From 5807c703bdf5d7792476c4ef075746a2971ff749 Mon Sep 17 00:00:00 2001
From: ShiangYong <ptell8@gmail.com>
Date: Tue, 31 Jan 2017 09:10:47 -0600
Subject: [PATCH] Fourth Submission

---
 ShiangYong/facies_classification_cnn.ipynb |  96 ++-
 ShiangYong/predicted_facies_ver04.csv      | 831 +++++++++++++++++++++
 2 files changed, 873 insertions(+), 54 deletions(-)
 create mode 100644 ShiangYong/predicted_facies_ver04.csv

diff --git a/ShiangYong/facies_classification_cnn.ipynb b/ShiangYong/facies_classification_cnn.ipynb
index ac576b3..31f0411 100644
--- a/ShiangYong/facies_classification_cnn.ipynb
+++ b/ShiangYong/facies_classification_cnn.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -55,19 +55,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Samples= 4149    Test Samples= 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def prepare_feature_vectors(data, features, window_width):\n",
     "\n",
@@ -87,10 +79,17 @@
     "\n",
     "        output = np.append(output, tmp, axis=0)\n",
     "\n",
-    "    return output[1:]\n",
-    "\n",
-    "\n",
-    "\n",
+    "    return output[1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
     "# Window around central value and list the six features we are using\n",
     "window_width = 15\n",
     "feature_list = ['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'NM_M', 'RELPOS']\n",
@@ -340,8 +339,6 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
     "# Plot PE of all wells (original plus imputed) \n",
     "plt.plot(data['PE'])"
    ]
@@ -371,14 +368,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Samples= 8298    Test Samples= 830\n"
+      "Training Samples= 4149    Test Samples= 830\n"
      ]
     }
    ],
@@ -393,10 +390,6 @@
     "X_train = prepare_feature_vectors(data, feature_list, window_width)\n",
     "X_test = prepare_feature_vectors(blind_wells, feature_list, window_width)\n",
     "\n",
-    "# reflect about central value to emphasize symmetry, a crude data augmentation\n",
-    "X_train = np.append(X_train, X_train[:, ::-1, :], axis=0)\n",
-    "Y_train = np.append(Y_train, Y_train, axis=0)\n",
-    "\n",
     "num_train_samples = np.asarray(np.shape(X_train))[0]\n",
     "num_test_samples = np.asarray(np.shape(X_test))[0]\n",
     "\n",
@@ -405,27 +398,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "____________________________________________________________________________________________________\nLayer (type)                     Output Shape          Param #     Connected to                     \n====================================================================================================\nconvolution1d_1 (Convolution1D)  (None, 15, 15)        120         convolution1d_input_1[0][0]      \n____________________________________________________________________________________________________\nactivation_1 (Activation)        (None, 15, 15)        0           convolution1d_1[0][0]            \n____________________________________________________________________________________________________\nconvolution1d_2 (Convolution1D)  (None, 15, 15)        240         activation_1[0][0]               \n____________________________________________________________________________________________________\nactivation_2 (Activation)        (None, 15, 15)        0           convolution1d_2[0][0]            \n____________________________________________________________________________________________________\nconvolution1d_3 (Convolution1D)  (None, 13, 15)        690         activation_2[0][0]               \n____________________________________________________________________________________________________\nactivation_3 (Activation)        (None, 13, 15)        0           convolution1d_3[0][0]            \n____________________________________________________________________________________________________\ndropout_1 (Dropout)              (None, 13, 15)        0           activation_3[0][0]               \n____________________________________________________________________________________________________\nflatten_1 (Flatten)              (None, 195)           0           dropout_1[0][0]                  \n____________________________________________________________________________________________________\ndense_1 (Dense)                  (None, 60)            11760       flatten_1[0][0]                  \n____________________________________________________________________________________________________\nbatchnormalization_1 (BatchNorma (None, 60)            240         dense_1[0][0]                    \n____________________________________________________________________________________________________\nactivation_4 (Activation)        (None, 60)            0           batchnormalization_1[0][0]       \n____________________________________________________________________________________________________\ndropout_2 (Dropout)              (None, 60)            0           activation_4[0][0]               \n____________________________________________________________________________________________________\ndense_2 (Dense)                  (None, 60)            3660        dropout_2[0][0]                  \n____________________________________________________________________________________________________\nbatchnormalization_2 (BatchNorma (None, 60)            240         dense_2[0][0]                    \n____________________________________________________________________________________________________\nactivation_5 (Activation)        (None, 60)            0           batchnormalization_2[0][0]       \n____________________________________________________________________________________________________\ndropout_3 (Dropout)              (None, 60)            0           activation_5[0][0]               \n____________________________________________________________________________________________________\ndense_3 (Dense)                  (None, 9)             549         dropout_3[0][0]                  \n====================================================================================================\nTotal params: 17,499\nTrainable params: 17,259\nNon-trainable params: 240\n____________________________________________________________________________________________________\n"
+      "____________________________________________________________________________________________________\nLayer (type)                     Output Shape          Param #     Connected to                     \n====================================================================================================\nconvolution1d_1 (Convolution1D)  (None, 15, 12)        96          convolution1d_input_1[0][0]      \n____________________________________________________________________________________________________\nactivation_1 (Activation)        (None, 15, 12)        0           convolution1d_1[0][0]            \n____________________________________________________________________________________________________\nconvolution1d_2 (Convolution1D)  (None, 15, 7)         91          activation_1[0][0]               \n____________________________________________________________________________________________________\nactivation_2 (Activation)        (None, 15, 7)         0           convolution1d_2[0][0]            \n____________________________________________________________________________________________________\nconvolution1d_3 (Convolution1D)  (None, 13, 12)        264         activation_2[0][0]               \n____________________________________________________________________________________________________\nactivation_3 (Activation)        (None, 13, 12)        0           convolution1d_3[0][0]            \n____________________________________________________________________________________________________\ndropout_1 (Dropout)              (None, 13, 12)        0           activation_3[0][0]               \n____________________________________________________________________________________________________\nflatten_1 (Flatten)              (None, 156)           0           dropout_1[0][0]                  \n____________________________________________________________________________________________________\ndense_1 (Dense)                  (None, 48)            7536        flatten_1[0][0]                  \n____________________________________________________________________________________________________\nbatchnormalization_1 (BatchNorma (None, 48)            192         dense_1[0][0]                    \n____________________________________________________________________________________________________\nactivation_4 (Activation)        (None, 48)            0           batchnormalization_1[0][0]       \n____________________________________________________________________________________________________\ndropout_2 (Dropout)              (None, 48)            0           activation_4[0][0]               \n____________________________________________________________________________________________________\ndense_2 (Dense)                  (None, 9)             441         dropout_2[0][0]                  \n====================================================================================================\nTotal params: 8,620\nTrainable params: 8,524\nNon-trainable params: 96\n____________________________________________________________________________________________________\n"
      ]
     }
    ],
    "source": [
     "# define neural network to classify facies\n",
-    "num_filters = 15\n",
-    "dropout_prob = 0.6666\n",
+    "num_filters = 12\n",
+    "dropout_prob = 0.6\n",
     "\n",
     "convnet = Sequential()\n",
     "convnet.add(Convolution1D(num_filters, 1, border_mode='valid',\n",
     "                          input_shape=(window_width, len(feature_list))))\n",
     "convnet.add(Activation('relu'))\n",
-    "convnet.add(Convolution1D(num_filters, 1, border_mode='valid'))\n",
+    "convnet.add(Convolution1D(7, 1, border_mode='valid'))\n",
     "convnet.add(Activation('relu'))\n",
     "convnet.add(Convolution1D(num_filters, 3, border_mode='valid'))\n",
     "convnet.add(Activation('relu'))\n",
@@ -437,11 +430,6 @@
     "convnet.add(Activation('sigmoid'))\n",
     "convnet.add(Dropout(dropout_prob))\n",
     "\n",
-    "convnet.add(Dense(4 * num_filters))\n",
-    "convnet.add(normalization.BatchNormalization())\n",
-    "convnet.add(Activation('sigmoid'))\n",
-    "convnet.add(Dropout(dropout_prob))\n",
-    "\n",
     "convnet.add(Dense(num_classes, activation='softmax'))\n",
     "convnet.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
     "convnet.summary()\n",
@@ -452,12 +440,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# define training parameters and prepare arrays to store training metrics\n",
-    "epochs_per_fold = 3000\n",
+    "epochs_per_fold = 1500\n",
     "num_fold = 6\n",
     "roll_stride = np.ceil(num_train_samples/num_fold).astype(int)\n",
     "\n",
@@ -475,49 +463,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.691130392866\n"
+      "F1 Score = 0.664015425404\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.660159074476\n"
+      "F1 Score = 0.662328271873\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.668353820198\n"
+      "F1 Score = 0.671487105327\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.678958785249\n"
+      "F1 Score = 0.700168715353\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.659194986744\n"
+      "F1 Score = 0.698240539889\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 Score = 0.678476741383\nAverage F1 Score = 0.672712300153\n"
+      "F1 Score = 0.660400096409\nAverage F1 Score = 0.676106692376\n"
      ]
     }
    ],
@@ -545,24 +533,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0xe2ee8d0>"
+       "<matplotlib.legend.Legend at 0xfb48c88>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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A67ZJOX5BPQF/UxDOBeorpdzbQ7fAUYrxj6FoS2zICT5d7Gl1TzP2pms8W9yy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\nIdmidU0KgQqLPNfZHVElnn4fyRKtalIAVFiIiGSNKgvJfyo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diUHM5K712lniXZ5pk4Xa/7Dth7j82uWAddltnnX1q+uIgdb8ruau+BByEBv3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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xd968d30>"
+       "<matplotlib.figure.Figure at 0xdd13b70>"
       ]
      },
      "metadata": {},
@@ -578,24 +566,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0xe36cdd8>"
+       "<matplotlib.legend.Legend at 0xf520cc0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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J3oD6MbPXhtcaEQXCwIKINGFgoR+DWG14rZkTA4s4wRtQP2b2REThw8AiTjCwoGjhtaYf\ng1htLPyFMiX+t8UJZvb6Wa1Gp4ASBQMLbZivmRMDizjByF4/ZvbaMLPXj9eaNszXzIn/bXGCN6B+\nLLHQhoGFfrzWtOG1Zk78OYoT9huQT0TaMbPXhkGsfrw/tbFfazxv5sIsIk7wBtSPgYU2fIrUj/en\nNvZrjfeouTCwiBP2wII3oHY8Z9qwxEI/Bhba8IHJnJhFxAlG9vrxnGnDzF4/XmvaMF8zJwYWcYKZ\nvX7MtLRh6Zh+vD+1Yb5mTgws4gQje/14zrRhYKEffyC1Yb5mTgws4gQze/14zrRhZq8fAwttWGJh\nTgws4gRvQP34A6kNg1j9eH9qwyDWnBhYxAnegPrxnGnDIFY/XmvacHwec2JgESf4FKkfz5k2vNb0\n4w+kNrzWzImBRZzgU6R+zLS0YWZP0cISC3NiYBEnWBWiH8+ZNrzW9OMPpDZ8YDInBhZxgk+R+jHT\n0obXmn48Z9owiDUnBhZxgpG9fsy0tOG1ph/PmTa81syJgUWcYGSvH8+ZNiyx0I8/kNowXzMnBhZx\ngpG9fsy0tGFmrx/vT22Yr5kTA4s4wcxeP2Za2rDEQj+eM22Yr5kTA4s4wcxeP54zbXit6ccgVhuW\nWJgTA4s4wRtQP54zbXit6cdzpg1LLMyJgUWc4A1I0cISC/0YWGjDAbLMiYFFnGBgQdHCa007+w8j\nz5k2vNbMiYFFnGDxtHY8V/qwxEI/XnPaMF8zJwYWcYKRPUULrzX9+AOpDa81c2JgESf4FEnRcqrq\n78Bl4/gjqQPvT21YYmFOyUYngMKDNyBFy/p2nQAAVuszBqfEfHh/asMSC3NiiUWc4A1I0cZrTT2r\ntALDq6H07J+MToqpsFeIOTGwiBPbmz4EjBLM7LWyFANgrqUHrzX1ikqLgLRjsHYcZ3RSTKUobRfQ\nJJOBhckwsIgTB+pNBcDIXosSawkwoizQYZLRSTEXqWQbDCzUswhbVitKjU2Iyaxp3xa4sxevNZNh\nYBFneAOqV2q1ZfLnzzU2ISYjpNI0i0GsegK2Mn3BG1SLkjLHAfBaMxsGFnGGgYV6wl6Ba+FTpBb2\nwILXmg4ssdCF15q5aAoshBDPCCH+FEKcFEIcEkLMFUKcp2K/y4UQWUKIM0KIrUKIAfqTTIEwsleP\nT5H6CJkEgJm9LgxidWG+Zi5aSyw6AXgLQHsAPQCUAbBQCJHqbwchRCMACwAsAnAhgEkAPhBCXKEj\nveQP673141OkJhawxEI3BrG68FozF03jWEgpr3Z9L4S4G8BhABkAfvez20MAdkgph9vebxFCXAZg\nKICfNaWW/BIyCVJYeQPqwcxeGwax+jGI1UTJ10pZYmEyobaxqAylr96xANt0APCLx7JMAJeEeGxy\nIaAUT/MG1IHF05rYq5B4renAa00bBrGmpDuwEErLtzcB/C6l3Bhg09oADnksOwSgohCinN7jkzvB\nG1A/llhoxGtNN5ZYaMIHJnMKZUjvKQCaA+gYprR4GTp0KCpVquS2rF+/fujXr1+kDmla9huQmb0O\nzOw1EbbAorRUAvYGsKQOSyw04QNTcDNnzsTMmTPdluXm5hqUGoWuwEII8TaAqwF0klIeDLL5fwBq\neSyrBeCklLIw0I4TJ05E27Zt9SQx4dhb6jOy14ElFprYA4sSqxWwBbSkFm9QLVhiEZyvh+3s7Gxk\nZGQYlCIdVSG2oOI6AF2llHtU7LISQHePZVfallOYCBZP68enSE3sT5GlpbzYNGMQqw1LLExJ6zgW\nUwDcAeB2AKeFELVsfyku24wVQnzisttUAI2FEK8KIZoKIR4GcDOACWFIPzko/5WM7HVgZq+JoypE\n8rxpxmtNE5ZYmJPWEosHAVQEsATAAZe/W122qQOgvv2NlHIXgN5Qxr1YA6Wb6SAppWdPEQqBe703\nacNzpoXzWuOPpGYMLDRhGwtz0jqORdBAREo50MeypVDGuqAIcWT2Vjao04yZvUbK9VXC3F47Xmua\nCJbEmhLnCokTjsiexdPaMbPXxNmeh7m9ZrzWNGJJrBkxsIgTjpb6LJ7Wjpm9JrzWQsBrTRO25zEn\nBhZxwlkVwhtQM2b2mjhKLJjZa8drTRNnvsaeW2bCwCJuMLDQjZm9JgxiQ8BrTZOCMvsAAPP2TzU4\nJaQFA4s4wcw+BMzsNeG1FgJea7psP7XG6CSQBgws4gQz+xAws9eEbSxCwGtNF1a7mQsDizjBwCIE\ngi3OtVDmH+S1pguvNV1K2MbCVBhYxAkGFiHgU6QmzrEF+COpGSe804UlFubCwCJOWBhYhIA/kFoI\nDpClH0ssdLHyWjMVBhZxgl0AQ8ASC01YOqadtAevvNZ0KZUs6TETBhZxg/XeWjGz14eBRQh4renC\nByZzYWARJ9hSPwScNl0TIThokW4MLHRhYGEuDCzihEXYq0KY2WvHem8tLI5qN5437XjO9GBViLkw\nsIgTAkkA2KBOFz5FasKqkBDwWtOFJRbmwsAiTjCzDwEze014rYXAwnOmhX3WZnZtNhcGFnHCwl4h\n+jGw0MQiGFhoxd9FfZyzm7IqxEwYWMQJPkVq58js2XhTE15rFC3sRm9ODCziBDP7EHDQIk2Y2VO0\n8FozJwYWcUII3oAUHQxiKVo4fLw5MbCIE8zsKVocT5G81ijCWGJhTgws4oSFmT1FCYNYih6Oz2NG\nDCziBiN7ig5nS31eaxRZjhIL8FozEwYWcYJPkRQtLJ6maOG1Zk4MLOKExTbyJp8iKdIYxFK0OBtv\n8lozEwYWccI5CRnrIvVgo3P12HiTosURWHCOFVNhYBEnOLtpaBhYaME2FhQdrAoxJwYWcYLF06Hh\naVPPPn8DrzWKNPvkiuwVYi4MLOIEA4vQ8LSpx3pvihYhBQD2CjEbBhZxg1UhoWBgoR67m1K0ONtY\n8FozEwYWcYLF06HhaVOPjTdDw/Y86tmrQlg6Zi4MLOIEq0JCw9OmnoBSPM0SC314ranHAbLMiYFF\nnHD0CmGupQtPm3ossQgNe4Srx/Y85sTAIk44BshiGwtd+BupBYPYUDCwUM9eOsYSC3OJ6cCCdZHq\nsSokNDxt6tkzezYUVs912m8GFloo1xobb5pLTAcWvAG14FNkKHja1LMHscUlPGl6MF9Tj403zSmm\nAwtm9uqxxEI716dInjb1HO15GFjowsBCPXY3NaeYDix4A6rnqIvkL6QuPG3qscQiNMzX1GNgYU4x\nHViUlBidAvPgJGShYWChHuelCU1xMRuPqWVvlM7Gm+YS04EFM3v1HE+RzOx14bWmHkssQlNSysBC\nrRRZFQCQVlrH4JSQFjEdWPDhWz0+RYaGgYV6vNZCU1LCwEKtWtY2AICmeQ8YnBLSgoFFnHA03mRm\nrwur3bRgD6RQsMRCC1vX5hJhcDpICwYWcYJPkaEpLDQ6BebBqpDQFLGNhWr2RunFxQYnhDRhYBEn\n7P29j5dZb3BKzImBhXr2Ce+KrTxpehQVMSDTisGYuTCwiBP2zH5r7bEGp8SczpwxOgXmYS+x2NDk\nPoNTYk7FbGOhgb3EgufMTBhYxAkR2/+VMY+BhXq81kLDp2/1HMPHsw2UqcR0DsHAQj1m9qFhYKGe\nPbMnffj0rR1Lecwlpn+NGFhowcw+FAwsKFqKitnGQis23jQXBhZxgk+RoWFgQdHCEgvteM7MJaYD\nC7bUp2hhYKFd2sGeRifBlFisrx0HFTOXmA4smNlTtDCI1a5sTlujk2BKDCzUs5+pYjbeNJWYDixY\nr0bRwiBWu1KOvKnL4sUMLLRiVYi5aA4shBCdhBDzhRD7hRBWIcS1QbbvYtvO9a9UCFEz2LH4FEnR\nwsBCOwYW6kmX38V163netCoWp41OAmmgp8QiHcAaAA/DWVIVjARwLoDatr86UsrDwXZiiYV6jOe1\nc83sGVioZz9tVsnW1XpcfwPvVq2sPZ7gRIEmojmwkFL+JKUcIaWcB219HI9IKQ/b/9TsUFSkNXVE\n+jCw0O5M8w+NToIp7c7fYHQSTIkl2OYRrTYWAsAaIcQBIcRCIcSlanZiYEHRwsBCh5Rco1NgSuvO\nfG90EkyJ96h5RCOwOAjgAQA3AbgRwF4AS4QQrYPtyMBCH8mSVs3mp9xidBIoQRRxfGoNnJkZAwvz\nSI70AaSUWwFsdVn0hxCiCYChAAYE2nfu3KHYvr2S27J+/fqhX79+YU9nPMnLAypWNDoV5rIjZY7R\nSTCl0lIgKcnoVJhLUQnbpujBqhDfZs6ciZkzZ7oty801tjQx4oGFH38C6BhsozbdBuObD2+NQnLi\nSPY9OHaMgQVFR2EhkJZmdCrMhUN668MSC998PWxnZ2cjIyPDoBQZN45FayhVJAEdKdkRhaTEGSFx\nmj2zKBr2XIqTJ41OhDlIlyJ9lljow8DCPDSXWAgh0gGcA2ePkMZCiAsBHJNS7hVCjANQV0o5wLb9\nYwB2AtgAIAXAfQC6Argi2LFKSjj/hWbCyhtQJclOujrZzlthJeTmArVrG5saszmcwzYWmq0cijMc\nQd409JRYXATgbwBZUHKYNwBkAxhtW18bQH2X7cvatlkLYAmAlgC6SymXBDuQOFNVR/ISnLDyKZKi\nI/0Qjh83OhHmk5TEqhDNRCneecfoRJBamksspJS/IUBAIqUc6PH+dQCva08aUFrAhgLqSce/jz8O\nZGcbmhhKBHWz8eyzwOLFRifEXIqtLLHQLKkYdeoYnQhSK6bnCjmCjUYnwXyEFVu3Bt+MvLGbrnan\nThmdAvM5VYfjWGhlSTmNs84yOhWkVkwHFgUlzLU0a/UFG2/qlJVldArM58rrjhmdBNOROeex66RG\n1pafYtIko1NBasV0YPHfNpZ9UfTsYCckzV4uqWZ0EsznwEU4csToRJgP70/ziOnAAuXyjE6BSbFM\nX4/UVKNTQAlhT0e8/bbRiSCKnNgOLLqMQUGB0YkwIcHAQo8bbzQ6BZQQ2r/FLuEU12I7sAAwf77R\nKTAhwQF49OAUDvpwOmuNGi5jewE9BC80s4j5wKJ+/eDbkIcqO3DihNGJoHiWnF/P8fqHHwxMCCUO\nUcpeSCYR84HFmDFGp8CE2nyE3buNTgTFM1Ga4ni9aLG2qrfCkkIMmjcIxwoSt0dJmzZGp8CELKVY\nsMDoRJAaMR9YdOhgdApMqMFyPPGE0YmguCadw+2/88dUTbsu2bUEH635CJP+SNz6gL//1ra9VVrx\n/dbvIRN5sJXaa8CJrc0h5gOL0aODb0MeGiznjJN6VNjP9gI6FPd8GI9nPq55P87Vot70NdNxzcxr\n8OuuX41OinHuvcToFJBKMR9Y3HV3sdFJMA1LYRXH6/m/5BiYEpO69xJs5GCvqtUp7xxnZsIfE1Tv\nJwQnF4SlWFNj4cOnDwMAcs/kRihBROET84HFjn0cRlItS1Fl55tHzjMuIWZVaS/27zc6EebxVMen\nQto/oYv1B3XEnDnad2NQRmYQ+4HFocNGJ8E0hExyvknltJN6PP+80Skwj2vOu8btvdpAQUD5cUzo\nqpB6f6FcOaMTQRQZMR9YFNTLNDoJprVvn9EpMJ/Vq41OgXmcXeVst/dqA4VEfuq+KP0Gx+sBA9Tv\nl8ilO0kFNd3eF7N2PObFfGCRt68+Z53UacQIo1NA8cwi3LMPq2TL12AuTO/leJ1Xqn7CEHvQZi/t\nSSSW4opu799/36CEkGoxH1iUJJ3Ezz8bnQrzqF/ROaLYsmUGJsREWqZdaXQS4sLxAla/BVNOpDvf\n3HS75v0TubQHANAkk2MbmUBMBxbJSAHScjBBfYPzhDetzzTH6+3bDUyIibQvf4vRSYgLNcfXDL4R\nXNpYJHpRZOoxlsYG4VW9VvsfHDpkTFpIvZgOLKqlVQPSDyOTzSxUq5pa1egkmE4ZkeLyTnLOkChJ\n6MabACCsqkeSTPggzC41cUdrNZOYDixqlq8KpLNXiBbV06q7veecIcG51VvXXY1p0/xvS6FL+OJ8\nO2HFtdeq2zSR21gAQMuaLZUXl70KADjNUQhiWkwHFusOrwPafGx0MkzFM7Ao5USn2iSfwdNPG50I\n82hUuZHfdTPXzURRaVH0EmM2STw3anU4y31uh+HDDUoIqRLTgYXDOT+yi5FKFcpWcHv/0y8FOHSK\nlZKqCSshZojNAAAgAElEQVTy8oxOhHmM6Oy769Ga/9bg9m9ux6u/v+q1LhGfun1WZdTYHP2EmFS/\nFu6ThHz3nUEJIVXMEVj0v5pdjFTyLGbu/1Mv1H6jtkGpiW0+M3thta2LcmJMql7Fej6XFxQXAABO\nnPFfF5eo7QbKly2veR/7uUrUaqSWtVq6vd+716CEkCoxHVg0q9HM8Xrw4MgfT4wWeHDBg5E/ULSk\nHgMaLTU6FeZiaxy2dq3B6Yhhro0uu5/d3W3d5dMvxzO/PKOqYWaiNt5sX6+923t7L4ei0iJsydkS\ncN9ELO0BvKt4ASA/34CEkCoxHVjULh/9J+33st6L+jEjpttzRqfAfJLPAACmTzc2GWaRZElye//b\n7t/wyvJXMO73cX73SdSnbrubmt3kfCOsqG3L5oZlDsP575yfsCU5Pvk6Fc2+BgCkp/tYRzEhpgOL\njDoZRifBNHw+/VXaE/2EmFSlcpWUF0lKY5433zQwMXFgwVb//SgT9anb7qbmLoGFRbneTp0Csg9m\nA/B9Lydq6Y7C43rpe7MxySDVYjqwuKW5+8BFx9iFWZvzfjA6BTHN9cHwnavfUV5cP9CxzMoRqikC\nUpNTnW8syqApX3zhLMnxNTR6orex8OnsRRCjhaM9D8WOmA4skpOS3d5Xq2ZQQuJAYaHRKYhtZZPK\nei2rW09y/gtfwvTw7K/IP68wD2OXjY2rKgHXr+LWeFMoKx54ACgt4Yikmlw0FQCQk59jcELIU0wH\nFm7KKCOi8J7zw+W85DzpfaP17RvFtJjQlU285ws51Hg8ksYk+diawsFf8f6Y38bgucXPIetgVpRT\nFB2upQ71bhsLnLcAgMSqg8sBJHq1h3b8TYg95gksGi0BABzmQJxBVUvzLtqZ99NJA1JiHullnS3B\npkyxvbhgtjGJiSO+iu59LTuQdwBvrXoLAByDasVnaZH7d9/feBxwex/gVme1r2eJxap9qzDqt1G2\nvVkVAgArVjhft2zpfzsyhnkCizuuAQBHC2rS6NLXMX8Npzv1TSDZ4qx2e+ghA5NiQs9e9qzfdb6K\n9X39ON459048+tOjKLGWGNKOQIwWeDzz8agf16H5146XngHV3fPudrxO5DYWrWu3drzu4DIQ50k+\nM8Uc8wQWAFBhv9EpMK92b+O6eZ2xZON6o1NiOuwv74vzB25019F+t5rwxwSMXTY26KfZG+C5Bh3R\nbmvw5qrY6ArEqhDfXuz6ouO1EIBrEzzOHRJbYj6wGNphqPNNOSU05cRaOqQqJ63rVTx5njwfAv/5\n7x/ceqvzPfvLB+Za2uPLc4vdx1OxP3W7Bg6OSbaEcE6rHuIPbKm1FMWl6ucCiJVGk/v2xUY6Yk3v\nc3u7vb/SpVlU+fJsaxFLYj6weKTdI843l70CAKhSxaDExDw1xaS8+4Lp9HEnTP7wEJB+xLGsZ08D\nExSnfI7XEMZfh26fdkPZl7x7+xjJHsTuH+a/9LXp+RJ7/AxBk8htLFyrgUqsJV4B/4QJUU4Q+RXz\ngcXZVc52vmn9KdD4FwDAftaKBFS/Yn3fK4REnz7RTYvZ5BXlKfOrVN5tWyKx8GcrjuYf1f2ZUkqU\nWuN3qtlRXUYFXL/16FYAwOwNs3Gq6FQUUgQs3a1tOPtoVkHUKV/H/0phRcOGUUuKKa09tBZzNs5x\nW/bEE8pAY7Hm32P/YuG/C41ORlTFfGDh5a4rAACDBhmcjhj37W3f+lkjsWABkF5eYurqqZzWWq1O\nL6P669U1Fa27mrRqEpJfTI7Tng7AiC6+Zzm1W7l3JXLyc9B3Tl8Myxzmtd61pMJXVUm8CdwIU/ne\nl10GlJbG93nQK2Oac1Tm85o6l1eoAPz2W2SO+c2mb/D3wb8179d8SnP0/DyxijzNF1jYZGYanYLY\n1rZOW98rblWGw82vsQQPff8QJv0xKYqpMrGzFwMAyqaU4MiRINv68PUmpdV/vP5IBOutMH91Flbu\nXQkAOFqgv+QnIdgGzVq+HLhw3PXYctQ5MVki9wrx5/3Zu4DqzinoL788Mse5afZNaDvNT74aQCI+\nvJkisDj5tEd/oqrbAQAlJQYkxuzSbYNnJStDcR7Pj8GyQ4MMuXiI7xXCPRioWcuqueTh32P/AgB2\nntipK21m983+t3DtrGvdlvlqvOlaHZG4vSMkkH4IgMSG0nluawQE9ubuxYhfR8RtkBrIwNYDvZZ1\n+aQzMKSZ27I//4xWisgXUwQWFcpVcF9ge+rmeAM6NfwNkMqTz7ixHO7bbkg734FF9oZTwNlLnAtG\nJiFpTBKOanjwPnjqIABgya4lPtdbpRVitMBn/3ym/kNjzD8P/qNqO/sPor/AIVEaKJ5V8SzfK56u\nCjxZG6i11mtV1sEsDJw3EC8ufRF5RXkRTmHseaHzC6q2a98emDYtwokhv0wRWHiprWRgH3xgcDrM\nauDlztdCIiXFsJTElPOqnedz+aQtj7q8c/7oVa+u/Rj+njLtDTs/+Nu8F3WrWq1UbWcPKCb+MdFn\nQ05/xf3HCuJrFsI1D6wJvIGtZNbVM4uewaKdiwAkTgDmyq0xv4eVK93fP/AAsMD/JLsUQaYJLI4/\nddx9wcXKuMu5ufo/c/ux7Y4JbA7mHdT/QTFq3UPr/K+s7Z6pPfOMus+0P1l/se6LEFIWm/z9oP13\n6j/nm1T3YoqqVZUGdv5sOLwBh087x6GP9+L9g48Hv49cJ43ad3IfAN8Bl+uyBVsXoNpr1bDpyKYw\npDI2+Bp6341FW12vVVrxxMIncCDvQAipMi/X0Tjt+vTh5JVGME1gUTmlsvuC3oOB1GNo3Fj/Z577\n1rloMaUFAKDuhLohpC42XVDjAv8rr3ja9kLJvF95ReljH2x4XPuT9dTVU8OQQnP4ecfPzjePuxdf\nHz/rCyS/JPDVhjnwpcW7LdDy3cSZzKBWei1N27+09CU0fbups42FlD4HyMo6oExItuvErvAkNEYE\nLOW55TZNn7U3dy/eWPkGHl9o4NDkKnX/tDvKjy0fdDstgfj0NdN9Dp547Bhw6JCW1FGoTBNY+FQm\nH8eOAR99pP8jDp2O3ytOVQvyLi8BFZxPOJUqKQ2fSqy+n5YcXQE9bviHv38Y9393v/7E+pBXmIfJ\nqyYb3kgtYEPNm+4AANz68aMYPRoo8tEA3LXEIhyOFxzH7hO7g29oACEEVg5aGXxDmxnrZjjGuPBk\n9P97NCzop7+s3rOY30ylYYt3LsbpYn3jcPsLXgfOG4hKlYAff/ReV7s2sHSp/tE5z5Sc8btu6e6l\nfvPLRGWqwOKNK99wX1BfmWaYY1r4t/ZB7wZgXuqvcHvb/pqNKPNiGdwz7x6vTR1Pkx536Lur38X7\n2e/rT6gPI34dgcd+egwbj2wM6+cG8n4fDd/hOtcW6hKjRgHlygXeJRw/li3ebYFGkxqF/DmR0uEs\nH2XSKvkK4tYfXo/3st5T/RmTV012lHDEuvqV/Axkp8Lttwvc5lKoYb+24r3txYaHNwRc36uX7x6D\nXboAFguwerX2YxaW+G7hvvP4TnSZ3gXjlo3Dz//+jFbvqmtnFO9MFVgMu8RjYB2XdgIbI/zb89CC\nhyBGm++GbVlLRTH8rbcAnV8Cyv8HXPo6cKcyCNnHaz722tT1qej15a97jYNx+PRhfLLmk9ASbZNf\nrMz+Fc1Bpa465yr1G7eZ7nzt0iVVCGCZn4lkw/FUaYY69E2DtbWFsP8oprycgvErx7utu+TDSxy9\natR47KfHcNH7F2k6vhoFxQVuA6TpGSzJl2PD9TZKlfjyS2DsK8UoLLK6zbcSz4K1Ten4UUdk7vgB\nAwb4Xn/xxco9OmUKsNuj4G/6mul+e255+mXHL2g8WamL35O7B08vehrrDgdo15ZATBVYBHKBn+YE\nm45sCktDw6lZcd6moNsLwL0dgCuHAxWdP1xbtvjfZfgvw/G/zP+5Lbtr7l24e97dpi3Grlexns49\n3b9v585KxuW1lc7zMuDbAW6jDca686ufr2n7rIPeJQxXfHYF9ubuDXmAoZ3Hd+Km2TeFXFydNjYN\nl318GQClKL/ttLZYsNW9PmLuprk+S0sCBZRVUnVOfiQkYCnBc4VlkdLvTuz0M0TK5FWTsfO4mcdP\n0RYordi7Ao/++CimT/fuKQIAsBQDPYdh8NA8NGoE7NjhHAp84LyB6PpJV1w781ofO7r7cv2Xjtdm\nqoaKBtMFFl0adnG8vsejpD411Xv71u+1xh3f3BHhVMW2w0+orOOv7F1vf36vJRCjBf479R/unX8v\nzp6kdPfydyPlFuYGXK9HpG7asH6u8P6swYO9NzuQdwBfb/w64EcdzT8KMVrgx23OyuJP//kU2Qez\nQ05mNBU8VxDS/oWlhXjrz7fcgjF7XXdxaTE+yP7AbV3fOX1x3lveXYZH/TYK32z6Jiw/rn/uV0Ze\n2n9Smaxob+5et/U3zr5RV2lJ6Qgd88g81AoYUUZ53eoL9HhaefjZu8f9h/ixnx7DjbNv1P75Mcxf\nnmYvVf73+L+4/7v70eCCA1jnVogggUvHA5dMBNq/BQBo0kQZCvyrr5xbfbf1O81pinQVVKm1FB0+\n6GCKaj7TBRZf3+rMlI9aNgHJBUoECuDMGaXxYX6+c3vXp52dx3dixtoZUUtrrKiRXkP/zucpN1id\nZjvx4d8fBuwe6MrMc2LMu21e8I08lC+vLkh5adlLuPmrm72WuwY59nM8b4v2dMSSlOQULL1b20Rg\nnjyvM3uVyDt/vYP7vrsPTd92ThQxe8NsbDu2zeszjhcoXdUvnHphSGnxmb4wBacWYUH+s/nBN3Tl\n+SDQ8XUAwLKlAr16AVdc4VwVSmnNjLUzHAGVpyOnjxgyuZ6aPO397PdRb0I9tGjh0miz3TtAj2eV\n1w3c6ytvvVXdsV9b/pqGlIbPsYJjWLV/FUb9NsqQ42thusDCtX5t3pZ5wPNpwIiyjoacJ0/6H5Oh\n26fd0H9u/2gkM+aceMpHPyw1HD1G3KPxnBzvTV2Fsyok2o3Rrm0avBjU0ymZg8c+U9/AMBB7Hfl7\nWe/FZGtzLT+mnRp2QpMqTXQfa/zK8Si2Ots12K8F+8BavgIJT/ZgpKCkAIdPH0ZOfg7W/BdkcCoP\nnoFyJCZKSy2Tivva3heGTxLIzAR++cU5TfvRHP33UP+5/dH+g/Zey63Siprja2LkkpG6PzsU468Y\nH3wjF1ICzTs75xTBuT8F3WfWLKDAo+DtqV+e0nTccDNDNbPpAgu/brpd+bf2Gkx+/xiEAMaNc9/k\nZGGQQRriWKWUSvj8hs+179hylvLvvZe4Ld521DkqoL1Y2JVVKvNp+GpouO7QOk3/F2apv5z074OQ\nEnj33eDb7t8PNGwIHAzSJjGUqdpjxfZHvUeQ1GuT7XfBNdis+0Zd1bNH1hpfC5d+eCnavNdG03Hf\nWOHskfb68tcdr8N9bb53TRiCU+kdRBw8YEHDhsC8ecAjjygzp65fD+SFMCq4/Qdu8c7F+j/EZsba\nGVh/eL2mfYZeMlTzcS7p5NFttMObfrcVAujXD3hB3SjiDsF++BdsXaBrtlN/Xf3txxzywxBsyQnQ\nKC6KTBlY9GvRz3uhsALnLQAebAM8pZRqPPus+yZmiPT0UpPB3dHqDpRNKhueA6Y7p/h0rVO21/9Z\npRXjV4xHvQn1kJOfg+3HnD8uraa2Qp+Zfdw+rtGbjWKqcWLh8/onUJkiWwGjAj8hnnUWsGcPcMMN\nwMKFwPz5zu3jsbugHBmee++tycq5ce35cPDUQSz8d6HffTzPp5pSjqLSIlww5QJHz4+1h53dtof/\nMtzx+pEfH1GXcD9p8VovBM4853/MBO1s510K7NkDXH898PbbysypLVsCFSuG4wih/9/2n9tf80By\nFmHBuO7jgm9oM2PtDHz494fuC3sNBSrtUV4n+bjn62TjjTm/obLH+Iy+5ldS2xtn0PxBAa9XfwJd\nO0WlRXjnr3dw/4LwjiWklykDC19Rfc3aVqCO/8Zti3YswvEzx/2uj1UFxQWoOK4i/tj3R3g+L8QG\ndb64DnltL7aWkFi5T2mSPXDeQJz71rlu+3h21duduxvZB7Mdy/OL8zEtW90sQl+u/xKHTukf6MzX\nDas3AHty4ZOOLmfBSiMAYNUqoGdP4BZbs4vCQuBbl6YVb6x8w/eOJhSW4OLsRRAC2LZXZ9WeSvtP\n7sfGIxvx2gqlPt3zGrlz7p1e+3gOorT/5H6s2LvCa7tgyiWXQ7XUUMahtqdVugwL7v9Hae9e4P77\ngXfeAebOBXr0UJaXWEswdtlYv/u5jpZaai0NW/dbLZ6+7Omg2ySNSUJhSSFeXf6q7w2GNgQggfO+\n9173QIb73Eo2V1zhPa+P2gfXeH7AtdMcWAghOgkh5gsh9gshrEKIoBXSQojLhRBZQogzQoitQgg/\nPYzV8ZrtFMDhggNAV5e6vhFJQLfnHW97fNYjlEMaZt/JfcgrysOUv3z0XdTBIiy4uO7FYfmsQNLH\npuPbBUoQ88cepeGX1aWa2t9TTttpbQEAq/atUn2s276+DTfNvklnSv1T3ZvGhesYDLVra9jR1qtk\n1X9LMeJl54/m6yted+tr71nfGwqrtCKvMM+rCHrdoXWOad7DzWvOH60umAO0+RDTt70efFubvw78\npfkwntenmgnQ9uTucbxetnsZzpp4Fjp+1FHzsQHgyJNHgm8USLlcpaHiCFuAXOdvpRRtlADafKSM\nWXPJG8AVT6JBA+D994EhQ4AbbwQWLVIGk5q3eR6eW/yc4yN/3LQEi3Ys8jqUhMS438eh7bS27vPq\nRMmBYYHHdbFKK1JeTgk4xkR+PlDtHPW9hjzHqcnedtDRZXrX7sgEDpFo1xMpekos0gGsAfAwPDvv\n+yCEaARgAYBFAC4EMAnAB0KIKwLsFjqLFej8ss9Vw4cDB1SMMbRk15KAUfjg7wej2yfd9KbQMH/e\n92fQ0evC4pxMAEDOUeUyOav7dzj7WSUOLSmRboGGJ9dixZz8HHT+uDNmb5jtlnk/8N0DuH7W9QAQ\nkdKokHrTBFPmtDJyZ5KPMRpaubeFadTI+bp1a+drz3kRSkuBCROtOFWgbtyHl5e+jIqvVETLd1vi\nuy1K75+i0iK0mtoK57x1TpC99VXXVE6prG402ECuuze0/TWwl1R8v83H06zNntw9WLxzsVsPlc7T\nO4d2XCEw/7b5+nZu/QnwTGXgaj/VNNcNAp6oA/R8Aujo0QDy6sFAx1exdClw863uDYevnt0VPT7r\nga9tHfPsP3CrD6zGlqNK3b5n99toqFOhTkijvQJASgpwNOMJ94XXPOB/h4r73N7+vcfZtqFxYyVA\nu9bPI7evhyrXifmCMUObM82BhZTyJynlCCnlPKjLXR4CsENKOVxKuUVK+Q6AOQC0t7wJk9dfB+q5\njIP01YavfG7X9ZOujidoX6asnoJfd/3qtmzn8Z34a7/2JyR/AjXYOVl40u9Qs8E0r9Ecf97ruwtZ\npBy8/FrsKqf8gJ0ptOKG+zfho68O4rJhb3lt61r03P3T7li2Zxn6zumLhm82xK87lXM+LXtawC6Z\nRaVF2JyzGd9v/R4TV05E66nKr/KR00dUd4ed3Guy6u/naXPOZv8rnyuvjNzZ8VUgxSNCaON/8put\nLtNqVKkCtG2rFGFv367UnT++aAgqvBZkXHEAp4tO44ftPzje2yf3Gvlr8Bb+f5V9HcUVfc/voUbL\nWi3xag8/xdJRdPPsm3Ew7yBOnHE//38f/BtNJis9WbYd24YjpwOXHjR8syEGfBtSIaxPfZr2Cb5R\nuLWb4jJBoW83f/gIsrOBp55W8iSrtDru13YftEO1V2o6tt22Dfj998gl127FPdqrnFz5/LG+KEBV\n7D2XeXyA60+hxNy5wHffqZubZOfxnajxeg3MWj8r4HZmansVjTYWHQD84rEsE8AlPrZVTU8xtT+3\nznHvwPy///nZUIXGkxuj3QftQkyRoqC4IGAf8UqvVEKX6V1clmi78C6ud7H+bqhapPvImMsUYH79\n5hi0riGWV3rUbZUQ7u0MPF3x2RVYujV4fW65l8qh2TvNcM3MazBs4TD8c+gf5Bfno+b4mrjso8uw\neJcya+mWguV+P+OR9toa57lq9k6z4Bt1GwE87Tnyovr/x7//Voqwzz3Xdt22VMZpWboU+OEHJej4\n7z/3aqj84nyUH1ferd2OPYDdlbtL9bFDMbzj8PD0gAjB15u+xqD5g1DlVef5X394vdvDxOoDq9Hi\n3RZBP8s+9ki4yZESb13lHXiHXfOvgjY4dmj/NjK+TsfEouaORZ995lx9rPAIhABSrnoB5/X+Hp06\nKcvPOw946SX1SSooLsBpi7qh3IUQWNhfe4NIux+2/RB8I1degwn6PncWl1/Yiy8Giou9qzLsPefU\nPpCu2rcq5of1j0ZgURuAZ8u6QwAqCiGCP1r5Ecli6kkfHEZaWsQ+PqhSayne/etdpI1Nc4yY569e\nbdV+9W0RfKmUUgmv9TBmwBcAQFKxz8VvTvSfyZXKUnT50L3NTF5hHsRoActo5ZL+abvvPuoFxUoj\nhZX7VqLvt0o1ysaCwN3lcp5UX0ypm+vInX7OiSopSjfeLl2A3r2VoKNOHSApCWjVSgk2Dp8IMKuk\nj66Ky/csD9v8L67uz7gfW4foL/kIh81H3Lvn+eqZEO7ZabUa0m4ILqjhZ86CcKi7GrhgtvuyJkF+\npMvmA1Vd2uF4XjcVDqCww0vAHdcAAESVndi2Tem6OcxjyidXM2cq05wDwA1f3oCNyeq7yF/RRH/t\numcvNU+LNmkY7dLHKLyAMvlZ2bLAUVsP8t69galTgXW2Jk5nCpXqTECZYdpzllZ78H/8zPGYn+ws\n2egEBDJ06FBUqlTJbVm/fv3Qr5/S3XT/sP2oN0HH3A53XA3MCBChph1BwRFncV5alVxARSmGvx8z\nrWasm4GHf3gYABwze0ayXu3Jjk9i2CXDkPxijFwOVz4OHG0afDsXe08qdbv283TVDN+TiZVK7xIg\nESS+DjbpUTi8sPBlvKhmkMrGP+s+xrp1SmaGNADD3ddt3gycaaUMCATbb+vIkUqbjhvXKsW+vc8a\ngOrVdR/ep3OrnYtTz5xC+XHlw/vBKu3clw94twWPOesfXo9dJ3Y5htQPq/t9NOa+/RpgnY9u/f60\n/tT9fYpLm6d6q4D7OgC/jAWqb8HErwcBtukYHE2pRtkOaxuOqEYN4MhgpY2WlraKun8Tguj+affA\nG1QL3oXZSflCP/yg/KE+gEHAlHeAtGXK/Ezf2UYU9/fdjxY4x7eZOXMmZnwxA9gKrE9bj2u/uRa5\nubka0hN+0Six+A9ALY9ltQCclFIGbCAwceJEzJ8/3+3PHlQAQN0KdfWl6Nwf3XqMeJHup6Xgf85O\nzKLdOxBJxXDtsrxmjXJD/Lw6eKvideuUm+nvACX59lk93ZIU4ZbASZYkFL8QwpNyOF06AegToOEU\nAJQ95XdVoO7kZ4/1rqZSU3epay4HDV5c+qLfdT+4xsB3Xel8XScbaJIZluO/87ZQ5tpxefIc8+Ze\n3OgyxUSNGu5tk8IlvWw6jg43aCCwCpHvxbDj+A7UeDM95M9pVLkRCp8vxK0XqBx7OhRJxd7BgiYu\n99R9toaVPZ5VGpbeE6Rh6yiBIxc6R7cs0ZAt6f5NCCv9efX48c6gAgC6d1d6rDz/PDB9uu99evTo\nhznffA3cDpS5owzmz5+PiRMn6k5DOEQjsFgJwDPcu9K2PGQ3N/eed0EVPz1GAADN5wC1/vG9rvcQ\nIMO9UU+bNkoR3oTPna3dKz15MX7a/Kvn3si2DbXxZ3TbTaqSbEmGHCnRuEpjo5MSXLK+GS/zy3pP\ntKYmG7AIi2GB11X+ZnJ/IAO4s1fkDpzuXQWgpjeVHlVTq0KOlJAjJT69PpQftNgz6rPwBH+AMr7K\nrJtmYdW9oVWBRpyPKjVN2n6g+zP0T0MfHkVFwIv+nxP8VpWgvPdYPIsXA+npwMsvA0P/534+hABy\nc4GaNYHu3ZV1h07rH88nnPSMY5EuhLhQCGHv+NbY9r6+bf04IYRrhexU2zavCiGaCiEeBnAzgAkh\npx7A1N5TcdU5/nJenbqNAB5q7X99WY866lECuKsHcLFzavWT5Vfjqi+7QQjlAli/XpntcuZMZX1B\nAbBpk1LaAQCHDinFXst2L/MaaMeXX3Z4tocNn38fjcwYBrHq+DF1GViyJRl5z4QwBrJOwcZQSK91\nCLh8lNekSpq0mqGMfeDKXwYYYXdeeCdynza2KDecXBs2hoMQAu3qtcPBxw+iY31942REXIqG/7+7\n/FQz6Lz+qqRW0T6hWxidLj6NZ54txZp/rPhhubN7PLqMBiq7lGpf8aRjAk0Ayj3oGszXXQ2c49rQ\nwjufso8IumKF81x99ZV7qYcR9JRYXATgbwBZUB723gCQDWC0bX1tKLVGAAAp5S4AvQH0gDL+xVAA\ng6SUYfllrJZWDd/f7r+PeURYSoAaG92XNfYeOMZVy5bAlMxMZJYbBGRMw9DPp6F5c6W0Y9IkZTCl\nwUNPofP0zhiW6d26Ke+UxOIdvyJzu/L0c8VnzoZK6/yP+6KbHCnD2vMmqqppaxB4+pT6J6PyZcuH\nPsiTRtVeC9zG4+q3HgEuH40Xpq52LCsoUIYNB5TrCwDQ6jOlvttXhl1/JdB7MNwzL4/tyuUC3f3M\n8BdmFctVdJRgzO07NyrHjJgKzp4Ny8LY9bJ2+dr4/Z7fDQl2g7q9t/ptG/tqPB1aiUdqmVQUPl+I\nhy56KKTP0aPKq1WQ/GIyFpwYh6t/buhc0XUUGg2/Bcn2pmwdxwPneLTLS3V5iLj/YqD/1crrMqeB\nS9Q9i996Rz5GTQrf/Dx66BnH4jcppUVKmeTxd49t/UApZTePfZZKKTOklKlSynOllGGN4YUQKHpe\nX9G4Lt2fAwarbaXtkjn3uw5o+5HSfqDPA8r8JhX34X8jlaj23fdKbHt4Z/wLFkh0/6wbes3o5dWG\noBs9wsEAABYASURBVFUr4K/wDZ3hUCO9BuRIiQX9FoT/wyPpEW0NP9NStd0GlVMqI//ZfNzd+m5N\n+0WKfUyOF/90BqQpKcpQzVICjz4KfLpoFXDjXcB9HfDNN34+qNUM5c9OeIz10Wks0OmVMKc+uOvP\nvx5ypDS8e6puXZzl4v9sC3FETR/Kly0fE+OCuEnTWR3xgo+h9Kvr6zlUNqkspvSeggcygrTXipD5\nW70HOLOkH0f5Ch75u2ugP6QZ0MzHDXrNQ8Dlo72X+3L9AOCWvhpSGn6mnCvElzJJZTCyizHT9wZ0\nTiZwwZfAWSvh9QQ4MgkYVt82Vj3U1yn66W9eHKHYqvd5vWOnYWcE1Kyp/ekotUwqPr7uY6x5QNv0\n25GgZhjlj/c6Sxo6qi09F1b3IMRi7BTu92fcDzlS4vUr1A/nHWvSu+kfcC2Q4R2HQ46UuK7pdRH5\n/EjxapNu72qdFr7GvFOvmYoZN84IvmGY/bnfuyHd7hO73QZk+3YeMOgt967crR56FXPnedxrlYN3\nDHCo7ad9YBTFTWABAKMuHwU5UqJro65GJ8Wp+7PALbcB914auMHhs+kIXPwXoL7R17DQYWZv2PlI\nO/0DRsWsVP1VGxfWvhCbBm8KY2K0W77Xe4CvxTsXo/83/ZFfnI/CkkK3dhp5heqKzpevkLjhBuf7\nuwbExlDCQzsMxbzbAoygFsNEuQBjiITBt7d9i3UPRaBuNELE6OiMJnl7y9uN63nkwrO7u0UAm3Lc\nq9VTUwXWVnJO/rb7xB506hTsPLmsD7XhbBjEVWBh9+MdPxpW/OWljsoZ/8rmA/2Czufm2wu6xxnT\nbPJVkyFHSlhHWDG221i82DVQ82dzUDu8tz/nVz8fcqSMeJdULbp/2h0z1s1A+th0tJraym0ugh3H\nd6j6DKu0ug13Xy3yw3mokmRJwrVNr3W0wZAjJX4d8Cve7/M+MupkGJ28gApE5Adba1GzBeRIiXd7\nvxvxY5lJ1dSqKHy+EIvvCjwgXjRdO+tarxlwhRDYfcLZe63hmw2xbI93w2wplb8th3aheWuX7vc6\nq47CKS4Di3LJ5TD1mqmhzxAYbY1+878uCqUSWggh8EynZ/B85+dx5Mkj2PDwBsiREieeOoF/HvwH\nFctVNDqJqoVrDH6LsECOlDg6/Cha1YqdkfG2Ht2K/Xn7He9LrOqqNKSUbsPdx/Ksipc3uhz3tr0X\nq+9fjTPPncG+oZEZYjtUlkCDrITZgxc9iL8f+Btju/mf+jzRlE0qi65nd8VXt/ieHyoWCAis2Kd+\n7pOm756Nyg8bMK9MADEy1GJkVE+rDjlSyQynrp6K7IPZeD/7fYNTpVOzb41OgV/V06qjepoyJGOl\nlEpoldLK0V1w2e5lyC3MRZ+ZfVA5pTKmXTMNfZr2QerLqUYmOaKqplbFPw/+gx3Hd2Dp7qUYOG+g\n0Ulyc/UXV6vazrMRsRlmVQSUB4t6FetBjpTYcXwHUpJTIjIaox4WEd1nuda1W6N17dZ4ppOzjc3O\n4zvReLIJxqqJoJub3ww5UuLQqUOoP7E+iq2x04ZMCBF4AkObC6ZcgKqpVQHAq9TDaHEdWLh68KIH\nHf9mTIvt4lJdks+g1lnBx7+Itk4NlRmI7AGenRwpkVeYh23HtmH+lvkY/ZvKFs8RICL0FNm4SmM0\nrtIYPZv0xN3z7sbCf/VPkmQEzyqiSasm+dkydtkHe7up2U24t+29eHzh445h8o0QqWtNi7OrnO24\nHzfnbMb8LfMxc/1MrPlvDR666CF8v+177MndE+RT4kOt8rVQ9EIRpJSYljUND37/oNFJUl2CauR1\nHIyIxeJNIURbAFlZWVlo29b/tOV6Hc0/iqqpVbEndw/2ntyLTh93CvsxjOL5A24WRaVF+Pjvj3HX\nhXdhxd4V6PFZj+A7hUmjyo2w8zENra5D8N7q92Ii81Jj8V2L0e3Tbn7Xm/VaA4A5G+cg60AWHuvw\nGBZsXYDuZ3fH04uexuwNs4PvHIKySWVR+HzAmQxiyvI9y/HZ2s9waf1LcX718zHi1xHI/Dd8I4mq\nFc1rbfHOxcHnBol1BwAoA0RnSCmzo334hAwsPD258EmcLj6NnPwcfLUxduve1DBzZu/pdNFp5Bfn\nI7cwF+e+dW7EjtOgUgPs/p/3UN+RcqroFLp90g1/HYjA4CNh9P3t36P3F/4HOoqna83Trzt/RUpy\nClrVaoXeX/TGb7t/w6guo3Cm5AxeWa5/LI8uDbtgyd1LwpdQA5RaS7Fk1xLUTK+JVlOj05bIiGvt\n0KlD6PBhB+w6sSvqxw4ZAwtv0Q4sPGVuz8T8LfOxaOcibDm6JfgOMSReM/vjBcex68QutK7dGkII\nFBQXIH1sOiQkvu37Le777j4cydfXWLdJlSbY/qgxI9VlTMtA9sGo3/dhEa/XWjAH8g5g45GNOHTq\nEPrP7a9p322PbMM5Vc+JUMqMUVBcgKyDWfjv1H9oXKUxJq+ajE/++QTt6rXzOZaDHkZfa0WlRVh9\nYDU6fhSjQ6h7YmDhzejAwm5LzhZ0/7Q7RnQZgUd+fARFpbHVM8MXo2/AaCooLsCmnE1oW0e5RopK\nizBw3kCMuXwMapWvhVX7VmHfyX34fN3nAedWmdxrMh5pb8z4HFJK5OTnoOb4moYcPxSJdK1pceT0\nEew7uQ9CCLR5r43bukQ9Z3/s+wNtarfBjHUzMGj+IM37x9J5K7GWIOtAFjp82MHopPjHwMJbrAQW\nns6UnEFRaRG6fdINF9W9CO9lxd4Qw7F0A8aq4tJi9PisB97s+SZa1WqFJEuS0UkCoAQZ3T7thiW7\nlhidFFV4ralXalXGOImVa81IxaXFSLIk4aftP6GwpBD78/Zj/Irx2J3rvzoylq+1qq9WxfEz0Z0/\nKCgGFt5iNbAIZOC8gZi/ZT6aVW/mcyTEaInlG5DUKSwpxKmiU6j+utKFt3zZ8jhVdCrIXtHHa40i\n5d9j/6JexXo4XnAcFctVRHrZdKOTFJSUEpNWTcLQzKFGJ8XwwCJhuptG2sfXfez2/sjpIzhTcgb1\nK9XHsYJjWH1gNXp+3tOg1JGZlEsuh3LJ5VD4fCFKrCVIK5MGADh8+jBunn2zz1H4iOJJk6pNAAB1\nKtQxOCXqCSHwvw7/Q6PKjVA2qSwaVW6EC6aonawyvrDEIoryCvNw17d34dKzLkXLWi1x1Yyrwn4M\nPkXGv+LSYiRbknH8zHFsOLwBaw+txR/7/8Dnaz+Pajp4rREFdzDvIDL/zcS5Vc/FZR9fFp2DsirE\nW7wGFp6KS4uxct9KJIkknFftPDz202PIyc/Bzzt+1vV5zOjJVV5hHlLLpCLZkozUl1NxWYPLkFeY\nh1X7V4Xl83m9EWn377F/8X72+/jrwF9YvDNC85YwsPCWKIFFIL/s+AXJlmR0atAJw38ejrtb340q\nqVWQuT0T87bMw3dbv8OKe1bgwtoX4o0Vb6BLoy7o3LCz0ckmk8kvzkdxaTEqlquIEb+OwEvLXgIA\ndKzfMWhbIQYWRKHZfWI3ThWdwoSVE/DRmo9Qt0JdHMg7EPoHM7DwxsCCyBhDfxqKjg064ubmNzuW\n5Z7JxamiU6hboS5OnDmBqq9VxeOXPI7xV443MKVE8Sm/OB8/bf8J434fhwtqXIBZ62ehsFTjaK0M\nLLwxsCAiInL6bst3GPXbKGQfzMaVTa4MPPcQe4UQERFRIH2a9kGfps7p0aWUeG7xcxj3+zgDU+Ub\nSyyIiIjiwIbDG/BB9gfold4LvTr3AgwqsbBE+4BEREQUfhfUvAATe01EjfQahqaDgQURERGFDQML\nIiIiChsGFkRERBQ2DCyIiIgobBhYEBERUdgwsCAiIqKwYWBBREREYcPAgoiIiMKGgQURERGFDQML\nIiIiChsGFkRERBQ2DCyIiIgobBhYEBERUdgwsCAiIqKwYWBBREREYcPAgoiIiMKGgQURERGFDQML\nIiIiChsGFkRERBQ2DCyIiIgobBhYEBERUdgwsCAiIqKwYWBBREREYcPAgoiIiMKGgQURERGFDQML\nIiIiChsGFkRERBQ2DCyIiIgobBhYEBERUdgwsIiCmTNnGp2EsOL3iV3x9F0Afp9YFk/fBYi/72Mk\nXYGFEGKwEGKnEKJACPGHEOLiANt2EUJYPf5KhRA19SfbXOLtguX3iV3x9F0Afp9YFk/fBYi/72Mk\nzYGFEKIvgDcAjATQBsA/ADKFENUD7CYBnAugtu2vjpTysPbkEhERUSzTU2IxFMB7UspPpZSbATwI\nIB/APUH2OyKlPGz/03FcIiIiinGaAgshRBkAGQAW2ZdJKSWAXwBcEmhXAGuEEAeEEAuFEJfqSSwR\nERHFtmSN21cHkATgkMfyQwCa+tnnIIAHAKwGUA7AfQCWCCHaSSnX+NknBQA2bdqkMXmxKTc3F9nZ\n2UYnI2z4fWJXPH0XgN8nlsXTdwHi6/u4/HamGHF8oRQ4qNxYiDoA9gO4REq5ymX5qwA6SykDlVq4\nfs4SALullAP8rL8dwAzVCSMiIiJPd0gpv4j2QbWWWOQAKAVQy2N5LQD/aficPwF0DLA+E8AdAHYB\nOKPhc4mIiBJdCoBGUH5Lo05TYCGlLBZCZAHoDmA+AAghhO39ZA0f1RpKFYm/4xwFEPUoi4iIKE6s\nMOrAWkssAGACgOm2AONPKL1E0gBMBwAhxDgAde3VHEKIxwDsBLABShR1H4CuAK4INfFEREQUWzQH\nFlLK2bYxK8ZAqQJZA6CnlPKIbZPaAOq77FIWyrgXdaF0S10LoLuUcmkoCSciIqLYo6nxJhEREVEg\nnCuEiIiIwoaBBREREYVNzAUWWiY4i2KanhFC/CmEOCmEOCSEmCuEOM/HdmNso4vmCyF+FkKc47G+\nnBDiHSFEjhAiTwgxx3MyNiFEFSHEDCFErhDiuBDiAyFEegS/29O2ieEmmPW7CCHqCiE+s6UlXwjx\njxCirRm/jxDCIoR4UQixw5bW7UKI531sF3PfRwjRSQgxXwix33ZNXWtUuoUQ9YUQ3wshTgsh/hNC\nvCaE0DrSsN/vI4RIFkK8KoRYK4Q4ZdvmE6GM9WO67+Nj26m2bR6Nxe+j8lprJoSYJ4Q4Yfs/WiWE\nOCvWvoua7yOESBdCvC2E2Gu7dzYIIR7w2CZmvg+klDHzB6AvlHEr7gJwPoD3ABwDUN3gdP0A4E4A\nzQC0BLAAyhgbqS7bPGVL6zUAWgD4FsC/AMq6bPOubb8uUCZwWwFgmcexfgSQDeAiAJcC2Arg8wh9\nr4sB7ADwN4AJZvwuACpD6XX0AZTh5hsC6AHgbJN+n2cBHAbQC0ADADcCOAlgSKx/H1uaxwC4Dsp4\nN9d6rI9KuqE8MK2D0oe/JYCetnP6Uri+D4CKts+/CcoEi+0A/AHgT4/PMMX3+X975xaiVRXF8d8y\nTakwCbWhklAr6aJjaGWhpllKppUvJkEhId3TirAigi5U5oNp2TwkFdk9owxjKBAlSidJDTU1EbRU\nRMEyUwuddPWw9sk9x5lxmM7Md3atH5yHc86e2ev/fedbe519W7lyEzCfsB2YWkY9LXjW+mL7Lr0A\nDAB6h+eue9m0tFDPa6HuYZhfmALUA+NKqae1TqQtDuyHOSc6F2AHML3StuXs7A4cBYZG13YCD0Xn\nXYE/gYnR+SFgQlSmX/g/l4fzC8P5pVGZMcBfQFXBGk4DNgHXAEtpGFgkowWYAXx1gjIp6VkEzMtd\n+xiYn5Ke8L/zzrFd7Aaux5xu3IjcBewFOhalp5Eyg7FG4ZxU9QBnA9uCXVuJAouy6mniWXsfeKuZ\nvymllmb0rAOeyF1bCTxTRj2lGQqR1ic4qwTdsFTwvwKISG9smW1s++/ACo7ZPhhb3huX2YT9iLMy\nQ4C9qvp9VNfiUNcVBWt4FVikqkviiwlqGQ+sFJGPxIapVovIlIT1LAdGicj5wf5qbJfa2kT1UAG7\nhwDrVHVPVOZL4HTg4oIkNUbmF34L54NISI+ICDAfmKmqjSVqSkJP0HEDsFlEvgh+4VsRuSk1LRHL\ngRtF5CwAERmJ9ZRlO2uWSk9pAguaT3BW1f7mNE54aGcD36jqhnC5CvtymrP9TOBwcKZNlanCup3+\nQVWPYAFMYZ+BiEzCdj99vJHbSWkB+gD3YL0vo7HuwJdF5LbIjpT0zAA+BH4UkcPAKmC2qn4Q2ZGS\nnoz2tLuqiXqgjXyJiHTGvrv3VPVAVFdKeh7D7J3bxP1U9PTEemQfxQLy64BPgU9EZFhUTwpaMh4A\nNgI7gl+oBe5T1WVRXaXR05qdN//v1AAX0Xyuk9ISJi/NBq5V1fpK21MAHbBx7SfD+RoRuQS4G3i7\ncma1mluAW4FJwAYsAJwjIjtVNUU9/3lEpCOwAAuc7q2wOa1CRAYBU7Gx+dTJXpgXqmqWamKtiFyF\n+YWvK2PWv2Iq1qswDuuFGA7UBL+wpNm/rABl6rEoKsFZmyEic4GxwAhVjXOd7MLmgzRn+y7gZBHp\neoIy+Vm8JwFnUNxnMAjoAawWkXoRqccm+0wLkfDuhLSA5ZzJd9tuxCY4ZXakpGcmMENVF6jqelV9\nF3iJY71LqenJaE+7dzVRDxSsLQoqegGjo96KrK5U9AzF/ML2yC+cC8wSkS1RXSno2YPNGziRX0hB\nCyLSBXgOeFhVa1X1B1WtwXo2H4nqKo2e0gQW4e05S3AGNEhwVrFkKpEtc7EZuyNVdVt8T1W3Yh96\nbHtXLMLMbF+FPexxmX7Yg14XLtUB3UQkfmsYhTnkFRTDYmy270CgOhwrgXeAalXdkpAWgGXYJKWY\nfsDPkNx3A5Z350ju2lHCbzVBPVTA7jqgv1jqgYzRwD6sF6gQoqCiD5amYG+uSEp65mOrJ6qjYycW\n6I5JSU9oS77jeL9wAcEvpKIl0Ckceb9whGNteLn0tGbWalsdwEQsn0i83PQXoEeF7arBZsUOw6K3\n7OgSlZkebB2PNdwLgc00XEpXg820HoH1HCzj+OVAtVhDfxk23LIJeLuN9eVXhSSjBZvwdwh7o++L\nDSPsByYlqudNrKtzLPbGOAEbF32+7HqAU7EGaSAWDD0Yznu1p92Ys12DLa0bgDWMu4Fni9KDDSN/\nhjVU/WnoFzqlpqeJ8g1WhZRJTwuetZuxrQumYH7hfuAwcGXZtLRQz1Isz9bVWDr0yVhbeWcp9bTW\nibTVgY1R/oQtQ6sDBpfApqNYdJg/bs+VewqL8v/AZtKel7vfGXgF66rbj73t9MyV6Yb1HuzDgpl5\nwCltrG8JUWCRmhasEV4bbF0P3NFImST0BAczKziIg1jD+zS5pV5l1IM5vcZ+K2+0t91Y4/85cABz\njC8CHYrSgwV9+XvZ+fDU9DRRfgvHBxal0NPCZ20ytk/DQWzvhnFl1NISPdgQxuvY3iIHsd6DaWXV\n40nIHMdxHMcpjNLMsXAcx3EcJ308sHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzA8\nsHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzA8sHAcx3EcpzD+Bph6\na3BvCJP9AAAAAElFTkSuQmCC\n",
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HZBEs3LXjkDx1mNe0YyChjjOQ4HfU2BhIWAQjd+3YAU4d5jXtGEio4wxaGUgYG4sEi2Dh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\nBgoctAYUSl4WHxOvrVkojFyjBqTE7B2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YcbI/rq93Bx4ePQj3NOuO6TEPhOXYZslrZfkl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\nCCFqIyMhhBBCiNrISAghhBCiNv8C+gZHORXP9gAAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xd968cf8>"
+       "<matplotlib.figure.Figure at 0xdd13400>"
       ]
      },
      "metadata": {},
@@ -609,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -618,7 +606,7 @@
     "\n",
     "# Append predictions to dataframe and write to file\n",
     "blind_wells['Facies'] = Y_test\n",
-    "blind_wells.to_csv('./ShiangYong/predicted_facies_ver03.csv', index=False)"
+    "blind_wells.to_csv('./ShiangYong/predicted_facies_ver04.csv', index=False)"
    ]
   },
   {
diff --git a/ShiangYong/predicted_facies_ver04.csv b/ShiangYong/predicted_facies_ver04.csv
new file mode 100644
index 0000000..b91b09a
--- /dev/null
+++ b/ShiangYong/predicted_facies_ver04.csv
@@ -0,0 +1,831 @@
+Formation,Well Name,Depth,GR,ILD_log10,DeltaPHI,PHIND,PE,NM_M,RELPOS,Facies
+A1 SH,STUART,2808.0,66.27600000000001,0.63,3.3,10.65,3.591,1,1.0,2
+A1 SH,STUART,2808.5,77.252,0.585,6.5,11.95,3.341,1,0.978,2
+A1 SH,STUART,2809.0,82.899,0.5660000000000001,9.4,13.6,3.0639999999999996,1,0.956,2
+A1 SH,STUART,2809.5,80.671,0.593,9.5,13.25,2.977,1,0.9329999999999999,2
+A1 SH,STUART,2810.0,75.971,0.638,8.7,12.35,3.02,1,0.9109999999999999,2
+A1 SH,STUART,2810.5,73.955,0.667,6.9,12.25,3.0860000000000003,1,0.889,2
+A1 SH,STUART,2811.0,77.962,0.674,6.5,12.45,3.092,1,0.867,2
+A1 SH,STUART,2811.5,83.89399999999999,0.667,6.3,12.65,3.123,1,0.8440000000000001,2
+A1 SH,STUART,2812.0,84.42399999999999,0.653,6.7,13.05,3.1210000000000004,1,0.8220000000000001,2
+A1 SH,STUART,2812.5,83.16,0.642,7.3,12.95,3.127,1,0.8,2
+A1 SH,STUART,2813.0,79.063,0.6509999999999999,7.3,12.05,3.147,1,0.778,2
+A1 SH,STUART,2813.5,69.002,0.677,6.2,10.8,3.096,1,0.7559999999999999,2
+A1 SH,STUART,2814.0,63.983000000000004,0.69,4.4,9.7,3.103,1,0.733,2
+A1 SH,STUART,2814.5,61.797,0.675,3.5,9.15,3.1010000000000004,1,0.711,2
+A1 SH,STUART,2815.0,61.372,0.6459999999999999,2.8,9.3,3.065,1,0.6890000000000001,2
+A1 SH,STUART,2815.5,63.535,0.621,2.8,9.8,2.9819999999999998,1,0.667,2
+A1 SH,STUART,2816.0,65.126,0.6,3.3,10.55,2.9139999999999997,1,0.644,2
+A1 SH,STUART,2816.5,75.93,0.5760000000000001,3.4,11.9,2.845,1,0.6,2
+A1 SH,STUART,2817.0,85.07700000000001,0.584,4.4,12.9,2.8539999999999996,1,0.578,2
+A1 SH,STUART,2817.5,89.459,0.598,6.6,13.5,2.986,1,0.556,2
+A1 SH,STUART,2818.0,88.619,0.61,7.2,14.8,2.988,1,0.5329999999999999,2
+A1 SH,STUART,2818.5,81.593,0.636,6.4,13.9,2.998,1,0.511,2
+A1 SH,STUART,2819.0,66.595,0.7020000000000001,2.8,11.4,2.988,1,0.489,2
+A1 SH,STUART,2819.5,55.081,0.789,2.7,8.15,3.028,1,0.467,2
+A1 SH,STUART,2820.0,48.111999999999995,0.84,1.0,7.5,3.073,1,0.444,2
+A1 SH,STUART,2820.5,43.73,0.846,0.4,7.1,3.1460000000000004,1,0.42200000000000004,2
+A1 SH,STUART,2821.0,44.097,0.84,0.7,6.65,3.205,1,0.4,2
+A1 SH,STUART,2821.5,46.839,0.8420000000000001,0.8,6.6,3.2539999999999996,1,0.37799999999999995,2
+A1 SH,STUART,2822.0,50.348,0.843,1.1,6.75,3.23,1,0.35600000000000004,2
+A1 SH,STUART,2822.5,57.129,0.8220000000000001,2.2,7.3,3.237,1,0.33299999999999996,2
+A1 SH,STUART,2823.0,64.465,0.777,4.4,8.4,3.259,1,0.311,2
+A1 SH,STUART,2823.5,70.267,0.7290000000000001,7.1,9.85,3.2889999999999997,1,0.289,2
+A1 SH,STUART,2824.0,76.566,0.664,10.7,11.55,3.3810000000000002,1,0.244,2
+A1 SH,STUART,2824.5,76.778,0.643,10.7,12.25,3.452,1,0.222,2
+A1 SH,STUART,2825.0,73.971,0.632,9.7,12.55,3.3960000000000004,1,0.2,2
+A1 SH,STUART,2825.5,74.314,0.622,8.8,13.1,3.1439999999999997,1,0.17800000000000002,2
+A1 SH,STUART,2826.0,77.031,0.583,4.8,16.2,3.034,1,0.156,2
+A1 SH,STUART,2826.5,74.469,0.517,4.8,19.2,2.931,1,0.133,2
+A1 SH,STUART,2827.0,73.327,0.489,6.3,20.35,2.88,1,0.111,2
+A1 SH,STUART,2827.5,74.575,0.526,4.9,19.25,2.795,1,0.08900000000000001,2
+A1 SH,STUART,2828.0,70.536,0.5579999999999999,6.5,17.05,3.009,1,0.067,2
+A1 SH,STUART,2828.5,62.93899999999999,0.528,7.6,18.8,3.073,1,0.044000000000000004,3
+A1 SH,STUART,2829.0,57.137,0.511,10.9,19.15,3.313,1,0.022000000000000002,3
+A1 LM,STUART,2829.5,47.345,0.584,7.0,16.3,3.5269999999999997,2,1.0,8
+A1 LM,STUART,2830.0,35.733000000000004,0.73,6.4,10.2,3.928,2,0.987,8
+A1 LM,STUART,2830.5,29.326999999999998,0.873,2.7,7.85,4.33,2,0.9740000000000001,8
+A1 LM,STUART,2831.0,28.241999999999997,0.963,1.4,6.3,4.413,2,0.961,8
+A1 LM,STUART,2831.5,34.558,1.018,1.8,5.6,4.511,2,0.9470000000000001,8
+A1 LM,STUART,2832.0,43.754,1.054,1.8,5.2,4.412,2,0.934,8
+A1 LM,STUART,2832.5,53.611999999999995,1.067,1.5,5.05,4.226,2,0.9209999999999999,6
+A1 LM,STUART,2833.0,60.718999999999994,1.0390000000000001,1.9,4.85,3.931,2,0.9079999999999999,6
+A1 LM,STUART,2833.5,66.538,0.94,2.7,5.45,3.7030000000000003,2,0.895,6
+A1 LM,STUART,2834.0,75.52199999999999,0.8009999999999999,4.0,7.3,3.5010000000000003,2,0.882,6
+A1 LM,STUART,2834.5,95.979,0.6920000000000001,6.5,9.55,3.346,2,0.868,6
+A1 LM,STUART,2835.0,130.26,0.644,8.3,11.15,3.242,2,0.855,4
+A1 LM,STUART,2835.5,160.167,0.643,9.3,12.15,3.2939999999999996,2,0.8420000000000001,4
+A1 LM,STUART,2836.0,176.528,0.6729999999999999,9.1,12.05,3.298,2,0.8290000000000001,4
+A1 LM,STUART,2836.5,175.622,0.72,8.9,12.15,3.3489999999999998,2,0.816,4
+A1 LM,STUART,2837.0,153.965,0.7559999999999999,7.6,11.9,3.31,2,0.8029999999999999,4
+A1 LM,STUART,2837.5,120.07600000000001,0.7659999999999999,7.2,12.2,3.365,2,0.789,4
+A1 LM,STUART,2838.0,91.434,0.787,7.1,12.15,3.392,2,0.7759999999999999,4
+A1 LM,STUART,2838.5,69.312,0.8740000000000001,4.6,10.4,3.293,2,0.763,4
+A1 LM,STUART,2839.0,54.828,1.0290000000000001,0.8,8.2,3.395,2,0.75,6
+A1 LM,STUART,2839.5,50.854,1.149,0.3,6.85,3.5,2,0.737,6
+A1 LM,STUART,2840.0,54.632,1.09,1.0,7.4,3.576,2,0.7240000000000001,6
+A1 LM,STUART,2840.5,56.835,0.924,4.1,8.45,3.555,2,0.711,6
+A1 LM,STUART,2841.0,60.393,0.7879999999999999,7.1,10.05,3.591,2,0.6970000000000001,6
+A1 LM,STUART,2841.5,58.94,0.733,8.1,11.15,3.576,2,0.684,6
+A1 LM,STUART,2842.0,49.718999999999994,0.754,6.1,10.75,3.6689999999999996,2,0.6709999999999999,6
+A1 LM,STUART,2842.5,41.403999999999996,0.8190000000000001,3.5,9.55,3.872,2,0.6579999999999999,6
+A1 LM,STUART,2843.0,36.084,0.882,1.6,8.2,4.125,2,0.645,6
+A1 LM,STUART,2843.5,29.344,0.897,0.1,7.25,4.23,2,0.632,6
+A1 LM,STUART,2844.0,24.072,0.841,-0.6,6.8,4.302,2,0.618,6
+A1 LM,STUART,2844.5,22.62,0.733,-2.5,7.65,4.348,2,0.605,8
+A1 LM,STUART,2845.0,22.408,0.597,-3.8,9.5,4.29,2,0.5920000000000001,8
+A1 LM,STUART,2845.5,21.184,0.469,-2.7,11.05,4.255,2,0.579,8
+A1 LM,STUART,2846.0,21.796,0.375,-2.3,13.35,4.213,2,0.5660000000000001,8
+A1 LM,STUART,2846.5,23.925,0.314,-1.8,15.1,4.363,2,0.5529999999999999,8
+A1 LM,STUART,2847.0,24.464000000000002,0.278,-1.3,16.15,4.495,2,0.539,8
+A1 LM,STUART,2847.5,26.039,0.257,-1.9,16.55,4.602,2,0.526,8
+A1 LM,STUART,2848.0,27.491,0.247,-2.5,16.45,4.544,2,0.513,8
+A1 LM,STUART,2848.5,28.193,0.249,-2.4,16.3,4.587,2,0.5,8
+A1 LM,STUART,2849.0,28.634,0.265,-1.9,16.15,4.678,2,0.48700000000000004,8
+A1 LM,STUART,2849.5,29.809,0.299,-1.0,15.2,4.8660000000000005,2,0.474,8
+A1 LM,STUART,2850.0,31.171999999999997,0.349,-1.5,14.15,4.783,2,0.461,8
+A1 LM,STUART,2850.5,33.448,0.418,-1.1,12.65,4.688,2,0.447,8
+A1 LM,STUART,2851.0,33.514,0.503,-1.2,11.3,4.628,2,0.434,8
+A1 LM,STUART,2851.5,35.146,0.593,-0.5,10.05,4.387,2,0.42100000000000004,8
+A1 LM,STUART,2852.0,35.953,0.6709999999999999,-0.7,9.45,4.291,2,0.408,8
+A1 LM,STUART,2852.5,35.178000000000004,0.7290000000000001,-0.6,9.0,4.178,2,0.395,8
+A1 LM,STUART,2853.0,36.109,0.773,0.1,8.35,4.203,2,0.382,6
+A1 LM,STUART,2853.5,38.769,0.82,1.3,7.55,4.262,2,0.368,6
+A1 LM,STUART,2854.0,39.510999999999996,0.873,1.3,7.05,4.387,2,0.355,6
+A1 LM,STUART,2854.5,40.523,0.927,2.6,6.5,4.006,2,0.342,6
+A1 LM,STUART,2855.0,42.693999999999996,0.9640000000000001,2.8,6.8,3.718,2,0.32899999999999996,6
+A1 LM,STUART,2855.5,45.297,0.9590000000000001,3.0,7.3,3.594,2,0.316,6
+A1 LM,STUART,2856.0,48.145,0.915,3.8,8.2,3.46,2,0.303,6
+A1 LM,STUART,2856.5,51.041000000000004,0.862,5.9,8.75,3.333,2,0.289,6
+A1 LM,STUART,2857.0,56.125,0.825,7.4,9.3,3.3360000000000003,2,0.276,6
+A1 LM,STUART,2857.5,62.205,0.807,6.4,9.9,3.362,2,0.263,6
+A1 LM,STUART,2858.0,64.865,0.794,4.7,10.25,3.2439999999999998,2,0.25,6
+A1 LM,STUART,2858.5,68.186,0.7709999999999999,4.1,10.25,3.174,2,0.237,6
+A1 LM,STUART,2859.0,72.421,0.743,4.1,10.55,3.174,2,0.22399999999999998,4
+A1 LM,STUART,2859.5,74.322,0.73,4.5,10.65,3.187,2,0.21100000000000002,4
+A1 LM,STUART,2860.0,72.543,0.748,4.1,10.35,3.359,2,0.19699999999999998,4
+A1 LM,STUART,2860.5,65.672,0.8140000000000001,3.5,9.05,3.6180000000000003,2,0.184,6
+A1 LM,STUART,2861.0,57.153,0.951,1.7,6.75,3.9019999999999997,2,0.171,6
+A1 LM,STUART,2861.5,46.056000000000004,1.148,0.1,4.55,4.221,2,0.158,8
+A1 LM,STUART,2862.0,37.961,1.324,0.0,3.3,4.561,2,0.145,8
+A1 LM,STUART,2862.5,33.579,1.402,-0.2,2.8,4.7989999999999995,2,0.132,8
+A1 LM,STUART,2863.0,31.212,1.439,-0.4,2.7,4.874,2,0.11800000000000001,8
+A1 LM,STUART,2863.5,30.176,1.486,-0.5,2.55,4.787,2,0.105,8
+A1 LM,STUART,2864.0,29.858,1.507,-0.7,2.75,4.622,2,0.092,6
+A1 LM,STUART,2864.5,29.923000000000002,1.433,-0.6,3.3,4.375,2,0.079,6
+A1 LM,STUART,2865.0,34.428000000000004,1.2790000000000001,0.0,4.0,4.115,2,0.066,6
+A1 LM,STUART,2865.5,39.935,1.1340000000000001,0.8,4.6,3.9789999999999996,2,0.053,6
+A1 LM,STUART,2866.0,39.935,1.1340000000000001,0.8,4.6,3.9789999999999996,2,0.053,6
+A1 LM,STUART,2866.5,46.823,1.05,1.3,5.15,3.727,2,0.039,6
+A1 LM,STUART,2867.0,57.968999999999994,1.01,2.0,5.2,3.537,2,0.026000000000000002,6
+A1 LM,STUART,2867.5,71.24600000000001,0.9540000000000001,2.3,5.65,3.4819999999999998,2,0.013000000000000001,2
+B1 SH,STUART,2868.0,82.54799999999999,0.843,3.2,7.2,3.532,1,1.0,2
+B1 SH,STUART,2868.5,92.119,0.718,4.9,8.95,3.484,1,0.968,2
+B1 SH,STUART,2869.0,93.564,0.635,7.0,11.1,3.4019999999999997,1,0.935,2
+B1 SH,STUART,2869.5,86.26,0.615,9.0,11.9,3.486,1,0.903,2
+B1 SH,STUART,2870.0,77.097,0.637,9.5,11.25,3.56,1,0.871,2
+B1 SH,STUART,2870.5,67.68,0.6509999999999999,9.2,10.7,3.4730000000000003,1,0.8390000000000001,2
+B1 SH,STUART,2871.0,64.04899999999999,0.639,8.5,10.65,3.4010000000000002,1,0.8059999999999999,2
+B1 SH,STUART,2871.5,67.566,0.621,7.5,11.45,3.307,1,0.774,2
+B1 SH,STUART,2872.0,69.72800000000001,0.604,7.1,12.45,3.28,1,0.742,2
+B1 SH,STUART,2872.5,67.96600000000001,0.5820000000000001,8.3,13.45,3.253,1,0.71,2
+B1 SH,STUART,2873.0,65.803,0.565,9.6,13.5,3.284,1,0.677,2
+B1 SH,STUART,2873.5,62.351000000000006,0.575,10.1,11.85,3.405,1,0.645,2
+B1 SH,STUART,2874.0,59.512,0.621,10.1,9.65,3.5039999999999996,1,0.613,2
+B1 SH,STUART,2874.5,61.176,0.691,9.9,8.15,3.5869999999999997,1,0.581,2
+B1 SH,STUART,2875.0,65.75399999999999,0.7509999999999999,9.6,7.7,3.605,1,0.5479999999999999,2
+B1 SH,STUART,2875.5,66.66,0.768,9.0,8.2,3.505,1,0.516,2
+B1 SH,STUART,2876.0,64.604,0.742,8.0,8.9,3.411,1,0.484,2
+B1 SH,STUART,2876.5,61.699,0.696,7.0,9.2,3.319,1,0.452,2
+B1 SH,STUART,2877.0,58.353,0.657,5.6,9.3,3.265,1,0.419,2
+B1 SH,STUART,2877.5,55.928999999999995,0.64,4.2,9.2,3.167,1,0.387,2
+B1 SH,STUART,2878.0,57.413999999999994,0.64,4.0,9.1,3.181,1,0.355,2
+B1 SH,STUART,2878.5,60.393,0.64,3.8,9.3,3.133,1,0.32299999999999995,2
+B1 SH,STUART,2879.0,65.90100000000001,0.636,4.2,9.9,3.1630000000000003,1,0.29,2
+B1 SH,STUART,2879.5,71.385,0.635,5.8,10.8,3.1310000000000002,1,0.258,2
+B1 SH,STUART,2880.0,75.816,0.625,4.9,13.55,2.997,1,0.226,2
+B1 SH,STUART,2880.5,75.334,0.5870000000000001,5.6,15.9,2.938,1,0.19399999999999998,2
+B1 SH,STUART,2881.0,72.69,0.5579999999999999,4.8,18.5,2.969,1,0.129,2
+B1 SH,STUART,2881.5,68.635,0.5579999999999999,6.9,19.45,3.1039999999999996,1,0.09699999999999999,3
+B1 SH,STUART,2882.0,60.695,0.52,7.5,21.25,3.147,1,0.065,3
+B1 SH,STUART,2882.5,51.645,0.501,5.6,18.1,3.3539999999999996,1,0.032,3
+B1 LM,STUART,2883.0,41.918,0.5479999999999999,7.0,10.9,3.764,2,1.0,8
+B1 LM,STUART,2883.5,32.991,0.633,7.7,6.35,4.109,2,0.968,8
+B1 LM,STUART,2884.0,28.258000000000003,0.705,5.0,6.1,4.154,2,0.935,8
+B1 LM,STUART,2884.5,26.635,0.769,4.1,5.35,4.321000000000001,2,0.903,8
+B1 LM,STUART,2885.0,25.541,0.847,3.6,4.8,4.476,2,0.871,8
+B1 LM,STUART,2885.5,23.795,0.948,2.3,4.45,4.565,2,0.8390000000000001,8
+B1 LM,STUART,2886.0,20.719,1.061,2.2,3.5,4.615,2,0.8059999999999999,8
+B1 LM,STUART,2886.5,18.499000000000002,1.139,2.4,3.2,4.696000000000001,2,0.774,6
+B1 LM,STUART,2887.0,19.445999999999998,1.1179999999999999,1.5,4.65,4.668,2,0.742,6
+B1 LM,STUART,2887.5,21.388,1.0190000000000001,1.0,7.1,4.579,2,0.71,8
+B1 LM,STUART,2888.0,24.178,0.9079999999999999,1.2,9.2,4.292,2,0.677,8
+B1 LM,STUART,2888.5,27.638,0.812,0.0,11.1,4.046,2,0.645,8
+B1 LM,STUART,2889.0,31.669,0.7240000000000001,0.9,11.45,3.822,2,0.613,8
+B1 LM,STUART,2889.5,39.389,0.627,3.4,11.2,3.6289999999999996,2,0.581,6
+B1 LM,STUART,2890.0,49.385,0.535,6.4,11.5,3.517,2,0.5479999999999999,6
+B1 LM,STUART,2890.5,58.516000000000005,0.488,9.0,11.4,3.4739999999999998,2,0.516,6
+B1 LM,STUART,2891.0,66.619,0.511,9.2,10.8,3.613,2,0.484,6
+B1 LM,STUART,2891.5,67.296,0.602,6.4,9.5,3.862,2,0.452,6
+B1 LM,STUART,2892.0,57.798,0.745,3.7,8.25,4.052,2,0.419,6
+B1 LM,STUART,2892.5,48.169,0.912,1.1,7.45,4.2410000000000005,2,0.387,6
+B1 LM,STUART,2893.0,41.437,1.045,0.4,6.3,4.476,2,0.355,6
+B1 LM,STUART,2893.5,39.348,1.117,0.1,5.35,4.712,2,0.32299999999999995,6
+B1 LM,STUART,2894.0,49.312,1.1440000000000001,1.0,4.5,4.56,2,0.29,6
+B1 LM,STUART,2894.5,61.983999999999995,1.143,0.7,4.85,4.605,2,0.258,6
+B1 LM,STUART,2895.0,73.506,1.122,0.9,5.75,4.574,2,0.226,6
+B1 LM,STUART,2895.5,74.208,1.095,0.3,6.85,4.478,2,0.19399999999999998,6
+B1 LM,STUART,2896.0,67.819,1.072,-0.7,7.55,4.444,2,0.161,6
+B1 LM,STUART,2896.5,61.625,1.057,-1.2,7.4,4.439,2,0.129,6
+B1 LM,STUART,2897.0,61.625,1.057,-1.2,7.4,4.439,2,0.129,6
+B1 LM,STUART,2897.5,55.481,1.041,-0.9,6.55,4.335,2,0.09699999999999999,8
+B1 LM,STUART,2898.0,57.251000000000005,0.98,-0.1,5.75,3.907,2,0.065,8
+B1 LM,STUART,2898.5,67.28,0.84,-0.6,7.1,3.58,2,0.032,8
+B2 SH,STUART,2899.0,76.02,0.6859999999999999,0.7,9.45,3.23,1,1.0,3
+B2 SH,STUART,2899.5,84.45700000000001,0.603,2.5,12.45,2.967,1,0.9470000000000001,3
+B2 SH,STUART,2900.0,89.01899999999999,0.595,4.6,14.9,2.7939999999999996,1,0.895,3
+B2 SH,STUART,2900.5,89.93299999999999,0.591,3.9,18.55,2.7889999999999997,1,0.8420000000000001,3
+B2 SH,STUART,2901.0,89.38600000000001,0.539,4.5,22.55,2.861,1,0.789,3
+B2 SH,STUART,2901.5,89.427,0.479,6.2,25.4,2.935,1,0.737,3
+B2 SH,STUART,2902.0,87.51700000000001,0.461,7.6,24.4,3.128,1,0.684,3
+B2 SH,STUART,2902.5,83.74700000000001,0.485,8.7,23.15,3.284,1,0.632,3
+B2 SH,STUART,2903.0,78.304,0.541,6.1,21.25,3.332,1,0.579,3
+B2 SH,STUART,2903.5,71.858,0.635,8.5,15.95,3.485,1,0.526,3
+B2 SH,STUART,2904.0,64.70100000000001,0.737,5.0,15.7,3.471,1,0.474,3
+B2 SH,STUART,2904.5,64.293,0.752,1.7,17.15,3.45,1,0.42100000000000004,3
+B2 SH,STUART,2905.0,67.158,0.6859999999999999,-0.3,19.95,3.2760000000000002,1,0.368,2
+B2 SH,STUART,2905.5,72.47800000000001,0.631,0.2,20.4,3.1710000000000003,1,0.316,2
+B2 SH,STUART,2906.0,78.068,0.614,5.0,18.1,3.252,1,0.21100000000000002,2
+B2 SH,STUART,2906.5,73.008,0.5920000000000001,2.4,20.1,3.175,1,0.158,2
+B2 SH,STUART,2907.0,64.64399999999999,0.5429999999999999,1.7,22.35,3.302,1,0.105,3
+B2 SH,STUART,2907.5,49.801,0.5379999999999999,0.0,20.8,3.551,1,0.053,2
+B2 LM,STUART,2908.0,35.937,0.628,-2.8,16.8,3.96,2,1.0,8
+B2 LM,STUART,2908.5,24.203000000000003,0.743,-0.3,12.35,4.454,2,0.963,8
+B2 LM,STUART,2909.0,15.985999999999999,0.777,-0.2,11.5,4.657,2,0.9259999999999999,8
+B2 LM,STUART,2909.5,12.036,0.773,-0.5,10.55,4.593999999999999,2,0.889,8
+B2 LM,STUART,2910.0,12.745999999999999,0.787,-1.2,9.2,4.437,2,0.852,8
+B2 LM,STUART,2910.5,14.843,0.809,-1.6,7.7,4.351,2,0.815,8
+B2 LM,STUART,2911.0,17.087,0.8059999999999999,-1.0,6.8,4.242,2,0.778,8
+B2 LM,STUART,2911.5,17.977,0.765,0.3,6.45,4.105,2,0.741,8
+B2 LM,STUART,2912.0,18.834,0.701,-0.1,7.15,4.279,2,0.7040000000000001,8
+B2 LM,STUART,2912.5,19.258,0.644,0.4,7.4,4.669,2,0.667,8
+B2 LM,STUART,2913.0,19.062,0.61,0.6,7.8,5.19,2,0.63,8
+B2 LM,STUART,2913.5,18.637999999999998,0.6,1.8,7.6,5.527,2,0.593,8
+B2 LM,STUART,2914.0,19.462,0.598,3.4,6.8,6.321000000000001,2,0.556,8
+B2 LM,STUART,2914.5,19.552,0.581,3.1,6.95,6.16,2,0.519,8
+B2 LM,STUART,2915.0,20.653000000000002,0.529,0.9,8.65,5.865,2,0.48100000000000004,8
+B2 LM,STUART,2915.5,23.371,0.45,-1.0,11.3,5.315,2,0.444,8
+B2 LM,STUART,2916.0,25.468000000000004,0.391,-1.3,13.75,4.918,2,0.40700000000000003,8
+B2 LM,STUART,2916.5,26.291999999999998,0.377,-2.3,14.85,4.521,2,0.37,8
+B2 LM,STUART,2917.0,27.736,0.41100000000000003,-3.6,14.1,4.4430000000000005,2,0.33299999999999996,8
+B2 LM,STUART,2917.5,29.784000000000002,0.48200000000000004,-3.1,11.85,4.3469999999999995,2,0.29600000000000004,8
+B2 LM,STUART,2918.0,31.041,0.578,-2.2,9.4,4.416,2,0.259,8
+B2 LM,STUART,2918.5,30.837,0.6829999999999999,-0.4,7.4,4.394,2,0.222,8
+B2 LM,STUART,2919.0,32.64,0.807,1.0,5.6,4.343,2,0.185,6
+B2 LM,STUART,2919.5,33.571,0.9420000000000001,1.6,4.5,4.086,2,0.14800000000000002,6
+B2 LM,STUART,2920.0,39.56,1.003,1.9,4.55,3.8110000000000004,2,0.111,6
+B2 LM,STUART,2920.5,47.475,0.9309999999999999,1.5,6.15,3.625,2,0.07400000000000001,6
+B2 LM,STUART,2921.0,56.443000000000005,0.821,2.3,8.05,3.3930000000000002,2,0.037000000000000005,6
+B3 SH,STUART,2921.5,64.79899999999999,0.7509999999999999,3.4,9.6,3.12,1,1.0,3
+B3 SH,STUART,2922.0,69.72,0.711,3.0,12.4,3.03,1,0.95,3
+B3 SH,STUART,2922.5,72.51100000000001,0.637,3.5,16.35,2.984,1,0.9,3
+B3 SH,STUART,2923.0,74.69800000000001,0.525,5.4,20.8,2.915,1,0.85,3
+B3 SH,STUART,2923.5,74.69800000000001,0.525,5.4,20.8,2.915,1,0.85,3
+B3 SH,STUART,2924.0,73.425,0.45799999999999996,5.2,21.6,2.92,1,0.8,3
+B3 SH,STUART,2924.5,71.328,0.486,3.1,17.35,2.995,1,0.75,2
+B3 SH,STUART,2925.0,68.937,0.5870000000000001,4.1,13.25,3.122,1,0.7,2
+B3 SH,STUART,2925.5,66.562,0.6709999999999999,1.3,13.25,3.2430000000000003,1,0.65,2
+B3 SH,STUART,2926.0,64.63600000000001,0.6629999999999999,3.2,13.3,3.324,1,0.6,2
+B3 SH,STUART,2926.5,66.619,0.625,4.0,13.6,3.33,1,0.55,2
+B3 SH,STUART,2927.0,66.619,0.625,4.0,13.6,3.33,1,0.55,2
+B3 SH,STUART,2927.5,65.624,0.635,3.2,12.4,3.3089999999999997,1,0.5,2
+B3 SH,STUART,2928.0,65.322,0.68,3.3,9.65,3.315,1,0.45,2
+B3 SH,STUART,2928.5,67.035,0.7070000000000001,2.4,8.6,3.2319999999999998,1,0.4,2
+B3 SH,STUART,2929.0,68.194,0.693,3.0,8.8,3.157,1,0.35,2
+B3 SH,STUART,2929.5,69.149,0.6459999999999999,3.0,10.4,3.034,1,0.3,2
+B3 SH,STUART,2930.0,72.20100000000001,0.588,3.2,13.2,3.052,1,0.25,2
+B3 SH,STUART,2930.5,73.77600000000001,0.542,4.4,16.0,3.1319999999999997,1,0.2,2
+B3 SH,STUART,2931.0,72.609,0.525,5.5,17.55,3.31,1,0.15,2
+B3 SH,STUART,2931.5,66.86399999999999,0.55,5.3,16.55,3.625,1,0.1,2
+B3 SH,STUART,2932.0,53.702,0.62,5.7,13.75,4.003,1,0.05,2
+B3 LM,STUART,2932.5,40.474000000000004,0.713,4.7,11.15,4.2780000000000005,2,1.0,8
+B3 LM,STUART,2933.0,28.12,0.774,2.2,10.5,4.537,2,0.9,8
+B3 LM,STUART,2933.5,20.213,0.769,-1.1,10.95,4.6819999999999995,2,0.8,8
+B3 LM,STUART,2934.0,19.192999999999998,0.732,-1.0,11.1,4.546,2,0.7,8
+B3 LM,STUART,2934.5,20.449,0.6990000000000001,-0.7,10.65,4.386,2,0.6,8
+B3 LM,STUART,2935.0,21.355,0.679,-0.1,9.95,4.28,2,0.5,8
+B3 LM,STUART,2935.5,21.641,0.6679999999999999,1.3,8.95,4.221,2,0.4,8
+B3 LM,STUART,2936.0,24.203000000000003,0.653,2.6,8.3,4.099,2,0.3,8
+B3 LM,STUART,2936.5,34.574,0.613,2.4,9.0,3.66,2,0.2,8
+B3 LM,STUART,2937.0,45.231,0.5479999999999999,2.3,10.45,3.4960000000000004,2,0.1,8
+B4 SH,STUART,2937.5,56.427,0.498,2.9,11.35,3.338,1,1.0,2
+B4 SH,STUART,2938.0,67.06,0.504,1.7,11.65,3.135,1,0.9440000000000001,2
+B4 SH,STUART,2938.5,70.83800000000001,0.5579999999999999,2.0,10.8,3.012,1,0.889,2
+B4 SH,STUART,2939.0,69.932,0.612,3.3,10.05,3.0660000000000003,1,0.833,2
+B4 SH,STUART,2939.5,74.52600000000001,0.633,4.1,10.25,3.109,1,0.778,2
+B4 SH,STUART,2940.0,77.611,0.626,5.0,11.0,3.063,1,0.722,2
+B4 SH,STUART,2940.5,78.59,0.605,5.9,11.55,3.092,1,0.667,2
+B4 SH,STUART,2941.0,76.729,0.591,5.7,11.95,3.051,1,0.611,2
+B4 SH,STUART,2941.5,74.11,0.608,3.7,10.95,3.07,1,0.556,2
+B4 SH,STUART,2942.0,66.407,0.653,2.2,9.6,2.997,1,0.5,2
+B4 SH,STUART,2942.5,64.081,0.693,1.4,8.6,3.093,1,0.444,2
+B4 SH,STUART,2943.0,65.885,0.705,0.9,8.55,3.1060000000000003,1,0.389,2
+B4 SH,STUART,2943.5,70.29899999999999,0.696,1.4,9.0,3.085,1,0.33299999999999996,2
+B4 SH,STUART,2944.0,70.29899999999999,0.696,1.4,9.0,3.085,1,0.33299999999999996,2
+B4 SH,STUART,2944.5,74.551,0.677,3.1,9.65,3.0660000000000003,1,0.278,2
+B4 SH,STUART,2945.0,79.65899999999999,0.643,3.4,12.5,2.9019999999999997,1,0.222,2
+B4 SH,STUART,2945.5,80.402,0.573,-2.8,19.3,2.9819999999999998,1,0.16699999999999998,2
+B4 SH,STUART,2946.0,74.649,0.483,-0.5,23.95,3.14,1,0.111,2
+B4 SH,STUART,2946.5,63.428999999999995,0.461,0.2,24.2,3.3939999999999997,1,0.055999999999999994,3
+B4 LM,STUART,2947.0,47.916000000000004,0.598,-7.6,18.9,3.762,2,1.0,8
+B4 LM,STUART,2947.5,32.469,0.912,-1.4,9.8,4.314,2,0.929,8
+B4 LM,STUART,2948.0,20.718000000000004,1.222,-2.9,7.85,4.863,2,0.857,8
+B4 LM,STUART,2948.5,15.015,1.2329999999999999,-1.6,6.4,4.813,2,0.7859999999999999,8
+B4 LM,STUART,2949.0,14.084000000000001,1.084,-1.1,6.55,4.644,2,0.7140000000000001,8
+B4 LM,STUART,2949.5,15.937000000000001,0.9620000000000001,-0.4,7.4,4.6339999999999995,2,0.643,8
+B4 LM,STUART,2950.0,19.348,0.9009999999999999,-1.0,7.9,4.545,2,0.5710000000000001,8
+B4 LM,STUART,2950.5,21.469,0.9059999999999999,-1.1,6.75,4.5760000000000005,2,0.5,8
+B4 LM,STUART,2951.0,22.587,0.9690000000000001,-1.4,5.5,4.527,2,0.429,8
+B4 LM,STUART,2951.5,25.721,1.079,-0.9,4.45,4.512,2,0.35700000000000004,8
+B4 LM,STUART,2952.0,29.45,1.2,0.0,4.0,4.437,2,0.28600000000000003,8
+B4 LM,STUART,2952.5,36.313,1.169,-3.3,6.95,3.97,2,0.214,6
+B4 LM,STUART,2953.0,49.49100000000001,0.909,-8.9,13.85,3.695,2,0.14300000000000002,6
+B5 SH,STUART,2953.5,69.222,0.469,-6.0,27.9,3.3510000000000004,1,1.0,3
+B5 SH,STUART,2954.0,70.968,0.444,-2.5,28.85,3.49,1,0.75,3
+B5 SH,STUART,2954.5,65.37899999999999,0.501,-0.3,22.75,3.9,1,0.5,3
+B5 SH,STUART,2955.0,54.093,0.56,-0.5,16.75,4.126,1,0.25,3
+B5 LM,STUART,2955.5,41.633,0.541,3.9,11.55,4.482,2,1.0,8
+B5 LM,STUART,2956.0,33.644,0.439,3.8,11.7,4.707,2,0.976,8
+B5 LM,STUART,2956.5,28.976999999999997,0.321,1.3,14.45,4.447,2,0.951,8
+B5 LM,STUART,2957.0,24.611,0.23199999999999998,-1.1,17.25,4.481,2,0.927,8
+B5 LM,STUART,2957.5,24.154,0.184,-2.9,19.65,4.327,2,0.902,8
+B5 LM,STUART,2958.0,25.639,0.17300000000000001,-2.4,20.5,4.302,2,0.878,8
+B5 LM,STUART,2958.5,25.956999999999997,0.191,-2.4,19.9,4.294,2,0.8540000000000001,8
+B5 LM,STUART,2959.0,38.965,0.266,-2.1,17.05,4.228,2,0.805,8
+B5 LM,STUART,2959.5,45.354,0.301,0.2,14.8,4.127,2,0.78,8
+B5 LM,STUART,2960.0,53.645,0.327,2.6,12.6,3.908,2,0.7559999999999999,6
+B5 LM,STUART,2960.5,60.163999999999994,0.33799999999999997,3.8,12.2,3.74,2,0.732,6
+B5 LM,STUART,2961.0,64.032,0.33399999999999996,5.5,13.15,3.5380000000000003,2,0.7070000000000001,4
+B5 LM,STUART,2961.5,66.774,0.32299999999999995,7.9,14.75,3.5580000000000003,2,0.6829999999999999,4
+B5 LM,STUART,2962.0,68.423,0.313,8.1,15.75,3.4760000000000004,2,0.659,4
+B5 LM,STUART,2962.5,63.501999999999995,0.318,6.1,14.75,3.682,2,0.634,4
+B5 LM,STUART,2963.0,57.903999999999996,0.349,2.8,12.9,3.937,2,0.61,4
+B5 LM,STUART,2963.5,52.935,0.415,2.3,10.25,4.133,2,0.585,4
+B5 LM,STUART,2964.0,45.125,0.507,2.3,8.45,4.248,2,0.561,6
+B5 LM,STUART,2964.5,39.927,0.605,3.5,6.75,4.468,2,0.537,6
+B5 LM,STUART,2965.0,36.215,0.703,2.4,6.0,4.575,2,0.512,6
+B5 LM,STUART,2965.5,31.253,0.787,2.3,4.45,4.5569999999999995,2,0.488,6
+B5 LM,STUART,2966.0,28.445999999999998,0.8320000000000001,2.2,3.4,4.573,2,0.46299999999999997,6
+B5 LM,STUART,2966.5,27.296,0.821,1.2,4.4,4.79,2,0.439,6
+B5 LM,STUART,2967.0,28.944000000000003,0.755,-0.4,7.3,4.887,2,0.415,6
+B5 LM,STUART,2967.5,31.596,0.659,-1.6,11.0,4.824,2,0.39,8
+B5 LM,STUART,2968.0,33.211999999999996,0.574,-3.1,14.45,4.765,2,0.366,8
+B5 LM,STUART,2968.5,34.689,0.517,-3.0,16.4,4.766,2,0.341,8
+B5 LM,STUART,2969.0,39.266,0.493,-1.9,16.15,4.705,2,0.317,8
+B5 LM,STUART,2969.5,50.103,0.499,-2.0,14.6,4.453,2,0.293,8
+B5 LM,STUART,2970.0,65.167,0.525,-1.6,13.1,4.3660000000000005,2,0.268,8
+B5 LM,STUART,2970.5,76.436,0.557,-0.6,11.7,4.289,2,0.244,8
+B5 LM,STUART,2971.0,92.88600000000001,0.628,-0.2,9.4,4.272,2,0.195,8
+B5 LM,STUART,2971.5,99.64299999999999,0.6829999999999999,-0.3,7.95,4.395,2,0.171,8
+B5 LM,STUART,2972.0,101.585,0.757,0.0,6.5,4.51,2,0.146,8
+B5 LM,STUART,2972.5,95.775,0.85,1.6,4.9,4.53,2,0.122,8
+B5 LM,STUART,2973.0,87.036,0.951,2.1,3.95,4.456,2,0.098,6
+B5 LM,STUART,2973.5,72.331,1.0190000000000001,2.6,3.6,4.056,2,0.073,6
+B5 LM,STUART,2974.0,58.858999999999995,0.991,3.4,3.9,3.7119999999999997,2,0.049,8
+B5 LM,STUART,2974.5,60.931999999999995,0.857,1.3,7.35,3.3710000000000004,2,0.024,8
+C SH,STUART,2975.0,67.655,0.675,-0.8,14.4,3.1310000000000002,1,1.0,3
+C SH,STUART,2975.5,77.219,0.528,6.8,20.9,3.008,1,0.977,3
+C SH,STUART,2976.0,82.115,0.45899999999999996,10.6,24.2,2.988,1,0.953,3
+C SH,STUART,2976.5,84.865,0.446,9.4,23.7,3.134,1,0.93,3
+C SH,STUART,2977.0,84.384,0.449,13.3,21.45,3.409,1,0.907,3
+C SH,STUART,2977.5,81.77199999999999,0.44299999999999995,14.8,20.9,3.483,1,0.884,3
+C SH,STUART,2978.0,78.998,0.444,16.5,20.65,3.438,1,0.8370000000000001,3
+C SH,STUART,2978.5,79.896,0.457,14.9,20.15,3.423,1,0.8140000000000001,3
+C SH,STUART,2979.0,79.194,0.462,14.8,19.9,3.45,1,0.7909999999999999,3
+C SH,STUART,2979.5,78.99,0.451,13.5,19.65,3.3930000000000002,1,0.767,3
+C SH,STUART,2980.0,79.21,0.441,15.1,18.95,3.475,1,0.7440000000000001,3
+C SH,STUART,2980.5,78.1,0.451,15.6,17.5,3.478,1,0.721,3
+C SH,STUART,2981.0,78.59,0.489,14.3,16.75,3.505,1,0.698,3
+C SH,STUART,2981.5,79.439,0.544,12.7,15.25,3.478,1,0.674,2
+C SH,STUART,2982.0,81.29899999999999,0.5760000000000001,11.5,14.65,3.4530000000000003,1,0.6509999999999999,2
+C SH,STUART,2982.5,78.933,0.494,11.9,14.85,3.4789999999999996,1,0.605,2
+C SH,STUART,2983.0,79.471,0.441,13.4,16.3,3.4939999999999998,1,0.581,2
+C SH,STUART,2983.5,81.895,0.431,14.2,16.8,3.4410000000000003,1,0.5579999999999999,2
+C SH,STUART,2984.0,82.76799999999999,0.46799999999999997,14.3,16.05,3.4930000000000003,1,0.535,2
+C SH,STUART,2984.5,79.02199999999999,0.537,12.9,15.05,3.5,1,0.512,2
+C SH,STUART,2985.0,76.74600000000001,0.601,11.4,13.8,3.4410000000000003,1,0.488,2
+C SH,STUART,2985.5,73.066,0.638,11.1,13.25,3.426,1,0.465,2
+C SH,STUART,2986.0,70.35600000000001,0.6509999999999999,11.0,13.1,3.447,1,0.442,2
+C SH,STUART,2986.5,74.608,0.621,10.5,14.85,3.3989999999999996,1,0.395,2
+C SH,STUART,2987.0,73.008,0.585,11.1,15.15,3.3930000000000002,1,0.37200000000000005,2
+C SH,STUART,2987.5,70.438,0.546,12.1,15.15,3.4389999999999996,1,0.349,2
+C SH,STUART,2988.0,68.08,0.5,13.5,14.65,3.478,1,0.326,2
+C SH,STUART,2988.5,66.75,0.46,13.6,15.2,3.4610000000000003,1,0.302,2
+C SH,STUART,2989.0,69.45100000000001,0.455,13.4,14.8,3.4939999999999998,1,0.27899999999999997,2
+C SH,STUART,2989.5,75.946,0.494,14.2,14.0,3.5410000000000004,1,0.256,2
+C SH,STUART,2990.0,80.344,0.556,11.8,13.6,3.522,1,0.233,2
+C SH,STUART,2990.5,83.772,0.616,10.4,12.9,3.4760000000000004,1,0.18600000000000003,2
+C SH,STUART,2991.0,84.76700000000001,0.618,10.7,12.55,3.39,1,0.163,2
+C SH,STUART,2991.5,84.09,0.616,9.4,13.4,3.2889999999999997,1,0.14,2
+C SH,STUART,2992.0,86.62799999999999,0.601,8.8,14.4,3.19,1,0.11599999999999999,2
+C SH,STUART,2992.5,90.43,0.556,2.0,21.4,3.045,1,0.09300000000000001,2
+C SH,STUART,2993.0,87.07700000000001,0.498,2.0,26.4,3.088,1,0.07,3
+C SH,STUART,2993.5,76.623,0.479,5.3,28.45,3.2239999999999998,1,0.047,3
+C SH,STUART,2994.0,62.768,0.546,3.2,26.3,3.3339999999999996,1,0.023,3
+C LM,STUART,2994.5,43.738,0.703,1.8,19.3,3.8760000000000003,2,1.0,8
+C LM,STUART,2995.0,27.859,0.855,6.9,11.75,4.508,2,0.993,8
+C LM,STUART,2995.5,18.034000000000002,0.879,5.3,9.55,4.815,2,0.985,8
+C LM,STUART,2996.0,13.203,0.83,3.0,9.1,4.863,2,0.978,8
+C LM,STUART,2996.5,12.762,0.767,2.0,8.9,5.024,2,0.97,8
+C LM,STUART,2997.0,14.843,0.7020000000000001,1.8,9.2,4.6160000000000005,2,0.963,8
+C LM,STUART,2997.5,19.087,0.644,0.4,10.4,4.172,2,0.955,8
+C LM,STUART,2998.0,19.087,0.644,0.4,10.4,4.172,2,0.955,8
+C LM,STUART,2998.5,25.206999999999997,0.601,-0.2,10.7,3.97,2,0.948,8
+C LM,STUART,2999.0,27.997,0.5870000000000001,1.1,10.25,3.9330000000000003,2,0.94,8
+C LM,STUART,2999.5,28.822,0.602,2.2,10.0,3.875,2,0.9329999999999999,8
+C LM,STUART,3000.0,28.618000000000002,0.622,1.9,10.35,3.969,2,0.925,8
+C LM,STUART,3000.5,24.815,0.612,0.7,11.15,4.118,2,0.9179999999999999,8
+C LM,STUART,3001.0,23.371,0.575,0.2,11.8,4.243,2,0.91,8
+C LM,STUART,3001.5,23.052,0.542,0.1,12.35,4.196000000000001,2,0.903,8
+C LM,STUART,3002.0,23.109,0.522,-0.1,12.15,4.192,2,0.8959999999999999,8
+C LM,STUART,3002.5,24.138,0.511,-0.3,12.05,3.978,2,0.888,8
+C LM,STUART,3003.0,24.448,0.511,-0.1,11.75,3.975,2,0.8809999999999999,8
+C LM,STUART,3003.5,25.419,0.523,0.0,11.6,3.9619999999999997,2,0.873,8
+C LM,STUART,3004.0,26.724,0.56,-0.7,11.35,3.99,2,0.866,8
+C LM,STUART,3004.5,29.213,0.623,-0.8,10.7,3.885,2,0.858,8
+C LM,STUART,3005.0,34.036,0.6859999999999999,0.4,9.4,3.8160000000000003,2,0.851,8
+C LM,STUART,3005.5,38.157,0.716,1.7,7.75,3.66,2,0.843,8
+C LM,STUART,3006.0,40.45,0.701,1.8,6.8,3.5439999999999996,2,0.836,6
+C LM,STUART,3006.5,41.576,0.662,3.5,6.45,3.392,2,0.828,6
+C LM,STUART,3007.0,40.123000000000005,0.633,3.7,7.35,3.24,2,0.821,6
+C LM,STUART,3007.5,36.663000000000004,0.632,3.4,8.1,3.2119999999999997,2,0.813,6
+C LM,STUART,3008.0,32.705999999999996,0.65,3.8,8.3,3.253,2,0.8059999999999999,6
+C LM,STUART,3008.5,30.323,0.672,3.4,8.7,3.2889999999999997,2,0.799,6
+C LM,STUART,3009.0,28.601,0.6940000000000001,3.0,9.4,3.3139999999999996,2,0.7909999999999999,6
+C LM,STUART,3009.5,28.169,0.721,2.4,9.7,3.262,2,0.784,7
+C LM,STUART,3010.0,29.441999999999997,0.7559999999999999,1.4,9.8,3.31,2,0.7759999999999999,7
+C LM,STUART,3010.5,30.878,0.795,0.7,9.55,3.359,2,0.769,7
+C LM,STUART,3011.0,32.150999999999996,0.8320000000000001,0.1,9.35,3.409,2,0.7609999999999999,7
+C LM,STUART,3011.5,33.677,0.856,0.4,9.1,3.417,2,0.754,6
+C LM,STUART,3012.0,33.701,0.872,1.1,8.95,3.444,2,0.746,6
+C LM,STUART,3012.5,35.105,0.889,1.5,8.75,3.466,2,0.7390000000000001,6
+C LM,STUART,3013.0,37.104,0.914,1.6,8.6,3.49,2,0.731,6
+C LM,STUART,3013.5,37.349000000000004,0.9390000000000001,2.1,7.95,3.418,2,0.7240000000000001,6
+C LM,STUART,3014.0,37.936,0.9520000000000001,2.4,7.7,3.5039999999999996,2,0.716,6
+C LM,STUART,3014.5,39.086999999999996,0.953,2.1,7.75,3.511,2,0.7090000000000001,6
+C LM,STUART,3015.0,38.059,0.951,1.7,7.55,3.55,2,0.701,6
+C LM,STUART,3015.5,37.202,0.9520000000000001,2.2,7.3,3.603,2,0.6940000000000001,6
+C LM,STUART,3016.0,37.218,0.951,1.8,7.5,3.675,2,0.687,6
+C LM,STUART,3016.5,37.781,0.9490000000000001,1.4,7.6,3.741,2,0.679,6
+C LM,STUART,3017.0,40.164,0.948,1.5,7.35,3.735,2,0.672,6
+C LM,STUART,3017.5,40.955999999999996,0.95,1.6,7.3,3.747,2,0.664,6
+C LM,STUART,3018.0,41.013000000000005,0.9520000000000001,1.5,7.25,3.64,2,0.657,6
+C LM,STUART,3018.5,42.547,0.95,1.9,7.15,3.6830000000000003,2,0.649,6
+C LM,STUART,3019.0,41.192,0.945,2.0,7.0,3.6439999999999997,2,0.642,6
+C LM,STUART,3019.5,42.726000000000006,0.941,2.3,7.15,3.7039999999999997,2,0.634,6
+C LM,STUART,3020.0,44.407,0.9420000000000001,3.4,7.0,3.695,2,0.627,6
+C LM,STUART,3020.5,45.387,0.946,3.8,6.8,3.7880000000000003,2,0.619,6
+C LM,STUART,3021.0,45.052,0.945,2.6,7.5,3.917,2,0.612,6
+C LM,STUART,3021.5,46.129,0.935,2.4,7.4,3.948,2,0.604,6
+C LM,STUART,3022.0,47.826,0.925,2.5,7.05,4.016,2,0.597,6
+C LM,STUART,3022.5,50.053999999999995,0.925,2.3,6.75,3.935,2,0.59,6
+C LM,STUART,3023.0,55.562,0.9329999999999999,1.8,6.9,3.9739999999999998,2,0.5820000000000001,6
+C LM,STUART,3023.5,63.028999999999996,0.9470000000000001,2.0,6.8,3.859,2,0.575,6
+C LM,STUART,3024.0,75.399,0.966,1.9,6.45,3.681,2,0.5670000000000001,6
+C LM,STUART,3024.5,102.116,0.987,0.2,6.9,3.5610000000000004,2,0.56,6
+C LM,STUART,3025.0,140.778,0.993,-0.4,7.0,3.513,2,0.552,6
+C LM,STUART,3025.5,183.358,0.981,-0.3,7.35,3.437,2,0.545,4
+C LM,STUART,3026.0,213.99900000000002,0.977,0.2,7.8,3.412,2,0.537,4
+C LM,STUART,3026.5,220.41299999999998,1.004,0.0,8.0,3.528,2,0.53,4
+C LM,STUART,3027.0,200.30599999999998,1.0270000000000001,0.0,8.0,3.6710000000000003,2,0.522,4
+C LM,STUART,3027.5,162.149,0.9990000000000001,-0.2,8.5,3.7460000000000004,2,0.515,4
+C LM,STUART,3028.0,121.20200000000001,0.95,0.2,9.2,3.892,2,0.507,4
+C LM,STUART,3028.5,92.805,0.9159999999999999,1.3,9.95,3.924,2,0.5,4
+C LM,STUART,3029.0,77.758,0.894,1.9,10.65,3.9530000000000003,2,0.493,4
+C LM,STUART,3029.5,67.623,0.879,2.9,10.85,3.889,2,0.485,4
+C LM,STUART,3030.0,62.376000000000005,0.873,2.5,11.45,3.799,2,0.478,4
+C LM,STUART,3030.5,59.463,0.873,2.1,11.25,3.773,2,0.47,4
+C LM,STUART,3031.0,55.211000000000006,0.872,2.7,10.45,3.784,2,0.46299999999999997,4
+C LM,STUART,3031.5,51.727,0.866,2.3,10.35,3.787,2,0.455,4
+C LM,STUART,3032.0,52.38,0.858,2.1,10.75,3.7760000000000002,2,0.44799999999999995,4
+C LM,STUART,3032.5,53.122,0.851,2.5,11.05,3.7239999999999998,2,0.44,4
+C LM,STUART,3033.0,51.245,0.8440000000000001,2.7,11.25,3.697,2,0.433,4
+C LM,STUART,3033.5,52.208,0.836,1.5,11.75,3.653,2,0.425,4
+C LM,STUART,3034.0,54.95,0.826,1.3,12.05,3.647,2,0.418,4
+C LM,STUART,3034.5,54.43600000000001,0.8170000000000001,2.0,12.0,3.65,2,0.41,4
+C LM,STUART,3035.0,54.81100000000001,0.81,1.8,12.1,3.678,2,0.40299999999999997,4
+C LM,STUART,3035.5,55.603,0.8009999999999999,1.6,12.2,3.6439999999999997,2,0.396,4
+C LM,STUART,3036.0,54.288999999999994,0.785,1.3,12.75,3.656,2,0.38799999999999996,4
+C LM,STUART,3036.5,52.38,0.768,2.0,13.0,3.5980000000000003,2,0.381,4
+C LM,STUART,3037.0,53.645,0.758,2.0,13.7,3.582,2,0.373,4
+C LM,STUART,3037.5,53.93,0.757,2.1,13.75,3.5810000000000004,2,0.366,4
+C LM,STUART,3038.0,56.071999999999996,0.755,2.9,13.75,3.645,2,0.358,4
+C LM,STUART,3038.5,59.016999999999996,0.75,4.0,13.7,3.694,2,0.35100000000000003,4
+C LM,STUART,3039.0,62.144,0.742,4.4,14.1,3.65,2,0.34299999999999997,4
+C LM,STUART,3039.5,64.814,0.73,4.8,14.2,3.66,2,0.336,4
+C LM,STUART,3040.0,66.705,0.7140000000000001,5.6,14.6,3.6889999999999996,2,0.32799999999999996,4
+C LM,STUART,3040.5,67.568,0.695,6.5,15.45,3.674,2,0.321,4
+C LM,STUART,3041.0,67.683,0.6759999999999999,6.7,15.65,3.603,2,0.313,4
+C LM,STUART,3041.5,67.683,0.662,6.0,15.4,3.562,2,0.306,4
+C LM,STUART,3042.0,67.683,0.6659999999999999,5.7,15.25,3.57,2,0.299,4
+C LM,STUART,3042.5,67.683,0.701,6.0,15.2,3.603,2,0.29100000000000004,4
+C LM,STUART,3043.0,67.683,0.778,5.1,15.65,3.537,2,0.284,4
+C LM,STUART,3043.5,67.683,0.882,4.9,15.75,3.5469999999999997,2,0.276,4
+C LM,STUART,3044.0,67.683,0.973,4.4,15.8,3.533,2,0.26899999999999996,4
+C LM,STUART,3044.5,67.683,1.0170000000000001,3.5,16.25,3.495,2,0.261,4
+A1 LM,CRAWFORD,2972.5,49.675,0.845,3.905,11.175,3.265,2,1.0,8
+A1 LM,CRAWFORD,2973.0,34.435,0.879,3.085,8.175,3.8310000000000004,2,0.991,8
+A1 LM,CRAWFORD,2973.5,26.178,0.92,2.615,4.945,4.306,2,0.981,8
+A1 LM,CRAWFORD,2974.0,19.463,0.9670000000000001,0.82,3.82,4.578,2,0.972,8
+A1 LM,CRAWFORD,2974.5,19.26,0.995,0.32,3.63,4.643,2,0.9620000000000001,8
+A1 LM,CRAWFORD,2975.0,19.985,1.008,0.06,4.32,4.614,2,0.953,8
+A1 LM,CRAWFORD,2975.5,22.298000000000002,1.002,-0.01,5.5,4.4910000000000005,2,0.943,8
+A1 LM,CRAWFORD,2976.0,24.611,0.956,0.05,6.87,4.369,2,0.934,8
+A1 LM,CRAWFORD,2976.5,24.677,0.8240000000000001,0.31,6.94,4.047,2,0.915,8
+A1 LM,CRAWFORD,2977.0,24.945999999999998,0.667,0.965,5.915,3.8930000000000002,2,0.9059999999999999,6
+A1 LM,CRAWFORD,2977.5,29.31,0.5589999999999999,2.11,6.15,3.52,2,0.8959999999999999,6
+A1 LM,CRAWFORD,2978.0,37.321999999999996,0.522,3.375,8.445,3.125,2,0.887,6
+A1 LM,CRAWFORD,2978.5,42.141999999999996,0.51,3.985,10.785,2.843,2,0.877,6
+A1 LM,CRAWFORD,2979.0,52.434,0.49200000000000005,4.42,13.15,2.674,2,0.868,5
+A1 LM,CRAWFORD,2979.5,63.181000000000004,0.473,5.18,14.13,2.6439999999999997,2,0.858,4
+A1 LM,CRAWFORD,2980.0,69.405,0.465,5.52,14.56,2.6630000000000003,2,0.8490000000000001,4
+A1 LM,CRAWFORD,2980.5,77.785,0.475,5.82,14.94,2.68,2,0.84,4
+A1 LM,CRAWFORD,2981.0,83.57700000000001,0.469,5.965,15.395,2.6660000000000004,2,0.83,4
+A1 LM,CRAWFORD,2981.5,84.05799999999999,0.513,6.025,15.855,2.622,2,0.821,4
+A1 LM,CRAWFORD,2982.0,81.82,0.544,5.95,16.05,2.609,2,0.8109999999999999,4
+A1 LM,CRAWFORD,2982.5,80.257,0.563,5.74,16.1,2.6260000000000003,2,0.802,4
+A1 LM,CRAWFORD,2983.0,79.833,0.579,5.135,15.715,2.662,2,0.792,4
+A1 LM,CRAWFORD,2983.5,80.32300000000001,0.583,4.565,15.275,2.693,2,0.7829999999999999,4
+A1 LM,CRAWFORD,2984.0,84.704,0.59,4.365,15.165,2.7110000000000003,2,0.774,4
+A1 LM,CRAWFORD,2984.5,96.67399999999999,0.596,4.725,15.565,2.7439999999999998,2,0.764,4
+A1 LM,CRAWFORD,2985.0,112.662,0.609,4.88,15.71,2.824,2,0.755,4
+A1 LM,CRAWFORD,2985.5,131.484,0.634,5.11,15.6,2.935,2,0.745,4
+A1 LM,CRAWFORD,2986.0,138.16899999999998,0.659,5.33,15.1,3.0,2,0.736,4
+A1 LM,CRAWFORD,2986.5,135.045,0.682,5.1,14.46,3.048,2,0.726,4
+A1 LM,CRAWFORD,2987.0,117.726,0.7070000000000001,4.415,13.315,3.097,2,0.7170000000000001,4
+A1 LM,CRAWFORD,2987.5,98.382,0.7290000000000001,3.395,11.695,3.193,2,0.708,4
+A1 LM,CRAWFORD,2988.0,91.348,0.7709999999999999,3.235,10.985,3.3080000000000003,2,0.698,4
+A1 LM,CRAWFORD,2988.5,87.05,0.831,3.21,10.64,3.4930000000000003,2,0.6890000000000001,4
+A1 LM,CRAWFORD,2989.0,82.98,0.9279999999999999,2.835,10.115,3.5260000000000002,2,0.679,4
+A1 LM,CRAWFORD,2989.5,67.264,1.0190000000000001,0.475,8.435,3.217,2,0.67,4
+A1 LM,CRAWFORD,2990.0,57.068999999999996,1.047,-2.385,7.115,3.157,2,0.66,6
+A1 LM,CRAWFORD,2990.5,46.873999999999995,1.0659999999999998,-3.97,6.6,3.159,2,0.6509999999999999,6
+A1 LM,CRAWFORD,2991.0,39.325,1.06,-3.69,7.15,3.2239999999999998,2,0.642,6
+A1 LM,CRAWFORD,2991.5,43.946000000000005,1.042,-2.73,7.29,3.272,2,0.632,6
+A1 LM,CRAWFORD,2992.0,51.73,1.005,-0.9,7.98,3.29,2,0.623,6
+A1 LM,CRAWFORD,2992.5,58.373999999999995,0.956,1.46,9.41,3.2769999999999997,2,0.613,6
+A1 LM,CRAWFORD,2993.0,67.75399999999999,0.904,3.985,11.545,3.2960000000000003,2,0.604,4
+A1 LM,CRAWFORD,2993.5,71.206,0.8740000000000001,5.045,12.915,3.3280000000000003,2,0.594,4
+A1 LM,CRAWFORD,2994.0,69.188,0.852,4.86,12.85,3.33,2,0.585,4
+A1 LM,CRAWFORD,2994.5,58.452,0.841,3.455,11.265,3.332,2,0.575,6
+A1 LM,CRAWFORD,2995.0,38.715,0.816,2.185,9.855,3.365,2,0.5660000000000001,6
+A1 LM,CRAWFORD,2995.5,30.541,0.8009999999999999,0.96,8.76,3.4760000000000004,2,0.557,8
+A1 LM,CRAWFORD,2996.0,21.684,0.792,-0.04,8.28,3.7739999999999996,2,0.547,8
+A1 LM,CRAWFORD,2996.5,21.252,0.7929999999999999,-0.505,9.225,4.041,2,0.5379999999999999,8
+A1 LM,CRAWFORD,2997.0,23.116999999999997,0.7929999999999999,-0.555,9.895,4.292,2,0.528,8
+A1 LM,CRAWFORD,2997.5,28.85,0.7909999999999999,-0.46,9.89,4.371,2,0.519,8
+A1 LM,CRAWFORD,2998.0,36.226,0.7879999999999999,-0.085,9.175,4.112,2,0.509,8
+A1 LM,CRAWFORD,2998.5,42.83,0.8009999999999999,0.41,8.49,3.91,2,0.5,6
+A1 LM,CRAWFORD,2999.0,46.846000000000004,0.82,1.255,8.275,3.793,2,0.491,6
+A1 LM,CRAWFORD,2999.5,48.133,0.845,1.77,8.29,3.486,2,0.48100000000000004,6
+A1 LM,CRAWFORD,3000.0,43.162,0.8740000000000001,1.35,8.64,3.263,2,0.47200000000000003,6
+A1 LM,CRAWFORD,3000.5,36.586,0.92,1.01,8.74,3.187,2,0.462,6
+A1 LM,CRAWFORD,3001.0,32.746,0.955,0.625,8.535,3.173,2,0.45299999999999996,6
+A1 LM,CRAWFORD,3001.5,30.956999999999997,0.9740000000000001,0.565,8.555,3.315,2,0.44299999999999995,6
+A1 LM,CRAWFORD,3002.0,30.765,0.981,0.545,8.575,3.4410000000000003,2,0.434,6
+A1 LM,CRAWFORD,3002.5,31.265,0.987,0.625,8.425,3.6180000000000003,2,0.425,6
+A1 LM,CRAWFORD,3003.0,33.124,0.9940000000000001,0.755,8.045,3.943,2,0.415,6
+A1 LM,CRAWFORD,3003.5,35.211,0.997,0.635,7.555,4.07,2,0.406,6
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