AC_INFO_GAN_SEMI_SUPERVISED.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "AC INFO GAN SEMI SUPERVISED.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QczsvPlllBtA"
      },
      "source": [
        "####auxillary classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdRnWc_mlBtA"
      },
      "source": [
        "# example of fitting an auxiliary classifier gan (ac-gan) on fashion mnsit\n",
        "from numpy import zeros\n",
        "from numpy import ones\n",
        "from numpy import expand_dims\n",
        "from numpy.random import randn\n",
        "from numpy.random import randint\n",
        "from keras.datasets.fashion_mnist import load_data\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Model\n",
        "from keras.layers import Input\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Reshape\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import Conv2DTranspose\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Concatenate\n",
        "from keras.initializers import RandomNormal\n",
        "from matplotlib import pyplot\n",
        "\n",
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yHIA3AilBtE"
      },
      "source": [
        "# define the standalone discriminator model\n",
        "def define_discriminator(in_shape=(28,28,1), n_classes=10):\n",
        "\t# weight initialization\n",
        "\tinit = RandomNormal(stddev=0.02)\n",
        "\t# image input\n",
        "\tin_image = Input(shape=in_shape)\n",
        "\t# downsample to 14x14\n",
        "\tfe = Conv2D(32, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(in_image)\n",
        "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
        "\tfe = Dropout(0.5)(fe)\n",
        "\t# normal\n",
        "\tfe = Conv2D(64, (3,3), padding='same', kernel_initializer=init)(fe)\n",
        "\tfe = BatchNormalization()(fe)\n",
        "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
        "\tfe = Dropout(0.5)(fe)\n",
        "\t# downsample to 7x7\n",
        "\tfe = Conv2D(128, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(fe)\n",
        "\tfe = BatchNormalization()(fe)\n",
        "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
        "\tfe = Dropout(0.5)(fe)\n",
        "\t# normal\n",
        "\tfe = Conv2D(256, (3,3), padding='same', kernel_initializer=init)(fe)\n",
        "\tfe = BatchNormalization()(fe)\n",
        "\tfe = LeakyReLU(alpha=0.2)(fe)\n",
        "\tfe = Dropout(0.5)(fe)\n",
        "\t# flatten feature maps\n",
        "\tfe = Flatten()(fe)\n",
        "\t# real/fake output\n",
        "\tout1 = Dense(1, activation='sigmoid')(fe)\n",
        "\t# class label output\n",
        "\tout2 = Dense(n_classes, activation='softmax')(fe)\n",
        "\t# define model\n",
        "\tmodel = Model(in_image, [out1, out2])\n",
        "\t# compile model\n",
        "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
        "\tmodel.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], optimizer=opt)\n",
        "\treturn model"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ML8izzeMlBtH",
        "outputId": "cfcb6d91-1bb3-41a4-8179-3e4edc11fcb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        }
      },
      "source": [
        "# define the discriminator model\n",
        "model = define_discriminator()\n",
        "# summarize the model\n",
        "model.summary()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 14, 14, 32)   320         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)         (None, 14, 14, 32)   0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 14, 14, 32)   0           leaky_re_lu[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 14, 14, 64)   18496       dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 14, 14, 64)   256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)       (None, 14, 14, 64)   0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 14, 14, 64)   0           leaky_re_lu_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 7, 7, 128)    73856       dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 7, 7, 128)    512         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)       (None, 7, 7, 128)    0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 7, 7, 128)    0           leaky_re_lu_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 7, 7, 256)    295168      dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 7, 7, 256)    1024        conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)       (None, 7, 7, 256)    0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 7, 7, 256)    0           leaky_re_lu_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 12544)        0           dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 1)            12545       flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           125450      flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 527,627\n",
            "Trainable params: 526,731\n",
            "Non-trainable params: 896\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fCTHcdOlBtK",
        "outputId": "a72849c0-bc49-4469-cd44-6d828ecd6a3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# plot the model\n",
        "plot_model(model, to_file='discriminator_plot_of_AC.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpesWdAylBtN"
      },
      "source": [
        "# define the standalone generator model\n",
        "def define_generator(latent_dim, n_classes=10):\n",
        "\t# weight initialization\n",
        "\tinit = RandomNormal(stddev=0.02)\n",
        "\t# label input\n",
        "\tin_label = Input(shape=(1,))\n",
        "\t# embedding for categorical input\n",
        "\tli = Embedding(n_classes, 50)(in_label)\n",
        "\t# linear multiplication\n",
        "\tn_nodes = 7 * 7\n",
        "\tli = Dense(n_nodes, kernel_initializer=init)(li)\n",
        "\t# reshape to additional channel\n",
        "\tli = Reshape((7, 7, 1))(li)\n",
        "\t# image generator input\n",
        "\tin_lat = Input(shape=(latent_dim,))\n",
        "\t# foundation for 7x7 image\n",
        "\tn_nodes = 384 * 7 * 7\n",
        "\tgen = Dense(n_nodes, kernel_initializer=init)(in_lat)\n",
        "\tgen = Activation('relu')(gen)\n",
        "\tgen = Reshape((7, 7, 384))(gen)\n",
        "\t# merge image gen and label input\n",
        "\tmerge = Concatenate()([gen, li])\n",
        "\t# upsample to 14x14\n",
        "\tgen = Conv2DTranspose(192, (5,5), strides=(2,2), padding='same', kernel_initializer=init)(merge)\n",
        "\tgen = BatchNormalization()(gen)\n",
        "\tgen = Activation('relu')(gen)\n",
        "\t# upsample to 28x28\n",
        "\tgen = Conv2DTranspose(1, (5,5), strides=(2,2), padding='same', kernel_initializer=init)(gen)\n",
        "\tout_layer = Activation('tanh')(gen)\n",
        "\t# define model\n",
        "\tmodel = Model([in_lat, in_label], out_layer)\n",
        "\treturn model"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcqK53CmlBtP",
        "outputId": "1e871633-7da8-4117-95f5-5bd00d6283c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "# define the size of the latent space\n",
        "latent_dim = 100\n",
        "# define the generator model\n",
        "model = define_generator(latent_dim)\n",
        "# summarize the model\n",
        "model.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 100)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 18816)        1900416     input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding (Embedding)           (None, 1, 50)        500         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 18816)        0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 1, 49)        2499        embedding[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "reshape_1 (Reshape)             (None, 7, 7, 384)    0           activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape (Reshape)               (None, 7, 7, 1)      0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 7, 7, 385)    0           reshape_1[0][0]                  \n",
            "                                                                 reshape[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose (Conv2DTranspo (None, 14, 14, 192)  1848192     concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 14, 14, 192)  768         conv2d_transpose[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 14, 14, 192)  0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTrans (None, 28, 28, 1)    4801        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 28, 28, 1)    0           conv2d_transpose_1[0][0]         \n",
            "==================================================================================================\n",
            "Total params: 3,757,176\n",
            "Trainable params: 3,756,792\n",
            "Non-trainable params: 384\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGnlVYOAlBtS",
        "outputId": "b69b64d0-dcea-4690-c17c-7c76c22bf669",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# plot the model\n",
        "plot_model(model, to_file='generator_plot_OF_AC.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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dCKEdO3bs2LGDWt/e3l61AKKjo4VCoRp7MAzgCXc2b96s70AMES3rXa0es2fPnj148GB+fv6YMWO0tIl+0TsRzZo1i/ZH6O3bt1NSUvC1LVrCmVYbDLHiEYvFLS0tjo6O2t6Qi4uLsbFxaWmptjeEtLNTBQUFv/zyy+bNm6urq5csWbJ06dKvv/56zJgxhw8f/vzzz6nVIiIitm3b9tVXX40dO5bH41F3atvY2CCEDh06FB0drX4wQqFwxYoV6vdjyM6fP48Qov1uqoZ+FY9WE9Hhw4evX79+8+bNUaNGaaP/gdA+ETk6Oo6EIzQlJYXGu4kzrTYYYsWTn59PkuSsWbPwSwaDMdAZ2iF59erVf/zHf5w+fZpaUlZWJpVKx44dq37ng9LGTv3yyy9cLhch9PDhQ7FYvHHjRicnJ9TnbKelpWVYWNjZs2fNzMw+/fRTavnYsWNZLFZxcbGaYQBAS1pKRCRJfvHFF83NzdnZ2QyGrjMwJCIwkhnKvFoymay5uVkikZSUlERHR/P5/IiICNzk4uLS1NSUnZ0tFosbGxufPn0q/0YrK6u6urqqqqq2tjbFhy6Xy71x48bNmzfx6db79+9/9NFHXC53y5YteIW8vDzlHwrV706JxeL6+vr8/HycaPh8PkLo22+/7erqKisro54+pWzYsKG7u/vKlSvyQ7GxWKw1a9acOXMmLS2ttbVVKpXW1tYa4HiMAOiMDhLRb7/99re//e3EiRMmJiaEnAMHDuAVIBFBIgLaMtSHu5R5Lu7w4cN4jAcOhxMcHHz06FE8IdmECRMqKiqOHz/O4/EQQuPGjXv8+DFJkuvWrTMxMXFwcGAwGDweb/HixRUVFVRvr169CggIYLFYAoHgP/7jP/7yl78ghFxcXPADlvfu3Rs3bhybzfb19X3x4oXiwIKDgwUCwahRo0xNTZ2dncPDwx8+fEi1Xr161czMbO/evX3fWFRU5OHhYWRkhBB64403EhMTdbZTx44dc3Z2Hui/7+LFi7jDmJgYKysrCwuL0NBQPPqIs7Mz9QwqSZLTpk2LjY3ttV/d3d0xMTF8Pp/BYNjY2Cxbtqy0tHT//v1sNhshNHbsWPmn9xVA8HT6iKf778CgWzTMRPTw4cN+j+Xk5GS8AiQilRPRCDlC4el0lWlrPJ4hwQOua7ZPvTO0nQoMDKysrNRS51DxAAOseIbK0I5ZjTC0ndJqIhohRyhUPCozlKta+AlGmtH7TlEnoktKSvDPOP3GA4CB0/sxqw163ylIRMBAGErFo75Hjx4RAwsPD9d3gHoQExNTVlb2+PHjNWvW7NmzR9/hjBTr16+nvnirV6+Wb/r2229jY2NlMtmSJUv4fD6LxXJwcAgJCSkpKVG+f5lMdujQoX6neCwsLPTx8eFwOPb29jExMd3d3cq0Xrp0af/+/fJ/F7Ozs6ldwLM+ASVBIuoLEpFe6CURGXoyGepJIY2fT4uNjcVDZo0fP/78+fMa7FmPDGSntm/fbmRkNHbsWGo0dy1BcFVLDr6OkJeX98cff3R1dVHLd+7cuWjRInzjvLW19a1btzo6OiorK+fOnWtubi4/AL8Cjx8/9vHxQQhNnTq1V9Ovv/7KZrPj4uLa29t//PHH0aNHr1mzRsnWlJQUf39/auICmUxWW1tbUFAQGBhITRSgmO6/A5rdooEcs5plIDuls0QEV7Xk6SsRqZ9MaH4fDxjutP3XTiQSCYVCvXelfMXj4ODQa+G+fftcXV07OztJkhSLxQsXLqSafvrpJ4RQYmLioD0XFxcvXbr05MmTXl5efRNNWFiYQCCQyWT4ZXJyMkEQ1FREiltJkoyMjBQKhWKxWL7PqKioEVLxABrQQcVjCLlI+YpHL4mIVDuZ0P8+HgAUSE9Pb2hoMLSulFdeXh4XF7d7924Wi4UQYjAYly9fplrx4CUVFRWD9jN16tSsrKxVq1aZmpr2apJIJLm5uf7+/tQgKAsWLCBJMicnZ9BWLD4+vri4WDfjjwMwTA3rXKSDRIQZbDKBigfoCEmSBw8exDMaWlpaLl68mJo6JzIykslk4geJEUKbNm3icrkEQbx8+RIhFB0dvXXr1oqKCoIgXFxcUlNTWSyWra3t+vXr7e3tWSyWt7c3NfLHkLpCCF27dk2zY5/0KzU1lSTJ4ODgfls7OzsRQvihYpVVVla2t7fjAVEw/CwxvjCvuBWztLT09/dPSUkhSVKdSAAwcCM2F+kgEWEGm0yg4gE6Eh8fHxsbu3379oaGhoKCgpqaGj8/v/r6eoRQamqq/IjpR48e3b17N/UyJSVl0aJFzs7OJEmWl5dHRkZGRESIRKKoqKiqqqp79+5JJJK5c+fW1NQMtSv0r8dYZDKZVvc9NzfXzc0Nj5vSFz6Z7Ovrq84mXrx4gRCSn5CSxWKx2Wz8CStupUybNu3Zs2cPHjxQJxIADNyIzUU6SEQUw0wmUPEAXejs7Dx48ODSpUtXr15tbm7u6en55Zdfvnz58vjx46p1yGAw8E80d3f3tLS0tra2jIwMFfoJCgpqbW2Ni4tTLQxldHR0PHnypN/R2+rr68+ePRsVFSUUCgf64aUk/OAVnoyaYmJign+3KW6lTJgwASE00BB5ANDAiM1FuklEFMNMJoY4rxagn9LS0vb29hkzZlBLZs6cyWQy+45Dr4IZM2ZwOBzqvLShaWhoIEmy399VQqGwo6NjxYoVe/fuNTExUWcr+MK8RCKRX9jT04NHrVXcSsFB9jrxAwCdjNhcpJtERDHMZAIVD9CFlpYWhFCvaaItLCza2to00r+pqWljY6NGutK4rq4uhFC/t/jZ2tqmp6d7eHiovxV8u0Brayu1RCQSdXV12dvbD9pKwQUQDhgAWhqxuUg3iYhimMkErmoBXbCwsEAI9copLS0tjo6O6ncuFos11ZU24CO/33FvbWxs8CejPoFAYGZmJj8TJL45YMqUKYO2Unp6eqiAAaClEZuLdJOIKIaZTOAcD9CFyZMnjxo16ueff6aW3Llzp6en580338QvGQyG4hmnFcjPzydJctasWep3pQ22trYEQbx+/bpvk/yjoWpiMBiBgYEFBQUymQzPNJmXl0cQBL4qr7iVgoO0s7PTVFQAGJoRm4t0k4gohplM4BwP0AUWi7V169aLFy+ePHmytbX14cOHGzZssLe3X7duHV7BxcWlqakpOztbLBY3NjbKn41ACFlZWdXV1VVVVbW1teEMIpPJmpubJRJJSUlJdHQ0n8+PiIhQoau8vDxtPxHK4XCcnJxqa2t7LS8vL7ezswsLC5NfGB4ebmdnd+/ePRU2FBcXV19fv2vXro6Ojtu3bycnJ0dERLi5uSnTiuEgPT09Vdg6AMPCiM1FOktEmGEmE6h4gI7s2rUrKSkpISFh9OjR/v7+48ePz8/P53K5uHXjxo0BAQErV650c3Pbs2cPPhcqFArxc54bNmywtbV1d3cPDAxsampCCHV1dXl6erLZbD8/P1dX1++//566Pj3UrnQgKCiotLS014NR/Y5U0dPT09DQID8woLyioiJfX98xY8bcuXPnwYMH9vb2Pj4+BQUFuNXDw+P69es3btywtrZetmzZ2rVrjx07Rr1XcSt29+5dBweHXpe6AKCZEZuLdJOIMANNJkMdpBlmmQB9Id2O94/ni9HZ5igqzzJRVlbGYDC++eabQd8rlUr9/PzS09NVj1JVL1++ZLFYBw4ckF8Is0yAYUT382rpJRepPMuEzhKRmskEZpkA4N/0e/+d4ejs7Lx+/XpZWRm+fc/FxSUhISEhIaG9vV3Bu6RSaXZ2dltbm15m2I6Pj/fy8oqMjEQIkSRZV1dXWFiIb3AGAAzEkHORvhKRwSYTqHgA0Lympqb58+e7urquXbsWL4mNjQ0NDQ0PD+/3zkEsPz8/KysrLy9voEFRtefgwYPFxcVXr17Fo3Hk5OQ4ODj4+fnl5ubqOBIAgKboJREZcjKBigcMM9u2bcvIyHj9+rVAILhw4YK+w+nHl19+SZ1EPXnyJLU8MTExMjJy3759A71xzpw5p06doibi0ZmcnJzu7u78/HxLS0u8ZPHixdQu4GmAAAC9GHgu0ksiMvBkAk+ng2EmKSkpKSlJ31GoaN68efPmzdN3FL2FhISEhIToOwoAhpnhm4u0l4gMPJnAOR4AAAAA0B9UPAAAAACgP6h4AAAAAEB/UPEAAAAAgP5UvHP53Llzmo0DDHe3b9/Wdwhah8dNhy+/4RgJ3zqgvBFyhOKvPY13s7a2VluzsQ51yEI82iMAAPSi+zGXAQC0pKUxlwlIHEBlK1asQLT+qQEAMATnzp0LCwuDv1ZATXAfDwAAAADoDyoeAAAAANAfVDwAAAAAoD+oeAAAAABAf1DxAAAAAID+oOIBAAAAAP1BxQMAAAAA+oOKBwAAAAD0BxUPAAAAAOgPKh4AAAAA0B9UPAAAAACgP6h4AAAAAEB/UPEAAAAAgP6g4gEAAAAA/UHFAwAAAAD6g4oHAAAAAPQHFQ8AAAAA6A8qHgAAAADQH1Q8AAAAAKA/qHgAAAAAQH9Q8QAAAACA/qDiAQAAAAD9QcUDAAAAAPqDigcAAAAA9AcVDwAAAADoDyoeAAAAANAfVDwAAAAAoD+oeAAAAABAf1DxAAAAAID+oOIBAAAAAP1BxQMAAAAA+oOKBwAAAAD0BxUPAAAAAOiPIElS3zGAYePUqVPp6ekymQy/fPLkCUJIIBDgl0ZGRh9//PGqVav0Fh8AgBZqa2s/+ugjqVSKXzY3Nz958mT69OnUCm5ubv/4xz/0FB0Yrhj6DgAMJ56ent9//32vhdXV1dS/U1JSdBsRAICGHB0dnz59WlFRIb/whx9+oP49e/ZsnQcFhj24qgWGYMqUKW5ubgO1uri4TJkyRZfxAADo6sMPPzQxMRmoNTw8XJfBAHqAigcMzQcffNBvGjIxMVmzZo3u4wEA0NKqVaskEkm/TR4eHu7u7jqOB9AAVDxgaFauXNlvGhKLxStWrNB9PAAAWnJ2dp4yZQpBEL2Wm5iYfPTRR3oJCQx3UPGAoXFycpo+fXqvNEQQxIwZM1xcXPQVFQCAfj788ENjY+NeCyUSSWhoqF7iAcMdVDxgyPqmIWNj4w8//FBf8QAAaGnlypXUk6GYkZHRrFmzxo8fr6eIwPAGFQ8YsvDw8F5pSCaTwSUtAIBm2dvb+/j4GBn9398pIyMj+HEFVAYVDxgyW1tbf39/6jSPsbHx22+/bWdnp9+oAAD088EHH8i/JEly6dKl+goGDHdQ8QBVfPDBB/JjV/bKSgAAoBHLly+X/3H17rvv2tra6jckMHxBxQNUsWzZMgbjf4evNDIyWrx4sX7jAQDQkqWl5dy5c3HRQ5Lk6tWr9R0RGMag4gGq4PF48+fPZzAYDAYjMDDQwsJC3xEBAOhp9erV+MZBExMT+HEF1AEVD1DR6tWrpVKpVCqFibQAANoTHBxsamqKEFq0aNGoUaP0HQ4YxqDiASpatGgRh8Nhs9kLFy7UdywAANricrn41A5c0gLqIrVG33sGANAMyAwAgOFo+fLl8slHu3OnR0dHC4VCrW4CqO/27dspKSmZmZlDfWNxcTFBEFOnTtVGVNoQFhYG38khwd8NjXcL/wtgSKRSaWZm5vvvv6/Ce0fIUX/o0CGE0ObNm/UdiAHBn4k8Qns/uQiCyMzMhIHpDN+5c+fCwsJU+CbgCbaoh7YMH3wnh0rl74YC8L8AVNDV1cVisVR44wj5vuGZN86fP6/vQAxI389k2PytAgZoGNU6AIBhTbVyBwB5cOcyAAAAAOgPKh4AAAAA0B9UPAAAAACgP6h4AAAAAEB/UPEAFV29etXc3Pzy5cv6DkQ/vv3229jYWJlMtmTJEj6fz2KxHBwcQkJCSkpKlO9EJpMdOnTI29u7b1NhYaGPjw+Hw7G3t4+Jienu7lam9dKlS/v375dKpersGgBghOc3edrLdbrPV1DxABWN5KHkdu3alZqaum3bNplMduvWrdOnTzc1NUARnOYAACAASURBVBUWFnZ2ds6ePbuurk6ZTsrKymbPnr1lyxaRSNSrqbS0dN68eXPmzGlsbLx48eLXX3+9YcMGZVqDg4NZLNacOXNaWlo0tbMAjEAjOb/J02qu00O+0uxoqr1GVs3MzNRe/0BT8NiD+o5iQCKRSCgUaqQrjXwn9+3b5+rq2tnZSZKkWCxeuHAh1fTTTz8hhBITEwftpLi4eOnSpSdPnvTy8po6dWqv1rCwMIFAIJPJ8Mvk5GSCIH7//XdlWkmSjIyMFAqFYrFYjb38X9r4bkBmALpk4N83TeW35cuX9xpfWH06yHWkRvNVL30/EzjHAwxdenp6Q0ODvqP4X+Xl5XFxcbt378ajgzAYDPnz3k5OTgihioqKQfuZOnVqVlbWqlWr8BSJ8iQSSW5urr+/P0EQeMmCBQtIkszJyRm0FYuPjy8uLtbGWMkAAM0yqPwmTwe5DtNlvoKKB6iisLCQz+cTBHHkyBGEUFpaGpfL5XA4OTk5CxYs4PF4jo6OZ86cwSunpqayWCxbW9v169fb29uzWCxvb+87d+7g1sjISCaT+cYbb+CXmzZt4nK5BEG8fPkSIRQdHb1169aKigqCIFxcXBBC165d4/F4iYmJeththFJTU0mSDA4O7re1s7MTIcTj8dTZRGVlZXt7O5/Pp5Y4OzsjhPBVc8WtmKWlpb+/f0pKCgln5gEYuhGb3+TpINdhusxXUPEAVfj6+v7444/Uy40bN27evLmzs9PMzCwzM7OiosLJyenTTz8Vi8UIocjIyIiICJFIFBUVVVVVde/ePYlEMnfu3JqaGoRQamqq/ADwR48e3b17N/UyJSVl0aJFzs7OJEmWl5cjhPBtbjKZTGc7Ky83N9fNzY3D4fTbis/0+vr6qrOJFy9eIITMzMyoJSwWi81m19fXD9pKmTZt2rNnzx48eKBOJACMTCM2v8nTQa6j6CxfQcUDNMnb25vH49nY2ISHh3d0dFRXV1NNDAZj0qRJpqam7u7uaWlpbW1tGRkZKmwiKCiotbU1Li5Oc1Erq6Oj48mTJ/icSi/19fVnz56NiooSCoUD/SpSEn7wytjYWH6hiYkJ/lGluJUyYcIEhNDDhw/ViQQAII/e+U2ebnIdRWf5CuZFAlrBZDIRQvg3UF8zZszgcDiPHj3SbVDqamhoIEmy3x89QqGwo6NjxYoVe/fuNTExUWcr+Ko5nqWV0tPTw2azB22l4CB7nfgBAGgELfObPN3kOorO8hVUPEA/TE1NGxsb9R3F0HR1dSGE+r3/ztbWNj093cPDQ/2t4Ev+ra2t1BKRSNTV1WVvbz9oKwUXQDhgAICODcf8Jk83uY6is3wFV7WAHojF4paWFkdHR30HMjT4sOx3vCwbGxsLCwuNbEUgEJiZmT19+pRagi/wT5kyZdBWSk9PDxUwAECXhml+k6ebXEfRWb6CczxAD/Lz80mSnDVrFn7JYDAGOj9sUGxtbQmCeP36dd8mDY7NymAwAgMDCwoKZDKZkZERQigvL48gCHzJXHErBQdpZ2enqagAAEoapvlNnm5yHUVn+QrO8QAdkclkzc3NEomkpKQkOjqaz+dHRETgJhcXl6ampuzsbLFY3NjYKH8CAyFkZWVVV1dXVVXV1tYmFovz8vL09fQmh8NxcnKqra3ttby8vNzOzi4sLEx+YXh4uJ2d3b1791TYUFxcXH19/a5duzo6Om7fvp2cnBwREeHm5qZMK4aD9PT0VGHrAIChokF+k6ezXIfpLF9BxQNUceTIkZkzZyKEYmJiQkJC0tLSDh06hBCaMmVKZWXliRMntm7dihCaP39+WVkZfktXV5enpyebzfbz83N1df3++++pi8QbN24MCAhYuXKlm5vbnj178LlNoVCIH+/csGGDra2tu7t7YGBgU1OTXvaXEhQUVFpa2uvBqH6Hkejp6WloaJAfGFBeUVGRr6/vmDFj7ty58+DBA3t7ex8fn4KCAtzq4eFx/fr1GzduWFtbL1u2bO3atceOHaPeq7gVu3v3roODQ69LXQAAZYzY/CZPN7kO012+0vi4zhRk2GN7A4oOZplYt26dlZWVVjehDPW/k2VlZQwG45tvvhl0TalU6ufnl56ers7mVPPy5UsWi3XgwAH1u4JZJsBwp4PvmyHkN43PMqGzXKfBfNULzDIB9IYeE3q7uLgkJCQkJCS0t7crWE0qlWZnZ7e1tYWHh+ssNkp8fLyXl1dkZKTuNw3AyESP/CZPZ7lOl/nKgCqeTz75xMzMjCCI4uJifcfyv/bv3z9x4kQ2m83lcidOnBgXFyf/VLACWVlZTk5OhBwmk2lra/v2228nJyc3NzdrO3KgPbGxsaGhoeHh4f3e1ofl5+dnZWXl5eUNNGKp9hw8eLC4uPjq1auaGipjmJo5c6axsbGXl5c6nShOSn1br169am5uro1bO+X1TS+U8ePHq9AhjT8roA4d5Dod5ysDqni++uqrEydO6DuKf3Pr1q1PP/20urq6vr5+z549+/fvX758uTJvXLZsWWVlpbOzs7m5OUmSMpmsoaHh3LlzAoEgJibGw8Pj559/1nbwhmPbtm0ZGRmvX78WCAQXLlzQdzgakJiYGBkZuW/fvoFWmDNnzqlTp6jJdHQmJyenu7s7Pz/f0tJSx5s2NHfv3g0ICFCzE8VJqW8rqZOJzHqlF5IkJRKJSCSqr69X7a8OjT8rHaBffpOn1Vyn+3wFT6crwmQyN23ahEe5DQ0NPX/+/Pnz558/f95rtLdBEQRhYWHx9ttvv/3220FBQWFhYUFBQY8fPzY3N9dO4IYlKSkpKSlJ31Fo2Lx58+bNm6fvKHoLCQkJCQnRdxQGhJphXjeCgoIU/BrWHmNjYzabzWazXV1dVe5khHxWGkfL/CZPe7lO9/nKgM7xIJ0fcoO6ePEiLncwBwcHhJDii5qDWr58eURERENDw5dffqlufAAAhdQ/Va44KWkwZZEkef78+ePHj6vTSXZ2tsrvHWmfFRiB9FzxkCSZnJzs5uZmampqbm7+l7/8Rb5VKpXu3LmTz+ez2ewpU6bgx0bS0tK4XC6Hw8nJyVmwYAGPx3N0dDxz5gz1rh9++OGtt97icDg8Hs/T0xPfedNvV0NVVlZmYWExbtw4/PLatWuqDZyAx2nIy8szzN0EQO/6/SanpKRwuVwjI6M333zTzs7OxMSEy+VOnz7dz89v7NixLBbLwsLi888/l++nvLx84sSJXC4XPzZcWFioeBNosKSkoLWwsJDP5xMEceTIEaTEISyVSpOSktzc3Nhs9ujRowUCQVJSEjXPtsrpBRtRnxUAytL482AUpMQzgdu3bycI4u9//3tzc7NIJDp69ChC6P79+7j1z3/+s6mp6YULF5qbm7dt22ZkZHT37l38LoTQd9999/r164aGBj8/Py6X29PTQ5Jke3s7j8fbv39/Z2fnixcvli5d2tjYqKArZfT09NTW1h4+fNjU1FT+Ub0rV66YmZklJCQM9Eb5C+3ycHUyduxYA9lNHTydbiCU+U4Cefp6On2gb/KuXbsQQnfu3Ono6Hj58uX8+fMRQrm5uY2NjR0dHfhxj+LiYtzJnDlznJycnjx5IhaLf/311//3//4fi8V6/Pix4k0oTkqKW/EIK4cPH6ZWHugQJkkyMTHR2Ng4JydHJBL98ssvdnZ2b7/9NvUJDDW9REVFPXz4UH6FkfNZKTZCjnqNP51OA30/E31WPCKRiMPhzJ07l1qCi3p8SHR2dnI4nPDwcGplU1PTjRs3kv86Njo7O3ETPpDKy8tJkvz1118RQleuXJHfkIKulIGHvra2tv7P//xP6ghUxkAVD0mS+M4eA9lNqHjAQPRS8Sj4JuO/4m1tbbjpv//7vxFC1F/6n376CSF09uxZ/HLOnDlTp06lui0pKUEI/fnPf1awCcVJSXErOcBf8X4PYZIkZ86c+dZbb1Fd/elPfzIyMuru7lbuUySdnZ17/Xztt+KBz2qEHPVQ8fTV9zPR553L5eXlIpFozpw5/bb+8ccfIpFo8uTJ+CWbzX7jjTcePXrUd00mk4kQwhOXODk52drarl69OioqKiIiAj+rqXxX/aqpqWlpabl//35sbOzx48dv3rxpa2s7tF39dx0dHSRJ8ng8g9rNc+fOqbNTw8Xt27f1HcJwopePa6gHhUQiwS/xnSgDzWHk6elpbm6O/5YPtAnFSUlx66DkD2GEUFdXl/xtglKp1MTExNjYWPkOzc3NW1pa8L+jo6OV2frI/KxGwlGPJ2oYIWlcSbW1tb3mc9VnxYP/h2xsbPpt7ejoQAjt2LFjx44d1MJBH5Jis9k3b9784osvEhMTExISVqxYkZGRoVpXFBMTExsbm3nz5gkEAldX16SkpJSUFCXf26/Hjx8jhCZOnIgMaTd7TZVCVykpKWr+9wFtU/ObrICJiQn+IzrQJhQnJcWtQxUYGJicnJyTkzNv3rzS0tLs7OyFCxcOqeKRp/FvNZ0+q5Fz1I+QNK68XgPK6PPOZVyzd3d399uKD5VDhw7Jn5JSplT38PC4fPlyXV1dTExMZmbmgQMHVO6qFxcXF2Nj49LS0qG+sZdr164hhBYsWIAMaTc1fD7RIKGRcX5bg/Ry87umDtheJBJJU1MTn89XsAnFSUlx61DFx8e/8847ERERPB5v6dKlK1asMJwByWj2WY2Eox6uavXVd/w8fVY8kydPNjIy+uGHH/ptxQ8UDHX85bq6ut9++w0hZGNjs2/fvunTp//222+qdfXq1av3339ffklZWZlUKh07duyQ+unlxYsXhw4dcnR0XLt2LTKA3QTA0Gjpm/z999/LZLLp06cr2ITipKS4dahKS0srKioaGxvFYnF1dXVaWpr647A9f/58zZo16sc2Ej4rMALps+KxsbFZtmzZhQsX0tPTW1tbS0pK5MdXYLFYa9asOXPmTFpaWmtrq1Qqra2tff78ueI+6+rq1q9f/+jRo56envv37z99+nTWrFmqdcXlcm/cuHHz5s3W1laxWHz//v2PPvqIy+Vu2bIFr5CXlzfo46MkSba3t8tkMpIkGxsbMzMzfXx8jI2Ns7Oz8X08et9NAAyNBr/JPT09r1+/lkgk9+7di4yMHDduHB4bYqBNKE5KiluH6rPPPuPz+QON76VMepFHkmRnZ2dWVhZOLCoYvp8VAMrS3gklpMS5xLa2tk8++cTa2nrUqFG+vr47d+5ECDk6Oj548IAkye7u7piYGD6fz2Aw8PFTWlp69OhRPJL6hAkTKioqjh8/jo/wcePGPX78uKqqytvb29LS0tjYeMyYMdu3b5dIJAN1NeguBAcHCwSCUaNGmZqaOjs7h4eHyz8NcfXqVTMzs7179/Z946VLl6ZMmcLhcJhMppGREfrXsMtvvfVWQkLCq1ev5FfW+27Cs1pgIPp6Or3fb3JKSgo+KMaPH3/r1q2//vWveNRyOzu7U6dOnT17Fj9WaWlpeebMGZIkMzIyAgICbG1tGQyGtbX1ypUrnz59qngT5GBJSUHr4cOH8Vj7HA4nODhY8SFMkuTNmzetra2pVGxiYjJp0qSsrCwcnoL0cvHixb4PalF27NhBkuSI+qzU/77RAFzV6suwnk4HBgIqHjAQfVU8I8HRo0ejo6Opl93d3Zs3bzY1NRWJRHqMyjCp81mNkO8bVDx9GdbT6QAAMDK9ePEiMjJS/uYYJpPJ5/PFYrFYLGaz2XqMzdDAZwU0xbDm1dKlR48eEQMLDw/Xd4AAANpis9kmJibp6en19fVisbiuru6rr77auXNneHi4yjfi0BV8VkBTRm7FM3HiRAVnw86ePavvAIGeffvtt7GxsTKZbMmSJXw+n8ViOTg4hISE4GHZlCSTyQ4dOuTt7d236fTp0zNnzjQzMxs3btyaNWtevHihfKvinsVicVJSkouLC5PJtLCwmDx5clVVFULo0qVL+/fvl0qlyscPtMTc3PzGjRu//vqrq6srm812d3fPyMj461//ikdGBvLgs9IGbec3Bfbu3dvrFAM1viVWWFjo4+PD4XDs7e1jYmKoMQ40kMG0dwkNjYyrpzQA9/H0tXPnzkWLFuHH9KytrW/dutXR0VFZWTl37lxzc/Nnz54p08njx499fHwQQvLj92O4pN6/fz8eztvJycnLy0ssFivTqrhnkiSXLFni5uZWVFSEfxAHBwdTd9ynpKT4+/s3NzcrEz8J9/GA4W+EfN+GdB+PtvObYnv27OlVh3h4eFCtv/76K5vNjouLa29v//HHH0ePHr1mzRqqdUgZDO5cBv3QQcUjEomEQqHeu1LyO7lv3z5XV1c8xY9YLF64cCHVhGcjSkxMHLST4uLipUuXnjx50svLq29GCAgIGDNmDB62gCRJPIN0YWGhMq2Kez5z5gxBECUlJQMFFhkZKRQK5esnBaDiAcOdtr9vBpLclK94dJDfFNuzZ4/8nNy9hIWFCQQCKvslJycTBPH7779TKyifwfp+JiP3qhbQpfT09IaGBkPrql/l5eVxcXG7d+/Gg8YyGIzLly9TrU5OTgihioqKQfuZOnVqVlbWqlWrTE1N+7bW1NTY29sTBIFf4mEtnz59qkyr4p6PHTs2ffp0T0/PgQKLj48vLi4eIYPuA6Btwyi5IV3lN5VJJJLc3Fx/f38q+y1YsIAkyZycHGoddTIYVDxAWSRJHjx4cNKkSaamppaWlosXL6Ymd4yMjGQymXiADYTQpk2buFwuQRAvX75ECEVHR2/durWiooIgCBcXl9TUVBaLZWtru379ent7exaL5e3tfefOHRW6Qghdu3ZtSAO1DSo1NZUkyeDg4H5bOzs7EULq3y/p5OQkn9rwbTo43QzaqkBPT09RUZGXl5eCdSwtLf39/VNSUkiSVC14AGhmhCQ3pKv8prLKysr29nY8vQmGh56Sv7tInQwGFQ9QVnx8fGxs7Pbt2xsaGgoKCmpqavz8/Orr6xFCqampK1asoNY8evTo7t27qZcpKSmLFi1ydnYmSbK8vDwyMjIiIkIkEkVFRVVVVd27d08ikcydO7empmaoXSGE8F1sMplMU7uZm5vr5uaGx0PrC5/19fX1VXMr27Zte/HixeHDh9va2vDYeu+9996sWbOUaVWgrq6up6fnl19+CQgIwAl30qRJR48e7ZUapk2b9uzZswcPHqi5FwDQwwhJbkhX+W1QsbGxlpaWTCZTIBAsXrz47t27eDn+dWdmZkatyWKx2Gw2/r+gqJzBoOIBSuns7Dx48ODSpUtXr15tbm7u6en55Zdfvnz5UuWR4xkMBv5F5e7unpaW1tbWlpGRoUI/QUFBra2tcXFxqoXRS0dHx5MnT/od0La+vv7s2bNRUVFCoXCgX0jK8/f3j4mJiYyM5PF4kydPbmtr++qrr5RsVQAPw29jY5OYmFhaWlpfX7948eLPPvvs9OnT8qtNmDABIfTw4UM19wIAGhghyQ3pML8p9tFHH126dKmmpqa9vf3MmTPV1dX+/v54im78WJaxsbH8+iYmJvjkE0XlDAYVD1BKaWlpe3v7jBkzqCUzZ85kMpnUCVt1zJgxg8PhUKeR9aihoYEkyX5/AAmFwqioqMWLF+fl5ZmYmKi5oe3btx8/fvy7775rb2+vrKz09vYWCoX4h+CgrQrga+oeHh7e3t5WVlbm5ua7d+82NzfvlbvxDvb62QTAyDRCkhvSYX5TbOzYsdOmTRs1ahSTyZw1a1ZGRkZnZ+fRo0cRQvjuIolEIr9+T09Pr3EmVc5gUPEApbS0tCCERo0aJb/QwsKira1NI/2bmpo2NjZqpCt1dHV1oX/VDb3Y2trevHnz8OHDeH4idTx//nz//v1/+tOf3nnnHS6XKxAITpw4UVdXl5ycPGirYvb29gghfFsAxmQyx40b1+tWRJw+8M4CMMKNkOSGdJXfhsrT09PY2Pjx48cIIXyTU2trK9UqEom6urpwZqOonMGg4gFKsbCwQAj1SgEtLS2Ojo7qdy4WizXVlZrwgdTvCFc2Njb4Q1BfWVmZVCodM2YMtYTH41lZWeHzuopbFRs1atSECRN+++03+YUSiaRXFuvp6UH/2lkARrgRktyQrvLbUMlkMplMhuswgUBgZmZGPZeKEMK3NE2ZMkX+LSpnMKh4gFImT548atSon3/+mVpy586dnp6eN998E79kMBhisVi1zvPz80mSpO7MVacrNdna2hIE8fr1675Nly9fdnBw0MhWcPp7/vw5taStra2pqQk/ha64dVBhYWH379+vrKzEL0Ui0dOnT3s9rI53EM+eDcAIN0KSG9JVfhvUe++9J//y7t27JEkKhUKEEIPBCAwMLCgooO7XzsvLIwii161FKmcwqHiAUlgs1tatWy9evHjy5MnW1taHDx9u2LDB3t5+3bp1eAUXF5empqbs7GyxWNzY2ChfpCOErKys6urqqqqq2tra8AEvk8mam5slEklJSUl0dDSfz4+IiFChq7y8PA0+wMnhcJycnGpra3stLy8vt7OzCwsLk18YHh5uZ2d37969oW5FIBAEBAScOHGioKCgs7OzpqYGf4wff/zxoK2D2rJly7hx4yIiIqqrq1+9ehUTE9PZ2fnFF1/Ir4N3UMGYPQCMHCMkuSFd5bdB3/vs2bOzZ8+2tLSIxeLbt29/8sknfD5/w4YNuDUuLq6+vn7Xrl0dHR23b99OTk6OiIhwc3OT70HlDAYVD1DWrl27kpKSEhISRo8e7e/vP378+Pz8fC6Xi1s3btwYEBCwcuVKNze3PXv24PON1P22GzZssLW1dXd3DwwMbGpqQgh1dXV5enqy2Ww/Pz9XV9fvv/+euro81K40KygoqLS0tNejAf0O/NDT09PQ0CA/NJa8oqIiX1/fMWPG3Llz58GDB/b29j4+PgUFBQghgiDOnz8fHh7+8ccfW1pauru7V1dXZ2Vl+fn5DdqquGeEkKWl5a1btxwdHb28vBwcHH766afc3NxeI/TcvXvXwcGh14liAEasEZLckE7y26DvnT9//o4dOxwdHTkczooVK3x8fIqKiqytrXGrh4fH9evXb9y4YW1tvWzZsrVr1x47dqxXD6pnMKUGhVYJgrHkhwndz6u1bt06KysrXW4RU+Y7WVZWxmAwFAyCTpFKpX5+funp6RqKTkdevnzJYrEOHDigzMowywQY7nT8fdNXclNylgnd5Det5kblMxjMMgEMhcHO4O3i4pKQkJCQkIDHthmIVCrNzs5ua2sLDw/XWWwaER8f7+XlFRkZqe9AAKAng01uSCf5Tdu5UZ0MBhUPAL3FxsaGhoaGh4f3e4sflp+fn5WVlZeXN9DopYbp4MGDxcXFV69e1faQGwAAw6Tt/KbV3KhmBoOKB+jatm3bMjIyXr9+LRAILly4oO9w+peYmBgZGblv376BVpgzZ86pU6eoKXKGhZycnO7u7vz8fEtLS33HAgANDYvkhrSc37SXG9XPYAzNBgTAoJKSkpKSkvQdxeDmzZs3b948fUehSSEhISEhIfqOAgDaGi7JDQ3P/KZ+BoNzPAAAAACgP6h4AAAAAEB/UPEAAAAAgP6g4gEAAAAA/RFkf4MtaqZrgpg1a5aBzKAGFKitrS0qKlq+fLm+A9G6CxcuwHdySPB3Q7NZAjID0KURctQXFRUhhKgJvABCqKioaNasWefPn6eWaLHiCQ0N1VLPwEDcv38fITRt2jR9BwK0Sz5lqA8yAxiqFy9e3L9/f8GCBfoOBAwzQqFwy5Yt1EstVjyA9lasWIEQOnfunL4DAQDQ2blz58LCwuCvFVAT3McDAAAAAPqDigcAAAAA9AcVDwAAAADoDyoeAAAAANAfVDwAAAAAoD+oeAAAAABAf1DxAAAAAID+oOIBAAAAAP1BxQMAAAAA+oOKBwAAAAD0BxUPAAAAAOgPKh4AAAAA0B9UPAAAAACgP6h4AAAAAEB/UPEAAAAAgP6g4gEAAAAA/UHFAwAAAAD6g4oHAAAAAPQHFQ8AAAAA6A8qHgAAAADQH1Q8AAAAAKA/qHgAAAAAQH9Q8QAAAACA/qDiAQAAAAD9QcUDAAAAAPqDigcAAAAA9AcVDwAAAADoDyoeAAAAANAfVDwAAAAAoD+oeAAAAABAf1DxAAAAAID+oOIBAAAAAP0x9B0AGE5EIlF3dzf1sqenByHU3NxMLTE1NeVwOHqIDABAI2KxuL29nXrZ0dGB/j3VEARhYWGhh8jAcEaQJKnvGMCwkZaWtmnTJgUrHD16dOPGjTqLBwBAS/X19Q4ODlKpdKAVAgICbt68qcuQAA3AVS0wBKGhocbGxgO1Ghsbh4aG6jIeAAAt2dnZzZ4928io/79QBEGsXLlSxyEBGoCKBwyBjY3NnDlz+i16jI2N3333XRsbG91HBQCgnw8++GCgJmNj46VLl+oyGEAPUPGAoVm9enW/V0JJkly9erXu4wEA0NKyZcsYjH7uNDU2Np4/f761tbXuQwLDHVQ8YGgWL15sYmLSdzmDwQgODtZ9PAAAWuLxeAsWLOhb9MCPK6AyqHjA0JiZmS1atKhX0cNgMEJCQng8nr6iAgDQz+rVq/vevMxkMhcuXKiXeMBwBxUPGLJVq1ZJJBL5JVKpdNWqVfqKBwBASwsXLuw12oWJicmSJUu4XK6+QgLDGlQ8YMgCAwNHjRolv4TL5c6fP19f8QAAaInFYi1dulT+jLJYLIYfV0BlUPGAIWMymaGhoUwmE780MTEJCwszNTXVb1QAAPp5//33xWIx9ZLH482dO1eP8YBhDSoeoIr3338fD7iMEBKLxe+//75+4wEA0NK7775rZWWF/21iYrJy5UrqtxYAQwUVD1BFQEAANfTO6NGj/f399RsPAICWGAzGypUr8YUt+HEF1AQVD1CFkZHR+++/z2QyTUxMVq1apWAgZgAAUMfKlSvxhS07OztfX199hwOGMah4gIpWrlzZ09MDv7oAAFrl7e3t4OCAEPrwww8Hkb7OGAAAIABJREFUmncCAGUMOHd6bW3tjz/+qMtQwPBCkiQe9vTJkydVVVX6DgcYLm9vb0dHR51tDnIX/cycOfPZs2fW1tbnzp3TdyxAk1asWKHT7ZEDyMzM1GkcAACayszMHCjPaAPkLgCGC11mBpIkBzzHQ0Wjm90Gw9Fvv/2GEHJ3dycIIjMzU9fVus7hmeHPnz+v70CGE4Ig9LJdyF3DgvLH1IULF5YvX679iLTi3LlzYWFh8J2Uhz8THW90kIoHAAXc3d31HQIAYEQYvuUOMBxwFxgAAAAA6A8qHgAAAADQH1Q8AAAAAKA/qHgAAAAAQH9Q8QAAAACA/qDiAXpz9epVc3Pzy5cv6zsQbfn2229jY2NlMtmSJUv4fD6LxXJwcAgJCSkpKVG+E5lMdujQIW9v775Np0+fnjlzppmZ2bhx49asWfPixQvlWxX3LBaLk5KSXFxcmEymhYXF5MmT8SCTly5d2r9/v1QqVT5+ADQOUocyFBzgCuzdu5f4d5MnT5ZfobCw0MfHh8Ph2Nvbx8TEdHd34+XDIjlAxQP0ht6jU+zatSs1NXXbtm0ymezWrVunT59uamoqLCzs7OycPXt2XV2dMp2UlZXNnj17y5YtIpGoV1NmZuaqVatCQ0Nra2tzcnIKCgoWLFggkUiUaVXcM0IoLCzsn//856lTp0Qi0e+//+7s7Nze3o4QCg4OZrFYc+bMaWlpUfFzAUBtkDoGpfgAV1lpaem8efPmzJnT2Nh48eLFr7/+esOGDbhpeCSHgYYmxOOW6mgcRDDMIZ2PqzskIpFIKBSq38/y5cuXL1+uzJr79u1zdXXt7OwkSVIsFi9cuJBq+umnnxBCiYmJg3ZSXFy8dOnSkydPenl5TZ06tVdrQEDAmDFjZDIZfnnkyBGEUGFhoTKtins+c+YMQRAlJSUDBRYZGSkUCsVi8aC7QOrjuwG5axhR/pjSC02lDuW/kzpIHYrt2bPnm2++Gag1LCxMIBBQiSU5OZkgiN9//51aQfnkoJfjFM7xAPpLT09vaGjQ2ebKy8vj4uJ2797NYrEQQgwGQ/70u5OTE0KooqJi0H6mTp2alZW1atUqU1PTvq01NTX29vbUiMZjx45FCD19+lSZVsU9Hzt2bPr06Z6engMFFh8fX1xcnJKSMuguADCs0TJ1qEwikeTm5vr7+1OJZcGCBSRJ5uTkUOsYeHKAigfoR2FhIZ/PJwgCn35IS0vjcrkcDicnJ2fBggU8Hs/R0fHMmTN45dTUVBaLZWtru379ent7exaL5e3tfefOHdwaGRnJZDLfeOMN/HLTpk1cLpcgiJcvXyKEoqOjt27dWlFRQRCEi4sLQujatWs8Hi8xMVFLu5aamkqSZHBwcL+tnZ2dCCEej6fmVpycnORzMb5NB+fEQVsV6OnpKSoq8vLyUrCOpaWlv79/SkoKSeuLC8AwQerQ0tYHVVlZ2d7ezufzqSXOzs4IIfm7iww8OUDFA/TD19dXfoLrjRs3bt68ubOz08zMLDMzs6KiwsnJ6dNPPxWLxQihyMjIiIgIkUgUFRVVVVV17949iUQyd+7cmpoahFBqaqr8lF5Hjx7dvXs39TIlJWXRokXOzs4kSZaXlyOE8L11MplMS7uWm5vr5ubG4XD6bcWnpn19fdXcyrZt2168eHH48OG2trbS0tKUlJT33ntv1qxZyrQqUFdX19PT88svvwQEBOC/EJMmTTp69Giv/DVt2rRnz549ePBAzb0AYKggdWhp65TY2FhLS0smkykQCBYvXnz37l28HP9wMjMzo9ZksVhsNru+vl7+7YacHKDiAYbF29ubx+PZ2NiEh4d3dHRUV1dTTQwGY9KkSaampu7u7mlpaW1tbRkZGSpsIigoqLW1NS4uTnNR/5+Ojo4nT57gnz691NfXnz17NioqSigUDvQzTnn+/v4xMTGRkZE8Hm/y5MltbW1fffWVkq0K4DuUbWxsEhMTS0tL6+vrFy9e/Nlnn50+fVp+tQkTJiCEHj58qOZeAKApkDo04qOPPrp06VJNTU17e/uZM2eqq6v9/f1LS0sRQvixLGNjY/n1TUxM8MkniiEnB6h4gIFiMpkIIfxDra8ZM2ZwOJxHjx7pNqjBNTQ0kCTZ7680oVAYFRW1ePHivLw8ExMTNTe0ffv248ePf/fdd+3t7ZWVld7e3kKhEP9yHbRVAXzh38PDw9vb28rKytzcfPfu3ebm5sePH5dfDe9gr992ABgCSB3qGDt27LRp00aNGsVkMmfNmpWRkdHZ2Xn06FGEEL67SP6RT4RQT08Pm82WX2LIyQEqHjBcmZqaNjY26juK3rq6utC/6oZebG1tb968efjwYXNzczW38vz58/379//pT3965513uFyuQCA4ceJEXV1dcnLyoK2K2dvbI4TwfQwYk8kcN25cr/slcY7DOwvA8DKSU8dQeXp6GhsbP378GCGEb3hqbW2lWkUiUVdXF04aFENODlDxgGFJLBa3tLQ4OjrqO5De8NHe7zBcNjY2FhYWGtlKWVmZVCodM2YMtYTH41lZWeGTz4pbFRs1atSECRN+++03+YUSiaRXqu3p6UH/2lkAhpERnjqGSiaTyWQyXIcJBAIzMzPqkU+EEL69acqUKfJvMeTkABUPGJby8/NJkqRuxWUwGAOdxNYxW1tbgiBev37dt+ny5csODg4a2QrO18+fP6eWtLW1NTU14afQFbcOKiws7P79+5WVlfilSCR6+vRpr4fV8Q7a2dmpuycA6NYITx2Deu+99+Rf3r17lyRJoVCIEGIwGIGBgQUFBdS923l5eQRB9Lq1yJCTA1Q8YNiQyWTNzc0SiaSkpCQ6OprP50dEROAmFxeXpqam7OxssVjc2Ngo/ysEIWRlZVVXV1dVVdXW1iYWi/Py8rT3iCmHw3Fycqqtre21vLy83M7OLiwsTH5heHi4nZ3dvXv3hroVgUAQEBBw4sSJgoKCzs7OmpqadevWIYQ+/vjjQVsHtWXLlnHjxkVERFRXV7969SomJqazs/OLL76QXwfvoIIxewAwHJA6lH/vs2fPzp4929LSIhaLb9++/cknn/D5fGpg5bi4uPr6+l27dnV0dNy+fTs5OTkiIsLNzU2+B0NODlDxAP04cuTIzJkzEUIxMTEhISFpaWmHDh1CCE2ZMqWysvLEiRNbt25FCM2fP7+srAy/paury9PTk81m+/n5ubq6fv/999Q1740bNwYEBKxcudLNzW3Pnj34hCp1r+6GDRtsbW3d3d0DAwObmpq0vWtBQUGlpaW9nl/od3SKnp6ehoYG+fG75BUVFfn6+o4ZM+bOnTsPHjywt7f38fEpKChACBEEcf78+fDw8I8//tjS0tLd3b26ujorK8vPz2/QVsU9I4QsLS1v3brl6Ojo5eXl4ODw008/5ebm9hqh5+7duw4ODr3OZgOgA5A6kBqpY9D3zp8/f8eOHY6OjhwOZ8WKFT4+PkVFRdbW1rjVw8Pj+vXrN27csLa2XrZs2dq1a48dO9arB4NODgMNxgwjtQPlIe3PJLBu3TorKyutbmJQSo6IX1ZWxmAwFIzUTpFKpX5+funp6ZqITndevnzJYrEOHDigzMo6+G70ArlrGNHBLBOGkDqU/E7qJnVoNe0onxxglgkAFDHwWXkpLi4uCQkJCQkJeGybgUil0uzs7La2tvDwcJ3FphHx8fFeXl6RkZH6DgQApUDq0Mh7lWHgyUHPFc/Vq1fNzc3lpw4xzD4VO336NEEQ3t7e6nRCj48CYLGxsaGhoeHh4f3eh4jl5+dnZWXl5eUNNMSqYTp48GBxcfHVq1e1PS6ILn3yySdmZmYEQRQXF+s7lv+VkJDg7u7O4/FMTU1dXFw+//xzxX8FKVlZWU5OToQcJpNpa2v79ttvJycnNzc3aztyoA5tpw6tph3DTw56rnhILUy9oY0+FTt9+rSzs/Pt27fxo3qqocdHoSXbtm3LyMh4/fq1QCC4cOGCvsNRSmJiYmRk5L59+wZaYc6cOadOnaLm9BkWcnJyuru78/PzLS0t9R2LJn311VcnTpzQdxT/5ubNm5999llVVdXLly+TkpJSUlJCQ0OVeeOyZcsqKyudnZ3Nzc1JkpTJZA0NDefOnRMIBDExMR4eHj///LO2gzcckDo0+F7FhkdyGOhyl5ausYlEIqFQaPh9Ku/ly5cCgeDkyZMIobi4OOXfSKePAun8Xg290ME9B/Sj+++GarkLzz15//59bYSkgqCgIIlEQr3E0z9VV1cr+Xaq4pF3/vx5IyMjW1vblpYWjQWqnhFyTMG9ZX2NiPt40tPT5ad0Ntg+lXfu3LmgoKDg4GAWi4XvOFPyjfT7KAAYvgiC0HcI/+bKlSvyExiNHj0aISQSidTpc/ny5REREQ0NDV9++aW68QEwDKlb8dy6dcvd3d3c3JzFYnl6el6/fp1q+uabb2bMmMFisbhc7vjx4/fs2RMdHb1169aKigqCIFxcXAoLC/l8PkEQR44cQQhNmjSJIAgjI6M333wTH9iff/457vm//uu/BtqW4j4RQiRJHjx4EE8jZ2lpuXjxYmpGlbS0NC6Xy+FwcnJyFixYwOPxHB0d8U897Nq1a4OOvnD69OmlS5eamZnNmzevqqrq1q1bfdehwUcBAM2QJJmcnOzm5mZqampubv6Xv/xFvlUqle7cuZPP57PZ7ClTpuDfo4MeJj/88MNbb73F4XB4PJ6npycej7/frobq2bNnbDZbIBDgl8qkpn7hcWjy8vIMczcB0K6BTv4oecbp/Pnz8fHxTU1Nr169mjVrlrW1NV6OB0jYt2/fq1evmpqa/vGPf6xatYokyWXLljk7O1Nvx2MeHD58mCRJiUQyfvx4Pp8vfy538+bNhw4dUrwtBX2SJLlz504mk/nNN9+0tLSUlJRMnz599OjRL168wK3bt29HCH333XevX79uaGjw8/Pjcrk9PT249cqVK2ZmZgkJCQPt/tOnT21sbHDA33zzDULo448/7rUOPT4KxRBc1QID0P13Q8nctX37doIg/v73vzc3N4tEIjxXInVV6/+zd6cBTVzrw8BPIEDYwmIBWQRZREQQ3AVBil63WkVtKbhcq9WKWwOKXhRcUAuItoKotFek9qKiKHhFVNSCpYgKrggGoYBFQGQTkECCCWTeD/O/86YsIQTIJOH5fTJzZs48M2aeHGbOnLN9+3YVFZXExMTGxsbAwEAFBQV88Fkhl0lLSwudTg8PD+dwONXV1UuXLq2rqxNSlehaW1s1NTUZDAaxpNfU1O1TLQzD8NbJiBEjpOQwh8g1BU+1uiLlnAxkP57Q0FCEUG1tLZfL1dbWdnd3J4ra29sjIyOx3n6S8cbBpUuX8I+tra2mpqYfPnwQsi/hdbLZbA0NDW9vb6L00aNHCCEiU+AXNofDwT/iWa+kpETEQw4LC1uzZg3+7w8fPqioqNDpdDabTawwRE4FtHhAT6SzxcNms9XU1GbPnk0sEezHw+Fw1NTUiIuFzWarqKhs2rQJE3qZvHz5EiF0/fp1wR0JqUp0QUFB1tbWzc3Nom/SU4sHwzAKhaKtrS0lhzlErilo8XRFyjmhDuDtIvyFtI6Ojry8vKamJsHpORQVFX19fXutYd26dcHBwcRbCefOnVu8eDGdTheyL+EVMpnMlpaWSZMmEUsmT56srKyck5PT7frKysoIIdGnWYmPj8cbHAghOp0+Z86clJSU5ORkYqiDoXMqIiIiLl++LOLKMio7OxshJOIrM0CalZSUsNnsWbNmdVtaVFTEZrPt7Ozwj6qqqsOHDyceAQsSvEwsLCz09fVXrlzp6+u7evXqkSNH9qmqnly5cuXSpUt37tzR1NTswxH2oLW1FcMwPJNIyWFmZ2fL/TWFT7wg94fZJ13n05CA/vbjuXHjxqeffqqnp6eiovKvf/0LX4jfOBVjrlcNDY3169c/ePAAv/3w008/CQ5k1O2+hGtqasKrFVyora3NYrH6GltXL1++zM/PX7hwITHuBT7yTVxcHLHOEDkVAMgWPNvq6el1W9ra2ooQ2r17N3Fpv3nzptdew6qqqnfv3nVxcQkJCbGwsPD29uZwOOJVRbh48eKhQ4cyMjLwhkX//fnnnwghGxsbJE2HCYBk9OseT3l5+ZIlS5YuXfrLL78YGRkdP34c//U1MjJCCNXX14tRJ4PBiIyMjIiI2Lhx44gRIywtLYXvSzi8qdHpR72pqQmfXLqfzp8/v2zZsvj4eGJJY2OjsbHxnTt3qqur8QEPhsipQAht3boVf4FWjuF/osn9rayBJW3vQOFoNBpC6OPHj92W4i2hiIgIPz+/PlU7duzYlJSUurq6o0ePHjp0aOzYsfjtXjGqQggdP3789u3bd+/e7fSHSn/cunULITR//nwkNYc5bdo0ub+mLl265OXlJfeH2Sf4OZHwTvt1jyc/P5/H423atMnCwoJGoxGpbeTIkbq6unfu3BGjThMTk6+++ioxMXHPnj2CF09P+xLOzs5OQ0NDcMStnJwcLpc7ceJEMWIThGHYxYsXN2/eLLhQR0fH09Ozo6ODaAYNhVMBgMyxs7NTUFD4448/ui0dMWIEjUbr6/jLVVVVBQUFCCE9Pb2wsLAJEyYUFBSIVxWGYQEBAfn5+VevXh3A5k51dXVERISJick333yDpOAwAZCwfrV4TE1NEUJpaWltbW3FxcVEjxAVFZXAwMDMzEwGg/H27Vs+n89isfCLRFdXt6qqqqysjMVi9dRHxN/fv729vbGxcebMmb3uS3idNBrN39//ypUr586da25uzs/P37hxo6GhoY+PjygHmJqa2tMroA8ePKDT6dOnT++0fOPGjUjgwZbcnAoA5Iment4XX3yRmJgYGxvb3Nycl5d36tQpopRGo61Zs+bChQvR0dHNzc0dHR2VlZXv3r0TXmdVVdWGDRsKCwu5XO7z58/fvHkzbdo08aoqKCg4fPhwTEyMkpKS4HwRP/zwA76CkNREwDCspaWFz+djGFZXV5eQkDB9+nRFRcWrV6/i/XhIP0wAJK2nLs0i9qMOCAjQ1dXV1tb29PTEx32xtLTEBwY9ceKEvb09jUaj0Wjjx48/efIkhmHPnj0zMzNTVVV1cXHZvXs3/uhHTU1t0aJFgtW6u7ufPn1axH0Jr5PP5x85cmTUqFFKSko6OjpLliwpKirCKzx58iQ+t8ioUaNKS0tPnTqFJwIzM7M///wTw7CbN29qamp+//33nSJZu3aturo6lUp1cHB49uwZsfzgwYOGhob4iTU2NsYPWT5OhXAI3tUCPZD8d0PE3MVisdatWzds2DANDQ0XF5e9e/cihExMTF68eIFh2MePHwMCAkxNTalUKt48YjKZwi+TsrIyZ2dnHR0dRUVFIyOjoKAgfHSJbqsSHlt+fn636frIkSP4Cj2lJgzDrl27Nm7cODU1NWVlZQUFBYQQ/nLWlClTDhw48P79e8GVyT1MbMhcU/CuVleknBMK1sMYwfgztp5KARBEoVASEhKgHw/oSvLfDchdMmSIXFPwneyKlHNC8kyiAAAAAAASAC0eAAZLWlrarl27+Hz+kiVLTE1NaTSasbGxh4dHXl6e6JXw+fyIiAhnZ+euRfHx8ZMnT9bU1DQzM1uzZk11dbXopcJr5vF4oaGhVlZWysrK2tradnZ2ZWVlCKFr166Fh4f3OvgTEK6wsJDSM2I0LzBkDXbqEFFbW5uNjc3u3buJJTweb+/evRYWFsrKysbGxtu3b+dwOHiRTCQHaPEAMCj27dsXFRUVGBjI5/Pv3bsXHx/f0NCQlZXF4XBmzJhRVVUlSiXFxcUzZszYtm1b16FNEhISVqxY4enpWVlZmZycnJmZOX/+/Pb2dlFKhdeMEPLy8oqLizt//jybzX716pWlpWVLSwtCCJ8xd9asWfjwTkA8NjY2QroaXLx4kewAAZkGO3WILigoqKioSHCJn5/fkSNHQkND379/f/78+ZiYmHXr1uFFspEcerrqoKcVEB0a5N6pbDbbycmJ9KpE72UZFhZmbW2ND9LP4/E+//xzoggfUjIkJKTXSnJzc5cuXXru3DlHR0cHB4dOpe7u7kZGRvibOBiG4R3Ys7KyRCkVXvOFCxcoFEpeXl5PgTEYDCcnJx6P1+shYFLccxlIAwn0XJaG7CH6d1ICqUNE9+/fnzNnDkIoKCgIX1JaWqqgoLB+/XpiHfz2T0FBAbFE9ORAynUK93iADIiNja2trZW2qnpSUlKyZ8+e/fv348PcUalUfDBunIWFBUKotLS013ocHBySkpJWrFihoqLStbSiosLQ0JAYjWnEiBEIoTdv3ohSKrzmn376acKECfb29j0FFhwcnJubGxkZ2eshAEA6GcoekkkdouBwODt27Oh0jT9+/JjP50+dOpVYMm/ePITQ7du3iSVSnhygxQMkBMOwo0ePjhkzRkVFRUdHZ/HixcS0OwwGQ1lZGX+XHiG0efNmdXV1CoWCj1Xt5+fn7+9fWlpKoVCsrKyioqJoNJq+vv6GDRsMDQ1pNJqzszMxKFGfqkII3bp1q9dxTfoqKioKw7BFixZ1W4o/9u52irQ+sbCwEEy+eDcdPCf2WioEl8vNzs52dHQUso6Ojo6bmxs+Ia54wQPQJ0Mke0gmdYgiKCho8+bNnaZhwQc7UFVVJZaMGjUKIfTq1StiiZQnB2jxAAkJDg7etWtXUFBQbW1tZmZmRUWFq6trTU0NQigqKkrw7eWTJ0/u37+f+BgZGblw4UJ8TviSkhIGg7F69Wo2m+3r61tWVvbs2bP29vbZs2fjE8X3qSr0vxlY+Xz+AB7pjRs3Ro8ejY9o0hV+a9rFxaWfewkMDKyurj5+/DiLxWIymZGRkXPnzp02bZoopUJUVVVxudynT5+6u7vjPwljxozBR5ASXG38+PFv37598eJFP48CAFEMkewhmdTRq/v375eWli5fvrzTcnw6NsH2zbBhwxBCdXV1gqtJc3KAFg+QBA6Hc/To0aVLl65cuVJLS8ve3v7nn3+ur68XHOi2T6hUKv4Hn62tbXR0NIvFOnPmjBj1LFiwoLm5ec+ePeKF0VVra+tff/1FTIImqKam5uLFi76+vk5OTj39GSc6Nze3gIAABoNBp9Pt7OxYLNbp06dFLBUC76Gsp6cXEhLCZDJramoWL168ZcsWwfnj0P/+tutpoDwABtAQyR4SSx3CcTgcPz+/6OjorkX29vbz5s07efLk3bt329raqqurr1y5QqFQOo3vL83JAVo8QBKYTGZLS8ukSZOIJZMnT1ZWVhacIkNskyZNUlNTI+5yk6u2thbDsG7/SnNycvL19V28eHFqaqqSklI/dxQUFHTq1Kn09PSWlpbXr187Ozs7OTnhf6r2WioE/uB/7Nixzs7Ourq6Wlpa+/fv19LS6vTrgh8g/kc2AINqiGQPiaUO4QIDA9evX29sbNxt6cWLFz09PVetWqWrqzt9+vT//ve/GIbhd3oI0pwcoMUDJAF/X7HTnIja2tqd5nIXm4qKSqc7q2Rpa2tD/2s3dKKvr3/37t3jx49raWn1cy/v3r0LDw9fv379zJkz1dXVzc3NY2Jiqqqqjhw50mupcPg0KXjHBZyysrKZmVmn/pL4s3z8YAEYVEMke0gmdQiXlZWVn59PvHDelZaW1s8//1xZWclms0tLS3/88UeEkJGRkeA60pwcoMUDJEFbWxsh1ClDNTU1mZiY9L9yHo83UFX1H361dzsMl56eHn4e+q+4uLijo0Mw0dDpdF1dXSaT2WupcBoaGqNGjcInuyW0t7d3SrVcLhf9vQ8jAINkiGQPyaQO4WJjY9PT0xUUFPDBMPGeyyEhIRQK5cmTJ13Xf/z4MULI3d1dcKE0Jwdo8QBJsLOz09DQELxmcnJyuFzuxIkT8Y9UKrWnCeR7lZGRgWEY0S23P1X1n76+PoVC+fDhQ9eilJSUnu4V9xWeoAXnpmaxWA0NDfhb6MJLe+Xl5fX8+fPXr1/jH9ls9ps3bzq9rI4foIGBQX+PBIDeDJHsIZnUIdyZM2cER6/Bb33h4/EIPlUkxMTEmJubu7m5CS6U5uQALR4gCTQazd/f/8qVK+fOnWtubs7Pz9+4caOhoaGPjw++gpWVVUNDw9WrV3k8Xl1dHTFyDE5XV7eqqqqsrIzFYuH5iM/nNzY2tre35+Xl+fn5mZqarl69WoyqUlNTB/b9UjU1NQsLi8rKyk7LS0pKDAwMvLy8BBd6e3sbGBg8e/asr3sxNzd3d3ePiYnJzMzkcDgVFRX4mVy7dm2vpb3atm2bmZnZ6tWry8vL379/HxAQwOFwdu7cKbgOfoBCxuwBYKAMkewhmdTRz22nTJny5s2b9vb2srKy7du3p6WlxcbGKisrC64jzckBWjxAQvbt2xcaGnrgwIFPPvnEzc1t5MiRGRkZ6urqeOmmTZvc3d2XLVs2evTogwcP4ndEic62Gzdu1NfXt7W1/eyzzxoaGhBCbW1t9vb2qqqqrq6u1tbWv//+O/H8u69VDbgFCxYwmUxiuhlct6NTcLnc2tra5OTkbuvJzs52cXExMjLKycl58eKFoaHh9OnTMzMzEUIUCuXy5cve3t5r167V0dGxtbUtLy9PSkpydXXttVR4zQghHR2de/fumZiYODo6GhsbP3r06MaNG51G6Hn8+LGxsfG4ceP6fbYA6N0QyR4SSB29biuctra2o6OjqqrqhAkTCgsL79271+mRFpLy5NDTYMwwUjsQHZLsTAI+Pj66uroS2x1BxBHxi4uLqVTq2bNne12zo6PD1dU1NjZ2IKKTnPr6ehqN9sMPP4iysoS/GxjkLpkigVkmOiEle4j4nZRM6hjUtCN6coBZJgAQlTTP0GtlZXXgwIEDBw7gY9v0pKOj4+rVqywWS+bmyg4ODnZ0dGQwGGQHAoA4pDZ7SCB1DHbakfLkAC0eAAberl27PD09vb29u+2HiMvQ2FuLAAAgAElEQVTIyEhKSkpNTe1piFXpdPTo0dzc3Js3bw72uCAADEGDnToGNe1If3KAFg+QMYGBgWfOnPnw4YO5uXliYiLZ4fQoJCSEwWCEhYX1tMKsWbPOnz9PTOIjE5KTkz9+/JiRkaGjo0N2LAD0mUxkj0FNHYOXdmQiOVDJDgCAvgkNDQ0NDSU7CpHMmTNnzpw5ZEcxkDw8PDw8PMiOAgAxyUr2kMXUIRPJAe7xAAAAAED+QYsHAAAAAPIPWjwAAAAAkH/Q4gEAAACA/IMWDwAAAADkXy/valEoFMnEAWSdl5dXp5lf5BVcFDIB/ptkyBD5zxoihynNKFh3c3YghCorKx88eCDhaIBsiYiIQAht3bqV7ECAVHN2dsanc5cMyF3y5+HDh5GRkfi8BECefPXVV5LcXY8tHgB6hX9ZL126RHYgAAB5dunSJS8vL/i1Av0E/XgAAAAAIP+gxQMAAAAA+QctHgAAAADIP2jxAAAAAED+QYsHAAAAAPIPWjwAAAAAkH/Q4gEAAACA/IMWDwAAAADkH7R4AAAAACD/oMUDAAAAAPkHLR4AAAAAyD9o8QAAAABA/kGLBwAAAADyD1o8AAAAAJB/0OIBAAAAgPyDFg8AAAAA5B+0eAAAAAAg/6DFAwAAAAD5By0eAAAAAMg/aPEAAAAAQP5BiwcAAAAA8g9aPAAAAACQf9DiAQAAAID8gxYPAAAAAOQftHgAAAAAIP+gxQMAAAAA+QctHgAAAADIP2jxAAAAAED+QYsHAAAAAPIPWjwAAAAAkH/Q4gEAAACA/IMWDwAAAADkH5XsAIAsycnJefHiBfHx9evXCKFTp04RSxwcHKZOnUpCZAAAOVJXV/ff//6X+PjkyRP091Sjqam5bNkyEiIDsoyCYRjZMQCZcf369YULFyoqKiooKCCE8C8PhUJBCPH5/I6OjpSUlM8//5zkKAEAMu7jx4/6+votLS2Kioro76kGIcTj8b7++utff/2VxAiBLIIWD+gDHo/3ySefNDc3d1tKp9Pr6uqUlZUlHBUAQP6sXbv23LlzXC6329Lbt2/PmTNHwiEBWQf9eEAfKCkpLVu2rNs2jZAiAADoq+XLl/fU3NHW1p45c6aE4wFyAFo8oG+WLVvWbRri8XjLly+XfDwAALnk7u6up6fXdbmSktLKlSupVOiECvoMWjygb1xdXQ0MDLou19PTc3FxkXw8AAC5pKCgsGLFCiUlpU7LeTwe9FkG4oEWD+gbBQWFf/7zn52eXikrK69evRrvzgwAAANi2bJlPB6v00IjIyMnJydS4gGyDn6iQJ91fbDF5XLhry4AwMCaMmWKmZmZ4BJlZeWvv/6aeGkLgD6BFg/oswkTJlhZWQkusbCwGD9+PFnxAADk1T//+U/BB1vwxxXoD2jxAHGsXLmSSEP4X13kxgMAkEsrVqwQfLBlZWVlb29PYjxApkGLB4hDMA1xuVxvb29y4wEAyCUbGxtbW1v8MZaSktKaNWvIjgjIMGjxAHFYWVk5ODhQKBQKheLg4GBtbU12RAAA+bRq1Sp85OX29nZ4pAX6A1o8QEx4GlJUVFy1ahXZsQAA5NayZcs6OjoQQhMmTDA3Nyc7HCDDoMUDxOTt7Y3PpeXl5UV2LAAAuWVqaorPTwz9BUE//W3YyocPHx49epSsUIDM0dXVRQj5+fmRHQiQGdu2bev/YCqenp4DEgyQFR8/fqRQKHfu3MnMzCQ7FiA5Tk5O27ZtG8AK/3aPp6KiIjExcQBrBzIqMTGxsrKy19VMTU07jZYhW7Kzs7Ozs8mOYghJTEysqKgYkHpE+X4C6SfiNWhiYmJgYECj0SQQ0mCorKyE39a+ys7Ofvjw4cDW2c3UJJcvXx7YfQCZQ6FQtm7d+tVXXwlfraGhAf3vTo8swm8VwBdeYgZw4DhRvp9A+ol+DZaUlHQaBkyGXLp0ycvLC1JNnwzGrVyYjA2IT3bbOgAA2SK7zR0gPaDnMgAAAADkH7R4AAAAACD/oMUDAAAAAPkHLR4AAAAAyD9o8YABc/PmTS0trZSUFLIDIUdaWtquXbv4fP6SJUtMTU1pNJqxsbGHh0deXp7olfD5/IiICGdn565F8fHxkydP1tTUNDMzW7NmTXV1teilwmvm8XihoaFWVlbKysra2tp2dnZlZWUIoWvXroWHh+PD3QIgPYZaqhns3CKitrY2Gxub3bt3E0t4PN7evXstLCyUlZWNjY23b9/O4XDwIunMHtDiAQMGwzCyQyDNvn37oqKiAgMD+Xz+vXv34uPjGxoasrKyOBzOjBkzqqqqRKmkuLh4xowZ27ZtY7PZnYoSEhJWrFjh6elZWVmZnJycmZk5f/789vZ2UUqF14wQ8vLyiouLO3/+PJvNfvXqlaWlZUtLC0Jo0aJFNBpt1qxZTU1NYp4XAAbBkEo1g51bRBcUFFRUVCS4xM/P78iRI6Ghoe/fvz9//nxMTMy6devwIinNHpiAhISETkvA0IQQSkhIIDuKHrHZbCcnp/7X8+WXX3755Zf9rycsLMza2prD4WAYxuPxPv/8c6Lo0aNHCKGQkJBeK8nNzV26dOm5c+ccHR0dHBw6lbq7uxsZGfH5fPzjiRMnEEJZWVmilAqv+cKFCxQKJS8vr6fAGAyGk5MTj8fr9RB6NVDfKyn/fgLRDdQ1OEgGKtWI/dsqgdwiovv378+ZMwchFBQUhC8pLS1VUFBYv349sQ5++6egoIBY0p/sMRjfDbjHA2RPbGxsbW0t2VH8n5KSkj179uzfvx8fEJZKpQrebLewsEAIlZaW9lqPg4NDUlLSihUrVFRUupZWVFQYGhoSI/iNGDECIfTmzRtRSoXX/NNPP02YMMHe3r6nwIKDg3NzcyMjI3s9BADkDLmpRjK5RRQcDmfHjh2dksDjx4/5fD4+5Rlu3rx5CKHbt28TS6Qte0CLBwyMrKwsU1NTCoWC32CIjo5WV1dXU1NLTk6eP38+nU43MTG5cOECvnJUVBSNRtPX19+wYYOhoSGNRnN2ds7JycFLGQyGsrLy8OHD8Y+bN29WV1enUCj19fUIIT8/P39//9LSUgqFgg9KduvWLTqdHhISQsJhIxQVFYVh2KJFi7otxZ9q0+n0fu7FwsJCMPPi3XTwlNdrqRBcLjc7O9vR0VHIOjo6Om5ubpGRkdhQepQApNbQSTWSyS2iCAoK2rx5s56enuBCBQUFhJCqqiqxZNSoUQihV69eEUukLXtAiwcMDBcXlwcPHhAfN23atHXrVg6Ho6mpmZCQUFpaamFh8e233/J4PIQQg8FYvXo1m8329fUtKyt79uxZe3v77Nmz8UmXoqKiBCcQOHny5P79+4mPkZGRCxcutLS0xDCspKQEIYR3juPz+RI7WEE3btwYPXq0mppat6X4nWcXF5d+7iUwMLC6uvr48eMsFovJZEZGRs6dO3fatGmilApRVVXF5XKfPn3q7u6O/x6MGTPm5MmTndLT+PHj3759++LFi34eBQD9N3RSjWRyS6/u379fWlq6fPnyTsttbGzQ39s3w4YNQwjV1dUJriZV2QNaPGBwOTs70+l0PT09b2/v1tbW8vJyoohKpY4ZM0ZFRcXW1jY6OprFYp05c0aMXSxYsKC5uXnPnj0DF7WoWltb//rrL0tLy65FNTU1Fy9e9PX1dXJy6umvNNG5ubkFBAQwGAw6nW5nZ8disU6fPi1iqRB4D2U9Pb2QkBAmk1lTU7N48eItW7bEx8cLrob/6Zafn9/PowBg8MhZqpFYbhGOw+H4+flFR0d3LbK3t583b97Jkyfv3r3b1tZWXV195coVCoWCtzUJUpU9oMUDJERZWRkh1OliIEyaNElNTa2wsFCyQfVXbW0thmHd/hHm5OTk6+u7ePHi1NRUJSWlfu4oKCjo1KlT6enpLS0tr1+/dnZ2dnJyIuYhF14qBP5cf+zYsc7Ozrq6ulpaWvv379fS0jp16pTgavgB1tTU9PMoAJAA+Ug1EsstwgUGBq5fv97Y2Ljb0osXL3p6eq5atUpXV3f69On//e9/MQzD7/QQpCp7QIsHSAsVFZVOt0OlX1tbG/pfu6ETfX39u3fvHj9+XEtLq597effuXXh4+Pr162fOnKmurm5ubh4TE1NVVXXkyJFeS4UzNDRECOG9FnDKyspmZmadukPij+rxgwVA1slEqpFMbhEuKysrPz+feOG8Ky0trZ9//rmyspLNZpeWlv74448IISMjI8F1pCp7QIsHSAUej9fU1GRiYkJ2IH2DX8zdjrKlp6enra09IHspLi7u6OgQzCN0Ol1XV5fJZPZaKpyGhsaoUaMKCgoEF7a3t3fKpFwuF/29iyIAMkpWUo1kcotwsbGx6enpCgoKFAqFQqHgPZdDQkIoFMqTJ0+6rv/48WOEkLu7u+BCqcoe0OIBUiEjIwPDMKKzLZVK7emmtFTR19enUCgfPnzoWpSSktLTreC+wrPzu3fviCUsFquhoQF/C114aa+8vLyeP3/++vVr/CObzX7z5k2nl9XxAzQwMOjvkQBANllJNZLJLcKdOXNGcDAb/MYYPh7PpEmTuq4fExNjbm7u5uYmuFCqsge0eABp+Hx+Y2Nje3t7Xl6en5+fqanp6tWr8SIrK6uGhoarV6/yeLy6ujpiaBmcrq5uVVVVWVkZi8Xi8XipqalkvZ2upqZmYWFRWVnZaXlJSYmBgYGXl5fgQm9vbwMDg2fPnvV1L+bm5u7u7jExMZmZmRwOp6KiwsfHByG0du3aXkt7tW3bNjMzs9WrV5eXl79//z4gIIDD4ezcuVNwHfwAhYzZA4A0k8VUI5nc0s9tp0yZ8ubNm/b29rKysu3bt6elpcXGxuL9qAhSlT2gxQMGxokTJyZPnowQCggI8PDwiI6OjoiIQAiNGzfu9evXMTEx/v7+CKF58+YVFxfjm7S1tdnb26uqqrq6ulpbW//+++/EQ+tNmza5u7svW7Zs9OjRBw8exO+IEr1xN27cqK+vb2tr+9lnnzU0NJByvIQFCxYwmUxiNhlct4NPcLnc2tra5OTkbuvJzs52cXExMjLKycl58eKFoaHh9OnTMzMzEUIUCuXy5cve3t5r167V0dGxtbUtLy9PSkpydXXttVR4zQghHR2de/fumZiYODo6GhsbP3r06MaNG51G6Hn8+LGxsfG4ceP6fbYA6K+hk2okkFt63VY4bW1tR0dHVVXVCRMmFBYW3rt3r9MjLSRt2UPwnhXMMgFwaPBH8ffx8dHV1R3UXfRqQEYxLy4uplKpZ8+e7XXNjo4OV1fX2NjYfu5Rwurr62k02g8//ND/qgbqeyWB7yeQDAnMMiENqUa831bJ5JZBzUv9yR4wywSQK9I2ra54rKysDhw4cODAAXxsm550dHRcvXqVxWJ5e3tLLLYBERwc7OjoyGAwyA4EADHJaKqRQG4Z7LwkbdkDWjwA9NeuXbs8PT29vb277WaIy8jISEpKSk1N7WkEVel09OjR3NzcmzdvDvawHwCArgY7twxqXpLC7CHpFs+aNWtoNBqFQpGSt/MRQnw+PyIiwtnZWfRNkpKSLCwsKAJoNJq5ufk333zz119/iReGFJ6ZwRMYGHjmzJkPHz6Ym5snJiaSHc4ACAkJYTAYYWFhPa0wa9as8+fPEzP4yITk5OSPHz9mZGTo6OiQHYtESc/FCKmmn+Qg1Qxqbhm8vCSl2UPwEZdk+vEEBQUhhDgczmDvSBR//vnn9OnTEUIODg593dbS0lJLSwvDsI6Ojpqamri4ODU1NX19/fr6evGCkZ4zg4ZGPwkJ9CEAggbqeyWB76f0XIyYXKeaIXINQh9ZMUA/ngH24sWLnTt3bty4Ufjc0b1SUFDQ19f/5z//uWXLltra2rS0tIGKEAAACJBqAOgP0lo8FAqFrF0THBwckpKSVqxY0e1I3mKwsrJCCFVXV/enEmk4MwAAJMUXI6QaAMTQ5xbP4cOH1dTUNDU1a2tr/f39jY2Ni4qKOjo69u7da2pqqqqqOm7cOPwOHkLojz/+mDJlipqaGp1Ot7e3b25u/r+9KijcuHFj/vz5WlpahoaGv/zyC1H/vXv3bG1ttbS0aDSavb397du3EUJRUVE0Gk1fX3/Dhg2GhoY0Gs3Z2TknJ4fYqqcA+uPWrVt9HWwKH/7BwcGh18Bk+swAIOVkK01BqgFAQgQfcYn4rBF/Buzr63v8+PGlS5e+evVq+/btKioqiYmJjY2NgYGBCgoKjx8/bmlpodPp4eHhHA6nurp66dKldXV1xObp6elNTU0NDQ2fffaZiopKa2srXvnly5eDg4MbGhrev38/bdq0YcOG4ct9fHzU1dULCgra2tqYTObkyZM1NTXLy8vx0m4DEP3Z3tSpU7v247l+/bqmpuaBAwd62op4uI5hWGNj46+//qqmprZgwQLBdWT0zCDoxwMGwUB9r0SpR4bS1FBONUPkGoR+PGIYjO+G+C0eotcbh8NRU1Pz9vbGP7LZbBUVlU2bNr18+RIhdP36deGbx8XFIYRevnzZdUehoaEIodraWgzDfHx8iGsewzB8xrL9+/cLCUCEw/8/3bZ4emVpaSnYdqRQKN9//z2XyyVWkN0zAy0eMBgk3+KRjzQlx6lmiFyD0OIRg5T2XC4qKmKz2XZ2dvhHVVXV4cOHFxYWWlhY6Ovrr1y5Mjg4uKysrKfN8Tf1u53LDS/qdvCoSZMmqampFRYWCgmgvwcmAuL637FjB4ZhWlpaggMPyPSZ8fLyosi7xMTExMREsqMYQkT54g0Smb4YkVynmqFwDeLTYJEdhYwZjNEEqP2vorW1FSG0e/fu3bt3EwsNDQ1VVVXv3r27c+fOkJCQAwcOfPXVV2fOnOl1yvgbN24cOXKEyWQ2NzcLn9JWRUUFn8q1pwD6c1B9tWfPnrNnzwYGBnp4eBBzVsv0mfHz83NychJlTdmFT8ezdetWsgMZKjpNfyhJMn0xCpK/VDNt2jS5vwYfPnwYGRkJfZv6BM/PA0zwho94T7X+/PNPPLie1q+trcWnYsYfVHfa/MKFCwih58+fYxj25s0bJSUlLy+v0tJSDodz+PBhhNC7d++wLjdUuVwuhUJZvXq1KAH0SuynWoIh/frrrwihzz//nFgiu2cGwVMtMAgG6nslSj3ylKbkONUMkWsQnmqJQUqfao0YMYJGo+Xm5nZaXlVVVVBQgBDS09MLCwubMGEC/lGI/Px8Ho+3adMmCwsLfFTQntbMyMjAMGzatGlCApCwVatWTZ069fr165cuXcKXwJkBQErI08UIqQYA8QxAi4dGo61Zs+bChQvR0dHNzc0dHR2VlZXv3r2rqqrasGFDYWEhl8t9/vz5mzdv8GtDCFNTU4RQWlpaW1tbcXGx4KuPCCE+n9/Y2Nje3p6Xl+fn52dqarp69WohAfTzuFJTU/v0yiiFQomKiqJQKAwGo7GxUUhgsn5mAJA50nwxQqoBQEIEb/iIcuctPDwcfww8YsQIYhb7jx8/BgQEmJqaUqlUPT29L774gslklpWVOTs76+joKCoqGhkZBQUFtbe3E5uPGjWqtLT03Llz+KQbJiYm+JsCAQEBurq62tranp6eJ06cQAhZWlqWl5f7+PgoKSkZGxtTqVQ6nb548eLS0lIiqm4D6PUG18OHD6dPn048bB4+fLizs/Mff/yBl968eVNTU/P777/vuuH9+/etra3xrYyMjDZs2EAU4de/trZ2WFiY7J4ZBE+1wCAYqO9Vr/XIVpoayqlmiFyD8FRLDNLydjopfHx8dHV1yY5CGg3GmYEWDxgMEmvxkEW+09RgHN0QuQal+bdVaklpPx6J6fYNSYDgzAAgNeT7YpTvowNyT5ZaPH1SWFgo5EV/b29vsgMEsictLW3Xrl18Pn/JkiWmpqY0Gs3Y2NjDwyMvL0+UzT/99NOuX0UNDY3B3jY8PNzGxkZVVVVdXd3GxmbPnj3EVAO4+Ph4fPxcMzOzNWvW9DRbU1tbm42NDfH68bVr18LDw+EnEIABJ6OpBsfn8yMiIpydnQUXSkm6kI0WT2Bg4JkzZz58+GBubi7iqEQ2NjZCbm1dvHhxsGOWDDHODBDPvn37oqKiAgMD+Xz+vXv34uPjGxoasrKyOBzOjBkzqqqqxKvWxcVF7JBE3PbevXvffvtteXl5TU3NwYMHw8PDv/zyS6I0ISFhxYoVnp6elZWVycnJmZmZ8+fPb29v71pPUFBQUVER8XHRokU0Gm3WrFlNTU1iH4I8ke+LUb6PTqrIbqpBCBUXF8+YMWPbtm1sNltwubSkC8F2ADxrBDg0yP0k2Gy2k5MT6VWJ/pw4LCzM2toaH7+Ex+MJDoXy6NEjhFBISEivlcydO7e5uVlwiY+PT3p6uigB9GfbJUuWECOvYBjm6emJEKqqqsI/uru7GxkZ8fl8/CPeUzUrK6tTJffv358zZw5CKCgoSHA5g8FwcnLi8XiiRDJQ36vB/n4CiZFAPx5pyDai/7bKdKrJzc1dunTpuXPnHB0dux3irk/pYqj34wFyIzY2tra2Vtqq6klJScmePXv2799Po9EQQlQqNSUlhSi1sLBACJWWlvZaz61btzQ1NYmPFRUVL1++nDlzpigx9GfbK1eu4JHjjI2NEUItLS1EVYaGhsSwK/gwvm/evBGsgcPh7NixIzIysmvlwcHBubm53RYBIA1kKNvIeqpxcHBISkpasWKFiopKtyuQni6gxQPEhGHY0aNHx4wZo6KioqOjs3jxYmIaHQaDoaysPHz4cPzj5s2b1dXVKRRKfX09QsjPz8/f37+0tJRCoVhZWUVFRdFoNH19/Q0bNhgaGtJoNGdnZ2IUkD5VhRC6detWn4Y2EUVUVBSGYYsWLeq2lMPhIITodHpfqz106JCvr694IfVn2+LiYm1tbTMzM/yjhYWFYBLHO/HguZUQFBS0efNmPT29rrXp6Oi4ublFRkZiGCZePAD0aohkGzlLNV2Rny4Eb/jAUy2AQyI8Ndi7d6+ysvLZs2ebmpry8vImTJjwySefVFdX46UrVqwwMDAgVj5y5AhCqK6uDv/4xRdfWFpaEqU+Pj7q6uoFBQVtbW1MJhPvRVteXi5GVdevX9fU1MSH0u+ViHdNLSwsbG1teypNSkpCCCUmJoqyR0JlZaWtrW1HR0efturPtlwut7Ky8vjx4yoqKsQQNRiGZWRkKCkpRUVFNTc3v3z5csyYMXPnzhXcMCsra9GiRRiG4TModXqqhWHYrl270P/mKBBOlO+VKAaqHkA6Ea9BWc82Iv62ykeqwYRO3CR6uoCnWkBacDico0ePLl26dOXKlVpaWvb29j///HN9ff2pU6fEq5BKpeJ/wNna2kZHR7NYrDNnzohRz4IFC5qbm/fs2SNeGF21trb+9ddflpaWXYtqamouXrzo6+vr5OTU059lPTl06NB3332noCDOBSjetiNGjDAxMQkODj58+LDgjJ5ubm4BAQEMBoNOp9vZ2bFYrNOnTxOlHA7Hz88vOjpaSM2jRo1CCOXn5/fxOAAQyRDJNnKTaoQjN11AiweIg8lktrS0TJo0iVgyefJkZWXlTmPSi2fSpElqamrEXWty1dbWYhimpqbWtcjJycnX13fx4sWpqalKSkqi11lVVXXt2jV8zNy+EnvbioqK2tra+Pj4//znP+PHjyeeZAUFBZ06dSo9Pb2lpeX169fOzs5OTk4VFRV4aWBg4Pr16/GuPz3BT05NTU2fDwYAEQyRbCM3qUY4ctMFtHiAOPA3DDsN0qCtrc1isQakfhUVFfwZCuna2toQQt12xNPX17979+7x48e1tLT6VGd4ePi3334r2JtYAtsqKSnp6enNmTPn4sWLTCYzNDQUIfTu3bvw8PD169fPnDlTXV3d3Nw8JiamqqoKv5mflZWVn5+/bt064TXjUxbgJwqAATdEso3cpBrhyE0XVFL2CmSdtrY2QqhTxmlqajIxMel/5Tweb6Cq6j/8+ux24Cw9PT38PPRJdXV1fHy84MA2ktmWYGVlpaioyGQyEULFxcUdHR1GRkZEKZ1O19XVxUtjY2PT09M73dMOCQkJCQl5/Pgx8Tc3l8tF/ztRAAy4IZJt5C/VdIvcdAH3eIA47OzsNDQ0njx5QizJycnhcrkTJ07EP1KpVB6PJ17lGRkZGIYRkzz3p6r+09fXp1AoHz586FqUkpIi/HFPt8LDw1euXKmrqytGMGJs+/79++XLlwsuwVs5+FvoeKIXnOOaxWI1NDTgpWfOnBHs9CfYc1nwEQN+cgwMDMQ4IgB6NUSyjaynGhGRmy6gxQPEQaPR/P39r1y5cu7cuebm5vz8/I0bNxoaGvr4+OArWFlZNTQ0XL16lcfj1dXVdRrfRVdXt6qqqqysjMVi4fmFz+c3Nja2t7fn5eX5+fmZmpoSz4/7VFVqaurAvi+qpqZmYWFRWVnZaXlJSYmBgYFgF2CEkLe3t4GBwbNnz3qqraam5pdfftm6dWvXokHaVl1d/c6dO3fv3m1ububxeM+fP//666/V1dW3bduGEDI3N3d3d4+JicnMzORwOBUVFfj/4Nq1a3sKoyv85Njb24u+CQCiGyLZRtZTjYjITRfQ4gFi2rdvX2ho6IEDBz755BM3N7eRI0dmZGSoq6vjpZs2bXJ3d1+2bNno0aMPHjyI38MkusRu3LhRX1/f1tb2s88+a2hoQAi1tbXZ29urqqq6urpaW1v//vvvxPPsvlY14BYsWMBkMvHBMAhYd+NJcLnc2tra5OTknqo6fPjwokWLTE1NJbYtjUabPn36unXrjI2NNTU1PT09R44cmZ2dbWdnhxCiUCiXL1/29vZeu3atjo6Ora1teXl5UlKSq6trT2F09fjxY2Nj43Hjxom+CQB9MkSyjUynGoRQdna2i4uLkZFRTk7OixcvDA0Np0+fnpmZKbgOyelC8K41jMcDcEiy4534+Pjo6upKbHcEEcd7KC4upqMlzR8AACAASURBVFKpgmPY9KSjo8PV1TU2NlaMYMjatp/q6+tpNNoPP/wgysoD9b2S8PcTDB4JzDLRCSnZRsTfVrlPNX1KFzAeD5BbpM+pK4SVldWBAwcOHDhAzMzQrY6OjqtXr7JYLG9v777ugqxt+y84ONjR0ZHBYEh+1wCIR2qzjdynGtLTBbR4AOjdrl27PD09vb29u+1XiMvIyEhKSkpNTe12RA3hyNq2n44ePZqbm3vz5s0+jRECAOiJHKcaaUgX0OIBJAsMDDxz5syHDx/Mzc0TExPJDqdHISEhDAYjLCyspxVmzZp1/vx5YlKePiFr2/5ITk7++PFjRkaGjo6OhHcNgHhkItvIZaqRknQB4/EAkoWGhuKj4Um/OXPmzJkzh+wopIWHh4eHhwfZUQDQB7KSbeQv1UhJuoB7PAAAAACQf9DiAQAAAID8gxYPAAAAAOQftHgAAAAAIP+66bl86dIlyccBpM3Dhw/JDqG/MAyjUChCVsDHO4cvvCySg++ntOn1ehkMQ+QaxL+ucn+YA6uysnLgZ3gVHI4QHxcSAAAGyUCNuQwAkHsDPuYyBdIHkFcJCQnbt29vaWnZt2/fli1bqFQYiwGAv/n48WNERERISIient7Ro0cXL15MdkQADCJo8QB5xmazDx8+HB4ebmZmFhkZOW/ePLIjAkBapKWlMRiMN2/e7NixY+fOnTQajeyIABhc0HMZyDM1NbXg4OCXL1+OGzdu/vz5Cxcu/Ouvv8gOCgCSFRcXf/7557Nnz7a0tCwoKAgODobmDhgKoMUD5J+lpeWlS5fS0tJev349duzYnTt3Cp+oDwB51draGhwcbG9v//bt2z/++CMlJcXMzIzsoACQEHiqBYYQHo8XHR29d+9eOp0eEhKyatUqsiMCQEIwDDt79mxAQACXy927d++WLVsUFRXJDgoAiYJ7PGAIUVJS8vX1LSwsdHd3X7169cyZM1++fEl2UAAMuqdPn7q4uKxZs2b27NmFhYW+vr7Q3AFDELR4wJBjaGgYFxeXk5PDZrPHjx/v6+v74cMHsoMCYFBUV1f7+PhMmTJFWVn5+fPncXFxenp6ZAcFADmgxQOGqMmTJz948CA2NvbChQuWlpbHjh3j8/lkBwXAgOHxeMeOHRs9evTNmzfPnDlz9+7dcePGkR0UAGSCfjxgqGtsbAwODj558uT48eOjoqKcnJzIjgiA/kpLS/Pz83v9+jWDwdi9e7eGhgbZEQFAPrjHA4Y6HR2dY8eOPX36VFVVdfr06atWraqpqSE7KADEVFJS8tVXX82ePdvc3JzJZB46dAiaOwDgoMUDAEIIOTg4ZGZmJicnZ2Zm2tjYhIeHc7lcsoMCoA/wN8/t7Ozy8/Nv3bqVkpJibm5OdlAASBF4qgXA3wgO03zs2LG5c+eSHREAvcAwLDExcfv27SwWa9++fZs3b4Y5VQDoCu7xAPA3+DDN+fn5o0aNmjdv3sKFC8vKysgOCoAePXv2zNXV1dvb283NDX/zHJo7AHQLWjwAdMPKyiolJeW3334rLS21tbUNDg5ua2sjOygA/ub9+/e+vr5Tpkzh8XgPHz6Mi4vT19cnOygApBe0eADo0T/+8Y8XL16EhYUdPXrU2to6Li6O7IgAQAih9vb2Y8eOWVpaJiYm/vLLL9nZ2VOmTCE7KACkHbR4ABCGGKb5008/Xb169axZs5hMJtlBgSHt7t2748eP/9e//vX1118XFhauWrWKQqGQHRQAMgBaPAD0zsjIKC4uLjs7u6WlBR+mubm5meygwJBTUVGxatWqWbNmjRw58tWrV8eOHdPU1CQ7KABkBrR4ABDVlClTHj58ePr06QsXLowePfrUqVMwTDOQDDabHRwcbG1tnZOTc/PmzZSUFAsLC7KDAkDGwNvpAPSZ4DDNx48fnzZtGtkRAXmWkpLy3XffffjwYefOnVu3blVWViY7IgBkEtzjAaDP8GGanzx5QgzTXFtbS3ZQQA7l5ubOmDHDw8NjxowZhYWFAQEB0NwBQGzQ4gFATI6OjpmZmVevXv3jjz9Gjx597Nix9vZ2soMCcqKhocHX13fSpEltbW0PHjyIi4szMDAgOygAZBu0eADol4ULF7569crX1zcgIMDe3v7OnTtkRwRkW3t7+6lTp0aPHn358uXo6Ojs7Gx4bArAgIAWDwD9hQ/T/PLlSysrq7lz5y5cuPDNmzdkBwVkUkZGxsSJE7ds2bJ8+fLCwsL169crKECWBmBgwLUEwMCAYZpBf1RWVq5atcrd3V1fX//FixfHjh2j0+lkBwWAXIEWDwADCR+mOTQ09OjRo3Z2dpcvXyY7IiDtOBxOeHj4mDFjsrOzr1+//ttvv40ZM4bsoACQQ9DiAWCAEcM0Ozs7e3l5/eMf/4BhmkFPUlJSbG1tDx486O/vn5+fv2DBArIjAkBuQYsHgEGBD9OckZFRV1cHwzSDrgoLC+fNm+fh4eHq6lpSUhIcHKyiokJ2UADIM2jxADCIZsyY8fz589OnT8fHx9vY2MAwzQAh1NjY6Ovra29vX19fn5WVFRcXN3z4cLKDAkD+QYsHgMGloKCwatWqoqIiT0/PTZs2TZs2LScnh+ygADn4fH5cXNzo0aMvXbp08uTJR48eOTs7kx0UAEMFtHgAkARdXd1jx449fvxYRUXF2dkZhmkegv74448JEyasW7du2bJlRUVF8OY5ABIG1xsAkjN+/PjMzMyLFy9mZGTY2NjAMM1DxNu3b/E3z/X09J4/fw5vngNACmjxACBRFArF09Pz1atXDAYDH6b5t99+IzsoMFi4XO6xY8dsbGwePnyYkJDw22+/jR07luygABiioMUDAAnU1dWDg4Pz8/MtLS3nzJkDwzTLpZSUFBsbm6CgIH9//5cvX3p6epIdEQBDGrR4ACDNqFGjrl+/fu3atYKCAhimWZ4UFRXNnz/fw8Nj0qRJBQUF8OY5ANIAWjwAkAyfixSGaZYPTU1NO3fuHDduXG1t7b179y5dumRqakp2UAAAhKDFA4A0UFZW9vX1ffXqFT5M8+zZswsKCsgOCvQN8eb56dOnDx8+/OjRo+nTp5MdFADg/4MWDwDSwtjYOC4u7vfff6+trXV0dBQyTPO7d++ePn0q4fCGuPb29rt37/ZU+vjxY2dn57Vr13p7e5eWlvr6+ioqKkoyPABAr6DFA4B0cXNze/r06YkTJ/BhmuPi4jAM67TOjh07Pvvss8rKSlIiHJp8fX2XLFnSdRSlqqqqVatWTZ06VV1dHX/zXEtLi5QIAQDCQYsHAKlDpVLXr1+PD9P8zTffTJ069dGjR0TpgwcP4uPj6+vr58+f39raSmKcQ0dUVNRPP/3U2tq6a9cuYiGPx8PfPM/IyPj111/T09Pt7OxIDBIAIByl65+PAADp8ezZMwaD8fDhwxUrVvz444/Dhg2bOHHiy5cv29vbqVTqrFmzbty4AQ9QBlVqaurnn3+OT4hGoVCys7OnTJmSkpKydevWd+/e7dixY+fOnTQajewwAQC9gBYPANIOw7C4uLidO3fyeLwlS5bExsYSl62iouLWrVuPHDlCboRyjMlkTp06lcPh4C0eKpVqb2+vr69/584db2/vw4cPm5iYkB0jAEAk0OIBQDY0NzcHBgb++uuvbDa702X7008/bdiwgazA5Ni7d+8mTpxYV1cnOBkIhUKZNm3a4cOHXVxcSIwNANBX0I8HANlAp9MxDONyuV3/StmyZUt6ejopUckxDoezcOHC+vr6rnOfFRUVjRs3jpSoAABigxYPALKByWT+/PPPPB6vaxGGYUuWLCkqKpJ8VPKKz+d7e3u/ePGi6wnHMKy5uTksLIyUwAAAYoOnWgDIAAzDXF1dc3JyepprnUqlmpiYPHnyZNiwYRKOTS7961//+vHHH/G+O91SUlIqKCiwsrKSZFQAgP6AezwAyICEhIT79+8jhHp6Lau9vf3t27ceHh5cLleyocmh06dPHzlyREhzR1lZmcfjbdu2TZJRAQD6Ce7xACADWlpacnNznz59+vTp05ycnJKSEj6fT6VSEUKCd32oVOry5cv/85//kBepzEtPT587d25HR4fgQiqV2tHRgWGYmpqao6Pj1KlTx48fP2HChLFjx5IVJwCgr6DFA4DsaW1txRtAz549y8nJKS4u7ujoUFJS6ujo4PP5hw4dCggIIDtGmVRYWDhlyhQWi6WsrIzfLaPT6RMnTpwyZcqECRMmTJhgaWlJoVDIDhMAIA5o8UgU5EoAAADwy0sKKtkBDDl+fn5OTk5kRwHkGY/Hq66uHjFiRKflERERCKGtW7eSEZTkPHz4MDIyMiEhoa8bvn//nkqlwqxYYFDh30+yoxiioMUjaU5OTl999RXZUYCh6PLlywihofD1i4yMHAqHCWQUtHjIAu9qAQAAAED+QYsHAAAAAPIPWjwAAAAAkH/Q4gEAAACA/IMWDwAAAADkH7R4AADC3Lx5U0tLKyUlhexABtiGDRso/7Ny5UrBorS0tF27dvH5/CVLlpiamtJoNGNjYw8Pj7y8PFFq/vTTTyldaGhoDPa24eHhNjY2qqqq6urqNjY2e/bsaW5uFlwhPj5+8uTJmpqaZmZma9asqa6u7raetrY2Gxub3bt34x+vXbsWHh7eaRBqEcnomcTx+fyIiAhnZ2fBhV3PxtWrV4n6P/nkE9HrB5IHLR4AgDByPFSarq5uampqUVFRbGwssXDfvn1RUVGBgYF8Pv/evXvx8fENDQ1ZWVkcDmfGjBlVVVXi7cvFxUXsOEXc9t69e99++215eXlNTc3BgwfDw8O//PJLojQhIWHFihWenp6VlZXJycmZmZnz58/vdmLaoKCgoqIi4uOiRYtoNNqsWbOampr6FLbsnkmEUHFx8YwZM7Zt28ZmswWXdz0bHh4elZWVmZmZn332mdiBAQnBgAQhhBISEsiOAgxRX3755Zdffkl2FD1is9lOTk79rwcfe7DX1Xx8fIyNjTstDAsLs7a25nA4GIbxeLzPP/+cKHr06BFCKCQkpNea586d29zc3Glf6enpogTfn22XLFmCR47z9PRECFVVVeEf3d3djYyM+Hw+/vHEiRMIoaysrE6V3L9/f86cOQihoKAgweUMBsPJyYnH44kSCSbjZzI3N3fp0qXnzp1zdHR0cHDoukK3Z8PX13fYsGG9Vi7i9xMMBrjHAwCQCrGxsbW1tSQGUFJSsmfPnv3799NoNIQQlUoVfJZnYWGBECotLe21nlu3bmlqahIfKyoqXr58OXPmTFFi6M+2V65cwSPHGRsbI4RaWlqIqgwNDYmJbvAhud+8eSNYA4fD2bFjR7fj4wUHB+fm5oo4dJ6sn0kHB4ekpKQVK1aoqKh0u0KfzgaQHtDiAQD0KCsry9TUlEKh4LcEoqOj1dXV1dTUkpOT58+fT6fTTUxMLly4gK8cFRVFo9H09fU3bNhgaGhIo9GcnZ1zcnLwUgaDoaysPHz4cPzj5s2b1dXVKRRKfX09QsjPz8/f37+0tJRCoVhZWSGEbt26RafTQ0JCJHawUVFRGIYtWrSo21IOh4MQotPpfa320KFDvr6+4oXUn22Li4u1tbXNzMzwjxYWFoINSrwTD974IAQFBW3evFlPT69rbTo6Om5ubpGRkZgITznl7Ex21aezAaQHtHgAAD1ycXF58OAB8XHTpk1bt27lcDiampoJCQmlpaUWFhbffvstj8dDCDEYjNWrV7PZbF9f37KysmfPnrW3t8+ePbuiogIhFBUVJTjzw8mTJ/fv3098jIyMXLhwoaWlJYZhJSUlCCG8cyifz5fYwd64cWP06NFqamrdluLPYvraieTt27cZGRlffPGFGPGIty2Px3v79u2JEyfS0tKOHz+urKyMLw8MDKyurj5+/DiLxWIymZGRkXPnzp02bRqx4f3790tLS5cvX95TzePHj3/79u2LFy96jUE+zqRwop8NID2gxQMA6DNnZ2c6na6np+ft7d3a2lpeXk4UUanUMWPGqKio2NraRkdHs1isM2fOiLGLBQsWNDc379mzZ+CiFqa1tfWvv/6ytLTsWlRTU3Px4kVfX18nJ6ee7lv05NChQ999952CgjiZVrxtR4wYYWJiEhwcfPjwYS8vL2K5m5tbQEAAg8Gg0+l2dnYsFuv06dNEKYfD8fPzi46OFlLzqFGjEEL5+fnCA5CbMymciGcDSBVo8QAAxIffQsDv8XQ1adIkNTW1wsJCyQYljtraWgzDur0t4eTk5Ovru3jx4tTUVCUlJdHrrKqqunbt2urVq8WIR+xtKyoqamtr4+Pj//Of/4wfP554khUUFHTq1Kn09PSWlpbXr187Ozs7OTnht98QQoGBgevXr8e7/vQEPzk1NTXCA5CbMymciGcDSBVo8QAABpGKikpdXR3ZUfSura0NIdRtT1V9ff27d+8eP35cS0urT3WGh4d/++23gr2JJbCtkpKSnp7enDlzLl68yGQyQ0NDEULv3r0LDw9fv379zJkz1dXVzc3NY2Jiqqqqjhw5ghDKysrKz89ft26d8JpVVVXR/06UEHJzJoUT8WwAqUIlOwAAgNzi8XhNTU0mJiZkB9I7/Aes23H29PT0tLW1+1phdXV1fHy84MA2ktmWYGVlpaioyGQyEULFxcUdHR1GRkZEKZ1O19XVxUtjY2PT09M7PfQJCQkJCQl5/PjxpEmT8CVcLhf970QJIX9nslsing0gVeAeDwBgsGRkZGAYRnSPpVKpPT3/Ip2+vj6FQvnw4UPXopSUFOGPe7oVHh6+cuVKXV1dMYIRY9v379936nSMt3Lwt9DxRue7d++IUhaL1dDQgJeeOXNGcMwS/J4cPh4P0dxBCOEnx8DAQHgksn4mRSTi2QBSBVo8AICBxOfzGxsb29vb8/Ly/Pz8TE1NiS4UVlZWDQ0NV69e5fF4dXV1nQaD0dXVraqqKisrY7FYPB4vNTVVkm+nq6mpWVhYVFZWdlpeUlJiYGAg2AUYIeTt7W1gYPDs2bOeaqupqfnll1+2bt3atWiQtlVXV79z587du3ebm5t5PN7z58+//vprdXX1bdu2IYTMzc3d3d1jYmIyMzM5HE5FRYWPjw9CaO3atT2F0RV+cuzt7YVHIutnUkSCZwPICmjxAAB6dOLEicmTJyOEAgICPDw8oqOjIyIiEELjxo17/fp1TEyMv78/QmjevHnFxcX4Jm1tbfb29qqqqq6urtbW1r///jvRpWPTpk3u7u7Lli0bPXr0wYMH8ScCRP/ZjRs36uvr29rafvbZZw0NDZI/2AULFjCZTHy0GEK3A65wudza2trk5OSeqjp8+PCiRYtMTU0lti2NRps+ffq6deuMjY01NTU9PT1HjhyZnZ1tZ2eHEKJQKJcvX/b29l67dq2Ojo6trW15eXlSUpKrq2tPYXT1+PFjY2PjcePG9XoUMn0mEULZ2dkuLi5GRkY5OTkvXrwwNDScPn16Zmam4DqCZwPIDEkO8AwQzDIByCOBWSZ8fHx0dXUHdRe9EnuWieLiYiqVevbs2V637ejocHV1jY2NFSM8srbtp/r6ehqN9sMPP4gSidyfyU5nAwezTEg/uMcDABhI4k2yTQoOh3P79u3i4mK8F6qVldWBAwcOHDhAzMzQrY6OjqtXr7JYLG9v777ukaxt+y84ONjR0ZHBYIgSidyfScGzgWFYVVVVVlYWPnImkGbQ4gEyrKio6Lvvvhs7dqympiaVStXS0rK2tl6wYMHDhw/JDg3IgIaGhnnz5llbW3/zzTf4kl27dnl6enp7e3fb8RaXkZGRlJSUmpra05jCQpC1bT8dPXo0Nzf35s2b+CA6okQix2ey09lITk42NjZ2dXW9ceOGGLUBiSL7JtPQguCp1sA5ffq0kpLSjBkzbt261djY2NbWVlpaevHiRWdn53//+99kRyeNBvup1q5du/ABCUeOHHn58uXB25Fw/X9qcPv27YCAgIGKR9ZdvXo1NDS0vb1djG3l70z252zg4KkWiSgYTIQmQRQKJSEhQXB2IenE4XBmzZolOKGStFWOdy10c3O7ffs2lfq3YaXw5xRbtmzpX5h9Jv0nzdPTEyF0+fLlAQpKSl26dMnLywsyG5BO8P0kEYxACLoRGxsrOM2yFFb+/fffd3R0hIWFdWruIITmzp07d+7cftYvBuk/aQAAMJRBPx5pdPbs2UmTJtFoNHV19ZEjRx48eBAhhGHY0aNH8TkadXR0Fi9eTExXFB0dra6urqamlpycPH/+fDqdbmJicuHChV7rvHfvnq2trZaWFo1Gs7e3v337NkLIz8/P39+/tLSUQqFYWVkhhDo6Ovbu3Wtqaqqqqjpu3Dj8rmyvO+1P5QihW7du9TQcC5fLTU9PHzZs2JQpU4SfyaF20gAAAPSI1GdqQw4SoR8PPt5JWFjY+/fvGxoa/v3vf69YsQLDsL179yorK589e7apqSkvL2/ChAmffPJJdXU1vlVQUBBCKD09/cOHD7W1ta6ururq6lwuV3idly9fDg4ObmhoeP/+/bRp04hXK7/44gtLS0sipO3bt6uoqCQmJjY2NgYGBiooKDx+/LjXnfaz8uvXr2tqah44cKDrKfrzzz8RQtOmTev1hA+1kyacBN5OlwbQTwJIM/h+kgjOu0T12uLhcrna2tru7u7Ekvb29sjISDabraGh4e3tTSx/9OgRQohoEOC/oxwOB/948uRJhFBJSYmQOjvtGp9xEJ/3WPD3lcPhqKmpEbtms9kqKiqbNm0SvtP+Vy7EkydPEEL/+Mc/hK8GJ60TaPEAQDr4fpII+vFIl7y8vKamJsFuKIqKir6+vk+ePGlpaRGc42by5MnKyso5OTnd1oO/MoPPYdRTnZ02wd+07DqYSlFREZvNxkduRQipqqoOHz6ceDbU004HvHJBGhoaCCE2my18NSaTCSetk8rKykuXLomypuzCxyaQ+8MEMgrGziARtHikS3NzM0Ko6/TCTU1N6H+/9ARtbW0WiyV2nQihGzduHDlyhMlk4nPxdLt5a2srQmj37t27d+8mFhoaGva638GrfOTIkTQaDX+2JQSctK6ys7M7TWwkr4bIYQIARAc9l6WLkZERQqi+vr7Tcvynt9NPdVNTEz4lsnh1lpeXL1myZPjw4Tk5OR8+fAgPD+92cz09PYRQRESE4L3BXv9MGdTKVVRU5s6dW19ff//+/a6lDQ0N69atQ3DSugNPtQAgF7xnQCJo8UiXkSNH6urq3rlzp9NyOzs7DQ0NvP8KLicnh8vlTpw4Uew68/PzeTzepk2bLCwsaDQahULpdvMRI0bQaLTc3Nw+HcigVo4QCg4OVlFR2bZtW6fZChFCL1++xF9Zh5MGAACAAC0e6aKiohIYGJiZmclgMN6+fcvn81ksVkFBAY1G8/f3v3Llyrlz55qbm/Pz8zdu3GhoaOjj4yN2nfiUwmlpaW1tbcXFxYK9W3R1dauqqsrKylgslqKi4po1ay5cuBAdHd3c3NzR0VFZWfnu3TvhO+1/5ampqT29nY4QcnR0PH/+/MuXL11dXW/evPnhwwcej/fXX3/FxMSsXbsW7wEzBE8aAACAHpF9h29oQaLNMnHixAl7e3sajUaj0caPH3/y5EkMw/h8/pEjR0aNGqWkpKSjo7NkyZKioiJ8/ZMnT+ITxIwaNaq0tPTUqVN0Oh0hZGZm9ueffwqpMyAgQFdXV1tb29PT88SJEwghS0vL8vLyZ8+emZmZqaqquri4VFdXf/z4MSAgwNTUlEql6unpffHFF0wms9ed9qdyDMNu3rypqan5/fffCzlR5eXl27dvt7e319DQUFRU1NbWHj9+/Nq1a+/fv4+vMNROmnDwrhYApIPvJ4lglgmJkpVZJoBcglkmACAdfD9JBE+1AAAAACD/oMUDAAAAAPkHLR4AAJBGaWlpu3bt4vP5S5YsMTU1pdFoxsbGHh4eeXl5omz+6aefUrroNDzVYGwbHh5uY2Ojqqqqrq5uY2OzZ88efHQrQnx8/OTJkzU1Nc3MzNasWVNdXU0Uff/99512Soy0ee3atfDw8K4DcgIgOmjxAACA1Nm37/+xd+cBTZz5/8CfcCQh4ZajHCKXR6koWq2CULRurcoqagXxqLVbLaItoHgUUYsoKOoiC8JaLaX9egEqP/BCrSKrttXiKoJYLaAIggKKQIAggczvj9nNppzhSALD+/VXM8dnPjNMk4/zPPM830RFRW3atEksFl+/fv3YsWOVlZU3btwQCoXvv/9+aWlp98I6Ozt3OyUZ971+/fqKFSuKiorKysq2b98eHh4+f/58ydrExMTFixd7eHg8e/YsNTX12rVrM2bMaGpq6jTs7NmzuVzu1KlT6ZFFAboBFQ8A9BqhUOjk5NTXQvU7u3btSkhISEpK0tLSIoQ4Ojo6OzvzeDwrK6vQ0NDq6uoffvih0yBcLrempkb6RRVvb+8NGzbIkkBP9mWz2atXrzY0NNTU1PTw8JgzZ85PP/0kGT3h22+/NTU1Xb9+vY6OjoODw9q1a7OysqQHYjh8+LD0ce/fvy9Z5efnN3r06JkzZ8pSIQG0hooHAHpNXFxceXl5XwvVv+Tn52/ZsmXbtm1cLpcQoqamdubMGclaa2trQkhBQUGncS5cuEAXTLTi4uL79+9/8MEHsuTQk32Tk5PpzGlmZmaEkNraWkkoExMTyeiagwcPJoQ8ffpUlsiEkODg4KysrMjISBm3B5CGigcA/oSiqIiIiLfffpvD4ejp6c2ZM0cyTamvry+bzX7rrbfoj6tXr+bz+SwWi56Ow9/fPyAgoKCggMVi2draRkVFcblcIyOjlStXmpiYcLlcJycnyb/muxSKEHLhwoUORqRkkqioKIqiZs+e3eZaepBxehinLtm1a1frqXAVsG9eXp6uru6QIUPoj9bW1tKFLN2Jhy7jZKGnp+fq6hoZGYm3u6E75DvcD/wZkW0EO1WMjAAAIABJREFUQgB5kHEEwq1bt7LZ7MOHD1dVVWVnZ48dO9bAwODFixf02sWLFxsbG0s23rNnDyGkoqKC/vjxxx/b2NhI1np7e/P5/AcPHjQ0NOTm5tL9VYuKiroR6uzZs1paWiEhIZ3m399HeLO2trazs2tv7alTpwghJ0+e7FLMZ8+e2dnZNTc3dyOf7u3b2Nj47Nmz6OhoDocj3VCVkZGhrq4eFRVVU1Nz//79t99++6OPPpKs3b59u7m5ua6urrq6uqWlpbu7+2+//dYicmBgICHk7t273TiXvqC/35/9Gp7xAMD/CIXCiIiIefPmLVmyREdHx97e/sCBAy9fvjx48GD3AqqpqdGPi+zs7GJjYwUCQXx8fDfiuLm51dTUbNmypXtp9Bd1dXVPnjyxsbFpvaqsrCwhIcHPz8/R0bG9J0Dt2bVr11dffaWi0p0v/O7tO3jwYHNz8+Dg4N27d0vPY+/q6rpx40ZfX19tbe2RI0cKBILvvvtOsvbTTz89ffp0cXFxbW3t8ePHi4qKXF1dc3NzpSMPHTqUEJKTk9ONc4EBDhUPAPxPbm5ubW3tuHHjJEvGjx/PZrOl+5Z227hx43g8nqSNDForLy+nKIqeiqQFR0dHPz+/OXPmpKWl0TPHyai0tPT06dPLli3rRj7d3re4uLi8vPzYsWM//vjjmDFjJC1ZQUFBBw8evHLlSm1t7ePHj52cnBwdHYuLi+m1gwcPHjNmjKamJpvNnjhxYnx8vFAojImJkY5MX5yysrJunA4McKh4AOB/6Fd/W4y8oqurKxAIeiU+h8OpqKjolVCM1NDQQAjhcDitVxkZGaWnp0dHR+vo6HQpZnh4+IoVK6R7EytgX3V1dUNDw2nTpiUkJOTm5oaFhRFCnj9/Hh4e/sUXX3zwwQd8Pt/KyurQoUOlpaV0g2Zr9vb2qqqqf/zxh/RCDQ0N8t8LBdAlaspOAAD6EF1dXUJIi/qmqqrK3Ny858FFIlFvhWIq+ue8zXH2DA0N6b9Ol7x48eLYsWOPHj3qRjI92VfC1tZWVVWVbpnKy8trbm42NTWVrNXW1tbX12/RbiUhFovFYnGL+q+xsZH890IBdAme8QDA/4wcOVJTU/P27duSJbdu3WpsbHz33Xfpj2pqaiKRqHvBMzIyKIqaOHFiz0MxlZGREYvFqq6ubr3qzJkz9JveXRIeHr5kyRJ9ff1uJNONfV+9erVo0SLpJXSVQ7+FThe7krF5CCECgaCyspJeSwj56KOPpPfNzMykKMrR0VF6IX1xjI2Nu3YyAKh4AEAal8sNCAhITk4+cuRITU1NTk6Oj4+PiYmJt7c3vYGtrW1lZWVKSopIJKqoqGgxkoq+vn5paWlhYaFAIKCrGbFY/Pr166ampuzsbH9/fwsLC0mnkC6FSktLGwhvp/N4PGtr62fPnrVYnp+fb2xsLN0FmBDi5eVlbGx8586d9qKVlZV9//33a9asab1KTvvy+fxLly6lp6fX1NSIRKK7d+9++umnfD5/7dq1hBArK6spU6YcOnTo2rVrQqGwuLiYvq8+//xzeveSkpKEhISqqiqRSPTrr78uX77cwsLCx8dH+hD0xbG3t28vc4D2oOIBgD/55ptvwsLCQkJCDAwMXF1dLS0tMzIy+Hw+vXbVqlVTpkxZuHDh8OHDt2/fTjcuSDqf+vj4GBkZ2dnZzZw5s7KykhDS0NBgb2+voaHh4uIybNiwq1evShopuhpqgHBzc8vNzaXH3ZGg2hp+prGxsby8PDU1tb1Qu3fvnj17toWFhcL25XK5kyZNWr58uZmZmZaWloeHh6Wl5c2bN+npsVgs1okTJ7y8vD7//HM9PT07O7uioqJTp065uLjQu0+fPn3z5s3m5uY8Hs/T03PSpEk3b94cNGiQ9CEyMzPNzMxGjRrVXuYA7VLuy/EDDcF4PKA8Mo7H04u8vb319fUVeUSq/493kpeXp6am1mKyhTY1Nze7uLjExcV14yjK2reHXr58yeVy9+7dq/hD95b+fn/2a3jGAwByhMmuu8rW1jYkJCQkJEQyM0ObmpubU1JSBAKBl5dXVw+hrH17Ljg42MHBwdfXV/GHBgZAxQMA0LcEBgZ6eHh4eXm12YWZlpGRcerUqbS0tDYH7+mYsvbtoYiIiKysrPPnz3dpOCIACVQ8ACAXmzZtio+Pr66utrKyOnnypLLT6WdCQ0N9fX137tzZ3gZTp049evSoZGKyLlHWvj2Rmpr65s2bjIwMPT09BR8aGAPj8QCAXISFhdHjzkH3TJs2bdq0acrOoq9wd3d3d3dXdhbQv+EZDwAAADAfKh4AAABgPlQ8AAAAwHyoeAAAAID5WFRbQ3mCnLBYrIkTJ2ImRVCKmzdvEkIk01ox1bNnz27evDl//nxlJwLQBvr+xC+vUqDiUSgPDw9lpwCgTHfv3iWEjBkzRtmJACjTiRMnlJ3CQISKBwAUx9PTkxCSlJSk7EQAYMBBPx4AAABgPlQ8AAAAwHyoeAAAAID5UPEAAAAA86HiAQAAAOZDxQMAAADMh4oHAAAAmA8VDwAAADAfKh4AAABgPlQ8AAAAwHyoeAAAAID5UPEAAAAA86HiAQAAAOZDxQMAAADMh4oHAAAAmA8VDwAAADAfKh4AAABgPlQ8AAAAwHyoeAAAAID5UPEAAAAA86HiAQAAAOZDxQMAAADMh4oHAAAAmA8VDwAAADAfKh4AAABgPlQ8AAAAwHyoeAAAAID5UPEAAAAA86HiAQAAAOZDxQMAAADMh4oHAAAAmA8VDwAAADCfmrITAAAmq6+vf/PmjeRjY2MjIeT169eSJRwOh8fjKSEzABhgWBRFKTsHAGCs2NjY1atXd7BBTEzMqlWrFJYPAAxYqHgAQI4qKipMTEyam5vbXKuqqvr8+XNDQ0MFZwUAAxD68QCAHBkaGk6dOlVVVbX1KlVV1b/85S8odwBAMVDxAIB8LVmypM1nyRRFLVmyRPH5AMDAhFYtAJAvgUBgaGgo3X+ZxmazKyoqtLW1lZIVAAw0eMYDAPKlpaU1a9YsdXV16YVqamru7u4odwBAYVDxAIDcLV68uKmpSXpJc3Pz4sWLlZUPAAxAaNUCALlrbGw0MDAQCASSJZqami9fvuRwOErMCgAGFDzjAQC5Y7PZHh4ebDab/qiurr5gwQKUOwCgSKh4AEARFi1aRA+4TAgRiUSLFi1Sbj4AMNCgVQsAFEEsFr/11lsVFRWEEAMDgxcvXrQ5SA8AgJzgGQ8AKIKKisqiRYvYbLa6uvrixYtR7gCAgqHiAQAFWbhwYWNjI5q0AEApMHd6L0tKSlJ2CgB9FEVRgwYNIoQ8efKksLBQ2ekA9FGenp7KToGZ0I+nl7FYLGWnAAAA/Rh+l+UEz3h6X2JiIip0gDY9ePCAEGJnZ6fsRAghJCkpacGCBQPh14XFYuF7qV+g70llZ8FYqHgAQHH6SK0DAAMQei4DAAAA86HiAQAAAOZDxQMAAADMh4oHAAAAmA8VDwAAADAfKh4AgC44f/68jo7OmTNnlJ2Icly+fDkwMFAsFs+dO9fCwoLL5ZqZmbm7u2dnZ8uy++TJk1mtaGpqynvf8PDwESNGaGho8Pn8ESNGbNmypaamRnqDY8eOjR8/XktLa8iQIZ999tmLFy8kq3bs2NHioCNHjqRXnT59Ojw8vLm5WZYcQOlQ8QAAdMFAGL+nPd98801UVNSmTZvEYvH169ePHTtWWVl548YNoVD4/vvvl5aWdi+ss7Nzt1OScd/r16+vWLGiqKiorKxs+/bt4eHh8+fPl6xNTExcvHixh4fHs2fPUlNTr127NmPGjKampk7Dzp49m8vlTp06taqqqtunAAqDigcAoAvc3Nyqq6tnzZol7wMJhUInJyd5H0V2u3btSkhISEpK0tLSIoQ4Ojo6OzvzeDwrK6vQ0NDq6uoffvih0yBcLrempoaS4u3tvWHDBlkS6Mm+bDZ79erVhoaGmpqaHh4ec+bM+emnn54/f06v/fbbb01NTdevX6+jo+Pg4LB27dqsrKxbt25Jdj98+LD0ce/fvy9Z5efnN3r06JkzZ8pSIYFyoeIBAOiL4uLiysvLlZ3Ff+Tn52/ZsmXbtm1cLpcQoqamJt2uZ21tTQgpKCjoNM6FCxfogolWXFx8//79Dz74QJYcerJvcnIynTnNzMyMEFJbWysJZWJiIpkjaPDgwYSQp0+fyhKZEBIcHJyVlRUZGSnj9qAsqHgAAGR148YNCwsLFou1f/9+QkhsbCyfz+fxeKmpqTNmzNDW1jY3Nz9+/Di9cVRUFJfLNTIyWrlypYmJCZfLdXJykjw58PX1ZbPZb731Fv1x9erVfD6fxWK9fPmSEOLv7x8QEFBQUMBisWxtbQkhFy5c0NbWDg0NVcJpExIVFUVR1OzZs9tcKxQKCSHa2tpdDbtr1y4/P7/updSTffPy8nR1dYcMGUJ/tLa2li4u6U48dBknCz09PVdX18jIyIHc4tkvoOIBAJCVs7PzL7/8Ivm4atWqNWvWCIVCLS2txMTEgoICa2vrFStWiEQiQoivr++yZcvq6+v9/PwKCwvv3LnT1NT04YcfFhcXE0KioqKkJ7qKiYnZtm2b5GNkZOSsWbNsbGwoisrPzyeE0N1jxWKxwk5W2rlz54YPH87j8dpc+9tvv5Gud8cpKSnJyMj4+OOPu5FP9/YViUQlJSX79++/fPlydHQ0m82ml2/atOnFixfR0dECgSA3NzcyMvKjjz6aOHGiZMfAwEA9PT02m21lZTVnzpzMzMwWkceMGVNSUnLv3r1unAsoDCoeAICecnJy0tbWNjQ09PLyqqurKyoqkqxSU1N7++23ORyOnZ1dbGysQCCIj4/vxiHc3Nxqamq2bNnSe1nLqq6u7smTJzY2Nq1XlZWVJSQk+Pn5OTo6tvcEqD27du366quvVFS68zPUvX0HDx5sbm4eHBy8e/du6Qk7XV1dN27c6Ovrq62tPXLkSIFA8N1330nWfvrpp6dPny4uLq6trT1+/HhRUZGrq2tubq505KFDhxJCcnJyunEuoDCoeAAAeg392IB+xtPauHHjeDzew4cPFZtUT5WXl1MU1eYDHkdHRz8/vzlz5qSlpamrq8ses7S09PTp08uWLetGPt3et7i4uLy8/NixYz/++OOYMWMkLVlBQUEHDx68cuVKbW3t48ePnZycHB0d6UdxhJDBgwePGTNGU1OTzWZPnDgxPj5eKBTGxMRIR6YvTllZWTdOBxQGFQ8AgOJwOJyKigplZ9E1DQ0NhBAOh9N6lZGRUXp6enR0tI6OTpdihoeHr1ixQro3sQL2VVdXNzQ0nDZtWkJCQm5ublhYGCHk+fPn4eHhX3zxxQcffMDn862srA4dOlRaWrpnz542g9jb26uqqv7xxx/SCzU0NMh/LxT0WWrKTgAAYKAQiURVVVXm5ubKTqRr6J/zNsfZMzQ01NXV7WrAFy9eHDt27NGjR91Ipif7Stja2qqqqtItU3l5ec3NzaamppK12tra+vr6LdqtJMRisVgsblH/NTY2kv9eKOiz8IwHAEBBMjIyKIqSdIlVU1Nrr/2rTzEyMmKxWNXV1a1XnTlzhn7Tu0vCw8OXLFmir6/fjWS6se+rV68WLVokvYSucui30OkCVDI2DyFEIBBUVlbSawkhH330kfS+mZmZFEU5OjpKL6QvjrGxcddOBhQLFQ8AgByJxeLXr183NTVlZ2f7+/tbWFhIOqDY2tpWVlampKSIRKKKiooWA8Do6+uXlpYWFhYKBAKRSJSWlqast9N5PJ61tfWzZ89aLM/Pzzc2NpbuAkwI8fLyMjY2vnPnTnvRysrKvv/++zVr1rReJad9+Xz+pUuX0tPTa2pqRCLR3bt3P/30Uz6fv3btWkKIlZXVlClTDh06dO3aNaFQWFxc7O3tTQj5/PPP6d1LSkoSEhKqqqpEItGvv/66fPlyCwsLHx8f6UPQF8fe3r69zKEvQMUDACCr/fv3jx8/nhCyceNGd3f32NjYffv2EUJGjRr1+PHjQ4cOBQQEEEKmT5+el5dH79LQ0GBvb6+hoeHi4jJs2LCrV69KGkRWrVo1ZcqUhQsXDh8+fPv27XSbiKTPrI+Pj5GRkZ2d3cyZMysrK5VyvhJubm65ubn0uDsSbQ4/09jYWF5enpqa2l6o3bt3z54928LCQmH7crncSZMmLV++3MzMTEtLy8PDw9LS8ubNm/T0WCwW68SJE15eXp9//rmenp6dnV1RUdGpU6dcXFzo3adPn75582Zzc3Mej+fp6Tlp0qSbN28OGjRI+hCZmZlmZmajRo1qL3PoEyjoVYSQxMREZWcBAJ1LTEyU93egt7e3vr6+XA8hi55/L+Xl5ampqbWYbKFNzc3NLi4ucXFx3TiKsvbtoZcvX3K53L179/Y8lALuyYEMz3gAAOSIGRNr29rahoSEhISESGZmaFNzc3NKSopAIPDy8urqIZS1b88FBwc7ODj4+voq/tDQJah4BoSQkBA7OzttbW0Oh2Nra7thw4b2vraWL1+upaXFYrGysrIUnGT/8ujRo6+++uqdd97R0tJSU1PT0dEZNmyYm5vbr7/+Ku9Dd/DXPHXqlLW1NUsKm802MjKaPHnynj17Xr9+LQni5eXF6tDZs2flfSLQvwQGBnp4eHh5ebXZhZmWkZFx6tSptLS09kZn7oCy9u2hiIiIrKys8+fPd2k4IlAOZT9kYhrSJ1u1XF1dY2JiXr16VVNTk5iYqK6uPn369PY2pmcFunv3riIz7F++++47dXX1999//8KFC69fv25oaCgoKEhISHBycvr222/lffRO/5o2NjY6OjoURdF9Zq9evbps2TIWi2ViYkK/ZkJR1IIFCy5dukR3xqTfUpk9e3ZjY2NdXV15efmKFSvOnDkj7xNROnm3IAQGBtIDElpaWp44cUJ+B+pUL34vXbx4cePGjb0SigFSUlLCwsKampp6KyBateQK4/EMCJqamt7e3qqqqoQQT0/PU6dOJSUlFRcXS16/7DahUDh16lTpmYYY7+bNm97e3q6urhcvXlRT+8//QdbW1tbW1rq6upL+qvIj+1+TxWLp6upOnjx58uTJbm5uCxYscHNz++OPP3R0dFgs1qRJk6T/QcxisdTV1dXV1Xk83rvvvivvsxgIwsLC6DHumGTatGnTpk1TdhZ9hbu7u7u7u7KzAFmhVWtAOHv2LP0DSTMwMCCE1NfXt7kxi8WSPXJcXJz0nMMDwY4dO5qbm3fu3CkpdyQ++uijL7/8Ut4JdOmvKTF//vxly5aVl5cfOHCAEHL8+PEOnv97e3v/9a9/7aV8AQD6BFQ8ynH48OFx48ZxuVw+n29pabl9+3ZCCEVRERER9KSDenp6c+bMkcy/Exsby+fzeTxeamrqjBkztLW1zc3N6eYnQsjbb7/NYrFUVFTeffdd+pdvw4YNOjo6XC73hx9+aH30kpISDQ0NKysr+iNFUXv27Bk+fDiHw9HR0Vm/fr2MZ+Hv7x8QEFBQUMBisWxtbXfv3s3j8bS0tMrLywMCAszMzB49enT9+nU7Ozs6GXt7+4sXL3Z6OoSQf/3rX++99x6Px9PW1ra3t6+pqYmKiuJyuUZGRitXrjQxMeFyuU5OTrdu3ZLs0sHVazMgIaS5uXnr1q0WFhYaGhqjRo2inycTQi5cuNDewCeNjY1XrlwZNGjQe++91/HFUdZfswP0MDBpaWmdbkkIYcxfEwDgP5TapsZARIb2cnoAj507d7569aqysvLbb79dvHgxRVFbt25ls9mHDx+uqqrKzs4eO3asgYHBixcv6L2CgoIIIVeuXKmuri4vL3dxceHz+Y2NjRRFNTU1WVpaWlhYSDcnr1mzZt++fa2PXldXp6Wl5evrK1kSFBTEYrH+/ve/v379ur6+np4hT8Z+PB9//LGNjY10KEKIn59fdHT0vHnzfv/99xMnTgQHB1dWVr569WrixImDBg3q9HRqa2u1tbXDw8OFQuGLFy/mzZtXUVFBUZS3tzefz3/w4EFDQ0Nubu748eO1tLSKiorogB1cvfYCrlu3jsPhnDx58vXr15s2bVJRUaG7uZw9e1ZLSyskJKT1+dKT6UycOLHTK6OsvyYl1Y+nBboyGDx4cIvldD8ed3f3FsuZ8dfswMDpMyHL9xL0BQPnnlQKXNle1uk3S2Njo66u7pQpUyRLmpqaIiMj6+vrNTU1vby8JMt/++03Qojkd5f+UREKhfRHui7Jz8+nP9JVVFJSEv2xrq7OwsKiurq6dQJBQUHDhg2rqamhP9bX1/N4vA8//FCyQZd6LrdZ8UiSbIHu00DPw9zB6dy/f58Qcvbs2Ra7e3t7S/+QZ2ZmEkK2bdtGn0UHV6/NgEKhkMfjSXapr6/ncDirVq3q+Hxv375NCPnLX/7S8WbK+mvS2qt4KIqie/a0WNhxxcPgv+bA+XVBxdNfDJx7UinQc1nRsrOzq6qqpCdqUVVV9fPzu337dm1t7bhx4yTLx48fz2azpR/1S6PfAZFMyrN8+fLg4ODIyEgPDw9CyJEjR+bMmaOtrd1ir+Tk5KSkpEuXLmlpadFL8vPz6+vrp06d2nun2C767c02hyeRPh1ra2sjI6MlS5b4+fktW7bM0tKyzWjjxo3j8Xh0Y0dubm4HV6/NgI8ePaqvr6cHXSWEaGhovPXWW9JNJ23S1NQkMnSa6Tifjk+f9OCv2bG6ujqKolrH6R4G/DVp9EVmvH379p04cULZWUAnWk/lAb0I/XgUjW5ZaD3bcFVVFfnvD6qErq6uQCCQJaympuYXX3zxyy+/0P8a/uc//9l6OKyEhIRdu3ZlZGRI/+rQ/4MZGhp29URkdO7cucmTJxsaGnI4nA0bNsiyi4aGRnp6urOzc2hoqLW1tZeXV4ux7SU4HE5FRQXp7Oq1GbCuro4QsnnzZskINE+fPu20lLG0tORyuXTbVgeU9dfsGJ32iBEjZNy+NYb9NQFgQMEzHkUzNTUlhLx8+bLFcroGavGLWFVVRc/rKwtfX9/IyMh9+/b5+PgMHjzYxsZGem10dPTFixfT09Nb/JBwuVxCyJs3b7p4HjIpKiqaO3fuvHnzvv/+e1NT0+joaBl/Jt95550zZ85UVFRERETs2rXrnXfe2bJlS4ttRCKR5Pp0evVaB6THZt23b5+/v7/sZ8ThcD766KPU1NSff/550qRJLdZWVlZu2LDhu+++U9Zfs2MXLlwghMyYMUP2XaQx769JGwhPPlgs1po1azw9PZWdCHQiKSmpxcys0IvwjEfRLC0t9fX1L1261GL5yJEjNTU16W4itFu3bjU2Nso+Moq5ubmnp+fJkye3bNki/b1PUdTGjRtzcnJSUlJa/0COHDlSRUXlX//6V7fOphM5OTkikWjVqlXW1tZcLlfG995LS0sfPHhACDE0NNy5c+fYsWPpjy1kZGRQFDVx4kTS2dVrM+DgwYO5XG43hpYODg7mcDhr165t/azi/v379CvryvprduDFixf79u0zNzf/29/+Jvte0hj51wSAgQMVj6JxOJxNmzZdu3bN19e3pKRELBYLBIIHDx5wudyAgIDk5OQjR47U1NTk5OT4+PiYmJh4e3vLHjwgIKCpqen169cffPCBZOGDBw9279596NAhdXV16WkE9u7dSwgxNDT8+OOPT548GRcXV1NTk52dffDgQdmPqK+vX1paWlhYKBAIJN1QJOgpji9fvtzQ0JCXl9deL5YWSktLV65c+fDhw8bGxrt37z59+pT+ISSE0IMINzU1ZWdn+/v7W1hY0G9cd3z12gzI5XI/++yz48ePx8bG1tTUNDc3P3v2jO7Dm5aW1t7b6YQQBweHo0eP3r9/38XF5fz589XV1SKR6MmTJ4cOHfr888/p3i3K+mtKUBRVW1srFospiqqoqEhMTJw0aZKqqmpKSkq3+/H0078mAMB/KLHXNCMR2d6J2L9/v729PZfL5XK5Y8aMiYmJoShKLBbv2bNn6NCh6urqenp6c+fOffToEb19TEwMPV7c0KFDCwoKDh48SP9uDRky5I8//pCOPGXKlO+++056SU5OTpt/+j179tAbCASC5cuXDxo0SFNT09nZeevWrYQQc3Pze/fudXoid+7cGTJkiIaGhrOz89q1azU0NAghgwcPlsyxvHHjRn19fV1dXQ8Pj/379xNCbGxsvv766w5Op7Cw0MnJSU9PT1VV1dTUNCgoiH5P29vbW11d3czMTE1NTVtbe86cOQUFBZJMOrh67QV88+bNxo0bLSws1NTU6MovNzeXoqjz589raWnt2LGjgxMvKipat26dvb29pqamqqqqrq7umDFjPv/8859//rnTfOT31zx9+vSoUaN4PB6bzVZRUSH/HXb5vffeCwkJefXqVYuzqKmpef/99/X19QkhKioqtra2oaGh9Krw8HBm/DU7MHDei5HxewmUbuDck0rBoiiq16onIITFYiUmJqK9XB5Wrlx54sSJV69eKTsR6AV94a9J95kYCN+B+F7qLwbOPakUaNWC/qTNd6Ghn8JfEwAUCRUPtOvhw4es9tFvxwAAw1y+fDkwMFAsFs+dO9fCwoLL5ZqZmbm7u2dnZ8seRCwW79u3z8nJqYNtGhoaRowYsXnz5i6lp/jIIpFo69at1tbWbDbbzMxs3bp10m8thISE2NnZaWtrczgcW1vbDRs21NbW0qtOnz4dHh6Oyr4PUXKrGuMQtJfLR2BgID2unaWl5YkTJ5SdDvRIH/lrDpw+E7J/L23dunXWrFk1NTUikWjQoEHXr1+vq6t7/Pjxhx9+qKOjU1JSIkuQP/74gx67YfTo0R1stnbtWkJIUFCQTOegvMirVq3icrnHjx+vqam5evWqtrb2okWLJGtdXV1jYmJevXpVU1OTmJiorq4+ffp0ydrIyEhXV9fXr1/LmMbAuSeVAle2l6HiAegvFPDrUl9f7+iuHJ9LAAAgAElEQVToqPRQMn4v7dy5c9iwYfRsISKR6K9//atkFT0YpqRjeweysrLmzZt35MgRBweHDuqSn3/+edq0aV2qS5QSuaCgQEVF5YsvvpAsoR8dPXjwgP7o5uYmPQUe3VlKMkMcRVG+vr6Ojo4ikUiWTFDxyBVatQAA5CUuLq68vLyvhWpTfn7+li1btm3bRo9KqqamdubMGclaa2trQkhBQUGncUaPHn3q1KnFixdzOJz2thEKhevXr4+MjOxShkqJnJmZKRaLJ0yYIFkyffp0QsjFixfpj2fPnlVVVZWsNTAwIH+eiCY4ODgrK6urKYE8oOIBAOgIRVERERFvv/02h8PR09ObM2eOZMYuX19fNpv91ltv0R9Xr17N5/NZLBY9qLq/v39AQEBBQQGLxbK1tY2KiuJyuUZGRitXrjQxMeFyuU5OTpJhjboUihBy4cKFDkaN6oaoqCiKombPnt3mWrrnSm9NyhYUFLR69Wp5TG7T65HpUR7okRpoQ4cOJYT8/vvvbW5fUlKioaFhZWUlWaKnp+fq6hoZGUnhDSxlQ8UDANCR4ODgwMDAoKCg8vLya9euFRcXu7i4lJWVEUKioqKkX/mOiYnZtm2b5GNkZOSsWbNsbGwoisrPz/f19V22bFl9fb2fn19hYeGdO3eampo+/PDD4uLiroYi/33TTSwW99Zpnjt3bvjw4fTQSq3RrVrOzs49P9DPP/9cUFCwaNGinodSQGR6Hjrp+mbQoEGEEHoOuBbq6+vT09NXrFhBd1OTGDNmTElJyb1793oxMegGVDwAAO0SCoURERHz5s1bsmSJjo6Ovb39gQMHXr582aWhyaWpqanRj4vs7OxiY2MFAkF8fHw34ri5udXU1LSeoax76urqnjx50mL6NlpZWVlCQoKfn5+jo2N7T4BkJxQK/f39Y2NjexhHYZHt7e2nT58eExOTnp7e0NDw4sWL5ORkFovVeoh5QkhYWJiJicmOHTtaLKcfC7U3fCgoDGYSBQBoV25ubm1t7bhx4yRLxo8fz2azZZxko2Pjxo3j8XiSNjIlKi8vpyiqzQc8jo6OdXV1np6eO3bsoCdR6YlNmzZ98cUXZmZmPYyjyMgJCQkbN25cunRpZWWliYnJhAkTKIqin/RIS05OTkpKunTpkpaWVotV9IWlnwuCEqHiAQBoV1VVFSGkxaSturq6LaZ27zYOh9Nm+4iCNTQ00Mm0XmVkZBQXF/fOO+/0/Cg3btzIycmJiIjoeSiFRSaE6OjoHDhwQPLx+fPnx48fNzU1ld4mISEhIiIiIyOjxXIa3Q2IvsigRGjVAgBol66uLiGkRX1TVVVlbm7e8+Aikai3QvUQ/ZPc5lh5hoaG9EXoubi4uCtXrqioqNCjmNL9i0NDQ1ks1u3bt/tm5NYyMzMJIVOmTJEsiY6OPnLkSHp6epvlDiGksbGR/Ln7MygFKh4AgHaNHDlSU1NT+lfz1q1bjY2N7777Lv1RTU2tzS4dssjIyKAoSjKZfE9C9ZCRkRGLxaqurm696syZM73VVBQfHy89OAr9cIseNUe63bBPRW7t0KFDVlZWrq6uhBCKojZu3JiTk5OSktLiQaA0+sIaGxv3bibQVah4AADaxeVyAwICkpOTjxw5UlNTk5OT4+PjY2Ji4u3tTW9ga2tbWVmZkpIiEokqKiqePn0qvbu+vn5paWlhYaFAIKCrGbFY/Pr166ampuzsbH9/fwsLi2XLlnUjVFpaWi++nc7j8aytrZ89e9ZieX5+vrGx8YIFC6QXenl5GRsb37lzp1cO3fcjv/fee0+fPm1qaiosLFy3bt3ly5fj4uLot7EePHiwe/fuQ4cOqaurS0/Cs3fvXukI9IW1t7fvlXOBbkPFAwDQkW+++SYsLCwkJMTAwMDV1dXS0jIjI4PP59NrV61aNWXKlIULFw4fPnz79u10y4WjoyP9zrmPj4+RkZGdnd3MmTMrKysJIQ0NDfb29hoaGi4uLsOGDbt69aqk90xXQ/UuNze33Nxc6RmjCCFtDiHT2NhYXl6empraZpybN286OzubmpreunXr3r17JiYmkyZNunbtmiw59M3Iurq6Dg4OGhoaY8eOffjw4fXr1yVNWjIOsZOZmWlmZjZq1ChZNgY5kvOYzgMOwSwTAP2E4kf09/b21tfXV+QRabJ8L+Xl5ampqR0+fLjTaM3NzS4uLnFxcb2UXf+O3KmXL19yudy9e/fKsjFmmZArPOMBAFCcPjuTtq2tbUhISEhIiGTq7zY1NzenpKQIBAIvL6/eTaA/RpZFcHCwg4ODr6+v4g8NLaDiAQAAQggJDAz08PDw8vJqswszLSMj49SpU2lpae2Nztxt/TFypyIiIrKyss6fP9/zoYyg51DxAAAowqZNm+Lj46urq62srE6ePKnsdNoWGhrq6+u7c+fO9jaYOnXq0aNHJfN/9aL+GLljqampb968ycjI0NPTU/ChoU0YgRAAQBHCwsLCwsKUnUXnpk2bNm3aNGVnwQTu7u7u7u7KzgL+B894AAAAgPlQ8QAAAADzoeIBAAAA5kPFAwAAAMyHigcAAACYj0XJNkg2yIjFYik7BQAA6MfwuywneDu9l9FjhANAm/bt20cIWbNmjbITAYABB894AEBxPD09CSFJSUnKTgQABhz04wEAAADmQ8UDAAAAzIeKBwAAAJgPFQ8AAAAwHyoeAAAAYD5UPAAAAMB8qHgAAACA+VDxAAAAAPOh4gEAAADmQ8UDAAAAzIeKBwAAAJgPFQ8AAAAwHyoeAAAAYD5UPAAAAMB8qHgAAACA+VDxAAAAAPOh4gEAAADmQ8UDAAAAzIeKBwAAAJgPFQ8AAAAwHyoeAAAAYD5UPAAAAMB8qHgAAACA+VDxAAAAAPOh4gEAAADmQ8UDAAAAzIeKBwAAAJgPFQ8AAAAwHyoeAAAAYD5UPAAAAMB8qHgAAACA+VDxAAAAAPOpKTsBAGCyW7du3bt3T/Lx8ePHhJCDBw9KlowePXrChAlKyAwABhgWRVHKzgEAGOvs2bOzZs1SVVVVUVEhhNBfOCwWixAiFoubm5vPnDnz17/+VclZAsAAgIoHAORIJBIZGBjU1NS0uVZbW7uiooLNZis4KwAYgNCPBwDkSF1dfeHChW3WNB2sAgDodah4AEC+Fi5c2NjY2Hq5SCRatGiR4vMBgIEJrVoAIF9isdjU1LSsrKzFckNDwxcvXtD9ewAA5A3fNQAgXyoqKp988kmL1is2m71s2TKUOwCgMPi6AQC5a92w1djYuHDhQmXlAwADEFq1AEARhg4dmp+fL/lobW1dUFCgxHwAYKDBMx4AUIQlS5aoq6vT/81msz/99FPl5gMAAw2e8QCAIuTn5w8dOlTy8dGjR8OGDVNiPgAw0OAZDwAogq2t7ejRo1ksFovFGj16NModAFAwVDwAoCBLly5VVVVVVVVdunSpsnMBgAEHrVoAoCClpaWDBw+mKKq4uNjMzEzZ6QDAwIKK5z9+/fXXiIgIZWcBwHAZGRmEkMmTJys5DwCmW7t2raOjo7Kz6FvQqvUfxcXFJ0+eVHYWAAxnYWExZMgQZWfRy06ePPns2TNlZyF3N2/evHnzprKzAJmcPHmyuLhY2Vn0OWrKTqBvOXHihLJTAGCyyspKQoi+vr6yE+lNLBZrzZo1np6eyk5Evjw8PAi+JPsJFoul7BT6IlQ8AKA4DKt1AKAfQasWAAAAMB8qHgAAAGA+VDwAAADAfKh4AAAAgPlQ8QAAKMH58+d1dHTOnDmj7ETk5fLly4GBgWKxeO7cuRYWFlwu18zMzN3dPTs7W/YgYrF43759Tk5OHWzT0NAwYsSIzZs3dyk9xUcWiURbt261trZms9lmZmbr1q0TCoWStSEhIXZ2dtra2hwOx9bWdsOGDbW1tfSq06dPh4eHNzc3dykNaA0VDwCAEjB79NdvvvkmKipq06ZNYrH4+vXrx44dq6ysvHHjhlAofP/990tLS2UJkpeX9/77769du7a+vr6DzYKCgh49etSl9JQS2d/ff8+ePWFhYa9evTp69OihQ4eWL18uWZuenv7ll18WFha+fPkyLCwsMjKSHg6AEDJ79mwulzt16tSqqqouJQMtoOIBAFACNze36urqWbNmyftAQqGw4ycZvW7Xrl0JCQlJSUlaWlqEEEdHR2dnZx6PZ2VlFRoaWl1d/cMPP3Qa5N69e19//bWPj4+Dg0MHm/3yyy/379/vUnpKifz48eMDBw4sXbrUy8tLS0tr8uTJvr6+x44d+/333+kNNDU1vb299fX1tbS0PD09586de+HCBckogn5+fqNHj545c2ZTU1OXUgJpqHgAAJgsLi6uvLxcYYfLz8/fsmXLtm3buFwuIURNTU265c7a2poQUlBQ0Gmc0aNHnzp1avHixRwOp71thELh+vXrIyMju5ShUiJnZmaKxeIJEyZIlkyfPp0QcvHiRfrj2bNnVVVVJWsNDAwIIdIPioKDg7OysrqaEkhDxQMAoGg3btywsLBgsVj79+8nhMTGxvL5fB6Pl5qaOmPGDG1tbXNz8+PHj9MbR0VFcblcIyOjlStXmpiYcLlcJyenW7du0Wt9fX3ZbPZbb71Ff1y9ejWfz2exWC9fviSE+Pv7BwQEFBQUsFgsW1tbQsiFCxe0tbVDQ0PldGpRUVEURc2ePbvNtXTPFW1t7V45VlBQ0OrVqw0NDXslmlwjq6ioEEI0NDQkS4YOHUoIkTzjaaGkpERDQ8PKykqyRE9Pz9XVNTIyktntoXKFigcAQNGcnZ1/+eUXycdVq1atWbNGKBRqaWklJiYWFBRYW1uvWLFCJBIRQnx9fZctW1ZfX+/n51dYWHjnzp2mpqYPP/yQbvKIioqSnuAiJiZm27Ztko+RkZGzZs2ysbGhKCo/P58QQneAFYvFcjq1c+fODR8+nMfjtbn2t99+I4Q4Ozv3/EA///xzQUHBokWLeh5KAZFHjBhB/lzfDBo0iBBSUVHReuP6+vr09PQVK1aw2Wzp5WPGjCkpKbl3714vJjagoOIBAOgrnJyctLW1DQ0Nvby86urqioqKJKvU1NTefvttDodjZ2cXGxsrEAji4+O7cQg3N7eampotW7b0Xtb/U1dX9+TJExsbm9arysrKEhIS/Pz8HB0d23sCJDuhUOjv7x8bG9vDOAqLbG9vP3369JiYmPT09IaGhhcvXiQnJ7NYLLqobSEsLMzExGTHjh0tltOPhXJycno3t4ED82oBAPQ59D/u2/w5JISMGzeOx+M9fPhQsUl1rry8nKKoNh/wODo61tXVeXp67tixQ11dvYcH2rRp0xdffGFmZtbDOIqMnJCQsHHjxqVLl1ZWVpqYmEyYMIGiKPpJj7Tk5OSkpKRLly7R/b6l0Re2rKys13MbIFDxAAD0PxwOp80GEeVqaGgghLTZI9jIyCguLu6dd97p+VFu3LiRk5MTERHR81AKi0wI0dHROXDggOTj8+fPjx8/bmpqKr1NQkJCRERERkZGi+U0uhsQfZGhG9CqBQDQz4hEoqqqKnNzc2Un0hL9k9zmWHmGhoa6urq9cpS4uLgrV66oqKiwWCwWi0X3Lw4NDWWxWLdv3+6bkVvLzMwkhEyZMkWyJDo6+siRI+np6W2WO4SQxsZG8ufuz9AlqHgAAPqZjIwMiqImTpxIf1RTU2uv/UvBjIyMWCxWdXV161Vnzpzpraai+Ph4Sgr9rCsoKIiiqHHjxvXNyK0dOnTIysrK1dWVEEJR1MaNG3NyclJSUjQ1Ndvbhb6wxsbGvZvJwIGKBwCgHxCLxa9fv25qasrOzvb397ewsFi2bBm9ytbWtrKyMiUlRSQSVVRUPH36VHpHfX390tLSwsJCgUAgEonS0tLk93Y6j8eztrZ+9uxZi+X5+fnGxsYLFiyQXujl5WVsbHznzp1eT6NvRn7vvfeePn3a1NRUWFi4bt26y5cvx8XF0R22Hjx4sHv37kOHDqmrq7Ok7N27VzoCfWHt7e175VwGIFQ8AACKtn///vHjxxNCNm7c6O7uHhsbu2/fPkLIqFGjHj9+fOjQoYCAAELI9OnT8/Ly6F0aGhrs7e01NDRcXFyGDRt29epVSXeZVatWTZkyZeHChcOHD9++fTvd6uHo6Ei/vu7j42NkZGRnZzdz5szKykp5n5qbm1tubq70jFGknSk1Ghsby8vLU1NT24xz8+ZNZ2dnU1PTW7du3bt3z8TEZNKkSdeuXZMlh74ZWVdX18HBQUNDY+zYsQ8fPrx+/bqkSUvGIXYyMzPNzMxGjRoly8bQBgooiqKoxMREXA0A6AZCSGJiolwPQc8/INdDdGr+/Pnz58/vdLO8vDw1NbXDhw93umVzc7OLi0tcXFxvZNfvI3fq5cuXXC537969smysgHuyP8IzHgCAfqC/TJ1ta2sbEhISEhIimfq7Tc3NzSkpKQKBwMvLq3cT6I+RZREcHOzg4ODr66v4QzMGKh4AAOhNgYGBHh4eXl5ebXZhpmVkZJw6dSotLa290Zm7rT9G7lRERERWVtb58+d7PpTRQIaKpwvGjx+vqqra8XS73fbZZ59xuVwWi8WMsRb27t1Lv7UhGX/i/PnzOjo60nMK9kTvRutAeHj4iBEjNDQ0+Hz+iBEjtmzZUlNTI8uOp06dsra2lu6EqKamZmBg8Je//CU5Obm30uv4tpHO4ZNPPpFeNW3aNC0tLVVV1XfeeUceHTxlwZibRN42bdoUHx9fXV1tZWV18uRJZacjk9DQUF9f3507d7a3wdSpU48ePSqZDqwX9cfIHUtNTX3z5k1GRoaenp6CD80wqHi6IDMzU3rshN4VHx+/bt06OQVXvHXr1klPG0Rk7pono96N1oHr16+vWLGiqKiorKxs+/bt4eHh8+fPl2XHjz/++PHjxzY2Njo6OnQTckVFRWJiYklJyccff0z3G+u5jm8bSQ6DBg06cuTIuXPnJKsuXbp04sSJWbNm5ebmjh07tleS6SrG3CTyFhYW9ubNG4qinjx5IuPt1xdMmzZt165dys6CCdzd3QMDA6VnVofuQcXTZSwWq6u7CIVCJycneSTTj7i5uVVXV8+aNat7u7e4hj2MJjs2m01Poaypqenh4TFnzpyffvrp+fPn3Qilp6c3derUf/zjH4SQpKSkTrfvxdsmKipKRUXF29u7g1aGvqCf3iQA0C+g4umybjSjxsXFlZeXy7hxNyqqgaBL17AXJScnc7lcyUd6CLWOu2R2zNLSkhBSVVXV6Za9eNs4OTn5+/uXlJQw6Tlia8q6SQCgX0DF02X5+fkjRozg8/n0wBg3btyQrLp+/bqdnZ2Ojg6Xy7W3t7948SIhxN/fPyAgoKCggMVi2dra0lsePnx43LhxXC6Xz+dbWlpu376dXq6ionLu3LkZM2bo6OiYmJh8//33sqQUGxvL5/N5PF5qauqMGTO0tbXNzc2PHz8u2YCiqIiICHriZT09vTlz5kjmINy9ezePx9PS0iovLw8ICDAzM/Px8eHz+SoqKu+++66xsbG6ujqfzx87dqyLi8vgwYO5XK6uru6GDRs6PusWbty4YWFhwWKx9u/fT19DVis//fSTjNewRbSOT7DTi9MleXl5urq6Q4YMoT9euHChq4O5ZWdnE0LogVZpirltduzYMWzYsO++++7y5cttJoabpLduEgDoo5TyTnwfJON4PFOnTrW2tn7y5IlIJLp///6ECRO4XO4ff/xBrz1x4kRwcHBlZeWrV68mTpw4aNAgevnHH39sY2MjCUIPNbZz585Xr15VVlZ+++23ixcvpigqKCiIEHLlypWqqqrKysqZM2dyOJy6ujpZ8pfsW11dXV5e7uLiwufzGxsb6bVbt25ls9mHDx+uqqrKzs4eO3asgYHBixcvpPf18/OLjo6eN2/e77///s033xBCbt26VVdX9/Lly+nTpxNCzp07V1FRUVdXR78emZWV1fFZ0yOn/fOf/6Q/0oOhRUdH06u+/vpr+tSeP3+up6fn5OTU3Nws+zWUjibjCbZ3cWTR2Nj47Nmz6OhoDocjPdDI2bNntbS0QkJC2ttRuh9PfX19WlrakCFDpk2bVltbK9lG3reNjY3NkydPKIr65ZdfVFRULC0t6aOnpaW5u7tLNsNN0u2bhAyMsU9kHI8H+oIBck92FSqe/5C94hk9erTkI/3v9XXr1rXeMiwsjBBSXl5O/fmLuLGxUVdXd8qUKZItm5qaIiMjqf9+5wqFQnr5//3f/xFC7t+/L0v+LfaNiYkhhOTn51MUVV9fr6mp6eXlJdn4t99+I4RIfqdb7EtRFP1jJhAI6I8//vgjISQnJ0d694SEhI7PuoMfM2lz587lcrkPHz7sOFoHP2ZdPUHpiyMjeiKbQYMG/eMf/+hSqWRjY9Pi3xj29vY//vgj3RFV9lPuyW0jqXgoiqJH8v3yyy+pP1c8uEl6cpMMkF8XVDz9yAC5J7tKrVsPhuA/7O3tdXR06LqnBbq7T+tBw7Kzs6uqqj766CPJElVVVT8/v/YidG+CQHquFnrf3Nzc2tpa6Wnwxo8fz2azb9261aVoTU1NnSbW3lm3Jykp6f/9v/8XHh4+fPjwbkfr6glKXxwZFRcXV1VV3b17NzAw8ODBg+np6UZGRjLuq6OjQ/faaWpqKisru3Tpkq+vb1hY2I0bNwwMDFpsLO/bZseOHWfPno2JiWkxwxFukha6epMsWLCgxSVlKnQ0hP4LFU9PqaurS74Wz507t2fPntzc3Jqamva+K+nRXHR1dRWWIf1z22I+Xl1dXYFA0CvxZTnrNr169eqrr74aP348/eCh29HkfYKEEHV1dUNDw2nTpllZWQ0bNiwsLCwyMrKrQdTU1MzMzD777LPm5uYVK1bs3Lnz73//O1HsbcPlcuPj452dnf/2t7+Fh4dLluMm6SF/f39HR8deCdVn0e2qa9asUXYi0LkBUn93FSqeHmlqaqqsrLSwsCCEFBUVzZ07d968ed9//72pqWl0dLR0z00JU1NTQsjLly8VliT9M9nim72qqsrc3LznwWU86zb5+flVVVWlp6dLxpnoXjS5nmALtra2qqqqubm5PQlCT3384MEDoozbxtHRce3atXv37t2+fTt96xLcJD3m6Ojo6enZK6H6rBMnThBCGH+azICKp014V6tHrl69KhaL6QHccnJyRCLRqlWrrK2t6WFw29zF0tJSX1//0qVLCkty5MiRmpqat2/fliy5detWY2Pju+++2/PgMp51a+fOnTt69OiWLVveeecdesn69eu7F01+J/jq1atFixZJL8nLy2tubh48eHBPwv773/8mhNBtNEq5bbZv3z5ixIi7d+9KluAmAQDGQ8XTZY2NjdXV1U1NTXfu3PH19R0yZMiyZcsIIfQ/ly9fvtzQ0JCXlyfdP0BfX7+0tLSwsFAgEKioqGzatOnatWu+vr4lJSVisVggEND/3JcTLpcbEBCQnJx85MiRmpqanJwcHx8fExMTb2/vngfv4Kw7UFNTs3LlSgcHh6+//poQ0tDQcPv27aysLBmvYYuGDPmdIJ/Pv3TpUnp6Ot16cvfu3U8//ZTP569du5beIC0tTZa304VCoVgspiiqtLQ0Pj5+8+bNBgYGdOuAUm4bum1LeghX3CQAwHzK7jrdV8j4rlZ8fPyUKVOMjIzU1NQGDRq0cOHCp0+fStZu3LhRX19fV1fXw8ODHgXExsamqKjozp07Q4YM0dDQcHZ2pt+G3b9/v729PZfL5XK5Y8aMiYmJCQ8P19DQIIQMHTq0oKDgyJEj9BQq5ubmnb6uFRMTQ89sR+978OBBbW1tQsiQIUPoN+fFYvGePXuGDh2qrq6up6c3d+7cR48e0ftKjjt48GD6vevIyEg6mqWl5fXr13ft2qWjo0MIMTY2Pnr0aEJCAv3ikp6e3vHjx9s7a39/f3ozPp8/b9686OhoejIaHo83e/bsvXv3tr4VZ86cKeM13Lx5s3S0jk+w04vTsdmzZ1tZWWlqanI4HBsbGy8vL8nrSBRFnT9/XktLa8eOHa13TE5Obv2iFofDGTp06KpVq4qKihRw20hyMDAwoN/PkrZ+/Xrpt9Nxk3T7JiED470YvKvVjwyQe7KrWBRTpp7poaSkpAULFuBqAEBXsVisxMRExndw8fDwIP/tzQN93AC5J7sKrVoAAADAfKh4+oGHDx+2Hm5fwsvLS9kJ9le4sADyc/ny5cDAQLFYPHfuXAsLCy6Xa2Zm5u7u3uYAZu0Ri8X79u3reErdhoaGESNGbN68uUvpKT6ySCTaunWrtbU1m802MzNbt26dUCiUrA0JCbGzs9PW1uZwOLa2ths2bJDM33f69Onw8HDZh7CC9uDt9H5gxIgRaG6TB1xYADn55ptv7t69e/ToUbFYfP369ZSUlLFjx5aVlXl7e7///vsPHjygB1zoWF5e3mefffbzzz+PHj26g82CgoIePXrUpfSUEtnf3//777+Pj493c3P797//7e7u/vz586NHj9Jr09PTv/zySy8vL3V19bS0tCVLluTk5KSlpRFCZs+e/eTJk6lTp6akpChyLDfmwTMeAIA+TSgUdvwoQimhOrBr166EhISkpCQtLS1CiKOjo7OzM4/Hs7KyCg0Nra6u/uGHHzoNcu/eva+//trHx8fBwaGDzX755Zf79+93KT2lRH78+PGBAweWLl3q5eWlpaU1efJkX1/fY8eO/f777/QGmpqa3t7e+vr6Wlpanp6ec+fOvXDhAj1TCiHEz89v9OjRM2fOlAxrDt2AigcAoE+Li4srLy/va6Hak5+fv2XLlm3btnG5XEKImpramTNnJGutra0JIQUFBZ3GGT169KlTpxYvXszhcNrbRigUrl+/vqsDoCslcmZmplgsnjBhgmQJPfnuxYsX6Y9nz56VHjCCnoKmvr5esiQ4ODgrK6sbo72DBCoeAAC5oygqIiLi7bff5nA4etBhHI4AACAASURBVHp6c+bMefjwIb3K19eXzWbT79ITQlavXs3n81ksFj3Etr+/f0BAQEFBAYvFsrW1jYqK4nK5RkZGK1euNDEx4XK5Tk5OkkGJuhSKEHLhwgVZBpTqkqioKIqiZs+e3eZauucK/fJ/zwUFBa1evdrQ0LBXosk1soqKCiGEHuWBNnToUEKI5BlPCyUlJRoaGlZWVpIlenp6rq6u9PzBvZXVQIOKBwBA7oKDgwMDA4OCgsrLy69du1ZcXOzi4lJWVkYIiYqKkn6LOCYmZtu2bZKPkZGRs2bNoueEz8/P9/X1XbZsWX19vZ+fX2Fh4Z07d5qamj788EO6+aNLoch/Z2AVi8W9eKbnzp0bPnw4Pb5Ra/SU9c7Ozj0/0M8//1xQUNBiVPReIY/II0aMIH+ubwYNGkQIqaioaL1xfX19enr6ihUr6BltJcaMGVNSUnLv3r1eTGxAQcUDACBfQqEwIiJi3rx5S5Ys0dHRsbe3P3DgwMuXLw8ePNi9gGpqavTjIjs7u9jYWIFAEB8f3404bm5uNTU1W7Zs6V4ardXV1T158qT12JuEkLKysoSEBD8/P0dHx/aeAMlOKBT6+/vHxsb2MI7CItvb20+fPj0mJiY9Pb2hoeHFixfJycksFqvNqXDDwsJMTEx27NjRYjn9WCgnJ6d3cxs48K4WAIB85ebm1tbWjhs3TrJk/PjxbDZbxgk3OjZu3DgejydpI1Ou8vJyiqLafMDj6OhYV1fn6em5Y8cOdXX1Hh5o06ZNX3zxhZmZWQ/jKDJyQkLCxo0bly5dWllZaWJiMmHCBIqi6Cc90pKTk5OSki5dukT3+5ZGX1j60SB0AyoeAAD5qqqqIoRoampKL9TV1W0xl3u3cTicNhtHFK+hoYEQ0maPYCMjo7i4OMmksD1x48aNnJyciIiInodSWGRCiI6OzoEDByQfnz9/fvz48RZv6SckJERERGRkZLT59j7dDYi+yNANaNUCAJAvegyVFvVNVVWVubl5z4OLRKLeCtVz9E9ym2PlGRoa9tZYMnFxcVeuXFFRUaEHC6X7F4eGhrJYrNu3b/fNyK1lZmYSQqZMmSJZEh0dfeTIkfT09PYGK2psbCR/7v4MXYKKBwBAvkaOHKmpqSn9k3nr1q3GxsZ3332X/qimptZmfw5ZZGRkUBQ1ceLEnofqOSMjIxaLVV1d3XrVmTNnequpKD4+Xnp6SPr5VlBQEEVR0k2HfSpya4cOHbKysnJ1dSWEUBS1cePGnJyclJSUFs8CpdEXlp5/F7oBFQ8AgHxxudyAgIDk5OQjR47U1NTk5OT4+PiYmJh4e3vTG9ja2lZWVqakpIhEooqKiqdPn0rvrq+vX1paWlhYKBAI6GpGLBa/fv26qakpOzvb39/fwsJi2bJl3QiVlpbWu2+n83g8a2vrZ8+etVien59vbGy8YMEC6YVeXl7GxsZ37tzpraP38cjvvffe06dPm5qaCgsL161bd/ny5bi4OPptrAcPHuzevfvQoUPq6urSc93s3btXOgJ9Ye3t7XvlXAYgVDwAAHL3zTffhIWFhYSEGBgYuLq6WlpaZmRk8Pl8eu2qVaumTJmycOHC4cOHb9++nW62cHR0pN859/HxMTIysrOzmzlzZmVlJSGkoaHB3t5eQ0PDxcVl2LBhV69elXSd6WqoXufm5pabmys9YxQhpM0hZBobG8vLy1NTU9uMc/PmTWdnZ1NT01u3bt27d8/ExGTSpEnXrl2TJYe+GVlXV9fBwUFDQ2Ps2LEPHz68fv26pElLxiF2MjMzzczMRo0aJcvG0AYKKIqiqMTERFwNAOgGQkhiYqLCDkfPRaCww0nMnz9//vz5nW6Wl5enpqZ2+PDhTrdsbm52cXGJi4vrjez6feROvXz5ksvl7t27V5aNFXxP9hd4xgMA0M/05Wm0bW1tQ0JCQkJCJFN/t6m5uTklJUUgEHh5efVuAv0xsiyCg4MdHBx8fX0Vf2jGQMUDAAC9KTAw0MPDw8vLq80uzLSMjIxTp06lpaW1Nzpzt/XHyJ2KiIjIyso6f/58z4cyGshQ8QAA9BubNm2Kj4+vrq62srI6efKkstNpV2hoqK+v786dO9vbYOrUqUePHpVMAdaL+mPkjqWmpr558yYjI0NPT0/Bh2YYjEAIANBvhIWFhYWFKTsLmUybNm3atGnKzoIJ3N3d3d3dlZ0FE+AZDwAAADAfKh4AAABgPlQ8AAAAwHyoeAAAAID50HP5T5KSkpSdAgD0P7/++quyU5A7eooDfElC/8WiZBvcmvGSkpJazPkCAADQTyUmJnp6eio7i74FFQ8AKA79FYznBACgeOjHAwAAAMyHigcAAACYDxUPAAAAMB8qHgAAAGA+VDwAAADAfKh4AAAAgPlQ8QAAAADzoeIBAAAA5kPFAwAAAMyHigcAAACYDxUPAAAAMB8qHgAAAGA+VDwAAADAfKh4AAAAgPlQ8QAAAADzoeIBAAAA5kPFAwAAAMyHigcAAACYDxUPAAAAMB8qHgAAAGA+VDwAAADAfKh4AAAAgPlQ8QAAAADzoeIBAAAA5kPFAwAAAMyHigcAAACYDxUPAAAAMB8qHgAAAGA+VDwAAADAfKh4AAAAgPlQ8QAAAADzoeIBAAAA5kPFAwAAAMzHoihK2TkAAGMdPXo0Li5OLBbTH588eUIIsbKyoj+qqKh8/vnnixcvVlp+ADBgoOIBADnKzs4ePXp0Bxvcu3dv1KhRCssHAAYsVDwAIF8jRox49OhRm6tsbW3z8vIUnA8ADEzoxwMA8vXJJ5+oq6u3Xq6urv7ZZ58pPh8AGJjwjAcA5Ovx48e2trZtftXk5eXZ2toqPiUAGIDwjAcA5Mva2nrs2LEsFkt6IYvFGjduHModAFAYVDwAIHdLly5VVVWVXqKqqrp06VJl5QMAAxBatQBA7srLy01MTCTvqBNCVFRUSktLjY2NlZgVAAwoeMYDAHJnZGTk6uoqecyjqqo6efJklDsAoEioeABAET755BPpJ8qffPKJEpMBgAEIrVr/n707jYriWvfHvwsauhm6GWSQQWRyIiLOCoJIPDEqUcQExOj1muuAaH4NipEI0SAKRvECC4VjJByzoiKguCAaUZcSokZFs4yCGA1gcEIFZGoZQkPX/0X9T98+jA30AN3fz6vUruqnnq5U6Ce1d+0NAIrQ0NBgamra2tpKCNHS0qqsrDQ0NFR2UgCgRvCMBwAUgcfjzZ8/n8VisVishQsXotwBAAVDxQMACrJy5cr29vb29nYspAUAiodeLQBQkJaWFhMTE5qmq6urdXR0lJ0OAKgXVDxqJzMzc9myZcrOAgBACTIyMvz9/ZWdBSgHS9kJgHJkZGQoOwVQccuWLQsJCXF1dZVsvHfvHkVRPa+mPrTEx8cTQjZv3qzsRKB3+J89NYeKR03h/3JA3pYtW+bq6trhTlu6dCkhhMVSnb88p06dIvgPaohAxaPmVOfvDgAMfqpU6wDA0IJ3tQAAAED1oeIBAAAA1YeKBwAAAFQfKh4AAABQfah4AGAQOX/+vIGBwdmzZ5WdiLxcvnx5+/btIpHI19fXxsaGw+FYWVn5+PgUFhZKH0QkEsXHx7u5ufVwTEtLy9ixY7/66qs+paf4yEKhcOfOnfb29tra2lZWVlu3bm1ubhbvjYqKcnJy4vF4bDbb0dFx27Zt7969Y3b9+OOP+/bta29v71MaoM5Q8QDAIKLac6J+/fXXiYmJ4eHhIpHo2rVraWlpNTU1169fb25unj17dkVFhTRBSkpKZs+evWXLlqamph4Oi4iIePz4cZ/SU0rkkJCQ2NjYmJiYt2/fnjhxIiUlZe3ateK9eXl5n3/+eXl5eXV1dUxMTEJCgp+fH7Nr8eLFHA5n7ty5dXV1fUoG1BYqHgAYRLy9vevr6xctWiTvEzU3N/f8JEPmvvnmm/T09MzMTC6XSwhxdXV1d3fX1dW1s7OLjo6ur6///vvvew1y//79L7/8MigoaOLEiT0cduPGjQcPHvQpPaVEfvLkyeHDh1etWhUQEMDlcufMmcPn89PS0v744w/mAH19/cDAQGNjYy6X6+/v7+vre+HChefPnzN7g4ODXVxcFi5c2NbW1qeUQD2h4gEAdZSamlpZWamw05WWlu7YsWPXrl0cDocQwmKxJHvu7O3tCSFlZWW9xnFxccnKylqxYgWbze7umObm5i+++CIhIaFPGSol8p07d0Qi0YwZM8Qt8+fPJ4RcvHiR2Tx37pympqZ4r4mJCSFE8kFRZGTkvXv3+poSqCdUPAAwWFy/ft3GxoaiqEOHDhFCkpOT9fT0dHV1c3JyFixYwOPxrK2tT548yRycmJjI4XDMzMw2bNhgYWHB4XDc3NwKCgqYvXw+X1tbe/jw4czmpk2b9PT0KIqqrq4mhISEhISGhpaVlVEU5ejoSAi5cOECj8eLjo6W01dLTEykaXrx4sVd7mVGrvB4PJmcKyIiYtOmTaampjKJJtfIGhoahBDJZWVHjRpFCBE/4+ng5cuXOjo6dnZ24hYjIyNPT8+EhATV7g8FmUDFAwCDhbu7+40bN8SbGzdu3Lx5c3NzM5fLzcjIKCsrs7e3X7dunVAoJITw+fzVq1c3NTUFBweXl5ffvXu3ra3tgw8+YLo8EhMTJVd+SEpK2rVrl3gzISFh0aJFDg4ONE2XlpYSQpgBsCKRSE5f7aeffhozZoyurm6Xe2/fvk0IcXd3H/iJfv3117Kysk8//XTgoRQQeezYseQ/65thw4YRQqqqqjof3NTUlJeXt27dOm1tbcn2SZMmvXz58v79+zJMDFQSKh4AGOzc3Nx4PJ6pqWlAQEBjY+OzZ8/Eu1gs1rhx49hstpOTU3JyskAgOHr0aD9O4e3t3dDQsGPHDtll/X8aGxv/+usvBweHzrvevHmTnp4eHBzs6ura3RMg6TU3N4eEhCQnJw8wjsIiOzs7z58/PykpKS8vr6Wl5fXr12fOnKEoiilqO4iJibGwsNizZ0+HduaxUFFRkWxzA9WDNW4AYMhg/ue+y59DQsjUqVN1dXUfPXqk2KR6V1lZSdN0lw94XF1dGxsb/f399+zZo6WlNcAThYeHr1+/3srKaoBxFBk5PT09LCxs1apVNTU1FhYWM2bMoGmaedIj6cyZM5mZmZcuXWLGfUtiLuybN29knhuoGFQ8AKA62Gx2lx0iytXS0kII6XJEsJmZWWpq6nvvvTfws1y/fr2oqCguLm7goRQWmRBiYGBw+PBh8earV69OnjxpaWkpeUx6enpcXFx+fn6HdgYzDIi5yAA9QK8WAKgIoVBYV1dnbW2t7EQ6Yn6Su5wrz9TU1NDQUCZnSU1NvXLlioaGBkVRFEUx44ujo6Mpivrtt98GZ+TO7ty5Qwjx8vIStxw8ePD48eN5eXldljuEkNbWVvKfw58BuoSKBwBURH5+Pk3TM2fOZDZZLFZ3/V8KZmZmRlFUfX19511nz56VVVfR0aNHaQnMs66IiAiapqdOnTo4I3eWkpJiZ2fn6elJCKFpOiwsrKioKDs7W19fv7uPMBfW3NxctpmA6kHFAwBDmEgkqq2tbWtrKywsDAkJsbGxWb16NbPL0dGxpqYmOztbKBRWVVU9ffpU8oPGxsYVFRXl5eUCgUAoFObm5srv7XRdXV17e/sXL150aC8tLTU3N1+2bJlkY0BAgLm5+d27d2WexuCMPH369KdPn7a1tZWXl2/duvXy5cupqanMgK2HDx/u378/JSVFS0uLknDgwAHJCMyFdXZ2lsl3ARWGigcABotDhw5NmzaNEBIWFubj45OcnBwfH08ImTBhwpMnT1JSUkJDQwkh8+fPLykpYT7S0tLi7Oyso6Pj4eExevTon3/+WTxcZuPGjV5eXsuXLx8zZszu3buZXg9XV1fm9fWgoCAzMzMnJ6eFCxfW1NTI+6t5e3sXFxdLrhhFullSo7W1tbKyMicnp8s4t27dcnd3t7S0LCgouH//voWFxaxZs65evSpNDoMzsqGh4cSJE3V0dCZPnvzo0aNr166Ju7SknGLnzp07VlZWEyZMkOZgUGs0qJmMjAz8ewcFIIRkZGTI9RTM+gNyPUWvPvnkk08++aTXw0pKSlgs1rFjx3o9sr293cPDIzU1VRbZDfnIvaquruZwOAcOHJDmYAXckzCY4RkPAAxhQ2XpbEdHx6ioqKioKPHS311qb2/Pzs4WCAQBAQGyTWAoRpZGZGTkxIkT+Xy+4k8NQw4qHpCL8+fPGxgYSK4cNDhj9kAkEsXHx/d7scm0tDSKoga4VqUKXEYQ2759u5+fX0BAQJdDmBn5+flZWVm5ubndzc7cb0Mxcq/i4uLu3bt3/vz5gU9lBOoAFQ/IBS2HNW7kEbM7JSUls2fP3rJli+SahX2Slpbm4OBw8+ZNZhGD/hnql1GuwsPDjx49Wl9fb2dnd/r0aWWnI5Xo6Gg+n793797uDpg7d+6JEyfEy4HJ0FCM3LOcnJy///47Pz/fyMhIwaeGoUq5nWqgeHIax9PU1OTq6jr4Y0rj3r17S5cuPX78+MSJE11cXPoRobq62s7O7vjx44SQHTt2SP9BVbqMRD3GTEg5jgcGAzW5J6E7eMYDspGamlpZWTn4Y0rDxcUlKytrxYoVXU6SK43MzExvb+/FixdzOBxmsKqUH1SlywgAMKig4oGuXbt2zcnJycDAgMPhODs7X7x4Ubzr2LFjU6dO5XA4enp6tra2u3fvDgkJCQ0NLSsroyjK0dHx+vXrNjY2FEUdOnSIEDJu3DiKojQ0NKZMmcJ0Em3bto2J/P3333d3rp5jEkJomo6Li2NWkTQyMlqyZIl4QaXk5GQ9PT1dXd2cnJwFCxbweDxra+uTJ0/K5MpcuHCh14lb0tLSli5dyuVy582bV15efu3atc7HqPllBABQNCU/YwKFk7JX69SpU5GRkTU1NW/fvp05c+awYcOYdmZ+lL179759+7ampubbb79dsWIFTdMff/yxg4OD+OPMlCcHDx6kabqtrc3W1tbGxqatrU18wObNm+Pj43s+Vw8xaZreuXOntrb2sWPH6urqCgsLJ0+ebGJi8vr1a2ZvREQEIeTKlSv19fWVlZUeHh56enqtra19ulYzZszo3Kt17tw5LpcbFRXV3aeePn1qamrKfNljx44RQtasWdPhGHW4jEQ9ehDQqzWEqMk9Cd1BxaN2+jGOJyYmhhBSWVnZ2tpqaGjo5eUl3tXW1paQkED39rPK/MBnZmYym42NjTY2NvX19T2cq+eYTU1N+vr6AQEB4r23b98mhIgLEeanurm5mdlMSkoihJSWlvbpi3dZ8fRq7969n332GfPP9fX1bDabx+M1NTWJD1CTy6gmvy6oeIYQNbknoTtYOx16x7z52d7eXlhYWFdX9+GHH4p3aWpqBgcH9xph7dq1kZGRCQkJfn5+hJDjx48vWbKEx+P1cK6eAxYXF797905yTZ9p06Zpa2sXFBR0eTwzab1iVllKS0tjCg5CCI/Hmzdv3tmzZ3NycsSzlajPZbx586aURw5dzBIHmZmZyk4EAHqBige69tNPP8XGxhYXFzc0NIh/4RoaGggh/VjqWV9ff/369bGxsbdv354+ffo///lPydeJuzxXz+rq6piwko2GhoYCgaCvucnWgwcPioqKFi1a1KH9hx9+EFc86nMZExISEhISZBJqkOuwNhYADEIYuQxdePbsma+v7/DhwwsKCurr6/ft28e0W1paEkKqq6v7EZPP52tpacXHx1+9enXEiBEODg49n6tnTLnQ4Ye5rq7O2tq6H7nJ0IkTJ5YvXy75HLWmpkZHR+fSpUuvX79mjlGfy6gOPQjo1RpCZHJXw9CFige6UFRUJBQKN27caG9vz+FwKIpi2m1tbY2NjS9dutSPmNbW1v7+/qdPn96xY0dISEiv5+rZ+PHj9fX1f/vtN3FLQUFBa2vrlClT+pGbrNA0nZ6evmnTJslGIyMjPz+/9vb2tLQ0pgWXEQBA8VDxQBdsbGwIIZcvX25paSkpKRGP6mCz2eHh4VevXuXz+S9fvhSJRAKB4OHDh4QQY2PjioqK8vJygUDQXZdKaGhoW1tbbW3t+++/3+u5eo7J4XBCQ0PPnDlz/PjxhoaGoqKioKAgCwuLwMBAWV+MjnJzc7t7O/3GjRs8Hm/WrFkd2oOCggghP/zwA7OJywgAoATKfsoIiiblu1phYWHGxsaGhoZ+fn7M3C0ODg7Pnj2jafrQoUPOzs4cDofD4UyaNCkpKYmm6bt3744cOVJHR8fd3f2rr75ippzX1dVdvHixZFgvL6/vvvtOynP1HFMkEsXGxo4aNUpLS8vIyMjX1/fx48dMwKSkJGaJn1GjRpWVlR05coQZ3jty5Mg///yz1+9+8+bNWbNmWVhYMP+NDB8+3M3N7ZdffmH2nj9/nsvl7tmzp8On1qxZo6enx2KxXFxc7t69K27fvXu3OJSVlRVzudThMhL0asEgoyb3JHSHotG1qWYyMzOXLVuGf+8gbxRFZWRk+Pv7KzsR+WLemzt16pSyE4Heqck9Cd1BrxYAAACoPlQ8oF4ePXpEdU/8AjkAAKgYVDygXsaOHdtDL296erqyEwQVd/ny5e3bt4tEIl9fXxsbGw6HY2Vl5ePjU1hYKH0QkUgUHx/v5ubWwzEtLS1jx4796quv+pSe4iMLhcKdO3fa29tra2tbWVlt3bq1ublZvDcqKsrJyYnH47HZbEdHx23btr17947Z9eOPP+7bt6/XaTYBxFDxAAAoyNdff52YmBgeHi4Sia5du5aWllZTU3P9+vXm5ubZs2dXVFRIE6SkpGT27NlbtmxhFpTtTkRExOPHj/uUnlIih4SExMbGxsTEvH379sSJEykpKWvXrhXvzcvL+/zzz8vLy6urq2NiYsQTjhNCFi9ezOFw5s6dy0ykCdArVDwAMCQ1Nzf3/ChCKaF68M0336Snp2dmZnK5XEKIq6uru7u7rq6unZ1ddHR0fX39999/32uQ+/fvf/nll0FBQRMnTuzhsBs3bjx48KBP6Skl8pMnTw4fPrxq1aqAgAAulztnzhw+n5+WlvbHH38wB+jr6wcGBhobG3O5XH9/f19f3wsXLjBrwxFCgoODXVxcFi5c2NbW1qeUQD2h4gGAISk1NbWysnKwhepOaWnpjh07du3axeFwCCEsFuvs2bPivfb29oSQsrKyXuO4uLhkZWWtWLGCzWZ3d0xzc/MXX3zR1/U9lBL5zp07IpFoxowZ4pb58+cTQi5evMhsnjt3TlNTU7zXxMSEECL5oCgyMvLevXtqspgJDBAqHgBQGpqm4+Lixo0bx2azjYyMlixZ8ujRI2YXn8/X1tZmZg8ihGzatElPT4+iKGZ1jpCQkNDQ0LKyMoqiHB0dExMTORyOmZnZhg0bLCwsOByOm5ubeBrGPoUihFy4cKG7SSb7LTExkabpxYsXd7mXGbnS5aKw/RAREbFp0yZTU1OZRJNrZA0NDUKIjo6OuGXUqFGEEPEzng5evnypo6NjZ2cnbjEyMvL09ExISMCMG9ArVDwAoDSRkZHbt2+PiIiorKy8evXq8+fPPTw83rx5QwhJTEyUnDclKSlp165d4s2EhIRFixY5ODjQNF1aWsrn81evXt3U1BQcHFxeXn737t22trYPPviA6f7oUyjy7zXnRSKRDL/pTz/9NGbMGGZGx85u375NCHF3dx/4iX799deysrJPP/104KEUEHns2LHkP+ubYcOGEUKqqqo6H9zU1JSXl7du3TptbW3J9kmTJr18+fL+/fsyTAxUEioeAFCO5ubmuLi4pUuXrly50sDAwNnZ+fDhw9XV1UeOHOlfQBaLxTwucnJySk5OFggER48e7Uccb2/vhoaGHTt29C+NzhobG//66y/xsq+S3rx5k56eHhwc7Orq2t0TIOk1NzeHhIQkJycPMI7CIjs7O8+fPz8pKSkvL6+lpeX169dnzpyhKKrLJVZiYmIsLCz27NnToZ15LFRUVCTb3ED1sJSdAACoqeLi4nfv3k2dOlXcMm3aNG1tbclFwfpt6tSpurq64j4y5aqsrKRpussHPK6uro2Njf7+/nv27NHS0hrgicLDw9evX29lZTXAOIqMnJ6eHhYWtmrVqpqaGgsLixkzZtA0zTzpkXTmzJnMzMxLly4x474lMReWeTQI0ANUPACgHMxLxfr6+pKNhoaGAoFAJvHZbHaXnSOK19LSQgjpckSwmZlZamrqe++9N/CzXL9+vaioKC4ubuChFBaZEGJgYHD48GHx5qtXr06ePGlpaSl5THp6elxcXH5+fod2BjMMiLnIAD1ArxYAKIehoSEhpEN9U1dXZ21tPfDgQqFQVqEGjvlJ7nKuPFNTU+Y6DFxqauqVK1c0NDSYCcSZ8cXR0dEURf3222+DM3Jnd+7cIYR4eXmJWw4ePHj8+PG8vLwuyx1CSGtrK/nP4c8AXULFAwDKMX78eH19fcmfzIKCgtbW1ilTpjCbLBary/Ec0sjPz6dpeubMmQMPNXBmZmYURdXX13fedfbsWVl1FR09elRyAnHm+VZERARN05Jdh4MqcmcpKSl2dnaenp6EEJqmw8LCioqKsrOzOzwLlMRcWHNzc9lmAqoHFQ8AKAeHwwkNDT1z5szx48cbGhqKioqCgoIsLCwCAwOZAxwdHWtqarKzs4VCYVVV1dOnTyU/bmxsXFFRUV5eLhAImGpGJBLV1ta2tbUVFhaGhITY2NisXr26H6Fyc3Nl+3a6rq6uvb39ixcvOrSXlpaam5svW7ZMsjEgIMDc3Pzu3buyOvsgjzx9+vSnT5+2tbWVl5dv3br18uXLqampzNtYDx8+3L9/f0pKipaWluT6dwcOHJCMwFxYZ2dnmXwXUGGoeABAab7++uuYmJioqCgTExNPT09bW9v8/Hw9PT1m78aNG728vJYvXz5mzJjdu3cz3RauRxyMYgAAIABJREFUrq7MO+dBQUFmZmZOTk4LFy6sqakhhLS0tDg7O+vo6Hh4eIwePfrnn38WD53payiZ8/b2Li4ullwxihDS5RQyra2tlZWVOTk5Xca5deuWu7u7paVlQUHB/fv3LSwsZs2adfXqVWlyGJyRDQ0NJ06cqKOjM3ny5EePHl27dk3cpSXlFDt37tyxsrKaMGGCNAeDWuthVUVQSRkZGfj3DgpACMnIyFDY6Zi1CBR2OrFPPvnkk08+6fWwkpISFot17NixXo9sb2/38PBITU2VRXZDPnKvqqurORzOgQMHpDlYwfckDDZ4xgMAKmIwL6Pt6OgYFRUVFRUlXvq7S+3t7dnZ2QKBICAgQLYJDMXI0oiMjJw4cSKfz1f8qWHIQcUDAKAI27dv9/PzCwgI6HIIMyM/Pz8rKys3N7e72Zn7bShG7lVcXNy9e/fOnz8/8KmMQB2g4gGAIS88PPzo0aP19fV2dnanT59Wdjrdio6O5vP5e/fu7e6AuXPnnjhxQrwEmAwNxcg9y8nJ+fvvv/Pz842MjBR8ahiiMAMhAAx5MTExMTExys5CKvPmzZs3b56ys1AFPj4+Pj4+ys4ChhI84wEAAADVh4oHAAAAVB8qHgAAAFB9qHgAAABA9WHkspry8/NTdgqg+uLj40+dOqXsLOTr1q1bBP9BAQwFFC3dNN6gMm7evBkXF6fsLEBN/f7774SQSZMmKTsRUFNbtmxxdXVVdhagHKh4AEBx/P39CSGZmZnKTgQA1A7G8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpDxQMAAACqDxUPAAAAqD5UPAAAAKD6UPEAAACA6kPFAwAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpDxQMAAACqDxUPAAAAqD5UPAAAAKD6UPEAAACA6kPFAwAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpDxQMAAACqDxUPAAAAqD5UPAAAAKD6WMpOAABUWVNT099//y3ebG1tJYTU1taKW9hstq6urhIyAwA1Q9E0rewcAEBlJScnb9q0qYcDkpKSNm7cqLB8AEBtoeIBADmqqqqysLBob2/vcq+mpuarV69MTU0VnBUAqCGM4wEAOTI1NZ07d66mpmbnXZqamv/4xz9Q7gCAYqDiAQD5WrlyZZfPkmmaXrlypeLzAQD1hF4tAJAvgUBgamoqOX6Zoa2tXVVVxePxlJIVAKgbPOMBAPnicrmLFi3S0tKSbGSxWD4+Pih3AEBhUPEAgNytWLGira1NsqW9vX3FihXKygcA1BB6tQBA7lpbW01MTAQCgbhFX1+/urqazWYrMSsAUCt4xgMAcqetre3n56etrc1samlpLVu2DOUOACgSKh4AUIRPP/2UmXCZECIUCj/99FPl5gMA6ga9WgCgCCKRaPjw4VVVVYQQExOT169fdzlJDwCAnOAZDwAogoaGxqeffqqtra2lpbVixQqUOwCgYKh4AEBBli9f3traii4tAFAKrJ3eZ5mZmcpOAWBIoml62LBhhJC//vqrvLxc2ekADEn+/v7KTmGowjiePqMoStkpAACAmsKvdr/hGU9/ZGRkoMoG6IeHDx8SQpycnJSdSO8yMzOXLVumDr8uFEXhb9qQwNyTys5iCEPFAwCKMyRqHQBQSRi5DAAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwCAzJw/f97AwODs2bPKTkReLl++vH37dpFI5Ovra2Njw+FwrKysfHx8CgsLpQ8iEoni4+Pd3Nx6OKalpWXs2LFfffVVn9JTfGShULhz5057e3ttbW0rK6utW7c2NzeL90ZFRTk5OfF4PDab7ejouG3btnfv3jG7fvzxx3379rW3t/cpDRgIVDwAADKj2vP3fP3114mJieHh4SKR6Nq1a2lpaTU1NdevX29ubp49e3ZFRYU0QUpKSmbPnr1ly5ampqYeDouIiHj8+HGf0lNK5JCQkNjY2JiYmLdv3544cSIlJWXt2rXivXl5eZ9//nl5eXl1dXVMTExCQoKfnx+za/HixRwOZ+7cuXV1dX1KBvoNFQ8AgMx4e3vX19cvWrRI3idqbm7u+UmGzH3zzTfp6emZmZlcLpcQ4urq6u7urqura2dnFx0dXV9f//333/ca5P79+19++WVQUNDEiRN7OOzGjRsPHjzoU3pKifzkyZPDhw+vWrUqICCAy+XOmTOHz+enpaX98ccfzAH6+vqBgYHGxsZcLtff39/X1/fChQvPnz9n9gYHB7u4uCxcuLCtra1PKUH/oOIBABh6UlNTKysrFXa60tLSHTt27Nq1i8PhEEJYLJZkz529vT0hpKysrNc4Li4uWVlZK1asYLPZ3R3T3Nz8xRdfJCQk9ClDpUS+c+eOSCSaMWOGuGX+/PmEkIsXLzKb586d09TUFO81MTEhhEg+KIqMjLx3715fU4L+QcUDACAb169ft7GxoSjq0KFDhJDk5GQ9PT1dXd2cnJwFCxbweDxra+uTJ08yBycmJnI4HDMzsw0bNlhYWHA4HDc3t4KCAmYvn8/X1tYePnw4s7lp0yY9PT2KoqqrqwkhISEhoaGhZWVlFEU5OjoSQi5cuMDj8aKjo+X01RITE2maXrx4cZd7mZErPB5PJueKiIjYtGmTqampTKLJNbKGhgYhREdHR9wyatQoQoj4GU8HL1++1NHRsbOzE7cYGRl5enomJCSodn/oIIGKBwBANtzd3W/cuCHe3Lhx4+bNm5ubm7lcbkZGRllZmb29/bp164RCISGEz+evXr26qakpODi4vLz87t27bW1tH3zwAdPlkZiYKLnQVVJS0q5du8SbCQkJixYtcnBwoGm6tLSUEMIMgBWJRHL6aj/99NOYMWN0dXW73Hv79m1CiLu7+8BP9Ouvv5aVlX366acDD6WAyGPHjiX/Wd8MGzaMEFJVVdX54Kampry8vHXr1mlra0u2T5o06eXLl/fv35dhYtAlVDwAAPLl5ubG4/FMTU0DAgIaGxufPXsm3sViscaNG8dms52cnJKTkwUCwdGjR/txCm9v74aGhh07dsgu6//T2Nj4119/OTg4dN715s2b9PT04OBgV1fX7p4ASa+5uTkkJCQ5OXmAcRQW2dnZef78+UlJSXl5eS0tLa9fvz5z5gxFUUxR20FMTIyFhcWePXs6tDOPhYqKimSbG3SGlUQBABSE+Z/7Ln8OCSFTp07V1dV99OiRYpPqXWVlJU3TXT7gcXV1bWxs9Pf337Nnj5aW1gBPFB4evn79eisrqwHGUWTk9PT0sLCwVatW1dTUWFhYzJgxg6Zp5kmPpDNnzmRmZl66dIkZ9y2JubBv3ryReW7QASoeAIDBgs1md9kholwtLS2EkC5HBJuZmaWmpr733nsDP8v169eLiori4uIGHkphkQkhBgYGhw8fFm++evXq5MmTlpaWksekp6fHxcXl5+d3aGcww4CYiwxyhV4tAIBBQSgU1tXVWVtbKzuRjpif5C7nyjM1NTU0NJTJWVJTU69cuaKhoUFRFEVRzPji6OhoiqJ+++23wRm5szt37hBCvLy8xC0HDx48fvx4Xl5el+UOIaS1tZX85/BnkBNUPAAAg0J+fj5N0zNnzmQ2WSxWd/1fCmZmZkZRVH19feddZ8+elVVX0dGjR2kJzLOuiIgImqanTp06OCN3lpKSYmdn5+npSQihaTosLKyoqCg7O1tfX7+7jzAX1tzcXLaZQGeoeAAAlEYkEtXW1ra1tRUWFoaEhNjY2KxevZrZ5ejoWFNTk52dLRQKq6qqnj59KvlBY2PjioqK8vJygUAgFApzc3Pl93a6rq6uvb39ixcvOrSXlpaam5svW7ZMsjEgIMDc3Pzu3bsyT2NwRp4+ffrTp0/b2trKy8u3bt16+fLl1NRUZsDWw4cP9+/fn5KSoqWlRUk4cOCAZATmwjo7O8vku0APUPEAAMjGoUOHpk2bRggJCwvz8fFJTk6Oj48nhEyYMOHJkycpKSmhoaGEkPnz55eUlDAfaWlpcXZ21tHR8fDwGD169M8//yweLrNx40YvL6/ly5ePGTNm9+7dTK+Hq6sr8/p6UFCQmZmZk5PTwoULa2pq5P3VvL29i4uLJVeMIt0sqdHa2lpZWZmTk9NlnFu3brm7u1taWhYUFNy/f9/CwmLWrFlXr16VJofBGdnQ0HDixIk6OjqTJ09+9OjRtWvXxF1aUk6xc+fOHSsrqwkTJkhzMAwIDX1ECMnIyFB2FgAgXxkZGfL+C8msPyDXU0hDmr9pJSUlLBbr2LFjvUZrb2/38PBITU2VUXZDO3KvqqurORzOgQMHpDlYAfekasMzHgAApRkqS2c7OjpGRUVFRUWJl/7uUnt7e3Z2tkAgCAgIkG0CQzGyNCIjIydOnMjn8xV/ajWEikdFREVFOTk58Xg8Npvt6Oi4bdu27v4wrV27lsvlUhR17949BSc5tIhEovj4+P4t1vj48eP/9//+33vvvcflclksloGBwejRo729vW/evCnzPDvo4U7Iysqyt7eXHE+gra1tZmY2Z86c2NjY2tpacZCAgACqR+fOnZP3F4HBZvv27X5+fgEBAV0OYWbk5+dnZWXl5uZ2Nztzvw3FyL2Ki4u7d+/e+fPnBz6VEUhF2Q+Zhh4yKHu1PD09k5KS3r5929DQkJGRoaWlNX/+/O4OZlb2+f333xWZ4dDy559/zpo1ixDi4uLS189+9913Wlpas2fPvnDhQm1tbUtLS1lZWXp6upub27fffiuPbCX1eic4ODgYGBjQNM2Mmf35559Xr15NUZSFhcWdO3eYY5YtW3bp0qW6ujqhUPjq1StCyOLFi1tbWxsbGysrK9etW3f27Fl5fxGlk3cPwvbt25nxrba2tqdOnZLfiXrVp79pFy9eDAsLk2s+aiI7OzsmJqatrU36j6BXa4AwA6GK0NfXDwwMZBbp9ff3z8rKyszMfP78+YgRIwYYubm5ee7cuZKrBam8+/fvR0VFBQUFNTY20n1c3u/WrVuBgYGenp4XL15ksf7//77s7e3t7e0NDQ3F41XlR/o7gaIoQ0PDOXPmzJkzx9vbe9myZd7e3n/++aeBgQFFUbNmzZL8X16KorS0tLS0tHR1dadMmSLvb6EOYmJiYmJilJ1Fn82bN2/evHnKzkIV+Pj4+Pj4KDsL9YJeLRVx7tw55keOYWJiQghpamrq8mCKoqSPnJqaWllZOcD0hhYXF5esrKwVK1Z0Oclsz/bs2dPe3r53715xuSP24Ycffv755zLKsVt9uhPEPvnkk9WrV1dWVjKzx548ebKHJ/yBgYEfffSRjPIFAFAQVDzycuzYsalTp3I4HD09PVtb2927dxNCaJqOi4tjFg40MjJasmSJeA2d5ORkPT09XV3dnJycBQsW8Hg8a2trpvuJEDJu3DiKojQ0NKZMmcL8em3bts3AwIDD4Xz//fedz/7y5UsdHR07Oztmk6bp2NjYMWPGsNlsAwODL774QspvERISEhoaWlZWRlGUo6Pj/v37dXV1uVxuZWVlaGiolZXV48ePr1275uTkxCTj7Ox88eLFXr8OIeSXX36ZPn26rq4uj8dzdnZuaGhITEzkcDhmZmYbNmywsLDgcDhubm4FBQXij/Rw9boMSAhpb2/fuXOnjY2Njo7OhAkTmGfCA3ThwoXuJj5pbW29cuXKsGHDpk+f3nMQZd0JPWCmgcnNze31SEII7gQAGHqU2aU2NBEp+ryZSTj27t379u3bmpqab7/9dsWKFTRN79y5U1tb+9ixY3V1dYWFhZMnTzYxMXn9+jXzqYiICELIlStX6uvrKysrPTw89PT0WltbaZpua2uztbW1sbGR7PTdvHlzfHx857M3NjZyuVw+ny9uiYiIoCjqf//3f2tra5uampKSkojU43g+/vhjBwcHyVCEkODg4IMHDy5duvSPP/44depUZGRkTU3N27dvZ86cOWzYsF6/zrt373g83r59+5qbm1+/fr106dKqqiqapgMDA/X09B4+fNjS0lJcXDxt2jQul/vs2TMmYA9Xr7uAW7duZbPZp0+frq2tDQ8P19DQEA9VkcaMGTM6j+M5d+4cl8uNiorqfPyff/5JCJk5c2avkZV1J9AS43g6YCqDESNGdGhnxvH4+Ph0aFf5O0F9xkxI8zcNBgP1uSflBNeuz3r969Da2mpoaOjl5SVuaWtrS0hIaGpq0tfXDwgIELffvn2bECL+7WR+GJqbm5lNpi4pLS1lNpkqKjMzk9lsbGy0sbGpr6/vnEBERMTo0aMbGhqYzaamJl1d3Q8++EB8QJ9GLndZ8YiT7IAZl8CstNzD13nw4AEh5Ny5cx0+HhgYKPljzKxQs2vXLuZb9HD1ugzY3Nysq6sr/khTUxObzd64caM035rRZcXTA2aNnn/84x89H6asO4HRXcVD0zQzsqdDY88VjwrfCerz64KKZ6hQn3tSTjByWfYKCwvr6uo+/PBDcYumpmZwcPBvv/327t07yWVcpk2bpq2tLfm4XhLzHod4YZ21a9dGRkYmJCT4+fkRQo4fP75kyRIej9fhU2fOnMnMzLx06RKXy2VaSktLm5qa5s6dK7uv2C3mHcsupxiR/Dr29vZmZmYrV64MDg5evXq1ra1tl9GmTp2qq6vLdFgUFxf3cPW6DPj48eOmpqbx48czx+vo6AwfPlyy+0PmmKVzeh000/N36UxWd0LPmGHaneP0j8rcCcxFVnnx8fGnTp1SdhbQi84LfUCfYByP7DG9A53XE66rqyP//lEUMzQ0FAgE0oTV19dfv379jRs3mP+j/ec//9l50qr09PRvvvkmPz9f8peD+Y+EWS5YHn766ac5c+aYmpqy2ext27ZJ8xEdHZ28vDx3d/fo6Gh7e/uAgIAOs9eLsdlsZtm/nq9elwEbGxsJIV999ZV4FpmnT5/2Wo4MhK2tLYfDYfq2eqCsO6FnTNpjx46V8vjOcCcAwGCGZzyyZ2lpSQiprq7u0M7UQB1+1erq6qytraWMzOfzExIS4uPjg4KCRowY4eDgILn34MGDFy9ezMvL6/BjwOFwCCF///13H7+HVJ49e+br67t06dJ//etflpaWBw8elPKn7r333jt79mxVVVVcXNw333zz3nvv7dixo8MxQqFQfH16vXqdAzIzqMbHx4eEhAz8m0qDzWZ/+OGHOTk5v/76KzOdj6Samppt27Z99913yroTenbhwgVCyIIFC6T/iCRVvRPU4ckHRVGbN2/29/dXdiLQi8zMzA7rtkKf4BmP7Nna2hobG1+6dKlD+/jx4/X19ZmhHoyCgoLW1lbpZzextrb29/c/ffr0jh07JP920zQdFhZWVFSUnZ3d+Udu/PjxGhoav/zyS7++TS+KioqEQuHGjRvt7e05HI6U771XVFQ8fPiQEGJqarp3797Jkyczmx3k5+fTND1z5kzS29XrMuCIESM4HI6Cp5aOjIxks9lbtmzp/KziwYMHzCvryroTevD69ev4+Hhra+v/+Z//kf5TknAnAMAgh4pH9thsdnh4+NWrV/l8/suXL0UikUAgePjwIYfDCQ0NPXPmzPHjxxsaGoqKioKCgiwsLAIDA6UPHhoa2tbWVltb+/7774sbHz58uH///pSUFC0tLcmlAA4cOEAIMTU1/fjjj0+fPp2amtrQ0FBYWHjkyBHpz2hsbFxRUVFeXi4QCMRDScRsbGwIIZcvX25paSkpKeluJEoHFRUVGzZsePToUWtr6++///706VPmx4wQwkwE3NbWVlhYGBISYmNjw7w13fPV6zIgh8P57LPPTp48mZyc3NDQ0N7e/uLFC2Yc7kDk5uZ293Y6IWTixIknTpx48OCBh4fH+fPn6+vrhULhX3/9lZKSsmbNGmZ0i7LuBDGapt+9eycSiWiarqqqysjImDVrlqamZnZ2dr/H8ajhnQAAQ4wSR00PUUS69xoOHTrk7OzM4XA4HM6kSZOSkpJomhaJRLGxsaNGjdLS0jIyMvL19X38+DFzfFJSEjPn26hRo8rKyo4cOcL89owcOfLPP/+UjOzl5fXdd99JthQVFXX5Lzc2NpY5QCAQrF27dtiwYfr6+u7u7jt37iSEWFtb379/v9cvcvfu3ZEjR+ro6Li7u2/ZskVHR4cQMmLECPEqymFhYcbGxoaGhn5+focOHSKEODg4fPnllz18nfLycjc3NyMjI01NTUtLy4iICOZd68DAQC0tLSsrKxaLxePxlixZUlZWJs6kh6vXXcC///47LCzMxsaGxWIxlV9xcXGvX/nmzZuzZs2ysLBgLuPw4cPd3Nx++eUXZu/58+e5XO6ePXt6iPDs2bOtW7c6Ozvr6+tramoaGhpOmjRpzZo1v/76a6/fRX53wo8//jhhwgRdXV1tbW0NDQ3y72mXp0+fHhUV9fbt2w7foqGhYfbs2cbGxoQQDQ0NR0fH6OhoZte+fftU/k5Qn/dipPybBkqnPveknFB0HyfRB4qiMjIy0OctDxs2bDh16tTbt2+VnQgo2WC4E5gxE+rwFxJ/04YK9bkn5QS9WjC4dPk+M6gh3AkAIFuoeNTao0ePqO4xb7ioGDX8ygAKc/ny5e3bt4tEIl9fXxsbGw6HY2Vl5ePjU1hYKM3Ho6KinJyceDwem812dHTctm3bu3fvJA9IS0tjJuAeOXLkZ5999vr1aykTk19khkgkio+Pd3Nzk2z88ccf9+3bh9p9EFFyr9oQRNDnLR/bt29n5qaztbU9deqUstMBpRkkd4L6jJmQ1d+0nTt3Llq0qKGhQSgUDhs27Nq1a42NjU+ePPnggw8MDAxevnzZawRPT8+kpKS3b982NDRkZGRoaWnNnz9fvDc9PZ0Qsm/fvrq6ut9//93e3n7ixIlCoVCa3OQXmabpP//8k5mNovMU7QkJCZ6enrW1tVKG6pn63JNygmvXZ6h4ANSBAn5dmpqaXF1dlR5KJn/T9u7dO3r0aGYtEaFQ+NFHH4l3MVNlioe998Db21tywThmaJF4PTUvLy9LS0vmHUOappnR8devX5cmPflFvnfv3tKlS48fPz5x4sQuF6Xh8/murq7S1089QMUzQOjVAgBQjtTU1MrKysEWqh9KS0t37Nixa9cuZr5TFot19uxZ8V57e3tCSFlZWa9xzp07p6mpKd40MTEhEsu2PH/+3MLCQjzV04gRIwghT58+lSZD+UV2cXHJyspasWIFm83u8oDIyMh79+4lJCRIEw3kChUPAED/0TQdFxc3btw4NpttZGS0ZMkS8YpdfD5fW1t7+PDhzOamTZv09PQoimImZA8JCQkNDS0rK6MoytHRMTExkcPhmJmZbdiwwcLCgsPhuLm5iac16lMoQsiFCxd6mDVK5hITE2maXrx4cZd7mdk4+zHV08uXL3V0dOzs7JhNe3t7yaqOGWrDlFODJ3JnRkZGnp6eCQkJNN6xUjolP2Maggh6tQDUgJQ9CDt37tTW1j527FhdXV1hYeHkyZNNTExev37N7F2xYoW5ubn44NjYWEJIVVUVs/nxxx87ODiI9wYGBurp6T18+LClpaW4uJgZSCvueelTqHPnznG5XGY9+V4N/G+avb29k5NTd3uzsrIIIadPn+5TzMbGRi6Xy+fzxS35+flaWlqJiYkNDQ0PHjwYN27chx9+2I9s5RR5xowZXfZq0TS9fft2Qsjvv//ej2wloVdrgPCMBwCgn5qbm+Pi4pYuXbpy5UoDAwNnZ+fDhw9XV1f3aVpzSSwWi3lc5OTklJycLBAIjh492o843t7eDQ0NnVcok4fGxsa//vqrw+JujDdv3qSnpwcHB7u6unb3BKg7MTExFhYWe/bsEbd4enqGhYXx+Xwejzd+/HiBQPDdd9/1I2H5Re7OqFGjCCHdTRAKCoOKBwCgn4qLi9+9ezd16lRxy7Rp07S1taVcZKNnU6dO1dXVFfeRDVqVlZU0TTMza3fg6uoaHBy8ZMmS3NxcZokVKZ05cyYzM/PixYtcLlfcGBERceTIkStXrrx79+7Jkydubm6urq7Pnz/vU7byi9wD5uK8efNGVgGhf1DxAAD0U11dHSGkw6KthoaGHZZ27zc2m11VVSWTUPLT0tJCCOly3K6ZmVleXt7BgwcNDAykD5ienv7NN9/k5+fb2tqKG1+9erVv377169e///77enp6dnZ2KSkpFRUVTO+e0iP3jFmShblQoEQsZScAADBUGRoaEkI61Dd1dXXW1tYDDy4UCmUVSq6Yn/Mu59kzNTVlLpH0Dh48ePHixby8vA51ZElJSXt7u6WlpbiFx+MZGxsXFxcrPXKvWltbyb8vFCgRKh4AgH4aP368vr7+b7/9Jm4pKChobW2dMmUKs8lisYRCYf+C5+fn0zQtXkx+IKHkyszMjKKo+vr6zrsk31HvFU3TX375ZW1tbXZ2NovV8beJqfwkV7wXCAQ1NTXMm+TKiiwl5uKYm5vLKiD0D3q1AAD6icPhhIaGnjlz5vjx4w0NDUVFRUFBQRYWFoGBgcwBjo6ONTU12dnZQqGwqqqqwxQvxsbGFRUV5eXlAoGAqWZEIlFtbW1bW1thYWFISIiNjc3q1av7ESo3N1dhb6fr6ura29u/ePGiQ3tpaam5ufmyZcskGwMCAszNze/evds5zsOHD/fv35+SkqKlpSW59suBAwcIIXZ2dl5eXikpKVevXm1ubn7+/DlzkdesWaPEyFJiLo6zs3O/I4BMoOIBAOi/r7/+OiYmJioqysTExNPT09bWNj8/X09Pj9m7ceNGLy+v5cuXjxkzZvfu3Uy/hnhUbFBQkJmZmZOT08KFC2tqagghLS0tzs7OOjo6Hh4eo0eP/vnnn8XjY/oaSpG8vb2Li4uZeXe4/O68AAAc00lEQVTE6K6mn2ltba2srMzJyem8q8vjxSiKOnXqVEBAwJo1a4yMjJycnJ49e5aVleXh4aHEyISQW7duubu7W1paFhQU3L9/38LCYtasWVevXpU85s6dO1ZWVhMmTOghDVAEJb4ZP0QRzMcDoAYUP/dJYGCgsbGxIs/IGPjftJKSEhaLdezYsV6PbG9v9/DwSE1NHcjphlbk6upqDodz4MCBgWeC+XgGCM94AAAGiyG6zrajo2NUVFRUVFSHBck7aG9vz87OFggEAQEBsk1gMEeOjIycOHEin8+XbWLQD6h4AABgoLZv3+7n5xcQENDlEGZGfn5+VlZWbm5ul5P3DMSgjRwXF3fv3r3z58/3aToikBNUPAAAyhceHn706NH6+no7O7vTp08rO53+iI6O5vP5e/fu7e6AuXPnnjhxQrw6mAwNzsg5OTl///13fn6+kZGRzBODfsDb6QAAyhcTExMTE6PsLAZq3rx58+bNU3YWg4WPj4+Pj4+ys4D/g2c8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+ii6x+m3oTOKopSdAgAAqCn8avcb3k7vM2aebwDoh/j4eELI5s2blZ0IAKgdPOMBAMXx9/cnhGRmZio7EQBQOxjHAwAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpDxQMAAACqDxUPAAAAqD5UPAAAAKD6UPEAAACA6kPFAwAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpDxQMAAACqDxUPAAAAqD5UPAAAAKD6UPEAAACA6kPFAwAAAKoPFQ8AAACoPlQ8AAAAoPpQ8QAAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAAAAgOpjKTsBAFBlBQUF9+/fF28+efKEEHLkyBFxi4uLy4wZM5SQGQCoGYqmaWXnAAAq69y5c4sWLdLU1NTQ0CCEMH9wKIoihIhEovb29rNnz3700UdKzhIA1AAqHgCQI6FQaGJi0tDQ0OVeHo9XVVWlra2t4KwAQA1hHA8AyJGWltby5cu7rGl62AUAIHOoeABAvpYvX97a2tq5XSgUfvrpp4rPBwDUE3q1AEC+RCKRpaXlmzdvOrSbmpq+fv2aGd8DACBv+FsDAPKloaHxX//1Xx16r7S1tVevXo1yBwAUBn9uAEDuOndstba2Ll++XFn5AIAaQq8WACjCqFGjSktLxZv29vZlZWVKzAcA1A2e8QCAIqxcuVJLS4v5Z21t7f/+7/9Wbj4AoG7wjAcAFKG0tHTUqFHizcePH48ePVqJ+QCAusEzHgBQBEdHRxcXF4qiKIpycXFBuQMACoaKBwAUZNWqVZqampqamqtWrVJ2LgCgdtCrBQAKUlFRMWLECJqmnz9/bmVlpex0AEC9oOKB/3Dz5s24uDhlZwEqKz8/nxAyZ84cJecBqmvLli2urq7KzgIGI/RqwX94/vz56dOnlZ0FqCwbG5uRI0cSQk6fPv3ixQtlpyN3t27dunXrlrKzUCOnT59+/vy5srOAQYql7ARgMDp16pSyUwDVVFNTQwgxNjamKGrz5s3+/v7Kzki+/Pz8CP6DUiCKopSdAgxeqHgAQHGMjY2VnQIAqCn0agEAAIDqQ8UDAAAAqg8VDwAAAKg+VDwAAACg+lDxAMCQcf78eQMDg7Nnzyo7EXm5fPny9u3bRSKRr6+vjY0Nh8OxsrLy8fEpLCyU5uNRUVFOTk48Ho/NZjs6Om7btu3du3eSB6SlpU2bNo3L5Y4cOfKzzz57/fq1lInJLzJDJBLFx8e7ublJNv7444/79u1rb2/vUyiA7qDiAYAhQ7VnTP36668TExPDw8NFItG1a9fS0tJqamquX7/e3Nw8e/bsioqKXiPk5eV9/vnn5eXl1dXVMTExCQkJzOvxjIyMjBUrVvj5+b148SInJ+fq1asLFixoa2uTJjf5RSaElJSUzJ49e8uWLU1NTZLtixcv5nA4c+fOraurkzIUQE9oAAkZGRm4K0ABCCEZGRnKzqJbTU1Nrq6uA4/zySeffPLJJ9IcuXfv3tGjRzc3N9M0LRQKP/roI/Gu27dvE0Kio6N7DeLt7d3W1ibeZKY7evbsGbPp5eVlaWkpEomYzUOHDhFCrl+/Lk168ot87969pUuXHj9+fOLEiS4uLp0P4PP5rq6uQqFQmmiD/L4C5cIzHgCAjlJTUysrKxV2utLS0h07duzatYvD4RBCWCyWZM+dvb09IaSsrKzXOOfOndPU1BRvmpiYEELED06eP39uYWEhnqNvxIgRhJCnT59Kk6H8Iru4uGRlZa1YsYLNZnd5QGRk5L179xISEqSJBtADVDwAMDRcv37dxsaGoijmEUJycrKenp6urm5OTs6CBQt4PJ61tfXJkyeZgxMTEzkcjpmZ2YYNGywsLDgcjpubW0FBAbOXz+dra2sPHz6c2dy0aZOenh5FUdXV1YSQkJCQ0NDQsrIyiqIcHR0JIRcuXODxeNHR0XL6aomJiTRNL168uMu9zc3NhBAej9fXsC9fvtTR0bGzs2M27e3tJcs4ZqgNU04NnsidGRkZeXp6JiQk0CrdpwkKgIoHAIYGd3f3GzduiDc3bty4efPm5uZmLpebkZFRVlZmb2+/bt06oVBICOHz+atXr25qagoODi4vL797925bW9sHH3zALLqUmJgoucBFUlLSrl27xJsJCQmLFi1ycHCgabq0tJQQwgyeFYlEcvpqP/3005gxY3R1dbvcy/Rqubu79ylmU1NTXl7eunXrtLW1mZbw8PDXr18fPHhQIBAUFxcnJCR8+OGHM2fO7Gu28ovcnUmTJr18+fL+/fuyCgjqCRUPAAxtbm5uPB7P1NQ0ICCgsbHx2bNn4l0sFmvcuHFsNtvJySk5OVkgEBw9erQfp/D29m5oaNixY4fssv4/jY2Nf/31l4ODQ+ddb968SU9PDw4OdnV17e4JUHdiYmIsLCz27NkjbvH09AwLC+Pz+Tweb/z48QKB4LvvvutHwvKL3J1Ro0YRQoqKimQYE9QQKh4AUBHMIwfmGU9nU6dO1dXVffTokWKT6l1lZSVN010+4HF1dQ0ODl6yZElubq6Wlpb0Mc+cOZOZmXnx4kUulytujIiIOHLkyJUrV969e/fkyRM3NzdXV9e+rjQuv8g9YC7OmzdvZBUQ1BMqHgBQF2w2u6qqStlZdNTS0kII6XLcrpmZWV5e3sGDBw0MDKQPmJ6e/s033+Tn59va2oobX716tW/fvvXr17///vt6enp2dnYpKSkVFRWxsbGDIXLPdHR0yL8vFEC/Ye10AFALQqGwrq7O2tpa2Yl0xPycdznPnqmpqaGhYZ+iHTx48OLFi3l5efr6+pLtJSUl7e3tlpaW4hYej2dsbFxcXKz0yL1qbW0l/75QAP2GigcA1EJ+fj5N0+LhtCwWq7v+LwUzMzOjKKq+vr7zrj7NLk3T9JdffllbW5udnc1idfzbzpR6r169ErcIBIKamhrmTXJlRZYSc3HMzc1lFRDUE3q1AEBliUSi2tratra2wsLCkJAQGxub1atXM7scHR1ramqys7OFQmFVVVWHyWOMjY0rKirKy8sFAoFQKMzNzZXf2+m6urr29vYvXrzo0F5aWmpubr5s2TLJxoCAAHNz87t373aO8/Dhw/3796ekpGhpaVESDhw4QAixs7Pz8vJKSUm5evVqc3Pz8+fPAwMDCSFr1qxRYmQpMRfH2dm53xEACCoeABgqDh06NG3aNEJIWFiYj49PcnJyfHw8IWTChAlPnjxJSUkJDQ0lhMyfP7+kpIT5SEtLi7Ozs46OjoeHx+jRo3/++WfxcJmNGzd6eXktX758zJgxu3fvZnpMxONtg4KCzMzMnJycFi5cWFNTI++v5u3tXVxczMy7I9bl9DOtra2VlZU5OTmdd/U8XQ1FUadOnQoICFizZo2RkZGTk9OzZ8+ysrI8PDyUGJkQcuvWLXd3d0tLy4KCgvv371tYWMyaNevq1auSx9y5c8fKymrChAk9pAHQO2VN9gyDE1aZAMUg8l8NIDAw0NjYWK6n6JWUq0yUlJSwWKxjx471emR7e7uHh0dqaqosshsakaurqzkczoEDB6Q5WAH3FQxdeMYDACprqCy77ejoGBUVFRUV1WFB8g7a29uzs7MFAkFAQIBsExjMkSMjIydOnMjn82WbGKghVDwAAMq3fft2Pz+/gICALocwM/Lz87OysnJzc7ubnbnfBm3kuLi4e/funT9/vk/TEQF0CRUPKMH58+cNDAz69B6KUmJ2KSoqysnJicfjsdlsR0fHbdu29fz/5V1KS0ujKMrNzW0gmQzpyyhv4eHhR48era+vt7OzO336tLLTkUp0dDSfz9+7d293B8ydO/fEiRPi5cBkaHBGzsnJ+fvvv/Pz842MjGSeGKghvJ0OSkDLYUVAecTsUl5e3ueffx4QEKClpZWbm7ty5cqioqLc3Nw+BUlLS3NwcLh582ZpaSmzVmU/DOnLKG8xMTExMTHKzqLP5s2bN2/ePGVnMVj4+Pj4+PgoOwtQHXjGA4rQ3Nws+TzD29u7vr5+0aJFgy2mNPT19ZkhsVwu19/f39fX98KFC32aUP/t27cPHz5klq784YcfpP+gKl1GAAAFQ8UDipCamlpZWTn4Y0rj3Llzmpqa4k0TExNCSFNTk/QRMjMzvb29Fy9ezOFwmNdzpPygKl1GAAAFQ8UD/XHt2jUnJycDAwMOh+Ps7Hzx4kXxrmPHjk2dOpXD4ejp6dna2u7evTskJCQ0NLSsrIyiKEdHx+vXr9vY2FAUdejQIULIuHHjKIrS0NCYMmUKUzds27aNifz99993d66eYxJCaJqOi4tj1s02MjJasmSJeAnJ5ORkPT09XV3dnJycBQsW8Hg8a2vrkydP9u9SvHz5UkdHx87Ojtm8cOFCr1PVpaWlLV26lMvlzps3r7y8/Nq1a52PUbfLCAAgd0p8Mx4GISnn4zl16lRkZGRNTc3bt29nzpw5bNgwpp2ZEW7v3r1v376tqan59ttvV6xYQdP0xx9/7ODgIP440wd08OBBmqbb2tpsbW1tbGza2trEB2zevDk+Pr7nc/UQk6bpnTt3amtrHzt2rK6urrCwcPLkySYmJq9fv2b2RkREEEKuXLlSX19fWVnp4eGhp6fX2tra18vV2NjI5XL5fL645dy5c1wuNyoqqruPPH361NTUlPmyx44dI4SsWbOmwzHqcBmJesybIuV8PCAranJfQf+g4oH/0I8ZCJnxoZWVla2trYaGhl5eXuJdbW1tCQkJdG8/q8wPfGZmJrPZ2NhoY2NTX1/fw7l6jtnU1KSvrx8QECDee/v2bUKIuBBhfqqbm5uZzaSkJEJIaWlpn744E2f06NENDQ3Sf2Tv3r2fffYZ88/19fVsNpvH4zU1NYkPUJPLqCa/TKh4FExN7ivoH/RqwUAx82S0t7cXFhbW1dV9+OGH4l2amprBwcG9Rli7dq2BgUFCQgKzefz48SVLlvB4vB7O1XPA4uLid+/eTZ06Vdwybdo0bW3tgoKCLo/X1tYmhPR1XckzZ85kZmZevHiRy+VK/ymmS4v5Zx6PN2/evIaGBskJ+NXnMi5btoxSdadPnz59+rSys1AjUt57oJ7wdjr0x08//RQbG1tcXNzQ0CD+hWtoaCCEGBoa9jWavr7++vXrY2Njb9++PX369H/+85+SE6h0ea6e1dXVMWElGw0NDQUCQV9z6056enpcXFx+fr6lpaX0n3rw4EFRUVHnN6F++OEH8Yy06nMZQ0JCXF1dZRJq0GIevG3evFnZiaiLDguvAkhCxQN99uzZM19f36VLl/7rX/+ytLQ8ePDgtm3bCCHMb391dXU/YvL5/ISEhPj4+KCgoBEjRjg4OPR8rp4x5UKHH+a6ujpra+t+5NbZwYMHL168mJeX16Ea6NWJEyeWL1+elpYmbqmtrbWysrp06dLr16+ZWdrU5zK6urr6+/vLJNSgderUKUKIyn/NwQMVD/QAvVrQZ0VFRUKhcOPGjfb29hwOR/wk2dbW1tjY+NKlS/2IaW1t7e/vf/r06R07doSEhPR6rp6NHz9eX1//t99+E7cUFBS0trZOmTKlH7lJomk6LCysqKgoOzu7r+UOTdPp6embNm2SbDQyMvLz82tvbxeXQepwGQEAFA8VD/SZjY0NIeTy5cstLS0lJSXiUR1sNjs8PPzq1at8Pv/ly5cikUggEDx8+JAQYmxsXFFRUV5eLhAIuutSCQ0NbWtrq62tff/993s9V88xORxOaGjomTNnjh8/3tDQUFRUFBQUZGFhERgYOMDv/vDhw/3796ekpGhpaUmOHjhw4ABzQG5ubndvp9+4cYPH482aNatDe1BQEJGYilAdLiMAgBIoe+g0DC5SvqsVFhZmbGxsaGjo5+fHzN3i4ODw7NkzmqYPHTrk7OzM4XA4HM6kSZOSkpJomr579+7IkSN1dHTc3d2/+uorpvtGV1d38eLFkmG9vLy+++47Kc/Vc0yRSBQbGztq1CgtLS0jIyNfX9/Hjx8zAZOSkphFDUeNGlVWVnbkyBFmeO/IkSP//PPPnr94UVFRl/8dxcbGMgecP3+ey+Xu2bOnwwfXrFmjp6fHYrFcXFzu3r0rbt+9e7eFhQUTxMrKirlcKn8ZabV5pwbvaimYmtxX0D8UrSrL6IBMZGZmLlu2DHcFyBtFURkZGSo/wMXPz4/8ezQPKICa3FfQP+jVAgAAANWHigfg/zx69KiHqT7EL5ADyMnly5e3b98uEol8fX1tbGw4HI6VlZWPj09hYaE0H4+KinJycuLxeOz/r727DWnqfeMAfo+mm66pE9uYPbmxLIemlFKzVEwwykgRrIW9KITMiikJlYZpljNLLCZEaOILTdScaEnmi2yYUFKUWZNALUvzmfkwbeZ05//i8Dv/MTc353RrXp93O+fw9eIw9ebc97kvCoXH4125cmV6elr7goqKiqCgIDqdvn379rNnzw4NDZlY2Ool4zQazf3797Wb2iKEnj17lpeXZ3TjKABMBCMeAP5v165dS8wBV1ZWWrtAYM8yMzMlEkl6erpGo3nz5k1FRYVCoWhtbVWpVKGhoQMDA0YTmpubL1261NvbOzY2JhaLHzx4gE+r4aqqquLj4+Pi4vr7++vr61taWo4cOTI/P29KbauXjBDq6uoKDQ29fPmyTkdevNtuREQEvjUUACu1RuuFwD/CjC4TAJgBrfIK0z9//ggEAqtHmb5yOTc319vbG2/ZoVarjx07RpzCm3vk5OQYDYmKitJurIYvZ8FfKcAwLDw83NPTU6PR4B/xBeytra2mlLd6ye3t7bGxseXl5QEBAf7+/osvEIlEAoFArVabkrba3yvwT4NnPAAAO1RSUjIyMmJrUYZ0d3dnZGTcvHmTSqUihMhk8vPnz4mzXC4XIdTT02M0p6GhYcOGDcRHDw8PhBDx4KSvr4/NZhO7MW3duhUh9PPnT1MqXL1kf39/qVQaHx9PoVD0XpCVldXe3k60TwHAbDDiAQDYKAzDCgoKfHx8KBQKg8GIiYn59u0bfkokEjk6OuLv0iOELl68SKPRSCQSvld1SkpKampqT08PiUTi8XgSiYRKpTKZzPPnz7PZbCqVGhwcTGxKtKwohNDLly8NbblkNolEgmHY8ePH9Z5VqVQIIb0t0pb2+/dvJycnDoeDf+RyudpDN3ypDT6csp3kxRgMRlhYGN5M1yKBYP2y8jMmYGNgVgusDWTC7MONGzccHR3LysomJiY6Ojr27Nnj4eExNDSEn42Pj2exWMTF9+7dQwiNjo7iH3V6wicmJtJotM7OztnZWblcjq+xJSZllhXV0NBAp9OJBvJLM3FWi8vl8vl8Q2elUilCqKamxpSfSJiZmaHT6SKRiDgik8kcHBwkEsnU1NTXr199fHwOHz68rMxVTd63b5/eWS0Mw9LS0hBCnz59MhpiyvcKrFvwjAcAYItUKlVBQUFsbOzp06ddXV39/PwePXo0NjZWVFRkXiCZTMYfF/H5/IcPHyqVytLSUjNyoqKipqamMjIyzCtjsZmZmR8/fhBN0LQNDw9XVlYmJycLBAJDT4AMEYvFbDb79u3bxJGwsLCrV6+KRCIXFxdfX1+lUvn48WMzCl69ZEN27NiBEDK0/ycAJoIRDwDAFsnl8unp6cDAQOJIUFCQo6OjdosMswUGBjo7OxNzZNY1MjKCYRi+gbUOgUCQnJwcExPT2Njo4OBgemZtbW11dXVTUxOdTicOXr9+vaio6NWrV9PT09+/fw8ODhYIBH19fcuqdvWSl4DfnOHhYUsFgvUJRjwAAFuEv5Cs067Vzc1Np5e72SgUyujoqEWiVmh2dhYhpHfdLpPJbG5uLiwsdHV1NT2wsrLyzp07MpnMy8uLODg4OJiXl3fu3LlDhw7RaDQOh1NcXDwwMIBP4Vk9eWlOTk7ovxsFgNnI1i4AAAD0cHNzQwjpjG8mJia2bNmy8nC1Wm2pqJXD/53r3Wdv06ZN+H0wXWFhYVNTU3Nzs85gsaura2FhwdPTkzji4uLi7u4ul8utnmzU3Nwc+u9GAWA2GPEAAGyRr6/vxo0bP3z4QBxpa2ubm5vbu3cv/pFMJhtqIG+UTCbDMGz//v0rj1o5JpNJIpEmJycXn9J+R90oDMOuXbs2Pj5eV1dHJuv+bceHd4ODg8QRpVKpUCjwN8mtlWwi/OawWCxLBYL1CWa1AAC2iEqlpqam1tbWlpeXT01NffnyJSkpic1mJyYm4hfweDyFQlFXV6dWq0dHR3V2f3F3dx8YGOjt7VUqlfhoRqPRjI+Pz8/Pd3R0pKSkbNu27cyZM2ZENTY2WvbtdGdnZy6X29/fr3O8u7ubxWKdPHlS+6BQKGSxWB8/flyc09nZeffu3eLiYgcHB+3uKPn5+QghDocTHh5eXFzc0tKiUqn6+vrwO5mQkGDFZBPhN8fPz8/sBAAQjHgAADYrMzNTLBZnZ2d7eHiEhYV5eXnJZDIajYafvXDhQnh4+KlTp3bu3Hnr1i18yoNYMJuUlMRkMvl8/tGjRxUKBUJodnbWz8/PyckpJCTE29v79evXxNKZ5UZZXFRUlFwux/fdIWD6tp+Zm5sbGRmpr69ffErv9QQSifT06VOhUJiQkMBgMPh8/q9fv6RSaUhIiBWTEULv3r07ePCgp6dnW1vb58+f2Wz2gQMHWlpatK95//795s2bd+/evUQZABhnxTfjgQ2C/XjA2kBru29KYmKiu7v7mv04gon78XR1dZHJ5LKyMqNXLiwshISElJSUWKK6fyN5bGyMSqXm5+ebcvEaf6/AvwWe8QAA1gVbbsHN4/Gys7Ozs7N1GpLrWFhYqKurUyqVQqHQsgXYcnJWVlZAQIBIJLJsYWAdghEPAABYX1paWlxcnFAo1LuEGSeTyaRSaWNjo97Ne1bCZpMLCgra29tfvHixrO2IANALRjwAADuXnp5eWlo6OTnJ4XBqamqsXY5BOTk5IpEoNzfX0AURERFPnjwhWoBZkG0m19fX//37VyaTMRgMixcG1iF4Ox0AYOfEYrFYLLZ2FSaJjIyMjIy0dhW2Ijo6Ojo62tpVAPsBz3gAAAAAYP9gxAMAAAAA+wcjHgAAAADYPxjxAAAAAMD+wcploEd1dbW1SwD27+3bt9YuYdXh7RHgFwoAW0DCltw+HKw31dXVOn18AADgH1JVVXXixAlrVwFsEYx4AAAAAGD/YB0PAAAAAOwfjHgAAAAAYP9gxAMAAAAA+wcjHgAAAADYv/8ByWC/DxWuWLQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdpZbQDflBtV"
      },
      "source": [
        "# define the combined generator and discriminator model, for updating the generator\n",
        "def define_gan(g_model, d_model):\n",
        "\t# make weights in the discriminator not trainable\n",
        "\td_model.trainable = False\n",
        "\t# connect the outputs of the generator to the inputs of the discriminator\n",
        "\tgan_output = d_model(g_model.output)\n",
        "\t# define gan model as taking noise and label and outputting real/fake and label outputs\n",
        "\tmodel = Model(g_model.input, gan_output)\n",
        "\t# compile model\n",
        "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
        "\tmodel.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], optimizer=opt)\n",
        "\treturn model"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeKRiaoIlBtX",
        "outputId": "67929ef7-45d2-49a0-cc24-6eaab0efd009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "# define the size of the latent space\n",
        "latent_dim = 100\n",
        "# define the generator model\n",
        "g_model = define_generator(latent_dim)\n",
        "#define the discriminator\n",
        "d_model= define_discriminator()\n",
        "model=define_gan(g_model,d_model)\n",
        "# summarize the model\n",
        "model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_9\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_5 (InputLayer)            [(None, 100)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_4 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 18816)        1900416     input_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 1, 50)        500         input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 18816)        0           dense_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 1, 49)        2499        embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "reshape_3 (Reshape)             (None, 7, 7, 384)    0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_2 (Reshape)             (None, 7, 7, 1)      0           dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 7, 7, 385)    0           reshape_3[0][0]                  \n",
            "                                                                 reshape_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTrans (None, 14, 14, 192)  1848192     concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 14, 14, 192)  768         conv2d_transpose_2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 14, 14, 192)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTrans (None, 28, 28, 1)    4801        activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 28, 28, 1)    0           conv2d_transpose_3[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "functional_7 (Functional)       [(None, 1), (None, 1 527627      activation_5[0][0]               \n",
            "==================================================================================================\n",
            "Total params: 4,284,803\n",
            "Trainable params: 3,756,792\n",
            "Non-trainable params: 528,011\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZaEUi1TlBtZ",
        "outputId": "e46e1271-a2af-4cd2-8909-38853c9eff2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# plot the model\n",
        "plot_model(model, to_file='gan_plot_OF_AC.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyxB02WRlBtb"
      },
      "source": [
        "# load images\n",
        "def load_real_samples():\n",
        "\t# load dataset\n",
        "\t(trainX, trainy), (_, _) = load_data()\n",
        "\t# expand to 3d, e.g. add channels\n",
        "\tX = expand_dims(trainX, axis=-1)\n",
        "\t# convert from ints to floats\n",
        "\tX = X.astype('float32')\n",
        "\t# scale from [0,255] to [-1,1]\n",
        "\tX = (X - 127.5) / 127.5\n",
        "\tprint(X.shape, trainy.shape)\n",
        "\treturn [X, trainy]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShIR_gIelBtd"
      },
      "source": [
        "# select real samples\n",
        "def generate_real_samples(dataset, n_samples):\n",
        "\t# split into images and labels\n",
        "\timages, labels = dataset\n",
        "\t# choose random instances\n",
        "\tix = randint(0, images.shape[0], n_samples)\n",
        "\t# select images and labels\n",
        "\tX, labels = images[ix], labels[ix]\n",
        "\t# generate class labels\n",
        "\ty = ones((n_samples, 1))\n",
        "\treturn [X, labels], y"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEV8zVt1lBtf"
      },
      "source": [
        "# generate points in latent space as input for the generator\n",
        "def generate_latent_points(latent_dim, n_samples, n_classes=10):\n",
        "\t# generate points in the latent space\n",
        "\tx_input = randn(latent_dim * n_samples)\n",
        "\t# reshape into a batch of inputs for the network\n",
        "\tz_input = x_input.reshape(n_samples, latent_dim)\n",
        "\t# generate labels\n",
        "\tlabels = randint(0, n_classes, n_samples)\n",
        "\treturn [z_input, labels]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qn2SRJMBlBth"
      },
      "source": [
        "# use the generator to generate n fake examples, with class labels\n",
        "def generate_fake_samples(generator, latent_dim, n_samples):\n",
        "\t# generate points in latent space\n",
        "\tz_input, labels_input = generate_latent_points(latent_dim, n_samples)\n",
        "\t# predict outputs\n",
        "\timages = generator.predict([z_input, labels_input])\n",
        "\t# create class labels\n",
        "\ty = zeros((n_samples, 1))\n",
        "\treturn [images, labels_input], y"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHnSdmDdlBtk"
      },
      "source": [
        "# generate samples and save as a plot and save the model\n",
        "def summarize_performance(step, g_model, latent_dim, n_samples=100):\n",
        "\t# prepare fake examples\n",
        "\t[X, _], _ = generate_fake_samples(g_model, latent_dim, n_samples)\n",
        "\t# scale from [-1,1] to [0,1]\n",
        "\tX = (X + 1) / 2.0\n",
        "\t# plot images\n",
        "\tfor i in range(100):\n",
        "\t\t# define subplot\n",
        "\t\tpyplot.subplot(10, 10, 1 + i)\n",
        "\t\t# turn off axis\n",
        "\t\tpyplot.axis('off')\n",
        "\t\t# plot raw pixel data\n",
        "\t\tpyplot.imshow(X[i, :, :, 0], cmap='gray_r')\n",
        "\t# save plot to file\n",
        "\tfilename1 = 'AC_generated_plot_%04d.png' % (step+1)\n",
        "\tpyplot.savefig(filename1)\n",
        "\tpyplot.close()\n",
        "\t# save the generator model\n",
        "\tfilename2 = 'AC_model_%04d.h5' % (step+1)\n",
        "\tg_model.save(filename2)\n",
        "\tprint('>Saved: %s and %s' % (filename1, filename2))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDvKRR6xlBtm"
      },
      "source": [
        "# train the generator and discriminator\n",
        "def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=10, n_batch=100):\n",
        "\t# calculate the number of batches per training epoch\n",
        "\tbat_per_epo = int(dataset[0].shape[0] / n_batch)\n",
        "\t# calculate the number of training iterations\n",
        "\tn_steps = bat_per_epo * n_epochs\n",
        "\t# calculate the size of half a batch of samples\n",
        "\thalf_batch = int(n_batch / 2)\n",
        "\t# manually enumerate epochs\n",
        "\tfor i in range(n_steps):\n",
        "\t\t# get randomly selected 'real' samples\n",
        "\t\t[X_real, labels_real], y_real = generate_real_samples(dataset, half_batch)\n",
        "\t\t# update discriminator model weights\n",
        "\t\t_,d_r1,d_r2 = d_model.train_on_batch(X_real, [y_real, labels_real])\n",
        "\t\t# generate 'fake' examples\n",
        "\t\t[X_fake, labels_fake], y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
        "\t\t# update discriminator model weights\n",
        "\t\t_,d_f,d_f2 = d_model.train_on_batch(X_fake, [y_fake, labels_fake])\n",
        "\t\t# prepare points in latent space as input for the generator\n",
        "\t\t[z_input, z_labels] = generate_latent_points(latent_dim, n_batch)\n",
        "\t\t# create inverted labels for the fake samples\n",
        "\t\ty_gan = ones((n_batch, 1))\n",
        "\t\t# update the generator via the discriminator's error\n",
        "\t\t_,g_1,g_2 = gan_model.train_on_batch([z_input, z_labels], [y_gan, z_labels])\n",
        "\t\t# summarize loss on this batch\n",
        "\t\tprint('>%d, dr[%.3f,%.3f], df[%.3f,%.3f], g[%.3f,%.3f]' % (i+1, d_r1,d_r2, d_f,d_f2, g_1,g_2))\n",
        "\t\t# evaluate the model performance every 'epoch'\n",
        "\t\tif (i+1) % (bat_per_epo * 10) == 0:\n",
        "\t\t\tsummarize_performance(i, g_model, latent_dim)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjuMmEG-lBtn",
        "outputId": "ee349fbf-ea09-42c4-9bf0-4d28f0a34cc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# size of the latent space\n",
        "latent_dim = 100\n",
        "# create the discriminator\n",
        "discriminator = define_discriminator()\n",
        "# create the generator\n",
        "generator = define_generator(latent_dim)\n",
        "# create the gan\n",
        "gan_model = define_gan(generator, discriminator)\n",
        "# load image data\n",
        "dataset = load_real_samples()\n",
        "# train model\n",
        "train(generator, discriminator, gan_model, dataset, latent_dim)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            ">1003, dr[0.000,0.594], df[0.000,0.028], g[1.119,0.051]\n",
            ">1004, dr[0.004,0.405], df[0.000,0.026], g[1.268,0.042]\n",
            ">1005, dr[0.000,0.529], df[0.001,0.019], g[0.741,0.091]\n",
            ">1006, dr[0.001,0.578], df[0.000,0.015], g[1.096,0.046]\n",
            ">1007, dr[0.000,0.510], df[0.000,0.036], g[1.061,0.044]\n",
            ">1008, dr[0.000,0.309], df[0.000,0.020], g[0.959,0.045]\n",
            ">1009, dr[0.001,0.688], df[0.000,0.030], g[0.933,0.050]\n",
            ">1010, dr[0.001,0.958], df[0.000,0.083], g[1.010,0.057]\n",
            ">1011, dr[0.001,0.633], df[0.000,0.024], g[0.853,0.054]\n",
            ">1012, dr[0.000,0.509], df[0.000,0.025], g[1.203,0.046]\n",
            ">1013, dr[0.001,0.562], df[0.001,0.019], g[0.732,0.050]\n",
            ">1014, dr[0.001,0.590], df[0.001,0.048], g[0.844,0.027]\n",
            ">1015, dr[0.001,0.444], df[0.001,0.031], g[1.090,0.041]\n",
            ">1016, dr[0.000,0.449], df[0.000,0.020], g[1.448,0.043]\n",
            ">1017, dr[0.002,1.086], df[0.002,0.021], g[1.228,0.054]\n",
            ">1018, dr[0.001,0.510], df[0.004,0.014], g[1.477,0.038]\n",
            ">1019, dr[0.000,0.644], df[0.003,0.035], g[1.209,0.045]\n",
            ">1020, dr[0.002,0.525], df[0.000,0.024], g[1.302,0.025]\n",
            ">1021, dr[0.001,0.676], df[0.000,0.017], g[1.586,0.059]\n",
            ">1022, dr[0.001,0.976], df[0.008,0.023], g[1.611,0.062]\n",
            ">1023, dr[0.001,0.536], df[0.001,0.021], g[1.211,0.038]\n",
            ">1024, dr[0.007,0.696], df[0.000,0.042], g[1.095,0.037]\n",
            ">1025, dr[0.001,0.616], df[0.006,0.014], g[1.305,0.026]\n",
            ">1026, dr[0.008,0.337], df[0.025,0.024], g[1.423,0.120]\n",
            ">1027, dr[0.002,0.816], df[0.000,0.009], g[1.636,0.046]\n",
            ">1028, dr[0.000,0.322], df[0.000,0.007], g[1.993,0.060]\n",
            ">1029, dr[0.001,0.654], df[0.001,0.028], g[1.946,0.060]\n",
            ">1030, dr[0.003,0.580], df[0.000,0.042], g[1.809,0.026]\n",
            ">1031, dr[0.001,0.314], df[0.004,0.036], g[2.353,0.056]\n",
            ">1032, dr[0.004,0.724], df[0.002,0.031], g[1.514,0.067]\n",
            ">1033, dr[0.001,0.547], df[0.000,0.009], g[1.551,0.069]\n",
            ">1034, dr[0.000,0.478], df[0.001,0.021], g[1.866,0.064]\n",
            ">1035, dr[0.001,0.783], df[0.000,0.015], g[2.138,0.039]\n",
            ">1036, dr[0.003,0.771], df[0.001,0.019], g[1.986,0.064]\n",
            ">1037, dr[0.002,0.794], df[0.000,0.041], g[2.063,0.045]\n",
            ">1038, dr[0.001,0.480], df[0.000,0.020], g[1.429,0.075]\n",
            ">1039, dr[0.002,0.623], df[0.005,0.023], g[1.150,0.124]\n",
            ">1040, dr[0.001,0.541], df[0.002,0.036], g[0.870,0.090]\n",
            ">1041, dr[0.001,0.496], df[0.004,0.008], g[1.533,0.035]\n",
            ">1042, dr[0.001,0.427], df[0.000,0.009], g[1.575,0.032]\n",
            ">1043, dr[0.000,0.366], df[0.001,0.028], g[1.506,0.040]\n",
            ">1044, dr[0.008,0.614], df[0.000,0.006], g[1.525,0.052]\n",
            ">1045, dr[0.006,0.541], df[0.000,0.039], g[1.326,0.059]\n",
            ">1046, dr[0.001,0.580], df[0.003,0.023], g[1.424,0.026]\n",
            ">1047, dr[0.003,0.658], df[0.002,0.011], g[1.333,0.066]\n",
            ">1048, dr[0.000,0.598], df[0.000,0.027], g[1.608,0.035]\n",
            ">1049, dr[0.003,0.558], df[0.001,0.028], g[1.711,0.022]\n",
            ">1050, dr[0.001,0.827], df[0.002,0.031], g[1.448,0.014]\n",
            ">1051, dr[0.000,0.550], df[0.001,0.023], g[1.717,0.037]\n",
            ">1052, dr[0.006,0.696], df[0.001,0.015], g[1.647,0.042]\n",
            ">1053, dr[0.002,0.530], df[0.006,0.032], g[1.427,0.054]\n",
            ">1054, dr[0.000,0.606], df[0.006,0.011], g[1.749,0.043]\n",
            ">1055, dr[0.001,0.801], df[0.001,0.051], g[1.706,0.023]\n",
            ">1056, dr[0.001,0.542], df[0.001,0.038], g[1.517,0.031]\n",
            ">1057, dr[0.000,0.535], df[0.001,0.032], g[1.346,0.038]\n",
            ">1058, dr[0.001,1.064], df[0.002,0.025], g[1.571,0.036]\n",
            ">1059, dr[0.001,0.411], df[0.003,0.036], g[1.914,0.018]\n",
            ">1060, dr[0.001,0.403], df[0.006,0.025], g[2.298,0.042]\n",
            ">1061, dr[0.003,0.612], df[0.020,0.069], g[2.671,0.020]\n",
            ">1062, dr[0.001,0.615], df[0.001,0.015], g[2.788,0.029]\n",
            ">1063, dr[0.013,0.472], df[0.002,0.029], g[2.643,0.035]\n",
            ">1064, dr[0.002,0.485], df[0.010,0.059], g[2.506,0.018]\n",
            ">1065, dr[0.027,0.818], df[0.014,0.042], g[2.621,0.025]\n",
            ">1066, dr[0.001,0.660], df[0.022,0.029], g[2.425,0.068]\n",
            ">1067, dr[0.002,0.661], df[0.011,0.049], g[2.572,0.038]\n",
            ">1068, dr[0.004,0.890], df[0.005,0.043], g[2.833,0.056]\n",
            ">1069, dr[0.006,0.392], df[0.001,0.026], g[3.079,0.061]\n",
            ">1070, dr[0.004,0.374], df[0.000,0.029], g[2.641,0.029]\n",
            ">1071, dr[0.003,0.440], df[0.000,0.043], g[2.251,0.066]\n",
            ">1072, dr[0.001,0.691], df[0.000,0.027], g[2.399,0.023]\n",
            ">1073, dr[0.005,0.506], df[0.000,0.013], g[2.275,0.017]\n",
            ">1074, dr[0.001,0.341], df[0.000,0.032], g[2.256,0.046]\n",
            ">1075, dr[0.005,0.637], df[0.000,0.032], g[1.879,0.051]\n",
            ">1076, dr[0.001,0.519], df[0.000,0.005], g[1.723,0.050]\n",
            ">1077, dr[0.001,0.463], df[0.000,0.023], g[1.472,0.082]\n",
            ">1078, dr[0.002,0.773], df[0.001,0.024], g[1.632,0.028]\n",
            ">1079, dr[0.000,0.620], df[0.005,0.012], g[1.809,0.037]\n",
            ">1080, dr[0.001,0.769], df[0.000,0.028], g[2.306,0.036]\n",
            ">1081, dr[0.003,0.349], df[0.000,0.038], g[1.730,0.034]\n",
            ">1082, dr[0.002,1.084], df[0.000,0.029], g[1.560,0.120]\n",
            ">1083, dr[0.005,0.640], df[0.001,0.025], g[1.692,0.096]\n",
            ">1084, dr[0.003,0.679], df[0.000,0.017], g[1.416,0.039]\n",
            ">1085, dr[0.000,0.571], df[0.002,0.009], g[1.005,0.032]\n",
            ">1086, dr[0.001,0.332], df[0.007,0.009], g[1.255,0.035]\n",
            ">1087, dr[0.000,0.494], df[0.000,0.024], g[1.341,0.034]\n",
            ">1088, dr[0.000,0.472], df[0.000,0.015], g[1.210,0.066]\n",
            ">1089, dr[0.000,0.596], df[0.000,0.017], g[1.235,0.029]\n",
            ">1090, dr[0.000,0.802], df[0.013,0.017], g[1.345,0.050]\n",
            ">1091, dr[0.000,0.648], df[0.001,0.044], g[1.480,0.049]\n",
            ">1092, dr[0.001,0.452], df[0.003,0.023], g[1.414,0.044]\n",
            ">1093, dr[0.000,0.330], df[0.000,0.024], g[1.598,0.037]\n",
            ">1094, dr[0.003,0.616], df[0.000,0.017], g[1.819,0.045]\n",
            ">1095, dr[0.001,0.630], df[0.001,0.033], g[1.627,0.035]\n",
            ">1096, dr[0.010,0.701], df[0.000,0.026], g[1.367,0.060]\n",
            ">1097, dr[0.005,0.822], df[0.001,0.018], g[1.249,0.056]\n",
            ">1098, dr[0.000,0.847], df[0.000,0.027], g[1.361,0.037]\n",
            ">1099, dr[0.000,0.603], df[0.003,0.012], g[1.573,0.053]\n",
            ">1100, dr[0.013,0.520], df[0.001,0.012], g[1.277,0.044]\n",
            ">1101, dr[0.002,0.780], df[0.007,0.020], g[1.297,0.067]\n",
            ">1102, dr[0.005,0.922], df[0.000,0.009], g[1.220,0.038]\n",
            ">1103, dr[0.003,0.728], df[0.009,0.016], g[1.429,0.057]\n",
            ">1104, dr[0.003,0.352], df[0.001,0.031], g[1.540,0.048]\n",
            ">1105, dr[0.001,0.651], df[0.000,0.007], g[1.447,0.040]\n",
            ">1106, dr[0.001,0.364], df[0.003,0.015], g[1.201,0.029]\n",
            ">1107, dr[0.004,0.806], df[0.009,0.029], g[1.235,0.139]\n",
            ">1108, dr[0.027,0.385], df[0.021,0.010], g[1.244,0.028]\n",
            ">1109, dr[0.008,1.198], df[0.013,0.008], g[1.586,0.043]\n",
            ">1110, dr[0.005,0.611], df[0.001,0.007], g[1.996,0.050]\n",
            ">1111, dr[0.006,0.494], df[0.001,0.034], g[1.933,0.060]\n",
            ">1112, dr[0.000,0.421], df[0.001,0.018], g[2.365,0.039]\n",
            ">1113, dr[0.022,0.527], df[0.001,0.011], g[1.526,0.046]\n",
            ">1114, dr[0.001,0.725], df[0.008,0.012], g[1.092,0.038]\n",
            ">1115, dr[0.001,0.566], df[0.001,0.010], g[1.315,0.059]\n",
            ">1116, dr[0.000,0.627], df[0.001,0.011], g[1.433,0.045]\n",
            ">1117, dr[0.000,0.643], df[0.004,0.009], g[1.441,0.037]\n",
            ">1118, dr[0.005,0.656], df[0.006,0.041], g[1.333,0.046]\n",
            ">1119, dr[0.001,0.625], df[0.001,0.021], g[1.388,0.022]\n",
            ">1120, dr[0.001,0.671], df[0.001,0.018], g[1.266,0.034]\n",
            ">1121, dr[0.000,0.593], df[0.000,0.017], g[1.348,0.050]\n",
            ">1122, dr[0.001,0.783], df[0.000,0.009], g[1.487,0.051]\n",
            ">1123, dr[0.019,0.676], df[0.002,0.013], g[1.445,0.023]\n",
            ">1124, dr[0.001,0.357], df[0.001,0.012], g[1.512,0.037]\n",
            ">1125, dr[0.003,0.882], df[0.001,0.011], g[1.458,0.020]\n",
            ">1126, dr[0.001,0.503], df[0.004,0.019], g[1.216,0.033]\n",
            ">1127, dr[0.003,0.611], df[0.000,0.010], g[1.187,0.061]\n",
            ">1128, dr[0.001,0.576], df[0.001,0.014], g[0.943,0.031]\n",
            ">1129, dr[0.001,0.353], df[0.000,0.011], g[1.122,0.038]\n",
            ">1130, dr[0.003,0.391], df[0.000,0.020], g[1.509,0.026]\n",
            ">1131, dr[0.003,0.488], df[0.006,0.011], g[1.349,0.028]\n",
            ">1132, dr[0.001,0.636], df[0.001,0.035], g[1.430,0.138]\n",
            ">1133, dr[0.003,0.657], df[0.005,0.045], g[1.084,0.020]\n",
            ">1134, dr[0.008,0.699], df[0.014,0.019], g[1.307,0.016]\n",
            ">1135, dr[0.002,0.753], df[0.004,0.008], g[1.087,0.030]\n",
            ">1136, dr[0.004,1.006], df[0.004,0.024], g[1.141,0.042]\n",
            ">1137, dr[0.004,0.441], df[0.001,0.005], g[0.886,0.036]\n",
            ">1138, dr[0.001,0.764], df[0.001,0.008], g[1.260,0.031]\n",
            ">1139, dr[0.008,0.480], df[0.000,0.029], g[1.484,0.032]\n",
            ">1140, dr[0.000,0.778], df[0.000,0.016], g[1.108,0.036]\n",
            ">1141, dr[0.001,0.354], df[0.000,0.016], g[1.057,0.096]\n",
            ">1142, dr[0.003,0.857], df[0.000,0.038], g[1.218,0.054]\n",
            ">1143, dr[0.001,0.660], df[0.000,0.007], g[1.271,0.019]\n",
            ">1144, dr[0.002,0.527], df[0.000,0.017], g[1.271,0.039]\n",
            ">1145, dr[0.001,0.526], df[0.000,0.012], g[1.285,0.056]\n",
            ">1146, dr[0.001,0.887], df[0.000,0.026], g[1.017,0.041]\n",
            ">1147, dr[0.002,0.682], df[0.001,0.029], g[0.623,0.067]\n",
            ">1148, dr[0.000,0.532], df[0.000,0.030], g[0.718,0.034]\n",
            ">1149, dr[0.001,0.611], df[0.001,0.026], g[0.635,0.024]\n",
            ">1150, dr[0.000,0.397], df[0.005,0.027], g[0.859,0.052]\n",
            ">1151, dr[0.006,0.343], df[0.002,0.015], g[0.859,0.046]\n",
            ">1152, dr[0.000,0.606], df[0.001,0.023], g[1.204,0.027]\n",
            ">1153, dr[0.002,0.949], df[0.002,0.028], g[1.278,0.050]\n",
            ">1154, dr[0.001,0.483], df[0.000,0.012], g[1.450,0.034]\n",
            ">1155, dr[0.001,0.566], df[0.000,0.026], g[1.201,0.063]\n",
            ">1156, dr[0.000,0.333], df[0.000,0.016], g[1.102,0.068]\n",
            ">1157, dr[0.004,1.081], df[0.000,0.011], g[0.885,0.052]\n",
            ">1158, dr[0.001,0.674], df[0.043,0.017], g[1.332,0.038]\n",
            ">1159, dr[0.001,0.526], df[0.001,0.013], g[2.060,0.016]\n",
            ">1160, dr[0.002,0.696], df[0.000,0.012], g[1.831,0.040]\n",
            ">1161, dr[0.005,0.516], df[0.000,0.010], g[1.706,0.037]\n",
            ">1162, dr[0.014,0.827], df[0.001,0.014], g[1.225,0.088]\n",
            ">1163, dr[0.002,0.829], df[0.001,0.018], g[1.205,0.024]\n",
            ">1164, dr[0.002,0.568], df[0.001,0.010], g[1.090,0.040]\n",
            ">1165, dr[0.001,0.480], df[0.000,0.026], g[1.307,0.073]\n",
            ">1166, dr[0.006,0.531], df[0.000,0.024], g[1.371,0.037]\n",
            ">1167, dr[0.000,0.512], df[0.001,0.008], g[1.406,0.033]\n",
            ">1168, dr[0.000,0.479], df[0.001,0.036], g[1.784,0.054]\n",
            ">1169, dr[0.002,0.380], df[0.001,0.012], g[1.612,0.047]\n",
            ">1170, dr[0.001,0.997], df[0.001,0.011], g[1.788,0.025]\n",
            ">1171, dr[0.001,0.660], df[0.000,0.004], g[1.314,0.047]\n",
            ">1172, dr[0.002,0.539], df[0.002,0.009], g[1.026,0.026]\n",
            ">1173, dr[0.002,0.714], df[0.000,0.033], g[0.972,0.054]\n",
            ">1174, dr[0.001,0.332], df[0.000,0.008], g[1.000,0.030]\n",
            ">1175, dr[0.000,0.619], df[0.001,0.011], g[1.028,0.034]\n",
            ">1176, dr[0.002,0.596], df[0.000,0.016], g[1.071,0.048]\n",
            ">1177, dr[0.001,0.436], df[0.001,0.019], g[1.035,0.050]\n",
            ">1178, dr[0.001,0.290], df[0.001,0.010], g[1.082,0.040]\n",
            ">1179, dr[0.004,0.737], df[0.000,0.013], g[0.938,0.017]\n",
            ">1180, dr[0.023,0.565], df[0.005,0.021], g[0.755,0.061]\n",
            ">1181, dr[0.002,0.724], df[0.021,0.017], g[0.979,0.065]\n",
            ">1182, dr[0.000,0.614], df[0.000,0.016], g[1.343,0.024]\n",
            ">1183, dr[0.001,0.323], df[0.001,0.012], g[1.764,0.027]\n",
            ">1184, dr[0.001,0.530], df[0.001,0.005], g[1.423,0.023]\n",
            ">1185, dr[0.000,0.456], df[0.001,0.023], g[1.075,0.043]\n",
            ">1186, dr[0.002,0.373], df[0.000,0.014], g[1.434,0.026]\n",
            ">1187, dr[0.002,0.805], df[0.002,0.017], g[1.634,0.020]\n",
            ">1188, dr[0.002,0.558], df[0.001,0.046], g[1.282,0.037]\n",
            ">1189, dr[0.001,0.337], df[0.002,0.013], g[1.066,0.036]\n",
            ">1190, dr[0.001,0.673], df[0.001,0.008], g[1.255,0.048]\n",
            ">1191, dr[0.007,0.477], df[0.000,0.042], g[1.143,0.038]\n",
            ">1192, dr[0.002,0.503], df[0.007,0.010], g[0.978,0.031]\n",
            ">1193, dr[0.000,0.530], df[0.027,0.008], g[1.224,0.074]\n",
            ">1194, dr[0.002,0.904], df[0.001,0.018], g[1.984,0.048]\n",
            ">1195, dr[0.003,0.657], df[0.000,0.017], g[1.836,0.015]\n",
            ">1196, dr[0.025,0.517], df[0.000,0.021], g[1.650,0.054]\n",
            ">1197, dr[0.001,0.672], df[0.000,0.020], g[1.569,0.058]\n",
            ">1198, dr[0.001,0.656], df[0.000,0.024], g[1.199,0.060]\n",
            ">1199, dr[0.000,0.425], df[0.000,0.013], g[1.120,0.019]\n",
            ">1200, dr[0.000,0.628], df[0.001,0.011], g[1.210,0.051]\n",
            ">1201, dr[0.000,0.579], df[0.004,0.008], g[1.040,0.051]\n",
            ">1202, dr[0.001,0.556], df[0.038,0.010], g[1.769,0.021]\n",
            ">1203, dr[0.001,0.618], df[0.000,0.012], g[2.218,0.044]\n",
            ">1204, dr[0.071,0.879], df[0.025,0.023], g[0.795,0.066]\n",
            ">1205, dr[0.000,0.548], df[0.000,0.033], g[0.620,0.035]\n",
            ">1206, dr[0.000,0.717], df[0.000,0.012], g[0.783,0.046]\n",
            ">1207, dr[0.001,0.967], df[0.013,0.014], g[1.436,0.043]\n",
            ">1208, dr[0.004,0.507], df[0.000,0.026], g[1.328,0.053]\n",
            ">1209, dr[0.005,0.797], df[0.000,0.012], g[1.225,0.084]\n",
            ">1210, dr[0.000,0.252], df[0.000,0.021], g[1.606,0.076]\n",
            ">1211, dr[0.001,0.497], df[0.000,0.015], g[1.380,0.080]\n",
            ">1212, dr[0.000,0.985], df[0.001,0.013], g[0.994,0.036]\n",
            ">1213, dr[0.003,0.644], df[0.032,0.022], g[1.592,0.016]\n",
            ">1214, dr[0.001,0.539], df[0.000,0.014], g[2.343,0.039]\n",
            ">1215, dr[0.004,0.772], df[0.000,0.024], g[1.991,0.034]\n",
            ">1216, dr[0.001,0.527], df[0.000,0.019], g[2.042,0.078]\n",
            ">1217, dr[0.005,0.649], df[0.000,0.053], g[1.943,0.093]\n",
            ">1218, dr[0.001,0.601], df[0.000,0.014], g[2.152,0.053]\n",
            ">1219, dr[0.003,0.575], df[0.001,0.013], g[1.994,0.039]\n",
            ">1220, dr[0.002,0.479], df[0.000,0.025], g[1.956,0.143]\n",
            ">1221, dr[0.003,0.496], df[0.001,0.017], g[1.887,0.038]\n",
            ">1222, dr[0.002,0.419], df[0.000,0.016], g[1.330,0.050]\n",
            ">1223, dr[0.002,0.686], df[0.001,0.011], g[1.403,0.028]\n",
            ">1224, dr[0.002,0.686], df[0.000,0.013], g[1.526,0.090]\n",
            ">1225, dr[0.000,0.336], df[0.000,0.014], g[1.540,0.075]\n",
            ">1226, dr[0.001,0.632], df[0.000,0.011], g[1.290,0.115]\n",
            ">1227, dr[0.001,0.715], df[0.000,0.011], g[1.709,0.059]\n",
            ">1228, dr[0.006,0.817], df[0.000,0.021], g[1.662,0.074]\n",
            ">1229, dr[0.001,0.982], df[0.000,0.016], g[1.689,0.056]\n",
            ">1230, dr[0.001,0.716], df[0.000,0.047], g[1.587,0.089]\n",
            ">1231, dr[0.023,0.558], df[0.001,0.016], g[1.063,0.068]\n",
            ">1232, dr[0.001,0.730], df[0.001,0.022], g[0.966,0.029]\n",
            ">1233, dr[0.001,0.911], df[0.002,0.012], g[0.787,0.041]\n",
            ">1234, dr[0.001,1.056], df[0.003,0.007], g[0.845,0.049]\n",
            ">1235, dr[0.001,0.496], df[0.001,0.016], g[0.413,0.088]\n",
            ">1236, dr[0.000,0.553], df[0.007,0.008], g[0.514,0.055]\n",
            ">1237, dr[0.000,0.774], df[0.003,0.017], g[0.693,0.078]\n",
            ">1238, dr[0.001,0.486], df[0.001,0.013], g[0.631,0.041]\n",
            ">1239, dr[0.004,0.462], df[0.002,0.009], g[0.794,0.021]\n",
            ">1240, dr[0.001,0.698], df[0.012,0.009], g[1.248,0.016]\n",
            ">1241, dr[0.001,0.506], df[0.000,0.017], g[1.091,0.032]\n",
            ">1242, dr[0.001,1.141], df[0.001,0.010], g[1.524,0.026]\n",
            ">1243, dr[0.002,0.565], df[0.001,0.017], g[1.297,0.111]\n",
            ">1244, dr[0.004,0.384], df[0.000,0.014], g[1.166,0.107]\n",
            ">1245, dr[0.000,0.868], df[0.001,0.006], g[0.979,0.064]\n",
            ">1246, dr[0.001,0.824], df[0.012,0.014], g[1.908,0.047]\n",
            ">1247, dr[0.004,0.438], df[0.000,0.010], g[1.636,0.057]\n",
            ">1248, dr[0.001,0.696], df[0.009,0.017], g[1.914,0.030]\n",
            ">1249, dr[0.001,0.507], df[0.002,0.016], g[2.438,0.034]\n",
            ">1250, dr[0.002,0.531], df[0.001,0.010], g[1.957,0.021]\n",
            ">1251, dr[0.006,0.977], df[0.000,0.007], g[2.399,0.030]\n",
            ">1252, dr[0.003,0.932], df[0.000,0.034], g[1.952,0.052]\n",
            ">1253, dr[0.001,0.872], df[0.009,0.028], g[2.139,0.037]\n",
            ">1254, dr[0.001,0.667], df[0.000,0.012], g[2.076,0.109]\n",
            ">1255, dr[0.017,0.487], df[0.000,0.009], g[1.387,0.066]\n",
            ">1256, dr[0.001,0.380], df[0.004,0.016], g[1.327,0.063]\n",
            ">1257, dr[0.001,0.387], df[0.001,0.013], g[1.646,0.042]\n",
            ">1258, dr[0.000,0.890], df[0.000,0.016], g[1.483,0.092]\n",
            ">1259, dr[0.000,0.587], df[0.000,0.008], g[1.038,0.096]\n",
            ">1260, dr[0.002,0.814], df[0.000,0.026], g[0.792,0.080]\n",
            ">1261, dr[0.000,0.588], df[0.000,0.018], g[1.030,0.097]\n",
            ">1262, dr[0.001,0.428], df[0.000,0.015], g[0.931,0.035]\n",
            ">1263, dr[0.001,0.638], df[0.000,0.012], g[0.754,0.052]\n",
            ">1264, dr[0.001,0.532], df[0.001,0.023], g[0.847,0.074]\n",
            ">1265, dr[0.002,0.329], df[0.000,0.013], g[0.936,0.021]\n",
            ">1266, dr[0.000,0.486], df[0.000,0.008], g[0.793,0.044]\n",
            ">1267, dr[0.000,0.552], df[0.000,0.012], g[0.978,0.025]\n",
            ">1268, dr[0.003,1.050], df[0.000,0.012], g[1.219,0.040]\n",
            ">1269, dr[0.000,0.629], df[0.001,0.008], g[1.104,0.033]\n",
            ">1270, dr[0.000,0.702], df[0.000,0.007], g[0.938,0.064]\n",
            ">1271, dr[0.000,0.591], df[0.000,0.013], g[0.896,0.034]\n",
            ">1272, dr[0.001,0.545], df[0.000,0.009], g[0.955,0.043]\n",
            ">1273, dr[0.006,0.590], df[0.001,0.008], g[0.629,0.043]\n",
            ">1274, dr[0.001,0.992], df[0.000,0.023], g[0.934,0.062]\n",
            ">1275, dr[0.001,0.590], df[0.000,0.013], g[0.984,0.064]\n",
            ">1276, dr[0.001,0.502], df[0.000,0.019], g[0.915,0.038]\n",
            ">1277, dr[0.001,0.298], df[0.000,0.016], g[0.779,0.032]\n",
            ">1278, dr[0.001,0.562], df[0.000,0.034], g[0.915,0.034]\n",
            ">1279, dr[0.000,0.597], df[0.005,0.012], g[0.735,0.034]\n",
            ">1280, dr[0.002,0.703], df[0.006,0.026], g[1.088,0.024]\n",
            ">1281, dr[0.002,1.091], df[0.000,0.021], g[0.666,0.040]\n",
            ">1282, dr[0.001,0.768], df[0.018,0.024], g[1.158,0.091]\n",
            ">1283, dr[0.001,0.526], df[0.000,0.034], g[1.371,0.041]\n",
            ">1284, dr[0.000,0.738], df[0.001,0.014], g[1.243,0.027]\n",
            ">1285, dr[0.003,0.593], df[0.000,0.031], g[1.427,0.035]\n",
            ">1286, dr[0.001,0.582], df[0.000,0.023], g[1.319,0.039]\n",
            ">1287, dr[0.008,0.586], df[0.000,0.044], g[1.333,0.075]\n",
            ">1288, dr[0.006,0.604], df[0.000,0.023], g[0.807,0.056]\n",
            ">1289, dr[0.007,0.595], df[0.000,0.024], g[0.905,0.162]\n",
            ">1290, dr[0.002,0.668], df[0.002,0.028], g[0.735,0.055]\n",
            ">1291, dr[0.000,0.328], df[0.001,0.009], g[0.950,0.031]\n",
            ">1292, dr[0.000,0.616], df[0.002,0.018], g[0.923,0.040]\n",
            ">1293, dr[0.003,0.902], df[0.000,0.009], g[1.085,0.069]\n",
            ">1294, dr[0.009,0.468], df[0.000,0.020], g[0.783,0.091]\n",
            ">1295, dr[0.001,0.304], df[0.000,0.021], g[0.812,0.043]\n",
            ">1296, dr[0.001,0.400], df[0.000,0.009], g[0.812,0.090]\n",
            ">1297, dr[0.000,0.559], df[0.003,0.031], g[1.105,0.037]\n",
            ">1298, dr[0.000,0.757], df[0.000,0.024], g[0.948,0.039]\n",
            ">1299, dr[0.000,0.385], df[0.005,0.019], g[0.892,0.092]\n",
            ">1300, dr[0.001,0.404], df[0.002,0.024], g[1.126,0.069]\n",
            ">1301, dr[0.002,0.725], df[0.001,0.010], g[1.179,0.057]\n",
            ">1302, dr[0.000,0.712], df[0.000,0.012], g[1.087,0.050]\n",
            ">1303, dr[0.001,0.550], df[0.000,0.013], g[1.246,0.049]\n",
            ">1304, dr[0.001,0.728], df[0.000,0.016], g[1.253,0.063]\n",
            ">1305, dr[0.004,0.761], df[0.000,0.007], g[1.080,0.052]\n",
            ">1306, dr[0.000,0.671], df[0.000,0.017], g[1.174,0.037]\n",
            ">1307, dr[0.002,0.477], df[0.000,0.024], g[1.047,0.079]\n",
            ">1308, dr[0.002,0.930], df[0.001,0.009], g[1.210,0.052]\n",
            ">1309, dr[0.000,0.706], df[0.000,0.025], g[1.000,0.098]\n",
            ">1310, dr[0.000,0.469], df[0.000,0.032], g[1.410,0.139]\n",
            ">1311, dr[0.001,0.502], df[0.000,0.024], g[1.259,0.082]\n",
            ">1312, dr[0.000,0.649], df[0.000,0.009], g[1.287,0.118]\n",
            ">1313, dr[0.002,0.429], df[0.000,0.024], g[0.817,0.089]\n",
            ">1314, dr[0.001,0.860], df[0.000,0.045], g[1.559,0.054]\n",
            ">1315, dr[0.001,0.612], df[0.001,0.016], g[1.214,0.070]\n",
            ">1316, dr[0.001,0.653], df[0.000,0.020], g[1.209,0.118]\n",
            ">1317, dr[0.001,0.603], df[0.000,0.024], g[1.137,0.036]\n",
            ">1318, dr[0.002,0.705], df[0.000,0.006], g[1.098,0.026]\n",
            ">1319, dr[0.008,0.721], df[0.000,0.014], g[1.553,0.049]\n",
            ">1320, dr[0.000,0.815], df[0.009,0.015], g[1.340,0.109]\n",
            ">1321, dr[0.001,0.469], df[0.001,0.009], g[1.585,0.054]\n",
            ">1322, dr[0.002,0.615], df[0.000,0.008], g[1.511,0.055]\n",
            ">1323, dr[0.000,0.337], df[0.000,0.005], g[1.568,0.050]\n",
            ">1324, dr[0.001,0.514], df[0.000,0.010], g[1.588,0.064]\n",
            ">1325, dr[0.005,0.597], df[0.001,0.008], g[1.412,0.033]\n",
            ">1326, dr[0.003,0.459], df[0.000,0.019], g[1.020,0.081]\n",
            ">1327, dr[0.000,0.526], df[0.000,0.010], g[1.407,0.081]\n",
            ">1328, dr[0.001,0.724], df[0.001,0.010], g[1.403,0.044]\n",
            ">1329, dr[0.000,0.591], df[0.000,0.011], g[1.667,0.035]\n",
            ">1330, dr[0.004,0.421], df[0.000,0.015], g[1.568,0.035]\n",
            ">1331, dr[0.002,0.483], df[0.000,0.010], g[1.505,0.035]\n",
            ">1332, dr[0.000,0.587], df[0.001,0.015], g[1.380,0.043]\n",
            ">1333, dr[0.000,0.652], df[0.000,0.015], g[1.601,0.040]\n",
            ">1334, dr[0.000,0.882], df[0.000,0.014], g[1.240,0.043]\n",
            ">1335, dr[0.000,0.737], df[0.000,0.016], g[1.148,0.079]\n",
            ">1336, dr[0.000,0.425], df[0.001,0.024], g[1.091,0.073]\n",
            ">1337, dr[0.000,0.565], df[0.000,0.008], g[0.899,0.079]\n",
            ">1338, dr[0.000,0.819], df[0.000,0.013], g[0.729,0.126]\n",
            ">1339, dr[0.000,0.515], df[0.000,0.025], g[0.676,0.054]\n",
            ">1340, dr[0.001,0.840], df[0.002,0.040], g[0.630,0.071]\n",
            ">1341, dr[0.000,0.399], df[0.002,0.014], g[0.495,0.081]\n",
            ">1342, dr[0.003,0.785], df[0.002,0.013], g[0.801,0.060]\n",
            ">1343, dr[0.000,0.473], df[0.002,0.017], g[0.745,0.031]\n",
            ">1344, dr[0.002,0.654], df[0.001,0.010], g[0.962,0.051]\n",
            ">1345, dr[0.000,0.506], df[0.001,0.012], g[0.777,0.056]\n",
            ">1346, dr[0.001,0.271], df[0.004,0.022], g[0.702,0.063]\n",
            ">1347, dr[0.000,0.704], df[0.000,0.041], g[0.831,0.030]\n",
            ">1348, dr[0.000,0.632], df[0.000,0.009], g[0.980,0.046]\n",
            ">1349, dr[0.000,0.814], df[0.001,0.008], g[1.036,0.056]\n",
            ">1350, dr[0.000,0.413], df[0.000,0.013], g[0.837,0.142]\n",
            ">1351, dr[0.003,0.506], df[0.000,0.019], g[0.984,0.057]\n",
            ">1352, dr[0.000,0.530], df[0.002,0.010], g[0.851,0.050]\n",
            ">1353, dr[0.001,0.752], df[0.000,0.012], g[0.869,0.083]\n",
            ">1354, dr[0.000,0.628], df[0.000,0.007], g[0.891,0.068]\n",
            ">1355, dr[0.000,0.498], df[0.001,0.013], g[0.821,0.011]\n",
            ">1356, dr[0.001,0.443], df[0.001,0.012], g[0.863,0.037]\n",
            ">1357, dr[0.001,0.427], df[0.000,0.012], g[1.003,0.085]\n",
            ">1358, dr[0.000,0.387], df[0.000,0.008], g[1.054,0.035]\n",
            ">1359, dr[0.001,0.783], df[0.000,0.016], g[0.896,0.094]\n",
            ">1360, dr[0.000,0.331], df[0.000,0.011], g[1.316,0.062]\n",
            ">1361, dr[0.001,0.773], df[0.000,0.014], g[0.872,0.049]\n",
            ">1362, dr[0.000,0.399], df[0.000,0.009], g[0.968,0.036]\n",
            ">1363, dr[0.001,0.695], df[0.000,0.009], g[0.902,0.022]\n",
            ">1364, dr[0.000,0.818], df[0.000,0.018], g[0.938,0.057]\n",
            ">1365, dr[0.000,0.491], df[0.000,0.026], g[1.003,0.020]\n",
            ">1366, dr[0.001,0.534], df[0.000,0.022], g[0.813,0.029]\n",
            ">1367, dr[0.001,0.492], df[0.000,0.021], g[0.716,0.043]\n",
            ">1368, dr[0.005,0.641], df[0.000,0.018], g[0.545,0.067]\n",
            ">1369, dr[0.001,0.774], df[0.000,0.022], g[0.576,0.066]\n",
            ">1370, dr[0.002,0.294], df[0.000,0.015], g[0.569,0.021]\n",
            ">1371, dr[0.002,0.880], df[0.001,0.006], g[0.738,0.064]\n",
            ">1372, dr[0.001,0.434], df[0.001,0.009], g[0.610,0.027]\n",
            ">1373, dr[0.001,0.328], df[0.001,0.009], g[0.725,0.021]\n",
            ">1374, dr[0.001,0.586], df[0.000,0.009], g[0.806,0.041]\n",
            ">1375, dr[0.001,0.934], df[0.001,0.019], g[0.935,0.027]\n",
            ">1376, dr[0.007,0.619], df[0.000,0.011], g[0.640,0.049]\n",
            ">1377, dr[0.002,0.313], df[0.002,0.006], g[0.658,0.031]\n",
            ">1378, dr[0.000,0.439], df[0.000,0.005], g[0.752,0.043]\n",
            ">1379, dr[0.001,0.347], df[0.000,0.006], g[0.763,0.057]\n",
            ">1380, dr[0.001,0.611], df[0.000,0.010], g[1.089,0.024]\n",
            ">1381, dr[0.000,0.437], df[0.000,0.025], g[0.730,0.019]\n",
            ">1382, dr[0.001,1.065], df[0.001,0.010], g[0.841,0.054]\n",
            ">1383, dr[0.000,0.542], df[0.000,0.005], g[0.840,0.036]\n",
            ">1384, dr[0.001,0.631], df[0.002,0.014], g[1.235,0.035]\n",
            ">1385, dr[0.000,0.668], df[0.000,0.011], g[1.008,0.047]\n",
            ">1386, dr[0.004,0.755], df[0.000,0.013], g[0.880,0.049]\n",
            ">1387, dr[0.000,0.577], df[0.001,0.005], g[0.965,0.011]\n",
            ">1388, dr[0.001,0.286], df[0.001,0.007], g[1.119,0.019]\n",
            ">1389, dr[0.001,0.730], df[0.001,0.007], g[1.003,0.038]\n",
            ">1390, dr[0.001,0.550], df[0.000,0.004], g[0.990,0.033]\n",
            ">1391, dr[0.000,0.752], df[0.004,0.008], g[0.855,0.021]\n",
            ">1392, dr[0.000,0.639], df[0.001,0.008], g[1.086,0.020]\n",
            ">1393, dr[0.000,0.210], df[0.001,0.011], g[1.272,0.012]\n",
            ">1394, dr[0.000,0.925], df[0.001,0.010], g[0.956,0.029]\n",
            ">1395, dr[0.000,0.840], df[0.000,0.016], g[0.862,0.041]\n",
            ">1396, dr[0.000,0.545], df[0.000,0.006], g[0.611,0.016]\n",
            ">1397, dr[0.001,1.148], df[0.000,0.014], g[0.889,0.010]\n",
            ">1398, dr[0.000,0.983], df[0.000,0.013], g[0.774,0.022]\n",
            ">1399, dr[0.001,0.858], df[0.001,0.013], g[1.056,0.024]\n",
            ">1400, dr[0.000,0.695], df[0.000,0.033], g[0.890,0.048]\n",
            ">1401, dr[0.000,0.407], df[0.002,0.013], g[1.004,0.028]\n",
            ">1402, dr[0.000,0.474], df[0.001,0.008], g[1.015,0.033]\n",
            ">1403, dr[0.000,0.436], df[0.002,0.017], g[0.929,0.026]\n",
            ">1404, dr[0.000,0.379], df[0.001,0.012], g[1.031,0.029]\n",
            ">1405, dr[0.000,0.538], df[0.000,0.009], g[0.967,0.025]\n",
            ">1406, dr[0.000,0.494], df[0.001,0.018], g[0.852,0.022]\n",
            ">1407, dr[0.001,1.094], df[0.002,0.036], g[0.842,0.060]\n",
            ">1408, dr[0.000,0.771], df[0.005,0.021], g[0.948,0.042]\n",
            ">1409, dr[0.001,0.332], df[0.000,0.015], g[1.068,0.019]\n",
            ">1410, dr[0.001,0.571], df[0.000,0.011], g[1.206,0.035]\n",
            ">1411, dr[0.002,0.615], df[0.001,0.019], g[1.239,0.051]\n",
            ">1412, dr[0.001,0.780], df[0.001,0.020], g[0.818,0.053]\n",
            ">1413, dr[0.002,0.626], df[0.003,0.009], g[0.868,0.059]\n",
            ">1414, dr[0.000,0.408], df[0.001,0.027], g[0.799,0.028]\n",
            ">1415, dr[0.001,0.454], df[0.027,0.016], g[1.206,0.023]\n",
            ">1416, dr[0.009,0.558], df[0.001,0.011], g[1.847,0.026]\n",
            ">1417, dr[0.002,0.478], df[0.000,0.014], g[1.896,0.027]\n",
            ">1418, dr[0.003,0.550], df[0.000,0.009], g[1.530,0.024]\n",
            ">1419, dr[0.003,0.862], df[0.000,0.010], g[1.628,0.029]\n",
            ">1420, dr[0.008,0.637], df[0.000,0.009], g[1.428,0.032]\n",
            ">1421, dr[0.001,0.543], df[0.000,0.018], g[1.698,0.024]\n",
            ">1422, dr[0.002,0.520], df[0.000,0.010], g[1.579,0.019]\n",
            ">1423, dr[0.000,0.665], df[0.000,0.043], g[1.232,0.053]\n",
            ">1424, dr[0.004,0.344], df[0.000,0.023], g[1.106,0.064]\n",
            ">1425, dr[0.000,0.360], df[0.000,0.007], g[0.936,0.038]\n",
            ">1426, dr[0.002,0.352], df[0.000,0.009], g[1.443,0.039]\n",
            ">1427, dr[0.000,0.725], df[0.000,0.009], g[1.006,0.018]\n",
            ">1428, dr[0.001,0.524], df[0.001,0.014], g[1.018,0.046]\n",
            ">1429, dr[0.002,0.565], df[0.000,0.024], g[1.186,0.043]\n",
            ">1430, dr[0.000,0.702], df[0.001,0.014], g[1.363,0.027]\n",
            ">1431, dr[0.000,0.600], df[0.000,0.012], g[1.039,0.038]\n",
            ">1432, dr[0.001,0.522], df[0.000,0.029], g[1.081,0.037]\n",
            ">1433, dr[0.005,0.766], df[0.000,0.010], g[1.114,0.042]\n",
            ">1434, dr[0.001,0.808], df[0.000,0.064], g[0.976,0.080]\n",
            ">1435, dr[0.003,0.799], df[0.000,0.032], g[1.069,0.060]\n",
            ">1436, dr[0.003,0.283], df[0.000,0.010], g[1.379,0.055]\n",
            ">1437, dr[0.001,0.297], df[0.000,0.016], g[1.560,0.024]\n",
            ">1438, dr[0.003,0.609], df[0.001,0.011], g[1.031,0.013]\n",
            ">1439, dr[0.001,0.328], df[0.002,0.008], g[1.023,0.014]\n",
            ">1440, dr[0.001,0.459], df[0.003,0.004], g[1.043,0.016]\n",
            ">1441, dr[0.000,0.489], df[0.000,0.025], g[1.207,0.025]\n",
            ">1442, dr[0.001,0.584], df[0.000,0.011], g[1.145,0.027]\n",
            ">1443, dr[0.000,0.583], df[0.005,0.033], g[1.506,0.031]\n",
            ">1444, dr[0.015,0.719], df[0.000,0.008], g[1.257,0.025]\n",
            ">1445, dr[0.000,0.971], df[0.000,0.007], g[0.907,0.031]\n",
            ">1446, dr[0.000,0.597], df[0.000,0.009], g[0.740,0.021]\n",
            ">1447, dr[0.001,0.864], df[0.002,0.031], g[1.038,0.045]\n",
            ">1448, dr[0.000,0.596], df[0.000,0.014], g[1.279,0.031]\n",
            ">1449, dr[0.000,0.827], df[0.001,0.022], g[1.011,0.034]\n",
            ">1450, dr[0.007,0.746], df[0.008,0.019], g[0.786,0.022]\n",
            ">1451, dr[0.002,0.956], df[0.003,0.033], g[0.771,0.029]\n",
            ">1452, dr[0.000,0.383], df[0.003,0.011], g[1.034,0.022]\n",
            ">1453, dr[0.000,0.584], df[0.012,0.013], g[1.397,0.023]\n",
            ">1454, dr[0.001,0.680], df[0.001,0.011], g[0.967,0.058]\n",
            ">1455, dr[0.001,0.882], df[0.000,0.026], g[1.130,0.045]\n",
            ">1456, dr[0.017,0.437], df[0.011,0.007], g[1.425,0.070]\n",
            ">1457, dr[0.002,0.396], df[0.000,0.010], g[1.400,0.054]\n",
            ">1458, dr[0.000,0.626], df[0.000,0.006], g[1.586,0.029]\n",
            ">1459, dr[0.008,0.603], df[0.002,0.024], g[0.912,0.027]\n",
            ">1460, dr[0.001,0.671], df[0.000,0.010], g[0.672,0.042]\n",
            ">1461, dr[0.001,0.932], df[0.001,0.011], g[0.815,0.042]\n",
            ">1462, dr[0.001,0.483], df[0.000,0.009], g[0.768,0.036]\n",
            ">1463, dr[0.003,0.553], df[0.004,0.019], g[0.684,0.021]\n",
            ">1464, dr[0.000,0.883], df[0.008,0.011], g[0.668,0.053]\n",
            ">1465, dr[0.000,0.777], df[0.002,0.012], g[0.804,0.019]\n",
            ">1466, dr[0.000,0.828], df[0.000,0.009], g[0.995,0.017]\n",
            ">1467, dr[0.001,0.629], df[0.001,0.007], g[1.190,0.033]\n",
            ">1468, dr[0.003,0.404], df[0.000,0.019], g[0.938,0.016]\n",
            ">1469, dr[0.001,0.611], df[0.000,0.017], g[1.042,0.056]\n",
            ">1470, dr[0.017,0.445], df[0.004,0.011], g[1.081,0.042]\n",
            ">1471, dr[0.000,0.596], df[0.000,0.015], g[1.193,0.045]\n",
            ">1472, dr[0.000,0.621], df[0.004,0.013], g[1.490,0.050]\n",
            ">1473, dr[0.002,0.563], df[0.001,0.023], g[1.216,0.018]\n",
            ">1474, dr[0.002,0.761], df[0.004,0.010], g[1.633,0.014]\n",
            ">1475, dr[0.001,0.358], df[0.001,0.018], g[1.504,0.021]\n",
            ">1476, dr[0.013,0.503], df[0.032,0.003], g[1.724,0.046]\n",
            ">1477, dr[0.002,0.501], df[0.001,0.016], g[1.620,0.033]\n",
            ">1478, dr[0.004,0.302], df[0.002,0.010], g[1.919,0.034]\n",
            ">1479, dr[0.004,0.479], df[0.002,0.039], g[1.621,0.046]\n",
            ">1480, dr[0.001,0.672], df[0.018,0.016], g[1.754,0.023]\n",
            ">1481, dr[0.004,0.421], df[0.001,0.015], g[2.139,0.020]\n",
            ">1482, dr[0.009,0.613], df[0.002,0.005], g[1.657,0.014]\n",
            ">1483, dr[0.001,0.441], df[0.000,0.007], g[2.030,0.020]\n",
            ">1484, dr[0.001,0.523], df[0.000,0.011], g[2.071,0.025]\n",
            ">1485, dr[0.001,0.741], df[0.001,0.019], g[1.425,0.014]\n",
            ">1486, dr[0.001,0.570], df[0.001,0.014], g[1.087,0.107]\n",
            ">1487, dr[0.000,0.434], df[0.000,0.006], g[1.008,0.027]\n",
            ">1488, dr[0.004,0.707], df[0.000,0.019], g[1.268,0.027]\n",
            ">1489, dr[0.004,0.677], df[0.000,0.055], g[0.752,0.054]\n",
            ">1490, dr[0.001,0.798], df[0.000,0.013], g[0.933,0.043]\n",
            ">1491, dr[0.001,0.648], df[0.000,0.008], g[0.731,0.048]\n",
            ">1492, dr[0.000,0.787], df[0.000,0.009], g[0.690,0.024]\n",
            ">1493, dr[0.000,0.660], df[0.000,0.041], g[0.642,0.021]\n",
            ">1494, dr[0.000,0.676], df[0.000,0.017], g[0.821,0.015]\n",
            ">1495, dr[0.001,0.718], df[0.000,0.020], g[1.089,0.027]\n",
            ">1496, dr[0.000,0.427], df[0.000,0.015], g[1.003,0.034]\n",
            ">1497, dr[0.002,0.623], df[0.000,0.011], g[1.295,0.034]\n",
            ">1498, dr[0.001,0.631], df[0.000,0.016], g[1.731,0.040]\n",
            ">1499, dr[0.001,0.920], df[0.000,0.046], g[1.351,0.019]\n",
            ">1500, dr[0.000,0.845], df[0.000,0.040], g[1.142,0.038]\n",
            ">1501, dr[0.001,0.444], df[0.000,0.013], g[1.031,0.068]\n",
            ">1502, dr[0.000,0.492], df[0.000,0.012], g[0.934,0.038]\n",
            ">1503, dr[0.001,0.545], df[0.000,0.012], g[1.078,0.042]\n",
            ">1504, dr[0.001,0.928], df[0.000,0.052], g[0.843,0.078]\n",
            ">1505, dr[0.000,0.552], df[0.000,0.017], g[1.069,0.042]\n",
            ">1506, dr[0.001,0.657], df[0.000,0.025], g[1.063,0.052]\n",
            ">1507, dr[0.001,0.600], df[0.000,0.025], g[0.768,0.031]\n",
            ">1508, dr[0.002,0.778], df[0.000,0.015], g[0.820,0.065]\n",
            ">1509, dr[0.000,0.909], df[0.000,0.032], g[0.597,0.049]\n",
            ">1510, dr[0.000,0.618], df[0.000,0.034], g[0.662,0.040]\n",
            ">1511, dr[0.009,0.811], df[0.000,0.013], g[0.711,0.046]\n",
            ">1512, dr[0.001,0.602], df[0.006,0.015], g[0.670,0.080]\n",
            ">1513, dr[0.002,0.247], df[0.003,0.008], g[0.780,0.021]\n",
            ">1514, dr[0.000,1.249], df[0.000,0.007], g[0.863,0.050]\n",
            ">1515, dr[0.000,0.789], df[0.000,0.064], g[0.811,0.026]\n",
            ">1516, dr[0.000,0.280], df[0.000,0.011], g[0.748,0.028]\n",
            ">1517, dr[0.000,0.684], df[0.001,0.026], g[1.161,0.018]\n",
            ">1518, dr[0.000,0.407], df[0.000,0.016], g[1.240,0.033]\n",
            ">1519, dr[0.008,0.600], df[0.000,0.013], g[1.211,0.036]\n",
            ">1520, dr[0.000,0.472], df[0.001,0.009], g[0.820,0.036]\n",
            ">1521, dr[0.000,0.363], df[0.001,0.013], g[0.945,0.011]\n",
            ">1522, dr[0.000,0.462], df[0.001,0.020], g[0.885,0.028]\n",
            ">1523, dr[0.000,0.608], df[0.000,0.024], g[1.003,0.035]\n",
            ">1524, dr[0.001,0.852], df[0.001,0.014], g[0.849,0.038]\n",
            ">1525, dr[0.001,0.518], df[0.000,0.013], g[0.979,0.024]\n",
            ">1526, dr[0.001,0.651], df[0.001,0.011], g[1.251,0.012]\n",
            ">1527, dr[0.001,0.743], df[0.001,0.016], g[1.236,0.018]\n",
            ">1528, dr[0.001,0.732], df[0.000,0.012], g[1.296,0.027]\n",
            ">1529, dr[0.001,0.546], df[0.000,0.006], g[1.332,0.011]\n",
            ">1530, dr[0.000,0.381], df[0.000,0.011], g[1.163,0.052]\n",
            ">1531, dr[0.000,0.554], df[0.000,0.005], g[0.812,0.036]\n",
            ">1532, dr[0.002,0.594], df[0.000,0.019], g[0.996,0.018]\n",
            ">1533, dr[0.001,0.647], df[0.000,0.011], g[0.988,0.019]\n",
            ">1534, dr[0.001,0.466], df[0.010,0.007], g[1.610,0.029]\n",
            ">1535, dr[0.001,0.495], df[0.000,0.007], g[1.561,0.015]\n",
            ">1536, dr[0.001,0.609], df[0.001,0.012], g[1.950,0.026]\n",
            ">1537, dr[0.001,0.953], df[0.000,0.008], g[1.824,0.032]\n",
            ">1538, dr[0.000,0.609], df[0.000,0.013], g[1.683,0.019]\n",
            ">1539, dr[0.000,0.883], df[0.002,0.007], g[1.147,0.019]\n",
            ">1540, dr[0.003,0.546], df[0.001,0.013], g[0.904,0.024]\n",
            ">1541, dr[0.001,0.844], df[0.004,0.008], g[1.037,0.040]\n",
            ">1542, dr[0.000,0.613], df[0.016,0.010], g[2.096,0.031]\n",
            ">1543, dr[0.001,0.388], df[0.014,0.010], g[2.586,0.025]\n",
            ">1544, dr[0.048,0.438], df[0.005,0.021], g[0.816,0.019]\n",
            ">1545, dr[0.000,0.248], df[0.015,0.011], g[1.161,0.019]\n",
            ">1546, dr[0.000,0.414], df[0.003,0.006], g[1.175,0.026]\n",
            ">1547, dr[0.021,0.686], df[0.008,0.015], g[1.402,0.018]\n",
            ">1548, dr[0.000,0.505], df[0.001,0.013], g[1.308,0.020]\n",
            ">1549, dr[0.002,0.406], df[0.001,0.007], g[1.123,0.037]\n",
            ">1550, dr[0.001,0.432], df[0.000,0.012], g[1.470,0.029]\n",
            ">1551, dr[0.000,0.479], df[0.001,0.017], g[0.951,0.030]\n",
            ">1552, dr[0.001,0.744], df[0.006,0.007], g[0.942,0.032]\n",
            ">1553, dr[0.002,0.487], df[0.001,0.019], g[1.220,0.030]\n",
            ">1554, dr[0.000,0.804], df[0.001,0.012], g[1.475,0.028]\n",
            ">1555, dr[0.001,0.653], df[0.000,0.014], g[0.929,0.032]\n",
            ">1556, dr[0.002,0.469], df[0.002,0.035], g[1.017,0.028]\n",
            ">1557, dr[0.002,0.542], df[0.000,0.027], g[1.403,0.034]\n",
            ">1558, dr[0.002,0.583], df[0.004,0.008], g[1.282,0.019]\n",
            ">1559, dr[0.002,0.492], df[0.005,0.012], g[1.440,0.047]\n",
            ">1560, dr[0.007,0.642], df[0.009,0.020], g[2.050,0.043]\n",
            ">1561, dr[0.001,0.511], df[0.007,0.014], g[2.527,0.041]\n",
            ">1562, dr[0.015,0.732], df[0.001,0.008], g[1.845,0.030]\n",
            ">1563, dr[0.001,0.383], df[0.002,0.034], g[1.619,0.049]\n",
            ">1564, dr[0.012,0.739], df[0.006,0.010], g[1.841,0.038]\n",
            ">1565, dr[0.001,0.547], df[0.000,0.019], g[1.703,0.060]\n",
            ">1566, dr[0.003,0.769], df[0.004,0.016], g[1.374,0.056]\n",
            ">1567, dr[0.000,0.499], df[0.002,0.016], g[1.733,0.067]\n",
            ">1568, dr[0.000,0.576], df[0.014,0.020], g[2.544,0.052]\n",
            ">1569, dr[0.002,0.593], df[0.001,0.014], g[2.322,0.044]\n",
            ">1570, dr[0.006,0.473], df[0.000,0.006], g[1.656,0.026]\n",
            ">1571, dr[0.001,0.842], df[0.000,0.013], g[1.786,0.038]\n",
            ">1572, dr[0.001,0.718], df[0.000,0.019], g[1.936,0.037]\n",
            ">1573, dr[0.001,0.461], df[0.000,0.013], g[1.634,0.024]\n",
            ">1574, dr[0.002,0.639], df[0.000,0.008], g[1.403,0.043]\n",
            ">1575, dr[0.000,0.748], df[0.001,0.012], g[1.742,0.037]\n",
            ">1576, dr[0.001,0.465], df[0.000,0.018], g[1.771,0.034]\n",
            ">1577, dr[0.002,0.472], df[0.002,0.020], g[1.577,0.033]\n",
            ">1578, dr[0.001,0.478], df[0.001,0.012], g[1.491,0.026]\n",
            ">1579, dr[0.002,0.802], df[0.000,0.022], g[1.602,0.049]\n",
            ">1580, dr[0.001,0.716], df[0.000,0.013], g[1.678,0.038]\n",
            ">1581, dr[0.000,0.583], df[0.000,0.015], g[1.613,0.064]\n",
            ">1582, dr[0.001,0.808], df[0.000,0.013], g[1.779,0.035]\n",
            ">1583, dr[0.001,0.748], df[0.000,0.006], g[1.102,0.077]\n",
            ">1584, dr[0.000,0.631], df[0.000,0.007], g[1.338,0.068]\n",
            ">1585, dr[0.006,0.730], df[0.000,0.012], g[1.105,0.104]\n",
            ">1586, dr[0.002,0.328], df[0.000,0.018], g[1.085,0.067]\n",
            ">1587, dr[0.002,0.898], df[0.000,0.007], g[1.241,0.051]\n",
            ">1588, dr[0.001,0.524], df[0.000,0.022], g[1.261,0.048]\n",
            ">1589, dr[0.000,0.450], df[0.000,0.008], g[1.351,0.029]\n",
            ">1590, dr[0.000,0.432], df[0.000,0.008], g[1.135,0.037]\n",
            ">1591, dr[0.002,0.646], df[0.000,0.015], g[1.444,0.044]\n",
            ">1592, dr[0.001,0.324], df[0.000,0.015], g[1.033,0.025]\n",
            ">1593, dr[0.000,0.690], df[0.000,0.011], g[0.964,0.031]\n",
            ">1594, dr[0.001,0.606], df[0.000,0.014], g[1.294,0.031]\n",
            ">1595, dr[0.001,0.670], df[0.000,0.012], g[1.088,0.059]\n",
            ">1596, dr[0.001,0.544], df[0.000,0.019], g[1.273,0.037]\n",
            ">1597, dr[0.002,0.906], df[0.000,0.026], g[0.777,0.036]\n",
            ">1598, dr[0.000,0.456], df[0.000,0.010], g[0.834,0.062]\n",
            ">1599, dr[0.003,0.906], df[0.000,0.015], g[1.025,0.027]\n",
            ">1600, dr[0.000,0.481], df[0.000,0.015], g[1.146,0.058]\n",
            ">1601, dr[0.001,0.515], df[0.000,0.011], g[1.410,0.097]\n",
            ">1602, dr[0.001,0.471], df[0.000,0.014], g[1.426,0.035]\n",
            ">1603, dr[0.002,0.404], df[0.000,0.009], g[1.183,0.052]\n",
            ">1604, dr[0.000,0.550], df[0.000,0.009], g[0.960,0.028]\n",
            ">1605, dr[0.001,0.780], df[0.000,0.012], g[0.707,0.045]\n",
            ">1606, dr[0.000,0.607], df[0.000,0.008], g[0.933,0.064]\n",
            ">1607, dr[0.000,0.725], df[0.001,0.009], g[0.720,0.110]\n",
            ">1608, dr[0.001,0.646], df[0.006,0.009], g[0.900,0.069]\n",
            ">1609, dr[0.002,0.312], df[0.000,0.012], g[1.209,0.018]\n",
            ">1610, dr[0.000,0.566], df[0.000,0.009], g[1.073,0.029]\n",
            ">1611, dr[0.002,0.676], df[0.000,0.019], g[0.930,0.060]\n",
            ">1612, dr[0.000,0.771], df[0.000,0.008], g[0.937,0.047]\n",
            ">1613, dr[0.001,0.638], df[0.000,0.010], g[0.863,0.019]\n",
            ">1614, dr[0.001,0.574], df[0.000,0.010], g[0.881,0.035]\n",
            ">1615, dr[0.001,0.465], df[0.001,0.014], g[1.004,0.045]\n",
            ">1616, dr[0.000,0.780], df[0.000,0.008], g[1.207,0.040]\n",
            ">1617, dr[0.000,0.506], df[0.000,0.013], g[0.955,0.047]\n",
            ">1618, dr[0.000,0.719], df[0.003,0.015], g[1.307,0.031]\n",
            ">1619, dr[0.001,0.457], df[0.000,0.007], g[1.088,0.056]\n",
            ">1620, dr[0.001,0.567], df[0.000,0.012], g[0.890,0.029]\n",
            ">1621, dr[0.009,0.440], df[0.000,0.008], g[0.858,0.036]\n",
            ">1622, dr[0.000,0.314], df[0.000,0.007], g[0.645,0.054]\n",
            ">1623, dr[0.002,0.484], df[0.002,0.007], g[0.854,0.026]\n",
            ">1624, dr[0.000,0.390], df[0.000,0.022], g[1.086,0.026]\n",
            ">1625, dr[0.002,0.557], df[0.000,0.022], g[0.824,0.046]\n",
            ">1626, dr[0.000,0.228], df[0.000,0.010], g[0.658,0.037]\n",
            ">1627, dr[0.000,0.349], df[0.000,0.017], g[0.864,0.025]\n",
            ">1628, dr[0.000,0.734], df[0.005,0.016], g[0.951,0.018]\n",
            ">1629, dr[0.000,0.636], df[0.000,0.011], g[0.599,0.055]\n",
            ">1630, dr[0.002,0.824], df[0.000,0.029], g[0.568,0.035]\n",
            ">1631, dr[0.000,0.448], df[0.015,0.035], g[0.785,0.055]\n",
            ">1632, dr[0.000,0.460], df[0.002,0.012], g[1.288,0.012]\n",
            ">1633, dr[0.001,0.559], df[0.000,0.013], g[1.893,0.022]\n",
            ">1634, dr[0.000,0.576], df[0.000,0.006], g[1.853,0.027]\n",
            ">1635, dr[0.002,0.584], df[0.000,0.004], g[1.757,0.025]\n",
            ">1636, dr[0.001,0.349], df[0.001,0.010], g[1.747,0.023]\n",
            ">1637, dr[0.000,0.738], df[0.000,0.014], g[1.664,0.021]\n",
            ">1638, dr[0.000,0.656], df[0.000,0.009], g[1.286,0.034]\n",
            ">1639, dr[0.000,0.594], df[0.000,0.007], g[1.428,0.016]\n",
            ">1640, dr[0.000,0.535], df[0.001,0.008], g[1.199,0.032]\n",
            ">1641, dr[0.000,0.288], df[0.000,0.012], g[1.130,0.035]\n",
            ">1642, dr[0.000,0.628], df[0.000,0.010], g[1.195,0.024]\n",
            ">1643, dr[0.000,0.641], df[0.000,0.012], g[1.359,0.035]\n",
            ">1644, dr[0.001,0.817], df[0.001,0.028], g[1.448,0.020]\n",
            ">1645, dr[0.000,0.635], df[0.008,0.006], g[1.784,0.010]\n",
            ">1646, dr[0.003,0.304], df[0.002,0.005], g[1.651,0.014]\n",
            ">1647, dr[0.000,0.575], df[0.000,0.014], g[1.689,0.012]\n",
            ">1648, dr[0.000,0.416], df[0.000,0.032], g[1.899,0.012]\n",
            ">1649, dr[0.001,0.333], df[0.000,0.056], g[1.764,0.020]\n",
            ">1650, dr[0.001,0.603], df[0.000,0.008], g[1.252,0.013]\n",
            ">1651, dr[0.000,0.645], df[0.000,0.012], g[1.050,0.032]\n",
            ">1652, dr[0.001,0.630], df[0.000,0.032], g[0.879,0.025]\n",
            ">1653, dr[0.001,0.155], df[0.000,0.013], g[0.823,0.051]\n",
            ">1654, dr[0.002,0.740], df[0.000,0.011], g[1.281,0.047]\n",
            ">1655, dr[0.000,0.595], df[0.000,0.012], g[0.877,0.024]\n",
            ">1656, dr[0.000,0.322], df[0.001,0.008], g[1.275,0.023]\n",
            ">1657, dr[0.000,0.563], df[0.000,0.025], g[1.257,0.036]\n",
            ">1658, dr[0.000,0.499], df[0.000,0.008], g[1.489,0.040]\n",
            ">1659, dr[0.001,0.295], df[0.001,0.010], g[1.411,0.029]\n",
            ">1660, dr[0.001,0.352], df[0.000,0.006], g[1.266,0.036]\n",
            ">1661, dr[0.001,0.669], df[0.000,0.010], g[1.090,0.011]\n",
            ">1662, dr[0.001,0.595], df[0.000,0.021], g[1.383,0.014]\n",
            ">1663, dr[0.002,0.493], df[0.000,0.027], g[1.385,0.021]\n",
            ">1664, dr[0.000,1.003], df[0.002,0.010], g[1.057,0.017]\n",
            ">1665, dr[0.000,0.618], df[0.002,0.021], g[1.209,0.037]\n",
            ">1666, dr[0.001,0.538], df[0.001,0.021], g[1.184,0.042]\n",
            ">1667, dr[0.002,0.559], df[0.000,0.009], g[1.233,0.023]\n",
            ">1668, dr[0.001,0.366], df[0.000,0.011], g[1.302,0.042]\n",
            ">1669, dr[0.002,0.768], df[0.003,0.021], g[1.050,0.015]\n",
            ">1670, dr[0.000,0.533], df[0.001,0.016], g[1.169,0.012]\n",
            ">1671, dr[0.001,0.687], df[0.004,0.007], g[0.809,0.021]\n",
            ">1672, dr[0.002,0.393], df[0.006,0.009], g[1.278,0.033]\n",
            ">1673, dr[0.011,0.560], df[0.000,0.006], g[1.589,0.017]\n",
            ">1674, dr[0.001,0.637], df[0.000,0.013], g[1.517,0.020]\n",
            ">1675, dr[0.000,0.326], df[0.000,0.010], g[1.318,0.039]\n",
            ">1676, dr[0.000,0.436], df[0.001,0.016], g[1.264,0.019]\n",
            ">1677, dr[0.000,0.487], df[0.000,0.007], g[1.537,0.026]\n",
            ">1678, dr[0.000,0.517], df[0.000,0.024], g[1.395,0.042]\n",
            ">1679, dr[0.002,0.837], df[0.002,0.015], g[1.438,0.014]\n",
            ">1680, dr[0.001,0.636], df[0.000,0.007], g[1.380,0.011]\n",
            ">1681, dr[0.001,0.533], df[0.000,0.007], g[1.719,0.029]\n",
            ">1682, dr[0.007,0.533], df[0.000,0.010], g[1.399,0.015]\n",
            ">1683, dr[0.000,0.847], df[0.000,0.017], g[1.033,0.010]\n",
            ">1684, dr[0.000,0.453], df[0.000,0.013], g[1.211,0.020]\n",
            ">1685, dr[0.001,0.729], df[0.000,0.008], g[0.930,0.030]\n",
            ">1686, dr[0.000,0.492], df[0.006,0.011], g[1.261,0.032]\n",
            ">1687, dr[0.001,0.578], df[0.012,0.013], g[2.047,0.018]\n",
            ">1688, dr[0.007,0.614], df[0.000,0.008], g[1.894,0.022]\n",
            ">1689, dr[0.000,0.680], df[0.001,0.007], g[1.678,0.020]\n",
            ">1690, dr[0.001,0.476], df[0.004,0.006], g[1.499,0.034]\n",
            ">1691, dr[0.000,0.321], df[0.016,0.018], g[1.869,0.017]\n",
            ">1692, dr[0.001,0.472], df[0.001,0.045], g[1.907,0.047]\n",
            ">1693, dr[0.008,0.795], df[0.000,0.016], g[2.454,0.023]\n",
            ">1694, dr[0.001,0.394], df[0.009,0.012], g[2.302,0.038]\n",
            ">1695, dr[0.006,0.503], df[0.003,0.015], g[2.472,0.035]\n",
            ">1696, dr[0.001,0.534], df[0.000,0.014], g[1.857,0.042]\n",
            ">1697, dr[0.003,0.409], df[0.002,0.021], g[1.680,0.076]\n",
            ">1698, dr[0.001,0.418], df[0.000,0.005], g[1.611,0.038]\n",
            ">1699, dr[0.000,0.636], df[0.000,0.013], g[1.498,0.032]\n",
            ">1700, dr[0.034,0.460], df[0.001,0.013], g[0.964,0.040]\n",
            ">1701, dr[0.001,0.625], df[0.007,0.008], g[0.925,0.024]\n",
            ">1702, dr[0.000,0.850], df[0.002,0.010], g[0.966,0.020]\n",
            ">1703, dr[0.004,0.952], df[0.000,0.012], g[1.261,0.029]\n",
            ">1704, dr[0.000,0.520], df[0.002,0.009], g[1.013,0.039]\n",
            ">1705, dr[0.000,0.865], df[0.015,0.017], g[0.935,0.035]\n",
            ">1706, dr[0.000,0.439], df[0.000,0.016], g[1.379,0.046]\n",
            ">1707, dr[0.001,0.807], df[0.001,0.012], g[1.194,0.040]\n",
            ">1708, dr[0.003,0.549], df[0.000,0.010], g[1.594,0.040]\n",
            ">1709, dr[0.000,0.490], df[0.000,0.022], g[2.006,0.025]\n",
            ">1710, dr[0.003,0.679], df[0.000,0.006], g[1.774,0.036]\n",
            ">1711, dr[0.002,0.334], df[0.001,0.007], g[1.717,0.051]\n",
            ">1712, dr[0.007,0.611], df[0.001,0.013], g[1.127,0.029]\n",
            ">1713, dr[0.001,0.547], df[0.000,0.015], g[1.533,0.054]\n",
            ">1714, dr[0.001,0.791], df[0.000,0.011], g[1.726,0.024]\n",
            ">1715, dr[0.001,0.700], df[0.000,0.005], g[1.600,0.043]\n",
            ">1716, dr[0.007,0.543], df[0.005,0.004], g[0.895,0.027]\n",
            ">1717, dr[0.002,0.266], df[0.001,0.022], g[1.079,0.025]\n",
            ">1718, dr[0.008,0.599], df[0.000,0.006], g[0.903,0.021]\n",
            ">1719, dr[0.003,0.665], df[0.050,0.020], g[1.785,0.053]\n",
            ">1720, dr[0.008,0.508], df[0.000,0.011], g[2.697,0.040]\n",
            ">1721, dr[0.031,0.377], df[0.000,0.040], g[2.504,0.020]\n",
            ">1722, dr[0.000,0.542], df[0.000,0.024], g[2.775,0.030]\n",
            ">1723, dr[0.000,0.823], df[0.000,0.008], g[2.647,0.030]\n",
            ">1724, dr[0.001,0.666], df[0.000,0.029], g[2.468,0.023]\n",
            ">1725, dr[0.002,0.489], df[0.000,0.018], g[2.509,0.012]\n",
            ">1726, dr[0.020,0.398], df[0.002,0.020], g[1.012,0.059]\n",
            ">1727, dr[0.004,0.333], df[0.008,0.035], g[1.171,0.021]\n",
            ">1728, dr[0.001,0.430], df[0.004,0.029], g[1.256,0.024]\n",
            ">1729, dr[0.000,0.408], df[0.003,0.012], g[1.805,0.015]\n",
            ">1730, dr[0.002,0.726], df[0.076,0.029], g[3.347,0.030]\n",
            ">1731, dr[0.001,0.417], df[0.000,0.028], g[4.269,0.019]\n",
            ">1732, dr[0.081,0.568], df[0.000,0.010], g[2.526,0.021]\n",
            ">1733, dr[0.001,0.615], df[0.000,0.027], g[1.636,0.051]\n",
            ">1734, dr[0.001,0.489], df[0.000,0.021], g[1.625,0.049]\n",
            ">1735, dr[0.000,0.522], df[0.000,0.007], g[1.582,0.031]\n",
            ">1736, dr[0.000,0.687], df[0.000,0.029], g[1.408,0.032]\n",
            ">1737, dr[0.002,1.121], df[0.007,0.009], g[1.169,0.053]\n",
            ">1738, dr[0.001,0.529], df[0.001,0.010], g[1.405,0.014]\n",
            ">1739, dr[0.000,0.649], df[0.002,0.015], g[1.412,0.017]\n",
            ">1740, dr[0.000,0.416], df[0.010,0.011], g[1.443,0.018]\n",
            ">1741, dr[0.003,0.588], df[0.000,0.010], g[1.846,0.016]\n",
            ">1742, dr[0.003,0.310], df[0.004,0.007], g[1.806,0.026]\n",
            ">1743, dr[0.030,0.522], df[0.005,0.006], g[1.863,0.016]\n",
            ">1744, dr[0.000,0.518], df[0.000,0.016], g[1.645,0.038]\n",
            ">1745, dr[0.003,0.745], df[0.003,0.010], g[1.620,0.018]\n",
            ">1746, dr[0.001,0.724], df[0.001,0.008], g[2.091,0.058]\n",
            ">1747, dr[0.001,0.895], df[0.000,0.026], g[2.237,0.042]\n",
            ">1748, dr[0.000,0.481], df[0.000,0.007], g[1.302,0.015]\n",
            ">1749, dr[0.001,0.700], df[0.003,0.010], g[1.230,0.027]\n",
            ">1750, dr[0.000,0.461], df[0.005,0.017], g[1.152,0.057]\n",
            ">1751, dr[0.000,0.831], df[0.004,0.006], g[1.481,0.018]\n",
            ">1752, dr[0.000,0.801], df[0.008,0.013], g[1.749,0.019]\n",
            ">1753, dr[0.000,0.499], df[0.001,0.015], g[1.652,0.023]\n",
            ">1754, dr[0.002,0.348], df[0.035,0.009], g[2.508,0.035]\n",
            ">1755, dr[0.002,0.918], df[0.000,0.011], g[2.468,0.052]\n",
            ">1756, dr[0.005,0.810], df[0.000,0.012], g[2.409,0.021]\n",
            ">1757, dr[0.012,0.662], df[0.000,0.004], g[1.777,0.051]\n",
            ">1758, dr[0.000,0.532], df[0.000,0.013], g[1.874,0.045]\n",
            ">1759, dr[0.000,0.410], df[0.000,0.007], g[1.461,0.062]\n",
            ">1760, dr[0.000,0.588], df[0.000,0.011], g[2.297,0.034]\n",
            ">1761, dr[0.005,0.407], df[0.000,0.011], g[1.909,0.018]\n",
            ">1762, dr[0.028,0.767], df[0.001,0.015], g[1.064,0.041]\n",
            ">1763, dr[0.000,0.825], df[0.016,0.015], g[1.180,0.058]\n",
            ">1764, dr[0.000,0.300], df[0.001,0.043], g[1.644,0.032]\n",
            ">1765, dr[0.000,0.801], df[0.000,0.011], g[2.199,0.069]\n",
            ">1766, dr[0.001,0.734], df[0.000,0.009], g[1.981,0.051]\n",
            ">1767, dr[0.001,0.615], df[0.001,0.011], g[2.145,0.025]\n",
            ">1768, dr[0.003,0.553], df[0.000,0.010], g[1.792,0.045]\n",
            ">1769, dr[0.000,0.348], df[0.000,0.011], g[1.899,0.069]\n",
            ">1770, dr[0.010,0.424], df[0.000,0.076], g[1.762,0.064]\n",
            ">1771, dr[0.001,0.506], df[0.000,0.013], g[1.612,0.035]\n",
            ">1772, dr[0.001,0.630], df[0.000,0.097], g[1.754,0.071]\n",
            ">1773, dr[0.000,0.425], df[0.000,0.013], g[1.954,0.019]\n",
            ">1774, dr[0.002,0.665], df[0.000,0.009], g[1.809,0.059]\n",
            ">1775, dr[0.005,0.474], df[0.000,0.008], g[1.543,0.034]\n",
            ">1776, dr[0.000,0.626], df[0.000,0.011], g[1.564,0.028]\n",
            ">1777, dr[0.000,0.697], df[0.000,0.020], g[1.556,0.030]\n",
            ">1778, dr[0.001,0.742], df[0.000,0.024], g[1.152,0.028]\n",
            ">1779, dr[0.000,0.742], df[0.000,0.005], g[1.411,0.076]\n",
            ">1780, dr[0.000,0.742], df[0.001,0.013], g[1.295,0.031]\n",
            ">1781, dr[0.001,0.577], df[0.005,0.011], g[1.198,0.018]\n",
            ">1782, dr[0.000,0.462], df[0.001,0.013], g[1.294,0.039]\n",
            ">1783, dr[0.000,0.772], df[0.001,0.019], g[1.246,0.055]\n",
            ">1784, dr[0.000,0.454], df[0.000,0.014], g[1.530,0.035]\n",
            ">1785, dr[0.000,0.631], df[0.001,0.025], g[1.331,0.034]\n",
            ">1786, dr[0.000,0.931], df[0.000,0.015], g[1.035,0.030]\n",
            ">1787, dr[0.000,0.382], df[0.000,0.010], g[1.300,0.046]\n",
            ">1788, dr[0.000,0.558], df[0.000,0.017], g[1.391,0.018]\n",
            ">1789, dr[0.002,0.506], df[0.000,0.034], g[1.569,0.038]\n",
            ">1790, dr[0.000,0.463], df[0.000,0.011], g[1.735,0.120]\n",
            ">1791, dr[0.000,0.394], df[0.000,0.011], g[1.576,0.023]\n",
            ">1792, dr[0.003,0.110], df[0.000,0.005], g[1.602,0.016]\n",
            ">1793, dr[0.000,0.474], df[0.000,0.014], g[1.650,0.060]\n",
            ">1794, dr[0.000,0.562], df[0.000,0.009], g[1.705,0.026]\n",
            ">1795, dr[0.000,0.538], df[0.000,0.011], g[1.469,0.028]\n",
            ">1796, dr[0.000,0.388], df[0.000,0.009], g[1.448,0.038]\n",
            ">1797, dr[0.001,0.510], df[0.001,0.028], g[1.664,0.040]\n",
            ">1798, dr[0.000,0.611], df[0.000,0.011], g[2.047,0.016]\n",
            ">1799, dr[0.001,0.626], df[0.000,0.020], g[1.949,0.035]\n",
            ">1800, dr[0.000,0.565], df[0.000,0.062], g[1.699,0.045]\n",
            ">1801, dr[0.011,1.070], df[0.004,0.025], g[1.931,0.056]\n",
            ">1802, dr[0.000,0.696], df[0.001,0.014], g[1.221,0.052]\n",
            ">1803, dr[0.000,0.760], df[0.001,0.013], g[1.574,0.031]\n",
            ">1804, dr[0.000,0.579], df[0.000,0.010], g[1.992,0.029]\n",
            ">1805, dr[0.000,0.358], df[0.000,0.017], g[1.862,0.018]\n",
            ">1806, dr[0.000,0.410], df[0.000,0.018], g[2.010,0.032]\n",
            ">1807, dr[0.000,0.368], df[0.000,0.022], g[1.993,0.060]\n",
            ">1808, dr[0.000,0.699], df[0.000,0.013], g[1.786,0.022]\n",
            ">1809, dr[0.000,0.504], df[0.000,0.012], g[1.812,0.015]\n",
            ">1810, dr[0.001,0.441], df[0.001,0.007], g[2.092,0.023]\n",
            ">1811, dr[0.000,0.555], df[0.001,0.029], g[1.656,0.017]\n",
            ">1812, dr[0.000,0.608], df[0.001,0.026], g[1.693,0.023]\n",
            ">1813, dr[0.000,0.656], df[0.001,0.023], g[1.432,0.035]\n",
            ">1814, dr[0.000,0.467], df[0.001,0.009], g[1.516,0.028]\n",
            ">1815, dr[0.001,0.437], df[0.000,0.027], g[1.371,0.026]\n",
            ">1816, dr[0.001,1.093], df[0.000,0.010], g[1.373,0.048]\n",
            ">1817, dr[0.000,0.619], df[0.001,0.020], g[1.200,0.046]\n",
            ">1818, dr[0.001,0.469], df[0.002,0.032], g[0.985,0.029]\n",
            ">1819, dr[0.000,0.769], df[0.000,0.018], g[1.007,0.045]\n",
            ">1820, dr[0.000,0.587], df[0.000,0.026], g[0.901,0.084]\n",
            ">1821, dr[0.000,0.665], df[0.000,0.015], g[1.098,0.019]\n",
            ">1822, dr[0.002,0.855], df[0.002,0.004], g[0.750,0.074]\n",
            ">1823, dr[0.001,0.393], df[0.004,0.013], g[0.743,0.032]\n",
            ">1824, dr[0.001,0.225], df[0.000,0.019], g[1.180,0.025]\n",
            ">1825, dr[0.001,0.711], df[0.000,0.011], g[1.529,0.025]\n",
            ">1826, dr[0.000,0.536], df[0.000,0.032], g[1.196,0.048]\n",
            ">1827, dr[0.000,0.411], df[0.000,0.040], g[1.250,0.010]\n",
            ">1828, dr[0.000,0.405], df[0.000,0.024], g[1.233,0.030]\n",
            ">1829, dr[0.001,0.822], df[0.001,0.011], g[0.898,0.029]\n",
            ">1830, dr[0.003,0.605], df[0.001,0.016], g[1.657,0.046]\n",
            ">1831, dr[0.002,0.378], df[0.001,0.006], g[1.835,0.033]\n",
            ">1832, dr[0.001,0.524], df[0.000,0.026], g[1.934,0.017]\n",
            ">1833, dr[0.001,0.442], df[0.006,0.015], g[1.446,0.032]\n",
            ">1834, dr[0.001,0.503], df[0.048,0.028], g[2.856,0.040]\n",
            ">1835, dr[0.003,0.263], df[0.001,0.005], g[5.122,0.037]\n",
            ">1836, dr[0.336,0.484], df[0.102,0.024], g[0.294,0.021]\n",
            ">1837, dr[0.000,0.261], df[0.473,0.027], g[9.251,0.054]\n",
            ">1838, dr[0.533,0.728], df[0.000,0.013], g[1.140,0.011]\n",
            ">1839, dr[0.000,0.896], df[0.000,0.101], g[0.239,0.049]\n",
            ">1840, dr[0.000,0.712], df[0.004,0.069], g[0.155,0.056]\n",
            ">1841, dr[0.000,0.784], df[0.000,0.029], g[0.163,0.055]\n",
            ">1842, dr[0.000,0.625], df[0.001,0.047], g[0.098,0.032]\n",
            ">1843, dr[0.000,1.048], df[0.001,0.037], g[0.054,0.048]\n",
            ">1844, dr[0.000,0.271], df[0.004,0.028], g[0.075,0.014]\n",
            ">1845, dr[0.000,0.567], df[0.004,0.020], g[0.110,0.035]\n",
            ">1846, dr[0.000,0.706], df[0.001,0.030], g[0.120,0.037]\n",
            ">1847, dr[0.000,0.504], df[0.006,0.024], g[0.041,0.041]\n",
            ">1848, dr[0.000,0.408], df[0.000,0.010], g[0.081,0.014]\n",
            ">1849, dr[0.000,0.627], df[0.045,0.027], g[0.111,0.011]\n",
            ">1850, dr[0.000,0.631], df[0.000,0.010], g[0.241,0.052]\n",
            ">1851, dr[0.000,0.980], df[0.000,0.041], g[0.176,0.023]\n",
            ">1852, dr[0.000,1.072], df[0.000,0.019], g[0.146,0.022]\n",
            ">1853, dr[0.000,0.452], df[0.003,0.025], g[0.236,0.015]\n",
            ">1854, dr[0.000,0.364], df[0.001,0.012], g[0.254,0.022]\n",
            ">1855, dr[0.000,0.355], df[0.000,0.016], g[0.121,0.021]\n",
            ">1856, dr[0.000,0.366], df[0.001,0.038], g[0.071,0.040]\n",
            ">1857, dr[0.000,0.446], df[0.000,0.008], g[0.137,0.020]\n",
            ">1858, dr[0.003,0.491], df[0.000,0.007], g[0.064,0.019]\n",
            ">1859, dr[0.000,0.576], df[0.002,0.031], g[0.109,0.040]\n",
            ">1860, dr[0.000,0.828], df[0.006,0.016], g[0.144,0.022]\n",
            ">1861, dr[0.000,0.943], df[0.002,0.017], g[0.208,0.023]\n",
            ">1862, dr[0.000,1.051], df[0.037,0.017], g[0.357,0.026]\n",
            ">1863, dr[0.000,0.408], df[0.000,0.016], g[0.561,0.029]\n",
            ">1864, dr[0.000,0.539], df[0.000,0.022], g[0.566,0.080]\n",
            ">1865, dr[0.000,0.466], df[0.001,0.045], g[0.770,0.038]\n",
            ">1866, dr[0.000,0.502], df[0.001,0.024], g[0.514,0.064]\n",
            ">1867, dr[0.001,0.610], df[0.003,0.011], g[0.338,0.014]\n",
            ">1868, dr[0.000,0.640], df[0.002,0.022], g[0.494,0.077]\n",
            ">1869, dr[0.000,0.620], df[0.006,0.050], g[0.443,0.060]\n",
            ">1870, dr[0.000,0.519], df[0.011,0.011], g[0.579,0.024]\n",
            ">1871, dr[0.000,0.637], df[0.001,0.020], g[0.863,0.032]\n",
            ">1872, dr[0.001,0.483], df[0.004,0.046], g[0.614,0.034]\n",
            ">1873, dr[0.001,0.658], df[0.005,0.079], g[1.018,0.048]\n",
            ">1874, dr[0.000,0.882], df[0.003,0.039], g[0.901,0.048]\n",
            ">1875, dr[0.000,0.483], df[0.001,0.044], g[0.987,0.023]\n",
            ">1876, dr[0.001,0.374], df[0.001,0.052], g[0.709,0.085]\n",
            ">1877, dr[0.002,0.464], df[0.029,0.042], g[1.260,0.028]\n",
            ">1878, dr[0.005,0.405], df[0.000,0.008], g[1.844,0.016]\n",
            ">1879, dr[0.004,0.520], df[0.000,0.026], g[2.031,0.026]\n",
            ">1880, dr[0.000,0.549], df[0.000,0.042], g[1.815,0.038]\n",
            ">1881, dr[0.000,0.683], df[0.000,0.013], g[1.768,0.011]\n",
            ">1882, dr[0.005,0.566], df[0.000,0.012], g[1.800,0.023]\n",
            ">1883, dr[0.000,0.360], df[0.001,0.018], g[1.107,0.019]\n",
            ">1884, dr[0.000,0.580], df[0.002,0.014], g[1.479,0.021]\n",
            ">1885, dr[0.000,0.306], df[0.009,0.062], g[1.766,0.044]\n",
            ">1886, dr[0.000,0.779], df[0.001,0.043], g[1.711,0.013]\n",
            ">1887, dr[0.002,0.431], df[0.002,0.015], g[1.759,0.027]\n",
            ">1888, dr[0.001,0.636], df[0.001,0.044], g[1.829,0.046]\n",
            ">1889, dr[0.000,0.588], df[0.001,0.015], g[1.777,0.045]\n",
            ">1890, dr[0.000,0.344], df[0.000,0.028], g[1.580,0.024]\n",
            ">1891, dr[0.001,0.867], df[0.001,0.026], g[1.168,0.037]\n",
            ">1892, dr[0.000,0.914], df[0.003,0.089], g[1.486,0.055]\n",
            ">1893, dr[0.000,0.722], df[0.002,0.018], g[1.366,0.057]\n",
            ">1894, dr[0.000,0.446], df[0.001,0.031], g[1.324,0.035]\n",
            ">1895, dr[0.003,0.653], df[0.000,0.062], g[1.091,0.050]\n",
            ">1896, dr[0.008,0.433], df[0.001,0.024], g[0.831,0.053]\n",
            ">1897, dr[0.000,0.575], df[0.000,0.044], g[0.610,0.048]\n",
            ">1898, dr[0.000,0.703], df[0.003,0.032], g[0.579,0.046]\n",
            ">1899, dr[0.000,0.542], df[0.001,0.030], g[0.605,0.067]\n",
            ">1900, dr[0.001,0.609], df[0.003,0.061], g[0.714,0.068]\n",
            ">1901, dr[0.002,0.744], df[0.001,0.050], g[0.819,0.051]\n",
            ">1902, dr[0.001,0.949], df[0.000,0.028], g[0.846,0.041]\n",
            ">1903, dr[0.001,0.552], df[0.000,0.082], g[0.828,0.038]\n",
            ">1904, dr[0.000,0.579], df[0.000,0.035], g[0.716,0.024]\n",
            ">1905, dr[0.001,0.559], df[0.000,0.039], g[0.609,0.065]\n",
            ">1906, dr[0.000,0.819], df[0.000,0.041], g[0.841,0.118]\n",
            ">1907, dr[0.000,0.433], df[0.001,0.041], g[0.618,0.064]\n",
            ">1908, dr[0.001,0.429], df[0.001,0.032], g[0.664,0.031]\n",
            ">1909, dr[0.000,0.691], df[0.000,0.067], g[0.573,0.023]\n",
            ">1910, dr[0.000,0.457], df[0.001,0.021], g[0.805,0.057]\n",
            ">1911, dr[0.000,0.709], df[0.001,0.022], g[0.613,0.060]\n",
            ">1912, dr[0.001,0.655], df[0.001,0.045], g[0.644,0.024]\n",
            ">1913, dr[0.000,0.420], df[0.000,0.059], g[0.720,0.050]\n",
            ">1914, dr[0.001,0.296], df[0.000,0.042], g[0.837,0.102]\n",
            ">1915, dr[0.000,0.451], df[0.000,0.052], g[0.713,0.030]\n",
            ">1916, dr[0.003,0.439], df[0.000,0.082], g[0.677,0.032]\n",
            ">1917, dr[0.000,0.499], df[0.000,0.035], g[0.676,0.041]\n",
            ">1918, dr[0.000,0.388], df[0.000,0.023], g[0.932,0.049]\n",
            ">1919, dr[0.001,0.570], df[0.000,0.068], g[0.667,0.070]\n",
            ">1920, dr[0.000,0.367], df[0.002,0.055], g[0.709,0.066]\n",
            ">1921, dr[0.000,0.595], df[0.000,0.079], g[1.228,0.032]\n",
            ">1922, dr[0.001,0.300], df[0.000,0.015], g[1.171,0.054]\n",
            ">1923, dr[0.000,0.398], df[0.000,0.028], g[1.029,0.045]\n",
            ">1924, dr[0.000,0.183], df[0.000,0.025], g[0.955,0.060]\n",
            ">1925, dr[0.000,0.499], df[0.000,0.043], g[0.936,0.052]\n",
            ">1926, dr[0.001,0.784], df[0.000,0.029], g[1.569,0.113]\n",
            ">1927, dr[0.006,0.490], df[0.000,0.088], g[1.163,0.028]\n",
            ">1928, dr[0.005,0.650], df[0.000,0.041], g[0.879,0.064]\n",
            ">1929, dr[0.000,0.357], df[0.000,0.088], g[0.809,0.160]\n",
            ">1930, dr[0.000,0.199], df[0.000,0.027], g[0.725,0.072]\n",
            ">1931, dr[0.002,0.304], df[0.000,0.078], g[0.791,0.104]\n",
            ">1932, dr[0.000,0.641], df[0.000,0.071], g[0.676,0.086]\n",
            ">1933, dr[0.000,0.505], df[0.000,0.022], g[0.761,0.050]\n",
            ">1934, dr[0.000,0.654], df[0.000,0.032], g[0.634,0.042]\n",
            ">1935, dr[0.000,0.551], df[0.000,0.053], g[0.585,0.054]\n",
            ">1936, dr[0.000,0.455], df[0.000,0.037], g[0.601,0.052]\n",
            ">1937, dr[0.000,0.665], df[0.000,0.038], g[0.654,0.096]\n",
            ">1938, dr[0.000,0.504], df[0.000,0.030], g[0.818,0.040]\n",
            ">1939, dr[0.001,0.805], df[0.000,0.157], g[0.808,0.060]\n",
            ">1940, dr[0.000,0.433], df[0.000,0.055], g[0.640,0.075]\n",
            ">1941, dr[0.000,0.553], df[0.000,0.113], g[0.633,0.120]\n",
            ">1942, dr[0.000,0.739], df[0.000,0.069], g[1.078,0.191]\n",
            ">1943, dr[0.000,0.516], df[0.000,0.054], g[0.779,0.064]\n",
            ">1944, dr[0.000,0.708], df[0.000,0.046], g[0.978,0.079]\n",
            ">1945, dr[0.001,0.460], df[0.000,0.037], g[1.045,0.146]\n",
            ">1946, dr[0.001,0.605], df[0.000,0.048], g[1.387,0.087]\n",
            ">1947, dr[0.001,0.969], df[0.000,0.056], g[0.933,0.103]\n",
            ">1948, dr[0.000,0.462], df[0.000,0.020], g[1.178,0.067]\n",
            ">1949, dr[0.000,0.418], df[0.000,0.046], g[0.808,0.051]\n",
            ">1950, dr[0.004,0.612], df[0.000,0.069], g[0.821,0.052]\n",
            ">1951, dr[0.000,0.350], df[0.000,0.091], g[0.519,0.065]\n",
            ">1952, dr[0.000,0.554], df[0.000,0.051], g[0.666,0.069]\n",
            ">1953, dr[0.000,0.527], df[0.000,0.065], g[0.722,0.041]\n",
            ">1954, dr[0.000,0.774], df[0.000,0.063], g[0.794,0.127]\n",
            ">1955, dr[0.000,0.510], df[0.000,0.028], g[0.932,0.084]\n",
            ">1956, dr[0.001,0.582], df[0.000,0.078], g[0.890,0.057]\n",
            ">1957, dr[0.000,0.291], df[0.000,0.038], g[0.892,0.142]\n",
            ">1958, dr[0.000,0.611], df[0.000,0.049], g[0.557,0.060]\n",
            ">1959, dr[0.000,0.404], df[0.000,0.018], g[0.641,0.071]\n",
            ">1960, dr[0.000,0.837], df[0.000,0.036], g[0.647,0.081]\n",
            ">1961, dr[0.001,0.748], df[0.000,0.065], g[0.600,0.038]\n",
            ">1962, dr[0.002,0.320], df[0.000,0.032], g[0.794,0.085]\n",
            ">1963, dr[0.000,0.763], df[0.000,0.058], g[0.708,0.059]\n",
            ">1964, dr[0.000,0.658], df[0.000,0.037], g[0.787,0.142]\n",
            ">1965, dr[0.000,0.484], df[0.000,0.036], g[0.625,0.085]\n",
            ">1966, dr[0.000,0.415], df[0.000,0.034], g[0.513,0.075]\n",
            ">1967, dr[0.000,0.652], df[0.000,0.037], g[0.401,0.035]\n",
            ">1968, dr[0.000,0.290], df[0.000,0.035], g[0.566,0.034]\n",
            ">1969, dr[0.000,0.558], df[0.000,0.056], g[0.518,0.044]\n",
            ">1970, dr[0.000,0.434], df[0.000,0.057], g[0.490,0.063]\n",
            ">1971, dr[0.000,0.262], df[0.000,0.033], g[0.558,0.087]\n",
            ">1972, dr[0.000,0.472], df[0.000,0.041], g[0.513,0.053]\n",
            ">1973, dr[0.001,0.422], df[0.000,0.040], g[0.439,0.067]\n",
            ">1974, dr[0.000,0.349], df[0.000,0.055], g[0.470,0.036]\n",
            ">1975, dr[0.000,0.684], df[0.000,0.031], g[0.581,0.024]\n",
            ">1976, dr[0.000,0.825], df[0.000,0.092], g[0.480,0.050]\n",
            ">1977, dr[0.000,0.310], df[0.000,0.061], g[0.375,0.035]\n",
            ">1978, dr[0.000,0.935], df[0.000,0.023], g[0.220,0.031]\n",
            ">1979, dr[0.000,0.805], df[0.000,0.012], g[0.310,0.048]\n",
            ">1980, dr[0.000,0.806], df[0.000,0.037], g[0.419,0.063]\n",
            ">1981, dr[0.000,0.921], df[0.000,0.032], g[0.287,0.038]\n",
            ">1982, dr[0.000,0.442], df[0.000,0.019], g[0.264,0.040]\n",
            ">1983, dr[0.000,0.378], df[0.000,0.054], g[0.322,0.038]\n",
            ">1984, dr[0.000,0.563], df[0.000,0.015], g[0.284,0.023]\n",
            ">1985, dr[0.001,0.514], df[0.000,0.028], g[0.241,0.031]\n",
            ">1986, dr[0.000,0.514], df[0.000,0.019], g[0.348,0.018]\n",
            ">1987, dr[0.001,0.274], df[0.000,0.015], g[0.203,0.052]\n",
            ">1988, dr[0.001,0.462], df[0.000,0.013], g[0.317,0.032]\n",
            ">1989, dr[0.000,0.586], df[0.000,0.012], g[0.471,0.032]\n",
            ">1990, dr[0.000,0.372], df[0.001,0.018], g[0.237,0.066]\n",
            ">1991, dr[0.000,0.360], df[0.000,0.024], g[0.315,0.043]\n",
            ">1992, dr[0.000,0.472], df[0.000,0.058], g[0.378,0.020]\n",
            ">1993, dr[0.000,0.462], df[0.000,0.026], g[0.309,0.033]\n",
            ">1994, dr[0.000,0.671], df[0.000,0.015], g[0.252,0.086]\n",
            ">1995, dr[0.000,0.586], df[0.000,0.036], g[0.230,0.036]\n",
            ">1996, dr[0.000,0.824], df[0.000,0.026], g[0.347,0.043]\n",
            ">1997, dr[0.000,0.640], df[0.001,0.074], g[0.361,0.027]\n",
            ">1998, dr[0.000,0.605], df[0.001,0.024], g[0.553,0.035]\n",
            ">1999, dr[0.000,0.331], df[0.000,0.012], g[0.337,0.055]\n",
            ">2000, dr[0.000,0.407], df[0.000,0.022], g[0.304,0.057]\n",
            ">2001, dr[0.000,0.359], df[0.001,0.018], g[0.270,0.037]\n",
            ">2002, dr[0.000,0.483], df[0.004,0.023], g[0.446,0.103]\n",
            ">2003, dr[0.000,0.510], df[0.005,0.026], g[0.675,0.043]\n",
            ">2004, dr[0.000,0.441], df[0.000,0.011], g[0.575,0.041]\n",
            ">2005, dr[0.000,0.669], df[0.000,0.012], g[0.571,0.047]\n",
            ">2006, dr[0.000,0.514], df[0.000,0.015], g[0.566,0.033]\n",
            ">2007, dr[0.000,0.591], df[0.001,0.017], g[0.705,0.024]\n",
            ">2008, dr[0.000,0.330], df[0.000,0.019], g[0.782,0.027]\n",
            ">2009, dr[0.000,0.562], df[0.000,0.032], g[0.624,0.038]\n",
            ">2010, dr[0.006,0.815], df[0.000,0.048], g[0.797,0.059]\n",
            ">2011, dr[0.000,0.812], df[0.001,0.028], g[0.726,0.070]\n",
            ">2012, dr[0.000,0.529], df[0.000,0.029], g[0.916,0.033]\n",
            ">2013, dr[0.000,0.407], df[0.000,0.018], g[0.700,0.042]\n",
            ">2014, dr[0.000,0.532], df[0.000,0.015], g[0.823,0.028]\n",
            ">2015, dr[0.000,0.534], df[0.000,0.043], g[0.795,0.029]\n",
            ">2016, dr[0.000,0.520], df[0.000,0.037], g[0.711,0.030]\n",
            ">2017, dr[0.000,0.424], df[0.000,0.016], g[0.658,0.048]\n",
            ">2018, dr[0.000,0.572], df[0.000,0.031], g[0.644,0.041]\n",
            ">2019, dr[0.000,0.288], df[0.002,0.016], g[0.733,0.039]\n",
            ">2020, dr[0.000,0.629], df[0.001,0.024], g[0.711,0.028]\n",
            ">2021, dr[0.000,0.622], df[0.000,0.022], g[0.760,0.072]\n",
            ">2022, dr[0.000,0.580], df[0.000,0.052], g[0.705,0.069]\n",
            ">2023, dr[0.000,0.650], df[0.000,0.056], g[0.620,0.038]\n",
            ">2024, dr[0.000,0.443], df[0.001,0.023], g[0.644,0.045]\n",
            ">2025, dr[0.000,0.637], df[0.001,0.027], g[0.586,0.041]\n",
            ">2026, dr[0.001,0.661], df[0.001,0.017], g[0.654,0.064]\n",
            ">2027, dr[0.000,0.415], df[0.001,0.009], g[0.754,0.037]\n",
            ">2028, dr[0.002,0.605], df[0.000,0.022], g[0.665,0.055]\n",
            ">2029, dr[0.000,0.656], df[0.000,0.041], g[0.711,0.036]\n",
            ">2030, dr[0.000,0.493], df[0.002,0.022], g[0.911,0.040]\n",
            ">2031, dr[0.000,0.780], df[0.001,0.028], g[0.722,0.039]\n",
            ">2032, dr[0.000,0.928], df[0.000,0.017], g[0.747,0.021]\n",
            ">2033, dr[0.000,0.486], df[0.002,0.071], g[0.996,0.040]\n",
            ">2034, dr[0.000,0.470], df[0.000,0.016], g[1.098,0.058]\n",
            ">2035, dr[0.001,0.727], df[0.000,0.026], g[1.168,0.111]\n",
            ">2036, dr[0.001,0.391], df[0.000,0.072], g[0.847,0.039]\n",
            ">2037, dr[0.000,0.873], df[0.000,0.021], g[0.577,0.020]\n",
            ">2038, dr[0.000,0.669], df[0.000,0.022], g[0.665,0.076]\n",
            ">2039, dr[0.000,0.679], df[0.000,0.044], g[0.490,0.050]\n",
            ">2040, dr[0.000,0.358], df[0.000,0.020], g[0.547,0.027]\n",
            ">2041, dr[0.000,0.497], df[0.000,0.055], g[0.633,0.023]\n",
            ">2042, dr[0.001,0.428], df[0.002,0.033], g[0.683,0.057]\n",
            ">2043, dr[0.000,0.400], df[0.000,0.018], g[0.729,0.025]\n",
            ">2044, dr[0.000,0.514], df[0.000,0.020], g[0.669,0.055]\n",
            ">2045, dr[0.006,1.152], df[0.000,0.027], g[0.569,0.035]\n",
            ">2046, dr[0.000,0.394], df[0.000,0.040], g[0.799,0.024]\n",
            ">2047, dr[0.000,0.619], df[0.000,0.016], g[0.763,0.063]\n",
            ">2048, dr[0.000,0.354], df[0.001,0.042], g[0.407,0.028]\n",
            ">2049, dr[0.000,0.663], df[0.006,0.031], g[0.808,0.039]\n",
            ">2050, dr[0.000,0.527], df[0.001,0.025], g[0.661,0.054]\n",
            ">2051, dr[0.000,0.552], df[0.001,0.034], g[1.060,0.039]\n",
            ">2052, dr[0.000,0.484], df[0.001,0.028], g[0.857,0.046]\n",
            ">2053, dr[0.000,0.415], df[0.000,0.025], g[1.185,0.029]\n",
            ">2054, dr[0.000,0.763], df[0.000,0.051], g[0.983,0.075]\n",
            ">2055, dr[0.000,0.454], df[0.000,0.039], g[1.067,0.056]\n",
            ">2056, dr[0.000,0.363], df[0.005,0.011], g[1.125,0.042]\n",
            ">2057, dr[0.001,0.568], df[0.000,0.022], g[1.143,0.043]\n",
            ">2058, dr[0.001,0.534], df[0.000,0.013], g[1.112,0.038]\n",
            ">2059, dr[0.002,0.271], df[0.000,0.029], g[1.227,0.051]\n",
            ">2060, dr[0.000,0.671], df[0.000,0.017], g[1.002,0.050]\n",
            ">2061, dr[0.000,0.398], df[0.000,0.016], g[1.074,0.080]\n",
            ">2062, dr[0.000,0.503], df[0.000,0.020], g[0.918,0.064]\n",
            ">2063, dr[0.000,0.347], df[0.000,0.010], g[1.266,0.038]\n",
            ">2064, dr[0.001,0.409], df[0.000,0.018], g[1.046,0.033]\n",
            ">2065, dr[0.000,0.556], df[0.000,0.014], g[1.168,0.025]\n",
            ">2066, dr[0.001,0.749], df[0.000,0.015], g[1.104,0.052]\n",
            ">2067, dr[0.000,0.468], df[0.000,0.034], g[1.060,0.045]\n",
            ">2068, dr[0.001,0.468], df[0.000,0.039], g[1.106,0.122]\n",
            ">2069, dr[0.000,0.613], df[0.001,0.050], g[1.016,0.096]\n",
            ">2070, dr[0.001,0.362], df[0.000,0.036], g[1.111,0.018]\n",
            ">2071, dr[0.000,0.937], df[0.000,0.055], g[1.133,0.026]\n",
            ">2072, dr[0.000,0.731], df[0.000,0.007], g[1.100,0.077]\n",
            ">2073, dr[0.000,0.522], df[0.001,0.007], g[1.191,0.056]\n",
            ">2074, dr[0.000,0.675], df[0.000,0.028], g[1.175,0.034]\n",
            ">2075, dr[0.001,0.529], df[0.001,0.010], g[0.748,0.037]\n",
            ">2076, dr[0.000,0.190], df[0.001,0.013], g[1.155,0.047]\n",
            ">2077, dr[0.000,0.520], df[0.002,0.035], g[1.063,0.036]\n",
            ">2078, dr[0.000,0.501], df[0.000,0.019], g[1.365,0.064]\n",
            ">2079, dr[0.000,0.419], df[0.001,0.021], g[1.121,0.052]\n",
            ">2080, dr[0.000,0.602], df[0.000,0.016], g[1.114,0.028]\n",
            ">2081, dr[0.000,0.484], df[0.000,0.020], g[1.110,0.045]\n",
            ">2082, dr[0.000,0.588], df[0.000,0.007], g[1.262,0.058]\n",
            ">2083, dr[0.000,0.551], df[0.000,0.022], g[1.176,0.109]\n",
            ">2084, dr[0.000,0.961], df[0.000,0.016], g[1.137,0.026]\n",
            ">2085, dr[0.000,0.352], df[0.001,0.033], g[0.844,0.078]\n",
            ">2086, dr[0.001,0.391], df[0.000,0.081], g[1.029,0.041]\n",
            ">2087, dr[0.000,0.643], df[0.000,0.032], g[1.197,0.061]\n",
            ">2088, dr[0.000,1.019], df[0.000,0.015], g[1.099,0.052]\n",
            ">2089, dr[0.000,0.506], df[0.002,0.019], g[0.963,0.020]\n",
            ">2090, dr[0.002,0.321], df[0.001,0.011], g[0.968,0.053]\n",
            ">2091, dr[0.000,0.770], df[0.002,0.035], g[0.745,0.069]\n",
            ">2092, dr[0.000,0.554], df[0.009,0.011], g[1.147,0.048]\n",
            ">2093, dr[0.000,0.467], df[0.002,0.018], g[1.134,0.035]\n",
            ">2094, dr[0.000,0.789], df[0.000,0.044], g[1.220,0.093]\n",
            ">2095, dr[0.000,0.577], df[0.000,0.029], g[1.331,0.037]\n",
            ">2096, dr[0.001,0.384], df[0.000,0.014], g[1.114,0.067]\n",
            ">2097, dr[0.000,0.435], df[0.000,0.047], g[1.229,0.086]\n",
            ">2098, dr[0.000,0.834], df[0.000,0.020], g[1.108,0.131]\n",
            ">2099, dr[0.000,0.426], df[0.001,0.017], g[1.208,0.042]\n",
            ">2100, dr[0.027,0.221], df[0.000,0.023], g[1.239,0.041]\n",
            ">2101, dr[0.000,0.419], df[0.000,0.011], g[1.042,0.038]\n",
            ">2102, dr[0.001,0.533], df[0.001,0.041], g[1.091,0.125]\n",
            ">2103, dr[0.001,0.778], df[0.000,0.025], g[0.879,0.062]\n",
            ">2104, dr[0.000,0.726], df[0.000,0.013], g[0.805,0.043]\n",
            ">2105, dr[0.000,0.469], df[0.001,0.028], g[0.973,0.042]\n",
            ">2106, dr[0.007,0.507], df[0.001,0.011], g[1.033,0.037]\n",
            ">2107, dr[0.000,0.411], df[0.001,0.015], g[1.063,0.022]\n",
            ">2108, dr[0.000,0.474], df[0.001,0.018], g[1.023,0.029]\n",
            ">2109, dr[0.000,0.710], df[0.000,0.061], g[1.042,0.080]\n",
            ">2110, dr[0.001,0.579], df[0.000,0.042], g[1.067,0.036]\n",
            ">2111, dr[0.000,0.623], df[0.000,0.010], g[1.171,0.052]\n",
            ">2112, dr[0.000,0.506], df[0.000,0.031], g[1.165,0.036]\n",
            ">2113, dr[0.000,0.314], df[0.001,0.023], g[0.962,0.044]\n",
            ">2114, dr[0.000,0.531], df[0.000,0.011], g[1.304,0.049]\n",
            ">2115, dr[0.001,0.699], df[0.000,0.009], g[1.273,0.047]\n",
            ">2116, dr[0.000,0.398], df[0.000,0.007], g[1.293,0.030]\n",
            ">2117, dr[0.000,0.763], df[0.000,0.021], g[1.101,0.061]\n",
            ">2118, dr[0.000,0.412], df[0.001,0.009], g[1.135,0.044]\n",
            ">2119, dr[0.000,0.856], df[0.000,0.009], g[1.277,0.028]\n",
            ">2120, dr[0.000,0.548], df[0.000,0.013], g[1.300,0.045]\n",
            ">2121, dr[0.001,0.461], df[0.000,0.033], g[1.369,0.033]\n",
            ">2122, dr[0.001,0.472], df[0.000,0.022], g[1.260,0.039]\n",
            ">2123, dr[0.000,0.342], df[0.001,0.024], g[1.111,0.039]\n",
            ">2124, dr[0.001,0.686], df[0.001,0.012], g[1.321,0.060]\n",
            ">2125, dr[0.000,0.674], df[0.000,0.024], g[1.259,0.058]\n",
            ">2126, dr[0.000,0.628], df[0.000,0.019], g[0.964,0.073]\n",
            ">2127, dr[0.001,0.644], df[0.001,0.023], g[1.100,0.022]\n",
            ">2128, dr[0.000,0.510], df[0.000,0.022], g[1.116,0.033]\n",
            ">2129, dr[0.000,0.485], df[0.002,0.022], g[0.955,0.027]\n",
            ">2130, dr[0.001,0.742], df[0.002,0.012], g[1.284,0.046]\n",
            ">2131, dr[0.000,0.189], df[0.001,0.023], g[1.401,0.028]\n",
            ">2132, dr[0.000,0.622], df[0.002,0.023], g[1.474,0.031]\n",
            ">2133, dr[0.000,0.596], df[0.000,0.012], g[1.402,0.022]\n",
            ">2134, dr[0.000,0.489], df[0.001,0.007], g[1.670,0.030]\n",
            ">2135, dr[0.000,0.588], df[0.000,0.008], g[1.287,0.036]\n",
            ">2136, dr[0.002,0.680], df[0.000,0.030], g[1.311,0.051]\n",
            ">2137, dr[0.000,0.495], df[0.000,0.014], g[1.316,0.047]\n",
            ">2138, dr[0.005,0.493], df[0.000,0.005], g[1.083,0.042]\n",
            ">2139, dr[0.000,0.723], df[0.000,0.009], g[1.271,0.033]\n",
            ">2140, dr[0.000,0.369], df[0.000,0.013], g[1.369,0.033]\n",
            ">2141, dr[0.002,0.469], df[0.000,0.010], g[1.379,0.025]\n",
            ">2142, dr[0.000,0.594], df[0.000,0.015], g[1.097,0.031]\n",
            ">2143, dr[0.001,0.792], df[0.000,0.011], g[1.198,0.055]\n",
            ">2144, dr[0.000,0.225], df[0.000,0.014], g[1.256,0.033]\n",
            ">2145, dr[0.001,0.527], df[0.000,0.032], g[1.361,0.053]\n",
            ">2146, dr[0.002,0.446], df[0.000,0.006], g[1.235,0.029]\n",
            ">2147, dr[0.001,0.600], df[0.000,0.021], g[1.412,0.058]\n",
            ">2148, dr[0.000,0.464], df[0.000,0.005], g[1.241,0.031]\n",
            ">2149, dr[0.000,0.764], df[0.002,0.013], g[0.964,0.033]\n",
            ">2150, dr[0.010,0.818], df[0.001,0.038], g[1.427,0.040]\n",
            ">2151, dr[0.001,0.534], df[0.000,0.011], g[1.050,0.061]\n",
            ">2152, dr[0.000,1.010], df[0.000,0.012], g[0.900,0.042]\n",
            ">2153, dr[0.001,0.658], df[0.000,0.022], g[0.801,0.064]\n",
            ">2154, dr[0.000,0.695], df[0.001,0.007], g[0.989,0.080]\n",
            ">2155, dr[0.000,0.319], df[0.001,0.012], g[1.114,0.051]\n",
            ">2156, dr[0.001,0.652], df[0.001,0.024], g[0.870,0.062]\n",
            ">2157, dr[0.000,0.724], df[0.010,0.030], g[1.544,0.033]\n",
            ">2158, dr[0.001,0.415], df[0.000,0.023], g[1.759,0.065]\n",
            ">2159, dr[0.002,0.716], df[0.000,0.027], g[1.828,0.048]\n",
            ">2160, dr[0.001,0.472], df[0.000,0.017], g[1.937,0.065]\n",
            ">2161, dr[0.015,0.537], df[0.000,0.016], g[1.467,0.046]\n",
            ">2162, dr[0.000,0.401], df[0.000,0.030], g[1.616,0.082]\n",
            ">2163, dr[0.000,0.260], df[0.000,0.022], g[1.835,0.068]\n",
            ">2164, dr[0.000,0.519], df[0.000,0.009], g[1.423,0.024]\n",
            ">2165, dr[0.004,0.373], df[0.000,0.005], g[1.664,0.025]\n",
            ">2166, dr[0.001,0.619], df[0.000,0.035], g[1.528,0.063]\n",
            ">2167, dr[0.000,0.801], df[0.000,0.015], g[1.636,0.101]\n",
            ">2168, dr[0.000,0.430], df[0.001,0.009], g[1.623,0.081]\n",
            ">2169, dr[0.000,0.533], df[0.002,0.008], g[1.491,0.044]\n",
            ">2170, dr[0.000,0.401], df[0.001,0.021], g[1.304,0.039]\n",
            ">2171, dr[0.000,0.618], df[0.000,0.017], g[1.239,0.037]\n",
            ">2172, dr[0.000,0.537], df[0.000,0.043], g[1.168,0.032]\n",
            ">2173, dr[0.000,0.610], df[0.001,0.006], g[1.360,0.051]\n",
            ">2174, dr[0.000,0.470], df[0.000,0.019], g[1.354,0.079]\n",
            ">2175, dr[0.002,0.544], df[0.001,0.043], g[1.446,0.060]\n",
            ">2176, dr[0.001,0.508], df[0.000,0.022], g[1.195,0.037]\n",
            ">2177, dr[0.000,0.401], df[0.000,0.018], g[1.275,0.085]\n",
            ">2178, dr[0.001,0.443], df[0.000,0.008], g[1.276,0.043]\n",
            ">2179, dr[0.000,0.709], df[0.001,0.008], g[1.521,0.083]\n",
            ">2180, dr[0.000,0.429], df[0.000,0.018], g[1.335,0.045]\n",
            ">2181, dr[0.000,0.408], df[0.000,0.010], g[1.765,0.027]\n",
            ">2182, dr[0.001,0.399], df[0.000,0.010], g[1.497,0.048]\n",
            ">2183, dr[0.000,0.953], df[0.000,0.012], g[1.476,0.041]\n",
            ">2184, dr[0.000,0.470], df[0.000,0.017], g[1.701,0.020]\n",
            ">2185, dr[0.001,0.452], df[0.000,0.017], g[1.939,0.098]\n",
            ">2186, dr[0.002,0.364], df[0.000,0.035], g[1.361,0.054]\n",
            ">2187, dr[0.000,0.388], df[0.000,0.067], g[1.242,0.047]\n",
            ">2188, dr[0.008,0.595], df[0.000,0.027], g[1.101,0.034]\n",
            ">2189, dr[0.000,0.643], df[0.000,0.010], g[1.388,0.042]\n",
            ">2190, dr[0.000,0.318], df[0.001,0.012], g[0.972,0.057]\n",
            ">2191, dr[0.001,0.482], df[0.003,0.016], g[1.125,0.029]\n",
            ">2192, dr[0.000,0.496], df[0.000,0.025], g[1.571,0.075]\n",
            ">2193, dr[0.002,0.725], df[0.004,0.012], g[1.456,0.041]\n",
            ">2194, dr[0.005,0.539], df[0.000,0.053], g[1.933,0.105]\n",
            ">2195, dr[0.000,0.397], df[0.000,0.021], g[1.867,0.031]\n",
            ">2196, dr[0.000,0.317], df[0.003,0.017], g[1.913,0.062]\n",
            ">2197, dr[0.000,1.056], df[0.001,0.021], g[2.346,0.033]\n",
            ">2198, dr[0.000,0.473], df[0.000,0.049], g[2.047,0.038]\n",
            ">2199, dr[0.000,0.699], df[0.000,0.045], g[1.689,0.047]\n",
            ">2200, dr[0.000,0.324], df[0.001,0.028], g[1.649,0.231]\n",
            ">2201, dr[0.000,0.561], df[0.000,0.012], g[1.673,0.028]\n",
            ">2202, dr[0.000,0.431], df[0.001,0.009], g[2.149,0.049]\n",
            ">2203, dr[0.000,0.552], df[0.001,0.021], g[1.953,0.068]\n",
            ">2204, dr[0.000,0.638], df[0.000,0.011], g[1.762,0.087]\n",
            ">2205, dr[0.001,0.422], df[0.000,0.016], g[1.528,0.078]\n",
            ">2206, dr[0.000,0.476], df[0.000,0.014], g[1.576,0.074]\n",
            ">2207, dr[0.000,0.408], df[0.000,0.008], g[1.148,0.074]\n",
            ">2208, dr[0.000,0.667], df[0.001,0.024], g[1.210,0.070]\n",
            ">2209, dr[0.001,0.195], df[0.001,0.016], g[0.940,0.068]\n",
            ">2210, dr[0.001,0.602], df[0.001,0.047], g[0.800,0.126]\n",
            ">2211, dr[0.002,0.511], df[0.004,0.016], g[0.676,0.069]\n",
            ">2212, dr[0.001,0.778], df[0.050,0.055], g[2.829,0.020]\n",
            ">2213, dr[0.001,0.753], df[0.000,0.010], g[4.043,0.072]\n",
            ">2214, dr[0.167,0.471], df[0.178,0.021], g[3.668,0.030]\n",
            ">2215, dr[0.001,0.378], df[0.000,0.026], g[7.418,0.076]\n",
            ">2216, dr[0.005,0.441], df[0.000,0.079], g[7.242,0.273]\n",
            ">2217, dr[0.081,0.871], df[0.000,0.058], g[2.920,0.203]\n",
            ">2218, dr[0.000,0.332], df[0.000,0.059], g[2.092,0.276]\n",
            ">2219, dr[0.000,0.750], df[0.000,0.038], g[1.894,0.112]\n",
            ">2220, dr[0.000,0.858], df[0.000,0.076], g[1.820,0.088]\n",
            ">2221, dr[0.000,0.708], df[0.000,0.029], g[1.890,0.048]\n",
            ">2222, dr[0.000,0.296], df[0.000,0.034], g[1.425,0.153]\n",
            ">2223, dr[0.000,0.510], df[0.000,0.034], g[1.389,0.086]\n",
            ">2224, dr[0.000,0.750], df[0.000,0.075], g[1.791,0.119]\n",
            ">2225, dr[0.000,0.766], df[0.000,0.031], g[1.740,0.147]\n",
            ">2226, dr[0.000,0.335], df[0.000,0.046], g[1.356,0.120]\n",
            ">2227, dr[0.000,0.954], df[0.000,0.100], g[1.348,0.171]\n",
            ">2228, dr[0.000,0.248], df[0.000,0.035], g[1.093,0.138]\n",
            ">2229, dr[0.000,0.764], df[0.000,0.020], g[1.200,0.126]\n",
            ">2230, dr[0.000,0.757], df[0.000,0.024], g[1.380,0.076]\n",
            ">2231, dr[0.000,0.519], df[0.003,0.023], g[1.409,0.085]\n",
            ">2232, dr[0.000,0.335], df[0.000,0.016], g[1.285,0.075]\n",
            ">2233, dr[0.000,0.371], df[0.000,0.036], g[1.181,0.056]\n",
            ">2234, dr[0.000,0.428], df[0.000,0.037], g[0.861,0.080]\n",
            ">2235, dr[0.000,0.761], df[0.002,0.021], g[1.059,0.078]\n",
            ">2236, dr[0.000,0.574], df[0.000,0.015], g[1.016,0.109]\n",
            ">2237, dr[0.000,0.385], df[0.005,0.025], g[0.951,0.087]\n",
            ">2238, dr[0.001,0.685], df[0.001,0.019], g[0.941,0.124]\n",
            ">2239, dr[0.000,0.427], df[0.001,0.015], g[1.140,0.044]\n",
            ">2240, dr[0.000,0.361], df[0.000,0.023], g[1.016,0.051]\n",
            ">2241, dr[0.000,0.690], df[0.000,0.023], g[1.162,0.056]\n",
            ">2242, dr[0.001,0.471], df[0.000,0.036], g[0.772,0.092]\n",
            ">2243, dr[0.001,0.434], df[0.000,0.011], g[0.969,0.050]\n",
            ">2244, dr[0.000,0.303], df[0.000,0.021], g[0.991,0.046]\n",
            ">2245, dr[0.000,0.343], df[0.001,0.017], g[0.984,0.060]\n",
            ">2246, dr[0.000,0.580], df[0.003,0.016], g[1.229,0.052]\n",
            ">2247, dr[0.000,0.581], df[0.003,0.025], g[1.119,0.047]\n",
            ">2248, dr[0.000,0.588], df[0.000,0.033], g[1.609,0.074]\n",
            ">2249, dr[0.002,0.438], df[0.000,0.030], g[1.444,0.043]\n",
            ">2250, dr[0.000,0.410], df[0.000,0.020], g[1.180,0.059]\n",
            ">2251, dr[0.000,0.757], df[0.000,0.016], g[1.411,0.072]\n",
            ">2252, dr[0.000,0.514], df[0.000,0.036], g[1.906,0.041]\n",
            ">2253, dr[0.001,0.625], df[0.000,0.032], g[2.114,0.083]\n",
            ">2254, dr[0.000,0.506], df[0.000,0.015], g[2.112,0.047]\n",
            ">2255, dr[0.000,0.700], df[0.000,0.022], g[1.791,0.097]\n",
            ">2256, dr[0.000,0.295], df[0.000,0.057], g[1.458,0.092]\n",
            ">2257, dr[0.007,0.749], df[0.000,0.031], g[1.063,0.119]\n",
            ">2258, dr[0.000,0.380], df[0.000,0.020], g[0.910,0.101]\n",
            ">2259, dr[0.004,0.723], df[0.000,0.013], g[0.705,0.055]\n",
            ">2260, dr[0.000,0.606], df[0.000,0.014], g[0.647,0.057]\n",
            ">2261, dr[0.000,0.382], df[0.000,0.035], g[0.648,0.043]\n",
            ">2262, dr[0.000,0.234], df[0.000,0.017], g[0.702,0.028]\n",
            ">2263, dr[0.000,0.770], df[0.000,0.016], g[0.642,0.050]\n",
            ">2264, dr[0.001,0.526], df[0.000,0.014], g[0.655,0.060]\n",
            ">2265, dr[0.000,0.816], df[0.000,0.019], g[0.638,0.059]\n",
            ">2266, dr[0.000,0.440], df[0.001,0.022], g[0.554,0.101]\n",
            ">2267, dr[0.000,0.707], df[0.002,0.012], g[1.208,0.045]\n",
            ">2268, dr[0.001,0.699], df[0.000,0.013], g[0.965,0.048]\n",
            ">2269, dr[0.000,0.226], df[0.000,0.051], g[0.908,0.041]\n",
            ">2270, dr[0.000,0.553], df[0.001,0.013], g[0.720,0.050]\n",
            ">2271, dr[0.000,0.363], df[0.000,0.013], g[0.796,0.041]\n",
            ">2272, dr[0.000,0.812], df[0.000,0.016], g[0.694,0.054]\n",
            ">2273, dr[0.000,0.404], df[0.003,0.020], g[0.546,0.076]\n",
            ">2274, dr[0.000,0.525], df[0.000,0.023], g[0.484,0.045]\n",
            ">2275, dr[0.000,0.998], df[0.000,0.022], g[0.626,0.026]\n",
            ">2276, dr[0.000,0.176], df[0.000,0.024], g[0.520,0.045]\n",
            ">2277, dr[0.000,0.185], df[0.000,0.013], g[0.359,0.020]\n",
            ">2278, dr[0.000,0.370], df[0.002,0.016], g[0.444,0.025]\n",
            ">2279, dr[0.001,0.512], df[0.001,0.022], g[0.385,0.033]\n",
            ">2280, dr[0.000,0.497], df[0.000,0.015], g[0.617,0.028]\n",
            ">2281, dr[0.000,0.355], df[0.000,0.019], g[0.347,0.035]\n",
            ">2282, dr[0.000,0.521], df[0.001,0.009], g[0.495,0.042]\n",
            ">2283, dr[0.000,0.533], df[0.001,0.009], g[0.444,0.073]\n",
            ">2284, dr[0.000,0.642], df[0.000,0.020], g[0.359,0.045]\n",
            ">2285, dr[0.001,0.472], df[0.003,0.026], g[0.397,0.040]\n",
            ">2286, dr[0.001,0.631], df[0.001,0.029], g[0.574,0.082]\n",
            ">2287, dr[0.001,0.543], df[0.001,0.023], g[0.419,0.058]\n",
            ">2288, dr[0.001,0.520], df[0.005,0.018], g[0.490,0.083]\n",
            ">2289, dr[0.000,0.469], df[0.008,0.013], g[0.831,0.022]\n",
            ">2290, dr[0.007,0.364], df[0.001,0.009], g[1.216,0.027]\n",
            ">2291, dr[0.001,0.717], df[0.000,0.012], g[1.037,0.032]\n",
            ">2292, dr[0.001,0.283], df[0.004,0.022], g[1.037,0.095]\n",
            ">2293, dr[0.001,0.822], df[0.001,0.014], g[0.995,0.097]\n",
            ">2294, dr[0.000,0.572], df[0.001,0.041], g[0.900,0.017]\n",
            ">2295, dr[0.007,0.334], df[0.002,0.017], g[0.523,0.017]\n",
            ">2296, dr[0.004,0.477], df[0.003,0.011], g[0.237,0.029]\n",
            ">2297, dr[0.001,0.550], df[0.004,0.014], g[0.165,0.044]\n",
            ">2298, dr[0.001,0.632], df[0.039,0.014], g[0.340,0.038]\n",
            ">2299, dr[0.000,0.790], df[0.000,0.005], g[0.641,0.032]\n",
            ">2300, dr[0.002,0.447], df[0.000,0.007], g[0.567,0.032]\n",
            ">2301, dr[0.009,0.561], df[0.004,0.008], g[0.527,0.029]\n",
            ">2302, dr[0.000,0.703], df[0.000,0.014], g[0.583,0.033]\n",
            ">2303, dr[0.000,0.709], df[0.003,0.007], g[0.402,0.018]\n",
            ">2304, dr[0.002,0.677], df[0.008,0.006], g[0.607,0.010]\n",
            ">2305, dr[0.000,0.437], df[0.004,0.019], g[0.899,0.017]\n",
            ">2306, dr[0.000,0.487], df[0.002,0.005], g[1.173,0.030]\n",
            ">2307, dr[0.000,0.393], df[0.009,0.007], g[1.513,0.012]\n",
            ">2308, dr[0.000,0.183], df[0.001,0.016], g[1.449,0.020]\n",
            ">2309, dr[0.006,0.533], df[0.002,0.008], g[1.565,0.020]\n",
            ">2310, dr[0.005,0.775], df[0.000,0.003], g[1.339,0.039]\n",
            ">2311, dr[0.001,0.521], df[0.000,0.007], g[1.380,0.036]\n",
            ">2312, dr[0.001,0.581], df[0.001,0.008], g[1.248,0.094]\n",
            ">2313, dr[0.000,0.548], df[0.000,0.010], g[1.260,0.100]\n",
            ">2314, dr[0.002,0.536], df[0.001,0.044], g[1.133,0.083]\n",
            ">2315, dr[0.000,0.423], df[0.005,0.008], g[1.297,0.044]\n",
            ">2316, dr[0.000,0.384], df[0.003,0.003], g[1.479,0.028]\n",
            ">2317, dr[0.004,0.290], df[0.000,0.005], g[1.394,0.025]\n",
            ">2318, dr[0.004,0.554], df[0.000,0.012], g[1.359,0.109]\n",
            ">2319, dr[0.001,0.338], df[0.000,0.011], g[1.226,0.148]\n",
            ">2320, dr[0.016,0.293], df[0.002,0.013], g[1.258,0.045]\n",
            ">2321, dr[0.000,0.424], df[0.000,0.005], g[1.294,0.042]\n",
            ">2322, dr[0.002,0.476], df[0.000,0.014], g[1.056,0.051]\n",
            ">2323, dr[0.000,0.443], df[0.001,0.011], g[0.988,0.067]\n",
            ">2324, dr[0.001,0.510], df[0.000,0.008], g[1.198,0.043]\n",
            ">2325, dr[0.002,0.635], df[0.000,0.007], g[0.975,0.068]\n",
            ">2326, dr[0.001,0.587], df[0.002,0.012], g[0.998,0.039]\n",
            ">2327, dr[0.003,0.318], df[0.001,0.001], g[0.900,0.076]\n",
            ">2328, dr[0.000,0.409], df[0.000,0.003], g[1.208,0.060]\n",
            ">2329, dr[0.001,0.403], df[0.000,0.004], g[1.205,0.030]\n",
            ">2330, dr[0.000,0.388], df[0.000,0.011], g[1.309,0.044]\n",
            ">2331, dr[0.000,0.398], df[0.001,0.014], g[1.531,0.085]\n",
            ">2332, dr[0.003,0.822], df[0.000,0.002], g[1.369,0.023]\n",
            ">2333, dr[0.001,0.554], df[0.000,0.002], g[1.433,0.056]\n",
            ">2334, dr[0.000,0.727], df[0.000,0.005], g[1.364,0.052]\n",
            ">2335, dr[0.001,0.410], df[0.000,0.012], g[1.508,0.050]\n",
            ">2336, dr[0.001,0.392], df[0.000,0.004], g[1.440,0.041]\n",
            ">2337, dr[0.000,0.574], df[0.001,0.016], g[1.319,0.046]\n",
            ">2338, dr[0.000,0.838], df[0.000,0.002], g[1.315,0.023]\n",
            ">2339, dr[0.000,0.751], df[0.005,0.004], g[1.710,0.021]\n",
            ">2340, dr[0.001,1.000], df[0.000,0.013], g[1.651,0.030]\n",
            ">2341, dr[0.001,0.475], df[0.001,0.007], g[1.403,0.036]\n",
            ">2342, dr[0.000,0.430], df[0.000,0.002], g[1.561,0.048]\n",
            ">2343, dr[0.001,0.516], df[0.001,0.004], g[1.600,0.028]\n",
            ">2344, dr[0.001,0.655], df[0.000,0.005], g[1.483,0.062]\n",
            ">2345, dr[0.001,0.491], df[0.000,0.005], g[1.460,0.042]\n",
            ">2346, dr[0.001,0.267], df[0.001,0.007], g[1.261,0.062]\n",
            ">2347, dr[0.000,0.396], df[0.002,0.009], g[1.353,0.014]\n",
            ">2348, dr[0.000,0.607], df[0.012,0.029], g[1.866,0.018]\n",
            ">2349, dr[0.000,0.692], df[0.001,0.002], g[2.235,0.032]\n",
            ">2350, dr[0.000,0.333], df[0.004,0.006], g[2.611,0.067]\n",
            ">2351, dr[0.005,0.969], df[0.001,0.011], g[2.264,0.046]\n",
            ">2352, dr[0.003,0.457], df[0.001,0.028], g[2.194,0.072]\n",
            ">2353, dr[0.006,0.343], df[0.001,0.006], g[2.359,0.058]\n",
            ">2354, dr[0.000,0.708], df[0.004,0.003], g[2.371,0.090]\n",
            ">2355, dr[0.000,0.987], df[0.001,0.002], g[2.349,0.057]\n",
            ">2356, dr[0.002,0.698], df[0.001,0.001], g[2.392,0.048]\n",
            ">2357, dr[0.002,0.540], df[0.000,0.003], g[2.362,0.064]\n",
            ">2358, dr[0.001,0.282], df[0.000,0.003], g[1.827,0.157]\n",
            ">2359, dr[0.001,0.456], df[0.000,0.020], g[1.818,0.063]\n",
            ">2360, dr[0.000,0.492], df[0.000,0.005], g[2.096,0.072]\n",
            ">2361, dr[0.003,0.836], df[0.000,0.003], g[1.975,0.201]\n",
            ">2362, dr[0.001,0.668], df[0.001,0.019], g[2.358,0.097]\n",
            ">2363, dr[0.000,0.333], df[0.000,0.006], g[2.354,0.051]\n",
            ">2364, dr[0.000,0.383], df[0.001,0.001], g[2.298,0.029]\n",
            ">2365, dr[0.001,0.744], df[0.001,0.025], g[1.876,0.065]\n",
            ">2366, dr[0.001,0.178], df[0.000,0.010], g[2.024,0.036]\n",
            ">2367, dr[0.000,0.435], df[0.000,0.003], g[2.080,0.055]\n",
            ">2368, dr[0.000,0.487], df[0.000,0.004], g[1.898,0.098]\n",
            ">2369, dr[0.000,0.254], df[0.000,0.003], g[1.775,0.065]\n",
            ">2370, dr[0.004,0.532], df[0.000,0.011], g[1.446,0.175]\n",
            ">2371, dr[0.000,0.597], df[0.000,0.003], g[1.743,0.135]\n",
            ">2372, dr[0.000,0.499], df[0.012,0.005], g[2.125,0.025]\n",
            ">2373, dr[0.001,0.298], df[0.002,0.002], g[2.484,0.044]\n",
            ">2374, dr[0.000,0.312], df[0.003,0.008], g[2.680,0.020]\n",
            ">2375, dr[0.000,0.688], df[0.000,0.005], g[3.116,0.029]\n",
            ">2376, dr[0.000,0.385], df[0.000,0.014], g[2.752,0.042]\n",
            ">2377, dr[0.002,0.631], df[0.000,0.009], g[2.337,0.107]\n",
            ">2378, dr[0.000,0.652], df[0.000,0.008], g[2.451,0.142]\n",
            ">2379, dr[0.001,0.426], df[0.000,0.005], g[2.213,0.066]\n",
            ">2380, dr[0.000,0.667], df[0.000,0.005], g[2.352,0.051]\n",
            ">2381, dr[0.001,0.423], df[0.000,0.012], g[2.487,0.096]\n",
            ">2382, dr[0.000,0.532], df[0.000,0.005], g[2.445,0.077]\n",
            ">2383, dr[0.000,0.412], df[0.000,0.007], g[2.435,0.056]\n",
            ">2384, dr[0.006,0.581], df[0.000,0.003], g[2.485,0.033]\n",
            ">2385, dr[0.002,0.588], df[0.000,0.012], g[2.643,0.059]\n",
            ">2386, dr[0.003,0.598], df[0.000,0.021], g[2.852,0.052]\n",
            ">2387, dr[0.001,0.715], df[0.001,0.008], g[2.600,0.048]\n",
            ">2388, dr[0.008,0.549], df[0.001,0.004], g[2.484,0.038]\n",
            ">2389, dr[0.000,0.462], df[0.000,0.015], g[2.834,0.037]\n",
            ">2390, dr[0.000,0.828], df[0.007,0.008], g[2.441,0.014]\n",
            ">2391, dr[0.000,0.437], df[0.021,0.025], g[3.111,0.028]\n",
            ">2392, dr[0.000,0.259], df[0.001,0.021], g[3.222,0.033]\n",
            ">2393, dr[0.000,0.507], df[0.000,0.082], g[3.493,0.035]\n",
            ">2394, dr[0.000,0.674], df[0.000,0.008], g[3.507,0.138]\n",
            ">2395, dr[0.002,0.326], df[0.000,0.012], g[3.307,0.063]\n",
            ">2396, dr[0.001,0.681], df[0.000,0.008], g[3.424,0.140]\n",
            ">2397, dr[0.000,0.376], df[0.000,0.019], g[3.442,0.085]\n",
            ">2398, dr[0.001,0.806], df[0.000,0.022], g[3.818,0.128]\n",
            ">2399, dr[0.001,0.406], df[0.000,0.012], g[4.089,0.049]\n",
            ">2400, dr[0.000,0.444], df[0.000,0.016], g[4.250,0.071]\n",
            ">2401, dr[0.000,0.348], df[0.000,0.012], g[4.024,0.061]\n",
            ">2402, dr[0.000,0.649], df[0.000,0.062], g[3.896,0.065]\n",
            ">2403, dr[0.000,0.402], df[0.000,0.007], g[3.634,0.101]\n",
            ">2404, dr[0.000,0.473], df[0.000,0.012], g[3.727,0.064]\n",
            ">2405, dr[0.004,0.339], df[0.000,0.027], g[3.873,0.067]\n",
            ">2406, dr[0.001,0.635], df[0.000,0.010], g[3.455,0.053]\n",
            ">2407, dr[0.001,0.485], df[0.000,0.018], g[3.145,0.073]\n",
            ">2408, dr[0.001,0.268], df[0.000,0.037], g[3.196,0.125]\n",
            ">2409, dr[0.001,0.604], df[0.000,0.015], g[3.160,0.254]\n",
            ">2410, dr[0.001,0.462], df[0.000,0.009], g[3.034,0.059]\n",
            ">2411, dr[0.000,0.259], df[0.000,0.014], g[3.138,0.083]\n",
            ">2412, dr[0.000,0.363], df[0.000,0.035], g[2.858,0.111]\n",
            ">2413, dr[0.000,0.504], df[0.000,0.020], g[2.750,0.079]\n",
            ">2414, dr[0.009,0.384], df[0.000,0.032], g[2.782,0.107]\n",
            ">2415, dr[0.000,0.310], df[0.000,0.020], g[2.965,0.099]\n",
            ">2416, dr[0.000,0.304], df[0.000,0.041], g[2.850,0.098]\n",
            ">2417, dr[0.000,0.732], df[0.000,0.040], g[2.887,0.085]\n",
            ">2418, dr[0.001,0.250], df[0.000,0.034], g[2.604,0.126]\n",
            ">2419, dr[0.000,0.748], df[0.000,0.037], g[2.998,0.059]\n",
            ">2420, dr[0.001,0.398], df[0.000,0.042], g[2.695,0.023]\n",
            ">2421, dr[0.000,0.191], df[0.000,0.031], g[2.665,0.035]\n",
            ">2422, dr[0.000,0.469], df[0.000,0.039], g[2.614,0.067]\n",
            ">2423, dr[0.001,0.364], df[0.000,0.080], g[2.810,0.061]\n",
            ">2424, dr[0.010,0.496], df[0.000,0.045], g[2.818,0.063]\n",
            ">2425, dr[0.000,0.570], df[0.000,0.039], g[2.691,0.058]\n",
            ">2426, dr[0.002,0.562], df[0.000,0.028], g[2.510,0.032]\n",
            ">2427, dr[0.000,0.309], df[0.000,0.019], g[2.013,0.059]\n",
            ">2428, dr[0.000,0.414], df[0.000,0.016], g[2.239,0.033]\n",
            ">2429, dr[0.000,0.560], df[0.000,0.024], g[1.851,0.053]\n",
            ">2430, dr[0.001,0.590], df[0.000,0.026], g[1.895,0.070]\n",
            ">2431, dr[0.000,0.510], df[0.000,0.031], g[1.445,0.071]\n",
            ">2432, dr[0.000,0.405], df[0.000,0.032], g[1.724,0.057]\n",
            ">2433, dr[0.001,0.619], df[0.000,0.029], g[1.445,0.055]\n",
            ">2434, dr[0.000,0.558], df[0.000,0.021], g[1.562,0.117]\n",
            ">2435, dr[0.000,0.389], df[0.001,0.026], g[1.291,0.069]\n",
            ">2436, dr[0.000,0.838], df[0.000,0.019], g[0.483,0.058]\n",
            ">2437, dr[0.000,0.399], df[0.001,0.013], g[0.365,0.029]\n",
            ">2438, dr[0.000,0.392], df[0.003,0.008], g[0.255,0.061]\n",
            ">2439, dr[0.000,0.836], df[0.008,0.023], g[0.363,0.044]\n",
            ">2440, dr[0.000,0.825], df[0.002,0.035], g[0.431,0.039]\n",
            ">2441, dr[0.002,0.607], df[0.002,0.014], g[0.347,0.021]\n",
            ">2442, dr[0.000,0.552], df[0.001,0.009], g[0.760,0.033]\n",
            ">2443, dr[0.000,0.627], df[0.004,0.004], g[0.551,0.023]\n",
            ">2444, dr[0.000,0.371], df[0.000,0.014], g[0.818,0.015]\n",
            ">2445, dr[0.000,0.221], df[0.000,0.016], g[0.881,0.018]\n",
            ">2446, dr[0.000,0.536], df[0.000,0.005], g[0.690,0.026]\n",
            ">2447, dr[0.001,0.757], df[0.000,0.005], g[0.868,0.050]\n",
            ">2448, dr[0.000,0.319], df[0.000,0.007], g[0.749,0.021]\n",
            ">2449, dr[0.000,0.403], df[0.000,0.011], g[0.813,0.025]\n",
            ">2450, dr[0.000,0.574], df[0.000,0.008], g[0.487,0.052]\n",
            ">2451, dr[0.001,0.521], df[0.000,0.006], g[0.591,0.042]\n",
            ">2452, dr[0.000,0.506], df[0.000,0.021], g[0.643,0.026]\n",
            ">2453, dr[0.001,0.240], df[0.000,0.022], g[0.547,0.050]\n",
            ">2454, dr[0.001,0.688], df[0.009,0.015], g[0.511,0.058]\n",
            ">2455, dr[0.000,0.524], df[0.000,0.005], g[0.896,0.032]\n",
            ">2456, dr[0.000,0.889], df[0.000,0.029], g[0.816,0.027]\n",
            ">2457, dr[0.000,0.315], df[0.000,0.015], g[0.784,0.031]\n",
            ">2458, dr[0.000,0.619], df[0.000,0.010], g[0.768,0.026]\n",
            ">2459, dr[0.000,0.540], df[0.000,0.008], g[0.744,0.038]\n",
            ">2460, dr[0.000,0.553], df[0.000,0.007], g[0.519,0.037]\n",
            ">2461, dr[0.000,0.600], df[0.000,0.007], g[0.605,0.050]\n",
            ">2462, dr[0.000,0.493], df[0.000,0.010], g[0.488,0.037]\n",
            ">2463, dr[0.000,0.596], df[0.000,0.009], g[0.570,0.024]\n",
            ">2464, dr[0.001,0.520], df[0.000,0.010], g[0.487,0.040]\n",
            ">2465, dr[0.001,0.622], df[0.000,0.004], g[0.446,0.028]\n",
            ">2466, dr[0.000,0.413], df[0.001,0.004], g[0.622,0.026]\n",
            ">2467, dr[0.000,0.434], df[0.000,0.006], g[0.650,0.023]\n",
            ">2468, dr[0.000,0.412], df[0.000,0.007], g[0.472,0.023]\n",
            ">2469, dr[0.001,0.483], df[0.010,0.006], g[0.798,0.019]\n",
            ">2470, dr[0.004,0.546], df[0.000,0.015], g[0.895,0.021]\n",
            ">2471, dr[0.001,0.359], df[0.000,0.006], g[0.991,0.034]\n",
            ">2472, dr[0.000,0.460], df[0.000,0.004], g[0.811,0.024]\n",
            ">2473, dr[0.001,0.359], df[0.000,0.014], g[0.932,0.063]\n",
            ">2474, dr[0.001,0.597], df[0.000,0.011], g[1.048,0.028]\n",
            ">2475, dr[0.000,0.683], df[0.000,0.004], g[1.434,0.037]\n",
            ">2476, dr[0.001,0.341], df[0.000,0.003], g[1.576,0.043]\n",
            ">2477, dr[0.000,0.534], df[0.000,0.006], g[1.191,0.030]\n",
            ">2478, dr[0.000,0.663], df[0.000,0.005], g[1.255,0.024]\n",
            ">2479, dr[0.001,0.699], df[0.000,0.005], g[1.329,0.030]\n",
            ">2480, dr[0.000,0.624], df[0.000,0.004], g[1.500,0.020]\n",
            ">2481, dr[0.001,0.356], df[0.000,0.006], g[1.639,0.033]\n",
            ">2482, dr[0.000,0.786], df[0.000,0.005], g[1.675,0.042]\n",
            ">2483, dr[0.003,0.300], df[0.000,0.006], g[1.778,0.019]\n",
            ">2484, dr[0.000,0.486], df[0.000,0.008], g[1.811,0.025]\n",
            ">2485, dr[0.000,0.342], df[0.000,0.005], g[1.970,0.031]\n",
            ">2486, dr[0.001,0.597], df[0.000,0.011], g[1.585,0.037]\n",
            ">2487, dr[0.001,0.485], df[0.000,0.012], g[1.578,0.045]\n",
            ">2488, dr[0.000,0.722], df[0.000,0.006], g[1.410,0.039]\n",
            ">2489, dr[0.001,0.324], df[0.001,0.008], g[1.436,0.037]\n",
            ">2490, dr[0.000,0.689], df[0.001,0.014], g[1.197,0.053]\n",
            ">2491, dr[0.000,0.383], df[0.000,0.004], g[1.010,0.036]\n",
            ">2492, dr[0.000,0.343], df[0.001,0.005], g[1.256,0.056]\n",
            ">2493, dr[0.000,0.465], df[0.000,0.007], g[1.171,0.089]\n",
            ">2494, dr[0.000,0.546], df[0.000,0.005], g[1.327,0.057]\n",
            ">2495, dr[0.001,0.517], df[0.002,0.013], g[1.257,0.072]\n",
            ">2496, dr[0.001,0.874], df[0.000,0.014], g[1.588,0.043]\n",
            ">2497, dr[0.000,0.482], df[0.000,0.014], g[1.580,0.102]\n",
            ">2498, dr[0.000,0.430], df[0.000,0.036], g[1.667,0.034]\n",
            ">2499, dr[0.000,0.761], df[0.000,0.008], g[1.686,0.036]\n",
            ">2500, dr[0.000,0.656], df[0.000,0.010], g[1.680,0.027]\n",
            ">2501, dr[0.000,0.731], df[0.000,0.003], g[1.259,0.060]\n",
            ">2502, dr[0.000,0.421], df[0.000,0.024], g[1.274,0.033]\n",
            ">2503, dr[0.001,0.144], df[0.000,0.009], g[1.182,0.055]\n",
            ">2504, dr[0.000,0.630], df[0.000,0.010], g[1.030,0.033]\n",
            ">2505, dr[0.001,0.422], df[0.000,0.012], g[1.432,0.083]\n",
            ">2506, dr[0.001,0.737], df[0.000,0.009], g[1.494,0.043]\n",
            ">2507, dr[0.000,0.416], df[0.000,0.010], g[1.284,0.060]\n",
            ">2508, dr[0.001,0.456], df[0.000,0.009], g[1.364,0.043]\n",
            ">2509, dr[0.001,0.530], df[0.000,0.011], g[1.417,0.048]\n",
            ">2510, dr[0.000,0.592], df[0.000,0.008], g[1.532,0.069]\n",
            ">2511, dr[0.000,0.686], df[0.000,0.023], g[1.458,0.023]\n",
            ">2512, dr[0.000,0.642], df[0.000,0.012], g[1.681,0.035]\n",
            ">2513, dr[0.000,0.415], df[0.000,0.041], g[1.698,0.050]\n",
            ">2514, dr[0.000,0.455], df[0.000,0.008], g[1.416,0.082]\n",
            ">2515, dr[0.000,0.684], df[0.000,0.014], g[1.253,0.051]\n",
            ">2516, dr[0.000,0.383], df[0.000,0.016], g[1.377,0.053]\n",
            ">2517, dr[0.001,0.773], df[0.000,0.010], g[1.282,0.041]\n",
            ">2518, dr[0.000,0.562], df[0.000,0.014], g[1.242,0.126]\n",
            ">2519, dr[0.000,0.495], df[0.000,0.015], g[1.044,0.112]\n",
            ">2520, dr[0.000,0.540], df[0.000,0.019], g[1.545,0.102]\n",
            ">2521, dr[0.000,0.434], df[0.000,0.010], g[1.633,0.071]\n",
            ">2522, dr[0.000,0.526], df[0.000,0.014], g[1.399,0.054]\n",
            ">2523, dr[0.000,0.311], df[0.000,0.033], g[1.461,0.069]\n",
            ">2524, dr[0.000,0.288], df[0.000,0.029], g[1.631,0.037]\n",
            ">2525, dr[0.001,0.556], df[0.000,0.016], g[1.745,0.106]\n",
            ">2526, dr[0.000,0.397], df[0.000,0.054], g[1.176,0.079]\n",
            ">2527, dr[0.000,0.327], df[0.000,0.059], g[1.195,0.114]\n",
            ">2528, dr[0.001,0.415], df[0.000,0.015], g[1.335,0.089]\n",
            ">2529, dr[0.001,0.464], df[0.000,0.012], g[1.084,0.081]\n",
            ">2530, dr[0.000,0.450], df[0.000,0.020], g[1.555,0.045]\n",
            ">2531, dr[0.000,0.458], df[0.000,0.040], g[0.958,0.058]\n",
            ">2532, dr[0.000,0.359], df[0.000,0.054], g[1.455,0.093]\n",
            ">2533, dr[0.000,0.380], df[0.000,0.024], g[1.070,0.032]\n",
            ">2534, dr[0.000,0.406], df[0.000,0.021], g[1.249,0.064]\n",
            ">2535, dr[0.001,0.594], df[0.000,0.029], g[1.556,0.033]\n",
            ">2536, dr[0.000,0.442], df[0.000,0.023], g[1.127,0.050]\n",
            ">2537, dr[0.000,0.631], df[0.000,0.031], g[0.759,0.036]\n",
            ">2538, dr[0.000,0.331], df[0.000,0.012], g[0.780,0.062]\n",
            ">2539, dr[0.000,0.306], df[0.000,0.013], g[0.781,0.101]\n",
            ">2540, dr[0.000,0.569], df[0.000,0.018], g[0.578,0.111]\n",
            ">2541, dr[0.001,0.346], df[0.000,0.013], g[0.798,0.071]\n",
            ">2542, dr[0.000,0.593], df[0.000,0.021], g[0.807,0.032]\n",
            ">2543, dr[0.000,0.484], df[0.000,0.023], g[1.180,0.059]\n",
            ">2544, dr[0.000,0.454], df[0.000,0.044], g[0.956,0.044]\n",
            ">2545, dr[0.001,0.584], df[0.000,0.018], g[1.024,0.036]\n",
            ">2546, dr[0.000,0.744], df[0.000,0.028], g[0.802,0.037]\n",
            ">2547, dr[0.001,0.779], df[0.000,0.026], g[0.760,0.102]\n",
            ">2548, dr[0.000,0.467], df[0.000,0.026], g[0.778,0.042]\n",
            ">2549, dr[0.000,0.819], df[0.000,0.034], g[0.895,0.088]\n",
            ">2550, dr[0.000,0.537], df[0.000,0.052], g[0.817,0.034]\n",
            ">2551, dr[0.001,0.797], df[0.000,0.021], g[0.696,0.129]\n",
            ">2552, dr[0.000,0.408], df[0.000,0.031], g[0.636,0.076]\n",
            ">2553, dr[0.000,0.328], df[0.000,0.028], g[0.531,0.068]\n",
            ">2554, dr[0.001,0.229], df[0.000,0.011], g[0.543,0.092]\n",
            ">2555, dr[0.000,0.394], df[0.000,0.011], g[0.589,0.051]\n",
            ">2556, dr[0.000,1.056], df[0.000,0.012], g[0.459,0.046]\n",
            ">2557, dr[0.000,0.432], df[0.000,0.014], g[0.457,0.064]\n",
            ">2558, dr[0.000,0.512], df[0.000,0.012], g[0.464,0.056]\n",
            ">2559, dr[0.000,0.774], df[0.000,0.014], g[0.549,0.045]\n",
            ">2560, dr[0.000,0.214], df[0.000,0.013], g[0.425,0.070]\n",
            ">2561, dr[0.001,0.293], df[0.001,0.013], g[0.597,0.025]\n",
            ">2562, dr[0.000,0.915], df[0.001,0.008], g[0.838,0.046]\n",
            ">2563, dr[0.000,0.642], df[0.000,0.022], g[0.958,0.033]\n",
            ">2564, dr[0.000,0.386], df[0.000,0.020], g[0.836,0.053]\n",
            ">2565, dr[0.000,0.355], df[0.000,0.023], g[0.836,0.049]\n",
            ">2566, dr[0.000,0.462], df[0.001,0.019], g[0.668,0.064]\n",
            ">2567, dr[0.000,0.320], df[0.001,0.021], g[0.746,0.051]\n",
            ">2568, dr[0.000,0.508], df[0.000,0.007], g[0.636,0.058]\n",
            ">2569, dr[0.001,0.294], df[0.000,0.027], g[0.795,0.059]\n",
            ">2570, dr[0.000,0.304], df[0.001,0.011], g[1.218,0.048]\n",
            ">2571, dr[0.001,0.556], df[0.000,0.050], g[1.227,0.047]\n",
            ">2572, dr[0.000,0.660], df[0.000,0.035], g[1.172,0.043]\n",
            ">2573, dr[0.000,0.229], df[0.000,0.039], g[1.288,0.025]\n",
            ">2574, dr[0.000,0.897], df[0.000,0.053], g[1.335,0.034]\n",
            ">2575, dr[0.000,0.371], df[0.000,0.032], g[1.358,0.027]\n",
            ">2576, dr[0.000,0.707], df[0.000,0.018], g[1.275,0.028]\n",
            ">2577, dr[0.000,0.539], df[0.000,0.035], g[1.325,0.039]\n",
            ">2578, dr[0.000,0.244], df[0.000,0.010], g[1.148,0.051]\n",
            ">2579, dr[0.000,0.555], df[0.000,0.010], g[1.021,0.039]\n",
            ">2580, dr[0.000,0.443], df[0.000,0.015], g[0.822,0.040]\n",
            ">2581, dr[0.001,0.920], df[0.000,0.021], g[1.127,0.069]\n",
            ">2582, dr[0.000,0.356], df[0.000,0.026], g[1.038,0.035]\n",
            ">2583, dr[0.000,0.364], df[0.000,0.024], g[1.054,0.057]\n",
            ">2584, dr[0.001,0.517], df[0.000,0.024], g[1.106,0.035]\n",
            ">2585, dr[0.001,0.590], df[0.001,0.029], g[1.085,0.070]\n",
            ">2586, dr[0.000,0.320], df[0.000,0.035], g[0.956,0.050]\n",
            ">2587, dr[0.000,0.549], df[0.000,0.016], g[0.999,0.066]\n",
            ">2588, dr[0.000,0.403], df[0.000,0.026], g[0.940,0.078]\n",
            ">2589, dr[0.002,0.609], df[0.000,0.014], g[0.873,0.066]\n",
            ">2590, dr[0.000,0.777], df[0.000,0.013], g[1.038,0.081]\n",
            ">2591, dr[0.001,0.554], df[0.000,0.028], g[0.913,0.109]\n",
            ">2592, dr[0.000,0.296], df[0.000,0.018], g[1.371,0.057]\n",
            ">2593, dr[0.000,0.452], df[0.000,0.024], g[1.192,0.051]\n",
            ">2594, dr[0.000,0.631], df[0.000,0.028], g[1.556,0.042]\n",
            ">2595, dr[0.000,0.877], df[0.000,0.015], g[1.395,0.031]\n",
            ">2596, dr[0.000,0.611], df[0.000,0.013], g[1.180,0.045]\n",
            ">2597, dr[0.000,0.446], df[0.000,0.025], g[1.282,0.033]\n",
            ">2598, dr[0.000,0.386], df[0.000,0.025], g[1.011,0.029]\n",
            ">2599, dr[0.000,0.353], df[0.000,0.017], g[0.831,0.059]\n",
            ">2600, dr[0.000,0.351], df[0.000,0.016], g[1.123,0.038]\n",
            ">2601, dr[0.000,0.182], df[0.000,0.037], g[1.238,0.061]\n",
            ">2602, dr[0.000,0.486], df[0.000,0.023], g[0.971,0.074]\n",
            ">2603, dr[0.000,0.273], df[0.000,0.016], g[1.025,0.077]\n",
            ">2604, dr[0.000,0.626], df[0.000,0.029], g[0.807,0.108]\n",
            ">2605, dr[0.001,0.372], df[0.000,0.021], g[0.719,0.118]\n",
            ">2606, dr[0.001,0.536], df[0.000,0.022], g[0.621,0.040]\n",
            ">2607, dr[0.000,0.786], df[0.000,0.025], g[0.812,0.128]\n",
            ">2608, dr[0.000,0.582], df[0.000,0.024], g[0.929,0.067]\n",
            ">2609, dr[0.000,0.800], df[0.000,0.011], g[1.167,0.048]\n",
            ">2610, dr[0.000,0.586], df[0.000,0.021], g[1.358,0.087]\n",
            ">2611, dr[0.000,0.584], df[0.000,0.037], g[1.125,0.048]\n",
            ">2612, dr[0.000,0.434], df[0.000,0.016], g[1.015,0.061]\n",
            ">2613, dr[0.001,0.914], df[0.000,0.014], g[1.002,0.047]\n",
            ">2614, dr[0.001,0.576], df[0.000,0.017], g[0.998,0.041]\n",
            ">2615, dr[0.000,0.505], df[0.000,0.015], g[1.112,0.041]\n",
            ">2616, dr[0.000,0.252], df[0.000,0.007], g[0.630,0.053]\n",
            ">2617, dr[0.000,0.384], df[0.000,0.009], g[0.810,0.062]\n",
            ">2618, dr[0.000,0.277], df[0.000,0.008], g[0.588,0.037]\n",
            ">2619, dr[0.001,0.413], df[0.001,0.022], g[0.600,0.052]\n",
            ">2620, dr[0.000,0.355], df[0.004,0.024], g[0.784,0.058]\n",
            ">2621, dr[0.000,0.661], df[0.001,0.028], g[0.588,0.034]\n",
            ">2622, dr[0.001,0.700], df[0.000,0.032], g[0.860,0.027]\n",
            ">2623, dr[0.001,0.344], df[0.001,0.008], g[1.084,0.027]\n",
            ">2624, dr[0.001,0.736], df[0.001,0.005], g[1.163,0.068]\n",
            ">2625, dr[0.000,0.648], df[0.000,0.013], g[1.153,0.037]\n",
            ">2626, dr[0.003,0.463], df[0.000,0.009], g[1.033,0.046]\n",
            ">2627, dr[0.001,0.475], df[0.000,0.006], g[0.830,0.025]\n",
            ">2628, dr[0.000,0.923], df[0.000,0.013], g[0.863,0.026]\n",
            ">2629, dr[0.000,0.102], df[0.002,0.003], g[1.008,0.023]\n",
            ">2630, dr[0.000,0.684], df[0.000,0.009], g[1.206,0.022]\n",
            ">2631, dr[0.000,0.476], df[0.001,0.009], g[1.126,0.023]\n",
            ">2632, dr[0.000,0.219], df[0.000,0.005], g[0.975,0.034]\n",
            ">2633, dr[0.001,0.365], df[0.000,0.007], g[1.187,0.025]\n",
            ">2634, dr[0.000,0.851], df[0.000,0.009], g[1.141,0.044]\n",
            ">2635, dr[0.000,0.394], df[0.000,0.005], g[0.822,0.095]\n",
            ">2636, dr[0.002,0.228], df[0.001,0.008], g[1.182,0.026]\n",
            ">2637, dr[0.000,0.614], df[0.003,0.007], g[1.246,0.051]\n",
            ">2638, dr[0.000,0.459], df[0.000,0.010], g[0.844,0.052]\n",
            ">2639, dr[0.000,0.879], df[0.000,0.005], g[1.030,0.032]\n",
            ">2640, dr[0.000,0.749], df[0.000,0.022], g[1.559,0.023]\n",
            ">2641, dr[0.000,0.852], df[0.000,0.018], g[1.642,0.025]\n",
            ">2642, dr[0.001,0.808], df[0.000,0.008], g[2.143,0.017]\n",
            ">2643, dr[0.001,0.399], df[0.000,0.016], g[1.827,0.021]\n",
            ">2644, dr[0.000,0.520], df[0.000,0.026], g[2.037,0.043]\n",
            ">2645, dr[0.002,0.491], df[0.000,0.011], g[1.598,0.085]\n",
            ">2646, dr[0.001,0.432], df[0.000,0.007], g[1.613,0.048]\n",
            ">2647, dr[0.000,0.675], df[0.000,0.017], g[1.840,0.073]\n",
            ">2648, dr[0.001,0.451], df[0.000,0.024], g[1.567,0.045]\n",
            ">2649, dr[0.000,0.618], df[0.000,0.018], g[1.543,0.069]\n",
            ">2650, dr[0.001,0.749], df[0.000,0.010], g[1.948,0.064]\n",
            ">2651, dr[0.000,0.618], df[0.000,0.018], g[1.977,0.029]\n",
            ">2652, dr[0.001,0.634], df[0.000,0.004], g[1.509,0.040]\n",
            ">2653, dr[0.000,0.443], df[0.000,0.011], g[1.282,0.021]\n",
            ">2654, dr[0.000,0.525], df[0.000,0.011], g[1.412,0.012]\n",
            ">2655, dr[0.001,0.569], df[0.000,0.008], g[1.282,0.031]\n",
            ">2656, dr[0.000,0.537], df[0.001,0.012], g[1.190,0.025]\n",
            ">2657, dr[0.000,0.371], df[0.000,0.036], g[1.480,0.021]\n",
            ">2658, dr[0.000,0.441], df[0.000,0.014], g[1.730,0.022]\n",
            ">2659, dr[0.000,0.365], df[0.000,0.008], g[1.847,0.022]\n",
            ">2660, dr[0.000,0.578], df[0.000,0.025], g[1.915,0.011]\n",
            ">2661, dr[0.002,0.725], df[0.000,0.012], g[1.507,0.012]\n",
            ">2662, dr[0.000,0.513], df[0.001,0.007], g[1.841,0.031]\n",
            ">2663, dr[0.000,0.352], df[0.001,0.017], g[1.581,0.014]\n",
            ">2664, dr[0.010,0.570], df[0.001,0.007], g[1.450,0.024]\n",
            ">2665, dr[0.000,0.285], df[0.000,0.004], g[1.505,0.013]\n",
            ">2666, dr[0.000,0.998], df[0.000,0.009], g[1.845,0.022]\n",
            ">2667, dr[0.000,0.344], df[0.001,0.007], g[2.179,0.016]\n",
            ">2668, dr[0.000,0.402], df[0.004,0.009], g[2.143,0.008]\n",
            ">2669, dr[0.000,0.951], df[0.016,0.014], g[2.991,0.012]\n",
            ">2670, dr[0.000,0.679], df[0.001,0.019], g[3.560,0.022]\n",
            ">2671, dr[0.002,0.631], df[0.000,0.014], g[3.794,0.015]\n",
            ">2672, dr[0.001,0.567], df[0.000,0.006], g[3.686,0.017]\n",
            ">2673, dr[0.000,0.700], df[0.000,0.008], g[3.475,0.021]\n",
            ">2674, dr[0.000,0.426], df[0.000,0.013], g[3.160,0.039]\n",
            ">2675, dr[0.000,0.365], df[0.000,0.010], g[3.608,0.045]\n",
            ">2676, dr[0.000,0.745], df[0.000,0.030], g[3.058,0.031]\n",
            ">2677, dr[0.000,0.418], df[0.000,0.027], g[2.552,0.065]\n",
            ">2678, dr[0.000,0.483], df[0.000,0.010], g[2.781,0.033]\n",
            ">2679, dr[0.000,0.373], df[0.000,0.011], g[2.717,0.031]\n",
            ">2680, dr[0.001,0.264], df[0.000,0.034], g[2.086,0.062]\n",
            ">2681, dr[0.000,0.495], df[0.000,0.014], g[1.773,0.068]\n",
            ">2682, dr[0.001,0.399], df[0.000,0.030], g[1.378,0.082]\n",
            ">2683, dr[0.001,0.712], df[0.000,0.014], g[1.530,0.081]\n",
            ">2684, dr[0.000,0.359], df[0.000,0.009], g[0.876,0.095]\n",
            ">2685, dr[0.003,0.561], df[0.000,0.027], g[0.803,0.046]\n",
            ">2686, dr[0.000,0.416], df[0.000,0.010], g[0.648,0.022]\n",
            ">2687, dr[0.000,0.383], df[0.004,0.034], g[1.037,0.045]\n",
            ">2688, dr[0.000,0.469], df[0.000,0.013], g[1.088,0.059]\n",
            ">2689, dr[0.001,0.388], df[0.003,0.010], g[0.940,0.025]\n",
            ">2690, dr[0.000,0.452], df[0.000,0.015], g[1.544,0.015]\n",
            ">2691, dr[0.013,0.725], df[0.001,0.009], g[1.917,0.024]\n",
            ">2692, dr[0.001,0.395], df[0.000,0.008], g[1.247,0.036]\n",
            ">2693, dr[0.001,0.452], df[0.000,0.010], g[1.517,0.043]\n",
            ">2694, dr[0.001,0.455], df[0.000,0.017], g[2.216,0.020]\n",
            ">2695, dr[0.000,1.118], df[0.000,0.011], g[2.209,0.028]\n",
            ">2696, dr[0.000,0.983], df[0.000,0.009], g[1.932,0.034]\n",
            ">2697, dr[0.000,0.611], df[0.000,0.011], g[1.944,0.047]\n",
            ">2698, dr[0.000,0.332], df[0.000,0.013], g[2.060,0.022]\n",
            ">2699, dr[0.000,0.618], df[0.000,0.023], g[1.642,0.027]\n",
            ">2700, dr[0.000,0.549], df[0.000,0.007], g[1.424,0.047]\n",
            ">2701, dr[0.000,0.568], df[0.000,0.011], g[1.159,0.040]\n",
            ">2702, dr[0.000,0.527], df[0.000,0.006], g[0.978,0.046]\n",
            ">2703, dr[0.001,0.562], df[0.000,0.004], g[1.297,0.016]\n",
            ">2704, dr[0.000,0.263], df[0.002,0.011], g[0.949,0.040]\n",
            ">2705, dr[0.002,0.571], df[0.000,0.008], g[0.902,0.025]\n",
            ">2706, dr[0.001,0.552], df[0.002,0.007], g[0.874,0.066]\n",
            ">2707, dr[0.000,0.643], df[0.000,0.004], g[1.111,0.037]\n",
            ">2708, dr[0.000,0.335], df[0.000,0.004], g[0.979,0.029]\n",
            ">2709, dr[0.000,0.235], df[0.001,0.005], g[1.511,0.019]\n",
            ">2710, dr[0.014,0.315], df[0.000,0.007], g[1.365,0.022]\n",
            ">2711, dr[0.000,0.655], df[0.000,0.013], g[1.298,0.018]\n",
            ">2712, dr[0.000,0.298], df[0.001,0.011], g[1.487,0.039]\n",
            ">2713, dr[0.002,0.690], df[0.003,0.010], g[1.601,0.016]\n",
            ">2714, dr[0.000,0.794], df[0.001,0.018], g[1.381,0.024]\n",
            ">2715, dr[0.000,0.237], df[0.001,0.019], g[1.255,0.016]\n",
            ">2716, dr[0.001,0.608], df[0.001,0.018], g[1.561,0.015]\n",
            ">2717, dr[0.000,0.237], df[0.010,0.007], g[1.902,0.007]\n",
            ">2718, dr[0.002,0.608], df[0.003,0.022], g[3.338,0.005]\n",
            ">2719, dr[0.000,0.588], df[0.001,0.063], g[3.468,0.005]\n",
            ">2720, dr[0.001,0.523], df[0.014,0.176], g[3.360,0.004]\n",
            ">2721, dr[0.001,0.646], df[0.001,0.028], g[3.032,0.007]\n",
            ">2722, dr[0.002,0.669], df[0.000,0.016], g[2.058,0.034]\n",
            ">2723, dr[0.003,0.300], df[0.000,0.030], g[1.816,0.021]\n",
            ">2724, dr[0.001,0.621], df[0.000,0.014], g[1.555,0.021]\n",
            ">2725, dr[0.001,0.524], df[0.000,0.034], g[1.517,0.025]\n",
            ">2726, dr[0.010,0.573], df[0.001,0.018], g[1.045,0.020]\n",
            ">2727, dr[0.005,0.473], df[0.013,0.013], g[1.462,0.022]\n",
            ">2728, dr[0.000,0.528], df[0.001,0.012], g[1.764,0.024]\n",
            ">2729, dr[0.002,0.316], df[0.005,0.012], g[2.312,0.050]\n",
            ">2730, dr[0.002,0.416], df[0.000,0.046], g[2.142,0.048]\n",
            ">2731, dr[0.002,0.690], df[0.000,0.023], g[1.836,0.013]\n",
            ">2732, dr[0.027,0.997], df[0.015,0.012], g[2.233,0.045]\n",
            ">2733, dr[0.001,0.342], df[0.000,0.024], g[1.931,0.029]\n",
            ">2734, dr[0.000,0.958], df[0.001,0.015], g[2.246,0.026]\n",
            ">2735, dr[0.001,0.448], df[0.001,0.025], g[1.632,0.041]\n",
            ">2736, dr[0.001,0.430], df[0.002,0.030], g[1.421,0.022]\n",
            ">2737, dr[0.000,0.321], df[0.000,0.014], g[1.766,0.027]\n",
            ">2738, dr[0.000,0.438], df[0.000,0.006], g[1.863,0.028]\n",
            ">2739, dr[0.000,0.292], df[0.000,0.003], g[2.104,0.206]\n",
            ">2740, dr[0.001,0.666], df[0.000,0.076], g[2.372,0.039]\n",
            ">2741, dr[0.001,0.496], df[0.000,0.010], g[2.578,0.018]\n",
            ">2742, dr[0.001,0.442], df[0.000,0.063], g[3.109,0.069]\n",
            ">2743, dr[0.000,0.376], df[0.000,0.054], g[2.899,0.065]\n",
            ">2744, dr[0.002,0.558], df[0.000,0.105], g[2.552,0.103]\n",
            ">2745, dr[0.001,0.360], df[0.000,0.155], g[3.082,0.060]\n",
            ">2746, dr[0.002,0.480], df[0.000,0.186], g[2.994,0.103]\n",
            ">2747, dr[0.000,0.665], df[0.000,0.256], g[3.146,0.267]\n",
            ">2748, dr[0.008,0.416], df[0.000,0.407], g[2.635,0.299]\n",
            ">2749, dr[0.004,0.455], df[0.000,0.304], g[0.868,0.391]\n",
            ">2750, dr[0.001,0.420], df[0.000,0.101], g[0.559,0.175]\n",
            ">2751, dr[0.000,0.813], df[0.000,0.134], g[0.708,0.109]\n",
            ">2752, dr[0.000,0.490], df[0.000,0.080], g[0.644,0.208]\n",
            ">2753, dr[0.000,0.422], df[0.000,0.102], g[0.797,0.168]\n",
            ">2754, dr[0.000,0.333], df[0.000,0.132], g[0.955,0.173]\n",
            ">2755, dr[0.000,0.448], df[0.000,0.059], g[0.800,0.193]\n",
            ">2756, dr[0.000,0.548], df[0.000,0.034], g[0.640,0.205]\n",
            ">2757, dr[0.000,0.393], df[0.000,0.109], g[0.495,0.145]\n",
            ">2758, dr[0.000,0.531], df[0.000,0.041], g[0.539,0.064]\n",
            ">2759, dr[0.000,0.526], df[0.000,0.117], g[0.873,0.103]\n",
            ">2760, dr[0.000,0.298], df[0.000,0.135], g[0.514,0.054]\n",
            ">2761, dr[0.000,0.654], df[0.000,0.164], g[0.498,0.055]\n",
            ">2762, dr[0.001,0.433], df[0.000,0.088], g[0.356,0.095]\n",
            ">2763, dr[0.000,0.817], df[0.000,0.082], g[0.445,0.060]\n",
            ">2764, dr[0.000,0.884], df[0.000,0.069], g[0.276,0.197]\n",
            ">2765, dr[0.000,0.567], df[0.000,0.129], g[0.394,0.142]\n",
            ">2766, dr[0.001,0.816], df[0.000,0.098], g[0.769,0.095]\n",
            ">2767, dr[0.001,0.986], df[0.000,0.153], g[0.663,0.183]\n",
            ">2768, dr[0.000,0.595], df[0.000,0.112], g[0.687,0.080]\n",
            ">2769, dr[0.000,0.770], df[0.000,0.080], g[0.370,0.044]\n",
            ">2770, dr[0.000,0.604], df[0.000,0.050], g[0.400,0.052]\n",
            ">2771, dr[0.001,0.594], df[0.000,0.087], g[0.536,0.055]\n",
            ">2772, dr[0.000,0.371], df[0.000,0.036], g[0.459,0.055]\n",
            ">2773, dr[0.000,0.268], df[0.000,0.157], g[0.222,0.071]\n",
            ">2774, dr[0.001,0.495], df[0.000,0.131], g[0.186,0.068]\n",
            ">2775, dr[0.000,0.673], df[0.000,0.106], g[0.162,0.061]\n",
            ">2776, dr[0.000,0.431], df[0.000,0.052], g[0.145,0.038]\n",
            ">2777, dr[0.000,0.582], df[0.000,0.072], g[0.257,0.049]\n",
            ">2778, dr[0.001,0.479], df[0.000,0.096], g[0.317,0.068]\n",
            ">2779, dr[0.000,0.430], df[0.000,0.135], g[0.169,0.053]\n",
            ">2780, dr[0.000,0.519], df[0.000,0.080], g[0.122,0.069]\n",
            ">2781, dr[0.000,0.602], df[0.000,0.055], g[0.161,0.116]\n",
            ">2782, dr[0.000,0.604], df[0.000,0.048], g[0.170,0.068]\n",
            ">2783, dr[0.000,0.432], df[0.000,0.042], g[0.161,0.035]\n",
            ">2784, dr[0.000,0.629], df[0.000,0.050], g[0.231,0.098]\n",
            ">2785, dr[0.000,0.505], df[0.000,0.020], g[0.120,0.096]\n",
            ">2786, dr[0.000,0.735], df[0.001,0.038], g[0.099,0.098]\n",
            ">2787, dr[0.000,0.320], df[0.000,0.012], g[0.139,0.054]\n",
            ">2788, dr[0.000,0.483], df[0.000,0.012], g[0.229,0.027]\n",
            ">2789, dr[0.000,0.513], df[0.000,0.007], g[0.251,0.051]\n",
            ">2790, dr[0.000,0.433], df[0.000,0.011], g[0.120,0.063]\n",
            ">2791, dr[0.000,0.449], df[0.000,0.033], g[0.127,0.033]\n",
            ">2792, dr[0.000,0.558], df[0.000,0.015], g[0.225,0.075]\n",
            ">2793, dr[0.001,0.399], df[0.000,0.014], g[0.249,0.065]\n",
            ">2794, dr[0.001,0.711], df[0.000,0.025], g[0.196,0.042]\n",
            ">2795, dr[0.000,0.304], df[0.000,0.008], g[0.440,0.010]\n",
            ">2796, dr[0.000,0.386], df[0.000,0.013], g[0.557,0.024]\n",
            ">2797, dr[0.000,0.426], df[0.000,0.018], g[0.460,0.032]\n",
            ">2798, dr[0.000,0.548], df[0.000,0.017], g[0.350,0.030]\n",
            ">2799, dr[0.000,0.654], df[0.000,0.040], g[0.251,0.022]\n",
            ">2800, dr[0.000,0.824], df[0.000,0.010], g[0.259,0.025]\n",
            ">2801, dr[0.000,0.638], df[0.000,0.030], g[0.219,0.029]\n",
            ">2802, dr[0.000,0.571], df[0.000,0.034], g[0.181,0.035]\n",
            ">2803, dr[0.000,0.410], df[0.000,0.013], g[0.229,0.013]\n",
            ">2804, dr[0.000,0.482], df[0.000,0.017], g[0.187,0.039]\n",
            ">2805, dr[0.001,0.479], df[0.000,0.006], g[0.179,0.021]\n",
            ">2806, dr[0.000,0.289], df[0.000,0.015], g[0.197,0.028]\n",
            ">2807, dr[0.000,0.334], df[0.000,0.009], g[0.314,0.029]\n",
            ">2808, dr[0.000,0.138], df[0.000,0.018], g[0.230,0.019]\n",
            ">2809, dr[0.000,0.750], df[0.000,0.033], g[0.194,0.024]\n",
            ">2810, dr[0.001,0.594], df[0.000,0.010], g[0.105,0.022]\n",
            ">2811, dr[0.000,0.322], df[0.000,0.011], g[0.081,0.027]\n",
            ">2812, dr[0.000,0.725], df[0.000,0.005], g[0.066,0.061]\n",
            ">2813, dr[0.000,0.407], df[0.000,0.007], g[0.027,0.029]\n",
            ">2814, dr[0.000,0.436], df[0.000,0.013], g[0.188,0.023]\n",
            ">2815, dr[0.000,0.542], df[0.000,0.014], g[0.216,0.061]\n",
            ">2816, dr[0.000,0.841], df[0.000,0.005], g[0.210,0.036]\n",
            ">2817, dr[0.000,0.832], df[0.000,0.020], g[0.178,0.047]\n",
            ">2818, dr[0.000,0.396], df[0.000,0.022], g[0.126,0.024]\n",
            ">2819, dr[0.000,0.416], df[0.001,0.008], g[0.206,0.032]\n",
            ">2820, dr[0.000,0.312], df[0.002,0.013], g[0.114,0.015]\n",
            ">2821, dr[0.000,0.561], df[0.001,0.025], g[0.288,0.030]\n",
            ">2822, dr[0.000,0.214], df[0.000,0.007], g[0.324,0.036]\n",
            ">2823, dr[0.000,0.334], df[0.000,0.003], g[0.147,0.018]\n",
            ">2824, dr[0.000,0.608], df[0.000,0.007], g[0.239,0.013]\n",
            ">2825, dr[0.000,0.463], df[0.000,0.007], g[0.231,0.012]\n",
            ">2826, dr[0.000,0.607], df[0.000,0.008], g[0.277,0.036]\n",
            ">2827, dr[0.000,0.355], df[0.000,0.011], g[0.178,0.014]\n",
            ">2828, dr[0.001,0.511], df[0.001,0.021], g[0.231,0.014]\n",
            ">2829, dr[0.000,0.594], df[0.000,0.013], g[0.131,0.016]\n",
            ">2830, dr[0.001,0.559], df[0.000,0.008], g[0.221,0.015]\n",
            ">2831, dr[0.001,0.641], df[0.000,0.007], g[0.243,0.017]\n",
            ">2832, dr[0.000,0.509], df[0.001,0.008], g[0.316,0.025]\n",
            ">2833, dr[0.000,0.407], df[0.001,0.016], g[0.418,0.028]\n",
            ">2834, dr[0.000,0.642], df[0.000,0.021], g[0.657,0.044]\n",
            ">2835, dr[0.000,0.539], df[0.000,0.010], g[0.743,0.043]\n",
            ">2836, dr[0.000,0.470], df[0.000,0.012], g[0.716,0.052]\n",
            ">2837, dr[0.000,0.592], df[0.000,0.011], g[0.586,0.033]\n",
            ">2838, dr[0.000,0.444], df[0.000,0.019], g[0.708,0.028]\n",
            ">2839, dr[0.000,0.748], df[0.000,0.009], g[0.432,0.024]\n",
            ">2840, dr[0.000,0.712], df[0.000,0.008], g[0.440,0.045]\n",
            ">2841, dr[0.000,0.372], df[0.001,0.046], g[0.831,0.051]\n",
            ">2842, dr[0.000,0.538], df[0.000,0.008], g[0.593,0.037]\n",
            ">2843, dr[0.000,0.481], df[0.000,0.010], g[0.440,0.042]\n",
            ">2844, dr[0.000,0.978], df[0.000,0.009], g[0.567,0.041]\n",
            ">2845, dr[0.000,0.509], df[0.000,0.006], g[0.691,0.074]\n",
            ">2846, dr[0.000,0.340], df[0.000,0.011], g[0.568,0.046]\n",
            ">2847, dr[0.000,0.485], df[0.000,0.013], g[0.512,0.055]\n",
            ">2848, dr[0.000,0.887], df[0.000,0.017], g[0.269,0.051]\n",
            ">2849, dr[0.000,0.765], df[0.001,0.009], g[0.335,0.037]\n",
            ">2850, dr[0.000,0.440], df[0.000,0.003], g[0.257,0.064]\n",
            ">2851, dr[0.000,0.505], df[0.001,0.007], g[0.368,0.085]\n",
            ">2852, dr[0.001,0.572], df[0.000,0.010], g[0.380,0.080]\n",
            ">2853, dr[0.001,0.616], df[0.000,0.011], g[0.483,0.066]\n",
            ">2854, dr[0.001,0.564], df[0.000,0.003], g[0.423,0.039]\n",
            ">2855, dr[0.000,0.487], df[0.000,0.021], g[0.589,0.045]\n",
            ">2856, dr[0.000,0.434], df[0.000,0.009], g[0.561,0.032]\n",
            ">2857, dr[0.000,0.501], df[0.000,0.013], g[0.557,0.023]\n",
            ">2858, dr[0.001,0.566], df[0.000,0.003], g[0.588,0.101]\n",
            ">2859, dr[0.000,0.604], df[0.000,0.011], g[0.726,0.047]\n",
            ">2860, dr[0.000,1.098], df[0.000,0.007], g[0.290,0.016]\n",
            ">2861, dr[0.000,0.632], df[0.000,0.006], g[0.359,0.049]\n",
            ">2862, dr[0.000,0.290], df[0.001,0.006], g[0.268,0.039]\n",
            ">2863, dr[0.000,0.298], df[0.000,0.009], g[0.334,0.043]\n",
            ">2864, dr[0.000,0.490], df[0.001,0.012], g[0.318,0.048]\n",
            ">2865, dr[0.000,0.571], df[0.000,0.010], g[0.458,0.040]\n",
            ">2866, dr[0.004,0.213], df[0.001,0.007], g[0.488,0.012]\n",
            ">2867, dr[0.002,0.648], df[0.000,0.005], g[0.507,0.039]\n",
            ">2868, dr[0.000,0.519], df[0.001,0.012], g[0.494,0.018]\n",
            ">2869, dr[0.000,0.499], df[0.000,0.005], g[0.701,0.023]\n",
            ">2870, dr[0.000,0.595], df[0.000,0.007], g[0.885,0.102]\n",
            ">2871, dr[0.001,0.425], df[0.000,0.006], g[0.731,0.080]\n",
            ">2872, dr[0.000,0.386], df[0.000,0.015], g[0.692,0.068]\n",
            ">2873, dr[0.000,0.256], df[0.000,0.003], g[0.783,0.048]\n",
            ">2874, dr[0.000,0.782], df[0.000,0.004], g[0.716,0.045]\n",
            ">2875, dr[0.000,0.228], df[0.000,0.006], g[0.323,0.053]\n",
            ">2876, dr[0.000,0.458], df[0.000,0.023], g[0.451,0.033]\n",
            ">2877, dr[0.000,0.418], df[0.001,0.009], g[0.216,0.041]\n",
            ">2878, dr[0.000,0.361], df[0.000,0.012], g[0.459,0.033]\n",
            ">2879, dr[0.000,0.700], df[0.001,0.004], g[0.268,0.025]\n",
            ">2880, dr[0.000,0.884], df[0.000,0.003], g[0.445,0.019]\n",
            ">2881, dr[0.000,0.407], df[0.000,0.004], g[0.235,0.043]\n",
            ">2882, dr[0.000,0.634], df[0.001,0.005], g[0.230,0.022]\n",
            ">2883, dr[0.001,0.737], df[0.000,0.005], g[0.256,0.018]\n",
            ">2884, dr[0.001,0.681], df[0.001,0.011], g[0.257,0.035]\n",
            ">2885, dr[0.000,0.473], df[0.003,0.006], g[0.228,0.025]\n",
            ">2886, dr[0.000,0.395], df[0.001,0.027], g[0.455,0.072]\n",
            ">2887, dr[0.000,0.518], df[0.001,0.005], g[0.541,0.017]\n",
            ">2888, dr[0.001,0.298], df[0.000,0.005], g[0.493,0.033]\n",
            ">2889, dr[0.000,0.370], df[0.000,0.015], g[0.445,0.013]\n",
            ">2890, dr[0.001,0.464], df[0.002,0.004], g[0.659,0.026]\n",
            ">2891, dr[0.000,0.604], df[0.000,0.025], g[0.356,0.034]\n",
            ">2892, dr[0.000,0.427], df[0.000,0.008], g[0.503,0.024]\n",
            ">2893, dr[0.000,0.947], df[0.000,0.009], g[0.749,0.055]\n",
            ">2894, dr[0.000,0.541], df[0.001,0.018], g[0.909,0.030]\n",
            ">2895, dr[0.000,0.329], df[0.000,0.003], g[1.250,0.019]\n",
            ">2896, dr[0.000,0.557], df[0.000,0.004], g[1.002,0.053]\n",
            ">2897, dr[0.000,0.423], df[0.000,0.024], g[0.979,0.049]\n",
            ">2898, dr[0.000,0.319], df[0.000,0.006], g[0.979,0.054]\n",
            ">2899, dr[0.000,0.845], df[0.000,0.004], g[0.936,0.064]\n",
            ">2900, dr[0.001,0.530], df[0.000,0.008], g[1.272,0.036]\n",
            ">2901, dr[0.000,0.333], df[0.000,0.007], g[0.878,0.055]\n",
            ">2902, dr[0.000,0.595], df[0.000,0.039], g[0.903,0.029]\n",
            ">2903, dr[0.000,0.404], df[0.000,0.005], g[0.619,0.072]\n",
            ">2904, dr[0.010,0.525], df[0.000,0.006], g[0.625,0.055]\n",
            ">2905, dr[0.000,0.666], df[0.000,0.047], g[0.640,0.064]\n",
            ">2906, dr[0.000,0.470], df[0.000,0.005], g[0.628,0.053]\n",
            ">2907, dr[0.000,0.505], df[0.000,0.020], g[0.500,0.048]\n",
            ">2908, dr[0.000,0.936], df[0.000,0.012], g[0.545,0.054]\n",
            ">2909, dr[0.000,0.515], df[0.000,0.012], g[0.363,0.049]\n",
            ">2910, dr[0.000,0.383], df[0.000,0.007], g[0.316,0.043]\n",
            ">2911, dr[0.000,0.492], df[0.000,0.008], g[0.380,0.031]\n",
            ">2912, dr[0.000,0.726], df[0.000,0.009], g[0.498,0.064]\n",
            ">2913, dr[0.000,0.636], df[0.000,0.013], g[0.799,0.092]\n",
            ">2914, dr[0.000,0.368], df[0.000,0.007], g[0.958,0.062]\n",
            ">2915, dr[0.000,0.377], df[0.000,0.017], g[1.285,0.043]\n",
            ">2916, dr[0.000,0.151], df[0.000,0.016], g[0.941,0.053]\n",
            ">2917, dr[0.000,0.775], df[0.000,0.008], g[0.858,0.056]\n",
            ">2918, dr[0.000,0.420], df[0.000,0.006], g[0.864,0.066]\n",
            ">2919, dr[0.000,0.309], df[0.000,0.013], g[0.854,0.078]\n",
            ">2920, dr[0.000,0.776], df[0.000,0.013], g[0.732,0.064]\n",
            ">2921, dr[0.000,0.240], df[0.000,0.011], g[0.686,0.092]\n",
            ">2922, dr[0.000,1.133], df[0.000,0.014], g[0.615,0.035]\n",
            ">2923, dr[0.000,0.255], df[0.000,0.008], g[0.700,0.058]\n",
            ">2924, dr[0.000,0.558], df[0.000,0.011], g[0.587,0.038]\n",
            ">2925, dr[0.000,0.371], df[0.000,0.014], g[0.534,0.017]\n",
            ">2926, dr[0.000,0.410], df[0.000,0.009], g[0.556,0.041]\n",
            ">2927, dr[0.000,0.391], df[0.000,0.010], g[0.459,0.044]\n",
            ">2928, dr[0.000,0.483], df[0.000,0.012], g[0.633,0.036]\n",
            ">2929, dr[0.000,0.471], df[0.000,0.020], g[0.370,0.032]\n",
            ">2930, dr[0.000,0.691], df[0.000,0.004], g[0.827,0.048]\n",
            ">2931, dr[0.000,0.400], df[0.000,0.007], g[0.695,0.060]\n",
            ">2932, dr[0.000,0.442], df[0.000,0.006], g[0.748,0.049]\n",
            ">2933, dr[0.000,0.316], df[0.000,0.007], g[0.428,0.034]\n",
            ">2934, dr[0.000,0.556], df[0.001,0.006], g[0.270,0.043]\n",
            ">2935, dr[0.000,0.413], df[0.002,0.024], g[0.506,0.027]\n",
            ">2936, dr[0.000,0.338], df[0.004,0.015], g[0.971,0.051]\n",
            ">2937, dr[0.000,0.689], df[0.000,0.011], g[1.051,0.028]\n",
            ">2938, dr[0.000,0.471], df[0.000,0.011], g[1.773,0.031]\n",
            ">2939, dr[0.000,0.557], df[0.000,0.004], g[1.547,0.021]\n",
            ">2940, dr[0.000,0.391], df[0.000,0.003], g[1.582,0.036]\n",
            ">2941, dr[0.000,0.447], df[0.000,0.014], g[1.166,0.040]\n",
            ">2942, dr[0.000,0.429], df[0.000,0.005], g[1.101,0.031]\n",
            ">2943, dr[0.000,0.568], df[0.000,0.016], g[1.395,0.066]\n",
            ">2944, dr[0.000,0.408], df[0.000,0.007], g[1.297,0.024]\n",
            ">2945, dr[0.001,0.554], df[0.000,0.004], g[1.039,0.045]\n",
            ">2946, dr[0.001,0.755], df[0.001,0.004], g[0.813,0.064]\n",
            ">2947, dr[0.000,0.290], df[0.000,0.006], g[1.051,0.043]\n",
            ">2948, dr[0.000,0.451], df[0.000,0.008], g[0.748,0.045]\n",
            ">2949, dr[0.000,0.741], df[0.000,0.003], g[0.773,0.033]\n",
            ">2950, dr[0.000,0.750], df[0.000,0.008], g[0.732,0.093]\n",
            ">2951, dr[0.001,0.492], df[0.000,0.006], g[0.530,0.065]\n",
            ">2952, dr[0.000,0.515], df[0.000,0.015], g[0.584,0.036]\n",
            ">2953, dr[0.000,0.979], df[0.000,0.021], g[0.528,0.035]\n",
            ">2954, dr[0.000,0.696], df[0.000,0.013], g[0.681,0.039]\n",
            ">2955, dr[0.000,0.704], df[0.000,0.004], g[1.018,0.038]\n",
            ">2956, dr[0.000,0.414], df[0.000,0.005], g[0.923,0.023]\n",
            ">2957, dr[0.000,0.926], df[0.001,0.003], g[1.139,0.013]\n",
            ">2958, dr[0.000,0.394], df[0.000,0.020], g[0.789,0.019]\n",
            ">2959, dr[0.000,0.287], df[0.000,0.003], g[0.848,0.055]\n",
            ">2960, dr[0.000,0.296], df[0.000,0.008], g[0.954,0.041]\n",
            ">2961, dr[0.000,0.350], df[0.000,0.005], g[0.691,0.030]\n",
            ">2962, dr[0.001,0.394], df[0.000,0.007], g[0.938,0.032]\n",
            ">2963, dr[0.000,0.364], df[0.001,0.012], g[1.164,0.036]\n",
            ">2964, dr[0.001,0.803], df[0.000,0.005], g[1.303,0.023]\n",
            ">2965, dr[0.000,0.495], df[0.000,0.009], g[1.449,0.040]\n",
            ">2966, dr[0.000,0.265], df[0.000,0.012], g[1.306,0.026]\n",
            ">2967, dr[0.000,0.366], df[0.000,0.007], g[1.921,0.040]\n",
            ">2968, dr[0.000,0.639], df[0.000,0.006], g[1.781,0.052]\n",
            ">2969, dr[0.001,0.411], df[0.000,0.007], g[1.914,0.033]\n",
            ">2970, dr[0.000,0.804], df[0.000,0.007], g[1.990,0.057]\n",
            ">2971, dr[0.001,0.313], df[0.000,0.009], g[2.031,0.043]\n",
            ">2972, dr[0.000,0.650], df[0.000,0.012], g[1.771,0.053]\n",
            ">2973, dr[0.000,0.598], df[0.000,0.017], g[1.490,0.041]\n",
            ">2974, dr[0.000,0.356], df[0.000,0.005], g[1.546,0.019]\n",
            ">2975, dr[0.000,0.450], df[0.000,0.012], g[1.201,0.079]\n",
            ">2976, dr[0.000,0.319], df[0.000,0.012], g[1.007,0.041]\n",
            ">2977, dr[0.000,0.679], df[0.001,0.009], g[1.481,0.022]\n",
            ">2978, dr[0.004,0.399], df[0.000,0.007], g[1.602,0.022]\n",
            ">2979, dr[0.000,0.275], df[0.000,0.005], g[1.526,0.028]\n",
            ">2980, dr[0.000,0.383], df[0.000,0.010], g[1.359,0.025]\n",
            ">2981, dr[0.000,0.456], df[0.000,0.006], g[1.096,0.022]\n",
            ">2982, dr[0.001,0.645], df[0.000,0.009], g[1.331,0.034]\n",
            ">2983, dr[0.000,0.401], df[0.000,0.011], g[1.332,0.022]\n",
            ">2984, dr[0.000,0.455], df[0.000,0.008], g[1.103,0.048]\n",
            ">2985, dr[0.000,0.450], df[0.000,0.019], g[1.039,0.030]\n",
            ">2986, dr[0.003,0.718], df[0.002,0.007], g[0.995,0.020]\n",
            ">2987, dr[0.000,0.413], df[0.000,0.007], g[0.834,0.046]\n",
            ">2988, dr[0.001,0.444], df[0.001,0.013], g[0.706,0.031]\n",
            ">2989, dr[0.000,0.341], df[0.003,0.018], g[0.892,0.034]\n",
            ">2990, dr[0.000,0.366], df[0.002,0.021], g[1.233,0.021]\n",
            ">2991, dr[0.000,0.430], df[0.003,0.014], g[1.448,0.037]\n",
            ">2992, dr[0.000,0.690], df[0.000,0.018], g[2.049,0.084]\n",
            ">2993, dr[0.012,0.518], df[0.000,0.012], g[1.759,0.047]\n",
            ">2994, dr[0.002,0.486], df[0.000,0.009], g[1.988,0.025]\n",
            ">2995, dr[0.001,0.318], df[0.000,0.006], g[1.724,0.026]\n",
            ">2996, dr[0.000,0.693], df[0.000,0.004], g[1.534,0.029]\n",
            ">2997, dr[0.000,0.517], df[0.001,0.006], g[1.792,0.020]\n",
            ">2998, dr[0.000,0.446], df[0.001,0.004], g[1.732,0.017]\n",
            ">2999, dr[0.000,0.308], df[0.001,0.007], g[2.110,0.026]\n",
            ">3000, dr[0.000,0.740], df[0.000,0.007], g[1.555,0.025]\n",
            ">3001, dr[0.000,0.563], df[0.001,0.003], g[1.568,0.019]\n",
            ">3002, dr[0.001,0.392], df[0.000,0.004], g[1.947,0.026]\n",
            ">3003, dr[0.000,0.328], df[0.001,0.004], g[1.583,0.023]\n",
            ">3004, dr[0.001,0.520], df[0.001,0.006], g[1.142,0.077]\n",
            ">3005, dr[0.000,0.669], df[0.003,0.007], g[1.887,0.062]\n",
            ">3006, dr[0.002,0.430], df[0.002,0.009], g[2.494,0.088]\n",
            ">3007, dr[0.001,0.573], df[0.000,0.014], g[3.180,0.028]\n",
            ">3008, dr[0.001,0.612], df[0.000,0.004], g[3.276,0.072]\n",
            ">3009, dr[0.001,0.584], df[0.000,0.004], g[3.389,0.020]\n",
            ">3010, dr[0.002,0.436], df[0.000,0.011], g[3.177,0.036]\n",
            ">3011, dr[0.004,0.367], df[0.000,0.010], g[2.579,0.040]\n",
            ">3012, dr[0.001,0.405], df[0.000,0.017], g[2.123,0.042]\n",
            ">3013, dr[0.000,0.182], df[0.000,0.006], g[1.584,0.097]\n",
            ">3014, dr[0.000,0.585], df[0.000,0.020], g[2.050,0.032]\n",
            ">3015, dr[0.000,0.483], df[0.002,0.002], g[1.729,0.071]\n",
            ">3016, dr[0.000,0.716], df[0.004,0.003], g[1.822,0.089]\n",
            ">3017, dr[0.000,0.389], df[0.001,0.009], g[2.126,0.044]\n",
            ">3018, dr[0.000,0.605], df[0.000,0.008], g[2.829,0.033]\n",
            ">3019, dr[0.000,0.720], df[0.000,0.007], g[2.248,0.071]\n",
            ">3020, dr[0.000,0.474], df[0.000,0.010], g[3.104,0.072]\n",
            ">3021, dr[0.000,0.165], df[0.000,0.004], g[3.079,0.066]\n",
            ">3022, dr[0.000,0.995], df[0.000,0.005], g[3.549,0.063]\n",
            ">3023, dr[0.000,0.550], df[0.000,0.011], g[3.557,0.084]\n",
            ">3024, dr[0.001,0.487], df[0.000,0.012], g[3.360,0.086]\n",
            ">3025, dr[0.000,0.469], df[0.000,0.005], g[3.253,0.097]\n",
            ">3026, dr[0.000,0.496], df[0.000,0.006], g[2.839,0.040]\n",
            ">3027, dr[0.000,0.486], df[0.000,0.008], g[2.530,0.032]\n",
            ">3028, dr[0.000,0.548], df[0.001,0.016], g[2.882,0.048]\n",
            ">3029, dr[0.000,0.756], df[0.003,0.007], g[2.484,0.058]\n",
            ">3030, dr[0.000,0.368], df[0.008,0.006], g[2.691,0.037]\n",
            ">3031, dr[0.001,0.226], df[0.003,0.007], g[3.038,0.027]\n",
            ">3032, dr[0.001,0.431], df[0.005,0.012], g[3.354,0.026]\n",
            ">3033, dr[0.000,0.433], df[0.036,0.007], g[5.609,0.023]\n",
            ">3034, dr[0.004,0.847], df[0.000,0.029], g[6.133,0.107]\n",
            ">3035, dr[0.017,0.490], df[0.000,0.003], g[4.848,0.078]\n",
            ">3036, dr[0.002,0.703], df[0.000,0.011], g[5.201,0.051]\n",
            ">3037, dr[0.001,0.666], df[0.000,0.016], g[4.583,0.111]\n",
            ">3038, dr[0.001,0.203], df[0.000,0.002], g[4.499,0.211]\n",
            ">3039, dr[0.003,0.519], df[0.000,0.007], g[4.238,0.066]\n",
            ">3040, dr[0.003,0.467], df[0.000,0.010], g[3.387,0.173]\n",
            ">3041, dr[0.001,0.977], df[0.000,0.012], g[3.445,0.148]\n",
            ">3042, dr[0.001,0.350], df[0.000,0.009], g[3.755,0.063]\n",
            ">3043, dr[0.002,0.496], df[0.000,0.012], g[3.252,0.063]\n",
            ">3044, dr[0.002,0.319], df[0.000,0.004], g[2.602,0.107]\n",
            ">3045, dr[0.001,0.378], df[0.001,0.011], g[1.892,0.076]\n",
            ">3046, dr[0.000,0.595], df[0.000,0.008], g[1.647,0.219]\n",
            ">3047, dr[0.000,0.500], df[0.001,0.013], g[1.618,0.184]\n",
            ">3048, dr[0.000,0.866], df[0.000,0.013], g[2.142,0.111]\n",
            ">3049, dr[0.000,0.227], df[0.000,0.031], g[1.957,0.086]\n",
            ">3050, dr[0.001,0.447], df[0.002,0.018], g[2.646,0.036]\n",
            ">3051, dr[0.000,0.573], df[0.003,0.005], g[2.170,0.065]\n",
            ">3052, dr[0.002,0.614], df[0.000,0.023], g[2.236,0.058]\n",
            ">3053, dr[0.001,0.303], df[0.007,0.005], g[3.047,0.063]\n",
            ">3054, dr[0.000,0.679], df[0.000,0.003], g[3.830,0.065]\n",
            ">3055, dr[0.001,0.324], df[0.000,0.003], g[4.125,0.076]\n",
            ">3056, dr[0.002,0.790], df[0.000,0.007], g[3.504,0.069]\n",
            ">3057, dr[0.000,0.416], df[0.000,0.015], g[3.686,0.091]\n",
            ">3058, dr[0.001,0.380], df[0.000,0.004], g[2.990,0.106]\n",
            ">3059, dr[0.000,0.175], df[0.000,0.015], g[2.570,0.152]\n",
            ">3060, dr[0.031,0.519], df[0.001,0.011], g[1.502,0.060]\n",
            ">3061, dr[0.000,0.329], df[0.003,0.001], g[1.706,0.032]\n",
            ">3062, dr[0.001,0.435], df[0.001,0.005], g[1.895,0.057]\n",
            ">3063, dr[0.001,0.415], df[0.000,0.018], g[2.057,0.024]\n",
            ">3064, dr[0.000,0.722], df[0.001,0.001], g[2.201,0.015]\n",
            ">3065, dr[0.000,0.624], df[0.000,0.018], g[2.416,0.049]\n",
            ">3066, dr[0.001,0.347], df[0.000,0.021], g[2.129,0.059]\n",
            ">3067, dr[0.001,0.697], df[0.002,0.016], g[1.731,0.061]\n",
            ">3068, dr[0.000,0.703], df[0.000,0.003], g[2.119,0.022]\n",
            ">3069, dr[0.000,0.505], df[0.001,0.019], g[2.542,0.033]\n",
            ">3070, dr[0.001,0.573], df[0.001,0.067], g[2.228,0.025]\n",
            ">3071, dr[0.000,0.488], df[0.002,0.011], g[3.045,0.044]\n",
            ">3072, dr[0.000,0.464], df[0.003,0.013], g[3.716,0.025]\n",
            ">3073, dr[0.003,0.437], df[0.024,0.044], g[5.232,0.029]\n",
            ">3074, dr[0.001,0.453], df[0.000,0.028], g[6.521,0.025]\n",
            ">3075, dr[0.029,0.287], df[0.000,0.066], g[5.074,0.015]\n",
            ">3076, dr[0.000,0.904], df[0.000,0.033], g[4.184,0.009]\n",
            ">3077, dr[0.002,0.635], df[0.000,0.017], g[3.071,0.032]\n",
            ">3078, dr[0.001,1.267], df[0.000,0.009], g[2.134,0.038]\n",
            ">3079, dr[0.001,0.407], df[0.000,0.004], g[1.544,0.031]\n",
            ">3080, dr[0.001,0.431], df[0.000,0.010], g[1.530,0.017]\n",
            ">3081, dr[0.000,0.534], df[0.000,0.034], g[1.256,0.019]\n",
            ">3082, dr[0.001,0.452], df[0.000,0.016], g[1.321,0.030]\n",
            ">3083, dr[0.001,0.367], df[0.000,0.009], g[1.046,0.029]\n",
            ">3084, dr[0.014,0.703], df[0.000,0.005], g[0.894,0.032]\n",
            ">3085, dr[0.000,0.502], df[0.000,0.008], g[0.813,0.034]\n",
            ">3086, dr[0.000,0.181], df[0.000,0.010], g[0.565,0.011]\n",
            ">3087, dr[0.000,0.499], df[0.000,0.010], g[0.644,0.022]\n",
            ">3088, dr[0.000,0.265], df[0.000,0.005], g[0.445,0.023]\n",
            ">3089, dr[0.000,0.478], df[0.000,0.003], g[0.601,0.029]\n",
            ">3090, dr[0.000,0.543], df[0.000,0.003], g[0.413,0.043]\n",
            ">3091, dr[0.000,0.658], df[0.000,0.003], g[0.355,0.054]\n",
            ">3092, dr[0.000,0.397], df[0.000,0.003], g[0.450,0.040]\n",
            ">3093, dr[0.001,0.716], df[0.000,0.001], g[0.534,0.034]\n",
            ">3094, dr[0.001,0.890], df[0.000,0.007], g[0.535,0.024]\n",
            ">3095, dr[0.000,0.271], df[0.000,0.005], g[0.472,0.023]\n",
            ">3096, dr[0.000,0.265], df[0.000,0.002], g[0.660,0.022]\n",
            ">3097, dr[0.000,0.559], df[0.000,0.005], g[0.533,0.065]\n",
            ">3098, dr[0.000,0.746], df[0.000,0.009], g[0.310,0.023]\n",
            ">3099, dr[0.000,0.390], df[0.000,0.004], g[0.398,0.042]\n",
            ">3100, dr[0.000,0.533], df[0.000,0.002], g[0.328,0.021]\n",
            ">3101, dr[0.000,0.677], df[0.000,0.004], g[0.300,0.017]\n",
            ">3102, dr[0.000,0.630], df[0.000,0.002], g[0.338,0.019]\n",
            ">3103, dr[0.000,0.542], df[0.000,0.002], g[0.429,0.010]\n",
            ">3104, dr[0.000,0.329], df[0.000,0.003], g[0.346,0.008]\n",
            ">3105, dr[0.000,0.641], df[0.000,0.012], g[0.362,0.017]\n",
            ">3106, dr[0.001,0.616], df[0.000,0.007], g[0.338,0.048]\n",
            ">3107, dr[0.000,0.546], df[0.000,0.012], g[0.397,0.060]\n",
            ">3108, dr[0.000,0.264], df[0.000,0.006], g[0.242,0.036]\n",
            ">3109, dr[0.000,0.383], df[0.000,0.005], g[0.419,0.021]\n",
            ">3110, dr[0.000,0.483], df[0.000,0.002], g[0.269,0.027]\n",
            ">3111, dr[0.000,0.712], df[0.000,0.003], g[0.415,0.035]\n",
            ">3112, dr[0.001,0.400], df[0.001,0.005], g[0.439,0.035]\n",
            ">3113, dr[0.000,0.468], df[0.000,0.002], g[0.414,0.054]\n",
            ">3114, dr[0.000,0.473], df[0.000,0.003], g[0.334,0.021]\n",
            ">3115, dr[0.001,0.475], df[0.000,0.004], g[0.403,0.009]\n",
            ">3116, dr[0.001,0.358], df[0.000,0.002], g[0.548,0.024]\n",
            ">3117, dr[0.001,0.815], df[0.000,0.003], g[0.351,0.019]\n",
            ">3118, dr[0.000,0.557], df[0.000,0.004], g[0.295,0.030]\n",
            ">3119, dr[0.001,0.625], df[0.000,0.003], g[0.355,0.042]\n",
            ">3120, dr[0.004,0.777], df[0.002,0.002], g[0.419,0.027]\n",
            ">3121, dr[0.002,0.296], df[0.000,0.004], g[0.445,0.030]\n",
            ">3122, dr[0.002,0.309], df[0.000,0.007], g[0.403,0.014]\n",
            ">3123, dr[0.000,0.573], df[0.001,0.004], g[0.444,0.015]\n",
            ">3124, dr[0.000,0.235], df[0.000,0.003], g[0.426,0.010]\n",
            ">3125, dr[0.000,0.417], df[0.000,0.003], g[0.316,0.022]\n",
            ">3126, dr[0.001,0.340], df[0.000,0.002], g[0.355,0.015]\n",
            ">3127, dr[0.000,0.554], df[0.001,0.008], g[0.119,0.029]\n",
            ">3128, dr[0.001,0.554], df[0.000,0.005], g[0.167,0.036]\n",
            ">3129, dr[0.000,0.478], df[0.000,0.004], g[0.237,0.056]\n",
            ">3130, dr[0.000,0.509], df[0.000,0.002], g[0.303,0.039]\n",
            ">3131, dr[0.000,0.355], df[0.000,0.003], g[0.418,0.040]\n",
            ">3132, dr[0.001,0.743], df[0.000,0.002], g[0.271,0.025]\n",
            ">3133, dr[0.000,0.643], df[0.000,0.005], g[0.467,0.034]\n",
            ">3134, dr[0.000,0.338], df[0.000,0.004], g[0.236,0.016]\n",
            ">3135, dr[0.000,0.388], df[0.000,0.006], g[0.266,0.022]\n",
            ">3136, dr[0.000,0.435], df[0.000,0.004], g[0.182,0.024]\n",
            ">3137, dr[0.000,0.327], df[0.000,0.002], g[0.244,0.013]\n",
            ">3138, dr[0.000,0.741], df[0.000,0.003], g[0.244,0.022]\n",
            ">3139, dr[0.000,0.360], df[0.000,0.001], g[0.325,0.029]\n",
            ">3140, dr[0.000,0.409], df[0.000,0.002], g[0.223,0.053]\n",
            ">3141, dr[0.000,0.556], df[0.000,0.003], g[0.264,0.008]\n",
            ">3142, dr[0.000,0.282], df[0.000,0.005], g[0.300,0.012]\n",
            ">3143, dr[0.000,0.441], df[0.000,0.002], g[0.322,0.015]\n",
            ">3144, dr[0.000,0.542], df[0.000,0.008], g[0.381,0.009]\n",
            ">3145, dr[0.000,0.402], df[0.000,0.005], g[0.453,0.014]\n",
            ">3146, dr[0.001,0.304], df[0.000,0.004], g[0.541,0.009]\n",
            ">3147, dr[0.000,0.728], df[0.000,0.002], g[0.446,0.009]\n",
            ">3148, dr[0.000,0.298], df[0.000,0.003], g[0.379,0.028]\n",
            ">3149, dr[0.001,0.784], df[0.001,0.005], g[0.512,0.021]\n",
            ">3150, dr[0.000,0.391], df[0.000,0.006], g[0.818,0.009]\n",
            ">3151, dr[0.001,0.554], df[0.000,0.003], g[0.800,0.015]\n",
            ">3152, dr[0.000,0.569], df[0.000,0.007], g[0.819,0.008]\n",
            ">3153, dr[0.000,0.256], df[0.000,0.004], g[0.895,0.014]\n",
            ">3154, dr[0.000,0.475], df[0.000,0.002], g[0.813,0.013]\n",
            ">3155, dr[0.001,0.588], df[0.000,0.002], g[0.768,0.019]\n",
            ">3156, dr[0.000,0.393], df[0.000,0.015], g[0.773,0.023]\n",
            ">3157, dr[0.001,0.538], df[0.000,0.009], g[0.848,0.015]\n",
            ">3158, dr[0.000,0.322], df[0.000,0.005], g[0.697,0.023]\n",
            ">3159, dr[0.000,0.913], df[0.000,0.005], g[0.677,0.041]\n",
            ">3160, dr[0.000,0.440], df[0.000,0.009], g[0.589,0.036]\n",
            ">3161, dr[0.000,0.606], df[0.000,0.007], g[0.559,0.022]\n",
            ">3162, dr[0.001,0.496], df[0.000,0.006], g[0.618,0.042]\n",
            ">3163, dr[0.000,0.529], df[0.000,0.002], g[0.670,0.021]\n",
            ">3164, dr[0.000,0.630], df[0.001,0.019], g[0.625,0.033]\n",
            ">3165, dr[0.000,0.489], df[0.001,0.007], g[0.627,0.029]\n",
            ">3166, dr[0.000,0.453], df[0.000,0.012], g[0.541,0.047]\n",
            ">3167, dr[0.000,0.550], df[0.003,0.004], g[0.492,0.027]\n",
            ">3168, dr[0.000,0.303], df[0.000,0.012], g[0.615,0.042]\n",
            ">3169, dr[0.002,0.386], df[0.000,0.006], g[0.726,0.024]\n",
            ">3170, dr[0.000,0.483], df[0.000,0.003], g[0.677,0.040]\n",
            ">3171, dr[0.000,0.433], df[0.000,0.011], g[0.656,0.024]\n",
            ">3172, dr[0.000,0.430], df[0.002,0.003], g[0.805,0.021]\n",
            ">3173, dr[0.000,0.297], df[0.000,0.008], g[0.904,0.028]\n",
            ">3174, dr[0.000,0.351], df[0.000,0.003], g[1.023,0.022]\n",
            ">3175, dr[0.000,0.586], df[0.000,0.008], g[1.043,0.046]\n",
            ">3176, dr[0.000,0.506], df[0.001,0.013], g[1.121,0.020]\n",
            ">3177, dr[0.000,0.571], df[0.000,0.006], g[1.062,0.031]\n",
            ">3178, dr[0.000,0.281], df[0.000,0.015], g[1.106,0.035]\n",
            ">3179, dr[0.000,0.387], df[0.001,0.009], g[1.020,0.025]\n",
            ">3180, dr[0.000,0.675], df[0.000,0.008], g[1.141,0.012]\n",
            ">3181, dr[0.000,0.402], df[0.000,0.006], g[0.961,0.025]\n",
            ">3182, dr[0.000,0.448], df[0.000,0.013], g[0.912,0.031]\n",
            ">3183, dr[0.000,0.518], df[0.001,0.008], g[0.880,0.033]\n",
            ">3184, dr[0.000,0.429], df[0.000,0.005], g[0.945,0.028]\n",
            ">3185, dr[0.000,0.267], df[0.000,0.022], g[1.131,0.049]\n",
            ">3186, dr[0.000,0.339], df[0.000,0.017], g[1.239,0.050]\n",
            ">3187, dr[0.000,0.727], df[0.001,0.008], g[1.119,0.016]\n",
            ">3188, dr[0.000,0.517], df[0.001,0.013], g[1.159,0.036]\n",
            ">3189, dr[0.000,0.665], df[0.002,0.016], g[1.716,0.035]\n",
            ">3190, dr[0.000,0.332], df[0.000,0.012], g[1.586,0.014]\n",
            ">3191, dr[0.000,0.688], df[0.000,0.013], g[1.620,0.034]\n",
            ">3192, dr[0.000,0.687], df[0.001,0.013], g[1.490,0.018]\n",
            ">3193, dr[0.000,0.860], df[0.000,0.017], g[1.514,0.021]\n",
            ">3194, dr[0.000,0.515], df[0.000,0.009], g[1.836,0.022]\n",
            ">3195, dr[0.000,0.649], df[0.000,0.008], g[1.258,0.018]\n",
            ">3196, dr[0.000,0.665], df[0.000,0.020], g[1.038,0.017]\n",
            ">3197, dr[0.000,0.489], df[0.000,0.012], g[1.526,0.032]\n",
            ">3198, dr[0.000,0.370], df[0.001,0.028], g[1.593,0.028]\n",
            ">3199, dr[0.002,0.451], df[0.001,0.009], g[1.662,0.021]\n",
            ">3200, dr[0.001,0.197], df[0.001,0.004], g[1.581,0.013]\n",
            ">3201, dr[0.000,0.458], df[0.000,0.012], g[2.006,0.022]\n",
            ">3202, dr[0.000,0.647], df[0.000,0.018], g[2.040,0.014]\n",
            ">3203, dr[0.000,0.537], df[0.000,0.009], g[1.866,0.029]\n",
            ">3204, dr[0.000,0.638], df[0.002,0.014], g[1.590,0.013]\n",
            ">3205, dr[0.002,0.380], df[0.000,0.019], g[1.701,0.027]\n",
            ">3206, dr[0.000,0.682], df[0.000,0.017], g[1.835,0.043]\n",
            ">3207, dr[0.000,0.211], df[0.000,0.018], g[1.649,0.046]\n",
            ">3208, dr[0.000,0.391], df[0.000,0.008], g[1.425,0.028]\n",
            ">3209, dr[0.000,0.466], df[0.000,0.030], g[1.412,0.017]\n",
            ">3210, dr[0.000,0.437], df[0.000,0.020], g[1.157,0.072]\n",
            ">3211, dr[0.000,0.331], df[0.000,0.018], g[1.469,0.061]\n",
            ">3212, dr[0.001,0.405], df[0.000,0.009], g[1.115,0.038]\n",
            ">3213, dr[0.002,0.422], df[0.000,0.015], g[1.261,0.042]\n",
            ">3214, dr[0.000,0.571], df[0.000,0.009], g[1.194,0.017]\n",
            ">3215, dr[0.000,0.287], df[0.000,0.018], g[1.247,0.012]\n",
            ">3216, dr[0.001,0.423], df[0.000,0.013], g[1.220,0.017]\n",
            ">3217, dr[0.000,0.472], df[0.000,0.012], g[1.336,0.012]\n",
            ">3218, dr[0.001,0.321], df[0.000,0.010], g[1.216,0.015]\n",
            ">3219, dr[0.000,0.514], df[0.000,0.025], g[1.352,0.017]\n",
            ">3220, dr[0.000,0.540], df[0.000,0.011], g[1.217,0.010]\n",
            ">3221, dr[0.001,0.644], df[0.000,0.006], g[1.355,0.043]\n",
            ">3222, dr[0.000,0.674], df[0.000,0.008], g[1.237,0.051]\n",
            ">3223, dr[0.000,0.459], df[0.000,0.010], g[1.321,0.040]\n",
            ">3224, dr[0.001,0.351], df[0.000,0.007], g[1.159,0.032]\n",
            ">3225, dr[0.000,0.466], df[0.000,0.007], g[0.988,0.025]\n",
            ">3226, dr[0.000,0.468], df[0.000,0.005], g[1.337,0.029]\n",
            ">3227, dr[0.000,0.755], df[0.000,0.007], g[1.254,0.021]\n",
            ">3228, dr[0.000,0.617], df[0.000,0.006], g[1.115,0.013]\n",
            ">3229, dr[0.000,0.435], df[0.000,0.002], g[1.090,0.019]\n",
            ">3230, dr[0.000,0.500], df[0.000,0.006], g[1.106,0.009]\n",
            ">3231, dr[0.000,0.347], df[0.000,0.005], g[1.115,0.020]\n",
            ">3232, dr[0.000,0.543], df[0.000,0.006], g[1.196,0.007]\n",
            ">3233, dr[0.000,0.658], df[0.000,0.005], g[0.902,0.012]\n",
            ">3234, dr[0.000,0.932], df[0.000,0.029], g[0.853,0.017]\n",
            ">3235, dr[0.000,0.425], df[0.000,0.008], g[0.864,0.016]\n",
            ">3236, dr[0.000,0.691], df[0.000,0.008], g[1.004,0.014]\n",
            ">3237, dr[0.000,0.451], df[0.000,0.006], g[0.969,0.013]\n",
            ">3238, dr[0.000,0.521], df[0.000,0.005], g[1.052,0.024]\n",
            ">3239, dr[0.000,0.373], df[0.001,0.029], g[0.938,0.084]\n",
            ">3240, dr[0.000,0.448], df[0.000,0.010], g[1.222,0.053]\n",
            ">3241, dr[0.000,0.243], df[0.000,0.007], g[1.091,0.038]\n",
            ">3242, dr[0.000,0.690], df[0.001,0.006], g[1.083,0.020]\n",
            ">3243, dr[0.000,0.314], df[0.003,0.006], g[1.347,0.011]\n",
            ">3244, dr[0.000,0.300], df[0.001,0.007], g[1.825,0.040]\n",
            ">3245, dr[0.000,0.809], df[0.000,0.012], g[1.879,0.011]\n",
            ">3246, dr[0.000,0.537], df[0.000,0.004], g[1.929,0.013]\n",
            ">3247, dr[0.001,0.370], df[0.000,0.003], g[1.799,0.017]\n",
            ">3248, dr[0.000,0.640], df[0.000,0.003], g[1.652,0.017]\n",
            ">3249, dr[0.000,0.731], df[0.000,0.007], g[1.468,0.042]\n",
            ">3250, dr[0.000,0.514], df[0.000,0.002], g[1.315,0.025]\n",
            ">3251, dr[0.000,0.552], df[0.000,0.007], g[1.371,0.027]\n",
            ">3252, dr[0.002,0.218], df[0.000,0.006], g[1.303,0.018]\n",
            ">3253, dr[0.000,0.544], df[0.000,0.006], g[1.510,0.019]\n",
            ">3254, dr[0.001,0.802], df[0.000,0.006], g[1.308,0.044]\n",
            ">3255, dr[0.000,0.333], df[0.000,0.024], g[1.539,0.023]\n",
            ">3256, dr[0.000,0.538], df[0.000,0.004], g[1.584,0.021]\n",
            ">3257, dr[0.000,0.414], df[0.000,0.006], g[1.320,0.021]\n",
            ">3258, dr[0.003,0.472], df[0.000,0.003], g[1.395,0.019]\n",
            ">3259, dr[0.002,0.315], df[0.000,0.017], g[1.339,0.027]\n",
            ">3260, dr[0.002,0.233], df[0.000,0.009], g[1.086,0.015]\n",
            ">3261, dr[0.001,0.464], df[0.000,0.005], g[0.946,0.021]\n",
            ">3262, dr[0.001,0.256], df[0.000,0.004], g[1.001,0.024]\n",
            ">3263, dr[0.001,0.513], df[0.000,0.004], g[0.843,0.036]\n",
            ">3264, dr[0.001,0.590], df[0.000,0.010], g[1.118,0.029]\n",
            ">3265, dr[0.000,0.475], df[0.000,0.004], g[0.943,0.023]\n",
            ">3266, dr[0.000,0.739], df[0.000,0.008], g[1.049,0.034]\n",
            ">3267, dr[0.000,0.504], df[0.000,0.033], g[1.103,0.040]\n",
            ">3268, dr[0.002,0.396], df[0.000,0.003], g[1.262,0.064]\n",
            ">3269, dr[0.000,0.371], df[0.007,0.012], g[1.332,0.043]\n",
            ">3270, dr[0.000,0.412], df[0.000,0.018], g[1.733,0.029]\n",
            ">3271, dr[0.000,0.364], df[0.000,0.007], g[1.512,0.022]\n",
            ">3272, dr[0.001,0.437], df[0.000,0.002], g[1.738,0.025]\n",
            ">3273, dr[0.000,0.478], df[0.000,0.009], g[1.612,0.031]\n",
            ">3274, dr[0.000,0.361], df[0.000,0.013], g[1.803,0.024]\n",
            ">3275, dr[0.003,0.411], df[0.000,0.006], g[1.370,0.023]\n",
            ">3276, dr[0.000,0.345], df[0.000,0.006], g[1.520,0.026]\n",
            ">3277, dr[0.000,0.426], df[0.000,0.005], g[1.289,0.040]\n",
            ">3278, dr[0.000,0.235], df[0.000,0.014], g[1.460,0.032]\n",
            ">3279, dr[0.000,0.511], df[0.000,0.004], g[1.315,0.060]\n",
            ">3280, dr[0.000,0.388], df[0.000,0.015], g[1.299,0.047]\n",
            ">3281, dr[0.000,0.261], df[0.000,0.008], g[1.192,0.016]\n",
            ">3282, dr[0.000,0.352], df[0.000,0.016], g[1.157,0.035]\n",
            ">3283, dr[0.000,0.414], df[0.000,0.010], g[1.168,0.020]\n",
            ">3284, dr[0.000,0.354], df[0.000,0.014], g[1.254,0.037]\n",
            ">3285, dr[0.000,0.305], df[0.000,0.016], g[0.827,0.038]\n",
            ">3286, dr[0.000,0.531], df[0.000,0.010], g[1.297,0.047]\n",
            ">3287, dr[0.001,0.550], df[0.001,0.006], g[1.148,0.041]\n",
            ">3288, dr[0.000,0.889], df[0.000,0.006], g[1.384,0.061]\n",
            ">3289, dr[0.001,0.727], df[0.000,0.016], g[1.342,0.050]\n",
            ">3290, dr[0.000,0.595], df[0.000,0.015], g[0.893,0.076]\n",
            ">3291, dr[0.001,0.443], df[0.000,0.006], g[1.040,0.075]\n",
            ">3292, dr[0.000,0.464], df[0.000,0.015], g[0.955,0.044]\n",
            ">3293, dr[0.001,0.390], df[0.001,0.008], g[0.976,0.029]\n",
            ">3294, dr[0.001,0.368], df[0.000,0.007], g[1.241,0.027]\n",
            ">3295, dr[0.000,1.003], df[0.000,0.026], g[1.108,0.035]\n",
            ">3296, dr[0.000,0.291], df[0.000,0.006], g[1.054,0.057]\n",
            ">3297, dr[0.000,0.464], df[0.000,0.004], g[1.005,0.029]\n",
            ">3298, dr[0.000,0.555], df[0.000,0.004], g[0.996,0.054]\n",
            ">3299, dr[0.000,0.502], df[0.000,0.006], g[0.718,0.040]\n",
            ">3300, dr[0.000,0.483], df[0.000,0.005], g[0.676,0.050]\n",
            ">3301, dr[0.000,0.363], df[0.000,0.003], g[0.739,0.050]\n",
            ">3302, dr[0.000,0.512], df[0.000,0.003], g[0.735,0.034]\n",
            ">3303, dr[0.000,0.581], df[0.000,0.007], g[0.794,0.027]\n",
            ">3304, dr[0.000,0.536], df[0.000,0.005], g[0.753,0.024]\n",
            ">3305, dr[0.000,0.352], df[0.000,0.004], g[1.023,0.021]\n",
            ">3306, dr[0.000,0.250], df[0.000,0.004], g[0.913,0.033]\n",
            ">3307, dr[0.000,0.411], df[0.000,0.003], g[0.919,0.036]\n",
            ">3308, dr[0.000,0.358], df[0.000,0.014], g[1.232,0.020]\n",
            ">3309, dr[0.000,0.478], df[0.000,0.001], g[1.193,0.022]\n",
            ">3310, dr[0.000,0.255], df[0.001,0.005], g[0.921,0.024]\n",
            ">3311, dr[0.000,0.752], df[0.001,0.021], g[0.703,0.028]\n",
            ">3312, dr[0.000,0.477], df[0.000,0.002], g[0.974,0.046]\n",
            ">3313, dr[0.000,0.701], df[0.000,0.004], g[0.949,0.020]\n",
            ">3314, dr[0.000,0.297], df[0.000,0.017], g[1.322,0.038]\n",
            ">3315, dr[0.000,0.368], df[0.000,0.013], g[0.927,0.008]\n",
            ">3316, dr[0.000,0.494], df[0.000,0.005], g[1.097,0.013]\n",
            ">3317, dr[0.000,0.659], df[0.000,0.004], g[1.314,0.018]\n",
            ">3318, dr[0.001,0.243], df[0.000,0.008], g[1.340,0.023]\n",
            ">3319, dr[0.001,0.498], df[0.000,0.005], g[1.301,0.012]\n",
            ">3320, dr[0.000,0.393], df[0.000,0.004], g[1.365,0.027]\n",
            ">3321, dr[0.001,0.340], df[0.000,0.006], g[1.186,0.024]\n",
            ">3322, dr[0.000,0.330], df[0.000,0.008], g[1.245,0.013]\n",
            ">3323, dr[0.000,0.595], df[0.001,0.005], g[1.083,0.030]\n",
            ">3324, dr[0.000,0.641], df[0.000,0.003], g[1.122,0.018]\n",
            ">3325, dr[0.001,0.390], df[0.000,0.007], g[1.035,0.017]\n",
            ">3326, dr[0.000,0.490], df[0.000,0.005], g[0.843,0.019]\n",
            ">3327, dr[0.000,0.360], df[0.001,0.003], g[1.013,0.023]\n",
            ">3328, dr[0.000,0.327], df[0.000,0.006], g[0.896,0.010]\n",
            ">3329, dr[0.000,0.259], df[0.000,0.002], g[0.702,0.012]\n",
            ">3330, dr[0.000,0.655], df[0.001,0.005], g[1.138,0.011]\n",
            ">3331, dr[0.000,0.605], df[0.000,0.005], g[1.106,0.015]\n",
            ">3332, dr[0.001,0.248], df[0.000,0.003], g[1.180,0.023]\n",
            ">3333, dr[0.000,0.443], df[0.000,0.014], g[1.025,0.016]\n",
            ">3334, dr[0.000,0.473], df[0.001,0.007], g[0.872,0.032]\n",
            ">3335, dr[0.001,0.893], df[0.001,0.015], g[1.006,0.015]\n",
            ">3336, dr[0.000,0.560], df[0.000,0.044], g[1.307,0.051]\n",
            ">3337, dr[0.001,0.548], df[0.001,0.019], g[1.263,0.035]\n",
            ">3338, dr[0.006,0.396], df[0.001,0.007], g[1.579,0.027]\n",
            ">3339, dr[0.000,0.360], df[0.000,0.006], g[1.629,0.020]\n",
            ">3340, dr[0.001,0.547], df[0.000,0.005], g[1.774,0.014]\n",
            ">3341, dr[0.000,0.329], df[0.000,0.003], g[1.780,0.012]\n",
            ">3342, dr[0.000,0.384], df[0.000,0.011], g[1.523,0.059]\n",
            ">3343, dr[0.001,0.352], df[0.000,0.008], g[1.363,0.008]\n",
            ">3344, dr[0.000,0.468], df[0.000,0.010], g[1.477,0.039]\n",
            ">3345, dr[0.000,0.592], df[0.004,0.005], g[2.045,0.031]\n",
            ">3346, dr[0.001,0.281], df[0.000,0.004], g[2.105,0.010]\n",
            ">3347, dr[0.002,0.367], df[0.000,0.006], g[2.130,0.007]\n",
            ">3348, dr[0.001,0.555], df[0.000,0.016], g[2.058,0.031]\n",
            ">3349, dr[0.000,0.321], df[0.001,0.014], g[2.279,0.012]\n",
            ">3350, dr[0.001,0.573], df[0.000,0.022], g[2.129,0.033]\n",
            ">3351, dr[0.000,0.569], df[0.000,0.003], g[2.035,0.010]\n",
            ">3352, dr[0.000,0.270], df[0.000,0.006], g[2.034,0.016]\n",
            ">3353, dr[0.000,0.449], df[0.000,0.009], g[1.864,0.020]\n",
            ">3354, dr[0.001,0.241], df[0.000,0.012], g[2.009,0.023]\n",
            ">3355, dr[0.000,0.462], df[0.000,0.006], g[2.129,0.040]\n",
            ">3356, dr[0.000,0.391], df[0.000,0.014], g[2.003,0.024]\n",
            ">3357, dr[0.001,0.474], df[0.000,0.006], g[1.588,0.024]\n",
            ">3358, dr[0.000,0.398], df[0.000,0.017], g[1.610,0.017]\n",
            ">3359, dr[0.000,0.456], df[0.000,0.006], g[1.238,0.020]\n",
            ">3360, dr[0.001,0.353], df[0.000,0.019], g[1.273,0.054]\n",
            ">3361, dr[0.000,0.345], df[0.001,0.040], g[1.089,0.059]\n",
            ">3362, dr[0.002,0.446], df[0.000,0.013], g[1.198,0.018]\n",
            ">3363, dr[0.000,0.324], df[0.000,0.009], g[1.318,0.046]\n",
            ">3364, dr[0.001,0.389], df[0.000,0.006], g[1.209,0.015]\n",
            ">3365, dr[0.000,0.673], df[0.000,0.010], g[1.168,0.036]\n",
            ">3366, dr[0.000,0.272], df[0.000,0.011], g[1.053,0.029]\n",
            ">3367, dr[0.000,0.333], df[0.000,0.007], g[1.251,0.027]\n",
            ">3368, dr[0.001,0.561], df[0.000,0.018], g[1.258,0.021]\n",
            ">3369, dr[0.000,0.544], df[0.000,0.022], g[1.034,0.028]\n",
            ">3370, dr[0.000,0.371], df[0.001,0.006], g[1.234,0.044]\n",
            ">3371, dr[0.000,0.470], df[0.000,0.011], g[1.326,0.039]\n",
            ">3372, dr[0.000,0.363], df[0.000,0.009], g[1.659,0.076]\n",
            ">3373, dr[0.001,1.196], df[0.000,0.014], g[1.836,0.052]\n",
            ">3374, dr[0.000,0.490], df[0.000,0.013], g[1.470,0.035]\n",
            ">3375, dr[0.002,0.519], df[0.000,0.014], g[1.250,0.032]\n",
            ">3376, dr[0.000,0.551], df[0.002,0.003], g[1.548,0.030]\n",
            ">3377, dr[0.000,0.667], df[0.004,0.007], g[1.671,0.037]\n",
            ">3378, dr[0.001,0.570], df[0.001,0.010], g[1.401,0.069]\n",
            ">3379, dr[0.000,0.951], df[0.000,0.020], g[1.526,0.043]\n",
            ">3380, dr[0.000,0.429], df[0.000,0.027], g[1.660,0.036]\n",
            ">3381, dr[0.000,0.390], df[0.000,0.013], g[1.639,0.067]\n",
            ">3382, dr[0.000,0.642], df[0.000,0.012], g[1.297,0.046]\n",
            ">3383, dr[0.000,0.273], df[0.000,0.010], g[1.644,0.064]\n",
            ">3384, dr[0.000,0.357], df[0.001,0.018], g[1.626,0.056]\n",
            ">3385, dr[0.001,0.418], df[0.000,0.007], g[1.992,0.034]\n",
            ">3386, dr[0.000,0.387], df[0.000,0.008], g[1.877,0.034]\n",
            ">3387, dr[0.001,0.508], df[0.000,0.044], g[1.693,0.025]\n",
            ">3388, dr[0.000,0.403], df[0.000,0.008], g[1.958,0.057]\n",
            ">3389, dr[0.000,0.340], df[0.000,0.016], g[1.781,0.036]\n",
            ">3390, dr[0.003,0.643], df[0.000,0.012], g[1.235,0.029]\n",
            ">3391, dr[0.000,0.585], df[0.000,0.011], g[1.521,0.045]\n",
            ">3392, dr[0.000,0.537], df[0.001,0.012], g[1.487,0.041]\n",
            ">3393, dr[0.000,0.454], df[0.000,0.028], g[1.687,0.034]\n",
            ">3394, dr[0.002,0.345], df[0.001,0.018], g[1.356,0.024]\n",
            ">3395, dr[0.000,0.866], df[0.006,0.017], g[1.745,0.128]\n",
            ">3396, dr[0.001,0.555], df[0.001,0.022], g[2.098,0.094]\n",
            ">3397, dr[0.003,0.478], df[0.000,0.017], g[2.257,0.094]\n",
            ">3398, dr[0.000,0.597], df[0.000,0.010], g[1.788,0.044]\n",
            ">3399, dr[0.000,0.508], df[0.001,0.008], g[1.485,0.047]\n",
            ">3400, dr[0.000,0.408], df[0.000,0.017], g[1.478,0.031]\n",
            ">3401, dr[0.000,0.655], df[0.002,0.008], g[1.724,0.043]\n",
            ">3402, dr[0.000,0.297], df[0.000,0.007], g[1.949,0.034]\n",
            ">3403, dr[0.001,0.675], df[0.000,0.005], g[1.753,0.045]\n",
            ">3404, dr[0.000,0.287], df[0.000,0.011], g[0.987,0.069]\n",
            ">3405, dr[0.000,0.404], df[0.001,0.015], g[0.778,0.030]\n",
            ">3406, dr[0.000,0.472], df[0.002,0.010], g[0.482,0.037]\n",
            ">3407, dr[0.000,0.371], df[0.005,0.012], g[1.102,0.015]\n",
            ">3408, dr[0.000,0.499], df[0.000,0.008], g[1.167,0.025]\n",
            ">3409, dr[0.004,0.348], df[0.000,0.006], g[1.165,0.043]\n",
            ">3410, dr[0.000,0.375], df[0.000,0.005], g[1.393,0.045]\n",
            ">3411, dr[0.001,0.195], df[0.000,0.009], g[1.072,0.037]\n",
            ">3412, dr[0.000,0.309], df[0.000,0.007], g[0.843,0.024]\n",
            ">3413, dr[0.000,0.274], df[0.001,0.006], g[0.730,0.028]\n",
            ">3414, dr[0.000,0.716], df[0.003,0.008], g[0.692,0.041]\n",
            ">3415, dr[0.003,0.441], df[0.000,0.006], g[1.008,0.038]\n",
            ">3416, dr[0.002,0.389], df[0.003,0.008], g[1.309,0.026]\n",
            ">3417, dr[0.001,0.409], df[0.000,0.003], g[1.813,0.033]\n",
            ">3418, dr[0.007,0.454], df[0.000,0.004], g[1.519,0.048]\n",
            ">3419, dr[0.001,0.314], df[0.000,0.004], g[1.525,0.067]\n",
            ">3420, dr[0.002,0.329], df[0.000,0.005], g[1.343,0.131]\n",
            ">3421, dr[0.000,0.539], df[0.000,0.007], g[1.348,0.085]\n",
            ">3422, dr[0.001,0.475], df[0.000,0.029], g[2.220,0.065]\n",
            ">3423, dr[0.001,0.515], df[0.000,0.007], g[1.794,0.058]\n",
            ">3424, dr[0.001,0.497], df[0.003,0.017], g[1.714,0.092]\n",
            ">3425, dr[0.001,0.585], df[0.006,0.047], g[3.014,0.059]\n",
            ">3426, dr[0.001,0.437], df[0.000,0.013], g[3.158,0.036]\n",
            ">3427, dr[0.005,0.546], df[0.000,0.011], g[2.472,0.107]\n",
            ">3428, dr[0.000,0.667], df[0.001,0.005], g[1.740,0.074]\n",
            ">3429, dr[0.003,0.641], df[0.000,0.008], g[1.338,0.162]\n",
            ">3430, dr[0.002,0.545], df[0.000,0.010], g[1.552,0.110]\n",
            ">3431, dr[0.001,0.284], df[0.001,0.004], g[1.218,0.109]\n",
            ">3432, dr[0.001,0.402], df[0.001,0.002], g[1.493,0.093]\n",
            ">3433, dr[0.000,0.227], df[0.000,0.004], g[1.600,0.036]\n",
            ">3434, dr[0.000,0.435], df[0.008,0.009], g[2.826,0.015]\n",
            ">3435, dr[0.000,0.508], df[0.000,0.016], g[4.560,0.017]\n",
            ">3436, dr[0.003,0.437], df[0.000,0.039], g[2.701,0.021]\n",
            ">3437, dr[0.001,0.371], df[0.000,0.005], g[2.709,0.035]\n",
            ">3438, dr[0.000,0.741], df[0.000,0.004], g[3.259,0.027]\n",
            ">3439, dr[0.000,0.344], df[0.000,0.005], g[3.503,0.021]\n",
            ">3440, dr[0.001,0.815], df[0.000,0.006], g[3.092,0.010]\n",
            ">3441, dr[0.000,0.561], df[0.000,0.007], g[2.743,0.020]\n",
            ">3442, dr[0.001,0.359], df[0.000,0.009], g[2.349,0.066]\n",
            ">3443, dr[0.002,0.324], df[0.000,0.051], g[1.722,0.027]\n",
            ">3444, dr[0.001,0.790], df[0.000,0.020], g[2.103,0.014]\n",
            ">3445, dr[0.001,0.587], df[0.000,0.026], g[2.663,0.039]\n",
            ">3446, dr[0.001,0.537], df[0.000,0.024], g[2.436,0.020]\n",
            ">3447, dr[0.000,0.609], df[0.000,0.017], g[2.134,0.027]\n",
            ">3448, dr[0.001,0.629], df[0.000,0.053], g[1.310,0.078]\n",
            ">3449, dr[0.000,0.356], df[0.009,0.021], g[2.897,0.012]\n",
            ">3450, dr[0.001,0.330], df[0.000,0.009], g[4.157,0.019]\n",
            ">3451, dr[0.000,0.356], df[0.000,0.010], g[3.800,0.017]\n",
            ">3452, dr[0.002,0.531], df[0.002,0.009], g[2.569,0.020]\n",
            ">3453, dr[0.006,0.352], df[0.050,0.109], g[5.924,0.055]\n",
            ">3454, dr[0.006,0.297], df[0.000,0.032], g[6.839,0.053]\n",
            ">3455, dr[0.017,0.155], df[0.000,0.020], g[4.917,0.142]\n",
            ">3456, dr[0.162,0.711], df[0.000,0.006], g[0.194,0.276]\n",
            ">3457, dr[0.000,0.389], df[0.000,0.016], g[0.090,0.204]\n",
            ">3458, dr[0.000,0.300], df[0.000,0.032], g[0.091,0.096]\n",
            ">3459, dr[0.000,0.570], df[0.000,0.032], g[0.238,0.032]\n",
            ">3460, dr[0.000,0.464], df[0.000,0.004], g[0.140,0.029]\n",
            ">3461, dr[0.000,0.367], df[0.000,0.003], g[0.157,0.010]\n",
            ">3462, dr[0.000,0.460], df[0.000,0.002], g[0.217,0.032]\n",
            ">3463, dr[0.000,0.339], df[0.000,0.004], g[0.326,0.072]\n",
            ">3464, dr[0.003,0.561], df[0.000,0.007], g[0.288,0.025]\n",
            ">3465, dr[0.000,0.376], df[0.000,0.004], g[0.362,0.047]\n",
            ">3466, dr[0.000,0.393], df[0.000,0.003], g[0.442,0.045]\n",
            ">3467, dr[0.000,0.629], df[0.000,0.009], g[0.270,0.042]\n",
            ">3468, dr[0.000,0.768], df[0.000,0.025], g[0.235,0.025]\n",
            ">3469, dr[0.000,0.242], df[0.000,0.017], g[0.214,0.067]\n",
            ">3470, dr[0.001,0.645], df[0.000,0.007], g[0.255,0.042]\n",
            ">3471, dr[0.000,0.443], df[0.000,0.004], g[0.149,0.022]\n",
            ">3472, dr[0.000,0.706], df[0.000,0.028], g[0.095,0.032]\n",
            ">3473, dr[0.000,0.693], df[0.000,0.005], g[0.120,0.031]\n",
            ">3474, dr[0.000,0.247], df[0.000,0.008], g[0.116,0.020]\n",
            ">3475, dr[0.000,0.515], df[0.000,0.004], g[0.119,0.017]\n",
            ">3476, dr[0.000,0.675], df[0.000,0.004], g[0.125,0.018]\n",
            ">3477, dr[0.000,0.525], df[0.000,0.013], g[0.106,0.032]\n",
            ">3478, dr[0.000,0.642], df[0.000,0.004], g[0.102,0.021]\n",
            ">3479, dr[0.000,0.401], df[0.000,0.004], g[0.204,0.024]\n",
            ">3480, dr[0.001,0.553], df[0.000,0.016], g[0.240,0.007]\n",
            ">3481, dr[0.000,0.337], df[0.000,0.008], g[0.378,0.020]\n",
            ">3482, dr[0.000,0.306], df[0.000,0.012], g[0.242,0.035]\n",
            ">3483, dr[0.000,0.300], df[0.000,0.006], g[0.283,0.038]\n",
            ">3484, dr[0.000,0.496], df[0.000,0.008], g[0.340,0.084]\n",
            ">3485, dr[0.000,0.443], df[0.000,0.026], g[0.347,0.032]\n",
            ">3486, dr[0.000,0.370], df[0.000,0.020], g[0.429,0.034]\n",
            ">3487, dr[0.000,0.263], df[0.000,0.015], g[0.201,0.039]\n",
            ">3488, dr[0.000,0.435], df[0.000,0.034], g[0.173,0.035]\n",
            ">3489, dr[0.000,0.357], df[0.000,0.012], g[0.234,0.035]\n",
            ">3490, dr[0.000,0.477], df[0.000,0.014], g[0.256,0.028]\n",
            ">3491, dr[0.000,0.720], df[0.000,0.007], g[0.214,0.039]\n",
            ">3492, dr[0.000,0.304], df[0.000,0.029], g[0.291,0.082]\n",
            ">3493, dr[0.000,0.600], df[0.000,0.014], g[0.330,0.054]\n",
            ">3494, dr[0.000,0.317], df[0.000,0.007], g[0.232,0.030]\n",
            ">3495, dr[0.000,0.526], df[0.000,0.014], g[0.281,0.042]\n",
            ">3496, dr[0.000,0.298], df[0.000,0.057], g[0.293,0.054]\n",
            ">3497, dr[0.000,0.448], df[0.000,0.037], g[0.187,0.041]\n",
            ">3498, dr[0.000,0.477], df[0.000,0.039], g[0.178,0.053]\n",
            ">3499, dr[0.001,0.447], df[0.000,0.034], g[0.265,0.046]\n",
            ">3500, dr[0.000,0.343], df[0.000,0.056], g[0.246,0.054]\n",
            ">3501, dr[0.000,0.283], df[0.000,0.042], g[0.338,0.082]\n",
            ">3502, dr[0.000,0.586], df[0.000,0.048], g[0.298,0.049]\n",
            ">3503, dr[0.000,0.529], df[0.000,0.023], g[0.339,0.051]\n",
            ">3504, dr[0.000,0.635], df[0.000,0.016], g[0.238,0.096]\n",
            ">3505, dr[0.000,0.648], df[0.000,0.023], g[0.169,0.094]\n",
            ">3506, dr[0.000,0.528], df[0.000,0.015], g[0.274,0.160]\n",
            ">3507, dr[0.000,0.436], df[0.000,0.007], g[0.417,0.028]\n",
            ">3508, dr[0.000,0.824], df[0.000,0.030], g[0.452,0.060]\n",
            ">3509, dr[0.000,0.383], df[0.000,0.016], g[0.684,0.043]\n",
            ">3510, dr[0.000,0.923], df[0.000,0.027], g[0.447,0.092]\n",
            ">3511, dr[0.000,0.611], df[0.000,0.035], g[0.326,0.091]\n",
            ">3512, dr[0.000,0.208], df[0.000,0.011], g[0.291,0.080]\n",
            ">3513, dr[0.000,0.364], df[0.000,0.021], g[0.239,0.051]\n",
            ">3514, dr[0.000,0.478], df[0.000,0.040], g[0.191,0.056]\n",
            ">3515, dr[0.000,0.374], df[0.000,0.021], g[0.159,0.039]\n",
            ">3516, dr[0.000,0.514], df[0.000,0.016], g[0.173,0.039]\n",
            ">3517, dr[0.000,0.249], df[0.000,0.016], g[0.099,0.028]\n",
            ">3518, dr[0.000,0.464], df[0.000,0.024], g[0.200,0.023]\n",
            ">3519, dr[0.000,0.528], df[0.000,0.015], g[0.149,0.055]\n",
            ">3520, dr[0.000,0.693], df[0.000,0.018], g[0.132,0.039]\n",
            ">3521, dr[0.000,0.388], df[0.000,0.018], g[0.113,0.038]\n",
            ">3522, dr[0.000,0.392], df[0.001,0.021], g[0.092,0.058]\n",
            ">3523, dr[0.000,0.342], df[0.000,0.035], g[0.091,0.029]\n",
            ">3524, dr[0.000,0.591], df[0.000,0.008], g[0.247,0.040]\n",
            ">3525, dr[0.000,0.338], df[0.000,0.010], g[0.289,0.025]\n",
            ">3526, dr[0.000,0.365], df[0.000,0.014], g[0.253,0.027]\n",
            ">3527, dr[0.000,0.319], df[0.000,0.028], g[0.144,0.034]\n",
            ">3528, dr[0.000,0.201], df[0.000,0.020], g[0.253,0.029]\n",
            ">3529, dr[0.000,0.306], df[0.000,0.029], g[0.107,0.033]\n",
            ">3530, dr[0.003,0.660], df[0.000,0.058], g[0.150,0.073]\n",
            ">3531, dr[0.000,0.660], df[0.000,0.011], g[0.085,0.027]\n",
            ">3532, dr[0.000,0.279], df[0.000,0.032], g[0.129,0.019]\n",
            ">3533, dr[0.000,0.546], df[0.000,0.007], g[0.154,0.022]\n",
            ">3534, dr[0.000,0.469], df[0.000,0.012], g[0.233,0.039]\n",
            ">3535, dr[0.000,0.332], df[0.000,0.018], g[0.295,0.018]\n",
            ">3536, dr[0.000,0.478], df[0.000,0.013], g[0.184,0.026]\n",
            ">3537, dr[0.000,0.368], df[0.000,0.011], g[0.157,0.023]\n",
            ">3538, dr[0.000,0.620], df[0.000,0.027], g[0.130,0.068]\n",
            ">3539, dr[0.000,0.580], df[0.000,0.023], g[0.110,0.066]\n",
            ">3540, dr[0.000,0.758], df[0.000,0.023], g[0.201,0.073]\n",
            ">3541, dr[0.000,0.487], df[0.000,0.027], g[0.224,0.037]\n",
            ">3542, dr[0.000,0.753], df[0.011,0.025], g[0.273,0.026]\n",
            ">3543, dr[0.000,0.516], df[0.000,0.030], g[0.712,0.047]\n",
            ">3544, dr[0.000,0.387], df[0.000,0.067], g[0.606,0.051]\n",
            ">3545, dr[0.000,0.486], df[0.000,0.020], g[0.701,0.030]\n",
            ">3546, dr[0.000,0.299], df[0.000,0.034], g[0.458,0.087]\n",
            ">3547, dr[0.000,0.440], df[0.000,0.029], g[0.784,0.098]\n",
            ">3548, dr[0.000,0.387], df[0.000,0.043], g[1.104,0.037]\n",
            ">3549, dr[0.000,0.265], df[0.000,0.042], g[1.234,0.061]\n",
            ">3550, dr[0.000,0.589], df[0.000,0.053], g[1.483,0.115]\n",
            ">3551, dr[0.000,0.373], df[0.000,0.025], g[1.097,0.111]\n",
            ">3552, dr[0.000,0.211], df[0.000,0.135], g[1.026,0.202]\n",
            ">3553, dr[0.000,0.321], df[0.000,0.040], g[1.027,0.158]\n",
            ">3554, dr[0.000,0.326], df[0.000,0.055], g[0.772,0.166]\n",
            ">3555, dr[0.000,0.349], df[0.000,0.054], g[0.612,0.082]\n",
            ">3556, dr[0.000,0.505], df[0.000,0.022], g[0.658,0.162]\n",
            ">3557, dr[0.000,0.585], df[0.000,0.022], g[0.527,0.071]\n",
            ">3558, dr[0.000,0.480], df[0.000,0.031], g[0.904,0.055]\n",
            ">3559, dr[0.000,0.419], df[0.000,0.043], g[1.050,0.055]\n",
            ">3560, dr[0.000,0.745], df[0.000,0.045], g[0.944,0.144]\n",
            ">3561, dr[0.000,0.597], df[0.000,0.058], g[0.917,0.099]\n",
            ">3562, dr[0.000,0.431], df[0.000,0.072], g[0.691,0.121]\n",
            ">3563, dr[0.000,0.322], df[0.000,0.036], g[0.733,0.141]\n",
            ">3564, dr[0.000,0.442], df[0.000,0.031], g[1.003,0.098]\n",
            ">3565, dr[0.000,0.654], df[0.000,0.024], g[0.973,0.134]\n",
            ">3566, dr[0.000,0.412], df[0.000,0.038], g[0.968,0.114]\n",
            ">3567, dr[0.000,0.427], df[0.000,0.028], g[0.649,0.082]\n",
            ">3568, dr[0.000,0.380], df[0.000,0.020], g[0.498,0.142]\n",
            ">3569, dr[0.000,0.503], df[0.000,0.029], g[0.593,0.101]\n",
            ">3570, dr[0.000,0.533], df[0.000,0.021], g[0.862,0.052]\n",
            ">3571, dr[0.000,0.692], df[0.000,0.045], g[0.536,0.093]\n",
            ">3572, dr[0.000,0.560], df[0.000,0.042], g[0.588,0.122]\n",
            ">3573, dr[0.000,0.427], df[0.000,0.035], g[0.533,0.075]\n",
            ">3574, dr[0.000,0.334], df[0.000,0.038], g[0.455,0.060]\n",
            ">3575, dr[0.000,0.559], df[0.000,0.022], g[0.379,0.107]\n",
            ">3576, dr[0.000,0.505], df[0.000,0.025], g[0.397,0.086]\n",
            ">3577, dr[0.000,0.401], df[0.000,0.007], g[0.328,0.086]\n",
            ">3578, dr[0.000,0.495], df[0.000,0.015], g[0.337,0.048]\n",
            ">3579, dr[0.000,0.800], df[0.000,0.014], g[0.279,0.079]\n",
            ">3580, dr[0.000,0.276], df[0.000,0.046], g[0.239,0.086]\n",
            ">3581, dr[0.000,0.610], df[0.000,0.026], g[0.168,0.134]\n",
            ">3582, dr[0.000,0.413], df[0.000,0.011], g[0.340,0.067]\n",
            ">3583, dr[0.000,0.205], df[0.000,0.037], g[0.220,0.074]\n",
            ">3584, dr[0.000,0.404], df[0.000,0.006], g[0.121,0.157]\n",
            ">3585, dr[0.000,0.513], df[0.000,0.007], g[0.147,0.020]\n",
            ">3586, dr[0.000,0.170], df[0.001,0.024], g[0.209,0.025]\n",
            ">3587, dr[0.000,0.385], df[0.000,0.003], g[0.261,0.028]\n",
            ">3588, dr[0.000,0.344], df[0.003,0.009], g[0.390,0.068]\n",
            ">3589, dr[0.000,0.437], df[0.000,0.008], g[0.431,0.040]\n",
            ">3590, dr[0.000,0.524], df[0.000,0.020], g[0.270,0.050]\n",
            ">3591, dr[0.000,0.417], df[0.000,0.018], g[0.375,0.056]\n",
            ">3592, dr[0.000,0.346], df[0.000,0.008], g[0.375,0.058]\n",
            ">3593, dr[0.000,0.620], df[0.000,0.006], g[0.335,0.089]\n",
            ">3594, dr[0.000,0.392], df[0.000,0.010], g[0.435,0.030]\n",
            ">3595, dr[0.001,0.500], df[0.000,0.012], g[0.743,0.044]\n",
            ">3596, dr[0.000,0.300], df[0.000,0.026], g[0.792,0.084]\n",
            ">3597, dr[0.000,0.379], df[0.000,0.009], g[0.595,0.072]\n",
            ">3598, dr[0.000,0.488], df[0.000,0.011], g[0.713,0.045]\n",
            ">3599, dr[0.000,0.531], df[0.000,0.009], g[0.498,0.063]\n",
            ">3600, dr[0.000,0.552], df[0.000,0.013], g[0.402,0.093]\n",
            ">3601, dr[0.000,0.601], df[0.000,0.007], g[0.430,0.155]\n",
            ">3602, dr[0.000,0.469], df[0.000,0.010], g[0.540,0.071]\n",
            ">3603, dr[0.000,0.763], df[0.000,0.005], g[0.526,0.098]\n",
            ">3604, dr[0.000,0.446], df[0.000,0.008], g[0.615,0.049]\n",
            ">3605, dr[0.000,0.289], df[0.000,0.010], g[0.648,0.054]\n",
            ">3606, dr[0.000,0.414], df[0.000,0.007], g[0.422,0.027]\n",
            ">3607, dr[0.000,0.489], df[0.000,0.003], g[0.457,0.051]\n",
            ">3608, dr[0.000,0.424], df[0.000,0.008], g[0.492,0.041]\n",
            ">3609, dr[0.000,0.377], df[0.000,0.007], g[0.343,0.061]\n",
            ">3610, dr[0.000,0.412], df[0.000,0.013], g[0.250,0.065]\n",
            ">3611, dr[0.000,0.535], df[0.000,0.007], g[0.254,0.085]\n",
            ">3612, dr[0.000,0.579], df[0.000,0.004], g[0.294,0.029]\n",
            ">3613, dr[0.000,0.470], df[0.000,0.005], g[0.243,0.052]\n",
            ">3614, dr[0.000,0.417], df[0.000,0.018], g[0.385,0.044]\n",
            ">3615, dr[0.000,0.425], df[0.000,0.003], g[0.385,0.044]\n",
            ">3616, dr[0.000,0.652], df[0.000,0.003], g[0.340,0.080]\n",
            ">3617, dr[0.000,0.645], df[0.000,0.004], g[0.262,0.106]\n",
            ">3618, dr[0.000,0.448], df[0.000,0.015], g[0.465,0.052]\n",
            ">3619, dr[0.000,0.374], df[0.000,0.004], g[0.456,0.022]\n",
            ">3620, dr[0.000,0.716], df[0.000,0.002], g[1.013,0.031]\n",
            ">3621, dr[0.000,0.498], df[0.000,0.004], g[1.216,0.047]\n",
            ">3622, dr[0.000,0.285], df[0.000,0.005], g[1.048,0.040]\n",
            ">3623, dr[0.000,0.514], df[0.000,0.006], g[0.938,0.086]\n",
            ">3624, dr[0.000,0.292], df[0.000,0.032], g[0.648,0.040]\n",
            ">3625, dr[0.000,0.433], df[0.000,0.011], g[0.404,0.072]\n",
            ">3626, dr[0.000,0.422], df[0.000,0.009], g[0.393,0.092]\n",
            ">3627, dr[0.000,0.483], df[0.000,0.005], g[0.328,0.031]\n",
            ">3628, dr[0.000,0.607], df[0.000,0.007], g[0.369,0.035]\n",
            ">3629, dr[0.000,0.332], df[0.000,0.035], g[0.295,0.068]\n",
            ">3630, dr[0.000,0.506], df[0.000,0.004], g[0.289,0.060]\n",
            ">3631, dr[0.000,0.402], df[0.000,0.005], g[0.303,0.037]\n",
            ">3632, dr[0.000,0.337], df[0.000,0.010], g[0.349,0.120]\n",
            ">3633, dr[0.000,0.429], df[0.000,0.003], g[0.254,0.057]\n",
            ">3634, dr[0.000,0.569], df[0.002,0.009], g[0.483,0.055]\n",
            ">3635, dr[0.000,0.388], df[0.000,0.009], g[0.620,0.027]\n",
            ">3636, dr[0.000,0.302], df[0.000,0.010], g[0.606,0.042]\n",
            ">3637, dr[0.000,0.503], df[0.000,0.005], g[0.556,0.063]\n",
            ">3638, dr[0.000,0.442], df[0.000,0.007], g[0.535,0.019]\n",
            ">3639, dr[0.000,0.460], df[0.000,0.009], g[0.474,0.039]\n",
            ">3640, dr[0.000,0.444], df[0.000,0.006], g[0.369,0.089]\n",
            ">3641, dr[0.002,0.369], df[0.000,0.011], g[0.518,0.045]\n",
            ">3642, dr[0.000,0.530], df[0.000,0.005], g[0.339,0.035]\n",
            ">3643, dr[0.000,0.635], df[0.000,0.010], g[0.314,0.040]\n",
            ">3644, dr[0.000,0.397], df[0.000,0.003], g[0.310,0.053]\n",
            ">3645, dr[0.000,0.396], df[0.000,0.004], g[0.290,0.054]\n",
            ">3646, dr[0.000,0.507], df[0.000,0.005], g[0.234,0.037]\n",
            ">3647, dr[0.000,0.604], df[0.001,0.016], g[0.214,0.041]\n",
            ">3648, dr[0.000,0.574], df[0.001,0.019], g[0.207,0.063]\n",
            ">3649, dr[0.000,0.305], df[0.001,0.005], g[0.304,0.027]\n",
            ">3650, dr[0.000,0.277], df[0.000,0.006], g[0.348,0.026]\n",
            ">3651, dr[0.000,0.684], df[0.001,0.005], g[0.589,0.019]\n",
            ">3652, dr[0.000,1.029], df[0.000,0.003], g[0.509,0.027]\n",
            ">3653, dr[0.000,0.540], df[0.000,0.006], g[0.652,0.037]\n",
            ">3654, dr[0.000,0.577], df[0.000,0.015], g[0.317,0.062]\n",
            ">3655, dr[0.000,0.543], df[0.001,0.011], g[0.329,0.042]\n",
            ">3656, dr[0.000,0.730], df[0.001,0.012], g[0.471,0.066]\n",
            ">3657, dr[0.000,0.609], df[0.000,0.004], g[0.459,0.039]\n",
            ">3658, dr[0.000,0.470], df[0.001,0.018], g[0.475,0.070]\n",
            ">3659, dr[0.000,0.351], df[0.002,0.027], g[0.290,0.097]\n",
            ">3660, dr[0.000,0.487], df[0.001,0.005], g[0.547,0.037]\n",
            ">3661, dr[0.000,0.399], df[0.000,0.003], g[0.451,0.050]\n",
            ">3662, dr[0.000,0.501], df[0.000,0.004], g[0.544,0.027]\n",
            ">3663, dr[0.000,0.451], df[0.000,0.006], g[0.757,0.046]\n",
            ">3664, dr[0.000,0.417], df[0.000,0.010], g[0.711,0.054]\n",
            ">3665, dr[0.000,0.479], df[0.000,0.010], g[0.581,0.043]\n",
            ">3666, dr[0.000,0.608], df[0.000,0.006], g[0.574,0.051]\n",
            ">3667, dr[0.000,0.275], df[0.001,0.011], g[0.498,0.079]\n",
            ">3668, dr[0.000,0.418], df[0.002,0.005], g[0.402,0.054]\n",
            ">3669, dr[0.000,0.531], df[0.001,0.006], g[0.646,0.080]\n",
            ">3670, dr[0.000,0.497], df[0.000,0.006], g[0.875,0.049]\n",
            ">3671, dr[0.000,0.496], df[0.000,0.028], g[0.676,0.045]\n",
            ">3672, dr[0.000,0.560], df[0.000,0.019], g[0.853,0.051]\n",
            ">3673, dr[0.003,0.749], df[0.001,0.002], g[0.459,0.140]\n",
            ">3674, dr[0.001,0.507], df[0.001,0.004], g[0.753,0.040]\n",
            ">3675, dr[0.001,0.309], df[0.002,0.004], g[0.850,0.044]\n",
            ">3676, dr[0.000,0.598], df[0.001,0.018], g[0.934,0.031]\n",
            ">3677, dr[0.001,0.554], df[0.002,0.017], g[1.492,0.068]\n",
            ">3678, dr[0.001,0.432], df[0.001,0.029], g[1.188,0.064]\n",
            ">3679, dr[0.004,0.510], df[0.003,0.014], g[1.930,0.097]\n",
            ">3680, dr[0.000,0.732], df[0.001,0.011], g[2.107,0.145]\n",
            ">3681, dr[0.000,0.591], df[0.001,0.010], g[1.639,0.078]\n",
            ">3682, dr[0.001,0.431], df[0.000,0.015], g[2.238,0.081]\n",
            ">3683, dr[0.000,0.488], df[0.001,0.004], g[1.024,0.060]\n",
            ">3684, dr[0.000,0.507], df[0.000,0.006], g[1.056,0.080]\n",
            ">3685, dr[0.000,0.581], df[0.000,0.006], g[1.451,0.064]\n",
            ">3686, dr[0.003,0.151], df[0.000,0.013], g[1.049,0.050]\n",
            ">3687, dr[0.001,0.319], df[0.001,0.006], g[0.918,0.048]\n",
            ">3688, dr[0.001,0.323], df[0.000,0.011], g[0.974,0.065]\n",
            ">3689, dr[0.003,0.488], df[0.001,0.003], g[0.898,0.095]\n",
            ">3690, dr[0.000,0.373], df[0.000,0.003], g[0.947,0.135]\n",
            ">3691, dr[0.002,0.282], df[0.001,0.001], g[1.438,0.054]\n",
            ">3692, dr[0.001,0.503], df[0.001,0.001], g[1.189,0.029]\n",
            ">3693, dr[0.001,0.509], df[0.001,0.002], g[1.350,0.037]\n",
            ">3694, dr[0.000,0.503], df[0.000,0.008], g[1.632,0.093]\n",
            ">3695, dr[0.000,0.576], df[0.000,0.007], g[1.779,0.092]\n",
            ">3696, dr[0.000,0.590], df[0.000,0.006], g[0.879,0.112]\n",
            ">3697, dr[0.000,0.490], df[0.002,0.005], g[0.664,0.058]\n",
            ">3698, dr[0.000,0.360], df[0.001,0.002], g[0.680,0.099]\n",
            ">3699, dr[0.001,0.265], df[0.005,0.003], g[1.211,0.033]\n",
            ">3700, dr[0.001,0.750], df[0.002,0.011], g[2.245,0.043]\n",
            ">3701, dr[0.001,0.465], df[0.000,0.015], g[2.044,0.112]\n",
            ">3702, dr[0.005,0.583], df[0.000,0.006], g[1.633,0.152]\n",
            ">3703, dr[0.000,0.230], df[0.000,0.012], g[1.080,0.108]\n",
            ">3704, dr[0.000,0.413], df[0.002,0.010], g[0.953,0.066]\n",
            ">3705, dr[0.000,0.325], df[0.001,0.015], g[1.421,0.062]\n",
            ">3706, dr[0.001,0.468], df[0.000,0.016], g[1.037,0.062]\n",
            ">3707, dr[0.000,0.522], df[0.002,0.003], g[1.756,0.075]\n",
            ">3708, dr[0.000,0.485], df[0.000,0.007], g[2.125,0.051]\n",
            ">3709, dr[0.000,0.575], df[0.000,0.013], g[3.058,0.034]\n",
            ">3710, dr[0.001,0.491], df[0.001,0.009], g[2.809,0.100]\n",
            ">3711, dr[0.000,0.669], df[0.000,0.004], g[3.123,0.131]\n",
            ">3712, dr[0.000,0.215], df[0.001,0.004], g[3.181,0.059]\n",
            ">3713, dr[0.000,0.373], df[0.000,0.005], g[3.283,0.063]\n",
            ">3714, dr[0.004,0.402], df[0.001,0.002], g[2.103,0.029]\n",
            ">3715, dr[0.000,0.442], df[0.004,0.006], g[2.482,0.075]\n",
            ">3716, dr[0.001,0.923], df[0.014,0.018], g[4.470,0.142]\n",
            ">3717, dr[0.002,0.406], df[0.000,0.006], g[6.688,0.068]\n",
            ">3718, dr[0.006,0.494], df[0.000,0.012], g[5.443,0.088]\n",
            ">3719, dr[0.001,0.470], df[0.001,0.004], g[4.189,0.163]\n",
            ">3720, dr[0.001,0.479], df[0.000,0.005], g[3.475,0.071]\n",
            ">3721, dr[0.001,0.412], df[0.003,0.004], g[3.152,0.097]\n",
            ">3722, dr[0.003,0.316], df[0.002,0.005], g[2.605,0.139]\n",
            ">3723, dr[0.015,0.444], df[0.026,0.006], g[4.608,0.116]\n",
            ">3724, dr[0.002,0.494], df[0.000,0.002], g[6.893,0.089]\n",
            ">3725, dr[0.001,0.351], df[0.000,0.006], g[6.913,0.128]\n",
            ">3726, dr[0.006,0.487], df[0.000,0.008], g[6.614,0.174]\n",
            ">3727, dr[0.023,0.414], df[0.000,0.002], g[4.508,0.306]\n",
            ">3728, dr[0.000,0.357], df[0.000,0.001], g[3.581,0.172]\n",
            ">3729, dr[0.001,0.511], df[0.000,0.003], g[3.569,0.071]\n",
            ">3730, dr[0.003,0.763], df[0.000,0.003], g[3.043,0.077]\n",
            ">3731, dr[0.001,0.250], df[0.000,0.002], g[2.304,0.106]\n",
            ">3732, dr[0.000,0.317], df[0.000,0.001], g[2.444,0.149]\n",
            ">3733, dr[0.001,0.588], df[0.000,0.005], g[1.975,0.069]\n",
            ">3734, dr[0.000,0.514], df[0.000,0.002], g[1.709,0.108]\n",
            ">3735, dr[0.000,0.338], df[0.000,0.002], g[1.572,0.073]\n",
            ">3736, dr[0.000,0.277], df[0.000,0.002], g[1.345,0.107]\n",
            ">3737, dr[0.000,0.541], df[0.000,0.009], g[1.850,0.097]\n",
            ">3738, dr[0.000,0.164], df[0.000,0.005], g[1.843,0.141]\n",
            ">3739, dr[0.000,0.463], df[0.000,0.005], g[1.940,0.114]\n",
            ">3740, dr[0.000,0.395], df[0.000,0.004], g[1.902,0.066]\n",
            ">3741, dr[0.000,0.458], df[0.000,0.009], g[1.937,0.074]\n",
            ">3742, dr[0.000,0.444], df[0.000,0.002], g[2.399,0.079]\n",
            ">3743, dr[0.001,0.620], df[0.000,0.004], g[1.885,0.071]\n",
            ">3744, dr[0.002,0.374], df[0.000,0.005], g[1.710,0.139]\n",
            ">3745, dr[0.000,0.681], df[0.000,0.007], g[2.112,0.101]\n",
            ">3746, dr[0.000,0.370], df[0.000,0.003], g[2.374,0.117]\n",
            ">3747, dr[0.000,0.388], df[0.000,0.004], g[1.933,0.067]\n",
            ">3748, dr[0.002,0.306], df[0.000,0.009], g[2.003,0.084]\n",
            ">3749, dr[0.000,0.742], df[0.000,0.006], g[1.508,0.155]\n",
            ">3750, dr[0.000,0.780], df[0.000,0.002], g[1.719,0.065]\n",
            ">3751, dr[0.000,0.576], df[0.000,0.004], g[2.399,0.085]\n",
            ">3752, dr[0.000,0.475], df[0.000,0.022], g[2.430,0.084]\n",
            ">3753, dr[0.000,0.815], df[0.000,0.011], g[2.761,0.080]\n",
            ">3754, dr[0.000,1.011], df[0.000,0.007], g[2.370,0.065]\n",
            ">3755, dr[0.000,0.959], df[0.000,0.006], g[1.392,0.091]\n",
            ">3756, dr[0.000,0.335], df[0.000,0.013], g[1.183,0.068]\n",
            ">3757, dr[0.000,0.512], df[0.000,0.005], g[1.102,0.029]\n",
            ">3758, dr[0.000,0.420], df[0.000,0.055], g[1.427,0.060]\n",
            ">3759, dr[0.000,0.634], df[0.000,0.015], g[0.812,0.074]\n",
            ">3760, dr[0.000,0.629], df[0.000,0.010], g[1.262,0.035]\n",
            ">3761, dr[0.006,0.206], df[0.000,0.013], g[0.954,0.062]\n",
            ">3762, dr[0.001,0.491], df[0.000,0.035], g[1.332,0.090]\n",
            ">3763, dr[0.000,0.375], df[0.000,0.016], g[1.144,0.069]\n",
            ">3764, dr[0.000,0.518], df[0.000,0.013], g[1.681,0.067]\n",
            ">3765, dr[0.000,0.554], df[0.000,0.022], g[1.037,0.049]\n",
            ">3766, dr[0.000,0.505], df[0.000,0.018], g[1.597,0.061]\n",
            ">3767, dr[0.000,0.460], df[0.000,0.009], g[1.178,0.055]\n",
            ">3768, dr[0.000,0.335], df[0.000,0.012], g[1.789,0.031]\n",
            ">3769, dr[0.000,0.472], df[0.000,0.012], g[1.532,0.053]\n",
            ">3770, dr[0.000,0.470], df[0.000,0.011], g[1.307,0.084]\n",
            ">3771, dr[0.000,0.379], df[0.000,0.031], g[1.218,0.073]\n",
            ">3772, dr[0.000,0.534], df[0.002,0.022], g[1.437,0.047]\n",
            ">3773, dr[0.000,0.544], df[0.000,0.015], g[1.633,0.060]\n",
            ">3774, dr[0.000,0.676], df[0.000,0.024], g[2.011,0.073]\n",
            ">3775, dr[0.000,0.333], df[0.000,0.053], g[1.862,0.074]\n",
            ">3776, dr[0.000,0.318], df[0.000,0.015], g[1.680,0.053]\n",
            ">3777, dr[0.000,0.348], df[0.000,0.028], g[2.075,0.068]\n",
            ">3778, dr[0.000,0.478], df[0.000,0.031], g[1.650,0.073]\n",
            ">3779, dr[0.000,0.309], df[0.000,0.031], g[1.640,0.049]\n",
            ">3780, dr[0.000,0.227], df[0.001,0.032], g[1.624,0.040]\n",
            ">3781, dr[0.000,0.432], df[0.000,0.012], g[1.171,0.111]\n",
            ">3782, dr[0.000,0.343], df[0.000,0.047], g[1.201,0.061]\n",
            ">3783, dr[0.000,0.533], df[0.000,0.010], g[1.139,0.093]\n",
            ">3784, dr[0.000,0.364], df[0.000,0.046], g[0.808,0.049]\n",
            ">3785, dr[0.000,0.565], df[0.000,0.018], g[0.849,0.046]\n",
            ">3786, dr[0.000,0.610], df[0.000,0.016], g[0.561,0.035]\n",
            ">3787, dr[0.000,0.411], df[0.000,0.012], g[0.986,0.060]\n",
            ">3788, dr[0.000,0.377], df[0.000,0.017], g[1.355,0.030]\n",
            ">3789, dr[0.000,0.388], df[0.000,0.032], g[0.723,0.059]\n",
            ">3790, dr[0.000,0.508], df[0.000,0.051], g[0.677,0.071]\n",
            ">3791, dr[0.000,0.424], df[0.000,0.031], g[0.895,0.063]\n",
            ">3792, dr[0.000,0.554], df[0.000,0.042], g[0.905,0.057]\n",
            ">3793, dr[0.000,0.515], df[0.000,0.026], g[0.812,0.070]\n",
            ">3794, dr[0.001,0.481], df[0.000,0.022], g[0.459,0.051]\n",
            ">3795, dr[0.000,0.198], df[0.001,0.013], g[0.821,0.078]\n",
            ">3796, dr[0.000,0.481], df[0.000,0.015], g[0.822,0.049]\n",
            ">3797, dr[0.000,0.120], df[0.000,0.013], g[0.577,0.104]\n",
            ">3798, dr[0.000,0.518], df[0.000,0.027], g[0.323,0.083]\n",
            ">3799, dr[0.000,0.492], df[0.000,0.021], g[0.308,0.074]\n",
            ">3800, dr[0.000,0.275], df[0.000,0.038], g[0.183,0.053]\n",
            ">3801, dr[0.000,0.461], df[0.000,0.020], g[0.205,0.057]\n",
            ">3802, dr[0.000,0.287], df[0.000,0.010], g[0.230,0.032]\n",
            ">3803, dr[0.000,0.666], df[0.000,0.009], g[0.219,0.027]\n",
            ">3804, dr[0.000,0.212], df[0.000,0.017], g[0.254,0.019]\n",
            ">3805, dr[0.000,0.613], df[0.000,0.025], g[0.208,0.030]\n",
            ">3806, dr[0.000,0.282], df[0.000,0.012], g[0.193,0.019]\n",
            ">3807, dr[0.000,0.475], df[0.000,0.020], g[0.160,0.037]\n",
            ">3808, dr[0.000,0.495], df[0.000,0.012], g[0.187,0.040]\n",
            ">3809, dr[0.000,0.619], df[0.000,0.015], g[0.333,0.027]\n",
            ">3810, dr[0.001,0.727], df[0.000,0.030], g[0.162,0.013]\n",
            ">3811, dr[0.000,0.417], df[0.000,0.008], g[0.139,0.011]\n",
            ">3812, dr[0.000,0.201], df[0.000,0.026], g[0.121,0.012]\n",
            ">3813, dr[0.000,0.563], df[0.000,0.004], g[0.128,0.028]\n",
            ">3814, dr[0.000,0.716], df[0.000,0.021], g[0.163,0.023]\n",
            ">3815, dr[0.000,0.498], df[0.000,0.023], g[0.190,0.023]\n",
            ">3816, dr[0.000,0.896], df[0.000,0.012], g[0.155,0.031]\n",
            ">3817, dr[0.000,0.257], df[0.000,0.002], g[0.190,0.039]\n",
            ">3818, dr[0.000,0.386], df[0.000,0.010], g[0.137,0.017]\n",
            ">3819, dr[0.000,0.629], df[0.000,0.049], g[0.172,0.029]\n",
            ">3820, dr[0.000,0.394], df[0.000,0.013], g[0.154,0.019]\n",
            ">3821, dr[0.000,0.562], df[0.000,0.005], g[0.132,0.015]\n",
            ">3822, dr[0.000,0.475], df[0.000,0.015], g[0.137,0.024]\n",
            ">3823, dr[0.000,0.366], df[0.000,0.006], g[0.117,0.031]\n",
            ">3824, dr[0.000,0.350], df[0.001,0.004], g[0.184,0.025]\n",
            ">3825, dr[0.000,0.672], df[0.000,0.022], g[0.205,0.035]\n",
            ">3826, dr[0.000,0.469], df[0.000,0.010], g[0.421,0.028]\n",
            ">3827, dr[0.000,0.461], df[0.000,0.007], g[0.329,0.037]\n",
            ">3828, dr[0.000,0.492], df[0.000,0.005], g[0.280,0.026]\n",
            ">3829, dr[0.000,0.584], df[0.000,0.013], g[0.288,0.011]\n",
            ">3830, dr[0.000,0.902], df[0.000,0.005], g[0.395,0.015]\n",
            ">3831, dr[0.000,0.507], df[0.000,0.005], g[0.498,0.024]\n",
            ">3832, dr[0.000,0.414], df[0.000,0.007], g[0.410,0.057]\n",
            ">3833, dr[0.000,0.275], df[0.000,0.009], g[0.395,0.020]\n",
            ">3834, dr[0.000,0.319], df[0.000,0.006], g[0.550,0.024]\n",
            ">3835, dr[0.000,0.797], df[0.000,0.010], g[0.518,0.022]\n",
            ">3836, dr[0.000,0.585], df[0.000,0.013], g[0.538,0.035]\n",
            ">3837, dr[0.000,0.456], df[0.000,0.006], g[0.541,0.046]\n",
            ">3838, dr[0.000,0.484], df[0.000,0.018], g[0.645,0.047]\n",
            ">3839, dr[0.000,0.846], df[0.000,0.013], g[0.544,0.039]\n",
            ">3840, dr[0.004,0.740], df[0.000,0.018], g[0.574,0.030]\n",
            ">3841, dr[0.000,0.483], df[0.000,0.017], g[0.547,0.032]\n",
            ">3842, dr[0.000,0.272], df[0.000,0.011], g[0.508,0.028]\n",
            ">3843, dr[0.000,0.294], df[0.000,0.012], g[0.504,0.021]\n",
            ">3844, dr[0.000,0.501], df[0.000,0.022], g[0.688,0.031]\n",
            ">3845, dr[0.000,0.420], df[0.001,0.017], g[0.662,0.038]\n",
            ">3846, dr[0.000,0.450], df[0.004,0.014], g[1.343,0.023]\n",
            ">3847, dr[0.000,0.523], df[0.000,0.010], g[1.297,0.048]\n",
            ">3848, dr[0.000,0.450], df[0.000,0.028], g[0.823,0.027]\n",
            ">3849, dr[0.000,0.517], df[0.000,0.011], g[0.899,0.049]\n",
            ">3850, dr[0.000,0.307], df[0.000,0.021], g[0.830,0.038]\n",
            ">3851, dr[0.000,0.669], df[0.000,0.013], g[1.120,0.021]\n",
            ">3852, dr[0.000,0.541], df[0.000,0.022], g[1.203,0.037]\n",
            ">3853, dr[0.000,0.382], df[0.000,0.025], g[1.203,0.044]\n",
            ">3854, dr[0.000,0.680], df[0.000,0.034], g[1.068,0.026]\n",
            ">3855, dr[0.000,0.334], df[0.000,0.085], g[0.774,0.036]\n",
            ">3856, dr[0.000,0.500], df[0.000,0.024], g[0.733,0.046]\n",
            ">3857, dr[0.000,0.331], df[0.000,0.009], g[0.927,0.025]\n",
            ">3858, dr[0.000,0.494], df[0.000,0.013], g[0.942,0.035]\n",
            ">3859, dr[0.001,0.280], df[0.000,0.034], g[0.816,0.022]\n",
            ">3860, dr[0.000,0.443], df[0.000,0.031], g[1.208,0.037]\n",
            ">3861, dr[0.000,0.668], df[0.000,0.010], g[1.072,0.036]\n",
            ">3862, dr[0.000,0.576], df[0.000,0.038], g[1.588,0.034]\n",
            ">3863, dr[0.000,0.452], df[0.000,0.011], g[1.464,0.043]\n",
            ">3864, dr[0.000,0.305], df[0.000,0.017], g[1.442,0.046]\n",
            ">3865, dr[0.000,0.246], df[0.000,0.014], g[1.555,0.025]\n",
            ">3866, dr[0.000,0.330], df[0.000,0.011], g[1.692,0.029]\n",
            ">3867, dr[0.000,0.408], df[0.000,0.028], g[1.525,0.013]\n",
            ">3868, dr[0.000,0.625], df[0.000,0.014], g[1.374,0.050]\n",
            ">3869, dr[0.000,0.582], df[0.000,0.017], g[1.213,0.034]\n",
            ">3870, dr[0.000,0.478], df[0.000,0.007], g[1.037,0.030]\n",
            ">3871, dr[0.000,0.530], df[0.000,0.008], g[1.138,0.043]\n",
            ">3872, dr[0.001,0.267], df[0.000,0.027], g[0.990,0.025]\n",
            ">3873, dr[0.000,0.525], df[0.003,0.014], g[1.042,0.019]\n",
            ">3874, dr[0.000,0.342], df[0.000,0.020], g[1.079,0.015]\n",
            ">3875, dr[0.000,0.338], df[0.000,0.013], g[1.283,0.023]\n",
            ">3876, dr[0.000,0.626], df[0.000,0.011], g[1.086,0.026]\n",
            ">3877, dr[0.000,0.484], df[0.000,0.023], g[1.257,0.038]\n",
            ">3878, dr[0.000,0.409], df[0.002,0.007], g[1.532,0.016]\n",
            ">3879, dr[0.000,0.552], df[0.000,0.010], g[1.467,0.018]\n",
            ">3880, dr[0.000,0.942], df[0.000,0.016], g[1.398,0.020]\n",
            ">3881, dr[0.000,0.554], df[0.000,0.007], g[1.552,0.031]\n",
            ">3882, dr[0.000,0.409], df[0.000,0.019], g[1.332,0.039]\n",
            ">3883, dr[0.000,0.281], df[0.000,0.007], g[1.180,0.025]\n",
            ">3884, dr[0.000,0.973], df[0.001,0.010], g[1.272,0.043]\n",
            ">3885, dr[0.000,0.540], df[0.000,0.031], g[1.379,0.028]\n",
            ">3886, dr[0.000,0.751], df[0.000,0.029], g[1.629,0.022]\n",
            ">3887, dr[0.000,0.829], df[0.000,0.021], g[2.181,0.060]\n",
            ">3888, dr[0.001,0.270], df[0.000,0.051], g[2.563,0.118]\n",
            ">3889, dr[0.000,0.499], df[0.000,0.022], g[1.858,0.098]\n",
            ">3890, dr[0.000,0.514], df[0.000,0.011], g[1.321,0.034]\n",
            ">3891, dr[0.000,0.499], df[0.000,0.011], g[1.491,0.042]\n",
            ">3892, dr[0.000,0.438], df[0.000,0.012], g[0.967,0.029]\n",
            ">3893, dr[0.000,0.296], df[0.000,0.024], g[1.221,0.021]\n",
            ">3894, dr[0.000,0.627], df[0.000,0.012], g[1.064,0.055]\n",
            ">3895, dr[0.000,0.581], df[0.000,0.017], g[0.940,0.030]\n",
            ">3896, dr[0.000,0.310], df[0.000,0.010], g[0.684,0.023]\n",
            ">3897, dr[0.000,0.510], df[0.000,0.018], g[0.563,0.046]\n",
            ">3898, dr[0.000,0.348], df[0.000,0.018], g[0.879,0.045]\n",
            ">3899, dr[0.000,0.445], df[0.000,0.021], g[0.783,0.050]\n",
            ">3900, dr[0.000,0.807], df[0.000,0.015], g[1.065,0.023]\n",
            ">3901, dr[0.000,0.356], df[0.000,0.007], g[1.256,0.025]\n",
            ">3902, dr[0.000,0.525], df[0.000,0.012], g[1.750,0.022]\n",
            ">3903, dr[0.000,0.453], df[0.000,0.014], g[1.314,0.044]\n",
            ">3904, dr[0.000,0.364], df[0.000,0.017], g[1.301,0.022]\n",
            ">3905, dr[0.000,0.453], df[0.000,0.016], g[1.404,0.027]\n",
            ">3906, dr[0.000,0.369], df[0.000,0.031], g[1.426,0.046]\n",
            ">3907, dr[0.000,0.391], df[0.000,0.014], g[0.907,0.040]\n",
            ">3908, dr[0.000,0.367], df[0.000,0.008], g[1.381,0.037]\n",
            ">3909, dr[0.000,0.486], df[0.000,0.015], g[1.160,0.018]\n",
            ">3910, dr[0.000,0.240], df[0.001,0.007], g[0.936,0.025]\n",
            ">3911, dr[0.000,0.728], df[0.000,0.008], g[1.194,0.024]\n",
            ">3912, dr[0.000,0.284], df[0.000,0.018], g[0.895,0.032]\n",
            ">3913, dr[0.000,0.195], df[0.000,0.006], g[1.329,0.067]\n",
            ">3914, dr[0.000,0.519], df[0.000,0.018], g[1.409,0.042]\n",
            ">3915, dr[0.000,0.440], df[0.000,0.010], g[0.920,0.042]\n",
            ">3916, dr[0.000,0.229], df[0.000,0.016], g[0.806,0.025]\n",
            ">3917, dr[0.000,0.698], df[0.000,0.010], g[1.066,0.039]\n",
            ">3918, dr[0.000,0.224], df[0.000,0.013], g[0.843,0.026]\n",
            ">3919, dr[0.000,0.312], df[0.000,0.010], g[0.757,0.035]\n",
            ">3920, dr[0.001,0.258], df[0.000,0.014], g[0.757,0.044]\n",
            ">3921, dr[0.000,0.230], df[0.000,0.018], g[0.822,0.018]\n",
            ">3922, dr[0.000,0.335], df[0.000,0.026], g[0.923,0.064]\n",
            ">3923, dr[0.001,0.440], df[0.000,0.009], g[1.014,0.037]\n",
            ">3924, dr[0.000,0.440], df[0.000,0.020], g[1.167,0.021]\n",
            ">3925, dr[0.000,0.580], df[0.000,0.006], g[1.089,0.025]\n",
            ">3926, dr[0.000,0.575], df[0.000,0.019], g[0.899,0.025]\n",
            ">3927, dr[0.000,0.371], df[0.000,0.011], g[0.797,0.016]\n",
            ">3928, dr[0.000,0.832], df[0.000,0.011], g[0.980,0.020]\n",
            ">3929, dr[0.000,0.404], df[0.000,0.010], g[1.032,0.018]\n",
            ">3930, dr[0.000,0.333], df[0.000,0.006], g[0.854,0.018]\n",
            ">3931, dr[0.003,0.610], df[0.000,0.007], g[0.814,0.026]\n",
            ">3932, dr[0.000,0.412], df[0.000,0.010], g[0.722,0.023]\n",
            ">3933, dr[0.000,0.326], df[0.000,0.012], g[0.888,0.027]\n",
            ">3934, dr[0.000,0.299], df[0.000,0.016], g[1.104,0.045]\n",
            ">3935, dr[0.000,0.563], df[0.000,0.007], g[1.437,0.034]\n",
            ">3936, dr[0.000,0.480], df[0.000,0.024], g[1.405,0.021]\n",
            ">3937, dr[0.000,0.726], df[0.000,0.015], g[1.537,0.038]\n",
            ">3938, dr[0.000,0.759], df[0.000,0.006], g[1.387,0.030]\n",
            ">3939, dr[0.000,0.505], df[0.000,0.018], g[1.591,0.037]\n",
            ">3940, dr[0.000,0.459], df[0.000,0.017], g[1.549,0.049]\n",
            ">3941, dr[0.000,0.450], df[0.000,0.008], g[1.282,0.052]\n",
            ">3942, dr[0.000,0.690], df[0.000,0.011], g[1.403,0.048]\n",
            ">3943, dr[0.000,0.373], df[0.000,0.006], g[1.575,0.050]\n",
            ">3944, dr[0.000,0.416], df[0.000,0.020], g[1.168,0.028]\n",
            ">3945, dr[0.000,0.329], df[0.000,0.013], g[1.201,0.025]\n",
            ">3946, dr[0.000,0.728], df[0.000,0.007], g[1.262,0.013]\n",
            ">3947, dr[0.000,0.584], df[0.000,0.012], g[1.402,0.020]\n",
            ">3948, dr[0.000,0.425], df[0.000,0.021], g[1.786,0.024]\n",
            ">3949, dr[0.000,0.759], df[0.000,0.017], g[1.776,0.066]\n",
            ">3950, dr[0.000,0.538], df[0.000,0.019], g[1.610,0.015]\n",
            ">3951, dr[0.000,0.541], df[0.000,0.015], g[1.669,0.024]\n",
            ">3952, dr[0.000,0.402], df[0.003,0.005], g[2.015,0.020]\n",
            ">3953, dr[0.000,0.484], df[0.001,0.020], g[2.003,0.034]\n",
            ">3954, dr[0.000,0.642], df[0.001,0.012], g[2.224,0.046]\n",
            ">3955, dr[0.000,0.426], df[0.000,0.013], g[2.496,0.040]\n",
            ">3956, dr[0.000,0.409], df[0.000,0.018], g[2.151,0.035]\n",
            ">3957, dr[0.000,0.260], df[0.000,0.006], g[2.233,0.035]\n",
            ">3958, dr[0.000,0.584], df[0.000,0.020], g[2.115,0.051]\n",
            ">3959, dr[0.000,0.531], df[0.000,0.021], g[1.880,0.030]\n",
            ">3960, dr[0.000,0.367], df[0.000,0.062], g[1.609,0.023]\n",
            ">3961, dr[0.000,0.358], df[0.000,0.034], g[1.637,0.049]\n",
            ">3962, dr[0.000,0.382], df[0.000,0.020], g[1.479,0.063]\n",
            ">3963, dr[0.001,0.377], df[0.000,0.028], g[1.434,0.041]\n",
            ">3964, dr[0.000,0.549], df[0.000,0.008], g[0.977,0.084]\n",
            ">3965, dr[0.000,0.900], df[0.000,0.033], g[1.089,0.035]\n",
            ">3966, dr[0.000,0.356], df[0.000,0.021], g[1.177,0.043]\n",
            ">3967, dr[0.000,0.266], df[0.000,0.019], g[1.221,0.027]\n",
            ">3968, dr[0.000,0.419], df[0.000,0.008], g[1.211,0.036]\n",
            ">3969, dr[0.000,0.514], df[0.001,0.007], g[1.285,0.040]\n",
            ">3970, dr[0.000,0.328], df[0.000,0.011], g[1.558,0.040]\n",
            ">3971, dr[0.001,0.750], df[0.000,0.016], g[1.419,0.038]\n",
            ">3972, dr[0.000,0.577], df[0.001,0.017], g[1.540,0.028]\n",
            ">3973, dr[0.000,0.521], df[0.000,0.032], g[1.498,0.038]\n",
            ">3974, dr[0.000,0.549], df[0.000,0.010], g[1.496,0.059]\n",
            ">3975, dr[0.000,0.373], df[0.000,0.012], g[1.262,0.039]\n",
            ">3976, dr[0.000,0.491], df[0.000,0.006], g[1.378,0.027]\n",
            ">3977, dr[0.000,0.240], df[0.001,0.011], g[1.729,0.047]\n",
            ">3978, dr[0.000,0.408], df[0.000,0.006], g[1.425,0.043]\n",
            ">3979, dr[0.000,0.737], df[0.000,0.024], g[1.463,0.032]\n",
            ">3980, dr[0.000,0.788], df[0.000,0.012], g[1.459,0.107]\n",
            ">3981, dr[0.000,0.547], df[0.000,0.014], g[1.270,0.056]\n",
            ">3982, dr[0.000,0.530], df[0.000,0.008], g[1.156,0.105]\n",
            ">3983, dr[0.000,0.540], df[0.000,0.022], g[1.112,0.038]\n",
            ">3984, dr[0.000,0.345], df[0.000,0.005], g[1.332,0.022]\n",
            ">3985, dr[0.000,0.586], df[0.000,0.013], g[1.014,0.021]\n",
            ">3986, dr[0.000,0.488], df[0.000,0.010], g[0.924,0.031]\n",
            ">3987, dr[0.000,0.404], df[0.000,0.003], g[1.077,0.027]\n",
            ">3988, dr[0.000,0.419], df[0.000,0.015], g[1.297,0.026]\n",
            ">3989, dr[0.000,0.862], df[0.000,0.015], g[0.980,0.038]\n",
            ">3990, dr[0.000,0.508], df[0.000,0.010], g[1.016,0.041]\n",
            ">3991, dr[0.000,0.469], df[0.001,0.009], g[0.858,0.043]\n",
            ">3992, dr[0.000,0.346], df[0.001,0.003], g[1.183,0.039]\n",
            ">3993, dr[0.000,0.332], df[0.000,0.014], g[1.074,0.041]\n",
            ">3994, dr[0.000,0.371], df[0.000,0.005], g[1.336,0.036]\n",
            ">3995, dr[0.000,0.356], df[0.001,0.003], g[1.182,0.038]\n",
            ">3996, dr[0.000,0.595], df[0.000,0.002], g[1.052,0.031]\n",
            ">3997, dr[0.000,0.218], df[0.000,0.004], g[1.338,0.027]\n",
            ">3998, dr[0.000,0.459], df[0.000,0.008], g[1.249,0.035]\n",
            ">3999, dr[0.000,0.675], df[0.000,0.003], g[1.414,0.052]\n",
            ">4000, dr[0.000,0.315], df[0.001,0.004], g[1.237,0.072]\n",
            ">4001, dr[0.000,0.339], df[0.000,0.007], g[1.123,0.044]\n",
            ">4002, dr[0.000,0.469], df[0.002,0.007], g[1.553,0.037]\n",
            ">4003, dr[0.000,0.226], df[0.000,0.020], g[1.588,0.027]\n",
            ">4004, dr[0.000,0.336], df[0.003,0.015], g[1.490,0.091]\n",
            ">4005, dr[0.000,0.332], df[0.015,0.004], g[2.496,0.037]\n",
            ">4006, dr[0.001,0.568], df[0.001,0.006], g[3.820,0.017]\n",
            ">4007, dr[0.001,0.505], df[0.013,0.025], g[4.355,0.086]\n",
            ">4008, dr[0.008,0.429], df[0.000,0.011], g[3.789,0.176]\n",
            ">4009, dr[0.002,0.451], df[0.000,0.003], g[3.789,0.082]\n",
            ">4010, dr[0.025,0.511], df[0.000,0.026], g[3.816,0.063]\n",
            ">4011, dr[0.001,0.311], df[0.000,0.009], g[3.103,0.028]\n",
            ">4012, dr[0.001,0.410], df[0.000,0.016], g[1.947,0.030]\n",
            ">4013, dr[0.000,0.182], df[0.000,0.051], g[1.772,0.079]\n",
            ">4014, dr[0.001,0.399], df[0.000,0.006], g[1.507,0.070]\n",
            ">4015, dr[0.000,0.324], df[0.002,0.007], g[1.559,0.025]\n",
            ">4016, dr[0.001,0.363], df[0.000,0.017], g[1.199,0.050]\n",
            ">4017, dr[0.001,0.464], df[0.002,0.066], g[0.757,0.025]\n",
            ">4018, dr[0.000,0.453], df[0.006,0.010], g[0.693,0.026]\n",
            ">4019, dr[0.000,0.627], df[0.001,0.022], g[1.159,0.049]\n",
            ">4020, dr[0.001,0.817], df[0.001,0.011], g[1.512,0.029]\n",
            ">4021, dr[0.001,0.195], df[0.000,0.021], g[1.057,0.051]\n",
            ">4022, dr[0.000,0.536], df[0.001,0.023], g[0.776,0.056]\n",
            ">4023, dr[0.001,0.217], df[0.001,0.009], g[1.024,0.063]\n",
            ">4024, dr[0.005,0.910], df[0.002,0.026], g[1.090,0.027]\n",
            ">4025, dr[0.007,0.318], df[0.017,0.009], g[2.377,0.009]\n",
            ">4026, dr[0.000,0.683], df[0.000,0.010], g[3.010,0.043]\n",
            ">4027, dr[0.001,0.430], df[0.000,0.011], g[2.770,0.020]\n",
            ">4028, dr[0.002,0.668], df[0.000,0.007], g[1.802,0.008]\n",
            ">4029, dr[0.001,0.581], df[0.000,0.005], g[2.311,0.017]\n",
            ">4030, dr[0.000,0.640], df[0.000,0.012], g[1.710,0.024]\n",
            ">4031, dr[0.001,0.549], df[0.000,0.006], g[1.348,0.024]\n",
            ">4032, dr[0.002,0.313], df[0.000,0.036], g[0.966,0.047]\n",
            ">4033, dr[0.002,0.386], df[0.000,0.013], g[0.568,0.025]\n",
            ">4034, dr[0.000,0.451], df[0.000,0.008], g[0.505,0.024]\n",
            ">4035, dr[0.000,0.363], df[0.000,0.017], g[0.327,0.013]\n",
            ">4036, dr[0.001,0.240], df[0.000,0.010], g[0.236,0.022]\n",
            ">4037, dr[0.000,0.342], df[0.000,0.010], g[0.215,0.024]\n",
            ">4038, dr[0.000,0.551], df[0.000,0.027], g[0.244,0.025]\n",
            ">4039, dr[0.001,0.311], df[0.000,0.010], g[0.291,0.019]\n",
            ">4040, dr[0.000,0.564], df[0.000,0.007], g[0.295,0.010]\n",
            ">4041, dr[0.001,0.593], df[0.000,0.005], g[0.336,0.020]\n",
            ">4042, dr[0.000,0.359], df[0.000,0.023], g[0.390,0.011]\n",
            ">4043, dr[0.001,0.240], df[0.000,0.047], g[0.405,0.011]\n",
            ">4044, dr[0.001,0.696], df[0.000,0.006], g[0.276,0.012]\n",
            ">4045, dr[0.003,0.408], df[0.001,0.007], g[0.287,0.020]\n",
            ">4046, dr[0.000,0.620], df[0.002,0.011], g[0.215,0.011]\n",
            ">4047, dr[0.000,0.373], df[0.000,0.008], g[0.181,0.017]\n",
            ">4048, dr[0.001,0.628], df[0.001,0.018], g[0.448,0.008]\n",
            ">4049, dr[0.000,0.454], df[0.001,0.020], g[0.423,0.022]\n",
            ">4050, dr[0.000,0.660], df[0.000,0.013], g[0.282,0.024]\n",
            ">4051, dr[0.001,0.362], df[0.000,0.007], g[0.307,0.006]\n",
            ">4052, dr[0.000,0.619], df[0.001,0.007], g[0.210,0.020]\n",
            ">4053, dr[0.001,0.272], df[0.001,0.013], g[0.448,0.014]\n",
            ">4054, dr[0.000,0.339], df[0.001,0.009], g[0.470,0.017]\n",
            ">4055, dr[0.002,0.521], df[0.000,0.004], g[0.465,0.008]\n",
            ">4056, dr[0.000,0.227], df[0.000,0.006], g[0.331,0.010]\n",
            ">4057, dr[0.000,0.291], df[0.000,0.004], g[0.397,0.022]\n",
            ">4058, dr[0.000,0.317], df[0.002,0.003], g[0.291,0.016]\n",
            ">4059, dr[0.000,0.374], df[0.001,0.002], g[0.676,0.004]\n",
            ">4060, dr[0.001,0.504], df[0.000,0.004], g[0.667,0.011]\n",
            ">4061, dr[0.001,0.302], df[0.000,0.004], g[0.595,0.013]\n",
            ">4062, dr[0.002,0.423], df[0.000,0.009], g[0.516,0.013]\n",
            ">4063, dr[0.000,0.502], df[0.000,0.018], g[0.390,0.025]\n",
            ">4064, dr[0.000,0.377], df[0.000,0.002], g[0.405,0.028]\n",
            ">4065, dr[0.000,0.740], df[0.000,0.004], g[0.888,0.014]\n",
            ">4066, dr[0.000,0.412], df[0.000,0.003], g[1.098,0.012]\n",
            ">4067, dr[0.001,0.478], df[0.000,0.003], g[0.992,0.010]\n",
            ">4068, dr[0.001,0.385], df[0.000,0.004], g[0.922,0.016]\n",
            ">4069, dr[0.001,0.836], df[0.000,0.006], g[0.927,0.015]\n",
            ">4070, dr[0.001,0.366], df[0.000,0.002], g[1.066,0.012]\n",
            ">4071, dr[0.001,0.327], df[0.000,0.005], g[0.949,0.016]\n",
            ">4072, dr[0.000,0.119], df[0.000,0.005], g[0.977,0.014]\n",
            ">4073, dr[0.001,0.298], df[0.000,0.011], g[1.424,0.015]\n",
            ">4074, dr[0.001,0.631], df[0.000,0.005], g[1.061,0.015]\n",
            ">4075, dr[0.004,0.342], df[0.000,0.003], g[1.034,0.028]\n",
            ">4076, dr[0.000,0.399], df[0.000,0.007], g[1.119,0.013]\n",
            ">4077, dr[0.001,0.268], df[0.000,0.003], g[0.717,0.013]\n",
            ">4078, dr[0.001,0.385], df[0.001,0.008], g[0.692,0.021]\n",
            ">4079, dr[0.000,0.075], df[0.001,0.016], g[0.837,0.023]\n",
            ">4080, dr[0.000,0.179], df[0.000,0.005], g[0.925,0.015]\n",
            ">4081, dr[0.000,0.416], df[0.000,0.005], g[0.828,0.011]\n",
            ">4082, dr[0.000,0.484], df[0.000,0.003], g[0.667,0.014]\n",
            ">4083, dr[0.000,0.364], df[0.000,0.009], g[0.904,0.016]\n",
            ">4084, dr[0.000,0.658], df[0.000,0.003], g[1.058,0.015]\n",
            ">4085, dr[0.000,0.415], df[0.000,0.007], g[1.161,0.019]\n",
            ">4086, dr[0.000,0.250], df[0.000,0.027], g[0.952,0.022]\n",
            ">4087, dr[0.000,0.589], df[0.000,0.006], g[0.692,0.029]\n",
            ">4088, dr[0.000,0.556], df[0.001,0.003], g[0.518,0.036]\n",
            ">4089, dr[0.000,0.532], df[0.000,0.004], g[0.959,0.006]\n",
            ">4090, dr[0.000,0.268], df[0.000,0.006], g[0.772,0.011]\n",
            ">4091, dr[0.000,0.426], df[0.000,0.003], g[0.906,0.031]\n",
            ">4092, dr[0.000,0.258], df[0.000,0.003], g[0.614,0.014]\n",
            ">4093, dr[0.001,0.450], df[0.000,0.006], g[0.287,0.020]\n",
            ">4094, dr[0.001,0.515], df[0.000,0.007], g[0.280,0.022]\n",
            ">4095, dr[0.000,0.544], df[0.004,0.005], g[0.233,0.035]\n",
            ">4096, dr[0.000,0.528], df[0.000,0.005], g[0.659,0.013]\n",
            ">4097, dr[0.000,0.648], df[0.000,0.007], g[0.946,0.012]\n",
            ">4098, dr[0.000,0.357], df[0.000,0.005], g[1.105,0.019]\n",
            ">4099, dr[0.000,0.411], df[0.000,0.004], g[1.267,0.014]\n",
            ">4100, dr[0.000,0.840], df[0.000,0.005], g[1.110,0.031]\n",
            ">4101, dr[0.000,0.605], df[0.000,0.002], g[0.695,0.014]\n",
            ">4102, dr[0.001,0.420], df[0.000,0.002], g[0.714,0.020]\n",
            ">4103, dr[0.000,0.500], df[0.000,0.003], g[0.991,0.015]\n",
            ">4104, dr[0.000,0.586], df[0.000,0.002], g[0.787,0.010]\n",
            ">4105, dr[0.000,0.227], df[0.000,0.003], g[0.959,0.013]\n",
            ">4106, dr[0.000,0.470], df[0.000,0.002], g[0.860,0.007]\n",
            ">4107, dr[0.002,0.353], df[0.000,0.004], g[1.271,0.010]\n",
            ">4108, dr[0.000,0.446], df[0.000,0.006], g[0.675,0.034]\n",
            ">4109, dr[0.000,0.395], df[0.000,0.011], g[0.715,0.020]\n",
            ">4110, dr[0.001,0.572], df[0.000,0.006], g[0.515,0.021]\n",
            ">4111, dr[0.000,0.213], df[0.000,0.015], g[0.637,0.024]\n",
            ">4112, dr[0.000,0.198], df[0.000,0.004], g[0.838,0.013]\n",
            ">4113, dr[0.000,0.439], df[0.000,0.007], g[1.207,0.015]\n",
            ">4114, dr[0.000,0.380], df[0.000,0.003], g[0.809,0.027]\n",
            ">4115, dr[0.000,0.193], df[0.000,0.004], g[0.945,0.014]\n",
            ">4116, dr[0.000,0.520], df[0.000,0.013], g[0.743,0.031]\n",
            ">4117, dr[0.000,0.175], df[0.000,0.005], g[0.800,0.037]\n",
            ">4118, dr[0.000,0.447], df[0.000,0.003], g[0.915,0.047]\n",
            ">4119, dr[0.000,0.445], df[0.000,0.005], g[0.889,0.075]\n",
            ">4120, dr[0.000,0.685], df[0.000,0.003], g[0.611,0.021]\n",
            ">4121, dr[0.001,0.355], df[0.000,0.008], g[0.468,0.037]\n",
            ">4122, dr[0.000,0.743], df[0.000,0.007], g[0.626,0.024]\n",
            ">4123, dr[0.000,0.364], df[0.000,0.004], g[0.657,0.014]\n",
            ">4124, dr[0.000,0.226], df[0.000,0.003], g[0.965,0.016]\n",
            ">4125, dr[0.000,0.763], df[0.002,0.003], g[0.787,0.017]\n",
            ">4126, dr[0.000,0.684], df[0.000,0.008], g[0.678,0.017]\n",
            ">4127, dr[0.000,0.446], df[0.001,0.013], g[0.571,0.026]\n",
            ">4128, dr[0.000,0.352], df[0.000,0.007], g[0.588,0.019]\n",
            ">4129, dr[0.000,0.741], df[0.000,0.012], g[0.553,0.018]\n",
            ">4130, dr[0.000,0.340], df[0.000,0.008], g[0.651,0.027]\n",
            ">4131, dr[0.000,0.550], df[0.000,0.005], g[0.438,0.018]\n",
            ">4132, dr[0.000,0.186], df[0.000,0.002], g[0.593,0.024]\n",
            ">4133, dr[0.000,0.443], df[0.000,0.005], g[0.510,0.020]\n",
            ">4134, dr[0.000,0.455], df[0.000,0.006], g[0.457,0.015]\n",
            ">4135, dr[0.000,0.561], df[0.000,0.005], g[0.488,0.019]\n",
            ">4136, dr[0.000,0.425], df[0.000,0.003], g[0.481,0.026]\n",
            ">4137, dr[0.000,0.348], df[0.000,0.006], g[0.606,0.033]\n",
            ">4138, dr[0.000,0.547], df[0.000,0.004], g[0.391,0.027]\n",
            ">4139, dr[0.000,0.394], df[0.001,0.001], g[0.398,0.023]\n",
            ">4140, dr[0.004,0.302], df[0.000,0.005], g[0.599,0.016]\n",
            ">4141, dr[0.000,0.620], df[0.002,0.002], g[0.651,0.023]\n",
            ">4142, dr[0.000,0.419], df[0.000,0.005], g[0.664,0.016]\n",
            ">4143, dr[0.001,0.368], df[0.000,0.003], g[0.924,0.043]\n",
            ">4144, dr[0.010,0.492], df[0.000,0.002], g[0.585,0.028]\n",
            ">4145, dr[0.000,0.619], df[0.000,0.003], g[0.522,0.019]\n",
            ">4146, dr[0.000,0.325], df[0.000,0.004], g[0.518,0.012]\n",
            ">4147, dr[0.000,0.457], df[0.000,0.007], g[0.743,0.013]\n",
            ">4148, dr[0.000,0.190], df[0.000,0.002], g[0.826,0.022]\n",
            ">4149, dr[0.001,0.434], df[0.000,0.002], g[0.887,0.028]\n",
            ">4150, dr[0.000,0.591], df[0.000,0.004], g[1.042,0.044]\n",
            ">4151, dr[0.000,0.595], df[0.000,0.003], g[1.023,0.025]\n",
            ">4152, dr[0.000,0.258], df[0.000,0.004], g[0.866,0.013]\n",
            ">4153, dr[0.000,0.502], df[0.000,0.002], g[1.111,0.015]\n",
            ">4154, dr[0.007,0.262], df[0.000,0.003], g[0.850,0.019]\n",
            ">4155, dr[0.001,0.481], df[0.000,0.004], g[1.099,0.024]\n",
            ">4156, dr[0.000,0.495], df[0.000,0.012], g[0.918,0.044]\n",
            ">4157, dr[0.000,0.329], df[0.000,0.002], g[0.980,0.037]\n",
            ">4158, dr[0.000,0.322], df[0.000,0.002], g[1.073,0.025]\n",
            ">4159, dr[0.000,0.414], df[0.000,0.007], g[1.102,0.022]\n",
            ">4160, dr[0.001,0.183], df[0.000,0.004], g[0.868,0.008]\n",
            ">4161, dr[0.000,0.358], df[0.000,0.005], g[1.119,0.018]\n",
            ">4162, dr[0.000,0.534], df[0.000,0.003], g[0.840,0.022]\n",
            ">4163, dr[0.000,0.477], df[0.000,0.003], g[0.752,0.047]\n",
            ">4164, dr[0.001,0.726], df[0.000,0.010], g[0.960,0.076]\n",
            ">4165, dr[0.000,0.407], df[0.000,0.004], g[0.493,0.051]\n",
            ">4166, dr[0.000,0.148], df[0.000,0.015], g[0.864,0.018]\n",
            ">4167, dr[0.000,0.555], df[0.000,0.005], g[1.342,0.020]\n",
            ">4168, dr[0.000,0.407], df[0.000,0.003], g[1.221,0.018]\n",
            ">4169, dr[0.000,0.235], df[0.000,0.002], g[0.979,0.024]\n",
            ">4170, dr[0.000,0.376], df[0.000,0.002], g[0.877,0.032]\n",
            ">4171, dr[0.000,0.325], df[0.000,0.006], g[0.743,0.027]\n",
            ">4172, dr[0.000,0.525], df[0.000,0.008], g[0.607,0.021]\n",
            ">4173, dr[0.001,0.648], df[0.000,0.004], g[0.786,0.024]\n",
            ">4174, dr[0.000,0.312], df[0.000,0.005], g[0.625,0.015]\n",
            ">4175, dr[0.000,0.321], df[0.000,0.014], g[0.609,0.015]\n",
            ">4176, dr[0.000,0.696], df[0.000,0.007], g[0.684,0.033]\n",
            ">4177, dr[0.000,0.585], df[0.000,0.014], g[0.862,0.013]\n",
            ">4178, dr[0.000,0.224], df[0.000,0.004], g[0.655,0.036]\n",
            ">4179, dr[0.000,0.304], df[0.000,0.015], g[0.807,0.024]\n",
            ">4180, dr[0.000,0.406], df[0.000,0.007], g[0.798,0.017]\n",
            ">4181, dr[0.000,0.283], df[0.000,0.005], g[1.061,0.015]\n",
            ">4182, dr[0.000,0.419], df[0.000,0.008], g[0.835,0.029]\n",
            ">4183, dr[0.000,0.570], df[0.000,0.003], g[0.795,0.022]\n",
            ">4184, dr[0.000,0.409], df[0.000,0.005], g[0.651,0.027]\n",
            ">4185, dr[0.000,0.563], df[0.000,0.050], g[0.681,0.045]\n",
            ">4186, dr[0.000,0.347], df[0.000,0.011], g[0.912,0.033]\n",
            ">4187, dr[0.000,0.275], df[0.000,0.008], g[0.834,0.018]\n",
            ">4188, dr[0.000,0.512], df[0.000,0.003], g[0.713,0.023]\n",
            ">4189, dr[0.000,0.421], df[0.000,0.010], g[0.703,0.017]\n",
            ">4190, dr[0.000,0.671], df[0.000,0.016], g[0.611,0.047]\n",
            ">4191, dr[0.000,0.551], df[0.000,0.009], g[0.412,0.034]\n",
            ">4192, dr[0.000,0.663], df[0.000,0.002], g[0.535,0.027]\n",
            ">4193, dr[0.000,0.778], df[0.001,0.014], g[0.328,0.021]\n",
            ">4194, dr[0.001,0.301], df[0.001,0.005], g[0.532,0.011]\n",
            ">4195, dr[0.000,0.319], df[0.001,0.008], g[0.483,0.007]\n",
            ">4196, dr[0.000,0.823], df[0.000,0.003], g[1.157,0.020]\n",
            ">4197, dr[0.000,0.800], df[0.000,0.005], g[1.071,0.026]\n",
            ">4198, dr[0.000,0.439], df[0.000,0.011], g[0.790,0.048]\n",
            ">4199, dr[0.000,0.243], df[0.000,0.007], g[0.809,0.021]\n",
            ">4200, dr[0.001,0.310], df[0.000,0.004], g[0.859,0.023]\n",
            ">4201, dr[0.000,0.339], df[0.000,0.005], g[0.722,0.065]\n",
            ">4202, dr[0.000,0.348], df[0.000,0.006], g[0.995,0.019]\n",
            ">4203, dr[0.000,0.667], df[0.000,0.003], g[0.829,0.010]\n",
            ">4204, dr[0.000,0.447], df[0.000,0.007], g[1.304,0.030]\n",
            ">4205, dr[0.001,0.419], df[0.000,0.003], g[1.804,0.018]\n",
            ">4206, dr[0.000,0.422], df[0.000,0.006], g[2.118,0.027]\n",
            ">4207, dr[0.000,0.807], df[0.000,0.007], g[1.545,0.033]\n",
            ">4208, dr[0.000,0.411], df[0.000,0.011], g[1.154,0.017]\n",
            ">4209, dr[0.000,0.469], df[0.000,0.007], g[1.114,0.024]\n",
            ">4210, dr[0.000,0.583], df[0.000,0.011], g[1.030,0.041]\n",
            ">4211, dr[0.000,0.302], df[0.000,0.002], g[1.223,0.012]\n",
            ">4212, dr[0.001,0.459], df[0.000,0.006], g[0.940,0.013]\n",
            ">4213, dr[0.000,0.336], df[0.000,0.005], g[0.888,0.011]\n",
            ">4214, dr[0.000,0.388], df[0.000,0.003], g[0.774,0.008]\n",
            ">4215, dr[0.000,0.658], df[0.000,0.015], g[0.741,0.014]\n",
            ">4216, dr[0.000,0.181], df[0.000,0.010], g[0.644,0.014]\n",
            ">4217, dr[0.000,0.418], df[0.000,0.004], g[0.884,0.013]\n",
            ">4218, dr[0.000,0.381], df[0.000,0.019], g[1.030,0.026]\n",
            ">4219, dr[0.000,0.371], df[0.000,0.012], g[0.795,0.010]\n",
            ">4220, dr[0.000,0.385], df[0.000,0.005], g[0.682,0.021]\n",
            ">4221, dr[0.000,0.437], df[0.000,0.009], g[0.732,0.018]\n",
            ">4222, dr[0.000,0.794], df[0.000,0.008], g[1.120,0.034]\n",
            ">4223, dr[0.000,0.661], df[0.000,0.008], g[0.710,0.044]\n",
            ">4224, dr[0.000,0.570], df[0.000,0.017], g[1.036,0.032]\n",
            ">4225, dr[0.000,0.245], df[0.001,0.032], g[0.929,0.043]\n",
            ">4226, dr[0.000,0.478], df[0.000,0.016], g[1.113,0.029]\n",
            ">4227, dr[0.000,0.304], df[0.000,0.006], g[0.904,0.031]\n",
            ">4228, dr[0.000,0.542], df[0.000,0.007], g[0.794,0.019]\n",
            ">4229, dr[0.000,0.423], df[0.000,0.016], g[0.762,0.017]\n",
            ">4230, dr[0.000,0.445], df[0.000,0.004], g[1.124,0.011]\n",
            ">4231, dr[0.000,0.895], df[0.000,0.009], g[1.293,0.046]\n",
            ">4232, dr[0.000,0.480], df[0.000,0.017], g[1.090,0.048]\n",
            ">4233, dr[0.000,0.722], df[0.000,0.003], g[1.302,0.023]\n",
            ">4234, dr[0.000,0.494], df[0.000,0.007], g[1.211,0.019]\n",
            ">4235, dr[0.000,0.377], df[0.000,0.007], g[1.259,0.031]\n",
            ">4236, dr[0.000,0.275], df[0.000,0.007], g[1.143,0.015]\n",
            ">4237, dr[0.000,0.206], df[0.000,0.005], g[0.893,0.026]\n",
            ">4238, dr[0.000,0.359], df[0.002,0.011], g[0.750,0.036]\n",
            ">4239, dr[0.000,0.459], df[0.000,0.011], g[0.988,0.009]\n",
            ">4240, dr[0.000,0.381], df[0.000,0.009], g[1.205,0.020]\n",
            ">4241, dr[0.000,0.177], df[0.000,0.008], g[1.541,0.009]\n",
            ">4242, dr[0.000,0.624], df[0.000,0.018], g[1.424,0.039]\n",
            ">4243, dr[0.000,0.215], df[0.000,0.011], g[1.708,0.036]\n",
            ">4244, dr[0.000,0.509], df[0.000,0.011], g[1.654,0.016]\n",
            ">4245, dr[0.000,0.246], df[0.001,0.031], g[1.622,0.028]\n",
            ">4246, dr[0.000,0.344], df[0.000,0.008], g[2.228,0.033]\n",
            ">4247, dr[0.002,0.436], df[0.000,0.007], g[1.845,0.033]\n",
            ">4248, dr[0.002,0.675], df[0.000,0.010], g[1.495,0.024]\n",
            ">4249, dr[0.000,0.183], df[0.000,0.012], g[1.421,0.033]\n",
            ">4250, dr[0.000,0.526], df[0.000,0.024], g[1.535,0.019]\n",
            ">4251, dr[0.000,0.326], df[0.000,0.020], g[1.412,0.019]\n",
            ">4252, dr[0.001,0.305], df[0.000,0.007], g[1.272,0.024]\n",
            ">4253, dr[0.000,0.778], df[0.000,0.006], g[1.825,0.017]\n",
            ">4254, dr[0.000,0.291], df[0.000,0.004], g[1.310,0.024]\n",
            ">4255, dr[0.000,0.365], df[0.000,0.006], g[1.784,0.012]\n",
            ">4256, dr[0.001,0.520], df[0.000,0.007], g[1.153,0.027]\n",
            ">4257, dr[0.000,0.651], df[0.000,0.004], g[0.967,0.020]\n",
            ">4258, dr[0.000,0.611], df[0.000,0.009], g[1.411,0.024]\n",
            ">4259, dr[0.000,0.516], df[0.000,0.005], g[0.829,0.023]\n",
            ">4260, dr[0.000,0.388], df[0.000,0.019], g[1.162,0.012]\n",
            ">4261, dr[0.000,0.265], df[0.000,0.006], g[1.198,0.030]\n",
            ">4262, dr[0.000,0.340], df[0.001,0.021], g[0.924,0.017]\n",
            ">4263, dr[0.000,0.462], df[0.000,0.027], g[1.694,0.034]\n",
            ">4264, dr[0.001,0.520], df[0.000,0.011], g[2.165,0.015]\n",
            ">4265, dr[0.000,0.325], df[0.000,0.008], g[1.772,0.047]\n",
            ">4266, dr[0.000,0.351], df[0.000,0.006], g[1.919,0.042]\n",
            ">4267, dr[0.000,0.826], df[0.000,0.030], g[1.415,0.027]\n",
            ">4268, dr[0.001,0.346], df[0.000,0.018], g[1.436,0.031]\n",
            ">4269, dr[0.001,0.486], df[0.000,0.015], g[1.205,0.026]\n",
            ">4270, dr[0.001,0.423], df[0.000,0.009], g[0.975,0.047]\n",
            ">4271, dr[0.000,0.363], df[0.000,0.004], g[1.346,0.047]\n",
            ">4272, dr[0.001,0.597], df[0.000,0.014], g[1.030,0.022]\n",
            ">4273, dr[0.000,0.309], df[0.000,0.008], g[1.030,0.034]\n",
            ">4274, dr[0.000,0.246], df[0.000,0.009], g[1.162,0.024]\n",
            ">4275, dr[0.000,0.620], df[0.000,0.009], g[0.760,0.021]\n",
            ">4276, dr[0.000,0.458], df[0.001,0.007], g[1.248,0.022]\n",
            ">4277, dr[0.000,0.323], df[0.000,0.033], g[1.555,0.067]\n",
            ">4278, dr[0.001,0.273], df[0.000,0.007], g[1.094,0.035]\n",
            ">4279, dr[0.000,0.483], df[0.000,0.012], g[0.951,0.035]\n",
            ">4280, dr[0.000,0.575], df[0.000,0.002], g[0.827,0.035]\n",
            ">4281, dr[0.000,0.473], df[0.000,0.011], g[0.741,0.028]\n",
            ">4282, dr[0.000,0.821], df[0.000,0.011], g[0.587,0.019]\n",
            ">4283, dr[0.000,0.383], df[0.000,0.017], g[0.593,0.041]\n",
            ">4284, dr[0.000,0.510], df[0.002,0.014], g[0.761,0.023]\n",
            ">4285, dr[0.001,0.212], df[0.000,0.010], g[0.906,0.029]\n",
            ">4286, dr[0.000,0.443], df[0.000,0.013], g[0.995,0.029]\n",
            ">4287, dr[0.001,0.224], df[0.000,0.002], g[0.849,0.026]\n",
            ">4288, dr[0.000,0.424], df[0.000,0.021], g[1.125,0.026]\n",
            ">4289, dr[0.000,0.433], df[0.000,0.023], g[1.269,0.040]\n",
            ">4290, dr[0.000,0.374], df[0.000,0.005], g[1.284,0.051]\n",
            ">4291, dr[0.000,0.349], df[0.000,0.037], g[1.210,0.040]\n",
            ">4292, dr[0.000,0.464], df[0.000,0.019], g[1.255,0.053]\n",
            ">4293, dr[0.000,0.435], df[0.000,0.014], g[1.321,0.035]\n",
            ">4294, dr[0.000,0.369], df[0.000,0.009], g[0.609,0.036]\n",
            ">4295, dr[0.001,0.321], df[0.001,0.023], g[0.876,0.041]\n",
            ">4296, dr[0.000,0.425], df[0.000,0.059], g[1.179,0.043]\n",
            ">4297, dr[0.000,0.420], df[0.000,0.009], g[2.495,0.020]\n",
            ">4298, dr[0.001,0.406], df[0.000,0.082], g[2.462,0.071]\n",
            ">4299, dr[0.000,0.415], df[0.000,0.021], g[2.061,0.063]\n",
            ">4300, dr[0.002,0.500], df[0.000,0.039], g[1.238,0.044]\n",
            ">4301, dr[0.000,0.546], df[0.000,0.068], g[0.828,0.115]\n",
            ">4302, dr[0.002,0.428], df[0.002,0.026], g[1.311,0.081]\n",
            ">4303, dr[0.000,0.710], df[0.001,0.012], g[2.355,0.045]\n",
            ">4304, dr[0.001,0.403], df[0.000,0.020], g[2.414,0.055]\n",
            ">4305, dr[0.000,0.522], df[0.000,0.034], g[2.034,0.091]\n",
            ">4306, dr[0.001,0.439], df[0.000,0.038], g[1.213,0.034]\n",
            ">4307, dr[0.000,0.452], df[0.000,0.046], g[0.707,0.068]\n",
            ">4308, dr[0.000,0.541], df[0.000,0.123], g[1.012,0.071]\n",
            ">4309, dr[0.000,0.465], df[0.000,0.091], g[0.625,0.068]\n",
            ">4310, dr[0.000,0.539], df[0.000,0.116], g[0.705,0.038]\n",
            ">4311, dr[0.000,0.680], df[0.001,0.082], g[0.315,0.024]\n",
            ">4312, dr[0.000,0.356], df[0.064,0.028], g[5.925,0.032]\n",
            ">4313, dr[0.002,0.539], df[0.000,0.072], g[11.615,0.034]\n",
            ">4314, dr[0.074,0.511], df[0.000,0.035], g[3.299,0.051]\n",
            ">4315, dr[0.000,0.670], df[0.000,0.056], g[2.215,0.045]\n",
            ">4316, dr[0.000,0.326], df[0.000,0.065], g[1.243,0.057]\n",
            ">4317, dr[0.000,0.291], df[0.000,0.064], g[1.399,0.078]\n",
            ">4318, dr[0.000,0.356], df[0.000,0.088], g[1.507,0.057]\n",
            ">4319, dr[0.000,0.553], df[0.000,0.054], g[1.333,0.060]\n",
            ">4320, dr[0.000,0.741], df[0.000,0.032], g[1.656,0.051]\n",
            ">4321, dr[0.000,0.530], df[0.000,0.048], g[1.341,0.073]\n",
            ">4322, dr[0.000,0.668], df[0.002,0.057], g[0.974,0.102]\n",
            ">4323, dr[0.000,0.643], df[0.000,0.041], g[0.770,0.136]\n",
            ">4324, dr[0.000,0.496], df[0.009,0.085], g[2.243,0.061]\n",
            ">4325, dr[0.000,0.813], df[0.000,0.064], g[1.974,0.047]\n",
            ">4326, dr[0.000,0.582], df[0.000,0.041], g[2.340,0.089]\n",
            ">4327, dr[0.000,0.560], df[0.000,0.036], g[1.863,0.158]\n",
            ">4328, dr[0.000,0.344], df[0.000,0.048], g[2.276,0.071]\n",
            ">4329, dr[0.000,0.490], df[0.000,0.093], g[2.650,0.031]\n",
            ">4330, dr[0.000,0.351], df[0.000,0.104], g[2.456,0.106]\n",
            ">4331, dr[0.000,0.572], df[0.000,0.031], g[1.905,0.103]\n",
            ">4332, dr[0.000,0.364], df[0.000,0.052], g[1.409,0.057]\n",
            ">4333, dr[0.000,0.296], df[0.000,0.034], g[1.206,0.045]\n",
            ">4334, dr[0.000,0.239], df[0.000,0.030], g[0.944,0.061]\n",
            ">4335, dr[0.000,0.667], df[0.000,0.013], g[0.996,0.055]\n",
            ">4336, dr[0.000,0.381], df[0.000,0.031], g[1.338,0.103]\n",
            ">4337, dr[0.000,0.315], df[0.000,0.043], g[1.475,0.098]\n",
            ">4338, dr[0.000,0.426], df[0.000,0.037], g[0.932,0.083]\n",
            ">4339, dr[0.000,0.763], df[0.000,0.080], g[1.324,0.070]\n",
            ">4340, dr[0.000,0.371], df[0.000,0.050], g[1.229,0.039]\n",
            ">4341, dr[0.000,0.648], df[0.000,0.036], g[0.527,0.054]\n",
            ">4342, dr[0.000,0.459], df[0.000,0.052], g[0.467,0.060]\n",
            ">4343, dr[0.000,0.302], df[0.000,0.044], g[0.644,0.027]\n",
            ">4344, dr[0.000,0.399], df[0.000,0.052], g[0.357,0.026]\n",
            ">4345, dr[0.000,0.454], df[0.000,0.020], g[0.263,0.028]\n",
            ">4346, dr[0.000,0.430], df[0.000,0.042], g[0.182,0.039]\n",
            ">4347, dr[0.000,0.425], df[0.000,0.038], g[0.331,0.029]\n",
            ">4348, dr[0.000,0.665], df[0.000,0.054], g[0.521,0.069]\n",
            ">4349, dr[0.000,0.254], df[0.000,0.069], g[0.408,0.062]\n",
            ">4350, dr[0.000,0.535], df[0.001,0.047], g[0.403,0.027]\n",
            ">4351, dr[0.000,0.417], df[0.000,0.040], g[0.136,0.028]\n",
            ">4352, dr[0.000,0.574], df[0.003,0.006], g[0.215,0.018]\n",
            ">4353, dr[0.000,0.486], df[0.000,0.029], g[0.207,0.014]\n",
            ">4354, dr[0.000,0.507], df[0.000,0.015], g[0.348,0.029]\n",
            ">4355, dr[0.000,0.571], df[0.000,0.030], g[0.274,0.019]\n",
            ">4356, dr[0.000,0.672], df[0.000,0.042], g[0.522,0.018]\n",
            ">4357, dr[0.000,0.455], df[0.000,0.027], g[0.265,0.018]\n",
            ">4358, dr[0.000,0.386], df[0.000,0.038], g[0.263,0.021]\n",
            ">4359, dr[0.000,0.497], df[0.000,0.006], g[0.250,0.019]\n",
            ">4360, dr[0.000,0.332], df[0.000,0.010], g[0.247,0.008]\n",
            ">4361, dr[0.000,0.524], df[0.000,0.006], g[0.180,0.013]\n",
            ">4362, dr[0.000,0.464], df[0.000,0.021], g[0.149,0.051]\n",
            ">4363, dr[0.000,0.200], df[0.000,0.014], g[0.270,0.064]\n",
            ">4364, dr[0.000,0.425], df[0.000,0.012], g[0.174,0.016]\n",
            ">4365, dr[0.000,0.379], df[0.000,0.021], g[0.141,0.017]\n",
            ">4366, dr[0.001,0.441], df[0.000,0.009], g[0.096,0.017]\n",
            ">4367, dr[0.000,0.328], df[0.000,0.030], g[0.180,0.015]\n",
            ">4368, dr[0.000,0.571], df[0.000,0.007], g[0.193,0.018]\n",
            ">4369, dr[0.000,0.551], df[0.000,0.036], g[0.095,0.015]\n",
            ">4370, dr[0.000,0.372], df[0.000,0.017], g[0.120,0.026]\n",
            ">4371, dr[0.000,0.454], df[0.000,0.017], g[0.144,0.016]\n",
            ">4372, dr[0.000,0.214], df[0.000,0.025], g[0.159,0.024]\n",
            ">4373, dr[0.000,0.376], df[0.000,0.024], g[0.178,0.024]\n",
            ">4374, dr[0.000,0.311], df[0.000,0.014], g[0.170,0.015]\n",
            ">4375, dr[0.000,0.430], df[0.000,0.011], g[0.202,0.026]\n",
            ">4376, dr[0.000,0.307], df[0.000,0.018], g[0.163,0.018]\n",
            ">4377, dr[0.000,0.478], df[0.000,0.077], g[0.061,0.018]\n",
            ">4378, dr[0.000,0.208], df[0.000,0.017], g[0.076,0.018]\n",
            ">4379, dr[0.000,0.564], df[0.000,0.012], g[0.088,0.013]\n",
            ">4380, dr[0.000,0.418], df[0.000,0.020], g[0.025,0.020]\n",
            ">4381, dr[0.000,0.538], df[0.007,0.012], g[0.073,0.016]\n",
            ">4382, dr[0.000,0.488], df[0.000,0.006], g[0.068,0.012]\n",
            ">4383, dr[0.000,0.425], df[0.000,0.014], g[0.084,0.011]\n",
            ">4384, dr[0.000,0.331], df[0.001,0.006], g[0.095,0.016]\n",
            ">4385, dr[0.000,0.688], df[0.001,0.018], g[0.112,0.018]\n",
            ">4386, dr[0.001,0.397], df[0.002,0.016], g[0.061,0.015]\n",
            ">4387, dr[0.000,0.458], df[0.000,0.013], g[0.110,0.017]\n",
            ">4388, dr[0.000,0.431], df[0.001,0.010], g[0.061,0.022]\n",
            ">4389, dr[0.000,0.458], df[0.001,0.010], g[0.086,0.006]\n",
            ">4390, dr[0.000,0.649], df[0.000,0.025], g[0.140,0.007]\n",
            ">4391, dr[0.000,0.287], df[0.000,0.007], g[0.077,0.009]\n",
            ">4392, dr[0.000,0.726], df[0.000,0.008], g[0.139,0.013]\n",
            ">4393, dr[0.000,0.790], df[0.000,0.005], g[0.202,0.010]\n",
            ">4394, dr[0.000,0.370], df[0.000,0.018], g[0.342,0.014]\n",
            ">4395, dr[0.000,0.623], df[0.000,0.018], g[0.272,0.026]\n",
            ">4396, dr[0.000,0.480], df[0.000,0.007], g[0.253,0.010]\n",
            ">4397, dr[0.000,0.535], df[0.000,0.014], g[0.196,0.010]\n",
            ">4398, dr[0.000,0.477], df[0.000,0.014], g[0.295,0.007]\n",
            ">4399, dr[0.000,0.493], df[0.000,0.041], g[0.277,0.012]\n",
            ">4400, dr[0.000,0.542], df[0.000,0.001], g[0.248,0.009]\n",
            ">4401, dr[0.000,0.379], df[0.001,0.023], g[0.231,0.012]\n",
            ">4402, dr[0.000,0.483], df[0.000,0.016], g[0.248,0.016]\n",
            ">4403, dr[0.000,0.411], df[0.000,0.016], g[0.226,0.011]\n",
            ">4404, dr[0.000,0.538], df[0.000,0.011], g[0.175,0.011]\n",
            ">4405, dr[0.002,0.562], df[0.000,0.019], g[0.248,0.005]\n",
            ">4406, dr[0.000,0.402], df[0.000,0.006], g[0.149,0.014]\n",
            ">4407, dr[0.000,0.481], df[0.000,0.006], g[0.194,0.023]\n",
            ">4408, dr[0.000,0.466], df[0.000,0.004], g[0.207,0.013]\n",
            ">4409, dr[0.000,0.464], df[0.000,0.026], g[0.136,0.009]\n",
            ">4410, dr[0.000,0.642], df[0.000,0.006], g[0.129,0.021]\n",
            ">4411, dr[0.000,0.697], df[0.001,0.022], g[0.130,0.011]\n",
            ">4412, dr[0.000,0.548], df[0.001,0.008], g[0.211,0.015]\n",
            ">4413, dr[0.000,0.652], df[0.000,0.007], g[0.292,0.024]\n",
            ">4414, dr[0.000,0.414], df[0.000,0.010], g[0.372,0.032]\n",
            ">4415, dr[0.000,0.264], df[0.000,0.019], g[0.350,0.017]\n",
            ">4416, dr[0.000,0.632], df[0.000,0.006], g[0.372,0.008]\n",
            ">4417, dr[0.000,0.564], df[0.000,0.006], g[0.326,0.036]\n",
            ">4418, dr[0.000,0.349], df[0.000,0.011], g[0.266,0.020]\n",
            ">4419, dr[0.000,0.354], df[0.001,0.004], g[0.300,0.010]\n",
            ">4420, dr[0.000,0.438], df[0.001,0.006], g[0.309,0.010]\n",
            ">4421, dr[0.000,0.593], df[0.000,0.005], g[0.459,0.022]\n",
            ">4422, dr[0.000,0.353], df[0.001,0.006], g[0.242,0.013]\n",
            ">4423, dr[0.002,0.488], df[0.000,0.006], g[0.362,0.009]\n",
            ">4424, dr[0.000,0.461], df[0.000,0.006], g[0.446,0.008]\n",
            ">4425, dr[0.000,0.356], df[0.000,0.004], g[0.446,0.020]\n",
            ">4426, dr[0.000,0.468], df[0.000,0.012], g[0.373,0.008]\n",
            ">4427, dr[0.000,0.497], df[0.000,0.004], g[0.243,0.032]\n",
            ">4428, dr[0.000,0.268], df[0.000,0.011], g[0.347,0.006]\n",
            ">4429, dr[0.000,0.638], df[0.001,0.006], g[0.217,0.017]\n",
            ">4430, dr[0.001,0.832], df[0.000,0.002], g[0.286,0.025]\n",
            ">4431, dr[0.000,0.446], df[0.003,0.002], g[0.330,0.020]\n",
            ">4432, dr[0.000,0.620], df[0.000,0.010], g[0.355,0.012]\n",
            ">4433, dr[0.000,0.483], df[0.000,0.004], g[0.440,0.009]\n",
            ">4434, dr[0.000,0.799], df[0.001,0.005], g[0.396,0.006]\n",
            ">4435, dr[0.000,0.516], df[0.000,0.005], g[0.434,0.018]\n",
            ">4436, dr[0.000,0.403], df[0.001,0.022], g[0.545,0.053]\n",
            ">4437, dr[0.000,0.619], df[0.000,0.004], g[0.410,0.011]\n",
            ">4438, dr[0.000,0.470], df[0.000,0.021], g[0.516,0.042]\n",
            ">4439, dr[0.000,0.320], df[0.000,0.002], g[0.643,0.014]\n",
            ">4440, dr[0.000,0.498], df[0.001,0.009], g[0.700,0.008]\n",
            ">4441, dr[0.000,0.487], df[0.000,0.003], g[0.607,0.008]\n",
            ">4442, dr[0.000,0.350], df[0.000,0.005], g[0.706,0.009]\n",
            ">4443, dr[0.000,0.350], df[0.001,0.009], g[0.521,0.059]\n",
            ">4444, dr[0.000,0.663], df[0.001,0.020], g[0.551,0.047]\n",
            ">4445, dr[0.000,0.462], df[0.000,0.003], g[0.658,0.022]\n",
            ">4446, dr[0.001,0.706], df[0.000,0.002], g[0.482,0.010]\n",
            ">4447, dr[0.000,0.476], df[0.000,0.009], g[0.536,0.031]\n",
            ">4448, dr[0.000,0.734], df[0.001,0.014], g[0.558,0.042]\n",
            ">4449, dr[0.000,0.570], df[0.000,0.005], g[0.605,0.034]\n",
            ">4450, dr[0.000,0.399], df[0.000,0.004], g[0.597,0.016]\n",
            ">4451, dr[0.000,0.337], df[0.000,0.010], g[0.548,0.053]\n",
            ">4452, dr[0.000,0.337], df[0.000,0.052], g[0.592,0.042]\n",
            ">4453, dr[0.001,0.489], df[0.000,0.013], g[0.791,0.099]\n",
            ">4454, dr[0.000,0.544], df[0.000,0.009], g[0.722,0.029]\n",
            ">4455, dr[0.000,0.287], df[0.000,0.008], g[0.881,0.018]\n",
            ">4456, dr[0.000,0.512], df[0.000,0.011], g[0.985,0.068]\n",
            ">4457, dr[0.000,0.629], df[0.000,0.008], g[0.621,0.025]\n",
            ">4458, dr[0.000,0.317], df[0.000,0.002], g[0.534,0.038]\n",
            ">4459, dr[0.002,0.340], df[0.000,0.002], g[0.489,0.019]\n",
            ">4460, dr[0.000,0.519], df[0.000,0.004], g[0.620,0.057]\n",
            ">4461, dr[0.000,0.578], df[0.000,0.006], g[0.532,0.042]\n",
            ">4462, dr[0.005,0.649], df[0.000,0.007], g[0.735,0.028]\n",
            ">4463, dr[0.000,0.406], df[0.000,0.007], g[0.736,0.026]\n",
            ">4464, dr[0.001,0.302], df[0.000,0.005], g[0.642,0.038]\n",
            ">4465, dr[0.000,0.600], df[0.001,0.004], g[0.638,0.021]\n",
            ">4466, dr[0.000,0.282], df[0.000,0.006], g[0.542,0.042]\n",
            ">4467, dr[0.000,0.510], df[0.000,0.006], g[0.628,0.041]\n",
            ">4468, dr[0.000,0.433], df[0.002,0.007], g[0.568,0.052]\n",
            ">4469, dr[0.000,0.328], df[0.000,0.006], g[0.911,0.031]\n",
            ">4470, dr[0.000,0.302], df[0.000,0.004], g[0.919,0.045]\n",
            ">4471, dr[0.000,0.336], df[0.000,0.007], g[0.864,0.055]\n",
            ">4472, dr[0.000,0.432], df[0.000,0.005], g[1.021,0.035]\n",
            ">4473, dr[0.000,0.202], df[0.000,0.010], g[1.035,0.039]\n",
            ">4474, dr[0.004,0.492], df[0.000,0.024], g[0.931,0.052]\n",
            ">4475, dr[0.000,0.314], df[0.000,0.007], g[0.896,0.054]\n",
            ">4476, dr[0.000,0.327], df[0.000,0.006], g[0.797,0.052]\n",
            ">4477, dr[0.000,0.458], df[0.000,0.003], g[0.846,0.034]\n",
            ">4478, dr[0.000,0.353], df[0.001,0.003], g[0.778,0.040]\n",
            ">4479, dr[0.000,0.574], df[0.001,0.004], g[0.954,0.037]\n",
            ">4480, dr[0.000,0.342], df[0.000,0.001], g[0.994,0.061]\n",
            ">4481, dr[0.001,0.193], df[0.000,0.003], g[1.147,0.024]\n",
            ">4482, dr[0.000,0.381], df[0.000,0.017], g[1.141,0.029]\n",
            ">4483, dr[0.001,0.299], df[0.000,0.008], g[1.055,0.093]\n",
            ">4484, dr[0.000,0.339], df[0.002,0.008], g[0.811,0.028]\n",
            ">4485, dr[0.000,0.254], df[0.000,0.006], g[1.114,0.030]\n",
            ">4486, dr[0.000,0.291], df[0.000,0.004], g[1.197,0.027]\n",
            ">4487, dr[0.000,0.551], df[0.000,0.002], g[1.127,0.060]\n",
            ">4488, dr[0.000,0.359], df[0.000,0.007], g[1.091,0.095]\n",
            ">4489, dr[0.000,0.594], df[0.002,0.004], g[1.080,0.105]\n",
            ">4490, dr[0.000,0.234], df[0.000,0.016], g[1.342,0.081]\n",
            ">4491, dr[0.000,0.797], df[0.001,0.006], g[1.196,0.113]\n",
            ">4492, dr[0.000,0.496], df[0.000,0.013], g[1.234,0.072]\n",
            ">4493, dr[0.000,0.169], df[0.000,0.009], g[1.158,0.144]\n",
            ">4494, dr[0.000,0.183], df[0.000,0.013], g[1.292,0.151]\n",
            ">4495, dr[0.000,0.529], df[0.000,0.018], g[1.169,0.112]\n",
            ">4496, dr[0.000,0.611], df[0.000,0.017], g[1.167,0.048]\n",
            ">4497, dr[0.000,0.793], df[0.000,0.017], g[0.872,0.040]\n",
            ">4498, dr[0.001,0.580], df[0.000,0.006], g[0.854,0.076]\n",
            ">4499, dr[0.000,0.944], df[0.000,0.008], g[0.958,0.121]\n",
            ">4500, dr[0.000,0.460], df[0.000,0.010], g[0.730,0.126]\n",
            ">4501, dr[0.000,0.340], df[0.000,0.007], g[0.819,0.051]\n",
            ">4502, dr[0.001,0.655], df[0.000,0.010], g[0.811,0.080]\n",
            ">4503, dr[0.000,0.312], df[0.000,0.030], g[0.949,0.057]\n",
            ">4504, dr[0.000,0.426], df[0.000,0.007], g[1.175,0.075]\n",
            ">4505, dr[0.000,0.473], df[0.000,0.012], g[1.393,0.091]\n",
            ">4506, dr[0.000,0.345], df[0.000,0.004], g[1.541,0.091]\n",
            ">4507, dr[0.000,0.260], df[0.000,0.012], g[1.592,0.174]\n",
            ">4508, dr[0.000,0.471], df[0.000,0.023], g[1.587,0.057]\n",
            ">4509, dr[0.000,0.547], df[0.000,0.012], g[1.794,0.071]\n",
            ">4510, dr[0.000,0.387], df[0.000,0.009], g[1.555,0.086]\n",
            ">4511, dr[0.000,0.733], df[0.000,0.025], g[1.035,0.100]\n",
            ">4512, dr[0.000,0.261], df[0.000,0.020], g[1.025,0.101]\n",
            ">4513, dr[0.004,0.637], df[0.000,0.025], g[0.735,0.154]\n",
            ">4514, dr[0.000,0.370], df[0.000,0.013], g[0.772,0.172]\n",
            ">4515, dr[0.000,0.663], df[0.000,0.019], g[0.474,0.095]\n",
            ">4516, dr[0.000,0.545], df[0.000,0.011], g[0.362,0.090]\n",
            ">4517, dr[0.000,0.397], df[0.000,0.009], g[0.227,0.104]\n",
            ">4518, dr[0.000,0.644], df[0.000,0.038], g[0.461,0.104]\n",
            ">4519, dr[0.000,0.167], df[0.000,0.017], g[0.383,0.053]\n",
            ">4520, dr[0.000,0.557], df[0.000,0.008], g[0.415,0.095]\n",
            ">4521, dr[0.000,0.454], df[0.000,0.007], g[0.560,0.088]\n",
            ">4522, dr[0.000,0.596], df[0.000,0.049], g[0.469,0.062]\n",
            ">4523, dr[0.000,0.340], df[0.000,0.012], g[0.583,0.048]\n",
            ">4524, dr[0.002,0.548], df[0.000,0.006], g[0.661,0.088]\n",
            ">4525, dr[0.000,0.675], df[0.000,0.017], g[0.636,0.092]\n",
            ">4526, dr[0.000,0.651], df[0.000,0.013], g[0.840,0.124]\n",
            ">4527, dr[0.000,0.383], df[0.000,0.019], g[0.867,0.091]\n",
            ">4528, dr[0.000,0.351], df[0.000,0.010], g[0.830,0.055]\n",
            ">4529, dr[0.001,0.296], df[0.000,0.012], g[0.804,0.055]\n",
            ">4530, dr[0.000,0.537], df[0.000,0.011], g[0.690,0.046]\n",
            ">4531, dr[0.000,0.449], df[0.000,0.030], g[0.646,0.097]\n",
            ">4532, dr[0.000,0.601], df[0.000,0.006], g[0.720,0.075]\n",
            ">4533, dr[0.000,0.309], df[0.000,0.015], g[0.471,0.079]\n",
            ">4534, dr[0.000,0.671], df[0.000,0.026], g[0.401,0.049]\n",
            ">4535, dr[0.000,0.371], df[0.000,0.007], g[0.453,0.079]\n",
            ">4536, dr[0.000,0.312], df[0.000,0.014], g[0.292,0.040]\n",
            ">4537, dr[0.001,0.651], df[0.000,0.005], g[0.325,0.046]\n",
            ">4538, dr[0.000,0.547], df[0.000,0.012], g[0.306,0.084]\n",
            ">4539, dr[0.000,0.610], df[0.000,0.006], g[0.268,0.081]\n",
            ">4540, dr[0.000,0.691], df[0.000,0.005], g[0.279,0.080]\n",
            ">4541, dr[0.000,0.399], df[0.000,0.008], g[0.288,0.061]\n",
            ">4542, dr[0.000,0.466], df[0.000,0.013], g[0.319,0.052]\n",
            ">4543, dr[0.000,0.335], df[0.000,0.011], g[0.315,0.032]\n",
            ">4544, dr[0.000,0.408], df[0.000,0.004], g[0.379,0.047]\n",
            ">4545, dr[0.000,0.461], df[0.000,0.004], g[0.476,0.068]\n",
            ">4546, dr[0.000,0.300], df[0.000,0.017], g[0.407,0.062]\n",
            ">4547, dr[0.002,0.238], df[0.000,0.018], g[0.444,0.058]\n",
            ">4548, dr[0.000,0.261], df[0.000,0.005], g[0.433,0.045]\n",
            ">4549, dr[0.000,0.407], df[0.000,0.003], g[0.404,0.038]\n",
            ">4550, dr[0.000,0.485], df[0.000,0.007], g[0.352,0.073]\n",
            ">4551, dr[0.000,0.543], df[0.000,0.015], g[0.378,0.041]\n",
            ">4552, dr[0.000,0.286], df[0.000,0.006], g[0.346,0.036]\n",
            ">4553, dr[0.000,0.752], df[0.000,0.005], g[0.358,0.048]\n",
            ">4554, dr[0.000,0.604], df[0.000,0.009], g[0.374,0.091]\n",
            ">4555, dr[0.000,0.384], df[0.000,0.009], g[0.585,0.078]\n",
            ">4556, dr[0.000,0.325], df[0.000,0.013], g[0.369,0.058]\n",
            ">4557, dr[0.000,0.607], df[0.000,0.013], g[0.464,0.050]\n",
            ">4558, dr[0.001,0.321], df[0.000,0.012], g[0.567,0.047]\n",
            ">4559, dr[0.000,0.433], df[0.000,0.005], g[0.486,0.047]\n",
            ">4560, dr[0.000,0.406], df[0.000,0.017], g[0.552,0.075]\n",
            ">4561, dr[0.001,0.390], df[0.000,0.012], g[0.397,0.054]\n",
            ">4562, dr[0.000,0.411], df[0.001,0.004], g[0.380,0.039]\n",
            ">4563, dr[0.000,0.295], df[0.000,0.006], g[0.547,0.075]\n",
            ">4564, dr[0.000,0.347], df[0.002,0.009], g[0.407,0.051]\n",
            ">4565, dr[0.000,0.172], df[0.000,0.005], g[0.565,0.058]\n",
            ">4566, dr[0.037,0.584], df[0.000,0.004], g[0.508,0.048]\n",
            ">4567, dr[0.000,0.488], df[0.000,0.006], g[0.440,0.038]\n",
            ">4568, dr[0.000,0.366], df[0.000,0.005], g[0.409,0.062]\n",
            ">4569, dr[0.000,0.547], df[0.001,0.013], g[0.485,0.050]\n",
            ">4570, dr[0.000,0.429], df[0.000,0.007], g[0.602,0.039]\n",
            ">4571, dr[0.000,0.333], df[0.000,0.006], g[0.585,0.026]\n",
            ">4572, dr[0.000,0.368], df[0.000,0.015], g[0.596,0.026]\n",
            ">4573, dr[0.000,0.343], df[0.000,0.004], g[0.679,0.031]\n",
            ">4574, dr[0.000,0.643], df[0.000,0.004], g[0.472,0.029]\n",
            ">4575, dr[0.000,0.437], df[0.000,0.003], g[0.557,0.026]\n",
            ">4576, dr[0.000,0.346], df[0.000,0.018], g[0.672,0.041]\n",
            ">4577, dr[0.000,0.269], df[0.000,0.004], g[0.644,0.023]\n",
            ">4578, dr[0.000,0.518], df[0.000,0.015], g[0.565,0.036]\n",
            ">4579, dr[0.000,0.226], df[0.000,0.026], g[0.429,0.013]\n",
            ">4580, dr[0.000,0.681], df[0.000,0.007], g[0.552,0.023]\n",
            ">4581, dr[0.000,0.241], df[0.001,0.004], g[0.523,0.028]\n",
            ">4582, dr[0.000,0.295], df[0.000,0.013], g[0.648,0.026]\n",
            ">4583, dr[0.000,0.438], df[0.000,0.005], g[0.458,0.015]\n",
            ">4584, dr[0.000,0.157], df[0.000,0.007], g[0.567,0.046]\n",
            ">4585, dr[0.000,0.232], df[0.000,0.010], g[0.426,0.013]\n",
            ">4586, dr[0.000,0.312], df[0.000,0.003], g[0.349,0.022]\n",
            ">4587, dr[0.000,0.679], df[0.000,0.004], g[0.461,0.019]\n",
            ">4588, dr[0.000,0.297], df[0.002,0.005], g[0.879,0.027]\n",
            ">4589, dr[0.000,0.460], df[0.000,0.008], g[0.598,0.028]\n",
            ">4590, dr[0.000,0.903], df[0.000,0.007], g[0.465,0.015]\n",
            ">4591, dr[0.000,0.712], df[0.000,0.008], g[0.421,0.033]\n",
            ">4592, dr[0.000,0.434], df[0.000,0.004], g[0.586,0.038]\n",
            ">4593, dr[0.000,0.222], df[0.001,0.008], g[0.435,0.046]\n",
            ">4594, dr[0.000,0.622], df[0.001,0.057], g[0.328,0.026]\n",
            ">4595, dr[0.000,0.298], df[0.000,0.004], g[0.368,0.069]\n",
            ">4596, dr[0.000,0.450], df[0.000,0.014], g[0.230,0.024]\n",
            ">4597, dr[0.000,0.346], df[0.000,0.002], g[0.270,0.035]\n",
            ">4598, dr[0.000,0.742], df[0.000,0.010], g[0.550,0.028]\n",
            ">4599, dr[0.000,0.430], df[0.001,0.012], g[0.320,0.050]\n",
            ">4600, dr[0.000,0.294], df[0.004,0.005], g[0.359,0.023]\n",
            ">4601, dr[0.000,0.308], df[0.001,0.031], g[0.471,0.021]\n",
            ">4602, dr[0.000,0.572], df[0.001,0.005], g[0.754,0.019]\n",
            ">4603, dr[0.000,0.337], df[0.000,0.007], g[0.730,0.020]\n",
            ">4604, dr[0.000,0.303], df[0.001,0.027], g[1.042,0.025]\n",
            ">4605, dr[0.000,0.500], df[0.001,0.003], g[0.925,0.014]\n",
            ">4606, dr[0.000,0.426], df[0.002,0.002], g[0.933,0.045]\n",
            ">4607, dr[0.000,0.296], df[0.002,0.003], g[1.158,0.030]\n",
            ">4608, dr[0.000,0.311], df[0.001,0.009], g[0.950,0.054]\n",
            ">4609, dr[0.002,0.261], df[0.002,0.003], g[0.973,0.014]\n",
            ">4610, dr[0.000,0.319], df[0.001,0.004], g[0.979,0.020]\n",
            ">4611, dr[0.000,0.306], df[0.002,0.009], g[0.971,0.013]\n",
            ">4612, dr[0.000,0.141], df[0.006,0.004], g[1.465,0.011]\n",
            ">4613, dr[0.000,0.217], df[0.001,0.004], g[2.078,0.026]\n",
            ">4614, dr[0.001,0.613], df[0.000,0.002], g[2.023,0.023]\n",
            ">4615, dr[0.000,0.532], df[0.000,0.005], g[2.247,0.030]\n",
            ">4616, dr[0.000,0.376], df[0.001,0.004], g[1.847,0.056]\n",
            ">4617, dr[0.000,0.435], df[0.000,0.024], g[1.640,0.053]\n",
            ">4618, dr[0.000,0.394], df[0.002,0.005], g[1.598,0.063]\n",
            ">4619, dr[0.000,0.373], df[0.000,0.003], g[1.692,0.043]\n",
            ">4620, dr[0.000,0.689], df[0.000,0.011], g[1.612,0.075]\n",
            ">4621, dr[0.000,0.705], df[0.000,0.002], g[1.351,0.062]\n",
            ">4622, dr[0.000,0.510], df[0.000,0.007], g[1.257,0.042]\n",
            ">4623, dr[0.000,0.548], df[0.000,0.009], g[1.177,0.061]\n",
            ">4624, dr[0.000,0.608], df[0.000,0.010], g[1.080,0.022]\n",
            ">4625, dr[0.000,0.519], df[0.000,0.002], g[0.925,0.056]\n",
            ">4626, dr[0.001,0.355], df[0.000,0.004], g[1.048,0.031]\n",
            ">4627, dr[0.000,0.424], df[0.000,0.022], g[1.110,0.025]\n",
            ">4628, dr[0.001,0.249], df[0.000,0.003], g[1.153,0.045]\n",
            ">4629, dr[0.000,0.258], df[0.000,0.001], g[0.944,0.030]\n",
            ">4630, dr[0.000,0.391], df[0.000,0.002], g[1.124,0.057]\n",
            ">4631, dr[0.001,0.718], df[0.000,0.001], g[1.148,0.040]\n",
            ">4632, dr[0.000,0.508], df[0.000,0.002], g[0.934,0.040]\n",
            ">4633, dr[0.002,0.570], df[0.000,0.002], g[0.816,0.020]\n",
            ">4634, dr[0.000,0.406], df[0.000,0.002], g[0.777,0.030]\n",
            ">4635, dr[0.000,0.254], df[0.000,0.001], g[0.839,0.034]\n",
            ">4636, dr[0.000,0.441], df[0.000,0.004], g[0.796,0.045]\n",
            ">4637, dr[0.000,0.462], df[0.000,0.002], g[0.889,0.021]\n",
            ">4638, dr[0.000,0.217], df[0.000,0.001], g[0.941,0.022]\n",
            ">4639, dr[0.000,0.435], df[0.000,0.001], g[0.797,0.032]\n",
            ">4640, dr[0.000,0.451], df[0.000,0.002], g[0.969,0.010]\n",
            ">4641, dr[0.000,0.378], df[0.000,0.003], g[0.942,0.013]\n",
            ">4642, dr[0.000,0.665], df[0.000,0.002], g[0.775,0.031]\n",
            ">4643, dr[0.000,0.431], df[0.000,0.003], g[0.830,0.015]\n",
            ">4644, dr[0.000,0.577], df[0.000,0.001], g[0.790,0.026]\n",
            ">4645, dr[0.000,0.583], df[0.000,0.002], g[0.817,0.015]\n",
            ">4646, dr[0.000,0.386], df[0.000,0.001], g[1.033,0.033]\n",
            ">4647, dr[0.000,0.545], df[0.000,0.003], g[0.973,0.023]\n",
            ">4648, dr[0.006,0.303], df[0.000,0.002], g[0.489,0.029]\n",
            ">4649, dr[0.001,0.420], df[0.001,0.007], g[0.599,0.012]\n",
            ">4650, dr[0.000,0.513], df[0.002,0.004], g[1.076,0.007]\n",
            ">4651, dr[0.000,0.316], df[0.000,0.002], g[0.828,0.015]\n",
            ">4652, dr[0.000,0.397], df[0.000,0.002], g[1.149,0.014]\n",
            ">4653, dr[0.000,0.605], df[0.000,0.002], g[1.151,0.018]\n",
            ">4654, dr[0.000,0.514], df[0.000,0.001], g[1.380,0.018]\n",
            ">4655, dr[0.000,0.520], df[0.000,0.001], g[0.875,0.032]\n",
            ">4656, dr[0.000,0.390], df[0.000,0.003], g[1.287,0.014]\n",
            ">4657, dr[0.000,0.579], df[0.000,0.003], g[1.224,0.028]\n",
            ">4658, dr[0.000,0.261], df[0.000,0.002], g[0.962,0.022]\n",
            ">4659, dr[0.000,0.209], df[0.000,0.002], g[1.047,0.014]\n",
            ">4660, dr[0.000,0.419], df[0.000,0.003], g[0.962,0.014]\n",
            ">4661, dr[0.000,0.439], df[0.000,0.004], g[1.198,0.029]\n",
            ">4662, dr[0.000,0.528], df[0.000,0.007], g[0.983,0.040]\n",
            ">4663, dr[0.000,0.257], df[0.000,0.007], g[1.284,0.020]\n",
            ">4664, dr[0.000,0.724], df[0.000,0.009], g[1.147,0.028]\n",
            ">4665, dr[0.000,0.463], df[0.000,0.003], g[0.977,0.029]\n",
            ">4666, dr[0.000,0.492], df[0.002,0.004], g[1.065,0.055]\n",
            ">4667, dr[0.000,0.454], df[0.000,0.005], g[0.993,0.026]\n",
            ">4668, dr[0.000,0.476], df[0.000,0.008], g[0.801,0.043]\n",
            ">4669, dr[0.000,0.407], df[0.000,0.004], g[0.895,0.020]\n",
            ">4670, dr[0.000,0.434], df[0.000,0.004], g[1.031,0.035]\n",
            ">4671, dr[0.000,0.523], df[0.000,0.005], g[0.861,0.046]\n",
            ">4672, dr[0.000,0.539], df[0.000,0.002], g[0.973,0.017]\n",
            ">4673, dr[0.000,0.442], df[0.000,0.003], g[0.726,0.021]\n",
            ">4674, dr[0.000,0.663], df[0.000,0.011], g[0.699,0.022]\n",
            ">4675, dr[0.000,0.570], df[0.000,0.005], g[0.670,0.031]\n",
            ">4676, dr[0.000,0.660], df[0.000,0.013], g[0.727,0.055]\n",
            ">4677, dr[0.000,0.428], df[0.000,0.011], g[0.619,0.050]\n",
            ">4678, dr[0.001,0.441], df[0.000,0.018], g[0.751,0.059]\n",
            ">4679, dr[0.000,0.321], df[0.000,0.008], g[0.735,0.046]\n",
            ">4680, dr[0.001,0.716], df[0.000,0.016], g[0.738,0.040]\n",
            ">4681, dr[0.000,0.416], df[0.000,0.014], g[0.513,0.039]\n",
            ">4682, dr[0.000,0.328], df[0.000,0.006], g[0.746,0.053]\n",
            ">4683, dr[0.000,0.384], df[0.000,0.010], g[0.662,0.064]\n",
            ">4684, dr[0.000,0.473], df[0.000,0.008], g[0.733,0.045]\n",
            ">4685, dr[0.000,0.325], df[0.000,0.010], g[0.855,0.039]\n",
            ">4686, dr[0.001,0.380], df[0.000,0.014], g[0.760,0.033]\n",
            ">4687, dr[0.000,0.839], df[0.000,0.008], g[0.867,0.037]\n",
            ">4688, dr[0.000,0.348], df[0.000,0.015], g[0.679,0.048]\n",
            ">4689, dr[0.000,0.383], df[0.000,0.008], g[0.828,0.039]\n",
            ">4690, dr[0.000,0.525], df[0.000,0.017], g[0.700,0.034]\n",
            ">4691, dr[0.001,0.374], df[0.000,0.006], g[0.566,0.044]\n",
            ">4692, dr[0.000,0.398], df[0.000,0.005], g[0.733,0.033]\n",
            ">4693, dr[0.001,0.341], df[0.000,0.007], g[0.537,0.025]\n",
            ">4694, dr[0.001,0.513], df[0.000,0.018], g[0.727,0.047]\n",
            ">4695, dr[0.000,0.253], df[0.000,0.011], g[0.811,0.018]\n",
            ">4696, dr[0.000,0.380], df[0.000,0.009], g[0.747,0.026]\n",
            ">4697, dr[0.000,0.476], df[0.000,0.007], g[1.112,0.080]\n",
            ">4698, dr[0.001,0.524], df[0.000,0.005], g[0.696,0.034]\n",
            ">4699, dr[0.000,0.456], df[0.000,0.009], g[0.531,0.032]\n",
            ">4700, dr[0.000,0.350], df[0.000,0.012], g[0.777,0.035]\n",
            ">4701, dr[0.000,0.922], df[0.000,0.012], g[0.520,0.040]\n",
            ">4702, dr[0.000,0.566], df[0.000,0.019], g[0.571,0.039]\n",
            ">4703, dr[0.000,0.540], df[0.000,0.006], g[0.593,0.061]\n",
            ">4704, dr[0.000,0.268], df[0.000,0.009], g[0.717,0.043]\n",
            ">4705, dr[0.000,0.643], df[0.000,0.006], g[0.904,0.084]\n",
            ">4706, dr[0.000,0.515], df[0.000,0.016], g[0.728,0.020]\n",
            ">4707, dr[0.000,0.700], df[0.000,0.006], g[0.677,0.017]\n",
            ">4708, dr[0.000,0.448], df[0.000,0.014], g[0.588,0.029]\n",
            ">4709, dr[0.000,0.196], df[0.000,0.005], g[0.668,0.031]\n",
            ">4710, dr[0.000,0.313], df[0.000,0.004], g[0.824,0.031]\n",
            ">4711, dr[0.000,0.363], df[0.000,0.004], g[0.443,0.027]\n",
            ">4712, dr[0.000,0.432], df[0.000,0.010], g[0.412,0.065]\n",
            ">4713, dr[0.000,0.561], df[0.000,0.009], g[0.361,0.030]\n",
            ">4714, dr[0.000,0.369], df[0.000,0.002], g[0.539,0.033]\n",
            ">4715, dr[0.000,0.485], df[0.000,0.003], g[0.409,0.032]\n",
            ">4716, dr[0.000,0.451], df[0.000,0.015], g[0.417,0.028]\n",
            ">4717, dr[0.000,0.319], df[0.000,0.004], g[0.499,0.034]\n",
            ">4718, dr[0.000,0.455], df[0.000,0.005], g[0.595,0.030]\n",
            ">4719, dr[0.000,0.639], df[0.000,0.007], g[0.660,0.062]\n",
            ">4720, dr[0.000,0.456], df[0.000,0.019], g[0.371,0.050]\n",
            ">4721, dr[0.000,0.199], df[0.000,0.008], g[0.467,0.047]\n",
            ">4722, dr[0.000,0.530], df[0.000,0.003], g[0.363,0.027]\n",
            ">4723, dr[0.000,0.428], df[0.000,0.005], g[0.511,0.048]\n",
            ">4724, dr[0.000,0.435], df[0.000,0.005], g[0.428,0.041]\n",
            ">4725, dr[0.002,0.367], df[0.000,0.017], g[0.440,0.035]\n",
            ">4726, dr[0.000,0.642], df[0.000,0.007], g[0.433,0.039]\n",
            ">4727, dr[0.000,0.519], df[0.000,0.006], g[0.418,0.022]\n",
            ">4728, dr[0.000,0.324], df[0.001,0.002], g[0.641,0.015]\n",
            ">4729, dr[0.000,0.367], df[0.000,0.010], g[0.928,0.028]\n",
            ">4730, dr[0.000,0.760], df[0.000,0.007], g[1.015,0.029]\n",
            ">4731, dr[0.000,0.514], df[0.000,0.008], g[1.285,0.040]\n",
            ">4732, dr[0.002,0.389], df[0.000,0.017], g[1.077,0.036]\n",
            ">4733, dr[0.000,0.307], df[0.000,0.024], g[1.102,0.037]\n",
            ">4734, dr[0.000,0.434], df[0.000,0.005], g[1.191,0.028]\n",
            ">4735, dr[0.000,0.389], df[0.000,0.010], g[0.844,0.018]\n",
            ">4736, dr[0.000,0.466], df[0.000,0.012], g[0.919,0.024]\n",
            ">4737, dr[0.000,0.461], df[0.000,0.004], g[0.741,0.018]\n",
            ">4738, dr[0.000,0.608], df[0.000,0.005], g[0.850,0.048]\n",
            ">4739, dr[0.000,0.441], df[0.000,0.016], g[0.862,0.056]\n",
            ">4740, dr[0.000,0.648], df[0.000,0.004], g[1.243,0.050]\n",
            ">4741, dr[0.000,0.267], df[0.000,0.004], g[0.943,0.039]\n",
            ">4742, dr[0.000,0.483], df[0.000,0.011], g[0.983,0.039]\n",
            ">4743, dr[0.000,0.257], df[0.000,0.005], g[1.163,0.031]\n",
            ">4744, dr[0.000,0.336], df[0.000,0.005], g[1.004,0.032]\n",
            ">4745, dr[0.000,0.375], df[0.000,0.010], g[1.005,0.038]\n",
            ">4746, dr[0.000,0.489], df[0.000,0.005], g[1.262,0.036]\n",
            ">4747, dr[0.000,0.586], df[0.000,0.007], g[1.354,0.046]\n",
            ">4748, dr[0.000,0.379], df[0.000,0.008], g[1.309,0.040]\n",
            ">4749, dr[0.000,0.549], df[0.000,0.012], g[0.976,0.025]\n",
            ">4750, dr[0.000,0.511], df[0.000,0.012], g[1.105,0.047]\n",
            ">4751, dr[0.000,0.428], df[0.000,0.004], g[1.354,0.031]\n",
            ">4752, dr[0.000,0.388], df[0.000,0.009], g[1.494,0.071]\n",
            ">4753, dr[0.000,0.255], df[0.000,0.011], g[1.412,0.036]\n",
            ">4754, dr[0.000,0.399], df[0.001,0.006], g[1.430,0.035]\n",
            ">4755, dr[0.000,0.386], df[0.001,0.011], g[1.139,0.040]\n",
            ">4756, dr[0.000,0.284], df[0.000,0.006], g[1.291,0.037]\n",
            ">4757, dr[0.000,0.276], df[0.000,0.015], g[1.256,0.030]\n",
            ">4758, dr[0.000,0.859], df[0.000,0.027], g[1.089,0.030]\n",
            ">4759, dr[0.000,0.217], df[0.000,0.014], g[1.046,0.064]\n",
            ">4760, dr[0.000,0.405], df[0.000,0.007], g[1.002,0.041]\n",
            ">4761, dr[0.000,0.263], df[0.000,0.005], g[0.783,0.021]\n",
            ">4762, dr[0.000,0.279], df[0.000,0.005], g[0.811,0.029]\n",
            ">4763, dr[0.000,0.514], df[0.000,0.018], g[0.981,0.030]\n",
            ">4764, dr[0.000,0.388], df[0.000,0.014], g[0.857,0.028]\n",
            ">4765, dr[0.000,0.537], df[0.000,0.021], g[0.791,0.035]\n",
            ">4766, dr[0.000,0.438], df[0.000,0.016], g[0.851,0.075]\n",
            ">4767, dr[0.000,0.444], df[0.000,0.011], g[0.711,0.049]\n",
            ">4768, dr[0.000,0.624], df[0.000,0.009], g[0.794,0.039]\n",
            ">4769, dr[0.000,0.430], df[0.000,0.010], g[0.876,0.023]\n",
            ">4770, dr[0.000,0.409], df[0.000,0.016], g[0.981,0.026]\n",
            ">4771, dr[0.000,0.370], df[0.000,0.012], g[1.054,0.033]\n",
            ">4772, dr[0.000,0.414], df[0.000,0.006], g[1.078,0.086]\n",
            ">4773, dr[0.000,0.407], df[0.000,0.015], g[1.054,0.052]\n",
            ">4774, dr[0.000,0.235], df[0.000,0.006], g[0.860,0.049]\n",
            ">4775, dr[0.000,0.344], df[0.000,0.010], g[0.730,0.021]\n",
            ">4776, dr[0.000,0.373], df[0.000,0.004], g[0.604,0.045]\n",
            ">4777, dr[0.000,0.635], df[0.000,0.011], g[0.790,0.018]\n",
            ">4778, dr[0.000,0.496], df[0.000,0.006], g[0.720,0.026]\n",
            ">4779, dr[0.000,0.604], df[0.000,0.013], g[0.901,0.033]\n",
            ">4780, dr[0.001,0.423], df[0.000,0.005], g[0.734,0.032]\n",
            ">4781, dr[0.000,0.355], df[0.000,0.024], g[0.823,0.049]\n",
            ">4782, dr[0.000,0.542], df[0.000,0.007], g[0.979,0.038]\n",
            ">4783, dr[0.000,0.284], df[0.000,0.017], g[1.072,0.049]\n",
            ">4784, dr[0.000,0.380], df[0.000,0.010], g[1.077,0.042]\n",
            ">4785, dr[0.000,0.347], df[0.000,0.010], g[0.771,0.047]\n",
            ">4786, dr[0.000,0.308], df[0.000,0.008], g[0.834,0.033]\n",
            ">4787, dr[0.000,0.597], df[0.000,0.008], g[0.790,0.038]\n",
            ">4788, dr[0.000,0.224], df[0.000,0.007], g[0.806,0.046]\n",
            ">4789, dr[0.000,0.404], df[0.000,0.005], g[0.764,0.043]\n",
            ">4790, dr[0.003,0.421], df[0.000,0.019], g[0.663,0.046]\n",
            ">4791, dr[0.000,0.506], df[0.000,0.003], g[0.503,0.026]\n",
            ">4792, dr[0.000,0.396], df[0.000,0.008], g[0.486,0.029]\n",
            ">4793, dr[0.000,0.213], df[0.000,0.007], g[0.661,0.035]\n",
            ">4794, dr[0.000,0.496], df[0.000,0.013], g[0.525,0.023]\n",
            ">4795, dr[0.000,0.217], df[0.000,0.015], g[0.568,0.045]\n",
            ">4796, dr[0.000,0.287], df[0.000,0.018], g[0.521,0.015]\n",
            ">4797, dr[0.000,0.423], df[0.000,0.007], g[0.448,0.044]\n",
            ">4798, dr[0.000,0.353], df[0.000,0.012], g[0.623,0.032]\n",
            ">4799, dr[0.000,0.356], df[0.000,0.007], g[0.546,0.024]\n",
            ">4800, dr[0.000,0.412], df[0.000,0.004], g[0.630,0.049]\n",
            ">4801, dr[0.000,0.544], df[0.000,0.009], g[0.716,0.067]\n",
            ">4802, dr[0.000,0.480], df[0.000,0.016], g[0.910,0.031]\n",
            ">4803, dr[0.000,0.412], df[0.000,0.007], g[0.798,0.034]\n",
            ">4804, dr[0.000,0.477], df[0.000,0.003], g[1.022,0.028]\n",
            ">4805, dr[0.000,0.532], df[0.001,0.003], g[0.936,0.033]\n",
            ">4806, dr[0.000,0.275], df[0.000,0.014], g[0.881,0.025]\n",
            ">4807, dr[0.000,0.145], df[0.001,0.018], g[1.179,0.027]\n",
            ">4808, dr[0.000,0.705], df[0.000,0.008], g[1.058,0.050]\n",
            ">4809, dr[0.000,0.670], df[0.000,0.011], g[1.466,0.029]\n",
            ">4810, dr[0.000,0.304], df[0.000,0.006], g[1.454,0.055]\n",
            ">4811, dr[0.000,0.550], df[0.000,0.045], g[1.336,0.032]\n",
            ">4812, dr[0.000,0.508], df[0.000,0.017], g[1.447,0.061]\n",
            ">4813, dr[0.000,0.306], df[0.000,0.008], g[1.355,0.074]\n",
            ">4814, dr[0.000,0.474], df[0.000,0.025], g[0.789,0.075]\n",
            ">4815, dr[0.000,0.458], df[0.000,0.026], g[1.053,0.028]\n",
            ">4816, dr[0.000,0.531], df[0.000,0.033], g[1.212,0.037]\n",
            ">4817, dr[0.000,0.461], df[0.000,0.014], g[0.901,0.036]\n",
            ">4818, dr[0.000,0.368], df[0.000,0.015], g[0.860,0.049]\n",
            ">4819, dr[0.000,0.692], df[0.000,0.014], g[0.642,0.042]\n",
            ">4820, dr[0.000,0.369], df[0.000,0.013], g[0.741,0.035]\n",
            ">4821, dr[0.000,0.518], df[0.000,0.014], g[0.744,0.075]\n",
            ">4822, dr[0.000,0.363], df[0.000,0.012], g[0.801,0.065]\n",
            ">4823, dr[0.000,0.100], df[0.000,0.007], g[0.907,0.069]\n",
            ">4824, dr[0.000,0.833], df[0.000,0.027], g[0.927,0.061]\n",
            ">4825, dr[0.000,0.359], df[0.001,0.020], g[1.141,0.039]\n",
            ">4826, dr[0.000,0.123], df[0.000,0.011], g[1.263,0.065]\n",
            ">4827, dr[0.000,0.563], df[0.000,0.014], g[1.138,0.022]\n",
            ">4828, dr[0.000,0.180], df[0.000,0.017], g[1.043,0.040]\n",
            ">4829, dr[0.000,0.413], df[0.000,0.009], g[0.779,0.047]\n",
            ">4830, dr[0.000,0.813], df[0.000,0.016], g[1.016,0.050]\n",
            ">4831, dr[0.000,0.415], df[0.000,0.020], g[0.732,0.074]\n",
            ">4832, dr[0.000,0.500], df[0.000,0.017], g[0.862,0.058]\n",
            ">4833, dr[0.000,0.300], df[0.000,0.026], g[0.984,0.046]\n",
            ">4834, dr[0.000,0.479], df[0.000,0.011], g[0.875,0.038]\n",
            ">4835, dr[0.000,0.563], df[0.000,0.023], g[1.297,0.042]\n",
            ">4836, dr[0.000,0.309], df[0.000,0.021], g[1.056,0.059]\n",
            ">4837, dr[0.000,0.345], df[0.000,0.013], g[1.214,0.103]\n",
            ">4838, dr[0.000,0.486], df[0.000,0.015], g[0.766,0.038]\n",
            ">4839, dr[0.000,0.600], df[0.000,0.011], g[0.931,0.070]\n",
            ">4840, dr[0.000,0.545], df[0.000,0.023], g[1.074,0.046]\n",
            ">4841, dr[0.000,0.478], df[0.000,0.011], g[1.199,0.057]\n",
            ">4842, dr[0.000,0.667], df[0.000,0.007], g[1.472,0.061]\n",
            ">4843, dr[0.000,0.399], df[0.000,0.012], g[1.119,0.056]\n",
            ">4844, dr[0.000,0.738], df[0.000,0.007], g[0.995,0.044]\n",
            ">4845, dr[0.000,0.346], df[0.000,0.018], g[0.990,0.035]\n",
            ">4846, dr[0.000,0.507], df[0.000,0.015], g[0.739,0.026]\n",
            ">4847, dr[0.000,0.311], df[0.000,0.004], g[0.875,0.044]\n",
            ">4848, dr[0.000,0.369], df[0.000,0.015], g[1.072,0.030]\n",
            ">4849, dr[0.000,0.575], df[0.000,0.007], g[1.116,0.054]\n",
            ">4850, dr[0.000,0.258], df[0.000,0.008], g[1.157,0.037]\n",
            ">4851, dr[0.000,0.742], df[0.000,0.014], g[1.055,0.053]\n",
            ">4852, dr[0.000,0.349], df[0.000,0.006], g[1.112,0.028]\n",
            ">4853, dr[0.000,0.505], df[0.000,0.008], g[1.159,0.070]\n",
            ">4854, dr[0.000,0.311], df[0.000,0.010], g[1.172,0.037]\n",
            ">4855, dr[0.000,0.588], df[0.000,0.009], g[1.136,0.054]\n",
            ">4856, dr[0.000,0.337], df[0.000,0.005], g[1.078,0.039]\n",
            ">4857, dr[0.000,0.613], df[0.000,0.014], g[1.140,0.043]\n",
            ">4858, dr[0.000,0.580], df[0.000,0.005], g[1.291,0.054]\n",
            ">4859, dr[0.000,0.554], df[0.000,0.009], g[1.064,0.043]\n",
            ">4860, dr[0.000,0.349], df[0.000,0.011], g[1.084,0.037]\n",
            ">4861, dr[0.000,0.627], df[0.000,0.014], g[1.000,0.044]\n",
            ">4862, dr[0.000,0.626], df[0.000,0.007], g[1.138,0.041]\n",
            ">4863, dr[0.000,0.375], df[0.001,0.019], g[1.323,0.026]\n",
            ">4864, dr[0.000,0.257], df[0.000,0.007], g[1.232,0.043]\n",
            ">4865, dr[0.000,0.417], df[0.000,0.004], g[1.491,0.040]\n",
            ">4866, dr[0.000,0.332], df[0.000,0.011], g[0.939,0.050]\n",
            ">4867, dr[0.000,0.298], df[0.000,0.013], g[1.320,0.040]\n",
            ">4868, dr[0.000,0.464], df[0.000,0.013], g[1.062,0.068]\n",
            ">4869, dr[0.000,0.463], df[0.002,0.004], g[1.184,0.033]\n",
            ">4870, dr[0.000,0.358], df[0.000,0.007], g[1.104,0.036]\n",
            ">4871, dr[0.000,0.322], df[0.000,0.025], g[1.197,0.034]\n",
            ">4872, dr[0.000,0.482], df[0.000,0.009], g[1.073,0.050]\n",
            ">4873, dr[0.000,0.158], df[0.000,0.025], g[0.841,0.043]\n",
            ">4874, dr[0.000,0.432], df[0.000,0.011], g[0.950,0.035]\n",
            ">4875, dr[0.000,0.355], df[0.000,0.004], g[0.853,0.050]\n",
            ">4876, dr[0.000,0.936], df[0.000,0.009], g[0.923,0.054]\n",
            ">4877, dr[0.000,0.498], df[0.000,0.011], g[1.266,0.060]\n",
            ">4878, dr[0.000,0.469], df[0.000,0.013], g[1.152,0.081]\n",
            ">4879, dr[0.000,0.336], df[0.000,0.006], g[1.517,0.026]\n",
            ">4880, dr[0.000,0.396], df[0.000,0.005], g[1.244,0.032]\n",
            ">4881, dr[0.000,0.356], df[0.000,0.012], g[0.831,0.061]\n",
            ">4882, dr[0.000,0.308], df[0.000,0.005], g[1.024,0.059]\n",
            ">4883, dr[0.000,0.346], df[0.000,0.005], g[0.804,0.028]\n",
            ">4884, dr[0.000,0.303], df[0.000,0.025], g[0.908,0.041]\n",
            ">4885, dr[0.000,0.462], df[0.000,0.001], g[0.634,0.058]\n",
            ">4886, dr[0.000,0.240], df[0.000,0.024], g[0.902,0.112]\n",
            ">4887, dr[0.000,0.494], df[0.000,0.009], g[0.893,0.084]\n",
            ">4888, dr[0.000,0.946], df[0.000,0.003], g[1.010,0.047]\n",
            ">4889, dr[0.000,0.352], df[0.000,0.005], g[1.060,0.035]\n",
            ">4890, dr[0.000,0.454], df[0.000,0.004], g[1.179,0.032]\n",
            ">4891, dr[0.000,0.472], df[0.000,0.008], g[0.925,0.033]\n",
            ">4892, dr[0.000,0.309], df[0.000,0.010], g[0.919,0.042]\n",
            ">4893, dr[0.000,0.654], df[0.000,0.004], g[1.072,0.038]\n",
            ">4894, dr[0.000,0.288], df[0.000,0.007], g[0.902,0.032]\n",
            ">4895, dr[0.000,0.334], df[0.000,0.004], g[0.839,0.043]\n",
            ">4896, dr[0.000,0.352], df[0.000,0.003], g[0.913,0.042]\n",
            ">4897, dr[0.000,0.478], df[0.000,0.022], g[0.983,0.040]\n",
            ">4898, dr[0.000,0.473], df[0.000,0.012], g[0.962,0.038]\n",
            ">4899, dr[0.000,0.414], df[0.000,0.008], g[0.978,0.029]\n",
            ">4900, dr[0.000,0.542], df[0.000,0.007], g[0.760,0.053]\n",
            ">4901, dr[0.000,0.285], df[0.000,0.014], g[0.892,0.027]\n",
            ">4902, dr[0.000,0.265], df[0.000,0.016], g[1.069,0.045]\n",
            ">4903, dr[0.001,0.415], df[0.000,0.002], g[1.048,0.079]\n",
            ">4904, dr[0.000,0.524], df[0.000,0.027], g[0.870,0.070]\n",
            ">4905, dr[0.000,0.604], df[0.000,0.008], g[0.771,0.034]\n",
            ">4906, dr[0.000,0.560], df[0.001,0.009], g[0.638,0.024]\n",
            ">4907, dr[0.000,0.343], df[0.000,0.003], g[0.681,0.052]\n",
            ">4908, dr[0.000,0.267], df[0.000,0.006], g[0.997,0.046]\n",
            ">4909, dr[0.000,0.456], df[0.000,0.014], g[0.988,0.043]\n",
            ">4910, dr[0.000,0.712], df[0.000,0.004], g[0.693,0.038]\n",
            ">4911, dr[0.000,0.305], df[0.000,0.002], g[0.915,0.036]\n",
            ">4912, dr[0.000,0.592], df[0.000,0.007], g[0.782,0.040]\n",
            ">4913, dr[0.000,0.331], df[0.000,0.016], g[0.810,0.070]\n",
            ">4914, dr[0.000,0.338], df[0.000,0.006], g[1.142,0.038]\n",
            ">4915, dr[0.000,0.650], df[0.000,0.003], g[1.277,0.055]\n",
            ">4916, dr[0.000,0.409], df[0.000,0.008], g[1.105,0.068]\n",
            ">4917, dr[0.000,0.323], df[0.000,0.004], g[0.896,0.037]\n",
            ">4918, dr[0.000,0.594], df[0.000,0.003], g[1.019,0.074]\n",
            ">4919, dr[0.000,0.386], df[0.000,0.002], g[0.958,0.031]\n",
            ">4920, dr[0.000,0.520], df[0.000,0.005], g[0.845,0.032]\n",
            ">4921, dr[0.000,0.456], df[0.000,0.001], g[0.816,0.026]\n",
            ">4922, dr[0.000,0.356], df[0.000,0.005], g[0.931,0.022]\n",
            ">4923, dr[0.000,0.464], df[0.000,0.006], g[1.145,0.034]\n",
            ">4924, dr[0.000,0.308], df[0.000,0.002], g[0.715,0.042]\n",
            ">4925, dr[0.000,0.672], df[0.000,0.006], g[1.027,0.042]\n",
            ">4926, dr[0.000,0.276], df[0.000,0.003], g[0.925,0.043]\n",
            ">4927, dr[0.000,0.557], df[0.000,0.007], g[0.909,0.045]\n",
            ">4928, dr[0.000,0.568], df[0.000,0.008], g[0.903,0.032]\n",
            ">4929, dr[0.000,0.348], df[0.000,0.004], g[0.726,0.050]\n",
            ">4930, dr[0.001,0.520], df[0.000,0.001], g[0.652,0.039]\n",
            ">4931, dr[0.000,0.310], df[0.000,0.005], g[0.699,0.071]\n",
            ">4932, dr[0.000,0.728], df[0.000,0.004], g[0.660,0.031]\n",
            ">4933, dr[0.000,0.423], df[0.000,0.002], g[0.644,0.041]\n",
            ">4934, dr[0.000,0.202], df[0.000,0.039], g[0.564,0.029]\n",
            ">4935, dr[0.000,0.482], df[0.000,0.018], g[0.630,0.019]\n",
            ">4936, dr[0.000,0.311], df[0.000,0.004], g[0.476,0.037]\n",
            ">4937, dr[0.000,0.540], df[0.000,0.003], g[0.607,0.035]\n",
            ">4938, dr[0.001,0.553], df[0.000,0.003], g[0.707,0.038]\n",
            ">4939, dr[0.000,0.542], df[0.000,0.004], g[0.596,0.036]\n",
            ">4940, dr[0.000,0.208], df[0.000,0.002], g[0.526,0.040]\n",
            ">4941, dr[0.000,0.739], df[0.000,0.003], g[0.546,0.031]\n",
            ">4942, dr[0.000,0.390], df[0.000,0.006], g[0.543,0.048]\n",
            ">4943, dr[0.000,0.396], df[0.000,0.005], g[0.455,0.030]\n",
            ">4944, dr[0.000,0.747], df[0.000,0.004], g[0.405,0.059]\n",
            ">4945, dr[0.000,0.343], df[0.001,0.007], g[0.526,0.034]\n",
            ">4946, dr[0.000,0.330], df[0.000,0.002], g[0.905,0.017]\n",
            ">4947, dr[0.000,0.664], df[0.000,0.005], g[0.871,0.042]\n",
            ">4948, dr[0.000,0.396], df[0.000,0.001], g[0.828,0.037]\n",
            ">4949, dr[0.000,0.424], df[0.000,0.003], g[0.639,0.023]\n",
            ">4950, dr[0.000,0.573], df[0.000,0.003], g[0.859,0.032]\n",
            ">4951, dr[0.002,0.349], df[0.003,0.003], g[1.115,0.041]\n",
            ">4952, dr[0.001,0.501], df[0.000,0.007], g[1.198,0.047]\n",
            ">4953, dr[0.001,0.411], df[0.000,0.005], g[1.515,0.029]\n",
            ">4954, dr[0.001,0.159], df[0.000,0.005], g[1.235,0.053]\n",
            ">4955, dr[0.001,0.374], df[0.000,0.011], g[1.338,0.065]\n",
            ">4956, dr[0.000,0.588], df[0.001,0.003], g[1.550,0.033]\n",
            ">4957, dr[0.002,0.445], df[0.000,0.005], g[1.397,0.035]\n",
            ">4958, dr[0.000,0.252], df[0.000,0.005], g[1.708,0.016]\n",
            ">4959, dr[0.000,0.375], df[0.000,0.002], g[1.370,0.039]\n",
            ">4960, dr[0.000,0.428], df[0.000,0.003], g[1.519,0.051]\n",
            ">4961, dr[0.000,0.533], df[0.000,0.014], g[1.641,0.033]\n",
            ">4962, dr[0.001,0.319], df[0.000,0.002], g[1.458,0.027]\n",
            ">4963, dr[0.000,0.522], df[0.000,0.004], g[1.224,0.037]\n",
            ">4964, dr[0.001,0.453], df[0.000,0.003], g[1.139,0.028]\n",
            ">4965, dr[0.000,0.330], df[0.000,0.003], g[0.810,0.030]\n",
            ">4966, dr[0.000,0.318], df[0.000,0.003], g[0.746,0.028]\n",
            ">4967, dr[0.000,0.438], df[0.000,0.005], g[0.382,0.054]\n",
            ">4968, dr[0.000,0.459], df[0.001,0.004], g[0.198,0.019]\n",
            ">4969, dr[0.001,0.462], df[0.001,0.001], g[0.442,0.014]\n",
            ">4970, dr[0.000,0.408], df[0.000,0.002], g[0.339,0.033]\n",
            ">4971, dr[0.001,0.255], df[0.001,0.003], g[0.312,0.038]\n",
            ">4972, dr[0.000,0.388], df[0.000,0.003], g[0.359,0.033]\n",
            ">4973, dr[0.002,0.299], df[0.000,0.004], g[0.368,0.019]\n",
            ">4974, dr[0.000,0.741], df[0.005,0.001], g[0.370,0.033]\n",
            ">4975, dr[0.000,0.388], df[0.000,0.002], g[0.542,0.024]\n",
            ">4976, dr[0.000,0.550], df[0.000,0.002], g[0.415,0.033]\n",
            ">4977, dr[0.000,0.617], df[0.000,0.002], g[0.616,0.021]\n",
            ">4978, dr[0.001,0.395], df[0.000,0.002], g[0.476,0.045]\n",
            ">4979, dr[0.000,0.300], df[0.000,0.003], g[0.696,0.038]\n",
            ">4980, dr[0.000,0.398], df[0.000,0.003], g[0.570,0.038]\n",
            ">4981, dr[0.001,0.549], df[0.000,0.004], g[0.541,0.043]\n",
            ">4982, dr[0.000,0.582], df[0.000,0.001], g[0.663,0.020]\n",
            ">4983, dr[0.000,0.342], df[0.000,0.001], g[0.419,0.023]\n",
            ">4984, dr[0.000,0.395], df[0.000,0.001], g[0.821,0.027]\n",
            ">4985, dr[0.000,0.507], df[0.000,0.002], g[0.668,0.022]\n",
            ">4986, dr[0.000,0.440], df[0.000,0.001], g[0.681,0.048]\n",
            ">4987, dr[0.000,0.225], df[0.000,0.004], g[0.503,0.039]\n",
            ">4988, dr[0.001,0.511], df[0.000,0.001], g[0.368,0.027]\n",
            ">4989, dr[0.001,0.565], df[0.000,0.004], g[0.245,0.087]\n",
            ">4990, dr[0.000,0.385], df[0.000,0.001], g[0.464,0.035]\n",
            ">4991, dr[0.000,0.392], df[0.000,0.002], g[0.397,0.052]\n",
            ">4992, dr[0.000,0.494], df[0.001,0.001], g[0.373,0.023]\n",
            ">4993, dr[0.001,0.405], df[0.000,0.002], g[0.519,0.037]\n",
            ">4994, dr[0.000,0.388], df[0.000,0.001], g[0.543,0.043]\n",
            ">4995, dr[0.000,0.424], df[0.000,0.006], g[0.675,0.031]\n",
            ">4996, dr[0.001,0.473], df[0.000,0.001], g[0.440,0.024]\n",
            ">4997, dr[0.000,0.576], df[0.000,0.002], g[0.374,0.012]\n",
            ">4998, dr[0.000,0.428], df[0.001,0.001], g[0.463,0.050]\n",
            ">4999, dr[0.000,0.576], df[0.000,0.001], g[0.697,0.022]\n",
            ">5000, dr[0.000,0.369], df[0.000,0.001], g[0.931,0.025]\n",
            ">5001, dr[0.000,0.189], df[0.000,0.001], g[0.681,0.018]\n",
            ">5002, dr[0.000,0.314], df[0.000,0.002], g[0.734,0.017]\n",
            ">5003, dr[0.000,0.598], df[0.000,0.001], g[0.668,0.039]\n",
            ">5004, dr[0.001,0.372], df[0.000,0.001], g[1.006,0.024]\n",
            ">5005, dr[0.000,0.356], df[0.000,0.001], g[0.872,0.045]\n",
            ">5006, dr[0.000,0.725], df[0.000,0.002], g[0.720,0.051]\n",
            ">5007, dr[0.000,0.540], df[0.000,0.006], g[0.420,0.073]\n",
            ">5008, dr[0.000,0.305], df[0.000,0.001], g[0.601,0.044]\n",
            ">5009, dr[0.000,0.349], df[0.000,0.002], g[0.502,0.042]\n",
            ">5010, dr[0.000,0.520], df[0.000,0.001], g[0.327,0.041]\n",
            ">5011, dr[0.000,0.728], df[0.000,0.003], g[0.364,0.042]\n",
            ">5012, dr[0.000,0.609], df[0.000,0.000], g[0.225,0.041]\n",
            ">5013, dr[0.000,0.428], df[0.000,0.001], g[0.191,0.021]\n",
            ">5014, dr[0.000,0.910], df[0.000,0.006], g[0.389,0.029]\n",
            ">5015, dr[0.000,0.433], df[0.000,0.001], g[0.399,0.020]\n",
            ">5016, dr[0.000,0.227], df[0.000,0.002], g[0.415,0.076]\n",
            ">5017, dr[0.000,0.493], df[0.000,0.001], g[0.503,0.020]\n",
            ">5018, dr[0.000,0.376], df[0.000,0.001], g[0.487,0.021]\n",
            ">5019, dr[0.000,0.379], df[0.000,0.001], g[0.451,0.023]\n",
            ">5020, dr[0.000,0.303], df[0.000,0.001], g[0.278,0.033]\n",
            ">5021, dr[0.000,0.384], df[0.000,0.002], g[0.229,0.031]\n",
            ">5022, dr[0.000,0.457], df[0.000,0.000], g[0.391,0.019]\n",
            ">5023, dr[0.000,0.420], df[0.000,0.001], g[0.370,0.027]\n",
            ">5024, dr[0.000,0.520], df[0.000,0.001], g[0.342,0.043]\n",
            ">5025, dr[0.000,0.451], df[0.000,0.001], g[0.411,0.022]\n",
            ">5026, dr[0.000,0.426], df[0.000,0.001], g[0.135,0.041]\n",
            ">5027, dr[0.000,0.521], df[0.001,0.001], g[0.175,0.045]\n",
            ">5028, dr[0.000,0.335], df[0.001,0.001], g[0.191,0.024]\n",
            ">5029, dr[0.000,0.737], df[0.000,0.001], g[0.301,0.017]\n",
            ">5030, dr[0.000,0.210], df[0.000,0.001], g[0.339,0.011]\n",
            ">5031, dr[0.000,0.208], df[0.000,0.002], g[0.329,0.023]\n",
            ">5032, dr[0.000,0.876], df[0.000,0.001], g[0.393,0.014]\n",
            ">5033, dr[0.000,0.119], df[0.000,0.004], g[0.295,0.023]\n",
            ">5034, dr[0.000,0.212], df[0.000,0.004], g[0.322,0.045]\n",
            ">5035, dr[0.000,0.493], df[0.000,0.001], g[0.291,0.024]\n",
            ">5036, dr[0.000,0.253], df[0.000,0.002], g[0.334,0.020]\n",
            ">5037, dr[0.000,0.418], df[0.000,0.001], g[0.351,0.022]\n",
            ">5038, dr[0.000,0.566], df[0.000,0.001], g[0.421,0.015]\n",
            ">5039, dr[0.000,0.260], df[0.000,0.001], g[0.547,0.047]\n",
            ">5040, dr[0.001,0.345], df[0.000,0.003], g[0.368,0.029]\n",
            ">5041, dr[0.000,0.588], df[0.000,0.001], g[0.380,0.028]\n",
            ">5042, dr[0.000,0.654], df[0.000,0.002], g[0.337,0.033]\n",
            ">5043, dr[0.000,0.436], df[0.002,0.002], g[0.325,0.048]\n",
            ">5044, dr[0.000,0.362], df[0.000,0.002], g[0.345,0.034]\n",
            ">5045, dr[0.000,0.555], df[0.000,0.002], g[0.374,0.056]\n",
            ">5046, dr[0.000,0.190], df[0.000,0.001], g[0.422,0.025]\n",
            ">5047, dr[0.000,0.442], df[0.000,0.001], g[0.491,0.029]\n",
            ">5048, dr[0.001,0.252], df[0.000,0.019], g[0.373,0.025]\n",
            ">5049, dr[0.001,0.677], df[0.000,0.001], g[0.461,0.030]\n",
            ">5050, dr[0.001,0.589], df[0.000,0.002], g[0.394,0.034]\n",
            ">5051, dr[0.000,0.470], df[0.000,0.004], g[0.492,0.046]\n",
            ">5052, dr[0.000,0.327], df[0.000,0.003], g[0.404,0.043]\n",
            ">5053, dr[0.000,0.460], df[0.000,0.006], g[0.375,0.033]\n",
            ">5054, dr[0.000,0.264], df[0.000,0.001], g[0.510,0.029]\n",
            ">5055, dr[0.000,0.552], df[0.000,0.001], g[0.702,0.054]\n",
            ">5056, dr[0.000,0.402], df[0.000,0.003], g[0.509,0.056]\n",
            ">5057, dr[0.000,0.474], df[0.000,0.001], g[0.184,0.039]\n",
            ">5058, dr[0.000,0.396], df[0.000,0.003], g[0.350,0.067]\n",
            ">5059, dr[0.001,0.456], df[0.000,0.002], g[0.339,0.046]\n",
            ">5060, dr[0.000,0.743], df[0.000,0.004], g[0.257,0.022]\n",
            ">5061, dr[0.000,0.457], df[0.001,0.002], g[0.413,0.035]\n",
            ">5062, dr[0.000,0.824], df[0.000,0.002], g[0.399,0.015]\n",
            ">5063, dr[0.001,0.347], df[0.000,0.002], g[0.326,0.025]\n",
            ">5064, dr[0.000,0.448], df[0.000,0.001], g[0.335,0.018]\n",
            ">5065, dr[0.000,0.628], df[0.000,0.001], g[0.370,0.026]\n",
            ">5066, dr[0.000,0.373], df[0.000,0.001], g[0.319,0.021]\n",
            ">5067, dr[0.000,0.351], df[0.000,0.008], g[0.256,0.020]\n",
            ">5068, dr[0.000,0.512], df[0.000,0.002], g[0.245,0.026]\n",
            ">5069, dr[0.001,0.221], df[0.000,0.004], g[0.336,0.032]\n",
            ">5070, dr[0.003,0.590], df[0.000,0.002], g[0.222,0.037]\n",
            ">5071, dr[0.001,0.648], df[0.000,0.001], g[0.290,0.052]\n",
            ">5072, dr[0.000,0.421], df[0.001,0.001], g[0.352,0.047]\n",
            ">5073, dr[0.001,0.382], df[0.000,0.001], g[0.255,0.043]\n",
            ">5074, dr[0.001,0.408], df[0.000,0.002], g[0.214,0.029]\n",
            ">5075, dr[0.000,0.591], df[0.000,0.006], g[0.159,0.021]\n",
            ">5076, dr[0.000,0.845], df[0.000,0.004], g[0.370,0.027]\n",
            ">5077, dr[0.000,0.399], df[0.001,0.001], g[0.357,0.045]\n",
            ">5078, dr[0.000,0.416], df[0.000,0.001], g[0.522,0.030]\n",
            ">5079, dr[0.000,0.536], df[0.000,0.001], g[0.484,0.040]\n",
            ">5080, dr[0.000,0.541], df[0.000,0.002], g[0.373,0.025]\n",
            ">5081, dr[0.000,0.350], df[0.000,0.001], g[0.178,0.029]\n",
            ">5082, dr[0.000,0.312], df[0.000,0.003], g[0.292,0.025]\n",
            ">5083, dr[0.000,0.596], df[0.001,0.002], g[0.291,0.066]\n",
            ">5084, dr[0.002,0.292], df[0.000,0.003], g[0.238,0.055]\n",
            ">5085, dr[0.000,0.295], df[0.001,0.001], g[0.182,0.048]\n",
            ">5086, dr[0.000,0.207], df[0.002,0.001], g[0.423,0.055]\n",
            ">5087, dr[0.001,0.473], df[0.000,0.004], g[0.413,0.062]\n",
            ">5088, dr[0.000,0.260], df[0.000,0.005], g[0.719,0.038]\n",
            ">5089, dr[0.001,0.475], df[0.007,0.009], g[0.969,0.057]\n",
            ">5090, dr[0.001,0.614], df[0.000,0.009], g[1.493,0.034]\n",
            ">5091, dr[0.000,0.582], df[0.000,0.007], g[1.923,0.030]\n",
            ">5092, dr[0.004,0.287], df[0.000,0.003], g[2.353,0.029]\n",
            ">5093, dr[0.000,0.366], df[0.000,0.006], g[2.073,0.046]\n",
            ">5094, dr[0.001,0.327], df[0.000,0.002], g[1.775,0.061]\n",
            ">5095, dr[0.000,0.327], df[0.000,0.002], g[1.372,0.067]\n",
            ">5096, dr[0.000,0.244], df[0.000,0.004], g[1.177,0.078]\n",
            ">5097, dr[0.000,0.539], df[0.000,0.005], g[1.581,0.067]\n",
            ">5098, dr[0.000,0.465], df[0.000,0.009], g[1.366,0.085]\n",
            ">5099, dr[0.000,0.317], df[0.000,0.002], g[1.325,0.059]\n",
            ">5100, dr[0.000,0.511], df[0.000,0.012], g[1.206,0.049]\n",
            ">5101, dr[0.000,0.471], df[0.000,0.004], g[1.210,0.068]\n",
            ">5102, dr[0.000,0.581], df[0.000,0.010], g[1.363,0.059]\n",
            ">5103, dr[0.000,0.439], df[0.000,0.026], g[1.213,0.045]\n",
            ">5104, dr[0.000,0.431], df[0.000,0.010], g[1.312,0.027]\n",
            ">5105, dr[0.001,0.460], df[0.000,0.012], g[1.085,0.125]\n",
            ">5106, dr[0.013,0.439], df[0.000,0.001], g[0.675,0.049]\n",
            ">5107, dr[0.000,0.569], df[0.000,0.008], g[0.365,0.048]\n",
            ">5108, dr[0.000,0.499], df[0.000,0.006], g[0.407,0.057]\n",
            ">5109, dr[0.001,0.642], df[0.001,0.013], g[0.529,0.033]\n",
            ">5110, dr[0.000,0.462], df[0.000,0.017], g[0.506,0.124]\n",
            ">5111, dr[0.000,0.304], df[0.000,0.005], g[0.403,0.043]\n",
            ">5112, dr[0.000,0.331], df[0.003,0.014], g[0.662,0.030]\n",
            ">5113, dr[0.000,0.314], df[0.001,0.012], g[0.944,0.089]\n",
            ">5114, dr[0.000,0.647], df[0.009,0.056], g[1.641,0.065]\n",
            ">5115, dr[0.000,0.685], df[0.000,0.018], g[2.165,0.056]\n",
            ">5116, dr[0.000,0.450], df[0.000,0.014], g[2.458,0.092]\n",
            ">5117, dr[0.000,0.272], df[0.000,0.011], g[2.038,0.195]\n",
            ">5118, dr[0.000,0.214], df[0.000,0.014], g[1.544,0.084]\n",
            ">5119, dr[0.000,0.698], df[0.000,0.017], g[1.750,0.097]\n",
            ">5120, dr[0.000,0.454], df[0.000,0.009], g[1.423,0.179]\n",
            ">5121, dr[0.000,0.361], df[0.000,0.021], g[1.265,0.107]\n",
            ">5122, dr[0.000,0.311], df[0.000,0.006], g[1.482,0.077]\n",
            ">5123, dr[0.001,0.421], df[0.000,0.003], g[1.518,0.058]\n",
            ">5124, dr[0.001,0.620], df[0.000,0.003], g[1.265,0.082]\n",
            ">5125, dr[0.000,0.421], df[0.000,0.004], g[1.381,0.040]\n",
            ">5126, dr[0.000,0.306], df[0.000,0.004], g[1.160,0.044]\n",
            ">5127, dr[0.000,0.466], df[0.000,0.003], g[1.182,0.062]\n",
            ">5128, dr[0.000,0.184], df[0.000,0.009], g[0.871,0.074]\n",
            ">5129, dr[0.000,0.307], df[0.000,0.003], g[1.179,0.065]\n",
            ">5130, dr[0.000,0.293], df[0.000,0.005], g[1.242,0.081]\n",
            ">5131, dr[0.000,0.566], df[0.000,0.003], g[1.075,0.039]\n",
            ">5132, dr[0.001,0.535], df[0.000,0.002], g[0.712,0.064]\n",
            ">5133, dr[0.000,0.330], df[0.000,0.002], g[0.782,0.043]\n",
            ">5134, dr[0.000,0.645], df[0.000,0.006], g[0.988,0.040]\n",
            ">5135, dr[0.000,0.258], df[0.000,0.005], g[0.968,0.073]\n",
            ">5136, dr[0.000,0.225], df[0.000,0.004], g[0.773,0.048]\n",
            ">5137, dr[0.000,0.588], df[0.000,0.005], g[0.679,0.127]\n",
            ">5138, dr[0.000,1.192], df[0.000,0.008], g[0.818,0.094]\n",
            ">5139, dr[0.000,0.317], df[0.000,0.003], g[0.781,0.065]\n",
            ">5140, dr[0.000,0.351], df[0.000,0.005], g[0.666,0.044]\n",
            ">5141, dr[0.000,0.365], df[0.000,0.004], g[0.725,0.077]\n",
            ">5142, dr[0.000,0.385], df[0.000,0.005], g[0.404,0.050]\n",
            ">5143, dr[0.000,0.493], df[0.000,0.003], g[0.550,0.044]\n",
            ">5144, dr[0.000,0.618], df[0.000,0.002], g[0.544,0.037]\n",
            ">5145, dr[0.001,0.302], df[0.000,0.010], g[0.681,0.037]\n",
            ">5146, dr[0.000,0.386], df[0.000,0.003], g[0.607,0.023]\n",
            ">5147, dr[0.000,0.347], df[0.000,0.009], g[0.580,0.017]\n",
            ">5148, dr[0.000,0.526], df[0.000,0.006], g[0.401,0.047]\n",
            ">5149, dr[0.000,0.379], df[0.000,0.008], g[0.496,0.033]\n",
            ">5150, dr[0.000,0.153], df[0.000,0.007], g[0.536,0.029]\n",
            ">5151, dr[0.000,0.257], df[0.000,0.009], g[0.439,0.013]\n",
            ">5152, dr[0.000,0.421], df[0.000,0.006], g[0.414,0.033]\n",
            ">5153, dr[0.000,0.498], df[0.000,0.015], g[0.264,0.061]\n",
            ">5154, dr[0.000,0.373], df[0.000,0.012], g[0.228,0.054]\n",
            ">5155, dr[0.000,0.269], df[0.000,0.003], g[0.181,0.040]\n",
            ">5156, dr[0.000,0.230], df[0.000,0.008], g[0.137,0.029]\n",
            ">5157, dr[0.000,0.348], df[0.000,0.002], g[0.287,0.015]\n",
            ">5158, dr[0.000,0.518], df[0.000,0.008], g[0.324,0.053]\n",
            ">5159, dr[0.000,0.521], df[0.000,0.006], g[0.355,0.042]\n",
            ">5160, dr[0.000,0.420], df[0.000,0.004], g[0.245,0.047]\n",
            ">5161, dr[0.000,0.617], df[0.000,0.003], g[0.228,0.048]\n",
            ">5162, dr[0.000,0.688], df[0.000,0.004], g[0.283,0.018]\n",
            ">5163, dr[0.000,0.224], df[0.000,0.008], g[0.129,0.019]\n",
            ">5164, dr[0.000,0.293], df[0.000,0.003], g[0.239,0.018]\n",
            ">5165, dr[0.000,0.253], df[0.000,0.006], g[0.132,0.027]\n",
            ">5166, dr[0.000,0.364], df[0.000,0.005], g[0.245,0.031]\n",
            ">5167, dr[0.000,0.482], df[0.000,0.019], g[0.273,0.043]\n",
            ">5168, dr[0.000,0.631], df[0.000,0.002], g[0.251,0.026]\n",
            ">5169, dr[0.000,0.316], df[0.000,0.015], g[0.236,0.014]\n",
            ">5170, dr[0.000,0.478], df[0.000,0.002], g[0.361,0.021]\n",
            ">5171, dr[0.000,0.334], df[0.000,0.007], g[0.252,0.032]\n",
            ">5172, dr[0.000,0.545], df[0.000,0.003], g[0.214,0.020]\n",
            ">5173, dr[0.000,0.485], df[0.000,0.003], g[0.182,0.050]\n",
            ">5174, dr[0.000,0.358], df[0.000,0.003], g[0.211,0.016]\n",
            ">5175, dr[0.000,0.522], df[0.000,0.010], g[0.262,0.019]\n",
            ">5176, dr[0.000,0.370], df[0.000,0.002], g[0.441,0.044]\n",
            ">5177, dr[0.003,0.334], df[0.000,0.004], g[0.319,0.028]\n",
            ">5178, dr[0.000,0.452], df[0.000,0.004], g[0.399,0.040]\n",
            ">5179, dr[0.000,0.465], df[0.000,0.003], g[0.510,0.031]\n",
            ">5180, dr[0.000,0.652], df[0.000,0.005], g[0.299,0.059]\n",
            ">5181, dr[0.000,0.287], df[0.000,0.004], g[0.332,0.060]\n",
            ">5182, dr[0.000,0.300], df[0.000,0.006], g[0.535,0.029]\n",
            ">5183, dr[0.002,0.422], df[0.000,0.003], g[0.513,0.022]\n",
            ">5184, dr[0.001,0.798], df[0.000,0.005], g[0.420,0.030]\n",
            ">5185, dr[0.001,0.384], df[0.000,0.004], g[0.363,0.033]\n",
            ">5186, dr[0.000,0.584], df[0.000,0.003], g[0.296,0.045]\n",
            ">5187, dr[0.000,0.382], df[0.000,0.005], g[0.265,0.027]\n",
            ">5188, dr[0.000,0.302], df[0.001,0.006], g[0.333,0.035]\n",
            ">5189, dr[0.000,0.290], df[0.000,0.003], g[0.269,0.026]\n",
            ">5190, dr[0.000,0.273], df[0.000,0.004], g[0.375,0.027]\n",
            ">5191, dr[0.000,0.260], df[0.000,0.004], g[0.533,0.063]\n",
            ">5192, dr[0.000,0.392], df[0.000,0.004], g[0.582,0.021]\n",
            ">5193, dr[0.000,0.429], df[0.000,0.002], g[0.420,0.063]\n",
            ">5194, dr[0.000,0.722], df[0.000,0.005], g[0.246,0.024]\n",
            ">5195, dr[0.000,0.362], df[0.000,0.003], g[0.331,0.017]\n",
            ">5196, dr[0.000,0.726], df[0.000,0.003], g[0.606,0.012]\n",
            ">5197, dr[0.000,0.961], df[0.000,0.033], g[0.671,0.017]\n",
            ">5198, dr[0.000,0.493], df[0.000,0.002], g[0.623,0.032]\n",
            ">5199, dr[0.000,0.268], df[0.000,0.003], g[0.406,0.046]\n",
            ">5200, dr[0.001,0.410], df[0.000,0.003], g[0.555,0.019]\n",
            ">5201, dr[0.000,0.299], df[0.000,0.006], g[0.281,0.035]\n",
            ">5202, dr[0.000,0.406], df[0.000,0.003], g[0.305,0.034]\n",
            ">5203, dr[0.000,0.365], df[0.000,0.008], g[0.547,0.024]\n",
            ">5204, dr[0.000,0.405], df[0.000,0.011], g[0.902,0.038]\n",
            ">5205, dr[0.001,0.237], df[0.000,0.011], g[0.783,0.045]\n",
            ">5206, dr[0.000,0.676], df[0.001,0.008], g[0.713,0.042]\n",
            ">5207, dr[0.000,0.626], df[0.000,0.006], g[0.609,0.034]\n",
            ">5208, dr[0.000,0.267], df[0.001,0.003], g[0.878,0.039]\n",
            ">5209, dr[0.000,0.224], df[0.000,0.005], g[1.163,0.085]\n",
            ">5210, dr[0.000,0.253], df[0.000,0.005], g[0.799,0.062]\n",
            ">5211, dr[0.000,0.525], df[0.000,0.025], g[0.990,0.074]\n",
            ">5212, dr[0.000,0.475], df[0.000,0.008], g[0.935,0.082]\n",
            ">5213, dr[0.000,0.478], df[0.000,0.008], g[0.748,0.053]\n",
            ">5214, dr[0.000,0.470], df[0.000,0.018], g[0.927,0.050]\n",
            ">5215, dr[0.001,0.784], df[0.000,0.006], g[1.090,0.049]\n",
            ">5216, dr[0.000,0.767], df[0.000,0.005], g[1.420,0.036]\n",
            ">5217, dr[0.000,0.319], df[0.000,0.006], g[1.461,0.041]\n",
            ">5218, dr[0.000,0.419], df[0.000,0.003], g[0.826,0.114]\n",
            ">5219, dr[0.000,0.495], df[0.000,0.007], g[0.833,0.055]\n",
            ">5220, dr[0.000,0.288], df[0.000,0.006], g[0.680,0.071]\n",
            ">5221, dr[0.000,0.477], df[0.000,0.011], g[0.569,0.066]\n",
            ">5222, dr[0.000,0.277], df[0.000,0.005], g[0.817,0.043]\n",
            ">5223, dr[0.001,0.349], df[0.000,0.005], g[0.507,0.070]\n",
            ">5224, dr[0.000,0.530], df[0.000,0.002], g[0.243,0.039]\n",
            ">5225, dr[0.000,0.530], df[0.000,0.003], g[0.201,0.038]\n",
            ">5226, dr[0.000,0.418], df[0.000,0.002], g[0.199,0.022]\n",
            ">5227, dr[0.000,0.534], df[0.000,0.004], g[0.152,0.033]\n",
            ">5228, dr[0.001,0.554], df[0.000,0.003], g[0.144,0.016]\n",
            ">5229, dr[0.000,0.368], df[0.000,0.003], g[0.186,0.018]\n",
            ">5230, dr[0.000,0.319], df[0.000,0.005], g[0.238,0.013]\n",
            ">5231, dr[0.000,0.362], df[0.000,0.004], g[0.258,0.013]\n",
            ">5232, dr[0.000,0.467], df[0.000,0.004], g[0.392,0.021]\n",
            ">5233, dr[0.000,0.848], df[0.000,0.003], g[0.420,0.034]\n",
            ">5234, dr[0.000,0.448], df[0.000,0.003], g[0.516,0.029]\n",
            ">5235, dr[0.000,0.807], df[0.000,0.009], g[0.443,0.034]\n",
            ">5236, dr[0.000,0.472], df[0.000,0.003], g[0.279,0.053]\n",
            ">5237, dr[0.000,0.497], df[0.001,0.005], g[0.400,0.027]\n",
            ">5238, dr[0.000,0.509], df[0.000,0.005], g[0.595,0.062]\n",
            ">5239, dr[0.000,0.330], df[0.000,0.005], g[0.610,0.034]\n",
            ">5240, dr[0.000,0.449], df[0.000,0.002], g[0.523,0.064]\n",
            ">5241, dr[0.000,0.354], df[0.000,0.007], g[0.716,0.039]\n",
            ">5242, dr[0.001,0.615], df[0.000,0.006], g[0.276,0.020]\n",
            ">5243, dr[0.000,0.482], df[0.000,0.003], g[0.285,0.024]\n",
            ">5244, dr[0.000,0.360], df[0.000,0.016], g[0.287,0.050]\n",
            ">5245, dr[0.000,0.399], df[0.000,0.009], g[0.317,0.023]\n",
            ">5246, dr[0.000,0.608], df[0.000,0.005], g[0.157,0.026]\n",
            ">5247, dr[0.000,0.376], df[0.000,0.006], g[0.357,0.046]\n",
            ">5248, dr[0.000,0.350], df[0.001,0.003], g[0.316,0.041]\n",
            ">5249, dr[0.000,0.424], df[0.000,0.002], g[0.448,0.021]\n",
            ">5250, dr[0.000,0.383], df[0.000,0.005], g[0.270,0.045]\n",
            ">5251, dr[0.000,0.551], df[0.000,0.001], g[0.403,0.021]\n",
            ">5252, dr[0.000,0.636], df[0.000,0.004], g[0.394,0.021]\n",
            ">5253, dr[0.000,0.369], df[0.000,0.004], g[0.293,0.033]\n",
            ">5254, dr[0.000,0.622], df[0.000,0.002], g[0.568,0.013]\n",
            ">5255, dr[0.000,0.633], df[0.000,0.003], g[0.422,0.023]\n",
            ">5256, dr[0.000,0.409], df[0.000,0.003], g[0.359,0.024]\n",
            ">5257, dr[0.000,0.215], df[0.000,0.004], g[0.275,0.015]\n",
            ">5258, dr[0.000,0.518], df[0.000,0.003], g[0.228,0.007]\n",
            ">5259, dr[0.000,0.281], df[0.000,0.002], g[0.347,0.012]\n",
            ">5260, dr[0.000,0.551], df[0.000,0.004], g[0.381,0.034]\n",
            ">5261, dr[0.000,0.412], df[0.000,0.006], g[0.491,0.021]\n",
            ">5262, dr[0.000,0.331], df[0.000,0.016], g[0.296,0.023]\n",
            ">5263, dr[0.000,0.303], df[0.000,0.007], g[0.329,0.038]\n",
            ">5264, dr[0.000,0.518], df[0.000,0.004], g[0.227,0.024]\n",
            ">5265, dr[0.001,0.677], df[0.000,0.013], g[0.243,0.036]\n",
            ">5266, dr[0.000,0.395], df[0.000,0.006], g[0.465,0.015]\n",
            ">5267, dr[0.000,0.550], df[0.000,0.003], g[0.432,0.035]\n",
            ">5268, dr[0.000,0.684], df[0.000,0.003], g[0.440,0.008]\n",
            ">5269, dr[0.000,0.394], df[0.000,0.004], g[0.388,0.008]\n",
            ">5270, dr[0.000,0.214], df[0.000,0.006], g[0.291,0.008]\n",
            ">5271, dr[0.000,0.473], df[0.000,0.004], g[0.456,0.021]\n",
            ">5272, dr[0.000,0.678], df[0.000,0.009], g[0.561,0.028]\n",
            ">5273, dr[0.000,0.341], df[0.000,0.003], g[0.924,0.021]\n",
            ">5274, dr[0.000,0.329], df[0.000,0.003], g[1.017,0.016]\n",
            ">5275, dr[0.000,0.401], df[0.000,0.007], g[1.259,0.033]\n",
            ">5276, dr[0.000,0.332], df[0.000,0.003], g[1.144,0.022]\n",
            ">5277, dr[0.000,0.337], df[0.000,0.013], g[1.209,0.046]\n",
            ">5278, dr[0.000,0.393], df[0.000,0.004], g[1.160,0.036]\n",
            ">5279, dr[0.000,0.389], df[0.000,0.004], g[0.866,0.029]\n",
            ">5280, dr[0.000,0.711], df[0.000,0.008], g[1.109,0.029]\n",
            ">5281, dr[0.000,0.341], df[0.000,0.005], g[0.880,0.040]\n",
            ">5282, dr[0.000,0.417], df[0.000,0.004], g[0.800,0.028]\n",
            ">5283, dr[0.000,0.373], df[0.000,0.005], g[0.493,0.073]\n",
            ">5284, dr[0.000,0.354], df[0.000,0.004], g[0.891,0.029]\n",
            ">5285, dr[0.000,0.205], df[0.000,0.004], g[0.574,0.052]\n",
            ">5286, dr[0.000,0.410], df[0.000,0.009], g[0.422,0.050]\n",
            ">5287, dr[0.001,0.329], df[0.000,0.003], g[0.370,0.032]\n",
            ">5288, dr[0.000,0.506], df[0.000,0.010], g[0.451,0.047]\n",
            ">5289, dr[0.000,0.360], df[0.000,0.004], g[0.522,0.033]\n",
            ">5290, dr[0.000,0.335], df[0.001,0.005], g[0.498,0.045]\n",
            ">5291, dr[0.000,0.325], df[0.000,0.004], g[0.635,0.037]\n",
            ">5292, dr[0.000,0.477], df[0.000,0.004], g[0.654,0.024]\n",
            ">5293, dr[0.000,0.402], df[0.000,0.002], g[0.476,0.031]\n",
            ">5294, dr[0.000,0.310], df[0.000,0.006], g[0.475,0.034]\n",
            ">5295, dr[0.000,0.551], df[0.000,0.005], g[0.617,0.017]\n",
            ">5296, dr[0.000,0.498], df[0.000,0.004], g[0.573,0.015]\n",
            ">5297, dr[0.000,0.499], df[0.000,0.003], g[0.636,0.027]\n",
            ">5298, dr[0.000,0.239], df[0.000,0.005], g[0.620,0.028]\n",
            ">5299, dr[0.000,0.595], df[0.000,0.005], g[0.418,0.032]\n",
            ">5300, dr[0.001,0.349], df[0.000,0.003], g[0.564,0.027]\n",
            ">5301, dr[0.000,0.480], df[0.000,0.004], g[0.676,0.040]\n",
            ">5302, dr[0.000,0.381], df[0.000,0.003], g[0.530,0.029]\n",
            ">5303, dr[0.000,0.461], df[0.000,0.003], g[0.480,0.019]\n",
            ">5304, dr[0.000,0.358], df[0.000,0.004], g[0.504,0.021]\n",
            ">5305, dr[0.000,0.768], df[0.000,0.003], g[0.586,0.021]\n",
            ">5306, dr[0.000,0.115], df[0.000,0.005], g[0.605,0.038]\n",
            ">5307, dr[0.000,0.311], df[0.000,0.011], g[0.630,0.036]\n",
            ">5308, dr[0.000,0.516], df[0.000,0.009], g[0.645,0.018]\n",
            ">5309, dr[0.000,0.279], df[0.000,0.007], g[0.337,0.027]\n",
            ">5310, dr[0.000,0.434], df[0.000,0.009], g[0.393,0.038]\n",
            ">5311, dr[0.000,0.411], df[0.000,0.005], g[0.304,0.024]\n",
            ">5312, dr[0.000,0.399], df[0.000,0.010], g[0.508,0.020]\n",
            ">5313, dr[0.000,0.422], df[0.000,0.003], g[0.306,0.011]\n",
            ">5314, dr[0.000,0.364], df[0.000,0.002], g[0.607,0.026]\n",
            ">5315, dr[0.000,0.396], df[0.000,0.007], g[0.510,0.018]\n",
            ">5316, dr[0.000,0.196], df[0.000,0.009], g[0.652,0.007]\n",
            ">5317, dr[0.000,0.261], df[0.000,0.007], g[0.495,0.017]\n",
            ">5318, dr[0.000,0.709], df[0.000,0.003], g[0.748,0.027]\n",
            ">5319, dr[0.000,0.236], df[0.000,0.004], g[0.459,0.021]\n",
            ">5320, dr[0.000,0.520], df[0.000,0.005], g[0.667,0.026]\n",
            ">5321, dr[0.000,0.385], df[0.000,0.003], g[0.755,0.015]\n",
            ">5322, dr[0.000,0.353], df[0.000,0.003], g[0.725,0.019]\n",
            ">5323, dr[0.000,0.844], df[0.000,0.003], g[1.106,0.017]\n",
            ">5324, dr[0.000,0.325], df[0.000,0.007], g[1.222,0.033]\n",
            ">5325, dr[0.000,0.569], df[0.000,0.003], g[1.365,0.028]\n",
            ">5326, dr[0.000,0.296], df[0.000,0.004], g[1.535,0.059]\n",
            ">5327, dr[0.000,0.713], df[0.000,0.003], g[1.576,0.028]\n",
            ">5328, dr[0.000,0.391], df[0.000,0.002], g[1.875,0.039]\n",
            ">5329, dr[0.000,0.442], df[0.000,0.008], g[2.276,0.078]\n",
            ">5330, dr[0.000,0.648], df[0.000,0.003], g[1.895,0.058]\n",
            ">5331, dr[0.000,0.354], df[0.000,0.013], g[1.648,0.049]\n",
            ">5332, dr[0.000,0.433], df[0.000,0.004], g[1.445,0.037]\n",
            ">5333, dr[0.000,0.248], df[0.000,0.004], g[1.389,0.078]\n",
            ">5334, dr[0.000,0.260], df[0.000,0.007], g[1.791,0.051]\n",
            ">5335, dr[0.000,0.725], df[0.000,0.005], g[1.641,0.036]\n",
            ">5336, dr[0.000,0.489], df[0.000,0.003], g[1.897,0.054]\n",
            ">5337, dr[0.000,0.571], df[0.000,0.005], g[1.567,0.085]\n",
            ">5338, dr[0.000,0.296], df[0.000,0.004], g[1.349,0.070]\n",
            ">5339, dr[0.000,0.422], df[0.000,0.004], g[1.250,0.070]\n",
            ">5340, dr[0.000,0.207], df[0.000,0.012], g[1.166,0.059]\n",
            ">5341, dr[0.000,0.438], df[0.000,0.005], g[1.043,0.051]\n",
            ">5342, dr[0.001,0.512], df[0.000,0.003], g[0.812,0.080]\n",
            ">5343, dr[0.000,0.512], df[0.000,0.005], g[1.225,0.069]\n",
            ">5344, dr[0.000,0.310], df[0.000,0.010], g[1.587,0.038]\n",
            ">5345, dr[0.000,0.678], df[0.000,0.003], g[1.155,0.049]\n",
            ">5346, dr[0.000,0.330], df[0.000,0.003], g[1.079,0.078]\n",
            ">5347, dr[0.000,0.452], df[0.000,0.004], g[0.706,0.047]\n",
            ">5348, dr[0.000,0.229], df[0.000,0.002], g[0.630,0.024]\n",
            ">5349, dr[0.000,0.292], df[0.000,0.002], g[0.867,0.073]\n",
            ">5350, dr[0.000,0.869], df[0.000,0.003], g[1.204,0.079]\n",
            ">5351, dr[0.000,0.549], df[0.000,0.006], g[1.196,0.022]\n",
            ">5352, dr[0.000,0.374], df[0.000,0.004], g[1.267,0.028]\n",
            ">5353, dr[0.000,0.429], df[0.000,0.004], g[0.921,0.074]\n",
            ">5354, dr[0.000,0.215], df[0.000,0.004], g[0.763,0.044]\n",
            ">5355, dr[0.000,0.349], df[0.000,0.003], g[0.608,0.045]\n",
            ">5356, dr[0.000,0.187], df[0.001,0.002], g[0.889,0.047]\n",
            ">5357, dr[0.000,0.297], df[0.000,0.003], g[0.688,0.040]\n",
            ">5358, dr[0.000,0.280], df[0.000,0.008], g[0.914,0.058]\n",
            ">5359, dr[0.000,0.340], df[0.000,0.010], g[0.721,0.021]\n",
            ">5360, dr[0.000,0.653], df[0.000,0.003], g[1.262,0.018]\n",
            ">5361, dr[0.000,0.238], df[0.000,0.006], g[1.132,0.020]\n",
            ">5362, dr[0.000,0.765], df[0.000,0.003], g[1.184,0.047]\n",
            ">5363, dr[0.000,0.375], df[0.000,0.003], g[1.641,0.045]\n",
            ">5364, dr[0.000,0.287], df[0.000,0.008], g[1.761,0.069]\n",
            ">5365, dr[0.001,0.424], df[0.000,0.003], g[1.680,0.077]\n",
            ">5366, dr[0.000,0.462], df[0.000,0.003], g[1.720,0.070]\n",
            ">5367, dr[0.000,0.477], df[0.000,0.002], g[1.586,0.021]\n",
            ">5368, dr[0.000,0.541], df[0.000,0.004], g[1.343,0.042]\n",
            ">5369, dr[0.000,0.517], df[0.000,0.004], g[1.243,0.068]\n",
            ">5370, dr[0.000,0.207], df[0.000,0.004], g[1.075,0.034]\n",
            ">5371, dr[0.000,0.319], df[0.000,0.001], g[1.162,0.052]\n",
            ">5372, dr[0.000,0.260], df[0.000,0.004], g[0.795,0.018]\n",
            ">5373, dr[0.000,0.544], df[0.000,0.006], g[0.951,0.034]\n",
            ">5374, dr[0.000,0.287], df[0.000,0.018], g[0.866,0.027]\n",
            ">5375, dr[0.000,0.344], df[0.000,0.009], g[0.795,0.027]\n",
            ">5376, dr[0.000,0.515], df[0.000,0.003], g[0.905,0.036]\n",
            ">5377, dr[0.000,0.432], df[0.000,0.016], g[1.278,0.068]\n",
            ">5378, dr[0.000,0.436], df[0.000,0.006], g[1.194,0.034]\n",
            ">5379, dr[0.000,0.561], df[0.000,0.004], g[1.100,0.032]\n",
            ">5380, dr[0.000,0.416], df[0.000,0.004], g[0.854,0.056]\n",
            ">5381, dr[0.000,0.470], df[0.000,0.003], g[1.007,0.055]\n",
            ">5382, dr[0.000,0.391], df[0.000,0.005], g[0.957,0.018]\n",
            ">5383, dr[0.000,0.408], df[0.000,0.004], g[1.178,0.033]\n",
            ">5384, dr[0.000,0.319], df[0.000,0.003], g[1.329,0.042]\n",
            ">5385, dr[0.000,0.496], df[0.000,0.006], g[0.856,0.062]\n",
            ">5386, dr[0.000,0.375], df[0.000,0.002], g[1.051,0.042]\n",
            ">5387, dr[0.000,0.431], df[0.000,0.003], g[0.694,0.046]\n",
            ">5388, dr[0.000,0.276], df[0.000,0.002], g[0.559,0.024]\n",
            ">5389, dr[0.000,0.485], df[0.000,0.003], g[0.591,0.059]\n",
            ">5390, dr[0.000,0.332], df[0.000,0.003], g[0.516,0.029]\n",
            ">5391, dr[0.000,0.366], df[0.000,0.003], g[0.468,0.024]\n",
            ">5392, dr[0.000,0.249], df[0.000,0.006], g[0.426,0.035]\n",
            ">5393, dr[0.000,0.291], df[0.000,0.002], g[0.630,0.015]\n",
            ">5394, dr[0.000,0.318], df[0.000,0.002], g[0.521,0.022]\n",
            ">5395, dr[0.000,0.426], df[0.000,0.004], g[0.484,0.021]\n",
            ">5396, dr[0.000,0.439], df[0.000,0.002], g[0.388,0.023]\n",
            ">5397, dr[0.000,0.389], df[0.000,0.003], g[0.437,0.026]\n",
            ">5398, dr[0.000,0.332], df[0.000,0.001], g[0.472,0.040]\n",
            ">5399, dr[0.000,0.248], df[0.000,0.005], g[0.353,0.040]\n",
            ">5400, dr[0.000,0.402], df[0.000,0.002], g[0.402,0.041]\n",
            ">5401, dr[0.000,0.373], df[0.000,0.002], g[0.367,0.040]\n",
            ">5402, dr[0.000,0.365], df[0.000,0.005], g[0.316,0.014]\n",
            ">5403, dr[0.000,0.455], df[0.000,0.002], g[0.387,0.010]\n",
            ">5404, dr[0.000,0.280], df[0.000,0.001], g[0.550,0.010]\n",
            ">5405, dr[0.000,0.355], df[0.000,0.018], g[0.652,0.040]\n",
            ">5406, dr[0.000,0.502], df[0.000,0.004], g[0.727,0.021]\n",
            ">5407, dr[0.000,0.213], df[0.000,0.002], g[0.706,0.028]\n",
            ">5408, dr[0.000,0.227], df[0.000,0.002], g[0.560,0.032]\n",
            ">5409, dr[0.000,0.269], df[0.000,0.003], g[0.762,0.036]\n",
            ">5410, dr[0.000,0.488], df[0.000,0.002], g[0.767,0.016]\n",
            ">5411, dr[0.000,0.183], df[0.000,0.020], g[0.807,0.022]\n",
            ">5412, dr[0.000,0.444], df[0.000,0.003], g[1.001,0.051]\n",
            ">5413, dr[0.000,0.385], df[0.000,0.004], g[1.344,0.041]\n",
            ">5414, dr[0.000,0.725], df[0.000,0.002], g[0.981,0.023]\n",
            ">5415, dr[0.000,0.234], df[0.000,0.003], g[1.419,0.014]\n",
            ">5416, dr[0.000,0.404], df[0.000,0.002], g[1.428,0.033]\n",
            ">5417, dr[0.000,0.250], df[0.000,0.002], g[1.218,0.061]\n",
            ">5418, dr[0.000,0.318], df[0.000,0.003], g[1.092,0.031]\n",
            ">5419, dr[0.000,0.330], df[0.000,0.010], g[0.882,0.065]\n",
            ">5420, dr[0.000,0.330], df[0.000,0.003], g[0.851,0.049]\n",
            ">5421, dr[0.000,0.153], df[0.000,0.004], g[0.806,0.031]\n",
            ">5422, dr[0.000,0.387], df[0.000,0.010], g[0.667,0.048]\n",
            ">5423, dr[0.000,0.592], df[0.000,0.006], g[0.696,0.025]\n",
            ">5424, dr[0.000,0.589], df[0.000,0.002], g[0.693,0.038]\n",
            ">5425, dr[0.000,0.480], df[0.000,0.005], g[1.237,0.046]\n",
            ">5426, dr[0.000,0.414], df[0.000,0.005], g[0.958,0.062]\n",
            ">5427, dr[0.000,0.448], df[0.000,0.004], g[0.993,0.033]\n",
            ">5428, dr[0.000,0.276], df[0.000,0.002], g[1.083,0.029]\n",
            ">5429, dr[0.000,0.436], df[0.000,0.002], g[0.756,0.024]\n",
            ">5430, dr[0.000,0.358], df[0.000,0.003], g[0.809,0.026]\n",
            ">5431, dr[0.000,0.496], df[0.000,0.004], g[0.698,0.035]\n",
            ">5432, dr[0.000,0.283], df[0.000,0.002], g[1.079,0.079]\n",
            ">5433, dr[0.000,0.485], df[0.000,0.003], g[1.071,0.070]\n",
            ">5434, dr[0.000,0.459], df[0.000,0.015], g[1.086,0.035]\n",
            ">5435, dr[0.000,0.324], df[0.000,0.004], g[1.061,0.021]\n",
            ">5436, dr[0.000,0.467], df[0.000,0.004], g[0.828,0.059]\n",
            ">5437, dr[0.000,0.350], df[0.000,0.010], g[1.120,0.047]\n",
            ">5438, dr[0.000,0.404], df[0.000,0.006], g[1.118,0.031]\n",
            ">5439, dr[0.000,0.194], df[0.000,0.004], g[1.099,0.018]\n",
            ">5440, dr[0.000,0.212], df[0.000,0.004], g[0.950,0.036]\n",
            ">5441, dr[0.000,0.487], df[0.000,0.025], g[0.713,0.065]\n",
            ">5442, dr[0.000,0.398], df[0.000,0.002], g[0.712,0.063]\n",
            ">5443, dr[0.000,0.387], df[0.000,0.003], g[0.718,0.035]\n",
            ">5444, dr[0.000,0.533], df[0.000,0.005], g[0.858,0.063]\n",
            ">5445, dr[0.000,1.044], df[0.000,0.005], g[1.367,0.063]\n",
            ">5446, dr[0.000,0.383], df[0.000,0.001], g[1.574,0.042]\n",
            ">5447, dr[0.000,0.462], df[0.000,0.008], g[1.311,0.038]\n",
            ">5448, dr[0.000,0.265], df[0.000,0.003], g[1.247,0.025]\n",
            ">5449, dr[0.000,0.669], df[0.000,0.002], g[0.711,0.020]\n",
            ">5450, dr[0.000,0.294], df[0.000,0.001], g[0.843,0.063]\n",
            ">5451, dr[0.000,0.394], df[0.000,0.003], g[0.895,0.015]\n",
            ">5452, dr[0.000,0.210], df[0.000,0.003], g[1.150,0.042]\n",
            ">5453, dr[0.000,0.363], df[0.000,0.003], g[1.023,0.028]\n",
            ">5454, dr[0.000,0.437], df[0.000,0.008], g[1.016,0.023]\n",
            ">5455, dr[0.000,0.495], df[0.000,0.008], g[0.785,0.045]\n",
            ">5456, dr[0.000,0.314], df[0.000,0.003], g[1.082,0.028]\n",
            ">5457, dr[0.000,0.339], df[0.000,0.003], g[0.829,0.032]\n",
            ">5458, dr[0.000,0.346], df[0.000,0.005], g[0.731,0.027]\n",
            ">5459, dr[0.000,0.442], df[0.000,0.002], g[1.051,0.033]\n",
            ">5460, dr[0.000,0.469], df[0.000,0.006], g[1.231,0.019]\n",
            ">5461, dr[0.000,0.745], df[0.000,0.003], g[1.429,0.024]\n",
            ">5462, dr[0.000,0.368], df[0.000,0.010], g[1.398,0.054]\n",
            ">5463, dr[0.000,0.259], df[0.000,0.011], g[1.752,0.043]\n",
            ">5464, dr[0.000,0.490], df[0.000,0.010], g[1.379,0.016]\n",
            ">5465, dr[0.000,0.496], df[0.000,0.013], g[1.091,0.045]\n",
            ">5466, dr[0.000,0.318], df[0.000,0.005], g[1.301,0.040]\n",
            ">5467, dr[0.000,0.409], df[0.000,0.012], g[1.555,0.044]\n",
            ">5468, dr[0.000,0.493], df[0.000,0.003], g[1.608,0.047]\n",
            ">5469, dr[0.000,0.474], df[0.000,0.017], g[1.084,0.015]\n",
            ">5470, dr[0.000,0.827], df[0.000,0.004], g[0.788,0.022]\n",
            ">5471, dr[0.000,0.223], df[0.000,0.004], g[0.732,0.032]\n",
            ">5472, dr[0.000,0.642], df[0.000,0.010], g[1.312,0.038]\n",
            ">5473, dr[0.000,0.328], df[0.000,0.005], g[1.392,0.025]\n",
            ">5474, dr[0.000,0.435], df[0.000,0.008], g[1.282,0.063]\n",
            ">5475, dr[0.000,0.564], df[0.000,0.005], g[1.373,0.054]\n",
            ">5476, dr[0.000,0.557], df[0.000,0.013], g[1.231,0.040]\n",
            ">5477, dr[0.000,0.720], df[0.000,0.004], g[1.423,0.081]\n",
            ">5478, dr[0.000,0.290], df[0.000,0.003], g[1.146,0.061]\n",
            ">5479, dr[0.000,0.679], df[0.000,0.008], g[1.101,0.014]\n",
            ">5480, dr[0.001,0.355], df[0.000,0.006], g[0.999,0.029]\n",
            ">5481, dr[0.000,0.564], df[0.000,0.003], g[0.945,0.025]\n",
            ">5482, dr[0.000,0.445], df[0.000,0.003], g[0.903,0.027]\n",
            ">5483, dr[0.000,0.307], df[0.000,0.007], g[0.625,0.041]\n",
            ">5484, dr[0.000,0.434], df[0.000,0.005], g[0.625,0.035]\n",
            ">5485, dr[0.000,0.571], df[0.000,0.004], g[0.837,0.026]\n",
            ">5486, dr[0.000,0.474], df[0.000,0.003], g[0.683,0.030]\n",
            ">5487, dr[0.000,0.331], df[0.000,0.003], g[0.713,0.080]\n",
            ">5488, dr[0.000,0.225], df[0.000,0.006], g[1.000,0.113]\n",
            ">5489, dr[0.000,0.489], df[0.000,0.004], g[1.046,0.027]\n",
            ">5490, dr[0.000,0.352], df[0.000,0.014], g[1.119,0.022]\n",
            ">5491, dr[0.000,0.221], df[0.000,0.003], g[0.818,0.034]\n",
            ">5492, dr[0.000,0.864], df[0.000,0.020], g[0.902,0.026]\n",
            ">5493, dr[0.000,0.227], df[0.001,0.004], g[1.052,0.036]\n",
            ">5494, dr[0.000,0.326], df[0.000,0.006], g[1.015,0.017]\n",
            ">5495, dr[0.000,0.637], df[0.000,0.003], g[1.629,0.035]\n",
            ">5496, dr[0.000,0.250], df[0.000,0.004], g[1.814,0.020]\n",
            ">5497, dr[0.000,0.202], df[0.006,0.012], g[2.297,0.020]\n",
            ">5498, dr[0.000,0.421], df[0.000,0.013], g[3.231,0.041]\n",
            ">5499, dr[0.000,0.359], df[0.000,0.012], g[3.215,0.034]\n",
            ">5500, dr[0.000,0.348], df[0.000,0.006], g[3.802,0.022]\n",
            ">5501, dr[0.000,0.260], df[0.000,0.005], g[3.781,0.037]\n",
            ">5502, dr[0.000,0.512], df[0.000,0.002], g[2.997,0.047]\n",
            ">5503, dr[0.000,0.510], df[0.000,0.003], g[2.165,0.039]\n",
            ">5504, dr[0.000,0.366], df[0.000,0.013], g[1.864,0.032]\n",
            ">5505, dr[0.000,0.147], df[0.000,0.003], g[1.932,0.064]\n",
            ">5506, dr[0.000,0.537], df[0.000,0.002], g[2.361,0.047]\n",
            ">5507, dr[0.000,0.305], df[0.000,0.009], g[2.357,0.027]\n",
            ">5508, dr[0.000,0.443], df[0.000,0.015], g[2.270,0.033]\n",
            ">5509, dr[0.003,0.348], df[0.000,0.015], g[1.978,0.087]\n",
            ">5510, dr[0.000,0.752], df[0.000,0.008], g[1.332,0.070]\n",
            ">5511, dr[0.001,0.299], df[0.000,0.034], g[0.903,0.060]\n",
            ">5512, dr[0.000,0.435], df[0.000,0.036], g[0.996,0.030]\n",
            ">5513, dr[0.000,0.194], df[0.000,0.008], g[0.727,0.027]\n",
            ">5514, dr[0.000,0.472], df[0.000,0.028], g[1.038,0.014]\n",
            ">5515, dr[0.000,0.511], df[0.000,0.009], g[1.050,0.033]\n",
            ">5516, dr[0.000,0.409], df[0.000,0.017], g[1.151,0.034]\n",
            ">5517, dr[0.000,0.298], df[0.000,0.007], g[1.172,0.048]\n",
            ">5518, dr[0.000,0.456], df[0.000,0.019], g[0.986,0.044]\n",
            ">5519, dr[0.000,0.366], df[0.000,0.025], g[1.018,0.029]\n",
            ">5520, dr[0.000,0.514], df[0.000,0.016], g[1.115,0.037]\n",
            ">5521, dr[0.000,0.568], df[0.000,0.008], g[0.754,0.031]\n",
            ">5522, dr[0.000,0.383], df[0.000,0.012], g[0.465,0.046]\n",
            ">5523, dr[0.000,0.334], df[0.000,0.014], g[0.308,0.032]\n",
            ">5524, dr[0.000,0.296], df[0.000,0.013], g[0.392,0.037]\n",
            ">5525, dr[0.000,0.341], df[0.000,0.009], g[0.463,0.025]\n",
            ">5526, dr[0.000,0.503], df[0.000,0.021], g[0.575,0.017]\n",
            ">5527, dr[0.000,0.354], df[0.000,0.005], g[0.648,0.031]\n",
            ">5528, dr[0.000,0.523], df[0.000,0.008], g[0.820,0.040]\n",
            ">5529, dr[0.000,0.296], df[0.000,0.020], g[0.875,0.030]\n",
            ">5530, dr[0.000,0.532], df[0.000,0.016], g[0.700,0.039]\n",
            ">5531, dr[0.000,0.540], df[0.000,0.035], g[0.908,0.026]\n",
            ">5532, dr[0.000,0.700], df[0.000,0.019], g[0.941,0.038]\n",
            ">5533, dr[0.000,0.124], df[0.000,0.021], g[1.235,0.030]\n",
            ">5534, dr[0.000,0.516], df[0.000,0.006], g[0.948,0.022]\n",
            ">5535, dr[0.000,0.384], df[0.000,0.012], g[1.193,0.020]\n",
            ">5536, dr[0.000,0.318], df[0.000,0.009], g[1.245,0.041]\n",
            ">5537, dr[0.000,0.449], df[0.000,0.020], g[1.194,0.046]\n",
            ">5538, dr[0.000,0.300], df[0.000,0.019], g[0.784,0.033]\n",
            ">5539, dr[0.000,0.232], df[0.001,0.008], g[0.542,0.057]\n",
            ">5540, dr[0.000,0.339], df[0.011,0.020], g[2.530,0.027]\n",
            ">5541, dr[0.000,0.293], df[0.000,0.015], g[5.029,0.042]\n",
            ">5542, dr[0.001,0.294], df[0.000,0.018], g[4.511,0.051]\n",
            ">5543, dr[0.000,0.445], df[0.000,0.022], g[3.874,0.082]\n",
            ">5544, dr[0.002,0.386], df[0.071,0.125], g[14.657,0.117]\n",
            ">5545, dr[0.071,0.255], df[0.000,0.019], g[11.822,0.077]\n",
            ">5546, dr[0.101,0.391], df[0.000,0.042], g[2.456,0.037]\n",
            ">5547, dr[0.000,0.600], df[0.000,0.027], g[0.924,0.044]\n",
            ">5548, dr[0.000,0.377], df[0.000,0.025], g[0.461,0.071]\n",
            ">5549, dr[0.000,0.335], df[0.000,0.015], g[0.484,0.128]\n",
            ">5550, dr[0.000,0.454], df[0.000,0.013], g[0.266,0.166]\n",
            ">5551, dr[0.000,0.384], df[0.000,0.026], g[0.118,0.061]\n",
            ">5552, dr[0.000,0.359], df[0.000,0.024], g[0.177,0.066]\n",
            ">5553, dr[0.000,0.528], df[0.000,0.008], g[0.067,0.038]\n",
            ">5554, dr[0.000,0.263], df[0.000,0.017], g[0.022,0.023]\n",
            ">5555, dr[0.000,0.530], df[0.000,0.009], g[0.027,0.042]\n",
            ">5556, dr[0.000,0.299], df[0.000,0.005], g[0.008,0.030]\n",
            ">5557, dr[0.000,0.537], df[0.000,0.005], g[0.004,0.033]\n",
            ">5558, dr[0.000,0.359], df[0.000,0.009], g[0.003,0.019]\n",
            ">5559, dr[0.000,0.376], df[0.000,0.004], g[0.009,0.027]\n",
            ">5560, dr[0.000,0.723], df[0.000,0.005], g[0.012,0.015]\n",
            ">5561, dr[0.000,0.304], df[0.000,0.003], g[0.016,0.015]\n",
            ">5562, dr[0.000,0.168], df[0.000,0.028], g[0.009,0.012]\n",
            ">5563, dr[0.000,0.362], df[0.000,0.008], g[0.011,0.027]\n",
            ">5564, dr[0.000,0.562], df[0.000,0.002], g[0.033,0.011]\n",
            ">5565, dr[0.000,0.465], df[0.000,0.003], g[0.018,0.011]\n",
            ">5566, dr[0.000,0.312], df[0.000,0.005], g[0.010,0.019]\n",
            ">5567, dr[0.000,0.516], df[0.000,0.012], g[0.007,0.024]\n",
            ">5568, dr[0.000,0.375], df[0.001,0.002], g[0.013,0.029]\n",
            ">5569, dr[0.000,0.269], df[0.000,0.005], g[0.007,0.019]\n",
            ">5570, dr[0.000,0.618], df[0.000,0.011], g[0.020,0.013]\n",
            ">5571, dr[0.000,0.584], df[0.000,0.003], g[0.009,0.020]\n",
            ">5572, dr[0.000,0.329], df[0.000,0.053], g[0.009,0.021]\n",
            ">5573, dr[0.000,0.496], df[0.000,0.003], g[0.017,0.008]\n",
            ">5574, dr[0.000,0.223], df[0.000,0.002], g[0.004,0.017]\n",
            ">5575, dr[0.000,0.489], df[0.001,0.004], g[0.013,0.014]\n",
            ">5576, dr[0.000,0.543], df[0.000,0.002], g[0.020,0.031]\n",
            ">5577, dr[0.000,0.355], df[0.000,0.004], g[0.002,0.016]\n",
            ">5578, dr[0.000,0.543], df[0.000,0.006], g[0.004,0.015]\n",
            ">5579, dr[0.000,0.545], df[0.005,0.019], g[0.008,0.026]\n",
            ">5580, dr[0.000,0.629], df[0.000,0.002], g[0.008,0.010]\n",
            ">5581, dr[0.000,0.218], df[0.000,0.003], g[0.006,0.026]\n",
            ">5582, dr[0.000,0.690], df[0.000,0.012], g[0.008,0.016]\n",
            ">5583, dr[0.000,0.531], df[0.000,0.002], g[0.029,0.006]\n",
            ">5584, dr[0.000,0.661], df[0.000,0.001], g[0.007,0.006]\n",
            ">5585, dr[0.000,0.197], df[0.000,0.005], g[0.026,0.010]\n",
            ">5586, dr[0.000,0.191], df[0.000,0.002], g[0.025,0.008]\n",
            ">5587, dr[0.000,0.448], df[0.000,0.014], g[0.035,0.008]\n",
            ">5588, dr[0.000,0.656], df[0.000,0.002], g[0.012,0.006]\n",
            ">5589, dr[0.000,0.262], df[0.000,0.003], g[0.005,0.009]\n",
            ">5590, dr[0.000,0.364], df[0.000,0.001], g[0.009,0.011]\n",
            ">5591, dr[0.000,0.256], df[0.001,0.001], g[0.022,0.016]\n",
            ">5592, dr[0.000,0.221], df[0.000,0.001], g[0.008,0.005]\n",
            ">5593, dr[0.000,0.263], df[0.000,0.001], g[0.022,0.007]\n",
            ">5594, dr[0.000,0.601], df[0.000,0.001], g[0.011,0.011]\n",
            ">5595, dr[0.000,0.378], df[0.000,0.001], g[0.025,0.006]\n",
            ">5596, dr[0.000,0.365], df[0.000,0.001], g[0.025,0.009]\n",
            ">5597, dr[0.000,0.275], df[0.000,0.005], g[0.028,0.004]\n",
            ">5598, dr[0.000,0.253], df[0.000,0.001], g[0.036,0.007]\n",
            ">5599, dr[0.000,0.374], df[0.000,0.002], g[0.040,0.011]\n",
            ">5600, dr[0.000,0.512], df[0.000,0.001], g[0.024,0.006]\n",
            ">5601, dr[0.000,0.409], df[0.000,0.001], g[0.012,0.007]\n",
            ">5602, dr[0.000,0.369], df[0.000,0.001], g[0.030,0.007]\n",
            ">5603, dr[0.000,0.634], df[0.000,0.001], g[0.035,0.004]\n",
            ">5604, dr[0.000,0.467], df[0.000,0.006], g[0.018,0.015]\n",
            ">5605, dr[0.000,0.218], df[0.000,0.001], g[0.015,0.011]\n",
            ">5606, dr[0.000,0.406], df[0.001,0.002], g[0.012,0.013]\n",
            ">5607, dr[0.000,0.295], df[0.002,0.011], g[0.020,0.009]\n",
            ">5608, dr[0.000,0.455], df[0.001,0.003], g[0.013,0.021]\n",
            ">5609, dr[0.000,0.246], df[0.000,0.001], g[0.021,0.009]\n",
            ">5610, dr[0.000,0.402], df[0.000,0.001], g[0.012,0.009]\n",
            ">5611, dr[0.000,0.340], df[0.001,0.002], g[0.040,0.011]\n",
            ">5612, dr[0.000,0.472], df[0.000,0.001], g[0.060,0.011]\n",
            ">5613, dr[0.000,0.507], df[0.000,0.001], g[0.064,0.006]\n",
            ">5614, dr[0.000,0.354], df[0.000,0.002], g[0.011,0.011]\n",
            ">5615, dr[0.000,0.570], df[0.000,0.003], g[0.027,0.010]\n",
            ">5616, dr[0.000,0.331], df[0.000,0.001], g[0.039,0.026]\n",
            ">5617, dr[0.000,0.375], df[0.000,0.005], g[0.071,0.007]\n",
            ">5618, dr[0.000,0.531], df[0.000,0.001], g[0.080,0.006]\n",
            ">5619, dr[0.000,0.496], df[0.000,0.001], g[0.021,0.010]\n",
            ">5620, dr[0.000,0.351], df[0.000,0.001], g[0.029,0.007]\n",
            ">5621, dr[0.000,0.358], df[0.000,0.001], g[0.026,0.006]\n",
            ">5622, dr[0.000,0.202], df[0.000,0.001], g[0.053,0.009]\n",
            ">5623, dr[0.000,0.621], df[0.000,0.001], g[0.040,0.013]\n",
            ">5624, dr[0.000,0.442], df[0.000,0.003], g[0.017,0.009]\n",
            ">5625, dr[0.000,0.426], df[0.000,0.002], g[0.052,0.009]\n",
            ">5626, dr[0.000,0.407], df[0.000,0.003], g[0.015,0.007]\n",
            ">5627, dr[0.006,0.422], df[0.000,0.006], g[0.011,0.005]\n",
            ">5628, dr[0.000,0.389], df[0.003,0.001], g[0.023,0.004]\n",
            ">5629, dr[0.000,0.131], df[0.000,0.010], g[0.028,0.005]\n",
            ">5630, dr[0.000,0.499], df[0.000,0.001], g[0.087,0.008]\n",
            ">5631, dr[0.000,0.431], df[0.000,0.001], g[0.088,0.036]\n",
            ">5632, dr[0.000,0.311], df[0.000,0.032], g[0.057,0.012]\n",
            ">5633, dr[0.000,0.342], df[0.000,0.001], g[0.062,0.010]\n",
            ">5634, dr[0.000,0.803], df[0.000,0.001], g[0.046,0.008]\n",
            ">5635, dr[0.000,0.215], df[0.001,0.001], g[0.071,0.016]\n",
            ">5636, dr[0.006,0.387], df[0.000,0.002], g[0.074,0.026]\n",
            ">5637, dr[0.000,0.409], df[0.000,0.001], g[0.074,0.017]\n",
            ">5638, dr[0.000,0.393], df[0.000,0.002], g[0.175,0.008]\n",
            ">5639, dr[0.000,0.425], df[0.000,0.010], g[0.242,0.024]\n",
            ">5640, dr[0.000,0.345], df[0.000,0.002], g[0.149,0.014]\n",
            ">5641, dr[0.000,0.850], df[0.000,0.002], g[0.125,0.008]\n",
            ">5642, dr[0.000,0.624], df[0.000,0.004], g[0.174,0.025]\n",
            ">5643, dr[0.000,0.590], df[0.000,0.002], g[0.097,0.017]\n",
            ">5644, dr[0.000,0.337], df[0.000,0.001], g[0.126,0.019]\n",
            ">5645, dr[0.000,0.604], df[0.000,0.035], g[0.127,0.019]\n",
            ">5646, dr[0.000,0.417], df[0.001,0.007], g[0.190,0.042]\n",
            ">5647, dr[0.000,0.352], df[0.001,0.024], g[0.221,0.016]\n",
            ">5648, dr[0.000,0.223], df[0.001,0.006], g[0.298,0.023]\n",
            ">5649, dr[0.000,0.611], df[0.000,0.003], g[0.323,0.043]\n",
            ">5650, dr[0.000,0.488], df[0.000,0.004], g[0.193,0.014]\n",
            ">5651, dr[0.000,0.475], df[0.000,0.002], g[0.214,0.029]\n",
            ">5652, dr[0.000,0.258], df[0.000,0.003], g[0.365,0.023]\n",
            ">5653, dr[0.000,0.563], df[0.000,0.002], g[0.265,0.012]\n",
            ">5654, dr[0.000,0.473], df[0.000,0.007], g[0.222,0.017]\n",
            ">5655, dr[0.000,0.692], df[0.000,0.002], g[0.174,0.024]\n",
            ">5656, dr[0.000,0.395], df[0.000,0.002], g[0.156,0.066]\n",
            ">5657, dr[0.000,0.326], df[0.000,0.006], g[0.176,0.017]\n",
            ">5658, dr[0.000,0.534], df[0.000,0.008], g[0.195,0.032]\n",
            ">5659, dr[0.000,0.381], df[0.000,0.004], g[0.346,0.022]\n",
            ">5660, dr[0.000,0.754], df[0.000,0.011], g[0.256,0.013]\n",
            ">5661, dr[0.000,0.518], df[0.000,0.025], g[0.353,0.013]\n",
            ">5662, dr[0.000,0.453], df[0.000,0.007], g[0.281,0.010]\n",
            ">5663, dr[0.000,0.403], df[0.000,0.005], g[0.346,0.014]\n",
            ">5664, dr[0.000,0.563], df[0.000,0.002], g[0.310,0.019]\n",
            ">5665, dr[0.000,0.444], df[0.000,0.005], g[0.187,0.019]\n",
            ">5666, dr[0.000,0.498], df[0.000,0.001], g[0.187,0.023]\n",
            ">5667, dr[0.000,0.258], df[0.000,0.016], g[0.327,0.014]\n",
            ">5668, dr[0.000,0.287], df[0.000,0.002], g[0.261,0.029]\n",
            ">5669, dr[0.000,0.534], df[0.000,0.002], g[0.274,0.043]\n",
            ">5670, dr[0.000,0.745], df[0.000,0.001], g[0.239,0.056]\n",
            ">5671, dr[0.000,0.738], df[0.000,0.009], g[0.223,0.024]\n",
            ">5672, dr[0.000,0.649], df[0.000,0.002], g[0.258,0.052]\n",
            ">5673, dr[0.000,0.245], df[0.000,0.004], g[0.315,0.047]\n",
            ">5674, dr[0.000,0.574], df[0.000,0.007], g[0.401,0.058]\n",
            ">5675, dr[0.000,0.307], df[0.000,0.008], g[0.749,0.029]\n",
            ">5676, dr[0.000,0.886], df[0.000,0.024], g[0.591,0.031]\n",
            ">5677, dr[0.000,0.379], df[0.000,0.003], g[0.321,0.032]\n",
            ">5678, dr[0.000,0.422], df[0.000,0.003], g[0.398,0.083]\n",
            ">5679, dr[0.000,0.480], df[0.000,0.023], g[0.416,0.030]\n",
            ">5680, dr[0.000,0.435], df[0.000,0.003], g[0.428,0.020]\n",
            ">5681, dr[0.000,0.393], df[0.000,0.005], g[0.282,0.019]\n",
            ">5682, dr[0.000,0.122], df[0.000,0.004], g[0.309,0.027]\n",
            ">5683, dr[0.000,0.294], df[0.000,0.003], g[0.279,0.024]\n",
            ">5684, dr[0.001,0.487], df[0.000,0.005], g[0.333,0.017]\n",
            ">5685, dr[0.000,0.347], df[0.000,0.002], g[0.230,0.027]\n",
            ">5686, dr[0.000,0.619], df[0.000,0.005], g[0.218,0.023]\n",
            ">5687, dr[0.000,0.520], df[0.001,0.002], g[0.214,0.024]\n",
            ">5688, dr[0.000,0.174], df[0.000,0.003], g[0.278,0.015]\n",
            ">5689, dr[0.000,0.332], df[0.000,0.004], g[0.225,0.018]\n",
            ">5690, dr[0.000,0.168], df[0.000,0.003], g[0.175,0.020]\n",
            ">5691, dr[0.000,0.566], df[0.000,0.005], g[0.131,0.025]\n",
            ">5692, dr[0.000,0.381], df[0.000,0.002], g[0.164,0.023]\n",
            ">5693, dr[0.000,0.399], df[0.000,0.002], g[0.102,0.044]\n",
            ">5694, dr[0.000,0.350], df[0.000,0.007], g[0.187,0.036]\n",
            ">5695, dr[0.000,0.506], df[0.000,0.002], g[0.080,0.030]\n",
            ">5696, dr[0.000,0.333], df[0.001,0.003], g[0.252,0.036]\n",
            ">5697, dr[0.000,0.274], df[0.000,0.001], g[0.141,0.020]\n",
            ">5698, dr[0.000,0.291], df[0.000,0.007], g[0.259,0.022]\n",
            ">5699, dr[0.000,0.343], df[0.000,0.004], g[0.135,0.030]\n",
            ">5700, dr[0.000,0.374], df[0.000,0.016], g[0.257,0.013]\n",
            ">5701, dr[0.000,0.322], df[0.001,0.006], g[0.252,0.016]\n",
            ">5702, dr[0.000,0.552], df[0.000,0.003], g[0.313,0.021]\n",
            ">5703, dr[0.000,0.650], df[0.002,0.006], g[0.328,0.018]\n",
            ">5704, dr[0.000,0.464], df[0.000,0.006], g[0.514,0.009]\n",
            ">5705, dr[0.000,0.353], df[0.000,0.013], g[0.259,0.017]\n",
            ">5706, dr[0.000,0.505], df[0.000,0.007], g[0.308,0.013]\n",
            ">5707, dr[0.000,0.452], df[0.000,0.012], g[0.314,0.014]\n",
            ">5708, dr[0.000,0.502], df[0.000,0.007], g[0.409,0.029]\n",
            ">5709, dr[0.000,0.362], df[0.000,0.005], g[0.293,0.036]\n",
            ">5710, dr[0.000,0.288], df[0.000,0.004], g[0.400,0.020]\n",
            ">5711, dr[0.000,0.327], df[0.000,0.002], g[0.361,0.016]\n",
            ">5712, dr[0.000,0.448], df[0.000,0.005], g[0.366,0.023]\n",
            ">5713, dr[0.000,0.398], df[0.000,0.009], g[0.462,0.031]\n",
            ">5714, dr[0.000,0.284], df[0.000,0.009], g[0.503,0.042]\n",
            ">5715, dr[0.000,0.597], df[0.000,0.011], g[0.655,0.028]\n",
            ">5716, dr[0.000,0.509], df[0.000,0.006], g[0.374,0.026]\n",
            ">5717, dr[0.000,0.255], df[0.000,0.016], g[0.486,0.065]\n",
            ">5718, dr[0.000,0.398], df[0.000,0.006], g[0.563,0.039]\n",
            ">5719, dr[0.000,0.462], df[0.000,0.006], g[0.539,0.024]\n",
            ">5720, dr[0.000,0.533], df[0.000,0.011], g[0.505,0.019]\n",
            ">5721, dr[0.000,0.370], df[0.000,0.008], g[0.470,0.060]\n",
            ">5722, dr[0.000,0.366], df[0.000,0.012], g[0.685,0.024]\n",
            ">5723, dr[0.000,0.321], df[0.000,0.019], g[0.541,0.034]\n",
            ">5724, dr[0.000,0.478], df[0.000,0.011], g[0.553,0.021]\n",
            ">5725, dr[0.000,0.540], df[0.000,0.005], g[0.582,0.030]\n",
            ">5726, dr[0.000,0.206], df[0.000,0.005], g[0.558,0.024]\n",
            ">5727, dr[0.000,0.550], df[0.000,0.045], g[0.626,0.029]\n",
            ">5728, dr[0.000,0.216], df[0.000,0.008], g[0.535,0.032]\n",
            ">5729, dr[0.000,0.373], df[0.000,0.010], g[0.483,0.046]\n",
            ">5730, dr[0.000,0.155], df[0.000,0.021], g[0.372,0.032]\n",
            ">5731, dr[0.000,0.363], df[0.000,0.016], g[0.452,0.042]\n",
            ">5732, dr[0.000,0.552], df[0.000,0.007], g[0.389,0.024]\n",
            ">5733, dr[0.000,0.445], df[0.000,0.012], g[0.317,0.036]\n",
            ">5734, dr[0.000,0.661], df[0.000,0.011], g[0.598,0.047]\n",
            ">5735, dr[0.000,0.494], df[0.000,0.008], g[0.662,0.024]\n",
            ">5736, dr[0.000,0.535], df[0.000,0.011], g[0.689,0.058]\n",
            ">5737, dr[0.000,0.510], df[0.004,0.030], g[1.212,0.032]\n",
            ">5738, dr[0.000,0.410], df[0.000,0.006], g[1.555,0.040]\n",
            ">5739, dr[0.000,0.450], df[0.000,0.010], g[1.472,0.101]\n",
            ">5740, dr[0.031,0.571], df[0.000,0.012], g[1.168,0.039]\n",
            ">5741, dr[0.000,0.572], df[0.000,0.014], g[1.191,0.084]\n",
            ">5742, dr[0.000,0.353], df[0.000,0.015], g[0.995,0.053]\n",
            ">5743, dr[0.000,0.564], df[0.000,0.012], g[0.766,0.031]\n",
            ">5744, dr[0.000,0.157], df[0.001,0.013], g[0.922,0.032]\n",
            ">5745, dr[0.000,0.260], df[0.000,0.019], g[1.083,0.028]\n",
            ">5746, dr[0.000,0.504], df[0.000,0.006], g[0.920,0.017]\n",
            ">5747, dr[0.000,0.506], df[0.000,0.008], g[0.977,0.038]\n",
            ">5748, dr[0.000,0.266], df[0.001,0.012], g[0.974,0.025]\n",
            ">5749, dr[0.000,0.639], df[0.001,0.009], g[1.052,0.019]\n",
            ">5750, dr[0.000,0.675], df[0.000,0.013], g[1.245,0.029]\n",
            ">5751, dr[0.000,0.236], df[0.000,0.005], g[1.077,0.076]\n",
            ">5752, dr[0.000,0.285], df[0.000,0.006], g[1.053,0.048]\n",
            ">5753, dr[0.000,0.451], df[0.000,0.006], g[1.142,0.055]\n",
            ">5754, dr[0.000,0.428], df[0.000,0.037], g[1.421,0.069]\n",
            ">5755, dr[0.000,0.517], df[0.000,0.006], g[1.282,0.053]\n",
            ">5756, dr[0.000,0.368], df[0.000,0.013], g[0.973,0.028]\n",
            ">5757, dr[0.000,0.426], df[0.000,0.015], g[1.272,0.069]\n",
            ">5758, dr[0.000,0.320], df[0.000,0.059], g[1.303,0.028]\n",
            ">5759, dr[0.000,0.655], df[0.001,0.016], g[1.347,0.119]\n",
            ">5760, dr[0.000,0.624], df[0.000,0.011], g[1.466,0.065]\n",
            ">5761, dr[0.000,0.351], df[0.000,0.012], g[1.326,0.019]\n",
            ">5762, dr[0.000,0.387], df[0.000,0.016], g[1.205,0.023]\n",
            ">5763, dr[0.000,0.455], df[0.000,0.023], g[1.354,0.046]\n",
            ">5764, dr[0.000,0.336], df[0.000,0.019], g[1.389,0.114]\n",
            ">5765, dr[0.000,0.208], df[0.000,0.024], g[1.578,0.031]\n",
            ">5766, dr[0.000,0.436], df[0.001,0.011], g[1.524,0.046]\n",
            ">5767, dr[0.000,0.300], df[0.000,0.017], g[1.340,0.047]\n",
            ">5768, dr[0.000,0.402], df[0.000,0.012], g[1.230,0.024]\n",
            ">5769, dr[0.000,0.408], df[0.000,0.013], g[1.595,0.013]\n",
            ">5770, dr[0.000,0.238], df[0.000,0.010], g[1.824,0.043]\n",
            ">5771, dr[0.000,0.293], df[0.000,0.018], g[1.651,0.030]\n",
            ">5772, dr[0.000,0.351], df[0.000,0.023], g[2.110,0.058]\n",
            ">5773, dr[0.000,0.539], df[0.000,0.010], g[2.327,0.036]\n",
            ">5774, dr[0.000,0.323], df[0.001,0.012], g[2.039,0.050]\n",
            ">5775, dr[0.000,0.491], df[0.001,0.013], g[1.639,0.067]\n",
            ">5776, dr[0.000,0.232], df[0.000,0.036], g[1.954,0.046]\n",
            ">5777, dr[0.000,0.460], df[0.000,0.029], g[1.825,0.048]\n",
            ">5778, dr[0.000,0.369], df[0.001,0.023], g[1.878,0.061]\n",
            ">5779, dr[0.000,0.230], df[0.000,0.023], g[1.760,0.040]\n",
            ">5780, dr[0.000,0.495], df[0.001,0.012], g[1.753,0.124]\n",
            ">5781, dr[0.000,0.411], df[0.002,0.016], g[1.842,0.070]\n",
            ">5782, dr[0.000,0.454], df[0.000,0.072], g[2.439,0.051]\n",
            ">5783, dr[0.000,0.410], df[0.000,0.013], g[2.375,0.064]\n",
            ">5784, dr[0.000,0.621], df[0.000,0.013], g[2.070,0.057]\n",
            ">5785, dr[0.000,0.345], df[0.000,0.022], g[2.109,0.092]\n",
            ">5786, dr[0.000,0.661], df[0.001,0.017], g[2.479,0.061]\n",
            ">5787, dr[0.000,0.467], df[0.001,0.016], g[1.939,0.047]\n",
            ">5788, dr[0.000,0.281], df[0.000,0.016], g[1.826,0.084]\n",
            ">5789, dr[0.000,0.255], df[0.004,0.042], g[2.482,0.054]\n",
            ">5790, dr[0.000,0.196], df[0.001,0.042], g[2.777,0.039]\n",
            ">5791, dr[0.000,0.568], df[0.000,0.039], g[2.966,0.056]\n",
            ">5792, dr[0.000,0.249], df[0.002,0.036], g[2.870,0.059]\n",
            ">5793, dr[0.000,0.452], df[0.002,0.017], g[2.700,0.073]\n",
            ">5794, dr[0.000,0.444], df[0.003,0.060], g[3.738,0.060]\n",
            ">5795, dr[0.000,0.204], df[0.001,0.015], g[4.780,0.052]\n",
            ">5796, dr[0.000,0.439], df[0.000,0.014], g[4.520,0.126]\n",
            ">5797, dr[0.000,0.344], df[0.000,0.021], g[4.039,0.098]\n",
            ">5798, dr[0.000,0.479], df[0.001,0.016], g[4.050,0.148]\n",
            ">5799, dr[0.000,0.329], df[0.000,0.058], g[3.665,0.249]\n",
            ">5800, dr[0.000,0.198], df[0.002,0.032], g[3.485,0.099]\n",
            ">5801, dr[0.000,0.421], df[0.007,0.012], g[5.017,0.089]\n",
            ">5802, dr[0.001,0.794], df[0.001,0.094], g[6.652,0.075]\n",
            ">5803, dr[0.000,0.455], df[0.000,0.025], g[7.104,0.118]\n",
            ">5804, dr[0.001,0.405], df[0.000,0.029], g[6.702,0.153]\n",
            ">5805, dr[0.002,0.231], df[0.000,0.038], g[6.062,0.138]\n",
            ">5806, dr[0.001,0.458], df[0.000,0.026], g[6.022,0.165]\n",
            ">5807, dr[0.001,0.303], df[0.000,0.012], g[5.605,0.131]\n",
            ">5808, dr[0.001,0.272], df[0.000,0.011], g[5.322,0.145]\n",
            ">5809, dr[0.000,0.485], df[0.000,0.017], g[4.962,0.112]\n",
            ">5810, dr[0.001,0.255], df[0.000,0.019], g[4.934,0.119]\n",
            ">5811, dr[0.001,0.201], df[0.000,0.051], g[4.514,0.160]\n",
            ">5812, dr[0.002,0.356], df[0.000,0.018], g[4.604,0.110]\n",
            ">5813, dr[0.000,0.273], df[0.000,0.063], g[4.803,0.188]\n",
            ">5814, dr[0.000,0.533], df[0.000,0.064], g[4.338,0.167]\n",
            ">5815, dr[0.000,0.540], df[0.001,0.020], g[4.465,0.189]\n",
            ">5816, dr[0.000,0.231], df[0.000,0.043], g[4.521,0.105]\n",
            ">5817, dr[0.000,0.757], df[0.000,0.037], g[4.633,0.099]\n",
            ">5818, dr[0.002,0.463], df[0.000,0.053], g[4.382,0.171]\n",
            ">5819, dr[0.001,0.250], df[0.000,0.018], g[4.063,0.140]\n",
            ">5820, dr[0.001,0.361], df[0.000,0.022], g[3.849,0.189]\n",
            ">5821, dr[0.001,0.227], df[0.003,0.038], g[4.593,0.240]\n",
            ">5822, dr[0.004,0.611], df[0.000,0.014], g[4.871,0.148]\n",
            ">5823, dr[0.000,0.490], df[0.002,0.022], g[4.896,0.170]\n",
            ">5824, dr[0.000,0.327], df[0.001,0.066], g[5.297,0.276]\n",
            ">5825, dr[0.000,0.254], df[0.000,0.017], g[5.895,0.182]\n",
            ">5826, dr[0.000,0.371], df[0.000,0.015], g[6.012,0.114]\n",
            ">5827, dr[0.000,0.297], df[0.000,0.005], g[6.103,0.113]\n",
            ">5828, dr[0.001,0.562], df[0.000,0.012], g[5.930,0.118]\n",
            ">5829, dr[0.000,0.386], df[0.000,0.007], g[5.545,0.113]\n",
            ">5830, dr[0.000,0.319], df[0.000,0.006], g[5.350,0.108]\n",
            ">5831, dr[0.000,0.599], df[0.000,0.005], g[5.066,0.160]\n",
            ">5832, dr[0.000,0.238], df[0.000,0.010], g[5.185,0.126]\n",
            ">5833, dr[0.000,0.749], df[0.000,0.010], g[5.303,0.136]\n",
            ">5834, dr[0.000,0.347], df[0.000,0.025], g[4.782,0.160]\n",
            ">5835, dr[0.000,0.471], df[0.000,0.006], g[5.177,0.082]\n",
            ">5836, dr[0.000,0.483], df[0.000,0.004], g[4.814,0.133]\n",
            ">5837, dr[0.000,0.291], df[0.000,0.004], g[4.922,0.124]\n",
            ">5838, dr[0.000,0.472], df[0.000,0.007], g[4.973,0.107]\n",
            ">5839, dr[0.000,0.350], df[0.000,0.018], g[4.693,0.120]\n",
            ">5840, dr[0.000,0.357], df[0.000,0.040], g[4.658,0.161]\n",
            ">5841, dr[0.000,0.322], df[0.000,0.020], g[3.906,0.087]\n",
            ">5842, dr[0.000,0.429], df[0.000,0.028], g[4.503,0.124]\n",
            ">5843, dr[0.000,0.262], df[0.000,0.010], g[4.246,0.101]\n",
            ">5844, dr[0.000,0.442], df[0.000,0.043], g[4.238,0.195]\n",
            ">5845, dr[0.000,0.201], df[0.000,0.013], g[4.534,0.119]\n",
            ">5846, dr[0.000,0.719], df[0.000,0.004], g[4.666,0.092]\n",
            ">5847, dr[0.000,0.413], df[0.000,0.008], g[5.024,0.095]\n",
            ">5848, dr[0.000,0.329], df[0.000,0.009], g[5.079,0.129]\n",
            ">5849, dr[0.000,0.416], df[0.000,0.005], g[4.494,0.097]\n",
            ">5850, dr[0.000,0.326], df[0.000,0.009], g[5.128,0.148]\n",
            ">5851, dr[0.000,0.739], df[0.000,0.022], g[5.163,0.062]\n",
            ">5852, dr[0.002,0.770], df[0.000,0.014], g[4.830,0.152]\n",
            ">5853, dr[0.000,0.517], df[0.000,0.065], g[4.456,0.088]\n",
            ">5854, dr[0.000,0.137], df[0.000,0.011], g[4.355,0.077]\n",
            ">5855, dr[0.000,0.693], df[0.000,0.011], g[4.170,0.088]\n",
            ">5856, dr[0.001,0.319], df[0.000,0.004], g[4.136,0.101]\n",
            ">5857, dr[0.000,0.312], df[0.000,0.008], g[3.825,0.118]\n",
            ">5858, dr[0.000,0.478], df[0.000,0.011], g[3.382,0.077]\n",
            ">5859, dr[0.000,0.593], df[0.000,0.007], g[3.195,0.160]\n",
            ">5860, dr[0.001,0.316], df[0.000,0.009], g[3.321,0.064]\n",
            ">5861, dr[0.000,0.528], df[0.000,0.021], g[2.808,0.084]\n",
            ">5862, dr[0.000,0.249], df[0.000,0.017], g[2.735,0.101]\n",
            ">5863, dr[0.000,0.530], df[0.000,0.011], g[2.029,0.142]\n",
            ">5864, dr[0.000,0.466], df[0.000,0.005], g[2.272,0.069]\n",
            ">5865, dr[0.000,0.619], df[0.000,0.026], g[1.870,0.127]\n",
            ">5866, dr[0.000,0.440], df[0.000,0.015], g[2.164,0.039]\n",
            ">5867, dr[0.000,0.899], df[0.000,0.007], g[1.609,0.084]\n",
            ">5868, dr[0.000,0.601], df[0.000,0.014], g[1.959,0.097]\n",
            ">5869, dr[0.000,0.257], df[0.000,0.011], g[1.378,0.066]\n",
            ">5870, dr[0.000,0.465], df[0.000,0.012], g[1.016,0.065]\n",
            ">5871, dr[0.000,0.430], df[0.000,0.016], g[0.946,0.098]\n",
            ">5872, dr[0.000,0.692], df[0.000,0.060], g[0.768,0.117]\n",
            ">5873, dr[0.000,0.182], df[0.001,0.015], g[1.234,0.100]\n",
            ">5874, dr[0.000,0.391], df[0.000,0.030], g[1.394,0.124]\n",
            ">5875, dr[0.000,0.428], df[0.002,0.012], g[1.369,0.050]\n",
            ">5876, dr[0.000,0.455], df[0.000,0.035], g[1.848,0.124]\n",
            ">5877, dr[0.000,0.306], df[0.000,0.009], g[1.609,0.106]\n",
            ">5878, dr[0.000,0.491], df[0.000,0.013], g[1.138,0.146]\n",
            ">5879, dr[0.000,0.628], df[0.000,0.038], g[1.314,0.205]\n",
            ">5880, dr[0.000,0.439], df[0.000,0.008], g[1.486,0.047]\n",
            ">5881, dr[0.000,0.430], df[0.000,0.027], g[1.558,0.138]\n",
            ">5882, dr[0.000,0.418], df[0.000,0.009], g[1.256,0.139]\n",
            ">5883, dr[0.000,0.389], df[0.000,0.004], g[1.734,0.141]\n",
            ">5884, dr[0.000,0.327], df[0.000,0.007], g[1.320,0.140]\n",
            ">5885, dr[0.000,0.450], df[0.000,0.004], g[1.585,0.088]\n",
            ">5886, dr[0.000,0.417], df[0.000,0.013], g[1.596,0.100]\n",
            ">5887, dr[0.000,0.606], df[0.000,0.005], g[1.563,0.038]\n",
            ">5888, dr[0.000,0.394], df[0.000,0.019], g[1.227,0.110]\n",
            ">5889, dr[0.000,0.286], df[0.000,0.004], g[1.386,0.104]\n",
            ">5890, dr[0.000,0.349], df[0.000,0.007], g[1.319,0.115]\n",
            ">5891, dr[0.000,0.153], df[0.001,0.009], g[1.227,0.096]\n",
            ">5892, dr[0.000,0.289], df[0.000,0.010], g[1.353,0.045]\n",
            ">5893, dr[0.000,0.426], df[0.000,0.005], g[1.600,0.067]\n",
            ">5894, dr[0.000,0.569], df[0.000,0.006], g[1.553,0.058]\n",
            ">5895, dr[0.000,0.538], df[0.000,0.006], g[0.557,0.092]\n",
            ">5896, dr[0.000,0.367], df[0.000,0.022], g[0.774,0.034]\n",
            ">5897, dr[0.000,0.497], df[0.001,0.007], g[0.431,0.052]\n",
            ">5898, dr[0.000,0.528], df[0.000,0.007], g[0.708,0.130]\n",
            ">5899, dr[0.000,0.782], df[0.003,0.013], g[0.735,0.050]\n",
            ">5900, dr[0.000,0.785], df[0.000,0.007], g[0.869,0.063]\n",
            ">5901, dr[0.001,0.339], df[0.000,0.013], g[1.017,0.042]\n",
            ">5902, dr[0.000,0.536], df[0.000,0.008], g[0.685,0.060]\n",
            ">5903, dr[0.000,0.329], df[0.000,0.006], g[0.996,0.094]\n",
            ">5904, dr[0.001,0.312], df[0.000,0.012], g[0.941,0.044]\n",
            ">5905, dr[0.000,0.432], df[0.000,0.020], g[0.748,0.067]\n",
            ">5906, dr[0.000,0.608], df[0.000,0.006], g[0.746,0.036]\n",
            ">5907, dr[0.000,0.352], df[0.000,0.038], g[0.775,0.052]\n",
            ">5908, dr[0.000,0.382], df[0.000,0.013], g[0.587,0.060]\n",
            ">5909, dr[0.000,0.272], df[0.000,0.035], g[0.730,0.020]\n",
            ">5910, dr[0.000,0.214], df[0.000,0.034], g[0.565,0.031]\n",
            ">5911, dr[0.000,0.138], df[0.000,0.006], g[0.653,0.054]\n",
            ">5912, dr[0.000,0.717], df[0.000,0.010], g[0.461,0.041]\n",
            ">5913, dr[0.000,0.205], df[0.000,0.037], g[0.337,0.021]\n",
            ">5914, dr[0.000,0.361], df[0.000,0.010], g[0.386,0.039]\n",
            ">5915, dr[0.000,0.477], df[0.000,0.011], g[0.303,0.050]\n",
            ">5916, dr[0.000,0.705], df[0.000,0.020], g[0.359,0.045]\n",
            ">5917, dr[0.000,0.275], df[0.000,0.047], g[0.259,0.070]\n",
            ">5918, dr[0.000,0.302], df[0.000,0.022], g[0.283,0.020]\n",
            ">5919, dr[0.000,0.445], df[0.000,0.017], g[0.379,0.014]\n",
            ">5920, dr[0.000,0.286], df[0.000,0.031], g[0.316,0.031]\n",
            ">5921, dr[0.000,0.462], df[0.000,0.006], g[0.344,0.024]\n",
            ">5922, dr[0.000,0.709], df[0.000,0.012], g[0.366,0.021]\n",
            ">5923, dr[0.000,0.668], df[0.000,0.015], g[0.152,0.024]\n",
            ">5924, dr[0.000,0.408], df[0.000,0.009], g[0.305,0.013]\n",
            ">5925, dr[0.000,0.419], df[0.000,0.030], g[0.134,0.010]\n",
            ">5926, dr[0.000,0.277], df[0.000,0.036], g[0.349,0.013]\n",
            ">5927, dr[0.000,0.357], df[0.000,0.028], g[0.347,0.017]\n",
            ">5928, dr[0.000,0.506], df[0.000,0.011], g[0.248,0.014]\n",
            ">5929, dr[0.001,0.411], df[0.000,0.021], g[0.162,0.016]\n",
            ">5930, dr[0.000,0.525], df[0.000,0.020], g[0.265,0.018]\n",
            ">5931, dr[0.000,0.408], df[0.000,0.032], g[0.240,0.020]\n",
            ">5932, dr[0.000,0.572], df[0.000,0.023], g[0.192,0.018]\n",
            ">5933, dr[0.000,0.861], df[0.000,0.036], g[0.309,0.033]\n",
            ">5934, dr[0.000,0.347], df[0.000,0.020], g[0.385,0.036]\n",
            ">5935, dr[0.001,0.442], df[0.000,0.076], g[0.319,0.011]\n",
            ">5936, dr[0.000,0.307], df[0.001,0.041], g[0.288,0.040]\n",
            ">5937, dr[0.000,0.353], df[0.000,0.031], g[0.358,0.014]\n",
            ">5938, dr[0.000,0.649], df[0.000,0.048], g[0.580,0.013]\n",
            ">5939, dr[0.000,0.455], df[0.000,0.020], g[0.516,0.009]\n",
            ">5940, dr[0.000,0.398], df[0.000,0.031], g[0.579,0.011]\n",
            ">5941, dr[0.000,0.440], df[0.000,0.015], g[0.463,0.020]\n",
            ">5942, dr[0.000,0.428], df[0.000,0.036], g[0.405,0.012]\n",
            ">5943, dr[0.000,0.342], df[0.000,0.033], g[0.302,0.037]\n",
            ">5944, dr[0.000,0.870], df[0.000,0.134], g[0.410,0.006]\n",
            ">5945, dr[0.000,0.359], df[0.000,0.061], g[0.506,0.014]\n",
            ">5946, dr[0.000,0.382], df[0.000,0.017], g[0.634,0.028]\n",
            ">5947, dr[0.000,0.679], df[0.000,0.008], g[0.543,0.014]\n",
            ">5948, dr[0.000,0.392], df[0.000,0.032], g[0.471,0.018]\n",
            ">5949, dr[0.000,0.436], df[0.000,0.016], g[0.576,0.021]\n",
            ">5950, dr[0.000,0.204], df[0.001,0.023], g[0.594,0.025]\n",
            ">5951, dr[0.000,0.269], df[0.000,0.026], g[0.710,0.023]\n",
            ">5952, dr[0.000,0.509], df[0.000,0.014], g[0.550,0.017]\n",
            ">5953, dr[0.000,0.468], df[0.000,0.018], g[0.565,0.073]\n",
            ">5954, dr[0.000,0.225], df[0.000,0.018], g[0.678,0.022]\n",
            ">5955, dr[0.000,0.369], df[0.001,0.014], g[0.642,0.021]\n",
            ">5956, dr[0.000,0.310], df[0.001,0.037], g[0.734,0.057]\n",
            ">5957, dr[0.000,0.163], df[0.000,0.021], g[0.866,0.016]\n",
            ">5958, dr[0.000,0.656], df[0.000,0.029], g[0.697,0.026]\n",
            ">5959, dr[0.000,0.397], df[0.000,0.042], g[0.772,0.034]\n",
            ">5960, dr[0.000,0.609], df[0.000,0.039], g[0.612,0.015]\n",
            ">5961, dr[0.000,0.328], df[0.000,0.019], g[0.806,0.017]\n",
            ">5962, dr[0.000,0.204], df[0.000,0.019], g[0.840,0.014]\n",
            ">5963, dr[0.000,0.465], df[0.000,0.044], g[0.899,0.018]\n",
            ">5964, dr[0.001,0.188], df[0.000,0.049], g[1.019,0.039]\n",
            ">5965, dr[0.000,0.450], df[0.000,0.067], g[1.041,0.069]\n",
            ">5966, dr[0.000,0.720], df[0.000,0.035], g[0.642,0.025]\n",
            ">5967, dr[0.000,0.210], df[0.000,0.105], g[0.616,0.023]\n",
            ">5968, dr[0.001,0.543], df[0.000,0.058], g[0.746,0.031]\n",
            ">5969, dr[0.000,0.397], df[0.000,0.061], g[0.674,0.044]\n",
            ">5970, dr[0.001,0.305], df[0.000,0.035], g[1.088,0.043]\n",
            ">5971, dr[0.000,0.758], df[0.000,0.046], g[0.656,0.118]\n",
            ">5972, dr[0.000,0.178], df[0.005,0.098], g[1.292,0.062]\n",
            ">5973, dr[0.000,0.317], df[0.000,0.094], g[1.360,0.120]\n",
            ">5974, dr[0.000,0.409], df[0.000,0.155], g[1.379,0.034]\n",
            ">5975, dr[0.000,0.596], df[0.000,0.262], g[0.951,0.051]\n",
            ">5976, dr[0.000,0.253], df[0.000,0.060], g[0.927,0.088]\n",
            ">5977, dr[0.000,0.192], df[0.000,0.110], g[0.783,0.037]\n",
            ">5978, dr[0.000,0.638], df[0.000,0.052], g[0.700,0.042]\n",
            ">5979, dr[0.002,0.358], df[0.000,0.062], g[0.785,0.027]\n",
            ">5980, dr[0.000,0.358], df[0.000,0.151], g[0.825,0.029]\n",
            ">5981, dr[0.000,0.662], df[0.000,0.067], g[1.011,0.033]\n",
            ">5982, dr[0.000,0.480], df[0.000,0.094], g[1.006,0.031]\n",
            ">5983, dr[0.000,0.209], df[0.000,0.115], g[0.892,0.046]\n",
            ">5984, dr[0.000,0.366], df[0.000,0.191], g[0.966,0.037]\n",
            ">5985, dr[0.000,0.490], df[0.000,0.062], g[1.144,0.115]\n",
            ">5986, dr[0.000,0.685], df[0.000,0.103], g[1.353,0.073]\n",
            ">5987, dr[0.000,0.344], df[0.000,0.048], g[1.177,0.072]\n",
            ">5988, dr[0.000,0.130], df[0.000,0.054], g[1.140,0.082]\n",
            ">5989, dr[0.000,0.372], df[0.000,0.023], g[0.930,0.063]\n",
            ">5990, dr[0.000,0.451], df[0.000,0.080], g[1.161,0.118]\n",
            ">5991, dr[0.000,0.243], df[0.000,0.087], g[1.670,0.080]\n",
            ">5992, dr[0.000,0.410], df[0.000,0.038], g[1.029,0.062]\n",
            ">5993, dr[0.000,0.268], df[0.000,0.075], g[1.154,0.037]\n",
            ">5994, dr[0.000,0.254], df[0.000,0.109], g[1.513,0.035]\n",
            ">5995, dr[0.000,0.454], df[0.000,0.031], g[1.507,0.046]\n",
            ">5996, dr[0.000,0.496], df[0.000,0.097], g[1.522,0.060]\n",
            ">5997, dr[0.000,0.525], df[0.000,0.102], g[1.369,0.069]\n",
            ">5998, dr[0.000,0.140], df[0.000,0.033], g[1.407,0.048]\n",
            ">5999, dr[0.000,0.572], df[0.000,0.046], g[1.570,0.039]\n",
            ">6000, dr[0.000,0.326], df[0.000,0.136], g[1.724,0.045]\n",
            ">Saved: AC_generated_plot_6000.png and AC_model_6000.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZPiqC0xlBtr",
        "outputId": "5d5271f2-acc8-4331-f8a1-5035befe704f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# example of loading the generator model and generating images\n",
        "from math import sqrt\n",
        "from numpy import asarray\n",
        "from numpy.random import randn\n",
        "from keras.models import load_model\n",
        "from matplotlib import pyplot\n",
        "\n",
        "# generate points in latent space as input for the generator\n",
        "def generate_latent_points(latent_dim, n_samples, n_class):\n",
        "\t# generate points in the latent space\n",
        "\tx_input = randn(latent_dim * n_samples)\n",
        "\t# reshape into a batch of inputs for the network\n",
        "\tz_input = x_input.reshape(n_samples, latent_dim)\n",
        "\t# generate labels\n",
        "\tlabels = asarray([n_class for _ in range(n_samples)])\n",
        "\treturn [z_input, labels]\n",
        "\n",
        "# create and save a plot of generated images\n",
        "def save_plot(examples, n_examples):\n",
        "\t# plot images\n",
        "\tfor i in range(n_examples):\n",
        "\t\t# define subplot\n",
        "\t\tpyplot.subplot(sqrt(n_examples), sqrt(n_examples), 1 + i)\n",
        "\t\t# turn off axis\n",
        "\t\tpyplot.axis('off')\n",
        "\t\t# plot raw pixel data\n",
        "\t\tpyplot.imshow(examples[i, :, :, 0], cmap='gray_r')\n",
        "\tpyplot.show()\n",
        "\n",
        "# load model\n",
        "model = load_model('/content/AC_model_6000.h5')\n",
        "latent_dim = 100\n",
        "n_examples = 100 # must be a square\n",
        "n_class = 5 # sneaker\n",
        "# generate images\n",
        "latent_points, labels = generate_latent_points(latent_dim, n_examples, n_class)\n",
        "# generate images\n",
        "X  = model.predict([latent_points, labels])\n",
        "# scale from [-1,1] to [0,1]\n",
        "X = (X + 1) / 2.0\n",
        "# plot the result\n",
        "save_plot(X, n_examples)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13O4wcTulBtt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPQDizzclBtv"
      },
      "source": [
        "##INFO GAN"
      ]
    }
  ]
}