From 735ceab579b0e535d840b655aa37fb8e3d139bdf Mon Sep 17 00:00:00 2001
From: Richard Sowers <r-sowers@illinois.edu>
Date: Sat, 16 Mar 2019 13:00:44 -0500
Subject: [PATCH] updated error analyses notebooks

---
 ErrorAnalysis/ErrorAnalysis.ipynb             | 480 ++++-------
 ErrorAnalysis/ErrorAnalysis_fall.ipynb        | 745 ------------------
 ErrorAnalysis/ErrorAnalysis_seasonal.ipynb    | 552 +++++++++++++
 ErrorAnalysis/compare.png                     | Bin 46209 -> 0 bytes
 ErrorAnalysis/error_by_penalty_fall.png       | Bin 29066 -> 0 bytes
 ErrorAnalysis/error_by_rank_seasonal.png      | Bin 0 -> 40676 bytes
 ErrorAnalysis/error_by_sparsity_seasonal.png  | Bin 0 -> 27327 bytes
 ...l.png => sparsity_by_penalty_seasonal.png} | Bin
 8 files changed, 726 insertions(+), 1051 deletions(-)
 delete mode 100644 ErrorAnalysis/ErrorAnalysis_fall.ipynb
 create mode 100644 ErrorAnalysis/ErrorAnalysis_seasonal.ipynb
 delete mode 100644 ErrorAnalysis/compare.png
 delete mode 100644 ErrorAnalysis/error_by_penalty_fall.png
 create mode 100644 ErrorAnalysis/error_by_rank_seasonal.png
 create mode 100644 ErrorAnalysis/error_by_sparsity_seasonal.png
 rename ErrorAnalysis/{sparsity_by_penalty_fall.png => sparsity_by_penalty_seasonal.png} (100%)

diff --git a/ErrorAnalysis/ErrorAnalysis.ipynb b/ErrorAnalysis/ErrorAnalysis.ipynb
index ca3b52e..fc3bfc0 100644
--- a/ErrorAnalysis/ErrorAnalysis.ipynb
+++ b/ErrorAnalysis/ErrorAnalysis.ipynb
@@ -35,7 +35,7 @@
     "import matplotlib.pylab as plt\n",
     "%matplotlib inline\n",
     "import scipy.interpolate\n",
-    "import scipy.optimize "
+    "import scipy.optimize"
    ]
   },
   {
@@ -51,10 +51,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fname=\"LevelCurveData2\"\n",
     "colorsequence=['b', 'g', 'r', 'c', 'm', 'y', 'k']"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname=\"LevelCurveData2\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -64,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -204,205 +212,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   rank  beta  no_iterations  pre_error  post_error  pre_sparsity  \\\n",
-      "0    40     0            263  26.638031   41.724601      0.673253   \n",
-      "1    40  1000            174  26.958008   41.843305      0.693565   \n",
-      "2    40  2000            176  26.952959   41.781512      0.715966   \n",
-      "3    40  3000            177  26.990673   41.414115      0.730479   \n",
-      "4    40  4000            179  27.039437   41.120108      0.741846   \n",
-      "\n",
-      "   post_sparsity  spikey_mean  spikey_std  H_zero_percent  \n",
-      "0       0.838865     0.720289    0.122731       88.617507  \n",
-      "1       0.851688     0.664605    0.112649       89.375543  \n",
-      "2       0.865042     0.629207    0.113267       90.285621  \n",
-      "3       0.872522     0.608351    0.115246       90.801477  \n",
-      "4       0.878487     0.594085    0.117988       91.222850  \n",
-      "Index(['rank', 'beta', 'no_iterations', 'pre_error', 'post_error',\n",
-      "       'pre_sparsity', 'post_sparsity', 'spikey_mean', 'spikey_std',\n",
-      "       'H_zero_percent'],\n",
-      "      dtype='object')\n"
+      "               error  sparsity\n",
+      "rank beta                     \n",
+      "40   0     26.638031  0.673253\n",
+      "     1000  26.958008  0.693565\n",
+      "     2000  26.952959  0.715966\n",
+      "     3000  26.990673  0.730479\n",
+      "     4000  27.039437  0.741846\n",
+      "Index(['error', 'sparsity'], dtype='object')\n"
      ]
     }
    ],
    "source": [
     "data=data_raw.copy()\n",
+    "data=data.set_index(keys=[\"rank\",\"beta\"])\n",
+    "data=data[[\"pre_error\",\"pre_sparsity\"]]\n",
+    "data=data.rename(mapper={\"pre_error\":\"error\",\"pre_sparsity\":\"sparsity\"},axis=\"columns\")\n",
     "print(data.head())\n",
     "print(data.columns)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>no_iterations</th>\n",
-       "      <th>pre_error</th>\n",
-       "      <th>post_error</th>\n",
-       "      <th>pre_sparsity</th>\n",
-       "      <th>post_sparsity</th>\n",
-       "      <th>spikey_mean</th>\n",
-       "      <th>spikey_std</th>\n",
-       "      <th>H_zero_percent</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>rank</th>\n",
-       "      <th>beta</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <th>40</th>\n",
-       "      <th>0</th>\n",
-       "      <td>263</td>\n",
-       "      <td>26.638031</td>\n",
-       "      <td>41.724601</td>\n",
-       "      <td>0.673253</td>\n",
-       "      <td>0.838865</td>\n",
-       "      <td>0.720289</td>\n",
-       "      <td>0.122731</td>\n",
-       "      <td>88.617507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>40</th>\n",
-       "      <th>1000</th>\n",
-       "      <td>174</td>\n",
-       "      <td>26.958008</td>\n",
-       "      <td>41.843305</td>\n",
-       "      <td>0.693565</td>\n",
-       "      <td>0.851688</td>\n",
-       "      <td>0.664605</td>\n",
-       "      <td>0.112649</td>\n",
-       "      <td>89.375543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>40</th>\n",
-       "      <th>2000</th>\n",
-       "      <td>176</td>\n",
-       "      <td>26.952959</td>\n",
-       "      <td>41.781512</td>\n",
-       "      <td>0.715966</td>\n",
-       "      <td>0.865042</td>\n",
-       "      <td>0.629207</td>\n",
-       "      <td>0.113267</td>\n",
-       "      <td>90.285621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>40</th>\n",
-       "      <th>3000</th>\n",
-       "      <td>177</td>\n",
-       "      <td>26.990673</td>\n",
-       "      <td>41.414115</td>\n",
-       "      <td>0.730479</td>\n",
-       "      <td>0.872522</td>\n",
-       "      <td>0.608351</td>\n",
-       "      <td>0.115246</td>\n",
-       "      <td>90.801477</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <th>40</th>\n",
-       "      <th>4000</th>\n",
-       "      <td>179</td>\n",
-       "      <td>27.039437</td>\n",
-       "      <td>41.120108</td>\n",
-       "      <td>0.741846</td>\n",
-       "      <td>0.878487</td>\n",
-       "      <td>0.594085</td>\n",
-       "      <td>0.117988</td>\n",
-       "      <td>91.222850</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             no_iterations  pre_error  post_error  pre_sparsity  \\\n",
-       "  rank beta                                                       \n",
-       "0 40   0               263  26.638031   41.724601      0.673253   \n",
-       "1 40   1000            174  26.958008   41.843305      0.693565   \n",
-       "2 40   2000            176  26.952959   41.781512      0.715966   \n",
-       "3 40   3000            177  26.990673   41.414115      0.730479   \n",
-       "4 40   4000            179  27.039437   41.120108      0.741846   \n",
-       "\n",
-       "             post_sparsity  spikey_mean  spikey_std  H_zero_percent  \n",
-       "  rank beta                                                          \n",
-       "0 40   0          0.838865     0.720289    0.122731       88.617507  \n",
-       "1 40   1000       0.851688     0.664605    0.112649       89.375543  \n",
-       "2 40   2000       0.865042     0.629207    0.113267       90.285621  \n",
-       "3 40   3000       0.872522     0.608351    0.115246       90.801477  \n",
-       "4 40   4000       0.878487     0.594085    0.117988       91.222850  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data=data.set_index([\"rank\",\"beta\"],drop=True,append=True)\n",
-    "data.head()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[40 50 60 70 80 90]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "rankvals=pandas.unique(data.index.get_level_values(\"rank\"))\n",
-    "print(rankvals)\n",
-    "data_by_rank=data.groupby(by=\"rank\")"
+    "class processor:\n",
+    "    def __init__(self,df):\n",
+    "        self.rank_vals=pandas.unique(df.index.get_level_values(\"rank\"))\n",
+    "        self.df=df.dropna(axis=\"index\")\n",
+    "        \n",
+    "    def by_penalty(self,rank):\n",
+    "        temp=self.df.groupby(by=\"rank\").get_group(rank)\n",
+    "        return temp.reset_index(level=\"rank\",drop=True)\n",
+    "    \n",
+    "    def sparsity_by_penalty(self,rank):\n",
+    "        temp=self.by_penalty(rank)[\"sparsity\"]\n",
+    "        return temp\n",
+    "    \n",
+    "    def error_by_sparsity(self,rank):\n",
+    "        temp=self.by_penalty(rank)\n",
+    "        temp=temp.set_index(keys=\"sparsity\",drop=True)[\"error\"]\n",
+    "        temp.sort_index(axis=\"index\",inplace=True)\n",
+    "        return temp\n",
+    "    \n",
+    "p=processor(data)"
    ]
   },
   {
@@ -412,7 +274,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -423,11 +285,9 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"beta\",axis=0)\n",
-    "    beta=df_sorted.index.get_level_values(\"beta\")\n",
-    "    sparsity=df_sorted[\"post_sparsity\"]\n",
-    "    plt.plot(beta,sparsity,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
+    "for n,rank in enumerate(p.rank_vals):\n",
+    "    df=p.sparsity_by_penalty(rank)\n",
+    "    plt.plot(df.index,df.values,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
     "plt.legend()\n",
     "plt.xlabel(\"beta\")\n",
     "plt.ylabel(\"sparsity\")\n",
@@ -443,7 +303,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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PtbPdRcBFAEOHDg2pjeFAaxPXfvVVE+JQyv9isUS2vK7OvzNvft/Q0P55KmXy9WRk7HkdPhymTNnbuefmGhGwyZAFQYgoIf0LKqXiMCKwXGv9ole5DTgNOKitbbXWi4HFAPn5+bqtetGM3Q7vv2+c/2uvmTi3Uibfvc1mxMHtNq++SyjL26oLkJjY2on37Qt5ea3L2ntNSTGiIghCzyGUo4YUsATYoLW+02f1scAPWusdoTp+pCgshNdfN87/3XeNGKSkwPHHmzz3M2ZEb1xba+lkFYTeSChbBIcD5wDrlFJrPWXXaa3fAObQTlioJ6G1Gcny2mvG+a9aZcqHDTMJzWbOhKlTe0Z8W0RAEHonoRw19Cng17VoreeF6rjhoL4e3nvPOP/XXjOtAKVg8mST037mTNhvP3GsgiD0DKSbLkB27tzj+N9913SapqbCr39tUhjPmAHZ2ZG2UhAEofOIELSB221CPs0dvatXm/K8PLjwwj0hn/j4iJopCILQbUQIvKivN3f7r75qOnyLiswImMmT4R//MM5/3DgJ+QiCEFv0eiHYsWNPyOe99/aEfE44YU/IJysr0lYKgiCEjl4nBG63Gdnz6qtmWesZzzR8OFx8sbnrP/JICfkIgtB76BVCUFfXOuRTXGxCPlOmwK23mjv/sWMl5CMIQu8kpoXghRdgyRIzu7exEdLSTMhn5kzzKiEfQRCEGBeC1avhp59g/nzj/I84QkI+giAIvijtmw4yCsnPz9cFBQWd3s7hgLg4CfkIQmd44tsnGJM5hoMHHRxpU4RuopRapbXu8OGsMZ0eLD5eREAQOsPa4rVc8MoF3PzpzZE2RQgjMS0EgtCb2Vm9k5rGmoDr1zpqmf38bLKSs3h45sMhtEyINkQIBCEGqWqo4oAHD+Cad68JeJtL37iUTRWbePK0J8lKlpEUvQkRAkGIQe7/+n4q7BV8+sunAdV//JvHefybx7nxVzcyNW9qiK0Tog0RAkGIMeocddy18i4sysJ3pd9R31Tfbv0fy37kf1//X3417Ffc8KsbwmSlEE2IEAhCjLF41WLK6su4esrVuLWbNUVr2qzb4GxgzgtzSLQlsvy05dgsMT2iXGgDEQJBiCEanA3c9vltTMubxh8O/QMAXxd+3Wb9q9+5mrXFa1l6ylIGpw0Ol5lClCHyLwgxxKNrHqWotognTnuC3NRcBqUOoqDQ/xycFT+s4F9f/YsFhy7gpNEnhdlSIZqQFoEgxAhNriZu/exWJg+ezLS8aQDkD8z32yL4peoXzn/5fA7KPYhbjr0l3KYKUYYIgSDECMvXLWdb1TauP/J6lGcm5cEDD+an8p+obKhsqed0O5n7wlya3E08fcbTJNh6wAO1hZAiQiAIMYDL7eLmT25mwoAJzBg1o6W8OU3E6qLVLWWLPlzEZ9s/46GTHmJkv5Fht1WIPkQIBCEGeO7759hYsZEbjryhpTUAcFDuQQB8vdOEh977+T1u/uRmzp9wPnPHz42IrUL0IUIgCD0ct3Zz0yc3MTZrLKeOPbXVuszkTIb3Hc7XhV9TUlfC2S+dzZisMdw7/d4IWStEIzJqSBB6OK/++CrrS9az7NRlWNTe93YHDzyYz7d/zrkvnctu+27ePvtt+sT3iYClQrQiLQJB6MForfn7J39neN/hzNl/jt86+QPz2V69nbc3v83dJ9zNATkHhNlKIdqRFoEg9GD+u/m/FBQW8PDMh9ucFXzIoEMAOH3s6Vx80MXhNE/oIYgQCEIPRWvN3z7+G4PTBnPugee2We+IoUfw6KxHOW3saa06kgWhmZCFhpRSQ5RSHyilNiilvlNK/dFr3WVKqR895f8MlQ2CEMt8vO1jPtv+GVdPuZp4a9vPYLUoC/MmzCMtIS2M1gk9iVC2CJzAQq31aqVUKrBKKfUOkAPMAg7QWjcqpfqH0AZBiFlu+uQm+vfpz+8m/S7Spgg9nJAJgda6CCjyvK9RSm0ABgEXArdorRs960pCZYMgxCpvbHyDd35+h38e+0+S4pIibY7QwwnLqCGlVB4wEfgSGA0cqZT6Uin1kVLK7xOylVIXKaUKlFIFpaWl4TBTEHoEj6x5hFlPz2L//vsz/+D5kTZHiAFCLgRKqRTgBWCB1roa0wrpCxwGXAU8q/z0YGmtF2ut87XW+dnZ2aE2UxCiHrd2c91713HBKxcwLW8an/72U1LiUyJtlhADhHTUkFIqDiMCy7XWL3qKdwAvaq018JVSyg1kAXLbLwhtYG+yM+/leTz73bNcNOki7ptxH3HWuEib1XNwu8Ei06baIpSjhhSwBNigtb7Ta9UK4GhPndFAPFAWKjsEoadTUlfC0Y8fzbPfPcttx93Ggyc9KCIQKFrDM8/AvvvCjz9G2pqoJZQtgsOBc4B1Sqm1nrLrgEeAR5RS6wEHcJ6ndSAIgg8bSjdw4pMnUlRbxPO/eZ7Tx50eaZN6Dl9+CZdfDl98AQccAHV1kbYoagnlqKFPgbZmr5wdquMKQixQ2VDJs989yzXvXkO8NZ4Pz/uQQwcfGmmzega//ALXXgtPPgk5OfCf/8C8eWC1RtqyqEVmFgtClOBwOXhr01ss+3YZr/74Ko2uRiYOmMiLs18kLyMv0uZFP7W1cMstcMcdJiR03XXwpz9BamqkLYt6RAgEIYJorfly55cs+2YZz3z3DOX2crKSs7hw0oWcc+A5HDzwYEkL0REuFyxdCjfcAMXFMHcu3HwzDBsWact6DCIEghABNlVsYvm3y3li3RNsqthEoi2RWWNmcfYBZ/PrEb+WzuBAef99uOIK+OYbmDwZXnoJDjss0lb1OEQIBCGMVDVUMf/1+Ty1/ikUimn7TOO6I67j9HGnSy6gzmC3mzv/FSvMnf/TT8OZZ4K0nrqECIEghImCwgJmPz+bbZXmAfMXH3QxQ9KHRNqsnskrrxgRuPFG0zGcJGk2uoMIgQCYWHVhTSGrilaxqnAVq4pWsa5kHRZlIS0hjdT4VFITUkmNT2312e86n/dJtqReHefWWnP3yru55t1rGJAygI/mfcThQw+PtFk9m5UrjfO/8UaIkzBadxEh6IV4O/2CwoIW57+rbhdg0haPzRrLEUOPwKIs1DTWUOOooay+jC27t1DjqGkpCwSLsuwlEq1EpL11PqKSEp/i93GM0Up5fTm/ffm3vPrTq5w85mQenfUo/ZL6Rdqsns/KlZCfLyIQJEQIYpxmp9/i8Ntw+ieMPIGDcg/ioIEHcWDOgQE909at3dQ56qhx1FDdWN0iDjWNns9eguH9ufl9UU1Rq21d2hXQOaXEp7TfCvGzLjkuGa01Go1bu1sWrVt/dmt3h3UCXe90O3l49cOU1JVwzwn3cNkhl/XqllHQaGyE1avhj3/suK4QECIEMYTWmp01O1tCO/6c/rjscV1y+v6wKItxuAmpDEwd2G3bG5wNe4lKoAKzrXJbq/qNrsZu2RMsxmSO4fPzP+eggQdF2pQexRdVVXxfX88Fubl7r1yzBhwOGR0UREQIeij+nH5BYQEldebxDsF2+qFGKUVSXBJJcUn079P9ZxU5XI5WAlLfVI9SCouytCwKn88hWG+z2KQV0AWmrFkD4F8IVq40ryIEQUOEoAfg6/Sbwzy+Tn/6yOk9wumHg3hrPJnJmWQmZ0baFCHYrFwJQ4fCwO61QoU9iBBEGd5O3zuu35bTzx+Yz4EDDiQ5LjnClgtCmFi5UloDQUaEIII0O/2CwoJWIR5/Tj9/YD4H5R4kTl/o3RQVwbZt0lEcZEQIwoTWmh3VO1qN0/fn9GeMmmHCO+L0BaE1JSXw8MPmvbQIgooIQQjw5/QLCgsorTcPYbMoC/tl7ydOXxA64rXX4L33zLJunSkbOhQmToysXTGGCEEQqGms4b0t75m4fpEJ8/g6/RNHnyhOXxDao6HBPETmvffg2GNN2cyZkJgIhx9uMooecwxMmgS22Hddzc/rCseos9j/NsPA9urtnPrMqViVlXHZ48TpC0IgOJ1mYljzHf9nnxkxsFr3CMF778GUKUYMehn19d+zZs2RjBv3NP36HR/SY4kQBIHmSUPi9AWhA5xO88SwN9+Ejz6CqipTPn48XHIJHH00TJ1qBAJwT5uGpZfOw6ipWYXTuZuEhMEhP5YIQRCwWqxMHjI50mYIQvRz7bVw++0wfLhJG33MMTBtGvT3P4nQpXWvFgKLpQ/JyWNCfiwRAkEQwsNLLxkRmD8f/v3vgDZxak1vTStXU7OKlJQJKBX6Zy33nDSOgiD0XDZtMg+Qz8+Hu+4KeLMHCgspdThCZ1eUorWL2to1pKbmh+V4IgSCIIQWux3OOMN0Aj/3HCQkBLzpws2b2d4YHQkEw0l9/Q+43fWkpoYnWaGEhgRBCC2XXWaeKfzaa5CX1+nN43phH0FNzSqAsAmBtAgEQQgdjz4KS5bA9dfDiSd2aRe2XioE4eooBhECQRBCQUWF6RS+4AIzKugvf+nyrnqnEBSEraMYRAgEQQgmLhcsXgyjR5u8QH/4g3nIvLXrDq23hYZMR/HasHUUQwBCoJSyKqUu7+yOlVJDlFIfKKU2KKW+U0r90VO+SCm1Uym11rPM6IrhgiBEGV99ZZLBXXwxjBtnJoXdfTekpbW72Te1tbxcVtbm+t7WIgh3RzEEIARaaxcwqwv7dgILtdZjgcOAS5VS4zzr7tJaT/Asb3Rh34IgRAulpXDhhUYEdu6E5cvNrOEDDgho8/k//cSlP/3U5vreJgQ1NQVA+DqKIfBRQ58ppe4DngHqmgu11qvb2kBrXQQUed7XKKU2AIO6YasgCNGEywUPPgg33AC1tXDFFfDnP3fYAvBms93OF9XVZHolkXN5kq01E2eJzgj2lhu34Kp1MfKukUHdb7g7iiFwIZjief2rV5kGjg5kY6VUHjAR+BI4HPi9UupcoADTatjtZ5uLgIsAhg4dGqCZgiCEhc8/h0svhbVrTX6gf/3LhIM6yRO7dgHQ6OX8m9zuVnWitUVQ+mIpukmHRAjC2VEMAXYWa62n+VkCFYEU4AVggda6GngAGAFMwLQY7mjjmIu11vla6/zs7OyATkYQhBCza5eZIXz44VBWBs8+C+++2yUR0FqzrLgYAIeX83f4tgiiUAi0S2PfZMdRHNxZz263M+wdxRCgECil0pVSdyqlCjzLHUqp9AC2i8OIwHKt9YsAWutdWmuX1toNPAwc0p0TEAQhDLhccO+9ZjTQk0+a5HEbNsBvfgNddNQrq6vZ3NDAsIQEHFq35N9v8hGCaGwRNGxrQDs0rhoXrjpX0PYbiY5iCHz46CNADXCmZ6kGHm1vA2WeprAE2KC1vtOrPNer2qnA+s4YLAhCmNm8GY46yjwnePJkWL/ePCQmJaVbu122axeJFgtzPJlHmwWgJ4SG6n+qb3nfWBS8FBi1teGdUdxMoH0EI7TWp3t9/otSam0H2xwOnAOs86p7HfA/SqkJmD6GrcDFnbBXEIRwoTU89BBceaV5Itjjj8PZZ3e5BeCNw+3mmZISTsnKIivO5BdtdLuJt1j2Cg1FYxpq+0/2lveOYgfJI4PzHJJIdBRD4EJgV0odobX+FEApdThgb28DT11/v6AMFxWEaGfnTjMr+O234bjjTJqIIUOCtvs3ysupcDo5JyeHTXbjShxthIaihfK3ykk7JI24fnGtWgSOouD0E2it2b37PVJT88PaUQyBh4YuAe5XSm1VSm0F7kPu5AUh9tDazAPYf3/45BO4/34jBkEUATBhof5xcRzfty8JnuGhjZ6QkG9oKBpw1blYN2MdRQ8XAWD/0U7iPubxmZU177J+/ektfRxdpaamgPr678nJmdtteztLhy0CpZQFGKO1PlAplQbgGf0jCEIsUVpq8gO98ILpC3jsMRg1KuiH2d3UxGvl5cwfOBCbxUK8J/TTPHLINzQUDbjqXKD39AfU/1RP+hHpNG5vpMbyPjVlL+J0VhAXl9nlYxQXP4rFkkj//rODZXbABDKz2A383vO+WkRAEGIMt9vkA9p/f3jlFfjHP0xrIAQiAPBsaSkOrTlnwACAPS2CKA4NuerNyKCm0iZcdheNvzSSvG8y8QPiaXKVANDYWNT1/bsaKCl5iqys07DZOhyQGXQC7SN4Ryl1JXsj9vvKAAAgAElEQVTPLK4IiVWCIISOpiaTB+jjj43D/+wzky30wAPhnXcCTg3RVZYVFzM2OZlJnlFHiR4hqHY6jXlRGBpy2z1hq9Im7JtMn0by6GTic+OxK5MnyeEoAvbv0v7Ly1/B6axkwIB5wTC30wQqBOd7Xi/1KtPA8OCaIwhC0Kmrg5UrjdP/5BPzvt7T2TlqFJxyCkydCnPmQHx8SE352W7ns+pqbt5nH5QnJHSoJyXF2xUVHJyWFpWhIXe9J2xV4mgZMZQ0Oon4AfHUJXgLQdcoLn6UhIQh9O0b0DzdoBNoH8HZWuvPwmCPIAjdpbwcPv10j+NfvRqcTrBYzF3/734HRx4JRxwBnvBMuFjuSSkxNyenpWxQQgJT0tJ4oayMG/LyojM0ZN8TGmoeMZQ0Kon43HjcSSYw0lUhaGzcSUXFfxk69NqwjxZqpkMh0Fq7lVK3A5PDYI8gCJ1l+/Y9Tv+TT+C770x5fDwccghcdZVx/FOmQHr448/NaK1ZtmsXU9PTGZaY2GrdGdnZXLF5M5vq61ulm4gWmlsETaVN1P9YT/zAeGwpNuJybZBWCXS9j6C4eBngjlhYCAIPDf1XKXU68KLu7hgpQRC6jtbw44/G4TfH+LdtM+tSU00OoLlzjeM/+GDwcbiR5KuaGjba7VzjJ4nk6R4heKGsjLHJwZmcFUya+wh0k6bm6xqSxxgbbQPtYDOtBYejuNP71VpTXLyU9PQjSE4ObvK6zhCoEFwBJAMupVQDZqKY1loHnm9WEISuoTUUFMATT5gkb55EbfTvbxz+5Zeb1wMP7NaTwELNsuJiEi0WzvCTRHJoYiKHpKbyfGkpf4rCbMPNo4YA6r+vJ/1I07Ky5O4ZRNmV0FB19ZfY7T8ydOhV3TeyGwQqBOnAWcA+Wuu/KqWGArkdbCMIQnfYssVM7nriCdMKSEiAk06C6dON4x81KijpHsKBw+3m6ZISTs7MJN3m3+2ckZ3N1T//zE/19X7XR5LmFkEzyaNNi0BlVUIjWHW/LgmBmTuQTHb2mUGxs6sEKgT3A27M8wf+iklA9wJwcIjsEoTeSUWFuet/4gkzrBPMiJ4rr4QzzoCMjMja10Xeqqig3JNSoi1O9wjB0yUlYbQsMLxbBGBGDAGQvhtKIN4+Foftm87t02WnpORpsrNPx2ZLDZapXSJQIThUaz1JKbUGQGu9WykV2nFmgtBbaGiA1183zv/11804/3HjzMSuuXMhCkMlnWXZrl1kx8Xx63792qwzPCmJSSkprK6tDaNlgdFWi8Ddx4wYsu0ehT35M9zuRiyWhID2WVa2ApermgEDfhtcY7tAoELQpMy4Jg2glMrGtBAiRlNTEzt27KChoSGSZkSExMREBg8eTJwna6PQA3G7TUfvE0/Ac89BVZUZynnZZSbD54QJPSbs0xGVTU28WlbGRQMHdvjYyTOys6NeCJRNteQZcrpLwa1QpTkwCFyuuoCFwMwdGEZGxtSQ2NwZAhWCe4GXgP5KqZuAM4AbQmZVAOzYsYPU1FTy8vJaJqb0BrTWlJeXs2PHDvYZMsSMDY/SZ7oKfvj+e+P8ly+HX36BPn3gtNPgnHPMIx+juLO3qzxXWkqj1u2GhZo5PTub67ZsCYNVncNV70LFKyzxFuIHxmOJM/85h6ME6jJwlxthcLnqiItru9XTTEPDdnbvfpdhw27ETNWKLAEJgdZ6uVJqFXAMZsTQKVrrDSG1rAMaGhp6nQgAKKXIzMyktLQULrwQli41QhAX1/4SH99xnUjXt1pj5i64FUVF8PTTRgBWrzbnefzxJvQza5YRgxhm2a5djElKIj+14zj46ORkxvfpw7q6ug7rhhO33Y0lyUJ8dnzL0FGApqYSLPZ+uEpN69zlCszuXbseBzQDBpwXCnM7TaAtArTWPwA/hNCWTtPbRKCZlvM+9VTIyzMx5Y4Wh6P157q6zm0TrukjwRCaaBC9ujp46SXj/N9914SC8vPhnntg9mwI4O44Fthqt/NJVRV/90op0RHn5ORw9c8/h9iyzuGud2NNsrLv0n2x9dvjNh2OXdicWTTtMmVud8dCsGfuwFSSkqIjS0/AQiBEISefbJZw4HJ1TXBCvU1jI9TWBr6NK3jPlw2IvDy47jo46yzYd9/wHjsKeMKTUuIsz+MoA2HhkCGcnp3NiC+/DJVZncZld2FJtpB+eOuZ2U1NJdjYl/pi40oDaRFUV3+O3b6JoUOvD4mtXUGEoBsopbjiiiu44447ALj99tupra1l0aJFAe+jurqasWPHcuqpp3LfffcBsGrVKubNm4fdbmfGjBncc889kW/9WK1miaKZql1C6/AIFMCxx5qZvpH+7SJEc0qJX6Wnk5eUFPB2FqUYEOLkd53FXW9CQ744HCWk2I6EGtNB7HJ1PAeiqOhRLJY+ZGefEXQ7u4oIQTdISEjgxRdf5NprryUrK6tL+7jxxhuZOrX1qIH58+ezePFiDjvsMGbMmMFbb73F9OnTg2GyoJQJ7USZo4lFCmpq+Mlu56ouPN3MGmXi6ba7sSa37sh3uRpwuaqJTxoADeYGqaPQkMtVR2nps/Tv/xtstpSQ2dtZYkIIFiyAtWuDu88JE+Duu9uvY7PZuOiii7jrrru46aabOn2MVatWsWvXLk444QQKCgoAKCoqorq6msmTTY6/c889lxUrVogQCD2OZbt2kaCU35QSHWGLMiFw1bv2ahE0NZmJbwmpOWA3LZ6OQkOlpS/ictVExdwBbyI/bqmHc+mll7J8+XKqqqpalS9fvpwJEybstZxxhmkOut1uFi5cyG233dZqu507dzJ48OCWz4MHD2bnzp2hPxFBCCJNnpQSM7OyyOjCfJdoc0z+WgQOhxGCpL4DW1oEHQlBcfFSEhOHk55+RGgM7SIx0SLo6M49lKSlpXHuuedy7733kuQVBz3rrLM466yz2tzu3//+NzNmzGCIT7PZX3LXiPcPCEInebuigtKmpoDmDvgj2q755uGj3jS3CJL650KDuRFsTwgaGrZRWfk+eXl/iYq5A97EhBBEmgULFjBp0iR++9s9zb3ly5fvdbcPMHLkSJ5//nm++OILPvnkE/79739TW1uLw+EgJSWFP/7xj+zYsaOl/o4dOxg4cGBYzkMQgsWyXbvItNk4oZ2UEj0JV70ZNeSNw2FGRCWmD0SpJjTt9xEUFz8GEDVzB7wRIQgC/fr148wzz2TJkiWcf755qmdHLYLly5e3vF+6dCkFBQXccsstAKSmprJy5UoOPfRQHn/8cS677LLQnoAgBJEqp5OXy8r4XW4u8TEy691tN/MIvGnpI0jIISGnhAZnfJstAq3dFBcvJSPjaBITh4Xc3s4SG79SFLBw4ULKysqCsq8HHniA3/3ud4wcOZIRI0ZIR7HQo3i+OaVENx+DOSElhftGjQqSVd3Df4ugBIslGau1D/ED4lGOpDaFoKrqUxoatkRdJ3Ez0iLoBrVeybFycnKo72Ie9Xnz5jFv3ryWz/n5+axfv7675glCRFhWXMyopCQOCSClRHusyc8PkkXdp60+gvh4M1EuPjceGhLbFILi4kexWlPJzj415LZ2hZC1CJRSQ5RSHyilNiilvlNK/dFn/ZVKKa2U6toAfEEQoo5tDQ18VFXFOTk5Udfh21W0S6Md2s+ooV3ExZnO8ITcBHR9ot8+AqezlpKS58jOPhOrNTrzSoUyNOQEFmqtxwKHAZcqpcaBEQngOOCXEB5fEIQws9yTUuLsGMql5LKbtCS+LYLGxp17WgQD4qEuAadj7xTapaXP43bXkZsbnWEhCKEQaK2LtNarPe9rgA3AIM/qu4Cr8TzfQBCEno9Lax4vLuaI9HT26URKiWjHXW+eReDdImho2E59/fekpU0B9oSGnA17C0Fx8VKSkka21I1GwtJZrJTKAyYCXyqlTgZ2aq3bfa6bUuoipVSBUqqgtLQ0DFYKgtAd7ty+nR/tdi6NseHOzQ+l8W4RlJe/AkBW1imARwjsSXu1COz2n6mq+ogBA+ZFdags5EKglErBPN94ASZcdD3w546201ov1lrna63zs7swRV0QhPCxrraWG7Zs4dSsLGZ3ItNoT6D5ecXeo4bKyl4mKWk0ffqYjLLxAzydxc7WfQRm7oAiJ+fcsNnbFUIqBEqpOIwILNdavwiMAPYBvlFKbQUGA6uVUt0bZyYIQsRwuN2cs2EDGTYbD40eHdV3vl2huUXQPI+gqamSysoPWloDsCc05PbKPmrmDjxG377HkpjY+cR74SSUo4YUsATYoLW+E0BrvU5r3V9rnae1zgN2AJO01sWhsiOUKKVYuHBhy+fbb7+9UymorVZrSw6ik72eK7BlyxYOPfRQRo0axezZs3E4HME0WxCCyqKtW/mmro7/jBlDdgxmdW0JDXlaBBUVb6K1s7UQZMeTkJOBttlbyiorP6SxcVvUzh3wJpQtgsOBc4CjlVJrPcuMEB4v7DSnoe7qRLKkpCTWrl3L2rVreeWVV1rKr7nmGi6//HI2btxI3759WbJkSbBMFoSg8nlVFbf+8gsXDBjAzC6mYo92WkJDnj6CsrIVxMXlkJZ2aEsdZVX0P3kI2ranRVBcvBSrNa2VYEQrIZtQprX+FPN84/bq5AXjWAveWsDa4uDmoZ4wYAJ3n9B+NrvupqH2h9aa999/nyeffBKA8847j0WLFjF//vyg7F8QgkWt08m5GzYwNDGRO0eOjLQ5IaMlNJRsxe1upKLiDfr3/5+9EsdZLH1wuxvQ2uV57sDz5OScg9Ua/SOoJMVEN+lqGmqAhoYG8vPzOeyww1ixYgUA5eXlZGRkYLMZjZY01EK0ctXPP/NzQwOP7bsvabbYTVLg3SLYvfsDXK5asrJm7VWvebKYy1VPaelzuN32HhEWghhJMdHRnXso6WoaaoBffvmFgQMH8vPPP3P00Uczfvx40tLS9qoXa51vsYZLa8qbmtjlcFDieW1eypqasCpFH6vVLBZLy/tkr/e+65rXR+tv/2Z5OQ8WFnLlkCH8KiMj0uaEFO8WQVnZCiyWPmRkHLNXvT1CUOeZOzCmVfgomokJIYg0XUlDDbSklx4+fDhHHXUUa9as4fTTT6eyshKn04nNZpM01BHC4XZT6uPUWzn5ZsfvcFDa1ITbzz7ilCI7Lg6X1tS53dS5XJ2eQdlKLDoQDn+fk9tZZ+tiZtDypiYu+PFH9u/Th7/l5XVpHz2J5gllKhHKd75Cv34nYLXu/ezuZiGoq/uGqqpPGT78lqgVcl9ECIJAV9JQ7969m+TkZBISEigrK+Ozzz7j6quvRinFtGnTeP7555kzZw6PPfYYs2bt3QwVOk+9y9WhU29+v9vp9LuPZIuFnPh4cuLjGZ6YyGFpaeZzXFxLeX/P+wybrZUj0FrT4BGEZmFoWdxu6r3e+67z/byzsXGv9U1+HmrUHvFttFQ6+vx6eTllTU28MX48iVZrxwfqwdi32tn97m4A6vUqHI6iNjt/LRYjBDt33g9YyMk5O1xmdhsRgiCxcOFC7rvvvoDrb9iwgYsvvhiLxYLb7eZPf/oT48aNA+DWW29lzpw53HDDDUycOJELLrggVGb3aLTWVDmdezn1Eh8H3+z4a10uv/vJsNnIiYujf3w8+/fpwzEZGcah+zj4nPh4+nTD8SmlSLJaSbJaCcX4mqZ2RKb5fX0AIlPe1MQvPuvt7tZtntuGD2dCN7OLRivapSl/s5zCBwqpeLMCFPSf25+KumWAlczME/1u19wiKC9/jX79fk1CwiC/9aIREYJu0J001FOmTGHdunV+1w0fPpyvvvqq2/b1RNxe8XZ/Tr3E532jv0d7AlnNDjwujkPbuWvvHx9PQow8PCXOYiHDYiEUEXu31qbF4najtWZAQkIIjhJZGosbKV5STOHiQhp/aSQ+N55hNwwj98JcEock8tVXL5ORMZW4uL5+t9+TWVT3mE7iZkQIhJDT5HZT0oFTb17airfblGpx3jnx8eyXnNzmXXumzdbl+LfgH4tSpNhspETakCCjtabyw0oKHyik7KUytFOTcUwGI+8cSebJmVjizHVUX/8T9fUbGDiw7WHczUJgs2WQmXlym/WiERECoUvYm+Pt7Tj15pBNRRvx9qTmeHtcHMMSEzkkLa3Fqfs6+L4+8XZB6A5Nu5vY9fguCh8spP6Hemx9bQy6bBADLx5I8pjkveqXlb0MQFZW2w6+uY+gf/+5fjuToxkRAgEwd0bVHufu69R9h0Tuaifenma1tjjvccnJHOWJt/sLy6RYreLchbBS/XU1hQ8UUvJ0CW67m9RDU9l36b5kn5m91zOJvSkrW0FKysR2nzeclDSSwYMXMHjwglCYHlJECAQA5v3wA497HirijQIy4+JaOlMPbueuvX9cXMyPIhF6JrXra9l02SYqP6zE0sdCzjk5DLxkIKkTO+7wdjh2UV39BXl5i9qtZ7HYGDnyriBZHF5ECAQAzsjOZnyfPnvdtWfHxUm8XeixOKudbF20lR337sCWbmPk3SMZMG8AtvTAXV9Z2auA9jubOFYQIRAAmJmVxcxIGyEIQUJrTclTJWxeuBnHLge5F+Yy/ObhxGXGdXpfZWUrSEzMo0+fA0JgaXQgt3rdoLtpqH/55ReOP/54xo4dy7hx49i6dSsgaagFoTvUrq9l7bS1bDhrAwmDE5j05STGPDSmSyLgdNaye/e7ZGbOiun+LBGCbtDdNNTnnnsuV111FRs2bOCrr76iv+fJTpKGWhA6j7PayaaFmyiYUEDdujpGPzSaSSsnkXbw3vm7AmX37rfRurFHpJLuDrERGlqwANYGNw01EybA3aFLQ/3999/jdDo57rjjAEhJMSO0JQ21IHSOljDQlZtxFHcvDORLWdnL2Gz9SE8/IgiWRi+xIQQR5NJLL+WAAw7g6quvblXeUdK5n376iYyMDE477TS2bNnCscceyy233MLu3bslDbUgBEjdd3X8dOlPVH1URWp+Kvuv2J+0Q7reAvDG7W6ivPw1MjNnYrHEtquMjbPr4M49lHQ1DbXT6eSTTz5hzZo1DB06lNmzZ7N06dJWj6xsJpZjk4LQFZw1Trb+ZSs779mJNc3K6IdGk3tBLsoavP9KVdWnOJ27Yz4sBLEiBBGmK2moBw8ezMSJExk+fDgAp5xyCitXruT888+XNNSC0AbapSleVsyW67fgKHKQ+7tc9rl5H+Kzgv+sZPPsgUT69Ts+6PuONkQIgkBX0lAffPDB7N69m9LSUrKzs3n//ffJz8+XNNSC4AetNRVvVvDzn36mbl0dqQensv+L+5N2aHDCQP6OV1b2Mn37HueVTC52kVFDQWLhwoWdGj1ktVq5/fbbOeaYYxg/fjxaay688ELApKG+8847GTlyJOXl5ZKGWujVVH9dzTdHf8O6E9fhtrsZ9+w4Jn05KWQiAFBZ+QGNjdvIzj6j48oxgLQIukF30lADHHfccXz77bd7lffmNNSC0Ix9s52fr/uZ0mdLicuOY9R9o8i9MBdLfOjvX3fu/BdxcVlkZ58Z8mNFAyIEgiBEFY4SB9v+to3CBwtR8Yphfx7GkCuHYEsNj7tqaNhGWdkrDB16TY/LItpVRAgEQYgKXHUutt+5ne3/3I7L7mLghQMZ9n/DSBgQ3ofg7Nz5AEC7zx6INUQIBEGIKG6nm+IlxWxdtBVHsYOs07IYfvNwv88FCDUul52ioofJyjqFxMQhYT9+pBAhEAQhIrib3JS9VMbW/9tK/Q/1pB+Rzn4v7kf65PSI2VRS8hROZwWDBl0WMRsiQciEQCk1BHgcGAC4gcVa63uUUn8DZnnKSoB5WuvCUNkhCEJ00bCtgcKHCyleUoyj2EHy2GT2f3l/MmdmRnTypNaanTv/RZ8++5ORMTVidkSCULYInMBCrfVqpVQqsEop9Q5wm9b6RgCl1B+APwOXhNAOQRAijNvppuKNCgofKqTizQpQkDkjk9yLc8mcnhnUGcFdpbr6c2pr1zJ69IO9bjZ/yMZhaa2LtNarPe9rgA3AIK11tVe1PoAOlQ2hpjtpqD/44AMmTJjQsiQmJrJixQpA0lALsUPjzka2/mUrX+7zJetnrad2TS3DbhjGYVsOY/yr48k6KSsqRABgx45/YbNlkJNzdqRNCTthmVCmlMoDJgJfej7fpJTaDpyFaRH42+YipVSBUqqgtLQ0HGZ2mu6koZ42bRpr165l7dq1vP/++yQnJ3P88WYqu6ShFnoy2qUpf7Ocdaes44thX7B10VaS90tmvxf347Bth7HPX/chcWh0DctsbCykrOwFBgw4v1fMJPYl5J3FSqkU4AVgQXNrQGt9PXC9Uupa4PfA//lup7VeDCwGyM/Pb7fVsGDjRtZ6Te4KBhNSUrh71Kh263QnDbU3zz//PNOnTyc5OVnSUAs9lsbiRoofKabo4SIatjYQ1z+OoVcNJffCXJKGJ3W8gwhSWPgQWrsYNOh/I21KRAipECil4jAisFxr/aKfKk8Cr+NHCHoKXU1D7c3TTz/NFVdcAUB5ebmkoRZ6DNqtqfygksIHCylbUYZ2ajKOzmD4rcPJOiUrLLOAu4vb7aCw8CH69ZtBUtKISJsTEUI5akgBS4ANWus7vcpHaa03ej6eDPzQ3WN1dOceSrqahrqZoqIi1q1bx69//WvAjFzwpbd1XAnRj6PUQfHSYooWF2HfZMfWz8agPw5i4EUDSR4d/vH/3aG09DmamnYxeHDvGjLqTShbBIcD5wDrlFLNjw+7DrhAKTUGM3x0GzEwYqgraaibefbZZzn11FOJizNPU8rKypI01EJUorWm6uMqCh8qpPSFUrRDk35EOnmL8sg6PQtrojXSJnaJnTvvIylpFH37HhdpUyJGyIRAa/0p4O9W9o1QHTNSdCUNdTNPPfUU//jHP1o+SxpqIdpoqmii+PFiih4qov6HeqzpVgZePJCBFw+kz349u2O1urqA6uqVjBx5D0pFfxgrVPTeMw8ynU1DDbB161a2b9/O1KmtJ69IGmoh0mitqfqiig3nbeCLQV+w+fLNWNOtjHl0DFMKpzDq3lE9XgTAZBm1WlMYMGBepE2JKJJioht0Nw11Xl6e345gSUMtBAO3042ryoWz0rlnqXK2/uyvrMqJc7cTV7ULa4qVAfMGkHtxLqkTUiN9SkHF4SihpORpcnN/h80Wumcb9ARECAQhSnE3ujt22O04dXedu/0DKLCl27Bl2LCmW7Fl2EjcJxFbhinrs38f+s/uH7b0z+GmqOg/aO1g0KDfR9qUiBObv7AgRBitNe56d+BO3Oezq8qFu6F9R65sqpUTt2XYSM5NbnHurRbfsnQb1lQrytI7R6S53U4KCx+gb99j6dNnbKTNiTgiBILgB+3WuGpcgYdSfJy4s9KJdrafPUUlqL0cduKwxPYduFeZJdkiQ4u7SFnZChobdzBq1P2RNiUqECEQYhK3042ruoP4uB8n3xJTr3J2mAXL0sfSyjnH58STPCa53bvw5vfWdGuPHW4ZC+zceR+JiXlkZp4YaVOiAhECocfjbnTzzbHftHbota4Ot2sJqaT73I2348BbHHmaFUucDLrraWit2b37XaqqPmL48H+ilIgxiBAIMYCKV1gSLSSNSuowNt7i/FNtUZP1UggtbreDysqPKCt7mfLyV2hs3I7NlklurgzLbkaEoBsopbjiiiu44447AJOGura2NuBU1FdffTWvv/46breb4447jnvuuQelFKtWrWLevHnY7XZmzJjRUi74RynFge8cGGkzhCiiqamSioo3KCt7hYqKN3G5qrFYkujb93jy8haRmTmTuLh+kTYzapC2bTfoThrqzz//nM8++4xvv/2W9evX8/XXX/PRRx8BMH/+fBYvXszGjRvZuHEjb731VrBNF4SYw27fyo4d97J27TF8/nk2GzacRWXlh/Tvfyb77/8Khx9exvjxK8jNPZ/4+OxImxtVxESLYOOCjdSuDW4a6pQJKYy6O3RpqJVSNDQ04HA40FrT1NRETk4ORUVFVFdXM3nyZADOPfdcVqxYwfTp07t8LoIQi2jtpqZmFeXlr1BW9jJ1desASE4ex5AhV5KZOYu0tEN6deqIQIkJIYgkXU1DPXnyZKZNm0Zubi5aa37/+98zduxYCgoKGDx4cEt9SUMtCHtwuRqorPzAE+9/FYejELCQnn4EI0bcQWbmySQnj4y0mT2OmBCCju7cQ0lX01Bv2rSJDRs2sGPHDgCOO+44Pv7441b7aEb6B4TeTFNTOeXlr1NW9gq7d7+Ny1WLxdKHfv1OICtrFpmZM4iLy4y0mT2amBCCSNOVNNQvvfQShx12GCkpKQBMnz6dlStXcs4557SIAyBpqIVeSX39ppaQT1XVp4Cb+PiB5OScTWbmyWRkTMNqja7HXfZkRAiCQFfSUA8dOpSHH36Ya6+9Fq01H330EQsWLCA3N5fU1FRWrlzJoYceyuOPP85ll/XeB2YIvQOt3VRXf9ni/OvrNwDQp88BDBt2PZmZJ5OaOkni/SFChCBILFy4kPvuuy/g+meccQbvv/8+48ePRynFCSecwMyZMwF44IEHWoaPTp8+XTqKhZjE5bKze/e7nnj/azQ17UIpG+npUxk48BIyM08mKSkv0mb2CkQIukF30lBbrVYeeughv+vy8/NZv359t+0ThGjD4SihvPw1T7z/v7jddqzWNPr1m05W1iz69TuBuLi+kTaz1yFCIAhCUHE6a2lo2Izdvsmz7Hnf2LgD0CQkDCU39wIyM2eRkfErLJb4SJvdqxEhEASh0zQ1Vfh19Hb7ZpqadrWqGxfXn6SkEWRkHEVy8r706zeDlJQDZTRcFCFCIAjCXmitcTiKWzl677t8p7OyVf2EhMEkJY0kM/MkkpJGepYRJCWN6PVP/+oJiBAIQi9FaxeNjTvavLN3u737vKwkJuaRlDSC/v3nejn6kSQm7oPVuvf8F6HnIEIgCDGM266FBgoAAAusSURBVO2goWGrX0ff0LAFrR0tdZVKIClpOElJI+nb95iWO/vExBEkJg7DYomL4JkIoUSEQBB6OC5XPXb7zy1O3juE09DwC7DnkZdWawpJSSPp02d/srJOaXVnn5AwSMbp91JECLpBd9NQX3PNNbz++usA3HjjjcyePRuALVu2MGfOHCoqKpg0aRLLli0jPl5GVfRmmpoqvRx861eTb2cPNlsmSUkjSEubQk7OOa1i9nFx/aWTVtgLEYJu0JyG+tprryUrK6tT277++uusXr2atWvX0tjYyNSpU5k+fTppaWlcc801XH755cyZM4dLLrmEJUuWMH/+/BCdhRANmAy0pXuFb/Z0zpa3qh8fn0tS0kj69Tu+VQjHOHsZhy90jpgQgo0bF1Bbuzao+0xJmcCoUXe3W6c7aai///57pk6dis1mw2azceCBB/LWW2/xm9/8hvfff58nn3wSgPPOO49FixaJEMQAWrtpbCz0G8Kx2zfjctV41baQmDiUxMQRZGef0SqEk5Q0HKu1T8TOQ4g9QiYESqkhwOPAAEyQcrHW+h6l1G3ATMABbAZ+q7WubHtP0U1X01AfeOCB/OUvf+GKK66gvr6eDz74gHHjxlFeXk5GRgY2m/lpJA11z8LtdtLYuM1vCKeh4Wfc7oaWukrFkZi4D0lJI0lPP7JVCCcxMQ+LJSGCZyL0JkLZInACC7XWq5VSqcAqpdQ7wDvAtVprp1LqVuBa4JruHKijO/dQ0tU01Mcffzxff/01U6ZMITs7m8mTJ2Oz2dBa71VXYrrRhcvVQEPDljZmzm5Da2dLXYsliaSkkSQnjyYzc4ZXCGckiYlD5OHpQlQQMiHQWhcBRZ73NUqpDcAgrfV/vaqtBM4IlQ3hoitpqAGuv/56rr/+egDmzp3LqFGjyMrKorKyEqfTic1mkzTUEcLprPE7kcpu39ySJqEZqzWdpKSRpKbm07//HK8Qzgji43NFyIWoJyx9BEqpPGAi8KXPqvOBZ9rY5iLgIjApm6OZrqShdrlcVFZWkpmZybfffsu3337L8ccfj1KKadOm8fzzzzNnzhwee+wxZs2aFa5T6TVorXE6K/yGcOz2TTQ1lbSqb9IkjCQj4yifmbMjsdn6ibMXejQhFwKlVArwArBAa13tVX49Jny03N92WuvFwGKA/Pz8veMlUUZn01A3NTVx5JFHAia89MQTT7T0C9x6663MmTOHG264gYkTJ3LBBReExObeypo1v6Kubp2fNAlDSEoaQVbWya1COCZNQmqErBWE0BNSIVBKxWFEYLnW+kWv8vOAk4BjtL+geA+hO2moExMT+f777/2uGz58OF999VW37RP8k5y8L336jPfpnJU0CULvJZSjhhSwBNigtb7Tq/wETOfwVK114J5TEILEmDGLI22CIEQVoWwRHA6cA6xTSjUP8r8OuBdIAN7xxFVXaq0vCaEdgiAIQjuEctTQp4C/HrQ3gniMXtlJ14OjaYIgRCE9NsNUYmIi5eXlvc4paq0pLy8nMTEx0qYIghAj9NgUE4MHD2bHjh2UlpZG2pSwk5iYyODBgyNthiAIMUKPFYK4uDj22WefSJshCILQ4+mxoSFBEAQhOIgQCIIg9HJECARBEHo5qieMulFKlQLbgrCrLKAsCPuJduQ8Yws5z9gh3Oc4TGud3VGlHiEEwUIpVaC1zo+0HaFGzjO2kPOMHaL1HCU0JAiC0MsRIRAEQejl9DYh6C3ZxuQ8Yws5z9ghKs+xV/URCIIgCHvT21oEgiAIgg8iBIIgCL2cmBECpdQJSqkflVKblFJ/8rN+qFLqA6XUGqXUt0qpGX7W1yqlrgyf1Z2nO+eplDpAKfWFUuo7pdQ6pVTUpjDt6nkqpeKUUo95zm+DUura8FsfGAGc4zCl1Hue8/tQKTXYa915SqmNnuW88FreObp6nkqpCV7X67dKqdnhtz5wuvN7etanKaV2KqUCf+ZtsNBa9/gFsAKbgeFAPPANMM6nzmJgvuf9OGCrz/oXgOeAKyN9PqE4T0yCwW+BAz2fMwFrpM8pBOc5F3ja8z4Z2ArkRfqc/r+9swuxqori+O8vkxJ+myCZkUZGadqUMmUkhoWJlWZKoklpPVRCUJQZFTEKUZlhmk8hJBkW2JOg+RUVZmqROWo+qJnkaCFlZH6EX6uHvc3DdcTxnjn33OasH1zOuufsc+763z2XtffZc9YqU+NS4LFoDwcWR7sbsCduu0a7a96aMtB5PdA32j2BX4EueWtqaZ2J4/OAJcCCSvvfWmYEdcBuM9tjZieAT4AxJW0M6BTtzsCBswckPUj4Mf1YAV/TkEbnCGCrmTUAmNkfZna6Aj6XQxqdBrSXVANcDpwADmfv8iXTHI39gM+j/UXi+L3AGjM7ZGZ/AmuAkRXwuRzK1mlmO81sV7QPAAeBiz4lmxNp+hNJg4AewOoK+HoerSUQXAXsS7xvjPuS1AOTJTUSqqQ9AyCpPaGG8szs3UxN2ToJoyuTtErSZkkvZu1sCtLo/BQ4Shg9/gLMMbNDmXpbHs3R2ACMi/ZYoKOkK5p5brWQRud/SKojjLR/ysjPtJStU1Ib4B1geuZeXoDWEgiaqldZ+n+xE4FFZtYLGAUsjh0wE5hrZkcy9rElSKOzBrgTeCRux0q6O0tnU5BGZx1wmnAroQ/wvKRrs3S2TJqj8QVgmKQfgGHAfuBUM8+tFtLoDBeQrgQWA1PN7ExWjqYkjc5pwAoz20dO/G8L05TQCFydeN+LxK2fyBPE6bOZbYgLpd2B24DxkmYDXYAzkv4xs8ov2FycNDobga/M7HcASSuAWzk3Va0m0uicBKw0s5PAQUnrgcGEW3/VxEU1xtshDwFI6gCMM7O/4izorpJzv8zS2RSUrTO+7wQsB141s40V8bg80vTnEGCopGlAB6CtpCNmdt6Cc2bkvcjSQgs1NYQfeh/OLdT0L2nzGTAl2jcSOkklbeqp7sXisnUSFhU3ExZQa4C1wH15a8pA5wzgg2i3B3YAA/PWVKbG7kCbaL8OzIp2N+Dn2Kddo90tb00Z6GxLGKg8m7eOLHWWtJlCDovFuX+BLdgRo4CdhHuIr8R9s4DR0e4HrI8dtAUY0cQ1qjoQpNUJTCYsiG8HZuetJQudhBHV0qhzBzA9by0pNI4HdsU2C4F2iXMfB3bH19S8tWShM/69noz9e/ZVm7eeLPozcY1cAoGnmHAcxyk4rWWx2HEcxykTDwSO4zgFxwOB4zhOwfFA4DiOU3A8EDiO4xQcDwSOkyGSvonb3pIm5e2P4zSFBwLHSUlMcNckZnZHNHsTnnp2nKrDA4FTOCS1l7RcUoOk7ZImSNor6S1J38bXdbHtA5I2xboHayX1iPvrJb0vaTXwoaT+8bwtMd9839jubA6rNwlpBLZIek7SOkm1CZ/WSxpY4a/CcQAPBE4xGQkcMLObzewmYGXcf9jM6oAFwLtx39fA7WZ2CyG1cDJr6yBgjJlNAp4C5plZLSG3UWPJZ74ErDOzWjObS3iydAqApOsJT5lubWGdjtMsPBA4RWQbcE+cAQy1mOAM+DixHRLtXsAqSdsIaYL7J66zzMyOR3sD8LKkGcA1if0XYilwv6TLCOkiFqVS5Dgp8EDgFA4z20kYzW8D3pD02tlDyWZx+x4h98sA4EkgWd7zaOKaS4DRwHFC4Bh+ER+OEQrKjAEeJlSmcpxc8EDgFA5JPYFjZvYRMIeQjhtgQmK7IdqdCXnjAS5YGzjWPNhjZvOBZUDp/f6/gY4l+xYC84HvrDqL5zgFobXUI3CcS2EA8LakM4Tslk8TKpu1k7SJMECaGNvWA0sl7Qc2EtIMN8UEQsW0k8BvhKyTSbYCpyQ1EArqzDWz7yUdJqTNdpzc8OyjjgNI2gsMtli4p0Kf2ZNQUOYGq97KW04B8FtDjpMDkh4FNhHy1nsQcHLFZwSO4zgFx2cEjuM4BccDgeM4TsHxQOA4jlNwPBA4juMUHA8EjuM4BedfSKd9NtpRl44AAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -454,11 +314,9 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"post_sparsity\",axis=0)\n",
-    "    sparsity=df_sorted[\"post_sparsity\"]\n",
-    "    error=df_sorted[\"pre_error\"]\n",
-    "    plt.plot(sparsity,error,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
+    "for n,rank in enumerate(p.rank_vals):\n",
+    "    df=p.error_by_sparsity(rank)\n",
+    "    plt.plot(df.index,df.values,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
     "plt.legend()\n",
     "plt.xlabel(\"sparsity\")\n",
     "plt.ylabel(\"error\")\n",
@@ -471,27 +329,61 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[40 50 60 70 80 90]\n",
+      "sparsity\n",
+      "0.673253    26.638031\n",
+      "0.693565    26.958008\n",
+      "0.715966    26.952959\n",
+      "0.730479    26.990673\n",
+      "0.741846    27.039437\n",
+      "0.750373    27.107603\n",
+      "0.756349    27.200291\n",
+      "0.760755    27.313106\n",
+      "0.762338    27.471069\n",
+      "0.773470    27.645194\n",
+      "Name: error, dtype: float64\n",
+      "10\n",
+      "     fun: 1.6255461079946334e-05\n",
+      "   maxcv: 5.662382632564277e-20\n",
+      " message: 'Maximum number of function evaluations has been exceeded.'\n",
+      "    nfev: 1000\n",
+      "  status: 2\n",
+      " success: False\n",
+      "       x: array([ 2.66357476e+01,  3.20771539e-01, -5.66238263e-20,  3.35167266e-02,\n",
+      "        4.91532331e-02,  6.91959376e-02,  9.02470128e-02,  1.17152754e-01,\n",
+      "        1.54223354e-01,  1.75703846e-01])\n",
+      "y_approx [26.63574756 26.95651909 26.95651909 26.99003582 27.03918905 27.10838499\n",
+      " 27.198632   27.31578476 27.47000811 27.64571196]\n"
+     ]
+    }
+   ],
    "source": [
     "class monotone_invert:\n",
-    "    def __init__(self,knots,sign=\"increasing\"):\n",
-    "        knots=[(t,y) for (t,y) in knots if not numpy.isnan(y)]\n",
-    "        if len(knots)<2:\n",
-    "            return\n",
-    "        print(knots)\n",
-    "        self.tvals=numpy.array([t for t,_ in knots])\n",
-    "        self.yvals=numpy.array([y for _,y in knots])\n",
-    "        self.N=len(knots)\n",
+    "    def __init__(self,df,sign=\"increasing\"):\n",
+    "        self.df=df\n",
+    "        if len(self.df)<2:\n",
+    "            return None\n",
+    "        self.N=len(self.df)\n",
+    "        self.tvals=numpy.array(self.df.index)\n",
+    "        self.yvals=numpy.array(self.df.to_numpy())\n",
     "        self.L=numpy.tril(numpy.ones(shape=(self.N,self.N)),k=0)\n",
+    "        self.ctr=1\n",
+    "        x0=[numpy.mean(self.yvals)/self.N]*self.N\n",
+    "        \n",
     "        def objective(d):\n",
     "            error=self.yvals-self.L.dot(d)\n",
     "            return 0.5*error.dot(error)\n",
     "        \n",
-    "        def jacobian(d):\n",
+    "        def jacobian(self,d): #not used\n",
     "            error=self.yvals-self.L.dot(d)\n",
     "            return self.L.T.dot(error)\n",
     "        \n",
-    "        def hessian(d):\n",
+    "        def hessian(self,d): # not used\n",
     "            return self.L.T*dot(self.L)\n",
     "        \n",
     "        print(self.N)\n",
@@ -499,11 +391,11 @@
     "        if (sign==\"decreasing\"):\n",
     "            pm=-1\n",
     "        constraints={\"type\":\"ineq\",\"fun\":lambda x:pm*x}\n",
-    "        res=scipy.optimize.minimize(objective,self.yvals,method=\"COBYLA\",jac=jacobian,hessp=hessian,constraints=constraints)\n",
+    "        res=scipy.optimize.minimize(objective,x0=x0,method=\"COBYLA\",constraints=constraints)\n",
     "        print(res)\n",
     "        d_best=res.x\n",
     "        self.y_approx_vals=self.L.dot(d_best)\n",
-    "        print(self.y_approx_vals)\n",
+    "        print(\"y_approx\",self.y_approx_vals)\n",
     "        \n",
     "        self.linapprox=scipy.interpolate.interp1d(self.tvals,self.y_approx_vals,copy=True,bounds_error=True)\n",
     "        \n",
@@ -511,13 +403,16 @@
     "        if not (min(self.tvals)<=t<=max(self.tvals)):\n",
     "            return numpy.nan\n",
     "        return self.linapprox(t).item()\n",
-    "        \n",
-    "    def invert(self,yval):\n",
-    "        if not (min(self.y_approx_vals)<yval<max(self.y_approx_vals)):\n",
-    "            return numpy.nan\n",
+    "\n",
     "        \n",
     "        tval=scipy.optimize.brentq(lambda x:self.linapprox(x)-yval,min(self.tvals),max(self.tvals))\n",
-    "        return tval"
+    "        return tval\n",
+    "    \n",
+    "    \n",
+    "print(p.rank_vals)\n",
+    "t=p.error_by_sparsity(40)\n",
+    "print(t)\n",
+    "mi=monotone_invert(t)"
    ]
   },
   {
@@ -529,103 +424,83 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0.8388654329999999, 26.638031400000003), (0.851687515, 26.95800752), (0.865041935, 26.95295884), (0.872521978, 26.9906727), (0.8784872659999999, 27.03943659), (0.882699528, 27.107602500000002), (0.8852063379999999, 27.20029113), (0.887280473, 27.31310563), (0.887846563, 27.47106941), (0.8952729859999999, 27.64519409)]\n",
       "10\n",
-      "     fun: 3.9907231233324056e-05\n",
-      "   maxcv: 0.0\n",
+      "     fun: 1.6255461079946334e-05\n",
+      "   maxcv: 5.662382632564277e-20\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([2.66318755e+01, 3.25678725e-01, 2.73237182e-19, 3.06800653e-02,\n",
-      "       5.19658345e-02, 6.74240742e-02, 9.13026763e-02, 1.15527896e-01,\n",
-      "       1.53538201e-01, 1.78160839e-01])\n",
-      "[26.63187549 26.95755422 26.95755422 26.98823428 27.04020012 27.10762419\n",
-      " 27.19892687 27.31445476 27.46799296 27.6461538 ]\n",
-      "[(0.8480086590000001, 25.36197488), (0.866354897, 25.66748831), (0.883902203, 25.59166646), (0.894206458, 25.59756563), (0.8962998659999999, 25.72661958), (0.8997734470000001, 25.83122995), (0.902259747, 26.009027800000002), (0.905187923, 26.51357444), (0.9061295290000001, 26.131918), (0.910054027, 26.57639081)]\n",
+      "       x: array([ 2.66357476e+01,  3.20771539e-01, -5.66238263e-20,  3.35167266e-02,\n",
+      "        4.91532331e-02,  6.91959376e-02,  9.02470128e-02,  1.17152754e-01,\n",
+      "        1.54223354e-01,  1.75703846e-01])\n",
+      "y_approx [26.63574756 26.95651909 26.95651909 26.99003582 27.03918905 27.10838499\n",
+      " 27.198632   27.31578476 27.47000811 27.64571196]\n",
       "10\n",
-      "     fun: 0.0381960880797453\n",
-      "   maxcv: 8.076674749081945e-20\n",
-      " message: 'Maximum number of function evaluations has been exceeded.'\n",
-      "    nfev: 1000\n",
-      "  status: 2\n",
-      " success: False\n",
-      "       x: array([ 2.53605026e+01,  2.58639770e-01, -8.07667475e-20, -8.04712496e-20,\n",
-      "        1.06958723e-01,  1.05712436e-01,  1.76972516e-01,  3.14482456e-01,\n",
-      "       -7.75706452e-20,  2.52716362e-01])\n",
-      "[25.36050258 25.61914235 25.61914235 25.61914235 25.72610107 25.83181351\n",
-      " 26.00878602 26.32326848 26.32326848 26.57598484]\n",
-      "[(0.858227836, 24.40184251), (0.8936744459999999, 24.34322987), (0.901314398, 24.57246013), (0.908879227, 24.723011399999997), (0.912043176, 24.90329832), (0.9165858040000001, 25.1288706), (0.9182241640000001, 25.30703604), (0.923680153, 25.42553739), (0.924444265, 25.71551156), (0.9282573409999999, 25.89497942)]\n",
+      "     fun: 0.03819477316121524\n",
+      "   maxcv: 1.198445148951187e-19\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 933\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.53613775e+01,  2.57777488e-01, -1.19844515e-19, -9.31120426e-20,\n",
+      "        1.06878245e-01,  1.05609000e-01,  1.77478621e-01,  3.13538992e-01,\n",
+      "       -7.59952272e-20,  2.53789542e-01])\n",
+      "y_approx [25.36137745 25.61915494 25.61915494 25.61915494 25.72603318 25.83164218\n",
+      " 26.00912081 26.3226598  26.3226598  26.57644934]\n",
       "10\n",
-      "     fun: 0.0011194936676139564\n",
-      "   maxcv: 5.421010862427523e-20\n",
+      "     fun: 0.0009375981199570231\n",
+      "   maxcv: 0.0\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.43736750e+01, -5.42101086e-20,  1.93589363e-01,  1.53508625e-01,\n",
-      "        1.90583506e-01,  2.14610546e-01,  1.77058767e-01,  1.33020749e-01,\n",
-      "        2.62726480e-01,  1.98775127e-01])\n",
-      "[24.37367498 24.37367498 24.56726434 24.72077297 24.91135647 25.12596702\n",
-      " 25.30302579 25.43604654 25.69877302 25.89754814]\n",
-      "[(0.864803955, 23.53705497), (0.894401055, 23.75652443), (0.9127284999999999, 23.69114643), (0.915190938, 23.9887707), (0.919820815, 25.05158084), (0.9199216659999999, 24.72009516), (0.9212544559999999, 24.38145484), (0.92155345, 25.57379936), (0.921779327, 24.12994877), (0.922003501, 25.22024878)]\n",
+      "       x: array([2.43753658e+01, 5.42101086e-20, 1.89628414e-01, 1.57529453e-01,\n",
+      "       1.85263483e-01, 2.17361575e-01, 1.87570213e-01, 1.08768572e-01,\n",
+      "       2.92542210e-01, 1.80181789e-01])\n",
+      "y_approx [24.37536581 24.37536581 24.56499423 24.72252368 24.90778716 25.12514874\n",
+      " 25.31271895 25.42148752 25.71402973 25.89421152]\n",
       "10\n",
-      "     fun: 0.6345178126830088\n",
-      "   maxcv: 9.958200192064222e-20\n",
+      "     fun: 0.27413886552017336\n",
+      "   maxcv: 1.1225665900990498e-19\n",
       " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 838\n",
+      "    nfev: 940\n",
       "  status: 1\n",
       " success: True\n",
-      "       x: array([ 2.35360475e+01,  1.88507889e-01, -5.46425148e-23,  2.63328157e-01,\n",
-      "        7.30080483e-01, -1.47216331e-20, -9.51861366e-20,  1.34104994e-01,\n",
-      "       -9.95820019e-20,  3.68571615e-01])\n",
-      "[23.53604747 23.72455536 23.72455536 23.98788351 24.717964   24.717964\n",
-      " 24.717964   24.85206899 24.85206899 25.22064061]\n",
-      "[(0.8812751440000001, 22.63627827), (0.910435722, 22.78596345), (0.919387968, 23.08736296), (0.924051983, 23.29840336), (0.9287888209999999, 23.53549908), (0.9337104159999999, 23.70320873), (0.934204577, 24.81826615), (0.9346189340000001, 24.40188262), (0.935699287, 25.00547559), (0.936971005, 23.90587189)]\n",
+      "       x: array([ 2.35362838e+01,  1.87737809e-01, -1.00492293e-19,  2.64352718e-01,\n",
+      "        5.82321864e-01, -1.01957081e-19, -1.12256659e-19, -1.02201785e-19,\n",
+      "        8.26119953e-01, -9.51815297e-20])\n",
+      "y_approx [23.53628378 23.72402159 23.72402159 23.98837431 24.57069617 24.57069617\n",
+      " 24.57069617 24.57069617 25.39681613 25.39681613]\n",
       "10\n",
-      "     fun: 0.35779439724992584\n",
-      "   maxcv: 1.0373675707956813e-19\n",
+      "     fun: 0.3576867655188462\n",
+      "   maxcv: 1.8531210618781645e-19\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.26261217e+01,  1.75369742e-01,  2.74289707e-01,  2.23787808e-01,\n",
-      "        2.36184907e-01,  1.65598069e-01,  8.29639207e-01, -9.24425351e-20,\n",
-      "       -7.31015805e-20, -1.03736757e-19])\n",
-      "[22.62612173 22.80149147 23.07578118 23.29956899 23.53575389 23.70135196\n",
-      " 24.53099117 24.53099117 24.53099117 24.53099117]\n",
-      "[(0.885119069, 21.94604631), (0.917812982, 22.32535596), (0.929556328, 22.62522228), (0.93281993, 23.26554288), (0.935662702, 24.02740774), (0.93647063, 24.80141684), (0.9368171040000001, 24.50401453), (0.937286512, 23.58883198), (0.942963516, 24.84511375), (0.944176777, 25.35023203)]\n",
+      "       x: array([ 2.26285131e+01,  1.68756030e-01,  2.81782839e-01,  2.23825614e-01,\n",
+      "        2.31126762e-01,  1.70087422e-01,  8.28078758e-01,  7.84731625e-20,\n",
+      "       -1.85312106e-19,  8.19319247e-20])\n",
+      "y_approx [22.62851311 22.79726914 23.07905198 23.30287759 23.53400435 23.70409177\n",
+      " 24.53217053 24.53217053 24.53217053 24.53217053]\n",
       "10\n",
-      "     fun: 0.3994864023989464\n",
-      "   maxcv: 1.0167621502852552e-20\n",
+      "     fun: 0.4633227065485485\n",
+      "   maxcv: 1.293259367001177e-19\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.19377574e+01,  3.95531285e-01,  2.86071990e-01,  6.48382417e-01,\n",
-      "        7.58119324e-01,  2.73038471e-01, -1.01676215e-20,  6.58935032e-20,\n",
-      "        5.43938383e-01,  5.09026078e-01])\n",
-      "[21.9377574  22.33328868 22.61936067 23.26774309 24.02586241 24.29890088\n",
-      " 24.29890088 24.29890088 24.84283927 25.35186534]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\richa\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py:502: RuntimeWarning: Method COBYLA does not use gradient information (jac).\n",
-      "  RuntimeWarning)\n",
-      "C:\\Users\\richa\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py:513: RuntimeWarning: Method COBYLA does not use Hessian-vector product information (hessp).\n",
-      "  'information (hessp).' % method, RuntimeWarning)\n"
+      "       x: array([ 2.19370906e+01,  4.01216563e-01,  2.81569900e-01,  6.47781167e-01,\n",
+      "        7.59788026e-01,  2.70814044e-01, -1.17691865e-19, -1.20557309e-19,\n",
+      "        8.00494343e-01, -1.29325937e-19])\n",
+      "y_approx [21.93709064 22.3383072  22.6198771  23.26765827 24.02744629 24.29826034\n",
+      " 24.29826034 24.29826034 25.09875468 25.09875468]\n"
      ]
     }
    ],
    "source": [
-    "fdict={}\n",
-    "for rank,df in data_by_rank:\n",
-    "    df_sorted=df.sort_values(by=\"post_sparsity\",axis=0)\n",
-    "    knots=list(zip(df_sorted[\"post_sparsity\"],df_sorted[\"pre_error\"]))\n",
-    "    fdict[rank]=monotone_invert(knots)"
+    "fdict={rank:monotone_invert(p.error_by_sparsity(rank)) for rank in p.rank_vals}"
    ]
   },
   {
@@ -635,7 +510,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -646,11 +521,10 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"post_sparsity\",axis=0)\n",
-    "    knots=list(zip(df_sorted[\"post_sparsity\"],df_sorted[\"pre_error\"]))\n",
+    "for n,rank in enumerate(p.rank_vals):\n",
+    "    df=p.error_by_sparsity(rank)\n",
+    "    plt.plot(df.index,df.values,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
     "    temp=fdict[rank]\n",
-    "    plt.plot(temp.tvals,temp.yvals,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
     "    try:\n",
     "        plt.plot(temp.tvals,temp.y_approx_vals,label=\"N={:} increasing\".format(rank),linewidth=5,linestyle=\"-.\",color=colorsequence[n])\n",
     "    except Exception:\n",
@@ -672,12 +546,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.86   0.8775 0.895  0.9125 0.93  ]\n"
+      "[0.7        0.72555556 0.75111111 0.77666667 0.80222222 0.82777778\n",
+      " 0.85333333 0.87888889 0.90444444 0.93      ]\n"
      ]
     }
    ],
    "source": [
-    "sparsityvals=numpy.linspace(start=0.86,stop=0.93,num=5)\n",
+    "sparsityvals=numpy.linspace(start=0.70,stop=0.93,num=10)\n",
     "print(sparsityvals)"
    ]
   },
@@ -688,7 +563,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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uDYOCgkJmz57tFxER4XXlPPfdd99FHx8fDdCpU6fUmTNn1pw6darf0aNH3a9sdyQ0NDTpjz/+qJiZmanmz59fbfDgwecB+vTpc/lKd0PO15XuD61tT6uUcnitL7/8ssoDDzyQ5Opq6WxwcPx17ti1imXMg1IqEGgFbM+xuQsQp7U+6uCY0UqpXUqpXQkJCfm/WEo8aUsews14GYA6pmyWxMYRmGXbH5WZncmEPyew4eSG/J9fCCHETa958+aZu3fvPtysWbP0qVOn1n7uuedqAhgM//tYvPKBPH78+ICnnnoqPjIy8vDcuXNPZmZmXt3J29vbfOXvY8aMSfzpp5+OeXl5me+///6g1atXl8+rDuXLlzd36dIleenSpZVWr15dZeTIkYkA12t5CAgIuKal4cyZM+61atVyOOhi5cqVVYYMGXK1S6JOnTrGM2fOXD0+JiYmz+PtKfLgQSnlg6V7YqLWOjlH0SDyaHXQWi/QWrfVWrf19c1/OvpTKYp/Uq4deOqXnc3i2DiCMrNs9jeZTUzePNnuDA0hhBC3pujoaLfy5cubn3rqqcSJEyfG7d27txzAV199VQVg0aJFlVu1apUKcPnyZZeAgAAjwJIlSxzOTDh8+LB748aNM1955ZX4e+655+LevXu9cpZXrFgxOyUl5ZqBiWPGjDn/4osv+rdo0SL1yhiE67U83HXXXanR0dGeERER7hkZGWrlypVVBgwYcNFenfbt2+eRnJzs0qNHj6sDAB988MFLmzdvrpCQkOCSkJDgsnnz5goPPvjgJWfuX5EuT62UcsMSOHyrtV6ZY7sr0B8o9MEyAX6+/N5zAYt/m8pw1/VXt1czm/niXDxj/Hw56OFxzTHZOpuXt7xMhimDgUEDC7tKQggh7HA0mLE4hIWFeU2ZMqWOwWDA1dVVf/rppycHDRp0W2ZmpmrevHmw2WxW3333XRTA1KlTzw4aNOi2GjVqZLVt2zb11KlTHvbO+fXXX1f5/vvvq7q6umpfX1/jjBkzzuYsb9++fbqrq6tu1KhRyODBg89PmzYtvkuXLmne3t7Zw4cPz/dSyG5ubsycOfPUfffdF5Sdnc3gwYPPt23bNgNg4sSJtdq1a5f62GOPXQL48ssvq/br1y8xZ4tKjRo1sp9//vmzbdq0aQzwwgsvnL0SuOSXstf3URisYxq+BBK11hNzld0HTNFa35Wfc7Vt21bv2rXLqesv2XaCk2tm8qrrNxhydAWlKMX4Gr6EednMagHghXYvMDRkqFPXEkKI0kgpFaa1blvS9cht37590S1atCh1eQNq167dbNeuXeE1a9a0HShXRKKjo926du3a6Pjx4wddXJyaLVnk9u3bV61FixaB9sqKstuiMzAU6K6U2mt9PWAte5SiGCiZQ2jnetTv/RxPGp8lXf9vEKqP1syLS+D2NPszYd7b+R6f7fvM7oASIYQQorDMnTu3aseOHRu/9tprMaUtcLieImt5KEw30vJwxdLtp/hu1SoWuX+Ar/pfl04W8Fz1amz0Lmf3uJFNR/JM62ecHoEqhBClhbQ8iIIoqZaHUmFwhwCG9H+Ih7LeINJ8daYo7sDM+PPcn5Jq97hFBxfxzo53MGuz3XIhhBCirLrlgweA/7Tz59mBPRlonM627CZXt7sBMxIu0P9yit3jlkYsZfrf08k2OzWORAghhLillYngAWBAmzq88Z/OjDC9yPemO69udwGmnU/ksUuX7R7347EfmbJlCkazU1NghRBCiFtWkU7VLG0ebFUbF4Ni4nJXThmrM9ntB8ASQb2YmISXNvN5pYo2x62NXkt6djof3PUBHi52Z+gIIYQQZUaZaXm4ok+LWswZ1Jp5egATs54iS1tGuCrgmaRLPJ1od50NNp3exIQNE0g35ZmvRAghhLhGaUvJHRkZ6d6kSZPGwcHBIQ0aNGjy3nvv5X8lRqsyFzwAPNCsJp881ppfVReGZr3MRe19tWzUpWReuJBk97h/Yv9hzO9jSMmyP0ZCCCFE2XQzpeQOCAgw7tq1KyIiIuJwWFhY+Mcff+wXHR3tVGKsMtVtkdO9TfyY91gbnvpWMSBrOovd3iPAYMmhMTT5MuXMZl6vVgWda6rm7vjdPPHbE3x292dU9LDt4hBCCJFP0ysWeUpupl+SlNxcm5Lb09Pz6hoN6enpymx2flZhmWx5uKJnSA0+G9qG0y7+PJT1BnvMDa6WDUhJZUbCBVzsrINx6MIhhq8fzvl0maYshBA3o7Kckhvg2LFjbkFBQSH16tVr/vTTT58LDAwsXYmxSrtuwdVZ+HhbUlwr82jWK6zJbn+1rFdqGjPjz+NmJ4A4mnSU4euGcy7VYaAnhBCilCrLKbkBGjRoYIyMjDwcHh5+cOnSpdVOnz7tVE9EmQ8eAO4K8mXRsHYoN0/GGZ/mM1Ovq2U90tKZE5eAh51mnejkaELXhXL68unirK4QQogCKsspuXMKDAw0NmrUKP2PP/7Is665SfBgdUfDanwR2g5PNzdmmB7jFeNwsrVlvEPn9AzmxSVQzk4AEZMSQ+jaUKIuRRV3lYUQQtygspyS+/jx424pKSkKICEhwWXXrl0+TZo0yXDm/pXZAZP23H5bNZYMb8fwJTv5JutuzuhqfOI2G2+VSbuMTBaei2dMjepcdrk25opPj2f4uuEsuHsBjao0KqHaCyHETcbBYMbiUJZTcu/fv9/rxRdfrKOUQmvN+PHjz7Vv396pdQhu+cRYN2JXdCKhi3eSkmmiiYpmkfv7+CnL9M0j7m6M9qtOop0MaOXdyzO/53ya+zYvtroKIYQjkhjLOZKS+1plOjHWjWgbWIWvRranvIcrh3QgD2W+QbjZH4BGWUYWx8ZR3WT7bF3Ousyo30ax89zO4q6yEEKIm8zNnJJbggcHWgdU5psnOlDB05VYqvJw1jQ2Z1taFOobTSyJjaO20TaASDOlMfaPsWyN2VrcVRZCCFEAMTExB4qz1WH8+PEXzp07t3/EiBH2VyYsxSR4yEML/0p8+0RHKnq5kUI5RhqfY6mpGwD+pmyWxMYRmGU7wDUzO5MJf05gw8kNxV1lovYmELYuutivK4QQouyQ4OE6mtWpyNJRHahczg0TrrxseoJ3jI8C4JedzeLYOBpmZdkcZzKbmLx5Mr9G/VpsdT26M451Cw7y76oo9v5xqtiuK4QQomyR4CEfmtSqyLLRHanq7Q4o5mf3ZXzWBDK1G9XMZhbHxtM0M9PmuGydzZQtU1gRuaLI63h421l+++IQ2mwZALvth2Mc2hJT5NcVQghR9kjwkE/BfhVYNroj1Xws63L8Yu7E4KyXSdQ+VDSbWRgbT+sM22myGs30f6bzzeFviqxuZ48msfHrCMg1cWbT0iMc2S4rYAohhChcEjw4IahGeb4b3RHf8pYpvmG6EQ9lvUGU2Q8frZl3LoFO6fanyr67810W7l9YJPWqeVslGneuaVugYcOSwxzfHV8k1xVCCHF9pS0lN4CLi0ubK2Xdu3dvYO/4vEjw4KQG1S0BRI0KlgDipPajf9br7DA3opzWzIlLoGtqmt1jZ++Zzce7P7a7LnlBKIOi62PBNGxXw6ZMa/ht0SGiD5S6KdVCCHHLuJlScgN4eHiYr5T9+eefx5ytg6wweQNu8/Vh+ehODFr4L7GXMrhIeYZmTeF9t8/o6/IPs+LP87JvVdb5eNsc+/mBz0k3pfNCuxcwqMKL3QwGRY/Qxpiysjmx79pAwZytWffZQXqPb06d4EIJeoUQosCafdmsyFNyHxh2QFJyc21K7sIgLQ83KLCaN8tHd6J2JcvS5Zm484xxHHNN/XAD3km4wIOXU+we+234t7z+z+tkm7MLtU4uLgbufaIpASG2AUK2ycyv8w4Qe/xSoV5TCCFuRmU9JXdWVpahadOmjVu0aBH89ddfV3L2/knwUAABVcvx3eiO+FexBBAaAx+YHuFF4yi0NvD6+UQGX7ps99iVR1cyZesUjGanUqhfl4ubgfvGNKNWQ9tnwZSZzS9z9pJwyn6dhBCirCjrKbmPHTu2/+DBg+HLli2Leumll/wPHTpkN1+HIxI8FJB/lXJ8N7oTdauWu7pteXY3hhtfIFV78VJiEiMv2v+2v/bEWiZvmkxWtu06EQXh5u5Cr3HNqVGvgk1ZVkY2qz/ey4UY+60iQghRFpT1lNyBgYFGgJCQkKyOHTte3rFjRzn7R9snwUMhqF3Ji+9Gd6Retf+Ncdhibs7ArGmc1VWZmHSJCYl2s6Wy8fRGJvw5gXSTUwnNrsvd05Xe41tQtY6PTVlGqpHVH+/lYpz9gZ1CCHGrK8spuRMSElzS09MVQGxsrOuuXbt8mjdv7tSHkAyYLCQ1K1oCiEEL/yUqwfIeHdEBPJT5Bl+4v8/oS9F4ac17VSvbHPv32b8Z8/sYPunxCT7uth/2N8rT242+T7dk1azdJJ27NlBIS87ip4/28NBzralQ1cvBGYQQoug4GsxYHMpySu69e/d6jhs3ru6VlNwTJ04816ZNG9uFivIgKbkLWfzlDB5buJ2j8f/rFihHBnPc5tDDZQ/fl/fmzapV0ErZHNusWjPm9ZxHRY+KhVqnlKRMfpwZRvJ522ejgq8X/Se3xruSU91dQoibgKTkdo6k5L6WpOQuRtXLe7JsdEca1fhfV1canow2TuJL0908fDmVtxIu4GInaDtw/gAj1o/gfHrh/p/yqexBv4mt8KlsGyAkJ6Tz00d7SL9cuOMuhBBC5E1SctuhlPJXSm1USoUrpQ4ppZ7JUTZBKXXEuv29oqpDSanm48Gy0R1pXPN/AxazcWGaKZQ3jUPolZLOB/HncbUTQEQmRTJ83XDOpRbustIVqnnRb2IrvCrYzuZJOpfG6tl7yUgt3JkfQghxM5GU3PlXlC0PJmCy1rox0BEYp5QKUUp1A/oBzbXWTYAPirAOJaaKtztLn+hAk1o5ZzwoFmU/wFjjRDqnZjM7LgEPs9nm2OjkaELXhXL68ulCrVOlGuXo90xLPLxth7qcP53CL3P3kZVRbP9vhBBC3KSKLHjQWsdqrXdb/34ZCAdqA2OBd7TWmdayWzbxQmVvd5Y+0ZHmda4dw7De3I5Hs16hcZo78+IS8LITQMSkxBC6LpSoS1GFWqeqtX3o+3RL3D1tm8jiTiSz5tP9mLIKd/EqIYQQt5ZiGfOglAoEWgHbgSCgi1Jqu1Jqs1KqXXHUoaRULOfG1yM70NL/2kWb9ukGPJj1BlXSq7LwXDzls20DiPi0eIavDeVI4pFCrVP1uhXoPb4Fru62b39M5EXWfnaAbKNtfYQQQggohuBBKeUDrAAmaq2TsUwPrYylK+N54P+Usp16oJQarZTapZTalZCQUNTVLFIVvdz4emR72tS9dprmGV2dhzKnk57egC/OxVE52/Ybf2JmEiPWDedAwoFCrVPNBpV44KnmuLjaPgKnDiXy26JDmO0ENEIIIUSRBg9KKTcsgcO3WuuV1s1ngJXaYgdgBqrlPlZrvUBr3VZr3dbX17coq1ksynu68eWI9rQLvDaASMaHoVkvEZ7ekSWxcfiabMccJBsvM+q3kew6V7jTVf2Dq3Dfk00xGGynjUbtTeCPJeGYzaV/Kq8QQpRmpTEl95gxY+o0aNCgSf369ZuEhob6m+10n+elKGdbKGAREK61npWjaBXQ3bpPEOAOlLr5vkXBx8OVJcPb07H+tc+OEVeeNY7h5/S+fBkbTy2jbQCRakpn7O9Psi1mW6HWKbBZNe4e2QQ7y05wdGccm5ceKfQU4kIIcau5mVJy//777947duzwiYiIOBQZGXlo79693mvWrMlzKe3cinKFyc7AUOCAUmqvddvLwBfAF0qpg0AWMEyXoU8nbw9XFoe254mvdrLt2IUcJYoPTQM5lV6dxee+YLRfVU66uV1zbIY5iwkbxvF+15n0COhRaHVq0KY6JmNjNiwJtyk7vPUsru4G7ni4IXZ6l4QQ4oaFBzcu8pTcjSPCJSU316bkVkqRmZmpMjIylNZamUwmlVduDHuKcrbFVq210lo311q3tL7WaK2ztNZDtNZNtdattdZ/FlUdSisvdxcWDWtHl4Y2vTWsyL6TyWnPsyA+lQZZtgs3GXU2kzc+y5qoXwu1TsEda3LX4EZbsNKqAAAgAElEQVR2y/b/eYbtPxXurA8hhChJZTkld8+ePVM7d+58uWbNmi1q1arVvFu3bsmtW7d2anlqWWGyhHi6ubDw8bbcFWQ7nuMfcxOGXn6VuUkuhGRm2pRno3lpy0usPPJ9odap6Z216Tywgd2ysHUn2bU2ulCvJ4QQJaUsp+Q+ePCgR2RkpOeZM2f2nzlzZv+WLVvKr1271qnEShI8lCBPNxcWPN6GHsHVbcqO69r0S3yFd1Or0irDNiDUwLR/3+Db/YsKtU4tewbQvk89u2Xbf4pi34bCXbhKCCFKQllOyb18+fJK7dq1S61YsaK5YsWK5p49e17atm2bt6Pj7ZHgoYR5uLowb0gb7g6pYVN2norcF/c8U7ND6JhuvwvsnT0f8fnOmYVap7YPBNLqngC7ZVu/P8rhrWftlgkhxM2iLKfkDggIyNq2bVt5o9FIZmam2rZtW/mQkBCnui0kJXcp4O5q4NPHWvP0sj2sPXjteJcMPLj/1BN83/BXPFN/Z5N3OZvjPz68hLT0JCZ0ebNQBjUqpej00G2Ysswc2HTGpnzjtxG4uBlo1MHu7CAhhMgXR4MZi0NZTsk9fPjwpI0bN1Zo1KhRE6UU3bp1uzR48OBLztw/ScldihizzUxcvpdf98faLV/SdDe/Ji9hnZ0AAmCIXxdeuOeTQpsVoc2aP7+JIOJv2/oog+K+UU2p3+rmX4NDiFuVpOR2jqTkvpak5L5JuLkY+PiRlvRrWctueejB1txZbQr9Uu23Ln1zbgtvrnyEbHPh5KZQBkW3IcE0aGs7JkObNes/P8jJgxfsHCmEEOJ6JCW3KDSuLgZm/acl/VvVtls+YXdNQmq+z6PptgGCV4am44cH+ObZLmQZneq+cshgUPQcHkJgc9tppeZszdrPDhBz5KbLJiuEEDYkJXf+SfBQCrkYFO8/3IKH29SxW/7ydjd8a33K8Kwci0hpzVO/mvE/D+3XJ7H+4fZcPlc4azO4uBi4d1QT/BtXtinLNpr55dP9nItyqrtMCCHETUyCh1LKxaB4d0BzBrX3t1v+9t+pKL/PGIflA73PDk2HyP+NX2kQYWTPQ72I2rS0UOrj6ubC/WObU7NBRZsyU2Y2P8/ZR8Kpy4VyLSGEEKWbBA+lmMGgeOvBZgzpaH/a5Mfb4rlU9ROmXajF4I22SU18kyB5wpv8M2tModTHzd2F3uNaUD2wgk1ZVrqJ1bP3kng21c6RQgghbiUSPJRyBoPizX5NCb090G75vK2niKj3Lkl32B9k6WGESgs283toF0zpaQWuj7uXK30mtKBqbdvFyDJSjPz08R4uxhf8OkIIIUovCR5uAkoppvUJYURn+ys/fr49hg19ZnN+dGfSHaxuXuff82zp1Z7EIwWfVu3p7UbfZ1pSqYbtlNG0S1n89NEeLicWzoBNIYS42ZVUSu6jR4+6d+jQIahx48YhQUFBIcuXL7/a7zxlyhS/gICApoGBgU1XrFhh25x8HRI83CSUUrzauzFP3lnfbvmSv6NZX/8Zyn30AuccrH/mdzabqEeHcGT5RwWuT7kK7vSb2IoK1WyywJKSmMlPH+4h9ZJtXg4hhLgVlcaU3K+99lrN/v37J4WHhx9etmxZ1KRJkwIAwsLCPFeuXFnlyJEjh9atWxc5ceLEAJPJuUkmssLkTUQpxUv3B+NiUHy66bhN+Tf/niK7fRue/34Vf41/mEaHbR9m73QwTfuMf3Zto+O7y1GGG48ffSp70G9iK36cuZuUpGsDhUsJ6fz00V4emtwKLx+Hyd6EEGXYJ2P+LPKU3OPmdy+zKbmVUiQnJ7sAJCUluVSvXt0I8MMPP1Tq379/opeXlw4ODs6qW7du5qZNm7x79uyZ70Fr0vJwk1FK8fy9jXi6u/3sl8t2nGbGtgweWL6LI31rY7LzDhuASj8f5K9+7clMcJj+PV8qVPOi7zMt8SrvZlOWFJvK6o/3kpnmVJp4IYQocmUhJfeMGTPOfv/991Vq1KjRvH///g1nz559CiAmJsbd39//6vG1atXKOn36tFPf8iR4uAkppZh0TyOe7Rlkt/z7sDO8tPIwfd75neSX+5LoIK9b9aOp7O3VnbitawpUn8p+3vSb2AqPcrYNWedPp/DL3H1kZRTbuitCCHFdZSEl9+LFi6sMGjToQlxc3P6VK1ceDQ0NrZedne10Sm97JHi4iT3TsyHP39vIbtnKPTFM+r+9dBg0g1oL3+dooP18FxWSNfFPTubQhy/YfaDyq2ptH/o83RI3T9slVs9FJbNm3n5MWYWzbHZuN0N+FiFE6VIWUnJ/88031YYOHZoI0LNnz9TMzEzDuXPnXOvUqXNNS8PZs2fd69Sp41QTsQQPN7lx3Rrw0v3Bdst+2nuWP8LjqNeyN92X/07YHV5293PNBsNnP7P98bsxp934NMsagRXoPb4Fru62j1XMkYusW3CQbJPtehQFcergPpZPf4mEU9GFel4hxK2tLKTkrlWrVtaaNWsqAOzevdszKytL1axZ0zRgwICLK1eurJKenq4iIiLco6OjPbt27erUIj0yYPIWMOau23A1KP77a/g125/p0ZD7mtYEwKdibR5bsIsfZw6gzrcRlLczk7Lizhh23NuRZp8vw7tRkxuqS60GlXhgbHN+/WS/TaBw8uAFfl90iHueaILBpeBxa7bJyIYv5pMYc5qvX3yaVvf14faHH8OjnP2so0KI0sXRYMbiUBZScn/44YenR40aFfjJJ5/UUEoxf/78aIPBQNu2bTMefPDBxKCgoCYuLi7MmjXrpKurc+GApOS+hSzedoLXfz4MwPhuDZh8T5Dd9Nz/rn2XpHeXEOhgrGSmO1SdOgn/R0bdcF1O7D/PuvkHMJttn6+gDjXoOSwEZShY6vAdP/3AlqVLrtnmXakydw0ZQfAdXQstNbkQNytJye0cScl9LUnJXUYM71yPN/s1YWzX2xwGDgAd73+RZosXsrOl/bffIwtSps1i/+Rh6DzmLuelXvNq9BwRgr0qRG6PY/OyIwUaq5B8PoF/V3xnsz31YhJr5s7k/16fwnnpyhBClGI3c0puaXkowzLSk/hm+v20/fUSHg7i7JT6FWnxxSrc/ewuYHZdEf/EsuHLcLtlLXr403lggxtqIYiLOsavs98nKTbG4T7KYKD1/X3pNHCwdGWIMklaHkRBSMuDsMvTqzIjZ/zNiUkdiLXNtg2AT9QlDvbqQdKWDTd0jeBONbnzUftTSvdtOM2On0/c0Hlr1G/A4+/P5Y5Bw3D1sNv9iDabCft1FYsnjSF822aZlSGEEIVEgocyThkMPDRiCZVmPcse+5/xeKWaOTt6PFEfvIY2Oz9bolnXOtze3/6iVrvWRBO2LtrpcwK4urnR4cGHGT5rHg073O5wv9SkRNbMfp/v35zKhTOnbuhaQggh/keCBwFA606j6bHg//ijmyvZdnoRDBoyP/+eA0P6kJ2c7PT5W90TQLve9hN7/bsqiv0bTzt9zisqVKtO30kv03/K61Tyq+lwv9OH9vPVCxPY/M0XZBVChlEhhCirJHgQV9Xwa8aYj/5lS6gfSd7293HbHcX+++4k/dAhp8/frlcgLe8OsFu2ZflRDm87a7csv+q1bMOwDz6l8yNDcXW335Vhzs5m188rWTxpLEf+2SJdGUIIcQMkeBDXcPPwZuyLGzk/9W7C/e3v45mYybFHBhK3dLFT51ZKcXv/22h6V2275Ru/iSByZ8Fybbi6udGx/yOEzvyUBu06OtwvJfECv3z0Lj/89xUuxNx4q4cQQlxPaUvJvXHjxnJXVq1s1KhRyFdffVXJ2evLbAvh0KGD/8ef702n5w7Hz4jLvZ1p8O5cDJ62qbkd0WbNn1+HE/GPbaCgDIr7RjelfkvfG6pzblF7drJx8QIuxsU63Mfg4kqb3g/Ssf8juHvaX4VTiJuRzLYoPkajETc32wSBOf3yyy/lZ86cWWPjxo3HCnItk8lEvXr1mq5fvz6yfv36xhYtWjReunRpVJs2ba5Z/m/QoEF1W7Zsmfbiiy8mhIWFefbt27dhTEzMgcuXLxs8PT3Nbm5unDx50q1Vq1YhcXFx+3LXP6/ZFrLCpHCoSdP/UPOj1iyY+TB3/5JBuUzbfbLXb+NgeE+CFy3D3d9BU0UuyqDoNrQxpiwzx8LirynTZs36zw/Sa2xzApo4XAU23+q3akdAkxbs/HkFO378HpMxy2Yfc7aJnT/9QPjWTXR7/AkadugsC0wJUQxmPtK7yFNyT17+i6Tk5tqU3OXLl7868j09PV3dyO876bYQeapSpQHPTf+HHU/VJ7q6/X3cTl0gos/9JP/xR77PazAoeo4IIbB5NZsys0mzdv4Bzh5NutFqX8PV3Z1OAwYROutTbmvbweF+KRfO8/OH77Di7ddIPHumUK4thCidynJKboA///zTu0GDBk1at27d5MMPPzx5vVaT3IoseFBK+SulNiqlwpVSh5RSz1i3T1dKxSil9lpfDxRVHUThcHXzZOyTv2J4dSBbmtqPUN0ysokZP4HTM6ajTflb2dXFxcC9o5pQJ9h2kQmT0cwvc/dz7sSlAtU9p4rV/Xjw+Vd58IXXqFi9hsP9Tu7fw5fPjWfLsi8xZthJAiKEuOmV5ZTcAN27d089duzYoa1bt4a///77NdPS0pxqfijKlgcTMFlr3RjoCIxTSoVYyz7UWre0vtYUYR1EIbr37jfpOmMm399rIMvBSqopXy4nYnB/TBcu5Oucrm4uPDC2OTUbVLQpM2Zm88ucfSScvlyQatu4rU17hs38lE4DB+HiINo2Z5vYsep7Fk8ay9Htf8usDCFuMWU5JXfOfVq3bp1Rrly57F27djk14KvIggetdazWerf175eBcMD+MHtx0whqeD8T31jHj4/7EG/7eW+x/yjhve4mbffufJ3TzcOF3uNaUL2u7f+zzDQTqz/eS2KsU9lir39Ndw9uf/gxQj/4lHqtHI8nu3whgdWz3mbljGl5LoUthLi5lOWU3BEREe5Ga96iyMhI9xMnTng2bNjQdkBYHoplwKRSKhBoBWwHOgPjlVKPA7uwtE7YdG4rpUYDowECAuyvDSBKRoWK/rz23D98XmcQp5ceos1x22/lrhfTOTHkMao/9xzVho+47gBEdy9X+jzdklWzdnMh5tpAISPFyE8f7aH/c62p6Fu4OSoq+dXkoRencTxsBxuXfEZyQrzd/aL37ebL58bRts8AOjz0MG4e+Z9dIoSwz9FgxuJQllNyb9iwwad37941XV1dtcFg0DNnzjzlbCbRIp+qqZTyATYDb2mtVyqlagDnAQ28CdTUWo/I6xwyVbP02vz3TLYsWcRDWzQGB4+SR/fO1H3vY1x8HKw8lUNachY/ztzNxTjbFSDLV/HkoedaU75K0XxwGzMz2PHTD+z86Qey8xi3UcG3Ol2HjaJB244yK0OUajJV0zmSkvtaJZYYSynlBqwAvtVarwTQWsdprbO11mZgIdC+KOsgitZdt09m6NTPWPywC8kOeswy/9zGkX73kXn06HXPV66CO/0mtqR8VdsA4XJiBj99tIfUS3bmjBYCNw9POv9nCMNmfkpgS8czyJIT4ln9wVv8+O7rXDzneP0IIYTIi6Tktndiy1eyL4FErfXEHNtraq1jrX9/FuigtX40r3NJy0Ppl5YSz4yvHqTNiiQaORgaYHZ3oc5b71CxT+/rnu9SQjo/ztxN6kXbQKFKLW8emtQaTx/nphY5Q2vNsV3/snHJAi6fT3C4n4ubG+36DqT9gwNxc7AkthAlRVoeREGUVMtDZ2Ao0D3XtMz3lFIHlFL7gW7As0VYB1FMyvlU540xW7k4oSNr29hvyjdkZXP2+ec5O+01zFl5j82p6OtFv4kt8SpvGyAknk1l9ey9ZKYXXcuiUoqG7ToxfNY8Ojz0CC6u9ocHZRuN/LtiGUsmPcXxsO1FVh8hhChNZHlqUej+DZvP8uWzeXytxtNm8pCFa+MGBH7yGW61auV5rvNnUlg1azeZabaBQs3bKtLn6Za4eRR9c1/i2Rj+XDyfk/v35Llf/dbt6Bb6JJVq2F1qXohiJS0PoiBKbMyDKJs6thnD8xO/YuHjbpxxMKnJFH6Mo/16k7J1W57nqlbHhz4TWuLmaRsgxB6/xJp5+zEZswuj2nmqUqs2A15+gz6TplC+quO8G1G7d7Jk8lj+/n4pxqyiGZshhBAl7brBg1LKxTo2QYh8q1WrLbPGb2bbKD+2NbbfjaEup3Nq1BMkzJ2LNpvt7gNQo14Feo9rgaub7eN6JiKJdQsOkm1yfHxhUUoR1KEzw2fNo32/gRhcHHdl/PPDUr58bhxRu3cWeb2EEKK4XTd40FpnA/2KoS7iFuPpVZlpj2/AZ3R3lvRUmOw8bUrD+bmfcPKJkZiSHOeyqNWwEg+MbY7B1TYQOXngAr9/cQhzdtEHEABunp50GRzK4+/PIaBZS4f7XYo7x4/vvs6q99/kUnzBUo0LIW5epS0l95EjR9w9PT1bX1m5cvDgwU4vppTfbottSqm5SqkuSqnWV17OXkyUPcpg4NH75/Lo6JeZNdjAeQeLtab//S/H+vUh/cABh+fyD6nCfaOaYjDYBhDHdyewfuEh0pKdWiStQKrW9mfg1DfpPfElfKo4zgB6fNd2lkx6in9WLMN0nYGiQoib05UVG+25sjLk0aNHPZYvX17g4MFkMvHss88GrFmzJjIyMvLQihUrqoSFhdnMb3/ttddq9u/fPyk8PPzwsmXLoiZNmnQ1SPD398+8snLl0qVLT+U+9nryu8Lk7dY/38ixTQPdnb2gKJtaNR/Ce36teKXKELqvzqR5tO1AXR1/gRODHqXmK69S6ZFH7C7AVK+FLz1HhPDbokOWJzCHqL0JxBxNovOAhgR38iuWBZyUUjTqdAf1WrXh3xXfEfbrKszZtmMwTMYs/v6/bzm8+U+6D38yzyWxhSgrzry0pchTctd5p4uk5ObalNyFIV8tD1rrbnZeEjgIp/hWb8LckVs5NqwuP3R2MA7CZObc9Nc5+8KLmNNsV5kEaNi2Bt2HBtsty0w18edX4fz00R67q1QWFXdPL+58bDiPvzcX/ybNHe53MS6Wle9M56cP/utwKWwhRNEr6ym5z5w54964ceOQdu3aNVq3bp2Ps/cvX8GDUqqiUmqWUmqX9TVTKeUoLZIQDrl5eDN10FqaD+7L+w8bSHGw0nTyzz8T9fBAMk+csFve+PZa3PlokMPrxBy5yHdv7mDXmuhiGUx5RdU6/jz86lv0euYFfCo7bp08tvNfFk8ay78rl2PKo7lTCFE0ynJK7oCAAOOJEyf2h4eHH541a9bp0NDQ+omJiU7Nvszvzl8Al4H/WF/JwGJnLiRETv16vMPkx95k5jADxx0siWA8foKoAf1JXv+b3fJmXetw94gQPMo5mPVgMrN9dRT/9/ZOYo9fKqyqX5dSiuDb72T4h/Np26c/BgfLzpqyMtm2/Gu+en4c0XtLLD+QEGVSWU7J7eXlpf38/LIBunTpkhYQEJB58OBBp5IG5Td4uE1rPU1rHWV9vQ7Ud+ZCQuTWJLg/84at5ueh3vzeysH4hLQMYp55hrh330Pb+YYe1N6PwdM70rBdDYfXSTybysoPwti89EiRrkqZm7tXOe4aMoKh786mTkhTh/slxZ5lxYxprJ71NsnnpStDiOJQllNynz171tVkTf53+PBh9+joaI9GjRo5tTBNfgdMpiul7tBabwVQSnUG8hzQIUR+VK5yG588vpXZFQczt/YRRq0z42Hn8z1x8WLS9+2l9ocf4Vaj+jVl5Sq4c8/IJgR39GPT0iNcvpBhewINB/+KIWpfAnc+GkT9lr7FlhGzmn9d/vPaDCK2bWbz14tIvWh/SurR7X9zYm8YHfs/StveD+LiWnS5O4QoDRwNZiwOZTkl92+//ebz3//+t7aLi4t2cXHRH3300ckrgUt+5Wt5aqVUC+Ar4Mo4hyRgmNZ6vzMXu1GyPHXZsH7Lmyz4+zvGrTJT08GSD4Yqlanz4Ud4d7CfjNWYmc3OX06wd8NptNnxs12vRTXufDQIn8pFk97bkcy0NP754Vt2r/05z4WxKteqQ4/hY6jb3PE6EkJcjyxP7RxJyX2tAi1PrZQyAI201i2A5kBzrXWr4gocRNlxb5dXeXfALD593IXtQfZbBcyJSZwaHsr5hQvtDhpy83Dh9gENePiltvgGOO5uPLHvPEunb2f/xtOY8wgyCptHuXJ0fXwUQ9+dTe3gJg73Szp7hh/eeoWfP3yHyxdK3e9YIUQhuOVTciul/tJa31kM9bFLWh7KluRLp3l51cNU2H6ZwRvNuDh4RH169KDWjLdxqVDBbrnZrDmw8Qz/ro7ClOm4Ra56YAW6DQmmWh2nZysViNaa8K2b2Pz1ItIu2XRXXuXm4UnHAY/Splc/6coQTpGWB1EQhZEY63el1HNKKX+lVJUrr8KrohD/U6GiP7OHbMX/npa8OchAkrf9/VI2bODEgIFkRETYLTcYFC16+DN4WgfqNnO8AmR8dDLfv72Tf348hjGr6JNsXaGUIqRLN0Z89Bmt7++LpZHPljEzgy1Ll/DV8xM4eWBvsdVPCCEcyW/Lg73J9lprXSwzLqTloez6698PmbHzc55YbSbktIOd3N2pOX06lfo/5PA8WmuO707gr+WRpOexhHWFap50HRyMf0jxx8bx0VFs+GI+Z48cznO/Rp26cNfjIylfpVox1UzcrKTlQRREXi0P1w0erGMeOmmt886dXIQkeCjbTp7cwqTfn6Ldlmz6bXf8vHrf2YWqoaGU69TJ4UyKjFQj/6w6zuEtZ+2WX9Gogx+dBzbAq7zNom1FSmvN4b/+5K9vF+fdleHpRaeBg2h9f19cXPM7aUqUNRI8iIIoUPAAoJT6R2vdqbArll8SPIi0lHheWzWQxMMXGPermXJ5zEj2CAqiyrDHqdC7NwYPuzOqOHvsIpu+iSDpnOMlrD293eg8sAGNOhZPnoycMlJT2Lb8G/b9tgatHc/KqBkUTJ9nX5JWCGGXBA+iIApjzMNvSqkBqrh/gwphVc6nOu8P3sSdt3fi5WEGTvo63jczMpLYqa9wrHsPEubMxXTe9ndUrQaVeGRqe9r3qWc3zTdYWik2fBnOTx/t5WJ88eXJAPD09qHHiDEMeecjagbZz+MBEBsZwTcvTeT0YcfZSIUQJau0peTOzMxU/fv3DwwKCgqpX79+kylTpjhY59ex/AYPk4D/AzKVUslKqctKqWRnLyZEQSiDgdDen/NG16d5f6iBzU3zjmWzL1zg/CefcKxbd86+PJWMI5HXlLu4GWjXqx6PvtKeWg0rOTxPzJEkvntzB2HrosnOLr48GQDVA+sz6PX3uHfMM3iVtz+rJO3SRb5/cyphv66yO31VCFH0bqaU3IsXL66clZVliIyMPLxv377wr776yvfIkSNO9dHmt7O0IvAYUE9r/YZSKgCo6cyFhCgsHVo/yTd+bZjoOpL9gSb+s9VMDcfDA9BGI5dWruTSypWU69SRKsOG4XPnnSjrGvaV/bx58NlWhP8Ty98rjpGZZrs+TLbRzL+roojcEUe3IcH41S++vHDKYKBpt7tp0K4TW5d/zb7f10CuIEGbzWz66nNijx7hnjFP4+7p5eBsQpQe06dPL/KU3NOnT5eU3FybklspRVpamsFoNJKamqrc3Nx0pUqVnJpqlt+Wh0+AjsAg678vA3OduZAQhalmrbZ8NXgztdrUZuJoAx/0NxBe5/rHpf3zL2fGjCWqV2+Sli27mvZbGRQhnWvlK0/GivfD2LysePNkAHj6+NBz5Fgemf4O3g4ydh75ZwtLp04mKTamWOsmxM2mLKfkDg0NTSpXrpy5evXqLerVq9d8/Pjx55xdnjq/wUMHrfU4IANAa50EFO8wdCFy8fCsxCuPrmd1o+H4N6rIm8M8eSnUhS0hCtN1nuysEyc49/obHO3WnfiZszDGxQH/y5PRe3wLyldxsHS1hoObY1g2/V+i9iQU8k91fXWCmzBkxkfUDg6xW37hzCm+mfIsx3ZtL+aaCXHzKMspuTdv3lzOYDDoc+fO7T927NiBuXPn+h0+fNipz/T8Bg9GpZQLoK2V9AWKt/NXCHuUIqDzc0wfupW1/dfSqfsQFvQvx/ixLvzYSZFyndQV5kuXuLBwIcd69CRm8nOkH7AMPKzbtCqDpnWgZU9/HA0TTr2UxdrPDrBm3n5Skuwk4ypCPpWr8PCrb9P6/r52y7PS0/jp/TfZ+t3XmM3Ft/CVEDeLspyS++uvv6567733XvLw8NC1a9c2tWvXLuXvv/92sByfffkNHmYDPwLVlVJvAVuBt525kBBFymDAz9uPl9q/xLoB63jw9pGsvrsCY8e5sPBeA2evN0TJZCL511+Jfvg/RA9+jOT1v+HqCp0HNuThKe2unyfj9e0c2HSmWPNkuLi60i10NA9MeA5Xd/tTUrf/uJwf33md9MsyvlmInMpySu6AgICsjRs3VjCbzSQnJxt2797t3axZM6e+AeVrwKTW+lulVBjQA1DAg1rrcGcuJERxqeZVjWfbPMuIpiNYGrGUb8t/yx+tLtLyuKbXTk3z6Lw/4NN37yZm927catem8tAhVBk4kIEvtmH/xjNsXx2FKcu20c2Ykc1f30VyZPs5ug0Jpmrt4suT0fiOrlTzr8vqmW9zMS7Wpjx6326+mfIsfSdNoUb9BsVWLyGux9FgxuJQllNyv/DCC/GPPvpoYFBQUBOtNYMHDz5/vUGaueVrkaiSJotEiYJINaby/ZHvWXJoCRcyLhAQr+m108wdhzRu+WjRN3h7U2ngACoPHUqGZ1X++i6SkwcvON7foGh5TwDtHgjE1b34MuVlpKawdu5MonbvtFvu4uZGz5FP0bTb3cVWJ1GyZJEo50hK7msVxiJRQty0vN28CW0ayroB63i5w8sY6057ttkAACAASURBVNViXi8Xnhrnwvd3KC6Vy/t4c2oqiV9+xfF77iX59Rfp1tHIPU80wauC/fFFZrNm97qTLHtzB6fDE4vgJ7LP09uHB59/ldsffgx7AzWyjUbWz/+Y3xfOxZTHnHQhRPG45VNylzRpeRCFyZht5JeoX1h0cBEnk0/iZtLccUjzwE4zdfM5ecKzaVO8Bw/jUEp9Dv99Ls99G3W05snwKb4JSif27GLNnA/ISE2xW16zQSP6TJpC+aqyrPWtTFoeREEUOLdFSZPgQRSFbHM2v5/8nQUHFnA06ShoTbNoy7iI1sfz9//CtUYNTP1GsjupPhcTHCfc8PR2o/PDDWjUofjyZFyMO8fqWW+TEB1lt9yrQkV6P/MiAU2bF0t9RPGT4EEUhAQPQuTBrM1sPr2ZhQcWcuC8ZapmzQuWloiuBzQe+ej91OV8ONf9KY6k1yWvmZF1gitz1+BGVKp+nb6SQmLMzOCPzz/l8F9/2i1XBgNdBofStvdDxZ78SxQ9CR5EQZRI8KCU8ge+AvywrAmxQGv9cY7y54D3AV+tdZ4PkQQPojhorfk39l8WHljIznOWQYfe6ZqeezX3hZmpevn650gtV4Oj7caSqBxn7rLk1Aik5d0BuLgU/bAjrTX7flvDxi8XYs62HwkFdbyDe8c8jbtX8QQ1onhI8CAKoqSCh5pATa31bqVUeSAMyxTPw9bA4nMgGGgjwYMobfbE72HB/gVsjdkKgEu2pmOEptcOMw3yHuKARnG2ZieONxyIyWB//QWAqrW96fpY8eXJOBsZzs+zZpCSZH8QZ5Xa/vR7bipVauVjnW9xU5DgQRREicy20FrHaq13W/9+GQgHaluLPwRewLpipRClTavqrZjXcx7Ley/n7rp3Y3YxsK2JgZdDXXh1iAvbGynMDlr5FZrasX/T4d/pVI93HPReiLHkyfjru0iyiiFPRq2gxgx552PqNG5qtzwx5jTfvvwsR3f8XeR1EaIsKamU3JGRke6dOnUKCgoKCmnfvn2j48ePu10pmzNnTtW6des2rVu3btM5c+Y4XPjKkWIZ86CUCgT+ApoCXYEeWutnlFLRQFt7LQ9KqdHAaICAgIA2J0+eLPJ6CuHI8YvH+fzA56w9sZZsbRnU4HtR88AuM932acplOT72fJUmHAl6lExPx78zvCt5cOejQdRv6bi7o7Bkm0xsWbqYsF9/crhP+wcfpvMjQzAYbq7pY+Ja0vJQfIxGI25ubnnu88svv5SfOXNmjY0bNx4ryLVMJhP16tVrun79+sj69esbW7Ro0Xjp0qVRbdq0uWaVyPvvv7/+Aw88cGnChAkXVq9eXf6LL76otmrVqhNxcXEubdq0CQkLCztsMBho1apVyJ49ew77+vpeM2KrRAdMKqV8gM3AW8A6YCNwj9b6Ul7BQ07SbSFKi9OXT/PFwS/46dhPGM2WtRK8MjXd92nu32Wm+iX7x5lcPDgR2IvTdbqBctzgV7+lL10eCcKnsuPujsISsW0z6z+bjSnT/iyRgGYt6fX085SrUHzpx0XhKu3Bw4Y/byvylNw9uh8vNSm5W7RoERwVFeVZu3btrIKk5P7jjz+8p0+fXmvr1q1HAaZMmeIHMGPGjGs6VRs0aNBk/fr1kbfddpvRbDZToUKFVin/396dh0dd3Xscf5+Z7HtIyMqSjSQEZAtLAkoqWKsW2Stg1d72aq3Wq9Rb63W5t8uttvZapbVPe221rWXxVmUVFHCDIrIvsohAQLZsBLJO9pk5948ZbICZyUySH9m+r+fxIZnfdn4MEz855/zO12LZ9/LLL/fbvHlz+LJly04D3HnnnYMLCgpq77///svGNLtskSillD+wHFiqtV4BpAOpwKfO4DAA2KuUctnlIkR3MzB8ID/O/zHvzn6Xu4beRZA5iIZAxbrxJh7+nplfzzLxuYspA362JoacWMHYPb8irPaM2/Of3F/Osp9u5+Cmc2iD62RkTyrgm8+8QHRiksvtZw7uZ8kTCyk9cdzQdgjRFfpCSe6hQ4fWL1u2LBpg8eLFUXV1dabS0lJzUVGR/4ABA748Pjk5ubmoqMhzt8kVDAsPyvHc16vAEa31CwBa64Na6zitdYrWOgU4B4zRWrcxBU2I7iU+NJ7Hxz/O+jnrufe6ewnzD8NuUuzINvFfd/vxxLfMfOyiNHiE5Sxj9/4PGYUrMNlc/8Z/qU7G8v/Zw8Ui14s8dZbYgYP55rMvkj42z+X22gvl/N+Pf8TBDzca2g4hrrW+UJL7pZdeOrdly5bwoUOH5mzatCk8Li6uxd/f393x3v/lYWzPwyTgbmCKUmq/87/bDLyeENdcTHAMj4x5hA1zN/DQqIeICowC4ESS4rczzDz0gJlVeZeXBjdpO4POfcCEXT+n38XDbs9d9kUNbzyzi+2rTmBtNq6sdmBIKDP+/Umun3+P22WtN778Wzb+8SVZ1lr0Gn2hJHdKSkrLxo0bTxw5cuSzRYsWFQHExMTYBgwY0HLu3Lkvjy8qKnJ5vCdeVdVsD631xzgqcHraJ8Wo6wtxLUUERHD/yPu5O+du3jzmKMJ1oeECFRGKZTeaWT5JM/mQoyBXknNUMbixgpEHf8/5uFyOZcylJSDiqvPa7Zo9609TuOc8Bd/MYmB2p0zUvooymZgw6w7iU9NZ99LzNFquXtTi4AcbKD91ktsffZKIWOMndgphpFOnTvnHxcVZH3zwwYrw8HD7a6+9FgOOktzPPvtsaUdKcg8bNuz8yZMnA/fv3x88ffr0Lz9M7kpyz5kzJ2PcuHGW1iW5b7/99s/cXad1Se6UlJSWFStW9Fu6dOlVS8mWlJT4xcXFWc1mM08//XTiggULLgDMnDmz+mc/+1lyeXm5GWDz5s0RL7744jlf/v4MCw9C9EUh/iF8a9i3mJ89n1XHV/HnQ3+muK6YpgDFe2MU749Wl5UGV0D8+T30qzhCYfpMShInuTxvdXkDaxbtJzsvgYkG1slIGZXLXb9YxJpfP8v5Uyeu2l564jhL/uMRpi18nEHDRxrSBtF3uJvMeC30hZLc69evD//JT36SrJRiwoQJtX/961/PAMTHx9see+yx4tzc3KEAP/rRj4ovBRdvyfLUQhioxd7COyff4ZWDr3Cq5tRl21yVBq+MzOBo1p3Uh8S7PWdQiJnr52WROT7esCWlW5qb+OCVP3B48/sutytl4voF9zBu+hxZ1rob6+5PW3R1O64kJbkvJyW5hegi/iZ/ZmTMYNWMVTxf8DxZ0VlfbjsTp64qDR5dXci43c+ScuodlN31z6/Gehvv/+UzVv18C1Xn641pd0AgX3vgEW669/uYzFd3UGptZ8uyv/L2i7+gucGYNrRWu/kszQZPHhXiWpOS3AaTngfRW2it2VK0hZcPvMyB8gOXbbtUGvzru+wMKoe6kAQ+z7qT6sh0t+czYWNUQQLj5uTgF2DMD5+S40dZ88KzWCouutzeL2kA03/4FDHJAw25fktZHWUv7gUgICWCsIlJBA+LRZmlx6Mt0vMgOkKqagrRzWit2Vm6kz8d+BM7SndcufHL0uCjT0Bx4kROpM/E6ue+aFVokI2Cb48idaQxExnrq6tYu+g5zn520OV2/6BgbnlwIZkTXM/Z6IjKlcep23H509zmyABC85MIHZeAOdSnx9P7FAkPoiMkPAjRjX1a/il/OvAnNp/bfNW2JGdp8PzPwzmd+g3Ox43xeK5BQ0Ip+NYIImKDPe7XHnabjS2vv8but1e43Wfc9DlcP/8eTJ3UBWuvb6HkFzvRLXbXO/iZCB0dR9ikJPwTQjvlmr2JhAfRERIehOgBPq/4nD8d+BPvnX4PfUXNuLB6R2nw/MIcigfO81gnw2zSjLk1hTG3pODn3/lDGUe3fcyGPyyipanR5fZBw0fw9Uce75RlrWu3nKN63Rde7RuYHknYxGSChvZDmWRIAyQ8iI6R8CBED3Ky+iSvHnyVdSfXfVmE6xKzTXPDIX8KCr/G+YSpaA+FqyKi/Jh81zAGD/e5YF6bLp47w+rnn6GypMjl9rCYWKY/+gSJGVkut3tLt9io31dO7dYirGXeTcw09wsiLD+R0LEJmIL79tPoEh5ER8jTFkL0IGmRaTxz/TOsnbWWOzLvwN/0zzF9m1mxaaSVX9+6FlX9LNGVR92ep6bKytrffco7v/+Umose6+z4LGbAIL757ItkjMt3ud1y8QJ///HjHPhgfYeuo/zNhI5PIH7hGGLvu46gnJg2lp4DW0Uj1eu+oOQXO6hcVUiLQU+kCOGt7laS+9ixYwHDhg0bmp2dnZORkTHsV7/6lc+TpaTnQYhu7nz9eV47/BpvHnuTBuvlIWBUoZ3v7LuBwv430+xcGtsVsx+M/Xoao28ahNm/835n0Fqza81yPn79b2jtel7C8BtvZup3vodfQOcsbGWtaMSyrZi6XaXoRu/WtQnMjCZsUhJBQ6L71JCG9DxcOz2pJHdjY6PSWhMcHKyrq6tNOTk5w7Zu3fp5SkrKZUtUe+p56Nt9ekL0AHEhcTw27jHuve5elhxZwutHXqe2xbHirWniWCY//jwZz/+G/TvrODvgRpdDGTYr7Fh9ks8/KaZgQTYDczpnmWulFONnzCU+NYO1v/0VjbU1V+1z6KONlJ/+gumPPkFE/7gOX9OvXxBRX08j4qbB1O8tw/JJMdZyzz0rTccqaTpWiV9sMGETkwjJjcMUKD/+ulrCR/sNL8ldeuOoblOS+6mnnko+efJkUHZ2dk5HSnJv2rQpdPDgwU05OTnNALNnz6546623onJzcy97LOn48ePBt91221mAadOm1d55550ZAEFBQV/2GjQ0NCi73c2EZA9k2EKIHiI6KJp/G/1vbJi7gYdHP0x0YDT3jbgPc1gYA3/yFFN/OouJxX8lquqY23NUlzey5rf7Wf/HQ1gqXU94bI/BI0Zx9y8XEZ82xOX2spPHWfzEQk4f2N9p1zQFmgnLTyL+B7nEfmc4QVnRbR5jvdBA1ZoTlDy7k6q3T2Dt5OEc0XP05ZLcAIWFhf6ZmZk5qampIx5++OHSK3sd2iLhQYgeJjwgnPtG3MeGuRuYlPTPdRVCxo1jxFuvclOuhZzPXyOgqdrtOU7sPc/SH29n78bT2Ky+/9bhSkRsHPN/+hzXTbnZ5fbG2hqWP/tf7Fj1psuSwO2lTIqgzGhivz2c+H/PJWxiEqqNBbN0kw3L1mJKn9/NhdcO01hY2altEt1fXy7JDZCRkdFy7Nixz44cOXJo2bJlsWfPnvWpK07CgxA9VLBf8FV1JUxBQcQ/9kPyXnqMyZWvM/Dshyjtel6AtdnOthUn+PvPd3LuaGWntMkvIICb73+Yr373Icx+rpe1/vj111jz62dpqu/8iYz+/UOImp5O4pPjiZyWhrlfkOcDNDQeqeDCK4coW7QXy44S7AaWPxfdR18uyX3lPllZWQ3vv/++x7ZeScKDEL1Q8HXDyXxzKROnJTNu//NEVrmfn1VZWs/qF/ex8dXD1FU1dcr1R0y9hXk/fY7wGNeTuAt3bWPpkz/g4rkznXK9K5mC/Ai/PpmEH44l5p4cAjPcTya9xFpWT9XKQkp+sZOqd7/A2onDOqL7OXXqlH94eLj9wQcfrFi4cGHZ/v37Q8BRkhugIyW5n3766fM333xz1f79+y9brc1dSe7HH3984MiRI+tal+T21PPQuiR3Y2OjWrFiRb85c+ZUXdmekpISP5vNkRVal+Q+ceKEv8ViUQDl5eXm3bt3hw0bNsynf/AyY0iIXkr5+xP7wAOEf/WrxD71NF8c2Uph+ixaAiJc7n98VxmnDlxg/O2pXHfjAMzmjv1ukZiRxV2/XMS63zzHmUMHrtpeWVLE0icf5WsPLCQr//oOXcsdZVIE58QQnBNDS1kdlk+Kqd973v2KlYBusGLZfA7LP84RPCyGsEnJBKRESPVQA7ibzHgt9OWS3AcOHAh+/PHHByil0Frz0EMPlY4fP96nCUDyqKYQfYC22ahcupSi3/4vJxNu4lzyZFDuw0G/pFAKFmSSNKTtSYhtsdtsfPz3xexa/ZbbfXKnzWLynf/Sactae2xPfQt1u8qwbCvG5mVPi39SKGETkwkZ2R/ViY+6Gk0e1fSNlOS+nCwSJUQfp8xm+t1zD1mr3mBU/7OM2/McEdUn3e5fUVzHyl/v472/HKauumNDGSazmcl3/gvTH30S/yDXNTf2rF3JWz9/mvrqq3peO50pxJ/wggEkPDaOmLuGEpDquiemtZbiOirfOkbJL3dSvfEUtprOGd4RfZuU5DaY9DwI0Xm01lSvWEHpL39FUegwTqTNoCXA/VypgCAz46encV1BMqYODmVcLDrLmuefoaL4nMvtYf1iuP0HT5CUmd2h6/iqudiCZWsx9Z+eB6sXPxNNiuDrYgmblETgoLbDR1eRngfREVLbQghxlZay85T+7GdUbt7GydTpFCVN8jiUETMgjIIFWSSmd6zgVXNDPev/sIjjOz5xud1k9mPKt7/LiJtuvebzDGyWZup2lmLZXoK9prntAwD/geGET0wi+LpYlF/36sztxuHh5HXXXVdpMpm6//+A+ii73a4OHjwYPXLkyDRX2yU8CNGHaa2p3bCB0v/+OZXNoRwdMo/aiBSPx2RPTCR/ZjohEe1fblprze63V7Bl2Wtul7UeVnATU+99AP8Al3PTDKVtdhoOXcCytZjmM7VeHWMKDyAsL5HQCQmYwzpnKe6O6sbhYU1CQkJO//79qyVAdD92u12Vl5dHlpaWfjZy5MjprvaR8CCEwFpZyflf/pKq1W9TnDiRE2nTsfqHud0/MMSPCdPTGDY5GVMHakWcOfQpaxc9R4OLZa0B4lLTmf7ok0TGxbf7Gh3VfLYWy9Yi6g9eAJsXPy/NipCR/R1PaSS7/zu8FrpreNizZ0+cn5/fK8BwZO5dd2QHDlmt1ntzc3PPu9pBwoMQ4kuWf/yDkh//hPoLNZxInUFJqxUsXek/KJzJCzJJSG3/UEbNhXLefvEXlBa6XlY7KCycrz/8GCkjx7T7Gp3BVtOMZUcJdTtKsFu8W8k3ICWCsIlJBA+LRZmv/aOe3TU8iJ5PwoMQ4jI2i4XyF16gctnrVEekcGzIPGrDB3k8JmdSInmz0gluZ3e9taWFj/76Mgfed1PCWykm3XEXE2Z+A2Xq2l9UtdVO/aflWD4ppqXI4tUx5shAQvMTCR2XgDnUc+XFziThQRhFwoMQwqX6Xbsoefo/aTp9hqKk6zmZOh2rf4jb/QND/cifmU7OpKR2l70+9NF7vP/q77G1uP7NPn1sHl+97/uERnV8/YmO0lrTfLoGy9ZiGg5fcHT0tkH5mwgZHUfYxCT8E0INb6OEB2EUCQ9CCLfsjY1c+N3vuPjnv9BsDqEwbSalifkej4kbHE7BnVnEDW7fI4xlJwtZ88Kz1JS7HGrFzz+AEV+9lXHT5xAW3TmlxTvKWtVE3fZi6naWYq/3bn2hwPRIwiYmEzS0X7vDVlskPAijSHgQQrSp4eAhSp5+mqajR6mKSONY5jwsYQPcH6Bg2A3J5M1II6gd3fT1NdW889LznD6wz+0+fv4BjLjpFsbNmNttQoRusVG/r5zarUVYy7wr/GXuF0RYfhKhY+MxBXduxQAJD8IoEh6EEF7RLS1cfOUVLvz+D9isNoqSJnMydRo2P9erRgIEhfmTPyudofmJPv92bbfb+OSNpexY+YbH/cz+/oyYegvjZswhvF+sT9cwitaappPVWLYW03jkInjxY1YFmAjJjXcMafR3PzzkCwkPwigSHoQQPmkqLKTkqadp+PRTmgIiOJE2k9KECR6PSUiLYPL8LPoP8qnqLwDHd21jw+8X0VRf53E/s78/1035GuNnzu02IQLAWtGIZVsxdbtK0Y3elfsOzIwmbFISQUOiOzSkIeFBGEXCgxDCZ5cKbZ1/cRG6oYHKyAyOZc6jLjTJ7TFKwfCCAUyYnkpgiG9DGfU11exeu5L969fS0uS5crDZz4/hU77G+BlziYh1XRK8K9ibbNTvLcPySTHWcu8KGPrFBhM2MYmQ3DhMgb4PaUh4EEYxLDwopQYCfwMScMxD/qPW+jdKqf8GZjhfOw/8i9a62P2ZJDwI0V01nztHyX/+J/XbtmNXJs4lF/BFyjRsfkFujwkO92finAyyJiT4vPx0fU01e9auZN+GdbQ0ev4fsNnPj+E33sz4md/oViFC2zVNhVVYthbReLTSq2NUoJmIqYMIn+xhnomr4yQ8CIMYGR4SgUSt9V6lVDiwB5gJnNNa1zj3eRjI0Vp/z9O5JDwI0X1dKrRV9svnsNfW0hQQSWH6LMrix3k8LjEjkoIFWcS0YxXG+ppq9qxbxb71a9sMESazH9dN+aozRMT5fC0jtZTXY/mkmPo959HNnoc0omakE5bvvmfHFQkPwijXbNhCKbUa+J3W+r1Wrz0BDNJaP+DpWAkPQnR/lwptWT74AICKqEyODbmD+tBEt8cok2LEVwYw/vZUAtrxpEFDbQ171q1m3/o1NDe0HSKGf+UmJsy6g4j+3StE2But1O12DGnYKq4ellGBZhKfHO/z0IWEB2GUaxIelFIpwD+A4VrrGqXUM8A9QDVwo9a63MUx3wW+CzBo0KDc06dPG95OIUTHtC60Zbt4Ebsyc3bAjZxKuQ2b2X2Bq5CIACbNzWDIuPh2VdJssNSyd90q9r7rXYgY9pWpTJh5R5fWzHBF2zWNn1dg+aSYpsKqL18Puz6ZqGkuixt6JOFBGMXw8KCUCgM2A89orVdcse0JIEhr/WNP55CeByF6lkuFtqpXrwGgMTCKwvQ5nI/zXJ8iOTOKG+ZnEpPUvoJSDZZa9r6zmr3vrKG5wfM6CyazmWEFU5kw6w4i4xLadT0jtZTVOYY09pUT/8ho/GLcPxLrjoQHYRRDw4NSyh9YC2zQWr/gYvtgYJ3Werin80h4EKJnulRoy1pSAkBFdLZjKCPE/W/8JpNixNSBjPt6CgFB7Vs0qdFiYe+7jhDR1iOeJrOZnMlTmDBrHlHx3S9E2JttmALM7TpWwoMwipETJhXwGlChtV7Y6vUhWuvjzq//DSjQWs/1dC4JD0L0XK0LbQHYlR9nBk7h1OBbsZvdF9IKjQpk0twMMnLj2jWUAdBYZ2HvO2vY++5qmuo8hwhlMpEzeQp5s+YRleB+nkZPIuFBGMXI8HA9sAU4yD9LxjwJ/CuQ5XztNPA9rXWRp3NJeBCi57tUaKvZOX+pIbAfhRlzKO8/yuNxA7KjmTw/k+gOFJJqqq9j77tr2LNulXch4oYbmTB7HtEJvj3d0N1IeBBGkUWihBDXTOtCW9gdv1Nc7JfDsSF30BDsfi0Gk1kx6qZBjL0tBf/A9nXhgyNE7Hv3bfasW0Vjnedy2kqZGHrDV8ibPY/oxOR2X7MrSXgQRpHwIIS45loX2gKwmfw4M/AmTg/6WptDGcNuSCJrQgIRsb5PILykqb6efeudIcJS63FfpUxkX19A3ux59EvybZGmribhQRhFwoMQoku0LrSlW1oAaAiK4XjGXC7Ejmjz+OSsKLLzE0kfHdfu3ojmhnr2rV/L7nWraKyt8bivUiayJ01mwux5xCQPbNf1rjUJD8IoEh6EEF2qdaGtSy7EDOdYxjdoDG67wJV/oJn0Mf3Jzk8kKSOqXYWkmhvq2bdhHbvXrmwzRKAU2RMnkzd7PjEDuneIkPAgjCLhQQjR5a4stAVgM/lzetDNnBn0Vewm7wppRcQGkZWXSHZe+4Y1mhsb2L9hHbvfXkGDFyEiK/8G8ufMJ2bAIJ+vdS1IeBBGkfAghOg2WhfauqQ+uD+FaTMdQxnK5PW5kjMdwxppo/v7vF5Ec2MDn258h11vr6ChptrzzkqRlXc9eXPmEztwsE/XMZqEB2EUCQ9CiG7lykJblzQGRlEaP4HShAkeF5m6kl+gmYx2Dmu0NDay/7132P32CuqrqzzvrBSZEyaRP2c+sYNSvL6GkSQ8CKNIeBBCdEtXFtq6RAM1EamUJORRFpeLzc/74YnwmCCy8xLIyksksr/3x7U0NfLpe++ya83ytkMEkDlhEnlzF9C/i0OEhAdhFAkPQohu68pCW1eymfwpjx1BaUIeFdHZPg1rJA1xPq0xxvthjZamRg68v55da5ZTV1XZ5v5Dxk8kb8584lJ8L2rVGSQ8CKNIeBBCdHv2+nqq16yhYvESmk+ccLmPY1hjPKUJeb4Pa4zuT1Z+IslDvBvWaGlu4uD769m5Zjl1lRVt7p8xLp/8uQuueYiQ8CCMIuFBCNFjaK2p37aNisVLsGzaBC5+fjmGNVIoScjjfFwuVr8Qr88f3i+IrPwEsr0c1mhpbuLgBxvYufotr0JE+tg88ucuID413es2dYSEB2EUCQ9CiB6p+cwZKpcuo2r5cuwW10tN20z+XIgdQUm7hzUSSB8T1+awhrW5mQMfbGDX6jexeBUiJpA/ZwHxaRlet6c9JDwIo0h4EEL0aPa6OqpWr6Zy8RKav/jC7X6OYY1xzmEN70tv+wWYSB8TR7YXwxrW5mYOfrSRnavexFJx9RyNK6Xljmfi3DsNCxESHoRRJDwIIXoFbbdT98k2KhcvxrJ5s/v9gJrwFEoT8iiLH4vVl6c1+gWRlZdAdn4Ckf3dD4dYW1o49OFGdqx+E8vFC22eN23MOPLnLCAhI9PrtnhDwoMwioQHIUSv03zqFBXLllG9fAV2DyW4bSY/LsQ4hzX6DfVpWCMxI5Ls/EQyct0Pa1hbWji86T12rHyT2ovlbZ4zdfRY8ucuIDEjy+t2eCLhQRhFwoMQoteyWeqoXrWKyiVLaD51yuO+TQGRjqc1Bt5AXUCM19fwCzCRPjqO7PwEkjOjXQ5rOELE++xY9Qa1F9oOESmjcsmfs4CkzGyv2+GKhAdhFAkPQoheT9vt1G3dSsXixdT9Y4vnfYHa8MGUDriesoTxtGjvl7YODfD/KAAACzlJREFU6xdIdl4iWXkJRMVdPaxhs7ZweNMH7Fj1BjXl59s8X8rIMeTPXUBS5lCv29CahAdhFAkPQog+penkF1QuW0b1ihXY6+s97ntpWKM851bKTUmungx168thjTFxBARfHkBs1hYOb/6QHSvfoKa8rM1zDR4xmvy5d5Kc5VuIkPAgjCLhQQjRJ9ksFqpXrKRi6RJaTp9pc/+mgEjKs2+mNHkSNQ3eVfkE8PM3keasrTHgimENm9XKZ//4kB0r/071ec8hYthXbuKWBxZ6fV2Q8CCMI+FBCNGnabudui1bqPjbYuq2bm17f8ASl8XF8XdwzppEc6Pd62uFRQc6ntbISyQq/p/DGjarlSNbPmL7yr9TXVZ61XHKZOI7L75MVEKi19cCCQ/COBIehBDCqenECSqXLqVq1Wp0G0Ma4FiEqu6GOyhJnkRxifZtWCPdMayRnhtHoHNYw2a1cuTjTexY8Xeqykq+3HdYwU3c8qBvvQ4g4UEYR8KDEEJcwVZTQ/XKlVQsWUrL2bNeHWPPGE7VDd/kdH08lWUNXl/Lz99E2mjHsEZyVjQmk8Jus3Hk401sX/F/VJ8v49sv/i/RCUk+34eEB2EUCQ9CCOGGttmwbP4HlUsWU/fJNq+OUWFh6Gl3U5o8iRNH6miqt3p9vbDoQLImJJCd7xjWsNtslBw/SnJ2TrvaL+FBGEXCgxBCeKGpsJCKJUuoXr0G3eBFz4JSBBdMoa5gHl9cjODMZxVou/c/bxPSIsnOTyBjbPyXwxq+kvAgjCLhQQghfGCrrqZq+Qoqly6lpajIq2MC0tMJvuMeSmPHcHTPRSqK3a96eSWzv4m0Uf0ZXpBMUkaUT22V8CCMIuFBCCHaQdtsWDZtomLxEuq3b/fqGFN4OJFz5mKbMpsTJ+0c21VKU513wxrjpqUyflqqT22U8CCMIuFBCCE6qPHYMSqXLKV6zRp0Y2PbByhF2JQpRN75Tc4HpXF0eymnD3se1rj75/lExHpfxMtxGQkPwhgSHoQQopPYqqqoWr6ciqVLsRaXtH0AEDgkg+i77sa/4GaOH6zh820lVw1rJGdGMfPRMT63R8KDMIqEByGE6GTaaqX2o4+oXLyE+p07vTrGFBlJ1Nw5RM1fQI2O5Mi2Eo7tdAxrTP3WULLzfVsgCiQ8CONIeBBCCAM1Hj1K5ZIlVK95G93U1PYBJhPhU6cQfdfdBI7O5fShiwzM6ee27LcnEh6EUQwLD0qpgcDfgATADvxRa/0bpdT/ALcDzcAJ4Nta6ypP55LwIITo6ayVlVS99RaVy17HWuLlkEZmJtF330XktGmYgn2b7wASHoRxjAwPiUCi1nqvUioc2APMBAYAH2qtrUqp5wC01o97OpeEByFEb6GtVmo/+JDKxYup9/LnmjkyktjvP0i/e+7x6VoSHoRRTEadWGtdorXe6/y6FjgCJGutN2qtLz2btB1HmBBCiD5B+fkR8bWbGbxkMakrVxA5ZzYqIMDjMbbqakB53EeIa8mw8NCaUioFGA3suGLTd4B3r0UbhBCiuwkaOpSkZ54hY/Mm+v/gB/jFx7vczxQSQuTsWde4dUK4Z3h4UEqFAcuBhVrrmlavPwVYgaVujvuuUmq3Ump3eXm50c0UQogu4xcdTez93yXj/fdIXvQiwbm5l22PnD0bc1hYF7VOiKsZ+rSFUsofWAts0Fq/0Or1bwHfA6ZqrduseytzHoQQfU3D4cNULllKzTvvkLpqJYGpvq0uCTLnQRjHyAmTCngNqNBaL2z1+i3AC0CB1tqrLgUJD0KIvspmsbS710HCgzBK+0q1eWcScDdwUCm13/nak8BvgUDgPUe+YLvW+nsGtkMIIXosGa4Q3ZFh4UFr/TGupwe/Y9Q1hRBCCGG8a/K0hRBCCCF6DwkPQgghhPCJhAchhBBC+ETCgxBCCCF80iOqaiqlyoHT7Tw8FrjQic3pCeSe+wa5576hI/c8WGvdvzMbIwT0kPDQEUqp3X3tOWe5575B7rlv6Iv3LLo/GbYQQgghhE8kPAghhBDCJ30hPPyxqxvQBeSe+wa5576hL96z6OZ6/ZwHIYQQQnSuvtDzIIQQQohOJOFBCCGEED7pdeFBKWVWSu1TSq11fp+qlNqhlDqulPq7Uiqgq9vY2ZRSp5RSB5VS+5VSu52v9VNKvee87/eUUtFd3c7OopSKUkq9pZT6XCl1RCmV38vvN8v53l76r0YptbA33zOAUuoHSqnDSqlDSqnXlVJBvf3zrJR6xHm/h5VSC52v9er3WfRMvS48AI8AR1p9/xzwotZ6CFAJ/GuXtMp4N2qtR7V6Hvw/gA+c9/2B8/ve4jfAeq11NjASx/vda+9Xa33U+d6OAnKBemAlvfielVLJwMPAWK31cMAMzKcXf56VUsOB+4DxOP5dT1NKDaEXv8+i5+pV4UEpNQD4OvCK83sFTAHecu7yGjCza1p3zc3Acb/Qi+5bKRUBTAZeBdBaN2utq+il9+vCVOCE1vo0vf+e/YBgpZQfEAKU0Ls/z0OB7Vrreq21FdgMzKL3v8+iB+pV4QFYBPwIsDu/jwGqnB9EgHNAclc0zGAa2KiU2qOU+q7ztXitdQmA88+4Lmtd50oDyoG/OIenXlFKhdJ77/dK84HXnV/32nvWWhcBzwNncISGamAPvfvzfAiYrJSKUUqFALcBA+nF77PouXpNeFBKTQPOa633tH7Zxa698dnUSVrrMcCtwPeVUpO7ukEG8gPGAH/QWo8G6ugj3bjO8f3pwJtd3RajOcf1ZwCpQBIQiuPf95V6zedZa30Ex7DMe8B64FPA6vEgIbpIrwkPwCRgulLqFPB/OLo3FwFRzm5PgAFAcdc0zzha62Lnn+dxjIWPB8qUUokAzj/Pd10LO9U54JzWeofz+7dwhIneer+t3Qrs1VqXOb/vzfd8E/CF1rpca90CrAAm0ss/z1rrV7XWY7TWk4EK4Di9+30WPVSvCQ9a6ye01gO01ik4unY/1Fp/E/gImOvc7VvA6i5qoiGUUqFKqfBLXwM34+j+XIPjfqEX3bfWuhQ4q5TKcr40FfiMXnq/V1jAP4csoHff8xkgTykV4py7dOl97u2f5zjnn4OA2Tje7978PoseqleuMKmU+grwQ631NKVUGo6eiH7APuAurXVTV7avMznvb6XzWz9gmdb6GaVUDPAGMAjHD+JvaK0ruqiZnUopNQrHpNgA4CTwbRxBuFfeL4BzDPwskKa1rna+1mvfYwCl1E+BeTi67vcB9+KY49CbP89bcMzVagEe1Vp/0NvfZ9Ez9crwIIQQQgjj9JphCyGEEEJcGxIehBBCCOETCQ9CCCGE8ImEByGEEEL4RMKDEEIIIXwi4UEIAyilfqKU+mFXt0MIIYwg4UGINigH+awIIYST/EAUwgWlVIpS6ohS6vfAXuBVpdRupdRh5+JFl/Y7pZT6qVJqr1LqoFIq28W57lNKvauUCr6W9yCEEEaR8CCEe1nA35wFuP5daz0WGAEUKKVGtNrvgrMw2R+Ay4YqlFIPAbcDM7XWDdeo3UIIYSgJD0K4d1prvd359R1Kqb04lkQeBuS02m+F8889QEqr1+/GUcxqTm9aQlkIISQ8COFeHYBSKhVHj8JUrfUIYB0Q1Gq/S8HAhqO+yCWHcISJAYa3VAghriEJD0K0LQJHkKhWSsXj6E3wxj7gfmCNUirJqMYJIcS1JuFBiDZorT/FEQQOA38Gtvpw7Mc4ei3WKaVijWmhEEJcW1JVUwghhBA+kZ4HIYQQQvhEwoMQQgghfCLhQQghhBA+kfAghBBCCJ9IeBBCCCGETyQ8CCGEEMInEh6EEEII4ZP/By45BBexG3isAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -698,26 +573,19 @@
     }
    ],
    "source": [
-    "plt.figure()\n",
-    "for sparsity in sparsityvals:\n",
-    "    errvals=[fdict[rank].inc_approx(sparsity)  for rank in rankvals]\n",
-    "    plt.plot(rankvals,errvals,linewidth=5,label=\"sparsity={:.2f}\".format(sparsity))\n",
-    "    \n",
-    "plt.legend()\n",
-    "plt.xlabel(\"rank\")\n",
-    "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of rank\",fontsize=\"xx-large\")\n",
-    "plt.show()\n",
-    "plt.close()\n",
+    "    plt.figure()\n",
+    "    for sparsity in sparsityvals:\n",
+    "        errvals=[fdict[rank].inc_approx(sparsity)  for rank in p.rank_vals]\n",
+    "        plt.plot(p.rank_vals,errvals,linewidth=5,label=\"sparsity={:.2f}\".format(sparsity))\n",
+    "\n",
+    "    plt.legend(bbox_to_anchor=(1.1, 1))\n",
+    "    plt.xlabel(\"rank\")\n",
+    "    plt.ylabel(\"error\")\n",
+    "    plt.title(\"error as a function of rank\",fontsize=\"xx-large\")\n",
+    "    plt.show()\n",
+    "    plt.close()\n",
     "    "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/ErrorAnalysis/ErrorAnalysis_fall.ipynb b/ErrorAnalysis/ErrorAnalysis_fall.ipynb
deleted file mode 100644
index 65c165b..0000000
--- a/ErrorAnalysis/ErrorAnalysis_fall.ipynb
+++ /dev/null
@@ -1,745 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"logo.png\" alt=\"University of Illinois\" style=\"width: 200px;\"/>\n",
-    "\n",
-    "### Error Analysis fall###\n",
-    "by: Richard Sowers\n",
-    "* <r-sowers@illinois.edu>\n",
-    "* <https://publish.illinois.edu/r-sowers/>\n",
-    "\n",
-    "Copyright 2019 University of Illinois Board of Trustees. All Rights Reserved. Licensed under the MIT license\n",
-    "\n",
-    "### Explanation###\n",
-    "This code plots error analysis for Manhattan Traffic Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas\n",
-    "import numpy\n",
-    "import matplotlib.pylab as plt\n",
-    "%matplotlib inline\n",
-    "import scipy.interpolate\n",
-    "import scipy.optimize "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def saver(fname):\n",
-    "    plt.savefig(fname+\".png\",bbox_inches=\"tight\")\n",
-    "    \n",
-    "params={\n",
-    "    #\"font.size\":20,\n",
-    "    \"figure.titlesize\":\"large\",\n",
-    "    \"lines.linewidth\":3,\n",
-    "    #\"legend.fontsize\":\"small\",\n",
-    "    #\"xtick.labelsize\":\"x-small\",\n",
-    "    #\"ytick.labelsize\":\"x-small\",\n",
-    "    #\"axes.labelsize\": 'small',\n",
-    "}\n",
-    "plt.rcParams.update(params) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#fname=\"LevelCurveData2\"\n",
-    "fname=\"fall_values_COMBINED\"\n",
-    "colorsequence=['b', 'g', 'r', 'c', 'm', 'y', 'k']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "read data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   rank    beta   error_year_preAxing   error_year_postAxing  \\\n",
-      "0  40.0     0.0             26.638031              41.724601   \n",
-      "1   NaN     NaN                   NaN                    NaN   \n",
-      "2  40.0  1000.0             26.958008              41.843305   \n",
-      "3   NaN     NaN                   NaN                    NaN   \n",
-      "4  40.0  2000.0             26.952959              41.781512   \n",
-      "\n",
-      "    error_fall_preAxing   error_fall_postAxing   sparsity_preAxing  \\\n",
-      "0             27.410104              42.350878            0.675348   \n",
-      "1                   NaN                    NaN                 NaN   \n",
-      "2             27.733881              42.377424            0.694921   \n",
-      "3                   NaN                    NaN                 NaN   \n",
-      "4             27.722486              42.350216            0.716475   \n",
-      "\n",
-      "    sparsity_postAxing  \n",
-      "0             0.838865  \n",
-      "1                  NaN  \n",
-      "2             0.851688  \n",
-      "3                  NaN  \n",
-      "4             0.865042  \n"
-     ]
-    }
-   ],
-   "source": [
-    "data_raw=pandas.read_csv(fname+\".csv\",na_values=['nan',' nan'])\n",
-    "print(data_raw.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   rank    beta  error_year_preAxing  error_year_postAxing  \\\n",
-      "0    40     0.0            26.638031             41.724601   \n",
-      "2    40  1000.0            26.958008             41.843305   \n",
-      "4    40  2000.0            26.952959             41.781512   \n",
-      "6    40  3000.0            26.990673             41.414115   \n",
-      "8    40  4000.0            27.039437             41.120108   \n",
-      "\n",
-      "   error_fall_preAxing  error_fall_postAxing  sparsity_preAxing  \\\n",
-      "0            27.410104             42.350878           0.675348   \n",
-      "2            27.733881             42.377424           0.694921   \n",
-      "4            27.722486             42.350216           0.716475   \n",
-      "6            27.758684             42.045900           0.730274   \n",
-      "8            27.807777             41.763680           0.740969   \n",
-      "\n",
-      "   sparsity_postAxing  \n",
-      "0            0.838865  \n",
-      "2            0.851688  \n",
-      "4            0.865042  \n",
-      "6            0.872522  \n",
-      "8            0.878487  \n",
-      "Index(['rank', 'beta', 'error_year_preAxing', 'error_year_postAxing',\n",
-      "       'error_fall_preAxing', 'error_fall_postAxing', 'sparsity_preAxing',\n",
-      "       'sparsity_postAxing'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "data=data_raw.copy()\n",
-    "data.columns=[colname.strip() for colname in data.columns]\n",
-    "data=data.dropna(axis='index',subset=['rank','beta'])\n",
-    "data[\"rank\"]=data[\"rank\"].astype('int')\n",
-    "print(data.head())\n",
-    "print(data.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>error_year_preAxing</th>\n",
-       "      <th>error_year_postAxing</th>\n",
-       "      <th>error_fall_preAxing</th>\n",
-       "      <th>error_fall_postAxing</th>\n",
-       "      <th>sparsity_preAxing</th>\n",
-       "      <th>sparsity_postAxing</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>rank</th>\n",
-       "      <th>beta</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <th>40</th>\n",
-       "      <th>0.0</th>\n",
-       "      <td>26.638031</td>\n",
-       "      <td>41.724601</td>\n",
-       "      <td>27.410104</td>\n",
-       "      <td>42.350878</td>\n",
-       "      <td>0.675348</td>\n",
-       "      <td>0.838865</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>40</th>\n",
-       "      <th>1000.0</th>\n",
-       "      <td>26.958008</td>\n",
-       "      <td>41.843305</td>\n",
-       "      <td>27.733881</td>\n",
-       "      <td>42.377424</td>\n",
-       "      <td>0.694921</td>\n",
-       "      <td>0.851688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <th>40</th>\n",
-       "      <th>2000.0</th>\n",
-       "      <td>26.952959</td>\n",
-       "      <td>41.781512</td>\n",
-       "      <td>27.722486</td>\n",
-       "      <td>42.350216</td>\n",
-       "      <td>0.716475</td>\n",
-       "      <td>0.865042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <th>40</th>\n",
-       "      <th>3000.0</th>\n",
-       "      <td>26.990673</td>\n",
-       "      <td>41.414115</td>\n",
-       "      <td>27.758684</td>\n",
-       "      <td>42.045900</td>\n",
-       "      <td>0.730274</td>\n",
-       "      <td>0.872522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <th>40</th>\n",
-       "      <th>4000.0</th>\n",
-       "      <td>27.039437</td>\n",
-       "      <td>41.120108</td>\n",
-       "      <td>27.807777</td>\n",
-       "      <td>41.763680</td>\n",
-       "      <td>0.740969</td>\n",
-       "      <td>0.878487</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               error_year_preAxing  error_year_postAxing  error_fall_preAxing  \\\n",
-       "  rank beta                                                                     \n",
-       "0 40   0.0               26.638031             41.724601            27.410104   \n",
-       "2 40   1000.0            26.958008             41.843305            27.733881   \n",
-       "4 40   2000.0            26.952959             41.781512            27.722486   \n",
-       "6 40   3000.0            26.990673             41.414115            27.758684   \n",
-       "8 40   4000.0            27.039437             41.120108            27.807777   \n",
-       "\n",
-       "               error_fall_postAxing  sparsity_preAxing  sparsity_postAxing  \n",
-       "  rank beta                                                                 \n",
-       "0 40   0.0                42.350878           0.675348            0.838865  \n",
-       "2 40   1000.0             42.377424           0.694921            0.851688  \n",
-       "4 40   2000.0             42.350216           0.716475            0.865042  \n",
-       "6 40   3000.0             42.045900           0.730274            0.872522  \n",
-       "8 40   4000.0             41.763680           0.740969            0.878487  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data=data.set_index([\"rank\",\"beta\"],drop=True,append=True)\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[40 50 60 70 80 90]\n"
-     ]
-    }
-   ],
-   "source": [
-    "rankvals=pandas.unique(data.index.get_level_values(\"rank\"))\n",
-    "print(rankvals)\n",
-    "data_by_rank=data.groupby(by=\"rank\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"beta\",axis=0)\n",
-    "    beta=df_sorted.index.get_level_values(\"beta\")\n",
-    "    plt.plot(beta,df_sorted[\"sparsity_preAxing\"],label=\"N={:}\".format(rank),color=colorsequence[n])\n",
-    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
-    "plt.xlabel(\"beta\")\n",
-    "plt.ylabel(\"sparsity\")\n",
-    "plt.title(\"sparsity as a function of penalty\",fontsize=\"xx-large\")\n",
-    "saver(\"sparsity_by_penalty_fall\")\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"beta\",axis=0)\n",
-    "    beta=df_sorted.index.get_level_values(\"beta\")\n",
-    "    plt.plot(beta,df_sorted[\"error_year_preAxing\"],label=\"N={:}; yearly\".format(rank),color=colorsequence[n])\n",
-    "    plt.plot(beta,df_sorted[\"error_fall_preAxing\"],label=\"N={:}; fall\".format(rank),color=colorsequence[n],linestyle=\"--\")\n",
-    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
-    "plt.xlabel(\"beta\")\n",
-    "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of penalty\",fontsize=\"xx-large\")\n",
-    "saver(\"error_by_penalty_fall\")\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"sparsity_preAxing\",axis=0)\n",
-    "    sparsity=df_sorted[\"sparsity_preAxing\"]\n",
-    "    error=df_sorted[\"error_year_preAxing\"]\n",
-    "    plt.plot(sparsity,error,label=\"error for year; N={:}\".format(rank),color=colorsequence[n])\n",
-    "    error=df_sorted[\"error_fall_preAxing\"]\n",
-    "    plt.plot(sparsity,error,label=\"error for fall; N={:}\".format(rank),color=colorsequence[n],linestyle=\"--\")\n",
-    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
-    "plt.xlabel(\"sparsity\")\n",
-    "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of sparsity\",fontsize=\"xx-large\")\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class monotone_invert:\n",
-    "    def __init__(self,knots,sign=\"increasing\"):\n",
-    "        knots=[(t,y) for (t,y) in knots if not numpy.isnan(y)]\n",
-    "        self.tvals=numpy.array([t for t,_ in knots])\n",
-    "        self.yvals=numpy.array([y for _,y in knots])\n",
-    "        if len(knots)<2:\n",
-    "            return\n",
-    "        print(knots)\n",
-    "        self.N=len(knots)\n",
-    "        self.L=numpy.tril(numpy.ones(shape=(self.N,self.N)),k=0)\n",
-    "        def objective(d):\n",
-    "            error=self.yvals-self.L.dot(d)\n",
-    "            return 0.5*error.dot(error)\n",
-    "        \n",
-    "        def jacobian(d):\n",
-    "            error=self.yvals-self.L.dot(d)\n",
-    "            return self.L.T.dot(error)\n",
-    "        \n",
-    "        def hessian(d):\n",
-    "            return self.L.T*dot(self.L)\n",
-    "        \n",
-    "        print(self.N)\n",
-    "        pm=1\n",
-    "        if (sign==\"decreasing\"):\n",
-    "            pm=-1\n",
-    "        constraints={\"type\":\"ineq\",\"fun\":lambda x:pm*x}\n",
-    "        res=scipy.optimize.minimize(objective,self.yvals,method=\"COBYLA\",jac=jacobian,hessp=hessian,constraints=constraints)\n",
-    "        print(res)\n",
-    "        d_best=res.x\n",
-    "        self.y_approx_vals=self.L.dot(d_best)\n",
-    "        print(self.y_approx_vals)\n",
-    "        \n",
-    "        self.linapprox=scipy.interpolate.interp1d(self.tvals,self.y_approx_vals,copy=True,bounds_error=True)\n",
-    "        \n",
-    "    def inc_approx(self,t):\n",
-    "        if not (min(self.tvals)<=t<=max(self.tvals)):\n",
-    "            return numpy.nan\n",
-    "        return self.linapprox(t).item()\n",
-    "        \n",
-    "    def invert(self,yval):\n",
-    "        if not (min(self.y_approx_vals)<yval<max(self.y_approx_vals)):\n",
-    "            return numpy.nan\n",
-    "        \n",
-    "        tval=scipy.optimize.brentq(lambda x:self.linapprox(x)-yval,min(self.tvals),max(self.tvals))\n",
-    "        return tval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0.675347565, 26.638031400000003), (0.69492121, 26.95800752), (0.716474972, 26.95295884), (0.730273818, 26.9906727), (0.740968785, 27.03943659), (0.74893877, 27.107602500000002), (0.75453444, 27.20029113), (0.7587797490000001, 27.31310563), (0.760752467, 27.47106941)]\n",
-      "9\n",
-      "     fun: 1.589069232902657e-05\n",
-      "   maxcv: 1.1714129932787732e-22\n",
-      " message: 'Maximum number of function evaluations has been exceeded.'\n",
-      "    nfev: 1000\n",
-      "  status: 2\n",
-      " success: False\n",
-      "       x: array([ 2.66367149e+01,  3.19387678e-01, -1.17141299e-22,  3.43967640e-02,\n",
-      "        4.77312051e-02,  7.16016171e-02,  8.80990180e-02,  1.17142741e-01,\n",
-      "        1.55197506e-01])\n",
-      "[26.63671487 26.95610255 26.95610255 26.99049932 27.03823052 27.10983214\n",
-      " 27.19793116 27.3150739  27.4702714 ]\n",
-      "[(0.675347565, 27.41010373), (0.69492121, 27.7338814), (0.716474972, 27.72248626), (0.730273818, 27.75868365), (0.740968785, 27.80777743), (0.74893877, 27.87734269), (0.75453444, 27.97266789), (0.7587797490000001, 28.08882583), (0.760752467, 28.24975867)]\n",
-      "9\n",
-      "     fun: 3.3555985192558115e-05\n",
-      "   maxcv: 1.056228847044682e-21\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 925\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.74089590e+01,  3.19681918e-01, -1.05622885e-21,  2.96353009e-02,\n",
-      "        4.94569732e-02,  6.99943103e-02,  9.49563122e-02,  1.15805959e-01,\n",
-      "        1.61441809e-01])\n",
-      "[27.40895903 27.72864095 27.72864095 27.75827625 27.80773322 27.87772753\n",
-      " 27.97268384 28.0884898  28.24993161]\n",
-      "[(0.697145437, 25.36197488), (0.7270037020000001, 25.66748831), (0.756782009, 25.59166646), (0.7759766770000001, 25.59756563), (0.7797476329999999, 25.72661958), (0.786361773, 25.83122995), (0.790195474, 26.009027800000002), (0.7977495370000001, 26.131918)]\n",
-      "8\n",
-      "     fun: 0.001779331789776059\n",
-      "   maxcv: 4.81482486096809e-35\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 773\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.53618119e+01,  2.57192580e-01,  2.48789406e-22, -4.81482486e-35,\n",
-      "        1.07119491e-01,  1.05616166e-01,  1.76812242e-01,  1.23846944e-01])\n",
-      "[25.36181191 25.61900449 25.61900449 25.61900449 25.72612398 25.83174015\n",
-      " 26.00855239 26.13239933]\n",
-      "[(0.697145437, 26.13994062), (0.7270037020000001, 26.46534067), (0.756782009, 26.39263461), (0.7759766770000001, 26.39611458), (0.7797476329999999, 26.53562081), (0.786361773, 26.63429755), (0.790195474, 26.80672575), (0.7977495370000001, 26.924473499999998)]\n",
-      "8\n",
-      "     fun: 0.0016829650180806496\n",
-      "   maxcv: 2.933341066542872e-20\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 614\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.61392012e+01,  2.78966438e-01, -2.86991418e-20, -2.93334107e-20,\n",
-      "        1.16691415e-01,  9.94120451e-02,  1.73527404e-01,  1.16385664e-01])\n",
-      "[26.13920124 26.41816768 26.41816768 26.41816768 26.53485909 26.63427114\n",
-      " 26.80779854 26.9241842 ]\n",
-      "[(0.717233078, 24.40184251), (0.777131857, 24.34322987), (0.7888837040000001, 24.57246013), (0.802405687, 24.723011399999997), (0.8087048170000001, 24.90329832)]\n",
-      "5\n",
-      "     fun: 0.0008588758858584533\n",
-      "   maxcv: 0.0\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 349\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([2.43725999e+01, 6.93889390e-21, 1.99935763e-01, 1.50420915e-01,\n",
-      "       1.80460811e-01])\n",
-      "[24.37259987 24.37259987 24.57253563 24.72295654 24.90341736]\n",
-      "[(0.717233078, 25.06797662), (0.777131857, 24.97875261), (0.7888837040000001, 25.21572288), (0.802405687, 25.38155257), (0.8087048170000001, 25.57105101)]\n",
-      "5\n",
-      "     fun: 0.0019903012548098827\n",
-      "   maxcv: 3.251143348871942e-22\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 287\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.50234024e+01, -3.25114335e-22,  1.92585094e-01,  1.65357254e-01,\n",
-      "        1.89862745e-01])\n",
-      "[25.02340236 25.02340236 25.21598745 25.38134471 25.57120745]\n",
-      "[(0.731976361, 23.53705497), (0.780960478, 23.75652443), (0.81319072, 23.69114643), (0.817408027, 23.9887707)]\n",
-      "4\n",
-      "     fun: 0.0010689019281735874\n",
-      "   maxcv: 6.93889390390561e-22\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 194\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.35366868e+01,  1.87410627e-01, -6.93889390e-22,  2.64049073e-01])\n",
-      "[23.53668685 23.72409748 23.72409748 23.98814655]\n",
-      "[(0.731976361, 24.23175905), (0.780960478, 24.44915912), (0.81319072, 24.37480928), (0.817408027, 24.67894239)]\n",
-      "4\n",
-      "     fun: 0.0013822522157020327\n",
-      "   maxcv: 1.8441555372789396e-20\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 197\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([ 2.42311963e+01,  1.81130319e-01, -1.84415554e-20,  2.66553445e-01])\n",
-      "[24.23119633 24.41232665 24.41232665 24.67888009]\n",
-      "[(0.759708954, 22.63627827), (0.809178187, 22.78596345)]\n",
-      "2\n",
-      "     fun: 1.7337922373565135e-08\n",
-      "   maxcv: 0.0\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 78\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([22.63632997,  0.14981237])\n",
-      "[22.63632997 22.78614234]\n",
-      "[(0.759708954, 23.27287597), (0.809178187, 23.38852745)]\n",
-      "2\n",
-      "     fun: 1.0087493901844637e-08\n",
-      "   maxcv: 0.0\n",
-      " message: 'Optimization terminated successfully.'\n",
-      "    nfev: 81\n",
-      "  status: 1\n",
-      " success: True\n",
-      "       x: array([23.27274469,  0.11572852])\n",
-      "[23.27274469 23.38847321]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\richa\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py:502: RuntimeWarning: Method COBYLA does not use gradient information (jac).\n",
-      "  RuntimeWarning)\n",
-      "C:\\Users\\richa\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py:513: RuntimeWarning: Method COBYLA does not use Hessian-vector product information (hessp).\n",
-      "  'information (hessp).' % method, RuntimeWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "fdict_year={}\n",
-    "fdict_fall={}\n",
-    "for rank,df in data_by_rank:\n",
-    "    df_sorted=df.sort_values(by=\"sparsity_preAxing\",axis=0)\n",
-    "    knots=list(zip(df_sorted[\"sparsity_preAxing\"],df_sorted[\"error_year_preAxing\"]))\n",
-    "    fdict_year[rank]=monotone_invert(knots)\n",
-    "    knots=list(zip(df_sorted[\"sparsity_preAxing\"],df_sorted[\"error_fall_preAxing\"]))\n",
-    "    fdict_fall[rank]=monotone_invert(knots)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "for n,(rank,df) in enumerate(data_by_rank):\n",
-    "    df_sorted=df.sort_values(by=\"sparsity_preAxing\",axis=0)\n",
-    "    knots=list(zip(df_sorted[\"sparsity_preAxing\"],df_sorted[\"error_year_preAxing\"]))\n",
-    "    temp=fdict_year[rank]\n",
-    "    plt.plot(temp.tvals,temp.yvals,label=\"error for year; N={:}\".format(rank),color=colorsequence[n])\n",
-    "    try:\n",
-    "        plt.plot(temp.tvals,temp.y_approx_vals,label=\"error for year; N={:} increasing\".format(rank),linewidth=5,linestyle=\"-.\",color=colorsequence[n])\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    temp=fdict_fall[rank]\n",
-    "    plt.plot(temp.tvals,temp.yvals,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
-    "    try:\n",
-    "        plt.plot(temp.tvals,temp.y_approx_vals,label=\"error for fall; N={:} increasing\".format(rank),linewidth=5,linestyle=\"-.\",color=colorsequence[n])\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "plt.legend(bbox_to_anchor=(1.5, 1))\n",
-    "plt.xlabel(\"sparsity\")\n",
-    "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of sparsity\",fontsize=\"xx-large\")\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.68  0.715 0.75  0.785 0.82 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sparsityvals=numpy.linspace(start=0.68,stop=0.82,num=5)\n",
-    "print(sparsityvals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "for n,sparsity in enumerate(sparsityvals):\n",
-    "    errvals=[fdict_year[rank].inc_approx(sparsity)  for rank in rankvals]\n",
-    "    try:\n",
-    "        plt.plot(rankvals,errvals,linewidth=5,label=\"error for year; sparsity={:.2f}\".format(sparsity),color=colorsequence[n])\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    \n",
-    "    errvals=[fdict_fall[rank].inc_approx(sparsity)  for rank in rankvals]\n",
-    "    try:\n",
-    "        plt.plot(rankvals,errvals,linewidth=5,label=\"error for fall; sparsity={:.2f}\".format(sparsity),color=colorsequence[n],linestyle=\":\")\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    \n",
-    "plt.legend(bbox_to_anchor=(1.6, 1))\n",
-    "plt.xlabel(\"rank\")\n",
-    "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of rank\",fontsize=\"xx-large\")\n",
-    "plt.show()\n",
-    "plt.close()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ErrorAnalysis/ErrorAnalysis_seasonal.ipynb b/ErrorAnalysis/ErrorAnalysis_seasonal.ipynb
new file mode 100644
index 0000000..00462d2
--- /dev/null
+++ b/ErrorAnalysis/ErrorAnalysis_seasonal.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"logo.png\" alt=\"University of Illinois\" style=\"width: 200px;\"/>\n",
+    "\n",
+    "### Seasonal Error Analysis ###\n",
+    "by: Richard Sowers\n",
+    "* <r-sowers@illinois.edu>\n",
+    "* <https://publish.illinois.edu/r-sowers/>\n",
+    "\n",
+    "Copyright 2019 University of Illinois Board of Trustees. All Rights Reserved. Licensed under the MIT license\n",
+    "\n",
+    "### Explanation###\n",
+    "This code plots error analysis for Manhattan Traffic Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "import numpy\n",
+    "import matplotlib.pylab as plt\n",
+    "%matplotlib inline\n",
+    "import scipy.interpolate\n",
+    "import scipy.optimize "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def saver(fname):\n",
+    "    plt.savefig(fname+\".png\",bbox_inches=\"tight\")\n",
+    "    \n",
+    "params={\n",
+    "    #\"font.size\":20,\n",
+    "    \"figure.titlesize\":\"large\",\n",
+    "    \"lines.linewidth\":3,\n",
+    "    #\"legend.fontsize\":\"small\",\n",
+    "    #\"xtick.labelsize\":\"x-small\",\n",
+    "    #\"ytick.labelsize\":\"x-small\",\n",
+    "    #\"axes.labelsize\": 'small',\n",
+    "}\n",
+    "plt.rcParams.update(params) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colorsequence=['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
+    "stylesequence=[\"-\",\":\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class processor:\n",
+    "    def __init__(self,df):\n",
+    "        self.rank_vals=pandas.unique(df.index.get_level_values(\"rank\"))\n",
+    "        self.df=df.dropna(axis=\"index\")\n",
+    "        \n",
+    "    def by_penalty(self,rank):\n",
+    "        temp=self.df.groupby(by=\"rank\").get_group(rank)\n",
+    "        return temp.reset_index(level=\"rank\",drop=True)\n",
+    "    \n",
+    "    def sparsity_by_penalty(self,rank):\n",
+    "        temp=self.by_penalty(rank)[\"sparsity\"]\n",
+    "        return temp\n",
+    "    \n",
+    "    def error_by_sparsity(self,rank):\n",
+    "        temp=self.by_penalty(rank)\n",
+    "        temp=temp.set_index(keys=\"sparsity\",drop=True)[\"error\"]\n",
+    "        temp.sort_index(axis=\"index\",inplace=True)\n",
+    "        return temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class monotone_invert:\n",
+    "    def __init__(self,df,sign=\"increasing\"):\n",
+    "        self.df=df\n",
+    "        self.tvals=numpy.array(self.df.index)\n",
+    "        self.yvals=numpy.array(self.df.to_numpy())\n",
+    "        if len(self.df)<2:\n",
+    "            return None\n",
+    "        self.N=len(self.df)\n",
+    "        self.L=numpy.tril(numpy.ones(shape=(self.N,self.N)),k=0)\n",
+    "        self.ctr=1\n",
+    "        x0=[numpy.mean(self.yvals)/self.N]*self.N\n",
+    "        \n",
+    "        def objective(d):\n",
+    "            error=self.yvals-self.L.dot(d)\n",
+    "            return 0.5*error.dot(error)\n",
+    "        \n",
+    "        def jacobian(self,d): #not used\n",
+    "            error=self.yvals-self.L.dot(d)\n",
+    "            return self.L.T.dot(error)\n",
+    "        \n",
+    "        def hessian(self,d): # not used\n",
+    "            return self.L.T*dot(self.L)\n",
+    "        \n",
+    "        print(self.N)\n",
+    "        pm=1\n",
+    "        if (sign==\"decreasing\"):\n",
+    "            pm=-1\n",
+    "        constraints={\"type\":\"ineq\",\"fun\":lambda x:pm*x}\n",
+    "        res=scipy.optimize.minimize(objective,x0=x0,method=\"COBYLA\",constraints=constraints)\n",
+    "        print(res)\n",
+    "        d_best=res.x\n",
+    "        self.y_approx_vals=self.L.dot(d_best)\n",
+    "        print(\"y_approx\",self.y_approx_vals)\n",
+    "        \n",
+    "        self.linapprox=scipy.interpolate.interp1d(self.tvals,self.y_approx_vals,copy=True,bounds_error=True)\n",
+    "        \n",
+    "    def inc_approx(self,t):\n",
+    "        if not (min(self.tvals)<=t<=max(self.tvals)):\n",
+    "            return numpy.nan\n",
+    "        return self.linapprox(t).item()\n",
+    "\n",
+    "        \n",
+    "        tval=scipy.optimize.brentq(lambda x:self.linapprox(x)-yval,min(self.tvals),max(self.tvals))\n",
+    "        return tval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fname=\"LevelCurveData2\"\n",
+    "fname=\"fall_values_COMBINED\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   rank    beta   error_year_preAxing   error_year_postAxing  \\\n",
+      "0  40.0     0.0             26.638031              41.724601   \n",
+      "1   NaN     NaN                   NaN                    NaN   \n",
+      "2  40.0  1000.0             26.958008              41.843305   \n",
+      "3   NaN     NaN                   NaN                    NaN   \n",
+      "4  40.0  2000.0             26.952959              41.781512   \n",
+      "\n",
+      "    error_fall_preAxing   error_fall_postAxing   sparsity_preAxing  \\\n",
+      "0             27.410104              42.350878            0.675348   \n",
+      "1                   NaN                    NaN                 NaN   \n",
+      "2             27.733881              42.377424            0.694921   \n",
+      "3                   NaN                    NaN                 NaN   \n",
+      "4             27.722486              42.350216            0.716475   \n",
+      "\n",
+      "    sparsity_postAxing  \n",
+      "0             0.838865  \n",
+      "1                  NaN  \n",
+      "2             0.851688  \n",
+      "3                  NaN  \n",
+      "4             0.865042  \n"
+     ]
+    }
+   ],
+   "source": [
+    "data_raw=pandas.read_csv(fname+\".csv\",na_values=['nan',' nan'])\n",
+    "print(data_raw.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             error_year_preAxing  error_year_postAxing  error_fall_preAxing  \\\n",
+      "rank beta                                                                     \n",
+      "40   0.0               26.638031             41.724601            27.410104   \n",
+      "     1000.0            26.958008             41.843305            27.733881   \n",
+      "     2000.0            26.952959             41.781512            27.722486   \n",
+      "     3000.0            26.990673             41.414115            27.758684   \n",
+      "     4000.0            27.039437             41.120108            27.807777   \n",
+      "\n",
+      "             error_fall_postAxing  sparsity_preAxing  sparsity_postAxing  \n",
+      "rank beta                                                                 \n",
+      "40   0.0                42.350878           0.675348            0.838865  \n",
+      "     1000.0             42.377424           0.694921            0.851688  \n",
+      "     2000.0             42.350216           0.716475            0.865042  \n",
+      "     3000.0             42.045900           0.730274            0.872522  \n",
+      "     4000.0             41.763680           0.740969            0.878487  \n",
+      "Index(['error_year_preAxing', 'error_year_postAxing', 'error_fall_preAxing',\n",
+      "       'error_fall_postAxing', 'sparsity_preAxing', 'sparsity_postAxing'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "data=data_raw.copy()\n",
+    "data.columns=[colname.strip() for colname in data.columns]\n",
+    "data=data.dropna(axis='index',subset=['rank','beta'])\n",
+    "data[\"rank\"]=data[\"rank\"].astype('int')\n",
+    "data=data.set_index(keys=[\"rank\",\"beta\"])\n",
+    "print(data.head())\n",
+    "print(data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataDict={}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_year=data[[\"error_year_preAxing\",\"sparsity_preAxing\"]]\n",
+    "data_year=data_year.rename(mapper={\"error_year_preAxing\":\"error\",\"sparsity_preAxing\":\"sparsity\"},axis=\"columns\")\n",
+    "data_year.head()\n",
+    "dataDict[\"year\"]=data_year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_fall=data[[\"error_fall_preAxing\",\"sparsity_preAxing\"]]\n",
+    "data_fall=data_fall.rename(mapper={\"error_fall_preAxing\":\"error\",\"sparsity_preAxing\":\"sparsity\"},axis=\"columns\")\n",
+    "data_fall.head()\n",
+    "dataDict[\"fall\"]=data_fall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pDict={season:processor(dataDict[season]) for season in dataDict.keys()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "for n,rank in enumerate(pDict[\"year\"].rank_vals):\n",
+    "    df=pDict[\"year\"].sparsity_by_penalty(rank)\n",
+    "    plt.plot(df.index,df.values,label=\"N={:}\".format(rank),color=colorsequence[n])\n",
+    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
+    "plt.xlabel(\"beta\")\n",
+    "plt.ylabel(\"sparsity\")\n",
+    "plt.title(\"sparsity as a function of penalty\",fontsize=\"xx-large\")\n",
+    "saver(\"sparsity_by_penalty_seasonal\")\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "for n,season in enumerate(dataDict.keys()):\n",
+    "    for nn,rank in enumerate(pDict[season].rank_vals):\n",
+    "        df=pDict[season].error_by_sparsity(rank)\n",
+    "        plt.plot(df.index,df.values,label=season+\"; N={:}\".format(rank),color=colorsequence[nn],linestyle=stylesequence[n])\n",
+    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
+    "plt.xlabel(\"sparsity\")\n",
+    "plt.ylabel(\"error\")\n",
+    "plt.title(\"error as a function of sparsity\",fontsize=\"xx-large\")\n",
+    "saver(\"error_by_sparsity_seasonal\")\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n",
+      "     fun: 7.132430141822445e-06\n",
+      "   maxcv: 0.0\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 894\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([2.66372674e+01, 3.18616516e-01, 4.40280354e-21, 3.40886982e-02,\n",
+      "       4.96443675e-02, 6.78453293e-02, 9.29968697e-02, 1.12435298e-01,\n",
+      "       1.58159535e-01])\n",
+      "y_approx [26.63726738 26.9558839  26.9558839  26.9899726  27.03961696 27.10746229\n",
+      " 27.20045916 27.31289446 27.471054  ]\n",
+      "8\n",
+      "     fun: 0.0017795021108989624\n",
+      "   maxcv: 9.899114871240379e-20\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 583\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.53616629e+01,  2.57533934e-01, -6.93976136e-20, -9.89911487e-20,\n",
+      "        1.07118695e-01,  1.05529569e-01,  1.76549560e-01,  1.23894889e-01])\n",
+      "y_approx [25.36166289 25.61919682 25.61919682 25.61919682 25.72631552 25.83184509\n",
+      " 26.00839465 26.13228954]\n",
+      "5\n",
+      "     fun: 0.0008590621772018529\n",
+      "   maxcv: 3.3881317890170426e-21\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 376\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.43728220e+01, -3.38813179e-21,  1.99339048e-01,  1.51237338e-01,\n",
+      "        1.79932175e-01])\n",
+      "y_approx [24.37282196 24.37282196 24.57216101 24.72339835 24.90333052]\n",
+      "4\n",
+      "     fun: 0.001068647311509867\n",
+      "   maxcv: 6.776263578034251e-21\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 167\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.35373237e+01,  1.86633477e-01, -6.77626358e-21,  2.65040154e-01])\n",
+      "y_approx [23.53732367 23.72395715 23.72395715 23.9889973 ]\n",
+      "2\n",
+      "     fun: 6.7002469141090226e-09\n",
+      "   maxcv: 0.0\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 79\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([22.63634145,  0.14971901])\n",
+      "y_approx [22.63634145 22.78606045]\n",
+      "9\n",
+      "     fun: 3.510115132378873e-05\n",
+      "   maxcv: 1.4546810998747153e-20\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 811\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.74085714e+01,  3.20392504e-01, -1.45468110e-20,  2.92111455e-02,\n",
+      "        4.93564043e-02,  7.08905340e-02,  9.42394896e-02,  1.16246374e-01,\n",
+      "        1.61321703e-01])\n",
+      "y_approx [27.40857144 27.72896394 27.72896394 27.75817509 27.80753149 27.87842202\n",
+      " 27.97266151 28.08890789 28.25022959]\n",
+      "8\n",
+      "     fun: 0.0016831224316709828\n",
+      "   maxcv: 1.3135861272349847e-20\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 656\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.61398064e+01,  2.78284812e-01, -1.24419719e-20, -1.31358613e-20,\n",
+      "        1.16613513e-01,  1.00491128e-01,  1.70526604e-01,  1.18523312e-01])\n",
+      "y_approx [26.1398064  26.41809121 26.41809121 26.41809121 26.53470472 26.63519585\n",
+      " 26.80572245 26.92424577]\n",
+      "5\n",
+      "     fun: 0.0019902916617093335\n",
+      "   maxcv: -0.0\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 330\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([25.02340161,  0.        ,  0.19259593,  0.16557375,  0.18968663])\n",
+      "y_approx [25.02340161 25.02340161 25.21599754 25.3815713  25.57125793]\n",
+      "4\n",
+      "     fun: 0.0013820258944647282\n",
+      "   maxcv: 4.68583827096163e-22\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 262\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([ 2.42317132e+01,  1.80481528e-01, -4.68583827e-22,  2.66855950e-01])\n",
+      "y_approx [24.23171316 24.41219469 24.41219469 24.67905064]\n",
+      "2\n",
+      "     fun: 9.01398407261569e-09\n",
+      "   maxcv: 0.0\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 59\n",
+      "  status: 1\n",
+      " success: True\n",
+      "       x: array([23.27293882,  0.11570728])\n",
+      "y_approx [23.27293882 23.3886461 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "fDict={}\n",
+    "for season in dataDict.keys():\n",
+    "    fDict[season]={rank:monotone_invert(pDict[season].error_by_sparsity(rank)) for rank in pDict[season].rank_vals}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.68   0.7175 0.755  0.7925 0.83  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sparsityvals=numpy.linspace(start=0.68,stop=0.83,num=5)\n",
+    "print(sparsityvals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "for n,season in enumerate(dataDict.keys()):\n",
+    "    for nn,sparsity in enumerate(sparsityvals):\n",
+    "        errvals=[fDict[season][rank].inc_approx(sparsity)  for rank in pDict[season].rank_vals]\n",
+    "        plt.plot(pDict[season].rank_vals,errvals,linewidth=5,label=season+\"; sparsity={:.2f}\".format(sparsity),color=colorsequence[nn],\n",
+    "                linestyle=stylesequence[n])\n",
+    "plt.legend(bbox_to_anchor=(1.1, 1))\n",
+    "plt.xlabel(\"rank\")\n",
+    "plt.ylabel(\"error\")\n",
+    "plt.title(\"error as a function of rank\",fontsize=\"xx-large\")\n",
+    "saver(\"error_by_rank_seasonal\")\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ErrorAnalysis/compare.png b/ErrorAnalysis/compare.png
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diff --git a/ErrorAnalysis/sparsity_by_penalty_fall.png b/ErrorAnalysis/sparsity_by_penalty_seasonal.png
similarity index 100%
rename from ErrorAnalysis/sparsity_by_penalty_fall.png
rename to ErrorAnalysis/sparsity_by_penalty_seasonal.png
-- 
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