{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Si Ratio 1.5\n", "Nutrient comparisons with edited dataset using surface instead of 2m for depth. (" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys, os\n", "sys.path.append('/ocean/vdo/MEOPAR/tools/SalishSeaTools/')\n", "import numpy as np\n", "import netCDF4 as nc\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import datetime\n", "import xarray as xr\n", "from salishsea_tools import tidetools, geo_tools, viz_tools, nc_tools\n", "import pytz\n", "import os\n", "import glob\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "HTML('''\n", "
''')\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#load model grid stuff\n", "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n", "bathy, X, Y = tidetools.get_bathy_data(grid)\n", "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:170: RuntimeWarning: invalid value encountered in greater\n", " (np.logical_and(model_lons > lon - tols[grid]['tol_lon'],\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:171: RuntimeWarning: invalid value encountered in less\n", " model_lons < lon + tols[grid]['tol_lon'])),\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:172: RuntimeWarning: invalid value encountered in greater\n", " (np.logical_and(model_lats > lat - tols[grid]['tol_lat'],\n", "/data/vdo/MEOPAR/tools/SalishSeaTools/salishsea_tools/geo_tools.py:173: RuntimeWarning: invalid value encountered in less\n", " model_lats < lat + tols[grid]['tol_lat']))\n" ] } ], "source": [ "# Load CitSci data and clean it up\n", "nutrients_2015 = pd.read_csv(\n", " '/ocean/eolson/MEOPAR/obs/PSFCitSci/PSFbottledata2015_CN_edits_EOCor2.csv')\n", "Yinds = np.array([])\n", "Xinds = np.array([])\n", "for n in nutrients_2015.index:\n", " Yind, Xind = geo_tools.find_closest_model_point(nutrients_2015['lon'][n], \n", " nutrients_2015['lat'][n], \n", " X, Y, land_mask = bathy.mask)\n", " Yinds = np.append(Yinds, Yind)\n", " Xinds = np.append(Xinds, Xind)\n", "nutrients_2015 = nutrients_2015.assign(Yind = Yinds)\n", "nutrients_2015 = nutrients_2015.assign(Xind = Xinds)\n", "nutrients_2015 = nutrients_2015.dropna(subset=['Yind'])\n", "nutrients_2015 = nutrients_2015[~nutrients_2015.flagged]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(890, 13)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nutrients_2015.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load model data and load it into arrays\n", "model_nutrients = sorted(glob.glob(\n", " '/data/vdo/MEOPAR/completed-runs/SiRatio1.5/test*/SalishSea*1d*ptrc*'))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "times is done\n", "Si is done\n", "Nitrate is done\n" ] } ], "source": [ "with nc_tools.scDataset(model_nutrients) as f: #takes a while to run, prone to killing kernal\n", " times = f.variables['time_counter'][:]\n", " print('times is done')\n", " model_si = f.variables['silicon'][:, :19, ...]\n", " print('Si is done')\n", " model_n023 = f.variables['nitrate'][:, :19, ...]\n", " print('Nitrate is done')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h = nc.Dataset(model_nutrients[0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert into datetime\n", "converted_timesa = nc.num2date(times, h.variables['time_counter'].units)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# mask everything outside of daterange of modelled results \n", "# apply same mask to everything in CS data\n", "dates = nutrients_2015['date'].values\n", "dates = np.array([pd.to_datetime(dates[n]) for n in range(890)])\n", "dates = np.ma.masked_outside(dates, datetime.datetime(2015,1,31), datetime.datetime(2015,5,2))\n", "Yinds = np.ma.masked_array(nutrients_2015['Yind'].values, mask = dates.mask)\n", "Xinds = np.ma.masked_array(nutrients_2015['Xind'].values, mask = dates.mask)\n", "depths = np.ma.masked_array(nutrients_2015['depth'].values, mask = dates.mask)\n", "cs_si = np.ma.masked_array(nutrients_2015['si'].values, mask = dates.mask)\n", "cs_no23 = np.ma.masked_array(nutrients_2015['no23'].values, mask = dates.mask)\n", "stations = np.ma.masked_array(nutrients_2015['station'].values, mask = dates.mask)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "273" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ma.count(cs_si)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:19: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:11: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", "/home/vdo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:12: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] } ], "source": [ "list_of_model_si = np.ma.masked_array(np.zeros((890)), mask = True)\n", "list_of_model_ni = np.ma.masked_array(np.zeros((890)), mask = True)\n", "t = 0\n", "for n in range(890):\n", " if dates.mask[n] == False:\n", " Yind = Yinds[n]\n", " Xind = Xinds[n]\n", " date = dates[n]\n", " if ((depths[n] == 20) and (mesh.variables['tmask'][0,18,Yind,Xind] == 1)):\n", " index = np.argmin(np.abs(converted_timesa - date))\n", " s_val = model_si[index, 18,Yind, Xind]\n", " n_val = model_n023[index, 18, Yind, Xind]\n", " list_of_model_si.mask[t] = False\n", " list_of_model_si[t] = s_val\n", " list_of_model_ni.mask[t] = False\n", " list_of_model_ni[t] = n_val\n", " if ((depths[n] == 2) and (mesh.variables['tmask'][0,0,Yind,Xind] == 1)):\n", " index = np.argmin(np.abs(converted_timesa - date))\n", " s_val = model_si[index, 0,Yind, Xind]\n", " n_val = model_n023[index, 0, Yind, Xind]\n", " list_of_model_si.mask[t] = False\n", " list_of_model_si[t] = s_val\n", " list_of_model_ni.mask[t] = False\n", " list_of_model_ni[t] = n_val\n", " t = t + 1" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cs_no23.mask = list_of_model_ni.mask\n", "cs_si.mask = list_of_model_si.mask" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stations = np.ma.masked_array(stations, mask = list_of_model_si.mask)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "267" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ma.count(cs_no23)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cb_model_si = np.array([])\n", "cb_model_ni = np.array([])\n", "cb_cs_si = np.array([])\n", "cb_cs_ni = np.array([])\n", "cb_depths = np.array([])\n", "for n in range(890):\n", " if stations.mask[n] == False:\n", " if stations[n][:2] == 'CB':\n", " cb_model_si = np.append(cb_model_si, list_of_model_si[n])\n", " cb_model_ni = np.append(cb_model_ni, list_of_model_ni[n])\n", " cb_cs_si = np.append(cb_cs_si, cs_si[n])\n", " cb_cs_ni = np.append(cb_cs_ni, cs_no23[n])\n", " cb_depths = np.append(cb_depths, depths[n])\n", " list_of_model_ni.mask[n] = True\n", " list_of_model_si.mask[n] = True\n", " cs_no23.mask[n] = True\n", " cs_si.mask[n] = True" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bias = 7.23641975594\n", "RMSE = 9.27139787146\n", "Willmott = 0.672883640877\n" ] }, { "data": { "image/png": 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gQJg3z+7m0KF+R5QfNJErFS6TOxrF0ys7nfsRDNqOaYWF9nGiiSiRZoFkOhem\nKu5o6428iAovXU+Y0NLpr7DQPt57bztSCsCwYZlzzsVh3To47jj497/h0Ufh97/3O6L8oYlcqXCZ\n2lYeb6/sdO1HeFwdOtjG0EQTUaJJOVYpv737tFMVd6T2LqLef9++Zoz9/eKLrfsODBuWfAxptnYt\nHHssvP22HbXt1FP9jii/aCJXKpzft4/FEm8JO137ER4XwI47JretVPX4b+/Cp724k7knva2LqGAQ\nJk9uaSM3xt6b1dxs/66vz6xaIBd+/BGOPhrmz7ejtg0Z4ndE+UcTuVKR/Lx9LJZEq51TvR+RCS5T\nazDau/BpL9km07+grYuoQMAOOB4ulMRDf5eUxLGj/vrhBzjqKKiuhqeftj3VVfppIlcqG2RCTUGs\nBOd3XNG0d4HRXrINXQTU1SU23nusi6hQXOG3yxUUtFS1FxTYHutZ4Pvv4YgjbB+9adNsT3XlD03k\nalM5Ns5z2nh93PyuKYhVyvU7rmjcXGC0lWw7dGhJrg8/nLr288jbA2tq7O/27q/PMN99Z5P44sV2\n6NVjj/U7ovymiVy1lmPjPKdNPhy3dFajp+KiKNELjPJyOOYYm6HAjvueylnYosXV1v31GWb1anuq\n//e/8Oyztmpd+UsTeS5JxZdfJt9+5Va04+B1aTkXjlt70lWNnuxFUSo+6+22827d0bi96PC5tuyb\nb+xH88UXMHOmHX9H+U8Tea5IVYkwUzsvuRVrbGuvS8vZftzcSkc1ejIXReGff2EhjByZWLX4sGEt\ns7AVFdnHfte6RN4yd9ZZab3nfOVKOPxwe+v9Cy/YydtUZijwOwCVItG+/BIRKnWNGZOd1cPRjkOq\njk1bsv24eSk0H3kw6G750EVRYWH8F0WRn/UDD9jkN3GindfcbQzl5XZdt97aciGRjvOoLeHbr69v\n2Te3+5SEFSvsx7Bsmb3tXZN4ZtESea5IZYkwEzsvuRXrOKSjtJzNxy1R7VX1Rs5H7uYiJ5kq/NDn\nX1dnO6qF7s2+8MKWXuJ//WvL3N/txeH2lrVkuK0uj7ZvaWjGWb7cJu5Vq+Dll+G3v/VsUypBniVy\nEekIzAM2c7YzzRhzk4j8AngSKAWWAqcYY37wKo68kam3AaVbrOOQSccmV+4KcFPV7JQiJdb0orGO\nRTId1ebMsZ3TJk+292wXFNjfxthkftFFLXN/J7LuVH528VTXh+9b+ExpHjbjfP21TeLffWdnMsvm\n0zWXeVmcCG6kAAAgAElEQVQirwcON8bUikgR8KaIvAj8DphjjLldRK4BrgGu9jCO/JGPJcJooh2H\nTDk2frezppKbtuzw+chTPfBKLKHPetiwltu8LrywZSCWpqbES7FuziMvx8SP3DcPLwZXrerIWWfZ\nQV9eeQV+8xtPNqNSwLNEbowxQK3zsMj5McAJQIXz/BQggCZylS9yqXe7m6pmpxS5dNIkdhk5MvbA\nK14ci8ike9FFdlubbZaesefdXJwkWl3v8YXpF1/ApZeW0dBgd6FvX882pVJATPi8uKleuUghsADY\nDfirMeZqEVljjOnmvC7AD6HHEe+tBCoBunfv3nfq1KmexVlbW0vnzp09W7+fcnXfsnW/ui5aRJ9R\no5DGRkxREQvHj2dt794bX8+2/eq6aBHdqqtZU1bWaj8iRduv9o5FsttM1fvaE75vOz7+ODtPmoQ0\nN9NcUMDSkSP5+owzXMXV2LUrRWvXpjy+eC1f3onLL+9DXV0B48d/wO6717b/piySLf9j/fr1W2CM\n2d/VwsYYz3+AbsBcYC9gTcRrP7T3/r59+xovzZ0719P1+ylX9y2r9+utt4y57Tb7O0JW71cbYu5X\nG8ci5vKdOhlTWGh/u32fh1rtW6LxZch+ffKJMdtvb8zWWxvz4IPv+hKD17LlfwyYb1zm2LT0WjfG\nrBGRucDRwDci0sMYs1JEegDfpiMGpTJGprTXZ4J4j0WmN00k2iEuA/Zr8WJ7n3hzM8ydC99991Na\nt68S59l95CKyjYiEqtA7AUcAnwDPAcOdxYYDz3oVg1LKJ8Fg2/dtt/d6rOVLShK/xzzRWONVXg7X\nXpvYbXOp3K84LFrUsslAAPbaK62bV0nyskTeA5jitJMXAE8ZY2aKSBB4SkTOBr4CTvEwBqVUusUa\nXa+t19tKepHLT5hgJxtJpsd2qGd55IQlft1F4OPtox98YA9vUZEtie+5Z9o2rVLEy17rHwD7Rnm+\nBujv1XaVUj5rr5o43mrkyOVramyJN1HhFwahKURj3eeeTj40uVRX2/HSO3a0SXz33dO6eZUiOkSr\nUiq12qsmjrcaOdXVzuEXBk1NNpn7VKXtpwULbJv45pvD669rEs9mOkSrUiq1olUTh49LHm81crLV\nzpEDtETeu51MVX1oHPnNNsusTnftePddOPJI6NbNlsR33tnviFQyNJErpVKvvWrieKuRE612jtUe\nn4r26ETGkc8Ab79t5xAvKbFJfKed/I5IJUur1vNdqnvsKpUpgkEYPdpOMhKaMSxUM5BIz/JI0caR\ndxOTj/9v//63LYlvs42tTtcknhu0RJ7Pcmncb5V7kplcJnRuh2YKA9uhraQkdfG1NY58WzH59P/2\nxhtwzDGw/fa2JL7DDmnbtPKYlsjzmd/zKysVy8SJcOihcMMNic25HTq3w4egLiiwbeGpEhpHfuRI\nd0nZx/+3QACOPhp69bIlcU3iuUUTeT7zeRAKpaIKBu0EJxs22FJ0eJW4W6Fzu8D5iiso8GaylPJy\nO5a6m5K1T/9vc+bAscdCaaktiffokZbNqjTSqvV8pnOY56dMnw89ELCl1pCCAqioILgsSGBpgIrS\nCsp7xdHTvaQk+QFkErBJvD78v82eDSecALvtZje97baeb1L5QBN5vtNxv9uW6UkvXtnQL6Kiwpae\n6+tt6fX++wn2hP5V/WloaqC4sJg5w+a4S+Y+7VtwWTB6vGmM6cUXYfBgO1LbnDmw9dZp2azygVat\nKxVLKOn96U+JtdNmoqqqll7cmdovIlRyveUW26BbWUlgaYCGpgaaTBMNTQ0ElgZavSW4LMjYN8YS\nXOZ8Rj73Dm8vXq/NnAknngi//jW89pom8VynJXKVufwuDWfAjFQpFQzC5MktHcAKC/3tF9HW5xtR\ncq0oraC4sJj6pnoKpICSzVt6n29S+t1vAuVD/B0/fZN4V66xFxbJnssu/ieefRZOPhn69LFV61tt\nlfjmVHbQErnKTJlQGs61zoCBgO1ABiACI0f6d2ES5+db3qucCUdPoEAKaGpu4tKXLt1Y+t6k9Ltg\nuu93Y7SOdwOXfnAnwX8k2AM/xMUx+9e/4KSTYL/94JVXNInnC03k+SAbB33JhFvjQlW8Y8ZkZlty\nvMIvTDp2hGHD/Isl8vOtqmr3HK1ZX4MxhmaaW1VXh0q/hVJIcWExFX2HZMQFWEu8hoYCCOwYx8Ax\n0bTzP/H003DKKfCb39iSeLduye6ByhZatZ7jui5aBFdemdmdm6KJHA/br9JwLnUGzKS7FMI/38JC\nW+W/YUOb52goYYeq0CtKKwBb+p0zbE7rHuJz9vZ9PzfGu6Ge4uZmKr4uSO5cbuN/4p//hKFD7a7O\nmgVduqRiD1S20ESe47pVV2dnO2+akk5ctzTlgjgvTDw7PuGf79dfw4MPtnuORk3YYa+1ii+VF2AJ\n9tVoFW9tCeVbJHkLXIz/iUcfhREj7Pg5zz8PnTsntnqVvTSR57g1ZWWJlWz97mgGnpeGY94ipIAY\nx2c5qZlsJLSOa6+1j6dMcXWObpKwE92u29iTvF2vVbzHxB1xlBW2/p945BHb1eHww+G55+yUpCr/\naCLPcWt7946/ZJsN9xqnQLRbhDSRt9jk+MyrovzcKcmdF17ORpbIdtsTq13a74tc4KGHoLISjjgC\nnnkGOnXyLRTlM03k+SDekm2u3XYVQ6w2V2VtcnyWkvx5Eevc8rovQqLndGS7dElJywVBYSGMHEnX\nvfZKex+OBx6A88+346fPmGH7Lqr8pYlcbSpTOpp5rK02V790XbTIlh5TUdoLVSUnOETpJsdnOXCr\nuyrwmPw6txLdbmRtQfgFQVMTPPAAfYqL7f1eabrY/etf7VD0AwfCtGl2EDyV3zSRq01lUu9mjyXV\n5ppqwSB9Ro1q3XsbEvscQlXJ9fV24pHQpCHJtPH2ou3zwk0bdKLnVrJ9NpI5pyNrC4qLW6ZHNQZp\nbExbrdW998Kll9rx0596yoailCZyFV20qs5M6ACXywIBChobbeIN3Vs9JcE26VDJsbnZPm5uTk0z\nSawq8HjaoOOtRk9Vn41UVN+HLgiqqjbeMmc6dEhLzcLdd8OoUTBkiL3drKjI802qLJG/iTw8Kan2\npbsDXD5eNFRU0FxURGGoRA6Jt0mHqpLDS+ReVmV72a8i2uAx6Tw3Is/F0M+wYRAIsLBrV/YLxeHR\neXvHHXDNNXbAl8ce0ySuWsvPRB6RlLqOG6cJvT3p7ACXJ73mN1FezsLx49lv7dqW89HlbVnR1pWW\naTzD2+HdtEG7TXThy4W3b4vYe86Nia+pINH+Am2di87vbpMmtYxI58F5e+utcMMNcPrp9hqmQ35+\na6s25OcpEZGUulVX+x1R5ktnJ6U86TUfzdrevVsf22T6KnjdEzwyyU2Y0HaSdHuBFm25UHV2aOAY\nsLUNkedGtAuFyP4CIrZIG/7eWBcY4edifT2MHm1/ysvte/r1Y+eGBjsqy1lnpfy8vflmu7mhQ21N\nfmFhUqtTOSo/E3lEUlpTVuZ3RJkvnR3g8qTXvCuZPERs5AVXTY0d4MXt8rESXbTlrr3W/g7N3Aab\nzt4W60Ihsr+AMS1V9KGEHOsCI7KJ4tVX4Y03Wi4s6usRsK+vWpWy89YYuOkmO8z/iBH2nnFN4iqW\n/EzkEUlpbX293xFlh3QllTzqNZ/V4r3gcrt8rOUqKmx1en29bfO///7YpefwC4XQ+kI9zSOFv6+u\nriXBQ8u5OHq0TeLNbUx8st12KTlvjYHrr7dzyJxzjr1nvECnt1JtyM9EDvDhhy1tZnvs4Xc0KlIm\nl0SzUSo6YUXr9BVP4nKzfGgb0arp23t/tAuA0PouvhhmzoSPP7bLFhW1zP5WUWEbnpuabBadNMm+\nFr7d0aNtSTzy4mLSJJobGykIrS/J89YYuPpqGDcOzjsP/vY3TeKqffmZyCdOtP8lALNn0+Pyy/O7\n+lbltlR0Hoy1jngTV1u3NZaU2Juk24oz9P5gEP7wB/tceAJ1En2wrITAF1VU3DSJ8i83tFSrx4rp\nrLNs0dcYm9Ajq/1jXUQEAiydNIldUjC3uzH29rJ77oELL4S//MU25yvVnvxM5NOnt3q4zbx5PgWi\nVBqkovNgijsgbpxVrbaE8iFO8i4osOtv55734P1XE3h6HBVfGDva3KRJrYZ6DfbETvayoY7i0wxz\npmCXC9fYaKvQQ/u27752nNO2qv2jXYSUl/N1fT27pCCJX3KJTd6XXGKTuSZx5VZ+JvIhQ2D27I0P\nVx96KL/wMRylPBVPW3asKvhUdEB01h0sK6H/e5faMdxNAXO2aaL862abzQoKbAaL2EYo8a9Zsoi7\nv32c5grY7BBskl7RemS1jZO9YGgogEBplEQO8PDD9iKgqcldr3uPNDfbIVf//ne4/HK46y5N4io+\n+ZnIKyvt7+nTYcgQVu6xB3v6G1HmyseBWXKN27bs9u6ZTqIjV9dFi+DKK6GhgcChQsNhzTTRTAOG\nwK4FlK8Q2059zDGw3XYEB+1LYEOAimX2/f2r+lPfVE+zaYYCQKDeOEn626JWSb/VZC8YKpbGqFbf\nsMH+DvVib6/XvQeam+3kJw8+CFddBbffrklcxS8/EznYZB5K6NF6oKr8HZglF7lpy26v+jyJjlzd\nqqs3rrtiSQHFhxXSIGJnVbtyAuz5vi0dP/88wdJC+m8vNJgNFBcWM7zPcBqaGmwSBxDA2HxeseOh\ncOftreJqNdnLwjWUL7+zJRCRlpJ/UVFLm7gPtzk2NcG559r7w6+7Dm65RZO4Skz+JnLVvjwemCUv\neXj//pqyso3rLl9dzJzfTCDQuaZl1rnqsRtnFAvs0ExDM7ZqvKkBgOLC4o0lckHoIML9e11B+Z/v\nsBecY8e2qinYONnLIUDxrrb2rawM1q6193tvt11Lr3UfapyammDkSNtMf+ONtlO8JnGVKE3kKjYd\nmCW/eHj//trevVutu7y8nFZrD90C1txMxfIOFBcUbCyRD+szjGF9hhFYGqBk8xJq1oddALipNaqs\nhL33bjXRCcXFKbldLBEbNsDw4fDEE3bkthtvTOvmVQ7SRK5i04FZEpOqfgV+9E/wMrG1t25nsJby\n5cKc/e9rXWKH6NPNuqk1CiX78AFhfKphamy0w60++aQdQ/2669K6eZWjNJGrtunALPFJVb+CaOuB\n3L2oCgRaBmRpaqK8uoZyNx3P3NQahZJ9KIlH6RWfDo2NduKT6dPtbGZXXZXWzascpok8GdqjW0VK\nVb+CyPUkMzd5Nki0GcdNrVH4ugsLbeN0+MhtadDQAKeeCs88A+PH29vMlEoVTeSJ0h7dKppU9SuI\nXA/kdsfDZJpx2qo1amvI1zSpr4eTT4bnn4d774U//jGtm1d5QBN5orRHt4omVf0KItcDic9Nni1S\n3YyTARfbdXV2/KlZs+Cvo77ggp+ehGCFfleolNJEnijt0Z1+4U0ZmSxVCSlyPX53PMy2pqS2ZjVL\ng59/hsGD4eWX4YGrllD5l721Bk95QhN5orRHd3pFlK66jhuX+Qk91fzseJgBpdu4tTermYfWr4cT\nTrCH6eGHYeQ3T2kNnvKMTpCXjPJyO6Sj/kN6L6Ipo1t1td8R5ZdoTUmZLjSrWWikldCsZh776ScY\nONAm8cmTbd+6jTV4hYVag6dSThO5yg4RX4Rrysr8jii/ZGsiGjbMzmqWprhra+HYY+H11+HRR+3A\nL0BLDd6YMdlRm6Gyilatq+wQ0ZSxtr7e74jyS7Y2JaUx7rVrbRJ/+207atupp0aJJVuOm8oqniVy\nEekFVAHdAQNMNMbcKyKjgXOB1c6i1xljZnkVh8oh4V+E2VC1m2uyNRGlIe4ff4Sjj4b582HqVDjp\nJE83p1QrXpbINwCjjDHviUgXYIGIvOK8do8x5i4Pt62USoVs66nug9raDhxxBLz/Pjz1lO2prlQ6\neZbIjTErgZXO3+tEZDGwg1fbU0qlWDb2VE+z77+HUaP68OWXdujVQYP8jkjlo7R0dhORUmBf4B3n\nqYtF5AMRmSQiW6UjBqVUnKINEzt2rE3wipoae53z5ZdbMGOGJnHlHzGhiQS82oBIZ+B14FZjzL9E\npDvwHbbdfAzQwxgzMsr7KoFKgO7du/edOnWqZzHW1tbSuXNnz9bvp1zdN90v73VdtIg+o0YhjY2Y\nwkIEkKYmmouKWDh+vJ2a1KVM2q9kdF20iG7V1Xy560FUPngqy5ZtzvXX/4fDDvvZ79BSLlc+s0jZ\nsl/9+vVbYIzZ39XCxhjPfoAi4GXg8hivlwIftbeevn37Gi/NnTvX0/X7KVf3TfcrTd56y5jbbjPm\n/PONKSw0Buzv226LvvwDDxhz5JH2d5iM269EvPWWMZ06mVUFPUxv+ch0LG4yr7ySI/sWhe6Xv4D5\nxmWu9axqXUQEeBhYbIy5O+z5HmGLDQY+8ioG5QgGtUo0H6Xicw8NejRsWPv3kU+cCOedB7Nn298T\nJya+3UwUCLCqfiv6Nb/Kl6aUF4ZOZcAAv4NSytte678FhgIfikhoGK7rgNNFpAxbtb4UOM/DGPJe\n10WL4MortcNSvkl1RzU392NPn77p48rKTePK0l7w/+t9BIeb37GcHZhVPJjDzr7Z75CUArzttf4m\nIFFe0nvG06hbdbWO8ZzroiVHL2bna+9+7CFDbGk8/HFknFnaC375cjj8iv1Z2amJl858gv8bcXPW\nxK5yn47slovCvtjXlJXpLG25LFZy9GN2vlDpe/p0m8QjS+NZOvXv119Dv36wejXMfrWQ8vKhfoek\n0ikLapE0keeayC/2ceOyc2hN5U6s5OjXkKqVlZsm8JAsnPp36VKbxH/4AV55BQ480O+IVFplSS2S\nJvJcE22WsAsvzMiTT6VAW8kx04ZUzbLx2r/4wibxtWvh1Vdhf3c3AqlckiW1SJrIc03EF7vOEpbj\nsiw5ZtzFRQyffw6HH26nJJ0zB/bbz++IlC+ypBZJE3mu0VnCskeq2t6yJDlmi//+1ybxujp47TXo\n08fviJRvsuRCWRN5LsqgWcIyrZ9IwvGkekdS2faWaQc5i33yiU3iGzbA3Lmw995+R6R8lwUXyprI\nlWcyrZ9IwvF4sSOpanvLtIOcxT7+2CZxsEk8jhFolfJVWiZNUfkpWq7Kyni82JFQ21tbI6X5FVse\n+vBD+xEUFNhDqElcZRMtkSvPZFo/kYTj8WJHUtX2lmkHOQstXGgrNTbbzJbE99jD74iUio8mcuWZ\nTOsnknA8Xu1Ism1vobbxCRPsnJrpOsg51Cb/3ntwxBGw+eY2ie+2m98RKRU/TeTKU5nWTyTheDJt\nR/xqG8+hNvn5820S79rVJvFddvE7IqUSo23k0ehsYVktLz4+v9rGc6RN/p13YMAA2GrzOl4/9W/s\n8k0unywq12mJPFIOlTjyUd58fH61jedAm/xbb8HRR8O2Xet47bt92PHuL+D+XD5ZVK7TEnmkHClx\n5Ku8+fhC7fZjxqQ3Afm13RR580046ijYbjsInPkQO274Ig9OFpXrtEQeKQdKHPlMP740iNZfIFYH\nOOf5rl27+v5hvP46HHcc9OxpR2zb/qu+cJ+eLCr7aSKPlGldrTNANnVSzpuPL5PaEGLFEvZ8nw4d\n7IDlPsX42mswcCCUltq/t9sO2D5fThaV6zSRR5NpPZR9lEn5wq28+PgyaVamWLGEPS/G+Bbj7Nlw\nwgn21rI5c2DbbcNezIuTReU6bSNXbcqbNudsk6qR4byMJex5U1TkS4wvvQSDBtlBXl57LSKJK5Uj\ntESu2qRtzhkqk9oQYsUS9vzCrl3ZL80xvvAC/O53drjVV16BkpK0bl6ptNFErtrkNl9kUzt6zsik\nauFYsTjPr01XVY5zIj5XOJiTbvglffrYqvWttmr9up6oKpdoIlftai9fZGM7etppAvGecyLOqD+G\nU5qvYL9fr+PlV7rQrVvr1/VEVblG28hV0rQdvR2hBPKnP9nfOT3knI+qqnj654Gc3PwkBzCf2Sc9\n2JLEQU9UlbM0kaukZVK/q4ykCcR7wSBTH1zH6TxBOUFeLj6eLY+OKG3riapylFatq6RlUr+rjKQ9\nBlMnRhPF4xNWM6xpCv/Hm7zAQDqPPHPTE1FPVJWjNJGrlMikflcZRxNIasRo454yBc56+ngqCl7n\neQaxxWZNMGxY9HXoiapykCZypdJBE0jyojRRPPxxOeeeC/37C89e14nN375WL5ZU3tE2cqVUdoho\n45645hTOOQeOPBKeew4279jsd4RK+UJL5Eqp7BDWRPG3mlO58M5dOPZYmD4dOr6vt5ap/KUlcqVU\n9igv574truXC8bswaBD861/QsSN6Z4DKa65L5CKyA7BT+HuMMfO8CEoppaK55x64/HIYPBimXvo2\nxXfPtVXuemeAymOuErmI3AGcCnwMNDlPG0ATuVIqLcaNg6uugpNOgif++DZFRx3euipd7wxQecpt\nifxEYE9jTL2XwSilVDRjx8J118Fpp8Gjj0KHcXM3rUq/9lpN4CovuW0j/wIo8jIQpZSK5s9/tkn8\njDOcJN4BHaVNqTBuS+TrgWoRmQNsLJUbY/7oSVRKqbxnDNx0E4wZY8d3mTTJ5m2g9SA7JSUtndu0\nRK7ykNtE/pzzo5Q/dPawvGIM3HAD3HYbjBwJEyeGJfGQ0Hmgt52pPOcqkRtjpohIJ2BHY8ynHsek\nVGvRhuZUmSvJiy5j4Jpr4M47obIS/v53KIjVCBjttjNN5CrPuO21fjxwF1AM7CwiZcCfjTGDvAzO\nU+FfNsozKSlI65d19khyzm9j4Ior4O674YIL4C9/aSOJg952phTuq9ZHA78BAgDGmGoR2cWjmLwX\n8WXTddw4/QLwQJLf6S2ifVnX6w0UGSmJiy5j4NJL4b774I9/hAkTQKSdN+mENEq57rXeaIz5MeK5\n7B3YOOLLplt1td8R5aSUDbYV+rIeM6b9q4GJE+Goo+xvlX4J9iZvboaLLrJJ/LLLXCbxkPJyvfVM\n5TW3JfJFIvJ7oFBEdgf+CLzlXVgeiyjhrSkr8zuinJSyWs9gEKqq2l9u4kQ47zz79+zZ9ndlZYIb\nzRHp7iSYQAm5udlWoz/wAFx5JdxxRxxJXCnlOpFfDFyPvfXsCeBl4BavgvJcxJfNWq2m9URKaj2D\nQfvmhgb7ePJkmDs3+rLTp2/6OJ8TecraNuIUx5Stzc32I3r4YVuovvVWTeJKxcttr/X1wPUicqvz\nd/YL/7LRCRY8k/Q03IEANDa2PG6r3XXIkJaSeOhxPsvwToJNTXD22TBlCvyp35vcPLAQkcyJT6ls\n4aqNXEQOFpGPgU+cx31E5G+eRqYU2NJ4Udiggm3V0VdW2vrZI4+0v/O5NA4ZPfrZhg0wfLhN4jd3\nGMOf51UgA/rbWgSlVFzcVq3fAxyFMyiMMWahiBzqWVTpFAyy4+OPw2abZVRpxUtZNbZKebkNNtRG\nPmxYy3PRVFZqAg/J0B7dGzbA0KEwdSrccsTrXP/azRlba6BUNnA9jakxZpm0brxqirVs1nDaEHeu\nr4fHH8+LUaG8bjZ1e5EQ18VE0vXzeSzDjl1jox0z/emnbae2qw4phjf1PnClkuE2kS8TkYMBIyJF\nwCXA4rbeICK9gCqgO3bK04nGmHtF5BfAk0ApsBQ4xRjzQ2LhJ8lpQ5Tm5rwpDSTdbNpGBnZ7kRBr\noLYMKziqFGtosLOXzZgB48fbecUhM2sNlMombhP5+cC9wA7ACmA2cGE779kAjDLGvCciXYAFIvIK\nMAKYY4y5XUSuAa4Brk4k+KQ5bYjN9fUU5ElpIKlbwtrJ1G4vEiKXq6qybaU6XHbuamgQTj4ZnnsO\n7r3XDviyUYbVGiiVbdrt7CYihcBQY8wZxpjuxphtjTFnGmNq2nqfMWalMeY95+912BL8DsAJwBRn\nsSnYuc794bQhLh05Mm+yRzxjq2yinRFe3PatilwOUjRwjMpI9fVw00178dxzcP/9EUlcKZU0Mca0\nv5DIf4wxByS8EZFSYB6wF/C1Maab87wAP4QeR7ynEqgE6N69e9+pU6cmuvl21dbW0rlzZ8/W76dU\n7lvXRYvoM2oU0tiIKSpi4fjxrO3du9UyixZ1pbq6G2Vla+jde23MdYUvBzBqVB8aG4WiIsP48Qvb\nfC/k7meWa/vV0FDAn/7Um3ffLeGyyz5l0KCVfoeUcrn2mYXofvmrX79+C4wx+7tZ1m0ivwcowrZt\n/xR6PlTibue9nYHXgVuNMf8SkTXhiVtEfjDGbNXWOvbff38zf/78duNMVCAQoCJHq9WT3jenTTxY\nMpBAzd5UlHxIec3MlLdnxtuTPlc/s1zar/Xr4cQT4dVXYdSoTxg37pd+h+SJXPrMwul++UtEXCdy\nt23koTFM/xz2nAEObyeQImA68Lgx5l/O09+ISA9jzEoR6QF86zIGlW5Om3iwfj/6N19CQ4GheLO9\nmTNn75S3QmgzaW756ScYNMgOwjdpEpSWrgJyM5GrDJdV99smxu3Ibv3iXbFTbf4wsNgYc3fYS88B\nw4Hbnd/PxrtulTxX57bTJh5oPoQGimlqlnzp3K+SUFsLAwfCG2/Yjoxnnqn9HpRPYt0ik2Pczkd+\neZSnfwQWGGNiTR32W2Ao8KGIhJa5DpvAnxKRs4GvgFPiC1kly/W95E6vtIr6NyhubqChoJDiYklp\n5/48uFjOK+vWwbHH2s/1scfg9NP9jkjltQwfpjhV3Fat7+/8PO88Hgh8AJwvIk8bY+6MfIMx5k0g\n1vQH/eMNVCUvlDS//trlue10cS8PBJhTssS2kVdsumyiydivOT1STq9GAFi7Fo4+Gt59F/75Tzj5\nZL8jUnkv2v22OThJlttE3hPYzxhTCyAiNwEvAIcCC4BNErnKLOFJs0MHe+sXuLiX3Gm8Lgeipahk\nknFOXCznzNWIO8FlQaoW2uFyh/UZRnkvu69r1tgkvmABPPUU/O53fkaplCPaMMU52M7jNpFvi53C\nNHUIqU0AACAASURBVKQR6G6M+VlEcu/yJgeFJ02Ac8+FHXdMvhCZTDJO2XzlfsqJq5FNBZcFCSwN\nUFFasTFZB5cFqZhSQUOTnVJ2cvVk5g6fyy87l3PkkbBwIdzyj0/4dJsZBJe1vE8pX+VBT1q3ifxx\n4B0ReRZbXT4QeEJEtgA+9io4lTqRSTM090iq1xtPMs7QOT3ikxNXI60FlwXpX9WfhqYGiguLmTNs\nDuW9ygksDdDY1DKlbENTA7MWvsOFN5azaBHc9sBibvxfXxpWtH6fUspbbnutjxGRF7Ed2ADON8aE\nbuw+w5PIVEyJNMl6lTSTXW/WXyznxNVIa4GlARqaGmgyTTQ0NRBYGqC8VzkVpRUUFRZtLJEX1fXg\nyavP5esl8MwzUN35GRqWbfo+pZS34pn9bD4wX0Qqw5K4SrNkmmS9SppZn4yTlWMHoKK0guLC4o0l\n8orSCgBbKh8eoGphFT+v6cKbt97EsqVb8Nxzdgr4bsuiv08p5S3XiTzM+cDEVAei3MnRJlmVQcp7\nlTNn2JxN2shDr+1cVE7//vC/r2DmTHth2d77lFLeSSSRx7qlTKVBDjbJqgxU3qs8aiL+3//g8MNh\n+XKYNWvT8y/W+5RS3nE7IMzOxpgvnYfHR3lOpUm8TbJdFy2y9fE50n6r/LNiBfTrBytXwosvwiGH\n+B2RUgrcl8inA/sBGGOWO89NA/p6EZRqm+sm2WCQPqNGwYYNeXGPs/LOsmU2iX/7Lbz8Mhx8sN8R\nKaVC2kzkIvJLoDewpYiED/HQFejoZWC5Li2DgQUCFDQ2QnOzNqirhH31lU3iNTUwezYcdJDfESml\nwrVXIt8Te894N5wqdcc64Fyvgsp1aRsMrKKC5qIiCkMlcm1QV3H68kubxH/80U5HesABfkeklIrU\nZiI3xjwLPCsi5caYYJpiynlp63leXs7C8ePZb+1abSNXcVuyxCbx2lp7sbnffn5HpJSKxm0b+WAR\nWQT8DLwE7ANcZox5zLPIclg6e56v7d1bS+Iqbp99ZpN4XR289hqUlfkdkVIqlgKXyx1pjFmLrWZf\nCuwGXOlVULku1PN8zJjM7X8WDMLYsfa3yi+ffgqHHWYvNOfO1SSuVKZzWyIvcn4fBzxtjPlRRG8n\nT0YmDwaWZxN6qTAff2zvEzfGJvHevf2OSCnVHrcl8udF5BPs7WZzRGQboM67sJSforXhq9z30Ue2\nFUbEfuaaxJXKDq4SuTHmGuBgYH9jTCPwE3CCl4Ep/4Ta8AsLtbN7vli40LaJFxXZJP6rX/kdkVLK\nrXiGaN0eGCAi4fePV6U4HpUBcnBCL9WG99+HAQNg881tdfpuu/kdkVIqHm6HaL0JqAB+DcwCjgHe\nRBN5zsrkNnyVOvPnwxFHQNeuNonvsovfESml4uW2jfwkoD+wyhhzFtAH2NKzqJRSnnv3XVsS79YN\nXn9dk7hS2cptIv/ZGNMMbBCRrsC3QC/vwlJKeSkYtCXxkhKbxEtL/Y5IKZUot23k80WkG/AgsACo\nBfQOY6Wy0JtvwjHHwHbb2er0nj39jkgplQxXidwYc4Hz5z9E5CWgqzHmA+/CUkp54fXX4bjjYIcd\nbBLffnu/I1JKJctV1bqIDBaRLQGMMUuBr0XkRC8DU0ql1muv2ZL4jjvaOxI0iSuVG9y2kd9kjPkx\n9MAYswa4yZuQlFKp9sortiS+6662JN6jh98RKaVSxW0ij7ZcPPegK6USkYJB719+GY4/HvbYw5bK\nu3dPYXxKKd/F09ntbuCvzuMLsZ3elFJeScGg97NmweDB8Otf2/nES0o8ilUp5Ru3JfKLgUbgSeen\nDpvMlVJeSXLQ++efhxNPhL33ttcAmsSVyk1uS+TbGWOu9jQSpVRrSUxcP2MGnHoq7LuvrVrv1s2z\nKJVSPnObyCeJSE/gP8AbwDxjzIfehaWUSnTQ+2nT4PTTYf/94aWXYEsdg1GpnOb2PvLDRKQYOAA7\n5voLItLZGPMLL4NTKu/FOej9k0/CGWfAQQfZ9vGuXT2MTSmVEdxOmvJ/wCHOTzdgJrZkrnJNMJif\n057lwH4/8QQMHQr/93/wwgvQubPfESml0sFt1XoA20t9LDDLGNPgWUTKPynoJZ2VcmC/q6rgrLPg\nsMNsJ7cttvA7IqVUurjttb418GegHHhJRF4VkTHehaV8kWQv6ayV5fs9eTKMGAGHHw4zZ2oSVyrf\nuG0jXyMiX2BnPOsJHAwUeRmY8kESvaSzWhbv94MPQmUlHHWU7aneqZPfEYUJb65QSnnGbRv5F8An\nwJvA34GztHo9ByXYSzrrZel+/+Mf8Ic/wLHHwvTp0LGj3xGFiWiu6DpunCZ0pTzito18N2c+cpXr\n4uwlnTOybL/vvx8uvtgOvfr007DZZn5HFCGiuaJbdbXfESmVs9y2kW8vIjNE5FvnZ7pzX7lSKs0m\nTLBJ/MQT7T3jGZfEoaW5orAQiotZU1bmd0RK5Sy3JfLJwBPAyc7jM53njvAiKJXFcuA2rkx2111w\n5ZUwZAj8859QlKk9VSKaK9bW1/sdkVI5y20i38YYMzns8SMicqkXAakslgO3cWWy22+Ha6+1Q68+\n+mgGJ/GQ8OaKLLsTQKls4rZqvUZEzhSRQufnTKDGy8BUFsq227hSMEVoutxyi03iv/89PPZYFiRx\npVTauC2RjwT+AtwDGOAt4CyvglJZKptu48qS2gNj4Oab7c/Qofae8cJCv6NSSmUSt/eRfwUM8jgW\nle2y6TauaLUHGRavMfCnP8Gtt8JZfRfyYOV6CgszK0allP/aTOQicmMbLxtjjI7ulitS1UktW27j\nyvDaA2NsVfodd8A5hZN54P1KCo4sytiaA6WUf9orkf8U5bktgLOBEiBmIheRScBA4FtjzF7Oc6OB\nc4HVzmLXGWNmxRmzSrUsqWZOqQyuPTDG9kwfPx7O/817/HV+JQXNG6DBZGTNgVLKX20mcmPM+NDf\nItIFuATbNj4VGB/rfY5HgPuBqojn7zHG3BV3pMo7WVDN7IkMrD0wBi67DO69Fy66CO47vR4ZUGST\neAbWHCil/NduG7mI/AK4HDgDmALsZ4z5ob33GWPmiUhpsgGqNMjwauZ80dwM9923O888A5dcAvfc\nAyKZW3OglMoM7bWRjwN+B0wE9jbG1KZgmxeLyDBgPjDKzUWB8lgGVzPni+ZmuOACeOaZHRg1CsaN\nAxHnxQysOVBKZQ4xxsR+UaQZqAc2YG872/gStrNb1zZXbkvkM8PayLsD3znrGgP0MMaMjPHeSqAS\noHv37n2nTp3qbo8SUFtbS+fOnT1bv59ydd9yab+am2H8+D2ZNasHQ4Z8zoUXLm9J4jkilz6vSLm6\nb7pf/urXr98CY8z+rhY2xnj2A5QCH8X7WuRP3759jZfmzp3r6fr9lKv7liv7tWGDMSNGGAPG3HCD\nMa+9NtfvkDyRK59XNLm6b7pf/gLmG5e51u3IbikhIj3CHg4GPkrn9pXKJE1NMGIEPPIIjB4NY8aQ\ncyVxpZT33I7sFjcR+SdQAWwtIsuBm4AKESnDVq0vBc7zavtKZbING2DYMDvxyZgxcMMNfkeklMpW\nniVyY8zpUZ5+2KvtKZUtGhvhjDPsPOJjx8I11/gdkVIqm3mWyJVSm2pogNNPh3/9y05JOmqU3xEp\npbKdJnKl0qS+3k5B+uyzMGGCvVdcKaWSpYlcqTSoq4OTToIXXoD774cLL/Q7IqVUrtBErpTH6upg\n8GB46SX4+9/h/PP9jkgplUvSevuZUvnm559h0CB4+WV48MH4k3gwaDvEBYPexKeUyn5aIlfKI+vX\nw/HHw9y58PDDcNZZ8b0/HyelU0rFT0vkSnmgthaOO84OX19VFX8Sh+iT0imlVCQtkSuVYuvW2ST+\n73/DY4/Z280SoZPSKaXc0ESuVAqtXQvHHAPvvGNHbTvllMTXpZPSKaXc0EQeLhjUb02VsB9/hKOO\nggUL4MknYciQ5NepM5gqpdqjiTxEexapJPzwg03i1dUwbRqccILfESml8oV2dgvRnkUqQd9/DwMG\nwMKFduhVTeJKqXTSRB4S6llUWKg9i5Rr331nK3IWLYIZM2DgQL8jUkrlG61aD9GeRSpOq1fbJP7Z\nZ/Dcc3DkkX5HpJTKR5rIw2nPIuXSN9/YJP7FFzBzpv1bKaX8oFXrKiH5PHToypXQrx98+SXMmqVJ\nXCnlLy2Rq7jlcwf/FSvg8MPt7xdfhEMP9TsipVS++//27j3OqrLe4/jnx4CI4hyRYjTkiCmZaYJi\nFlkxo3IRTcK8ZokvT2JmaGVqaSpqiS9NM9NKSpR4oWiSmJfkOqMW4wlQQFFRJEW8h0dhUrnM/M4f\nz5rcjnPbsy9rr7W/79eL177M3mv/Htc43/08a63nUY9cspaqE/yzGFp46aVw+sSrr4ZFUBTiIlIK\n1COXrKVm6tAshhZefDEMp69fD3Pnwhe+UORaRUTaoCCXrKXmBP/WhhZaacw//xlC/J13YP58+Nzn\nil6piEibFOTSJak4wb8TQwvPPx+OiW/cGEJ86NCiVyki0i4FuZSvDoYWnnsuhPh778HChTBkSCxV\nioi0S0Eu5a2NoYVVq8Jw+pYtIcT32y+G2kREOkFnrUvW8nUNealei/7UUzB8eDh0XlurEBeR0qYe\nuWQlX9eQl+q16E8+Gerq1i2E+N57x12RiEj71COXrOTrGvJSvBZ9xYownF5REepRiItIEijIJSv5\nWiQun4vN5WOI/vFbl3PIsHfpaZt46CHYa6+ub0tEpJg0tC5Zydc15PnaTj6G6JfesoIRpw6gN/+i\ntvFw9vjXH2BQCYzzi4h0goJcspava8jzsZ1OzunSpn/8A0Z+91PsyGvUUsPuW1/KfiMiIjHS0Lok\nWi5D9I8+CiNGwE47wUM9R7F7xUsJn3NWRMqReuSSaF0dov/73+Hww6GqChYu3JYB625NwZyzIlKO\nFOSSeNkO0T/8MIwZA/37h8le+vcHBqRhzlkRKUcaWpeyUlsbeuIDBoQOeP/+cVckIpIbBbmUjfnz\n4YgjYODAEOK77BJ3RSIiuVOQS1mYMwe++lXYc8/QK6+qirsiEZH8UJBL6j3wAIwdGyZ5WbgQ+vWL\nuyIRkfxRkOeqVFf+EADuuw/GjYN99gkh/rGPxV2RiEh+6az1XJTqyh8CwOzZcNxxMHgwzJ0LffrE\nXZGISP6pR56LUlz5QwCYNQuOPRYOOADmzVOIi0h6Kchzkc+VPyRv7rwTjj8eDjoo9MR33DHuikRE\nCkdD67nI18ofkje33Qbf+hYcfDDcfz/ssEPcFYmIFJaCPFf5WkFEcjZ9OpxyCnzlK+Ekt+23j7si\nEZHC09C6pMKtt8L48VBTE3riCnERKRcKckm8P/wBTj01rGR2772w3XZxVyQiUjwFC3Izm2pmb5jZ\nkxnP7WRm88zsuehW5xJLTn73OzjtNBg1Cu65B3r1irsiEZHiKmSP/FZgdIvnfgwscPdBwILosUiX\n3HADnHFGmD999mzYdtu4KxIRKb6CBbm7Pwy81eLpscC06P404GuF+nxJt7vu2pWJE8PUq7NmQc+e\ncVckIhKPYh8jr3L3V6P7rwFaukKyds01cOONe3L00eGacYW4iJQzc/fCbdxsIHCfu+8bPX7b3XfM\n+Pn/uXurx8nNbAIwAaCqqmrozJkzC1ZnQ0MDvXv3Ltj245S2tt1++wCmTNmDgw9+hUmTnqN798L9\n/sYhbfurWVrbBeltm9oVr5qamqXufmCnXuzuBfsHDASezHi8Ctglur8LsKoz2xk6dKgXUm1tbUG3\nH6c0te1nP3MH9xNPdJ8/vy7ucgoiTfsrU1rb5Z7etqld8QKWeCeztthD638Bxkf3xwP3FPnzJaEu\nvRR++tMwa9v06VBRka6euIhIVxXy8rPbgXpgLzNbZ2b/A1wJjDCz54DDoscibXKHiy+GSZPCrG23\n3BKmthcRkaBgU7S6+4lt/OjQQn2mpIs7XHABXHklfPvbcNNN0E1TGImIfIjmWpeS5A7nnQe/+AWc\nfjr85jcKcRGR1ijIpeS4ww9/CNddB2eeCb/+NZjFXZWISGlSH0dKijucdVYI8bPPVoiLiHREQS4f\nqK+HyZPDbQyamkIP/IYb4Jxz4Je/VIiLiHREQ+sS1NfDoYfC5s2wzTawYEFR11lvaoLvfAd+/3s4\n//zwfSK2EK+vh7o6qK7WWvMiUvIU5BLU1YUQb2wMt3V1RQuxxkaYMAGmToULL4TLL485xGP8QiMi\nki0NrUtQXR2Cq6Ii3FZXF+VjGxvDWuJTp4brxWMNcWj9C42ISAlTj1yCYcNC77OIQ8pbt8L48XDb\nbXDZZXDRRQX/yI41f6Fp7pEX6QuNiEhXKcjlA8OGfRDgBT5OvGVLmG71jjvgiivgJz/J+0d0TQxf\naEREcqEgl48q8HHiLVvgxBPDOuJXXQXnnpu3TedH5hcaEZESp2Pk8lEFPE68eTMcd1wI8WuvLcEQ\nFxFJGPXI5aMKdJx40yY49li49164/nqYODEvmxURKWsKcvmoAhwnfv99+PrX4YEHwrzpZ5yR8yZF\nRAQFubQlj8eJ33sPxo2DOXNgyhQ47bS8bFZERFCQS4G9+y6MHRs6+DffHK4ZFxGR/FGQS8H8+9/w\n1a+GEfpbb4WTT467IhGR9FGQS0Fs3AhHHgl/+xtMnw4nnRR3RSIi6aQgl7zbsAHGjIFHH4UZM+CE\nE+KuSEQkvRTkklfvvAOjR8OSJTBzJhxzTNwViYikm4Jc8ubtt2HkSHj8cbjzznCmuoiIFJaCXPLi\nrbdCiK9YEWZtO+qouCsSESkPCnLJ2fr1cNhh8PTTMHt2OD4uIiLFoSCXnLz5ZgjxVavgnntg1Ki4\nKxIRKS8Kcumy118Pi6Q9/zzcd18IdBERKS4FuXTJa6/BIYfAiy/C/feH+yIiUnxaxrTc1NfD5Mnh\ntoteeSWspbJ2bVgERSEuIhIf9cjLSX19GAtvXp50wYKsF0ZZtw5qakKP/MEH4UtfKlCtIiLSKeqR\nl5O6uhDijY3htq4uq7evXQvDh4dj43PnKsRFREqBgrycVFeHnnhFRbitru70W194IYT4+vUwf37e\nVjgVEZEcaWi9nAwbFobT6+pCiHcyjdesCcPpGzeGtw8dWtAqRUQkCwrycjNsWFbd6dWrQ4i/+24I\n8f33L2BtIiKSNQW5tOnZZ0OIb94MCxfC4MFxVyQiIi0pyKVVzzwTQryxEWprYd99465IRERao5Pd\n5CNWrgwntrmHw+kKcRGR0qUglw9ZsSKcB1dREUL8M5+JuyI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (8,8))\n", "ax.plot(cs_no23, list_of_model_ni, 'r.')\n", "ax.plot(cb_cs_ni[cb_depths == 2], cb_model_ni[cb_depths == 2], 'b.', label = 'CB surface')\n", "ax.plot(cb_cs_ni[cb_depths == 20], cb_model_ni[cb_depths == 20], 'g.',label = 'CB 20 m' )\n", "ax.plot(np.arange(0,38), np.arange(0,38), 'b-')\n", "ax.grid('on')\n", "plt.legend()\n", "ax.set_title('Citizen Science Data vs Nowcast-green: Nitrate')\n", "ax.set_xlabel('Citizen Science')\n", "ax.set_ylabel('Nowcast-green');\n", "print('bias = ' + str(-np.mean(cs_no23) + np.mean(list_of_model_ni)))\n", "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_ni - cs_no23)**2) /\n", " 267)))\n", "xbar = np.mean(cs_no23)\n", "print('Willmott = ' + str(1-(np.sum((list_of_model_ni - cs_no23)**2) / \n", " np.sum((np.abs(list_of_model_ni - xbar) \n", " + np.abs(cs_no23 - xbar))**2))))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bias = 10.626370048\n", "RMSE = 15.1542056878\n", "Willmott = 0.592321581353\n" ] }, { "data": { "image/png": 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/GPn9OnZo+mD9C01N8JvfwPjx4SSsWGzDC6IoyDTSnkgBBF0iP8Ba+7ExZmvg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\n1tpXvb9XAm8D2wNHAdO9t00Hjg4qhsDE4y6JNze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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (8,8))\n", "ax.plot(cs_si, list_of_model_si, 'r.')\n", "ax.plot(cb_cs_si[cb_depths == 2], cb_model_si[cb_depths == 2], 'b.', label = 'CB surface')\n", "ax.plot(cb_cs_si[cb_depths == 20], cb_model_si[cb_depths == 20], 'g.',label = 'CB 20 m' )\n", "ax.plot(np.arange(0,56), np.arange(0,56), 'b-')\n", "ax.grid('on')\n", "plt.legend()\n", "ax.set_title('Citizen Science Data vs Nowcast-green: Si')\n", "ax.set_xlabel('Citizen Science')\n", "ax.set_ylabel('Nowcast-green');\n", "print('bias = ' + str(-np.mean(cs_si) + np.mean(list_of_model_si)))\n", "print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_si - cs_si)**2) /\n", " 267)))\n", "xbar = np.mean(cs_si)\n", "print('Willmott = ' + str(1-(np.sum((list_of_model_si - cs_si)**2) / \n", " np.sum((np.abs(list_of_model_si - xbar) \n", " + np.abs(cs_si - xbar))**2))))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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86U9/OqV4DhW4Z511FuFwmKeffhoAn8/HwIEDeeaZZ4D0E5+bm5uZNWsW//Vf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\n5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heXH+0hhdVCXziMdkFEt99NtbUam9vGyqyVlGSWRPT5jlrtfPP5Whq6BrmtajH3fbaU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\nKyISoYaCQ+xu3E19dz0zU2dyZc6VxLhCufmt6hN8b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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,2, figsize = (16,8))\n", "ax[0].plot(cs_no23[nutrients_2015['depth'].values == 2], \n", " cs_si[nutrients_2015['depth'].values == 2], \n", " 'b.', alpha = 0.5)\n", "ax[0].plot(cs_no23[nutrients_2015['depth'].values == 20], \n", " cs_si[nutrients_2015['depth'].values == 20], \n", " 'g.', alpha = 0.5)\n", "ax[0].plot(cb_cs_ni[cb_depths == 20], cb_cs_si[cb_depths == 20], \n", " '.', alpha = 0.5, color = 'saddlebrown')\n", "ax[0].plot(cb_cs_ni[cb_depths == 2], cb_cs_si[cb_depths == 2], \n", " '.', alpha = 0.5, color = 'purple')\n", "ax[0].plot(np.unique(cs_no23), np.poly1d(np.polyfit(cs_no23, cs_si, 1))(np.unique(cs_no23)))\n", "x = np.arange(0,50)\n", "#ax[0].plot(x,x, 'r-', alpha = 0.3)\n", "ax[0].plot(x, 2*x, 'g-', alpha = 0.3)\n", "ax[0].plot(x, 2*x+30, 'y-', alpha = 0.3)\n", "ax[1].plot(list_of_model_ni[nutrients_2015['depth'].values == 2], \n", " list_of_model_si[nutrients_2015['depth'].values == 2], 'b.', \n", " alpha = 0.5, label = 'surface')\n", "ax[1].plot(list_of_model_ni[nutrients_2015['depth'].values == 20], \n", " list_of_model_si[nutrients_2015['depth'].values == 20], 'g.', \n", " alpha = 0.5, label = '20m')\n", "ax[1].plot(cb_model_ni[cb_depths == 2], cb_model_si[cb_depths == 2], \n", " '.', alpha = 0.5, color = 'purple', label = 'CB surface')\n", "ax[1].plot(cb_model_ni[cb_depths == 20], cb_model_si[cb_depths == 20], \n", " '.', alpha = 0.5, color = 'saddlebrown', label = 'CB 20m')\n", "ax[1].plot(np.unique(list_of_model_ni), \n", " np.poly1d(np.polyfit(list_of_model_ni, \n", " list_of_model_si, 1))(np.unique(list_of_model_ni)))\n", "x = np.arange(0,53)\n", "#ax[1].plot(x,x, 'r-', alpha = 0.3, label = 'slope = 1')\n", "ax[1].plot(x, 2*x, 'g-', alpha = 0.3, label = 'slope = 2')\n", "ax[1].plot(x, 2*x+30, 'y-', alpha = 0.3, label = '2*N + 30')\n", "ax[0].set_title('Citizen Science', fontsize = 16)\n", "ax[1].set_title('Model', fontsize = 16)\n", "for ax in ax:\n", " ax.grid('on')\n", " ax.set_ylabel('Si', fontsize = 14)\n", " ax.set_xlabel('N', fontsize = 14)\n", " ax.set_ylim(0,105)\n", " ax.set_xlim(0,40)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CitSci slope = 1.20089704365\n", "CitSci y int = 16.3134925754\n", "model slope = 1.6960524897\n", "model y int = 0.979972798278\n" ] } ], "source": [ "m1, b1 = np.polyfit(cs_no23, cs_si, 1)\n", "print('CitSci slope = ' + str(m1))\n", "print('CitSci y int = ' + str(b1))\n", "m2, b2 = np.polyfit(list_of_model_ni, list_of_model_si, 1)\n", "print('model slope = ' + str(m2))\n", "print('model y int = ' + str(b2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }