MyoPS(ResUnet+GAN).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMZSWCxCFvLmvfxCvdXYUUh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rik030/MyoPS-2020/blob/main/MyoPS(ResUnet%2BGAN).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0bBCMM4vvCU",
        "outputId": "9d7b9065-6301-48b1-fbae-30b387145909"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "base_dir = '/content/drive/My Drive/Colab Notebooks'\n",
        "os.chdir(base_dir)"
      ],
      "metadata": {
        "id": "uaxOExpBv_og"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPool2D, UpSampling2D, Concatenate\n",
        "from tensorflow.keras.models import Model"
      ],
      "metadata": {
        "id": "ACeYpqy8wHo_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import zeros\n",
        "from numpy import ones\n",
        "import numpy as np\n",
        "from numpy.random import randint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.initializers import RandomNormal\n",
        "from keras.models import Model\n",
        "from keras.models import Input\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import Conv2DTranspose\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Concatenate\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import BatchNormalization\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "metadata": {
        "id": "FDFO9jHCzRWl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Reshape\n",
        "from keras.layers import Flatten\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "jbLr1xEm9TK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tqdm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBGFy6ybqeTU",
        "outputId": "b4cbec42-4a41-45af-9656-83462cc00951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.64.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from glob import glob\n",
        "def load_data(path):\n",
        "    \"\"\" X = Images and Y = masks \"\"\"\n",
        "\n",
        "    X = sorted(glob(os.path.join(path, \"images\", \"*.npy\")))\n",
        "    Y = sorted(glob(os.path.join(path, \"gts\", \"*.npy\")))\n",
        "\n",
        "    return (X,Y)\n",
        "\n",
        "data_path='/content/drive/MyDrive/Colab Notebooks/myo_208/'\n",
        "(X, Y)= load_data(data_path)    "
      ],
      "metadata": {
        "id": "oA2uaEYTyWsN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Augmentation\n",
        "from tqdm import tqdm\n",
        "from albumentations import HorizontalFlip, VerticalFlip, Rotate\n",
        "\n",
        "def augment_data(images, masks, save_path, augment=True):\n",
        "    H = 208\n",
        "    W = 208\n",
        "\n",
        "    for idx, (x, y) in tqdm(enumerate(zip(images, masks)), total=len(images)):\n",
        "        \"\"\" Extracting names \"\"\"\n",
        "        name = x.split(\"/\")[-1].split(\".\")[0]\n",
        "\n",
        "        \"\"\" Reading image and mask \"\"\"\n",
        "        x = np.load(x)\n",
        "        y = np.load(y)\n",
        "        #print(x)\n",
        "        if augment == True:\n",
        "            aug = HorizontalFlip(p=1.0)\n",
        "            augmented = aug(image=x, mask=y)\n",
        "            x1 = augmented[\"image\"]\n",
        "            y1 = augmented[\"mask\"]\n",
        "\n",
        "            aug = VerticalFlip(p=1.0)\n",
        "            augmented = aug(image=x, mask=y)\n",
        "            x2 = augmented[\"image\"]\n",
        "            y2 = augmented[\"mask\"]\n",
        "\n",
        "            aug=Rotate(limit=120, p=1.0)\n",
        "            augmented = aug(image=x, mask=y)\n",
        "            x3 = augmented[\"image\"]\n",
        "            y3 = augmented[\"mask\"]\n",
        "\n",
        "            # aug=Affine(scale=0.2, p=1.0)\n",
        "            # augmented = aug(image=x, mask=y)\n",
        "            # x5 = augmented[\"image\"]\n",
        "            # y5 = augmented[\"mask\"]\n",
        "\n",
        "            X = [x, x1, x2, x3]\n",
        "            Y = [y, y1, y2, y3]\n",
        "\n",
        "        else:\n",
        "            X = [x]\n",
        "            Y = [y]\n",
        "\n",
        "        index = 0\n",
        "        for i, m in zip(X, Y):\n",
        "            '''i = cv2.resize(i, (W, H))\n",
        "            m = cv2.resize(m, (W, H))'''\n",
        "           # print(len(X))\n",
        "            if len(X) == 1:\n",
        "                tmp_image_name = f\"{name}.npy\"\n",
        "                tmp_mask_name = f\"{name}.npy\"\n",
        "            else:\n",
        "                tmp_image_name = f\"{name}_{index}.npy\"\n",
        "                tmp_mask_name = f\"{name}_{index}.npy\"\n",
        "\n",
        "            image_path = os.path.join(save_path, \"images\", tmp_image_name)\n",
        "            mask_path = os.path.join(save_path, \"gts\", tmp_mask_name)\n",
        "            # cv2.imwrite(image_path, i)\n",
        "            # cv2.imwrite(mask_path, m)\n",
        "            np.save(image_path,i)\n",
        "            np.save(mask_path,m)\n",
        "            '''raise Exception(\"Could not write image\")'''\n",
        "\n",
        "            index += 1\n",
        "\n",
        "augment_data(X, Y, \"/content/drive/MyDrive/Colab Notebooks/aug_myo_208/\", augment=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3wtxAurpWd_",
        "outputId": "349a2403-2de8-4a1c-cdae-3b75b2f4328e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 102/102 [10:33<00:00,  6.21s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img=np.load(\"/content/drive/MyDrive/Colab Notebooks/aug_myo_208/images/id_101_3.npy\")\n",
        "gt=np.load(\"/content/drive/MyDrive/Colab Notebooks/aug_myo_208/gts/id_101_3.npy\")\n",
        "plt.imshow(img[:,:,0],cmap='gray')\n",
        "plt.show()\n",
        "plt.imshow(gt[:,:], cmap='gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "jhEEp3EYKAkn",
        "outputId": "640cb2d4-a94c-49f3-f1bb-de13ed9b71d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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2dHx8rIODAx0cHAz/z2azUUTe5wVIC6XlQh/SX64otItQWaGu60auxmw2097e3hCk3NvbG9wALk/md8YGfNajN3ZdXl4OW719zr8Bi0CxKlYgaUm4V1HryrITLLzTlbGOtKYElgQCU3UCmfunqpuTLXxV1wxMtmIGZILOj3nS9SGzSOaRQOC/VeDs9FJiBl3XfVzSvybpf5f0RyR9R9d13yjpF/TAHt5+GeVsUuJmHmksOFRuruzzgNmHsxBzFWD6/J5dkTRE8yth4hoCrso0EFTnPfpEY8cHDAxUfgIAAcIzCWxTWudViZYv3YHq3ooiVy4DA3CVa5B50G9Pep73Mo6SFr5SRt9bTZtmW83EWF7WqXKhqgVMLP+zBgZd1x1L+luS/ou+7591XfdDkr5XD3GE75X0A5K+uXhueKPSOpHOTUi5m8+pCs7Q13cU2Ermg1W4jmJvb2/kPzrZItzf3+vq6mrw/3moiKQlMDAr8Dyz4wVWdioOhdDsg0yAYGAgoIWTFmBIilsJIBWCFo2AwGtOqRBTdDnLTxeqAhX3GRU6lahSbroYmXeloFNAwDGhTrCf8vnMs+pv13lVel9g0HXdjh6A4K/3ff+3Hwv/NK7/iKSfblRyeKPSwcHBeqbkc5C2t7d1fHysw8PDYZ6fFjGprhmAX7dm/57g4A09h4eHw6Dnuxv95/w9X2zf3c+0/EgCDfcNuE0+lIR77C0wud2brxVzkDNpPvuCIEPFTfruZyrKyxmLylen0lWBRipmuglTAbmq//N+UnJ/r1bQZltZTrZXWgCxgZ6zSrkEPfsgASbdpXXS+5lN6CT9ZUm/3Pf9X8TvX9Q/xBMk6Wsl/dJ7LeNzlRx0Oz4+1unp6bAJ6ubmRpeXl6P5dGnZuvR9P1Dwy8vLIVbgVXvz+Xyg7Pv7+4OyUYBpcUxjPQ1oy+Fg3VSASVrMVzP+4PUOeQyaWQH9567rhnq73RQ+rkvImYgUSlq/6l0TLtfPtgJ5VDQuUGP7+SajtIy0tBXlz5mVbEOym2QiCQY5JqwD+8l959OkKuBK5U5wYf6szypQeD/M4I9I+uOS/q+u6/7Px9++R9I3dF33CT24Cb8u6dveRxmf1WQhcODv5OREx8fHg4Aycm6l4Hw4fU/OydPf9jPz+VxHR0dDvqbe6b9yAL3OXNKQNw8WSZCSxvRTWiwyYp39fT5/OBPh7OxscFv29/cHEOOGKNeH04JmRGQHtKAECtf55uZm+M3XzaCSPUjjtQlUutY0ZOXnp4LRB2ffe5zoPlSWtmIKCQSsO/uDz/ieBPYpJa5cBjMVBh1ZXiu9n9mE/1VS5ah8aNYUJIXjST/cJ9B13aBojqTbxzZrIDOQNBJ2g4FnELjSz5bJltfKSavDQbTF2tnZGVHIXKFogaCSmPH4ZCPHErzY6P7+XhcXF3r27Nlw7d133x3AoNrPkO0gCFCB2OdkBexPt+H6+npk7XOlY44dZ2GoyBlcbAEBD2YxIFcWVqqXMFe/5bV0dVgPrh7kjAH7VVoEWvOPZVUA0oonZPq8W4FYUVkmWlgrV9d1o0i6zxPw4DHYk5aQVpnz/o7wexEQn7WVZp60HHxBjMvhOgWCgRcb+TXyT5480ZMnT3R6eqqTkxMdHBwM69Zvb291fn6ut99+e4hR7O/vD4em5hoDzpywLskKqKj0jx3MNAD47/z8fLQEmdOY6YfTElKp3FdULtcxKTeZmam5+52zE4wL0J2rlC1jHGw//9iHlCUbJBsZxqqqxUnMs2Il66TPGzCwIFmYMxo/m80GS+6Am33lruuG9ypcXFzo6upKfd8PQThG5Dk4LoeuBPcleHUit+pWiO/8mJI2+1kepeVZisPDQ52cnOjNN9/UG2+8MXyenp4OQcz7+/vh2DTOTOzt7Q0bk8gQWB4DirlvgBavGhPOXviPMyJbW1u6vr4e3KJ8s1UqNfuqBQBVPXgfwYBuSrUgqCrH8pSK6zwrhuI+8gI1bjM3yJo5VS4E6/4iQUOmVxoM2EG5iSYVzgLgQJ//Ny33Bh0HEGmhPVB2HSqK6cE9ODjQ6empnj59qqdPn2p/f38AItctYw9pAahoTmQpBh6Xd3R0pCdPnujNN9/URz7yEb311lt64403dHx8PGxcMiuwz842ctns1dXVaMESlZxMi0pAyj8VVfdvBEYDkutEhtKaP0+3pCo35aSK/JN9uK2eVqZccTyYVzKDVtn+3QFiuqncOJYvu/E4JBCQGU65MJleSTCgC5AdxM6nX+ZEyug3Dtvq0+/nMWsMfqVC2JpwH8DJyYlOT0/15MkT7e3tDQyEkXrOLDBPCqu0mAa00BgMuMbATOStt94agODJkyeDi5KuhtkEGY8VMV+9zhhJ5ctmHIVWnf+z7QYA6QFEzQasDOmyUBkY/SdYVONfyQ3dG7aRMZKDg4Mh1uCt3MxjSvE4tgQNB2sPDw+HLcdmklx0dnl5qWfPnuns7Ky5YYzfGQhdlV4ZMKjQsUpE6Iq+WvhTATk7wKChtBByKw0VywNKn5BBPDID+8bZlsptkJbBhu86MBh4BeLp6alOT0+HNROmoy7Xi4xsbXhCTgY8maaEjD42A6oGvmRradW8pmF3d3d43mstyA7onhBUCQjpKqS1TtpNecnAJM+G4JhX5TjPZAmUOzO44+NjHR8fj86mJDBeX19rZ2dnMEx2VyuXKdM6QcQPNRikG8DOyIEmYjJOUC1nzc5kh1PJGfjjZp7036XFKkv/TgV2flbODFrRX03aakrpYJ8DghYmrzz0hinXyVN3XuJ8eXk5Wmac5Vc01KnVfxkEZWwgFbqKlRCsDdIGKQMnGZnzdL65QCyVdN3YTM7KSIuzF8isKsDJRDbqfA0EDuraVaCcWKbu7u5Gs1xkoyw72cI66UMJBrQE6+zTlpbnchlxZr4cWN6XVJEnClHw/KzvN5NwSiZSARPraH+dMw2sm90PTxUaDHj0udcmGLQ8O2AguLi4GOgulYYR/rToZF8ZzyBwUmBtzQgIdgGkxWlMFHb2h9/P4JOfqAQEA9f3+vpakkY0PmUiUypwxYQMSi1XNJlEtoN50j2w++jxIqhSztIVqsaB47Fu+lCBAel4NS3YokkVPWRgi1aNvmve6+dtxdN9yAg6lwIzGCQt9i44LpHBKGl8SrCFz+CXboiZQXWakfvOZdla+nVrni3gJhnff35+PsyipL/uNhPgOFPDxMVXBgGCi5WGpyxXbCFnSzy+8/l81M/s767rRu+TrBhCizFQwTgDxT7KWEPlWlQBRMsVX3nPBWB2q2xULi8vR2ORAfEK3FbFMJg+FGCQwRFS8JwWqhpP96AaFMYJ0qoxQCctNgRJi1WAXJ1H6k767hgBTxMyVWfsIaexqGiVwHL/geklLbLzJehYkK+urvT8+fNhlsQujvvVYMEpVU8vMphIhanqaEZiq052YaVOX7y1xJhsjWURRJNh+HuCWPr1LoNTiM6bsQl/GmQq17JiBXm9FXdyGZ7O9nczuPPz8+EVeslGkplI9Vuxq7SRYJABQCqEO5nW2wMjTa/NrgYlrQUpuJXRCsw5Zg9ezgLYUtNXrxYZWUFdd+fJgKDz47Lf/Es3I4N2jKmYqkuL7dBnZ2c6Pz8fTVcxcu/1BZzrT4aQACDVm51yuXROP3I6jePDsaHiZTzI5frT/rVdJZ7PYIveArAEOLICK61ZVtYpASABk0rLYODFxcWQz+3t7QDEdqUcc3Fsp3Jns/+njGSmjQEDxgGo6LkaTxofKDGfzwf/MQeR1jGBgANMga4oPpmBn6WCcUAMBEdHRzo8PBz+zA4cxDMYVDTYn6wThZpAkPSwmrqTHhTP06L038/PzwcwIIX3d8cXOE5cMZh9xwVLdKfMCnLBEvuZ5z+kPJABVildwXTPPMNyfn4+mi5O2SIzct1TmcgM0i+vgCRTMgIzNLs6lkluI+eYJrMhAFUMxfL2ge1NeJmp7/ulPfbSYlluTgFSeSw4BISqQ7I8T/+lD2mB5Co4P8NnK2tg390AcHx8PICCjxzjYOfMQEUbb29vNZs97BtwG9OFoc/K+jHQRxpqILi8vBzoP9+FwNkFj4vHIIN1qQgU6Ox/fnJhFPuabIIr7qpAWMZ8Evg59UpwchmOkzDvZAFVWkfJeU/eW01n2y1jTMl97ABoVW7OiPE+3msZmkobAQZOtLZunAGBAbwXSQkOpOM5HZRKyYHNKSrX0ffbajuAZ/eAU32cgXB7qdQWWvp6LsuW1eXkGQXsP/chP+3nGwDOzs6GWAEDemQGtEgsi+U45XSZab8BhPf5k4yAeTO4mmBAYMrgZSUbvs6DYPkMT3xKwGd7PG6VQlZxKOfjz+oZuiAGacpctezaz1ZlpWtY9cNU2ggwSGvIGIAHhOv+negzk8IlZeR1p7QGvjcDVJy+IlWlO8GzBg4ODgYm4FVkGdRj0IfKQZrsOrrMu7u7EWupXBj61GQHpvUGgWfPng2vbifVd70cxabV5BuWXbf0+RkzqV7fln3N06DZxzxYlcLN8eBsSitaz3oa0NIN2NraGoKnZHsVgFG+nKiYaUB4T7oZlDHXh65ui+1UwNKKC7T+b6WNAAOntNL+rfIXbXFSQHOwnCqG0GIZZA6Zt8tmjMDuAY8X41x/DjzrVP1Vwmfa6PJpgavoO90Cvgnp+fPnevfdd/Xs2TOdn5+PqL7ZVzV3XQGxpJEyGgyonBw3xkGyjf7MWR2WR8XO8aAFz/IzHy8Ld7sy5lLR7TQS/q3lq3N8fW8CQj6fss98MvE+6kfWa1WshWmjwMAd3vfLmyyS+vieKnqeaNoqa+p6C5D8LJfrmgkYDOwW0DXgs5KWlDmVh8CQS27dTlrHVEIuz+XMAA84NRWXNMQjcip1lX/surNsW3dH3P1bMjALK2NF+Zn3+zsV1oqVbgPL5m+MadCN6bpuFBTmuCdgvwgYcNxdZ5afsasEDj7PPCu26Xs489Uaw0wbAwZUDqcEg7TsFAT/TwqetC2VMoUrByNdFccurHycQuReA64rz4M32NZsM4UvfWELe7Vc2G5KFb3POAA3GhnUsk7uU0mDe5YgmwCQQbeK/kuLdzXwdyt/+r05bq5vKmoyAdYtracVhUyC/UgXpZrdark9+X/FRNk//N11IiBkXgwa053susX5mpYLBntzPKfSyzgd+dclPZd0L+mu7/t/veu6NyX9TUkf18PRZ1/XrzgunUphwW/5QjkoVSdWg5foOHWtqh+tsEGAbgEZQbV4pgUGToxP+D4qNt9XwCW8ztuCxc1HuVafx6vxzU4JPi0XqeqXakyYEuDSFfK4sUwCCWdQ+Mf+8yeVJetYuXn8n2DASD63Cift53i1AIBlV2yC8ZeqnpwJ4YY0A8L9/f1oLQjz9Vivk14WM/h3+r7/F/j+XZL+Qd/339d13Xc9fv8zqzLJVYVJnRLxabGSFUhjoVvlO1V0LpPBwIpfbQzKCH8Vp0h3hyDh3/xcbh+utjmnReWsgYNjnJ7y87u7u0sWy+yDMwapqBWo0lpV8YDsx3TxSG3dbuZBZlG5J2nxk0KzjQnsZis+N8FAkHsoXI91jEiLFSSF5zQ1AcL14+yRgYFyZ3m6uLgYpom5cIzt5XqRKn1QbsLXSPqqx///mqR/qDXAgIlBosoiJDK32ENSfl/LlJatuocDlMzASM36eKCpHMwr25X0z4rJxUBOGbhzeV6swulDr7Zjuyh06Y4x+JibkiqKzj/OdFTjyKXaVTyB7IYgxug/lT1/Y/+xnQSEyk3zJii322BgQOUKzaof8/9WqowO+9PX8nwDukdcy+KzMC2DniEyS6C8rEovAwx6SX+/67pe0v/YP7wP4aP94rj039bDy1nbGRTWPX1Haew75bOJvrzeUv5WovVJpKaLwIBhrg9YtXCF7aEi2c+TFqvQeNwYlyrTurpMbj6yINu6p/XNvrBLMrVDsepX18lsyTSWkX9usMqAZ1JoSSML7WPZmBJMuVYj949MJQKJz00wCLstjP3kXgS3cZ1UyS3ZrfN0f/ocisPDQ81msyHu4/q6z1kP1yWN0DquwssAg3+j7/tPdV33BZJ+tuu6/5sX+77vH4FilDq8UcmojGd8j6Tp9/BVMU5TwMsAACAASURBVALeW1FaXq8Gk1aawuYpqWoKkS7OlI+dboHz5zZlD6ItI49UY90MQFY2zxh4ibEtmstkW9MaOdEtyYVFrLO0EFr3i/vGy6652jKXRieoOz/GSei35wKcauZoFbNju8ks6XY4kb2QcVgpDQhZjyqRDVSxBrp7rotnqQwGknR1dTX0A7dycw+Nz3pIkJkySk7vGwz6vv/U4+fvdF33k5K+QtKnu8eXqXRd90WSfqd4bnij0t7eXp/BI3cQvz8+VwYWK+Gib+18Mj/Up7yHtDfXEtBquDxa08yT7UtW4Gk4BhGTYVhIHKOwctuVMBswGPDtPFza7XpmnMVlTs3ju08ofO4XbsYyW2K/VO5cjjnXOiQocSEV/WIqZQVwVblWZgYQ3daUoewnHgLD/ClHjHNxvJmvn0u3J3e7+v0alhGf0Slp2Izl/5Ndp1s3ld7v69WOJM36h7cwH0n69yT9eUk/JelPSPq+x8+/O5WPhTAHpkKzCgiysykcFnJ2duVTcmD8aQCwlSAbIAhYsLhFNzs/aa2VkpbCvjrbKi2m4ywk9CFdpgOGBAJuq2b/ELRySXFr2pHTqQxsZeyE+zBITZM1pYBWgb9UfK6b4EEmeWowZSOnLV0Xl+Hp4mx/Ghq7h+meJd3P3/J+14njSyBMkHVQmkF1skW/f1PSkotHIFjlLknvnxl8VNJPPirWtqS/0ff9/9R13c9L+omu675F0m9I+rqpTEjdSNla0fIWEPB+gkHSMKaK5pEVWAFpkU3PvYnIeVBwc6BZlyqgWCF43/cjX9h1sJJ6VaKBwNFknh7E8rpu/GYoCiWpf7IWg2Ges8jzFnNatTpXIfucUXSXzWdcR1JjA4DBmKDu1ZQJsL7O7045FglEBhn3v8fC7pnbkHWhvCV4VPVwXdjXNDYERi4k432ut8GArtkHDgZ9339S0r9a/P67kr76BfNaEkJS5YoRSNObNpK6+f/WvLeBiK5BHpRBYZEWNLLabkshoFUmOLBOuc6AfcK5c5flaLf/prYUExCdMiLPQBmn3Hgwav4RJHLbMkE+rX7f9wO7ScZWKZWBLxlWjiv3WdCapnzQfWJiDMYMxH1GsJO0BBbuO7IfyoHzptxkoqtkFjCfLw6a4Yt8XP8EXdefRmnVjkVpg1YgOrUCe8kGqpSI7HzSJXBi/lQCCnauIExrxc+kllleVS4TlwOz/gyqWYjMCDhrQMtIis8yaY0T7LjBKN2CXEvB/qmOdEtqmouBaJETLHJMae3n8/lAm5M1sn8JrDkOBFtuDnK9CO5uA7db20enK2ZAbe3voCFxHdPFoBxJD2BzcXGhruuGvSV8uxXZdLrWWX5LFpk2DgzcOaumGNM/o8X1PZUfKo1PnrFS0C+n1Uvl9POkkvSFSetJKZMF+P/KpcjFMwwUWmB85kCeaiwtnyqc1LTrusHd4AKqbHfFkLhTkrGIqo8ZAOUMSFp3/qWlTstGFy7ZovPgghsrewUIVDrnTatOa0q30WVXR7iRiTFISVDnWFDe6Qp5CtFKzzUPFQNssS9f39raWukqbAwYEM0Z9U6fnnQrI7JOFNIMnNGCpFtA2ku/LYU+680yc+FQFUTjc1X76BawTPcR3+rM6SYqRNYx87el9wyAA3+cIUlAzTl89mPlE9OypuuT7guZV+aVRsHPp4GwstqfZr/z3taMg6P1aaGZGC84ODgYnSDtRHlJ97cySP5uUHd73BdeP8LjzrINFVtmLGZ7e3tUxyptBBh4IGjhOW/PDq38RCpACnDSUSdfSxBI94AWzfcwbuB6plVPpaEfmzECCh/rn4ufzA54jh8PJaVSusy0HltbW8NUoE9j4tFsueGJ/Vb5806k8sl0EgAzb99j+k/fW1pMMyY7yDdgm9Xd3NyMQLjruiUfmu1hn9E9IJhtbW0N1xj15yvrfc3ywnIsu3RfWC7dP7IassIqOJ2JY+AyzADPz8+X7mfaGDDwQgkrY2v6JV0EX0uqJGnpk/m4k/KPtD6FjCvrpOWltknv2T4CW+WTGlz8V9E+CwVPJmJ8xHWq6Gi1QCjPaMwpU7Yv/dspoM4AbTK0it7bKnO3p/NiZJzlUbENngZtlukThMjM0mi4LLaN9N/0XdLgJjBw2HXdaC9AdValy3UiYHHq1OUaXNhnlE3nx74k2FgmPeuzKm0cGFTUvzVovsbgVFrjtGr0edMPTgXMaLppdAbKXFdSX1pQ19nfrWAe/K2trdH8MGcmEgz8TAYsfY/70Mn14joJrhT08e3pElEp0r1irIOspOr7jA+wD5Ih2a/lmCQYuE0cU363BXdKI5J9lnskuMWbbTF7lRYxBoITnzX7qs6z4LhkPXlMnPP2CVcJgMlwciwsx1yvsCptBBhIGubspXpKLhWC8+i2BpWw0cL7XkbA0wq5LCoRF9fQZ8zOpy/MOrMdrp8Vyj7uzs7OyP8kTU3FtCJW00WpKAQCK3++z4/WJ61j9rcTf6eSZvnJ2pzIItwWn53ApcBWCAZH2becemYf2CJSeQhqBnCCOEHd+dBNcH2dRwYPLcfX19fa398fnnP7CURuawLC9vZ2GRdIULEMttzjNGQZ66rSRoABUY2/pW/kwad/ln46QYLKQMX3J9lEMhF3JBfUEAi4wIjgwUTBzU8KCKPs0oIakzLmDIT/56efzYCfwcxLhukSkLoTQDPGkf5tUul02dj//rPVTJaQ4EYK7t+TWfFZ1zPdN2kRPOMUKtvpxDH0vWRfWV8CFNuZ5XPc+b9l1QwhmS5ZDGMuNDDMN+vOIHi6ra20EWAgLW8goqDTAnCpJa2fAzbe9EQg4LJO+vVJbas4Qc6hGwDy9eSMN2TcIcuRlg/YcFvtNpDapmUmUFaxFebPPiCwsU1+nm5JWnOCBMvxtSqwRaHmd9/j71w3kG5JBQaux9SOygSzVPZcnEQDYUNQ+egGAp5kzDjP7e3tkFcaOfaF+4NjaGZgBSYLpOucLMv1zAVi1AuW20obAwbScrQ6/VQeSeXGU4GTltO6cxlxBrJyMHKFXdJVU0GCEpWp8t1aPnWyiVzXQAGq/MSpe1y2AS2nDSsgTKrfAhvXlcJKhWbKepJFmTEwaMzxTwWicahcmLSoSbcJQpyhcB0oO/z0mBgMquXXu7u7I1eH7pfLqAAz68170xWrDAgNUsWA11l9KG0gGEjLc+9Gbx4uQepPpfX9OR3YekUakTv9LAsHfWdureXAUuFpYbNt6eO5LqSfDExW0fzMK5WQSmxrx4VEGZOh1UxBS4rMuhto6KIxICgtFv3QBeB0rPOrAD3lINmVkwE4+4ssg+U6b/dTgmyyO/YLFx7xhbcXFxc6ODgYjFUVRK7qnePpMlhPxkUITNlGuoYZ5E6ArtJGgUEKNAWBFsDzpjxkhGcKuINz4RA7n8LojjNoMEbgshkn4Bt9U4mSVjKoUwXXpGWw4YGc7Bum9OGZHJj0faS/rEu6A5U7w/Jp4emjMwLu6Dfrn0E7xjXS9UhGxD6o4jtUCltlg0+6JGwv2YMZnoGL4+J2uO+4xsDGyOs2+FYqxhUqtsT+5rhUljyDtFX9GL+gTBEwVqWNAwN2nIWMv21tbWl/f1/Hx8c6OTkZIuOeTeB9tlyVhXb+VIbcIlx1ei4aYTSaFD8Dm6l86Q5ldJ4Dme2v2pHuAf9v1aHlviQjY338u+9lflbyPKiGATD2TwJPugOp0A4as/68dzabDYzNSuX73C5aTSoJgYvz/ARTMiz/blZKt9KbiRjXYL9Wwbx0Tdh3BlCPv8EsGUglM+sCgbRBYFBRqvRXt7a2hqmxJ0+e6MmTJ6M3Gxsh3YFcP5CCnHQ+d+OlwFTTVwYAWl2CQosxUJA5UOkSVHEApxa19XdOt1aKV+WXTIR1SX8+XYiMOVgBkx2khavYSBUncBkEhHR3PEWbiWyAjIayQLbgaW6/RYouAtdAUFboUprdJfNwWU4JCi6L9co2sO7uY/Z3gu+HEgxSATKRETx9+lRvvPGGnj59qpOTk2G6iO8GoKK2qCFpWh5Bnf5kheYsI4GA96dSE4jSJ6yUvCq7op0EQbaJ1jAVm0LE/lkFRBkMsw9e1Z/jy/rzejWPzjzSZSRzczvsa5MpJWiYeeU0o68ZvPKAGrIDzkjYJZvNFi+7MSPgSU0EQctKrjGoxjiVmYFFjkMyoIyfrDIG0oaCQWVF9/b2Bkbw9OnT4e/k5GSISF9eXi752umve6AkDUtgc6DpA2dnS+PVflT+KkqfA8l6OeVSW7oZruNUnzm5/Gq/Rbo8zMO/Vy6B25vWNQXR5TOxLcnycqzTBXDfVoLt+rZYj7/7Gqf6DFw2CJ7FYL9kX7i+rOPW1tYwzc14leMG3kyW+0dooKo65/hWU7atccoxdR+smzYGDKTlhUb00w4PD3VycjL8nZ6e6vT0VMfHx4Ov6OCVE4W0mubJmAEHKTu7UogMDrb870q4PEgU+KSktmBc/lrl7ZSgRjDgnHbGKzJukSBDKl4FpbLdLj8pPZ9JP5qAQHB1PTnTwj4gI2GeOfY2GP7NfVz53ZWy+T5/zzMZ3b58ZwXfYuU2pfvK/iNwVtOiFTBW7lYar3XSxoBB0lJO8zlO4B12PJfQFC23ykpjQKFPx6gxmQEHqKJoOWjJYCpabCaSAUhbLIIA9+g7D5dvt6ayzM6P7adCtdYu0Oq5X6aYAceqYl99vzj+i7SVeaa1ZVuT9rveBDBb9pSZCnBZd9YngWAqpjLlLuU9XozksyZazICgx3ra1ZDGO1lzfNOlptLz/3S1VqX3DAZd1/0hPbxCzekPSPqvJT2V9K2SPvP4+/f0ff8za+S39Le9/XAIpffc+w0ynLZxh3FKLhWMC0D4IhKXm9H2tOjS8t4CPt9KKaSMDdhS0QKSGVT+c+5TaJXXAoqsa8tFqNwI98uqNrM//TzrbFCTVLKQlq/vPsu6tNwIqT4xm/nmuFexnsrNy3q5HTxjgpvOKKce9wRoujwpe9nnLRcumUBr3FvpPYNB3/e/IukTj4VtSfqUpJ+U9E2SfrDv++9/kfxSiIyiXBZ8f3+v8/Pz0VFU/stNPvTt/EfrQ8Bg9N+WJxWqUj7SxqJ/Rs/lfRQ019P3kyqTXuc+gJZyki5nuS0rWFnAygpRaNPNqsAs+1taDs6R2leK7pQWnkDQaq9TzmqktWSd6Q7xGT+XTMX5J9NhG5J10XXNPsy2V+OVz1SuAtMUs3F6WW7CV0v6tb7vf2NdFMqUUXhaKdMvR2dns4c96lbgvb29wT/jS0P8SR9WWrY+raCO762EktH7KUEkNeX9GXBMt4X9YEaRfeP8+enrmdw2xkryemWdqHBUAMYhEmSp3OzHKmXsgRa3pehsSzKDqu1p3fmd45BA1sozGUSVMiDtNnEWgH3KumRZTATNVqzKfdQyRK30ssDg6yX9GL5/R9d13yjpFyR9Z1+8gbnDG5Wk5RNk7+4ezoT3gPv75eXlECNwzMDLQK+vr4fOzg0aleWj4nHA0vqSKfg5aRz8y4FFO0fC1ooOkx34f7s43N7tvmE9WtZ+ylpUApLK2GJRXP+eCmtXprJylYV0MiPKOlb9T2VifgR7lp11qIJvNApcNFUpVBVgJADmJrD7+/EZFLlwzeNvFzgBPoPfaTwqNsG6+5lV6WW8kn1X0n8s6bsff/ohSd8rqX/8/AFJ35zP9Xij0mw26+mrc/mvz32ze+Ajrba2tvT8+XPt7+8P9xsMsuHu/FzjTdaQvloKWXYyGUFaGlpQDpqfS0vDctIy+n8yBN+fc+3F2IzoO8tzXfP+KepsQfdfxSCcf9YrXY6k6wlorT5OlykV038ZeExFqYyC+9SzA5VV5v+UNY/LbLZ4Z6NBwWDAsw+S0aTF92drcx2fzzhHys2U+8D0MpjBvy/pF/u+//RjBT7tC13X/Yikn14nk6pz3EH39/e6uLgYGrmzszM6194Mwp3NdxxwsChABIKkcRn9T+qbg5nt4OC1fFEqEctg/twUVb13sQKXSsEpXFVAjIm0k4LkumWfsQ05w1CVkayIbkkyGVJ09hmXC9PKpiI4X/YFy2dcxisX+74f4lNeh1BN7WZd84Rkjr9ZbfZ/Kq3zNcjkgjnKXf4R9JiXx2eVWyO9HDD4BsFF6B7fsfj49Wsl/dI6mZARtBC467ql/QPz+Xx4Jz0tgVE1LWJFEV2OBY0zFQlMWWdaLLo6fT/enednc10BZ0NoISyMPg6dU1VUUrIQUnXOUad/mbMFqZgZGKQQ56KsHDN/VkrKfqfrxP5LhlhZOpadiW2Z8u3pGrDtrgsZpKQlAGZinbiT1slrD8xcWc9kVQmWbA/rzmlRrltgv9CNq4A/08t41+IflfRt+Pm/67ruE3pwE349rjVTdgqTG8zlyCcnJ6NXVVtJ7GejjqO8UqlToAkElTBaETiFSUpbCUsObH46b5ZpasmdcNXBLmlJLRRkQbnslco9RR8Z7EyArtyplhWuwMD5WxktuOyLSogJCAlSLJugluWzH3I3YtaNxoL7XrLMdDu4pd7vQySQsl84FnmNU5IV0+GhPpZFuoF2UyrgzPR+X692Lumt+O2Pv5e8cuA5KF6K/MYbb+iNN97QycnJcMacZxB4JnwqQvrBFErec3+/OMGomgN3Pf1sUk5/5oBVittKCQSeRcnpuFbfGRDpS/v3rNtUHbLurFv66+7XltX0Z6WMlb9LkKkCrswjZ2RcFyp2gqzLYh0MLs4nZwPMGn0fD4nJxCCrpIHF+nxDMomUIfcV655yyGfYjgQDxkA+cGbwQSVSOC9F9uakt956SwcHB5I0esOQn3NyTMEWJyO0RGAPcroIrVQpSyobgSGXvGY+/J71oPC4/ql0OdAUNlrt9MGzz6pgFIGAfwQB58PZEObJ6DyvTwEjgSdXjLrcBFkrdmv5ecVQUh6siAQqGwluhOL1KtDKuuQJRC0GzHbwHsa5qrHLmBBnm9jGVWkjwKDyJ3lwhN/4c3R0NLgAHiD6R2YRjgx792LfL6YanX/6odWqxpawJvjQEhEU6DZUyJwK64FkXQgCjHS7zb42FdxKi1gxF/9P2s3YhNudfZPPO2UAkYCQFNz1atXdf1nPnIpk3lxARkXK8rLezpOzJZa1dNGYTNnpctCnp19PpU+QymsViGUbuAM3ma4NUdVXmTYCDJjced5O7PP99/f3B2R2jMAHWfDlHzlVyOWvuWlG0gAaOXsgLU8lMa1r3WhFq2sukzGB6qhsDmgCpwWhsva0Lv5uYKgCbO4fBqiSWSTFZv6VxU5BTv88+9PPsKyW8rZSghSZS7XcmnUkW0gLT6X1JxkVWYDvz81J6U5SFtPlzLplYlzBdXG9LVtkSlNpo8DAgRee5HtwcDD8uXPpt+ZZBKR2GTfIQFHeU/mnSY95zf8n2vu6807BpxBwDtog5/Y5fyodwaWKyFdCk+4C70mgspCaGRBEEgiqoJYtdvYLhTF9+vR72ccVbXYeLVbCccsAo5+hL16BHeMWfp6b3XIsXadcxEamkpu42K+Mj5AhtEAhwdYyzvZw7cM6ALoxYGCaxXcVGAx8rJk0Xi9g8PA5iD7e2h2TvipTDjgFvSVYU/SavrOfcapiGv6dzMBAwJdvUom4VdYp6baVMQNolQWv6HOyEfYV252sgP2ebWWALuMQzK+KtrsPKnZVsRoqR17LeqRSJxAwcGv59L18PuNRHBtJIzeB19KNq1gK657xnox3sA88LtztuiptDBike2AwqA7noCXhoaepnCl8TpUVSMEkylb+IS2e78+FTmmJM28KHqPnWU4GCwk8eZ0UM90cC10VhHR+LoPWk0qTR4f7umMdWRfnR9aS9cqgG2MVXMBTjQHblkDDsc52tBgIxyEXEvk+5lVZdcpTLt82sOViN1P81lZ892eWzXsykEgwWCeIuDFgkEDAI6nTArGj6RZIY0WXlhUmaW/eW/nRvpb3TPm+FAxaEAt/pvQf3SdEdVK+KWvA1HINGEQluLGe+WzW2+BCF4uKnH2QQMayq3uz3IxHsD2VZSUASioBl3VItkRQpbFhEJcWmywz689AovuBU8AECo5bC7TYTraBC93oIn+oYgaOFfC02RYQSMvTV1ym21IUJwoxgSDzTfeAyEvfkKyAz6VQWAgscBUI5fQVFSV97alEoUw2xTomG8o4ANvP/mAb0uLTetF39fd0IVwHKi2VmwG4jBWwzDyJuPL/kwUSAP3p8WWfeQOTx5CuX9YpZY/xFbsb/ss4gfOu3BDmWckn+5d6s86CI2lDwGA2mw3vQMgXgqZF9yeDie6ABAoPbBWs4SpDJyp0KnLSMAomB4uKX1mItNxkORxUChitB6l0lss+YmCSEWVSyCyrldi/VVwg+4DCWoFuts3WNym673fZfNaJND1pdl5rsUDKC2k8XQKPDfM3QBOQWgwyAbBiWfk9jQj7l6wlWV+ODdnRVNoYMHCgkECQYECUIypWtDsHolKuSiEoAJUVqRSnpZiZR1Jo3+vyeUZApgqEksW4Ln3/8Ir73d1dXV9fj9ytyqq6XhlncLlZjvuNfnRS+GRHnM5Mt4rjYJbH9SMcz8rq+Y8vLKnAgHXIlOOXwJ3LxNlvqZBkjtV6igRtsoMEH/ZLBYppRFJm+fuqtDFgwLMNk4JLi47jhhH6WjnAScXTx5uijDkQUj11ReVmZ08pQVI85p9AkGU4n62txRt8W4PedZ2urq7KE5IrEGFZea0FHulmpAvAfPJaZd39P19o67K4XiSViVH/VKpkZlUf+Lt/swLndKgZlu/NoLVdAMuk31uRB9LyJK5s6yp3uMWAKxbzomljwMDrCDx9Q+XmoPNddrnM088RAIzyjKJnp1eKPjUgrHcCiZ93nSsK6e9OFqwK0KqUgpjJCrW1tTUAgv3uitInra/Ky9gBr02l7MOKVfFeUnpSXAp81pd7JabKr1gdGZc0Xl6dY0ewcHmWKQKI+zpXInJVpIOQrj/7uJK77HuCPkErZfFF0kaAgdHTQCDVUXGDAc9A9N5zCjrBwAPGKG4LOZNqtwbD/6fVz2BnLmKq8ssgYd5ToX22MfvL/3Ol5u7urm5ubiRpZN2qOrTiKNlvU34v6XCVsj8J0tn/rBNdsgSPCtirWEbWNV3IVcCeLpsNjs8/oIvg/DNAS2OUC4aYCErZf+yXFsC/CFPYCDCQlpmA0ZYUyod9+NVVjsYaEFJwu+7htWm5tDcTrQ87z3lU1JPXpeUltlNxCQoV862EkD5vWoX8389RUHxalGMxBICMcvf9YtNWS4CqzUjZL9lGWuXquSmlTtcgFSatqVOLAVGBqnolAFWuCMcyWSPjBawj3QUG/jj+vrcaz2xHVU+6ailb66SNAIO+74ddYdJYkWj5ck8C/UQOhBPpXNKvik4xr8rKVS6G83P5CWp5D1PGCHKlYAVCLaEhYNCa5d4HKz+nbWlps97srxwzMqSp1Z4ppPl81dbKDapA079X1DgZG+WEwJUAxYAkd4/aGBGwnFflulGWklVkmytAq5gY/9x3OT3pcqb6rEobAQbz+Xw4AKLrFivfiNyMGfAgE+ZBV6CisK1O9/P+5HUqf2tRieuX7IDXnZKas36sx1SqrAmj3RmQSgtHeuvnk47yt6r+aTlTQfLZyhd3Yl1WMbgElezfzDfdNzIV3lMxQAOodyxyAxndCtbNiX3v57kL1c+2DFXmaUPHN2T1fT/MbHANBJkH+25V2hgwODs7G+1MpIWwhfdAVLv63FguTskZiRYdqxA6qbjdlMpKS+O1/hROCmQlgBmoyog3/5xH+pstMHDdyRCS4dBfTaFkHaR6HQbzd9uosEnXM2+CQN5bgRPHq6ojyzAD4n0GbSYyhWRWPH+S29ur2SFSdTNXg4gPqUmXj+3Mg2Z5rkbXdcNJ4N6mf39/P5z/ybFlvhyLVYZmLTDouu6vSPoPJf1O3/f/yuNvb+rhjUof18PxZl/X9/3b3UPv/CVJ/4GkC0l/su/7X5zKfz6f6/z8XPP5fHR2HGkWLYu0jIQOEkr1TjEqcXbWFGuYAoL04z1lxHrSf0ylTvrq/5N+uj2sH/3XSsmdXO9qLttAUG2ASrqZ070EIwYck36ndc77fG/60SkD1bQry6nAgEwtqXwCsYGimnFgn+Ux6mmFK5fDh/b6RC4ffc+g4mw209HRkQ4ODjSbPbwX5PLycmDBs9ls2NJvo+lrBiv2F/uw6p8qrcsM/qqk/0HSj+K375L0D/q+/76u677r8fuf0cNpyV/2+PeVejg6/StXFXB5eTkSoLSWVnbPILgj0vdi4ykMRtqqU/I5l5kUrgKDRNtUBrozyVhSAf0cFYjInptlcrVdggotd95DQMhU9VFFX0lh3faMP1CROBYEO+ZfMYscx1WUN5kY70+26OtsnwPTlrWUs8qlzDLIoBjr4oIqM5ft7W0dHBzorbfe0pMnTzSbzfT222/rnXfeWQIE7+FxP3GdTdU/7NdVqxDXAoO+7/+Xrus+Hj9/jaSvevz/r0n6h3oAg6+R9KP9Q41+ruu6p934xOQq/5FiuaFUFC7mkBYvpuDgpiJReJM+t+i473f5lWJUzMApI9W+P1frJeVmPSohTuvqPDNwlwBDxU/hrYCosphOlSKyTm5jZd0NjMkQMvF6FTBzm9hnLCd/y/q0WITL4zJ3s1T3vU/OshVurfXgbxmvySlKA86TJ0/0sY99TB/5yEfUdYtZMJbttjNPAjIVfpUbVaX3EzP4KBT8tyV99PH/L5b0z3Hfbz7+NgKDrnijEjcrkcbu7e0N1IgKenV1NViOfKEIlaASbtJxKgFdihRuCngV+MlEIav+8t4EsUpo03K2yqwEMlmBFZR19P8V42GeVX1bfcX+JkPIctIVpLtHgMwNaVWZU6CTQOUy/QwD02QmLcZWtTW/V3s03LbDw0N95CMf0Rd8wRfoC7/wC4d+OT8/aJ5xVgAAIABJREFUH6bRnY8Bqeu60RmZFatOA7cqvZQAYt/3fdd1L7QGso83Kvng0+PjY+3v7w/+mX0lvifBLMFnAlb7GEhNkx65s9O/r5hCWqRMLcVlnXJPewpqy7+r6tDya6v2Oe/KvalYiGmnBbUSairzun3E/FPhXV/fw7ZXYMBxS+rOutGfZx9nv7CsBOl0g5jYh3Y/03V0GckKCCpmBgcHBzo+Ptbp6am6rtPp6emwLoRBdL5AJo/0T0aZTHdVej9g8GnT/67rvkjS7zz+/ilJX4L7Pvb4WzOZFh0cHOj09FT7+/sjJeZJMR5kC4JX1XEgySqk+jSgqg68XlkagxEHmSktP0GAYGAhSuFpKTgXqpCm0iJO0X/GB1welZ3P+1oyDypzi7q3GIE/04qSHVRMyZ8t2s92sf5UUspE1SesB9de+DefsJWBYPaB2QrdUbMXH3fP8rMPHFfwjEPXjWc9yEIMBpyqrPoqWe4HDQY/JelPSPq+x8+/i9+/o+u6H9dD4PDdqXiB09bWlnZ3d7W/v6+jo6PRacZEe79rMYM77gS6BRV9pEASMOh7Vz4mBYB73ZkSCJIRpJIlXa1iFYyi08rwtwwaZr7JChwcYx3oGlUW0f3G8aCitWIoFY2uYjEVEDJ25Hxyl6Lzc99x7G1Nq37OJdeSRhuIEijdb/6jK5PuoPOaz+fDLIKPtKPsWfFvb291dnam3/u93xtA5+233x7ewJTvWmgxDfZlAsNLA4Ou635MD8HCj3Rd95uS/hs9gMBPdF33LZJ+Q9LXPd7+M3qYVvxVPUwtftOq/InqbqCVnVaRyLlOnh50CnBSqPSlczVXTk9SOf0MFTepLdlARnwr5aVly7/K6jNwlLSZDIrMgH3u/1NB3ecJCP5MICBd5b3pFiRDaLkVWTd/cllwshHGP7iak3nl/1OyxIVu+a5L9jvrQKru16pdXl4OrwD0vYx7zGYzPX/+XL/927+t8/Nz7ezs6NmzZ3rnnXeGt2ll3SlPKcsVo1snrTub8A2NS19d3NtL+tNrlY7kgb68vBwUiNtvLQS+l8BBmpl0n9al5fuh7qMBMy2rdvtV21czTpC71qgQlTBPDXimZCup/EwEiCk6n25PWkj+lmwk1zHQFeLYUHD5PK9nu8mI+G4LPjfFBKt+y/+z3wk8nBrkQqBkDFRELwg6OzvT+fn5MD3I+uYBO3d3d3r+/Lm6rtPl5aUuLi4GdlCBbPZRjhWBdB12sBErEKUH4TAQ2KL6dev2oaqoeAJCCpS0HDxyYodSKLPzyRIICHyeIMDPVrQ9aTUVO2ke+6iqP2muU1rmVFamCozSlaHVJYtIIWwJXPaBP+liZN+lktNSc9EZgSdXWjIlQHvcHINhyj0dLpPlkvJn0PD29lYXFxe6uLgYrLv7jTJh11fSsD/H8u5nKPMtGaEsJVtKttBKGwEG9NuNwF61JWlYesl3IEpjYbLQc5AypbBXvqvzqAKFCQot5U82kBa2Usx0CaZAhPXMQKBTFZQkiFK4EvjS8rfo9br0k4n+f7ourG+6R7S4DJ71/eJt17TmCQgZx8mViO4Luhf0z5mn88q+ZR2vr691fn6u8/PzkYuQhsr3O6bAuEMaPV5P2WoxzRcZo40AA2k56OEIK5MbmzMHFPwUcOYt1XPAvI+UliAgaYjw+nsGC6nAL2o5nTjQGQnns5WF4DPug+zTimFVcYEEhFY/JoNpWaAKVCowqNhJlkHB95izDAKaxzCNRtbL93Onq4GgFRz1vQngdnefP3++tFaAbctxMbB5/AhUrKM/U8amZHsdUNgYMKBr4AVG9MUo2KTU9r2oxGkRKsXK4As7ltaoAgt+5v9OGcBKgW8BQ8UMGBMgABmc0rKmwFPoCAhZRsstaQHGVEphz5QKmwDeYiT5nbLhz8qXNqOjDBHAK7eMzLBy6VgPbpC7vb0dgoY81qyqU8ozv5PqJzC/qKKvkzYGDLquG8Dg8PBweDsSN2GY4tFP7vt+mG50SsElpctAY0X3aCVI/3KwqiAOUzILJgJCq/wUkLzuwGo+z7bzd9JoP5dgmPc7uT/y/tbzFWC0lL3lAxOM0j2ismReHuN8HXnLraP74HxpgXP5rwHCdeOMlWX24uJCl5eXo1O5UsHZHvdZgnLFSiqWkb+nvK/DSjcGDCQNb13269SSGbSoLyO89PdTcZmozHzOeVYCVuXj1Ors/L1SUP5e+foMdvE5AkLmJy0fWZY+sNlFWqJVaYoZvAhroEtH4Jvyf6nAeY/7ioFeHoDjPqlmfbiwLRWdc/oEVV+zrJnNcubBdXacIkEl+4Sp5Z5M9an7gGcetAKqmTYGDKh4XoCUvpIbXCme12x7YDO/RNz09Z1Io50YdKLAciajRYXzdz+f1/g9A0UMWLViFNmPdHGoYGQGzoP+NFlJVW+ntEbJcPJ5+rgVoHKcLMCtKTVG3DkOpP9Uclp69qf/527YtMqc/qt2iZphGQju7u6GGQSDtevhPDlWlFP+5jFZFwzYLr+ZzGB/c3MzbJ2eShsBBkQvdzgVgHTP93hgfY/jBnmABBXIiR1HX4+obyXh87zXCsMOznl6tk+q6Rz9XQoKfXoviSV7YR/5j8uUpQW1dxkJCASVym0hfc0/l5PPVO6Cx4p5MX+OieudVpexGi9Go4KS4XiNihWdSpUg63ucp/OyoueaBtfPMmtZ2draGs2GXV9fq+s6HRwcDOPHtTLsP+eZAF4Zk0zZZr+E2KeN39zc6Pnz503ZZNoIMJAW0ytevpkvX+VKsBx4AwIpXUU1aU1JpdIiVVQ3g3RWsgzgJYonEBhEWoNMQSNroeVmmzIglgHXll/t8xAJjC3qmjETti2ZAYGHrgfBogIMgizzZiCW/ZHsKO9hOXzOxoX38Ch5Gp0WvSYQcDxns4dDSXgsn11e51P1H8F0ynBkovtoF/vw8FAnJyc6OTnR9va2Li4udHt7O0zTT6WNAgOf7vL8+fPhXYv5zsUEAr6glQPJAFD6/haIihlkAIvP8yQjafWS1sqdsaLwe+ZnhSZVzqkkCpdpruvHMivQoTUzgEy5ChUo0G1rJQbuEpyqfpsCSD+brprHjhaXIMc8KhcrmaMZgY2OLarvo1wlc5zP5wMzMMiYrpu10dXlmDNNXeMYmA1YB8gKjo6OBlkwS1qVNgIMLPheqOFzDbga0SlBwP7R9vb2aACrQE0FClJttTI+4cGvmITvT8tWtbNS/pYVoNVJmupkMGCMRVp2Wfh7ClS1Mi/bktaW5WfftVLVPj9L8KH/n8ljUFF99idBW6r3jbANHkvvHrSPXQE3x4MGhQvmuq4bgIDPrDIg2e6KAdpVcrDd8m92wE1+q8aEaSPAQFqcXHR5eTk6v0DScMwTLY3BYG9vb3hrkNdxM2bg7wzKpXKxo6vOJxgkdWUeVpgpXy+BiQLGe5KlOGW9SY8NCOzTysrYiqarwDawbmxPta6Bn+skAhUZRhWkpPtSuR4Z86iS+zKBIGn6/f39MCXo48ayDykL0uJQHrteXltA9pmHqbJ9KXP8ZNyLbfV2/8PDQx0dHQ1uDhmsl/ZfXl6OjlqbShsDBtIibnBxcTF6P6CtgbSwlgaDfJU7T7O9vb0d4g0OJHFTCH1lxx0qFuGUYJBWkYNr4Wr57ZX7UPmQrgPjHwyq+bqP6KKv7URlZgzi7u5uCHTRZagUnCCXDKpFQasYAxOVKtkB+5V9RndmFStJxauClOxX7zI8Ozsbth0nENPY0L2Yz+cDo5A0AAT3U2R5TNmn+RvdAscGnj59quPjY+3s7Az1NxhdXV1pNpsN+yPyeLwqbQQYUNAsoJeXlyPE8/HQFBaCgRkCqZrdCA+GtHBJnBdX8VFQCAYUeAtHKlcOJgNpldXnd37mbEFF25k4m8KDPOkqVc+5r7uuG3blpbvARKvI9toC5jPpVjBmUAUKq2cyVlHR9ClfOAGVcScGo91PNkSeGszDQ3LNAevG04ztpzNwSBBhn7Ke7KNsO91jrsfx2R/cRMVTmLkSclXaCDCQxn4bAYHbh1M5+r7X7u6ubm9vh84hIOzu7g4IKY2BwNbdQODor++jwpI1kF0wjpADyDzIEtjWKoZQUUZaR/YXE2cFKsrte1hWNSuTC3IoqEmRfa8DlwzoMbXouxMBpgLKylK6LqmU2ad83nW3fPG0Yv92fX29xApoCJLNSIvtytxybPbKfQnZl1k/l1GxnJYrlfWyi3B+fj4AwjpAIG0QGEiLAXYQh4t9jLSSRlFSd8DBwYFubm60v78/zPdeX19re3t7oMJ+lv6X87R1zWh8FWzkYKbVraie88n7KwpNy5D3VN+r5DZSgKpn+35xyAbBJAN0VbvpihDYcj3HVPwkE90Q9gnrx3ynFD//p4vHKWy+LckMKXfHuh+pdOwj5mn3M+vBeFUrvkE3sArYsl8MWmYKNzc3Ojs7G5hNxjzWkZuNAYN0FW5ubkYCajCwf2TB81yun/OzDpyY/to6k9JW7zNIAWzVNVF6VWcnRa6EOClz+s8su2UleA+BLYWP3z0l5nKrmEAFcsk0pMVshZWHAp79RJaV8QACSAIMZeVFkt0DuwN8lwHPSag2x7HOrpPrYjAwsDrC73YlqGT/Z7/4Osea7MbWX1pMdxscDAY+d7E1/lVaCQZd/Tal/17SfyTpRtKvSfqmvu/f6bru45J+WdKvPD7+c33ff/vKWmg5cu2IbAYL7TZYqT3TQGWiL2hQYd4JBLlph4lWKKkp6006X1nRFshku8lg2K58LsHCQOPotem7+7A15UnXh4tT2Fbu/3fdpvqL7ac1Jagw/3QNaJU5W+L+Yr4V5U5XjcuJOXVoX5r+NtcWtCwqjZTZFU8k8py/r+UsAuWiAocWM3RbrB93d3e6urpaWnZsRvCiaR1m8Fe1/Daln5X03X3f33Vd999K+m49vEBFkn6t7/tPvHBNNFYMCycDJ7RGfT8+1ntvb2/ELO7v74ddYxZwxwW4boHxA9+TaOzUErxMBI8WIlcgksHIzNsKUQU1aTnsPnHunfdlG0j5fR+nZD2PnS5WxW74O4EgLTnBIutSTd0ZTDJek1FyMhsCgRmAP+1Pc69GBeC00BWQ8lg0vujk6upqWDuTS+SzHMpDBRrsF/bb9fX1aEbDOlMBQVX/TCvBoC/eptT3/d/H15+T9J+syudFkimmBzEV4/7+Xvv7+6OFSZ5nPzo6GgkzT0yqLLQ7kK4HfbycwaDFynq1KFmLyvszaXnLhfD/jGMkZaal59r9TJULxJWPZAvuZy8NZ/vZlorNVNHzZFmsT4KB8/BfupPJ1nJ6kjGojAkQqFrjw//ZJgKNXVL3Qd/3ury8HBSWvnurPJbVYm9kneyPnDJdR/6q9DJiBt+shxewOn1p13X/RNIzSX+27/t/tE4miZCm51ZoJwvB0dGRbm9vRwuUzBB8j4+gNkJXYODOte+Vgs0NLBlMa7ECpxSo6lrew/Zz4Gl1KyCi6+P2J5Bl2dmetOTceLO3t6e7u7vh8BlJo11+rZmH7JOpfnLbElQJcLT4BC4yiZxG9B+nE3NxWo7F1HcbEcumI/e2yMlEyDoqRW0xTPZVuhRVfukiVX09ld4XGHRd919JupP01x9/+i1Jv7/v+9/tuu4PS/o7Xdd9ed/3z4pnR69Xm2oUz423tbOf5tmD/f39QSD9hpqTk5NBiIjeLoMCYx9bWggUlctCSoHlACUiV8pe9EH5f1LwdB+yv9geLtXmuyhpaSsw8e++j/v1DawG2r7vh/8NmOmnr7KCBNQK4BJwWy5Gxg78O4N7+Up1xge4Lbnq13TFCHJ3d3c6Pz8fTSlWfddaeZhjx3ZWfdGauaoSmVbVriq9ZzDouu5P6iGw+NX9Y0l9319Lun78/x93Xfdrkv6gpF/I5/t4vRornJ3lgXOElH4aNyfZt5U0AAL9Qlu7yqqmokuLVYqo85KlpfWjcLsdUynZReVy0OK1ruWz1TQhBY4K6HwqempFya28HgOv+uz7foie53Rjy+rytwyGpqvh/Ei1ExzS5fD/ZAGMD5AZpJKxzlkO2+ej0L3Cj2W636qZCQI6gTD7acrKZ3+yvzOvD5QZdF33xyT9l5L+7b7vL/D775P0e33f33dd9wf08Fr2T66bL4U8I+5W5AxucUAdPDOt395+eNW1B5T+Iq0mpysdeOu6bon6+tP5VdbKA5hr4PnpNlUDWgkgy0oL0kpdt1gbT9bjaxTAZA5ZbgbAyBTsppldVWyj1VbXJYXYv/PeiglkflRG92MuMPJiogoMWlPJzj9nJa6urgbGycBzAuEqZUygSMa0ihUQDAkwef+qeqwztVi9Tem7Je1J+tnHSnkK8d+S9Oe7rruVNJf07X3f/96qMrKBblwlQKn8jFSbHXhdeNc9vMFZevBpvdDk6upqEGoKq10DLnayheVgsWPTUhkIfK1F8RgE9L35WYFHdb1iJAYBHh/XCiZWwJL0PP11B+b29vaGPSDJQth3U4pGZuay0xqmAlf9wn52PR1A5uwB4w0tFuB6uQ8sL5y+819lFHL8WinHcN3nKDeVfGVdVrFUab3ZhOptSn+5ce/fkvS3Vpa6Rqo6hdYqO9xWwAEubx+1kHru9+rqanjDTboNFEorVLXunvVxua6T88gttIwGW/Ayz2xfJZwVQPq+BC3GD6gALiPXH7gPMr7AmAmf5yIvl002QlCdAoQM+lWuWBoCJy6sYr9x3YTBwEDg9k65MM7P9xpYWjsanU9LMXPM2OcJYgn4qfAtluVrLVldlTZmBWJL8av7qIT+zQJAoPDmJp95MJ/Ph5NnuIHDgcVcTOKUU4ykoB4Y+rO+pxqYKZR2vatpKJeZlJvIb0Gnm8Ml21QWKvZUn1dAmHP9drkkjUCAS8nJGBJs+JdrGNJFYHsr6ptAwDcaZXuTFVaK6fYa9LiE2XnkuFbuQY5ZNY6sX9bD7a+eS9aarI73TqWNA4OWoFf3Z0Ap546pELbw3uHot92cnZ0NgsIlnHRTcrFP/m8w8eKl9GkzdpDxCLY1Fc11ScHN55yvy6eL48NPUqlatDi/Vy6KP9P1SIZAd6uaMfAznMrNACIBIdvKexjf4FJjBppZR/9ZNlrBQgOeXQ27TJ7K5lRjFc/I9vJ3GhP/RsbAACRTAg0BjczzRdyWjQCDVX7NVCOyw9Lq8Fg0/llRJI0OgOCCG+bjzs6FNS6j8mN9LQelij8kPXTKYKV3B2bfuVx/ss55KKikYaGVAczMovJbU5hb4CxpYEu0YrmQy+1wXnQrWB/WoRLoikEwwMeAMZWN7Uo3sFq/4FWsBgMbFR922vf9ABapvK14TFpxtneKYWSf071jv+VYVasSM20EGEjtRSi8Vgm9fzd6syMsYE5WDM8y0DKcnZ0NwuOlpKSlpGy06rS+LNd7KVyHKV+yUj6yiYx2p5K08qQyeupPGr9Zyd9bKYN7/J355XUKn/eZSONdo/yNpwKlUCdLaNWXgGdaT5boerv8jFW43gYULlbiUeOWn+PjY81ms4F5kK1UQMaUxiCvTQFByw1JYMwyVgHCxoABUwUM2bmVlXKDuWIxp5AcM9ja2hpW0jmPs7OzEagkIEganaRLgWVMwbTc9+Ygsb4prDkTYStb9dEqYTBzycBj0u4MhDFPBjqpUDn1m23ys5XfmszBwFRNTbq8qowpGk1gyHn8nPGwi2AWwJWKBhXLhJdle9UrgdV1oPviuuSYZr9xnFsM2Xnw0/cnWNpVTEY2lTYSDKRphXdix/I616wz4n1zc6Ojo6NhxmE2Wz4uzH/0MaVFYIpbqauttemaVLEB5ulyKgvsQSW9rwAlaWeWRwZERWI+lVL5WbsPCSzJDjhWFVtg+5PtZFttxRhPYB5053KKMMGD8QDGNHjqto0A3QvKjt2Xyj3gkuNqjDJVMrsO28tn2ZeMVTHY6PZPAYzTxoKBU6sRiapMRHkCgf+Oj491f38/LGH2mQgULAOCn5ce/GGv0ffcOn3Oajptyj8kbXOdM6pNxaUlTOWbAgInB+iqfqQlT9fIz/ozaT7b4LwYyGRdEkiYBym668KpyqS83HpMq1ytD2H96S56XHnSkZWbf5KGgKENCGcXPC6uX/VXyWper8aNY+X6Uz7Y9ykr7tcPJRis8nvWYQb8NOLnEmYLnt/4zENSqAh0PQgyPN+Oa/PTetqiJDXNtmaknYNN377rFm+PavmhTH6GSsxkpWHEnQE3ClsCcEbgOW7O28+y3Kqe7isquKTRJh9S+mx7Mkn2t+uQQOBYTt8vTg7iNKT7wvlw1ybPRUhWkMyLspz9VIFBy1XiuhkbK+7bocz6kwxrVdo4MJCWV5UlIPA+aXmhBhMVip1HgfJ6BO567Pt+WMaba83JIJLuVvPV9oer7b9pNXyN9aYl4FLbKiBEoEmXhBSZIMCNTWkR3daW387/W2C9yl+tQGWK+fH+nP6korOP0z3wPTzo5PLycjQjIC3clN3d3YFJkkVk0DoV0+XnmJB9VW5Btt+xioODg2HNjOvsBXQtljEVIGbaKDBISl1RrCqOIC03mkLCAE92jAdjb29v6HQHFmez2ZK1ZIBRWkTNTcGtdFTevu8HSsopNboXFFgGtOyzp+WjklWWh/1C+mp2wGi6lSNfY2e2wJdy5IwGBZp9UbkE7PPqO+vDACNnIJjYl/6eL97JenFBlK2rDxD1LJLL4loJb4Kbz+dLh6ayrenWObH/VwFBFfPwWR1PnjzRwcGB7u7udHZ2NrQxY1wpA+sAwsaAQeXzOmWArQUI0lhIUyityEyk8F7CTCtD2kpf0t8ZmHIMgYyA/i8pPmksFZYLYLhUOqPvlV+fFiGvp5vCPvAcv6fSfFYEfWbmn8t6qdCt2MAUtadrQYbEOicj4NoBBm4JqBVTISvwceI+74IvbM39KX2/WE+QcYJ0Y1ttbV3POqbR2Nvb09HRkU5OTnR0dDSMCV06stgsu1Ue08aBQSaiLAGjFSHNzqZQOh+enpRMxMJgYKCFJz20kvuTlsTl+RnnkVY5y8+Al8GIv7tNbguXTlfKmv1b+f2ur4HMDIGsiG6LracFsgUE1bgwBtGyVgkm6Wqkm8ff7ZJx8ZDdnFxbMJ/Ph4NJHKz0uPM+97eBMplaBQLpUuX4sK35HAHb4+Kpai+tn81m2t/fH94vwrF6r2kjwCCV28qTEdIp9lClfI70OxmCB/vw8HC0YpFl3d/fa29vbwAEvo/Px7J7EBmcovL4Pit/WsRsG1flua8qRUqBq/zwdCvcz1Q0zojwHs7O2IWQlgOLnD3w9WpcMthVtYX/U7nZBjIz9zHZmAGrWnLscfNbkDglzGQlc0zFrNPXWsBG16BqWyXHqQsVM2LA0vekUWP+67ACaUPAQFo+A6+iNpVvnPSsiiXwXlJz+4u+z5bc6xC4gpA01Hv4vYPNoOB3PUiLI8EoKAYAB3wMCvnHeqfFWDXQqWQt4chpzaTlmSyABgIKZwVMVt606KxbZeFb41/52a365qE3XdeN3t2ZTM7jQqubMsFZKNJ4titTSwkpw6nABAICrRmtD/k1q3GcI/vE/6+qC9PGgAGVeh0fh9Tb352PUwZ3pOUlvQYEI+719bUODw91dXU1HLrqRUYMQHkVmoXJL9CwC0KL68E3I7FSm9Km25DK4jyqNqYvbgVN8Mg8qj5rAVMG9QwE3hpN6u8+9R6KBEPOeTPwSHeMeTo/+sN0tzwebDuPQbcrNZ/PdXBwIGkRaPTLS8/OzpamDqUFG2FQlf1RjUkVF5hyGVrGi/Jt2bSMue/ITjN+wfFrBSozbQwYTKUK6aQ6MJIdn4hZdZSpvtcTXF9fD69r89kIfK8jWQP9fSO2B4cKTuVweZw7dj2dJ+n5OuDo9q7Tj6ssFvuHMRBJozdQGSAIXvf390Osg/XnfDepvoWbC7ys+C6Tc/60oPTxreAGEQf5XFcDRcYD/AJTry5lm+jmMVbiOpGpsN9ayrcuXU+QtBxl0JAxK6YM2qaRaaWNA4PKp6oodDKIZADV9fTF/Gmh9AzB9fX18J5GU0fO8zK24GnIra2t0QYnDpBjAwlCTru7u8PAc1EJLSuDdJlafnnlVjG1fq9cDFp3TpMmfbegbm9vjxSG1zlL4vLMGtz/7gtba4Ooo/i2mna3eGrz7e2tnj9/PuoHW3ffb8vuo/V5QIu/Z1/6/wRA90sqXKt/mS/bwd+YN8EzZaJiJsy/Yiyt9F7fqPTnJH2rpM883vY9fd//zOO175b0LZLuJf3nfd//vVVlSNOnC1dgMBWAqhKfyYUq7ihaAFspLlJx8NCW6/j4eAALI3j1Yo6kuNJ4gLnyzsJvocw9Axmwcx6VAhMMKnepmgXI/syAGxkMhTndE1vhtLDuDyuny6bVZj34diKDLfvO9d/d3dXh4eGwNuT58+ejNwvZBSSddpzg8PBwtEeFhiH98ZYhYn9PKWH2b7oNfJauk/s+mWKlM6xDGqGp9F7fqCRJP9j3/ffzh67r/mVJXy/pyyX9S5L+567r/mDf95PzHancae1zGrFFd6sZibyPAsw/zkuTgpnKe146aRmXM9tqehu0BYp0WBq/b4ArI60kXO9Ay8s+YXtaAsFIe4zT0F/8zjzz/wQY9jnrloFI9pUpOP393ITEWQCu3XB/0H0w1bfAO4bjzWjPni1O6L+/vx9W7O3v74/cNy9DZ8DR042MX6SyZX/yezVOmUfV39X3ylVssWY+T8PxUsCgL96oNJG+RtKP9w9Hpv9/Xdf9qqSvkPS/rXpwCgySTlrQkgJX96YypbWkQhAQCAoWQK5TpyBybprTkRZqU04fxGoanfUhQ8hdkdlWKloKWj6Tz7K/Ws9lmWkNc9wynxwfj0G1gYuA63y4apDTtdwx6HwzuJerDP28GQOPdzcgcNMS2UT1Rubs31TGFitoGbGplGzEbcxyK7lnWqdQt5DiAAAgAElEQVTM9xMz+I6u675RD+9E+M6+79+W9MV6eN2a028+/rZWSppVNciDW3XEVH6p6FOKUlFt32fXwco/n89Hr3rjexu8htygwJV8LNe/G3AMQNyt53qRVkvL8YKKKvp3tmsV08oxSFBtpSkLRLfBSmtlzF2Q3GVqQDAYk1lZea+vr3V5eam+7wc2xulgg/DFxcVwYrZZmIPDlAeOGcGA7az6Ng1SC0Sn+j7LmLrm/3MNSTUNvSq9VzD4IUnfK6l//PwBPbxmbe3UxRuVnBJJK6vlTq9Wp1X5WIirqa6K+lWUy8Jk35OLTiw0nrri1Juf97QQp4ZsuejHkpoSdKw0zr8CsqreRb8P1ysLNgUIU4n9UVmpBCKOn6SlKb35/GEenbMunoXJ6VhJAxjMZrNRrGA2mw2nWl1cXAzuApWFjI7jyaPV2XctBpb9abmbcilaip7XK3lusQN/Ui8+MGbQ9/2nUfEfkfTTj18/JelLcOvHHn+r8hjeqNR13VDTygpWnezvlVXJjpPabwSuOluqV+tZcXkAau6pn8/nw8IVrmRz/gwq5tShhdG/5dmNVbuyrygolWJX/VyMzdK9U8JUuVp8rmWZKLRWck8XXl1dDe5C9rdfOU9A8DXPSBh0uduQy5Ttvhm4eZ1AQCY0BY4GLd5b3bcKpNe5Pw0iy0rAdb6rGJ303t+o9EV93//W49evlfRLj///lKS/0XXdX9RDAPHLJP0fL5Dvki+ZVpCdxkGsACE7qJqWIVNgh+WKSP5vC0UamdNnXL3IuXke2Z3ug7QIspkheDel27pO32VbKnDwM0wpwOvQywzuVnVi/lSatJoZiORUH1c8ekaBr4p3H/P8g62trWFxmKceHbdx39/e3g5BXwd8uSNxCgTzGsGiYpotBsb+4jNT7gXLzPKoK1Ogn+m9vlHpq7qu+4Qe3IRfl/Rtj5X4p13X/YSkf6aHF7L+6X7FTEKUtQQEOdWU99oCV6v4soNI3eibczNLDqA7lgqcefDv7u5Oh4eHw8KkVGL7vrZOCUgUVE93dV030NhcruqUtJ+fzJd91xqDqUTrV7lZGXmv8s6xTHB2W7iFOGn8bPawUcf7+y0HdLm4SOzw8HB4jrtP7X540ZiBgK5GtncKBPgbn6Ec0zV80f7PumSfrgMgrfRS36j0eP9fkPQXXrQi7DBSt2QGXHDC5wgIUZ8RM5C0BAAuKzuSAs6yfY1rADJQ6D0Otl5UaD9rYCAd9TUuVLKLQMGitWf5VfzEbc9rFG5STl9jP1TjlcpRUdGcNqzYQo4Jp0QZ4HNfejaGYEBW5vpxatLjzNWROUtgYMj3LLDN/qyMRpVcZo69ge1FlDddN48Z+7JixOumjVmByMZxmm6dYJm/8xlp2XJKi1OHUvhICSsW4frlIiDnSRfCwnRzczMsaOGhG647fWH/pYXPbcTZZlqaSkhbzCrzYX78rRKsyipV+bcCuwm4BFIqqNcA8HARn0RF6m+az9WJXHYsacTCHIdwINh953xaU4mt9lX+uq9zA5QBnnsdKvmrxoP9XtUrgcC/retuSBsEBskG8viqlj9W+XUprE7Mg8DTypNTfLbgpPnM18rsQWYQygyBQsHNTy6bb3Ri+cyLvrT7LcGL/ZkWvOqrqe9uX5Xy94p5+D7GQdK9ontAN4EHjVjppcVBMnbFXBZnELh5yQpIJXRswNccXzBokClVLkDFcBLIueaBm6A8FTqbLVasStMvwGWe7PtVSv4izGMjwKBiBJ4fZhBJWp4VSKUlxWyxh75f7GSjvy6N6b+pPC2NFZIgVMUPDAzc+HR4eKj5vH53gwXJ1o2KxClN151A6T/2SVqU7IdW3IAzA1J9sm5lNfN/CmvGaLLfM17i/ibNtzKltWVQkQDpeAvjCNVblqz8XLuQYJAuWStVNN7uhwHNhiX72eUSVCogyL5t1WEVG6zSRoCBNJ4ZsNXkyUEeUO56k5ZpZt8vVpxZWeg2pA/LfDjw/CQNT3eBfrDvp8DbXdjf3x9NQ3Zdp4ODgxEguB+8aIYKyePa7T+7j6gEbkul6FNuFvtkKrbARKGthDDZAF0hsgCOI9thg+BAIA2FZYPj72e8AKxaC0LmYeXy9alZl5biVTSeQV4qN8HNMkVgpNxlnhXDIzhV9+QYrUobAQb21xIIOL/ujqIfKI0Xu1jQ/FtOe5E254BRcCuhd7LV8v+pEGQN9PczRuE8CQhsH+mqhVXSaH2C8+aBHOybDKhWAr3KbZhKKYCZLxkS+4HlVkE0Ko7fU5AHzZAR2NrP5/OlE43m88UORI+PgYVyYGCp+qAFguv0BV1IBz4pK+nKrepr1o9Ak3WywVonb6eNAANJS0BgAc9FLPSZW0ptQGhF16tpS04dWjhcJgW3QmrWjQosjYOLXKRkZZc0rJG3T+moNgNqXJXI/Pb29gbGZEWhwqQlyTY5UZha/dayWFVEm2BFy5tsrAqOOk++vSrdIufpGADPiEhGQJlhAHJ3d3d0AArHnHXM9rdcsJSHDFY6/ypOxTymXLwqtQCB47GOy7ARYGBFICPI+XnfZ0VNZPd1N5wUjIkgQb+NFNWuCBcAVcEd1ovTcgQEAhSFnz60p8eoBIyQcyEN/ycDIZNyO9KtcaqEp6XcbGPV5hYjSEZUKb20HAPiFCCnBFmHjMnwlOoqb8cQOJvT9/1ovYGT+5L957I9vq1AKe/nGHkXK9mtGUtr7wPb4DwzeJhl+f90Ez6UYJD+IJWbflVFrdKC+wANaTx4FgjfQ3rI+uTyX6aqg1OJLLC8P1kCZwoYVKQlcwyhWovAuISDVD4IxPcZUFtMoHJzKgvoNvIz78m2Ve4R+yPZg+uQQEAg5PhW7zlM5iFpyC+n+PhyVefprec+Jp6uoPNPFpP9yr6QxjEfn+HAsUvXserjvN5ScN+bjPBDBQZ0DTgTkFZQah/GUTEDzzLwGQtntUgplbrrutFKNdeJdWOqosREflpCKs719fUwXUYfOc/3I8uwQNuapeIRXKo6ZrtbqRLS1vjQalPRkh1RGZxPziaR7rP/0t2aYm6eeTD7ckRfGp+tyD0PtNpULqaqvJay0k2qZgTShWtR/WqMssz8nyC2Km0UGCQQ+FqiJH8zDfPz7gR2fm5qSSqaZVbK4rqwHhRuP0d/m1aNz7jefC3W3d3d4M8yRkIFoSL4k/Xx775GMHDbk3qTCTA+49SyhFR+KvuUe+B7+Luf8RhwTFwO2+fn013KcZnNZsOiJP85cMjpYjMpz/oYYPOUpLSyq3zzVHC6dOzLyv3yZxqbdax7K31omIG0OK8/qT0DeqSDVCK+BozrBzy4uZnJgJBuCWmpyyCVowVMK+f/GUSs0D6tgi1S3s96MbhlRsOpRn8ysOaYQ4JfLs9lf7B/yLCcMkBKRWcAln1WLTKq4jAV6PuTZSQIsm7Oh2zg4OBgCBYmwNA9otx5FsN9WsUWWEfKSv7eMiCVy5X9kKCzLhhU5X9owKAaEP+eU4lJt0kV85AKU9Xc7OK8zShIya0UBAILJH1U1iFpbDXPzHZy+tHPUNBJryWNWE9ack9D0loaTNiXDqLRBUmmxL5Oq5uxGvdJZfUNcjm1muDiulZToBkfqfYT+BnGV8wIfAz64eHhKBCcIJUuBw9N9SEoyWIqBlC5jJlSDtJAVMCQwLGqnMxna2tLT5480enpqT75yU9O1m8jwEDSSDjTMicFlRZWij6qn2NQkMLMT//PbaxJUSksdkdyN1uLHmY72E4rtetA4Ejh5M5Ff9p6UYENBNWZiy6XoMGlvc6jitVQgBOYU9ETDNgW9gWVIV20KlBG0E2jwXwcdzo8PNTR0dEQg7J85Atlucyb9XSZCVTJaCqXoErZnlbcpZKHVXGCTK7zkydPdHJyouPj4+a9mTYCDLLTSUMZOfZA2vIlUprWUSGzw3k9fXOueLMQUWC4gCZpLQU882e6v1/slKRQ0NraBaAQuk6MT5j621Wi7+s2VwEksyUquL9z9iHrz23UtJC5qtBKxUg5GVOyDPZF5f87JYBmHgw8kp3c3T28KOfq6mo41JZgwNfkMbZCF4VuEdM6jMD3Vb/RaEzlmwCR13Z2dnR8fKzT01MdHR2VILIqbQwYcBC5UIVTSF5pJmmwrhR40tC0ZOmvUXhTcO7v74cjy1h+zgdTEFlW+tBMtHQEIV6n5WIAjMyFawqSyWSU3uDqFWmShsU40ngpOPuyAgMqZMYJ2M+08MkAWqCQ/cQ6EKz8HBPHmTLiOX6/Bs+bwShfXKvA2IrXm+QOwxfx41uKXn2vmEKyA/7v17SfnJzo4OBgJEfvJW0cGFCIqYikeb7OxtO6VghPgU3fl26IrzNukPPAFsak2M7XLgXb0hpwzkCwnnd3dzo/Px+mw7hld39/fwQ+3OPPjTl0qSzkXMhFZeb9TlRIj01F6ysG5LZWG8ZW3Zd191jlYiDWj21wn1rRr66uhkNL6D6RdRrs3U8OOHIsCXjvxfJWaYoVVKxjZ2dHR0dHOj4+HgzEy6rLRoCBtHx4Y1L0nK+Wxv4V4w0WEE8d0fLzOSuxE61jyx+kNScYUFDpRlgwEwRSYS1g7gfTVtNYg8LR0ZEkLcU3XC//2fJTkcgmUvkra0VFz5kSsqDsn4wtZGKf0mVinrbKHF8mBnadF8+HsOHwexcTCJwfg7aULzLIDOhmn3Fsp1LLwq/qJzOABICXBQJO7/WNSn9T0h96vOWppHf6vv9E13Ufl/TLkn7l8drP9X3/7etWhopCJsAAVCo8pwJ93QOaQUmuguO8NrfGEhAojM6beyjoHjDmQeFOHzij2v7uaUAPtK0SYxWMXfg5L2OmkroN/s2Wju9iSLpbJfdtAmMVL3HitCVjAWx/jl/rXn6mK9Jaa0B3z4ySKzjTPamYDccjQZGJRqNyc/LeF1He2Wym4+NjHR8fDy7AOizr/aT39Ealvu//U//fdd0PSHoX9/9a3/efeNGKcL48gYC+WhWFp+KSbjNyb2XlmgEGDK0sFsz5fL5kTTJQ1dojYYXJjS+uFwWW9XEAlGyDMxoUTAcavWKRQOT2kmrTD65cgiq5z3OtQcYOqBAU2qmVpBmTMDCwf5KV0RXIe+jSZd9yxoOywzhD1W7Wu0qUP36umyrAODo6GmZDuFjus5He1xuVuofWf52kf/f9VMKK4IAPgaCiZ1QYo7u0/FYm+9F8xvcZJKTFHngrlrSI+nva0cKUypqzAvQnEyTYVgZH88RkP0swMSA60OXAmBfW5AGsdIFyxmBd4XUf0fWhJeesgvNL98ltyj7ybwQIBk7pFjqlsmcsh1F/Uv50L12Pih0xr3VSqw9bv1fA4vUQfpkv3bnPZnq/Jf6bkj7d9/3/i9++tOu6fyLpmaQ/2/f9P1ono4zwcsCl5am79E2T8uVgJBVNKyVpoNOerpM0CmBSQFLBnKfrxwBmKsbOzs7S2gUezc26U4HpUpgGe1rs/v5eR0dHo5eR0N9OBtOyNiybbhKDq2zTOn3izypOkbNGDObRmqflJxBMBdxYh/wt+3rq/lZfpTszBbS8J5dKm6F+LtP7BYNvkPRj+P5bkn5/3/e/23XdH5b0d7qu+/K+75/lgx3eqMT14VysUk3lRB6DWzCbzZamFUmzfA+tmYUz58J5io6PLDMgZPCL6x2oGIxJpK/noJDbbEHg9BWFzG2wMvge18dgkQuanKrAa8tXZx3talHpuVKTv1cATQX+/9u7uhDrrrP8vJnJN3POnBlqbClBg02kXvRKY6kFS24UtbmJeiG5USO9EVpQUDDam+JVFRQqSKHSgooYhSr2QsFaFPGi1aakSWqNTTRiQ02UyDdz/udnebH3s+fZz6y9z5lvxpwzc/YLhzmz99l7r7X2Ws963p/1Ln54PAcEs9msameCpYOJenf02Vpu18/bqP6yA7/t2tzztRz8kIEqC9V0a6uWewaDiNgG8JMAvp/HUrHh6qz8/mxEvALge1Dsx1iTJDsq9Xq9pC9aVQSdZeXaC51bow51huKHHVtdjXxpHmrMF8SXxzBepZ3a+dUN6c933dmFRkDO8k0UmPVmW2h4LPd/ZLkJjGozoFuQ7eV2D30GgJpa5GqCGiLdwKaMQN+lZh1WlcgNfao26SB2hrBogOeMkS5XYQX+XrzubCv2H83YxI1x1k2uwgx+GMC/pJS+yQMR8Q4Ab6aUTiPiERQ7KrUHRJeihjLP8lPeuxq8OktwgGgHdWag1J0DV++rMxS33NKAHr5E1e1V3CC1iBUoXVfbxtbWVm0w+IAgoNE4qHq1RmfyPJkOcHHzE40mzBn61BbjzEoBRNtDdXWWTwe4llfVQrcRuWFw2VleZREAuMqo/SE34y/7nIioDNK05RAM1hEAVO5pR6WU0qcBPIm6igAAjwH49Yg4BnAG4OdTSm8uekZKF2PZ+X8O4ZVFqM/fyl0bcMoG1NdO0VmKNFhdXzTsAPl9GJUiq3qS05+Ben5CnXXV6+G6MAGCg50GTp6bzWZV+QgkbBfaQBSoeC+ltq57s3M74GmbEoxy1nwN93W3qKoPrq4tS+svwwCUsjsbygHCIjDQ8zrrc6HU7u7u2gOAyr3uqISU0lOZY58F8NnLFoJg4BQZqM+6ucGhs5ezB6DOOBaxDP6WwSscSJopR0GG13A2Jnj4jKugpM931sLncrCql8SNZ1pfzrhMpU4wAVBlCtb2YFASr+f9+CxXvxzQ+DyPf8h9fGGQDni3+vugZBlUcu2YO+fnc9+bwC/37BxAsF8wKrDf71c7Pt9EWYsIxJRSzZqulFR1YWUKriq4DuczkNofdBAqmLBzO7WnquARcbyHzrpaLgDZkOkc3dbQZf5WmZA+m22h9eWgVGBRdUAHNlD33hAcVJ8HUIXksrxsR13w4xvGKGD5cbVj+HtbBAD6zl10cOdYgN9f309OFdH78hzPK+MbDAbY29u754VB6yZrAQY0gqk7jh1Xc9bNZrMandTr3VLNjqhUVmdC/tb1al9vwEFEdyAlpwK4WgOgGmy8D3AxdBg4D4VmG6je7+UnS6KoCkF2wXqxM2ukpVJ9bTO9nmqS1oeqyGg0wng8ri380ffSRP998DfN4n68DQB8xvd7eT2b7A8OJHpM+0Wv18NgMKjyJNwmWYvacGbjLEZjoIbQUidVq7h2ajdO6VoEFYKB2wv0PBkJYw6A81Tu+rucl0DvxVlaO5onWFH3nlr3mU2X9VJXIO/rA0jdjf5MD2RRYFF7iwKk3p/3Ho/HGA6HODo6wmQyuWD5V/B1FYDP1TLozOuD0fuIltsHfNPv/bs+e9F9FQD29/ext7d36wBAZS1qRjUhpVTbQYcpqzhDkcLrTKu018N7gYsvPdcxlB6raqK+df1NSue+f7+PfveAHIKTqhjqIdDt5CKiMggCqGi/swMFE6XorHvTgKftQEGIwVa0JyhLUmPgZDLBeDzGaDSqJZXR++f0f30fy/71+2m9XO/PSe4aP65lY1vt7OxUKsCqg4HeKlkbMNAOSFZAi6wayGhYUwrHe7hv3mMAFgEC76EsQ2dh9ULkmIHTWzfKqX1CRQcrPRdtHRyoRweqTk8G5YCltg31XjjrICioesTr1eOiwUG5gdVWdh/MufN+P72O33Mg5M/OqQ45cNna2qpUgE0CAJW1AAPg3F/NwevUVu0IGvnGjp/zQtx33/kGnNRlFRicGuqMq/qu6vv6bBel/fpbPlvZgboI1diondcHplL3iPpSaQYvaaiz2zWUPXEwkQlp5mAe5/14Xc43v4iON7XTIkDQZ/h1bfd1QGi7JiKq5CCDweBWqwDLyNrUnoM0F3nHgaA6dc5Qpdco3b///vtrM74yB+3gQD0gh9fowKQ06cM5xqCARTsA60Vqfnp6vmeiGt30fgQbbsSh6sDZ2VltI9ac3UNndra5sic1TNLYSDBNKVXBM4ymoweoCRyXlSZVrsnWkPttExDoe+E55gboAKAua9ESOkOoG0pnHx3c7sv2zqgUlwkym5iD654AajOkBjYpU1EwahI1prkNgYCgOQwJBl4W/vWOq8ZJGlbVjsIwai6E0eg+N7jSJkEGwzX03HSEG7z0+31MJpPaJiMOsm2Sm+39e5vNYZEq0GSjoBGwA4BmWYtW4WzMgaU+atW9Vbf12URFgUA3clX2kKPQOtO7fqpglAvQoejsy4AbCpkNQYk+++Pj45rxUOuqawhUDdG1AayzGsAiogJCGmIJQBoHwHpPp9Oq7SMCg8Ggsl/0ej3cd18RZz8YDGrsLSIwnU4v2FnaRAd0jsa3gUHTO89Jr9fDwcEB9vf3K4aziTKfz3H37t2Fv1srMOBL1oVD7Nxq9GoCgoioeSP0Q91XPQQefOSdWRmGJkFRQHI7wfHxMSaTSS3yEUBtV5+UEqbTKcbjcS06j+Xg/QhmDhS5ACGWV8viMfLU+ynT6RSnp6cYj8cAzoGM5WM7absOBoMLXort7e0K1HJxICpNOv0i70HuWp7T3925cwcHBwc4ODjA7u5utgybIPP5HEdHRzg8PMR0Ol0KQNcKDHRVG5mBU7qcMUg7JamxrjZkuKhb9HVgqDfCAaMJDHz2poGQwMNIPf6GNJVxBEdHRxgOhxUoEACpFim7YVk8cs7rwLbUcunHA5YYPES2wOefnZ1V15ApEFTUmOmAwHrkbD9aTv/fjYk5vV8BQhkeGQvL2GZkvM1yfHxcxYCMRqNWdpWTtQMDVxU0gk8NdGr4oivSQUB15l6vV/3enw1c3CADOKf1mn3G1xuoX5qDlaoA1wpwMHFnGwZRMUvRcDjEcDjEaDTC6elptRMwgUS3RPOOrm2hng+PjmQ70XOg6wZokNWcDZPJBDs7OxgOhxUgMRknw8PV9cr2ISB4gpJldH8/5+9IZXd3twKAfr+/0NNwW2U+n1d9ZzKZXLBTXUbWBgw4m+pLVR1eZ37dAUldY1wxRrWABjAavkjR3ZgH5PVYDir1YmjwjpZfVQoAODg4wNnZWbVFFzsud/o5PT2tla3f7+Pw8LBiQlQfOLByblCCqLYFUA9tpoFvNptVgKFeCK2btjvVHW4+QqBQUNzZ2UG/3wdQ995MJhNsbW3VEpE6o9E2z7VnzkDIttrb26vA12M2NkEIAMPhsAoHvwwDaJK1AQMmD2FHUFqotJ0d7s6dO9V5goTq5fwNIxn50RmtqVP6rK+dzqMHFYFZ/u3tbfR6PaSUKr2Vac77/X7FIFiXXq9XW/XW7/er+P/RaHQhu6+2m6sxqs8DqIyUukDIMwbnoi4JIky6wo1IWD+NjSDI5trN8xh4u7WxALYfZ3+qaLkw8Nsu8/kc4/G4UutUFQMu5me4F1kLMADOBzoHmgcXUdR/DqAGBp5SSgGCFnWNyGvqjGogVNuCd1RdC6AuS4JbRFRgQHBSHVjXYhDE6AKjPeHw8LBmV3DjnBoqT0/Pk6Ro+SKi1nkIBDpQ2a4EYt6TQDAej7G1Vaxu9DgMvj8CurYlr/FFS7nFS2wb9YJQNdlEADg5OalUAH33buQG7h0AVNYGDIB68k8dhOzYusJPvQ8aXMRB57sQ0ZbgBjfvjD6zup7Osqi7kcFEPK92DYqGDOs6CI0NoFrDqLjRaITBYIDDw8MKFHRxEDuFdhINPFJ7AMum6cU0gQrrT9bCATudTjEajarBz/wITs/VoElVgv+reqL5GLQODAemOueqz6bIyclJtfaD75rvSSeSttiKJra1CDDWCgzUQq//u7DTUnI2A//LpdA+aJcxVlEIBAoiLIvaGnLuMoqvkVCDJDu/Bvj0+/2KJitLUKrIQaXZhpSO06WoKoLuCcE6eHnJIkajUeUdoQqm6hMBU+MneF9lQP5ROxAZgAP2Joi6msfjcS1DNEXVZz3m98kx3msDg4h4CMUGKu8EkAB8KqX0iYh4AMCfAHgXgFcB/FRK6X+jePInADwOYAzgqZTSVxY9B6gHAvnM7LRdqaOmm9KUU5qEUmMKvLHaDFm00LsNwxvbYw+07AAuDFBFdn8mZ0WqPqzT3t4e7t69i+FwWO0fSP++AwNjNY6Pj2shyXQfKhgQKDSYSiMTySh4H419UPcjr1W1wNuI9cu5PjdJUkpVNmi6oGmopeQmE1cP+L2pP2ufXBQ2vswbOAHwSymlr0TEPoBnI+LzAJ4C8IWU0scj4mkATwP4FQAfRJEI9d0AfgDAJ8u/rcJCu7FOZ0z3bwO4MGj0w85LFqEzpTbcIhTVAe6eCO3gqmL4NX5PBRYFGj5PAU/tJLu7u9jf369oJGcTBQYdkLQN6JbuucAgLavHW/AY10SwXQkMfD/8vQOBxmvoO940ACDTolGW7E5jO4B2dkpxUGhivNofF8kyORC/hWI/BKSUjiLi6wC+A8ATKBKlAsDvA/g7FGDwBIA/SEVpvhgRb4uIB8v7NIoOfB1IOkv6zKWWdLUNqGtRr3EjVs6i7fYBP64DV6ku7QYcFG0uyNwzdPA5s6Dhk0a6fr9fDX56HKg+kGaSKSgoKP12gyfL0sScdKDT07C9vV25G72dCZBqy9lEV6AabDn7KwPwCWPRoM2pBk0swSenRXIpaI6IdwH4PgBfAvBOGeD/hUKNAAqg+E+57JvlsaXAQDuLRxXqANPOpgFHND7llu7qev9cOi4frDlLtzc83Wyc5dS/rwyGv1fXpHcEn6n1Q5Bke/R6Pcxms9rWXBp8QuqpOQodiLSzqN7vZfV6kCnwLw2C/B1BmmrapjEANdTO5/NarEaOjfEa7Ws5W1aTHUqZpxsaefxawSAiBigyH/9iSunQKpIi4lK+jZAdlVQNoKiFXRccseNqzIEyAXXfqXtNXWl0p+lKRjaoA1KT+1GpvK6mVCDKqTU59cG/a9l1QCq9Vq8JbQoEBDIE90s7+9CyODC4d6DJC6PtpoC8iclB2Ldms1ktRqQJACg5Nsbjbdf573xS0/OL7AXAkvH7PzcAAAcpSURBVGAQEfejAII/Sin9WXn4ddL/iHgQwBvl8dcAPCSXf2d5zCtQ21GJs4faDjy8WKMBlX5ycCiYcPbXdF1Eaz2nvnJ9vjamN6QOToIBU7KxfBqxmLO88z56T4+r8E7iA1afT7bQ7/erPIXskAoKmhbOwUjr7rO8MhotDwOser3exgIAjYFsd4YFt8kie9JlJddX/NwiWcabEAA+DeDrKaXfllOfA/CzAD5e/v0LOf6RiHgGheHw7iJ7ATsVhbOMGgLZKQkSCgRqJCR1VbWAxhqlzb6M1w0uKm3eAx2QHiKt7EYBy9daaL19pm5jJfq/G1JpVxiNRhiNRlUQk+Yi8FgFlk3rqGxEy8z3s4lLg7WP0V4zmUwW2qDa7rfMb1yVbVMZ+Owc2DTJMszgBwH8NIAXIuK58tivoQCBP42IDwH4DxRbswPAX6JwK76MwrX4c4sewM7MynK2YQy6u6LUmq0d1l1q/NBw4xt7KihQdAYEcMGmoGXWGV/BwAOJ1O7hoODAk7NbOLV3IFDWoSHO8/kce3t7mEwmVfASGcNkMqmpD+7d8PtvbW3VIjs3TbR9uC7g8PCwYlRtsojitw3UnEqZs+OoyzoHCNcCBimlfwDQVJsfyvw+AfjwwiebcDATCA4ODtDr9WpeBLUTKBsAzt1Z7OAaf09WoEY1X2oLXHxpOTqtA8XZgaoGBDW1a/iKypz6oN/12arD5+IUHBjodaAbi+BKhqDGRqoPCga8L4OeNjU3AFkAVQAGYPlv+LdJ71+GIaj4hKDvWfsN+wknQqC+/6WXo03Wxsyry291cQrDY5U1cLDpS6ELxyPsXE0gKPC4Zvf1QZgLnFFRMNCXo2BA9UZDpBUYlE1oAI9/2uiedpSUUqVyue2FXgeGOpMpqAciIqrfMPfCJsrJyQnG4zGOjo4wHo8r17GzQ6B9OXZO2JeXmbH9eWpQp6FXY2fcWNjmoXBZCzBgqKvOprpzra7Y8xegcfQc4JrKW1fdeaQXwUDvzfv7YppcJwBQm9k9ClGNjLoYiXSbf3VNhQOEUkI3MuV0x5yBT2MUuPaBS6pJd5ntaBOjASlcF0AA4NJxSo6qLwMEbV6CZa5hGRh2z37ECZG7YHECU6DxiaJN1uKtp5QqMNjd3a0KTTUgNxCUGhEIVCXQSC8N+eT/alB0oOEzcoFAep5lzIUiOyBwnb+CAtkBDaV6Tr0nalj0zpebrTRykMdUhaHqxQAltvsmytnZWS1oS2dYsiJX1/wdL+O2c7kMIKSUKvapyXo4UQLnyYAuq46oxL24Ma5bIuK/AYwA/M+qy3KN8nZ09Vl3uW11WlSf70opvaPp5FqAAQBExJdTSu9ddTmuS7r6rL/ctjpdtT6baR3qpJNOLkgHBp100gmA9QKDT626ANcsXX3WX25bna5Un7WxGXTSSSerlXViBp100skKZeVgEBE/FhEvRcTLUWRMupESEa9GxAsR8VxEfLk89kBEfD4ivlH+/bZVl7NJIuIzEfFGRLwox7Llj0J+p3xnz0fEo6sreV4a6vOxiHitfEfPRcTjcu5Xy/q8FBE/uppSN0tEPBQRfxsR/xwRX4uIXyiPX9870qWPb/UHwBaAVwA8AuAOgK8CeM8qy3SFurwK4O127DcBPF1+fxrAb6y6nC3lfwzAowBeXFR+FAvR/grFmpX3A/jSqsu/ZH0+BuCXM799T9n3dgA8XPbJrVXXwcr4IIBHy+/7AP61LPe1vaNVM4P3AXg5pfRvKaU5gGdQpE27LfIEipRwKP/++ArL0ioppb8H8KYdbip/ldoupfRFAG+LIqfF2khDfZrkCQDPpJRmKaV/R7Hi9n3/b4W7B0kpfSuViYVTSkcANP3gtbyjVYNBU4q0mygJwF9HxLNRZHECmlPD3RS5bGq7myAfKWnzZ0Rtu1H1iaulH2yUVYPBbZIPpJQeRZEd+sMR8ZieTAV3u7Gum5te/lI+CeC7AXwvipycv7Xa4lxewtIP6rmrvqNVg8FSKdJugqSUXiv/vgHgz1HQzNdJzaKeGu6mSFP5b+R7Sym9nlI6TSmdAfg9nKsCN6I+0ZJ+sDx/pXe0ajD4JwDvjoiHI+IOgCdRpE27URIRe1HsKYGI2APwIwBexHlqOKCeGu6mSFP5PwfgZ0qL9fuxRGq7dRDTmX8CxTsCivo8GRE7EfEwij0//vGtLl+bRLEcsS39IHDVd7QGVtLHUVhGXwHw0VWX5x7r8AgKa/RXAXyN9QDw7QC+AOAbAP4GwAOrLmtLHf4YBXU+RqFffqip/Cgs1L9bvrMXALx31eVfsj5/WJb3+XKwPCi//2hZn5cAfHDV5c/U5wMoVIDnATxXfh6/znfURSB20kknAFavJnTSSSdrIh0YdNJJJwA6MOikk05K6cCgk046AdCBQSeddFJKBwaddNIJgA4MOumkk1I6MOikk04AAP8HueQZFU7tlYsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(os.listdir(\"/content/drive/MyDrive/Colab Notebooks/aug_myo_208/gts\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLs37i5_8LNV",
        "outputId": "7d3b825c-0ddf-445f-88e3-fa7512aa9802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "408"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from os import listdir\n",
        "from numpy import asarray, load\n",
        "from numpy import vstack\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.preprocessing.image import load_img\n",
        "from numpy import savez_compressed\n",
        "from matplotlib import pyplot\n",
        "import numpy as np\n",
        "\n",
        "# load all images in a directory into memory\n",
        "def load_images(path,size=(208,208)):\n",
        "    src_list= list()\n",
        "    # enumerate filenames in directory, assume all are images\n",
        "    for filename in sorted(listdir(path)):\n",
        "      # load and resize the image\n",
        "      img = np.load(path +'/'+ filename)\n",
        "      src_list.append(img)\n",
        "    return np.asarray(src_list)\n",
        " \n",
        "def load_gts(path,size=(208,208)):\n",
        "    tar_list = list()\n",
        "    # enumerate filenames in directory, assume all are images\n",
        "    for filename in sorted(listdir(path)):\n",
        "      # load and resize the image\n",
        "      img = np.load(path +'/'+ filename)\n",
        "      img=np.expand_dims(img, axis=2)\n",
        "      tar_list.append(img)\n",
        "    return np.asarray(tar_list)\n",
        " \n",
        "# dataset path\n",
        "image_path='/content/drive/MyDrive/Colab Notebooks/myo_208/images'\n",
        "\n",
        "gt_path='/content/drive/MyDrive/Colab Notebooks/myo_208/gts'\n",
        "# load dataset\n",
        "[src_images, tar_images] = [load_images(image_path), load_gts(gt_path)]\n",
        "print('Loaded: ', src_images.shape, tar_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUCxYU_DBbUe",
        "outputId": "8d48975c-14e9-4e2a-92e6-7bbf38acd3b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded:  (102, 208, 208, 3) (102, 208, 208, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(src_images[101,:,:,0],cmap='gray')\n",
        "plt.show()\n",
        "plt.imshow(tar_images[101,:,:,0],cmap='gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "HLmBUvOfg9ar",
        "outputId": "d7d4aff3-d1e7-495c-e9c0-cc542e6a340c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting\n",
        "n_samples = 6\n",
        "for i in range(n_samples):\n",
        "\tpyplot.subplot(2, n_samples, 1 + i)\n",
        "\tpyplot.axis('off')\n",
        "\tpyplot.imshow(src_images[i,:,:,0], cmap='gray')\n",
        "pyplot.show()\n",
        "# plot target image\n",
        "for i in range(n_samples):\n",
        "\tpyplot.subplot(2, n_samples, 1 + n_samples + i)\n",
        "\tpyplot.imshow(tar_images[i,:,:,0], cmap='gray')\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "3zVN6w73J6nI",
        "outputId": "285ddd73-e3b3-4cbf-e78e-ee4979864d68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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ZfSo9GnnOX71eh8PhkH3CaMxqtcLtdsPlcsFisUi4rp7XcrmMUCgkSk2r1SKRSGAwGGBtbQ3D4RD1eh3JZBInJyfyO4PBIIqfxpFJLBXnJV56kWnxQ8L10uv1ODs7Q6/Xw3A4lH1mNpvR6/Uk6TczM4N8Po+DgwM0Gg3x4Jlr4LktFovweDywWCyoVquSYATOHZlEIoFcLodOpyPGm167Xq9HtVpFv99HrVZDp9OB3+8fYZFoNBpJGP+QXDoDnU4H33zzDdbW1rC0tCQeWLPZlA3ND+OD9ft9OWxUPNz4PMT0CLlonU4HxWIRLpcLZrNZaB/MGnq9XjidTskiHx8f4/DwECaTSag6Ho8HuVxuooNKYchiNpvFm+NzU6mq36teGL0+vo9KpVEPLSkzuVxOQlGGQIFAQDYT54oYZz6fx/HxMVKpFNxuN4bDIUql0thZfeD8IHi9Xnz00UeIRqMSikWjUTx69AiFQgFvv/02Njc3kclkkEqlUCqV0O/3BTbiupEhwDFSYajYtmp8h8MhCoUCjEYjLBYLarWaGGR+tVotHB8fw2AwIJ/Po1wuo9/vw+VyjW1ImegoFouSyVdDYK4n14fPyYTaRYNqsVhkDQ0Gwwh2OxgMEAqF4Ha7kclkUKvVxEhbLBZRpN1uF41GA+VyWbz9g4MDnJycyH4m5DauVCoV2YfcXw6HA9FoFOFwGJVKZWQsVqsVPp9PvENGFTSA5XIZg8EA5XJZcjBfffXVSOTWarVgMpmwtLSEubk5ZDIZPH/+HMlkEnq9HjabTeacnwuM4riTRC3UK3xGGkQqtVAoJIY7l8thb28Pm5ubMBqN2N3dFS/bYrEIzt/tdmGxWBCPx7G4uDhilA0GA+LxOLxeL9rtNhqNBvL5vESYhISMRqPAQWSBuN1u5PN5geM8Hs8rx3ap0iXOt7W1haWlJaysrMBms2F/fx/ValWwRTWRwklXLYw62bRaLpdLrLLqsc7MzMjgVWs6Pz8PnU6Hk5MTPHv2TDy32dlZ5HI5CRletzE7s/WcVComHlzSfPj+3ExqEowWXv07VZh84UKm02khYvNQ09OlR8j3ZyjOg2y1WiWMukwGgwFOTk6wvb2Nv/qrv5KQ2WazQavVIhAIYGFhAc+fPxcaEa03+Zu1Wk2ggm63K1DJlStX0Ov1kEgkUK1WBbsj/qt6xBwjDzL3Cz2YdDqNYrEoFJ7LNrAqTLotLi6iVCrBZrPh448/RqPRwNHRkcw151fFGHmYmNgiJmyxWBAKhTA3Nwez2SyRjcvlEr7tL3/5S6TTady4cQPBYBCNRgPD4VCoSqqBdrvdktBrNBrodrvCJBlXiCfTgPBZvV4vgsGg4KfNZhNGo1GgBHJz6RUTKiQsksvlsLOzg263i3K5LGvEvUcsniH7/Pw82u02crmcGF9GvOqaqjphXMoYCyMajQaq1ap8ps/nk7Vgvimfz2Nvbw+DwQALCwuir5jktlgsaDabMsdarRYHBwdwu92w2WxoNBpYXV3FYDBAoVCQvAZ1D6mIhDs4/06nE2+88Qa8Xq94yNFoVMb/Q3Kp0qXSdDqdODw8RL/fx+bmJpxOJ548eYJSqTRSDcLJVV+r0kjo+aqZZIZynU4HZ2dnqFar2NjYGDkUnDziom63e8TbMpvNKJfLuHr1KrLZ7MS4rt1ux+bm5ne8eOKvTMSoyRcKwysqI9X4MORWM+kMUYl7Aud8SIfDgW63O5IkY/hKJVav1wW6mVT6/T6SyaQYPWJl4XBYIoVf/OIXgnPV63VJMmQyGRQKBRgMBqHpVCoVGXen08Hi4iIODw8FYiCnUTUMOp1OaFU8gFSWg8EAlUpFElH0VKioL5Ner4dUKoX33nsPH3/8Mfb392E0GrG9vY1isSgMFyaOAIj3QyYGf6ZShkhV4r5Woyufz4d6vY50Oo0HDx7gz/7sz4Sb63Q6JWLodrsy3/V6Xc4LlfgkSlflxDJRq9VqUS6XEYvFEAwGJYnGYhFi9cTjyU3NZrOSWCqXy+L4cD7pJVJpF4tFwTbNZjOWl5dhMpmEGsdzTRiNXzRmKsvnVcLzR4/SaDQiGAwiEolIxECnJBKJCKXv0aNHMBgM2NjYwLNnz1AulyWfpMJVNKwAJJr7+c9/jp2dHbx48QLFYlF4zuoccK/MzMzgxo0biEQiSKfTAvflcrn/PqYLQA5QKBRCo9FALBbD3Nwcrl27hsPDQySTSRiNxu94XSrzAIDggPy5wWCQJAInuNfrodFoIJlMwmq1wuVywWQyjYQvoVAIzWYTlUpFklhWqxXHx8e4evWqEPUnldu3b+POnTv4+uuvsbu7K0kRUnAYbl0UejHfVwXE77khKczeM/wi5n0xAaBW/jCbSwbFZRZVFXqbxG7dbrdkZ2OxGLxeLyKRCCqVilCBmMFeXV0dqfIaDAYolUridR8cHOCTTz7BrVu34PP5UKvVEI1GcXZ2JpAB8TUqexWX6/f7Qp8i2Z0Y9iQhKSGucrkMAJifn4fL5YLVakUgEBA+Jj+TyUhS9MhXpsJnyN1ut0dC51arJawNYt937tyB2WzGv//7v0sBhN/vh8ViwfHxsTBfeNibzaYwbybFrlUnxu/3Y3Z2FjMzM2IYmGtwOBzCzyVLQvXmASCfzwvEQyfnIo5JCI0OBXFhsglmZmag1WoRj8dFSQGQ3IXKnbZarWONkeNg5EkObCaTweHhocBTPp8PHo8HN2/elAKFk5MTbG1tjeSUXC6XOAHq2SoWizCbzdjb24Pf78fNmzfh9/uRTCYF16WHHQ6HpVCG7BoqbJPJBJ/PJ8m9V8mlSpdeHKlF0WgUgUBA4APiWqlUCtVqVTyyi3ioap1VChbwMjxn2V0wGBxhMXg8HpjNZuzs7ECn0yESiQhxvlgswu12o91uo1gsCl46qVQqFfzqV7/CW2+9hRs3biCXy8lmJRE+n8+LErg4JgDyc5Wqo+JR3MD0Dvn+AMRjIc+PtKtqtYpGoyH4LakvDHkmlUajgXQ6jWAwiGaziePjY8Tjcdy4cUNoMIVCAWazGcFgULwEKkhuVkYBxBODwSD29/dl7WhMotEo6vW6JJfIYGFCi1GPVquVzDj3A8PVcYURCPE4hqDr6+vIZDIjhp2HhkwFYtAX+b2qk8BnKRaLI56fVquFz+eTHEUmkxnxEslSUDP8asj9fdHTq4Se48LCwgj9LhAIiDep1WqFF02IkHPM+WbJLwDJ6s/NzSEQCEgikmtOpkY2m0WlUhEPkgo5GAyi3W4jlUrJGno8Hola+B7jrifxU71ej2g0CpvNhhcvXiCbzQI4P69er1dYBeVyGfF4HPF4XHi0ZrMZCwsLYkwYsR4fH0txCqO+Xq+H/f198Wg3NzelBNlkMsHhcIixsdlsI5WW1HeVSgVms/nSKHQsnq7dbhernE6nodfrcefOHbjdbiELz87Owu/3o1wuS6WMqlSZYFMTFlRSpK04nU5ks1loNBoZJLmTACQJcHR0JBlNj8eDbreLZDIppYCvK5VKBZ9//rnAGPPz8yNjD4VCqFQqYlyIX6mGheOlx0SqGOdD5VMyfK3VaojFYqjX6wLMVyoVnJ2dibFrtVooFovyxcqZcYXzyDp1GoPT01P5DGZdGe7TipfLZXi9XglDOT5WNJVKJfh8Phk7sTC/3y+GxefzCWWM3ovKFOD7sQCHEMckmC5hHHqy3W5XEqucQwqTgcViUZwI1WASXuDc0DgA5/CC3W6H0+nE7du3pXLq5OQE2WxWlAVD1EKhIJEBoZV+vy+JZsJZk6wlDTkVP5UqewNQIfDZ6QwxuqRSokdrMBgQDAaxuLgIq9Uqf0tl2el0YLfbEQqF0Gq1kEwmcXx8LO9FpV8oFCQ6I20OgBipywoHKHxf6ovHjx8LX5y4qkoH63a7yGazKJVKmJmZQSQSgc/nQ7/fR7FYlGIXs9kMv98v3jdx3+FwiNPTU5jNZmxsbMhZzmQywkSZmZmRqETF97mX2Djnv81e6Pf7yGaz8qY6nQ65XA6pVAoLCwuIRqNoNpvY2tpCOp2G3++Hz+eTDc4MZL/fFyqQWvHEChgAQjTf3d1FIpGQ7G40GhWrSQWnJmCIBa6trQn74HWF4W+v18PCwgICgYBQg+x2O+r1Ou7fvw+t9rxKjUwCkt6ZoFGxYHptHC9wzjtmWEKvjOG/w+EQzK1SqQgHtlarCc1uUmoc4QlCN2rICJx7b16vF/V6Xeg0tPpMqJD/yATNYHDeNyGRSIhHzJCMLAfugV6vB7/fj+HwvCSWHoIKSVUqFRQKBcG3aZgmWTsedh7wZDIpe1HlffP5uV48ZBaLRcJoKg06BzTqVqtVsMyNjQ3cvHkTz549w69//Wt0Oh3s7+8Ld5xlxyTRn52dSdEPP9Plck3k0RsMhhGFTW4qxwO89MrVqE+Fx6rVquwrrVYLj8eDUCgk88PioHa7jXK5LBGBTqdDIBCA0+mE1WrF4eGhJMhcLhdCoZDQyQgj0dEIh8NYXl4ea4w07o1GA/v7+yiXy2KwuPdZTUZdotPp4Ha7ce3aNQSDQWi15+XYTqcTlUoF2WwWT58+RbVaRb1el5wB9zMVdL1eh9lsloY73D80PHT2uBZqYVGz2cTjx49fObaxey8wbKG3WiqV4HA4EA6H4XK5sLGxIfxb4p/BYFCynfl8Hna7XbAVlrECEA8vHA7D5/OJUmf1VjweH3kWcnbpGTUaDUSjUWxsbIx0IZpUqMwZhm1tbcFutyMajWIwGGBvb09YHJzos7MzDIdDaSAyOzs74rnR26IHcHZ2Jlg1NxV5z0ywkMpClgLLL5nNV72ASYTh3f7+vnCnS6USTk9P4XQ6USgUEA6HcXp6OgIhkHZDJUrvQqfT4fDwEIVCQYpZ6GGSGUFviSGfRnNe7s3Nzf1EShYxyNnZWSnMGLe6kBFEtVpFKpWSqj+dTodgMCi5CWLW3EflcnnEWHJN1OSWw+HA2toa3G63JKiI4xGTttvtWF9fx8nJCeLxuBgterbRaBStVku4npzfScYInCtPfhbXgNED8LJ4gklr/pxsEVLYMpmMPDf3b6fTEVbDxUY1LJNlpMPXMZFkMBiwtLQkCVt65Iw8WM4+jnQ6HcHMyTXmnuMcsOkQmU4sIiJTht683+9HMBhENBrF4uIi9vb2sLu7i6OjI4lkqOMajYbotkajIU6HGvF6vV7JIzEx7PF4oNFokEwm8dlnn71ybGNhurQsXMRisSg10QyZjUYjIpEInj9/jkwmgytXrsDlckkvSqPRiLfffhs+n0+yjt98843ABLVaDTs7O9Dr9bLBSUchbsMGFvSmeZAtFgv8fr8kCyZVRupYibGxEubhw4diLalEWK1F2lYwGMTc3By8Xq90ZTo+PkalUpFNwqYrxEQZJtlsNrhcLvE8OJ80KlQAZrMZgUAAAASrnMS4kMJE7y+dTsPr9cJoNEolUqlUEsNlsViEKcG5YN8JYoqJRALPnz8fwT49Hg9sNpskdzqdDjKZDE5PT5HL5cSDpnGt1WoCv1QqFTgcDql+bLVasg/GEeLt/EzCVjMzM7Db7aIAiCuzTwQA2cPtdhu1Wg3FYlG8XJPJhOXlZUQiEUmuUuESAvvoo49w584d7O/v48qVK/jmm28Qj8ext7cn+PFwOMQHH3yA3/72t/KsVB6TRGekh3Fd6U0CGOGaM8LkuSD0wt4ZhJzoERIiY35Fzc+wZF+j0ci+ZiKPjX8I0fh8PoHheJYACFtiHFHpm2w/yecHXha5HBwcSM8V/j0dHrvdLjgsx8YS7uXlZcTjcWxvbyOVSkk7gmq1itPTUwwG56Xu7Czn8Xjg9XpxenqKTCYjeoJlwcw77e3tiQf+QzKWdmKdst1uh8FgQKFQECCddA4qivn5eckK12o1JBIJaDQaRKNRYRssLCzg0aNHSKfT6Pf7gkcBkMwrezisrq4KblWr1VAul1GpVLCwsIBwOAwAI70w1SzzpMLNoeJ3pVIJX3/9NT788EM4HA7pKMRuSzMzM1LWaTAYxLM/PDwUj4+wg0r4pgJgyMfwk7iU3+/H/Py8hEI2m00y773eeeMNZtzHERq6YDCIs7Mz4TV7PB4sLi5KeXGxWITdbkcmkxkhpjOioI+h50sAACAASURBVJI6Pj7Gt99+K9ldjUaDK1euYG1tDYPBQMK7Wq2G2dlZzM7O4uTkBC9evBgpr3S5XBIhEXclXn58fIxEInFp1yYKOZXE/BgSc28xTFSpSL1eD8FgUDwjVkqyp6xGo0EwGMTMzAwsFsvIOlChBoNBzM7OwmAw4MaNG7h27Rpu3bqFL774QvIewWAQABCNRkey3K+zV1noQjpfrVYT/q/a8IfUN2LJVL5kEFBRu91u8WYZBXCOGNWwradazcdmVCpzifxo5hvUhlDfx3D6IeF+YCcxtgGoVCqSgKVnyVC/0+nA5/OJPiJERbiAsBYLWHw+Hz788EP0+328ePEChUJBCpDU6rmzszPhsW9sbCCbzSKTychnMJrVaDQCg7xKxvJ0aYWJa5K+VSgUhENJfI5hIrEgekC1Wg3BYBCDwQD379/H6empLDw3gs1mEz4jDyYB8Lffflue4dNPP0U+nxfrEggExKKT3vM6cjHBRy8inU7j6dOnuHPnDlwulyjKQCAgVo4GSM3YMmlE74hKlFac4+FhtlqtI0RrANjc3BTPg4tLOGYSXFer1eLOnTtCAI/H47h16xacTqdwGg8PDxEMBgXCKZfL4hEzYcjn+MMf/iBz1e124fF4sLGxAY/Hg3w+j1arJckTnU4Hj8cDh8MBq9WKR48eyabO5/Ow2WwCrdAz4dww+hhHiPnpdDp4vV7x3iuVCmq12kjTcRo6Gg16xWrfDBoctf8Ecc58Po9kMolCoYCFhQXB7Qln0BiyexV/1uv1EI1Gcf/+fQAQYzxJIg04x7/n5ubQ6XTQbDYRDAZFIZIFwrPF5CCjFNLXVP75RaqXys5hhRvfk4k1PjcTcnRC6OHabDY4HA6pLlxeXh7bSSCvmDCYzWZDLpeT8l4+C3Mw6pliK0iXyyX5llwuJ+XahHO4R9bX17G0tITd3V387ne/Q6VSQTAYlARkvV7Hl19+iZ2dHalRYEdERj2MHJaWlvCP//iPrxzbWJ4uSzcrlYqEnCRjA5DwkMkGhgG04rSIqVQKvV5P2A3Ehdil5+bNm4LT/PGPf0Sr1UKlUsHR0RFmZmbw13/913j48CHcbjdOTk5QKBRgs9kQi8Xg8XgEW34dxcsNxDBFlcFggFgshmvXrqHRaCCXywkVhxggOZLhcHikFFmr1WJlZQXz8/MwGAzSMCWbzcLpdAr+xGIJcnGZ8CLMUCqVsLa2hrW1Ncme379/X3DxccbHDkqs1uEzc/MyzOJhZYkqMXhu1kKhgGazKQdQr9djfX1d+isMh0NUKhW43W44HA4pHhgMBpibm0OxWMSzZ8+EauV0OqV/Lr0pvV4veCmN4ThjDIVCwr20WCzCmCD+T++NcItWqxWYih4ZlRajMJbGEoNmwop0IqvVilwuJ0UCzLgTRgoGg4jFYlLFt7y8jGfPnkmVFJNgkwjpXlSqrM4yGo3CvwYg60YDrbJsmPxVvXoaFZa/MuFIOIHRAr10zimb57jdbgSDQYmEaazIuhn3XObzeRgMBmnp+uzZM1itVoFzAIzkmZgHoS7xeDzQarXSG5f4MwspCDux5J3MF44nk8nI+q2urgpT4tGjR6hWq7h+/bq0aWVlLA3XZcU8lypdDsxqtcqiuN1uCWHYJYxNa7g4NpsNxWIR5XIZTqdTwmGG1P3++V1D7733nnhS8/PzuHbtGuLxOAqFAmKxmGAz9+/fx9LSEhYXF2Gz2SRsISWLoD0P2evIxdJdvidw7nWx7JUYIz0kHpxOp4NYLCaQSrfbxVtvvYVr167JpvT5fNBqtYjFYqKcScSm12i327G0tIRIJAKXy4U//vGP0kYxFAohEAhIK8RxhREH6/wfP36Mjz/+eIS2pb6fWmHIeaZVZ8UfqU6RSARvvvkmhsPzBtEul0u6Z/G1Ho9HatoJNZC6ViqVJAxmAkWn041gpuMIQ17u2evXr0ttPpWI1WpFt9uVysdAIACDwQCr1SrGmiwZUoDofXON6DFT8ao8X76u2+1KtRoApFIpdDodtNtteL1e+P1+JBIJABDu7LjCZCEhInJoHQ6HcKEZxpO+RWNDJU3GwUU6HOFCJuPUZu80ptxP9JR5nnkWfD4fZmdnRZnxtZlMZuxxsgqQ1FHOscp9ByB5EXreXCfOSS6XkypKu92OUqmETCaD5eVlgfJevHiBdrstN4KQm9/rnXf1Oz4+ht/vh8PhwPXr1zEYDLC1tYVEIgG9Xo+lpSV5ZuanXiVjebrE9Fi7TR4a6TDpdFp62XJB1Hp9i8UiSTAeNJ1Oh/X1dVy9elWa2wSDQWlUTk8klUpJB/9Hjx7h+vXrcDqdIzxSYpwkbdvt9onDNYZgtHS0/BwPQ3ziR8yU87Uej0eSYbTmZrMZMzMzAh2wC7/JZMKdO3eQSCSwt7cHh8OBvb09AC8P1OLiojTAZrcqNtf2er0Tt7BkiJtOpyUpQkWuhtaEicgFZtJU5RczUUD4hV2byERgkooYIRUAEzJer1cYDJx3r9crHG16VGdnZ7DZbJfyHlVhaFsul2G1WnHz5k3cvXtXvHW19JNrx/FRMREaYUKRzgT/nmPhv/xeJduzrJb3tjEk5lVCjGra7fbEjdr5jMfHx1KWSiyViTG13JVenLo2VMZMlnHsZN4QGyZrAYAk3BhKM2/DiI8shUKhIOfR4XCIt89k7DgSCoWk7JxeMwCJgjQaDXK5nPRv0Wq1KBQK0qt7d3cX9XpdOqvpdDpsbW2h3+/jzp07qNfr2N7elnwRDZ/L5RJHgWtCrD+VSqFer+PDDz/EL37xC+zv7+Pg4AC1Wg2hUEhKyi87l2M1vAHOwxm32421tTW5sqLdbku7N9KmAEjSKx6PSx2y0+nE/Py8WDtigLTODL1XVlag1+vlUsC9vT1JePT7fTmAaraXWX9CHuSJTiq0mqSfcBPSU1CZGkyQVSoV4QGSgsVN63A4hHNKRcMG4WxHt7u7K6FvrVbDcHje9YkNVYg7sm8rLXkymZTGH+OIyl8l9shOZSaTSS7noxIEXhZ5sBKPdDYqXYbp3PQMR91uNw4PD1GtVgVbY4EAP4PQBkNjYn38P4n/VGLjjpFJJqPx/BLCpaUlbGxsYGdnB1arFSaTSQ4uKVFMMhHnJDOEz8IID4BEOXw9jQs5xVTCg8FA5pu4tqqMyYBh/mBS3jU5palUCl6vF8lkUvBkOiWcT+5lMm4IGTB6udiHV8XRyeWuVqviMRLT5ZzwMzOZjMwHo06VxkWK3jgSDAaF6XB4eCh9Vuh5s0Me+0zQO+cc0IECIOfNbDbj6tWr0Gg0EmXk83mJsAhJsdkNf8aeGczX3Lt3DxsbG9jY2IDb7cYf/vAHSRAzQvinf/qnHxzbWMURZAusrq5KdpAVRgzHTk9PhUHQ6XTg8XgQDodl0gOBwAjOy43ucDgQCoVQKpUEk2LjisFgIBfUcfIymczILa4ULsakpaPqOGmpSO+hEgRedsViUcRgMJBLBVk2y02hlgfy2hp+8TCzu9fm5iZSqZQ0HuENBNlsVvoYAC/7ClDxTSrtdht3794FcN73gaXEi4uLIwyCQqEgn6fVaqW5S7FYlIPH+aanxtaAVDwWi0Xq/uld2e12VKtV8VrU7nSMiCg8/CpFaBwhH5d78Pj4GC6XC/Pz80gmk9jf38fS0hLcbrf0Lebr1IIbdb5VBUTlSqXKog++nr/nfmEfYdVoDQYDHB0dSYRGZ2FSJ4GeeSaTQSgUwszMjCS8mKSkAuW/akGROk6eF76Oc8FxqUaBZ4LnhREnGQEqds4zxN+p2PJlwrvaiJcTMuK6WK1WvPXWW7BYLNLqktEm2T88Z/SWr1y5IkwFKlaeJV6IYLPZEAqFoNVqsbCwgHq9jgcPHgiLyGQyoVgsYn9/X640IsPqxYsXqNVqkgT/wbUbZwL40KRJLC0tCU9QxbpIKrbb7XKYHz16hOFwiFgshkKhIB5CtVoduTGCA2A3K71ej0gkgkePHgmBmddhE6Lg4qrdgF63Go3Z5VqtJllgbjgeNPbvdDgconS5+QiNqN2c1PCNdDLVEyqVStJKjyE9N5fReH4PGr18PuPreEXAuRd4dnYm7BLi7aziU3mQjCoorC4irt3tdkdu/mUpsdVqle5a6rjJ5GAbTxpoetw8xDRIbC6t4srjiMPhkEsxq9UqDg8P0Wq1cO3aNUSjUWSzWRwfHwvLgvimmkBimM2x0dvnzxnt0AMmDYqJ0Hg8LtcukftKCAHASKtEKruLCv8yofIZDM774+7v72NjY0OiT6fTKWwNtbk2P4dwAACJ6i6+L/CSWkcvlQpd5e9yj7N0eDAYyDlWFTJzL+N6umQv8VySScDP1+v1Ura7vr4u0SBvjwiHwxI9Z7NZrKyswG634/DwEMViUQq89Hq9JKbZIIkXWdJZNJvNODg4wJdffil4+enpKWZnZ7GysoJ79+4hlUrJs10WfV46A8wi854iXpDIbjsMTdhtPZvNSthLikipVJKu7uzoRMD/3r17qNfriP5X1RfLQD0eDx4+fIiTkxPJEPt8Pjx//lx6H3Cj0LvgYXkdT5chi0ajkVBGBezJKSVDg1gVbwrgVTv5fB6FQkG8l3v37knTnmazKRBMNpvFP//zP4shURkP3W4Xq6urcjsGyxXJdJgk5FbHx3vX2HylUqlIDwEWPpCPycokegJarVbwKioMNrFJpVL48ssv8ZOf/ESuOQEgzYfI76aCYkEAPZZCoSB3VFG5qftvXGFjIiZ22P4zFoshEolgcXFRavhnZmZG6Etsv0jDQ6NSLBYxPz8v7SuJiVLpMpEEnHv/xAep/ImDUonQ+GSzWVFSk5Q6c07Ufh9s+u50OqU/Ab84l9zLdCgYWRBnJjSiYtZUmOQ71+t1oXCxcxzzKeyjDLws57darTJu9V61cYSREIuPMpmMXNdDY72zsyPze+3aNczPzyObzSKXyyGRSIjTsr6+LiyG09NTRKNRlMtl1Ot1oezZ7XYEAgHMzc2h2Wzi7bffFkeJ0Uo0GsXDhw9l3kqlEtLpNNrtNsLhMFKpFAaDlze8/OD6XTZ4ZrypzNLptISVCwsL0gSDlCNeQGi1WnHjxg3cuHEDOzs7CAQC0Ol0ePr0qYQCv/vd7xCLxQAA6XRa6v55gSOttcViwcbGBuLxOB48eCCfwUXkhvo+ute4QiVGPPFieEmvgXhxJBKRRi/sm0owHzgPVwqFAjwejzSE/pd/+Resr69jY2ND5vHo6EgSW8BL5W4wGPDs2TNUKhVRDmrYOOk4ybQgRQwAyuUy3G43vF4v9vb2JAnJ3sQqS4NencfjQTQaRTqdFpy/2+1id3dX8H4mszKZjPQFcDgcsNlsOD4+ljJjJu+oKInL0ZNRFfw4sr6+jk6ng6+++goej0cwdN7QS/iKCkDlraoNuI1Go2Tpk8kkAoEAgsGgYJNqCM4Iy2g0wuFwYH19HYlEQooumPdggoqUPCpsNVk7rtA7Yy6j3+8jFothbW0NRqMR5XIZkUhEQnoWe3Cc/HzuI2KWhCHoqVFJM3FHiijZSWbzy5ugSbNj+Szfk5GC6qWOI8wH6XQ6hMNh7O7uSlRL40Aoo1QqwWAwSF/op0+fIpFIYG5uDqurq1LJSXhgYWFBrryiLjKZTHC5XAiHw9IjZn5+Xooo5ubmEAqFEAqF8Pz5c5ycnIixo1Hiubms6m4sN4LhOzdZPp+HVnt+NcnMzIxwS4vFItLpNE5PT1Gr1TA3N4dIJIJr164hkUjA6/UKRswmFrRAW1tbgucaDAYp/2Vv1tnZWfzxj3+U0EA9lBSV4jWpcHx6vV467XPjUCnSozWbzYhEIuj3+9K712g0Ym9vT6AAYpLNZhP37t0Tj+6zzz7D7u6ulDyzugWAVLO8//77OD4+xs7OjmBr7BmgktYnEaPRiM3NTSSTScl0p1Ip5HK5kc5maoNxXjHDeaaH5/F44PP5pIsW/4b8ZY1GI5nkpaUlOJ1O4Y8eHBygXC5LMoZd6WgMqBi4rgwrxxFm8m/evCmKltRFGgAWfagXLNL7460Vc3NzACCN3JPJpEQCLIRgkpWJVSpss9mMxcVFSS4bjUak02nxGLnO3HNMQk2C0xO6YCK30+kgn89jd3cXV69eRb/fR7VaxXA4FKoT9zOhQJ4hKtREIoFoNCpetFqRxv8zqUb4hkI+K7nrOzs7AtWoTgsdmXEkmUxKMRVvg1DxamLg7HTHq3MWFxdRrVbx5MkTCfWdTqe0qlxZWZHXUVfQeHW7XWxtbYlhOjs7w8zMDMrlMra3tyUfMTc3J42qyOaxWCyYmZkR5+FVMtZtwAsLC0KkZkFDq9XC7u4uCoUCFhcXpbcAE0mZTAaxWExoXK1WCwcHBwiFQjCbzTg7O8PJyQl8Pp80v+Gh02q1qFQqaDabWFlZQTQaxddff42joyMAEO+CG/91PL+LwiQYG2QwHK5UKhJCkgtJC+v3+wV/9Xg8mJ+fx9nZGfb392WTnp2doV6v49/+7d8kCRWPx8UaUtETU11dXUWhUMCTJ0/kIFIh0TJzzJOOjyEbOY3E8jgueiNUQiweUJuI8Fm8Xq8krBhmptNpoXlRCTE6OD09RTwel0s2WfTBdVTxUjIoLnJIL5MHDx7I87GF4enpqYSQmUwGVqtVGmGTC8xiH5/Ph1QqJeE2AIm8wuGw5BA4F1QihKW4F2hE2bCI65jP55FKpUYSbpxbNbl1mbDMlWwMQhosY52fn5dq0EajIR6jSlmj0q3VapJfODs7QzgcFsNOvJZKjk2auB4Gg0GgQ3rJp6enODs7w9zcHJaWlnDv3r2RfMUksBiTjSsrKwiHw9jf3x/B0mlcYrEY9vf3cePGDXg8HqytrUkp/sHBgVQ6sgUB+cqE0wKBgDShZwKc0TQv4iVnfHt7W/oKv/HGGzg4OEClUkEul5Pz9d/m6RILJC2I1xPv7e3JdRg6nU48XofDIVf4UOGenp7CarUKk4Gt1549e4ZCoSBEc1pthrNra2uwWq344osvpB0hu/7QS1C5smqmeVIhVkXQno2KSYFh4cPq6qooG/JlAQiTgTii2lCadzzxGVn1xA3NKqw7d+6g1Wrhyy+/lEYaKnGeofDrJtL4jN1uV8jfzOqura3JAeQBI7WKnhwPn0ajkVsRGF7xQNdqNezv78Nms2Fubg7JZFKSqNzIxDEZQfAzgZdULCrPSdaTXiuNBY0BFSOdBcIhTPQw0w0APp8PZ2dnAF5ycDudjtx/xb3AJJzqddErVwsN2G/aarVie3t7pDcI+ezcy+MKMVLS8wgplEolJJNJ6XtAVgkjQCprKn2v1ys0J61Wi2w2KxcTMDGm3rTNNaEnx3khz7tUKmF7e1v2NxPiaqQ0CUbPPWWz2bC+vo7j/7qFhC0pVW81nU7D7XZLE/bbt2/D4/HIvYAsSCEBgB6y1WqVCsnBYCC30vj9frl0NJFISLTCPTE/P49bt27hwYMHAhONm8i/dAaYVGC5rs/ng8FgwMLCAra3t1EqlcQDZSlutVqVCh8yFHZ3d8UTaLVamJ+fRyQSQSKRkO5Fg8FA+i/0ej0Jf4kpcyHUja0q2v8OvMDsNT2OdrstoT3bUer1eqlu4uclEgkB+I1GI7LZrHTeDwQCyGazyOfzgusxUcPDAAAzMzMIhUKIx+PY3d2VEIrYOddBfdZJheE/PVMmOL755hvBdfv9Pg4PD7G2toZgMCg3yzIUZ4jX6523baRnyCRMtVrF8fExksmkJErJ8CDlht5iq9WSyjY1Kcr5p8KdhK3x53/+5xIqEvoAzhN65Flms1nBedVLGElp9Hg8ciUSeysAkMRXJBKRFoVcI2b3HQ6HQE30fpgszGQyUk6vVnJxbJNQ48j4UBu8kK7FSziXl5fRbrfFgKgFLsSv7XY75ubm5G8ASIKJzbjp6QMvG0E1m02pIFRpoKlUShJwTCir+3wSZ4HRBBN0q6ureP78ubR5ZXEPS4OHwyGKxSKMRiMWFxcxPz8Pr9eLaDSKk5MT7O7uyjNms1lxANUiIa32/MohvV6PdDqN7e1t4ckzSWownF/pvrGxgcPDQyn7Zv+YcSirlyrdbreLWCwmWUQyF+bn57G4uIgnT55I6MCL/xYWFuQyw+vXr8vkkG/HUll6Bkxc6fV66W5FxUTrSjyZYR+V9EVl/LpKlweIOKPBYJBr0ZnJ52KQY0wKEikxTNQALxsA3b59G8fHx8hms9KvgIorFArJAX3w4AHS6bSEwioOpmaqX1d6vR6Oj49hMplkLpeWlpDNZvHVV1/B6XTC5/Ph7t27MJlMeOuttyTU5NVIajSh0WikYQhDslKphG+//VbGSXiBnhUb5uj157cq8DlYuaYqJR66i2yGVwnxVgDS5MZoNGJ/fx8nJycYDAZwOp2SCGKpciwWk5tPbDabJE8Is5CiGIvFoNFoZF6YZCb0VCqVpDCIPStUBUw8lMqLTgKjj3GFninpey6Xa+TePOZWFhYWBC4h3ZHeJqMLKlZWKqp0Rr6OiodKkPCLSj9Lp9Nylrmu/Fet7JpE8bIIqNc7v1Dg5s2bSCQSGA6HomiZ3yCkValUsLe3J+Xk7FHBs+Tz+XBwcCAOIPMyLOxhTkltCNRsNgUaZInw9vY2nj9/LslpskXGKcwaq/cCe2Oq1m1hYQFXr15FLBaTgRcKBdTrdekvwGyt0Xh+kyczwvRouEi0jmqYpXJTefAuMhYYhqrshUnCl4vCogiGCYQzHA6HZL2bzaa0nWMTICpFeuRUzoFAAOFwGPl8Xgj5XBTWzvPacobvNBrExUk9UxWPSmUbV4hrMXFDRdput/HkyRMsLS3h3Xffhd/vx8nJCZaXl+FwOMRbIaVKJal7vV6srKxIf4JsNitJBfZcpSdLhUscl2wQbmY2peH4VCrguPLkyRN4vV6BfegIbG5uYjA4r5cntsdET71elyQbPTxyzdmnmXPVarVwcnIiiUCXyyVlsYzIeD6YkK3X63jy5InwuTkuKiOGo5MoI2KPTNyxq5vqgKTTaamIpFdN7JqODNfSZDIhFAqhXC5LUorRCKEasnpoNPnMvV5P+MH8HBWKYJVXv9+fqG8wjQLvXgwEArh27Rp2dnawtbU1Qokj3529umOxmJSUq9z4YDCI1dVVXL9+HY8fP8bKyorcJMGCJ46LyTSWQbNCkre5EPbgujLCstvt/+/cBswJV2+F6HQ6iEQiWFlZwbNnz2RxSXHiguzt7UkGMRgMolAoiHJUyyNfpWwvesQqjMDXUtm9bvEAwyZ+VrlchkajEQzbbrdLJRyruYgVEmOmZ8bnTCaTSCaTMBgMSCQSspiELqjE1PFw3gih8IBSgVFeR+murKwIHc9kMuHmzZuYn59HIpHA4eEhbty4gbfeegu//OUvUSgU8POf/1zCViZduBG5dpubm8LX5XqxaYrKKSYThdQkJuhMJpMYHWLqqoc77p1aAPB//s//gdPpxMrKClZXV6Wnr8fjwebmJra2tpDL5TA/Py83ZbACibg1E1DsCc3DR9qVxWKRMDObzY5UWqmULHKfnz9/LhGauk+pfNVy4nGFio0XJx4eHgoXmj2PB4OB4NDsocHmPgBGHBgmllwulzBqCoUCdDqdJAmpkFQue71el34jamTCM+hyuXDlyhXpzKc6TpcJ90yxWMTp6SkikQgikQjeffddKXmmQ9Lr9bCzswOv1wuPxyN7k9ABHadMJoN0Oo3r16/D5/NBo9Fgbm4OVqtVkpw884zAer3eCARK/JqlxSpn2+FwYHZ29tLbMcZSumoYz678/LBwOIxsNit0JxV8Z+MRhuO8EI4YJz0ZteSQiTI1pObfqRUyFyt6JglDf2iMhBCIY5FoTq9HrRBjkYiavaZHpHru9Jz5c34WjQhpRTyYKl2HhodjVSGGSb1AHgIyR1j44PP50Ov1cHBwgLt37+JnP/sZ3nnnHfz+97/HJ598IonDXq8n1EHerpBIJDAzMyNXs6ysrIh3ydCMtwWoN9PyQLF8GoDAMoQ/VOU+rhBH5OFiQ3GTyQS/34+f/vSn+M1vfoPT01PMzMzgypUrWF9fF4qcikGWSiVRipFIBHNzc6jX6/j2228xHA6l6o5rC0Aa0RsMBsHn1ShNZQOQZ8p1nIS9MBwOR8Jf4ufkCrPZP6Mylscy30BDpkYijLTYJY5wSKFQQCAQkEo3g8GA09NTgZBY3s3cgxpas6CKymlSZ4jR5IsXL3Djxg1Eo1G8+eabOD4+xt27d4VCxv3CtgS8Ep5JNxrTUqkkxVi5XA7NZhO5XE6gF+Z1tre3YTAY5IonQmjE59Wo1mazYX5+XiKkXC4nvcJ/SCa6I42bptPpIJVKwWAwYHFxEQsLCzI4NfkDQJpssDcuE1J2ux2VSkU2JZWPCjPQ+l9Mll1kKaibmQmDSYUUHLVpBZW5emUQlQ9DYypGhnyqx8KNdnHDcT75MzUxSI/o4us4R6+L7Wo0GmEsuFwuSfARj+52u/jiiy9QKBSwsrKCjz76CE+fPsW9e/ekvR69OWbFI5GINLfhVdfffvutVHsRJ6MnwPnk86thKo0ujcFFwzqOkKTe6/Wwvb0tYfc777wDm82G5eVlvP/++/jNb36Do6Mj3L59W65iOjo6Qrlcxvz8vBw0slEYomYyGezv76PRaKBarUpS+fDwECaTCZubm9BqtXj06BFSqZQYDnqSxDvVDmCck0nYC8vLyxJtkV1EnJOYo15/fvcbO5HpdDopCAgEAnA4HELNU5N53L9MePMWiN3dXamq1Ov10k6xUqmM4Laqg6buFf5s0giNyV3e3xcKhfDee+/h9PQUyWRS+MFMDjNBS2yeThz3UzKZRDwel0Y/hEV5HrkWrGRUvXdWxrLUn/DNwsIC+v0+dnZ2UCqVLr3BeiztpCoFWpVutyv9JHlr7vHxsVwJwt6h6sJyA7KJuRqSAt/FKtWFfd1Z1AAADURJREFUpKegLhwVHkM6t9s9EkJNIlrt+YVz9Lw4TmZiiTdyfDwkKl+YhHVVoapKkt9zLDzYKvdRhU0oF1+vzs24wqIUGkFyWHmzwsHBAfL5PI6PjzE7Oys8S2b8E4mEhK02mw1ra2vw+/1otVp44403MBwOhSrWarWktwNDb41G851eGWwIRMNGz0vFHifldapJ2VKphN///vcYDof4+OOP4fP5sL6+jqOjI+zt7cnFjF6vFzs7O6hWq3jnnXfQ6/XEQ2PJN6uaQqGQ5CYikYh4ebz9YmdnR+7QYgKR31NUnJ5w1CTjfPPNN6X4RMXoue9cLpd8RqlUwvHxsVA6WSlYrValFSQA6Q/B5KDNZhvhnTJR5ff7pQc0cU5gdN9SSV00JIx+xxFVF5ydnSEWi+GDDz7AcDjE8vIy3nrrLfzmN7+R6ImGgNxoFsKw0TjHxtt9aZxY6KG2qKSSZnRKmIV7LBAISN9r3otGfcdy81fJ2P10gZcHnZuEPU9JC/N4PKhUKiO0CU40PTe1jaPqQRPYVzO6dPlZeskFVuvfSWXy+XxYXFyUfravY1FbrZbgjHwGEr5TqZSElIQM+Nn8lyGHqlzVrP9FyIBh9kXFrEIJqmdIrEz1LMaVTqeDhw8finU3Go0oFAq4f/8+7HY7jo6OpGF8LBbD4eEhnj59iuXlZaysrGB2dlYgId6X9uLFCzSbTeTzeZRKJfGkWEobiURQr9elOIYHgyWsXCsqSlJz1CTpJGE3DyAVjl5/3u/5d7/7HXq9Hn7xi19gZWUFP/nJT5BKpRCLxdBut6UwpN/vy03UrEDiPuB1Q2TimM3mkSb7lUpFknHEsS/uQe4NMlvoJU4qDocDTqcTkUhEkqHValXWUFUozMUQ02XupV6vj1w8yvwCAIFjWMXGTmZ8b1IPOSY1mc2G8BqNRi4hVQ3oJPuW+5/JyL//+7+H3++HVqvFrVu3sL+/jxcvXog+ACCMGXVe2MOaLAMaEHK2CRHSgVOflfuC/ZiXl5cRDodxeHgoeRqTySQQA5kQr5Kx++lyEtQQkZVj6XRaumzx+g8S04mH0OrRivK6bTadII5KxUQrQy+byTxuEv5LmoZGo5FO9eTLTSL9/nnzZTYH9/l8cLvdQpXhIVSxKzYnZ/02raXquXMOuZAq1vsqw3Dx9xepca8jVGbMTufzeTx//hxWq1WUH0Mn4vHxeBz37t2Ta6fptVFp8vBx8/L74fBlsQSNILmMXq8X8XgcrVZLlBTfmzglvcBJjOdF/JTGvFqt4ssvv4TL5cJf/MVf4Pr166jVavj0008lQcZm6b1eDw6HA36/X/i4w+FwpMkSlQrXmlggnQv+jgqJe1Xta8sWnhwfud/jyOHhoXRUY+TFPd/tdvHixQuheVmtVvHGaQzZ01nl2KqhtVZ7fi8Yx8b3IB2OPGGOUYXjCGMBEGYP8HL/jrueqjIfDAZIpVLI5/NYWFgAAKysrODtt98WLjVvBFErNpln6XQ6AiMR66Xh0el0wjtWE56EDhmNcS5tNhuSyaSUvLOBF71lGuFXydjX9ahyMfTNZDIwm82IRqPodDo4PDzEcDiUnrHcvN1uV+gUDC9JKifYXygU5NAR/2Uix2AwjCQBeNDJj6TyHrex90VhlzTyddvtNgKBgChg9SogevPFYlGex263S/KH2C/nUJ1P4Ic3oapoVMYGN7UKs7wOxEAKD+lNoVAIm5ubctsqP09NeDGiUUNJGhCOXQ3riQ3m8/kRXJq0IdLvVCxTjQoIX5GzPEnGm0qAyo1J2mKxiEePHuGNN97AzZs38cEHHyCbzeLXv/61QF9sB8mDxFuCWRzA8dO4ci5UxoyqZNTcBOfzomeo1WoFix5Xnj59KnQ1n8+HcDgsChgYjWosFotQwtSIk6wUVah4SefUarVCnxsMBkgkEjg6OvqOA8D1ooPF/tfqunzf95etpTqP+XxeEmpsuv/mm2/i8PAQX375pewl4uVUrgDkLKpJbrPZLNCWw+EQMgDhBHq1VN5c21QqJR3PgsGgJBuNRiNmZmawubmJ995775VjG5u9QGVBigtDKC5iPB6H3W7H7Ows9Ho9Dg4OhMtmNpvh9XrFa1AbOPd6PaytrWFhYQHlchkOhwOpVArFYlEWlO49J5FQBG8lZkPiSSGF7xNmlVm22mq1EA6HsbKygt3dXTkoKixAagqzyEygUPi3wHc3ncpGUL0jRhTqYVU9qEkVrsViQSQSwcnJCbxerzRXX15elpJftf8FgJH5VzFuNamnEv2Blz1ZecBJNAdeXoFOwjvXUavVjtDnyH6gAnsdVgoVGxOFWq0WW1tb+Pbbb3Ht2jWEQiG8//77UrfP6jkqaiow9htgfoLzr86LisuqeL2aJKP3rzbzURXLJCyNs7MzaXrv9XpRqVQEfyfm2ev1cPfuXbm9Y3Z2VvoiLC0t4eTkBPv7+5LA5NiIe7INZ6fTkaow/owGk0k8FldwLoCXFYWq0uJ8jCvqWahWq7h//z5++tOfIhQKCXPq7bffRjwex9HRkZQ502hQX6g5Jf6fXj/3HhkMPFd0Jq1WqzTZJ1uBjXjoHUejUYRCIVy9ehWRSAQbGxuvHNdY/XQZLlycDAAyqYPBQEosQ6EQNJrzEll6LAxpGNrysj8ebIa3vGiPLAiC/wTteQcUN69aUKA2zJlUmJBQyerD4VDKPd1utxQ6kOKl4s9caLX3KDexWqVDUb+/CCN8nzegHgz1s8cVvV6P27dvo9VqybXqm5ubCIfD0geC80ClQc+A0I76XBybyuBQw3t6sOTlcl3UDkyqt6dGU2w8pHI+J5GLc0ivbTAY4PPPP8fNmzfx7rvv4saNGxgMBvjXf/1X6Q+i1WqF+kcKVTAYlB4DfGa1Dv+HWDaql66W/nK+WNzABj3jCvcAs/XFYhH5fB43btyAz+eDTqfDlStX0Ov18PDhQ9RqNZyenkrHtGAwCLvdPtK/QFU26rqoZ4trp0JkhI74pdIi1cq3ixHeOKLqmH6/j4ODA+RyOSwuLqLX68n1O/F4HPl8XowDDTjnic/B/czvmXSksY3+V5c16iZG18FgEP3++UW6TLTV63Upn37jjTcQjUYRDofh9XovhYouVbq8gnhcITaq1+svzeJRyOek6HTnDXR+TKHl/CFhSMks9mUyCUb3YwgN3a1bt/DZZ59J3wM2m2fzZpZMqriqWjUIjCpLtSwbeBmiMmHHn6n4n6qYqSBZ3UPuqsoKmcQ7uhiWcuz8PpVK4bPPPsPVq1fhdDpx9epV/OVf/iU++eQTCS/VQhCz2SzPqsIXqjcLvOQXE1Lia9SoQU2iEuMmrWmSIpDvwy0PDw/R6XRw8+ZNhMNh6HQ6rK2tYTgc4uHDhwK/EfZhb41YLDZSdMT54lzye8JMKkQCQO4XU7131eNX50n1hCcRvk+hUMDh4SHeffddqRIdDoe4efMmYrEYHjx4II4AK+Ro8FUYi3PI3gsWi0UMIiNo9p7gzSpbW1typRgLfcxmM2ZnZ6WEnEVgl0FFr18zO5U/Ken3+1Idd/XqVfFui8Uijo+PpeUmb83ga4CX0Yx6CLl51f9/X7JPPXj8O3pB/D2AEYV0MQq4rD/p98nFqEL1xh4+fIitrS289957sFqteOedd1Aul/HZZ59JJENiPQ0Tr5BXu27x+YCXVWKUi0pHNQY0Ypw/QjeTCBUlve3BYICTkxOBB0js39jYgEajwTfffCOJ7d3dXemV7Ha7US6XR7BoVamqpfaqwlRzC6oi5nPRYKqR2SS9Fy7CcYw6Hz9+jL/927+VO/jMZjOuXLmC9957D+l0Wm6nYTKQ88NnVo1VqVQSOhkhE9Jd0+k0jo6OUCwWsbCwgEajgb29Pej1eqytrQlc4XQ64fF4pOqNfPZXyVTp/i8RhsTsrqR64uyxyr4Ef6oSjUble3XjUwGoIfBvf/tbZDKZkXCTkY7q2QIvexGrVxHx56pyuJgkUz3tiweRh1+v18vXuBIKhUb+rypKlt6TrkWFvri4KN4u8yqDwUBu12CO5uL7ft/Yvu93lO97j0mhMPV91Hlkk/EXL17ggw8+EM+dEcvp6SkKhYJQP0lJJI+fX4QWyFm32+2Sc7qYEP36669Rr9fxwQcfoFarSWMqKmiyJrxeL0KhkOS8XiVTpTuV/2skEomM/bf1eh1fffXVd37u9/t/8DU2m036sf5PyqueETjnK5+cnIz8TKPR/OCzvy7b5/9LYe8LFbZiVd3nn3+O2dlZKbRiLxh2Ptzf35fkPW/LYPRCz1dV5owAeKEnc0ekhKVSKdy9exdarRZXrlzBzMyMwBfsD0E+Or3fV8lU6U5lKlP5/53cunXre3+u1Wpx//59PHr0SH52EbtXI4HLDJQqGo3mBzm26m0Q7OkLnF9oenh4iHv37o3QCH/729/+8OeMy5ubylSmMpWp/Pfl9UqbpjKVqUxlKq8lU6U7lalMZSo/okyV7lSmMpWp/IgyVbpTmcpUpvIjylTpTmUqU5nKjyhTpTuVqUxlKj+i/D/07euY/Y3k/AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install segmentation_models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6a1NmMua5nr",
        "outputId": "092bd5b9-3291-44b6-8ff9-05f124575a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting segmentation_models\n",
            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 7.1 MB/s \n",
            "\u001b[?25hCollecting image-classifiers==1.0.0\n",
            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
            "Collecting efficientnet==1.0.0\n",
            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from efficientnet==1.0.0->segmentation_models) (0.18.3)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation_models) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation_models) (1.21.6)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->segmentation_models) (1.5.2)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (1.3.0)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2021.11.2)\n",
            "Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (1.4.1)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (3.2.2)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (7.1.2)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2.4.1)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->efficientnet==1.0.0->segmentation_models) (2.6.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (1.4.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet==1.0.0->segmentation_models) (1.15.0)\n",
            "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
            "Successfully installed efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 segmentation-models-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from segmentation_models.losses import CategoricalCELoss\n",
        "# class_weights=np.load(\"/content/drive/MyDrive/Colab Notebooks/Vox2Vox/class_weights.npy\")\n",
        "class_weights=[0.01,0.15,0.14,0.1,0.35,0.25]\n",
        "class_weights=np.asarray(class_weights)\n",
        "loss1=CategoricalCELoss(class_weights=class_weights)"
      ],
      "metadata": {
        "id": "75QQ4_s4bBNj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2ab26e5-e2cc-4fe5-e21f-46fdb566eafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Segmentation Models: using `keras` framework.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smooth = 1e-15\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_true = tf.keras.layers.Flatten()(y_true)\n",
        "    y_pred = tf.keras.layers.Flatten()(y_pred)\n",
        "    intersection = tf.reduce_sum(y_true * y_pred)\n",
        "    return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)"
      ],
      "metadata": {
        "id": "ozsADD4ojrWB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"  \n",
        "Generator:    \n",
        "The encoder-decoder architecture consists of:\n",
        "encoder:\n",
        "C64-C128-C256-C512-C512-C512-C512-C512\n",
        "decoder:\n",
        "CD512-CD512-CD512-C512-C256-C128-C64\n",
        "Discriminator\n",
        "C64-C128-C256-C512\n",
        "After the last layer, a convolution is applied to map to\n",
        "a 1-dimensional output, followed by a Sigmoid function.    \n",
        "\"\"\"\n",
        "\n",
        "from numpy import ones\n",
        "from numpy.random import randint\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.initializers import RandomNormal\n",
        "from keras.models import Model\n",
        "from keras.models import Input\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import Conv2DTranspose\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Concatenate\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import BatchNormalization\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras import models, layers, regularizers\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "\n",
        "#############################################################################\n",
        "#Define generator, discriminator, gan and other helper functions\n",
        "#We will use functional way of defining model and not sequential\n",
        "#as we have multiple inputs; both images and corresponding labels. \n",
        "########################################################################\n",
        "\n",
        "#Since pix2pix is a conditional GAN, it takes 2 inputs - image and corresponding label\n",
        "#For pix2pix the label will be another image. \n",
        "\n",
        "# define the standalone discriminator model\n",
        "#Given an input image, the Discriminator outputs the likelihood of the image being real.\n",
        "    #Binary classification - true or false (1 or 0). So using sigmoid activation.\n",
        "#Think of discriminator as a binary classifier that is classifying images as real/fake.\n",
        "\n",
        "# From the paper C64-C128-C256-C512\n",
        "#After the last layer, conv to 1-dimensional output, followed by a Sigmoid function. \n",
        "\n",
        "def bn_act(x, act=True):\n",
        "    x = tf.keras.layers.BatchNormalization()(x)\n",
        "    if act == True:\n",
        "        x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "    return x\n",
        "\n",
        "def conv_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
        "    conv = bn_act(x)\n",
        "    conv = tf.keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)\n",
        "    return conv\n",
        "\n",
        "def stem(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
        "    conv = tf.keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)\n",
        "    conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
        "    \n",
        "    shortcut = tf.keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
        "    shortcut = bn_act(shortcut, act=False)\n",
        "    \n",
        "    output = tf.keras.layers.Add()([conv, shortcut])\n",
        "    return output\n",
        "\n",
        "def residual_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
        "    res = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
        "    res = conv_block(res, filters, kernel_size=kernel_size, padding=padding, strides=1)\n",
        "    \n",
        "    shortcut = tf.keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
        "    shortcut = bn_act(shortcut, act=False)\n",
        "    \n",
        "    output = tf.keras.layers.Add()([shortcut, res])\n",
        "    return output\n",
        "\n",
        "def upsample_concat_block(x, xskip):\n",
        "    u = tf.keras.layers.UpSampling2D((2, 2))(x)\n",
        "    c = tf.keras.layers.Concatenate()([u, xskip])\n",
        "    return c\n",
        "\n",
        "def define_discriminator(image_shape):\n",
        "      \n",
        "    # weight initialization\n",
        "    init = RandomNormal(stddev=0.02) #As described in the original paper\n",
        "      \n",
        "    # source image input\n",
        "    in_target_image = Input(shape=image_shape)  #Image we want to convert to another image\n",
        "    # target image input\n",
        "    # in_target_image = Input(shape=image_shape)  #Image we want to generate after training\n",
        "    # print(\"Input_shape\", in_src_image.shape)\n",
        "    # print(\"Target_shape\", in_target_image.shape)\n",
        "    # concatenate images, channel-wise\n",
        "    # merged = Concatenate()([in_src_image, in_target_image])\n",
        "      \n",
        "    # C64: 4x4 kernel Stride 2x2\n",
        "    d = Conv2D(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(in_target_image)\n",
        "    d = LeakyReLU(alpha=0.2)(d)\n",
        "    # C128: 4x4 kernel Stride 2x2\n",
        "    d = Conv2D(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)\n",
        "    d = BatchNormalization()(d)\n",
        "    d = LeakyReLU(alpha=0.2)(d)\n",
        "    # C256: 4x4 kernel Stride 2x2\n",
        "    d = Conv2D(256, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)\n",
        "    d = BatchNormalization()(d)\n",
        "    d = LeakyReLU(alpha=0.2)(d)\n",
        "    # C512: 4x4 kernel Stride 2x2 \n",
        "      # Not in the original paper. Comment this block if you want.\n",
        "    d = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)\n",
        "    d = BatchNormalization()(d)\n",
        "    d = LeakyReLU(alpha=0.2)(d)\n",
        "    # second last output layer : 4x4 kernel but Stride 1x1\n",
        "    d = Conv2D(512, (4,4), padding='same', kernel_initializer=init)(d)\n",
        "    d = BatchNormalization()(d)\n",
        "    d = LeakyReLU(alpha=0.2)(d)\n",
        "    # patch output\n",
        "    d = Conv2D(6, (4,4), padding='same', kernel_initializer=init)(d)\n",
        "    patch_out = Activation('sigmoid')(d)\n",
        "    # define model\n",
        "    # print(patch_out.shape)\n",
        "    model = Model(in_target_image, patch_out)\n",
        "    # print(model.summary())\n",
        "    # dot_img_file = 'dmodel.png'\n",
        "    # plot_model(model, to_file=dot_img_file, show_shapes=True)\n",
        "    # compile model\n",
        "      #The model is trained with a batch size of one image and Adam opt. \n",
        "      #with a small learning rate and 0.5 beta. \n",
        "      #The loss for the discriminator is weighted by 50% for each model update.\n",
        "      \n",
        "    opt = Adam(lr=0.0002, beta_1=0.5)\n",
        "    model.compile(loss='binary_crossentropy', optimizer=opt, loss_weights=[0.5])\n",
        "    return model\n",
        "\n",
        "def define_generator(image_shape=(208,208,3)):\n",
        "    f = [64, 128, 256, 512, 1024]\n",
        "    inputs = layers.Input(image_shape, dtype=tf.float32)\n",
        "    \n",
        "    ## Encoder\n",
        "    e0 = inputs\n",
        "    e1 = stem(e0, f[0])\n",
        "    e2 = residual_block(e1, f[1], strides=2)\n",
        "    e3 = residual_block(e2, f[2], strides=2)\n",
        "    e4 = residual_block(e3, f[3], strides=2)\n",
        "    e5 = residual_block(e4, f[4], strides=2)\n",
        "    \n",
        "    ## Bridge\n",
        "    b0 = conv_block(e5, f[4], strides=1)\n",
        "    b1 = conv_block(b0, f[4], strides=1)\n",
        "    \n",
        "    ## Decoder\n",
        "    u1 = upsample_concat_block(b1, e4)\n",
        "    d1 = residual_block(u1, f[4])\n",
        "    \n",
        "    u2 = upsample_concat_block(d1, e3)\n",
        "    d2 = residual_block(u2, f[3])\n",
        "    \n",
        "    u3 = upsample_concat_block(d2, e2)\n",
        "    d3 = residual_block(u3, f[2])\n",
        "    \n",
        "    u4 = upsample_concat_block(d3, e1)\n",
        "    d4 = residual_block(u4, f[1])\n",
        "    \n",
        "    outputs = tf.keras.layers.Conv2D(6, (1, 1), padding=\"same\", activation=\"softmax\")(d4)\n",
        "    model = tf.keras.models.Model(inputs, outputs)\n",
        "    return model\n",
        "\n",
        "# # define the standalone generator model - U-net\n",
        "# def define_generator(image_shape=(208,208,3)):\n",
        "#     # weight initialization\n",
        "#     init = RandomNormal(stddev=0.02)\n",
        "#     # image input\n",
        "#     # in_image = Input(shape=image_shape)\n",
        "#     print(\"Entered gen\")\n",
        "#     FILTER_NUM = 64 # number of filters for the first layer\n",
        "#     FILTER_SIZE = 3 # size of the convolutional filter\n",
        "#     UP_SAMP_SIZE = 2 # size of upsampling filters\n",
        "#     dropout_rate=0.0\n",
        "#     batch_norm=True\n",
        "#     NUM_CLASSES=6\n",
        "#     inputs = layers.Input(image_shape, dtype=tf.float32)\n",
        "  \n",
        "#     # Downsampling layers\n",
        "#     # DownRes 1, convolution + pooling\n",
        "#     conv_128 = conv_block(inputs, FILTER_SIZE, FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     pool_64 = layers.MaxPooling2D(pool_size=(2,2))(conv_128)\n",
        "#     # DownRes 2\n",
        "#     conv_64 = conv_block(pool_64, FILTER_SIZE, 2*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     pool_32 = layers.MaxPooling2D(pool_size=(2,2))(conv_64)\n",
        "#     # DownRes 3\n",
        "#     conv_32 = conv_block(pool_32, FILTER_SIZE, 4*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     pool_16 = layers.MaxPooling2D(pool_size=(2,2))(conv_32)\n",
        "#     # DownRes 4\n",
        "#     conv_16 = conv_block(pool_16, FILTER_SIZE, 8*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     pool_8 = layers.MaxPooling2D(pool_size=(2,2))(conv_16)\n",
        "#     # DownRes 5, convolution only\n",
        "#     conv_8 = conv_block(pool_8, FILTER_SIZE, 16*FILTER_NUM, dropout_rate, batch_norm)\n",
        "\n",
        "#     # Upsampling layers\n",
        "   \n",
        "#     up_16 = layers.UpSampling2D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(conv_8)\n",
        "#     up_16 = layers.concatenate([up_16, conv_16], axis=3)\n",
        "#     up_conv_16 = conv_block(up_16, FILTER_SIZE, 8*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     # UpRes 7\n",
        "    \n",
        "#     up_32 = layers.UpSampling2D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_16)\n",
        "#     up_32 = layers.concatenate([up_32, conv_32], axis=3)\n",
        "#     up_conv_32 = conv_block(up_32, FILTER_SIZE, 4*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     # UpRes 8\n",
        "    \n",
        "#     up_64 = layers.UpSampling2D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_32)\n",
        "#     up_64 = layers.concatenate([up_64, conv_64], axis=3)\n",
        "#     up_conv_64 = conv_block(up_64, FILTER_SIZE, 2*FILTER_NUM, dropout_rate, batch_norm)\n",
        "#     # UpRes 9\n",
        "   \n",
        "#     up_128 = layers.UpSampling2D(size=(UP_SAMP_SIZE, UP_SAMP_SIZE), data_format=\"channels_last\")(up_conv_64)\n",
        "#     up_128 = layers.concatenate([up_128, conv_128], axis=3)\n",
        "#     up_conv_128 = conv_block(up_128, FILTER_SIZE, FILTER_NUM, dropout_rate, batch_norm)\n",
        "\n",
        "#     # 1*1 convolutional layers\n",
        "   \n",
        "#     conv_final = layers.Conv2D(NUM_CLASSES, kernel_size=(1,1))(up_conv_128)\n",
        "#     conv_final = layers.BatchNormalization(axis=3)(conv_final)\n",
        "#     conv_final = layers.Activation('softmax')(conv_final)  #Change to softmax for multichannel\n",
        "\n",
        "#     # Model \n",
        "#     model = models.Model(inputs, conv_final, name=\"UNet\")\n",
        "#     # print(model.summary())\n",
        "#     return model\n",
        "\n",
        "# gen_model = define_generator((256,256,3))\n",
        "# plot_model(gen_model, to_file='gen_model.png', show_shapes=True)\n",
        "\n",
        "\n",
        "# define the combined generator and discriminator model, for updating the generator\n",
        "def define_gan(g_model, d_model, image_shape):\n",
        "    # make weights in the discriminator not trainable\n",
        "    for layer in d_model.layers:\n",
        "      if not isinstance(layer, BatchNormalization):\n",
        "        layer.trainable = False       #Descriminator layers set to untrainable in the combined GAN but \n",
        "                                                  #standalone descriminator will be trainable.\n",
        "              \n",
        "    # define the source image\n",
        "    in_src = Input(shape=image_shape)\n",
        "    # suppy the image as input to the generator \n",
        "    gen_out = g_model(in_src)\n",
        "    # print(gen_out.shape)\n",
        "    # print(\"gen_out\", type(gen_out))\n",
        "    # supply the input image and generated image as inputs to the discriminator\n",
        "    dis_out = d_model(gen_out)\n",
        "    # src image as input, generated image and disc. output as outputs\n",
        "    model = Model(in_src, [dis_out, gen_out])\n",
        "    # print(model.summary())\n",
        "    # dot_img_file = '/tmp/model_1.png'\n",
        "    # plot_model(model, to_file=dot_img_file, show_shapes=True)\n",
        "    # print(plot_model(model))\n",
        "    # compile model\n",
        "    opt = Adam(lr=0.0002, beta_1=0.5)\n",
        "      \n",
        "      #Total loss is the weighted sum of adversarial loss (BCE) and L1 loss (MAE)\n",
        "      #Authors suggested weighting BCE vs L1 as 1:100.\n",
        "    model.compile(loss=[loss1,'mae'], \n",
        "                optimizer=opt, loss_weights=[1,100])\n",
        "    return model\n",
        "\n",
        "# select a batch of random samples, returns images and target\n",
        "def generate_real_samples(dataset, n_samples, patch_shape):\n",
        "    # unpack dataset\n",
        "    trainA, trainB = dataset\n",
        "    # choose random instances\n",
        "    ix = randint(0, trainA.shape[0], n_samples)\n",
        "    # retrieve selected images\n",
        "    X1, X2 = trainA[ix], trainB[ix]\n",
        "    XX=tf.keras.utils.to_categorical(X2, num_classes=6)\n",
        "    # generate 'real' class labels (1)\n",
        "    y = ones((n_samples, patch_shape, patch_shape, 6))\n",
        "    return [X1, XX], y\n",
        "\n",
        "# generate a batch of images, returns images and targets\n",
        "def generate_fake_samples(g_model, samples, patch_shape):\n",
        "    # generate fake instance\n",
        "    X = g_model.predict(samples)\n",
        "    # print(X.shape)\n",
        "    # create 'fake' class labels (0)\n",
        "    # print(\"X shape\", X.shape)\n",
        "    y = zeros((len(X), patch_shape, patch_shape, 6))\n",
        "    return X, y"
      ],
      "metadata": {
        "id": "iPuqno_cyEyC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generate samples and save as a plot and save the model\n",
        "#GAN models do not converge, we just want to find a good balance between\n",
        "#the generator and the discriminator. Therefore, it makes sense to periodically\n",
        "#save the generator model and check how good the generated image looks. \n",
        "def summarize_performance(step, g_model, dataset, n_samples=3):\n",
        "    # select a sample of input images\n",
        "    [X_realA, X_realB], _ = generate_real_samples(dataset, n_samples, 1)\n",
        "    # generate a batch of fake samples\n",
        "    X_fakeB, _ = generate_fake_samples(g_model, X_realA, 1)\n",
        "    # X_fakeB=np.squeeze(X_fakeB)\n",
        "    # print(X_fakeB.shape)\n",
        "    # plt.imshow(X_fakeB[0,:,:])\n",
        "    # scale all pixels from [-1,1] to [0,1]\n",
        "    X_realA = (X_realA + 1) / 2.0\n",
        "    # X_realB = (X_realB + 1) / 2.0\n",
        "    # X_fakeB = (X_fakeB + 1) / 2.0\n",
        "    # plot real source images\n",
        "    for i in range(n_samples):\n",
        "      plt.subplot(3, n_samples, 1 + i)\n",
        "      plt.axis('off')\n",
        "      plt.imshow(X_realA[i,:,:,0], cmap='gray')\n",
        "    # plot generated target image\n",
        "    for i in range(n_samples):\n",
        "      plt.subplot(3, n_samples, 1 + n_samples + i)\n",
        "      plt.axis('off')\n",
        "      plt.imshow(X_fakeB[i,:,:,0], cmap='gray')\n",
        "    # plot real target image\n",
        "    for i in range(n_samples):\n",
        "      plt.subplot(3, n_samples, 1 + n_samples*2 + i)\n",
        "      plt.axis('off')\n",
        "      # print(\"XRB\", X_realB.shape)\n",
        "      realB=np.argmax(X_realB, axis=-1)\n",
        "      # print(\"XRA\", realB.shape)\n",
        "      plt.imshow(realB[i,:,:], cmap='gray')\n",
        "    # save plot to file\n",
        "    filename1 = 'plot_%06d.png' % (step+1)\n",
        "    plt.savefig(filename1)\n",
        "    plt.close()\n",
        "    # save the generator model\n",
        "    filename2 = 'model_%06d.h5' % (step+1)\n",
        "    g_model.save(filename2)\n",
        "    print('>Saved: %s and %s' % (filename1, filename2))"
      ],
      "metadata": {
        "id": "q4A8IqfHVpVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_shape = src_images.shape[1:]\n",
        "# image_shape=(256,256,1)\n",
        "gt_shape=(208,208,6)\n",
        "# print(gt_shape)\n",
        "# define the models\n",
        "d_model = define_discriminator(gt_shape)\n",
        "g_model = define_generator(image_shape)\n",
        "# define the composite model\n",
        "gan_model = define_gan(g_model, d_model, image_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULejQ1PmDwzC",
        "outputId": "eec42b7f-ec57-4527-c5a2-f2171cc6e42b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=src_images[0:360,:,:,:]\n",
        "Y_train=tar_images[0:360,:,:,:]\n",
        "X_valid=src_images[360:,:,:,:]\n",
        "Y_valid=tar_images[360:,:,:,:]"
      ],
      "metadata": {
        "id": "YEavmOzm1kTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define data\n",
        "# load and prepare training images (full dataset)\n",
        "tdata = [src_images, tar_images]\n",
        "\n",
        "def normalise_image(data):\n",
        "    '''\n",
        "    make image zero mean and unit standard deviation\n",
        "    '''\n",
        "    X1, X2 = data[0], data[1]\n",
        "    img_o = np.float32(X1.copy())\n",
        "    m = np.mean(img_o)\n",
        "    s = np.std(img_o)\n",
        "    XX=np.divide((img_o - m), s)\n",
        "    return [XX, X2]\t\n",
        "\n",
        "train_dataset = normalise_image(tdata)"
      ],
      "metadata": {
        "id": "UWv6zE9oxDIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define data\n",
        "# load and prepare training images\n",
        "tdata = [X_train, Y_train]\n",
        "\n",
        "def normalise_image(data):\n",
        "    '''\n",
        "    make image zero mean and unit standard deviation\n",
        "    '''\n",
        "    X1, X2 = data[0], data[1]\n",
        "    img_o = np.float32(X1.copy())\n",
        "    m = np.mean(img_o)\n",
        "    s = np.std(img_o)\n",
        "    XX=np.divide((img_o - m), s)\n",
        "    return [XX, X2]\t\n",
        "\n",
        "train_dataset = normalise_image(tdata)"
      ],
      "metadata": {
        "id": "0IbIYgnrEJkt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define data\n",
        "# load and prepare validation images\n",
        "vdata = [X_valid, Y_valid]\n",
        "\n",
        "def normalise_image(data):\n",
        "    '''\n",
        "    make image zero mean and unit standard deviation\n",
        "    '''\n",
        "    X1, X2 = data[0], data[1]\n",
        "    img_o = np.float32(X1.copy())\n",
        "    m = np.mean(img_o)\n",
        "    s = np.std(img_o)\n",
        "    XX=np.divide((img_o - m), s)\n",
        "    return [XX, X2]\t\n",
        "\n",
        "valid_dataset = normalise_image(vdata)"
      ],
      "metadata": {
        "id": "AWVIAr4A4EaM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train pix2pix models\n",
        "def train(d_model, g_model, gan_model, dataset, n_epochs=100, n_batch=1):\n",
        "    # determine the output square shape of the discriminator\n",
        "    print(\"dmodel: \", d_model.output_shape[1])\n",
        "    n_patch = d_model.output_shape[1]\n",
        "    # unpack dataset\n",
        "    trainA, trainB = dataset\n",
        "    # calculate the number of batches per training epoch\n",
        "    bat_per_epo = int(len(trainA) / n_batch)\n",
        "    # calculate the number of training iterations\n",
        "    n_steps = bat_per_epo * n_epochs\n",
        "    # manually enumerate epochs\n",
        "    # print(\"Executed......\")\n",
        "    for i in range(n_steps):\n",
        "      # select a batch of real samples\n",
        "      [X_realA, X_realB], y_real = generate_real_samples(dataset, n_batch, n_patch)\n",
        "      # print(y_real.shape)\n",
        "      # generate a batch of fake samples\n",
        "      X_fakeB, y_fake = generate_fake_samples(g_model, X_realA, n_patch)\n",
        "      # update discriminator for real samples\n",
        "      d_loss1 = d_model.train_on_batch(X_realB, y_real)\n",
        "      # update discriminator for generated samples\n",
        "      d_loss2 = d_model.train_on_batch(X_fakeB, y_fake)\n",
        "      # update the generator\n",
        "      g_loss,_,_ = gan_model.train_on_batch(X_realA, [y_real, X_realB])\n",
        "      # summarize performance\n",
        "      print('>%d, d1[%.3f] d2[%.3f] g[%.3f]' % (i+1, d_loss1, d_loss2, g_loss))\n",
        "      # summarize model performance\n",
        "      if (i+1) % (bat_per_epo * 20) == 0:\n",
        "        summarize_performance(i, g_model, dataset)"
      ],
      "metadata": {
        "id": "UANsySLiM7FJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime \n",
        "start1 = datetime.now() \n",
        "\n",
        "train(d_model, g_model, gan_model, train_dataset, n_epochs=100, n_batch=1) \n",
        "#Reports parameters for each batch (total 1096) for each epoch.\n",
        "#For 10 epochs we should see 10960\n",
        "\n",
        "stop1 = datetime.now()\n",
        "#Execution time of the model \n",
        "execution_time = stop1-start1\n",
        "print(\"Execution time is: \", execution_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NYOGXDEVaqk",
        "outputId": "18f2ebfe-747d-4d4c-b365-8feb72551550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            ">5208, d1[0.000] d2[0.000] g[1.284]\n",
            ">5209, d1[0.000] d2[0.000] g[1.548]\n",
            ">5210, d1[0.000] d2[0.000] g[0.711]\n",
            ">5211, d1[0.000] d2[0.000] g[0.872]\n",
            ">5212, d1[0.000] d2[0.000] g[0.851]\n",
            ">5213, d1[0.005] d2[0.001] g[0.833]\n",
            ">5214, d1[0.000] d2[0.000] g[1.537]\n",
            ">5215, d1[0.000] d2[0.000] g[1.343]\n",
            ">5216, d1[0.000] d2[0.000] g[1.132]\n",
            ">5217, d1[0.000] d2[0.000] g[0.852]\n",
            ">5218, d1[0.008] d2[0.001] g[0.898]\n",
            ">5219, d1[0.000] d2[0.000] g[1.946]\n",
            ">5220, d1[0.000] d2[0.001] g[1.337]\n",
            ">5221, d1[0.000] d2[0.001] g[1.167]\n",
            ">5222, d1[0.000] d2[0.000] g[1.037]\n",
            ">5223, d1[0.000] d2[0.000] g[1.162]\n",
            ">5224, d1[0.000] d2[0.001] g[1.164]\n",
            ">5225, d1[0.000] d2[0.000] g[1.208]\n",
            ">5226, d1[0.000] d2[0.001] g[0.876]\n",
            ">5227, d1[0.000] d2[0.000] g[1.914]\n",
            ">5228, d1[0.000] d2[0.002] g[0.762]\n",
            ">5229, d1[0.000] d2[0.000] g[1.450]\n",
            ">5230, d1[0.000] d2[0.000] g[0.913]\n",
            ">5231, d1[0.000] d2[0.000] g[0.794]\n",
            ">5232, d1[0.000] d2[0.000] g[1.688]\n",
            ">5233, d1[0.000] d2[0.001] g[1.145]\n",
            ">5234, d1[0.000] d2[0.001] g[0.823]\n",
            ">5235, d1[0.002] d2[0.000] g[0.775]\n",
            ">5236, d1[0.000] d2[0.001] g[1.112]\n",
            ">5237, d1[0.000] d2[0.000] g[0.909]\n",
            ">5238, d1[0.000] d2[0.000] g[1.461]\n",
            ">5239, d1[0.000] d2[0.000] g[1.274]\n",
            ">5240, d1[0.000] d2[0.000] g[1.487]\n",
            ">5241, d1[0.000] d2[0.000] g[0.786]\n",
            ">5242, d1[0.000] d2[0.000] g[0.704]\n",
            ">5243, d1[0.000] d2[0.000] g[1.679]\n",
            ">5244, d1[0.000] d2[0.000] g[1.094]\n",
            ">5245, d1[0.004] d2[0.001] g[0.872]\n",
            ">5246, d1[0.000] d2[0.000] g[0.985]\n",
            ">5247, d1[0.000] d2[0.000] g[1.551]\n",
            ">5248, d1[0.009] d2[0.002] g[0.876]\n",
            ">5249, d1[0.000] d2[0.001] g[1.019]\n",
            ">5250, d1[0.000] d2[0.002] g[1.637]\n",
            ">5251, d1[0.000] d2[0.001] g[1.857]\n",
            ">5252, d1[0.000] d2[0.000] g[0.725]\n",
            ">5253, d1[0.004] d2[0.003] g[0.813]\n",
            ">5254, d1[0.000] d2[0.000] g[1.142]\n",
            ">5255, d1[0.000] d2[0.001] g[1.891]\n",
            ">5256, d1[0.000] d2[0.000] g[1.053]\n",
            ">5257, d1[0.000] d2[0.002] g[1.247]\n",
            ">5258, d1[0.000] d2[0.000] g[1.402]\n",
            ">5259, d1[0.000] d2[0.000] g[0.647]\n",
            ">5260, d1[0.000] d2[0.000] g[1.003]\n",
            ">5261, d1[0.000] d2[0.000] g[0.951]\n",
            ">5262, d1[0.003] d2[0.000] g[0.753]\n",
            ">5263, d1[0.000] d2[0.000] g[1.180]\n",
            ">5264, d1[0.000] d2[0.000] g[1.623]\n",
            ">5265, d1[0.000] d2[0.000] g[1.005]\n",
            ">5266, d1[0.000] d2[0.001] g[1.111]\n",
            ">5267, d1[0.000] d2[0.001] g[1.482]\n",
            ">5268, d1[0.000] d2[0.000] g[1.167]\n",
            ">5269, d1[0.003] d2[0.031] g[0.594]\n",
            ">5270, d1[0.000] d2[0.053] g[0.769]\n",
            ">5271, d1[0.000] d2[0.000] g[1.095]\n",
            ">5272, d1[0.000] d2[0.000] g[1.715]\n",
            ">5273, d1[0.000] d2[0.000] g[0.822]\n",
            ">5274, d1[0.000] d2[0.000] g[1.292]\n",
            ">5275, d1[0.000] d2[0.000] g[1.186]\n",
            ">5276, d1[0.000] d2[0.000] g[0.955]\n",
            ">5277, d1[0.001] d2[0.000] g[1.095]\n",
            ">5278, d1[0.001] d2[0.000] g[0.658]\n",
            ">5279, d1[0.000] d2[0.000] g[0.694]\n",
            ">5280, d1[0.000] d2[0.000] g[1.183]\n",
            ">5281, d1[0.000] d2[0.000] g[1.064]\n",
            ">5282, d1[0.000] d2[0.000] g[1.361]\n",
            ">5283, d1[0.000] d2[0.000] g[0.751]\n",
            ">5284, d1[0.000] d2[0.000] g[1.956]\n",
            ">5285, d1[0.001] d2[0.000] g[1.054]\n",
            ">5286, d1[0.000] d2[0.000] g[1.405]\n",
            ">5287, d1[0.000] d2[0.000] g[1.164]\n",
            ">5288, d1[0.000] d2[0.000] g[0.972]\n",
            ">5289, d1[0.000] d2[0.000] g[1.051]\n",
            ">5290, d1[0.000] d2[0.000] g[0.854]\n",
            ">5291, d1[0.000] d2[0.000] g[0.991]\n",
            ">5292, d1[0.000] d2[0.000] g[1.005]\n",
            ">5293, d1[0.000] d2[0.000] g[0.877]\n",
            ">5294, d1[0.000] d2[0.000] g[0.921]\n",
            ">5295, d1[0.000] d2[0.000] g[0.849]\n",
            ">5296, d1[0.000] d2[0.000] g[1.423]\n",
            ">5297, d1[0.000] d2[0.000] g[1.530]\n",
            ">5298, d1[0.001] d2[0.000] g[0.836]\n",
            ">5299, d1[0.000] d2[0.000] g[0.846]\n",
            ">5300, d1[0.000] d2[0.000] g[1.068]\n",
            ">5301, d1[0.068] d2[0.001] g[0.881]\n",
            ">5302, d1[0.000] d2[0.000] g[0.927]\n",
            ">5303, d1[0.000] d2[0.000] g[0.839]\n",
            ">5304, d1[0.000] d2[0.001] g[0.762]\n",
            ">5305, d1[0.000] d2[0.001] g[0.866]\n",
            ">5306, d1[0.000] d2[0.001] g[1.422]\n",
            ">5307, d1[0.023] d2[0.005] g[0.800]\n",
            ">5308, d1[0.000] d2[0.001] g[0.957]\n",
            ">5309, d1[0.000] d2[0.000] g[0.759]\n",
            ">5310, d1[0.000] d2[0.001] g[1.609]\n",
            ">5311, d1[0.004] d2[0.000] g[0.700]\n",
            ">5312, d1[0.002] d2[0.004] g[0.410]\n",
            ">5313, d1[0.000] d2[0.001] g[0.929]\n",
            ">5314, d1[0.000] d2[0.000] g[1.031]\n",
            ">5315, d1[0.000] d2[0.000] g[0.936]\n",
            ">5316, d1[0.000] d2[0.001] g[1.086]\n",
            ">5317, d1[0.000] d2[0.000] g[1.674]\n",
            ">5318, d1[0.000] d2[0.001] g[0.687]\n",
            ">5319, d1[0.004] d2[0.001] g[0.782]\n",
            ">5320, d1[0.006] d2[0.002] g[0.678]\n",
            ">5321, d1[0.000] d2[0.006] g[0.650]\n",
            ">5322, d1[0.000] d2[0.000] g[1.979]\n",
            ">5323, d1[0.000] d2[0.000] g[1.341]\n",
            ">5324, d1[0.000] d2[0.001] g[1.039]\n",
            ">5325, d1[0.000] d2[0.000] g[0.854]\n",
            ">5326, d1[0.000] d2[0.000] g[1.963]\n",
            ">5327, d1[0.000] d2[0.000] g[0.945]\n",
            ">5328, d1[0.000] d2[0.000] g[0.778]\n",
            ">5329, d1[0.009] d2[0.001] g[0.857]\n",
            ">5330, d1[0.000] d2[0.000] g[1.586]\n",
            ">5331, d1[0.000] d2[0.000] g[1.478]\n",
            ">5332, d1[0.019] d2[0.001] g[1.018]\n",
            ">5333, d1[0.000] d2[0.001] g[0.778]\n",
            ">5334, d1[0.000] d2[0.001] g[0.738]\n",
            ">5335, d1[0.000] d2[0.000] g[0.993]\n",
            ">5336, d1[0.004] d2[0.001] g[0.799]\n",
            ">5337, d1[0.000] d2[0.000] g[0.974]\n",
            ">5338, d1[0.001] d2[0.004] g[0.827]\n",
            ">5339, d1[0.000] d2[0.000] g[0.953]\n",
            ">5340, d1[0.000] d2[0.001] g[1.309]\n",
            ">5341, d1[0.003] d2[0.001] g[0.685]\n",
            ">5342, d1[0.000] d2[0.000] g[0.878]\n",
            ">5343, d1[0.000] d2[0.000] g[0.848]\n",
            ">5344, d1[0.000] d2[0.000] g[1.051]\n",
            ">5345, d1[0.000] d2[0.001] g[1.363]\n",
            ">5346, d1[0.000] d2[0.000] g[2.091]\n",
            ">5347, d1[0.000] d2[0.000] g[0.972]\n",
            ">5348, d1[0.000] d2[0.000] g[1.035]\n",
            ">5349, d1[0.000] d2[0.001] g[0.663]\n",
            ">5350, d1[0.000] d2[0.000] g[1.876]\n",
            ">5351, d1[0.000] d2[0.000] g[0.903]\n",
            ">5352, d1[0.000] d2[0.000] g[1.916]\n",
            ">5353, d1[0.000] d2[0.001] g[1.056]\n",
            ">5354, d1[0.000] d2[0.000] g[1.033]\n",
            ">5355, d1[0.000] d2[0.001] g[0.772]\n",
            ">5356, d1[0.000] d2[0.013] g[0.963]\n",
            ">5357, d1[0.000] d2[0.000] g[1.934]\n",
            ">5358, d1[0.000] d2[0.000] g[1.446]\n",
            ">5359, d1[0.005] d2[0.001] g[0.697]\n",
            ">5360, d1[0.000] d2[0.000] g[0.868]\n",
            ">5361, d1[0.000] d2[0.001] g[0.946]\n",
            ">5362, d1[0.000] d2[0.000] g[1.028]\n",
            ">5363, d1[0.000] d2[0.001] g[1.265]\n",
            ">5364, d1[0.000] d2[0.000] g[1.291]\n",
            ">5365, d1[0.000] d2[0.000] g[1.800]\n",
            ">5366, d1[0.000] d2[0.001] g[0.868]\n",
            ">5367, d1[0.000] d2[0.001] g[1.155]\n",
            ">5368, d1[0.000] d2[0.000] g[0.934]\n",
            ">5369, d1[0.000] d2[0.001] g[1.607]\n",
            ">5370, d1[0.000] d2[0.000] g[0.677]\n",
            ">5371, d1[0.000] d2[0.000] g[0.891]\n",
            ">5372, d1[0.000] d2[0.000] g[1.186]\n",
            ">5373, d1[0.000] d2[0.001] g[1.704]\n",
            ">5374, d1[0.000] d2[0.000] g[0.923]\n",
            ">5375, d1[0.000] d2[0.000] g[1.004]\n",
            ">5376, d1[0.000] d2[0.000] g[1.676]\n",
            ">5377, d1[0.008] d2[0.000] g[0.876]\n",
            ">5378, d1[0.002] d2[0.001] g[0.668]\n",
            ">5379, d1[0.000] d2[0.000] g[1.025]\n",
            ">5380, d1[0.000] d2[0.000] g[0.933]\n",
            ">5381, d1[0.000] d2[0.001] g[0.966]\n",
            ">5382, d1[0.000] d2[0.000] g[1.682]\n",
            ">5383, d1[0.000] d2[0.000] g[1.677]\n",
            ">5384, d1[0.000] d2[0.000] g[0.974]\n",
            ">5385, d1[0.002] d2[0.000] g[0.486]\n",
            ">5386, d1[0.000] d2[0.000] g[0.811]\n",
            ">5387, d1[0.000] d2[0.000] g[0.776]\n",
            ">5388, d1[0.000] d2[0.001] g[0.827]\n",
            ">5389, d1[0.000] d2[0.000] g[0.659]\n",
            ">5390, d1[0.000] d2[0.001] g[0.682]\n",
            ">5391, d1[0.010] d2[0.003] g[0.628]\n",
            ">5392, d1[0.000] d2[0.001] g[1.648]\n",
            ">5393, d1[0.000] d2[0.001] g[1.200]\n",
            ">5394, d1[0.015] d2[0.001] g[0.743]\n",
            ">5395, d1[0.000] d2[0.000] g[1.158]\n",
            ">5396, d1[0.003] d2[0.001] g[1.041]\n",
            ">5397, d1[0.000] d2[0.000] g[0.929]\n",
            ">5398, d1[0.000] d2[0.001] g[1.174]\n",
            ">5399, d1[0.000] d2[0.000] g[0.985]\n",
            ">5400, d1[0.000] d2[0.001] g[1.597]\n",
            ">5401, d1[0.000] d2[0.000] g[0.858]\n",
            ">5402, d1[0.000] d2[0.001] g[1.059]\n",
            ">5403, d1[0.000] d2[0.000] g[1.640]\n",
            ">5404, d1[0.000] d2[0.002] g[1.172]\n",
            ">5405, d1[0.003] d2[0.003] g[0.784]\n",
            ">5406, d1[0.002] d2[0.002] g[0.620]\n",
            ">5407, d1[0.001] d2[0.002] g[0.784]\n",
            ">5408, d1[0.000] d2[0.001] g[1.651]\n",
            ">5409, d1[0.000] d2[0.001] g[0.986]\n",
            ">5410, d1[0.000] d2[0.000] g[0.892]\n",
            ">5411, d1[0.008] d2[0.001] g[0.971]\n",
            ">5412, d1[0.000] d2[0.000] g[1.149]\n",
            ">5413, d1[0.000] d2[0.000] g[1.500]\n",
            ">5414, d1[0.000] d2[0.000] g[0.832]\n",
            ">5415, d1[0.000] d2[0.001] g[1.161]\n",
            ">5416, d1[0.002] d2[0.001] g[0.766]\n",
            ">5417, d1[0.000] d2[0.000] g[0.882]\n",
            ">5418, d1[0.000] d2[0.000] g[1.957]\n",
            ">5419, d1[0.000] d2[0.000] g[1.095]\n",
            ">5420, d1[0.000] d2[0.000] g[0.954]\n",
            ">5421, d1[0.000] d2[0.000] g[1.295]\n",
            ">5422, d1[0.000] d2[0.001] g[0.625]\n",
            ">5423, d1[0.000] d2[0.001] g[1.205]\n",
            ">5424, d1[0.000] d2[0.000] g[0.712]\n",
            ">5425, d1[0.000] d2[0.000] g[1.877]\n",
            ">5426, d1[0.000] d2[0.001] g[1.352]\n",
            ">5427, d1[0.000] d2[0.000] g[1.792]\n",
            ">5428, d1[0.000] d2[0.001] g[1.450]\n",
            ">5429, d1[0.000] d2[0.000] g[0.709]\n",
            ">5430, d1[0.000] d2[0.000] g[1.003]\n",
            ">5431, d1[0.000] d2[0.000] g[0.698]\n",
            ">5432, d1[0.000] d2[0.000] g[0.701]\n",
            ">5433, d1[0.000] d2[0.000] g[0.991]\n",
            ">5434, d1[0.000] d2[0.000] g[1.241]\n",
            ">5435, d1[0.000] d2[0.000] g[0.826]\n",
            ">5436, d1[0.000] d2[0.000] g[0.824]\n",
            ">5437, d1[0.000] d2[0.000] g[1.577]\n",
            ">5438, d1[0.000] d2[0.000] g[0.792]\n",
            ">5439, d1[0.000] d2[0.000] g[0.749]\n",
            ">5440, d1[0.000] d2[0.000] g[1.423]\n",
            ">5441, d1[0.006] d2[0.001] g[0.657]\n",
            ">5442, d1[0.000] d2[0.000] g[0.676]\n",
            ">5443, d1[0.000] d2[0.001] g[0.947]\n",
            ">5444, d1[0.000] d2[0.001] g[0.944]\n",
            ">5445, d1[0.000] d2[0.000] g[1.245]\n",
            ">5446, d1[0.000] d2[0.000] g[0.935]\n",
            ">5447, d1[0.000] d2[0.000] g[0.944]\n",
            ">5448, d1[0.000] d2[0.000] g[0.640]\n",
            ">5449, d1[0.000] d2[0.007] g[1.159]\n",
            ">5450, d1[0.000] d2[0.000] g[0.940]\n",
            ">5451, d1[0.002] d2[0.001] g[0.663]\n",
            ">5452, d1[0.000] d2[0.000] g[1.637]\n",
            ">5453, d1[0.000] d2[0.000] g[2.117]\n",
            ">5454, d1[0.000] d2[0.000] g[2.290]\n",
            ">5455, d1[0.000] d2[0.000] g[0.643]\n",
            ">5456, d1[0.000] d2[0.001] g[1.329]\n",
            ">5457, d1[0.000] d2[0.000] g[1.443]\n",
            ">5458, d1[0.000] d2[0.001] g[1.956]\n",
            ">5459, d1[0.000] d2[0.000] g[1.284]\n",
            ">5460, d1[0.000] d2[0.000] g[0.989]\n",
            ">5461, d1[0.000] d2[0.000] g[1.859]\n",
            ">5462, d1[0.000] d2[0.000] g[0.711]\n",
            ">5463, d1[0.000] d2[0.000] g[1.872]\n",
            ">5464, d1[0.000] d2[0.000] g[1.794]\n",
            ">5465, d1[0.000] d2[0.000] g[0.877]\n",
            ">5466, d1[0.000] d2[0.000] g[1.446]\n",
            ">5467, d1[0.000] d2[0.000] g[1.659]\n",
            ">5468, d1[0.000] d2[0.001] g[1.497]\n",
            ">5469, d1[0.000] d2[0.000] g[1.341]\n",
            ">5470, d1[0.000] d2[0.000] g[2.018]\n",
            ">5471, d1[0.000] d2[0.001] g[1.224]\n",
            ">5472, d1[0.000] d2[0.000] g[1.034]\n",
            ">5473, d1[0.000] d2[0.000] g[0.638]\n",
            ">5474, d1[0.000] d2[0.000] g[1.229]\n",
            ">5475, d1[0.000] d2[0.000] g[1.071]\n",
            ">5476, d1[0.002] d2[0.001] g[0.428]\n",
            ">5477, d1[0.000] d2[0.000] g[0.874]\n",
            ">5478, d1[0.000] d2[0.000] g[1.997]\n",
            ">5479, d1[0.000] d2[0.000] g[1.015]\n",
            ">5480, d1[0.000] d2[0.000] g[1.561]\n",
            ">5481, d1[0.000] d2[0.000] g[1.011]\n",
            ">5482, d1[0.000] d2[0.000] g[2.373]\n",
            ">5483, d1[0.000] d2[0.000] g[0.819]\n",
            ">5484, d1[0.000] d2[0.001] g[1.642]\n",
            ">5485, d1[0.000] d2[0.000] g[2.052]\n",
            ">5486, d1[0.000] d2[0.000] g[2.320]\n",
            ">5487, d1[0.000] d2[0.000] g[1.462]\n",
            ">5488, d1[0.000] d2[0.000] g[0.919]\n",
            ">5489, d1[0.000] d2[0.000] g[1.477]\n",
            ">5490, d1[0.000] d2[0.000] g[0.844]\n",
            ">5491, d1[0.000] d2[0.000] g[0.795]\n",
            ">5492, d1[0.000] d2[0.000] g[1.982]\n",
            ">5493, d1[0.002] d2[0.000] g[0.677]\n",
            ">5494, d1[0.000] d2[0.000] g[0.956]\n",
            ">5495, d1[0.000] d2[0.000] g[1.345]\n",
            ">5496, d1[0.000] d2[0.000] g[1.810]\n",
            ">5497, d1[0.000] d2[0.000] g[0.933]\n",
            ">5498, d1[0.000] d2[0.000] g[0.621]\n",
            ">5499, d1[0.000] d2[0.000] g[1.697]\n",
            ">5500, d1[0.000] d2[0.000] g[2.333]\n",
            ">5501, d1[0.000] d2[0.000] g[0.838]\n",
            ">5502, d1[0.003] d2[0.001] g[0.882]\n",
            ">5503, d1[0.003] d2[0.000] g[0.661]\n",
            ">5504, d1[0.000] d2[0.000] g[0.865]\n",
            ">5505, d1[0.000] d2[0.000] g[1.517]\n",
            ">5506, d1[0.000] d2[0.000] g[0.796]\n",
            ">5507, d1[0.003] d2[0.003] g[0.630]\n",
            ">5508, d1[0.000] d2[0.001] g[0.732]\n",
            ">5509, d1[0.000] d2[0.000] g[1.867]\n",
            ">5510, d1[0.003] d2[0.001] g[0.684]\n",
            ">5511, d1[0.000] d2[0.001] g[1.262]\n",
            ">5512, d1[0.000] d2[0.000] g[0.856]\n",
            ">5513, d1[0.000] d2[0.000] g[1.214]\n",
            ">5514, d1[0.000] d2[0.001] g[1.580]\n",
            ">5515, d1[0.002] d2[0.001] g[0.484]\n",
            ">5516, d1[0.000] d2[0.000] g[0.982]\n",
            ">5517, d1[0.000] d2[0.000] g[0.894]\n",
            ">5518, d1[0.000] d2[0.000] g[0.843]\n",
            ">5519, d1[0.000] d2[0.000] g[1.302]\n",
            ">5520, d1[0.000] d2[0.001] g[2.224]\n",
            ">5521, d1[0.000] d2[0.000] g[1.421]\n",
            ">5522, d1[0.000] d2[0.000] g[1.099]\n",
            ">5523, d1[0.000] d2[0.000] g[0.713]\n",
            ">5524, d1[0.000] d2[0.000] g[0.993]\n",
            ">5525, d1[0.000] d2[0.000] g[0.985]\n",
            ">5526, d1[0.000] d2[0.001] g[1.349]\n",
            ">5527, d1[0.000] d2[0.000] g[1.617]\n",
            ">5528, d1[0.000] d2[0.000] g[1.458]\n",
            ">5529, d1[0.000] d2[0.000] g[0.918]\n",
            ">5530, d1[0.003] d2[0.001] g[0.654]\n",
            ">5531, d1[0.000] d2[0.000] g[1.179]\n",
            ">5532, d1[0.000] d2[0.000] g[0.710]\n",
            ">5533, d1[0.000] d2[0.000] g[0.992]\n",
            ">5534, d1[0.000] d2[0.001] g[1.042]\n",
            ">5535, d1[0.005] d2[0.000] g[0.717]\n",
            ">5536, d1[0.000] d2[0.000] g[0.961]\n",
            ">5537, d1[0.002] d2[0.001] g[0.613]\n",
            ">5538, d1[0.000] d2[0.000] g[1.032]\n",
            ">5539, d1[0.000] d2[0.000] g[1.675]\n",
            ">5540, d1[0.000] d2[0.002] g[1.393]\n",
            ">5541, d1[0.000] d2[0.001] g[0.990]\n",
            ">5542, d1[0.000] d2[0.000] g[0.976]\n",
            ">5543, d1[0.004] d2[0.000] g[0.752]\n",
            ">5544, d1[0.000] d2[0.000] g[0.859]\n",
            ">5545, d1[0.002] d2[0.000] g[0.460]\n",
            ">5546, d1[0.000] d2[0.002] g[1.737]\n",
            ">5547, d1[0.000] d2[0.001] g[1.474]\n",
            ">5548, d1[0.003] d2[0.000] g[0.776]\n",
            ">5549, d1[0.000] d2[0.000] g[0.776]\n",
            ">5550, d1[0.000] d2[0.001] g[1.528]\n",
            ">5551, d1[0.000] d2[0.000] g[1.695]\n",
            ">5552, d1[0.000] d2[0.000] g[0.838]\n",
            ">5553, d1[0.002] d2[0.020] g[0.397]\n",
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            ">5555, d1[0.000] d2[0.000] g[1.004]\n",
            ">5556, d1[0.000] d2[0.000] g[0.785]\n",
            ">5557, d1[0.000] d2[0.000] g[0.796]\n",
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            ">5559, d1[0.000] d2[0.000] g[0.722]\n",
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            ">5608, d1[0.000] d2[0.001] g[1.217]\n",
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            ">5610, d1[0.000] d2[0.001] g[1.396]\n",
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            ">5756, d1[0.000] d2[0.000] g[0.929]\n",
            ">5757, d1[0.000] d2[0.000] g[1.138]\n",
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            ">5762, d1[0.000] d2[0.000] g[1.207]\n",
            ">5763, d1[0.000] d2[0.000] g[1.737]\n",
            ">5764, d1[0.000] d2[0.000] g[1.041]\n",
            ">5765, d1[0.000] d2[0.000] g[0.869]\n",
            ">5766, d1[0.000] d2[0.000] g[1.369]\n",
            ">5767, d1[0.000] d2[0.000] g[1.958]\n",
            ">5768, d1[0.000] d2[0.000] g[0.956]\n",
            ">5769, d1[0.008] d2[0.000] g[0.743]\n",
            ">5770, d1[0.000] d2[0.000] g[0.686]\n",
            ">5771, d1[0.000] d2[0.000] g[1.239]\n",
            ">5772, d1[0.000] d2[0.000] g[0.774]\n",
            ">5773, d1[0.000] d2[0.000] g[1.446]\n",
            ">5774, d1[0.000] d2[0.000] g[0.859]\n",
            ">5775, d1[0.000] d2[0.000] g[0.851]\n",
            ">5776, d1[0.000] d2[0.000] g[0.675]\n",
            ">5777, d1[0.000] d2[0.000] g[1.054]\n",
            ">5778, d1[0.000] d2[0.000] g[0.960]\n",
            ">5779, d1[0.000] d2[0.000] g[1.049]\n",
            ">5780, d1[0.000] d2[0.000] g[1.510]\n",
            ">5781, d1[0.000] d2[0.000] g[1.427]\n",
            ">5782, d1[0.000] d2[0.000] g[0.863]\n",
            ">5783, d1[0.000] d2[0.000] g[0.766]\n",
            ">5784, d1[0.000] d2[0.000] g[1.022]\n",
            ">5785, d1[0.000] d2[0.000] g[0.584]\n",
            ">5786, d1[0.000] d2[0.000] g[0.806]\n",
            ">5787, d1[0.000] d2[0.000] g[1.055]\n",
            ">5788, d1[0.000] d2[0.000] g[0.900]\n",
            ">5789, d1[0.000] d2[0.000] g[1.598]\n",
            ">5790, d1[0.003] d2[0.000] g[0.754]\n",
            ">5791, d1[0.000] d2[0.000] g[2.239]\n",
            ">5792, d1[0.003] d2[0.002] g[0.688]\n",
            ">5793, d1[0.011] d2[0.001] g[0.600]\n",
            ">5794, d1[0.000] d2[0.000] g[1.920]\n",
            ">5795, d1[0.000] d2[0.000] g[0.879]\n",
            ">5796, d1[0.000] d2[0.001] g[1.895]\n",
            ">5797, d1[0.000] d2[0.001] g[0.946]\n",
            ">5798, d1[0.000] d2[0.000] g[1.445]\n",
            ">5799, d1[0.000] d2[0.000] g[1.232]\n",
            ">5800, d1[0.000] d2[0.000] g[0.848]\n",
            ">5801, d1[0.003] d2[0.000] g[0.558]\n",
            ">5802, d1[0.000] d2[0.001] g[1.217]\n",
            ">5803, d1[0.000] d2[0.000] g[0.950]\n",
            ">5804, d1[0.002] d2[0.003] g[0.675]\n",
            ">5805, d1[0.000] d2[0.000] g[1.036]\n",
            ">5806, d1[0.000] d2[0.000] g[0.847]\n",
            ">5807, d1[0.000] d2[0.000] g[1.068]\n",
            ">5808, d1[0.000] d2[0.000] g[1.893]\n",
            ">5809, d1[0.000] d2[0.000] g[1.238]\n",
            ">5810, d1[0.000] d2[0.000] g[1.043]\n",
            ">5811, d1[0.000] d2[0.000] g[1.144]\n",
            ">5812, d1[0.000] d2[0.000] g[1.510]\n",
            ">5813, d1[0.000] d2[0.000] g[0.913]\n",
            ">5814, d1[0.004] d2[0.001] g[0.514]\n",
            ">5815, d1[0.000] d2[0.000] g[1.020]\n",
            ">5816, d1[0.000] d2[0.001] g[0.890]\n",
            ">5817, d1[0.000] d2[0.000] g[1.670]\n",
            ">5818, d1[0.000] d2[0.000] g[0.651]\n",
            ">5819, d1[0.000] d2[0.005] g[0.735]\n",
            ">5820, d1[0.000] d2[0.000] g[1.685]\n",
            ">5821, d1[0.000] d2[0.000] g[1.871]\n",
            ">5822, d1[0.000] d2[0.000] g[0.884]\n",
            ">5823, d1[0.000] d2[0.002] g[1.394]\n",
            ">5824, d1[0.000] d2[0.000] g[0.898]\n",
            ">5825, d1[0.004] d2[0.000] g[0.689]\n",
            ">5826, d1[0.000] d2[0.001] g[2.135]\n",
            ">5827, d1[0.000] d2[0.000] g[0.920]\n",
            ">5828, d1[0.000] d2[0.000] g[1.238]\n",
            ">5829, d1[0.000] d2[0.000] g[1.259]\n",
            ">5830, d1[0.000] d2[0.000] g[1.087]\n",
            ">5831, d1[0.000] d2[0.000] g[1.910]\n",
            ">5832, d1[0.000] d2[0.000] g[0.953]\n",
            ">5833, d1[0.017] d2[0.001] g[0.711]\n",
            ">5834, d1[0.000] d2[0.000] g[0.898]\n",
            ">5835, d1[0.000] d2[0.001] g[1.491]\n",
            ">5836, d1[0.000] d2[0.001] g[2.206]\n",
            ">5837, d1[0.000] d2[0.000] g[1.930]\n",
            ">5838, d1[0.000] d2[0.001] g[0.986]\n",
            ">5839, d1[0.000] d2[0.000] g[1.385]\n",
            ">5840, d1[0.001] d2[0.001] g[0.643]\n",
            ">5841, d1[0.000] d2[0.000] g[1.156]\n",
            ">5842, d1[0.001] d2[0.001] g[0.515]\n",
            ">5843, d1[0.001] d2[0.001] g[0.712]\n",
            ">5844, d1[0.000] d2[0.000] g[0.698]\n",
            ">5845, d1[0.002] d2[0.001] g[0.879]\n",
            ">5846, d1[0.000] d2[0.000] g[1.514]\n",
            ">5847, d1[0.000] d2[0.000] g[0.845]\n",
            ">5848, d1[0.000] d2[0.002] g[0.844]\n",
            ">5849, d1[0.000] d2[0.000] g[1.194]\n",
            ">5850, d1[0.000] d2[0.000] g[1.124]\n",
            ">5851, d1[0.000] d2[0.000] g[1.908]\n",
            ">5852, d1[0.000] d2[0.000] g[1.232]\n",
            ">5853, d1[0.000] d2[0.000] g[0.908]\n",
            ">5854, d1[0.000] d2[0.000] g[0.932]\n",
            ">5855, d1[0.001] d2[0.000] g[0.788]\n",
            ">5856, d1[0.000] d2[0.000] g[1.443]\n",
            ">5857, d1[0.003] d2[0.002] g[0.850]\n",
            ">5858, d1[0.000] d2[0.000] g[1.241]\n",
            ">5859, d1[0.002] d2[0.001] g[1.010]\n",
            ">5860, d1[0.000] d2[0.004] g[0.996]\n",
            ">5861, d1[0.000] d2[0.000] g[1.042]\n",
            ">5862, d1[0.000] d2[0.000] g[0.880]\n",
            ">5863, d1[0.000] d2[0.001] g[2.091]\n",
            ">5864, d1[0.000] d2[0.001] g[1.699]\n",
            ">5865, d1[0.000] d2[0.001] g[1.247]\n",
            ">5866, d1[0.000] d2[0.000] g[1.710]\n",
            ">5867, d1[0.001] d2[0.001] g[0.694]\n",
            ">5868, d1[0.000] d2[0.000] g[0.814]\n",
            ">5869, d1[0.000] d2[0.001] g[0.867]\n",
            ">5870, d1[0.000] d2[0.000] g[1.196]\n",
            ">5871, d1[0.002] d2[0.001] g[0.735]\n",
            ">5872, d1[0.000] d2[0.000] g[1.191]\n",
            ">5873, d1[0.000] d2[0.000] g[1.652]\n",
            ">5874, d1[0.003] d2[0.007] g[1.047]\n",
            ">5875, d1[0.000] d2[0.000] g[0.973]\n",
            ">5876, d1[0.000] d2[0.000] g[1.441]\n",
            ">5877, d1[0.000] d2[0.000] g[2.200]\n",
            ">5878, d1[0.000] d2[0.000] g[0.958]\n",
            ">5879, d1[0.000] d2[0.000] g[0.971]\n",
            ">5880, d1[0.004] d2[0.000] g[0.518]\n",
            ">5881, d1[0.000] d2[0.000] g[2.052]\n",
            ">5882, d1[0.002] d2[0.000] g[0.588]\n",
            ">5883, d1[0.002] d2[0.001] g[0.556]\n",
            ">5884, d1[0.000] d2[0.000] g[1.248]\n",
            ">5885, d1[0.000] d2[0.000] g[1.153]\n",
            ">5886, d1[0.000] d2[0.000] g[0.869]\n",
            ">5887, d1[0.000] d2[0.000] g[1.072]\n",
            ">5888, d1[0.001] d2[0.000] g[0.676]\n",
            ">5889, d1[0.000] d2[0.000] g[0.898]\n",
            ">5890, d1[0.001] d2[0.003] g[0.744]\n",
            ">5891, d1[0.000] d2[0.000] g[1.232]\n",
            ">5892, d1[0.004] d2[0.001] g[0.931]\n",
            ">5893, d1[0.000] d2[0.000] g[1.942]\n",
            ">5894, d1[0.000] d2[0.000] g[1.156]\n",
            ">5895, d1[0.005] d2[0.001] g[0.693]\n",
            ">5896, d1[0.000] d2[0.000] g[1.259]\n",
            ">5897, d1[0.000] d2[0.000] g[1.062]\n",
            ">5898, d1[0.000] d2[0.000] g[0.664]\n",
            ">5899, d1[0.000] d2[0.000] g[1.001]\n",
            ">5900, d1[0.000] d2[0.001] g[2.182]\n",
            ">5901, d1[0.000] d2[0.000] g[0.902]\n",
            ">5902, d1[0.000] d2[0.000] g[0.998]\n",
            ">5903, d1[0.000] d2[0.000] g[1.463]\n",
            ">5904, d1[0.000] d2[0.000] g[1.021]\n",
            ">5905, d1[0.000] d2[0.000] g[1.895]\n",
            ">5906, d1[0.000] d2[0.000] g[0.822]\n",
            ">5907, d1[0.000] d2[0.000] g[1.730]\n",
            ">5908, d1[0.000] d2[0.000] g[0.962]\n",
            ">5909, d1[0.001] d2[0.001] g[0.706]\n",
            ">5910, d1[0.000] d2[0.001] g[1.632]\n",
            ">5911, d1[0.000] d2[0.000] g[1.451]\n",
            ">5912, d1[0.000] d2[0.000] g[0.745]\n",
            ">5913, d1[0.000] d2[0.000] g[0.660]\n",
            ">5914, d1[0.000] d2[0.000] g[1.050]\n",
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            ">5917, d1[0.000] d2[0.000] g[1.667]\n",
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            ">5921, d1[0.001] d2[0.000] g[0.693]\n",
            ">5922, d1[0.000] d2[0.000] g[1.129]\n",
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            ">5924, d1[0.000] d2[0.000] g[1.119]\n",
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            ">5930, d1[0.000] d2[0.000] g[0.891]\n",
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            ">5939, d1[0.000] d2[0.001] g[0.946]\n",
            ">5940, d1[0.000] d2[0.000] g[1.388]\n",
            ">5941, d1[0.000] d2[0.000] g[1.489]\n",
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            ">5943, d1[0.000] d2[0.000] g[1.238]\n",
            ">5944, d1[0.000] d2[0.000] g[0.888]\n",
            ">5945, d1[0.000] d2[0.001] g[2.169]\n",
            ">5946, d1[0.000] d2[0.000] g[1.202]\n",
            ">5947, d1[0.000] d2[0.000] g[1.016]\n",
            ">5948, d1[0.001] d2[0.000] g[0.909]\n",
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            ">5950, d1[0.000] d2[0.000] g[1.477]\n",
            ">5951, d1[0.002] d2[0.019] g[0.615]\n",
            ">5952, d1[0.000] d2[0.000] g[1.909]\n",
            ">5953, d1[0.000] d2[0.000] g[1.422]\n",
            ">5954, d1[0.000] d2[0.000] g[0.976]\n",
            ">5955, d1[0.000] d2[0.000] g[0.631]\n",
            ">5956, d1[0.000] d2[0.000] g[1.137]\n",
            ">5957, d1[0.000] d2[0.000] g[1.164]\n",
            ">5958, d1[0.014] d2[0.001] g[0.636]\n",
            ">5959, d1[0.000] d2[0.000] g[1.258]\n",
            ">5960, d1[0.000] d2[0.000] g[0.829]\n",
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            ">5964, d1[0.000] d2[0.000] g[0.999]\n",
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            ">5969, d1[0.000] d2[0.001] g[1.174]\n",
            ">5970, d1[0.000] d2[0.000] g[1.729]\n",
            ">5971, d1[0.000] d2[0.000] g[0.701]\n",
            ">5972, d1[0.000] d2[0.000] g[1.256]\n",
            ">5973, d1[0.001] d2[0.000] g[0.675]\n",
            ">5974, d1[0.000] d2[0.000] g[1.483]\n",
            ">5975, d1[0.000] d2[0.000] g[1.215]\n",
            ">5976, d1[0.000] d2[0.000] g[0.870]\n",
            ">5977, d1[0.001] d2[0.000] g[0.646]\n",
            ">5978, d1[0.001] d2[0.001] g[0.637]\n",
            ">5979, d1[0.000] d2[0.000] g[0.996]\n",
            ">5980, d1[0.001] d2[0.001] g[0.600]\n",
            ">5981, d1[0.000] d2[0.000] g[1.984]\n",
            ">5982, d1[0.004] d2[0.003] g[0.860]\n",
            ">5983, d1[0.000] d2[0.001] g[0.865]\n",
            ">5984, d1[0.000] d2[0.000] g[1.702]\n",
            ">5985, d1[0.001] d2[0.000] g[0.748]\n",
            ">5986, d1[0.000] d2[0.000] g[1.001]\n",
            ">5987, d1[0.000] d2[0.000] g[1.702]\n",
            ">5988, d1[0.000] d2[0.000] g[0.899]\n",
            ">5989, d1[0.001] d2[0.000] g[0.701]\n",
            ">5990, d1[0.000] d2[0.000] g[1.411]\n",
            ">5991, d1[0.000] d2[0.000] g[1.137]\n",
            ">5992, d1[0.000] d2[0.000] g[1.063]\n",
            ">5993, d1[0.000] d2[0.000] g[1.596]\n",
            ">5994, d1[0.000] d2[0.000] g[0.628]\n",
            ">5995, d1[0.000] d2[0.000] g[1.147]\n",
            ">5996, d1[0.000] d2[0.000] g[0.704]\n",
            ">5997, d1[0.000] d2[0.000] g[1.667]\n",
            ">5998, d1[0.000] d2[0.000] g[1.121]\n",
            ">5999, d1[0.000] d2[0.000] g[0.950]\n",
            ">6000, d1[0.000] d2[0.001] g[2.068]\n",
            ">6001, d1[0.004] d2[0.002] g[0.989]\n",
            ">6002, d1[0.000] d2[0.000] g[0.997]\n",
            ">6003, d1[0.000] d2[0.000] g[0.848]\n",
            ">6004, d1[0.000] d2[0.000] g[0.979]\n",
            ">6005, d1[0.000] d2[0.000] g[1.015]\n",
            ">6006, d1[0.000] d2[0.000] g[0.795]\n",
            ">6007, d1[0.000] d2[0.000] g[1.683]\n",
            ">6008, d1[0.000] d2[0.000] g[1.858]\n",
            ">6009, d1[0.000] d2[0.000] g[0.930]\n",
            ">6010, d1[0.000] d2[0.000] g[1.202]\n",
            ">6011, d1[0.000] d2[0.001] g[0.870]\n",
            ">6012, d1[0.000] d2[0.000] g[1.892]\n",
            ">6013, d1[0.000] d2[0.000] g[1.195]\n",
            ">6014, d1[0.000] d2[0.000] g[0.789]\n",
            ">6015, d1[0.000] d2[0.000] g[1.123]\n",
            ">6016, d1[0.002] d2[0.000] g[0.693]\n",
            ">6017, d1[0.000] d2[0.000] g[0.932]\n",
            ">6018, d1[0.000] d2[0.000] g[0.900]\n",
            ">6019, d1[0.000] d2[0.000] g[2.277]\n",
            ">6020, d1[0.000] d2[0.000] g[1.428]\n",
            ">6021, d1[0.065] d2[0.003] g[0.715]\n",
            ">6022, d1[0.000] d2[0.039] g[1.260]\n",
            ">6023, d1[0.000] d2[0.010] g[1.049]\n",
            ">6024, d1[0.001] d2[0.002] g[0.487]\n",
            ">6025, d1[0.001] d2[0.001] g[1.739]\n",
            ">6026, d1[0.000] d2[0.001] g[1.386]\n",
            ">6027, d1[0.001] d2[0.221] g[0.391]\n",
            ">6028, d1[0.236] d2[0.189] g[0.714]\n",
            ">6029, d1[0.061] d2[0.004] g[0.612]\n",
            ">6030, d1[0.113] d2[0.008] g[0.767]\n",
            ">6031, d1[0.055] d2[0.404] g[0.573]\n",
            ">6032, d1[0.616] d2[0.805] g[0.733]\n",
            ">6033, d1[0.005] d2[0.014] g[1.971]\n",
            ">6034, d1[0.019] d2[0.002] g[1.485]\n",
            ">6035, d1[0.002] d2[0.007] g[1.203]\n",
            ">6036, d1[0.002] d2[0.013] g[1.089]\n",
            ">6037, d1[0.422] d2[1.202] g[0.594]\n",
            ">6038, d1[0.004] d2[0.001] g[0.708]\n",
            ">6039, d1[0.005] d2[0.001] g[1.647]\n",
            ">6040, d1[0.015] d2[0.000] g[0.884]\n",
            ">6041, d1[0.008] d2[0.001] g[1.201]\n",
            ">6042, d1[0.007] d2[0.000] g[1.139]\n",
            ">6043, d1[0.003] d2[0.001] g[1.110]\n",
            ">6044, d1[0.004] d2[0.001] g[2.260]\n",
            ">6045, d1[1.179] d2[0.139] g[0.556]\n",
            ">6046, d1[0.001] d2[0.058] g[0.926]\n",
            ">6047, d1[0.001] d2[0.005] g[0.944]\n",
            ">6048, d1[0.001] d2[0.004] g[1.996]\n",
            ">6049, d1[0.000] d2[0.004] g[1.482]\n",
            ">6050, d1[0.001] d2[0.021] g[0.955]\n",
            ">6051, d1[0.002] d2[0.003] g[0.727]\n",
            ">6052, d1[0.000] d2[0.002] g[2.336]\n",
            ">6053, d1[0.030] d2[0.003] g[0.810]\n",
            ">6054, d1[0.002] d2[0.002] g[1.055]\n",
            ">6055, d1[0.002] d2[0.003] g[0.961]\n",
            ">6056, d1[0.001] d2[0.005] g[1.042]\n",
            ">6057, d1[0.001] d2[0.006] g[0.991]\n",
            ">6058, d1[0.001] d2[0.002] g[0.852]\n",
            ">6059, d1[0.748] d2[1.016] g[0.835]\n",
            ">6060, d1[0.000] d2[0.008] g[1.479]\n",
            ">6061, d1[0.001] d2[0.009] g[1.126]\n",
            ">6062, d1[0.001] d2[0.007] g[1.831]\n",
            ">6063, d1[0.003] d2[0.003] g[0.865]\n",
            ">6064, d1[0.030] d2[0.015] g[0.599]\n",
            ">6065, d1[0.001] d2[0.006] g[1.367]\n",
            ">6066, d1[0.001] d2[0.003] g[0.992]\n",
            ">6067, d1[0.002] d2[0.003] g[0.973]\n",
            ">6068, d1[0.002] d2[0.002] g[1.005]\n",
            ">6069, d1[0.004] d2[0.002] g[0.669]\n",
            ">6070, d1[0.002] d2[0.001] g[0.914]\n",
            ">6071, d1[0.003] d2[0.002] g[0.739]\n",
            ">6072, d1[0.001] d2[0.001] g[0.961]\n",
            ">6073, d1[0.001] d2[0.002] g[0.886]\n",
            ">6074, d1[0.001] d2[0.001] g[1.788]\n",
            ">6075, d1[0.001] d2[0.002] g[1.427]\n",
            ">6076, d1[0.001] d2[0.003] g[1.349]\n",
            ">6077, d1[0.001] d2[0.001] g[1.260]\n",
            ">6078, d1[0.002] d2[0.001] g[0.856]\n",
            ">6079, d1[0.002] d2[0.001] g[1.044]\n",
            ">6080, d1[0.062] d2[0.002] g[0.746]\n",
            ">6081, d1[0.000] d2[0.001] g[1.737]\n",
            ">6082, d1[0.001] d2[0.002] g[0.829]\n",
            ">6083, d1[0.001] d2[0.005] g[1.174]\n",
            ">6084, d1[0.001] d2[0.001] g[0.699]\n",
            ">6085, d1[0.001] d2[0.002] g[0.825]\n",
            ">6086, d1[0.002] d2[0.006] g[0.646]\n",
            ">6087, d1[0.001] d2[0.001] g[0.854]\n",
            ">6088, d1[0.000] d2[0.003] g[1.436]\n",
            ">6089, d1[0.001] d2[0.001] g[0.944]\n",
            ">6090, d1[0.012] d2[0.001] g[0.772]\n",
            ">6091, d1[0.000] d2[0.001] g[1.291]\n",
            ">6092, d1[0.001] d2[0.003] g[0.933]\n",
            ">6093, d1[0.001] d2[0.001] g[1.529]\n",
            ">6094, d1[0.001] d2[0.002] g[1.466]\n",
            ">6095, d1[0.001] d2[0.002] g[0.988]\n",
            ">6096, d1[0.001] d2[0.001] g[1.869]\n",
            ">6097, d1[0.001] d2[0.002] g[1.396]\n",
            ">6098, d1[0.001] d2[0.002] g[0.689]\n",
            ">6099, d1[0.000] d2[0.002] g[1.889]\n",
            ">6100, d1[0.001] d2[0.001] g[0.868]\n",
            ">6101, d1[0.001] d2[0.001] g[0.937]\n",
            ">6102, d1[0.001] d2[0.001] g[1.289]\n",
            ">6103, d1[0.001] d2[0.001] g[0.696]\n",
            ">6104, d1[0.001] d2[0.001] g[1.635]\n",
            ">6105, d1[0.068] d2[0.056] g[0.421]\n",
            ">6106, d1[0.001] d2[0.001] g[0.716]\n",
            ">6107, d1[0.001] d2[0.001] g[0.913]\n",
            ">6108, d1[0.552] d2[0.063] g[0.621]\n",
            ">6109, d1[0.000] d2[0.036] g[1.034]\n",
            ">6110, d1[0.000] d2[0.361] g[1.692]\n",
            ">6111, d1[0.031] d2[0.006] g[0.638]\n",
            ">6112, d1[0.003] d2[0.003] g[0.772]\n",
            ">6113, d1[0.004] d2[0.007] g[1.220]\n",
            ">6114, d1[0.165] d2[0.034] g[0.785]\n",
            ">6115, d1[0.001] d2[0.003] g[1.780]\n",
            ">6116, d1[0.000] d2[0.006] g[1.073]\n",
            ">6117, d1[0.001] d2[0.016] g[1.109]\n",
            ">6118, d1[0.001] d2[0.285] g[1.758]\n",
            ">6119, d1[0.001] d2[0.002] g[0.946]\n",
            ">6120, d1[0.002] d2[0.003] g[1.185]\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            ">Saved: plot_006120.png and model_006120.h5\n",
            ">6121, d1[0.002] d2[0.003] g[1.012]\n",
            ">6122, d1[0.016] d2[0.001] g[0.878]\n",
            ">6123, d1[0.001] d2[0.000] g[1.711]\n",
            ">6124, d1[0.006] d2[0.001] g[1.018]\n",
            ">6125, d1[0.002] d2[0.001] g[0.862]\n",
            ">6126, d1[0.001] d2[0.001] g[1.446]\n",
            ">6127, d1[0.005] d2[0.001] g[0.825]\n",
            ">6128, d1[0.047] d2[0.001] g[0.544]\n",
            ">6129, d1[0.002] d2[0.001] g[0.738]\n",
            ">6130, d1[0.001] d2[0.001] g[2.201]\n",
            ">6131, d1[0.001] d2[0.001] g[1.453]\n",
            ">6132, d1[0.001] d2[0.001] g[1.129]\n",
            ">6133, d1[0.001] d2[0.001] g[0.966]\n",
            ">6134, d1[0.001] d2[0.000] g[1.171]\n",
            ">6135, d1[0.001] d2[0.001] g[1.100]\n",
            ">6136, d1[0.001] d2[0.001] g[0.996]\n",
            ">6137, d1[0.002] d2[0.001] g[1.037]\n",
            ">6138, d1[0.001] d2[0.000] g[1.100]\n",
            ">6139, d1[0.001] d2[0.001] g[0.770]\n",
            ">6140, d1[0.001] d2[0.001] g[1.104]\n",
            ">6141, d1[0.002] d2[0.002] g[0.885]\n",
            ">6142, d1[0.003] d2[0.001] g[0.589]\n",
            ">6143, d1[0.000] d2[0.000] g[1.879]\n",
            ">6144, d1[0.001] d2[0.004] g[1.328]\n",
            ">6145, d1[0.001] d2[0.001] g[1.262]\n",
            ">6146, d1[0.002] d2[0.001] g[0.858]\n",
            ">6147, d1[0.001] d2[0.001] g[1.101]\n",
            ">6148, d1[0.001] d2[0.001] g[1.363]\n",
            ">6149, d1[0.001] d2[0.001] g[0.774]\n",
            ">6150, d1[0.001] d2[0.000] g[0.892]\n",
            ">6151, d1[0.001] d2[0.001] g[0.996]\n",
            ">6152, d1[0.001] d2[0.001] g[0.771]\n",
            ">6153, d1[0.000] d2[0.001] g[0.998]\n",
            ">6154, d1[0.000] d2[0.001] g[1.363]\n",
            ">6155, d1[0.001] d2[0.016] g[0.637]\n",
            ">6156, d1[0.001] d2[0.001] g[0.898]\n",
            ">6157, d1[0.001] d2[0.000] g[1.634]\n",
            ">6158, d1[0.000] d2[0.000] g[1.378]\n",
            ">6159, d1[1.187] d2[0.002] g[0.556]\n",
            ">6160, d1[0.000] d2[0.231] g[1.177]\n",
            ">6161, d1[0.000] d2[0.019] g[1.218]\n",
            ">6162, d1[0.001] d2[0.003] g[3.149]\n",
            ">6163, d1[0.001] d2[0.006] g[0.995]\n",
            ">6164, d1[0.000] d2[0.005] g[1.067]\n",
            ">6165, d1[0.000] d2[0.005] g[1.259]\n",
            ">6166, d1[0.000] d2[0.012] g[2.004]\n",
            ">6167, d1[0.000] d2[0.019] g[1.627]\n",
            ">6168, d1[0.006] d2[0.023] g[0.825]\n",
            ">6169, d1[0.001] d2[0.007] g[1.527]\n",
            ">6170, d1[0.000] d2[0.005] g[2.403]\n",
            ">6171, d1[0.000] d2[0.006] g[2.127]\n",
            ">6172, d1[0.001] d2[0.009] g[1.867]\n",
            ">6173, d1[0.007] d2[0.003] g[0.783]\n",
            ">6174, d1[0.001] d2[0.008] g[1.782]\n",
            ">6175, d1[0.000] d2[0.007] g[2.150]\n",
            ">6176, d1[0.001] d2[0.010] g[1.517]\n",
            ">6177, d1[0.000] d2[0.006] g[1.547]\n",
            ">6178, d1[0.001] d2[0.005] g[1.683]\n",
            ">6179, d1[0.000] d2[0.014] g[1.890]\n",
            ">6180, d1[0.018] d2[0.060] g[1.621]\n",
            ">6181, d1[0.002] d2[0.003] g[1.284]\n",
            ">6182, d1[0.003] d2[0.042] g[0.634]\n",
            ">6183, d1[0.000] d2[0.001] g[1.507]\n",
            ">6184, d1[0.019] d2[0.007] g[0.950]\n",
            ">6185, d1[0.000] d2[0.002] g[1.187]\n",
            ">6186, d1[0.000] d2[0.002] g[0.985]\n",
            ">6187, d1[0.001] d2[0.002] g[1.415]\n",
            ">6188, d1[0.001] d2[0.001] g[1.164]\n",
            ">6189, d1[0.000] d2[0.002] g[1.073]\n",
            ">6190, d1[0.001] d2[0.006] g[1.113]\n",
            ">6191, d1[0.000] d2[0.002] g[2.320]\n",
            ">6192, d1[0.001] d2[0.002] g[1.704]\n",
            ">6193, d1[0.000] d2[0.003] g[2.011]\n",
            ">6194, d1[0.000] d2[0.001] g[1.564]\n",
            ">6195, d1[0.000] d2[0.002] g[1.560]\n",
            ">6196, d1[0.000] d2[0.001] g[1.848]\n",
            ">6197, d1[0.012] d2[0.002] g[0.990]\n",
            ">6198, d1[0.031] d2[0.002] g[0.840]\n",
            ">6199, d1[0.001] d2[0.011] g[1.342]\n",
            ">6200, d1[0.001] d2[0.001] g[1.215]\n",
            ">6201, d1[0.000] d2[0.001] g[1.165]\n",
            ">6202, d1[0.001] d2[0.001] g[1.409]\n",
            ">6203, d1[0.002] d2[0.001] g[0.982]\n",
            ">6204, d1[0.039] d2[0.001] g[0.789]\n",
            ">6205, d1[0.007] d2[0.429] g[1.100]\n",
            ">6206, d1[0.000] d2[0.000] g[2.470]\n",
            ">6207, d1[0.001] d2[0.000] g[1.693]\n",
            ">6208, d1[0.000] d2[0.000] g[1.924]\n",
            ">6209, d1[0.003] d2[0.000] g[1.878]\n",
            ">6210, d1[0.027] d2[0.001] g[0.886]\n",
            ">6211, d1[0.002] d2[0.000] g[1.240]\n",
            ">6212, d1[0.002] d2[0.000] g[1.127]\n",
            ">6213, d1[0.020] d2[0.001] g[0.832]\n",
            ">6214, d1[0.001] d2[0.000] g[0.859]\n",
            ">6215, d1[0.001] d2[0.000] g[1.167]\n",
            ">6216, d1[0.118] d2[0.000] g[0.746]\n",
            ">6217, d1[0.000] d2[0.001] g[1.576]\n",
            ">6218, d1[0.001] d2[0.000] g[1.051]\n",
            ">6219, d1[0.001] d2[0.001] g[1.091]\n",
            ">6220, d1[0.000] d2[0.001] g[1.800]\n",
            ">6221, d1[0.002] d2[0.001] g[1.124]\n",
            ">6222, d1[0.001] d2[0.001] g[1.639]\n",
            ">6223, d1[0.118] d2[0.001] g[0.937]\n",
            ">6224, d1[0.000] d2[0.001] g[2.033]\n",
            ">6225, d1[0.001] d2[0.001] g[0.793]\n",
            ">6226, d1[0.000] d2[0.024] g[1.243]\n",
            ">6227, d1[0.000] d2[0.010] g[0.880]\n",
            ">6228, d1[0.006] d2[0.001] g[0.709]\n",
            ">6229, d1[0.000] d2[0.001] g[1.194]\n",
            ">6230, d1[0.001] d2[0.001] g[1.109]\n",
            ">6231, d1[0.001] d2[0.001] g[1.054]\n",
            ">6232, d1[0.000] d2[0.001] g[1.697]\n",
            ">6233, d1[0.000] d2[0.000] g[1.256]\n",
            ">6234, d1[0.001] d2[0.001] g[0.705]\n",
            ">6235, d1[0.000] d2[0.001] g[1.085]\n",
            ">6236, d1[0.000] d2[0.002] g[1.580]\n",
            ">6237, d1[0.001] d2[0.003] g[1.259]\n",
            ">6238, d1[0.394] d2[0.005] g[0.504]\n",
            ">6239, d1[0.000] d2[0.020] g[1.769]\n",
            ">6240, d1[0.000] d2[0.012] g[0.991]\n",
            ">6241, d1[0.001] d2[0.508] g[0.495]\n",
            ">6242, d1[0.000] d2[0.003] g[2.457]\n",
            ">6243, d1[0.001] d2[0.002] g[1.636]\n",
            ">6244, d1[0.001] d2[0.003] g[1.757]\n",
            ">6245, d1[0.002] d2[0.002] g[1.930]\n",
            ">6246, d1[0.607] d2[0.111] g[0.937]\n",
            ">6247, d1[0.001] d2[0.197] g[1.064]\n",
            ">6248, d1[0.001] d2[0.011] g[2.098]\n",
            ">6249, d1[0.001] d2[0.003] g[1.502]\n",
            ">6250, d1[0.001] d2[0.323] g[1.117]\n",
            ">6251, d1[0.012] d2[0.006] g[0.900]\n",
            ">6252, d1[0.001] d2[0.002] g[1.757]\n",
            ">6253, d1[0.007] d2[0.007] g[1.374]\n",
            ">6254, d1[0.131] d2[0.002] g[0.720]\n",
            ">6255, d1[0.001] d2[0.001] g[1.707]\n",
            ">6256, d1[0.001] d2[0.000] g[1.691]\n",
            ">6257, d1[0.070] d2[0.001] g[0.900]\n",
            ">6258, d1[0.001] d2[0.001] g[1.206]\n",
            ">6259, d1[0.001] d2[0.006] g[0.758]\n",
            ">6260, d1[0.001] d2[0.005] g[1.245]\n",
            ">6261, d1[0.000] d2[0.005] g[1.817]\n",
            ">6262, d1[0.000] d2[0.002] g[1.533]\n",
            ">6263, d1[0.001] d2[0.003] g[1.204]\n",
            ">6264, d1[0.295] d2[2.064] g[0.875]\n",
            ">6265, d1[0.001] d2[0.001] g[1.005]\n",
            ">6266, d1[0.010] d2[0.004] g[2.041]\n",
            ">6267, d1[0.019] d2[0.001] g[2.083]\n",
            ">6268, d1[0.001] d2[0.000] g[1.110]\n",
            ">6269, d1[0.003] d2[0.000] g[1.498]\n",
            ">6270, d1[0.001] d2[0.001] g[1.705]\n",
            ">6271, d1[0.119] d2[0.001] g[0.827]\n",
            ">6272, d1[0.002] d2[0.001] g[0.872]\n",
            ">6273, d1[0.002] d2[0.004] g[1.277]\n",
            ">6274, d1[0.001] d2[0.001] g[1.342]\n",
            ">6275, d1[0.002] d2[0.001] g[1.181]\n",
            ">6276, d1[0.001] d2[0.001] g[1.685]\n",
            ">6277, d1[0.628] d2[0.038] g[0.812]\n",
            ">6278, d1[0.001] d2[0.028] g[1.075]\n",
            ">6279, d1[0.004] d2[0.103] g[0.938]\n",
            ">6280, d1[0.001] d2[0.007] g[1.959]\n",
            ">6281, d1[0.003] d2[0.001] g[0.749]\n",
            ">6282, d1[0.004] d2[0.004] g[0.809]\n",
            ">6283, d1[0.001] d2[0.003] g[1.042]\n",
            ">6284, d1[0.001] d2[0.001] g[1.892]\n",
            ">6285, d1[0.024] d2[0.007] g[0.891]\n",
            ">6286, d1[0.006] d2[0.050] g[1.118]\n",
            ">6287, d1[0.001] d2[0.001] g[1.807]\n",
            ">6288, d1[0.001] d2[0.003] g[0.975]\n",
            ">6289, d1[0.000] d2[0.005] g[1.542]\n",
            ">6290, d1[0.103] d2[0.002] g[1.093]\n",
            ">6291, d1[0.001] d2[1.205] g[1.436]\n",
            ">6292, d1[0.107] d2[0.003] g[0.767]\n",
            ">6293, d1[0.006] d2[0.009] g[1.333]\n",
            ">6294, d1[0.003] d2[0.000] g[1.961]\n",
            ">6295, d1[0.003] d2[0.000] g[1.513]\n",
            ">6296, d1[0.003] d2[0.000] g[1.761]\n",
            ">6297, d1[0.062] d2[0.000] g[0.739]\n",
            ">6298, d1[0.067] d2[0.000] g[0.852]\n",
            ">6299, d1[0.623] d2[0.011] g[1.278]\n",
            ">6300, d1[0.000] d2[0.003] g[1.355]\n",
            ">6301, d1[0.000] d2[0.001] g[1.803]\n",
            ">6302, d1[0.000] d2[0.012] g[2.336]\n",
            ">6303, d1[0.001] d2[0.009] g[1.642]\n",
            ">6304, d1[0.001] d2[0.023] g[0.818]\n",
            ">6305, d1[0.001] d2[0.012] g[0.652]\n",
            ">6306, d1[0.000] d2[0.001] g[2.268]\n",
            ">6307, d1[0.001] d2[0.010] g[1.123]\n",
            ">6308, d1[0.000] d2[0.001] g[1.620]\n",
            ">6309, d1[0.000] d2[0.001] g[1.991]\n",
            ">6310, d1[0.001] d2[0.061] g[1.040]\n",
            ">6311, d1[0.001] d2[0.000] g[1.389]\n",
            ">6312, d1[0.000] d2[0.002] g[1.310]\n",
            ">6313, d1[0.061] d2[0.002] g[0.744]\n",
            ">6314, d1[0.001] d2[0.007] g[0.946]\n",
            ">6315, d1[0.001] d2[0.001] g[1.263]\n",
            ">6316, d1[0.000] d2[0.005] g[1.344]\n",
            ">6317, d1[0.001] d2[0.001] g[1.178]\n",
            ">6318, d1[0.002] d2[0.001] g[1.111]\n",
            ">6319, d1[0.000] d2[0.001] g[1.910]\n",
            ">6320, d1[0.000] d2[0.001] g[1.373]\n",
            ">6321, d1[0.001] d2[0.001] g[0.779]\n",
            ">6322, d1[0.000] d2[0.000] g[2.122]\n",
            ">6323, d1[0.000] d2[0.005] g[1.222]\n",
            ">6324, d1[0.000] d2[0.001] g[1.613]\n",
            ">6325, d1[0.003] d2[0.001] g[0.897]\n",
            ">6326, d1[0.001] d2[0.001] g[0.875]\n",
            ">6327, d1[0.000] d2[0.001] g[1.338]\n",
            ">6328, d1[0.001] d2[0.000] g[1.109]\n",
            ">6329, d1[0.001] d2[0.000] g[0.993]\n",
            ">6330, d1[0.001] d2[0.000] g[1.361]\n",
            ">6331, d1[0.000] d2[0.001] g[1.178]\n",
            ">6332, d1[0.001] d2[0.002] g[0.668]\n",
            ">6333, d1[0.010] d2[0.017] g[0.622]\n",
            ">6334, d1[0.001] d2[0.000] g[1.223]\n",
            ">6335, d1[0.001] d2[0.000] g[1.182]\n",
            ">6336, d1[0.000] d2[0.014] g[1.601]\n",
            ">6337, d1[0.000] d2[0.001] g[1.471]\n",
            ">6338, d1[0.001] d2[0.000] g[1.001]\n",
            ">6339, d1[0.027] d2[0.001] g[1.333]\n",
            ">6340, d1[0.000] d2[0.000] g[1.076]\n",
            ">6341, d1[0.000] d2[0.001] g[1.096]\n",
            ">6342, d1[0.001] d2[0.001] g[1.015]\n",
            ">6343, d1[0.000] d2[0.000] g[1.457]\n",
            ">6344, d1[0.000] d2[0.000] g[1.227]\n",
            ">6345, d1[0.001] d2[0.000] g[1.035]\n",
            ">6346, d1[0.000] d2[0.001] g[1.421]\n",
            ">6347, d1[0.001] d2[0.000] g[1.598]\n",
            ">6348, d1[0.001] d2[0.001] g[0.961]\n",
            ">6349, d1[0.002] d2[0.001] g[0.932]\n",
            ">6350, d1[0.000] d2[0.000] g[1.528]\n",
            ">6351, d1[0.000] d2[0.001] g[1.731]\n",
            ">6352, d1[0.000] d2[0.001] g[1.287]\n",
            ">6353, d1[0.001] d2[0.000] g[1.274]\n",
            ">6354, d1[0.000] d2[0.001] g[1.715]\n",
            ">6355, d1[0.000] d2[0.001] g[2.318]\n",
            ">6356, d1[0.000] d2[0.002] g[1.563]\n",
            ">6357, d1[0.002] d2[0.001] g[0.862]\n",
            ">6358, d1[0.000] d2[0.002] g[1.298]\n",
            ">6359, d1[0.000] d2[0.000] g[1.332]\n",
            ">6360, d1[0.000] d2[0.001] g[1.399]\n",
            ">6361, d1[0.001] d2[1.302] g[0.932]\n",
            ">6362, d1[0.000] d2[0.000] g[2.133]\n",
            ">6363, d1[1.871] d2[0.004] g[0.801]\n",
            ">6364, d1[0.001] d2[0.003] g[1.532]\n",
            ">6365, d1[0.001] d2[0.002] g[1.269]\n",
            ">6366, d1[0.000] d2[0.004] g[0.999]\n",
            ">6367, d1[0.001] d2[0.074] g[1.002]\n",
            ">6368, d1[0.001] d2[0.002] g[0.976]\n",
            ">6369, d1[0.002] d2[0.001] g[0.852]\n",
            ">6370, d1[0.001] d2[0.001] g[0.942]\n",
            ">6371, d1[0.001] d2[0.001] g[0.954]\n",
            ">6372, d1[0.001] d2[0.009] g[1.388]\n",
            ">6373, d1[0.002] d2[0.001] g[0.788]\n",
            ">6374, d1[0.000] d2[0.001] g[2.045]\n",
            ">6375, d1[0.001] d2[0.001] g[0.713]\n",
            ">6376, d1[0.001] d2[0.003] g[1.146]\n",
            ">6377, d1[0.001] d2[0.001] g[0.833]\n",
            ">6378, d1[0.001] d2[0.005] g[1.683]\n",
            ">6379, d1[0.001] d2[0.001] g[1.012]\n",
            ">6380, d1[0.053] d2[0.001] g[0.845]\n",
            ">6381, d1[0.001] d2[0.001] g[1.150]\n",
            ">6382, d1[0.001] d2[0.003] g[0.942]\n",
            ">6383, d1[0.000] d2[0.001] g[1.425]\n",
            ">6384, d1[0.002] d2[0.026] g[0.752]\n",
            ">6385, d1[0.001] d2[0.001] g[1.203]\n",
            ">6386, d1[0.001] d2[0.001] g[1.039]\n",
            ">6387, d1[0.001] d2[0.001] g[1.013]\n",
            ">6388, d1[0.001] d2[0.001] g[0.954]\n",
            ">6389, d1[0.008] d2[0.001] g[0.506]\n",
            ">6390, d1[0.001] d2[0.038] g[0.960]\n",
            ">6391, d1[0.001] d2[0.000] g[1.239]\n",
            ">6392, d1[0.001] d2[0.000] g[0.990]\n",
            ">6393, d1[0.004] d2[0.001] g[0.752]\n",
            ">6394, d1[0.003] d2[0.001] g[0.847]\n",
            ">6395, d1[0.076] d2[0.001] g[0.737]\n",
            ">6396, d1[0.001] d2[0.001] g[1.294]\n",
            ">6397, d1[0.001] d2[0.001] g[1.296]\n",
            ">6398, d1[0.001] d2[0.002] g[0.897]\n",
            ">6399, d1[0.001] d2[0.000] g[1.056]\n",
            ">6400, d1[0.000] d2[0.001] g[2.372]\n",
            ">6401, d1[0.000] d2[0.001] g[1.425]\n",
            ">6402, d1[0.001] d2[0.001] g[0.827]\n",
            ">6403, d1[0.001] d2[0.000] g[1.008]\n",
            ">6404, d1[0.000] d2[0.000] g[1.162]\n",
            ">6405, d1[0.001] d2[0.000] g[0.742]\n",
            ">6406, d1[0.002] d2[0.001] g[0.763]\n",
            ">6407, d1[0.000] d2[0.002] g[1.250]\n",
            ">6408, d1[0.000] d2[0.001] g[1.752]\n",
            ">6409, d1[0.000] d2[0.002] g[1.503]\n",
            ">6410, d1[0.001] d2[0.001] g[0.967]\n",
            ">6411, d1[0.039] d2[0.001] g[1.106]\n",
            ">6412, d1[0.007] d2[0.059] g[0.683]\n",
            ">6413, d1[0.001] d2[0.000] g[0.906]\n",
            ">6414, d1[0.001] d2[0.016] g[1.138]\n",
            ">6415, d1[0.000] d2[0.000] g[1.999]\n",
            ">6416, d1[0.000] d2[0.007] g[1.107]\n",
            ">6417, d1[0.001] d2[0.000] g[1.340]\n",
            ">6418, d1[0.002] d2[0.002] g[0.682]\n",
            ">6419, d1[0.001] d2[0.003] g[1.100]\n",
            ">6420, d1[0.001] d2[0.000] g[0.905]\n",
            ">6421, d1[0.003] d2[0.002] g[0.762]\n",
            ">6422, d1[0.001] d2[0.000] g[1.059]\n",
            ">6423, d1[0.000] d2[0.001] g[1.426]\n",
            ">6424, d1[0.001] d2[0.000] g[1.503]\n",
            ">6425, d1[0.001] d2[0.001] g[1.097]\n",
            ">6426, d1[0.002] d2[0.000] g[0.742]\n",
            ">6427, d1[0.018] d2[0.005] g[0.714]\n",
            ">6428, d1[0.000] d2[0.001] g[1.003]\n",
            ">6429, d1[0.001] d2[0.000] g[0.777]\n",
            ">6430, d1[0.000] d2[0.001] g[1.883]\n",
            ">6431, d1[0.001] d2[0.001] g[1.213]\n",
            ">6432, d1[0.001] d2[0.002] g[1.051]\n",
            ">6433, d1[0.000] d2[0.000] g[2.413]\n",
            ">6434, d1[0.002] d2[0.000] g[1.045]\n",
            ">6435, d1[0.002] d2[0.000] g[0.862]\n",
            ">6436, d1[0.001] d2[0.003] g[1.068]\n",
            ">6437, d1[0.001] d2[0.001] g[0.956]\n",
            ">6438, d1[0.001] d2[0.000] g[0.969]\n",
            ">6439, d1[0.000] d2[0.000] g[1.347]\n",
            ">6440, d1[0.000] d2[0.000] g[1.444]\n",
            ">6441, d1[0.000] d2[0.001] g[1.341]\n",
            ">6442, d1[0.000] d2[0.000] g[2.066]\n",
            ">6443, d1[0.000] d2[0.001] g[1.860]\n",
            ">6444, d1[0.001] d2[0.000] g[1.220]\n",
            ">6445, d1[0.001] d2[0.001] g[0.944]\n",
            ">6446, d1[0.000] d2[0.001] g[1.280]\n",
            ">6447, d1[0.001] d2[0.001] g[0.864]\n",
            ">6448, d1[0.005] d2[0.000] g[0.745]\n",
            ">6449, d1[0.017] d2[0.001] g[0.917]\n",
            ">6450, d1[0.000] d2[0.001] g[0.898]\n",
            ">6451, d1[0.001] d2[0.000] g[1.502]\n",
            ">6452, d1[0.000] d2[0.000] g[0.991]\n",
            ">6453, d1[0.000] d2[0.001] g[1.955]\n",
            ">6454, d1[0.001] d2[0.000] g[0.964]\n",
            ">6455, d1[0.000] d2[0.001] g[1.160]\n",
            ">6456, d1[0.001] d2[0.002] g[0.770]\n",
            ">6457, d1[0.000] d2[0.000] g[1.384]\n",
            ">6458, d1[0.001] d2[0.000] g[0.756]\n",
            ">6459, d1[0.018] d2[0.001] g[1.063]\n",
            ">6460, d1[0.000] d2[0.000] g[0.977]\n",
            ">6461, d1[0.000] d2[0.001] g[0.902]\n",
            ">6462, d1[0.000] d2[0.001] g[0.781]\n",
            ">6463, d1[0.000] d2[0.002] g[1.344]\n",
            ">6464, d1[0.000] d2[0.000] g[1.388]\n",
            ">6465, d1[0.001] d2[0.000] g[1.246]\n",
            ">6466, d1[0.027] d2[0.002] g[1.096]\n",
            ">6467, d1[0.000] d2[0.000] g[0.920]\n",
            ">6468, d1[0.000] d2[0.000] g[1.563]\n",
            ">6469, d1[0.000] d2[0.001] g[0.836]\n",
            ">6470, d1[0.000] d2[0.001] g[0.903]\n",
            ">6471, d1[0.000] d2[0.001] g[1.030]\n",
            ">6472, d1[0.000] d2[0.001] g[1.033]\n",
            ">6473, d1[0.000] d2[0.000] g[1.260]\n",
            ">6474, d1[0.021] d2[0.002] g[0.735]\n",
            ">6475, d1[0.000] d2[0.001] g[0.819]\n",
            ">6476, d1[0.001] d2[0.000] g[0.628]\n",
            ">6477, d1[0.000] d2[0.169] g[1.363]\n",
            ">6478, d1[0.000] d2[0.000] g[1.011]\n",
            ">6479, d1[0.000] d2[0.001] g[1.486]\n",
            ">6480, d1[0.001] d2[0.001] g[0.963]\n",
            ">6481, d1[0.002] d2[0.000] g[1.124]\n",
            ">6482, d1[0.078] d2[0.001] g[0.643]\n",
            ">6483, d1[0.046] d2[0.002] g[0.865]\n",
            ">6484, d1[0.000] d2[0.001] g[1.650]\n",
            ">6485, d1[0.001] d2[0.000] g[0.831]\n",
            ">6486, d1[0.015] d2[0.004] g[0.898]\n",
            ">6487, d1[0.000] d2[0.001] g[1.291]\n",
            ">6488, d1[0.000] d2[0.001] g[0.902]\n",
            ">6489, d1[0.000] d2[0.000] g[1.517]\n",
            ">6490, d1[0.022] d2[0.001] g[0.702]\n",
            ">6491, d1[0.000] d2[0.000] g[1.964]\n",
            ">6492, d1[0.000] d2[0.001] g[1.593]\n",
            ">6493, d1[0.000] d2[0.000] g[0.806]\n",
            ">6494, d1[0.000] d2[0.000] g[1.456]\n",
            ">6495, d1[0.000] d2[0.000] g[0.898]\n",
            ">6496, d1[0.000] d2[0.000] g[1.222]\n",
            ">6497, d1[0.000] d2[0.001] g[1.629]\n",
            ">6498, d1[0.000] d2[0.000] g[1.839]\n",
            ">6499, d1[0.000] d2[0.000] g[1.165]\n",
            ">6500, d1[0.000] d2[0.001] g[1.512]\n",
            ">6501, d1[0.000] d2[0.000] g[1.684]\n",
            ">6502, d1[0.010] d2[0.004] g[0.856]\n",
            ">6503, d1[0.000] d2[0.000] g[1.489]\n",
            ">6504, d1[0.005] d2[0.002] g[1.016]\n",
            ">6505, d1[0.000] d2[0.000] g[1.231]\n",
            ">6506, d1[0.000] d2[0.000] g[1.971]\n",
            ">6507, d1[0.000] d2[0.000] g[1.426]\n",
            ">6508, d1[0.000] d2[0.000] g[0.834]\n",
            ">6509, d1[0.000] d2[0.001] g[1.562]\n",
            ">6510, d1[0.000] d2[0.000] g[0.964]\n",
            ">6511, d1[0.000] d2[0.000] g[1.456]\n",
            ">6512, d1[0.001] d2[0.000] g[0.684]\n",
            ">6513, d1[0.000] d2[0.000] g[1.195]\n",
            ">6514, d1[0.001] d2[0.004] g[0.764]\n",
            ">6515, d1[0.000] d2[0.000] g[1.734]\n",
            ">6516, d1[0.000] d2[0.000] g[1.152]\n",
            ">6517, d1[0.042] d2[0.000] g[0.842]\n",
            ">6518, d1[0.000] d2[0.000] g[2.100]\n",
            ">6519, d1[0.000] d2[0.000] g[1.397]\n",
            ">6520, d1[0.000] d2[0.001] g[1.269]\n",
            ">6521, d1[0.000] d2[0.002] g[0.865]\n",
            ">6522, d1[0.000] d2[0.000] g[0.741]\n",
            ">6523, d1[0.000] d2[0.000] g[0.814]\n",
            ">6524, d1[0.003] d2[0.006] g[0.628]\n",
            ">6525, d1[0.020] d2[0.002] g[0.809]\n",
            ">6526, d1[0.000] d2[0.001] g[1.455]\n",
            ">6527, d1[0.001] d2[0.000] g[0.747]\n",
            ">6528, d1[0.000] d2[0.000] g[1.271]\n",
            ">6529, d1[0.000] d2[0.001] g[0.957]\n",
            ">6530, d1[0.000] d2[0.000] g[1.987]\n",
            ">6531, d1[0.000] d2[0.001] g[1.455]\n",
            ">6532, d1[0.000] d2[0.001] g[1.505]\n",
            ">6533, d1[0.001] d2[0.000] g[1.122]\n",
            ">6534, d1[0.000] d2[0.011] g[1.108]\n",
            ">6535, d1[0.000] d2[0.001] g[0.943]\n",
            ">6536, d1[0.001] d2[0.000] g[0.775]\n",
            ">6537, d1[0.000] d2[0.001] g[0.941]\n",
            ">6538, d1[0.007] d2[0.003] g[0.805]\n",
            ">6539, d1[0.126] d2[0.001] g[0.718]\n",
            ">6540, d1[0.000] d2[0.001] g[1.218]\n",
            ">6541, d1[0.001] d2[0.007] g[0.693]\n",
            ">6542, d1[0.000] d2[0.001] g[1.639]\n",
            ">6543, d1[0.000] d2[0.001] g[0.936]\n",
            ">6544, d1[0.000] d2[0.001] g[0.972]\n",
            ">6545, d1[0.000] d2[0.001] g[1.127]\n",
            ">6546, d1[0.000] d2[0.001] g[1.194]\n",
            ">6547, d1[0.003] d2[0.018] g[0.736]\n",
            ">6548, d1[0.000] d2[0.001] g[0.892]\n",
            ">6549, d1[0.000] d2[0.001] g[1.428]\n",
            ">6550, d1[0.000] d2[0.001] g[1.539]\n",
            ">6551, d1[0.000] d2[0.000] g[1.060]\n",
            ">6552, d1[0.000] d2[0.001] g[1.628]\n",
            ">6553, d1[0.000] d2[0.001] g[0.950]\n",
            ">6554, d1[0.000] d2[0.001] g[1.435]\n",
            ">6555, d1[0.000] d2[0.005] g[0.735]\n",
            ">6556, d1[0.000] d2[0.001] g[0.787]\n",
            ">6557, d1[0.000] d2[0.002] g[1.153]\n",
            ">6558, d1[0.000] d2[0.001] g[0.961]\n",
            ">6559, d1[0.000] d2[0.001] g[1.682]\n",
            ">6560, d1[0.000] d2[0.001] g[1.258]\n",
            ">6561, d1[0.000] d2[0.001] g[1.342]\n",
            ">6562, d1[0.000] d2[0.001] g[1.475]\n",
            ">6563, d1[0.000] d2[0.000] g[2.062]\n",
            ">6564, d1[0.000] d2[0.000] g[1.476]\n",
            ">6565, d1[0.000] d2[0.001] g[0.985]\n",
            ">6566, d1[0.003] d2[0.003] g[0.712]\n",
            ">6567, d1[0.000] d2[0.000] g[1.246]\n",
            ">6568, d1[0.000] d2[0.001] g[1.401]\n",
            ">6569, d1[0.006] d2[0.000] g[0.823]\n",
            ">6570, d1[0.000] d2[0.002] g[1.335]\n",
            ">6571, d1[0.000] d2[0.001] g[0.907]\n",
            ">6572, d1[0.000] d2[0.001] g[1.674]\n",
            ">6573, d1[0.002] d2[0.003] g[0.839]\n",
            ">6574, d1[0.000] d2[0.001] g[1.063]\n",
            ">6575, d1[0.006] d2[0.010] g[0.831]\n",
            ">6576, d1[0.000] d2[0.000] g[1.345]\n",
            ">6577, d1[0.000] d2[0.001] g[0.701]\n",
            ">6578, d1[0.001] d2[0.000] g[0.694]\n",
            ">6579, d1[0.000] d2[0.000] g[1.427]\n",
            ">6580, d1[0.000] d2[0.001] g[0.801]\n",
            ">6581, d1[0.000] d2[0.003] g[0.867]\n",
            ">6582, d1[0.000] d2[0.000] g[0.794]\n",
            ">6583, d1[0.006] d2[0.004] g[0.661]\n",
            ">6584, d1[0.000] d2[0.000] g[1.983]\n",
            ">6585, d1[0.006] d2[0.000] g[0.663]\n",
            ">6586, d1[0.000] d2[0.001] g[1.001]\n",
            ">6587, d1[0.000] d2[0.001] g[1.386]\n",
            ">6588, d1[0.000] d2[0.001] g[0.877]\n",
            ">6589, d1[0.000] d2[0.000] g[1.892]\n",
            ">6590, d1[0.000] d2[0.002] g[1.100]\n",
            ">6591, d1[0.000] d2[0.000] g[1.952]\n",
            ">6592, d1[0.000] d2[0.003] g[1.041]\n",
            ">6593, d1[0.005] d2[0.001] g[0.731]\n",
            ">6594, d1[0.000] d2[0.000] g[1.356]\n",
            ">6595, d1[0.000] d2[0.000] g[0.836]\n",
            ">6596, d1[0.000] d2[0.000] g[1.208]\n",
            ">6597, d1[0.000] d2[0.000] g[1.745]\n",
            ">6598, d1[0.000] d2[0.000] g[1.092]\n",
            ">6599, d1[0.001] d2[0.601] g[0.432]\n",
            ">6600, d1[0.000] d2[0.000] g[0.914]\n",
            ">6601, d1[0.001] d2[0.000] g[1.238]\n",
            ">6602, d1[0.004] d2[0.000] g[1.121]\n",
            ">6603, d1[0.001] d2[0.000] g[0.887]\n",
            ">6604, d1[0.001] d2[0.000] g[1.246]\n",
            ">6605, d1[3.857] d2[0.002] g[0.434]\n",
            ">6606, d1[0.000] d2[0.021] g[1.077]\n",
            ">6607, d1[0.001] d2[0.011] g[1.144]\n",
            ">6608, d1[0.000] d2[0.004] g[0.868]\n",
            ">6609, d1[0.001] d2[0.004] g[0.994]\n",
            ">6610, d1[0.000] d2[0.002] g[2.004]\n",
            ">6611, d1[0.000] d2[0.004] g[1.503]\n",
            ">6612, d1[0.000] d2[0.003] g[0.724]\n",
            ">6613, d1[0.004] d2[0.004] g[0.642]\n",
            ">6614, d1[0.001] d2[0.003] g[1.035]\n",
            ">6615, d1[0.000] d2[0.006] g[1.361]\n",
            ">6616, d1[0.000] d2[0.003] g[0.945]\n",
            ">6617, d1[0.001] d2[0.003] g[0.994]\n",
            ">6618, d1[0.000] d2[0.009] g[1.236]\n",
            ">6619, d1[0.003] d2[0.003] g[0.660]\n",
            ">6620, d1[0.001] d2[0.002] g[0.928]\n",
            ">6621, d1[0.027] d2[0.002] g[0.785]\n",
            ">6622, d1[0.008] d2[0.006] g[0.472]\n",
            ">6623, d1[0.014] d2[0.002] g[0.741]\n",
            ">6624, d1[0.000] d2[0.002] g[0.881]\n",
            ">6625, d1[0.000] d2[0.002] g[1.128]\n",
            ">6626, d1[0.001] d2[0.002] g[0.997]\n",
            ">6627, d1[0.001] d2[0.003] g[0.698]\n",
            ">6628, d1[0.005] d2[0.002] g[0.501]\n",
            ">6629, d1[0.000] d2[0.005] g[1.852]\n",
            ">6630, d1[0.000] d2[0.003] g[1.289]\n",
            ">6631, d1[0.000] d2[0.003] g[1.344]\n",
            ">6632, d1[0.000] d2[0.002] g[0.922]\n",
            ">6633, d1[0.000] d2[0.002] g[1.419]\n",
            ">6634, d1[0.024] d2[0.003] g[0.641]\n",
            ">6635, d1[0.001] d2[0.001] g[1.197]\n",
            ">6636, d1[0.000] d2[0.003] g[1.258]\n",
            ">6637, d1[0.000] d2[0.003] g[1.139]\n",
            ">6638, d1[0.011] d2[0.006] g[0.684]\n",
            ">6639, d1[0.001] d2[0.001] g[1.109]\n",
            ">6640, d1[0.000] d2[0.001] g[0.869]\n",
            ">6641, d1[0.000] d2[0.002] g[0.988]\n",
            ">6642, d1[0.000] d2[0.001] g[0.939]\n",
            ">6643, d1[0.000] d2[0.001] g[1.744]\n",
            ">6644, d1[0.000] d2[0.020] g[0.776]\n",
            ">6645, d1[0.000] d2[0.001] g[0.807]\n",
            ">6646, d1[0.000] d2[0.002] g[1.869]\n",
            ">6647, d1[0.001] d2[0.005] g[0.723]\n",
            ">6648, d1[0.000] d2[0.001] g[1.786]\n",
            ">6649, d1[0.000] d2[0.001] g[1.972]\n",
            ">6650, d1[0.000] d2[0.001] g[1.850]\n",
            ">6651, d1[0.002] d2[0.001] g[0.700]\n",
            ">6652, d1[0.000] d2[0.001] g[1.075]\n",
            ">6653, d1[0.001] d2[0.002] g[0.746]\n",
            ">6654, d1[0.000] d2[0.001] g[1.483]\n",
            ">6655, d1[0.000] d2[0.002] g[1.039]\n",
            ">6656, d1[0.000] d2[0.002] g[0.926]\n",
            ">6657, d1[0.001] d2[0.001] g[0.652]\n",
            ">6658, d1[0.000] d2[0.001] g[0.931]\n",
            ">6659, d1[0.000] d2[0.002] g[0.894]\n",
            ">6660, d1[0.001] d2[0.001] g[0.776]\n",
            ">6661, d1[0.000] d2[0.001] g[0.814]\n",
            ">6662, d1[0.000] d2[0.001] g[0.938]\n",
            ">6663, d1[0.000] d2[0.001] g[0.956]\n",
            ">6664, d1[0.000] d2[0.001] g[0.930]\n",
            ">6665, d1[0.000] d2[0.001] g[0.952]\n",
            ">6666, d1[0.002] d2[0.001] g[0.729]\n",
            ">6667, d1[0.000] d2[0.001] g[0.962]\n",
            ">6668, d1[0.007] d2[0.002] g[0.387]\n",
            ">6669, d1[0.000] d2[0.002] g[1.830]\n",
            ">6670, d1[0.000] d2[0.001] g[1.390]\n",
            ">6671, d1[0.000] d2[0.006] g[1.153]\n",
            ">6672, d1[0.011] d2[0.002] g[0.593]\n",
            ">6673, d1[0.001] d2[0.001] g[1.027]\n",
            ">6674, d1[0.001] d2[0.001] g[0.614]\n",
            ">6675, d1[0.000] d2[0.001] g[1.758]\n",
            ">6676, d1[0.000] d2[0.001] g[0.951]\n",
            ">6677, d1[0.000] d2[0.012] g[1.102]\n",
            ">6678, d1[0.000] d2[0.001] g[0.779]\n",
            ">6679, d1[0.000] d2[0.001] g[0.887]\n",
            ">6680, d1[0.011] d2[0.001] g[0.559]\n",
            ">6681, d1[0.000] d2[0.001] g[1.329]\n",
            ">6682, d1[0.001] d2[0.003] g[0.718]\n",
            ">6683, d1[0.000] d2[0.001] g[1.625]\n",
            ">6684, d1[0.000] d2[0.001] g[1.076]\n",
            ">6685, d1[0.001] d2[0.001] g[0.595]\n",
            ">6686, d1[0.000] d2[0.026] g[0.869]\n",
            ">6687, d1[0.007] d2[0.001] g[0.626]\n",
            ">6688, d1[0.000] d2[0.001] g[1.143]\n",
            ">6689, d1[0.000] d2[0.001] g[1.705]\n",
            ">6690, d1[0.001] d2[0.001] g[0.758]\n",
            ">6691, d1[0.000] d2[0.001] g[1.386]\n",
            ">6692, d1[0.000] d2[0.000] g[0.924]\n",
            ">6693, d1[0.000] d2[0.006] g[1.150]\n",
            ">6694, d1[0.000] d2[0.001] g[1.230]\n",
            ">6695, d1[0.000] d2[0.001] g[0.896]\n",
            ">6696, d1[0.000] d2[0.001] g[0.931]\n",
            ">6697, d1[0.000] d2[0.001] g[1.296]\n",
            ">6698, d1[0.001] d2[0.001] g[0.820]\n",
            ">6699, d1[0.000] d2[0.001] g[0.833]\n",
            ">6700, d1[0.000] d2[0.001] g[1.483]\n",
            ">6701, d1[0.007] d2[0.001] g[0.508]\n",
            ">6702, d1[0.000] d2[0.001] g[0.937]\n",
            ">6703, d1[0.042] d2[0.001] g[0.578]\n",
            ">6704, d1[0.000] d2[0.001] g[0.962]\n",
            ">6705, d1[0.000] d2[0.001] g[1.388]\n",
            ">6706, d1[0.000] d2[0.001] g[1.258]\n",
            ">6707, d1[0.000] d2[0.001] g[1.331]\n",
            ">6708, d1[0.001] d2[0.000] g[0.886]\n",
            ">6709, d1[0.000] d2[0.001] g[0.845]\n",
            ">6710, d1[0.000] d2[0.001] g[1.664]\n",
            ">6711, d1[0.000] d2[0.001] g[1.138]\n",
            ">6712, d1[0.000] d2[0.001] g[1.226]\n",
            ">6713, d1[0.000] d2[0.001] g[1.787]\n",
            ">6714, d1[0.000] d2[0.002] g[0.705]\n",
            ">6715, d1[0.001] d2[0.001] g[0.621]\n",
            ">6716, d1[0.000] d2[0.000] g[0.659]\n",
            ">6717, d1[0.000] d2[0.001] g[1.150]\n",
            ">6718, d1[0.001] d2[0.001] g[1.115]\n",
            ">6719, d1[0.000] d2[0.000] g[1.169]\n",
            ">6720, d1[0.025] d2[0.001] g[0.686]\n",
            ">6721, d1[0.000] d2[0.001] g[0.839]\n",
            ">6722, d1[0.000] d2[0.000] g[1.023]\n",
            ">6723, d1[0.005] d2[0.154] g[0.546]\n",
            ">6724, d1[0.000] d2[0.000] g[1.542]\n",
            ">6725, d1[0.000] d2[0.000] g[1.831]\n",
            ">6726, d1[0.001] d2[0.000] g[0.795]\n",
            ">6727, d1[0.001] d2[0.001] g[1.178]\n",
            ">6728, d1[0.001] d2[0.000] g[0.900]\n",
            ">6729, d1[0.000] d2[0.000] g[0.784]\n",
            ">6730, d1[0.025] d2[0.000] g[0.664]\n",
            ">6731, d1[0.000] d2[0.000] g[1.856]\n",
            ">6732, d1[0.000] d2[0.000] g[1.415]\n",
            ">6733, d1[0.111] d2[0.008] g[0.727]\n",
            ">6734, d1[0.000] d2[0.001] g[1.479]\n",
            ">6735, d1[0.000] d2[0.001] g[0.968]\n",
            ">6736, d1[0.006] d2[0.016] g[0.566]\n",
            ">6737, d1[0.001] d2[0.001] g[0.796]\n",
            ">6738, d1[0.002] d2[0.001] g[0.605]\n",
            ">6739, d1[0.000] d2[0.001] g[2.090]\n",
            ">6740, d1[0.000] d2[0.001] g[1.252]\n",
            ">6741, d1[0.000] d2[0.001] g[0.836]\n",
            ">6742, d1[0.000] d2[0.001] g[1.249]\n",
            ">6743, d1[0.000] d2[0.001] g[1.046]\n",
            ">6744, d1[0.000] d2[0.001] g[0.788]\n",
            ">6745, d1[0.000] d2[0.001] g[1.845]\n",
            ">6746, d1[0.000] d2[0.001] g[0.848]\n",
            ">6747, d1[0.010] d2[0.002] g[0.738]\n",
            ">6748, d1[0.000] d2[0.002] g[1.805]\n",
            ">6749, d1[0.001] d2[0.001] g[0.707]\n",
            ">6750, d1[0.000] d2[0.001] g[1.891]\n",
            ">6751, d1[0.000] d2[0.001] g[0.848]\n",
            ">6752, d1[0.000] d2[0.001] g[0.990]\n",
            ">6753, d1[0.000] d2[0.002] g[1.115]\n",
            ">6754, d1[0.000] d2[0.001] g[0.887]\n",
            ">6755, d1[0.000] d2[0.000] g[1.365]\n",
            ">6756, d1[0.001] d2[0.001] g[0.706]\n",
            ">6757, d1[0.085] d2[0.002] g[0.693]\n",
            ">6758, d1[0.005] d2[0.002] g[0.858]\n",
            ">6759, d1[0.000] d2[0.002] g[0.780]\n",
            ">6760, d1[0.000] d2[0.001] g[0.671]\n",
            ">6761, d1[0.000] d2[0.001] g[1.665]\n",
            ">6762, d1[0.000] d2[0.002] g[1.217]\n",
            ">6763, d1[0.000] d2[0.001] g[0.751]\n",
            ">6764, d1[0.003] d2[0.002] g[0.411]\n",
            ">6765, d1[0.000] d2[0.001] g[1.167]\n",
            ">6766, d1[0.000] d2[0.001] g[1.068]\n",
            ">6767, d1[0.000] d2[0.001] g[1.221]\n",
            ">6768, d1[0.000] d2[0.022] g[0.807]\n",
            ">6769, d1[0.000] d2[0.001] g[0.763]\n",
            ">6770, d1[0.000] d2[0.001] g[1.757]\n",
            ">6771, d1[0.000] d2[0.005] g[0.950]\n",
            ">6772, d1[0.006] d2[0.002] g[0.780]\n",
            ">6773, d1[0.000] d2[0.000] g[1.507]\n",
            ">6774, d1[0.000] d2[0.003] g[0.855]\n",
            ">6775, d1[0.000] d2[0.000] g[0.974]\n",
            ">6776, d1[0.000] d2[0.000] g[0.778]\n",
            ">6777, d1[0.000] d2[0.001] g[0.774]\n",
            ">6778, d1[0.000] d2[0.001] g[0.928]\n",
            ">6779, d1[0.000] d2[0.000] g[0.649]\n",
            ">6780, d1[0.000] d2[0.001] g[1.562]\n",
            ">6781, d1[0.000] d2[0.001] g[0.767]\n",
            ">6782, d1[0.000] d2[0.000] g[0.865]\n",
            ">6783, d1[0.006] d2[0.005] g[0.800]\n",
            ">6784, d1[0.003] d2[0.006] g[0.650]\n",
            ">6785, d1[0.000] d2[0.001] g[1.892]\n",
            ">6786, d1[0.000] d2[0.004] g[0.975]\n",
            ">6787, d1[0.000] d2[0.001] g[1.010]\n",
            ">6788, d1[0.000] d2[0.001] g[1.186]\n",
            ">6789, d1[0.000] d2[0.000] g[1.839]\n",
            ">6790, d1[0.000] d2[0.002] g[0.929]\n",
            ">6791, d1[0.000] d2[0.000] g[0.839]\n",
            ">6792, d1[0.000] d2[0.001] g[2.242]\n",
            ">6793, d1[0.000] d2[0.001] g[1.780]\n",
            ">6794, d1[0.000] d2[0.001] g[1.321]\n",
            ">6795, d1[0.000] d2[0.000] g[1.145]\n",
            ">6796, d1[0.000] d2[0.000] g[0.912]\n",
            ">6797, d1[0.000] d2[0.001] g[1.047]\n",
            ">6798, d1[0.000] d2[0.000] g[1.203]\n",
            ">6799, d1[0.000] d2[0.001] g[0.903]\n",
            ">6800, d1[0.000] d2[0.000] g[1.004]\n",
            ">6801, d1[0.000] d2[0.000] g[1.157]\n",
            ">6802, d1[0.000] d2[0.000] g[1.758]\n",
            ">6803, d1[0.001] d2[0.000] g[0.645]\n",
            ">6804, d1[0.000] d2[0.010] g[1.085]\n",
            ">6805, d1[0.000] d2[0.000] g[1.154]\n",
            ">6806, d1[0.008] d2[0.001] g[0.679]\n",
            ">6807, d1[0.000] d2[0.000] g[1.003]\n",
            ">6808, d1[0.000] d2[0.000] g[0.931]\n",
            ">6809, d1[0.000] d2[0.001] g[0.832]\n",
            ">6810, d1[0.000] d2[0.001] g[0.911]\n",
            ">6811, d1[0.000] d2[0.001] g[0.896]\n",
            ">6812, d1[0.000] d2[0.001] g[0.849]\n",
            ">6813, d1[0.004] d2[0.001] g[0.658]\n",
            ">6814, d1[0.000] d2[0.000] g[1.295]\n",
            ">6815, d1[0.000] d2[0.001] g[1.997]\n",
            ">6816, d1[0.000] d2[0.001] g[1.444]\n",
            ">6817, d1[0.000] d2[0.000] g[1.159]\n",
            ">6818, d1[0.000] d2[0.000] g[0.948]\n",
            ">6819, d1[0.007] d2[0.001] g[0.666]\n",
            ">6820, d1[0.005] d2[0.095] g[0.536]\n",
            ">6821, d1[0.000] d2[0.000] g[0.927]\n",
            ">6822, d1[0.000] d2[0.000] g[1.455]\n",
            ">6823, d1[0.227] d2[0.001] g[0.552]\n",
            ">6824, d1[0.000] d2[0.009] g[0.945]\n",
            ">6825, d1[0.000] d2[0.004] g[1.452]\n",
            ">6826, d1[0.003] d2[0.009] g[0.816]\n",
            ">6827, d1[0.000] d2[0.011] g[0.912]\n",
            ">6828, d1[0.000] d2[0.001] g[0.956]\n",
            ">6829, d1[0.000] d2[0.001] g[1.158]\n",
            ">6830, d1[0.004] d2[0.014] g[0.423]\n",
            ">6831, d1[0.000] d2[0.001] g[0.937]\n",
            ">6832, d1[0.011] d2[0.002] g[0.629]\n",
            ">6833, d1[0.000] d2[0.001] g[1.241]\n",
            ">6834, d1[0.000] d2[0.002] g[1.497]\n",
            ">6835, d1[0.000] d2[0.002] g[0.727]\n",
            ">6836, d1[0.000] d2[0.002] g[0.887]\n",
            ">6837, d1[0.000] d2[0.000] g[1.529]\n",
            ">6838, d1[0.000] d2[0.007] g[1.400]\n",
            ">6839, d1[0.002] d2[0.000] g[0.684]\n",
            ">6840, d1[0.000] d2[0.001] g[0.805]\n",
            ">6841, d1[0.000] d2[0.000] g[1.623]\n",
            ">6842, d1[0.000] d2[0.001] g[0.805]\n",
            ">6843, d1[0.000] d2[0.002] g[2.161]\n",
            ">6844, d1[0.000] d2[0.000] g[1.058]\n",
            ">6845, d1[0.000] d2[0.002] g[0.881]\n",
            ">6846, d1[0.000] d2[0.001] g[1.645]\n",
            ">6847, d1[0.000] d2[0.001] g[0.874]\n",
            ">6848, d1[0.000] d2[0.003] g[1.869]\n",
            ">6849, d1[0.000] d2[0.001] g[0.850]\n",
            ">6850, d1[0.009] d2[0.007] g[0.757]\n",
            ">6851, d1[0.000] d2[0.000] g[0.792]\n",
            ">6852, d1[0.000] d2[0.000] g[0.877]\n",
            ">6853, d1[0.000] d2[0.001] g[0.810]\n",
            ">6854, d1[0.000] d2[0.000] g[1.138]\n",
            ">6855, d1[0.000] d2[0.001] g[1.066]\n",
            ">6856, d1[0.004] d2[0.008] g[0.569]\n",
            ">6857, d1[0.010] d2[0.001] g[0.724]\n",
            ">6858, d1[0.000] d2[0.000] g[0.976]\n",
            ">6859, d1[0.000] d2[0.003] g[0.580]\n",
            ">6860, d1[0.000] d2[0.001] g[1.662]\n",
            ">6861, d1[0.000] d2[0.004] g[1.344]\n",
            ">6862, d1[0.000] d2[0.000] g[1.970]\n",
            ">6863, d1[0.000] d2[0.000] g[0.920]\n",
            ">6864, d1[0.003] d2[0.002] g[0.645]\n",
            ">6865, d1[0.000] d2[0.001] g[0.807]\n",
            ">6866, d1[0.000] d2[0.000] g[1.516]\n",
            ">6867, d1[0.000] d2[0.000] g[0.792]\n",
            ">6868, d1[0.000] d2[0.003] g[1.119]\n",
            ">6869, d1[0.000] d2[0.000] g[0.873]\n",
            ">6870, d1[0.000] d2[0.000] g[1.372]\n",
            ">6871, d1[0.006] d2[0.002] g[0.541]\n",
            ">6872, d1[0.000] d2[0.001] g[0.799]\n",
            ">6873, d1[0.005] d2[0.001] g[0.539]\n",
            ">6874, d1[0.000] d2[0.000] g[1.472]\n",
            ">6875, d1[0.000] d2[0.000] g[1.233]\n",
            ">6876, d1[0.000] d2[0.000] g[1.116]\n",
            ">6877, d1[0.001] d2[0.000] g[0.649]\n",
            ">6878, d1[0.000] d2[0.001] g[0.652]\n",
            ">6879, d1[0.003] d2[0.001] g[0.836]\n",
            ">6880, d1[0.000] d2[0.000] g[1.179]\n",
            ">6881, d1[0.000] d2[0.001] g[0.917]\n",
            ">6882, d1[0.000] d2[0.001] g[0.968]\n",
            ">6883, d1[0.002] d2[0.001] g[0.491]\n",
            ">6884, d1[0.008] d2[0.000] g[0.600]\n",
            ">6885, d1[0.000] d2[0.001] g[0.673]\n",
            ">6886, d1[0.000] d2[0.000] g[1.858]\n",
            ">6887, d1[0.000] d2[0.000] g[1.344]\n",
            ">6888, d1[0.000] d2[0.001] g[1.888]\n",
            ">6889, d1[0.000] d2[0.001] g[1.978]\n",
            ">6890, d1[0.000] d2[0.001] g[1.012]\n",
            ">6891, d1[0.000] d2[0.000] g[1.285]\n",
            ">6892, d1[0.000] d2[0.001] g[1.048]\n",
            ">6893, d1[0.000] d2[0.000] g[0.890]\n",
            ">6894, d1[0.000] d2[0.000] g[1.252]\n",
            ">6895, d1[0.000] d2[0.001] g[0.869]\n",
            ">6896, d1[0.000] d2[0.000] g[0.860]\n",
            ">6897, d1[0.000] d2[0.001] g[1.938]\n",
            ">6898, d1[0.000] d2[0.000] g[0.641]\n",
            ">6899, d1[0.000] d2[0.001] g[0.783]\n",
            ">6900, d1[0.000] d2[0.000] g[1.394]\n",
            ">6901, d1[0.000] d2[0.000] g[0.962]\n",
            ">6902, d1[0.000] d2[0.000] g[0.764]\n",
            ">6903, d1[0.000] d2[0.000] g[1.450]\n",
            ">6904, d1[0.000] d2[0.000] g[0.836]\n",
            ">6905, d1[0.000] d2[0.000] g[1.877]\n",
            ">6906, d1[0.010] d2[0.006] g[0.656]\n",
            ">6907, d1[0.000] d2[0.001] g[0.981]\n",
            ">6908, d1[0.000] d2[0.001] g[1.803]\n",
            ">6909, d1[0.000] d2[0.000] g[0.850]\n",
            ">6910, d1[0.000] d2[0.001] g[1.707]\n",
            ">6911, d1[0.000] d2[0.000] g[1.223]\n",
            ">6912, d1[0.006] d2[0.004] g[0.648]\n",
            ">6913, d1[0.003] d2[0.001] g[0.685]\n",
            ">6914, d1[0.000] d2[0.001] g[0.656]\n",
            ">6915, d1[0.000] d2[0.000] g[1.020]\n",
            ">6916, d1[0.000] d2[0.000] g[0.916]\n",
            ">6917, d1[0.000] d2[0.000] g[1.195]\n",
            ">6918, d1[0.000] d2[0.000] g[1.152]\n",
            ">6919, d1[0.000] d2[0.007] g[0.872]\n",
            ">6920, d1[0.000] d2[0.003] g[0.919]\n",
            ">6921, d1[0.000] d2[0.000] g[0.879]\n",
            ">6922, d1[0.000] d2[0.000] g[0.783]\n",
            ">6923, d1[0.000] d2[0.000] g[1.504]\n",
            ">6924, d1[0.000] d2[0.000] g[1.455]\n",
            ">6925, d1[0.008] d2[0.000] g[0.737]\n",
            ">6926, d1[0.000] d2[0.000] g[1.422]\n",
            ">6927, d1[0.000] d2[0.000] g[0.678]\n",
            ">6928, d1[0.008] d2[0.003] g[0.642]\n",
            ">6929, d1[0.000] d2[0.000] g[1.217]\n",
            ">6930, d1[0.008] d2[0.000] g[0.608]\n",
            ">6931, d1[0.000] d2[0.000] g[0.890]\n",
            ">6932, d1[0.000] d2[0.000] g[0.937]\n",
            ">6933, d1[0.000] d2[0.000] g[1.239]\n",
            ">6934, d1[0.003] d2[0.001] g[0.424]\n",
            ">6935, d1[0.000] d2[0.000] g[2.154]\n",
            ">6936, d1[0.000] d2[0.001] g[1.012]\n",
            ">6937, d1[0.000] d2[0.000] g[1.147]\n",
            ">6938, d1[0.000] d2[0.000] g[0.637]\n",
            ">6939, d1[0.000] d2[0.000] g[1.155]\n",
            ">6940, d1[0.000] d2[0.001] g[1.169]\n",
            ">6941, d1[0.000] d2[0.000] g[1.434]\n",
            ">6942, d1[0.000] d2[0.000] g[1.122]\n",
            ">6943, d1[0.000] d2[0.001] g[0.939]\n",
            ">6944, d1[0.000] d2[0.000] g[0.862]\n",
            ">6945, d1[0.002] d2[0.006] g[0.931]\n",
            ">6946, d1[0.000] d2[0.000] g[1.313]\n",
            ">6947, d1[0.000] d2[0.002] g[0.857]\n",
            ">6948, d1[0.000] d2[0.000] g[1.447]\n",
            ">6949, d1[0.000] d2[0.000] g[0.937]\n",
            ">6950, d1[0.000] d2[0.000] g[0.699]\n",
            ">6951, d1[0.000] d2[0.000] g[1.472]\n",
            ">6952, d1[0.000] d2[0.002] g[1.799]\n",
            ">6953, d1[0.000] d2[0.000] g[0.878]\n",
            ">6954, d1[0.000] d2[0.000] g[1.423]\n",
            ">6955, d1[0.000] d2[0.000] g[0.912]\n",
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            ">6957, d1[0.000] d2[0.001] g[1.103]\n",
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            ">6960, d1[0.000] d2[0.000] g[0.678]\n",
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            ">6963, d1[0.000] d2[0.000] g[0.776]\n",
            ">6964, d1[0.000] d2[0.000] g[1.394]\n",
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            ">6998, d1[0.000] d2[0.000] g[1.159]\n",
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            ">7152, d1[0.000] d2[0.000] g[0.934]\n",
            ">7153, d1[0.000] d2[0.000] g[1.117]\n",
            ">7154, d1[0.000] d2[0.000] g[1.643]\n",
            ">7155, d1[0.012] d2[0.001] g[0.720]\n",
            ">7156, d1[0.021] d2[0.001] g[0.670]\n",
            ">7157, d1[0.000] d2[0.000] g[0.916]\n",
            ">7158, d1[0.000] d2[0.000] g[0.774]\n",
            ">7159, d1[0.000] d2[0.000] g[0.970]\n",
            ">7160, d1[0.003] d2[0.001] g[0.676]\n",
            ">7161, d1[0.000] d2[0.000] g[0.950]\n",
            ">7162, d1[0.000] d2[0.001] g[1.004]\n",
            ">7163, d1[0.000] d2[0.000] g[0.653]\n",
            ">7164, d1[0.000] d2[0.000] g[1.707]\n",
            ">7165, d1[0.005] d2[0.002] g[0.704]\n",
            ">7166, d1[0.000] d2[0.000] g[0.991]\n",
            ">7167, d1[0.000] d2[0.000] g[0.994]\n",
            ">7168, d1[0.003] d2[0.000] g[0.519]\n",
            ">7169, d1[0.000] d2[0.000] g[1.395]\n",
            ">7170, d1[0.000] d2[0.000] g[0.790]\n",
            ">7171, d1[0.000] d2[0.000] g[1.145]\n",
            ">7172, d1[0.000] d2[0.000] g[0.606]\n",
            ">7173, d1[0.000] d2[0.000] g[0.780]\n",
            ">7174, d1[0.000] d2[0.000] g[0.992]\n",
            ">7175, d1[0.000] d2[0.000] g[1.908]\n",
            ">7176, d1[0.000] d2[0.001] g[0.822]\n",
            ">7177, d1[0.000] d2[0.000] g[0.789]\n",
            ">7178, d1[0.000] d2[0.000] g[0.954]\n",
            ">7179, d1[0.000] d2[0.000] g[0.846]\n",
            ">7180, d1[0.000] d2[0.000] g[0.902]\n",
            ">7181, d1[0.000] d2[0.000] g[1.869]\n",
            ">7182, d1[0.000] d2[0.000] g[0.991]\n",
            ">7183, d1[0.000] d2[0.000] g[0.957]\n",
            ">7184, d1[0.000] d2[0.000] g[0.800]\n",
            ">7185, d1[0.000] d2[0.000] g[0.756]\n",
            ">7186, d1[0.000] d2[0.000] g[0.654]\n",
            ">7187, d1[0.000] d2[0.000] g[1.147]\n",
            ">7188, d1[0.000] d2[0.000] g[0.934]\n",
            ">7189, d1[0.000] d2[0.000] g[0.807]\n",
            ">7190, d1[0.000] d2[0.000] g[1.834]\n",
            ">7191, d1[0.000] d2[0.000] g[1.816]\n",
            ">7192, d1[0.000] d2[0.000] g[2.175]\n",
            ">7193, d1[0.000] d2[0.000] g[1.195]\n",
            ">7194, d1[0.003] d2[0.000] g[0.485]\n",
            ">7195, d1[0.000] d2[0.000] g[1.050]\n",
            ">7196, d1[0.000] d2[0.000] g[0.940]\n",
            ">7197, d1[0.000] d2[0.000] g[1.317]\n",
            ">7198, d1[0.000] d2[0.001] g[1.250]\n",
            ">7199, d1[0.000] d2[0.001] g[0.939]\n",
            ">7200, d1[0.000] d2[0.001] g[0.920]\n",
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            ">7202, d1[0.000] d2[0.000] g[0.739]\n",
            ">7203, d1[0.000] d2[0.000] g[1.194]\n",
            ">7204, d1[0.004] d2[0.000] g[0.676]\n",
            ">7205, d1[0.004] d2[0.001] g[0.712]\n",
            ">7206, d1[0.000] d2[0.000] g[1.204]\n",
            ">7207, d1[0.000] d2[0.000] g[1.368]\n",
            ">7208, d1[0.000] d2[0.000] g[1.376]\n",
            ">7209, d1[0.000] d2[0.000] g[1.651]\n",
            ">7210, d1[0.000] d2[0.000] g[1.258]\n",
            ">7211, d1[0.000] d2[0.001] g[1.243]\n",
            ">7212, d1[0.000] d2[0.000] g[2.192]\n",
            ">7213, d1[0.000] d2[0.000] g[0.649]\n",
            ">7214, d1[0.000] d2[0.000] g[0.754]\n",
            ">7215, d1[0.000] d2[0.010] g[0.921]\n",
            ">7216, d1[0.000] d2[0.000] g[0.945]\n",
            ">7217, d1[0.004] d2[0.000] g[0.733]\n",
            ">7218, d1[0.000] d2[0.000] g[0.818]\n",
            ">7219, d1[0.000] d2[0.000] g[1.128]\n",
            ">7220, d1[0.000] d2[0.000] g[0.823]\n",
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            ">7223, d1[0.000] d2[0.000] g[1.070]\n",
            ">7224, d1[0.000] d2[0.000] g[0.837]\n",
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            ">7409, d1[0.000] d2[0.000] g[0.814]\n",
            ">7410, d1[0.001] d2[0.000] g[0.700]\n",
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            ">7414, d1[0.000] d2[0.000] g[1.240]\n",
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            ">7416, d1[0.000] d2[0.000] g[1.657]\n",
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            ">7418, d1[0.000] d2[0.000] g[1.302]\n",
            ">7419, d1[0.000] d2[0.000] g[0.983]\n",
            ">7420, d1[0.000] d2[0.000] g[0.861]\n",
            ">7421, d1[0.000] d2[0.078] g[0.909]\n",
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            ">7423, d1[0.000] d2[0.000] g[0.906]\n",
            ">7424, d1[0.000] d2[0.001] g[0.834]\n",
            ">7425, d1[0.000] d2[0.000] g[0.881]\n",
            ">7426, d1[0.075] d2[0.000] g[0.641]\n",
            ">7427, d1[0.000] d2[0.001] g[1.106]\n",
            ">7428, d1[0.000] d2[0.001] g[0.822]\n",
            ">7429, d1[0.000] d2[0.000] g[0.947]\n",
            ">7430, d1[0.000] d2[0.073] g[0.755]\n",
            ">7431, d1[0.000] d2[0.000] g[1.115]\n",
            ">7432, d1[0.000] d2[0.002] g[2.214]\n",
            ">7433, d1[0.000] d2[0.000] g[1.770]\n",
            ">7434, d1[0.027] d2[0.000] g[0.724]\n",
            ">7435, d1[0.002] d2[0.001] g[0.960]\n",
            ">7436, d1[0.000] d2[0.001] g[0.857]\n",
            ">7437, d1[0.000] d2[0.001] g[0.939]\n",
            ">7438, d1[0.000] d2[0.000] g[0.932]\n",
            ">7439, d1[0.000] d2[0.001] g[0.914]\n",
            ">7440, d1[0.000] d2[0.000] g[1.687]\n",
            ">7441, d1[0.000] d2[0.000] g[0.849]\n",
            ">7442, d1[0.000] d2[0.001] g[0.830]\n",
            ">7443, d1[0.000] d2[0.001] g[0.942]\n",
            ">7444, d1[0.000] d2[0.000] g[1.827]\n",
            ">7445, d1[0.000] d2[0.002] g[1.207]\n",
            ">7446, d1[0.028] d2[0.001] g[0.662]\n",
            ">7447, d1[0.071] d2[0.000] g[0.612]\n",
            ">7448, d1[0.000] d2[0.000] g[0.692]\n",
            ">7449, d1[0.009] d2[0.002] g[0.484]\n",
            ">7450, d1[0.000] d2[0.025] g[1.612]\n",
            ">7451, d1[0.000] d2[0.000] g[0.684]\n",
            ">7452, d1[0.000] d2[0.001] g[1.695]\n",
            ">7453, d1[0.000] d2[0.003] g[1.367]\n",
            ">7454, d1[0.001] d2[0.000] g[0.730]\n",
            ">7455, d1[0.000] d2[0.001] g[1.307]\n",
            ">7456, d1[0.000] d2[0.006] g[2.152]\n",
            ">7457, d1[0.000] d2[0.000] g[1.119]\n",
            ">7458, d1[0.004] d2[0.001] g[0.600]\n",
            ">7459, d1[0.000] d2[0.000] g[1.084]\n",
            ">7460, d1[0.002] d2[0.001] g[0.654]\n",
            ">7461, d1[0.002] d2[0.000] g[0.705]\n",
            ">7462, d1[0.000] d2[0.000] g[0.695]\n",
            ">7463, d1[0.004] d2[0.000] g[0.786]\n",
            ">7464, d1[0.002] d2[0.000] g[0.819]\n",
            ">7465, d1[0.000] d2[0.000] g[0.824]\n",
            ">7466, d1[0.000] d2[0.000] g[1.758]\n",
            ">7467, d1[0.000] d2[0.000] g[1.673]\n",
            ">7468, d1[0.000] d2[0.000] g[0.936]\n",
            ">7469, d1[0.000] d2[0.000] g[0.985]\n",
            ">7470, d1[0.000] d2[0.001] g[0.991]\n",
            ">7471, d1[0.000] d2[0.000] g[1.377]\n",
            ">7472, d1[0.000] d2[0.000] g[1.164]\n",
            ">7473, d1[0.000] d2[0.001] g[0.900]\n",
            ">7474, d1[0.000] d2[0.001] g[0.679]\n",
            ">7475, d1[0.000] d2[0.000] g[1.208]\n",
            ">7476, d1[0.000] d2[0.000] g[1.914]\n",
            ">7477, d1[0.000] d2[0.001] g[0.880]\n",
            ">7478, d1[0.000] d2[0.002] g[1.595]\n",
            ">7479, d1[0.000] d2[0.000] g[0.809]\n",
            ">7480, d1[0.000] d2[0.000] g[0.960]\n",
            ">7481, d1[0.000] d2[0.000] g[0.789]\n",
            ">7482, d1[0.002] d2[0.001] g[0.520]\n",
            ">7483, d1[0.000] d2[0.000] g[1.398]\n",
            ">7484, d1[0.002] d2[0.192] g[0.367]\n",
            ">7485, d1[0.000] d2[0.000] g[1.380]\n",
            ">7486, d1[0.001] d2[0.003] g[1.906]\n",
            ">7487, d1[0.786] d2[0.111] g[0.581]\n",
            ">7488, d1[0.000] d2[0.002] g[1.115]\n",
            ">7489, d1[0.000] d2[0.203] g[0.991]\n",
            ">7490, d1[0.000] d2[0.001] g[0.916]\n",
            ">7491, d1[0.001] d2[0.000] g[0.742]\n",
            ">7492, d1[0.000] d2[0.000] g[1.708]\n",
            ">7493, d1[0.000] d2[0.001] g[0.932]\n",
            ">7494, d1[0.000] d2[0.001] g[1.916]\n",
            ">7495, d1[0.000] d2[0.002] g[0.830]\n",
            ">7496, d1[0.000] d2[0.001] g[0.985]\n",
            ">7497, d1[0.930] d2[0.124] g[0.411]\n",
            ">7498, d1[0.002] d2[0.020] g[0.492]\n",
            ">7499, d1[0.000] d2[0.005] g[1.327]\n",
            ">7500, d1[0.000] d2[0.009] g[0.715]\n",
            ">7501, d1[0.000] d2[0.007] g[1.871]\n",
            ">7502, d1[0.000] d2[0.009] g[0.857]\n",
            ">7503, d1[0.000] d2[0.004] g[1.822]\n",
            ">7504, d1[0.000] d2[0.002] g[1.681]\n",
            ">7505, d1[0.000] d2[0.001] g[1.002]\n",
            ">7506, d1[0.000] d2[0.002] g[1.117]\n",
            ">7507, d1[0.000] d2[0.004] g[1.235]\n",
            ">7508, d1[0.000] d2[0.002] g[0.898]\n",
            ">7509, d1[0.000] d2[0.002] g[1.105]\n",
            ">7510, d1[0.000] d2[0.002] g[0.804]\n",
            ">7511, d1[0.000] d2[0.307] g[0.773]\n",
            ">7512, d1[0.001] d2[0.003] g[0.759]\n",
            ">7513, d1[0.002] d2[0.006] g[0.954]\n",
            ">7514, d1[0.002] d2[0.000] g[0.638]\n",
            ">7515, d1[0.000] d2[0.000] g[1.384]\n",
            ">7516, d1[0.001] d2[0.001] g[0.822]\n",
            ">7517, d1[0.000] d2[0.001] g[1.335]\n",
            ">7518, d1[0.000] d2[0.001] g[1.920]\n",
            ">7519, d1[0.000] d2[0.001] g[0.946]\n",
            ">7520, d1[0.429] d2[0.011] g[0.513]\n",
            ">7521, d1[0.000] d2[0.005] g[1.900]\n",
            ">7522, d1[0.000] d2[0.003] g[0.933]\n",
            ">7523, d1[0.000] d2[0.032] g[1.003]\n",
            ">7524, d1[0.000] d2[0.005] g[1.143]\n",
            ">7525, d1[0.000] d2[0.004] g[1.093]\n",
            ">7526, d1[0.000] d2[0.004] g[0.923]\n",
            ">7527, d1[0.000] d2[0.001] g[1.440]\n",
            ">7528, d1[0.000] d2[0.022] g[1.146]\n",
            ">7529, d1[0.000] d2[0.003] g[0.891]\n",
            ">7530, d1[0.000] d2[0.001] g[1.228]\n",
            ">7531, d1[0.000] d2[0.000] g[1.965]\n",
            ">7532, d1[0.000] d2[0.000] g[0.876]\n",
            ">7533, d1[0.000] d2[0.001] g[0.931]\n",
            ">7534, d1[0.000] d2[0.006] g[0.779]\n",
            ">7535, d1[0.000] d2[0.002] g[0.921]\n",
            ">7536, d1[0.000] d2[0.024] g[0.852]\n",
            ">7537, d1[0.001] d2[0.001] g[0.591]\n",
            ">7538, d1[0.000] d2[0.000] g[1.649]\n",
            ">7539, d1[0.000] d2[0.045] g[1.649]\n",
            ">7540, d1[0.000] d2[0.000] g[0.894]\n",
            ">7541, d1[0.022] d2[0.001] g[0.688]\n",
            ">7542, d1[0.000] d2[0.001] g[1.777]\n",
            ">7543, d1[0.000] d2[0.000] g[1.704]\n",
            ">7544, d1[0.000] d2[0.000] g[0.761]\n",
            ">7545, d1[0.000] d2[0.001] g[0.891]\n",
            ">7546, d1[0.000] d2[0.001] g[0.840]\n",
            ">7547, d1[0.000] d2[0.001] g[0.988]\n",
            ">7548, d1[0.001] d2[0.001] g[0.784]\n",
            ">7549, d1[0.000] d2[0.000] g[0.598]\n",
            ">7550, d1[0.000] d2[0.000] g[0.930]\n",
            ">7551, d1[0.000] d2[0.000] g[1.337]\n",
            ">7552, d1[0.019] d2[0.000] g[0.676]\n",
            ">7553, d1[0.001] d2[0.000] g[0.934]\n",
            ">7554, d1[0.003] d2[0.000] g[0.649]\n",
            ">7555, d1[0.001] d2[0.001] g[0.638]\n",
            ">7556, d1[0.000] d2[0.001] g[0.946]\n",
            ">7557, d1[0.000] d2[0.001] g[0.887]\n",
            ">7558, d1[0.000] d2[0.000] g[1.230]\n",
            ">7559, d1[0.000] d2[0.001] g[1.837]\n",
            ">7560, d1[0.000] d2[0.000] g[1.669]\n",
            ">7561, d1[0.000] d2[0.000] g[1.425]\n",
            ">7562, d1[0.000] d2[0.000] g[0.890]\n",
            ">7563, d1[0.000] d2[0.001] g[1.788]\n",
            ">7564, d1[0.000] d2[0.016] g[1.524]\n",
            ">7565, d1[0.000] d2[0.001] g[1.219]\n",
            ">7566, d1[0.000] d2[0.000] g[1.691]\n",
            ">7567, d1[0.000] d2[0.000] g[1.853]\n",
            ">7568, d1[0.000] d2[0.000] g[1.867]\n",
            ">7569, d1[0.003] d2[0.001] g[0.800]\n",
            ">7570, d1[0.000] d2[0.000] g[1.305]\n",
            ">7571, d1[0.005] d2[0.001] g[0.687]\n",
            ">7572, d1[0.000] d2[0.027] g[1.095]\n",
            ">7573, d1[0.001] d2[0.000] g[0.736]\n",
            ">7574, d1[0.000] d2[0.000] g[1.018]\n",
            ">7575, d1[0.001] d2[0.000] g[0.723]\n",
            ">7576, d1[0.000] d2[0.001] g[1.242]\n",
            ">7577, d1[0.000] d2[0.001] g[0.869]\n",
            ">7578, d1[0.000] d2[0.000] g[1.121]\n",
            ">7579, d1[0.001] d2[0.000] g[0.909]\n",
            ">7580, d1[0.001] d2[0.001] g[0.731]\n",
            ">7581, d1[0.000] d2[0.000] g[0.922]\n",
            ">7582, d1[0.000] d2[0.000] g[0.748]\n",
            ">7583, d1[0.000] d2[0.000] g[1.802]\n",
            ">7584, d1[0.000] d2[0.001] g[0.856]\n",
            ">7585, d1[0.000] d2[0.002] g[2.163]\n",
            ">7586, d1[0.000] d2[0.000] g[1.852]\n",
            ">7587, d1[0.000] d2[0.000] g[0.822]\n",
            ">7588, d1[0.000] d2[0.000] g[1.394]\n",
            ">7589, d1[0.000] d2[0.000] g[1.864]\n",
            ">7590, d1[0.000] d2[0.000] g[1.586]\n",
            ">7591, d1[0.000] d2[0.000] g[1.396]\n",
            ">7592, d1[0.002] d2[0.001] g[0.526]\n",
            ">7593, d1[0.001] d2[0.000] g[0.657]\n",
            ">7594, d1[0.000] d2[0.001] g[1.863]\n",
            ">7595, d1[0.007] d2[0.001] g[0.547]\n",
            ">7596, d1[0.001] d2[0.000] g[0.894]\n",
            ">7597, d1[0.001] d2[2.097] g[0.649]\n",
            ">7598, d1[0.261] d2[0.000] g[0.728]\n",
            ">7599, d1[0.801] d2[0.003] g[0.655]\n",
            ">7600, d1[0.001] d2[0.002] g[0.701]\n",
            ">7601, d1[0.001] d2[0.012] g[1.191]\n",
            ">7602, d1[0.001] d2[0.029] g[0.869]\n",
            ">7603, d1[0.042] d2[0.012] g[0.612]\n",
            ">7604, d1[0.001] d2[0.004] g[0.945]\n",
            ">7605, d1[0.001] d2[0.003] g[1.053]\n",
            ">7606, d1[0.000] d2[0.007] g[1.058]\n",
            ">7607, d1[0.000] d2[0.002] g[1.008]\n",
            ">7608, d1[0.001] d2[0.000] g[1.039]\n",
            ">7609, d1[0.001] d2[0.001] g[0.845]\n",
            ">7610, d1[0.000] d2[0.004] g[0.690]\n",
            ">7611, d1[0.001] d2[0.001] g[1.037]\n",
            ">7612, d1[0.001] d2[0.004] g[0.596]\n",
            ">7613, d1[0.000] d2[0.005] g[1.874]\n",
            ">7614, d1[0.001] d2[0.002] g[0.788]\n",
            ">7615, d1[0.005] d2[0.002] g[0.414]\n",
            ">7616, d1[0.000] d2[0.002] g[1.859]\n",
            ">7617, d1[0.006] d2[0.001] g[0.496]\n",
            ">7618, d1[0.000] d2[0.001] g[1.472]\n",
            ">7619, d1[0.000] d2[0.001] g[1.228]\n",
            ">7620, d1[0.000] d2[0.001] g[1.012]\n",
            ">7621, d1[0.000] d2[0.001] g[1.821]\n",
            ">7622, d1[0.000] d2[0.001] g[1.818]\n",
            ">7623, d1[0.000] d2[0.001] g[1.430]\n",
            ">7624, d1[0.000] d2[0.011] g[1.506]\n",
            ">7625, d1[0.001] d2[0.001] g[0.725]\n",
            ">7626, d1[0.000] d2[0.001] g[0.799]\n",
            ">7627, d1[0.000] d2[0.000] g[1.825]\n",
            ">7628, d1[0.000] d2[0.002] g[1.759]\n",
            ">7629, d1[0.000] d2[0.001] g[1.082]\n",
            ">7630, d1[0.000] d2[0.001] g[1.382]\n",
            ">7631, d1[0.001] d2[0.001] g[0.693]\n",
            ">7632, d1[0.001] d2[0.000] g[0.784]\n",
            ">7633, d1[0.001] d2[0.000] g[1.062]\n",
            ">7634, d1[0.000] d2[0.001] g[0.838]\n",
            ">7635, d1[0.001] d2[0.001] g[0.631]\n",
            ">7636, d1[0.000] d2[0.000] g[0.920]\n",
            ">7637, d1[0.000] d2[0.000] g[1.384]\n",
            ">7638, d1[0.000] d2[0.001] g[0.752]\n",
            ">7639, d1[0.001] d2[0.001] g[0.769]\n",
            ">7640, d1[0.000] d2[0.000] g[0.921]\n",
            ">7641, d1[0.000] d2[0.002] g[1.872]\n",
            ">7642, d1[0.000] d2[0.005] g[0.741]\n",
            ">7643, d1[0.000] d2[0.000] g[1.434]\n",
            ">7644, d1[0.002] d2[0.015] g[0.705]\n",
            ">7645, d1[0.019] d2[0.002] g[0.761]\n",
            ">7646, d1[0.000] d2[0.000] g[1.485]\n",
            ">7647, d1[0.001] d2[0.001] g[0.646]\n",
            ">7648, d1[0.000] d2[0.001] g[0.951]\n",
            ">7649, d1[0.000] d2[0.000] g[0.938]\n",
            ">7650, d1[0.001] d2[0.000] g[1.110]\n",
            ">7651, d1[0.006] d2[0.000] g[0.669]\n",
            ">7652, d1[0.000] d2[0.000] g[1.228]\n",
            ">7653, d1[0.000] d2[0.000] g[0.928]\n",
            ">7654, d1[0.000] d2[0.002] g[1.173]\n",
            ">7655, d1[0.000] d2[0.001] g[0.763]\n",
            ">7656, d1[0.000] d2[0.000] g[0.914]\n",
            ">7657, d1[0.000] d2[0.003] g[1.594]\n",
            ">7658, d1[0.000] d2[0.001] g[1.053]\n",
            ">7659, d1[0.009] d2[0.001] g[0.532]\n",
            ">7660, d1[0.000] d2[0.001] g[1.698]\n",
            ">7661, d1[0.008] d2[0.014] g[0.502]\n",
            ">7662, d1[0.000] d2[0.000] g[1.604]\n",
            ">7663, d1[0.000] d2[0.001] g[0.877]\n",
            ">7664, d1[0.000] d2[0.001] g[0.851]\n",
            ">7665, d1[0.000] d2[0.000] g[1.109]\n",
            ">7666, d1[0.000] d2[0.000] g[1.230]\n",
            ">7667, d1[0.002] d2[0.000] g[0.571]\n",
            ">7668, d1[0.000] d2[0.000] g[0.791]\n",
            ">7669, d1[0.000] d2[0.002] g[0.808]\n",
            ">7670, d1[0.000] d2[0.000] g[0.853]\n",
            ">7671, d1[0.001] d2[0.000] g[0.802]\n",
            ">7672, d1[0.009] d2[0.001] g[0.540]\n",
            ">7673, d1[0.000] d2[0.000] g[0.978]\n",
            ">7674, d1[0.000] d2[0.001] g[1.001]\n",
            ">7675, d1[0.000] d2[0.005] g[1.244]\n",
            ">7676, d1[0.018] d2[0.001] g[0.608]\n",
            ">7677, d1[0.001] d2[0.000] g[0.694]\n",
            ">7678, d1[0.007] d2[0.002] g[0.563]\n",
            ">7679, d1[0.000] d2[0.001] g[1.154]\n",
            ">7680, d1[0.000] d2[0.000] g[0.592]\n",
            ">7681, d1[0.001] d2[0.000] g[0.704]\n",
            ">7682, d1[0.001] d2[0.000] g[0.848]\n",
            ">7683, d1[0.001] d2[0.000] g[0.794]\n",
            ">7684, d1[0.000] d2[0.000] g[0.863]\n",
            ">7685, d1[0.000] d2[0.000] g[0.815]\n",
            ">7686, d1[0.000] d2[0.000] g[0.862]\n",
            ">7687, d1[0.001] d2[0.000] g[1.034]\n",
            ">7688, d1[0.000] d2[0.000] g[1.809]\n",
            ">7689, d1[0.010] d2[0.004] g[0.786]\n",
            ">7690, d1[0.000] d2[0.000] g[1.236]\n",
            ">7691, d1[0.000] d2[0.000] g[1.167]\n",
            ">7692, d1[0.000] d2[0.000] g[0.788]\n",
            ">7693, d1[0.000] d2[0.000] g[1.918]\n",
            ">7694, d1[0.005] d2[0.000] g[0.482]\n",
            ">7695, d1[0.001] d2[0.000] g[0.710]\n",
            ">7696, d1[0.000] d2[0.001] g[1.059]\n",
            ">7697, d1[0.001] d2[0.000] g[0.739]\n",
            ">7698, d1[0.000] d2[0.000] g[0.871]\n",
            ">7699, d1[0.000] d2[0.000] g[1.238]\n",
            ">7700, d1[0.000] d2[0.000] g[0.822]\n",
            ">7701, d1[0.000] d2[0.001] g[0.673]\n",
            ">7702, d1[0.007] d2[0.000] g[0.681]\n",
            ">7703, d1[0.000] d2[0.000] g[0.693]\n",
            ">7704, d1[0.000] d2[0.001] g[1.627]\n",
            ">7705, d1[0.000] d2[0.000] g[1.456]\n",
            ">7706, d1[0.016] d2[0.000] g[0.635]\n",
            ">7707, d1[0.001] d2[0.007] g[0.641]\n",
            ">7708, d1[0.001] d2[0.017] g[0.585]\n",
            ">7709, d1[0.000] d2[0.007] g[1.066]\n",
            ">7710, d1[0.000] d2[0.000] g[0.852]\n",
            ">7711, d1[0.002] d2[0.001] g[0.714]\n",
            ">7712, d1[0.001] d2[0.000] g[0.906]\n",
            ">7713, d1[0.001] d2[0.000] g[0.836]\n",
            ">7714, d1[0.000] d2[0.003] g[1.067]\n",
            ">7715, d1[0.000] d2[0.000] g[0.848]\n",
            ">7716, d1[0.000] d2[0.000] g[0.925]\n",
            ">7717, d1[0.000] d2[0.000] g[1.222]\n",
            ">7718, d1[0.000] d2[0.000] g[1.206]\n",
            ">7719, d1[0.007] d2[0.000] g[0.609]\n",
            ">7720, d1[0.000] d2[0.000] g[1.666]\n",
            ">7721, d1[0.055] d2[0.001] g[0.617]\n",
            ">7722, d1[0.000] d2[0.001] g[1.620]\n",
            ">7723, d1[0.000] d2[0.000] g[0.733]\n",
            ">7724, d1[0.001] d2[0.000] g[0.605]\n",
            ">7725, d1[0.000] d2[0.000] g[1.705]\n",
            ">7726, d1[0.015] d2[0.001] g[0.582]\n",
            ">7727, d1[0.007] d2[0.000] g[0.717]\n",
            ">7728, d1[0.008] d2[0.001] g[0.539]\n",
            ">7729, d1[0.000] d2[0.001] g[1.298]\n",
            ">7730, d1[0.000] d2[0.000] g[0.738]\n",
            ">7731, d1[0.000] d2[0.000] g[0.826]\n",
            ">7732, d1[0.000] d2[0.001] g[0.985]\n",
            ">7733, d1[0.002] d2[0.000] g[0.678]\n",
            ">7734, d1[0.000] d2[0.000] g[1.837]\n",
            ">7735, d1[0.000] d2[0.007] g[0.864]\n",
            ">7736, d1[0.000] d2[0.001] g[1.408]\n",
            ">7737, d1[0.000] d2[0.000] g[0.951]\n",
            ">7738, d1[0.000] d2[0.079] g[1.641]\n",
            ">7739, d1[0.000] d2[0.000] g[0.918]\n",
            ">7740, d1[0.000] d2[0.001] g[1.046]\n",
            ">7741, d1[0.000] d2[0.000] g[1.403]\n",
            ">7742, d1[0.000] d2[0.000] g[2.210]\n",
            ">7743, d1[0.000] d2[0.000] g[1.643]\n",
            ">7744, d1[0.000] d2[0.000] g[0.658]\n",
            ">7745, d1[0.000] d2[0.000] g[1.446]\n",
            ">7746, d1[0.000] d2[0.000] g[1.199]\n",
            ">7747, d1[0.000] d2[0.000] g[0.838]\n",
            ">7748, d1[0.014] d2[0.003] g[0.598]\n",
            ">7749, d1[0.040] d2[0.005] g[0.558]\n",
            ">7750, d1[0.001] d2[0.000] g[0.623]\n",
            ">7751, d1[0.000] d2[0.001] g[1.326]\n",
            ">7752, d1[0.008] d2[0.000] g[0.588]\n",
            ">7753, d1[0.000] d2[0.000] g[0.857]\n",
            ">7754, d1[0.004] d2[0.001] g[0.535]\n",
            ">7755, d1[0.000] d2[0.000] g[1.145]\n",
            ">7756, d1[0.005] d2[0.001] g[0.679]\n",
            ">7757, d1[0.000] d2[0.001] g[1.082]\n",
            ">7758, d1[0.002] d2[0.002] g[0.460]\n",
            ">7759, d1[0.000] d2[0.000] g[1.188]\n",
            ">7760, d1[0.000] d2[0.000] g[0.802]\n",
            ">7761, d1[0.000] d2[0.000] g[1.648]\n",
            ">7762, d1[0.000] d2[0.000] g[1.039]\n",
            ">7763, d1[0.001] d2[0.001] g[0.655]\n",
            ">7764, d1[0.000] d2[0.000] g[1.725]\n",
            ">7765, d1[0.000] d2[0.000] g[0.717]\n",
            ">7766, d1[0.000] d2[0.000] g[0.897]\n",
            ">7767, d1[0.000] d2[0.000] g[1.897]\n",
            ">7768, d1[0.000] d2[0.000] g[0.824]\n",
            ">7769, d1[0.000] d2[0.000] g[0.896]\n",
            ">7770, d1[0.000] d2[0.000] g[0.750]\n",
            ">7771, d1[0.000] d2[0.000] g[1.306]\n",
            ">7772, d1[0.000] d2[0.000] g[1.602]\n",
            ">7773, d1[0.000] d2[0.001] g[0.931]\n",
            ">7774, d1[0.000] d2[0.001] g[0.894]\n",
            ">7775, d1[0.000] d2[0.000] g[0.709]\n",
            ">7776, d1[0.000] d2[0.000] g[0.884]\n",
            ">7777, d1[0.000] d2[0.000] g[1.466]\n",
            ">7778, d1[0.008] d2[0.000] g[0.567]\n",
            ">7779, d1[0.000] d2[0.000] g[0.817]\n",
            ">7780, d1[0.000] d2[0.000] g[1.942]\n",
            ">7781, d1[0.000] d2[0.000] g[0.788]\n",
            ">7782, d1[0.000] d2[0.001] g[1.053]\n",
            ">7783, d1[0.000] d2[0.000] g[0.646]\n",
            ">7784, d1[0.000] d2[0.000] g[1.009]\n",
            ">7785, d1[0.000] d2[0.000] g[0.893]\n",
            ">7786, d1[0.000] d2[0.000] g[0.854]\n",
            ">7787, d1[0.000] d2[0.001] g[2.182]\n",
            ">7788, d1[0.003] d2[0.000] g[0.482]\n",
            ">7789, d1[0.000] d2[0.000] g[0.811]\n",
            ">7790, d1[0.000] d2[0.000] g[1.778]\n",
            ">7791, d1[0.000] d2[0.000] g[1.849]\n",
            ">7792, d1[0.002] d2[0.361] g[0.397]\n",
            ">7793, d1[0.001] d2[0.000] g[0.893]\n",
            ">7794, d1[0.001] d2[0.000] g[1.463]\n",
            ">7795, d1[0.001] d2[0.000] g[1.062]\n",
            ">7796, d1[0.001] d2[0.000] g[2.156]\n",
            ">7797, d1[0.585] d2[0.009] g[0.648]\n",
            ">7798, d1[0.000] d2[0.010] g[0.917]\n",
            ">7799, d1[0.000] d2[0.001] g[1.091]\n",
            ">7800, d1[0.000] d2[0.007] g[0.994]\n",
            ">7801, d1[0.000] d2[0.001] g[1.462]\n",
            ">7802, d1[0.000] d2[0.001] g[0.921]\n",
            ">7803, d1[0.004] d2[0.020] g[0.668]\n",
            ">7804, d1[0.000] d2[0.001] g[1.272]\n",
            ">7805, d1[0.000] d2[0.000] g[1.897]\n",
            ">7806, d1[0.000] d2[0.001] g[0.676]\n",
            ">7807, d1[0.000] d2[0.000] g[0.780]\n",
            ">7808, d1[0.000] d2[0.000] g[1.005]\n",
            ">7809, d1[0.000] d2[0.002] g[1.402]\n",
            ">7810, d1[0.000] d2[0.000] g[0.962]\n",
            ">7811, d1[0.000] d2[0.000] g[0.924]\n",
            ">7812, d1[0.000] d2[0.000] g[1.155]\n",
            ">7813, d1[0.000] d2[0.000] g[1.388]\n",
            ">7814, d1[0.000] d2[0.001] g[0.635]\n",
            ">7815, d1[0.000] d2[0.000] g[0.887]\n",
            ">7816, d1[0.000] d2[0.000] g[0.924]\n",
            ">7817, d1[0.000] d2[0.001] g[0.738]\n",
            ">7818, d1[0.000] d2[0.001] g[1.132]\n",
            ">7819, d1[0.003] d2[0.003] g[0.490]\n",
            ">7820, d1[0.000] d2[0.000] g[1.372]\n",
            ">7821, d1[0.000] d2[0.000] g[0.620]\n",
            ">7822, d1[0.000] d2[0.000] g[0.725]\n",
            ">7823, d1[0.000] d2[0.000] g[1.855]\n",
            ">7824, d1[0.000] d2[0.001] g[0.974]\n",
            ">7825, d1[0.000] d2[0.001] g[1.723]\n",
            ">7826, d1[0.000] d2[0.001] g[1.877]\n",
            ">7827, d1[0.000] d2[0.000] g[1.824]\n",
            ">7828, d1[0.000] d2[0.002] g[0.766]\n",
            ">7829, d1[0.000] d2[0.003] g[0.869]\n",
            ">7830, d1[0.000] d2[0.000] g[0.842]\n",
            ">7831, d1[0.000] d2[0.000] g[0.988]\n",
            ">7832, d1[0.017] d2[0.004] g[0.602]\n",
            ">7833, d1[0.000] d2[0.000] g[0.923]\n",
            ">7834, d1[0.003] d2[0.002] g[0.677]\n",
            ">7835, d1[0.009] d2[0.000] g[0.661]\n",
            ">7836, d1[0.004] d2[0.001] g[0.561]\n",
            ">7837, d1[0.000] d2[0.000] g[0.794]\n",
            ">7838, d1[0.000] d2[0.000] g[1.422]\n",
            ">7839, d1[0.000] d2[0.000] g[0.826]\n",
            ">7840, d1[0.000] d2[0.000] g[0.894]\n",
            ">7841, d1[0.033] d2[0.005] g[0.772]\n",
            ">7842, d1[0.005] d2[0.003] g[0.547]\n",
            ">7843, d1[0.000] d2[0.001] g[0.649]\n",
            ">7844, d1[0.000] d2[0.000] g[0.786]\n",
            ">7845, d1[0.002] d2[0.001] g[0.483]\n",
            ">7846, d1[0.000] d2[0.000] g[1.757]\n",
            ">7847, d1[0.000] d2[0.008] g[2.180]\n",
            ">7848, d1[0.000] d2[0.000] g[0.959]\n",
            ">7849, d1[0.003] d2[0.009] g[0.666]\n",
            ">7850, d1[0.001] d2[0.000] g[0.662]\n",
            ">7851, d1[0.000] d2[0.000] g[1.448]\n",
            ">7852, d1[0.000] d2[0.000] g[1.377]\n",
            ">7853, d1[0.000] d2[0.000] g[0.812]\n",
            ">7854, d1[0.000] d2[0.002] g[0.937]\n",
            ">7855, d1[0.000] d2[0.000] g[0.742]\n",
            ">7856, d1[0.000] d2[0.002] g[2.178]\n",
            ">7857, d1[0.000] d2[0.000] g[1.168]\n",
            ">7858, d1[0.000] d2[0.000] g[1.100]\n",
            ">7859, d1[0.000] d2[0.000] g[1.175]\n",
            ">7860, d1[0.000] d2[0.000] g[1.134]\n",
            ">7861, d1[0.000] d2[0.002] g[0.742]\n",
            ">7862, d1[0.000] d2[0.001] g[1.217]\n",
            ">7863, d1[0.000] d2[0.000] g[0.622]\n",
            ">7864, d1[0.000] d2[0.000] g[0.879]\n",
            ">7865, d1[0.000] d2[0.000] g[1.013]\n",
            ">7866, d1[0.000] d2[0.000] g[1.404]\n",
            ">7867, d1[0.000] d2[0.000] g[0.764]\n",
            ">7868, d1[0.000] d2[0.001] g[0.803]\n",
            ">7869, d1[0.000] d2[0.000] g[0.873]\n",
            ">7870, d1[0.000] d2[0.001] g[1.037]\n",
            ">7871, d1[0.000] d2[0.000] g[0.861]\n",
            ">7872, d1[0.000] d2[0.000] g[1.866]\n",
            ">7873, d1[0.000] d2[0.000] g[1.057]\n",
            ">7874, d1[0.000] d2[0.000] g[1.447]\n",
            ">7875, d1[0.000] d2[0.000] g[1.089]\n",
            ">7876, d1[0.003] d2[0.001] g[0.469]\n",
            ">7877, d1[0.000] d2[0.000] g[1.093]\n",
            ">7878, d1[0.000] d2[0.001] g[0.689]\n",
            ">7879, d1[0.000] d2[0.000] g[0.853]\n",
            ">7880, d1[0.000] d2[0.000] g[1.268]\n",
            ">7881, d1[0.000] d2[0.001] g[0.702]\n",
            ">7882, d1[0.011] d2[0.000] g[0.659]\n",
            ">7883, d1[0.001] d2[0.011] g[0.529]\n",
            ">7884, d1[0.000] d2[0.000] g[1.106]\n",
            ">7885, d1[0.000] d2[0.001] g[0.924]\n",
            ">7886, d1[0.009] d2[0.002] g[0.615]\n",
            ">7887, d1[0.000] d2[0.001] g[1.208]\n",
            ">7888, d1[0.192] d2[0.025] g[0.659]\n",
            ">7889, d1[0.002] d2[0.000] g[0.384]\n",
            ">7890, d1[0.000] d2[0.001] g[1.416]\n",
            ">7891, d1[0.000] d2[0.017] g[1.002]\n",
            ">7892, d1[0.000] d2[0.116] g[1.379]\n",
            ">7893, d1[0.001] d2[0.002] g[0.670]\n",
            ">7894, d1[0.000] d2[0.001] g[1.223]\n",
            ">7895, d1[0.002] d2[0.002] g[0.690]\n",
            ">7896, d1[0.002] d2[0.001] g[0.710]\n",
            ">7897, d1[0.015] d2[0.001] g[0.781]\n",
            ">7898, d1[0.000] d2[0.003] g[1.097]\n",
            ">7899, d1[0.000] d2[0.000] g[1.192]\n",
            ">7900, d1[0.000] d2[0.001] g[0.748]\n",
            ">7901, d1[0.000] d2[0.000] g[0.739]\n",
            ">7902, d1[0.000] d2[0.001] g[0.624]\n",
            ">7903, d1[0.000] d2[0.000] g[1.103]\n",
            ">7904, d1[0.000] d2[0.000] g[1.900]\n",
            ">7905, d1[0.000] d2[0.000] g[0.820]\n",
            ">7906, d1[0.008] d2[0.001] g[0.778]\n",
            ">7907, d1[0.000] d2[0.000] g[1.115]\n",
            ">7908, d1[0.000] d2[0.000] g[0.970]\n",
            ">7909, d1[0.025] d2[0.000] g[0.580]\n",
            ">7910, d1[0.000] d2[0.000] g[1.657]\n",
            ">7911, d1[0.000] d2[0.000] g[0.765]\n",
            ">7912, d1[0.000] d2[0.000] g[1.999]\n",
            ">7913, d1[0.000] d2[0.000] g[0.863]\n",
            ">7914, d1[0.000] d2[0.001] g[0.745]\n",
            ">7915, d1[0.000] d2[0.005] g[0.986]\n",
            ">7916, d1[0.000] d2[0.001] g[0.967]\n",
            ">7917, d1[0.001] d2[0.000] g[0.603]\n",
            ">7918, d1[0.000] d2[0.000] g[0.812]\n",
            ">7919, d1[0.000] d2[0.002] g[0.844]\n",
            ">7920, d1[0.000] d2[0.000] g[0.932]\n",
            ">7921, d1[0.000] d2[0.000] g[1.655]\n",
            ">7922, d1[0.000] d2[0.000] g[0.953]\n",
            ">7923, d1[0.007] d2[0.001] g[0.632]\n",
            ">7924, d1[0.000] d2[0.000] g[1.841]\n",
            ">7925, d1[0.000] d2[0.002] g[0.945]\n",
            ">7926, d1[0.008] d2[0.001] g[0.596]\n",
            ">7927, d1[0.000] d2[0.001] g[0.810]\n",
            ">7928, d1[0.000] d2[0.000] g[1.117]\n",
            ">7929, d1[0.000] d2[0.000] g[0.650]\n",
            ">7930, d1[0.000] d2[0.001] g[0.919]\n",
            ">7931, d1[0.003] d2[0.001] g[0.715]\n",
            ">7932, d1[0.000] d2[0.001] g[1.130]\n",
            ">7933, d1[0.005] d2[0.001] g[0.656]\n",
            ">7934, d1[0.000] d2[0.001] g[0.903]\n",
            ">7935, d1[0.000] d2[0.000] g[0.805]\n",
            ">7936, d1[0.000] d2[0.001] g[0.777]\n",
            ">7937, d1[0.000] d2[0.000] g[1.140]\n",
            ">7938, d1[0.000] d2[0.004] g[0.872]\n",
            ">7939, d1[0.003] d2[0.001] g[0.556]\n",
            ">7940, d1[0.000] d2[0.000] g[0.973]\n",
            ">7941, d1[0.003] d2[0.000] g[0.546]\n",
            ">7942, d1[0.000] d2[0.000] g[0.838]\n",
            ">7943, d1[0.000] d2[0.000] g[0.761]\n",
            ">7944, d1[0.000] d2[0.001] g[0.672]\n",
            ">7945, d1[0.000] d2[0.001] g[1.381]\n",
            ">7946, d1[0.000] d2[0.000] g[0.684]\n",
            ">7947, d1[0.000] d2[0.001] g[1.059]\n",
            ">7948, d1[0.000] d2[0.000] g[1.403]\n",
            ">7949, d1[0.000] d2[0.000] g[0.773]\n",
            ">7950, d1[0.000] d2[0.000] g[0.971]\n",
            ">7951, d1[0.000] d2[0.001] g[1.135]\n",
            ">7952, d1[0.003] d2[0.006] g[0.564]\n",
            ">7953, d1[0.000] d2[0.000] g[0.846]\n",
            ">7954, d1[0.003] d2[0.000] g[0.719]\n",
            ">7955, d1[0.000] d2[0.000] g[1.068]\n",
            ">7956, d1[0.000] d2[0.000] g[1.344]\n",
            ">7957, d1[0.000] d2[0.000] g[0.804]\n",
            ">7958, d1[0.000] d2[0.000] g[0.962]\n",
            ">7959, d1[0.004] d2[0.001] g[0.569]\n",
            ">7960, d1[0.000] d2[0.000] g[0.667]\n",
            ">7961, d1[0.000] d2[0.000] g[1.394]\n",
            ">7962, d1[0.000] d2[0.000] g[1.600]\n",
            ">7963, d1[0.000] d2[0.000] g[1.734]\n",
            ">7964, d1[0.000] d2[0.000] g[0.925]\n",
            ">7965, d1[0.000] d2[0.000] g[1.237]\n",
            ">7966, d1[0.000] d2[0.000] g[1.217]\n",
            ">7967, d1[0.001] d2[0.000] g[0.697]\n",
            ">7968, d1[0.001] d2[0.096] g[0.534]\n",
            ">7969, d1[0.000] d2[0.000] g[0.820]\n",
            ">7970, d1[0.000] d2[0.000] g[1.858]\n",
            ">7971, d1[0.000] d2[0.000] g[1.513]\n",
            ">7972, d1[0.000] d2[0.000] g[1.078]\n",
            ">7973, d1[0.000] d2[0.000] g[1.087]\n",
            ">7974, d1[0.000] d2[0.000] g[1.020]\n",
            ">7975, d1[0.000] d2[0.000] g[0.864]\n",
            ">7976, d1[0.000] d2[0.000] g[0.952]\n",
            ">7977, d1[0.000] d2[0.000] g[1.876]\n",
            ">7978, d1[0.000] d2[0.000] g[1.567]\n",
            ">7979, d1[0.000] d2[0.000] g[0.774]\n",
            ">7980, d1[0.084] d2[0.000] g[0.568]\n",
            ">7981, d1[0.000] d2[0.001] g[1.964]\n",
            ">7982, d1[0.000] d2[0.000] g[0.922]\n",
            ">7983, d1[0.000] d2[0.000] g[0.763]\n",
            ">7984, d1[0.000] d2[0.000] g[0.724]\n",
            ">7985, d1[0.000] d2[0.001] g[1.123]\n",
            ">7986, d1[0.000] d2[0.000] g[0.654]\n",
            ">7987, d1[0.000] d2[0.000] g[0.929]\n",
            ">7988, d1[0.000] d2[0.000] g[0.866]\n",
            ">7989, d1[0.000] d2[0.000] g[1.344]\n",
            ">7990, d1[0.000] d2[0.000] g[0.849]\n",
            ">7991, d1[0.002] d2[0.002] g[0.675]\n",
            ">7992, d1[0.000] d2[0.002] g[1.174]\n",
            ">7993, d1[0.015] d2[0.002] g[0.690]\n",
            ">7994, d1[0.000] d2[0.000] g[0.998]\n",
            ">7995, d1[0.000] d2[0.001] g[0.965]\n",
            ">7996, d1[0.000] d2[0.000] g[0.667]\n",
            ">7997, d1[0.001] d2[0.000] g[0.574]\n",
            ">7998, d1[0.000] d2[0.000] g[0.991]\n",
            ">7999, d1[0.000] d2[0.000] g[1.098]\n",
            ">8000, d1[0.000] d2[0.001] g[0.933]\n",
            ">8001, d1[0.000] d2[0.000] g[0.804]\n",
            ">8002, d1[0.000] d2[0.002] g[1.049]\n",
            ">8003, d1[0.000] d2[0.000] g[1.416]\n",
            ">8004, d1[0.004] d2[0.001] g[0.531]\n",
            ">8005, d1[0.000] d2[0.000] g[1.640]\n",
            ">8006, d1[0.000] d2[0.000] g[1.070]\n",
            ">8007, d1[0.000] d2[0.000] g[1.361]\n",
            ">8008, d1[0.000] d2[0.000] g[0.948]\n",
            ">8009, d1[0.000] d2[0.000] g[1.395]\n",
            ">8010, d1[0.000] d2[0.000] g[0.738]\n",
            ">8011, d1[0.000] d2[0.000] g[0.760]\n",
            ">8012, d1[0.000] d2[0.002] g[0.879]\n",
            ">8013, d1[0.000] d2[0.000] g[0.768]\n",
            ">8014, d1[0.000] d2[0.000] g[0.928]\n",
            ">8015, d1[0.000] d2[0.000] g[0.848]\n",
            ">8016, d1[0.001] d2[0.000] g[0.658]\n",
            ">8017, d1[0.000] d2[0.000] g[1.652]\n",
            ">8018, d1[0.000] d2[0.000] g[0.680]\n",
            ">8019, d1[0.000] d2[0.000] g[0.964]\n",
            ">8020, d1[0.000] d2[0.000] g[1.530]\n",
            ">8021, d1[0.000] d2[0.000] g[1.820]\n",
            ">8022, d1[0.000] d2[0.000] g[1.428]\n",
            ">8023, d1[0.010] d2[0.001] g[0.667]\n",
            ">8024, d1[0.000] d2[0.004] g[0.803]\n",
            ">8025, d1[0.000] d2[0.000] g[1.377]\n",
            ">8026, d1[0.000] d2[0.000] g[1.316]\n",
            ">8027, d1[0.000] d2[0.000] g[0.958]\n",
            ">8028, d1[0.004] d2[0.000] g[0.671]\n",
            ">8029, d1[0.000] d2[0.000] g[0.784]\n",
            ">8030, d1[0.000] d2[0.000] g[0.628]\n",
            ">8031, d1[0.000] d2[0.000] g[1.629]\n",
            ">8032, d1[0.000] d2[0.000] g[1.594]\n",
            ">8033, d1[0.000] d2[0.000] g[0.877]\n",
            ">8034, d1[0.003] d2[0.000] g[0.535]\n",
            ">8035, d1[0.000] d2[0.001] g[0.992]\n",
            ">8036, d1[0.000] d2[0.001] g[2.170]\n",
            ">8037, d1[0.000] d2[0.000] g[1.845]\n",
            ">8038, d1[0.000] d2[0.000] g[1.742]\n",
            ">8039, d1[0.000] d2[0.000] g[0.639]\n",
            ">8040, d1[0.000] d2[0.001] g[0.917]\n",
            ">8041, d1[0.000] d2[0.000] g[0.752]\n",
            ">8042, d1[0.006] d2[0.004] g[0.701]\n",
            ">8043, d1[0.000] d2[0.000] g[1.197]\n",
            ">8044, d1[0.000] d2[0.000] g[0.918]\n",
            ">8045, d1[0.000] d2[0.000] g[1.066]\n",
            ">8046, d1[0.000] d2[0.000] g[1.167]\n",
            ">8047, d1[0.000] d2[0.000] g[1.704]\n",
            ">8048, d1[0.000] d2[0.000] g[1.192]\n",
            ">8049, d1[0.003] d2[0.000] g[0.508]\n",
            ">8050, d1[0.000] d2[0.000] g[1.385]\n",
            ">8051, d1[0.000] d2[0.000] g[1.754]\n",
            ">8052, d1[0.000] d2[0.000] g[1.091]\n",
            ">8053, d1[0.000] d2[0.000] g[1.862]\n",
            ">8054, d1[0.008] d2[0.000] g[0.595]\n",
            ">8055, d1[0.000] d2[0.000] g[0.860]\n",
            ">8056, d1[0.000] d2[0.001] g[1.226]\n",
            ">8057, d1[0.006] d2[0.072] g[0.667]\n",
            ">8058, d1[0.000] d2[0.000] g[1.422]\n",
            ">8059, d1[0.000] d2[0.000] g[1.077]\n",
            ">8060, d1[0.007] d2[0.000] g[0.528]\n",
            ">8061, d1[0.000] d2[0.001] g[1.081]\n",
            ">8062, d1[0.000] d2[0.000] g[0.955]\n",
            ">8063, d1[0.007] d2[0.000] g[0.662]\n",
            ">8064, d1[0.000] d2[0.000] g[1.006]\n",
            ">8065, d1[0.000] d2[0.000] g[0.875]\n",
            ">8066, d1[0.000] d2[0.000] g[0.660]\n",
            ">8067, d1[0.024] d2[0.001] g[0.381]\n",
            ">8068, d1[0.011] d2[0.001] g[0.552]\n",
            ">8069, d1[0.000] d2[0.000] g[1.373]\n",
            ">8070, d1[0.006] d2[0.001] g[0.562]\n",
            ">8071, d1[0.000] d2[0.000] g[0.670]\n",
            ">8072, d1[0.000] d2[0.000] g[0.727]\n",
            ">8073, d1[0.000] d2[0.000] g[1.356]\n",
            ">8074, d1[0.000] d2[0.000] g[1.257]\n",
            ">8075, d1[0.000] d2[0.000] g[0.749]\n",
            ">8076, d1[0.000] d2[0.000] g[0.747]\n",
            ">8077, d1[0.000] d2[0.000] g[0.664]\n",
            ">8078, d1[0.000] d2[0.000] g[0.876]\n",
            ">8079, d1[0.000] d2[0.000] g[1.753]\n",
            ">8080, d1[0.000] d2[0.000] g[1.040]\n",
            ">8081, d1[0.000] d2[0.000] g[0.856]\n",
            ">8082, d1[0.000] d2[0.000] g[0.879]\n",
            ">8083, d1[0.000] d2[0.000] g[0.719]\n",
            ">8084, d1[0.000] d2[0.000] g[0.854]\n",
            ">8085, d1[0.000] d2[0.000] g[1.503]\n",
            ">8086, d1[0.000] d2[0.000] g[2.180]\n",
            ">8087, d1[0.000] d2[0.000] g[1.223]\n",
            ">8088, d1[0.000] d2[0.000] g[1.190]\n",
            ">8089, d1[0.000] d2[0.000] g[0.822]\n",
            ">8090, d1[0.003] d2[0.000] g[0.583]\n",
            ">8091, d1[0.004] d2[0.000] g[0.710]\n",
            ">8092, d1[0.000] d2[0.000] g[0.675]\n",
            ">8093, d1[0.000] d2[0.000] g[0.725]\n",
            ">8094, d1[0.000] d2[0.000] g[1.370]\n",
            ">8095, d1[0.000] d2[0.000] g[1.364]\n",
            ">8096, d1[0.000] d2[0.000] g[0.669]\n",
            ">8097, d1[0.002] d2[0.001] g[0.594]\n",
            ">8098, d1[0.000] d2[0.000] g[1.388]\n",
            ">8099, d1[0.000] d2[0.000] g[0.933]\n",
            ">8100, d1[0.003] d2[0.000] g[0.725]\n",
            ">8101, d1[0.000] d2[0.000] g[0.714]\n",
            ">8102, d1[0.000] d2[0.000] g[1.366]\n",
            ">8103, d1[0.000] d2[0.000] g[1.857]\n",
            ">8104, d1[0.000] d2[0.000] g[1.376]\n",
            ">8105, d1[0.000] d2[0.000] g[1.154]\n",
            ">8106, d1[0.000] d2[0.000] g[0.872]\n",
            ">8107, d1[0.000] d2[0.000] g[1.744]\n",
            ">8108, d1[0.000] d2[0.000] g[0.599]\n",
            ">8109, d1[0.000] d2[0.000] g[0.904]\n",
            ">8110, d1[0.000] d2[0.000] g[0.878]\n",
            ">8111, d1[0.004] d2[0.000] g[0.500]\n",
            ">8112, d1[0.000] d2[0.001] g[0.840]\n",
            ">8113, d1[0.000] d2[0.000] g[0.748]\n",
            ">8114, d1[0.000] d2[0.000] g[0.936]\n",
            ">8115, d1[0.000] d2[0.001] g[0.968]\n",
            ">8116, d1[0.003] d2[0.000] g[0.631]\n",
            ">8117, d1[0.003] d2[0.000] g[0.632]\n",
            ">8118, d1[0.000] d2[0.000] g[1.469]\n",
            ">8119, d1[0.000] d2[0.000] g[0.890]\n",
            ">8120, d1[0.000] d2[0.000] g[1.071]\n",
            ">8121, d1[0.000] d2[0.000] g[0.884]\n",
            ">8122, d1[0.000] d2[0.000] g[0.717]\n",
            ">8123, d1[0.000] d2[0.000] g[0.958]\n",
            ">8124, d1[0.003] d2[0.000] g[0.674]\n",
            ">8125, d1[0.000] d2[0.000] g[1.151]\n",
            ">8126, d1[0.000] d2[0.000] g[1.691]\n",
            ">8127, d1[0.000] d2[0.000] g[0.638]\n",
            ">8128, d1[0.000] d2[0.000] g[0.970]\n",
            ">8129, d1[0.000] d2[0.000] g[1.684]\n",
            ">8130, d1[0.000] d2[0.000] g[0.804]\n",
            ">8131, d1[0.000] d2[0.000] g[1.824]\n",
            ">8132, d1[0.002] d2[0.000] g[0.697]\n",
            ">8133, d1[0.002] d2[0.002] g[0.634]\n",
            ">8134, d1[0.019] d2[0.001] g[0.593]\n",
            ">8135, d1[0.000] d2[0.000] g[0.781]\n",
            ">8136, d1[0.000] d2[0.000] g[1.439]\n",
            ">8137, d1[0.000] d2[0.000] g[1.071]\n",
            ">8138, d1[0.000] d2[0.000] g[1.391]\n",
            ">8139, d1[0.000] d2[0.000] g[1.451]\n",
            ">8140, d1[0.000] d2[0.000] g[0.825]\n",
            ">8141, d1[0.000] d2[0.002] g[0.819]\n",
            ">8142, d1[0.000] d2[0.000] g[1.645]\n",
            ">8143, d1[0.000] d2[0.000] g[0.881]\n",
            ">8144, d1[0.000] d2[0.000] g[0.762]\n",
            ">8145, d1[0.000] d2[0.000] g[1.379]\n",
            ">8146, d1[0.003] d2[0.000] g[0.499]\n",
            ">8147, d1[0.000] d2[0.000] g[0.811]\n",
            ">8148, d1[0.000] d2[0.000] g[1.828]\n",
            ">8149, d1[0.000] d2[0.000] g[1.391]\n",
            ">8150, d1[0.000] d2[0.000] g[1.057]\n",
            ">8151, d1[0.000] d2[0.000] g[0.861]\n",
            ">8152, d1[0.000] d2[0.000] g[0.811]\n",
            ">8153, d1[0.000] d2[0.000] g[0.704]\n",
            ">8154, d1[0.000] d2[0.000] g[1.158]\n",
            ">8155, d1[0.000] d2[0.000] g[0.863]\n",
            ">8156, d1[0.004] d2[0.001] g[0.538]\n",
            ">8157, d1[0.000] d2[0.000] g[1.363]\n",
            ">8158, d1[0.000] d2[0.000] g[0.912]\n",
            ">8159, d1[0.000] d2[0.000] g[0.917]\n",
            ">8160, d1[0.000] d2[0.000] g[0.848]\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            ">Saved: plot_008160.png and model_008160.h5\n",
            ">8161, d1[0.003] d2[0.000] g[0.514]\n",
            ">8162, d1[0.000] d2[0.000] g[1.153]\n",
            ">8163, d1[0.000] d2[0.000] g[1.439]\n",
            ">8164, d1[0.000] d2[0.000] g[0.601]\n",
            ">8165, d1[0.003] d2[0.000] g[0.529]\n",
            ">8166, d1[0.003] d2[0.000] g[0.491]\n",
            ">8167, d1[0.000] d2[0.000] g[0.801]\n",
            ">8168, d1[0.000] d2[0.000] g[0.778]\n",
            ">8169, d1[0.005] d2[0.001] g[0.586]\n",
            ">8170, d1[0.002] d2[0.000] g[0.525]\n",
            ">8171, d1[0.000] d2[0.000] g[1.028]\n",
            ">8172, d1[0.000] d2[0.005] g[2.140]\n",
            ">8173, d1[0.000] d2[0.001] g[0.913]\n",
            ">8174, d1[0.007] d2[0.000] g[0.561]\n",
            ">8175, d1[0.000] d2[0.000] g[0.830]\n",
            ">8176, d1[0.095] d2[0.022] g[0.642]\n",
            ">8177, d1[0.000] d2[0.001] g[1.295]\n",
            ">8178, d1[0.000] d2[0.006] g[0.815]\n",
            ">8179, d1[0.001] d2[0.000] g[0.749]\n",
            ">8180, d1[0.000] d2[0.009] g[0.705]\n",
            ">8181, d1[0.000] d2[0.010] g[0.914]\n",
            ">8182, d1[0.000] d2[0.000] g[1.195]\n",
            ">8183, d1[0.000] d2[0.000] g[1.668]\n",
            ">8184, d1[0.000] d2[0.000] g[0.869]\n",
            ">8185, d1[0.000] d2[0.000] g[0.814]\n",
            ">8186, d1[0.001] d2[0.267] g[0.649]\n",
            ">8187, d1[0.000] d2[0.002] g[1.121]\n",
            ">8188, d1[0.000] d2[0.000] g[0.970]\n",
            ">8189, d1[0.001] d2[0.000] g[0.942]\n",
            ">8190, d1[0.001] d2[0.000] g[1.640]\n",
            ">8191, d1[0.000] d2[0.000] g[1.689]\n",
            ">8192, d1[0.001] d2[0.000] g[0.870]\n",
            ">8193, d1[0.001] d2[0.000] g[0.822]\n",
            ">8194, d1[0.001] d2[0.000] g[1.069]\n",
            ">8195, d1[0.000] d2[0.000] g[0.894]\n",
            ">8196, d1[0.001] d2[0.000] g[0.849]\n",
            ">8197, d1[0.001] d2[0.000] g[1.151]\n",
            ">8198, d1[0.962] d2[0.051] g[0.540]\n",
            ">8199, d1[0.000] d2[0.009] g[0.641]\n",
            ">8200, d1[0.000] d2[0.108] g[1.062]\n",
            ">8201, d1[0.001] d2[0.000] g[0.851]\n",
            ">8202, d1[0.013] d2[0.001] g[0.428]\n",
            ">8203, d1[0.000] d2[0.002] g[0.917]\n",
            ">8204, d1[0.000] d2[0.002] g[0.966]\n",
            ">8205, d1[0.001] d2[0.004] g[0.610]\n",
            ">8206, d1[0.000] d2[0.000] g[0.824]\n",
            ">8207, d1[0.001] d2[0.001] g[0.603]\n",
            ">8208, d1[0.000] d2[0.001] g[0.765]\n",
            ">8209, d1[0.000] d2[0.001] g[1.218]\n",
            ">8210, d1[0.000] d2[0.001] g[1.023]\n",
            ">8211, d1[0.000] d2[0.000] g[0.887]\n",
            ">8212, d1[0.000] d2[0.001] g[0.744]\n",
            ">8213, d1[0.000] d2[0.001] g[1.019]\n",
            ">8214, d1[0.000] d2[0.001] g[0.791]\n",
            ">8215, d1[0.000] d2[0.000] g[0.948]\n",
            ">8216, d1[0.000] d2[0.000] g[0.615]\n",
            ">8217, d1[0.000] d2[0.000] g[0.587]\n",
            ">8218, d1[0.000] d2[0.000] g[0.993]\n",
            ">8219, d1[0.000] d2[0.001] g[1.179]\n",
            ">8220, d1[0.000] d2[0.000] g[1.223]\n",
            ">8221, d1[0.000] d2[0.001] g[1.835]\n",
            ">8222, d1[0.000] d2[0.001] g[0.631]\n",
            ">8223, d1[0.000] d2[0.000] g[1.694]\n",
            ">8224, d1[0.000] d2[0.000] g[1.412]\n",
            ">8225, d1[0.002] d2[0.001] g[0.687]\n",
            ">8226, d1[0.000] d2[0.000] g[0.979]\n",
            ">8227, d1[0.000] d2[0.003] g[0.902]\n",
            ">8228, d1[0.000] d2[0.000] g[0.805]\n",
            ">8229, d1[0.000] d2[0.000] g[1.438]\n",
            ">8230, d1[0.000] d2[0.000] g[0.906]\n",
            ">8231, d1[0.026] d2[0.000] g[0.515]\n",
            ">8232, d1[0.000] d2[0.001] g[1.178]\n",
            ">8233, d1[0.000] d2[0.000] g[0.728]\n",
            ">8234, d1[0.000] d2[0.017] g[0.534]\n",
            ">8235, d1[0.000] d2[0.000] g[0.971]\n",
            ">8236, d1[0.000] d2[0.000] g[2.184]\n",
            ">8237, d1[0.000] d2[0.000] g[1.417]\n",
            ">8238, d1[0.000] d2[0.000] g[0.857]\n",
            ">8239, d1[0.001] d2[0.000] g[0.761]\n",
            ">8240, d1[0.000] d2[0.002] g[0.858]\n",
            ">8241, d1[0.000] d2[0.001] g[0.938]\n",
            ">8242, d1[0.000] d2[0.001] g[0.684]\n",
            ">8243, d1[0.000] d2[0.000] g[1.321]\n",
            ">8244, d1[0.000] d2[0.000] g[1.348]\n",
            ">8245, d1[0.000] d2[0.000] g[1.015]\n",
            ">8246, d1[0.000] d2[0.002] g[0.693]\n",
            ">8247, d1[0.003] d2[0.000] g[0.669]\n",
            ">8248, d1[0.000] d2[0.000] g[0.864]\n",
            ">8249, d1[0.000] d2[0.001] g[0.837]\n",
            ">8250, d1[0.000] d2[0.009] g[1.716]\n",
            ">8251, d1[0.000] d2[0.000] g[1.638]\n",
            ">8252, d1[0.000] d2[0.000] g[1.139]\n",
            ">8253, d1[0.000] d2[0.000] g[0.848]\n",
            ">8254, d1[0.000] d2[0.000] g[0.814]\n",
            ">8255, d1[0.000] d2[0.001] g[0.625]\n",
            ">8256, d1[0.000] d2[0.000] g[0.737]\n",
            ">8257, d1[0.000] d2[0.001] g[1.114]\n",
            ">8258, d1[0.001] d2[0.000] g[0.614]\n",
            ">8259, d1[0.012] d2[0.000] g[0.568]\n",
            ">8260, d1[0.000] d2[0.175] g[0.793]\n",
            ">8261, d1[0.000] d2[0.000] g[1.486]\n",
            ">8262, d1[0.001] d2[0.000] g[0.824]\n",
            ">8263, d1[0.000] d2[0.000] g[1.405]\n",
            ">8264, d1[0.001] d2[0.000] g[1.025]\n",
            ">8265, d1[0.004] d2[0.000] g[1.008]\n",
            ">8266, d1[0.001] d2[0.000] g[0.910]\n",
            ">8267, d1[0.001] d2[0.000] g[1.757]\n",
            ">8268, d1[0.001] d2[0.000] g[0.736]\n",
            ">8269, d1[0.012] d2[0.000] g[0.840]\n",
            ">8270, d1[0.001] d2[0.000] g[1.110]\n",
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            ">8273, d1[0.000] d2[0.000] g[1.054]\n",
            ">8274, d1[0.000] d2[0.000] g[1.434]\n",
            ">8275, d1[0.000] d2[0.000] g[1.222]\n",
            ">8276, d1[0.000] d2[0.000] g[1.592]\n",
            ">8277, d1[0.000] d2[0.000] g[0.870]\n",
            ">8278, d1[0.000] d2[0.000] g[1.103]\n",
            ">8279, d1[0.000] d2[0.000] g[1.784]\n",
            ">8280, d1[0.000] d2[0.000] g[0.799]\n",
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            ">8282, d1[0.000] d2[0.000] g[1.437]\n",
            ">8283, d1[0.000] d2[0.000] g[1.444]\n",
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            ">8285, d1[0.000] d2[0.000] g[1.385]\n",
            ">8286, d1[0.000] d2[0.000] g[0.817]\n",
            ">8287, d1[0.062] d2[0.020] g[0.600]\n",
            ">8288, d1[0.000] d2[0.000] g[1.008]\n",
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            ">8290, d1[0.000] d2[0.000] g[0.979]\n",
            ">8291, d1[0.000] d2[0.001] g[1.627]\n",
            ">8292, d1[0.001] d2[0.000] g[0.531]\n",
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            ">8294, d1[0.000] d2[0.000] g[1.059]\n",
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            ">8296, d1[0.000] d2[0.000] g[0.739]\n",
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            ">8298, d1[0.000] d2[0.000] g[1.230]\n",
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            ">8301, d1[0.000] d2[0.000] g[1.427]\n",
            ">8302, d1[0.000] d2[0.000] g[0.889]\n",
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            ">8306, d1[0.000] d2[0.000] g[1.427]\n",
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            ">8539, d1[0.000] d2[0.000] g[1.654]\n",
            ">8540, d1[0.009] d2[0.004] g[0.595]\n",
            ">8541, d1[0.000] d2[0.000] g[1.954]\n",
            ">8542, d1[0.000] d2[0.000] g[1.407]\n",
            ">8543, d1[0.000] d2[0.001] g[0.766]\n",
            ">8544, d1[0.002] d2[0.004] g[0.359]\n",
            ">8545, d1[0.000] d2[0.001] g[1.602]\n",
            ">8546, d1[0.000] d2[0.000] g[0.743]\n",
            ">8547, d1[0.001] d2[0.000] g[0.737]\n",
            ">8548, d1[0.000] d2[0.000] g[0.785]\n",
            ">8549, d1[0.000] d2[0.000] g[1.357]\n",
            ">8550, d1[0.006] d2[0.000] g[0.487]\n",
            ">8551, d1[0.000] d2[0.000] g[1.616]\n",
            ">8552, d1[0.000] d2[0.000] g[1.012]\n",
            ">8553, d1[0.000] d2[0.000] g[1.848]\n",
            ">8554, d1[0.002] d2[0.002] g[0.529]\n",
            ">8555, d1[0.000] d2[0.000] g[1.090]\n",
            ">8556, d1[0.000] d2[0.001] g[0.669]\n",
            ">8557, d1[0.000] d2[0.000] g[0.988]\n",
            ">8558, d1[0.000] d2[0.000] g[0.730]\n",
            ">8559, d1[0.000] d2[0.000] g[0.752]\n",
            ">8560, d1[0.006] d2[0.001] g[0.503]\n",
            ">8561, d1[0.000] d2[0.000] g[0.865]\n",
            ">8562, d1[0.003] d2[0.000] g[0.652]\n",
            ">8563, d1[0.000] d2[0.001] g[0.912]\n",
            ">8564, d1[0.009] d2[0.003] g[0.570]\n",
            ">8565, d1[0.002] d2[0.001] g[0.648]\n",
            ">8566, d1[0.000] d2[0.000] g[0.895]\n",
            ">8567, d1[0.000] d2[0.001] g[0.863]\n",
            ">8568, d1[0.000] d2[0.000] g[1.737]\n",
            ">8569, d1[0.000] d2[0.000] g[0.923]\n",
            ">8570, d1[0.000] d2[0.000] g[0.957]\n",
            ">8571, d1[0.000] d2[0.000] g[1.443]\n",
            ">8572, d1[0.000] d2[0.000] g[1.662]\n",
            ">8573, d1[0.000] d2[0.000] g[1.617]\n",
            ">8574, d1[0.000] d2[0.000] g[1.208]\n",
            ">8575, d1[0.000] d2[0.000] g[0.877]\n",
            ">8576, d1[0.007] d2[0.001] g[0.607]\n",
            ">8577, d1[0.000] d2[0.001] g[0.855]\n",
            ">8578, d1[0.000] d2[0.000] g[1.081]\n",
            ">8579, d1[0.001] d2[0.000] g[0.667]\n",
            ">8580, d1[0.000] d2[0.000] g[0.826]\n",
            ">8581, d1[0.002] d2[0.001] g[0.797]\n",
            ">8582, d1[0.001] d2[0.003] g[0.571]\n",
            ">8583, d1[0.001] d2[0.001] g[0.617]\n",
            ">8584, d1[0.000] d2[0.000] g[0.846]\n",
            ">8585, d1[0.000] d2[0.000] g[0.900]\n",
            ">8586, d1[0.000] d2[0.000] g[1.831]\n",
            ">8587, d1[0.000] d2[0.000] g[0.886]\n",
            ">8588, d1[0.002] d2[0.001] g[0.411]\n",
            ">8589, d1[0.000] d2[0.000] g[0.707]\n",
            ">8590, d1[0.002] d2[0.000] g[0.648]\n",
            ">8591, d1[0.000] d2[0.001] g[2.219]\n",
            ">8592, d1[0.000] d2[0.001] g[1.056]\n",
            ">8593, d1[0.000] d2[0.001] g[0.882]\n",
            ">8594, d1[0.002] d2[0.003] g[0.616]\n",
            ">8595, d1[0.000] d2[0.000] g[1.420]\n",
            ">8596, d1[0.000] d2[0.000] g[1.843]\n",
            ">8597, d1[0.000] d2[0.000] g[0.849]\n",
            ">8598, d1[0.019] d2[0.001] g[0.608]\n",
            ">8599, d1[0.000] d2[0.000] g[1.100]\n",
            ">8600, d1[0.000] d2[0.000] g[0.751]\n",
            ">8601, d1[0.000] d2[0.000] g[1.085]\n",
            ">8602, d1[0.000] d2[0.000] g[0.679]\n",
            ">8603, d1[0.000] d2[0.002] g[0.552]\n",
            ">8604, d1[0.000] d2[0.001] g[0.994]\n",
            ">8605, d1[0.000] d2[0.000] g[1.052]\n",
            ">8606, d1[0.001] d2[0.000] g[0.494]\n",
            ">8607, d1[0.000] d2[0.000] g[0.645]\n",
            ">8608, d1[0.000] d2[0.000] g[1.103]\n",
            ">8609, d1[0.000] d2[0.000] g[0.910]\n",
            ">8610, d1[0.005] d2[0.001] g[0.683]\n",
            ">8611, d1[0.000] d2[0.000] g[0.645]\n",
            ">8612, d1[0.000] d2[0.000] g[1.495]\n",
            ">8613, d1[0.000] d2[0.051] g[0.979]\n",
            ">8614, d1[0.000] d2[0.001] g[0.902]\n",
            ">8615, d1[0.000] d2[0.000] g[0.963]\n",
            ">8616, d1[0.000] d2[0.000] g[1.105]\n",
            ">8617, d1[0.000] d2[0.000] g[1.081]\n",
            ">8618, d1[0.000] d2[0.000] g[0.809]\n",
            ">8619, d1[0.000] d2[0.000] g[0.900]\n",
            ">8620, d1[0.000] d2[0.000] g[0.793]\n",
            ">8621, d1[0.000] d2[0.000] g[1.099]\n",
            ">8622, d1[0.000] d2[0.000] g[0.948]\n",
            ">8623, d1[0.010] d2[0.001] g[0.748]\n",
            ">8624, d1[0.003] d2[0.000] g[0.623]\n",
            ">8625, d1[0.000] d2[0.000] g[1.733]\n",
            ">8626, d1[0.000] d2[0.000] g[1.491]\n",
            ">8627, d1[0.000] d2[0.000] g[1.157]\n",
            ">8628, d1[0.000] d2[0.000] g[1.901]\n",
            ">8629, d1[0.000] d2[0.000] g[1.132]\n",
            ">8630, d1[0.000] d2[0.000] g[1.079]\n",
            ">8631, d1[0.000] d2[0.000] g[2.169]\n",
            ">8632, d1[0.000] d2[0.000] g[1.070]\n",
            ">8633, d1[0.018] d2[0.000] g[0.587]\n",
            ">8634, d1[0.000] d2[0.000] g[1.220]\n",
            ">8635, d1[0.016] d2[0.001] g[0.619]\n",
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            ">8638, d1[0.000] d2[0.000] g[0.757]\n",
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            ">8640, d1[0.000] d2[0.000] g[1.661]\n",
            ">8641, d1[0.000] d2[0.000] g[0.913]\n",
            ">8642, d1[0.000] d2[0.000] g[1.387]\n",
            ">8643, d1[0.000] d2[0.000] g[1.695]\n",
            ">8644, d1[0.000] d2[0.001] g[1.926]\n",
            ">8645, d1[0.000] d2[0.000] g[1.291]\n",
            ">8646, d1[0.000] d2[0.000] g[1.068]\n",
            ">8647, d1[0.000] d2[0.000] g[1.867]\n",
            ">8648, d1[0.000] d2[0.000] g[1.506]\n",
            ">8649, d1[0.000] d2[0.000] g[1.042]\n",
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            ">8651, d1[0.000] d2[0.001] g[0.702]\n",
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            ">8653, d1[0.000] d2[0.000] g[1.450]\n",
            ">8654, d1[0.001] d2[0.000] g[0.680]\n",
            ">8655, d1[0.000] d2[0.000] g[0.834]\n",
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            ">8659, d1[0.000] d2[0.007] g[0.892]\n",
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            ">8663, d1[0.000] d2[0.000] g[1.140]\n",
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            ">8665, d1[0.000] d2[0.000] g[0.977]\n",
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            ">8674, d1[0.000] d2[0.000] g[0.607]\n",
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            ">8779, d1[0.000] d2[0.000] g[0.948]\n",
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            ">8788, d1[0.000] d2[0.000] g[1.318]\n",
            ">8789, d1[0.000] d2[0.000] g[0.861]\n",
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            ">8795, d1[0.000] d2[0.001] g[1.363]\n",
            ">8796, d1[0.000] d2[0.005] g[1.811]\n",
            ">8797, d1[0.000] d2[0.002] g[0.807]\n",
            ">8798, d1[0.001] d2[0.001] g[0.664]\n",
            ">8799, d1[0.000] d2[0.007] g[0.877]\n",
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            ">8803, d1[0.000] d2[0.001] g[1.159]\n",
            ">8804, d1[0.000] d2[0.001] g[0.718]\n",
            ">8805, d1[0.000] d2[0.000] g[0.754]\n",
            ">8806, d1[0.000] d2[0.001] g[1.365]\n",
            ">8807, d1[0.012] d2[0.004] g[0.658]\n",
            ">8808, d1[0.000] d2[0.001] g[0.913]\n",
            ">8809, d1[0.001] d2[0.001] g[0.974]\n",
            ">8810, d1[0.010] d2[0.004] g[0.524]\n",
            ">8811, d1[0.000] d2[0.001] g[1.499]\n",
            ">8812, d1[0.000] d2[0.000] g[1.129]\n",
            ">8813, d1[0.000] d2[0.000] g[0.920]\n",
            ">8814, d1[0.013] d2[0.301] g[0.627]\n",
            ">8815, d1[0.002] d2[0.000] g[1.435]\n",
            ">8816, d1[0.001] d2[0.000] g[1.033]\n",
            ">8817, d1[0.001] d2[0.000] g[1.807]\n",
            ">8818, d1[0.001] d2[0.000] g[0.743]\n",
            ">8819, d1[0.002] d2[0.000] g[1.375]\n",
            ">8820, d1[0.002] d2[0.000] g[1.712]\n",
            ">8821, d1[0.001] d2[0.000] g[1.285]\n",
            ">8822, d1[0.002] d2[0.000] g[0.854]\n",
            ">8823, d1[0.001] d2[0.000] g[0.870]\n",
            ">8824, d1[0.002] d2[0.000] g[0.960]\n",
            ">8825, d1[0.001] d2[0.000] g[1.934]\n",
            ">8826, d1[0.002] d2[0.000] g[0.953]\n",
            ">8827, d1[0.000] d2[0.001] g[0.924]\n",
            ">8828, d1[0.001] d2[0.001] g[1.804]\n",
            ">8829, d1[0.466] d2[0.001] g[0.557]\n",
            ">8830, d1[0.000] d2[0.003] g[1.308]\n",
            ">8831, d1[0.000] d2[0.004] g[1.351]\n",
            ">8832, d1[0.000] d2[0.005] g[1.102]\n",
            ">8833, d1[0.000] d2[0.002] g[0.860]\n",
            ">8834, d1[0.000] d2[0.001] g[1.848]\n",
            ">8835, d1[0.001] d2[0.001] g[0.617]\n",
            ">8836, d1[0.018] d2[0.000] g[0.533]\n",
            ">8837, d1[0.000] d2[0.001] g[0.943]\n",
            ">8838, d1[0.000] d2[0.002] g[1.446]\n",
            ">8839, d1[0.000] d2[0.003] g[0.928]\n",
            ">8840, d1[0.000] d2[0.002] g[0.748]\n",
            ">8841, d1[0.000] d2[0.001] g[0.785]\n",
            ">8842, d1[0.000] d2[0.001] g[1.423]\n",
            ">8843, d1[0.000] d2[0.000] g[1.102]\n",
            ">8844, d1[0.000] d2[0.002] g[1.449]\n",
            ">8845, d1[0.000] d2[0.001] g[0.811]\n",
            ">8846, d1[0.000] d2[0.001] g[0.875]\n",
            ">8847, d1[0.000] d2[0.001] g[1.227]\n",
            ">8848, d1[0.000] d2[0.002] g[1.493]\n",
            ">8849, d1[0.000] d2[0.001] g[0.903]\n",
            ">8850, d1[0.000] d2[0.001] g[1.366]\n",
            ">8851, d1[0.000] d2[0.001] g[0.868]\n",
            ">8852, d1[0.000] d2[0.001] g[2.162]\n",
            ">8853, d1[0.000] d2[0.001] g[1.095]\n",
            ">8854, d1[0.000] d2[0.001] g[0.935]\n",
            ">8855, d1[0.000] d2[0.001] g[1.358]\n",
            ">8856, d1[0.000] d2[0.001] g[2.001]\n",
            ">8857, d1[0.000] d2[0.001] g[1.583]\n",
            ">8858, d1[0.000] d2[0.000] g[0.924]\n",
            ">8859, d1[0.000] d2[0.001] g[1.125]\n",
            ">8860, d1[0.030] d2[0.002] g[0.590]\n",
            ">8861, d1[0.000] d2[0.001] g[1.145]\n",
            ">8862, d1[0.000] d2[0.001] g[1.047]\n",
            ">8863, d1[0.000] d2[0.001] g[0.950]\n",
            ">8864, d1[0.000] d2[0.000] g[0.944]\n",
            ">8865, d1[0.000] d2[0.001] g[1.146]\n",
            ">8866, d1[0.003] d2[0.010] g[0.510]\n",
            ">8867, d1[0.000] d2[0.001] g[0.673]\n",
            ">8868, d1[0.001] d2[0.000] g[0.585]\n",
            ">8869, d1[0.002] d2[0.001] g[0.629]\n",
            ">8870, d1[0.000] d2[0.001] g[0.979]\n",
            ">8871, d1[0.000] d2[0.001] g[1.027]\n",
            ">8872, d1[0.000] d2[0.001] g[1.127]\n",
            ">8873, d1[0.000] d2[0.001] g[1.064]\n",
            ">8874, d1[0.000] d2[0.000] g[0.932]\n",
            ">8875, d1[0.000] d2[0.001] g[1.384]\n",
            ">8876, d1[0.000] d2[0.000] g[1.038]\n",
            ">8877, d1[0.000] d2[0.002] g[0.787]\n",
            ">8878, d1[0.000] d2[0.000] g[0.817]\n",
            ">8879, d1[0.000] d2[0.000] g[0.825]\n",
            ">8880, d1[0.000] d2[0.000] g[0.802]\n",
            ">8881, d1[0.008] d2[0.003] g[0.597]\n",
            ">8882, d1[0.000] d2[0.000] g[1.852]\n",
            ">8883, d1[0.000] d2[0.000] g[0.905]\n",
            ">8884, d1[0.000] d2[0.000] g[0.768]\n",
            ">8885, d1[0.001] d2[0.001] g[0.534]\n",
            ">8886, d1[0.000] d2[0.001] g[0.761]\n",
            ">8887, d1[0.000] d2[0.000] g[1.599]\n",
            ">8888, d1[0.000] d2[0.000] g[0.861]\n",
            ">8889, d1[0.003] d2[0.001] g[0.645]\n",
            ">8890, d1[0.000] d2[0.003] g[0.892]\n",
            ">8891, d1[0.000] d2[0.000] g[0.909]\n",
            ">8892, d1[0.000] d2[0.001] g[1.092]\n",
            ">8893, d1[0.000] d2[0.001] g[0.771]\n",
            ">8894, d1[0.000] d2[0.000] g[1.484]\n",
            ">8895, d1[0.000] d2[0.001] g[1.127]\n",
            ">8896, d1[0.000] d2[0.000] g[1.017]\n",
            ">8897, d1[0.003] d2[0.001] g[0.399]\n",
            ">8898, d1[0.000] d2[0.000] g[1.940]\n",
            ">8899, d1[0.000] d2[0.001] g[0.779]\n",
            ">8900, d1[0.000] d2[0.000] g[0.965]\n",
            ">8901, d1[0.000] d2[0.000] g[1.951]\n",
            ">8902, d1[0.000] d2[0.001] g[1.139]\n",
            ">8903, d1[0.000] d2[0.000] g[1.365]\n",
            ">8904, d1[0.000] d2[0.000] g[1.177]\n",
            ">8905, d1[0.000] d2[0.000] g[0.704]\n",
            ">8906, d1[0.000] d2[0.000] g[0.864]\n",
            ">8907, d1[0.000] d2[0.000] g[0.970]\n",
            ">8908, d1[0.000] d2[0.000] g[1.406]\n",
            ">8909, d1[0.000] d2[0.000] g[0.928]\n",
            ">8910, d1[0.000] d2[0.001] g[1.418]\n",
            ">8911, d1[0.000] d2[0.001] g[0.835]\n",
            ">8912, d1[0.008] d2[0.011] g[0.593]\n",
            ">8913, d1[0.000] d2[0.000] g[0.771]\n",
            ">8914, d1[0.001] d2[0.189] g[0.511]\n",
            ">8915, d1[0.000] d2[0.000] g[1.336]\n",
            ">8916, d1[0.000] d2[0.000] g[0.770]\n",
            ">8917, d1[0.000] d2[0.000] g[2.101]\n",
            ">8918, d1[0.000] d2[0.000] g[1.199]\n",
            ">8919, d1[0.001] d2[0.000] g[1.194]\n",
            ">8920, d1[0.015] d2[0.000] g[0.717]\n",
            ">8921, d1[0.000] d2[0.000] g[2.152]\n",
            ">8922, d1[0.000] d2[0.000] g[0.906]\n",
            ">8923, d1[0.000] d2[0.000] g[1.156]\n",
            ">8924, d1[0.266] d2[0.025] g[0.592]\n",
            ">8925, d1[0.002] d2[0.001] g[0.593]\n",
            ">8926, d1[0.004] d2[0.022] g[0.592]\n",
            ">8927, d1[0.000] d2[0.001] g[1.378]\n",
            ">8928, d1[0.001] d2[0.000] g[0.780]\n",
            ">8929, d1[0.000] d2[0.000] g[1.356]\n",
            ">8930, d1[0.000] d2[0.000] g[1.875]\n",
            ">8931, d1[0.000] d2[0.001] g[0.963]\n",
            ">8932, d1[0.000] d2[0.000] g[0.795]\n",
            ">8933, d1[0.000] d2[0.000] g[1.702]\n",
            ">8934, d1[0.000] d2[0.000] g[1.933]\n",
            ">8935, d1[0.009] d2[0.001] g[0.566]\n",
            ">8936, d1[0.000] d2[0.000] g[0.747]\n",
            ">8937, d1[0.000] d2[0.000] g[0.722]\n",
            ">8938, d1[0.029] d2[0.002] g[0.662]\n",
            ">8939, d1[0.001] d2[0.345] g[0.580]\n",
            ">8940, d1[0.000] d2[0.000] g[2.171]\n",
            ">8941, d1[0.000] d2[0.000] g[0.871]\n",
            ">8942, d1[0.001] d2[0.000] g[1.081]\n",
            ">8943, d1[0.001] d2[0.000] g[0.946]\n",
            ">8944, d1[0.001] d2[0.000] g[0.867]\n",
            ">8945, d1[0.006] d2[0.000] g[0.606]\n",
            ">8946, d1[0.000] d2[0.000] g[1.226]\n",
            ">8947, d1[0.825] d2[0.001] g[0.548]\n",
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            ">8950, d1[0.000] d2[0.059] g[0.742]\n",
            ">8951, d1[0.001] d2[0.002] g[0.623]\n",
            ">8952, d1[0.000] d2[0.002] g[1.048]\n",
            ">8953, d1[0.000] d2[0.075] g[0.644]\n",
            ">8954, d1[0.000] d2[0.001] g[1.601]\n",
            ">8955, d1[0.000] d2[0.000] g[1.667]\n",
            ">8956, d1[0.000] d2[0.000] g[1.077]\n",
            ">8957, d1[0.003] d2[0.001] g[0.493]\n",
            ">8958, d1[0.000] d2[0.001] g[0.832]\n",
            ">8959, d1[0.000] d2[0.001] g[0.993]\n",
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            ">8961, d1[0.000] d2[0.001] g[1.329]\n",
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            ">8964, d1[0.001] d2[0.000] g[0.684]\n",
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            ">9040, d1[0.000] d2[0.000] g[1.118]\n",
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            ">9042, d1[0.000] d2[0.002] g[1.076]\n",
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            ">9044, d1[0.000] d2[0.000] g[0.867]\n",
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            ">9046, d1[0.000] d2[0.000] g[1.781]\n",
            ">9047, d1[0.000] d2[0.002] g[1.869]\n",
            ">9048, d1[0.000] d2[0.001] g[0.581]\n",
            ">9049, d1[0.000] d2[0.000] g[0.843]\n",
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            ">9051, d1[0.000] d2[0.000] g[1.181]\n",
            ">9052, d1[0.000] d2[0.015] g[1.032]\n",
            ">9053, d1[0.000] d2[0.001] g[1.478]\n",
            ">9054, d1[0.000] d2[0.001] g[0.834]\n",
            ">9055, d1[0.000] d2[0.001] g[0.905]\n",
            ">9056, d1[0.000] d2[0.001] g[1.226]\n",
            ">9057, d1[0.000] d2[0.001] g[0.899]\n",
            ">9058, d1[0.000] d2[0.000] g[0.956]\n",
            ">9059, d1[0.011] d2[0.001] g[0.613]\n",
            ">9060, d1[0.000] d2[0.001] g[1.953]\n",
            ">9061, d1[0.000] d2[0.001] g[1.738]\n",
            ">9062, d1[0.000] d2[0.001] g[1.289]\n",
            ">9063, d1[0.000] d2[0.000] g[1.614]\n",
            ">9064, d1[0.000] d2[0.001] g[1.116]\n",
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            ">9067, d1[0.000] d2[0.000] g[1.294]\n",
            ">9068, d1[0.000] d2[0.001] g[1.258]\n",
            ">9069, d1[0.000] d2[0.000] g[0.992]\n",
            ">9070, d1[0.000] d2[0.002] g[1.140]\n",
            ">9071, d1[0.000] d2[0.000] g[0.861]\n",
            ">9072, d1[0.001] d2[0.000] g[0.520]\n",
            ">9073, d1[0.000] d2[0.002] g[0.822]\n",
            ">9074, d1[0.000] d2[0.000] g[1.744]\n",
            ">9075, d1[0.000] d2[0.001] g[0.779]\n",
            ">9076, d1[0.000] d2[0.001] g[0.710]\n",
            ">9077, d1[0.000] d2[0.001] g[0.949]\n",
            ">9078, d1[0.000] d2[0.000] g[0.775]\n",
            ">9079, d1[0.000] d2[0.000] g[0.960]\n",
            ">9080, d1[0.006] d2[0.000] g[0.633]\n",
            ">9081, d1[0.000] d2[0.000] g[1.872]\n",
            ">9082, d1[0.000] d2[0.000] g[1.099]\n",
            ">9083, d1[0.000] d2[0.001] g[0.952]\n",
            ">9084, d1[0.000] d2[0.001] g[0.869]\n",
            ">9085, d1[0.000] d2[0.000] g[0.898]\n",
            ">9086, d1[0.000] d2[0.000] g[1.366]\n",
            ">9087, d1[0.000] d2[0.000] g[0.786]\n",
            ">9088, d1[0.000] d2[0.000] g[0.855]\n",
            ">9089, d1[0.000] d2[0.000] g[0.886]\n",
            ">9090, d1[0.000] d2[0.000] g[1.176]\n",
            ">9091, d1[0.000] d2[0.000] g[1.873]\n",
            ">9092, d1[0.000] d2[0.000] g[0.792]\n",
            ">9093, d1[0.000] d2[0.000] g[0.902]\n",
            ">9094, d1[0.000] d2[0.000] g[0.695]\n",
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            ">9096, d1[0.038] d2[0.002] g[0.569]\n",
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            ">9375, d1[0.000] d2[0.001] g[1.076]\n",
            ">9376, d1[0.021] d2[0.005] g[0.680]\n",
            ">9377, d1[0.000] d2[0.006] g[0.845]\n",
            ">9378, d1[0.000] d2[0.001] g[1.034]\n",
            ">9379, d1[0.000] d2[0.001] g[1.012]\n",
            ">9380, d1[0.000] d2[0.000] g[2.069]\n",
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            ">9382, d1[0.000] d2[0.001] g[0.714]\n",
            ">9383, d1[0.000] d2[0.000] g[0.760]\n",
            ">9384, d1[0.000] d2[0.000] g[0.875]\n",
            ">9385, d1[0.000] d2[0.001] g[0.808]\n",
            ">9386, d1[0.000] d2[0.000] g[0.603]\n",
            ">9387, d1[0.000] d2[0.001] g[0.820]\n",
            ">9388, d1[0.000] d2[0.000] g[1.473]\n",
            ">9389, d1[0.000] d2[0.001] g[1.260]\n",
            ">9390, d1[0.000] d2[0.001] g[1.363]\n",
            ">9391, d1[0.000] d2[0.002] g[1.065]\n",
            ">9392, d1[0.000] d2[0.001] g[1.821]\n",
            ">9393, d1[0.000] d2[0.001] g[0.712]\n",
            ">9394, d1[0.000] d2[0.001] g[0.922]\n",
            ">9395, d1[0.000] d2[0.000] g[1.330]\n",
            ">9396, d1[0.006] d2[0.001] g[0.414]\n",
            ">9397, d1[0.000] d2[0.000] g[0.785]\n",
            ">9398, d1[0.000] d2[0.000] g[2.030]\n",
            ">9399, d1[0.000] d2[0.001] g[0.791]\n",
            ">9400, d1[0.000] d2[0.000] g[0.755]\n",
            ">9401, d1[0.000] d2[0.001] g[0.722]\n",
            ">9402, d1[0.000] d2[0.001] g[0.848]\n",
            ">9403, d1[0.000] d2[0.001] g[0.743]\n",
            ">9404, d1[0.000] d2[0.001] g[0.914]\n",
            ">9405, d1[0.000] d2[0.001] g[0.911]\n",
            ">9406, d1[0.002] d2[0.002] g[0.511]\n",
            ">9407, d1[0.002] d2[0.002] g[0.493]\n",
            ">9408, d1[0.000] d2[0.000] g[1.262]\n",
            ">9409, d1[0.000] d2[0.001] g[0.763]\n",
            ">9410, d1[0.000] d2[0.001] g[1.084]\n",
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            ">9413, d1[0.000] d2[0.000] g[1.166]\n",
            ">9414, d1[0.000] d2[0.001] g[1.593]\n",
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            ">9416, d1[0.000] d2[0.000] g[0.785]\n",
            ">9417, d1[0.000] d2[0.000] g[0.857]\n",
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            ">9426, d1[0.000] d2[0.000] g[0.693]\n",
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            ">9429, d1[0.000] d2[0.000] g[0.789]\n",
            ">9430, d1[0.000] d2[0.000] g[1.317]\n",
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            ">9444, d1[0.000] d2[0.000] g[1.033]\n",
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            ">9651, d1[0.000] d2[0.001] g[1.718]\n",
            ">9652, d1[0.000] d2[0.000] g[1.433]\n",
            ">9653, d1[0.003] d2[0.002] g[0.550]\n",
            ">9654, d1[0.000] d2[0.000] g[1.759]\n",
            ">9655, d1[0.000] d2[0.000] g[1.287]\n",
            ">9656, d1[0.000] d2[0.001] g[0.810]\n",
            ">9657, d1[0.000] d2[0.001] g[2.166]\n",
            ">9658, d1[0.000] d2[0.001] g[1.426]\n",
            ">9659, d1[0.000] d2[0.001] g[0.911]\n",
            ">9660, d1[0.005] d2[0.001] g[0.613]\n",
            ">9661, d1[0.000] d2[0.000] g[1.164]\n",
            ">9662, d1[0.000] d2[0.002] g[1.107]\n",
            ">9663, d1[0.000] d2[0.000] g[1.665]\n",
            ">9664, d1[0.000] d2[0.001] g[1.060]\n",
            ">9665, d1[0.000] d2[0.002] g[1.909]\n",
            ">9666, d1[0.000] d2[0.000] g[1.112]\n",
            ">9667, d1[0.000] d2[0.001] g[0.909]\n",
            ">9668, d1[0.000] d2[0.000] g[0.965]\n",
            ">9669, d1[0.000] d2[0.000] g[0.922]\n",
            ">9670, d1[0.000] d2[0.001] g[0.912]\n",
            ">9671, d1[0.000] d2[0.001] g[1.864]\n",
            ">9672, d1[0.001] d2[0.000] g[0.787]\n",
            ">9673, d1[0.000] d2[0.001] g[1.332]\n",
            ">9674, d1[0.000] d2[0.001] g[1.022]\n",
            ">9675, d1[0.011] d2[0.001] g[0.512]\n",
            ">9676, d1[0.000] d2[0.000] g[1.067]\n",
            ">9677, d1[0.001] d2[0.000] g[1.451]\n",
            ">9678, d1[0.036] d2[0.001] g[0.795]\n",
            ">9679, d1[0.000] d2[0.000] g[0.962]\n",
            ">9680, d1[0.000] d2[0.000] g[0.753]\n",
            ">9681, d1[0.000] d2[0.004] g[1.068]\n",
            ">9682, d1[0.746] d2[0.012] g[0.465]\n",
            ">9683, d1[0.000] d2[0.285] g[0.989]\n",
            ">9684, d1[0.000] d2[0.016] g[0.843]\n",
            ">9685, d1[0.001] d2[0.299] g[0.563]\n",
            ">9686, d1[0.000] d2[0.013] g[1.451]\n",
            ">9687, d1[0.061] d2[0.026] g[0.674]\n",
            ">9688, d1[0.000] d2[0.053] g[0.941]\n",
            ">9689, d1[0.016] d2[0.001] g[1.049]\n",
            ">9690, d1[0.000] d2[0.001] g[1.155]\n",
            ">9691, d1[0.018] d2[0.039] g[0.597]\n",
            ">9692, d1[0.001] d2[0.000] g[0.682]\n",
            ">9693, d1[0.049] d2[0.005] g[0.772]\n",
            ">9694, d1[0.000] d2[0.001] g[0.885]\n",
            ">9695, d1[0.000] d2[0.001] g[0.818]\n",
            ">9696, d1[0.001] d2[0.000] g[2.137]\n",
            ">9697, d1[0.000] d2[0.001] g[0.851]\n",
            ">9698, d1[0.000] d2[0.001] g[1.087]\n",
            ">9699, d1[0.006] d2[0.001] g[0.631]\n",
            ">9700, d1[0.000] d2[0.001] g[1.653]\n",
            ">9701, d1[0.000] d2[0.001] g[0.764]\n",
            ">9702, d1[0.001] d2[0.001] g[0.962]\n",
            ">9703, d1[0.000] d2[0.004] g[1.727]\n",
            ">9704, d1[0.001] d2[0.001] g[0.672]\n",
            ">9705, d1[0.000] d2[0.001] g[1.096]\n",
            ">9706, d1[0.000] d2[0.001] g[1.095]\n",
            ">9707, d1[0.000] d2[0.001] g[1.188]\n",
            ">9708, d1[0.000] d2[0.000] g[0.708]\n",
            ">9709, d1[0.000] d2[0.001] g[1.920]\n",
            ">9710, d1[0.000] d2[0.001] g[0.785]\n",
            ">9711, d1[0.000] d2[0.000] g[1.347]\n",
            ">9712, d1[0.000] d2[0.000] g[1.085]\n",
            ">9713, d1[0.001] d2[0.001] g[1.307]\n",
            ">9714, d1[0.000] d2[0.001] g[0.865]\n",
            ">9715, d1[0.001] d2[0.000] g[1.022]\n",
            ">9716, d1[0.000] d2[0.001] g[1.051]\n",
            ">9717, d1[0.022] d2[0.001] g[0.522]\n",
            ">9718, d1[0.000] d2[0.001] g[0.851]\n",
            ">9719, d1[0.000] d2[0.001] g[0.957]\n",
            ">9720, d1[0.000] d2[0.001] g[1.041]\n",
            ">9721, d1[0.000] d2[0.001] g[0.937]\n",
            ">9722, d1[0.015] d2[0.001] g[0.513]\n",
            ">9723, d1[0.000] d2[0.000] g[1.404]\n",
            ">9724, d1[0.008] d2[0.001] g[0.617]\n",
            ">9725, d1[0.000] d2[0.003] g[1.044]\n",
            ">9726, d1[0.000] d2[0.000] g[0.902]\n",
            ">9727, d1[0.000] d2[0.001] g[0.800]\n",
            ">9728, d1[0.000] d2[0.001] g[1.081]\n",
            ">9729, d1[0.000] d2[0.001] g[1.172]\n",
            ">9730, d1[0.000] d2[0.001] g[0.733]\n",
            ">9731, d1[0.000] d2[0.001] g[0.960]\n",
            ">9732, d1[0.018] d2[0.001] g[0.821]\n",
            ">9733, d1[0.000] d2[0.000] g[0.841]\n",
            ">9734, d1[0.000] d2[0.001] g[0.915]\n",
            ">9735, d1[0.000] d2[0.001] g[1.451]\n",
            ">9736, d1[0.000] d2[0.001] g[1.732]\n",
            ">9737, d1[0.000] d2[0.001] g[0.822]\n",
            ">9738, d1[0.000] d2[0.001] g[1.862]\n",
            ">9739, d1[0.000] d2[0.001] g[1.622]\n",
            ">9740, d1[0.000] d2[0.000] g[1.450]\n",
            ">9741, d1[0.000] d2[0.000] g[0.855]\n",
            ">9742, d1[0.000] d2[0.001] g[0.660]\n",
            ">9743, d1[0.019] d2[0.001] g[0.635]\n",
            ">9744, d1[0.000] d2[0.002] g[0.949]\n",
            ">9745, d1[0.000] d2[0.001] g[0.593]\n",
            ">9746, d1[0.000] d2[0.001] g[2.132]\n",
            ">9747, d1[0.000] d2[0.006] g[0.894]\n",
            ">9748, d1[0.000] d2[0.003] g[0.851]\n",
            ">9749, d1[0.000] d2[0.001] g[0.933]\n",
            ">9750, d1[0.000] d2[0.001] g[1.320]\n",
            ">9751, d1[0.000] d2[0.000] g[1.078]\n",
            ">9752, d1[0.001] d2[0.319] g[0.385]\n",
            ">9753, d1[0.000] d2[0.001] g[0.744]\n",
            ">9754, d1[0.001] d2[0.000] g[1.259]\n",
            ">9755, d1[0.197] d2[0.001] g[0.610]\n",
            ">9756, d1[0.001] d2[0.001] g[1.200]\n",
            ">9757, d1[0.000] d2[0.001] g[1.130]\n",
            ">9758, d1[0.000] d2[0.001] g[1.778]\n",
            ">9759, d1[0.001] d2[0.001] g[1.148]\n",
            ">9760, d1[0.001] d2[0.000] g[0.689]\n",
            ">9761, d1[0.001] d2[0.000] g[0.721]\n",
            ">9762, d1[0.000] d2[0.001] g[1.389]\n",
            ">9763, d1[0.000] d2[0.000] g[1.382]\n",
            ">9764, d1[0.001] d2[0.000] g[0.746]\n",
            ">9765, d1[0.000] d2[0.001] g[0.794]\n",
            ">9766, d1[0.001] d2[0.001] g[1.019]\n",
            ">9767, d1[0.001] d2[0.001] g[0.969]\n",
            ">9768, d1[0.000] d2[0.001] g[1.612]\n",
            ">9769, d1[0.055] d2[0.000] g[0.516]\n",
            ">9770, d1[0.000] d2[0.000] g[1.323]\n",
            ">9771, d1[0.000] d2[0.001] g[1.218]\n",
            ">9772, d1[0.000] d2[0.000] g[1.622]\n",
            ">9773, d1[0.000] d2[0.001] g[1.625]\n",
            ">9774, d1[0.000] d2[0.001] g[1.377]\n",
            ">9775, d1[0.006] d2[0.004] g[0.508]\n",
            ">9776, d1[0.000] d2[0.000] g[0.845]\n",
            ">9777, d1[0.000] d2[0.001] g[1.621]\n",
            ">9778, d1[0.000] d2[0.000] g[0.876]\n",
            ">9779, d1[0.000] d2[0.001] g[0.913]\n",
            ">9780, d1[0.000] d2[0.000] g[1.065]\n",
            ">9781, d1[0.019] d2[0.000] g[0.732]\n",
            ">9782, d1[0.000] d2[0.004] g[1.045]\n",
            ">9783, d1[0.000] d2[0.001] g[0.729]\n",
            ">9784, d1[0.001] d2[0.000] g[0.627]\n",
            ">9785, d1[0.000] d2[0.000] g[0.871]\n",
            ">9786, d1[0.000] d2[0.001] g[0.686]\n",
            ">9787, d1[0.000] d2[0.000] g[0.802]\n",
            ">9788, d1[0.000] d2[0.000] g[2.039]\n",
            ">9789, d1[0.000] d2[0.001] g[1.042]\n",
            ">9790, d1[0.001] d2[0.003] g[0.618]\n",
            ">9791, d1[0.009] d2[0.001] g[0.559]\n",
            ">9792, d1[0.000] d2[0.000] g[2.125]\n",
            ">9793, d1[0.000] d2[0.001] g[1.403]\n",
            ">9794, d1[0.000] d2[0.000] g[1.249]\n",
            ">9795, d1[0.000] d2[0.000] g[1.217]\n",
            ">9796, d1[0.000] d2[0.000] g[0.892]\n",
            ">9797, d1[0.011] d2[0.000] g[0.387]\n",
            ">9798, d1[0.000] d2[0.000] g[1.608]\n",
            ">9799, d1[0.000] d2[0.000] g[1.801]\n",
            ">9800, d1[0.000] d2[0.002] g[0.755]\n",
            ">9801, d1[0.067] d2[0.000] g[0.634]\n",
            ">9802, d1[0.008] d2[0.001] g[0.497]\n",
            ">9803, d1[0.000] d2[0.000] g[0.683]\n",
            ">9804, d1[0.000] d2[0.002] g[1.594]\n",
            ">9805, d1[0.001] d2[0.001] g[0.612]\n",
            ">9806, d1[0.000] d2[0.003] g[1.040]\n",
            ">9807, d1[0.000] d2[0.001] g[1.220]\n",
            ">9808, d1[0.000] d2[0.002] g[1.371]\n",
            ">9809, d1[0.003] d2[0.001] g[0.565]\n",
            ">9810, d1[0.000] d2[0.001] g[1.003]\n",
            ">9811, d1[0.000] d2[0.000] g[0.694]\n",
            ">9812, d1[0.002] d2[0.001] g[0.630]\n",
            ">9813, d1[0.000] d2[0.001] g[1.585]\n",
            ">9814, d1[0.002] d2[0.001] g[0.381]\n",
            ">9815, d1[0.000] d2[0.001] g[1.066]\n",
            ">9816, d1[0.000] d2[0.001] g[0.725]\n",
            ">9817, d1[0.000] d2[0.001] g[0.730]\n",
            ">9818, d1[0.000] d2[0.001] g[0.732]\n",
            ">9819, d1[0.000] d2[0.000] g[0.843]\n",
            ">9820, d1[0.000] d2[0.001] g[0.891]\n",
            ">9821, d1[0.000] d2[0.001] g[1.797]\n",
            ">9822, d1[0.000] d2[0.012] g[0.741]\n",
            ">9823, d1[0.000] d2[0.001] g[0.700]\n",
            ">9824, d1[0.000] d2[0.001] g[0.785]\n",
            ">9825, d1[0.000] d2[0.001] g[1.030]\n",
            ">9826, d1[0.000] d2[0.000] g[0.641]\n",
            ">9827, d1[0.000] d2[0.000] g[0.681]\n",
            ">9828, d1[0.000] d2[0.000] g[0.839]\n",
            ">9829, d1[0.000] d2[0.001] g[1.457]\n",
            ">9830, d1[0.000] d2[0.001] g[0.752]\n",
            ">9831, d1[0.000] d2[0.001] g[1.588]\n",
            ">9832, d1[0.000] d2[0.001] g[1.037]\n",
            ">9833, d1[0.000] d2[0.001] g[1.067]\n",
            ">9834, d1[0.000] d2[0.000] g[0.819]\n",
            ">9835, d1[0.003] d2[0.001] g[0.583]\n",
            ">9836, d1[0.000] d2[0.000] g[1.041]\n",
            ">9837, d1[0.000] d2[0.001] g[0.676]\n",
            ">9838, d1[0.000] d2[0.000] g[0.916]\n",
            ">9839, d1[0.000] d2[0.000] g[1.600]\n",
            ">9840, d1[0.000] d2[0.000] g[1.385]\n",
            ">9841, d1[0.000] d2[0.001] g[1.103]\n",
            ">9842, d1[0.000] d2[0.000] g[0.778]\n",
            ">9843, d1[0.000] d2[0.000] g[0.891]\n",
            ">9844, d1[0.000] d2[0.000] g[1.176]\n",
            ">9845, d1[0.000] d2[0.000] g[1.658]\n",
            ">9846, d1[0.000] d2[0.000] g[0.710]\n",
            ">9847, d1[0.000] d2[0.000] g[1.681]\n",
            ">9848, d1[0.000] d2[0.001] g[0.667]\n",
            ">9849, d1[0.000] d2[0.001] g[1.094]\n",
            ">9850, d1[0.000] d2[0.001] g[1.700]\n",
            ">9851, d1[0.000] d2[0.000] g[1.712]\n",
            ">9852, d1[0.000] d2[0.000] g[1.601]\n",
            ">9853, d1[0.000] d2[0.001] g[0.882]\n",
            ">9854, d1[0.000] d2[0.000] g[0.908]\n",
            ">9855, d1[0.007] d2[0.001] g[0.593]\n",
            ">9856, d1[0.000] d2[0.012] g[2.117]\n",
            ">9857, d1[0.000] d2[0.000] g[1.032]\n",
            ">9858, d1[0.000] d2[0.000] g[1.132]\n",
            ">9859, d1[0.000] d2[0.001] g[0.735]\n",
            ">9860, d1[0.000] d2[0.001] g[1.708]\n",
            ">9861, d1[0.002] d2[0.000] g[0.523]\n",
            ">9862, d1[0.000] d2[0.000] g[1.628]\n",
            ">9863, d1[0.000] d2[0.000] g[0.900]\n",
            ">9864, d1[0.000] d2[0.000] g[1.701]\n",
            ">9865, d1[0.000] d2[0.001] g[1.841]\n",
            ">9866, d1[0.000] d2[0.001] g[1.098]\n",
            ">9867, d1[0.000] d2[0.001] g[1.371]\n",
            ">9868, d1[0.000] d2[0.001] g[1.014]\n",
            ">9869, d1[0.000] d2[0.000] g[0.801]\n",
            ">9870, d1[0.000] d2[0.000] g[1.857]\n",
            ">9871, d1[0.000] d2[0.000] g[0.996]\n",
            ">9872, d1[0.000] d2[0.007] g[0.946]\n",
            ">9873, d1[0.000] d2[0.001] g[1.449]\n",
            ">9874, d1[0.000] d2[0.002] g[0.664]\n",
            ">9875, d1[0.000] d2[0.001] g[0.876]\n",
            ">9876, d1[0.000] d2[0.000] g[0.905]\n",
            ">9877, d1[0.000] d2[0.001] g[0.967]\n",
            ">9878, d1[0.005] d2[0.002] g[0.516]\n",
            ">9879, d1[0.000] d2[0.000] g[0.767]\n",
            ">9880, d1[0.000] d2[0.000] g[1.343]\n",
            ">9881, d1[0.000] d2[0.001] g[0.861]\n",
            ">9882, d1[0.000] d2[0.000] g[1.361]\n",
            ">9883, d1[0.000] d2[0.001] g[0.772]\n",
            ">9884, d1[0.002] d2[0.000] g[0.497]\n",
            ">9885, d1[0.000] d2[0.000] g[1.857]\n",
            ">9886, d1[0.000] d2[0.000] g[0.803]\n",
            ">9887, d1[0.000] d2[0.000] g[1.124]\n",
            ">9888, d1[0.002] d2[0.001] g[0.530]\n",
            ">9889, d1[0.000] d2[0.000] g[1.859]\n",
            ">9890, d1[0.000] d2[0.000] g[0.878]\n",
            ">9891, d1[0.000] d2[0.000] g[0.735]\n",
            ">9892, d1[0.000] d2[0.000] g[0.637]\n",
            ">9893, d1[0.000] d2[0.001] g[0.919]\n",
            ">9894, d1[0.000] d2[0.001] g[1.271]\n",
            ">9895, d1[0.000] d2[0.000] g[0.824]\n",
            ">9896, d1[0.000] d2[0.000] g[1.713]\n",
            ">9897, d1[0.000] d2[0.000] g[0.778]\n",
            ">9898, d1[0.000] d2[0.000] g[0.830]\n",
            ">9899, d1[0.005] d2[0.000] g[0.496]\n",
            ">9900, d1[0.000] d2[0.000] g[1.400]\n",
            ">9901, d1[0.000] d2[0.001] g[1.881]\n",
            ">9902, d1[0.010] d2[0.001] g[0.705]\n",
            ">9903, d1[0.124] d2[0.003] g[0.569]\n",
            ">9904, d1[0.000] d2[0.004] g[0.960]\n",
            ">9905, d1[0.000] d2[0.001] g[1.832]\n",
            ">9906, d1[0.000] d2[0.029] g[1.014]\n",
            ">9907, d1[0.000] d2[0.001] g[1.073]\n",
            ">9908, d1[0.000] d2[0.003] g[2.157]\n",
            ">9909, d1[0.000] d2[0.006] g[0.750]\n",
            ">9910, d1[0.000] d2[0.002] g[1.344]\n",
            ">9911, d1[0.000] d2[0.001] g[1.367]\n",
            ">9912, d1[0.000] d2[0.001] g[1.255]\n",
            ">9913, d1[0.000] d2[0.002] g[0.853]\n",
            ">9914, d1[0.001] d2[0.002] g[0.530]\n",
            ">9915, d1[0.000] d2[0.000] g[1.393]\n",
            ">9916, d1[0.000] d2[0.010] g[1.030]\n",
            ">9917, d1[0.000] d2[0.000] g[1.057]\n",
            ">9918, d1[0.002] d2[0.002] g[0.515]\n",
            ">9919, d1[0.000] d2[0.000] g[1.029]\n",
            ">9920, d1[0.000] d2[0.001] g[1.322]\n",
            ">9921, d1[0.000] d2[0.000] g[0.638]\n",
            ">9922, d1[0.000] d2[0.000] g[1.727]\n",
            ">9923, d1[0.000] d2[0.000] g[1.819]\n",
            ">9924, d1[0.000] d2[0.000] g[1.351]\n",
            ">9925, d1[0.000] d2[0.000] g[0.723]\n",
            ">9926, d1[0.002] d2[0.009] g[0.626]\n",
            ">9927, d1[0.000] d2[0.001] g[1.412]\n",
            ">9928, d1[0.000] d2[0.002] g[0.915]\n",
            ">9929, d1[0.003] d2[0.005] g[0.616]\n",
            ">9930, d1[0.000] d2[0.000] g[2.056]\n",
            ">9931, d1[0.002] d2[0.001] g[0.608]\n",
            ">9932, d1[0.000] d2[0.000] g[1.894]\n",
            ">9933, d1[0.000] d2[0.000] g[1.053]\n",
            ">9934, d1[0.000] d2[0.000] g[0.744]\n",
            ">9935, d1[0.000] d2[0.000] g[0.659]\n",
            ">9936, d1[0.000] d2[0.001] g[1.441]\n",
            ">9937, d1[0.000] d2[0.000] g[1.809]\n",
            ">9938, d1[0.000] d2[0.001] g[0.792]\n",
            ">9939, d1[0.000] d2[0.004] g[1.318]\n",
            ">9940, d1[0.000] d2[0.000] g[1.338]\n",
            ">9941, d1[0.000] d2[0.000] g[0.940]\n",
            ">9942, d1[0.000] d2[0.001] g[0.748]\n",
            ">9943, d1[0.000] d2[0.000] g[1.069]\n",
            ">9944, d1[0.000] d2[0.000] g[1.127]\n",
            ">9945, d1[0.000] d2[0.000] g[2.230]\n",
            ">9946, d1[0.000] d2[0.001] g[0.728]\n",
            ">9947, d1[0.000] d2[0.003] g[0.664]\n",
            ">9948, d1[0.006] d2[0.000] g[0.647]\n",
            ">9949, d1[0.000] d2[0.000] g[0.749]\n",
            ">9950, d1[0.000] d2[0.000] g[1.565]\n",
            ">9951, d1[0.000] d2[0.000] g[1.837]\n",
            ">9952, d1[0.000] d2[0.000] g[1.794]\n",
            ">9953, d1[0.001] d2[0.000] g[0.495]\n",
            ">9954, d1[0.006] d2[0.000] g[0.580]\n",
            ">9955, d1[0.000] d2[0.000] g[0.721]\n",
            ">9956, d1[0.000] d2[0.000] g[0.695]\n",
            ">9957, d1[0.000] d2[0.000] g[0.566]\n",
            ">9958, d1[0.000] d2[0.000] g[1.033]\n",
            ">9959, d1[0.000] d2[0.001] g[0.856]\n",
            ">9960, d1[0.004] d2[0.001] g[0.547]\n",
            ">9961, d1[0.000] d2[0.000] g[0.982]\n",
            ">9962, d1[0.000] d2[0.000] g[1.118]\n",
            ">9963, d1[0.000] d2[0.000] g[0.691]\n",
            ">9964, d1[0.000] d2[0.000] g[0.684]\n",
            ">9965, d1[0.000] d2[0.000] g[0.712]\n",
            ">9966, d1[0.000] d2[0.002] g[1.015]\n",
            ">9967, d1[0.000] d2[0.000] g[0.886]\n",
            ">9968, d1[0.000] d2[0.003] g[0.776]\n",
            ">9969, d1[0.000] d2[0.000] g[0.738]\n",
            ">9970, d1[0.000] d2[0.000] g[0.623]\n",
            ">9971, d1[0.000] d2[0.000] g[1.359]\n",
            ">9972, d1[0.000] d2[0.001] g[0.917]\n",
            ">9973, d1[0.000] d2[0.000] g[0.570]\n",
            ">9974, d1[0.000] d2[0.000] g[0.767]\n",
            ">9975, d1[0.000] d2[0.000] g[0.746]\n",
            ">9976, d1[0.000] d2[0.000] g[0.853]\n",
            ">9977, d1[0.000] d2[0.000] g[1.675]\n",
            ">9978, d1[0.000] d2[0.002] g[0.866]\n",
            ">9979, d1[0.000] d2[0.001] g[0.748]\n",
            ">9980, d1[0.002] d2[0.001] g[0.568]\n",
            ">9981, d1[0.000] d2[0.001] g[0.968]\n",
            ">9982, d1[0.000] d2[0.000] g[1.018]\n",
            ">9983, d1[0.000] d2[0.000] g[1.103]\n",
            ">9984, d1[0.000] d2[0.004] g[0.577]\n",
            ">9985, d1[0.000] d2[0.001] g[0.663]\n",
            ">9986, d1[0.000] d2[0.000] g[1.597]\n",
            ">9987, d1[0.000] d2[0.000] g[1.815]\n",
            ">9988, d1[0.000] d2[0.003] g[2.138]\n",
            ">9989, d1[0.000] d2[0.000] g[0.675]\n",
            ">9990, d1[0.000] d2[0.000] g[1.423]\n",
            ">9991, d1[0.000] d2[0.000] g[0.767]\n",
            ">9992, d1[0.000] d2[0.000] g[1.680]\n",
            ">9993, d1[0.000] d2[0.000] g[0.679]\n",
            ">9994, d1[0.000] d2[0.001] g[0.643]\n",
            ">9995, d1[0.000] d2[0.000] g[0.755]\n",
            ">9996, d1[0.000] d2[0.004] g[0.759]\n",
            ">9997, d1[0.000] d2[0.000] g[1.817]\n",
            ">9998, d1[0.000] d2[0.001] g[0.738]\n",
            ">9999, d1[0.000] d2[0.000] g[1.809]\n",
            ">10000, d1[0.000] d2[0.000] g[0.767]\n",
            ">10001, d1[0.000] d2[0.000] g[0.859]\n",
            ">10002, d1[0.000] d2[0.000] g[1.829]\n",
            ">10003, d1[0.000] d2[0.000] g[1.552]\n",
            ">10004, d1[0.001] d2[0.000] g[0.653]\n",
            ">10005, d1[0.004] d2[0.002] g[0.629]\n",
            ">10006, d1[0.003] d2[0.000] g[0.393]\n",
            ">10007, d1[0.003] d2[0.000] g[0.621]\n",
            ">10008, d1[0.000] d2[0.000] g[0.621]\n",
            ">10009, d1[0.006] d2[0.009] g[0.490]\n",
            ">10010, d1[0.000] d2[0.000] g[0.964]\n",
            ">10011, d1[0.000] d2[0.001] g[1.026]\n",
            ">10012, d1[0.000] d2[0.000] g[0.863]\n",
            ">10013, d1[0.000] d2[0.000] g[0.791]\n",
            ">10014, d1[0.000] d2[0.000] g[1.403]\n",
            ">10015, d1[0.000] d2[0.000] g[1.249]\n",
            ">10016, d1[0.000] d2[0.000] g[0.734]\n",
            ">10017, d1[0.000] d2[0.000] g[0.742]\n",
            ">10018, d1[0.001] d2[0.013] g[0.642]\n",
            ">10019, d1[0.000] d2[0.000] g[0.943]\n",
            ">10020, d1[0.012] d2[0.000] g[0.593]\n",
            ">10021, d1[0.016] d2[0.000] g[0.558]\n",
            ">10022, d1[0.000] d2[0.000] g[0.710]\n",
            ">10023, d1[0.003] d2[0.001] g[0.483]\n",
            ">10024, d1[0.000] d2[0.000] g[0.867]\n",
            ">10025, d1[0.000] d2[0.000] g[0.897]\n",
            ">10026, d1[0.000] d2[0.024] g[0.617]\n",
            ">10027, d1[0.000] d2[0.001] g[1.017]\n",
            ">10028, d1[0.000] d2[0.000] g[0.781]\n",
            ">10029, d1[0.000] d2[0.000] g[0.953]\n",
            ">10030, d1[0.000] d2[0.000] g[0.904]\n",
            ">10031, d1[0.000] d2[0.000] g[1.637]\n",
            ">10032, d1[0.004] d2[0.000] g[0.619]\n",
            ">10033, d1[0.000] d2[0.000] g[0.762]\n",
            ">10034, d1[0.000] d2[0.000] g[1.869]\n",
            ">10035, d1[0.000] d2[0.000] g[1.330]\n",
            ">10036, d1[0.000] d2[0.000] g[1.822]\n",
            ">10037, d1[0.000] d2[0.000] g[1.068]\n",
            ">10038, d1[0.000] d2[0.000] g[1.836]\n",
            ">10039, d1[0.000] d2[0.005] g[0.829]\n",
            ">10040, d1[0.000] d2[0.001] g[0.801]\n",
            ">10041, d1[0.000] d2[0.000] g[1.293]\n",
            ">10042, d1[0.000] d2[0.000] g[0.938]\n",
            ">10043, d1[0.006] d2[0.000] g[0.501]\n",
            ">10044, d1[0.000] d2[0.000] g[0.818]\n",
            ">10045, d1[0.000] d2[0.000] g[0.879]\n",
            ">10046, d1[0.000] d2[0.000] g[1.222]\n",
            ">10047, d1[0.000] d2[0.000] g[0.783]\n",
            ">10048, d1[0.000] d2[0.000] g[0.709]\n",
            ">10049, d1[0.000] d2[0.000] g[1.331]\n",
            ">10050, d1[0.000] d2[0.000] g[0.733]\n",
            ">10051, d1[0.000] d2[0.000] g[0.684]\n",
            ">10052, d1[0.000] d2[0.000] g[1.292]\n",
            ">10053, d1[0.000] d2[0.000] g[1.102]\n",
            ">10054, d1[0.000] d2[0.000] g[1.178]\n",
            ">10055, d1[0.000] d2[0.000] g[1.129]\n",
            ">10056, d1[0.000] d2[0.000] g[1.169]\n",
            ">10057, d1[0.000] d2[0.000] g[1.212]\n",
            ">10058, d1[0.004] d2[0.002] g[0.625]\n",
            ">10059, d1[0.000] d2[0.009] g[0.880]\n",
            ">10060, d1[0.000] d2[0.000] g[1.137]\n",
            ">10061, d1[0.000] d2[0.000] g[0.713]\n",
            ">10062, d1[0.000] d2[0.000] g[0.831]\n",
            ">10063, d1[0.000] d2[0.000] g[1.043]\n",
            ">10064, d1[0.000] d2[0.000] g[0.801]\n",
            ">10065, d1[0.000] d2[0.000] g[0.653]\n",
            ">10066, d1[0.000] d2[0.000] g[0.974]\n",
            ">10067, d1[0.000] d2[0.000] g[1.585]\n",
            ">10068, d1[0.000] d2[0.000] g[1.374]\n",
            ">10069, d1[0.000] d2[0.000] g[0.827]\n",
            ">10070, d1[0.000] d2[0.000] g[0.668]\n",
            ">10071, d1[0.002] d2[0.001] g[0.543]\n",
            ">10072, d1[0.000] d2[0.000] g[0.922]\n",
            ">10073, d1[0.000] d2[0.000] g[1.788]\n",
            ">10074, d1[0.000] d2[0.000] g[0.804]\n",
            ">10075, d1[0.000] d2[0.000] g[0.952]\n",
            ">10076, d1[0.000] d2[0.000] g[0.963]\n",
            ">10077, d1[0.000] d2[0.000] g[1.357]\n",
            ">10078, d1[0.000] d2[0.000] g[0.698]\n",
            ">10079, d1[0.000] d2[0.000] g[0.875]\n",
            ">10080, d1[0.000] d2[0.000] g[1.225]\n",
            ">10081, d1[0.000] d2[0.000] g[1.062]\n",
            ">10082, d1[0.000] d2[0.000] g[0.863]\n",
            ">10083, d1[0.000] d2[0.000] g[1.055]\n",
            ">10084, d1[0.000] d2[0.000] g[0.831]\n",
            ">10085, d1[0.000] d2[0.000] g[0.902]\n",
            ">10086, d1[0.000] d2[0.000] g[1.030]\n",
            ">10087, d1[0.000] d2[0.000] g[0.750]\n",
            ">10088, d1[0.000] d2[0.000] g[0.779]\n",
            ">10089, d1[0.000] d2[0.000] g[0.645]\n",
            ">10090, d1[0.005] d2[0.000] g[0.613]\n",
            ">10091, d1[0.000] d2[0.000] g[0.741]\n",
            ">10092, d1[0.000] d2[0.000] g[1.524]\n",
            ">10093, d1[0.012] d2[0.000] g[0.541]\n",
            ">10094, d1[0.000] d2[0.000] g[0.747]\n",
            ">10095, d1[0.000] d2[0.000] g[1.004]\n",
            ">10096, d1[0.000] d2[0.000] g[1.673]\n",
            ">10097, d1[0.000] d2[0.000] g[1.412]\n",
            ">10098, d1[0.000] d2[0.000] g[1.160]\n",
            ">10099, d1[0.000] d2[0.000] g[0.846]\n",
            ">10100, d1[0.000] d2[0.000] g[1.610]\n",
            ">10101, d1[0.000] d2[0.000] g[1.666]\n",
            ">10102, d1[0.000] d2[0.000] g[0.758]\n",
            ">10103, d1[0.003] d2[0.000] g[0.557]\n",
            ">10104, d1[0.013] d2[0.000] g[0.437]\n",
            ">10105, d1[0.000] d2[0.000] g[0.750]\n",
            ">10106, d1[0.000] d2[0.001] g[1.013]\n",
            ">10107, d1[0.000] d2[0.000] g[1.233]\n",
            ">10108, d1[0.000] d2[0.001] g[0.760]\n",
            ">10109, d1[0.000] d2[0.000] g[1.617]\n",
            ">10110, d1[0.001] d2[0.004] g[0.536]\n",
            ">10111, d1[0.003] d2[0.000] g[0.405]\n",
            ">10112, d1[0.000] d2[0.000] g[1.426]\n",
            ">10113, d1[0.000] d2[0.000] g[0.748]\n",
            ">10114, d1[0.002] d2[0.000] g[0.473]\n",
            ">10115, d1[0.000] d2[0.000] g[1.253]\n",
            ">10116, d1[0.000] d2[0.000] g[0.809]\n",
            ">10117, d1[0.000] d2[0.000] g[1.258]\n",
            ">10118, d1[0.000] d2[0.000] g[1.315]\n",
            ">10119, d1[0.000] d2[0.000] g[1.026]\n",
            ">10120, d1[0.002] d2[0.000] g[0.648]\n",
            ">10121, d1[0.000] d2[0.001] g[1.028]\n",
            ">10122, d1[0.000] d2[0.000] g[1.786]\n",
            ">10123, d1[0.003] d2[0.000] g[0.523]\n",
            ">10124, d1[0.000] d2[0.000] g[0.713]\n",
            ">10125, d1[0.000] d2[0.000] g[0.905]\n",
            ">10126, d1[0.000] d2[0.000] g[1.379]\n",
            ">10127, d1[0.000] d2[0.000] g[1.631]\n",
            ">10128, d1[0.003] d2[0.000] g[0.461]\n",
            ">10129, d1[0.000] d2[0.000] g[1.698]\n",
            ">10130, d1[0.000] d2[0.001] g[0.956]\n",
            ">10131, d1[0.000] d2[0.001] g[1.206]\n",
            ">10132, d1[0.000] d2[0.000] g[1.308]\n",
            ">10133, d1[0.000] d2[0.000] g[1.793]\n",
            ">10134, d1[0.000] d2[0.000] g[1.203]\n",
            ">10135, d1[0.004] d2[0.007] g[0.445]\n",
            ">10136, d1[0.000] d2[0.000] g[0.794]\n",
            ">10137, d1[0.000] d2[0.000] g[1.315]\n",
            ">10138, d1[0.000] d2[0.000] g[0.877]\n",
            ">10139, d1[0.000] d2[0.000] g[0.722]\n",
            ">10140, d1[0.000] d2[0.000] g[0.708]\n",
            ">10141, d1[0.005] d2[0.000] g[0.486]\n",
            ">10142, d1[0.000] d2[0.000] g[1.267]\n",
            ">10143, d1[0.000] d2[0.000] g[0.597]\n",
            ">10144, d1[0.000] d2[0.001] g[0.791]\n",
            ">10145, d1[0.000] d2[0.000] g[1.385]\n",
            ">10146, d1[0.000] d2[0.000] g[0.968]\n",
            ">10147, d1[0.000] d2[0.000] g[1.022]\n",
            ">10148, d1[0.000] d2[0.000] g[1.819]\n",
            ">10149, d1[0.003] d2[0.000] g[0.528]\n",
            ">10150, d1[0.000] d2[0.001] g[0.574]\n",
            ">10151, d1[0.000] d2[0.000] g[0.857]\n",
            ">10152, d1[0.000] d2[0.000] g[0.778]\n",
            ">10153, d1[0.002] d2[0.341] g[0.521]\n",
            ">10154, d1[0.000] d2[0.000] g[1.829]\n",
            ">10155, d1[0.649] d2[0.009] g[0.594]\n",
            ">10156, d1[0.000] d2[0.009] g[1.573]\n",
            ">10157, d1[0.000] d2[0.007] g[0.598]\n",
            ">10158, d1[0.000] d2[0.001] g[1.655]\n",
            ">10159, d1[0.000] d2[0.002] g[0.921]\n",
            ">10160, d1[0.000] d2[0.004] g[0.762]\n",
            ">10161, d1[0.000] d2[0.033] g[0.724]\n",
            ">10162, d1[0.000] d2[0.000] g[1.024]\n",
            ">10163, d1[0.017] d2[0.001] g[0.507]\n",
            ">10164, d1[0.000] d2[0.001] g[1.157]\n",
            ">10165, d1[0.000] d2[0.000] g[1.076]\n",
            ">10166, d1[0.000] d2[0.001] g[0.912]\n",
            ">10167, d1[0.000] d2[0.001] g[0.742]\n",
            ">10168, d1[2.005] d2[0.215] g[0.467]\n",
            ">10169, d1[0.000] d2[1.053] g[0.711]\n",
            ">10170, d1[0.000] d2[0.033] g[1.007]\n",
            ">10171, d1[0.000] d2[0.004] g[1.123]\n",
            ">10172, d1[0.001] d2[0.070] g[1.025]\n",
            ">10173, d1[0.000] d2[0.003] g[1.334]\n",
            ">10174, d1[0.000] d2[0.003] g[0.792]\n",
            ">10175, d1[0.001] d2[0.002] g[1.438]\n",
            ">10176, d1[0.029] d2[0.002] g[0.488]\n",
            ">10177, d1[0.000] d2[0.001] g[2.076]\n",
            ">10178, d1[0.000] d2[0.007] g[0.813]\n",
            ">10179, d1[0.000] d2[0.001] g[0.679]\n",
            ">10180, d1[0.000] d2[0.002] g[0.961]\n",
            ">10181, d1[0.000] d2[0.001] g[1.461]\n",
            ">10182, d1[0.000] d2[0.001] g[0.864]\n",
            ">10183, d1[0.000] d2[0.001] g[1.047]\n",
            ">10184, d1[0.000] d2[0.001] g[0.871]\n",
            ">10185, d1[0.000] d2[0.002] g[0.644]\n",
            ">10186, d1[0.001] d2[0.001] g[1.366]\n",
            ">10187, d1[0.000] d2[0.001] g[0.703]\n",
            ">10188, d1[0.000] d2[0.001] g[0.745]\n",
            ">10189, d1[0.000] d2[0.016] g[0.863]\n",
            ">10190, d1[0.000] d2[0.000] g[0.938]\n",
            ">10191, d1[0.001] d2[0.001] g[0.791]\n",
            ">10192, d1[0.000] d2[0.000] g[1.877]\n",
            ">10193, d1[0.000] d2[0.001] g[0.735]\n",
            ">10194, d1[0.005] d2[0.008] g[0.502]\n",
            ">10195, d1[0.001] d2[0.000] g[0.799]\n",
            ">10196, d1[0.012] d2[0.001] g[0.630]\n",
            ">10197, d1[0.000] d2[0.000] g[1.857]\n",
            ">10198, d1[0.000] d2[0.000] g[1.137]\n",
            ">10199, d1[0.001] d2[0.001] g[1.657]\n",
            ">10200, d1[0.000] d2[0.001] g[1.253]\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            ">Saved: plot_010200.png and model_010200.h5\n",
            "Execution time is:  1:01:28.970539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "from numpy.random import randint\n",
        "model = load_model('/content/drive/MyDrive/Colab Notebooks/model_010200.h5')"
      ],
      "metadata": {
        "id": "IPJ1S4IBa42W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1111d030-dd26-44d9-fc6c-fd1f8dfe8e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[X1, X2] = train_dataset\n",
        "# select random example\n",
        "ix = randint(0, len(X1), 1)\n",
        "src_image, tar_image = X1[ix], X2[ix]\n",
        "# generate image from source\n",
        "gen_image = model.predict(src_image)\n",
        "# plot all three images\n",
        "gn=np.argmax(gen_image, axis=3)"
      ],
      "metadata": {
        "id": "JRPbAn58ppWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJeH9NftT92h",
        "outputId": "e2197d0d-ef86-4f5c-a883-37e710bd2f8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([46])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=np.empty((len(X1),208,208))\n",
        "gts=np.empty((len(X1),208,208))\n",
        "for i in range(len(X1)):\n",
        "  t_im=np.squeeze(X2[i], axis=2)\n",
        "  src=np.expand_dims(X1[i], axis=0)\n",
        "  tar=np.expand_dims(t_im, axis=0)\n",
        "  gen_im = model.predict(src)\n",
        "  gn_im=np.argmax(gen_im, axis=3)\n",
        "  gts[i]=tar\n",
        "  preds[i]=gn_im"
      ],
      "metadata": {
        "id": "wuq9ZVUY3ZRJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(preds[56,:,:])\n",
        "plt.show()\n",
        "plt.imshow(gts[56,:,:])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "CCzpjdPRHVhj",
        "outputId": "e6f25e80-6de4-4afe-fa88-621dc6dd29b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(gn[0,:,:])\n",
        "plt.show()\n",
        "tar_im=np.squeeze(tar_image, axis=3)\n",
        "plt.imshow(tar_im[0,:,:])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "AGTVjLrrivdt",
        "outputId": "101f080a-7428-4bf7-e87b-c1fedef6fee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dice_coef_multilabel(y_true, y_pred):\n",
        "    ytrue=np.array(tf.keras.utils.to_categorical(y_true, num_classes=6))\n",
        "    ypred=np.array(tf.keras.utils.to_categorical(y_pred, num_classes=6))\n",
        "    dice=[]\n",
        "    for i in range(6):\n",
        "        dice.append(dice_coef(ytrue[0,:,:,i], ypred[0,:,:,i]).numpy())\n",
        "    return dice "
      ],
      "metadata": {
        "id": "YGSOZv4iPAYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dice_coef_multi(y_true, y_pred):\n",
        "    ytrue=np.array(tf.keras.utils.to_categorical(y_true, num_classes=6))\n",
        "    ypred=np.array(tf.keras.utils.to_categorical(y_pred, num_classes=6))\n",
        "    # print(ypred.shape)\n",
        "    dice=[]\n",
        "    for i in range(6):\n",
        "        dice.append(dice_coef(ytrue[:,:,i], ypred[:,:,i]).numpy())\n",
        "    return dice "
      ],
      "metadata": {
        "id": "52pR4HzhtTKd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res=np.empty((len(X1),6))\n",
        "for i in range(len(X1)):\n",
        "    res[i]=dice_coef_multi(gts[i], preds[i])"
      ],
      "metadata": {
        "id": "Oj5kYgs6qmvc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "results=pd.DataFrame(res)"
      ],
      "metadata": {
        "id": "gPKc9LjUBvpo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results.to_csv(\"result_ResUNet_100ep_16PG_WOAug_208_shape_full_dataset.csv\")"
      ],
      "metadata": {
        "id": "XjlLearSCde9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GGhKU_xCEIds"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}