIiKg8RT0pKSkpeOCBB2yW6XQ6hISEICUlpcrtACA0NNRmecuWLbF582YUFBTA19dXSVOstNran49Oo5FtvnoaT68P8PwaWZ/78/QaWZ/7c4UaFYUUg8EAnU5Xbrler0dWVlaV23l7e8PHx8dmuU6ngxACWVlZNQopsiwhKMhxE83odH4O27cr8PT6AM+vkfW5P0+vkfW5PzVrdOtbkM1mAYOh9m+N0mhk6HR+MBjyYTJ53qhtT68P8PwaWZ/78/QaWZ/7c1SNOp2f3b0zikKKTqdDdnZ2ueVZWVnQ6/VVbldUVITCwkKb3hSDwQBJkqrctjqOvD/dZDJ77P3vgOfXB3h+jazP/Xl6jazP/alZo6ILTaGhoeXGnmRnZyM9Pb3ceJOy2wHA2bNnbZanpKTglltuqfF4FCIiIvJcikJKdHQ0Dh8+DIPBYF22d+9eyLKMqKioSrfr3LkzAgICsGfPHuuy4uJi7Nu3D9HR0TVoNhEREXk6RZd7YmNjsWXLFiQkJCA+Ph6pqalYunQpYmNjbeZIiYuLw6VLl7B//34AgI+PD+Lj45GYmIjg4GCEhYVh69atyMzMxMMPP1y7FREREZFHUBRS9Ho9Nm/ejIULFyIhIQH+/v4YMWIEpk+fbrOe2WyGyWSyWfboo49CCIENGzZYp8Vfv349br311huvgoiIiDyOJIRw249PNZnMuHo1t9b3q9XKCAryR0ZGrkcOiPL0+gDPr5H1uT9Pr5H1uT9H1Rgc7G/33T2eOwsNERERuTW37kkRQsBsdkzzNRrZY+99Bzy/PsDza2R97s/Ta2R97s8RNcqyZPenK7t1SCEiIiLPxcs9RERE5JIYUoiIiMglMaQQERGRS2JIISIiIpfEkEJEREQuiSGFiIiIXBJDChEREbkkhhQiIiJySQwpRERE5JIYUoiIiMglMaQQERGRS2JIISIiIpfEkEJEREQuiSHlOmfOnMGECRMQERGBqKgoLF26FEVFRWo3q1r//ve/ER4eXu7fK6+8YrPejh07MGjQILRv3x5Dhw7FF198UW5f2dnZmDNnDrp3745OnTph6tSpSEtLc1YpAIBz585h3rx5GDZsGNq0aYMhQ4ZUuF5t1nPixAmMHj0aHTp0QL9+/bBmzRo46gPC7anvn//8Z4Xn9MyZMy5f3549e/DYY48hOjoaERERGDZsGHbu3FnueO56/uyt0Z3P4Zdffolx48ahZ8+eaNeuHQYMGIAXX3wR2dnZNut9/vnnGDp0KNq3b49BgwZh165d5fZVVFSEl156CVFRUYiIiMCECROQkpJSbj1n/v61p75Zs2ZVeP6Sk5Ndvr6ycnNzER0djfDwcPzwww8233P596EgIYQQmZmZIioqSjz44IMiOTlZ7NixQ3Tp0kUsWLBA7aZVa9euXSIsLEwkJyeLkydPWv9dunTJus7HH38swsPDxWuvvSaOHDki5s6dK9q0aSNOnjxps6+JEyeK6Oho8cknn4hPP/1UDBkyRAwdOlQUFxc7rZ79+/eL6Oho8cQTT4ghQ4aIe+65p9w6tVnPH3/8ISIiIkRCQoI4fPiw2Lhxo2jbtq1Yt26davWNGzdOxMbG2pzPkydPioKCApevb9SoUWL69Onik08+EYcPHxavvPKKaNWqlUhMTLSu487nz94a3fkc/uc//xEvvfSS2Lt3rzh69KjYsmWL6N69u5gwYYJ1nePHj4vWrVuLuXPniiNHjojXXntNhIeHiz179tjsa+7cuaJLly5ix44dIjk5WYwdO1b06dNHGAwG6zrO/v1rT30zZ84UAwYMKHf+rm+3q9ZX1tKlS0WvXr1EWFiY+P77763L3eF9yJBSavXq1SIiIkJkZGRYl23btk20bt1a/P333+o1zA6WkHLlypVK17n77rvFjBkzbJaNHj1aPPLII9bnJ06cEGFhYeKrr76yLjtz5owIDw8Xn3zySe03vBImk8n6eObMmRX+J16b9cydO1f069dPFBYWWpe9+uqromvXrjbLaos99Y0bN05MmjSpyv24an0V/Rw+99xzonPnztba3fn8CWFfje58Divy3nvvibCwMOvvw4kTJ4rRo0fbrDNjxgwRExNjff7XX3+J1q1bi23btlmXZWRkiIiICLFmzRrrMlf4/Vu2vsrem9dzh/pOnz4tIiIixNatW8uFFHd4H/JyT6nk5GRERkYiMDDQuiwmJgZmsxmHDh1Sr2G14MKFC/jjjz8QExNjs3zw4ME4cuSItcsxOTkZOp0OUVFR1nVCQ0PRunXrcl2cjiTLVf9Y1nY9ycnJGDBgALy9vW32ZTAYcPLkydooyUZ19dnLVesLDg4ut6x169bIyclBXl6e258/oPoa7eXKNZZl+d1YXFyMoqIiHDt2DP/4xz9s1hk8eDDOnDmDixcvAgAOHjwIs9lss15gYCCioqLK1af279/r67OXO9T3wgsvIDY2Fi1atLBZ7i7vQ4aUUikpKQgNDbVZptPpEBISUuH1RVc0ZMgQtG7dGgMGDEBSUhJMJhMAWNtf9oe0ZcuWKC4uxoULF6zrtWjRApIk2awXGhrqUq9BbdaTl5eHv/76q9y5Dw0NhSRJqtb9v//9DxEREWjfvj3GjRuH48eP23zfner75ptv0KhRIwQEBHjs+bu+Rgt3P4cmkwmFhYU4deoU3nzzTfTv3x9NmzbF+fPnUVxcXK5NLVu2tNZl+dqgQQPo9fpy613fbrV+/1ZWn8W5c+fQpUsXtGvXDsOHD8enn35qs72r17d371789ttvSEhIKPc9d3kfamu8pYcxGAzQ6XTlluv1emRlZanQIvuFhITgiSeeQMeOHSFJEj7//HO8/vrrSE1Nxbx586ztL1uf5bnl+waDAfXr1y+3f71ejx9//NHBVdivNuuxDJQruy9vb2/4+fmpdu67deuGYcOGoXnz5khLS8P69esxYcIEbNmyBZ06dQLgPvV9/fXX2L17N2bOnAnAM89f2RoBzziH/fr1Q2pqKgCgT58+ePXVVwHc+DnU6XQ27Vbr929l9QElPWPt27fH7bffjuzsbGzduhUJCQl44403rD0nrlxffn4+lixZgunTp9sEZwt3eR8ypHiAPn36oE+fPtbnvXv3ho+PDzZv3ozJkyer2DKqqalTp9o879u3L4YMGYKVK1di7dq1KrVKub///hvTp09Hjx498NBDD6ndHIeorEZPOIdr1qxBfn4+Tp8+jVWrVmHy5MnYuHGj2s2qNZXVp9FoEBcXZ7Nu//79ERsbi+XLl5e7zOWKVq1ahQYNGuCBBx5Quyk3hJd7Sul0unK31wElabJsV547iImJgclkws8//2xtf9n6DAYDAFi/r9PpkJOTU25frvYa1GY9lr8Qyu6rqKgI+fn5LlN3vXr1cOedd+LUqVPWZa5en8FgwKOPPorAwEAkJiZax+J40vmrrMaKuOM5bNWqFTp16oSRI0di5cqVOHbsGPbv33/D59BgMNi0W63fv5XVVxFZlnH33XfjzJkzKCgosLbbFev7888/sWHDBkydOhXZ2dkwGAzWsVJ5eXnIzc11m/chQ0qpisZdZGdnIz09vdx1NndjaX/Z+lJSUuDl5YVbb73Vut7Zs2fL3dd+9uxZl3oNarOeevXqoXHjxuX2ZdnOleouy5XrKygoQHx8PLKzs7Fu3Tqb7mJPOX9V1WgvV6/xeuHh4fDy8sL58+fRrFkzeHl5VXgOgWvnODQ0FJcvXy7X3V92jIYr/P69vj57uWp9Fy9eRHFxMSZNmoRu3bqhW7du1l71hx56CBMmTHCb9yFDSqno6GgcPnzYmiKBkkFHsizbjGp2F7t374ZGo0GbNm1w6623onnz5ti7d2+5dSIjI62jsaOjo5GVlYUjR45Y1zl79ix++uknREdHO7X9VanteqKjo/HZZ5/ZjOrfvXs3dDqddeyA2vLy8nDgwAG0b9/eusxV6zMajXjyySeRkpKCdevWoVGjRjbf94TzV12NFXGnc1iR7777DsXFxWjatCm8vb3Ro0cP/Pe//7VZZ/fu3WjZsqV18Gnv3r0hyzL27dtnXScrKwsHDx4sV5/av3+vr68iZrMZe/fuxR133AFfX18Arltf69at8dZbb9n8mz17NgBgwYIFmD9/vvu8D2t887KHsUy2M27cOPHVV1+JnTt3iq5du7rFZG4TJ04USUlJ4sCBA+LAgQNi7ty5Ijw8XCxatMi6zkcffSTCw8PFG2+8IY4ePSrmzZsn2rRpI06cOFFuX3feeafYvXu3+Oyzz1SZzC0vL0/s2bNH7NmzR4wbN07ceeed1ueW+Slqsx7LJERPPPGEOHz4sNi0aZNDJ8qqrr7jx4+L+Ph4sXPnTnHkyBHxwQcfiPvuu0+0bdtWfPfddy5f33PPPSfCwsLEhg0byk2EZZkvwZ3Pnz01uvs5TEhIEKtWrRKff/65OHz4sNiwYYOIiooS9957r/UcWiZzmz9/vjh69Kh44403RHh4uNi9e7fNvubOnSu6du0qdu7cKb766isxbty4Sic7c9bv3+rqu3jxohg3bpzYunWrOHz4sNizZ4946KGHRHh4uNi3b5/L11eRo0ePlpsnxR3ehwwp1zl9+rSIi4sTHTp0EJGRkWLJkiVOmyjpRixcuFDcfffdokOHDqJdu3ZiyJAhYvPmzcJsNtust337djFw4EDRtm1bMWTIEPH555+X25fBYBCzZ88WXbt2FREREeLxxx93+mR2Fy5cEGFhYRX+O3r0qHW92qznm2++ESNHjhTt2rUT0dHRIikpqdzr56z6/vjjDzFx4kQRFRUl2rZtK7p27SoeffTRcv+5uWp9/fr1q7S+CxcuWNdz1/NnT43ufg6TkpLEsGHDRKdOnURERIS45557xOuvvy6ys7Nt1rPMPtq2bVsxcOBAsWPHjnL7KiwsFEuWLBGRkZGiQ4cOYvz48eL06dPl1nPm79/q6svIyBCTJ08W0dHRom3btiIiIkKMGzdOJCcnu0V9FakopAjh+u9DSQgHfsAFERERUQ1xTAoRERG5JIYUIiIickkMKUREROSSGFKIiIjIJTGkEBERkUtiSCEiIiKXxJBCRERELokhhYiIiFwSQwoRERG5JIYUIiIickkMKUREROSS/h/4pKnCqCKzHwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ps_exp0 = calc_sequential_pvalues(counts[1], prob_assignment)\n",
        "ps_chi2_exp0 = calc_chi2_pvalues(counts[1], assignments[1])\n",
        "plt.plot(ps_exp0, label=\"Sequential p-value\")\n",
        "plt.plot(ps_chi2_exp0, label=\"Chi-square test\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "zbiB_dOyHQA6"
      },
      "outputs": [],
      "source": [
        "ps = [calc_sequential_pvalues(counts[i], prob_assignment)[-1] for i in range(counts.shape[0])]\n",
        "ps_chi2 = [chi2_pvalue(counts[i, :, -1], assignments[i, :, -1]) for i in range(counts.shape[0])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "A_WhDqG0HW7N",
        "outputId": "b129a2c3-30b0-43f6-9870-24feb072de38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([812.,  80.,  41.,  28.,   6.,  11.,   3.,   6.,   5.,   8.]),\n",
              " array([2.42105187e-09, 1.00000002e-01, 2.00000002e-01, 3.00000002e-01,\n",
              "        4.00000001e-01, 5.00000001e-01, 6.00000001e-01, 7.00000001e-01,\n",
              "        8.00000000e-01, 9.00000000e-01, 1.00000000e+00]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(ps, alpha=0.7)\n",
        "plt.hist(ps_chi2, alpha=0.7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMYq-EMLHa61",
        "outputId": "b037c4d4-5958-470d-cfa2-66180d696891"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.202, 0.715)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "(np.array(ps) < 0.05).mean(), (np.array(ps_chi2) < 0.05).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "q_ictAswRaSr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyODUA/qKHAPadJnETpGU4OW",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}