From 7bb8c1dacadbd0295cb5b3ac7e6cc6e0b11665d6 Mon Sep 17 00:00:00 2001 From: Alan Richardson Date: Wed, 1 Feb 2017 00:00:36 +0000 Subject: [PATCH] submission 3 --- ar4/ar4_submission3.csv | 831 ++++++++++++++++++++++++++++++++++ ar4/ar4_submission3.ipynb | 497 ++++++++++++++++++++ ar4/ar4_submission3.py | 626 +++++++++++++++++++++++++ ar4/dtw_dist_fce.npy | Bin 0 -> 1048 bytes ar4/dtw_distformation_fce.npy | Bin 0 -> 13632 bytes 5 files changed, 1954 insertions(+) create mode 100644 ar4/ar4_submission3.csv create mode 100644 ar4/ar4_submission3.ipynb create mode 100644 ar4/ar4_submission3.py create mode 100644 ar4/dtw_dist_fce.npy create mode 100644 ar4/dtw_distformation_fce.npy diff --git a/ar4/ar4_submission3.csv b/ar4/ar4_submission3.csv new file mode 100644 index 0000000..887ea42 --- /dev/null +++ b/ar4/ar4_submission3.csv @@ -0,0 +1,831 @@ +,Well Name,Depth,Facies +4069,STUART,2808.0,3.0 +4070,STUART,2808.5,3.0 +4071,STUART,2809.0,3.0 +4072,STUART,2809.5,3.0 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+1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2016 SEG ML Contest entry by Alan Richardson (Ausar Geophysical)\n", + "\n", + "This notebook discusses some of the ideas contained in my submission. The majority of the code in my submission is in the [`ar4_submission3.py`](https://github.com/seg/2016-ml-contest/blob/master/ar4/ar4_submission3.py) script." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.interpolate\n", + "\n", + "from ar4_submission3 import run, get_numwells, get_wellnames\n", + "\n", + "run_ml = False # run the ML estimators, if false just loads results from file\n", + "solve_rgt = False # run the RGT solver - takes about 30mins, run_ml must be True\n", + "\n", + "if run_ml:\n", + " data = run_all(solve_rgt)\n", + "else:\n", + " data = pd.read_csv('ar4_submission3.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "matplotlib.style.use('ggplot')\n", + "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n", + "cmap_facies = colors.ListedColormap(facies_colors[0:len(facies_colors)], 'indexed')\n", + "\n", + "def plotwellsim(data,f,y=None,title=None):\n", + " wells = data['Well Name'].unique()\n", + " nwells = len(wells)\n", + " dfg = data.groupby('Well Name',sort=False)\n", + " fig, ax = plt.subplots(nrows=1, ncols=nwells, figsize=(12, 9), sharey=True)\n", + " if (title):\n", + " plt.suptitle(title)\n", + " if (y):\n", + " miny = data[y].min()\n", + " maxy = data[y].max()\n", + " ax[0].set_ylabel(y)\n", + " else:\n", + " miny = 0\n", + " maxy = dfg.size().max()\n", + " ax[0].set_ylabel('Depth from top of well')\n", + " vmin=data[f].min()\n", + " vmax=data[f].max()\n", + " if (f=='Facies') | (f=='NeighbFacies'):\n", + " cmap = cmap_facies\n", + " else:\n", + " cmap = 'viridis'\n", + " for wellidx,(name,group) in enumerate(dfg):\n", + " if y:\n", + " welldinterp = scipy.interpolate.interp1d(group[y], group[f], bounds_error=False)\n", + " nf = len(data[y].unique())\n", + " else:\n", + " welldinterp = scipy.interpolate.interp1d(np.arange(0,len(group[f])), group[f], bounds_error=False)\n", + " nf = (maxy-miny)/0.5\n", + " fnew = np.linspace(miny, maxy, nf) \n", + " ynew = welldinterp(fnew)\n", + " ynew = ynew[:, np.newaxis]\n", + " ax[wellidx].set_xticks([])\n", + " ax[wellidx].set_yticks([])\n", + " ax[wellidx].grid(False)\n", + " ax[wellidx].imshow(ynew, aspect='auto',vmin=vmin,vmax=vmax,cmap=cmap)\n", + " ax[wellidx].set_xlabel(name,rotation='vertical')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Many contestants have experimented with different estimators. At the time of writing, ensemble tree methods are clearly dominating the top of the leaderboard. This is likely to be because they are (reportedly) less likely to suffer from overfitting than other approaches. As the training set is small, and at least one of the validation wells shows notable differences compared to the training wells (discussed below), overfitting the training dataset is likely to be severely detrimental to validation performance.\n", + "\n", + "Some feature engineering has also proved useful to other entrants. The most successful entries are currently (at the time of writing) all based on the submission 2 of Paolo Bestagini. One of the notable features of Paolo's submission is that it not does contain very sophisticated feature engineering: primarily a simple augmentation of each sample's features by those of the sample above and below in depth. Other entrants, such as Bird Team and geoLEARN, have invested great effort into developing new features, but it has currently not been as successful as Paolo's simple approach. This is again likely to be due to detailed feature sets causing overfitting.\n", + "\n", + "My submission thus only considers ensemble tree estimators. Although a significant number of new features are created through feature engineering, I remained conscious of the risk of overfitting and attempted to mitigate this by using cautious estimator parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature engineering\n", + "\n", + "One of the features provided with the data is the depth (probably height above sea level) at which each sample was measured. This information is discarded by many contestants, as in its current form it does not contain much predictive value. This is due to the differing amounts of uplift at each well causing formations to be at different depths in different wells. This is demonstrated in the figure below. This figure also shows that one of the validation wells (Crawford) has experienced more uplift than any of the training wells. This indicates that it may not be close to the training wells, and so they may not be good predictors of this validation well." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EREREqsfSS0RERESqx22IiahSMoqNaztLAGhoHi87AhERPSFe6SUiIiIi1WPpJSIiIiLV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\nAQEBaty4sb788ks5HA6dOnVK+/btcx5zYyHMz89XXl6eOnf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "well_width = 100\n", + "mind = data['Depth'].min()\n", + "maxd = data['Depth'].max()\n", + "fim = np.nan*np.ones([int((maxd-mind)*2)+1,get_numwells(data)*well_width])\n", + "dfg = data.groupby('Well Name',sort=False)\n", + "plt.figure(figsize=(12, 9))\n", + "plt.title('Wells cover different depths')\n", + "ax=plt.subplot(111)\n", + "ax.grid(False)\n", + "ax.get_xaxis().set_visible(False)\n", + "ax.set_ylabel('Depth')\n", + "plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", \n", + " labelbottom=\"off\", left=\"off\", right=\"off\", labelleft=\"off\")\n", + "ax.text(well_width*7,1000,'Colours represent\\nformation class')\n", + "for i,(name,group) in enumerate(dfg):\n", + " if (maxd-group['Depth'].max())*2 > 600:\n", + " ty = (maxd-group['Depth'].max())*2-50\n", + " tva='bottom'\n", + " else:\n", + " ty = (maxd-group['Depth'].min())*2+50\n", + " tva='top'\n", + " ax.text(well_width*(i+0.5),ty,name,va=tva,rotation='vertical')\n", + " for j in range(len(group)):\n", + " fim[-int((group.loc[group.index[j],'Depth']-maxd)*2),i*well_width:(i+1)*well_width]=group.loc[group.index[j],'FormationClass']\n", + " \n", + "plt.imshow(fim,cmap='viridis',aspect='auto')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure also demonstrates that most of the wells follow a somewhat consistent pattern with depth in the well. One new feature that I create is thus the depth below the surface, by subtracting the surface depth from the depth measurement at each sample. This creates a feature that captures the element of consistency between the wells with depth below the surface.\n", + "\n", + "One important feature that was not provided with the data is the location of each well. It is plausible that this feature would have some predictive power, as nearby wells are more likely to be similar. The amount of uplift may be an indicator for location, if we assume that the formations can be approximated as being planar. I performed inversions to find 1D and 2D positions for each well using this idea to extract this information, but in the end decided to simply create a feature for each well containing the top depth of the third formation from the surface (the first that is present in all wells without interference from the top of the well)).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZGfDy8kJCQgICAwNrjefj4wMfHx+Dsjt37tR4rqq8rCGXUKvy8rJaP7s+jYnd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\nyhVcuXJFPC5VIgIME3uzZs3M2gXTsmV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax=data.groupby('Well Name',sort=False)['Formation3Depth'].first().plot.bar(title='Top of Formation 3 Depth')\n", + "ax.get_xaxis().tick_bottom()\n", + "ax.get_yaxis().tick_left()\n", + "ax.grid(axis='x')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The wells are too far apart to allow matching of facies between wells. It is thus not possible to build a 3D lithofacies model with which one could interpolate facies in wells without core samples. Nevertheless, one can still try to extract some information by matching wells. To achieve this, I use an approach similar to that described by Wheeler and Hale ([CWP Report](http://cwp.mines.edu/Documents/cwpreports/cwp826.pdf)). Dynamic warping (usually referred to as dynamic time warping in the signal processing community) attempts to match two sequences by shifting and stretching/compressing them to get the best fit. The amount of stretching/compressing is variable with position along the sequence. This idea seems well suited to matching well logs as varying amounts of uplift and compaction are likely to result in a well log being shifted and stretched/compressed relative to corresponding portions of another well log.\n", + "\n", + "To implement this dynamic warping strategy, I use the FastDTW Python package to calculate the dynamic warping distance and best path between each pair of wells. I use all available features (so PE is not used for matching for the wells that lack it, and Facies is not used for matching the validation wells). I constrain the warping to only match portions of the wells that are in the same formation. As described by Wheeler and Hale, it is now necessary to form and solve a system of equations to find the best warping that satisfies all pairs. The position of each point after warping will be referred to as its relative geologic time (RGT). Points with the same RGT in different wells should thus correspond. The distance measure reported by the dynamic time warping tool is supposed to represent how closely pairs of wells match. Following Wheeler and Hale, I use this in the system of equations to weight more strongly rows corresponding to wells that match closely. In order to find a solution that results in the logs continuing to be monotonically increasing with depth, I solve for the difference in RGT between adjacent depths. I use SciPy's constrained linear least squares tool to find a solution where these RGT differences are always at least 1.\n", + "\n", + "The results of dynamic warping are demonstrated by the matching of RELPOS and GR values between wells. The Facies values for the training set are also shown." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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e0yxJxRajCEF6wjfIzpHFSRiACt9yhkI8kuAC4z6pW8BuwBS+W5rMMY36nFOO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14dvL/WlIJtu/ffLbJK+3f+DHSWBT4etLfYc2fnTKmT9XNJ5uUJZ5P48/7dz0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\nv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UBJKUFhh+gM1Q09cR3fWwwhdyixVO+EIFATZ/MTAgQoLmvIQe7pDlqcUNVE6g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JB+xriVvWywABXdMoeHzAwtcnjTEgJ6hGXKUa2oQgID2+0DGOLWdmHl8WwO7G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\n1Kc+9SkddNBBoc3xSpIkw8Z649dp5OMf//gGv//cc8/prrv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xY8cwc+ZMmJqaso7Z5yor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IiKtzPtde/ZF2BGNaaAeIAU6+h7n6Ou7I82bE17LMaV9rRzAngsK//Yv9FoME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dI1icypq8yHaEwPRo2M52BMShGm262Y4AAEBUjBW+m+dNMTUrA6IvAAeuzQ0x\nR/xyZ31T7Eeyed5U2xECxPqOhD6ffnloihuNNzTcROZLQ52xwvfKVSNNzcqAJ6Kekq4OfvGx4G+7\nYWKIOUx73HYAxKkO49vbjhCMQfkxTZ4X2+RO4LztNmOFb7+uLU3NKq5kDhpqO0JgekyOvuB350In\n+mWW/Fzuk/d8F2IOxBtfj2m+nsN8bOEf+Mw/bUcITI8xf4h6Wl/WtbHCt/Z5lU3NKq5kN8i2HcEK\nX08SPi63j8vss9t+es52hADFdmHro5lXuTG2a4+l0TddX7ewX4hJTIu+8HVlXUslr28ebgMARK3P\n+Q1tR4BBPl7Y+rqN+7KuKXwBAAB+4esdal+Usx0AAAAAMIHCFwAAAF6g8AUAAIAX6OMLAIga/R8B\nN1Wtl2o7ghEUvgBQChSAAFxS6cQKtiMYQeELoEwoAAG4ZOPcHbYjBCa9m+0E8YfCFwAAFIkLW7iG\nwheh4GAJ1+Wfnmc7AgAgRozqAAAAAC9ELHzvueceEzkAAACAUEXs6vDjjz+ayOEsbvkDAADEh4iF\nb1JSkokccIwrT8XyRCwA+IUGK7dFLHz37dunBx98sMRphg4dGlggAEgIu20HgEkUQ4AbIha+ycnJ\nuvDCC01kgUM4ScB5x9kOAACIVcTCt3z58urQoYOBKAAAAEB4Iha++fn5JnIATqClGwCA+BVxOLMe\nPXqYyAEAAACEKmKL7yWXXKIVK1aoUaNGkqS33npLeXn/fWPRpZdeqrS0tPASAgAAAAGIWPj+61//\nUnJyckHh+/bbb+ucc86RJP3www/Kzc1Vnz59wk0JAHEmabVDL75sajtA/GOIRsANEQvfzz77TIMH\nD/7vHyhfXpmZmZKkzZs3a/jw4RS+AACn0X8fcEPEJostW7aoRo0aBZ87depU8N81atTQ5s2bw0kG\nAAAABChii68k7dixQ5UrH7ravfbaawt9DwA+yj89L/JEAIC4ErHFt0GDBpoxY0aRv82YMUP169cP\nPBQAAAAQtIgtvr1799bQoUO1detWnXPOOapataq2bdumefPmacaMGXrggQdM5ESCoTUMAADEm4iF\nb3p6uv7yl7/otdde0/vvv6/8/HwlJSWpXr16Gjx4sM444wwTOQHEKVeedpd44h0AXBdVH98GDRro\noYce0r59+/Tzzz/r+OOPV8WKFbVmzRqNHDlSAwYMCDsnAAAAUCYRC999+/bp7bff1urVq1W7dm31\n7t1b27ZiDIV7AAAgAElEQVRt0yuvvKLFixerffv2Uc2IViEAABDvqFfcFrHwfeGFF/Ttt9+qadOm\nWrRokdasWaMNGzaoffv2uuWWWwpGe0DRfN2BNo3fFV4Qg9JjHNjflfUdy7pmfFP4wMd9G35xZRuX\nSt7OIxa+ixcv1mOPPaYqVaqoS5cuuv3225WVlaWGDRsGmREAEoqvb27z5eQIwE0RC9+9e/eqSpUq\nkg69sCI1NZWiFyhGl2XNbEcIxJe2AwCIC65c6MRykfPSjKXhBTHsYdsB4lDEwvfgwYNatmxZoe+O\n/nz22WdHnFH7L6LrC5wI1sUwra/L7atKbfvajmBc/bHNbUcIzLoYTo6uFARSbEXB6HcXhBfEsFEj\nop/WleWOZZkld85hsZy/pvb4ILQcpsVS+LqyrqWS13fEwrdKlSp69tlnCz6npaUV+pyUlKSnnnoq\nYojH6lwWcRoXPXn9nbYjwKCBa3NtRzCObdwvk9LdGcVnVAzTurLcsSyz5Oe528fjuOTPuo5Y+D79\n9NMmcjjrwP/71naE4HT/te0EiENs4/BBjTZ0CAZcENU4vgAA+MzXVkDANRS+CAVDXAFuouXTLynt\nTrMdAQgUhS8AlIKvF3e0fPpl2LcbbUcIRG/Vsx0BcYLCF0CZVMpsZTsCgJBwoQPXUPgCAIAijTjN\njTIhw3YAxA03tmgAABC4rO5uvJQHOMyhd24CAAAAxaPwBQAAgBfo6hAyHvwBAACIDxS+IcuavMh2\nhMD0aNjOdgQAgEGunMM4f+EwCt+QHdi5xXYEAEAZ+Tq6AQ+3wTVu7MlxbMeK2bYjBKi77QAAYMVf\nrmhiO4IVe8bMtx0hGM90tJ0AccJY4Vvh8jNMzSquXLlqpO0IAXrCdoC452urEOC6u/7vt7YjBOKq\nv8+yHQGwythZ+q5/jDI1q9D16hp9Adiva8sQkyDeVPv3A7YjBGPQzKgndaUPoEQ/wGhklV9tO0Jg\nYrnAe/rWaaHliGc+Xsy7ssxSbMu9qMuvQsthWknLbWztZjfINjWruFL7vMq2I8CgF9rssh3BuA7j\n29uOEJxB+bYTxL33Kj1jO0KAbox6yiED7gwxhzk93ns5pul7/evSkJIYNmh31JP6ekzbN7heiEEM\nu6L45TZW+FIAwgc+bufc1fCLj9u4JE1tssJ2BCvuGfgb2xGM8/WY5styu9OeD8AKXwshAG6qWr+S\n7QgIEYUvAADALyrVTLEdASHilcUAAADwAoUvAAAAvEDhCwAAAC/QxxehyD+eYaEAuIOHOOE6X7Zx\nCl+E40QKX7gt//Q82xEAADGiqwMAAAC8QOELAAAAL1D4AgAAwAsUvgAAAPACD7cBAfpp8S7bEQLx\nq262EwAAEDwKXyBAB37mSX8AAOIVXR0AAADgBQpfAAAAeIGuDkCAfHnzDQAAiYgWXwAAAHiBwhcA\nAABeoKsDAJTG6iRJUpKk/KN+Ku13Sb/8/9Hfhf33q6kAwAsUvgBQGqcfKh+PLjbL8l2Qf1dZ/n4A\ncBVdHQAAAOAFCl8AAAB4gcIXAAAAXqDwBQAAgBcofAEAAOAFRnUAAABF2jh3h+0IgUjvZjsB4gUt\nvgAAAPAChS8AAAC8QFcHhCJptSPXVLzRCgAAZzhSnQAAAAAlo8UXCBAPggAAEL9o8QUAAIAXaPEF\nAAD4hSt37iTu3hWFwhehcOXAwUEDgMQxDXAFhS+AMnGlIJBiKwqcGblEYvQSAN6g8AUARM3XCx0A\nbqDwBYBSoAD0y+h3F9iOEIhRI2KbfvUP28MJYlh6DNO6ssxSbMu9d2tuaDlMO66E34wVvoOGzDA1\nq9CNiuEk4ety+3qS8HG5XVlmKfb17SNf1/ekM+4NL4hBo2Kc/rqtvw0lh2nrYph2QP0XQsth2uIY\npm0yuXVoOUxbd33xvxkrfOs+u97UrOKKr8s9KX2A7QiBiPUk4ety+8jXAnBmnavDCxLHxjxyv+0I\nVowZ/qDtCMYNXOtOy2csfFnXxgrfE8d/bmpW4WtbJ+pJfV3uGm38vHfq43K7UuxLsRX8vi735kqn\nhJYjnu0ZM992hGA80zGmyX1c7nsrZIWXw7AMRb/czqxrqcT1TR9fIEBZ3ZvZjmCcj8W+z1jfABIZ\nhS8QIGeumGNoHfGx2PcZ6xtAIqPwBVAmzhT7Usy3gX3E+obrHt+fZTsCQkThCwClMLLXLbYjAABi\nROELAKWQO9ehEVuubWA7AQAYQeELAEAElTJb2Y4AIAAUvgAARJA1eZHtCIHo0bCd7QiAVRS+AMok\nK2WN7QiByYhh2hGnuXP4jGW5/7ThndBymBbLGKe+WnrZ/9iOAEOSG59oO4IR7hy5AVix+7ultiMA\nods8b4rtCAGJrcX33SVu9GUffll61NMmVUsNMUn8emTbVtsRAlPS+yWNFb6+to4AAIDEkb91r+0I\nCJE71SgAIHS+vrnN1+X2saWbBxndZqzw3fujO/0AgeJwwATc1GF8e9sRgjEo33aCuHfH85fbjhCY\nHn+fZTtC3DFW+F76we9NzcqAa6Oe0tcuHj62EkhS5qChIeUwq8fkJ6Ke9spVI0NMYlr0y73n41dC\nzGFa9Nu5O/u2FMty9+vaMsQc8cvHlu6nb51mO4IVvuzbxqoyXw8azrQSSDG1FGQ3yA4xSPxypwiM\nvgD0dd9+7+yFtiNYMeXUf9uOEKDot/Pa51UOMQfiycyrqtiOEJgeS6M/b7tz/pJK2rfdaY6MU74W\nBb5iffvD10Ko6vF+PvEOf/h6HPdluSl8AQBAkbK6N7MdAQhUOdsBAAAAABOMtfj6elsQAAAA8cFY\n4btx7g5Tswpdun8PuQI4Csc0v7iyvlnXKI4vDZT08QUAAPDc5H8PsR0hMBklXOBR+AIAAHhu/pyL\nbEcITIbyiv2NwhcAAMBzd1zawnYEIyh8AQBAkQ78Z73tCMFo6Ef/VURG4QsAAIqUO3+j7QjBuLGh\n7QSIE4zjCwAAAC/Q4gsApdB74em2IwQmx3YAADCEwjdko99dYDtCYEaNsJ0g/rmyvlnXkV2wcZLt\nCDBo/io3bvmn2w6QAFw5jkscy4tirPD1dUOamZEdXpA41v6L9rYjBGJdjNNPafJQKDlMGxXDtK6s\nayn29Q1/3JPbx3aEQFxtO0ACmJQ+wHaEwMRyLPeFscLX1w0pq3uz0HLEsxpt/Hw90JND+tuOYNyY\n4Q/ajmCFrxe1vjZi+Lqd+8jX85cvjBW+vm5Ie8bMtx0hOM90jHpSXwt+Z9Z3DOvamWWW2MZRLGe2\n8xi2cV+xb7uNPr4AgKj5evcOcF3nbdfbjhCYr0v4jcIXABA1X+/eAa57KLmR7QhGMI4vAAAAvECL\nLwAgavR/BJDIKHwBAFFz5iEviQe9olAps5XtCECgKHwBoBQoAAG4xJeLHPr4AgAAwAu0+AIAAHhu\n3DMP2I4QmB5j/l7sbxS+AMrEl9tj8BvbuT987cb0aXJOiEHiB4UvgDLx9SQBv2RNXmQ7QiB6NGwX\n0/TO7N8x7Ntc5LiNwhcAgAg2z5tiO0JAYit8762QFU4MwzIUfeH78LM3hpjErB6jJ0Y97dO3Tgsx\nSfyg8AUAAPjF/be9aDsCQkThCwSIW2QAAMQvCl+Ewsd+YQDcVaNNN9sRAATAWOHLay4BN9HKDQBI\nFMYKX2daACVaAQHAM385qabtCAACQFeHkJU79QTbEQAAZXTX0j/ajhCIqzTLdgTAKgrfkA1P2mM7\nQmD62A4AAABQBhS+AMrElYH9pdgH9wfgHo5pbqPwBVAmPLgKwCUD1+bajoAQUfiGjKIAAAAgPlD4\nAgAA/MKV1zRLsb2q2RcUviFjGDcAAID4UM52AAAAAMAECl8AAAB4ga4OAICo8YpqAImMwhcAEDXG\nOIXrHt+fZTsCQkThC6BMMgcNtR0hMD0mP2E7QtzbPG+K7QgBovDFsbir4TYK35CNOM2df+IM2wES\ngCutYbSEAYXVyK9uOwKAALhTlcUpWkcAuKRGm262I1hRrtYA2xFgCMOQus1Y4ZvS6XRTs4orvp4k\nALiJ17kCbnrpmfttRwhMjzGjiv3NWOE77Mt1pmYVut6qG/W0vLIYAIDE4Wsf36sP+tFQZ6zw9bWV\nIP/n/bYjAEBgeG7BLz6OcODKsxpSbM9r+PKqZmNHMF8PlntfXBJaDuNa0VcI8N0dHc+0HcEKX+/e\n+dr6CXcZq0Y7jG9valbhG5RvOwEQN477n8a2I1iR0v5XtiNYkTZume0IwWkT/cW8K62AjNgSma8X\nOaPOfdF2BCPcaYYFYEWlWtH3eXfJ3a8/aztCYHpfzfjFkbgzQg+FbySuXORIsV3o9P/PjSEmMav3\n1bOK/Y3CN2S+3ibydbl9PDnm5R6QkiTl69D/64j/PvL/VcR38TZ9DMrn7YvtDzjC1337ylUjbUcI\nCBc5kbhzHJe40DmWscK3X9eWpmYVV3y9cnRlubktGNnWBe/ajhCgTlFP2e3bp0PMYdpTUU/pyr4t\nsX8DR/LlQUZafEPm65WjO8sd24nRx1Yhd5ZZojUsMnf2bSmW/fu8BqeEmAPxxNdjmi+DELizlHHK\n1x3IneWOrRDy8c6Gj8ss+bvc7uzbUiz7d6v0WiHmiF+Zg4bajhCIHpOjX9e+7tu+DEJA4RsyX3cg\nX5cbcB37tl+yG2TbjgBDfNm3y9kOAAAAAJhA4QsAAAAvUPgCAADACxS+AAAA8AKFLwAAALxA4QsA\nAAAvUPgCAADACxS+AAAA8AIvsAAAAPBc7fMq245gBIUvAJSCLycJAHAJhS8AlMLGuTtsRwhMejfb\nCQDADArfkNEqBLjppJZptiPAINY34AYKXwAoheSKPBvsE1/Xt4+NN1XOSLUdASGi8AUARM3HQgh+\nOe6kCrYjIER+XsICAADAO7T4AgCixkN9gJt82bcpfAGUCbe+AQCJgsIXCBBFIAAA8Ys+vgAAAPAC\nLb4AgKhxVwNAIqPFFwAAAF6gxRcAAMBzlfpPtR3BCApfAGXiyxA4AIDEZ6zwpV8Y4Cb2bQBAoqCP\nLwAAALxAVwcgQK7c9ueWPwDARbT4AgAAwAsUvgAAAPCCsa4OrtwClrgNDAAAkIjo4xuyA7sO2o4A\nAAAAUfiG7qclP9uOEJgq19hOAABAuDbNc+cOdV3uUB+DwhcAAOAX+dyodRqFL4Ay+XnjftsRAnOc\n7QAAgFBR+AIokx3f7rUdITAn2g6QAHhQ2S+urG/WNQ6j8AUC9MJHS2xHCMQjtgMgbrX/or3tCIFZ\nF8O0vhaArqzvWNb16HcXhJbDtFEjbCeIP8YKX183JJY7scV60Hjnyg/DCWJYLIWvK+taim19Dxoy\nI7wgho2KoRi66bbM8ILEscty77YdIRCfxzi9j+t7Zka27QgIkbHCd1L6AFOzCt2oGKb1dbl9PXAM\nXJtrO4Jxvm7jvi53/SnfhJbDuM6nRz3pA3tqh5cjjjmzvmNY1z4ex31CV4eQ1WjjZ8eirO7NbEcA\nQuXrvg0AiYzCN2QUgH6plNnKdgQYwr4NAImHwjdke8bMtx0hOM90jHrS/dNXh5fDpIZNbCdAnHJm\nG5fYzgF4g8IXoTi4/CfbEazImrzIdoRA9GjYznaEuOfrNg4AiYzCFwCACOjGBLiBwhcAgAi4mwO4\ngcIXAADgF7Tuu81Y4evr0D9re9ezHQEI1WN1LrMdAUBI7q2QZTtCIDIU/cPZcJuxwtfXAaHHf/Kt\n7QiBGXjhadFPnJwUXhAAAELi62hMvqCrA0Lxp7XTbEcIRIYutB0BAKwpt7q17QhAoIwVvuXb1DE1\nq7iyed4U2xECxEMRgO/+tOEd2xECw+3vyMYMf9B2BCBQxgrfAW//r6lZhe6q656Ielr6P/rFnQud\n6C9yRpzmzo2jDNsBgDiTOWio7QiB6DE5+vM23ObOGQsAAASq+jm/tR3BOEZ1cBujOoTM19Ywbo/5\nw51WbomWbqCwB1rW/eW/kiTlH/FLUZ8VxTQ2/0x0XGnllmjpLgqjOoQsq3sz2xEAhMDXYxr8kvfD\nbtsRgEC502QBxAEf72z4uMw+u3LVSNsRAkRrWCR/Xfe97QiBuEoNop62SeffhZgEthkrfJ/8dQVT\nswodtwUBAHDTzR3q246AEBkrfDfOedvUrMJ3X1vbCYC4Mah8mu0IMGhS+gDbEQIzynYAAMbR1QGh\nyJq8yHaEQPRoyNjFkQzP3WU7QmD62A6QAOja4pc9m76xHSEg0R/L5636McQcZvVoWNl2hLhD4Rsy\nVwpAiSIQRfN1VAdfsb79svu7pbYjGPeveSttRwjMo5en244Qdyh8AZQJLYCAu3zcv7cvnWE7QoAu\ntx0g7lD4AigTWgABAImCwhdAmfjYIgQASEwUviGjNQwAACA+UPgCQCn8acM7tiMEJkMdbUcA4gZ3\nsdxG4QugTHZ9487IJdzVAApzZ/9m38YhFL4AymTfj9/ZjgAgJD7u33RRdBuFLwAAKFJS+Qq2IxhH\nVwe3UfgCKBNOEoC7qrfsYjsCECgK35BRFAAAAMQHCl8AAFAkd/q7Rt/X1Z1llujjeywKXwBA1JLz\nDtiOAAClRuELAIja8Y072Y4AAKVG4QugTLK6N7MdAQalpFWzHcGKDuPb244QjEH5thMAVlH4AiiT\nO56/3HaEwPT4+yzbEeLewLW5tiNYMTMj23YEAAGg8EUo8vNpVQDgDu5sAG4wVvheuWqkqVkZ8ETU\nU/r6dOiWT6eGmMOk2G5vNnr72pByGDZoje0Ecc/XY5qvXLmzwV2NyNi33UaLb8jYgfxy0p61tiMA\nAIBiUPgCAZqUPsB2hECMsh0AiDMnnjzMdgQAATBW+LpSEEgUBSgeb+qD6+bNdmc4swzxLALgG1p8\ngQC506c7+v7cj+/PCi8GECcYzgxwA4UvECB3+nRH3597cdf/CTGHWRm2AyBuuXLXkjuW8B2FL4Ay\neX/petsRAjPi8nTbERCnfLyoBVxE4QsEyMdB7t3p3iHF0sUDAJB4ytkOAAAAAJhA4QsAAAAv0NUB\nQJm40/dRiqX/oysPO0k88ATAHxS+AFAKvhb8AJDIKHwBAAB+wd0ct1H4AkAp+DiCh89cKYYohOA7\nCl8AACLgdeT+qJj7s+0ICJGxwvfiNS+ZmpUB9IdD0dwZ05bxbAH46fLvnrcdIUDP2Q4Qd4wVvpUP\nbDE1K8QBdx78ie0iJ7sBt78BAIhXdHUAgFIYuDbXdgQAQIx4gQUAAAC8YKzF19cnoH1dbgAAgHhD\nVweEYmn1trYjAKEacZo7h88M2wEAwBBjR+7u674wNSsDon/i3Z2n/KVYlvums4aGmAOwz9d9GwAS\nmbHC939TXjE1q9AN1c1RT/vlDQtDTALY58rA/lKMg/vn54UVAwAQEnfu1QFx4Ipyg21HCMSKGKb1\ndWD/L29cbDuCFQP/vd52BCuyujezHQFAACh8gQAN2V7VdgQAIWhTZZftCAGpbDsAYBWFb8gm/3uI\n7QiByfCzYQ8o0sa5O2xHCEw6+zYATzCOLwAAALxAiy9CMWXBH2xHCESGvrUdAXFq3s7HbEcITLrt\nAABgCIUvQvHk23NsR4AhDOsFuOvTZ+6xHQEIlLHC9/H9WaZmBcAgTowAgERBiy8QoA5DzrQdAQAA\nFIPCFwBKgYscAEg8FL4AAAC/8PUlLb4wVvjuStlsalYAAADAMYwVvpcPPN/UrAAAAIBj8AILAAAA\neCEpPz8/33YIAAAAIGy0+AIAAMALFL4AAADwAoUvAAAAvEDhCwAAAC9Q+AIAAMALFL4JaP/+/dq6\ndav2799vOwpC8v333x/zv59++kl5eXm2o1mTl5ennJwc2zFC8Z///KfQ5w0bNhT6/M4775iMAwRu\n27ZtJf7+zTffGEoC3zGcWQJZtmyZXnvtNX377bfKz89XUlKSfv3rXysjI0ONGze2HQ8Buvrqq4v8\nPjk5Wb/5zW9000036bjjjjOcyo7vvvtO2dnZmj17tvLy8vTCCy/YjhS46667TuPGjSv4fMMNN2js\n2LHF/g4kmqO34X79+mn06NHF/u6Kjz/+WG3btrUdA0cw9ua2IE2cODHiNL169TKQxJxVq1Zp+PDh\n6tSpk6655hpVr15dW7Zs0bx58zRixAhlZWXpjDPOsB0zFC+++KJuvPHGgs/Tp09Xx44dCz4//vjj\nuvfee21EC80bb7xxzHcHDx7U999/rwkTJujVV1/VLbfcYiGZGdu3b9fs2bM1a9Ysfffdd0pKStIN\nN9ygCy+80Ha0UERqf6B9wi3jx4/XVVddpfLlE/IUXCpHb8M7d+4s8XdX/O///q/XhW9OTo4+//xz\n7dq1S2lpaWrYsKGaN29uNVNC7nUbN24s9rdFixZp165dzhW+U6dOVbdu3XTVVVcVfFenTh2dffbZ\nqly5sqZOnaoBAwZYTBie7OzsQoXvK6+8UqjwXbp0qY1YxiUnJ6tOnTq65ZZbnCv0D/vPf/6j7Oxs\nLV68WKeccoratm2r++67T3/5y1/0m9/8RhUqVLAdMRRJSUll+j1R3XHHHSUuW1JSksaMGWMwkRlf\nf/217rvvPt1+++2qV6+e7ThG+LqNu1rQR5Kbm6vhw4fryy+/VN26dVWtWjWtX79e7733nurVq6fB\ngwdbu/BLyMI3MzPzmO8WLFigN954Q5UrV9ZNN91kIVW4vvzyS1133XVF/tapUycNHjzYcCJzfD1w\nFKdSpUrat2+f7Rih+Pvf/660tDTdfffdOuecc2zHMSo/P7/Qtn70Zxf98Y9/LPL7b775RlOnTlW5\ncm4+hvLAAw9o+vTpevTRR9W+fXv16dPH2Ys63+Xl5WnZsmUlTnP22WcbSmPOtGnTtHPnTj355JOq\nWbNmwfc//fST/va3v2natGnq3r27lWwJWfgeadmyZZowYYK2b9+uXr166YILLnDyYLl7925Vr169\nyN+qV6+u3bt3G05kjqstAaU1d+5cnXbaabZjhOK2225Tdna2Ro4cqfT0dLVt21bnnXee89vA3r17\n1adPn0LfHf3ZRUc/m7Bu3Tq98cYbWr58ua644gp16dLFUrLwdezYUS1bttSYMWPUv39/nXTSSYV+\nHzp0qKVk4di3b58efPDBgs979+4t+Jyfn+/sw9oHDhzQc889V+xFbFJSkp566inDqcI3b948XX/9\n9YWKXkmqWbNmQX9uCt8Yffnll3r99de1ceNG9ezZUx07dvSqv9TRXC4MDh48WOiK+egraBdHOhgz\nZswx6zQ3N1c//vijNmzYoEGDBllKFq4OHTqoQ4cO+vHHH5Wdna33339fL7/8siRp4cKFateunZMX\nti6e+GLxww8/6I033lBOTo46d+6s2267zYuHN+fNm6dvvvlGHTt21Kmnnmo7TqiObt0/ur/+kd3X\nXJKamurl/r1x48Zinzs644wztGnTJsOJ/ishK8VHH31UX331lbp166aBAwcW3CI6sgBy7eS4d+9e\n3XbbbcX+7uqtb0mqUqWKnn322YLPaWlphT5XrlzZRqxQ1apV65jvkpOT1aJFCzVr1szJZT7SiSee\nqF69eqlXr176/PPPlZ2drXHjxun111/X888/bzte4E488cRifzt48KCeffZZ3XnnnQYTmbFlyxZN\nnDhRc+bMUadOnTRq1Cjnt21J2rRpk5599lnt3btXDzzwgE4//XTbkULXoUOHYn/Ly8vTzJkzjWVB\n+PLz84vtvmO7W09CDmdW3FBPRyrqqfhEtmLFiojTNGrUyEASwI4DBw7os88+03nnnWc7ilEHDhzQ\ntdde69wxTZKuueYapaamqkuXLsV25XKxJfD666/Xb3/7W3Xr1k3Jycm241jn8jbet2/fgrtWPrnm\nmmt00003FdvF48UXX9Srr75qONUhCdni6+NtA4raouXm5iozM7NQCzDclJKS4l3R67p69eopKSlJ\ny5cvL3YaFwvfYcOGOd+1AYeUVPTm5ubqww8/1KWXXmowkRn16tXTrFmzSvzdloQsfEu6LQi/5Ofn\na8uWLbZjACiFrKws2xGsOPXUU5Wfn6/t27erSpUqSkpK0qJFi5STk6Nf/epXuuiii2xHRICWLl2q\n1atXq1atWmrdurUOHjyof//735oyZYrS0tKcLHzjed9OyMI3mtsh0XSHAIB4MH369GJ/O3jwoMEk\n8WHXrl36+OOPlZ2dreHDh9uOE7gVK1boiSee0K5du3TSSSfp6quv1iuvvKIGDRpo3rx5+umnn5wb\n1eP7778v9rcDBw4YTGLW5MmTNWnSJJ122mlau3atOnfurOXLlyslJUW33nqrWrRoYTuicdu3b9fU\nqVP1+9//3sr8E7Lw3bx5s+0IAEKwdu1aZ4dqK8ns2bNL/N2Hrk4HDx5UTk6OsrOztXDhQlWvXl0X\nX3yx7ViheOWVV3TNNdeobdu2mjlzpp577jk9+uijOvXUU7V+/Xo98sgjzhW+/fr1sx3Big8//FBD\nhw5V3bp19eWXX+r+++9X3759ddlll9mOFqr8/HzNmDGjoKX7kksu0b59+/TPf/5TH330kdVjWkIW\nvrfffnuJv5d0ZemqNWvW6Fe/+pXtGKEoamivw1wcykw69LT7559/XtCn9R//+Idyc3MLfu/Tp0+x\nDwMlsiFDhuiKK65Qz549nRuZpSR33nmnatSoYTuGFd98841mzpypOXPmKC8vT+ecc45SUlI0bNgw\nValSxXa8UGzYsKGg7/JFF12kl19+uaDP7ymnnHLM63xd4OKDa9HYuXOn6tatK0mqX7++UlJS1LVr\nV8upwvfKK69o7ty5BXcxvv76a3311VeqV6+e/vrXv1qtVxKy8C3JgQMH1K9fPyd3st27d2vTpk2q\nWbNmwZA/q1ev1sSJE7Vw4UK99tprlhOGo6ihvY7k2uupJWnKlCk6+eSTCz5//PHHBQfL9evXa8qU\nKbrhhhtsxQvN8OHD9Y9//EPz5s3T7bffrl//+te2IxkxYMAAjRs3znYM4+655x59//33at68uW65\n5S3vKmMAACAASURBVBa1aNFCKSkpWrhwoe1oxpQrV04pKSmFvnN5XHYfHfkWxsPr2uXhV6VDr58f\nOnSoTj75ZK1fv14DBgzQ3Xffrd/85je2o7lX+LoqJydHf//737Vv3z6VL19emZmZWrFihWbPnq1O\nnTo5+T77w6644gqlpqYW+/uqVasMpjFj0aJFevjhhws+JycnF9z63LFjR6E3ILmkTp06ysrK0gcf\nfKC//vWvateu3TFPv7v4lH8CjioZiH379qlcuXKqUKGCKlas6M1LiA4cOFCocWb//v2FPh95d8cl\n8+fP17p161S/fn01aNBATz31lHJycnTqqaeqX79+hS72XRHNWxldbag7vD5POeUUVahQIS6KXonC\nN2FMmDBBffv2Vbt27TR9+nQ9/fTTBa+7TEtLsx0vVI888oiGDBlS5KDXX3zxhR599FGNHTvWQrLw\nbN++vdBA/kc+rFm5cmXnR7Jo3bq1PvnkE82bN0/ffvttod9cLHyTkpIKtQoVxcVWoaeeekorVqxQ\ndna2nnzySVWoUEHnnnuuDhw44HSr5/nnn1/oWZWiPrvmzTff1IwZM1S/fn29//77qlevnlJSUtS/\nf3/NmTNHY8eO1Z///GfbMQPn4/Cr0qGL+R9++KHgmJacnFzosyRrFzoUvgnihx9+KBji5pJLLtG4\nceN02223qWLFipaTha9y5coaMWKE/vznPxe6Jbh8+XI99thj6tu3r8V04Shfvry2bNlS0I/3yOFu\ntmzZ4nTL2EcffaTx48erQ4cOhd7M6LKiWoWO5mKrkHTowb1GjRrpD3/4gz755BPNmjVLe/bsUVZW\nljp37qzOnTvbjhi4O+64w3YE42bMmKGHHnpIJ554ojZu3Ki77rpLL730kipVqqRGjRo5+29S0vCr\nu3bt0pw5c5zcxvft26fMzMxC3x392dYxLSHPniW9utdVR14llStXTqmpqV4UvZJ011136fHHH9fj\njz+u++67T+XLl9fixYs1cuRI3XjjjWrfvr3tiIE7++yz9c477xQ53Mu0adN09tlnW0gVvocffljb\ntm3ToEGDin3Pu4sqVKigkSNH2o5hVYUKFdSuXTu1a9dOW7ZsUXZ2tt5//30ni4Lhw4erYcOGatSo\nkdLT0714e9vu3bsLisDatWsrNTVVlSpVkiSlpqY6273jaHl5ecrJydHMmTO1cOFC1apVy8ltPJ4v\n1BOy8D36qsEH+/btK9Svc+/evcf08xw6dKjpWEaUL19e9957rx599FGNHDlSHTp00FNPPaVbb73V\nyVuC0qE+YIMHD9bGjRvVpk0bVa1aVVu3btWnn36qlStX6pFHHrEdMRT16tVTr169nG7RLkq5cuV4\nMc8RqlevriuuuEKLFi2yHSUUDRo00PLly/X2228rLy9P9erVU8OGDdWwYUPVr1/fi7scLnbdKck3\n33yj7OxszZ07V/v379eBAwc0YMAAtWrVyna00G3cuFE7d+5U5cqVIz6sbkJSfoI+VXF4hIPatWsX\nXDW6bObMmRGn6dChQ+g5bNq/f78eeeQRffXVV+rfv7/OOecc25FCtWnTJv3zn//U0qVLtXPnTqWl\npalx48bq3bu3ateubTteKA4Pd3PY/v37CxUBn376qZPrvW/fviW+2tRHBw4c0LXXXhvXLUdllZeX\np2+//Vaff/65Vq5cqS+++EK7d+9W3bp1Cz3c6oKrr7660BCMR3blkqStW7dqwoQJNqKFaurUqcrO\nztamTZvUpEkTtW3bVq1atVJmZqb+9re/OTtknyTNmzdPL7/8sn766aeC72rWrKnf//73Vh90S8hm\nlZycHD355JPav3+/UlNTdd999zl76/cw14vakhzZteXwG37Gjh1b6IG2Z5991niusNWqVcu7uxvD\nhg0rNKzXrbfeWmg9P/30004Wvj5168B/lStXTunp6apdu7Zq1aqlWrVqKTs7W2vXrrUdLXCujkQT\nyWuvvaa0tDTdcccdOvfcc51+aPNIOTk5euaZZ9SzZ0+de+65qlatmrZu3aq5c+fqueeeU0pKilq2\nbGklW0IWvm+88YauueYaXXjhhfroo480YcIEDRs2zHasUL344ou68cYbCz5Pnz690NPtjz/+uO69\n914b0ULnW/Hns0g3oBL0BlVELg7Jh+Lt2LFDK1as0IoVK7Ry5Urt3LlT9evX15lnnqlBgwbp9NNP\ntx0xcD68fbAoDzzwgLKzs/X8889r3LhxOv/889W2bVvnC+BJkybplltuKdQd8aSTTlL37t1Vs2ZN\nTZo0icI3Ft9//33BU+6dO3fWW2+9ZTlR+LKzswsVvq+88kqhwnfp0qU2Yhnh6wHTR5FOBq6fLHxT\nUjeGgwcPGkxi1s0336xTTjlFXbt2VdeuXeOi32PYpk+fHnEaF4cqPOuss3TWWWfpD3/4g+bNm6fs\n7Gy9++67ys/P1wcffKDOnTvrhBNOsB0zcGvXri327lybNm30j3/8w3Ci/0rIwvfIVp/k5GSnD5CH\nudrSFY2JEydGnMbFt7fBH/v374843uedd95pKI05R45dWxQXR2yRDvV3XblypSZMmKBTTz1VZ555\npho2bKgGDRqU+LKeRDZ79uyI07hY+B5WsWLFglFLfvrpJ82aNUuzZs3S5MmT9eqrr9qOF7iUlBTt\n2bPnmLcSStLPP/9s9QHmhCx8fRzhwOeWro0bNxb726JFi7Rr1y4KX0fs3bu3UJ/u3bt3F/q8b98+\nG7FCl5SU5ORbqyK5/fbbbUewomfPnpIOPdy2evVqrVy5Uh988IGeeeYZVatWTWeeeaauv/56uyED\n5msf36LUrFlTPXv2VM+ePfXVV1/ZjhOKpk2bavz48frjH/94zG+vv/66mjZtaiHVIQlZ+B79D3nh\nhRdaSmLOwYMHtWzZsoLPeXl5x3x2VVF9fBcsWKA33nhDlStX1k033WQhVbh87dPt68kxJSVFvXv3\nth3DmuXLl2vJkiXauXOnTjjhBDVu3Nj5B5alQw+31a1bt+DBtsMPt7333nvOFb5Fyc3N1Zo1a3Ty\nySfr+OOPtx0nFEV150lOTtaJJ56o5s2bFxrFxiXXXnut7r//ft17771q06ZNwcNtn376qXbv3q2H\nHnrIWraELHx9HOGgSpUqhUYuSEtLK/T5yNfbumzZsmWaMGGCtm/frl69eumCCy5wcjxIX/t0R+rP\nHenWeKLytStTbm6uRo4cqcWLF6tevXqqWrWqNmzYoGnTpqlJkya65557nBzT+fDDbStXrtTKlSu1\ndu1aVa9eXQ0bNtTVV1/t5HMNu3fv1j//+U+tW7dO9evX10UXXaQHHnhAP/zwgypUqKD77rtPTZo0\nsR0zcEUds3Jzc7V48WK99NJLGjRokOrXr28hWbiqV6+uESNGaNq0aVq0aFHBRW3Lli11+eWXKy0t\nzVq2hDyiZGdnR5zGtb5hTz/9tO0IVn355Zd6/fXXtXHjRvXs2VMdO3Z08oR4mK+FUJ8+fdSrV69i\nu64MGDCg0HBnrrjgggtK/P3nn392skXszTff1LZt2zR69GjVqFGj4PuffvpJI0eO1JtvvqmMjAyL\nCcNx8803q1atWmrYsKEuu+wyNWrUyPkXmPzf//2fdu3apdatW+uzzz7T3Llz1aVLF3Xq1EkzZszQ\nhAkTnCx8S+rO8/HHH+vVV1+12voZprS0NPXp0yfi69hNS8jK4ZlnnlGtWrVUtWrVIguEpKQk5wrf\nI23YsEG7du1SWlqa6tSpYztO6B599FF99dVX6tatmwYOHFjwQoMju3e41urra5/u5ORkzZkzR198\n8YX69+9/TKuAqxcEN9988zHfHX61aXZ2tnJycvTaa69ZSBauOXPmaNCgQYWKXulQH8jbbrtNw4cP\nd7Lwff7551W1alXbMYxasmSJnnrqKaWmpuq8887TzTffrEsvvVTlypXTJZdc4uTLKyI599xz9eKL\nL9qOEYo//elPeuyxxwo+T5s2TZdffrnFRP+VkIVvly5d9Mknnyg1NVXt27dX69ati3xy0DXZ2dl6\n9dVXtWPHjoLvqlSpooyMDKe7fyxcuFDSoYHAizv5u/Z2J1/7dJcvX17Dhw/Xc889p4EDB+ruu+8u\n9HIHHy4Ivv32W2VnZ2vOnDnasWOHzj//fOce1j1sx44dxV68n3LKKdq5c6fhRGbMmjVLv/3tbws+\nL1mypFBr57hx43TdddfZiBaaAwcOFIxYkZaWptTU1IIGi3Llyjl7UVuSPXv2OPt66k2bNhX6PGnS\nJArfsrj++uvVt29fLVq0SNnZ2XrppZfUokULdejQQWeeeabteKFYsmSJXnjhBfXu3bugo/iWLVs0\nb948jR07VtWrV3fyNpGkiMM8ucjnPt2pqam666679O677+rhhx9WRkaGOnfubDtWqLZt26bZs2dr\n5syZ2rBhgxo3bqxrr71WL7/8sq677jpnX2tavXp1ffPNN0W+uW7VqlWqVq3a/2/vTqOiPM83gF8M\ni4CIRKgLuMSFUVARqBhFDtTi0roQTQEXjJbEpYrWBpOa2kCMmkZMrLEYVzyilWPdsCqaEz2hDBF3\nRVwAUZG4izgiIMgyw/+Df6csg1hl5mHe9/qd0w/zPvPhIlDnnvd9nvsWkMrw9uzZU6vwXblyZa0J\nhcnJyZIrfKurq5Gfn68rcPW9liJ9Nyk0Gg0ePnyI7du3w8vLS0Aqw2vONylMsvAFnn9D9Pb2hre3\nN0pLS5GYmIhFixbhs88+k+Rp4O+//x4TJkzAyJEjddfatWuHoKAgWFlZ4dChQ5ItfF+2962kpARp\naWmSK4wa29NdUVFhpCTijBw5Ej169MDKlStx5coVzJw5U7IfjrNmzYKtrS2Cg4Ph6+urK3SluL2h\npsDAQMTGxmLevHno1q2b7vr169exevVqDB06VGA6w5HjhMLy8vJ6HXrkMJVz4sSJeq9bWFjgnXfe\nwfvvv2/kRMZTXV1d62+57mtRWxRNtvAFnp8STUtLg0qlQlFREX73u99JctQj8PyDQF8/POD5PqE9\ne/YYOZE4L/Y+pqSkID09He3bt5dc4fuy/VAVFRWIiYlBVFSUkVMZn1KpRExMDGJjY/GXv/xFssNq\n/Pz8cOrUKRw4cACPHz+Gn58fOnfuLDqWwQUFBaGgoAALFy6Eo6OjruVRQUEBhg0bhjFjxoiOaBBy\nnFAote1or0rfE0tzc3M4ODhI7mxKTc+ePat3qK3ua1F/EyZZ+J45cwapqanIzs5G//79MXnyZMlu\ncXihvLy8wcedrVu3lmxj/5pyc3OhUqlw7NgxVFRUoLKyEpGRkejfv7/oaE3uwIEDsLKywvDhw2td\nLysrw1dffSXZfWFOTk71rtnb22PhwoXYuXOnZMeTR0REYNq0aThx4gRSU1Oxb98+dOzYEWVlZSgu\nLpbsVgcA+OCDDzBy5EhcvHhR1/KoT58+6NChg+hoBiPXx/5ytGzZMqxYsUJ0DKNrzlsUzapN8P9h\n48ePh7OzM7y9vRssAMaPH2/kVIY1depUxMfHN/gPYnh4uCTbPAHA/v37oVKpcP/+fXh4eMDPzw/9\n+/fH3Llz8fXXX0uyKLh16xYWL16MSZMm6Qa0lJaWYunSpWjVqhU+/vhjWRzolKuaI00fPHgAHx8f\nREZGio7V5Bo7tGdmZobo6GgjpTGeV/l8kusdUqmZMmUKtm7dKjqG0V27dg3dunVrlne1TfKOr7+/\nP8zMzCR74lcffY8N5CIhIQF2dnaIiIjAoEGDJPkYsK5OnTph4cKFWLp0KSwtLeHp6YklS5bA0dER\nkZGRku1hLMce3frUHGmak5PzSv9dTFFD/YvVajW+//57yT7JYlErH3L4vNLniy++gJmZGZRKJdzc\n3ODu7g5XV9dm8dllknd85ejhw4eNvkeqDdAvX74MlUqFkydPwtraGoMHD4afnx9iYmKwfPlySd7x\nfeHatWv429/+hpYtW6Jbt26YN29es/wG3VTGjx/faI9uKbb2KigowMWLF/WOX09JSUHfvn3r9bqV\nouLiYuzduxc//vgjfH19ERwcLMmfW653uuVowoQJ6Nmz50vfI8V/0zQaDa5fv47s7GxkZmbiypUr\nqKysRPfu3XWFsKgD+SZZ+L5KD1MpFwdyVV5ejpMnT0KlUuHy5cuorq5GSEgIRowYgVatWomO16Rq\n3hG6du0acnJydM3eX5Dadh4AiI+Px4kTJ9ClSxdZ9ehet24dunXrVm9PNwAcOXIEubm5mDlzpoBk\nxlFaWor9+/fjhx9+gLe3N0JCQtC+fXvRsQwmOTlZ7/Wad7q3bdtm5FRkCGFhYXoH1NQk5T78L1RX\nV+PmzZs4d+4cDh06hKKiImFPPkyy8JXj/qg1a9a8dN3MzAyzZs0yUhrxau6BLCgokNyHRGO/b+Dl\nozBNmVar1fXozsrKknyPbgCYM2cOvv76a9jY2NRbe/bsGebPny/JseUVFRU4ePAgkpKS4O7ujtDQ\nUHTq1El0LKOTw53uiIiIlz72NzMzQ2xsrBETGcfUqVMle/7mVZSUlCArKwtZWVnIzMzEo0eP0KNH\nD7i5udXqZW1M4jdbvIbmfFrQUNq0aaP3ekVFBVQqFUpKSmRV+NbcA3n16lXRcZqcVIvaVyG3Ht3A\n8wlmLVq00LtmZWUl2fMMERER0Gq1CAoKQvfu3fHkyRM8efKk1nuk+jsH6t/pjomJkeyd7obacebm\n5mL//v2SfUprgvcWm0RcXByys7NRXl4OpVKJXr16YciQIc3ii61JFr6N7WW9efOmkZIYT92DbRqN\nBkeOHMHevXvRtWtXSR98qzmqVy70/czm5ub4xS9+obfll9TIqUc3ALz11lvIy8urNcThhby8PDg4\nOAhIZXgvuvIcPnxY77qZmZkkb3TUvdO9ePHiZlEQGFLfvn1rvb59+zZ27NiBy5cvY8yYMfjtb38r\nKJlhBQUF4ebNm7q+3E+ePMGWLVtw69YtuLq6YsqUKbpRzlKiUqng5OSEgIAAuLm5wdXVtdm04TTJ\nrQ7A8w/G+/fvw8nJSTe+NS8vD7t370Z6erpkJx5ptVqkpKRgz549cHR0xIQJE+Du7i46lkFFRETU\nev3o0aNajwGl+OFY92cGnn/ZefLkCXr06IGPPvqowacApqxuj25/f39Jb3F4YefOnTh37hz+/Oc/\n1/q9qtVqfPPNN/Dy8kJISIjAhNSUpk+fXutOtz5SvdOdn5+PHTt24Ny5cxgxYgSCgoJga2srOpbB\nREdHIzg4WHeQa/ny5Xj8+DECAgKQlpaGLl26YNq0aYJTNr26h9uuX7+Odu3aoVevXnBzc0PPnj1h\nZ2cnJJtJFr7nzp3Dt99+i/LyclhYWGDu3LnIzMzETz/9hMDAQIwcOVKSRUFaWhp27twJW1tbhIaG\nSnbGd2PCw8NrzbWXk/LyciQkJKCwsFCSfV3l2KMbAKqqqrBixQpcunQJPXr0gIODAwoLC3Ht2jX0\n7dsX8+fPh7m5ueiY1ET0fbGtSYpf5tVqNXbv3o20tDQEBgZi7NixuptWUvbhhx9i3bp1sLS0xNOn\nTzFt2jSsWLECzs7OKCgoQFRUFNauXSs6psHVPdxWUlKC7du3C8liklsd/vWvf2HKlCnw9/dHcnIy\nvvvuO/zyl79EbGyssG8QhvbJJ59ArVbj3XffxYABA2BmZoYHDx7Uek+7du0EpSNjadGiBSZNmoQ/\n/vGPoqMYhBx7dAOAhYUFFixYgAsXLuDSpUsoLi6Gq6sr3nvvvXqPiMn0SfGgYmPmzp0La2trjBkz\nBm3atMGZM2fqvefXv/61gGSGpdFodL1rr169CgcHBzg7OwN4flbl6dOnIuMZXM3DbVlZWcjLy0Pr\n1q0xcOBAYZlMsvDNz8/H0KFDAQDDhw/Hli1bMGvWrAYPh0jBi33LCQkJDW7jkFonC9LP3NwcGo1G\ndAyDaOxOmNR5eHgI621JZEiurq4wMzPD5cuXG3yPFAvfTp064fjx4/D19UVaWlqtL7JqtVqy2zzi\n4uKQlZWFO3fuwMnJCe7u7hg+fDjc3NyEH+A0ycK35u4MhUIBa2trSRe9AIta+q+DBw/qPQQlBezR\nTSRNixYtEh1BiLCwMMTExGDjxo1QKBRYsmSJbu3YsWONDrcwVVqtFuPGjYObm1uza81nknt8605C\nycnJgVKprPUeKU5CacitW7egUqkwefJk0VEMIjo6ulb/Rzn8vuv+zMDzfaAFBQWwsrLCp59+ChcX\nF0HpDEeOPbqJ5KykpARHjx6FSqXCV199JTqOQZSVleHevXvo0KFDrV7dd+/ehbW1tSTPJL2M6JrF\nJO/41u0HqG/Mp9QVFRXp/rHIy8uT9EG3uo+/5PD71vfIz9zcHE5OTs1m3rkhSO1ADxHVp9FocO7c\nOahUKqSnp6NNmzYYNmyY6FgGY2Njo/cp3Yu9vnJQs2b5+eef4enpKSyLSd7xlauqqiqcPXsWKpUK\n58+fh6OjIx4/fozFixdL9tE38LzBuYWFRa0+iPHx8bh9+7ak+yA2RKvVYteuXZLsbpCVlQU3N7cG\n17dv346JEycaMRERNZXc3FykpKQgLS0NWq0WAwYMwMmTJ7Fq1Sq0bt1adDxqYs21ZjHJ20avMtBA\naj0Q4+LicPz4cZibm2PgwIFYtGgRlEolZsyY0ez2zzS1+Ph4BAcH6wrf9evX4/HjxwgMDERaWhq2\nbdsmyT6IDdFoNEhMTJRk4bt8+XIsXLgQrq6u9da2bNmCkydPSrLwjY2Nfek4V+D5WGMiUzV//nw8\nePAAXl5emDFjBry9vWFpaYn09HTR0cgAmnPNYpKFb92ed3IYaHDkyBHY2dkhJCQEgwcPluxJUH3u\n3Lmjuwv49OlTpKen6/og9u/fH1FRUbIqfKVs2rRpWLZsGT777DN07dpVdz0uLg4ZGRmSPSBT95Tz\nvn378O677wpKQ9T0ysvLoVAoYGVlhRYtWkh2uxY915xrFpP8y6vbAzE8PFzyfRFjY2ORmpqK/fv3\nIz4+Hl5eXvDz85PFHHC590GUk8GDB6OyshJffvkloqOj0blzZ6xduxbZ2dn4/PPPJTuuue5UtkOH\nDnFSG0nK6tWrkZmZCZVKhZUrV8LKygqDBg1CZWVlo087yPQ055rFJAtfOWrbti2Cg4MRHByMrKws\nqFQqrFu3DmVlZdi+fTtGjx6Njh07io5pEHLsg/iy7TxVVVVGTGJ8v/rVr1BZWYmlS5fC1dUV9+7d\nwxdffAEHBwfR0YjoDbi7u8Pd3R0ffvghTpw4gdTUVJSVlWHRokUYMWIERowYIToiNZHmXLNI4nCb\nXEfYVlRU4NSpU1CpVLh06ZKw8X+Glp2djZiYGADQ9UF8ccc3KSkJV69exUcffSQyYpN7lUEOUnzK\nUbPg/+GHH3Dx4kVMmzatVtErtf37+sj13zSSH7VaDZVKhdTUVKxcuVJ0HDKg5lKzsPCVCLVaLele\ngOyDKA+NFfxS3L8PoN748QULFmD58uW1HgtyJDkRSUVBQYGwrWsmWfjKcaBBTEwMZsyYgbfeeqve\nWmZmJtavX49Vq1YJSEZEb4qDO0jqXuUz+fPPPzdCEjKm4uJitGzZUjdxs7CwEPv27cOPP/6IrVu3\nCslkknt85TjQoF27dpg/fz7CwsIQGBgIACgtLcXWrVtx5swZvP/++4ITEtHrYlFLUpeZmQlnZ2f4\n+fnpvYFD0pKTk4OVK1dCrVajVatWiIyMRG5uLnbt2oV+/fohOjpaWDaTvOMr14EGOTk5WLt2Ldq0\naYPBgwdjx44dcHNzwwcffAB7e3vR8YiIiPT6+eefoVKpcOzYMXTu3Bn+/v4YMGAArKysREcjA4iK\nikLv3r3h5+cHlUqF5ORkdO7cGdOnTxc+sc4kC9/o6GgEBwfDw8MDwPOm948fP0ZAQADS0tLQpUsX\nyfZ1LSgowIIFC1BSUoJRo0ZhypQpoiMRERG9Eq1Wi4yMDKhUKmRmZsLb2xsTJkxg1xaJCQ8Px6ZN\nm6BQKFBVVYXJkycjLi4OdnZ2oqNBITrA69A30GDu3Ln4zW9+g3nz5uHs2bOCExpGSkoKFixYgHfe\neQdz5szB0aNHsXbtWvaxJSIik6BQKODl5YXQ0FAMHDgQKSkp9Q53kunTarW6fb0WFhawsbFpFkUv\nYKJ7fOU40ODLL79Efn4+IiMj0bt3bwCAl5cXNm/ejMjISISHh2PgwIGCUxIREelXUlKCtLQ0pKam\noqSkBP7+/vjHP/6Btm3bio5GTayioqJWB57y8vJ6HXlEjWE3ycJXjgMNOnXqhE8++aTWfig7OzvM\nnTsX586dQ1xcHAtfIgm5e/cubt++jbfffpuFAZm8b775BlevXoWPjw+mTp1arxMTSct7771X6/W4\nceMEJanPJPf4ynGgQWOePXsmyQN9RHKwZcsWdO3aFf7+/gAAlUqFtWvXomXLlnj27Bk+/vhjeHl5\nCU5J9PrGjx8Pe3t73dNafdauXWvERGRI2dnZOHv2LMLCwuqtbdu2DQMGDBD25cck7/j26tULa9as\n0TvQwNvbG76+vgLTGcb+/fsRFBSke33hwgXd4T7geTukqVOniohGRG/o9OnTGDVqlO719u3bER4e\njhEjRiAlJQW7d+9m4UsmjT165WXv3r0NjqDu06cPEhMT8emnnxo51XMmebgNAGxsbNCtW7daRS8A\nODs7S3KK1549e2q9rjvaMTk52ZhxiKgJFRcX66YY3bx5E8XFxbp+5f7+/rh7967IeERvzN3dvdH/\nkXTk5eXB09NT71rfvn1x48YNIyf6L5O84ytHje1IMcEdK0T0/2xtbVFYWAgHBwdkZ2eje/fusLS0\nBABUVVUJTkf05l7l5kzd4VRkusrKylBVVaW3T7NGo0FZWZmAVM+x8DURNUc0v846ETVfgwYNwqpV\nq+Dj44OkpCSMHTtWt3bt2jW0a9dOYDqiN/fTTz/Vep2dnY1evXrVusbCVzpcXFyQkZEBHx+fZPBD\nLQAACaNJREFUemsZGRlwcXERkOo5Fr4morq6Gvn5+bo7u/peE5FpmjRpEvbu3YsLFy5g6NChGDZs\nmG4tLy8PQ4cOFZiO6M3V3eMbHh7Ofb8SNmrUKGzYsAFarRY+Pj5QKBTQarU4ffo0Nm3aJHT4lkl2\ndZCj8ePHN/qeHTt2GCEJERHRmwkPD8fmzZtFxyADSkpKws6dO1FZWQl7e3sUFRXB0tISoaGhGD16\ntLBcLHyJiATLzc2FhYUFOnfuDAAoKipCfHw8bt26BVdXV0yZMoXtCklSWPjKQ2lpKXJyclBSUgI7\nOzsolUrhsxZMtqsD/VdVVRUfGRGZsPj4eBQWFuper1u3Dvfu3UNgYCBu3bqFbdu2CUxHRPR6bG1t\n4enpCT8/P3h6egovegHu8ZWE6upqZGdni45BRK/pzp07cHNzAwA8ffoU6enpWLFiBZydndG/f39E\nRUVh2rRpglMSvb5Zs2bVel1aWlrvGgdYkDGw8CUiEkyj0egmWl29ehUODg66aZROTk54+vSpyHhE\nb2zu3LmiIxABYOFLRCRcp06dcPz4cfj6+iItLQ19+/bVranV6mbxeJDoTXBABTUXLHxNxMs6Nmg0\nGiMmIaKmFhYWhpiYGGzcuBEKhQJLlizRrR07dgw9e/YUmI7ozaWkpOD8+fP405/+VG/t22+/hbe3\nN/z9/QUkI7lh4WsiHj169NL1gIAAIyUhoqbWq1cvrFmzBvfu3UOHDh1qjWL39vaGr6+vwHREb+7I\nkSMN7lMfO3YsNm7cyMKXjIKFr4mYPXv2S9e1Wq2RkhCRIdjY2MDZ2Rk5OTkoKiqCvb09XF1ddXt9\niUzZ/fv30bVrV71rb7/9Nu7fv2/kRCRXLHxN3M2bN6FSqXD06FGsX79edBwiek0HDx7Ejh07UFlZ\niVatWqG4uLhZNHsnagparVbXy7WukpIS3rwho2Hha4KKiopw9OhRqFQq5OXloVevXvj9738vOhYR\nvaaUlBT8+9//xh/+8AcMHDhQN97zxIkT2Lx5M1q2bIkhQ4aIjkn02pRKJZKTkxEUFFRv7T//+Q+U\nSqWAVCRHLHxNRFVVFc6cOYOUlBRkZGSgffv2GDx4MB4+fIjIyEi0bt1adEQiek0HDx5EREQEPD09\nddcUCgV8fX1ha2uLf/7znyx8yaSFhIRg8eLFKCgowMCBA+Hg4IDCwkK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotwellsim(data,'RELPOS',title='RELPOS by depth')\n", + "plotwellsim(data,'RELPOS','RGT',title='RELPOS by RGT')\n", + "plotwellsim(data,'GR',title='GR by depth')\n", + "plotwellsim(data,'GR','RGT',title='GR by RGT')\n", + "plotwellsim(data,'Facies',title='Facies by depth')\n", + "plotwellsim(data,'Facies','RGT',title='Facies by RGT')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I also used the dynamic warping distance to create another feature estimating the location of each well, by inverting for this using a least squares inversion. The results, shown below, do not appear to be very consistent with the Formation 3 Depth plot shown above. This may be due to inaccuracies in the approximation that the wells are in a line, or in the approximation that the formations are planar. The X1D values that I find are almost monotonic with the order of wells in the provided data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3N1dmGmxMTAyMjY3Rs2dPnpI1X46OjoKfTaZKdEZBGi0+Ph579uzBvn37YGFh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\nZSxevBiOjo7YuXMn35EaZe7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax=plt.subplot(111)\n", + "plt.axis('off')\n", + "plt.ylim(-1,1)\n", + "plt.title('Well Position (X1D)')\n", + "for i,wellname in enumerate(get_wellnames(data)):\n", + " x=data.loc[data['Well Name']==wellname, 'X1D'].values[0]\n", + " plt.scatter(x=-x,y=0)\n", + " if i%2==0:\n", + " tva='top'\n", + " ty=-0.1\n", + " else:\n", + " tva='bottom'\n", + " ty=0.1\n", + " plt.text(-x,ty,wellname,rotation='vertical',ha='center',va=tva)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The similarity of wells logs at the same RGT means that I can estimate the facies using a nearest neighbour estimator. The results are shown below, with the true facies for the training wells also shown for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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I1/0c7qLwRSR8PVj6eFvQVxSAfvH1otaZ/TyBffzu1UURBoFtFL4Re+rXg2xH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\nKCgIEyZMwIIFC/DZZ5/xsig4d+4cUlNTMWvWLM0BLa2trfj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o0lN72I4AAEDojBW++Y8sMTWr6NFnBiXIzS+wHQGI1KRm7twoHGE7AADj6OML\nAAAAL7hz6Q4AiJwv/QAPR/99wA0UvkCIfH2LGQAAlQGFLxAiZ8Z/pB87AEkvdTvOdoRQxNOf25Vl\nlujHXhwK34hxe8wvyae5c8CM1YrzT7YdAUBEPlm/3XYE44Z2bm47AiJE4Rux22e/YztCaNIn9I95\nWl8L/nt3unGSGBbHtH99bEZkOUy7p/8U2xES3h82vGQ7QmhGKPY7GxlzsyNMYk56ai/bEQCrKHwj\ntn3pa7YjhCj2wpeThD98eb87/LYla57tCCHhmIbi+dJgReELAOXAg4x+4QLPH6403EjxNd74stzG\nCl9friQOx8EScJMzDzJKPMwYA1+HcQNcY6zw9eVKAgDgoFrcIAVcwJ4cMW6HwnW37OEw4hNf72JN\ncmTAlniHt+Ic5g9f1jVnrIhxO9QvPt4ODbbm2I4ARM7HfVty6BzG+atMvtyZp/AFAMTMl1YhAG6i\n8AUAxMyZFkCJVkAUy9fW/TsHtLUdwQgKXwAAyvB027q2I4SCV9iiJDmPLbEdITylXNRS+EZsUjN3\n/sQcMAFU/dmJtiNYcWn12rYjAAiBsarMnbfeSPG8+abPrN4R5jBsXGA7QcLzdbxqH/m6rv+47lvb\nEUJzoVrFPG3B5r0RJkEi8XXf9qWhzthSDlk11dSsDJgS85RjBpweYY7E5c6FTnxjNrvyVCxjVZdt\n9LgJtiOEJn1u7Me0Le/NjTCJabFv5+/O6hxhDnOGx9mA4WMR6MpxXIrvWN7bk4Y6Y4WvrwWgr9y5\n0Im9IJAcauGP4+T4hw0vRRjErBGK/WGnzFaZESZJXJk/fdN2BCt8PYe5coEXz8WdM8dxKa5j+T9T\nxkYYxKwHS/nOnXZtJBRfTxI+LrevBWCjEXVsR7CicXc3HvJCbHzcv308jkv+rOsqtgMAAAAAJlD4\nAgAAwAsUvgAAAPACfXwRCfoBAgCAREPhC4SIgh8AgMRF4QsA5ZC02qGeYu1tBwAAM4wVvrSEAQAA\nJCZf6jSHmiwAAACAklH4AgAAwAv08QUAxOzbD3fZjhCakwfFPq0vt4EB1xkrfDcu2mlqVpFLieNg\nCQAuKTgQ2I4AAOVGVwcAAAB4ga4OAFAOQfMC2xEAAHGixRcAAABeYBxfAACAH1CvuI0WXwAAAHiB\nPr4RS0q2ncAOV0bxiHcED1+XG/6gNcwv+3PzbUcAQkXhG7FGXTlJ+ISiwCMbkmwnCE/72CfloT6/\nPPbfbNvItU41AAAgAElEQVQRQjHNdgAkDApfRIICEK5LOuBQ4QsAnqDwBVAhXOQA7no+ZaztCKGg\nxRc/ovAFgHJo+UQH2xFCs44+3UAhV57VkHheozi8srgc2JAAwC9bPtljO0Ioasd5/mrQlRMe3EKL\nLwCUw6SkZ21HCNGUmKdMWu3QKJhxPNR3YCejGwAuoPAFgHIYnNHSdgQruHvnl4zBabYjAKFy6NId\nAAAAKBktvhHztXXEleWmRQiAz0aPm2A7QijS58benQduo/AFAADFGrJqqu0IIaHwxUEUvkCIlv57\nk+0IofgZLd0AJI0ZcLrtCDDElTu1Uul3a40Vvmev6GpqVpH7Oo5pH3z5o8hymDZtUuzTurLc8Syz\nJF114oPRBDFsWRzT+nKwPNz4izKjC2LYtDhWeO/Pe0cXxLB1cUzr6zHNx+X2dRv3ZbmNFb5X/u5m\nU7NKKK689UaK7803r57r0lBPsbu1dn3bEYz71UdNbEcIzfu2A1QCk3uOtB3BioUj3LnQiYcr57B4\nzl++buPTJ95pO4IRxgrf1k2OMTUrJIAa9d0phuJRsGq77QjGbajTwnYEGOTjNg6/sI27jeHMAAAA\n4AUebgMAAMXilcVwDYVvxHzpMwMAcM/Na/NsR4Ah+6Z/aDtCeB7uW+JXFL4R82VDgr+4uIMPbmvV\n1HYEACEwVvhSAAJuYt+GD65/81LbEUJxwS/fjGv631fPiCaIYSPEvo2DaPEFgHJgYH8AqHwofAEA\nADznSuu+VHoLP4UvAACA59oVtLYdwQgKXwAAAM9dnHeh7QhGUPgCAFCGyQcybEeAITWG+9Hy6SsK\nXwAAUKzjG95jO4Jx+2evsB0hPL0a2U6QcCh8AQAAPFflJ/VsRzCCwhcAABQrY3Ca7QgwpMYvWtiO\nYISxwndSM3dq7BG2AwAJ5A8bXrIdITQMco+SuHIO4/wF37mxJyewWqM72Y4Agzg5wnWubOMS2zng\nI3eOYAkqY2627QihSU/tZTsCElCDroNsRwAAICbGCt8tWfNMzcqA2AvAm9fmRZgjcbmzvin2y7Il\na77tCCFifZeFPp9+uWueG403NNyUzZeGOmOF75BVU03NyoApMU9JVwe/+Fjw99jwXIQ5TJtsOwAS\nVJ9ZvW1HCMe4IK7JC+Kb3Amct91mrPAdM+B0U7NKKKPHTbAdITTpc2Mv+N250Il9mSU/l7vhvm8i\nzIFE4+sxzddzmI8t/Dc//A/bEUKTPv2KmKf1ZV0bK3wbd69ralYJJbNVpu0IVvh6kvBxuX1cZp9d\n+/2jtiOEKL4LWx8tvNCNsV3Tl8XedH3JkjERJjEt9sLXlXUtlb6+ebgNABCz4Wek2o4Ag3y8sPV1\nG/dlXVP4AgAA/MDXO9S+qGI7AAAAAGAChS8AAAC8QOELAAAAL9DHFwAQM/o/Am46pkVN2xGMoPAF\ngHKgAATgklrHV7cdwQgKXwAVQgEIwCUbF+20HSE0KYNsJ0g8FL4AAKBYXNjCNRS+iAQHS7guaF5g\nOwIAIE6M6gAAAAAvlFn43njjjSZyAAAAAJEqs6vDd999ZyKHs7jlDwAAkBjKLHyTkpJM5IBjXHkq\nlidiAcAvNFi5rczCd//+/brzzjtLnWbChAmhBQKASmGv7QAwiWIIcEOZhW9ycrLOPPNME1ngEE4S\ncF5t2wEAAPEqs/CtWrWq+vTpYyAKAAAAEJ0yC98gCEzkAJxASzcAAImrzOHM0tPTTeQAAAAAIlVm\ni+8555yjFStWqHXr1pKkf/7znyoo+P83Fp177rmqU6dOdAkBAACAEJRZ+P7rX/9ScnJyYeH7wgsv\nqEuXLpKkzZs3Ky8vT8OHD482JQAkmKTVDr34sr3tAImPIRoBN5RZ+H7wwQcaP378//+DqlU1evRo\nSdKWLVs0ceJECl8AgNPovw+4ocwmi61bt6pBgwaFn88666zC/27QoIG2bNkSTTIAAAAgRGW2+ErS\nzp07Vbfuwavdiy66qMjPAcBHQfOCsicCACSUMlt8W7VqpQULFhT73YIFC9SyZcvQQwEAAABhK7PF\n94ILLtCECRO0bds2denSRcccc4y2b9+urKwsLViwQHfccYeJnKhkaA0DAACJpszCNyUlRbfeeque\neeYZvfLKKwqCQElJSWrRooXGjx+vU0891UROAAnKlafdJZ54BwDXxdTHt1WrVrrrrru0f/9+7dmz\nR0cddZRq1KihNWvWaOrUqRo7dmzUOQEAAIAKKbPw3b9/v1544QWtXr1ajRs31gUXXKDt27frqaee\n0tKlS9W7d++YZkSrEAAASHTUK24rs/CdOXOmvv76a7Vv317Z2dlas2aNNmzYoN69e+uqq64qHO0B\nxfN1B9o0a3d0QQxKiXNgf1fWdzzrmvFN4QMf9234xZVtXCp9Oy+z8F26dKnuv/9+1atXT/3799eo\nUaOUkZGh1NTUMDMCQKXi65vbfDk5AnBTmYVvTk6O6tWrJ+ngCytq1qxJ0QuUoP8nabYjhGKl7QAA\nEoIrFzrxXOT8dcGy6IIYdrftAAmozMI3Pz9fn3zySZGfHf65TZs2Zc6o9+ex9QWuDNbFMa2vy+2r\nWj0uth3BuJZPdLAdITTr4jg5ulIQSPEVBQ++/FF0QQybNin2aV1Z7niWWXLnHBbP+Wt++n8jy2Fa\nPIWvK+taKn19l1n41qtXT4888kjh5zp16hT5nJSUpBkzZpQZ4v4mA8ucxkUPXHqd7Qgw6Oa1ebYj\nGMc27pfnU9wZxWdaHNO6stzxLLPk57nbx+O45M+6LrPwfeihh0zkcFbuf762HSE8g0+2nQAJiG0c\nPmjQlQ7BgAtiGscXAACf+doKCLiGwheRYIgrwE20fPqlWq9mtiMAoaLwBYBy8PXijpZPv9zz9Ubb\nEUJxgVrYjoAEQeELoEJqje5kOwKAiHChA9dQ+AIAgGJNauZGmTDCdgAkDDe2aAAAELqMwW68lAf4\nkUPv3AQAAABKRuELAAAAL9DVIWI8+AMAAJAYKHwjljE323aE0KSn9rIdAQBgkCvnMM5f+BGFb8Ry\nd221HQEAUEG+jm7Aw21wjRt7cgLbueIt2xFCNNh2AACw4tbz29mOYMW+6R/ajhCOh/vaToAEYazw\nrX7eqaZmlVCGrJpqO0KIptgOkPB8bRUCXPe7//2F7QihuPDPb9qOAFhl7Cz9u79MMzWryA0dEHsB\nOGbA6REmQaI59tU7bEcIx7iFMU/qSh9AiX6Ascioutp2hNDEc4H30NUvRpYjkfl4Me/KMkvxLXd2\n/5Miy2FaacttbO1mtso0NauE0rh7XdsRYNDMrrttRzCuz6zetiOEZ1xgO0HC+3eth21HCNHlMU95\n29jrIsxhTvq//xbX9EP/dW5ESQwbtzfmSX09pu0f3yLCIIadX/JyGyt8KQDhAx+3c+5q+MXHbVyS\n5rdbYTuCFTfe3M12BON8Pab5stzutOcDsMLXQgiAm45pWct2BESIwhcAAOAHtY6rZjsCIsQriwEA\nAOAFCl8AAAB4gcIXAAAAXqCPLyIRHMWwUADcwUOccJ0v2ziFL6JxPIUv3BY0L7AdAQAQJ7o6AAAA\nwAsUvgAAAPAChS8AAAC8QOELAAAAL/BwGxCi75futh0hFCcNsp0AAIDwUfgCIcrdw5P+AAAkKro6\nAAAAwAsUvgAAAPACXR2AEPny5hsAACojWnwBAADgBQpfAAAAeIGuDgBQHquTJElJkoLDvirvz5J+\n+P/Dfxb171d7AYAXKHwBoDyaHywfDy82K/KzMH9XRX4/ALiKrg4AAADwAoUvAAAAvEDhCwAAAC9Q\n+AIAAMALFL4AAADwAqM6AACAYm1ctNN2hFCkDLKdAImCFl8AAAB4gcIXAAAAXqCrAyKRtNqRayre\naAUAgDMcqU4AAACA0tHiC4SIB0EAAEhctPgCAADAC7T4AgAA/MCVO3cSd++KQ+GLSLhy4OCgAUDi\nmAa4gsIXQIW4UhBI8RUFzoxcIjF6CQBvUPgCAGLm64UOADdQ+AJAOVAA+uXBlz+yHSEU0ybFN/3q\nzTuiCWJYShzTurLMUnzLnbMtL7IcptUu5Ttjhe+42xaYmlXkpsVxkvB1uX09Sfi43K4ssxT/+vaR\nr+v7+VN/H10Qg6bFOf0l234RSQ7T1sUx7diWMyPLYdrSOKZtN7dzZDlMW3dpyd8ZK3xPeWS9qVkl\nFF+X+/mUsbYjhCLek4Svy+0jXwvAhU2GRRckgU2/93bbEayYPvFO2xGMu3mtOy2f8fBlXRsrfI+f\n9ZmpWUWvR5OYJ/V1uRt09fPeqY/L7UqxL8VX8Pu63FtqnRhZjkS2b/qHtiOE4+G+cU3u43L/vnpG\ndDkMG6HYl9uZdS2Vur7p4wuEKGNwmu0IxvlY7PuM9Q2gMqPwBULkzBVzHK0jPhb7PmN9A6jMKHwB\nVIgzxb4U921gH7G+4brJBzJsR0CEKHwBoBymDr3KdgQAQJwofAGgHPIWOTRiy0WtbCcAACMofAEA\nKEOt0Z1sRwAQAgpfAADKkDE323aEUKSn9rIdAbCKwhdAhWRUW2M7QmhGxDHtpGbuHD7jWe4/bHgp\nshymxTPGqa+WDfyJ7QgwJLnt8bYjGOHOkRuAFXu/WWY7AhC5LVnzbEcISXwtvi9/7EZf9okDU2Ke\nNunYmhEmSVz3bt9mO0JoSnu/pLHC19fWEQAAUHkE23JsR0CE3KlGAQCR8/XNbb4ut48t3TzI6DZj\nhW/Od+70AwRKwgETcFOfWb1tRwjHuMB2goT328fOsx0hNOl/ftN2hIRjrPA997+/NjUrAy6KeUpf\nu3j42EogSaPHTYgoh1npc6fEPO2QVVMjTGJa7Mu97+2nIsxhWuzbuTv7thTPco8ZcHqEORKXjy3d\nD139ou0IVviybxurynw9aDjTSiDF1VKQ2SozwiCJy50iMPYC0Nd9+99tltiOYMW8pq/ajhCi2Lfz\nxt3rRpgDiWThhfVsRwhN+rLYz9vunL+k0vZtd5ojE5SvRYGvWN/+8LUQOuYoP594hz98PY77stwU\nvgAAoFgZg9NsRwBCVcV2AAAAAMAEYy2+vt4WBAAAQGIwVvhuXLTT1Kwil+LfQ64ADsMxzS+urG/W\nNUriSwMlfXwBAAA8N/fV22xHCM2IUi7wKHwBAAA89+E7Z9uOEJoRKijxOwpfAAAAz/323I62IxhB\n4QsAAIqV++562xHCkepH/1WUjcIXAAAUK+/DjbYjhOPyVNsJkCAYxxcAAABeoMUXAMrhgiXNbUcI\nzWLbAQDAEArfiD348ke2I4Rm2iTbCRKfK+ubdV22nhuftx0BBn24yo1b/im2A1QCrhzHJY7lxTFW\n+Pq6IS0ckRldkATW+/PetiOEYl2c089rd1ckOUybFse0rqxrKf71DX/cmDfcdoRQDLMdoBJ4PmWs\n7QihiedY7gtjha+vG1LG4LTIciSyBl39fD3QA7ddbzuCcdMn3mk7ghW+XtT62ojh63buI1/PX74w\nVvj6uiHtm/6h7QjhebhvzJP6WvA7s77jWNfOLLPENo4SObOdx7GN+4p922308QUAxMzXu3eA6/pt\nv9R2hNB8Wcp3FL4AgJj5evcOcN1dya1tRzCCcXwBAADgBVp8AQAxo/8jgMqMwhcAEDNnHvKSeNAr\nBrVGd7IdAQgVhS8AlAMFIACX+HKRQx9fAAAAeIEWXwAAAM89+fAdtiOEJn36n0v8jsIXQIX4cnsM\nfmM794ev3ZjeT14cYZDEQeELoEJ8PUnALxlzs21HCEV6aq+4pndm/45j3+Yix20UvgAAlGFL1jzb\nEUISX+H7++oZ0cQwbIRiL3zvfuTyCJOYlf7gczFP+9DVL0aYJHFQ+AIAAPzg9msftx0BEaLwBULE\nLTIAABIXhS8i4WO/MADuatB1kO0IAEJgrPDlNZeAm2jlBgBUFsYKX2daACVaAQHAM7eecJztCABC\nQFeHiFVperTtCACACvrdsmtsRwjFhXrTdgTAKgrfiE1M2mc7QmiG2w4AAABQARS+ACrElYH9pfgH\n9wfgHo5pbqPwBVAhPLgKwCU3r82zHQERovCNGEUBAABAYqDwBQAA+IErr2mW4ntVsy8ofCPGMG4A\nAACJoYrtAAAAAIAJFL4AAADwAl0dAAAx4xXVACozCl8AQMwY4xSum3wgw3YERIjCF0CFjB43wXaE\n0KTPnWI7QsLbkjXPdoQQUfjiSNzVcBuFb8QmNXPnTzzCdoBKwJXWMFrCgKIaBPVtRwAQAneqsgRF\n6wgAlzToOsh2BCuqNBprOwIMYRhStxkrfKud1dzUrBKKrycJAG7ida6Am/768O22I4Qmffq0Er8z\nVvjes3KdqVlF7gKdEvO0vLIYAIDKw9c+vsPy/WioM1b4+tpKEOw5YDsCAISG5xb84uMIB648qyHF\n97yGL69qNnYE8/VgmfP4x5HlMK4TfYUA3/22709tR7DC17t3vrZ+wl3GqtE+s3qbmlX0xgW2EwAJ\no/ZP2tqOYEW13ifZjmBFnSc/sR0hPF1jv5h3pRWQEVvK5utFzrSfPW47ghHuNMMCsKJWo9j7vLvk\nhmcfsR0hNBcMY/zisrgzQg+Fb1lcuciR4rvQuf7dyyNMYtYFw94s8TsK34j5epvI1+X28eRYkJcr\nJUkKdPD/dch/H/r/KuZniTZ9HKoW7I/vHzjC1317yKqptiOEhIucsrhzHJe40DmSscJ3zIDTTc0q\nofh65ejKcnNbsGzbPnrZdoQQnRXzlIO+fijCHKbNiHlKV/Ztif0bOJQvDzLS4hsxX68c3Vnu+E6M\nPrYKubPMEq1hZXNn35bi2b+7tzoxwhxIJL4e03wZhMCdpUxQvu5A7ix3fIWQj3c2fFxmyd/ldmff\nluLZvzulNIowR+IaPW6C7QihSJ8b+7r2dd/2ZRACCt+I+boD+brcgOvYt/2S2SrTdgQY4su+XcV2\nAAAAAMAECl8AAAB4gcIXAAAAXqDwBQAAgBcofAEAAOAFCl8AAAB4gcIXAAAAXqDwBQAAgBd4gQUA\nAIDnGnevazuCERS+AFAOvpwkAMAlFL4AUA4bF+20HSE0KYNsJwAAMyh8I0arEOCmE06vYzsCDGJ9\nA26g8AWAckiuwbPBPvF1ffvYeFPv1Jq2IyBCFL4AgJj5WAjBL7VPqG47AiLk5yUsAAAAvEOLLwAg\nZjzUB7jJl32bwhdAhXDrGwBQWVD4AiGiCAQAIHHRxxcAAABeoMUXABAz7moAqMxo8QUAAIAXaPEF\nAADwXK3r59uOYASFL4AK8WUIHABA5Wes8KVfGOAm9m0AQGVBH18AAAB4ga4OQIhcue3PLX8AgIto\n8QUAAIAXKHwBAADgBWNdHVy5BSxxGxgAAKAyoo9vxHJ359uOAAAAAFH4Ru77j/fYjhCaeiNtJwAA\nIFqbsty5Q30Kd6iPQOELAADwg4AbtU6j8AVQIXs2HrAdITS1bQcAAESKwhdAhez8Osd2hNAcbztA\nJcCDyn5xZX2zrvEjCl8gRDNf/9h2hFDcazsAElbvz3vbjhCadXFM62sB6Mr6jmddP/jyR5HlMG3a\nJNsJEo+xwtfXDYnlrtziPWi8NOS1aIIYFk/h68q6luJb3+NuWxBdEMOmxVEMXXnt6OiCJLCBeTfY\njhCKz+Kc3sf1vXBEpu0IiJCxwvf5lLGmZhW5aXFM6+ty+3rguHltnu0Ixvm6jfu63C3nfRVZDuP6\nNY950jv2NY4uRwJzZn3Hsa59PI77hK4OEWvQ1c+ORRmD02xHACLl674NAJUZhW/EKAD9Umt0J9sR\nYAj7NgBUPhS+Eds3/UPbEcLzcN+YJz3wxurocpiU2s52AiQoZ7Zxie0cgDcofBGJ/OXf245gRcbc\nbNsRQpGe2st2hITn6zYOAJUZhS8AAGWgGxPgBgpfAADKwN0cwA0UvgAAAD+gdd9txgpfX4f+WXtB\nC9sRgEjd32Sg7QgAIvL76hm2I4RihGJ/OBtuM1b4+jog9Kz3vrYdITQ3n9ks9omTk6ILAgBARHwd\njckXdHVAJP6w9kXbEUIxQmfajgAA1lRZ3dl2BCBUxgrfql2bmJpVQtmSNc92hBDxUATguz9seMl2\nhNBw+7ts0yfeaTsCECpjhe/YF/7H1Kwid+ElU2Kelv6PfnHnQif2i5xJzdy5cTTCdgAgwYweN8F2\nhFCkz439vA23uXPGAgAAoarf5Re2IxjHqA5uY1SHiPnaGsbtMX+408ot0dINFHXH6af88F9JkoJD\nvinus2KYxua/iY0rrdwSLd3FYVSHiGUMTrMdAUAEfD2mwS8Fm/fajgCEyp0mCyAB+Hhnw8dl9tmQ\nVVNtRwgRrWFl+eO6b21HCMWFahXztO36/SrCJLDNWOH7wMnVTc0qctwWBADATb/p09J2BETIWOG7\n8Z0XTM0qejf1sJ0ASBjjqtaxHQEGPZ8y1naE0EyzHQCAcXR1QCQy5mbbjhCK9FTGLi7LxLzdtiOE\nZrjtAJUAXVv8sm/TV7YjhCT2Y3nWqu8izGFWempd2xESDoVvxFwpACWKQBTP11EdfMX69sveb5bZ\njmDcv7I+tR0hNPedl2I7QsKh8AVQIbQAAu7ycf/esWyB7QghOs92gIRD4QugQmgBBABUFhS+ACrE\nxxYhAEDlROEbMVrDAAAAEgOFLwCUwx82vGQ7QmhGqK/tCEDC4C6W2yh8AVTI7q/cGbmEuxpAUe7s\n3+zbOIjCF0CF7P/uG9sRAETEx/2bLopuo/AFAADFSqpa3XYE4+jq4DYKXwAVwkkCcFf90/vbjgCE\nisI3YhQFAAAAiYHCFwAAFMud/q6x93V1Z5kl+vgeicIXABCz5IJc2xEAoNwofAEAMTuq7Vm2IwBA\nuVH4AqiQjMFptiPAoGp1jrUdwYo+s3rbjhCOcYHtBIBVFL4AKuS3j51nO0Jo0v/8pu0ICe/mtXm2\nI1ixcESm7QgAQkDhi0gEAa0KANzBnQ3ADcYK3yGrppqalQFTYp7S16dDt74/P8IcJsV3e7P1CxdF\nlMOwcWtsJ0h4vh7TfOXKnQ3uapSNfdtttPhGjB3ILyfsW2s7AgAAKAGFLxCi51PG2o4Qimm2AwAJ\n5viG99iOACAExgpfVwoCiaIAJeNNfXBd1lvuDGc2QjyLAPiGFl8gRO706Y69P/fkAxnRxQASBMOZ\nAW6g8AVC5E6f7tj7cy8d8JMIc5g1wnYAJCxX7lpyxxK+o/AFUCGvLFtvO0JoJp2XYjsCEpSPF7WA\niyh8gRD5OMi9O907pHi6eAAAKp8qtgMAAAAAJlD4AgAAwAt0dQBQIe70fZTi6f/oysNOEg88AfAH\nhS8AlIOvBT8AVGYUvgAAAD/gbo7bKHwBoBx8HMHDZ64UQxRC8B2FLwAAZeB15P6okbfHdgREyFjh\n+/M1fzU1KwPoD4fiuTOmLePZAvDTed88ZjtCiB61HSDhGCt86+ZuNTUrJAB3HvyJ7yInsxW3vwEA\nSFR0dQCAcrh5bZ7tCACAOPECCwAAAHjBWIuvr09A+7rcAAAAiYauDojEsvo9bEcAIjWpmTuHzxG2\nAwCAIcaO3IPXfW5qVgbE/sS7O0/5S/Es95WnTYgwB2Cfr/s2AFRmxgrf/6n2lKlZRW6CfhPztCsv\nWxJhEsA+Vwb2l+Ic3D8oiCoGACAi7tyrAxLA+VXG244QihVxTOvrwP4rL19qO4IVN7+63nYEKzIG\np9mOACAEFL5AiG7bcYztCAAi0LXebtsRQlLXdgDAKgrfiM199TbbEUIzws+GPaBYGxfttB0hNCns\n2wA8wTi+AAAA8AItvojEvI+usB0hFCP0te0ISFBZu+63HSE0KbYDAIAhFL6IxAMvvGM7AgxhWC/A\nXe8/fKPtCECojBW+kw9kmJoVAIM4MQIAKgtafIEQ9bntp7YjAACAElD4AkA5cJEDAJUPhS8AAMAP\nfH1Jiy+MFb67q20xNSsAAADgCMYK3/NuPsPUrAAAAIAj8AILAAAAeCEpCILAdggAAAAgarT4AgAA\nwAsUvgAAAPAChS8AAAC8QOELAAAAL1D4AgAAwAsUvpXQgQMHtG3bNh04cMB2FETk22+/PeJ/33//\nvQoKCmxHs6agoECLFy+2HSMS7777bpHPGzZsKPL5pZdeMhkHCN327dtL/f6rr74ylAS+YzizSuST\nTz7RM888o6+//lpBECgpKUknn3yyRowYobZt29qOhxANGzas2J8nJyerW7duuvLKK1W7dm3Dqez4\n5ptvlJmZqbfeeksFBQWaOXOm7Uihu+SSS/Tkk08Wfr7sssv0xBNPlPg9UNkcvg2PGTNGDz74YInf\nu+Ltt99Wjx49bMfAIYy9uS1Mzz33XJnTDB061EASc1atWqWJEyfqrLPO0siRI1W/fn1t3bpVWVlZ\nmjRpkjIyMnTqqafajhmJxx9/XJdffnnh5zfeeEN9+/Yt/Dx58mT9/ve/txEtMnPmzDniZ/n5+fr2\n2281e/ZsPf3007rqqqssJDNjx44deuutt/Tmm2/qm2++UVJSki677DKdeeaZtqNFoqz2B9on3DJr\n1ixdeOGFqlq1Up6Cy+XwbXjXrl2lfu+K//mf//G68F28eLE+++wz7d69W3Xq1FFqaqo6dOhgNVOl\n3Os2btxY4nfZ2dnavXu3c4Xv/PnzNWjQIF144YWFP2vSpInatGmjunXrav78+Ro7dqzFhNHJzMws\nUvg+9dRTRQrfZcuW2YhlXHJyspo0aaKrrrrKuUL/R++++64yMzO1dOlSnXjiierRo4duuukm3Xrr\nrerWrZuqV69uO2IkkpKSKvR9ZfXb3/621GVLSkrS9OnTDSYy48svv9RNN92kUaNGqUWLFrbjGOHr\nNu5qQV+WvLw8TZw4UStXrtQpp5yiY489VuvXr9e///1vtWjRQuPHj7d24VcpC9/Ro0cf8bOPPvpI\nc+bMUd26dXXllVdaSBWtlStX6pJLLin2u7POOkvjx483nMgcXw8cJalVq5b2799vO0Yk/vznP6tO\nnZ8eBvsAACAASURBVDq64YYb1KVLF9txjAqCoMi2fvhnF11zzTXF/vyrr77S/PnzVaWKm4+h3HHH\nHXrjjTd03333qXfv3ho+fLizF3W+Kygo0CeffFLqNG3atDGUxpwXX3xRu3bt0gMPPKDjjjuu8Off\nf/+9/vSnP+nFF1/U4MGDrWSrlIXvoT755BPNnj1bO3bs0NChQ9WzZ08nD5Z79+5V/fr1i/2ufv36\n2rt3r+FE5rjaElBeixYtUrNmzWzHiMS1116rzMxMTZ06VSkpKerRo4e6d+/u/DaQk5Oj4cOHF/nZ\n4Z9ddPizCevWrdOcOXO0fPlynX/++erfv7+lZNHr27evTj/9dE2fPl3XX3+9TjjhhCLfT5gwwVKy\naOzfv1933nln4eecnJzCz0EQOPuwdm5urh599NESL2KTkpI0Y8YMw6mil5WVpUsvvbRI0StJxx13\nXGF/bgrfOK1cuVLPPvusNm7cqF/+8pfq27evV/2lDudyYZCfn1/kivnwK2gXRzqYPn36Ees0Ly9P\n3333nTZs2KBx48ZZShatPn36qE+fPvruu++UmZmpV155RX/7298kSUuWLFGvXr2cvLB18cQXj82b\nN2vOnDlavHix+vXrp2uvvdaLhzezsrL01VdfqW/fvmratKntOJE6vHX/8P76h3Zfc0nNmjW93L83\nbtxY4nNHp556qjZt2mQ40f+rlJXifffdpy+++EKDBg3SzTffXHiL6NACyLWTY05Ojq699toSv3f1\n1rck1atXT4888kjh5zp16hT5XLduXRuxItWoUaMjfpacnKyOHTsqLS3NyWU+1PHHH6+hQ4dq6NCh\n+uyzz5SZmaknn3xSzz77rB577DHb8UJ3/PHHl/hdfn6+HnnkEV133XUGE5mxdetWPffcc3rnnXd0\n1llnadq0ac5v25K0adMmPfLII8rJydEdd9yh5s2b244UuT59+pT4XUFBgRYuXGgsC6IXBEGJ3Xds\nd+uplMOZlTTU06GKeyq+MluxYkWZ07Ru3dpAEsCO3NxcffDBB+revbvtKEbl5ubqoosucu6YJkkj\nR45UzZo11b9//xK7crnYEnjppZfqF7/4hQYNGqTk5GTbcaxzeRu/+OKLC+9a+WTkyJG68sorS+zi\n8fjjj+vpp582nOqgStni6+NtA4ra4uXl5Wn06NFFWoDhpmrVqnlX9LquRYsWSkpK0vLly0ucxsXC\n95577nG+awMOKq3ozcvL02uvvaZzzz3XYCIzWrRooTfffLPU722plIVvabcF4ZcgCLR161bbMQCU\nQ0ZGhu0IVjRt2lRBEGjHjh2qV6+ekpKSlJ2drcWLF+ukk07S2WefbTsiQrRs2TKtXr1ajRo1UufO\nnZWfn69XX31V8+bNU506dZwsfBN5366UhW8st0Ni6Q4BAIngjTfeKPG7/Px8g0kSw+7du/X2228r\nMzNTEydOtB0ndCtWrNCUKVO0e/dunXDCCRo2bJieeuoptWrVSllZWfr++++dG9Xj22+/LfG73Nxc\ng0nMmjt3rp5//nk1a9ZMa9euVb9+/bR8+XJVq1ZNV199tTp27Gg7onE7duzQ/Pnz9etf/9rK/Ctl\n4btlyxbbEQBEYO3atc4O1Vaat956q9TvfejqlJ+fr8WLFyszM1NLlixR/fr19fOf/9x2rEg89dRT\nGjlypHr06KGFCxfq0Ucf1X333aemTZtq/fr1uvfee50rfMeMGWM7ghWvvfaaJkyYoFNOOUUrV67U\n7bffrosvvlgDBw60HS1SQRBowYIFhS3d55xzjvbv369//OMfev31160e0ypl4Ttq1KhSvy/tytJV\na9as0UknnWQ7RiSKG9rrRy4OZSYdfNr9s88+K+zT+pe//EV5eXmF3w8fPrzEh4Eqs9tuu03nn3++\nfvnLXzo3MktprrvuOjVo0MB2DCu++uorLVy4UO+8844KCgrUpUsXVatWTffcc4/q1atnO14kNmzY\nUNh3+eyzz9bf/va3wj6/J5544hGv83WBiw+uxWLXrl065ZRTJEktW7ZUtWrVNGDAAMupovfUU09p\n0aJFhXcxvvzyS33xxRdq0aKF/vjHP1qtVypl4Vua3NxcjRkzxsmdbO/evdq0aZOOO+64wiF/Vq9e\nreeee05LlizRM888YzlhNIob2utQrr2eWpLmzZunhg0bFn5+++23Cw+W69ev17x583TZZZfZiheZ\niRMn6i9/+YuysrI0atQonXzyybYjGTF27Fg9+eSTtmMYd+ONN+rbb79Vhw4ddNVVV6ljx46qVq2a\nlixZYjuaMVWqVFG1atWK/Mzlcdl9dOhbGH9c1y4PvyodfP38hAkT1LBhQ61fv15jx47VDTfcoG7d\nutmO5l7h66rFixfrz3/+s/bv36+qVatq9OjRWrFihd566y2dddZZTr7P/kfnn3++atasWeL3q1at\nMpjGjOzsbN19992Fn5OTkwtvfe7cubPIG5Bc0qRJE2VkZOi///2v/vjHP6pXr15HPP3u4lP+lXBU\nyVDs379fVapUUfXq1VWjRg1vXkKUm5tbpHHmwIEDRT4fenfHJR9++KHWrVunli1bqlWrVpoxY4YW\nL16spk2basyYMUUu9l0Ry1sZXW2o+3F9nnjiiapevXpCFL0ShW+lMXv2bF188cXq1auX3njjDT30\n0EOFr7usU6eO7XiRuvfee3XbbbcVO+j1559/rvvuu09PPPGEhWTR2bFjR5GB/A99WLNu3brOj2TR\nuXNnvffee8rKytLXX39d5DsXC9+kpKQirULFcbFVaMaMGVqxYoUyMzP1wAMPqHr16vrZz36m3Nxc\np1s9zzjjjCLPqhT32TV///vftWDBArVs2VKvvPKKWrRooWrVqun666/XO++8oyeeeEK33HKL7Zih\n83H4VengxfzmzZsLj2nJyclFPkuydqFD4VtJbN68uXCIm3POOUdPPvmkrr32WtWoUcNysujVrVtX\nkyZN0i233FLkluDy5ct1//336+KLL7aYLhpVq1bV1q1bC/vxHjrczdatW51uGXv99dc1a9Ys9enT\np8ibGV1WXKvQ4VxsFZIOPrjXunVrXXHFFXrvvff05ptvat++fcrIyFC/fv3Ur18/2xFD99vf/tZ2\nBOMWLFigu+66S8cff7w2btyo3/3ud/rrX/+qWrVqqXXr1s7+TUobfnX37t165513nNzG9+/fr9Gj\nRxf52eGfbR3TKuXZs7RX97rq0KukKlWqqGbNml4UvZL0u9/9TpMnT9bkyZN10003qWrVqlq6dKmm\nTp2qyy+/XL1797YdMXRt2rTRSy+9VOxwLy+++KLatGljIVX07r77bm3fvl3jxo0r8T3vLqpevbqm\nTp1qO4ZV1atXV69evdSrVy9t3bpVmZmZeuWVV5wsCiZOnKjU1FS1bt1aKSkpXry9be/evYVFYOPG\njVWzZk3VqlVLklSzZk1nu3ccrqCgQIsXL9bChQu1ZMkSNWrUyMltPJEv1Ctl4Xv4VYMP9u/fX6Rf\nZ05OzhH9PCdMmGA6lhFVq1bV73//e913332aOnWq+vTpoxkzZujqq6928pagdLAP2Pjx47Vx40Z1\n7dpVxxxzjLZt26b3339fn376qe69917bESPRokULDR061OkW7eJUqVKFF/Mcon79+jr//POVnZ1t\nO0okWrVqpeXLl+uFF15QQUGBWrRoodTUVKWmpqply5Ze3OVwsetOab766itlZmZq0aJFOnDggHJz\nczV27Fh16tTJdrTIbdy4Ubt27VLdunXLfFjdhKSgkj5V8eMIB40bNy68anTZwoULy5ymT58+keew\n6cCBA7r33nv1xRdf6Prrr1eXLl1sR4rUpk2b9I9//EPLli3Trl27VKdOHbVt21YXXHCBGjdubDte\nJH4c7uZHBw4cKFIEvP/++06u94svvrjUV5v6KDc3VxdddFFCtxxVVEFBgb7++mt99tln+vTTT/X5\n559r7969OuWUU4o83OqCYcOGFRmC8dCuXJK0bds2zZ4920a0SM2fP1+ZmZnatGmT2rVrpx49eqhT\np04aPXq0/vSnPzk7ZJ8kZWVl6W9/+5u+//77wp8dd9xx+vWvf231QbdK2ayyePFiPfDAAzpw4IBq\n1qz5f+3da1hU5fo/8C/DQUROAimCZ2UEDBQSD0CQaVJiZGxQU9Mo0RTdFmqmO8jQtmKZFZ7FBION\noGIZ2lVeEQtFxQMIKiAqkpgHREQYQWBm+L/gz8TAIP6UmYdZ6/5cVy/WrHnxVYy551n3cz9YtmwZ\nbx/9NuF7UfskzVtbmk742b17t9KGtq1bt2o8l7pZW1sL7unGmjVrlMZ6zZs3T+nnvHnzZl4WvkJq\n6yD/EIlEGDRoEHr16gVra2tYW1uD4ziUlJSwjtbh+DqJpj3x8fEwNjZGSEgIxowZw+tNm81lZWVh\ny5Yt8Pf3x5gxY9C9e3c8ePAAJ06cwLZt26Cvr4+XXnqJSTatLHwTExMxY8YMjB07Fn/88Qf27t2L\nNWvWsI6lVj/88APef/99xXVqaqrS7vavv/4aS5cuZRFN7YRW/AlZew+gtPQBVbv4OJKPtK2yshJ5\neXnIy8tDfn4+qqqqIBaLYW9vjxUrVqB///6sI3Y4IZw+qEp4eDg4jsP27dsRGxsLDw8PeHp68r4A\nPnDgAObOnavUjtijRw9MnjwZVlZWOHDgABW+/xd3795V7HL38fFBcnIy40Tqx3GcUuH7448/KhW+\nFy5cYBFLI4T6C1OI2vsw4PuHhdA8qY1BJpNpMIlmBQcHw9bWFhMnTsTEiRM7Rd+juqWmprb7Hj6O\nKhw6dCiGDh2KDz74AJmZmeA4DkeOHEFDQwOOHj0KHx8fmJiYsI7Z4UpKStp8Ojdq1Cjs2LFDw4n+\noZWFb/NVH11dXV7/gmzC15Wup7F///5238PH09uIcNTV1bU773PhwoUaSqM5zWfXqsLHiS1AY79r\nfn4+9u7di969e8Pe3h4ODg4YMmTIEw/r0WbHjh1r9z18LHybdOnSRTG1pKysDOnp6UhPT8dPP/2E\nuLg41vE6nL6+PmpqalqdSggAjx49YrqBWSsLXyFOOBDyStft27fbvHf+/HlIJBIqfHni8ePHSj3d\n1dXVSte1tbUsYqmdjo4OL0+tas+CBQtYR2DC398fQOPmtuLiYuTn5+Po0aPYsmULunfvDnt7e7z3\n3ntsQ3Ywofb4qmJlZQV/f3/4+/vjypUrrOOoxbBhw/C///0PH374Yat7CQkJGDZsGINUjbSy8G35\nFzl27FhGSTRHJpPh4sWLimu5XN7qmq9U9fieO3cOiYmJMDU1xZw5cxikUi+h9nQL9cNRX18fgYGB\nrGMwc+nSJeTm5qKqqgomJiZwcnLi/YZloHFz28CBAxUb25o2t/3666+8K3xVkUqluHHjBnr27Ilu\n3bqxjqMWqtp5dHV18cILL8DFxUVpig2fzJw5E2FhYVi6dClGjRql2Nx2+vRpVFdXIyIiglk2rSx8\nhTjhwMzMTGlygbGxsdJ18+Nt+ezixYvYu3cvHj58iICAALz88su8nAcp1J7u9vq523s0rq2E2sok\nlUrxzTffICcnB3Z2djA3N8etW7eQkpICZ2dnLFmyhJcznZs2t+Xn5yM/Px8lJSWwsLCAg4MDpk6d\nyst9DdXV1di3bx9u3rwJsViM8ePHIzw8HKWlpTAwMMCyZcvg7OzMOmaHU/U7SyqVIicnBzExMVix\nYgXEYjGDZOplYWGByMhIpKSk4Pz584ovtS+99BImTZoEY2NjZtm08jcKx3HtvodvvWGbN29mHYGp\nwsJCJCQk4Pbt2/D398err77Kyw/EJkIthKZNm4aAgIA2W1dCQ0OVxp3xxcsvv/zE+48ePeLlilhS\nUhIqKirw/fffw9LSUvF6WVkZvvnmGyQlJWH69OkME6pHcHAwrK2t4eDgAF9fXzg6OvL+AJPo6GhI\nJBK4ubnhzJkzOHHiBN544w2MGzcOf/75J/bu3cvLwvdJ7TzHjx9HXFwc09VPdTI2Nsa0adPaPY5d\n07SyctiyZQusra1hbm6uskDQ0dHhXeHb3K1btyCRSGBsbAwbGxvWcdRu3bp1uHLlCt566y0sX75c\ncaBB8/YOvq36CrWnW1dXFxkZGbh8+TIWL17calWAr18IgoODW73WdLQpx3HIyspCfHw8g2TqlZGR\ngRUrVigVvUBjD+T8+fOxdu1aXha+27dvh7m5OesYGpWbm4tNmzbB0NAQ7u7uCA4Oxuuvvw6RSIQJ\nEybw8vCK9owZMwY//PAD6xhq8cknn2D9+vWK65SUFEyaNIlhon9oZeH7xhtv4NSpUzA0NIS3tzfc\n3NxU7hzkG47jEBcXh8rKSsVrZmZmmD59Oq/bP7KzswE0DgJv68Ofb6c7CbWnW09PD2vXrsW2bduw\nfPlyfPzxx0qHOwjhC8H169fBcRwyMjJQWVkJDw8P3m3WbVJZWdnml3dbW1tUVVVpOJFmpKenw8/P\nT3Gdm5urtNoZGxuL2bNns4imNvX19YqJFcbGxjA0NFQsWIhEIt5+qX2Smpoa3h5PfefOHaXrAwcO\nUOH7PN577z3MmjUL58+fB8dxiImJgaurK1555RXY29uzjqcWubm52LVrFwIDAxWN4uXl5cjMzMTu\n3bthYWHBy8dEANod88RHQu7pNjQ0xEcffYQjR45g9erVmD59Onx8fFjHUquKigocO3YMaWlpuHXr\nFpycnDBz5kzs2bMHs2fP5u2xphYWFigqKlJ5ct21a9fQvXt3BqnU78CBA0qF78aNG5VOKExNTeVd\n4dvQ0IDS0lJFgavqmo9ULVLIZDLcu3cPCQkJcHFxYZBK/TrzIoVWFr5A4zdEV1dXuLq6orq6GsnJ\nyVi1ahU+++wzXu4G/vXXXzFt2jRMnDhR8VrPnj3h5+cHAwMDHDlyhLeF75N63yQSCTIyMnhXGLXX\n011XV6ehJOxMnDgRgwcPxsaNG3H58mXMmzePtx+O8+fPh5GREQICAuDu7q4odPnY3tDcuHHjEBUV\nhcWLF2PgwIGK169du4ZNmzZh/PjxDNOpjxBPKKytrW01oUcIp3K+8847Kl/X09PDqFGj8O6772o4\nkeY0NDQo/Vtuec2qRVFrC1+gcZdoRkYGOI5DZWUl/vWvf/HyqEeg8YNA1Tw8oLFP6MCBAxpOxE5T\n72NaWhqys7NhbW3Nu8L3Sf1QdXV1iIyMRFhYmIZTaZ5YLEZkZCSioqKwYsUK3h5W4+npidOnT+OX\nX37BgwcP4Onpib59+7KOpXZ+fn4oKyvDypUrYWlpqRh5VFZWhtdeew1vvvkm64hqIcQTCvnWjva0\nVD2x1NXVhbm5Oe/2pjT3+PHjVpvaWl6z+jehlYXv2bNnkZ6ejoKCAowYMQIzZ87kbYtDk9ra2jYf\nd5qZmfF2sH9zRUVF4DgOJ06cQF1dHerr6xEaGooRI0awjtbhfvnlFxgYGGDChAlKr9fU1GDt2rW8\n7QuzsrJq9ZqpqSlWrlyJpKQk3h5PHhISgjlz5uDUqVNIT0/Hzz//jN69e6OmpgZVVVW8bXUAgPff\nfx8TJ07EhQsXFCOPXnzxRfTq1Yt1NLUR6mN/IVq3bh02bNjAOobGdeYWRZ0GLfw/bOrUqbCxsYGr\nq2ubBcDUqVM1nEq9Zs+ejZiYmDZ/IQYFBfFyzBMAHDp0CBzH4c6dO3B2doanpydGjBiBRYsW4auv\nvuJlUVBSUoKIiAhMnz5dcUBLdXU11qxZAxMTEyxdulQQGzqFqvmRpnfv3oWbmxtCQ0NZx+pw7W3a\n09HRQXh4uIbSaM7TfD4JdYWUb2bNmoU9e/awjqFxV69excCBAzvlqrZWrvh6eXlBR0eHtzt+VVH1\n2EAo4uPjYWxsjJCQEIwZM4aXjwFb6tOnD1auXIk1a9ZAX18fw4cPx+rVq2FpaYnQ0FDezjAW4oxu\nVZofaVpYWPhUfy/aqK35xeXl5fj11195+ySLilrhEMLnlSpffPEFdHR0IBaL4eDgAEdHR9jZ2XWK\nzy6tXPEVonv37rX7Hr4OQL906RI4jkNmZiYMDQ3h4eEBT09PREZGYv369bxc8W1y9epV/Pe//0W3\nbt0wcOBALF68uFN+g+4oU6dObXdGNx9He5WVleHChQsqj19PS0uDk5NTq1m3fFRVVYWDBw/ijz/+\ngLu7OwICAnj55xbqSrcQTZs2DUOGDHnie/j4O00mk+HatWsoKChAXl4eLl++jPr6egwaNEhRCLPa\nkK+Vhe/TzDDlc3EgVLW1tcjMzATHcbh06RIaGhoQGBgIHx8fmJiYsI7XoZqvCF29ehWFhYWKYe9N\n+NbOAwAxMTE4deoU+vXrJ6gZ3du2bcPAgQNb9XQDwNGjR1FUVIR58+YxSKYZ1dXVOHToEH777Te4\nuroiMDAQ1tbWrGOpTWpqqsrXm690x8XFaTgVUYcZM2aoPKCmOT7P4W/S0NCAGzduICsrC0eOHEFl\nZSWzJx9aWfgKsT9qy5YtT7yvo6OD+fPnaygNe817IMvKynj3IdHezxt48lGY2kwulytmdOfn5/N+\nRjcALFy4EF999RW6du3a6t7jx4+xZMkSXh5bXldXh8OHDyMlJQWOjo6YMmUK+vTpwzqWxglhpTsk\nJOSJj/11dHQQFRWlwUSaMXv2bN7uv3kaEokE+fn5yM/PR15eHu7fv4/BgwfDwcFBaZa1JrFvtngG\nnXm3oLpYWFiofL2urg4cx0EikQiq8G3eA3nlyhXWcTocX4vapyG0Gd1A4wlmXbp0UXnPwMCAt/sZ\nQkJCIJfL4efnh0GDBuHhw4d4+PCh0nv4+jMHWq90R0ZG8nalu61xnEVFRTh06BBvn9Jq4dpih4iO\njkZBQQFqa2shFothb2+PsWPHdoovtlpZ+LbXy3rjxg0NJdGclhvbZDIZjh49ioMHD2LAgAG83vjW\n/KheoVD1Z9bV1cULL7ygcuQX3whpRjcAdO/eHcXFxUqHODQpLi6Gubk5g1Tq1zSV5/fff1d5X0dH\nh5cLHS1XuiMiIjpFQaBOTk5OStc3b95EYmIiLl26hDfffBNvvPEGo2Tq5efnhxs3bijmcj98+BCx\nsbEoKSmBnZ0dZs2apTjKmU84joOVlRW8vb3h4OAAOzu7TjOGUytbHYDGD8Y7d+7AyspKcXxrcXEx\n9u/fj+zsbN6eeCSXy5GWloYDBw7A0tIS06ZNg6OjI+tYahUSEqJ0ff/+faXHgHz8cGz5ZwYav+w8\nfPgQgwcPxscff9zmUwBt1nJGt5eXF69bHJokJSUhKysLn3zyidLPtby8HF9//TVcXFwQGBjIMCHp\nSMHBwUor3arwdaW7tLQUiYmJyMrKgo+PD/z8/GBkZMQ6ltqEh4cjICBAsZFr/fr1ePDgAby9vZGR\nkYF+/fphzpw5jFN2vJab265du4aePXvC3t4eDg4OGDJkCIyNjZlk08rCNysrC99++y1qa2uhp6eH\nRYsWIS8vD8eOHcO4ceMwceJEXhYFGRkZSEpKgpGREaZMmcLbM77bExQUpHSuvZDU1tYiPj4eFRUV\nvJzrKsQZ3QAglUqxYcMGXLx4EYMHD4a5uTkqKipw9epVODk5YcmSJdDV1WUdk3QQVV9sm+Pjl/ny\n8nLs378fGRkZGDduHCZPnqxYtOKzDz74ANu2bYO+vj4ePXqEOXPmYMOGDbCxsUFZWRnCwsKwdetW\n1jHVruXmNolEgoSEBCZZtLLVYe/evZg1axa8vLyQmpqKzZs346WXXkJUVBSzbxDqtmzZMpSXl+Ot\nt97CyJEjoaOjg7t37yq9p2fPnozSEU3p0qULpk+fjn//+9+so6iFEGd0A4Cenh6WL1+O3NxcXLx4\nEVVVVbCzs4O/v3+rR8RE+/Fxo2J7Fi1aBENDQ7z55puwsLDA2bNnW73n1VdfZZBMvWQymWJ27ZUr\nV2Bubg4bGxsAjXtVHj16xDKe2jXf3Jafn4/i4mKYmZlh9OjRzDJpZeFbWlqK8ePHAwAmTJiA2NhY\nzJ8/v83NIXzQ1LccHx/fZhsH3yZZENV0dXUhk8lYx1CL9lbC+M7Z2ZnZbEtC1MnOzg46Ojq4dOlS\nm+/hY+Hbp08fnDx5Eu7u7sjIyFD6IlteXs7bNo/o6Gjk5+fj77//hpWVFRwdHTFhwgQ4ODgw38Cp\nlYVv8+4MkUgEQ0NDXhe9ABW15B+HDx9WuQmKD2hGNyH8tGrVKtYRmJgxYwYiIyOxc+dOiEQirF69\nWnHvxIkT7R5uoa3kcjnefvttODg4dLrRfFrZ49vyJJTCwkKIxWKl9/DxJJS2lJSUgOM4zJw5k3UU\ntQgPD1ea/yiEn3fLPzPQ2AdaVlYGAwMDfPrpp7C1tWWUTn2EOKObECGTSCQ4fvw4OI7D2rVrWcdR\ni5qaGty+fRu9evVSmtV969YtGBoa8nJP0pOwrlm0csW35TxAVcd88l1lZaXil0VxcTGvN7q1fPwl\nhJ+3qkd+urq6sLKy6jTnnasD3zb0EEJak8lkyMrKAsdxyM7OhoWFBV577TXWsdSma9euKp/SNfX6\nCkHzmuWvv/7C8OHDmWXRyhVfoZJKpTh37hw4jsP58+dhaWmJBw8eICIigrePvoHGAed6enpKcxBj\nYmJw8+ZNXs9BbItcLse+fft4Od0gPz8fDg4Obd5PSEjAO++8o8FEhJCOUlRUhLS0NGRkZEAul2Pk\nyJHIzMzEd999BzMzM9bxSAfrrDWLVi4bPc2BBnybgRgdHY2TJ09CV1cXo0ePxqpVqyAWizF37txO\n1z/T0WJiYhAQEKAofLdv344HDx5g3LhxyMjIQFxcHC/nILZFJpMhOTmZl4Xv+vXrsXLlStjZ2bW6\nFxsbi8zMTF4WvlFRUU88zhVoPNaYEG21ZMkS3L17Fy4uLpg7dy5cXV2hr6+P7Oxs1tGIGnTmmkUr\nC9+WM++EcKDB0aNHYWxsjMDAQHh4ePB2J6gqf//9t2IV8NGjR8jOzlbMQRwxYgTCwsIEVfjyXDo9\nWgAAD+9JREFU2Zw5c7Bu3Tp89tlnGDBggOL16Oho5OTk8HaDTMtdzj///DPeeustRmkI6Xi1tbUQ\niUQwMDBAly5deNuuRRp15ppFK//ltZyBGBQUxPu5iFFRUUhPT8ehQ4cQExMDFxcXeHp6CuIccKHP\nQRQSDw8P1NfX48svv0R4eDj69u2LrVu3oqCgAJ9//jlvj2tueSrbkSNH6KQ2wiubNm1CXl4eOI7D\nxo0bYWBggDFjxqC+vr7dpx1E+3TmmkUrC18h6tGjBwICAhAQEID8/HxwHIdt27ahpqYGCQkJmDRp\nEnr37s06ploIcQ7ik9p5pFKpBpNo3iuvvIL6+nqsWbMGdnZ2uH37Nr744guYm5uzjkYIeQ6Ojo5w\ndHTEBx98gFOnTiE9PR01NTVYtWoVfHx84OPjwzoi6SCduWbhxeY2oR5hW1dXh9OnT4PjOFy8eJHZ\n8X/qVlBQgMjISABQzEFsWvFNSUnBlStX8PHHH7OM2OGe5iAHPj7laF7w//bbb7hw4QLmzJmjVPTy\nrX9fFaH+TiPCU15eDo7jkJ6ejo0bN7KOQ9Sos9QsVPjyRHl5Oa9nAdIcRGFor+DnY/8+gFbHjy9f\nvhzr169XeixIR5ITQviirKyMWeuaVha+QjzQIDIyEnPnzkX37t1b3cvLy8P27dvx3XffMUhGCHle\ndHAH4bun+Uz+/PPPNZCEaFJVVRW6deumOHGzoqICP//8M/744w/s2bOHSSat7PEV4oEGPXv2xJIl\nSzBjxgyMGzcOAFBdXY09e/bg7NmzePfddxknJIQ8KypqCd/l5eXBxsYGnp6eKhdwCL8UFhZi48aN\nKC8vh4mJCUJDQ1FUVIR9+/Zh2LBhCA8PZ5ZNK1d8hXqgQWFhIbZu3QoLCwt4eHggMTERDg4OeP/9\n92Fqaso6HiGEEKLSX3/9BY7jcOLECfTt2xdeXl4YOXIkDAwMWEcjahAWFoahQ4fC09MTHMchNTUV\nffv2RXBwMPMT67Sy8A0PD0dAQACcnZ0BNA69f/DgAby9vZGRkYF+/frxdq5rWVkZli9fDolEAl9f\nX8yaNYt1JEIIIeSpyOVy5OTkgOM45OXlwdXVFdOmTaOpLTwTFBSEXbt2QSQSQSqVYubMmYiOjoax\nsTHraBCxDvAsVB1osGjRIrz++utYvHgxzp07xziheqSlpWH58uUYNWo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotwellsim(data,'NeighbFacies','RGT','Facies found by KNN classifier')\n", + "plotwellsim(data,'Facies','RGT','True facies (except validation wells)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I created what I refer to as 'interval' features. These capture information about the current interval, such as the distance from the beginning of it, size, and what fraction of the way through the interval the sample was taken. These features are made for the intervals of Formation, NM_M (non-marine/marine), and, when predicting PE, Facies. The features are calculated using the metrics Depth and RGT. Another interesting interval feature I created is compaction, which I calculate by dividing the size of the interval in RGT by its size in depth, capturing how much time has been compressed into that depth." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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90aNHq3///rr22mv10EMPlbm9pUuXavr06Vq8eLG8Xq+GDx+uDh06+NT5wx/+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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 9))\n", + "plt.tick_params(axis=\"x\", which=\"both\", bottom=\"off\", top=\"off\", \n", + " labelbottom=\"off\", left=\"off\", right=\"off\", labelleft=\"off\")\n", + "wellnames = get_wellnames(data).tolist()\n", + "num_wells = get_numwells(data)\n", + "formations = data['FormationClass'].unique().tolist()\n", + "num_formations = len(formations)\n", + "colors=plt.cm.viridis(np.linspace(0,1,len(wellnames)))\n", + "dfg=data.groupby(['Well Name','FormationClass'], sort=False)\n", + "for i,(name,group) in enumerate(dfg):\n", + " widx = wellnames.index(name[0])\n", + " fidx = formations.index(name[1])\n", + " plt.bar(fidx*num_wells+widx,group['FormationClassCompaction'].values[0],color=colors[widx])\n", + "plt.ylim(0,0.1)\n", + "plt.title('Formation compaction')\n", + "plt.text(70,0.09,'Color represents well, each cycle along the x axis is one formation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "My 'measurement' features consist of a median filter, derivative, and second derivative, of each type of log measurement. I also have a sharpened version of each measurement constructed by subtracting the second derivative from the measurement. The motivation for the sharpening filter is a crude attempt at deconvolving the smoothing effect of the measurement tools.\n", + "\n", + "The final class of new features I introduce are 'interval measurement' features. These capture information about the well log measurements over an interval. The features are the mean, difference from the mean, standard deviation, and fraction of the standard deviation from the mean. A motivation for creating these features is the idea that the well log measurements may not be well calibrated between the wells, so using the raw values may not be very predictive. What fraction of the standard deviation over the interval the measurement is away from the mean over that interval, should be less affected by the calibration, however. The intervals I use are the Formation, the NM_M interval, each whole well, and a local Gaussian window around the sample depth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction\n", + "\n", + "Two of the training wells do not have PE measurement values. Scikit-Learn does not allow estimators to be created when the training data contains missing values. The missing PE values must thus be filled-in (\"imputed\") so that PE can be used in the facies prediction. A popular strategy for this is to use the mean or median. As well as its simplicity, this approach may be less susceptible to overfitting than another approach; using an estimator to predict the missing values. Despite the risk of overfitting, the estimator approach has the attraction of using all of the data, and so has the potential to yield more accurate results.\n", + "\n", + "I perform the PE prediction using two steps. The first is to make an initial prediction of the facies values in the two validation wells using all of the data except the PE values. This allows all of the data, including that in the validation wells, to be used for the prediction of PE.\n", + "\n", + "Once the PE data is complete, all of the data, now including PE, can be used for a second, and hopefully more accurate, prediction of the facies in the validation wells." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v_rows = (data['Well Name'] == 'STUART') | (data['Well Name'] == 'CRAWFORD')\n", + "plotwellsim(data.loc[v_rows,:],'Facies',title='Predicted facies')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [Root]", + "language": "python", + "name": "Python [Root]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ar4/ar4_submission3.py b/ar4/ar4_submission3.py new file mode 100644 index 0000000..b594d9b --- /dev/null +++ b/ar4/ar4_submission3.py @@ -0,0 +1,626 @@ +# Alan Richardson (Ausar Geophysical) +# 2017/01/31 + +import numpy as np +import scipy.signal +import pandas as pd +from sklearn import preprocessing, metrics +from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor +from sklearn.base import clone +from matplotlib import pyplot as plt +import scipy.optimize +from scipy.optimize import lsq_linear +import fastdtw +from scipy.sparse import csr_matrix +from scipy.sparse.linalg import lsqr +from scipy.signal import medfilt, gaussian +import xgboost as xgb +from xgboost.sklearn import XGBClassifier, XGBRegressor + +eps = 1e-5 + +def load_data(): + train_data = pd.read_csv('../facies_vectors.csv'); + train_data = train_data[train_data['Well Name'] != 'Recruit F9'].reset_index(drop=True) + validation_data = pd.read_csv('../validation_data_nofacies.csv') + return pd.concat([train_data, validation_data]).reset_index(drop=True) + +def get_wellnames(data): + return data['Well Name'].unique() + +def get_numwells(data): + return len(get_wellnames(data)) + +def set_well_value(data, wellname, colname, val): + data.loc[data['Well Name']==wellname, colname] = val + +def get_well_value(data, wellname, colname): + return data.loc[data['Well Name']==wellname, colname].values[0] + +def make_label_encoders(data, names): + les=[] + for name in names: + le=preprocessing.LabelEncoder() + le.fit(data[name]) + les.append({'name': name, 'le': le}) + return les + +def apply_label_encoders(data, les): + for le in les: + data['%sClass' % le['name']]=le['le'].transform(data[le['name']]) + data.drop(le['name'], axis=1, inplace=True) + +def make_onehot_encoders(data, names): + ohes = [] + for name in names: + ohe=preprocessing.OneHotEncoder() + ohe.fit(data[name]) + ohes.append({'name': name, 'ohe': ohe}) + return ohes + +def apply_onehot_encoders(data, ohes): + for ohe in ohes: + ohdata = pd.DataFrame(ohe['ohe'].transform(data[ohe['name']]).toarray()) + data=data.join(ohdata) + data.drop(ohe['name'],axis=1,inplace=True) + return data + +def make_scalers(data, names, stype='Robust'): + scalers = [] + for name in names: + if (stype == 'Robust'): + scaler = preprocessing.RobustScaler() + elif (stype == 'Standard'): + scaler = preprocessing.StandardScaler() + else: + raise ValueError('unknown stype') + scaler.fit(data[name].dropna(axis=0, inplace=False).values.reshape(-1, 1)) + scalers.append({'name': name, 'scaler': scaler}) + return scalers + +def apply_scalers(data, scalers): + for scaler in scalers: + data.loc[~data[scaler['name']].isnull(), scaler['name']] = scaler['scaler'].transform(data[scaler['name']].dropna(axis=0, inplace=False).values.reshape(-1,1)) + +def neigh_interp(data): + odata = load_data() + wellnames = get_wellnames(data) + formations = data['FormationClass'].unique() + distformation=np.load('dtw_distformation_fce.npy') + distformation[pd.isnull(distformation)]=0.0 + # distformation is upper triangular, so add the transpose to make it full + distformationf = np.zeros([len(wellnames),len(wellnames),len(formations)]) + for fidx in range(len(formations)): + distformationf[:,:,fidx] = distformation[:,:,fidx]+distformation[:,:,fidx].T + # We don't have facies for wells 9 or 10, so we don't want any other well + # to have these as one of their nearest neighbours + distformationf[:,9,:]=np.inf + distformationf[:,10,:]=np.inf + # We also don't want a well to be its own neighbour + distformationf[distformationf==0.0]=np.inf + data['NeighbFacies']=0 + + k=8 + clf = KNeighborsClassifier(n_neighbors = k, weights = 'distance', leaf_size = 1, p = 1) + cols = ['GR', 'ILD_log10', 'PHIND', 'RELPOS', 'NM_MClass', 'RGT'] + for wellidx in range(len(wellnames)): + for fidx in formations: + # Find the k 'nearest' (as determined by dtw) wells + neighb = np.argsort(distformationf[wellidx,:,formations.tolist().index(fidx)])[:k] + # Find the rows in data for these wells + neighb_rows = np.array([False]*len(data)) + for nidx in neighb: + neighb_rows = neighb_rows | (data['Well Name']==wellnames[nidx]) + # Select only those rows with formation 'fidx' + neighb_rows = neighb_rows & (data['FormationClass']==fidx) + # Rows for the chosen formation in the current well + my_rows = (data['Well Name']==wellnames[wellidx]) & (data['FormationClass']==fidx) + # Fit and predict + if (np.sum(neighb_rows)>0) & (np.sum(my_rows)>0): + clf.fit(data.loc[neighb_rows, cols].values, odata.loc[neighb_rows, 'Facies'].values.ravel()) + data.loc[my_rows, 'NeighbFacies'] = clf.predict(data.loc[my_rows, cols].values) + +# Start of functions associated with finding RGT + +def get_pts_per_well(data): + npts_in_well = data.groupby('Well Name', sort=False).size().values + cum_pts = np.append([0],np.cumsum(npts_in_well)) + return npts_in_well, cum_pts + +def build_Adtw(data, wells, nwells, npts_in_well, cum_pts, cols): + formations = data['FormationClass'].unique() + max_num_pairs = int(nwells * (nwells-1) / 2 * np.max(npts_in_well)) + max_nz_in_row = int(np.max(npts_in_well) * 2) + max_num_rows = max_num_pairs + max_num_nonzero = max_num_rows * max_nz_in_row + dist = np.zeros([len(wells),len(wells)]) + distformation = np.zeros([len(wells),len(wells),len(formations)]) + indices = np.zeros(max_num_nonzero,dtype=int) + indptr = np.zeros(max_num_rows+1,dtype=int) + Adata = np.zeros(max_num_nonzero) + b = np.zeros(max_num_rows) + bounds = np.ones(len(data)) + nz_rows = 0 + nz_indices = 0 + + def add_shift_sum(Adata, indices, nz_indices, i, path, cum_pts, wellidx, idx): + col0 = cum_pts[wellidx] + col1 = cum_pts[wellidx] + path[i][idx] + num_added_indices = col1 - col0 + 1 + indices[nz_indices:nz_indices+num_added_indices] = np.arange(col0, col1+1) + #1-2*idx so when idx=0 put +1 in Adata, when idx=1 put -1 in Adata + Adata[nz_indices:nz_indices+num_added_indices] = np.ones(num_added_indices)*(1-2*idx) + return num_added_indices + + def add_row (Adata, indices, indptr, b, nz_rows, nz_indices, i, path, cum_pts, wellidxs): + num_added_indices = 0 + indptr[nz_rows] = nz_indices + for idx in [0,1]: + num_added_indices = add_shift_sum(Adata, indices, nz_indices, i, path, cum_pts, wellidxs[idx], idx) + nz_indices = nz_indices + num_added_indices + b[nz_rows] = 0.0 + return nz_indices + + weightsum = 0.0 + for well1idx in range(nwells-1): + for well2idx in range(well1idx+1, nwells): + w1df = data.loc[data['Well Name'] == wells[well1idx], cols + ['FormationClass']] + w2df = data.loc[data['Well Name'] == wells[well2idx], cols + ['FormationClass']] + w1formations = w1df['FormationClass'].unique() + w2formations = w2df['FormationClass'].unique() + nzcols = [] + path = [] + for col in cols: + if (np.all(np.isfinite(w1df[col])) & np.all(np.isfinite(w2df[col]))): + nzcols.append(col) + for formation in formations: + if (formation in w1formations) & (formation in w2formations): + w1f = w1df.loc[w1df['FormationClass'] == formation, nzcols] + w2f = w2df.loc[w2df['FormationClass'] == formation, nzcols] + w1 = np.array(w1f.values) + w2 = np.array(w2f.values) + dist_tmp, path_tmp = fastdtw.dtw(w1, w2, 2) + dist[well1idx,well2idx] += dist_tmp + distformation[well1idx,well2idx,formations.tolist().index(formation)] = dist_tmp + for pair in path_tmp: + idx1 = w1f.index[pair[0]]-w1df.index[0] + idx2 = w2f.index[pair[1]]-w2df.index[0] + path.append((idx1, idx2)) + bounds[cum_pts[well1idx]] = np.max([bounds[cum_pts[well1idx]], path[0][1]]) + bounds[cum_pts[well2idx]] = np.max([bounds[cum_pts[well2idx]], path[0][0]]) + #NOTE delete + #np.save('path_%d_%d_fce.npy' % (well1idx, well2idx), path, allow_pickle = False) + pre_nz_rows = nz_rows + pre_nz_indices = nz_indices + added_1=-1 + added_2=-1 + for i in range(len(path)): + if ((path[i][0] != added_1) & (path[i][1] != added_2)): + if ((i > 0) & (i < len(path)-1)): + if (((path[i][0] != path[i-1][0]) & (path[i][1] != path[i+1][1])) | ((path[i][0] != path[i+1][0]) & (path[i][1] != path[i-1][1]))): + nz_indices = add_row(Adata, indices, indptr, b, nz_rows, nz_indices, i, path, cum_pts, [well1idx, well2idx]) + nz_rows = nz_rows + 1 + added_1 = path[i][0] + added_2 = path[i][1] + elif (i>0): + if ((path[i][0] != path[i-1][0]) & (path[i][1] != path[i-1][1])): + nz_indices = add_row(Adata, indices, indptr, b, nz_rows, nz_indices, i, path, cum_pts, [well1idx, well2idx]) + nz_rows = nz_rows + 1 + added_1 = path[i][0] + added_2 = path[i][1] + else: + if ((path[i][0] != path[i+1][0]) & (path[i][1] != path[i+1][1])): + nz_indices = add_row(Adata, indices, indptr, b, nz_rows, nz_indices, i, path, cum_pts, [well1idx, well2idx]) + nz_rows = nz_rows + 1 + added_1 = path[i][0] + added_2 = path[i][1] + num_matched_pairs = nz_rows - pre_nz_rows + 1 + p = 2.0 + weight = num_matched_pairs * (num_matched_pairs/dist[well1idx, well2idx])**(2.0/p) + weightsum = weightsum + weight + Adata[pre_nz_indices : nz_indices] = Adata[pre_nz_indices : nz_indices] * weight + + Adata[:nz_indices] = Adata[:nz_indices] / weightsum + indptr[nz_rows] = nz_indices + indptr = indptr[:nz_rows+1] + np.save('dtw_dist_fce.npy', dist) + np.save('dtw_distformation_fce.npy', distformation) + return Adata, indices, indptr, b, bounds, nz_rows, nz_indices + +def create_Ab(Adata, indices, indptr, b, nz_rows, nz_indices): + Adata = Adata[:nz_indices] + indices = indices[:nz_indices] + b = b[:nz_rows] + A = csr_matrix((Adata, indices, indptr), dtype=float) + return A, b, Adata, indices + +def solve_Ax(A, b, bounds, data, wells, nwells, npts_in_well, cum_pts, reg_start_row, its=1): + res = lsq_linear(A,b,bounds=(bounds, 100.0*np.ones(len(data))),verbose=2,lsmr_tol='auto',max_iter=its) + wellnames = data['Well Name'].unique() + k = 0 + for i, wellname in enumerate(wellnames): + wl = len(data.loc[data['Well Name'] == wellname]) + rgt = np.cumsum(res.x[k:k+wl]) + data.loc[data['Well Name'] == wellname, 'RGT'] = rgt + k = k+wl + +def find_rgt(data, names, its): + wellnames = get_wellnames(data) + numwells = get_numwells(data) + npts_in_well, cum_pts = get_pts_per_well(data) + Adata, indices, indptr, b, bounds, dtw_rows, dtw_indices = build_Adtw(data, wellnames, numwells, npts_in_well, cum_pts, names) + A, b, Adata, indices = create_Ab(Adata, indices, indptr, b, dtw_rows, dtw_indices) + solve_Ax(A, b, bounds, data, wellnames, numwells, npts_in_well, cum_pts, dtw_rows, its) + +# End of RGT functions + +# Start of feature engineering functions + +def find_dist(data): + wellnames = get_wellnames(data) + numwells = get_numwells(data) + dist = np.load('dtw_dist_fce.npy') + dist[pd.isnull(dist)]=0.0 + distf = dist + dist.T + numpairs = int(numwells * (numwells-1) / 2) + A = np.zeros([numpairs, numwells], dtype=int) + b = np.zeros(numpairs) + row = 0 + for well1idx in range(numwells-1): + for well2idx in range(well1idx+1, numwells): + A[row, well1idx] = 1 + A[row, well2idx] = -1 + b[row] = distf[well1idx, well2idx] + row += 1 + dist = lsqr(A,b) + for well1idx in range(numwells): + set_well_value(data, wellnames[well1idx], 'X1D', dist[0][well1idx]) + +def interval_cols(intervals): + cols = [] + for interval in intervals: + for metric in ['Depth','RGT']: + cols.append('%sFromPrev%sChange' % (metric, interval)) + cols.append('%sToNext%sChange' % (metric, interval)) + cols.append('%sToNearest%sChange' % (metric, interval)) + cols.append('FracThrough%s%s' % (metric, interval)) + cols.append('%sSize%s' % (interval, metric)) + cols.append('Next%s' % interval) + cols.append('Prev%s' % interval) + cols.append('%sCompaction' % interval) + return cols + +def interval_fe(data, intervals): + for interval in intervals: + for metric in ['Depth','RGT']: + df = data.groupby(['Well Name',interval],sort=False)[metric].min().reset_index() + df.columns = ['Well Name',interval,'%sPrev%sChange' % (metric, interval)] + data = pd.merge(data,df,how='left',on = ['Well Name',interval]) + + df = data.groupby(['Well Name',interval],sort=False)[metric].max().reset_index() + df.columns = ['Well Name',interval,'Max%sBefore%sChange' % (metric, interval)] + data = pd.merge(data,df,how='left',on = ['Well Name',interval]) + + # Set next change to be prev change of next interval. This will cause 'NaN' at the end of each well, so fill those with the max of the interval + df = data.groupby(['Well Name',interval],sort=False)['%sPrev%sChange' % (metric, interval)].first().reset_index() + df['%sNext%sChange' % (metric, interval)] = df['%sPrev%sChange' % (metric, interval)].shift(-1).reset_index(drop=True) + df.drop('%sPrev%sChange' % (metric, interval),axis=1,inplace=True) + df = df.groupby(['Well Name',interval],sort=False).first() + for wellname in df.index.levels[0]: + df.loc[wellname,df.loc[wellname].index[-1]] = np.nan + df = df.reset_index() + data = pd.merge(data,df,how='left',on = ['Well Name', interval]) + data.loc[data['%sNext%sChange' % (metric, interval)].isnull(),'%sNext%sChange' % (metric, interval)] = data.loc[data['%sNext%sChange' % (metric, interval)].isnull(),'Max%sBefore%sChange' % (metric, interval)] + + #IntervalSizeMetric + data['%sSize%s'%(interval,metric)] = data['%sNext%sChange'%(metric,interval)] - data['%sPrev%sChange'%(metric,interval)] + #MetricFromPrevIntervalChange + data['%sFromPrev%sChange' % (metric,interval)] = data[metric] - data['%sPrev%sChange' % (metric,interval)] + #MetricToNextIntervalChange + data['%sToNext%sChange' % (metric,interval)] = data['%sNext%sChange' % (metric,interval)] - data[metric] + #MetricToNearestIntervalChange + data['%sToNearest%sChange' % (metric,interval)] = data[['%sToNext%sChange' % (metric,interval), '%sFromPrev%sChange' % (metric,interval)]].min(axis=1) + #FracThroughMetricInterval + data['FracThrough%s%s' % (metric,interval)] = (data[metric] - data['%sPrev%sChange'%(metric,interval)]) / (data['%sSize%s'%(interval,metric)]+eps) + + #Next/PrevInterval + intervalClass = data.groupby(['Well Name', interval],sort=False)[interval].first() + intervalClass.name = 'Shift%s' %interval + nextIntervalClass = intervalClass.shift(-1).reset_index() + prevIntervalClass = intervalClass.shift(1).reset_index() + nextIntervalClass.columns = ['Well Name',interval,'Next%s'%interval] + prevIntervalClass.columns = ['Well Name',interval,'Prev%s'%interval] + nextIntervalClass.loc[nextIntervalClass['Next%s'%interval].isnull(),'Next%s'%interval] = nextIntervalClass.loc[nextIntervalClass['Next%s'%interval].isnull(),interval] + prevIntervalClass.loc[prevIntervalClass['Prev%s'%interval].isnull(),'Prev%s'%interval] = prevIntervalClass.loc[prevIntervalClass['Prev%s'%interval].isnull(),interval] + data = pd.merge(data,nextIntervalClass,how='left',on = ['Well Name', interval]) + data = pd.merge(data,prevIntervalClass,how='left',on = ['Well Name', interval]) + + #Compaction + data['%sCompaction'%interval] = data['%sSizeRGT'%interval] / (data['%sSizeDepth'%interval] + eps) + + return data + +def measurement_cols(ms): + cols = [] + for m in ms: + cols.append('MedFilt%s' % m) + cols.append('Diff%s' % m) + cols.append('Diff2%s' % m) + cols.append('Sharp%s' % m) + return cols + +def measurement_fe(data, ms): + + dfg = data.groupby('Well Name') + + for m in ms: + + #MedFilt NOTE WINDOW CHOICE + for name,group in dfg[m]: + data.loc[data['Well Name']==name,'MedFilt%s'%m] = medfilt(group,15) + + #Diff + for name,group in dfg[m]: + data.loc[data['Well Name']==name,'Diff%s'%m] = np.gradient(group) + + #Diff2 + for name,group in dfg['Diff%s'%m]: + data.loc[data['Well Name']==name,'Diff2%s'%m] = np.gradient(group) + + #Sharp + data['Sharp%s' %m] = data[m] - data['Diff2%s' % m] + + return data + +def interval_measurement_cols(intervals, ms): + cols = [] + for interval in intervals: + for m in ms: + cols.append('Mean%s%s' % (interval, m)) + cols.append('DiffMean%s%s' % (interval, m)) + if (interval != 'Local'): + cols.append('Std%s%s' % (interval, m)) + cols.append('FracStd%s%s' % (interval, m)) + return cols + +def interval_measurement_fe(data, intervals, ms): + for interval in intervals: + for m in ms: + + # Get dataframe group and rows + dfg = None + def rows(data, name): + return None + if (interval == 'Well') | (interval == 'Local'): + dfg = data.groupby('Well Name') + def rows(data, name): + return data['Well Name']==name + else: + dfg = data.groupby(['Well Name', interval]) + def rows(data, name): + return (data['Well Name']==name[0]) & (data[interval]==name[1]) + + # Compute mean and standard deviation + if (interval != 'Local'): + #MeanInterval + for name,group in dfg[m]: + data.loc[rows(data, name),'Mean%s%s'% (interval, m)] = np.mean(group) + + #StdInterval + for name,group in dfg[m]: + data.loc[rows(data, name),'Std%s%s'% (interval, m)] = np.std(group) + else: + #MeanLocal NOTE WINDOW CHOICE + gauss = gaussian(5,1) + gauss /= np.sum(gauss) + for name,group in dfg[m]: + data.loc[rows(data, name),'MeanLocal%s'%m] = np.convolve(group,gauss,'same') + + #DiffMeanInterval + data['DiffMean%s%s'% (interval, m)] = data[m] - data['Mean%s%s'% (interval, m)] + + #FracStdInterval + if (interval != 'Local'): + data['FracStd%s%s'% (interval, m)] = data['DiffMean%s%s'% (interval, m)] / (data['Std%s%s'% (interval, m)]+eps) + + return data + +def basic_feature_engineering(data): + cols = ['X1D', 'Formation3Depth', 'DepthFromSurf', 'WellFracMarine', 'FormationFracMarine', 'DepthFromSurf_divby_RGT', 'FormationSizeDepth_rel_av', 'FormationSizeRGT_rel_av', 'DiffRGT', 'IGR', 'VShaleClavier'] + + # Give unique values to each NM_M interval so they can be distinguished below + # Very hacky method for doing it... + nmclasssep = np.zeros(len(data['NM_MClass'])) + nmclasssep[1:] = np.cumsum(np.abs(np.diff(data['NM_MClass'].values))) + nmclasssep[0] = nmclasssep[1] + data['NM_MClassSep'] = nmclasssep + + intervals = ['FormationClass', 'NM_MClassSep'] + intervals_measurement = intervals + ['Well', 'Local'] + cols += interval_cols(intervals) + + ms=[u'GR', u'ILD_log10', u'DeltaPHI', u'PHIND', u'RELPOS'] + cols += measurement_cols(ms) + cols += interval_measurement_cols(intervals_measurement, ms) + + # X1D + find_dist(data) + + # Formation3Depth + df = data.loc[data['FormationClass']==3].groupby(['Well Name'],sort=False)['Depth'].min().reset_index() + df.columns = ['Well Name','Formation3Depth'] + data = pd.merge(data,df,how='left',on = 'Well Name') + + # DepthFromSurf + df = data.groupby(['Well Name'],sort=False)['Depth'].min().reset_index() + df.columns = ['Well Name','SurfDepth'] + data = pd.merge(data,df,how='left',on = ['Well Name']) + data['DepthFromSurf'] = data['Depth']-data['SurfDepth'] + data.drop('SurfDepth',axis=1,inplace=True) + + # WellFracMarine + df = data.groupby(['Well Name'],sort=False)['NM_MClass'].mean().reset_index() + df.columns = ['Well Name','WellFracMarine'] + data = pd.merge(data,df,how='left',on = ['Well Name']) + + # FormationFracMarine + df = data.groupby(['Well Name', 'FormationClass'],sort=False)['NM_MClass'].mean().reset_index() + df.columns = ['Well Name','FormationClass','FormationFracMarine'] + data = pd.merge(data,df,how='left',on = ['Well Name', 'FormationClass']) + + #DepthFromSurf_divby_RGT + data['DepthFromSurf_divby_RGT'] = data['DepthFromSurf']/data['RGT'] + + #DiffRGT + wellrgt = data.groupby(['Well Name'],sort=False)['RGT'] + for name,group in wellrgt: + data.loc[data['Well Name']==name,'DiffRGT'] = np.gradient(group) + + # Intervals + data = interval_fe(data, intervals) + + # Remove useless columns + cols.remove('NextNM_MClassSep') + cols.remove('PrevNM_MClassSep') + + # FormationSizeDepth_rel_av + mss=data.groupby(['Well Name','FormationClass'])['FormationClassSizeDepth'].first().reset_index().groupby('FormationClass').mean().values + data['FormationSizeDepth_rel_av']=data['FormationClassSizeDepth'].values/mss[data['FormationClass'].values.astype(int)].ravel() + # FormationSizeRGT_rel_av + mss=data.groupby(['Well Name','FormationClass'])['FormationClassSizeRGT'].first().reset_index().groupby('FormationClass').mean().values + data['FormationSizeRGT_rel_av']=data['FormationClassSizeRGT'].values/mss[data['FormationClass'].values.astype(int)].ravel() + + #Measurements + data = measurement_fe(data, ms) + data = interval_measurement_fe(data, intervals_measurement, ms) + + #IGR + data['IGR'] = (data['MedFiltGR']-data['MedFiltGR'].min())/(data['MedFiltGR'].max()-data['MedFiltGR'].min()) + + #VShaleClavier + data['VShaleClavier'] = 1.7 * np.sqrt(3.38 - (data['IGR']+0.7)**2) + + return cols, data + +def predict_pe_feature_engineering(data): + cols = [] + intervals = ['Facies'] + cols += interval_cols(intervals) + + ms=[u'GR', u'ILD_log10', u'DeltaPHI', u'PHIND', u'RELPOS'] + cols += interval_measurement_cols(intervals, ms) + + data = interval_fe(data, intervals) + data = interval_measurement_fe(data, intervals, ms) + + return cols, data + +def predict_facies2_feature_engineering(data): + cols = [] + intervals = ['FormationClass', 'NM_MClassSep', 'Well', 'Local'] + + ms=['PE'] + cols += measurement_cols(ms) + cols += interval_measurement_cols(intervals, ms) + + data = measurement_fe(data, ms) + data = interval_measurement_fe(data, intervals, ms) + + return cols, data + +def make_classifier(data, Xcols, Ycols, rows, clf): + clf.fit(data.loc[~rows, Xcols], data.loc[~rows, Ycols]) + return clf + +def classify(data, clf, Xcols, Ycols, rows): + data.loc[rows, Ycols] = clf.predict(data.loc[rows, Xcols]) + +def make_regressor(data, Xcols, Ycols, rows, reg): + reg.fit(data.loc[~rows, Xcols], data.loc[~rows, Ycols]) + return reg + +def regress(data, reg, Xcols, Ycols, rows): + data.loc[rows, Ycols] = reg.predict(data.loc[rows, Xcols]) + +#NOTE seeds +def run(solve_rgt=False): + # Load + preprocessing + odata = load_data() + if (solve_rgt): + data = load_data() + le = make_label_encoders(data, ['Formation', 'NM_M']) + apply_label_encoders(data, le) + scalers = make_scalers(data, ['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'RELPOS', 'PE', 'FormationClass', u'NM_MClass', u'Facies']) + apply_scalers(data, scalers) + #NOTE Max its + find_rgt(data, [u'DeltaPHI', u'Facies', u'GR', u'ILD_log10', u'NM_MClass', u'PE', u'PHIND', u'RELPOS'], 1) + else: + data = pd.read_csv('dtw_out_fce_14000.csv') + data.drop(u'Unnamed: 0', axis=1, inplace=True) + # Reset Facies back to their unscaled values + data['Facies']=odata['Facies'].values + scalers = make_scalers(data, ['RGT'], stype='Standard') + apply_scalers(data, scalers) + neigh_interp(data) + cols = ['DeltaPHI', 'GR', 'ILD_log10', 'PHIND', 'RELPOS', 'FormationClass', 'NM_MClass', 'RGT', 'NeighbFacies'] + basic_cols, data = basic_feature_engineering(data) + cols += basic_cols + + seed1=0 + seed2=0 + seed3=0 + + facies_rows_to_predict = data['Facies'].isnull() + pe_rows_to_predict = data['PE'].isnull() + + clf1 = XGBClassifier(base_score=0.5, colsample_bylevel=0.5, colsample_bytree=0.6, gamma=0.01, learning_rate=0.025, max_delta_step=0, max_depth=2, min_child_weight=7, missing=None, n_estimators=500, nthread=-1, objective='multi:softprob', reg_alpha=2, reg_lambda=20, scale_pos_weight=1, seed=seed1, silent=True, subsample=0.2) + + clf2 = XGBClassifier(base_score=0.5, colsample_bylevel=0.3, colsample_bytree=0.8, + gamma=0.01, learning_rate=0.05, max_delta_step=0, max_depth=3, + min_child_weight=1, missing=None, n_estimators=500, nthread=-1, + objective='multi:softprob', reg_alpha=0, reg_lambda=1, + scale_pos_weight=1, seed=seed2, silent=True, subsample=0.5) + + reg1 = XGBRegressor(base_score=0.5, colsample_bylevel=0.5, colsample_bytree=0.1, + gamma=0, learning_rate=0.05, max_delta_step=0, max_depth=1, + min_child_weight=10, missing=None, n_estimators=500, nthread=-1, + objective='reg:linear', reg_alpha=10, reg_lambda=10, + scale_pos_weight=1, seed=seed3, silent=True, subsample=0.1) + + # Predict facies #1 + Ycol = 'Facies' + Xcols = cols + clf = make_classifier(data, Xcols, Ycol, facies_rows_to_predict, clf1) + classify(data, clf, Xcols, Ycol, facies_rows_to_predict) + for wellname in get_wellnames(data): + wd = data.loc[data['Well Name'] == wellname, 'Facies'] + wd = medfilt(wd, kernel_size=5) + data.loc[data['Well Name'] == wellname, 'Facies1'] = wd + cols += ['Facies1'] + + # Predict PE + predict_pe_cols, data = predict_pe_feature_engineering(data) + cols += predict_pe_cols + Ycol = 'PE' + Xcols = cols + reg = make_regressor(data, Xcols, Ycol, pe_rows_to_predict, reg1) + regress(data, reg, Xcols, Ycol, pe_rows_to_predict) + cols += ['PE'] + + # Predict facies #2 + predict_facies2_cols, data = predict_facies2_feature_engineering(data) + cols += predict_facies2_cols + Ycol = 'Facies' + Xcols = cols + clf = make_classifier(data, Xcols, Ycol, facies_rows_to_predict, clf2) + classify(data, clf, Xcols, Ycol, facies_rows_to_predict) + for wellname in get_wellnames(data): + wd = data.loc[data['Well Name'] == wellname, 'Facies'] + wd = medfilt(wd, kernel_size=7) + data.loc[data['Well Name'] == wellname, 'Facies'] = wd + + data = data.loc[(data['Well Name'] == 'STUART') | (data['Well Name'] == 'CRAWFORD'),['Well Name','Depth','Facies']] + data.to_csv('ar4_submission3.csv') + +if __name__ == "__main__": + run() diff --git a/ar4/dtw_dist_fce.npy 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