Tyxz/foolinglime.ipynb

## foolinglime.ipynb
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "output_auto_scroll": true,
    "toc-autonumbering": true,
    "colab": {
      "name": "FoolingLime.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/Tyxz/c03780c97562b17b723cfe08a5d23b26/foolinglime.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3Da0WcAbz4N",
        "colab_type": "text"
      },
      "source": [
        "# Seminar On Explainable & Fair Machine Learning Methods\n",
        "Adversarially Attacking LIME -- Can we construct an Adversary?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GA0U78iccDTJ",
        "colab_type": "text"
      },
      "source": [
        "## Setup System\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7cb53b1bvQw",
        "colab_type": "text"
      },
      "source": [
        "### Colabs Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPWq_8OCuZnN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "if not (os.path.exists(\"/content/config.yaml\") \n",
        "        or os.path.exists(\"/content/asset/german_finance\")):\n",
        "    # Code in this cell from https://stackoverflow.com/a/50459396\n",
        "    # Zip file for assets\n",
        "    file_id = \"1EGoU9Z7VkHpyJAwr3Cqd5yt5DBx8olxS\"\n",
        "    !pip install -U -q PyDrive\n",
        "\n",
        "    from pydrive.auth import GoogleAuth\n",
        "    from pydrive.drive import GoogleDrive\n",
        "    from google.colab import auth\n",
        "    from oauth2client.client import GoogleCredentials\n",
        "\n",
        "    # 1. Authenticate and create the PyDrive client.\n",
        "    auth.authenticate_user()\n",
        "    gauth = GoogleAuth()\n",
        "    gauth.credentials = GoogleCredentials.get_application_default()\n",
        "    drive = GoogleDrive(gauth)\n",
        "\n",
        "    # PyDrive reference:\n",
        "    # https://googledrive.github.io/PyDrive/docs/build/html/index.html\n",
        "\n",
        "\n",
        "    from google.colab import auth\n",
        "    auth.authenticate_user()\n",
        "\n",
        "    from googleapiclient.discovery import build\n",
        "    drive_service = build('drive', 'v3')\n",
        "\n",
        "    # Replace the assignment below with your file ID\n",
        "    # to download a different file.\n",
        "    #\n",
        "    # A file ID looks like: 1gLBqEWEBQDYbKCDigHnUXNTkzl-OslSO\n",
        "\n",
        "    import io\n",
        "    from googleapiclient.http import MediaIoBaseDownload\n",
        "\n",
        "    request = drive_service.files().get_media(fileId=file_id)\n",
        "    downloaded = io.BytesIO()\n",
        "    downloader = MediaIoBaseDownload(downloaded, request)\n",
        "    done = False\n",
        "    while done is False:\n",
        "        # _ is a placeholder for a progress object that we ignore.\n",
        "        # (Our file is small, so we skip reporting progress.)\n",
        "        _, done = downloader.next_chunk()\n",
        "\n",
        "    fileId = drive.CreateFile({'id': file_id }) #DRIVE_FILE_ID is file id example: 1iytA1n2z4go3uVCwE_vIKouTKyIDjEq\n",
        "    print(fileId['title'])  \n",
        "    fileId.GetContentFile(fileId['title'])  # Save Drive file as a local file\n",
        "\n",
        "    !unzip {fileId['title']}\n",
        "    !pip install --upgrade -r requirements.txt"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLh-360Pb6hy",
        "colab_type": "text"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5NJypNruVTs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from copy import deepcopy\n",
        "import yaml\n",
        "from dotmap import DotMap\n",
        "from functools import partial\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from lime.lime_tabular import LimeTabularExplainer"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Izx97EiguVT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten, InputLayer\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "from sklearn import svm, datasets, decomposition\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold, KFold, GridSearchCV\n",
        "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.utils import check_random_state\n",
        "from sklearn.metrics import pairwise_distances\n",
        "from sklearn.linear_model import Ridge, lars_path\n",
        "\n",
        "import scipy as sp\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style=\"dark\")\n",
        "import json\n",
        "from IPython.display import Image"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mm0wVIpduVUC",
        "colab_type": "text"
      },
      "source": [
        "### Load configuration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqdLNzS_uVUD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(r\"/content/config.yaml\") as file:\n",
        "    config = DotMap(yaml.load(file, Loader=yaml.FullLoader))"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1rZ-PPSH3xW",
        "colab_type": "text"
      },
      "source": [
        "## Why do we need LIME?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqFx6kUpcOFb",
        "colab_type": "text"
      },
      "source": [
        "## How does LIME work?\n",
        "Let's create a random forest classifier for the Iris dataset as our blackbox model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYhQrcHQJ0ji",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        },
        "outputId": "9911cc1d-c8ea-4a29-e0c8-0b55cdf3e86f"
      },
      "source": [
        "# Load dataset with only two features for simplicity\n",
        "iris = sns.load_dataset(\"iris\")\n",
        "# Limit dataset to contain only two features\n",
        "X = iris.iloc[:, :2]\n",
        "y = deepcopy(iris.species)\n",
        "y.loc[y==\"setosa\"] = 0\n",
        "y.loc[y==\"versicolor\"] = 1\n",
        "y.loc[y==\"virginica\"] = 2\n",
        "y = y.astype(int)\n",
        "\n",
        "class_names = iris.species.unique()\n",
        "feature_names=X.columns.values.tolist()\n",
        "setosa = iris.loc[iris.species == class_names[0]]\n",
        "versicolor = iris.loc[iris.species == class_names[1]]\n",
        "virginica = iris.loc[iris.species == class_names[2]]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100).fit(X.values, y)\n",
        "\n",
        "# create a mesh to plot in\n",
        "h = .02\n",
        "x_min, x_max = X.iloc[:, 0].min() - 1, X.iloc[:, 0].max() + 1\n",
        "y_min, y_max = X.iloc[:, 1].min() - 1, X.iloc[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                     np.arange(y_min, y_max, h))\n",
        "\n",
        "\n",
        "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "plot_df = iris.iloc[:, :2]\n",
        "plot_df[\"species\"] = iris.species\n",
        "colors = [\"blue\", \"black\", \"red\"]\n",
        "sns.pairplot(plot_df, vars=plot_df.columns[:-1], hue=\"species\", palette=colors, kind=\"reg\", height=6)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fb8e48d2f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 957.85x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nA17U-FamUh4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934
        },
        "outputId": "fdf57489-2d63-44c0-f064-095e26bac609"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,16))\n",
        "cont = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "# Plot also the training points\n",
        "colors = [\"aqua\", \"green\", \"yellow\"]\n",
        "for i, c in enumerate(colors):\n",
        "    ax.scatter(iris[y == i].sepal_length, iris[y == i].sepal_width, c=c, label=class_names[i])\n",
        "ax.set_xlabel('Sepal length')\n",
        "ax.set_ylabel('Sepal width')\n",
        "ax.set_xlim(xx.min(), xx.max())\n",
        "ax.set_ylim(yy.min(), yy.max())\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.legend()\n",
        "ax.set_title(\"Prediction for iris dataset\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOGAjaIla1eT",
        "colab_type": "text"
      },
      "source": [
        "The blackbox is able to classify any data point to the three possible classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrPlJQnMDe2F",
        "colab_type": "text"
      },
      "source": [
        "### **L**ocal fidelity\n",
        "If we pick one sample, the explanation should be locally faithful. It does not imply, that feature, beeing globally importet, are also importend locally. LIME does only look at the local sample and the features responsible for it.\n",
        "\n",
        "The first step for LIME is to pick a instance of interest $x_*$ to explain our black box model $f():\\mathcal{X}\\rightarrow\\mathcal{R}$ from the p-dimensional space $\\mathcal{X}$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tX8AA1B1lVu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "276d495e-8662-4d00-acc2-84dfc9d3ebac"
      },
      "source": [
        "def instance_of_interest(seed=20, verbose=True):\n",
        "    np.random.seed(seed)\n",
        "    i = np.random.randint(0, X.shape[0])\n",
        "    sample_point = X.iloc[i,:]\n",
        "    prediction = clf.predict_proba(sample_point.values.reshape(1, -1)).flatten()\n",
        "    if verbose:\n",
        "        print(sample_point,\"\\n\")\n",
        "        print(\"pobapility\\tclass\\n\",\"--------------------\")\n",
        "        for i, c in enumerate(class_names):\n",
        "            print(f\"{prediction[i]*100:.2f}%\\t\\t{c}\")\n",
        "    return sample_point\n",
        "sample_point = instance_of_interest()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sepal_length    5.7\n",
            "sepal_width     2.8\n",
            "Name: 99, dtype: float64 \n",
            "\n",
            "pobapility\tclass\n",
            " --------------------\n",
            "1.00%\t\tsetosa\n",
            "92.00%\t\tversicolor\n",
            "7.00%\t\tvirginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udNshuU2-J3Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "d04cafed-7b72-4bd3-b29f-a6e3e7f21ce3"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,10))\n",
        "cont = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.5)\n",
        "for i, c in enumerate(colors):\n",
        "    ax.scatter(iris[y == i].sepal_length, iris[y == i].sepal_width, c=c, label=class_names[i])\n",
        "circle = plt.Circle((sample_point), 0.05, color='red')\n",
        "plt.gcf().gca().add_artist(circle)\n",
        "ax.set_xlabel('Sepal length')\n",
        "ax.set_ylabel('Sepal width')\n",
        "ax.set_xlim(xx.min(), xx.max())\n",
        "ax.set_ylim(yy.min(), yy.max())\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.legend()\n",
        "ax.set_title(\"Prediction for iris dataset\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xeZ-7VrD2BAm",
        "colab_type": "text"
      },
      "source": [
        "The next step ist to generate a neighbourhood around the the instance of interest $$\\Pi_{x_*}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ac3BXOVvuPa_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_neighbourhood(training_data, sample_point, num_samples=5000):\n",
        "    \"\"\"Generates a neighborhood around a prediction.\n",
        "        For numerical features, perturb them by sampling from a Normal(0,1) and\n",
        "        doing the inverse operation of mean-centering and scaling, according to\n",
        "        the means and stds in the training data. For categorical features,\n",
        "        perturb by sampling according to the training distribution, and making\n",
        "        a binary feature that is 1 when the value is the same as the instance\n",
        "        being explained.\n",
        "        https://github.com/marcotcr/lime/blob/master/lime/lime_tabular.py\n",
        "    \"\"\"\n",
        "    num_cols = sample_point.shape[0]\n",
        "    categorical_features = range(num_cols)\n",
        "    data = np.zeros((num_samples, num_cols))\n",
        "    instance_sample = sample_point\n",
        "    scaler = StandardScaler(with_mean=False)\n",
        "    scaler.fit(training_data)\n",
        "    scale = scaler.scale_\n",
        "    mean = scaler.mean_\n",
        "\n",
        "    random_state = check_random_state(None)\n",
        "    data = random_state.normal(\n",
        "        0, 1, num_samples * num_cols).reshape(\n",
        "        num_samples, num_cols)\n",
        "\n",
        "    data = data * scale\n",
        "    data[:, 0] += instance_sample[0]\n",
        "    data[:, 1] += instance_sample[1]\n",
        "\n",
        "    data[0] = deepcopy(sample_point)\n",
        "    inverse = deepcopy(data)\n",
        "    return data, inverse"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i59QdZkAzBRJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "9099ae3e-d4ca-4189-eaa9-46bd35f6b5dc"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,10))\n",
        "data, inverse = generate_neighbourhood(X.values, sample_point)\n",
        "cont = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.5)\n",
        "plt.scatter(inverse[:, 0], inverse[:, 1], c=\"grey\")\n",
        "for i, c in enumerate(colors):\n",
        "    ax.scatter(iris[y == i].sepal_length, iris[y == i].sepal_width, c=c, label=class_names[i])\n",
        "circle = plt.Circle((sample_point), 0.05, color='red')\n",
        "plt.gcf().gca().add_artist(circle)\n",
        "ax.set_xlabel('Sepal length')\n",
        "ax.set_ylabel('Sepal width')\n",
        "ax.set_xlim(xx.min(), xx.max())\n",
        "ax.set_ylim(yy.min(), yy.max())\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.legend()\n",
        "plt.title(\"Generation of neighbourhood with underlying distribution\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_A9aCqvCj6m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "38678f3c-4d00-403f-b95d-29c759470d4f"
      },
      "source": [
        "plt.figure(figsize=(16,10))\n",
        "\n",
        "data, inverse = generate_neighbourhood(X.values, sample_point)\n",
        "\n",
        "plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.25)\n",
        "plt.scatter(inverse[:, 0], inverse[:, 1], cmap='viridis')\n",
        "circle = plt.Circle((sample_point), 0.05, color='red')\n",
        "plt.gcf().gca().add_artist(circle)\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.xticks(())\n",
        "plt.yticks(())\n",
        "plt.title(\"Generation of neighbourhood\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipapfGNqq1pY",
        "colab_type": "text"
      },
      "source": [
        "The generated neightbourhood gets weighted based on the distance to the point to be explained. As the L in LIME stands for local.\n",
        "LIME uses an exponential smoothing kernel to achieve a distance weighting with a default width of 0.75: $$\\sqrt{e^{\\frac{-d^2}{w^2}}}$$ where $d$ is the scaled euclidean distance of the sampled points and $w$ the kernel weight."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1CZUDQfsvie",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LIME(object):\n",
        "    def __init__(self, training_data, feature_names=None, class_names=None, \n",
        "                 kernel_width=None, kernel = None, random_state=1,\n",
        "                 verbose=True):\n",
        "        self.training_data = training_data\n",
        "        # Create a exponential smoothing kernel\n",
        "        kernel_width = float(\n",
        "             self.__kernel_width() if kernel_width is None else kernel_width\n",
        "        )\n",
        "        kernel = self.__kernel if kernel is None else kernel\n",
        "        # partial creates a new function from an other with set arguments\n",
        "        self.kernel = partial(kernel, kernel_width=kernel_width)\n",
        "        self.feature_names = feature_names\n",
        "        self.class_names = class_names\n",
        "\n",
        "        # scale data to get statistics\n",
        "        self.scaler = StandardScaler(with_mean=False)\n",
        "        self.scaler.fit(training_data)\n",
        "\n",
        "        self.random_state = check_random_state(random_state)\n",
        "\n",
        "        self.verbose = verbose\n",
        "        \n",
        "    def __kernel_width(self):\n",
        "        return np.sqrt(self.training_data.shape[1]) * .75\n",
        "\n",
        "    def __kernel(self, distance, kernel_width):\n",
        "        return np.sqrt(np.exp(-(distance**2) / kernel_width ** 2))\n",
        "\n",
        "    def get_weights(self, data):\n",
        "        scaled_data = (data - self.scaler.mean_) / self.scaler.scale_\n",
        "        # Compute the distance matrix from the scaled data\n",
        "        distances = pairwise_distances(\n",
        "                scaled_data,\n",
        "                scaled_data[0].reshape(1, -1),\n",
        "                metric=\"euclidean\"\n",
        "        ).ravel()\n",
        "        self.weights = lime.kernel(distances)\n",
        "        return self.weights\n",
        "\n",
        "    def _data_inverse(self, sample_point, num_samples=5000):\n",
        "        return generate_neighbourhood(self.training_data, sample_point, num_samples)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QynKT65fCQoV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "7006db52-9606-47c2-defc-1d92f523a3ae"
      },
      "source": [
        "lime = LIME(X.values, feature_names=feature_names, class_names=class_names)\n",
        "weights = lime.get_weights(data)\n",
        "weights_max = np.max(weights)\n",
        "sizes = [int(9 * w / weights_max) for w in weights]\n",
        "plt.figure(figsize=(16,10))\n",
        "\n",
        "plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.25)\n",
        "colors = range(10)\n",
        "plt.scatter(inverse[:, 0], inverse[:, 1], s=[(s+1) * 10 for s in sizes], c=[colors[i] for i in sizes], cmap='viridis')\n",
        "circle = plt.Circle((sample_point), 0.05, color='red')\n",
        "plt.gcf().gca().add_artist(circle)\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.xticks(())\n",
        "plt.yticks(())\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVK2B4wBDjnV",
        "colab_type": "text"
      },
      "source": [
        "### Easy to **I**nterpret\n",
        "To find out, which feature influences the classification in what way, LIME creates a easily interpretable model $g()$ from the space $G$ of interpretable models to locally approximate the black-box model $f()$ for each class.  \n",
        "The loss function $L$ is the fidelity of the approximation- to the black-box model in the neighbourhood. Additionally, the regressor $\\Omega(g)$ is introduced, to penalize complexity of model $g()$:$$\\hat{g}=\\underset{g\\in G}{\\operatorname{argmin}}L(f,g,\\Pi_{x_*})+\\Omega(g)$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aloJpwYhDjAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model(self, sample_point, predict_fn, class_label, returnModel=False):\n",
        "    data, inverse = self._data_inverse(sample_point)\n",
        "    weights = self.get_weights(data)\n",
        "    # Predict the propability of each class for the neighbourhood\n",
        "    yss = predict_fn(inverse)\n",
        "    labels_column = yss[:, class_label]\n",
        "    easy_model = Ridge(alpha=1, fit_intercept=True,\n",
        "                       random_state=self.random_state)\n",
        "    easy_model.fit(data, labels_column, sample_weight=weights)\n",
        "    prediction_score = easy_model.score(data, labels_column,\n",
        "                                        sample_weight=weights)\n",
        "    # data[0] is the instance of interest\n",
        "    local_pred = easy_model.predict(data[0].reshape(1, -1))\n",
        "    if self.verbose:\n",
        "        print(f\"Class: {self.class_names[class_label]}\")\n",
        "        print(f\"Intercept: {easy_model.intercept_}\")\n",
        "        print(f\"Model prediction: {local_pred[0]:.2f}\")\n",
        "        print(f\"Ground truth: {labels_column[0]}\")\n",
        "    if returnModel:\n",
        "        return easy_model\n",
        "    else:\n",
        "        return (easy_model.intercept_,\n",
        "                sorted(zip([0, 1], easy_model.coef_),\n",
        "                       key=lambda x: np.abs(x[1]), reverse=True),\n",
        "                prediction_score, local_pred[0])\n",
        "\n",
        "LIME.create_model = create_model"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tXTS3vBMeYu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "6a825406-442a-47d0-9b80-e052417b158e"
      },
      "source": [
        "lime = LIME(X.values, feature_names=feature_names, class_names=class_names)\n",
        "lime.verbose = 0\n",
        "lime_model = lime.create_model(sample_point, clf.predict_proba, 1, True)\n",
        "lime.verbose = 1\n",
        "lime.create_model(sample_point, clf.predict_proba, 1)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class: versicolor\n",
            "Intercept: 1.2584049200213503\n",
            "Model prediction: 0.54\n",
            "Ground truth: 0.92\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.2584049200213503,\n",
              " [(1, -0.30910555769489345), (0, 0.02661997126998786)],\n",
              " 0.10901962702205492,\n",
              " 0.5446431947145794)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9fH3r1qShrZ",
        "colab_type": "text"
      },
      "source": [
        "The model $f()$ might operate on a different variable space as $g()$, where $f(x):\\mathcal{X}\\rightarrow\\mathcal{R}$ has the dimension $p$, and $g(x):\\mathcal{X}'\\rightarrow\\mathcal{R}$ has the dimension $q$ of the interpretable space $\\mathcal{X}'$.  \n",
        "LIME introduces differnet methods of a function $h()$ to transform $\\mathcal{X}$ to $\\mathcal{X}'$.  \n",
        "For simplicity sake of this example, we choose to limit the variable space $\\mathcal{X}$ to be of $k=2$ non zero coefficents (features: sepal length and width). Additionally, we defined $\\mathcal{X} = \\mathcal{X}'$ and not implemented the function $h()$, because it is unneccesary to explain LIME."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6LIlqsoytq8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Explainer(object):\n",
        "    def __init__(self, feature_names):\n",
        "        self.feature_names = feature_names\n",
        "        self.intercept = DotMap()\n",
        "        self.local_exp = DotMap()\n",
        "        self.score = -1\n",
        "        self.local_pred = -1\n",
        "    \n",
        "    def as_map(self):\n",
        "        obj = {}\n",
        "        for label in self.local_exp:\n",
        "            temp = {}\n",
        "            for value in self.local_exp[label]:\n",
        "                temp[self.feature_names[value[0]]] = round(value[1],4)\n",
        "            obj[label] = temp\n",
        "        return obj\n",
        "            \n",
        "\n",
        "    def __repr__(self):\n",
        "        return json.dumps(self.as_map(), indent=2)\n",
        "    def __str__(self):\n",
        "        return self.__repr__()\n",
        "\n",
        "\n",
        "def explain_instance(self, sample_point, predict_fn):\n",
        "    \"\"\"Generates explanations for a prediction.\n",
        "        First, we generate neighborhood data by randomly perturbing features\n",
        "        from the instance (see __data_inverse). We then learn locally weighted\n",
        "        linear models on this neighborhood data to explain each of the classes\n",
        "        in an interpretable way.\n",
        "        https://github.com/marcotcr/lime/blob/master/lime/lime_tabular.py\n",
        "    \"\"\"\n",
        "    explainer = Explainer(self.feature_names)\n",
        "    for label in range(self.training_data.shape[1]+1):\n",
        "        key = self.class_names[label]\n",
        "        (explainer.intercept[key],\n",
        "             explainer.local_exp[key],\n",
        "             explainer.score, explainer.local_pred) = self.create_model(\n",
        "                 sample_point, predict_fn, label)\n",
        "    return explainer\n",
        "\n",
        "LIME.explain_instance = explain_instance"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Igbdf_ZJWSBR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "19adbb19-c422-4eaa-dc48-0a30936da360"
      },
      "source": [
        "lime = LIME(X.values, feature_names=feature_names, class_names=class_names, verbose=False)\n",
        "lime.explain_instance(sample_point, clf.predict_proba)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{\n",
              "  \"setosa\": {\n",
              "    \"sepal_width\": 0.3649,\n",
              "    \"sepal_length\": -0.3266\n",
              "  },\n",
              "  \"versicolor\": {\n",
              "    \"sepal_width\": -0.3068,\n",
              "    \"sepal_length\": 0.0228\n",
              "  },\n",
              "  \"virginica\": {\n",
              "    \"sepal_length\": 0.312,\n",
              "    \"sepal_width\": -0.0516\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLst2yaHWr86",
        "colab_type": "text"
      },
      "source": [
        "The LIME explainer returns a map of parameters in percentage to explain the influence of the features on the classification.  \n",
        "If the value is negative, the label is likely not the correct classification, if they are posivite, the label is likely to be the correct classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHyuVRF6Dw3l",
        "colab_type": "text"
      },
      "source": [
        "### Model-Agnostic\n",
        "The M in LIME stands for its model agnosticness. If we use a support vector maschine with a RBF kernel, LIME ist still able to explain the influence of the features on the classification, because the glass-box model $g()$ is still a easy interpretable linear model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZdvSMc4D2j8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "00f0984e-51c0-487e-c896-d8f722debe28"
      },
      "source": [
        "# Create SVM\n",
        "clf_svm = svm.SVC(kernel='rbf', gamma=0.7, C=1.0, probability=True).fit(X.values,y)\n",
        "Z_svm = clf_svm.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z_svm = Z_svm.reshape(xx.shape)\n",
        "\n",
        "# Choose random point\n",
        "sample_point_svm = instance_of_interest(0)\n",
        "\n",
        "plt.figure(figsize=(16,10))\n",
        "\n",
        "cont = plt.contourf(xx, yy, Z_svm, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "colors = [\"aqua\", \"green\", \"yellow\"]\n",
        "for i, c in enumerate(colors):\n",
        "    plt.scatter(iris[y == i].sepal_length, iris[y == i].sepal_width, c=c, label=class_names[i])\n",
        "circle = plt.Circle((sample_point_svm), 0.05, color='red')\n",
        "plt.gcf().gca().add_artist(circle)\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.xticks(())\n",
        "plt.yticks(())\n",
        "plt.legend(class_names)\n",
        "plt.title(\"Prediction for iris dataset\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sepal_length    4.6\n",
            "sepal_width     3.2\n",
            "Name: 47, dtype: float64 \n",
            "\n",
            "pobapility\tclass\n",
            " --------------------\n",
            "100.00%\t\tsetosa\n",
            "0.00%\t\tversicolor\n",
            "0.00%\t\tvirginica\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "388vUdeAgZ0x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "be1a5393-8876-4542-ce41-8071a2d7d9b5"
      },
      "source": [
        "lime.explain_instance(sample_point_svm, clf_svm.predict_proba)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{\n",
              "  \"setosa\": {\n",
              "    \"sepal_width\": 0.3056,\n",
              "    \"sepal_length\": -0.1587\n",
              "  },\n",
              "  \"versicolor\": {\n",
              "    \"sepal_width\": -0.2648,\n",
              "    \"sepal_length\": 0.1604\n",
              "  },\n",
              "  \"virginica\": {\n",
              "    \"sepal_width\": -0.0589,\n",
              "    \"sepal_length\": -0.009\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Up_DIDFdD25j",
        "colab_type": "text"
      },
      "source": [
        "### Explaining"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5cuITDD_2mj",
        "colab_type": "text"
      },
      "source": [
        "The same example with the official LIME framework from its creator Marco Tulio Ribeiro et. al., one can install with `pip install lime`. (https://github.com/marcotcr/lime)  \n",
        "The result looks like this for the random forest classification:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qt_ylgIG2vox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "94520856-ca76-4cf4-d751-7ada9e44e23c"
      },
      "source": [
        "explainer = LimeTabularExplainer(X, feature_names=X.columns, class_names=class_names, discretize_continuous=False)\n",
        "explanation = explainer.explain_instance(sample_point, clf.predict_proba, labels=range(3))\n",
        "explanation.show_in_notebook()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "        <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF8\">\n",
              "        <head><script>var lime =\n",
              "/******/ (function(modules) { // webpackBootstrap\n",
              "/******/ \t// The module cache\n",
              "/******/ \tvar installedModules = {};\n",
              "/******/\n",
              "/******/ \t// The require function\n",
              "/******/ \tfunction __webpack_require__(moduleId) {\n",
              "/******/\n",
              "/******/ \t\t// Check if module is in cache\n",
              "/******/ \t\tif(installedModules[moduleId])\n",
              "/******/ \t\t\treturn installedModules[moduleId].exports;\n",
              "/******/\n",
              "/******/ \t\t// Create a new module (and put it into the cache)\n",
              "/******/ \t\tvar module = installedModules[moduleId] = {\n",
              "/******/ \t\t\texports: {},\n",
              "/******/ \t\t\tid: moduleId,\n",
              "/******/ \t\t\tloaded: false\n",
              "/******/ \t\t};\n",
              "/******/\n",
              "/******/ \t\t// Execute the module function\n",
              "/******/ \t\tmodules[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n",
              "/******/\n",
              "/******/ \t\t// Flag the module as loaded\n",
              "/******/ \t\tmodule.loaded = true;\n",
              "/******/\n",
              "/******/ \t\t// Return the exports of the module\n",
              "/******/ \t\treturn module.exports;\n",
              "/******/ \t}\n",
              "/******/\n",
              "/******/\n",
              "/******/ \t// expose the modules object (__webpack_modules__)\n",
              "/******/ \t__webpack_require__.m = modules;\n",
              "/******/\n",
              "/******/ \t// expose the module cache\n",
              "/******/ \t__webpack_require__.c = installedModules;\n",
              "/******/\n",
              "/******/ \t// __webpack_public_path__\n",
              "/******/ \t__webpack_require__.p = \"\";\n",
              "/******/\n",
              "/******/ \t// Load entry module and return exports\n",
              "/******/ \treturn __webpack_require__(0);\n",
              "/******/ })\n",
              "/************************************************************************/\n",
              "/******/ ([\n",
              "/* 0 */\n",
              "/***/ (function(module, exports, __webpack_require__) {\n",
              "\n",
              "\t/* WEBPACK VAR INJECTION */(function(global) {'use strict';\n",
              "\t\n",
              "\tObject.defineProperty(exports, \"__esModule\", {\n",
              "\t  value: true\n",
              "\t});\n",
              "\texports.PredictedValue = exports.PredictProba = exports.Barchart = exports.Explanation = undefined;\n",
              "\t\n",
              "\tvar _explanation = __webpack_require__(1);\n",
              "\t\n",
              "\tvar _explanation2 = _interopRequireDefault(_explanation);\n",
              "\t\n",
              "\tvar _bar_chart = __webpack_require__(3);\n",
              "\t\n",
              "\tvar _bar_chart2 = _interopRequireDefault(_bar_chart);\n",
              "\t\n",
              "\tvar _predict_proba = __webpack_require__(6);\n",
              "\t\n",
              "\tvar _predict_proba2 = _interopRequireDefault(_predict_proba);\n",
              "\t\n",
              "\tvar _predicted_value = __webpack_require__(7);\n",
              "\t\n",
              "\tvar _predicted_value2 = _interopRequireDefault(_predicted_value);\n",
              "\t\n",
              "\tfunction _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }\n",
              "\t\n",
              "\tif (!global._babelPolyfill) {\n",
              "\t  __webpack_require__(8);\n",
              "\t}\n",
              "\t\n",
              "\t__webpack_require__(339);\n",
              "\t\n",
              "\texports.Explanation = _explanation2.default;\n",
              "\texports.Barchart = _bar_chart2.default;\n",
              "\texports.PredictProba = _predict_proba2.default;\n",
              "\texports.PredictedValue = _predicted_value2.default;\n",
              "\t//require('style-loader');\n",
              "\t/* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }())))\n",
              "\n",
              "/***/ }),\n",
              "/* 1 */\n",
              "/***/ (function(module, exports, __webpack_require__) {\n",
              "\n",
              "\t'use strict';\n",
              "\t\n",
              "\tObject.defineProperty(exports, \"__esModule\", {\n",
              "\t  value: true\n",
              "\t});\n",
              "\t\n",
              "\tvar _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i[\"return\"]) _i[\"return\"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError(\"Invalid attempt to destructure non-iterable instance\"); } }; }();\n",
              "\t\n",
              "\tvar _d2 = __webpack_require__(2);\n",
              "\t\n",
              "\tvar _d3 = _interopRequireDefault(_d2);\n",
              "\t\n",
              "\tvar _bar_chart = __webpack_require__(3);\n",
              "\t\n",
              "\tvar _bar_chart2 = _interopRequireDefault(_bar_chart);\n",
              "\t\n",
              "\tvar _lodash = __webpack_require__(4);\n",
              "\t\n",
              "\tfunction _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }\n",
              "\t\n",
              "\tfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n",
              "\t\n",
              "\tvar Explanation = function () {\n",
              "\t  function Explanation(class_names) {\n",
              "\t    _classCallCheck(this, Explanation);\n",
              "\t\n",
              "\t    this.names = class_names;\n",
              "\t    if (class_names.length < 10) {\n",
              "\t      this.colors = _d3.default.scale.category10().domain(this.names);\n",
              "\t      this.colors_i = _d3.default.scale.category10().domain((0, _lodash.range)(this.names.length));\n",
              "\t    } else {\n",
              "\t      this.colors = _d3.default.scale.category20().domain(this.names);\n",
              "\t      this.colors_i = _d3.default.scale.category20().domain((0, _lodash.range)(this.names.length));\n",
              "\t    }\n",
              "\t  }\n",
              "\t  // exp: [(feature-name, weight), ...]\n",
              "\t  // label: int\n",
              "\t  // div: d3 selection\n",
              "\t\n",
              "\t\n",
              "\t  Explanation.prototype.show = function show(exp, label, div) {\n",
              "\t    var svg = div.append('svg').style('width', '100%');\n",
              "\t    var colors = ['#5F9EA0', this.colors_i(label)];\n",
              "\t    var names = ['NOT ' + this.names[label], this.names[label]];\n",
              "\t    if (this.names.length == 2) {\n",
              "\t      colors = [this.colors_i(0), this.colors_i(1)];\n",
              "\t      names = this.names;\n",
              "\t    }\n",
              "\t    var plot = new _bar_chart2.default(svg, exp, true, names, colors, true, 10);\n",
              "\t    svg.style('height', plot.svg_height + 'px');\n",
              "\t  };\n",
              "\t  // exp has all ocurrences of words, with start index and weight:\n",
              "\t  // exp = [('word', 132, -0.13), ('word3', 111, 1.3)\n",
              "\t\n",
              "\t\n",
              "\t  Explanation.prototype.show_raw_text = function show_raw_text(exp, label, raw, div) {\n",
              "\t    var opacity = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : true;\n",
              "\t\n",
              "\t    //let colors=['#5F9EA0', this.colors(this.exp['class'])];\n",
              "\t    var colors = ['#5F9EA0', this.colors_i(label)];\n",
              "\t    if (this.names.length == 2) {\n",
              "\t      colors = [this.colors_i(0), this.colors_i(1)];\n",
              "\t    }\n",
              "\t    var word_lists = [[], []];\n",
              "\t    var max_weight = -1;\n",
              "\t    var _iteratorNormalCompletion = true;\n",
              "\t    var _didIteratorError = false;\n",
              "\t    var _iteratorError = undefined;\n",
              "\t\n",
              "\t    try {\n",
              "\t      for (var _iterator = exp[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) {\n",
              "\t        var _step$value = _slicedToArray(_step.value, 3),\n",
              "\t            word = _step$value[0],\n",
              "\t            start = _step$value[1],\n",
              "\t            weight = _step$value[2];\n",
              "\t\n",
              "\t        if (weight > 0) {\n",
              "\t          word_lists[1].push([start, start + word.length, weight]);\n",
              "\t        } else {\n",
              "\t          word_lists[0].push([start, start + word.length, -weight]);\n",
              "\t        }\n",
              "\t        max_weight = Math.max(max_weight, Math.abs(weight));\n",
              "\t      }\n",
              "\t    } catch (err) {\n",
              "\t      _didIteratorError = true;\n",
              "\t      _iteratorError = err;\n",
              "\t    } finally {\n",
              "\t      try {\n",
              "\t        if (!_iteratorNormalCompletion && _iterator.return) {\n",
              "\t          _iterator.return();\n",
              "\t        }\n",
              "\t      } finally {\n",
              "\t        if (_didIteratorError) {\n",
              "\t          throw _iteratorError;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t\n",
              "\t    if (!opacity) {\n",
              "\t      max_weight = 0;\n",
              "\t    }\n",
              "\t    this.display_raw_text(div, raw, word_lists, colors, max_weight, true);\n",
              "\t  };\n",
              "\t  // exp is list of (feature_name, value, weight)\n",
              "\t\n",
              "\t\n",
              "\t  Explanation.prototype.show_raw_tabular = function show_raw_tabular(exp, label, div) {\n",
              "\t    div.classed('lime', true).classed('table_div', true);\n",
              "\t    var colors = ['#5F9EA0', this.colors_i(label)];\n",
              "\t    if (this.names.length == 2) {\n",
              "\t      colors = [this.colors_i(0), this.colors_i(1)];\n",
              "\t    }\n",
              "\t    var table = div.append('table');\n",
              "\t    var thead = table.append('tr');\n",
              "\t    thead.append('td').text('Feature');\n",
              "\t    thead.append('td').text('Value');\n",
              "\t    thead.style('color', 'black').style('font-size', '20px');\n",
              "\t    var _iteratorNormalCompletion2 = true;\n",
              "\t    var _didIteratorError2 = false;\n",
              "\t    var _iteratorError2 = undefined;\n",
              "\t\n",
              "\t    try {\n",
              "\t      for (var _iterator2 = exp[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) {\n",
              "\t        var _step2$value = _slicedToArray(_step2.value, 3),\n",
              "\t            fname = _step2$value[0],\n",
              "\t            value = _step2$value[1],\n",
              "\t            weight = _step2$value[2];\n",
              "\t\n",
              "\t        var tr = table.append('tr');\n",
              "\t        tr.style('border-style', 'hidden');\n",
              "\t        tr.append('td').text(fname);\n",
              "\t        tr.append('td').text(value);\n",
              "\t        if (weight > 0) {\n",
              "\t          tr.style('background-color', colors[1]);\n",
              "\t        } else if (weight < 0) {\n",
              "\t          tr.style('background-color', colors[0]);\n",
              "\t        } else {\n",
              "\t          tr.style('color', 'black');\n",
              "\t        }\n",
              "\t      }\n",
              "\t    } catch (err) {\n",
              "\t      _didIteratorError2 = true;\n",
              "\t      _iteratorError2 = err;\n",
              "\t    } finally {\n",
              "\t      try {\n",
              "\t        if (!_iteratorNormalCompletion2 && _iterator2.return) {\n",
              "\t          _iterator2.return();\n",
              "\t        }\n",
              "\t      } finally {\n",
              "\t        if (_didIteratorError2) {\n",
              "\t          throw _iteratorError2;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t  };\n",
              "\t\n",
              "\t  Explanation.prototype.hexToRgb = function hexToRgb(hex) {\n",
              "\t    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})$/i.exec(hex);\n",
              "\t    return result ? {\n",
              "\t      r: parseInt(result[1], 16),\n",
              "\t      g: parseInt(result[2], 16),\n",
              "\t      b: parseInt(result[3], 16)\n",
              "\t    } : null;\n",
              "\t  };\n",
              "\t\n",
              "\t  Explanation.prototype.applyAlpha = function applyAlpha(hex, alpha) {\n",
              "\t    var components = this.hexToRgb(hex);\n",
              "\t    return 'rgba(' + components.r + \",\" + components.g + \",\" + components.b + \",\" + alpha.toFixed(3) + \")\";\n",
              "\t  };\n",
              "\t  // sord_lists is an array of arrays, of length (colors). if with_positions is true,\n",
              "\t  // word_lists is an array of [start,end] positions instead\n",
              "\t\n",
              "\t\n",
              "\t  Explanation.prototype.display_raw_text = function display_raw_text(div, raw_text) {\n",
              "\t    var word_lists = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : [];\n",
              "\t    var colors = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : [];\n",
              "\t    var max_weight = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : 1;\n",
              "\t    var positions = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false;\n",
              "\t\n",
              "\t    div.classed('lime', true).classed('text_div', true);\n",
              "\t    div.append('h3').text('Text with highlighted words');\n",
              "\t    var highlight_tag = 'span';\n",
              "\t    var text_span = div.append('span').style('white-space', 'pre-wrap').text(raw_text);\n",
              "\t    var position_lists = word_lists;\n",
              "\t    if (!positions) {\n",
              "\t      position_lists = this.wordlists_to_positions(word_lists, raw_text);\n",
              "\t    }\n",
              "\t    var objects = [];\n",
              "\t    var _iteratorNormalCompletion3 = true;\n",
              "\t    var _didIteratorError3 = false;\n",
              "\t    var _iteratorError3 = undefined;\n",
              "\t\n",
              "\t    try {\n",
              "\t      var _loop = function _loop() {\n",
              "\t        var i = _step3.value;\n",
              "\t\n",
              "\t        position_lists[i].map(function (x) {\n",
              "\t          return objects.push({ 'label': i, 'start': x[0], 'end': x[1], 'alpha': max_weight === 0 ? 1 : x[2] / max_weight });\n",
              "\t        });\n",
              "\t      };\n",
              "\t\n",
              "\t      for (var _iterator3 = (0, _lodash.range)(position_lists.length)[Symbol.iterator](), _step3; !(_iteratorNormalCompletion3 = (_step3 = _iterator3.next()).done); _iteratorNormalCompletion3 = true) {\n",
              "\t        _loop();\n",
              "\t      }\n",
              "\t    } catch (err) {\n",
              "\t      _didIteratorError3 = true;\n",
              "\t      _iteratorError3 = err;\n",
              "\t    } finally {\n",
              "\t      try {\n",
              "\t        if (!_iteratorNormalCompletion3 && _iterator3.return) {\n",
              "\t          _iterator3.return();\n",
              "\t        }\n",
              "\t      } finally {\n",
              "\t        if (_didIteratorError3) {\n",
              "\t          throw _iteratorError3;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t\n",
              "\t    objects = (0, _lodash.sortBy)(objects, function (x) {\n",
              "\t      return x['start'];\n",
              "\t    });\n",
              "\t    var node = text_span.node().childNodes[0];\n",
              "\t    var subtract = 0;\n",
              "\t    var _iteratorNormalCompletion4 = true;\n",
              "\t    var _didIteratorError4 = false;\n",
              "\t    var _iteratorError4 = undefined;\n",
              "\t\n",
              "\t    try {\n",
              "\t      for (var _iterator4 = objects[Symbol.iterator](), _step4; !(_iteratorNormalCompletion4 = (_step4 = _iterator4.next()).done); _iteratorNormalCompletion4 = true) {\n",
              "\t        var obj = _step4.value;\n",
              "\t\n",
              "\t        var word = raw_text.slice(obj.start, obj.end);\n",
              "\t        var start = obj.start - subtract;\n",
              "\t        var end = obj.end - subtract;\n",
              "\t        var match = document.createElement(highlight_tag);\n",
              "\t        match.appendChild(document.createTextNode(word));\n",
              "\t        match.style.backgroundColor = this.applyAlpha(colors[obj.label], obj.alpha);\n",
              "\t        var after = node.splitText(start);\n",
              "\t        after.nodeValue = after.nodeValue.substring(word.length);\n",
              "\t        node.parentNode.insertBefore(match, after);\n",
              "\t        subtract += end;\n",
              "\t        node = after;\n",
              "\t      }\n",
              "\t    } catch (err) {\n",
              "\t      _didIteratorError4 = true;\n",
              "\t      _iteratorError4 = err;\n",
              "\t    } finally {\n",
              "\t      try {\n",
              "\t        if (!_iteratorNormalCompletion4 && _iterator4.return) {\n",
              "\t          _iterator4.return();\n",
              "\t        }\n",
              "\t      } finally {\n",
              "\t        if (_didIteratorError4) {\n",
              "\t          throw _iteratorError4;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t  };\n",
              "\t\n",
              "\t  Explanation.prototype.wordlists_to_positions = function wordlists_to_positions(word_lists, raw_text) {\n",
              "\t    var ret = [];\n",
              "\t    var _iteratorNormalCompletion5 = true;\n",
              "\t    var _didIteratorError5 = false;\n",
              "\t    var _iteratorError5 = undefined;\n",
              "\t\n",
              "\t    try {\n",
              "\t      for (var _iterator5 = word_lists[Symbol.iterator](), _step5; !(_iteratorNormalCompletion5 = (_step5 = _iterator5.next()).done); _iteratorNormalCompletion5 = true) {\n",
              "\t        var words = _step5.value;\n",
              "\t\n",
              "\t        if (words.length === 0) {\n",
              "\t          ret.push([]);\n",
              "\t          continue;\n",
              "\t        }\n",
              "\t        var re = new RegExp(\"\\\\b(\" + words.join('|') + \")\\\\b\", 'gm');\n",
              "\t        var temp = void 0;\n",
              "\t        var list = [];\n",
              "\t        while ((temp = re.exec(raw_text)) !== null) {\n",
              "\t          list.push([temp.index, temp.index + temp[0].length]);\n",
              "\t        }\n",
              "\t        ret.push(list);\n",
              "\t      }\n",
              "\t    } catch (err) {\n",
              "\t      _didIteratorError5 = true;\n",
              "\t      _iteratorError5 = err;\n",
              "\t    } finally {\n",
              "\t      try {\n",
              "\t        if (!_iteratorNormalCompletion5 && _iterator5.return) {\n",
              "\t          _iterator5.return();\n",
              "\t        }\n",
              "\t      } finally {\n",
              "\t        if (_didIteratorError5) {\n",
              "\t          throw _iteratorError5;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t\n",
              "\t    return ret;\n",
              "\t  };\n",
              "\t\n",
              "\t  return Explanation;\n",
              "\t}();\n",
              "\t\n",
              "\texports.default = Explanation;\n",
              "\n",
              "/***/ }),\n",
              "/* 2 */\n",
              "/***/ (function(module, exports, __webpack_require__) {\n",
              "\n",
              "\tvar __WEBPACK_AMD_DEFINE_FACTORY__, __WEBPACK_AMD_DEFINE_RESULT__;!function() {\n",
              "\t  var d3 = {\n",
              "\t    version: \"3.5.17\"\n",
              "\t  };\n",
              "\t  var d3_arraySlice = [].slice, d3_array = function(list) {\n",
              "\t    return d3_arraySlice.call(list);\n",
              "\t  };\n",
              "\t  var d3_document = this.document;\n",
              "\t  function d3_documentElement(node) {\n",
              "\t    return node && (node.ownerDocument || node.document || node).documentElement;\n",
              "\t  }\n",
              "\t  function d3_window(node) {\n",
              "\t    return node && (node.ownerDocument && node.ownerDocument.defaultView || node.document && node || node.defaultView);\n",
              "\t  }\n",
              "\t  if (d3_document) {\n",
              "\t    try {\n",
              "\t      d3_array(d3_document.documentElement.childNodes)[0].nodeType;\n",
              "\t    } catch (e) {\n",
              "\t      d3_array = function(list) {\n",
              "\t        var i = list.length, array = new Array(i);\n",
              "\t        while (i--) array[i] = list[i];\n",
              "\t        return array;\n",
              "\t      };\n",
              "\t    }\n",
              "\t  }\n",
              "\t  if (!Date.now) Date.now = function() {\n",
              "\t    return +new Date();\n",
              "\t  };\n",
              "\t  if (d3_document) {\n",
              "\t    try {\n",
              "\t      d3_document.createElement(\"DIV\").style.setProperty(\"opacity\", 0, \"\");\n",
              "\t    } catch (error) {\n",
              "\t      var d3_element_prototype = this.Element.prototype, d3_element_setAttribute = d3_element_prototype.setAttribute, d3_element_setAttributeNS = d3_element_prototype.setAttributeNS, d3_style_prototype = this.CSSStyleDeclaration.prototype, d3_style_setProperty = d3_style_prototype.setProperty;\n",
              "\t      d3_element_prototype.setAttribute = function(name, value) {\n",
              "\t        d3_element_setAttribute.call(this, name, value + \"\");\n",
              "\t      };\n",
              "\t      d3_element_prototype.setAttributeNS = function(space, local, value) {\n",
              "\t        d3_element_setAttributeNS.call(this, space, local, value + \"\");\n",
              "\t      };\n",
              "\t      d3_style_prototype.setProperty = function(name, value, priority) {\n",
              "\t        d3_style_setProperty.call(this, name, value + \"\", priority);\n",
              "\t      };\n",
              "\t    }\n",
              "\t  }\n",
              "\t  d3.ascending = d3_ascending;\n",
              "\t  function d3_ascending(a, b) {\n",
              "\t    return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN;\n",
              "\t  }\n",
              "\t  d3.descending = function(a, b) {\n",
              "\t    return b < a ? -1 : b > a ? 1 : b >= a ? 0 : NaN;\n",
              "\t  };\n",
              "\t  d3.min = function(array, f) {\n",
              "\t    var i = -1, n = array.length, a, b;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if ((b = array[i]) != null && b >= b) {\n",
              "\t        a = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = array[i]) != null && a > b) a = b;\n",
              "\t    } else {\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n",
              "\t        a = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null && a > b) a = b;\n",
              "\t    }\n",
              "\t    return a;\n",
              "\t  };\n",
              "\t  d3.max = function(array, f) {\n",
              "\t    var i = -1, n = array.length, a, b;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if ((b = array[i]) != null && b >= b) {\n",
              "\t        a = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = array[i]) != null && b > a) a = b;\n",
              "\t    } else {\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n",
              "\t        a = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null && b > a) a = b;\n",
              "\t    }\n",
              "\t    return a;\n",
              "\t  };\n",
              "\t  d3.extent = function(array, f) {\n",
              "\t    var i = -1, n = array.length, a, b, c;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if ((b = array[i]) != null && b >= b) {\n",
              "\t        a = c = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = array[i]) != null) {\n",
              "\t        if (a > b) a = b;\n",
              "\t        if (c < b) c = b;\n",
              "\t      }\n",
              "\t    } else {\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n",
              "\t        a = c = b;\n",
              "\t        break;\n",
              "\t      }\n",
              "\t      while (++i < n) if ((b = f.call(array, array[i], i)) != null) {\n",
              "\t        if (a > b) a = b;\n",
              "\t        if (c < b) c = b;\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return [ a, c ];\n",
              "\t  };\n",
              "\t  function d3_number(x) {\n",
              "\t    return x === null ? NaN : +x;\n",
              "\t  }\n",
              "\t  function d3_numeric(x) {\n",
              "\t    return !isNaN(x);\n",
              "\t  }\n",
              "\t  d3.sum = function(array, f) {\n",
              "\t    var s = 0, n = array.length, a, i = -1;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if (d3_numeric(a = +array[i])) s += a;\n",
              "\t    } else {\n",
              "\t      while (++i < n) if (d3_numeric(a = +f.call(array, array[i], i))) s += a;\n",
              "\t    }\n",
              "\t    return s;\n",
              "\t  };\n",
              "\t  d3.mean = function(array, f) {\n",
              "\t    var s = 0, n = array.length, a, i = -1, j = n;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if (d3_numeric(a = d3_number(array[i]))) s += a; else --j;\n",
              "\t    } else {\n",
              "\t      while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) s += a; else --j;\n",
              "\t    }\n",
              "\t    if (j) return s / j;\n",
              "\t  };\n",
              "\t  d3.quantile = function(values, p) {\n",
              "\t    var H = (values.length - 1) * p + 1, h = Math.floor(H), v = +values[h - 1], e = H - h;\n",
              "\t    return e ? v + e * (values[h] - v) : v;\n",
              "\t  };\n",
              "\t  d3.median = function(array, f) {\n",
              "\t    var numbers = [], n = array.length, a, i = -1;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) if (d3_numeric(a = d3_number(array[i]))) numbers.push(a);\n",
              "\t    } else {\n",
              "\t      while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) numbers.push(a);\n",
              "\t    }\n",
              "\t    if (numbers.length) return d3.quantile(numbers.sort(d3_ascending), .5);\n",
              "\t  };\n",
              "\t  d3.variance = function(array, f) {\n",
              "\t    var n = array.length, m = 0, a, d, s = 0, i = -1, j = 0;\n",
              "\t    if (arguments.length === 1) {\n",
              "\t      while (++i < n) {\n",
              "\t        if (d3_numeric(a = d3_number(array[i]))) {\n",
              "\t          d = a - m;\n",
              "\t          m += d / ++j;\n",
              "\t          s += d * (a - m);\n",
              "\t        }\n",
              "\t      }\n",
              "\t    } else {\n",
              "\t      while (++i < n) {\n",
              "\t        if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) {\n",
              "\t          d = a - m;\n",
              "\t          m += d / ++j;\n",
              "\t          s += d * (a - m);\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    if (j > 1) return s / (j - 1);\n",
              "\t  };\n",
              "\t  d3.deviation = function() {\n",
              "\t    var v = d3.variance.apply(this, arguments);\n",
              "\t    return v ? Math.sqrt(v) : v;\n",
              "\t  };\n",
              "\t  function d3_bisector(compare) {\n",
              "\t    return {\n",
              "\t      left: function(a, x, lo, hi) {\n",
              "\t        if (arguments.length < 3) lo = 0;\n",
              "\t        if (arguments.length < 4) hi = a.length;\n",
              "\t        while (lo < hi) {\n",
              "\t          var mid = lo + hi >>> 1;\n",
              "\t          if (compare(a[mid], x) < 0) lo = mid + 1; else hi = mid;\n",
              "\t        }\n",
              "\t        return lo;\n",
              "\t      },\n",
              "\t      right: function(a, x, lo, hi) {\n",
              "\t        if (arguments.length < 3) lo = 0;\n",
              "\t        if (arguments.length < 4) hi = a.length;\n",
              "\t        while (lo < hi) {\n",
              "\t          var mid = lo + hi >>> 1;\n",
              "\t          if (compare(a[mid], x) > 0) hi = mid; else lo = mid + 1;\n",
              "\t        }\n",
              "\t        return lo;\n",
              "\t      }\n",
              "\t    };\n",
              "\t  }\n",
              "\t  var d3_bisect = d3_bisector(d3_ascending);\n",
              "\t  d3.bisectLeft = d3_bisect.left;\n",
              "\t  d3.bisect = d3.bisectRight = d3_bisect.right;\n",
              "\t  d3.bisector = function(f) {\n",
              "\t    return d3_bisector(f.length === 1 ? function(d, x) {\n",
              "\t      return d3_ascending(f(d), x);\n",
              "\t    } : f);\n",
              "\t  };\n",
              "\t  d3.shuffle = function(array, i0, i1) {\n",
              "\t    if ((m = arguments.length) < 3) {\n",
              "\t      i1 = array.length;\n",
              "\t      if (m < 2) i0 = 0;\n",
              "\t    }\n",
              "\t    var m = i1 - i0, t, i;\n",
              "\t    while (m) {\n",
              "\t      i = Math.random() * m-- | 0;\n",
              "\t      t = array[m + i0], array[m + i0] = array[i + i0], array[i + i0] = t;\n",
              "\t    }\n",
              "\t    return array;\n",
              "\t  };\n",
              "\t  d3.permute = function(array, indexes) {\n",
              "\t    var i = indexes.length, permutes = new Array(i);\n",
              "\t    while (i--) permutes[i] = array[indexes[i]];\n",
              "\t    return permutes;\n",
              "\t  };\n",
              "\t  d3.pairs = function(array) {\n",
              "\t    var i = 0, n = array.length - 1, p0, p1 = array[0], pairs = new Array(n < 0 ? 0 : n);\n",
              "\t    while (i < n) pairs[i] = [ p0 = p1, p1 = array[++i] ];\n",
              "\t    return pairs;\n",
              "\t  };\n",
              "\t  d3.transpose = function(matrix) {\n",
              "\t    if (!(n = matrix.length)) return [];\n",
              "\t    for (var i = -1, m = d3.min(matrix, d3_transposeLength), transpose = new Array(m); ++i < m; ) {\n",
              "\t      for (var j = -1, n, row = transpose[i] = new Array(n); ++j < n; ) {\n",
              "\t        row[j] = matrix[j][i];\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return transpose;\n",
              "\t  };\n",
              "\t  function d3_transposeLength(d) {\n",
              "\t    return d.length;\n",
              "\t  }\n",
              "\t  d3.zip = function() {\n",
              "\t    return d3.transpose(arguments);\n",
              "\t  };\n",
              "\t  d3.keys = function(map) {\n",
              "\t    var keys = [];\n",
              "\t    for (var key in map) keys.push(key);\n",
              "\t    return keys;\n",
              "\t  };\n",
              "\t  d3.values = function(map) {\n",
              "\t    var values = [];\n",
              "\t    for (var key in map) values.push(map[key]);\n",
              "\t    return values;\n",
              "\t  };\n",
              "\t  d3.entries = function(map) {\n",
              "\t    var entries = [];\n",
              "\t    for (var key in map) entries.push({\n",
              "\t      key: key,\n",
              "\t      value: map[key]\n",
              "\t    });\n",
              "\t    return entries;\n",
              "\t  };\n",
              "\t  d3.merge = function(arrays) {\n",
              "\t    var n = arrays.length, m, i = -1, j = 0, merged, array;\n",
              "\t    while (++i < n) j += arrays[i].length;\n",
              "\t    merged = new Array(j);\n",
              "\t    while (--n >= 0) {\n",
              "\t      array = arrays[n];\n",
              "\t      m = array.length;\n",
              "\t      while (--m >= 0) {\n",
              "\t        merged[--j] = array[m];\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return merged;\n",
              "\t  };\n",
              "\t  var abs = Math.abs;\n",
              "\t  d3.range = function(start, stop, step) {\n",
              "\t    if (arguments.length < 3) {\n",
              "\t      step = 1;\n",
              "\t      if (arguments.length < 2) {\n",
              "\t        stop = start;\n",
              "\t        start = 0;\n",
              "\t      }\n",
              "\t    }\n",
              "\t    if ((stop - start) / step === Infinity) throw new Error(\"infinite range\");\n",
              "\t    var range = [], k = d3_range_integerScale(abs(step)), i = -1, j;\n",
              "\t    start *= k, stop *= k, step *= k;\n",
              "\t    if (step < 0) while ((j = start + step * ++i) > stop) range.push(j / k); else while ((j = start + step * ++i) < stop) range.push(j / k);\n",
              "\t    return range;\n",
              "\t  };\n",
              "\t  function d3_range_integerScale(x) {\n",
              "\t    var k = 1;\n",
              "\t    while (x * k % 1) k *= 10;\n",
              "\t    return k;\n",
              "\t  }\n",
              "\t  function d3_class(ctor, properties) {\n",
              "\t    for (var key in properties) {\n",
              "\t      Object.defineProperty(ctor.prototype, key, {\n",
              "\t        value: properties[key],\n",
              "\t        enumerable: false\n",
              "\t      });\n",
              "\t    }\n",
              "\t  }\n",
              "\t  d3.map = function(object, f) {\n",
              "\t    var map = new d3_Map();\n",
              "\t    if (object instanceof d3_Map) {\n",
              "\t      object.forEach(function(key, value) {\n",
              "\t        map.set(key, value);\n",
              "\t      });\n",
              "\t    } else if (Array.isArray(object)) {\n",
              "\t      var i = -1, n = object.length, o;\n",
              "\t      if (arguments.length === 1) while (++i < n) map.set(i, object[i]); else while (++i < n) map.set(f.call(object, o = object[i], i), o);\n",
              "\t    } else {\n",
              "\t      for (var key in object) map.set(key, object[key]);\n",
              "\t    }\n",
              "\t    return map;\n",
              "\t  };\n",
              "\t  function d3_Map() {\n",
              "\t    this._ = Object.create(null);\n",
              "\t  }\n",
              "\t  var d3_map_proto = \"__proto__\", d3_map_zero = \"\\x00\";\n",
              "\t  d3_class(d3_Map, {\n",
              "\t    has: d3_map_has,\n",
              "\t    get: function(key) {\n",
              "\t      return this._[d3_map_escape(key)];\n",
              "\t    },\n",
              "\t    set: function(key, value) {\n",
              "\t      return this._[d3_map_escape(key)] = value;\n",
              "\t    },\n",
              "\t    remove: d3_map_remove,\n",
              "\t    keys: d3_map_keys,\n",
              "\t    values: function() {\n",
              "\t      var values = [];\n",
              "\t      for (var key in this._) values.push(this._[key]);\n",
              "\t      return values;\n",
              "\t    },\n",
              "\t    entries: function() {\n",
              "\t      var entries = [];\n",
              "\t      for (var key in this._) entries.push({\n",
              "\t        key: d3_map_unescape(key),\n",
              "\t        value: this._[key]\n",
              "\t      });\n",
              "\t      return entries;\n",
              "\t    },\n",
              "\t    size: d3_map_size,\n",
              "\t    empty: d3_map_empty,\n",
              "\t    forEach: function(f) {\n",
              "\t      for (var key in this._) f.call(this, d3_map_unescape(key), this._[key]);\n",
              "\t    }\n",
              "\t  });\n",
              "\t  function d3_map_escape(key) {\n",
              "\t    return (key += \"\") === d3_map_proto || key[0] === d3_map_zero ? d3_map_zero + key : key;\n",
              "\t  }\n",
              "\t  function d3_map_unescape(key) {\n",
              "\t    return (key += \"\")[0] === d3_map_zero ? key.slice(1) : key;\n",
              "\t  }\n",
              "\t  function d3_map_has(key) {\n",
              "\t    return d3_map_escape(key) in this._;\n",
              "\t  }\n",
              "\t  function d3_map_remove(key) {\n",
              "\t    return (key = d3_map_escape(key)) in this._ && delete this._[key];\n",
              "\t  }\n",
              "\t  function d3_map_keys() {\n",
              "\t    var keys = [];\n",
              "\t    for (var key in this._) keys.push(d3_map_unescape(key));\n",
              "\t    return keys;\n",
              "\t  }\n",
              "\t  function d3_map_size() {\n",
              "\t    var size = 0;\n",
              "\t    for (var key in this._) ++size;\n",
              "\t    return size;\n",
              "\t  }\n",
              "\t  function d3_map_empty() {\n",
              "\t    for (var key in this._) return false;\n",
              "\t    return true;\n",
              "\t  }\n",
              "\t  d3.nest = function() {\n",
              "\t    var nest = {}, keys = [], sortKeys = [], sortValues, rollup;\n",
              "\t    function map(mapType, array, depth) {\n",
              "\t      if (depth >= keys.length) return rollup ? rollup.call(nest, array) : sortValues ? array.sort(sortValues) : array;\n",
              "\t      var i = -1, n = array.length, key = keys[depth++], keyValue, object, setter, valuesByKey = new d3_Map(), values;\n",
              "\t      while (++i < n) {\n",
              "\t        if (values = valuesByKey.get(keyValue = key(object = array[i]))) {\n",
              "\t          values.push(object);\n",
              "\t        } else {\n",
              "\t          valuesByKey.set(keyValue, [ object ]);\n",
              "\t        }\n",
              "\t      }\n",
              "\t      if (mapType) {\n",
              "\t        object = mapType();\n",
              "\t        setter = function(keyValue, values) {\n",
              "\t          object.set(keyValue, map(mapType, values, depth));\n",
              "\t        };\n",
              "\t      } else {\n",
              "\t        object = {};\n",
              "\t        setter = function(keyValue, values) {\n",
              "\t          object[keyValue] = map(mapType, values, depth);\n",
              "\t        };\n",
              "\t      }\n",
              "\t      valuesByKey.forEach(setter);\n",
              "\t      return object;\n",
              "\t    }\n",
              "\t    function entries(map, depth) {\n",
              "\t      if (depth >= keys.length) return map;\n",
              "\t      var array = [], sortKey = sortKeys[depth++];\n",
              "\t      map.forEach(function(key, keyMap) {\n",
              "\t        array.push({\n",
              "\t          key: key,\n",
              "\t          values: entries(keyMap, depth)\n",
              "\t        });\n",
              "\t      });\n",
              "\t      return sortKey ? array.sort(function(a, b) {\n",
              "\t        return sortKey(a.key, b.key);\n",
              "\t      }) : array;\n",
              "\t    }\n",
              "\t    nest.map = function(array, mapType) {\n",
              "\t      return map(mapType, array, 0);\n",
              "\t    };\n",
              "\t    nest.entries = function(array) {\n",
              "\t      return entries(map(d3.map, array, 0), 0);\n",
              "\t    };\n",
              "\t    nest.key = function(d) {\n",
              "\t      keys.push(d);\n",
              "\t      return nest;\n",
              "\t    };\n",
              "\t    nest.sortKeys = function(order) {\n",
              "\t      sortKeys[keys.length - 1] = order;\n",
              "\t      return nest;\n",
              "\t    };\n",
              "\t    nest.sortValues = function(order) {\n",
              "\t      sortValues = order;\n",
              "\t      return nest;\n",
              "\t    };\n",
              "\t    nest.rollup = function(f) {\n",
              "\t      rollup = f;\n",
              "\t      return nest;\n",
              "\t    };\n",
              "\t    return nest;\n",
              "\t  };\n",
              "\t  d3.set = function(array) {\n",
              "\t    var set = new d3_Set();\n",
              "\t    if (array) for (var i = 0, n = array.length; i < n; ++i) set.add(array[i]);\n",
              "\t    return set;\n",
              "\t  };\n",
              "\t  function d3_Set() {\n",
              "\t    this._ = Object.create(null);\n",
              "\t  }\n",
              "\t  d3_class(d3_Set, {\n",
              "\t    has: d3_map_has,\n",
              "\t    add: function(key) {\n",
              "\t      this._[d3_map_escape(key += \"\")] = true;\n",
              "\t      return key;\n",
              "\t    },\n",
              "\t    remove: d3_map_remove,\n",
              "\t    values: d3_map_keys,\n",
              "\t    size: d3_map_size,\n",
              "\t    empty: d3_map_empty,\n",
              "\t    forEach: function(f) {\n",
              "\t      for (var key in this._) f.call(this, d3_map_unescape(key));\n",
              "\t    }\n",
              "\t  });\n",
              "\t  d3.behavior = {};\n",
              "\t  function d3_identity(d) {\n",
              "\t    return d;\n",
              "\t  }\n",
              "\t  d3.rebind = function(target, source) {\n",
              "\t    var i = 1, n = arguments.length, method;\n",
              "\t    while (++i < n) target[method = arguments[i]] = d3_rebind(target, source, source[method]);\n",
              "\t    return target;\n",
              "\t  };\n",
              "\t  function d3_rebind(target, source, method) {\n",
              "\t    return function() {\n",
              "\t      var value = method.apply(source, arguments);\n",
              "\t      return value === source ? target : value;\n",
              "\t    };\n",
              "\t  }\n",
              "\t  function d3_vendorSymbol(object, name) {\n",
              "\t    if (name in object) return name;\n",
              "\t    name = name.charAt(0).toUpperCase() + name.slice(1);\n",
              "\t    for (var i = 0, n = d3_vendorPrefixes.length; i < n; ++i) {\n",
              "\t      var prefixName = d3_vendorPrefixes[i] + name;\n",
              "\t      if (prefixName in object) return prefixName;\n",
              "\t    }\n",
              "\t  }\n",
              "\t  var d3_vendorPrefixes = [ \"webkit\", \"ms\", \"moz\", \"Moz\", \"o\", \"O\" ];\n",
              "\t  function d3_noop() {}\n",
              "\t  d3.dispatch = function() {\n",
              "\t    var dispatch = new d3_dispatch(), i = -1, n = arguments.length;\n",
              "\t    while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch);\n",
              "\t    return dispatch;\n",
              "\t  };\n",
              "\t  function d3_dispatch() {}\n",
              "\t  d3_dispatch.prototype.on = function(type, listener) {\n",
              "\t    var i = type.indexOf(\".\"), name = \"\";\n",
              "\t    if (i >= 0) {\n",
              "\t      name = type.slice(i + 1);\n",
              "\t      type = type.slice(0, i);\n",
              "\t    }\n",
              "\t    if (type) return arguments.length < 2 ? this[type].on(name) : this[type].on(name, listener);\n",
              "\t    if (arguments.length === 2) {\n",
              "\t      if (listener == null) for (type in this) {\n",
              "\t        if (this.hasOwnProperty(type)) this[type].on(name, null);\n",
              "\t      }\n",
              "\t      return this;\n",
              "\t    }\n",
              "\t  };\n",
              "\t  function d3_dispatch_event(dispatch) {\n",
              "\t    var listeners = [], listenerByName = new d3_Map();\n",
              "\t    function event() {\n",
              "\t      var z = listeners, i = -1, n = z.length, l;\n",
              "\t      while (++i < n) if (l = z[i].on) l.apply(this, arguments);\n",
              "\t      return dispatch;\n",
              "\t    }\n",
              "\t    event.on = function(name, listener) {\n",
              "\t      var l = listenerByName.get(name), i;\n",
              "\t      if (arguments.length < 2) return l && l.on;\n",
              "\t      if (l) {\n",
              "\t        l.on = null;\n",
              "\t        listeners = listeners.slice(0, i = listeners.indexOf(l)).concat(listeners.slice(i + 1));\n",
              "\t        listenerByName.remove(name);\n",
              "\t      }\n",
              "\t      if (listener) listeners.push(listenerByName.set(name, {\n",
              "\t        on: listener\n",
              "\t      }));\n",
              "\t      return dispatch;\n",
              "\t    };\n",
              "\t    return event;\n",
              "\t  }\n",
              "\t  d3.event = null;\n",
              "\t  function d3_eventPreventDefault() {\n",
              "\t    d3.event.preventDefault();\n",
              "\t  }\n",
              "\t  function d3_eventSource() {\n",
              "\t    var e = d3.event, s;\n",
              "\t    while (s = e.sourceEvent) e = s;\n",
              "\t    return e;\n",
              "\t  }\n",
              "\t  function d3_eventDispatch(target) {\n",
              "\t    var dispatch = new d3_dispatch(), i = 0, n = arguments.length;\n",
              "\t    while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch);\n",
              "\t    dispatch.of = function(thiz, argumentz) {\n",
              "\t      return function(e1) {\n",
              "\t        try {\n",
              "\t          var e0 = e1.sourceEvent = d3.event;\n",
              "\t          e1.target = target;\n",
              "\t          d3.event = e1;\n",
              "\t          dispatch[e1.type].apply(thiz, argumentz);\n",
              "\t        } finally {\n",
              "\t          d3.event = e0;\n",
              "\t        }\n",
              "\t      };\n",
              "\t    };\n",
              "\t    return dispatch;\n",
              "\t  }\n",
              "\t  d3.requote = function(s) {\n",
              "\t    return s.replace(d3_requote_re, \"\\\\$&\");\n",
              "\t  };\n",
              "\t  var d3_requote_re = /[\\\\\\^\\$\\*\\+\\?\\|\\[\\]\\(\\)\\.\\{\\}]/g;\n",
              "\t  var d3_subclass = {}.__proto__ ? function(object, prototype) {\n",
              "\t    object.__proto__ = prototype;\n",
              "\t  } : function(object, prototype) {\n",
              "\t    for (var property in prototype) object[property] = prototype[property];\n",
              "\t  };\n",
              "\t  function d3_selection(groups) {\n",
              "\t    d3_subclass(groups, d3_selectionPrototype);\n",
              "\t    return groups;\n",
              "\t  }\n",
              "\t  var d3_select = function(s, n) {\n",
              "\t    return n.querySelector(s);\n",
              "\t  }, d3_selectAll = function(s, n) {\n",
              "\t    return n.querySelectorAll(s);\n",
              "\t  }, d3_selectMatches = function(n, s) {\n",
              "\t    var d3_selectMatcher = n.matches || n[d3_vendorSymbol(n, \"matchesSelector\")];\n",
              "\t    d3_selectMatches = function(n, s) {\n",
              "\t      return d3_selectMatcher.call(n, s);\n",
              "\t    };\n",
              "\t    return d3_selectMatches(n, s);\n",
              "\t  };\n",
              "\t  if (typeof Sizzle === \"function\") {\n",
              "\t    d3_select = function(s, n) {\n",
              "\t      return Sizzle(s, n)[0] || null;\n",
              "\t    };\n",
              "\t    d3_selectAll = Sizzle;\n",
              "\t    d3_selectMatches = Sizzle.matchesSelector;\n",
              "\t  }\n",
              "\t  d3.selection = function() {\n",
              "\t    return d3.select(d3_document.documentElement);\n",
              "\t  };\n",
              "\t  var d3_selectionPrototype = d3.selection.prototype = [];\n",
              "\t  d3_selectionPrototype.select = function(selector) {\n",
              "\t    var subgroups = [], subgroup, subnode, group, node;\n",
              "\t    selector = d3_selection_selector(selector);\n",
              "\t    for (var j = -1, m = this.length; ++j < m; ) {\n",
              "\t      subgroups.push(subgroup = []);\n",
              "\t      subgroup.parentNode = (group = this[j]).parentNode;\n",
              "\t      for (var i = -1, n = group.length; ++i < n; ) {\n",
              "\t        if (node = group[i]) {\n",
              "\t          subgroup.push(subnode = selector.call(node, node.__data__, i, j));\n",
              "\t          if (subnode && \"__data__\" in node) subnode.__data__ = node.__data__;\n",
              "\t        } else {\n",
              "\t          subgroup.push(null);\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return d3_selection(subgroups);\n",
              "\t  };\n",
              "\t  function d3_selection_selector(selector) {\n",
              "\t    return typeof selector === \"function\" ? selector : function() {\n",
              "\t      return d3_select(selector, this);\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.selectAll = function(selector) {\n",
              "\t    var subgroups = [], subgroup, node;\n",
              "\t    selector = d3_selection_selectorAll(selector);\n",
              "\t    for (var j = -1, m = this.length; ++j < m; ) {\n",
              "\t      for (var group = this[j], i = -1, n = group.length; ++i < n; ) {\n",
              "\t        if (node = group[i]) {\n",
              "\t          subgroups.push(subgroup = d3_array(selector.call(node, node.__data__, i, j)));\n",
              "\t          subgroup.parentNode = node;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return d3_selection(subgroups);\n",
              "\t  };\n",
              "\t  function d3_selection_selectorAll(selector) {\n",
              "\t    return typeof selector === \"function\" ? selector : function() {\n",
              "\t      return d3_selectAll(selector, this);\n",
              "\t    };\n",
              "\t  }\n",
              "\t  var d3_nsXhtml = \"http://www.w3.org/1999/xhtml\";\n",
              "\t  var d3_nsPrefix = {\n",
              "\t    svg: \"http://www.w3.org/2000/svg\",\n",
              "\t    xhtml: d3_nsXhtml,\n",
              "\t    xlink: \"http://www.w3.org/1999/xlink\",\n",
              "\t    xml: \"http://www.w3.org/XML/1998/namespace\",\n",
              "\t    xmlns: \"http://www.w3.org/2000/xmlns/\"\n",
              "\t  };\n",
              "\t  d3.ns = {\n",
              "\t    prefix: d3_nsPrefix,\n",
              "\t    qualify: function(name) {\n",
              "\t      var i = name.indexOf(\":\"), prefix = name;\n",
              "\t      if (i >= 0 && (prefix = name.slice(0, i)) !== \"xmlns\") name = name.slice(i + 1);\n",
              "\t      return d3_nsPrefix.hasOwnProperty(prefix) ? {\n",
              "\t        space: d3_nsPrefix[prefix],\n",
              "\t        local: name\n",
              "\t      } : name;\n",
              "\t    }\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.attr = function(name, value) {\n",
              "\t    if (arguments.length < 2) {\n",
              "\t      if (typeof name === \"string\") {\n",
              "\t        var node = this.node();\n",
              "\t        name = d3.ns.qualify(name);\n",
              "\t        return name.local ? node.getAttributeNS(name.space, name.local) : node.getAttribute(name);\n",
              "\t      }\n",
              "\t      for (value in name) this.each(d3_selection_attr(value, name[value]));\n",
              "\t      return this;\n",
              "\t    }\n",
              "\t    return this.each(d3_selection_attr(name, value));\n",
              "\t  };\n",
              "\t  function d3_selection_attr(name, value) {\n",
              "\t    name = d3.ns.qualify(name);\n",
              "\t    function attrNull() {\n",
              "\t      this.removeAttribute(name);\n",
              "\t    }\n",
              "\t    function attrNullNS() {\n",
              "\t      this.removeAttributeNS(name.space, name.local);\n",
              "\t    }\n",
              "\t    function attrConstant() {\n",
              "\t      this.setAttribute(name, value);\n",
              "\t    }\n",
              "\t    function attrConstantNS() {\n",
              "\t      this.setAttributeNS(name.space, name.local, value);\n",
              "\t    }\n",
              "\t    function attrFunction() {\n",
              "\t      var x = value.apply(this, arguments);\n",
              "\t      if (x == null) this.removeAttribute(name); else this.setAttribute(name, x);\n",
              "\t    }\n",
              "\t    function attrFunctionNS() {\n",
              "\t      var x = value.apply(this, arguments);\n",
              "\t      if (x == null) this.removeAttributeNS(name.space, name.local); else this.setAttributeNS(name.space, name.local, x);\n",
              "\t    }\n",
              "\t    return value == null ? name.local ? attrNullNS : attrNull : typeof value === \"function\" ? name.local ? attrFunctionNS : attrFunction : name.local ? attrConstantNS : attrConstant;\n",
              "\t  }\n",
              "\t  function d3_collapse(s) {\n",
              "\t    return s.trim().replace(/\\s+/g, \" \");\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.classed = function(name, value) {\n",
              "\t    if (arguments.length < 2) {\n",
              "\t      if (typeof name === \"string\") {\n",
              "\t        var node = this.node(), n = (name = d3_selection_classes(name)).length, i = -1;\n",
              "\t        if (value = node.classList) {\n",
              "\t          while (++i < n) if (!value.contains(name[i])) return false;\n",
              "\t        } else {\n",
              "\t          value = node.getAttribute(\"class\");\n",
              "\t          while (++i < n) if (!d3_selection_classedRe(name[i]).test(value)) return false;\n",
              "\t        }\n",
              "\t        return true;\n",
              "\t      }\n",
              "\t      for (value in name) this.each(d3_selection_classed(value, name[value]));\n",
              "\t      return this;\n",
              "\t    }\n",
              "\t    return this.each(d3_selection_classed(name, value));\n",
              "\t  };\n",
              "\t  function d3_selection_classedRe(name) {\n",
              "\t    return new RegExp(\"(?:^|\\\\s+)\" + d3.requote(name) + \"(?:\\\\s+|$)\", \"g\");\n",
              "\t  }\n",
              "\t  function d3_selection_classes(name) {\n",
              "\t    return (name + \"\").trim().split(/^|\\s+/);\n",
              "\t  }\n",
              "\t  function d3_selection_classed(name, value) {\n",
              "\t    name = d3_selection_classes(name).map(d3_selection_classedName);\n",
              "\t    var n = name.length;\n",
              "\t    function classedConstant() {\n",
              "\t      var i = -1;\n",
              "\t      while (++i < n) name[i](this, value);\n",
              "\t    }\n",
              "\t    function classedFunction() {\n",
              "\t      var i = -1, x = value.apply(this, arguments);\n",
              "\t      while (++i < n) name[i](this, x);\n",
              "\t    }\n",
              "\t    return typeof value === \"function\" ? classedFunction : classedConstant;\n",
              "\t  }\n",
              "\t  function d3_selection_classedName(name) {\n",
              "\t    var re = d3_selection_classedRe(name);\n",
              "\t    return function(node, value) {\n",
              "\t      if (c = node.classList) return value ? c.add(name) : c.remove(name);\n",
              "\t      var c = node.getAttribute(\"class\") || \"\";\n",
              "\t      if (value) {\n",
              "\t        re.lastIndex = 0;\n",
              "\t        if (!re.test(c)) node.setAttribute(\"class\", d3_collapse(c + \" \" + name));\n",
              "\t      } else {\n",
              "\t        node.setAttribute(\"class\", d3_collapse(c.replace(re, \" \")));\n",
              "\t      }\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.style = function(name, value, priority) {\n",
              "\t    var n = arguments.length;\n",
              "\t    if (n < 3) {\n",
              "\t      if (typeof name !== \"string\") {\n",
              "\t        if (n < 2) value = \"\";\n",
              "\t        for (priority in name) this.each(d3_selection_style(priority, name[priority], value));\n",
              "\t        return this;\n",
              "\t      }\n",
              "\t      if (n < 2) {\n",
              "\t        var node = this.node();\n",
              "\t        return d3_window(node).getComputedStyle(node, null).getPropertyValue(name);\n",
              "\t      }\n",
              "\t      priority = \"\";\n",
              "\t    }\n",
              "\t    return this.each(d3_selection_style(name, value, priority));\n",
              "\t  };\n",
              "\t  function d3_selection_style(name, value, priority) {\n",
              "\t    function styleNull() {\n",
              "\t      this.style.removeProperty(name);\n",
              "\t    }\n",
              "\t    function styleConstant() {\n",
              "\t      this.style.setProperty(name, value, priority);\n",
              "\t    }\n",
              "\t    function styleFunction() {\n",
              "\t      var x = value.apply(this, arguments);\n",
              "\t      if (x == null) this.style.removeProperty(name); else this.style.setProperty(name, x, priority);\n",
              "\t    }\n",
              "\t    return value == null ? styleNull : typeof value === \"function\" ? styleFunction : styleConstant;\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.property = function(name, value) {\n",
              "\t    if (arguments.length < 2) {\n",
              "\t      if (typeof name === \"string\") return this.node()[name];\n",
              "\t      for (value in name) this.each(d3_selection_property(value, name[value]));\n",
              "\t      return this;\n",
              "\t    }\n",
              "\t    return this.each(d3_selection_property(name, value));\n",
              "\t  };\n",
              "\t  function d3_selection_property(name, value) {\n",
              "\t    function propertyNull() {\n",
              "\t      delete this[name];\n",
              "\t    }\n",
              "\t    function propertyConstant() {\n",
              "\t      this[name] = value;\n",
              "\t    }\n",
              "\t    function propertyFunction() {\n",
              "\t      var x = value.apply(this, arguments);\n",
              "\t      if (x == null) delete this[name]; else this[name] = x;\n",
              "\t    }\n",
              "\t    return value == null ? propertyNull : typeof value === \"function\" ? propertyFunction : propertyConstant;\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.text = function(value) {\n",
              "\t    return arguments.length ? this.each(typeof value === \"function\" ? function() {\n",
              "\t      var v = value.apply(this, arguments);\n",
              "\t      this.textContent = v == null ? \"\" : v;\n",
              "\t    } : value == null ? function() {\n",
              "\t      this.textContent = \"\";\n",
              "\t    } : function() {\n",
              "\t      this.textContent = value;\n",
              "\t    }) : this.node().textContent;\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.html = function(value) {\n",
              "\t    return arguments.length ? this.each(typeof value === \"function\" ? function() {\n",
              "\t      var v = value.apply(this, arguments);\n",
              "\t      this.innerHTML = v == null ? \"\" : v;\n",
              "\t    } : value == null ? function() {\n",
              "\t      this.innerHTML = \"\";\n",
              "\t    } : function() {\n",
              "\t      this.innerHTML = value;\n",
              "\t    }) : this.node().innerHTML;\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.append = function(name) {\n",
              "\t    name = d3_selection_creator(name);\n",
              "\t    return this.select(function() {\n",
              "\t      return this.appendChild(name.apply(this, arguments));\n",
              "\t    });\n",
              "\t  };\n",
              "\t  function d3_selection_creator(name) {\n",
              "\t    function create() {\n",
              "\t      var document = this.ownerDocument, namespace = this.namespaceURI;\n",
              "\t      return namespace === d3_nsXhtml && document.documentElement.namespaceURI === d3_nsXhtml ? document.createElement(name) : document.createElementNS(namespace, name);\n",
              "\t    }\n",
              "\t    function createNS() {\n",
              "\t      return this.ownerDocument.createElementNS(name.space, name.local);\n",
              "\t    }\n",
              "\t    return typeof name === \"function\" ? name : (name = d3.ns.qualify(name)).local ? createNS : create;\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.insert = function(name, before) {\n",
              "\t    name = d3_selection_creator(name);\n",
              "\t    before = d3_selection_selector(before);\n",
              "\t    return this.select(function() {\n",
              "\t      return this.insertBefore(name.apply(this, arguments), before.apply(this, arguments) || null);\n",
              "\t    });\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.remove = function() {\n",
              "\t    return this.each(d3_selectionRemove);\n",
              "\t  };\n",
              "\t  function d3_selectionRemove() {\n",
              "\t    var parent = this.parentNode;\n",
              "\t    if (parent) parent.removeChild(this);\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.data = function(value, key) {\n",
              "\t    var i = -1, n = this.length, group, node;\n",
              "\t    if (!arguments.length) {\n",
              "\t      value = new Array(n = (group = this[0]).length);\n",
              "\t      while (++i < n) {\n",
              "\t        if (node = group[i]) {\n",
              "\t          value[i] = node.__data__;\n",
              "\t        }\n",
              "\t      }\n",
              "\t      return value;\n",
              "\t    }\n",
              "\t    function bind(group, groupData) {\n",
              "\t      var i, n = group.length, m = groupData.length, n0 = Math.min(n, m), updateNodes = new Array(m), enterNodes = new Array(m), exitNodes = new Array(n), node, nodeData;\n",
              "\t      if (key) {\n",
              "\t        var nodeByKeyValue = new d3_Map(), keyValues = new Array(n), keyValue;\n",
              "\t        for (i = -1; ++i < n; ) {\n",
              "\t          if (node = group[i]) {\n",
              "\t            if (nodeByKeyValue.has(keyValue = key.call(node, node.__data__, i))) {\n",
              "\t              exitNodes[i] = node;\n",
              "\t            } else {\n",
              "\t              nodeByKeyValue.set(keyValue, node);\n",
              "\t            }\n",
              "\t            keyValues[i] = keyValue;\n",
              "\t          }\n",
              "\t        }\n",
              "\t        for (i = -1; ++i < m; ) {\n",
              "\t          if (!(node = nodeByKeyValue.get(keyValue = key.call(groupData, nodeData = groupData[i], i)))) {\n",
              "\t            enterNodes[i] = d3_selection_dataNode(nodeData);\n",
              "\t          } else if (node !== true) {\n",
              "\t            updateNodes[i] = node;\n",
              "\t            node.__data__ = nodeData;\n",
              "\t          }\n",
              "\t          nodeByKeyValue.set(keyValue, true);\n",
              "\t        }\n",
              "\t        for (i = -1; ++i < n; ) {\n",
              "\t          if (i in keyValues && nodeByKeyValue.get(keyValues[i]) !== true) {\n",
              "\t            exitNodes[i] = group[i];\n",
              "\t          }\n",
              "\t        }\n",
              "\t      } else {\n",
              "\t        for (i = -1; ++i < n0; ) {\n",
              "\t          node = group[i];\n",
              "\t          nodeData = groupData[i];\n",
              "\t          if (node) {\n",
              "\t            node.__data__ = nodeData;\n",
              "\t            updateNodes[i] = node;\n",
              "\t          } else {\n",
              "\t            enterNodes[i] = d3_selection_dataNode(nodeData);\n",
              "\t          }\n",
              "\t        }\n",
              "\t        for (;i < m; ++i) {\n",
              "\t          enterNodes[i] = d3_selection_dataNode(groupData[i]);\n",
              "\t        }\n",
              "\t        for (;i < n; ++i) {\n",
              "\t          exitNodes[i] = group[i];\n",
              "\t        }\n",
              "\t      }\n",
              "\t      enterNodes.update = updateNodes;\n",
              "\t      enterNodes.parentNode = updateNodes.parentNode = exitNodes.parentNode = group.parentNode;\n",
              "\t      enter.push(enterNodes);\n",
              "\t      update.push(updateNodes);\n",
              "\t      exit.push(exitNodes);\n",
              "\t    }\n",
              "\t    var enter = d3_selection_enter([]), update = d3_selection([]), exit = d3_selection([]);\n",
              "\t    if (typeof value === \"function\") {\n",
              "\t      while (++i < n) {\n",
              "\t        bind(group = this[i], value.call(group, group.parentNode.__data__, i));\n",
              "\t      }\n",
              "\t    } else {\n",
              "\t      while (++i < n) {\n",
              "\t        bind(group = this[i], value);\n",
              "\t      }\n",
              "\t    }\n",
              "\t    update.enter = function() {\n",
              "\t      return enter;\n",
              "\t    };\n",
              "\t    update.exit = function() {\n",
              "\t      return exit;\n",
              "\t    };\n",
              "\t    return update;\n",
              "\t  };\n",
              "\t  function d3_selection_dataNode(data) {\n",
              "\t    return {\n",
              "\t      __data__: data\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.datum = function(value) {\n",
              "\t    return arguments.length ? this.property(\"__data__\", value) : this.property(\"__data__\");\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.filter = function(filter) {\n",
              "\t    var subgroups = [], subgroup, group, node;\n",
              "\t    if (typeof filter !== \"function\") filter = d3_selection_filter(filter);\n",
              "\t    for (var j = 0, m = this.length; j < m; j++) {\n",
              "\t      subgroups.push(subgroup = []);\n",
              "\t      subgroup.parentNode = (group = this[j]).parentNode;\n",
              "\t      for (var i = 0, n = group.length; i < n; i++) {\n",
              "\t        if ((node = group[i]) && filter.call(node, node.__data__, i, j)) {\n",
              "\t          subgroup.push(node);\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return d3_selection(subgroups);\n",
              "\t  };\n",
              "\t  function d3_selection_filter(selector) {\n",
              "\t    return function() {\n",
              "\t      return d3_selectMatches(this, selector);\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.order = function() {\n",
              "\t    for (var j = -1, m = this.length; ++j < m; ) {\n",
              "\t      for (var group = this[j], i = group.length - 1, next = group[i], node; --i >= 0; ) {\n",
              "\t        if (node = group[i]) {\n",
              "\t          if (next && next !== node.nextSibling) next.parentNode.insertBefore(node, next);\n",
              "\t          next = node;\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return this;\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.sort = function(comparator) {\n",
              "\t    comparator = d3_selection_sortComparator.apply(this, arguments);\n",
              "\t    for (var j = -1, m = this.length; ++j < m; ) this[j].sort(comparator);\n",
              "\t    return this.order();\n",
              "\t  };\n",
              "\t  function d3_selection_sortComparator(comparator) {\n",
              "\t    if (!arguments.length) comparator = d3_ascending;\n",
              "\t    return function(a, b) {\n",
              "\t      return a && b ? comparator(a.__data__, b.__data__) : !a - !b;\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.each = function(callback) {\n",
              "\t    return d3_selection_each(this, function(node, i, j) {\n",
              "\t      callback.call(node, node.__data__, i, j);\n",
              "\t    });\n",
              "\t  };\n",
              "\t  function d3_selection_each(groups, callback) {\n",
              "\t    for (var j = 0, m = groups.length; j < m; j++) {\n",
              "\t      for (var group = groups[j], i = 0, n = group.length, node; i < n; i++) {\n",
              "\t        if (node = group[i]) callback(node, i, j);\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return groups;\n",
              "\t  }\n",
              "\t  d3_selectionPrototype.call = function(callback) {\n",
              "\t    var args = d3_array(arguments);\n",
              "\t    callback.apply(args[0] = this, args);\n",
              "\t    return this;\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.empty = function() {\n",
              "\t    return !this.node();\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.node = function() {\n",
              "\t    for (var j = 0, m = this.length; j < m; j++) {\n",
              "\t      for (var group = this[j], i = 0, n = group.length; i < n; i++) {\n",
              "\t        var node = group[i];\n",
              "\t        if (node) return node;\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return null;\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.size = function() {\n",
              "\t    var n = 0;\n",
              "\t    d3_selection_each(this, function() {\n",
              "\t      ++n;\n",
              "\t    });\n",
              "\t    return n;\n",
              "\t  };\n",
              "\t  function d3_selection_enter(selection) {\n",
              "\t    d3_subclass(selection, d3_selection_enterPrototype);\n",
              "\t    return selection;\n",
              "\t  }\n",
              "\t  var d3_selection_enterPrototype = [];\n",
              "\t  d3.selection.enter = d3_selection_enter;\n",
              "\t  d3.selection.enter.prototype = d3_selection_enterPrototype;\n",
              "\t  d3_selection_enterPrototype.append = d3_selectionPrototype.append;\n",
              "\t  d3_selection_enterPrototype.empty = d3_selectionPrototype.empty;\n",
              "\t  d3_selection_enterPrototype.node = d3_selectionPrototype.node;\n",
              "\t  d3_selection_enterPrototype.call = d3_selectionPrototype.call;\n",
              "\t  d3_selection_enterPrototype.size = d3_selectionPrototype.size;\n",
              "\t  d3_selection_enterPrototype.select = function(selector) {\n",
              "\t    var subgroups = [], subgroup, subnode, upgroup, group, node;\n",
              "\t    for (var j = -1, m = this.length; ++j < m; ) {\n",
              "\t      upgroup = (group = this[j]).update;\n",
              "\t      subgroups.push(subgroup = []);\n",
              "\t      subgroup.parentNode = group.parentNode;\n",
              "\t      for (var i = -1, n = group.length; ++i < n; ) {\n",
              "\t        if (node = group[i]) {\n",
              "\t          subgroup.push(upgroup[i] = subnode = selector.call(group.parentNode, node.__data__, i, j));\n",
              "\t          subnode.__data__ = node.__data__;\n",
              "\t        } else {\n",
              "\t          subgroup.push(null);\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return d3_selection(subgroups);\n",
              "\t  };\n",
              "\t  d3_selection_enterPrototype.insert = function(name, before) {\n",
              "\t    if (arguments.length < 2) before = d3_selection_enterInsertBefore(this);\n",
              "\t    return d3_selectionPrototype.insert.call(this, name, before);\n",
              "\t  };\n",
              "\t  function d3_selection_enterInsertBefore(enter) {\n",
              "\t    var i0, j0;\n",
              "\t    return function(d, i, j) {\n",
              "\t      var group = enter[j].update, n = group.length, node;\n",
              "\t      if (j != j0) j0 = j, i0 = 0;\n",
              "\t      if (i >= i0) i0 = i + 1;\n",
              "\t      while (!(node = group[i0]) && ++i0 < n) ;\n",
              "\t      return node;\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3.select = function(node) {\n",
              "\t    var group;\n",
              "\t    if (typeof node === \"string\") {\n",
              "\t      group = [ d3_select(node, d3_document) ];\n",
              "\t      group.parentNode = d3_document.documentElement;\n",
              "\t    } else {\n",
              "\t      group = [ node ];\n",
              "\t      group.parentNode = d3_documentElement(node);\n",
              "\t    }\n",
              "\t    return d3_selection([ group ]);\n",
              "\t  };\n",
              "\t  d3.selectAll = function(nodes) {\n",
              "\t    var group;\n",
              "\t    if (typeof nodes === \"string\") {\n",
              "\t      group = d3_array(d3_selectAll(nodes, d3_document));\n",
              "\t      group.parentNode = d3_document.documentElement;\n",
              "\t    } else {\n",
              "\t      group = d3_array(nodes);\n",
              "\t      group.parentNode = null;\n",
              "\t    }\n",
              "\t    return d3_selection([ group ]);\n",
              "\t  };\n",
              "\t  d3_selectionPrototype.on = function(type, listener, capture) {\n",
              "\t    var n = arguments.length;\n",
              "\t    if (n < 3) {\n",
              "\t      if (typeof type !== \"string\") {\n",
              "\t        if (n < 2) listener = false;\n",
              "\t        for (capture in type) this.each(d3_selection_on(capture, type[capture], listener));\n",
              "\t        return this;\n",
              "\t      }\n",
              "\t      if (n < 2) return (n = this.node()[\"__on\" + type]) && n._;\n",
              "\t      capture = false;\n",
              "\t    }\n",
              "\t    return this.each(d3_selection_on(type, listener, capture));\n",
              "\t  };\n",
              "\t  function d3_selection_on(type, listener, capture) {\n",
              "\t    var name = \"__on\" + type, i = type.indexOf(\".\"), wrap = d3_selection_onListener;\n",
              "\t    if (i > 0) type = type.slice(0, i);\n",
              "\t    var filter = d3_selection_onFilters.get(type);\n",
              "\t    if (filter) type = filter, wrap = d3_selection_onFilter;\n",
              "\t    function onRemove() {\n",
              "\t      var l = this[name];\n",
              "\t      if (l) {\n",
              "\t        this.removeEventListener(type, l, l.$);\n",
              "\t        delete this[name];\n",
              "\t      }\n",
              "\t    }\n",
              "\t    function onAdd() {\n",
              "\t      var l = wrap(listener, d3_array(arguments));\n",
              "\t      onRemove.call(this);\n",
              "\t      this.addEventListener(type, this[name] = l, l.$ = capture);\n",
              "\t      l._ = listener;\n",
              "\t    }\n",
              "\t    function removeAll() {\n",
              "\t      var re = new RegExp(\"^__on([^.]+)\" + d3.requote(type) + \"$\"), match;\n",
              "\t      for (var name in this) {\n",
              "\t        if (match = name.match(re)) {\n",
              "\t          var l = this[name];\n",
              "\t          this.removeEventListener(match[1], l, l.$);\n",
              "\t          delete this[name];\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    return i ? listener ? onAdd : onRemove : listener ? d3_noop : removeAll;\n",
              "\t  }\n",
              "\t  var d3_selection_onFilters = d3.map({\n",
              "\t    mouseenter: \"mouseover\",\n",
              "\t    mouseleave: \"mouseout\"\n",
              "\t  });\n",
              "\t  if (d3_document) {\n",
              "\t    d3_selection_onFilters.forEach(function(k) {\n",
              "\t      if (\"on\" + k in d3_document) d3_selection_onFilters.remove(k);\n",
              "\t    });\n",
              "\t  }\n",
              "\t  function d3_selection_onListener(listener, argumentz) {\n",
              "\t    return function(e) {\n",
              "\t      var o = d3.event;\n",
              "\t      d3.event = e;\n",
              "\t      argumentz[0] = this.__data__;\n",
              "\t      try {\n",
              "\t        listener.apply(this, argumentz);\n",
              "\t      } finally {\n",
              "\t        d3.event = o;\n",
              "\t      }\n",
              "\t    };\n",
              "\t  }\n",
              "\t  function d3_selection_onFilter(listener, argumentz) {\n",
              "\t    var l = d3_selection_onListener(listener, argumentz);\n",
              "\t    return function(e) {\n",
              "\t      var target = this, related = e.relatedTarget;\n",
              "\t      if (!related || related !== target && !(related.compareDocumentPosition(target) & 8)) {\n",
              "\t        l.call(target, e);\n",
              "\t      }\n",
              "\t    };\n",
              "\t  }\n",
              "\t  var d3_event_dragSelect, d3_event_dragId = 0;\n",
              "\t  function d3_event_dragSuppress(node) {\n",
              "\t    var name = \".dragsuppress-\" + ++d3_event_dragId, click = \"click\" + name, w = d3.select(d3_window(node)).on(\"touchmove\" + name, d3_eventPreventDefault).on(\"dragstart\" + name, d3_eventPreventDefault).on(\"selectstart\" + name, d3_eventPreventDefault);\n",
              "\t    if (d3_event_dragSelect == null) {\n",
              "\t      d3_event_dragSelect = \"onselectstart\" in node ? false : d3_vendorSymbol(node.style, \"userSelect\");\n",
              "\t    }\n",
              "\t    if (d3_event_dragSelect) {\n",
              "\t      var style = d3_documentElement(node).style, select = style[d3_event_dragSelect];\n",
              "\t      style[d3_event_dragSelect] = \"none\";\n",
              "\t    }\n",
              "\t    return function(suppressClick) {\n",
              "\t      w.on(name, null);\n",
              "\t      if (d3_event_dragSelect) style[d3_event_dragSelect] = select;\n",
              "\t      if (suppressClick) {\n",
              "\t        var off = function() {\n",
              "\t          w.on(click, null);\n",
              "\t        };\n",
              "\t        w.on(click, function() {\n",
              "\t          d3_eventPreventDefault();\n",
              "\t          off();\n",
              "\t        }, true);\n",
              "\t        setTimeout(off, 0);\n",
              "\t      }\n",
              "\t    };\n",
              "\t  }\n",
              "\t  d3.mouse = function(container) {\n",
              "\t    return d3_mousePoint(container, d3_eventSource());\n",
              "\t  };\n",
              "\t  var d3_mouse_bug44083 = this.navigator && /WebKit/.test(this.navigator.userAgent) ? -1 : 0;\n",
              "\t  function d3_mousePoint(container, e) {\n",
              "\t    if (e.changedTouches) e = e.changedTouches[0];\n",
              "\t    var svg = container.ownerSVGElement || container;\n",
              "\t    if (svg.createSVGPoint) {\n",
              "\t      var point = svg.createSVGPoint();\n",
              "\t      if (d3_mouse_bug44083 < 0) {\n",
              "\t        var window = d3_window(container);\n",
              "\t        if (window.scrollX || window.scrollY) {\n",
              "\t          svg = d3.select(\"body\").append(\"svg\").style({\n",
              "\t            position: \"absolute\",\n",
              "\t            top: 0,\n",
              "\t            left: 0,\n",
              "\t            margin: 0,\n",
              "\t            padding: 0,\n",
              "\t            border: \"none\"\n",
              "\t          }, \"important\");\n",
              "\t          var ctm = svg[0][0].getScreenCTM();\n",
              "\t          d3_mouse_bug44083 = !(ctm.f || ctm.e);\n",
              "\t          svg.remove();\n",
              "\t        }\n",
              "\t      }\n",
              "\t      if (d3_mouse_bug44083) point.x = e.pageX, point.y = e.pageY; else point.x = e.clientX, \n",
              "\t      point.y = e.clientY;\n",
              "\t      point = point.matrixTransform(container.getScreenCTM().inverse());\n",
              "\t      return [ point.x, point.y ];\n",
              "\t    }\n",
              "\t    var rect = container.getBoundingClientRect();\n",
              "\t    return [ e.clientX - rect.left - container.clientLeft, e.clientY - rect.top - container.clientTop ];\n",
              "\t  }\n",
              "\t  d3.touch = function(container, touches, identifier) {\n",
              "\t    if (arguments.length < 3) identifier = touches, touches = d3_eventSource().changedTouches;\n",
              "\t    if (touches) for (var i = 0, n = touches.length, touch; i < n; ++i) {\n",
              "\t      if ((touch = touches[i]).identifier === identifier) {\n",
              "\t        return d3_mousePoint(container, touch);\n",
              "\t      }\n",
              "\t    }\n",
              "\t  };\n",
              "\t  d3.behavior.drag = function() {\n",
              "\t    var event = d3_eventDispatch(drag, \"drag\", \"dragstart\", \"dragend\"), origin = null, mousedown = dragstart(d3_noop, d3.mouse, d3_window, \"mousemove\", \"mouseup\"), touchstart = dragstart(d3_behavior_dragTouchId, d3.touch, d3_identity, \"touchmove\", \"touchend\");\n",
              "\t    function drag() {\n",
              "\t      this.on(\"mousedown.drag\", mousedown).on(\"touchstart.drag\", touchstart);\n",
              "\t    }\n",
              "\t    function dragstart(id, position, subject, move, end) {\n",
              "\t      return function() {\n",
              "\t        var that = this, target = d3.event.target.correspondingElement || d3.event.target, parent = that.parentNode, dispatch = event.of(that, arguments), dragged = 0, dragId = id(), dragName = \".drag\" + (dragId == null ? \"\" : \"-\" + dragId), dragOffset, dragSubject = d3.select(subject(target)).on(move + dragName, moved).on(end + dragName, ended), dragRestore = d3_event_dragSuppress(target), position0 = position(parent, dragId);\n",
              "\t        if (origin) {\n",
              "\t          dragOffset = origin.apply(that, arguments);\n",
              "\t          dragOffset = [ dragOffset.x - position0[0], dragOffset.y - position0[1] ];\n",
              "\t        } else {\n",
              "\t          dragOffset = [ 0, 0 ];\n",
              "\t        }\n",
              "\t        dispatch({\n",
              "\t          type: \"dragstart\"\n",
              "\t        });\n",
              "\t        function moved() {\n",
              "\t          var position1 = position(parent, dragId), dx, dy;\n",
              "\t          if (!position1) return;\n",
              "\t          dx = position1[0] - position0[0];\n",
              "\t          dy = position1[1] - position0[1];\n",
              "\t          dragged |= dx | dy;\n",
              "\t          position0 = position1;\n",
              "\t          dispatch({\n",
              "\t            type: \"drag\",\n",
              "\t            x: position1[0] + dragOffset[0],\n",
              "\t            y: position1[1] + dragOffset[1],\n",
              "\t            dx: dx,\n",
              "\t            dy: dy\n",
              "\t          });\n",
              "\t        }\n",
              "\t        function ended() {\n",
              "\t          if (!position(parent, dragId)) return;\n",
              "\t          dragSubject.on(move + dragName, null).on(end + dragName, null);\n",
              "\t          dragRestore(dragged);\n",
              "\t          dispatch({\n",
              "\t            type: \"dragend\"\n",
              "\t          });\n",
              "\t        }\n",
              "\t      };\n",
              "\t    }\n",
              "\t    drag.origin = function(x) {\n",
              "\t      if (!arguments.length) return origin;\n",
              "\t      origin = x;\n",
              "\t      return drag;\n",
              "\t    };\n",
              "\t    return d3.rebind(drag, event, \"on\");\n",
              "\t  };\n",
              "\t  function d3_behavior_dragTouchId() {\n",
              "\t    return d3.event.changedTouches[0].identifier;\n",
              "\t  }\n",
              "\t  d3.touches = function(container, touches) {\n",
              "\t    if (arguments.length < 2) touches = d3_eventSource().touches;\n",
              "\t    return touches ? d3_array(touches).map(function(touch) {\n",
              "\t      var point = d3_mousePoint(container, touch);\n",
              "\t      point.identifier = touch.identifier;\n",
              "\t      return point;\n",
              "\t    }) : [];\n",
              "\t  };\n",
              "\t  var ε = 1e-6, ε2 = ε * ε, π = Math.PI, τ = 2 * π, τε = τ - ε, halfπ = π / 2, d3_radians = π / 180, d3_degrees = 180 / π;\n",
              "\t  function d3_sgn(x) {\n",
              "\t    return x > 0 ? 1 : x < 0 ? -1 : 0;\n",
              "\t  }\n",
              "\t  function d3_cross2d(a, b, c) {\n",
              "\t    return (b[0] - a[0]) * (c[1] - a[1]) - (b[1] - a[1]) * (c[0] - a[0]);\n",
              "\t  }\n",
              "\t  function d3_acos(x) {\n",
              "\t    return x > 1 ? 0 : x < -1 ? π : Math.acos(x);\n",
              "\t  }\n",
              "\t  function d3_asin(x) {\n",
              "\t    return x > 1 ? halfπ : x < -1 ? -halfπ : Math.asin(x);\n",
              "\t  }\n",
              "\t  function d3_sinh(x) {\n",
              "\t    return ((x = Math.exp(x)) - 1 / x) / 2;\n",
              "\t  }\n",
              "\t  function d3_cosh(x) {\n",
              "\t    return ((x = Math.exp(x)) + 1 / x) / 2;\n",
              "\t  }\n",
              "\t  function d3_tanh(x) {\n",
              "\t    return ((x = Math.exp(2 * x)) - 1) / (x + 1);\n",
              "\t  }\n",
              "\t  function d3_haversin(x) {\n",
              "\t    return (x = Math.sin(x / 2)) * x;\n",
              "\t  }\n",
              "\t  var ρ = Math.SQRT2, ρ2 = 2, ρ4 = 4;\n",
              "\t  d3.interpolateZoom = function(p0, p1) {\n",
              "\t    var ux0 = p0[0], uy0 = p0[1], w0 = p0[2], ux1 = p1[0], uy1 = p1[1], w1 = p1[2], dx = ux1 - ux0, dy = uy1 - uy0, d2 = dx * dx + dy * dy, i, S;\n",
              "\t    if (d2 < ε2) {\n",
              "\t      S = Math.log(w1 / w0) / ρ;\n",
              "\t      i = function(t) {\n",
              "\t        return [ ux0 + t * dx, uy0 + t * dy, w0 * Math.exp(ρ * t * S) ];\n",
              "\t      };\n",
              "\t    } else {\n",
              "\t      var d1 = Math.sqrt(d2), b0 = (w1 * w1 - w0 * w0 + ρ4 * d2) / (2 * w0 * ρ2 * d1), b1 = (w1 * w1 - w0 * w0 - ρ4 * d2) / (2 * w1 * ρ2 * d1), r0 = Math.log(Math.sqrt(b0 * b0 + 1) - b0), r1 = Math.log(Math.sqrt(b1 * b1 + 1) - b1);\n",
              "\t      S = (r1 - r0) / ρ;\n",
              "\t      i = function(t) {\n",
              "\t        var s = t * S, coshr0 = d3_cosh(r0), u = w0 / (ρ2 * d1) * (coshr0 * d3_tanh(ρ * s + r0) - d3_sinh(r0));\n",
              "\t        return [ ux0 + u * dx, uy0 + u * dy, w0 * coshr0 / d3_cosh(ρ * s + r0) ];\n",
              "\t      };\n",
              "\t    }\n",
              "\t    i.duration = S * 1e3;\n",
              "\t    return i;\n",
              "\t  };\n",
              "\t  d3.behavior.zoom = function() {\n",
              "\t    var view = {\n",
              "\t      x: 0,\n",
              "\t      y: 0,\n",
              "\t      k: 1\n",
              "\t    }, translate0, center0, center, size = [ 960, 500 ], scaleExtent = d3_behavior_zoomInfinity, duration = 250, zooming = 0, mousedown = \"mousedown.zoom\", mousemove = \"mousemove.zoom\", mouseup = \"mouseup.zoom\", mousewheelTimer, touchstart = \"touchstart.zoom\", touchtime, event = d3_eventDispatch(zoom, \"zoomstart\", \"zoom\", \"zoomend\"), x0, x1, y0, y1;\n",
              "\t    if (!d3_behavior_zoomWheel) {\n",
              "\t      d3_behavior_zoomWheel = \"onwheel\" in d3_document ? (d3_behavior_zoomDelta = function() {\n",
              "\t        return -d3.event.deltaY * (d3.event.deltaMode ? 120 : 1);\n",
              "\t      }, \"wheel\") : \"onmousewheel\" in d3_document ? (d3_behavior_zoomDelta = function() {\n",
              "\t        return d3.event.wheelDelta;\n",
              "\t      }, \"mousewheel\") : (d3_behavior_zoomDelta = function() {\n",
              "\t        return -d3.event.detail;\n",
              "\t      }, \"MozMousePixelScroll\");\n",
              "\t    }\n",
              "\t    function zoom(g) {\n",
              "\t      g.on(mousedown, mousedowned).on(d3_behavior_zoomWheel + \".zoom\", mousewheeled).on(\"dblclick.zoom\", dblclicked).on(touchstart, touchstarted);\n",
              "\t    }\n",
              "\t    zoom.event = function(g) {\n",
              "\t      g.each(function() {\n",
              "\t        var dispatch = event.of(this, arguments), view1 = view;\n",
              "\t        if (d3_transitionInheritId) {\n",
              "\t          d3.select(this).transition().each(\"start.zoom\", function() {\n",
              "\t            view = this.__chart__ || {\n",
              "\t              x: 0,\n",
              "\t              y: 0,\n",
              "\t              k: 1\n",
              "\t            };\n",
              "\t            zoomstarted(dispatch);\n",
              "\t          }).tween(\"zoom:zoom\", function() {\n",
              "\t            var dx = size[0], dy = size[1], cx = center0 ? center0[0] : dx / 2, cy = center0 ? center0[1] : dy / 2, i = d3.interpolateZoom([ (cx - view.x) / view.k, (cy - view.y) / view.k, dx / view.k ], [ (cx - view1.x) / view1.k, (cy - view1.y) / view1.k, dx / view1.k ]);\n",
              "\t            return function(t) {\n",
              "\t              var l = i(t), k = dx / l[2];\n",
              "\t              this.__chart__ = view = {\n",
              "\t                x: cx - l[0] * k,\n",
              "\t                y: cy - l[1] * k,\n",
              "\t                k: k\n",
              "\t              };\n",
              "\t              zoomed(dispatch);\n",
              "\t            };\n",
              "\t          }).each(\"interrupt.zoom\", function() {\n",
              "\t            zoomended(dispatch);\n",
              "\t          }).each(\"end.zoom\", function() {\n",
              "\t            zoomended(dispatch);\n",
              "\t          });\n",
              "\t        } else {\n",
              "\t          this.__chart__ = view;\n",
              "\t          zoomstarted(dispatch);\n",
              "\t          zoomed(dispatch);\n",
              "\t          zoomended(dispatch);\n",
              "\t        }\n",
              "\t      });\n",
              "\t    };\n",
              "\t    zoom.translate = function(_) {\n",
              "\t      if (!arguments.length) return [ view.x, view.y ];\n",
              "\t      view = {\n",
              "\t        x: +_[0],\n",
              "\t        y: +_[1],\n",
              "\t        k: view.k\n",
              "\t      };\n",
              "\t      rescale();\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.scale = function(_) {\n",
              "\t      if (!arguments.length) return view.k;\n",
              "\t      view = {\n",
              "\t        x: view.x,\n",
              "\t        y: view.y,\n",
              "\t        k: null\n",
              "\t      };\n",
              "\t      scaleTo(+_);\n",
              "\t      rescale();\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.scaleExtent = function(_) {\n",
              "\t      if (!arguments.length) return scaleExtent;\n",
              "\t      scaleExtent = _ == null ? d3_behavior_zoomInfinity : [ +_[0], +_[1] ];\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.center = function(_) {\n",
              "\t      if (!arguments.length) return center;\n",
              "\t      center = _ && [ +_[0], +_[1] ];\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.size = function(_) {\n",
              "\t      if (!arguments.length) return size;\n",
              "\t      size = _ && [ +_[0], +_[1] ];\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.duration = function(_) {\n",
              "\t      if (!arguments.length) return duration;\n",
              "\t      duration = +_;\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.x = function(z) {\n",
              "\t      if (!arguments.length) return x1;\n",
              "\t      x1 = z;\n",
              "\t      x0 = z.copy();\n",
              "\t      view = {\n",
              "\t        x: 0,\n",
              "\t        y: 0,\n",
              "\t        k: 1\n",
              "\t      };\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    zoom.y = function(z) {\n",
              "\t      if (!arguments.length) return y1;\n",
              "\t      y1 = z;\n",
              "\t      y0 = z.copy();\n",
              "\t      view = {\n",
              "\t        x: 0,\n",
              "\t        y: 0,\n",
              "\t        k: 1\n",
              "\t      };\n",
              "\t      return zoom;\n",
              "\t    };\n",
              "\t    function location(p) {\n",
              "\t      return [ (p[0] - view.x) / view.k, (p[1] - view.y) / view.k ];\n",
              "\t    }\n",
              "\t    function point(l) {\n",
              "\t      return [ l[0] * view.k + view.x, l[1] * view.k + view.y ];\n",
              "\t    }\n",
              "\t    function scaleTo(s) {\n",
              "\t      view.k = Math.max(scaleExtent[0], Math.min(scaleExtent[1], s));\n",
              "\t    }\n",
              "\t    function translateTo(p, l) {\n",
              "\t      l = point(l);\n",
              "\t      view.x += p[0] - l[0];\n",
              "\t      view.y += p[1] - l[1];\n",
              "\t    }\n",
              "\t    function zoomTo(that, p, l, k) {\n",
              "\t      that.__chart__ = {\n",
              "\t        x: view.x,\n",
              "\t        y: view.y,\n",
              "\t        k: view.k\n",
              "\t      };\n",
              "\t      scaleTo(Math.pow(2, k));\n",
              "\t      translateTo(center0 = p, l);\n",
              "\t      that = d3.select(that);\n",
              "\t      if (duration > 0) that = that.transition().duration(duration);\n",
              "\t      that.call(zoom.event);\n",
              "\t    }\n",
              "\t    function rescale() {\n",
              "\t      if (x1) x1.domain(x0.range().map(function(x) {\n",
              "\t        return (x - view.x) / view.k;\n",
              "\t      }).map(x0.invert));\n",
              "\t      if (y1) y1.domain(y0.range().map(function(y) {\n",
              "\t        return (y - view.y) / view.k;\n",
              "\t      }).map(y0.invert));\n",
              "\t    }\n",
              "\t    function zoomstarted(dispatch) {\n",
              "\t      if (!zooming++) dispatch({\n",
              "\t        type: \"zoomstart\"\n",
              "\t      });\n",
              "\t    }\n",
              "\t    function zoomed(dispatch) {\n",
              "\t      rescale();\n",
              "\t      dispatch({\n",
              "\t        type: \"zoom\",\n",
              "\t        scale: view.k,\n",
              "\t        translate: [ view.x, view.y ]\n",
              "\t      });\n",
              "\t    }\n",
              "\t    function zoomended(dispatch) {\n",
              "\t      if (!--zooming) dispatch({\n",
              "\t        type: \"zoomend\"\n",
              "\t      }), center0 = null;\n",
              "\t    }\n",
              "\t    function mousedowned() {\n",
              "\t      var that = this, dispatch = event.of(that, arguments), dragged = 0, subject = d3.select(d3_window(that)).on(mousemove, moved).on(mouseup, ended), location0 = location(d3.mouse(that)), dragRestore = d3_event_dragSuppress(that);\n",
              "\t      d3_selection_interrupt.call(that);\n",
              "\t      zoomstarted(dispatch);\n",
              "\t      function moved() {\n",
              "\t        dragged = 1;\n",
              "\t        translateTo(d3.mouse(that), location0);\n",
              "\t        zoomed(dispatch);\n",
              "\t      }\n",
              "\t      function ended() {\n",
              "\t        subject.on(mousemove, null).on(mouseup, null);\n",
              "\t        dragRestore(dragged);\n",
              "\t        zoomended(dispatch);\n",
              "\t      }\n",
              "\t    }\n",
              "\t    function touchstarted() {\n",
              "\t      var that = this, dispatch = event.of(that, arguments), locations0 = {}, distance0 = 0, scale0, zoomName = \".zoom-\" + d3.event.changedTouches[0].identifier, touchmove = \"touchmove\" + zoomName, touchend = \"touchend\" + zoomName, targets = [], subject = d3.select(that), dragRestore = d3_event_dragSuppress(that);\n",
              "\t      started();\n",
              "\t      zoomstarted(dispatch);\n",
              "\t      subject.on(mousedown, null).on(touchstart, started);\n",
              "\t      function relocate() {\n",
              "\t        var touches = d3.touches(that);\n",
              "\t        scale0 = view.k;\n",
              "\t        touches.forEach(function(t) {\n",
              "\t          if (t.identifier in locations0) locations0[t.identifier] = location(t);\n",
              "\t        });\n",
              "\t        return touches;\n",
              "\t      }\n",
              "\t      function started() {\n",
              "\t        var target = d3.event.target;\n",
              "\t        d3.select(target).on(touchmove, moved).on(touchend, ended);\n",
              "\t        targets.push(target);\n",
              "\t        var changed = d3.event.changedTouches;\n",
              "\t        for (var i = 0, n = changed.length; i < n; ++i) {\n",
              "\t          locations0[changed[i].identifier] = null;\n",
              "\t        }\n",
              "\t        var touches = relocate(), now = Date.now();\n",
              "\t        if (touches.length === 1) {\n",
              "\t          if (now - touchtime < 500) {\n",
              "\t            var p = touches[0];\n",
              "\t            zoomTo(that, p, locations0[p.identifier], Math.floor(Math.log(view.k) / Math.LN2) + 1);\n",
              "\t            d3_eventPreventDefault();\n",
              "\t          }\n",
              "\t          touchtime = now;\n",
              "\t        } else if (touches.length > 1) {\n",
              "\t          var p = touches[0], q = touches[1], dx = p[0] - q[0], dy = p[1] - q[1];\n",
              "\t          distance0 = dx * dx + dy * dy;\n",
              "\t        }\n",
              "\t      }\n",
              "\t      function moved() {\n",
              "\t        var touches = d3.touches(that), p0, l0, p1, l1;\n",
              "\t        d3_selection_interrupt.call(that);\n",
              "\t        for (var i = 0, n = touches.length; i < n; ++i, l1 = null) {\n",
              "\t          p1 = touches[i];\n",
              "\t          if (l1 = locations0[p1.identifier]) {\n",
              "\t            if (l0) break;\n",
              "\t            p0 = p1, l0 = l1;\n",
              "\t          }\n",
              "\t        }\n",
              "\t        if (l1) {\n",
              "\t          var distance1 = (distance1 = p1[0] - p0[0]) * distance1 + (distance1 = p1[1] - p0[1]) * distance1, scale1 = distance0 && Math.sqrt(distance1 / distance0);\n",
              "\t          p0 = [ (p0[0] + p1[0]) / 2, (p0[1] + p1[1]) / 2 ];\n",
              "\t          l0 = [ (l0[0] + l1[0]) / 2, (l0[1] + l1[1]) / 2 ];\n",
              "\t          scaleTo(scale1 * scale0);\n",
              "\t        }\n",
              "\t        touchtime = null;\n",
              "\t        translateTo(p0, l0);\n",
              "\t        zoomed(dispatch);\n",
              "\t      }\n",
              "\t      function ended() {\n",
              "\t        if (d3.event.touches.length) {\n",
              "\t          var changed = d3.event.changedTouches;\n",
              "\t          for (var i = 0, n = changed.length; i < n; ++i) {\n",
              "\t            delete locations0[changed[i].identifier];\n",
              "\t          }\n",
              "\t          for (var identifier in locations0) {\n",
              "\t            return void relocate();\n",
              "\t          }\n",
              "\t        }\n",
              "\t        d3.selectAll(targets).on(zoomName, null);\n",
              "\t        subject.on(mousedown, mousedowned).on(touchstart, touchstarted);\n",
              "\t        dragRestore();\n",
              "\t        zoomended(dispatch);\n",
              "\t      }\n",
              "\t    }\n",
              "\t    function mousewheeled() {\n",
              "\t      var dispatch = event.of(this, arguments);\n",
              "\t      if (mousewheelTimer) clearTimeout(mousewheelTimer); else d3_selection_interrupt.call(this), \n",
              "\t      translate0 = location(center0 = center || d3.mouse(this)), zoomstarted(dispatch);\n",
              "\t      mousewheelTimer = setTimeout(function() {\n",
              "\t        mousewheelTimer = null;\n",
              "\t        zoomended(dispatch);\n",
              "\t      }, 50);\n",
              "\t      d3_eventPreventDefault();\n",
              "\t      scaleTo(Math.pow(2, d3_behavior_zoomDelta() * .002) * view.k);\n",
              "\t      translateTo(center0, translate0);\n",
              "\t      zoomed(dispatch);\n",
              "\t    }\n",
              "\t    function dblclicked() {\n",
              "\t      var p = d3.mouse(this), k = Math.log(view.k) / Math.LN2;\n",
              "\t      zoomTo(this, p, location(p), d3.event.shiftKey ? Math.ceil(k) - 1 : Math.floor(k) + 1);\n",
              "\t    }\n",
              "\t    return d3.rebind(zoom, event, \"on\");\n",
              "\t  };\n",
              "\t  var d3_behavior_zoomInfinity = [ 0, Infinity ], d3_behavior_zoomDelta, d3_behavior_zoomWheel;\n",
              "\t  d3.color = d3_color;\n",
              "\t  function d3_color() {}\n",
              "\t  d3_color.prototype.toString = function() {\n",
              "\t    return this.rgb() + \"\";\n",
              "\t  };\n",
              "\t  d3.hsl = d3_hsl;\n",
              "\t  function d3_hsl(h, s, l) {\n",
              "\t    return this instanceof d3_hsl ? void (this.h = +h, this.s = +s, this.l = +l) : arguments.length < 2 ? h instanceof d3_hsl ? new d3_hsl(h.h, h.s, h.l) : d3_rgb_parse(\"\" + h, d3_rgb_hsl, d3_hsl) : new d3_hsl(h, s, l);\n",
              "\t  }\n",
              "\t  var d3_hslPrototype = d3_hsl.prototype = new d3_color();\n",
              "\t  d3_hslPrototype.brighter = function(k) {\n",
              "\t    k = Math.pow(.7, arguments.length ? k : 1);\n",
              "\t    return new d3_hsl(this.h, this.s, this.l / k);\n",
              "\t  };\n",
              "\t  d3_hslPrototype.darker = function(k) {\n",
              "\t    k = Math.pow(.7, arguments.length ? k : 1);\n",
              "\t    return new d3_hsl(this.h, this.s, k * this.l);\n",
              "\t  };\n",
              "\t  d3_hslPrototype.rgb = function() {\n",
              "\t    return d3_hsl_rgb(this.h, this.s, this.l);\n",
              "\t  };\n",
              "\t  function d3_hsl_rgb(h, s, l) {\n",
              "\t    var m1, m2;\n",
              "\t    h = isNaN(h) ? 0 : (h %= 360) < 0 ? h + 360 : h;\n",
              "\t    s = isNaN(s) ? 0 : s < 0 ? 0 : s > 1 ? 1 : s;\n",
              "\t    l = l < 0 ? 0 : l > 1 ? 1 : l;\n",
              "\t    m2 = l <= .5 ? l * (1 + s) : l + s - l * s;\n",
              "\t    m1 = 2 * l - m2;\n",
              "\t    function v(h) {\n",
              "\t      if (h > 360) h -= 360; else if (h < 0) h += 360;\n",
              "\t      if (h < 60) return m1 + (m2 - m1) * h / 60;\n",
              "\t      if (h < 180) return m2;\n",
              "\t      if (h < 240) return m1 + (m2 - m1) * (240 - h) / 60;\n",
              "\t      return m1;\n",
              "\t    }\n",
              "\t    function vv(h) {\n",
              "\t      return Math.round(v(h) * 255);\n",
              "\t    }\n",
              "\t    return new d3_rgb(vv(h + 120), vv(h), vv(h - 120));\n",
              "\t  }\n",
              "\t  d3.hcl = d3_hcl;\n",
              "\t  function d3_hcl(h, c, l) {\n",
              "\t    return this instanceof d3_hcl ? void (this.h = +h, this.c = +c, this.l = +l) : arguments.length < 2 ? h instanceof d3_hcl ? new d3_hcl(h.h, h.c, h.l) : h instanceof d3_lab ? d3_lab_hcl(h.l, h.a, h.b) : d3_lab_hcl((h = d3_rgb_lab((h = d3.rgb(h)).r, h.g, h.b)).l, h.a, h.b) : new d3_hcl(h, c, l);\n",
              "\t  }\n",
              "\t  var d3_hclPrototype = d3_hcl.prototype = new d3_color();\n",
              "\t  d3_hclPrototype.brighter = function(k) {\n",
              "\t    return new d3_hcl(this.h, this.c, Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)));\n",
              "\t  };\n",
              "\t  d3_hclPrototype.darker = function(k) {\n",
              "\t    return new d3_hcl(this.h, this.c, Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)));\n",
              "\t  };\n",
              "\t  d3_hclPrototype.rgb = function() {\n",
              "\t    return d3_hcl_lab(this.h, this.c, this.l).rgb();\n",
              "\t  };\n",
              "\t  function d3_hcl_lab(h, c, l) {\n",
              "\t    if (isNaN(h)) h = 0;\n",
              "\t    if (isNaN(c)) c = 0;\n",
              "\t    return new d3_lab(l, Math.cos(h *= d3_radians) * c, Math.sin(h) * c);\n",
              "\t  }\n",
              "\t  d3.lab = d3_lab;\n",
              "\t  function d3_lab(l, a, b) {\n",
              "\t    return this instanceof d3_lab ? void (this.l = +l, this.a = +a, this.b = +b) : arguments.length < 2 ? l instanceof d3_lab ? new d3_lab(l.l, l.a, l.b) : l instanceof d3_hcl ? d3_hcl_lab(l.h, l.c, l.l) : d3_rgb_lab((l = d3_rgb(l)).r, l.g, l.b) : new d3_lab(l, a, b);\n",
              "\t  }\n",
              "\t  var d3_lab_K = 18;\n",
              "\t  var d3_lab_X = .95047, d3_lab_Y = 1, d3_lab_Z = 1.08883;\n",
              "\t  var d3_labPrototype = d3_lab.prototype = new d3_color();\n",
              "\t  d3_labPrototype.brighter = function(k) {\n",
              "\t    return new d3_lab(Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)), this.a, this.b);\n",
              "\t  };\n",
              "\t  d3_labPrototype.darker = function(k) {\n",
              "\t    return new d3_lab(Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)), this.a, this.b);\n",
              "\t  };\n",
              "\t  d3_labPrototype.rgb = function() {\n",
              "\t    return d3_lab_rgb(this.l, this.a, this.b);\n",
              "\t  };\n",
              "\t  function d3_lab_rgb(l, a, b) {\n",
              "\t    var y = (l + 16) / 116, x = y + a / 500, z = y - b / 200;\n",
              "\t    x = d3_lab_xyz(x) * d3_lab_X;\n",
              "\t    y = d3_lab_xyz(y) * d3_lab_Y;\n",
              "\t    z = d3_lab_xyz(z) * d3_lab_Z;\n",
              "\t    return new d3_rgb(d3_xyz_rgb(3.2404542 * x - 1.5371385 * y - .4985314 * z), d3_xyz_rgb(-.969266 * x + 1.8760108 * y + .041556 * z), d3_xyz_rgb(.0556434 * x - .2040259 * y + 1.0572252 * z));\n",
              "\t  }\n",
              "\t  function d3_lab_hcl(l, a, b) {\n",
              "\t    return l > 0 ? new d3_hcl(Math.atan2(b, a) * d3_degrees, Math.sqrt(a * a + b * b), l) : new d3_hcl(NaN, NaN, l);\n",
              "\t  }\n",
              "\t  function d3_lab_xyz(x) {\n",
              "\t    return x > .206893034 ? x * x * x : (x - 4 / 29) / 7.787037;\n",
              "\t  }\n",
              "\t  function d3_xyz_lab(x) {\n",
              "\t    return x > .008856 ? Math.pow(x, 1 / 3) : 7.787037 * x + 4 / 29;\n",
              "\t  }\n",
              "\t  function d3_xyz_rgb(r) {\n",
              "\t    return Math.round(255 * (r <= .00304 ? 12.92 * r : 1.055 * Math.pow(r, 1 / 2.4) - .055));\n",
              "\t  }\n",
              "\t  d3.rgb = d3_rgb;\n",
              "\t  function d3_rgb(r, g, b) {\n",
              "\t    return this instanceof d3_rgb ? void (this.r = ~~r, this.g = ~~g, this.b = ~~b) : arguments.length < 2 ? r instanceof d3_rgb ? new d3_rgb(r.r, r.g, r.b) : d3_rgb_parse(\"\" + r, d3_rgb, d3_hsl_rgb) : new d3_rgb(r, g, b);\n",
              "\t  }\n",
              "\t  function d3_rgbNumber(value) {\n",
              "\t    return new d3_rgb(value >> 16, value >> 8 & 255, value & 255);\n",
              "\t  }\n",
              "\t  function d3_rgbString(value) {\n",
              "\t    return d3_rgbNumber(value) + \"\";\n",
              "\t  }\n",
              "\t  var d3_rgbPrototype = d3_rgb.prototype = new d3_color();\n",
              "\t  d3_rgbPrototype.brighter = function(k) {\n",
              "\t    k = Math.pow(.7, arguments.length ? k : 1);\n",
              "\t    var r = this.r, g = this.g, b = this.b, i = 30;\n",
              "\t    if (!r && !g && !b) return new d3_rgb(i, i, i);\n",
              "\t    if (r && r < i) r = i;\n",
              "\t    if (g && g < i) g = i;\n",
              "\t    if (b && b < i) b = i;\n",
              "\t    return new d3_rgb(Math.min(255, r / k), Math.min(255, g / k), Math.min(255, b / k));\n",
              "\t  };\n",
              "\t  d3_rgbPrototype.darker = function(k) {\n",
              "\t    k = Math.pow(.7, arguments.length ? k : 1);\n",
              "\t    return new d3_rgb(k * this.r, k * this.g, k * this.b);\n",
              "\t  };\n",
              "\t  d3_rgbPrototype.hsl = function() {\n",
              "\t    return d3_rgb_hsl(this.r, this.g, this.b);\n",
              "\t  };\n",
              "\t  d3_rgbPrototype.toString = function() {\n",
              "\t    return \"#\" + d3_rgb_hex(this.r) + d3_rgb_hex(this.g) + d3_rgb_hex(this.b);\n",
              "\t  };\n",
              "\t  function d3_rgb_hex(v) {\n",
              "\t    return v < 16 ? \"0\" + Math.max(0, v).toString(16) : Math.min(255, v).toString(16);\n",
              "\t  }\n",
              "\t  function d3_rgb_parse(format, rgb, hsl) {\n",
              "\t    var r = 0, g = 0, b = 0, m1, m2, color;\n",
              "\t    m1 = /([a-z]+)\\((.*)\\)/.exec(format = format.toLowerCase());\n",
              "\t    if (m1) {\n",
              "\t      m2 = m1[2].split(\",\");\n",
              "\t      switch (m1[1]) {\n",
              "\t       case \"hsl\":\n",
              "\t        {\n",
              "\t          return hsl(parseFloat(m2[0]), parseFloat(m2[1]) / 100, parseFloat(m2[2]) / 100);\n",
              "\t        }\n",
              "\t\n",
              "\t       case \"rgb\":\n",
              "\t        {\n",
              "\t          return rgb(d3_rgb_parseNumber(m2[0]), d3_rgb_parseNumber(m2[1]), d3_rgb_parseNumber(m2[2]));\n",
              "\t        }\n",
              "\t      }\n",
              "\t    }\n",
              "\t    if (color = d3_rgb_names.get(format)) {\n",
              "\t      return rgb(color.r, color.g, color.b);\n",
              "\t    }\n",
              "\t    if (format != null && format.charAt(0) === \"#\" && !isNaN(color = parseInt(format.slice(1), 16))) {\n",
              "\t      if (format.length === 4) {\n",
              "\t
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