PS1casjobs/Accuracy_SDSS_Debiased.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of statistics of 4 models for the SDSS test set\n",
    "\n",
    "In this notebook we examine the ROC curve, the FoM, the accuracy, and the ROC AUC for 4 models: the ML model, the simple model, the PS1 model, and the SDSS model, for the SDSS test set. \n",
    "The uncertainty for each statistic is derived by 100 bootstrap resampling. \n",
    "\n",
    "\n",
    "The performance of the ML model is the best than that of altanative models except the accuracy. \n",
    "The bias in the SDSS test set towards point sources at the faint end and the hard star--galaxy cut employed in the SDSS model makes the reversal of the performance. \n",
    "This bias affects the ROC curve. \n",
    "The kink and the cross of the curve around the TPR=0.015 are due to the bias. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os,math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "rcParams[\"font.family\"] = \"sans-serif\"\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "from matplotlib import gridspec as grs\n",
    "%matplotlib notebook\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from matplotlib import cm\n",
    "from astropy.table import Table\n",
    "import seaborn as sns\n",
    "import statsmodels.nonparametric.api as smnp\n",
    "from statsmodels.nonparametric.kernel_density import KDEMultivariate\n",
    "from scipy.special import expit\n",
    "from scipy import stats\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, accuracy_score, auc, make_scorer\n",
    "from sklearn.model_selection import GridSearchCV, KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hst_tbl = Table.read(\"HST_COSMOS_features_adamamiller.fit\").to_pandas()\n",
    "sdss_tbl = Table.read(\"sdssSP_MLfeats_adamamiller.fit\").to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss_tbl = sdss_tbl[~sdss_tbl.duplicated('objid')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hst_det = np.where(hst_tbl.nDetections > 0)\n",
    "hst_kron_mag = np.array(-2.5*np.log10(hst_tbl['wwKronFlux'].loc[hst_det]/3631))\n",
    "\n",
    "sdss_det = np.where(sdss_tbl.nDetections > 0)\n",
    "sdss_photo_det = np.logical_and(np.isfinite(sdss_tbl.type),\n",
    "                                sdss_tbl.countRatio > 0)\n",
    "sdss_in_common = np.logical_and(np.isfinite(sdss_tbl.iPSFminusKron), \n",
    "                                sdss_photo_det)\n",
    "low_z_gal = np.logical_and(sdss_tbl.z < 1e-4, sdss_tbl['class'] == 'GALAXY')\n",
    "low_z_qso = np.logical_and(sdss_tbl.z < 1, sdss_tbl['class'] == '   QSO')\n",
    "\n",
    "sdss_test_set = sdss_in_common & ~low_z_gal & ~low_z_qso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['wwpsfChiSq', 'wwExtNSigma', 'wwpsfLikelihood',\n",
    "            'wwPSFKronRatio', 'wwPSFKronDist',  'wwPSFApRatio',\n",
    "            'wwmomentRH', 'wwmomentXX', 'wwmomentXY', 'wwmomentYY', \n",
    "            'wwKronRad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hst_ml_train_X = np.array(hst_tbl[features].loc[hst_det])\n",
    "hst_ml_train_y = np.array(hst_tbl[\"MU_CLASS\"].loc[hst_det] - 1)\n",
    "sdss_ml_test_X = np.array(sdss_tbl[features].loc[sdss_test_set])\n",
    "sdss_spec_class = np.array(sdss_tbl['class'].loc[sdss_test_set])\n",
    "sdss_ml_test_y = np.ones_like(sdss_spec_class).astype(int)\n",
    "sdss_ml_test_y[np.where(sdss_spec_class == \"GALAXY\")] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import star_galaxy_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_obj = star_galaxy_models.RandomForestModel()\n",
    "rf_obj.read_rf_from_pickle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_predict = rf_obj.rf_clf_.predict_proba(sdss_ml_test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_fpr, ML_tpr, ML_thre = roc_curve(sdss_ml_test_y, ML_predict[:,1])\n",
    "sdss_fpr, sdss_tpr,sdss_thre = roc_curve(sdss_ml_test_y, sdss_tbl[\"countRatio\"].loc[sdss_test_set])\n",
    "dist_fpr, dist_tpr, dist_thre = roc_curve(sdss_ml_test_y, sdss_tbl[\"wwPSFKronDist\"].loc[sdss_test_set])\n",
    "i_fpr, i_tpr, i_thre = roc_curve(sdss_ml_test_y, -1.*sdss_tbl[\"iPSFminusKron\"].loc[sdss_test_set])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps1_preds = -1.*sdss_tbl[\"iPSFminusKron\"].loc[sdss_test_set]\n",
    "simple_preds = sdss_tbl[\"wwPSFKronDist\"].loc[sdss_test_set]\n",
    "rf_preds = ML_predict[:,1]\n",
    "sdss_preds = sdss_tbl[\"countRatio\"].loc[sdss_test_set]\n",
    "y_sdss = sdss_ml_test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interp\n",
    "from sklearn.metrics import roc_curve, accuracy_score, auc, roc_auc_score\n",
    "def calc_fom(fpr, tpr, thresh, fpr_at=0.005):\n",
    "    return interp(fpr_at, fpr, thresh), interp(fpr_at, fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_summary_stats(y_sdss, ps1_preds, simple_preds, rf_preds, sdss_preds, \n",
    "                       ps1_ct = -0.05, \n",
    "                       simple_ct = 9.2e-7,\n",
    "                       rf_ct = 0.5, \n",
    "                       sdss_ct = 10**(-0.185/2.5)):\n",
    "    ps1_fpr, ps1_tpr, ps1_thresh = roc_curve(y_sdss, ps1_preds)\n",
    "    ps1_fom = calc_fom(ps1_fpr, ps1_tpr, ps1_thresh)\n",
    "    ps1_auc = roc_auc_score(y_sdss, ps1_preds)\n",
    "    ps1_acc = accuracy_score(y_sdss, -1*ps1_preds <= ps1_ct)\n",
    "    \n",
    "    simple_fpr, simple_tpr, simple_thresh = roc_curve(y_sdss, simple_preds)\n",
    "    simple_fom = calc_fom(simple_fpr, simple_tpr, simple_thresh)\n",
    "    simple_auc = roc_auc_score(y_sdss, simple_preds)\n",
    "    simple_acc = accuracy_score(y_sdss, simple_preds >= simple_ct)\n",
    "    \n",
    "    rf_fpr, rf_tpr, rf_thresh = roc_curve(y_sdss, rf_preds)\n",
    "    rf_fom = calc_fom(rf_fpr, rf_tpr, rf_thresh)\n",
    "    rf_auc = roc_auc_score(y_sdss, rf_preds)\n",
    "    rf_acc = accuracy_score(y_sdss, rf_preds >= rf_ct)\n",
    "    \n",
    "    sdss_fpr, sdss_tpr, sdss_thresh = roc_curve(y_sdss, sdss_preds)\n",
    "    sdss_fom = calc_fom(sdss_fpr, sdss_tpr, sdss_thresh)\n",
    "    sdss_auc = roc_auc_score(y_sdss, sdss_preds)\n",
    "    sdss_acc = accuracy_score(y_sdss, sdss_preds >= sdss_ct)\n",
    "    \n",
    "    return  rf_auc, rf_acc, rf_fom,  simple_auc, simple_acc, simple_fom, ps1_auc, ps1_acc, ps1_fom, sdss_auc, sdss_acc, sdss_fom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_stats = calc_summary_stats(y_sdss, ps1_preds, simple_preds, rf_preds, sdss_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    RF & 0.843 & 0.963  & 0.987  \\\\\n",
      "    simple & 0.798  & 0.956 & 0.985 \\\\\n",
      "    PS1 & 0.290 & 0.900 & 0.984 \\\\\n",
      "    SDSS & 0.777 & 0.971 & 0.987 \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(r\"\"\"\n",
    "    RF & {2:.3f} & {1:.3f}  & {0:.3f}  \\\\\n",
    "    simple & {5:.3f}  & {4:.3f} & {3:.3f} \\\\\n",
    "    PS1 & {8:.3f} & {7:.3f} & {6:.3f} \\\\\n",
    "    SDSS & {11:.3f} & {10:.3f} & {9:.3f} \\\\\n",
    "\"\"\".format(*summary_stats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bootstrap resampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "kron_mag = np.array(-2.5*np.log10(sdss_tbl['wwKronFlux'].loc[sdss_test_set]/3631))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_stats_bootstrap(gt, preds, ct=0.5, fom_at=[0.005, 0.01, 0.02, 0.05, 0.1], Nboot=100, mag_max = 30):\n",
    "    mag_mask = np.where(kron_mag  <= mag_max)\n",
    "    acc = accuracy_score(gt[mag_mask], preds[mag_mask] >= ct)\n",
    "    auc = roc_auc_score(gt[mag_mask], preds[mag_mask])\n",
    "    fpr, tpr, thresh = roc_curve(gt[mag_mask], preds[mag_mask])\n",
    "    fom_thresh = np.array([calc_fom(fpr, tpr, thresh, f) for f in fom_at])\n",
    "    thresh = fom_thresh[:,0]\n",
    "    fom = fom_thresh[:,1]\n",
    "    acc_std_arr = np.empty(Nboot)\n",
    "    auc_std_arr = np.empty_like(acc_std_arr)\n",
    "    fom_std_arr = np.empty((Nboot, len(fom_at)))\n",
    "    thresh_std_arr = np.empty_like(fom_std_arr)\n",
    "    for i in range(Nboot):\n",
    "        if(i%1==0):\n",
    "            print(\"%d.\"%i, end='')\n",
    "        boot_sources = np.random.choice(mag_mask[0], len(mag_mask[0]), replace=True)\n",
    "        auc_std_arr[i] = roc_auc_score(gt[boot_sources], preds[boot_sources])\n",
    "        acc_std_arr[i] = accuracy_score(gt[boot_sources], preds[boot_sources] >= ct)\n",
    "        _fpr, _tpr, _thresh = roc_curve(gt[boot_sources], preds[boot_sources])\n",
    "        _fom_thresh = np.array([calc_fom(_fpr, _tpr, _thresh, f) for f in fom_at])\n",
    "        thresh_std_arr[i,:] = _fom_thresh[:,0]\n",
    "        fom_std_arr[i,:] = _fom_thresh[:,1]\n",
    "    acc_std = np.std(acc_std_arr)\n",
    "    auc_std = np.std(auc_std_arr)\n",
    "    fom_std = np.std(fom_std_arr, axis=0)\n",
    "    thresh_std = np.std(thresh_std_arr, axis=0)\n",
    "    return {'Num': len(kron_mag[mag_mask]), \n",
    "                'Acc': acc*100, \n",
    "                'AUC': auc, \n",
    "                'FoM': fom, \n",
    "                'Thresh': thresh, \n",
    "                'AccSTD': acc_std*100, \n",
    "                'AUCSTD': auc_std, \n",
    "                'FoMSTD': fom_std, \n",
    "                'ThreshSTD': thresh_std}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps1_ct = -0.05, \n",
    "simple_ct = 9.2e-7\n",
    "rf_ct = 0.5\n",
    "sdss_ct = 10**(-0.185/2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99."
     ]
    }
   ],
   "source": [
    "stat_rf = summary_stats_bootstrap(y_sdss, rf_preds, ct=rf_ct, Nboot=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUC': 0.98713117838108511,\n",
       " 'AUCSTD': 6.6746500963364271e-05,\n",
       " 'Acc': 96.25373440899898,\n",
       " 'AccSTD': 0.010122492154387774,\n",
       " 'FoM': array([ 0.84308974,  0.91322055,  0.94742827,  0.96477697,  0.97594995]),\n",
       " 'FoMSTD': array([ 0.00112882,  0.00053063,  0.00021365,  0.00017044,  0.0001365 ]),\n",
       " 'Num': 3592938,\n",
       " 'Thresh': array([ 0.810375  ,  0.50862524,  0.26413417,  0.135     ,  0.06066667]),\n",
       " 'ThreshSTD': array([ 0.00272024,  0.00285574,  0.00100327,  0.00034501,  0.00014297])}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99.0.1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99.0.1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99."
     ]
    }
   ],
   "source": [
    "stat_ps1 = summary_stats_bootstrap(y_sdss, ps1_preds.values, ct=ps1_ct, Nboot=100)\n",
    "stat_simple = summary_stats_bootstrap(y_sdss, simple_preds.values, ct=simple_ct, Nboot=100)\n",
    "stat_sdss = summary_stats_bootstrap(y_sdss, sdss_preds.values, ct=sdss_ct, Nboot=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUC': 0.98411794372530115,\n",
       " 'AUCSTD': 6.7086071564212565e-05,\n",
       " 'Acc': 96.117383600830294,\n",
       " 'AccSTD': 0.0090463838760812768,\n",
       " 'FoM': array([ 0.28994216,  0.7613966 ,  0.935268  ,  0.9685331 ,  0.97898192]),\n",
       " 'FoMSTD': array([ 0.0036232 ,  0.00401264,  0.00034549,  0.00013789,  0.00012308]),\n",
       " 'Num': 3592938,\n",
       " 'Thresh': array([ 0.13290072,  0.04500008, -0.07390013, -0.24310112, -0.35819817]),\n",
       " 'ThreshSTD': array([ 0.00089204,  0.00100816,  0.00079227,  0.00042546,  0.00033153])}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_ps1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUC': 0.98503017551718985,\n",
       " 'AUCSTD': 8.3192975159283847e-05,\n",
       " 'Acc': 95.568751812583457,\n",
       " 'AccSTD': 0.011220871543247714,\n",
       " 'FoM': array([ 0.79792979,  0.89413228,  0.95014616,  0.96744146,  0.97444493]),\n",
       " 'FoMSTD': array([ 0.00158545,  0.00080701,  0.0002654 ,  0.00017194,  0.00015392]),\n",
       " 'Num': 3592938,\n",
       " 'Thresh': array([  1.70552150e-06,   9.31750294e-07,   5.13090947e-08,\n",
       "         -2.12707404e-06,  -4.57542294e-06]),\n",
       " 'ThreshSTD': array([  1.24870938e-08,   6.69814634e-09,   7.64245548e-09,\n",
       "          9.21674138e-09,   8.17511288e-09])}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUC': 0.98660473889196332,\n",
       " 'AUCSTD': 7.8183918684798814e-05,\n",
       " 'Acc': 97.128533807151697,\n",
       " 'AccSTD': 0.0088219189858879405,\n",
       " 'FoM': array([ 0.77720695,  0.90346533,  0.95586257,  0.97331779,  0.97965236]),\n",
       " 'FoMSTD': array([ 0.00297895,  0.00087134,  0.00030467,  0.00015363,  0.00012823]),\n",
       " 'Num': 3592938,\n",
       " 'Thresh': array([ 0.96247655,  0.9231685 ,  0.87565661,  0.68230901,  0.60383077]),\n",
       " 'ThreshSTD': array([ 0.00053257,  0.00047051,  0.00059737,  0.00040919,  0.00024205])}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_sdss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "cmap = plt.get_cmap(\"Dark2\")\n",
    "\n",
    "color_dict = {'ml': cmap(0.33), \n",
    "              'sdss': cmap(0.66), \n",
    "              'simple': cmap(0.),\n",
    "              'ps1': cmap(1.)}\n",
    "\n",
    "# apple colors\n",
    "color_dict = {'ml': \"#0072c6\", #\"#1C1858\",\n",
    "              'sdss': \"#5BC236\", #\"#00C78E\",\n",
    "              'simple': \"#C864AF\", #\"#C70039\",\n",
    "              'ps1': \"#C65400\"}\n",
    "\n",
    "# color blind friendly\n",
    "color_dict = {'ml': '#0072b2', \n",
    "              'sdss': '#d55e00', \n",
    "              'simple': '#cc79a7',\n",
    "              'ps1': '#009e73'}\n",
    "\n",
    "# color brewer\n",
    "color_dict = {'ml': '#7570b3', \n",
    "              'sdss': '#d95f02', \n",
    "              'simple': '#1b9e77',\n",
    "              'ps1': '#34495e'}\n",
    "\n",
    "col_star_sdss = '#e41a1c'\n",
    "col_star_ps1 = '#001e43'\n",
    "\n",
    "ls_dict = {'ml': '-', \n",
    "              'sdss': '-.', \n",
    "              'simple': '--',\n",
    "              'ps1': '--'}\n",
    "\n",
    "lw_dict = {'ml': 1.75, \n",
    "              'sdss': 1.5, \n",
    "              'simple': 1.5, \n",
    "              'ps1': 1.5}\n",
    "\n",
    "ylims = [0, 1.02]\n",
    "xlims = [1.7e-4, 1]\n",
    "\n",
    "fig, main_ax = plt.subplots(figsize=(7,5))\n",
    "\n",
    "\n",
    "axins = inset_axes(main_ax, width=\"43.5%\",  \n",
    "                   height=\"53%\", loc=3,\n",
    "                   bbox_to_anchor=(0.55, 0.07, 1., 1.),\n",
    "                   bbox_transform=main_ax.transAxes)\n",
    "\n",
    "main_ax.grid(alpha=0.5, lw=0.5, c='grey', linestyle=':') \n",
    "main_ax.tick_params(which=\"both\", top=True, right=True)\n",
    "main_ax.minorticks_on()\n",
    "\n",
    "main_ax.plot(ML_fpr, ML_tpr, color=color_dict['ml'], label='RF model', ls=ls_dict['ml'], lw=lw_dict['ml'], alpha=0.9, zorder=4)\n",
    "main_ax.plot(dist_fpr, dist_tpr, color=color_dict['simple'], label='Simple model', ls=ls_dict['simple'], lw=lw_dict['simple'], alpha=0.9, zorder=3)\n",
    "main_ax.plot(i_fpr, i_tpr, color=color_dict['ps1'], label='PS1 model', ls=ls_dict['ps1'], lw=lw_dict['ps1'], dashes=(8, 4), alpha=0.9, zorder=2)\n",
    "main_ax.plot(sdss_fpr, sdss_tpr, color=color_dict['sdss'], label='SDSS photo', ls=ls_dict['sdss'], lw=lw_dict['sdss'], alpha=0.9, zorder=1)\n",
    "main_ax.plot(sdss_fpr[np.argmin(np.abs(sdss_thre-10**(-0.185/2.5)))], \n",
    "        sdss_tpr[np.argmin(np.abs(sdss_thre-10**(-0.185/2.5)))], '*', markersize=10, color=col_star_sdss, zorder=5)\n",
    "main_ax.plot(i_fpr[np.argmin(np.abs(i_thre+0.05))], \n",
    "        i_tpr[np.argmin(np.abs(i_thre+0.05))], '*', markersize=10, color=col_star_ps1, zorder=5)\n",
    "\n",
    "main_ax.vlines([5e-3], 1e-3, 1.02, \n",
    "          color='DarkSlateGrey', lw=2., linestyles=\":\", zorder=1)\n",
    "main_ax.text(4.9e-3, 0.1, 'FoM', \n",
    "        color='DarkSlateGrey', \n",
    "        rotation=90, ha=\"right\", fontsize=14)\n",
    "\n",
    "main_ax.set_xlim(xlims); main_ax.set_ylim(ylims)\n",
    "main_ax.set_xscale('log')\n",
    "main_ax.tick_params(labelsize = 15)\n",
    "main_ax.set_xlabel('False Positive Rate', fontsize=15)\n",
    "main_ax.set_ylabel('True Positive Rate', fontsize=15)\n",
    "\n",
    "main_ax.legend(loc=3, borderaxespad=0, fontsize=13, \n",
    "               handlelength=1.5, \n",
    "               bbox_to_anchor=(0.0125, 0.725, 1., 0.102))\n",
    "\n",
    "\n",
    "ylims = [0.875, 0.965]\n",
    "xlims = np.array([7.5e-3, 2.75e-2])\n",
    "\n",
    "origin = 'lower'\n",
    "axins.tick_params(which=\"both\", top=True)\n",
    "axins.minorticks_on()\n",
    "\n",
    "axins.plot(ML_fpr, ML_tpr, color=color_dict['ml'], label='RF model', ls=ls_dict['ml'], lw=lw_dict['ml'], alpha=0.9, zorder=5)\n",
    "axins.plot(dist_fpr, dist_tpr, color=color_dict['simple'], label='Simple model', ls=ls_dict['simple'], lw=lw_dict['simple'], alpha=0.9, zorder=4)\n",
    "axins.plot(i_fpr, i_tpr, color=color_dict['ps1'], label='PS1 model', ls=ls_dict['ps1'], lw=lw_dict['ps1'], dashes=(8, 4), alpha=0.9, zorder=3)\n",
    "axins.plot(sdss_fpr, sdss_tpr, color=color_dict['sdss'], label='SDSS photo', ls=ls_dict['sdss'], lw=lw_dict['sdss'], alpha=0.9, zorder=2)\n",
    "axins.plot(sdss_fpr[np.argmin(np.abs(sdss_thre-10**(-0.185/2.5)))], \n",
    "        sdss_tpr[np.argmin(np.abs(sdss_thre-10**(-0.185/2.5)))], '*', markersize=10, color=col_star, zorder=6)\n",
    "axins.plot(i_fpr[np.argmin(np.abs(i_thre+0.05))], \n",
    "        i_tpr[np.argmin(np.abs(i_thre+0.05))], '*', markersize=10, color=col_star_ps1, zorder=5)\n",
    "\n",
    "\"\"\"\n",
    "axins.vlines([5e-3], 1e-3, 1, \n",
    "          color='DarkSlateGrey', lw=2., linestyles=\":\", zorder=1)\n",
    "axins.text(5e-3, 0.59, 'FoM', \n",
    "        color='DarkSlateGrey', \n",
    "        rotation=90, ha=\"right\", fontsize=14)\n",
    "\"\"\"\n",
    "axins.set_xlim(xlims); axins.set_ylim(ylims)\n",
    "#axins.set_yticks([0.85,0.90, 0.95])\n",
    "axins.tick_params(labelsize = 15)\n",
    "\n",
    "fig.subplots_adjust(right=0.97,top=0.98,bottom=0.11,left=0.1)\n",
    "fig.savefig('../paper/Figures/ROC_curves_log_inset2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy curve for the original SDSS test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_decision_thresh = 0.5\n",
    "sdss_decision_thresh = 10**(-0.185/2.5)\n",
    "simple_decision_thresh = 9.2e-07 #  maximize acc on training set\n",
    "ps1_decision_thresh = 0.05\n",
    "\n",
    "ml_labels = np.logical_not(ML_predict[:,1] < ml_decision_thresh).astype(int)\n",
    "sdss_labels = np.logical_not(np.array(sdss_tbl[\"countRatio\"].ix[sdss_test_set]) < sdss_decision_thresh).astype(int)\n",
    "simple_labels = np.logical_not(np.array(sdss_tbl[\"wwPSFKronDist\"].ix[sdss_test_set]) < simple_decision_thresh).astype(int)\n",
    "ps1_labels = np.logical_not(-1*np.array(sdss_tbl[\"iPSFminusKron\"].ix[sdss_test_set]) < ps1_decision_thresh).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/y_tachibana/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "binwidth = 0.5\n",
    "Nboot = 100\n",
    "mag_array = np.arange(13 , 23+binwidth, binwidth)\n",
    "kron_mag = np.array(-2.5*np.log10(sdss_tbl['wwKronFlux'].ix[sdss_test_set]/3631))\n",
    "\n",
    "sdss_acc_arr = np.zeros_like(mag_array)\n",
    "simple_acc_arr = np.zeros_like(mag_array)\n",
    "ps1_acc_arr = np.zeros_like(mag_array)\n",
    "ml_acc_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "\n",
    "for bin_num, binedge in enumerate(mag_array):\n",
    "    bin_sources = np.where((kron_mag >= binedge) & (kron_mag < binedge + binwidth))\n",
    "    sdss_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_sources], \n",
    "                                           sdss_labels[bin_sources])\n",
    "    simple_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_sources], \n",
    "                                             simple_labels[bin_sources])\n",
    "    ps1_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_sources], \n",
    "                                          ps1_labels[bin_sources])\n",
    "    ml_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_sources], \n",
    "                                         ml_labels[bin_sources])\n",
    "    sdss_boot_acc = np.empty(Nboot1\n",
    "    simple_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "    ps1_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "    ml_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "    for i in range(Nboot):\n",
    "        boot_sources = np.random.choice(bin_sources[0], len(bin_sources[0]), \n",
    "                                        replace=True)\n",
    "        sdss_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                           sdss_labels[boot_sources])\n",
    "        simple_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                             simple_labels[boot_sources])\n",
    "        ps1_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                          ps1_labels[boot_sources])\n",
    "        ml_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                         ml_labels[boot_sources])\n",
    "\n",
    "    sdss_boot_scatt[:,bin_num] = np.percentile(sdss_boot_acc, [16, 84])\n",
    "    simple_boot_scatt[:,bin_num] = np.percentile(simple_boot_acc, [16, 84])\n",
    "    ps1_boot_scatt[:,bin_num] = np.percentile(ps1_boot_acc, [16, 84])\n",
    "    ml_boot_scatt[:,bin_num] = np.percentile(ml_boot_acc, [16, 84])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KernelDensity\n",
    "kde_grid = np.linspace(13,23.5,200)\n",
    "sdss_stars = np.where(sdss_ml_test_y == 1)\n",
    "sdss_gal = np.where(sdss_ml_test_y == 0)\n",
    "sdss_kde_gal_norm = len(sdss_gal[0])/len(sdss_ml_test_y)\n",
    "sdss_kde_star_norm = 1 - sdss_kde_gal_norm\n",
    "\n",
    "kde_sdss = KernelDensity(bandwidth=1.059*np.std(kron_mag, ddof=1)*len(kron_mag)**(-0.2),\n",
    "                         rtol=1E-4)\n",
    "kde_sdss.fit(kron_mag[:, np.newaxis])\n",
    "\n",
    "kde_sdss_stars = KernelDensity(bandwidth=1.059*np.std(kron_mag[sdss_stars], ddof=1)*len(kron_mag[sdss_stars])**(-0.2),\n",
    "                               rtol=1E-4)\n",
    "kde_sdss_stars.fit(kron_mag[sdss_stars[0], np.newaxis])\n",
    "\n",
    "kde_sdss_gal = KernelDensity(bandwidth=1.059*np.std(kron_mag[sdss_gal], ddof=1)*len(kron_mag[sdss_gal])**(-0.2),\n",
    "                             rtol=1E-4)\n",
    "kde_sdss_gal.fit(kron_mag[sdss_gal[0], np.newaxis])\n",
    "\n",
    "pdf_sdss = np.exp(kde_sdss.score_samples(kde_grid[:, np.newaxis]))\n",
    "pdf_sdss_stars = np.exp(kde_sdss_stars.score_samples(kde_grid[:, np.newaxis]))\n",
    "pdf_sdss_gal = np.exp(kde_sdss_gal.score_samples(kde_grid[:, np.newaxis]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NSql5Sqliq9Ua61CEEKLXsNlsWK1WLJa2C+WZTCZqampiFFVs/OhHP2L58uXU\n19fHOpRuSzOnc87wQkoat7ckPc0SbC+wLKVUsVJqXkcHxXXio7VerbUuysrKinUoQgjRa9TU1GAy\nmdoNaSUnJ1NfX4/H44lRZNGXkpLCT37yE37/+9/HOpQeq3ZWBGxPoL3ArFrrIq316o4OiuvERwgh\nRFs+n4+GhoaANS3NiVBdXWJNmb7iiivYsGEDBw8ejHUoPRJsL7Bg7YlKEh8hhEggDocDrTWGEfjX\nf1JSEtXV1STCxJdmhmFwzz33cM8998Q6lB6Zn7+QZKPtNhcmZWZ+/sIYRdQ7SeIjhBAJpL6+vsNZ\nW2azGY/Hg8vlimJUsTdr1izKysr48ssvYx1Kt03LncOCcXeQbckDFNmWPE7KPp38fuNjHVqvIrO6\nhBAiQWitqaur63TqttYap9PZrvg53v32t79l8eLFrFq1KtahdNu03DltZnB5tYdndj1M4ZhFmI2+\nN2U/EqTHRwghEoTT6cTr9bZMYQ/GZDJhs9miFFXvMWnSJGpra8nLy8MwDPLz81mxYkWsw+oRkzIz\nZ9gVrNnft59HOEniI4QQCaKxsTGk48xmc8jHxpMVK1awadMmysvL0Vqzd+9eioqK+nzyMzh1OAMs\ng/ja+mmsQ+kVJPERQogEEcowF/h7fNxuN16vNwpR9R6LFy/Gbre3abPZbL16J/dQnZZ7Lpur3pFd\n3pHERwghEoLH48HpdGI2h17a6XQ6Oz8ojpSWlnapvS9RSnH+8GtZve9vsQ4l5iTxEUKIBNCdJMbh\ncEQgkt5r5MiRXWrvazKTBzA+61g+Prw+1qHElCQ+QgiRALqaxCRinc+SJUva7VOWlpbGkiVLYhRR\n+B0/8HuUNGynxnU41qHEjCQ+QgiRABobG7s0zNU8syuRFjIsLCykuLiYUaNGoZTCYrFQXFxMYWFh\nrEMLq3kj/ENeifS9bU0SHyGEiHNaa+x2e5cSH8Mw0FrjdrsjGFnvU1hYSElJCT6fj3PPPZfTTjst\n1iGFXYoplWm5c1hf1nfXK+oJSXyEECLOuVwutNYdrtgcTKIVOLd22WWX8eKLL8Y6jIgYmzERm7eB\ng7aSWIcSdXGd+Cil5imliq1Wa6xDEUKImHE6nd0a1lBKJeRChs3OO+88Vq/ucKPvPm3usCtZd/BF\nPL646dXLUkoVK6XmdXRQXCc+WuvVWuuirKysWIcihBAxY7PZOl2tORCz2ZxwM7taS09PJzc3lz17\n9sQ6lIgwKTNnD7uc1w48G+tQwsWqtS7SWneYrcZ14iOEEKLrhc3NDMPodm9RvJg/f37cDncB5KWO\nJDNpAN/UfR7rUKJGEh8hhIjYpUMoAAAgAElEQVRjXq8Xt9vdrR4fwzDw+XwJt4Jza+eccw5r1qyJ\ndRgRNWPwPDZWvondkxjLF0jiI4QQcSwcxcmJNrOrtbS0NIYMGcLOnTtjHUrEKKU4f8S1vLI/MVZ1\nlsRHCCHiWDhqdBI58QG4/PLLWblyZazDiKis5GwKMiaxpeqdWIcScZL4CCFEHOtuYXMzpVRCT2kH\nmDNnDmvXro11GBE3JXs6O+u/pNZVFetQIkoSHyGEiGNdXbjwSCaTqd2O5YkmNTWVUaNGsX379liH\nEnEXjLgu7ld1lsRHCCHilNfrxev1Yhjd/1VvMpkSvscH/LO74n24CyDFlMYpOWeyofyVWIcSMZL4\nCCFEnHK5XD2+hmEYLQlUIjv77LP597//HeswomJc5mTq3bUcspXGOpSIkMRHCCHiVDjX4En0AmeL\nxcLYsWP58ssvYx1KVJwzrJA3Dr6Ax+eJdShhJ4mPEELEKYfD0aPC5tYSPfGB+F/MsDWzYebMofN5\n/cBzsQ4l7CTxEUKIOGW328OW+IRj2KyvO/PMM1m3bl1cF/62NjRtFGnmdHbWfRHrUMJKEh8hhIhD\nPp8Pp9MZlsRHZnb5JScnc9RRR/HFF/GVCHRkVt6FfFj5bxze+NmsVhIfIYSIQ81DU0qpHl9LZnZ9\n6/LLL+eFF16IdRhRo5Ri3ohreWVf/KzqLImPEELEoXDW5BiGgdvtxufzhe2afdXs2bN5++23E2a4\nC2BA8iBGp0/g06r3Yx1KWMR14qOUmqeUKrZarbEORQghoiocW1U0a+41kgJnSEpKYtKkSXz22Wex\nDiWqThw0k+11/8Xqqo51KB3JUkoVK6XmdXRQXCc+WuvVWuuirKysWIcihBBR1dMVmwPxeOJvanN3\nJMpihkc6f8S1bVZ13la7JcYRtWPVWhdprVd3dFB4XxVCCCF6BbvdTlJSUlivKT0+fjNnzuTOO+9E\nax2WGqq+Is2czomDZvHXHb/j89qPqHKWkW3JY37+Qqblzol1eCGL6x4fIYRIRB6PB5/PF9JWFZ4Q\n63Zks9Jvmc1mjjvuOLZs6XU9HhF32FnGuxWvUuUsA6DKWcayHQ/wQcXrMY4sdJL4CCFEnAl1zZ1D\nDfW88PWXbCk7hLeTBMhkMslaPq0k6nDXypLH8Oq2Q54un4OVJY/FKKKuk8RHCCHijMvl6nTWkdXp\n4N39pZgNxVdVlby5dze2DoayDMOQHp9Wpk+fzrvvvptQs7sAqpzlXWrvjSTxEUKIONPZVhVOj4e3\nS0vwaY3FZKZfUhKVdhsflx0Meo5hGC1DaMLfAzZ16lQ2bdoU61CiKtsyuEvtvZEkPkIIEWc6S3y2\nVlbQ6HaT2jTrSylFmjmJAw31uILswt5cxCszu741f/78hFrMEGB+/kKSjZQ2bUkqmeGpY7B7GmMU\nVddI4iOEEHFEa93hVhU+rdltrSH1iMcNpdBac7ChvsPrS+Lzre9973t8+OGHCdULNi13DgvG3UG2\nJQ9QZFvyuH78YoqOupMX9z7O/sbdsQ6xU1Gfzq6UOgb4I/BdoBZ4CrhXax34z4xvz5sK/BaYAijg\nE2Cx1npjZCMWQoi+w+PxdDjNuryxEbfPR3KAqe4mpdhZW01+Vv8Ory/8DMPg5JNP5qOPPuLUU0+N\ndThRMy13TsDp61ePuYXX9j/L3sZvevX09pB6fJRS5ymletw7pJQaALwJaOAC4D7gZ8C9nZw3ouk8\nM/B94Jqmf7+hlBrV07iEECJedDbzao+1hmArz1hMJsobG3EESW5kSnt7ibZ3V0cMZeK8EdeQkdSf\nlSWP4fL1zp+VUJOZl4EDSqkHlVITenC/m4BU4GKt9Tqt9eP4k56fKqUyOzjvXCCj6bw1Wus1wEVA\nOnBOD+IRQoi40tEigx6fj9I6KylBVnRu7iXaX18X8HGZ2dXeKaecwsaNGxNquKsz3xlwCqcPuZhn\ndz9CuX1/rMNpJ9TEZyxQDMwHvlBK/UcpdUMnyUogc4F/a61bv6qex58MzejgvCTAAzS0amtoakuc\nZTOFEKITDocj6MKFZY0NeLXG6GC1YXPTcFcgspZPe4ZhMG3aNN5/Pz428AyXbMtgrh5zKx9UvM6W\nqndiHU4bISU+WusSrfXdWuvRwJnATmApcEgp9Xel1KwQ73c08PUR1y4FbE2PBfPPpmP+oJTKVUrl\nNt2/BngxxHsLIUTcs9vtQQubd9XWYHTyp2KyyUSV3Y7d077nqHmX9kRbu6YzibqYYWfMRhIXj7oe\nr/byUukyPL6u1YdFai+wLtftaK3f1lpfA4wHtgCFwJtKqT1KqVuVUh0VTA/AX9B8pJqmx4Ld8yAw\nC7gEKG/6uBg4W2td2dXnIIQQ8aijGV1ur5cDDfVYTB3PaVFKoZSiJsDu7qpp5pcUOLd10kknsXnz\nZrxBlgJIdCcNms13c85kxe6HqXZWhHzeV9ZPIhJPlxMfpdQMpdRyYDswCfgzcBb+npd7gac7uUSg\nPxVUkPbmew4B/oE/0Zrb9LEFWKOUGhnknCKl1A6lVGVpaWknIQkhRN/XXN8TaEZXtcMOnQxzNfNp\nTbXdHvRxSXzaUkq1rOQsAstLHcmVo3/MuoMv8kXNx5G6zUilVGXTe/9mpVRRoINCndU1Sil1l1Jq\nF/A2MAIoAoZorf9Ha/2W1vp24Fr8s7WCqQECzZPMInBPULPb8M/iulRr/brW+nX8vT9e4OeBTtBa\nF2utx2mtc0aODJgbCSFEXOmosLnSZsMX/O/LNsxKUW4LvhidJD7tyXBX55JNKVw++kfUuCp5bf8K\nfDrsBeGlWuucpvf+qVrr4kAHhdrjsxu4AXgWKNBan661fk5rfWR5/5dAR+t3f80RtTxNU9X7cUTt\nzxGOBr7UWre8qrXWrqb7jQ3xOQghRFzrqPD4QGM9SSHs1g6QZBhU2W1Ba3mkwLm9KVOm8Nlnn0lS\nGILTBp/DxP4n8szupdS7O+rziIxQE595wCit9Z1a6z3BDtJaf6O17qjQeS1wtlIqo1Xb5YAd6Kjs\ney8wSSmV3NyglLLgH2orCSF+IYSIe8EKm70+H1V2O0lG8G0sWjMZBm6fD1uAAmeTySRT2gNQSjFz\n5kw2bNgQ61D6hFHp47l01I28vG85O+u2RvXeoSY+7wEBdyBTSg1RSqWHeJ3HASfwL6XUGU3jb/cA\nD7We4q6U2qmUWtbqvKeAocBLSqlzlVLnAauAIfin2QshRMILlvjUOh1AaPU9zZSCWkf7BMcwDOnx\nCUIWM+yaNHM6haMXsadhO28e/GfUZguGmvgsw7/KciD34E9MOqW1rgFOB0zAavzF0EuBu4841Nx0\nTPN5W4A5+Bcx/Dv+Auo04Eyt9X9DfA5CCBG3fD4fbrc74Bo+h+02fF18U/H5oNpha9cuiU9wxx57\nLF988UWHtVaiLaUUZw69lFHp43l2z6PYPA18UPE6izZdwEulT7Fo0wV8UPF6WO8Z6l5d0/GvuhzI\na8Bjod5Qa70NmN3JMfkB2t4C3gr1PkIIkUg6mtF1oL4ecxd3HUoyKcoaG5mc07bdMAx8Ph9er7fD\nHeATkVKK008/nbfeeos5c3rvXlW90bjMyeSljuDRr37FduunuLU/ua5ylrFsxwMAYdv/K9RXQhb+\nBQQDcdDBGjxCCCEiL1gvg9aaSruNJFMXEx/DRLXDHnT4QYp4A7v88stldlc3ZST155B9b0vS08zl\nc7CyJOT+lU6F+krYgX+/rEDOAXaFJxwhhBDdEazguM7lxOvzYepij4+hFF6fj8YgCZUkPoFNmjSJ\nr7/+WoYDu6nKWd6l9u4I9ZXwR+DHSqn/p5SaqJQa2PT5f4EfAY+ELSIhhBBdFqyw+bDNFuLqPQEo\n1VQY3Z7UsQSmlOKss85i3bp1sQ6lT8q2BJxHFbS9O0Ldq+tJ/AXINwOfA5VNn38E/LrpcSGEEDHi\ncDgCJj5ljY10YTJXGz6tqQqwgrNSShKfDlx22WUy3NVN8/MXkmyktGlLNlKYn78wbPcItbgZrfVv\nlFJ/BL4LZANVwH+01tawRSOEEKLLvF4vbrebtLS0do8dtttCXrjwSEmGojLACs4mkwlHgL28hN/E\niRPZtWsXDoeDlJSUzk8QLZoLmFeWPEaVs4xsSx7z8xeGrbAZupD4ADQlOeGdVyaEEKJH3G53wNlc\nHp+PBreLNHOXftW3MCkDq0vW8umOs88+mzfeeIPzzz8/1qH0OdNy5zAtdw7/3Pskl4y6IezXD/nV\noPyvqmn4d2Vvl8Jqrf8SxriEEEKEKNiwk9XpRKnAU9xDYVKKRo8Hj8+HuVWvkWEYOBwOtNbdvna8\nmz9/Pvfdd58kPr1QSImPUmow/jV0jsG/i3rzT3rrmjlJfIQQIgYcDkfABKTGYacni+EqpTAUNLpd\nZFlS2rSDf2ZXUlJS928Qx4466ihKS0ux2+2kpqbGOhzRSqgDv38ArPh3ZVfAyUA+cCf+qe7jIxFc\nTyml5imliq1WKUMSQsSvYIXNh+02etwfoxUNQYa1ZEp7x+bOncvatWtjHUYiyVJKFSul5nV0UKiJ\nzwz8yc+hpv8rrXWp1vq3wDP00t4erfVqrXVRVlZWrEMRQoiICTaVvdLW/cLmZj581Evi0y3z58+X\n2V3RZdVaF2mtV3d0UKiviP5ApdbaB9QBua0e+xA4tXsxCiGE6AmPx4PP52u3R5fX56PO5WxTm9Md\nhlLUONtPaW++twiuoKCAgwcPYrMF2/hAxEKor4g9+HdCB/gSKGz12DygOpxBCSGECE2wwuY6V88K\nm5uZlUFNgKnrhmEEXS1afOvcc89lzZo1sQ6jT5qQdUJErhtq4vMacFbTv38DXKKU2q+U2gP8BP/K\nzkIIIaLM6XQG3E+r1umk+0s2f8tsGNS52t/DZDJJ4hMCGe7qvmP6T4nIdUOa1aW1/mWrf69VSp0K\nXASkAuu01lK9JYQQMRCssLmqJ1tVtOLfs0vj9HpJabUekKzlE5rRo0dTWVlJQ0MD6enpsQ5HEEKP\nj1LKopRarJQ6trlNa71Za71Ya/1TSXqEECJ2ghU2V/RgxeYjGUrR4G6b5BiGgdfrxefzheUe8Wze\nvHm8+uqrsQ5DNOn0VaG1dgKL8Rc4CyGE6CV8Ph9Op7Nd4uPTmlqno8eFzc201jKlvQdk767eJdRX\nxUYgMoNtQgghuqW5sPnIAuYGlwu0xgjTqso+NNYg9TyS+HRu5MiR1NbWUldXF+tQBKEnPrcDC5VS\nP1ZKjVFK9VNKpbX+iGSQQggh2gtWY2N1Oen5yoXf8s/saj+lXWstiU+Izj//fFav7nB5GRElXenx\nGQs8in+l5jqg/ogPIYQQURRsq4pah51wlt6YDMM/S+wIMqU9dJdeeikvvvhirMMQhL5J6Q8Jy8RI\nIYQQ4RKssPmw3Y7ZCF+Xj1kpGt0ufEcMn8mU9tANHz6choYGamtr6d9fSmZjKdTp7MsjHIcQQogu\n0FrjcDgCbhJa7bCHrbAZ/DVECmh0u8lITm5plx6frrngggt4+eWXufbaa2MdSkIL3ytDCCFE1ATb\nqsLl9WL3eDCFqbC5hVLYAkxpd7vdARdQFO1deuml/OMf/4h1GAkvpB4fpVQlnQx1aa1zO3pcCCFE\n+AQrbK5zOTGU6vFWFUfSWtN4xPYYzffweDwBe55EW0OGDMHhcFBdXc3AgQNjHU7CCrXG58+0T3wG\nArOBTGBZOIMKl6at6ecVFBTEOhQhhAiroIlPkC0sekqjO9ylXRKf0Fx00UWsWrWKH/7wh7EOJR5l\nKaWKgdUd7dAeao3PPYHalT/dXwn0yvmMTU989dSpU2+IdSxCCBFOwQqbqwNMOw8HszKwOttvVgr+\n9YRSU1Mjct94c8kll/CDH/xAEp/IsGqtizo7qEc1Ptr/Z8VTwI97ch0hhBBdY7PZgszosoW1sLmZ\nSSnqgvT4BNshXrT35ptvsn79egzDID8/nxUrVsQ6pIQTjlfHGCC506OEEEKEhcfjwePxtEt8tNbU\nOhxh26OrNZNh0Ohun/jIlPbQrVixgqKiIhwOB1pr9u7dS1FRkSQ/URZqcfPNAZqTgQlAISCrMgkh\nRJQESzTsHg/eMG5V0ZoC3D4fLq+X5FYJl0xpD93ixYux2Wxt2mw2G4sXL6awsDBGUSWeUIub/xSg\nzQnsB/4C3Bu2iIQQQnQoWKJhdTqJQM4D+GdwGUrR6Ha3SXyae3y01mGfSRZvSktLu9QuIiPU4mZZ\n70cIIXqJxsZGzOb2v76tTkdYt6poR2tsbjcDUlJampRSaK3xer0BYxLfGjlyJHv37g3YLqJHEhoh\nhOhDtNZBZ3RV2e2YIvhb3YcOWOcDskt7KJYsWUJaWts9vdPS0liyZEmMIkpMIb1ElFJLlFJPBHns\ncaXU/eENSwghRCButzvgis0AVY7IzOhqplDUuQIPs0ni07nCwkKKi4sZNWoUSiksFguPPPKI1PdE\nWaivkCuB94I89h5wVXjCEUII0ZFgCxf6tH+BQbOKXOJjUgprkPqiYHGJtgoLCykpKcHn8/HAAw/I\n+kcxEOqA7FDgQJDHDjY9Hjfcbjf79+/H4Qi8WJeIDykpKQwfPlxWnBV9it1uD1hEXO9yoiCiBcZm\nwwi4erPM7OqeSy65hFtvvVV6fKIs1MSnDDgBWB/gsROAyrBF1Avs37+fjIwM8vPzZZZCnNJaU1VV\nxf79+xk9enSswxEiZMEKm+tcLv+c8wgyKUWj29VuBpes5dM9I0eOpKqqisbGRvr16xfrcBJGqH2i\nK4G7lFLntm5USp0D3Ak8H+7AYsnhcJCdnS1JTxxTSpGdnS29eqJP8fl8OByOgIXNtQ57ZGd08W1v\nkuOIeh7DMHC5XLJLezfMnTuXtWvXxjqMhBJq4nMXsBFYrZSqVEp93rRj+2rgP/iTn7giSU/8k++x\n6Gua62gC/ewettsxG5H/mTaUotHTdosKwzDw+Xx4vd6I3z/eXHLJJfzzn/+MdRgJJaTER2vt0Fqf\nBczFvxP7xqbPc7TWc7XW0scZwPLly2MdghAijnTUQ1njsEd0Rlcz3bSWTyCyZ1fXFRQUSE1plHXp\nVaK1/rfW+pda6xuaPq+LVGDhoJSap5QqtlqtMbm/JD4dM5vNbNiwIaRjN2zYIIujiYTX0NAQcJjL\n7fVi83gwRaEX04emIchaPpL4dM+ZZ57JunW9+u20r8hSShUrpeZ1dFCo6/hcoZS6LchjP1dKze9O\nhJGmtV6ttS7KysqKdSgRMXPmTJRSrFy5sk37xo0bUUqRn5/f0nbddddx/fXXRzlCIUS4aK07LGw2\nVHSGb40gU9qVUlLg3E0y3BU2Vq11kdZ6dUcHhdrj80sgWD+cDbijK5HFuxUrVpCfn88777xDfn5+\nRHfenTBhAk8++WSbtieffJIJEyZE7J4iMdntdnbv3s2ePXvYt28fdrs91iEllOb9sAItXFjnchKt\numKzMqhztn87MJlMMlzTTccccwzffPON9JhFSaiJzzjgiyCPfdX0uMCf9BQVFbXsx7J3716Kiooi\nlvxcfPHFfPrpp+zevRuA+vp6/vnPf/KDH/yg29e85557OP300/nFL35BTk4O2dnZPPTQQ+zdu5fZ\ns2eTkZHBlClT+Oqrr1rOsdlsLFq0iBEjRjBo0CAuvPDCNhvv1dfXc+211zJw4EBGjRrF3/72t3b3\nXbVqFVOmTKF///5MmDAhogmj6BqXy8X+/fvxer34fD7sdjuHDh3CF+lpRKJFR4lmjcMORCfzMSlF\ng6v9G7Ss5dN9SilmzZrF+vWBVowR4RZq4mMDhgd5bAT+ndoFsHjxYmw2W5s2m83G4sWLI3K/lJQU\nCgsLWbZsGQDPPfccM2bMYMiQIT267rvvvsu4ceMoKyvjmWee4bbbbmPBggX8+c9/prq6mgkTJrBo\n0aKW42+99VY++ugjPvroI/bu3cugQYOYN29eyyyPW265hR07drBt2zY+//xzXn755TYzQNatW8eC\nBQt4+OGHqa6u5m9/+xs//vGPeffdd3v0PETPeTwe9u/fj9aa5ORkzGYzFosFp9NJbW1trMNLGA0N\nDUHr3A7bI7tVRWuGUji8HnxHdDGZTCY8Ho8kw90kw13RE2q16JvAnUqpf2utK5oblVI5wGLgjUgE\n15tcccUVlJWVdXpcoJ13m9tnzpzZ6fl5eXk8/3zXlkW64YYbOOuss7j33nspLi7m3nvvpaampkvX\nONL48eNbaoLmzp1LdnY2Z599dssQ2lVXXdWy2qjP5+Ppp5/mlVdeYdiwYQA8/PDDDBw4kE2bNnHy\nySezYsUK1qxZQ15eHgAPPvggL730Usv9HnnkERYtWsRpp50GwEknncTVV1/N008/zfTp03v0XETP\nlJWV4Xa7SWm1Izf4k+7KykrS09NJTk6OUXSJwefz0djY2O57AP7an1qHI2qJj1IKBdjcbtIDfN/d\nbjcWiyUqscST448/ns8//xyv1xuwgF2ET6iJzy+Aj4BdSqnXgUPAEOBswArcHpnweo9Qk5H8/PyA\nyc+oUaNCnsHUVZMmTWLUqFHcf//9lJeXM2fOHJ577rkeXfPIHqO0tLQ2bWlpadTX1wNQWVmJw+Fg\nzJgxLY+np6eTm5vLvn37GDNmDE6ns02x9ZGrJe/Zs4f169fz0EMPtbR5vd6WREjEhs1mo76+PuB+\nQs21JhUVFQwfHqxDWIRD8xBSoOJlh9eL2+cjyYjerEelFHaPJD7hpJRi2rRpvP/++8yYMSPW4cS1\nUNfxKQWOBf6Ef2hrbtPnPwLH4d/SQgBLliwhLS2tTVtaWhpLliyJ6H2Lioq4//77WbBgQdT/WsjJ\nycFisbBnz56WtoaGBioqKhgxYgQ5OTkkJydTUlLS8njrY8GfGN5zzz3U1ta2fNTX1/Paa69F62mI\nI2itqaiowGw2B50tZLFYqK+vl6LMCOuovicae3QdSdbyiQwZ7oqOkPtGtdaVWus7tNanaK3HAacC\nbwG/QxKfFoWFhRQXFzNq1CjA/4ZeXFwc8U3orrzySt544402dTdH8nq9OByONh/hWGLeMAy+//3v\nc+edd3Lw4EFsNhs/+9nPOProoznppJMwDIOrrrqKu+++m/Lycurq6rjjjrYTAW+55RYefvhh3nvv\nPbxeLy6Xiy1btrB58+Yexye6p6GhAbvd3uEwVvObbWNjY7TCSkj19fVB63tqHQ58USpsbubTBFzL\nx2QyyWy/Hjj55JPZtGmT1ElFWJcHhZVSJyulHgb2A+uAC4mzvbp6qrCwkJKSEmbMmEFJSUlUdt5N\nSUnhjDPOYMCAAUGPWb58OampqW0+Nm7cGJb7L126lKlTp3LiiScycuRIDh06xCuvvNLS+/TII48w\nevRojj76aCZPnsy8efPa9EydddZZFBcXc9tttzFo0CCGDBnCrbfeSkNDQ1jiE12jtaaysjKkneuT\nkpKoq6uLQlSJyePxYLfbgyY+lXYbRpS3XzEZBFzLR2Z29YxhGJx44ols2rQp1qHENRXKX/xKqUnA\nlcAVQD7gApKBnwJ/1lp7gp/d7lrH4B8i+y5QCzwF3Ku1DrrJi1LqHuDuIA//Smv9QEf3nDp1qu5K\nz8FXX30VlnVwZs6cGbG6HhEe4fpex5u6ujoOHDjQbtg2EK01DoeDgoICWV07AqxWK4cOHQpYZwXw\nys7tOD0ekqI4xO3yesm0WJgzuqBNu9Yap9PJ+PHjZS+8btqwYQNr1qzh//2//xfrUPocpdQWrfXU\nzo4L2uOjlBqjlPqVUmor8F/g5/jX7Pk+/nV7FPBpF5OeAfhniGngAuA+4GfAvZ2c+hT+RKn1x4NN\nj8m2tkKEkdaa6urqkHp7wD/cpbWWIY4IsVqtQev2vD4f9S5X1GZ0NTMpRaOr/VBX88+CxxPy24I4\nwmmnncb7778vO91HUEd/nu3En6BsBG4E/qm1rgFQSnV3D4ibgFTgYq11HbBOKZUJ3KOU+t+mtna0\n1vvxD621UErdCXyttf6sm7FE3HXXXRfrEIToMrvdjsPhCNrDEIjZbMZqtZKRkRHByBKPx+PBZrMF\nnMYOUO9yRb2wGZp3aPfg9fkwBUi63G53yImzaMtkMjF58mQ+++wzjj/++FiHE5c6+jNhL/5enUnA\nTOBUpVRP+7HnAv8+IsF5Hn8yFPL8PaXUQOBMoGdztiNMEh/RF1VXVwfcFqEjSUlJNDY2tlmUUvRc\nc9F4sMTG6nJGuazZTymFgcIepGfHFaA3SIROZndFVtDfblrr0cA04G/A6cBqoFwp9WTT/7vzejsa\n+PqI+5TiXxn66C5c51IgCSmqFiKsnE4nDQ0NXV6QUIa7IsNqtXZYN1Vtj95WFe0ojc0TeOsK+Tno\nGdm+IrI6/LNOa/0frfX/AMPwL1b4MnAJ8I+mQ25QSnVaSNTKAPwFzUeqaXosVFcAn2itvwl2gFKq\nSCm1QylV2XrPKCFEcDU1Nf6VebsxdKKUkmntYdQ8zNVR4lNpb4x6fU8zrQm4lo/ZbJbEp4eSk5Mp\nKChg27ZtsQ6lrxmplKpseu/frJQqCnRQqAsY+rTW67TWPwTygIuBF4GLgI1Kqa86vMARlwvQpoK0\ntz9QqSH4h8U6HObSWhdrrcdprXNGjhzZhfCESEwejwer1drtVXebh7tEeDQv5RAsCdVaUxPFrSra\n3x8aAgxpGYaBy+WStWh6SIa7uqVUa53T9N4/VWtdHOigLr9itNYurfUqrfUVwGD8s7x2hnh6DdA/\nQHsWgXuCApmPP1F6IcTjhRAhsFqtaK27XSjb/IYndT495/P5OHz4cIcFwg6PB4/Ph0nFJvExGf4a\noyM1//xInU/PnHXWWbzxRtxvgxkTPXrFaK0btdYrtNbzQjzla46o5VFKjQD6cUTtTweuAN7XWu8L\nPdLY2Fa7JdYhCBESn89HVVVVjzYbbX7Dczgc4QorYTU0NOB2uzsc5rK6nMRyqRyTMqgPkPiAvzdK\nEp+eSUlJYdiwYezatTn7eRoAACAASURBVCvWocSdaP+psBY4WynVes7r5YAdeKezk5VS+cAp9PLZ\nXM2+sn4S6xCECElDQ0PYdoWW+o6e0Vpz+PDhTpPQWoeDWI4mmZQKONQFUuAcLjLcFRnRTnweB5zA\nv5RSZzQVHt0DPNR6irtSaqdSalmA868APHxbXC36qOXLl1NQUND5gSLitNZUVVWFZd2VpKQk2Wak\nhxobG3E6nZ2ugl1pt2GKzSgX4F/Lx+H14g2QfUmBc3jMnTuXtWtljd5wi+rLpmkBxNMBE/7p8fcC\nS2m/HYW56ZgjXQG8pbWujGScfcXu3bu57LLLyMvLIz09nREjRnDRRRe1dDEvX74cwzBIT08nMzOT\n3NxcZs+ezbJly9oUHvp8Pu6//37GjRtHRkYG2dnZTJs2rc10yrfffpvp06czYMAAsrKyOOqoo1i8\neHHUn3NrSinef//9mMYQD5oXLAxH4mMymXA4HFLY2k1d2SOtym4jyYjeNhVHUkphKLAFWMun+edA\nVh/umfT0dAYMGMC+fb2+sqNPifrGOlrrbcDsTo7JD9J+XCRiCrcPKl5nZcljVDnLeLf8VebnL2Ra\n7pyw3+ecc87hrLPOYvv27WRmZnLgwAFeffXVNr9sxowZw86d/trzhoaGlh3cX3vttZYu1AcffJBn\nn32WV155hQkTJlBfX88HH3zQsnLvnj17OO+883jiiSe44oorUEqxfft2PvlEhvLiQVVVVViGuODb\nOh+n09mllZ+FX1VVFQ6Ho9M90lxeLw1uN/1ivjeawuZ2k3HEsFzzz4Hb7e5R3ZiAiy++mH/9618s\nWrQo1qHEjRh2lManDypeZ9mOB6hylgFQ5Sxj2Y4H+KDi9bDep6qqiu3bt3PTTTeRlZWFUorhw4dz\n0003BZ2OnJ6ezsUXX8yKFSv417/+xbp16wD48MMPmTdvXstmnRkZGcyZM4dTTjkFgE8++YSMjAyu\nueYakpKSMJvNTJw4kWuuuSZofNdddx2FhYVcc801ZGZmMnbsWJYvX97uuEcffZThw4czYMAAbrzx\nxjYzgj7//HNmz57NgAEDGDNmDL/5zW9aHj/22GMB/8yH9PR0rr/++pavy/e//32GDBlCXl4e1157\nLdXV1V386iaO7i5Y2BkZ5ui6xsZGKisrg25P0Vqt04HRzfWWwklrjc0TvIhZCpx77rzzzuPVV1+N\ndRhxRRKfMFtZ8hguX9tZLS6fg5Ulj4X1PtnZ2UycOJHrr7+ep59+mm3btoXcrTx9+nSGDh3KW2+9\n1fL/p556igceeID33nuv3VosU6dOpaGhgWuuuYZVq1aF3O26cuVKzj77bKqrq3n88cdZuHAhH374\nYcvje/fupby8nF27dvHxxx/z4osv8vzz/sW4rVYrZ555JrNmzaKsrIw1a9bw17/+lYceegiA//73\nvwC88cYbNDQ08NRTTwFQWFhITU0N27Zt46uvvuLw4cMdJmiJrnl7inC+gZrNZlnPp4vcbjcHDx4k\nOTk5pO1Cahz/n703D5OkLvN9P29E5FbVO90NdDfQSIOI6Kj0cccFRoRGXGBkZi7qcUS4ema89xzR\n61XncR256swwznauw2yOo47jGdERpAVEQAFRNhsbgd5rr8qsrNwzIzOW3/kjMqurq2vJqsq9fp+n\n8+msiMiINzMyM775riX8DggjKRS5ecSNiGgB3AA2bNhANBplfHy83ab0DO32k3YNP524g4Q9tuh2\nNU/PXMu/O/D3iz5+S/R0XnfqW+qy6f777+eWW27hK1/5Cvv372fDhg186EMf4o//+I8XvZDt2LGD\nZDIJwEc+8hFOP/10vvWtb/HlL3+ZYrHI5Zdfzt/8zd9wxhlncNZZZ/GLX/yCr3zlK3zkIx/hyJEj\nnHfeeXzxi1/k7W9/+7zHeOUrX8m73vUuAN70pjdxzTXX8LWvfY1Xv/rVAMRiMT73uc9hmia7du3i\n0ksv5bHHHuO6667jhz/8IeFwePq5vOAFL+BjH/sYt9xyCx/96EfnPN7o6Ch33XUXBw4cYOPGoBH4\nLbfcwvnnn8/Y2Binn356Xa/rasFxHDKZTF0ehqVgWRbFYnFFPYFWE8VikdHRUZRSiyY015goFDA7\n4LU1xSA9T/sC0zS18GkQb3/72/n+97/PBz7wgXab0hNo4VMn9YqRn07cMaf4OSVyGtecdUNDbdq8\neTM333wzN998M8Vike985zvccMMNbN++nfe9730LPnZ4eJg3vvGNQPDL7F3vete0SHn88cd53/ve\nx3XXXcdPf/pTAC688MJpr0o8Hufmm2/mne98J08//TTnnXfenMfYuXPnSX/PzAvaunXrCbkl/f39\n5HI5AIaGhti5c+cJF85zzjlnQW9Tbd3ZZ599wmNq67TwOZF0OugZWq84iRcK/HxsmKhpsj4S5YJT\ntrBujrBqbW5XpVJZdhfo1YDruqRSqelGhUt5rRKlIqE2dWyeiSVCdh6PT034aAG8ct72trfx7ne/\nWwufBtH+T06Pce3ODxI2TvwFHTaiXLvzg009bl9fH+9973t58YtfzK9+9asFt33wwQcZHR3lkkvm\nzjG/6KKLeP/73z/vfrZu3crnP/95XNdl//798x7n2LFjJ/29Y8eOhZ9IlTPOOIOBgYETwndHjhzh\njDPOmP579pdpbd3M4x45cuSEdZoAz/OYmpqq+2KbLZe5b+gYJcchUy5zMDXFw6ND84ZXlVKUy3M3\nt1ut+L6Pbduk02kGBwc5dOgQyWSSaDS6pIo623WxXbczPD6GQd4pz/k+MAxDNzJsEJs3b55uO6FZ\nOVr4NJjXbL2c68/9OKdETgMCT8/153684VVdqVSKj3/84+zfvx/HcXBdl+9+97vs37+fiy++eM7H\nFAoFvv/973Pdddfxtre9jcsuuwwIwkF79+4lk8kAcPDgQb7+9a9P7+dnP/sZf/VXf8Xg4CC+75PL\n5fjSl75ELBZj9+75Z9Q+8sgj/Nu//Rue5/GTn/yE7373u7znPe+p6/ldeeWV2LbNzTffTKVS4bnn\nnuNLX/oS119//fQ2p512GgcPHpz+e9u2bVx22WXcdNNNpNNpUqkUN910E1dccYX29swik8ng+35d\n+SQl1+HegaP4ShG1LMKmyZpQiMlSiaFcZs7HmKZJsVhstNldR7lcZmpqioGBAQ4cOMCxY8cYHx+n\nXC4TjUaJxWJ1nYOZpMs2IvV76pqJAJ6vcBZoX6DDXY3hrW99Kz/4wQ/abUZPoIVPE3jN1sv5y5f/\nJ+848/385cv/syml7OFwmHg8ztVXX82mTZvYsmULf/Inf8Jf//Vf8853vnN6uyNHjrBmzRrWrl3L\nWWedxVe+8hU+8YlPnNANdN26dXz+85/nec97HmvWrOG3f/u3ueiii/iXf/kXADZu3Mj999/Pq1/9\natatW8fznvc8HnnkEe68804WGgB77bXXcuedd7Jx40auv/56/vZv/5bXvva1dT2/9evXc/fdd/Pj\nH/+YU089lTe/+c285z3v4cMf/vD0Nl/4whf41Kc+NV0RBvCNb3yDtWvXcv7553P++eezYcMGvv71\nry/pte11PM9jcnKyLm+PUooHR4Youg6xGfknIkLYMHhsfAx3ngZ2qzXBWSlFsVhkaGiIo0ePEo/H\ncRxnWujEYjHC4fCyhctUqTMSmyF4H4gIBWf+cNdqfR80mne84x1873vfa7cZPYGshgZTu3fvVo89\n9ljd2z/zzDPTpd0r4bsDf9/wvJ5u4b3vfS+WZU3nBXUqjTrX3UQqlWJiYqKuPjvjhTw/HjhKv2XN\neaEuOA4v2XoaL9y85aR1pVKJc889t2E9groBx3GYmJggl8thWRahUKjhnpmfDBwlXiwQbXsPn4Ci\n6/L6HWeyfe26k9b5vo/jOJx77rkd4aHqdi699FK+973vsW7dya+1BkTkcaXU/GGIKj3t8RGRq0Tk\n1loIp9W8YP3L2nJcjWY+alO/6+nbo5Ti8fExrAX6xURNk19PxuccWwCsqjyfbDbL0aNHKRQKK/bq\nzIdSiqTdGYnNNXxfkZ/H42MYBr7v6zyfBqF7+izKehG5VUQWHJzeOZ+eJqCUul0pdeP69evbcvwL\nNlzUluNqNPORzWbrHkY6lMuSKpeILLCtWb2wJUpz5/Oshkntvu8Tj8cZGRnBsiyi0WjTvBsl16Xi\n+Rgd5D0xBDKLCNzV8D5oBddccw233XZbu83oZDJKqRuVUrcvtFFn+Eo1PcdcXZo17cX3fRKJRF3e\nHl8pnpgYI2yYi17EFTCczXJa/5oTltfyfDZt2rQSszsa13UZHR2d9vI0O5yTsksdk9hcwzRkQeFj\nmib5fJ52/QDtJc4880ySySSFQoH+/v52m9O19LTHR6PRHCeTyeC6bl3ensFshrzjEK5j24hpMpDL\nnFTSXKvs6tU8wkqlwuDgIKVSib6+vpaIkYliAb/DXk5TDHKV+YVPTQD36vug1Vx++eX86EeNHYG0\n2tDCZx70h7T3WU3neKmVXE8l4nXnkZgi2K570q/+Wh8Xx3GWZXMnY9s2AwMDuK7b8M7XCzGSyxHu\noPweCM5/yXXnrTTTeT6N5ZprrjmhKlezdDrrE9QhRKNRksnkqrowrjZqzcBaedFqJ5lMpu7cnvFC\ngVylXPcFVkRQKEbzuTnX91qCc6lUYmBgABFpaWfqiueRrZQ7KrEZqiXtQHERgav7+TSGXbt2MTQ0\n1HOfq1aic3zmYMeOHQwPD5NIJNptiqaJRKPRujtJdzM1b0+9E9h/PTmx5MnfIcPkWDbNBbPK2kWE\nYrHI2rVrl2Rzp1IsFhkcHCQUCtU9V6tRJEvFjsvvmUag6Dqsmec9ZlkWmUyGDRs2tNiw3uRNb3oT\n99xzD295S32jlDQnooXPHIRCoRPmPWk03UwqlcL3/bq8PVN2iUSxSN8SL+phwyBl25Rc94RGh6FQ\niHw+z6mnnrpkuzuNQqHA0NBQW0QPwESxSKc6oZUKejrNh2VZlEolHMdZ0ngOzdxcc801/Nmf/ZkW\nPsuks3ymGo2moTiOQzKZrDsk8/Rk4OVcqlehtv34rHCXaZrTI1W6mVKp1FbRAzCaz3ZcmKuGUmrB\nBOfa4FrdxbkxXHDBBRw4cKAn8+daQWd+ijQaTUOoDTWsZx5UvlJhMJs5wWOzFAQYK+TnXNfN+Qi2\nbTM4OIhlWW0TPY7nkbLtjhU+pmGQKS/cqycUCtGuZrK9hojwhje8gfvvv7/dpnQlnfkp0mg0K6Zc\nLpNKper29jw3NYmCZTfHC5smE3MIn1qeTzdSqVQYGhrCNM22hmim7BJCh+b3AJYImfLCVVuWZVEs\nFrWXokHo6q7lo4WPRtOjJBIJDMOo62JZ9lwOpKaIWcufq2WKUHRdSrPCWt06sNR1XYaHh1FKtT0v\nJV4s4NOhCT4EHp98pbxgJez0QNMufC90Ii996UvZt28fnue125SuQwsfjaYHKRQK5HK5ur09h1Mp\nPKUwZflfCbULW8o+sWzZNE1s2+6qL2jf9xkZGcFxnJaWrM/HSD5HyOjcYa+GCJ5S2IucY8uySKVS\nLbKqtxERXvOa1/DQQw+125SuQwsfjabH8H2f8fHxuieDe77P08kE0QZMUfd9RXxWWKtmQ7fk+Sil\nGB8fp1gsdkSfp4rnMVUqdVzjwtkYIuQXSHCGIM/Htm09u6tB6HDX8ujsT5JGo1ky6XSaSqVSd3hm\nIJuh4nlYDbiwhk3jpMquGt3SwG5ycpJ0Ok0sFmu3KQCMV/OmOjW/p4av5p/SPhPDMKaT7jUr4xWv\neAW//OUv8X2/3aZ0FT0tfETkKhG5VVcSaFYLjuOQSCTqDs/4SrEvMdGwaiHLMJgql/BmfRF3S55P\nNpslkUi0ZOBovRzLpukQUxZBkbEX9+pFIhFyuZweYdEADMNg9+7dPProo+02pVNYLyK3ishVC23U\n08JHKXW7UupGPRVYs1qodRuvp1khwHAuS7HOYaT1UKsIS88Ka9Uqejr5l2mpVGJkZIRoNFpX+X8r\n8HyfkVyOiNn5vWZNMZiyF/fq1XLBtNenMehw1wlklFI3KqVuX2ijzvh0azSaFVMoFMhkMnV7e1TV\n22MZjXUn+EqRLHVXnk+tbD0UCtUtGltBvFhEoZbdYqCVWIZx0qDa+YhEImQyGV3a3gAuvvhiHnzw\nQT1bcglo4aPR9AC+7zM2NlZ3QjMEuSOZcplwg6uFTDHmbWTYieEuz/MYHh4GaHvZ+mwGc5mOHVMx\nm6CdgXNSmHMutNencZimyYUXXsi+ffvabUrXoIWPRtMDJJNJXNet+8IdeHviWEscRloPYcMgXiyc\n9As0FAqRzWYbeqyVUhOMlUqlI8rWZ6KUYjCbaUi1XSuoTWlfaGbXTCKRCKlUqmubW3YSOty1NLTw\n0Wi6HNu2mZycXNKFO14sMFkqEmnCRdU0DCqeR9E98QJoWRaVSqVjklqVUsTjcXK5XMdUcM0kaZdw\nPA+zQ/KN6kKoq7ILAqEUCoUYHR3tqh5Pncgb3/hG7rvvvnab0TV00SdKo9HMptazx7KsuhNylVI8\nGZ/AbIK3p4YgpObo1aKU6piy9lQqxdTUVEeKHoCj6XQH92qeG9+H7BLyuEKhEK7rMjk52USrep9w\nOMw555zDM888025TugItfDSaLiadTlMqlQiHw3U/ZqLq7WlmCMVHMTlHCMOyrI4Id+VyOSYmJjqq\nbH0mFc/jUHqK6ApGiLQDQyC1yLDS2USjUZLJZEe8L7oZHe6qHy18NJoupVwuE4/Hl9RdWCnFr+Lj\nTcntmUnIMBgvnpzgHAqF2l7WXiwWGRkZIRKJdEzZ+myOZtL4Kxwh0g4swyC9xK7MIkI0GmV0dFTn\n+6yAyy67jLvvvrvdZnQF3fWp0mg0QCBgxsbGMAxjSRfviSbm9swkZBikbBt/VoKziLQ13FUulzuy\nbH0mvlL8Jpno+BEVc2EZBtlFhpXOhWmahEIhhoaGOrblQacTjUbZtm0bR44cabcpHU/3fbI0Gg2p\nVIpSqbSkhGalFI9PjGFJfRPbV4IhgkKRnWN2k4iQz89d7t5Mar16DMPAsjq3IeB4IU/RcQh1qDBb\nCAFc36eyjGTlWp7a4OCgnuW1THS4qz608NFouozlhLgg6NKctu2me3tqKMWcCc7hcJhMJtPScJfj\nOAwODuL7/pLyodrBbyYTXdGwcC5q/XlydVZ2zSYcDiMiHDt2rC3iuNu54oor2Lt3b7vN6Hi08NFo\nuoha3xnTNJcU4vKV4vGJcSyj+d6eGkJQNj8bwzDwfb9lFzbHcRgaGsL3/Y7r1TObsXyO8WKha3r3\nzIlS5FfQsiAUChEOhxkaGmJqakp3JF4Ca9asYcOGDdMNOTVzo4WPRtNF1EJcS/VaHMukKTiVlnl7\nIMjzic/TqdmyLKampppuQ030OI7T8aLH9X0eGRshZDQ38bzZKKhrZtdCmKZJNBplYmKCsbEx3edn\nCVx99dXcdttt7Tajo9HCR6PpEmzbXlaIy/V9noyPtzxZtpbo6swR0gqFQti23dRcjlp4y3GcJb9m\n7WD/ZJyC43TFQNKFsAyDRAOqswzDIBaLkc1mGRgY6JjGl53OW97yFu644452m9HR9LTwEZGrROTW\nTCbTblM0mhVRC3EtpVFhjQNTSWzXbXmyrIhgiJBZoK9Lsz6blUqFwcFBXNftCtGTsm2enkzQ18FJ\n1/USVPSVGhKiEhFisRie53H06FFyuVwDLOxtfvjDH/Lggw9iGAY7d+7km9/8ZrtNaiXrReRWEblq\noY16WvgopW5XSt24fv36dpui0ayIZDKJbdtLDnHZrstTiYmWhrhm4imfqXlK1yORCOl0uuFhDNu2\nGRgYwPO8rhA9uUqF+waPYlaFYrdjiOCj6p7ZVQ/hcJhQKMTw8DCJRELn/czDN7/5TW688UZKpUB4\nDgwMcOONN64m8ZNRSt2olLp9oY16WvhoNL1AsVhkcnJyWRfx/Yk4nlJYbeoJY4rB+DyT2g3DQCnV\n0FyfYrHIwMAAItLxOT0QiJ67jx3G9jyiPeDtOY7M2cpgJdTyfiYnJxkZGdF5P3PwyU9+8qQmkMVi\nkU9+8pNtsqgz0cJHo+lgPM9jdHSUUCi05BBXrlLmQCpJrI0X1LBhMDHHpPYakUiEZDLZkKZ1mUyG\ngYEBLMuqe0p9u1BKMZbPcfexw1Q8rydCXDPxlT9nK4OVUsv7yefzOu9nDgYHB5e0fLWihY9G08HE\n43E8z1vyhVwpxePjYyC0NXxiiFDxvHnDHrXO0+Pj48sOX/i+TzweZ3R0lEgk0tHNCQEyZZsHhga4\nd/Aoru+1VZg2C0uMOVsZNIJa3o/ruhw7dkyPuZjBmWeeuaTlqxUtfDSaDiWbzZJOp5cVshkv5BnO\nZ4m1uUJIRBAWLm+ORCIUi8VlJTrXkpiTySSxWKwjx1AopchVyjw3leSHhw9yx+GDjORz9Fuhrq/g\nmg/LMFZc0r4YkUgE0zQZHBwklUrpvB/gC1/4An19fScs6+vr4wtf+EKbLOpMevNTp9F0OZVKhbGx\nMSKRyJJ7uni+zy/HRgm1sFnhQiggXihw5rr5iwwikQhjY2OICPUUI/i+TyaTIR6PIyInfdm3G18p\nkqUiA5kMg7kMJdcFgoqnPsvqiPPSTEwRiq5L2XObKu5qVY7j4+OUy2W2bt3asYNnW8F1110HBLk+\ng4ODhEIhbr311unlmgAtfDSaDqNWui4iy/JgHExNkXMqrOmQPJewYTA2T4JzjVri6sjICJVKhc2b\nN88pDnzfp1AoEI/HqVQq07/6O4WS43AoPcWzU0kc38NXEDXNVSF2ZlIbXZEtl9nS19zLTC3vJ51O\nUy6X2bZtW8fneDWT6667blrofPCDH+SFL3xhmy3qPLTw0Wg6jGQySbFYXJYXo+S6/Cox3lEjD6Yb\nGXregr2EahewyclJ0uk069evp7+/H6UUSikKhQLZbBbP8wiHwx3l5clXKuyfjHMkk0IpiJgmfdbq\nvfhCEOJL2zZb+vqbfqxa3o9t2xw9epRt27axZs2aph+309mzZw933nknL3nJS9ptSkexen2CGk0H\nks/nl126DvDExBie377y9bk4nuezeJWPYRj09fVhmiZTU1MMDQ0xPDzMyMgImUyGUChEX19fxyQw\nl12XJybG+MHh5zicThE1LfpDoY56/duFCEw2Oc9nNtFoFNM0GRoaIpFItHQQbidyySWXcO+997bb\njI6j5Z9OEblARO4VkaKIjIrI50Skrp+nInK1iDwqIiURSYrIj0Sk+T8nNJoW4DgOo6OjhMPhZeUp\nTBTyHM2kO7I02kcxWaq/ysc0TWKx2Am3aDTaMfkbSikOp1P856HneCY5OS14eqEBYaMIGQaTbai4\nsixrut/PwMBAQ1oldCv9/f3TLSM0x2npt4iIbAR+TJDv+Dbgc8BNwGfreOz7gW8Be4ErgPcDB9Hh\nOk0P4Ps+o6OjKKWW5c3wfJ9HRoc7dsClJQZjLZrG3mzSZZsfHT3Mz0eHEBEteObBkvlntTWbmufQ\ndV2OHj1KMplctd6fyy+/nLvvvrvdZnQUrf759AEgBlytlLpHKfVVAtHzYRFZN9+DRGQz8BfAh5RS\nn1JK3a+U+p5S6kNKKT2IS9P1TE5OUiwWlx3i+k1yklwHD7gMmyaJUhG/i0uOfaV4ejLBnUcOkirb\n9FshQh3igepEggRnmCq1r89OOBwmEokQj8cZGBigNM/4lF6mluejOU6rP7VXAHcppbIzln2bQAy9\nfoHHXVv9/1+aZZhG0y6y2ex0H5rlkLZtfj05QayDEppnY4iglFpwYGknU3Aq3HPsME9OjBExVl+V\n1nLxlWpaI8N6men9OXbsGBMTE6tq3MWuXbs4fPjwqnrOi9Fq4XM+8OzMBUqpQaBYXTcfrwCeA64X\nkWERcUTkFyLy6uaZqtE0H9u2pzsOL+dC6ivFQ6NDCGB2uPdBoRgvtPciuBxGclnuOHyQpF2iPxTq\n+Ne5kwgZJiP5zpioHg6HicVipFIpjhw5QiaTWTVND1/+8pfz6KOPttuMjqHVn+CNQHqO5anquvk4\nDXg+8MfAx4CrgALwIxE5da4HiMiNInJQRBJ6TommE3Ech+HhYSzLWnYvmmeSCVK2TbRDQ1wzsQyD\nwWz3RKaVUjwVn+C+oWMYQJ8V0l6eJRI2DJKlEm6H5NfUyt5N02R0dHTVhL9WUbjrTBFJVK/9j4nI\njXNt1I6fLnNJbJlneQ0DWANcr5T6plLqR8DbAQ/4ozkPotStSqlzlVJb9JwSTafhed70hOnlNltL\nl232xYMQVzdckMOGyWSpiNMFLnfH83hgaICnJuP0WaEF+w9p5qfWyiDV4rL2xTBNk76+PhzH4dix\nYwwPD/d09dfrX/96HnjggXab0QoGlVJbqtf+3UqpW+faqNXCJwVsmGP5eub2BNWYqv5/f21BNU/o\nceCCRhmn0bQCpRQTExPYtr3sZGbX9/nZ8GDQ3blLQi+1i2Cijcmu9VB0HO46drg6T8vSFVsrxIe2\n5/nMRy38VSgUOHr0KGNjYz058T0SibBx40bGx8fbbUpH0OpvzGeZlcsjImcA/czK/ZnFMwQeodnf\nQELwudJougKlFPF4nEwms2zRA7AvPk62bHfdZG8FjOY6I+djLlJ2ib1HD5GtlOkP6dBWIwgZ0jF5\nPnMhIkSjUaLRKJlMhiNHjjA+Pt5zAuiKK65g79697TajI2i18NkLvFlE1s5Y9rtACVjID3cHgch5\nY22BiKwHLgL2NcFOjaYpJJPJ6Qqu5V5Ux/I5nplKEuvCkQhh02Qwn+3IpNLxfJ67jh3G8bxVP26i\nkYQMk8lSCa9D8nzmo5b/06sC6IorrlgteT6L0mrh81WgDNwmIr9dTTz6DHDLzBJ3ETkkIv9Y+1sp\n9Rjwn8A/ish/FZErgR8ADvC3rXwCGs1ySaVSxOPxFYmekuPw4MgQYcPoyhCMJULJccg7TrtNOYHB\nbJp7B49iIES7aBkldgAAIABJREFUzIvW6QTvU0WqS1oZzPQApdNpDh8+3BMhsDPPPJPx8XGcDvvs\ntYOWCh+lVAq4FDCB2wmaF/4F8OlZm1rVbWbyLuD7wC3AfxCInkuq+9RoOppMJsPY2BixWGzZYxd8\npfjp8CAVzyPcpcm2NcE3sci09lZyYCrJT4cHiZhm176unY6vOuuc10PNAxSLxchmsxw5cqTrBdBr\nXvMaHn744Xab0XZa/tNGKfUb4JJFttk5x7I88MHqTaPpGrLZLKOjoyueNbUvPkGiVKC/y8MwpgiD\n2Qy7Nm5qqx1KKfZPJtiXGCdmWl2TJN6NhA2DI5k0L9y8td2mLJmaB0gpRTabJZPJsGnTJjZt2tQx\nw3LrZc+ePfzwhz/k9a9fqF9w76M/6RpNE8nlctMNCpfbqwdgKJvh6WS8J3rJhE2TsUKekuu2zQal\nFE9MjLMvMU6fpZsSNpuQYZAtl8lVurdkfGYIbGpqiiNHjpBKpToyX20+XvWqV/HQQw+124y2oz/t\nGk2TyOVyjIyMEA6HVyR6UrbNgyNDREyzK/N6ZlN7DkNtambo+T4/Hx3mmWSCPksPGG0FIoJCMZTN\nLr5xh1MLgVmWxfj4OAMDA9h2d+QvhUIhtm3bxsDAQLtNaSta+Gg0TaBRosd2Xe4bPIoQVMf0CiHD\n4LlUsuW/lh3f56fDAxxJp/RU9RYTNkwOp3snJXN2E8REItEVE+D37Nmz6svatfDRaBpMo0SP6/v8\ndGiAkuv2XKVRLfSRbmG33JLr8uNjR4LGhLpHT8sJGQbZSplsj3VIrk2An5ycZHBwsOOTn3U/Hy18\nNJqGks1mGR4eXrHoUUrx89Fh4qUCfT0meqAW+oCjmdZ4ANJlm71HDpKyS/T3QJ5UNyLVsvahXOtC\nnMr38SsVvFIJN1/AzeWO3wpFPLuM77or9jzWJsBXKhWOHDlCtoNDeqeeeiqZTKZrwnPNoPe+UTWa\nNpHNZhkZGVlxInMt8XYgm+7pi3TUNDmUmuIlW09rashpJJflZyODoKBvmXPRNI0hVA13LbW6SymF\n8jyU66JcF99xUY6D77j4TgW/XMa3y/jlCn6ljF9xUI6D8n0QAaT27/g+j+8cEcGIRDD7+7HWryW0\ndi3W2rWY/X1L+vxFIpHpOXylUoktW7asqJKzWdRmd735zW9utyltQQsfjaYBpNNpxsbGVix6AJ6Z\nmgwSb3s8HGMZBgXHYTCbYef6uUb4rQxfKfYn4jw1GSdiGISs3smR6gbE97FcF8v1MD0f0/MQ18Nz\nHMaLDjEIxIznge+jfB/lK/C94/er65VXzZ0xAAQhSJZGVW8iwWdFDDCC+xIOI1DXZ6gmrJxUispk\nkuoDERHCp5xC5LSthE/ZhFnHmBnTNInFYqRSKWzbZvv27R1X9r5nzx6+9a1vrVrhI91UirdUROQq\n4Kpdu3bdcPDgwXabo+lRpqammJiYWHGfHoBDqSkeGR0mZq2OvjIVz8M0DN626/lYDXy+tuvy8OgQ\no/mcrtxqNkoRKVeI2mWiJZtYySZccTA9DzX9ukvgWUHhKwiZJmsjEaTmhpm5Xc0zI3J8eU3YtPqp\n+X7gOapeJ0MbNxA7YweRrVsw6hAz5XIZwzDYsWPHimbzNRrP87j44ot7rpmhiBwC7gNuV0rdPt92\nnSVDG0z1id++e/fuG9pti6Y3SSaTTExMrKgjc41jmTSPjK0e0QNBT5+843BgKskFm7c0ZJ8ThTw/\nGx6k7Hk9HSpsG0oRqjj0F4qsyxaIFUsIClFB+Mg3BGUYOJY1Q9CcSEUp1oQ6v3+SGAYSiQCBV8jN\nZMmm9iOmSeysM+g74wzM2PyCJhKJUKlUGBgYYPv27axZs6ZVpi+IaZrs2rWLgwcPcu6557bbnEaS\nUUrduNhGnf2u02g6FKUUiUSiYaJnMJvmoZEhoquwg3DUNHkqMbHihoae77MvPs6PB47iKaUrtxqJ\nUkTsMqckkpxzaIBdhwY4bSxBrFjCMw1cy8IJWbghC980A0/PIq99zuns6qfZiAhGOIwZjSKGQfHw\nMSYfeJDs08/gLZAoHA6HsSyLoaEh0ul0Cy1emFoX59XI6vqG1WgaQE30JBKJhomenw0HDQobGe7p\nFizDwFOKffHxZVfXpGybvUcP8evJBDHLIqJnbjWEUMXhlMQU5xw8xtmHB9kaT2K6Ho5l4oYsPMtc\nVODMhSFC0XHwujTVQkwTMxbFCIcoDQ0z+cCD5A4cwp9HvFuWRTQaZXR0lMnJyY7o9nzZZZdx1113\ntduMttDToS6NptEopZiYmCCVStHXt7SKj7k4lgk8PatV9NSIWRaHUlP0h8JcuHlL3a+r43n8ejLO\nM8lJDBH6LUt7eVaI4XmszebZmMoQKwU9dzzDwF2myJmLoLAdcpUyGyKdk/uyVMQwMKNRlO9TPHIU\ne3iEtS84n8hpW096HxqGQSwWIx6P4/s+W7bU/z5vBps2baJSqZDP5zsmBNcqtPDRaOrE933Gx8fJ\nZDLEYrEVf2kdmEryy/FRoqtc9EDgAegLhdiXGMcUWTTfx/N9BrIZnpgYo+y5xHQC88pQiljJZkMq\nw/pMDhQoQ3AaKHZmY4pQcBz6QiHCXd6VvCaAfNcl86t9hLdsYd2FLzipCqzW7yeZTOL7Pqeeempb\nxc+ll17KT37yE9761re2zYZ2sLq/bTWaOvF9n9HR0YaIHqUUTyXi/GJshJgWPdMYIsQsiycmxnhg\naIBEsXhCSEApRaZs85vJBLcdfJafjw6jlKI/FNaiZ5mYrseGqTTnHBpg59Fh1mdyuGY1jGU2T/RA\ntZgLmLJt2h/4aQyGZWFEo1QmkyR/9jClkdGTwlq1WV+pVIqJiYm2hr2uvPJK7rzzzrYdv11oj49G\nswi1hmTFYnHFosfzfX45Psrh1JSeFTUHphj0h0KM5LIM57KBN6A6nDVXKeN6PgqImCb9HdSM0PB8\nInaZSLlMrGQTtSuYnovl+tRa5SkxcEImTihEKRbFjkYoRyM4ofmrn5rCHN4dv8nenfkwRHB9j1yl\nzLpwpKXHbhYighmNoFyP7FP7qUwmWXvB+Rgz3q818TM1NYWIsHXryaGxVvDiF7+Yffv2oapNHFcL\nWvhoNAvgui7Dw8PYtk0sFlvRviqex0+HBxgvFHTF0QJINeyllMLzfYqehyIIjYQ7ROyI7xO1y/Tn\ni6zN5YnaFZQQlHQL+CIoEVzzRG+e5XqEKi5r8oXpHje+YVDsi5Ff00+xL0olEm6KALEcl7XZHJuS\nacKOixJwm+zVqQdTDHKVCmHTJGr2ziVJLBPDjGKPjeOk0qx/2UsIrVt7fL0IfX19J4ifltsowoUX\nXsj+/ft50Yte1PLjt4veeZdpNA2mUqkwPDyM4zgrFj2Zss39g8fIO45OwK0TEcHqoNfJdF3680XW\nZfOsyReDjsEszVvii4ABHsdzWsT36c8XWJMrBH3+xKDQF6Wwpp9SX5RyJIxaTjhUKUJOYPP6TJa+\nol3ts2O0xbszH9Mhr1KJzX39hHso9Bt4f6L4lQqpn/+CtRdeQGz7thPWx2IxkskklmWxadOmltu4\nZ88e7rzzzlUlfHq6c3ON3bt3q8cee6zdZmi6iHK5zNDQEL7vE4mszAU/nMvys+FBIKhe0nQJShGu\nOPTnC6zP5IhWK5yU0NT8F1EKw/MxlEJVy58qkRB2NEopFsEJh3EtE8808Y1ANohSmJ6H6XqEKxX6\nijZ9xRIhJyiv9kXwTKNjxM5c+NXRE1v7+rCkd8RPDeV5+JUKfTvPYs3zz0VmCDzf9ymVSuzYsYN1\n69a11K5cLsfVV1/NPffc09LjNgMReVwptXux7fS3sEYzi1KpxODgIIZhrEj0+ErxVHyC/ck4EdMk\n1OWVK6uCav7LmlwgdizHDcYsNLice0ETRPAsE2+GTZbjsa6SY30mi5oWBXP/aK2F27wO8+wshiGC\npxSJYpHNsT5CPeT5gaD3jxGNUjw2gFsosP63XjSd92MYBtFolJGRESzLoq+vr2V2rV27FtM0SafT\nbNjQ+Jl5nYgWPhrNDHK5HCMjI4RCoRUNFiw5Dj8bGSReLOhZUZ1MNRzUVyiyNlegv1BEVDB7wTdb\nJ3YWRATfFPxVUIRriuArRaJY4JRYX881ohSR6aqv1COPsmH3y6ZHXpimSTgcZnh4mJ07dxIOh1tm\n12WXXcbdd9/Ntdde27JjtpPe/yRpNHUyNTXF8PDwdIv55TKWz3H7kYMkikX6tejpLKqjF9anMmwb\nHufcA0fZdfAYp4/G6c8Xqo36qqMXjM4ODfUqtc/LZKlIwXXabE3jqeX9uMUiUz//BW4+P73Oqub/\nDQ8P43neAntpLLU8n9WC9vhoVj21ERTJZHJFE9Y93+epRJynkwnChtFR5darkmqOTqRcIVqy6S+U\niFZnKnVrOGi1YIiggJRdwg37rAtH6LUzZEYi+OUKUz//JRv/y8sIVcNM4XCYUqnE2NgY27dvb0kh\nxPOf/3yee+45fN9f8QiebqCnhY+IXAVctWvXrnaboulQXNdlbGyMfD6/oh49uUqFB4cHSZaK9On+\nPC1FfJ+Q4xKuOIQqFaJ2mVipTLhyfAimKBV4czqgfFtTH0Kt1L2M43lsisUwekz+GJEwvuOQ+uXj\nrH/ZS4hsPgWAWCxGLpcjkUi0pMxdRLjooot44okn2L170dzgTma9iNwK3K6Uun2+jXRVl2bVYtv2\ntEs5Gl3+vKDBbJqHR0fwlSJmmrpUvYGI72M5LpbnYbkuluPOEDkOYcfF8P2g+olA4CgRfDGCiid9\nLnoCTyksw2BzLIbZgxVfvuuiPI/1L/0toluDcS1KKYrFYssqvfbu3cujjz7Kpz71qaYfq1nUW9Wl\nhY9m1aGUIpVKEY/HpxMKl4Pn+zw+McaBVFJXba0EpQg5DpFy4LGJlcpEyhVCjoPp1URNUNctSqEI\nmgMqEZQRhES0wOl9PKUwRHqy4guq4sd1A/FzauDl8TyPSqXCzp07V/TjrB5KpRJ79uzhvvvua+px\nmokuZ9do5qBSqTAxMUEulyMWiy07np2vVHhgeICUXdJVW0vA8Dwi5QrhcoVYySZWsomUTwxJBR4b\nQen8G80Mer3iy7AsFELmyX1QFT+maWJZ1nSl10qKLhYjFouxZs0a4vF4W7pItxItfDSrAt/3SaVS\nJBIJRIT+/v5l72skl+XBkSE8pei39OiJk6iWiIecIBwVKQd5N8H8Ki8Y1TCj67HOu9HUi1EVP5Ol\nIpuiUWJWbxUQiGVioE4QP6FQCNu2GRsbY8eOHU39vrn88su56667ePe73920Y3QCWvhoehqlFLlc\njng8juM4K6ra8pXi14kJfj2ZIGIYRFZhF2bxFYYfdAgO8m48LMclXKkQrjhB7k11DhRQFThBSMqX\n9gzC1PQWtYqvpG2zIaJYE2pdv5tWIJaFAYH4edlLiG7dQjQaJZ/PMzk5yZYtW5p27D179vCJT3xC\nCx+NphtRSlEoFEgkEti2TTgcXlE31JLr8tDIIOP5fPdWbSlVHW3gY/g+pudheD7mjPuW6wWjDzyv\nuo2P+ME2hu9Ph6JmMp13o8WNpkUEFV9CumzjK9Uzk91riGUhQOaJX8FFLyW6ZTOxWIzJyUkikUjT\nkp3PPvtshoaGcF23qWG1dtO7z0yzKvF9f/qXUblcJhQKrbj9+2SpyANDA9iu296p6kphuUFFk+V6\nmG5Q4RQIlePCxPADgVO7BX/7GH4QXpqZLKwIetoIQbdiJRIMykSmt1PVv91aToUWNZoOoFbunq1U\n8JRiQyTaU8XuhmXhE4gfueilRDafQiQSYXR0lHA43LRk51e+8pU88sgjvPa1r23K/jsBLXw0PYHr\numSzWZLJJK7rNkTwKKV4dmqSJybGsQxpbUPCavO9aCkYNhkr2kRm9KWhKlYUx4VK8H8NmQ43KQTf\nMPAMtGjR9BQCWCIUnAqe7/dcr59p8fP4k6zf/TIip2xqerJzrYuzFj4aTYdi2zbpdJpMJoNSinA4\n3JAZN7br8sjoMMP5LDHTwmx2+axSRMoVYsUSa3IF+oul6ZlRQDAkUycBazRzYomB7XnTA07NHvqc\nGJaFrxSZx55kw395GeFNG7Ftm5GREc4444yGd1p+7Wtfy6c//emG7rPT0MJH03XUwllTU1OUSiUM\nwyAcDjfsC2A8n+fBkUHKnte8qq2qRydWLM0ajlkt5zbNk3JpNBrN/FgiuL5PvFjouV4/RiiE7zik\nH3uCjS/fTXTDeorFIuPj45x++ukN/Y4Kh8Ns3bqVkZERtm/f3rD9dhJa+Gi6hpnhLM/zsCxrRWMm\nTtq/7/NUYoLfJBOEDbOxoa0ZQmdNvkh/oYjh+9W8GrTQ0WgaQK3XT7xY4JRYjKjZO5e4mvhJ/fIx\nNr58N7H160in04RCoYZXetXCXTfccEND99sp9M67QtOzVCoVUqkU6XS6oeGsmUyWijw0PETOqTSm\nIeHM0FW+SH+hhCj/hOGYbg9XTWg07cIQwSfo9bMuHGFtDw04PS5+Hmfjyy8itm4tiUSCUCjEhuqQ\n00ZwxRVX8Id/+Ida+Gg0raZUKjE1NUU2m214OKuG7br8Kj7BofQUlghrlunlMTwvGI5ZLLEmXyJW\nKgUraqErw0T10K9PjaaTMRBEhGylTMXz2BiN9Uzez0zPz/qLXkps4wbGxsYwDKNhZe7btm0jmUxS\nLpeJRHqrVQBo4aPpMGqD+ZLJJIVCAdM0GxrOquH6PodSU+xLTOD4Pn2WVbeXpzZ2IWKX6SsGVVe1\npn2ilE5E1mg6gKDiy6DseUwUC2yKRnsm9FUTP5nHnmDdy15CZNNGRkZGMAyDNWvWNOQYF198MQ8+\n+CCXXnppQ/bXSfTGu0DT9fi+Ty6Xm/6V0ej8nRqO53E4neLXkxNUPJ+IaRKZz8szY3hmpOrNidll\nLNdFiYEoHyWCp2dKaTQdSy3vZ7JUZE0ozLpIpCdK3o1QCN91yTz+JOtefCHhrVsYHh5mx44dDRE/\ne/bs4bbbbutJ4dPT09lF5Crgql27dt1w8ODBdpujmYNKpUImkyGVSuH7PqFQqCm9KXKVMgempjiY\nSuIpRcQ0sWaEzcT3CZcrRMuV6d45Jw7PBF8E35Bqk7/u/+LUaFYTimDsjIiwMRLpmTlfyvPwKxXW\nnHcu4TN34LpuQ8SP53m87nWv46GHHmqQpc1HRA4B9wG3K6Vun2+7nvb4VJ/47bt37+7NDK0uxfd9\nisUiU1NTFItFACKRSMPzdxzPYySf5dmpJMlqzk3UNOlTELYrRMpBqCpWKhEpO3p4pkbTw9TGXPhK\nkbRtwkaF9ZFo1095F9PEiETIHzhIrFSi77xzGRoaYvv27SvK+TFNk7POOovDhw9zzjnnNNDippJR\nSt242EY9LXw0nYNSCtu2yWazZDKZ6XL0aDTa0HBWxfMYL+Q5mk4zmssQrjj0OS5nOC59dpmoXT4+\nRHNGc0AdqtJoVgeGCAZBnl+iVCRimqwLR7paAIlhYESjlIZGcHN51v7WhQwPD3PaaaexadOmZe93\nz5497N27lz/6oz9qoLXtp6dDXTV2796tHnvssXabsepQSlEul8nlcqTTaVzXbXh1lu/7pPM5JqdS\nJKamqORy9NtlohWHiOfPmBJOtbpKh6o0Gs1xvOo10DIM1oTCRC2rayvAlFL45QpG2GLti1+E3xfj\nlFNOYfPmzcv6zk0kEvzBH/wBd9xxRxOsbTwi8rhSavdi22mPj6ah1MROoVAgnU7jOA4iQigUWnHv\nHeX7uPkCmXSK3GSSUiqNKhQwPB9fYJMK9IwSA9/QU8I1Gs3i1ESO5/ukyzaUIWKaxKwQEcvEku7p\nAC0imNHIdMVX3/POZtL3KZVKbNu2jdAS23Vs2bKFQqFAsVhc8ezDTkILH82K8TwP27bJ5/Nks1k8\nzwOC1uexWGxZ+1RK4ds25XSG7OQk9mQSJ5fD8RVUJ4n7hqBME1V1UXuNekIajWbVMbOdRcXzsD0P\nKQe5QWHTJGJaWIaBZRiYhnR0ZZgRCqFMk+LhI1iTk3DeLo5VKpx22mmsXbt2Sfu65JJLuO+++7jy\nyiubZG3r0cJHs2Qcx6FcLlMqlcjn85TLZYAVeXaU5+HmchSmUuTjCZxUCqfi4Cp/WuT4hoGEzBO+\nbjr3q0ej0XQrtTwgCFIBAyHkItVvHAUYgGkYmGIQMmT6vimCYUh1H+37hqrl/bi5PPnHniR65pkM\nlmw2bj6FrVu31l09u2fPHv7pn/5JCx/N6sHzPCqVCuVymWKxSKFQwHVdRILOqMtJUFZK4RaKFNNp\n8pNJKlNTePkCju+D8vHFwDUMxDQQOS50usfhrNFoegUh+FE3W8QogvCYh0/ZA4WaFka19bVKskAU\nzfhfDAwRTCO43yx5JCKYkQjK97EHB5HRMdznnUUum2XL1q1s2LBh0dyfl770pTzxxBOoaiuAXkAL\nHw0QiBHHcaa9OcViEdu2cV13ehvTNLEsa0keHd/zKeSz5FIZ7HQKZyoN+QK+71NLrPcMA2UIErKm\nvwC6t75Co9GsBmqC6MQlJ1INzON4HpXpZScKJAg8TJZhEDIMQoZJqBpSW/HMwJplhoEZjeK7LuXn\nDuEMDGGfdQbJ7dvYeuqprF27dl4BZBgG559/Ps8++ywveMELGmJPu2m58BGRC4C/Bl4FpIF/AD6r\nlJo3RUNEdgJH51j170qp32uCmT2LUgrP86ZFjm3blEolbNtmZoVfTeTUkwzn+j5Fp0KxUKSUz1PK\nZnEyGcgXCJXs6Z8+osCbDlkdFzmCDllpNJreY/o7rg6B5HgeFc8DnOllpgghwyBiWoTMQBiZK0i2\nNiwLLAvfcSgfOETlyDHy204nuu10tmzfxrp16+b8YVub1q6FzzIQkY3Aj4HfAG8DzgH+nCCK8cd1\n7OIjwMw2kpONtrEX8H0f13XxPA/XdadDVZVKhUqlgu/709vWwlWRSGRON6avFLbrUnAqgUgqFikV\ni9jFEl6xiFEqESpXiFZcBBVoHKUIV0c5eJZ10n51yEqj0WiOc7L3KOB4fpF3wg/FkGkSMc1p75Bp\nLC1cZoRCEAqhPA9ncAhncIjCurWETjuVNdtO55RTT6Wvr29aBF122WVce+213HTTTSt8pp1Bqz0+\nHwBiwNVKqSxwj4isAz4jIl+uLluI55RSjzTdyg7H9/1pUVMTNjNvtaqqmZimiWEYhEIhfIJfF2XP\npVIpU8lmqdg2lbJN2S7j2DZ+yUbKZQzHJeS5hLxqkrFACAjXnEOGBOXjlhn0x2G6LyCgPTkajUaz\nXI7nFx2n5h0qzxBDACHDIGyahE0TyzCx6qg8E9PENE2UUqh8gfJzh7CfO0gyFiO0+RRiW7ew/rRT\nWbtxI57nkc1mGzYBvp20WvhcAdw1S+B8G/gS8Hpg3tkaq4WaqJlL3NjlMqVymXKlgqcUnvJxfYWr\nfPxqCMt3XZxKBafi4FQqeI6D71RQFQej4mA4DpbnEXI9LN9HFCg5LlZCCiIolASCBgka/nmWNd0T\np/dbXmo0Gk1nMp8Ycn0fx/cpOCeGymbmDpmGYNQqz0ROCMVJ1bujlEK5Ls7wCM7wCGnfR8JhLjj1\ndP7hT/+Mq666ir4NG+jbsJ7ImjXT8xW7KfG51cLnfOAnMxcopQZFpFhdt5jw+WcR2QTEgX8DPqmU\nKtVz4Fp4p5bHopTC8/1gcJ0fCAff91EolAJf+ShV3a563/N9fOXj+yrYXvnBPqrb+9V91v6v3VzP\nC26+Nx3HdTwP1/Onl7meh1vdH54Hrg+eC56H4XmI52N4HmHPJ1wVLiHPw/I8TM/H9NVxQVLzyiAI\nCqnm2NQ6FisCMePPEDPT5wPw0Wg0Gk23sFCorHbNUTgn+H9qJflSFUE1MTR9M41gnTIR3+Plz9vF\nv//kHl65bcf0MSEIm0kkghWNYPXFCMX6CPXFCEUimJEIoXAYMxzCDFVvVggzZGGYJoZlYRjGdJVw\nq2i18NlIkNA8m1R13XyUgb8F7gaywBuAjxHkCL1tsYNmEgn2fvVWYLa3Qp2woH5PhjpxWzV77Umb\nV/N7gzUChKs3qa6fXqeCBVJdNvN/Y47xIn7VI+OLnBRXUjU7p8c2qOkhnDMXazQajab3WPQ7Xh3/\nsTvfD97aPjZs2sjBkRFSth2IlGrBimHbGJnMSaJKVbdRVH9wy/E9BverEYTa/RPyv+eLLsicd5dK\nS2d1iYgDfEQp9Zezlo8AX1NKfXIJ+/og8D+BlyqlfjXH+huBjwIbgHXAr1di+yKsBzJN3P+ZwGAT\n999s+5u9/1YcQ5+D9u6/21//VhxDn4Pe3n+zX3/o/tfoRQTOkXT1OLcqpW49aSulVMtuBCGqT8+x\nPA98dIn72kIgCN9Xx7a3Nvl5NXv/iS63v6n71+dgVey/q19/fQ46wv5u339TX/8eeY3q2n+rK4uf\nJcjlmUZEzgD6q+uWgpr1/0I0O2m62fufKzzYSLr99WnFMfQ5aO/+u/31b8Ux9Dno7f03+/WH7n+N\n6tp/q0NdHycIP52llMpVl30E+Bxwmlq8nH3mvj4A/P/AbymlnmqGvZ2CiDymlNrdbjtWM/octBf9\n+rcffQ7ai379G0erk5u/CvxfwG0i8iXgecBngFtmih4ROQQ8oJS6vvr3Z4C1BM0Ls8DrCATUbb0u\neqqcHKPUtBp9DtqLfv3bjz4H7UW//g2ipR4fmB5Z8TecOLLiM2rGyAoROQbcr5R6b/Xv3yPo2nwu\nQQPEQeBbwBeUUuVW2q/RaDQajaZ7abnw0Wg0Go1Go2kXemxSByAiu0Tk70Rkn4h4InL/Itt/RUSU\niPxZi0zseeo9ByLyIhG5Q0QyIpITkV+KyEUtNrcnqecciMjpIvLPIjIiInkReVJErmuDuT2FiLxT\nRH4w43V9XER+f47tbhCRgyJiV7e5tB329iKLnQMRWScin61+52REZFxEvici57XT7m5EC5/O4IXA\nHuBA9TbhiJixAAALMUlEQVQv1VDh+whynTSNY9FzICIvAR4mCNH+LvBOgiqCWIts7HUWPAciYgA/\nIBhv8/8QNC99BPiGiLyjhXb2Ih8maCvyP4C3AvcB3xKRD9U2qKYcfBX4OsH4oaeBO0Tkwtab25Ms\ndg7OBG4A7gJ+B/g/gdOBX1SrozV1okNdHYCIGEopv3r/P4DNSqk3zLPtj4GfA+8G/kMp9ZGWGdrD\n1HMOROQR4IhS6v9og4k9z2LnQETOB54B3qqUun3G8ieAg0qp322xyT2DiGxWSk3OWvYt4FVKqbOr\nfz8HPKSUel/1bwPYB+xTSr2r1Tb3GoudAxHpB3w1Y0xTdYTTIPCnSqnPttbi7kV7fDqA2pf9YojI\n7wAvAL7YXItWH4udg6qn7RXAX7fGotVHHZ+DUPX/2Z1f0+gJLCti9gW3ypPAVgAReR5wHvCdGY/x\ngf9F4P3RrJDFzoFSqqBmzaZUSk0BA7VtNPWhhU+XICIx4M+B/1cpVWi3PauQV1T/31jNQXFF5LCI\nXN9Wq1YX+4FfAJ8TkXOrOQ/vBV5DEILRNJZXA7+p3q81np3daPYZYJOIbGmZVauLmefgJKqv+66F\nttGcjBY+3cPHgTHgG+02ZJVyWvX/rwPfBN4E/Aj4BxHZ0zarVhEqiMtfQfC9dYDqLB6CsTU/aadt\nvUY1afltBMOh4fgQ6dndg1Oz1msaxBznYC7+nCAv6NstMapHaHUDQ80yEJGzCfoYXaJ0Ula7qP1I\n+Ael1Jer9+8TkRcQiNI722PW6qGaU/KvwCkEyeVxgmTofxSRpFLqR+20r1cQkZ0EfdL+Uyn1tVmr\nZ3//yDzLNStgkXNQ2+aDwLuAa5RSyZYZ1wNo4dMdfBHYCzwrIhuqywwgUv07owVR05mq/n/frOU/\nIajC0DSftwBXAucppQ5Wl91frWj5MoEHTrMCqsmyewkSZmcmLNc8Oxs4Mceq9n3UijlSq4IFzsHM\nbd5KkG/4MaXU91poXk+gQ13dwfOBqwm+fGq3M4A/qt7f3j7TVg3PzLNcgLqS0zUr5nygOEP01HgS\nOKcN9vQUItIH3AGEgStn5RLWcnvOn/Ww84EppVSiBSb2PIucg9o2ryYIbX1VKfWnLTaxJ9Aen+7g\n/cCaWcu+DTxAMKhVf+k0n4cJROalBH00alxKUNKraT4DQJ+IPF8p9dyM5RcBx9pjUm8gIhZBhda5\nwGuUUvGZ65VSR0TkAEHvqruqjzGqf+9tsbk9yWLnoLrNCwmE0Y8I5l5qloEWPh1AVeXXEmS3A+uq\npesAdyqlHpvjMTYwpJS6vzVW9jZ1nIOiiHwO+LKIpIFHgWsIBua+vuUG9yCLnYPqbRD4fvVcJAhC\nX9cCf9hic3uN/0nw2v/fBFVar5yx7snqTMTPEDSLPEYwMPq/ElykdV+rxrDgOQDWEwiePPBXwMtF\nprs4ZJVSurKrTnQDww6gmsh2dJ7VZyuljs3xmGPoBoYNo95zICIfBj5EcGF+Dvi0Uuq2FpjY89Rz\nDkRkF/D/EZSwrwMOE1wwbtV5bsun+n1y1jyrZ77/bwA+RhBqfxr4qFLq3lbY2Ossdg6AnZycY1jj\ngfma3mpORgsfjUaj0Wg0qwad3KzRaDQajWbVoIWPRqPRaDSaVYMWPhqNRqPRaFYNWvhoNBqNRqNZ\nNWjho9FoNBqNZtWghY9Go9FoNJpVgxY+Go1mSYjIe0VEicjsbuKzt7tfRP5jxt+Xich/X+Yx31A9\n5oWzln+guvzTy9nvMm2pPf+siMTmWH9vdf3XWmWTRqOpHy18NBpNs/hvBJPra1wGLEv4zIWIvJeg\neeEXlVKfbdR+l2ICQefomTadStDJO98GezQaTR1o4aPRaJqCUuo3cwwUbQgi8vvAPwJ/qZT6+ALb\nmSISboYNwO3A781adi1BN+nnTt5co9F0Alr4aDSrHBG5pBqa2TZj2c9FxBORDTOW/VpEvjDjoWeL\nyD0iUhCRZ0Xk6ln7nQ51ichngJuAs6rHOiEUJCKvFZEHRKQoIkkR+XsRWTuPvVcDXwf+Tin1P2at\n+5qIPCYibxeRpwEbeEV13UuqYaiiiKRE5JtVD03tsTurdl0rIn8nIhkRGRaRz1YHcs7m28CVs+z8\nvery2TafLyLfFpGh6vGfFpH/Pnu/IvJiEXlYROzqNnuqz+drs/ep0WiWhxY+Go3m54ADXAzTw0Iv\nAioEM7EQkU3AC4GfzXjct4AfAO8ADgLfFpEd8xzjH6rbjwOvqt4+X933a4B7q+t+hyActgf45zn2\ncyXwb8C/Mv9g0p3Alwlmeu0BjorIFuB+oI9gqOaHCEJS98zhEfoyQajqd4BvAJ+q3p/N/UAKeHv1\neZxZfV4nCR+Oz3b7b1Wb/h74LMHcK6qP7yOYfB4Dfh/4E+AvgDPneZ4ajWYZ6OnsGs0qRylVEpHH\nCYTPvwOvBLLAj6vLfgi8FlDAw0DNs/MXSql/Aqg+fgJ4C/DVOY4xLCJjQFkp9cis1V8EHlZK/W5t\ngYiMAPeKyIVKqf2ztn0ceP8CQ0lPAX5bKfWrGfv7YvXum5VS2eqyA8AvgGsIxFSNnyqlbqrev0dE\nLq8+5+/MOo4P/C8CL8+/Vv9/Sin1zIyp2bXnfy+BuEOClQ8SiLAbCAQawB9Ubd+tlBqpbnu4aqNG\no2kQ2uOj0Wgg8ORcXL3/uurfD8xatq8mGqrcXbujlEoCcWA+j8+cVL0crwK+IyJW7UYgDBwCz9NM\n7q4ue+8Cux2ZKXqqvBy4e6b9SqlfAscIRN3/bud+QmwMoziOf0+TjIWE/E+xMRY2NGNnIpFILFho\nIislWShGbEzKAmWj2Vj4k8IkFkhkoWbGvyQLQ0JJimHIlNCMOBbnuXl7vXeamTui7u9T03Sfee59\n3mcW05nnnPPk18h6Qvl9nQOWpROxwjQXgJnVppTZC6CP2NsBIl1Y+ge0AXhQCnoyz/iuzNoiMgwK\nfEQEoB2Yl2p6FhGBTwdQb2a1mbGs3tzrfqB2iOuOB2qI7qzvma8+YBQwMzd/F3EqdczMVpf5zKJA\nYVqZ8XfAhNzYoPfl7neAN8BeYH56tiIHgZ3AMSLV1UCkssh89lSgp+C9RWMiMkxKdYkIwC2iPXsx\nkeraDTwmal2WAguAw39h3V4ihdYCXC34+Zvc65/AJiIl1GZmy929MzenKAX2FphcMD6FSJ1Voo0I\nau65+8syc9YDR939UGnAzFbl5nQDdQXvnVTh84lIhgIfEcHdP5lZF7AD+AE8dHc3s06gmfhbkQ8w\nhuqPkxN3/2Jmd4E6d98/yGftT51dN4FLZtaYqwMqcg/YamZj3f0zgJk1EIXQle7rFBGwnBlgzhji\nFIu0dg1/tsLfB5rMbEamxmchEZyJyAhRqktEStqJWp7b7v4jjXWksefu3l3h5z8FpqSbj+vNbFYa\nbwbWmdlpM1uT2us3m9l5M5tT9EEpeFkJfASupY6qgRxJ36+nNZqAi8Aj4EIlm0r3Fa1193zxc9YN\nYJuZbUwnPZeB0bk5J4APwJXUjr+B6CrrIU66RGQEKPARkZJSDU97wVilpyIQXVEniXbx+0R6i5Sq\naiRSOqeJoKAZeM0Ahb3u/p64DbqGCGgmDjC3B1hC3OtzFmgl9rbM3fsr29agbE/rtQLHgS5+d3OV\nnvErsAL4RqTPWojfQy/RZSciI8DKd4SKiMi/ZGazgWfAFncvutdIRIZIgY+IyH/CzPYQBd2viIsL\n9wDjgLm5qwREZJhU3Cwi8v9wYB8wnSiG7gB2KugRGTk68REREZGqoeJmERERqRoKfERERKRqKPAR\nERGRqqHAR0RERKqGAh8RERGpGgp8REREpGr8AisyTGsj+PgUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17cc9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "color_dict = {'ml': \"black\", #\"#1C1858\",\n",
    "              'sdss': \"#5BC236\", #\"#00C78E\",\n",
    "              'simple': \"#C864AF\", #\"#C70039\",\n",
    "              'ps1': \"#C65400\"}\n",
    "\n",
    "mag_bin_centers = mag_array + binwidth/2\n",
    "cmap_star = sns.cubehelix_palette(rot=0.5, light=0.7,dark=0.3,as_cmap=True)\n",
    "cmap_gal = sns.cubehelix_palette(start=0.3,rot=-0.5,light=0.7,dark=0.3,as_cmap=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "ax.errorbar(mag_bin_centers, ml_acc_arr, \n",
    "            yerr=np.abs(ml_boot_scatt - ml_acc_arr), \n",
    "            ls='-', lw=.75, fmt='o',\n",
    "            color=color_dict['ml'], label=\"ML model\", zorder=2)\n",
    "\"\"\"\n",
    "ax.errorbar(mag_bin_centers, simple_acc_arr, \n",
    "            yerr=np.abs(simple_boot_scatt - simple_acc_arr), \n",
    "            ls ='-', lw=.5, fmt='o',\n",
    "            color=color_dict['simple'], label=\"Simple model\")\n",
    "ax.errorbar(mag_bin_centers, ps1_acc_arr, \n",
    "            yerr=np.abs(ps1_boot_scatt - ps1_acc_arr), \n",
    "            ls ='-', lw=.5, dashes=(8, 4), fmt='o',\n",
    "            color=color_dict['ps1'], label=r'm$_{\\rm iPSF}-$m$_{\\rm iKron}$')\n",
    "\"\"\"\n",
    "ax.errorbar(mag_bin_centers, sdss_acc_arr, \n",
    "            yerr=np.abs(sdss_boot_scatt - sdss_acc_arr), \n",
    "            ls='-', lw=.5, fmt='o',\n",
    "            color=color_dict['sdss'], label=\"SDSS photo\", zorder=1)\n",
    "\n",
    "# add KDE plots\n",
    "ax.fill(kde_grid, pdf_sdss + 0.5, alpha=0.4, color=\"0.7\")\n",
    "ax.fill(kde_grid, pdf_sdss_gal*sdss_kde_gal_norm + 0.5, alpha=0.7, color=cmap_gal(0.25))\n",
    "ax.fill(kde_grid, pdf_sdss_stars*sdss_kde_star_norm + 0.5, alpha=0.7, color=cmap_star(0.25))\n",
    "\n",
    "ax.set_ylim(0.5,1.01)\n",
    "ax.set_xlim(13, 23.5)\n",
    "ax.tick_params(which=\"both\", top=True, right=True, labelsize=15)\n",
    "ax.set_xlabel('whiteKronMag', fontsize=15)\n",
    "ax.set_ylabel('Accuracy', fontsize=15)\n",
    "\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.025))\n",
    "ax.xaxis.set_major_locator(MultipleLocator(2))\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.5))\n",
    "\n",
    "ax.legend(bbox_to_anchor=(0.01, 0.3, 1., 0.102), loc=3, fontsize=13)\n",
    "\n",
    "fig.subplots_adjust(top=0.98,right=0.98,left=0.1,bottom=0.12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debiasing by resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "binwidth = 0.5\n",
    "Nboot = 100\n",
    "\n",
    "# bootstrap star arrays\n",
    "sdss_star_acc_arr = np.zeros_like(mag_array)\n",
    "simple_star_acc_arr = np.zeros_like(mag_array)\n",
    "ps1_star_acc_arr = np.zeros_like(mag_array)\n",
    "ml_star_acc_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_star_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_star_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_star_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_star_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "# bootstrap galaxy arrays\n",
    "sdss_gal_acc_arr = np.zeros_like(mag_array)\n",
    "simple_gal_acc_arr = np.zeros_like(mag_array)\n",
    "ps1_gal_acc_arr = np.zeros_like(mag_array)\n",
    "ml_gal_acc_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_gal_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_gal_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_gal_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_gal_boot_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "for bin_num, binedge in enumerate(mag_array):\n",
    "    bin_stars = np.where((kron_mag >= binedge) & (kron_mag < binedge + binwidth) & \n",
    "                         (sdss_ml_test_y == 1))\n",
    "    sdss_star_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_stars], \n",
    "                                           sdss_labels[bin_stars])\n",
    "    simple_star_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_stars], \n",
    "                                             simple_labels[bin_stars])\n",
    "    ps1_star_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_stars], \n",
    "                                          ps1_labels[bin_stars])\n",
    "    ml_star_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_stars], \n",
    "                                         ml_labels[bin_stars])\n",
    "    \n",
    "    bin_gals = np.where((kron_mag >= binedge) & (kron_mag < binedge + binwidth) & \n",
    "                        (sdss_ml_test_y == 0))\n",
    "    sdss_gal_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_gals], \n",
    "                                           sdss_labels[bin_gals])\n",
    "    simple_gal_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_gals], \n",
    "                                             simple_labels[bin_gals])\n",
    "    ps1_gal_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_gals], \n",
    "                                          ps1_labels[bin_gals])\n",
    "    ml_gal_acc_arr[bin_num] = accuracy_score(sdss_ml_test_y[bin_gals], \n",
    "                                         ml_labels[bin_gals])    \n",
    "    \n",
    "    # get the bootstrap accuracies\n",
    "    \n",
    "    sdss_star_boot_acc = np.empty(Nboot)\n",
    "    simple_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    ps1_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    ml_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    \n",
    "    sdss_gal_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    simple_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "    ps1_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "    ml_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "\n",
    "    for i in range(Nboot):\n",
    "        star_boot_sources = np.random.choice(bin_stars[0], len(bin_stars[0]), \n",
    "                                             replace=True)\n",
    "        sdss_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                           sdss_labels[star_boot_sources])\n",
    "        simple_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                             simple_labels[star_boot_sources])\n",
    "        ps1_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                          ps1_labels[star_boot_sources])\n",
    "        ml_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                         ml_labels[star_boot_sources])\n",
    "        \n",
    "        gal_boot_sources = np.random.choice(bin_gals[0], len(bin_gals[0]), \n",
    "                                            replace=True)\n",
    "        sdss_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                           sdss_labels[gal_boot_sources])\n",
    "        simple_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                             simple_labels[gal_boot_sources])\n",
    "        ps1_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                          ps1_labels[gal_boot_sources])\n",
    "        ml_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                         ml_labels[gal_boot_sources])\n",
    "\n",
    "\n",
    "    sdss_star_boot_scatt[:,bin_num] = np.percentile(sdss_star_boot_acc, [16, 84])\n",
    "    simple_star_boot_scatt[:,bin_num] = np.percentile(simple_star_boot_acc, [16, 84])\n",
    "    ps1_star_boot_scatt[:,bin_num] = np.percentile(ps1_star_boot_acc, [16, 84])\n",
    "    ml_star_boot_scatt[:,bin_num] = np.percentile(ml_star_boot_acc, [16, 84])    \n",
    "    \n",
    "    sdss_gal_boot_scatt[:,bin_num] = np.percentile(sdss_gal_boot_acc, [16, 84])\n",
    "    simple_gal_boot_scatt[:,bin_num] = np.percentile(simple_gal_boot_acc, [16, 84])\n",
    "    ps1_gal_boot_scatt[:,bin_num] = np.percentile(ps1_gal_boot_acc, [16, 84])\n",
    "    ml_gal_boot_scatt[:,bin_num] = np.percentile(ml_gal_boot_acc, [16, 84])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126 3 23645 22763 23645 562\n",
      "161 6 36897 45170 36897 1375\n",
      "206 10 50611 94001 50611 2456\n",
      "288 29 66261 166928 66261 6672\n",
      "325 57 70723 293069 70723 12403\n",
      "401 93 79154 174356 79154 18357\n",
      "472 176 89247 131564 89247 33278\n",
      "633 343 107231 188220 107231 58104\n",
      "731 653 106618 255031 106618 95241\n",
      "926 1057 111399 528090 111399 127158\n",
      "1007 1756 133584 237271 133584 232942\n",
      "1200 2811 137765 76111 32491 76111\n",
      "1397 4375 137181 30476 9731 30476\n",
      "1416 6133 97725 19126 4415 19126\n",
      "1120 6738 31678 7312 1215 7312\n",
      "765 5704 4325 1319 176 1319\n",
      "370 3312 478 125 13 125\n"
     ]
    }
   ],
   "source": [
    "binwidth = 0.5\n",
    "Nboot = 100\n",
    "\n",
    "sdss_resamp_arr = np.zeros_like(mag_array)\n",
    "simple_resamp_arr = np.zeros_like(mag_array)\n",
    "ps1_resamp_arr = np.zeros_like(mag_array)\n",
    "ml_resamp_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "# bootstrap star arrays\n",
    "sdss_star_resamp_arr = np.zeros_like(mag_array)\n",
    "simple_star_resamp_arr = np.zeros_like(mag_array)\n",
    "ps1_star_resamp_arr = np.zeros_like(mag_array)\n",
    "ml_star_resamp_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_star_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_star_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_star_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_star_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "# bootstrap galaxy arrays\n",
    "sdss_gal_resamp_arr = np.zeros_like(mag_array)\n",
    "simple_gal_resamp_arr = np.zeros_like(mag_array)\n",
    "ps1_gal_resamp_arr = np.zeros_like(mag_array)\n",
    "ml_gal_resamp_arr = np.zeros_like(mag_array)\n",
    "\n",
    "sdss_gal_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "simple_gal_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ps1_gal_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "ml_gal_resamp_scatt = np.vstack((np.zeros_like(mag_array), np.zeros_like(mag_array)))\n",
    "\n",
    "for bin_num, binedge in enumerate(mag_array):\n",
    "    if bin_num <= 3:\n",
    "        continue\n",
    "    \n",
    "    bin_stars = np.where((kron_mag >= binedge) & (kron_mag < binedge + binwidth) & \n",
    "                         (sdss_ml_test_y == 1))\n",
    "    \n",
    "    bin_gals = np.where((kron_mag >= binedge) & (kron_mag < binedge + binwidth) & \n",
    "                        (sdss_ml_test_y == 0))\n",
    "\n",
    "    hst_stars = np.where((hst_kron_mag >= binedge) & (hst_kron_mag < binedge + binwidth) & \n",
    "                         (hst_ml_train_y == 1))\n",
    "    hst_gals = np.where((hst_kron_mag >= binedge) & (hst_kron_mag < binedge + binwidth) & \n",
    "                        (hst_ml_train_y == 0))\n",
    "    \n",
    "    # figure out the number of stars and galaxies to select\n",
    "    if len(hst_stars[0])/len(hst_gals[0]) > len(bin_stars[0])/len(bin_gals[0]):\n",
    "        n_star_resamp = len(bin_stars[0])\n",
    "        n_gal_resamp = int(len(bin_stars[0])*len(hst_gals[0])/len(hst_stars[0]))\n",
    "    else:\n",
    "        n_star_resamp = int(len(bin_gals[0])*len(hst_stars[0])/len(hst_gals[0]))\n",
    "        n_gal_resamp = len(bin_gals[0])\n",
    "    \n",
    "    print(len(hst_stars[0]), len(hst_gals[0]), \n",
    "          len(bin_stars[0]), len(bin_gals[0]),\n",
    "          n_star_resamp, n_gal_resamp)\n",
    "    \n",
    "    # get the bootstrap accuracies    \n",
    "    sdss_boot_acc = np.empty(Nboot)\n",
    "    simple_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "    ps1_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "    ml_boot_acc = np.empty_like(sdss_boot_acc)\n",
    "\n",
    "    sdss_star_boot_acc = np.empty(Nboot)\n",
    "    simple_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    ps1_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    ml_star_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    \n",
    "    sdss_gal_boot_acc = np.empty_like(sdss_star_boot_acc)\n",
    "    simple_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "    ps1_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "    ml_gal_boot_acc = np.empty_like(sdss_gal_boot_acc)\n",
    "\n",
    "    for i in range(Nboot):\n",
    "        star_boot_sources = np.random.choice(bin_stars[0], n_star_resamp, \n",
    "                                             replace=True)\n",
    "        gal_boot_sources = np.random.choice(bin_gals[0], n_gal_resamp, \n",
    "                                            replace=True)\n",
    "        boot_sources = np.append(star_boot_sources, gal_boot_sources)\n",
    "\n",
    "        sdss_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                           sdss_labels[boot_sources])\n",
    "        simple_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                             simple_labels[boot_sources])\n",
    "        ps1_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                          ps1_labels[boot_sources])\n",
    "        ml_boot_acc[i] = accuracy_score(sdss_ml_test_y[boot_sources], \n",
    "                                         ml_labels[boot_sources])\n",
    "\n",
    "        sdss_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                           sdss_labels[star_boot_sources])\n",
    "        simple_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                             simple_labels[star_boot_sources])\n",
    "        ps1_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                          ps1_labels[star_boot_sources])\n",
    "        ml_star_boot_acc[i] = accuracy_score(sdss_ml_test_y[star_boot_sources], \n",
    "                                         ml_labels[star_boot_sources])\n",
    "        \n",
    "        sdss_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                           sdss_labels[gal_boot_sources])\n",
    "        simple_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                             simple_labels[gal_boot_sources])\n",
    "        ps1_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                          ps1_labels[gal_boot_sources])\n",
    "        ml_gal_boot_acc[i] = accuracy_score(sdss_ml_test_y[gal_boot_sources], \n",
    "                                         ml_labels[gal_boot_sources])\n",
    "\n",
    "\n",
    "    sdss_resamp_arr[bin_num] = np.mean(sdss_boot_acc)\n",
    "    simple_resamp_arr[bin_num] = np.mean(simple_boot_acc)\n",
    "    ps1_resamp_arr[bin_num] = np.mean(ps1_boot_acc)\n",
    "    ml_resamp_arr[bin_num] = np.mean(ml_boot_acc)\n",
    "    \n",
    "    sdss_resamp_scatt[:,bin_num] = np.percentile(sdss_boot_acc, [16, 84])\n",
    "    simple_resamp_scatt[:,bin_num] = np.percentile(simple_boot_acc, [16, 84])\n",
    "    ps1_resamp_scatt[:,bin_num] = np.percentile(ps1_boot_acc, [16, 84])\n",
    "    ml_resamp_scatt[:,bin_num] = np.percentile(ml_boot_acc, [16, 84])    \n",
    "\n",
    "    sdss_star_resamp_arr[bin_num] = np.mean(sdss_star_boot_acc)\n",
    "    simple_star_resamp_arr[bin_num] = np.mean(simple_star_boot_acc)\n",
    "    ps1_star_resamp_arr[bin_num] = np.mean(ps1_star_boot_acc)\n",
    "    ml_star_resamp_arr[bin_num] = np.mean(ml_star_boot_acc)\n",
    "    \n",
    "    sdss_star_resamp_scatt[:,bin_num] = np.percentile(sdss_star_boot_acc, [16, 84])\n",
    "    simple_star_resamp_scatt[:,bin_num] = np.percentile(simple_star_boot_acc, [16, 84])\n",
    "    ps1_star_resamp_scatt[:,bin_num] = np.percentile(ps1_star_boot_acc, [16, 84])\n",
    "    ml_star_resamp_scatt[:,bin_num] = np.percentile(ml_star_boot_acc, [16, 84])    \n",
    "\n",
    "    sdss_gal_resamp_arr[bin_num] = np.mean(sdss_gal_boot_acc)\n",
    "    simple_gal_resamp_arr[bin_num] = np.mean(simple_gal_boot_acc)\n",
    "    ps1_gal_resamp_arr[bin_num] = np.mean(ps1_gal_boot_acc)\n",
    "    ml_gal_resamp_arr[bin_num] = np.mean(ml_gal_boot_acc)\n",
    "    \n",
    "    sdss_gal_resamp_scatt[:,bin_num] = np.percentile(sdss_gal_boot_acc, [16, 84])\n",
    "    simple_gal_resamp_scatt[:,bin_num] = np.percentile(simple_gal_boot_acc, [16, 84])\n",
    "    ps1_gal_resamp_scatt[:,bin_num] = np.percentile(ps1_gal_boot_acc, [16, 84])\n",
    "    ml_gal_resamp_scatt[:,bin_num] = np.percentile(ml_gal_boot_acc, [16, 84])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "kde_grid = np.linspace(13,23.5,200)\n",
    "hst_stars = np.where(hst_ml_train_y == 1)\n",
    "hst_gal = np.where(hst_ml_train_y == 0)\n",
    "\n",
    "kde_hst = KernelDensity(bandwidth=1.059*np.std(hst_kron_mag, ddof=1)*len(hst_kron_mag)**(-0.2),\n",
    "                         rtol=1E-4)\n",
    "kde_hst.fit(hst_kron_mag[:, np.newaxis])\n",
    "\n",
    "kde_hst_stars = KernelDensity(bandwidth=1.059*np.std(hst_kron_mag[hst_stars], ddof=1)*len(hst_kron_mag[hst_stars])**(-0.2),\n",
    "                               rtol=1E-4)\n",
    "kde_hst_stars.fit(hst_kron_mag[hst_stars[0], np.newaxis])\n",
    "\n",
    "kde_hst_gal = KernelDensity(bandwidth=1.059*np.std(hst_kron_mag[hst_gal], ddof=1)*len(hst_kron_mag[hst_gal])**(-0.2),\n",
    "                             rtol=1E-4)\n",
    "kde_hst_gal.fit(hst_kron_mag[hst_gal[0], np.newaxis])\n",
    "\n",
    "pdf_hst = np.exp(kde_hst.score_samples(kde_grid[:, np.newaxis]))\n",
    "pdf_hst_stars = np.exp(kde_hst_stars.score_samples(kde_grid[:, np.newaxis]))\n",
    "pdf_hst_gal = np.exp(kde_hst_gal.score_samples(kde_grid[:, np.newaxis]))\n",
    "\n",
    "hst_kde_gal_norm = len(hst_gal[0])/len(hst_ml_train_y)\n",
    "hst_kde_star_norm = 1 - hst_kde_gal_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hq2MyMzP517/+xaZNm4rMvxJCsOTbb4lv1gyAeGdnQrZvJ61JE5L184sKXyE1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\nRIWi87Qi1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dRLOhp9fEFSqVQ6phjWDjOl4pZfy06haxrlDUpqG5+HYrHI/v37uzqWa/7s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\nxibl/wPkK93MuSLudwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a44fb79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'ml': \"black\", #\"#1C1858\",\n",
    "              'sdss': \"teal\",\n",
    "              'simple': \"#C864AF\", #\"#C70039\",\n",
    "              'ps1': \"#C65400\"}\n",
    "\n",
    "plt.figure(figsize=(12.5,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.errorbar(mag_bin_centers+0.1, ml_star_acc_arr, \n",
    "            yerr=np.abs(ml_star_boot_scatt - ml_star_acc_arr), \n",
    "            ls =ls_dict['ml'], lw=.75, fmt='^', ms=8,\n",
    "            mec=\"0.2\", mew=0.5,\n",
    "            color=color_dict['ml'], label=\"ML stars\", zorder=4)\n",
    "# ax.errorbar(mag_bin_centers, simple_star_acc_arr, \n",
    "#             yerr=np.abs(simple_star_boot_scatt - simple_star_acc_arr), \n",
    "#             ls =ls_dict['simple'], lw=.5, fmt='*', ms=10,\n",
    "#             mec=\"0.2\", mew=0.5,\n",
    "#             color=color_dict['simple'], label=\"Simple model\")\n",
    "# ax.errorbar(mag_bin_centers, ps1_star_acc_arr, \n",
    "#             yerr=np.abs(ps1_star_boot_scatt - ps1_star_acc_arr), \n",
    "#             ls =ls_dict['ps1'], lw=.5, dashes=(8, 4), fmt='*', ms=10,\n",
    "#             mec=\"0.2\", mew=0.5,\n",
    "#             color=color_dict['ps1'], label=r'm$_{\\rm iPSF}-$m$_{\\rm iKron}$')\n",
    "plt.errorbar(mag_bin_centers+0.1, sdss_star_acc_arr, \n",
    "            yerr=np.abs(sdss_star_boot_scatt - sdss_star_acc_arr), \n",
    "            ls=ls_dict['sdss'], lw=.5, fmt='^', ms=8,\n",
    "            mec=\"0.2\", mew=0.5,\n",
    "            color=color_dict['sdss'], label=\"SDSS stars\", zorder=2)\n",
    "\n",
    "\n",
    "plt.errorbar(mag_bin_centers-0.1, ml_gal_acc_arr, \n",
    "            yerr=np.abs(ml_gal_boot_scatt - ml_gal_acc_arr), \n",
    "            ls =ls_dict['ml'], lw=.75, fmt='s',\n",
    "            color=color_dict['ml'], label=\"ML galaxies\", zorder=3)\n",
    "# ax.errorbar(mag_bin_centers, simple_gal_acc_arr, \n",
    "#             yerr=np.abs(simple_gal_boot_scatt - simple_gal_acc_arr), \n",
    "#             ls =ls_dict['simple'], lw=.5, fmt=',',\n",
    "#             color=color_dict['simple'], label=\"Simple model\")\n",
    "# ax.errorbar(mag_bin_centers, ps1_gal_acc_arr, \n",
    "#             yerr=np.abs(ps1_gal_boot_scatt - ps1_gal_acc_arr), \n",
    "#             ls =ls_dict['ps1'], lw=.5, dashes=(8, 4), fmt=',',\n",
    "#             color=color_dict['ps1'], label=r'm$_{\\rm iPSF}-$m$_{\\rm iKron}$')\n",
    "plt.errorbar(mag_bin_centers-0.1, sdss_gal_acc_arr, \n",
    "            yerr=np.abs(sdss_gal_boot_scatt - sdss_gal_acc_arr), \n",
    "            ls=ls_dict['sdss'], lw=.5, fmt='s',\n",
    "            color=color_dict['sdss'], label=\"SDSS galaxies\", zorder=1)\n",
    "\n",
    "# add KDE plots\n",
    "plt.fill(kde_grid, pdf_sdss + 0.5, alpha=0.4, color=\"0.7\")\n",
    "plt.fill(kde_grid, pdf_sdss_gal*sdss_kde_gal_norm + 0.5, alpha=0.7, color=cmap_gal(0.25))\n",
    "plt.fill(kde_grid, pdf_sdss_stars*sdss_kde_star_norm + 0.5, alpha=0.7, color=cmap_star(0.25))\n",
    "\n",
    "plt.ylim(0.5,1.01)\n",
    "plt.xlim(13, 23.5)\n",
    "plt.tick_params(which=\"both\", top=True, right=True, labelsize=15)\n",
    "plt.xlabel('whiteKronMag', fontsize=15)\n",
    "plt.ylabel('Accuracy', fontsize=15)\n",
    "plt.minorticks_on()\n",
    "\n",
    "#plt.yaxis.set_minor_locator(MultipleLocator(0.025))\n",
    "#plt.xaxis.set_major_locator(MultipleLocator(2))\n",
    "#plt.xaxis.set_minor_locator(MultipleLocator(0.5))\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0.01, 0.4, 1., 0.102), loc=3, fontsize=13)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.errorbar(mag_bin_centers[4:], ml_resamp_arr[4:], \n",
    "            yerr=np.abs(ml_resamp_scatt - ml_resamp_arr)[:,4:], \n",
    "            ls =ls_dict['ml'], lw=.75, fmt='o',\n",
    "            color=color_dict['ml'], label=\"ML model\")\n",
    "\"\"\"\n",
    "ax.errorbar(mag_bin_centers[4:], simple_resamp_arr[4:], \n",
    "            yerr=np.abs(simple_resamp_scatt - simple_resamp_arr)[:,4:], \n",
    "            ls =ls_dict['simple'], lw=.5, fmt='o',\n",
    "            color=color_dict['simple'], label=\"Simple model\")\n",
    "ax.errorbar(mag_bin_centers[4:], ps1_resamp_arr[4:], \n",
    "            yerr=np.abs(ps1_resamp_scatt - ps1_resamp_arr)[:,4:], \n",
    "            ls =ls_dict['ps1'], lw=.5, dashes=(8, 4), fmt='o',\n",
    "            color=color_dict['ps1'], label=r'm$_{\\rm iPSF}-$m$_{\\rm iKron}$')\n",
    "\"\"\"\n",
    "plt.errorbar(mag_bin_centers[4:], sdss_resamp_arr[4:], \n",
    "            yerr=np.abs(sdss_resamp_scatt - sdss_resamp_arr)[:,4:], \n",
    "            ls=ls_dict['sdss'], lw=.5, fmt='o',\n",
    "            color=color_dict['sdss'], label=\"SDSS photo\")\n",
    "\n",
    "# add KDE plots\n",
    "plt.fill(np.append(kde_grid, 23.5), \n",
    "        np.append(pdf_hst, 0) + 0.5, alpha=0.4, color=\"0.7\")\n",
    "plt.fill(np.append(kde_grid, 23.5), \n",
    "        np.append(pdf_hst_gal*hst_kde_gal_norm, 0) + 0.5, alpha=0.7, color=cmap_gal(0.25))\n",
    "plt.fill(np.append(kde_grid, 23.5), \n",
    "        np.append(pdf_hst_stars*hst_kde_star_norm, 0) + 0.5, alpha=0.7, color=cmap_star(0.25))\n",
    "\n",
    "plt.ylim(0.5,1.01)\n",
    "plt.xlim(15, 23.5)\n",
    "plt.tick_params(which=\"both\", top=True, right=True, labelsize=15, labelleft='off')\n",
    "plt.xlabel('whiteKronMag', fontsize=15)\n",
    "#plt.ylabel('Accuracy', fontsize=15)\n",
    "plt.minorticks_on()\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0.01, 0.4, 1., 0.102), loc=3, fontsize=13)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('Accuracy_SDSS_ML_model.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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