From 9978bcf0604e37623ae47b53e1fb1c810b5c6689 Mon Sep 17 00:00:00 2001 From: David John Gagne Date: Fri, 8 Jan 2016 18:43:26 -0600 Subject: [PATCH] Fixed bootstrap shade ordering --- demos/unidata_users_workshop_2015.ipynb | 109 +++++++++++++----------- hagelslag/evaluation/MetricPlotter.py | 6 +- 2 files changed, 63 insertions(+), 52 deletions(-) diff --git a/demos/unidata_users_workshop_2015.ipynb b/demos/unidata_users_workshop_2015.ipynb index 45eb561..da3bf46 100644 --- a/demos/unidata_users_workshop_2015.ipynb +++ b/demos/unidata_users_workshop_2015.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##Severe Weather Forecasting with Python and Data Science Tools: Interactive Demo\n", + "## Severe Weather Forecasting with Python and Data Science Tools: Interactive Demo\n", "David John Gagne, University of Oklahoma and NCAR\n" ] }, @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##Introduction\n", + "## Introduction\n", "Severe weather forecasting has entered an age of unprecedented access to large model and observational datasets with even greater hordes of data in the pipeline. With multiple ensembles of convection-allowing models available and an increasing variety of observations derived from radar, satellite, surface, upper air, and crowd-sourcing, forecasters can easily be overwhelmed with guidance. Without ways to organize, synthesize, and visualize the data in a useful manner for forecasters, the pile of new models and observations will languish unused and will not fulfill their full potential. An even worse outcome would be to take the human forecasters completely out of the loop and trust the models, which is a way fraught with peril. Data science tools offer ways to synthesize essential information from many disparate data sources while also quantifying uncertainty. When forecasters use the tools properly, they can identify potential hazards and the associated spatial and time uncertainties more quickly by using the output of the tools to help target their domain knowledge. \n", "\n", "This module demonstrates how data science tools from the image processing and machine learning families can be used to create a forecast of severe hail. It aims to teach the advantages, challenges, and limitations of these tools through hands-on interaction. " @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -32,8 +32,8 @@ "import matplotlib.pyplot as plt\n", "from datetime import datetime, timedelta\n", "from mpl_toolkits.basemap import Basemap\n", - "from IPython.display import display\n", - "from IPython.html import widgets\n", + "from IPython.display import display, Image\n", + "from ipywidgets import widgets, interact\n", "from scipy.ndimage import gaussian_filter, find_objects\n", "from copy import deepcopy\n", "from glob import glob" @@ -49,16 +49,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from hagelslag.processing.EnhancedWatershedSegmenter import EnhancedWatershed\n", - "from hagelslag.data.ModelOutput import ModelOutput\n", - "from hagelslag.processing.ObjectMatcher import ObjectMatcher, shifted_centroid_distance, centroid_distance\n", - "from hagelslag.processing.STObject import STObject" + "from hagelslag.processing import EnhancedWatershed\n", + "from hagelslag.data import ModelOutput\n", + "from hagelslag.processing import ObjectMatcher, shifted_centroid_distance, centroid_distance\n", + "from hagelslag.processing import STObject" ] }, { @@ -70,19 +70,11 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading SSEF\n" - ] - } - ], + "outputs": [], "source": [ "model_path = \"../testdata/spring2015_unidata/\"\n", "ensemble_name = \"SSEF\"\n", @@ -114,16 +106,16 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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3v5KyzivAy618IVHhIMyxSfeFLcr7E1RTqIsnrO2i3IHUD7TOuQyY2ekEVQME\nbAvsASwG/hJe8RwFHGhmSzMLslp+DHUtYmYPSNoUeBgYDij8XfgZFv4emXJ/0vqFnzWtfC01mAI8\nYWb/iiwbF3YRWAGcYmZzy23AE9Z2lNRK4Je1nHMZCVtTF4U/5xaWSzoG2A+YkVFopfz46PKjj2CC\njs82+mqEpOMaub0GORC4NPL348BWZvaMpHcAV0t6q5k9n7YBH3TVbiLJqvUvKP4u3G7VczvnXBX+\nQjDpgGuE6GAyPx63NTM7GngAmCdpi6zjqcb9rOQqlnMVy7mHFwGqKmUnaT2CCUiuKCwzs9VhjWfM\n7G7gX8Cby23HW1jbVDRZBVDPpOY9WfTAWE33g5WzrJhAL+5DUw7zlg3nutM/gNdnHUTHih6P/Spb\n2zGzGZJmA7MlfdvMWl8aLrT+cGPHTcp3f92R4cBGAPz2JePelSsfq3Lz7wfuN7PHCwskjQGeMbO1\nkrYhSFaXlNuIJ6xtqiRBbeYBqtpv8bGWX5s5u3iXLZyOJl5omnJYsMAPqM51BTOztqrek/ekb3Sv\nEmOMNyoQa8zI6+txmNl9kt4JzJS0D3BILgcsVkHSZQR1Zl8taRlwqpldCBwAXBZbfVfgNElrCAaN\nfdbMnqUMT1jbTdoBqxXPVwNN7MEW9hf/toX9QeI6bU/Ugw8Sc87lTzscl5Jqzsa0pIuYa5hwWtd9\nJR0N3CXpI2a2LOu4amVmB6YsPyxh2VXAVbVs3xPWrNWTfLb6oFrN80UuS6lnEgaIPmxhP2vmLAdg\nxNQxsLgPoq3D5ZLhdjh5OOcqeVnSJma2IutA2k41XbASWlrVM8mT1jZkZj+RdANwjaQZZnZx1jHl\niSeseZF2YEpK6PKcyIUtsuqZFCSmExYwgtklq1j/AjS2t/K2vFyXc51gEUHtxauzDqRtJFziH6Tc\nhAmFY3B0G34sbQtm9qCkHYDLJO1NUBauLbsINJonrFnrxINI5DWpB2MasLivpIuAH0Cd6xq3E5Tv\n8YS1TvHW0moG2bZkQK5rirCLwP6SDgfuljTNzMoOSOoGXtaq3bRbkje6V+qZBBMmo4k9ife33Wty\n7c1LArXaTcDErIPIhWpLUqUdExf3AWEyWss+7MfYtmRmFxCUg/qdpE9nHU/WvIU17zrhQDO6V+rB\nin1Xa+kT65xra2b2lKT1s46jaeLjEKKJZGxZaUvpgoHL9mmX9+NJ6YQK065H1k9sWc17FQQ3iJkt\nDbsIXCyaCCzFAAAgAElEQVRpL2D/sAW263jC6lqjloEDzjWT72tZGClpSzOrtm5jexo0sctAUmr9\nC4otpABMmBxJYBcUB0sBA/to+Ftje0uS3uJ6VVQIUM+kwet5l6y2EvZhPVjSx4H5kg40sweyjqvV\nPGF1lSW1GDjnXPUOJ5h6caeOG0CScEyMJo1lR+tHE1jAFveFragLLK3vabECQCz5LVm/uJ0yfVk9\naW07Zna5pNsIPksXm9k5WcfUSp6wuvJiLQZeQ9U5Vysze0DSxcCvgCOyjqdpolVSEgzK1CdMHpS0\nFtetIdFlcV9x24WygknbS0xa45K6IvgxPzfM7DFJOwGXSFrfzKZnHVOreMLaDuLfhDP4Zuw1/Zxz\nQ2Fm50i6VtK+Zta5FQPKHJujl/YhPK5W6pdao6RjdWICnTYpTNoyT1pzI5xB7hDgBsATVpcThYNH\nmb5RTT2QRPtQOefc0OxH0Afvr2b2ZNbBZCKtVmoVisloFUluapIa/7uWagPe8pobZrZGUldVevKE\nNe9iB5R436hih3o/cDjnci48yX4K+CPw31nHk6lKx+yERHLINVXrOVeUW9/HN2TtRUlbmNkTWQfS\nCp6wtoOkcinOOdeGzOweSbMk/djMvpB1PLmVlgAO9TxQa9Ja5vmiDSjqIbn11ZPaZvpf4CBgRtaB\ntEJXNSe3vbAIv89c4pxrZ2Z2JjBe0tSsY2k7hclWhjLpSpVTflv/gkFX9dLGMwxaXu0EHT6Rx1BM\nhdjc5x3MW1jbjfcpdc51hn2BOyRNMbMVWQfTtuIDcofy+FiraLxxpFhSK/J31duuxLu21UTS9sA6\nM7sv61haxRPWbuUznjjnMmRmqyR9BrgG2C3reDpC/HheLoEtlyCW6Y6gnkl+3siHc4CDsw6ilTxh\n7VQ+mtM5l3NmNk/SXEnTzeyErOPJXK39PcslpNEuA2nr1dqq2YKKNK4ySbsBy81sWdaxtJInrM45\n5zJjZl+TdHPYNeC2rOPJ1BCStsFTsibOH9Cw52uYPMTQfr4D7J11EK3mCWsnqqYfkx8knHP5sQ/w\nV0nvMrNVWQeTO2lXzGI1XZOmhC3bz9RH8LcdSfsBD5nZM1nH0mqesHYqP/g459qEmT0v6UvAb4Fp\nWceTe/FEMzoYN6Vud9Tg1thJPq6hfZwE7Jp1EFnwhLVb+Ddp51y+LQRelXUQuVSuL2o80Ywc30uq\nyXjpqLYXDlKc161XITxhbWdptfQ8IXXOtZ/tgaVZB5FrScf2ahPRWqdhdbkiScCRwA5Zx5IVT1jz\nKi0ZrTQqtBxPZJ1z+bUd8FDWQbSdOkb5x7sO1Lwdl4WTgD+Y2dqsA8mKJ6w5V3bqu4LY5aKkfks+\nO5ZzLufGA7OyDqJreILaNiQNBz4KvDPrWLLkCWteRVtTF/cBYEQSz+j9CZ3sbeZs1sxZDsCIqWNg\nWpjw+kHKOZdPWwP3ZB2Eczn0XeCXZtbVXTo8YW0HEyYDYbJaqRB0zMPzVzGO5YxgdpC0+pSurl4+\ncM8110Zm9p+sg+gY/nntCJJGAbuZ2XFZx5I1T1jzbHSvin2Nyh1wYrX4AJjYB2ELK8CaOcWk1TT2\nlIYevCSp27/5dQU/6bnm8v3LuYgwWb0U+F7WseSBJ6ztIClRSFtW+FY9YTIjpvYzjuWl6yzuwzij\nkUnrOmAY0LUdwZ1zLnf8C2bbCSsB7AN8EngtwXn1ejO7ItPAcsIT1k4TGQWqIyfZyIkXYgv7gUIr\nK4iGJq2GJ6zOuaFbT9LN4e31gV3MbE2G8TjXdJJ2AY4A3kzQAHQP8E0zuy/TwHJoWNYBuDrUUHdP\nUw5DE3uKi9bMWR4ksIv7GlVIupCwOudc3czsPWa2u5ntDiwApmQcknMNJ+ktks6WdJuk24DPABeZ\n2S5mtquZfaFdk1VJF0h6QtK9kWXflPSopL7w54OR+06S9E9JD0iaWmn73sLaLuLJZTUd6sN1NOUw\n4MJin9ZCSysTFjSicoAnrM65RrsV+ABwU9aBODcUkl4HfBbYHRgO9BP0Sz2uA2uqXgj8GPhNZJkB\nM8xsRnRFSdsBBxDUX94SuFHSeDNbl7ZxT1jzIikBTairmlhPNTooK2E+aOtfgC3s58o5A/vB/ixn\n5MQ+GHp9Vk9YXXV81LKr3vUE/ficayuSNiK4xP8hgq4tzwN/AD7Y6VOqmtltksYm3JV0vP8wcFnY\n7WeppIeAnYB5adv3hDUPklpPwxN60iQAVW0DEmfGWhL+fnj+KsYv7EdDv+jmCasbzKeAdENgZk9J\nGp11HM5VImkE8HFgf2BTYDUwBzjQzJ7KMrYc+YKkTwHzCVqWnwVeT2ly+ihBS2sqT1jzIJpYxlqe\nklpUi0ns4r5ijdZB6xeS3tG9Ug/GxD6CKxGBu54exfhwW0OszeoJqxtQTaLqravOuc5xObAU+IKZ\nLc02lFz6KXBaePt04AcELdBJyp4/PGHNi0plquJ/h7NfFX+Hiav1LxhYxoXFEle2sL/YugpBS+ua\nOcsZOY3EbgQ18IS129XSmurJqqveGknrd/plVNf2Xm9m07IOYqhGrj+McTusX3adW55+kVufXgnA\nXStehpXlW0QBzKzYUibpV8C14Z+PAVtFVn1DuCyVJ6x5l3KCt4X9weCpqWOCBQtnB9UAIi2uq2cs\nCm8tGrwBgm4BExYPuR+rJ6zdLKGfNaT0tS6s70mrq87NwHWSvmpmf886GOfiJH0M6Jp9c7fNN2C3\nzTcA4Lx/P82cp1aWTTAhGHQWmcHuI0ChgsA1wKWSZhB0BXgzcGe5bXnC2qbWzFk+MO1qIWktmDAZ\nmzmbK+esYwmwDbDj5i+zDaNKWllL1J9EeMLqSqQmq3FDa9nPt3ircye+xiYzs9MlzQS+LemNwM5e\nl9XlzJHAx7IOIi8kXQbsBoyRtAz4BrC7pEkEucLDBBUTMLNFkq4kaFF7BTiq0oyZnrC2qRFTxwye\nxSrFEoCnS5PVbaBi83+VPGF1A0lqPDEr113AkzhXgZktAj4i6c/AhsAzGYfkHFCcNnWUD6waYGYH\nJiy+oMz6ZwJnVrt9T1jbTWEQ1TSCslQFEyYHfVfD/qua2MP+U4NW2LueHtyyuv/UYYw8drtBg7bq\n4AlrN6sl6ezWBLVMyTlXNT/OuLw5DpiZdRDdxBPWNqWeSdAzqWzZq5HHbscEYNyMRcXEdcfNX2bc\nDusz8tjtwgkFGOoJ1E8kLl3SvtUN9VjTBlG6evlxxuXNaIJ+l65FPGFtR/EWm4JCK2vhNkFiO3LC\nAiYs7mNCZD31TGrUCdRPJK6ytK4B3uroquPHGZcrZnaKpFMlXU8wKYDXnm4yT1jbVSwBKPYhjA54\nCZMAje2FsU2LxE8keZCXVsuhTBjgFQRcunwdZ/LyeXOZMrPTJH0CuEPS+8zs+axj6mSesLaraDKa\nLSN52jWXlXoTv3pbO4c6q1W0vrAnrS5ZvhLWwj7r+2rXM7NLJS0Fbpe0j08e0DyesLqhWkeeTiSd\nptqWnIRpeIf0nOWeq5HTrhaex0/8rrx8JaxQus96GbOuZma3S+oFrgMmZh1Pp/KE1Q1V/k4knaqW\nFp2hXrJMenwzElXnquPHGZdrZvaIpMckbWJmK7KOpxN5wuqGyk8kWUqbunco26nlvjSekLrGynfX\no2Z8qXPtaBkwCbgl60A6UcWENSyOm3frsg6gCu0QI9QepwHDJLXsZOKjMVNUGolf0Iji/lkPOkmK\n2ZPkTpZd16O0z4eXLnODLSboEuAJaxNU08I6q+lRDE07HCDaIUaoL871gPMaHUgZw1qYG9frLcC7\nzOyRIW+p0gmwXD+6NPHBVfWcZP3E7FrLr+S4drAImJp1EJ2qYsJqZh9oRSDOdQpJVxAUlW6cKgd1\nFCaSULS8Wblttmvi6ZdgO1/p//b3ZJWwtutnxGXhUODirIPoVP6N1bnGa31rULf2Mx3dq+KP6xyx\n/fmjH3nPR/HzlcsxSe8FNjCza7KOpVP5oCvnGq/xrX4V+pKWm6K36m05lxexffT3f9jnCpqZsPqM\na24IwjEcPwSmZB1LJ/OE1bnGa+6I5sKl/LTZzgoaWZvVuWw19zPliaobmp8D5/lMV83lCatzjdf4\nLgErZ5n1L4DFfcHfExYYDCSpnqy6Tvbxj0454KSvfOwAVpYZA+xJp8uApLcB483sM1nH0um8T5Bz\njdfYhDWerELp7ZTHNOz5ncuYJNat813a5dIFwIFZB9ENPGF1rvEak7CunGW29IzByapzXeay3916\nyTprl1LWrltI+jZwrZn9J+tYuoF3CXCu8ervb1doTYX0JHXC5OrKVsX5JVOXV5UnorB3vufLO5jZ\n39t20oh2LiPnBpG0FfB+M3tX1rF0C09YnWuO6k9MsSTVFvYP3tjEHpgwObhdS7LqJ0gXl/UsZZUk\nJ3YGjADatwZvHt9rNxSXAodkHUQ38YTVucarbhrJSN/UQpK6Zs7yQauNmDqm9gj85OiS5DXJq7y/\nngv8RNLOZra2ysfULu/JvMsFSV8E/mFmD2QdSzfxhNW5xqupS4At7E9MVEtU27rqJ1lXTrR6RBvt\nK2Z2p6TvA3/Gp750GZG0GfBt4J3AzhmH03U8YXWu8YZUMzLaolpTV4A2SkBchtp0PzGzKySNk3Sx\nmX2yeEcjW0Xb9L3pRGEx/mHA8MjPegm3h4W3hyUsGw6sMbMaZlZJjGU/4IsEx/VzzeyooWzP1ccT\nVucar7oqAaN7pR6MaTByYmnfVU3sCW6EySoMzGaVmLj6idZ1ATM7S9LPJP0CmA8MP+f7n2HdunWs\nXWscd9I+x1Ga5BRuK7JMkfviP2nLo49RZDtp68Zv++ezdhb+rAt/LPJ7bWxZ0vK14e/NJBmwr5lV\n3SVG0hbAWcBbgbuB/czs6Ya8MlcXT1ida7zqy1qN7pXG9sJY0J5gS88YdEBVzyRPVl1jtetIe8DM\nPidpLrA5cOdLL61h+PBhDBsmgJcZSFrWRW6vreL2K7Fl8fvit+M/r8RuF9etJVFyjSfp88ANkj5Q\n6X8h6RDg0wT70vfN7M+tiNFV5gmrc81R18lfY09JfJzG9rZ1kuFyILb/DPoS1F59W6cAfwXmf/Vr\nFxTjPeb4X2YXUTk+mCtTZnaupOeBuZJ2N7M1SeuFpaqOB95lZi+0NEhXkSesWQoPYoNOHH5Aa3fV\nVQmoVaX9Ip7Q+n7kChK+7CRO59smzMwkvQ/4m6T9zezBrGOqShu9x53GzH4t6Vngdkm7mtmqhHWW\nSXrKk9V88pmushKWNCrW32Qgcc1t6RnnXOdqs+NOmHB8Dvh61rFUNLpXnqxmz8z+CJxAkLRuknU8\nrjaesGYo2sKhnkn1zV7k8qg5Lay18JOjK6g3EV05y4o/+XUH8Masg3Dtw8xuIuijels4sCpunaQR\nLQ6rI0i6QNITku6NLPuepPsl3SPpqsIXBUljJa2S1Bf+nFdp+94lICthQqGxvRkH4ppgSGWt6uZJ\nqourlGxG95lak9McdEEJuwa0+mldmzOz+ZIOAG6UtI+ZLY3cfT+wO3BDFrG1uQuBHwO/iSybA5xg\nZusknQWcBJwY3veQmU2mSp6wOtd41VcJcK6Z4klkPMksTIMa608PFer+JiW2yVOqVtaAwV6S5CPx\nXS3M7H5JvcC1kv4JPEBQvupegskpPGGtkZndJmlsbFn0fbwDmFbv9j1hda7xsmlhda6S6ExXMdFk\ntfB3agWBZk2LWt92lwDvBm5vcESuw5nZI5K2B8YBexEkqq8Hrsk0sM51OHBZ5O9xkvqAFcApZja3\n3IM9YXWuObyF1bWHMBnNrA/90JPfm4AP4Qmrq0PYMr9E0kTgGjO7LuuYOpGkrwGrzezScNHjwFZm\n9oykdwBXS3qrmT2ftg1PWJ1rvHV4C6trN2VaX3NuFnBw1kG4tvd24Oisg8iKNhpeMi14kpsfepZb\n/rUCgDttNcCWVW1bOpTgS+X7CsvMbDWwOrx9t6R/AW8m6JaRyBNW5xrPuwS4/IoPtCosS0hWy86w\nlhNhC81ySfOA3wM/8P6srhaSRgGv+H5T3u5v2pTd37QpAOfOfZzrFz/7WKXHSNoL+Cqwm5m9FFk+\nBnjGzNZK2oYgWV1SbluesDrXeD7oyuVbfOallJbVhiWqTZ7pycwOkjScYPTxPEnLgK/ERn87l+bT\nBKPZ3RBIugzYDRgTfga/QVAVYCTB1LgAfzOzo8L1viVpDcFVyc+a2bPltu8Jq3ON5y2sLr8GzbBH\nfaWsklpqM2Rma4FvA9+WtDPwc0kbAB9ImtXIuYiPAPtlHUS7M7MDExZfkLLuTGBmLdv3hNW5xvOE\n1bW3avqzxkpiweBKA3HqmYQtPcOifxefr4HMbB6wp6TTgANJOWk6FxplZiuyDsKV5wmrc43nXQLy\nqNpWwGqTp/rLMGWrMGlJLS2racq9p4v7ALCF/WhiT3A7XFZghElr897Li4Af4gmrSyFpChX6Trp8\n8ITVucbzFta8WTnL0lr/BvXTrLa/ZTskq0kJZVot1ei6sfer2r6s6plUbGW1hf3F5dGktcTiviCJ\nnTAZWGDqmdTQ99XMlkjarFHbcx3p88B3sw7CVeYJq3ONtw5vYc2PofSvbNdW1HLqnd2qIOWxcZrY\nU5K0xpXct3B28Hta2PLb2PfcJA03s7WNmFXLdZwtzezvWQfhKvOE1TnX2cIEq5CMlczgVM1j21la\nuapCP9KES/QFqe9RmdbquJJW1QmTBz0fwJo5y4u3RzAbJvahKUNMWiOv+fOf3Zv3v3fSK6ycNfj+\ndv//ukZ4WdKrzeyprANx5XnC2i0a3X/PleN9WPMmsl9rbG+GgWSg8NqrOQYUEsoJk1MTe+tfUJp4\nTpicvK205aFoC+zD88NB/POXMW6H5YzkwtqS1jKv7VMH7cGMc67mw/vsXNWmXNf5BXAq8KWsA3Hl\necLarppVRqbJ9RK7hCesLn8iiWuxr2lSq2ck0axp4oBYEpv4mEgrN4Dog0gLKwTJ6/iF/TBhQeXu\nAVUcB3d853juWbiUK2fOZf9puwzc4cc3F7gST1bbgies7SKLOofVPKcf9JOsA46T9ImsAwllXiMz\ntC7rAIZgJbB+1kHE7ffhd08DkMTMq2//fZUP+z3AtH3/+6PFJS9F63U/kPCQy5K39MKzwLzByzd8\nAF71Z8JC4cleejZ4/IqXWDdiNS8+/crAfSf1s9Fv7octX8vv/7zPlWmb+OhH3pO+/Yi3jH8Dx3/t\nAg741PR+4F7gSp/UyAGYmUl6XNKbzOyhrONx6VTuQyvJzMwTkqxUmaQOagUpV0MxLcFMG01cKQZP\nWAeRdCbwKHBt1rGQv5bedt1f8vY+8tA/fvEvgHUWfA8Yv/3n3lzPdh6852f/BLCn7k9dR6/eNvn+\nhxcNXjZuOwBss7eUPEav3rZkNXvqfng4uN/++RSvzH2GRxcWZ25k7Odeh6bugl69LW+ZfOSEtNge\n6Pvp4tTAY55cvoKTv3ExZuv47fnHsfXWWwR3+HGsq0naBTjKzPLSyDAkteZukt7+tteNvmfBce+o\n+jnOnfs4X7p6yblm9oW6gqyDt7DmQdoc3mUu1UUvt6WW5aklOY1LK33jqvWMmS3LOgjXIHmqFpDy\n+Tb7bL3bCx6/8ZPFRYmX89+05eCqAON2DX5Hj1XjxkQe/4bEp7T+BbDpa+CVR4N+rD2jWbPhSsbv\nPGpgpQUrGdFzN5r2GuzFaxeXef/Ty3PFTBj/Bm694SwWLlzKwUfM4LWv3Yzzz/siG49Oe4TrBmY2\nV9L3so7DlecJa6tVSBaLJ4TFfSVlX6KjbatqSa3z+V1DeFmrTpKnz0wTY6mmn2q0zmrSwKuqqy8k\nGDF1DFBaNWDNnOWMnNhX7PtalbTjYqQP78SJY7ntxun86fq72OODJ/P3BQecDXzZvJ9AN1siaRcz\nm5t1IC6ZJ6ytVMXJJjoYQgwuATNoKsNySWvK8qS6izWV+nGV+MQBnaSeL4bNkIcYqlC2NmvCMaY4\nC1asZmuhcsC4GYsGqgYkSWp5rTThQ/hefmi/b+lD+30LSUcCd0u60MzOkTQC2B/4GPBqYHPgNDO7\nomRbPki1k3wL+AHgCWtOecLaKjWcbIoH9Wrn2U66P6FWYmqJGoDFfaybOXugJXdC42ed6SJeJaDT\nNOpzUG39z2qOF42qJVqh7FVqAhqrMFDS+hqVUCqrpEpBuE6hzNWIqWNKWlmhkMxeiKYclhx7tdJe\no9lPJf0M+K6kO4BVBKPJjjezByUNB+ZJetjM7kzcVp66jbiahf/nMQo7gGYdjxvME9Y8iLfgDPWg\nV0hWi1MellKstEz0pFNo5RB9QTNht9WsbIx1eAuri6olAW12HDUcX6J96YvHhuKX2smJJbCgTPIa\nUXIcCpPftNmx1sxZDnOWM2LhdDRtTzT2lIZ/gQiTlK8Wl4/u3b2wmpmtlfR+YK6kvczssUHb8mS1\nE9xJ0LJ+RaUVXet5wpqFWi9hlTuRlXtcSr+ykhNJwswzEM79TR/0eKtBHfzbuRtQKREdSqJaa2H9\nKi+Vl2ML+4OkNWVQaOF4M6gVlZQuAbGasJrYwwhg/NSB/qzF7gEU+rTWeVxKe43xRD6h1TmofqQz\ngWOAr/pxsSOdRlCX1RPWHPKEtVUaMGNL6voJ26650HdozZzlA7POsIzxJ/ejaQtsUN9ZV4l3CehG\n1X5+G9GaWstnsZp1E2JSz6Tg21eYSNaqbDck0pPbQv/9EQTHpHE7DJTAHegeMMTpWwtqOzbvCOw1\n5Od0uWRmT0laX9IIM1uTdTyulCesWWvEiavGeq1FKXN7A1zx9Ci2AThzGZz5K8afvFVwKa7SzDMO\nvEtAvtXT/aaaqxyNuqTfyP6tDZbYLSCi3BflmrsIECSuIyf2sHrGopJW1hFA3UlrdP2Vs6xsRQHg\nuedW8vPzr2ezzTYA2HavD7zjU8AhNT1nI8Xj9eNxo10HfB74YdaBuFKesGalxSN+B/VXLZwQphxW\nMk3iyIk9jJuxiG3mBMXI73p6FDtu/jJr5ixnBLNhGp60VuaDrrLQrEEv1UzC0arPc/R5ysXV5MoG\nxW4BoUotqbVI6tsa9fD8VdUnrTW+BwMxD1xV6p32Lfb8wDt5/D9PA0z67OF7NW7Am8uj7wN9km4y\ns3uyDsYN8IQ172o5INZycI4MyIpWJbD+BYw8FvZnUaRrQNiHbP4yxs25hJHH9jXuclxn8oQ1K81I\nWisNiqwjMRw0O13S9sr1dS/3nPUMrKok0r80rq5ktTCQK/YFOm2bI6aOYRyllQOCL9HAhAXJX6KH\nmLBb/wK+eub17PqO1/C14/cH4NSTDjx20HO0+jgY3x+9xbWhzOxlSbsBfwiT1lOzjskFPGHNSjMO\nKkO4NBk9gRowYmo/4wj6s971dDD7zI6bvxy0bsxYFI7WXWAae0rx4Jk4RWx3Wpd1AF2rmqSl3sE6\nle4r1+IZu69sH/M64kuso5zwvPW2egKJFUfi26xYHSDWWlqsCLAwLKkXG6AVrUk9MqWCAItTKppU\n838pc/9Nty/h7wsf56ZLD09/PVmpVH+7e4+9DWFmTwK7SJou6RZgXzN7Juu4up0nrN2mzIjd6MlG\n0/Zk5MQ+xi/shzOXccXTo1gSJq7MWccB85cxYWIfxhlWmJVrzZzlwWw1E4PW2y7vOtCtr7txar3s\n2uAWtppE6x4nXb2o5svkED4rZadnjn6hTOmzXq78XUE1yW40cR60ftpzp2wnKT5NYFAZPlvYHyxP\nS9SqrWO9claxG8Bzz63ky2f+ittv+i7asMy8rVke38rtU560NoSZnSBpJ+BmSd8ys6uyjqmbecLa\niapp0Ykd7NJafNbMWV5sYY264ulRfH1hPyycXVpZYP4yxp8cnli6l3cJaIRmXoVopOhnrZBMpdRA\njn/uynYNKKfa/rPxRDpJldOqVlNbFVIS24RpXIHBs/mVS2onTC6pWlD4kgzUPn1rksh7uskGuhk4\nZcOe/ee2ywxjJZKSWU9g62Jmd0raAfidpGnAwT6xQDY8Ye1U9UxGMLpX6mHgRDphMiOm9sOchMtw\nwINnLispN+OKvEpAHrT4BF1MpiJ/A6WtXeW+SDYgMar5kn/KZf40qa2n1TxPmWQ08VI/6dUICsnq\nw/NXMW6H9YPHF/qywtBaq6WzgL8V55SvdvrrrBLCCgPOfNrtoQtLXO0r6VCCKXwPMrNFGYfVdTxh\n7WQNKKytaXuy/5xLOGvO4G6ZVzw9ihMJBkSMnzqwPCh/1dXTunoLazdJuKQ8SMJUyXVLa72N3h9V\nWLdCctqw+CISS1XFkt60ZLUoPptWSv3okRP7gkFcQ++ONA0YX7Kk2vEBeUhgI0r+p95NYMjM7CJJ\ns4GrJF1rZmdmHVM38YS121VxABt57HacyKLEpPWsOes49die0jm+/aDog646XZnEpeIl8aTHVEra\n4gqDlKoRH4mfltA2SuT5kiYJiP5tJHQLSIot4f2LVg4YMXUMq2csCm/3B+X36p9W+gcEsx19bNA9\n1ba2Fv7O+FhYbTcOVz0z+w/wbklnS/pfggFZz2cdVzfwhLVbhC081Y7gLxmANeUwRiycnto14LS9\nFvL1709n2JEneLIaCM7DrnOUuawPZVonYwOEogoJZzXJaqGvZsGIqWNqSnITp1JNiLEomsTWMFgq\ndbspyat6JhXL6ZXEW1gWG9BZqCRQSHJH1vAeVsvMfiZpkqRTzey0QSvUUOPWlp5R2ureyuNj+FyN\n6CbhBjOzYyTtAvxV0klmdl3WMXU6T1g7XeFSZHiprKr+TIUD3djegUFZE3s4YPOgWkCSS77Sz8ET\nGzhdYvvzLgF5MNRLtEmtZtWqN9GrQzyhjSqX3Ka20tYZe+rzxFpR429i9Jhk/QuCn5mzS15Xoe5q\nWovsiKljgm1N7Blyn00z+5yk/5W0j5ldO2iFpH0ppYRY5n1IfQBW05jZXEk7AjMlfRQ43AdkNY8n\nrHZhk4gAACAASURBVJ0m6aBZ74kzmuwCEy59JyfOSO4aAANzfDMhnCWmew+KPuiq0eo92Q5lxqd4\naagqtlNyCTZ2OXtQclhhIFKaQmJWUC5ZrSQpwUxKYuMzW6U9ttrnKUk6Y62vQOr7UpwsIBarpu05\nsCxpsFt99gLuknS/mT1Uce1GT9Hr2oKZvQz0SjoSmC/pgKr2F1czT1g7SWxgR/HAHR3sUOsMOOEl\nucKJYeTPD+LUxX2snrGIK8PEdcfNX2bcDusHJ845y4M+ZBO7ejYsA4ZnHUTHGGoCUO8+WMPjoi1o\ng/oNVugjWk1f1JEpSWSSQkJbrIs8BEO91J6UTMdbfOPJa3zd+DYKfxe2MyiRjtaCLVySr2cyBrM1\nkvYB/iRpJzNbVfFBUPyCk9gVIMspXbvzWNwyZvZTSX8Cfi/pMjObkXVMrSbpAmBvoN/M3hYu2xy4\nAtgaWArsb2bPhvedBBwOrAW+aGZzym3fE9Z2UamFKWEUcmK/umoPmIWSKOGJJT4jzchjt+PgY0vL\nyxTNX8a4HZaXzobVXdbhCWtnqXFSgkqXgAe1wlL7AJl4P86S7S/sL7lEXljWKM3YVryfbSHuaIvq\noC4ChYQ8Vjkh+j4GCWx9X57NbJmkLwE3SJpS9+XeWgdkZZnYurqZ2SPhRAM/lTQH+HDVX3Q6w4XA\nj4HfRJadCNxgZt+VdEL494mStgMOALYDtgRulDTezFIHLXvCmnfVXAqNFP8v1wXASgZBpFy2TzkR\nX/KVfpYU/wpOMNsA+08dVjJatzCV611z1rHj/GWMZzY6clK3tbR6WatGyuO+U29MFSbqqKWvY+GD\nOqg26uK+QdOcQvnR+CVJYwMltZBWiqGgeLl/KAl3NJGts4uAmd0kaXuCk/Anaw/CdZPwS83nJO0B\n3CHpGDO7Keu4WsHMbpM0Nrb4f4Ddwtu/Bm4mSFo/DFwW1rhdKukhYCdgXtr2PWHNsxovhUaT1aSD\nf/wyXFCzcEHZGbEKj1vCYEuAK+es46CpQekrW9gP85cV71vy9CgOOHMZE7pvMJZXCXDJKnzprOXx\nxbJN8SQ4LektM9Vq4sx0hcQ3QS1JZL3dEqpNUJO6BRTFu2PE3+cqj0lm9kNJF0n6qpl9L3XFtP9j\nrf1b6x0g2D3H2NwLv+i8m6Bm60fN7KisY8rIFmb2RHj7CWCL8PbrKU1OHyVoaU3lCWue1XPwCQdy\n1FyjMRRttSnO3LNwNttAatL64JnhdKzT9mQ8s7nrKwMtsEDpLDTdcUD1hNUNTXSwWKXPTJ2fqcQ6\npdEpY9P63iZcto9qZHeBmpSZUCG1HmwNCayZHSrpVkl9ZnZjahx5m/HKZcbMXgT2lPSHsEvJbVnH\nNBQ3P/Qst/xrBQB3/vt5qJBgxpmZSSr3pa3sFzpPWDtBvJN/SktK2QLdKdQzCR05iYOnlZaZuTJW\nKSAYvRv0b91/6kCf1i6durU9uwR0StmbdnsdaUlThgNzSpLZaBIbl5AgagJNKelV6Jeb2o8VKs8C\nRpnkFZJbQEv/D3sSXObdx8weqbDuYO2wP7pmOAb4BcH+k0+j1qvY0PXeiT28N7z94z/+i+sXP/tY\nFVt+QtJrzez/JL2OQp9CeAzYKrLeG8JlqTxh7RQpfVHLjlZenDw6N2lyAY3tRcedwqgjg8FdB4eV\nAqKDrR48cxl3PV3YF0cV+7cWBlMEZ4KuKHnV/mWt2vXyYnyCjHZQaE3N6/sd+UKc1PKa+F7H6qqW\nVWVyG+3WFD3ujCOsz5rQb7eSqvaVSBIbNBBpX+CPknYzsxU1PaHrSuFgrA0ljQrLYHWTa4BDgOnh\n76sjyy+VNIOgpfbNwJ3lNuQJa5dJmts7urzqk+aEyYz8+WTGh62uD89fNWhSgbPmrGObOf3suPmy\ncMnfgaDVdcTU6aZpe9ZcaqtNeJeARqq2xTSaWLRj0ppnSaWa4veniE5Akihhm2lJruiDIdSdHbKV\ns8KrmjoKmCXpReAEM7unlm0kLs/7PuCG6mLgVOBrWQfSLJIuIxhgNUbSMoLXexZwpaQjCMtaAZjZ\nIklXAouAV4CjKlXh8IS1wyXNIFPon1qi2jqBo3tVOAFVW4LnrlgiW6ggMG7OJYw8tq8TJxpoz4Q1\n+v5HC+Vn+X+JXSmoph90WyWq7WQo+0G5aiQJ96X9Dw0YMbW/WJWkoDgLVjh4rGn7QBirmd0OTJE0\nHviBpFcD55rZpamPLde1gsgUqinP6drez4G/0cEJq5kdmHLX+1PWPxM4s9rte8LawUoOjNE5zRPn\nDl8QO1hWl0RqYg8jCC7LHTC/crm5ePK6esYiYFExcYXaJzjIofZMWNNkebm6Ustewrolf7t8SWpd\njO9fSbOTRfeDaTCC2anlslp5/DCzB4F9JG0AfE/SccBfgIvMbGFN26p2YFiU7+NtI2yaf1TSDmY2\nP+t42pEnrJ2oQumUxFG8C2cDkVG/lWoXju6VejB6JsGEBYycBhPiFQdi5WZsYT/jKRT/ThiMVSjJ\nRXUtaTnW/n1Y86TWqVhdPtVyKTxtWaGE18Sekq4BI6aOQdP2JKtJSsLR4EdJEvAtgv56e5esFO8L\nnCL+5Sy9RXZS+w0wdCcDZwMfyjqQduQJaxeK12Qtq9xBMD6ieCwlB1BNKV1XU4L7Rk5LrxfbIdqz\nSkBUUitXXK0tr7V0MchDdwRXvaR9IZ5MpbWc1qlY+5mgpF4euoKErWiXA+MTV4geM6uoyVousR3o\nJhO+bv/M5J6ZPShpM0kjwoL5rgaesHaipG/y8ZG9hULhCZUCah6AFX/uCvdpbG+Q3AKFJLam7eRf\nZ7SwVjPAqRHdBSL9oTugO0h3qqWVdIiK9aEJjmNtu89UOYDQdZwrCWZ6Oj3rQNqNJ6ydKqnvX9IB\ncmzrQkrUjiea6nTq66pf0v/aR0y7WiTVie1gg6bdTbnftZWzgdvxhLVmnrB2Mj/pZ6UzWlidc9mI\nVeyoetChy72w20i/pO1rKofmPGF1rgk6q0pAklpPjhUGhwypG4pznSxecrDcOq5dnAx8B/if+B2S\nphJ0GXgEON7MnmxxbLnlCatzjdf5CWuj+InWuer4Z6VjmNl9kl4jabiZrZU0CvgmQb3SB4ADgR2A\n34WVJ75vZtdmF3E+eMLqXON5l4AEtRT/d865Dnct8CNJY4FXA78GdorM9nQdcF04KcV0SV8H5gEn\nm9kLGcSbOU9YO1nSJSRPFFqh/cta1ctL6zjnXDWmA78hmNr3vrSVzOwp4P8BSDoYuF7SKuBb9v/b\nu/sgyaryjuO/J7C7YXgpJdiIBLLrH9uJjnGmeCmDbgSURWQwUFiCkZgQNMRExSiUCEIhUARCuaWi\nYhIsjEAFSBAqrtEdMC4FZTayOCMMq4PIoisiA64S4q7sFvvkj9s9e6e3e6an+/Y99577/VRN0duv\nzwzdp3/99LnnuN+fS6UFQWCNVXrr1DkL+t/oqo+WdymYcmBJmlbNjSYapwNXAwBBufuLkt61yNvc\nLOlmMztc0j+Y2RpJt7r7mkHUWDQE1pi1LM7/6FVbGhc8qJUXHSYbvtFt1dkEiOzF2WHtd81VnmcA\n0Dd3/4mkMxtzX++TRGBFydVHZ0PrzvFn9cDWZbMXPXD+jI46cItWHLlJSz98o9uJdxAmshPfHNZu\nFzInlAJAXi5WMq2gEgissWp+BVsbkeqTWjo8oXdokzZv3K4Hti7T45Ie37pMGt+lMzY+qPqDV3qo\nfbgjFF9gJYgCQNGslvRHoYvIC4E1Zs1dYRrBdWl9UivvWCddtSUJq2nTE+F3vYpLnAEvi61YAQB9\nMbPXSHo6tapA9AisVZAOrqdL9eEJXbhmk24f3yVJWnHkPsn0AWQlvg7roC2wsQAAYI4rJV0Suog8\nEVgrxmojanZbz5qekE/NyIZrYjpApuINrL2GSQIpAGSisZnAy939odC15InAWiWpLf6awdVWiQCR\nPXa6Wiyeg6iC1oMXed6jN++TdHfoIvJGYK0iBslBi3NZq34MjVnXKw0AVcFGG+jNuyQdH7qIvBFY\ngeztCl1AIQ3iTZmpBhikAXVEZzd1aWC7YnTLzA6VtN3dXwhdS97oAgHZo8OaBzq2yNu2tT7nZ7Hm\nC6U8n9Gdv1eyrWvl8KYKZI83HiAGQ2M2+9NOj6HVaiOdL+81DKMq6u5eufmrElMCgEGoToc15Lqs\nzXmxfJWKPHSah93Lc7C5sUvr/aN3FTigzcxOk/S90HWEQmAFshfvslatQr8phH58VEu70Nrrc7DT\n7XhOZyPOD7PnSXp76CJCIbAC2eOgKyBW8YWgOEX2/8nM9pe0l7s/G7qWUAisQPaqMyVgEOLsjAAY\npPjHjMsl3RC6iJB4UwWyx8YBAIAsHSPpy6GLCIkOK5C96sxhBZAt1hZGCzM7RtJP3L3Qq0eYWV3S\nramzXinpUkkvlfQeSc80zv+Yu39jsfdPhxXIHh1WAP1jeSskLpH0sdBFLMTdp9191N1HJR0haZuk\nryh5T1zTvKyXsCoRWIFBoMPaL96oUVV0VbPRzwYPBWJme0t6qbs/FrqWRXqzpMfcfYuS98O+n9dM\nCQAGgzedXqXfsDkAC1XEc74/rSG1+e9y/l3Pl3RX6CJ6cKakf22cdkkfMLN3S9oo6SPu/qvF3iEd\nViB7dFizUPLOCIBAUsHUZyblM5Mhq+nXqZKuDV3EYpjZUkmnSPq3xlnXS1ohaUTSU5I+2cv90mEF\nskdgzUI5uyEAiqDTzmTl8ytJnzGz9w/0oKtl+0j10Xmvsn7DZq3fsFmS9J1N2yTp0A5XPUnSg+7+\njCS5+0zzAjO7QdJXeymRDiuQPQ66AgD0zd3fIumHkr5rZkeGrOXY163QZR86Xpd96HiddOxKSXqy\nw1Xfqd3TAWRmh6QuO03Sw708Ph1WIHvsdAUAoQ2NmS0fC11F39z9U2Z2k6Tbzewnkv6yqEtcmdm+\nSg64em/q7GvMbERJM2ezpHN7uW8CK5A9droCgKIp8Rq37v4LSW8ys3MkTZjZee5+b+i6Wrn7ryUd\n1HLeu7O4b95UgewxJaCdCJaYAYCQ3P2LklZJutDMbjWzJaFryguBNQaEgKLhoKtOStbVABCRoTGb\n/Skxd3/e3U+SdKekjWZ2Uuia8kBgjQWhtUgIrO2U/E0CKI30ovm8N7QXwd/G3W+T9DpJ55rZXWa2\nLHRNg8Qc1hgQBIqGKQEAgIFz9+2STjWzkyU9YGZXhK5pUOiwAtljlQAA4dDEqBx3/5qkIySdELqW\nQaHDGpPWrzcYtEJhlQAAYTUXzud9ING6QkCEfxd33ynpr8zsvQteuYQIrJHwJ65MXozTE1J9VFYb\nCVxRpTElAEB4EYYyVBeBNQbpT44LbK2GXDAlAACKpF1451vJUuFryxgMjVmzo2q1kaS7ygsvJKYE\nAEDZlHzVgNjRYY1FJFvQRYIOK4BioqvYGX+LQqMLBGSPOawAyqHKXcVID76KFYEVyB4bBwAAkCGm\nBADZ26WYPgw2OzDNZXLS6E4A5dJ8zaZfy1Vd/mrbWveZyWR1HUmqTzrHgBQXgRXIXnxfsTGAA4jR\n9IR8akaSZPXAtWBe8XSBgOKIaw4rYRWIX4XnstpwTTZcC10GFkCHFcheXIE1LT0tgCALlFe7qQFV\nMzRmtkrJtACJDXcKjsAKZK+8B111E0YJqkA8eD0TVEuCwApkr7yBFQCqJB3Yq9xtLgHmsALZI7AC\nQFltW+uz4TV9GkERWIHslTewspA2ACRal/5CUARWIHsMbABQNm0+sPvMpJoHZRFawyKwAtkr3ioB\nfK0FoBuMFZ0PROPbp6AIrED2ijclgIEWwEKqHlTbsNrI7lUE+PsExSoBQPaKF1glQiuA7lRprOi0\nlF+bdWp9ZlJWazPlq0p/r4AIrDHo9KmPF1EoxQysWWl9vvE8A+JQtddy60FV7X7/luCahFbWbQ2B\nwFp2fEVRRPH+P9m2dnZXGIkFtwFEZIEP41YbmT0Aa05w7RR2kSkCa4ya22emX3y8mPJUvIOu+pXq\nLkiSpickST49IVslBmsA5ZPearpL6Q/pdFvzRWAtuzZBwZ+4MnkBTk/Ip2ZkwzWpPulWGyG45iOe\nKQEtHdXZoDo1I0nJcwsA2lkoDBbh/ajb0Drf9Yrwe1QAqwTEpvmCaoTVnePPJuGiETSQizgCa2tY\nTZkNqvXRHAsCEI0ihbxOG6bMM0VgzuoBmGVmT5jZQ2Y2YWbfaZx3oJndbWaPmtm4mb2kl/smsEZu\nyeqDkhMEizzFEVjbST2P7PQTRdceQDeaC/AXdhH+TvW0C63pMY/xr5VLOtbdR9396MZ5F0q6291X\nSvpm49+LxpSACFltRKqNSPWWg2N4YeVlV+gC+pIaoNt2ENLn8ZwC0EkPc0RLg7FvPq1/m7dJemPj\n9L9IWq8eQiuBNTbpryyWjwUspPJKPZg1uyCzgZXBGUAfmkfYl+5rdMa+xXJJ95jZi5L+0d3/WdLB\n7v504/KnJR3cyx0TWIHs7VJZp9vMM28VABZtzrxPtV+kv6jKUmdO1m/YrPUbNkuSvjP5U0k6tM3V\nXu/uT5nZyyTdbWY/SF/o7m5mPXXdCaxAxhovyNBlJLJY5J9BG0AWijKWdNrdKq/bF9He+yzY/T7u\nbSM67m3J6euu/6q+ce8Pn2y9jrs/1fjvM2Z2p6SjJT1tZi9395+b2SGSZnoqsZcbASiBdov8LzTQ\nDo3ZnK0HYxqQAaCb3a3aXZ8DrRZkZkOS9nL3581sX0mrJX1C0n9I+nNJ1zT+e1cv909gBWIU64EO\nAPIVYzexG62/L7tZdeNgSXc2vmHcW9It7j5uZhsl3W5m50h6QtI7erlzAisQMWs9or/55jPfm1DM\nR/YCQFNr17TduEdI7Zq7b5a0x7wCd98q6c393j+BFYhRc/DtdbBlkAYgxTcWxPb7VMiCgdXM1udQ\nB5CFvSXtL+mXoQuR9LsFeO3cq2S9u1lvfMPw7gvvn9rj8gF7UdJeOT4eEMKQpC2hiwgm3aUsWzgs\nW70Vs2Bgdfdjc6gD6JuZvVbSBe5+VuhaAFSTmb1a0sWh68jUIOexVnWOLBatnGtFAgCA4iF4YkCY\nw4rB4FMzAMRhseP4Yq7PewS6RGDF4jXCaDc7IllNeS8FwuAHAGXSaVWS5ntHrw0QGidRIbBicVKL\n0fsd67Rz/Nk5Fy9ZfdDsaRuuyacnpPqkW20kj0Fj14DvHwCQl9aVThaz8glL80WHwIreTE+0PbsZ\nYJesPkg+NdP49yYtWb1ONnyjS5KtOju5Mp96AcSn3MeGhDjKf761n1vP7za09ru0HwqHwIrupQeO\n+qhME1oyz9V3jj+rzRu3J//YuEXSFq286DBJNya3b24Bmu2AwuAEILTqdff6DbqtndQ2mt/udT3V\njLAaFQIrutf4xDr79X5trduqPa/mM5NJBzY1XeCBrcskSSub17ljnXaM3yJJWrL6GrfhWtJ57W+A\nYUoAAPQji5DXb2eT3fbQBoEVi9O6lV0btnxMqq31pfVJ1acn5FMzs0HVhmvasWaTrh5PZcvxGUkz\nOuPA92vlRUl4VX1UtvzjfDoGgDJoPUCq3wOeuui4oloIrBiMoTGz5WPScslWpVYVaMx9faWkx1tu\nctvWZdL5SXiVpnTGgTf4yosOk51+ohYRXss9fwwAQurnq/12wTLDObFW22ObelQIb+4YvJZBasnq\ng/SO1Qs/9W7bukxXnD+jy1fcpBdO/AP3J670BT5p9/4pfNtan/MDABXlM5NJg6HHsbDjkodZjK1D\nY8bc1Gqiw4p8tHRcl54+qUsb0wUevWpL0l2dx9Xju3TGETdo5UWHJasN1Eclzf3EPfXAZ3XF1bdK\n0pld1TTf4MnRpQB6E00jyGcmF7eWdvo4hz3up4/uaHq6AWurVhaBFflrCa/10yd14bm36PbxXXtM\nE2j16FVbtOLIZKmsxC2zl/kf7iN/bIt83eltA22nT/17DKQMhAD6U91vaVrHzzYBNvPHQCUQWBHW\n0JhZTb7szhGd1Zjjunv9Vu1eFkvSiiP3mT3dPL+5+sBRB76gJzY8p//dvk071mxSsvbrQXKtkw3X\nJEk+NTN7elYj2DZrGcBvCADx69T5TB/xv9hF/xmTkUJgRXiNQam5uoCtkpa9L7no95XqjDbCrCSt\nULLG61EHviApCa4HHrCj7d03b5M+3VyJoLUGAKi0RhMh6/vs6XatU7Oa4ZcpW5VEYEWxtBmEZgfP\n2ohUT8Lr0uEJ1bU7gK6U9P2n99P+4zv22B62rfro7qkADHwAsFu/C/8DA0BgRfGlBkJbPpZ86m6E\nzebGBevu+a5u3vgtTf1mH5338M7dt334yeR6+7xszl1e94VLPpP656cHUnc2YpsLd4W7/yJ0EcAA\n/ZakI83suhweayDjw/v/euwDzdOf/cLa6wb1OPPoOD5/8H1jyRWuP6XXcTuv3yWPx7nP3b+Sw+MU\nAoEV5dPmk/xb/uSUeyVd/bU7Lv3P1stOPv3yt+ZSV/aiOdq44V2S3irpptCFAAM0JWmHpK+FLqRX\nb119RDqwfj2nh+2qQ/uZ69euG/RjZCCvx7nCzO5099gaG20RWBELd/evq81A4f6JAOWglZkdHboG\nYNDcfaeZ/dLdvxG6lj7MjqPul+X/6Bx01RUze62k8yVdG7qWPMTWwQEAADFgE5eFXC3p7aGLyAuB\nFQCAbO0ws/1DFxEFQmtH7v6ipB+bNY/miBuBFQCAbD0i6U2hiyitNpsPzHv9am+r/RFJl4UuIg/M\nYQUAIFv3SVol6a7QhRRaa8BsM2e1uQ5311vEVmyNVnffYmZLzOx3Yl+BhQ4rUBTV7RAAsblH0mtC\nF1FqFQqdGbhG0idDFzFoBFagCAiqQDTc/TlJy0LXER3GyU42SxoOXcSgEVgBAED+hsZstpO6QEd1\ndovubu6zQhrLBd4q6eQC1HKYmX3LzB4xsykz+2Dj/MvM7KdmNtH4eUsv988cVgAAsudmtsTddy58\n1YqrWMjMipkdr2QqwKpGVz+0nZL+zt0nzWw/SQ+a2d1Kdv1a4+5r+rlzAitQJHEP3DH/bkCrH0k6\nRtK9oQsprC42CLDGNtzzXi/ucbMtMztV0sWSjnH37aHrkSR3/7mknzdO/5+ZfV/SoY2L+/5/xJQA\nAHnaFboAICfflnRc6CJy18sSU8xNXRQz+zMlO1wVJqy2MrPlkkYlbWic9QEz+56ZfdHMXtLLfdJh\nBYqggh0CIHLjkj4XuohctQbPfpeYYlzcg5n9raTTlEwDyCzoP/b4z7TqhI92ff2fPbW142WN6QD/\nLum8Rqf1ekmXNy6+Qsk0hnMWWyOBFQCAjDXWxzwgdB2FNjRmVVs3tR9mdrGk10k6IcuwKulH27fv\n+OP7v71poeuNKOmaNm1ovYKZLZF0h6Sb3f0uSXL3mdTlN0j6ai9FElgBAEB/2n2t300QJax2xcyu\nkfR77n5K1vft7r9WstnFQua9jpmZpC9K2uTun0qdf4i7P9X452mSHu6lTgIrAAADYmaWcTdsMNKB\nM+8QSZd1Xmb2eUlL3f3M0LUs4PWSzpL0kJlNNM67SNI7zWxEyWoBmyWd28udE1iB0BisgVg9LelV\nkh4JXchA9XrQVPp2XawYUEVmdpOkZ939b0LXshB3v1/tD+b/ehb3T2AFQuLoWCBm/yPpBJUhsPZ7\ncFQvY1njds1NAeYsYQWZ2bWSTpE0aWbru7zZtsFVFBaBFQiJbgIQs29K6v7Q6zJrDa3zjW3zfavE\nN06z3P0CSRcs9nZmFmUjhMAKhNDp66/WLgUDN1BmD0l6RegicrOY8apNR9ZnJumyoiMCK5C3NvO2\nWvfJjnTQZqMSVIq7e3LgNIB+8QYCBNYaViMX5VdVAHpntZFYP6QjQ3RYgTy1TgXYttatNjIntFpt\nhKkAQDyeN7ND3f3J0IUUUmqssxofaNEZgRXIS7slXBrS3YUkvE7uMXATZIFS+q6SlQK+FLiO4ug0\njjG+YR4EViCENsvA+MykND0hn5ppf5vhCameBFnCK1Aa6yW9UwRWoC8EViBvbYJmOqzuHH92j5ss\nWX1QcmI62TzE1fj6jNAKFN39ki4JXUQpsEoK5kFgBfLSOvi2zmOdnmh3KwAl5u47zYwDnBeS2kBA\ninalFPSBwAqE1pweUB+V1aWlw3tOC7DhWqDiAAAIj8AKFMHQmNnyseQrsdqIVJ87n9WnZpLQWh+V\nxBxWoGR2mNn+7v586EIKJzUNoHXFFCCNwAoUzLwHX6XmsEqTbss/TmgFiu8RSW+SdFfoQopudioA\nH8jRgsAKlEQ6wJoa811r7LsNlMB9klaJwLqn1hVTGM/QAYEVKJKhMbOa3JUKpQ3tOq7J3tulWS2g\nDDUCg3C3pHNDF1FY5Ri/EBiBFSiJSA682hW6ACBv7v68mS0LXQfiZmafk7QzdB2DQmAFiibVZW1n\nj+Ve6E4AAKSjJH1E0nmhCxkEAitQNK2LZ09PzF0dQCKkAuWzy8yWuHu0HTDkz8xM0uHu/mNJj0p6\nMXBJA8NixkBBzYbT1rAKoIwel3RM6CIQDzN7qaQNkm42s3uUzJV+T9iqBocOK1BQbdcjpLMKlNW3\nJR0n6d7QhaD8zGxU0pclne3uG83sVZI+L+llYSsbHAIrUCTp7QnTW7XWR3evCCARXIHyuVvS50IX\ngWislLRd0kfN7AJ33yTp2MYUgSgPbiWwAkXROne1Pjo3tGr3pgKqTzrzWYHycPctZnZA6DoQB3e/\nTdJtZna8ku79Kxrne5JZ48McVqBgrDay53zVZnBNBVi2MARQGtvW+uwPsnSppD8NXUQeCKxAUQyN\n2exPJ40DsCQOwgKAKjOzT0ta7+7rQ9eSB6YEAEXUWItVtZE5nVSrjUjlnQrAB2QAyICZnSTp1e7+\n5tC15IU3EKCIUl+bNacIzOmoli+sNvF1ICrJzPZSpAfDdKU5ZpV37CqaH0r6bTP7p8ZzK3p0RQk4\nCwAAAWdJREFUWIGyYKAHymw/SS+ELiKooTGbM4eVMa1n7v6YpDeY2V9IetDMznb3iQVuVmoEViCk\n1gMQ0l2I5mUM6kAMDpC0I3QRiIu7f8nMDpc0KonACiBj7Y6UbQ2mBFUgJvtL+k3oIhClwyV9K3QR\ng8YcViBvnZZ1YbkXIGb7KVnoHcjaIZJ+ELqIQaPDCuRloUBKRxWI2b6q+hzWNMa7vpnZqZLOl/Qr\nd38mdD2DRmAFQmLQBqqCDiv61lgR4BJJJ0t6SNLJ7v5c2KrywZQAIC/MUQWqbF8RWNEjMzvYzG6V\n9N+SnpN0tLufU5WwKtFhBfJFSAWqakgEVvRunaQPu/t/hS4kFAIrYvHT0AVgQTNKOgNAFW0T4xQf\n2nu3Q9KlZnbpAtfblkcxIZh75+NAzIyjlgEAAErE3aP7YDBvYAUAAABC46ArAAAAFBqBFQAAAIVG\nYAUAAEChEVgBAABQaARWAAAAFNr/A2Oh245sY/ENAAAAAElFTkSuQmCC\n", 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aOBMvLX8Iw4cPx8knn4w1a9Zk3N533+3APxo24ZzThuSht4QQM2VlZejs7ERr\na2vadUNp4bn88ssxatQonHTSSX53hRBSDJgqqL/zzju4/vrr8emnn2LYsGG49tprrWefGdvpcX6n\nn3kbpp02FjUThyWWE0Lyyvz587H77rvj4osvLr6g5VDn4SGEBJ7DDz8cTz/9NDo7O/Hkk0/i1FNP\nxY4dOzBlyhRcfPHFiWBkVdCU1WDdB7/G6PFXAmU9/ek4IRFk9uzZGD58OF588UXH9ejSIoQQQ7iY\nLDKxWAxTp07Fyy+/jBUrVmDbtm0YNWoUxo8fjxdeeCGlmbvuuit9DBAhxFN69uyJ9957D7fddpvj\net4LHpcZSHOBFh5CiOekcT91794dv/71r/HWW29h4cKFWLx4MY444gicdtpp2Lp1KwBg9OjR6N+/\nP+65555C9JgQolBZWen4ubeCJ48iR0X30RVkX4QQYmafffbBwoUL8fbbb2P69OlJxVTvv/9+PPTQ\nQ9i0aZOPPSSEmAmtS4sWHkJIEKipqUFHRwfq6uoAaOPTo48+iilTpvjcM0KIireCp6wm+S9P0KVF\nCAkSCxcuxKxZs+LvDz74YBx11FGYO3euj71SyHOYASFhIJQWHrq0CCFBok+fPqisrMQTTzwRX3bT\nTTfh5Zdfxrvvvutjz9JQgJhLQoJCKKel06VFCAkaDzzwAIYPH46pU6ciFtOeJZ988klMmjQJq1ev\nji/zBTuLO/MEkQgRSsFDCw8hJGiUlpZiypQpqK6uxpQpUzB+/HhUVFRg5syZOO+88/Dggw/63cXM\ncGP1oWAiISKUgocWHkJIEPnVr36FUaNGYcWKFZg9eza2bt0KIQQ+/fRTnHTSSfjxj3+MsWPH4ogj\nAl5JnWKHFCHpS0t8/1zywgCc5PPnz8c+++yDmTNn+t0VQghxxQcffICXXnoJb7/9NtasWROf1RVY\nnERPAO4DhFjhSWkJ2dQAUT5Iuwh8Ptnp0iKEhI39998f+++/P8477zyMGTPG7+6kJwqiJgD3M1I4\n0kbRyaYGy9cFweYJgy4tQkiYicVirqo7kzyhzkzjLLXI4MrCE7fuFAr15FOrEes4WnialyUJM1E+\niAqeEBIoDjjgALz++usYN26c310hBrT2FD1pBU9c6AToREiy8CjiSDY1AOvqIRubEitX1QOVDYE8\nDkJINBk+fDhee+214hA8YRQKZTWWD9MAGLBdxKS38ATwh80khkc2NgGNy3XhMxiiHIE8JkJIdDj6\n6KMxe/YwFC/jAAAgAElEQVRsv7vhDWEdTzPod4rXIIwijwR0WrqT+obJwmN8bqxfORgCCStPW+0W\nAEAJAIF6wMo157AvQgjxmoqKiniFdUJIYQim4AEcxYelhaesRrPeAJqoqWyAXLocJdW9k1bT4pGQ\nKpTMr130gxBCSIBRx3SrMV9dbve+eVlhY1hJ3ghlLS3bWVpqYHP5IIjJE3LfGaP3CSEkfJjHbmU2\nVtKM43RjvH5fkU0NhZ+pTDwluBYeBxxjeJJED4DJANbV2zemus8IIaRA/OAHP8CmTZuwzz77+N2V\nyGBOs0LLTbQoLguPmbIa7YSuHJx2PUKKAla/Dg2DBg3CSy+95Hc3woN6buc6k0p/CKbFJloUn4XH\njBHboyp5twKHQogQkidGjhyJZ599FmeeeabfXfGHTCaLmPKraZgsNFbt2MXtmB+C7WZdKdvRGhR+\nQil4Ms607OaCSjMzjJBQwHM3NIwZMwbXXXed393wHxfWGiPHWhKVg00iyCHfmv7emNhiadkxj/8W\n8T5JooeTXEJHMAWPVWS9Qt5qafFkJYQUiLKyMhx66KG4/fbbccUVV/jdncJjPGSa4iht3UyVg63j\nMZVlibuCRXxOWU2K8LHeX2Lb+Gfr6pMElq21h/l5Ak36aumFLtLpQjUvWLAA27Ztw9VXX12gThFC\niPd0dnbiiCOOwEMPPYSqqiq/u+MvVjOooIkLx1gbKxFkEbdpFilJYsZi+xTRY4MrV5d6D6MnIa94\nUi3dD+wqtLNaOiGkGIjFYnj66acxceJErF69Gl27BnpIzi8W1hcDO1EhmxrST0pR180ATwOarR7k\nKXwKTvCuLsW8aeePZbV0Qkix0KdPH8ydOxennnoq/vrXv/rdHf9xGcQMJAuhrAWK2VVmI6AcLTnZ\n1ONS16XoKQjpBY8fKjTNvmjhIYQUE1OnTsVzzz2HP/zhD/jFL37hd3eCj8Xsq0xnUSUJJJdWItt+\nWC13K3rsZpI5tU+yIq3gCWJyJlp4CCHFxiOPPIJhw4ahuroa+++/v9/dCQduBYGFmPDkvuZiOrsj\n6fqfZgIPyYzgubRcQAsPIaTYiMViWLp0KU466STU1dUhFgtlXthgkosoSUcuXpA0fUiaFeZUAyxd\nfTACwEWm5aBZdwBtYKDgIYQUG/vuuy9mzZqFadOm+d2V4seYpq5MV8/LPixwU5fLXAYjZX1mVc8Y\nd48QAVOLdGkRQoqVGTNmoK2tDY888ojfXYkWuQogs/vJaMNlm4aoscr1I8oH2Rsf0lV7V/sXcYGU\n3qUVMLED0KVFCClu/vKXv2Do0KEYO3Ys+vXr53d3oolTMHGObbqdep8yU9nOdRXA+3QQKUwMj8fT\n7mjhIYQUM127dsVf/vIXnHzyyVi5ciXjeTIlm7gau/tUJlPO3e432/shhU1O5C543AZLeSh6aOEh\nhBQ7lZWVOOusszBz5kwsXLjQ7+6EB/We5Pa+Y2zjJGbM1pSwuYbUUh4RJTfB49MPzqBlQkgUuPji\nizFp0iQsXboUkydP9rs74SCbG7pF3pzUEhe5dCogRFjsAIVyaXn8JdOlRQiJCkuXLsWQIUMwatQo\nlJcXw13XR5w8EmksN7ZV09PtJ+IiI0jkJnhUVVzAH5UuLUJIVCgtLcVjjz2GU045Ba+++qrf3Sku\nrISJEVjc32UVd4vPkwRRxN1IQSJ7wWP8iHYJnfL4A9OlRQiJEoceeiiEsCwATTLByYrjIH4AXQBZ\nrWtBytRyip5A4E7wWJkBnYKTjf95+oGFEHRpEUIIyQ6nLMWZbO+wbRCT9kYd98VDDZxETIGCmGnh\nIYRECT7g5RGz+MnkQV0RPhQ4wSczl1a6fAQFMtlR8BBCosQHH3yA3r17+92N4of5cYoad4LHwvwX\nD9BaV6/9rxwMQK+snucfny4tQkiU+Oc//4n99tvP724QEmqyiuExxI5cuhxttVv0pWtRUt0bmKzn\nK2DQMiGEeMK//vUvDBgwwO9uEBJq3Lu0LBIzGWyoawEAVGALSrA876KHgodkjQ9pFAjJlQ8++ABH\nHXWU392INsytE3oyq5YeL3ymVW4VVeWaVUehrXYLsK4+bc6CXKBLixASJTZt2oTDDjvM724QEmpy\nq5ZeORgC9agYsiVpsWxsgkA9UJ6fqem08JCs4ZMZCSGtra3o0aOH392INg5eDhIOMk88qCZiKgck\ngNLLNZHTVrslbvGRjU0AFkKMguc3GQoeQkiUaGlpwSuvvIKePXtil112wV577YWysjK/uxU9+MAU\nanIuLSH612iWnMoGlFbVx4UPAJQAyIfooeAhhESJmTNn4s9//jNaWlrQ0tKCjz76CHV1dX53i5BQ\n4VnxUFE+CBIAGpfHl7XVbsmL6GEMDyEkSpx//vk4//zz4+9HjRrlY28ICSfpg5abl7n2WxqBzFbI\npgbP/J+08BBCokxpaSm+++47v7tBSKhwN0sLSBYrNiJINjVANjZhQ10LNtS1YHFtJ9pqt+jxPN5B\nwUN8J4MHAUK8pqqqCi+++KLf3SAkVLgXPIDrQX7V1m5YtbUb1gNx0RPPyOwBsViMLi1SGIxzXi2K\naxb/hBSYsWPH4pVXXvG7G4QEim3btjl+njaGJ6XMvYpRaC2enwdAVT2G9tqIRVu7xVfbUNeCygw6\nnQ4hBC08JL9YCRmrZZy1QXxg/PjxuOeee/zuBiGBobq6Gu3t7Y7rpA9a1i0zqrwQ5YOUxILJgkg2\nNmGVInbWAxiqL0dlgycZmOnSInkjE4sNxQ7xiR49eqCtrc3vbhASCN577z2UlJSgtrYWQgjb9dzP\n0lKKhKYUDgUg9deJ2loJVm3thoHqghzT+1PwEE9RRI45Q3iKZdM4Z+nKIoSQQDB//nzMnTs37Xpp\nBU/rHWuTykcI1OuV0ROfG7W0AKBiyE6W7bTVbkFpVT3gQTV1Ch6SD9KKHSA1fkcVQFG2+ET9+H3g\nhBNOwLBhwzB06FDcdNNN2HXXXf3uEiG+sGHDBowYMSLteq6Clq2sNvEd1bVg1dZuWKQHKm+oa8HQ\nXjvc9zQLmIeHeIaNpSZJ7Li9kUfV6mMX0E3yyhVXXIGVK1di5MiRGD16NJYuXep3lwgpOC+88AJ+\n9KMfuVo3rYUnybpjkWOnYshOWFSbEB/G7CyVob12oKS6b5JlKBdo4SGeoVho4iLHrcBR16N1IwGt\nPQXl9NNPx1dffYV///vffneFkIJzxx134E9/+pOrddMKnrjIMcTKuvqk2J2S6t64Glvilh6z2Jna\nawcGzu1rm5AwGyh4iOdkeoPmDT2BuaiiOc6J31Xe4cxVEkXa29vx3XffoV+/fq7WTy94Rs1IiW1I\n+ryqHKVV5RjY2ATcvBFQZmgNNcTO5AmZPz07QMFDCg5v2s5YfT+sLl0wmJuMRJGGhgZ0dnais7MT\nsVj6CB3XeXgcqRwMUQlUVtVrwkdHVJVrn3kQqKxCwUN8Jd1NnOIoAb+LgkALD4kiQ4YMwfTp03HU\nUUfhxRdfRGlpqeP6mRcPNcXhJFluypdpRULNeDzoMWi5SAiby4PWChJQQmHhMV8/YbnuSaCZNWsW\nKioqMHz4cCxfvtxx3fQurUxmqxToBKaFp8gIUpCrV6LGKqaFkDwRCguP6uLkNUE8pKamBv369cO4\nceMc10tv4QngiUnBQ1LIVWB4ab2hJYgUmNCMiQG8n5Di4NBDD8WKFSvwwx/+0HadzF1aASA0F3eU\n8MJc7dXTXzqLUb4ECQdz4hOhcGnZQVcX8YjycufZ4JlVSw8IFDwhwI2oUAc2ryuQWyXBy0diPKN4\nLgdp4iOhcGkR4jO08BBvcDsFOR8+fKf9GtaebIUOhQwJAaG28DB9ASkQFDzEH9y6nXJ1TRnbm9ux\n2jYI4obBziQLAmfhsbs27c5pnuukAKQVPGPHji1ANzKjvb0dX3zxRSD7FgZ22mknvPDCC943nMmT\nWqYWIfO26v9sYgCCOMBaueCAYPaVBIrQPAQGaUYmiRxpBc8rr7xSgG6QQjJmzJj8Na6KEKsbtheD\nnXnQLJYB1E7AEWKFcn0FzqXF8iIkgITSpUUCToY3bNnUkJzvKerw5kDSYQryF631wbTw8FwmAYKC\nJ4IIIfztgDJYG6VLXIseDqCEpBATCKbgISRAUPAQ73ERaGwIHdeWHQodQhKYrgex0w50dr5ZuP3T\nVUUCyNatWx0/D2UeHhJCLHLgWIoduwGUcS2E2FLwoGXmniIB5LTTTnP8nBYekh+al8WtOAaqwEkR\nOxw8CcmaWOtqdOz4D7MWk8iyaNEi9OrVy3EdCh7iPYbYWVefWFY52DlOhxYcQrJGxARkp4WFhwKI\nRIDt27fjpptuQl1dHRYtWmS7HgUP8Q41PkcVO4SQvNJ1559ASl5zJJpMmzYNN9xwA0pLSx3Xo+Ah\nuWEKQgZgK3ZymnrOJ1NCbBFCoLPLvn53g5CCs2LFCrS0tOCEE05Iuy4FD8kcs8gxC5zKwUlvKXQI\nyQEX5UbiQcvFnLySWZqJic7OTsyePRuvvfaaq/UpeIg7LNxVsrEpZTVRVa59rouejMUOBzRSCMIU\n26KWYbG56Xft2hXbt29P3kalGMRC2PtPPOf888/HRRddhJ49e7pan4KHpEcJQjZETlvtlpTVSqp7\nZ78PDmaE2JPm+hg1ahTmzJmDd955B4cffnjG22cNi90Sn1izZg3ef/99/PGPf3S9DfPwRJCM8nWY\nxE5b7RZLsZNEptYdDpTET4rg/CstLcXf/vY3nHnmmdi8ebM/nShGNxoJJM8//zymTp2KJ554IqPt\naOEhOaFadURVOcUOCQdFeN716tULS5YswTHHHIO3334bZWVliQ/zZYkpwu+RBJMvv/wSc+fORUND\nAw466CC8/PLL2HPPPTNqg4IngmRaS0uUD4IEIFCPEmjurBShAyQFK7sqHcHBkhBPqaysxF133YVx\n48bhzTffRCwWXiO+lBKdnZ1ob29HR0cHOjo60N7eHl9mvDYv7+josHxtrGv8WS03XlstM39utVxK\nmfK58Vr9s1o33Z/xfTgtk1LG//yumdjc3Iz58+dj4sSJObWzePFi3HPPPRBC4JJLLsH999+fdVsU\nPMQZXZSIcgDlg4DKBpRORkpSwWzaJMR3rNwwIT8/x40bh/PPPx8DBgxA3759tYUdXypr/Arosrsv\nfcsUIQRisRiEEOjSpUv8tfrf6vN0r81/6nLzOlafdenSBaWlpUnL1c+slqmvu3btmvRaCJGyzPy6\na9eu8TaM18bnxmv1WP1mx44dGDNmDLZt24apU6dmtO3mzZtx9dVXY926dfjxj3+MZ555BrvttlvO\nfaLgIe4whE9//WbQH9n57EN+MyFFRLaFbEPAjBkzsH79enz22WeJoE4W/CQFpFu3bnj99ddx9NFH\n4+uvv8b555+fdps333wTV111Fbp3746rrroK1dXVnvZJOAWwCiFkQQvSkYIwduxYvPLKK/nfUSZT\nfzkYk3xjI9BtBU8RnItnnnkmBgwYgHnz5vndldzgbLDQ0tnZieOOOw5HHXUUrrrqKsd158yZg0GD\nBmHatGlZ708IASmlpYkrvA5ekjUFE7FGRWWnysoWVdQJKSSifFCy2CmiSuAPP/wwXn31Vcf6QqGi\nSH6XKBGLxfDCCy9g5cqVuOaaaxzXnTBhguskgln1JW8tE5IOCh0SRIrsvHz++eexYMECv7uRG0Uk\nQqPKkiVLsHnzZsyaNct2nTFjxuC9997LWx8oeCJIEALaLOGARvKBF1ZEo40QiqGuXRmqSYLBwoUL\nIYTAmWeeafl5SUkJOjs787Z/XgkRJQjTFilwiC9Y1ZuyKsWQLQGNN2lvb6f4Ib7z3//937jmmmtw\n8skn46mnnkr5PJ/3JVp4IgqD0UlksBIzZkGjvs9G7OTLAuRRmwMGDMDrr7/uQYcIyZ2bbroJRxxx\nBCZMmJBi0fnhD3+IxsbGvOyXcj+C6FHsfneDkMKhih6n3Dumqeoq8cBmq0KcaoHPfJBj8c+jjjoK\nL7zwAsaOHetdnwjJgauuugq777570jkZi8Xw5Zdf4vnnn0dVVZXn++S09Agybtw41NbW0rxNoksa\nceIoeAwK4a7yyD329ddf49RTT8WLL77oQacI8Z7f/va3mD17NhoaGtCvXz+Ul5dn1Y7TtHTe8SJK\nPgPDCAk7gUlC6JGo2nXXXbF9+3ZP2iLEazo7O/H0009jzpw5GDJkSN72wxieCCKEoOAh0cZJSAQo\n0JiQKLBo0SIMGzYs7/uh4IkgFDyEwDq3i00sjmxqiP+FlalTp2LIkCG48MILsW3bNr+7Q0icRx55\nBJdddlne90PBE0EYtEyIQrps4EhkY86Lq6tAuX0uvvhi1NXVYcSIEZg4cSKOPvpovPrqqwXZNyFO\nfPPNN9h3333zvh/G8EQQWngIgTfJCNO5v+zqyVlNiy+QK23atGmYNm0aPvroI8yZMwdXXHEFjjvu\nOMydOxelpaUF6QMhBu+99x722GOPguyLgieC0MJDSIKcqqXbzaIyCZrEPhz2m0mxXQ/Yd999sXjx\nYnR2duLOO+/E5MmT8dxzz+V1n4SYufPOO11VUvcCCp6IQgsPIRpphY7bHDsWVhpzzI9sagDW1ScW\nVA62jQuKi6M8C59YLIYrrrgCf/3rX/O6H0KsaGxsxH333VeQfVHwRBBaeIqQbNwzbm+kBXS35BWz\nNSZdMkJjvUy+W9N3JcoHJQsaXezIxibtc9TDDuMKLaTwaW1tpVuLFIzm5mZ07doVsVhhwokpeCII\nY3iKjOZlaWcPWVoxjBt5uhtp2MWOlWAxizi7WloWmZcdLUIWAsksegyxY7wWVTYJ1gxxFG/Hop8e\nMmTIEDz99NOYMmVK3vZBiMoDDzyAiRMnFmx/FDwRhIKniHBhfUjrsikWC04umESj63gec0mKD29M\nWSWb2CBVFKFxufZ/cn5Fz1lnnYUbbriBgocUjGeeeQbPPPNMwfZHwRNBKHiKCN2ikOI6QRY37aih\nipx1ya4ls8PX9rt0qL0Vb8v8u5gtOpWDk/avip222i3x1yVYDlTVQ4yCt7+ZfgyHHgB8+sm7zu49\nQjxkjz32wLvvvouf/OQnBdlf/gRPuumYvHh8gzE8RYZ+LYn+Nby+rLCbCu6EIUAqB6dd1SoQ2RYX\n7RmCSBU+G+pagLqNqBiyBaVYmJvoSfM9dHZ2FiymgkSba6+9Fr/85S+xbFlhclHlLnjcDiJWeScM\nODgXHAqeIoXXkj3ZCB8du6nrKWIH0N7bCBtbS5FNcDOgix3l9cDGJiBT0ePymAcesDeWvbAKxx83\n3F27hOTAwQcfjC+++KJg+0tfLf37AudlsLqA+dTqKZMmTcJ9992Hvffe2++uEOIfigjItGSEKlzs\n3GIp6CIonatRFVGysQlttVuSRA8ADJzbF2LyBK2tTJMfOrD50y8xbcbt2KVnGR667xL06tWT4y7J\nKxdeeCHGjRuHU045xZP2nKqluxY8sqmh6CoIR5Vjjz0WW7duRffu3f3uSmjZbbfd8NVXX/ndDU/Z\naaed0NLSkn7FoNDxZfL7Lrt72x4A2fZdZm00f2v/WdkPIEp6uGpGtn2ntfV9G+TWNgDA9m8TcXfd\nfxCD6FumtdndRUp+i2Nz4utvvsOHH3+FnXbaCUcffTQee+yxjLYnxC1NTU244IIL8NRTT3nSnpPg\nceXSMp5gkkSPU34KJ0GSSTbRAtWYiRolJSVYtGhRQWqXEOI5VuOCFw9BNlPQ7YLBLa1CdlYek3Un\nXXZnw8pjxPGYrTwVQ3ZCSXVv91YeIKvx9OHH/oa77n0Ot8w/A7+ao8ze4kMn8Yjy8nJ8+umnaG9v\nR9eu+Z1HlbtLywtzKi+egnLiiSfirrvuQv/+/f3uCgkSYZiens/Yv0xz7iAzV5hbC7nqIrObsWWQ\nsehRcSmAOjs7cd1Nj2PZC6vwy9mTMfXU0cE/T0iomD9/PnbddVdceumlObflZOHJXyh+8zJaaAKK\nEJbnAiHBvmYLNNEhk8roSeusq0/8ZdFWEhaWopLq3iip7p20rK12S/rYITucvkMlE3UsFsP1/zUd\nb674Lf7vb+/giHE34o033shun4RYMGfOHDz55JN53096+5Gb1OoOn2eVzIvkHebhIZYE9cndDyGW\naVkJFYuZWqobK+24qOfmEVXlyUkIFQwXV9KsLSvSCRsnlO+ge6+TsfDRk9HU1ISzzz4bLS0tePDB\nB1FRURFfffv27Vi0aBGWLFmCTz75BL///e8xYsQI532QyFNWVoZYLIZt27ahZ8+eedtPepdWuunL\nFmntHX3cBqY8F1mZZElWnHzyybjttttwwAEH+N0VQpxJJzjyNWa4EDopAsZsaakcbBkDBMAyb4/l\nuGmRkNBwbRmCJyWexwqvcvYo7bz77rv4xS9+gd122w0dHR34+uuv0aVLF4wYMQIzZ87EHnvsgZEj\nR+K5555LjhfMVwwWCTUPPPAANmzYgFtuuSWndnIOWrbFRQ2fFEzF89C4XHuKQf5rxRCNWCxGCw/J\nP5m4oAppwUkXq2TTF9uszIY1x2TRydainSR8jDYVa09Jde+UeJ622i3xTMyG0Mp5LE1Tg+zggw/G\nq6++in/84x/Ys2cj9v3RzJTVn3nmGUyaNAlvvvkmevRwN0ONRJNzzjkHRx55ZF73kX0MjyF2LHzW\n8df956X6sC0ScsnGJi1Ar6kh2DEERQJLS5C8k0lC0kKLnVxQHtiMP3W5imxqSLICOY2DVuIoaZlh\nBVLKUlQM2QkVQ3aKv2+r3YLWO9YmxtJcsRNMpu9w+PDhlmIHACoqKnDPPffghBNOyHw/JFLEYjH0\n7NkTH3/8cd72kZOFR5QPUir5WldjtnsqMvumZWMTBOpp6SkADFomeSPbzOvZkO2spAziVlIw1b1K\nwsKlZUV8rNTdXU6WoCSXmCF6UI8SZR11ynrFkJ3iYynKfZh1Z/G9jR5dg/b29sQCju3EhssuuwzX\nXXcdHnrooby0n17w2A0SStFCOxwL6uli5/2bNypLN2Lg3CbdLNvAuJ48QgsPccTNFPVMRIuXVpxs\nx4QcyzDExUflYE1QWGEhiNIJGhWr/DxWsT1qva2S6t6oQMLFpbm3oI+hyG0MNYs/jsckjxx77LG4\n/vrr89a+ewtPhgNWitgxDQSGhadiyE5YXNuJ9QD2A4CbN0IVPvGZB7zQPIMurSImk8Se6bY33+hU\nEeS1GyoTMeK1GMsB48EtqQK6YtlxE8fj1v2kCq74Ml14lcBk6cEW76qrq795umSz+nnS2tqK556v\nw+69foAf7rs/2tragpnniWIucPTp0wfvvPMODj/8cM/b9jytYYoLS704R81I+lxUlaMEwNC6jcDW\nbgCAVfp/3LwRA+cCwEI9CA88IT2C1dKJJW5uZk7r5RunAsQqRg6ZPPczxSWvih44ix3XMTam40ip\n4eXgYotbetwUGs31uzJtf/ypN6Jf33JIKfHN9/UoLy8P/vgdREEWQebNm4fzzjsPr7/+uuflj9zl\n4VFJc2GkTMM05aOIm2eN96jHwLlAhUWBvPdv3oiKIVtQejkY2+MhtPCEDD+L56r7zOGmmDbvjJsn\nbdXK5NSXHG5cGWVONgmcpM8sjjGTthPrNrjL26OjBjEDHru3HPuZ4Dd/eBX7VeyFBXf9IvmDXK2P\nhYCix3cOP/xwXHfddRg2bBgWLFiAkSNHetZ25haeNE95tjknbGYlSCSC8CqgiZ64lQcA6lpQccda\nlFQ3AZNtRA+rqWdELBajhaeYUM9/L1wXuWIhSDxLOmojdiyDf9MIo5xmMqnjWZokg672ZZ7qriYb\nrErO2WMcqyqEDGs5kFyCIp6ZeV1hJoQ0rP0Uz774Ht586vy87cNTrO5nzBPkOzU1NRg9ejROPPFE\nHHLIIbj77rs9aTd/lboMU6spqRZgE9/TuDwRfKdbelZt7ab91XZiaN1GDMTyhOgx2klK4GUqbkps\noeDxgWyFucvpwXl/OjXdHOwSjsbXdWsR8qDPKfu2em3TbwCWLnin/SS14bCNlTjJFCt3mdqfuLW8\nEsC6epQqAc2uyTLWS/3eW1tbceYv/4S/PXcLRPmu3u4v3xTABUoyo2fPnlixYgVuuukmHHHEEXjq\nqafQp0+fnNrMXvDYnCDmpFnp8kvEn1AmTwDW6dMt6zZikWrlAbB+azfsd2UTplfVa4m+oF3QyU8z\nhhWIJSycoEurCPA5jsYqVi/FymJjiU1aL5sbn5OV2eHGZZciwwm7sSQTAeM49dwi43K6iutGmylt\nmJIfGgHNRr4gbaq6h5Y2A/37Pum0W3DDvGkoD5vYIYHmmmuuwYknnojjjjsOl1xyCWbMmJF1W7lZ\neOwGl7IaiP4WJ3WOg/R6AK13rI2badtMcT8Dq6Fd/BQ8jlDw+ISXA30An0gzedBIl3/GFVaCKhex\nY2GNdsK1u8rlOvF2R80AKpP7KxubgMbl2ucOsUNGv4z9qQ+FeYvlKavB7bffjr37DcYJU2/UlqWL\nsYLHffACOyHtFMcWtGMoUg4++GCsXr0a06dPxzPPPIPFixejtLQ043Zyd2mpA28Wpvok91Q8v8VG\n200W13ZiipJzgmQOZ2kVCT4OtuYp0rYiwcEKbPd5LmQdl5PGleVEJsLHcr9pEhmaXVNOU+Hj3+26\n+rjYUaeql1YpsTyAJ+dQY2MjFi9ejLfeeiux0M2MvqALHxXlODwR6yRjYrEYHn/8cSxZsgRDhgzB\nn/70JwwdOjSjNryJ4cnFLG1+Uq0cjIFzm7DflU1Yb7HZemhF8wbO7QtAt+roxIvnBe0CChi08JCs\nUW5kbkSOJyUOssHKVWR+bUO++2w5tRzWotFVHI6dq8uCeEb7dUbNLeQ8Xp599tl4+OGHEYtZVCrK\nxBIZgpw4ccsZRY9vnHLKKRg3bhxOPPFE/OQnP8Gtt97qetv8BS1b4fIkFpMnYBqW4882omfR1m74\nda+gd94AAB/dSURBVFV5aoxQQC+SoGE5MBFiRwaVw5Owslw4WTSc2ldu/JYuHbclH8yzq6yW5xm3\nU8wdMzor6wBwPBbzVPXWO9YCgGfpPu677z78/Oc/x8qVK7MTPWmKlAYJw7IZ/90C2s9ip1evXnj1\n1VfxX//1XzjyyCPx9NNPa7me0iCcXBtCCJmx6yMbH6dV1fV19Vj3s9UpwcsqV1fHUHr/NIj+8zLr\nY8SZMWMGzj333LxXpiUhJ9sp3TauGFWoZDSDCImp1vFp1hm2mS7uJY5ZQFgJoSwEm+O+dLLK3aO7\nrpLaMR2r+rl5yrqYPAFejJ8PP/wwnnrqKTzzzDPOK+biwgyCsGAMT+B45513MGPGDFx99dU4/fTT\njZANy4KRngoe+eGNydtn4l4ynUiyqQFy6XLccKXzwEjRkzlnn302zj77bE8TOhGf8CoOwqqMhA22\nN2GnXDLITvCoN2gDs+ixW99qPdfCx0yWlikVy9gbtX0L7CxARkqO1Jmqva3LXOh9N6w7AFB6+UFa\ngLRHN+1LL70U3bt3d+diSCN8bK1gQRQYtPL4Tnt7O6ZOnYpYLIYlS5bYCp7cXVrqNNVcTMRKbICa\nTOvq6i34Ta19vMmGuhZUrvOpMnBIYdBykeBVwK/bkg1IY3HwQBRkg5UoUperwiedFchWlLg8LjdC\nzmqdFNeVPn7aJjC0EDuW7TcuTzqW0ssPStmHV9x999045phjsGjRIkydOtV5ZY8yePtOmPteRHTt\n2hVLly7FY489hiVLltiul52FxybTKYB4ssGMg4cdhJNcuhzv35yamwcApvbagYFz+2oXdTb7jSDn\nnHMOfv7zn2PMmDF+dyW6eGUaz9XCY94+1yLBBi7id+zicrJ1d9nhZBFywsoS42SlybTfrvdr41az\nEzwqxrEb42N8H1aWIw/Gzfb2dgwdOhSPPvooqqqqMm/AaTYUx3XiAu9cWg4ZVs1PItlk7lTNtIDp\nIjVNs1QD8Yz3cXMuhY8j5513Hn72s59h7NixfnclugQ1fb3VTJkMRJCrGU5mMWQXSJxuXx4JDCec\nhJhTrIwTdiLFKT4pjmk8dNtmvC1lRpgqKHJOBqnw2WefYfz48XjjjTfQs2fPzBtwujZCMJOL+IuT\n4HHv0nJIy64ui184WT55misQqyZZUVWO0qpyVAIpOSY21LUAdVqxUce6WwQAS0sEjqCcpzn2w1WF\ncKtiwhafObKu3jEex2lWVz6EUr7bjB+DIgjV4ypBgnRWHyB5erW6zIup1nvuuSfuu+8+jB8/Hm+9\n9VZms0LtxHW2M7kYX0MUMo7hSUqpbvM0JpNmOKR5crA5wZNdWIkLfz8A036XuNANS49RdNSy7hZP\n+CSYhycAhOmczKWvyvXtdDPNNEuzWTSZSy2YLRrGcsA5cDnt9HcXn6WjpLq3K1Fi1y+r/avxR45Y\nFDpVl2uiBzmfnyNGjMBZZ52FKVOmOMZU5IQbsWP8D9P1RvKGO5eWVSKxNNMhU3zdNnW1VFSXll0O\nHoOrq2MovfygJEvPqq3d4tvEY3uYjDCFCy64AKeccgp++tOf+t0VEmWyDfj0OODV8gEuQzdbIdxr\ndpjHXbuZapYi0NyW3Rid5fh5wQUXYK+99sK1117rvKLqEQiCSyuoWaBJWrxxaVk1bPOUY3nxr6uH\nnbQyLrJ45V+9ZowTG+paMLCxCWLyBJRW1aPijrVYpczmWrW1GypqtVTqrK2VDC08JBC4jRFyuul4\nMB0/fpO3y4NjJxBs3EtJ2xdACDnto612S0ZB2ymxmAZZCo377rsP48aNw7PPPovjjz/e3UYUGSRP\nZGzhAdwlHXONheXHsPS03rEWi2s7Uyw9+wEY2msHAMRnaMnGJrx/80as0t1gQ3vtSAQye5Rcq1i4\n6KKLcPzxx2PChAl+d4VkQ7GY6IOexC3b6fkGOUzRtxMxbtxxdrmLLPPzuCCjuB6L37C1tRVDhw7F\nkiVLMGDAAPdtEZIF3icedJqWbofdNFXYzOwyB0nrrq73b04uLGrE8KgV1A2Mi1yMmpHafoS56KKL\nUFNTg4kTJ/rdFe+IwuyNoAuETAmr28BibIpjGtPyLYzMmEWPOqM1SfRkmYMn46Bm/bf9+OOPcdxx\nx+Ef//gHysrKsto3IW7w3qWVZc4OAPbp1M2Dnv5e9Nd8uhKAqAQGYnnK7KxVW7thfa12oRvWH+Mi\n1waAhXqjCzllHRGopVUsFhAVNSFnsbhow/ob6XEm5orxBuqYZoxfTuQ8X9LkWjNEjzpGmutpFQz9\n2Pv1q8Fdd92F0aNH4+mnn8Y+++zjT39IpMlL8VDLmjBWn2Ux4KkxO+ZAZUCrpr5+azfsV9sJ1Dbp\nrq+EVahiyFqUVC+HqNLFT0RdXUUXw5NJVWa/ydR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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -131,15 +123,15 @@ } ], "source": [ - "lon_range = (-108, -90)\n", - "lat_range = (35, 45)\n", + "lon_range = (-105, -91)\n", + "lat_range = (35, 44)\n", "basemap = Basemap(projection=\"cyl\", \n", " resolution=\"l\",\n", " llcrnrlon=lon_range[0], \n", " urcrnrlon=lon_range[1],\n", " llcrnrlat=lat_range[0],\n", " urcrnrlat=lat_range[1])\n", - "plt.figure(figsize=(12,8))\n", + "plt.figure(figsize=(10,8))\n", "basemap.drawstates()\n", "plt.contourf(model_grid.lon, \n", " model_grid.lat, \n", @@ -147,9 +139,11 @@ " np.arange(25,225,25), \n", " extend=\"max\",\n", " cmap=\"YlOrRd\")\n", - "plt.colorbar(shrink=0.6, fraction=0.05, pad=0.02 )\n", + "#plt.colorbar(shrink=0.6, fraction=0.05, pad=0.02 )\n", "title_info = plt.title(\"Max Updraft Helicity {0}-{1}\".format(start_date.strftime(\"%d %B %y %H:%M\"), \n", - " end_date.strftime(\"%d %B %y %H:%M\")))" + " end_date.strftime(\"%d %B %y %H:%M\")),\n", + " fontweight=\"bold\", fontsize=14)\n", + "plt.savefig(\"uh_swaths.png\", dpi=200, bbox_inches=\"tight\")" ] }, { @@ -161,16 +155,16 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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LdGDD0iEiOo3tDwPrSDqgdJZukcIaERHtchnwrNIhIjqR7XcDy0s6rHSWbpDC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\nkiRJ6jQLqyRJkjrNwipJkqRO+y+5XELw8hA+jwAAAABJRU5ErkJggg==\n", 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CihUrsHz5chw7dgx58uRB9erVsW3bNjRq1Ai1atXC119/jYsXL+Khhx4CAFy/fh1t2rTB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\nI3N6OZr3mknt2rXDli1b7t9ODrZH7qV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -225,7 +219,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "###Storm Track Identification with the Enhanced Watershed\n", + "### Storm Track Identification with the Enhanced Watershed\n", "Our first data science tool is the enhanced watershed (Lakshmanan 2009), which is used for identifying features in gridded data. The original watershed transform identifies regions from an image or grid by finding local maxima and then growing objects from those maxima in discrete steps by looking for adjacent pixels with at least a certain intensity in an iterative fashion. The traditional watershed uses an intensity threshold as the stopping criterion for growth, which can produce unrealistic looking objects. The enhanced watershed first discretizes the data and then uses size and relative intensity thresholds to identify discrete objects. Buffer regions are also created around each object.\n", "\n", "The enhanced watershed has the following tunable parameters:\n", @@ -233,22 +227,22 @@ "* size: growth of an object is stopped after it reaches the specified number of grid points in area\n", "* delta: the maximum range of values contained within an object\n", "\n", - "#####Exercise: Manual Tuning\n", + "##### Exercise: Manual Tuning\n", "Pick a model time step and tune the enhanced watershed parameters until the objects look reasonable. Note how changing parameter values affects the shape of the objects. See how your chosen set of parameters handles other time steps. Finally, see what parameter settings produce particularly poor objects. If you find either a particularly good representation or a hilariously bad one, right-click the image, save it, and email the image to me at djgagne@ou.edu." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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BkipXXvVqZ+BHETGu6nqkHnQMMG2kGxlY1Sr2sEpqC5n5APB14Jyqa5F6SUTs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\nLkK9KyLOAJYGloqIoynmB9673jYGVkm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/iIECdMcdd+if//yn1QwUVvgFUwIA+MKMGTP0wAMPaNSoUdq0aZPKli2r7t27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\nxcfHa9asWWrYsKEOHjx4yfq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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -361,16 +355,16 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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rbmE1sy5Jp3ddmI4mULA/8OdCpywz6xb3AGPyDMAJq2XFLaxmVg2HAeMlnSxp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\nytHaCF5/5505NjT0QeAlwAZgy2dWrPjxGYsXzxs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OofXd3M/+tcX3/O23kW+tt4ReVH2Ysx02Xa2UagHlc3/5TtngxTzcpcAIpdRz9gYrrldr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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -390,7 +384,7 @@ " past_time_objs.append(obj)\n", " # If no objects existed in the last time step, then consider objects in current time step all new\n", " if len(past_time_objs) == 0:\n", - " tracked_model_objects.extend(copy(model_objects[h]))\n", + " tracked_model_objects.extend(deepcopy(model_objects[h]))\n", " # Match from previous time step with current time step\n", " elif len(past_time_objs) > 0 and len(model_objects[h]) > 0:\n", " assignments = object_matcher.match_objects(past_time_objs, model_objects[h], hour - 1, hour)\n", @@ -409,19 +403,27 @@ " object_matcher = ObjectMatcher([shifted_centroid_distance, centroid_distance], \n", " np.array([dist_weight, 1-dist_weight]), np.array([max_distance * 1000] * 2))\n", " tracked_model_objects = track_forecast_objects(model_objects, model_grid, object_matcher)\n", - " color_list = [\"red\", \"orange\", \"blue\", \"green\", \"purple\", \"darkgreen\", \"teal\", \"brown\"]\n", + " color_list = [\"violet\", \"cyan\", \"blue\", \"green\", \"purple\", \"darkgreen\", \"teal\", \"royalblue\"]\n", " color_arr = np.tile(color_list, len(tracked_model_objects) / len(color_list) + 1)\n", - " plt.figure(figsize=(12, 8))\n", + " plt.figure(figsize=(10, 8))\n", " basemap.drawstates()\n", + " plt.contourf(model_grid.lon, \n", + " model_grid.lat, \n", + " model_grid.data.max(axis=0), \n", + " np.arange(25,225,25), \n", + " extend=\"max\",\n", + " cmap=\"YlOrRd\")\n", + " plt.colorbar(shrink=0.6, fraction=0.05, pad=0.02 )\n", " for t, tracked_model_object in enumerate(tracked_model_objects):\n", " traj = tracked_model_object.trajectory()\n", " t_lon, t_lat = model_grid.proj(traj[0], traj[1], inverse=True)\n", - " plt.plot(t_lon, t_lat, marker='o', markersize=4, color=color_arr[t])\n", + " plt.plot(t_lon, t_lat, marker='o', markersize=4, color=color_arr[t], lw=2)\n", " #plt.barbs(t_lon, t_lat, tracked_model_object.u /3000, \n", " # tracked_model_object.v / 3000.0, length=6,\n", " # barbcolor=color_arr[t])\n", " plt.title(\"Forecast Tracks Shifted Centroid: {0:0.1f}, Centroid: {1:0.1f}, Max Distance: {2:3d} km\".format(\n", - " dist_weight, 1-dist_weight, max_distance))\n", + " dist_weight, 1-dist_weight, max_distance), fontweight=\"bold\", fontsize=14)\n", + " plt.savefig(\"storm_tracks.png\", dpi=200, bbox_inches=\"tight\")\n", "tracked_model_objects = None\n", "weight_slider = widgets.FloatSlider(min=0, max=1, step=1, value=1)\n", "dist_slider = widgets.IntSlider(min=10, max=1000, step=10, value=50)\n", @@ -433,7 +435,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##Part 2: Hail Size Prediction with Machine Learning\n", + "## Part 2: Hail Size Prediction with Machine Learning\n", "Once storm tracks have been identified, data can be extracted from within each step of the storm track from the other model fields. The forecast tracks are also associated with the observed tracks using a process similar to storm tracking. Storm track data and associated hail sizes have been extracted for a set of model runs from May 20 through 3 June 2015. We will use this data to find a relationship between the model output and hail size and try to account for the uncertainty in that relationship. \n", "\n", "First we will import some statistical and machine learning models from the scikit-learn package. The library supports a wide variety of machine learning models, which are described in great detail in the official [documentation](http://scikit-learn.org/stable/documentation.html). " @@ -2103,10 +2105,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##Closing\n", + "## Closing\n", "This notebook demonstrated some of the power found in data science tools to turn raw data into valuable forecast information. Please continue to experiment with the parameters in this notebook and even try some of the other models found in scikit-learn and elsewhere. If you have any questions or comments, please email me at djgagne@ou.edu or find me on Twitter (Username: @DJGagneDos).\n", "\n", - "##Additional Resources\n", + "## Additional Resources\n", "* [Personal webpage with hail forecasts and additional data](http://www.caps.ou.edu/~djgagne/)\n", "* [Scientific Python tutorial](https://scipy-lectures.github.io/)\n", "* [Scikit-learn documentation](http://scikit-learn.org/stable/documentation.html)\n", @@ -2116,6 +2118,15 @@ "* Valliappa Lakshmanan, Kurt Hondl, and Robert Rabin, 2009: An Efficient, General-Purpose Technique for Identifying Storm Cells in Geospatial Images. J. Atmos. Oceanic Technol., 26, 523–537.\n", "doi: http://dx.doi.org/10.1175/2008JTECHA1153.1 \n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -2134,7 +2145,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/hagelslag/evaluation/MetricPlotter.py b/hagelslag/evaluation/MetricPlotter.py index cb602b9..efae00c 100644 --- a/hagelslag/evaluation/MetricPlotter.py +++ b/hagelslag/evaluation/MetricPlotter.py @@ -48,7 +48,7 @@ def roc_curve(roc_objs, obj_labels, colors, markers, filename, figsize=(8, 8), for b, b_set in enumerate(bootstrap_sets): broc_curves = np.dstack([b_roc.roc_curve().values for b_roc in b_set]) pod_range = np.percentile(broc_curves[:,0], ci, axis=1) - pofd_range = np.percentile(1 - broc_curves[:,1], ci, axis=1) + pofd_range = np.percentile(broc_curves[:, 1], ci, axis=1) pod_poly = np.concatenate((pod_range[1], pod_range[0, ::-1])) pofd_poly = np.concatenate((pofd_range[1], pofd_range[0, ::-1])) pod_poly[np.isnan(pod_poly)] = 0 @@ -183,7 +183,7 @@ def reliability_diagram(rel_objs, obj_labels, colors, markers, filename, figsize for b, b_set in enumerate(bootstrap_sets): brel_curves = np.dstack([b_rel.reliability_curve().values for b_rel in b_set]) bin_range = np.percentile(brel_curves[:,0], ci, axis=1) - rel_range = np.percentile(1 - brel_curves[:,3], ci, axis=1) + rel_range = np.percentile(brel_curves[:, 3], ci, axis=1) bin_poly = np.concatenate((bin_range[1], bin_range[0, ::-1])) rel_poly = np.concatenate((rel_range[1], rel_range[0, ::-1])) bin_poly[np.isnan(bin_poly)] = 0 @@ -248,7 +248,7 @@ def attributes_diagram(rel_objs, obj_labels, colors, markers, filename, figsize= for b, b_set in enumerate(bootstrap_sets): brel_curves = np.dstack([b_rel.reliability_curve().values for b_rel in b_set]) bin_range = np.percentile(brel_curves[:,0], ci, axis=1) - rel_range = np.percentile(1 - brel_curves[:,3], ci, axis=1) + rel_range = np.percentile(brel_curves[:, 3], ci, axis=1) bin_poly = np.concatenate((bin_range[1], bin_range[0, ::-1])) rel_poly = np.concatenate((rel_range[1], rel_range[0, ::-1])) bin_poly[np.isnan(bin_poly)] = 0